diff --git a/src/qiskit_qec/codes/codebase/SubspaceCodesDatabaseChecks.ipynb b/src/qiskit_qec/codes/codebase/SubspaceCodesDatabaseChecks.ipynb
index 28f6f5a4..6eed878f 100644
--- a/src/qiskit_qec/codes/codebase/SubspaceCodesDatabaseChecks.ipynb
+++ b/src/qiskit_qec/codes/codebase/SubspaceCodesDatabaseChecks.ipynb
@@ -8,13 +8,30 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "\n",
     "np.set_printoptions(threshold=np.inf, linewidth=np.inf)\n",
     "\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
     "from qiskit_qec.info.properties import Properties\n",
-    "import qiskit_qec.codes.codebase as cb"
+    "import qiskit_qec.codes.codebase as cb\n",
+    "import math\n",
+    "\n",
+    "# This will set the default output format to index format and read product syntax\n",
+    "# Pauli strings from left-to-right\n",
+    "import qiskit_qec.utils.pauli_rep as pauli_rep\n",
+    "from qiskit_qec.operators.base_pauli import BasePauli\n",
+    "\n",
+    "# Set the output string syntax: choose between PRODUCT_SYNTAX, INDEX_SYNTAX and LATEX_SYNTAX\n",
+    "BasePauli.set_syntax(pauli_rep.INDEX_SYNTAX)\n",
+    "\n",
+    "# Set the default output qubit ordering: choose between left-to-right and right-to-left\n",
+    "BasePauli.set_qubit_order(\"left-to-right\")\n",
+    "\n",
+    "from qiskit_qec.operators.pauli import Pauli\n",
+    "from qiskit_qec.operators.pauli_list import PauliList\n",
+    "\n",
+    "import networkx as nx\n",
+    "import pandas as pd"
    ]
   },
   {
@@ -28,15 +45,147 @@
     "\n",
     "# Pretty print matrix: Thanks to Krzysztof Mizgała\n",
     "def lprint(array):\n",
-    "    matrix = \"\"\n",
+    "    matrix = ''\n",
     "    for row in array:\n",
     "        try:\n",
     "            for number in row:\n",
-    "                matrix += f\"{number}&\"\n",
+    "                matrix += f'{number}&'\n",
     "        except TypeError:\n",
-    "            matrix += f\"{row}&\"\n",
-    "        matrix = matrix[:-1] + r\"\\\\\"\n",
-    "    display(Math(r\"\\begin{bmatrix}\" + matrix + r\"\\end{bmatrix}\"))"
+    "            matrix += f'{row}&'\n",
+    "        matrix = matrix[:-1] + r'\\\\'\n",
+    "    display(Math(r'\\begin{bmatrix}'+matrix+r'\\end{bmatrix}'))\n",
+    "\n",
+    "\n",
+    "def item_latex_it(item, dollar=True):\n",
+    "    if item=='':\n",
+    "        return ''\n",
+    "    if dollar is True:\n",
+    "        formatted_item = '$'\n",
+    "    else:\n",
+    "        formatted_item = ''\n",
+    "    i = 0\n",
+    "    while i < len(item):\n",
+    "        if item[i].isalpha() and item[i].isupper():\n",
+    "             # Handle capital letter followed by digits\n",
+    "             j = i + 1\n",
+    "             while j < len(item) and item[j].isdigit():\n",
+    "                 j += 1\n",
+    "             formatted_item += f'{item[i]}' + '_{' + item[i + 1:j] + '}'\n",
+    "             i = j\n",
+    "        elif item[i] == '^':\n",
+    "             # Handle '^' and the following curly braces and content\n",
+    "             formatted_item += '^{' + item[i+1:] + '}'\n",
+    "             break  # Finished processing\n",
+    "        else:\n",
+    "             formatted_item += item[i]\n",
+    "             i += 1\n",
+    "    if dollar is True:\n",
+    "        formatted_item += '$'\n",
+    "    return formatted_item\n",
+    "\n",
+    "def latex_it(input, dollar=True):\n",
+    "    if not isinstance(input, list):\n",
+    "        return item_latex_it(input, dollar=dollar)\n",
+    "    if len(input)>0:\n",
+    "        return [item_latex_it(item, dollar=dollar) for item in input]\n",
+    "    return ''\n",
+    "    \n",
+    "\n",
+    "def latex_it_old(input_list):\n",
+    "    output_list = []\n",
+    "    \n",
+    "    for item in input_list:\n",
+    "        formatted_item = '$'\n",
+    "        i = 0\n",
+    "        \n",
+    "        while i < len(item):\n",
+    "            if item[i].isalpha() and item[i].isupper():\n",
+    "                # Handle capital letter followed by digits\n",
+    "                j = i + 1\n",
+    "                while j < len(item) and item[j].isdigit():\n",
+    "                    j += 1\n",
+    "                formatted_item += f'{item[i]}' + '_{' + item[i + 1:j] + '}'\n",
+    "                i = j\n",
+    "            elif item[i] == '^':\n",
+    "                # Handle '^' and the following curly braces and content\n",
+    "                formatted_item += '^{' + item[i+1:] + '}'\n",
+    "                break  # Finished processing\n",
+    "            else:\n",
+    "                formatted_item += item[i]\n",
+    "                i += 1\n",
+    "        formatted_item += '$'\n",
+    "        output_list.append(formatted_item)\n",
+    "    \n",
+    "    return output_list\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return idd[:2] + idd[3:]\n",
+    "\n",
+    "def add_node_to_graph(G, id):       \n",
+    "    G.add_nodes_from([id], layer=id[1], label=label_fmt(id))\n",
+    "\n",
+    "def add_attribute(att_dict, id, attr, val):\n",
+    "    try:\n",
+    "        att_dict[attr][id] = val\n",
+    "    except KeyError as e:\n",
+    "            att_dict[attr] = {}\n",
+    "            att_dict[attr][id] = val\n",
+    "\n",
+    "def add_edge_to_graph(G, id1, id2):\n",
+    "    G.add_edges_from([(id1,id2)]) \n",
+    "\n",
+    "def make_poly(wenum):\n",
+    "    def xpower(var, i):\n",
+    "        if i == 0: return ''\n",
+    "        return var + \"^{\" + f\"{i}\" + \"}\"\n",
+    "    def coeff_var(coeff, var, i):\n",
+    "        if coeff == 0:\n",
+    "            return ''\n",
+    "        if coeff == 1:\n",
+    "            if i == 0:\n",
+    "                return '1'\n",
+    "            return xpower(var, i)\n",
+    "        if coeff == -1:\n",
+    "            if i == 0:\n",
+    "                return '-1'\n",
+    "            return '-' + xpower(var, i)\n",
+    "        return str(coeff) + xpower(var, i)\n",
+    "    comp = [coeff_var(wenum[i], 'x', i) for i in range(len(wenum)) if wenum[i] != 0]\n",
+    "    return \" + \".join(comp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c3d9fd33-2bba-4fa4-bef2-4be3f933bea1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count_letters(input_string):\n",
+    "    count = 0\n",
+    "    for char in input_string:\n",
+    "        if char.isalpha(): \n",
+    "            count += 1\n",
+    "    return count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "04ddb753-bd8c-48ea-8aa4-7f16dc0bad11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_even_gen_set(generators):\n",
+    "    for gen in generators:\n",
+    "        if count_letters(gen)%2 == 1:\n",
+    "            return False\n",
+    "    return True\n",
+    "\n",
+    "def max_weight(generators):\n",
+    "    weights = [count_letters(item) for item in generators]\n",
+    "    return max(weights)\n",
+    "            "
    ]
   },
   {
@@ -47,9 +196,202 @@
     "# Count all codes in base database"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "47efa4e9-b905-4528-8fb9-f6651e50012f",
+   "metadata": {},
+   "source": [
+    "## Find the number of codes for each n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
+   "id": "550c6e11-7007-401a-a236-1933d4b183ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_codes_table = np.zeros((2,10), dtype=int) # for table in paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 742,
+   "id": "3a976b39-25e5-4366-975f-94c78775ccf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of codes for each n:\n",
+      " n = 1 : 2\n",
+      " n = 2 : 5\n",
+      " n = 3 : 12\n",
+      " n = 4 : 35\n",
+      " n = 5 : 112\n",
+      " n = 6 : 474\n",
+      " n = 7 : 2757\n",
+      " n = 8 : 28642\n",
+      " n = 9 : 721967\n",
+      "The database contains 754006 number of code equivalence classes\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Total number of codes for each n:\")\n",
+    "summary = 0\n",
+    "for n in range(1,10):\n",
+    "    total = 0\n",
+    "    num = 0\n",
+    "    for k in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, info_only=True, list_only=True)\n",
+    "        num = len(codes)\n",
+    "        total += num\n",
+    "    all_codes_table[0][n] = total\n",
+    "    print(f\" n = {n} : {total}\")\n",
+    "    summary += total\n",
+    "print(f\"The database contains {summary} number of code equivalence classes\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c895bc77-2705-4898-a7e4-d4e1984d7916",
+   "metadata": {},
+   "source": [
+    "## Find the number of codes for each n that are indecomposable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 743,
+   "id": "34a18863-5c02-4326-9e99-7cc8582e86e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of codes for each n that are indecomposable:\n",
+      " n = 1 : 2\n",
+      " n = 2 : 2\n",
+      " n = 3 : 4\n",
+      " n = 4 : 13\n",
+      " n = 5 : 46\n",
+      " n = 6 : 245\n",
+      " n = 7 : 1765\n",
+      " n = 8 : 22773\n",
+      " n = 9 : 662054\n",
+      "The database contains 686904 number of code equivalence classes\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Total number of codes for each n that are indecomposable:\")\n",
+    "summary = 0\n",
+    "for n in range(1,10):\n",
+    "    total = 0\n",
+    "    num = 0\n",
+    "    for k in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, is_decomposable=False, info_only=True, list_only=True)\n",
+    "        num = len(codes)\n",
+    "        total += num\n",
+    "    all_codes_table[1][n] = total\n",
+    "    print(f\" n = {n} : {total}\")\n",
+    "    summary += total\n",
+    "print(f\"The database contains {summary} number of code equivalence classes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 745,
+   "id": "63a3d3fc-c812-45db-bf76-4a945de59246",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "91.1006013214749"
+      ]
+     },
+     "execution_count": 745,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "100*686904/754006"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7276869b-1276-4cbb-8802-7dc78b8b2250",
+   "metadata": {},
+   "source": [
+    "## Paper Table: Number of codes : all and indecompsable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a3170192-6c53-4766-892c-73c856cf87ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&2&5&12&35&112&474&2757&28642&721967\\\\0&2&2&4&13&46&245&1765&22773&662054\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(all_codes_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "55c0f3bb-5a20-43b4-b865-edabecbf79e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "n & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      "$N_n$ & 2 & 5 & 12 & 35 & 112 & 474 & 2757 & 28642 & 721967 \\\\ \n",
+      "$M_n$ & 2 & 2 & 4 & 13 & 46 & 245 & 1765 & 22773 & 662054 \\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_max = 9\n",
+    "val = \"n & \" + \" & \".join(str(i) for i in range(1,n_max + 1)) + \" \\\\\\ \"\n",
+    "nk = \"$N_n$ & \" + \" & \".join(str(all_codes_table[0][i]) for i in range(1,n_max + 1)) + \" \\\\\\ \"\n",
+    "mk = \"$M_n$ & \" + \" & \".join(str(all_codes_table[1][i]) for i in range(1,n_max + 1)) + \" \\\\\\ \"\n",
+    "print(f\"{val}\\n\\midrule\\n{nk}\\n{mk}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d65a522e-ee3b-4c31-98f4-28e8e1415f1c",
+   "metadata": {},
+   "source": [
+    "## Number of Codes: fine grained"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
    "id": "3e291254-45df-44a0-8d94-98e27129d5b9",
    "metadata": {},
    "outputs": [
@@ -57,34 +399,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of codes: n = 0 : 0\n",
       "[[1,0]]: 1\n",
-      "Total number of codes: n = 1 : 1\n",
+      "[[1,1]]: 1\n",
+      "Total number of codes: n = 1 : 2\n",
       "[[2,0]]: 2\n",
       "[[2,1]]: 2\n",
-      "Total number of codes: n = 2 : 4\n",
+      "[[2,2]]: 1\n",
+      "Total number of codes: n = 2 : 5\n",
       "[[3,0]]: 3\n",
       "[[3,1]]: 5\n",
       "[[3,2]]: 3\n",
-      "Total number of codes: n = 3 : 11\n",
+      "[[3,3]]: 1\n",
+      "Total number of codes: n = 3 : 12\n",
       "[[4,0]]: 6\n",
       "[[4,1]]: 13\n",
       "[[4,2]]: 11\n",
       "[[4,3]]: 4\n",
-      "Total number of codes: n = 4 : 34\n",
+      "[[4,4]]: 1\n",
+      "Total number of codes: n = 4 : 35\n",
       "[[5,0]]: 11\n",
       "[[5,1]]: 36\n",
       "[[5,2]]: 40\n",
       "[[5,3]]: 19\n",
       "[[5,4]]: 5\n",
-      "Total number of codes: n = 5 : 111\n",
+      "[[5,5]]: 1\n",
+      "Total number of codes: n = 5 : 112\n",
       "[[6,0]]: 26\n",
       "[[6,1]]: 115\n",
       "[[6,2]]: 185\n",
       "[[6,3]]: 109\n",
       "[[6,4]]: 32\n",
       "[[6,5]]: 6\n",
-      "Total number of codes: n = 6 : 473\n",
+      "[[6,6]]: 1\n",
+      "Total number of codes: n = 6 : 474\n",
       "[[7,0]]: 59\n",
       "[[7,1]]: 448\n",
       "[[7,2]]: 1075\n",
@@ -92,7 +439,8 @@
       "[[7,4]]: 267\n",
       "[[7,5]]: 48\n",
       "[[7,6]]: 7\n",
-      "Total number of codes: n = 7 : 2756\n",
+      "[[7,7]]: 1\n",
+      "Total number of codes: n = 7 : 2757\n",
       "[[8,0]]: 182\n",
       "[[8,1]]: 2371\n",
       "[[8,2]]: 10010\n",
@@ -101,7 +449,8 @@
       "[[8,5]]: 614\n",
       "[[8,6]]: 71\n",
       "[[8,7]]: 8\n",
-      "Total number of codes: n = 8 : 28641\n",
+      "[[8,8]]: 1\n",
+      "Total number of codes: n = 8 : 28642\n",
       "[[9,0]]: 675\n",
       "[[9,1]]: 20128\n",
       "[[9,2]]: 181039\n",
@@ -111,23 +460,90 @@
       "[[9,6]]: 1344\n",
       "[[9,7]]: 99\n",
       "[[9,8]]: 9\n",
-      "Total number of codes: n = 9 : 721966\n"
+      "[[9,9]]: 1\n",
+      "Total number of codes: n = 9 : 721967\n"
      ]
     }
    ],
    "source": [
-    "for n in range(10):\n",
+    "all_codes_n_k = np.zeros((10,10), dtype=int)\n",
+    "for n in range(1,10):\n",
     "    total = 0\n",
     "    num = 0\n",
-    "    for k in range(n):\n",
-    "        codes = cb.all_small_codes(n, k, info_only=True, list_only=True)\n",
+    "    for k in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k,  info_only=True, list_only=True)\n",
     "        try:\n",
     "            num = len(codes)\n",
+    "            all_codes_n_k[n][k] = num\n",
     "            print(f\"[[{n},{k}]]: {num}\")\n",
     "        except TypeError:\n",
     "            pass\n",
     "        total = total + num\n",
-    "    print(f\"Total number of codes: n = {n} : {total}\")"
+    "    print(f\"Total number of codes: n = {n} : {total}\")\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "42611ae9-5057-428e-8b47-f49bfac00534",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0&0\\\\2&2&1&0&0&0&0&0&0&0\\\\3&5&3&1&0&0&0&0&0&0\\\\6&13&11&4&1&0&0&0&0&0\\\\11&36&40&19&5&1&0&0&0&0\\\\26&115&185&109&32&6&1&0&0&0\\\\59&448&1075&852&267&48&7&1&0&0\\\\182&2371&10010&11422&3963&614&71&8&1&0\\\\675&20128&181039&353569&146658&18445&1344&99&9&1\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(all_codes_n_k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d99dd84-8c00-45f9-8b62-8d89ff859429",
+   "metadata": {},
+   "source": [
+    "## Paper Table: Number of codes for each n and k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "e63a3bcd-e774-4fca-ad5d-c4d68ed0fd31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$n\\backslash k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      "1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "2 & 2 & 2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "3 & 3 & 5 & 3 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "4 & 6 & 13 & 11 & 4 & 1 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "5 & 11 & 36 & 40 & 19 & 5 & 1 & 0 & 0 & 0 & 0 \\\\ \n",
+      "6 & 26 & 115 & 185 & 109 & 32 & 6 & 1 & 0 & 0 & 0 \\\\ \n",
+      "7 & 59 & 448 & 1075 & 852 & 267 & 48 & 7 & 1 & 0 & 0 \\\\ \n",
+      "8 & 182 & 2371 & 10010 & 11422 & 3963 & 614 & 71 & 8 & 1 & 0 \\\\ \n",
+      "9 & 675 & 20128 & 181039 & 353569 & 146658 & 18445 & 1344 & 99 & 9 & 1 \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "n_max = 9\n",
+    "val = \"$n\\\\backslash k$ & \" + \" & \".join(str(i) for i in range(0,n_max + 1)) + \" \\\\\\ \"\n",
+    "print(f\"{val}\\n\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\"{n} & \" + \" & \".join(str(all_codes_n_k[n][k]) for k in range(n_max + 1)) + \" \\\\\\ \")"
    ]
   },
   {
@@ -135,12 +551,12 @@
    "id": "9fa33eb8-e204-44aa-bcee-ba4b1ff2027c",
    "metadata": {},
    "source": [
-    "## Count all codes that are not decomposbale"
+    "## Count all codes that are indecomposable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 67,
    "id": "a4826299-d4d5-483f-bf3a-c037e6a6d7f9",
    "metadata": {},
    "outputs": [
@@ -148,26 +564,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of codes: n = 0 : 0\n",
       "[[1,0]]: 1\n",
-      "Total number of codes: n = 1 : 1\n",
+      "[[1,1]]: 1\n",
+      "Total number of codes: n = 1 : 2\n",
       "[[2,0]]: 1\n",
       "[[2,1]]: 1\n",
+      "[[2,2]]: 0\n",
       "Total number of codes: n = 2 : 2\n",
       "[[3,0]]: 1\n",
       "[[3,1]]: 2\n",
       "[[3,2]]: 1\n",
+      "[[3,3]]: 0\n",
       "Total number of codes: n = 3 : 4\n",
       "[[4,0]]: 2\n",
       "[[4,1]]: 6\n",
       "[[4,2]]: 4\n",
       "[[4,3]]: 1\n",
+      "[[4,4]]: 0\n",
       "Total number of codes: n = 4 : 13\n",
       "[[5,0]]: 4\n",
       "[[5,1]]: 17\n",
       "[[5,2]]: 18\n",
       "[[5,3]]: 6\n",
       "[[5,4]]: 1\n",
+      "[[5,5]]: 0\n",
       "Total number of codes: n = 5 : 46\n",
       "[[6,0]]: 11\n",
       "[[6,1]]: 63\n",
@@ -175,6 +595,7 @@
       "[[6,3]]: 53\n",
       "[[6,4]]: 10\n",
       "[[6,5]]: 1\n",
+      "[[6,6]]: 0\n",
       "Total number of codes: n = 6 : 245\n",
       "[[7,0]]: 26\n",
       "[[7,1]]: 284\n",
@@ -183,6 +604,7 @@
       "[[7,4]]: 131\n",
       "[[7,5]]: 13\n",
       "[[7,6]]: 1\n",
+      "[[7,7]]: 0\n",
       "Total number of codes: n = 7 : 1765\n",
       "[[8,0]]: 101\n",
       "[[8,1]]: 1767\n",
@@ -192,6 +614,7 @@
       "[[8,5]]: 306\n",
       "[[8,6]]: 19\n",
       "[[8,7]]: 1\n",
+      "[[8,8]]: 0\n",
       "Total number of codes: n = 8 : 22773\n",
       "[[9,0]]: 440\n",
       "[[9,1]]: 17143\n",
@@ -202,23 +625,90 @@
       "[[9,6]]: 668\n",
       "[[9,7]]: 24\n",
       "[[9,8]]: 1\n",
+      "[[9,9]]: 0\n",
       "Total number of codes: n = 9 : 662054\n"
      ]
     }
    ],
    "source": [
-    "for n in range(10):\n",
+    "all_codes_n_k_indec = np.zeros((10,10), dtype=int)\n",
+    "for n in range(1,10):\n",
     "    total = 0\n",
     "    num = 0\n",
-    "    for k in range(n):\n",
+    "    for k in range(n+1):\n",
     "        codes = cb.all_small_codes(n, k, is_decomposable=False, info_only=True, list_only=True)\n",
     "        try:\n",
     "            num = len(codes)\n",
+    "            all_codes_n_k_indec[n][k] = num\n",
     "            print(f\"[[{n},{k}]]: {num}\")\n",
     "        except TypeError:\n",
     "            pass\n",
     "        total = total + num\n",
-    "    print(f\"Total number of codes: n = {n} : {total}\")"
+    "    print(f\"Total number of codes: n = {n} : {total}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "37d82ce7-c027-473b-9ed9-b8294d64aa09",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0&0\\\\1&2&1&0&0&0&0&0&0&0\\\\2&6&4&1&0&0&0&0&0&0\\\\4&17&18&6&1&0&0&0&0&0\\\\11&63&107&53&10&1&0&0&0&0\\\\26&284&754&556&131&13&1&0&0&0\\\\101&1767&8328&9417&2834&306&19&1&0&0\\\\440&17143&167595&331296&131035&13852&668&24&1&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(all_codes_n_k_indec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ab4cfd3-4279-4992-983d-a8dd22347f13",
+   "metadata": {},
+   "source": [
+    "## Paper Table: Number of codes for each n and k that are indecomposable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "af47a6db-c60d-44f4-be1b-0dc905e895a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$n\\backslash k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      "1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "2 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "3 & 1 & 2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "4 & 2 & 6 & 4 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "5 & 4 & 17 & 18 & 6 & 1 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "6 & 11 & 63 & 107 & 53 & 10 & 1 & 0 & 0 & 0 & 0 \\\\ \n",
+      "7 & 26 & 284 & 754 & 556 & 131 & 13 & 1 & 0 & 0 & 0 \\\\ \n",
+      "8 & 101 & 1767 & 8328 & 9417 & 2834 & 306 & 19 & 1 & 0 & 0 \\\\ \n",
+      "9 & 440 & 17143 & 167595 & 331296 & 131035 & 13852 & 668 & 24 & 1 & 0 \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "n_max = 9\n",
+    "val = \"$n\\\\backslash k$ & \" + \" & \".join(str(i) for i in range(0,n_max + 1)) + \" \\\\\\ \"\n",
+    "print(f\"{val}\\n\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\"{n} & \" + \" & \".join(str(all_codes_n_k_indec[n][k]) for k in range(n_max + 1)) + \" \\\\\\ \")"
    ]
   },
   {
@@ -226,47 +716,52 @@
    "id": "a54282b0-7026-4287-b714-b63a5ac47f5f",
    "metadata": {},
    "source": [
-    "## Count all codes that are decomposbale"
+    "## Count all codes that are decomposable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 300,
-   "id": "e8b9eabf-8e4e-4b22-a3d9-da4c9770218d",
+   "execution_count": 70,
+   "id": "83673f55-3ab9-4c73-8ad7-b8537e0b926e",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of codes: n = 0 : 0\n",
       "[[1,0]]: 0\n",
+      "[[1,1]]: 0\n",
       "Total number of codes: n = 1 : 0\n",
       "[[2,0]]: 1\n",
       "[[2,1]]: 1\n",
-      "Total number of codes: n = 2 : 2\n",
+      "[[2,2]]: 1\n",
+      "Total number of codes: n = 2 : 3\n",
       "[[3,0]]: 2\n",
       "[[3,1]]: 3\n",
       "[[3,2]]: 2\n",
-      "Total number of codes: n = 3 : 7\n",
+      "[[3,3]]: 1\n",
+      "Total number of codes: n = 3 : 8\n",
       "[[4,0]]: 4\n",
       "[[4,1]]: 7\n",
       "[[4,2]]: 7\n",
       "[[4,3]]: 3\n",
-      "Total number of codes: n = 4 : 21\n",
+      "[[4,4]]: 1\n",
+      "Total number of codes: n = 4 : 22\n",
       "[[5,0]]: 7\n",
       "[[5,1]]: 19\n",
       "[[5,2]]: 22\n",
       "[[5,3]]: 13\n",
       "[[5,4]]: 4\n",
-      "Total number of codes: n = 5 : 65\n",
+      "[[5,5]]: 1\n",
+      "Total number of codes: n = 5 : 66\n",
       "[[6,0]]: 15\n",
       "[[6,1]]: 52\n",
       "[[6,2]]: 78\n",
       "[[6,3]]: 56\n",
       "[[6,4]]: 22\n",
       "[[6,5]]: 5\n",
-      "Total number of codes: n = 6 : 228\n",
+      "[[6,6]]: 1\n",
+      "Total number of codes: n = 6 : 229\n",
       "[[7,0]]: 33\n",
       "[[7,1]]: 164\n",
       "[[7,2]]: 321\n",
@@ -274,7 +769,8 @@
       "[[7,4]]: 136\n",
       "[[7,5]]: 35\n",
       "[[7,6]]: 6\n",
-      "Total number of codes: n = 7 : 991\n",
+      "[[7,7]]: 1\n",
+      "Total number of codes: n = 7 : 992\n",
       "[[8,0]]: 81\n",
       "[[8,1]]: 604\n",
       "[[8,2]]: 1682\n",
@@ -283,7 +779,8 @@
       "[[8,5]]: 308\n",
       "[[8,6]]: 52\n",
       "[[8,7]]: 7\n",
-      "Total number of codes: n = 8 : 5868\n",
+      "[[8,8]]: 1\n",
+      "Total number of codes: n = 8 : 5869\n",
       "[[9,0]]: 235\n",
       "[[9,1]]: 2985\n",
       "[[9,2]]: 13444\n",
@@ -293,18 +790,21 @@
       "[[9,6]]: 676\n",
       "[[9,7]]: 75\n",
       "[[9,8]]: 8\n",
-      "Total number of codes: n = 9 : 59912\n"
+      "[[9,9]]: 1\n",
+      "Total number of codes: n = 9 : 59913\n"
      ]
     }
    ],
    "source": [
-    "for n in range(10):\n",
+    "all_codes_n_k_dec = np.zeros((10,10), dtype=int)\n",
+    "for n in range(1,10):\n",
     "    total = 0\n",
     "    num = 0\n",
-    "    for k in range(n):\n",
+    "    for k in range(n+1):\n",
     "        codes = cb.all_small_codes(n, k, is_decomposable=True, info_only=True, list_only=True)\n",
     "        try:\n",
     "            num = len(codes)\n",
+    "            all_codes_n_k_dec[n][k] = num\n",
     "            print(f\"[[{n},{k}]]: {num}\")\n",
     "        except TypeError:\n",
     "            pass\n",
@@ -312,95 +812,177 @@
     "    print(f\"Total number of codes: n = {n} : {total}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "04b4ed48-fe11-4c1a-9c4f-c5d6b94c9fdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\1&1&1&0&0&0&0&0&0&0\\\\2&3&2&1&0&0&0&0&0&0\\\\4&7&7&3&1&0&0&0&0&0\\\\7&19&22&13&4&1&0&0&0&0\\\\15&52&78&56&22&5&1&0&0&0\\\\33&164&321&296&136&35&6&1&0&0\\\\81&604&1682&2005&1129&308&52&7&1&0\\\\235&2985&13444&22273&15623&4593&676&75&8&1\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(all_codes_n_k_dec)"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "ddb13dff-7cc7-4e77-9ee2-b5a717a3d9a1",
+   "id": "0a99ac1b-da94-4e5f-b129-e45b9b57e1ba",
    "metadata": {},
    "source": [
-    "# Indecomposable codes : maximum distance"
+    "## Paper Table: Number of codes for each n and k that are decomposable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 315,
-   "id": "389e0db4-f46e-4a3c-b854-ea48b51051d2",
+   "execution_count": 72,
+   "id": "ab3c95d1-36e0-43af-8a1e-83c9532b7c18",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[2,1]] : 1\n",
-      "[[3,1]] : 1\n",
-      "[[3,2]] : 1\n",
-      "[[4,1]] : 2\n",
-      "[[4,2]] : 2\n",
-      "[[4,3]] : 1\n",
-      "[[5,1]] : 3\n",
-      "[[5,2]] : 2\n",
-      "[[5,3]] : 1\n",
-      "[[5,4]] : 1\n",
-      "[[6,1]] : 3\n",
-      "[[6,2]] : 2\n",
-      "[[6,3]] : 2\n",
-      "[[6,4]] : 2\n",
-      "[[6,5]] : 1\n",
-      "[[7,1]] : 3\n",
-      "[[7,2]] : 2\n",
-      "[[7,3]] : 2\n",
-      "[[7,4]] : 2\n",
-      "[[7,5]] : 1\n",
-      "[[7,6]] : 1\n",
-      "[[8,1]] : 3\n",
-      "[[8,2]] : 3\n",
-      "[[8,3]] : 3\n",
-      "[[8,4]] : 2\n",
-      "[[8,5]] : 2\n",
-      "[[8,6]] : 2\n",
-      "[[8,7]] : 1\n",
-      "[[9,1]] : 3\n",
-      "[[9,2]] : 3\n",
-      "[[9,3]] : 3\n",
-      "[[9,4]] : 2\n",
-      "[[9,5]] : 2\n",
-      "[[9,6]] : 2\n",
-      "[[9,7]] : 1\n",
-      "[[9,8]] : 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "mat = np.zeros((9, 9), dtype=int)\n",
-    "n_set = []\n",
-    "k_set = []\n",
-    "d_set = []\n",
+      "$n\\backslash k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      "1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "2 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "3 & 2 & 3 & 2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "4 & 4 & 7 & 7 & 3 & 1 & 0 & 0 & 0 & 0 & 0 \\\\ \n",
+      "5 & 7 & 19 & 22 & 13 & 4 & 1 & 0 & 0 & 0 & 0 \\\\ \n",
+      "6 & 15 & 52 & 78 & 56 & 22 & 5 & 1 & 0 & 0 & 0 \\\\ \n",
+      "7 & 33 & 164 & 321 & 296 & 136 & 35 & 6 & 1 & 0 & 0 \\\\ \n",
+      "8 & 81 & 604 & 1682 & 2005 & 1129 & 308 & 52 & 7 & 1 & 0 \\\\ \n",
+      "9 & 235 & 2985 & 13444 & 22273 & 15623 & 4593 & 676 & 75 & 8 & 1 \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "n_max = 9\n",
+    "val = \"$n\\\\backslash k$ & \" + \" & \".join(str(i) for i in range(0,n_max + 1)) + \" \\\\\\ \"\n",
+    "print(f\"{val}\\n\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\"{n} & \" + \" & \".join(str(all_codes_n_k_dec[n][k]) for k in range(n_max + 1)) + \" \\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "378a1198-ecd4-4b78-92b6-c7d8fad1ec88",
+   "metadata": {},
+   "source": [
+    "# Full Tables of codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 422,
+   "id": "c368291b-dd12-4f00-a806-8e010216dd31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def zero_dash(num):\n",
+    "    if num == 0:\n",
+    "        return '-'\n",
+    "    return str(num)\n",
+    "\n",
+    "def insert_pipe(string, offsets):\n",
+    "    result = []\n",
+    "    index = 0\n",
+    "    for num in offsets:\n",
+    "        result.append(string[index:index+num])\n",
+    "        index += num\n",
+    "        if index < len(string):\n",
+    "            result.append('|')\n",
+    "    return ''.join(result)\n",
+    "\n",
+    "def adjust_table(data, coln_adjust):\n",
+    "    table_data = []\n",
+    "    for k in range(10):\n",
+    "        table_data += [data[1:,k,1:coln_adjust[k]]]\n",
+    "    full_data = np.concatenate(table_data, axis=1)\n",
+    "    return full_data\n",
+    "\n",
+    "def make_full_table(data, coln_adjust, comb_var:str):\n",
+    "    full_data = adjust_table(data, coln_adjust)\n",
+    "\n",
+    "    offset = [0] + coln_adjust[1:]\n",
+    "    d_header = [comb_var]\n",
+    "    for k in range(10):\n",
+    "        d_header += [str(d) for d in range(1,4+offset[k]+1)]\n",
+    "\n",
+    "    offsets = [1]+[4+item for item in offset]\n",
+    "    tab_string = insert_pipe('l'*(full_data.shape[1]+1), offsets)\n",
+    "    k_column = \" & \".join([\"\\\\multicolumn{\" + str(4+offset[k]) + \"}{c|}{\" + str(k) + \"}\" for k in range(9)])\n",
+    "    k_column +=  \" & \\\\multicolumn{\" + str(4+offset[9]) + \"}{c}{\" + str(9) + \"}\"\n",
+    "    k_column = \"$k$ & \" + k_column + \" \\\\\\ \"\n",
+    "\n",
+    "    out_string = \"\\\\begin{tabular}{\" + tab_string + \"}\" + \"\\n\" \\\n",
+    "        + \"\\\\toprule\" + \"\\n\" \\\n",
+    "        + k_column + \"\\n\" \\\n",
+    "        + \" & \".join([str(i) for i in d_header]) + \" \\\\\\ \" + \"\\n\" \\\n",
+    "        + \"\\\\midrule\" + \"\\n\"\n",
+    "    for i in range(9):\n",
+    "        out_string += str(i+1) + \" & \" + \" & \".join([zero_dash(item) for item in full_data[i]]) + \" \\\\\\ \" + \"\\n\"\n",
+    "    out_string += \"\\\\bottomrule\" + \"\\n\" + \"\\\\end{tabular}\"\n",
+    "    \n",
+    "    return out_string\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c801cc1c-d606-4a4b-9890-45f5c13c06c9",
+   "metadata": {},
+   "source": [
+    "### All Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "9fbb1767-72f6-47c5-ade0-67f4e9b1da4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_all = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
     "for n in range(10):\n",
-    "    for k in range(1, 10):\n",
-    "        codes = cb.all_small_codes(n, k, is_decomposable=False, info_only=True, list_only=True)\n",
-    "        if len(codes) > 0:\n",
-    "            d_max = -1\n",
-    "            code_index = -1\n",
-    "            for code in codes:\n",
-    "                if code[\"d\"] > d_max:\n",
-    "                    d_max = code[\"d\"]\n",
-    "                    code_index = code[\"index\"]\n",
-    "            n_set += [n]\n",
-    "            k_set += [k]\n",
-    "            d_set += [d_max]\n",
-    "            mat[n - 1, k - 1] = d_max\n",
-    "            print(f\"[[{n},{k}]] : {d_max}\")"
+    "    for k in range(10):\n",
+    "        for d in range(5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, info_only=True, list_only=True)\n",
+    "            results_all[n][k][d] = len(codes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 321,
-   "id": "67380352-b3a7-4fd7-aa70-57f9fefcac96",
+   "execution_count": 419,
+   "id": "dfbf182d-e541-44bf-9b81-87658eb64a57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coln_adjust = [None, -1, -1, -1, -2, -2, -2, -3, -3, -3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 423,
+   "id": "4441a417-c253-4d6b-bdf3-0c2a87dc4523",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0\\\\2&2&1&0&0&0&0&0&0\\\\3&2&1&1&0&0&0&0&0\\\\3&2&2&2&1&0&0&0&0\\\\3&2&2&2&1&1&0&0&0\\\\3&3&3&2&2&2&1&0&0\\\\3&3&3&2&2&2&1&1&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}1&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&2&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\2&1&0&0&5&0&0&3&0&0&1&0&0&0&0&0&0&0&0&0&0&0\\\\3&3&0&0&11&2&0&10&1&0&4&0&0&1&0&0&0&0&0&0&0&0\\\\6&4&1&0&29&6&1&37&3&0&19&0&0&5&0&1&0&0&0&0&0&0\\\\11&13&1&1&78&35&2&156&29&0&104&5&0&31&1&6&0&1&0&0&0&0\\\\26&29&4&0&260&169&19&834&241&0&785&67&0&260&7&48&0&7&0&1&0&0\\\\59&107&11&5&1023&1170&178&6266&3724&20&9304&2117&1&3699&264&603&11&70&1&8&1&0\\\\182&416&69&8&5777&10742&3609&78567&98027&4445&222749&130598&222&122541&24117&17677&768&1331&13&99&9&1\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -411,122 +993,76 @@
     }
    ],
    "source": [
-    "lprint(mat)"
+    "lprint(adjust_table(results_all, coln_adjust))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
-   "id": "5b6964bf-789d-4ee1-9eaf-dea9017a06ab",
+   "execution_count": 424,
+   "id": "f9863635-33df-439c-a890-79a15efd7342",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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irbfeivvbRH9vKjr0+9H9Jrtment74XA48PTTT8ec39deey2A5NdlIvI66iyb0SuiKAIAfv7zn8c8pVKmTJmStf1Fk+i7JFpOclhh++STT0ZjYyPefPNNvP3223j11Vfxpz/9CXfddRfuvfdeAMBNN92E559/HqtXr8aCBQtgs9nAMAyuvPJK6VgOF1EUMXPmTDzyyCNxP4++MeQDoiiCYRj861//ivvbyR94fve732HlypV444038M477+Dmm2/GmjVr8Omnn6K6ujprfRrOuZXu77xp0yYpUOPrr79WWCm56iPDMHGvh+igoGzsK9t8//vfx5IlS/Daa6/hnXfewcMPP4wHH3wQGzZswAUXXJB027/+9a9YuXIlLrvsMvziF79AWVkZOI7DmjVrpActOdn4fvQ3//GPf4wVK1bEXWfWrFkpt0fJa6FJhZqaGrz//vvweDyKi7yxsVGx3qRJkwBEhrfOPffcpG1OnjwZe/bsGXK/u3fvhiiKCqumoaFB+jybUItM/uR64MABAJCiVGpqamK+d6I+mc1m/OAHP8APfvADhEIhXH755fjVr36F2267DQaDAa+88gpWrFiB3/3ud9I2gUAgYTRfU1MTzjrrLOm9x+NBZ2cnLrzwwoTfafLkydi1axfOOeectGffT548GQCwZ8+ehL8n/b6JjklJSQnMZjMMBgOMRiOamppi1ovedvLkySCEoLa2VrK8kjFz5kzMnDkTd9xxBz755BMsWrQITz75JB544IEhtx0J0vmdOzs7cdNNN2HZsmXQ6XTSQ1u2z/VoCgsL41oB0aMGuYB+t1TOjURUVlbiZz/7GX72s5+hp6cHp556Kn71q19JQpPo3H/llVcwadIkbNiwQbEOHYFJF3oPTHZvKy0thdVqhSAIQ94n0yGvh85S4cILLwTP81i7dq20TBAEPPHEE4r1ysrKsHTpUjz11FPo7OyMaae3t1f6+7vf/S527doVN4SRPh1ceOGF6Orqwt///nfpM57n8cQTT8BiseDMM88c9neT09HRoeiPy+XCiy++iDlz5qCiokLq02effYZt27ZJ63m9Xjz99NOYOHGiNKbc39+vaFun02HatGkghCAcDgOIPB1FPwk98cQTCZ8in376aWlbAFi7di14nk/61Pb9738fR48exZ///OeYz/x+vxQyG49TTz0VtbW1ePTRR2NuirTflZWVmDNnDl544QXFOnv27ME777wjiSDHcVi+fDlef/11tLe3S+vt378fGzduVLR9+eWXg+M43HvvvTHHhxAiHVuXywWe5xWfz5w5EyzLDhm6nU5483BJ53f+yU9+AlEU8eyzz+Lpp5+GRqPB9ddfn1OLAIiIe0NDg+Ia3bVrV8xwby6Qn0Py4ap3331X8nkmQhCEmCGusrIyVFVVKc4Bs9kcdyiMWijy47t9+3bF9Z0OpaWlOOOMM/Dcc88pznP5PjiOw3e/+128+uqrcQVJ/hukw5i3aC655BIsWrQIv/zlL9Ha2irNL4n3w/3xj3/E4sWLMXPmTPzkJz/BpEmT0N3djW3btuHIkSPYtWsXAOAXv/gFXnnlFVxxxRW47rrrMHfuXAwMDOB///d/8eSTT2L27Nm44YYb8NRTT2HlypXYsWMHJk6ciFdeeQUff/wxHn30UVit1qx+z5NOOgnXX389Pv/8c5SXl+O5555Dd3c3nn/+eWmdX/7yl/jb3/6GCy64ADfffDOKiorwwgsvoKWlBa+++qpkeS1btgwVFRVYtGgRysvLsX//fvzhD3/ARRddJPX74osvxl/+8hfYbDZMmzYN27Ztw3vvvYfi4uK4/QuFQjjnnHPw/e9/H42NjfjTn/6ExYsX49vf/nbC73T11VfjH//4B/7jP/4DH374IRYtWgRBENDQ0IB//OMf2LhxI+bNmxd3W5ZlsXbtWlxyySWYM2cOrr32WlRWVqKhoQF79+6VBOLhhx/GBRdcgAULFuD666+H3+/HE088AZvNpnC233vvvXj77bexZMkS/OxnP5MeGqZPn47du3dL602ePBkPPPAAbrvtNrS2tuKyyy6D1WpFS0sLXnvtNdxwww34+c9/jg8++AA33ngjrrjiCpx00kngeR5/+ctfpAs5GZ999hnOOuss3H333TnPm5Xq7/z888/jrbfewrp166RhvyeeeAI//vGPsXbtWvzsZz/LWR+vu+46PPLII1i+fDmuv/569PT04Mknn8T06dPhcrlytl/KmjVrcNFFF2Hx4sW47rrrMDAwIJ0bHo8n4XZutxvV1dX43ve+h9mzZ8NiseC9997D559/rrAg586di7///e+45ZZbcNppp8FiseCSSy7BxRdfjA0bNuA73/kOLrroIrS0tODJJ5/EtGnTku43GY8//jgWL16MU089FTfccANqa2vR2tqKt956Czt37gQA/OY3v8GHH36I+fPn4yc/+QmmTZuGgYEBfPnll3jvvfdSnnukIO04tRyQKLzZbDbHrHv33XfHzGvo7+8nV199NSkoKCA2m41cffXVUnipPJSTEEKam5vJNddcQyoqKohWqyXjxo0jF198MXnllVdi2rzxxhvJuHHjiE6nI9XV1WTFihWKkL/u7m5y7bXXkpKSEqLT6cjMmTNj9kdDDx9++GHFchrCGh3+Gu9Y1NTUkIsuuohs3LiRzJo1i+j1ejJ16tS4obPNzc3ke9/7HrHb7cRgMJDTTz+dvPnmm4p1nnrqKXLGGWeQ4uJiotfryeTJk8kvfvEL4nQ6pXUGBwel72axWMjy5ctJQ0MDqampIStWrIjp7+bNm8kNN9xACgsLicViIT/60Y8UIcWExA9TDYVC5MEHHyTTp08ner2eFBYWkrlz55J7771X0Z9EbN26lZx33nnEarUSs9lMZs2aRZ544gnFOu+99x5ZtGgRMRqNpKCggFxyySVk3759MW1t3ryZzJ07l+h0OjJp0iTy5JNPxj3fCCHk1VdfJYsXLyZms5mYzWYydepUsmrVKtLY2EgIicw7ue6668jkyZOJwWAgRUVF5KyzziLvvffekN8p3fDmeKHq9JyJBoAi3DaV3/nw4cPEZrORSy65JKa973znO8RsNpNDhw4RQhKHN6fSF0ISXy9//etfyaRJk4hOpyNz5swhGzduTBjePJxrLRGvvvoqOfnkk4leryfTpk0jGzZsiNk//U70dwsGg+QXv/gFmT17tnR+zp49WzEPkBBCPB4P+eEPf0jsdrsiZFoURfLrX/+a1NTUEL1eT0455RTy5ptvpvy9o/tD2bNnD/nOd74j3SPq6+vJnXfeqVinu7ubrFq1iowfP55otVpSUVFBzjnnHPL0008PeaziwRzrjEoeM3HiRMyYMQNvvvnmaHdFRUVFJW3GvI9GRUVFRSW/UYVGRUVFRSWnqEKjoqKiopJTVB+NioqKikpOUS0aFRUVFZWcogqNioqKikpOUYVGRUVFRSWnqEKjoqKiopJTVKFRUVFRUckpqtCoqKioqOQUVWhUVFRUVHKKKjQqKioqKjlFFRoVFRUVlZyiCo2KioqKSk5RhUZFRUVFJaeoQqOioqKiklNUoVFRUVFRySmq0KioqKio5BRVaFRUVFRUcooqNCoqKioqOUUVGhUVFRWVnKIKjYqKiopKTlGFRkVFRUUlp6hCo6KioqKSU1ShUVFRUVHJKarQqKioqKjkFFVoVFRUVFRyiio0KioqKio5RRUaFRUVFZWcogqNioqKikpOUYVGRUVFRSWnqEKjoqKiopJTVKFRUVFRUckpqtCoqKioqOQUVWhUVFRUVHKKKjQqKioqKjlFFRoVFRUVlZyiCo2KioqKSk5RhUZFRUVFJaeoQqOioqKiklNUoVFRUVFRySmq0KioqKio5BRVaFRUVFRUcooqNCoqKioqOUUVGhUVFRWVnKIKjYqKiopKTlGFRkVFRUUlp6hCo6KioqKSU1ShUVFRUVHJKarQqKioqKjkFFVoVFRUVFRyiio0KioqKio5RRUaFRUVFZWcogqNioqKikpOUYVGRUVFRSWnqEKjoqKiopJTVKFRUVFRUckpqtCoqKioqOQUVWhUVFRUVHKKKjQqKioqKjlFFRoVFRUVlZyiCo2KioqKSk5RhUZFRUVFJaeoQqOioqKiklNUoVFRUVFRySmq0KioqKio5BRVaFRUVFRUcooqNCoqKioqOUUVGhUVFRWVnKIKjYqKiopKTlGFRkVFRUUlp6hCo6KioqKSU1ShUVFRUVHJKarQqKioqKjkFFVoVFRUVFRyiio0KioqKio5RTPaHVBRUYkPIUR6AQDLRp4LGYYZzW6pqKSNKjQqKiOMXEAIIRBFUfG3/EXX1+l0YFkWDMNI/0e/AFWEVPITVWhUVLJEtIDIRYQKB/3b7/ejr68P48aNkywWSrRwyMWFEAJBEBTbMAyDUCgEn8+H4uJiAIgRI7qeispooAqNisoQ0Jt6tOURT0DkQ13ybQGlgASDQRw5cgQTJkyQPktGomEz2r7L5cKBAwewcOFCaZl8XSpWtC1VhFRGElVoVL6xxBOQeOLhcDjAsixMJpO0HbUuAMRYH6ncwOU3e0IIwuEwQqEQgsGg9JK/ZxgGJpMJJpMJZrNZ+lun08W0G/396N9yS0gVIZWRhCHRdruKyhiHntLx/B/RfhD5uokEpLGxEUajERMnTkz75isIQoxoBINBeDweOBwOGAwGBINBiKIIjuOg1+uh0+mg1+ull06ng0ajgc/ng9frhc/ng8/nQygUglarhclkAsdx8Hg8mDp1qiREWq12yGMU73jt3r0bM2bMgFarVUVIJSuoFo3KmCKeA52+D4VC4HkeGo0mRkAASCISbYEAySO65H4SCt1fPBGRL+N5HgzDxAiI0WiEx+NBfX29tIzjuIQ37mjLBQDC4bAkOj09PXC5XGhra4PP50M4HIZWq1VYP/Rvs9kMjeb4pR+9z97eXsXxSmYJRfuQVBFSiYcqNCp5QbIorGgrRL4+cPyG1tnZib6+PsyaNUtanm5IMB3GkguGx+OB1+uFy+WSloVCIQBQiIdOp4PRaITdblcIC7UM5DgcDgwMDKCoqCjjY6bVamGz2WCz2SSLZsGCBQCOi5DcAurt7ZVESKfTxRUgvV4vHS+5aMiPj/xvuSUoR24Z0u+vitA3F1VoVHJKIgFJFsob7UCP/juRgNCnao7j4vaDDmMl8oPQ94QQaDQaSUAEQYBOp0NxcbFCVGjIcT4iF6FoaIQaFSKPx4Oenh54vV7wPA8A2LFjBywWS4xPKJklFC1CR44cQVdXF+bNm6dYT7WEvnmoQqOSEZnMBaH/t7W1gWEYTJgwIeYGI79xp+MHoTfPYDCI9vb2uAIiCAJYlo3xf9hstphlcrHat28fjEYjqqurs3X4RhUqkna7XbGcEAK/348tW7Zg3LhxCAaDcLvd6O7uhs/nA8/z0Ov1MeJD/5Yfs2gxSccSUkXoxEMVGhUF6cwFSTWUN/pmQbdL5qwGIn4Q+TBWIkuE+kE4jgMhBE6nE3q9HmazGUVFRQoRSeYHScQ35ebGMIzkC6qsrFT8PnRIkQ7F0aHErq4ueL1eCIIgHXMqQH6/H4IgQBCEGBGSk4oI+f1++Hw+lJaWxogQbfOb8juNRVSh+YaQ7lyQUCiEtrY2TJkyJe5Q1nBmpdP9uN3upCG9cj9ItCM92grRarXo7OxEd3c3Zs6cmc1DJ/U538nljZaKkE6nQ2FhoeIzer7IfUIulwsOhwOBQADvvvsuDAZDXJ+Q0WhMSYRcLhdaW1tRXFycliUk/0xl9FCFZoyT6lyQaAtkqLkgoiiis7MT9fX1aQlItB8knpAEAgEAQE9PT0w0lsViUSzLZz/ISBLv5hr9ebb2A6QnWjSqTq/XK0Sovb0dPT09mDFjhsIn5HA40NHRAZ/PB0EQYDAY4g7FmUymmKHUZPOE0hmOi16ukltUocljhorCCofDcDgc0lh79IUWL5Q3VQuEjqvTdagfJJmABIPBGD8IFQxqgeh0OnR1dYFhGEydOjXh/kUigAGr3gTGOAzDwGAwwGAwxETYEUIQDAYVlpDD4cDRo0fh8/kgiiKMRqM0UTYQCKCnpyehCEW3Lf87+to4ePAgKisrYbFYYsLX410rKsNDFZpRYKgorOi5C4nmggQCAezatQtnnHEGWJbNKLtvIj+I3+8HAGzfvl3hB4meTBjtB6GTC5P1oau/HT4M4nDAC58wcPwlHv87ILpQyd2J8yvmpPxd5Baayugz1G+RighRAeru7oYgCDhw4AB8Ph8IIQktIaPROKQIdXZ2SsNwtJ+CIMT0UZ7IlLalilD6qEKTRYYTyhvP3Kf/JxIQ6qyN5+CmzttkPhC5H0Sr1SoEhM6nmDRpEgwGQ8L5IHLCYgA+oQe+0CB8Qj98wgC8UQLiFwYhGMORDfoTH0sWGvyr52ucVzYLnDp0NmbJ9GYsF6Hi4mKwLAtBEDB//nwQQhAIBBSW0MDAAA4fPgyfzwcAkiUU7RMyGAyStS5/OKNEW0L0WqUiJLeKVBFKHVVoUiATAaH/Hz58GIIgoLa2FkBqAjIUPM9LaUwAoK2tLe4sdTofJJ4VEj3RMPqCCwaDOHz4MEpKSkAgwi8OwhEeOCYgx4WEY3ToDu2DVxhAmPgyP8hRaBgD/EIIz7Vvxk8mnpW1dlWSk88WofzaMRqNMBqNUrZqCg3RlvuE+vr6pPdARIRosIvL5VJYQvJrMd7Dm3xYWi5CX3zxBaZPnw6j0QggNl3PN12EvtFCM5y5IHKGCuUVBEEx0S0RoigmtDzi+UFoKKrb7YbBYIDNZosRlXiTF+UEBBccQhd8oeNWh1foh4fvR29NO17q+gsCogsEYtztS7RT4OCPDPnd0oVj9AiKPP6360v8W83Sb+TFOZpk43gPFcCQblupwDDHk49GI4qiZAnt3LkTANDX14e2tjZpqJhuGx2UMJQIDQ4OKgJs6D1D3vdoS+ibJEInpNCkMxfE7XbD6/WirKxM2paSTEBSPSFoBFcqM9LD4bDCD0L/p34QuYhoNBrwPI+PPvoI06ZNixEUngThFXrgDfbDL3jgFboUfhCvMAC/MAAB4cSdNwAJ9EXCzXendBzShYUeHYFBAATr2rfg2pozh9xmLF2k2bYc8tkSyRbD/X1pBm4aSFBbWytlTqAiJJ8n1NPTA5/PB7/fL1lR8XxCBoNBGopLJBbyh9ToB1f5d4sOzz5RROiEE5qf/exnWLJkCS644IKYiy9eKK/X60VXVxcqKysVn6UCIUQaxkokIjSEs6urS+EHoaJRUFAQk9YkWR9EIsAvOuAID8AV6oHLvhtfuo/ATxwKx3qIeKVt7NwpGOR3gmGyfzMKEjdMbDF8YhKHSwYwjA4iCEp1VrzS8TlWTjgjpd/mm3DDTcRYvhENRa5/V7kIRUML1cl9QjRlj9/vl4ad9+7dC6vVGpM7bqgH1OigH0EQ0NjYCIPBIGXPoNuOVRE64YTm4MGDqKurS9kCiQ7jpfA8n3T4iv4tT+8uF4zCwkLo9XoMDAwgHA5j+vTpQ84HCYpuOPiuiANdEYkV8Yt4hX4ERKdyGKscGPAmbhMAwqQXAX4+jNpPhz6AGWDmSrIuNCCRYcFSfQF6Q2789cjHuHr84uzuI0Xy/SLOJpnMo0nWVjaPXTbbSqdvLMvCbDbDbDajtLRU8ZkoivD5fNi6dSvsdjvC4TC6urokS4gKWDxLKJkI0cSn9J4hF6F0LaHotkeDE05oOI6TfBjxoMNYVDAGBgbg9/uxd+9ehYjI/SByESkoKIhxpCfzv0TmA/jB4SgYoRcM6QVDesCK9O9eiIwd7tAn6BM4DAhaDIhaDAhaOEQNCIZ/gvjEDux1TMPcEiYnVg3HZP80IsdOTXpR/f3Ip/hx9aJRu2C+yZZSPpBt0coW8oJ448ePh8FgkD6jIiS3hGjKnkAgAI7jEvqERFHMyBLKVxE6YYRGEAT09PTA7Xbj888/h9/vx7e//e0YK0TuB6HOdEIIzGazZIXI/SBJfwQigiH9xwSkRxIOuZhMLe4GV+oB/HE2BwuRnQWt8CGKOKAoym8vEGDwmOgMR4AIRNj1QQTD82HQZd+qCYjurLdJSCR0uzvoAgD4xTD+fvRTXFm9IOv7UvnmkQvhim6PZVlYLBZYLJaYdQVBgN/vV/iEaLaEQCAAhmEwODgIl8sVI0R0eD0VEaJ/C4KAzz77DJMmTUJxcfGIi9AJITTLli3D+++/D1EUodPp0NDQgLa2Npxzzjkwm82SH0TuZKcHsLe3F62trZg4cWLSfTBiFzhhGzjhczDkyDFhGQCD2EleChKMlhGYIDITwIk7E27KMUAJF0YJp3TYUwEaFLToT1GAJprC+LS/AIvKsm/VuPlOMOBAhjoWaSCSyKk5EPbAyhngFgL4f0c+wQ/GfSvhyU8jfk40RFFMOXJxuGTz+I1G1Fk67WW7b+m0x3FcUhH68ssvpQmpNGOC1+tFMBgEx3EKP5BciOS1fyj0b/qQHZ3GJ54lRB/GVaGR8dhjj8FkMqGiogI/+tGPMHPmTNxyyy0pbZvqzYmwFeDZ74DXfBsMOQJWbFa8GNI2tOgcQ0QRwFjBkYaU1gcAQgAfYRAUOYTAQiAMjIyACVoetfBBzxDoGRE+Ehl+ixYgm86FNj+PufxpMGo/S3m/qSAgDJumGs4shjnz5Lh5V2UsRKOnEz4hhFc7Psf3xp2etf2MNtG54QKBQMzf4XDkQYNGPdGXxWKR5kRlm3wcpsrHPgHZ9WsBERHiOA5WqzXmAZjneYUlRCer0hIZGo0m7kRVk8mkiIyjRPc50RSO4ZKW0KxduxZr165Fa2srAGD69Om46667cMEFFyTc5uWXX8add96J1tZW1NXV4cEHH8SFF14ofU4Iwd13340///nPcDgcWLRoEdauXYu6urqU+3XyyScf/0IaTdxUEolI+ymY4UCYGghsDQScfXw5CYMhbbECJB4Fwxx33ouoBpggWNJ2fFPoQFAEkbGiN9wOgUSWcgygZQgMjAgjI8DMEphZPmn3jBBRzIUhP3oCARxiF049yYzusB2CxpdVHxAAGNgCOLPSUgRe5IBjwm1gj6er/8vhrWNGaOQiIhcQmlolEAhg8+bNYJjjSSlpFobCwkLpbzqMKy9U1t3djUOHDiEQCEiTcsPhMA4dOiQJUXQ+sJEm21bDaAUDpNIWMDLBChqNBlarFVarNeYznudjfEIDAwPwer1SBpB9+/ahoKAgxhqiDyu5GhVIS2iqq6vxm9/8BnV1dSCE4IUXXsCll16Kr776CtOnT49Z/5NPPsFVV12FNWvW4OKLL8b69etx2WWX4csvv8SMGTMAAA899BAef/xxvPDCC6itrcWdd96J5cuXY9++fQrHWspf6Nj8klTJ2oFltCDMFAjsFIVdc/RIM/yevZg6mQXDt4EwHrDECZHxAoxwTKBCx/pCYGJt8IsO4JgECATwERZ+aQyOSH8xABgGx6Qisj4vstCxgmI5C8DM8DjZ6MA0owPssW0JAEeaQ3CJIGSICTdpEiYsqNB4hKC03CME8XrHDlxWNTdmm5F84k0mInJLJJGIWK1WHD16FKeddlpKQxRarTbuTHh6czly5Ai6u7vhcrnQ2dkJr9cLQgiMRqNk+chfubCCxhLZHiLMNpkIoUajQUFBAQoKCmI+43keW7ZsQWVlpTSto7+/Hz6fD6FQCFqtVlG6oaSkRJpbmA3SEppLLrlE8f5Xv/oV1q5di08//TSu0Dz22GM4//zz8Ytf/AIAcP/99+Pdd9/FH/7wBzz55JMghODRRx/FHXfcgUsvvRQA8OKLL6K8vByvv/46rrzyyrS/EMdxaQkNkOOIIsYAT2ACeO0cQF7ni3jAioeiht+aYWP7YRvOQyg3tDXnF1n0HhMXh6iFS+TgETUIEBYhwoIQwBnWw6YLDtnW8TYHh9HpWELi8YPQ4R+UhBEA1h3eEldoskW0iHg8HoiiCI/HExNUEi0i8gSjBoMhYX64wcFBdHZ2SjnlMoXeXAoLC+FyuTBnzhwAkPKBeTweeL1eydlMx/m1Wm1cAcr34alskutggOGQbQuOBjaVlJTEVFYNh8MxlhBN15O1/We6oSAIePnll+H1erFgQfxIoG3btsX4SpYvX47XX38dANDS0oKuri6ce+650uc2mw3z58/Htm3bMhIajUajSP8wFLl2ICdsn7FA5GZB5GYplxMH9rsegyf0EYq4MIq5MIrYMIxsat/JIXBoD1rgZwhcogYuUQMf4RAgLMKEgRCxg4boNNDuLcJMXWdqXxKAW+iBFkaE44XXZUBQPN5HvxjCOEMhjgYiYubmA3iz6ytcXHFKzHZD/ZZURBJZIdEiYjAYEA6HodVqUVxcnJKI5AN0JrvRaIyZ+0ErZdIXTc3v9R6fkLVz584YX1C6wQgnyjyaVNoC8lu4gMTfWavVwmazSVkS5Ml+s0XaQvP1119jwYIFCAQCsFgseO211zBt2rS463Z1daG8vFyxrLy8HF1dXdLndFmiddJFo9FIztNUyLtIJcaOk6y34y+dP4SI45aZiRFQzIVQxIVhhoBuUQePqIFP5BAEA56wx6ZxZufkZBhgMGhEoT5V4SAo0FaiP3woK/sPRBlmhVqzJDQA8Fzb5hihoemFBgYG4opIIBCQyh3IrRC9Xp9URBobG6HRaFBdXZ2V7zbaaLVa2O32mCdbURThcDjw2WefwW63w+v1YnBwEB6PB+FwWEqHFB2QYDAYci64+TzUlS9DZ9luM6sWVbob1NfXY+fOnXA6nXjllVewYsUKbN68OaHYjDR0wmaqjJpFkwSO1eIk07lo8L0tLfMRDj7eiMO8ESGRg5fXoVCXHeshHhUGJ7b1T8KFVXtT3kbLGLO2/+AQP6GT92Pdzo04halQiAgQEQa5aFARSbXcwTcVlmWl7MM02zglFAoprKD+/n60t7dLecDiRcOlcx2mQr5aDCMZDJBvbaZK2kKj0+kwZcoUAMDcuXPx+eef47HHHsNTTz0Vs25FRQW6u5VJF7u7u1FRUSF9TpfRXGP0PR1rThetVotgMHXfQj4KDQDMt12HA773FFYNRccK2OctRqEu+1mTKXZdAIMhEwZDBhTqAiltw5PUj/tQOHwhxfsOZ1/MnKTX/Xtx7vjpkoh4PB4cPnwYp5+e/ai0vLJ645Ct/iVqh05wlpdqBo7PfqfRcDQtv9frBc/z4DhOGv2QixFNvzLcfmVCrn7LbAtNthlTQhMNTekSjwULFuD999/H6tWrpWXvvvuu5NOpra1FRUUF3n//fUlYXC4Xtm/fjp/+9KcZ9ScTiyaXZNo+x2pRZzoHjb6NcT/38ToMhow5s2o4RgTHiPi0bxIuqNqX0jYeoSdr+/dHXWcONgg9o0GQHBdetxjEAb0TZ5dGAlECgdQEUSU56Zyz8tnv8iFwQggaGhqkzOg0GzJNREknLEYPxZnN5oQh2fls0eTC+sh2aPpoPiylJTS33XYbLrjgAkyYMAFutxvr16/Hpk2bsHFj5GZ4zTXXYNy4cVizZg0A4D//8z9x5pln4ne/+x0uuugivPTSS/jiiy/w9NNPA4j80KtXr8YDDzyAuro6Kby5qqoKl112WUZfKN2os3y1aICIVdPkew9inImgxXov9jjGYUnZweF2MS4MA1SbBtHmLYYzZIAtBasmILqgEwsQYl3D3n+Q4SCvUSCCoNpYhGafUsyebPlAEppvOvk0HMgwDDiOkzIQyxEEAT6fT7KA6Lwgr9cLQRCkMFv5i+f5rFtt+Zq1IJdtjta8qrSEpqenB9dccw06Ozths9kwa9YsbNy4Eeeddx4AoL29XfFFFi5ciPXr1+OOO+7A7bffjrq6Orz++uvSHBoAuPXWW+H1enHDDTfA4XBg8eLFePvttzOaQwOMwITNDMi0fQ2rO2bVvBPzWbHegy8GJsARMsKeI6tmgmkAbd4SbOurxflV+1PaRg87Qhi+0Hj52Cg7syb2nOgPe7C5rwFnlkwd9j5Vsk+8mzmd9R496ZCGZMt9QV1dXVJYOcMw8Pv9Mb4go9GY1g00n9PZjLU2UyUtoXn22WeTfr5p06aYZVdccQWuuOKKhNswDIP77rsP9913XzpdSYhWqz1hLBoAmG+7Hk2+92OsmkKdDywIvnZUYUlZ83C7GZciXSSWvj9kgTOU2rwak8EKd2jI1YbEzcc+LITF+L/r2pb3cGbJ1Lx6oh+rjKYvRB6SXVJSovissbERHo8HlZWV8Hq9cLlcUhJKQkiMFURFSKvVJthbdi3AfBeFdKy4Uc8MMBbIx6Gz4aBhdZhiPAsH/O8plnMMQaHOh8O+IjhDR1Ma2koXk4YqBoNP+ydheeXQVk1I9Ax7vxx0cQt7dgfjJ7npDbnxSf8BnISiMTGxL9/Jxxswy7LQ6/WoqqpSLCeESLm/6DAcnRMUCoWg0+lixIdOks1nHw2Qm+CCMWHRjAU4jktrwiaQWydZNoTsW/afoMn/YUx25GK9F/0hC752jsPi0uxbNSwDWDR+eHgj+oIWuMM6WLXJzRW30IVIeFjmKWk0TPxh04GwF4VaMwbDsZXe/tDyLh6f+IOM96mS3yS6hhiGkfJ1JZqYSn1BAwMDOHz4sDTr/dNPP40bkJDJxNRsM5oWTS444YQm3Vxn8gp2ufoRUjkReZ6Pm7mXvizWerhtyuivYr0HcJej3VsEpy03Vs1Ecz/2OKsBMNjWNwnLKpNnnOZJEAWaKrj4joz3yTKJ07KU6wviCk130IWvPIcRm+VJJR3yOYw43esz0cTUQCCATZs2YfLkyZI11N/fD6/Xi3A4LKXnj34lmpg6FvwpqtBkmXSFhpJL8zc6T1Y8QREEQVESmk4uLCgogMFgQL32Frzm/neFVVOij9xwCRjscVZhUWl2ZuXLqTC6sOfYiFVv0JqSVWNkbHBhGEKDxAkfdWziU/a5no+xGqdlvN+xTDZv6vk4dJaL71deXh5TmyUcDivyw/X19aGtrQ0+n0+qAxPtCxIEISdCk4v2Uu1ntr/PCSc0mc6jyeSHpUWD5ClO4qU9EUURO3bsSCgiqVb0nMyfgYP+D6X3Vk0AOpZHSNSgzVuMmfYOFGiza9XYFO0x+LRvEs4bwqohwyysxiSxaLxC4oCEnrAbzewA5g9r7/HJ9wmb3wRGYs6bTqdDUVERioqKFJ/RkGx5RBydF0TvN1988UXcWkGZ9DsfLJpRTUGT72i12qwIDSEEPM8ntUKiLRG5aNhsNuj1eoRCIRw6dAgLFy4c9g+30P7vaPZvBqFZzRigSOdFV8AGAgZfO7Jv1eij6t/0BK3whrUwaxPnk/MLjuHtlCSOFIrO5BzN62IjfojE9ZFOZPItaCFfh+EyrYiZKCS7p6cHe/bsQVlZWdxaQfGi4YaqFZQPQpNNTjihSSXqTC4i1DHY2tqKcDgcV0TkAiIXESouyZyHTmdk3CkbP7CG1WOy8UyFVVOs96ArEMm6GrFqjqJAm71UMAwDlOud6A7a6BJs65+EcysaE27jEXqggQE8MrOuCBILjV8MKzI5R9MPP3Y72zHLNiHu5yojyzdhGE6n04HjuJiJqbRWEB2Kc7vd6OrqUtQKkouPvFaQKjR5Dsdx8Pv9+Oyzz1BcXAyDwRDXuS63RAAgGAzCZDJJIkLFJRt12rN5Yi+w/0Rh1VA/DXDMV+MYh4VZtmrGmwdlQgN0Bwrg4zUwaeILOoGIAk0FBvjWjPYnkuTHvCgqk3M0jzS/jXWn3pDRvvMFQghCoRACgYD0ZGy1WnNeNTNfhwjzOWw9WTXMeIXIUqkVxPM8Dh06hMLCQkmA0p2YGr3P0eSEEJpXX30Va9euxZEjR9DS0oJQKIRvf/vbuOOOO7B48WLo9XoYjUbY7fYYERFFEZs2bUJdXV3Oaq9n80fWskZMMi5Bs38zAKBYp4zAaj1m1VizaNWU6t1RSyIRaOdUHEi4jY41Z7y/oYRmqKN52N+Pva4jmF6Qv2n95SUNqJjQF30wIoRAp9NJWTI8Hg8IIdJTsPyVj8EA2U55kq/zXjJJvz9UraDPP/8cWq02plZQvGi4oSamyvuoWjTDoKamBj/84Q9RXV2NL7/8Eq+88go+/PDDoTfE8IIB0mk/myy0/zsO+T8CgQijJgwTF4RPiFhmEaumCgtKW7K2v3ii1RWwJbVqBJJ5egCecEgmJ/HCm6N55OC/8OypP8m4D8OBDs1GC4hcSGgN98bGRhgMBullt9ulv/V6PTiOAwDpRuL3++HxeKRXT0+PJEAsy2Lnzp0KAcq1BTRSjGb6lKHI6oPksZBshmEwceJEySckiqJiYiqdF0RDsoeqFTTaxy8toVmzZg02bNiAhoYGGI1GLFy4EA8++CDq6+sTbrN06VJs3rw5ZvmFF16It956CwCwcuVKvPDCC4rPly9fjrfffjtmu3jMmzcP8+bNAwD09/en+nUU5FJost22ljWi1rgYh/xbAESGz9p9xyO1WrwlmGHvyJpVo2FEaMCDV5wukQi0sxNYNe5hZHIOiywQJ5EopTPggIHRIkASByS0+vvQ6O5AvbUq4TqpEn2B0ozlcusjWkgEQYBGo1GIiM1mQ3l5OfR6PYLBIJqamhJWp03UDzo5UV7PnRCCQ4cOobOzEwUFBTECFM8CGosCdKJYNJm0ybKsJCDRpFIrSK/XgxCCzs5OKRghmVtgVMObN2/ejFWrVuG0004Dz/O4/fbbsWzZMuzbty/uAQCADRs2SE9vQEQIZs+eHZP/7Pzzz8fzzz8vvc+0lnq682ioOZnLMcxctL3I/h9o8W8FgYhivQftvuPhmHRezYKS7Fg1DBPx07R4lWZ+Z8AGP6+BMY5VExCdMLJ2+EVH2vvjSXKhEUEwzlgYk8k5mt8e/Cf+fMq/pb9/mTUSDAbhcDggCAJcLpe0jDqB5UJitVoV1kiyC3lwcDBrFzOtGKrX6zFp0iRpOU3PksgCiidA2ZwTMtpP0YnIZ38PJZ0+plIraGBgAP39/WhtbZVqBcknpsoDEoYahsuEtIQm2sJYt24dysrKsGPHDpxxxhlxt4mOR3/ppZdgMplihEav10uF0IYDy7IZVfYbS0NnALVqFuKQfyuK9bFDSS2eYsywZc+qGWd0xghNJAdaLc4qb4q7jYUry0hoQgIHIHk5botm6AeRQ75eNHu7MdmsrJMid7LHs0howS4qGKIoQqvVYty4cZKQ6HS6vLcIkllAyQSIEJJ3Q3DZFq2xZtFkgrxWkNFoREdHBxYsWCBdA/JghOhaQQsWLMj4YT8ew/LR0NDdaDFJxrPPPosrr7wyxgLatGkTysrKUFhYiLPPPhsPPPAAiouL0+5TumUCgNwm1sxl2wvtP8Uh38co1nnBgIDg+IlJwGKvsxLfKmnNyr6K9PGTZXb47QjwGhjiWDUsk9mTUSCFNGkhMbXf+IE9G/AL65kJnexUTAoLCxXWiXzybFNTREiz8SCUDyQToN7eXuzatQtWq1W6AWU6BJfNG3C259F806phytuTW8DR91hBEODxeKSS3tkiY6ERRRGrV6/GokWLFPVlkvHZZ59hz549MeUGzj//fFx++eWora1Fc3Mzbr/9dlxwwQXYtm2b5AxNlUxS0IzFoTMA0LEm2AJT4TTuh03rhyNsUnze4inBDFsHLEOkjEkFkyaRhRGxapbGsWrC4tBO+3gEUtCQDn/i8GY57bwD/VwQkysq4jrZU2W0w0NHAnoDYlkWkydPlpbLLSC3252yDygX/ctX8n3OS6rCRSemZtt6zVhoVq1ahT179mDr1q0pb/Pss89i5syZMTXdr7zySunvmTNnYtasWZg8eTI2bdqEc845J61+pZsZABi7Fg0AVDsvgtPYgGK9J0ZoRLDY66zC/CxYNSwIzFwAXiE2s/JRvx0BnoNBozzuLqETSDqPPz7e8NC/n1PwowB6uDD00ODfArvwp/qVafXhm0z0DSmZBUR9APEESKPRQK/Xg2GYYQ/B5btFMxbKBIxW+hkgQ6G58cYb8eabb2LLli2ork5troLX68VLL72UUoGzSZMmoaSkBAcPHkxbaGhmgHQObC6flOTh07nYj5YxogKnoFh/GM1xRrcOeUow3d4Bi2Z4Vg3DADXmAexzxYviYrC9vxZnlivLSvMkCCtXCbfQmda+HP4gIqUGkjPOUgyXZ+jknY2eTrT7+jHBlP5QrEpiaEST2WyOK0B79+4FELn2aanm4UTB5eMwXC7bGwtimCppCQ0hBDfddBNee+01bNq0CbW1tSlv+/LLLyMYDOLHP/7xkOseOXIE/f39qKysTKd7APLTR5NrpuOH2K+7K+5nIljsdWTHqqkwOhMIDXDEX4igwELPKR0sJKBFkowycSFaAxAaWhi1bOrDXw8ffAtPzLomvY6MIfJpaI8KEJ2UOGXKFADHBUgehJCqAI12yYFkjIV0MWNKaFatWoX169fjjTfegNVqRVdXFwDAZrNJzqNrrrkG48aNw5o1axTbPvvss7jssstinE8ejwf33nsvvvvd76KiogLNzc249dZbMWXKFCxfvjz9L5RnPppcWzQMw0DHmjCrYCY2dvEQSOzN95AnMq/GPEyrxq71J+sJtvdPwhllSqsmkxQ+Hj6138LLpx5Rt899FB3+QVQZC4deeYySbz6M6GtKbgGVlysjAYcSIEEQEAwG0dXVNewouFxci6rQJCetu8DatWsBRCZhynn++eexcuVKAEB7e3vMCdDY2IitW7finXfeiWmT4zjs3r0bL7zwAhwOB6qqqrBs2TLcf//9GYXX5ZtFQ8l1sMGSohvxrO7X6A1aYz4Xj0WgnV7cNqz9GLjkAn7YV4iQwEIns2pEjT/ZlJi4ePjUNjgaSJ7JOZqHmt7Eo7OuTq8zGJnzI18YjTDiVATo4MGDCIVCaG5ulgSIzv2wWq0pZ0Wm7WYT1aIZmrSHzoZi06ZNMcvq6+sTbms0GrFx48Z0upEUlmUzLnyWC0YixQ0hBAbWglqTFb0JHvKb3aWYYetIEj2Wyr6AEp0bfaFYMTu2Brb312JJ2fGy0m6hGxz0EFJw2kdaYOETBABDXxQBMYxxhiIcDQyk1PbX7iPoDjhRbrANvbLKqCMXoK6uLlitVkyaNCmuBUSzIgOIOwQXnZAyn2/iYyFcOl1OiFxncjKJOsvFGDBlJIo10QqepxsW4zPEjwIUwWKPs2rYVs0E80ASoQEO+4oQElugYyNWDYEIm6Yy5UzOHKNXzAcaikgm59SEBohYNb+b+aOU11fJnFzVo0lnCC6eANFJ3V6vFyaTadjXqBoMMDQnnNBkMnQG5K9FM9RMdprbiOM4GPXJ95ENq6bMEJ3JOaq/YPBZ30QsLjteqiCdTM5ckuqacfeXZjXPna529AbdKNUnFstvMvnsdB+qrVQFqL+/HzzP4+OPPwYQ3wJKR4BOtKGzXNwLT0ihyadgAEqi9qmTM1Gq+OiZ7LTMAZ3JfvjwYRQVFaGmpgYMw+DPn+2EIxzfaU/n1Zw2DKvGmkKp6HZfMcJiK7THrBohSfLLaDikJzSDofQnhT7c9CYemnFV2tvlM/laJiBbDKetaAGy2+3Yu3cvFi9eHJOKZ6ghuEQCdKLNo8k2qtAgd0JD08UDQF9fHwRBiBGSUCgEhmEU6U+SpYuPpqurCxzHSSdRvaUK2web464LAAfdpZg+DKtGy4hgIUBE4tBiAgaf9U+Uykp7hN6U22eQXk2gzoADRlYLv5j69/nS2Yr+oBvFaVg1YyEYIN+izoD8rrApz4gst4BoSv5UBSgYDOZEVL/xEzbzGY7jIIriiEzYpOnik1kkdBjv8OHDMBqNUrp4uXWi0+mG9cPKT/KTrcmFRgSLfc5KzCtuz2hfDANUmwbR7itJul6btxjzi1ugYQn84iAMjB0B4hi6/TSFRgRBlbEIzd7ulLchiGR2XjP9B2ntS+XEIdn1lq4A0UwIH3/88bCG4Cj5mqRzOJxwQkPnbQiCkHKcfSKLhqaLjyckdDkASTCi08XT5Vu2bMHs2bOlSonZJPrkmWoZuv7KQU8ZphV0wqTNzKqpNjmHFBpq1Sw8VoDNoilFIOxIofX0q5xauPTD4D93HMJgyItCXeaVQIdLPlog+TLcFa+t0S5fkEiAWlpa0NfXhwkTJsQIEB22S0eAVKEZA9BaCuFwOGldBbmTned59Pb2wuFwJE0XT+s3FBcXp5UufiRzqU21Dp1NQSAs9rsrMbcoM6umWBc/k3M0rd5inF7cCg1LoGFSExAxg1MyKGYQzg7gdwf/hQemfS/tbXMFIQThcFg6//x+v/Q/TXZIX7lM25/Pw1351hZFq9WivLw8ZQsomQCpQjMGoBaN0+mEw+GAwWAY0skuCAI4joPdbk+aLn44jFR2aKvGiHGGQhwNJM9u3OQuRX1BJywZ+GpSzTBAwOLz/hosKG1FiPhS2kYk6Z+S3UFn2tsAwKeDB+EO+2HVZjclejKokDgcDvA8jwMHDkiCQodatVotDAaDNNRaWloKhmHg8XjQ2toKj8cDURRhsVgk4fH7/RAEYdRvKLkkHwUQSHxtJxuCo8lIaUZsuQAZjUaIoojm5uZhDcFF91EVmmHS1dWFhx9+GO3t7Th0KOKArq+vxznnnINbb71VYZHEc7J/9dVXqKioyCi3WiqMdC61qdaqIYVGIBwanBWYV3w47X2yDIGBDSEgDm2ltHhLcFpxK9x8F1LJ5JyJ0AyGvSjWWtAfTs3SohAQ/PbgP3Hvyd9Nul46F6i8OqdcQOjfPM9Do9FI870YhkFRUZEkKvThJhqtVqsIlff5fHC73XC73XA4HBgcHEQoFMIHH3ygsHzorPlUyyLku+WQDUY7e7O8IFk8Aert7UVTU1OMAA0nDFsVmizAcRxCoRDmz5+Piy++GCtXrsTmzZsxa9asUbnAosn1Dxzd96mWSrzfu3fI7Q56ynBSQQ8K0qzCyTDAREs/GlxDCzMBix0DNZhf0gYrVwG30JV0fZ5wAFKofBZFmb4gbaEBgE8GmuDhA7BoUvOfySMHo4e3AoEAwuGwNNwqD/6okNXD0Wq1GBwcRENDA+rq6tLuszxclxZja2trQ29vL6ZMmSIJUEdHB9xuN8LhMEwmU4wAGY3GEZlQnA1G+0Y5FNnoGxUgOsIyZ84cAMcFiFbEdLvd6OzshNfrTVmA0g2OGvWoszVr1mDDhg1oaGiA0WjEwoUL8eCDD6K+vj7hNuvWrcO1116rWKbX6xEIHJ+TQQjB3XffjT//+c9wOBxYtGgR1q5dm9KFWFpaiieeeAIA4Pf7sXLlSlRWVqYsMrnMDEAZyXo3U61DBwQAEavm68EqLCprSXu/FQZnSkIDAM2eUswraoNRZx9SaMIii0yERstm9swkguD3B/+FO6d+J/JeFGOEpLe3V/LjhUIhsCwriYbRaITVakVZWZlCSEbjpsgwDOx2O+x2u7SMEIJgMCiJj9vtRnd3Nzwej3Rjk4sPz/MnvF9ltC2aVJC3J7eA5MgFiA7ByQWI5n+zWCzw+XyjOrSa9tW5efNmrFq1Cqeddhp4nsftt9+OZcuWYd++fTHlmeUUFBSgsbFReh/9ZR966CE8/vjjeOGFF1BbW4s777wTy5cvx759+9KK1qIBAOnOpcm1RTOSQ2dTzOXQMCx4MvQN+7C/CM5QJ2y6oSdiyinUJcvkrIRaNZdUDe28jghN+nj41PsTzeb+BpzzxWcgwTCCwWDMvCadTge9Xo9JkyZlJRw9VySaSEi/R2lpqbRcFEXp6djtdqOvrw8tLS0IBAJgGAZffPGFQoDMZnNGwQf56FcZC0k1U2kvHQEaGBhAKBTCu+++qxAgOrSaa+s2baF5++23Fe/XrVuHsrIy7NixA2eccUbC7RiGSVhznRCCRx99FHfccQcuvfRSAMCLL76I8vJyvP7664oKnENBL4Z00tDkOjPASA+d6VgNJpvL0OhJbj0AEavm8/4anFvZOOS6cgxcekEEzZ5SePmhC6CFxMyOVUfAkUEtzwgEwL+0Lbhl8jIYjUapKiSlubkZ4XAYNtuJk4yTZVlJROR0d3dj3759KC0thdvtRltbmyL4INoCij5WY4V8ToI5XOGKJ0DNzc3weDyYPHlyUguI/r60hlC2GLaPxumMRPwUFRUlXc/j8aCmpgaiKOLUU0/Fr3/9a0yfPh1AJA69q6sL5557rrS+zWbD/PnzsW3btrSEBkg/39lICM1IJ+2st1SlJDQA0Bu0whkypGXVsAxQqPNgMGQZemVEJopu6tVgfrEOAhJHrQWEzC6wgBhGMQzoR3qWGeVTTwtMBRbouTSrtJ1gcBwHjuNQU1MjLSOEwO/3S9aP0+nEkSNH4PP5oNVqY8THYrFAo9Hk7XDXWKhHk4vw5lQtoFAKRQfTZVhCI4oiVq9ejUWLFmHGjBkJ16uvr8dzzz2HWbNmwel04re//S0WLlyIvXv3orq6WiqgJo/AoO/pZ+mQbhqakXgiG0kfDRCZT/O/KR46ESy29dXi/Kr9ae27xjSQstAAkZDqc0oBp5g411ogffeMRLG+AP3BzIRGBMFjhzbi1rqLM+/ACUL09cAwDEwmE0wmk+IaFQRBejKmT8cHDhyQgg+ov0en00lzfzK91vI1gg0YGz6fZG1GC1AmSYmHYlhCs2rVKuzZswdbt8ZPTU9ZsGABFixYIL1fuHAhTj75ZDz11FO4//77h9OFuGQiNKI4jDtcCu2P1DwaSioZAuT0hyxwhvSw6VKPQBsqk3M0Ilh8OlCIk+2Jhabf5QOQmZ9Gk2LwRyLe692L1ZPPhy7DwILRZDRuxBzHwWazKYYU6URot9uNffv2ScXKooMP5FaQTpfaZN58tWhyVSYg222OyfDmG2+8EW+++Sa2bNmC6urqtLbVarU45ZRTcPBgpOwv9d10d3cr5rJ0d3dLIX6pwjCMVG8inW1yyWgMnU0wFsPM6eEVUhUOBp/2T8LyytStmoIUMjlHs8uhQX0BkMivLOr0QCiz1DiDfGqTQhMhEBF/OPQubplywbDaGcsM9zxlGAZ6vR56vR5GoxGVlZWorq6Whmeo9dPf34/W1lYEAgHo9XrFsBv9P1eZD/LdAlEzAyDS4ZtuugmvvfYaNm3ahNra2rR3KggCvv76a1x44YUAgNraWlRUVOD999+XhMXlcmH79u346U9/mnb7mVg0J1IwAN1nvaUSXzpbU26nL2iBO6SDVZfaGK2OFcBABEnDAhHAYqejGqcWHYn7eSDDYAAA6A65oCUswkzm1unGnt24adIyaNnj1tFYcXZnq5+5+L7y4Rn5w2Q4HFYMvx0+fBhutxuiKEplmq1WK0KhEMLh8KjfMBOhCk1y0haaVatWYf369XjjjTdgtVolH4rNZoPRGEnlcc0112DcuHFYs2YNAOC+++7Dt771LUyZMgUOhwMPP/ww2tra8G//9m8AIj/S6tWr8cADD6Curk4Kb66qqsJll12W/pfKoFTAWA4GSNR2vTU9oQEYfNI/OWWrhmGAcUYHjviTB4JEc8BVgTn2I3GtGg+fuUiIICglRnQzmVs2PBGxtuU93Dx5ecZtqKSOVqtFYWEhCgsLpWU0+IAKkMvlgt/vx/79+9HU1BRj/Vit1rjZFBKRCwsk29bXaApNLgQpbaFZu3YtAGDp0qWK5c8//zxWrlwJAGhvb1cc+MHBQfzkJz9BV1cXCgsLMXfuXHzyySeYNm2atM6tt94Kr9eLG264AQ6HA4sXL8bbb7+dUcbjdIXmRJuwSTk5TT8NcMyqCetg1aZm1VSb0hcaASx2OcbhlKKjMZ+5w8NzRBqykOzin9278NPacxVWjUr6ZHpDlwcflJWVAQC2bduGmpoamM1mSYC6u7tx8OBBhEIhaeKsXITMZnPc/ee7TyWXUWepMuqZAVI5qJs2bVK8//3vf4/f//73SbdhGAb33Xcf7rvvvnS7FEO6Phpg7Fo0QGzfaRbgamRSrpjBtr5JWFbZkNLaJfr0074AQKO7AnMKj0J+PjPgEBzmcQpnkFUgpg0i4OnWD7Fq0vFw+3yOesom+fw9WZaNCT4AIGU+oALU29sLjydyXsYLvQbye6hLHTobI+TjPJpsQit3BgIB+Hw+BAIBRRZgv98PURSh1WphY/VwIr1cZr1Ba8pWjVmTXtsUgXDY5RiHOYXHrRoNk35dmWgcTGbhzdH8X9eX+I+JZ4PLkUP6m8BIzaOhwQclJcdrJMnnhtCZ8W1tbVLJBZZl0dDQoMh8kGrKqnioQpOcE05oGIbJy2CAdNunQhIvAzBNCa/RaMCyLFiWBcMwUp0cmsyR4zi839CLj/rTm/UPMPi0bxLOS8Gq4RgCHRtGSEx/omODqwKz7cetGo4ZfmE4H8OjWGdBfygzS4sSJgKead+Ef5949rD7NNbIx7Qx6SIPPpBnJOF5Hk1NTRgYGIAoijhy5AjcbjcEQYhJPJpqahbVohmaE05ogPyLOgNin+7kWYCjMwDLswDL08fLa+UYjUZoNBq0tLTA7/cnTD461VKVgdAAPUErPLwOliFqzzBMZOJmk6c86XrxEAiHrx1VmFXYAQBgMXyLBgDKdQXDFhoAeL1zB26oOSsLPRo+qdzsTmSydaPUaDQwGo0wmUySj5gQgkAgoEg8SlOzcBwXd/hNXlQx330+tM1UywnkYv8npNBkMo8m2wdXngU4HA6jq6sLvb29kpjIswDL08mXl5crhGSok2Ooz1OpuJmgZWzrq8V5FUOLVKXJkZHQAMB+VyVm2jvAMACbQRnneGiy5MQPiTyea9uCszA+K+1908hX8YvuF8NEio0ZjUYp+ACIXMPy0Ouenh40NzcjGAxKJdtp0TmNRgNRFLMSfaZaNGOEkfDRiKIoVeuMN7wlzwLM8zx4nkdBQQFKSkokIclWOvlkfa+3VII9NtslXXoCBfDyWpiHqMJZlEYm52h4wmGPoxIzCzuBLAmNh8+OnwYAXu38DEvL0puQnE2o5UvTuiQ7X7J1LuXj0Nlo9ItlWRQUFKCgoECxnGY+oAEIHo8HDocDR44ciZt4NN1s36rQjBGyMXRGU2lED2vJhQSAVLmTPhEVFhZKFgrNbPvll1+iqqoqYfbq4TCUSBo5HSaYitHq68ukdWzrm4Rzh7Bq9GxmM/kp+1xVmGHvBJCdhJZHA4MZi2s0QZHHm569WKZLv0BZKtAIQb/fH/dFLV8anlpQUACr1Sr9b7VaczaDfrjks0UznJuuTqdDcXExiouLAUB6iCwvL1dUPT18+DB8Ph90Ol3c4bdEwQeq0IwRUhEa+QXu9Xrh8/nQ0NCgEBNCiEJI6PCWXEhSvchHOgWNnKmWqgyFBuhOwarRsAQ2jQ9O3pTRPnjCYZ+zAqcVZud0DIo8xhuKcDgwkJX2NnobcZ4287Tp1PqVC4jT6UQgEMCWLVsgCAJ0Op10XskfWKjlq9Pp4PV64XK54HK5cPToUcmJbbFYpAeOgYEBWK1WhQ9hNMlHiyZX816iq54CERGSD78dPXoUHo8H4XBYURNGXvUUGN0Jm7nghBQajuPA8zz6+/uh1WoT1nAXRVFh1mq1WunHpk73bDwtjkZSTTlTrVV4u2d3hq1HItDOGcKqGWcahNOVmdAAwB5nFU61Z+9CsGvNWROaIOHxTqAJ0zAt4To8zye0Sqj1K7d8zWYz/H4/5syZA4PBkHRmO7Vm6M1o3Lhx0nK/3w+Xy4XDhw/D4/Fg165dCAaDMBqNCuunoKAgpdox+WqFZJuRKhOg0WhSqnra1dUFr9cLlmWh0+lACEFbW5v0mw/3wUEVmizxzDPPYM+ePWhpacGuXbtw0003Qa/X429/+5viSTFeCHB7eztcLhcmT56ck77l8gdOzaLJNCAgQlegAD5eA5MmsZVoHiI6bSh4osEu57CaUECyMGwm513/AfyH7CEl+hUOh6WIJnp+lZWVScISbf0ODAxgcHAwpjZIOshn0Pv9fmi1WsyZMwehUAgul0tK30IjqLRarSQ6VIDizZ7PR8thLFg0qUJ9t4mqnh46dAhOpxO9vb04dOiQIvhAbgGlU/U03RQ0o54ZIF/Zv38/GIbB2WefjUOHDmHZsmX4z//8T0UOpWTk2zyabLY9yVwGPatBUEwv/5tsL9jWNwnnVBxIuIZVm9nETTm7HcNuQmIgPPzwZjkB8Hjk01dwprZWYZWUlJRI71OJEsyEdNvU6XQoKSlRTGAUBEESHlo50+2OlHmQ+3xo4srR6vtIkK/zXqjVWlBQAEKIlGA4FAopht+iq55G536LZ7mOOYtmzZo12LBhAxoaGmA0GrFw4UI8+OCDqK+vT7jNn//8Z7z44ovYs2cPAGDu3Ln49a9/jdNPP11aZ+XKlXjhhRcU2y1fvjymdHQifve730l/v/nmm6iurk5ZZEZjHs1Its0xLOrMFdjjjp8xORW6ArakVk2RzgtARKa1ZAAgnMVD1BVwwsTq4BOzVy3wU0MPbv/Wj7PWXjYZ8hzguJghHDp7nvp9uru74XA4IAgCtm7dqhh2S6duTC7I12g4IPfzaHQ6HYqKihRVjOmwabzgA+oCkIuPKIpjS2g2b96MVatW4bTTTgPP87j99tuxbNky7Nu3D2azOe42mzZtwlVXXYWFCxfCYDDgwQcfxLJly7B3715pvBkAzj//fDz//PPSe70+swl8NKY9VfIxM0A6badCvbVyWEJD69WcXR7fqtFzAqyaANwZBgRkGxEEJZwZ7VkUGq8QxGsdX+A7VfOy1uZoIp89X1UVScDa0dGBlpYW1NXVweVyweFwoL29HX6/Xxq+kYtPspnzo/0UnYhcXIsjbSHJh01TrXrq9XrR09OjsIKGU/U0HdIWmmgLY926dSgrK8OOHTtwxhlnxN3m//2//6d4/8wzz+DVV1/F+++/j2uuuUZartfrsxICTIMBUmUkCp/lklQunHQrbsaj02+Dn9fAmMCqKdH78kZoAECb/Yq0eOHwR3krNNk4zxgmUjiwrKxMMXkxHA4r/D7d3d3SzPnooIN0fAepku8WTb60l6jq6bZt21BSUgKtVitNPk1U9dRisWT8kJ+IYftonM6IB1du1g2Fz+dDOByO2WbTpk0oKytDYWEhzj77bDzwwANSrHo6fJNKOafaduYZAhR7w6f9tTirvCnup8V6D1q8JXE/Gw20JgOQXVcN3HwAb3Z+hYsrT8luw3lEvJucVqtVzB0Bjj89UwGiRcsIIbBYLPD7/ejt7ZWGctKpGZNLciEM2SQXosowDAoKChQP8jT4gFpAtOppZWUlZsyYkbX9A8MUGlEUsXr1aixatCitjv33f/83qqqqcO65x9Own3/++bj88stRW1uL5uZm3H777bjggguwbdu2tLOqZiI0uWa0h86qDIWwaYxw8pnP4geADr89oVVTovcOq+1s0xnIYhibjOfaN5/QQpMqiZ6eqd9n3759cDqd6O7uRigUgslkiol6S/XJ+USKOkuFkZhHIw+Zp1VPCSE5eegeltCsWrUKe/bswdatW1Pe5je/+Q1eeuklbNq0SVHU7Morr5T+njlzJmbNmoXJkydj06ZNOOecc9Lq1zfJRwOkfuFMtVZh+2DzMPeW2Kqx63xgIUIcRkBANnHyPpTorOgLubPcrh8bu3djefmsrLY7HLJ1fg23HfnExebmZtTV1aG0tBTBYFAKOnC5XDhy5Ah8Ph/0en2M32ck/Ab5LAyjnRkgF8c+Y6G58cYb8eabb2LLli2ork4tF9Rvf/tb/OY3v8F7772HWbOSX6STJk1CSUkJDh48mLbQpOujAcZ2eHOqTLVUZkFoIlZNgNfAEGXVcAxBod6H/mDmc0OyTZmuIOtCAwBPt32YV0KTTXJhOej1epSWlirmjfA8L/l8XC4XDh06BLfbHTfVTjajpvLdohltockFGVXYvOmmm/Daa69h06ZNqK2tTWm7hx56CL/61a+wceNGzJs3tDP1yJEj6O/vl0y6dMi3Us75EAwARCya7BCxapbGsWpKdJ68EppsZXKOxhH24b2uPTi3Irtj2Scayc59jUaDwsJCxTQEecZkeaodnuexa9cu2O12hfWT6Yz5fBaG0SwTkCvSFppVq1Zh/fr1eOONN2C1WtHV1QUAUg4wALjmmmswbtw4rFmzBgDw4IMP4q677sL69esxceJEaRsaWunxeHDvvffiu9/9LioqKtDc3Ixbb70VU6ZMwfLly9P/UmlmbwZyn3pjtIMBgOxEnlGO+u0I8BwMGuVxLtZ7gewbEBnjHqZPSvSxQL8WxKEB3BoQHwsEWYBn8EZVM85dnT9Ck4+hxOkiz5gsT7Xz/vvvY/z48VJqqZaWloxT7dCUPtki3y2kTNoc9cwAa9euBQAsXbpUsfz555/HypUrAQDt7e2KH3Lt2rUIhUL43ve+p9jm7rvvxj333AOO47B792688MILcDgcqKqqwrJly3D//fdnFGaXb4XPRnvojNbGCfv9KNNY0cNnQwkYbO+vxZnlBxVLi/VZDvMaJh0pZHIWXSwwoAVxaEE8HODjgBADCAyAxMe3/0hugg1Gk3xMG0OjpoqLixUp+zNNtTMW5tHke3h4umQ0dDYUmzZtUrxvbW1Nur7RaMTGjRvT7UpCOI4b9cJnI9k+ISSlpI5GoxHjYEFPlkyOI/5CBAUWeu544EWBNphxaedcQDM5ez1h2EMFMASM4HxahDwETa2DCAUFJBOTZARcIez+6jBmnXJiFUbLR8so3o0y01Q7Ho8HRqMRgiCkHdGaat+Gy2hbNNkmPwLbs0wmEzbz3UdDM75Gi4jb7ZbSzdOkjvRVUFCgyETNMAyOdnD4qqUzC98KiFg1k3BGmdKqKdF70eG3Z2kfqcESFsV8Adh+oERTCvg0CLkJnM4QbAVmtDR3oQchANGZAjL/bQgv4IN39p5wQpMtRiMTdCqpdnw+H5xOJzo6OqRJisNJtZPtm7goilmfc6QKTQ6gpQFSZSRCKVO56Gg1xXhWSSAQgCiK0Ov1kpBYLBYYDAb09vZi3rx5KTlGh5vJOZrDvkKERBY69rhVU6zLjdBoiQbFYRsKghboAnoQL4eAS4TDGUSfw4dOMXKMj8Kh2K7YnKNsBTzBvj0duWl7lMi2OORDPZroVDs8z0On02H8+PGS9TM4ODisVDvZRrVoxgCZDJ3lMjMAEPmhE1VTpOISDAbBsqzCKikuLpb+jlcfp6+vT6q7kwp1lgpoGBY8ydb3ZbC9rxZLyo6HTefCT8MEWQQ2FuIIAQDfsVdq9LtSXzctBIL+fje83iDM5uym7EiXEz3jcjahPhB6XWUj1U4+R7Hlqs10UIUG2T1J4lVT7OvrA8/z6OzshCAI0Gq1CjGRV1NMt7542inkWQ1qTWVo8nal+9US0u4rQkhskaya4hxkCCB6EaQiAHQahl45is5BN8x6LbzB4ZWcjulTmIAQ4O3/243vXnladto8VkLc5/PB5/NJ55BGo0FBQQFsNhsKCgryJp1LMrI9m38k5tFkkmonEAigp6cHHMdlJdWOKjRjhFxHnckd79Qa8fl8UvVOQFlNkQ53TZkyZchqiumSiX/pZGtlVoUm2qoxcDwsmgA8fPqikAxupgdCBkJDCFBVUoCmo/1Z7Q/4iLBu//hgWkJDLVtBENDR0SEJCv1fFEXp/DGZTFKKl8HBQbS2tiIYDMJsNiuEJ9cW+YlGOjfdoVLt7N27F06nE11dXcNOtUPbzjapCk2u/GonpNBotdphCQ19qkwUxRUOh8FxnMIqSVZNsbm5GeFweFjVFLNJvaUKwFdZbfNwHKsm20LDGAn8s/wg3Xpo/Ay4AMCGACYFh75Zn4MouGNCc6R9MOYjuWVCzxu5oNCZ7t3d3TAajbDZbKioqIDJZJIqv8qRO6iDwSCcTidcLhcGBgakOSUajQa7d++WBCiTp+t8LeWcb9aRPNVOY2Mj6uvrUVRUhEAgoMh2kEmqHdWiGSOkMnQmiqJ0A3A4HOB5Hrt375aWRTvezWYziouLYTKZ0q6mmA/Zm+VkL0PAcQgYfNZXi8XHrJoSvQdt3vQzbw+FqdqHwbAeITovRgA0AQLOD3ABBho/wPKxv0sgnGl10SQcq9QWCISxdfNuVFabFcIiCAL0er10zsjFxO/3o6mpCaeckn5yTr1eH5PGv6GhAS6XCyaTSTGh0WKxSDe3TMUnU0b75paIXDrv45VoTifVjtVqVYVmrEDDm3t6esCyLARBiDu3hGVZGAwGaLVaEEJQWFiIqqoqyfGejRh7IH+SalImGIth5vTwCsMvvyxH7qsp1uUmkzOrEaE1BxF26sHyACMABABvAsJWAoYHRB0BF4Bk9XB+oHMgywEKhEAWaId3/rkHP7z2VCkVO31ASXQO0blN2YKGtk+ZMkVaFggE4HK54HQ60d/fj0OHDiEUCkniQ4fdCgoKFP3MV3EYq/Vo0km1Q+f2+P1+KbX/cFLtpNpHOXmVVDPf2LJlC/75z3+iubkZ27ZtQ1dXF9544w08/PDDmDt3rnTh2+12GAwGmEwmyfHu8XiwY8cOjB+fm/kQubxwMxExlmFwkqUCXznbstoXAgZH3Kdiku0LFOl8YIgIwmR3hjPDAJZSHwa9eojRZ68AMBxANACvBXgroR2DJ+SDLsiA8SEiPgGAIdn7XY4e9mHq1KlpbZPr/Fj06ZpaPnQuFhWfvr4+aVjXbDbDZrNBEATJf5StB618Ix9SxiRKteP3+7Fz507odLphp9qR9w9I73wb9RQ0+crRo0fhcDhw+umnS+OmL730UkoF2XJtcQCjX48mmqmWqqwLDQA0ewswqUADjuUx227CTmfq85lShdUes2q8SgcrFwCEeNXEGUDUAwE9AWgGEwJwwWNDbn4GmgDABlPz98RjsN8Lvz8EozG9yX4jCcMwCcWH+ny6urrg8/nw3nvvxVg+Vqs1LfHJlhWSyY1yKPIxCSbDRMoz00zXEyZMAJB5qp3o/o2pobM1a9Zgw4YNaGhogNFoxMKFC/Hggw+ivr4+6XYvv/wy7rzzTrS2tqKurg4PPvggLrzwQulzQgjuvvtu/PnPf4bD4cCiRYuwdu1a1NXVpdSvq666CldddRUA4A9/+AM6OjpSrvqZLxM2MyWTtrNTcTOWnpAHrtAiFOg3Y7JFxM4cpAOTWzUUNgAI6czLZADBEHmh8NjxEwEuQKA5Jj5aNwGTRm2d99/ei4u/M7YKosnFp7y8HHq9Hj09PZg+fbpk+fT09KCpqQk8z8NisUjCY7PZYLFYxpzlk+8pY6L7Fy/VDs/zUsi1y+WKm2qH/m8ymbLex3RJW2g2b96MVatW4bTTTgPP87j99tuxbNky7Nu3D2ZzvMdJ4JNPPsFVV12FNWvW4OKLL8b69etx2WWX4csvv5Qqcz700EN4/PHH8cILL6C2thZ33nknli9fjn379ikKpKUC9cukyljOdZbpyVOfxUzO0Wwf4HBehQZFOg+A3Dzhs1oRGmMIvF8XcdIkz3+ZuJ0AoKHDaSEGbDji9wGJRLSRRN2P83Nu3XxgVIUmWzcShmGkoeby8nIAkZtfIBCQLJ/u7m5JfOTDOTTgIJulN7L9RJ4PQ2fDbU+j0QyZaof+RuFwZP7Yvn37FNZpuql2hkPaQvP2228r3q9btw5lZWXYsWMHzjjjjLjbPPbYYzj//PPxi1/8AgBw//33491338Uf/vAHPPnkkyCE4NFHH8Udd9yBSy+9FADw4osvory8HK+//rqi+mZKXyrDeTS5iszINx8NAJTqrTmpPgkAPUEP3KGFMBkPAJiQ9faBiFVjLfdisFUHzpdgyOwYbBDgfIhYKiEGTBhgj4lJsqEyNiiAiASiIc4Te5xD3trcl/4XGSPIxYfWnac+BWr5RIsPz/Po7u4GAEl88oV8HDobbnvRqXZoWx6PBx9//DFMJtOQqXZS9fuky7B9NE5nZGwk2TDVtm3bcMsttyiWLV++HK+//joAoKWlBV1dXTj33HOlz202G+bPn49t27ZlJDSjlRkgUfv5NnQGRPKebR3ITfGY7QManFvRB5umFk4+vdpAqcJqRWh0IfCCLiIm/shLE0xdTJK2LwKaAR7+qlihYeIc8lCIR0tzL2onl8Z+OEZI51yiPgWTyRRXfHbv3o3+/n60t7dDEARYrVZFpFuq4pPvFg2QvyloGIaRJotOnjxZGuZMlmpn3rx5aY8iDcWwhEYURaxevRqLFi2ShsDi0dXVJZnglPLycqkAGv0/2TrpkIlFA+Q21jzfhs6AyHyarQMHstib43QHPfCEF2BmAYOtA9kRGiHIQnRqwfs0EAMceJGFrhcwO5iMxSQpBNC5BPjjjTIm+D3/+cYurLrl3LifjRWGc07JxWfPnj2YMWMGLBYL/H6/NOzW2dmJxsbGGPGhPp9cWz75Xno5VxU75W0mSrXjdrtzMrF8WEKzatUq7NmzB1u3bs1Wf7KCVqvNyKLJp0mV6ZC5RZM7Pw0AbOkgON08ACDJuFYUeuhQxNhhEs1gBR0GnWE0dTogcgyI3LBgAa0X0Dlyd1NiBAIuSCKiEnXhx7NoAGDXl+05689YRS4+tDS7PIWL0+mUxEcURWk4hwoQfbrO56izfG8PGPo708ShuRD6jIXmxhtvxJtvvoktW7aguro66boVFRXSWC2lu7tbMrfp/93d3dKJSN/PmTMn7b6lW4+GMhaFZjgnZL2lYsjqk8NhECHANAnwKn97HbQw+nUo0pdAIxoQDjFw+Xn0ef04GgjgKAQALtkGcb6jABi7cjzkKRIwALROHmF71IS5BKnF+nrdCIV46HQjO3Mg2473XLclT+ESLT7U8jl69Cj2798vtdPQ0IDCwkLYbDZF5uRs9isf2gNy40MaU1FnhBDcdNNNeO2117Bp0ybU1tYOuc2CBQvw/vvvY/Xq1dKyd999FwsWLAAA1NbWoqKiAu+//74kLC6XC9u3b8dPf/rTdLuYcfbmfBSDVNrOtN8mjR7jjcVo82ffia2DBoWMHR0DBZhOChEMAg4fj15vAB1+PwCgGR4Amc3YN/QycVPNZBP22Cmk7w/HCA0jJj7mm97bj2UXzsxl18YM6U4SpOIjd2Y7nU58+umn4DhOIT7Rw27piE+2/T35btGMdraHtIVm1apVWL9+Pd544w1YrVbJh2Kz2WA0GgEA11xzDcaNG4c1a9YAAP7zP/8TZ555Jn73u9/hoosuwksvvYQvvvgCTz/9NIDIj7R69Wo88MADqKurk8Kbq6qqcNlll6X/pYbho8kV+dK2PPO0z+dDhWBCptM2NYRDIWuHlVjACXrwIRaegIA+bwCdPh86QLAXAwAAjmFQbS3ABKsN460FaB/ox0AGVicQcfjrY/NYZh0qJhp/nOOb5JBv+aBhVIQmmw7kfIGKDwCcdNJJ0Gg0IITA6/VKw25y8ZGHWRcUFMBiscSdwDiW5tHkW3uZkLbQrF27FgCwdOlSxfLnn38eK1euBAC0t7crniwWLlyI9evX44477sDtt9+Ouro6vP7664oAgltvvRVerxc33HADHA4HFi9ejLfffjuj6IdMo85OlKEzQRBiUs/T/0OhEDiOkxI9TjGUYnsosV+BJQyK2UJYiAUaQQ8hxMETENDvC6DH50cXIUAKlolACNpcTrS5js/gtGi1mFBgg1GjgY/nccTlgjscXWo5ChIZMsuJ8z8aPvKbsQLAhkSIuuPndDKLprmpJ+ddGwvkah4NwzBxw3i9Xq807EZrxgCIsXyyWVLhRLRo8iK8OZWTZ9OmTTHLrrjiClxxxRUJt2EYBvfddx/uu+++dLsUw2gWPkvUfraFhiYKdTgcIIRg//79kqBEi4nJZILdbpfea7Va6TvrPCX4266vUMTYYIEFWt4AIczBGxDR6/aizx9A5LbpPfbKHp5wGPv6jw/bMQDGWawoOzaTecDvx2G3S+EO0Q0CmsDIPJ2xApH6ZegNwzfueCYCJsnpFQzwONI+gOoJqWWmOJEZqSdpufjIc4fR2fNOpxOHDx/G3r17IYoimpub4XQ6JQGKl7olFXIlNNlkTFo0YwFajybVA0zXyVXxqEx/ZHnW6eiKi8FgEBzHSRafTqeD3W6XhEUuJsmoM5fjsfrrsLevFw39/Wh09WPA7wfLMNCxHMr1epiMJrBMxIZgGSby97H3Mf+DwbF/MpuD/kWn8BM4HE5YrRawHBcZhSIEJPIfBBKpXGnV6zHLUA6TTgsWwOF+Fwb6ZUECOUYuJlq3UlmSWTQA8M//3Ykbbjw7F93KKSMVDJBJO+leRwzDSKn3qfiIoohPPvkERUVFIISgvb0dLpdLypQsH3bLVHyGy2hbNLnghBSadH00QO6fvBJddNFiIv+figkVD1rThIaKarVahEIhfPzxx5g0aVJG34FhGEwrKcW0kuOTDLs8Huzq7canba34qqsTbS4n+FyIcNCveKtjWZSZLbDr9dBrNBBEEd5wGG1uJwYDAZxcVIK2usgwFuc/VgbAfywTcw6G0lj++HeODnMeSmi++nz4CUtpwkufzyfN4k6UVyybApGvYcTZgGVZcByHoqIiKdpVFEXFsBvNG0bFRz7sFl2s7EQcOssFJ6zQpDN0BuTWj0IIgSAI6O3tjREUuZhQQaHFsYxGo1TKIFm/6T6ydTJVWCyosFhwqqUALSYrZp16Kvb192FXTxd29XRjV08PBgL+oRuKAwugzGyBNhxGcYENLMfCFw5jIOBHn9+PI24XjsRJVjC9pAR7+yLDbKIu8grb4ifDTFT8LO2+yk6hSJizgLA9cskwQ+huT7cLgiCC44aOguJ5XrJY5S9aPM1gMEAQBCm1i91ul0oLj9ZT90iSi8wA8rZYlpUsHwqtF0OH3aj4yNP7U+HJZt/i9S/f2suEE1Jo0vXRAMMXGlqxM54Tnha5amlpUSQrpMIylJiMFrRPBo0Gp5ZX4NTyCumzwy4XdvV2HxOebhwcHIAgO34lBiOKjaZjw14MAgKPwUAAPT4vuryRwIHDfak5zQ0ch16fL/EKbCRzcyR787GbUlgpPBnVn4kyig0D4eNCM4TBTAiwdXMjzjz7ZACR8yMQCEgiMjg4iEAggK1btyIUCkGj0UiWqsViQVlZmfSwwXEctFqtNLve4XBI/gaWZWGz2cDzPLRaLYLBYFr16XNJrktvZEoqN165oNB5glR8qOXT2toKlysylLtjxw7Y7XZJgIxG47AzLGQLVWhyRCYTNlMRGnn552hBCQQCYFlWMcxFxSQYDKK1tRWnn376cL5Wwn4DI39Rjy8owPiCAlw8OVLGwRcOY09vD5ocA/iisxNfdHWicbA/K/uaXlKGHd2daW1DtEBYC4QLjhc/446VfJaG3MJJLj5CYgoEaLzHzRhGGPp4v/X6DhSWhKVzBIB0fnAcB41GgxkzZkgPG8mIN7teFEW43W44nU60t7fD5/Phww8/hMFggM1mkyyfgoKClMs3Z/s8ysYNLh8mHAJK8aEEAgFs2rQJFRUV8Hg8aGlpgdvtBsdxirxu6YiPatGMEWgwQLoQQlIWE3rDkD95Jsp82t+fnRvuUH3PNulYeSatFqdXjcPpVePwo2kzQQhBi9MhWTy7envQ4hhMOwdBTYENO3vSz3cnwUfKAGj8ABcE2BATGRITAYgEYNMI+xQBJiSC6FgpIi0Zh9scsNvtqKqqgslkgsFgkML+BwYG4PF4FGne04VaMzabTRpmO+mkk+B0OiXLp7W1FcFgEBaLRTHkliyn2GjflHJNtpNWAsD48eOl40kfAOiwGxUfjUYTE3AQT3xGU2hy9cB6QgqNRqNJGkEmH8agQiIIAnbt2oVQKBRXTOjfmaTRztcUNLmEYRhMshdikr0Q3zkpUuLYFQxid2/Ex7PpQAOOhEPwJXkgYABoWFYxJBcXEWD9gNZ/vK4Mwx+PGksUKMCIIkg6QgPA2BeGr0qfkkUTDAgw6u0oKSkYct1sodFoYpIl0joyDocDnZ2daGhoAADphketn2xm7M2mFZIPT+SJiPc95Q8AtDy83Pp0uVw4dOiQQnzk1o8oiqpFMxbgOA7hcBh79uyBKIooKipShAhTn4l8mItlWdTU1KC0tDTrNRlGQmhG26JJhQK9HourJ2Bx9QTM8voxa/ZsdPHh41ZPTzcOu4+HL59SXoEvu49bMzZOhwqNGfwAj/4WJ0QdF3HKZ1wKINnQWfzFWpcAVCWfRyPnrTd2YuUN8es0jRTyCprA8fkl1PKhT9xarVYKi+/r64PNZoNWqx2i9bFHNm+8qV4fcvGhCIKg8Pk0NzfD4/GAEIJ9+/ahqKhIEiGDwZBxn8fkhM18pbW1FY888giampqwb98+hEIhnHnmmbj66qtx/fXXw2g0oqSkRBKW6B+up6cHVqs163UYgJGxOvLV8ZoMBkB9UTHqi4rx/anTAAADAT92dXdjV2cnmg8OYJFQCa87hH6nD25fEN04llmA0yiiwjKBsJGgAaKN8/skOJw0zJnhUwv3/mJ7y4gITbp1ZGiUFXV0C4KguNnt3bsXfr9fmuxLb5K5yu6bjHz2WQzHcqN+nGjxef/991FUVIRgMCj9HhqNRhFmTbNaj4bPJxNOGKFhWRYajQbf/va38cMf/hDXXHMNurq6Ui5XmmurY6wOnY20gBUZjDirZiLOqpmIWz56A7t7e3O3MwbQ+ASEbbGXQaIyADTMOVWR6+pw5sWFPhQcx6GwsBCFhYXQ6XSYNWsWQqGQNOTW29uLgwcPgud5xZBbohDrfHHgjwTZ/I50nlRVVZVUF4bWiaGWT1NTEzweD3Q6Xcw8n3ijMflw/mUkNFu2bMHDDz+MHTt2oLOzE6+99lrS5JcrV67ECy+8ELN82rRp2Lt3LwDgnnvuwb333qv4vL6+XhpPHooJEybgkUceAQAcPnwYANJ68sqlGAC5v2GPhaGz6LaHolZvxpfoAclVXrNkfUjyvfUDYbApzl8VRYLtHzfjW4unpNm50Uen06G0tBSlpZHJvPI0/jTSjc4tkUe5ZXvILd8tmmzfxKPb5DgOdrtdEThCLVB5GW0qPtGWz5gVGq/Xi9mzZ+O6667D5ZdfPuT6jz32GH7zm99I73mex+zZs2Nyn02fPh3vvffe8c6lGJIZDd0uHA6n3EYuf4jRvmHnK4mOCZ0RbzFwmGqwYH8guznWFPvikFZhMwDQetPLkvDexr0jIjQjkbMvOo2/3MntcDjQ1dUFr9crDUG3t7ejuLg4rRDrXJOPaXaiGeq3lFugFJ7nFZYPFR963JuammKKyY0UGf3yF1xwAS644IKU148eh3z99dcxODiIa6+9VtkZjUZKCzEc6IFNt1TAWBSDsRQMEA19Kks0I16rD0Bo9QAVuTt+gp6FxiuCt0SldknytYfKChBN47705gCNJun+3nIn94QJEwBEHvAGBwfx5Zdfwul04siRIwgGg1IWZWr9xEvhn6hP2b6G8jnNTqbfV6PRxBWflpYWdHR0wO/3Sw8Cer0+7rBbrhiVR4xnn30W5557LmpqahTLm5qaUFVVBYPBgAULFmDNmjXSyZsO+Sg0Y3XobLhEz4inr1AohJ07dyadEe/zhrBh/dOYdVI1drtyU4CGaBho+/gYoUmWyyzdo+L1BDE46EVhYeolrUeT4f7utB49AMyaNQtarVYKsXY6nejo6FCEWMuH3IYTXZUq+Tx0lm3fFr2+jEYjZs2aBSByX5QPu3V2dkriY7fbMXfu3KzsW9GPrLc4BB0dHfjXv/6F9evXK5bPnz8f69atQ319PTo7O3HvvfdiyZIl2LNnjyIHUSpQoUknG/NY9tHk8sJMtd+hUChhvi7geCg5zeXm9Xpx8skno6SkJGGb1gIjKiptQLsPsGfj28SBYcCG45wnWc4h+s83duFHKxdmt1EZ+RZ1KO8PwzDSvDQ6YiEPsXY4HFJ0FfUxyF/54GNIRL4LDW1T3p5Go0FRURGKio6XsaDi4/P5Tozw5hdeeAF2uz0meEA+FDdr1izMnz8fNTU1+Mc//oHrr78+rX3QyI10swOMVYsmV+1Hn3CiKMYVE5/PB57nodPpJDFJNCOe0tramtKYfd3UCnz0YSNmTqjG1zmyagQDCyYsgmhTK2yWCZ990pxTockWI5WCJl6INfUxOBwOacjN7/fDaDSC53m0tbXBbrfDarUOK8R6NObRpNveSAc/UPEZTqaKpO3npNUEEELw3HPP4eqrrx4y7Nhut+Okk07CwYMH094PjXjJp6GzXJPN2h80Nf3AwAB4nsfOnTvh8/mkFDxUTEwmE4qKiqS/03X2ptLnuvpyfPRhI5jDPsA25OoZETZz0LkEBIvlQpPdfRw9MpjXT+ZyRquP8XwMwWAQnZ2dOHDgAHp6etDU1ARBECS/Ah12i07fn4x8zOdGycU9KB/OuxEVms2bN+PgwYMpWSgejwfNzc24+uqr094PfdrJJ6HJt6GzVFLTU+cgneiaaQqe4VA3NTLUcrR1ADOWjMMelyPr+xAMLIy9YQSLj4fkZtuiEQWCnTvaccq8mqFXPgHI1pO5Xq9HYWEhNBoNTjvtNEWItcPhQFtbG1wulzShUR5skOxhNl99NJR8DefOlIyExuPxKCyNlpYW7Ny5E0VFRZgwYQJuu+02HD16FC+++KJiu2effRbz58/HjBkzYtr8+c9/jksuuQQ1NTXo6OjA3XffDY7jcNVVV6XdP4Zh0q5JM5Z9NInaJ4QoUu9EO+OHSk3vdruxc+dOaWgjm6R64k+aXAqNlgMfFsAdCQC5SBvGAhqPoCxslkIus3R5559ff2OEJpvIb5TJQqzpkBuNrKKFAqnwFBQUgOO4vA4GoH5lNakmgC+++AJnnXWW9P6WW24BAKxYsQLr1q1DZ2cn2tvbFds4nU68+uqreOyxx+K2eeTIEVx11VXo7+9HaWkpFi9ejE8//VSaLJYu6dakGal8ZLl6sqDZYlNxxMsrdA7Vn9EWX61Og5raYjQf6MGRln7MWFKNPdn21TAMeAunCHPO9tAZAOz7+mj2G5WRzZT8+dJOKsTLIxYOh6Uot4GBAbS0tCAcDsNisUAQBHR3dwNAyiHWiRgLw+1j1qJZunRp0gO8bt26mGU2mw2+JMWrXnrppUy6kpB0yzmP1FyXTPeTzBEvCAIOHjwIs9mckiM+3X7ngnTaPqm+As0HIkXSNEf9QHpBiCkRskX8NJLQpF9lYkjcrgDc7gCs1pGdLDeajFb2Zq1Wi5KSEimqkRAihVjv2rULPT09aGlpAYCYIbd0h4fHWtTZaJAfU3WzDMMwaRc/y4dgAEIIQqEQvF5vjJgkc8Tv3LkTs2fPVhRkyhb5EDZbN7UC//q/3QCAw4f6MX3JOOzNsq+GN3Ew9gQARMb1czF0BgAb39yN710VvwAeDcSQ//70b41GI6UhoZMdc5XccrRvSrlAHmK9e/duzJo1CyaTCV6vVxpya25uhtvthl6vVwiPzWZLGOgyFsObR4MTUmgA5J2PBjh+EqXiiKdikoojfqzecFI93nX1ymwRuo4gYMluX0QtA02AgA2JEFMsbJYJ2z46iMt/MA+BQAA8z6O1tVUhKIIgwGg0StZpRUUFzObIRE+Hw4Hu7m40NjYCgHQjtNvtUinn4ZLtobN8Gs6Tt8cwDFiWlUKsad0YOp9EXjI7EAjAbDYrhIeGWOcqvDnbbapCkyMyGTrLdg4k6oj3eDwAgJ07d8Lv96fkiM9kf7lgtK08AKiqtsNs0cPridQRam/uw7Ql47Avm1YNAwgaQOuOhDkzfG6+d1trHzZv3iy993g8MJvNKC4uhtlsTvj70wSXwPHJjoODg3A4HGhoaJCsnlAohMLCwrRSvIwFRioFTbzJjMFgUBKe7u5uHDhwAKIooqCgADqdDoIgwOfzpVyqORlUFEbLosn2viknrNCkO3QGZHZTDYVC8Pv9McNdcke80WgEAJSXl8NisaTsiE+VXN1M8uUmxTAM6urLsXPH8QATfWcIyGZGF4ZBsFBzTGi0w651kwhRILBbJ2BCrR1NTU1xIzCHQj7ZkaZo+vrrrxEOh2EwGBRVNOXDbXa7PSWrJ9+skFwNT6WKXq9HWVkZysrKpO29Xq8U4RYOh/HRRx8pQqyp5ZNqmZJM+5Zqm6N9LatCc4xkFs1wZ8QLgoDNmzejvLw8JxULcznsN9pRZ5Qp9RUKoWk/2IuTl1Rhv8uZtf6ELRwMbcG0CptlwqZ3D2DFv8/Papscx0Gn0+Gkk04CEDlnPR4PHA4HHA6HlFTRbDZLFo/dbo9bSyabjPYNLhHD6RfDMLBYLLBYLFIet29961uKLNadnZ2SlSMXHhpinYiRKDswGpywQqPRaNLKdQZEQiIHBgbScsSnMyM+X27aqZJPN6CT6stjlhm7woApWz0CBCMLVgQ0XjEnUWeUr3cdAZBdoYmGZVmpBj21eoLBoCQ8R48exb59+8CyrMLiSfeaGQlykdY/2/No5DVjaLJgWjjO6XSiv78fhw4dQjgcjsliHS32qtCMEeiEzXgWTSJHvNfrBSEEPT09aTniU+0PMDZzqeVD1BlwPEOAnLamXkxdUoWGLFk1RBP5nXROHrksVuwY9CEYCOdwD/HR6/UoLy9HeXlEtOUTHR0OB44cOYJAIACtVgtRFIdl9eRrmpeRdN7HKxzn9/sl8aFizzCMJDy5SNWfqtAQQvJrwma+IwgCeJ7Hpk2bcOjQISxZskTyoSRyxHd1dcFkMmHKlOwXqMr108RY9dGkc1Lb7CaUlRegp9ulWG7qDgPGLHWIYRA2stC6c+SgkbH5/SZMrBvdy08+0ZE+he/ZswfBYBAajQZHjhzBvn37FE/rQ4X7RjNa82iGIpvtpeNop/edyspKAMeHOOXBBoIg4MMPP1SUT0jnmEdDCMlZZGqqnFBC86Mf/Qg7d+7EwYMHEQqF8Ne//hULFizA0qVLh5wRPzAwkPMb9lgbOpO3nQ8Xet3UihihaTvQi/rFlWh0uxJslR4hOwdzZxiCBuByOHz22bZWTKzLv/LOGo0GHMdh6tSpACI3QpfLJVk9NNzXarVKwlNYWBgTcZUvlnA0uRo6yxT5EOf48ePhdDrxxRdfYNasWYqS2YFAABaLJaZwXCoCog6dZZlly5bhhz/8Ierr63H55Zfjl7/8JS655JKUth0JqyMfQoXzoV1KusdjSn0ZPt58IPaDFgdQkp0ntrCFAxCGqGfB5TAg4Eh7dtPo5Cp1jNyHQ/H7/ZLwtLe3Y8+ePdBqtTHRbflyM88luYiIY1kWxcXFUvE4AIrCcdEh1vJgg3gh1vlw/DIWmi1btuDhhx/Gjh070NnZiddeey2mxoycTZs2KfKjUTo7OxXlm//4xz/i4YcfRldXF2bPno0nnngCp58efyZ1NCtWrJD+1mq1eTthM9uMRELQkTxReZ6H1+uNmSHv8fbFXb/vqB91tZVoyoJVI+iPCVaOfeJ8WERXhze3O8kRNGSfDv/QktxUfNra2hAKhUAIwb59+yTxycY8k+GSi5n32STRdWwwGGAwGCT/mjzE2uFwoKWlBW63W8ogkW+F4zIWGq/Xi9mzZ+O6667D5ZdfnvJ2jY2NilQpNDYdAP7+97/jlltuwZNPPon58+fj0UcfxfLly9HY2KhYLxXyKQXNSLQ/1qLOAGWUHxUVr9eLUCgErVYrZektLCxEdXU1pk+biddfaoUgxKqArU8AsuFHZSMaowmKIEi/bHM6fLm9C5elcOmM9k1iKDiOU9SRIYRgYGAAn3/+OYBIkTu32w2dTqeweoYK9ZWTz8EAozG5Uh5iPW7cOADHBV9eMtvn84HjOCk6lhaOy2RS+HDIWGguuOACRVXMVCkrK0tYxe2RRx7BT37yE1x77bUAgCeffBJvvfUWnnvuOfzyl79Maz+ZZAbIZWhnPoUKp8twLk5RFBEIBBRC4vP54Ha70djYCIPBIKVbKS8vh8lkgtlsTjjfaEJtMVoO9sYsb2nowZTFlTg4XKuGYRC2c9A7BIQNDLSB3D0ctB3Kjl+Jki8TgBmGkeaQTZs2DUDkJkifvgcHB6VsygUFBQrxoZOb5eTzQ9RoCU08ogUfiIRYf/XVV2BZFv39/WhubgbP85KPjVo9NM3RCZMZYM6cOQgGg5gxYwbuueceLFq0CEDkgOzYsQO33XabtC7Lsjj33HOxbdu2tPeTbq4zILcOzBPdoqFpOOIlhAQgCQidg9TW1oYJEyZIwy+pclJ9RVyhAQB7v0hzYg6LUEFEaMAxAHL3m7ldIYRCPHS6/HGV5mqYheM4RWoXGupLh9vo0I9Op1NMKC0oKMh6/Rggfy0kILsPjjqdTkoSWltbqwixpsEde/fulYISqqqqUFtbm7X9U0bsDK+srMSTTz6JefPmIRgM4plnnsHSpUuxfft2nHrqqejr64MgCNIYJKW8vFxKp5EOo53rLB5jLRiAIu93OByOsU68Xi8CgQA4jpPExGq1ory8HGazOW6pgqNHj2Ycebbxra/jftayvxtVpxSgQxxeeDJvigwrsOHcR0598M4+nH/xrJzvZ6QZ6lyXh/rSAmY8z0s3QPr0TZOMhsNhdHV1wW63w2DInzIL+WTRpNJmohBrmtUg0xDqoRgxoamvr0d9fb30fuHChWhubsbvf/97/OUvf8n6/liWzSuhyXXUWbZnTweDQbhckaGdpqYmKZ9bOByGTqeTBKW4uBjjx4+H2WyGTqfLuejVxckQIMfWL6CjMOkqQyJqI9+BCxEIWoDL4dzKLR80ZkVosvn7j9YQnEajUURbERIp29zW1oauri4pjb/BYFCEVtNMyqmQi2CAsSQ08aDzqaj1mAtG1WY//fTTsXXrVgCRuvQcx0mV7yjd3d2KqLRUybcyAfnoRxFFUcowHW2l0HIFQGTYg6arp/OQRotx44tgMung84Xift7T7kXtuDK0+IYR0XUskzPHA6KOBRfOne+u5WBPztoe6zAMIwWDuFwufOtb3wLP89JwW19fHw4ePAhRFBUlE2jxsnicKMEAo91muoyq0OzcuVMy33Q6HebOnYv3339fCpMWRRHvv/8+brzxxrTbzjehAUZv6EzuP5ELit/vV5jS0enqAWDz5s2ora3NurhkerxZlsGUk8qxe+fhhOsUDohoGc7oyrFMzqZePudhzsEgj/b2fkyYUDz0ymOIXGVv1mg0MZUzafEyh8OBpqYmeDweGI1GhdUTPbnxRAwGGMk20yVjofF4PDh48KD0vqWlBTt37kRRUREmTJiA2267DUePHsWLL74IAHj00UdRW1uL6dOnIxAI4JlnnsEHH3yAd955R2rjlltuwYoVKzBv3jycfvrpePTRR+H1eqUotLS+WB6Vcqbt5zoYIBQKxVgnXq9XSilCxcRms6GyslLynyT67lSo822W95T65ELT1tiHSQsrcMjrzngfYSsH9PLQBHIf5vyv13fh328+e9jt5Ft6/5G4ucnDfKurqwFE/IhUeHp6etDU1ARCiCK6ig4BDxe18FlqZCw0X3zxhWIC5i233AIgMmly3bp16OzsRHv78bTuoVAI/9//9//h6NGjMJlMmDVrFt577z1FGz/4wQ/Q29uLu+66C11dXZgzZw7efvvtmACBVDhR59EQEql9LhcUj8eDAwcOQBAE6PV6SVBKSkpQU1MjhQune7Lla0j2SXESbEZT4gQODcNeFwyRp1+GIJL/zJ870+arL1oV7+lvLLdAaTRWYWFhThIvyhntm9Jw0Wq1McksacmEvr7IpN8PPvhAKusht3oy+e6qRTM0GV+KS5cuTXrjXLduneL9rbfeiltvvXXIdm+88caMhsqiybehs3R/aOo/iR7u8vl8IITAaDRKgqLX6yVRyUXUSL5lho6XyTma5r1dsE8vgEOTWQQazeQMADlN5Qygt8eNlpZW+P3Hf2NRFKWSzmazGaIoSs5wk8kkiQ7NrpxvZDskeThtMczxQnHFxcXo6enBWWedBafTicHBQXR1dUnlsdMtFKcOnaVG/gTwZ5l8FJp47dOyBdFiIvefUCd8aWmp5D+RjzfTSJxsi8xon5yJKCwyo6TUgr5eT9L1JvJ67NT4MtsJwyBsYqH1iWBDufbdAR99eACLl06RfGQmk0n6jRmGkW54dFhocHBQSjOv0WjAsizMZjMcDgcKCgoyztabb8OkQPZFC4ifwl9eMoEWLjObzQrhibZ68n0eDaAKTU5J10cD5PbJnZ7I0ZMZg8EgtFqtJCh2ux3jxo2TKnSmmo4il+TjxVQ3tQJ9vQeTrnPkQC90Ew0IFWSWbiNo46D1ieDCuQ9zPtjoxjXXTZLei6IIQRCkY89xHBgmUmdJfoMURRFOpxP79++H3+/Hjh07IAiCNBxErZ50HkLyzdeTTRLddBmGiSkUFwqFJFHv6OhAQ0MDGIZRCI8gCKpFkwInrNCMho9GPrYebaXwPI/29nZYrVaYzWaUlZVJ4jJcp2SurLF8FrC6+gps+yi50BAS8dV0FCRdLSH8sUzOACDkOMz5YGM3wuGw4ph4HF5odVoYzHqEw2GwLCv9JvRvhmGkuSQGgwFTpkyB1+vF4OCgdIP0+/0oKChQCM9ITHoc7ZtbIlLtl06nQ1lZmZRnMbpQ3NGjR+H3+6HRaLBnz56slMfOB6E5IVLQjBS5zAwgimLc4S65/4QOfxQWFsJkMmH//v2YPHmyFJaZbcZaepvhkoqfBgCCAwForXqE7emf6lImZwCsmNtjEPCHseFP78Pd0Y/D+ztwpLEDPpcfzzb+HgaDQfoN6P/yiMBwOIxgMCgFCZjNZlgsFowfPz7SdiAgCQ/18xiNRkl4CgsLh3VzzDW5GDrLhHiF4vbv3w+PxwOtVisNZQ6nUFw+CE0uOKGFJp0kmfGEJlG6er/fL2VEpRc1TQgZ7T+hsCw7JlPQ5Krt4bY7ua4MLMtATEEAzJ0hODIQGprJmQXABUjOw5z//sxHQHun9P7ca86AtdACIHK8QqEQPB4PPB5PpGzCsb95npfS9vM8L1k69KXX61FZWSnNWZOH/3Z2dmL//v1SQkZa1EwUxWFVZcynYIBostkWx3EwGo1S1pNUCsXZ7XaYTKa4/VCFZoyR6tAZnX9C83U1NjYmTVc/btw4KdIr3R9vrKSgiSbfos4AwGDQYsLEYrQeil+jRg4XItA6+PStGuZ4JueRCHNGoU0hNJXzirB9+3ZpSDYcDkvZrml6eIvFArPZDI7jFFaPKIqK85NhGGm4LZ6fx+VyYXBwEG1tbejs7ERnZydsNptiuG00M0Jki1xHicUrFBcIBBTCs2fPHillP/WlFRQUQKPRqPNoxhparRbBYFB6nyhdvc/nA8/z0Gq1ICRS3S6VdPXpMhKZB3JBvlo0QMRPk4rQAIC5IzOrRsrkDIDkuuy64bivrmxKMSrry8DzPEKhEHieR0FBAYqKihKG3soTJwKIER4qPvH8PLRNj8cDg8GAyspKabht//798Pl8sFqtiuG2ZH6efD3XR8OXaTAYUFFRIaXSogEc8iqloVBIytlGh+azVShOFZoc4Pf70djYiMbGRrS0tODSSy/FjTfeKI2RytOtFBUVSb6U/v5+tLe3o66uLif9GktJNaPJ15tG3dRyvPuvPSmty4YJtE4eYVt6pzzN5AxELKOcDp8xDFBkAwacuGL1t6USGtSioTf+AwcOwOfzwWKxKFLqR9/4kwmP3OqRv+g6dLa93M8jryWza9cuyc+TaMLjN2HoLJO+sSwbUyiOHt/29nb4fD589NFHUnlseozTKRQ33D5mmxNKaH7+85/jkUcekSK7tFot5syZg4kTJ2LcuHFx09VT8m3CZrqMRf9POn2m5QnkL48vNWsGiIiD+WgIjjSFhmZyBjAiYc4oLYJZCOOMKxZIixiGkcon05T6wWBQuvHTCpbyG388P4BceGhmZI/HA7fbLfl8/H4/6urqwPO8Yh6PXq9XPJXT5JbyCY902KiwsDCrw0D57O/JxneU/76BQAB6vR4zZ85U+HoSFYpLZQpEqt85l/e/E0pobr75Ztxyyy2orKzEnXfeidbWVtx7770pbTsSQ1uqRXOcRCd+PEGh/jJanoAGX0ycWIt/vNiEgD+1O39GVg0D8FpAc2wX2Qxztln1cLqDyoVWE848cwEM5uRpZvR6PcrLy6X0TOFwWJrp3tHRgf3790tPxFarFVqtFoIgKIIJCCEwm82wWq1S0SuLxQKdTgdCiGLCc/RwG8dxiuSWcj8PrSfD8zy2b9+eV36efJ8GQEUhXnnsRIXioiPcoh+mx7RFs2XLFjz88MPYsWMHOjs78dprr0lZl+OxYcMGrF27Fjt37kQwGMT06dNxzz33YPny5dI699xzT4ww1NfXp1z4jE60AiI+mhMx11mitnNFrtqmKXY6Ojqkp+loQaHzjejf8W5Sk+vKsHf30ZT2mZFVwzAI2TXQ9EbOJTaLsQBivGOr0eCcFWem3ZZWq4XNZgPHcVLAgNPpRF9fH3p6IqUIGCZSYpmG55aWlg4ZdptsuA2I7+cBgP7+fuzevRvjxo2L6+ehlk+80s2J+pAtsi0Mw4nOi9deogml8QrFUatnYGAAhw4dknx5cst2TAuN1+vF7Nmzcd111+Hyyy8fcv0tW7bgvPPOw69//WvY7XY8//zzuOSSS7B9+3accsop0nrTp0/He++9d7yDGaZVYVlWLeWcB23LQ8Tlr2AwCI7jUFBQkJKgABFxojc8+ppUV5qy0AARq0bj4sEXpH5e0UzOAMAFRBAmkmxzuLhdAZSUWtHXezzL9MnTqzBldk3S7QRBUIQ30xedS0P9KzU1NVJUGsMw8Hg8ksXR2NiIffv2SXVcCgsL4873SMfPAyBmUml1dbWUVTkYDEp+ptbWVnz99dfQ6/WKAINEiS3zYR5NovZGy+ej0WhiymP7fD7J6qFzpgDgwIEDKC0tlXw92RTHlPqa6YYXXHABLrjggpTXf/TRRxXvf/3rX+ONN97A//3f/ymERqPRZFToLJp8zHV2IredTFBoiLjFYkFpaSkmTpyI9vZ22O12hRVKiSco8frEsizqp1bi/7Az9e8DwHI0BEcaQkMzOQPZz+ZsNCmP7/nfni39Ta2+aEHx+XzQaDSSoJSWlqK2thYWiyXp0BRNsVJTUyPdlKjw7Nu3D8FgUJrvQZ+Io7NWJBOecDgs+XsGBwel70DX1+l0MX4eOtzX3d2NxsZGKdMBfRUUZJjWIQn55qOJbi/T/jEMIz2sjRs3DkDkGH/wwQcwm82K8tiJCsXJHxqyyaj5aGg6B6rGlKamJlRVVcFgMGDBggVYs2ZN3JvRUOQyM0AmnChRZ/IkoJJT/tjTtHzOkbxEQbwUO0eOHFHk80omKDTShmVZ6UmM/j9tZnXa34cNEWjcPHhrirO1NVEXXhYfBnX648fGYOTAaPrw6aefQhAEBAIBAMdn+ttsNmnuTCbzuOTIb0rU4qCRbQ6HAwcPHoTX65Xy71HhMRqNEEVRYVG53e64FlV1dbX0+0ZHt1E/hLx0M70nyK2ecDgMvV4PjuPQ09ODwsLCYfl5cjGMlM/CpdFowDAMJkyYAIvFAkISF4qz2WyYNGlSTtITjZrQ/Pa3v4XH48H3v/99adn8+fOxbt061NfXo7OzE/feey+WLFmCPXv2wGq1ptV+vlk0QP4Ob8WDDs+IoojDhw9LFksgEFAISnFxMSZMmJA0Z1s8C4UQIvkOaISUfLyf/j2UiW8vNKGwyITBgdSzNDMALEdCcJyc4unPMAiZWei8kadzNothzi7n8X7PmFMKnS6S0SIYDIIQArvdLs2doT6YXEHnz9AMAsFgEL29vejr60NTUxOCwaB0ndBs0QUFBUNaVIkmksbz89AULxMnTpSsrsbGRrjdbjQ0NCjCuuXzeVK92eeTBTIS7UW3yTDxC8VRyzJXQ2qjIjTr16/HvffeizfeeENKWAdAMRQ3a9YszJ8/HzU1NfjHP/6B66+/Pq19fNMsmkyhZZ6pM56+AoGAlMZHEAQUFRVh/Pjx0s0k3j5TGfKir9raWvT29kpPrhqNBoWFhSgqKpLyw8n3QSexRadg8fl8KCrVYnAgve+drlUTsnGS0HBhoohEGw6D/QFwXCSVzr/9xwWoHHc8ykieHJOKfXRyzGwJTzgcVgzP0SEwQRCkAmH0N6FDZC6XC4FAAKFQSEpZQycdyhmOn4dWhNVoNJg1a5YirLutrU3y81BBTqWAWT4Lw2ikoNFqtSgpKUFRUVHOhuFHXGheeukl/Nu//RtefvllnHvuuUnXtdvtOOmkkxQlo1MlHyts5pKh+k4FJXrIiwoKtVCooFAL5ZNPPkFNTQ1sNpvUFp1lnkhQAGXKk3gWil6vl9qkM6UHBgak0FyNRiPNe+J5XqrPQ5/G5MNIrr7daG7cmtbxYgCYj4TgTNGq4c3HMzkDANGxQBbCnHlexPiaIhSXWCWRAZRPnuPHj0/oU4mOMBpqWCl62Iu+6PwN+dMunY+W6ClXnteLRj0RQqTUNYmssKGEh75nGEay7ujn0WHdifw8dLiPBjnQPuTjPJro9rLdvzEddZYJf/vb33DdddfhpZdewkUXXTTk+h6PB83Nzbj66qvT3lcmQ2e5ZiQsmniCQhOBUkGhWaWrq6slQUlkoQCRizk6hb1835kMedHZ0NEWitfrBcMwUkqgYDAojf0XFRWhuLg4Jv1J3cmZBY9waVg18kzOAMBkVrgzLtYCI86/eFbSdeL5VPx+v2TxNDY2wu/3K8KHjUYjgsGgQlC8Xi9YlpUEpbi4GDU1NdJcm3SQ5/WiQ120ZDK1wugkw2RiSB/yQqEQ3G635PNxu91SAtva2lrpepZPJE3m55Gnd6Hil4vktvns88llm+mQsdB4PB6FpdHS0oKdO3eiqKgIEyZMwG233YajR4/ixRdfBBAZLluxYgUee+wxzJ8/H11dXQAgOaGAyMz+Sy65BDU1Nejo6MDdd98NjuNw1VVXpf/F8sxHk+325UNJTqcToVAIfX190oVJb0r2Y4XU6ES8oYa86N8UjuPQ0NCgGK7R6/VpCYo867BcVERRhMlkkkJwS0tLYbFYYjJg01noAwMDaG9vx549eySxLCoqQs3EopQzOcuh82qcU1O4DFhAZAD22C64oKh4Pxy0Wg6nL5yc9nZ0NnlpaakUukxn6re3twM4PneGRpul69NIB4Y5XjKZWmF0kqFcDOVh7PIMBTzPS4EPVqsVlZWVsFqtUkQUPS+TTSSV+3noNnJB7u3tRTAYxNatWxVWT6Z5xXJhgWR7Xg6QutDk3dDZF198gbPOOkt6f8sttwAAVqxYgXXr1qGzs1M62QHg6aefBs/zWLVqFVatWiUtp+sDkUikq666Cv39/SgtLcXixYvx6aefSllm0yEfh84yaV9e+ybaQqEOWUEQoNPpUFtbmzSzdCKnfHQ/5RftvHnzpKGJw4cPY//+/bBYLFL8Pk03AkASlOihGZrGnj5FFxUVSeKSykWl0WgUs9DD4bB042hpaYHH40FRiQF9Pf60jy8XTNGqYRiEbccTbDIE4I0s2DTDnEusWhQZNdDxYYQcHjg6BnCyfRI4bujjQM8F+sQvH/bS6XTSTZ7eoLVarfTb0aElKtCpJMbMFnQY1Gq1Sr4nOhQqCIKUwaCkpATFxcUJb/ryZen4eehEx3HjxqG7uxsHDhxAXV0dBgcHpQcXnU6nOC5WqzWlm26++2jSFZpckbHQLF26NOmNk4oHZdOmTUO2+dJLL2XanRjyMTNAMoYSFJoI1GazoaqqSiEozc3N4HleMXyQiqBED3UlslDkVQZDoRB6e3vR09ODzs5OhMNhafxbEATFOH90GvtsodVqY/r01ac+9PU0p91WOlaNPJMzgIzCnPu+bkNfV+/xJjgW5604Q7EOHTKMFpREw17UWo2HwWBQpKmh1kV7ezv27t0Lg8GQlSd7eTCBvN+iKEppbmw2G6qrqxXWdXRtnMbGRmi12qTJOoHMAwwIIeA4TuHnEQRB6kNvby8OHDgAAAorPpuBF8lQhWaMkW7hMyD3WYrpBeD3+xU+CfkTHh1WKCgoQGVlJcxmc8KhDrmgJPKjJBIUAENaE/FmoNOINPoETas/0hsj7QMNgU71yXA46HQ6zD6lFh9vTl9ogGNWjZcHb05+OcgzOdPt0g5zLrEBMqGpXzgJzsAg3C1Ohc9KPoxEc7vRYcVMj6dWq1XUoZEnxqRBGNFP9tHRf3SoS+5HiQ4moENnqVit0X0SBEFKod/b24uDBw9Kzn157ZZ0Ituo78fj8WBgIBKeKJ9IyrKsws9DCFHM56F+HnnEX2FhoZQTLt+DCwBVaHLGaA+dyQWFhqkKgoC2trZhCUo8C0Wv1+Pw4cNwOp3SRVBcXCw95Q4lKPJIJLmw0AAC+Qz0iRMnJnyCpsMiAwMDGBgYQGtrKwBIvhRaliEbJz29eXi9XrjdboSE1DM5R8MAMB8e2qohnLLfLE/A6wBNKI2dmZS5vaacOR4tLS0ghECj0cBq/f/bO/P4qKq7/39mJpPMZCd7QkiABAiELQluIAglAan62Pqo1epPlNLnsSq4tLbS1mqrVi3V0opFbS3aKnVrLUWFsBkoAsqWhSWEAAECIctkZrLMklnu748838OZO5NJZnInMeG8X695RcbJnTM3c+/nfPcYjB49ms1ECiVylyTd5CnOU11dzXqn0fVktXa7J2kT0RerKhA0Go1HWxUK7vMpzXxlO986h75/JIB8ISm57uLi4pCYmOgV5+EfgHcXBT7OQ0WO5LYOCwtTbH6MsGiGGMEIDRD4H1ouKPzoZ5VKxVxe4eHhCA8PR25ubo9fyL66vIBLVfIqVXfVb0ZGBksRPnv2LI4ePcpuBFTwp1KpvFqa0FrJJUOTRGlH2lMCQU/nkEQpKyuL3SRaW1vR3NyMEydOQKvVesR3emuq6HQ6vVJxOzo64HA4oNfr2Q2vcMYEREQcht3e9785j8YuQdPphMuPVSNp4FU/I2nVQFcAlrNaDUTpgU4rkkcl4O5H7oBOp/OIOzU2NqK2tpb9LehBQXGlkcd+2tvbYbFYWGCaaqlsNhubpULfKV91M0qiVqtZcJ9u+p2dnTAYDGhpaWH1RXw2WXR0NGJjY5GUlKRIISngGecBujc6JpMJx44dg9FoxH/+8x9mDZIABnNulBYa3nLrC1+7ZICvO8EUbAI9/6FpV8MXCpKo8IJCbo6oqCgPQampqWGxFiCwfl7y+ImvL294eDhzQUiSBLPZjMbGRjbQjf/C6fV6xMbGsnhPdHR0SDKR+JsEpaeSGNbX1+Po0aPQ6XRMdCIiIrzScclNRwKWlpbG6jvkDSBzxqfiaFXfG2zyqABE92bVqFRwxGgQ1srthl1BWMFpScDJc7h+6XwWjJfHnXjhoQaUfKZdMMLDZwDyoiKP/fRU6c+nDvN1M3yMR+mGjXTdydOeyVXHz2Sh7057ezv7vmu1Wuj1ep9CE2ycB+i+3lJSUnDmzBmkpaWxjZ7RaERLSwtqa2u9zo2vpqW+Pq9Ibx5CBBqjoT8EP/JZbqFIksRcXnzHYX8mMy8oVBPCv5b+218/r56gY8rThjs6OjzWmpycDK1Wy9xNRqMRBoOBuWt6q6RWCpqxQem2NL+kqakJ58+fZ59Zr9cjPj4emZmZiIuL67NLZnxeWtBCA5BV44IrquegrztcDeCS0Ghskv80Z0kC5Oc2LgZh4WEo/n9zfP8OfAsPpXj3RXj4+Bp/gyZLMCYmBtHR0UhJSelz7IffOPB1MySI5Nbi4ymBtM0h61Ue/3G73UwER4wYwfp29fS9IGuDT3qgLtG0LnnsCfAvPHRO6f/Tg4RB7vLjz01rayvOnTvnFefhm1n6WocSBGrRhIphKzSUddYXC4X8/ACwa9cuSJLELBS6UZOg+KuS9mehREdH4+TJk2hpaWE3h8TERNbjqzfktSgkLE6n06MWJTExEdHR0X6PSxXdBoMBDQ0NqK6uhk6nY262hISEfg+o8lU/IxfB6OhoJCUlsbiPSqViN1PqEhAXF8fW5GuoE8/4vPR+rbm7B5od5gk9x0ZcelkQGv+X5mzxvamZMCkdx49d9HxSG4YZNxQgPqXvnYnlQXMSHoPBgFOnTqGzsxNarZZtsOx2O9tExMTEIDU1Fbm5uT4twWDh62aysrKYW4uEp76+Hg6HgxVL8sJjs9k8BIWKM8l6DSShQA5ZGyTSfPcAmgZKmx4+s81fgoHcsqKH3W5HVlaW10RSAF7nhh/HTXEeebo578JTisEWGWAYCw3FaGiqoK829rygkEursLDQ55eOCKSfF5/xlZmZiVGjRqG9vR0Gg4HFLOgGTzd52gnJLRSHwwGdTufRloQEJdC0S76iOycnB06nk+28Tp48icrKSsTGxnrEd/xd6DQVs6c4Cp+O25sI8m3krVYrE536+no4nU6PxAJ5Rtv4vP6Pl9DYJKgtLrgjfZ9Tpz6wc+3y5VpTqXDV7bMCXht9l+U7ftpsUBZUV1d3dgJl/tGNPtQ1M3yMjgo2Ozo60NjYyGKHfBCeLFs+7TkUcaiwsDCv7gFkTctdgHzbHNqA0nl2uVwsJpiQkMCSICgRwel0+o3zyMdx85ZXfX09jhw5wuKoAIKO8/CEQriCQSWFOqd3ADGZTNi9ezeOHDmC7du3Y+vWrQgLC8Ndd92FO+64wyOOIrdQXC4XduzYgdmzZ0Or1fYqKIC3qPS1Wh64NB2vqakJRqMRFouFmblU2R8fH88uXCV3or1hs9nYDZ5G8tLuLzIy0qtfljyOwj+Uqj3gM9oMBgNMJhMAMNFJSEhAZGQk7vvOGzC2dvbrvZw6lV+rJv6YBZquS98HdxigcvpOc46OiUCHbFxzZlYCXv3LvT0en3bP8poUi8UCrVbL3F7001edEu9qMxqNaG9vD2mxprz2h36SpUXrjYiIgMvlgtVqhdls9pi62dMMnFBC7mSDwcCuQ4rtajQads5oaFhf3Nn0k8/48iU8cgFwuVzYt28f2zAYjUYPAaTzE8h9oKOjA3v27EFJSUmvr3W73dBoNCE5/8PKoikvL8cPf/hD5OfnIyMjA3q9Hv/+97+Rn5/f40VFgkI7rfb2dkRHR3u8hheRQAWF744sD3LTBUhZXmFhYbDb7TCbzTAYDN2dif/PygkPDx8woaGGlxqNBnq9HmazmV2MwKW2JnFxccjNzfVIpQ4V/jLampqaWKFfcpqu30LTq1UTqYam69LOXO0EnOEqhHV5b0Y62u2IiQ1He9ulHGi+rxkfl/BX6Dhy5EjExMT0OQuwJ1cbxVL4Nj6BCg9tNOQBeofDgcjISK8OBf5m59hsNrYufgaO0tl2/lxflPqclJTExFuSJI8izrNnz3oNhfMXX+lLgoE8pVqj0bCUc3K38TGw8+fPs+F0fT0/oUouCJSgLZqdO3di5cqVOHDgABoaGvDxxx/jW9/6lt/fKSsrw2OPPYYjR45g1KhR+PnPf457773X4zWvvvoqVq5ciYsXL2LatGl45ZVXcOWVVwa8vn379uH666/H6dOnoVKpmLXAWyo8kiTh6NGjMJlMLBMqKSmpz4OW/LWxp2aW8t2+v5uzJEksjmIwGGA2m6HX65kLgG/9EizyZAJ+7ZIkeayV1h8eHo62tjZmWbS1tUGv13s0vOxvfKc3yA0qr5x3OBw4+GULdpc19P89/Fg1umYHoi54Fs/w82rk5IxPxsma7iLNsDAVlj48GdHROrhcLjgcDlb8KrdSQpk2zAtPa2trjxaPr0aXnZ2d0Gg0Huuln/21YLu6ulhDTF+WGDUL9YcvFyNlotFa+XX35ftKgkjroviKfChcbzd1XxYPYbfbUVVVxdzsgLfVQ/U8/s4PX6tmNpuxf/9+zJ8/v9fPSDVBXyuLprOzE9OmTcOSJUtwyy239Pr606dP44YbbsD999+Pd999F9u2bcPSpUuRnp6OhQsXAgDef/99PPbYY3jttddw1VVXYdWqVVi4cCGOHz/uMbemL1D2WGtrq9fQtJ4slBkzZsDpdLKb6PHjx9HV1cUKICl4z6c589X9/K67v9MQVapLzQHHjh3L1tXa2oqamhpYrVZWfJaYmIjY2Fi/7yGfN8JXoFMyAaW2+mpsyUNf6pycHJaG29raitraWlgsFsTGxjJ3Vl/cDT3BjzLmRcVqtXq4kNLT0y+tP6Eeu8v+EdT78WhsEtRWF9w+YjLOSO/Po/aT5tzVdcl1NnFKElJSEtj30+FwQKvVety0Qi3UgLfFY7fbWTp8TU0Ni/PQaymWkpKSwjpChGKnHB4e7tEexlfbHMogoyA+fbdJUCwWCzQaDRMUsgb7I946nc4jfsi3zqFU/fDwcA/h6al1Dm8RtrW1eQghJSBRvEdu9cjjPPz5OX/+PI4ePcpmO/VlbMRAoUiMRqVS9WrR/OQnP8Gnn36Kw4cPs+fuuOMOmEwmbNq0CUD3hM0rrrgCq1evBtB9oxk1ahSWLVuGJ554IqA1bdu2DTfddBOcTieuuuoqFBcXo7i4GFOnTu3TrotMbSo2bGtr87j4KJAZGxvrEfMZKDPVarUya6e1tRUqlcqjCFKe8SUfs8tbKkr2cCJxp3VRfIcSC3xdfL7qO0jAgUtV6HJr0Ne57uyw465vvwolIo8OnQptvqwat4SEKotHTEYCIKkBtQ+jJmNkHC6cNwMAVq6+0yM7jnbwJNadnZ3MNUJCrfTNggaX8TdnOtd0niMjI5nFazab0dbWNigNOQlqfWM2m9HS0sKGrtHtKyIighUoJycnh0wIe4LvqmAymWA2m9lmkTZtTqeTnXcaEsc/KAlJbvX4SzCQf0aa7cR/p2hwIW/1+PKGUIPTUIjTgMVo9uzZ4zXobOHChXjkkUcAdF9wBw4cwIoVK9j/V6vVKC4uxp49ewJ+v/nz56OzsxMnT57Exo0bUVpaipUrVyIuLo6Jzje+8Q3Exsaivr4e4eHhXt2H+Tb2mZmZLBDOV7t3dXWxGEpvJr2S0K6OMoso8NvY2AgArDg0Pj4e2dnZiI2NHZDdjU6nQ0ZGBjIyMpiPmSyxkydPsguMblJksfAZajExMcyy8lXv4I+o6AiMHJWA+rMBjtz0QZhNgtrqhluW0gy1Ci69GmFc52YVut1taou3wjU3dUATpsboMUleKdjyHbzdbmc3CGqrTxYi3Sj6ujGgm7M8BmSz2VgGI8UmejvXcsuiPzGe3uBdX/y6eddXVlYWYmJiEBkZyVKqTSYTTpw4gVOnTnmsayDqxMiVSN0UwsLCmOubj9fQPCFyy/v6W/ankJQ6N4wYMQIA0NraivLycqSlpcFkMuHChQvsO8ULT6g3DQMmNBcvXmQXE5Gamoq2tjbmd3S5XD5fU11dHdR7qlQq5ObmYtmyZVi2bBmsViv+9re/4b333sPy5cthMpmg0WjgcrnwxBNP4MYbb2Spi33xk9tsNrS0tMBgMLAeZomJiazduRK+Tn9xFAA+61HUajWrsTAYDDh//jzi4+OZm20gGl3STY6yeNRqNcLDw9l8EpVKxcYbjBgxAikpKUhOTlYk4WF8XpoiQtPdLcCGtvHeVo0z0lNoLv2Gt9A4HC6Myk7Awhv8DzcDujcQvIuGLESj0Yhjx47BbrezdGWqLaJUfnnGF9WGyfuSBTPkzF9yAQkPxeoCER4+BsRbV+QapXoaEhVf1yMNxQM8U5epQl+l8py42d+UYaqJ4d1elFhAxbAxMTHIyMhAbGwsIiIiPGqMaEIqFXDyWWWBdjAAPOfzyK0dSeruVp2VlYWsrCwA8KjnOXnyJNrb21mR9LRp04I+L/4YVllnvaHX69lubunSpUhNTcWFCxdQXV2NV199FWvXrsX8+fNRUlKC+fPne2WfydHpdMjMzERmZiYzWanly+HDh9luMTExsddiQ+CSS4P8t2RZ9RRH8bcD5QvWLBYLE526ujomiOTO6u9uRi6EvCuGhLCnFvF8LcORI0c86nf6cs58MT4vHds3H+3XZyLCrL6tGkekGjqD52s1dneP3ZwTk2IwZ35ewO8vtxAtFguamprQ0tKCM2fOwO12s35kFEvpy825vwQqPPHx8cwbwD+6uro8bs7UYiiYuCbgWSM2ZswYj7Y5NL8okLY58gw7evA1NYmJiWyT15OA+6oxohu+yWRiGXfR0dEewuPr2vQnPJTsRMlJTU1NLC5Er9fpdEhPT0d6errH366trS1kCSgDJjRpaWnMrUM0NjYiNjYWer0eGo0GGo3G52tod6cEy5cvx/Lly72et9ls+OKLL7Bx40a89NJLWLp0KYqKilBSUoIFCxagoKDAr8uCN1lzc3PR1dXFbu6VlZVwu90sMys+Pp65B8g6oYtOp9OxLzAFMYMpyuShhoCjRo3y6ApAQczIyEiPbLae3otfMy8q8sJMcsX4SygAvNus8AWa586dg9vt9ojv9LXz8/ggRzv7otuqsaNtvKdbVD4yAADULsAZoUKYvfvC14apkRYXjmgNMHFULPT6wCxcuQuJ7+NFLZAiIiJYYgHd6IHuIsVQd3/m4YWH5rs0NTXBZDLh4sWLbNdNKf0JCQkYO3YsK3gMFYG0zZFbH21tbR5JPtRpffz48X6LuvuCSqXyWcBJwkMp6GRp+GudQ2LKW1gdHR0sISIzM9OjfQ6dF/pv+tuRVRgKBjQZ4LPPPkNVVRV77rvf/S5aW1s9kgGuvPJKvPLKKwC6T2BWVhYeeuihgJMB+oMkSTh37hw2bdqETZs2Yfv27dBqtczaKS4uRlJSUq83PT7lubW1FSaTCVarlaVa8wHM2NhYNt52IHE4HCx4bzAYYLfb2UUXHh7OUon5gkFfhZlK3yzohkBJBUajkVV4U0abfLdHu0STyYxlS96DwxHYPKIe1wLANEEPt467sUgSRhyxQC2bFq6NU2NMhxXmC61oPtPMxkv/fu+zGD15VI+flYod5R2U5QWa/rKn+KJWOmeSJHn0Q1Pabcqvm8/64l1ftFmy2+1MDCktnl/bQCYXkMuO3Gy00SMiIiIQGxuL5ORkpKSkDEr2Fs0L4s8ZFXNTUTn1ZSRrlizamJgYj+SkvhSSUqGqPEtXCYIWmo6ODtTW1gIACgoK8PLLL2PevHlISEhAVlYWVqxYgfPnz+Ovf/0rgO705smTJ+PBBx/EkiVLsH37dixfvhyffvqpR3rz4sWL8frrr+PKK6/EqlWr8MEHH6C6utordjOQOBwOZu2UlpaiqqoKBQUFKCkpQUlJCQoLC1FXVweLxYK4uDgPS4U3mekRERHBWp0bDAaPFOqkpKSAA+D9gR8KRRceNRAFwMZFJyQkMLfGYBSAud1ulvBA9Ts6nY5ZTdQGh3b76948irN1ZsXe3xGpRts4T6sm5pQN4e2eSuPUSgj7T6XHc3lX5eLFbU8CuNTsUp71xQ8647OQgnUhAZcGeJHoGI1Gj+zEQKxEEjF5kL6rq8ujSJPvAODvuNT2iB7t7e3Q6XSKCw9tPmi9tOuXx1PoER4e7hHDMBqNXkWSA5U23NXVxdZLP2lGFMU4ATB3M7kBe/N+ULucmpoalJeXs0dVVRVWrFjhkZClFEELTVlZGebNm+f1/OLFi/HWW2/h3nvvRV1dnccI57KyMjz66KM4evQoMjMz8eSTT3oVbK5evZoVbE6fPh1/+MMfcNVVVwWzxJAgSRIqKyvx4osvYu/evR79m2bNmoUVK1awjJi+dMUlvzslFRiNRoSHhzPRSUhIUMRa8OX26ujoYD5yuRiS24XiTnRzp55l5AIM9XhbeeYU/bTZbAgLC2PJHE6nk8XEkpKS8NG6Q9jwz0PKrQPeVo3+YhciGx1er5OqqqHuvFQ7U/LwLExZMAFA982Db3bJp22H+lzyGZNkYVPNBd3gIyMje8z6AuCxblq7Et9PftonFZAGKjz8XB3ejcTHU/hHX8WCElhIePi2OeTS6k/iD1m0clEhMSQrhX7Se9FGgtZmMpngdDpZRllDQwMmTZqEmJgYVFdXo7y8HIcOHUJFRQWqqqogSRKmTp2K6dOno6ioCIWFhZg8eXJI+s0Nq15nA0VtbS0eeeQRTJkyBRMnToTD4UB1dTU+//xzHDp0CFOnTkVxcTEWLFiAK6+8MqDdj8vlYm38qQ1NXFwcSyrozaLw1yeLv8EF4/YiH7LcEqPYU3/TSOX1HbR2wLuWhr/ggEvxHXK1HasyYNP6M0Gvxef6ZFaNts2J2NN279e1tUJ79BwAQB8bgUc/+h60uu4xDTT7Rd6jbTCsRJfLhZaWFhZLsdlsHplK1HKFzneoEgt84U94qAs09QuUi6Gv+hQlRZxS0OkRSNscvhUOLywOh4NN3A1GDIFuoTUajdi/fz+++uorvPHGG6zGLiwsDGPHjsXs2bMxc+ZMFBUVYeLEicw6CjVCaBREkiQ0NjZi06ZNKC0txZYtW+B0OjFv3jwsWLAAJSUlSE9PD+gPyxdmGgwGaDQajzRlX4WOlK8vF5T+uGF8fVY+m81oNHqsLSEhoceLTT7RkdZPfafkYhjoDU6SJJysvYAf/uB9RT4rOy48rRqVU0LCEYvX6xwaJ7RfHAEA3Lzseix5/k72/ygZg7cqKD03lLEK/pzzD74/GcV+aHfd1tbG1kYbioGMoxCUndjc3Ayz2ewR56T2PbQRGwzR5rsw821h4uLiWCIP775zuVzsO84LSyBiSIJ1+PBhHDp0iLm/jh07hqioKEybNg0FBQXIzMyESqVCbW0tdu3ahZUrV7JQxUAihCaEOJ1O7Nu3j8V2Dhw4gEmTJjFr5+qrr+6TyU1+fbpBmc1m2O12VjWs1+vZ3JbY2NgB3XkSFEMh4Wlvb2c7YhIcvm2PSqXyslD8pYcGwz23roHZZFXseADgiFShbdylbC55J2cAcKskqPZUQq1S4Y+HXkBGbs9ZcJShRcLT1tbGYmJ0gw/0nMgbdcp3+3L3V083OLlVwXcHCHbKpz/k8RR6UEkCrZcyVXmXFsXseDfgQIqi2+1m55wC+FarlcU6qaCTYp19jY0Bl+JjFRUVKC8vZz+rq6sRFxeHgoICFBQUoKioCEVFRcjJyRnw6783hNAMEJIkoaWlBaWlpdi0aRM2b94Mu92O6667zsPaOX78OPR6vcdcmp7cXlqtFm1tbSy+I0kSc2MlJSUN+IXGB7nNZjOrASIoVTM1NRWJiYkhvxie+dnH2P/laUWPqddr0TJOB5uqe0cdXWdDhNnl9TpnfT0Kc1PxzCc/Cej4fG0RtaSR944jYZBnq9GDBojJXUj93e37Gj1Aokg3+L7GKujm6cvCCsaFxIuiL+Gh1kxK4HK5PNbc1tbmkU7MWyrUTYRvC2M2mz36otHrqf1MW1sbKioqmKVSUVGBmpoaJCcne4lKdnb2105UfCGEZpBwuVz48MMP8de//hUHDhxghVUAsHTpUtxzzz3sS9gXt5evbs9UH5OUlKRY4F4+d4RPxaVdm1wQaYgU72bTarXMxRaqMQPvv7MX697arfhxJ8zMwu7O7mrN6clJOLfVOxbk6mzHih9/E7O+fUW/3osyoPi4WEREBMu0o2w1uaUSioCunL72afOVXEBdC0IVT1FKeKjjAh9P4dOJeVHpa69Dvi/a2bNnsXbtWnz66acs7mQwGJCRkYGCggIUFhaisLAQRUVFzA02FBFCM4isXbsW//znPzF58mSMGTMGzc3NOHbsGLZt24aOjg7MmTOHFYxmZ2cH9CWT18dQ4D4QXzafqcYLC+/X50Wlr40Me3Kz8dls/d2lOZ1O7Nl1HL99trRfx/FFZFQEjHl6dDgdGJs8Auat9d6viVDjnfXLoAkL/KZJNzd5WxaVSsV8/jTniKxYegxEX6+esNvtLLnAbDbD4XCwrgUajcarzmOwkgt6Eh6NRuMlKlarldW78cISaLxTkiQ0NTV5WCqVlZWoq6tDVlYWRo4ciYiICLS1taGmpgZFRUUeGbtDHSE0X0PcbjcOHTrEkgr27NmDsWPHMtGZNWtWQG4AclMYDAa0tLSwADTfDcBXryy+QFOeWKBkFg/fRcFgMMDlcnmNZujpoub9+vyu2Wq1wu1S448vVfhqP9ZvRhUko9xtgTZMjRGVFrgcnu6z2757Fe5e4n9csz/XF93c+J2+/DzIi1pNJpNXRluoOorza+czv/h4CrmAqQM09faitVGftoGGElkuXryI1tZWDxcvFUSSVRaMdShJEhoaGnDw4EFUVFSwmMr58+eRk5Pj4f4qLCz0Kv52Op1obGzEyJEjFf3cg4kQmq85NOlvy5YtKC0tRWlpKYxGI2bPns0KRnNycvp8M+FHSLe2tnpl8FCTPyUKBoOBF0W+rojiAFRQxwuiy+XqsWjwB/euxYV6o+Lr1OnD0DJBB7skIcekgulMB/t/arUKb/zte0hOjWXP+ZtK6avGIxhXIt9eiJJG+Iw2f5mA/qAbs7yJZKBrpxR0crU5HA6vBqFKWzjyBpj0s6ury2PtUVFRLPZCFg+fcdeTq83tduPcuXMehY8VFRVoamrC+PHjmajMmDEDBQUFiI+PH7Lur/4ghGaI4Xa7UVVVhY0bN2LTpk3YvXs3srKymOjMmTMHer2e+YF5S4WGhvETHSmpwGKxsBsAn6Y8EGOa5ch3yxR4lu86ExISkJyc7Ddz6ncvbETZ1mMhWeeEWVnY3WHAtKhY1O++NNVz/MRELH3oapbWSueen0Gi5FTKnqCaLIrx8MH7njLa/MVT+BgQzWEKdu0kXnziA/Uco/XFxATWZVkuiPSTTyfm1+6vfsyXq+3MmTP4+OOPkZmZiYiICJw6dQoVFRUwmUzIy8vziKkUFBQMWheNryNCaIYwlADwySef4L333sMXX3zBekjZbDZ861vfwv33388uLF+FjnJ8xU+oxUVSUpLfTrfB0Jf58/xNAgC7MfXFzfbpvw7hjdWfK7ZenqiYCLSM12H0iBg0cnGaG2/Nxthx8R6xieTkZKSlpQ1IkL4nKG5HD4vFgsjISGa12u12r+mUfPZUKOMpfJ82Eh/q08a7sehvS98buZVFI8j5eEowDTBdLhdqa2s9qukPHjyIzs5O6HQ62Gw2jBgxAiUlJXjzzTcHreh2qCCEZhjw/vvv4/HHH8eUKVOQkZEBs9mMuro6VFRUICMjA8XFxSgpKcHcuXMDyt8HugO8fPxEkiQPayeQFOqe5s8Hu9PnYxQGgwEmk4m176GMtrqTBvzooXV9XmOgJE+KRWOsGuq9JgBAYnIU/rj2Xuh0EaxGhlxZlPTAd/EeiBiFv1gQtfFxOp1wuVyIjY1lCSNKbyoCXTO5sciFCoBtkux2O/ve8KISTHGvy+VCTU2NR0ylsrISXV1dmDx5MrNQCgsLMXXqVOh0OnR2dmL37t1oamrC3XffHZJzMJwQQjMM4Me98s91dHRg+/btrG6nvr4eM2fOZHU7eXl5AV+UFAOgcbpRUVEeSQWUZcRX/vMNGKmRIW+lKDV2l2/fQ0He6KgY/PZXe+F0KtPJWU50jA7N4yOQWGuHxWzDXffNwu13+e7N19XV5dEmx263s906Fdv29zyQ+0he59FbPIV+j+/8TGMaBjKjjc+4I2uls7MTGo2GiQh1FCcXL9+nrbcSAGoXxdeoUEd5ed+v/Pz8HkeGCwJDCM1lgiRJOH78OIvt7Ny5EykpKZg/fz4WLFiAuXPnBuxTdjgcaGpqQmNjI0wmE1wuFzQaDRuyJBeUUM8ekUPTKZ97ciPqzyjXyVnO+FnZsNkcuFBxEX9e932MSIjq9XfkN/bW1u6JoGTt0I3TH3w1Ol84SCOP5UWPgbY44RtwUosheY+2/uBwOLziKRaLhSWl8NaKfDPCF0FS4oNWq2XNJEeNGoUxY8bgyJEjHoH6I0eOIDw8HNOmTWOiUlRUhLy8vAHr+3U5IoTmMqWzsxNlZWXYuHEjNm/ejLq6OlxzzTUsqSA/P9/D2pF3USZrhZ8/HxHR7S6yWq0wm83Q6/UstjNixIgBFRmeP736OT75WLlOznJiYnVIuzIdyV1h+MkvbgrqGNRZmU9V1ul0HtaOPJ2YdvVyK6W3EeTBrs9sNjPhCTSjje9OTJ+Bvju8IFKNSiCQ9b5371589dVXePfdd3HmTHcRbXh4OGsmOWfOHBQVFWH8+PGDklZ9OTMkhWbnzp1YuXIlDhw4gIaGhl6Hrgn8I0kSamtrmbVTVlaGyMhIjB07FiqVChaLBc8++yybTSOPp/hqD+JyuTwKRq1WK+Lj45n/P5RuGL7RaHt7O/buOoUNH9WG5L2IabPG4NZvFWFqQVa/j0UDwpqbm1lXZXKP6nQ61s07Pj5eMbdjoJCbkoSHMtqom4VarfZo2U9za+SiEmgfNxKVyspKFqSvqKjw6PtVWFiIvLw8SJKEmpoalJWV4f7778d9990XorMh6I0hKTQbN27EF198gaKiItxyyy1CaBRk4cKF2Lx5M7thmM1mtLe3o6ioCIsWLcLChQsxderUgHfM1Om5paWFuWFIdBITE4Nqpsm3XPdlZdENzdoJ/Oyx9QEfvzdUKiAlNgIjdGokJUbi8T8sDnj9vjoqy4eJ0bRTmtRqMBjgcDhYKnBfxkeEAn79NA/FYrGwuiyaspmUlBRUxp0kSTCbzUxUyP114sQJ1veL0olnzJiBrKysIdH363JkSAoNT1/GSAv6TlVVFVJTU5GSkgKg+2I/ffq0h7UTFRXFMtnmz5+PESNGBHST41OoW1paWONIEp24uDiv4/GTKfkHxSPkHYnlwnX3LX9Ee5st6PMSpgYSIlWIC1MhrMuBjkYzGk81osvaPf53yfN34uZl1/v9zL7qU2j9gdR48PEdysiiyZkkPEo1kOTXz59/coNROjEfT4mOjvZKpabiTN4VKHfNtra2esRTysvLcerUKYwcOZIVPpKojBw5UsRThhBCaAQBYbPZsGvXLjb6oLq6GjNmzGDtcaZPnx6w/5uC9nwXaj4jymKx9Dse8csV/8TBfXV9Wk9MZBiSo7SIlFxwmDtgqjeg6UwzJLfvSyVcp8VfalYhJiEagO9eZfKCTbopKxFP4WfcUENViu9QYkGgw/d8JRlQgkeg6/eV+PDRRx/hq6++QlpaGlwuF06fPo2zZ89i9OjRmD59OrNUCgsLkZaWJkRliCOERhA0kiTh7Nmz2LRpEzZt2oTt27cjIiIC8+fPZ9aOvI+T/Pflrq/29u4BaLSjdzqd0Ol0SExMRGpqKkuhDpS/v70b7/1tr3wFSIqNQIJOA62jC12t7Wiua4KpMbAMtby5Y3HzT0tYR2Wbzca6L/AxiVD1HZPjdDo94icdHR2sxxhZjLQZoLYr8u7EVGjKC0swRYmSJOHChQse6cQVFRU4f/484uPjER4eDqPRiKioKDz88MN46qmnhKgMQy5boXn++efxz3/+E9XV1dDr9Zg5cyZefPFFTJgwITQLvQyw2+3Ys2cPPvvsM5SWluLw4cMoLCxESUkJZs+eDZfLhaNHj2L+/Plsxyx3HZHrhXbgDoeDJRS0tLSwTgB8F+q+cHDfafzpNxsRpwVUVhsszWY0nrwIa3vw7jTizpduRNaUkXC5XLDZbNDr9Wx9g5ltR9jtdrS2tqK5uRmtra1wOp3QarWsrkQuirGxsUElGfB9v/hAPd/3i1reFxYWMhdpV1cXvvzyS2i1Wlx99dUhOguCweSyFZrrr78ed9xxB6644go4nU789Kc/xeHDh3H06FFERfVeByHwjyRJeOGFF7B+/XocPXoU7e3tUKlUiI+Px0MPPYRvfvObGD16dEDtQeSdAIxGI3Q6HbupJyQk+HXbXTzdhENbq3Bo22FU7jiqiMiMnjwKv9/7LPu30+n0aJFD2Xa0PiWKMvtCV1eXV42K1WqFTqeDXq9n1hcN7OLdbH2J77jdbpw+fdrLUqG+X3w1vej7JbhshUZOc3MzUlJSsGPHDsyZM0eZxV3mPP300wgLC8P06dMxadIknDt3jnUpKC8vx7Rp09hY6yuuuCKokcXUCaClpYX1n6KkAn8p1E6HE8e/rMX+LZU4uKUSZ6rqEcylcP/v7sGi78/v8f9brVYmjK2trSxoH0wLH19Qexm5qNjtdpa5xrvA5H3ufHV81uv1SEhIQFNTEyZNmoSEhATU1tZ6iYrFYsGkSZOYmBQVFWHatGmi79cQJZReniEpNB0dHait7a6LKCgowMsvv4x58+YhISEBWVnB1THU1tZi3LhxqKqqwuTJk5VcrkCGJEm4ePEim7ezZcsWuN1uzJs3DwsWLEBxcTHS09MDvllZLBaWUNDa2sqmeCYlJSEhIYG10OHjQVSJrnZqcOFwE07tO4djX9TC3NzW6/vponVYe2IVImP6luHl66ZOU1ApG8ufRUYxLXnho8Ph8Mr8iomJCdhlR6K1f/9+7N27F6+99hoaGhqgVquhUqmQk5ODa6+9FjNmzEBRURGmTJkyaHU8AuUJpZdnSApNWVkZ5s2b5/X84sWL8dZbbwV8PLfbjf/6r/+CyWTCrl27FFihIBCcTie+/PJLJjwHDhxAfn4+61Jw9dVXBzSqgLKcGhoaWDDc5eoeTBYWFsYaW/ZUiS5JEk5XnsXBrVU4tLUK1XtPwCkbbAYAC5fMwwN/uDfoz+1wODzGNNtsNg83m1qtRkdHh4ewUMt7eXfiQDP9KD5z7Ngxj5hKVVUVVCoV6/uVnZ0Nu92OmpoabN++HWvWrBGJN5cJSnp5hqTQKM0PfvADbNy4Ebt27UJmZmZAv7tmzRqsWbMGdXV1AID8/Hz84he/wKJFi0Kw0uEPjbzdvHkzc7N1dXVh7ty5THj42eny+g7+hkxdDEhMqOLeYDAwFxbFd/wVE1o7bKjaeQyHtlbh4NYqXDzVBAD43e5fYezU7H5/ZqqxIRcgP/GRih7J1RZMOjRZKkeOHPFwf/F9v/hAfU99vyRJYqMPBMMfJb08l73QPPTQQ1i/fj127tyJMWPGBPz7GzZsgEajwbhx4yBJEt5++22sXLkShw4dQn5+fghWfHnhcrmwf/9+bNy4EZ999hkOHDiA1NRUxMTEwGKx4Prrr8fNN9/s1UTSX5IB78KiLtTR0dFMdOLj4/3ezBtONaJm/ylcd/s1QX0eedEjBeTlRZuUWGAwGNDW1sbcbJTN5uuGT+61yspKj8LHY8eOITo6GtOmTfOoph83bpwQjiFIqDe4Snt5LluhkSQJy5Ytw8cff4yysjKMGzdOsWMnJCRg5cqV+N73vqfYMS93/v73v+Puu+9GQkIC0tPT0dXVhQsXLsDpdLLYzoIFC5CVlRVwzIDa91N8x+VysYB9UlJS0FX2DofDq11/Z2cntFqtV3fi3mpsqNKe3GxdXV2oqalBXV0dcnNzYbVaUVVVhfLychw/fhzx8fFe1fRjx44VLVqGCaHe4PbHy+OLy1ZoHnjgAaxbtw7r16/3yKqIi4sL+sbicrnw4YcfYvHixTh06BAmTZqk1HIve4xGIywWCzIyMjzcZgcPHmSxnb179yInJ4d1KZg1a1bAWV2UQk2iYzKZWBdqfynUNNSNz/6yWq2IiIjwEhWaaBnousxmMyoqKnDo0CEcOHAAu3fvxsWLF6FSqaDVajFu3Dh8+9vfxve+9z3R9+syRKkNbn+9PL64bIWmpwt97dq1uPfeewM6VlVVFa655hrYbDZER0dj3bp1+OY3v6nAKgV9RZIkmEwmbN68GaWlpSgtLYXJZMKcOXNYbIe6UQcC775qaWmB3W5nmxGNRgObzcYaeer1ei9RCSSJgf8sBoOBiQrFVE6dOoXMzEyPFi1FRUVISUnBl19+ic2bNyM3NxeLFwfW3FMwtFFqgxtKL89lKzRK0tXVhbNnz8JsNuOjjz7Cn//8Z+zYsUNYNIOI2+1GRUUFa4+zZ88eZGdnM9GZPXt2r10F+BY5vKXicDig0WjYoLcRI0YgNTUVycnJQbW9b2pqYplf5eXlqKysxJkzZ1jfr6KiIuYCE32/BITSG9xQeHkIITQhoLi4GDk5OXj99deDPsYLL7yAFStW4OGHH8aqVauUW9xlCNXPbN26lbnZmpubMXv2bFYwOmbMGFRVVUGr1SIiIoIJC6UTy2tUSGhMJhNzs1ksFsTFxTE3m7wLgNvtRkNDg4eVUl5ejgsXLiA3N5fFVKhFS2JiohCVYYaS17XSG1wlvTxexxZCozzf+MY3kJWVFVRNDwDs27cPt99+O2JjYzFv3jwhNArjdruxZcsW/PnPf8aePXtw4cIFAN0X2q233oply5YhJSWFZX/1NdZhs9mY6BgMBvzud7+DwWBAWloarFYrqqur0dLSwvp+FRQUYMaMGSgoKPA5GkEwvAj1da3EBjdUDG63v2HAihUrsGjRImRlZaG9vR3r1q1DWVkZSktLgzpeR0cH7rrrLvzpT3/Cs88+2/svCAJGrVbj3LlzaG5uxq233opJkyaho6MDx44dw9atW1FcXIyZM2cyN1teXp5fsXG73Th16pRXM0mTyYT4+HicO3cORqMRubm5WLt2LRYtWiRE5TJjIK5rt9sNu90ekmP3FyE0/aSpqQn33HMPGhoaEBcXh6lTp6K0tBQlJSVBHe/BBx/EDTfcgOLiYiE0IWTp0qVYunSp1/NutxvHjx9nHah/9atfISUlhQ16u+6669DQ0MBEpbKykvX9ys/PR0FBAW655RY8++yzHn2/mpubsWXLFkyYMEGIzGWI0te10hvcUCOEpp+8+eabih3rvffew8GDB7Fv3z7FjikIDLVajYkTJ2LixIn44Q9/iI6ODnz++efYtGkTfv7zn+PkyZPQarWs8PHOO+/EypUrMXXqVL9py8nJyfjud787wJ9G0Feefvpp/PKXv/R4bsKECaiuru73sUNxXSu9wQ01Qmi+Jpw7dw4PP/wwtmzZ0q+OvqG8YC5HoqOjcdNNN+Gmm26CJEnYunUrZs2aNWBDzAQDR35+PrZu3cr+rcQcIaWuazlKbnAHAiE0XxMOHDiApqYmFBYWsudcLhd27tyJ1atXw26397lVSCguGEF3ssDXdcco6D9hYWFIS0tT9JhKXtdDGXEH+powf/58VFVVeTx33333IS8vDz/5yU8C+jKG4oIRCIY7J06cQEZGBnQ6Ha655ho8//zzQY8dIZS8rocyQmi+JsTExHh1SI2KikJiYmLAnVNDccEIBMOZq666Cm+99RYmTJiAhoYG/PKXv8Ts2bNx+PBhxMTEBH1cJa/roYxohjTMoAtm06ZNWLNmDU6fPo3Zs2ejvb19sJcmEPSb8+fP4+6770ZiYiL0ej2mTJmC/fv39/u4ixYtwm233YapU6di4cKF+Oyzz2AymfDBBx8osGqBKNgc5phMJmRnZ+Pll18W3aQFQxqj0YiCggLMmzcPP/jBD5CcnIwTJ04gJycHOTk5ir/fFVdcgeLiYjz//POKH/tyQ1g0w5z4+HiMHz+ejb4OlFDtIAWCQHnxxRcxatQorF27FldeeSXGjBmDBQsWhERkOjo6cPLkSaSnpyt+7MsRITTDnP5cMEajEbNmzYJWq8XGjRtx9OhRvPTSSxgxYkQIVioQ+Off//43ZsyYgdtuuw0pKSkoKCjAn/70J0WO/aMf/Qg7duxAXV0ddu/ejW9/+9vQaDS48847FTn+5Y5IBhhm/OhHP8JNN92E7OxsXLhwAU899VTQFwy/gySUmk8hEATKqVOnsGbNGjz22GP46U9/in379mH58uUIDw/v92iE+vp63HnnnTAYDEhOTsa1116LvXv3Ijk5WaHVX96IGM0w44477sDOnTs9LpjnnnsuKPfCpEmTsHDhQtTX12PHjh0YOXIkHnjgAXz/+98PwcoFw4HRo0fjzJkzXs8/8MADePXVV/t17PDwcMyYMQO7d+9mzy1fvhz79u3Dnj17+nVsQWgRFs0w47333lPsWKHcQQqGJ/v27YPL5WL/Pnz4MEpKSnDbbbf1+9jp6eleLfAnTpyIf/zjH/0+tiC0iBhNiJk7dy4eeeSRwV5GULjdbhQWFuLXv/41CgoK8D//8z/4/ve/j9deey2o440ePRoqlcrr8eCDDyq8csFgkZycjLS0NPb45JNPkJOTg+uuu67fx541axaOHz/u8VxNTQ2ys7P7fWxBaBFCI+iRnnaQZ8+eDep4+/btQ0NDA3ts2bIFABTZ7Qq+fnR1deGdd97BkiVLFOkL9+ijj2Lv3r349a9/jdraWqxbtw5vvPGG2KgMAYTrTNAjSu8g5YHVF154QbHdruDrx7/+9S+YTKZ+T2ckrrjiCnz88cdYsWIFfvWrX2HMmDFYtWoV7rrrLkWOLwgdwqIZYD799FPExcXh3XffHeyl9Eood5BK73YFfcPlcuHJJ5/EmDFjoNfrkZOTg2eeeQahyAl68803sWjRImRkZCh2zBtvvBFVVVWw2Ww4duyYSEwZKkiCkHLddddJDz/8sCRJkvTuu+9KMTEx0oYNGwZ3UQGwYcMGafLkyVJERISUl5cnvfHGG4oc9/3335c0Go10/vx5RY4n6BvPPfeclJiYKH3yySfS6dOnpQ8//FCKjo6Wfv/73yv6PnV1dZJarZb+9a9/KXpcwdBEpDeHmLlz52L69OkYN24cfvazn2H9+vXCVQRg4cKFCA8Px4YNGwZ7KZcVN954I1JTUz3mmfz3f/839Ho93nnnHcXe5+mnn8brr7+Oc+fOiTEVAuE6Gwg++ugjPProo9iyZYsQGQBnzpzB1q1bfY5S7isD6QIaTsycORPbtm1DTU0NAKCiogK7du3CokWLFHsPt9uNtWvXYvHixUJkBABEMsCAUFBQgIMHD+Ivf/kLZsyYcdnHJNauXYuUlBTccMMNQR/jxRdfxJo1a/D2228jPz8f+/fvx3333Ye4uDgsX75cwdUOL5544gm0tbUhLy8PGo0GLpcLzz33nKIB9a1bt+Ls2bNYsmSJYscUDG2ERTMA5OTk4PPPP8f69euxbNmywV7OoKLUbnf37t24+eabccMNN2D06NG49dZbsWDBAnz11VcKrnbgaW9vxyOPPILs7Gzo9XrMnDlT0VnzH3zwAd59912sW7cOBw8exNtvv43f/va3ePvttxV7jwULFkCSJIwfP16xYw4Gc+fOxfLly/HjH/8YCQkJSEtLw9NPPz3YyxqaDG6IaPjDJwNUV1dLaWlp7N+XI6WlpRIA6fjx4/06znPPPSdlZ2ez45SXl0spKSnSO++8o8QyB43bb79dmjRpkrRjxw7pxIkT0lNPPSXFxsZK9fX1ihw/MzNTWr16tcdzzzzzjDRhwgRFjj+cuO6666TY2Fjp6aeflmpqaqS3335bUqlU0ubNmwd7aUMO4TobQCZMmIDt27dj7ty50Gg0eOmllwZ7SQMO7Xb7y0C4gAYaq9WKf/zjH1i/fj3mzJkDoDuovmHDBqxZswbPPvtsv9/DYrFArfZ0ZGg0Grjd7n4fezgydepUPPXUUwCAcePGYfXq1di2bRtKSkoGeWVDCyE0IaasrMzj3xMnTkRjY+PgLGYYwbuA8vPzUV5ejkceeQQZGRlDtg+b0+mEy+WCTqfzeF6v12PXrl2KvMdNN92E5557DllZWcjPz8ehQ4fw8ssvi3hKD0ydOtXj3+np6Whqahqk1QxdhNAIhiSPP/44nnjiCdxxxx0AgClTpuDMmTN4/vnnh6zQxMTE4JprrsEzzzyDiRMnIjU1FX//+9+xZ88e5ObmKvIer7zyCp588kk88MADaGpqQkZGBv73f/8Xv/jFLxQ5/nBDq9V6/FulUgnrLwiE0AiGJMPVBfS3v/0NS5YswciRI6HRaFBYWIg777wTBw4cUOT4MTExWLVqFVatWqXI8QSCviCERjAkGa4uoJycHOzYsQOdnZ1oa2tDeno6vvOd72Ds2LGDvTSBIGiE0AiGJMPdBRQVFYWoqCgYjUaUlpbiN7/5zWAvSSAIGtGCRiD4GlFaWgpJkjBhwgTU1tbi8ccfh06nw3/+8x+veIFAMFQQBZsCwdcIs9mMBx98EHl5ebjnnntw7bXXorS0VIiMYEgjLBqBQCAQhBRh0QgEAoEgpAihEQgEAkFIEUIjEAgEgpAihEYgEAgEIUUIjUAgEAhCihAagUAgEIQUITQCgUAgCClCaAQCgUAQUoTQCAQCgSCkCKERCAQCQUgRQiMQCASCkCKERiAQCAQhRQiNQCAQCEKKEBqBQCAQhBQhNAKBQCAIKUJoBAKBQBBShNAIBAKBIKQIoREIBAJBSBFCIxAIBIKQIoRGIBAIBCFFCI1AIBAIQooQGoFAIBCEFCE0AoFAIAgpQmgEAoFAEFKE0AgEAoEgpAihEQgEAkFIEUIjEAgEgpAihEYgEAgEIUUIjUAgEAhCihAagUAgEIQUITQCgUAgCClCaAQCgUAQUoTQCAQCgSCkCKERCAQCQUgRQiMQCASCkCKERiAQCAQhRQiNQCAQCEKKEBqBQCAQhBQhNAKBQCAIKUJoBAKBQBBShNAIBAKBIKQIoREIBAJBSBFCIxAIBIKQ8v8BLmcT2k1h2kUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{l|llll|lll|lll|lll|ll|ll|ll|l|l|l}\n",
+      "\\toprule\n",
+      "$k$ & \\multicolumn{4}{c|}{0} & \\multicolumn{3}{c|}{1} & \\multicolumn{3}{c|}{2} & \\multicolumn{3}{c|}{3} & \\multicolumn{2}{c|}{4} & \\multicolumn{2}{c|}{5} & \\multicolumn{2}{c|}{6} & \\multicolumn{1}{c|}{7} & \\multicolumn{1}{c|}{8} & \\multicolumn{1}{c}{9} \\\\ \n",
+      "$n/d$ & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\midrule\n",
+      "1 & 1 & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & 1 & 1 & - & - & 2 & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & 2 & 1 & - & - & 5 & - & - & 3 & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & 3 & 3 & - & - & 11 & 2 & - & 10 & 1 & - & 4 & - & - & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & 6 & 4 & 1 & - & 29 & 6 & 1 & 37 & 3 & - & 19 & - & - & 5 & - & 1 & - & - & - & - & - & - \\\\ \n",
+      "6 & 11 & 13 & 1 & 1 & 78 & 35 & 2 & 156 & 29 & - & 104 & 5 & - & 31 & 1 & 6 & - & 1 & - & - & - & - \\\\ \n",
+      "7 & 26 & 29 & 4 & - & 260 & 169 & 19 & 834 & 241 & - & 785 & 67 & - & 260 & 7 & 48 & - & 7 & - & 1 & - & - \\\\ \n",
+      "8 & 59 & 107 & 11 & 5 & 1023 & 1170 & 178 & 6266 & 3724 & 20 & 9304 & 2117 & 1 & 3699 & 264 & 603 & 11 & 70 & 1 & 8 & 1 & - \\\\ \n",
+      "9 & 182 & 416 & 69 & 8 & 5777 & 10742 & 3609 & 78567 & 98027 & 4445 & 222749 & 130598 & 222 & 122541 & 24117 & 17677 & 768 & 1331 & 13 & 99 & 9 & 1 \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
+     ]
     }
    ],
    "source": [
-    "# Create a figure and axis\n",
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111, projection=\"3d\")\n",
-    "# Plot 3D surface\n",
-    "ax.plot_trisurf(n_set, k_set, d_set, cmap=\"viridis\", edgecolor=\"none\")\n",
-    "ax.view_init(elev=10.0, azim=50)  # Set the view angle. Top rotate change the azim value\n",
-    "# Set labels and title\n",
-    "ax.set_xlabel(\"n\")\n",
-    "ax.set_ylabel(\"k\")\n",
-    "ax.set_zlabel(\"d\")\n",
-    "ax.set_title(\"Indecomposable codes : maximum distance\")\n",
-    "\n",
-    "# Show plot\n",
-    "plt.show()"
+    "print(make_full_table(results_all, coln_adjust, \"$n/d$\"))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bb6faf25-63e9-4de1-a6a4-ebfd96124fd1",
+   "id": "4c59b653-0adb-45ae-b482-ae7eab9ddecb",
    "metadata": {},
    "source": [
-    "## Number of indecomposable codes of distance 1"
+    "### All indecomposable codes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 324,
-   "id": "9c622b55-be76-4404-ae64-c69b4b823913",
+   "execution_count": 748,
+   "id": "1cbad5d5-0270-4c53-b5b7-8a0069a04861",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2,1,2]]: 1\n",
-      "[[3,1,2]]: 2\n",
-      "[[3,2,2]]: 1\n",
-      "[[4,1,2]]: 4\n",
-      "[[4,2,2]]: 3\n",
-      "[[4,3,2]]: 1\n",
-      "[[5,1,2]]: 12\n",
-      "[[5,2,2]]: 16\n",
-      "[[5,3,2]]: 6\n",
-      "[[5,4,2]]: 1\n",
-      "[[6,1,2]]: 35\n",
-      "[[6,2,2]]: 82\n",
-      "[[6,3,2]]: 48\n",
-      "[[6,4,2]]: 9\n",
-      "[[6,5,2]]: 1\n",
-      "[[7,1,2]]: 140\n",
-      "[[7,2,2]]: 545\n",
-      "[[7,3,2]]: 494\n",
-      "[[7,4,2]]: 125\n",
-      "[[7,5,2]]: 13\n",
-      "[[7,6,2]]: 1\n",
-      "[[8,1,2]]: 646\n",
-      "[[8,2,2]]: 4858\n",
-      "[[8,3,2]]: 7373\n",
-      "[[8,4,2]]: 2579\n",
-      "[[8,5,2]]: 295\n",
-      "[[8,6,2]]: 18\n",
-      "[[8,7,2]]: 1\n",
-      "[[9,1,2]]: 4337\n",
-      "[[9,2,2]]: 69122\n",
-      "[[9,3,2]]: 202670\n",
-      "[[9,4,2]]: 107191\n",
-      "[[9,5,2]]: 13095\n",
-      "[[9,6,2]]: 656\n",
-      "[[9,7,2]]: 24\n",
-      "[[9,8,2]]: 1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "results_indec = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
     "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(n, k, d=1, is_decomposable=False, info_only=True, list_only=True)\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            print(f\"[[{n},{k},{2}]]: {num}\")\n",
-    "        except TypeError:\n",
-    "            pass"
+    "    for k in range(10):\n",
+    "        for d in range(5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "            results_indec[n][k][d] = len(codes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
-   "id": "380ff526-62c9-4a7d-be88-5452e4d81c3b",
+   "execution_count": 749,
+   "id": "2008a818-5c13-488c-baa8-81f69a027d0d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\2&1&0&0&0&0&0&0&0\\\\4&3&1&0&0&0&0&0&0\\\\12&16&6&1&0&0&0&0&0\\\\35&82&48&9&1&0&0&0&0\\\\140&545&494&125&13&1&0&0&0\\\\646&4858&7373&2579&295&18&1&0&0\\\\4337&69122&202670&107191&13095&656&24&1&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}1&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&2&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&2&0&0&4&2&0&3&1&0&1&0&0&0&0&0&0&0&0&0&0&0\\\\0&3&1&0&12&4&1&16&2&0&6&0&0&1&0&0&0&0&0&0&0&0\\\\0&9&1&1&35&27&1&82&25&0&48&5&0&9&1&1&0&0&0&0&0&0\\\\0&22&4&0&140&128&16&545&209&0&494&62&0&125&6&13&0&1&0&0&0&0\\\\0&85&11&5&646&964&157&4858&3450&20&7373&2043&1&2579&255&295&11&18&1&1&0&0\\\\0&363&69&8&4337&9395&3411&69122&94048&4425&202670&128405&221&107191&23844&13095&757&656&12&24&1&0\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -537,99 +1073,77 @@
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(n, k, d=1, is_decomposable=False, info_only=True, list_only=True)\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "lprint(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73955dd8-71e7-49d9-a460-cafc0106105b",
-   "metadata": {},
-   "source": [
-    "## Number of indecomposable codes of distance 2"
+    "coln_adjust = [None, -1, -1, -1, -2, -2, -2, -3, -3, -3]\n",
+    "lprint(adjust_table(results_indec, coln_adjust))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
-   "id": "80802720-a539-4030-b178-328581f69815",
+   "execution_count": 260,
+   "id": "97be3cb2-3c2f-4d7d-9b1a-ac505a57ab13",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[2,1,2]]: 0\n",
-      "[[3,1,2]]: 0\n",
-      "[[3,2,2]]: 0\n",
-      "[[4,1,2]]: 2\n",
-      "[[4,2,2]]: 1\n",
-      "[[4,3,2]]: 0\n",
-      "[[5,1,2]]: 4\n",
-      "[[5,2,2]]: 2\n",
-      "[[5,3,2]]: 0\n",
-      "[[5,4,2]]: 0\n",
-      "[[6,1,2]]: 27\n",
-      "[[6,2,2]]: 25\n",
-      "[[6,3,2]]: 5\n",
-      "[[6,4,2]]: 1\n",
-      "[[6,5,2]]: 0\n",
-      "[[7,1,2]]: 128\n",
-      "[[7,2,2]]: 209\n",
-      "[[7,3,2]]: 62\n",
-      "[[7,4,2]]: 6\n",
-      "[[7,5,2]]: 0\n",
-      "[[7,6,2]]: 0\n",
-      "[[8,1,2]]: 964\n",
-      "[[8,2,2]]: 3450\n",
-      "[[8,3,2]]: 2043\n",
-      "[[8,4,2]]: 255\n",
-      "[[8,5,2]]: 11\n",
-      "[[8,6,2]]: 1\n",
-      "[[8,7,2]]: 0\n",
-      "[[9,1,2]]: 9395\n",
-      "[[9,2,2]]: 94048\n",
-      "[[9,3,2]]: 128405\n",
-      "[[9,4,2]]: 23844\n",
-      "[[9,5,2]]: 757\n",
-      "[[9,6,2]]: 12\n",
-      "[[9,7,2]]: 0\n",
-      "[[9,8,2]]: 0\n"
+      "\\begin{tabular}{l|llll|lll|lll|lll|ll|ll|ll|l|l|l}\n",
+      "\\toprule\n",
+      "$k$ & \\multicolumn{4}{c|}{0} & \\multicolumn{3}{c|}{1} & \\multicolumn{3}{c|}{2} & \\multicolumn{3}{c|}{3} & \\multicolumn{2}{c|}{4} & \\multicolumn{2}{c|}{5} & \\multicolumn{2}{c|}{6} & \\multicolumn{1}{c|}{7} & \\multicolumn{1}{c|}{8} & \\multicolumn{1}{c}{9} \\\\ \n",
+      "$n/d$ & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\midrule\n",
+      "1 & 1 & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & - & 1 & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & - & 1 & - & - & 2 & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & - & 2 & - & - & 4 & 2 & - & 3 & 1 & - & 1 & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & - & 3 & 1 & - & 12 & 4 & 1 & 16 & 2 & - & 6 & - & - & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      "6 & - & 9 & 1 & 1 & 35 & 27 & 1 & 82 & 25 & - & 48 & 5 & - & 9 & 1 & 1 & - & - & - & - & - & - \\\\ \n",
+      "7 & - & 22 & 4 & - & 140 & 128 & 16 & 545 & 209 & - & 494 & 62 & - & 125 & 6 & 13 & - & 1 & - & - & - & - \\\\ \n",
+      "8 & - & 85 & 11 & 5 & 646 & 964 & 157 & 4858 & 3450 & 20 & 7373 & 2043 & 1 & 2579 & 255 & 295 & 11 & 18 & 1 & 1 & - & - \\\\ \n",
+      "9 & - & 363 & 69 & 8 & 4337 & 9395 & 3411 & 69122 & 94048 & 4425 & 202670 & 128405 & 221 & 107191 & 23844 & 13095 & 757 & 656 & 12 & 24 & 1 & - \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
      ]
     }
    ],
    "source": [
+    "print(make_full_table(results_indec, coln_adjust, \"$n/d$\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73f0ebd3-3331-4d75-9d6f-ae1f699f26d8",
+   "metadata": {},
+   "source": [
+    "## All indecomposable codes with k>0 and d>1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 750,
+   "id": "f0195d0a-619f-4f03-8d4f-83f25ebc0de4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_indec_kpos_erdect = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
     "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(n, k, d=2, is_decomposable=False, info_only=True, list_only=True)\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            print(f\"[[{n},{k},{2}]]: {num}\")\n",
-    "        except TypeError:\n",
-    "            pass"
+    "    for k in range(1,10):\n",
+    "        for d in range(2,5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "            results_indec_kpos_erdect[n][k][d] = len(codes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 322,
-   "id": "a16cad82-184a-4933-8f7d-70d50c6059db",
+   "execution_count": 772,
+   "id": "d82b95bf-e550-4283-92b4-4401de88e3e1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\2&1&0&0&0&0&0&0&0\\\\4&2&0&0&0&0&0&0&0\\\\27&25&5&1&0&0&0&0&0\\\\128&209&62&6&0&0&0&0&0\\\\964&3450&2043&255&11&1&0&0&0\\\\9395&94048&128405&23844&757&12&0&0&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&2&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&4&1&0&0&2&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&27&1&0&0&25&0&0&0&5&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&128&16&0&0&209&0&0&0&62&0&0&0&6&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&964&157&0&0&3450&20&0&0&2043&1&0&0&255&0&0&0&11&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&9395&3411&0&0&94048&4425&0&0&128405&221&0&0&23844&0&0&0&757&0&0&0&12&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -640,38 +1154,20 @@
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(n, k, d=2, is_decomposable=False, info_only=True, list_only=True)\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "lprint(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb28459e-c603-4971-9d49-ae0fdf161934",
-   "metadata": {},
-   "source": [
-    "## Number of indecomposable codes of distance 3"
+    "coln_adjust = [None, None, None, None, None, None, None, None, None, None]\n",
+    "lprint(adjust_table(results_indec_kpos_erdect, coln_adjust))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
-   "id": "99d7317b-294b-4df4-87d9-05e118b0db51",
+   "execution_count": 789,
+   "id": "7ad9f1a8-ad26-4e9b-943e-392495c87574",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\16&0&0&0&0&0&0&0&0\\\\157&20&1&0&0&0&0&0&0\\\\3411&4425&221&0&0&0&0&0&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}0\\\\0\\\\0\\\\3\\\\7\\\\59\\\\421\\\\6902\\\\264518\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -682,84 +1178,83 @@
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(n, k, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "\n",
-    "lprint(results)"
+    "table = adjust_table(results_indec_kpos_erdect, coln_adjust)\n",
+    "lprint(np.sum(table,axis =1))"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "4dc01ac9-07d4-4c4e-8636-4ae265b3d575",
+   "cell_type": "code",
+   "execution_count": 791,
+   "id": "d9254477-d381-4c7d-b327-ef74c5f53f84",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of indecomposable classes with k>0 and d>1 is 271910\n"
+     ]
+    }
+   ],
    "source": [
-    "## Number of indecomposable codes that are CSS"
+    "print(f\"Total number of indecomposable classes with k>0 and d>1 is {np.sum(table)}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
-   "id": "43ed8f31-00b6-4d09-8988-43ea3946c590",
+   "execution_count": 792,
+   "id": "d254247b-c697-467d-bb31-9e68a1b47e82",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\2&1&0&0&0&0&0&0&0\\\\5&4&1&0&0&0&0&0&0\\\\13&13&5&1&0&0&0&0&0\\\\37&60&32&9&1&0&0&0&0\\\\116&245&198&65&10&1&0&0&0\\\\398&1197&1290&603&122&15&1&0&0\\\\1532&6163&9280&5701&1571&214&17&1&0\\\\\\end{bmatrix}$"
-      ],
       "text/plain": [
-       "<IPython.core.display.Math object>"
+       "39.584861931216004"
       ]
      },
+     "execution_count": 792,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(\n",
-    "            n, k, is_decomposable=False, is_css=True, info_only=True, list_only=True\n",
-    "        )\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "\n",
-    "lprint(results)"
+    "100 * 271910/686904 "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "235ed07b-6075-4827-a720-1d3432a9d8da",
+   "id": "ce476bfb-f957-4f01-92cd-cf7a8d396e0e",
+   "metadata": {},
+   "source": [
+    "## All indecomposable equivalence classes with k>0 and d>1 that are CSS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 798,
+   "id": "d6b77439-111c-481c-9f5e-8ca85c8c5ea2",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Number of indecomposble codes that are CSS of distance 1"
+    "results_indec_kpos_erdect_css = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
+    "for n in range(10):\n",
+    "    for k in range(1,10):\n",
+    "        for d in range(2,5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, is_css=True, info_only=True, list_only=True)\n",
+    "            results_indec_kpos_erdect_css[n][k][d] = len(codes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
-   "id": "5d8f83f3-a436-4004-b997-48818ee964db",
+   "execution_count": 802,
+   "id": "b8915a29-dffb-4c8f-b103-4d17023fc95d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\2&1&0&0&0&0&0&0&0\\\\4&3&1&0&0&0&0&0&0\\\\10&12&5&1&0&0&0&0&0\\\\25&50&30&8&1&0&0&0&0\\\\74&208&185&63&10&1&0&0&0\\\\229&953&1176&572&119&14&1&0&0\\\\796&4688&8198&5396&1531&211&17&1&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&1&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&3&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&12&0&0&0&10&0&0&0&2&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&41&1&0&0&37&0&0&0&13&0&0&0&2&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&168&1&0&0&244&0&0&0&114&0&0&0&31&0&0&0&3&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&717&19&0&0&1475&0&0&0&1082&0&0&0&305&0&0&0&40&0&0&0&3&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -770,86 +1265,85 @@
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(\n",
-    "            n, k, d=1, is_decomposable=False, is_css=True, info_only=True, list_only=True\n",
-    "        )\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
+    "coln_adjust = [None, None, None, None, None, None, None, None, None, None]\n",
     "\n",
-    "lprint(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f18afb26-d615-4d11-ac67-18211b856452",
-   "metadata": {},
-   "source": [
-    "## Number of indecomposble codes that are CSS of distance 2"
+    "lprint(adjust_table(results_indec_kpos_erdect_css, coln_adjust))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
-   "id": "e9ee2c9a-a630-47d6-90b5-90448f9f8026",
+   "execution_count": 804,
+   "id": "ae235963-0eb3-41be-a0da-ff7dff199d06",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0\\\\3&1&0&0&0&0&0&0&0\\\\12&10&2&1&0&0&0&0&0\\\\41&37&13&2&0&0&0&0&0\\\\168&244&114&31&3&1&0&0&0\\\\717&1475&1082&305&40&3&0&0&0\\\\\\end{bmatrix}$"
-      ],
       "text/plain": [
-       "<IPython.core.display.Math object>"
+       "array([   0,    0,    0,    2,    4,   25,   94,  562, 3641])"
       ]
      },
+     "execution_count": 804,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(\n",
-    "            n, k, d=2, is_decomposable=False, is_css=True, info_only=True, list_only=True\n",
-    "        )\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "\n",
-    "lprint(results)"
+    "table = adjust_table(results_indec_kpos_erdect_css, coln_adjust)\n",
+    "np.sum(table, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 805,
+   "id": "22805111-0022-404d-b404-fd8f80746f90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 4328 indecomposbale CSS codes with k>0 and d>1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"There are {np.sum(table)} indecomposbale CSS codes with k>0 and d>1\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "05d20d8a-6f46-4e79-bdc0-d9eeaf653e34",
+   "id": "b7eeca71-004f-4e9a-9f8b-6afc992aa709",
    "metadata": {},
    "source": [
-    "## Number of indecomposble codes that are CSS of distance 3"
+    "### All css codes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 331,
-   "id": "ec5eac5b-710a-4137-8ca3-b030569db3ad",
+   "execution_count": 752,
+   "id": "941606e7-8855-44ed-856f-f59c14c04aa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_css = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
+    "for n in range(10):\n",
+    "    for k in range(10):\n",
+    "        for d in range(5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_css=True, info_only=True, list_only=True)\n",
+    "            results_css[n][k][d] = len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 753,
+   "id": "26ca0fe1-2bb1-40cd-9e08-3a2d3e509c7e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\19&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}1&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&2&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0\\\\2&1&0&0&5&0&0&3&0&1&0&0&0&0&0&0&0&0&0&0\\\\3&3&0&0&11&1&0&10&1&4&0&1&0&0&0&0&0&0&0&0\\\\6&4&0&0&27&4&0&32&2&18&0&5&0&1&0&0&0&0&0&0\\\\10&11&1&0&65&17&0&114&13&79&2&29&1&6&0&1&0&0&0&0\\\\22&20&1&0&175&62&1&417&52&392&15&168&3&43&0&7&0&1&0&0\\\\43&58&2&1&492&248&2&1691&311&2082&132&1153&36&326&3&61&1&8&1&0\\\\104&142&4&0&1539&1031&22&7494&1843&12627&1233&8886&345&3055&43&592&4&83&9&1\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -860,41 +1354,77 @@
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(\n",
-    "            n, k, d=3, is_decomposable=False, is_css=True, info_only=True, list_only=True\n",
-    "        )\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "\n",
-    "lprint(results)"
+    "coln_adjust = [None, -1, -2, -2, -2, -2, -2, -3, -3, -3]\n",
+    "lprint(adjust_table(results_css, coln_adjust))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "id": "e0dce7a9-c30b-4f12-a09f-8a5a2e683b6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{l|llll|lll|ll|ll|ll|ll|ll|l|l|l}\n",
+      "\\toprule\n",
+      "$k$ & \\multicolumn{4}{c|}{0} & \\multicolumn{3}{c|}{1} & \\multicolumn{2}{c|}{2} & \\multicolumn{2}{c|}{3} & \\multicolumn{2}{c|}{4} & \\multicolumn{2}{c|}{5} & \\multicolumn{2}{c|}{6} & \\multicolumn{1}{c|}{7} & \\multicolumn{1}{c|}{8} & \\multicolumn{1}{c}{9} \\\\ \n",
+      "$n/d$ & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\midrule\n",
+      "1 & 1 & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & 1 & 1 & - & - & 2 & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & 2 & 1 & - & - & 5 & - & - & 3 & - & 1 & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & 3 & 3 & - & - & 11 & 1 & - & 10 & 1 & 4 & - & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & 6 & 4 & - & - & 27 & 4 & - & 32 & 2 & 18 & - & 5 & - & 1 & - & - & - & - & - & - \\\\ \n",
+      "6 & 10 & 11 & 1 & - & 65 & 17 & - & 114 & 13 & 79 & 2 & 29 & 1 & 6 & - & 1 & - & - & - & - \\\\ \n",
+      "7 & 22 & 20 & 1 & - & 175 & 62 & 1 & 417 & 52 & 392 & 15 & 168 & 3 & 43 & - & 7 & - & 1 & - & - \\\\ \n",
+      "8 & 43 & 58 & 2 & 1 & 492 & 248 & 2 & 1691 & 311 & 2082 & 132 & 1153 & 36 & 326 & 3 & 61 & 1 & 8 & 1 & - \\\\ \n",
+      "9 & 104 & 142 & 4 & - & 1539 & 1031 & 22 & 7494 & 1843 & 12627 & 1233 & 8886 & 345 & 3055 & 43 & 592 & 4 & 83 & 9 & 1 \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(make_full_table(results_css, coln_adjust, \"$n/d$\"))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "00c9c272-a44c-4197-a15d-d2b8cddd6d7e",
+   "id": "b1b535b1-4835-4435-bd45-e4bbb69542d8",
    "metadata": {},
    "source": [
-    "# Number of codes that are GF(2) linear"
+    "### All indecomposable CSS codes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 333,
-   "id": "a4cce46f-1913-418e-a629-8a028a78ec42",
+   "execution_count": 269,
+   "id": "8bf24c78-efde-44fd-b084-d2c197bcca0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_css_indec = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
+    "for n in range(10):\n",
+    "    for k in range(10):\n",
+    "        for d in range(5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_css=True, is_decomposable=False, info_only=True, list_only=True)\n",
+    "            results_css_indec[n][k][d] = len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "id": "551a743a-648a-4fa2-94de-9bdec724de4b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\0&2&0&0&0&0&0&0&0\\\\2&0&2&0&0&0&0&0&0\\\\0&4&0&3&0&0&0&0&0\\\\4&0&5&0&3&0&0&0&0\\\\0&10&0&9&0&4&0&0&0\\\\7&0&16&0&11&0&4&0&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}1&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&2&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&2&0&0&4&1&0&3&1&1&0&0&0&0&0&0&0&0&0&0\\\\0&3&0&0&10&3&0&12&1&5&0&1&0&0&0&0&0&0&0&0\\\\0&7&1&0&25&12&0&50&10&30&2&8&1&1&0&0&0&0&0&0\\\\0&14&1&0&74&41&1&208&37&185&13&63&2&10&0&1&0&0&0&0\\\\0&40&2&1&229&168&1&953&244&1176&114&572&31&119&3&14&1&1&0&0\\\\0&106&4&0&796&717&19&4688&1475&8198&1082&5396&305&1531&40&211&3&17&1&0\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -905,205 +1435,126 @@
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(n, k, is_gf4linear=True, info_only=True, list_only=True)\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "\n",
-    "lprint(results)"
+    "coln_adjust = [None, -1, -2, -2, -2, -2, -2, -3, -3, -3]\n",
+    "lprint(adjust_table(results_css_indec, coln_adjust))"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "c363aa1d-e3b0-48e5-9a75-9c936c683997",
+   "cell_type": "code",
+   "execution_count": 274,
+   "id": "449c5a1a-5692-401c-94a9-043c7861ac03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{l|llll|lll|ll|ll|ll|ll|ll|l|l|l}\n",
+      "\\toprule\n",
+      "$k$ & \\multicolumn{4}{c|}{0} & \\multicolumn{3}{c|}{1} & \\multicolumn{2}{c|}{2} & \\multicolumn{2}{c|}{3} & \\multicolumn{2}{c|}{4} & \\multicolumn{2}{c|}{5} & \\multicolumn{2}{c|}{6} & \\multicolumn{1}{c|}{7} & \\multicolumn{1}{c|}{8} & \\multicolumn{1}{c}{9} \\\\ \n",
+      "$n/d$ & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\midrule\n",
+      "1 & 1 & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & - & 1 & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & - & 1 & - & - & 2 & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & - & 2 & - & - & 4 & 1 & - & 3 & 1 & 1 & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & - & 3 & - & - & 10 & 3 & - & 12 & 1 & 5 & - & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      "6 & - & 7 & 1 & - & 25 & 12 & - & 50 & 10 & 30 & 2 & 8 & 1 & 1 & - & - & - & - & - & - \\\\ \n",
+      "7 & - & 14 & 1 & - & 74 & 41 & 1 & 208 & 37 & 185 & 13 & 63 & 2 & 10 & - & 1 & - & - & - & - \\\\ \n",
+      "8 & - & 40 & 2 & 1 & 229 & 168 & 1 & 953 & 244 & 1176 & 114 & 572 & 31 & 119 & 3 & 14 & 1 & 1 & - & - \\\\ \n",
+      "9 & - & 106 & 4 & - & 796 & 717 & 19 & 4688 & 1475 & 8198 & 1082 & 5396 & 305 & 1531 & 40 & 211 & 3 & 17 & 1 & - \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(make_full_table(results_css_indec, coln_adjust, \"$n/d$\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 793,
+   "id": "2cb59500-e4b9-4a8b-9505-ec2007953ae1",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Number of codes that are GF(2) linear and indecomposable"
+    "table = adjust_table(results_css_indec, coln_adjust)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 335,
-   "id": "32614267-058f-4f38-bf06-60a9f6dc6af0",
+   "execution_count": 794,
+   "id": "5007f3a7-c0fe-4d61-876d-680a87a473be",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\0&1&0&1&0&0&0&0&0\\\\1&0&1&0&0&0&0&0&0\\\\0&4&0&2&0&1&0&0&0\\\\2&0&4&0&2&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
       "text/plain": [
-       "<IPython.core.display.Math object>"
+       "array([    2,     2,     4,    12,    35,   147,   650,  3669, 24589])"
       ]
      },
+     "execution_count": 794,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "results = np.zeros((9, 9), dtype=int)\n",
-    "\n",
-    "for n in range(10):\n",
-    "    num = 0\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(\n",
-    "            n, k, is_decomposable=False, is_gf4linear=True, info_only=True, list_only=True\n",
-    "        )\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            results[n - 1][k - 1] = num\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "\n",
-    "lprint(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f91d712a-c919-4391-85d0-b1324f791ac2",
-   "metadata": {},
-   "source": [
-    "## Number of codes that are GF(2) linear, indecomposable by maximum distance"
+    "np.sum(table, axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 336,
-   "id": "dfe7fb50-8408-46de-b929-4a3b7c32d552",
+   "execution_count": 796,
+   "id": "d2252846-6e1f-4128-8ed4-e38060468c52",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[4,2]] : 2\n",
-      "[[5,1]] : 3\n",
-      "[[6,2]] : 2\n",
-      "[[6,4]] : 2\n",
-      "[[7,1]] : 3\n",
-      "[[7,3]] : 2\n",
-      "[[8,2]] : 3\n",
-      "[[8,4]] : 2\n",
-      "[[8,6]] : 2\n",
-      "[[9,1]] : 3\n",
-      "[[9,3]] : 3\n",
-      "[[9,5]] : 2\n"
+      "There are 29110 indecomposable CSS codes in the database\n"
      ]
     }
    ],
    "source": [
-    "mat = np.zeros((9, 9), dtype=int)\n",
-    "n_set = []\n",
-    "k_set = []\n",
-    "d_set = []\n",
-    "for n in range(10):\n",
-    "    for k in range(1, 10):\n",
-    "        codes = cb.all_small_codes(\n",
-    "            n, k, is_decomposable=False, is_gf4linear=True, info_only=True, list_only=True\n",
-    "        )\n",
-    "        if len(codes) > 0:\n",
-    "            d_max = -1\n",
-    "            code_index = -1\n",
-    "            for code in codes:\n",
-    "                if code[\"d\"] > d_max:\n",
-    "                    d_max = code[\"d\"]\n",
-    "                    code_index = code[\"index\"]\n",
-    "            n_set += [n]\n",
-    "            k_set += [k]\n",
-    "            d_set += [d_max]\n",
-    "            mat[n - 1, k - 1] = d_max\n",
-    "            print(f\"[[{n},{k}]] : {d_max}\")"
+    "print(f\"There are {np.sum(np.sum(table, axis=1))} indecomposable CSS codes in the database\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9fac289d-204e-4da5-b03b-90e997111184",
+   "id": "ae49a1ff-ce4b-4de3-ab9d-8f84459b8517",
+   "metadata": {},
+   "source": [
+    "### GF(4)-linear code counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "9c8b50cc-588e-4c52-97c1-5743b296f813",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Distance counts on codes that are GF(2) linear, indecomposable"
+    "results_gf4 = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
+    "for n in range(10):\n",
+    "    for k in range(10):\n",
+    "        for d in range(5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_gf4linear=True, info_only=True, list_only=True)\n",
+    "            results_gf4[n][k][d] = len(codes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 338,
-   "id": "51aeaa6e-bd46-4c70-bc48-5a196c34967e",
+   "execution_count": 275,
+   "id": "bf2a4e6d-870a-4108-908b-f4a873fa2b61",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d=0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d=1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d=2\n"
-     ]
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&1&0&1&0&0&0&0&0\\\\0&0&1&0&0&0&0&0&0\\\\0&3&0&2&0&1&0&0&0\\\\0&0&3&0&2&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d=3\n"
-     ]
-    },
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0\\\\2&0&1&0&0&0&0&0&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&1&1&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&1&0&1&0&0&2&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&1&0&0&0&2&2&0&0&2&1&0&0&0&0&0&0&0\\\\0&0&0&0&2&0&2&0&0&4&1&0&0&3&0&0&0&0&0&0\\\\0&2&0&1&0&0&0&4&5&0&0&5&4&0&0&3&1&0&0&0\\\\0&0&0&0&3&0&4&0&0&10&5&0&0&9&2&0&0&4&0&0\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1111,89 +1562,82 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "coln_adjust = [None, -1, -2, -2, -2, -2, -2, -3, -3, -3]\n",
+    "lprint(adjust_table(results_gf4, coln_adjust))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "id": "ab9bbddd-3983-4b2f-a724-9b4c35110199",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "d=4\n"
+      "\\begin{tabular}{l|llll|lll|ll|ll|ll|ll|ll|l|l|l}\n",
+      "\\toprule\n",
+      "$k$ & \\multicolumn{4}{c|}{0} & \\multicolumn{3}{c|}{1} & \\multicolumn{2}{c|}{2} & \\multicolumn{2}{c|}{3} & \\multicolumn{2}{c|}{4} & \\multicolumn{2}{c|}{5} & \\multicolumn{2}{c|}{6} & \\multicolumn{1}{c|}{7} & \\multicolumn{1}{c|}{8} & \\multicolumn{1}{c}{9} \\\\ \n",
+      "$n/d$ & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\midrule\n",
+      "1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & - & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & - & 1 & - & - & - & - & - & 1 & 1 & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & - & - & - & - & 1 & - & 1 & - & - & 2 & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "6 & - & 1 & - & 1 & - & - & - & 2 & 2 & - & - & 2 & 1 & - & - & - & - & - & - & - \\\\ \n",
+      "7 & - & - & - & - & 2 & - & 2 & - & - & 4 & 1 & - & - & 3 & - & - & - & - & - & - \\\\ \n",
+      "8 & - & 2 & - & 1 & - & - & - & 4 & 5 & - & - & 5 & 4 & - & - & 3 & 1 & - & - & - \\\\ \n",
+      "9 & - & - & - & - & 3 & - & 4 & - & - & 10 & 5 & - & - & 9 & 2 & - & - & 4 & - & - \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
      ]
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "for d in range(5):\n",
-    "    results = np.zeros((9, 9), dtype=int)\n",
-    "    for n in range(10):\n",
-    "        num = 0\n",
-    "        for k in range(1, n):\n",
-    "            codes = cb.all_small_codes(\n",
-    "                n, k, d=d, is_decomposable=False, is_gf4linear=True, info_only=True, list_only=True\n",
-    "            )\n",
-    "            try:\n",
-    "                num = len(codes)\n",
-    "                results[n - 1][k - 1] = num\n",
-    "            except TypeError:\n",
-    "                pass\n",
-    "    print(f\"d={d}\")\n",
-    "    lprint(results)"
+    "print(make_full_table(results_gf4, coln_adjust, \"$n/d$\"))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "33e8ab82-a509-4d02-999c-fa25cb9545eb",
+   "id": "cea88b8b-00eb-45e8-be5e-6f877d71f5b8",
+   "metadata": {},
+   "source": [
+    "### Indecomposbale GF(4)-linear code counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "id": "619147be-c411-4e05-b658-31b50662a23a",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Distance counts on codes that are GF(2) linear and decomposable"
+    "results_gf4_indec = np.zeros((10, 10, 5), dtype=int)\n",
+    "\n",
+    "all_gfindcomp = []\n",
+    "for n in range(10):\n",
+    "    for k in range(10):\n",
+    "        for d in range(5):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_gf4linear=True, is_decomposable=False, info_only=True, list_only=True)\n",
+    "            results_gf4_indec[n][k][d] = len(codes)\n",
+    "            all_gfindcomp +=  codes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 339,
-   "id": "93c5721e-b263-4874-9a48-7c761b088a7b",
+   "execution_count": 276,
+   "id": "b1edb26c-4504-4862-ac78-98611498755c",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d=0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d=1\n"
-     ]
-    },
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0\\\\1&0&2&0&0&0&0&0&0\\\\0&2&0&2&0&0&0&0&0\\\\2&0&4&0&3&0&0&0&0\\\\0&4&0&5&0&3&0&0&0\\\\3&0&10&0&9&0&4&0&0\\\\\\end{bmatrix}$"
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&1&0&0&0&0&1&0&0&0&1&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&1&0&0&0&1&0&0&0&0&0&0&0&0&0\\\\0&0&0&1&0&0&0&0&3&0&0&0&2&0&0&0&1&0&0&0\\\\0&0&0&0&0&0&2&0&0&0&3&0&0&0&2&0&0&0&0&0\\\\\\end{bmatrix}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1201,59 +1645,21039 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "coln_adjust = [None, -1, -2, -2, -2, -2, -2, -3, -3, -3]\n",
+    "lprint(adjust_table(results_gf4_indec, coln_adjust))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "id": "7fa8a791-92b1-44fc-b74b-141e48ef40a7",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "d=2\n"
+      "\\begin{tabular}{l|llll|lll|ll|ll|ll|ll|ll|l|l|l}\n",
+      "\\toprule\n",
+      "$k$ & \\multicolumn{4}{c|}{0} & \\multicolumn{3}{c|}{1} & \\multicolumn{2}{c|}{2} & \\multicolumn{2}{c|}{3} & \\multicolumn{2}{c|}{4} & \\multicolumn{2}{c|}{5} & \\multicolumn{2}{c|}{6} & \\multicolumn{1}{c|}{7} & \\multicolumn{1}{c|}{8} & \\multicolumn{1}{c}{9} \\\\ \n",
+      "$n/d$ & 1 & 2 & 3 & 4 & 1 & 2 & 3 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\midrule\n",
+      "1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & - & - & - & - & - & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & - & - & - & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "6 & - & - & - & 1 & - & - & - & - & 1 & - & - & - & 1 & - & - & - & - & - & - & - \\\\ \n",
+      "7 & - & - & - & - & - & - & 1 & - & - & - & 1 & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "8 & - & - & - & 1 & - & - & - & - & 3 & - & - & - & 2 & - & - & - & 1 & - & - & - \\\\ \n",
+      "9 & - & - & - & - & - & - & 2 & - & - & - & 3 & - & - & - & 2 & - & - & - & - & - \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
      ]
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&2&0&2&0&0&0&0&0\\\\0&0&2&0&0&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "print(make_full_table(results_gf4_indec, coln_adjust, \"$n/d$\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 806,
+   "id": "dfaa4796-a9d5-47de-866e-0f96bd7a2a9c",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "d=3\n"
+      "There are only 24 indecomposable GF(4)-linear equivalence classes\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print(f\"There are only {np.sum(adjust_table(results_gf4_indec, coln_adjust))} indecomposable GF(4)-linear equivalence classes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 813,
+   "id": "609a1592-f4c0-451c-99b0-aa57067ae89a",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\2&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
       "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "d=4\n"
-     ]
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
-      ],
+       "[{aut_group_generators : ['S0S1', '(0,1)', 'H0H1'],\n",
+       " aut_group_size       : 12,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 1,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1', 'X0X1'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 2,\n",
+       " uuid                 : 9e7112a5-868b-4832-bd42-2eaf5f1182de,\n",
+       " weight_enumerator    : [1, 0, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['(2,3)', '(1,2)', 'S0S1S2S3', '(0,1)', 'H0H1H2H3'],\n",
+       " aut_group_size       : 144,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 9,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['X1X2', 'X1X3', 'Z0Z2', 'Z0Z3'],\n",
+       " n                    : 4,\n",
+       " uuid                 : 373b856e-a5af-4e9f-8524-997f1ccfe77e,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['H0V1V2S3S4^(1,2)', 'V0H1S2V3S4^(0,3)', 'H0V1V2S3S4^(0,1,3,2)', 'H0V1S2V3H4^(0,1,2,3)', 'H0V1V2S3S4^(3,4)'],\n",
+       " aut_group_size       : 360,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 21,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0Y1Z2Z3', 'Y0Z1Y2Z4', 'X0Z2X3Z4', 'X0Z1Z3X4'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z3X4', 'Z0Z1Z2Z3Z4'],\n",
+       " n                    : 5,\n",
+       " uuid                 : afef70ec-4dff-48ea-9361-3307ecc90878,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 15, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['V0V1S2S3V4V5^(4,5)', 'R4r5^(2,3)(4,5)', 'V0V1S2V3V4S5^(2,5,3,4)', 'V0H1H2S3S4H5^(3,4)', '(1,2)(3,4)', 'H0V1H2H3V4H5^(3,5)', 'R4r5^(0,1)(4,5)'],\n",
+       " aut_group_size       : 2160,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 4,\n",
+       " index                : 19,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0Z1Z2Z5', 'Z0Y1Z3Z5', 'Z0Y2Z4Z5', 'Z1Y3Z4Z5', 'Z2Z3Y4Z5', 'X0Z3Z4Y5'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 6,\n",
+       " uuid                 : c54f7a20-88d7-461e-830d-239f35fd5bc3,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 45, 0, 18],\n",
+       " },\n",
+       " {aut_group_generators : ['(4,5)', 'V0V1V2V3V4V5', '(2,3)', '(2,4)(3,5)', 'H0H1H2H3H4H5', '(0,1)', '(0,2)(1,3)'],\n",
+       " aut_group_size       : 288,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 126,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['X1X3X4', 'X1X3X5', 'Z0Z2Z4', 'Z0Z2Z5'],\n",
+       " n                    : 6,\n",
+       " uuid                 : 309ff5ca-e6e5-4ff2-a9a2-c1acf9e68c2c,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 9, 0, 6],\n",
+       " },\n",
+       " {aut_group_generators : ['(4,5)', '(3,4)', '(2,3)', '(1,2)', 'S0S1S2S3S4S5', '(0,1)', 'V0V1V2V3V4V5'],\n",
+       " aut_group_size       : 4320,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 29,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3Z4Z5', 'Y0Y1Y2Y3Y4Y5'],\n",
+       " k                    : 4,\n",
+       " logical_ops          : ['X1X2', 'X1X3', 'X1X4', 'X1X5', 'Z0Z2', 'Z0Z3', 'Z0Z4', 'Z0Z5'],\n",
+       " n                    : 6,\n",
+       " uuid                 : 7ba1bb8f-2185-44c5-af50-4ddcf3cf2226,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 0, 0, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['(3,4)(5,6)', '(3,5)(4,6)', 'V0V1V2V3V4V5V6', '(1,2)(5,6)', '(1,3)(2,4)', 'H0H1H2H3H4H5H6', '(0,1)(4,5)'],\n",
+       " aut_group_size       : 1008,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 226,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z3Z6', 'Z0Z2Z3Z5', 'Y1Y2Y3Y4', 'Z3Z4Z5Z6', 'Y0Y1Y4Y5', 'Y0Y2Y4Y6'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X2X4X5', 'Z1Z3Z5'],\n",
+       " n                    : 7,\n",
+       " uuid                 : 69b11699-9064-4ca3-8b7a-c21e72d0756b,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 21, 0, 42, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['R1r2^(1,2)', 'r0R1^(0,1)', 'H0V1H2H3S4S5V6^(4,5)', 'H0V1H2V3V4S5S6^(3,4)', 'H0V1H2V3V4S5S6^(5,6)', 'S0H1S2H3V4H5S6^(3,6,5,4)'],\n",
+       " aut_group_size       : 432,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 499,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X3X4Z5Z6', 'Z3Z4Y5Y6', 'Y0X1Y2Y3Z4Z5', 'X0Z1X2Z4X5Z6'],\n",
+       " k                    : 3,\n",
+       " logical_ops          : ['Z1X4Z5', 'Z1X2', 'Z1Z3Z5X6', 'Z1Z3Z4', 'Z0Z2', 'Z0Z1Z5Z6'],\n",
+       " n                    : 7,\n",
+       " uuid                 : 81af7aa6-3c72-467f-bf8a-176e37111bae,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 3, 0, 12, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['(4,5)(6,7)', '(4,6)(5,7)', '(2,3)(5,6)', '(2,4)(3,7)', 'V0V1V2V3V4V5V6V7', '(1,2)(6,7)', 'H0H1H2H3H4H5H6H7', '(0,1)(5,6)'],\n",
+       " aut_group_size       : 8064,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 4,\n",
+       " index                : 125,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X1X2X3', 'Z0Z1Z5Z6', 'Z0Z2Z5Z7', 'Z0Z3Z6Z7', 'Z4Z5Z6Z7', 'X1X2X4X5', 'X1X3X4X6', 'X2X3X4X7'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 8,\n",
+       " uuid                 : 66927953-9dab-4ae8-8ae8-77322912ab73,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 42, 0, 168, 0, 45],\n",
+       " },\n",
+       " {aut_group_generators : ['r1R2^(1,2)', 'S0V1S2V3V4H5H6V7^(5,6)', 'H0H1S2V3V4H5H6V7^(0,1)(5,6)', 'H0S1H2V3H4H5V6H7^(5,7)', 'R6r7^(4,5)(6,7)', 'H0S1H2H3V4V5H6H7^(6,7)', '(3,4)(5,6)'],\n",
+       " aut_group_size       : 2160,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 4492,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y3Y4Z5Z6', 'Z3Y5Z6Z7', 'Z4Z5Y6Z7', 'X3Z4Z6Y7', 'Y0Z1Y2Z3Z4Z7', 'Z0X1Z2Y3Z5Z7'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['Z0Z1X2', 'Z0Z3Z4Z5Z6X7', 'Z0Z2', 'Z1Z3Z4Z7'],\n",
+       " n                    : 8,\n",
+       " uuid                 : 3637c14a-652f-487d-844c-b1986233476d,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 15, 0, 30, 0, 18],\n",
+       " },\n",
+       " {aut_group_generators : ['r0R1^(0,1)', 'V0S1V2V3S4S5V6V7^(6,7)', 'R6r7^(4,5)(6,7)', 'V0S1V2V3S4V5V6S7^(4,7,5,6)', 'H0V1H2H3V4V5H6H7^(6,7)', 'R6r7^(2,3)(6,7)', 'S0H1V2V3V4V5S6S7^(2,4)(3,5)(6,7)'],\n",
+       " aut_group_size       : 288,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 4584,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X2X3Z4Z5', 'Z2Z3Y4Y5', 'Z4Z5Y6Z7', 'Y2Y3Z6X7', 'X0Z1Y2Z3Z4Z7', 'Z0Y1Z2Y4Z6Z7'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['Z0Z1X3Z4Z7', 'Z0Z1Z2Z4X5Z6Z7', 'Z0Z2Z3', 'Z1Z4Z5'],\n",
+       " n                    : 8,\n",
+       " uuid                 : 708cc1b7-fc90-4730-8caf-a162063923b6,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 9, 0, 42, 0, 12],\n",
+       " },\n",
+       " {aut_group_generators : ['(6,7)', '(4,5)', '(4,6)(5,7)', 'V0V1V2V3V4V5V6V7', '(2,3)', '(2,4)(3,5)', 'H0H1H2H3H4H5H6H7', '(0,1)', '(0,2)(1,3)'],\n",
+       " aut_group_size       : 2304,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 4926,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5', 'Z0Z1Z6Z7', 'X0X1X6X7'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['X1X3X5X6', 'X1X3X5X7', 'Z0Z2Z4Z6', 'Z0Z2Z4Z7'],\n",
+       " n                    : 8,\n",
+       " uuid                 : 5120f73b-17f4-4b7f-af58-702c1f14aa41,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 18, 0, 24, 0, 21],\n",
+       " },\n",
+       " {aut_group_generators : ['V0V1V2V3H4S5S6H7^(4,7)(5,6)', 'V0S1V2H3H4H5S6S7^(1,7)(3,5)', 'V0S1H2H3H4V5V6S7^(1,7)(5,6)', '(2,3)(5,6)', 'R0r1R7^(1,7,4)', '(0,1)(4,7)', 'R4r5r6R7^(0,2)(1,3)(4,6)(5,7)'],\n",
+       " aut_group_size       : 1728,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 4947,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0Y1Z4Z7', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'Z0Z1X4X7', 'Y0Z1Y2Z3Y4Z6', 'X0Z1X2Z3X5Z7'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['Z0Z1X3Z6', 'Z1Z5Z6X7', 'Z0Z1Z2Z3', 'Z0Z1Z4Z5Z6Z7'],\n",
+       " n                    : 8,\n",
+       " uuid                 : dd2f05f4-c705-4de0-97d7-8478312821be,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 6, 0, 48, 0, 9],\n",
+       " },\n",
+       " {aut_group_generators : ['(2,3)', '(6,7)', '(5,6)', 'V0V1V2V3V4V5V6V7', '(4,5)', 'H0H1H2H3H4H5H6H7', '(0,1)', '(0,2)(1,3)'],\n",
+       " aut_group_size       : 1152,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 2206,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z4Z5Z6Z7'],\n",
+       " k                    : 4,\n",
+       " logical_ops          : ['X1X3X4', 'X1X3X5', 'X1X3X6', 'X1X3X7', 'Z0Z2Z4', 'Z0Z2Z5', 'Z0Z2Z6', 'Z0Z2Z7'],\n",
+       " n                    : 8,\n",
+       " uuid                 : 0f5c1dea-450f-4f20-bfe4-f41c5ba94c3c,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 3, 0, 6, 0, 6],\n",
+       " },\n",
+       " {aut_group_generators : ['(6,7)', '(4,5)', 'V0V1S2S3S4S5S6S7^(4,6)(5,7)', '(2,3)', 'V0V1V2V3V4V5V6V7^(2,4)(3,5)', 'H0H1V2V3V4V5V6V7^(4,6)(5,7)', '(0,1)', 'V0V1V2V3V4V5S6S7^(0,2)(1,3)'],\n",
+       " aut_group_size       : 1152,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 3041,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z0Z1Z2Z3Z4Z5', 'Y0Y1Z2Z3Z6Z7'],\n",
+       " k                    : 4,\n",
+       " logical_ops          : ['X1Z2X3Z4', 'Z0X3Z4X5', 'Z0Z4X6', 'Z0Z4X7', 'Z0Z1', 'Z4Z5', 'Z2Z4Z6', 'Z2Z4Z7'],\n",
+       " n                    : 8,\n",
+       " uuid                 : 491bd914-f462-43df-8290-464f3f383695,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 0, 0, 12, 0, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['(6,7)', '(5,6)', '(4,5)', '(3,4)', '(2,3)', '(1,2)', 'S0S1S2S3S4S5S6S7', '(0,1)', 'H0H1H2H3H4H5H6H7'],\n",
+       " aut_group_size       : 241920,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 67,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3Z4Z5Z6Z7', 'X0X1X2X3X4X5X6X7'],\n",
+       " k                    : 6,\n",
+       " logical_ops          : ['X1X2', 'X1X3', 'X1X4', 'X1X5', 'X1X6', 'X1X7', 'Z0Z2', 'Z0Z3', 'Z0Z4', 'Z0Z5', 'Z0Z6', 'Z0Z7'],\n",
+       " n                    : 8,\n",
+       " uuid                 : d2812e9a-0222-40d3-8748-ff4fe98603cf,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 0, 0, 0, 0, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['(3,4)(5,6)', 'r3r4R5R6^(3,5)(4,6)', 'R2r3R4r6^(4,6,5)', '(2,3)(4,5)', 'V0V1V2V3H4H5S6H7H8^(4,5)(7,8)', 'R1R3r5R6r7^(1,8,7)(4,6,5)', 'H0H1V2S3S4H5H6S7S8^(5,6)(7,8)', '(0,1)(7,8)'],\n",
+       " aut_group_size       : 4320,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 9652,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0Y1Z7Z8', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'X2Z4X5Z6', 'X2Z3Z5X6', 'Z0Z1X7X8', 'X0Z1X2Z3Z4Z8', 'Z0Y2Z5Z6Y7Z8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z1Z2Z3Z4Z5Z6X8', 'Z0Z1Z2Z3Z4Z5Z6Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 4b3e443f-1604-454c-93fe-843146759a98,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 18, 0, 120, 0, 117, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['(2,3)(6,7)', 'r2r3R6R7^(2,6)(3,7)', 'V0V1V2H3S4S5S6H7S8^(3,7)', '(1,2)(5,6)', 'H0H1H2S3V4V5V6S7V8^(3,7)', '(0,1)(4,5)'],\n",
+       " aut_group_size       : 1152,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 10201,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X1Z4Z5', 'X0X2Z4Z6', 'X0X3Z4Z7', 'Z0Z1Y4Y5', 'Z0Z2Y4Y6', 'Z0Z3Y4Y7', 'Y0Y4Z5Z6Z7Z8', 'Y0Z1Z2Z3Z4Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z2Z3X8', 'Z0Z1Z2Z3Z4Z5Z6Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : a6a4813f-fc38-40f2-bc3c-1969666065a8,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 18, 0, 120, 0, 117, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['(7,8)', 'V0V1V2V3V4V5V6V7V8^(2,3)(4,5)', 'r4r5r6r7r8^(1,3,2)(4,5,6)', 'H0H1H2H3H4H5H6H7H8^(2,3)(4,5)', '(0,1)(2,3)'],\n",
+       " aut_group_size       : 144,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 118847,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Y1X2Y4Z5X6', 'X0X1Z4Y5X7X8', 'Y0Y2X4Y6Z7Z8', 'Y0X1Z2X4Y5Z6'],\n",
+       " k                    : 3,\n",
+       " logical_ops          : ['Z0Z2Z4Z5X6', 'Z1Z2Z5X7', 'Z1Z2Z5X8', 'Z0Z1Z6', 'Z0Z1Z4Z5Z7', 'Z0Z1Z4Z5Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : f10da7b6-7258-4773-bd7b-868ad773322c,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 3, 0, 30, 0, 30, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['(6,7)', '(5,6)', 'V0V1V2V3V4V5V6V7V8', '(1,3)(4,8)', '(1,4)(3,8)', 'H0H1H2H3H4H5H6H7H8', '(0,1)(2,3)'],\n",
+       " aut_group_size       : 864,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 131752,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X2X4X8', 'Y0Y1Y2Y3', 'Y1Y3Y4Y8', 'Y0Y1Y4Y5Y6Y7', 'Z1Z2Z5Z6Z7Z8'],\n",
+       " k                    : 3,\n",
+       " logical_ops          : ['X5X6', 'X5X7', 'X4X5X8', 'Z0Z2Z6', 'Z0Z2Z7', 'Z0Z1Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : ce19878f-5839-4365-9409-f23145f876ea,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 9, 0, 18, 0, 36, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['(7,8)', 'V0S1V2V3V4V5V6H7H8^(2,3)', 'R0r1R3^(1,3,2)', 'r0R2r3^(0,2,1)', 'V0H1V2H3H4H5H6S7S8^(1,3)', '(5,6)', 'V0H1V2H3H4V5V6V7V8^(1,3)(5,7)(6,8)'],\n",
+       " aut_group_size       : 576,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 163595,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0X1Y2Y3', 'Y5Y6Z7Z8', 'Z0Y1Z2Z3', 'Z5Z6X7X8', 'Z0Z1Y2Y4Y5Y6', 'X0X1Z2Z4X7X8'],\n",
+       " k                    : 3,\n",
+       " logical_ops          : ['Z0Z3Z5X6Z7', 'Z0Z2X4', 'Z2Z3Z5X8', 'Z5Z6', 'Z2Z3Z4', 'Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 97371997-73e6-4aea-94d7-956a4af695e9,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 6, 0, 24, 0, 33, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['r3R4r7R8^(3,5,4)(6,8,7)', 'V0H1V2S3V4S5S6S7V8^(3,6)(4,8)(5,7)', 'r1R2r3r4r5r6R8^(1,2)(3,7,4,8,5,6)', 'R3R6r7r8^(1,8,2,3)(4,7,6,5)', 'R0R1R2R3R4R5R6R7R8', 'V0V1H2S3H4H5H6S7H8^(0,1)(4,5)(6,8)'],\n",
+       " aut_group_size       : 1296,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 170235,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0X1X2Y3X4X5', 'Z0Z1X2X6Y7X8', 'Z0Z2Z3Z4Z6Y7', 'X1Z2Y3Z5Z7Z8', 'X0Z1Z2X3Z4Z5', 'X0X2X3X4X6Z7'],\n",
+       " k                    : 3,\n",
+       " logical_ops          : ['Z2Z4X6Z7', 'Z3Z4X5', 'Z0Z3X8', 'Z0Z1Z2Z3Z6', 'Z0Z1Z3Z5', 'Z2Z4Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 321e22c2-60af-4aa1-b154-740c1caec781,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 0, 0, 36, 0, 27, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['(7,8)', '(6,7)', '(5,6)', '(4,5)', 'H0H1H2H3V4V5V6V7V8^(1,2)', 'V0V1V2V3V4V5V6V7V8^(0,1)', 'V0V1V2V3V4V5V6V7V8^(2,3)', 'R0R1R2R3R4R5R6R7R8'],\n",
+       " aut_group_size       : 8640,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 8643,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X1X2X3', 'Z0Z1Z2Z3', 'Y0Z1X2X4X5X6X7X8', 'X0Y1Z2Z4Z5Z6Z7Z8'],\n",
+       " k                    : 5,\n",
+       " logical_ops          : ['Z0Z2X4', 'Z0Z2X5', 'Z0Z2X6', 'Z0Z2X7', 'Z0Z2X8', 'Z0Z1Z4', 'Z0Z1Z5', 'Z0Z1Z6', 'Z0Z1Z7', 'Z0Z1Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : cecb677a-dd59-4f92-8ee7-0b286b8a0aa8,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 3, 0, 0, 0, 12, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['(7,8)', '(6,7)', '(4,5)', '(3,4)', 'H0V1V2V3V4V5S6S7S8^(1,2)', 'r0R1R6R7R8^(0,2,1)', 'V0H1V2V3V4V5V6V7V8^(3,6)(4,7)(5,8)'],\n",
+       " aut_group_size       : 1296,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 14986,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0Z1Z2Z3Z4Z5', 'Y0Y1Z2Z6Z7Z8'],\n",
+       " k                    : 5,\n",
+       " logical_ops          : ['Z0Z1X4', 'Z0Z1X5', 'Z1Z3X6', 'Z1Z3X7', 'Z1Z3X8', 'Z3Z4', 'Z3Z5', 'Z2Z3Z6', 'Z2Z3Z7', 'Z2Z3Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : c9a29c3f-2763-4a93-b0c4-0a13ad27f13a,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 0, 0, 6, 0, 9, 0],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 813,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_gfindcomp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddb13dff-7cc7-4e77-9ee2-b5a717a3d9a1",
+   "metadata": {},
+   "source": [
+    "# Maximum distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ddb0574-9cc8-4f33-b9a7-0fccc0e17fca",
+   "metadata": {},
+   "source": [
+    "### All codes maximum distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "id": "389e0db4-f46e-4a3c-b854-ea48b51051d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat_all = np.zeros((10,10),dtype=int)\n",
+    "n_set = []\n",
+    "k_set = []\n",
+    "d_set = []\n",
+    "for n in range(10):\n",
+    "    for k in range(0,10):\n",
+    "        codes = cb.all_small_codes(n, k, info_only=True, list_only=True)\n",
+    "        if len(codes) > 0:\n",
+    "            d_max = -1\n",
+    "            code_index = -1\n",
+    "            for code in codes:\n",
+    "                if code['d'] > d_max:\n",
+    "                    d_max = code['d']\n",
+    "                    code_index = code['index']\n",
+    "            n_set += [n]\n",
+    "            k_set += [k]\n",
+    "            d_set += [d_max]\n",
+    "            mat_all[n,k] = d_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "id": "c99ce160-b1d3-4afe-8074-6696ef719e51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0&0\\\\2&1&1&0&0&0&0&0&0&0\\\\2&1&1&1&0&0&0&0&0&0\\\\2&2&2&1&1&0&0&0&0&0\\\\3&3&2&1&1&1&0&0&0&0\\\\4&3&2&2&2&1&1&0&0&0\\\\3&3&2&2&2&1&1&1&0&0\\\\4&3&3&3&2&2&2&1&1&0\\\\4&3&3&3&2&2&2&1&1&1\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(mat_all)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "id": "c06307e9-9a7d-4885-b227-9f4f76c165f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{c|llllllllll}\n",
+      "\\toprule\n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      " 1 &  1 & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 2 &  2 & 1 & 1 & - & - & - & - & - & - & - \\\\ \n",
+      " 3 &  2 & 1 & 1 & 1 & - & - & - & - & - & - \\\\ \n",
+      " 4 &  2 & 2 & 2 & 1 & 1 & - & - & - & - & - \\\\ \n",
+      " 5 &  3 & 3 & 2 & 1 & 1 & 1 & - & - & - & - \\\\ \n",
+      " 6 &  4 & 3 & 2 & 2 & 2 & 1 & 1 & - & - & - \\\\ \n",
+      " 7 &  3 & 3 & 2 & 2 & 2 & 1 & 1 & 1 & - & - \\\\ \n",
+      " 8 &  4 & 3 & 3 & 3 & 2 & 2 & 2 & 1 & 1 & - \\\\ \n",
+      " 9 &  4 & 3 & 3 & 3 & 2 & 2 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\\\begin{tabular}{c|llllllllll}\")\n",
+    "print(\"\\\\toprule\")\n",
+    "print(f\"$n/k$ & {' & '.join([str(i) for i in range(10)])} \\\\\\ \")\n",
+    "print(\"\\\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\" {n} &  {' & '.join([zero_dash(num) for num in mat_all[n]])} \\\\\\ \")\n",
+    "print(\"\\\\end{tabular}\")\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1c2d33-e1eb-4266-ae38-68df2b919f54",
+   "metadata": {},
+   "source": [
+    "### All indecomposable code maximum distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "id": "52ff5a01-4640-4deb-a3e1-b1dcf8951c9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat_all_indecom = np.zeros((10,10),dtype=int)\n",
+    "n_set = []\n",
+    "k_set = []\n",
+    "d_set = []\n",
+    "for n in range(10):\n",
+    "    for k in range(0,10):\n",
+    "        codes = cb.all_small_codes(n, k, is_decomposable=False, info_only=True, list_only=True)\n",
+    "        if len(codes) > 0:\n",
+    "            d_max = -1\n",
+    "            code_index = -1\n",
+    "            for code in codes:\n",
+    "                if code['d'] > d_max:\n",
+    "                    d_max = code['d']\n",
+    "                    code_index = code['index']\n",
+    "            n_set += [n]\n",
+    "            k_set += [k]\n",
+    "            d_set += [d_max]\n",
+    "            mat_all_indecom[n,k] = d_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "id": "d7233765-3129-4f2a-bb8c-52095dbbcfab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0&0\\\\2&1&0&0&0&0&0&0&0&0\\\\2&1&1&0&0&0&0&0&0&0\\\\2&2&2&1&0&0&0&0&0&0\\\\3&3&2&1&1&0&0&0&0&0\\\\4&3&2&2&2&1&0&0&0&0\\\\3&3&2&2&2&1&1&0&0&0\\\\4&3&3&3&2&2&2&1&0&0\\\\4&3&3&3&2&2&2&1&1&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(mat_all_indecom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "id": "fdeb589f-73ff-40e2-8e8a-6539920a1123",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{c|llllllllll}\n",
+      "\\toprule\n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      " 1 &  1 & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 2 &  2 & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 3 &  2 & 1 & 1 & - & - & - & - & - & - & - \\\\ \n",
+      " 4 &  2 & 2 & 2 & 1 & - & - & - & - & - & - \\\\ \n",
+      " 5 &  3 & 3 & 2 & 1 & 1 & - & - & - & - & - \\\\ \n",
+      " 6 &  4 & 3 & 2 & 2 & 2 & 1 & - & - & - & - \\\\ \n",
+      " 7 &  3 & 3 & 2 & 2 & 2 & 1 & 1 & - & - & - \\\\ \n",
+      " 8 &  4 & 3 & 3 & 3 & 2 & 2 & 2 & 1 & - & - \\\\ \n",
+      " 9 &  4 & 3 & 3 & 3 & 2 & 2 & 2 & 1 & 1 & - \\\\ \n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\\\begin{tabular}{c|llllllllll}\")\n",
+    "print(\"\\\\toprule\")\n",
+    "print(f\"$n/k$ & {' & '.join([str(i) for i in range(10)])} \\\\\\ \")\n",
+    "print(\"\\\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\" {n} &  {' & '.join([zero_dash(num) for num in mat_all_indecom[n]])} \\\\\\ \")\n",
+    "print(\"\\\\end{tabular}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33c4a09b-826b-4207-961c-33d7d52b1083",
+   "metadata": {},
+   "source": [
+    "### All CCS codes maximum distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 332,
+   "id": "8e5d624b-d2aa-47e0-acdd-06d9a71a8e7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat_all_css = np.zeros((10,10),dtype=int)\n",
+    "n_set = []\n",
+    "k_set = []\n",
+    "d_set = []\n",
+    "for n in range(10):\n",
+    "    for k in range(0,10):\n",
+    "        codes = cb.all_small_codes(n, k, is_css=True, info_only=True, list_only=True)\n",
+    "        if len(codes) > 0:\n",
+    "            d_max = -1\n",
+    "            code_index = -1\n",
+    "            for code in codes:\n",
+    "                if code['d'] > d_max:\n",
+    "                    d_max = code['d']\n",
+    "                    code_index = code['index']\n",
+    "            n_set += [n]\n",
+    "            k_set += [k]\n",
+    "            d_set += [d_max]\n",
+    "            mat_all_css[n,k] = d_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "id": "b7261aad-0b64-4098-b991-026fab63564f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0&0\\\\2&1&1&0&0&0&0&0&0&0\\\\2&1&1&1&0&0&0&0&0&0\\\\2&2&2&1&1&0&0&0&0&0\\\\2&2&2&1&1&1&0&0&0&0\\\\3&2&2&2&2&1&1&0&0&0\\\\3&3&2&2&2&1&1&1&0&0\\\\4&3&2&2&2&2&2&1&1&0\\\\3&3&2&2&2&2&2&1&1&1\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(mat_all_css)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 334,
+   "id": "63dec5b1-8e55-4217-bb0a-d7d8a140202c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{c|llllllllll}\n",
+      "\\toprule\n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      " 1 &  1 & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 2 &  2 & 1 & 1 & - & - & - & - & - & - & - \\\\ \n",
+      " 3 &  2 & 1 & 1 & 1 & - & - & - & - & - & - \\\\ \n",
+      " 4 &  2 & 2 & 2 & 1 & 1 & - & - & - & - & - \\\\ \n",
+      " 5 &  2 & 2 & 2 & 1 & 1 & 1 & - & - & - & - \\\\ \n",
+      " 6 &  3 & 2 & 2 & 2 & 2 & 1 & 1 & - & - & - \\\\ \n",
+      " 7 &  3 & 3 & 2 & 2 & 2 & 1 & 1 & 1 & - & - \\\\ \n",
+      " 8 &  4 & 3 & 2 & 2 & 2 & 2 & 2 & 1 & 1 & - \\\\ \n",
+      " 9 &  3 & 3 & 2 & 2 & 2 & 2 & 2 & 1 & 1 & 1 \\\\ \n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\\\begin{tabular}{c|llllllllll}\")\n",
+    "print(\"\\\\toprule\")\n",
+    "print(f\"$n/k$ & {' & '.join([str(i) for i in range(10)])} \\\\\\ \")\n",
+    "print(\"\\\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\" {n} &  {' & '.join([zero_dash(num) for num in mat_all_css[n]])} \\\\\\ \")\n",
+    "print(\"\\\\end{tabular}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f08f6bb-b208-43f3-b7e9-f3fc09c2b3aa",
+   "metadata": {},
+   "source": [
+    "### All indecomposable CSS codes maximum distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "id": "3ae73e50-cc35-47b4-bf56-0b122d066842",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat_all_indecom_css = np.zeros((10,10),dtype=int)\n",
+    "n_set = []\n",
+    "k_set = []\n",
+    "d_set = []\n",
+    "for n in range(10):\n",
+    "    for k in range(0,10):\n",
+    "        codes = cb.all_small_codes(n, k, is_css=True, is_decomposable=False, info_only=True, list_only=True)\n",
+    "        if len(codes) > 0:\n",
+    "            d_max = -1\n",
+    "            code_index = -1\n",
+    "            for code in codes:\n",
+    "                if code['d'] > d_max:\n",
+    "                    d_max = code['d']\n",
+    "                    code_index = code['index']\n",
+    "            n_set += [n]\n",
+    "            k_set += [k]\n",
+    "            d_set += [d_max]\n",
+    "            mat_all_indecom_css[n,k] = d_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "id": "9459a3ca-02a2-45c2-8b6b-4dcb5e887772",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\1&1&0&0&0&0&0&0&0&0\\\\2&1&0&0&0&0&0&0&0&0\\\\2&1&1&0&0&0&0&0&0&0\\\\2&2&2&1&0&0&0&0&0&0\\\\2&2&2&1&1&0&0&0&0&0\\\\3&2&2&2&2&1&0&0&0&0\\\\3&3&2&2&2&1&1&0&0&0\\\\4&3&2&2&2&2&2&1&0&0\\\\3&3&2&2&2&2&2&1&1&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(mat_all_indecom_css)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 349,
+   "id": "970ffe9f-4271-4e75-9e91-ebe88a5b4582",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{c|llllllllll}\n",
+      "\\toprule\n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      " 1 &  1 & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 2 &  2 & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 3 &  2 & 1 & 1 & - & - & - & - & - & - & - \\\\ \n",
+      " 4 &  2 & 2 & 2 & 1 & - & - & - & - & - & - \\\\ \n",
+      " 5 &  2 & 2 & 2 & 1 & 1 & - & - & - & - & - \\\\ \n",
+      " 6 &  3 & 2 & 2 & 2 & 2 & 1 & - & - & - & - \\\\ \n",
+      " 7 &  3 & 3 & 2 & 2 & 2 & 1 & 1 & - & - & - \\\\ \n",
+      " 8 &  4 & 3 & 2 & 2 & 2 & 2 & 2 & 1 & - & - \\\\ \n",
+      " 9 &  3 & 3 & 2 & 2 & 2 & 2 & 2 & 1 & 1 & - \\\\ \n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\\\begin{tabular}{c|llllllllll}\")\n",
+    "print(\"\\\\toprule\")\n",
+    "print(f\"$n/k$ & {' & '.join([str(i) for i in range(10)])} \\\\\\ \")\n",
+    "print(\"\\\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\" {n} &  {' & '.join([zero_dash(num) for num in mat_all_indecom_css[n]])} \\\\\\ \")\n",
+    "print(\"\\\\end{tabular}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7696f0f4-d96a-4a8b-a6a4-e448a04a6773",
+   "metadata": {},
+   "source": [
+    "### All GF(4)-linear codes maximum distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "id": "6172d658-9d03-4739-a71d-79daab06209a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat_all_gf4 = np.zeros((10,10),dtype=int)\n",
+    "n_set = []\n",
+    "k_set = []\n",
+    "d_set = []\n",
+    "for n in range(10):\n",
+    "    for k in range(0,10):\n",
+    "        codes = cb.all_small_codes(n, k, is_gf4linear=True, info_only=True, list_only=True)\n",
+    "        if len(codes) > 0:\n",
+    "            d_max = -1\n",
+    "            code_index = -1\n",
+    "            for code in codes:\n",
+    "                if code['d'] > d_max:\n",
+    "                    d_max = code['d']\n",
+    "                    code_index = code['index']\n",
+    "            n_set += [n]\n",
+    "            k_set += [k]\n",
+    "            d_set += [d_max]\n",
+    "            mat_all_gf4[n,k] = d_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "id": "fb7a13a2-89ea-4cb8-a49d-a4167877ef27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\2&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&0\\\\2&0&2&0&0&0&0&0&0&0\\\\0&3&0&1&0&0&0&0&0&0\\\\4&0&2&0&2&0&0&0&0&0\\\\0&3&0&2&0&1&0&0&0&0\\\\4&0&3&0&2&0&2&0&0&0\\\\0&3&0&3&0&2&0&1&0&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(mat_all_gf4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 344,
+   "id": "a6f5663a-a000-4f1e-a864-f111d6bcf5be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{c|llllllllll}\n",
+      "\\toprule\n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      " 1 &  - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      " 2 &  2 & - & - & - & - & - & - & - & - & - \\\\ \n",
+      " 3 &  - & 1 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 4 &  2 & - & 2 & - & - & - & - & - & - & - \\\\ \n",
+      " 5 &  - & 3 & - & 1 & - & - & - & - & - & - \\\\ \n",
+      " 6 &  4 & - & 2 & - & 2 & - & - & - & - & - \\\\ \n",
+      " 7 &  - & 3 & - & 2 & - & 1 & - & - & - & - \\\\ \n",
+      " 8 &  4 & - & 3 & - & 2 & - & 2 & - & - & - \\\\ \n",
+      " 9 &  - & 3 & - & 3 & - & 2 & - & 1 & - & - \\\\ \n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\\\begin{tabular}{c|llllllllll}\")\n",
+    "print(\"\\\\toprule\")\n",
+    "print(f\"$n/k$ & {' & '.join([str(i) for i in range(10)])} \\\\\\ \")\n",
+    "print(\"\\\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\" {n} &  {' & '.join([zero_dash(num) for num in mat_all_gf4[n]])} \\\\\\ \")\n",
+    "print(\"\\\\end{tabular}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53865912-fd8f-49f9-8f11-f4ce39c9ffe0",
+   "metadata": {},
+   "source": [
+    "### All indecomposable GF(4)-linear codes maximum distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "id": "a1215b6f-c418-49dc-8d5e-5cf58438b0be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat_all_indecom_gf4 = np.zeros((10,10),dtype=int)\n",
+    "n_set = []\n",
+    "k_set = []\n",
+    "d_set = []\n",
+    "for n in range(10):\n",
+    "    for k in range(0,10):\n",
+    "        codes = cb.all_small_codes(n, k, is_gf4linear=True, is_decomposable=False, info_only=True, list_only=True)\n",
+    "        if len(codes) > 0:\n",
+    "            d_max = -1\n",
+    "            code_index = -1\n",
+    "            for code in codes:\n",
+    "                if code['d'] > d_max:\n",
+    "                    d_max = code['d']\n",
+    "                    code_index = code['index']\n",
+    "            n_set += [n]\n",
+    "            k_set += [k]\n",
+    "            d_set += [d_max]\n",
+    "            mat_all_indecom_gf4[n,k] = d_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "id": "edbec62e-c39f-4980-8e75-427b024308c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\2&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&2&0&0&0&0&0&0&0\\\\0&3&0&0&0&0&0&0&0&0\\\\4&0&2&0&2&0&0&0&0&0\\\\0&3&0&2&0&0&0&0&0&0\\\\4&0&3&0&2&0&2&0&0&0\\\\0&3&0&3&0&2&0&0&0&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(mat_all_indecom_gf4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "id": "c86f2c4e-2686-463d-a8d1-d52bc5cf14a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{c|llllllllll}\n",
+      "\\toprule\n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\ \n",
+      "\\midrule\n",
+      " 1 &  - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      " 2 &  2 & - & - & - & - & - & - & - & - & - \\\\ \n",
+      " 3 &  - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      " 4 &  - & - & 2 & - & - & - & - & - & - & - \\\\ \n",
+      " 5 &  - & 3 & - & - & - & - & - & - & - & - \\\\ \n",
+      " 6 &  4 & - & 2 & - & 2 & - & - & - & - & - \\\\ \n",
+      " 7 &  - & 3 & - & 2 & - & - & - & - & - & - \\\\ \n",
+      " 8 &  4 & - & 3 & - & 2 & - & 2 & - & - & - \\\\ \n",
+      " 9 &  - & 3 & - & 3 & - & 2 & - & - & - & - \\\\ \n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\\\begin{tabular}{c|llllllllll}\")\n",
+    "print(\"\\\\toprule\")\n",
+    "print(f\"$n/k$ & {' & '.join([str(i) for i in range(10)])} \\\\\\ \")\n",
+    "print(\"\\\\midrule\")\n",
+    "for n in range(1,10):\n",
+    "    print(f\" {n} &  {' & '.join([zero_dash(num) for num in mat_all_indecom_gf4[n]])} \\\\\\ \")\n",
+    "print(\"\\\\end{tabular}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "890466ee-2d29-48f1-bd72-b46ae5beaa71",
+   "metadata": {},
+   "source": [
+    "# Indecomposable codes of a specific distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e868abdd-3889-41f4-9d76-fad648396777",
+   "metadata": {},
+   "source": [
+    "## Number of indecomposable codes of a specific distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 535,
+   "id": "8a1a1e73-a99a-4927-b4fb-bda4bdadf006",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes_indecom_d = np.zeros((5, 10,10), dtype=int)\n",
+    "\n",
+    "def distance_codes(d):\n",
+    "    for n in range(10):\n",
+    "        num = 0\n",
+    "        for k in range(0,n):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "            try:\n",
+    "                num = len(codes)\n",
+    "                codes_indecom_d[d][n][k]=num\n",
+    "            except TypeError:\n",
+    "                pass\n",
+    "\n",
+    "coln_adjust = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 536,
+   "id": "3746b630-9592-4edd-95d1-1eefe43bda3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for d in range(1,5):\n",
+    "    distance_codes(d)\n",
+    "coln_adjust = [None, -1,-3,-6,-9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 537,
+   "id": "380ff526-62c9-4a7d-be88-5452e4d81c3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0\\\\0&2&1&0&0&0&0&0&0\\\\0&4&3&1&0&0&0&0&0\\\\0&12&16&6&1&0&0&0&0\\\\0&35&82&48&9&1&0&0&0\\\\0&140&545&494&125&13&1&0&0\\\\0&646&4858&7373&2579&295&18&1&0\\\\0&4337&69122&202670&107191&13095&656&24&1\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0\\\\1&0&0&0&0&0&0\\\\2&2&1&0&0&0&0\\\\3&4&2&0&0&0&0\\\\9&27&25&5&1&0&0\\\\22&128&209&62&6&0&0\\\\85&964&3450&2043&255&11&1\\\\363&9395&94048&128405&23844&757&12\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0\\\\0&0&0&0\\\\0&0&0&0\\\\0&0&0&0\\\\0&0&0&0\\\\1&1&0&0\\\\1&1&0&0\\\\4&16&0&0\\\\11&157&20&1\\\\69&3411&4425&221\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\1\\\\0\\\\5\\\\8\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for d in range(1,5):\n",
+    "    lprint(codes_indecom_d[d][:,:coln_adjust[d]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 538,
+   "id": "8b1a44f2-fa99-477d-9456-d83691bb695b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_data = []\n",
+    "for d in range(1,5):\n",
+    "    table_data += [codes_indecom_d[d][:,:coln_adjust[d]]]\n",
+    "full_data = np.concatenate(table_data, axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 539,
+   "id": "62593279-0011-4f2b-9e2a-5af379493844",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0\\\\0&2&1&0&0&0&0&0&0&1&0&0&0&0&0&0&0&0&0&0&0\\\\0&4&3&1&0&0&0&0&0&2&2&1&0&0&0&0&0&0&0&0&0\\\\0&12&16&6&1&0&0&0&0&3&4&2&0&0&0&0&1&1&0&0&0\\\\0&35&82&48&9&1&0&0&0&9&27&25&5&1&0&0&1&1&0&0&1\\\\0&140&545&494&125&13&1&0&0&22&128&209&62&6&0&0&4&16&0&0&0\\\\0&646&4858&7373&2579&295&18&1&0&85&964&3450&2043&255&11&1&11&157&20&1&5\\\\0&4337&69122&202670&107191&13095&656&24&1&363&9395&94048&128405&23844&757&12&69&3411&4425&221&8\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(full_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 541,
+   "id": "6e61be8a-5f9a-4318-abf7-9140daa92e59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_full_table_distance(data, coln_adjust, comb_var:str, start=1, end=4):\n",
+    "    offset = coln_adjust\n",
+    "    offset[0] = -9\n",
+    "    k_header = [comb_var]\n",
+    "    for k in range(1,len(coln_adjust)):\n",
+    "        k_header += [str(k) for k in range(9+offset[k]+1)]\n",
+    "    offsets = [10+item for item in offset]\n",
+    "    tab_string = insert_pipe('l'*(data.shape[1]+1), offsets[:])\n",
+    "    d_column = \" & \".join([\"\\\\multicolumn{\" + str(10+offset[d]) + \"}{c|}{\" + str(d) + \"}\" for d in range(start,end)])\n",
+    "    d_column +=  \" & \\\\multicolumn{\" + str(10+offset[end]) + \"}{c}{\" + str(end) + \"}\"\n",
+    "    d_column = \"$d$ & \" + d_column + \" \\\\\\ \"\n",
+    "\n",
+    "    out_string = \"\\\\begin{tabular}{\" + tab_string + \"}\" + \"\\n\" \\\n",
+    "        + \"\\\\toprule\" + \"\\n\" \\\n",
+    "        + d_column + \"\\n\" \\\n",
+    "        + \" & \".join([str(i) for i in k_header]) + \" \\\\\\ \" + \"\\n\" \\\n",
+    "        + \"\\\\midrule\" + \"\\n\"\n",
+    "    for i in range(10):\n",
+    "        out_string += str(i) + \" & \" + \" & \".join([zero_dash(item) for item in data[i]]) + \" \\\\\\ \" + \"\\n\"\n",
+    "    out_string += \"\\\\bottomrule\" + \"\\n\" + \"\\\\end{tabular}\"\n",
+    "    \n",
+    "    return out_string\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 542,
+   "id": "eb8b81ee-46ac-4884-a1ac-8d6841ba3b38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{l|lllllllll|lllllll|llll|l}\n",
+      "\\toprule\n",
+      "$d$ & \\multicolumn{9}{c|}{1} & \\multicolumn{7}{c|}{2} & \\multicolumn{4}{c|}{3} & \\multicolumn{1}{c}{4} \\\\ \n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 0 & 1 & 2 & 3 & 0 \\\\ \n",
+      "\\midrule\n",
+      "0 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "1 & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & - & 1 & - & - & - & - & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & - & 2 & 1 & - & - & - & - & - & - & 1 & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & - & 4 & 3 & 1 & - & - & - & - & - & 2 & 2 & 1 & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & - & 12 & 16 & 6 & 1 & - & - & - & - & 3 & 4 & 2 & - & - & - & - & 1 & 1 & - & - & - \\\\ \n",
+      "6 & - & 35 & 82 & 48 & 9 & 1 & - & - & - & 9 & 27 & 25 & 5 & 1 & - & - & 1 & 1 & - & - & 1 \\\\ \n",
+      "7 & - & 140 & 545 & 494 & 125 & 13 & 1 & - & - & 22 & 128 & 209 & 62 & 6 & - & - & 4 & 16 & - & - & - \\\\ \n",
+      "8 & - & 646 & 4858 & 7373 & 2579 & 295 & 18 & 1 & - & 85 & 964 & 3450 & 2043 & 255 & 11 & 1 & 11 & 157 & 20 & 1 & 5 \\\\ \n",
+      "9 & - & 4337 & 69122 & 202670 & 107191 & 13095 & 656 & 24 & 1 & 363 & 9395 & 94048 & 128405 & 23844 & 757 & 12 & 69 & 3411 & 4425 & 221 & 8 \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(make_full_table_distance(full_data, coln_adjust, '$n/k$'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dc01ac9-07d4-4c4e-8636-4ae265b3d575",
+   "metadata": {},
+   "source": [
+    "## Number of indecomposable codes that are  CSS of a specific distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3d1bc5d-1498-456a-af06-e3ee52100573",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes_indecom_css_d = np.zeros((5, 10,10), dtype=int)\n",
+    "\n",
+    "def distance_codes_css(d):\n",
+    "    for n in range(10):\n",
+    "        num = 0\n",
+    "        for k in range(0,n):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, is_css=True, info_only=True, list_only=True)\n",
+    "            try:\n",
+    "                num = len(codes)\n",
+    "                codes_indecom_css_d[d][n][k]=num\n",
+    "            except TypeError:\n",
+    "                pass\n",
+    "\n",
+    "coln_adjust = []\n",
+    "\n",
+    "for d in range(1,5):\n",
+    "    distance_codes_css(d)\n",
+    "coln_adjust = [None, -1, -3, -8, -9]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adf4d5af-c093-44dd-9603-7c931b5181bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for d in range(1,5):\n",
+    "    lprint(codes_indecom_css_d[d][:,:coln_adjust[d]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 506,
+   "id": "71a82275-c3be-49bb-b9d8-711f15438a48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_data_css = []\n",
+    "for d in range(1,5):\n",
+    "    table_data_css += [codes_indecom_css_d[d][:,:coln_adjust[d]]]\n",
+    "full_data_css = np.concatenate(table_data_css, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 507,
+   "id": "759a2aec-7d8e-484e-b55b-6347f53d9864",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&1&0&0&0&0&0&0&0&0&0\\\\0&2&1&0&0&0&0&0&0&1&0&0&0&0&0&0&0&0&0\\\\0&4&3&1&0&0&0&0&0&2&1&1&0&0&0&0&0&0&0\\\\0&10&12&5&1&0&0&0&0&3&3&1&0&0&0&0&0&0&0\\\\0&25&50&30&8&1&0&0&0&7&12&10&2&1&0&0&1&0&0\\\\0&74&208&185&63&10&1&0&0&14&41&37&13&2&0&0&1&1&0\\\\0&229&953&1176&572&119&14&1&0&40&168&244&114&31&3&1&2&1&1\\\\0&796&4688&8198&5396&1531&211&17&1&106&717&1475&1082&305&40&3&4&19&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(full_data_css)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 508,
+   "id": "c160f25f-770b-482a-aee4-d34d41f7c1d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{l|lllllllll|lllllll|ll|l}\n",
+      "\\toprule\n",
+      "$d$ & \\multicolumn{9}{c|}{1} & \\multicolumn{7}{c|}{2} & \\multicolumn{2}{c|}{3} & \\multicolumn{1}{c}{4} \\\\ \n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 0 & 1 & 0 \\\\ \n",
+      "\\midrule\n",
+      "0 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "1 & 1 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & - & 1 & - & - & - & - & - & - & - & 1 & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & - & 2 & 1 & - & - & - & - & - & - & 1 & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & - & 4 & 3 & 1 & - & - & - & - & - & 2 & 1 & 1 & - & - & - & - & - & - & - \\\\ \n",
+      "5 & - & 10 & 12 & 5 & 1 & - & - & - & - & 3 & 3 & 1 & - & - & - & - & - & - & - \\\\ \n",
+      "6 & - & 25 & 50 & 30 & 8 & 1 & - & - & - & 7 & 12 & 10 & 2 & 1 & - & - & 1 & - & - \\\\ \n",
+      "7 & - & 74 & 208 & 185 & 63 & 10 & 1 & - & - & 14 & 41 & 37 & 13 & 2 & - & - & 1 & 1 & - \\\\ \n",
+      "8 & - & 229 & 953 & 1176 & 572 & 119 & 14 & 1 & - & 40 & 168 & 244 & 114 & 31 & 3 & 1 & 2 & 1 & 1 \\\\ \n",
+      "9 & - & 796 & 4688 & 8198 & 5396 & 1531 & 211 & 17 & 1 & 106 & 717 & 1475 & 1082 & 305 & 40 & 3 & 4 & 19 & - \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(make_full_table_distance(full_data_css, coln_adjust, '$n/k$'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c37b108-b7f5-4aac-974e-05cce402d737",
+   "metadata": {},
+   "source": [
+    "## Number of indecomposable codes that are GF(4)-linear of a specific distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 550,
+   "id": "d428656d-b680-4a97-85a7-06f7cb6cd552",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes_indecom_gf4_d = np.zeros((5, 10,10), dtype=int)\n",
+    "\n",
+    "def distance_codes_gf4(d):\n",
+    "    for n in range(10):\n",
+    "        num = 0\n",
+    "        for k in range(0,n):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, is_gf4linear=True, info_only=True, list_only=True)\n",
+    "            try:\n",
+    "                num = len(codes)\n",
+    "                codes_indecom_gf4_d[d][n][k]=num\n",
+    "            except TypeError:\n",
+    "                pass\n",
+    "\n",
+    "coln_adjust = []\n",
+    "\n",
+    "for d in range(1,5):\n",
+    "    distance_codes_gf4(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 558,
+   "id": "3fb9f939-50ec-49aa-bb96-e2c892158d8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coln_adjust = [None, 0, -3, -6, -9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 559,
+   "id": "d1b881dd-66a5-4f17-a033-0c6e2d73c738",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0\\\\0&0&0&0&0&0&0\\\\0&0&1&0&0&0&0\\\\0&0&0&0&0&0&0\\\\0&0&1&0&1&0&0\\\\0&0&0&1&0&0&0\\\\0&0&3&0&2&0&1\\\\0&0&0&3&0&2&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0\\\\0&0&0&0\\\\0&0&0&0\\\\0&0&0&0\\\\0&0&0&0\\\\0&1&0&0\\\\0&0&0&0\\\\0&1&0&0\\\\0&0&1&0\\\\0&2&0&1\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0\\\\0\\\\0\\\\0\\\\0\\\\0\\\\1\\\\0\\\\1\\\\0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for d in range(2,5):\n",
+    "    lprint(codes_indecom_gf4_d[d][:,:coln_adjust[d]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 560,
+   "id": "4dbe9f2b-5edf-434f-a173-bd23a799dd17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_data_gf4 = []\n",
+    "for d in range(2,5):\n",
+    "    table_data_gf4 += [codes_indecom_gf4_d[d][:,:coln_adjust[d]]]\n",
+    "full_data_gf4 = np.concatenate(table_data_gf4, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 561,
+   "id": "6988beb3-0a06-4c94-8885-3f29c5b1016d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0&0&0\\\\0&0&1&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&1&0&0&0\\\\0&0&1&0&1&0&0&0&0&0&0&1\\\\0&0&0&1&0&0&0&0&1&0&0&0\\\\0&0&3&0&2&0&1&0&0&1&0&1\\\\0&0&0&3&0&2&0&0&2&0&1&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(full_data_gf4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 562,
+   "id": "f13964bb-a44d-4196-8c09-10a7603f41dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_full_table_distance(data, coln_adjust, comb_var:str, start=1, end=4):\n",
+    "    offset = coln_adjust\n",
+    "    offset[0] = -9\n",
+    "    k_header = [comb_var]\n",
+    "    for k in range(1,len(coln_adjust)):\n",
+    "        k_header += [str(k) for k in range(9+offset[k]+1)]\n",
+    "    offsets = [10+item for item in offset]\n",
+    "    tab_string = insert_pipe('l'*(data.shape[1]+1), offsets[:])\n",
+    "    d_column = \" & \".join([\"\\\\multicolumn{\" + str(10+offset[d-1]) + \"}{c|}{\" + str(d) + \"}\" for d in range(start,end)])\n",
+    "    d_column +=  \" & \\\\multicolumn{\" + str(10+offset[end-1]) + \"}{c}{\" + str(end) + \"}\"\n",
+    "    d_column = \"$d$ & \" + d_column + \" \\\\\\ \"\n",
+    "\n",
+    "    out_string = \"\\\\begin{tabular}{\" + tab_string + \"}\" + \"\\n\" \\\n",
+    "        + \"\\\\toprule\" + \"\\n\" \\\n",
+    "        + d_column + \"\\n\" \\\n",
+    "        + \" & \".join([str(i) for i in k_header]) + \" \\\\\\ \" + \"\\n\" \\\n",
+    "        + \"\\\\midrule\" + \"\\n\"\n",
+    "    for i in range(10):\n",
+    "        out_string += str(i) + \" & \" + \" & \".join([zero_dash(item) for item in data[i]]) + \" \\\\\\ \" + \"\\n\"\n",
+    "    out_string += \"\\\\bottomrule\" + \"\\n\" + \"\\\\end{tabular}\"\n",
+    "    \n",
+    "    return out_string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 563,
+   "id": "08a7bc1c-1fc2-4e33-b513-e42c5f0bf4b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{l|lllllll|llll|l}\n",
+      "\\toprule\n",
+      "$d$ & \\multicolumn{7}{c|}{2} & \\multicolumn{4}{c|}{3} & \\multicolumn{1}{c}{4} \\\\ \n",
+      "$n/k$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 0 & 1 & 2 & 3 & 0 \\\\ \n",
+      "\\midrule\n",
+      "0 & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "1 & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "2 & 1 & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "3 & - & - & - & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "4 & - & - & 1 & - & - & - & - & - & - & - & - & - \\\\ \n",
+      "5 & - & - & - & - & - & - & - & - & 1 & - & - & - \\\\ \n",
+      "6 & - & - & 1 & - & 1 & - & - & - & - & - & - & 1 \\\\ \n",
+      "7 & - & - & - & 1 & - & - & - & - & 1 & - & - & - \\\\ \n",
+      "8 & - & - & 3 & - & 2 & - & 1 & - & - & 1 & - & 1 \\\\ \n",
+      "9 & - & - & - & 3 & - & 2 & - & - & 2 & - & 1 & - \\\\ \n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(make_full_table_distance(full_data_gf4, [None, -3, -6, -9], '$n/k$', start=2, end=4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e032c96-b54d-4719-abfd-a930b74d8063",
+   "metadata": {},
+   "source": [
+    "# List of Indecomposable Stabilizer Codes for Small values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 713,
+   "id": "0434b7d7-d944-4ba5-b55a-70b1904c5755",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_full_tables_old(n_range):\n",
+    "    title = \"$[[n,k]]$ & $\\mathrm{Idx}$ &  $d$ & $|\\mathrm{Aut}(S)|$ & $S$ & Logicals & $w(x)$ \\\\\\ \"\n",
+    "    print(title)\n",
+    "    for n in n_range:\n",
+    "        for k in range(n+1):\n",
+    "            if (n==1 and k==1) or (n>1 and n!=k):\n",
+    "                section_title = f\"\\\\midrule $[[{n},{k}]]$ \"\n",
+    "                for d in range(n-k+2):\n",
+    "                    codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "                    sorted_codes = sorted(codes, key=lambda x: x['aut_group_size'])\n",
+    "                    initial = True\n",
+    "                    for code in sorted_codes:\n",
+    "                        if initial is True:\n",
+    "                            code_line  = [section_title, str(code['index'])]\n",
+    "                            initial = False\n",
+    "                        else:\n",
+    "                            code_line  = ['',str(code['index'])]\n",
+    "                        code_line += [f\"{d}\"]\n",
+    "                        code_line += [str(code['aut_group_size'])]\n",
+    "                        code_line += [\"$\" + \", \".join(latex_it(code['isotropic_generators'], dollar=False)) + \"$\"]\n",
+    "                        logicals = code['logical_ops']\n",
+    "                        if len(logicals)>0:\n",
+    "                            code_line += [\"$\" + \", \".join(latex_it(logicals, dollar=False)) + \"$\"]\n",
+    "                        else:\n",
+    "                            code_line += ['']\n",
+    "                        code_line += [\"$\" + make_poly(code['weight_enumerator']) + \"$\"]\n",
+    "                        print(\" & \".join(code_line)+\" \\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 858,
+   "id": "18e60dc3-adf7-4031-ac42-041a8bc0629d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_full_tables(n_range, k_range=None, d_range=None):\n",
+    "    if isinstance(n_range, int):\n",
+    "        n_range = range(n_range, n_range+1)\n",
+    "        \n",
+    "    if k_range is None:\n",
+    "        def k_range_method(x):\n",
+    "            return range(x+1)\n",
+    "    else: \n",
+    "        def k_range_method(x):\n",
+    "            return k_range(x)\n",
+    "\n",
+    "    if d_range is None:\n",
+    "        def d_range_method(x,y):\n",
+    "            return range(x-y+2)\n",
+    "    else:\n",
+    "        def d_range_method(x,y):\n",
+    "            return d_range(x,y)\n",
+    "            \n",
+    "    title = \"$[[n,k]]$ & $\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & Logicals & $w(x)$ \\\\\\ \\n\"\n",
+    "    title += \"\\\\specialrule{1.5pt}{1pt}{1pt}\"\n",
+    "    print(title)\n",
+    "    for n in n_range:\n",
+    "        for k in k_range_method(n):\n",
+    "            if (n==1 and k==1) or (n>1 and n!=k):\n",
+    "                for d in d_range_method(n,k):\n",
+    "                    section_title = f\"\\\\midrule $[[{n},{k},{d}]]$ \"\n",
+    "                    initial = True\n",
+    "                    codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "                    sorted_codes = sorted(codes, key=lambda x: x['aut_group_size'])\n",
+    "                    for code in sorted_codes:\n",
+    "                        if initial is True:\n",
+    "                            code_line  = [section_title, str(code['index'])]\n",
+    "                            initial = False\n",
+    "                        else:\n",
+    "                            code_line  = ['',str(code['index'])]\n",
+    "                        code_line += [str(code['aut_group_size'])]\n",
+    "                        #code_line += [\"$\" + \", \".join(latex_it(code['isotropic_generators'], dollar=False)) + \"$\"]\n",
+    "\n",
+    "                        iso_gens = \", \".join(latex_it(code['isotropic_generators'], dollar=True)).strip()\n",
+    "                        iso_gens = \"$\\\\langle \" + iso_gens[1:-1] + \"\\\\rangle$\"\n",
+    "                        code_line += [iso_gens]\n",
+    "\n",
+    "                        logicals = code['logical_ops']\n",
+    "                        if len(logicals)>0:\n",
+    "                            code_line += [\"$\" + \", \".join(latex_it(logicals, dollar=False)) + \"$\"]\n",
+    "                        else:\n",
+    "                            code_line += ['']\n",
+    "                        code_line += [\"$\" + make_poly(code['weight_enumerator']) + \"$\"]\n",
+    "                        print(\" & \".join(code_line)+\" \\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84e9be69-f47f-4d87-a8f7-d78b1bc16c80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "make_full_tables(range(1,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4dd24cb-d37b-4afc-a7f4-97c1c6ec4012",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(0,1)\n",
+    "\n",
+    "make_full_tables(5, k_range=k_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59cf6207-41b2-4453-a483-127ae8b3a879",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(1,5)\n",
+    "\n",
+    "make_full_tables(5, k_range=k_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c755f258-f154-4a26-a4f9-35305dc912a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(1)\n",
+    "\n",
+    "make_full_tables(6, k_range=k_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "838d0fa9-fa68-4b07-978f-5d49eefda40d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(1,2)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(1,2)\n",
+    "\n",
+    "make_full_tables(6, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9bf8102-2a90-4aaf-ab06-89b26ed13df0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(1,2)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(2,4)\n",
+    "\n",
+    "make_full_tables(6, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cd270b8-f1a4-4029-8b53-5bdfe3408fe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(2,3)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(1,2)\n",
+    "\n",
+    "make_full_tables(6, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98db3120-eb55-4678-bbf8-c9cb753ab68e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(2,3)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(2,3)\n",
+    "\n",
+    "make_full_tables(6, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e39347f-cd33-4fb3-b7f3-6272a906399f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(3,4)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(1,3)\n",
+    "\n",
+    "make_full_tables(6, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "158e6bc8-e9d9-4721-b2c8-0ef98834e45b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(4,6)\n",
+    "\n",
+    "make_full_tables(6, k_range=k_range_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3546266c-cb2b-4e3c-b7ef-fcd5bf05b702",
+   "metadata": {},
+   "source": [
+    "## GF(4) Linear Codes that are indecomposable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1350,
+   "id": "4c289f44-4fd0-4e16-9d50-c50fc7f7cece",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2,0,2]]\n",
+      "[[4,2,2]]\n",
+      "[[5,1,3]]\n",
+      "[[6,0,4]]\n",
+      "[[6,2,2]]\n",
+      "[[6,4,2]]\n",
+      "[[7,1,3]]\n",
+      "[[7,3,2]]\n",
+      "[[8,0,4]]\n",
+      "[[8,2,2]]\n",
+      "[[8,2,2]]\n",
+      "[[8,2,2]]\n",
+      "[[8,2,3]]\n",
+      "[[8,4,2]]\n",
+      "[[8,4,2]]\n",
+      "[[8,6,2]]\n",
+      "[[9,1,3]]\n",
+      "[[9,1,3]]\n",
+      "[[9,3,2]]\n",
+      "[[9,3,2]]\n",
+      "[[9,3,2]]\n",
+      "[[9,3,3]]\n",
+      "[[9,5,2]]\n",
+      "[[9,5,2]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gf4_codes = []\n",
+    "\n",
+    "for n in range(10):\n",
+    "    for k in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, is_gf4linear=True, is_decomposable=False, info_only=True, list_only=True)\n",
+    "        gf4_codes += codes\n",
+    "        for code in codes:\n",
+    "            print(f\"[[{n},{k},{code['d']}]]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1356,
+   "id": "8c424c18-6c79-464a-8327-652082e6c122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_full_tables_GF4_indecom():\n",
+    "            \n",
+    "    title = \"$[[n,k]]$ & $\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & Logicals & $w(x)$ \\\\\\ \\n\"\n",
+    "    title += \"\\\\specialrule{1.5pt}{1pt}{1pt}\"\n",
+    "    print(title)\n",
+    "    for n in range(10):\n",
+    "        for k in range(n+1):\n",
+    "            for d in range(n+1):\n",
+    "                section_title = f\"\\\\midrule $[[{n},{k},{d}]]$ \"\n",
+    "                initial = True\n",
+    "                codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, is_gf4linear=True, info_only=True, list_only=True)\n",
+    "                sorted_codes = sorted(codes, key=lambda x: x['aut_group_size'])\n",
+    "                for code in sorted_codes:\n",
+    "                    if initial is True:\n",
+    "                        code_line  = [section_title, str(code['index'])]\n",
+    "                        initial = False\n",
+    "                    else:\n",
+    "                        code_line  = ['',str(code['index'])]\n",
+    "                    code_line += [str(code['aut_group_size'])]\n",
+    "                    iso_gens = \", \".join(latex_it(code['isotropic_generators'], dollar=True)).strip()\n",
+    "                    iso_gens = \"$\\\\langle \" + iso_gens[1:-1] + \"\\\\rangle$\"\n",
+    "                    code_line += [iso_gens]\n",
+    "                    #logicals = code['logical_ops']\n",
+    "                    #if len(logicals)>0:\n",
+    "                    #    code_line += [ \", \".join(latex_it(logicals, dollar=True)) ]\n",
+    "                    #else:\n",
+    "                    #    code_line += ['']\n",
+    "                    #code_line += [\"$\" + make_poly(code['weight_enumerator']) + \"$\"]\n",
+    "                    print(\" & \".join(code_line)+\" \\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1357,
+   "id": "8bb63b89-00f3-4b4f-ab72-699643e501e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$[[n,k]]$ & $\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & Logicals & $w(x)$ \\\\ \n",
+      "\\specialrule{1.5pt}{1pt}{1pt}\n",
+      "\\midrule $[[2,0,2]]$  & 1 & 12 & $\\langle Z_{0}Z_{1}$, $X_{0}X_{1}\\rangle$ \\\\ \n",
+      "\\midrule $[[4,2,2]]$  & 9 & 144 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}\\rangle$ \\\\ \n",
+      "\\midrule $[[5,1,3]]$  & 21 & 360 & $\\langle Y_{0}Y_{1}Z_{2}Z_{3}$, $Y_{0}Z_{1}Y_{2}Z_{4}$, $X_{0}Z_{2}X_{3}Z_{4}$, $X_{0}Z_{1}Z_{3}X_{4}\\rangle$ \\\\ \n",
+      "\\midrule $[[6,0,4]]$  & 19 & 2160 & $\\langle Y_{0}Z_{1}Z_{2}Z_{5}$, $Z_{0}Y_{1}Z_{3}Z_{5}$, $Z_{0}Y_{2}Z_{4}Z_{5}$, $Z_{1}Y_{3}Z_{4}Z_{5}$, $Z_{2}Z_{3}Y_{4}Z_{5}$, $X_{0}Z_{3}Z_{4}Y_{5}\\rangle$ \\\\ \n",
+      "\\midrule $[[6,2,2]]$  & 126 & 288 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}$, $Z_{0}Z_{1}Z_{4}Z_{5}$, $X_{0}X_{1}X_{4}X_{5}\\rangle$ \\\\ \n",
+      "\\midrule $[[6,4,2]]$  & 29 & 4320 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}Z_{4}Z_{5}$, $Y_{0}Y_{1}Y_{2}Y_{3}Y_{4}Y_{5}\\rangle$ \\\\ \n",
+      "\\midrule $[[7,1,3]]$  & 226 & 1008 & $\\langle Z_{0}Z_{1}Z_{3}Z_{6}$, $Z_{0}Z_{2}Z_{3}Z_{5}$, $Y_{1}Y_{2}Y_{3}Y_{4}$, $Z_{3}Z_{4}Z_{5}Z_{6}$, $Y_{0}Y_{1}Y_{4}Y_{5}$, $Y_{0}Y_{2}Y_{4}Y_{6}\\rangle$ \\\\ \n",
+      "\\midrule $[[7,3,2]]$  & 499 & 432 & $\\langle X_{3}X_{4}Z_{5}Z_{6}$, $Z_{3}Z_{4}Y_{5}Y_{6}$, $Y_{0}X_{1}Y_{2}Y_{3}Z_{4}Z_{5}$, $X_{0}Z_{1}X_{2}Z_{4}X_{5}Z_{6}\\rangle$ \\\\ \n",
+      "\\midrule $[[8,0,4]]$  & 125 & 8064 & $\\langle X_{0}X_{1}X_{2}X_{3}$, $Z_{0}Z_{1}Z_{5}Z_{6}$, $Z_{0}Z_{2}Z_{5}Z_{7}$, $Z_{0}Z_{3}Z_{6}Z_{7}$, $Z_{4}Z_{5}Z_{6}Z_{7}$, $X_{1}X_{2}X_{4}X_{5}$, $X_{1}X_{3}X_{4}X_{6}$, $X_{2}X_{3}X_{4}X_{7}\\rangle$ \\\\ \n",
+      "\\midrule $[[8,2,2]]$  & 4584 & 288 & $\\langle X_{2}X_{3}Z_{4}Z_{5}$, $Z_{2}Z_{3}Y_{4}Y_{5}$, $Z_{4}Z_{5}Y_{6}Z_{7}$, $Y_{2}Y_{3}Z_{6}X_{7}$, $X_{0}Z_{1}Y_{2}Z_{3}Z_{4}Z_{7}$, $Z_{0}Y_{1}Z_{2}Y_{4}Z_{6}Z_{7}\\rangle$ \\\\ \n",
+      " & 4492 & 2160 & $\\langle Y_{3}Y_{4}Z_{5}Z_{6}$, $Z_{3}Y_{5}Z_{6}Z_{7}$, $Z_{4}Z_{5}Y_{6}Z_{7}$, $X_{3}Z_{4}Z_{6}Y_{7}$, $Y_{0}Z_{1}Y_{2}Z_{3}Z_{4}Z_{7}$, $Z_{0}X_{1}Z_{2}Y_{3}Z_{5}Z_{7}\\rangle$ \\\\ \n",
+      " & 4926 & 2304 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}$, $Z_{0}Z_{1}Z_{4}Z_{5}$, $X_{0}X_{1}X_{4}X_{5}$, $Z_{0}Z_{1}Z_{6}Z_{7}$, $X_{0}X_{1}X_{6}X_{7}\\rangle$ \\\\ \n",
+      "\\midrule $[[8,2,3]]$  & 4947 & 1728 & $\\langle Y_{0}Y_{1}Z_{4}Z_{7}$, $X_{2}X_{3}Z_{5}Z_{6}$, $Z_{2}Z_{3}Y_{5}Y_{6}$, $Z_{0}Z_{1}X_{4}X_{7}$, $Y_{0}Z_{1}Y_{2}Z_{3}Y_{4}Z_{6}$, $X_{0}Z_{1}X_{2}Z_{3}X_{5}Z_{7}\\rangle$ \\\\ \n",
+      "\\midrule $[[8,4,2]]$  & 2206 & 1152 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}$, $X_{0}X_{1}X_{4}X_{5}X_{6}X_{7}$, $Z_{0}Z_{1}Z_{4}Z_{5}Z_{6}Z_{7}\\rangle$ \\\\ \n",
+      " & 3041 & 1152 & $\\langle Z_{0}Z_{1}X_{2}X_{3}X_{6}X_{7}$, $X_{0}X_{1}X_{2}X_{3}X_{4}X_{5}$, $Z_{0}Z_{1}Z_{2}Z_{3}Z_{4}Z_{5}$, $Y_{0}Y_{1}Z_{2}Z_{3}Z_{6}Z_{7}\\rangle$ \\\\ \n",
+      "\\midrule $[[8,6,2]]$  & 67 & 241920 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}Z_{4}Z_{5}Z_{6}Z_{7}$, $X_{0}X_{1}X_{2}X_{3}X_{4}X_{5}X_{6}X_{7}\\rangle$ \\\\ \n",
+      "\\midrule $[[9,1,3]]$  & 10201 & 1152 & $\\langle X_{0}X_{1}Z_{4}Z_{5}$, $X_{0}X_{2}Z_{4}Z_{6}$, $X_{0}X_{3}Z_{4}Z_{7}$, $Z_{0}Z_{1}Y_{4}Y_{5}$, $Z_{0}Z_{2}Y_{4}Y_{6}$, $Z_{0}Z_{3}Y_{4}Y_{7}$, $Y_{0}Y_{4}Z_{5}Z_{6}Z_{7}Z_{8}$, $Y_{0}Z_{1}Z_{2}Z_{3}Z_{4}Y_{8}\\rangle$ \\\\ \n",
+      " & 9652 & 4320 & $\\langle Y_{0}Y_{1}Z_{7}Z_{8}$, $Y_{2}Y_{3}Z_{4}Z_{5}$, $Y_{2}Z_{3}Y_{4}Z_{6}$, $X_{2}Z_{4}X_{5}Z_{6}$, $X_{2}Z_{3}Z_{5}X_{6}$, $Z_{0}Z_{1}X_{7}X_{8}$, $X_{0}Z_{1}X_{2}Z_{3}Z_{4}Z_{8}$, $Z_{0}Y_{2}Z_{5}Z_{6}Y_{7}Z_{8}\\rangle$ \\\\ \n",
+      "\\midrule $[[9,3,2]]$  & 118847 & 144 & $\\langle X_{0}X_{1}X_{2}X_{3}$, $Z_{0}Z_{1}Z_{2}Z_{3}$, $Z_{0}Y_{1}X_{2}Y_{4}Z_{5}X_{6}$, $X_{0}X_{1}Z_{4}Y_{5}X_{7}X_{8}$, $Y_{0}Y_{2}X_{4}Y_{6}Z_{7}Z_{8}$, $Y_{0}X_{1}Z_{2}X_{4}Y_{5}Z_{6}\\rangle$ \\\\ \n",
+      " & 163595 & 576 & $\\langle Y_{0}X_{1}Y_{2}Y_{3}$, $Y_{5}Y_{6}Z_{7}Z_{8}$, $Z_{0}Y_{1}Z_{2}Z_{3}$, $Z_{5}Z_{6}X_{7}X_{8}$, $Z_{0}Z_{1}Y_{2}Y_{4}Y_{5}Y_{6}$, $X_{0}X_{1}Z_{2}Z_{4}X_{7}X_{8}\\rangle$ \\\\ \n",
+      " & 131752 & 864 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{2}X_{4}X_{8}$, $Y_{0}Y_{1}Y_{2}Y_{3}$, $Y_{1}Y_{3}Y_{4}Y_{8}$, $Y_{0}Y_{1}Y_{4}Y_{5}Y_{6}Y_{7}$, $Z_{1}Z_{2}Z_{5}Z_{6}Z_{7}Z_{8}\\rangle$ \\\\ \n",
+      "\\midrule $[[9,3,3]]$  & 170235 & 1296 & $\\langle Y_{0}X_{1}X_{2}Y_{3}X_{4}X_{5}$, $Z_{0}Z_{1}X_{2}X_{6}Y_{7}X_{8}$, $Z_{0}Z_{2}Z_{3}Z_{4}Z_{6}Y_{7}$, $X_{1}Z_{2}Y_{3}Z_{5}Z_{7}Z_{8}$, $X_{0}Z_{1}Z_{2}X_{3}Z_{4}Z_{5}$, $X_{0}X_{2}X_{3}X_{4}X_{6}Z_{7}\\rangle$ \\\\ \n",
+      "\\midrule $[[9,5,2]]$  & 14986 & 1296 & $\\langle Y_{0}X_{1}X_{2}X_{3}X_{4}X_{5}$, $Z_{0}Z_{1}X_{2}X_{6}X_{7}X_{8}$, $X_{0}Z_{1}Z_{2}Z_{3}Z_{4}Z_{5}$, $Y_{0}Y_{1}Z_{2}Z_{6}Z_{7}Z_{8}\\rangle$ \\\\ \n",
+      " & 8643 & 8640 & $\\langle X_{0}X_{1}X_{2}X_{3}$, $Z_{0}Z_{1}Z_{2}Z_{3}$, $Y_{0}Z_{1}X_{2}X_{4}X_{5}X_{6}X_{7}X_{8}$, $X_{0}Y_{1}Z_{2}Z_{4}Z_{5}Z_{6}Z_{7}Z_{8}\\rangle$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "make_full_tables_GF4_indecom()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d5eb52e-364c-43af-afda-654029461653",
+   "metadata": {},
+   "source": [
+    "# Class of stabilizer codes: 4 qubits with d>=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1323,
+   "id": "73cb761e-86f8-48a5-804f-ce15042a6980",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4,0]]\n",
+      "[[4,1]]\n",
+      "[[4,2]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_codes = []\n",
+    "for n in range(4,5):\n",
+    "    for k in range(0,n+1):\n",
+    "        codes = cb.all_small_codes(n, k, d=2,info_only=True, list_only=True)\n",
+    "        num = 0\n",
+    "        try:\n",
+    "            num = len(codes)\n",
+    "            if num > 0:\n",
+    "                print(f\"[[{n},{k}]]\")\n",
+    "                all_codes = all_codes + codes\n",
+    "        except TypeError:\n",
+    "            pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1324,
+   "id": "a030bb78-42db-428a-8d13-c10557c688c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 1324,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(all_codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 862,
+   "id": "0da6dacc-0fca-4afb-9eb7-074df8dd2c0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$[[n,k]]$ & $\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & Logicals & $w(x)$ \\\\ \n",
+      "\\specialrule{1.5pt}{1pt}{1pt}\n",
+      "\\midrule $[[4,0,2]]$  & 2 & 32 & $\\langle X_{0}X_{2}$, $Z_{1}Z_{3}$, $Z_{0}Z_{2}Z_{3}$, $X_{1}X_{2}X_{3}\\rangle$ &  & $1 + 2x^{2} + 8x^{3} + 5x^{4}$ \\\\ \n",
+      " & 3 & 192 & $\\langle Z_{0}Z_{3}$, $Z_{1}Z_{3}$, $Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}\\rangle$ &  & $1 + 6x^{2} + 9x^{4}$ \\\\ \n",
+      "\\midrule $[[4,1,2]]$  & 8 & 24 & $\\langle Z_{0}X_{2}Z_{3}$, $Y_{0}X_{1}Y_{2}$, $Z_{1}Z_{2}X_{3}\\rangle$ & $X_{1}Z_{3}, Z_{0}Z_{1}$ & $1 + 4x^{3} + 3x^{4}$ \\\\ \n",
+      " & 6 & 32 & $\\langle X_{0}X_{1}$, $X_{2}X_{3}$, $Z_{0}Z_{1}Z_{2}Z_{3}\\rangle$ & $X_{1}X_{3}, Z_{2}Z_{3}$ & $1 + 2x^{2} + 5x^{4}$ \\\\ \n",
+      "\\midrule $[[4,2,2]]$  & 9 & 144 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}\\rangle$ & $X_{1}X_{2}, X_{1}X_{3}, Z_{0}Z_{2}, Z_{0}Z_{3}$ & $1 + 3x^{4}$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(0,3)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(2,4)\n",
+    "\n",
+    "make_full_tables(4, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1375,
+   "id": "cb049c07-7a32-447f-b018-0e225a003bcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_full_tables_indecom(n_range, k_range=None, d_range=None, nkd=True, limit=None):\n",
+    "    if isinstance(n_range, int):\n",
+    "        n_range = range(n_range, n_range+1)\n",
+    "        \n",
+    "    if k_range is None:\n",
+    "        def k_range_method(x):\n",
+    "            return range(x+1)\n",
+    "    else: \n",
+    "        def k_range_method(x):\n",
+    "            return k_range(x)\n",
+    "\n",
+    "    if d_range is None:\n",
+    "        def d_range_method(x,y):\n",
+    "            return range(x-y+2)\n",
+    "    else:\n",
+    "        def d_range_method(x,y):\n",
+    "            return d_range(x,y)\n",
+    "\n",
+    "    title = \"\"\n",
+    "    if nkd is True:\n",
+    "        title = \"$[[n,k,d]]$ & \"\n",
+    "    title += \"$\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\\\ \\n\"\n",
+    "    title += \"\\\\specialrule{1.5pt}{1pt}{1pt}\"\n",
+    "    print(title)\n",
+    "    for n in n_range:\n",
+    "        for k in k_range_method(n):\n",
+    "            if (n==1 and k==1) or (n>1 and n!=k):\n",
+    "                for d in d_range_method(n,k):\n",
+    "                    section_title = \"\\\\midrule \"\n",
+    "                    if nkd is True:\n",
+    "                        section_title = f\"\\\\midrule $[[{n},{k},{d}]]$ \"\n",
+    "                    initial = True\n",
+    "                    codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "                    sorted_codes = sorted(codes, key=lambda x: x['aut_group_size'])\n",
+    "                    for code in sorted_codes:\n",
+    "                        if initial is True:\n",
+    "                            if nkd is True:\n",
+    "                                code_line = [section_title, str(code['index'])]\n",
+    "                            else:\n",
+    "                                code_line = [str(code['index'])]\n",
+    "                            initial = False\n",
+    "                        else:\n",
+    "                            if nkd is True:\n",
+    "                                code_line  = ['',str(code['index'])]\n",
+    "                            else:\n",
+    "                                code_line = [str(code['index'])]\n",
+    "                        code_line += [str(code['aut_group_size'])]\n",
+    "                        iso_gens = \", \".join(latex_it(code['isotropic_generators'], dollar=True)).strip()\n",
+    "                        iso_gens = \"$\\\\langle \" + iso_gens[1:-1] + \"\\\\rangle$\"\n",
+    "                        code_line += [iso_gens]\n",
+    "                        #logicals = code['logical_ops']\n",
+    "                        #if len(logicals)>0:\n",
+    "                        #    code_line += [ \", \".join(latex_it(logicals, dollar=True)) ]\n",
+    "                        #else:\n",
+    "                        #    code_line += ['']\n",
+    "                        code_line += [\"$\" + make_poly(code['weight_enumerator']) + \"$\"]\n",
+    "                        print(\" & \".join(code_line)+\" \\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 868,
+   "id": "d32c4874-78cd-438e-a782-6613c02d7b44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$[[n,k,d]]$ & $\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\ \n",
+      "\\specialrule{1.5pt}{1pt}{1pt}\n",
+      "\\midrule $[[4,0,2]]$  & 2 & 32 & $\\langle X_{0}X_{2}$, $Z_{1}Z_{3}$, $Z_{0}Z_{2}Z_{3}$, $X_{1}X_{2}X_{3}\\rangle$ & $1 + 2x^{2} + 8x^{3} + 5x^{4}$ \\\\ \n",
+      " & 3 & 192 & $\\langle Z_{0}Z_{3}$, $Z_{1}Z_{3}$, $Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}\\rangle$ & $1 + 6x^{2} + 9x^{4}$ \\\\ \n",
+      "\\midrule $[[4,1,2]]$  & 8 & 24 & $\\langle Z_{0}X_{2}Z_{3}$, $Y_{0}X_{1}Y_{2}$, $Z_{1}Z_{2}X_{3}\\rangle$ & $1 + 4x^{3} + 3x^{4}$ \\\\ \n",
+      " & 6 & 32 & $\\langle X_{0}X_{1}$, $X_{2}X_{3}$, $Z_{0}Z_{1}Z_{2}Z_{3}\\rangle$ & $1 + 2x^{2} + 5x^{4}$ \\\\ \n",
+      "\\midrule $[[4,2,2]]$  & 9 & 144 & $\\langle Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}\\rangle$ & $1 + 3x^{4}$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(0,3)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(2,4)\n",
+    "\n",
+    "make_full_tables_indecom(4, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8a07b30-12cc-417f-ac73-cb1adadb8c6c",
+   "metadata": {},
+   "source": [
+    "# Class of indecomposable stabilizer codes with n<= 4, k>=1 and d>=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4f2b1c6-16c7-4369-b76d-86df6445f5e7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "8219dd1d-497f-41c4-b425-cebf71ba8aff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4,1]]\n",
+      "[[4,2]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_codes = []\n",
+    "for n in range(5):\n",
+    "    for k in range(1,n):\n",
+    "        codes = cb.all_small_codes(n, k, d=2, is_decomposable=False, info_only=True, list_only=True)\n",
+    "        num = 0\n",
+    "        try:\n",
+    "            num = len(codes)\n",
+    "            if num > 0:\n",
+    "                print(f\"[[{n},{k}]]\")\n",
+    "                all_codes = all_codes + codes\n",
+    "        except TypeError:\n",
+    "            pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "6e90dcad-f148-4f88-b18c-390d92b06566",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['V2V3', '(2,3)', 'V0V1', '(0,1)', '(0,2)(1,3)'],\n",
+       " aut_group_size       : 32,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 6,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X1', 'X2X3', 'Z0Z1Z2Z3'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X1X3', 'Z2Z3'],\n",
+       " n                    : 4,\n",
+       " uuid                 : c49160c8-795e-4558-9978-08b491cdd091,\n",
+       " weight_enumerator    : [1, 0, 2, 0, 5],\n",
+       " },\n",
+       " {aut_group_generators : ['V0H1S2H3^(1,3)', 'H0S1S2V3^(1,2)', '(0,1)(2,3)'],\n",
+       " aut_group_size       : 24,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 8,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0X2Z3', 'Y0X1Y2', 'Z1Z2X3'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X1Z3', 'Z0Z1'],\n",
+       " n                    : 4,\n",
+       " uuid                 : 51fc14fb-8309-4ff6-a51e-8801d0066f87,\n",
+       " weight_enumerator    : [1, 0, 0, 4, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['(2,3)', '(1,2)', 'S0S1S2S3', '(0,1)', 'H0H1H2H3'],\n",
+       " aut_group_size       : 144,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 9,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['X1X2', 'X1X3', 'Z0Z2', 'Z0Z3'],\n",
+       " n                    : 4,\n",
+       " uuid                 : 373b856e-a5af-4e9f-8524-997f1ccfe77e,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 3],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_codes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ca10d35-aa15-4444-9aa6-a5de2ce32620",
+   "metadata": {},
+   "source": [
+    "## Five qubit codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 889,
+   "id": "677a8445-5b80-4fd6-9aaf-20b04b205709",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5,1]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_5codes = []\n",
+    "n=5\n",
+    "for k in range(1,2):\n",
+    "    codes = cb.all_small_codes(n, k, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
+    "    num = 0\n",
+    "    try:\n",
+    "        num = len(codes)\n",
+    "        if num > 0:\n",
+    "            print(f\"[[{n},{k}]]\")\n",
+    "            all_5codes += codes\n",
+    "    except TypeError:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 890,
+   "id": "739eedb6-068e-4daf-8035-823e03b2bb1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['H0V1V2S3S4^(1,2)', 'V0H1S2V3S4^(0,3)', 'H0V1V2S3S4^(0,1,3,2)', 'H0V1S2V3H4^(0,1,2,3)', 'H0V1V2S3S4^(3,4)'],\n",
+       " aut_group_size       : 360,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 21,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0Y1Z2Z3', 'Y0Z1Y2Z4', 'X0Z2X3Z4', 'X0Z1Z3X4'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z3X4', 'Z0Z1Z2Z3Z4'],\n",
+       " n                    : 5,\n",
+       " uuid                 : afef70ec-4dff-48ea-9361-3307ecc90878,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 15, 0],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 890,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_5codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 877,
+   "id": "ec9b2645-bbda-48bb-a5bc-b2be7307010e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$[[n,k,d]]$ & $\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\ \n",
+      "\\specialrule{1.5pt}{1pt}{1pt}\n",
+      "\\midrule $[[5,0,2]]$  & 4 & 32 & $\\langle Z_{0}Z_{4}$, $Z_{1}Z_{2}$, $Z_{1}Z_{3}Z_{4}$, $X_{1}X_{2}X_{3}$, $X_{0}X_{3}X_{4}\\rangle$ & $1 + 2x^{2} + 8x^{3} + 13x^{4} + 8x^{5}$ \\\\ \n",
+      " & 2 & 96 & $\\langle Z_{0}Z_{4}$, $Z_{1}Z_{4}$, $X_{2}X_{3}$, $Z_{2}Z_{3}Z_{4}$, $X_{0}X_{1}X_{3}X_{4}\\rangle$ & $1 + 4x^{2} + 6x^{3} + 11x^{4} + 10x^{5}$ \\\\ \n",
+      " & 6 & 1920 & $\\langle Z_{0}Z_{4}$, $Z_{1}Z_{4}$, $Z_{2}Z_{4}$, $Z_{3}Z_{4}$, $X_{0}X_{1}X_{2}X_{3}X_{4}\\rangle$ & $1 + 10x^{2} + 5x^{4} + 16x^{5}$ \\\\ \n",
+      "\\midrule $[[5,0,3]]$  & 7 & 120 & $\\langle X_{0}Z_{1}Z_{2}$, $Z_{0}X_{1}Z_{3}$, $Z_{0}X_{2}Z_{4}$, $Z_{1}X_{3}Z_{4}$, $Z_{2}Z_{3}X_{4}\\rangle$ & $1 + 10x^{3} + 15x^{4} + 6x^{5}$ \\\\ \n",
+      "\\midrule $[[5,1,2]]$  & 18 & 8 & $\\langle Z_{0}Z_{1}Z_{3}$, $Z_{2}Z_{3}Z_{4}$, $X_{1}X_{2}X_{3}$, $X_{0}X_{3}X_{4}\\rangle$ & $1 + 4x^{3} + 7x^{4} + 4x^{5}$ \\\\ \n",
+      " & 20 & 8 & $\\langle X_{1}X_{2}$, $X_{1}Z_{3}Z_{4}$, $Y_{0}X_{3}Y_{4}$, $X_{0}Y_{1}Z_{2}Y_{3}\\rangle$ & $1 + x^{2} + 3x^{3} + 6x^{4} + 5x^{5}$ \\\\ \n",
+      " & 14 & 16 & $\\langle Z_{1}Z_{4}$, $X_{2}X_{3}$, $X_{0}X_{1}X_{4}$, $Z_{0}Z_{2}Z_{3}Z_{4}\\rangle$ & $1 + 2x^{2} + 2x^{3} + 5x^{4} + 6x^{5}$ \\\\ \n",
+      " & 9 & 96 & $\\langle Z_{0}Z_{4}$, $Z_{1}Z_{4}$, $Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}X_{4}\\rangle$ & $1 + 4x^{2} + 3x^{4} + 8x^{5}$ \\\\ \n",
+      "\\midrule $[[5,1,3]]$  & 21 & 360 & $\\langle Y_{0}Y_{1}Z_{2}Z_{3}$, $Y_{0}Z_{1}Y_{2}Z_{4}$, $X_{0}Z_{2}X_{3}Z_{4}$, $X_{0}Z_{1}Z_{3}X_{4}\\rangle$ & $1 + 15x^{4}$ \\\\ \n",
+      "\\midrule $[[5,2,2]]$  & 27 & 12 & $\\langle Z_{0}Z_{1}X_{4}$, $X_{0}X_{2}Z_{3}Z_{4}$, $X_{1}Z_{2}X_{3}Z_{4}\\rangle$ & $1 + x^{3} + 3x^{4} + 3x^{5}$ \\\\ \n",
+      " & 26 & 48 & $\\langle Z_{1}Z_{4}$, $Z_{0}Z_{2}Z_{3}Z_{4}$, $X_{0}X_{1}X_{2}X_{3}X_{4}\\rangle$ & $1 + x^{2} + 2x^{4} + 4x^{5}$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(0,6)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(2,4)\n",
+    "\n",
+    "make_full_tables_indecom(5, k_range=k_range_in, d_range=d_range_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8308e67e-3f2c-4bb8-83db-473045a66408",
+   "metadata": {},
+   "source": [
+    "#3 Six Qubit Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 913,
+   "id": "5c7cb38a-4717-488c-81d1-fee0f3e5ef20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[6,1]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_6codes = []\n",
+    "n=6\n",
+    "for k in range(1,6):\n",
+    "    codes = cb.all_small_codes(n, k, d=3, info_only=True, list_only=True)\n",
+    "    num = 0\n",
+    "    try:\n",
+    "        num = len(codes)\n",
+    "        if num > 0:\n",
+    "            print(f\"[[{n},{k}]]\")\n",
+    "            all_6codes += codes\n",
+    "    except TypeError:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 914,
+   "id": "20162632-3d88-47f2-8cec-599314a173e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['V0S5', 'H0H5^(0,5)', 'V1H2S3H4S5^(2,4)', 'H1H2V3V4S5^(3,4)', '(1,2)(3,4)'],\n",
+       " aut_group_size       : 96,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 68,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z5', 'X1X2Z3Z4', 'Y1Y3Z4Z5', 'X1Z2X4Z5', 'Z0Y1Z2Z3Y5'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z2X5', 'Z1Z2Z3Z4Z5'],\n",
+       " n                    : 6,\n",
+       " uuid                 : 9d94dfd4-e48b-4c65-9fc2-292322501c62,\n",
+       " weight_enumerator    : [1, 0, 1, 0, 11, 16, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['V5', 'V0V1H2H3S4^(2,3)', 'R1r3R4^(2,4,3)', '(1,2)(3,4)', 'H0V1V2S3S4^(3,4)', '(0,1)(2,3)'],\n",
+       " aut_group_size       : 720,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 87,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 1,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X5', 'Y0Y1Z2Z3', 'Y0Z1Y2Z4', 'X0Z2X3Z4', 'X0Z1Z3X4'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z3X4', 'Z0Z1Z2Z3Z4'],\n",
+       " n                    : 6,\n",
+       " uuid                 : 23ed352c-2a34-4d34-9a24-3d8722748979,\n",
+       " weight_enumerator    : [1, 1, 0, 0, 15, 15, 0],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 914,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_6codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 917,
+   "id": "6f866a8d-3417-49f3-959c-bef4c0bc704e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'X_{0}Z_{5}, X_{1}X_{2}Z_{3}Z_{4}, Y_{1}Y_{3}Z_{4}Z_{5}, X_{1}Z_{2}X_{4}Z_{5}, Z_{0}Y_{1}Z_{2}Z_{3}Y_{5}'"
+      ]
+     },
+     "execution_count": 917,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\", \".join(latex_it(['X0Z5', 'X1X2Z3Z4', 'Y1Y3Z4Z5', 'X1Z2X4Z5', 'Z0Y1Z2Z3Y5'], dollar=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 918,
+   "id": "ad8baf1a-c6d8-4b2c-b404-00ec6831e98d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1 + x^{2} + 11x^{4} + 16x^{5} + 3x^{6}'"
+      ]
+     },
+     "execution_count": 918,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_poly(all_6codes[0]['weight_enumerator'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd71f792-12ef-414d-9627-6cfe2b6989fd",
+   "metadata": {},
+   "source": [
+    "## 7 Cubic codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1100,
+   "id": "d829b8b7-879f-4351-99fe-ed9c8c2651e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[7,1,3]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_7codes = []\n",
+    "n=7\n",
+    "for k in range(1,7):\n",
+    "    codes = cb.all_small_codes(n, k, d=3, info_only=True, is_decomposable=False, list_only=True)\n",
+    "    num = 0\n",
+    "    try:\n",
+    "        num = len(codes)\n",
+    "        if num > 0:\n",
+    "            print(f\"[[{n},{k},3]]\")\n",
+    "            all_7codes += codes\n",
+    "    except TypeError:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1102,
+   "id": "d3ee82a4-043c-4e00-8314-bd959ab4cffa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 1102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(all_7codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1391,
+   "id": "57097e7c-39b3-4c53-b75b-86b288cf6fce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\ \n",
+      "\\specialrule{1.5pt}{1pt}{1pt}\n",
+      "185 & 4 & $\\langle Z_{3}X_{4}Z_{6}$, $Z_{2}Z_{4}X_{6}$, $Y_{0}Y_{1}Z_{2}Z_{5}$, $Y_{0}Z_{1}Y_{2}Z_{6}$, $Z_{0}X_{1}X_{3}Z_{4}$, $X_{0}Z_{2}Z_{3}X_{5}\\rangle$ & $1 + 2x^{3} + 9x^{4} + 24x^{5} + 22x^{6} + 6x^{7}$ \\\\ \n",
+      "200 & 6 & $\\langle Z_{1}Z_{3}X_{5}$, $Z_{2}Z_{4}X_{6}$, $Y_{0}Y_{1}Z_{2}Z_{5}$, $Y_{0}Z_{1}Y_{2}Z_{6}$, $Z_{0}X_{1}X_{3}Z_{4}$, $Z_{0}X_{2}Z_{3}X_{4}\\rangle$ & $1 + 2x^{3} + 9x^{4} + 24x^{5} + 22x^{6} + 6x^{7}$ \\\\ \n",
+      "221 & 16 & $\\langle X_{0}X_{1}Z_{3}Z_{4}$, $X_{0}X_{2}Z_{3}Z_{5}$, $Z_{0}Z_{1}Y_{3}Y_{4}$, $Z_{2}Z_{3}Z_{4}X_{5}$, $Z_{0}Z_{1}Z_{2}X_{6}$, $Y_{0}Y_{3}Z_{4}Z_{5}Z_{6}\\rangle$ & $1 + 13x^{4} + 24x^{5} + 18x^{6} + 8x^{7}$ \\\\ \n",
+      "240 & 16 & $\\langle X_{0}Z_{6}$, $Z_{1}Z_{3}X_{4}$, $Z_{2}Z_{3}X_{5}$, $X_{1}X_{2}Z_{4}Z_{5}$, $X_{1}X_{3}Z_{5}Z_{6}$, $Z_{0}Y_{1}Z_{2}Z_{4}Y_{6}\\rangle$ & $1 + x^{2} + 2x^{3} + 7x^{4} + 24x^{5} + 23x^{6} + 6x^{7}$ \\\\ \n",
+      "255 & 32 & $\\langle X_{0}Z_{4}$, $Y_{1}Y_{2}Z_{3}Z_{5}$, $Y_{1}Z_{2}Y_{3}Z_{6}$, $Z_{0}X_{4}Z_{5}Z_{6}$, $Z_{2}Z_{3}Y_{5}Y_{6}$, $X_{1}Z_{3}Z_{4}X_{5}Z_{6}\\rangle$ & $1 + x^{2} + 11x^{4} + 24x^{5} + 19x^{6} + 8x^{7}$ \\\\ \n",
+      "257 & 32 & $\\langle X_{0}Z_{6}$, $X_{1}X_{2}Z_{4}Z_{5}$, $X_{1}X_{3}Z_{5}Z_{6}$, $Y_{1}Z_{3}Y_{4}Z_{6}$, $Y_{2}Z_{3}Y_{5}Z_{6}$, $Z_{0}Z_{1}Z_{2}X_{6}\\rangle$ & $1 + x^{2} + 19x^{4} + 43x^{6}$ \\\\ \n",
+      "227 & 42 & $\\langle Y_{0}Y_{1}Z_{2}Z_{5}$, $Y_{0}Z_{1}Y_{2}Z_{6}$, $Z_{0}X_{1}X_{3}Z_{4}$, $Z_{0}X_{2}Z_{3}X_{4}$, $X_{0}Z_{2}Z_{3}X_{5}$, $X_{0}Z_{1}Z_{4}X_{6}\\rangle$ & $1 + 21x^{4} + 42x^{6}$ \\\\ \n",
+      "209 & 48 & $\\langle X_{0}X_{1}Z_{3}Z_{4}$, $X_{0}X_{2}Z_{3}Z_{5}$, $Z_{0}Z_{1}Y_{3}Y_{4}$, $Z_{0}Z_{2}Y_{3}Y_{5}$, $Z_{0}Z_{1}Z_{2}X_{6}$, $Y_{0}Y_{3}Z_{4}Z_{5}Z_{6}\\rangle$ & $1 + 13x^{4} + 24x^{5} + 18x^{6} + 8x^{7}$ \\\\ \n",
+      "164 & 64 & $\\langle X_{0}Z_{5}$, $X_{1}Z_{6}$, $Y_{2}Y_{3}Z_{4}Z_{5}$, $Y_{2}Z_{3}Y_{4}Z_{6}$, $Z_{0}X_{2}Z_{4}X_{5}Z_{6}$, $Z_{1}X_{2}Z_{3}Z_{5}X_{6}\\rangle$ & $1 + 2x^{2} + 9x^{4} + 24x^{5} + 20x^{6} + 8x^{7}$ \\\\ \n",
+      "166 & 64 & $\\langle X_{0}Z_{6}$, $X_{1}Z_{3}$, $Z_{3}X_{4}Z_{5}Z_{6}$, $Z_{1}X_{2}Y_{3}Y_{4}$, $Z_{2}Z_{4}X_{5}Z_{6}$, $Z_{0}Y_{2}Z_{4}Y_{6}\\rangle$ & $1 + 2x^{2} + 17x^{4} + 44x^{6}$ \\\\ \n",
+      "239 & 96 & $\\langle X_{0}Z_{1}$, $Z_{0}X_{1}Z_{6}$, $X_{2}X_{3}Z_{4}Z_{5}$, $Y_{2}Y_{4}Z_{5}Z_{6}$, $X_{2}Z_{3}X_{5}Z_{6}$, $Z_{1}Y_{2}Z_{3}Z_{4}Y_{6}\\rangle$ & $1 + x^{2} + 2x^{3} + 7x^{4} + 24x^{5} + 23x^{6} + 6x^{7}$ \\\\ \n",
+      "228 & 144 & $\\langle Y_{0}Y_{1}Z_{5}Z_{6}$, $Z_{0}X_{1}X_{2}Z_{3}$, $X_{0}Z_{1}Z_{2}X_{3}$, $X_{0}Z_{1}X_{4}Z_{6}$, $Z_{0}Z_{3}Z_{4}X_{5}$, $Z_{1}Z_{2}Z_{4}X_{6}\\rangle$ & $1 + 21x^{4} + 42x^{6}$ \\\\ \n",
+      "190 & 192 & $\\langle X_{0}Z_{6}$, $X_{1}Z_{5}$, $X_{2}Z_{4}$, $Z_{2}X_{3}X_{4}Z_{5}$, $Z_{1}Y_{3}Y_{5}Z_{6}$, $Z_{0}Z_{3}Z_{4}X_{6}\\rangle$ & $1 + 3x^{2} + 15x^{4} + 45x^{6}$ \\\\ \n",
+      "115 & 576 & $\\langle X_{0}Z_{6}$, $X_{1}Z_{6}$, $X_{2}X_{3}Z_{4}Z_{5}$, $Y_{2}Y_{4}Z_{5}Z_{6}$, $X_{2}Z_{3}X_{5}Z_{6}$, $Z_{0}Z_{1}Y_{2}Z_{3}Z_{4}Y_{6}\\rangle$ & $1 + 3x^{2} + 15x^{4} + 45x^{6}$ \\\\ \n",
+      "108 & 768 & $\\langle X_{0}Z_{4}$, $X_{1}Z_{4}$, $X_{2}Z_{5}$, $X_{3}Z_{6}$, $Z_{2}Z_{3}Y_{5}Y_{6}$, $Z_{0}Z_{1}Z_{2}X_{4}X_{5}Z_{6}\\rangle$ & $1 + 5x^{2} + 11x^{4} + 47x^{6}$ \\\\ \n",
+      "226 & 1008 & $\\langle Z_{0}Z_{1}Z_{3}Z_{6}$, $Z_{0}Z_{2}Z_{3}Z_{5}$, $Y_{1}Y_{2}Y_{3}Y_{4}$, $Z_{3}Z_{4}Z_{5}Z_{6}$, $Y_{0}Y_{1}Y_{4}Y_{5}$, $Y_{0}Y_{2}Y_{4}Y_{6}\\rangle$ & $1 + 21x^{4} + 42x^{6}$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(1,2)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(3,4)\n",
+    "\n",
+    "make_full_tables_indecom(7, k_range=k_range_in, d_range=d_range_in, nkd=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d7547f4-eac0-44b5-949e-c2035822edd2",
+   "metadata": {},
+   "source": [
+    "# [[9,1,3]] Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1392,
+   "id": "f4698d2e-f1b1-4dd0-9d48-38600a1b1241",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\ \n",
+      "\\specialrule{1.5pt}{1pt}{1pt}\n",
+      "8802 & 82944 & $\\langle Z_{0}Z_{6}$, $Z_{1}Z_{6}$, $Z_{2}Z_{7}$, $Z_{3}Z_{8}$, $Z_{4}Z_{8}$, $Z_{5}Z_{7}$, $X_{0}X_{1}X_{2}X_{5}X_{6}X_{7}$, $X_{0}X_{1}X_{3}X_{4}X_{6}X_{8}\\rangle$ & $1 + 9x^{2} + 27x^{4} + 75x^{6} + 144x^{8}$ \\\\ \n",
+      "4280 & 9216 & $\\langle Z_{0}Z_{7}$, $Z_{1}Z_{8}$, $X_{2}X_{6}$, $X_{3}X_{6}$, $Z_{4}Z_{5}$, $X_{0}X_{4}X_{5}X_{7}$, $X_{1}X_{4}X_{5}X_{8}$, $Z_{2}Z_{3}Z_{4}Z_{6}Z_{7}Z_{8}\\rangle$ & $1 + 6x^{2} + 24x^{4} + 90x^{6} + 135x^{8}$ \\\\ \n",
+      "4079 & 3072 & $\\langle Z_{0}Z_{8}$, $X_{1}X_{7}$, $X_{2}X_{7}$, $X_{3}X_{5}$, $X_{4}X_{6}$, $Z_{3}Z_{4}Z_{5}Z_{6}$, $X_{0}X_{5}X_{6}X_{8}$, $Z_{1}Z_{2}Z_{3}Z_{5}Z_{7}Z_{8}\\rangle$ & $1 + 6x^{2} + 24x^{4} + 90x^{6} + 135x^{8}$ \\\\ \n",
+      "4395 & 1152 & $\\langle Z_{0}Z_{8}$, $Z_{1}Z_{8}$, $Z_{2}Z_{3}Z_{6}Z_{7}$, $Z_{2}Z_{4}Z_{7}Z_{8}$, $Z_{5}Z_{6}Z_{7}Z_{8}$, $X_{2}X_{4}X_{5}X_{6}$, $X_{3}X_{4}X_{5}X_{7}$, $X_{0}X_{1}X_{2}X_{3}X_{5}X_{8}\\rangle$ & $1 + 3x^{2} + 21x^{4} + 105x^{6} + 126x^{8}$ \\\\ \n",
+      "8519 & 1024 & $\\langle Z_{0}Z_{8}$, $X_{1}X_{7}$, $Z_{2}Z_{6}$, $X_{3}X_{4}$, $Z_{3}Z_{4}Z_{5}Z_{6}$, $X_{2}X_{5}X_{6}X_{7}$, $Z_{1}Z_{5}Z_{7}Z_{8}$, $X_{0}X_{3}X_{5}X_{8}\\rangle$ & $1 + 4x^{2} + 22x^{4} + 100x^{6} + 129x^{8}$ \\\\ \n",
+      "4335 & 576 & $\\langle Z_{0}Z_{8}$, $Z_{1}Z_{8}$, $Z_{2}Z_{3}Z_{4}$, $Z_{3}Z_{5}Z_{6}$, $Z_{4}Z_{5}Z_{7}$, $X_{3}X_{4}X_{6}X_{7}$, $X_{2}X_{3}X_{5}X_{7}$, $X_{0}X_{1}X_{2}X_{3}X_{6}X_{8}\\rangle$ & $1 + 3x^{2} + 4x^{3} + 9x^{4} + 24x^{5} + 49x^{6} + 84x^{7} + 66x^{8} + 16x^{9}$ \\\\ \n",
+      "7419 & 384 & $\\langle Z_{0}Z_{8}$, $Z_{1}Z_{6}$, $Z_{2}Z_{7}$, $Z_{3}Z_{4}Z_{6}Z_{8}$, $Z_{3}Z_{5}Z_{7}Z_{8}$, $X_{1}X_{3}X_{5}X_{6}$, $X_{2}X_{3}X_{4}X_{7}$, $X_{0}X_{4}X_{5}X_{8}\\rangle$ & $1 + 3x^{2} + 21x^{4} + 105x^{6} + 126x^{8}$ \\\\ \n",
+      "8816 & 384 & $\\langle Z_{0}Z_{8}$, $Z_{1}Z_{6}$, $Z_{2}Z_{7}$, $X_{3}X_{4}X_{5}$, $Z_{3}Z_{4}Z_{6}Z_{8}$, $Z_{3}Z_{5}Z_{7}Z_{8}$, $Y_{1}Y_{2}X_{3}Y_{6}Y_{7}$, $Y_{0}Y_{1}X_{5}Y_{6}Y_{8}\\rangle$ & $1 + 3x^{2} + x^{3} + 15x^{4} + 27x^{5} + 37x^{6} + 87x^{7} + 72x^{8} + 13x^{9}$ \\\\ \n",
+      "9709 & 384 & $\\langle Y_{0}Y_{1}Y_{4}Y_{5}$, $Y_{0}Y_{2}Y_{4}Y_{6}$, $Y_{0}Y_{3}Y_{4}Y_{7}$, $X_{0}X_{1}X_{4}X_{5}$, $X_{0}X_{2}X_{4}X_{6}$, $X_{0}X_{3}X_{4}X_{7}$, $X_{0}X_{1}X_{2}X_{3}X_{8}$, $Z_{1}Z_{2}Z_{3}Z_{4}Z_{8}\\rangle$ & $1 + 18x^{4} + 16x^{5} + 56x^{6} + 96x^{7} + 53x^{8} + 16x^{9}$ \\\\ \n",
+      "5477 & 256 & $\\langle Z_{0}Z_{7}$, $Z_{1}Z_{8}$, $Z_{2}Z_{3}Z_{5}Z_{8}$, $Z_{2}Z_{4}Z_{5}Z_{7}$, $X_{3}X_{4}X_{5}X_{6}$, $Z_{5}Z_{6}Z_{7}Z_{8}$, $X_{0}X_{2}X_{3}X_{6}X_{7}$, $X_{1}X_{2}X_{4}X_{6}X_{8}\\rangle$ & $1 + 2x^{2} + 20x^{4} + 16x^{5} + 46x^{6} + 96x^{7} + 59x^{8} + 16x^{9}$ \\\\ \n",
+      "11001 & 192 & $\\langle Z_{0}Z_{1}$, $X_{0}X_{1}X_{4}$, $Z_{2}Z_{3}Z_{5}Z_{7}$, $X_{2}X_{4}X_{5}X_{6}$, $Z_{5}Z_{6}Z_{7}Z_{8}$, $X_{3}X_{4}X_{6}X_{7}$, $X_{2}X_{3}X_{6}X_{8}$, $Z_{1}Z_{2}Z_{4}Z_{7}Z_{8}\\rangle$ & $1 + x^{2} + 2x^{3} + 13x^{4} + 24x^{5} + 47x^{6} + 90x^{7} + 66x^{8} + 12x^{9}$ \\\\ \n",
+      "12079 & 192 & $\\langle Z_{0}Z_{8}$, $Z_{1}Z_{2}Z_{5}Z_{6}$, $Z_{1}Z_{3}Z_{4}Z_{6}$, $X_{1}X_{2}X_{4}X_{7}$, $X_{1}X_{3}X_{5}X_{7}$, $X_{2}X_{3}X_{6}X_{7}$, $X_{0}X_{3}X_{4}X_{8}$, $Z_{4}Z_{5}Z_{6}Z_{7}Z_{8}\\rangle$ & $1 + x^{2} + 19x^{4} + 16x^{5} + 51x^{6} + 96x^{7} + 56x^{8} + 16x^{9}$ \\\\ \n",
+      "9897 & 144 & $\\langle Z_{0}Z_{1}Z_{4}Z_{8}$, $Z_{0}Z_{2}Z_{5}Z_{7}$, $Z_{1}Z_{3}Z_{5}Z_{7}$, $X_{1}X_{2}X_{4}X_{5}$, $Z_{0}Z_{4}Z_{5}Z_{6}$, $X_{1}X_{3}X_{4}X_{6}$, $X_{0}X_{3}X_{4}X_{7}$, $X_{0}X_{3}X_{5}X_{8}\\rangle$ & $1 + 18x^{4} + 120x^{6} + 117x^{8}$ \\\\ \n",
+      "5781 & 128 & $\\langle Z_{0}Z_{8}$, $X_{1}X_{5}$, $Y_{2}Y_{3}Y_{6}Y_{7}$, $Z_{2}Z_{3}Z_{4}Z_{8}$, $X_{3}X_{4}X_{5}X_{6}$, $X_{2}X_{4}X_{5}X_{7}$, $Y_{1}Y_{2}Y_{4}Z_{5}Y_{7}$, $Y_{0}Y_{4}Y_{6}Y_{7}X_{8}\\rangle$ & $1 + 2x^{2} + 12x^{4} + 32x^{5} + 46x^{6} + 80x^{7} + 67x^{8} + 16x^{9}$ \\\\ \n",
+      "5784 & 128 & $\\langle Z_{0}Z_{8}$, $X_{1}X_{5}$, $Z_{2}Z_{3}Z_{6}Z_{8}$, $Z_{2}Z_{4}Z_{7}Z_{8}$, $Z_{1}Z_{5}Z_{6}Z_{7}$, $X_{2}X_{4}X_{5}X_{6}$, $X_{2}X_{3}X_{5}X_{7}$, $X_{0}X_{3}X_{4}X_{8}\\rangle$ & $1 + 2x^{2} + 20x^{4} + 110x^{6} + 123x^{8}$ \\\\ \n",
+      "12003 & 96 & $\\langle X_{0}X_{5}$, $Z_{1}Z_{4}Z_{6}$, $Z_{2}Z_{4}Z_{7}$, $Z_{3}Z_{4}Z_{8}$, $Y_{1}Y_{2}Y_{6}Y_{7}$, $Y_{1}Y_{3}Y_{6}Y_{8}$, $Z_{0}Z_{5}Z_{6}Z_{7}Z_{8}$, $X_{2}X_{3}X_{4}X_{5}X_{6}\\rangle$ & $1 + x^{2} + 3x^{3} + 9x^{4} + 25x^{5} + 55x^{6} + 85x^{7} + 62x^{8} + 15x^{9}$ \\\\ \n",
+      "6038 & 64 & $\\langle Z_{0}Z_{8}$, $X_{1}X_{2}$, $X_{3}X_{5}X_{7}$, $X_{4}X_{6}X_{7}$, $Z_{1}Z_{2}Z_{3}Z_{5}$, $Z_{1}Z_{2}Z_{4}Z_{6}$, $Z_{3}Z_{6}Z_{7}Z_{8}$, $X_{0}X_{2}X_{3}X_{4}X_{8}\\rangle$ & $1 + 2x^{2} + 2x^{3} + 12x^{4} + 26x^{5} + 46x^{6} + 86x^{7} + 67x^{8} + 14x^{9}$ \\\\ \n",
+      "8124 & 12 & $\\langle X_{2}X_{4}X_{5}$, $X_{1}X_{3}X_{6}$, $Z_{1}Z_{2}Z_{3}Z_{4}$, $Z_{2}Z_{3}Z_{5}Z_{6}$, $X_{0}X_{2}X_{3}X_{7}$, $Z_{0}Z_{4}Z_{5}Z_{7}$, $X_{0}X_{1}X_{4}X_{8}$, $Z_{0}Z_{2}Z_{5}Z_{8}\\rangle$ & $1 + 2x^{3} + 12x^{4} + 24x^{5} + 52x^{6} + 90x^{7} + 63x^{8} + 12x^{9}$ \\\\ \n",
+      "7810 & 8 & $\\langle Z_{1}Z_{2}Z_{4}$, $X_{1}X_{2}X_{7}$, $Y_{0}Y_{3}Y_{6}Y_{8}$, $X_{0}X_{3}X_{5}X_{7}$, $X_{2}X_{3}X_{4}X_{6}$, $Z_{0}Z_{4}Z_{5}Z_{6}$, $Z_{1}Z_{3}Z_{6}Z_{7}$, $X_{0}X_{2}X_{4}X_{8}\\rangle$ & $1 + 2x^{3} + 12x^{4} + 24x^{5} + 52x^{6} + 90x^{7} + 63x^{8} + 12x^{9}$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(1,2)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(3,4)\n",
+    "\n",
+    "make_partial_tables_indecom(9, k_range=k_range_in, d_range=d_range_in, nkd=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5077f3a1-b808-42e1-9e34-0a9cd409a5cb",
+   "metadata": {},
+   "source": [
+    "# [[9,2,3]] Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "id": "856fb89f-c8ea-4f63-96fe-4e4166432587",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4445"
+      ]
+     },
+     "execution_count": 345,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes = cb.all_small_codes(9, 2, d=3, info_only=True, list_only=True)\n",
+    "len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1371,
+   "id": "efecfa50-6325-48f7-81f7-be361efd800c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4425"
+      ]
+     },
+     "execution_count": 1371,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes = cb.all_small_codes(9, 2, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
+    "len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "id": "7fcab31a-6c08-44b0-9dfe-15ca107ae220",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 347,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes = cb.all_small_codes(9, 2, d=3, is_gf2linear=True, info_only=True, list_only=True)\n",
+    "len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "id": "f7380bb6-d6be-4dcc-88aa-8b36d49e5786",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "750"
+      ]
+     },
+     "execution_count": 348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes = cb.all_small_codes(9, 2, d=3, is_degenerate=True, info_only=True, list_only=True)\n",
+    "len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "id": "dcdb8481-b1b2-4c11-bf9c-63707b5a431b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 3178,\n",
+       " 2: 356,\n",
+       " 3: 4,\n",
+       " 4: 654,\n",
+       " 6: 13,\n",
+       " 8: 82,\n",
+       " 12: 10,\n",
+       " 16: 65,\n",
+       " 18: 1,\n",
+       " 24: 2,\n",
+       " 32: 25,\n",
+       " 36: 3,\n",
+       " 48: 3,\n",
+       " 64: 13,\n",
+       " 96: 4,\n",
+       " 128: 4,\n",
+       " 144: 1,\n",
+       " 192: 1,\n",
+       " 288: 1,\n",
+       " 384: 1,\n",
+       " 512: 1,\n",
+       " 768: 1,\n",
+       " 1152: 2}"
+      ]
+     },
+     "execution_count": 357,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes = cb.all_small_codes(9, 2, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
+    "\n",
+    "aut_sizes = {}\n",
+    "for code in codes:\n",
+    "    size = code['aut_group_size']\n",
+    "    try:\n",
+    "        aut_sizes[size] += 1\n",
+    "    except:\n",
+    "        aut_sizes[size] = 1\n",
+    "\n",
+    "sorted_aut_sizes = {k: aut_sizes[k] for k in sorted(aut_sizes)}\n",
+    "sorted_aut_sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1373,
+   "id": "d7129203-204b-439e-84dc-a9a7dac9f35c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes923aut = []\n",
+    "for code in codes:\n",
+    "    if code['aut_group_size'] >= 512:\n",
+    "        codes923aut += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1374,
+   "id": "d495ec13-7887-4c34-bb20-8554b8a0ce18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1374,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(codes923aut)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1389,
+   "id": "aee2fb56-54f6-406a-a8f0-e3718a345e4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_partial_tables_indecom(n_range, k_range=None, d_range=None, nkd=True, limit=None):\n",
+    "    if isinstance(n_range, int):\n",
+    "        n_range = range(n_range, n_range+1)\n",
+    "        \n",
+    "    if k_range is None:\n",
+    "        def k_range_method(x):\n",
+    "            return range(x+1)\n",
+    "    else: \n",
+    "        def k_range_method(x):\n",
+    "            return k_range(x)\n",
+    "\n",
+    "    if d_range is None:\n",
+    "        def d_range_method(x,y):\n",
+    "            return range(x-y+2)\n",
+    "    else:\n",
+    "        def d_range_method(x,y):\n",
+    "            return d_range(x,y)\n",
+    "\n",
+    "    title = \"\"\n",
+    "    if nkd is True:\n",
+    "        title = \"$[[n,k,d]]$ & \"\n",
+    "    title += \"$\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\\\ \\n\"\n",
+    "    title += \"\\\\specialrule{1.5pt}{1pt}{1pt}\"\n",
+    "    print(title)\n",
+    "    for n in n_range:\n",
+    "        for k in k_range_method(n):\n",
+    "            for d in d_range_method(n,k):\n",
+    "                section_title = \"\\\\midrule \"\n",
+    "                if nkd is True:\n",
+    "                    section_title = f\"\\\\midrule $[[{n},{k},{d}]]$ \"\n",
+    "                initial = True\n",
+    "                codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, is_css=True, info_only=True, list_only=True)\n",
+    "                sorted_codes = sorted(codes, key=lambda x: -x['aut_group_size'])\n",
+    "                count = 0\n",
+    "                for code in sorted_codes:\n",
+    "                    if initial is True:\n",
+    "                        if nkd is True:\n",
+    "                            code_line = [section_title, str(code['index'])]\n",
+    "                        else:\n",
+    "                            code_line = [str(code['index'])]\n",
+    "                        initial = False\n",
+    "                    else:\n",
+    "                        if nkd is True:\n",
+    "                            code_line  = ['',str(code['index'])]\n",
+    "                        else:\n",
+    "                            code_line = [str(code['index'])]\n",
+    "                    code_line += [str(code['aut_group_size'])]\n",
+    "                    iso_gens = \", \".join(latex_it(code['isotropic_generators'], dollar=True)).strip()\n",
+    "                    iso_gens = \"$\\\\langle \" + iso_gens[1:-1] + \"\\\\rangle$\"\n",
+    "                    code_line += [iso_gens]\n",
+    "                    #logicals = code['logical_ops']\n",
+    "                    #if len(logicals)>0:\n",
+    "                    #    code_line += [ \", \".join(latex_it(logicals, dollar=True)) ]\n",
+    "                    #else:\n",
+    "                    #    code_line += ['']\n",
+    "                    code_line += [\"$\" + make_poly(code['weight_enumerator']) + \"$\"]\n",
+    "                    print(\" & \".join(code_line)+\" \\\\\\ \")\n",
+    "                    count += 1\n",
+    "                    if limit is not None:\n",
+    "                        if count >= limit:\n",
+    "                            return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1388,
+   "id": "b7a3af93-2d11-4f40-b643-ebb0d88eaab7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\ \n",
+      "\\specialrule{1.5pt}{1pt}{1pt}\n",
+      "15551 & 1152 & $\\langle X_{0}Z_{8}$, $X_{1}Z_{7}$, $X_{2}Z_{7}$, $X_{3}X_{4}Z_{5}Z_{6}$, $Z_{3}Z_{4}Y_{5}Y_{6}$, $Z_{0}Z_{4}X_{5}Z_{6}X_{8}$, $Z_{1}Z_{2}Y_{3}Y_{5}Z_{6}X_{7}Z_{8}\\rangle$ & $1 + 4x^{2} + 6x^{4} + 8x^{5} + 12x^{6} + 56x^{7} + 41x^{8}$ \\\\ \n",
+      "80585 & 1152 & $\\langle X_{0}X_{1}Z_{4}Z_{5}$, $X_{2}X_{3}Z_{6}Z_{7}$, $Y_{0}Y_{4}Z_{5}Z_{8}$, $X_{0}Z_{1}X_{5}Z_{8}$, $Y_{2}Y_{6}Z_{7}Z_{8}$, $X_{2}Z_{3}X_{7}Z_{8}$, $Y_{0}Z_{1}Y_{2}Z_{3}Z_{4}Z_{6}X_{8}\\rangle$ & $1 + 14x^{4} + 16x^{6} + 64x^{7} + 33x^{8}$ \\\\ \n",
+      "22646 & 768 & $\\langle X_{0}Z_{7}$, $X_{1}Z_{8}$, $X_{3}X_{4}Z_{5}Z_{6}$, $Z_{2}Y_{3}Y_{5}Z_{6}$, $Z_{2}X_{3}Z_{4}X_{6}$, $Z_{0}Z_{1}Y_{7}Y_{8}$, $Z_{0}Y_{2}Y_{3}Z_{4}Z_{5}X_{7}Z_{8}\\rangle$ & $1 + 2x^{2} + 12x^{4} + 14x^{6} + 64x^{7} + 35x^{8}$ \\\\ \n",
+      "53565 & 512 & $\\langle X_{0}Z_{8}$, $X_{1}Z_{6}$, $X_{2}Z_{7}$, $X_{3}X_{4}$, $Z_{0}Y_{3}Z_{4}Z_{5}Y_{8}$, $Z_{2}Z_{3}Z_{4}X_{5}Z_{6}X_{7}$, $Z_{0}Z_{1}X_{5}X_{6}Z_{7}X_{8}\\rangle$ & $1 + 4x^{2} + 6x^{4} + 4x^{5} + 20x^{6} + 56x^{7} + 33x^{8} + 4x^{9}$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "def k_range_in(x):\n",
+    "    return range(2,3)\n",
+    "\n",
+    "def d_range_in(x,y):\n",
+    "    return range(3,4)\n",
+    "\n",
+    "make_partial_tables_indecom(9, k_range=k_range_in, d_range=d_range_in, nkd=False, limit=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "661a07fc-81b7-45e0-acfd-64d3f638bb86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(1, 0, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fc4a5bc0-ead0-49b8-b98c-e1f500d56fdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['V0'],\n",
+       " aut_group_size       : 2,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 1,\n",
+       " index                : 0,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 1,\n",
+       " uuid                 : 0d67535e-fa66-46c6-9be5-01bb3dac5512,\n",
+       " weight_enumerator    : [1, 1],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "60d8095f-03b7-4b36-bbd8-021b9c796a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Nnk(n,k):\n",
+    "    pd = 1\n",
+    "    gc = 1\n",
+    "    for i in range(k):\n",
+    "        gc = gc * (2**(n-i)-1)/(2**(k-i)-1)\n",
+    "    print(gc)\n",
+    "    for i in range(n-k):\n",
+    "        print((2**(n-i)+1))\n",
+    "        pd = pd * (2**(n-i)+1)\n",
+    "    print(pd)\n",
+    "    return gc*pd\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a4ae32d2-f81b-4d4b-8845-0bb771aa5dd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "3\n",
+      "3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Nnk(1,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "bd16e079-97a0-4060-b745-558b206685fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(1, 0, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "4c0fca6c-e6af-475a-84a5-9bd3185d4047",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['V0'],\n",
+       " aut_group_size       : 2,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 1,\n",
+       " index                : 0,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 1,\n",
+       " uuid                 : 0d67535e-fa66-46c6-9be5-01bb3dac5512,\n",
+       " weight_enumerator    : [1, 1],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "432c6e0b-5fb0-480d-8d56-0d5b4b10206f",
+   "metadata": {},
+   "source": [
+    "# Extremal Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1407,
+   "id": "195eedde-dedc-4710-afaa-c8009de47034",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0: max_rate = 0\n",
+      "[]\n",
+      "1: max_rate = 0\n",
+      "[]\n",
+      "2: max_rate = 0\n",
+      "[]\n",
+      "3: max_rate = 0\n",
+      "[]\n",
+      "4: max_rate = 0\n",
+      "[]\n",
+      "5: max_rate = 0.2\n",
+      "[(5, 1)]\n",
+      "6: max_rate = 0.16666666666666666\n",
+      "[(6, 1)]\n",
+      "7: max_rate = 0.14285714285714285\n",
+      "[(7, 1)]\n",
+      "8: max_rate = 0.375\n",
+      "[(8, 1), (8, 2), (8, 3)]\n",
+      "9: max_rate = 0.3333333333333333\n",
+      "[(9, 1), (9, 2), (9, 3)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(10):\n",
+    "    codes_rate = []\n",
+    "    for k in range(n+1):\n",
+    "        for d in range(3,4):\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "            codes_rate += codes\n",
+    "    max_rate = 0\n",
+    "    pairs = []\n",
+    "    for code in codes_rate:\n",
+    "        rate = code['k']/code['n']\n",
+    "        if rate > max_rate:\n",
+    "            max_rate = rate\n",
+    "            pairs += [(code['n'],code['k'])]\n",
+    "    print(f\"{n}: max_rate = {max_rate}\")\n",
+    "    print(pairs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "aad15c6f-5b23-40b6-ae63-609337c4ecbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_partial_tables_indecom_extreme(n_range, k_range=None, d_range=None, nkd=True, limit=None):\n",
+    "    if isinstance(n_range, int):\n",
+    "        n_range = range(n_range, n_range+1)\n",
+    "        \n",
+    "    if k_range is None:\n",
+    "        def k_range_method(x):\n",
+    "            return range(x+1)\n",
+    "    else: \n",
+    "        def k_range_method(x):\n",
+    "            return k_range(x)\n",
+    "\n",
+    "    if d_range is None:\n",
+    "        def d_range_method(x,y):\n",
+    "            return range(x-y+2)\n",
+    "    else:\n",
+    "        def d_range_method(x,y):\n",
+    "            return d_range(x,y)\n",
+    "\n",
+    "    title = \"\"\n",
+    "    if nkd is True:\n",
+    "        title = \"$[[n,k,d]]$ & \"\n",
+    "    title += \"$\\mathrm{Idx}$ & $|\\mathrm{Aut}(S)|$ & $S$ & $w(x)$ \\\\\\ \\n\"\n",
+    "    title += \"\\\\specialrule{1.5pt}{1pt}{1pt}\"\n",
+    "    print(title)\n",
+    "    for n in n_range:\n",
+    "        for k in k_range_method(n):\n",
+    "            for d in d_range_method(n,k):\n",
+    "                section_title = \"\\\\midrule \"\n",
+    "                if nkd is True:\n",
+    "                    section_title = f\"\\\\midrule $[[{n},{k},{d}]]$ \"\n",
+    "                initial = True\n",
+    "                codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "                sorted_codes = sorted(codes, key=lambda x: -x['aut_group_size'])\n",
+    "                count = 0\n",
+    "                for code in sorted_codes:\n",
+    "                    if initial is True:\n",
+    "                        if nkd is True:\n",
+    "                            code_line = [section_title, str(code['index'])]\n",
+    "                        else:\n",
+    "                            code_line = [str(code['index'])]\n",
+    "                        initial = False\n",
+    "                    else:\n",
+    "                        if nkd is True:\n",
+    "                            code_line  = ['',str(code['index'])]\n",
+    "                        else:\n",
+    "                            code_line = [str(code['index'])]\n",
+    "                    code_line += [str(code['aut_group_size'])]\n",
+    "                    iso_gens = \", \".join(latex_it(code['isotropic_generators'], dollar=True)).strip()\n",
+    "                    iso_gens = \"$\\\\langle \" + iso_gens[1:-1] + \"\\\\rangle$\"\n",
+    "                    code_line += [iso_gens]\n",
+    "                    #logicals = code['logical_ops']\n",
+    "                    #if len(logicals)>0:\n",
+    "                    #    code_line += [ \", \".join(latex_it(logicals, dollar=True)) ]\n",
+    "                    #else:\n",
+    "                    #    code_line += ['']\n",
+    "                    code_line += [\"$\" + make_poly(code['weight_enumerator']) + \"$\"]\n",
+    "                    print(\" & \".join(code_line)+\" \\\\\\ \")\n",
+    "                    count += 1\n",
+    "                    if limit is not None:\n",
+    "                        if count >= limit:\n",
+    "                            return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "8962fb43-cf2e-4870-9321-097b4eb960c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15.0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "72/8 + 72/12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c9b226a4-c5f6-427c-95fd-31315d106c7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(2, 0, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "928a3c16-3125-4ab6-b2cd-29b6f4f42536",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['V1', 'V0', '(0,1)'],\n",
+       " aut_group_size       : 8,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 1,\n",
+       " index                : 0,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 1,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0', 'X1'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 2,\n",
+       " uuid                 : 19bd6e1e-f908-4bf5-b089-2abed9863323,\n",
+       " weight_enumerator    : [1, 2, 1],\n",
+       " },\n",
+       " {aut_group_generators : ['S0S1', '(0,1)', 'H0H1'],\n",
+       " aut_group_size       : 12,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 1,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1', 'X0X1'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 2,\n",
+       " uuid                 : 9e7112a5-868b-4832-bd42-2eaf5f1182de,\n",
+       " weight_enumerator    : [1, 0, 3],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "7664323b-b0aa-4a72-9688-16544e1906ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(2, 2, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a96fe940-93d4-419d-bb50-651d2062fe3d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "b159861d-83bb-4ae9-8cda-3b783cf54169",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2,0,1]]\n",
+      "['X0', 'X1'] ['V1', 'V0', '(0,1)']\n",
+      "--------------------------------------------------------------\n",
+      "[[2,0,2]]\n",
+      "['Z0Z1', 'X0X1'] ['S0S1', '(0,1)', 'H0H1']\n",
+      "--------------------------------------------------------------\n",
+      "[[2,1,1]]\n",
+      "['X0X1'] ['V1', 'V0', '(0,1)']\n",
+      "['X1'] ['V0', 'H0', 'V1']\n",
+      "--------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "n=2\n",
+    "for k in range(n+1):\n",
+    "    for d in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, d=d, info_only=True, list_only=True)\n",
+    "        if len(codes)>0:\n",
+    "            print(f\"[[{n},{k},{d}]]\")\n",
+    "            for code in codes:\n",
+    "                print(code['isotropic_generators'], code['aut_group_generators'])\n",
+    "            print(\"--------------------------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "e7bbf6dd-cd90-46bc-a6c7-6ca62bd0a3e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3,0,1]]\n",
+      "['X_{0}', 'X_{1}', 'X_{2}'] ['V2', 'V1', 'V0', '(1,2)', '(0,1)']\n",
+      "['X_{2}', 'Z_{0}Z_{1}', 'X_{0}X_{1}'] ['V2', 'V0V1', 'H0H1', '(0,1)']\n",
+      "--------------------------------------------------------------\n",
+      "[[3,0,2]]\n",
+      "['X_{0}X_{1}', 'X_{0}X_{2}', 'Z_{0}Z_{1}Z_{2}'] ['V1V2', '(1,2)', 'V0V2', '(0,1)']\n",
+      "--------------------------------------------------------------\n",
+      "[[3,1,1]]\n",
+      "['X_{1}', 'X_{2}'] ['V0', 'H0', 'V2', 'V1', '(1,2)']\n",
+      "['X_{2}', 'X_{0}X_{1}'] ['V1', 'V2', 'V0', '(0,1)']\n",
+      "['X_{0}X_{1}', 'X_{0}X_{2}'] ['V2', 'V1', 'V0', '(1,2)', '(0,1)']\n",
+      "['Z_{0}Z_{1}', 'X_{0}X_{1}'] ['V2', 'H2', 'S0S1', '(0,1)', 'H0H1']\n",
+      "['X_{0}X_{2}', 'Z_{0}Z_{1}Z_{2}'] ['S1', 'V0V2', '(0,2)']\n",
+      "--------------------------------------------------------------\n",
+      "[[3,2,1]]\n",
+      "['X_{0}X_{2}'] ['V1', 'H1', 'V2', 'V0', '(0,2)']\n",
+      "['Z_{0}Z_{1}Z_{2}'] ['S2', 'S1', '(1,2)', 'S0', '(0,1)']\n",
+      "['X_{2}'] ['V1', 'H1', 'V0', 'H0', '(0,1)', 'V2']\n",
+      "--------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "n=3\n",
+    "for k in range(n+1):\n",
+    "    for d in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, d=d, info_only=True, list_only=True)\n",
+    "        if len(codes)>0:\n",
+    "            print(f\"[[{n},{k},{d}]]\")\n",
+    "            for code in codes:\n",
+    "                print(PauliList(code['isotropic_generators']), code['aut_group_generators'])\n",
+    "            print(\"--------------------------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "bfa3b1a5-00d8-4fb0-a31f-063b78df2614",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4,0,1]]\n",
+      "['X2', 'X3', 'Z0Z1', 'X0X1'] ['V3', 'V2', 'V0V1', '(2,3)', 'H0H1', '(0,1)']\n",
+      "['X0', 'X1', 'X2', 'X3'] ['V3', 'V2', 'V1', 'V0', '(2,3)', '(1,2)', '(0,1)']\n",
+      "['X3', 'X0X1', 'X0X2', 'Z0Z1Z2'] ['V3', 'V1V2', '(1,2)', 'V0V2', '(0,1)']\n",
+      "--------------------------------------------------------------\n",
+      "[[4,0,2]]\n",
+      "['X0X2', 'Z1Z3', 'Z0Z2Z3', 'X1X2X3'] ['S1S3', '(1,3)', 'V0V2', 'H0H1H2H3^(0,1)(2,3)']\n",
+      "['Z0Z3', 'Z1Z3', 'Z2Z3', 'X0X1X2X3'] ['S2S3', '(2,3)', 'S1S3', '(1,2)', 'S0S3', '(0,1)']\n",
+      "['Z0Z1', 'X0X1', 'Z2Z3', 'X2X3'] ['S2S3', '(2,3)', 'V0V1', 'H2H3', 'H0H1', '(0,1)', '(0,2)(1,3)']\n",
+      "--------------------------------------------------------------\n",
+      "[[4,1,1]]\n",
+      "['X0X1', 'Z2Z3', 'X2X3'] ['V1', 'V0', '(0,1)', 'V2V3', 'H2H3', '(2,3)']\n",
+      "['X0X2', 'Z1Z3', 'X1X2X3'] ['V2', 'V0', '(1,3)', '(0,2)', 'S1S3']\n",
+      "['X1', 'X2', 'X3'] ['V0', 'H0', 'V3', 'V2', 'V1', '(2,3)', '(1,2)']\n",
+      "['X0X1', 'X0X2', 'Z0Z1Z2'] ['V3', 'H3', 'V1V2', '(1,2)', 'V0V2', '(0,1)']\n",
+      "['X2', 'X3', 'X0X1'] ['V1', 'V3', 'V2', 'V0', '(0,1)', '(2,3)']\n",
+      "['X3', 'X0X1', 'X0X2'] ['V2', 'V1', 'V0', 'V3', '(1,2)', '(0,1)']\n",
+      "['Z0Z3', 'Z1Z3', 'Z2Z3'] ['S3', 'S2', 'S1', 'S0', '(2,3)', '(1,2)', '(0,1)']\n",
+      "['X3', 'Z0Z1', 'X0X1X2'] ['V2', 'V3', 'S0S1', '(0,1)']\n",
+      "['X3', 'Z0Z1', 'X0X1'] ['V2', 'H2', 'V3', 'V0V1', 'H0H1', '(0,1)']\n",
+      "['Z1Z3', 'Z0Z2Z3', 'X1X2X3'] ['S0', 'S1S3', '(1,3)']\n",
+      "['Z1Z3', 'Z2Z3', 'X0X1X2X3'] ['V0', 'S2S3', '(2,3)', 'S1S3', '(1,2)']\n",
+      "--------------------------------------------------------------\n",
+      "[[4,1,2]]\n",
+      "['X0X1', 'X2X3', 'Z0Z1Z2Z3'] ['V2V3', '(2,3)', 'V0V1', '(0,1)', '(0,2)(1,3)']\n",
+      "['Z0X2Z3', 'Y0X1Y2', 'Z1Z2X3'] ['V0H1S2H3^(1,3)', 'H0S1S2V3^(1,2)', '(0,1)(2,3)']\n",
+      "--------------------------------------------------------------\n",
+      "[[4,2,1]]\n",
+      "['X2', 'X3'] ['V1', 'H1', 'V0', 'H0', '(0,1)', 'V3', 'V2', '(2,3)']\n",
+      "['X3', 'X0X1'] ['V2', 'H2', 'V1', 'V3', 'V0', '(0,1)']\n",
+      "['Z1Z3', 'Z2Z3'] ['V0', 'H0', 'S3', 'S2', 'S1', '(2,3)', '(1,2)']\n",
+      "['X3', 'X0X1X2'] ['V2', 'V3', 'V1', '(1,2)', 'V0', '(0,1)']\n",
+      "['X0X1', 'X2X3'] ['V3', 'V1', 'V0', 'V2', '(2,3)', '(0,1)', '(0,2)(1,3)']\n",
+      "['Z1Z3', 'Z0Z2Z3'] ['S2', 'S3', 'S1', 'S0', '(0,2)', '(1,3)']\n",
+      "['Z2Z3', 'X2X3'] ['V1', 'H1', 'V0', 'H0', '(0,1)', 'S2S3', '(2,3)', 'H2H3']\n",
+      "['Z0Z2Z3', 'X1X2X3'] ['S0', 'V1', '(2,3)', 'H0H1H2H3^(0,1)']\n",
+      "['X2X3', 'Z0Z1Z2Z3'] ['S1', 'S0', '(0,1)', 'V2V3', '(2,3)']\n",
+      "['Z1Z3', 'X1X2X3'] ['V0', 'H0', 'V2', 'S1S3', '(1,3)']\n",
+      "--------------------------------------------------------------\n",
+      "[[4,2,2]]\n",
+      "['Z0Z1Z2Z3', 'X0X1X2X3'] ['(2,3)', '(1,2)', 'S0S1S2S3', '(0,1)', 'H0H1H2H3']\n",
+      "--------------------------------------------------------------\n",
+      "[[4,3,1]]\n",
+      "['X2X3'] ['V1', 'H1', 'V0', 'H0', '(0,1)', 'V3', 'V2', '(2,3)']\n",
+      "['X1X2X3'] ['V0', 'H0', 'V3', 'V2', '(2,3)', 'V1', '(1,2)']\n",
+      "['X0X1X2X3'] ['V3', 'V2', '(2,3)', 'V1', '(1,2)', 'V0', '(0,1)']\n",
+      "['X3'] ['V2', 'H2', 'V1', 'H1', '(1,2)', 'V0', 'H0', '(0,1)', 'V3']\n",
+      "--------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "n=4\n",
+    "for k in range(n+1):\n",
+    "    for d in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, d=d, info_only=True, list_only=True)\n",
+    "        if len(codes)>0:\n",
+    "            print(f\"[[{n},{k},{d}]]\")\n",
+    "            for code in codes:\n",
+    "                print(code['isotropic_generators'], code['aut_group_generators'])\n",
+    "            print(\"--------------------------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "f44bc17c-eeb5-43c2-8338-ae2b6e61de00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$[[4,0,2]]$ & \\\\ \n",
+      "\\midrule\n",
+      "2 & $X_{0}X_{2}$, $Z_{1}Z_{3}$, $Z_{0}Z_{2}Z_{3}$, $X_{1}X_{2}X_{3}$ &  & 32 & $S_{1}S_{3}$, $(1,3)$, $V_{0}V_{2}$, $H_{0}H_{1}H_{2}H_{3}^{(0,1)(2,3)}$\\\\ \n",
+      "3 & $Z_{0}Z_{3}$, $Z_{1}Z_{3}$, $Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}$ &  & 192 & $S_{2}S_{3}$, $(2,3)$, $S_{1}S_{3}$, $(1,2)$, $S_{0}S_{3}$, $(0,1)$\\\\ \n",
+      "\\midrule\n",
+      "$[[4,1,1]]$ & \\\\ \n",
+      "\\midrule\n",
+      "1 & $X_{0}X_{2}$, $Z_{1}Z_{3}$, $X_{1}X_{2}X_{3}$ & $X_{2}$, $Z_{0}Z_{1}Z_{2}$ & 32 & $V_{2}$, $V_{0}$, $(1,3)$, $(0,2)$, $S_{1}S_{3}$\\\\ \n",
+      "7 & $Z_{0}Z_{3}$, $Z_{1}Z_{3}$, $Z_{2}Z_{3}$ & $X_{0}X_{1}X_{2}X_{3}$, $Z_{3}$ & 384 & $S_{3}$, $S_{2}$, $S_{1}$, $S_{0}$, $(2,3)$, $(1,2)$, $(0,1)$\\\\ \n",
+      "11 & $Z_{1}Z_{3}$, $Z_{0}Z_{2}Z_{3}$, $X_{1}X_{2}X_{3}$ & $X_{0}X_{2}$, $Z_{1}Z_{2}$ & 8 & $S_{0}$, $S_{1}S_{3}$, $(1,3)$\\\\ \n",
+      "12 & $Z_{1}Z_{3}$, $Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}$ & $X_{1}X_{2}X_{3}$, $Z_{0}Z_{3}$ & 48 & $V_{0}$, $S_{2}S_{3}$, $(2,3)$, $S_{1}S_{3}$, $(1,2)$\\\\ \n",
+      "\\midrule\n",
+      "$[[4,1,2]]$ & \\\\ \n",
+      "\\midrule\n",
+      "6 & $X_{0}X_{1}$, $X_{2}X_{3}$, $Z_{0}Z_{1}Z_{2}Z_{3}$ & $X_{1}X_{3}$, $Z_{2}Z_{3}$ & 32 & $V_{2}V_{3}$, $(2,3)$, $V_{0}V_{1}$, $(0,1)$, $(0,2)(1,3)$\\\\ \n",
+      "8 & $Z_{0}X_{2}Z_{3}$, $Y_{0}X_{1}Y_{2}$, $Z_{1}Z_{2}X_{3}$ & $X_{1}Z_{3}$, $Z_{0}Z_{1}$ & 24 & $V_{0}H_{1}S_{2}H_{3}^{(1,3)}$, $H_{0}S_{1}S_{2}V_{3}^{(1,2)}$, $(0,1)(2,3)$\\\\ \n",
+      "\\midrule\n",
+      "$[[4,2,1]]$ & \\\\ \n",
+      "\\midrule\n",
+      "5 & $Z_{1}Z_{3}$, $Z_{0}Z_{2}Z_{3}$ & $X_{0}X_{2}$, $X_{0}X_{1}X_{3}$, $Z_{2}$, $Z_{3}$ & 64 & $S_{2}$, $S_{3}$, $S_{1}$, $S_{0}$, $(0,2)$, $(1,3)$\\\\ \n",
+      "7 & $Z_{0}Z_{2}Z_{3}$, $X_{1}X_{2}X_{3}$ & $X_{0}X_{2}$, $X_{0}X_{3}$, $Z_{1}Z_{2}$, $Z_{1}Z_{3}$ & 16 & $S_{0}$, $V_{1}$, $(2,3)$, $H_{0}H_{1}H_{2}H_{3}^{(0,1)}$\\\\ \n",
+      "8 & $X_{2}X_{3}$, $Z_{0}Z_{1}Z_{2}Z_{3}$ & $X_{0}X_{1}$, $X_{1}X_{3}$, $Z_{0}$, $Z_{2}Z_{3}$ & 32 & $S_{1}$, $S_{0}$, $(0,1)$, $V_{2}V_{3}$, $(2,3)$\\\\ \n",
+      "\\midrule\n",
+      "$[[4,2,2]]$ & \\\\ \n",
+      "\\midrule\n",
+      "9 & $Z_{0}Z_{1}Z_{2}Z_{3}$, $X_{0}X_{1}X_{2}X_{3}$ & $X_{1}X_{2}$, $X_{1}X_{3}$, $Z_{0}Z_{2}$, $Z_{0}Z_{3}$ & 144 & $(2,3)$, $(1,2)$, $S_{0}S_{1}S_{2}S_{3}$, $(0,1)$, $H_{0}H_{1}H_{2}H_{3}$\\\\ \n",
+      "\\midrule\n",
+      "$[[4,3,1]]$ & \\\\ \n",
+      "\\midrule\n",
+      "2 & $X_{0}X_{1}X_{2}X_{3}$ & $X_{1}$, $X_{2}$, $X_{3}$, $Z_{0}Z_{1}$, $Z_{0}Z_{2}$, $Z_{0}Z_{3}$ & 384 & $V_{3}$, $V_{2}$, $(2,3)$, $V_{1}$, $(1,2)$, $V_{0}$, $(0,1)$\\\\ \n",
+      "\\midrule\n"
+     ]
+    }
+   ],
+   "source": [
+    "n=4\n",
+    "for k in range(n+1):\n",
+    "    for d in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, d=d, info_only=True, is_decomposable=False, list_only=True)\n",
+    "        if len(codes)>0:\n",
+    "            print(f\"$[[{n},{k},{d}]]$ & \\\\\\ \")\n",
+    "            print(\"\\midrule\")\n",
+    "            for code in codes:\n",
+    "                output = f\"{code['index']} & \" \\\n",
+    "                         + f\"{', '.join(latex_it(code['isotropic_generators']))} & \" \\\n",
+    "                         + f\"{', '.join(latex_it(code['logical_ops']))} & \" \\\n",
+    "                         + f\"{code['aut_group_size']} & \" \\\n",
+    "                         + f\"{', '.join(latex_it(code['aut_group_generators']))}\" \\\n",
+    "                         + \"\\\\\\ \"\n",
+    "                print(output)\n",
+    "            print(\"\\midrule\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "5bc5624f-d718-4b41-9a75-858b279065ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(5, 1, d=1, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "95f08c4f-0a42-405d-8b71-38287138a3a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "1d42119e-8347-496e-94d6-1284df985133",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "19\n",
+      "22\n",
+      "23\n",
+      "24\n",
+      "27\n",
+      "28\n",
+      "29\n",
+      "30\n",
+      "31\n",
+      "32\n",
+      "33\n",
+      "34\n",
+      "35\n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in codes:\n",
+    "    print(code['index'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "c95e7614-8af2-4f55-b0a6-4799a5ae076a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1,0]] & 2\n",
+      "[[2,0]] & 12\n",
+      "[[2,1]] & 8\n",
+      "[[3,0]] & 24\n",
+      "[[3,1]] & 48\n",
+      "[[3,2]] & 48\n",
+      "[[4,0]] & 192\n",
+      "[[4,1]] & 384\n",
+      "[[4,2]] & 144\n",
+      "[[4,3]] & 384\n",
+      "[[5,0]] & 1920\n",
+      "[[5,1]] & 3840\n",
+      "[[5,2]] & 384\n",
+      "[[5,3]] & 384\n",
+      "[[5,4]] & 3840\n",
+      "[[6,0]] & 23040\n",
+      "[[6,1]] & 46080\n",
+      "[[6,2]] & 3072\n",
+      "[[6,3]] & 2304\n",
+      "[[6,4]] & 4320\n",
+      "[[6,5]] & 46080\n",
+      "[[7,0]] & 322560\n",
+      "[[7,1]] & 645120\n",
+      "[[7,2]] & 30720\n",
+      "[[7,3]] & 21504\n",
+      "[[7,4]] & 21504\n",
+      "[[7,5]] & 30720\n",
+      "[[7,6]] & 645120\n",
+      "[[8,0]] & 5160960\n",
+      "[[8,1]] & 10321920\n",
+      "[[8,2]] & 368640\n",
+      "[[8,3]] & 184320\n",
+      "[[8,4]] & 344064\n",
+      "[[8,5]] & 184320\n",
+      "[[8,6]] & 368640\n",
+      "[[8,7]] & 10321920\n",
+      "[[9,0]] & 92897280\n",
+      "[[9,1]] & 185794560\n",
+      "[[9,2]] & 5160960\n",
+      "[[9,3]] & 2211840\n",
+      "[[9,4]] & 1474560\n",
+      "[[9,5]] & 1474560\n",
+      "[[9,6]] & 2211840\n",
+      "[[9,7]] & 5160960\n",
+      "[[9,8]] & 185794560\n"
+     ]
+    }
+   ],
+   "source": [
+    "aut_size_max = np.zeros((10,10), dtype=int)\n",
+    "for n in range(10):\n",
+    "    for k in range(n+1):\n",
+    "        max_sym_n_k_code_aut_size  = 0\n",
+    "        max_sym_n_k_code  = None\n",
+    "        codes = cb.all_small_codes(n, k, info_only=True, is_decomposable=False, list_only=True)\n",
+    "        if len(codes)>0:\n",
+    "            for code in codes:\n",
+    "                if max_sym_n_k_code_aut_size < code['aut_group_size']:\n",
+    "                    max_sym_n_k_code = code\n",
+    "                    max_sym_n_k_code_aut_size  = code['aut_group_size']\n",
+    "        if max_sym_n_k_code is not None:\n",
+    "            print(f\"[[{n},{k}]] & {max_sym_n_k_code['aut_group_size']}\")\n",
+    "        aut_size_max[n][k] = max_sym_n_k_code_aut_size\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "id": "4465f15f-383c-4a4e-9529-2077fcf32804",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\2&0&0&0&0&0&0&0&0&0\\\\12&8&0&0&0&0&0&0&0&0\\\\24&48&48&0&0&0&0&0&0&0\\\\192&384&144&384&0&0&0&0&0&0\\\\1920&3840&384&384&3840&0&0&0&0&0\\\\23040&46080&3072&2304&4320&46080&0&0&0&0\\\\322560&645120&30720&21504&21504&30720&645120&0&0&0\\\\5160960&10321920&368640&184320&344064&184320&368640&10321920&0&0\\\\92897280&185794560&5160960&2211840&1474560&1474560&2211840&5160960&185794560&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(aut_size_max)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "id": "baa981c5-5053-4d55-9014-a0e08c8c870b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1,0]] & 0.3333333333333333\n",
+      "[[2,0]] & 0.16666666666666666\n",
+      "[[2,1]] & 0.1111111111111111\n",
+      "[[3,0]] & 0.018518518518518517\n",
+      "[[3,1]] & 0.037037037037037035\n",
+      "[[3,2]] & 0.037037037037037035\n",
+      "[[4,0]] & 0.006172839506172839\n",
+      "[[4,1]] & 0.012345679012345678\n",
+      "[[4,2]] & 0.004629629629629629\n",
+      "[[4,3]] & 0.012345679012345678\n",
+      "[[5,0]] & 0.00205761316872428\n",
+      "[[5,1]] & 0.00411522633744856\n",
+      "[[5,2]] & 0.00041152263374485596\n",
+      "[[5,3]] & 0.00041152263374485596\n",
+      "[[5,4]] & 0.00411522633744856\n",
+      "[[6,0]] & 0.0006858710562414266\n",
+      "[[6,1]] & 0.0013717421124828531\n",
+      "[[6,2]] & 9.144947416552355e-05\n",
+      "[[6,3]] & 6.858710562414266e-05\n",
+      "[[6,4]] & 0.0001286008230452675\n",
+      "[[6,5]] & 0.0013717421124828531\n",
+      "[[7,0]] & 0.00022862368541380886\n",
+      "[[7,1]] & 0.0004572473708276177\n",
+      "[[7,2]] & 2.1773684325124653e-05\n",
+      "[[7,3]] & 1.5241579027587257e-05\n",
+      "[[7,4]] & 1.5241579027587257e-05\n",
+      "[[7,5]] & 2.1773684325124653e-05\n",
+      "[[7,6]] & 0.0004572473708276177\n",
+      "[[8,0]] & 7.620789513793629e-05\n",
+      "[[8,1]] & 0.00015241579027587258\n",
+      "[[8,2]] & 5.443421081281163e-06\n",
+      "[[8,3]] & 2.7217105406405816e-06\n",
+      "[[8,4]] & 5.080526342529086e-06\n",
+      "[[8,5]] & 2.7217105406405816e-06\n",
+      "[[8,6]] & 5.443421081281163e-06\n",
+      "[[8,7]] & 0.00015241579027587258\n",
+      "[[9,0]] & 2.540263171264543e-05\n",
+      "[[9,1]] & 5.080526342529086e-05\n",
+      "[[9,2]] & 1.4112573173691905e-06\n",
+      "[[9,3]] & 6.048245645867959e-07\n",
+      "[[9,4]] & 4.032163763911973e-07\n",
+      "[[9,5]] & 4.032163763911973e-07\n",
+      "[[9,6]] & 6.048245645867959e-07\n",
+      "[[9,7]] & 1.4112573173691905e-06\n",
+      "[[9,8]] & 5.080526342529086e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "aut_size_max_per = np.zeros((10,10), dtype=float)\n",
+    "for n in range(10):\n",
+    "    max_val = (6**n)*math.factorial(n)\n",
+    "    for k in range(n+1):\n",
+    "        max_sym_n_k_code_aut_size  = 0\n",
+    "        max_sym_n_k_code  = None\n",
+    "        codes = cb.all_small_codes(n, k, info_only=True, is_decomposable=False, list_only=True)\n",
+    "        if len(codes)>0:\n",
+    "            for code in codes:\n",
+    "                if max_sym_n_k_code_aut_size < code['aut_group_size']/max_val:\n",
+    "                    max_sym_n_k_code = code\n",
+    "                    max_sym_n_k_code_aut_size  = code['aut_group_size']/max_val\n",
+    "        if max_sym_n_k_code is not None:\n",
+    "            print(f\"[[{n},{k}]] & {max_sym_n_k_code['aut_group_size']/max_val}\")\n",
+    "        aut_size_max_per[n][k] = max_sym_n_k_code_aut_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "e864e1cc-65d0-455d-9209-ad5f2eea34cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.080526342529086e-05"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "185794560/(6**9*math.factorial(9))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "ee132f1b-dd9b-4535-8733-52910ffdbbc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0.0&0.0&0.0&0.0&0.0&0.0&0.0&0.0&0.0&0.0\\\\0.3333333333333333&0.0&0.0&0.0&0.0&0.0&0.0&0.0&0.0&0.0\\\\0.16666666666666666&0.1111111111111111&0.0&0.0&0.0&0.0&0.0&0.0&0.0&0.0\\\\0.018518518518518517&0.037037037037037035&0.037037037037037035&0.0&0.0&0.0&0.0&0.0&0.0&0.0\\\\0.006172839506172839&0.012345679012345678&0.004629629629629629&0.012345679012345678&0.0&0.0&0.0&0.0&0.0&0.0\\\\0.00205761316872428&0.00411522633744856&0.00041152263374485596&0.00041152263374485596&0.00411522633744856&0.0&0.0&0.0&0.0&0.0\\\\0.0006858710562414266&0.0013717421124828531&9.144947416552355e-05&6.858710562414266e-05&0.0001286008230452675&0.0013717421124828531&0.0&0.0&0.0&0.0\\\\0.00022862368541380886&0.0004572473708276177&2.1773684325124653e-05&1.5241579027587257e-05&1.5241579027587257e-05&2.1773684325124653e-05&0.0004572473708276177&0.0&0.0&0.0\\\\7.620789513793629e-05&0.00015241579027587258&5.443421081281163e-06&2.7217105406405816e-06&5.080526342529086e-06&2.7217105406405816e-06&5.443421081281163e-06&0.00015241579027587258&0.0&0.0\\\\2.540263171264543e-05&5.080526342529086e-05&1.4112573173691905e-06&6.048245645867959e-07&4.032163763911973e-07&4.032163763911973e-07&6.048245645867959e-07&1.4112573173691905e-06&5.080526342529086e-05&0.0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lprint(aut_size_max_per)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "550a5e7b-0c42-4bae-a593-87f00ad4e151",
+   "metadata": {},
+   "source": [
+    "# Test for Figure 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "f974e34e-f560-4716-8769-436637730750",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5,1,2]]\n",
+      "aut_group_generators : ['(2,3)', '(1,4)', 'S0S4', 'S2S3', '(0,1)']\n",
+      "aut_group_size       : 96\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 9\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z4', 'Z1Z4', 'Z2Z3', 'X0X1X2X3X4']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X2X3', 'Z0Z3']\n",
+      "n                    : 5\n",
+      "uuid                 : f7172e66-8670-4290-a586-41de952e7ec2\n",
+      "weight_enumerator    : [1, 0, 4, 0, 3, 8]\n",
+      "\n",
+      "[[5,1,2]]\n",
+      "aut_group_generators : ['V2V3', '(2,3)', 'S1S4', '(1,4)']\n",
+      "aut_group_size       : 16\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 14\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z1Z4', 'X2X3', 'X0X1X4', 'Z0Z2Z3Z4']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X1X3X4', 'Z0Z4']\n",
+      "n                    : 5\n",
+      "uuid                 : 01435938-cc0c-4303-9afd-b9cdb97c9e0a\n",
+      "weight_enumerator    : [1, 0, 2, 2, 5, 6]\n",
+      "\n",
+      "[[5,1,2]]\n",
+      "aut_group_generators : ['H0H1H2H3H4^(1,4)', '(0,1)(2,4)']\n",
+      "aut_group_size       : 8\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 18\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z3', 'Z2Z3Z4', 'X1X2X3', 'X0X3X4']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X2X4', 'Z0Z4']\n",
+      "n                    : 5\n",
+      "uuid                 : cdbffe3c-84e0-4c9b-bbec-12304e462448\n",
+      "weight_enumerator    : [1, 0, 0, 4, 7, 4]\n",
+      "\n",
+      "[[5,1,2]]\n",
+      "aut_group_generators : ['(1,2)', 'V1V2', 'H0V1S3S4^(3,4)']\n",
+      "aut_group_size       : 8\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 20\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X1X2', 'X1Z3Z4', 'Y0X3Y4', 'X0Y1Z2Y3']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z1Z2X3', 'Z0Z3']\n",
+      "n                    : 5\n",
+      "uuid                 : 378a65ae-e0ea-4bef-8336-9d78cc5898ed\n",
+      "weight_enumerator    : [1, 0, 1, 3, 6, 5]\n",
+      "\n",
+      "[[5,2,2]]\n",
+      "aut_group_generators : ['(2,3)', '(0,2)', '(1,4)', 'S1S4', 'S0S2S3S4']\n",
+      "aut_group_size       : 48\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 26\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z1Z4', 'Z0Z2Z3Z4', 'X0X1X2X3X4']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['X2X3', 'X1X2X4', 'Z0Z3', 'Z0Z4']\n",
+      "n                    : 5\n",
+      "uuid                 : cff27c40-06b0-4d0b-9b8f-f3e4bdfaaf28\n",
+      "weight_enumerator    : [1, 0, 1, 0, 2, 4]\n",
+      "\n",
+      "[[5,2,2]]\n",
+      "aut_group_generators : ['H2H3^(2,3)', 'H0V2S3H4^(0,4)', 'H2H3^(0,1)']\n",
+      "aut_group_size       : 12\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 27\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1X4', 'X0X2Z3Z4', 'X1Z2X3Z4']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z0X3', 'Z1X2', 'Z1Z3', 'Z0Z2']\n",
+      "n                    : 5\n",
+      "uuid                 : bbcdb961-1bab-40bc-bbdc-788311786e89\n",
+      "weight_enumerator    : [1, 0, 0, 1, 3, 3]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "for k in range(1,6):\n",
+    "    codes = cb.all_small_codes(5, k ,d = 2, info_only=True, is_decomposable=False, list_only=True)\n",
+    "    if len(codes) > 0 :\n",
+    "        for code in codes:\n",
+    "            print(f\"[[5,{k},2]]\")\n",
+    "            print(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4ba5e8e3-228f-488d-aa0d-27625f0d7556",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f24d76fc-fe65-4583-967e-e40b0c2fba61",
+   "metadata": {},
+   "source": [
+    "# n=7 d=3 Codes Indecomposable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c58e07b8-d169-43d3-9f34-f39f633fd403",
+   "metadata": {},
+   "source": [
+    "# [[7,1,3]] Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1262,
+   "id": "d48fd735-08b8-4a26-8923-e9b70c248cc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(7, 1, d=3, is_decomposable=False, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1263,
+   "id": "9eb20607-e92b-4704-b5ef-117619d306e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_codes = sorted(codes, key=lambda x: -x['aut_group_size'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1266,
+   "id": "fbaa3b2b-a59c-48ae-ba69-9b95e0d52a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['(3,4)(5,6)', '(3,5)(4,6)', 'V0V1V2V3V4V5V6', '(1,2)(5,6)', '(1,3)(2,4)', 'H0H1H2H3H4H5H6', '(0,1)(4,5)']\n",
+      "aut_group_size       : 1008\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 226\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z3Z6', 'Z0Z2Z3Z5', 'Y1Y2Y3Y4', 'Z3Z4Z5Z6', 'Y0Y1Y4Y5', 'Y0Y2Y4Y6']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X2X4X5', 'Z1Z3Z5']\n",
+      "n                    : 7\n",
+      "uuid                 : 69b11699-9064-4ca3-8b7a-c21e72d0756b\n",
+      "weight_enumerator    : [1, 0, 0, 0, 21, 0, 42, 0]\n",
+      "\n",
+      "aut_group_generators : ['H2H3V4S5S6^(2,3)', 'V0S1S2V3H4H6^(4,6)', 'S4V5V6^(1,2)', 'H0H1H2H3^(5,6)', '(0,1)(2,3)(5,6)']\n",
+      "aut_group_size       : 144\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 228\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0Y1Z5Z6', 'Z0X1X2Z3', 'X0Z1Z2X3', 'X0Z1X4Z6', 'Z0Z3Z4X5', 'Z1Z2Z4X6']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X4Z5Z6', 'Z0Z1Z2Z3Z4']\n",
+      "n                    : 7\n",
+      "uuid                 : 61c1b550-2029-4e77-a2be-0eef5e1203a5\n",
+      "weight_enumerator    : [1, 0, 0, 0, 21, 0, 42, 0]\n",
+      "\n",
+      "aut_group_generators : ['S0S1S2H3H4H5V6', '(1,2)(4,5)', 'S0V1V2H3V4V5V6^(1,4)(2,5)', '(0,1)(3,4)']\n",
+      "aut_group_size       : 48\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 209\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0X1Z3Z4', 'X0X2Z3Z5', 'Z0Z1Y3Y4', 'Z0Z2Y3Y5', 'Z0Z1Z2X6', 'Y0Y3Z4Z5Z6']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z2Z3Z4X5', 'Z0Z1Z2Z3Z4Z5']\n",
+      "n                    : 7\n",
+      "uuid                 : 9d9b3bc7-a3e3-42e0-a7e8-187c48e8c9a9\n",
+      "weight_enumerator    : [1, 0, 0, 0, 13, 24, 18, 8]\n",
+      "\n",
+      "aut_group_generators : ['(1,2)(3,4)(5,6)', 'R0R1R2R3R4R5R6^(1,3,5,2,4,6)', '(0,1)(2,5)(3,6)']\n",
+      "aut_group_size       : 42\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 227\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0Y1Z2Z5', 'Y0Z1Y2Z6', 'Z0X1X3Z4', 'Z0X2Z3X4', 'X0Z2Z3X5', 'X0Z1Z4X6']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z2Z4X6', 'Z0Z1Z2Z3Z4Z5Z6']\n",
+      "n                    : 7\n",
+      "uuid                 : 86c7ea3e-6a6c-4e3e-932b-30d8c6d581d1\n",
+      "weight_enumerator    : [1, 0, 0, 0, 21, 0, 42, 0]\n",
+      "\n",
+      "aut_group_generators : ['S2V5V6^(3,4)', 'V0V1H2S3S4H5^(2,5)', '(0,1)(3,4)', 'H0H1H2H3H4H5^(0,3)(1,4)(2,5)']\n",
+      "aut_group_size       : 16\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 221\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0X1Z3Z4', 'X0X2Z3Z5', 'Z0Z1Y3Y4', 'Z2Z3Z4X5', 'Z0Z1Z2X6', 'Y0Y3Z4Z5Z6']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z1Z3X4Z5', 'Z0Z1Z2Z3Z4']\n",
+      "n                    : 7\n",
+      "uuid                 : 42033be1-0c77-44d2-abc7-cb2919ba8efa\n",
+      "weight_enumerator    : [1, 0, 0, 0, 13, 24, 18, 8]\n",
+      "\n",
+      "aut_group_generators : ['(1,2)(3,4)(5,6)', 'r0H1H2S3S4r5r6^(1,5,3)(2,6,4)']\n",
+      "aut_group_size       : 6\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 200\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z1Z3X5', 'Z2Z4X6', 'Y0Y1Z2Z5', 'Y0Z1Y2Z6', 'Z0X1X3Z4', 'Z0X2Z3X4']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z3X4Z6', 'Z0Z1Z2Z3Z4']\n",
+      "n                    : 7\n",
+      "uuid                 : 69a546ba-1104-4c9d-98b8-4d867e4dea9e\n",
+      "weight_enumerator    : [1, 0, 0, 2, 9, 24, 22, 6]\n",
+      "\n",
+      "aut_group_generators : ['V0H1S2S3V5^(2,3)(4,6)', '(0,5)(2,3)(4,6)']\n",
+      "aut_group_size       : 4\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 185\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z3X4Z6', 'Z2Z4X6', 'Y0Y1Z2Z5', 'Y0Z1Y2Z6', 'Z0X1X3Z4', 'X0Z2Z3X5']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z1Z3X5', 'Z0Z1Z2Z3Z5']\n",
+      "n                    : 7\n",
+      "uuid                 : 374bc230-1ab0-48bd-8ca9-a73652723d21\n",
+      "weight_enumerator    : [1, 0, 0, 2, 9, 24, 22, 6]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in sorted_codes:\n",
+    "    if code['is_degenerate'] == 0:\n",
+    "        print(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "32e2199e-e9ad-4774-8d8d-d45a3898ff23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "226 & 1008 & $1 + 21x^{4} + 42x^{6}$ & $\\langle Z_{0}Z_{1}Z_{3}Z_{6}, Z_{0}Z_{2}Z_{3}Z_{5}, Y_{1}Y_{2}Y_{3}Y_{4}, Z_{3}Z_{4}Z_{5}Z_{6},$ $Y_{1}Y_{2}Y_{3}Y_{4}, Z_{3}Z_{4}Z_{5}Z_{6}, Y_{0}Y_{1}Y_{4}Y_{5}, Y_{0}Y_{2}Y_{4}Y_{6}\\rangle$ \\\\ \n",
+      "108 & 768 & $1 + 5x^{2} + 11x^{4} + 47x^{6}$ & $\\langle X_{0}Z_{4}, X_{1}Z_{4}, X_{2}Z_{5}, X_{3}Z_{6},$ $X_{2}Z_{5}, X_{3}Z_{6}, Z_{2}Z_{3}Y_{5}Y_{6}, Z_{0}Z_{1}Z_{2}X_{4}X_{5}Z_{6}\\rangle$ \\\\ \n",
+      "115 & 576 & $1 + 3x^{2} + 15x^{4} + 45x^{6}$ & $\\langle X_{0}Z_{6}, X_{1}Z_{6}, X_{2}X_{3}Z_{4}Z_{5}, Y_{2}Y_{4}Z_{5}Z_{6},$ $X_{2}X_{3}Z_{4}Z_{5}, Y_{2}Y_{4}Z_{5}Z_{6}, X_{2}Z_{3}X_{5}Z_{6}, Z_{0}Z_{1}Y_{2}Z_{3}Z_{4}Y_{6}\\rangle$ \\\\ \n",
+      "190 & 192 & $1 + 3x^{2} + 15x^{4} + 45x^{6}$ & $\\langle X_{0}Z_{6}, X_{1}Z_{5}, X_{2}Z_{4}, Z_{2}X_{3}X_{4}Z_{5},$ $X_{2}Z_{4}, Z_{2}X_{3}X_{4}Z_{5}, Z_{1}Y_{3}Y_{5}Z_{6}, Z_{0}Z_{3}Z_{4}X_{6}\\rangle$ \\\\ \n",
+      "228 & 144 & $1 + 21x^{4} + 42x^{6}$ & $\\langle Y_{0}Y_{1}Z_{5}Z_{6}, Z_{0}X_{1}X_{2}Z_{3}, X_{0}Z_{1}Z_{2}X_{3}, X_{0}Z_{1}X_{4}Z_{6},$ $X_{0}Z_{1}Z_{2}X_{3}, X_{0}Z_{1}X_{4}Z_{6}, Z_{0}Z_{3}Z_{4}X_{5}, Z_{1}Z_{2}Z_{4}X_{6}\\rangle$ \\\\ \n",
+      "239 & 96 & $1 + x^{2} + 2x^{3} + 7x^{4} + 24x^{5} + 23x^{6} + 6x^{7}$ & $\\langle X_{0}Z_{1}, Z_{0}X_{1}Z_{6}, X_{2}X_{3}Z_{4}Z_{5}, Y_{2}Y_{4}Z_{5}Z_{6},$ $X_{2}X_{3}Z_{4}Z_{5}, Y_{2}Y_{4}Z_{5}Z_{6}, X_{2}Z_{3}X_{5}Z_{6}, Z_{1}Y_{2}Z_{3}Z_{4}Y_{6}\\rangle$ \\\\ \n",
+      "164 & 64 & $1 + 2x^{2} + 9x^{4} + 24x^{5} + 20x^{6} + 8x^{7}$ & $\\langle X_{0}Z_{5}, X_{1}Z_{6}, Y_{2}Y_{3}Z_{4}Z_{5}, Y_{2}Z_{3}Y_{4}Z_{6},$ $Y_{2}Y_{3}Z_{4}Z_{5}, Y_{2}Z_{3}Y_{4}Z_{6}, Z_{0}X_{2}Z_{4}X_{5}Z_{6}, Z_{1}X_{2}Z_{3}Z_{5}X_{6}\\rangle$ \\\\ \n",
+      "166 & 64 & $1 + 2x^{2} + 17x^{4} + 44x^{6}$ & $\\langle X_{0}Z_{6}, X_{1}Z_{3}, Z_{3}X_{4}Z_{5}Z_{6}, Z_{1}X_{2}Y_{3}Y_{4},$ $Z_{3}X_{4}Z_{5}Z_{6}, Z_{1}X_{2}Y_{3}Y_{4}, Z_{2}Z_{4}X_{5}Z_{6}, Z_{0}Y_{2}Z_{4}Y_{6}\\rangle$ \\\\ \n",
+      "209 & 48 & $1 + 13x^{4} + 24x^{5} + 18x^{6} + 8x^{7}$ & $\\langle X_{0}X_{1}Z_{3}Z_{4}, X_{0}X_{2}Z_{3}Z_{5}, Z_{0}Z_{1}Y_{3}Y_{4}, Z_{0}Z_{2}Y_{3}Y_{5},$ $Z_{0}Z_{1}Y_{3}Y_{4}, Z_{0}Z_{2}Y_{3}Y_{5}, Z_{0}Z_{1}Z_{2}X_{6}, Y_{0}Y_{3}Z_{4}Z_{5}Z_{6}\\rangle$ \\\\ \n",
+      "227 & 42 & $1 + 21x^{4} + 42x^{6}$ & $\\langle Y_{0}Y_{1}Z_{2}Z_{5}, Y_{0}Z_{1}Y_{2}Z_{6}, Z_{0}X_{1}X_{3}Z_{4}, Z_{0}X_{2}Z_{3}X_{4},$ $Z_{0}X_{1}X_{3}Z_{4}, Z_{0}X_{2}Z_{3}X_{4}, X_{0}Z_{2}Z_{3}X_{5}, X_{0}Z_{1}Z_{4}X_{6}\\rangle$ \\\\ \n",
+      "255 & 32 & $1 + x^{2} + 11x^{4} + 24x^{5} + 19x^{6} + 8x^{7}$ & $\\langle X_{0}Z_{4}, Y_{1}Y_{2}Z_{3}Z_{5}, Y_{1}Z_{2}Y_{3}Z_{6}, Z_{0}X_{4}Z_{5}Z_{6},$ $Y_{1}Z_{2}Y_{3}Z_{6}, Z_{0}X_{4}Z_{5}Z_{6}, Z_{2}Z_{3}Y_{5}Y_{6}, X_{1}Z_{3}Z_{4}X_{5}Z_{6}\\rangle$ \\\\ \n",
+      "257 & 32 & $1 + x^{2} + 19x^{4} + 43x^{6}$ & $\\langle X_{0}Z_{6}, X_{1}X_{2}Z_{4}Z_{5}, X_{1}X_{3}Z_{5}Z_{6}, Y_{1}Z_{3}Y_{4}Z_{6},$ $X_{1}X_{3}Z_{5}Z_{6}, Y_{1}Z_{3}Y_{4}Z_{6}, Y_{2}Z_{3}Y_{5}Z_{6}, Z_{0}Z_{1}Z_{2}X_{6}\\rangle$ \\\\ \n",
+      "221 & 16 & $1 + 13x^{4} + 24x^{5} + 18x^{6} + 8x^{7}$ & $\\langle X_{0}X_{1}Z_{3}Z_{4}, X_{0}X_{2}Z_{3}Z_{5}, Z_{0}Z_{1}Y_{3}Y_{4}, Z_{2}Z_{3}Z_{4}X_{5},$ $Z_{0}Z_{1}Y_{3}Y_{4}, Z_{2}Z_{3}Z_{4}X_{5}, Z_{0}Z_{1}Z_{2}X_{6}, Y_{0}Y_{3}Z_{4}Z_{5}Z_{6}\\rangle$ \\\\ \n",
+      "240 & 16 & $1 + x^{2} + 2x^{3} + 7x^{4} + 24x^{5} + 23x^{6} + 6x^{7}$ & $\\langle X_{0}Z_{6}, Z_{1}Z_{3}X_{4}, Z_{2}Z_{3}X_{5}, X_{1}X_{2}Z_{4}Z_{5},$ $Z_{2}Z_{3}X_{5}, X_{1}X_{2}Z_{4}Z_{5}, X_{1}X_{3}Z_{5}Z_{6}, Z_{0}Y_{1}Z_{2}Z_{4}Y_{6}\\rangle$ \\\\ \n",
+      "200 & 6 & $1 + 2x^{3} + 9x^{4} + 24x^{5} + 22x^{6} + 6x^{7}$ & $\\langle Z_{1}Z_{3}X_{5}, Z_{2}Z_{4}X_{6}, Y_{0}Y_{1}Z_{2}Z_{5}, Y_{0}Z_{1}Y_{2}Z_{6},$ $Y_{0}Y_{1}Z_{2}Z_{5}, Y_{0}Z_{1}Y_{2}Z_{6}, Z_{0}X_{1}X_{3}Z_{4}, Z_{0}X_{2}Z_{3}X_{4}\\rangle$ \\\\ \n",
+      "185 & 4 & $1 + 2x^{3} + 9x^{4} + 24x^{5} + 22x^{6} + 6x^{7}$ & $\\langle Z_{3}X_{4}Z_{6}, Z_{2}Z_{4}X_{6}, Y_{0}Y_{1}Z_{2}Z_{5}, Y_{0}Z_{1}Y_{2}Z_{6},$ $Y_{0}Y_{1}Z_{2}Z_{5}, Y_{0}Z_{1}Y_{2}Z_{6}, Z_{0}X_{1}X_{3}Z_{4}, X_{0}Z_{2}Z_{3}X_{5}\\rangle$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in sorted_codes:\n",
+    "    aut = code['aut_group_size']\n",
+    "    gen = '$\\\\langle ' + ', '.join(latex_it(code['isotropic_generators'][:4], dollar=False)) + ',$'\n",
+    "    gen += ' $' + ', '.join(latex_it(code['isotropic_generators'][-4:], dollar=False)) + '\\\\rangle$'\n",
+    "    w = make_poly(code['weight_enumerator'])\n",
+    "    index = code['index']\n",
+    "    print(f\"{index} & {aut} & ${w}$ & {gen} \\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47d604bf-0735-43fc-b9bc-3ac542295548",
+   "metadata": {},
+   "source": [
+    "# n=8, d=3 Codes Indecomposable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "332ac059-ec1e-4670-8d35-949acd5fa291",
+   "metadata": {},
+   "source": [
+    "# [[8,1,3]] Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1158,
+   "id": "f123447c-d6dc-4a23-ab44-d724461b4962",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "157"
+      ]
+     },
+     "execution_count": 1158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes_813_indcom = cb.all_small_codes(8, 1, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
+    "sorted_codes_813 = sorted(codes_813_indcom, key=lambda x: -x['aut_group_size'])\n",
+    "len(sorted_codes_813)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "2358a30b-b56b-4105-abf1-e0af4b119ca0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "855 6144 True\n",
+      "631 2304 True\n",
+      "1090 2048 True\n",
+      "1094 1024 True\n",
+      "346 512 True\n",
+      "493 384 True\n",
+      "564 384 True\n",
+      "140 256 True\n",
+      "595 192 True\n",
+      "748 128 True\n",
+      "152 64 True\n",
+      "154 64 True\n",
+      "164 64 True\n",
+      "1034 64 True\n",
+      "787 32 True\n",
+      "832 32 True\n",
+      "181 16 True\n",
+      "259 16 True\n",
+      "1146 16 True\n",
+      "1157 8 True\n",
+      "1247 4 True\n",
+      "21 codes found with planar tanner graphs\n"
+     ]
+    }
+   ],
+   "source": [
+    "planar_codes = []\n",
+    "count = 0\n",
+    "for code in sorted_codes_813 :\n",
+    "    if is_planar(code) is True:\n",
+    "        print(code['index'], code['aut_group_size'], is_planar(code))\n",
+    "        count = count +  1\n",
+    "        planar_codes += [code]\n",
+    "print(f\"{count} codes found with planar tanner graphs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1165,
+   "id": "9f0bd74a-ac15-4a1e-96cb-1a65184c455a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[8,1,3]] : 855 : 6144 : 1 : True\n",
+      "[[8,1,3]] : 894 : 4608 : 1 : False\n",
+      "[[8,1,3]] : 602 : 2880 : 1 : False\n",
+      "[[8,1,3]] : 631 : 2304 : 1 : True\n",
+      "[[8,1,3]] : 1090 : 2048 : 1 : True\n",
+      "[[8,1,3]] : 1094 : 1024 : 1 : True\n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in sorted_codes_813:\n",
+    "    if code['aut_group_size'] >= 1024:\n",
+    "        print(f\"[[8,1,3]] : {code['index']} : {code['aut_group_size']} : {code['is_degenerate']} : {is_planar(code)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1164,
+   "id": "663038fa-83fa-4f30-8b55-2bbb15e99298",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "855 & 6144 &  $\\langle X_{0}Z_{7}, X_{1}Z_{7}, X_{2}Z_{7}, X_{3}Z_{5},$ $X_{3}Z_{5}, X_{4}Z_{6}, Z_{3}Z_{4}Y_{5}Y_{6}, Z_{0}Z_{1}Z_{2}Z_{3}X_{5}Z_{6}X_{7}\\rangle$ & $1 + 8x^{2} + 18x^{4} + 32x^{6} + 64x^{7} + 5x^{8}$\\\\ \n",
+      "894 & 4608 &  $\\langle X_{0}Z_{7}, X_{1}Z_{7}, X_{2}Z_{7}, X_{3}X_{4}Z_{5}Z_{6},$ $X_{3}X_{4}Z_{5}Z_{6}, Y_{3}Y_{5}Z_{6}Z_{7}, X_{3}Z_{4}X_{6}Z_{7}, Z_{0}Z_{1}Z_{2}Y_{3}Z_{4}Z_{5}Y_{7}\\rangle$ & $1 + 6x^{2} + 20x^{4} + 34x^{6} + 64x^{7} + 3x^{8}$\\\\ \n",
+      "602 & 2880 &  $\\langle X_{0}Z_{7}, X_{1}Z_{7}, Y_{2}Y_{3}Z_{4}Z_{5}, Y_{2}Z_{3}Y_{4}Z_{6},$ $Y_{2}Z_{3}Y_{4}Z_{6}, X_{2}Z_{4}X_{5}Z_{6}, X_{2}Z_{3}Z_{5}X_{6}, Z_{0}Z_{1}X_{2}Z_{3}Z_{4}X_{7}\\rangle$ & $1 + 3x^{2} + 15x^{4} + 85x^{6} + 24x^{8}$\\\\ \n",
+      "631 & 2304 &  $\\langle X_{0}Z_{7}, X_{1}Z_{7}, X_{2}Z_{6}, X_{3}Z_{6},$ $X_{3}Z_{6}, X_{4}Z_{5}, Z_{0}Z_{1}Z_{4}Y_{5}Y_{7}, Z_{2}Z_{3}Z_{4}X_{5}X_{6}Z_{7}\\rangle$ & $1 + 7x^{2} + 15x^{4} + 8x^{5} + 33x^{6} + 56x^{7} + 8x^{8}$\\\\ \n",
+      "1090 & 2048 &  $\\langle X_{0}Z_{4}, X_{1}Z_{5}, X_{2}Z_{6}, X_{3}Z_{7},$ $X_{3}Z_{7}, Z_{0}Z_{1}Y_{4}Y_{5}, Z_{2}Z_{3}Y_{6}Y_{7}, Z_{0}Z_{2}X_{4}Z_{5}X_{6}Z_{7}\\rangle$ & $1 + 4x^{2} + 14x^{4} + 84x^{6} + 25x^{8}$\\\\ \n",
+      "1094 & 1024 &  $\\langle X_{0}Z_{6}, X_{1}Z_{7}, X_{2}Z_{5}, X_{3}X_{4},$ $X_{3}X_{4}, Z_{0}Y_{3}Z_{4}Z_{5}Y_{6}, Z_{0}Z_{2}X_{5}X_{6}Z_{7}, Z_{1}Z_{3}Z_{4}Z_{6}X_{7}\\rangle$ & $1 + 4x^{2} + 6x^{4} + 32x^{5} + 36x^{6} + 32x^{7} + 17x^{8}$\\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in sorted_codes_813:\n",
+    "    if code['aut_group_size'] >= 1024:\n",
+    "        aut = code['aut_group_size']\n",
+    "        gen = '$\\\\langle ' + ', '.join(latex_it(code['isotropic_generators'][:4], dollar=False)) + ',$'\n",
+    "        gen += ' $' + ', '.join(latex_it(code['isotropic_generators'][-4:], dollar=False)) + '\\\\rangle$'\n",
+    "        w = make_poly(code['weight_enumerator'])\n",
+    "        index = code['index']\n",
+    "        print(f\"{index} & {aut} &  {gen} & ${w}$\\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "e2b314ac-52ec-4f9f-bc64-d7845381a4d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V2S7', 'H2H7^(2,7)', 'V1S7', '(1,2)', 'V0S7', '(0,1)', 'V4S6', 'H4H6^(4,6)', 'V3S5', '(3,4)(5,6)']\n",
+      "aut_group_size       : 6144\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 855\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'X2Z7', 'X3Z5', 'X4Z6', 'Z3Z4Y5Y6', 'Z0Z1Z2Z3X5Z6X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z2X7', 'Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : b1beac76-46ec-47d6-9269-73a23db413d1\n",
+      "weight_enumerator    : [1, 0, 8, 0, 18, 0, 32, 64, 5]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "code = planar_codes[0]\n",
+    "print(code)\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "ed476085-a904-416c-adf3-ff1d96326c08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V1S7', 'V0S7', 'V3S6', 'V2S6', 'V4S5', 'H4H5^(4,5)', 'H1H7^(1,7)', 'H3H6^(3,6)', '(0,1)', '(2,3)']\n",
+      "aut_group_size       : 2304\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 631\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'X2Z6', 'X3Z6', 'X4Z5', 'Z0Z1Z4Y5Y7', 'Z2Z3Z4X5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z5X7', 'Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : baa2ddde-c0d7-4913-88c4-f4c3241a2d9a\n",
+      "weight_enumerator    : [1, 0, 7, 0, 15, 8, 33, 56, 8]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_codes[1]\n",
+    "print(code)\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "68c1ef9e-1614-496a-bd5a-ea4f5e02020d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V0S4', 'V1S5', 'H1H5^(1,5)', 'V3S7', 'H3H7^(3,7)', 'V2S6', '(2,3)(6,7)', '(0,1)(4,5)', '(0,2)(1,3)(4,6)(5,7)']\n",
+      "aut_group_size       : 2048\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1090\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z4', 'X1Z5', 'X2Z6', 'X3Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z3Z6X7', 'Z4Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : a3da7246-dc61-4d39-b2ff-4bb937216758\n",
+      "weight_enumerator    : [1, 0, 4, 0, 14, 0, 84, 0, 25]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_codes[2]\n",
+    "print(code)\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "8619c5a5-6ca9-45dc-ac31-34e6a999e738",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V0S6', 'V2S5', 'H2H5^(2,5)', 'V1S7', 'H1H7^(1,7)', 'V3V4', '(3,4)', 'H4H5^(0,1)(2,3)(4,5)(6,7)', 'r4S5R6S7^(0,3,1,2)(4,7,5,6)']\n",
+      "aut_group_size       : 1024\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1094\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z6', 'X1Z7', 'X2Z5', 'X3X4', 'Z0Y3Z4Z5Y6', 'Z0Z2X5X6Z7', 'Z1Z3Z4Z6X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z3Z4X6Z7', 'Z3Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : eebcc246-7e44-4e80-a401-8632fa112185\n",
+      "weight_enumerator    : [1, 0, 4, 0, 6, 32, 36, 32, 17]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_codes[3]\n",
+    "print(code)\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "28f8dddb-131b-4e51-9b58-53ac6abb1103",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V3S7', 'H3H7^(3,7)', 'V2S6', 'H2H6^(2,6)', 'V1S5', 'H1H5^(1,5)', 'V0S4', '(0,1)(4,5)']\n",
+      "aut_group_size       : 512\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 346\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z4', 'X1Z5', 'X2Z6', 'X3Z7', 'Z3Z6X7', 'Z0Z1Y4Y5', 'Z0Z2X4Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z2X6Z7', 'Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : 5aada1a9-a0f1-4f4d-923a-974f71338e7f\n",
+      "weight_enumerator    : [1, 0, 4, 4, 10, 8, 44, 52, 5]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_codes[4]\n",
+    "print(code)\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "c9e1a1f3-7c79-4c8d-a00c-7b681bca7cc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V1S7', 'H1H7^(1,7)', 'V0S7', '(0,1)', 'V3V4', '(3,4)', 'V2S5', 'H2H5^(2,5)']\n",
+      "aut_group_size       : 384\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 493\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'X2Z5', 'X3X4', 'Z2X5Z6Z7', 'Z3Z4Z5X6', 'Z0Z1X3Y6Y7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z3Z4Z5X7', 'Z3Z4Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 920cbad7-2530-4541-b9b7-be07bfd1303e\n",
+      "weight_enumerator    : [1, 0, 5, 0, 17, 8, 35, 56, 6]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_codes[5]\n",
+    "print(code)\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "88319240-1963-4956-8a9f-8e59110ade12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V2S6', 'H2H6^(2,6)', 'V1S6', '(1,2)', 'V0S7', 'H0H7^(0,7)', 'V3V4', '(3,4)']\n",
+      "aut_group_size       : 384\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 564\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z6', 'X3X4', 'Z3Z4X5', 'Z0X3Y5Y7', 'Z1Z2X3Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z3Z4X7', 'Z3Z4Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : b3d314a1-647e-47c0-9e8a-a3c835b99073\n",
+      "weight_enumerator    : [1, 0, 5, 2, 11, 12, 39, 50, 8]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_codes[6]\n",
+    "print(code)\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99b5d6e9-a872-4a6d-8493-2ad3e8987c54",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7f7d66a-5226-4959-8186-fd8e84dbce95",
+   "metadata": {},
+   "source": [
+    "# Note:\n",
+    "\n",
+    "The paper states that there are 22 indecomposable [[8,1,3]] codes with planar Tanner graphs. The above only finds 21. Is this a typo of somehthing changed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48b6936f-8c7d-438c-9484-5d06eda3ab2b",
+   "metadata": {},
+   "source": [
+    "## [[8,2,3]] Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1166,
+   "id": "5ad8343b-6064-415d-a572-1a07308880ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 1166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes_823_indcom = cb.all_small_codes(8, 2, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
+    "sorted_codes_823 = sorted(codes_823_indcom, key=lambda x: -x['aut_group_size'])\n",
+    "len(sorted_codes_823)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1167,
+   "id": "01b1d9f7-db82-4bab-842e-b50cefea594c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[8,2,3]] : 4947 : 1728 : 0 : False\n",
+      "[[8,2,3]] : 4948 : 48 : 0 : False\n",
+      "[[8,2,3]] : 4519 : 24 : 0 : False\n",
+      "[[8,2,3]] : 3745 : 16 : 0 : False\n",
+      "[[8,2,3]] : 3744 : 12 : 0 : False\n",
+      "[[8,2,3]] : 3310 : 6 : 0 : False\n",
+      "[[8,2,3]] : 4525 : 6 : 0 : False\n",
+      "[[8,2,3]] : 5277 : 4 : 1 : False\n",
+      "[[8,2,3]] : 4149 : 3 : 0 : False\n",
+      "[[8,2,3]] : 3710 : 2 : 0 : False\n",
+      "[[8,2,3]] : 3831 : 2 : 0 : False\n",
+      "[[8,2,3]] : 4091 : 2 : 0 : False\n",
+      "[[8,2,3]] : 4154 : 2 : 0 : False\n",
+      "[[8,2,3]] : 3354 : 1 : 0 : False\n",
+      "[[8,2,3]] : 3829 : 1 : 0 : False\n",
+      "[[8,2,3]] : 3952 : 1 : 0 : False\n",
+      "[[8,2,3]] : 3979 : 1 : 0 : False\n",
+      "[[8,2,3]] : 4337 : 1 : 0 : False\n",
+      "[[8,2,3]] : 4934 : 1 : 0 : False\n",
+      "[[8,2,3]] : 5834 : 1 : 0 : False\n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in sorted_codes_823:\n",
+    "    print(f\"[[8,2,3]] : {code['index']} : {code['aut_group_size']} : {code['is_degenerate']} : {is_planar(code)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1211,
+   "id": "f4820046-3395-44b3-aa04-63553009ea13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4947 & 1728 &  $\\langle Y_{0}Y_{1}Z_{4}Z_{7}, X_{2}X_{3}Z_{5}Z_{6}, Z_{2}Z_{3}Y_{5}Y_{6}, Z_{0}Z_{1}X_{4}X_{7},$ $Z_{2}Z_{3}Y_{5}Y_{6}, Z_{0}Z_{1}X_{4}X_{7}, Y_{0}Z_{1}Y_{2}Z_{3}Y_{4}Z_{6}, X_{0}Z_{1}X_{2}Z_{3}X_{5}Z_{7}\\rangle$ & $1 + 6x^{4} + 48x^{6} + 9x^{8}$\\\\ \n",
+      "4948 & 48 &  $\\langle Y_{0}Z_{1}Y_{2}Z_{7}, Y_{3}Y_{4}Z_{5}Z_{6}, X_{1}X_{2}X_{4}X_{5}, Z_{0}Y_{1}Z_{4}Y_{6},$ $X_{1}X_{2}X_{4}X_{5}, Z_{0}Y_{1}Z_{4}Y_{6}, Z_{2}Z_{3}Y_{5}Y_{7}, X_{0}Z_{1}Z_{2}X_{3}Z_{4}Z_{5}\\rangle$ & $1 + 6x^{4} + 48x^{6} + 9x^{8}$\\\\ \n",
+      "4519 & 24 &  $\\langle X_{1}X_{2}Z_{4}Z_{5}, Z_{1}Z_{2}X_{4}X_{5}, Y_{0}Y_{3}Z_{6}X_{7}, X_{0}X_{3}Z_{4}Z_{5}Z_{6},$ $Y_{0}Y_{3}Z_{6}X_{7}, X_{0}X_{3}Z_{4}Z_{5}Z_{6}, Y_{1}Z_{3}Y_{4}Z_{6}Z_{7}, Y_{0}Z_{2}Z_{3}X_{4}Y_{6}\\rangle$ & $1 + 4x^{4} + 12x^{5} + 24x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "3745 & 16 &  $\\langle X_{3}Z_{5}Z_{6}Z_{7}, Y_{0}Y_{1}Z_{2}X_{4}, X_{0}Z_{1}Z_{2}Z_{3}X_{5}, X_{2}Z_{3}Y_{4}Y_{5}Z_{6},$ $X_{0}Z_{1}Z_{2}Z_{3}X_{5}, X_{2}Z_{3}Y_{4}Y_{5}Z_{6}, X_{0}Z_{3}Z_{4}X_{6}Z_{7}, Z_{0}Z_{2}Z_{4}Y_{5}Y_{7}\\rangle$ & $1 + 2x^{4} + 16x^{5} + 24x^{6} + 16x^{7} + 5x^{8}$\\\\ \n",
+      "3744 & 12 &  $\\langle Y_{0}Y_{1}Z_{6}Z_{7}, Z_{2}X_{3}X_{4}Z_{5}, X_{0}Z_{1}Z_{2}Z_{3}X_{5}, Y_{2}Y_{3}Z_{4}X_{5}Z_{6},$ $X_{0}Z_{1}Z_{2}Z_{3}X_{5}, Y_{2}Y_{3}Z_{4}X_{5}Z_{6}, X_{0}Z_{3}Z_{4}X_{6}Z_{7}, Z_{0}Z_{2}Z_{4}Y_{5}Y_{7}\\rangle$ & $1 + 2x^{4} + 16x^{5} + 24x^{6} + 16x^{7} + 5x^{8}$\\\\ \n",
+      "3310 & 6 &  $\\langle X_{0}Z_{1}Z_{2}, X_{5}Z_{6}Z_{7}, X_{1}X_{2}X_{3}Z_{4}Z_{7}, Z_{0}X_{2}Y_{3}Y_{4}Z_{6},$ $X_{1}X_{2}X_{3}Z_{4}Z_{7}, Z_{0}X_{2}Y_{3}Y_{4}Z_{6}, Y_{1}X_{2}X_{4}Z_{5}Y_{6}, Z_{0}Y_{2}Z_{4}Z_{5}Y_{7}\\rangle$ & $1 + 2x^{3} + 12x^{5} + 28x^{6} + 18x^{7} + 3x^{8}$\\\\ \n",
+      "4525 & 6 &  $\\langle Y_{0}Z_{1}Y_{2}Z_{7}, Z_{0}X_{1}X_{3}Z_{4}, Y_{3}Y_{4}Z_{6}Z_{7}, Z_{0}X_{2}X_{5}Z_{6},$ $Y_{3}Y_{4}Z_{6}Z_{7}, Z_{0}X_{2}X_{5}Z_{6}, Z_{1}Z_{3}Z_{5}X_{6}, Z_{2}Z_{4}Z_{5}X_{7}\\rangle$ & $1 + 6x^{4} + 48x^{6} + 9x^{8}$\\\\ \n",
+      "5277 & 4 &  $\\langle X_{0}Z_{4}, X_{2}X_{3}Z_{5}Z_{6}, Z_{1}Z_{2}Z_{3}X_{7}, X_{1}Y_{2}Z_{4}Y_{5}Z_{6},$ $Z_{1}Z_{2}Z_{3}X_{7}, X_{1}Y_{2}Z_{4}Y_{5}Z_{6}, Y_{2}Z_{3}X_{5}Y_{6}Z_{7}, Z_{0}X_{4}Y_{5}Y_{6}X_{7}\\rangle$ & $1 + x^{2} + 2x^{4} + 12x^{5} + 25x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "4149 & 3 &  $\\langle X_{0}X_{1}Z_{4}Z_{7}, X_{0}X_{2}X_{3}Z_{4}, Z_{1}Z_{2}X_{4}X_{5}, Y_{1}Y_{3}X_{4}Z_{5}Z_{6},$ $Z_{1}Z_{2}X_{4}X_{5}, Y_{1}Y_{3}X_{4}Z_{5}Z_{6}, Z_{0}Z_{1}Y_{2}Z_{5}Y_{6}, Z_{0}Z_{3}Z_{4}Z_{5}X_{7}\\rangle$ & $1 + 4x^{4} + 12x^{5} + 24x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "3710 & 2 &  $\\langle Z_{1}Z_{2}X_{4}Z_{7}, Z_{0}Y_{2}Y_{5}X_{7}, Y_{0}Y_{2}X_{3}Z_{4}Z_{5}, Z_{0}Y_{1}X_{3}Y_{4}Z_{6},$ $Y_{0}Y_{2}X_{3}Z_{4}Z_{5}, Z_{0}Y_{1}X_{3}Y_{4}Z_{6}, X_{0}Z_{2}Y_{3}Y_{5}Z_{6}, Y_{1}Z_{4}Z_{5}Y_{6}Z_{7}\\rangle$ & $1 + 2x^{4} + 16x^{5} + 24x^{6} + 16x^{7} + 5x^{8}$\\\\ \n",
+      "3831 & 2 &  $\\langle Z_{1}Z_{2}X_{4}Z_{7}, Z_{0}Y_{2}Y_{5}X_{7}, Y_{0}Z_{2}Y_{3}Z_{5}Z_{7}, Z_{0}X_{1}Y_{2}Y_{3}Z_{6},$ $Y_{0}Z_{2}Y_{3}Z_{5}Z_{7}, Z_{0}X_{1}Y_{2}Y_{3}Z_{6}, X_{1}Z_{2}X_{3}Z_{4}X_{5}, X_{0}Z_{1}Z_{3}X_{6}Z_{7}\\rangle$ & $1 + 2x^{4} + 16x^{5} + 24x^{6} + 16x^{7} + 5x^{8}$\\\\ \n",
+      "4091 & 2 &  $\\langle Y_{0}Y_{1}X_{2}Z_{4}, X_{3}Z_{5}Z_{6}Z_{7}, X_{2}Z_{3}X_{5}Z_{6}, Z_{0}X_{1}Z_{2}X_{3}X_{4},$ $X_{2}Z_{3}X_{5}Z_{6}, Z_{0}X_{1}Z_{2}X_{3}X_{4}, X_{0}Z_{3}X_{4}Z_{5}X_{6}, X_{1}Z_{3}Y_{4}Z_{6}Y_{7}\\rangle$ & $1 + 4x^{4} + 12x^{5} + 24x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "4154 & 2 &  $\\langle Z_{2}X_{4}Z_{6}Z_{7}, Z_{1}Z_{3}Z_{4}X_{6}, Z_{0}Y_{2}X_{5}Y_{7}, Y_{0}X_{3}Y_{6}X_{7},$ $Z_{0}Y_{2}X_{5}Y_{7}, Y_{0}X_{3}Y_{6}X_{7}, X_{0}Z_{1}X_{3}Z_{5}Z_{6}, Y_{0}Y_{1}Y_{2}Y_{4}Z_{5}\\rangle$ & $1 + 4x^{4} + 12x^{5} + 24x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "3354 & 1 &  $\\langle Z_{1}Z_{2}X_{4}Z_{7}, Z_{0}X_{5}Z_{6}Z_{7}, X_{0}X_{2}Z_{4}Z_{5}Z_{7}, Z_{0}Y_{1}X_{3}Y_{4}Z_{6},$ $X_{0}X_{2}Z_{4}Z_{5}Z_{7}, Z_{0}Y_{1}X_{3}Y_{4}Z_{6}, Y_{1}X_{2}Z_{3}Z_{5}Y_{6}, X_{0}Y_{2}Z_{3}Z_{6}Y_{7}\\rangle$ & $1 + 2x^{4} + 16x^{5} + 24x^{6} + 16x^{7} + 5x^{8}$\\\\ \n",
+      "3829 & 1 &  $\\langle Y_{0}Y_{1}Z_{6}Z_{7}, Z_{2}Z_{3}X_{5}Z_{7}, Z_{1}Z_{3}Z_{4}X_{6}, X_{0}X_{2}Y_{3}Y_{6},$ $Z_{1}Z_{3}Z_{4}X_{6}, X_{0}X_{2}Y_{3}Y_{6}, Y_{2}Y_{4}Z_{5}Z_{6}Z_{7}, Z_{0}Z_{3}Z_{4}Z_{5}X_{7}\\rangle$ & $1 + 4x^{4} + 12x^{5} + 24x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "3952 & 1 &  $\\langle Z_{0}Z_{2}X_{3}Z_{7}, Y_{2}Z_{5}Z_{6}Y_{7}, X_{1}X_{2}Z_{3}Z_{6}Z_{7}, X_{0}Z_{1}Y_{2}Y_{4}Z_{5},$ $X_{1}X_{2}Z_{3}Z_{6}Z_{7}, X_{0}Z_{1}Y_{2}Y_{4}Z_{5}, Y_{0}X_{2}Z_{4}Y_{5}Z_{6}, Y_{1}Z_{4}Z_{5}Y_{6}Z_{7}\\rangle$ & $1 + 2x^{4} + 16x^{5} + 24x^{6} + 16x^{7} + 5x^{8}$\\\\ \n",
+      "3979 & 1 &  $\\langle X_{3}Z_{5}Z_{6}Z_{7}, Z_{2}Z_{3}Y_{4}Y_{5}, Z_{1}Y_{3}X_{4}Y_{6}, Z_{0}X_{4}X_{5}X_{7},$ $Z_{1}Y_{3}X_{4}Y_{6}, Z_{0}X_{4}X_{5}X_{7}, Z_{0}X_{1}X_{2}Z_{4}Z_{7}, X_{0}Z_{1}Y_{2}X_{3}Y_{4}\\rangle$ & $1 + 4x^{4} + 12x^{5} + 24x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "4337 & 1 &  $\\langle Z_{0}Z_{2}X_{3}Z_{7}, Z_{1}Z_{5}X_{6}Z_{7}, X_{3}X_{4}Y_{5}Y_{6}, X_{0}Y_{1}X_{4}Y_{7},$ $X_{3}X_{4}Y_{5}Y_{6}, X_{0}Y_{1}X_{4}Y_{7}, X_{1}X_{2}Z_{3}Z_{6}Z_{7}, X_{0}Z_{1}Y_{2}Y_{4}Z_{5}\\rangle$ & $1 + 4x^{4} + 12x^{5} + 24x^{6} + 20x^{7} + 3x^{8}$\\\\ \n",
+      "4934 & 1 &  $\\langle X_{0}Z_{6}Z_{7}, X_{1}X_{2}X_{3}Z_{7}, Y_{2}Y_{3}X_{4}Z_{5}Z_{6}, X_{0}Y_{1}Y_{2}Z_{4}X_{5},$ $Y_{2}Y_{3}X_{4}Z_{5}Z_{6}, X_{0}Y_{1}Y_{2}Z_{4}X_{5}, Y_{0}Z_{1}Y_{3}Z_{4}X_{6}, Z_{0}Z_{3}Z_{4}Z_{5}X_{7}\\rangle$ & $1 + x^{3} + x^{4} + 14x^{5} + 26x^{6} + 17x^{7} + 4x^{8}$\\\\ \n",
+      "5834 & 1 &  $\\langle X_{2}Z_{5}Z_{6}, X_{0}Y_{1}Z_{3}Y_{4}, Z_{0}X_{2}Z_{4}X_{7}, Y_{1}Y_{2}Y_{6}Y_{7},$ $Z_{0}X_{2}Z_{4}X_{7}, Y_{1}Y_{2}Y_{6}Y_{7}, X_{0}X_{1}X_{3}Z_{5}Z_{7}, X_{0}Y_{2}Y_{3}Z_{4}X_{5}\\rangle$ & $1 + x^{3} + 3x^{4} + 10x^{5} + 26x^{6} + 21x^{7} + 2x^{8}$\\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in sorted_codes_823:\n",
+    "    aut = code['aut_group_size']\n",
+    "    gen = '$\\\\langle ' + ', '.join(latex_it(code['isotropic_generators'][:4], dollar=False)) + ',$'\n",
+    "    gen += ' $' + ', '.join(latex_it(code['isotropic_generators'][-4:], dollar=False)) + '\\\\rangle$'\n",
+    "    w = make_poly(code['weight_enumerator'])\n",
+    "    index = code['index']\n",
+    "    print(f\"{index} & {aut} &  {gen} & ${w}$\\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3392a50-db6a-4d49-9026-a4b4d3af2421",
+   "metadata": {},
+   "source": [
+    "# n = 9 d=3 CSS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1282,
+   "id": "ed680cd5-617a-44b0-a639-8970a9b53ca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes_913 = cb.all_small_codes(9, 1, d=3, is_css=True,is_decomposable=False, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1283,
+   "id": "81011cce-72ab-4657-a6e0-5ee85650b2b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_codes = sorted(codes_913, key=lambda x: -x['aut_group_size'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1284,
+   "id": "abd7db60-3855-4e00-bf30-8b1abf7075c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19"
+      ]
+     },
+     "execution_count": 1284,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(sorted_codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1286,
+   "id": "6009fbd4-9f6c-4e07-a60a-8f17304797ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['S4S8', 'S3S8', 'S5S7', 'S2S7', 'S1S6', 'S0S6', '(1,6)', '(5,7)', '(4,8)', '(3,4)', '(2,3)(4,5)(7,8)', '(0,1)', '(0,2)(1,5)(6,7)']\n",
+      "aut_group_size       : 82944\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 8802\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z6', 'Z1Z6', 'Z2Z7', 'Z3Z8', 'Z4Z8', 'Z5Z7', 'X0X1X2X5X6X7', 'X0X1X3X4X6X8']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X3X4X8', 'Z0Z2Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : 120b6d9e-115c-40be-8c17-1d13ef32c190\n",
+      "weight_enumerator    : [1, 0, 9, 0, 27, 0, 75, 0, 144, 0]\n",
+      "\n",
+      "aut_group_generators : ['S0S7', 'V3V6', '(3,6)', 'V2V6', '(2,3)', 'S4S5', '(4,5)', 'S1S8', '(1,4)(5,8)', '(0,1)(7,8)']\n",
+      "aut_group_size       : 9216\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 4280\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z7', 'Z1Z8', 'X2X6', 'X3X6', 'Z4Z5', 'X0X4X5X7', 'X1X4X5X8', 'Z2Z3Z4Z6Z7Z8']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X4X5X6', 'Z2Z3Z6']\n",
+      "n                    : 9\n",
+      "uuid                 : c0cc3781-0de5-4a74-a039-7f509d1e02e4\n",
+      "weight_enumerator    : [1, 0, 6, 0, 24, 0, 90, 0, 135, 0]\n",
+      "\n",
+      "aut_group_generators : ['V1V7', 'V2V7', '(2,7)', 'S0S8', '(0,8)', 'V4V6', '(4,6)', 'V3V5', '(3,4)(5,6)', '(1,2)']\n",
+      "aut_group_size       : 3072\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 4079\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z8', 'X1X7', 'X2X7', 'X3X5', 'X4X6', 'Z3Z4Z5Z6', 'X0X5X6X8', 'Z1Z2Z3Z5Z7Z8']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X5X6X7', 'Z1Z2Z7']\n",
+      "n                    : 9\n",
+      "uuid                 : 60e6ec25-769d-4e53-8537-6df5a5d0f689\n",
+      "weight_enumerator    : [1, 0, 6, 0, 24, 0, 90, 0, 135, 0]\n",
+      "\n",
+      "aut_group_generators : ['S0S8', 'V3V4', '(3,4)', 'S2S6', '(2,6)', 'V1V7', '(1,7)', 'H0H1H2H3H4H5H6H7H8^(0,3,2,1)(4,6,7,8)']\n",
+      "aut_group_size       : 1024\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 8519\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z8', 'X1X7', 'Z2Z6', 'X3X4', 'Z3Z4Z5Z6', 'X2X5X6X7', 'Z1Z5Z7Z8', 'X0X3X5X8']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X4X5X7', 'Z1Z2Z7']\n",
+      "n                    : 9\n",
+      "uuid                 : 753d20ad-5cde-4541-8513-becbacb0f9e8\n",
+      "weight_enumerator    : [1, 0, 4, 0, 22, 0, 100, 0, 129, 0]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "planar_codes_913 = []\n",
+    "for code in sorted_codes:\n",
+    "    if is_planar(code) is True:\n",
+    "        print(code)\n",
+    "        planar_codes_913 += [code]\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1291,
+   "id": "05a1b924-ebb4-4dbd-9ea0-47bd269a40db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_codes_913[1]\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1281,
+   "id": "1f35d879-a079-43cd-88ad-b838def3abf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['S0S7', 'V3V6', '(3,6)', 'V2V6', '(2,3)', 'S4S5', '(4,5)', 'S1S8', '(1,4)(5,8)', '(0,1)(7,8)'],\n",
+       "aut_group_size       : 9216,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 3,\n",
+       "index                : 4280,\n",
+       "is_css               : 1,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 1,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['Z0Z7', 'Z1Z8', 'X2X6', 'X3X6', 'Z4Z5', 'X0X4X5X7', 'X1X4X5X8', 'Z2Z3Z4Z6Z7Z8'],\n",
+       "k                    : 1,\n",
+       "logical_ops          : ['X4X5X6', 'Z2Z3Z6'],\n",
+       "n                    : 9,\n",
+       "uuid                 : c0cc3781-0de5-4a74-a039-7f509d1e02e4,\n",
+       "weight_enumerator    : [1, 0, 6, 0, 24, 0, 90, 0, 135, 0],\n",
+       "}"
+      ]
+     },
+     "execution_count": 1281,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13a1456c-ffc5-4722-a4b9-11ff4d150e62",
+   "metadata": {},
+   "source": [
+    "# [[9,1,3]] Codes Table - Table 11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "363e73b9-6aef-4b39-9c65-8633901a7a47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8802 & 82944 & $1 + 9x^{2} + 27x^{4} + 75x^{6} + 144x^{8}$ & $\\langle Z_{0}Z_{6}, Z_{1}Z_{6}, Z_{2}Z_{7}, Z_{3}Z_{8},$ $Z_{4}Z_{8}, Z_{5}Z_{7}, X_{0}X_{1}X_{2}X_{5}X_{6}X_{7}, X_{0}X_{1}X_{3}X_{4}X_{6}X_{8}\\rangle$ \\\\ \n",
+      "4280 & 9216 & $1 + 6x^{2} + 24x^{4} + 90x^{6} + 135x^{8}$ & $\\langle Z_{0}Z_{7}, Z_{1}Z_{8}, X_{2}X_{6}, X_{3}X_{6},$ $Z_{4}Z_{5}, X_{0}X_{4}X_{5}X_{7}, X_{1}X_{4}X_{5}X_{8}, Z_{2}Z_{3}Z_{4}Z_{6}Z_{7}Z_{8}\\rangle$ \\\\ \n",
+      "4079 & 3072 & $1 + 6x^{2} + 24x^{4} + 90x^{6} + 135x^{8}$ & $\\langle Z_{0}Z_{8}, X_{1}X_{7}, X_{2}X_{7}, X_{3}X_{5},$ $X_{4}X_{6}, Z_{3}Z_{4}Z_{5}Z_{6}, X_{0}X_{5}X_{6}X_{8}, Z_{1}Z_{2}Z_{3}Z_{5}Z_{7}Z_{8}\\rangle$ \\\\ \n",
+      "4395 & 1152 & $1 + 3x^{2} + 21x^{4} + 105x^{6} + 126x^{8}$ & $\\langle Z_{0}Z_{8}, Z_{1}Z_{8}, Z_{2}Z_{3}Z_{6}Z_{7}, Z_{2}Z_{4}Z_{7}Z_{8},$ $Z_{5}Z_{6}Z_{7}Z_{8}, X_{2}X_{4}X_{5}X_{6}, X_{3}X_{4}X_{5}X_{7}, X_{0}X_{1}X_{2}X_{3}X_{5}X_{8}\\rangle$ \\\\ \n",
+      "8519 & 1024 & $1 + 4x^{2} + 22x^{4} + 100x^{6} + 129x^{8}$ & $\\langle Z_{0}Z_{8}, X_{1}X_{7}, Z_{2}Z_{6}, X_{3}X_{4},$ $Z_{3}Z_{4}Z_{5}Z_{6}, X_{2}X_{5}X_{6}X_{7}, Z_{1}Z_{5}Z_{7}Z_{8}, X_{0}X_{3}X_{5}X_{8}\\rangle$ \\\\ \n",
+      "4335 & 576 & $1 + 3x^{2} + 4x^{3} + 9x^{4} + 24x^{5} + 49x^{6} + 84x^{7} + 66x^{8} + 16x^{9}$ & $\\langle Z_{0}Z_{8}, Z_{1}Z_{8}, Z_{2}Z_{3}Z_{4}, Z_{3}Z_{5}Z_{6},$ $Z_{4}Z_{5}Z_{7}, X_{3}X_{4}X_{6}X_{7}, X_{2}X_{3}X_{5}X_{7}, X_{0}X_{1}X_{2}X_{3}X_{6}X_{8}\\rangle$ \\\\ \n",
+      "7419 & 384 & $1 + 3x^{2} + 21x^{4} + 105x^{6} + 126x^{8}$ & $\\langle Z_{0}Z_{8}, Z_{1}Z_{6}, Z_{2}Z_{7}, Z_{3}Z_{4}Z_{6}Z_{8},$ $Z_{3}Z_{5}Z_{7}Z_{8}, X_{1}X_{3}X_{5}X_{6}, X_{2}X_{3}X_{4}X_{7}, X_{0}X_{4}X_{5}X_{8}\\rangle$ \\\\ \n",
+      "8816 & 384 & $1 + 3x^{2} + x^{3} + 15x^{4} + 27x^{5} + 37x^{6} + 87x^{7} + 72x^{8} + 13x^{9}$ & $\\langle Z_{0}Z_{8}, Z_{1}Z_{6}, Z_{2}Z_{7}, X_{3}X_{4}X_{5},$ $Z_{3}Z_{4}Z_{6}Z_{8}, Z_{3}Z_{5}Z_{7}Z_{8}, Y_{1}Y_{2}X_{3}Y_{6}Y_{7}, Y_{0}Y_{1}X_{5}Y_{6}Y_{8}\\rangle$ \\\\ \n",
+      "9709 & 384 & $1 + 18x^{4} + 16x^{5} + 56x^{6} + 96x^{7} + 53x^{8} + 16x^{9}$ & $\\langle Y_{0}Y_{1}Y_{4}Y_{5}, Y_{0}Y_{2}Y_{4}Y_{6}, Y_{0}Y_{3}Y_{4}Y_{7}, X_{0}X_{1}X_{4}X_{5},$ $X_{0}X_{2}X_{4}X_{6}, X_{0}X_{3}X_{4}X_{7}, X_{0}X_{1}X_{2}X_{3}X_{8}, Z_{1}Z_{2}Z_{3}Z_{4}Z_{8}\\rangle$ \\\\ \n",
+      "5477 & 256 & $1 + 2x^{2} + 20x^{4} + 16x^{5} + 46x^{6} + 96x^{7} + 59x^{8} + 16x^{9}$ & $\\langle Z_{0}Z_{7}, Z_{1}Z_{8}, Z_{2}Z_{3}Z_{5}Z_{8}, Z_{2}Z_{4}Z_{5}Z_{7},$ $X_{3}X_{4}X_{5}X_{6}, Z_{5}Z_{6}Z_{7}Z_{8}, X_{0}X_{2}X_{3}X_{6}X_{7}, X_{1}X_{2}X_{4}X_{6}X_{8}\\rangle$ \\\\ \n",
+      "11001 & 192 & $1 + x^{2} + 2x^{3} + 13x^{4} + 24x^{5} + 47x^{6} + 90x^{7} + 66x^{8} + 12x^{9}$ & $\\langle Z_{0}Z_{1}, X_{0}X_{1}X_{4}, Z_{2}Z_{3}Z_{5}Z_{7}, X_{2}X_{4}X_{5}X_{6},$ $Z_{5}Z_{6}Z_{7}Z_{8}, X_{3}X_{4}X_{6}X_{7}, X_{2}X_{3}X_{6}X_{8}, Z_{1}Z_{2}Z_{4}Z_{7}Z_{8}\\rangle$ \\\\ \n",
+      "12079 & 192 & $1 + x^{2} + 19x^{4} + 16x^{5} + 51x^{6} + 96x^{7} + 56x^{8} + 16x^{9}$ & $\\langle Z_{0}Z_{8}, Z_{1}Z_{2}Z_{5}Z_{6}, Z_{1}Z_{3}Z_{4}Z_{6}, X_{1}X_{2}X_{4}X_{7},$ $X_{1}X_{3}X_{5}X_{7}, X_{2}X_{3}X_{6}X_{7}, X_{0}X_{3}X_{4}X_{8}, Z_{4}Z_{5}Z_{6}Z_{7}Z_{8}\\rangle$ \\\\ \n",
+      "9897 & 144 & $1 + 18x^{4} + 120x^{6} + 117x^{8}$ & $\\langle Z_{0}Z_{1}Z_{4}Z_{8}, Z_{0}Z_{2}Z_{5}Z_{7}, Z_{1}Z_{3}Z_{5}Z_{7}, X_{1}X_{2}X_{4}X_{5},$ $Z_{0}Z_{4}Z_{5}Z_{6}, X_{1}X_{3}X_{4}X_{6}, X_{0}X_{3}X_{4}X_{7}, X_{0}X_{3}X_{5}X_{8}\\rangle$ \\\\ \n",
+      "5781 & 128 & $1 + 2x^{2} + 12x^{4} + 32x^{5} + 46x^{6} + 80x^{7} + 67x^{8} + 16x^{9}$ & $\\langle Z_{0}Z_{8}, X_{1}X_{5}, Y_{2}Y_{3}Y_{6}Y_{7}, Z_{2}Z_{3}Z_{4}Z_{8},$ $X_{3}X_{4}X_{5}X_{6}, X_{2}X_{4}X_{5}X_{7}, Y_{1}Y_{2}Y_{4}Z_{5}Y_{7}, Y_{0}Y_{4}Y_{6}Y_{7}X_{8}\\rangle$ \\\\ \n",
+      "5784 & 128 & $1 + 2x^{2} + 20x^{4} + 110x^{6} + 123x^{8}$ & $\\langle Z_{0}Z_{8}, X_{1}X_{5}, Z_{2}Z_{3}Z_{6}Z_{8}, Z_{2}Z_{4}Z_{7}Z_{8},$ $Z_{1}Z_{5}Z_{6}Z_{7}, X_{2}X_{4}X_{5}X_{6}, X_{2}X_{3}X_{5}X_{7}, X_{0}X_{3}X_{4}X_{8}\\rangle$ \\\\ \n",
+      "12003 & 96 & $1 + x^{2} + 3x^{3} + 9x^{4} + 25x^{5} + 55x^{6} + 85x^{7} + 62x^{8} + 15x^{9}$ & $\\langle X_{0}X_{5}, Z_{1}Z_{4}Z_{6}, Z_{2}Z_{4}Z_{7}, Z_{3}Z_{4}Z_{8},$ $Y_{1}Y_{2}Y_{6}Y_{7}, Y_{1}Y_{3}Y_{6}Y_{8}, Z_{0}Z_{5}Z_{6}Z_{7}Z_{8}, X_{2}X_{3}X_{4}X_{5}X_{6}\\rangle$ \\\\ \n",
+      "6038 & 64 & $1 + 2x^{2} + 2x^{3} + 12x^{4} + 26x^{5} + 46x^{6} + 86x^{7} + 67x^{8} + 14x^{9}$ & $\\langle Z_{0}Z_{8}, X_{1}X_{2}, X_{3}X_{5}X_{7}, X_{4}X_{6}X_{7},$ $Z_{1}Z_{2}Z_{3}Z_{5}, Z_{1}Z_{2}Z_{4}Z_{6}, Z_{3}Z_{6}Z_{7}Z_{8}, X_{0}X_{2}X_{3}X_{4}X_{8}\\rangle$ \\\\ \n",
+      "8124 & 12 & $1 + 2x^{3} + 12x^{4} + 24x^{5} + 52x^{6} + 90x^{7} + 63x^{8} + 12x^{9}$ & $\\langle X_{2}X_{4}X_{5}, X_{1}X_{3}X_{6}, Z_{1}Z_{2}Z_{3}Z_{4}, Z_{2}Z_{3}Z_{5}Z_{6},$ $X_{0}X_{2}X_{3}X_{7}, Z_{0}Z_{4}Z_{5}Z_{7}, X_{0}X_{1}X_{4}X_{8}, Z_{0}Z_{2}Z_{5}Z_{8}\\rangle$ \\\\ \n",
+      "7810 & 8 & $1 + 2x^{3} + 12x^{4} + 24x^{5} + 52x^{6} + 90x^{7} + 63x^{8} + 12x^{9}$ & $\\langle Z_{1}Z_{2}Z_{4}, X_{1}X_{2}X_{7}, Y_{0}Y_{3}Y_{6}Y_{8}, X_{0}X_{3}X_{5}X_{7},$ $X_{2}X_{3}X_{4}X_{6}, Z_{0}Z_{4}Z_{5}Z_{6}, Z_{1}Z_{3}Z_{6}Z_{7}, X_{0}X_{2}X_{4}X_{8}\\rangle$ \\\\ \n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in sorted_codes:\n",
+    "    aut = code['aut_group_size']\n",
+    "    gen = '$\\\\langle ' + ', '.join(latex_it(code['isotropic_generators'][:4], dollar=False)) + ',$'\n",
+    "    gen += ' $' + ', '.join(latex_it(code['isotropic_generators'][-4:], dollar=False)) + '\\\\rangle$'\n",
+    "    w = make_poly(code['weight_enumerator'])\n",
+    "    index = code['index']\n",
+    "    print(f\"{index} & {aut} & ${w}$ & {gen} \\\\\\ \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "774f054d-caaa-4c52-b37b-967b63446946",
+   "metadata": {},
+   "source": [
+    "## [[9,2,3]] Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1287,
+   "id": "53c733fc-914d-432e-8652-2bd7c66a042e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes_923 = cb.all_small_codes(9, 2, d=3, is_decomposable=False, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1288,
+   "id": "808ff82b-eb19-4026-99da-e9a55c8519ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_codes_923 = sorted(codes_923, key=lambda x: -x['aut_group_size'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1309,
+   "id": "4e04793e-144d-486f-9a80-23436688018c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4425"
+      ]
+     },
+     "execution_count": 1309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(sorted_codes_923)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1308,
+   "id": "1046358b-ab2c-4beb-83cc-8f11bf59b2eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V1S6', 'H1H6^(1,6)', 'V2S7', 'H2H7^(2,7)', 'V3V4', '(3,4)', 'V0S8', 'H4H8^(0,3)(1,2)(4,8)(6,7)']\n",
+      "aut_group_size       : 512\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 53565\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z8', 'X1Z6', 'X2Z7', 'X3X4', 'Z0Y3Z4Z5Y8', 'Z2Z3Z4X5Z6X7', 'Z0Z1X5X6Z7X8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z2Z6X7', 'Z0Z3Z4X8', 'Z5Z6Z7', 'Z3Z4Z6Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : f8f2d94a-8701-4e60-a42c-e88b9a37a2a1\n",
+      "weight_enumerator    : [1, 0, 4, 0, 6, 4, 20, 56, 33, 4]\n",
+      "\n",
+      "aut_group_generators : ['V0S5', 'H0H5^(0,5)', 'V2S7', 'H2H7^(2,7)', 'V1S6', 'H1H6^(1,6)']\n",
+      "aut_group_size       : 64\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 3357\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z5', 'X1Z6', 'X2Z7', 'Z1Y3Y6Z8', 'Z3Z4Z5X8', 'Z2X3Y4Z6Y7', 'Z0Z2Z4X5X7Z8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z2Z4X7', 'Z1Z3X6', 'Z4Z5Z7', 'Z3Z4Z6']\n",
+      "n                    : 9\n",
+      "uuid                 : 1c30ea16-50ec-40cf-aa4b-9e987c693470\n",
+      "weight_enumerator    : [1, 0, 3, 0, 7, 4, 21, 56, 32, 4]\n",
+      "\n",
+      "aut_group_generators : ['V1S5', 'H1H5^(1,5)', 'V0S4', 'V2H3S6H7S8^(0,1)(3,7)(4,5)']\n",
+      "aut_group_size       : 32\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 22335\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z4', 'X1Z5', 'X3Z7Z8', 'Z3Z6X7', 'Z0Z2X3X4X6', 'Z1Y2X5Y6Z7', 'Z1Z2Z3Z4Y5Y8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z2X6Z7', 'Z2Z3Z4Z5X8', 'Z2Z4Z6', 'Z2Z5Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : 26bb0c81-2951-4b86-9b14-93a88c78703a\n",
+      "weight_enumerator    : [1, 0, 2, 2, 2, 8, 26, 50, 33, 4]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "count = 0\n",
+    "planar_923 = []\n",
+    "for code in sorted_codes_923:\n",
+    "    if is_planar(code) is True:\n",
+    "        print(code)\n",
+    "        planar_923 += [code]\n",
+    "        count += 1\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1307,
+   "id": "5ed52f35-8263-4032-aebc-721bc31bb8f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "code = planar_923[2]\n",
+    "G = nx.Graph()\n",
+    "G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1298,
+   "id": "68c3f540-8a50-4362-98d6-3e15b48003e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['V0S5', 'H0H5^(0,5)', 'V2S7', 'H2H7^(2,7)', 'V1S6', 'H1H6^(1,6)'],\n",
+       "aut_group_size       : 64,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 3,\n",
+       "index                : 3357,\n",
+       "is_css               : 0,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 1,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['X0Z5', 'X1Z6', 'X2Z7', 'Z1Y3Y6Z8', 'Z3Z4Z5X8', 'Z2X3Y4Z6Y7', 'Z0Z2Z4X5X7Z8'],\n",
+       "k                    : 2,\n",
+       "logical_ops          : ['Z2Z4X7', 'Z1Z3X6', 'Z4Z5Z7', 'Z3Z4Z6'],\n",
+       "n                    : 9,\n",
+       "uuid                 : 1c30ea16-50ec-40cf-aa4b-9e987c693470,\n",
+       "weight_enumerator    : [1, 0, 3, 0, 7, 4, 21, 56, 32, 4],\n",
+       "}"
+      ]
+     },
+     "execution_count": 1298,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1314,
+   "id": "a67507db-726b-4acb-abcf-aa5dd6308281",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V2S7', 'H2H7^(2,7)', 'V1S7', '(1,2)', 'V0S8', 'H0H8^(0,8)', 'R3r5R6^(4,6,5)', '(3,4)(5,6)']\n",
+      "aut_group_size       : 1152\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 15551\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z8', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'Z0Z4X5Z6X8', 'Z1Z2Y3Y5Z6X7Z8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z1Z2X7', 'Z0Z3Z4X8', 'Z3Z4Z7', 'Z3Z4Z5Z6Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : 3f463c93-83a8-462b-9e90-1cf4cdea9df7\n",
+      "weight_enumerator    : [1, 0, 4, 0, 6, 8, 12, 56, 41, 0]\n",
+      "\n",
+      "aut_group_generators : ['V2H3S6H7S8^(3,7)', 'V0H1V2H3S4H5S6H7^(1,5)(3,7)', 'R2r6R7^(3,7,6)', '(2,3)(6,7)', 'H0H1V2H3V4V5S6H7^(3,7)(4,5)', '(0,1)(4,5)', '(0,2)(1,3)(4,6)(5,7)']\n",
+      "aut_group_size       : 1152\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 80585\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0X1Z4Z5', 'X2X3Z6Z7', 'Y0Y4Z5Z8', 'X0Z1X5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Y0Z1Y2Z3Z4Z6X8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z3Z6X7', 'Z0Z1Z2Z3X8', 'Z0Z1Z2Z3Z4Z5Z6Z7', 'Z0Z1Z4Z5Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : b5eea235-55bc-4185-bf91-7cd7476d0d1e\n",
+      "weight_enumerator    : [1, 0, 0, 0, 14, 0, 16, 64, 33, 0]\n",
+      "\n",
+      "aut_group_generators : ['S2V3H4S5H6^(4,6)', 'V1S8', 'H1H8^(1,8)', 'V0S7', '(0,1)(7,8)', 'R3r5R6^(4,6,5)', '(3,4)(5,6)']\n",
+      "aut_group_size       : 768\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 22646\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z8', 'X3X4Z5Z6', 'Z2Y3Y5Z6', 'Z2X3Z4X6', 'Z0Z1Y7Y8', 'Z0Y2Y3Z4Z5X7Z8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z4Z5X6', 'Z1Z7X8', 'Z2Z3Z4Z5Z6', 'Z2Z7Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : ba7bd6ab-59ac-4617-91f6-f9fd52939745\n",
+      "weight_enumerator    : [1, 0, 2, 0, 12, 0, 14, 64, 35, 0]\n",
+      "\n",
+      "aut_group_generators : ['V1S6', 'H1H6^(1,6)', 'V2S7', 'H2H7^(2,7)', 'V3V4', '(3,4)', 'V0S8', 'H4H8^(0,3)(1,2)(4,8)(6,7)']\n",
+      "aut_group_size       : 512\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 53565\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z8', 'X1Z6', 'X2Z7', 'X3X4', 'Z0Y3Z4Z5Y8', 'Z2Z3Z4X5Z6X7', 'Z0Z1X5X6Z7X8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z2Z6X7', 'Z0Z3Z4X8', 'Z5Z6Z7', 'Z3Z4Z6Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : f8f2d94a-8701-4e60-a42c-e88b9a37a2a1\n",
+      "weight_enumerator    : [1, 0, 4, 0, 6, 4, 20, 56, 33, 4]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "for code in sorted_codes_923:\n",
+    "    if code['aut_group_size'] >= 512:\n",
+    "        print(code)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1316,
+   "id": "a007e8e9-971b-4594-8300-f524c7bb84f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for code in sorted_codes_923:\n",
+    "    if code['is_css'] == 1:\n",
+    "        print(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1317,
+   "id": "e185c2ce-cb92-4b05-8b02-e7ca2d4989fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4425"
+      ]
+     },
+     "execution_count": 1317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(sorted_codes_923)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f240941-3507-45f5-a264-b35ccdfeadb0",
+   "metadata": {},
+   "source": [
+    "# d = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "b230e619-4b5c-4020-a8c9-44142b0cafee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (2, 0, 1, 0))\n",
+    "add_attribute(node_attributes, (2, 0, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (2, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (2, 1, 1, 0), (2, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (2, 1, 1, 0), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (2, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (2, 1, 1, 1), (2, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (2, 1, 1, 1), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (2, 0, 2, 1))\n",
+    "add_attribute(node_attributes, (2, 0, 2, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (2, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (2, 1, 1, 0), (2, 0, 2, 1) )\n",
+    "add_attribute(node_attributes, (2, 1, 1, 0), 'is_decomposable', 0)\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x + 0.05, y+0.1, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=2')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c41a341-de5f-41b1-9348-58745b075851",
+   "metadata": {},
+   "source": [
+    "# n = 2 indecomposable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "680039ee-34ef-4906-8ccd-187a5b753ee6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (2, 0, 2, 1))\n",
+    "add_attribute(node_attributes, (2, 0, 2, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (2, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (2, 1, 1, 0), (2, 0, 2, 1) )\n",
+    "add_attribute(node_attributes, (2, 1, 1, 0), 'is_decomposable', 0)\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=8, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.0001, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=2 indecomposable')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c1e8b78-054a-4a91-aa64-1ec488b81b50",
+   "metadata": {},
+   "source": [
+    "# n=3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "2c11b222-bfc4-4458-821a-0a24cf12a9f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (3, 1, 1, 0))\n",
+    "add_attribute(node_attributes, (3, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 0), (3, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 2), (3, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 1, 1, 1))\n",
+    "add_attribute(node_attributes, (3, 1, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 0), (3, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 1), (3, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 2), (3, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 1, 1, 2))\n",
+    "add_attribute(node_attributes, (3, 1, 1, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 0), (3, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 0), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 1, 1, 3))\n",
+    "add_attribute(node_attributes, (3, 1, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 0), (3, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 0), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 1, 1, 4))\n",
+    "add_attribute(node_attributes, (3, 1, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 0), (3, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 1), (3, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 1), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 0, 1, 0))\n",
+    "add_attribute(node_attributes, (3, 0, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 0), (3, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 1), (3, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 1, 1, 2))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 2), (3, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 0, 2, 1))\n",
+    "add_attribute(node_attributes, (3, 0, 2, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 1, 1, 2))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 2), (3, 0, 2, 1) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 1, 1, 4))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 4), (3, 0, 2, 1) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 4), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 0, 1, 2))\n",
+    "add_attribute(node_attributes, (3, 0, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 1), (3, 0, 1, 2) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 1, 1, 3))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 3), (3, 0, 1, 2) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (3, 1, 1, 4))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 4), (3, 0, 1, 2) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 4), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=8, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.1, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=3')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc1147d8-73e4-4e67-ba26-4de6d694b4b1",
+   "metadata": {},
+   "source": [
+    "# n=3 indecomposable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "e96c6030-ec8c-4558-8d97-dcaf0fd90bd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (3, 0, 2, 1))\n",
+    "add_attribute(node_attributes, (3, 0, 2, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 1, 1, 2))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 2), (3, 0, 2, 1) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 1, 1, 4))\n",
+    "add_edge_to_graph(G, (3, 1, 1, 4), (3, 0, 2, 1) )\n",
+    "add_attribute(node_attributes, (3, 1, 1, 4), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (3, 1, 1, 4))\n",
+    "add_attribute(node_attributes, (3, 1, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (3, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (3, 2, 1, 1), (3, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (3, 2, 1, 1), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=8, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.1, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=3 indecomposable')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724164da-d389-45ac-acd2-2e751319df34",
+   "metadata": {},
+   "source": [
+    "# n=4 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "2702bdf2-3682-4837-bde0-d9f13c306e33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 0))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 4), (4, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 6))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 6), (4, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 1))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 4), (4, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 5), (4, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 5), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 7), (4, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 10), (4, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 10), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 2))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 0), (4, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 1), (4, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 2), (4, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 3))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 2), (4, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 10), (4, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 10), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 4))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 0), (4, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 1), (4, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 3), (4, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 4), (4, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 5), (4, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 5), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 5))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 1), (4, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 2), (4, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 5), (4, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 5), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 4), (4, 1, 2, 6) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 2, 6) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_edge_to_graph(G, (4, 2, 2, 9), (4, 1, 2, 6) )\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 7))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 2), (4, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 4), (4, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 4), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 2, 8))\n",
+    "add_attribute(node_attributes, (4, 1, 2, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 7), (4, 1, 2, 8) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_edge_to_graph(G, (4, 2, 2, 9), (4, 1, 2, 8) )\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 9))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 9), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 1), (4, 1, 1, 9) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 3), (4, 1, 1, 9) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 7), (4, 1, 1, 9) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 1, 9) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 10), (4, 1, 1, 9) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 10), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 10))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 10), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 1), (4, 1, 1, 10) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 6))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 6), (4, 1, 1, 10) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 10), (4, 1, 1, 10) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 10), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 11))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 5), (4, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 5), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 7), (4, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 10), (4, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 10), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 12))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 12), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 2), (4, 1, 1, 12) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 1, 12) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 0))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 3), (4, 2, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 3), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 1))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 1), (4, 2, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 3), (4, 2, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 3), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 2))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 2) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 3))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 1), (4, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 3), (4, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 3), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 4))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 5))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 5), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 1), (4, 2, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 1), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 6))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 6) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 1), (4, 2, 1, 7) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 1, 7) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 8) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 1, 8) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 2, 9) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 10))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 10), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 0), (4, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 1), (4, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 1), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 1, 0))\n",
+    "add_attribute(node_attributes, (4, 0, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 0), (4, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 1), (4, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 4))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 4), (4, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 9))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 9), (4, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 9), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 10))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 10), (4, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 10), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 1, 1))\n",
+    "add_attribute(node_attributes, (4, 0, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 2), (4, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 4))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 4), (4, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 5), (4, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 7), (4, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 7), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 2, 2))\n",
+    "add_attribute(node_attributes, (4, 0, 2, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 1), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 6), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 8))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 8), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 11))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 11), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 11), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 2, 3))\n",
+    "add_attribute(node_attributes, (4, 0, 2, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 6), (4, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 7), (4, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 12))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 12), (4, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 12), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 2, 4))\n",
+    "add_attribute(node_attributes, (4, 0, 2, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 0), (4, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 6), (4, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 1, 5))\n",
+    "add_attribute(node_attributes, (4, 0, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 3))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 3), (4, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 5), (4, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 9))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 9), (4, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 9), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (4, 1, 1, 11))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 11), (4, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 12))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 12), (4, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 12), 'is_decomposable', 0)\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x - 0.07, y + 0.02, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=4')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb6322e-7a70-4b82-8165-618eaf4fbfb6",
+   "metadata": {},
+   "source": [
+    "# Indecomposable n=4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 875,
+   "id": "98aaa198-aa48-48ad-87a7-cfd2afda021e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 2, 2))\n",
+    "add_attribute(node_attributes, (4, 0, 2, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 1), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 6), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 8))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 8), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 11))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 11), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 11), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 2, 3))\n",
+    "add_attribute(node_attributes, (4, 0, 2, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 6), (4, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 7), (4, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 1, 12))\n",
+    "add_edge_to_graph(G, (4, 1, 1, 12), (4, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (4, 1, 1, 12), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 1))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 1), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 5), (4, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 5), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 7), (4, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 2, 6) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_edge_to_graph(G, (4, 2, 2, 9), (4, 1, 2, 6) )\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 2, 8))\n",
+    "add_attribute(node_attributes, (4, 1, 2, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 7), (4, 1, 2, 8) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_edge_to_graph(G, (4, 2, 2, 9), (4, 1, 2, 8) )\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 11))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 5), (4, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 5), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 7), (4, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 1, 12))\n",
+    "add_attribute(node_attributes, (4, 1, 1, 12), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (4, 2, 1, 8), (4, 1, 1, 12) )\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 7))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 1, 7) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 1, 8))\n",
+    "add_attribute(node_attributes, (4, 2, 1, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 1, 8) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (4, 3, 1, 2), (4, 2, 2, 9) )\n",
+    "add_attribute(node_attributes, (4, 3, 1, 2), 'is_decomposable', 0)\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.05, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('Indecomposable, n=4')\n",
+    "plt.axis('off')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 876,
+   "id": "10af1ddc-0c38-4021-8f6c-0e27b278b8f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.savefig('pccode4ind.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "44acb5a3-0332-4c33-8b19-f097afbd9343",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 2, 2))\n",
+    "add_attribute(node_attributes, (4, 0, 2, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 6), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 8))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 8), (4, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 8), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 0, 2, 3))\n",
+    "add_attribute(node_attributes, (4, 0, 2, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_edge_to_graph(G, (4, 1, 2, 6), (4, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 2, 6))\n",
+    "add_attribute(node_attributes, (4, 1, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_edge_to_graph(G, (4, 2, 2, 9), (4, 1, 2, 6) )\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (4, 1, 2, 8))\n",
+    "add_attribute(node_attributes, (4, 1, 2, 8), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (4, 2, 2, 9))\n",
+    "add_edge_to_graph(G, (4, 2, 2, 9), (4, 1, 2, 8) )\n",
+    "add_attribute(node_attributes, (4, 2, 2, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.05, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('Indecompdosable, n=4, d=2')\n",
+    "plt.axis('off')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8890bb0c-6283-4117-b7c6-a69e1ec5a761",
+   "metadata": {},
+   "source": [
+    "# n=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "9cefb2fa-3f37-4909-a6bd-a6d16a8992cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 1, 0))\n",
+    "add_attribute(node_attributes, (5, 0, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 3), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 4), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 5), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 6), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 7), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 22), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 22), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 23), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 24), (5, 0, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 24), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 1, 1))\n",
+    "add_attribute(node_attributes, (5, 0, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 0), (5, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 1), (5, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 2), (5, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 3), (5, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 8), (5, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 10), (5, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 10), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 11), (5, 0, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 11), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 2))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 6), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 9), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 11), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 14), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 20), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 29))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 29), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 29), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 33), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 33), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 3))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 5), (5, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 9), (5, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 10), (5, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 10), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 14), (5, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 30))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 30), (5, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 30), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 4))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 7), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 14), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 18), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 20), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 28), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 28), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 31))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 31), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 32))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 32), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 35), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 1, 5))\n",
+    "add_attribute(node_attributes, (5, 0, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 4), (5, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 8), (5, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 12), (5, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 16), (5, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 16), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 17), (5, 0, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 17), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 6))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 9), (5, 0, 2, 6) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 17), (5, 0, 2, 6) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 17), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 34), (5, 0, 2, 6) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 34), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 3, 7))\n",
+    "add_attribute(node_attributes, (5, 0, 3, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 18), (5, 0, 3, 7) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 3, 21))\n",
+    "add_edge_to_graph(G, (5, 1, 3, 21), (5, 0, 3, 7) )\n",
+    "add_attribute(node_attributes, (5, 1, 3, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 27))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 27), (5, 0, 3, 7) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 27), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 1, 8))\n",
+    "add_attribute(node_attributes, (5, 0, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 1), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 13), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 22), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 22), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 2, 25))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 25), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 26), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 26), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 27))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 27), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 28), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 28), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 31))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 31), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 33), (5, 0, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 33), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 1, 9))\n",
+    "add_attribute(node_attributes, (5, 0, 1, 9), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 15), (5, 0, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 15), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 16), (5, 0, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 16), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 23), (5, 0, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 2, 25))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 25), (5, 0, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 29))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 29), (5, 0, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 29), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 34), (5, 0, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 34), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 35), (5, 0, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 0, 1, 10), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 0), (5, 0, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 19), (5, 0, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 19), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 2, 25))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 25), (5, 0, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 30))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 30), (5, 0, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 30), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 1, 1, 32))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 32), (5, 0, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 32), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 0))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 0), (5, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 8), (5, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 18), (5, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 18), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 30))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 30), (5, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 30), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 31))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 31), (5, 1, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 31), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 1))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 1), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 6), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 10), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 18), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 18), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 28), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 28), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 29))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 29), (5, 1, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 29), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 2))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 0), (5, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 1), (5, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 11), (5, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 28), (5, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 28), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 31))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 31), (5, 1, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 31), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 3))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 11), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 17), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 17), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 28), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 28), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 29))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 29), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 29), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 30))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 30), (5, 1, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 30), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 4))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 6), (5, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 11), (5, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 12), (5, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 5))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 7), (5, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 7), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 6))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 34), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 34), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 7))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 33), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 33), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 8))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 5), (5, 1, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 6), (5, 1, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 11), (5, 1, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 17), (5, 1, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 17), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 18), (5, 1, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 18), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 10), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 0), (5, 1, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 11))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 1), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 12))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 5), (5, 1, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 11), (5, 1, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 12), (5, 1, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 14), (5, 1, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 14), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 13))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 1), (5, 1, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 13), (5, 1, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 25))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 25), (5, 1, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 33), (5, 1, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 33), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 15))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 15), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 13), (5, 1, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 14), (5, 1, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 14), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 34), (5, 1, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 34), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 16))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 16), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 12), (5, 1, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 14), (5, 1, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 14), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 18), (5, 1, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 18), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 17))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 17), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 14), (5, 1, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 14), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 23), (5, 1, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 19))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 19), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 0), (5, 1, 1, 19) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 13), (5, 1, 1, 19) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 3, 21))\n",
+    "add_attribute(node_attributes, (5, 1, 3, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 3, 21) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 22))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 22), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 6), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 28), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 28), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 29))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 29), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 29), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 30))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 30), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 30), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 33), (5, 1, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 33), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 23))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 12), (5, 1, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 30))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 30), (5, 1, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 30), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 34), (5, 1, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 34), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 24))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 24), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 7), (5, 1, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 7), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 12), (5, 1, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 28), (5, 1, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 28), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 33), (5, 1, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 33), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 34), (5, 1, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 34), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 25))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 13), (5, 1, 2, 25) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 18), (5, 1, 2, 25) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 18), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 30))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 30), (5, 1, 2, 25) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 30), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 2, 32))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 32), (5, 1, 2, 25) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 32), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 2, 25) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 2, 25) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 2, 25) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 26))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 26), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 25))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 25), (5, 1, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 29))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 29), (5, 1, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 29), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 2, 32))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 32), (5, 1, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 32), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 27))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 10), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 25))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 25), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 28))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 28), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 33), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 33), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 29))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 29), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 34), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 34), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 30))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 30), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 0), (5, 1, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 8), (5, 1, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 31))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 13), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 32))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 2), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 13), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 33))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 33), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 1), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 34))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 34), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 14), (5, 1, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 14), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 35))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 13), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 0))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 11), (5, 2, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 1))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 2))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 5))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 0), (5, 2, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 6))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 3), (5, 2, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 7))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 7), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 8))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 11), (5, 2, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 11))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 0), (5, 2, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 3), (5, 2, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 12))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 13))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 16), (5, 2, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 16), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 14))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 14), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 14) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 14) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 17))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 17), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 0), (5, 2, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 3), (5, 2, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 6), (5, 2, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 18))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 18), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 6), (5, 2, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 19) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 19) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 19) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 20) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 20) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 23))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 23), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 23) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 24) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 25))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 25), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 25) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 16), (5, 2, 1, 25) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 16), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 2, 27) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 2, 27) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 2, 27) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 28))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 28), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 3), (5, 2, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 29))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 29), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 3), (5, 2, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 6), (5, 2, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 30))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 30), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 6), (5, 2, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 30) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 31))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 31), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 1), (5, 2, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 11), (5, 2, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 2, 32))\n",
+    "add_attribute(node_attributes, (5, 2, 2, 32), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 6), (5, 2, 2, 32) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 16), (5, 2, 2, 32) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 16), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 2, 32) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 33))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 33), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 5), (5, 2, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 17), (5, 2, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 34))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 34), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 2), (5, 2, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 16), (5, 2, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 16), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 12), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 4), (5, 2, 1, 37) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 37) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 37) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 16), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 16), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 8), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 13), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 0))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 4), (5, 3, 1, 0) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 4), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 1))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 4), (5, 3, 1, 1) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 4), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 2))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 2) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 3))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 3), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 4), (5, 3, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 4), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 4))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 4), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 5))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 5), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 5) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 6))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 6), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 4), (5, 3, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 4), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 8))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 8), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 8) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 11))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 11), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 12))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 12), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 12) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 13))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 13), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 13) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 14) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 14) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 16))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 16), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 17))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 17), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 0))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 0), (5, 3, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 0), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 1))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 1), (5, 3, 1, 17) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 1), 'is_decomposable', 1)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 2))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 2), (5, 3, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 2), 'is_decomposable', 1)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=5')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8578e892-1ec6-47a8-aea5-892827bc5a25",
+   "metadata": {},
+   "source": [
+    "# n=5 decomposable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "39e2b68d-a9f1-415e-b2f9-1915ab67f3b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 2))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 6))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 6), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 9), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 11))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 11), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 14), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 20), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 29))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 29), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 29), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 33))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 33), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 33), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 4))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 7), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 14), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 18), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 20), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 28))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 28), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 28), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 31))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 31), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 32))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 32), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 35), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 6))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 9), (5, 0, 2, 6) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 17))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 17), (5, 0, 2, 6) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 17), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 34))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 34), (5, 0, 2, 6) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 34), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 3, 7))\n",
+    "add_attribute(node_attributes, (5, 0, 3, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 18), (5, 0, 3, 7) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 3, 21))\n",
+    "add_edge_to_graph(G, (5, 1, 3, 21), (5, 0, 3, 7) )\n",
+    "add_attribute(node_attributes, (5, 1, 3, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 1, 27))\n",
+    "add_edge_to_graph(G, (5, 1, 1, 27), (5, 0, 3, 7) )\n",
+    "add_attribute(node_attributes, (5, 1, 1, 27), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 6))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 7))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 11))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 3, 21))\n",
+    "add_attribute(node_attributes, (5, 1, 3, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 3, 21) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 27))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 10), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 28))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 28), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 29))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 29), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 31))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 32))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 33))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 33), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 34))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 34), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 35))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 19) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 20) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 2, 26) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 2, 27) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 2, 27) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 2, 27) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 37) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 37) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 14) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=5 decomposable')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d2ee4a5-3406-420e-b6ea-5b467d83616d",
+   "metadata": {},
+   "source": [
+    "# n=5 indecomposable d=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "a9839606-0661-4a84-9886-69421db70f9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 6))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 6) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 7))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 7) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 11))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 11) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 27))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 27), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 10), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 27) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 28))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 28), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 3), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 16), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 28) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 29))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 29), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 24))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 24), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 24), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 29) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 31))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 22), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 35), (5, 1, 1, 31) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 32))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 9), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 39), (5, 1, 1, 32) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 33))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 33), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 4), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 21), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 36), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 33) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 34))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 34), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 20), (5, 1, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 34) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 1, 35))\n",
+    "add_attribute(node_attributes, (5, 1, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 15), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 19), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 37), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_edge_to_graph(G, (5, 2, 1, 38), (5, 1, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 3))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 3) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 4))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 4) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 9))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 9) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 15))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 15) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 16))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 16), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 16) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 19))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 19), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 19) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 20))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 20) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 21))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 21), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 21) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 22))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 22), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 9))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 9), (5, 2, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 22) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 35))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 35), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 35) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 36))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 15))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 15), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 36) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 37))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 37) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 37) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 38))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 7))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 7), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 38) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 2, 1, 39))\n",
+    "add_attribute(node_attributes, (5, 2, 1, 39), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 10), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 14), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_edge_to_graph(G, (5, 3, 1, 18), (5, 2, 1, 39) )\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 10))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 10) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 14))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 14) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "add_node_to_graph(G, (5, 3, 1, 18))\n",
+    "add_attribute(node_attributes, (5, 3, 1, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 4, 1, 3))\n",
+    "add_edge_to_graph(G, (5, 4, 1, 3), (5, 3, 1, 18) )\n",
+    "add_attribute(node_attributes, (5, 4, 1, 3), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=5 indecomposable d=1')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da032c38-5881-4cff-bc50-f351a8834930",
+   "metadata": {},
+   "source": [
+    "# n=5 indecomposable d=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "f21e4101-423f-4c30-981f-3a390361f59b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 2))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 2), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 9), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 14), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 20), (5, 0, 2, 2) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 4))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 4), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 14), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 18), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 20), (5, 0, 2, 4) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 2, 6))\n",
+    "add_attribute(node_attributes, (5, 0, 2, 6), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (5, 1, 2, 9), (5, 0, 2, 6) )\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 9))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 14))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 14), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 14) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 18))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (5, 1, 2, 20))\n",
+    "add_attribute(node_attributes, (5, 1, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 26))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 26), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 26), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 2, 2, 27))\n",
+    "add_edge_to_graph(G, (5, 2, 2, 27), (5, 1, 2, 20) )\n",
+    "add_attribute(node_attributes, (5, 2, 2, 27), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.05, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=5 indecomposable d=2')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "69aee641-84e2-443e-ae8a-7893daadd5c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (5, 0, 3, 7))\n",
+    "add_attribute(node_attributes, (5, 0, 3, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (5, 1, 3, 21))\n",
+    "add_edge_to_graph(G, (5, 1, 3, 21), (5, 0, 3, 7) )\n",
+    "add_attribute(node_attributes, (5, 1, 3, 21), 'is_decomposable', 0)\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.0001, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=5 indecomposable d=3')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b247276d-0489-4b19-89e0-ccb4e3dc0ee4",
+   "metadata": {},
+   "source": [
+    "# n=6 indecomposable d=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "bcb96935-1032-4409-9be5-b90d6ba76005",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'{idd[3]}'\n",
+    "    \n",
+    "add_node_to_graph(G, (6, 0, 2, 3))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 3), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 9), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 10))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 10), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 17))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 17), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 17), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 18))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 18), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 47))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 47), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 47), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 61))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 61), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 61), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 66))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 66), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 66), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 71))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 71), (6, 0, 2, 3) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 71), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 5))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 5), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 10))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 10), (6, 0, 2, 5) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 11))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 11), (6, 0, 2, 5) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 30))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 30), (6, 0, 2, 5) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 30), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 31))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 31), (6, 0, 2, 5) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 32))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 32), (6, 0, 2, 5) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 44))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 44), (6, 0, 2, 5) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 44), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 70))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 70), (6, 0, 2, 5) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 70), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 7))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 7), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 10))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 10), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 11))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 11), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 43))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 43), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 43), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 45))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 45), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 45), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 47))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 47), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 47), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 60))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 60), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 60), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 65))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 65), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 65), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 66))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 66), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 66), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 70))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 70), (6, 0, 2, 7) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 70), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 9))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 9))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 9), (6, 0, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 30))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 30), (6, 0, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 30), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 32))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 32), (6, 0, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 45))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 45), (6, 0, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 45), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 50))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 50), (6, 0, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 50), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 11))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 18))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 18), (6, 0, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 44))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 44), (6, 0, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 44), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 55))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 55), (6, 0, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 55), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 59))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 59), (6, 0, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 59), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 71))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 71), (6, 0, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 71), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 12))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 12), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 17))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 17), (6, 0, 2, 12) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 17), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 43))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 43), (6, 0, 2, 12) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 43), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 55))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 55), (6, 0, 2, 12) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 55), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 63))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 63), (6, 0, 2, 12) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 63), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 69))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 69), (6, 0, 2, 12) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 69), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 13))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 13), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 31))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 31), (6, 0, 2, 13) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 43))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 43), (6, 0, 2, 13) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 43), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 56))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 56), (6, 0, 2, 13) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 56), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 15))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 15), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 50))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 50), (6, 0, 2, 15) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 50), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 56))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 56), (6, 0, 2, 15) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 56), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 0, 2, 20))\n",
+    "add_attribute(node_attributes, (6, 0, 2, 20), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 37))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 37), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 52))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 52), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 60))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 60), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 60), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 65))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 65), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 65), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 67))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 67), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 67), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 69))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 69), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 69), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 70))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 70), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 70), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 1, 2, 71))\n",
+    "add_edge_to_graph(G, (6, 1, 2, 71), (6, 0, 2, 20) )\n",
+    "add_attribute(node_attributes, (6, 1, 2, 71), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 9))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 9), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 82), (6, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 82), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 93))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 93), (6, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 93), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 121))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 121), (6, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 121), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 122))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 122), (6, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 122), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 123))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 123), (6, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 123), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 128))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 128), (6, 1, 2, 9) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 128), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 10))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 10), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 10) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 128))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 128), (6, 1, 2, 10) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 128), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 130))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 130), (6, 1, 2, 10) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 130), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 133), (6, 1, 2, 10) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 11))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 11), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 56))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 56), (6, 1, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 56), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 99))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 99), (6, 1, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 99), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 121))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 121), (6, 1, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 121), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 122))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 122), (6, 1, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 122), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 123))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 123), (6, 1, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 123), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 130))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 130), (6, 1, 2, 11) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 130), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 17))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 17), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 52))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 52), (6, 1, 2, 17) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 82), (6, 1, 2, 17) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 82), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 111))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 111), (6, 1, 2, 17) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 111), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 129))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 129), (6, 1, 2, 17) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 129), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 131))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 131), (6, 1, 2, 17) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 131), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 18))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 18), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 120))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 120), (6, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 120), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 128))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 128), (6, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 128), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 130))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 130), (6, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 130), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 132))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 132), (6, 1, 2, 18) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 132), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 30))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 30), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 30) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 128))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 128), (6, 1, 2, 30) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 128), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 31))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 31), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 38))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 38), (6, 1, 2, 31) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 56))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 56), (6, 1, 2, 31) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 56), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 131))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 131), (6, 1, 2, 31) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 131), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 32))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 32), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 38))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 38), (6, 1, 2, 32) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 32) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 121))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 121), (6, 1, 2, 32) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 121), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 122))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 122), (6, 1, 2, 32) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 122), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 37))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 75), (6, 1, 2, 37) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 99))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 99), (6, 1, 2, 37) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 99), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 117))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 117), (6, 1, 2, 37) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 117), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 127))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 127), (6, 1, 2, 37) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 127), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 43))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 43), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 52))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 52), (6, 1, 2, 43) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 56))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 56), (6, 1, 2, 43) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 56), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 131))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 131), (6, 1, 2, 43) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 131), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 134))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 134), (6, 1, 2, 43) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 134), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 44))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 44), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 56))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 56), (6, 1, 2, 44) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 56), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 75), (6, 1, 2, 44) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 121))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 121), (6, 1, 2, 44) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 121), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 128))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 128), (6, 1, 2, 44) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 128), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 45))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 45), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 52))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 52), (6, 1, 2, 45) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 45) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 93))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 93), (6, 1, 2, 45) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 93), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 47))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 47), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 52))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 52), (6, 1, 2, 47) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 47) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 85), (6, 1, 2, 47) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 128))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 128), (6, 1, 2, 47) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 128), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 50))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 50), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 38))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 38), (6, 1, 2, 50) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 52))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 52), (6, 1, 2, 50) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 82), (6, 1, 2, 50) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 82), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 52))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 117))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 117), (6, 1, 2, 52) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 117), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 120))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 120), (6, 1, 2, 52) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 120), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 133), (6, 1, 2, 52) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 55))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 55), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 56))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 56), (6, 1, 2, 55) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 56), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 126))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 126), (6, 1, 2, 55) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 126), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 129))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 129), (6, 1, 2, 55) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 129), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 56))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 56), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 52))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 52), (6, 1, 2, 56) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 52), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 59))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 59), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 75), (6, 1, 2, 59) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 120))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 120), (6, 1, 2, 59) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 120), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 126))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 126), (6, 1, 2, 59) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 126), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 60))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 60), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 75), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 85), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 93))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 93), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 93), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 99))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 99), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 99), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 108))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 108), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 108), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 117))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 117), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 117), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 133), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 134))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 134), (6, 1, 2, 60) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 134), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 61))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 61), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 85), (6, 1, 2, 61) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 93))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 93), (6, 1, 2, 61) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 93), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 111))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 111), (6, 1, 2, 61) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 111), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 120))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 120), (6, 1, 2, 61) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 120), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 133), (6, 1, 2, 61) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 63))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 63), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 111))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 111), (6, 1, 2, 63) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 111), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 126))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 126), (6, 1, 2, 63) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 126), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 134))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 134), (6, 1, 2, 63) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 134), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 65))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 65), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 65) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 117))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 117), (6, 1, 2, 65) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 117), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 130))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 130), (6, 1, 2, 65) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 130), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 133), (6, 1, 2, 65) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 66))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 66), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 75), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 108))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 108), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 108), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 122))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 122), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 122), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 123))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 123), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 123), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 130))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 130), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 130), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 131))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 131), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 131), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 133), (6, 1, 2, 66) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 67))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 67), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 93))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 93), (6, 1, 2, 67) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 93), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 99))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 99), (6, 1, 2, 67) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 99), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 120))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 120), (6, 1, 2, 67) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 120), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 122))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 122), (6, 1, 2, 67) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 122), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 129))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 129), (6, 1, 2, 67) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 129), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 132))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 132), (6, 1, 2, 67) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 132), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 69))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 69), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 127))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 127), (6, 1, 2, 69) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 127), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 129))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 129), (6, 1, 2, 69) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 129), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 131))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 131), (6, 1, 2, 69) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 131), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 134))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 134), (6, 1, 2, 69) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 134), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 70))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 70), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 75), (6, 1, 2, 70) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 77), (6, 1, 2, 70) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 99))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 99), (6, 1, 2, 70) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 99), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 121))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 121), (6, 1, 2, 70) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 121), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 122))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 122), (6, 1, 2, 70) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 122), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 131))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 131), (6, 1, 2, 70) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 131), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 133), (6, 1, 2, 70) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 1, 2, 71))\n",
+    "add_attribute(node_attributes, (6, 1, 2, 71), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 75), (6, 1, 2, 71) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 85), (6, 1, 2, 71) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 108))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 108), (6, 1, 2, 71) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 108), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 121))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 121), (6, 1, 2, 71) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 121), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 129))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 129), (6, 1, 2, 71) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 129), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 130))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 130), (6, 1, 2, 71) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 130), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 2, 2, 132))\n",
+    "add_edge_to_graph(G, (6, 2, 2, 132), (6, 1, 2, 71) )\n",
+    "add_attribute(node_attributes, (6, 2, 2, 132), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 38))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 38), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 38) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 52))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 52), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 73))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 73), (6, 2, 2, 52) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 73), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 56))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 56), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 56) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 56) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 75))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 75), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 75) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 85), (6, 2, 2, 75) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 75) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 77))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 77), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 77) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 82))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 82), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 73))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 73), (6, 2, 2, 82) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 73), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 82) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 85))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 73))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 73), (6, 2, 2, 85) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 73), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 85) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 93))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 93), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 73))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 73), (6, 2, 2, 93) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 73), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 93) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 99))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 99), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 99) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 85), (6, 2, 2, 99) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 99) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 108))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 108), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 108) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 85), (6, 2, 2, 108) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 108) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 111))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 111), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 73))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 73), (6, 2, 2, 111) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 73), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 117))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 117), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 117) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 120))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 120), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 120) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 121))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 121), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 121) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 85), (6, 2, 2, 121) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 85), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 122))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 122), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 122) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 85), (6, 2, 2, 122) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 122) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 123))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 123), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 123) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 85))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 85), (6, 2, 2, 123) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 85), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 126))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 126), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 126) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 127))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 127), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 127) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 128))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 128), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 128) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 129))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 129), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 73))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 73), (6, 2, 2, 129) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 73), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 129) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 129) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 130))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 130), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 130) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 131))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 131), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 131) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 82), (6, 2, 2, 131) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 132))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 132), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 132) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 133))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 133), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 86))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 86), (6, 2, 2, 133) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 86), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 2, 2, 134))\n",
+    "add_attribute(node_attributes, (6, 2, 2, 134), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 73))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 73), (6, 2, 2, 134) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 73), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_edge_to_graph(G, (6, 3, 2, 76), (6, 2, 2, 134) )\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 3, 2, 76))\n",
+    "add_attribute(node_attributes, (6, 3, 2, 76), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 4, 2, 29))\n",
+    "add_edge_to_graph(G, (6, 4, 2, 29), (6, 3, 2, 76) )\n",
+    "add_attribute(node_attributes, (6, 4, 2, 29), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 3, 2, 82))\n",
+    "add_attribute(node_attributes, (6, 3, 2, 82), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 4, 2, 29))\n",
+    "add_edge_to_graph(G, (6, 4, 2, 29), (6, 3, 2, 82) )\n",
+    "add_attribute(node_attributes, (6, 4, 2, 29), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (6, 3, 2, 85))\n",
+    "add_attribute(node_attributes, (6, 3, 2, 85), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (6, 4, 2, 29))\n",
+    "add_edge_to_graph(G, (6, 4, 2, 29), (6, 3, 2, 85) )\n",
+    "add_attribute(node_attributes, (6, 4, 2, 29), 'is_decomposable', 0)\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.0001, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=6 indecomposable d=2')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4011aed-09b6-4c8c-bc5e-af0787ac05f9",
+   "metadata": {},
+   "source": [
+    "# n=7 indecomposable d=3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "13ed8922-2b1b-4595-8a2c-56ae6c7c6f92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "node_attributes = {}\n",
+    "\n",
+    "def label_fmt(idd):\n",
+    "    return f'[[{idd[0]},{idd[1]},{idd[2]}]]:{idd[3]}'\n",
+    "\n",
+    "add_node_to_graph(G, (7, 0, 3, 34))\n",
+    "add_attribute(node_attributes, (7, 0, 3, 34), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 209))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 209), (7, 0, 3, 34) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 209), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 221))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 221), (7, 0, 3, 34) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 221), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (7, 0, 3, 36))\n",
+    "add_attribute(node_attributes, (7, 0, 3, 36), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 209))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 209), (7, 0, 3, 36) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 209), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 226))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 226), (7, 0, 3, 36) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 226), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (7, 0, 3, 37))\n",
+    "add_attribute(node_attributes, (7, 0, 3, 37), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 185))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 185), (7, 0, 3, 37) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 185), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 200))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 200), (7, 0, 3, 37) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 200), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 227))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 227), (7, 0, 3, 37) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 227), 'is_decomposable', 0)\n",
+    "\n",
+    "add_node_to_graph(G, (7, 0, 3, 39))\n",
+    "add_attribute(node_attributes, (7, 0, 3, 39), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 185))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 185), (7, 0, 3, 39) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 185), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 221))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 221), (7, 0, 3, 39) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 221), 'is_decomposable', 0)\n",
+    "add_node_to_graph(G, (7, 1, 3, 228))\n",
+    "add_edge_to_graph(G, (7, 1, 3, 228), (7, 0, 3, 39) )\n",
+    "add_attribute(node_attributes, (7, 1, 3, 228), 'is_decomposable', 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Define node positions using multipartite_layout\n",
+    "pos = nx.multipartite_layout(G, subset_key='layer', align='horizontal')\n",
+    "\n",
+    "# Draw nodes and edges\n",
+    "nx.draw(G, pos, with_labels=False, node_color='lightblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=True)\n",
+    "\n",
+    "# Add node labels next to the nodes without box borders\n",
+    "node_labels = nx.get_node_attributes(G, 'label')\n",
+    "for node, label in node_labels.items():\n",
+    "    x, y = pos[node]\n",
+    "    plt.text(x+0.0001, y, label, verticalalignment='center', horizontalalignment='left', fontsize=8, bbox=dict(facecolor='none', edgecolor='none'))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.title('n=7 indecomposable d=3')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "3c3ed5d6-1db6-433a-bdfd-5827855cc09c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[8,1]]-894 of type StabSubSystemCode\n",
+      "-------------------------------------------------------------------------------\n",
+      "aut_group_generators : ['V2S7', 'V1S7', 'V0S7', 'H2H7^(2,7)', 'V3H4S5H6S7^(4,6)', '(1,2)', '(0,1)', 'H3H4V5V6S7^(5,6)', '(3,4)(5,6)']\n",
+      "aut_group_size       : 4608\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 894\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'X3Z4X6Z7', 'Z0Z1Z2Y3Z4Z5Y7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z2Z3Z4X7', 'Z3Z4Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 9d566873-fcd3-49df-8569-dd377e45776b\n",
+      "weight_enumerator    : [1, 0, 6, 0, 20, 0, 34, 64, 3]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "code = cb.small_code(8, 1, 894, info_only=True)\n",
+    "code.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 679,
+   "id": "d5ae1729-bf28-4d41-82e4-c53c096ae644",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_string(input_str):\n",
+    "    tuples_list = []\n",
+    "    i = 0\n",
+    "    while i < len(input_str):\n",
+    "        letter = input_str[i]\n",
+    "        i += 1\n",
+    "        num_str = \"\"\n",
+    "        while i < len(input_str) and input_str[i].isdigit():\n",
+    "            num_str += input_str[i]\n",
+    "            i += 1\n",
+    "        if num_str:\n",
+    "            tuples_list.append((letter, int(num_str)))\n",
+    "        else:\n",
+    "            raise ValueError(\"Invalid input format\")\n",
+    "    return tuples_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 680,
+   "id": "9efcd89d-e152-489e-9cbf-b5969dd041b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_tanner(gen, n):\n",
+    "    G = nx.Graph()\n",
+    "    nodes = range(n+len(gen))\n",
+    "    G.add_nodes_from(nodes)\n",
+    "    offset = n\n",
+    "    for stab in gen:\n",
+    "        p_string = parse_string(stab)\n",
+    "        connections = [item[1] for item in p_string]\n",
+    "        edges = [(offset, connection) for connection in connections]\n",
+    "        G.add_edges_from(edges)\n",
+    "        offset += 1\n",
+    "    return G\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcd98b06-22f8-4b7f-be5e-6210c05158df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = make_tanner(code['isotropic_generators'], 8)\n",
+    "\n",
+    "nx.draw(G, with_labels=True, node_color='skyblue', node_size=1000, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "beef4c2a-9d52-43e0-8100-f56febabc828",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nx.is_planar(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 676,
+   "id": "f98fcba3-cfc3-43f5-accc-b99cd7f8e116",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_planar(code):\n",
+    "    G = make_tanner(code['isotropic_generators'], code['n'])\n",
+    "    return nx.is_planar(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 677,
+   "id": "72043503-aac3-4837-a1b3-8175fc48e4b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_stabilizer_graph(gen, n):\n",
+    "    G = nx.Graph()\n",
+    "    nodes = range(n)\n",
+    "    G.add_nodes_from(nodes)\n",
+    "    for stab in gen:\n",
+    "        p_string = parse_string(stab)\n",
+    "        connections = [item[1] for item in p_string]\n",
+    "        edges = [(connections[i],connections[(i+1)%len(connections)]) for i in range(len(connections))]\n",
+    "        G.add_edges_from(edges)\n",
+    "    return G"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "8a9bfd02-c5d9-4c2e-8bf5-5da5ddc8c3eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['V2S6', 'H2H6^(2,6)', 'V1S6', '(1,2)', 'V0S7', 'H0H7^(0,7)', 'V3V4', '(3,4)'],\n",
+       "aut_group_size       : 384,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 3,\n",
+       "index                : 564,\n",
+       "is_css               : 0,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 1,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z6', 'X3X4', 'Z3Z4X5', 'Z0X3Y5Y7', 'Z1Z2X3Z5X6Z7'],\n",
+       "k                    : 1,\n",
+       "logical_ops          : ['Z0Z3Z4X7', 'Z3Z4Z6Z7'],\n",
+       "n                    : 8,\n",
+       "uuid                 : b3d314a1-647e-47c0-9e8a-a3c835b99073,\n",
+       "weight_enumerator    : [1, 0, 5, 2, 11, 12, 39, 50, 8],\n",
+       "}"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "2093aad1-797a-4e02-b43f-0909fffca73e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(4, 1, d=2, is_decomposable=False, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "ae442958-3905-43f2-ad97-300b7e464494",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['V2V3', '(2,3)', 'V0V1', '(0,1)', '(0,2)(1,3)'],\n",
+       " aut_group_size       : 32,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 6,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X1', 'X2X3', 'Z0Z1Z2Z3'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X1X3', 'Z2Z3'],\n",
+       " n                    : 4,\n",
+       " uuid                 : c49160c8-795e-4558-9978-08b491cdd091,\n",
+       " weight_enumerator    : [1, 0, 2, 0, 5],\n",
+       " },\n",
+       " {aut_group_generators : ['V0H1S2H3^(1,3)', 'H0S1S2V3^(1,2)', '(0,1)(2,3)'],\n",
+       " aut_group_size       : 24,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 8,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0X2Z3', 'Y0X1Y2', 'Z1Z2X3'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X1Z3', 'Z0Z1'],\n",
+       " n                    : 4,\n",
+       " uuid                 : 51fc14fb-8309-4ff6-a51e-8801d0066f87,\n",
+       " weight_enumerator    : [1, 0, 0, 4, 3],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8407d6e-1461-4382-b952-d0ef4852a360",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = make_stabilizer_graph(codes[1]['isotropic_generators'],codes[1]['n'])\n",
+    "nx.draw(G, with_labels=True, node_color='skyblue', node_size=1000, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n",
+    "\n",
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 695,
+   "id": "2877ad84-15d9-4bf2-beda-efafee912f0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(4, 0, d=2, is_decomposable=False, info_only=True, list_only=True) \\\n",
+    "    + cb.all_small_codes(4, 1, d=2, is_decomposable=False, info_only=True, list_only=True) \\\n",
+    "    + cb.all_small_codes(4, 2, d=2, is_decomposable=False, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 696,
+   "id": "1bbc878f-83ed-4ccd-a2a5-ad8b4f8fa6bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['S1S3', '(1,3)', 'V0V2', 'H0H1H2H3^(0,1)(2,3)'],\n",
+       " aut_group_size       : 32,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 2,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X2', 'Z1Z3', 'Z0Z2Z3', 'X1X2X3'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 4,\n",
+       " uuid                 : db5c4282-0f38-4540-951d-42382b281621,\n",
+       " weight_enumerator    : [1, 0, 2, 8, 5],\n",
+       " },\n",
+       " {aut_group_generators : ['S2S3', '(2,3)', 'S1S3', '(1,2)', 'S0S3', '(0,1)'],\n",
+       " aut_group_size       : 192,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 3,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z3', 'Z1Z3', 'Z2Z3', 'X0X1X2X3'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 4,\n",
+       " uuid                 : 30ab98d9-54a2-4c10-8646-16b074ec7960,\n",
+       " weight_enumerator    : [1, 0, 6, 0, 9],\n",
+       " },\n",
+       " {aut_group_generators : ['V2V3', '(2,3)', 'V0V1', '(0,1)', '(0,2)(1,3)'],\n",
+       " aut_group_size       : 32,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 6,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X1', 'X2X3', 'Z0Z1Z2Z3'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X1X3', 'Z2Z3'],\n",
+       " n                    : 4,\n",
+       " uuid                 : c49160c8-795e-4558-9978-08b491cdd091,\n",
+       " weight_enumerator    : [1, 0, 2, 0, 5],\n",
+       " },\n",
+       " {aut_group_generators : ['V0H1S2H3^(1,3)', 'H0S1S2V3^(1,2)', '(0,1)(2,3)'],\n",
+       " aut_group_size       : 24,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 8,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0X2Z3', 'Y0X1Y2', 'Z1Z2X3'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X1Z3', 'Z0Z1'],\n",
+       " n                    : 4,\n",
+       " uuid                 : 51fc14fb-8309-4ff6-a51e-8801d0066f87,\n",
+       " weight_enumerator    : [1, 0, 0, 4, 3],\n",
+       " },\n",
+       " {aut_group_generators : ['(2,3)', '(1,2)', 'S0S1S2S3', '(0,1)', 'H0H1H2H3'],\n",
+       " aut_group_size       : 144,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 9,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3'],\n",
+       " k                    : 2,\n",
+       " logical_ops          : ['X1X2', 'X1X3', 'Z0Z2', 'Z0Z3'],\n",
+       " n                    : 4,\n",
+       " uuid                 : 373b856e-a5af-4e9f-8524-997f1ccfe77e,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 3],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 696,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "155db87e-f57c-40a1-97bd-09aaa6980897",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['S1S3', '(1,3)', 'V0V2', 'H0H1H2H3^(0,1)(2,3)'],\n",
+       "aut_group_size       : 32,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 2,\n",
+       "index                : 2,\n",
+       "is_css               : 1,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 0,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['X0X2', 'Z1Z3', 'Z0Z2Z3', 'X1X2X3'],\n",
+       "k                    : 0,\n",
+       "logical_ops          : [],\n",
+       "n                    : 4,\n",
+       "uuid                 : db5c4282-0f38-4540-951d-42382b281621,\n",
+       "weight_enumerator    : [1, 0, 2, 8, 5],\n",
+       "}"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 697,
+   "id": "2ed055da-44b0-42ec-ae7f-e2bd444486c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = make_stabilizer_graph(codes[0]['isotropic_generators'],codes[0]['n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 698,
+   "id": "ead464d3-5187-4166-bcac-43aca8706c85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b32a51ed-e08f-4513-8f71-30f68d52d653",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "H = make_tanner(codes[0]['isotropic_generators'],codes[0]['n'])\n",
+    "\n",
+    "pos = nx.planar_layout(H)\n",
+    "nx.draw(H, with_labels=True, pos=pos, node_color='skyblue', \n",
+    "        node_size=50, font_size=12, font_color='black', \n",
+    "        font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "8fecd1e1-b62c-4f15-a1c2-241990f6c7ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['S2S3', '(2,3)', 'S1S3', '(1,2)', 'S0S3', '(0,1)'],\n",
+       "aut_group_size       : 192,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 2,\n",
+       "index                : 3,\n",
+       "is_css               : 1,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 0,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['Z0Z3', 'Z1Z3', 'Z2Z3', 'X0X1X2X3'],\n",
+       "k                    : 0,\n",
+       "logical_ops          : [],\n",
+       "n                    : 4,\n",
+       "uuid                 : 30ab98d9-54a2-4c10-8646-16b074ec7960,\n",
+       "weight_enumerator    : [1, 0, 6, 0, 9],\n",
+       "}"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 699,
+   "id": "c3cf89a4-b6cb-436c-87cd-36e13fd2d4f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "H = make_tanner(codes[1]['isotropic_generators'],codes[1]['n'])\n",
+    "\n",
+    "pos = nx.planar_layout(H)\n",
+    "nx.draw(H, with_labels=True, pos=pos, node_color='skyblue', \n",
+    "        node_size=50, font_size=12, font_color='black', \n",
+    "        font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "cbe0a867-bdb1-4667-a86d-e6666e91ace4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = make_stabilizer_graph(codes[1]['isotropic_generators'],codes[1]['n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "c12d8708-9e30-424f-a215-5ff19cc38a91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "8f41f711-78b6-4646-a8f3-95acd1117e8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['V2V3', '(2,3)', 'V0V1', '(0,1)', '(0,2)(1,3)'],\n",
+       "aut_group_size       : 32,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 2,\n",
+       "index                : 6,\n",
+       "is_css               : 1,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 0,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['X0X1', 'X2X3', 'Z0Z1Z2Z3'],\n",
+       "k                    : 1,\n",
+       "logical_ops          : ['X1X3', 'Z2Z3'],\n",
+       "n                    : 4,\n",
+       "uuid                 : c49160c8-795e-4558-9978-08b491cdd091,\n",
+       "weight_enumerator    : [1, 0, 2, 0, 5],\n",
+       "}"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "57b892c7-06dd-4290-a11b-3f2de30e1c8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = make_stabilizer_graph(codes[2]['isotropic_generators'],codes[2]['n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "e620badb-eff0-4faf-8ffb-8f83b7414577",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "c2970a9a-40c6-4a71-8043-2b6efb3c0ee2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['V0H1S2H3^(1,3)', 'H0S1S2V3^(1,2)', '(0,1)(2,3)'],\n",
+       "aut_group_size       : 24,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 2,\n",
+       "index                : 8,\n",
+       "is_css               : 0,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 0,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['Z0X2Z3', 'Y0X1Y2', 'Z1Z2X3'],\n",
+       "k                    : 1,\n",
+       "logical_ops          : ['X1Z3', 'Z0Z1'],\n",
+       "n                    : 4,\n",
+       "uuid                 : 51fc14fb-8309-4ff6-a51e-8801d0066f87,\n",
+       "weight_enumerator    : [1, 0, 0, 4, 3],\n",
+       "}"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 694,
+   "id": "b1e371e4-597b-4d36-81c5-1a73335fabb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[694], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m G \u001b[38;5;241m=\u001b[39m make_stabilizer_graph(\u001b[43mcodes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124misotropic_generators\u001b[39m\u001b[38;5;124m'\u001b[39m],codes[\u001b[38;5;241m3\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "G = make_stabilizer_graph(codes[3]['isotropic_generators'],codes[3]['n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "9aa9cc19-13f1-4e1b-b075-39bcd288081d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', \n",
+    "        node_size=50, font_size=12, font_color='black', \n",
+    "        font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "20cdd702-cf07-4867-a7bb-567b04a767de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['(2,3)', '(1,2)', 'S0S1S2S3', '(0,1)', 'H0H1H2H3'],\n",
+       "aut_group_size       : 144,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 2,\n",
+       "index                : 9,\n",
+       "is_css               : 1,\n",
+       "is_decomposable      : 0,\n",
+       "is_degenerate        : 0,\n",
+       "is_gf4linear         : 1,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3'],\n",
+       "k                    : 2,\n",
+       "logical_ops          : ['X1X2', 'X1X3', 'Z0Z2', 'Z0Z3'],\n",
+       "n                    : 4,\n",
+       "uuid                 : 373b856e-a5af-4e9f-8524-997f1ccfe77e,\n",
+       "weight_enumerator    : [1, 0, 0, 0, 3],\n",
+       "}"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes[4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "93f83a75-dd2f-4c98-8f5f-4a99bc141ec0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = make_stabilizer_graph(codes[4]['isotropic_generators'],codes[4]['n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "cb64f15b-3755-4b0d-91d2-4ca66cf53808",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ey/s+AKTgaOreuHGj1A3Mumz1yvjTP/lcfPYjH4iZQ4diMBjEVb/x72L51FT8/f1fmb3u3e+8or9DQgIMSqondQMn8rpXTsZzO/4iZg4diojDNePPfv8/HHfdv7/yexFnvLLr40EyJG+qJnUD8zl9ev4fCeR3hFA7g5KqeaobmM+nPvWpGA6HMRwOY/+hmdi7/1DsPzQTw+Ewdu7cGR/96Efjhz/8Yd/HhF4ZlFRL6gYWa3JiEK+YnJh9AGft2rWxbt262LZtW+zevbvfw0GPDEqqJHUDbVm/fn1MT0/H1q1bpW+qZVBSJakbaMv09HRs2LAhdu3aFTt27Oj7ONALg5LqSN1A26RvamdQUhWpGxgX6ZuaGZRUReoGxkX6pmYGJdWQuoFxk76plUFJFaRuoCvSNzUyKKmC1A10RfqmRgYlxZO6ga5J39TGoKRoUjfQF+mbmhiUFE3qBvoifVMTg5JiSd1A36RvamFQUiSpG0iF9E0NDEqKJHUDqZC+qYFBSXGkbiA10jelMygpitQNpEr6pmQGJUWRuoFUSd+UzKCkGFI3kDrpm1IZlBRB6gZyIX1TIoOSIkjdQC6kb0pkUJI9qRvIjfRNaQxKsiZ1A7mSvimJQUnWpG4gV9I3JTEoyZbUDeRO+qYUBiVZkrqBUkjflMCgJEtSN1AK6ZsSGJRkR+oGSiN9kzuDkqxI3UCppG9yZlCSFakbKJX0Tc4MSrIhdQOlk77JlUFJFqRuoBbSNzkyKMmC1A3UQvomRwYlyZO6gdpI3+TGoCRpUjdQK+mbnBiUJE3qBmolfZMTg5JkSd1A7aRvcmFQkiSpG+Aw6ZscGJQkSeoGOEz6JgcGJcmRugFeTvomdQYlSZG6AeYmfZMyg5KkSN0Ac5O+SZlBSTKkboCTk75JlUFJEqRugIWRvkmRQUkSpG6AhZG+SZFBSe+kboDFkb5JjUFJr6RugKWRvkmJQUmvpG6ApZG+SYlBSW+kboBmpG9SYVDSC6kboB3SNykwKOmF1A3QDumbFBiUdE7qBmiX9E3fDEo6JXUDjIf0TZ8MSjoldQOMh/RNnwxKOiN1A4yX9E1fDEo6IXUDdEP6pg8GJZ2QugG6IX3TB4OSsZO6AbolfdM1g5KxkroB+iF90yWDkrGSugH6IX3TJYOSsZG6AfolfdMVg5KxOJq6V65cKXUD9Ej6pgsGJWNxNHVv2LBB6gbokfRNFwxKWid1A6RF+mbcDEpaJXUDpEn6ZpwMSloldQOkSfpmnAxKWiN1A6RN+mZcDEpaIXUD5EH6ZhwMSlohdQPkQfpmHAxKGpO6AfIifdM2g5JGpG6APEnftMmgpBGpGyBP0jdtMihZMqkbIG/SN20xKFkSqRugDNI3bTAoWRKpG6AM0jdtMChZNKkboCzSN00ZlCyK1A1QJumbJgxKFkXqBiiT9E0TBiULJnUDlE36ZqkMShZE6gaog/TNUhiULIjUDVAH6ZulMCiZl9QNUBfpm8UyKDkpqRugTtI3i2FQclJSN0CdpG8Ww6DkhKRugLpJ3yyUQcmcpG4AIqRvFsagZE5SNwAR0jcLY1ByHKkbgGNJ38zHoORlpG4A5iJ9czIGJS8jdQMwF+mbkzEomSV1A3Ay0jcnYlASEVI3AAsjfTMXg5KIkLoBWBjpm7kYlEjdACyK9M0og7JyUjcAS3H11VdL38wyKCsndQOwFCtWrJC+mWVQVkzqBqAJ6ZujDMpKSd0AtEH6JsKgrJbUDUAbpG8iDMoqSd0AtEn6xqCsjNQNwDhI33UzKCsjdQMwDtJ33QzKikjdAIyT9F0vg7ISUjcAXZC+62RQVkLqBqAL0nedDMoKSN0AdEn6ro9BWTipG4A+SN91MSgLJ3UD0Afpuy4GZcGkbgD6JH3Xw6AslNQNQAqk7zoYlIWSugFIgfRdB4OyQFI3ACmRvstnUBZG6gYgRdJ32QzKwkjdAKRI+i6bQVkQqRuAlEnf5TIoCyF1A5AD6btMBmUhpG4AciB9l8mgLIDUDUBOpO/yGJSZk7oByJH0XRaDMnNSNwA5kr7LYlBmTOoGIGfSdzkMykxJ3QCUQPoug0GZKakbgBJI32UwKDMkdQNQEuk7fwZlZqRuAEokfefNoMyM1A1AiaTvvBmUGZG6ASiZ9J0vgzITUjcANZC+82RQZkLqBqAG0neeDMoMSN0A1ET6zo9BmTipG4AaSd95MSgTJ3UDUCPpOy8GZcKkbgBqJn3nw6BMlNQNANJ3LgzKREndACB958KgTJDUDQAvkb7TZ1AmRuoGgONJ32kzKBMjdQPA8aTvtBmUCZG6AeDEpO90GZSJkLoBYH7Sd5oMykRI3QAwP+k7TQZlAqRuAFg46Ts9BmXPpG4AWDzpOy0GZc+kbgBYvBUrVsS1114rfSfCoOyR1A0AS7dmzRrpOxEGZU+kbgBoTvpOg0HZE6kbAJqTvtNgUPZA6gaA9kjf/TMoOyZ1A0D7pO9+GZQdk7oBoH3Sd78Myg5J3QAwPtJ3fwzKjkjdADB+0nc/DMqOSN0AMH7Sdz8Myg5I3QDQHem7ewblmEndANA96btbBuWYSd0A0D3pu1sG5RhJ3QDQH+m7OwblmEjdANA/6bsbBuWYSN0A0D/puxsG5RhI3QCQDul7/AzKlkndAJAe6Xu8DMqWSd0AkB7pe7wMyhZJ3QCQLul7fAzKlkjdAJA+6Xs8DMqWSN0AkD7pezwMyhZI3QCQD+m7fQZlQ1I3AORH+m6XQdmQ1A0A+ZG+22VQNiB1A0C+pO/2GJRLJHUDQP6k73YYlEskdQNA/qTvdhiUSyB1A0A5pO/mDMpFkroBoDzSdzMG5SJJ3QBQHum7GYNyEaRuACiX9L10BuUCSd0AUD7pe2kMygWSugGgfNL30hiUCyB1A0A9pO/FMyjnIXUDQH2k78UxKOchdQNAfaTvxTEoT0LqBoB6Sd8LZ1CegNQNAEjfC2NQnoDUDQBI3wtjUM5B6gYAjpK+51f1oNy3b19s2rQpLr744pieno4zzjgjrrvuuti8ebPUDQDMOjZ9/+AHP4ibbrop3va2t8Upp5wSg8EgBoNBvP/97+/7mL1Z3vcB+nLw4MG45ppr4t5775392r59++KOO+6IO++8M2699VapGwCIiJfS95YtW+Kee+6Jj33sY30fKSnV3qH8xCc+MTsmX7P2oviV/3xrXPnPb4yIiEOHDsVHPvKR2LdvX59HBAAScjR9f+07u+KiN/9MXPFr/yre/M5fnv38+QMzPZ6uX9UOyt/7w/8++/r63/6v8Y+uekf83IduivN/5u0REfHoo4/GnXfe2dfxAIAEnXXZ2+Os6z4Q77vl9rj6X/5OvPaSdbOf7Xpuf9z/zAs9nq4/VQ7KB77/ZHzvwe9ERMSy5ZMv+5/hvEvfMvv6z770l52fDQBI0yN7D8SXntgXMRhEDOaeUPc88nw8uvdAxyfrX5WD8p6//rvZ16ec9uqYWLZs9v0rT181+/qbD36303MBAOm676kX5h1OE0euq011D+UcmBnGQ0/vmX2/bPnUyz5ftnxy9vWzP3ouHnns8Vg+6Ox4AECCDg4jdu5ZHsM4+SiYiYgH9+yPAzPDmJyoZ0BUNyj3HxrG5MpTZt8fPPDyB28OHXzpNvXkylfEJz/9PyP21fcvDQDgGCtWxoqrf31Blw7jyN4wKMs1tWwQp5997uz7H+95Ng4dPBjLlh/+T/HcM0/Nfnb62efGr7/vve5QAkDlDg4jbntyOO8dyoiIQRzeGzWpblBOTgzi0nNfE6vfcEE89b0HY+bgwXj0b+6P8y69LCIivv/N+2avvfytb43Xnf2TfR0VAEjI+S/+KB7asz9O9sOBJiLi/FOnqro7GVHpQzmXrV4Zb/lnvzr7/n//7o3xrXvvjG1/uCl2fuUvIiLi1NecHR94z7t6OiEAkJrLVq+MmYjY/8KP44Evfj4e+OLn47HvPDD7+e7HH41vfPHz8dT/vTsefvjh/g7ag8FwOBz2fYg+3PfEc/FL73xHfPdrf3XcZ8unVsTmP/pf8cHrf7GHkwEAqbr/mRfis1/92/j4O9500utuvfXWqn4VY5V3KCMiLjvrVXHP3XfFL//W78SZrz8/lk+tiFNOfXVc9rO/GJ+/96+MSQDgOOtWrYzrXv+qvo+RnGrvUB7rwMww9h8axtSyQXXf8wAALI398BKDEgCARqpN3gAAtMOgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABoxKAEAaMSgBACgEYMSAIBGDEoAABr5/yWkGMr1wlsfAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pos = nx.planar_layout(G)\n",
+    "nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', \n",
+    "        node_size=50, font_size=12, font_color='black', \n",
+    "        font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "1c74b3e9-9908-401e-9604-a3a246b4c3b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(4, 0, d=2, info_only=True, list_only=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "64819a2e-6960-4fce-bf14-d21e338451e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['S1S3', '(1,3)', 'V0V2', 'H0H1H2H3^(0,1)(2,3)'],\n",
+       " aut_group_size       : 32,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 2,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0X2', 'Z1Z3', 'Z0Z2Z3', 'X1X2X3'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 4,\n",
+       " uuid                 : db5c4282-0f38-4540-951d-42382b281621,\n",
+       " weight_enumerator    : [1, 0, 2, 8, 5],\n",
+       " },\n",
+       " {aut_group_generators : ['S2S3', '(2,3)', 'S1S3', '(1,2)', 'S0S3', '(0,1)'],\n",
+       " aut_group_size       : 192,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 3,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z3', 'Z1Z3', 'Z2Z3', 'X0X1X2X3'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 4,\n",
+       " uuid                 : 30ab98d9-54a2-4c10-8646-16b074ec7960,\n",
+       " weight_enumerator    : [1, 0, 6, 0, 9],\n",
+       " },\n",
+       " {aut_group_generators : ['S2S3', '(2,3)', 'V0V1', 'H2H3', 'H0H1', '(0,1)', '(0,2)(1,3)'],\n",
+       " aut_group_size       : 288,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 2,\n",
+       " index                : 4,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 1,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 1,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z1', 'X0X1', 'Z2Z3', 'X2X3'],\n",
+       " k                    : 0,\n",
+       " logical_ops          : [],\n",
+       " n                    : 4,\n",
+       " uuid                 : 5c6d15ec-3861-41c7-aa8f-f68a4527f7fe,\n",
+       " weight_enumerator    : [1, 0, 6, 0, 9],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "64d175bf-d254-4c47-ad19-de0e955e449b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "code = cb.small_code(9,1,8519, info_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64318b17-b77d-42a3-bc06-19ba38543b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[9,1]]-8519 of type StabSubSystemCode\n",
+      "-------------------------------------------------------------------------------\n",
+      "aut_group_generators : ['S0S8', 'V3V4', '(3,4)', 'S2S6', '(2,6)', 'V1V7', '(1,7)', 'H0H1H2H3H4H5H6H7H8^(0,3,2,1)(4,6,7,8)']\n",
+      "aut_group_size       : 1024\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 8519\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z8', 'X1X7', 'Z2Z6', 'X3X4', 'Z3Z4Z5Z6', 'X2X5X6X7', 'Z1Z5Z7Z8', 'X0X3X5X8']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X4X5X7', 'Z1Z2Z7']\n",
+      "n                    : 9\n",
+      "uuid                 : 753d20ad-5cde-4541-8513-becbacb0f9e8\n",
+      "weight_enumerator    : [1, 0, 4, 0, 22, 0, 100, 0, 129, 0]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "code.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4cc33737-9998-4d5c-bbe7-f673ee10eaac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "def save_dict_to_json(filename, data):\n",
+    "    with open(filename, 'w') as f:\n",
+    "        json.dump(data, f, indent=4)\n",
+    "\n",
+    "\n",
+    "def make_trivials(val):\n",
+    "    uuids = [\"9a1aa443-f00d-43da-8ee6-afc98036dd9c\",\n",
+    "         \"ac86a85a-dce3-45da-b5f9-6ba51709df70\",\n",
+    "         \"1e334ccc-1d59-4d7d-a6c5-ffeaf20c5a1d\",\n",
+    "         \"f8ed642d-e010-4b53-bd6c-ced2add1fc20\",\n",
+    "         \"5985113d-2c37-4f21-a8bd-a02afae6bcac\",\n",
+    "         \"a27ee9b6-6964-4c78-ad28-512dc5f5eea3\",\n",
+    "         \"ae525d70-70e6-4184-aea4-c01529f06adf\",\n",
+    "         \"9a4ba787-2e08-43ca-927f-eda8a306da9f\",\n",
+    "         \"703a36d1-7090-4357-9274-1658ac6abbdb\",\n",
+    "         \"bc9898de-4ec7-4370-adff-5a5bbfd4a4f3\"]\n",
+    "\n",
+    "    def aut_gen(n):\n",
+    "        gen = []\n",
+    "        for i in range(n):\n",
+    "            gen += [\"R\"+str(i),\"S\"+str(i)]\n",
+    "        if n > 1:\n",
+    "            gen += ['(0, 1)']\n",
+    "        if n > 2:\n",
+    "            gen += [str(tuple(range(n)))]\n",
+    "        return gen\n",
+    "    \n",
+    "    def decomp(n):\n",
+    "        if n == 1: \n",
+    "            return 0\n",
+    "        return 1\n",
+    "\n",
+    "    def logical(n):\n",
+    "        logicals = []\n",
+    "        for i in range(n):\n",
+    "            logicals += ['X'+str(i), 'Z'+str(i)]\n",
+    "        return logicals\n",
+    "        \n",
+    "    for n in range(1,val):\n",
+    "        data_dict = {}\n",
+    "        val_dict = {}\n",
+    "        val_dict[\"aut_group_generators\"] = aut_gen(n)\n",
+    "        val_dict[\"aut_group_size\"] = 6\n",
+    "        val_dict[\"code_type\"] = \"StabSubSystemCode\"\n",
+    "        val_dict[\"d\"] = 1\n",
+    "        val_dict[\"index\"] = 0\n",
+    "        val_dict[\"is_css\"] = 1\n",
+    "        val_dict[\"is_decomposable\"] = decomp(n)\n",
+    "        val_dict[\"is_degenerate\"] = 0\n",
+    "        val_dict[\"is_gf4linear\"] = 0\n",
+    "        val_dict[\"is_subsystem\"] = 1\n",
+    "        val_dict[\"isotropic_generators\"] = [\"I\"*n]\n",
+    "        val_dict[\"k\"] = 1\n",
+    "        val_dict[\"logical_ops\"] = logical(n)\n",
+    "        val_dict[\"n\"] = 1\n",
+    "        val_dict[\"uuid\"] = uuids[n]\n",
+    "        val_dict[\"weight_enumerator\"] = [1]\n",
+    "        data_dict[\"0\"] = val_dict\n",
+    "        print(f\"[[{n},{n},1]]\")\n",
+    "        pprint(data_dict)\n",
+    "        filename = f\"codes_n_{n}_k_{n}.json\"\n",
+    "        save_dict_to_json(filename, data_dict)\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ac1273b3-06fd-4495-9923-8648a6e97452",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1,1,1]]\n",
+      "{'0': {'aut_group_generators': ['R0', 'S0'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 0,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['I'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0', 'Z0'],\n",
+      "       'n': 1,\n",
+      "       'uuid': 'ac86a85a-dce3-45da-b5f9-6ba51709df70',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[2,2,1]]\n",
+      "{'0': {'aut_group_generators': ['R0', 'S0', 'R1', 'S1', '(0, 1)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['II'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0', 'Z0', 'X1', 'Z1'],\n",
+      "       'n': 1,\n",
+      "       'uuid': '1e334ccc-1d59-4d7d-a6c5-ffeaf20c5a1d',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[3,3,1]]\n",
+      "{'0': {'aut_group_generators': ['R0',\n",
+      "                                'S0',\n",
+      "                                'R1',\n",
+      "                                'S1',\n",
+      "                                'R2',\n",
+      "                                'S2',\n",
+      "                                '(0, 1)',\n",
+      "                                '(0, 1, 2)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['III'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0', 'Z0', 'X1', 'Z1', 'X2', 'Z2'],\n",
+      "       'n': 1,\n",
+      "       'uuid': 'f8ed642d-e010-4b53-bd6c-ced2add1fc20',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[4,4,1]]\n",
+      "{'0': {'aut_group_generators': ['R0',\n",
+      "                                'S0',\n",
+      "                                'R1',\n",
+      "                                'S1',\n",
+      "                                'R2',\n",
+      "                                'S2',\n",
+      "                                'R3',\n",
+      "                                'S3',\n",
+      "                                '(0, 1)',\n",
+      "                                '(0, 1, 2, 3)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['IIII'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0', 'Z0', 'X1', 'Z1', 'X2', 'Z2', 'X3', 'Z3'],\n",
+      "       'n': 1,\n",
+      "       'uuid': '5985113d-2c37-4f21-a8bd-a02afae6bcac',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[5,5,1]]\n",
+      "{'0': {'aut_group_generators': ['R0',\n",
+      "                                'S0',\n",
+      "                                'R1',\n",
+      "                                'S1',\n",
+      "                                'R2',\n",
+      "                                'S2',\n",
+      "                                'R3',\n",
+      "                                'S3',\n",
+      "                                'R4',\n",
+      "                                'S4',\n",
+      "                                '(0, 1)',\n",
+      "                                '(0, 1, 2, 3, 4)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['IIIII'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0',\n",
+      "                       'Z0',\n",
+      "                       'X1',\n",
+      "                       'Z1',\n",
+      "                       'X2',\n",
+      "                       'Z2',\n",
+      "                       'X3',\n",
+      "                       'Z3',\n",
+      "                       'X4',\n",
+      "                       'Z4'],\n",
+      "       'n': 1,\n",
+      "       'uuid': 'a27ee9b6-6964-4c78-ad28-512dc5f5eea3',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[6,6,1]]\n",
+      "{'0': {'aut_group_generators': ['R0',\n",
+      "                                'S0',\n",
+      "                                'R1',\n",
+      "                                'S1',\n",
+      "                                'R2',\n",
+      "                                'S2',\n",
+      "                                'R3',\n",
+      "                                'S3',\n",
+      "                                'R4',\n",
+      "                                'S4',\n",
+      "                                'R5',\n",
+      "                                'S5',\n",
+      "                                '(0, 1)',\n",
+      "                                '(0, 1, 2, 3, 4, 5)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['IIIIII'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0',\n",
+      "                       'Z0',\n",
+      "                       'X1',\n",
+      "                       'Z1',\n",
+      "                       'X2',\n",
+      "                       'Z2',\n",
+      "                       'X3',\n",
+      "                       'Z3',\n",
+      "                       'X4',\n",
+      "                       'Z4',\n",
+      "                       'X5',\n",
+      "                       'Z5'],\n",
+      "       'n': 1,\n",
+      "       'uuid': 'ae525d70-70e6-4184-aea4-c01529f06adf',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[7,7,1]]\n",
+      "{'0': {'aut_group_generators': ['R0',\n",
+      "                                'S0',\n",
+      "                                'R1',\n",
+      "                                'S1',\n",
+      "                                'R2',\n",
+      "                                'S2',\n",
+      "                                'R3',\n",
+      "                                'S3',\n",
+      "                                'R4',\n",
+      "                                'S4',\n",
+      "                                'R5',\n",
+      "                                'S5',\n",
+      "                                'R6',\n",
+      "                                'S6',\n",
+      "                                '(0, 1)',\n",
+      "                                '(0, 1, 2, 3, 4, 5, 6)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['IIIIIII'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0',\n",
+      "                       'Z0',\n",
+      "                       'X1',\n",
+      "                       'Z1',\n",
+      "                       'X2',\n",
+      "                       'Z2',\n",
+      "                       'X3',\n",
+      "                       'Z3',\n",
+      "                       'X4',\n",
+      "                       'Z4',\n",
+      "                       'X5',\n",
+      "                       'Z5',\n",
+      "                       'X6',\n",
+      "                       'Z6'],\n",
+      "       'n': 1,\n",
+      "       'uuid': '9a4ba787-2e08-43ca-927f-eda8a306da9f',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[8,8,1]]\n",
+      "{'0': {'aut_group_generators': ['R0',\n",
+      "                                'S0',\n",
+      "                                'R1',\n",
+      "                                'S1',\n",
+      "                                'R2',\n",
+      "                                'S2',\n",
+      "                                'R3',\n",
+      "                                'S3',\n",
+      "                                'R4',\n",
+      "                                'S4',\n",
+      "                                'R5',\n",
+      "                                'S5',\n",
+      "                                'R6',\n",
+      "                                'S6',\n",
+      "                                'R7',\n",
+      "                                'S7',\n",
+      "                                '(0, 1)',\n",
+      "                                '(0, 1, 2, 3, 4, 5, 6, 7)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['IIIIIIII'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0',\n",
+      "                       'Z0',\n",
+      "                       'X1',\n",
+      "                       'Z1',\n",
+      "                       'X2',\n",
+      "                       'Z2',\n",
+      "                       'X3',\n",
+      "                       'Z3',\n",
+      "                       'X4',\n",
+      "                       'Z4',\n",
+      "                       'X5',\n",
+      "                       'Z5',\n",
+      "                       'X6',\n",
+      "                       'Z6',\n",
+      "                       'X7',\n",
+      "                       'Z7'],\n",
+      "       'n': 1,\n",
+      "       'uuid': '703a36d1-7090-4357-9274-1658ac6abbdb',\n",
+      "       'weight_enumerator': [1]}}\n",
+      "[[9,9,1]]\n",
+      "{'0': {'aut_group_generators': ['R0',\n",
+      "                                'S0',\n",
+      "                                'R1',\n",
+      "                                'S1',\n",
+      "                                'R2',\n",
+      "                                'S2',\n",
+      "                                'R3',\n",
+      "                                'S3',\n",
+      "                                'R4',\n",
+      "                                'S4',\n",
+      "                                'R5',\n",
+      "                                'S5',\n",
+      "                                'R6',\n",
+      "                                'S6',\n",
+      "                                'R7',\n",
+      "                                'S7',\n",
+      "                                'R8',\n",
+      "                                'S8',\n",
+      "                                '(0, 1)',\n",
+      "                                '(0, 1, 2, 3, 4, 5, 6, 7, 8)'],\n",
+      "       'aut_group_size': 6,\n",
+      "       'code_type': 'StabSubSystemCode',\n",
+      "       'd': 1,\n",
+      "       'index': 0,\n",
+      "       'is_css': 1,\n",
+      "       'is_decomposable': 1,\n",
+      "       'is_degenerate': 0,\n",
+      "       'is_gf4linear': 0,\n",
+      "       'is_subsystem': 1,\n",
+      "       'isotropic_generators': ['IIIIIIIII'],\n",
+      "       'k': 1,\n",
+      "       'logical_ops': ['X0',\n",
+      "                       'Z0',\n",
+      "                       'X1',\n",
+      "                       'Z1',\n",
+      "                       'X2',\n",
+      "                       'Z2',\n",
+      "                       'X3',\n",
+      "                       'Z3',\n",
+      "                       'X4',\n",
+      "                       'Z4',\n",
+      "                       'X5',\n",
+      "                       'Z5',\n",
+      "                       'X6',\n",
+      "                       'Z6',\n",
+      "                       'X7',\n",
+      "                       'Z7',\n",
+      "                       'X8',\n",
+      "                       'Z8'],\n",
+      "       'n': 1,\n",
+      "       'uuid': 'bc9898de-4ec7-4370-adff-5a5bbfd4a4f3',\n",
+      "       'weight_enumerator': [1]}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "make_trivials(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "10d0a7be-069f-437f-a036-5256dc0e9db3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7b26dcfe-2eef-45e9-bd3a-d9083883b4b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 650,
+   "id": "7b6f5925-37c2-4b6b-8f58-995f085d3b96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "found_codes = []\n",
+    "for n in range(10):\n",
+    "    for k in range(10):\n",
+    "        for d in range(5):\n",
+    "            title = f\"[[{n},{k},{d}]]\"\n",
+    "            first_found = False\n",
+    "            codes = cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)\n",
+    "            for code in codes:\n",
+    "                if is_even_gen_set(code['isotropic_generators']) is True:\n",
+    "                    if first_found is False:\n",
+    "                        #print(title)\n",
+    "                        first_found = True\n",
+    "                    #print(f\"{code['index']} : {code['isotropic_generators']}\")\n",
+    "                    code['max_weight'] = max_weight(code['isotropic_generators'])\n",
+    "                    found_codes += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1318,
+   "id": "fc6e0ddb-6dff-4ce9-9963-02fe2a93f5c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21273"
+      ]
+     },
+     "execution_count": 1318,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(found_codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "206c0242-e834-461e-9715-b141c013516a",
+   "metadata": {},
+   "source": [
+    "So there are 21,273 out of the 686,904 codes that have even weight generating sets. So 3%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1319,
+   "id": "15a13a5b-0d24-4086-bcf1-7e42576d311c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_codes = sorted(found_codes, key=lambda x: x['max_weight'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1083,
+   "id": "6712b555-3788-47ba-af49-9eb829bcd953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_even = np.zeros((10,10,5),dtype=int)\n",
+    "for code in sorted_codes:\n",
+    "    count_even[code['n']][code['k']][code['d']] +=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1088,
+   "id": "f407c66b-f044-4c26-b027-76936477a211",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&0\\\\0&2&1&1&0&0&0&0&0&0\\\\0&3&3&2&0&0&0&0&0&0\\\\0&7&15&14&4&1&0&0&0&0\\\\0&15&49&64&22&4&0&0&0&0\\\\0&42&278&566&328&70&7&1&0&0\\\\0&134&1627&6063&5237&1224&107&7&0&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\1&1&1&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\3&8&10&3&1&0&0&0&0&0\\\\0&0&11&6&1&0&0&0&0&0\\\\11&82&277&236&64&6&1&0&0&0\\\\0&0&1069&2448&839&56&1&0&0&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&1&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&8&0&0&0&0&0&0&0&0\\\\0&10&3&1&0&0&0&0&0&0\\\\0&137&64&8&0&0&0&0&0&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{bmatrix}0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\1&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\3&0&0&0&0&0&0&0&0&0\\\\0&0&0&0&0&0&0&0&0&0\\\\\\end{bmatrix}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(1,5):\n",
+    "    lprint(count_even[:,:,i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1090,
+   "id": "8cc2f3ef-f58f-478f-b659-c3c20141dcd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 226: [[7,1, 3]] : 1008 :['Z0Z1Z3Z6', 'Z0Z2Z3Z5', 'Y1Y2Y3Y4', 'Z3Z4Z5Z6', 'Y0Y1Y4Y5', 'Y0Y2Y4Y6'] : False\n",
+      "4 :: 5784: [[9,1, 3]] : 128 :['Z0Z8', 'X1X5', 'Z2Z3Z6Z8', 'Z2Z4Z7Z8', 'Z1Z5Z6Z7', 'X2X4X5X6', 'X2X3X5X7', 'X0X3X4X8'] : False\n",
+      "4 :: 7419: [[9,1, 3]] : 384 :['Z0Z8', 'Z1Z6', 'Z2Z7', 'Z3Z4Z6Z8', 'Z3Z5Z7Z8', 'X1X3X5X6', 'X2X3X4X7', 'X0X4X5X8'] : False\n",
+      "4 :: 8519: [[9,1, 3]] : 1024 :['Z0Z8', 'X1X7', 'Z2Z6', 'X3X4', 'Z3Z4Z5Z6', 'X2X5X6X7', 'Z1Z5Z7Z8', 'X0X3X5X8'] : True\n",
+      "4 :: 9897: [[9,1, 3]] : 144 :['Z0Z1Z4Z8', 'Z0Z2Z5Z7', 'Z1Z3Z5Z7', 'X1X2X4X5', 'Z0Z4Z5Z6', 'X1X3X4X6', 'X0X3X4X7', 'X0X3X5X8'] : False\n",
+      "6 :: 4079: [[9,1, 3]] : 3072 :['Z0Z8', 'X1X7', 'X2X7', 'X3X5', 'X4X6', 'Z3Z4Z5Z6', 'X0X5X6X8', 'Z1Z2Z3Z5Z7Z8'] : True\n",
+      "6 :: 4280: [[9,1, 3]] : 9216 :['Z0Z7', 'Z1Z8', 'X2X6', 'X3X6', 'Z4Z5', 'X0X4X5X7', 'X1X4X5X8', 'Z2Z3Z4Z6Z7Z8'] : True\n",
+      "6 :: 4395: [[9,1, 3]] : 1152 :['Z0Z8', 'Z1Z8', 'Z2Z3Z6Z7', 'Z2Z4Z7Z8', 'Z5Z6Z7Z8', 'X2X4X5X6', 'X3X4X5X7', 'X0X1X2X3X5X8'] : False\n",
+      "6 :: 8802: [[9,1, 3]] : 82944 :['Z0Z6', 'Z1Z6', 'Z2Z7', 'Z3Z8', 'Z4Z8', 'Z5Z7', 'X0X1X2X5X6X7', 'X0X1X3X4X6X8'] : True\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "special_codes_d3_css = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['d'] == 3 and code['k'] > 0 and code['is_css']==1:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_d3_css += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1320,
+   "id": "a25a4b58-2d2b-4582-bb91-e0cfd15f3ccd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 4525: [[8,2, 3]] : 6 :['Y0Z1Y2Z7', 'Z0X1X3Z4', 'Y3Y4Z6Z7', 'Z0X2X5Z6', 'Z1Z3Z5X6', 'Z2Z4Z5X7'] : False\n",
+      "6 :: 4947: [[8,2, 3]] : 1728 :['Y0Y1Z4Z7', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'Z0Z1X4X7', 'Y0Z1Y2Z3Y4Z6', 'X0Z1X2Z3X5Z7'] : False\n",
+      "6 :: 4948: [[8,2, 3]] : 48 :['Y0Z1Y2Z7', 'Y3Y4Z5Z6', 'X1X2X4X5', 'Z0Y1Z4Y6', 'Z2Z3Y5Y7', 'X0Z1Z2X3Z4Z5'] : False\n",
+      "6 :: 5267: [[9,2, 3]] : 16 :['X0Z8', 'X1Z5', 'X2Y3Y6Z8', 'Z2Z4Z6X7', 'X3X4Z5Z6Z7Z8', 'Z1X2Z4X5Z7Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 24508: [[9,2, 3]] : 64 :['X0Z7', 'X1X2', 'X3X4Z5Z6', 'X3Z4X6Z8', 'X1Z3X5Z6Z7Z8', 'Z0Y1Z2X3Z5Y7', 'Z1Z2Y3Z4Z5Y8'] : False\n",
+      "6 :: 25670: [[9,2, 3]] : 128 :['X0Z7', 'X1X2', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'X1Z3X5Z6Z7Z8', 'Z0Y1Z2X3Z5Y7', 'Y1Z2Z3Z4Z7Y8'] : False\n",
+      "6 :: 31962: [[9,2, 3]] : 2 :['X2Z5Z6Z8', 'X0Z3X4Z6', 'X0Z2Z3X5', 'Y1Z2Y7X8', 'Y1Y3Z4Z5Z7Z8', 'X1Z2Z3Z4X6Z8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 32087: [[9,2, 3]] : 8 :['Z0X2Z7Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Y0Y2Z4X6', 'Z0X1X3Z4Z5Z6', 'X0Z1Z5Z6X7Z8', 'Y1Y2Z3Z4Z5X8'] : False\n",
+      "6 :: 32103: [[9,2, 3]] : 1 :['X1Z5Z6Z8', 'Y2Y3Z4Z5', 'X1X2X4Z7', 'Z0Y1X6Y7', 'Z0Y1Y2X8', 'Z1Y2Z3Y5Z7Z8', 'X0Y1X2Z3Z4Y6'] : False\n",
+      "6 :: 42867: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0Z1Z4X6', 'Y0Z4Z5Z6Y7Z8', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 44039: [[9,2, 3]] : 4 :['X2X3Z5Z6', 'X1Y2Y3X4', 'Z2Z3X5X6', 'Y1Z4Y7X8', 'Y1Y2Z3Z5Z7Z8', 'X0X1Z3Z4X5Z7', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 44085: [[9,2, 3]] : 1 :['X1Z2Z6Z8', 'Z1X2X3Z4', 'Z2X4X5Z6', 'Y0X3Z6Y7', 'Y5X6X7Y8', 'Y1Y2Z3X4Z5Z7', 'X0X2Z4Z5X6Z8'] : False\n",
+      "6 :: 44124: [[9,2, 3]] : 2 :['X1X2Z6Z7', 'Z0X1Z3X4', 'Y3Y4Z5Z6', 'Z3X5Z7Z8', 'Y0Y2Z4X6', 'X4Y6X7Y8', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 47765: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'X3Z4Z5Z8', 'Z2X4X5Z6', 'Y0Y3X4X7', 'Z0X2Y6Y7', 'X1Z2Z3X4Z7Z8', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 48283: [[9,2, 3]] : 2 :['Z0X2Z7Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Y1Y2Y3Y8', 'Z0X1X3Z4Z5Z6', 'Y0X1Z2Z4Y6Z7', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 48777: [[9,2, 3]] : 4 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z2X4X5Z6', 'Y0X3Z6Y7', 'X1Z2Z3X4Z7Z8', 'X0Y1Z2Z4Y6Z8', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 50090: [[9,2, 3]] : 1 :['Z2X3Z6Z7', 'X0Z2Z4X5', 'Y1X3X4Y5', 'X0Z3Z4X6', 'Z1X2Z3X4Z6Z8', 'Y0Z3Z5Z6Y7Z8', 'Z0Z1Z2Z5Z6X8'] : False\n",
+      "6 :: 52592: [[9,2, 3]] : 1 :['Z1X3Z6Z8', 'X4Z5Z6Z7', 'X1Y2Y3X4', 'Z2Z3X5X6', 'Z0Z1Y4Y7', 'Z0Y5Y6X8', 'X0X1Z3Z4X5Z7'] : False\n",
+      "6 :: 52777: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0X2X3X6', 'Y5X6X7Y8', 'X1Z2X3Z4Z5Z6', 'Y0Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 53693: [[9,2, 3]] : 4 :['X0Z1X2Z8', 'Z0X1X4Z6', 'X3X4Z7Z8', 'X5Z6Z7Z8', 'X0Z1Y3Z4Z5Y6', 'X1Z2Z3Z5X7Z8', 'Z0Y1Z2Z4Z5Y8'] : False\n",
+      "6 :: 54539: [[9,2, 3]] : 4 :['X2Z5Z6Z8', 'X0Z3X4Z6', 'X1Z2X5Z8', 'X0Z2Z4X6', 'Y1Y3Z4Z5Z7Z8', 'Z0Z1Z4Z5Z6X7', 'Y0Y2Z3Z4Z7X8'] : False\n",
+      "6 :: 56126: [[9,2, 3]] : 1 :['X2Z3Z5Z8', 'Z3X4Z6Z7', 'Y1Y2X4X5', 'Y0X1Z4Y7', 'Y0Y3Y6Y8', 'Y2X6Y7X8', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 56564: [[9,2, 3]] : 32 :['X0X1Z3Z8', 'X0Z3X4Z6', 'X0Z2Z3X5', 'Y1X2Y3Z4Z6Z7', 'X0Y2Z4Z5Y6Z8', 'Z0Z1Z4Z5Z6X7', 'Y0Y2Z3Z4Z7X8'] : False\n",
+      "6 :: 56691: [[9,2, 3]] : 2 :['X1X2Z3Z6', 'Z2X3Z4Z8', 'Y1Y2X4X5', 'Y0X1Z4Y7', 'Y0Y3Y6Y8', 'X1Z3X4Z5Z7Z8', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 57877: [[9,2, 3]] : 1 :['X1X2Z3Z6', 'Z2X3Z4Z8', 'Z1Z2X5Z7', 'Y1Y3Y4Y5', 'Y0X1Z4Y7', 'Z0Y1Y2X8', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 62350: [[9,2, 3]] : 4 :['X0X1X3X4', 'Z1Z4X5X6', 'X2X3Y5Y6', 'Z0Y3X6Y7', 'Y2X5Y7X8', 'X4Y6X7Y8', 'X0X2Z3Z4X5Z8'] : False\n",
+      "6 :: 64257: [[9,2, 3]] : 2 :['X2Z3Z5Z8', 'Z3X4Z6Z7', 'Y1Y2X4X5', 'X0Z1Z4X6', 'Y0X1Z4Y7', 'X1Z2X3Z4Z5Z6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 66983: [[9,2, 3]] : 1 :['X1X2Z6Z7', 'Z0X1Z3X4', 'Y3Y4Z5Z6', 'Z3X5Z7Z8', 'Z3X6X7X8', 'X0Z2Z4X6Z7Z8', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 68753: [[9,2, 3]] : 2 :['X1Z2Z6Z8', 'X3Z4Z5Z8', 'Z2Z3X5Z7', 'Y1X2X4Y5', 'X0Z1Z4X6', 'Y0X3Z6Y7', 'Z0Y2Z3Z5Z6Y8'] : False\n",
+      "6 :: 70678: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z2X4X5Z6', 'X1X3Y4Y5', 'X0X2X3X6', 'Y0Y3X4X7', 'Y5X6X7Y8', 'X1Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 70804: [[9,2, 3]] : 16 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Y0Y4Z5Z8', 'X2Z3X7Z8', 'Z1X2Z4X5Z6Z8', 'X0Z2Z4X6Z7Z8', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 70813: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0Z1Z4X6', 'Z0X2Y6Y7', 'X1Z2X3Z4Z5Z6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 72030: [[9,2, 3]] : 2 :['Z1X2Z5Z8', 'Z2Z3X5Z7', 'X1X4X5Z8', 'X0Z1Z4X6', 'Y0X3Z6Y7', 'X1Z2X3Z4Z5Z6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 72428: [[9,2, 3]] : 1 :['X2Z3Z5Z8', 'Z2X3Z4Z8', 'X1X2X4Z7', 'X0Z1Z4X6', 'Z0Y1Y2X8', 'Y1Z2Y5Z6Z7Z8', 'Y0Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 73016: [[9,2, 3]] : 1 :['X1Z2Z6Z8', 'Z1X2X3Z4', 'Z2X4X5Z6', 'Y0X3Z6Y7', 'Z0Y1Z3Y8', 'Y1Y2Z3X4Z5Z7', 'X0X2Z4Z5X6Z8'] : False\n",
+      "6 :: 73142: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'X3Z4Z5Z8', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0X2X3X6', 'Y0X1Z2Z4Z5Y7', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 73362: [[9,2, 3]] : 1 :['X1Z5Z6Z8', 'Z2X3Z4Z8', 'X1X2X4Z7', 'Z1Z2X5Z7', 'Z0Y1X6Y7', 'X0Y1X2Z3Z4Y6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 73374: [[9,2, 3]] : 1 :['Z0X2Z7Z8', 'X3Z4Z5Z8', 'Z0X1Z3X4', 'X4X5Z6Z7', 'Y0Y2Z4X6', 'Z3X6X7X8', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 73784: [[9,2, 3]] : 1 :['X2Z3Z5Z8', 'X1X2X4Z7', 'Y1Y3Y4Y5', 'Y0X1Z4Y7', 'Z0Y1Y2X8', 'X1Z2X3Z4Z5Z6', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 74247: [[9,2, 3]] : 4 :['X1Z2Z3Z7', 'X2X3Z5Z6', 'Z2Z3X5X6', 'Z0X1X4X8', 'Z1X2X4Z6Z7Z8', 'X0Z1Y2Z4Y5Z8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 74385: [[9,2, 3]] : 3 :['Z1X4Z5Z8', 'Z4X5Z6Z8', 'X1Y2Y3X5', 'X3Z5X6Z7', 'Z0Z3Y5Y8', 'X0X2Z3Z6Z7Z8', 'Y0Y1Z3Z4X7Z8'] : False\n",
+      "6 :: 74429: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z2X4X5Z6', 'X1X3Y4Y5', 'X0Z1Z4X6', 'Y0X3Z6Y7', 'Y5X6X7Y8', 'X1Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 74529: [[9,2, 3]] : 6 :['X0Z1Z3X5', 'X1Y3Y4X6', 'X2X5X6Z8', 'Y0X3Z4Y7', 'Y2X5Y7X8', 'X0Y1Y2Z4Z5Z7', 'X0X1Z2X3Z6Z7'] : False\n",
+      "6 :: 74933: [[9,2, 3]] : 2 :['Z2X4Z6Z7', 'X0X1X3X4', 'Z1Z4X5X6', 'X2X3Y5Y6', 'Z0Y3X6Y7', 'Y2X5Y7X8', 'Y1Z2Z3Y5Z7Z8'] : False\n",
+      "6 :: 75654: [[9,2, 3]] : 2 :['X3Z4Z6Z8', 'Z3X4Z5Z7', 'X0X2Y3Y4', 'Z1Z2Z3X6', 'Y0Z2X5Y7', 'Y1Y4Y6Y7', 'Z0Y1Z3Z6Z7Y8'] : False\n",
+      "6 :: 75674: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z2Z3X5Z7', 'X1X4X5Z8', 'Y0X3Z6Y7', 'Y5X6X7Y8', 'X0Y1Z2Z4Y6Z8'] : False\n",
+      "6 :: 75713: [[9,2, 3]] : 12 :['Y0Y1Z7Z8', 'Z0X1X3Z5', 'X0Z1Z2X4', 'Y2Y3Y4Y5', 'X2Z4X6Z7', 'Z0X2Z5Z6X7Z8', 'Z1Y2Z3Z4Z6Y8'] : False\n",
+      "6 :: 75720: [[9,2, 3]] : 48 :['X0X1Z4Z5', 'X2X3Z6Z7', 'X0Z1X5Z8', 'Z2Z3Y6Y7', 'Z0X2X4Z5Z6Z8', 'X0Z2Z4X6Z7Z8', 'Y0Z1Z2Z3Z4Y8'] : False\n",
+      "6 :: 75999: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z3X4Z6Z7', 'X1X4X5Z8', 'Z0X2Y6Y7', 'Y5X6X7Y8', 'X0Y1Z2Z4Y6Z8'] : False\n",
+      "6 :: 80000: [[9,2, 3]] : 48 :['X3Z4Z6Z8', 'Z3X4Z5Z7', 'X0X2Y3Y4', 'Y0Z2X5Y7', 'Z0Y2Y6X8', 'Y0X1Z4Y5Z6Z7', 'X0Y1Z2Z3Z5Y6'] : False\n",
+      "6 :: 80001: [[9,2, 3]] : 64 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0X2X4Z5Z6Z8', 'X0Z2Z4X6Z7Z8', 'Y0Z1Z2Z3Z4Y8'] : False\n",
+      "6 :: 83847: [[9,2, 3]] : 8 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z4X5X6Z8', 'Z2Z3X4X5Z6Z7', 'Z0Y1Z2Y3X7Z8', 'X1Z3Z4Z5Z7X8'] : False\n",
+      "6 :: 83870: [[9,2, 3]] : 16 :['X0Z4', 'X3Z6Z7Z8', 'Y2Y5Z6Z8', 'Y1Y2Y6Y8', 'Z0X2X4Z5Z6Z7', 'X1X2Z3Z4X6Z8', 'Y1Z2Z3Z5Y7Z8'] : False\n",
+      "6 :: 83891: [[9,2, 3]] : 8 :['X0Z7', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1X4Y5Y6', 'Z1Y2Y3X8', 'Y1Z2Z3Y4Z7Z8', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 84013: [[9,2, 3]] : 32 :['X0Z4', 'X2X3Z6Z7', 'X2X5Z7Z8', 'Z2Z3X6X7', 'Z0Z1X2X4Z6Z8', 'X1Z2Z4Z5X6Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 88105: [[9,2, 3]] : 32 :['X0Z4', 'X2X3Z6Z7', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z0Z1X2X4Z6Z8', 'X1Z4X5Z6Z7Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 89082: [[9,2, 3]] : 8 :['X0Z7', 'Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z4X5X6Z8', 'Y1X2Y5X8', 'Z1X2Z3Z4X5Z7', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 89286: [[9,2, 3]] : 4 :['X0Z7', 'Z1Y2Y3Z6', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1Y4X5Y8', 'X1Z2X4Z5Z6Z8', 'Y1Y2Z3Z4X5Z7'] : False\n",
+      "6 :: 90258: [[9,2, 3]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Y2Z5Y6Z8', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'Z0Z1X2X4Z6Z8', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 90766: [[9,2, 3]] : 12 :['X0Z8', 'X1X2Z3Z6', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'X1Z2X3X4Z7Z8', 'Y1X3Z5Z6Y7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 91131: [[9,2, 3]] : 8 :['X0Z8', 'Y3Y4Z6Z7', 'Y1Y2X5Z8', 'X3Z4X5Z7', 'X1Y2Z3Z5Y6Z7', 'Y1X2Z4Z5Z6Y7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 92647: [[9,2, 3]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Z2Z3X5Z7', 'Y1Z2X3Y7', 'X2X6X7X8', 'Z0X2X4Z5Z6Z7', 'X1X2Y3Z4Y6Z7'] : False\n",
+      "6 :: 93414: [[9,2, 3]] : 4 :['X0Z8', 'X1X2Z4Z7', 'X1Z5X6Z8', 'Y2X3Y4X6', 'Z3Y5Y6Z7', 'Y1X3Z4Z5Z6Y7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 93830: [[9,2, 3]] : 4 :['X0Z8', 'X3Z4Z5Z8', 'X4X5Z6Z7', 'Z1Z3Y4Y6', 'Z2Z3Y5Y7', 'Y1Y2Z3X4Z7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 94601: [[9,2, 3]] : 96 :['X0Z3', 'X1X2Z5Z6', 'Z1Z2X5X6', 'Z3X4X7Z8', 'Z0X3X4Z5Z6Z7', 'Z0Z1X3Z4X5Z8', 'Y1Z2Z3Y4Z6X8'] : False\n",
+      "6 :: 94752: [[9,2, 3]] : 8 :['X0Z8', 'Y2Y3Z5Z7', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'Y1X2X5Y6', 'Y1Y2Z3Z6X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 99377: [[9,2, 3]] : 4 :['X0Z8', 'X1X2Z4Z7', 'Z3X5Z7Z8', 'Y2Y3Y4Y5', 'Y1Z3X4Y6', 'Z1X3Z4Z6X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "special_codes_d3_923 = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['d'] == 3 and code['k'] == 2:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_d3_923 += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1076,
+   "id": "26ce89c5-8ce3-49d7-a5aa-3db9e19027ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 21: [[5,1, 3]] : 360 :['Y0Y1Z2Z3', 'Y0Z1Y2Z4', 'X0Z2X3Z4', 'X0Z1Z3X4'] : False\n",
+      "4 :: 166: [[7,1, 3]] : 64 :['X0Z6', 'X1Z3', 'Z3X4Z5Z6', 'Z1X2Y3Y4', 'Z2Z4X5Z6', 'Z0Y2Z4Y6'] : False\n",
+      "4 :: 190: [[7,1, 3]] : 192 :['X0Z6', 'X1Z5', 'X2Z4', 'Z2X3X4Z5', 'Z1Y3Y5Z6', 'Z0Z3Z4X6'] : True\n",
+      "4 :: 226: [[7,1, 3]] : 1008 :['Z0Z1Z3Z6', 'Z0Z2Z3Z5', 'Y1Y2Y3Y4', 'Z3Z4Z5Z6', 'Y0Y1Y4Y5', 'Y0Y2Y4Y6'] : False\n",
+      "4 :: 227: [[7,1, 3]] : 42 :['Y0Y1Z2Z5', 'Y0Z1Y2Z6', 'Z0X1X3Z4', 'Z0X2Z3X4', 'X0Z2Z3X5', 'X0Z1Z4X6'] : False\n",
+      "4 :: 228: [[7,1, 3]] : 144 :['Y0Y1Z5Z6', 'Z0X1X2Z3', 'X0Z1Z2X3', 'X0Z1X4Z6', 'Z0Z3Z4X5', 'Z1Z2Z4X6'] : False\n",
+      "4 :: 257: [[7,1, 3]] : 32 :['X0Z6', 'X1X2Z4Z5', 'X1X3Z5Z6', 'Y1Z3Y4Z6', 'Y2Z3Y5Z6', 'Z0Z1Z2X6'] : False\n",
+      "4 :: 975: [[8,1, 3]] : 12 :['Y0Y1Z2Z6', 'Y0Z1Y2Z7', 'Z0X1X3Z4', 'Z0X2Z3X4', 'Z0X1X5Z7', 'Z1Z3Z5X6', 'Z2Z4Z5X7'] : False\n",
+      "4 :: 1198: [[8,1, 3]] : 192 :['Y0Y1Z4Z7', 'Y2Y3Z5Z6', 'Z0X4Z5Z6', 'Z2Z4X5Z7', 'X1X3Y4Y5', 'Z3Z4X6Z7', 'Z1Z5Z6X7'] : False\n",
+      "4 :: 1201: [[8,1, 3]] : 16 :['Y0Y1Z2Z6', 'Y0Z1Y2Z7', 'Z0X1Z3X4', 'Y3Y4Z5Z6', 'Z0X2Z3X5', 'X0Z2Z4X6', 'X0Z1Z5X7'] : False\n",
+      "4 :: 1446: [[8,1, 3]] : 32 :['X0Z4', 'X1X2Z4Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Y2Y5Z6Z7', 'X2Z3X6Z7', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 4525: [[8,2, 3]] : 6 :['Y0Z1Y2Z7', 'Z0X1X3Z4', 'Y3Y4Z6Z7', 'Z0X2X5Z6', 'Z1Z3Z5X6', 'Z2Z4Z5X7'] : False\n",
+      "4 :: 1011: [[9,1, 3]] : 64 :['X0Z8', 'X1Z7', 'X2Z6', 'X3X4Z5Z7', 'Y4Y5Z6Z8', 'Z2X4X6Z8', 'Z1Y3Y7Z8', 'Z0Z3Z4X8'] : True\n",
+      "4 :: 1058: [[9,1, 3]] : 64 :['X0Z6', 'X1Z7', 'X2Z3', 'Z2X3X4Z5', 'Y4Y5Z7Z8', 'Z0X6Z7Z8', 'Z1Z5Z6X7', 'Z3Z4Z6X8'] : True\n",
+      "4 :: 1569: [[9,1, 3]] : 32 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z1Y4Y5Z7', 'Z3X6Z7Z8', 'Z2Z4Z6X7', 'Z0Z2Y3Y8'] : False\n",
+      "4 :: 2396: [[9,1, 3]] : 16 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'X2X4Z5Z7', 'Z2X5Z7Z8', 'Z1X6Z7Z8', 'X3Y5Z6Y7', 'Z0Z4Y7Y8'] : False\n",
+      "4 :: 2411: [[9,1, 3]] : 16 :['X0Z7', 'X1Z4', 'X2X3Z6Z8', 'Z1X4Z5Z8', 'Z4X5Z6Z8', 'Z3Z5X6Z8', 'Z0Z2Z3X7', 'Y2Y5Z7X8'] : False\n",
+      "4 :: 2476: [[9,1, 3]] : 16 :['X0Z8', 'X1Z7', 'Z3X4Z7Z8', 'Z3X5Z6Z8', 'X2X3Y4Y5', 'Z2Z5X6Z8', 'Z1X3X6X7', 'Z0X2X3X8'] : False\n",
+      "4 :: 3581: [[9,1, 3]] : 384 :['X0Z8', 'X1Z8', 'X2Z5', 'X3X4', 'Z5X6Z7Z8', 'Z2X3Y5Y6', 'Z3Z4Z6X7', 'Z0Z1X7X8'] : True\n",
+      "4 :: 3880: [[9,1, 3]] : 192 :['X0Z6', 'X1Z6', 'X2Z8', 'X3X4Z5Z7', 'X3Z4X5Z8', 'Z0Z1X6Z7', 'Z3Z5Z6X7', 'Z2Z3Z4X8'] : True\n",
+      "4 :: 4412: [[9,1, 3]] : 192 :['X0Z8', 'X1Z8', 'Y2Y3Z4Z6', 'Y2Z3Y4Z7', 'Z2X3X5Z7', 'Z3Z5X6Z8', 'Z4Z5X7Z8', 'Z0Z1X5X8'] : False\n",
+      "4 :: 5190: [[9,1, 3]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z5Z6', 'X4Z6Z7Z8', 'Z3X5Z7Z8', 'Z2Z4X6Z8', 'Z1Z4Z5X7', 'Z0X3Y6Y8'] : False\n",
+      "4 :: 5345: [[9,1, 3]] : 128 :['X0Z7', 'X1Z8', 'X2Z5', 'Z3X4Z7Z8', 'Z2Y3Y4X5', 'X3Y4Z5Y6', 'Z0Y3X6Y7', 'Z1Y3X6Y8'] : False\n",
+      "4 :: 5353: [[9,1, 3]] : 128 :['X0Z5', 'X1Z6', 'X2Z7', 'X3X4Z6Z7', 'Z0X3X5Z6', 'Z1Y3Y6Z8', 'Z2Y4Y7Z8', 'Z3Z4Z5X8'] : False\n",
+      "4 :: 5540: [[9,1, 3]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Z3Y4Z7', 'X2Z4X5Z8', 'Z2X4X6Z8', 'Z1Z4Z6X7', 'Z0Z5Z6X8'] : False\n",
+      "4 :: 5566: [[9,1, 3]] : 64 :['X0Z7', 'X1Z6', 'X4Z5Z7Z8', 'X2X3X4Z6', 'Z3Z4X5Z8', 'Z1Z3X6Z8', 'Z0Z2Z4X7', 'Z2X3Z4X8'] : False\n",
+      "4 :: 5575: [[9,1, 3]] : 32 :['X0Z8', 'X1Z5', 'Y3Y4Z5Z7', 'Z1X3X5Z7', 'Z2X6Z7Z8', 'Y2X3Z4Y6', 'Z3Z6X7Z8', 'Z0Y2Z7Y8'] : False\n",
+      "4 :: 5784: [[9,1, 3]] : 128 :['Z0Z8', 'X1X5', 'Z2Z3Z6Z8', 'Z2Z4Z7Z8', 'Z1Z5Z6Z7', 'X2X4X5X6', 'X2X3X5X7', 'X0X3X4X8'] : False\n",
+      "4 :: 5785: [[9,1, 3]] : 64 :['X0Z5', 'X1Z6', 'Y2Y3Z7Z8', 'X2Z3X4Z7', 'Z0X5Z6Z7', 'Z1Z5X6Z8', 'X2Z4Z5X7', 'X3Z4Z6X8'] : False\n",
+      "4 :: 5786: [[9,1, 3]] : 128 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z2Z3X4Z8', 'Z1X5Z6Z7', 'Y3Y4Z5X6', 'Y2Y4Z5X7', 'Z0X2Z7X8'] : False\n",
+      "4 :: 5861: [[9,1, 3]] : 64 :['X0Z8', 'X1Z7', 'X2Z4', 'Z2X3X4Z6', 'X3X5Z7Z8', 'Y3Z5Y6Z8', 'Z1Y5Z6Y7', 'Z0Z3Z4X8'] : False\n",
+      "4 :: 6662: [[9,1, 3]] : 8 :['X0X1Z7Z8', 'X2Z3Z7Z8', 'Z2X3Z4Z8', 'Z3X4Z6Z8', 'Z0Z1X5Z6', 'X0Z4X6Z7', 'Y0Z2Y5X7', 'Y1Y3Y5Y8'] : False\n",
+      "4 :: 6676: [[9,1, 3]] : 4 :['X0X1Z4Z7', 'X2Z3Z5Z8', 'Z2X3Z6Z7', 'Z1X4Z5Z6', 'X0Z2Z4X5', 'X0Z3Z4X6', 'Y0X2Z6Y7', 'Z0Z2X4X8'] : False\n",
+      "4 :: 7419: [[9,1, 3]] : 384 :['Z0Z8', 'Z1Z6', 'Z2Z7', 'Z3Z4Z6Z8', 'Z3Z5Z7Z8', 'X1X3X5X6', 'X2X3X4X7', 'X0X4X5X8'] : False\n",
+      "4 :: 7421: [[9,1, 3]] : 384 :['X0Z8', 'X1Z7', 'X2Z4', 'X5Z6Z7Z8', 'Z2X3X4X5', 'Z4Z5X6Z8', 'Z1Z3Z5X7', 'Z0Y3Y5X8'] : False\n",
+      "4 :: 8099: [[9,1, 3]] : 12 :['Z0X1Z6Z8', 'Z0X2Z7Z8', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Y0Y2Z4X6', 'Y0Y1Z5X7', 'Y1Y2Y3Y8'] : False\n",
+      "4 :: 8100: [[9,1, 3]] : 12 :['X0X1Z6Z8', 'X0X2Z5Z8', 'X2X3Z4Z7', 'Z3X4Z6Z8', 'Z2Z3X5Z8', 'Z1Z4X6Z8', 'Z0Z1Z2X7', 'Z0X3Z6X8'] : False\n",
+      "4 :: 8314: [[9,1, 3]] : 8 :['Y0Y1Z7Z8', 'Z2X3Z7Z8', 'X4Z6Z7Z8', 'Z2X5Z6Z8', 'Z4Z5X6Z8', 'Z1Z3Z4X7', 'X0X2X6X7', 'X1X2Y4Y8'] : False\n",
+      "4 :: 8519: [[9,1, 3]] : 1024 :['Z0Z8', 'X1X7', 'Z2Z6', 'X3X4', 'Z3Z4Z5Z6', 'X2X5X6X7', 'Z1Z5Z7Z8', 'X0X3X5X8'] : True\n",
+      "4 :: 8836: [[9,1, 3]] : 12 :['Z1X3Z5Z7', 'Z2X4Z6Z8', 'X0Z2Z3X5', 'X1X4X5Z7', 'X0Z1Z4X6', 'X2X3X6Z8', 'Y0X1Z5Y7', 'Y0X2Z6Y8'] : False\n",
+      "4 :: 9086: [[9,1, 3]] : 2 :['X1Z5Z6Z8', 'X2Z3Z5Z8', 'Z2X3Z4Z8', 'Z3X4Z6Z7', 'Z1Z2X5Z7', 'X0Z1Z4X6', 'Y0X1Z4Y7', 'Z0Y1Y2X8'] : False\n",
+      "4 :: 9234: [[9,1, 3]] : 4 :['Z1X2Z4Z8', 'X3Z5Z6Z8', 'Z2X4Z6Z7', 'X0X1X3X4', 'X0Z1Z3X5', 'X0Z3Z4X6', 'Y0X3Z4Y7', 'Z0Z2Y3Y8'] : False\n",
+      "4 :: 9239: [[9,1, 3]] : 4 :['X0X1Z5Z8', 'X3Z4Z5Z8', 'Z2Z3X4Z8', 'Z1Z3X5Z7', 'Z0Z1X6Z7', 'X0Z2Z5X7', 'Y2X3Z6Y7', 'Z0X2Z3X8'] : False\n",
+      "4 :: 9274: [[9,1, 3]] : 3 :['X0X1Z4Z8', 'Z2X3Z7Z8', 'X0Y2Y3Z6', 'Z1X4Z5Z8', 'Z4X5Z6Z8', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'Z0Z3Y5Y8'] : False\n",
+      "4 :: 9385: [[9,1, 3]] : 2 :['X1Z2Z6Z8', 'Z1X2Z5Z8', 'X3Z4Z5Z8', 'Z3X4Z6Z7', 'Z2Z3X5Z7', 'X0Z1Z4X6', 'Y0X3Z6Y7', 'Z0Y1Z3Y8'] : False\n",
+      "4 :: 9389: [[9,1, 3]] : 24 :['Y1Y2Z4Z7', 'X0X3Z6Z8', 'Z1X4Z6Z8', 'Y0Z3Y5Z7', 'X2Z3Y4Y6', 'Z0Z4X5X6', 'X0Z2Z6X7', 'Y0X2Y4X8'] : False\n",
+      "4 :: 9643: [[9,1, 3]] : 8 :['X1Z2Z3Z8', 'Z1X2Z4Z8', 'Z1X3Z5Z8', 'Z2X4Z6Z7', 'Z3X5Z6Z7', 'X0Z4Z5X6', 'Z0Y6Y7Z8', 'Z0Y1Z6Y8'] : False\n",
+      "4 :: 9879: [[9,1, 3]] : 36 :['X0X1Z5Z6', 'X1X2Z7Z8', 'X3Z4Z6Z8', 'Z3X4Z5Z7', 'Z0Z4X5Z7', 'Z1Z2Z3X6', 'X0Z2Z4X7', 'Y0Z1X4Y8'] : False\n",
+      "4 :: 9897: [[9,1, 3]] : 144 :['Z0Z1Z4Z8', 'Z0Z2Z5Z7', 'Z1Z3Z5Z7', 'X1X2X4X5', 'Z0Z4Z5Z6', 'X1X3X4X6', 'X0X3X4X7', 'X0X3X5X8'] : False\n",
+      "4 :: 9950: [[9,1, 3]] : 8 :['Z0X1Z5Z6', 'X2Z5Z7Z8', 'X3Z6Z7Z8', 'X4Z5Z6Z8', 'Z1Z2Z4X5', 'Z1Z3Z4X6', 'X0Z3X5X8', 'Z0Z4Y7Y8'] : False\n",
+      "4 :: 10001: [[9,1, 3]] : 2 :['Z1X2Z3Z8', 'Z2X3Z6Z8', 'X4Z5Z6Z7', 'X0X1X3X4', 'X0Z1Z4X5', 'X0Z3Z4X6', 'Y0Y4X7Z8', 'Z0Y3Z5Y8'] : False\n",
+      "4 :: 10125: [[9,1, 3]] : 18 :['Z1X3Z4Z8', 'Z2Z3X4Z7', 'X0Z1X5Z6', 'X2X3X5Z7', 'X0Z2Z5X6', 'X1X4X6Z8', 'Y0X2Z5Y7', 'Y0X1Z6Y8'] : False\n",
+      "4 :: 10216: [[9,1, 3]] : 8 :['X1Z2Z3Z7', 'Z1X2Z5Z8', 'Z1X3Z6Z8', 'X4Z5Z6Z7', 'X0Z2Z4X5', 'X0Z3Z4X6', 'Z0Z1Y4Y7', 'Z0Y2Y3X8'] : False\n",
+      "4 :: 10281: [[9,1, 3]] : 16 :['X0X1Z6Z7', 'X0X2Z3Z8', 'X1Z2X3Z8', 'X4Z5Z6Z8', 'Z4X5Z7Z8', 'Z0Z2Z4X6', 'Z1Z3Z5X7', 'Y0Z1Y4X8'] : False\n",
+      "4 :: 10283: [[9,1, 3]] : 72 :['X1Z2Z5Z7', 'Z1X2Z6Z8', 'X3Z4Z5Z8', 'Z3X4Z6Z7', 'X0Z1Z3X5', 'X0Z2Z4X6', 'Z0X2X3X7', 'Z0X1X4X8'] : False\n",
+      "4 :: 10284: [[9,1, 3]] : 24 :['Z1X2Z3Z8', 'Z2X3Z5Z8', 'Z1X4Z6Z7', 'X0Z3X5Z6', 'X1Y2Y4X5', 'X0Z4Z5X6', 'Z0Y6Y7Z8', 'Z0Y3Z6Y8'] : False\n",
+      "4 :: 11455: [[9,1, 3]] : 16 :['X0Z8', 'X1X3Z4Z7', 'X2X3Z5Z6', 'Z1X4Z5Z8', 'Z2Z4X5Z8', 'Z1Z3X6Z8', 'Z2Z3X7Z8', 'Z0X1X2X8'] : False\n",
+      "4 :: 11890: [[9,1, 3]] : 24 :['X0Z6', 'X1X2Z4Z5', 'X1X3Z7Z8', 'Y1Z3Y4Z8', 'Z2X5Z7Z8', 'Z0X6Z7Z8', 'Z3Z5Z6X7', 'Z1Y2Z6Y8'] : False\n",
+      "4 :: 12051: [[9,1, 3]] : 16 :['X0Z7', 'X3Z4Z7Z8', 'Z2Z3X4Z8', 'Z1X5Z6Z8', 'Y1X2X3Y5', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X2Z3Z5X8'] : False\n",
+      "4 :: 12374: [[9,1, 3]] : 32 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z2X4Z7Z8', 'Z3X5Z7Z8', 'Y1Z3X4Y6', 'Y2Y3X6X7', 'Z0Y1X7Y8'] : False\n",
+      "4 :: 12606: [[9,1, 3]] : 8 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'X1Z4Y5Y8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 12640: [[9,1, 3]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 12663: [[9,1, 3]] : 8 :['X0Z8', 'Y2Y3Z6Z7', 'Z1X4Z6Z8', 'Z1X5Z7Z8', 'Z2Z4X6Z8', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 12677: [[9,1, 3]] : 8 :['X0Z8', 'X3Z6Z7Z8', 'Z1X4Z7Z8', 'Z2X5Z6Z8', 'Y1Y2Y4Y5', 'Z1Z3Z5X6', 'Z2Z3Z4X7', 'Z0Y1Y6X8'] : False\n",
+      "4 :: 12717: [[9,1, 3]] : 32 :['X0Z8', 'X1X2Z6Z7', 'X3Z6Z7Z8', 'Z1Z2X4Z5', 'X1X5Z7Z8', 'Y2Y3Y4Y6', 'Y1Y3Y4Y7', 'Z0Y3Z5Y8'] : False\n",
+      "4 :: 12761: [[9,1, 3]] : 32 :['X0Z3', 'X1X2Z5Z6', 'Z0X3Z7Z8', 'X4Z5Z6Z8', 'Z1Z4X5Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7', 'Y2Y5Y7Y8'] : False\n",
+      "4 :: 12813: [[9,1, 3]] : 16 :['X0Z6', 'X1X2Z3Z7', 'X1Z2X3Z8', 'X1X4Z5Z8', 'Z4X5Z6Z8', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Y2Y5X7X8'] : False\n",
+      "4 :: 12836: [[9,1, 3]] : 16 :['X0Z8', 'X1X2Z3Z4', 'Y1Y3Z5Z6', 'Y2Y4Z5Z7', 'Z1Z2X5Z8', 'Z3X6Z7Z8', 'Z4Z6X7Z8', 'Z0X1Y6Y8'] : False\n",
+      "4 :: 12851: [[9,1, 3]] : 8 :['X0Z7', 'Y1Y2Z3Z8', 'X1X3Z6Z8', 'Z2X3X4Z5', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1X4Z7X8'] : False\n",
+      "4 :: 12852: [[9,1, 3]] : 16 :['X0Z4', 'X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 13430: [[9,1, 3]] : 4 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 13558: [[9,1, 3]] : 32 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Z1Z2Z3X8'] : False\n",
+      "6 :: 108: [[7,1, 3]] : 768 :['X0Z4', 'X1Z4', 'X2Z5', 'X3Z6', 'Z2Z3Y5Y6', 'Z0Z1Z2X4X5Z6'] : True\n",
+      "6 :: 115: [[7,1, 3]] : 576 :['X0Z6', 'X1Z6', 'X2X3Z4Z5', 'Y2Y4Z5Z6', 'X2Z3X5Z6', 'Z0Z1Y2Z3Z4Y6'] : False\n",
+      "6 :: 294: [[8,1, 3]] : 128 :['X0Z7', 'X1Z2', 'Z1X2X3Z5', 'Z1X2X4Z6', 'Y3Y5Z6Z7', 'X3Z4X6Z7', 'Z0Z1Y2Z3Z4Y7'] : False\n",
+      "6 :: 602: [[8,1, 3]] : 2880 :['X0Z7', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'X2Z4X5Z6', 'X2Z3Z5X6', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 983: [[8,1, 3]] : 768 :['X0Z6', 'X1Z7', 'X4X5', 'Y2Y3Z6Z7', 'X2Z3X4Z7', 'Z0Y2Z3Z4Z5Y6', 'Z1X2Z4Z5Z6X7'] : False\n",
+      "6 :: 1090: [[8,1, 3]] : 2048 :['X0Z4', 'X1Z5', 'X2Z6', 'X3Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7'] : True\n",
+      "6 :: 1420: [[8,1, 3]] : 64 :['X0Z7', 'X1X2Z4Z6', 'X1X3Z5Z6', 'X1Z2X4Z7', 'X1Z3X5Z7', 'Z1Z4Z5X6', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 1422: [[8,1, 3]] : 192 :['X0Z7', 'X1X2Z5Z6', 'X1X3Z6Z7', 'X4Z5Z6Z7', 'Z1Z3Z4X5', 'Z2Z3Z4X6', 'Z0Y1Z2Z4Z5Y7'] : False\n",
+      "6 :: 4947: [[8,2, 3]] : 1728 :['Y0Y1Z4Z7', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'Z0Z1X4X7', 'Y0Z1Y2Z3Y4Z6', 'X0Z1X2Z3X5Z7'] : False\n",
+      "6 :: 4948: [[8,2, 3]] : 48 :['Y0Z1Y2Z7', 'Y3Y4Z5Z6', 'X1X2X4X5', 'Z0Y1Z4Y6', 'Z2Z3Y5Y7', 'X0Z1Z2X3Z4Z5'] : False\n",
+      "6 :: 6822: [[8,3, 3]] : 168 :['X0Y1Y2X3Z4Z7', 'Z0X1Z3X4Z6Z7', 'X0Z1Z2X5Z6Z7', 'Y0Y2Z3Z5X6Z7', 'Y0Z1X2Z4Z5Y7'] : False\n",
+      "6 :: 632: [[9,1, 3]] : 256 :['X0Z8', 'X1Z7', 'X2Z5', 'X3Z6', 'Z2X4X5Z7', 'Z3X6Z7Z8', 'Z1Z4Z6X7', 'Z0Y4Z5Z6Z7Y8'] : True\n",
+      "6 :: 893: [[9,1, 3]] : 128 :['X0Z8', 'X1Z7', 'X2Z5', 'X3X4Z6Z7', 'Z2X3X5Z6', 'Y3Y6Z7Z8', 'Z1Z4Z6X7', 'Z0Y3Z4Z5Z6Y8'] : False\n",
+      "6 :: 1014: [[9,1, 3]] : 64 :['X0Z8', 'X1Z6', 'X2Z4', 'Z2Y3Y4Z7', 'X5Z6Z7Z8', 'Z1Z5X6Z8', 'Z3Z5X7Z8', 'Z0X3Z4Z5Z6X8'] : False\n",
+      "6 :: 1583: [[9,1, 3]] : 512 :['X0Z8', 'X1Z7', 'X2Z5', 'X3Z6', 'Z2X4X5Z7', 'Z3X4X6Z7', 'Z1Y4Y7Z8', 'Z0Y4Z5Z6Z7Y8'] : False\n",
+      "6 :: 1984: [[9,1, 3]] : 64 :['X0Z8', 'X1X2', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'X1Y3Z4Y5', 'Z4X6Z7Z8', 'Z0Z4X7X8', 'Y1Z2Z5Z6Y7Z8'] : False\n",
+      "6 :: 2033: [[9,1, 3]] : 128 :['X0Z8', 'X1Z2', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'Z1Y2Z3Z4Y7Z8', 'Z0Z1X2Y3Y4X8'] : False\n",
+      "6 :: 2097: [[9,1, 3]] : 64 :['X0Z8', 'X1Z4', 'Y2Y3Z5Z6', 'Z1X4Z7Z8', 'Z2X5Z7Z8', 'Z3X6Z7Z8', 'Z4Z5Z6X7', 'Z0X2Z3Z4Z6X8'] : False\n",
+      "6 :: 2204: [[9,1, 3]] : 16 :['X0Z8', 'X1Z7', 'X2X3Z5Z7', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Z2Z3X6Z8', 'Z0Y4Z6Y8', 'Z1Y2Z4Z6Y7Z8'] : False\n",
+      "6 :: 2312: [[9,1, 3]] : 64 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Z3Z4Y6Y7', 'Z0Z2Z4Z5Y6Y8'] : False\n",
+      "6 :: 2377: [[9,1, 3]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z8', 'X2X4Z5Z8', 'Z2X3X5Z6', 'Z5X6Z7Z8', 'Z1Z6X7Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 2403: [[9,1, 3]] : 128 :['X0Z8', 'X1Z6', 'X2X3Z4Z5', 'Y2Y4Z5Z7', 'X2Z3X5Z7', 'Z2Z3X7Z8', 'Z0Z6Z7X8', 'Z1X2Z4X6Z7Z8'] : False\n",
+      "6 :: 2438: [[9,1, 3]] : 128 :['X0Z8', 'X1Z5', 'X2X3Z5Z6', 'X2X4Z5Z7', 'Z1Y2Y5Z8', 'Y3Y6Z7Z8', 'X3Z4X7Z8', 'Z0Y2Z3Z4Z5Y8'] : False\n",
+      "6 :: 2765: [[9,1, 3]] : 512 :['X0Z8', 'X1Z6', 'X2Z7', 'X4X5', 'X3X4Z6Z7', 'Z1Y3Y6Z8', 'Z2Z4Z5X7', 'Z0Y3Z4Z5Z6Y8'] : True\n",
+      "6 :: 3391: [[9,1, 3]] : 384 :['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z7Z8', 'Y4Y6Z7Z8', 'Z2Z5Z6X7', 'Z0Z1Y3Z4Z5Y8'] : False\n",
+      "6 :: 3550: [[9,1, 3]] : 384 :['X0Z8', 'X1Z8', 'X2Z7', 'X3Z6', 'X4X5Z6Z8', 'Z3Z5X6Z8', 'Z2Z4Z5X7', 'Z0Z1Y4Z6Z7Y8'] : False\n",
+      "6 :: 3717: [[9,1, 3]] : 192 :['X0Z8', 'X1Z8', 'X2Z5', 'X3X4Z6Z7', 'Z2X4X5Z8', 'Y3Y6Z7Z8', 'Z4Z5Z6X7', 'Z0Z1Y3Z4Z6Y8'] : False\n",
+      "6 :: 3723: [[9,1, 3]] : 3072 :['X0Z8', 'X1Z8', 'X2Z5', 'X3Z6', 'X4Z7', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : True\n",
+      "6 :: 3799: [[9,1, 3]] : 96 :['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z7', 'Y4Y5Z6Z8', 'X3Z5X6Z8', 'Z2Z3Z6X7', 'Z0Z1Y3Z4Z7Y8'] : False\n",
+      "6 :: 3873: [[9,1, 3]] : 768 :['X0Z8', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z8', 'X3Z4X6Z8', 'Z0Z3Z4X8', 'Z1Z2X3Z5X7Z8'] : False\n",
+      "6 :: 3904: [[9,1, 3]] : 768 :['X0Z8', 'X1Z8', 'X2Z3', 'Z2X3X4Z6', 'Z2X3X5Z7', 'Y4Y6Z7Z8', 'X4Z5X7Z8', 'Z0Z1Z3Z4Z5X8'] : False\n",
+      "6 :: 3923: [[9,1, 3]] : 2304 :['X0Z8', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z8', 'X3Z4X6Z8', 'Z1Z2X7Z8', 'Z0Y3Z4Z5Z7Y8'] : False\n",
+      "6 :: 3978: [[9,1, 3]] : 2304 :['X0Z8', 'X1Z8', 'X2Z7', 'X3Z7', 'X4Z6', 'Z4Y5Y6Z8', 'Z0Z1Z5X8', 'Z2Z3X5Z6X7Z8'] : True\n",
+      "6 :: 4032: [[9,1, 3]] : 288 :['X0Z8', 'X1Z8', 'X2X3Z5Z8', 'X2X4Z6Z8', 'Y3Y5Z6Z7', 'Z4Z5X6Z8', 'Z2Z3Z4X7', 'Z0Z1Y2Z6Z7Y8'] : False\n",
+      "6 :: 4033: [[9,1, 3]] : 768 :['X0Z7', 'X1Z8', 'X2Z4', 'X3Z4', 'Y5Y6Z7Z8', 'Z0Z5X7Z8', 'Z1Z6Z7X8', 'Z2Z3X4X5Z6Z7'] : False\n",
+      "6 :: 4041: [[9,1, 3]] : 768 :['X0Z8', 'X1Z8', 'X2Z6', 'X3Z7', 'X4X5Z6Z7', 'Z2Y4Y6Z8', 'Z3Y5Y7Z8', 'Z0Z1Y4Z5Z6Y8'] : False\n",
+      "6 :: 4057: [[9,1, 3]] : 768 :['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'Z5X6Z7Z8', 'Z3X4Y5Y6', 'Z2Z4Z6X7', 'Z0Z1Y4Z6Z7Y8'] : False\n",
+      "6 :: 4079: [[9,1, 3]] : 3072 :['Z0Z8', 'X1X7', 'X2X7', 'X3X5', 'X4X6', 'Z3Z4Z5Z6', 'X0X5X6X8', 'Z1Z2Z3Z5Z7Z8'] : True\n",
+      "6 :: 4280: [[9,1, 3]] : 9216 :['Z0Z7', 'Z1Z8', 'X2X6', 'X3X6', 'Z4Z5', 'X0X4X5X7', 'X1X4X5X8', 'Z2Z3Z4Z6Z7Z8'] : True\n",
+      "6 :: 4354: [[9,1, 3]] : 48 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X2X4Z5Z8', 'X3Z4X5Z8', 'Y2Z4Y6Z8', 'Y3Z5Y7Z8', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 4372: [[9,1, 3]] : 384 :['X0Z8', 'X1Z8', 'X2X3Z5Z7', 'X2X4Z6Z7', 'X2Z3X5Z8', 'X2Z4X6Z8', 'Z2Z5Z6X7', 'Z0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 4395: [[9,1, 3]] : 1152 :['Z0Z8', 'Z1Z8', 'Z2Z3Z6Z7', 'Z2Z4Z7Z8', 'Z5Z6Z7Z8', 'X2X4X5X6', 'X3X4X5X7', 'X0X1X2X3X5X8'] : False\n",
+      "6 :: 4404: [[9,1, 3]] : 1152 :['X0Z8', 'X1Z8', 'Y2Y3Z4Z6', 'Y2Z3Y4Z7', 'Z2X3X5Z7', 'X2Z4Z5X6', 'X2Z3Z5X7', 'Z0Z1X2Z3Z4X8'] : False\n",
+      "6 :: 4440: [[9,1, 3]] : 1536 :['X0Z8', 'X1Z8', 'X2Z6', 'X3Z5', 'X4Z7', 'Z4Z6X7Z8', 'Z2Z3X5X6Z7Z8', 'Z0Z1Z3Y5Z7Y8'] : True\n",
+      "6 :: 4569: [[9,1, 3]] : 1536 :['X0Z8', 'X1Z8', 'X2Z7', 'X4X5', 'X3X4Z6Z7', 'Y3Y6Z7Z8', 'Z2X3Z4Z5X7Z8', 'Z0Z1Z3Z4Z5X8'] : False\n",
+      "6 :: 5248: [[9,1, 3]] : 128 :['X0Z8', 'X1Z6', 'X2Z7', 'X3X4Z6Z7', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0Y3Z4Z5Z6Y8'] : False\n",
+      "6 :: 5256: [[9,1, 3]] : 512 :['X0Z8', 'X1Z7', 'X2Z5', 'X3X4', 'Z2X5Z6Z8', 'Z5X6Z7Z8', 'Z1Y3Z4Z6Y7Z8', 'Z0Z3Z4Z5Z6X8'] : False\n",
+      "6 :: 5388: [[9,1, 3]] : 1024 :['X0Z8', 'X1Z6', 'X2Z7', 'X3X4', 'Y3Z4Y5Z8', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'Z1X3Z5X6Z7Z8'] : True\n",
+      "6 :: 5478: [[9,1, 3]] : 256 :['X0Z7', 'X1Z8', 'X2X3Z5Z8', 'X2X4Z5Z7', 'Z3Z4X5Z6', 'Z5X6Z7Z8', 'Z0Y2Z3Z6Y7Z8', 'Z1Y2Z4Z6Z7Y8'] : False\n",
+      "6 :: 5547: [[9,1, 3]] : 256 :['X0Z7', 'X1X2', 'X1X3Z5Z7', 'X1X4Z6Z7', 'Y3Y5Z6Z8', 'X3Z4X6Z8', 'Z0Z1Z2X7', 'Y1Z2Z3Z4Z7Y8'] : False\n",
+      "6 :: 5550: [[9,1, 3]] : 128 :['X0Z7', 'X1Z8', 'X2X3Z7Z8', 'X2X4Z6Z7', 'Z2Z3Z4X5', 'Z4X6Z7Z8', 'Z0Y2Z5Z6Y7Z8', 'Z1X2Z3Z5Z6X8'] : False\n",
+      "6 :: 5562: [[9,1, 3]] : 3840 :['X0Z7', 'X1Z8', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'X2Z4X5Z6', 'X2Z3Z5X6', 'Z0Z1Y7Y8', 'Z0X2Z3Z4X7Z8'] : False\n",
+      "6 :: 5576: [[9,1, 3]] : 256 :['X0Z4', 'X1Z5', 'X2X3Z6Z7', 'Z0X2X4Z6', 'Z1X2X5Z6', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Y2Z3Z4Z5Z6Y8'] : False\n",
+      "6 :: 5582: [[9,1, 3]] : 768 :['X0Z6', 'X1Z7', 'X2X3Z4Z5', 'Y2Y4Z5Z8', 'X2Z3X5Z8', 'Z0X6Z7Z8', 'Z1Z6X7Z8', 'Y2Z3Z4Z6Z7Y8'] : False\n",
+      "6 :: 5859: [[9,1, 3]] : 256 :['X0Z6', 'X1Z7', 'X2Z8', 'X3X4Z5Z8', 'X3Z4X5Z7', 'Z0X6Z7Z8', 'Z1Y3Z4Z6Y7Z8', 'Z2Y3Z5Z6Z7Y8'] : False\n",
+      "6 :: 6194: [[9,1, 3]] : 256 :['X0Z7', 'X1Z8', 'X2X3Z4Z5', 'Y2Y4Z7Z8', 'Y3Y5Z7Z8', 'X2Z4X6Z8', 'Z0Y2Z3Z4Z6Y7', 'Z1X2Z5Z6Z7X8'] : False\n",
+      "6 :: 6294: [[9,1, 3]] : 3072 :['X0Z8', 'X1Z8', 'X2Z8', 'X3Z6', 'X4Z7', 'Z3X5X6Z8', 'Z4Z5Z6X7', 'Z0Z1Z2Y5Z7Y8'] : True\n",
+      "6 :: 6330: [[9,1, 3]] : 1536 :['X0Z8', 'X1Z8', 'X2Z8', 'X3Z7', 'X4X5Z6Z7', 'Y4Y6Z7Z8', 'Z3Z5Z6X7', 'Z0Z1Z2Z4Z5X8'] : False\n",
+      "6 :: 6354: [[9,1, 3]] : 18432 :['X0Z8', 'X1Z8', 'X2Z8', 'X3Z7', 'X4Z7', 'X5Z6', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : True\n",
+      "6 :: 6411: [[9,1, 3]] : 1536 :['X0Z8', 'X1Z8', 'X2Z8', 'X3X4Z6Z7', 'X3X5Z7Z8', 'Y3Z5Y6Z8', 'Y4Z5Y7Z8', 'Z0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 7917: [[9,1, 3]] : 384 :['X0Z8', 'X1Z6', 'X2Z7', 'X3Z5Z6Z8', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Z1Z2Z3Z4Y6Y7', 'Z0Z1Z4Z5Y6Y8'] : False\n",
+      "6 :: 7937: [[9,1, 3]] : 2304 :['X0Z7', 'X1Z7', 'X2Z8', 'X3Z8', 'X4X5Z6Z8', 'X4Z5X6Z7', 'Z0Z1Y4Z5Y7Z8', 'Z2Z3Y4Z6Z7Y8'] : False\n",
+      "6 :: 8513: [[9,1, 3]] : 6144 :['X0Z7', 'X1Z8', 'X2X3', 'X4X5', 'X2X4Z7Z8', 'Y2Z3Z4Z5Y6Z7', 'Z0Y2Z3Z6Y7Z8', 'Z1X2Z4Z5Z6X8'] : False\n",
+      "6 :: 8802: [[9,1, 3]] : 82944 :['Z0Z6', 'Z1Z6', 'Z2Z7', 'Z3Z8', 'Z4Z8', 'Z5Z7', 'X0X1X2X5X6X7', 'X0X1X3X4X6X8'] : True\n",
+      "6 :: 9652: [[9,1, 3]] : 4320 :['Y0Y1Z7Z8', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'X2Z4X5Z6', 'X2Z3Z5X6', 'Z0Z1X7X8', 'X0Z1X2Z3Z4Z8', 'Z0Y2Z5Z6Y7Z8'] : False\n",
+      "6 :: 9733: [[9,1, 3]] : 192 :['X0X1Z7Z8', 'X0X2Z4Z7', 'X0X3Z5Z7', 'Z2X4Z7Z8', 'Z3X5Z7Z8', 'Z0Z1Y7Y8', 'Y0Z1Z2Z3Y6Z7', 'Y0Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 9882: [[9,1, 3]] : 32 :['X0X1Z6Z7', 'X2Z3Z4Z8', 'Z2X3Z5Z8', 'Z2X4Z6Z7', 'Z3X5Z6Z7', 'Y0X3Y4X6', 'Y1X3Y4X7', 'Y0Z1Z2Z3Z6Y8'] : False\n",
+      "6 :: 10077: [[9,1, 3]] : 288 :['Y0Y1Z7Z8', 'Z0X1X2Z4', 'Z0X1X3Z5', 'X0Z1Z2X4', 'X0Z1Z3X5', 'X0Z1X6Z8', 'Y0Z1Z4Z5Z6Y7', 'X0Z2Z3Z6Z7X8'] : False\n",
+      "6 :: 10078: [[9,1, 3]] : 96 :['Y0Y1Z7Z8', 'X0Z1X2Z8', 'X0Z1X3Z6', 'Z0X1X4Z6', 'X5Z6Z7Z8', 'Z3Z4Z5X6', 'Y0Z1Z2Z3Z5Y7', 'X0Z2Z4Z5Z7X8'] : False\n",
+      "6 :: 10080: [[9,1, 3]] : 32 :['Y0Y1Z2Z8', 'X0X2Z7Z8', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X0Z1Z2Z3Z4X7', 'Y0Z1Y3Z4Z6X8'] : False\n",
+      "6 :: 10201: [[9,1, 3]] : 1152 :['X0X1Z4Z5', 'X0X2Z4Z6', 'X0X3Z4Z7', 'Z0Z1Y4Y5', 'Z0Z2Y4Y6', 'Z0Z3Y4Y7', 'Y0Y4Z5Z6Z7Z8', 'Y0Z1Z2Z3Z4Y8'] : False\n",
+      "6 :: 10215: [[9,1, 3]] : 384 :['X1Z5Z7Z8', 'X2Z6Z7Z8', 'X3Z5Z6Z8', 'X4Z5Z6Z7', 'Z1Z3Z4X5', 'Z2Y6X7Y8', 'X0Z2Z3Z4X6Z7', 'Y0Z1Z2Z4Y7Z8'] : False\n",
+      "6 :: 10276: [[9,1, 3]] : 32 :['X0X1Z3Z8', 'X2Z5Z6Z8', 'X0Z3X4Z6', 'X0Z2Z3X5', 'X0Z2Z4X6', 'Y0X3Z6Y7', 'Y1Z2Y7X8', 'X0Z1X3Z4Z5Z7'] : False\n",
+      "6 :: 10278: [[9,1, 3]] : 128 :['X0X1Z4Z5', 'X0X2Z4Z6', 'X0X3Z4Z7', 'Y0Y4Z5Z8', 'X0Z1X5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Y0Z1Z2Z3Z4Y8'] : False\n",
+      "6 :: 11880: [[9,1, 3]] : 24 :['X0Z8', 'X1X2Z4Z8', 'X1X3Z6Z8', 'Y2Y4Z5Z7', 'Z4X5Z6Z8', 'Z3Z5X6Z8', 'Z1Z2Z3X7', 'Z0Y1Z5Z6Z7Y8'] : False\n",
+      "6 :: 12078: [[9,1, 3]] : 192 :['X0Z5', 'X1X2Z6Z7', 'X1X3Z6Z8', 'Z1Z2Z3X4', 'Z0Z1Y5Y6', 'Z0Z2Y5Y7', 'Z0Z3Y5Y8', 'Z0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 12292: [[9,1, 3]] : 16 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 12372: [[9,1, 3]] : 16 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 12518: [[9,1, 3]] : 96 :['X0Z8', 'Y1Y2Z7Z8', 'Z1X2X3Z5', 'Z1X2X4Z6', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'Z2Z3Z4X7', 'Z0Y1Z2Z5Z6Y8'] : False\n",
+      "6 :: 12700: [[9,1, 3]] : 8 :['X0Z7', 'X3Z4Z5Z8', 'Z2Z3X4Z8', 'Z1Z3X5Z8', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'X1X2Z3X8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 12762: [[9,1, 3]] : 32 :['X0Z1', 'X3Z4Z6Z7', 'Z3X4Z5Z8', 'Z4X5Z7Z8', 'Z1X2Z3X6', 'Z2X4Z6X7', 'Z2X3Z5X8', 'Z0X1X2Z6Z7Z8'] : False\n",
+      "6 :: 12789: [[9,1, 3]] : 8 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 12795: [[9,1, 3]] : 64 :['X0Z4', 'X2Z5Z7Z8', 'X3Z6Z7Z8', 'Z0X4Z6Z8', 'Z2X5Z6Z7', 'Y1Y2Z3X7', 'Y1Y2Y6Y8', 'X1Z3Z4Z5X6Z7'] : False\n",
+      "6 :: 12822: [[9,1, 3]] : 32 :['X0Z7', 'X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 12843: [[9,1, 3]] : 32 :['X0Z2', 'Z0Z1X2Z8', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 13523: [[9,1, 3]] : 96 :['X0Z7', 'X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 5267: [[9,2, 3]] : 16 :['X0Z8', 'X1Z5', 'X2Y3Y6Z8', 'Z2Z4Z6X7', 'X3X4Z5Z6Z7Z8', 'Z1X2Z4X5Z7Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 24508: [[9,2, 3]] : 64 :['X0Z7', 'X1X2', 'X3X4Z5Z6', 'X3Z4X6Z8', 'X1Z3X5Z6Z7Z8', 'Z0Y1Z2X3Z5Y7', 'Z1Z2Y3Z4Z5Y8'] : False\n",
+      "6 :: 25670: [[9,2, 3]] : 128 :['X0Z7', 'X1X2', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'X1Z3X5Z6Z7Z8', 'Z0Y1Z2X3Z5Y7', 'Y1Z2Z3Z4Z7Y8'] : False\n",
+      "6 :: 31962: [[9,2, 3]] : 2 :['X2Z5Z6Z8', 'X0Z3X4Z6', 'X0Z2Z3X5', 'Y1Z2Y7X8', 'Y1Y3Z4Z5Z7Z8', 'X1Z2Z3Z4X6Z8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 32087: [[9,2, 3]] : 8 :['Z0X2Z7Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Y0Y2Z4X6', 'Z0X1X3Z4Z5Z6', 'X0Z1Z5Z6X7Z8', 'Y1Y2Z3Z4Z5X8'] : False\n",
+      "6 :: 32103: [[9,2, 3]] : 1 :['X1Z5Z6Z8', 'Y2Y3Z4Z5', 'X1X2X4Z7', 'Z0Y1X6Y7', 'Z0Y1Y2X8', 'Z1Y2Z3Y5Z7Z8', 'X0Y1X2Z3Z4Y6'] : False\n",
+      "6 :: 42867: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0Z1Z4X6', 'Y0Z4Z5Z6Y7Z8', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 44039: [[9,2, 3]] : 4 :['X2X3Z5Z6', 'X1Y2Y3X4', 'Z2Z3X5X6', 'Y1Z4Y7X8', 'Y1Y2Z3Z5Z7Z8', 'X0X1Z3Z4X5Z7', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 44085: [[9,2, 3]] : 1 :['X1Z2Z6Z8', 'Z1X2X3Z4', 'Z2X4X5Z6', 'Y0X3Z6Y7', 'Y5X6X7Y8', 'Y1Y2Z3X4Z5Z7', 'X0X2Z4Z5X6Z8'] : False\n",
+      "6 :: 44124: [[9,2, 3]] : 2 :['X1X2Z6Z7', 'Z0X1Z3X4', 'Y3Y4Z5Z6', 'Z3X5Z7Z8', 'Y0Y2Z4X6', 'X4Y6X7Y8', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 47765: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'X3Z4Z5Z8', 'Z2X4X5Z6', 'Y0Y3X4X7', 'Z0X2Y6Y7', 'X1Z2Z3X4Z7Z8', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 48283: [[9,2, 3]] : 2 :['Z0X2Z7Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Y1Y2Y3Y8', 'Z0X1X3Z4Z5Z6', 'Y0X1Z2Z4Y6Z7', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 48777: [[9,2, 3]] : 4 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z2X4X5Z6', 'Y0X3Z6Y7', 'X1Z2Z3X4Z7Z8', 'X0Y1Z2Z4Y6Z8', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 50090: [[9,2, 3]] : 1 :['Z2X3Z6Z7', 'X0Z2Z4X5', 'Y1X3X4Y5', 'X0Z3Z4X6', 'Z1X2Z3X4Z6Z8', 'Y0Z3Z5Z6Y7Z8', 'Z0Z1Z2Z5Z6X8'] : False\n",
+      "6 :: 52592: [[9,2, 3]] : 1 :['Z1X3Z6Z8', 'X4Z5Z6Z7', 'X1Y2Y3X4', 'Z2Z3X5X6', 'Z0Z1Y4Y7', 'Z0Y5Y6X8', 'X0X1Z3Z4X5Z7'] : False\n",
+      "6 :: 52777: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0X2X3X6', 'Y5X6X7Y8', 'X1Z2X3Z4Z5Z6', 'Y0Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 53693: [[9,2, 3]] : 4 :['X0Z1X2Z8', 'Z0X1X4Z6', 'X3X4Z7Z8', 'X5Z6Z7Z8', 'X0Z1Y3Z4Z5Y6', 'X1Z2Z3Z5X7Z8', 'Z0Y1Z2Z4Z5Y8'] : False\n",
+      "6 :: 54539: [[9,2, 3]] : 4 :['X2Z5Z6Z8', 'X0Z3X4Z6', 'X1Z2X5Z8', 'X0Z2Z4X6', 'Y1Y3Z4Z5Z7Z8', 'Z0Z1Z4Z5Z6X7', 'Y0Y2Z3Z4Z7X8'] : False\n",
+      "6 :: 56126: [[9,2, 3]] : 1 :['X2Z3Z5Z8', 'Z3X4Z6Z7', 'Y1Y2X4X5', 'Y0X1Z4Y7', 'Y0Y3Y6Y8', 'Y2X6Y7X8', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 56564: [[9,2, 3]] : 32 :['X0X1Z3Z8', 'X0Z3X4Z6', 'X0Z2Z3X5', 'Y1X2Y3Z4Z6Z7', 'X0Y2Z4Z5Y6Z8', 'Z0Z1Z4Z5Z6X7', 'Y0Y2Z3Z4Z7X8'] : False\n",
+      "6 :: 56691: [[9,2, 3]] : 2 :['X1X2Z3Z6', 'Z2X3Z4Z8', 'Y1Y2X4X5', 'Y0X1Z4Y7', 'Y0Y3Y6Y8', 'X1Z3X4Z5Z7Z8', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 57877: [[9,2, 3]] : 1 :['X1X2Z3Z6', 'Z2X3Z4Z8', 'Z1Z2X5Z7', 'Y1Y3Y4Y5', 'Y0X1Z4Y7', 'Z0Y1Y2X8', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 62350: [[9,2, 3]] : 4 :['X0X1X3X4', 'Z1Z4X5X6', 'X2X3Y5Y6', 'Z0Y3X6Y7', 'Y2X5Y7X8', 'X4Y6X7Y8', 'X0X2Z3Z4X5Z8'] : False\n",
+      "6 :: 64257: [[9,2, 3]] : 2 :['X2Z3Z5Z8', 'Z3X4Z6Z7', 'Y1Y2X4X5', 'X0Z1Z4X6', 'Y0X1Z4Y7', 'X1Z2X3Z4Z5Z6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 66983: [[9,2, 3]] : 1 :['X1X2Z6Z7', 'Z0X1Z3X4', 'Y3Y4Z5Z6', 'Z3X5Z7Z8', 'Z3X6X7X8', 'X0Z2Z4X6Z7Z8', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 68753: [[9,2, 3]] : 2 :['X1Z2Z6Z8', 'X3Z4Z5Z8', 'Z2Z3X5Z7', 'Y1X2X4Y5', 'X0Z1Z4X6', 'Y0X3Z6Y7', 'Z0Y2Z3Z5Z6Y8'] : False\n",
+      "6 :: 70678: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z2X4X5Z6', 'X1X3Y4Y5', 'X0X2X3X6', 'Y0Y3X4X7', 'Y5X6X7Y8', 'X1Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 70804: [[9,2, 3]] : 16 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Y0Y4Z5Z8', 'X2Z3X7Z8', 'Z1X2Z4X5Z6Z8', 'X0Z2Z4X6Z7Z8', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 70813: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0Z1Z4X6', 'Z0X2Y6Y7', 'X1Z2X3Z4Z5Z6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 72030: [[9,2, 3]] : 2 :['Z1X2Z5Z8', 'Z2Z3X5Z7', 'X1X4X5Z8', 'X0Z1Z4X6', 'Y0X3Z6Y7', 'X1Z2X3Z4Z5Z6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 72428: [[9,2, 3]] : 1 :['X2Z3Z5Z8', 'Z2X3Z4Z8', 'X1X2X4Z7', 'X0Z1Z4X6', 'Z0Y1Y2X8', 'Y1Z2Y5Z6Z7Z8', 'Y0Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 73016: [[9,2, 3]] : 1 :['X1Z2Z6Z8', 'Z1X2X3Z4', 'Z2X4X5Z6', 'Y0X3Z6Y7', 'Z0Y1Z3Y8', 'Y1Y2Z3X4Z5Z7', 'X0X2Z4Z5X6Z8'] : False\n",
+      "6 :: 73142: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'X3Z4Z5Z8', 'Z3X4Z6Z7', 'X1X4X5Z8', 'X0X2X3X6', 'Y0X1Z2Z4Z5Y7', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 73362: [[9,2, 3]] : 1 :['X1Z5Z6Z8', 'Z2X3Z4Z8', 'X1X2X4Z7', 'Z1Z2X5Z7', 'Z0Y1X6Y7', 'X0Y1X2Z3Z4Y6', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 73374: [[9,2, 3]] : 1 :['Z0X2Z7Z8', 'X3Z4Z5Z8', 'Z0X1Z3X4', 'X4X5Z6Z7', 'Y0Y2Z4X6', 'Z3X6X7X8', 'X0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 73784: [[9,2, 3]] : 1 :['X2Z3Z5Z8', 'X1X2X4Z7', 'Y1Y3Y4Y5', 'Y0X1Z4Y7', 'Z0Y1Y2X8', 'X1Z2X3Z4Z5Z6', 'X0Y1Z4Z5Y6Z8'] : False\n",
+      "6 :: 74247: [[9,2, 3]] : 4 :['X1Z2Z3Z7', 'X2X3Z5Z6', 'Z2Z3X5X6', 'Z0X1X4X8', 'Z1X2X4Z6Z7Z8', 'X0Z1Y2Z4Y5Z8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 74385: [[9,2, 3]] : 3 :['Z1X4Z5Z8', 'Z4X5Z6Z8', 'X1Y2Y3X5', 'X3Z5X6Z7', 'Z0Z3Y5Y8', 'X0X2Z3Z6Z7Z8', 'Y0Y1Z3Z4X7Z8'] : False\n",
+      "6 :: 74429: [[9,2, 3]] : 1 :['Z1X2Z5Z8', 'Z2X4X5Z6', 'X1X3Y4Y5', 'X0Z1Z4X6', 'Y0X3Z6Y7', 'Y5X6X7Y8', 'X1Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 74529: [[9,2, 3]] : 6 :['X0Z1Z3X5', 'X1Y3Y4X6', 'X2X5X6Z8', 'Y0X3Z4Y7', 'Y2X5Y7X8', 'X0Y1Y2Z4Z5Z7', 'X0X1Z2X3Z6Z7'] : False\n",
+      "6 :: 74933: [[9,2, 3]] : 2 :['Z2X4Z6Z7', 'X0X1X3X4', 'Z1Z4X5X6', 'X2X3Y5Y6', 'Z0Y3X6Y7', 'Y2X5Y7X8', 'Y1Z2Z3Y5Z7Z8'] : False\n",
+      "6 :: 75654: [[9,2, 3]] : 2 :['X3Z4Z6Z8', 'Z3X4Z5Z7', 'X0X2Y3Y4', 'Z1Z2Z3X6', 'Y0Z2X5Y7', 'Y1Y4Y6Y7', 'Z0Y1Z3Z6Z7Y8'] : False\n",
+      "6 :: 75674: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z2Z3X5Z7', 'X1X4X5Z8', 'Y0X3Z6Y7', 'Y5X6X7Y8', 'X0Y1Z2Z4Y6Z8'] : False\n",
+      "6 :: 75713: [[9,2, 3]] : 12 :['Y0Y1Z7Z8', 'Z0X1X3Z5', 'X0Z1Z2X4', 'Y2Y3Y4Y5', 'X2Z4X6Z7', 'Z0X2Z5Z6X7Z8', 'Z1Y2Z3Z4Z6Y8'] : False\n",
+      "6 :: 75720: [[9,2, 3]] : 48 :['X0X1Z4Z5', 'X2X3Z6Z7', 'X0Z1X5Z8', 'Z2Z3Y6Y7', 'Z0X2X4Z5Z6Z8', 'X0Z2Z4X6Z7Z8', 'Y0Z1Z2Z3Z4Y8'] : False\n",
+      "6 :: 75999: [[9,2, 3]] : 1 :['Y1Y2Z5Z6', 'Z1X2X3Z4', 'Z3X4Z6Z7', 'X1X4X5Z8', 'Z0X2Y6Y7', 'Y5X6X7Y8', 'X0Y1Z2Z4Y6Z8'] : False\n",
+      "6 :: 80000: [[9,2, 3]] : 48 :['X3Z4Z6Z8', 'Z3X4Z5Z7', 'X0X2Y3Y4', 'Y0Z2X5Y7', 'Z0Y2Y6X8', 'Y0X1Z4Y5Z6Z7', 'X0Y1Z2Z3Z5Y6'] : False\n",
+      "6 :: 80001: [[9,2, 3]] : 64 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0X2X4Z5Z6Z8', 'X0Z2Z4X6Z7Z8', 'Y0Z1Z2Z3Z4Y8'] : False\n",
+      "6 :: 83847: [[9,2, 3]] : 8 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z4X5X6Z8', 'Z2Z3X4X5Z6Z7', 'Z0Y1Z2Y3X7Z8', 'X1Z3Z4Z5Z7X8'] : False\n",
+      "6 :: 83870: [[9,2, 3]] : 16 :['X0Z4', 'X3Z6Z7Z8', 'Y2Y5Z6Z8', 'Y1Y2Y6Y8', 'Z0X2X4Z5Z6Z7', 'X1X2Z3Z4X6Z8', 'Y1Z2Z3Z5Y7Z8'] : False\n",
+      "6 :: 83891: [[9,2, 3]] : 8 :['X0Z7', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1X4Y5Y6', 'Z1Y2Y3X8', 'Y1Z2Z3Y4Z7Z8', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 84013: [[9,2, 3]] : 32 :['X0Z4', 'X2X3Z6Z7', 'X2X5Z7Z8', 'Z2Z3X6X7', 'Z0Z1X2X4Z6Z8', 'X1Z2Z4Z5X6Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 88105: [[9,2, 3]] : 32 :['X0Z4', 'X2X3Z6Z7', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z0Z1X2X4Z6Z8', 'X1Z4X5Z6Z7Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 89082: [[9,2, 3]] : 8 :['X0Z7', 'Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z4X5X6Z8', 'Y1X2Y5X8', 'Z1X2Z3Z4X5Z7', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 89286: [[9,2, 3]] : 4 :['X0Z7', 'Z1Y2Y3Z6', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1Y4X5Y8', 'X1Z2X4Z5Z6Z8', 'Y1Y2Z3Z4X5Z7'] : False\n",
+      "6 :: 90258: [[9,2, 3]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Y2Z5Y6Z8', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'Z0Z1X2X4Z6Z8', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 90766: [[9,2, 3]] : 12 :['X0Z8', 'X1X2Z3Z6', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'X1Z2X3X4Z7Z8', 'Y1X3Z5Z6Y7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 91131: [[9,2, 3]] : 8 :['X0Z8', 'Y3Y4Z6Z7', 'Y1Y2X5Z8', 'X3Z4X5Z7', 'X1Y2Z3Z5Y6Z7', 'Y1X2Z4Z5Z6Y7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 92647: [[9,2, 3]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Z2Z3X5Z7', 'Y1Z2X3Y7', 'X2X6X7X8', 'Z0X2X4Z5Z6Z7', 'X1X2Y3Z4Y6Z7'] : False\n",
+      "6 :: 93414: [[9,2, 3]] : 4 :['X0Z8', 'X1X2Z4Z7', 'X1Z5X6Z8', 'Y2X3Y4X6', 'Z3Y5Y6Z7', 'Y1X3Z4Z5Z6Y7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 93830: [[9,2, 3]] : 4 :['X0Z8', 'X3Z4Z5Z8', 'X4X5Z6Z7', 'Z1Z3Y4Y6', 'Z2Z3Y5Y7', 'Y1Y2Z3X4Z7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 94601: [[9,2, 3]] : 96 :['X0Z3', 'X1X2Z5Z6', 'Z1Z2X5X6', 'Z3X4X7Z8', 'Z0X3X4Z5Z6Z7', 'Z0Z1X3Z4X5Z8', 'Y1Z2Z3Y4Z6X8'] : False\n",
+      "6 :: 94752: [[9,2, 3]] : 8 :['X0Z8', 'Y2Y3Z5Z7', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'Y1X2X5Y6', 'Y1Y2Z3Z6X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 99377: [[9,2, 3]] : 4 :['X0Z8', 'X1X2Z4Z7', 'Z3X5Z7Z8', 'Y2Y3Y4Y5', 'Y1Z3X4Y6', 'Z1X3Z4Z6X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 169211: [[9,3, 3]] : 6 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0Y2Z3Z5Z6Z8', 'X1Y2Z3X4Z6X7'] : False\n",
+      "6 :: 169549: [[9,3, 3]] : 6 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'X0Y1X2Z5Z7Z8', 'Z0X2Y3Z4Z5X7'] : False\n",
+      "6 :: 169613: [[9,3, 3]] : 6 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y1Z3Z4Z5X6Z8', 'Y0Z1Y4Z5X6X7'] : False\n",
+      "6 :: 169989: [[9,3, 3]] : 12 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y1Z3Z4Z5X6Z8', 'Y0Y2Y3Y5X6X7'] : False\n",
+      "6 :: 169997: [[9,3, 3]] : 6 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y1Z3Z4Z5X6Z8', 'Y0X1Y2Y3Z5X7'] : False\n",
+      "6 :: 170233: [[9,3, 3]] : 48 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z0Z2Z3Z4Z6X7', 'X1Z2X3Z5Z7Z8', 'Y0Y1Z3Z5Z6Z7', 'X0Y2Z3Y4Z5X6'] : False\n",
+      "6 :: 170234: [[9,3, 3]] : 324 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0X1Z2Z3Z4Z6', 'Y0Z1Y2Z5Z7Z8', 'Z0Z1Y3X4Z5X7', 'Y0Z3Y4Z5X6Z7'] : False\n",
+      "6 :: 170235: [[9,3, 3]] : 1296 :['Y0X1X2Y3X4X5', 'Z0Z1X2X6Y7X8', 'Z0Z2Z3Z4Z6Y7', 'X1Z2Y3Z5Z7Z8', 'X0Z1Z2X3Z4Z5', 'X0X2X3X4X6Z7'] : False\n",
+      "8 :: 7169: [[9,1, 3]] : 61440 :['X0Z8', 'X1Z8', 'X2Z8', 'X3Z8', 'X4Z6', 'X5Z7', 'Z4Z5Y6Y7', 'Z0Z1Z2Z3Z4X6Z7X8'] : True\n",
+      "8 :: 7180: [[9,1, 3]] : 46080 :['X0Z8', 'X1Z8', 'X2Z8', 'X3Z8', 'X4X5Z6Z7', 'Y4Y6Z7Z8', 'X4Z5X7Z8', 'Z0Z1Z2Z3Y4Z5Z6Y8'] : False\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "special_codes_d3 = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['d'] > 2 and code['k'] > 0:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_d3 += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1079,
+   "id": "972031c1-e142-4965-8a4f-98fdbc86fae1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "232"
+      ]
+     },
+     "execution_count": 1079,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(special_codes_d3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1091,
+   "id": "08e2563b-8adc-4964-82b0-e9c4960f87d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 6: [[4,1, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1Z2Z3'] : True\n",
+      "4 :: 9: [[4,2, 2]] : 144 :['Z0Z1Z2Z3', 'X0X1X2X3'] : True\n",
+      "4 :: 17: [[6,1, 2]] : 128 :['Z0Z1', 'Z2Z5', 'X3X4', 'Z0Z3Z4Z5', 'X0X1X2X5'] : True\n",
+      "4 :: 31: [[6,1, 2]] : 96 :['Z1Z5', 'Z2Z5', 'X3X4', 'Z0Z3Z4Z5', 'X0X1X2X5'] : True\n",
+      "4 :: 55: [[6,1, 2]] : 384 :['X0X1', 'X2X3', 'X4X5', 'Z0Z1Z2Z3', 'Z0Z1Z4Z5'] : True\n",
+      "4 :: 56: [[6,2, 2]] : 64 :['X0X1', 'Z3Z5', 'Z0Z1Z2Z4', 'X2Y3X4Y5'] : True\n",
+      "4 :: 126: [[6,2, 2]] : 288 :['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5'] : True\n",
+      "4 :: 129: [[6,2, 2]] : 64 :['X0X1', 'Z0Z1Z2Z4', 'Z0Z1Z3Z5', 'Y2Y3Y4Y5'] : True\n",
+      "4 :: 329: [[8,1, 2]] : 128 :['X0X4', 'X1X5', 'Z2Z3', 'Z0Z2Z4Z7', 'Z1Z2Z5Z6', 'X2X3X4X6', 'X2X3X5X7'] : False\n",
+      "4 :: 497: [[8,1, 2]] : 384 :['Z0Z1', 'Z3Z7', 'Z4Z7', 'X5X6', 'X0X1X2X6', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 498: [[8,1, 2]] : 384 :['X0X1', 'X2X6', 'Z3Z4', 'X5X6', 'Z0Z1Z3Z7', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 549: [[8,1, 2]] : 768 :['Z1Z2', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 554: [[8,1, 2]] : 768 :['Z1Z7', 'Z2Z7', 'X3X4', 'X5X6', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X0X1X2X7'] : True\n",
+      "4 :: 716: [[8,1, 2]] : 128 :['Z0Z6', 'Z1Z7', 'Z2Z3Z6Z7', 'Z4Z5Z6Z7', 'Y2Y3Y4Y5', 'X0X2X4X6', 'X1X3X4X7'] : False\n",
+      "4 :: 723: [[8,1, 2]] : 128 :['Z0Z7', 'Z1Z6', 'X2X4', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'X1X4X5X6', 'Y0X3X4Y7'] : True\n",
+      "4 :: 763: [[8,1, 2]] : 1024 :['Z0Z1', 'Z2Z7', 'X3X4', 'X5X6', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X0X1X2X7'] : True\n",
+      "4 :: 797: [[8,1, 2]] : 128 :['Z0Z6', 'Z1Z7', 'Y2Y3Y4Y5', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'Y0X3X4Y6', 'Y1X3X4Y7'] : False\n",
+      "4 :: 985: [[8,1, 2]] : 1152 :['Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1091: [[8,1, 2]] : 6144 :['X0X1', 'X2X3', 'X4X5', 'X6X7', 'Z0Z1Z2Z3', 'Z0Z1Z4Z5', 'Z0Z1Z6Z7'] : True\n",
+      "4 :: 1093: [[8,1, 2]] : 2048 :['Z1Z2', 'Z3Z7', 'Z0Z4', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1200: [[8,1, 2]] : 384 :['Z0Z1Z5Z6', 'Z0Z2Z5Z7', 'Z0Z3Z6Z7', 'Z4Z5Z6Z7', 'X0X3X4X5', 'X1X3X4X6', 'X2X3X4X7'] : False\n",
+      "4 :: 1448: [[8,1, 2]] : 96 :['X0X5', 'Z1Z2Z6Z7', 'Z1Z3Z4Z7', 'X1X2X4X5', 'Z0Z1Z5Z7', 'X1X3X5X6', 'X2X3X5X7'] : False\n",
+      "4 :: 352: [[8,2, 2]] : 64 :['Z0Z7', 'Z1Z2', 'X4X6', 'X1X2X3X5', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 532: [[8,2, 2]] : 32 :['Z3Z4', 'X5X6', 'X0X1X2X6', 'Z0Z1Z3Z7', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 746: [[8,2, 2]] : 128 :['Z1Z7', 'X2X6', 'X3X4', 'Z0Z3Z4Z7', 'Y2Z5Y6Z7', 'X0X1X5X7'] : True\n",
+      "4 :: 1284: [[8,2, 2]] : 192 :['Z0Z7', 'Z1Z7', 'Z3Z4', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X5X7'] : True\n",
+      "4 :: 1314: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'Z0Z3Z5Z7', 'Z0Z4Z6Z7', 'Y3Y4Y5Y6', 'X0X1X2X7'] : True\n",
+      "4 :: 1783: [[8,2, 2]] : 32 :['Z3Z7', 'Z5Z6', 'Z0Z1Z2Z6', 'Z0Z1Z4Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1796: [[8,2, 2]] : 64 :['Z3Z7', 'Z5Z6', 'Z0Z1Z6Z7', 'Z2Z4Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1838: [[8,2, 2]] : 64 :['Z4Z7', 'X5X6', 'X0X1X2X6', 'Z0Z1Z3Z7', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 1848: [[8,2, 2]] : 64 :['Z4Z7', 'X5X6', 'X0X1X2X6', 'Z0Z1Z3Z7', 'Z2Z3Y5Y6', 'X2X3Y4Y7'] : True\n",
+      "4 :: 1892: [[8,2, 2]] : 256 :['X0X1', 'Z3Z7', 'Z4Z5', 'Z0Z1Z2Z6', 'X2X4X5X6', 'X3X4X5X7'] : True\n",
+      "4 :: 1893: [[8,2, 2]] : 256 :['X1X2', 'X3X5', 'X4X6', 'Z0Z1Z2Z7', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 1947: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z0Z3Z5Z7', 'Z0Z4Z6Z7', 'Y3Y4Y5Y6', 'X0X1X2X7'] : True\n",
+      "4 :: 1948: [[8,2, 2]] : 256 :['Z0Z7', 'Z1Z2', 'X1X2X3X5', 'X1X2X4X6', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 1949: [[8,2, 2]] : 512 :['X0X1', 'X2X3', 'Z0Z1Z2Z3', 'Z0Z1Z4Z6', 'Z0Z1Z5Z7', 'Y4Y5Y6Y7'] : True\n",
+      "4 :: 2023: [[8,2, 2]] : 256 :['X0X1', 'Z4Z5', 'Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'X2X4X5X6', 'X3X4X5X7'] : True\n",
+      "4 :: 3960: [[8,2, 2]] : 32 :['Z0Z1Z6Z7', 'Z0Z2Z3Z7', 'Z4Z5Z6Z7', 'X2X3X4X5', 'X0X2X4X6', 'X1X3X4X7'] : False\n",
+      "4 :: 4322: [[8,2, 2]] : 16 :['Z1Z2Z6Z7', 'Z1Z3Z4Z7', 'X1X2X4X5', 'Z0Z1Z5Z7', 'X0X1X3X6', 'X0X2X3X7'] : False\n",
+      "4 :: 4520: [[8,2, 2]] : 64 :['Z0Z1Z6Z7', 'Y2Y3Y4Y5', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'X0X3X4X6', 'X1X3X4X7'] : False\n",
+      "4 :: 4926: [[8,2, 2]] : 2304 :['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5', 'Z0Z1Z6Z7', 'X0X1X6X7'] : True\n",
+      "4 :: 4927: [[8,2, 2]] : 768 :['Z0Z1Z6Z7', 'Z0Z2Z3Z6', 'Z2Z4Z6Z7', 'Z0Z2Z5Z7', 'X0X4X5X6', 'X1X2X3X7'] : False\n",
+      "4 :: 5275: [[8,2, 2]] : 48 :['X0X5', 'Z1Z2Z6Z7', 'Z3Z4Z6Z7', 'Z0Z5Z6Z7', 'X1X4X5X6', 'X2X3X5X7'] : False\n",
+      "4 :: 5457: [[8,2, 2]] : 16 :['X0X5', 'Z1Z2Z3Z4', 'Z0Z1Z2Z5', 'X1X4X5X6', 'X2X3X5X7', 'Z1Z3Z6Z7'] : False\n",
+      "4 :: 5547: [[8,2, 2]] : 64 :['Z5Z6', 'Z0Z2Z6Z7', 'Z0Z1Z3Z6', 'Z0Z1Z4Z7', 'X0X4X5X6', 'X1X2X3X7'] : False\n",
+      "4 :: 5551: [[8,2, 2]] : 384 :['X0X1', 'Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'Z0Z1Z4Z5', 'X2X4X5X6', 'X3X4X5X7'] : False\n",
+      "4 :: 1561: [[8,3, 2]] : 128 :['Z1Z5', 'Z3Z7', 'Z0Z2Z4Z6', 'X0Y1X4Y5', 'X2Y3X6Y7'] : True\n",
+      "4 :: 2314: [[8,3, 2]] : 8 :['Z0Z1Z3Z6', 'Z2Z4Z6Z7', 'Z0Z1Z5Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 4899: [[8,3, 2]] : 48 :['Z0Z1Z6Z7', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : False\n",
+      "4 :: 5813: [[8,3, 2]] : 64 :['Z0Z1Z2Z7', 'X1X2X3X5', 'X1X2X4X6', 'Z3Z4Z5Z6', 'X0X5X6X7'] : False\n",
+      "4 :: 6798: [[8,3, 2]] : 512 :['Z0Z1Z4Z5', 'Z0Z2Z4Z6', 'Z0Z3Z4Z7', 'Y0Y1Y4Y5', 'Y2Y3Y6Y7'] : True\n",
+      "4 :: 7044: [[8,3, 2]] : 32 :['Z1Z7', 'Z0Z3Z4Z7', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X5X7'] : True\n",
+      "4 :: 7048: [[8,3, 2]] : 16 :['X4X6', 'Z0Z1Z2Z7', 'X1X2X3X5', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 7053: [[8,3, 2]] : 128 :['Z3Z7', 'Z0Z1Z4Z5', 'Z0Z2Z4Z6', 'Y0Y1Y4Y5', 'X2Y3X6Y7'] : True\n",
+      "4 :: 2326: [[8,4, 2]] : 128 :['X4X5X6X7', 'X0X1X2X3', 'Z0Z1Z4Z5', 'Z2Z3Z6Z7'] : True\n",
+      "4 :: 22493: [[9,2, 2]] : 32 :['Z0Z8', 'Z1Z7', 'X2X3X4X5', 'Z2Z4Z6Z8', 'Z3Z5Z6Z7', 'X1X4X6X7', 'X0X5X6X8'] : True\n",
+      "4 :: 44074: [[9,2, 2]] : 12 :['Z0Z1Z6Z8', 'Z0Z2Z7Z8', 'Z3Z4Z6Z8', 'Z3Z5Z7Z8', 'X1X3X5X6', 'X2X3X4X7', 'X0X4X5X8'] : False\n",
+      "4 :: 94864: [[9,2, 2]] : 8 :['Z0Z7', 'Z1Z2Z4Z8', 'X2X3X4X5', 'Z3Z5Z7Z8', 'Z3Z4Z6Z7', 'X1X3X6X8', 'Y0X4Y7X8'] : False\n",
+      "4 :: 111894: [[9,3, 2]] : 4 :['Z0Z1Z2Z3', 'X4X5X6X7', 'Z0Z6Z7Z8', 'Z2Z4Z5Z8', 'X0X1X4X8', 'X2X3X6X8'] : False\n",
+      "4 :: 132216: [[9,3, 2]] : 12 :['Z0Z1Z2Z3', 'Z2Z5Z7Z8', 'X0X2X4X5', 'X1X2X6X7', 'X1X3X5X8', 'Z1Z4Z5Z6'] : False\n",
+      "6 :: 50: [[6,1, 2]] : 768 :['Z0Z5', 'Z1Z5', 'Z2Z5', 'Z3Z4', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 56: [[6,1, 2]] : 1152 :['Z0Z4', 'Z1Z5', 'Z2Z5', 'Z3Z4', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 38: [[6,2, 2]] : 288 :['Z1Z5', 'Z2Z5', 'Z0Z3Z4Z5', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 52: [[6,2, 2]] : 128 :['Z2Z5', 'Z3Z4', 'Z0Z1Z4Z5', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 82: [[6,2, 2]] : 384 :['Z0Z1', 'Z2Z4', 'Z3Z5', 'X0X1Y2Y3Y4Y5'] : True\n",
+      "6 :: 73: [[6,3, 2]] : 192 :['Z0Z1Z2Z4', 'Z0Z1Z3Z5', 'X0X1Y2Y3Y4Y5'] : True\n",
+      "6 :: 82: [[6,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'Z0Z1Z2Z3Z4Z5'] : True\n",
+      "6 :: 29: [[6,4, 2]] : 4320 :['Z0Z1Z2Z3Z4Z5', 'Y0Y1Y2Y3Y4Y5'] : True\n",
+      "6 :: 623: [[8,1, 2]] : 2304 :['X0X1', 'X2X6', 'Z3Z7', 'Z4Z7', 'X5X6', 'X3X4X6X7', 'Z0Z1Z2Z5Z6Z7'] : True\n",
+      "6 :: 629: [[8,1, 2]] : 2304 :['Z0Z7', 'Z1Z7', 'X2X6', 'X3X6', 'X4X5', 'X0X1X5X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 854: [[8,1, 2]] : 4608 :['X0X6', 'Z2Z7', 'Z3Z7', 'X4X6', 'X5X6', 'X1X2X3X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 856: [[8,1, 2]] : 6144 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z4', 'Z5Z6', 'X3X4X5X6', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 857: [[8,1, 2]] : 6144 :['X0X6', 'Z1Z2', 'Z3Z7', 'X4X6', 'X5X6', 'X1X2X3X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 875: [[8,1, 2]] : 3072 :['Z0Z1', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'X5X6', 'Z0Z5Z6Z7', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 959: [[8,1, 2]] : 7680 :['Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'X5X6', 'Z0Z5Z6Z7', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 988: [[8,1, 2]] : 4608 :['Z0Z1', 'Z0Z2', 'Z3Z7', 'Z4Z7', 'X5X6', 'Z0Z5Z6Z7', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 1247: [[8,2, 2]] : 1152 :['Z3Z7', 'Z4Z7', 'X5X6', 'X0X1X2X6', 'X3X4X6X7', 'Z0Z1Z2Z5Z6Z7'] : True\n",
+      "6 :: 1265: [[8,2, 2]] : 192 :['Z0Z7', 'Z1Z7', 'Z3Z4', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 1283: [[8,2, 2]] : 384 :['Z0Z7', 'Z1Z7', 'X3X6', 'X2X4X5X6', 'X0X1X5X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1297: [[8,2, 2]] : 768 :['Z0Z7', 'Z1Z7', 'X3X6', 'X4X5', 'Z2Z3Z4Z5Z6Z7', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 1300: [[8,2, 2]] : 768 :['Z0Z7', 'Z1Z7', 'X2X6', 'X3X4', 'X0X1X5X7', 'Y2Z3Z4Z5Y6Z7'] : True\n",
+      "6 :: 1313: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X3X4X5X6', 'X0X1X2X3X4X7'] : False\n",
+      "6 :: 1358: [[8,2, 2]] : 384 :['Z0Z7', 'X1X6', 'X2X6', 'X3X4', 'Y0X3X5Y7', 'Z1Z2Z3Z4Z5Z6'] : True\n",
+      "6 :: 1364: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'X4X5', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3Y6Y7'] : True\n",
+      "6 :: 1400: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'Z3Z6', 'X4X5', 'Z0Z4Z5Z7', 'X0Y1X2Y3Y6Y7'] : True\n",
+      "6 :: 1816: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X3X4X5X6', 'X0X1X2X3X4X7'] : False\n",
+      "6 :: 1854: [[8,2, 2]] : 256 :['X1X2', 'X3X5', 'X4X6', 'Z0Z1Z2Z7', 'X0X5X6X7', 'Z0Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1858: [[8,2, 2]] : 256 :['X0X1', 'Z4Z7', 'Z5Z6', 'Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'X2X3X4X5X6X7'] : True\n",
+      "6 :: 1859: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'X4X5', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3Y6Y7'] : True\n",
+      "6 :: 1879: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z3Z6', 'X4X5', 'Z0Z4Z5Z7', 'X0X1Y2Y3Y6Y7'] : True\n",
+      "6 :: 1903: [[8,2, 2]] : 1024 :['Z0Z1', 'Z2Z6', 'Z3Z7', 'Z4Z5', 'X3X4X5X7', 'X0X1X2X4X5X6'] : True\n",
+      "6 :: 1905: [[8,2, 2]] : 1024 :['Z0Z7', 'Z1Z2', 'X3X5', 'X4X6', 'Z3Z4Z5Z6', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 2072: [[8,2, 2]] : 1536 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'X4X5', 'Z3Z4Z5Z6', 'Y0X1X2X3X6Y7'] : True\n",
+      "6 :: 2076: [[8,2, 2]] : 3072 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'X3X4X5X6', 'Y3Y4Y5Y6', 'X0X1X2X3X5X7'] : True\n",
+      "6 :: 3058: [[8,2, 2]] : 2304 :['Z2Z7', 'Z3Z7', 'X4X6', 'X5X6', 'Z0Z1Y4Z5Y6Z7', 'X0X1Y2X3X6Y7'] : True\n",
+      "6 :: 3724: [[8,2, 2]] : 2048 :['X0X4', 'Z1Z5', 'X2X6', 'Z3Z7', 'Z0Z2Z4Z5Z6Z7', 'X1X3X4X5X6X7'] : True\n",
+      "6 :: 175: [[8,3, 2]] : 256 :['Z3Z6', 'X4X5', 'Z0Z1Z2Z7', 'Z0Z1Z4Z5', 'Y0Y1Y2Y3Y6Y7'] : True\n",
+      "6 :: 203: [[8,3, 2]] : 64 :['Z0Z7', 'Z1Z2', 'Z3Z4Z5Z6', 'Y0X4X5Y7', 'X1X2Y3Y4Y5Y6'] : True\n",
+      "6 :: 245: [[8,3, 2]] : 64 :['Z2Z7', 'X4X5', 'Z1Z3Z6Z7', 'Z0Z1Z4Z5', 'X0Y1X2Y3Y6Y7'] : True\n",
+      "6 :: 257: [[8,3, 2]] : 192 :['Z2Z7', 'X4X5', 'Z0Z3Z6Z7', 'Z1Z4Z5Z7', 'Y0X1X2Y3Y6Y7'] : True\n",
+      "6 :: 258: [[8,3, 2]] : 192 :['Z2Z7', 'X4X5', 'Z0Z1Z3Z6', 'Z1Z4Z5Z7', 'Y0Y1Y2Y3Y6Y7'] : True\n",
+      "6 :: 431: [[8,3, 2]] : 256 :['Z0Z1', 'Z2Z7', 'X4X5', 'Z0Z3Z4Z5Z6Z7', 'X0X1Y2X3X6Y7'] : True\n",
+      "6 :: 1071: [[8,3, 2]] : 576 :['Z1Z7', 'Z2Z7', 'X4X5', 'Z0Z3Z4Z5Z6Z7', 'X0Y1X2X3X6Y7'] : True\n",
+      "6 :: 1085: [[8,3, 2]] : 1152 :['Z0Z7', 'Z1Z7', 'X2X3X4X6', 'X0X1X5X7', 'Y2Y3Y4Z5Y6Z7'] : True\n",
+      "6 :: 1546: [[8,3, 2]] : 512 :['Z1Z5', 'Z3Z7', 'X0X2X4X6', 'Z0Z2Z4Z5Z6Z7', 'X1X3X4X5X6X7'] : True\n",
+      "6 :: 1553: [[8,3, 2]] : 1536 :['X3X5', 'X4X6', 'Z0Z1Z2Z7', 'Z3Z4Z5Z6', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 1554: [[8,3, 2]] : 1536 :['Z3Z7', 'Z4Z5', 'Z0Z1Z2Z6', 'X3X4X5X7', 'X0X1X2X4X5X6'] : True\n",
+      "6 :: 1569: [[8,3, 2]] : 256 :['Z2Z7', 'Z3Z6', 'X4X5', 'Z0Z1Z4Z5', 'X0X1Y2Y3Y6Y7'] : True\n",
+      "6 :: 1577: [[8,3, 2]] : 512 :['X0X4', 'X1X5', 'Z2Z3Z6Z7', 'Y2Y3Y6Y7', 'Y0Y1Z2Y4Y5Z6'] : True\n",
+      "6 :: 2315: [[8,3, 2]] : 256 :['Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'Z0Z1Z4Z5', 'X3X4X5X7', 'X0X1X2X4X5X6'] : False\n",
+      "6 :: 2368: [[8,3, 2]] : 64 :['Z0Z1Z2Z7', 'X1X2X3X5', 'X1X2X4X6', 'X0X5X6X7', 'Z0Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 5681: [[8,3, 2]] : 384 :['Z0Z7', 'Z1Z7', 'X2X4X5X6', 'Z2Z3Z4Z5Z6Z7', 'X0X1X3X5X6X7'] : True\n",
+      "6 :: 6865: [[8,3, 2]] : 128 :['X4X5', 'Z0Z1Z2Z7', 'Z1Z3Z6Z7', 'Z0Z1Z4Z5', 'X0Y1X2Y3Y6Y7'] : True\n",
+      "6 :: 6867: [[8,3, 2]] : 128 :['Z0Z1', 'Z3Z4Z6Z7', 'Z2Z3Z5Z7', 'X3X4X6X7', 'X0X1X2X4X5X6'] : False\n",
+      "6 :: 6928: [[8,3, 2]] : 768 :['X4X5', 'Z0Z1Z2Z7', 'Z0Z1Z3Z6', 'Z0Z1Z4Z5', 'X0X1Y2Y3Y6Y7'] : True\n",
+      "6 :: 6938: [[8,3, 2]] : 16 :['X4X6', 'Z0Z1Z2Z7', 'X1X2X3X5', 'X0X5X6X7', 'Z0Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 6973: [[8,3, 2]] : 128 :['X1X2', 'Z0Z1Z2Z7', 'X3X4X5X6', 'X0X3X4X7', 'Z0Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 7021: [[8,3, 2]] : 32 :['Z1Z7', 'Z0Z3Z4Z7', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X2X5X6X7'] : False\n",
+      "6 :: 1418: [[8,4, 2]] : 32 :['Z0Z1Z2Z3', 'X2X3X4X5', 'Z4Z5Z6Z7', 'X0X1X4X5X6X7'] : True\n",
+      "6 :: 1785: [[8,4, 2]] : 512 :['X4X5X6X7', 'Z0Z1Z2Z3', 'X0X1X2X3X4X5', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 2013: [[8,4, 2]] : 32 :['Z0Z1Z2Z3', 'X2X3X4X5', 'X0X1X4X5X6X7', 'Z0Z1Z4Z5Z6Z7'] : False\n",
+      "6 :: 2206: [[8,4, 2]] : 1152 :['Z0Z1Z2Z3', 'X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 2332: [[8,4, 2]] : 768 :['X4X5X6X7', 'Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 2624: [[8,4, 2]] : 128 :['X0X1', 'Z2Z3Z4Z5', 'X2X3X4X5X6X7', 'Z0Z1Z2Z3Z6Z7'] : True\n",
+      "6 :: 3050: [[8,4, 2]] : 768 :['X2X3', 'Z0Z1', 'X0X1X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 3060: [[8,4, 2]] : 384 :['Z0Z1', 'X0X1X2X3', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 3063: [[8,4, 2]] : 384 :['X0X1', 'Z2Z3Z4Z5', 'Z0Z1Z6Z7', 'X2X3X4X5X6X7'] : True\n",
+      "6 :: 3587: [[9,2, 2]] : 64 :['Z0Z8', 'X2X7', 'X4X6', 'X1X3X5X7', 'Z3Z4Z5Z6', 'X0X5X6X8', 'Z1Z2Z3Z5Z7Z8'] : True\n",
+      "6 :: 5846: [[9,2, 2]] : 512 :['Z0Z8', 'X1X6', 'Z2Z7', 'Z3Z4', 'X0X3X4X8', 'Z1Z3Z5Z6Z7Z8', 'X2X3X4X5X6X7'] : True\n",
+      "6 :: 7463: [[9,2, 2]] : 128 :['Z0Z8', 'X2X7', 'X1X3X5X7', 'X1X4X6X7', 'Z3Z4Z5Z6', 'X0X5X6X8', 'Z1Z2Z3Z5Z7Z8'] : False\n",
+      "6 :: 15777: [[9,2, 2]] : 768 :['Z0Z7', 'Z1Z8', 'Z2Z4', 'Z3Z4', 'Z5Z6Z7Z8', 'X0X2X3X4X6X7', 'X1X2X3X4X5X8'] : True\n",
+      "6 :: 16471: [[9,2, 2]] : 1152 :['Z0Z8', 'Z1Z8', 'Z3Z4Z6Z7', 'Z2Z3Z5Z7', 'X2X4X5X6', 'X2X3X5X7', 'X0X1X2X3X4X8'] : False\n",
+      "6 :: 16814: [[9,2, 2]] : 384 :['Z0Z8', 'X1X7', 'X2X7', 'Z3Z4Z5Z6', 'X3X4X5X6', 'X0X3X4X8', 'Z1Z2Z3Z5Z7Z8'] : False\n",
+      "6 :: 40502: [[9,2, 2]] : 768 :['Z1Z6', 'Z4Z8', 'Z5Z7', 'Z0Z2Z6Z7', 'Z0Z3Z6Z8', 'X0X1X2X5X6X7', 'X0X1X3X4X6X8'] : True\n",
+      "6 :: 89850: [[9,2, 2]] : 192 :['X0X1', 'Z3Z4Z7Z8', 'Y3Y4Y5Y6', 'Z5Z6Z7Z8', 'X2X4X5X7', 'X2X3X5X8', 'Z0Z1Z2Z3Z5Z8'] : False\n",
+      "6 :: 93706: [[9,2, 2]] : 16 :['Z0Z7', 'Z1Z2Z5Z7', 'Z1Z3Z6Z7', 'Y2Y3Y5Y6', 'X1X2X3X8', 'Z2Z4Z6Z8', 'Y0Y1Y2X4Y5X7'] : False\n",
+      "6 :: 2610: [[9,3, 2]] : 32 :['Z0Z1', 'X2X3', 'X4X5X6X7', 'Z2Z3Z6Z7', 'Z0Z2Z3Z4Z5Z8', 'X0X1X2X4X6X8'] : True\n",
+      "6 :: 15835: [[9,3, 2]] : 288 :['X0X1', 'X0X8', 'X2X3X4X5', 'X2X3X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 21930: [[9,3, 2]] : 32 :['Z0Z1', 'Z2Z3', 'Z0Z4Z5Z6', 'Z2Z4Z7Z8', 'X2X3X4X6X7X8', 'X0X1X2X3X5X7'] : False\n",
+      "6 :: 23343: [[9,3, 2]] : 128 :['Z0Z1', 'X2X3', 'X4X5X6X7', 'Z4Z5Z6Z7', 'Z0Z2Z3Z4Z5Z8', 'X0X1X2X4X6X8'] : False\n",
+      "6 :: 57484: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'X4X7', 'Z0Z1Z4Z5Z6Z7', 'X0X2X4X5X6X8', 'Z0Z1Z2Z3Z5Z8'] : True\n",
+      "6 :: 65123: [[9,3, 2]] : 6 :['Z0Z1Z2Z3', 'X0X2X4X8', 'X1X3X4X5', 'Z0Z1Z4Z6', 'X0X3X6X7', 'Z1Z2Z5Z6Z7Z8'] : False\n",
+      "6 :: 106358: [[9,3, 2]] : 144 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4X7', 'X1X4X5X8', 'Y1Z2Y4Y5Z7Y8', 'Z0Z1Z2Z3Z6Z8'] : False\n",
+      "6 :: 109476: [[9,3, 2]] : 16 :['X0X1X2X3', 'Z0Z2Z4Z8', 'Z0Z1Z4Z5', 'X0X2X4X8', 'X0X1X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 131752: [[9,3, 2]] : 864 :['Z0Z1Z2Z3', 'X0X2X4X8', 'Y0Y1Y2Y3', 'Y1Y3Y4Y8', 'Y0Y1Y4Y5Y6Y7', 'Z1Z2Z5Z6Z7Z8'] : False\n",
+      "6 :: 171115: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0X2X6X8', 'X2X3X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 173002: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'Y2Y3Y4Y5', 'X0Y4Y5Y6Y7X8', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 178937: [[9,3, 2]] : 8 :['Z0Z1', 'X2X3X4X5', 'X2X6X7X8', 'Z3Z4Z7Z8', 'X0X1X3X7', 'Z0Z2Z3Z4Z5Z6'] : False\n",
+      "6 :: 25911: [[9,4, 2]] : 48 :['X0X1X2X3', 'X0X1X4X5', 'X0X2X4X6X7X8', 'Z0Z1Z2Z3Z6Z7', 'Z0Z1Z4Z5Z6Z8'] : False\n",
+      "6 :: 46505: [[9,4, 2]] : 6 :['Z0Z1Z2Z3', 'Z0Z4Z5Z6', 'Z1Z4Z7Z8', 'X1X2X4X5X7X8', 'X0X1X2X3X6X7'] : False\n",
+      "6 :: 52859: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5Z6Z7', 'X0X2X3X6X7X8', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 10646: [[9,5, 2]] : 72 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z0Z2Z3Z5Z6Z8'] : False\n",
+      "8 :: 960: [[8,1, 2]] : 46080 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1042: [[8,1, 2]] : 92160 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1096: [[8,1, 2]] : 73728 :['Z0Z6', 'Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1390: [[8,2, 2]] : 1536 :['Z0Z1', 'Z2Z7', 'Z3Z7', 'Z5Z6', 'Z0Z4Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 2100: [[8,2, 2]] : 3072 :['Z1Z7', 'Z2Z7', 'Z3Z7', 'Z5Z6', 'Z0Z4Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 2107: [[8,2, 2]] : 6144 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z6', 'Z4Z5', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 2915: [[8,2, 2]] : 23040 :['Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'Z0Z5Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 3063: [[8,2, 2]] : 4608 :['Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 3066: [[8,2, 2]] : 4608 :['Z0Z1', 'Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 3723: [[8,2, 2]] : 12288 :['Z1Z2', 'Z3Z7', 'Z0Z4', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1067: [[8,3, 2]] : 1152 :['Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z2Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1080: [[8,3, 2]] : 768 :['Z1Z7', 'Z2Z7', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1090: [[8,3, 2]] : 1152 :['Z1Z7', 'Z2Z7', 'Z4Z5', 'Z0Z3Z6Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1507: [[8,3, 2]] : 256 :['Z3Z7', 'Z5Z6', 'Z0Z1Z2Z7', 'Z0Z1Z4Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1539: [[8,3, 2]] : 1024 :['Z0Z1', 'Z2Z7', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1567: [[8,3, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z4Z5', 'Z0Z3Z6Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1638: [[8,3, 2]] : 9216 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z4Z5Z6', 'Y0X1X2Y3Y4Y5Y6Y7'] : True\n",
+      "8 :: 4584: [[8,3, 2]] : 6144 :['Z0Z1', 'Z2Z7', 'Z3Z6', 'Z4Z5', 'X0X1Y2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 4882: [[8,3, 2]] : 21504 :['Z0Z1Z6Z7', 'Z0Z2Z3Z6', 'Z2Z4Z6Z7', 'Z0Z2Z5Z7', 'X0X1X2X3X4X5X6X7'] : False\n",
+      "8 :: 6930: [[8,3, 2]] : 768 :['Z3Z7', 'Z0Z1Z2Z7', 'Z1Z4Z6Z7', 'Z0Z1Z5Z6', 'X0X1X2X3X4X5X6X7'] : False\n",
+      "8 :: 352: [[8,4, 2]] : 5760 :['Z0Z2', 'Z0Z1', 'Z0Z3Z4Z5Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 553: [[8,4, 2]] : 1536 :['X0X1', 'X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 752: [[8,4, 2]] : 256 :['Z0Z1Z4Z5', 'Z0Z1Z2Z3', 'Z0Z2Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 2066: [[8,4, 2]] : 3072 :['X4X5X6X7', 'X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 2623: [[8,4, 2]] : 1152 :['X0X4', 'X4X5X6X7', 'X0X1X2X3', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 3052: [[8,4, 2]] : 1536 :['X2X3', 'X0X1', 'X0X2X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 3061: [[8,4, 2]] : 768 :['X0X1', 'X2X3X4X5', 'X2X3X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 3062: [[8,4, 2]] : 192 :['X0X1', 'X0X2X3X4', 'X2X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 326: [[8,5, 2]] : 4608 :['X4X5X6X7', 'X0X1X2X3', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 472: [[8,5, 2]] : 5760 :['X0X1', 'X2X3X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 541: [[8,5, 2]] : 768 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 67: [[8,6, 2]] : 241920 :['Z0Z1Z2Z3Z4Z5Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "special_codes_d2_css = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['d'] == 2 and code['k'] > 0 and code['is_css']==1:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_d2_css += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1077,
+   "id": "09a862fd-1ead-4092-91a5-82e914bca6b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 6: [[4,1, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1Z2Z3'] : True\n",
+      "4 :: 9: [[4,2, 2]] : 144 :['Z0Z1Z2Z3', 'X0X1X2X3'] : True\n",
+      "4 :: 17: [[6,1, 2]] : 128 :['Z0Z1', 'Z2Z5', 'X3X4', 'Z0Z3Z4Z5', 'X0X1X2X5'] : True\n",
+      "4 :: 31: [[6,1, 2]] : 96 :['Z1Z5', 'Z2Z5', 'X3X4', 'Z0Z3Z4Z5', 'X0X1X2X5'] : True\n",
+      "4 :: 43: [[6,1, 2]] : 32 :['X0Z4', 'X1Z5', 'Y2Y3Z4Z5', 'Z0Z2X4Z5', 'Z1Z3Z4X5'] : True\n",
+      "4 :: 55: [[6,1, 2]] : 384 :['X0X1', 'X2X3', 'X4X5', 'Z0Z1Z2Z3', 'Z0Z1Z4Z5'] : True\n",
+      "4 :: 63: [[6,1, 2]] : 48 :['X0X1Z3Z4', 'X0X2Z3Z5', 'Z0X3Z4Z5', 'Z1Z3X4Z5', 'Z2Z3Z4X5'] : False\n",
+      "4 :: 69: [[6,1, 2]] : 32 :['X0Z5', 'X1X2Z3Z4', 'Y1Y3Z4Z5', 'X1Z2X4Z5', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 56: [[6,2, 2]] : 64 :['X0X1', 'Z3Z5', 'Z0Z1Z2Z4', 'X2Y3X4Y5'] : True\n",
+      "4 :: 111: [[6,2, 2]] : 96 :['X0X1X2Z5', 'X1X3Z4Z5', 'X0X1Z3X4', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 126: [[6,2, 2]] : 288 :['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5'] : True\n",
+      "4 :: 127: [[6,2, 2]] : 72 :['X1X2Z3Z4', 'X0Y1Y3Z4', 'X1Z2X4Z5', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 129: [[6,2, 2]] : 64 :['X0X1', 'Z0Z1Z2Z4', 'Z0Z1Z3Z5', 'Y2Y3Y4Y5'] : True\n",
+      "4 :: 131: [[6,2, 2]] : 16 :['X2Z5', 'X0X3Z4Z5', 'X1Z3X4Z5', 'Z0Z1Z2X5'] : True\n",
+      "4 :: 134: [[6,2, 2]] : 12 :['X0Y1Y3Z4', 'Z1X2X3Z5', 'X0X1Z2X4', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 76: [[6,3, 2]] : 48 :['X0Z1X2Z4', 'Z0X1X3Z5', 'Z2Z3Y4Y5'] : True\n",
+      "4 :: 499: [[7,2, 2]] : 24 :['X0X1Z5Z6', 'X0X2Z4Z6', 'Z2X3X4Z5', 'Z1Y3Y5Z6', 'Z0Z3Z4X6'] : False\n",
+      "4 :: 563: [[7,2, 2]] : 4 :['X0X1X3Z5', 'X2X3Z4Z6', 'X0Y1Z3Y4', 'Y2Z3Y5Z6', 'Z0Z1Z2X6'] : False\n",
+      "4 :: 579: [[7,2, 2]] : 24 :['X1X2Z4Z5', 'X0X1X3Z5', 'Y1Z3Y4Z6', 'Y2Z3Y5Z6', 'Z0Z1Z2X6'] : False\n",
+      "4 :: 592: [[7,2, 2]] : 6 :['X1X2Z4Z5', 'X1X3Z5Z6', 'X0Y1Z3Y4', 'Y2Z3Y5Z6', 'Z0Z1Z2X6'] : False\n",
+      "4 :: 621: [[7,2, 2]] : 8 :['X0Z6', 'X1X3Z5Z6', 'Y1X2Y3X4', 'Z2Y3Z4Y5', 'Z0Z1Z2X6'] : True\n",
+      "4 :: 118: [[8,1, 2]] : 64 :['Z0X1', 'X4Z7', 'X0Z1X2Z5', 'X0Z1X3Z6', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 165: [[8,1, 2]] : 64 :['X0Z7', 'X1Z6', 'X2Z4', 'Z2X3X4Z5', 'Y3Y5Z6Z7', 'Z1X3X6Z7', 'Z0Z3Z4X7'] : True\n",
+      "4 :: 281: [[8,1, 2]] : 16 :['X0Z7', 'X1Z6', 'Y2Y3Z4Z7', 'X2X4Z5Z7', 'Z4X5Z6Z7', 'Z1Z5X6Z7', 'Z0X3Y5Y7'] : False\n",
+      "4 :: 296: [[8,1, 2]] : 32 :['X0Z7', 'X1Z4', 'X2X3Z5Z6', 'Z1X2X4Z7', 'Z3X5Z6Z7', 'Z2Z4Z5X6', 'Z0Z2Y3Y7'] : False\n",
+      "4 :: 328: [[8,1, 2]] : 128 :['X0Z7', 'X1Z5', 'X2Z6', 'X3X4Z5Z6', 'Z1Y3Y5Z7', 'Z2Y4Y6Z7', 'Z0Z3Z4X7'] : True\n",
+      "4 :: 329: [[8,1, 2]] : 128 :['X0X4', 'X1X5', 'Z2Z3', 'Z0Z2Z4Z7', 'Z1Z2Z5Z6', 'X2X3X4X6', 'X2X3X5X7'] : False\n",
+      "4 :: 357: [[8,1, 2]] : 128 :['X0Z6', 'X1Z4', 'X2Z5', 'Z1X3X4Z6', 'Z2X3X5Z6', 'Z0Y3Y6Z7', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 497: [[8,1, 2]] : 384 :['Z0Z1', 'Z3Z7', 'Z4Z7', 'X5X6', 'X0X1X2X6', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 498: [[8,1, 2]] : 384 :['X0X1', 'X2X6', 'Z3Z4', 'X5X6', 'Z0Z1Z3Z7', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 537: [[8,1, 2]] : 96 :['X0Z7', 'X1Z7', 'X2Z6', 'Y3Y4Z5Z7', 'X3X5Z6Z7', 'Z2Z3X4X6', 'Z0Z1Z3X7'] : True\n",
+      "4 :: 549: [[8,1, 2]] : 768 :['Z1Z2', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 554: [[8,1, 2]] : 768 :['Z1Z7', 'Z2Z7', 'X3X4', 'X5X6', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X0X1X2X7'] : True\n",
+      "4 :: 603: [[8,1, 2]] : 192 :['X0Z7', 'X1Z7', 'X2X3Z4Z5', 'Y2Y4Z5Z6', 'X2Z3X5Z6', 'Z2Z3X6Z7', 'Z0Z1Z6X7'] : False\n",
+      "4 :: 714: [[8,1, 2]] : 64 :['X0Z7', 'X1Z6', 'X3Z5Z6Z7', 'Z2X4Z6Z7', 'Z2Z3X5Z7', 'Z1Z3Z4X6', 'Z0X2Z3X7'] : False\n",
+      "4 :: 716: [[8,1, 2]] : 128 :['Z0Z6', 'Z1Z7', 'Z2Z3Z6Z7', 'Z4Z5Z6Z7', 'Y2Y3Y4Y5', 'X0X2X4X6', 'X1X3X4X7'] : False\n",
+      "4 :: 719: [[8,1, 2]] : 32 :['X0Z7', 'X1Z6', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'X2Z4X5Z7', 'Z1Z4X6Z7', 'Z0Z5Z6X7'] : False\n",
+      "4 :: 720: [[8,1, 2]] : 32 :['X0Z7', 'X1Z4', 'Z1Y3Y4Z6', 'Z2X5Z6Z7', 'Y2X3Z4Y5', 'Z3Z5X6Z7', 'Z0Y2Z6Y7'] : False\n",
+      "4 :: 723: [[8,1, 2]] : 128 :['Z0Z7', 'Z1Z6', 'X2X4', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'X1X4X5X6', 'Y0X3X4Y7'] : True\n",
+      "4 :: 728: [[8,1, 2]] : 128 :['Z0X1', 'X2Z5', 'X4Z7', 'X0Z1X3Z6', 'Z2X3X5Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 731: [[8,1, 2]] : 128 :['Z0X1', 'X4Z7', 'X5Z6', 'X0Z1X2Z6', 'X0Z1X3Z7', 'Z2Z5X6Z7', 'Z3Z4Z6X7'] : True\n",
+      "4 :: 738: [[8,1, 2]] : 128 :['X0Z3', 'X1Z6', 'X2Z7', 'Z3X5Z6Z7', 'Z0Y3X4Y5', 'Z1Y4Z5Y6', 'Z2Y4Z5Y7'] : False\n",
+      "4 :: 742: [[8,1, 2]] : 128 :['X0Z7', 'X1Z6', 'X2Z5', 'X4Z5Z6Z7', 'Z2Z4X5Z7', 'Z1Z3Z4X6', 'Z0Y3Y4X7'] : True\n",
+      "4 :: 756: [[8,1, 2]] : 128 :['Z0X1', 'X3Z6', 'X4Z7', 'X0Z1X2Z5', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 763: [[8,1, 2]] : 1024 :['Z0Z1', 'Z2Z7', 'X3X4', 'X5X6', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X0X1X2X7'] : True\n",
+      "4 :: 797: [[8,1, 2]] : 128 :['Z0Z6', 'Z1Z7', 'Y2Y3Y4Y5', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'Y0X3X4Y6', 'Y1X3X4Y7'] : False\n",
+      "4 :: 798: [[8,1, 2]] : 32 :['X0Z6', 'X1Z7', 'Y2Y3Z4Z6', 'Y2Z3Y4Z7', 'Z2X3X5Z7', 'Z0Z3Z5X6', 'Z1Z4Z5X7'] : False\n",
+      "4 :: 815: [[8,1, 2]] : 128 :['X0Z6', 'X1Z7', 'X2X3Z4Z5', 'Y2Y4Z6Z7', 'Y3Y5Z6Z7', 'Z0Z2Z3X6', 'Z1Z4Z5X7'] : True\n",
+      "4 :: 848: [[8,1, 2]] : 16 :['X0Z7', 'X1Z3', 'X2X4Z6Z7', 'Z1X3X4Z5', 'Z2Z4X5Z7', 'Z3Z4X6Z7', 'Z0Y2Z6Y7'] : False\n",
+      "4 :: 907: [[8,1, 2]] : 16 :['X0X1Z6Z7', 'X2Z4Z6Z7', 'X3Z5Z6Z7', 'X0Z2X4Z5', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Y2Y3X7'] : False\n",
+      "4 :: 909: [[8,1, 2]] : 8 :['X0X1Z6Z7', 'X0X2Z4Z6', 'X3Z5Z6Z7', 'Z2X4Z5Z7', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Y0Y2Y3Y7'] : False\n",
+      "4 :: 985: [[8,1, 2]] : 1152 :['Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1052: [[8,1, 2]] : 12 :['X0X1Z5Z7', 'X0X2Z6Z7', 'X3Z4Z5Z7', 'Z3X4Z6Z7', 'Z1Z3X5Z7', 'Z2Z4X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 1091: [[8,1, 2]] : 6144 :['X0X1', 'X2X3', 'X4X5', 'X6X7', 'Z0Z1Z2Z3', 'Z0Z1Z4Z5', 'Z0Z1Z6Z7'] : True\n",
+      "4 :: 1093: [[8,1, 2]] : 2048 :['Z1Z2', 'Z3Z7', 'Z0Z4', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1133: [[8,1, 2]] : 16 :['X0X1Z2Z3', 'X0Z1X2Z5', 'X0Z1X3Z6', 'X4Z5Z6Z7', 'Z2Z4X5Z7', 'Z3Z4X6Z7', 'Y0Y1Y4Y7'] : False\n",
+      "4 :: 1142: [[8,1, 2]] : 4 :['X0X1Z2Z3', 'X0Z1X2Z5', 'Z1X3Z6Z7', 'X4Z5Z6Z7', 'Z2Z4X5Z7', 'Z3Z4X6Z7', 'Y0Y1Y4Y7'] : False\n",
+      "4 :: 1152: [[8,1, 2]] : 48 :['X1Z2Z3Z7', 'X0Z1X2Z5', 'X0Z1X3Z6', 'X0X4Z5Z6', 'Z2Z4X5Z7', 'Z3Z4X6Z7', 'Y0Y1Y4Y7'] : False\n",
+      "4 :: 1190: [[8,1, 2]] : 16 :['X0X1Z2Z3', 'Z1X2Z5Z7', 'Z1X3Z6Z7', 'X0X4Z5Z6', 'Z2Z4X5Z7', 'Z3Z4X6Z7', 'Z0Y1Y4X7'] : False\n",
+      "4 :: 1199: [[8,1, 2]] : 128 :['X0X1Z4Z7', 'X0X2Z4Z5', 'X0X3Z4Z6', 'Z0X4Z5Z6', 'Z2Z4X5Z7', 'Z3Z4X6Z7', 'Z1Z5Z6X7'] : False\n",
+      "4 :: 1200: [[8,1, 2]] : 384 :['Z0Z1Z5Z6', 'Z0Z2Z5Z7', 'Z0Z3Z6Z7', 'Z4Z5Z6Z7', 'X0X3X4X5', 'X1X3X4X6', 'X2X3X4X7'] : False\n",
+      "4 :: 1357: [[8,1, 2]] : 16 :['X0Z7', 'X1X2Z4Z5', 'X3Z4Z5Z7', 'Z1Z3X4Z7', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 1376: [[8,1, 2]] : 8 :['X4X5', 'Y0Y1Z2Z3', 'Y0Z1Y2Z6', 'X0Z2X3Z7', 'Z0X2X4Z7', 'Z2Z4Z5X6', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 1419: [[8,1, 2]] : 8 :['X0Z7', 'Y1Y2Z4Z6', 'Z1X2X3Z5', 'Z2Z3X4Z7', 'Z3X5Z6Z7', 'Z1Z5X6Z7', 'Z0X3Z6X7'] : False\n",
+      "4 :: 1430: [[8,1, 2]] : 16 :['X0Z7', 'X3Z5Z6Z7', 'X1X2X3Z4', 'Z2X4Z6Z7', 'Z2Z3X5Z7', 'Z1Z3Z4X6', 'Z0Z5X6X7'] : False\n",
+      "4 :: 1434: [[8,1, 2]] : 192 :['Z0X1', 'X0Z1X2Z5', 'X0Z1X3Z6', 'X0Z1X4Z7', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 1438: [[8,1, 2]] : 16 :['X0Z1', 'Z0X1X2Z4', 'Z0X1X3Z5', 'Y2Y4Z6Z7', 'Y3Y5Z6Z7', 'X2Z5X6Z7', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 1439: [[8,1, 2]] : 8 :['X0Z7', 'X1X2Z5Z6', 'X1X3Z4Z7', 'X2Z3X4Z7', 'Y1Z3Y5Z7', 'Y2Z4Y6Z7', 'Z0Z1Z2X7'] : False\n",
+      "4 :: 1441: [[8,1, 2]] : 32 :['X0Z5', 'Y1Y2Z6Z7', 'Z1X2X3Z4', 'X1Z2Z3X4', 'Z0X5Z6Z7', 'Z1Z4Z5X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 1448: [[8,1, 2]] : 96 :['X0X5', 'Z1Z2Z6Z7', 'Z1Z3Z4Z7', 'X1X2X4X5', 'Z0Z1Z5Z7', 'X1X3X5X6', 'X2X3X5X7'] : False\n",
+      "4 :: 1538: [[8,1, 2]] : 8 :['X0X1Z2Z3', 'Z1X2Z5Z7', 'Z1X3Z6Z7', 'X4Z5Z6Z7', 'Z2Z4X5Z7', 'Z3Z4X6Z7', 'Y0Y1Y4Y7'] : False\n",
+      "4 :: 312: [[8,2, 2]] : 64 :['Z1X2', 'X4Z6', 'X0X1Z2Z7', 'X0X3Z5Z7', 'Z3Z4X5X6', 'Z0Z5Z6X7'] : True\n",
+      "4 :: 352: [[8,2, 2]] : 64 :['Z0Z7', 'Z1Z2', 'X4X6', 'X1X2X3X5', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 458: [[8,2, 2]] : 32 :['X1Z7', 'Z3X4', 'X0X2Z6Z7', 'X0X3Z4Z7', 'Z2Y5Y6Z7', 'Z0Z1Z5X7'] : True\n",
+      "4 :: 532: [[8,2, 2]] : 32 :['Z3Z4', 'X5X6', 'X0X1X2X6', 'Z0Z1Z3Z7', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 594: [[8,2, 2]] : 16 :['Z0X1', 'X3Z7', 'X0Z1X2Z6', 'X2Y4Y5Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7'] : True\n",
+      "4 :: 602: [[8,2, 2]] : 16 :['Z0X1', 'X3Z7', 'X0Z1X2Z6', 'Y4Y5Z6Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7'] : True\n",
+      "4 :: 746: [[8,2, 2]] : 128 :['Z1Z7', 'X2X6', 'X3X4', 'Z0Z3Z4Z7', 'Y2Z5Y6Z7', 'X0X1X5X7'] : True\n",
+      "4 :: 1284: [[8,2, 2]] : 192 :['Z0Z7', 'Z1Z7', 'Z3Z4', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X5X7'] : True\n",
+      "4 :: 1314: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'Z0Z3Z5Z7', 'Z0Z4Z6Z7', 'Y3Y4Y5Y6', 'X0X1X2X7'] : True\n",
+      "4 :: 1331: [[8,2, 2]] : 96 :['X3Z7', 'X4Z7', 'X0Z1X2Z6', 'Z0X1X5Z6', 'Z2Z5X6Z7', 'Z3Z4Z6X7'] : True\n",
+      "4 :: 1783: [[8,2, 2]] : 32 :['Z3Z7', 'Z5Z6', 'Z0Z1Z2Z6', 'Z0Z1Z4Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1788: [[8,2, 2]] : 32 :['X0Z6', 'X1Z7', 'Y2Y3Z4Z5', 'Z3X5Z6Z7', 'Z0X3Y4Y6', 'Z1X2Y5Y7'] : True\n",
+      "4 :: 1796: [[8,2, 2]] : 64 :['Z3Z7', 'Z5Z6', 'Z0Z1Z6Z7', 'Z2Z4Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 1835: [[8,2, 2]] : 32 :['X0Z7', 'X1Z4', 'Z1Y3Y4Z6', 'Y2X3Z4Y5', 'Z3Z5X6Z7', 'Z0X2X5X7'] : True\n",
+      "4 :: 1838: [[8,2, 2]] : 64 :['Z4Z7', 'X5X6', 'X0X1X2X6', 'Z0Z1Z3Z7', 'Z2Z5Z6Z7', 'X3X4X6X7'] : True\n",
+      "4 :: 1848: [[8,2, 2]] : 64 :['Z4Z7', 'X5X6', 'X0X1X2X6', 'Z0Z1Z3Z7', 'Z2Z3Y5Y6', 'X2X3Y4Y7'] : True\n",
+      "4 :: 1892: [[8,2, 2]] : 256 :['X0X1', 'Z3Z7', 'Z4Z5', 'Z0Z1Z2Z6', 'X2X4X5X6', 'X3X4X5X7'] : True\n",
+      "4 :: 1893: [[8,2, 2]] : 256 :['X1X2', 'X3X5', 'X4X6', 'Z0Z1Z2Z7', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 1898: [[8,2, 2]] : 32 :['X0Z7', 'X1Z3', 'Z1X3X4Z5', 'Y2Y4X5Z6', 'Z3Z4X6Z7', 'Z0Z2X4X7'] : True\n",
+      "4 :: 1946: [[8,2, 2]] : 32 :['X0Z7', 'X1Z6', 'X2X4Z5Z7', 'Y2Y3X5Z6', 'Z1Z4Y5Y6', 'Z0X3Y5Y7'] : True\n",
+      "4 :: 1947: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z0Z3Z5Z7', 'Z0Z4Z6Z7', 'Y3Y4Y5Y6', 'X0X1X2X7'] : True\n",
+      "4 :: 1948: [[8,2, 2]] : 256 :['Z0Z7', 'Z1Z2', 'X1X2X3X5', 'X1X2X4X6', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 1949: [[8,2, 2]] : 512 :['X0X1', 'X2X3', 'Z0Z1Z2Z3', 'Z0Z1Z4Z6', 'Z0Z1Z5Z7', 'Y4Y5Y6Y7'] : True\n",
+      "4 :: 2023: [[8,2, 2]] : 256 :['X0X1', 'Z4Z5', 'Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'X2X4X5X6', 'X3X4X5X7'] : True\n",
+      "4 :: 2503: [[8,2, 2]] : 8 :['Y1Y2Z6Z7', 'Y3Y4Z6Z7', 'Z0X5Z6Z7', 'X0Z1Z4X6', 'X2X3Z5X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 2519: [[8,2, 2]] : 8 :['X0Z1X2Z5', 'X0Z1X3Z6', 'Z0X1X4Z7', 'Z2X4X5Z6', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 2529: [[8,2, 2]] : 16 :['Y1Y2Y3Y4', 'Z0X5Z6Z7', 'Y0Y1Y2Y5', 'Z1Z4Z5X6', 'X0X2X3X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 2534: [[8,2, 2]] : 2 :['X1X2Z3Z6', 'Z0Y1Y3Z7', 'Y0Y2X3X4', 'Z0X2X5Z7', 'Z2Y4Y5X6', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 2564: [[8,2, 2]] : 16 :['Y1Y2Z6Z7', 'Y3Y4Z6Z7', 'Z0X5Z6Z7', 'Z1Z4Z5X6', 'X0X2X3X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 2584: [[8,2, 2]] : 2 :['Z1X2X3Z4', 'Y3Y4Z6Z7', 'Z0X5Z6Z7', 'Y0Y1Y2Y5', 'Z1Z4Z5X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 3048: [[8,2, 2]] : 768 :['Z0X1', 'X2Z6', 'X4X5', 'X0Z1X3Z7', 'Z3Z4Z5X7', 'Z2Y3X6Y7'] : True\n",
+      "4 :: 3233: [[8,2, 2]] : 8 :['Y1Y2Y3Y4', 'Z0X5Z6Z7', 'Y0Y1Y2Y5', 'X0Z1Z4X6', 'X2X3Z5X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 3365: [[8,2, 2]] : 24 :['Y0Y1X2Z5', 'X0Z1X3Z6', 'Z0X1X4Z7', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 3544: [[8,2, 2]] : 2 :['Z2Z3X4Z7', 'X0Z3X5Z6', 'Y1X2Y4X5', 'Z1Z5X6Z7', 'X0X2X3X6', 'Z0X3Z6X7'] : False\n",
+      "4 :: 3569: [[8,2, 2]] : 2 :['Z2Z3X4Z7', 'Z3X5Z6Z7', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'X0X2X3X6', 'Z0X3Z6X7'] : False\n",
+      "4 :: 3607: [[8,2, 2]] : 2 :['X0X1X2Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Z2X3X5Z7', 'Y3Z5Y6Z7', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 3613: [[8,2, 2]] : 8 :['X3Z4Z5Z7', 'X0X1X2X3', 'Z1Z3X4Z7', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 3787: [[8,2, 2]] : 8 :['X0X1Z6Z7', 'X0X3Z5Z6', 'Z2X4Z6Z7', 'Z2Z3X5Z7', 'Z1Z3Z4X6', 'Z0X2Z3X7'] : False\n",
+      "4 :: 3937: [[8,2, 2]] : 2 :['Z1X2X3Z5', 'X0Z2Z3X4', 'X0Z3X5Z6', 'Z1Z5X6Z7', 'Y1Y3Y4Y6', 'Z0X3Z6X7'] : False\n",
+      "4 :: 3957: [[8,2, 2]] : 2 :['Y0Y1Z2Z3', 'Y0Y2X3X4', 'Z0X2X5Z7', 'Z1X3X5Z6', 'Z2Z4Z5X6', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 3960: [[8,2, 2]] : 32 :['Z0Z1Z6Z7', 'Z0Z2Z3Z7', 'Z4Z5Z6Z7', 'X2X3X4X5', 'X0X2X4X6', 'X1X3X4X7'] : False\n",
+      "4 :: 3964: [[8,2, 2]] : 2 :['X0X3Z4Z5', 'X1X2X3Z7', 'X0Z1Z3X4', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Y0Y3Z6X7'] : False\n",
+      "4 :: 4085: [[8,2, 2]] : 2 :['Y1Y2Z4Z6', 'Z1X2X3Z5', 'X0Z2Z3X4', 'Z3X5Z6Z7', 'Z1Z5X6Z7', 'Z0X3Z6X7'] : False\n",
+      "4 :: 4140: [[8,2, 2]] : 2 :['X0Z2Z3X4', 'Z3X5Z6Z7', 'Y1X2Y4X5', 'Z1Z5X6Z7', 'X0X2X3X6', 'Z0X3Z6X7'] : False\n",
+      "4 :: 4146: [[8,2, 2]] : 8 :['X1X3Z4Z7', 'Z1Z2X4Z5', 'Y0X1Y5Z7', 'Z0X2X5Z6', 'X0Z1Y3Y6', 'X0Z2Y3Y7'] : False\n",
+      "4 :: 4152: [[8,2, 2]] : 2 :['X1X2Z3Z6', 'X0Z2X3Z7', 'Z0X2X4Z7', 'Z1X3X5Z6', 'Z2Y4Y5X6', 'Z3Z4Z5X7'] : True\n",
+      "4 :: 4159: [[8,2, 2]] : 4 :['X1X3Z4Z7', 'X0X2Z3X4', 'Y3Y4Z5Z6', 'X0Y1Z3Y5', 'Y2Z4Y6Z7', 'Z0Z1Z2X7'] : False\n",
+      "4 :: 4313: [[8,2, 2]] : 4 :['Y1Y2Z4Z6', 'Z1X2X3Z5', 'X0Z2Z3X4', 'X0Z3X5Z6', 'Z1Z5X6Z7', 'Z0X3Z6X7'] : False\n",
+      "4 :: 4315: [[8,2, 2]] : 2 :['Z0X1X2Z4', 'Y0Y1X3Z5', 'Y2Y3Y4Y5', 'X2Z5X6Z7', 'Y2X3X4Y6', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 4322: [[8,2, 2]] : 16 :['Z1Z2Z6Z7', 'Z1Z3Z4Z7', 'X1X2X4X5', 'Z0Z1Z5Z7', 'X0X1X3X6', 'X0X2X3X7'] : False\n",
+      "4 :: 4481: [[8,2, 2]] : 2 :['X0X3Z5Z6', 'X0Z2X4Z6', 'Z2Z3X5Z7', 'Z1Z3Z4X6', 'Y1Y2Y5Y6', 'Z0Z5X6X7'] : False\n",
+      "4 :: 4493: [[8,2, 2]] : 8 :['X1X2Z4Z5', 'X3Z4Z5Z7', 'X0Z1Z3X4', 'X0Z2Z3X5', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 4504: [[8,2, 2]] : 1 :['X1X2Z4Z5', 'X3Z4Z5Z7', 'X0Z1Z3X4', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 4520: [[8,2, 2]] : 64 :['Z0Z1Z6Z7', 'Y2Y3Y4Y5', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'X0X3X4X6', 'X1X3X4X7'] : False\n",
+      "4 :: 4523: [[8,2, 2]] : 1 :['X3Z5Z6Z7', 'X0Z2X4Z6', 'X1Y2Y4Z5', 'Z2Z3X5Z7', 'Z1Z3Z4X6', 'Z0Z5X6X7'] : False\n",
+      "4 :: 4534: [[8,2, 2]] : 6 :['Y0Z1Y2Z6', 'Z0Y1Y3Z7', 'Z0X2X4Z7', 'Z1X3X5Z6', 'Z2Z4Z5X6', 'Z3Z4Z5X7'] : True\n",
+      "4 :: 4537: [[8,2, 2]] : 24 :['X0Z1X2Z6', 'X0Z1X3Z7', 'Z0X1X4Z7', 'Z0X1X5Z6', 'Z2Z5X6Z7', 'Z3Z4Z6X7'] : False\n",
+      "4 :: 4541: [[8,2, 2]] : 384 :['X0X1X2Z7', 'X1X3Z4Z7', 'X0X1Z3X4', 'X1X5Z6Z7', 'X0X1Z5X6', 'Z0Z1Z2X7'] : False\n",
+      "4 :: 4542: [[8,2, 2]] : 16 :['Y1Y2Z6Z7', 'Z1X2X3Z4', 'X1Z2Z3X4', 'Z0X5Z6Z7', 'X0Z1Z4X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 4555: [[8,2, 2]] : 2 :['Y1Y2Z4Z6', 'Z2Z3X4Z7', 'Z3X5Z6Z7', 'Z1Z5X6Z7', 'X0X2X3X6', 'Z0X3Z6X7'] : False\n",
+      "4 :: 4573: [[8,2, 2]] : 2 :['X0X1X2Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Y2Y5Z6Z7', 'Y3Z5Y6Z7', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 4582: [[8,2, 2]] : 2 :['X3Z4Z5Z7', 'X0X1X2X3', 'X0Z1Z3X4', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 4586: [[8,2, 2]] : 4 :['X1X2Z5Z6', 'X0X1X3Z4', 'X2Z3X4Z7', 'X0Y1Z3Y5', 'Y2Z4Y6Z7', 'Z0Z1Z2X7'] : False\n",
+      "4 :: 4595: [[8,2, 2]] : 12 :['Y1Y2Z4Z6', 'X0Z2Z3X4', 'Z3X5Z6Z7', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Z0X3Z6X7'] : False\n",
+      "4 :: 4597: [[8,2, 2]] : 24 :['Y0Y1X2Z5', 'X0Z1X3Z6', 'Z0X1X4Z7', 'Y2X3Y5Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 4615: [[8,2, 2]] : 2 :['Y1Y2Z4Z6', 'Z2Z3X4Z7', 'X0Z3X5Z6', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Z0X3Z6X7'] : False\n",
+      "4 :: 4616: [[8,2, 2]] : 4 :['X3Z4Z5Z7', 'X0X1X2X3', 'X0Z1Z3X4', 'X0Z2Z3X5', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 4631: [[8,2, 2]] : 16 :['Z1X2X3Z4', 'X1Z2Z3X4', 'Z0X5Z6Z7', 'Y0Y1Y2Y5', 'X0Z1Z4X6', 'Z2Z3Z5X7'] : False\n",
+      "4 :: 4656: [[8,2, 2]] : 2 :['Y0Y1X2Z4', 'Y0Y1X3Z5', 'Y2Y4Z6Z7', 'Z3Z4Y5Y6', 'Z1Z2Z3X7', 'X0X4X5X7'] : False\n",
+      "4 :: 4863: [[8,2, 2]] : 8 :['X1X2Z4Z5', 'X0X3Z4Z5', 'Z1Z3X4Z7', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Y0Y3Z6X7'] : False\n",
+      "4 :: 4911: [[8,2, 2]] : 8 :['X0Z1X2Z5', 'Z0X1X3Z6', 'Z0X1X4Z7', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 4926: [[8,2, 2]] : 2304 :['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5', 'Z0Z1Z6Z7', 'X0X1X6X7'] : True\n",
+      "4 :: 4927: [[8,2, 2]] : 768 :['Z0Z1Z6Z7', 'Z0Z2Z3Z6', 'Z2Z4Z6Z7', 'Z0Z2Z5Z7', 'X0X4X5X6', 'X1X2X3X7'] : False\n",
+      "4 :: 4928: [[8,2, 2]] : 32 :['X0X1Z6Z7', 'Y2Y4Z6Z7', 'Y3Y5Z6Z7', 'Z0Z2Z3X6', 'Y0X4X5Y6', 'Z1Z4Z5X7'] : False\n",
+      "4 :: 4929: [[8,2, 2]] : 6 :['Y0Y1Z2Z3', 'Y0Y2X3X4', 'Z0X2X5Z7', 'Z1X3X5Z6', 'Z2Y4Y5X6', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 5275: [[8,2, 2]] : 48 :['X0X5', 'Z1Z2Z6Z7', 'Z3Z4Z6Z7', 'Z0Z5Z6Z7', 'X1X4X5X6', 'X2X3X5X7'] : False\n",
+      "4 :: 5347: [[8,2, 2]] : 16 :['X0Z7', 'Y1Y2Z4Z6', 'Z2Z3X4Z7', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Z0Y3X5Y7'] : True\n",
+      "4 :: 5349: [[8,2, 2]] : 16 :['X4Z7', 'X0Z1X2Z5', 'Z0X1X3Z6', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 5358: [[8,2, 2]] : 4 :['X0Z7', 'X3Z4Z5Z7', 'Y1X2Y3X4', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 5385: [[8,2, 2]] : 64 :['X2Z7', 'X0X3Z4Z7', 'X1Z3X4Z7', 'X0X5Z6Z7', 'X1Z5X6Z7', 'Z0Z1Z2X7'] : False\n",
+      "4 :: 5390: [[8,2, 2]] : 8 :['X4Z7', 'X0Z1X2Z5', 'Z0X1X3Z6', 'Z2X3X5Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 5457: [[8,2, 2]] : 16 :['X0X5', 'Z1Z2Z3Z4', 'Z0Z1Z2Z5', 'X1X4X5X6', 'X2X3X5X7', 'Z1Z3Z6Z7'] : False\n",
+      "4 :: 5473: [[8,2, 2]] : 16 :['X0Z5', 'Y1Y2Y3Y4', 'Z0X5Z6Z7', 'Z1Z4Z5X6', 'Z2Z3Z5X7', 'Y1Y3Y6Y7'] : True\n",
+      "4 :: 5492: [[8,2, 2]] : 8 :['X0Z7', 'Y1Y2Z4Z6', 'Z2Z3X4Z7', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Z0X3Z6X7'] : False\n",
+      "4 :: 5494: [[8,2, 2]] : 16 :['Z0X1', 'X0Z1X2Z6', 'X0Z1X3Z7', 'Y4Y5Z6Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7'] : True\n",
+      "4 :: 5500: [[8,2, 2]] : 8 :['X0Z7', 'X3Z4Z5Z7', 'Z1Z3X4Z7', 'Z2Z3X5Z7', 'Y1Y2X3X6', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 5506: [[8,2, 2]] : 8 :['X4X5', 'X1X2Z3Z6', 'Z0Y1Y3Z7', 'Y0Y2X3X4', 'Z3Z4Z5X7', 'Y0Y1X6X7'] : False\n",
+      "4 :: 5518: [[8,2, 2]] : 16 :['X4Z7', 'Y0Y1X2Z5', 'X0Z1X3Z6', 'Y2X3Y5Z7', 'X2Y3Y6Z7', 'Z4Z5Z6X7'] : False\n",
+      "4 :: 5521: [[8,2, 2]] : 4 :['X0Z5', 'Z1X2X3Z4', 'Y3Y4Z6Z7', 'Z0X5Z6Z7', 'Z2Z3Z5X7', 'Y1Y3Y6Y7'] : False\n",
+      "4 :: 5523: [[8,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Y2Y5Z6Z7', 'Y3Z5Y6Z7', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 5524: [[8,2, 2]] : 16 :['X5Z6', 'Y0Y1X2Z6', 'X0Z1X3Z7', 'Z0X1X4Z7', 'Z2Z5X6Z7', 'Z3Z4Z6X7'] : False\n",
+      "4 :: 5529: [[8,2, 2]] : 8 :['X0Z7', 'Y1Y2Z4Z6', 'Z1X2X3Z5', 'Z3X5Z6Z7', 'Y1Y3Y4Y6', 'Z0X3Z6X7'] : True\n",
+      "4 :: 5530: [[8,2, 2]] : 8 :['X0Z7', 'Y1Y2Z4Z6', 'Z3X5Z6Z7', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Z0X2Y6Y7'] : False\n",
+      "4 :: 5531: [[8,2, 2]] : 8 :['X0Z7', 'X3Z4Z5Z7', 'Y1X2Y3X4', 'X1Y2Y3X5', 'Y1Y2X3X6', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 5532: [[8,2, 2]] : 16 :['X4Z7', 'X0Z1X2Z6', 'Z0X1X3Z7', 'Z0X1X5Z6', 'Z2Z5X6Z7', 'Z3Z4Z6X7'] : False\n",
+      "4 :: 5534: [[8,2, 2]] : 32 :['Z1X2', 'X0X1Z2Z7', 'X3X4Z5Z6', 'X0Y3Y5Z6', 'X3Z4X6Z7', 'Z0Z3Z4X7'] : False\n",
+      "4 :: 5543: [[8,2, 2]] : 4 :['X0Z7', 'X1X2Z4Z5', 'Z1Y3Y4Z5', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : True\n",
+      "4 :: 5544: [[8,2, 2]] : 16 :['Z1X2', 'X0X1Z2Z7', 'X0Y3Y5Z6', 'Z3X4X5Z7', 'X0X3Z4X6', 'Z0Z3Z4X7'] : False\n",
+      "4 :: 5547: [[8,2, 2]] : 64 :['Z5Z6', 'Z0Z2Z6Z7', 'Z0Z1Z3Z6', 'Z0Z1Z4Z7', 'X0X4X5X6', 'X1X2X3X7'] : False\n",
+      "4 :: 5550: [[8,2, 2]] : 32 :['Z1X2', 'X0X1Z2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'X3Z4X6Z7', 'Z0Z3Z4X7'] : False\n",
+      "4 :: 5551: [[8,2, 2]] : 384 :['X0X1', 'Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'Z0Z1Z4Z5', 'X2X4X5X6', 'X3X4X5X7'] : False\n",
+      "4 :: 5552: [[8,2, 2]] : 128 :['Z1X2', 'X0X1Z2Z7', 'X0X3Z5Z7', 'X0X4Z6Z7', 'Z3Z4X5X6', 'Z0Z5Z6X7'] : False\n",
+      "4 :: 5700: [[8,2, 2]] : 8 :['X0Z7', 'Z1X2X3Z5', 'Z2X4X5Z6', 'Z1Z5X6Z7', 'Y1Y3Y4Y6', 'Z0Y3X5Y7'] : False\n",
+      "4 :: 5705: [[8,2, 2]] : 8 :['X0Z5', 'X2X3Z4Z6', 'Z0X2X5Z6', 'Z1Y3Z5Y6', 'Z2Y3Z5Y7', 'Y1Z3Y4X7'] : False\n",
+      "4 :: 5726: [[8,2, 2]] : 8 :['X0Z5', 'X1X3Z4Z7', 'Z1Z2X4Z5', 'Z0X2X5Z6', 'Y2Z3Y4X6', 'Z1Z2Y6Y7'] : False\n",
+      "4 :: 5730: [[8,2, 2]] : 16 :['X0Z4', 'X1X2Z4Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Z2Z3Y5Y6', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 5752: [[8,2, 2]] : 8 :['X0Z7', 'Z1X2X3Z5', 'Z2X4X5Z6', 'Z1Z5X6Z7', 'Y1Y3Y4Y6', 'Z0X3Z6X7'] : False\n",
+      "4 :: 5760: [[8,2, 2]] : 4 :['X0Z7', 'Z2X4X5Z6', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Y2Y3X4X6', 'Z0X3Z6X7'] : True\n",
+      "4 :: 5779: [[8,2, 2]] : 4 :['X0Z7', 'Z3X5Z6Z7', 'Y1X2Y4X5', 'Z1Z5X6Z7', 'Y2Y3X4X6', 'Z0X2Y6Y7'] : False\n",
+      "4 :: 1561: [[8,3, 2]] : 128 :['Z1Z5', 'Z3Z7', 'Z0Z2Z4Z6', 'X0Y1X4Y5', 'X2Y3X6Y7'] : True\n",
+      "4 :: 2314: [[8,3, 2]] : 8 :['Z0Z1Z3Z6', 'Z2Z4Z6Z7', 'Z0Z1Z5Z7', 'X0X4X5X6', 'X1X2X3X7'] : True\n",
+      "4 :: 3439: [[8,3, 2]] : 4 :['X0X3Z4Z5', 'Z2Z3X5Z7', 'Y1Y2Y4Y5', 'Z1Z2X6Z7', 'Y0Y3Z6X7'] : True\n",
+      "4 :: 3442: [[8,3, 2]] : 16 :['X1X2Z6Z7', 'X0X3Z4Z7', 'X1Z3X4Z7', 'Z2Y5Y6Z7', 'Z0Z1Z5X7'] : True\n",
+      "4 :: 3462: [[8,3, 2]] : 4 :['X0X1X2Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Z2Z3Y5Y6', 'Z1Z2Z3X7'] : False\n",
+      "4 :: 3656: [[8,3, 2]] : 2 :['X0X1X2Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Z2Z3Y5Y6', 'Z1Y3X5Y7'] : False\n",
+      "4 :: 4067: [[8,3, 2]] : 4 :['X0Z1X2Z6', 'Z0X1X3Z7', 'Y4Y5Z6Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7'] : True\n",
+      "4 :: 4139: [[8,3, 2]] : 96 :['Y0Y1X2Z6', 'X0Z1X3Z7', 'Z0X1X4X5', 'Z2Z4Z5X6', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 4621: [[8,3, 2]] : 64 :['X0Z1X2Z6', 'Z0X1X3Z7', 'Z0X1X4X5', 'Z2Z4Z5X6', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 4717: [[8,3, 2]] : 2 :['X3Z4Z5Z7', 'X0Z1Z3X4', 'X1Y2Y3X5', 'Z1Z2X6Z7', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 4899: [[8,3, 2]] : 48 :['Z0Z1Z6Z7', 'Z2Z4Z6Z7', 'Z3Z5Z6Z7', 'X0X4X5X6', 'X1X2X3X7'] : False\n",
+      "4 :: 5359: [[8,3, 2]] : 4 :['Y0Y1X2Z6', 'X0Z1X3Z7', 'X2X3Y4Y5', 'Z2Z4X6Z7', 'Z3Z5Z6X7'] : False\n",
+      "4 :: 5524: [[8,3, 2]] : 2 :['X0Z1X2Z6', 'Z0X1X3Z7', 'X3Y4Y5Z6', 'Z2Z4X6Z7', 'Z3Z5Z6X7'] : False\n",
+      "4 :: 5574: [[8,3, 2]] : 32 :['X0Y1Y2Z7', 'X1Z2X3Z5', 'Z1X2X4Z6', 'Z3Z4X5X6', 'Z0Z5Z6X7'] : False\n",
+      "4 :: 5619: [[8,3, 2]] : 8 :['X0Z1X2Z7', 'X1Z2X3Z5', 'X0X4Z6Z7', 'Z3Z4X5X6', 'Z0Z5Z6X7'] : False\n",
+      "4 :: 5666: [[8,3, 2]] : 6 :['X3Z4Z5Z7', 'X0Z1Z3X4', 'Z2Z3X5Z7', 'Y1Y2X3X6', 'Z0Y3Z6Y7'] : False\n",
+      "4 :: 5712: [[8,3, 2]] : 2 :['X1X2Z4Z5', 'Z2Z3X5Z7', 'Y1Y2X3X6', 'X0X4X5X6', 'Z0Y3Z6Y7'] : True\n",
+      "4 :: 5813: [[8,3, 2]] : 64 :['Z0Z1Z2Z7', 'X1X2X3X5', 'X1X2X4X6', 'Z3Z4Z5Z6', 'X0X5X6X7'] : False\n",
+      "4 :: 6798: [[8,3, 2]] : 512 :['Z0Z1Z4Z5', 'Z0Z2Z4Z6', 'Z0Z3Z4Z7', 'Y0Y1Y4Y5', 'Y2Y3Y6Y7'] : True\n",
+      "4 :: 6849: [[8,3, 2]] : 384 :['Y0Y1X2Z6', 'X0Z1X3Z7', 'Z0X1X4X5', 'Z2Y4Y5X6', 'Z3Z4Z5X7'] : False\n",
+      "4 :: 6851: [[8,3, 2]] : 48 :['Y1Y2Z4Z6', 'X0Z2Z3X4', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Z0Y3X5Y7'] : True\n",
+      "4 :: 6873: [[8,3, 2]] : 64 :['Z0X1', 'X0Z1X2Z6', 'X0Z1X3Z7', 'Z2X4Y5Y6', 'Z3Y4X5Y7'] : True\n",
+      "4 :: 6947: [[8,3, 2]] : 64 :['Z1X2', 'X0X1Z2Z7', 'Z3Z4Y5Y6', 'Z0Y4X5Y7', 'Y0Y3X6X7'] : True\n",
+      "4 :: 6979: [[8,3, 2]] : 16 :['Z1X2', 'X0X1Z2Z7', 'X0Y3Y5Z6', 'Y4Z5Y6Z7', 'Z0Z3Z4X7'] : True\n",
+      "4 :: 7015: [[8,3, 2]] : 16 :['Z1X2', 'X0X1Z2Z7', 'Y3Y5Z6Z7', 'Y4Z5Y6Z7', 'Z0Z3Z4X7'] : True\n",
+      "4 :: 7044: [[8,3, 2]] : 32 :['Z1Z7', 'Z0Z3Z4Z7', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X5X7'] : True\n",
+      "4 :: 7048: [[8,3, 2]] : 16 :['X4X6', 'Z0Z1Z2Z7', 'X1X2X3X5', 'Z3Z4Z5Z6', 'X0X5X6X7'] : True\n",
+      "4 :: 7053: [[8,3, 2]] : 128 :['Z3Z7', 'Z0Z1Z4Z5', 'Z0Z2Z4Z6', 'Y0Y1Y4Y5', 'X2Y3X6Y7'] : True\n",
+      "4 :: 2326: [[8,4, 2]] : 128 :['X4X5X6X7', 'X0X1X2X3', 'Z0Z1Z4Z5', 'Z2Z3Z6Z7'] : True\n",
+      "4 :: 3627: [[9,2, 2]] : 64 :['X1Z7', 'X2Z6', 'Z3X4', 'X0X3Z4Z8', 'Z2X5X6Z7', 'Z1Y5Y7Z8', 'Z0Z5Z6X8'] : True\n",
+      "4 :: 6776: [[9,2, 2]] : 32 :['X2Z6', 'Z3X4', 'X0X1Z7Z8', 'X0X3Z4Z8', 'Z2X5X6Z7', 'Z1Y5Y7Z8', 'Z0Z5Z6X8'] : True\n",
+      "4 :: 7666: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X4X5Z6Z7', 'Z2Z5X6Z8', 'Y2Y3Y4Y6', 'Z1X3X6X7', 'Z0Y4Y5X8'] : True\n",
+      "4 :: 7739: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Z3Y4Z7', 'Z2Y3Y5Z8', 'Z3X5X6Z7', 'Z1Z4Z6X7', 'Z0Z5Z6X8'] : True\n",
+      "4 :: 7951: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'X2X4Z5Z7', 'Z1X6Z7Z8', 'X3Y5Z6Y7', 'Z0Z4Y7Y8'] : True\n",
+      "4 :: 7960: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1Y4Y5Z7', 'Z3X6Z7Z8', 'Y3X4Z5Y6', 'Z2Z4Z6X7', 'Z0Y2Y6X8'] : True\n",
+      "4 :: 9033: [[9,2, 2]] : 16 :['X1Z5', 'Z2X3', 'X0X2Z3Z8', 'X0Z5X6Z7', 'Z4Z6X7Z8', 'Z1Y4X5Y7', 'Z0Y4Z6Y8'] : False\n",
+      "4 :: 9220: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X4Z5Z8', 'Z1Y4Y5Z7', 'Z3X6Z7Z8', 'Z2Z4Z6X7', 'Z0Z2Y3Y8'] : True\n",
+      "4 :: 9231: [[9,2, 2]] : 32 :['X1Z5', 'Z2X3', 'X0X2Z3Z8', 'Z5X6Z7Z8', 'Z1X4Y5Y6', 'Z4Z6X7Z8', 'Z0Y4Z6Y8'] : False\n",
+      "4 :: 16505: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'Y2Z3Y4Z7', 'Z2X4X5Z6', 'Z4Z5X7Z8', 'Y2X3Y6X7', 'Z0Z1X5X8'] : True\n",
+      "4 :: 22493: [[9,2, 2]] : 32 :['Z0Z8', 'Z1Z7', 'X2X3X4X5', 'Z2Z4Z6Z8', 'Z3Z5Z6Z7', 'X1X4X6X7', 'X0X5X6X8'] : True\n",
+      "4 :: 22586: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Y2Y3Z5X7', 'Z3Z4Y6Y7', 'Z0X6X7X8'] : False\n",
+      "4 :: 22631: [[9,2, 2]] : 32 :['X0Z7', 'X1Z4', 'Z4X5Z6Z8', 'X2Y3Z5Y6', 'Z1Z3X4X6', 'Z0Z2Z3X7', 'Y2Y5Z7X8'] : True\n",
+      "4 :: 25379: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Z3Y4Z7', 'X2Z4X5Z8', 'Y3X4Y5X6', 'Z1Z4Z6X7', 'Z0Z5Z6X8'] : False\n",
+      "4 :: 25715: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1Z3Y4Y5', 'Y2X3Z4Y6', 'Z3Z6X7Z8', 'X2X4Z5X7', 'Z0Y2Z7Y8'] : True\n",
+      "4 :: 25749: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Y3Y4Z5Z7', 'Z1X3X5Z7', 'Y2X3Z4Y6', 'Z3Z6X7Z8', 'Z0X2X6X8'] : True\n",
+      "4 :: 31385: [[9,2, 2]] : 2 :['X1X3Z7Z8', 'Y1Z3Y4Z8', 'Z2X5Z7Z8', 'Z0X6Z7Z8', 'Z3Z5Z6X7', 'X0Z1Y2Y8', 'Z2X4X7X8'] : False\n",
+      "4 :: 31593: [[9,2, 2]] : 1 :['Y2Y3Z5Z7', 'X0X4Z5Z6', 'X0Z3Z4X5', 'Z1Z4X6Z8', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 31619: [[9,2, 2]] : 1 :['X0X3Z6Z7', 'X0Z1X4Z7', 'Z2X5Z6Z8', 'Z2Z3Z4X7', 'Y4Y5Y6Y7', 'Y0Y1Y6Y8', 'Z0Y2Y7X8'] : False\n",
+      "4 :: 32401: [[9,2, 2]] : 1 :['Z1X2Z4Z8', 'Z2X4Z6Z8', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'X2X3Y5Y6', 'X1Z5X6X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 32449: [[9,2, 2]] : 8 :['X0X1Z5Z8', 'X0Z2X3Z6', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Y2Y4Z5X6', 'X1Y2Y3X7', 'Y0Y3Y4Y8'] : False\n",
+      "4 :: 32478: [[9,2, 2]] : 4 :['X0X1Z5Z6', 'Y2Y3Z7Z8', 'Z0X5Z6Z7', 'Z1Z5X6Z8', 'X0X2Z4X7', 'Z3Y4Z5Y7', 'X1X3Z4X8'] : False\n",
+      "4 :: 36661: [[9,2, 2]] : 1 :['X2Z5Z7Z8', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Y2Y3X5Z6', 'X0X1Z3X6', 'Y1Y2Z6X7', 'X3Y6Y7X8'] : False\n",
+      "4 :: 36793: [[9,2, 2]] : 1 :['Y1Z3Y4Z8', 'Z2X5Z7Z8', 'Y2X3Z4Y5', 'Z0X6Z7Z8', 'Y0X1X3Y6', 'Z3Z5Z6X7', 'Z1Y2Z6Y8'] : False\n",
+      "4 :: 37047: [[9,2, 2]] : 2 :['X0X1Z5Z6', 'Z2X3X4Z8', 'Z0X5Z6Z7', 'Z1Z5X6Z8', 'X0X2Z4X7', 'Z3Y4Z5Y7', 'X3Z4Z6X8'] : False\n",
+      "4 :: 42541: [[9,2, 2]] : 1 :['X4Z5Z6Z8', 'X0Z3Z4X5', 'X1X2Y4Y5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 42542: [[9,2, 2]] : 1 :['Y1Y3Z5Z6', 'Z1Z2X5Z8', 'X0Z3X6Z7', 'Z4Z6X7Z8', 'X3Y4X5Y7', 'X1X2Y6Y7', 'Y0X1Y6X8'] : False\n",
+      "4 :: 44019: [[9,2, 2]] : 1 :['X0X3Z6Z7', 'Z1X4Z7Z8', 'Z2X5Z6Z8', 'Y3X4Z5Y6', 'Z2Z3Z4X7', 'Y1Y2Y6Y7', 'Z0Y1Y6X8'] : False\n",
+      "4 :: 44022: [[9,2, 2]] : 4 :['X0X1Z5Z8', 'Z2Z3X4Z8', 'Z1X5Z6Z7', 'Y1X2X3Y5', 'Y3Y4Z5X6', 'X1Y2Y4X7', 'Y0X2Z7Y8'] : False\n",
+      "4 :: 44074: [[9,2, 2]] : 12 :['Z0Z1Z6Z8', 'Z0Z2Z7Z8', 'Z3Z4Z6Z8', 'Z3Z5Z7Z8', 'X1X3X5X6', 'X2X3X4X7', 'X0X4X5X8'] : False\n",
+      "4 :: 44077: [[9,2, 2]] : 12 :['X0X1Z7Z8', 'X0X2Z4Z8', 'X5Z6Z7Z8', 'Z2X3X4X5', 'Z4Z5X6Z8', 'Z1Z3Z5X7', 'Z0Y3Y5X8'] : False\n",
+      "4 :: 48202: [[9,2, 2]] : 1 :['Z1X3Z5Z6', 'Z2X4Z6Z8', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'Y1X2Y5X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 48407: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'Z3X4Z7Z8', 'X0Z3X5Z6', 'Z2Z5X6Z8', 'Y2Y3Y4Y6', 'Z1X3X6X7', 'Z0X2X3X8'] : False\n",
+      "4 :: 49554: [[9,2, 2]] : 4 :['X1X3Z4Z7', 'X2X3Z5Z6', 'X0Z1X4Z5', 'Z2Z4X5Z8', 'X0Z1Z3X6', 'Z2Z3X7Z8', 'Z0X1X2X8'] : False\n",
+      "4 :: 49900: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'X1Y2Z3Y4', 'X3X4Z5Z7', 'X0X2Z4X5', 'Z2X4X6Z8', 'Z1Z4Z6X7', 'Z0Z5Z6X8'] : False\n",
+      "4 :: 50699: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'Z3X4Z7Z8', 'X0Z3X5Z6', 'X2X3Y4Y5', 'Z2Z5X6Z8', 'Z1X3X6X7', 'Y0X2X3Y8'] : False\n",
+      "4 :: 53082: [[9,2, 2]] : 2 :['Z1X3Z5Z6', 'Z1Y2Y4Z6', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'Y1X2Y5X8', 'Y0Y4X7X8'] : False\n",
+      "4 :: 53262: [[9,2, 2]] : 1 :['X0X1Z5Z8', 'X0X2X4Z5', 'Z1Y4Y5Z7', 'Z3X6Z7Z8', 'X0X2Y3Y6', 'Z2Z4Z6X7', 'Z0Z2Y3Y8'] : False\n",
+      "4 :: 53699: [[9,2, 2]] : 2 :['X0X1Z7Z8', 'X0X2Z6Z8', 'X3X4Z5Z7', 'Y4Y5Z6Z8', 'Z2X4X6Z8', 'Z1Y3Y7Z8', 'Z0Z3Z4X8'] : False\n",
+      "4 :: 53834: [[9,2, 2]] : 4 :['X0X1Z5Z8', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'X1Y2Y4X6', 'Y2Y3Z5X7', 'Z0Y3Y4X8'] : False\n",
+      "4 :: 53968: [[9,2, 2]] : 4 :['X0X1Z7Z8', 'X0X2Z5Z7', 'Z3X4Z7Z8', 'Z2Y3Y4X5', 'X3Y4Z5Y6', 'Z0Y3X6Y7', 'Z1Y3X6Y8'] : False\n",
+      "4 :: 54188: [[9,2, 2]] : 1 :['Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'X1Z5X6X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 54501: [[9,2, 2]] : 1 :['X3Z4Z7Z8', 'X0Z2Y3Y4', 'Z1X5Z6Z8', 'X1Y2X3Y6', 'Z3X4Z5X6', 'Z0Z1Z3X7', 'X2Z3Z5X8'] : False\n",
+      "4 :: 56116: [[9,2, 2]] : 1 :['X0X1Z4Z7', 'Z1X4Z5Z8', 'Z4X5Z6Z8', 'X1X2X3X5', 'Z3Z5X6Z8', 'Z0Z2Z3X7', 'X0Y2Y5X8'] : False\n",
+      "4 :: 56139: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'Y2Y3Z5Z7', 'X0X4Z5Z6', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 56428: [[9,2, 2]] : 64 :['X0X1Z7Z8', 'X0X2Z6Z8', 'X0X3X4Z8', 'X2Y3Z4Y5', 'Z2X5X6Z7', 'Z1Y5Y7Z8', 'Y0X3X5Y8'] : False\n",
+      "4 :: 56474: [[9,2, 2]] : 1 :['X1X2Z4Z5', 'Y1Z3Y4Z8', 'Z2X5Z7Z8', 'Z0X6Z7Z8', 'Y0X1X3Y6', 'Z3Z5Z6X7', 'X0Z1Y2Y8'] : False\n",
+      "4 :: 56475: [[9,2, 2]] : 1 :['X3Z6Z7Z8', 'X0Z1X4Z7', 'Z2X5Z6Z8', 'Y1Y2Y4Y5', 'Y3X4Z5Y6', 'Z0Y1Y6X8', 'Y0Y2Y7Y8'] : False\n",
+      "4 :: 56500: [[9,2, 2]] : 1 :['Y2Y3Z5Z7', 'X4Z5Z6Z8', 'X0Z3Z4X5', 'X1X2Y4Y5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 56803: [[9,2, 2]] : 1 :['X3Z6Z7Z8', 'X0Z1X4Z7', 'Z2X5Z6Z8', 'Y1Y2Y4Y5', 'Z1Z3Z5X6', 'Z2Z3Z4X7', 'Y0Y1Y6Y8'] : False\n",
+      "4 :: 57964: [[9,2, 2]] : 1 :['X0X1Z6Z8', 'Z2Y3Y4Z7', 'Z2X5Z7Z8', 'X0Y2X4Y5', 'Z1X6Z7Z8', 'X3Y5Z6Y7', 'Z0Z4Y7Y8'] : False\n",
+      "4 :: 60064: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'Y2Y3Z5Z7', 'X0X4Z5Z6', 'X0Z3Z4X5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 60222: [[9,2, 2]] : 1 :['X4Z5Z6Z8', 'X0Z3Z4X5', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 60526: [[9,2, 2]] : 1 :['X1X3Z6Z8', 'Z4X5Z7Z8', 'Y1Y2Z4X6', 'X0Z3X5X6', 'Z0Z5X7Z8', 'X0Z1X4X8', 'X2Y5Y6X8'] : False\n",
+      "4 :: 60976: [[9,2, 2]] : 1 :['X2Z5Z7Z8', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Y2Y3X5Z6', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 60980: [[9,2, 2]] : 8 :['X2X3Z4Z5', 'Z2X4Z7Z8', 'X0X1Y2Y4', 'Z3X5Z7Z8', 'Y1Z3X4Y6', 'Y2Y3X6X7', 'Z0Y1X7Y8'] : False\n",
+      "4 :: 61062: [[9,2, 2]] : 1 :['Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'X1Z4Y5Y8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 61136: [[9,2, 2]] : 1 :['X0Z1X4Z6', 'Z1X5Z7Z8', 'Y2Y3X4X5', 'X0Z2Z4X6', 'Z3Z5X7Z8', 'Y1X2Y4X7', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 61160: [[9,2, 2]] : 2 :['Y2Y3Z5Z7', 'X0X4Z5Z6', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 62225: [[9,2, 2]] : 1 :['Z1Y2Y3Z6', 'Z2X3X4Z5', 'Z4X5Z7Z8', 'Y1Y2Z4X6', 'X0Z3X5X6', 'Z0Z5X7Z8', 'Z1X4Z7X8'] : False\n",
+      "4 :: 62385: [[9,2, 2]] : 2 :['X0X1Z7Z8', 'Y2Y3Z4Z5', 'X1Y2Z3Y4', 'X0X2Z4X5', 'Z2X4X6Z8', 'Z1Z4Z6X7', 'Z0Z5Z6X8'] : False\n",
+      "4 :: 62971: [[9,2, 2]] : 2 :['X2X3Z5Z6', 'X0Z1X4Z5', 'X0Z2Z4X5', 'Z1Z3X6Z8', 'Z2Z3X7Z8', 'X1Y3X5Y7', 'Y0X1X2Y8'] : False\n",
+      "4 :: 63139: [[9,2, 2]] : 1 :['Y1Y2Z3Z8', 'Z4X5Z7Z8', 'X1Y3Z4Y6', 'X0Z3X5X6', 'Z0Z5X7Z8', 'X0Z1X4X8', 'X2Y5Y6X8'] : False\n",
+      "4 :: 63433: [[9,2, 2]] : 2 :['X0X1Z7Z8', 'X0X2Z4Z8', 'Z2X3X4Z6', 'X3X5Z7Z8', 'Y3Z5Y6Z8', 'Z1Y5Z6Y7', 'Z0Z3Z4X8'] : False\n",
+      "4 :: 64295: [[9,2, 2]] : 1 :['X0X1Z5Z8', 'Y3Y4Z5Z7', 'Z1X3X5Z7', 'Z2X6Z7Z8', 'Z3Z6X7Z8', 'X1X2X4X7', 'Y0Y2Z7X8'] : False\n",
+      "4 :: 64645: [[9,2, 2]] : 1 :['X0X1X2Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X3X5Z6Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 64957: [[9,2, 2]] : 1 :['X2X3Z6Z7', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Z2Y6X8', 'Z5X6Y7Y8'] : False\n",
+      "4 :: 65333: [[9,2, 2]] : 12 :['Y1Y3Z5Z6', 'Y2Y4Z5Z7', 'X0Z1Z2X5', 'Z3X6Z7Z8', 'Y1X4Y5X6', 'Z4Z6X7Z8', 'Y0X1Y6X8'] : False\n",
+      "4 :: 65471: [[9,2, 2]] : 1 :['Y2Y3Z5Z7', 'X0X4Z5Z6', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 65514: [[9,2, 2]] : 2 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X0Z2X4Z7', 'Z3X5Z7Z8', 'Y1Z2X5Y6', 'Y2Y3X6X7', 'Z0Y1X7Y8'] : False\n",
+      "4 :: 67031: [[9,2, 2]] : 1 :['X0X1Z4Z7', 'X2X3Z6Z8', 'Z1X4Z5Z8', 'Z4X5Z6Z8', 'Y1Z3Y4X6', 'Z0Z2Z3X7', 'Y2Y5Z7X8'] : False\n",
+      "4 :: 67181: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'Z3X4Z7Z8', 'Z3X5Z6Z8', 'Z2Z5X6Z8', 'Z1X3X6X7', 'Y0X2X3Y8', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 67196: [[9,2, 2]] : 1 :['X0X3Z6Z7', 'Z1X4Z7Z8', 'Z2X5Z6Z8', 'Y1Y2Y4Y5', 'Y3X4Z5Y6', 'Z2Z3Z4X7', 'Y0Y1Y6Y8'] : False\n",
+      "4 :: 67630: [[9,2, 2]] : 2 :['Y2Y3Z6Z7', 'X0Z1X4Z6', 'Z1X5Z7Z8', 'X0Z2Z4X6', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 68299: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'X0X4Z5Z6', 'Z3Z4X5Z8', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 68378: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'X0X4Z5Z6', 'X0Z3Z4X5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 68441: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'Y2Y3Z5Z6', 'X0X4Z6Z7', 'Z3X5Z7Z8', 'Z2Z4X6Z8', 'Z1Z4Z5X7', 'Z0X3Y6Y8'] : False\n",
+      "4 :: 68563: [[9,2, 2]] : 4 :['X1X2Z3Z7', 'X1Z2X3Z8', 'Z4X5Z6Z8', 'X0X1Y4Y5', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Y2Y5X7X8'] : False\n",
+      "4 :: 68649: [[9,2, 2]] : 2 :['Y1Y3Z5Z6', 'Y2Y4Z5Z7', 'Z1Z2X5Z8', 'Z3X6Z7Z8', 'Z4Z6X7Z8', 'Y0X1Y6X8', 'Z0X2Y7Y8'] : False\n",
+      "4 :: 68791: [[9,2, 2]] : 1 :['X0X1Z4Z7', 'Z1X4Z5Z8', 'Z4X5Z6Z8', 'X1X2X3X5', 'X2Y3Z5Y6', 'Z0Z2Z3X7', 'X0Y2Y5X8'] : False\n",
+      "4 :: 68814: [[9,2, 2]] : 1 :['X2Z5Z7Z8', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 68853: [[9,2, 2]] : 2 :['X0X1Z6Z7', 'X4Z5Z7Z8', 'X1X2X3X4', 'Z3Z4X5Z8', 'Z1Z3X6Z8', 'Z0Z2Z4X7', 'Y2Y4Z6X8'] : False\n",
+      "4 :: 70061: [[9,2, 2]] : 2 :['X0Z1X4Z6', 'Z1X5Z7Z8', 'Y2Y3X4X5', 'Z2Z4X6Z8', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 70104: [[9,2, 2]] : 2 :['Y2Y3Z6Z7', 'X0Z1X4Z6', 'X0Z1X5Z7', 'Z2Z4X6Z8', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 70109: [[9,2, 2]] : 2 :['X1X2Z3Z4', 'Y2Y4Z5Z7', 'X0Z1Z2X5', 'Z3X6Z7Z8', 'Y3X4X5Y6', 'Z4Z6X7Z8', 'Z0X1Y6Y8'] : False\n",
+      "4 :: 70123: [[9,2, 2]] : 8 :['X0X1Z5Z8', 'X2X3Z6Z7', 'Z2Z3X4Z8', 'Z1X5Z6Z7', 'X1Y3Y4X6', 'Y2Y4Z5X7', 'Z0X2Z7X8'] : False\n",
+      "4 :: 70127: [[9,2, 2]] : 2 :['X1X3Z4Z7', 'X2X3Z5Z6', 'X0Z1X4Z5', 'X0Z2Z4X5', 'Z1Z3X6Z8', 'Z2Z3X7Z8', 'Z0X1X2X8'] : False\n",
+      "4 :: 70360: [[9,2, 2]] : 12 :['X3Z4Z7Z8', 'X0Z2Y3Y4', 'Z1X5Z6Z8', 'Y1X2X3Y5', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X2Z3Z5X8'] : False\n",
+      "4 :: 70413: [[9,2, 2]] : 1 :['X0Z1X4Z6', 'Z1X5Z7Z8', 'Y2Y3X4X5', 'Z2Z4X6Z8', 'Z3Z5X7Z8', 'X1Y2Y6X7', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 70447: [[9,2, 2]] : 2 :['Z1X3Z5Z6', 'Z2X4Z6Z8', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'X1Z4Y5Y8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 70476: [[9,2, 2]] : 2 :['Y2Y3Z6Z7', 'Z1X4Z6Z8', 'X0Z1X5Z7', 'X0Z2Z4X6', 'Z3Z5X7Z8', 'X1Y2Y6X7', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 70597: [[9,2, 2]] : 1 :['X4Z5Z6Z8', 'Z3Z4X5Z8', 'X0Z1Z4X6', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 70647: [[9,2, 2]] : 2 :['X0X1Z5Z8', 'Y3Y4Z5Z7', 'Z1X3X5Z7', 'X0Z2X6Z7', 'Y2X3Z4Y6', 'Z3Z6X7Z8', 'Y0Y2Z7X8'] : False\n",
+      "4 :: 70857: [[9,2, 2]] : 1 :['X3Z6Z7Z8', 'X0Z1X4Z7', 'Z2X5Z6Z8', 'Y1Y2Y4Y5', 'Y3X4Z5Y6', 'Z2Z3Z4X7', 'Z0Y1Y6X8'] : False\n",
+      "4 :: 70897: [[9,2, 2]] : 1 :['Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'X0Z1Z4X6', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 70950: [[9,2, 2]] : 1 :['X1X3Z5Z7', 'Z2X4Z7Z8', 'X0X1Y2Y4', 'Z3X5Z7Z8', 'Y1Z3X4Y6', 'Y4Y5X6X7', 'Z0Y1X7Y8'] : False\n",
+      "4 :: 71695: [[9,2, 2]] : 1 :['Z1X2Z4Z8', 'Z1X3Z5Z6', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'Z2Y4X5Y6', 'X1Z5X6X8', 'Y0Y4X7X8'] : False\n",
+      "4 :: 71718: [[9,2, 2]] : 1 :['X3Z6Z7Z8', 'X0X1X2X3', 'Z1Z2X4Z5', 'X0X1X5Z7', 'Y2Y3Y4Y6', 'Z1Z2X6X7', 'Z0Y3Z5Y8'] : False\n",
+      "4 :: 71877: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'Y2Y3Z5Z7', 'X0X4Z5Z6', 'X0Z3Z4X5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 72087: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'Y2Y3Z5Z7', 'X0X4Z5Z6', 'Z3Z4X5Z8', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 72145: [[9,2, 2]] : 2 :['Z1Y2Y3Z6', 'Z2X3X4Z5', 'Z4X5Z7Z8', 'Y1Y2Z4X6', 'X0Z3X5X6', 'Z0Z5X7Z8', 'X0Z1X4X8'] : False\n",
+      "4 :: 72291: [[9,2, 2]] : 2 :['X1X3Z4Z7', 'X2X3Z5Z6', 'Z1X4Z5Z8', 'X0Z2Z4X5', 'X0Z1Z3X6', 'Z2Z3X7Z8', 'Z0X1X2X8'] : False\n",
+      "4 :: 72300: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'X0X4Z5Z6', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Y0Y4Z7X8'] : False\n",
+      "4 :: 72544: [[9,2, 2]] : 8 :['X1X2Z6Z7', 'X3Z6Z7Z8', 'Z1Z2X4Z5', 'X0X1X5Z7', 'Y2Y3Y4Y6', 'Y1Y3Y4Y7', 'Z0Y3Z5Y8'] : False\n",
+      "4 :: 72628: [[9,2, 2]] : 2 :['Y2Y3Z6Z7', 'Z1X4Z6Z8', 'Z1X5Z7Z8', 'X0Z2Z4X6', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 73402: [[9,2, 2]] : 1 :['X1X2Z4Z6', 'X0X1X3Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Y2X3Y5X6', 'Z3Y5Z6Y7', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 73416: [[9,2, 2]] : 1 :['X0X1Z6Z8', 'X2X4Z5Z7', 'Z2X5Z7Z8', 'X0Y3Y4X5', 'Z1X6Z7Z8', 'X1X3Y5Y7', 'Z0Z4Y7Y8'] : False\n",
+      "4 :: 73508: [[9,2, 2]] : 2 :['X3Z4Z7Z8', 'X0Z2Y3Y4', 'Z1X5Z6Z8', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X1Y3Z6Y8', 'Y2X4Z5Y8'] : False\n",
+      "4 :: 74198: [[9,2, 2]] : 2 :['X3Z4Z7Z8', 'X0Z2Y3Y4', 'Y1X2X3Y5', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X1Y3Z6Y8', 'Y2X4Z5Y8'] : False\n",
+      "4 :: 74376: [[9,2, 2]] : 2 :['X2X3Z6Z7', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 74402: [[9,2, 2]] : 1 :['Z2X4Z6Z8', 'Y2X3Y4Z5', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'Y1X2Y5X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 74550: [[9,2, 2]] : 24 :['X1X2Z5Z6', 'Z0X3Z7Z8', 'Z1Z4X5Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7', 'X0X4X7Z8', 'Y2Y5Y7Y8'] : False\n",
+      "4 :: 74941: [[9,2, 2]] : 1 :['X0X1X2Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 75005: [[9,2, 2]] : 1 :['Y1Y2Z3Z8', 'X1X3Z6Z8', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'X0Z1X4X8', 'X2Y5Y6X8'] : False\n",
+      "4 :: 75042: [[9,2, 2]] : 1 :['X0X1Z6Z8', 'Y2Y3Z4Z5', 'Z2X5Z7Z8', 'X0Y2X4Y5', 'Z1X6Z7Z8', 'X1X3Y5Y7', 'Z0Z4Y7Y8'] : False\n",
+      "4 :: 75379: [[9,2, 2]] : 1 :['Z1X4Z6Z8', 'X0Z1X5Z7', 'Y2Y3X4X5', 'X0Z2Z4X6', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 75686: [[9,2, 2]] : 2 :['Y2Y3Z6Z7', 'Z1X4Z6Z8', 'X0Z1X5Z7', 'X0Z2Z4X6', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 75694: [[9,2, 2]] : 1 :['X1X2Z3Z4', 'Z1Z2X5Z8', 'X0Z3X6Z7', 'Y1X4Y5X6', 'Z4Z6X7Z8', 'X3Y4X5Y7', 'Z0X1Y6Y8'] : False\n",
+      "4 :: 75697: [[9,2, 2]] : 2 :['X2X3Z6Z7', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Y2Z3Y5Z8', 'X0X1Z3X6', 'Y1Y2Z6X7', 'Z5X6Y7Y8'] : False\n",
+      "4 :: 75708: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'X0Z3Z4X5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 75745: [[9,2, 2]] : 2 :['X2X3Z5Z6', 'Z1X4Z5Z8', 'X0Z2Z4X5', 'X0Z1Z3X6', 'Z2Z3X7Z8', 'X1Y3X5Y7', 'Y0X1X2Y8'] : False\n",
+      "4 :: 76046: [[9,2, 2]] : 1 :['X0Z1X4Z6', 'X0Z1X5Z7', 'Z2Z4X6Z8', 'Z3Z5X7Z8', 'Y1X2Y4X7', 'X1Y3X6Y7', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 76049: [[9,2, 2]] : 1 :['Y2Y3Z6Z7', 'X0Z1X4Z6', 'Z1X5Z7Z8', 'Z2Z4X6Z8', 'Z3Z5X7Z8', 'Y1X2Y4X7', 'Y0Y4Y5Y8'] : False\n",
+      "4 :: 76064: [[9,2, 2]] : 2 :['X0X3Z6Z7', 'Z1X4Z7Z8', 'Z2X5Z6Z8', 'Y3X4Z5Y6', 'Y3Z4X5Y7', 'Z0Y1Y6X8', 'Y0Y2Y7Y8'] : False\n",
+      "4 :: 76752: [[9,2, 2]] : 4 :['X0X1Z5Z6', 'X0X2Z5Z7', 'X3X4Z6Z7', 'Z0X3X5Z6', 'Z1Y3Y6Z8', 'Z2Y4Y7Z8', 'Z3Z4Z5X8'] : False\n",
+      "4 :: 76753: [[9,2, 2]] : 2 :['X2X3Z4Z5', 'X0Z2X4Z7', 'X1Y2Y4Z8', 'Z3X5Z7Z8', 'Y1Z3X4Y6', 'Y2Y3X6X7', 'Y0Y1X7X8'] : False\n",
+      "4 :: 76754: [[9,2, 2]] : 2 :['Y2Y3Z6Z7', 'X0Z1X4Z6', 'X0Z1X5Z7', 'Z2Z4X6Z8', 'Z3Z5X7Z8', 'X1Y2Y6X7', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 76755: [[9,2, 2]] : 2 :['Z2X4Z6Z8', 'Y2X3Y4Z5', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'X1Z4Y5Y8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 76785: [[9,2, 2]] : 1 :['X2Z5Z7Z8', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Y2Y3X5Z6', 'X0X1Z3X6', 'Y1Z2X3Y7', 'Z5X6Y7Y8'] : False\n",
+      "4 :: 79934: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'X0Z3Z4X5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 79940: [[9,2, 2]] : 2 :['X0X1Z6Z7', 'X0X2Z3Z6', 'Z2X3X4Z5', 'Y4Y5Z7Z8', 'Z0X6Z7Z8', 'Z1Z5Z6X7', 'Z3Z4Z6X8'] : False\n",
+      "4 :: 79975: [[9,2, 2]] : 2 :['X0X3Z6Z7', 'Z1X4Z7Z8', 'Z2X5Z6Z8', 'Z1Z3Z5X6', 'Z2Z3Z4X7', 'Y0Y1Y6Y8', 'Z0Y2Y7X8'] : False\n",
+      "4 :: 80776: [[9,2, 2]] : 2 :['X2X3Z6Z7', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Y2Z3Y5Z8', 'X1Z3Z4X6', 'Y1Y3X5X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 80826: [[9,2, 2]] : 2 :['X1X2Z3Z4', 'Y1Y3Z5Z6', 'Y2Y4Z5Z7', 'Z1Z2X5Z8', 'X0Z3X6Z7', 'Z4Z6X7Z8', 'Y0X1Y6X8'] : False\n",
+      "4 :: 81010: [[9,2, 2]] : 4 :['X0X1Z6Z7', 'X4Z5Z7Z8', 'X1X2X3X4', 'X0Z3Y4Y5', 'Z1Z3X6Z8', 'Z0Z2Z4X7', 'Y2Y4Z6X8'] : False\n",
+      "4 :: 84323: [[9,2, 2]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 84351: [[9,2, 2]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Y2Z3Y5Z8', 'X1Z3Z4X6', 'Y1Z2X3Y7', 'X2X6X7X8'] : False\n",
+      "4 :: 88214: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'Y2Z5Y6Z8', 'Z3Y5Z6Y7', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 88300: [[9,2, 2]] : 4 :['X0Z4', 'X2Z5Z7Z8', 'Z0X3X4Z5', 'Y2Y3X5Z6', 'X1Z3Z4X6', 'Y1Z2X3Y7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 88606: [[9,2, 2]] : 96 :['Z3X4', 'X0X1Z7Z8', 'X0X2Z6Z8', 'X0X3Z4Z8', 'Z2X5X6Z7', 'Z1Y5Y7Z8', 'Z0Z5Z6X8'] : False\n",
+      "4 :: 89829: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 91186: [[9,2, 2]] : 8 :['X0Z8', 'Z1X3X4Z6', 'Z2X5Z6Z8', 'Y1Y2Y4Y5', 'Z2Z3Z4X7', 'Y1Y2Y6Y7', 'Z0Y1Y6X8'] : False\n",
+      "4 :: 91217: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Z2X4Z6Z8', 'Z3Z4X6Z7', 'X2X3Y5Y6', 'X1Z5X6X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 91623: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Y1X3X4Y7', 'Z2Z4X6X7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 91662: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'Z3Z4X5Z8', 'Z1Y4Z5Y6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 91946: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3Z4X6Z7', 'X1Z4Y5Y8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 92344: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z1X2X5X6', 'Y1X2Y5X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 92730: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X2Y5X8', 'Z0Y4Y7X8'] : True\n",
+      "4 :: 92891: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X2Y5X8', 'Z0Y4Y7X8'] : True\n",
+      "4 :: 93364: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Y2X3Y5X6', 'Z3Y5Z6Y7', 'Z1Z2Z3X8'] : True\n",
+      "4 :: 93432: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z3X5Z7Z8', 'Y1Z3X4Y6', 'Y2Y3X6X7', 'Z0Y1X7Y8'] : False\n",
+      "4 :: 93816: [[9,2, 2]] : 4 :['X0Z8', 'X2X3Z4Z5', 'X1Y2Y4Z8', 'Z3X5Z7Z8', 'Y1Z2X5Y6', 'Y2Y3X6X7', 'Z0Y1X7Y8'] : False\n",
+      "4 :: 93905: [[9,2, 2]] : 4 :['X0Z8', 'Z1Z2X5Z8', 'Y3X4X5Y6', 'Z4Z6X7Z8', 'Y2X3Y5X7', 'X1X2Y6Y7', 'Z0X1Y6Y8'] : False\n",
+      "4 :: 93912: [[9,2, 2]] : 16 :['Z2X3', 'X0X1Z5Z8', 'X0X2Z3Z8', 'Z5X6Z7Z8', 'Z1X4Y5Y6', 'Z4Z6X7Z8', 'Z0Y4Z6Y8'] : False\n",
+      "4 :: 93927: [[9,2, 2]] : 16 :['Z1X2', 'X0X1Z2Z8', 'X0X3X5Z7', 'X4X5Z6Z8', 'X0Y3Z5Y6', 'Y4Z5Y7Z8', 'Z0Z3Z4X8'] : False\n",
+      "4 :: 93961: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'X1X2Y4Y5', 'Z1Z3X5X6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 94012: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 94043: [[9,2, 2]] : 8 :['X0Z7', 'X3Z4Z7Z8', 'Z1X5Z6Z8', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X1Y3Z6Y8', 'Y2X4Z5Y8'] : False\n",
+      "4 :: 94107: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z3Z4X6Z7', 'Z2Y4X5Y6', 'X1Z5X6X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 94129: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X3X5Z6Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 94144: [[9,2, 2]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'Z0X3X4Z5', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 94196: [[9,2, 2]] : 32 :['Z1X2', 'X0X1Z2Z8', 'X3X4Z6Z7', 'X0X3X5Z7', 'Y3Z5Y6Z8', 'Y4Z5Y7Z8', 'Z0Z3Z4X8'] : False\n",
+      "4 :: 94202: [[9,2, 2]] : 16 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'X1Z5X6X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 94203: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z4Z7', 'Z3X5Z7Z8', 'Y2Y3Y4Y5', 'Y1Z3X4Y6', 'Y2Y3X6X7', 'Z0Y1X7Y8'] : False\n",
+      "4 :: 94254: [[9,2, 2]] : 8 :['X0Z4', 'X2X3Z6Z7', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Z2Y6X8', 'Z5X6Y7Y8'] : False\n",
+      "4 :: 94309: [[9,2, 2]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'X1Z3Z4X6', 'Y1Y3X5X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 94363: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z1Z3X5X6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 94378: [[9,2, 2]] : 4 :['X0Z8', 'X4Z5Z6Z8', 'X1X2Y4Y5', 'Z1Z3X5X6', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 94505: [[9,2, 2]] : 32 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Z2Z3Y6Y7', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 94529: [[9,2, 2]] : 4 :['X0Z7', 'Z2X4Z6Z8', 'Y2X3Y4Z5', 'Z3X5Z7Z8', 'Z1X2X5X6', 'Y1X2Y5X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 94532: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Y2X3Y4Z5', 'Z3Z4X6Z7', 'X2X3Y5Y6', 'X1Z5X6X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 94669: [[9,2, 2]] : 4 :['X0Z8', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 94758: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'Z3Z4X5Z8', 'Z1Y4Z5Y6', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 94782: [[9,2, 2]] : 4 :['X0Z7', 'Z2X4Z6Z8', 'Y2X3Y4Z5', 'Z3Z4X6Z7', 'Z1X2X5X6', 'Y1X2Y5X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 94807: [[9,2, 2]] : 32 :['Z1X2', 'X0X1Z2Z8', 'X3X4Z6Z7', 'X3X5Z7Z8', 'Y3Z5Y6Z8', 'Y4Z5Y7Z8', 'Z0Z3Z4X8'] : False\n",
+      "4 :: 94864: [[9,2, 2]] : 8 :['Z0Z7', 'Z1Z2Z4Z8', 'X2X3X4X5', 'Z3Z5Z7Z8', 'Z3Z4Z6Z7', 'X1X3X6X8', 'Y0X4Y7X8'] : False\n",
+      "4 :: 95012: [[9,2, 2]] : 8 :['Z1X2', 'X0X1Z2Z8', 'X3X4Z6Z7', 'X3X5Z7Z8', 'X0Y3Z5Y6', 'Y4Z5Y7Z8', 'Z0Z3Z4X8'] : False\n",
+      "4 :: 95089: [[9,2, 2]] : 4 :['X0Z7', 'Z1Y2Y3Z6', 'Z2X3X4Z5', 'Y1Y2Z4X6', 'Z3X5X6Z7', 'Z0Z5X7Z8', 'Z1Y4X5Y8'] : False\n",
+      "4 :: 95105: [[9,2, 2]] : 4 :['X0Z4', 'X2Z5Z7Z8', 'Z0X4Z6Z8', 'Y2Y3X5Z6', 'X1Z3Z4X6', 'Y1Z2X3Y7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 95211: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z6', 'Z0Y1Y4Z8', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 95287: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'X2X3Y5Y6', 'X1Z5X6X8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 95296: [[9,2, 2]] : 4 :['X0Z7', 'Z2X4Z6Z8', 'Y2X3Y4Z5', 'Z3Z4X6Z7', 'Z1X2X5X6', 'X1Z4Y5Y8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 99818: [[9,2, 2]] : 8 :['X0Z8', 'Y2Y3Z5Z7', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8'] : True\n",
+      "4 :: 99861: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'X1Z4Y5Y8', 'Z0Y4Y7X8'] : False\n",
+      "4 :: 100226: [[9,2, 2]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Z0X4Z6Z8', 'Y2Z3Y5Z8', 'X1Z3Z4X6', 'Y1Y3X5X7', 'Y1Z2Y6X8'] : False\n",
+      "4 :: 100240: [[9,2, 2]] : 8 :['X0Z7', 'Z1Y2Y3Z6', 'Z2X3X4Z5', 'Z4X5Z7Z8', 'Y1Y2Z4X6', 'Z0Z5X7Z8', 'Z1X4Z7X8'] : False\n",
+      "4 :: 100986: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8'] : False\n",
+      "4 :: 101180: [[9,2, 2]] : 4 :['X0Z8', 'Z1X4Z6Z8', 'Y2Y3X4X5', 'Z3Z5X7Z8', 'Y1X2Y4X7', 'X1Y3X6Y7', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 101319: [[9,2, 2]] : 8 :['X0Z4', 'X1X2Z4Z6', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Y2X3Y5X6', 'Z3Y5Z6Y7', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 101503: [[9,2, 2]] : 8 :['X0Z8', 'Y2Y3Z6Z7', 'Z1X5Z7Z8', 'Z2Z4X6Z8', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Y4Y5X8'] : False\n",
+      "4 :: 101620: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z5Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'Y3Z6Y7Z8', 'Z1Z2Z3X8'] : False\n",
+      "4 :: 74150: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z3Z4Z7', 'Y0Z2Y6Y7', 'Z1Z3X5Z8'] : False\n",
+      "4 :: 87585: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Y1Y2Z4X5', 'X3Z4Y6X7', 'Y0Y3Z7Z8'] : False\n",
+      "4 :: 111894: [[9,3, 2]] : 4 :['Z0Z1Z2Z3', 'X4X5X6X7', 'Z0Z6Z7Z8', 'Z2Z4Z5Z8', 'X0X1X4X8', 'X2X3X6X8'] : False\n",
+      "4 :: 116769: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'X1Y2Z3Y4', 'Z1Y3Z5Z6', 'Y0Y2Z7Z8'] : False\n",
+      "4 :: 117903: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Z2Z4Z5', 'Z0X1Y2X8', 'Z0Y3Z5Z6'] : False\n",
+      "4 :: 121103: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z1Y3Z4Z5', 'X2Z6Z7Y8', 'Z0Z3Y4Z6'] : False\n",
+      "4 :: 123822: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X0Z4Z5Y6', 'Z0Z2X6X8', 'Z1Z3X7X8'] : False\n",
+      "4 :: 124444: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Z3Y4Y5', 'Z1Z2X7X8', 'X0X5Y6X8'] : False\n",
+      "4 :: 125036: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Y2Z3Y4Z5', 'X0Z6Z7Y8', 'Y0Y3X4X6'] : False\n",
+      "4 :: 128969: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z2Z3X4Y6', 'Z4Z5Z6Z8', 'Z0Z1Z7Z8', 'X2X6Y7Y8'] : False\n",
+      "4 :: 129274: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z2Z4Y5', 'Z1Y2X5X8', 'Y1Y3Y5Z6'] : False\n",
+      "4 :: 131725: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z2Y4Z5', 'Z1Y2X4X8', 'Y0Z3Y5Z6'] : False\n",
+      "4 :: 132091: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X0X4X5Z7', 'Y0Z1X6Y7', 'Y2Z3X5Z8'] : True\n",
+      "4 :: 132216: [[9,3, 2]] : 12 :['Z0Z1Z2Z3', 'Z2Z5Z7Z8', 'X0X2X4X5', 'X1X2X6X7', 'X1X3X5X8', 'Z1Z4Z5Z6'] : False\n",
+      "4 :: 132218: [[9,3, 2]] : 12 :['X0X1X2X3', 'X0X4X5X6', 'X1Z4Z6Z8', 'Z0Z2Z4X7', 'Z0Z1Z5X8', 'Z1Z3X4Y7'] : True\n",
+      "4 :: 132231: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0X1Z3Z4', 'Y1Z2Y4X8', 'Z2Z3Z5Z6', 'Y5Y6X7X8'] : False\n",
+      "4 :: 132847: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z0Z1Z4Z6', 'X2Z5Z6X8', 'Z2Z3Y4Y6'] : False\n",
+      "4 :: 134245: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y2Z3Z4Y5', 'X0Y6Y7Y8', 'Y0Z1Y5Z6'] : True\n",
+      "4 :: 137118: [[9,3, 2]] : 12 :['X0X1X2X3', 'Y0Y3Z7Z8', 'Z0Z3X4X5', 'Z1Z3X6X7', 'Z2Y3Z4Z6', 'X3Z5Y6X8'] : False\n",
+      "4 :: 137403: [[9,3, 2]] : 6 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Z0Z3Y4Y5', 'Z1Z3Y6Y7', 'Y0Z2X5Z8'] : False\n",
+      "4 :: 141104: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y1Z2Z4Z5', 'Z0X1Y2X8', 'X1X4Y6Z7'] : False\n",
+      "4 :: 177519: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'Y4Z5X6Y7', 'Z0Z1X6Z8'] : True\n",
+      "4 :: 178827: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Z2Z3X4X5', 'Y2Y3X6X7', 'Y4Y5X6X8', 'Z0Z1Z3Y7'] : True\n",
+      "6 :: 50: [[6,1, 2]] : 768 :['Z0Z5', 'Z1Z5', 'Z2Z5', 'Z3Z4', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 56: [[6,1, 2]] : 1152 :['Z0Z4', 'Z1Z5', 'Z2Z5', 'Z3Z4', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 38: [[6,2, 2]] : 288 :['Z1Z5', 'Z2Z5', 'Z0Z3Z4Z5', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 52: [[6,2, 2]] : 128 :['Z2Z5', 'Z3Z4', 'Z0Z1Z4Z5', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 82: [[6,2, 2]] : 384 :['Z0Z1', 'Z2Z4', 'Z3Z5', 'X0X1Y2Y3Y4Y5'] : True\n",
+      "6 :: 73: [[6,3, 2]] : 192 :['Z0Z1Z2Z4', 'Z0Z1Z3Z5', 'X0X1Y2Y3Y4Y5'] : True\n",
+      "6 :: 82: [[6,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'Z0Z1Z2Z3Z4Z5'] : True\n",
+      "6 :: 29: [[6,4, 2]] : 4320 :['Z0Z1Z2Z3Z4Z5', 'Y0Y1Y2Y3Y4Y5'] : True\n",
+      "6 :: 46: [[7,2, 2]] : 576 :['X0Z4', 'X1Z4', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'Z0Z1Z2X4X5Z6'] : False\n",
+      "6 :: 51: [[7,2, 2]] : 32 :['X1Z4', 'X3Z6', 'X0X2Z4Z5', 'Z2Z3Y5Y6', 'Z0Z1Z2X4X5Z6'] : True\n",
+      "6 :: 444: [[7,2, 2]] : 384 :['X1Z4', 'X2Z5', 'X3Z6', 'Y0Z1Z2Y4X5Z6', 'Z0Z1Z3X4Z5X6'] : True\n",
+      "6 :: 627: [[7,2, 2]] : 96 :['Z0X1', 'X3X4Z5Z6', 'Z2Y3Y5Z6', 'Z2X3Z4X6', 'X0Z1Y2Y3Z4Z5'] : False\n",
+      "6 :: 629: [[7,2, 2]] : 16 :['X0Z6', 'X2X3Z4Z6', 'Z1Y3Y4Z5', 'Y2Z3Y5Z6', 'Z0Y1Y2Z4Z5X6'] : False\n",
+      "6 :: 646: [[7,2, 2]] : 64 :['X1Z4', 'X0X2Z4Z5', 'X0X3Z4Z6', 'Z2Z3Y5Y6', 'Z0Z1Z2X4X5Z6'] : False\n",
+      "6 :: 398: [[7,3, 2]] : 96 :['X0X2Z4Z5', 'X0X3Z4Z6', 'Z0Y1Z2Y4X5Z6', 'Z0Z1Z3X4Z5X6'] : False\n",
+      "6 :: 425: [[7,3, 2]] : 8 :['X0X2Z4Z5', 'X1X3Z4Z6', 'Z2Z3Y5Y6', 'Z0Z1Z2X4X5Z6'] : False\n",
+      "6 :: 427: [[7,3, 2]] : 24 :['Z2Z3X4X5', 'Z2Y4Z5Y6', 'Y0Y1Y2Y3Z4Z5', 'X0Z1Y2Z3Y4Z6'] : False\n",
+      "6 :: 499: [[7,3, 2]] : 432 :['X3X4Z5Z6', 'Z3Z4Y5Y6', 'Y0X1Y2Y3Z4Z5', 'X0Z1X2Z4X5Z6'] : False\n",
+      "6 :: 502: [[7,3, 2]] : 6 :['X0X1X3Z5', 'Y2Z3Y5Z6', 'Z1Z2X4X5', 'Y0Y1Y2Z4Z5Y6'] : False\n",
+      "6 :: 570: [[7,3, 2]] : 32 :['X1Z4', 'Z2Z3Y5Y6', 'X0X2X3Z4Z5Z6', 'Z0Z1Z2X4X5Z6'] : False\n",
+      "6 :: 222: [[7,4, 2]] : 168 :['X0X2X3Z4Z5Z6', 'Z0Y1Z2Y4X5Z6', 'Z0Z1Z3X4Z5X6'] : False\n",
+      "6 :: 343: [[8,1, 2]] : 512 :['Z0X1', 'X2Z5', 'X3Z6', 'X4Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7', 'X0Z1Z2X5Z6Z7'] : False\n",
+      "6 :: 394: [[8,1, 2]] : 1536 :['Z0X1', 'X2Z5', 'X3Z6', 'X4Z7', 'Z2Z3Y5Y6', 'Z2Z4Y5Y7', 'X0Z1Z2X5Z6Z7'] : True\n",
+      "6 :: 492: [[8,1, 2]] : 384 :['X0Z7', 'X1Z7', 'X2Z6', 'X3Z5', 'Z3X4X5Z6', 'Z2Y4Y6Z7', 'Z0Z1Y4Z5Z6Y7'] : True\n",
+      "6 :: 495: [[8,1, 2]] : 384 :['X0Z7', 'X1Z7', 'X2Z6', 'X3Z5', 'Z3X5Z6Z7', 'Z2Z4Z5X6', 'Z0Z1Y4Z5Z6Y7'] : True\n",
+      "6 :: 532: [[8,1, 2]] : 192 :['X0Z7', 'X1Z7', 'X2Z6', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'Z2Z4Z5X6', 'Z0Z1Y3Z4Z5Y7'] : False\n",
+      "6 :: 534: [[8,1, 2]] : 96 :['X0Z7', 'X1Z7', 'X2Z6', 'Y3Y4Z5Z7', 'X3X5Z6Z7', 'Z2Z5X6Z7', 'Z0Z1Y3Z4Z6Y7'] : False\n",
+      "6 :: 555: [[8,1, 2]] : 768 :['X0Z7', 'X1Z6', 'X2Z6', 'X3X4', 'Y3Z4Y5Z7', 'Z0Z3Z4X7', 'Z1Z2X3Z5X6Z7'] : True\n",
+      "6 :: 599: [[8,1, 2]] : 192 :['X0Z7', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'Z3X5Z6Z7', 'Z4Z5X6Z7', 'Z0Z1Z2X3Z6X7'] : False\n",
+      "6 :: 601: [[8,1, 2]] : 96 :['X0Z7', 'X1Z7', 'X2X3Z5Z6', 'X2X4Z6Z7', 'Y2Z4Y5Z7', 'Y3Z4Y6Z7', 'Z0Z1Y2Z3Z5Y7'] : False\n",
+      "6 :: 608: [[8,1, 2]] : 384 :['X0Z7', 'X1Z7', 'X2Z6', 'X3Z4', 'Z4X5Z6Z7', 'Z2Z5X6Z7', 'Z0Z1Z3X4Z6X7'] : True\n",
+      "6 :: 623: [[8,1, 2]] : 2304 :['X0X1', 'X2X6', 'Z3Z7', 'Z4Z7', 'X5X6', 'X3X4X6X7', 'Z0Z1Z2Z5Z6Z7'] : True\n",
+      "6 :: 629: [[8,1, 2]] : 2304 :['Z0Z7', 'Z1Z7', 'X2X6', 'X3X6', 'X4X5', 'X0X1X5X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 717: [[8,1, 2]] : 256 :['X0Z7', 'X1Z6', 'X2Z4Z6Z7', 'X3Z5Z6Z7', 'Z2X4Z5Z7', 'Z3Z4X5Z7', 'Z0Z1Z4Z5Y6Y7'] : False\n",
+      "6 :: 726: [[8,1, 2]] : 128 :['X0Z7', 'X1Z3', 'X2Z5', 'Z3X4Z6Z7', 'Z2X5Z6Z7', 'Z4Z5X6Z7', 'Z0Z1X3Z5Z6X7'] : False\n",
+      "6 :: 744: [[8,1, 2]] : 512 :['Z0X1', 'X2Z5', 'X3Z6', 'X4Z7', 'Z2Z3Y5Y6', 'Z4Z5Z6X7', 'X0Z1Z2X5Z6Z7'] : True\n",
+      "6 :: 772: [[8,1, 2]] : 512 :['X0Z7', 'X1Z6', 'X2Z4', 'X3Z5', 'Z1Z4X6Z7', 'Z0Z5Z6X7', 'Z2Z3X4X5Z6Z7'] : True\n",
+      "6 :: 854: [[8,1, 2]] : 4608 :['X0X6', 'Z2Z7', 'Z3Z7', 'X4X6', 'X5X6', 'X1X2X3X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 856: [[8,1, 2]] : 6144 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z4', 'Z5Z6', 'X3X4X5X6', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 857: [[8,1, 2]] : 6144 :['X0X6', 'Z1Z2', 'Z3Z7', 'X4X6', 'X5X6', 'X1X2X3X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 875: [[8,1, 2]] : 3072 :['Z0Z1', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'X5X6', 'Z0Z5Z6Z7', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 886: [[8,1, 2]] : 768 :['X0Z7', 'X1Z7', 'X2Z7', 'X3Z5', 'Z3X4X5Z7', 'Y4Z5Y6Z7', 'Z0Z1Z2Y4Z6Y7'] : True\n",
+      "6 :: 893: [[8,1, 2]] : 1536 :['X0Z7', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'X3Z4X6Z7', 'Z0Z1Z2Z3Z4X7'] : False\n",
+      "6 :: 959: [[8,1, 2]] : 7680 :['Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'X5X6', 'Z0Z5Z6Z7', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 988: [[8,1, 2]] : 4608 :['Z0Z1', 'Z0Z2', 'Z3Z7', 'Z4Z7', 'X5X6', 'Z0Z5Z6Z7', 'X0X1X2X3X4X7'] : True\n",
+      "6 :: 990: [[8,1, 2]] : 4608 :['Z0X1', 'X2Z6', 'X3Z7', 'X4Z7', 'X5Z6', 'X0Z1Z2Z5X6Z7', 'X0Z1Z3Z4Z6X7'] : True\n",
+      "6 :: 1410: [[8,1, 2]] : 32 :['X0Z7', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z2X4Z5Z7', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Y1Z4Z5Z6Y7'] : False\n",
+      "6 :: 1413: [[8,1, 2]] : 32 :['X0Z7', 'Y1Y2Z3Z4', 'Y1Z2Y3Z5', 'Z2X4Z6Z7', 'Z3X5Z6Z7', 'Z4Z5X6Z7', 'Z0Z1X2Z5Z6X7'] : False\n",
+      "6 :: 1429: [[8,1, 2]] : 480 :['Z0X1', 'Z2X3Z5Z7', 'Z2X4Z6Z7', 'Z3X5Z6Z7', 'Z4Z5X6Z7', 'Y3Z4Z6Y7', 'X0Z1X2Z3Z4Z7'] : False\n",
+      "6 :: 1433: [[8,1, 2]] : 192 :['Z0X1', 'Y2Y3Z4Z5', 'Z2X4Z6Z7', 'Z3X5Z6Z7', 'Z4Z5X6Z7', 'X2X3Z6X7', 'X0Z1X2Z3Z4Z7'] : False\n",
+      "6 :: 239: [[8,2, 2]] : 32 :['X0Z6', 'X1Z7', 'Z3X5Z6Z7', 'X2X3Y4Y5', 'Z0X3Y4Y6', 'Z1Z2X3Z4Z6X7'] : False\n",
+      "6 :: 291: [[8,2, 2]] : 32 :['X0Z7', 'Z1X2', 'Y3Y5Z6Z7', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X1Z2X3X4Z5Z6'] : False\n",
+      "6 :: 296: [[8,2, 2]] : 32 :['X0Z7', 'Z1X2', 'Z3X4X5Z7', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X1Z2X3X4Z5Z6'] : False\n",
+      "6 :: 344: [[8,2, 2]] : 64 :['Z0X1', 'X2Z6', 'X3Z7', 'Y4Y5Z6Z7', 'Z3Z5Z6X7', 'X0Z1Z2Z4X6Z7'] : True\n",
+      "6 :: 427: [[8,2, 2]] : 16 :['X0Z6', 'X1Z7', 'Y2Y3Z4Z6', 'Y2Z3Y4Z7', 'Z1Z4Z5X7', 'Z0X2Z4Y5Y6Z7'] : True\n",
+      "6 :: 428: [[8,2, 2]] : 16 :['X0Z6', 'X1Z7', 'X3X4Z6Z7', 'X2Y3Y4X5', 'Z1Z4Z5X7', 'Z0Y2X3Z4Z5Y6'] : False\n",
+      "6 :: 454: [[8,2, 2]] : 64 :['X1Z7', 'X3Z6', 'X0X2Z6Z7', 'X0X4Z5Z7', 'Z0Z1Z5X7', 'Z2Z3Z4X5X6Z7'] : False\n",
+      "6 :: 457: [[8,2, 2]] : 16 :['X0Z7', 'X1Z6', 'X2X4Z5Z7', 'Y2Y3X5Z6', 'Z1Z5X6Z7', 'Z0X3Z4Z5Z6X7'] : False\n",
+      "6 :: 484: [[8,2, 2]] : 32 :['X0Z6', 'X1Z7', 'Y2Y3Z4Z6', 'Z2X4X5Z6', 'Z0Y2Y4Z5X6Z7', 'Z1Y2Z3X4Z5Y7'] : False\n",
+      "6 :: 489: [[8,2, 2]] : 16 :['X0Z7', 'X1Z4', 'Z3Z5X6Z7', 'X2Y3Z4Y6', 'Z0Y2Z6Y7', 'Z1Y2Z3X4Y5Z6'] : False\n",
+      "6 :: 504: [[8,2, 2]] : 32 :['X0Z7', 'X1Z6', 'Y2Y3Z4Z7', 'X2Y4Y5Z6', 'Z1Z5X6Z7', 'Z0Y2Z3Z5Z6Y7'] : False\n",
+      "6 :: 554: [[8,2, 2]] : 16 :['X2Z6', 'X3X4', 'X0X1Z6Z7', 'Y3Z4Y5Z7', 'Z0Z3Z4X7', 'Z1Z2X3Z5X6Z7'] : True\n",
+      "6 :: 567: [[8,2, 2]] : 32 :['X3Z6', 'X4Z7', 'X0Z1X2Z5', 'Z2X5Z6Z7', 'Z4Z5Z6X7', 'Z0X1Z3Z5X6Z7'] : False\n",
+      "6 :: 582: [[8,2, 2]] : 64 :['X4Z7', 'X5Z6', 'X0Z1X2Z6', 'X0Z1X3Z7', 'Z3Z4Z6X7', 'Z0X1Z2Z5X6Z7'] : False\n",
+      "6 :: 583: [[8,2, 2]] : 16 :['X0Z7', 'X1Z4', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z1X2X4X6', 'Z0Z2X3Z4Z5X7'] : False\n",
+      "6 :: 588: [[8,2, 2]] : 128 :['X0Z7', 'Z1X2', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'X3Z4X6Z7', 'Z0X1Z2Z3Z4X7'] : False\n",
+      "6 :: 595: [[8,2, 2]] : 32 :['X0Z7', 'Z1X2', 'X3X4Z5Z6', 'X3Z4X6Z7', 'Z0Z3Z4X7', 'X1Z2Y3Y5Z6Z7'] : False\n",
+      "6 :: 597: [[8,2, 2]] : 32 :['X3Z6', 'X4Z7', 'X0Z1X2Z5', 'Z2Z3Y5Y6', 'Z4Z5Z6X7', 'Z0X1Z2X5Z6Z7'] : True\n",
+      "6 :: 601: [[8,2, 2]] : 64 :['X0Z7', 'Z1X2', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'Z0Z3Z4X7', 'X1Z2Y3Y5Z6Z7'] : False\n",
+      "6 :: 743: [[8,2, 2]] : 128 :['X0Z4', 'X1Z5', 'X3Z7', 'Z2Z3Y6Y7', 'Z0Z1X2Y4Y5Z6', 'Z0Y2X4Z5Y6Z7'] : True\n",
+      "6 :: 748: [[8,2, 2]] : 128 :['Z0X1', 'X2Z6', 'X3Z7', 'Z2X4Y5Y6', 'Z3Y4X5Y7', 'X0Z1Y4Y5Z6Z7'] : True\n",
+      "6 :: 880: [[8,2, 2]] : 64 :['X0Z7', 'X2Z6', 'X3X4', 'Y3Z4Y5Z7', 'Z1Z2X3Z5X6Z7', 'Z0X1Z3Z4Z6X7'] : True\n",
+      "6 :: 883: [[8,2, 2]] : 64 :['Z0X1', 'X2Z6', 'X3Z7', 'Z2X4Y5Y6', 'Z3Z5Z6X7', 'X0Z1Y4Y5Z6Z7'] : True\n",
+      "6 :: 884: [[8,2, 2]] : 64 :['X1Z7', 'X3Z6', 'X4Z5', 'X0X2Z6Z7', 'Z0Z1Z5X7', 'Z2Z3Z4X5X6Z7'] : True\n",
+      "6 :: 1247: [[8,2, 2]] : 1152 :['Z3Z7', 'Z4Z7', 'X5X6', 'X0X1X2X6', 'X3X4X6X7', 'Z0Z1Z2Z5Z6Z7'] : True\n",
+      "6 :: 1262: [[8,2, 2]] : 96 :['X3Z7', 'X4Z6', 'X5Z6', 'X0X1Z6Z7', 'Z1Z2Z3X7', 'Z0X2Z4Z5X6Z7'] : True\n",
+      "6 :: 1265: [[8,2, 2]] : 192 :['Z0Z7', 'Z1Z7', 'Z3Z4', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 1269: [[8,2, 2]] : 192 :['X3Z7', 'X4Z7', 'X5Z6', 'X0Z1X2Z6', 'Z0X1Z2Z5X6Z7', 'Z0X1Z3Z4Z6X7'] : False\n",
+      "6 :: 1274: [[8,2, 2]] : 192 :['X3Z7', 'X4Z7', 'X5Z6', 'X0Z1X2Z6', 'Z2Z5X6Z7', 'Z0X1Z3Z4Z6X7'] : True\n",
+      "6 :: 1282: [[8,2, 2]] : 192 :['X1Z6', 'X2Z6', 'X3X4', 'X0Y3Z4Y5', 'Z0Z3Z4X7', 'Z1Z2X3Z5X6Z7'] : True\n",
+      "6 :: 1283: [[8,2, 2]] : 384 :['Z0Z7', 'Z1Z7', 'X3X6', 'X2X4X5X6', 'X0X1X5X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1295: [[8,2, 2]] : 288 :['X2Z6', 'X5Z6', 'Y0Y1X3Z7', 'X0Z1X4Z7', 'Z3Z4Z6X7', 'Z0X1Z2Z5X6Z7'] : False\n",
+      "6 :: 1297: [[8,2, 2]] : 768 :['Z0Z7', 'Z1Z7', 'X3X6', 'X4X5', 'Z2Z3Z4Z5Z6Z7', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 1298: [[8,2, 2]] : 768 :['X0Z7', 'X1Z7', 'X2Z6', 'Z3X4', 'Z2X3Z4Y5Y6Z7', 'Z0Z1X3Z4Z5X7'] : True\n",
+      "6 :: 1300: [[8,2, 2]] : 768 :['Z0Z7', 'Z1Z7', 'X2X6', 'X3X4', 'X0X1X5X7', 'Y2Z3Z4Z5Y6Z7'] : True\n",
+      "6 :: 1311: [[8,2, 2]] : 96 :['X0Z7', 'X1Z7', 'X2X3Z4Z5', 'X2Z3X5Z6', 'Z2Z3X6Z7', 'Z0Z1Y2Y4Z5X7'] : False\n",
+      "6 :: 1313: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X3X4X5X6', 'X0X1X2X3X4X7'] : False\n",
+      "6 :: 1315: [[8,2, 2]] : 48 :['X0Z7', 'X1Z7', 'X3X4Z5Z7', 'Y2Z4Y5Z7', 'X2Y3Y4X6', 'Z0Z1Z2Y3Z6Y7'] : True\n",
+      "6 :: 1316: [[8,2, 2]] : 48 :['X0Z7', 'X1Z7', 'X3X4Z5Z7', 'Y2Z4Y5Z7', 'Y3Z4Y6Z7', 'Z0Z1Z2Y3Z6Y7'] : False\n",
+      "6 :: 1329: [[8,2, 2]] : 48 :['X0Z7', 'X1Z7', 'X2X3Z5Z6', 'Y2Z4Y5Z7', 'Z3Y4Z5Y6', 'Z0Z1Y2Y3X4X7'] : True\n",
+      "6 :: 1338: [[8,2, 2]] : 192 :['X0Z7', 'X1Z7', 'Y2Y3Z4Z5', 'Z2Z3X4X5', 'Z2Y4Z5Y6', 'Z0Z1Z2X4Z6X7'] : False\n",
+      "6 :: 1358: [[8,2, 2]] : 384 :['Z0Z7', 'X1X6', 'X2X6', 'X3X4', 'Y0X3X5Y7', 'Z1Z2Z3Z4Z5Z6'] : True\n",
+      "6 :: 1364: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'X4X5', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3Y6Y7'] : True\n",
+      "6 :: 1365: [[8,2, 2]] : 192 :['X3Z7', 'X4Z6', 'X5Z6', 'X0X1X2Z6', 'Z1Z2Z3X7', 'Y0X1Z4Z5Y6Z7'] : True\n",
+      "6 :: 1371: [[8,2, 2]] : 192 :['X1X2', 'X3Z7', 'X4Z7', 'X0X5Z6Z7', 'X1Z5X6Z7', 'Z0Z1Z2Z3Z4X7'] : True\n",
+      "6 :: 1386: [[8,2, 2]] : 576 :['X3Z7', 'X4Z7', 'X0X1X2Z7', 'X1X5Z6Z7', 'X0X1Z5X6', 'Z0Z1Z2Z3Z4X7'] : False\n",
+      "6 :: 1400: [[8,2, 2]] : 384 :['Z1Z7', 'Z2Z7', 'Z3Z6', 'X4X5', 'Z0Z4Z5Z7', 'X0Y1X2Y3Y6Y7'] : True\n",
+      "6 :: 1418: [[8,2, 2]] : 384 :['X4Z6', 'X5Z6', 'X0X2Z6Z7', 'X0X1X3Z6', 'Z1Z2Z3X7', 'Y0X1Z4Z5Y6Z7'] : False\n",
+      "6 :: 1782: [[8,2, 2]] : 128 :['X4Z7', 'X5Z6', 'X0Z1X2Z6', 'X0Z1X3Z7', 'Z0X1Z2Z5X6Z7', 'Z0X1Z3Z4Z6X7'] : False\n",
+      "6 :: 1787: [[8,2, 2]] : 128 :['X2Z5', 'X4Z7', 'Y0Y1X3Z6', 'Z3Z5X6Z7', 'X0Z1Z2X5Z6Z7', 'Z0X1Z4Z5Z6X7'] : False\n",
+      "6 :: 1793: [[8,2, 2]] : 256 :['X2X3', 'X4Z7', 'X0X1X2Z7', 'X1X5Z6Z7', 'X0X1Z5X6', 'Z0Z1Z2Z3Z4X7'] : False\n",
+      "6 :: 1816: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z0Z3Z4Z7', 'Z0Z5Z6Z7', 'X3X4X5X6', 'X0X1X2X3X4X7'] : False\n",
+      "6 :: 1818: [[8,2, 2]] : 512 :['X0X4', 'X5Z6', 'X0X2Z6Z7', 'X0X1X3Z6', 'Z1Z2Z3X7', 'Y0X1Z4Z5Y6Z7'] : False\n",
+      "6 :: 1821: [[8,2, 2]] : 64 :['X0Z7', 'X1Z6', 'X2X3Z4Z5', 'Y2Y4Z5Z6', 'Z3Z4X5Z7', 'Z0Z1Z4Z5Y6Y7'] : True\n",
+      "6 :: 1826: [[8,2, 2]] : 32 :['X0Z7', 'X1Z4', 'Z2X5Z6Z7', 'X2Y3Z4Y6', 'Z1X2Z3X4Z5Z7', 'Z0Z2X3Z4Z5X7'] : False\n",
+      "6 :: 1836: [[8,2, 2]] : 128 :['X3Z6', 'X4Z7', 'Z0X1X2Z5', 'X2Z3X6Z7', 'Z4Z5Z6X7', 'X0Z1Z2X5Z6Z7'] : False\n",
+      "6 :: 1840: [[8,2, 2]] : 64 :['X1Z5', 'X3Z7', 'X0X2Z4Z6', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7'] : True\n",
+      "6 :: 1845: [[8,2, 2]] : 128 :['X2Z5', 'X4Z7', 'Y0Y1X3Z6', 'Z2Y3Y5X6', 'Z3Z4Y6Y7', 'X0Z1Z2X5Z6Z7'] : False\n",
+      "6 :: 1853: [[8,2, 2]] : 128 :['Z0X1', 'X2Z6', 'X3Z7', 'Y4Y5Z6Z7', 'X0Z1Z2Z4X6Z7', 'X0Z1Z3Z5Z6X7'] : True\n",
+      "6 :: 1854: [[8,2, 2]] : 256 :['X1X2', 'X3X5', 'X4X6', 'Z0Z1Z2Z7', 'X0X5X6X7', 'Z0Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1858: [[8,2, 2]] : 256 :['X0X1', 'Z4Z7', 'Z5Z6', 'Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'X2X3X4X5X6X7'] : True\n",
+      "6 :: 1859: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'X4X5', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3Y6Y7'] : True\n",
+      "6 :: 1861: [[8,2, 2]] : 256 :['X1X2', 'X3Z7', 'X5Z6', 'X0X1X4Z7', 'Z0Z4Z5X6', 'X0Y1Z2Z3Z6Y7'] : True\n",
+      "6 :: 1864: [[8,2, 2]] : 256 :['X2Z5', 'X3Z6', 'X4Z7', 'Z4Z5Z6X7', 'X0Z1Z2X5Z6Z7', 'Z0X1Z3Z5X6Z7'] : False\n",
+      "6 :: 1869: [[8,2, 2]] : 256 :['X0Z7', 'X2Z6', 'X3X4', 'Z0Z3Z4X7', 'X1Y3Z4Y5Z6Z7', 'Y1Z2X3Z5Y6Z7'] : True\n",
+      "6 :: 1870: [[8,2, 2]] : 128 :['Z0X1', 'X2Z6', 'X3Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7', 'X0Z1Y4Y5Z6Z7'] : True\n",
+      "6 :: 1879: [[8,2, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z3Z6', 'X4X5', 'Z0Z4Z5Z7', 'X0X1Y2Y3Y6Y7'] : True\n",
+      "6 :: 1903: [[8,2, 2]] : 1024 :['Z0Z1', 'Z2Z6', 'Z3Z7', 'Z4Z5', 'X3X4X5X7', 'X0X1X2X4X5X6'] : True\n",
+      "6 :: 1905: [[8,2, 2]] : 1024 :['Z0Z7', 'Z1Z2', 'X3X5', 'X4X6', 'Z3Z4Z5Z6', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 1906: [[8,2, 2]] : 1024 :['X0Z7', 'Z1X2', 'X3Z5', 'X4Z6', 'X1Z2Z3Z4X5X6', 'Z0X1Z2Z5Z6X7'] : True\n",
+      "6 :: 1926: [[8,2, 2]] : 256 :['X0Z4', 'X1Z5', 'X2X3Z6Z7', 'Z2Z3Y6Y7', 'Z0Z1X2Y4Y5Z6', 'Z0Y2X4Z5Y6Z7'] : False\n",
+      "6 :: 1930: [[8,2, 2]] : 32 :['X0Z7', 'X1Z6', 'X2Z4Z6Z7', 'Z2X3X4Z6', 'Z3Z4X5Z7', 'Z0Z1Z4Z5Y6Y7'] : False\n",
+      "6 :: 1931: [[8,2, 2]] : 128 :['X3Z6', 'X4Z7', 'X0Z1X2Z5', 'Z2Z3Y5Y6', 'Z2Z4Y5Y7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 1934: [[8,2, 2]] : 128 :['X3Z6', 'X4Z7', 'X0Z1X2Z5', 'Z2X5Z6Z7', 'Z3Z4Y6Y7', 'Z0X1Z3Z5X6Z7'] : True\n",
+      "6 :: 1936: [[8,2, 2]] : 32 :['X0Z7', 'X1Z4', 'Z1Y3Y4Z6', 'Z2Z3Y5Y6', 'Z0Y2Z6Y7', 'X2X3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1944: [[8,2, 2]] : 384 :['X3Z6', 'X4Z7', 'X0Z1X2Z5', 'Z3Z5X6Z7', 'Z4Z5Z6X7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 1951: [[8,2, 2]] : 768 :['X2Z6', 'X3Z7', 'X0X1Z4Z5', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 2021: [[8,2, 2]] : 128 :['X0Z7', 'X1Z4', 'X2X3Z5Z6', 'Z3X5Z6Z7', 'Z2Z4Z5X6', 'Z0Z1Y2Y3X4X7'] : False\n",
+      "6 :: 2059: [[8,2, 2]] : 64 :['X0Z7', 'X1Z6', 'X2X4Z5Z7', 'Z4X5Z6Z7', 'Z1Y2Y3Z4Z5X6', 'Z0Y2Z3Z5Z6Y7'] : False\n",
+      "6 :: 2067: [[8,2, 2]] : 32 :['X0Z7', 'X1Z4', 'Z3Z5X6Z7', 'X2Y3Z4Y6', 'Z0X2X5X7', 'Z1Y2Z3X4Y5Z6'] : False\n",
+      "6 :: 2068: [[8,2, 2]] : 32 :['X0Z7', 'X1Z6', 'Z2Y3Y4Z5', 'Y2Y3X5Z6', 'Z1Z5X6Z7', 'Z0X3Z4Z5Z6X7'] : True\n",
+      "6 :: 2072: [[8,2, 2]] : 1536 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'X4X5', 'Z3Z4Z5Z6', 'Y0X1X2X3X6Y7'] : True\n",
+      "6 :: 2076: [[8,2, 2]] : 3072 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'X3X4X5X6', 'Y3Y4Y5Y6', 'X0X1X2X3X5X7'] : True\n",
+      "6 :: 2096: [[8,2, 2]] : 768 :['X2Z7', 'X3Z7', 'X4Z7', 'X0X5Z6Z7', 'X1Z5X6Z7', 'Z0Z1Z2Z3Z4X7'] : True\n",
+      "6 :: 2527: [[8,2, 2]] : 48 :['X1X2Z5Z6', 'X1X3Z6Z7', 'X4Z5Z6Z7', 'Z2Z3Z4X6', 'X0Z1Z3Z4X5Z7', 'Z0Y1Z2Z4Z5Y7'] : False\n",
+      "6 :: 2533: [[8,2, 2]] : 4 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X0Z2X4Z5', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Y1Z4Z5Z6Y7'] : False\n",
+      "6 :: 2536: [[8,2, 2]] : 8 :['X1X3Z6Z7', 'X0X2X3Z5', 'X4Z5Z6Z7', 'Z2Z3Z4X6', 'Y1Y2Y5Y6', 'Z0Y1Z2Z4Z5Y7'] : False\n",
+      "6 :: 2579: [[8,2, 2]] : 16 :['X0X1Z6Z7', 'X0X2Z4Z6', 'X3Z5Z6Z7', 'Z2X4Z5Z7', 'Z3Z4X5Z7', 'Z0Z1Z4Z5Y6Y7'] : False\n",
+      "6 :: 2581: [[8,2, 2]] : 64 :['X0X1Z6Z7', 'X2Z4Z6Z7', 'X3Z5Z6Z7', 'X0Z2X4Z5', 'Z3Z4X5Z7', 'Z0Z1Z4Z5Y6Y7'] : False\n",
+      "6 :: 2589: [[8,2, 2]] : 16 :['X0X1Z2Z7', 'Z1X2X3Z5', 'Z1X2X4Z6', 'X0Y3Y5Z6', 'X3Z4X6Z7', 'Z0Z1Y2Z3Z4Y7'] : False\n",
+      "6 :: 3046: [[8,2, 2]] : 768 :['X0X1', 'X2X3', 'X4Z7', 'X0X5Z6Z7', 'X2Z5X6Z7', 'Z0Z1Z2Z3Z4X7'] : True\n",
+      "6 :: 3047: [[8,2, 2]] : 768 :['X2Z5', 'X3Z6', 'X4Z7', 'Y0Y1Z2X5Z6Z7', 'X0Z1Z3Z5X6Z7', 'Z0X1Z4Z5Z6X7'] : False\n",
+      "6 :: 3058: [[8,2, 2]] : 2304 :['Z2Z7', 'Z3Z7', 'X4X6', 'X5X6', 'Z0Z1Y4Z5Y6Z7', 'X0X1Y2X3X6Y7'] : True\n",
+      "6 :: 3096: [[8,2, 2]] : 128 :['X1X2', 'X3Z7', 'X5Z6', 'X0X1Z6Z7', 'Z0Z4Z5X6', 'Z1Z2Z3X4Z6X7'] : True\n",
+      "6 :: 3235: [[8,2, 2]] : 32 :['Z2X4Z6Z7', 'Z3X5Z6Z7', 'Z4Z5X6Z7', 'Z3Y4Z5Y7', 'X0Z1X2Z3Z4Z7', 'Z0X1Z2X3Z5Z7'] : False\n",
+      "6 :: 3237: [[8,2, 2]] : 16 :['X1X2Z4Z6', 'X1X3Z5Z6', 'X0X1Z2X4', 'X1Z3X5Z7', 'X0Z1Z4Z5X6Z7', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 3239: [[8,2, 2]] : 16 :['X1X2Z4Z6', 'X1X3Z5Z6', 'X0X1Z2X4', 'X1Z3X5Z7', 'Z1Z4Z5X6', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 3536: [[8,2, 2]] : 48 :['X0X1Z6Z7', 'Y2Y3Z6Z7', 'X1X2Z3X4', 'X2Z3X5Z7', 'Z0Y2Z3Z4Z5Y6', 'Z1X2Z4Z5Z6X7'] : False\n",
+      "6 :: 3546: [[8,2, 2]] : 24 :['Z3X5Z6Z7', 'X2X3Y4Y5', 'Z4Z5X6Z7', 'Y2X3X4Y7', 'Y0Y1X2Z3Z4Z7', 'X0Z1Z2X3Z5Z7'] : False\n",
+      "6 :: 3724: [[8,2, 2]] : 2048 :['X0X4', 'Z1Z5', 'X2X6', 'Z3Z7', 'Z0Z2Z4Z5Z6Z7', 'X1X3X4X5X6X7'] : True\n",
+      "6 :: 3961: [[8,2, 2]] : 8 :['X2X3Z4Z5', 'X1Z2X4Z7', 'X0Y2Y4Z6', 'X0X1Z3X5', 'Z1Z4Z5X6', 'Y0Y1Z2Z3Z6X7'] : False\n",
+      "6 :: 3970: [[8,2, 2]] : 6 :['X0X1X2Z4', 'X1X3Z5Z7', 'Z2X4Z5Z7', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Y1Z4Z5Z6Y7'] : False\n",
+      "6 :: 3977: [[8,2, 2]] : 12 :['Y1Z2Y3Z5', 'X0Z2X4Z6', 'Z3X5Z6Z7', 'Y1X2Y4X5', 'Z4Z5X6Z7', 'Y0Z1X2Z5Z6Y7'] : False\n",
+      "6 :: 4232: [[8,2, 2]] : 24 :['X2X3Z4Z5', 'X0Z2X4Z6', 'X0Z3X5Z6', 'Z4Z5X6Z7', 'Y1Y3Y4Y6', 'Z0Z1X2Z5Z6X7'] : False\n",
+      "6 :: 4261: [[8,2, 2]] : 8 :['X0X1X2Z4', 'X0X1X3Z5', 'Z2X4Z5Z7', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Y0Y1Z4Z5Z6X7'] : False\n",
+      "6 :: 4264: [[8,2, 2]] : 8 :['X2X3Z4Z5', 'X0X1Z2X4', 'Y2Y4Z6Z7', 'X0X1Z3X5', 'Z1Z4Z5X6', 'Y0Y1Z2Z3Z6X7'] : False\n",
+      "6 :: 4321: [[8,2, 2]] : 8 :['X0X1X3Z6', 'X2X3Z5Z7', 'X4Z5Z6Z7', 'Z1Z3Z4X5', 'Z2Z3Z4X6', 'Y0Y1Z2Z4Z5X7'] : False\n",
+      "6 :: 4330: [[8,2, 2]] : 2 :['X0X1X2Z4', 'X1X3Z5Z7', 'X0Z2X4Z5', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Y1Z4Z5Z6Y7'] : False\n",
+      "6 :: 4349: [[8,2, 2]] : 16 :['X3X4Z5Z6', 'Z3X5Z6Z7', 'Z4Z5X6Z7', 'Z2Y3Y4X7', 'X0Z1X2Z3Z4Z7', 'Z0X1Z2X3Z5Z7'] : False\n",
+      "6 :: 4419: [[8,2, 2]] : 64 :['X0Z1X2Z5', 'X0Z1X3Z6', 'X0Z1X4Z7', 'Z3Z5X6Z7', 'Z4Z5Z6X7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 4492: [[8,2, 2]] : 2160 :['Y3Y4Z5Z6', 'Z3Y5Z6Z7', 'Z4Z5Y6Z7', 'X3Z4Z6Y7', 'Y0Z1Y2Z3Z4Z7', 'Z0X1Z2Y3Z5Z7'] : False\n",
+      "6 :: 4499: [[8,2, 2]] : 48 :['X1X2Z4Z6', 'X1X3Z5Z6', 'X1Z2X4Z7', 'X1Z3X5Z7', 'X0Z1Z4Z5X6Z7', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 4584: [[8,2, 2]] : 288 :['X2X3Z4Z5', 'Z2Z3Y4Y5', 'Z4Z5Y6Z7', 'Y2Y3Z6X7', 'X0Z1Y2Z3Z4Z7', 'Z0Y1Z2Y4Z6Z7'] : False\n",
+      "6 :: 4585: [[8,2, 2]] : 8 :['X0X1Z2Z7', 'Y1Y2X3Z5', 'Z1X2X4Z6', 'X0Y3Y5Z6', 'X0X3Z4X6', 'Z0Z1Y2Z3Z4Y7'] : False\n",
+      "6 :: 4588: [[8,2, 2]] : 96 :['Z2Z3X4X5', 'Z4Z5X6Z7', 'X2Y3X4Y6', 'Y2X3X4Y7', 'Y0Y1X2Z3Z4Z7', 'X0Z1Z2X3Z5Z7'] : False\n",
+      "6 :: 4613: [[8,2, 2]] : 16 :['X1X2Z4Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Y2Y5Z6Z7', 'X2Z3X6Z7', 'X0Z1Z2Z3Z4X7'] : False\n",
+      "6 :: 4632: [[8,2, 2]] : 2 :['X1X2Z4Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'X2Z3X6Z7', 'Z1Z2Z3X7', 'X0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 4639: [[8,2, 2]] : 8 :['X0X1X2Z5', 'X0X1X3Z6', 'Z0Y1Y4Z7', 'Z2Z3Y5Y6', 'Z1Z2Z3X7', 'X1Z2Z4X5Z6Z7'] : False\n",
+      "6 :: 4651: [[8,2, 2]] : 192 :['X0Z1X2Z5', 'X0Z1X3Z6', 'X0Z1X4Z7', 'Z2Z3Y5Y6', 'Z2Z4Y5Y7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 4652: [[8,2, 2]] : 64 :['X0Z1X2Z5', 'X0Z1X3Z6', 'X0Z1X4Z7', 'Z2Z3Y5Y6', 'Z4Z5Z6X7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 4933: [[8,2, 2]] : 256 :['X0X1Z4Z5', 'X0X2Z4Z6', 'X0X3Z4Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 5055: [[8,2, 2]] : 32 :['X0Z5', 'Y1Y2Y3Y4', 'Z0Y1Y2X5', 'X2X3Z5X6', 'Z2Z3Z5X7', 'X1Z2X3Z4Z6Z7'] : False\n",
+      "6 :: 5057: [[8,2, 2]] : 32 :['X0Z5', 'Y1Y2Z6Z7', 'Y3Y4Z6Z7', 'X2X3Z5X6', 'Z2Z3Z5X7', 'Z0X1Z2X3Z4X5'] : False\n",
+      "6 :: 5063: [[8,2, 2]] : 16 :['X0Z7', 'X1X2Z4Z6', 'X1X3Z5Z6', 'X1Z3X5Z7', 'Y1Z2Y4Z5X6Z7', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 5074: [[8,2, 2]] : 32 :['X0Z7', 'Z1X2X3Z5', 'Z2X4X5Z6', 'Y2Y3X4X6', 'Z0Y3X5Y7', 'Y1X2Z3Y4Z6Z7'] : False\n",
+      "6 :: 5082: [[8,2, 2]] : 64 :['X4Z7', 'X0Z1X2Z5', 'X0Z1X3Z6', 'Z2X5Z6Z7', 'Z3Z5X6Z7', 'Z0X1Z4Z5Z6X7'] : False\n",
+      "6 :: 5084: [[8,2, 2]] : 32 :['X0Z4', 'X2X3Z5Z6', 'X2Z3X6Z7', 'Z1Z2Z3X7', 'Z0Z1X2X4Z5Z7', 'X1Z2Z4X5Z6Z7'] : False\n",
+      "6 :: 5253: [[8,2, 2]] : 16 :['Z0X1', 'Y2Y3Z4Z6', 'Z2X4X5Z6', 'Z3Y5Y6Z7', 'Z2Y4Z5Y7', 'X0Z1Y2Z3Y4Z7'] : False\n",
+      "6 :: 5254: [[8,2, 2]] : 64 :['Z0X1', 'Y2Y3Z4Z6', 'Y2Z3Y4Z7', 'Z3Y5Y6Z7', 'Z4Y5Z6Y7', 'X0Z1Z2X3X5Z7'] : False\n",
+      "6 :: 5255: [[8,2, 2]] : 32 :['X0Z7', 'X1X2Z5Z6', 'X1X3X4Z5', 'Z2Z3Z4X6', 'Y1Y3Z4X5Z6Z7', 'Z0Y1Y2X3Z4X7'] : False\n",
+      "6 :: 5300: [[8,2, 2]] : 32 :['X4Z7', 'X0Z1X2Z5', 'X0Z1X3Z6', 'Z3Z5X6Z7', 'Z4Z5Z6X7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 5302: [[8,2, 2]] : 4 :['X0Z7', 'X1X2X3Z7', 'Z1Y3Y4Z5', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 5306: [[8,2, 2]] : 48 :['X0Z7', 'X1X2X3Z7', 'Z1Z3X4Z7', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 5348: [[8,2, 2]] : 64 :['X4Z7', 'X0Z1X2Z5', 'X0Z1X3Z6', 'Z2Z3Y5Y6', 'Z2Z4Y5Y7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 5355: [[8,2, 2]] : 32 :['X4Z7', 'X0Z1X2Z5', 'X0Z1X3Z6', 'Z3Z5X6Z7', 'Z2Z4Y5Y7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 5365: [[8,2, 2]] : 8 :['X0Z7', 'Z1X2X3Z5', 'Z2X4X5Z6', 'Y2Y3X4X6', 'Z0X3Z6X7', 'Y1X2Z3Y4Z6Z7'] : False\n",
+      "6 :: 5370: [[8,2, 2]] : 8 :['X0Z4', 'X1X2Z4Z5', 'X1X3Z4Z6', 'Y2Y5Z6Z7', 'Z1Z2Z3X7', 'Z0Z1Z3X4Z5X6'] : False\n",
+      "6 :: 5391: [[8,2, 2]] : 8 :['X0Z7', 'X1X2Z4Z5', 'X3Z4Z5Z7', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Z1Y4Z5Z6Y7'] : False\n",
+      "6 :: 5409: [[8,2, 2]] : 16 :['X0Z7', 'X2X3Z4Z5', 'X1Z2X4Z7', 'Y3Y5Z6Z7', 'Z1Z4Z5X6', 'Z0Z1Y2Z3Z4Y7'] : False\n",
+      "6 :: 5424: [[8,2, 2]] : 8 :['X0Z7', 'X1X3Z5Z7', 'X1Y2Y4Z5', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Y1Z4Z5Z6Y7'] : False\n",
+      "6 :: 5426: [[8,2, 2]] : 8 :['X0Z7', 'X1X2X3Z7', 'Z1Z3X4Z7', 'Z2Z3X5Z7', 'X3Y4Y5X6', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 5427: [[8,2, 2]] : 8 :['X0Z7', 'X1X3Z5Z6', 'Y2Y4Z6Z7', 'X1Z3X5Z7', 'Z1Z4Z5X6', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 5436: [[8,2, 2]] : 8 :['X0Z7', 'Y1Y2Z4Z6', 'Z3X5Z6Z7', 'X1X3Y4Y5', 'X2X3X6Z7', 'Z0X1Z2Z4Z5X7'] : False\n",
+      "6 :: 5443: [[8,2, 2]] : 192 :['Z0X1', 'X3X4Z6Z7', 'Z2X3X5Z7', 'X2Z4Z5X6', 'X2Z3Z5X7', 'X0Z1Y2Y3Z4Z6'] : False\n",
+      "6 :: 5468: [[8,2, 2]] : 8 :['X0Z7', 'Y1Y2Z3Z4', 'Z2Z3X4X5', 'Z4Z5X6Z7', 'Y1Y3Y4Y6', 'Z0Z1X3Z4Z6X7'] : False\n",
+      "6 :: 5477: [[8,2, 2]] : 32 :['X1Z7', 'X3X4Z5Z6', 'Z2Y3Y5Z6', 'Z2X3Z4X6', 'X0Y2Y3Z4Z5Z7', 'Y0Z1X2Z3Z4Y7'] : False\n",
+      "6 :: 5486: [[8,2, 2]] : 8 :['X0Z4', 'X2X3Z5Z6', 'Z0Y1Y4Z7', 'Y2Y5Z6Z7', 'Z1Z2Z3X7', 'X1Z3Z4Z5X6Z7'] : False\n",
+      "6 :: 5489: [[8,2, 2]] : 16 :['Z0X1', 'X3X4Z6Z7', 'Z2X3X5Z7', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'X0Z1Y2Y3Z4Z6'] : False\n",
+      "6 :: 5498: [[8,2, 2]] : 32 :['X2Z6', 'X0X1Z6Z7', 'X0X3X4Z7', 'X0Y3Z4Y5', 'Z0Z3Z4X7', 'X0Z1Z2X3Z5X6'] : False\n",
+      "6 :: 5501: [[8,2, 2]] : 16 :['X0Z7', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Y1Z2Y4Z6X7'] : False\n",
+      "6 :: 5508: [[8,2, 2]] : 16 :['X0Z7', 'X1X2X3Z7', 'Z1Y3Y4Z5', 'Z2Y3Z4Y5', 'Z1Z2X6Z7', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 5533: [[8,2, 2]] : 48 :['X0Z7', 'X1X2Y3Y4', 'Y1Z3Y5Z7', 'Z1X4X5Z6', 'Y2Z4Y6Z7', 'Z0Y1Y2Z5Z6X7'] : False\n",
+      "6 :: 5540: [[8,2, 2]] : 64 :['X4Z7', 'X0Z1X2Z5', 'X0Z1X3Z6', 'Z2Z3Y5Y6', 'Z4Z5Z6X7', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 5541: [[8,2, 2]] : 64 :['Z0X1', 'X3X4Z6Z7', 'X2Y3Y4X5', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'X0Z1Y2Y3Z4Z6'] : False\n",
+      "6 :: 5548: [[8,2, 2]] : 64 :['X3Z7', 'X0X1Z4Z5', 'X0X2Z4Z6', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 5688: [[8,2, 2]] : 8 :['X0Z7', 'X1X3Z6Z7', 'X4Z5Z6Z7', 'Z2Z3Z4X6', 'Y1Y2Y5Y6', 'Z0Y1Z2Z4Z5Y7'] : False\n",
+      "6 :: 5742: [[8,2, 2]] : 16 :['X0Z7', 'X1X2Z5Z6', 'X4Z5Z6Z7', 'Z1Z3Z4X5', 'Z2Z3Z4X6', 'Z0Y1Y2X3Z4X7'] : False\n",
+      "6 :: 5743: [[8,2, 2]] : 16 :['X0Z7', 'X2X3Z4Z5', 'X1Z2X4Z7', 'X1Z3X5Z7', 'Z1Z4Z5X6', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 5754: [[8,2, 2]] : 4 :['X0Z7', 'X2X3Z4Z5', 'X1Y2Y4Z5', 'Z3Z4X5Z7', 'Z1Z2Z3X6', 'Z0Z1X2Z5Z6X7'] : False\n",
+      "6 :: 5762: [[8,2, 2]] : 8 :['X0Z4', 'X1X2Z4Z5', 'Z2X3X5Z7', 'Y3Z5Y6Z7', 'Z1Z2Z3X7', 'Z0Z1X3X4Z6Z7'] : False\n",
+      "6 :: 5772: [[8,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z5', 'Y2Y5Z6Z7', 'Y3Z5Y6Z7', 'Z1Z2Z3X7', 'Z0Z1X3X4Z6Z7'] : False\n",
+      "6 :: 173: [[8,3, 2]] : 64 :['X2Z7', 'X3Z6', 'X0X4Z5Z7', 'X1Z4X5Z7', 'Z0Z1Z2Z3Y6Y7'] : True\n",
+      "6 :: 174: [[8,3, 2]] : 64 :['X0Z7', 'Z1X2', 'Z3Z4Y5Y6', 'X1Z2Z3X4X5Z7', 'Z0Y3Y4Z5Z6X7'] : False\n",
+      "6 :: 175: [[8,3, 2]] : 256 :['Z3Z6', 'X4X5', 'Z0Z1Z2Z7', 'Z0Z1Z4Z5', 'Y0Y1Y2Y3Y6Y7'] : True\n",
+      "6 :: 199: [[8,3, 2]] : 64 :['X0Z7', 'Z1X2', 'Z3Z4Y5Y6', 'Z0Y4X5Y7', 'X1Z2Y3Y5Z6Z7'] : True\n",
+      "6 :: 203: [[8,3, 2]] : 64 :['Z0Z7', 'Z1Z2', 'Z3Z4Z5Z6', 'Y0X4X5Y7', 'X1X2Y3Y4Y5Y6'] : True\n",
+      "6 :: 239: [[8,3, 2]] : 32 :['X0Z7', 'Z1X2', 'X3Z4X6Z7', 'X1Z2Y3Y5Z6Z7', 'Z0Y3Y4Z5Z6X7'] : False\n",
+      "6 :: 243: [[8,3, 2]] : 32 :['X2Z6', 'X3X4', 'Z0X3Y5Y7', 'X0X1Y3Z4Y5Z6', 'Z1Z2X3Z5X6Z7'] : True\n",
+      "6 :: 245: [[8,3, 2]] : 64 :['Z2Z7', 'X4X5', 'Z1Z3Z6Z7', 'Z0Z1Z4Z5', 'X0Y1X2Y3Y6Y7'] : True\n",
+      "6 :: 256: [[8,3, 2]] : 96 :['X2Z6', 'X3Z7', 'Z3Z5Z6X7', 'X0Z1Y4Y5Z6Z7', 'Z0X1Z2Z4X6Z7'] : True\n",
+      "6 :: 257: [[8,3, 2]] : 192 :['Z2Z7', 'X4X5', 'Z0Z3Z6Z7', 'Z1Z4Z5Z7', 'Y0X1X2Y3Y6Y7'] : True\n",
+      "6 :: 258: [[8,3, 2]] : 192 :['Z2Z7', 'X4X5', 'Z0Z1Z3Z6', 'Z1Z4Z5Z7', 'Y0Y1Y2Y3Y6Y7'] : True\n",
+      "6 :: 264: [[8,3, 2]] : 32 :['X2Z6', 'X3X4', 'Z0Z3Z4X7', 'X0X1Y3Z4Y5Z6', 'Z1Z2X3Z5X6Z7'] : True\n",
+      "6 :: 268: [[8,3, 2]] : 32 :['X0Z7', 'Z1X2', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X1Z2Y3Y5Z6Z7'] : True\n",
+      "6 :: 431: [[8,3, 2]] : 256 :['Z0Z1', 'Z2Z7', 'X4X5', 'Z0Z3Z4Z5Z6Z7', 'X0X1Y2X3X6Y7'] : True\n",
+      "6 :: 1071: [[8,3, 2]] : 576 :['Z1Z7', 'Z2Z7', 'X4X5', 'Z0Z3Z4Z5Z6Z7', 'X0Y1X2X3X6Y7'] : True\n",
+      "6 :: 1076: [[8,3, 2]] : 96 :['X1Z7', 'X2Z7', 'X0X4Z5Z7', 'X3Z4X5Z6', 'Z0Z1Z2Z3Y6Y7'] : True\n",
+      "6 :: 1085: [[8,3, 2]] : 1152 :['Z0Z7', 'Z1Z7', 'X2X3X4X6', 'X0X1X5X7', 'Y2Y3Y4Z5Y6Z7'] : True\n",
+      "6 :: 1086: [[8,3, 2]] : 288 :['X0Z7', 'X1Z7', 'Z2Z3X4X5', 'X2Y3X4Y6', 'Z0Z1Z4Z5X6X7'] : True\n",
+      "6 :: 1508: [[8,3, 2]] : 128 :['X0X1', 'X2Z7', 'X0X4Z5Z7', 'X3Z4X5Z6', 'Z0Z1Z2Z3Y6Y7'] : True\n",
+      "6 :: 1512: [[8,3, 2]] : 64 :['X2Z6', 'X3Z7', 'Y4Y5Z6Z7', 'X0Z1Z2Z4X6Z7', 'Z0X1Z3Z5Z6X7'] : True\n",
+      "6 :: 1544: [[8,3, 2]] : 256 :['X3Z5', 'X4Z6', 'X0X1Z2Z7', 'Z1X2Z3Z4X5X6', 'Z0Z1X2Z5Z6X7'] : True\n",
+      "6 :: 1546: [[8,3, 2]] : 512 :['Z1Z5', 'Z3Z7', 'X0X2X4X6', 'Z0Z2Z4Z5Z6Z7', 'X1X3X4X5X6X7'] : True\n",
+      "6 :: 1548: [[8,3, 2]] : 512 :['X0Z7', 'Z1X2', 'Z3Z4Y5Y6', 'X1Z2X3X4Z5Z6', 'Z0X1Z2Z3Z4X7'] : True\n",
+      "6 :: 1549: [[8,3, 2]] : 128 :['X2Z6', 'X3Z7', 'Z0Z1Y4Y5', 'Y0X1Z2Y4X6Z7', 'Z0Z3X4Z5Z6X7'] : True\n",
+      "6 :: 1550: [[8,3, 2]] : 64 :['X2Z6', 'X3X4', 'X0X1Z6Z7', 'Z0X3Y5Y7', 'Z1Z2Z3Z4X5X6'] : True\n",
+      "6 :: 1553: [[8,3, 2]] : 1536 :['X3X5', 'X4X6', 'Z0Z1Z2Z7', 'Z3Z4Z5Z6', 'X0X1X2X5X6X7'] : True\n",
+      "6 :: 1554: [[8,3, 2]] : 1536 :['Z3Z7', 'Z4Z5', 'Z0Z1Z2Z6', 'X3X4X5X7', 'X0X1X2X4X5X6'] : True\n",
+      "6 :: 1569: [[8,3, 2]] : 256 :['Z2Z7', 'Z3Z6', 'X4X5', 'Z0Z1Z4Z5', 'X0X1Y2Y3Y6Y7'] : True\n",
+      "6 :: 1577: [[8,3, 2]] : 512 :['X0X4', 'X1X5', 'Z2Z3Z6Z7', 'Y2Y3Y6Y7', 'Y0Y1Z2Y4Y5Z6'] : True\n",
+      "6 :: 2235: [[8,3, 2]] : 128 :['X0X1X2Z7', 'X0X1X3Z6', 'X1X4Z5Z7', 'X0X1Z4X5', 'Z0Z1Z2Z3Y6Y7'] : False\n",
+      "6 :: 2236: [[8,3, 2]] : 16 :['X0Z1X2Z6', 'X0Z1X3Z7', 'Y4Y5Z6Z7', 'Z0X1Z2Z4X6Z7', 'Z0X1Z3Z5Z6X7'] : False\n",
+      "6 :: 2239: [[8,3, 2]] : 16 :['X3X4Z6Z7', 'Z2X3X5Z7', 'Z3Z4X6X7', 'Y0Y1Y2Y3Z4Z6', 'X0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 2243: [[8,3, 2]] : 32 :['X3X4Z6Z7', 'Y2Y4Y5Y6', 'Y2Y3Y5Y7', 'Z0X1Y2Y3Z4Z6', 'Y0Y1Z2X3X5Z7'] : False\n",
+      "6 :: 2266: [[8,3, 2]] : 8 :['X0X1Z6Z7', 'X0X3X4Z7', 'X0Y3Z4Y5', 'X0Z1Z2X3Z5X6', 'Z0X2Z3Z4Z6X7'] : False\n",
+      "6 :: 2268: [[8,3, 2]] : 64 :['X1X2X3Z7', 'X1X2X4Z6', 'X0X2X5Z7', 'Z0Z4Z5X6', 'X0Z1Y2Z3Z6Y7'] : False\n",
+      "6 :: 2269: [[8,3, 2]] : 32 :['Y2Y3Z4Z6', 'Y2Y4Y5Y6', 'Z4Y5Z6Y7', 'Z0X1Y2Z3Y4Z7', 'Y0Y1Z2X3X5Z7'] : False\n",
+      "6 :: 2315: [[8,3, 2]] : 256 :['Z0Z1Z2Z6', 'Z0Z1Z3Z7', 'Z0Z1Z4Z5', 'X3X4X5X7', 'X0X1X2X4X5X6'] : False\n",
+      "6 :: 2316: [[8,3, 2]] : 256 :['X0X1Z2Z7', 'X0X3Z5Z7', 'X0X4Z6Z7', 'Z1X2Z3Z4X5X6', 'Z0Z1X2Z5Z6X7'] : False\n",
+      "6 :: 2363: [[8,3, 2]] : 256 :['X0X1Z2Z7', 'X0X3Z5Z7', 'X0X4Z6Z7', 'Z3Z4X5X6', 'Z0Z1X2Z5Z6X7'] : False\n",
+      "6 :: 2366: [[8,3, 2]] : 16 :['X0Z1X2Z6', 'X0Z1X3Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7', 'Z0X1Y4Y5Z6Z7'] : False\n",
+      "6 :: 2368: [[8,3, 2]] : 64 :['Z0Z1Z2Z7', 'X1X2X3X5', 'X1X2X4X6', 'X0X5X6X7', 'Z0Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 2381: [[8,3, 2]] : 32 :['X0X1Z4Z5', 'Z2Z3Y6Y7', 'X0X2X3Z4Z6Z7', 'Y0Z1X2X4Y5Z6', 'Y0Y2Y4Z5Y6Z7'] : False\n",
+      "6 :: 2403: [[8,3, 2]] : 4 :['X0Z1X2Z7', 'X0Z3X4X5', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X1Z2X3X4Z5Z6'] : False\n",
+      "6 :: 2415: [[8,3, 2]] : 16 :['X0Z1X2Z7', 'X3X4Z5Z6', 'Z0Z3Z4X7', 'Y0Y5Y6Y7', 'X0X1Z2Y3Y5Z6'] : False\n",
+      "6 :: 2417: [[8,3, 2]] : 16 :['X2Y3Y4X5', 'Z3Y5Y6Z7', 'Z2Y4Z5Y7', 'Z0X1Y2Y3Z4Z6', 'Y0Y1Y2Z3Y4Z7'] : False\n",
+      "6 :: 2419: [[8,3, 2]] : 16 :['Y0Y1Y2Y5', 'Z0Y3Y4X5', 'Z1Z4Z5X6', 'X0X1X4X7', 'Z1X2Z3X4Z6Z7'] : False\n",
+      "6 :: 2952: [[8,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X5Z7', 'Y1Y2Y4Y5', 'Z1Z2X6Z7', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 3129: [[8,3, 2]] : 4 :['X0X1X2Z5', 'X1X3Z4Z6', 'Y2Y5Z6Z7', 'Z0Z1Z3X4Z5X6', 'Z0X1Z2Z3Y4Y7'] : False\n",
+      "6 :: 3260: [[8,3, 2]] : 768 :['Z0X1', 'X2Z6', 'X3Z7', 'X0Z1Z2X4Y5Y6', 'X0Z1Z3Y4X5Y7'] : True\n",
+      "6 :: 3434: [[8,3, 2]] : 16 :['X0Y1Y2Z7', 'Z3Z4Y5Y6', 'Z0Z3Z4X7', 'X0X3X4Z5Z6Z7', 'X1Z2Y3Y5Z6Z7'] : False\n",
+      "6 :: 3435: [[8,3, 2]] : 16 :['X0X1Z4Z5', 'X0X2Z4Z6', 'Z2Z3Y6Y7', 'Z0Z1X3Y4Y5Z7', 'Z0Z2X3X4Z5X6'] : False\n",
+      "6 :: 3436: [[8,3, 2]] : 16 :['X0Z1X2Z6', 'X0Z1X3Z7', 'Z2X4Y5Y6', 'Z3Y4X5Y7', 'Z0X1Y4Y5Z6Z7'] : False\n",
+      "6 :: 3611: [[8,3, 2]] : 16 :['X3X4Z6Z7', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'X0Z1Y2Y3Z4Z6', 'Z0X1Z2X3X5Z7'] : False\n",
+      "6 :: 3613: [[8,3, 2]] : 8 :['X2X3Z5Z7', 'X4Z5Z6Z7', 'X0Z1Z3Z4X5Z7', 'X1Y2Z3Z4Z5Y6', 'Z0Z1Y2Z4Z6Y7'] : False\n",
+      "6 :: 3614: [[8,3, 2]] : 16 :['Y2Y3Z4Z6', 'Z4Y5Z6Y7', 'Y0Y1Y2Z3Y4Z7', 'X0Z1Z2X3X5Z7', 'Y0Y1X2Z4Z5X6'] : False\n",
+      "6 :: 3643: [[8,3, 2]] : 2 :['X0X1X2Z5', 'Y3Z5Y6Z7', 'Z1Z2Z3X7', 'Z0Z1X3X4Z6Z7', 'X1Z2Z4X5Z6Z7'] : False\n",
+      "6 :: 3668: [[8,3, 2]] : 1 :['X3Z4Z5Z7', 'X1Y2Y3X5', 'Y1Y2X3X6', 'Z0Y3Z6Y7', 'X0Y1X2Z3Y4Z5'] : False\n",
+      "6 :: 4021: [[8,3, 2]] : 4 :['X2X3Z4Z5', 'X0X1Z2X4', 'Y3Y5Z6Z7', 'Z1Z4Z5X6', 'Z0Z1Y2Z3Z4Y7'] : False\n",
+      "6 :: 4035: [[8,3, 2]] : 96 :['X0Z1X2Z6', 'X0Z1X3Z7', 'Z0X1X4Z7', 'Z0X1X5Z6', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 4036: [[8,3, 2]] : 96 :['Y2Y4Y5Y6', 'Y2Y3Y5Y7', 'X0Z1Y2Y3Z4Z6', 'Y0Y1Y2Z3Y4Z7', 'X2Z3Z4X5Z6Z7'] : False\n",
+      "6 :: 4041: [[8,3, 2]] : 4 :['X2X3Z4Z5', 'X0X1Z2X4', 'X1Z3X5Z7', 'Z1Z4Z5X6', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 4057: [[8,3, 2]] : 2 :['X2X3X4Z6', 'Z1Z3Z4X5', 'X0X1X2Z5Z6Z7', 'X0X1Z2Y3Z4Y6', 'Y0Y1Y2X3Z4Y7'] : False\n",
+      "6 :: 4064: [[8,3, 2]] : 2 :['X1X2Z4Z5', 'Z0Y1Y4Z7', 'X2Z3X6Z7', 'X0X1Z2X5Z6Z7', 'Y1Z2Y3Z4Z6X7'] : False\n",
+      "6 :: 4071: [[8,3, 2]] : 4 :['Z2X4X5Z6', 'Z3Y5Y6Z7', 'Z2Y4Z5Y7', 'Y0Y1Y2Y3Z4Z6', 'X0Z1Y2Z3Y4Z7'] : False\n",
+      "6 :: 4072: [[8,3, 2]] : 4 :['X0Y1Y2Z7', 'X0Y3Y5Z6', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X1Z2X3X4Z5Z6'] : False\n",
+      "6 :: 4087: [[8,3, 2]] : 12 :['X3X4Z6Z7', 'Z2X3X5Z7', 'Z2Y4Z5Y7', 'X0Z1Y2Y3Z4Z6', 'Y0Y1X2Z4Z5X6'] : False\n",
+      "6 :: 4089: [[8,3, 2]] : 2 :['X2X3Z5Z7', 'X4Z5Z6Z7', 'Z1Z3Z4X5', 'X0X1Z2Y3Z4Y6', 'Z0Z1Y2Z4Z6Y7'] : False\n",
+      "6 :: 4147: [[8,3, 2]] : 8 :['X1X3Z5Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'X0X1X2Z4Z5Z6', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 4153: [[8,3, 2]] : 2 :['X3Z4Z5Z7', 'X1Y2Y3X5', 'Z1Z2X6Z7', 'X0X4X5X6', 'Z0X1X2Z3Z6X7'] : False\n",
+      "6 :: 4172: [[8,3, 2]] : 16 :['X0X1Z2Z7', 'X0Y3Y5Z6', 'Z3X4X5Z7', 'X0X3Z4X6', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 4184: [[8,3, 2]] : 2 :['X0X1X3Z6', 'X4Z5Z6Z7', 'Y1X2Z3Z4Y5Z6', 'X0Z2Z3Z4X6Z7', 'Y0Y1Z2Z4Z5X7'] : False\n",
+      "6 :: 4235: [[8,3, 2]] : 4 :['X0X1X2Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Z2Z3Y5Y6', 'X0Z1Z2Z3Z4X7'] : False\n",
+      "6 :: 4245: [[8,3, 2]] : 4 :['X4Z5Z6Z7', 'X0X1X2X4', 'Z1Z2X5X6', 'X0Z1Z3Z4X5Z7', 'Y0Y1Y2X3Z4Y7'] : False\n",
+      "6 :: 4470: [[8,3, 2]] : 4 :['X4Z5Z6Z7', 'X0X1X2X4', 'Z1Z3Z4X5', 'Z2Z3Z4X6', 'Y0Y1Y2X3Z4Y7'] : False\n",
+      "6 :: 4496: [[8,3, 2]] : 4 :['X2X3Z4Z5', 'X0X1Z2X4', 'X1Z3X5Z7', 'X0Z1Z4Z5X6Z7', 'Z0Y1Z2Z3Z6Y7'] : False\n",
+      "6 :: 4592: [[8,3, 2]] : 48 :['X0Y1Z3Y5', 'Y2Z4Y6Z7', 'X0X2X3Z4Z5Z6', 'X1Z3X4Z5Z6Z7', 'Y0Y1Y2Z5Z6Y7'] : False\n",
+      "6 :: 4593: [[8,3, 2]] : 128 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Y0Z1X2X4Y5Z6', 'Z0Z2X4Z5X6Z7', 'Y0X2Z3Y4Z5X7'] : False\n",
+      "6 :: 4630: [[8,3, 2]] : 4 :['X0X1Z2Z7', 'Z3X4X5Z7', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'Z1X2X3X4Z5Z6'] : False\n",
+      "6 :: 4664: [[8,3, 2]] : 2 :['Y3Z5Y6Z7', 'Z1Z2Z3X7', 'X0X2X3Z4Z5Z6', 'Y0Z1X2Y4Z5Z7', 'X1Z2Z4X5Z6Z7'] : False\n",
+      "6 :: 4680: [[8,3, 2]] : 16 :['Z2X4X5Z6', 'Y2Y3Y5Y7', 'Z3Z4X6X7', 'Z0X1Y2Y3Z4Z6', 'X0Z1Y2Z3Y4Z7'] : False\n",
+      "6 :: 4696: [[8,3, 2]] : 2 :['Z0Y1Y4Z7', 'Z2X3X5Z7', 'Z1Z2Z3X7', 'X0X2X3Z4Z5Z6', 'X1Z3Z4Z5X6Z7'] : False\n",
+      "6 :: 4812: [[8,3, 2]] : 2 :['X0X1Z6Z7', 'X2X3X4Z6', 'Z3Y4Y5Z7', 'Z0Z3Z4X7', 'Z1Y2X3Z5Y6Z7'] : False\n",
+      "6 :: 4859: [[8,3, 2]] : 512 :['X0X1Z4Z5', 'X0X2Z4Z6', 'X0X3Z4Z7', 'Z0Z2X4Z5X6Z7', 'Z1Z3Z4X5Z6X7'] : False\n",
+      "6 :: 4914: [[8,3, 2]] : 6 :['Z2Z3X4X5', 'Z2Y4Z5Y6', 'X0X1Y2Y3Z4Z5', 'X0Y2Z3Y4Z6Z7', 'Y0Z1X2Z3Z4Y7'] : False\n",
+      "6 :: 4935: [[8,3, 2]] : 12 :['X3X4Z5Z6', 'Z2X3Z4X6', 'X0Y2Y3Z4Z5Z7', 'X0X1X2Z4X5Z6', 'Y0Z1X2Z3Z4Y7'] : False\n",
+      "6 :: 4936: [[8,3, 2]] : 12 :['Z2X3X5Z7', 'Z1Z2Z3X7', 'X0X2X3Z4Z5Z6', 'Y0Z1X2Y4Z5Z7', 'X1Z3Z4Z5X6Z7'] : False\n",
+      "6 :: 4940: [[8,3, 2]] : 4 :['Z2X3X5Z7', 'Y3X4Y5X6', 'Z4Y5Z6Y7', 'X0Z1Y2Y3Z4Z6', 'Y0Y1Y2Z3Y4Z7'] : False\n",
+      "6 :: 4983: [[8,3, 2]] : 2 :['X0X1X3Z6', 'X4Z5Z6Z7', 'Z1Z3Z4X5', 'Y2Y3Y4Y6', 'Y0Y1Z2Z4Z5X7'] : False\n",
+      "6 :: 4999: [[8,3, 2]] : 4 :['X1X2X3Z7', 'X0Z1Z3X4', 'Z2Y3Z4Y5', 'Z1Z2X6Z7', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 5010: [[8,3, 2]] : 2 :['X1X3Z4Z6', 'Z0Y1Y4Z7', 'Z2X3X5Z7', 'Y3Z5Y6Z7', 'X0Y1Y2Z3Z5X7'] : False\n",
+      "6 :: 5061: [[8,3, 2]] : 2 :['X1X3Z4Z6', 'Z0Y1Y4Z7', 'Y3Z5Y6Z7', 'Z1Z2Z3X7', 'X0Y2Z4Y5Z6Z7'] : False\n",
+      "6 :: 5072: [[8,3, 2]] : 1 :['X1X3Z4Z6', 'Z2Z3Y5Y6', 'Z1Y3X5Y7', 'Y0Z1X2Y4Z5Z7', 'X0Y2Z4Y5Z6Z7'] : False\n",
+      "6 :: 5099: [[8,3, 2]] : 4 :['X0X1Z2Z7', 'X0X3Z4X6', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X0Z1X2Y3Y5Z6'] : False\n",
+      "6 :: 5105: [[8,3, 2]] : 4 :['X0X1X2Z5', 'Z0Y1Y4Z7', 'Z2Z3Y5Y6', 'Z1Y3X5Y7', 'X1Z2Z4X5Z6Z7'] : False\n",
+      "6 :: 5112: [[8,3, 2]] : 1 :['X0X1X3Z6', 'X4Z5Z6Z7', 'Z2Z3Z4X6', 'Z1X2Y3Z4Y5Z7', 'Y0Y1Z2Z4Z5X7'] : False\n",
+      "6 :: 5148: [[8,3, 2]] : 4 :['X2X3Z5Z6', 'Z0Y1Y4Z7', 'X2Z3X6Z7', 'X0X1Z2X5Z6Z7', 'Y1Y2Z3Z4Z5X7'] : False\n",
+      "6 :: 5169: [[8,3, 2]] : 2 :['X1X3Z4Z6', 'Z2Z3Y5Y6', 'Z1Z2Z3X7', 'Y0Z1X2Y4Z5Z7', 'X0Y2Z4Y5Z6Z7'] : False\n",
+      "6 :: 5212: [[8,3, 2]] : 8 :['X0Z1X2Z6', 'X0Z1X3Z7', 'Z2X4Y5Y6', 'Z3Z5Z6X7', 'Z0X1Y4Y5Z6Z7'] : False\n",
+      "6 :: 5246: [[8,3, 2]] : 1 :['X1X3Z4Z6', 'Z2X3X5Z7', 'Z1Z2Z3X7', 'Y0Z1X2Y4Z5Z7', 'X0X2Z3Z4X6Z7'] : False\n",
+      "6 :: 5261: [[8,3, 2]] : 4 :['X1X2Z4Z5', 'X1X3Z4Z6', 'X2Z3X6Z7', 'Y0Z1Z2Y4X5Z6', 'Z0X1Z2Z3Y4Y7'] : False\n",
+      "6 :: 5283: [[8,3, 2]] : 48 :['X0X2Z6Z7', 'X0X1X3Z6', 'X0X1X4Z7', 'Z0Z4Z5X6', 'Z1Z2Z3X5Z6X7'] : False\n",
+      "6 :: 5291: [[8,3, 2]] : 4 :['Z2X4X5Z6', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'Y0Y1Y2Y3Z4Z6', 'X0Z1Y2Z3Y4Z7'] : False\n",
+      "6 :: 5303: [[8,3, 2]] : 8 :['X1X2Z4Z5', 'X0X3Z4Z5', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Z1Y4Z5Z6Y7'] : False\n",
+      "6 :: 5314: [[8,3, 2]] : 2 :['X2X3Z5Z6', 'Z0Y1Y4Z7', 'Z1Z2Z3X7', 'X0Y2Z4Y5Z6Z7', 'X1Z3Z4Z5X6Z7'] : False\n",
+      "6 :: 5318: [[8,3, 2]] : 24 :['X3X4Z5Z6', 'Z3Z4Y5Y6', 'X0X1Y2Y3Z4Z5', 'X0X2Z4X5Z6Z7', 'Y0Z1X2Z3Z4Y7'] : False\n",
+      "6 :: 5320: [[8,3, 2]] : 64 :['X2X3Z6Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'X0X1X2Z4Z5Z6', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 5329: [[8,3, 2]] : 3 :['X1X3Z4Z6', 'Y3Z5Y6Z7', 'Z1Z2Z3X7', 'Y0Z1X2Y4Z5Z7', 'X0Y2Z4Y5Z6Z7'] : False\n",
+      "6 :: 5331: [[8,3, 2]] : 6 :['X2X3Z4Z5', 'X0X1Z3X5', 'Y3Y5Z6Z7', 'X0Y1Z2Y4Z5X6', 'Y0Y1Z2Z3Z6X7'] : False\n",
+      "6 :: 5346: [[8,3, 2]] : 12 :['X2Y3Y4X5', 'Z4Y5Z6Y7', 'Y2X3Y6X7', 'Z0X1Y2Y3Z4Z6', 'X0Z1Y2Z3Y4Z7'] : False\n",
+      "6 :: 5397: [[8,3, 2]] : 1 :['X3Z4Z5Z7', 'Z2Z3X5Z7', 'Z1Z2X6Z7', 'Z0Y3Z6Y7', 'X0Y1X2Z3Y4Z5'] : False\n",
+      "6 :: 5472: [[8,3, 2]] : 96 :['X0X1Z2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'X3Z4X6Z7', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 5474: [[8,3, 2]] : 8 :['X0Z1X2Z6', 'X0Z1X3Z7', 'Y4Y5Z6Z7', 'Z3Z5Z6X7', 'Z0X1Z2Z4X6Z7'] : False\n",
+      "6 :: 5479: [[8,3, 2]] : 2 :['X4Z5Z6Z7', 'X0X1X2X4', 'Z1Z3Z4X5', 'Y2Y3Y4Y6', 'Y0Y1Y2X3Z4Y7'] : False\n",
+      "6 :: 5481: [[8,3, 2]] : 2 :['X1X2Z4Z5', 'X1X3Z4Z6', 'Z0Y1Y4Z7', 'Z1Z2Z3X7', 'X0Z2Z3Z4Y5Y6'] : False\n",
+      "6 :: 5483: [[8,3, 2]] : 48 :['X3X4Z6Z7', 'Z3Y5Y6Z7', 'Z4Y5Z6Y7', 'Y0Y1Y2Y3Z4Z6', 'Z0X1Z2X3X5Z7'] : False\n",
+      "6 :: 5500: [[8,3, 2]] : 8 :['X0X2Z6Z7', 'X1X3Z6Z7', 'X1X4Z5Z7', 'Z0Z1Z5X7', 'Z2Z3Z4X5X6Z7'] : False\n",
+      "6 :: 5503: [[8,3, 2]] : 4 :['X0X1Z2Z7', 'Y3Y5Z6Z7', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'Z1X2X3X4Z5Z6'] : False\n",
+      "6 :: 5504: [[8,3, 2]] : 4 :['X3X4Z6Z7', 'Z2Y3Z5Y6', 'Z4Y5Z6Y7', 'Y0Y1Y2Y3Z4Z6', 'Z0X1Z2X3X5Z7'] : False\n",
+      "6 :: 5508: [[8,3, 2]] : 32 :['X0X1Z2Z7', 'X3X4Z5Z6', 'X0Y3Y5Z6', 'X3Z4X6Z7', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 5513: [[8,3, 2]] : 2 :['Z2Z3X4Z7', 'X1X3Y4Y5', 'Z1Z5X6Z7', 'Z0X3Z6X7', 'X0Y1Y2Z4Z6Z7'] : False\n",
+      "6 :: 5516: [[8,3, 2]] : 8 :['X0X3Z4Z5', 'X1X2X3Z7', 'X0Z1Z3X4', 'Z1Z2X6Z7', 'Z0Z2Z4Y5Z6Y7'] : False\n",
+      "6 :: 5525: [[8,3, 2]] : 1 :['X0X1X2Z5', 'X1X3Z4Z6', 'Y3Z5Y6Z7', 'Z1Z2Z3X7', 'Z0Z1Z2X4X5Z6'] : False\n",
+      "6 :: 5528: [[8,3, 2]] : 8 :['X0Z7', 'X1X3X4Z5', 'Y1X2Z3Z4Y5Z6', 'X1Z2Y3Z4Y6Z7', 'Z0Y1Z2Y4Z6X7'] : False\n",
+      "6 :: 5540: [[8,3, 2]] : 16 :['X0Y1Y2Z7', 'Z3Z4Y5Y6', 'Z0Y4X5Y7', 'Y0Y3X6X7', 'X1Z2Y3Y5Z6Z7'] : False\n",
+      "6 :: 5557: [[8,3, 2]] : 192 :['X0Z7', 'X1Z7', 'X2X3Z4Z6', 'Z2Z3X4Y5Y6Z7', 'Z0Z1Z3X4Z5X7'] : True\n",
+      "6 :: 5564: [[8,3, 2]] : 4 :['X0X1Z2Z7', 'X3X4Z5Z6', 'X3Z4X6Z7', 'Z0Z3Z4X7', 'Z1X2Y3Y5Z6Z7'] : False\n",
+      "6 :: 5567: [[8,3, 2]] : 16 :['X0X2Z4Z6', 'X1X3Z5Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 5587: [[8,3, 2]] : 16 :['X1Z5', 'Z2Z3Y6Y7', 'X0X2X3Z4Z6Z7', 'Y0Z1X2X4Y5Z6', 'Y0Y2Y4Z5Y6Z7'] : False\n",
+      "6 :: 5588: [[8,3, 2]] : 32 :['X0Z7', 'Z3Z4Y5Y6', 'Z1X2X3X4Z5Z6', 'Y1Y2Y3Y5Z6Z7', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 5681: [[8,3, 2]] : 384 :['Z0Z7', 'Z1Z7', 'X2X4X5X6', 'Z2Z3Z4Z5Z6Z7', 'X0X1X3X5X6X7'] : True\n",
+      "6 :: 5699: [[8,3, 2]] : 32 :['X3Z7', 'X0X1Z4Z5', 'Y0Z1X2X4Y5Z6', 'Z0Z2X4Z5X6Z7', 'Y0X2Z3Y4Z5X7'] : False\n",
+      "6 :: 5738: [[8,3, 2]] : 16 :['X0X1Z2Z7', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'Z0Z3Z4X7', 'Z1X2Y3Y5Z6Z7'] : False\n",
+      "6 :: 6840: [[8,3, 2]] : 64 :['X0Z7', 'X3X4', 'X1Y3Z4Y5Z6Z7', 'Y1Z2X3Z5Y6Z7', 'Z0X2Z3Z4Z6X7'] : False\n",
+      "6 :: 6842: [[8,3, 2]] : 64 :['X1Z5', 'X3Z7', 'Y0Z1X2X4Y5Z6', 'Z0Z2X4Z5X6Z7', 'Y0X2Z3Y4Z5X7'] : False\n",
+      "6 :: 6850: [[8,3, 2]] : 1152 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 6864: [[8,3, 2]] : 64 :['X2Z7', 'X0X1X3Z6', 'X1X4Z5Z7', 'X0X1Z4X5', 'Z0Z1Z2Z3Y6Y7'] : True\n",
+      "6 :: 6865: [[8,3, 2]] : 128 :['X4X5', 'Z0Z1Z2Z7', 'Z1Z3Z6Z7', 'Z0Z1Z4Z5', 'X0Y1X2Y3Y6Y7'] : True\n",
+      "6 :: 6867: [[8,3, 2]] : 128 :['Z0Z1', 'Z3Z4Z6Z7', 'Z2Z3Z5Z7', 'X3X4X6X7', 'X0X1X2X4X5X6'] : False\n",
+      "6 :: 6869: [[8,3, 2]] : 128 :['X4Z6', 'X0X1Z2Z7', 'X0X3Z5Z7', 'Z1X2Z3Z4X5X6', 'Z0Z1X2Z5Z6X7'] : False\n",
+      "6 :: 6872: [[8,3, 2]] : 384 :['X3Z6', 'X0X1X2Z7', 'X1X4Z5Z7', 'X0X1Z4X5', 'Z0Z1Z2Z3Y6Y7'] : False\n",
+      "6 :: 6874: [[8,3, 2]] : 32 :['X0Z7', 'Z3Z4Y5Y6', 'Z0Z3Z4X7', 'Y1Y2X3X4Z5Z6', 'X1Z2Y3Y5Z6Z7'] : False\n",
+      "6 :: 6876: [[8,3, 2]] : 32 :['Z1X2', 'X3X4Z5Z6', 'X3Z4X6Z7', 'Z0Z3Z4X7', 'X0X1Z2Y3Y5Z6'] : False\n",
+      "6 :: 6886: [[8,3, 2]] : 16 :['X1Z7', 'X3X4Z5Z6', 'Z2Y3Y5Z6', 'X0X2Z3Z5X6Z7', 'Y0Z1X2Z3Z4Y7'] : False\n",
+      "6 :: 6887: [[8,3, 2]] : 4 :['X0Z7', 'X1X2Z5Z6', 'Z2Z3Z4X6', 'Z1Z3Y4Y5Z6Z7', 'Z0Y1Y2X3Z4X7'] : False\n",
+      "6 :: 6896: [[8,3, 2]] : 8 :['X3X4', 'X0X1Z6Z7', 'Y3Z4Y5Z7', 'Z1Z2X3Z5X6Z7', 'Z0X2Z3Z4Z6X7'] : True\n",
+      "6 :: 6900: [[8,3, 2]] : 128 :['Z0X1', 'X3X4Z6Z7', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'X0Z1X2Y3Y4X5'] : False\n",
+      "6 :: 6902: [[8,3, 2]] : 128 :['X3Z7', 'X0X1Z4Z5', 'X0X2Z4Z6', 'Z0Z2X4Z5X6Z7', 'Z1Z3Z4X5Z6X7'] : False\n",
+      "6 :: 6903: [[8,3, 2]] : 128 :['Z0X1', 'Y2Y3Z4Z6', 'Z4Y5Z6Y7', 'X0Z1Z2X3X5Z7', 'Z2Z3X4Z5X6Z7'] : False\n",
+      "6 :: 6908: [[8,3, 2]] : 16 :['X0Z7', 'Z3Z4Y5Y6', 'Z0Y4X5Y7', 'Y1Y2X3X4Z5Z6', 'X1Z2Y3Y5Z6Z7'] : False\n",
+      "6 :: 6909: [[8,3, 2]] : 32 :['X3Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'X0X1X2Z4Z5Z6', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 6911: [[8,3, 2]] : 8 :['X3Z7', 'X0Z1X2Z6', 'Z2X4Y5Y6', 'Z3Z5Z6X7', 'Z0X1Y4Y5Z6Z7'] : True\n",
+      "6 :: 6923: [[8,3, 2]] : 16 :['X3Z7', 'Y0Y1X2Z6', 'Y2X4Y5X6', 'X0Z1Y4Y5Z6Z7', 'Z0X1Z3Z5Z6X7'] : False\n",
+      "6 :: 6926: [[8,3, 2]] : 48 :['Z0X1', 'Y2Y3Z4Z6', 'Z4Y5Z6Y7', 'X0Z1Z2X4X5Z6', 'Z2Z3X4Z5X6Z7'] : False\n",
+      "6 :: 6927: [[8,3, 2]] : 8 :['X1Z7', 'Z2Z3X4X5', 'Z2Y4Z5Y6', 'X0Y2Y3Z4Z5Z7', 'Z0Z1Z2X4Z6X7'] : False\n",
+      "6 :: 6928: [[8,3, 2]] : 768 :['X4X5', 'Z0Z1Z2Z7', 'Z0Z1Z3Z6', 'Z0Z1Z4Z5', 'X0X1Y2Y3Y6Y7'] : True\n",
+      "6 :: 6938: [[8,3, 2]] : 16 :['X4X6', 'Z0Z1Z2Z7', 'X1X2X3X5', 'X0X5X6X7', 'Z0Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 6940: [[8,3, 2]] : 16 :['X5Z6', 'X2X4Z6Z7', 'X0X1X3X4', 'Z1Z2Z3X7', 'Y0X1X2Z4Z5Y6'] : True\n",
+      "6 :: 6946: [[8,3, 2]] : 128 :['X4Z6', 'X0X1Z2Z7', 'X0X3Z5Z7', 'Z3Z4X5X6', 'Z0Z1X2Z5Z6X7'] : True\n",
+      "6 :: 6954: [[8,3, 2]] : 16 :['Z0X1', 'Z2X4X5Z6', 'Z3Y5Y6Z7', 'Z2Y4Z5Y7', 'X0Z1Y2Y3Z4Z6'] : False\n",
+      "6 :: 6969: [[8,3, 2]] : 16 :['X3Z7', 'Y0Y1X2Z6', 'Z2Z4X6Z7', 'X0Z1Y4Y5Z6Z7', 'Z0X1Z3Z5Z6X7'] : False\n",
+      "6 :: 6973: [[8,3, 2]] : 128 :['X1X2', 'Z0Z1Z2Z7', 'X3X4X5X6', 'X0X3X4X7', 'Z0Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 6975: [[8,3, 2]] : 16 :['X0Z7', 'X3X4Z5Z6', 'Z0Z3Z4X7', 'Y1Y2Y3Y5Z6Z7', 'X1Z2X3Z4X6Z7'] : False\n",
+      "6 :: 6983: [[8,3, 2]] : 8 :['X1Z5', 'X0X2Z4Z6', 'Z2Z3Y6Y7', 'Z0Z1X3Y4Y5Z7', 'Z0Z2X3X4Z5X6'] : False\n",
+      "6 :: 6985: [[8,3, 2]] : 16 :['X0Z7', 'Z3X4X5Z7', 'Y4Z5Y6Z7', 'X1Z2X3X4Z5Z6', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 6989: [[8,3, 2]] : 32 :['X0Z7', 'X3X4Z5Z6', 'X3Z4X6Z7', 'X1Z2Y3Y5Z6Z7', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 6990: [[8,3, 2]] : 24 :['Z0X1', 'Z2X4X5Z6', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'X0Z1Y2Z3Y4Z7'] : False\n",
+      "6 :: 6991: [[8,3, 2]] : 8 :['X3Z7', 'X0Z1X2Z6', 'Y4Y5Z6Z7', 'Z2Z4X6Z7', 'Z0X1Z3Z5Z6X7'] : True\n",
+      "6 :: 6992: [[8,3, 2]] : 24 :['X4Z5', 'X0X2Z6Z7', 'X1X3Z6Z7', 'Z0Z1Z5X7', 'Z2Z3Z4X5X6Z7'] : False\n",
+      "6 :: 6999: [[8,3, 2]] : 8 :['Z0X1', 'Z2X3X5Z7', 'Y3X4Y5X6', 'Z4Y5Z6Y7', 'X0Z1Y2Z3Y4Z7'] : True\n",
+      "6 :: 7000: [[8,3, 2]] : 16 :['X3Z7', 'X0Z1X2Z6', 'Z2X4Y5Y6', 'Z3Y4X5Y7', 'Z0X1Y4Y5Z6Z7'] : True\n",
+      "6 :: 7004: [[8,3, 2]] : 16 :['X3Z7', 'X0Z1X2Z6', 'X2Y4Y5Z7', 'Z3Z5Z6X7', 'Z0X1Z2Z4X6Z7'] : True\n",
+      "6 :: 7005: [[8,3, 2]] : 8 :['X3Z7', 'X0Z1X2Z6', 'Z2Z4X6Z7', 'Z3Z5Z6X7', 'Z0X1Y4Y5Z6Z7'] : True\n",
+      "6 :: 7006: [[8,3, 2]] : 16 :['X3Z7', 'X0Z1X2Z6', 'Z2Z4X6Z7', 'Z3Y4X5Y7', 'Z0X1Y4Y5Z6Z7'] : True\n",
+      "6 :: 7007: [[8,3, 2]] : 8 :['X0Z7', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X1Z2X3X4Z5Z6', 'Y1Y2Y3Y5Z6Z7'] : False\n",
+      "6 :: 7017: [[8,3, 2]] : 32 :['X2Z6', 'X0X1Z6Z7', 'X0X3X4Z7', 'Y0X3Y5X7', 'Z1Z2Z3Z4X5X6'] : False\n",
+      "6 :: 7018: [[8,3, 2]] : 32 :['X2Z7', 'X1X3Z6Z7', 'X0X4Z5Z7', 'X1Z4X5Z7', 'Z0Z1Z2Z3Y6Y7'] : True\n",
+      "6 :: 7020: [[8,3, 2]] : 32 :['X5Z6', 'X0X1X3Z6', 'X2X4Z6Z7', 'Z1Z2Z3X7', 'Y0X1Z4Z5Y6Z7'] : True\n",
+      "6 :: 7021: [[8,3, 2]] : 32 :['Z1Z7', 'Z0Z3Z4Z7', 'X2X3X4X6', 'Z2Z5Z6Z7', 'X0X1X2X5X6X7'] : False\n",
+      "6 :: 7026: [[8,3, 2]] : 64 :['X1Z7', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'X0Y2Y3Z4Z5Z7', 'Y0Z1X2Z3Z4Y7'] : False\n",
+      "6 :: 7029: [[8,3, 2]] : 192 :['Z0X1', 'Z2X4X5Z6', 'Y2Y3Y5Y7', 'Z3Z4X6X7', 'X0Z1X3X4Z6Z7'] : False\n",
+      "6 :: 7030: [[8,3, 2]] : 48 :['Z1X2', 'X0Y3Y5Z6', 'Y4Z5Y6Z7', 'Z0Z3Z4X7', 'X1Z2X3X4Z5Z6'] : False\n",
+      "6 :: 7034: [[8,3, 2]] : 16 :['Z0X1', 'Z2X4X5Z6', 'Y2Y3Y5Y7', 'Z3Z4X6X7', 'X0Z1Y2Y3Z4Z6'] : False\n",
+      "6 :: 7035: [[8,3, 2]] : 48 :['Z0X1', 'Z2X3X5Z7', 'Y3X4Y5X6', 'Z4Y5Z6Y7', 'X0Z1Y2Y3Z4Z6'] : False\n",
+      "6 :: 7073: [[8,3, 2]] : 8 :['X3Z7', 'X0Z1X2Z6', 'Y4Y5Z6Z7', 'Z0X1Z2Z4X6Z7', 'Z0X1Z3Z5Z6X7'] : False\n",
+      "6 :: 7083: [[8,3, 2]] : 8 :['X3X4', 'X0X1Z6Z7', 'Z0X3Y5Y7', 'X2Y3Z4Y5Z6Z7', 'Z1Y2X3Z5Y6Z7'] : False\n",
+      "6 :: 7084: [[8,3, 2]] : 4 :['X0Z4', 'X1X3Z4Z6', 'Y3Z5Y6Z7', 'Z0Y1Y2Y4Y5Z6', 'Z0X1Z2Z3Y4Y7'] : False\n",
+      "6 :: 7086: [[8,3, 2]] : 48 :['X0Z7', 'Z3X4X5Z7', 'Z0Z3Z4X7', 'Y1Y2X3X4Z5Z6', 'X1Z2X3Z4X6Z7'] : False\n",
+      "6 :: 7093: [[8,3, 2]] : 8 :['X0Z7', 'X4Z5Z6Z7', 'Z2Z3Z4X6', 'Y1Y2Y5Y6', 'Z0Z1Z2X3Y4Y7'] : True\n",
+      "6 :: 7094: [[8,3, 2]] : 4 :['X0Z4', 'X1X2Z4Z5', 'Z2Z3Y5Y6', 'Z1Y3X5Y7', 'Z0Z1X3X4Z6Z7'] : True\n",
+      "6 :: 7107: [[8,3, 2]] : 24 :['X0Z7', 'X4Z5Z6Z7', 'Y1X2Z3Z4Y5Z6', 'X1Z2Y3Z4Y6Z7', 'Z0Y1Z2Z4Z5Y7'] : False\n",
+      "6 :: 7108: [[8,3, 2]] : 16 :['X0Z7', 'X3Z4X6Z7', 'Z1X2X3X4Z5Z6', 'X1Z2Y3Y5Z6Z7', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 7116: [[8,3, 2]] : 8 :['X5Z6', 'X0X1Z6Z7', 'X2X4Z6Z7', 'Z1Z2Z3X7', 'Z0X3Z4Z5X6Z7'] : False\n",
+      "6 :: 7121: [[8,3, 2]] : 64 :['X1Z7', 'X0X2Z6Z7', 'X0X3Z4Z7', 'Z2Z3X4Y5Y6Z7', 'Z0Z1Z3X4Z5X7'] : False\n",
+      "6 :: 7122: [[8,3, 2]] : 64 :['X1Z7', 'X0X2Z6Z7', 'X0X3Z4Z7', 'Z0Z1Z5X7', 'Z2Z3X4Y5Y6Z7'] : False\n",
+      "6 :: 7124: [[8,3, 2]] : 8 :['X2Z6', 'X0X1Z6Z7', 'Y3Z4Y5Z7', 'Z0Z3Z4X7', 'Z1Z2X4Z5X6Z7'] : True\n",
+      "6 :: 7126: [[8,3, 2]] : 48 :['X3Z7', 'X0Z1X2Z6', 'Y4Y5Z6Z7', 'Z3Z5Z6X7', 'Z0X1Z2Z4X6Z7'] : True\n",
+      "6 :: 7142: [[8,3, 2]] : 64 :['X1Z7', 'X0X2Z6Z7', 'X0X4Z5Z7', 'Z2Z3Z4X5X6Z7', 'Z0Z1X3Z5Z6X7'] : False\n",
+      "6 :: 7452: [[8,3, 2]] : 8 :['X1X3X4Z5', 'X0X1X2Z5Z6Z7', 'X0Z1Z3Z4X5Z7', 'X1Z2Y3Z4Y6Z7', 'Y0Y1Y2X3Z4Y7'] : False\n",
+      "6 :: 690: [[8,4, 2]] : 64 :['X0X1X4X5', 'X0X1X2X3', 'Z0Z1X6X7', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 692: [[8,4, 2]] : 192 :['X0X1X4X5', 'X0X1X2X3', 'Z0Z1Z2Z3X6X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 761: [[8,4, 2]] : 16 :['X0X1X2X3', 'Z2Z3Z6Z7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 762: [[8,4, 2]] : 128 :['X4X5X6X7', 'X0X1X2X3', 'Z0Z1Z2Z3X4X5', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 771: [[8,4, 2]] : 32 :['X4X5X6X7', 'X0X1X2X3', 'Z2Z3Z6Z7', 'Z0Z1X2Z4Z5X6'] : True\n",
+      "6 :: 974: [[8,4, 2]] : 48 :['X0X4X5X6', 'X0X1X2X3', 'X0Z1Z2X7', 'Z0Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1091: [[8,4, 2]] : 64 :['X0X1X4X5', 'X0X1X2X3', 'Z0Z1X2X4X6X7', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 1120: [[8,4, 2]] : 12 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Z0Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 1331: [[8,4, 2]] : 8 :['X0X4X5X6', 'X0X1X2X3', 'X0Z1Z2Z4Z5X7', 'Z0X1Z3X4Z6Z7'] : True\n",
+      "6 :: 1379: [[8,4, 2]] : 24 :['X0X1X4X5', 'X0X1X2X3', 'Z0X1Z2Z4X6X7', 'X0Z1Z3Z5Z6Z7'] : False\n",
+      "6 :: 1415: [[8,4, 2]] : 8 :['X0X4X5X6', 'X0X1X2X3', 'X0Z1Z2Z4Z5X7', 'Z0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 1418: [[8,4, 2]] : 32 :['Z0Z1Z2Z3', 'X2X3X4X5', 'Z4Z5Z6Z7', 'X0X1X4X5X6X7'] : True\n",
+      "6 :: 1496: [[8,4, 2]] : 144 :['X0X1X2X3', 'Z0Z1X2X4', 'X0X1X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 1682: [[8,4, 2]] : 4 :['Z2X3Y4Z6', 'Y3X4Z5Z7', 'Z0Z1X2X3X6X7', 'X0X1X2X3X4X5'] : False\n",
+      "6 :: 1683: [[8,4, 2]] : 8 :['X0X1X2X3', 'Z2Z3Z4Z5', 'Z0Z1X4X5X6X7', 'Y0X1Y2X4Z6Z7'] : False\n",
+      "6 :: 1735: [[8,4, 2]] : 16 :['X0X4X5X6', 'X0X1X2X3', 'Z1Z2X4X7', 'Z0Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1782: [[8,4, 2]] : 1152 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X0X1X4X5X6X7', 'X0X2Z4Z5Z6Z7'] : False\n",
+      "6 :: 1784: [[8,4, 2]] : 64 :['X4X5X6X7', 'X0X1X2X3', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3X4Z6Z7'] : True\n",
+      "6 :: 1785: [[8,4, 2]] : 512 :['X4X5X6X7', 'Z0Z1Z2Z3', 'X0X1X2X3X4X5', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 1786: [[8,4, 2]] : 4 :['Z0Z1X4X5', 'X0X1X2X3', 'Y0X1Z2X4X6X7', 'Z0Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 1842: [[8,4, 2]] : 32 :['X4X5X6X7', 'X0X1X2X3', 'Z0Z1X2Z4Z5X6', 'Z0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 1924: [[8,4, 2]] : 72 :['X0X1X2X3', 'Z0X1Z2Z4', 'Z0Z1X4X5X6X7', 'X0Z1Z3Z5Z6Z7'] : True\n",
+      "6 :: 2012: [[8,4, 2]] : 8 :['X0X1X2X3', 'Z2Z3Z4Z5', 'Z0Z1X4X5X6X7', 'Z0X1Y2Y4Z6Z7'] : False\n",
+      "6 :: 2013: [[8,4, 2]] : 32 :['Z0Z1Z2Z3', 'X2X3X4X5', 'X0X1X4X5X6X7', 'Z0Z1Z4Z5Z6Z7'] : False\n",
+      "6 :: 2091: [[8,4, 2]] : 8 :['X0X4X5X6', 'X0X1X2X3', 'Z0Z3Z6Z7', 'X0Z1Z2Z4Z5X7'] : True\n",
+      "6 :: 2193: [[8,4, 2]] : 192 :['Z0Z1X4X5', 'X0X1X2X3', 'Y0Y1X6X7', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 2206: [[8,4, 2]] : 1152 :['Z0Z1Z2Z3', 'X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 2229: [[8,4, 2]] : 4 :['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6Z7', 'Y0X1Y2Z3Z4X6'] : False\n",
+      "6 :: 2230: [[8,4, 2]] : 24 :['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6Z7', 'Y0Y1Y2X3Z4Z6'] : False\n",
+      "6 :: 2332: [[8,4, 2]] : 768 :['X4X5X6X7', 'Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 2420: [[8,4, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z3Z6Z7'] : True\n",
+      "6 :: 2624: [[8,4, 2]] : 128 :['X0X1', 'Z2Z3Z4Z5', 'X2X3X4X5X6X7', 'Z0Z1Z2Z3Z6Z7'] : True\n",
+      "6 :: 2827: [[8,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 3040: [[8,4, 2]] : 48 :['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z0X1Z2Z3Z4Z6', 'X0Y1Z2Z4Z5Z7'] : False\n",
+      "6 :: 3041: [[8,4, 2]] : 1152 :['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z0Z1Z2Z3Z4Z5', 'Y0Y1Z2Z3Z6Z7'] : False\n",
+      "6 :: 3042: [[8,4, 2]] : 72 :['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z0Z1Z2Z3Z4Z5', 'Y0Z1Z2Y4Z6Z7'] : False\n",
+      "6 :: 3050: [[8,4, 2]] : 768 :['X2X3', 'Z0Z1', 'X0X1X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 3059: [[8,4, 2]] : 192 :['X0X1', 'Z0Z1Z6Z7', 'X0X2X3X4X5X6', 'X0Z2Z3Z4Z5X7'] : True\n",
+      "6 :: 3060: [[8,4, 2]] : 384 :['Z0Z1', 'X0X1X2X3', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 3063: [[8,4, 2]] : 384 :['X0X1', 'Z2Z3Z4Z5', 'Z0Z1Z6Z7', 'X2X3X4X5X6X7'] : True\n",
+      "6 :: 3066: [[8,4, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Z2Z3X6X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 3069: [[8,4, 2]] : 192 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 3070: [[8,4, 2]] : 64 :['X0X1', 'X0X2X3X4X5X6', 'X0Z2Z3Z4Z5X7', 'Z0Z1Y2Y3Z6Z7'] : False\n",
+      "6 :: 3072: [[8,4, 2]] : 48 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'Z0Z1Z4Z5Z6Z7'] : True\n",
+      "6 :: 3075: [[8,4, 2]] : 32 :['X0X1', 'X0X2X3X4X5X6', 'X0Z2Z3Z4Z5X7', 'Z0Z1X2X3Z6Z7'] : False\n",
+      "6 :: 3109: [[8,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z2Z3Z4Z5', 'Y0X1Z2Z4Z6Z7'] : False\n",
+      "6 :: 3110: [[8,4, 2]] : 16 :['Y0Z1Y2Y4', 'Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 3111: [[8,4, 2]] : 128 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z2Z3X4X5', 'Z0Z1Z4Z5Z6Z7'] : False\n",
+      "6 :: 3113: [[8,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 3116: [[8,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5X6', 'Y0X1Z3X4Z6Z7'] : False\n",
+      "6 :: 3123: [[8,4, 2]] : 128 :['X0X1X2X3', 'X0X1X4X5X6X7', 'X0X2Z4Z5Z6Z7', 'Z0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 3125: [[8,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z2Z3Z4Z5', 'Z0X1Z2Y4Z6Z7'] : False\n",
+      "6 :: 3137: [[8,4, 2]] : 2 :['X0Y2Z4X6', 'Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 3144: [[8,4, 2]] : 4 :['Z0Y2Z3Y4', 'Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 3152: [[8,4, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3X4Z6Z7'] : False\n",
+      "6 :: 392: [[8,5, 2]] : 96 :['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 537: [[8,5, 2]] : 384 :['Z0Z1Z2Z3X6X7', 'X0X1X2X3X4X5', 'Y0Y1Z4Z5Z6Z7'] : True\n",
+      "6 :: 539: [[8,5, 2]] : 192 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : True\n",
+      "6 :: 1911: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'X4Z6Z7Z8', 'Z3X5Z7Z8', 'Z1Z4Z5X7', 'X2Y3Z4Z5Y6Z8', 'Z0Z2X3Z4Z6X8'] : False\n",
+      "6 :: 2284: [[9,2, 2]] : 32 :['X0Z8', 'X2Z7', 'Y3Y5Z6Z8', 'Y4Z5Y6Z8', 'Z0Z3Z4X8', 'X1X3X4Z5Z6Z7', 'Y1Z2X3Z5Y7Z8'] : False\n",
+      "6 :: 2416: [[9,2, 2]] : 32 :['X0Z8', 'X2Z7', 'Z3X4X5Z8', 'Y4Z5Y6Z8', 'Z0Z3Z4X8', 'X1X3X4Z5Z6Z7', 'Y1Z2X3Z5Y7Z8'] : False\n",
+      "6 :: 2907: [[9,2, 2]] : 256 :['X0Z8', 'X1Z7', 'X2Z6', 'Z3X4', 'Z1Y5Y7Z8', 'Z0Z5Z6X8', 'Z2X3Z4X5X6Z7'] : True\n",
+      "6 :: 3112: [[9,2, 2]] : 64 :['X0Z5', 'X1Z6', 'X2Z7', 'X3X4Z6Z7', 'Z1Y3Y6Z8', 'Z3Z4Z5X8', 'Z0Z2Z4X5X7Z8'] : True\n",
+      "6 :: 3114: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z4', 'X3X5Z7Z8', 'Z1Z3X6X7', 'Z2Z3X4Z5X6Z8', 'Z0Z2Y3Y4Z6X8'] : False\n",
+      "6 :: 3175: [[9,2, 2]] : 64 :['X0Z7', 'X1Z8', 'X3Z4', 'Y5Y6Z7Z8', 'Z1Z6Z7X8', 'Z2Z3X4X5Z6Z7', 'Z0X2Z4Z5X7Z8'] : False\n",
+      "6 :: 3182: [[9,2, 2]] : 64 :['X0X1', 'X3Z7', 'X4Z6', 'X0X2Z7Z8', 'Z4Y5Y6Z8', 'Z0Z1Z5X8', 'Z2Z3X5Z6X7Z8'] : True\n",
+      "6 :: 3185: [[9,2, 2]] : 64 :['X0Z5', 'X1Z6', 'X2Z7', 'X3X4Z6Z7', 'Z1Y3Y6Z8', 'Z0Z2Z4X5X7Z8', 'Z0Y3Z4Y5Z6X8'] : False\n",
+      "6 :: 3208: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'X2Z7', 'X3X4Z6Z7', 'Z2Z4Z5X7', 'Z1Z3Y5Y6Z7Z8', 'Z0Y3Z4Y5Z7X8'] : False\n",
+      "6 :: 3214: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'X2Z7', 'X4Z5Z7Z8', 'Y3Z4Y5Z6', 'Z1Z2Z3Z4Y6Y7', 'Z0Z1Z4Z5Y6Y8'] : False\n",
+      "6 :: 3234: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z4', 'X3X5Z7Z8', 'Z1Y5Z6Y7', 'Z0Z3Z4X8', 'Z2Z3X4Z5X6Z8'] : True\n",
+      "6 :: 3313: [[9,2, 2]] : 64 :['X0Z6', 'X1Z7', 'X2Z8', 'X3Z4X5Z7', 'Z0Y4Y5X6', 'Z1Y3Z4Z6Y7Z8', 'Z2Y3Z5Z6Z7Y8'] : False\n",
+      "6 :: 3317: [[9,2, 2]] : 64 :['X0Z8', 'X1Z5', 'Z2X3', 'Z1X4Y5Y6', 'Z4Z6X7Z8', 'X2Z3Z5X6Z7Z8', 'Z0X2Z3Y4Z6Y8'] : False\n",
+      "6 :: 3360: [[9,2, 2]] : 64 :['X0Z8', 'X1Z5', 'Z2X3', 'Z4Z6X7Z8', 'Z1Y4X5Y7', 'Z0Y4Z6Y8', 'X2Z3Z5X6Z7Z8'] : False\n",
+      "6 :: 3383: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z4', 'Y3Z5Y6Z8', 'Z1Z3X6X7', 'Z0Z3Z4X8', 'Z2X4X5Z6Z7Z8'] : True\n",
+      "6 :: 3391: [[9,2, 2]] : 128 :['X0Z8', 'X1Z5', 'Z2X3', 'Z5X6Z7Z8', 'Z4Z6X7Z8', 'Z0Y4Z6Y8', 'Z1X2Z3X4Y5Y6'] : False\n",
+      "6 :: 3393: [[9,2, 2]] : 64 :['X0Z7', 'X1Z8', 'X3Z4', 'Z0X5Y6Y7', 'Z1Z6Z7X8', 'Y2Z3Y4X5Z6Z7', 'Z2Z3X4Z5X6Z8'] : False\n",
+      "6 :: 3395: [[9,2, 2]] : 128 :['X0Z8', 'X1Z7', 'X2Z4', 'Z3Y5Y6Z7', 'Z0Z3Z4X8', 'Z2X4X5Z6Z7Z8', 'Z1X3Z5Z6X7Z8'] : False\n",
+      "6 :: 3403: [[9,2, 2]] : 64 :['X0Z5', 'X1Z6', 'X2Z7', 'Z0X3X5Z6', 'Z3Z4Z5X8', 'Z1Z3X4X6Z7Z8', 'Z2X3Z4Z6X7Z8'] : False\n",
+      "6 :: 3413: [[9,2, 2]] : 128 :['X0Z6', 'X1Z7', 'X2Z8', 'X3X4Z5Z8', 'X3Z4X5Z7', 'Z0Z1Y3Z4Y6X7', 'Z2Y3Z5Z6Z7Y8'] : False\n",
+      "6 :: 3503: [[9,2, 2]] : 64 :['X1Z8', 'X2Z5', 'X4Z7', 'X0X3Z6Z8', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : True\n",
+      "6 :: 3532: [[9,2, 2]] : 128 :['X0Z8', 'X1Z7', 'X2Z6', 'X3X4Z5Z7', 'Y4Y5Z6Z8', 'Z1Y3Y7Z8', 'Z0Z2Z3Y4X6Y8'] : True\n",
+      "6 :: 3554: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'X2Z7', 'X3Z5Z6Z8', 'Z3Z4X5Z8', 'Z1Z2Y3Y4X6X7', 'Z0Z1Y4Y6Z7X8'] : True\n",
+      "6 :: 3560: [[9,2, 2]] : 128 :['X0Z6', 'X1Z7', 'X2Z8', 'X3X4Z5Z8', 'Z0X6Z7Z8', 'Z1Z3X5Z6X7Z8', 'Z2Z3Z4Y5Z6Y8'] : False\n",
+      "6 :: 3568: [[9,2, 2]] : 64 :['X1Z6', 'X2Z7', 'X3X4', 'Y3Z4Y5Z8', 'Z0Z3Z4X8', 'X0Z1X3Z5X6Z7', 'Z2X3Z5Z6X7Z8'] : False\n",
+      "6 :: 3575: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'X2Z7', 'Z3X5Z7Z8', 'Z2Y3Y4X7', 'Z1Y3X4Z5Y6Z7', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 3585: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z6', 'X3X4Z5Z7', 'Z2Z4Y5Y6', 'Z1Y3Y7Z8', 'Z0Y3X5Z6Z7Y8'] : True\n",
+      "6 :: 3587: [[9,2, 2]] : 64 :['Z0Z8', 'X2X7', 'X4X6', 'X1X3X5X7', 'Z3Z4Z5Z6', 'X0X5X6X8', 'Z1Z2Z3Z5Z7Z8'] : True\n",
+      "6 :: 3604: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'X2Z7', 'Z3X5Z7Z8', 'Z1Y3Y5X6', 'Z2Y3Y4X7', 'Z0Z4Z5Z6Z7X8'] : True\n",
+      "6 :: 3623: [[9,2, 2]] : 64 :['X1Z8', 'X3Z7', 'X4Z6', 'X0X2Z7Z8', 'Z4Y5Y6Z8', 'Z0Z1Z5X8', 'Z2Z3X5Z6X7Z8'] : True\n",
+      "6 :: 5065: [[9,2, 2]] : 16 :['X0Z7', 'X1Z8', 'X2X3Z4Z5', 'Y2Y4Z7Z8', 'Z3X5X6Z7', 'Z0Z2X5Z6X7Z8', 'Z1Z3Z4Y5Z6Y8'] : False\n",
+      "6 :: 5118: [[9,2, 2]] : 32 :['X1Z8', 'X3Z7', 'X0X2Z7Z8', 'X0X4Z6Z8', 'Z4Y5Y6Z8', 'Z0Z1Z5X8', 'Z2Z3X5Z6X7Z8'] : False\n",
+      "6 :: 5124: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X4Z5Z7Z8', 'X2Y3Y4X5', 'Z2Z3X6Z8', 'Z1Y2Z4Z6Y7Z8', 'Z0X2X3Z4Z6X8'] : False\n",
+      "6 :: 5302: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'X3X4Z6Z7', 'Y2X3X6Y7', 'Z1Z2X3X5Z7Z8', 'Z0X3Z4Z5X6X8'] : False\n",
+      "6 :: 5469: [[9,2, 2]] : 32 :['X0Z8', 'X1X2', 'Z3X5Z7Z8', 'Z4X6Z7Z8', 'X1Y3Y4Z5Z6Z7', 'Y1Z2Z5Z6Y7Z8', 'Z0Z1Z2X3Z6X8'] : False\n",
+      "6 :: 5493: [[9,2, 2]] : 64 :['X0Z8', 'X1X2', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z4X6Z7Z8', 'Y1Z2Z5Z6Y7Z8', 'Z0Z1Z2X3Z6X8'] : False\n",
+      "6 :: 5515: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Z2X5Z7Z8', 'Z1X6Z7Z8', 'Z0Z4Y7Y8', 'Z3Y4Z5Z6Y7Z8'] : False\n",
+      "6 :: 5521: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Z2X5Z7Z8', 'Z1X6Z7Z8', 'X3Y5Z6Y7', 'Z0Z3X4Z5Z6X8'] : False\n",
+      "6 :: 5524: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Z2X5Z7Z8', 'Z1X6Z7Z8', 'Z3Y4Z5Z6Y7Z8', 'Z0X2Z3Z6Z7X8'] : False\n",
+      "6 :: 5555: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'Z2Y3Y4Z7', 'Z2X5Z7Z8', 'Z1X6Z7Z8', 'X2Z3Z4Z6X7Z8', 'Z0Z3X4Z5Z6X8'] : False\n",
+      "6 :: 5563: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'X2X4Z5Z7', 'Z2X5Z7Z8', 'Z1X6Z7Z8', 'X2Z3Z4Z6X7Z8', 'Z0Y2X3Y4Z6X8'] : False\n",
+      "6 :: 5846: [[9,2, 2]] : 512 :['Z0Z8', 'X1X6', 'Z2Z7', 'Z3Z4', 'X0X3X4X8', 'Z1Z3Z5Z6Z7Z8', 'X2X3X4X5X6X7'] : True\n",
+      "6 :: 6075: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Z3Y4Z7', 'Z3X5X6Z7', 'Z1X2X5Z6X7Z8', 'Z0X2Z4Y5Z6Y8'] : False\n",
+      "6 :: 6445: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X4Z5Z8', 'X2Y3Y6Z8', 'Z2Z4Z6X7', 'Z1X3Z4X5Z6Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 6446: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X2X3Z5Z7', 'X4Z5Z7Z8', 'Z2Z4X5X6', 'Z1Y2Z4Z6Y7Z8', 'Z0Z3Y5Z6Z7Y8'] : False\n",
+      "6 :: 6452: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X4Z5Z8', 'Z1Y4Y5Z7', 'X2Y3Y6Z8', 'Z2Z4Z6X7', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 6468: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Z3Y4Z7', 'Z2Y3Y5Z8', 'Z2X4X6Z8', 'Z0Z5Z6X8', 'Z1Y2Y3Z5Z6X7'] : False\n",
+      "6 :: 6535: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'X2X4Z5Z7', 'Z2X5Z7Z8', 'Z1Z2Y3Y4X6Z8', 'X2Z3Z4Z6X7Z8', 'Z0Y2X3Y4Z6X8'] : False\n",
+      "6 :: 6550: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z3X6Z7Z8', 'Z1Y2X5Z6Y7Z8', 'Z0X2Z3Z4Y7Y8'] : False\n",
+      "6 :: 6569: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z3X6Z7Z8', 'Z2Z4Z6X7', 'Z0Z1Z3X5X7X8'] : False\n",
+      "6 :: 6588: [[9,2, 2]] : 16 :['X0Z8', 'X1Z2', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'Z4Z5X6Z8', 'Z1Y2Z3Z4Y7Z8', 'Z0Z1X2Y3Y4X8'] : False\n",
+      "6 :: 6593: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Z2Y3Y4Z7', 'Z2X5Z7Z8', 'Z0Z4Y7Y8', 'Z1X2X4Z5X6Z8', 'X2Z3Z4Z6X7Z8'] : False\n",
+      "6 :: 6616: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z3X6Z7Z8', 'Y3X4Z5Y6', 'Z2Z4Z6X7', 'Z0Z2Y3Y8', 'Z1X2Z4X5Z7Z8'] : False\n",
+      "6 :: 6766: [[9,2, 2]] : 32 :['X0Z4', 'X1Z5', 'X2X3Z6Z7', 'Z1X2X5Z6', 'X2Z3X7Z8', 'Z0Z2X4X6Z7Z8', 'Y2Z3Z4Z5Z6Y8'] : False\n",
+      "6 :: 6771: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'X3X4Z6Z7', 'Z1X5Z6Z7', 'Y2X3X6Y7', 'Z0X3Z4Z5X6X8'] : False\n",
+      "6 :: 6775: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z2Z3X4Z8', 'Z1X5Z6Z7', 'Y3Y4Z5X6', 'Z0Z3Z4Z5Y7Y8'] : False\n",
+      "6 :: 6872: [[9,2, 2]] : 192 :['X0Z6', 'X1Z7', 'X2X3Z4Z5', 'Z2Z3Y4Y5', 'Z1Z6X7Z8', 'Z0Y2Y4Z5X6Z7', 'Z3X4Z5Z6Z7X8'] : False\n",
+      "6 :: 6895: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Z3Y4Z7', 'Z2Y3Y5Z8', 'Z3X5X6Z7', 'Z1Y2Y3Z5Z6X7', 'Z0Y2Y3Z4Z6X8'] : False\n",
+      "6 :: 6901: [[9,2, 2]] : 16 :['X0Z8', 'X1X2', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'X1X3Z5X6', 'Z0Z4X7X8', 'Y1Z2Z5Z6Y7Z8'] : False\n",
+      "6 :: 6928: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Z3X4X5Z6', 'Z2Y4Y6Z7', 'Z1Z4Z5X7', 'Y2Y3X4Z5Z7Z8', 'Z0Z2X3Z4Z6X8'] : False\n",
+      "6 :: 7063: [[9,2, 2]] : 128 :['X0Z8', 'X3X4', 'X1X2Z6Z7', 'Y3Z4Y5Z8', 'Z1Z2Y6Y7', 'Z1X3Z5X6Z7Z8', 'Z0X1Z3Z4Z6X8'] : False\n",
+      "6 :: 7076: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'X2X4Z5Z7', 'Y3Y4X5Z8', 'Z1X6Z7Z8', 'Z2X3Z5Z6X7Z8', 'Z0X2Z3Z6Z7X8'] : False\n",
+      "6 :: 7088: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X3Z5Z6', 'Z1Y2Y5Z8', 'Z3X4X6Z8', 'Y4Z6Y7Z8', 'Z0Z2Z3Y4Z7Y8'] : False\n",
+      "6 :: 7132: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'X2X4Z5Z7', 'Z2X5Z7Z8', 'X3Y5Z6Y7', 'Z0Z1X2Z3Y6Y8'] : False\n",
+      "6 :: 7166: [[9,2, 2]] : 64 :['X0Z8', 'X1Z5', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'Z3Z4Y6Y7', 'Z1Y2Y3Z4X5Z7', 'Z0Y2Z3Z5Y6X8'] : False\n",
+      "6 :: 7167: [[9,2, 2]] : 64 :['X0Z8', 'Z1X2', 'X3X4Z6Z7', 'Y3Z5Y6Z8', 'Y4Z5Y7Z8', 'X1Z2X3X5Z7Z8', 'Z0X1Z2Z3Z4X8'] : False\n",
+      "6 :: 7168: [[9,2, 2]] : 64 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'X3X4Z6Z7', 'Z3Z4Y6Y7', 'Z1Z2X3X5Z7Z8', 'Z0Z2Z4Z5Y6Y8'] : False\n",
+      "6 :: 7170: [[9,2, 2]] : 64 :['X0Z8', 'X1Z5', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'Z3Z4Y6Y7', 'Z1Y2Y3Z4X5Z7', 'Z0Z2Z4Z5Y6Y8'] : False\n",
+      "6 :: 7203: [[9,2, 2]] : 256 :['X1Z8', 'Z2X3', 'X0X2Z3Z8', 'X0X4Z6Z8', 'X0X5Z7Z8', 'Z4Z5X6X7', 'Z0Z1Z4Y6Z7Y8'] : False\n",
+      "6 :: 7208: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Z2Z3X6Z8', 'Z1Y2Z4Z6Y7Z8', 'Z0X2X3Z4Z6X8'] : False\n",
+      "6 :: 7252: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Y3Y4Z5Z7', 'Z2X6Z7Z8', 'Z3Z6X7Z8', 'Z1X2Z4X5Z6Z8', 'Z0Z2X3Z4Z6X8'] : False\n",
+      "6 :: 7451: [[9,2, 2]] : 32 :['X0Z8', 'X1X2', 'Z3X5Z7Z8', 'Z4X6Z7Z8', 'Z0Z4X7X8', 'X1Y3Y4Z5Z6Z7', 'Z1Z2X3Z4Z6X7'] : False\n",
+      "6 :: 7456: [[9,2, 2]] : 64 :['X2Z7', 'X4Z6', 'X0X1Z7Z8', 'X0X3Z5Z8', 'Z3Z4X5X6', 'Z0Z5Z6X8', 'Z1Z2Z3X5X7Z8'] : False\n",
+      "6 :: 7458: [[9,2, 2]] : 64 :['X1Z8', 'X4Z7', 'X0X2Z5Z8', 'X0X3Z6Z8', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : False\n",
+      "6 :: 7463: [[9,2, 2]] : 128 :['Z0Z8', 'X2X7', 'X1X3X5X7', 'X1X4X6X7', 'Z3Z4Z5Z6', 'X0X5X6X8', 'Z1Z2Z3Z5Z7Z8'] : False\n",
+      "6 :: 7481: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Z3X5Z7Z8', 'Y2X3X4Y5', 'Z2Z4X6Z8', 'Z1Y2Y3Z4Z6X7', 'Z0X2Z3Z4Z5X8'] : False\n",
+      "6 :: 7535: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Z2X3X4Z7', 'Y3Z4Y5Z7', 'Z2Z3X7Z8', 'Z1X3Z5X6Z7Z8', 'Z0Y2Y4Z5Z6X8'] : False\n",
+      "6 :: 7549: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Y2X4Y5Z8', 'Z1Z2X5X6', 'Z0Z4Y7Y8', 'X2Z3Z4Z6X7Z8'] : False\n",
+      "6 :: 7643: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X3X4Z5Z7', 'X2Z4X5Z8', 'Z3X5X6Z7', 'Z1Y2Y3Z5Z6X7', 'Z0Y2Y3Z4Z6X8'] : False\n",
+      "6 :: 7644: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'X2Z4X5Z8', 'Z3X5X6Z7', 'Z1Y2Z3X4Z6Y7', 'Z0X3X4Z6Z7X8'] : False\n",
+      "6 :: 7672: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X3X4Z5Z7', 'X2Z4X5Z8', 'Z3X5X6Z7', 'Z0Z5Z6X8', 'Z1Y2Y3Z5Z6X7'] : False\n",
+      "6 :: 7810: [[9,2, 2]] : 128 :['X1Z8', 'X2Z5', 'X0X3Z6Z8', 'X0X4Z7Z8', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : False\n",
+      "6 :: 7828: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z3X6Z7Z8', 'Z1X2Z4X5Z7Z8', 'Y2Y4Z5Z6X7Z8', 'Z0Y2Y3X4Z5X8'] : False\n",
+      "6 :: 7834: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Y2Y3Z4Z6', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Z3Z4Y6Y7', 'Z0Z2Z4Z5Y6Y8'] : False\n",
+      "6 :: 7836: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'Y2Y3Z4Z6', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Z3Z4Y6Y7', 'Z0Y2Z3Z5Y6X8'] : False\n",
+      "6 :: 7840: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Z2X4X6Z8', 'Z3X5X6Z7', 'Z0Z5Z6X8', 'Z1Y2Z3X4Z6Y7'] : False\n",
+      "6 :: 7843: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'X2X3Z4Z5', 'Z2Z3Y4Y5', 'Z2Z3X7Z8', 'Z0Z6Z7X8', 'Z1X2Z4X6Z7Z8'] : False\n",
+      "6 :: 7857: [[9,2, 2]] : 32 :['X0Z8', 'Z1X2', 'X4X5Z6Z8', 'Z3Y5Y6Z7', 'Y4Z5Y7Z8', 'Z0Z3Z4X8', 'X1Z2X3X4Z6Z7'] : False\n",
+      "6 :: 7858: [[9,2, 2]] : 32 :['X0Z8', 'X2Z7', 'X3X4Z5Z6', 'X3Z4X6Z8', 'Z0Z3Z4X8', 'X1Y3Y5Z6Z7Z8', 'Z1Z2X3Z5X7Z8'] : False\n",
+      "6 :: 7888: [[9,2, 2]] : 16 :['X0Z8', 'X1X2', 'Z3X4Z6Z8', 'X1Y3Z4Y5', 'Z4X6Z7Z8', 'Z1Z2X3Z4Z6X7', 'Z0Y1Z2Z5Y6X8'] : False\n",
+      "6 :: 7902: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'X2Z4X5Z8', 'Z3X5X6Z7', 'Z0Z5Z6X8', 'Z1Y2Z3X4Z6Y7'] : False\n",
+      "6 :: 7952: [[9,2, 2]] : 64 :['X1Z8', 'Z2X3', 'X0X2Z3Z8', 'X0Y4Y6Z7', 'Z4X5X6Z8', 'X0X4Z5X7', 'Y0Z1Y4Z5Z6X8'] : False\n",
+      "6 :: 7955: [[9,2, 2]] : 32 :['X0Z7', 'X1Z8', 'X2X3Z4Z5', 'Y2Y4Z7Z8', 'X2Z4X6Z8', 'Z0Z2X5Z6X7Z8', 'Z1Z3Z4Y5Z6Y8'] : False\n",
+      "6 :: 8011: [[9,2, 2]] : 16 :['X0Z5', 'X1Z6', 'X2Z3X4Z7', 'Z1Z5X6Z8', 'X3Z4Z6X8', 'Z0Y2Y3X5Z6Z8', 'Z2Y3Z4Z5Y7Z8'] : False\n",
+      "6 :: 8023: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X3X4Z6Z7', 'Z1Y2Y5Z8', 'Y3Y6Z7Z8', 'X2Z4Z5Z6X7Z8', 'Z0Y2Z3Z4Z5Y8'] : False\n",
+      "6 :: 8024: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Y2X4Y5Z8', 'Z1X6Z7Z8', 'X2Z3Z4Z6X7Z8', 'Z0Z3X4Z5Z6X8'] : False\n",
+      "6 :: 8041: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'Z1Z3Z4X5X6X7', 'Z0Z1Y4Y5X6X8'] : False\n",
+      "6 :: 8044: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X4Z5Z8', 'Z3X6Z7Z8', 'Z2Z4Z6X7', 'Z0Y2Y6X8', 'Z1X3Z4X5Z6Z8'] : False\n",
+      "6 :: 8106: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Z3X5Z6Z8', 'X2X3Y4Y5', 'Z0X2X3X8', 'Y2X3Z4Y6Z7Z8', 'Z1Z2X3Z5X7Z8'] : False\n",
+      "6 :: 8218: [[9,2, 2]] : 128 :['X0Z8', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z8', 'X3Z4X6Z8', 'Z1Z2X3Z5X7Z8', 'Z0X1Z3Z4Z7X8'] : False\n",
+      "6 :: 8240: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Y3X4Z5Y6', 'Z0Z2Y3Y8', 'Z1X2Z4X5Z7Z8', 'Y2Y4Z5Z6X7Z8'] : False\n",
+      "6 :: 8302: [[9,2, 2]] : 128 :['X0Z8', 'Z1X2', 'X3X4Z6Z7', 'X3X5Z7Z8', 'Y3Z5Y6Z8', 'Y4Z5Y7Z8', 'Z0X1Z2Z3Z4X8'] : False\n",
+      "6 :: 8316: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Y3Y4Z5Z7', 'Y2X3Z4Y6', 'Z3Z6X7Z8', 'Z0X2X6X8', 'Z1X2Z4X5Z6Z8'] : False\n",
+      "6 :: 8321: [[9,2, 2]] : 64 :['X0Z8', 'Z1X2', 'X3X4Z6Z7', 'Y3Z5Y6Z8', 'Y4Z5Y7Z8', 'Z0Z3Z4X8', 'X1Z2X3X5Z7Z8'] : False\n",
+      "6 :: 8325: [[9,2, 2]] : 16 :['X1Z8', 'X3Z4', 'X0X2Z4Z7', 'Y5Y6Z7Z8', 'Z0Z5X7Z8', 'Z1Z6Z7X8', 'Z2Z3X4X5Z6Z7'] : False\n",
+      "6 :: 8373: [[9,2, 2]] : 16 :['X0Z8', 'Z1X2', 'X3X4Z6Z7', 'X3X5Z7Z8', 'Y4Z5Y7Z8', 'Z0Z3Z4X8', 'X1Z2Y3Z5Y6Z8'] : False\n",
+      "6 :: 8376: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z8', 'Z3Y4Y5Z6', 'Z5X6Z7Z8', 'Z1Z6X7Z8', 'Z0Z2Z4X5Z7X8'] : False\n",
+      "6 :: 8424: [[9,2, 2]] : 16 :['X0Z8', 'X1X2', 'Z3X4Z6Z8', 'Z3Z4X5X6', 'Z0Z4X7X8', 'X1X3Z4Z5Z7Z8', 'Y1Z2Z5Z6Y7Z8'] : False\n",
+      "6 :: 8509: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Z2Y3Y4Z7', 'Y2X4Y5Z8', 'Z1X6Z7Z8', 'X3Y5Z6Y7', 'Z0Z3X4Z5Z6X8'] : False\n",
+      "6 :: 8513: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1X3X5Z7', 'Z2X6Z7Z8', 'X2Y3Z4Y7', 'X2Z3X4Z5Z6Z8', 'Z0Z2X3Z4Z6X8'] : False\n",
+      "6 :: 8561: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Z2Y3Y4Z7', 'Y2X4Y5Z8', 'Z1Z2X5X6', 'Z2X3Z5Z6X7Z8', 'Z0X2Z3Z6Z7X8'] : False\n",
+      "6 :: 8605: [[9,2, 2]] : 128 :['X3Z6', 'X4X5', 'X0X1Z7Z8', 'X0X2Z6Z7', 'Z0Z4Z5X7', 'Z1Z4Z5X8', 'Z2Z3X4X6Z7Z8'] : False\n",
+      "6 :: 8606: [[9,2, 2]] : 16 :['X0Z8', 'X1X2', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'X1Y3Z4Y5', 'Z0Z4X7X8', 'Y1Z2Z4Z5Y6X7'] : False\n",
+      "6 :: 8634: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'X2X4Z5Z7', 'Z1X6Z7Z8', 'X2Z3Z4Z6X7Z8', 'Z0Y2Z3X5Z6Y8'] : False\n",
+      "6 :: 8646: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Z3X4Z7Z8', 'X2X3Y4Y5', 'Z2Z5X6Z8', 'Z0X2X3X8', 'Z1Y2Z4Z6Y7Z8'] : False\n",
+      "6 :: 8661: [[9,2, 2]] : 16 :['X0Z8', 'Z1X2', 'X3X5Z7Z8', 'Y3X4Y5X6', 'Z4Y5Z6Y7', 'Z0Z3Z4X8', 'X1Z2X3X4Z6Z7'] : False\n",
+      "6 :: 8665: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1Y4Y5Z7', 'X2Y3Y6Z8', 'Z2Z4Z6X7', 'X3X4Z5Z6Z7Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 8674: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Y2X4Y5Z8', 'Z1X6Z7Z8', 'Z0Z4Y7Y8', 'X2Z3Z4Z6X7Z8'] : True\n",
+      "6 :: 8713: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z5Z6', 'Z3X5Z7Z8', 'Z2Z4X6Z8', 'Z1Z4Z5X7', 'Z0Z2X3Y4Z7Y8'] : False\n",
+      "6 :: 8726: [[9,2, 2]] : 16 :['X0Z4', 'X1Z5', 'Z0X2X4Z6', 'Z1X3X5Z7', 'Z2X3X6Z8', 'Y3Z6Y7Z8', 'Z2Y3Z4Z5Z7Y8'] : False\n",
+      "6 :: 8732: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Y2X3X6Y7', 'Z0X3Z4Z5X6X8'] : False\n",
+      "6 :: 8778: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z1Y4Y5Z7', 'Z3X6Z7Z8', 'Y2Y4Z5Z6X7Z8', 'Z0Y2Y3X4Z5X8'] : False\n",
+      "6 :: 8830: [[9,2, 2]] : 16 :['X1Z8', 'X3Z4', 'X0X2Z4Z7', 'X0Y5Y6Z8', 'Z0Z5X7Z8', 'Z1Z6Z7X8', 'X0Z2Z3X4X5Z6'] : False\n",
+      "6 :: 8845: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'X2X3Z4Z5', 'Y2Y4Z5Z7', 'Z2Z3X7Z8', 'Z1Z3Z4X5X6Z8', 'Z0X2Z3X5Z6X8'] : False\n",
+      "6 :: 8852: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z0Y2Y6X8', 'Z1X2Z3Z4X5X6', 'Z1Y2X5Z6Y7Z8'] : False\n",
+      "6 :: 8867: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Z2Y3Y4Z7', 'Y2X4Y5Z8', 'Z1X6Z7Z8', 'Z2X3Z5Z6X7Z8', 'Z0X2Z3Z6Z7X8'] : False\n",
+      "6 :: 8887: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Z2Y3Y4Z5', 'Z5X6Z7Z8', 'Z1Z6X7Z8', 'X2Z3Z4X5Z6Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 8934: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z1Y4Y5Z7', 'Y3X4Z5Y6', 'Z0Y2Y6X8', 'Y2Y4Z5Z6X7Z8'] : False\n",
+      "6 :: 8938: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X3X4Z6Z7', 'Z1X5Z6Z7', 'Y2Y4Z5X6', 'Z0Y3Y4X8', 'X2Z3Z5Z6X7Z8'] : False\n",
+      "6 :: 8961: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Z2X5Z7Z8', 'Z0Z4Y7Y8', 'Z1X2X4Z5X6Z8', 'Z3Y4Z5Z6Y7Z8'] : False\n",
+      "6 :: 8980: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2Y3Y6Z8', 'Z2Z4Z6X7', 'Z0Z2Y3Y8', 'X3X4Z5Z6Z7Z8', 'Z1X2Z4X5Z7Z8'] : False\n",
+      "6 :: 9019: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X4X5Z6Z7', 'Z2Z5X6Z8', 'Y2Y3Y4Y6', 'Z0X2X3X8', 'Z1Y2Z4Z6Y7Z8'] : False\n",
+      "6 :: 9024: [[9,2, 2]] : 128 :['X0Z8', 'Z1X2', 'X3X4Z6Z7', 'Z3Y5Y6Z7', 'Z4Y5Z6Y7', 'Z0Z3Z4X8', 'X1Z2X3X5Z7Z8'] : False\n",
+      "6 :: 9037: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'X2X3X4Z8', 'Z3Z4X5Z8', 'Z0Y4Z6Y8', 'Y2Y3Z5X6Z7Z8', 'Z1Y2Y4Z5Z6X7'] : False\n",
+      "6 :: 9040: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Z3Y4Z7', 'Z2X4X6Z8', 'Z0Z5Z6X8', 'Z1X2X5Z6X7Z8'] : False\n",
+      "6 :: 9047: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1X5Z6Z7', 'Y2Y4Z5X7', 'Z2Z3Y6Y7', 'Z0X3Z6X8', 'Y2Y3X4Z6Z7Z8'] : False\n",
+      "6 :: 9051: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1Z3Y4Y5', 'Z3Z6X7Z8', 'X2Y3Z4Y7', 'Z0Y2Z7Y8', 'Y2Z3X4Z5Y6Z7'] : False\n",
+      "6 :: 9060: [[9,2, 2]] : 64 :['X0Z8', 'X1Z6', 'X2X3Z4Z5', 'X2Z3X5Z7', 'Z2Z3X7Z8', 'Z1X2Z4X6Z7Z8', 'Z0Y2Y4Z5Z6X8'] : False\n",
+      "6 :: 9073: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Y2Y4Z5Z7', 'Y3Z4Y5Z7', 'Z2Z3X7Z8', 'Z0Z6Z7X8', 'Z1X3Z5X6Z7Z8'] : False\n",
+      "6 :: 9085: [[9,2, 2]] : 32 :['X0Z8', 'Z1X2', 'X4X5Z6Z8', 'Z3Y5Y6Z7', 'Y4Z5Y7Z8', 'X1Z2X3X4Z6Z7', 'Z0X1Z2Z3Z4X8'] : False\n",
+      "6 :: 9090: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Z3X4Z7Z8', 'Z3X5Z6Z8', 'Y2Y3Y4Y6', 'Z0Y4Y5X8', 'Z1Z2X3Z5X7Z8'] : False\n",
+      "6 :: 9105: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Y2Y3Z4Z6', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'X2Y6Y7Z8', 'Z0Y2Z3Z5Y6X8'] : False\n",
+      "6 :: 9107: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z1Y4Y5Z7', 'Z3X6Z7Z8', 'Z2Z4Z6X7', 'Z0Y2Y3X4Z5X8'] : False\n",
+      "6 :: 9108: [[9,2, 2]] : 32 :['X1Z5', 'Z2X3', 'X0X2Z3Z8', 'Z5X6Z7Z8', 'Z4Z6X7Z8', 'Y0Y4Z6X8', 'X0Z1X4X5Z6Z7'] : False\n",
+      "6 :: 9109: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'Z2X3Z6Z8', 'Z1X5Z6Z7', 'Y2X4Y6X7', 'Z0Y4Z5Y6Z7X8'] : True\n",
+      "6 :: 9138: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'X2X4Z5Z7', 'Y3Y4X5Z8', 'Z1X6Z7Z8', 'X3Y5Z6Y7', 'Z0Y2X3Y4Z6X8'] : False\n",
+      "6 :: 9143: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X4Z5Z8', 'Z3X6Z7Z8', 'Z2Z4Z6X7', 'Z0Z2Y3Y8', 'Z1X3Z4X5Z6Z8'] : False\n",
+      "6 :: 9144: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X4Z5Z7Z8', 'X2Y3Y4X5', 'Z2Z3X6Z8', 'Z0Y4Z6Y8', 'Z1Y2Z4Z6Y7Z8'] : False\n",
+      "6 :: 9152: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2Z3Z4Z8', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Z3Z4Y6Y7', 'Z0X3Z4Z5X6X8'] : False\n",
+      "6 :: 9159: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1X3X5Z7', 'Z2X6Z7Z8', 'X2Y3Z4Y7', 'Z0Y2Z7Y8', 'X2Z3X4Z5Z6Z8'] : False\n",
+      "6 :: 9175: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z1X3X5Z7', 'Z3Z6X7Z8', 'X2Y3Z4Y7', 'Z0Y2Z7Y8', 'Y2Z3X4Z5Y6Z7'] : False\n",
+      "6 :: 9183: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'Z1X3X5Z7', 'Z3Z6X7Z8', 'X2Y3Z4Y7', 'Z0X2X6X8', 'Y2Z3X4Z5Y6Z7'] : False\n",
+      "6 :: 9184: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Z3X5Z6Z8', 'X2X3Y4Y5', 'Z1X3X6X7', 'Z0X2X3X8', 'Y2X3Z4Y6Z7Z8'] : False\n",
+      "6 :: 9186: [[9,2, 2]] : 32 :['X0Z5', 'X1Z6', 'X2Z3X4Z7', 'Z0X5Z6Z7', 'Z1Z5X6Z8', 'X3Z4Z6X8', 'Z2Y3Z4Z5Y7Z8'] : False\n",
+      "6 :: 9201: [[9,2, 2]] : 64 :['X0Z8', 'X2Z7', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'Z0Z3Z4X8', 'X1Y3Y5Z6Z7Z8', 'Y1Z2X3Z5Y7Z8'] : False\n",
+      "6 :: 9205: [[9,2, 2]] : 384 :['X0X1', 'Z2X3', 'X0X2Z3Z8', 'X4X5Z6Z7', 'Y4Y6Z7Z8', 'X4Z5X7Z8', 'Z0Z1Y4Z5Z6Y8'] : False\n",
+      "6 :: 9207: [[9,2, 2]] : 384 :['X1Z8', 'Z2X3', 'X0X2Z3Z8', 'X4X5Z6Z7', 'Y4Y6Z7Z8', 'X4Z5X7Z8', 'Z0Z1Y4Z5Z6Y8'] : False\n",
+      "6 :: 9208: [[9,2, 2]] : 64 :['X0Z7', 'X1Z6', 'X2X3X4Z6', 'Z3Z4X5Z8', 'Z0Z2Z4X7', 'Z2X3Z4X8', 'Z1X2Y3Z5Y6Z7'] : False\n",
+      "6 :: 9217: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Z2Y3Y4Z7', 'Y2X4Y5Z8', 'Z1X6Z7Z8', 'Z0Z4Y7Y8', 'Z2X3Z5Z6X7Z8'] : True\n",
+      "6 :: 9223: [[9,2, 2]] : 16 :['X2Z7', 'X3X4', 'X0X1Z6Z8', 'Y3Z4Y5Z8', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'Z1X3Z5X6Z7Z8'] : True\n",
+      "6 :: 9226: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'Z2Y3Y4Z7', 'Y2X4Y5Z8', 'Z1Z2X5X6', 'Z0Z4Y7Y8', 'Z2X3Z5Z6X7Z8'] : True\n",
+      "6 :: 11045: [[9,2, 2]] : 128 :['X0Z8', 'X1Z7', 'X2Z6', 'Z2X4X6Z8', 'Z1Y3Y7Z8', 'X3Z4X5Z6Z7Z8', 'Z0Y3Y4Z5Z7X8'] : False\n",
+      "6 :: 11049: [[9,2, 2]] : 256 :['X1Z8', 'X3Z6', 'X4X5', 'X0X2Z6Z7', 'Z0Z4Z5X7', 'Z1Z4Z5X8', 'Z2Z3X4X6Z7Z8'] : True\n",
+      "6 :: 11055: [[9,2, 2]] : 128 :['X0Z7', 'X1Z8', 'X3Z4', 'Z0X5Y6Y7', 'Z1Y5X6Y8', 'Y2Z3Y4X5Z6Z7', 'Z2Z3X4Z5X6Z8'] : True\n",
+      "6 :: 11247: [[9,2, 2]] : 256 :['X1Z8', 'X3Z6', 'X4Z7', 'X0X2Z5Z8', 'Z0Z1Z5X8', 'X0Z2Z3X5X6Z7', 'Z2Z4X5Z6X7Z8'] : False\n",
+      "6 :: 11269: [[9,2, 2]] : 256 :['X1Z6', 'X2Z7', 'X3X4', 'X0Y3Z4Y5', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'Z1X3Z5X6Z7Z8'] : True\n",
+      "6 :: 11270: [[9,2, 2]] : 256 :['X0Z7', 'X3Z6', 'X4X5', 'X1X2Z6Z8', 'Z1Z4Z5X8', 'Z0Y1X7Y8', 'Z2Z3X4X6Z7Z8'] : False\n",
+      "6 :: 11301: [[9,2, 2]] : 128 :['X0Z8', 'X2Z7', 'X3X4', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'X1Y3Z4Y5Z6Z8', 'Y1X3Z5Y6Z7Z8'] : True\n",
+      "6 :: 11789: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z4', 'Z3Y5Y6Z7', 'Z2X4X5Z6Z7Z8', 'Z1X3Z5Z6X7Z8', 'Z0Z2Y3Y4Z6X8'] : False\n",
+      "6 :: 11795: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z5', 'X3X4Z6Z7', 'Z2X3X5Z6', 'Z1Y3Z4X6Y7Z8', 'Z0Z4Z5X6Z7X8'] : False\n",
+      "6 :: 11802: [[9,2, 2]] : 128 :['X1Z8', 'Z2X3', 'X5Z7', 'X0X2Z3Z8', 'X0X4Z6Z8', 'Z4Z5X6X7', 'Z0Z1Z4Y6Z7Y8'] : True\n",
+      "6 :: 11832: [[9,2, 2]] : 64 :['X0Z8', 'X2Z7', 'X3X4', 'Y3Z4Y5Z8', 'Z1Z2Y6Y7', 'Z1X3Z5X6Z7Z8', 'Z0X1Z3Z4Z6X8'] : True\n",
+      "6 :: 11866: [[9,2, 2]] : 64 :['X0Z5', 'X1Z6', 'X2Z7', 'Z0X3X5Z6', 'Z1Z3X4X6Z7Z8', 'Z2X3Z4Z6X7Z8', 'Y3Y4Z5Z6Z7X8'] : False\n",
+      "6 :: 11932: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z4', 'Z2X3X4Z6', 'Z3Y5Y6Z7', 'Z1X3Z5Z6X7Z8', 'Z0Y3Z4X5Z7Y8'] : False\n",
+      "6 :: 11934: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X2Z4', 'Z2X3X4Z6', 'Y3Z5Y6Z8', 'Z1Y5Z6Y7', 'Z0Y3Z4X5Z7Y8'] : True\n",
+      "6 :: 11935: [[9,2, 2]] : 64 :['X1Z7', 'X2Z6', 'Z3X4', 'X0X3Z4Z8', 'X0Z1Y5Y7', 'Z0Z5Z6X8', 'X0Z2X5X6Z7Z8'] : True\n",
+      "6 :: 13103: [[9,2, 2]] : 512 :['X0Z8', 'X2Z7', 'X3Z5', 'X4Z6', 'X1Z3Z4X5X6Z7', 'Y1Z2Z3X5Y7Z8', 'Z0X1Z5Z6Z7X8'] : False\n",
+      "6 :: 13110: [[9,2, 2]] : 512 :['X1Z8', 'Z2X3', 'X4Z6', 'X5Z7', 'X0X2Z3Z8', 'X0Z4Z5X6X7Z8', 'Y0Z1Z4Y6Z7X8'] : True\n",
+      "6 :: 13113: [[9,2, 2]] : 512 :['X0Z8', 'X1Z6', 'X2Z7', 'X3X4', 'Z1Z2Y6Y7', 'Z0X3Y5Y8', 'Z1Z3Z4X5X6Z7'] : True\n",
+      "6 :: 13278: [[9,2, 2]] : 256 :['X0Z8', 'X1Z6', 'X2Z7', 'X3X4', 'Y3Z4Y5Z8', 'Z2X3Z5Z6X7Z8', 'Z0Z1X5X6Z7X8'] : True\n",
+      "6 :: 13279: [[9,2, 2]] : 256 :['X1Z8', 'X2Z5', 'X3Z6', 'X4Z7', 'Z0Z1Z5X8', 'X0Z2Z3X5X6Z7', 'Z2Z4X5Z6X7Z8'] : True\n",
+      "6 :: 13284: [[9,2, 2]] : 256 :['X0Z8', 'X1Z7', 'X2Z6', 'Z3X4', 'Z0Z5Z6X8', 'Z2X3Z4X5X6Z7', 'Z1X3Z4Y5Y7Z8'] : True\n",
+      "6 :: 14964: [[9,2, 2]] : 384 :['X0Z8', 'X1Z8', 'X3Z7', 'X4Z6', 'X2Z4Y5Y6Z7Z8', 'Y2Z3X5Z6Y7Z8', 'Z0Z1X2Z5Z7X8'] : False\n",
+      "6 :: 14987: [[9,2, 2]] : 384 :['X1Z8', 'X2Z8', 'X4Z7', 'X5Z6', 'X0X3Z7Z8', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : True\n",
+      "6 :: 15000: [[9,2, 2]] : 384 :['X0Z7', 'X1Z8', 'X2Z4', 'X3Z4', 'Z0X5Y6Y7', 'Z1Z6Z7X8', 'Z2Z3X4Z5X6Z8'] : True\n",
+      "6 :: 15027: [[9,2, 2]] : 384 :['X0Z7', 'X1Z8', 'X2Z4', 'X3Z4', 'Z0X5Y6Y7', 'Z1Y5X6Y8', 'Z2Z3X4Z5X6Z8'] : True\n",
+      "6 :: 15174: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z6', 'Y4Y6Z7Z8', 'Z2Y3X5Z6Y7Z8', 'Z0Z1Y3Z4Z5Y8'] : False\n",
+      "6 :: 15184: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2Z7', 'Y3Y5Z7Z8', 'Z3Z4X5X6', 'Z2Z5Z6X7', 'Z0Z1Z3Y4Z6Y8'] : True\n",
+      "6 :: 15220: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X3Z7', 'X2X4Z6Z7', 'Z4Y5Y6Z8', 'Z2Z3X5Z6X7Z8', 'Z0Z1X2Z5Z7X8'] : False\n",
+      "6 :: 15223: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2Z5', 'Z3X4X6Z8', 'Z4Z5Z6X7', 'Z2X3X5Z6Z7Z8', 'Z0Z1Z3Y4Z7Y8'] : False\n",
+      "6 :: 15230: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z7', 'Y4Y5Z6Z8', 'Z2Y3Z5Y6X7Z8', 'Z0Z1Y3Y4X6X8'] : False\n",
+      "6 :: 15241: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2Z5', 'Y3Y6Z7Z8', 'Z2Z3X5X6', 'Z4Z5Z6X7', 'Z0Z1Z3Y4Z7Y8'] : True\n",
+      "6 :: 15304: [[9,2, 2]] : 384 :['X1Z8', 'X2Z8', 'X4Z7', 'X0X3Z7Z8', 'X0X5Z6Z8', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : False\n",
+      "6 :: 15377: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2Z7', 'Y4Y5Z6Z8', 'X3Z5X6Z8', 'Z2Z3Z6X7', 'Z0Z1Y3Z4Z7Y8'] : True\n",
+      "6 :: 15407: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z7', 'Y4Y5Z6Z8', 'X4X6Z7Z8', 'Z2Y3X4Z5Z6Y7', 'Z0Z1Z3Y4Z5Y8'] : False\n",
+      "6 :: 15456: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z7', 'X4X6Z7Z8', 'X3Y4X5Y6', 'Z2Y3X4Z5Z6Y7', 'Z0Z1Z3Y4Z5Y8'] : False\n",
+      "6 :: 15464: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2Z5', 'X3X4Z6Z7', 'Z2Z3X5X6', 'Z2X3Z4Y5Y7Z8', 'Z0Z1Z4X6Z7X8'] : False\n",
+      "6 :: 15493: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z7', 'Y4Y5Z6Z8', 'X3Z5X6Z8', 'Z2Y3X4Z5Z6Y7', 'Z0Z1Y3Z4Z7Y8'] : False\n",
+      "6 :: 15500: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z7', 'Z4Y5Y6Z7', 'Z2Y3Z4X5Y7Z8', 'Z0Z1Z3X5Z6X8'] : False\n",
+      "6 :: 15509: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z5', 'X3X4Z6Z7', 'Z2X4X5Z8', 'Y3Z4Z5X6Y7Z8', 'Z0Z1Z4X6Z7X8'] : False\n",
+      "6 :: 15527: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X3Z7', 'X2X4Z6Z7', 'Z4Y5Y6Z8', 'Z0Z1Z5X8', 'Z2Z3X5Z6X7Z8'] : True\n",
+      "6 :: 15529: [[9,2, 2]] : 96 :['X0Z8', 'X1Z7', 'X2Z7', 'Y3Y5Z6Z8', 'Y4Z5Y6Z8', 'Z0Z3Z4X8', 'Z1Z2X4Z6X7Z8'] : False\n",
+      "6 :: 15544: [[9,2, 2]] : 768 :['X0Z8', 'X1Z8', 'X2Z5', 'X3X4Z6Z7', 'Z3Z4Y6Y7', 'Z2Z3X5X6Z7Z8', 'Z0Z1X3Z5Z6X8'] : False\n",
+      "6 :: 15550: [[9,2, 2]] : 1152 :['X0Z8', 'X1Z8', 'Z2X3', 'X4X5Z6Z7', 'Z4Z5Y6Y7', 'X2Z3Y4Y6Z7Z8', 'Z0Z1Z5X6Z7X8'] : False\n",
+      "6 :: 15559: [[9,2, 2]] : 192 :['X0Z7', 'X2Z4', 'X3Z4', 'X1Y5Y6Z7', 'Z0Z5X7Z8', 'Z1Z6Z7X8', 'Z2Z3X4Z5X6Z8'] : True\n",
+      "6 :: 15562: [[9,2, 2]] : 192 :['X0Z8', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z8', 'Z0Z3Z4X8', 'Z1Z2Z4Z5X6X7'] : False\n",
+      "6 :: 15564: [[9,2, 2]] : 768 :['X0Z8', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'Z0Z3Z4X8', 'Z1Z2Z3X5Z6X7'] : False\n",
+      "6 :: 15567: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X4Z7', 'X2X3Z5Z6', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : True\n",
+      "6 :: 15571: [[9,2, 2]] : 2304 :['X0Z8', 'X1Z8', 'X2Z5', 'X3X4Z6Z7', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : False\n",
+      "6 :: 15711: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X3Z4X5Z8', 'Y2Z4Y6Z8', 'Z3X4Z6X7', 'Z0Z1Z2Z5X7X8'] : False\n",
+      "6 :: 15717: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X3Z4X5Z8', 'Y4Y5Z6Z7', 'Y2Z4Y6Z8', 'X2Y3X4Y7', 'Z0Z1Z2Y3Z7Y8'] : False\n",
+      "6 :: 15718: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X4Z7Z8', 'X5Z6Z7Z8', 'Z3Z4Z5X7', 'Y2Y3Y6Y7', 'Z0Z1Z2Z3Z5X8'] : False\n",
+      "6 :: 15759: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X3Y4Y5', 'Y2Z4Y6Z8', 'Z2X5X6Z7', 'Y3Z5Y7Z8', 'Z0Z1Z2Y3Z7Y8'] : False\n",
+      "6 :: 15777: [[9,2, 2]] : 768 :['Z0Z7', 'Z1Z8', 'Z2Z4', 'Z3Z4', 'Z5Z6Z7Z8', 'X0X2X3X4X6X7', 'X1X2X3X4X5X8'] : True\n",
+      "6 :: 15782: [[9,2, 2]] : 1536 :['X0X1', 'X2Z8', 'X3Z7', 'X4Z7', 'X0X5Z6Z8', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : False\n",
+      "6 :: 15804: [[9,2, 2]] : 768 :['X0Z7', 'X2Z6', 'X3Z6', 'X4X5', 'Z1Z4Z5X8', 'Z0Y1X7Y8', 'Z2Z3X4X6Z7Z8'] : True\n",
+      "6 :: 15827: [[9,2, 2]] : 768 :['X0Z8', 'X1Z8', 'X3Z6', 'X4Z7', 'Z0Z1Z5X8', 'Y2Z3Y5X6Z7Z8', 'Z2Z4X5Z6X7Z8'] : True\n",
+      "6 :: 15905: [[9,2, 2]] : 384 :['X0Z8', 'X1Z8', 'X3Z7', 'X4Z6', 'Z4Y5Y6Z8', 'Z2Z3X5Z6X7Z8', 'Z0Z1X2Z5Z7X8'] : True\n",
+      "6 :: 15915: [[9,2, 2]] : 384 :['X0Z8', 'X1Z8', 'X2Z5', 'X4Z7', 'Z3Z4Y6Y7', 'Z2Z3X5X6Z7Z8', 'Z0Z1X3Z5Z6X8'] : True\n",
+      "6 :: 15982: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X4Z5Z8', 'Y4Y5Z6Z7', 'Y2X3X5Y6', 'Y3Z5Y7Z8', 'Z0Z1Z2Y3Z7Y8'] : False\n",
+      "6 :: 16007: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X2X4X5Z6', 'Z3Z4Z5X7', 'Y2Y4Z5X6Z7Z8', 'Z0Z1Z2Z3Z5X8'] : False\n",
+      "6 :: 16080: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X3X4Z6Z8', 'X5Z6Z7Z8', 'Y2Y4Y5Y6', 'Z3Z4Z5X7', 'Z0Z1Z2Z3Z5X8'] : False\n",
+      "6 :: 16083: [[9,2, 2]] : 24 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X3Z4X5Z8', 'Z2Y4Z5Y6', 'Y3Z5Y7Z8', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 16130: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Y2Z4Y6Z8', 'Z3Z4Y5Y7', 'Z0Z1Y3X5X6Y8'] : False\n",
+      "6 :: 16255: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X2X4Z5Z8', 'X3Z4X5Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 16282: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X2X4Z5Z8', 'X3Z4X5Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Z4X6X8'] : False\n",
+      "6 :: 16335: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'Y2Z3Y4Z7', 'Z2X4X5Z6', 'Z3Z4X6X7', 'Z0Z1X5X8', 'Y2X3Z4Z5Y6Z8'] : False\n",
+      "6 :: 16359: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X3Y4Y5', 'Z2Y4Z5Y6', 'X2Y3X4Y7', 'X3X4Z5Z6Z7Z8', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 16393: [[9,2, 2]] : 24 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z2X5X6Z7', 'Z3Z4Y5Y7', 'Z0Z1Z3Z4X6X8'] : False\n",
+      "6 :: 16397: [[9,2, 2]] : 24 :['X0Z8', 'X1Z8', 'X3Z4X5Z8', 'Y4Y5Z6Z7', 'Z2Y4Z5Y6', 'X2Y3X4Y7', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 16400: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X3Y4Y5', 'Y2Z4Y6Z8', 'Z2X5X6Z7', 'X2Y3X4Y7', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 16420: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X3Z4X5Z8', 'Y4Y5Z6Z7', 'Y2Z4Y6Z8', 'Y3Z5Y7Z8', 'Z0Z1Z2Y3Z7Y8'] : False\n",
+      "6 :: 16446: [[9,2, 2]] : 384 :['X0Z8', 'X1Z8', 'X2X3Z5Z6', 'X2X4Z5Z7', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : False\n",
+      "6 :: 16470: [[9,2, 2]] : 576 :['X1Z8', 'X2Z8', 'X4X5Z6Z7', 'Z3Y4Y6Z7', 'Z3X4Z5X7', 'Z0Z1Z2X8', 'X0Y3Y4Z5Z6Z8'] : False\n",
+      "6 :: 16471: [[9,2, 2]] : 1152 :['Z0Z8', 'Z1Z8', 'Z3Z4Z6Z7', 'Z2Z3Z5Z7', 'X2X4X5X6', 'X2X3X5X7', 'X0X1X2X3X4X8'] : False\n",
+      "6 :: 16479: [[9,2, 2]] : 96 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Y2Z4Y6Z8', 'Y3Z5Y7Z8', 'Z0Z1Y3X5X6Y8'] : False\n",
+      "6 :: 16484: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'Y2Y3Z4Z6', 'Y2Z3Y4Z7', 'Z2X3X5Z7', 'Z3Z4X6X7', 'Z0Z1Z3Z5X6X8'] : False\n",
+      "6 :: 16485: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X4Z5Z8', 'Y4Y5Z6Z7', 'Y2Z4Y6Z8', 'Z3X4Z6X7', 'Z0Z1Z2Y3Z7Y8'] : False\n",
+      "6 :: 16494: [[9,2, 2]] : 288 :['X1Z7', 'X2Z7', 'X3X4Z5Z6', 'X0Y3Y5Z6', 'X3Z4X6Z8', 'Z0Z3Z4X8', 'Z1Z2X3Z5X7Z8'] : False\n",
+      "6 :: 16498: [[9,2, 2]] : 384 :['X0Z8', 'X1Z8', 'X3X4Z6Z7', 'X2Y3Y4X5', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'Z0Z1Z2X3Z6X8'] : False\n",
+      "6 :: 16662: [[9,2, 2]] : 1536 :['X0X1', 'X2Z8', 'X3Z7', 'X4Z7', 'X5Z6', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : True\n",
+      "6 :: 16756: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z5', 'X3X4Z6Z7', 'Y3Y6Z7Z8', 'Z2X3Z4Y5Y7Z8', 'Z0Z1Y3Z5X7Y8'] : False\n",
+      "6 :: 16758: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'Z2X3', 'Z4X5X6Z8', 'Y5Z6Y7Z8', 'X2Z3X4X5Z6Z7', 'Z0Z1Z4Y5Z7Y8'] : False\n",
+      "6 :: 16790: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z5', 'Z2X4X5Z8', 'Y3Y6Z7Z8', 'Z4Z5Z6X7', 'Z0Z1Z3Y4Z7Y8'] : True\n",
+      "6 :: 16801: [[9,2, 2]] : 192 :['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z7', 'Y4Y5Z6Z8', 'Z2Z3Z6X7', 'Z0Z1Y3Y4X6X8'] : True\n",
+      "6 :: 16814: [[9,2, 2]] : 384 :['Z0Z8', 'X1X7', 'X2X7', 'Z3Z4Z5Z6', 'X3X4X5X6', 'X0X3X4X8', 'Z1Z2Z3Z5Z7Z8'] : False\n",
+      "6 :: 16904: [[9,2, 2]] : 1152 :['X1Z8', 'X2Z8', 'X3Z7', 'X4Z7', 'X0X5Z6Z8', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : False\n",
+      "6 :: 17123: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X4Z5Z8', 'X3Z4X5Z8', 'Y2Z4Y6Z8', 'Z3X4Z6X7', 'Z0Z1Z2Y3Z7Y8'] : False\n",
+      "6 :: 17190: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'Y2Y3Z4Z6', 'Z2X4X5Z6', 'Z3Y5Y6Z7', 'Z2Y4Z5Y7', 'Z0Z1Z2X4Z7X8'] : False\n",
+      "6 :: 17230: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'Y4Y5Z6Z7', 'Y2Z4Y6Z8', 'Y3Z5Y7Z8', 'Z0Z1Y2Z3Z6Y8'] : False\n",
+      "6 :: 17376: [[9,2, 2]] : 48 :['X0Z8', 'X1Z8', 'X2X3Z6Z7', 'Y4Y5Z6Z7', 'Y2Z4Y6Z8', 'Z3X4Z6X7', 'Z0Z1Y3X5X6Y8'] : False\n",
+      "6 :: 21994: [[9,2, 2]] : 128 :['X0Z7', 'X1Z8', 'X3Z4', 'Y5Y6Z7Z8', 'Z2Z3X4X5Z6Z7', 'Z0X2Z4Z5X7Z8', 'Z1X2Z4Z6Z7X8'] : False\n",
+      "6 :: 22018: [[9,2, 2]] : 128 :['X0Z7', 'X1Z8', 'X3Z4', 'Z0Z5X7Z8', 'Z1Z6Z7X8', 'Y2Z3Y4X5Z6Z7', 'Z2Z3X4Z5X6Z8'] : True\n",
+      "6 :: 22056: [[9,2, 2]] : 512 :['X0X1', 'X2Z8', 'X4Z7', 'X0X3Z7Z8', 'X0X5Z6Z8', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : False\n",
+      "6 :: 22065: [[9,2, 2]] : 128 :['X0Z8', 'X1Z6', 'X2Z7', 'X5Z6Z7Z8', 'Z1Y3X4Z5Y6Z7', 'Z2X3Y4Z5Z6Y7', 'Z0Z3Y4Z5Z7Y8'] : False\n",
+      "6 :: 22081: [[9,2, 2]] : 512 :['X0Z8', 'X2Z7', 'X3Z5', 'X4Z6', 'Z0Z5Z6X8', 'X1Z3Z4X5X6Z7', 'Y1Z2Z3X5Y7Z8'] : True\n",
+      "6 :: 22105: [[9,2, 2]] : 128 :['X0Z6', 'X1Z7', 'X2Z8', 'Y4Y5Z7Z8', 'Z0X3X4Z5X6Z7', 'Z1Y3Z4Z6Y7Z8', 'Z2Z3X4Z6Z7X8'] : False\n",
+      "6 :: 22108: [[9,2, 2]] : 128 :['X0Z8', 'X1Z5', 'Z2X3', 'Z5X6Z7Z8', 'Z1X4Y5Y6', 'Z0Y4Z6Y8', 'X2Z3Z4Z6X7Z8'] : False\n",
+      "6 :: 22113: [[9,2, 2]] : 128 :['X0Z8', 'X1Z7', 'X2Z4', 'Z2X3X4Z6', 'X3X5Z7Z8', 'Y3Z5Y6Z8', 'Z0Z1Z4X6X7X8'] : True\n",
+      "6 :: 22169: [[9,2, 2]] : 256 :['X2Z7', 'X3Z5', 'X4Z6', 'X0X1Z7Z8', 'Z0Z5Z6X8', 'X0Z3Z4X5X6Z8', 'X0Z1Z2Z3X5X7'] : False\n",
+      "6 :: 22173: [[9,2, 2]] : 256 :['X2Z7', 'X3Z5', 'X4Z6', 'X0X1Z7Z8', 'Z3Z4X5X6', 'Z0Z5Z6X8', 'Z1Z2Z3X5X7Z8'] : True\n",
+      "6 :: 22203: [[9,2, 2]] : 128 :['X0Z8', 'X1Z5', 'Z2X3', 'Z5X6Z7Z8', 'Z1Y4X5Y7', 'Z0Y4Z6Y8', 'Z1X2Z3X4Y5Y6'] : True\n",
+      "6 :: 22347: [[9,2, 2]] : 32 :['X0Z7', 'X1Z8', 'X2X3Z5Z8', 'Z3Z4X5Z6', 'Z5X6Z7Z8', 'Z0Y2Y3X4Z6X7', 'Z1Z2Y4Z5Z6Y8'] : False\n",
+      "6 :: 22361: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'X2X3Z5Z7', 'X4Z5Z7Z8', 'Z2Z4X5X6', 'Z1Y2Z3X5Z6Y7', 'Z0Z3Y5Z6Z7Y8'] : False\n",
+      "6 :: 22402: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Z2X4X6Z8', 'Z3X5X6Z7', 'Z1Y2Z3X4Z6Y7', 'Z0X3X4Z6Z7X8'] : False\n",
+      "6 :: 22498: [[9,2, 2]] : 128 :['X0Z8', 'X1Z7', 'Y2Y3Z5Z6', 'X4Z6Z7Z8', 'Z3X5Z7Z8', 'Z2Z4X6Z8', 'Z0Z1X2Z3X7X8'] : False\n",
+      "6 :: 22508: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X4Z5Z7Z8', 'X2Y3Y4X5', 'Y2Y3X4X6', 'Z1Z2X3Y4Z6Y7', 'Z0X2X3Z4Z6X8'] : False\n",
+      "6 :: 22526: [[9,2, 2]] : 768 :['X0Z8', 'X3X4', 'X1X2Z6Z7', 'Y3Z4Y5Z8', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'Z1X3Z5X6Z7Z8'] : False\n",
+      "6 :: 22536: [[9,2, 2]] : 64 :['X0Z4', 'X1Z5', 'Z0X2X4Z6', 'Z1X2X5Z6', 'Z2X3X6Z8', 'Y3Z6Y7Z8', 'Z2Y3Z4Z5Z7Y8'] : False\n",
+      "6 :: 22540: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'X2X3Z5Z7', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Z1Y2Z4Z6Y7Z8', 'Z0Y2Y3Z4Y6Y8'] : False\n",
+      "6 :: 22547: [[9,2, 2]] : 64 :['X0Z4', 'X1Z5', 'X2X3Z6Z7', 'Z0Z1X4X5', 'Y2Y6Z7Z8', 'Z0Z3X4Z6X7Z8', 'Z0Z2Z3Y4Z5Y8'] : False\n",
+      "6 :: 22550: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Y2Y3X4X6', 'Z1Z2X3Y4Z6Y7', 'Z0X2X3Z4Z6X8'] : False\n",
+      "6 :: 22554: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'Y2Y3Z4Z5', 'Z2X5Z7Z8', 'X3Y5Z6Y7', 'Z1X2X4Z5X6Z8', 'Z0Z3X4Z5Z6X8'] : False\n",
+      "6 :: 22559: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z2Z3X4Z8', 'Y3Y4Z5X6', 'Z1Y3Z4Y5X7Z8', 'Z0Z1X2X5Z6X8'] : False\n",
+      "6 :: 22612: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Y4X5X6', 'Z1Z4Z6X7', 'Z0Z5Z6X8', 'Z2Z3X4X5Z7Z8'] : False\n",
+      "6 :: 22626: [[9,2, 2]] : 64 :['X0Z7', 'X1X2', 'X1X3Z5Z7', 'Z3X4X5Z8', 'Y4Z5Y6Z8', 'Z0Z1Z2X7', 'Z1Z2Z3Y4Z6Y8'] : False\n",
+      "6 :: 22634: [[9,2, 2]] : 256 :['X0Z7', 'X1Z8', 'Y2Y3Z4Z5', 'Z2Z3X4X5', 'Z2Y4Z5Y6', 'Z0Z1Y7Y8', 'Z0Z2X4Z6X7Z8'] : False\n",
+      "6 :: 22638: [[9,2, 2]] : 256 :['X0Z8', 'X3X4', 'X1X2Z6Z7', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'X1Y3Z4Y5Z6Z8', 'Y1X3Z5Y6Z7Z8'] : False\n",
+      "6 :: 22647: [[9,2, 2]] : 768 :['Z0X1', 'Z2X3', 'X0Z1X2Z3', 'X5X6Z7Z8', 'Z4Y5Y7Z8', 'Z4X5Z6X8', 'X0Z1Y4Y5Z6Z7'] : False\n",
+      "6 :: 22648: [[9,2, 2]] : 768 :['X0X1', 'X2Z8', 'X4X5Z6Z7', 'Z3Y4Y6Z7', 'Z3X4Z5X7', 'Z0Z1Z2X8', 'X0Y3Y4Z5Z6Z8'] : False\n",
+      "6 :: 24387: [[9,2, 2]] : 64 :['X0Z7', 'X1Z8', 'X3X4Z7Z8', 'Z5X6Z7Z8', 'X2Y3Z4Y5Z6Z8', 'Z0Z2Y3Z5Z6Y7', 'Z1Z2Y4Z5Z6Y8'] : False\n",
+      "6 :: 24388: [[9,2, 2]] : 64 :['X0Z8', 'X1Z7', 'X4Z6Z7Z8', 'Z3X5Z7Z8', 'X2Y3Z4Z5Y6Z8', 'Z1Y2Y3Z4Z6X7', 'Z0Z2X3Z4Z6X8'] : False\n",
+      "6 :: 24489: [[9,2, 2]] : 128 :['X0Z7', 'X1X2', 'X1X3Z5Z7', 'X1X4Z6Z7', 'Z3Z4Y5Y6', 'Z1Z2Z4X5Z6X8', 'Z0X1Z3Z4Y7Y8'] : False\n",
+      "6 :: 24512: [[9,2, 2]] : 64 :['X0Z8', 'X1Z5', 'Y3Y4Z5Z7', 'Z2X6Z7Z8', 'Y2X3Z4Y6', 'Z3Z6X7Z8', 'Z0Z1Y2X3X5Y8'] : False\n",
+      "6 :: 24515: [[9,2, 2]] : 128 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'Z2Z3X4Z8', 'Y3Y4Z5X6', 'Y2Y4Z5X7', 'Z0Z1X2X5Z6X8'] : False\n",
+      "6 :: 24601: [[9,2, 2]] : 256 :['X0Z8', 'X1Z7', 'X2Z5', 'X3X4Z6Z7', 'Y3Y6Z7Z8', 'Z1Z2X3Z4X5X7', 'Z0Z2Z3Z4Y5Y8'] : False\n",
+      "6 :: 24705: [[9,2, 2]] : 256 :['X0Z8', 'X1Z5', 'Z2X3', 'Z5X6Z7Z8', 'Z4Z6X7Z8', 'Z1X2Z3X4Y5Y6', 'Z0X2Z3Y4Z6Y8'] : False\n",
+      "6 :: 24752: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Z3X4Z7Z8', 'Z2Z3Y5Y6', 'Z1X3X6X7', 'Z0X2X3X8', 'X2X3Z4Z5Z6Z7'] : False\n",
+      "6 :: 24875: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Z3Y4Z7', 'X2Z4X5Z8', 'Y3X4Y5X6', 'Z0Z5Z6X8', 'Z1Y2Y3Z5Z6X7'] : False\n",
+      "6 :: 25200: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Z3Y4Z7', 'X2Z4X5Z8', 'Y3X4Y5X6', 'Z1Y2Y3Z5Z6X7', 'Z0Y2Y3Z4Z6X8'] : False\n",
+      "6 :: 25403: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'X3X4Z5Z7', 'Z2Y3Y5Z8', 'Z2X4X6Z8', 'Z1Y2Y3Z5Z6X7', 'Z0Y2Y3Z4Z6X8'] : False\n",
+      "6 :: 25410: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z5Z6', 'X4Z6Z7Z8', 'Z2Z3Z4X5X6Z7', 'Z1Z3Z4Y5Y7Z8', 'Z0X2Z3Z4Z5X8'] : False\n",
+      "6 :: 25449: [[9,2, 2]] : 32 :['X0Z8', 'X1Z5', 'Y3Y4Z5Z7', 'Z2X6Z7Z8', 'Y2X3Z4Y6', 'Z0Y2Z7Y8', 'Z1X2Z3Z4X5X7'] : False\n",
+      "6 :: 25520: [[9,2, 2]] : 16 :['X0Z7', 'X1X2', 'X1X4Z6Z7', 'Z3X4X5Z8', 'Y4Z5Y6Z8', 'Z0Y1Z2X3Z5Y7', 'Y1Z2Z3Z4Z7Y8'] : False\n",
+      "6 :: 25552: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Y4X5X6', 'Z0Z5Z6X8', 'Z2Z3X4X5Z7Z8', 'Z1Y2Z3X4Z6Y7'] : False\n",
+      "6 :: 25559: [[9,2, 2]] : 64 :['X0Z7', 'X1X2', 'X1X3Z5Z7', 'X1X4Z6Z7', 'X3Z4X6Z8', 'Y1Z2Z3Z4Z7Y8', 'Z0Z4X5Z6X7X8'] : False\n",
+      "6 :: 25572: [[9,2, 2]] : 64 :['X0Z7', 'X1Z8', 'Y2Y4Z7Z8', 'Y3Y5Z7Z8', 'X3Z5X6Z8', 'Z0Z2Y3Z5Z6Y7', 'Z1X3Z4Z6Z7X8'] : False\n",
+      "6 :: 25604: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z5Z6', 'X4Z6Z7Z8', 'Z1Z4Z5X7', 'Z2Z3Z4X5X6Z7', 'Z0X2Z3Z4Z5X8'] : False\n",
+      "6 :: 25620: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Y2Y3X4X6', 'Z1Y2Z4Z6Y7Z8', 'Z0X2X3Z4Z6X8'] : False\n",
+      "6 :: 25646: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'Z3X6Z7Z8', 'Y3X4Z5Y6', 'Z2Z4Z6X7', 'Z0Y2Y6X8', 'Z1X2Z4X5Z7Z8'] : False\n",
+      "6 :: 25647: [[9,2, 2]] : 16 :['X0Z8', 'X1Z6', 'X2X4Z5Z7', 'Y3Y4X5Z8', 'Z1Z2X5X6', 'X3Y5Z6Y7', 'Z0Y2X3Y4Z6X8'] : False\n",
+      "6 :: 25697: [[9,2, 2]] : 128 :['X0Z8', 'Z1X2', 'X3X4Z6Z7', 'Z3Y5Y6Z7', 'Z4Y5Z6Y7', 'X1Z2X3X5Z7Z8', 'Z0X1Z2Z3Z4X8'] : False\n",
+      "6 :: 25698: [[9,2, 2]] : 128 :['X1Z6', 'X2Z7', 'X0X3X4Z8', 'X0Y3Z4Y5', 'Z0Z3Z4X8', 'Z1X3Z5X6Z7Z8', 'X0Z2X3Z5Z6X7'] : False\n",
+      "6 :: 25707: [[9,2, 2]] : 128 :['X0Z7', 'X1Z8', 'X3X4Z5Z6', 'Z2Y3Y5Z6', 'Z2X3Z4X6', 'Z0X2Z3Z4X7Z8', 'Z1Z2X3Z5Z7X8'] : False\n",
+      "6 :: 25720: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z2Z4Z6X7', 'Z0Y2Y6X8', 'Z1X2Z3Z4X5X6'] : False\n",
+      "6 :: 25730: [[9,2, 2]] : 32 :['X0Z8', 'X1Z6', 'X2X3Z4Z5', 'Y2Y4Z5Z7', 'Z2Z3X7Z8', 'Z0Z6Z7X8', 'Z1Z3Z4X5X6Z8'] : False\n",
+      "6 :: 25731: [[9,2, 2]] : 16 :['X0Z8', 'X1Z7', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Y2Y3X4X6', 'Z0Y4Z6Y8', 'Z1Z2X3Y4Z6Y7'] : False\n",
+      "6 :: 25742: [[9,2, 2]] : 16 :['X0Z8', 'X1Z5', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z1Y4Y5Z7', 'Z0Z2Y3Y8', 'Z2Z3Z4Y6Y7Z8'] : False\n",
+      "6 :: 25745: [[9,2, 2]] : 32 :['X0Z8', 'X1Z7', 'Y2Y3Z4Z5', 'Y2Z3Y4Z7', 'X2Z4X5Z8', 'Z0Z5Z6X8', 'Z1X2Z3Y6Y7Z8'] : False\n",
+      "6 :: 25938: [[9,2, 2]] : 1536 :['X0Z8', 'X1Z8', 'X2Z8', 'X4Z7', 'X3X5Z6Z7', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : True\n",
+      "6 :: 26015: [[9,2, 2]] : 384 :['X0Z8', 'X1Z8', 'X2Z8', 'X3X5Z7Z8', 'Y3X4Y5X6', 'Z4Y5Z6Y7', 'Z0Z1Z2Z3Z4X8'] : True\n",
+      "6 :: 31366: [[9,2, 2]] : 8 :['X0X1Z7Z8', 'X2X3Z7Z8', 'X2X4Z6Z7', 'Z4X6Z7Z8', 'X0Z2Z3Z4X5Z7', 'Z0Y2Z5Z6Y7Z8', 'Z1X2Z3Z5Z6X8'] : False\n",
+      "6 :: 31367: [[9,2, 2]] : 8 :['X2Z5Z7Z8', 'X3Z6Z7Z8', 'Z0X4Z6Z8', 'Z2X5Z6Z7', 'Y1Y2Z3X7', 'X0X1Z3Z5X6Z7', 'Z1Z2Z3Z4Z6X8'] : False\n",
+      "6 :: 31371: [[9,2, 2]] : 8 :['Y1Y2Z6Z7', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'X0Z1Z3Z5X6Z8', 'X0Z2Z4Z5X7Z8', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 31389: [[9,2, 2]] : 16 :['X0X1Z7Z8', 'X0X2Z5Z8', 'X0X3Z6Z8', 'Z2X4X5Z7', 'Z3X6Z7Z8', 'Z1Z4Z6X7', 'Z0Y4Z5Z6Z7Y8'] : False\n",
+      "6 :: 31667: [[9,2, 2]] : 4 :['X3Z4Z7Z8', 'Z2Z3X4Z8', 'Z1X5Z6Z8', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X2Z3Z5X8', 'X0X1X2Z4Z5Z6'] : False\n",
+      "6 :: 31737: [[9,2, 2]] : 4 :['X3Z6Z7Z8', 'Z0X4Z6Z8', 'Z2X5Z6Z7', 'Y0Y2Y4Y5', 'Y1Y3Y5Y7', 'Y1Y2Y6Y8', 'X0X1Z3Z5X6Z7'] : False\n",
+      "6 :: 31939: [[9,2, 2]] : 8 :['X0X1Z2Z8', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'X0Z3X5Z6', 'Z4Z5X6Z8', 'Z1Y2Z3Z4Y7Z8', 'Z0Z1X2Y3Y4X8'] : False\n",
+      "6 :: 32050: [[9,2, 2]] : 2 :['Y2Y4Z5Z7', 'X0Z1Z2X5', 'Z3X6Z7Z8', 'Y1X4Y5X6', 'Z4Z6X7Z8', 'Y0X1Y6X8', 'X0Y1Y3Z5Z6Z8'] : False\n",
+      "6 :: 32080: [[9,2, 2]] : 4 :['X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'X0Z1Z2Z3X4Z7', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 32301: [[9,2, 2]] : 16 :['X0X1Z7Z8', 'Y2Y4Z7Z8', 'Y3Y5Z7Z8', 'X1X2Z4X6', 'X3Z5X6Z8', 'Y0Y2Z3Z4Z6X7', 'X0Z1X2Z5Z6X8'] : False\n",
+      "6 :: 32314: [[9,2, 2]] : 2 :['X1X2Z6Z7', 'X3Z6Z7Z8', 'Z1Z2X4Z5', 'X0X1X5Z7', 'Y2Y3Y4Y6', 'Z0Y3Z5Y8', 'X0Y2Z3Z4Z5Y7'] : False\n",
+      "6 :: 32326: [[9,2, 2]] : 2 :['X3Z6Z7Z8', 'Z1X4Z7Z8', 'Z2X5Z6Z8', 'Y4Y5Y6Y7', 'Z0Y1Y6X8', 'Y0Y2Y7Y8', 'X0Z1Z3Z5X6Z8'] : False\n",
+      "6 :: 32346: [[9,2, 2]] : 4 :['X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'X0Y1Y2X5', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Y0X1Z2Y3Z5X8'] : False\n",
+      "6 :: 32480: [[9,2, 2]] : 1 :['X2X3Z4Z5', 'X1Z2X4Z8', 'X0Y2Y4Z7', 'Y3Y5Z6Z8', 'X0X1Z5X6', 'Z1Z4Z6X7', 'Y0Y1Z2Z3Z7X8'] : False\n",
+      "6 :: 36804: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'X1X2X3Z5', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Z2Z3X6Z8', 'Z0Y4Z6Y8', 'X0Z1Y2Z4Z6Y7'] : False\n",
+      "6 :: 36986: [[9,2, 2]] : 8 :['X0X1Z7Z8', 'X0Y2Y4Z8', 'Y3Y5Z7Z8', 'Z2Z3X4X5', 'X1X2Z4X6', 'Y0Y2Z3Z4Z6X7', 'X0Z1X2Z5Z6X8'] : False\n",
+      "6 :: 37069: [[9,2, 2]] : 1 :['X3Z4Z5Z8', 'Z1Z2X4X5', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'X0Z3X5X6', 'X1X2Z3X8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 39528: [[9,2, 2]] : 1 :['X0X4Z5Z6', 'X0Z3Z4X5', 'Z1Z4X6Z8', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Y0Y4Z7X8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 39640: [[9,2, 2]] : 1 :['Z4X5Z7Z8', 'Y3X4Y5Z6', 'Z1X2Z3X6', 'Z2X3Y4Y7', 'Z2Y3X4Y8', 'Z0X1X2Z6Z7Z8', 'X0Z1X3Z4Z6Z7'] : False\n",
+      "6 :: 39907: [[9,2, 2]] : 1 :['Z0Z1X2Z8', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'X0Z2X3Z5Z7Z8', 'X0Y1Z3Z4Y7Z8'] : False\n",
+      "6 :: 39910: [[9,2, 2]] : 1 :['X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'X0X1Z2Z5X6Z8'] : False\n",
+      "6 :: 40135: [[9,2, 2]] : 12 :['X0X1Z6Z8', 'X0X2Z7Z8', 'X3Z5Z6Z8', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Z1Z2Z3Z4Y6Y7', 'Z0Z1Z4Z5Y6Y8'] : False\n",
+      "6 :: 40186: [[9,2, 2]] : 4 :['X0X1Z7Z8', 'X0X2Z5Z8', 'X3X4Z6Z7', 'Z2X3X5Z6', 'Y3Y6Z7Z8', 'Z1Z4Z6X7', 'Z0Y3Z4Z5Z6Y8'] : False\n",
+      "6 :: 40290: [[9,2, 2]] : 2 :['Z1Y2Y3Z6', 'Z2X3X4Z5', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1X4Z7X8', 'X0Y1Y2Z3Z7Z8'] : False\n",
+      "6 :: 40401: [[9,2, 2]] : 1 :['X2X3Z5Z6', 'Z1X4Z5Z8', 'Z2Z4X5Z8', 'X0Z1Z3X6', 'Z2Z3X7Z8', 'Y0X1X2Y8', 'X0X1X3Z4Z7Z8'] : False\n",
+      "6 :: 40502: [[9,2, 2]] : 768 :['Z1Z6', 'Z4Z8', 'Z5Z7', 'Z0Z2Z6Z7', 'Z0Z3Z6Z8', 'X0X1X2X5X6X7', 'X0X1X3X4X6X8'] : True\n",
+      "6 :: 40525: [[9,2, 2]] : 768 :['X0Z8', 'X1Z7', 'X2Z4', 'X5Z6Z7Z8', 'Z4Z5X6Z8', 'Z1Z2Y3X4Y5X7', 'Z0Z2Z3X4Z5X8'] : False\n",
+      "6 :: 40526: [[9,2, 2]] : 768 :['X0Z8', 'X1Z7', 'X2Z6', 'Z3X4', 'Z2X3Z4X5X6Z7', 'Z1X3Z4Y5Y7Z8', 'Z0X3Z4Z5Z6X8'] : False\n",
+      "6 :: 40544: [[9,2, 2]] : 4608 :['X0X1', 'X0X2', 'X3Z7', 'X4Z7', 'X5Z6', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : True\n",
+      "6 :: 40919: [[9,2, 2]] : 128 :['X0Z8', 'X1Z6', 'X2Z7', 'X5Z6Z7Z8', 'Z2Z4Z5X7', 'Z1Y3X4Z5Y6Z7', 'Z0Z3Y4Z5Z7Y8'] : False\n",
+      "6 :: 40921: [[9,2, 2]] : 512 :['X0X1', 'X2Z8', 'X4Z7', 'X5Z6', 'X0X3Z7Z8', 'Z3Z4Z5X6X7Z8', 'Z0Z1Z2Z5Y6Y8'] : True\n",
+      "6 :: 40944: [[9,2, 2]] : 128 :['X0Z8', 'X1Z7', 'X2Z6', 'Z2Z4Y5Y6', 'X3Z4X5Z6Z7Z8', 'Z1Z3X4Z5X7Z8', 'Z0Y3Y4Z5Z7X8'] : False\n",
+      "6 :: 40952: [[9,2, 2]] : 128 :['X0Z8', 'X1Z7', 'X2Z6', 'Z2Z4Y5Y6', 'Z0Z3Z4X8', 'X3Z4X5Z6Z7Z8', 'Z1Z3X4Z5X7Z8'] : False\n",
+      "6 :: 42370: [[9,2, 2]] : 16 :['X2X3Z7Z8', 'Z0Z1Y5Y6', 'Z0Z2Y5Y7', 'Z0Z3Y5Y8', 'X0X1X2Z5Z6Z7', 'X0Z1Z2Z3X4Z5', 'Y0X1Z4Y5Z7Z8'] : False\n",
+      "6 :: 42371: [[9,2, 2]] : 16 :['X3Z6Z7Z8', 'Z0X4Z6Z8', 'Y2Y5Z6Z8', 'Z3Y6X7Y8', 'X0X2Z4Z5Z7Z8', 'X1Z3Z4Z5X6Z7', 'Y1Z2Z3Z5Y7Z8'] : False\n",
+      "6 :: 42546: [[9,2, 2]] : 2 :['X1X2Z5Z6', 'Z0X3Z7Z8', 'Z2Z4X6Z7', 'Z3Z5Z6X7', 'X0X4X7Z8', 'Y1Y6Y7Y8', 'X0Z1Z3Z4X5Z7'] : False\n",
+      "6 :: 42616: [[9,2, 2]] : 1 :['Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X2Y5X8', 'Y0Y4X7X8', 'X0Z1X2Z4Z7Z8'] : False\n",
+      "6 :: 42626: [[9,2, 2]] : 4 :['Z0Z1X2Z8', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'Z3Z4Y5Y6', 'X1Y3Y4X8', 'X0Z2Z3X5Z6Z8', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 42646: [[9,2, 2]] : 8 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z2X4Y5Y8', 'X0Z1Z2Z3X4Z7', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 42675: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'X2X3Z5Z7', 'X4Z5Z7Z8', 'X0Z3Z4X5', 'Z2Z3X6Z8', 'Z0Y4Z6Y8', 'Z1Y2Z4Z6Y7Z8'] : False\n",
+      "6 :: 43033: [[9,2, 2]] : 1 :['X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8', 'X0Z2Z3Z4X5Z7'] : False\n",
+      "6 :: 44001: [[9,2, 2]] : 8 :['X1X2Z5Z6', 'Z0X3Z7Z8', 'Z1Z2X5X6', 'Z3Z5Z6X7', 'X0X4X7Z8', 'X0Z1Z3Z4X5Z7', 'Y1Z2Z3Z4Z5Y8'] : False\n",
+      "6 :: 44678: [[9,2, 2]] : 2 :['X0X1Z7Z8', 'X0Y2Y3Z4', 'X0X2X4Z5', 'Z5X6Z7Z8', 'Z1Z6X7Z8', 'X2Z3Z4X5Z6Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 47441: [[9,2, 2]] : 4 :['X2X3Z6Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Z2Z3Y6Y7', 'Z1Z2Z3X8', 'X0X1X2Z4Z5Z6', 'X1Z2Z5X6Z7Z8'] : False\n",
+      "6 :: 47485: [[9,2, 2]] : 1 :['X3Z5Z6Z8', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'X0X1Z3X6', 'Y1Z2X3Y7', 'X2X6X7X8', 'X0X2Z4Z5Z7Z8'] : False\n",
+      "6 :: 47499: [[9,2, 2]] : 2 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Y2Y4X5X8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 47524: [[9,2, 2]] : 4 :['X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'X0X1X5Z6Z7Z8', 'X0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 47530: [[9,2, 2]] : 4 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X0X1X3Z6', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Y2Y4X5X8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 47652: [[9,2, 2]] : 4 :['X0Z1X4Z5', 'Z2Z4X5Z8', 'X0Z1Z3X6', 'Z2Z3X7Z8', 'Z0X1X2X8', 'X1X2Z4Z5Z6Z7', 'X0X1X3Z4Z7Z8'] : False\n",
+      "6 :: 48076: [[9,2, 2]] : 4 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'X0Y3Y5Z6', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 48079: [[9,2, 2]] : 12 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'X0Z1Z4Z6X7Z8', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 48135: [[9,2, 2]] : 2 :['Z2Z3X4Z8', 'Z1Z3X5Z8', 'X0Z1X6Z8', 'X0Y3Z6Y8', 'X0X1X2Z4Z5Z6', 'X0X3Z4Z5Z7Z8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 48156: [[9,2, 2]] : 2 :['X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'X0X1Z3X6', 'Y1Y2Z6X7', 'X0Z2Z3Z4X5Z7', 'Z1Z2Z3Z4Z6X8'] : False\n",
+      "6 :: 48159: [[9,2, 2]] : 2 :['X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'X1Z3Z4X6', 'Y1Y3X5X7', 'Y1Z2Y6X8', 'X0Z2Z3Z4X5Z7'] : False\n",
+      "6 :: 48266: [[9,2, 2]] : 1 :['Y1Y2Z6Z7', 'X0X3Z4Z5', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'X0Z1Z4X6', 'Z2Z5X7Z8', 'Y0X1Z2Y3Z7X8'] : False\n",
+      "6 :: 48766: [[9,2, 2]] : 2 :['X1Z2X3Z8', 'Z4X5Z6Z8', 'X0X1Y4Y5', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Y2Y5X7X8', 'X0X1X2Z3Z6Z7'] : False\n",
+      "6 :: 49436: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'X0Y2Y3Z4', 'X2X4Z5Z8', 'Z3Y4Y5Z6', 'Z5X6Z7Z8', 'Z1Z6X7Z8', 'Z0Y2Z3Z6Z7Y8'] : False\n",
+      "6 :: 49596: [[9,2, 2]] : 8 :['X1X2Z6Z7', 'X3Z6Z7Z8', 'X1X5Z7Z8', 'Z1Z2X6X7', 'Z0Y3Z5Y8', 'X0Z1Z2X4Z5Z8', 'Y1Z3Z4Z5Y6Z8'] : False\n",
+      "6 :: 50232: [[9,2, 2]] : 4 :['X0X1Z5Z8', 'X2Z3Z4Z8', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'X0X2Y6Y7', 'Y0Z2Z4Z5Y6X8'] : False\n",
+      "6 :: 50434: [[9,2, 2]] : 4 :['X1X2Z5Z6', 'X1X3Z5Z7', 'Y0X1Y4Z8', 'X0Y1Y5Z8', 'Z2Z3Y6Y7', 'Z1Z2Z3X8', 'X0Y2Z4Y6Z7Z8'] : False\n",
+      "6 :: 50636: [[9,2, 2]] : 4 :['X0X1Z6Z8', 'X0X2Z7Z8', 'X3X4Z6Z7', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0Y3Z4Z5Z6Y8'] : False\n",
+      "6 :: 52259: [[9,2, 2]] : 1 :['X1X3Z5Z7', 'X1Z2X4Z8', 'X0Y2Y4Z7', 'X0Y3Y5Z6', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 52427: [[9,2, 2]] : 4 :['X0X1Z5Z8', 'X2Z3Z4Z8', 'X0Z2X3Z6', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Z3Z4Y6Y7', 'Z0Z2Z4Z5Y6Y8'] : False\n",
+      "6 :: 52602: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'X0Y2Y3Z5Z7Z8'] : False\n",
+      "6 :: 52820: [[9,2, 2]] : 16 :['X0X1Z7Z8', 'X2X3Z5Z8', 'X2X4Z5Z7', 'Z5X6Z7Z8', 'X0Z3Z4X5Z6Z7', 'Z0Y2Z3Z6Y7Z8', 'Z1Y2Z4Z6Z7Y8'] : False\n",
+      "6 :: 52895: [[9,2, 2]] : 32 :['X0X1Z7Z8', 'X0X2Z5Z8', 'X0X3X4Z8', 'Z2X5Z6Z8', 'Z5X6Z7Z8', 'X0Z1Y3Z4Z6Y7', 'Z0Z3Z4Z5Z6X8'] : False\n",
+      "6 :: 52963: [[9,2, 2]] : 16 :['X2X3Z7Z8', 'Z1Z2Z3X4', 'Z0Z1Y5Y6', 'Z0Z2Y5Y7', 'Z0Z3Y5Y8', 'X0X1X2Z5Z6Z7', 'Y0X1Z4Y5Z7Z8'] : False\n",
+      "6 :: 53024: [[9,2, 2]] : 2 :['Z2Y3Y4Z5', 'Z1Z3X5Z8', 'X0Z1X6Z8', 'X0Y3Z6Y8', 'X1Y2X4Y8', 'X0X3Z4Z5Z7Z8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 53072: [[9,2, 2]] : 8 :['X0X1Z7Z8', 'X1X2X3Z5', 'X2X4Z5Z7', 'Z3Z4X5Z6', 'Z5X6Z7Z8', 'Z0Y2Z3Z6Y7Z8', 'Z1Y2Z4Z6Z7Y8'] : False\n",
+      "6 :: 53220: [[9,2, 2]] : 2 :['X0X3Z4Z5', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'X0Y1Y2Z6Z7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 53263: [[9,2, 2]] : 4 :['X0X1X2Z8', 'X0Z3X4Z6', 'Z3X5Z7Z8', 'X2Y3Z4Y5', 'Z4X6Z7Z8', 'Z0Z4X7X8', 'X0Y1Z2Z5Z6Y7'] : False\n",
+      "6 :: 53651: [[9,2, 2]] : 6 :['X1X3Z7Z8', 'Z2X5Z7Z8', 'Z0X6Z7Z8', 'Z3Z5Z6X7', 'X0Z1Y2Y8', 'X0X1X2Z4Z5Z6', 'X0Y1Z3Y4Z6Z8'] : False\n",
+      "6 :: 53857: [[9,2, 2]] : 1 :['Z0Z1X2Z8', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Z4Y5Y8', 'X0Z2X3Z5Z7Z8', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 53927: [[9,2, 2]] : 1 :['X3Z4Z5Z8', 'Z3X4Z6Z8', 'X0Z3X5Z7', 'Y1Y2X4X5', 'X0Z1Z4X6', 'Z2Z5X7Z8', 'Y0X1Z2Y3Z7X8'] : False\n",
+      "6 :: 54050: [[9,2, 2]] : 1 :['X0X4Z5Z6', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y0Y4Z7X8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 54312: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'X4Z5Z6Z8', 'X0Z3Z4X5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'X0Y2Y3Z5Z7Z8'] : False\n",
+      "6 :: 54356: [[9,2, 2]] : 1 :['X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'X0X1Z3X6', 'Y1Y3X5X7', 'Z5X6Y7Y8', 'X0Z2Z3Z4X5Z7'] : False\n",
+      "6 :: 54407: [[9,2, 2]] : 4 :['X0X1Z2Z8', 'X0X3Z5Z7', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'Z1Y2Z3Z4Y7Z8', 'Y0Z1X2Y3Y4Y8'] : False\n",
+      "6 :: 54534: [[9,2, 2]] : 1 :['Y1Y2Z6Z7', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'X0Z3X5Z7', 'X0Z1Z4X6', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 56002: [[9,2, 2]] : 2 :['Y1Y2Z6Z7', 'X0X3Z4Z6', 'X0Z3X4Z7', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Y0X1Z2Y3Z5X8'] : False\n",
+      "6 :: 56124: [[9,2, 2]] : 4 :['X3Z4Z7Z8', 'X0Z2Y3Y4', 'Z1X5Z6Z8', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X2Z3Z5X8', 'X0X1X2Z4Z5Z6'] : False\n",
+      "6 :: 56426: [[9,2, 2]] : 2 :['Y1Y2Z6Z7', 'X0X3Z4Z6', 'X0Z3X4Z7', 'X5Z6Z7Z8', 'Z1X4Y5Y6', 'Z2Z4Z5X7', 'Y0X1Z2Y3Z5X8'] : False\n",
+      "6 :: 56558: [[9,2, 2]] : 384 :['X0X1Z7Z8', 'X0X2X3Z7', 'X2X4Z7Z8', 'X0X2X5Z8', 'X0Y2Z3Z4Z5Y6', 'Y0Y2Z3Z6X7Z8', 'Y1X2Z4Z5Z6Y8'] : False\n",
+      "6 :: 56647: [[9,2, 2]] : 4 :['X0X1Z5Z8', 'X1X2X3Z6', 'X2X4Z5Z7', 'Z1Y2Y5Z8', 'X0Y3Y6Z7', 'X0X3Z4X7', 'Z0Y2Z3Z4Z5Y8'] : False\n",
+      "6 :: 56779: [[9,2, 2]] : 2 :['X3Z4Z5Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'X0Y1Y2Z6Z7Z8', 'Y0X1Z2Y3Z7X8'] : False\n",
+      "6 :: 56797: [[9,2, 2]] : 1 :['X0X1Z7Z8', 'X1X2X3Z5', 'X4Z5Z7Z8', 'X0Z3Z4X5', 'Z2Z3X6Z8', 'Z0Y4Z6Y8', 'X0Z1Y2Z4Z6Y7'] : False\n",
+      "6 :: 56936: [[9,2, 2]] : 1 :['X4Z5Z6Z8', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8', 'X0X1X2Z3Z6Z8'] : False\n",
+      "6 :: 56980: [[9,2, 2]] : 2 :['X0X1X2Z8', 'Z3X4Z6Z8', 'X0Z3X5Z7', 'X1Y3Z4Y5', 'Z4X6Z7Z8', 'Z0Z4X7X8', 'X0Y1Z2Z5Z6Y7'] : False\n",
+      "6 :: 57251: [[9,2, 2]] : 1 :['X3Z5Z6Z8', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'X0X1Z3X6', 'Y1Y2Z6X7', 'Z5X6Y7Y8', 'X0X2Z4Z5Z7Z8'] : False\n",
+      "6 :: 57555: [[9,2, 2]] : 4 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X0X1Z2X4', 'X0Y3Y5Z6', 'X1Z5X6Z8', 'X0Z1Z4Z6X7Z8', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 57929: [[9,2, 2]] : 2 :['X2Z5Z7Z8', 'Z0X4Z6Z8', 'Y0X3Y4Z7', 'Z2X5Z6Z7', 'Y1Y2Z3X7', 'Y1Y2Y6Y8', 'X1Z3Z4Z5X6Z7'] : False\n",
+      "6 :: 58138: [[9,2, 2]] : 8 :['X0X1Z5Z8', 'X2X3Z5Z6', 'X2X4Z5Z7', 'Z1Y2Y5Z8', 'X0Y3Y6Z7', 'X3Z4X7Z8', 'Z0Y2Z3Z4Z5Y8'] : False\n",
+      "6 :: 59430: [[9,2, 2]] : 1 :['Y1Y2Z6Z7', 'X0X3Z4Z6', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'Z2Z4Z5X7', 'X0Z1Z3Z5X6Z8', 'Y0X1Z2Y3Z5X8'] : False\n",
+      "6 :: 59529: [[9,2, 2]] : 2 :['X1X2Z4Z8', 'X0X1X3Z6', 'Z4X5Z6Z8', 'Z3Z5X6Z8', 'Z1Z2Z3X7', 'X0X1Z2X4Z5Z7', 'Y0Y1Z5Z6Z7X8'] : False\n",
+      "6 :: 59743: [[9,2, 2]] : 2 :['X0X3Z4Z6', 'X0Z3X4Z7', 'X5Z6Z7Z8', 'X0Y1Y2X5', 'Z1X4Y5Y6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 59894: [[9,2, 2]] : 8 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X0X1X3Z6', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 59940: [[9,2, 2]] : 2 :['X0X3Z4Z6', 'X0Z3X4Z7', 'X5Z6Z7Z8', 'X0Y1Y2X5', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 60348: [[9,2, 2]] : 1 :['Z0X3Z7Z8', 'X4Z5Z6Z8', 'Z2Z4X6Z7', 'Y1Y2Y5Y6', 'Z3Z5Z6X7', 'X0X1X2X7', 'X0Y1Z2Z4Z5Y8'] : False\n",
+      "6 :: 60784: [[9,2, 2]] : 1 :['X3Z4Z5Z8', 'Z1Z3X5Z8', 'Z1X6Z7Z8', 'Y3Z6Z7Y8', 'X1Y2X4Y8', 'X0X1X2Z4Z5Z6', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 61161: [[9,2, 2]] : 4 :['X0X1Z4Z8', 'Y2Y3Z5Z6', 'Z1X4Z7Z8', 'X0Z2X5Z7', 'Z3X6Z7Z8', 'Z4Z5Z6X7', 'Z0X2Z3Z4Z6X8'] : False\n",
+      "6 :: 61250: [[9,2, 2]] : 8 :['X2X3Z4Z5', 'Z2X4Z7Z8', 'X0X1Y2Y4', 'Z3X5Z7Z8', 'Z0Y1X7Y8', 'X0Y1Z2Z3Y6Z7', 'Y1Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 62201: [[9,2, 2]] : 2 :['X1Z2X3Z8', 'X1X4Z5Z8', 'Z4X5Z6Z8', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Y3Y5Y7Y8', 'X0X1X2Z3Z6Z7'] : False\n",
+      "6 :: 62255: [[9,2, 2]] : 2 :['Z1X2Z4Z8', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'X1Z4Y5Y8', 'Z0Y4Y7X8', 'X0Z1X3Z5Z6Z7'] : False\n",
+      "6 :: 62374: [[9,2, 2]] : 1 :['X0X3Z4Z5', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'X0Z1Z4X6', 'Z2Z5X7Z8', 'X0Y1Y2Z6Z7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 62441: [[9,2, 2]] : 1 :['Z1X2Z4Z8', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X3Y6X8', 'Z0Y4Y7X8', 'X0Z1X3Z5Z6Z7'] : False\n",
+      "6 :: 62464: [[9,2, 2]] : 4 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X0X1Z2X4', 'X0Y3Y5Z6', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 62468: [[9,2, 2]] : 8 :['X0X1Z6Z8', 'X2X3Z4Z5', 'X2Z3X5Z7', 'Z2Z3X7Z8', 'X0Y3X4Y7', 'Z0Z6Z7X8', 'Z1X2Z4X6Z7Z8'] : False\n",
+      "6 :: 62588: [[9,2, 2]] : 4 :['X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z1Y2Y3X8', 'X0Z2Z3Y5Y6Z7', 'Y0Z1Z2Y5X7Z8'] : False\n",
+      "6 :: 63158: [[9,2, 2]] : 8 :['X0X1Z5Z8', 'X0X2Z3Z4', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'Z3Z4Y6Y7', 'Z0Z2Z4Z5Y6Y8'] : False\n",
+      "6 :: 63222: [[9,2, 2]] : 1 :['Z2Z3X4Z8', 'Z1Y3Z4Y5', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'X0Z3X5X6', 'X0Y3Z6Y8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 63248: [[9,2, 2]] : 1 :['Y1Y2Z6Z7', 'X0X3Z4Z6', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Y0X1Z2Y3Z5X8'] : False\n",
+      "6 :: 63465: [[9,2, 2]] : 8 :['X1X2Z4Z7', 'X1X3Z5Z7', 'Z2X4Z7Z8', 'Z3X5Z7Z8', 'Z0Y1X7Y8', 'X0Y1Z2Z3Y6Z7', 'Y1Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 63649: [[9,2, 2]] : 4 :['X0X1Z4Z8', 'Z1X4Z7Z8', 'X0Z2X5Z7', 'Z3X6Z7Z8', 'X2X3Y5Y6', 'Z4Z5Z6X7', 'Y0X2Z3Z4Z6Y8'] : False\n",
+      "6 :: 64258: [[9,2, 2]] : 2 :['X0X1X2Z4', 'X1X3Z6Z8', 'Z4X5Z6Z8', 'Z3Z5X6Z8', 'X1Z2X4Z5Z7Z8', 'X0Z1Z2Z3X7Z8', 'Z0Y1Z5Z6Z7Y8'] : False\n",
+      "6 :: 64411: [[9,2, 2]] : 2 :['X1X2Z3Z4', 'Y2Y4Z5Z7', 'Z1Z2X5Z8', 'Z3X6Z7Z8', 'Z4Z6X7Z8', 'Z0X1Y6Y8', 'X0Y1Y3Z5Z6Z8'] : False\n",
+      "6 :: 64427: [[9,2, 2]] : 16 :['X2X3Z6Z7', 'Y0X1Y4Z8', 'X0Y1Y5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Z1Z2Z3X8', 'X0X1X2Z4Z5Z6'] : False\n",
+      "6 :: 64493: [[9,2, 2]] : 1 :['X3Z4Z7Z8', 'Z2Z3X4Z8', 'Z1X5Z6Z8', 'X1Y2X3Y6', 'Z0Z1Z3X7', 'X2Z3Z5X8', 'X0X1X2Z4Z5Z6'] : False\n",
+      "6 :: 64498: [[9,2, 2]] : 4 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X0X1Z2X4', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 64524: [[9,2, 2]] : 4 :['X0X1Z4Z8', 'Z1X4Z7Z8', 'Z2X5Z7Z8', 'Z3X6Z7Z8', 'Z4Z5Z6X7', 'X1Y2Y3X7', 'Y0X2Z3Z4Z6Y8'] : False\n",
+      "6 :: 64545: [[9,2, 2]] : 8 :['X3Z6Z7Z8', 'Z0X4Z6Z8', 'Y2Y5Z6Z8', 'Y1Y2Y6Y8', 'X0X2Z4Z5Z7Z8', 'X0X1Z3Z5X6Z7', 'Y1Z2Z3Z5Y7Z8'] : False\n",
+      "6 :: 64617: [[9,2, 2]] : 6 :['X0X1X2Z4', 'X0X1X3Z6', 'X0Z4X5Z6', 'Z3Z5X6Z8', 'X1Z2X4Z5Z7Z8', 'X0Z1Z2Z3X7Z8', 'Z0Y1Z5Z6Z7Y8'] : False\n",
+      "6 :: 64777: [[9,2, 2]] : 1 :['X0X3Z4Z6', 'Z3X4Z7Z8', 'Y1Y2Y3Y4', 'X5Z6Z7Z8', 'Z2Z4Z5X7', 'X0Z1Z3Z5X6Z8', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 64930: [[9,2, 2]] : 2 :['X1X2Z4Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X0X1Z5X6', 'X3X6Z7Z8', 'Z1Z4Z6X7', 'Y0Y1Z2Z3Z7X8'] : False\n",
+      "6 :: 65110: [[9,2, 2]] : 2 :['X0X1Z6Z8', 'X0X2Z4Z8', 'Z2Y3Y4Z7', 'X5Z6Z7Z8', 'Z1Z5X6Z8', 'Z3Z5X7Z8', 'Z0X3Z4Z5Z6X8'] : False\n",
+      "6 :: 65199: [[9,2, 2]] : 4 :['X0X1Z6Z8', 'Z2X3X4Z7', 'Y3Z4Y5Z7', 'Z2Z3X7Z8', 'X0Y2X5Y7', 'Z0Z6Z7X8', 'X0Z1X2Z4X6Z7'] : False\n",
+      "6 :: 65372: [[9,2, 2]] : 1 :['Z1Y2Y3Z6', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'X0Z1X4X8', 'X2Y5Y6X8', 'X0Y1Y2Z3Z7Z8'] : False\n",
+      "6 :: 65375: [[9,2, 2]] : 6 :['X3Z4Z5Z8', 'Z1Z3X5Z8', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'X1X2Z3X8', 'X0Z2Z3X4Z7Z8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 65455: [[9,2, 2]] : 8 :['X0X1X2Z6', 'X0X1X3Z7', 'Z0Y1Y4Z8', 'Z2Y5Y6Z7', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 65694: [[9,2, 2]] : 8 :['X0X1X2Z7', 'X1X3Z5Z7', 'X0X1X4Z6', 'Z3X4X5Z8', 'Y4Z5Y6Z8', 'Z0Z1Z2X7', 'X0Y1Z2Z3Z4Y8'] : False\n",
+      "6 :: 66272: [[9,2, 2]] : 2 :['X0X1Z5Z8', 'X2X3Z6Z7', 'X2X4Z5Z8', 'Z1Y4Y5Z7', 'Z3X6Z7Z8', 'Z0Z2Y3Y8', 'X0Z2Z4Z6X7Z8'] : False\n",
+      "6 :: 66419: [[9,2, 2]] : 4 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X0X1X3Z6', 'Z1Z2Z3X4', 'Z1Y2Y3X8', 'X0Z2Z3Y5Y6Z7', 'Y0Z1Z2Y5X7Z8'] : False\n",
+      "6 :: 66526: [[9,2, 2]] : 1 :['Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X2Y5X8', 'Z0Y4Y7X8', 'X0Z1X2Z4Z7Z8'] : False\n",
+      "6 :: 66558: [[9,2, 2]] : 1 :['X0X1X2Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'X0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 66623: [[9,2, 2]] : 32 :['X0X1Z6Z8', 'X0X2Z7Z8', 'X1X3X4Z7', 'X3X5Z6Z7', 'Z1Y3Y6Z8', 'Z2Z4Z5X7', 'Z0Y3Z4Z5Z6Y8'] : False\n",
+      "6 :: 66708: [[9,2, 2]] : 24 :['X0Y1Y2Z7', 'X3X4Z5Z6', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Z2X3Z5Z7Z8', 'X0Z2Z3Z4X7Z8', 'Z0Y1Z2Z5Z6Y8'] : False\n",
+      "6 :: 66866: [[9,2, 2]] : 64 :['X0X1Z7Z8', 'X3X4Z5Z6', 'Z2Y3Y5Z6', 'Z2X3Z4X6', 'Z0Z1Y7Y8', 'X0Y2Y3Z4Z5Z7', 'Y0X2Z3Z4Y7Z8'] : False\n",
+      "6 :: 66942: [[9,2, 2]] : 4 :['X1X2Z6Z7', 'X3Z6Z7Z8', 'Z1Z2X4Z5', 'X0X1X5Z7', 'Z1Z2X6X7', 'Z0Y3Z5Y8', 'X0Y1Z3Z4Z5Y6'] : False\n",
+      "6 :: 67082: [[9,2, 2]] : 4 :['X0X1X2Z6', 'X0X1X3Z7', 'Z0Y1Y4Z8', 'Z2Y5Y6Z7', 'Z3Y5Z6Y7', 'X1Z4X5Z6Z7Z8', 'X0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 67262: [[9,2, 2]] : 1 :['Y1Y2Z3Z8', 'Z4X5Z7Z8', 'X1Y3Z4Y6', 'X0Z3X5X6', 'Z0Z5X7Z8', 'Z1X4Z7X8', 'X1Z2X4Z5Z6Z8'] : False\n",
+      "6 :: 67429: [[9,2, 2]] : 4 :['X0X1Z5Z8', 'X2Z3Z4Z8', 'X0Z2X3Z6', 'Z2X4Z7Z8', 'Z1X5Z6Z7', 'X0X2Y6Y7', 'Y0Z2Z4Z5Y6X8'] : False\n",
+      "6 :: 67456: [[9,2, 2]] : 1 :['X2X3Z4Z5', 'X0X1Z2X4', 'Y2Y4Z7Z8', 'Y3Y5Z6Z8', 'X0X1Z5X6', 'Z1Z4Z6X7', 'Y0Y1Z2Z3Z7X8'] : False\n",
+      "6 :: 67492: [[9,2, 2]] : 1 :['X1X2Z4Z7', 'Z2X4Z7Z8', 'Z3X5Z7Z8', 'X0X1Y3Y5', 'Y4Y5X6X7', 'Y0Y1X7X8', 'X0Y1Z2Z3Y6Z7'] : False\n",
+      "6 :: 67529: [[9,2, 2]] : 16 :['X0X1Z6Z8', 'X2X3Z4Z5', 'Z2Z3Y4Y5', 'Z2Z3X7Z8', 'X0Y3X4Y7', 'Z0Z6Z7X8', 'X0Z1X2Z4X6Z7'] : False\n",
+      "6 :: 67557: [[9,2, 2]] : 16 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Z0X2X4Z6', 'Z1X2X5Z6', 'X2Z3X7Z8', 'X0Y2Z4Y6Z7Z8', 'Y2Z3Z4Z5Z6Y8'] : False\n",
+      "6 :: 67668: [[9,2, 2]] : 96 :['X0X1Z6Z7', 'X2X3Z4Z5', 'Z2Z3Y4Y5', 'Z0X6Z7Z8', 'Z1Z6X7Z8', 'X0Y2Y4Z5Z6Z8', 'X0Y2Z3Z4Z7Y8'] : False\n",
+      "6 :: 67783: [[9,2, 2]] : 4 :['X2X3Z4Z5', 'Z2X4Z7Z8', 'X0X1Y2Y4', 'Z3X5Z7Z8', 'Y2Y3X6X7', 'Y0Y1X7X8', 'X0Y1Z2Z3Y6Z7'] : False\n",
+      "6 :: 68304: [[9,2, 2]] : 1 :['X0X3Z4Z6', 'Z3X4Z7Z8', 'Y1Y2Y3Y4', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 68373: [[9,2, 2]] : 1 :['X1X3Z5Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Z2X3X6Z8', 'Y3Z6Y7Z8', 'Z1Z2Z3X8', 'X0X1X2Z4Z5Z6'] : False\n",
+      "6 :: 68493: [[9,2, 2]] : 16 :['X1X2Z5Z6', 'Z0X3Z7Z8', 'X4Z5Z6Z8', 'Z1Z2X5X6', 'Z3Z5Z6X7', 'X0Z1Z3Z4X5Z7', 'Y1Z2Z3Z4Z5Y8'] : False\n",
+      "6 :: 68494: [[9,2, 2]] : 16 :['X0X1X2Z7', 'X0X1X3Z5', 'X0X1X4Z6', 'X3Z4X6Z8', 'Z0Z1Z2X7', 'X1Z3X5Z6Z7Z8', 'X0Y1Z2Z3Z4Y8'] : False\n",
+      "6 :: 68582: [[9,2, 2]] : 2 :['Z1X2Z4Z8', 'Y2X3Y4Z5', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X3Y6X8', 'Y0Y4X7X8', 'X0Z1X3Z5Z6Z7'] : False\n",
+      "6 :: 68599: [[9,2, 2]] : 4 :['X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'X0Y1Y2X5', 'X0Z1Z3Z5X6Z8', 'X0Z2Z4Z5X7Z8', 'Y0X1Z2Y3Z5X8'] : False\n",
+      "6 :: 68687: [[9,2, 2]] : 8 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 68778: [[9,2, 2]] : 1 :['Z1X4Z6Z8', 'Z1X5Z7Z8', 'Z2Z4X6Z8', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Y4Y5X8', 'X0Y2Y3Z6Z7Z8'] : False\n",
+      "6 :: 68779: [[9,2, 2]] : 1 :['Z2Y3Y4Z5', 'Z1Z3X5Z8', 'Z1X6Z7Z8', 'X1X2Z3X8', 'X0Y3Z6Y8', 'Z4Y5Y6X8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 68833: [[9,2, 2]] : 1 :['X0X3Z4Z5', 'X0Z3X4Z6', 'Z3X5Z7Z8', 'Y1Y2X4X5', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 68834: [[9,2, 2]] : 1 :['Y1Y2Z6Z7', 'X3Z4Z5Z8', 'X0Z3X4Z6', 'Z3X5Z7Z8', 'X0Z1Z4X6', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 68842: [[9,2, 2]] : 2 :['X1X2Z4Z7', 'X0X1Z2X4', 'Y3Y5Z6Z8', 'X0X1Z5X6', 'X3X6Z7Z8', 'Z1Z4Z6X7', 'Y0Y1Z2Z3Z7X8'] : False\n",
+      "6 :: 69041: [[9,2, 2]] : 1 :['X1X2Z4Z8', 'X0X1X3Z6', 'X0Z4X5Z6', 'Z3Z5X6Z8', 'Z1Z2Z3X7', 'X0X1Z2X4Z5Z7', 'Y0Y1Z5Z6Z7X8'] : False\n",
+      "6 :: 69109: [[9,2, 2]] : 2 :['Z1Y2Y3Z6', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1X4Z7X8', 'X0Y1Y2Z3Z7Z8', 'X1Z2X4Z5Z6Z8'] : False\n",
+      "6 :: 69198: [[9,2, 2]] : 2 :['X1X3Z6Z8', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'X0Z1X4X8', 'X0Y1Y2Z3Z7Z8', 'Z1X2Z3X4Z5Z6'] : False\n",
+      "6 :: 69278: [[9,2, 2]] : 2 :['X0X1Z7Z8', 'X4Z6Z7Z8', 'Z3X5Z7Z8', 'Z2Z4X6Z8', 'Z1Z4Z5X7', 'Z0X3Y6Y8', 'X0Y2Y3Z5Z6Z8'] : False\n",
+      "6 :: 69481: [[9,2, 2]] : 1 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 69669: [[9,2, 2]] : 2 :['X3Z4Z5Z8', 'X0Z3X4Z6', 'X0Z3X5Z7', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'X0Y1Y2Z6Z7Z8', 'Y0X1Z2Y3Z7X8'] : False\n",
+      "6 :: 69674: [[9,2, 2]] : 4 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X0X1X3Z6', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'X2Y4Z6Y8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 70499: [[9,2, 2]] : 2 :['X0X3Z4Z5', 'X0Z3X4Z6', 'X0Z3X5Z7', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'X0Y1Y2Z6Z7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 70616: [[9,2, 2]] : 16 :['X0X1Z6Z7', 'Z2X3X4Z8', 'Y3Z4Y5Z8', 'Z0X6Z7Z8', 'Z1Z6X7Z8', 'X0X2X3Z4Z5Z6', 'X0Y2Z3Z4Z7Y8'] : False\n",
+      "6 :: 70784: [[9,2, 2]] : 1 :['Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Z0Y4Y7X8', 'Y0Y1Z2Y3Y7Z8'] : False\n",
+      "6 :: 70878: [[9,2, 2]] : 16 :['X0X1Z2Z8', 'X0X3Z5Z7', 'X0X4Z6Z7', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'Z1Y2Z3Z4Y7Z8', 'Z0Z1X2Y3Y4X8'] : False\n",
+      "6 :: 70892: [[9,2, 2]] : 2 :['Y1Y2Z3Z8', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'X0Z1X4X8', 'X0X1X3Z6Z7Z8', 'X1Z2X4Z5Z6Z8'] : False\n",
+      "6 :: 71005: [[9,2, 2]] : 1 :['X3Z4Z5Z8', 'Z1Z2X4X5', 'X0Z1X6Z8', 'Y1X2X3Y6', 'Z3X5X6Z7', 'Y3Z6Z7Y8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 71014: [[9,2, 2]] : 2880 :['X0X1Z7Z8', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'X2Z4X5Z6', 'X2Z3Z5X6', 'Z0Z1Y7Y8', 'Z0X2Z3Z4X7Z8'] : False\n",
+      "6 :: 71195: [[9,2, 2]] : 4 :['X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1X4Y5Y6', 'Z1Y2Y3X8', 'X0Z1Z2Z3X4Z7', 'Y0Z1Z2Y5X7Z8'] : False\n",
+      "6 :: 71693: [[9,2, 2]] : 4 :['X1X2Z3Z7', 'X1Z2X3Z8', 'Z4X5Z6Z8', 'X0X1Y4Y5', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'X0Y1Z2Z5Z7Y8'] : False\n",
+      "6 :: 71818: [[9,2, 2]] : 2 :['X3Z4Z7Z8', 'X0Z2Y3Y4', 'Z2Z5X6Z8', 'X1Y2X3Y6', 'Z0Z1Z3X7', 'X2Z3Z5X8', 'X0Z1X5Z6Z7Z8'] : False\n",
+      "6 :: 71937: [[9,2, 2]] : 2 :['X0Y1Y2Z7', 'X3X4Z5Z6', 'X0Z3X5Z6', 'Z4Z5X6Z8', 'Z2Z3Z4X7', 'X1Y3X6Y7', 'Z0Y1Z2Z5Z6Y8'] : False\n",
+      "6 :: 71987: [[9,2, 2]] : 4 :['X0X1Z7Z8', 'X0X2X3Z8', 'X0X2X4Z6', 'Z2Z3Z4X5', 'Z4X6Z7Z8', 'Y0Y2Z5Z6X7Z8', 'Y1X2Z3Z5Z6Y8'] : False\n",
+      "6 :: 72114: [[9,2, 2]] : 1 :['X3Z4Z5Z8', 'Z2Z3X4Z8', 'Z1X6Z7Z8', 'X0Z3X5X6', 'Y3Z6Z7Y8', 'Y1X2X5Y8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 72129: [[9,2, 2]] : 1 :['Z1X4Z6Z8', 'Z1X5Z7Z8', 'X0Z2Z4X6', 'Z3Z5X7Z8', 'X1Y2Y6X7', 'Z0Y4Y5X8', 'X1Z2X3Z4Z5Z7'] : False\n",
+      "6 :: 72387: [[9,2, 2]] : 4 :['X1X2Z5Z6', 'Z0X3Z7Z8', 'X4Z5Z6Z8', 'Z1Z4X5Z7', 'Z2Z4X6Z7', 'Z3Z5Z6X7', 'X0Y1Z2Z4Z5Y8'] : False\n",
+      "6 :: 72489: [[9,2, 2]] : 48 :['X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z2X4Y5Y8', 'X0Z1Z2Z3X4Z7', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 72613: [[9,2, 2]] : 1 :['X2X3Z5Z6', 'X0Z1X4Z5', 'Z2Z4X5Z8', 'Z1Z3X6Z8', 'Z2Z3X7Z8', 'Y0X1X2Y8', 'X0X1X3Z4Z7Z8'] : False\n",
+      "6 :: 72688: [[9,2, 2]] : 2 :['X2X3Z6Z7', 'Y0X1Y4Z8', 'X0Y1Y5Z8', 'X2Z3X7Z8', 'Z1Z2Z3X8', 'X0X1X2Z4Z5Z6', 'X1Z2Z5X6Z7Z8'] : False\n",
+      "6 :: 73043: [[9,2, 2]] : 2 :['X1X2Z4Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X0X1Z5X6', 'X3X6Z7Z8', 'X0Z1Z4Z6X7Z8', 'Y0Y1Z2Z3Z7X8'] : False\n",
+      "6 :: 73088: [[9,2, 2]] : 2 :['Y1Y2Z6Z7', 'X3Z4Z5Z8', 'X0Z3X4Z6', 'X0Z3X5Z7', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 73236: [[9,2, 2]] : 2 :['X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'X0Y1Y2X5', 'Z2Z4Z5X7', 'X0Z1Z3Z5X6Z8', 'Y0X1Z2Y3Z5X8'] : False\n",
+      "6 :: 73256: [[9,2, 2]] : 8 :['Z0Z1X2Z8', 'X3X4Z5Z6', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X0Z2X3Z5Z7Z8', 'X0Y1Z3Z4Y7Z8', 'X1Y3Z4Z6Z7Y8'] : False\n",
+      "6 :: 73272: [[9,2, 2]] : 2 :['Z0Z1X2Z8', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'X0Z2X3Z5Z7Z8', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 73302: [[9,2, 2]] : 4 :['Z3X4Z5Z8', 'X3X5Z6Z8', 'Z1X2Z3X6', 'Z2X4Z6X7', 'Z2X3Z5X8', 'Z0X1X2Z6Z7Z8', 'X0Z1X3Z4Z6Z7'] : False\n",
+      "6 :: 73321: [[9,2, 2]] : 4 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'X0Y3Y5Z6', 'X1Z5X6Z8', 'X0Z1Z4Z6X7Z8', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 73448: [[9,2, 2]] : 16 :['X3Z6Z7Z8', 'Z0X4Z6Z8', 'Y2Y5Z6Z8', 'Y1Y2Z3X7', 'Y1Y2Y6Y8', 'X0X2Z4Z5Z7Z8', 'X0X1Z3Z5X6Z7'] : False\n",
+      "6 :: 73530: [[9,2, 2]] : 32 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Z0X2X4Z6', 'Z1X2X5Z6', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Y2Z3Z4Z5Z6Y8'] : False\n",
+      "6 :: 73684: [[9,2, 2]] : 8 :['X0X1Z6Z7', 'X0X2Z6Z8', 'X3X4Z5Z8', 'X3Z4X5Z7', 'Z0X6Z7Z8', 'Z1Y3Z4Z6Y7Z8', 'Z2Y3Z5Z6Z7Y8'] : False\n",
+      "6 :: 73746: [[9,2, 2]] : 4 :['X1X2Z3Z7', 'X1Z2X3Z8', 'Z4X5Z6Z8', 'X0X1Y4Y5', 'Z0Z5X6Z8', 'X0Z1Z3Z4Z6X7', 'Y1Z2Z5Z6Z7Y8'] : False\n",
+      "6 :: 73747: [[9,2, 2]] : 1 :['X3Z6Z7Z8', 'X0Z1X4Z7', 'Z2X5Z6Z8', 'Y3X4Z5Y6', 'Y1Y2Y6Y7', 'Y0Y1Y6Y8', 'X1X2Z4Z5Z6Z7'] : False\n",
+      "6 :: 74175: [[9,2, 2]] : 2 :['X1X2Z6Z7', 'X3Z6Z7Z8', 'Z1Z2X4Z5', 'X1X5Z7Z8', 'Y1Y3Y4Y7', 'Z0Y3Z5Y8', 'X0Y1Z3Z4Z5Y6'] : False\n",
+      "6 :: 74197: [[9,2, 2]] : 2 :['Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y0Y4X7X8', 'X0Z2X4Z6Z7Z8', 'Y0Y1Z2Y3Y7Z8'] : False\n",
+      "6 :: 74237: [[9,2, 2]] : 16 :['Z0Z1X2Z8', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X0Y1Z3Z4Y7Z8', 'X0X1Z2Y3Y4X8'] : False\n",
+      "6 :: 74324: [[9,2, 2]] : 1 :['X0X4Z5Z6', 'X0Z3Z4X5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Y0Y4Z7X8', 'X0X1X2Z3Z6Z8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 74346: [[9,2, 2]] : 1 :['Y2Y3Z7Z8', 'X1X4Z5Z8', 'Z4X5Z6Z8', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'X0X1X2Z3Z6Z7', 'Y1Z2Z5Z6Z7Y8'] : False\n",
+      "6 :: 74395: [[9,2, 2]] : 1 :['X0X4Z5Z6', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'X0Z1Z4X6', 'Z1Z2X7Z8', 'Y0Y4Z7X8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 74409: [[9,2, 2]] : 1 :['X1X2Z3Z6', 'X4Z5Z6Z8', 'X0Z3Z4X5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'X0Y2Y3Z5Z7Z8'] : False\n",
+      "6 :: 74410: [[9,2, 2]] : 1 :['X1X3Z5Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'Y3Z6Y7Z8', 'Z1Z2Z3X8', 'X0X1X2Z4Z5Z6'] : False\n",
+      "6 :: 74448: [[9,2, 2]] : 4 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X0X1X3Z6', 'Z1X4Y5Y6', 'Z1Y2Y3X8', 'X2Y4Z6Y8', 'Y0Z1Z2Y5X7Z8'] : False\n",
+      "6 :: 74466: [[9,2, 2]] : 2 :['X1X2Z4Z7', 'X0X1Z2X4', 'Y3Y5Z6Z8', 'X0X1Z5X6', 'X3X6Z7Z8', 'X0Z1Z4Z6X7Z8', 'Y0Y1Z2Z3Z7X8'] : False\n",
+      "6 :: 74548: [[9,2, 2]] : 2 :['X3Z4Z5Z8', 'Z1Z3X5Z8', 'X0Z1X6Z8', 'X1X2Z3X8', 'Y3Z6Z7Y8', 'X0Z2Z3X4Z7Z8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 74815: [[9,2, 2]] : 2 :['X3Z4Z6Z7', 'Z3Y4Y5Z7', 'Z1X2Z3X6', 'Z2X3Z5X8', 'Z3Z4Y7Y8', 'Z0X1X2Z6Z7Z8', 'X0Z1Z3X4Z5Z8'] : False\n",
+      "6 :: 74858: [[9,2, 2]] : 8 :['Z3X4Z5Z8', 'Z4X5Z7Z8', 'Z1X2Z3X6', 'Z2X4Z6X7', 'Z2Y5Z6Y8', 'Z0X1X2Z6Z7Z8', 'X0Z1X3Z4Z6Z7'] : False\n",
+      "6 :: 74931: [[9,2, 2]] : 2 :['Y2Y3Z6Z7', 'X0Z1X4Z6', 'Z1X5Z7Z8', 'X0Z2Z4X6', 'Z3Z5X7Z8', 'Y0Y4Y5Y8', 'X1X2Z3Z4Z5Z6'] : False\n",
+      "6 :: 74937: [[9,2, 2]] : 2 :['Y1Y2Z6Z7', 'X0X3Z4Z5', 'X0Z3X4Z6', 'X0Z3X5Z7', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Y0X1Z2Y3Z7X8'] : False\n",
+      "6 :: 74946: [[9,2, 2]] : 4 :['X1X2Z4Z7', 'X1X3Z5Z7', 'X0X1Z2X4', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'X0Z1Z4Z6X7Z8', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 74954: [[9,2, 2]] : 8 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X0X1X3Z6', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'X0Z1Z2Z3X4Z7', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 74955: [[9,2, 2]] : 4 :['X0X1X2Z6', 'X0X1X3Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Z2Z3X6X7', 'X1Z2Z4Z5X6Z8', 'X0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 74956: [[9,2, 2]] : 4 :['X1X3Z6Z8', 'Z1Z2Z3X4', 'Y0Z1X5Y6', 'Z0Z2Y5Y7', 'Y1Y2X6X7', 'Z0Z3Y5Y8', 'Z0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 74957: [[9,2, 2]] : 4 :['X3Z4Z6Z7', 'Z3X4Z5Z8', 'Z1X2Z3X6', 'Z2X4Z6X7', 'Z2Y5Z6Y8', 'Z0X1X2Z6Z7Z8', 'X0Z1Z4X5Z7Z8'] : False\n",
+      "6 :: 75011: [[9,2, 2]] : 1 :['Y2Y3Z7Z8', 'Z2X3X4Z5', 'Z4X5Z6Z8', 'X0X1Y4Y5', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Y1Z2Z5Z6Z7Y8'] : False\n",
+      "6 :: 75216: [[9,2, 2]] : 16 :['X2X3Z6Z7', 'Y0X1Y4Z8', 'X0Y1Y5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'X0X1X2Z4Z5Z6', 'X0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 75235: [[9,2, 2]] : 1 :['X3Z6Z7Z8', 'X0X1X2X3', 'X0X1X5Z7', 'Y1Y3Y4Y7', 'Z1Z2X6X7', 'Z0Y3Z5Y8', 'X0Z1Z2X4Z5Z8'] : False\n",
+      "6 :: 75236: [[9,2, 2]] : 1 :['Y1Y2Z6Z7', 'X3Z4Z5Z8', 'X0Z3X4Z6', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 75259: [[9,2, 2]] : 8 :['Z0Z1X2Z8', 'X3X4Z5Z6', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'X0Z2X3Z5Z7Z8', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 75275: [[9,2, 2]] : 2 :['Z2Z3X4Z8', 'Z1Z3X5Z8', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'X0Y3Z6Y8', 'Z4Y5Y6X8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 75282: [[9,2, 2]] : 2 :['X1Z2X3Z8', 'Z4X5Z6Z8', 'X0X1Y4Y5', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Y3Y5Y7Y8', 'X0X1X2Z3Z6Z7'] : False\n",
+      "6 :: 75287: [[9,2, 2]] : 2 :['X2Z5Z7Z8', 'Z0X4Z6Z8', 'Y0X3Y4Z7', 'Z2X5Z6Z7', 'Y1Y3Y5Y7', 'Z3Y6X7Y8', 'X0X1Z3Z5X6Z7'] : False\n",
+      "6 :: 75288: [[9,2, 2]] : 1 :['Y1Y2Z3Z8', 'X1X3Z6Z8', 'Z4X5Z7Z8', 'X0Z3X5X6', 'Z0Z5X7Z8', 'X0Z1X4X8', 'X0Z2X3X4Z5Z7'] : False\n",
+      "6 :: 75290: [[9,2, 2]] : 2 :['Z1Y2Y4Z6', 'X0Z3X5Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'Y1X2Y5X8', 'Y0Y4X7X8', 'X0Z1X3Z5Z6Z7'] : False\n",
+      "6 :: 75294: [[9,2, 2]] : 2 :['X3Z4Z7Z8', 'Z2Z3X4Z8', 'Y1X2X3Y5', 'Z2Z5X6Z8', 'Z0Z1Z3X7', 'X1Y3Z6Y8', 'X0X1X2Z4Z5Z6'] : False\n",
+      "6 :: 75311: [[9,2, 2]] : 1 :['Y1Y2Z6Z7', 'X0X3Z4Z5', 'X0Z3X4Z6', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Y0X1Z2Y3Z7X8'] : False\n",
+      "6 :: 75332: [[9,2, 2]] : 4 :['X3Z4Z6Z7', 'Z4X5Z7Z8', 'Z1X2Z3X6', 'Z2Y3Y5X7', 'Z2Y3X4Y8', 'Z0X1X2Z6Z7Z8', 'X0Z1Z3X4Z5Z8'] : False\n",
+      "6 :: 75333: [[9,2, 2]] : 32 :['X0X1Z4Z5', 'X2X3Z6Z7', 'Y0X2Y4Z6', 'Y1X2Y5Z6', 'Z2Z3Y6Y7', 'X0Y2Z4Y6Z7Z8', 'X0Y2Z3Z5Z6Y8'] : False\n",
+      "6 :: 75367: [[9,2, 2]] : 16 :['X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'X0Z1Z2Z3X4Z7', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 75382: [[9,2, 2]] : 1 :['X1Z4Z7Z8', 'X0X1X2Z5', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'X2Y4Z6Y8', 'Z1X5X6X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 75392: [[9,2, 2]] : 8 :['X1X2Z6Z7', 'X3Z6Z7Z8', 'X0X1X5Z7', 'Z1Z2X6X7', 'Z0Y3Z5Y8', 'X0Z1Z2X4Z5Z8', 'Y1Z3Z4Z5Y6Z8'] : False\n",
+      "6 :: 75634: [[9,2, 2]] : 2 :['Y1Y2Z6Z7', 'X0X3Z4Z5', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Y0X1Z2Y3Z7X8'] : False\n",
+      "6 :: 75678: [[9,2, 2]] : 8 :['X0X1Z4Z5', 'Y0X2Y4Z6', 'Z0X3X4Z7', 'Y1X2Y5Z6', 'Z2X3X6Z8', 'Y3Z6Y7Z8', 'X0Y2Z3Z5Z6Y8'] : False\n",
+      "6 :: 75690: [[9,2, 2]] : 1 :['X0X1X2Z6', 'X0X1X3Z7', 'Z0Y1Y4Z8', 'Y2Z5Y6Z8', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 75711: [[9,2, 2]] : 192 :['X0X1Z6Z7', 'X2X3Z4Z5', 'Y2Y4Z5Z8', 'X2Z3X5Z8', 'Z0X6Z7Z8', 'Z1Z6X7Z8', 'Y2Z3Z4Z6Z7Y8'] : False\n",
+      "6 :: 75757: [[9,2, 2]] : 1 :['X3Z4Z5Z8', 'Z1Z2X4X5', 'Z1X6Z7Z8', 'X0Z3X5X6', 'Y3Z6Z7Y8', 'X1Y2X4Y8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 76005: [[9,2, 2]] : 1 :['Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'X1Z4Y5Y8', 'Z0Y4Y7X8', 'X0Z1X2Z4Z7Z8'] : False\n",
+      "6 :: 76017: [[9,2, 2]] : 8 :['X1X2Z5Z6', 'X1X3Z5Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'X0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 76031: [[9,2, 2]] : 8 :['X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'X0Z1Z2Z3Z4X8'] : False\n",
+      "6 :: 76050: [[9,2, 2]] : 2 :['X1X2Z3Z7', 'X1Z2X3Z8', 'X1X4Z5Z8', 'Z4X5Z6Z8', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'X0Y1Z2Z5Z7Y8'] : False\n",
+      "6 :: 76084: [[9,2, 2]] : 32 :['X0X1Z7Z8', 'X0X2Z5Z8', 'X0X3Z6Z8', 'Z2X4X5Z7', 'Z3X4X6Z7', 'Z1Y4Y7Z8', 'Z0Y4Z5Z6Z7Y8'] : False\n",
+      "6 :: 76716: [[9,2, 2]] : 2 :['X2X3Z6Z7', 'Z0X4Z6Z8', 'Y0X3Y4Z5', 'Z2Z3X5Z7', 'X0X1Z3X6', 'X2X6X7X8', 'Y1Z2Z5Z6Y7Z8'] : False\n",
+      "6 :: 76717: [[9,2, 2]] : 1 :['X3Z4Z5Z8', 'Z2Z3X4Z8', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'X0Z3X5X6', 'X1X2Z3X8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 76756: [[9,2, 2]] : 4 :['X0Z1Z2X5', 'Z3X6Z7Z8', 'Y3X4X5Y6', 'Z4Z6X7Z8', 'Z0X1Y6Y8', 'Y0X2Y7X8', 'X0Y1Y3Z5Z6Z8'] : False\n",
+      "6 :: 79927: [[9,2, 2]] : 2 :['Z2Y3Y4Z5', 'Z1Z3X5Z8', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'X0Y3Z6Y8', 'X1Y2X4Y8', 'Y0X1Z2Z5Y7Z8'] : False\n",
+      "6 :: 80582: [[9,2, 2]] : 32 :['X0X1X2Z7', 'X1X3Z5Z7', 'X1X4Z6Z7', 'Z3Z4Y5Y6', 'Z0Z1Z2X7', 'X0X1Z3X5Z6Z8', 'X0Y1Z2Z3Z4Y8'] : False\n",
+      "6 :: 80599: [[9,2, 2]] : 64 :['X0X1Z6Z8', 'X0X2Z7Z8', 'X0X3X4Z8', 'X0Y3Z4Y5', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'X0Z1X3Z5X6Z7'] : False\n",
+      "6 :: 83859: [[9,2, 2]] : 16 :['X0Z4', 'X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Y1Y2Z6X7', 'X1Z2Z4X5X6Z7', 'Z1Z2Z3Z4Z6X8'] : False\n",
+      "6 :: 83917: [[9,2, 2]] : 16 :['X0Z7', 'X3Z4Z5Z8', 'Z1Z2X4X5', 'Y1X2X3Y6', 'Z3X5X6Z7', 'Y3Z6Z7Y8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 83952: [[9,2, 2]] : 16 :['X0Z2', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'Z0Z1X2Z3X5Z6', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 83959: [[9,2, 2]] : 8 :['X0Z7', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z1Y2Y3X8', 'X1Y2Z3X5Y6Z7', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 83993: [[9,2, 2]] : 16 :['X1Z8', 'Y2Y3Z4Z6', 'Z2X4X5Z6', 'Z3Y5Y6Z7', 'Z2Y4Z5Y7', 'X0Y2Z3Y4Z7Z8', 'Y0Z1X2Z3Z4Y8'] : False\n",
+      "6 :: 84045: [[9,2, 2]] : 16 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z4X5X6Z8', 'Z0Y4Y7X8', 'Z2Z3X4X5Z6Z7', 'Z0X1Z3X4Z5X7'] : False\n",
+      "6 :: 84055: [[9,2, 2]] : 32 :['X0Z7', 'Y1Y2Z3Z8', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'X1Z2X4Z5Z6Z8', 'Z1Z2X3Z5Z7X8'] : False\n",
+      "6 :: 84063: [[9,2, 2]] : 8 :['X0Z7', 'Z1X2Z4Z8', 'Y2X3Y4Z5', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X3Y6X8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 84190: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Y2X3Y4Z5', 'Z3X5Z7Z8', 'X2X3Y5Y6', 'X1Z4Y5Y8', 'Z0Y1Y3X4Z6X7'] : False\n",
+      "6 :: 84313: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'Y1Z2Y4Z6X7Z8', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 84318: [[9,2, 2]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'Y1Z2X3Y7', 'X2X6X7X8', 'X1X2Y3Z4Y6Z7'] : False\n",
+      "6 :: 84326: [[9,2, 2]] : 48 :['Z0X1', 'Z2X3X4Z5', 'Y4Y5Z7Z8', 'Z2X3X6Z7', 'Z2Z5Z6X7', 'Z3Z4Z6X8', 'X0Z1Y2Y3Z7Z8'] : False\n",
+      "6 :: 84331: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y4Z5Z7', 'Y3X4X5Y6', 'Z4Z6X7Z8', 'X1X2Y6Y7', 'Z0X1Y6Y8', 'Z1X2X3Z4Z5Z6'] : False\n",
+      "6 :: 84357: [[9,2, 2]] : 64 :['X0Z8', 'Y1Y2Z7Z8', 'X3X4Z5Z6', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'Z2Z3Z4X7', 'Z0Z1X3Z6Z7X8'] : False\n",
+      "6 :: 86072: [[9,2, 2]] : 8 :['X0Z8', 'Z1X3X4Z6', 'Z2X5Z6Z8', 'Y3Z4X5Y7', 'Y1Y2Y6Y7', 'Z0Y1Y6X8', 'X1X2Z4Z5Z6Z7'] : False\n",
+      "6 :: 86552: [[9,2, 2]] : 8 :['X0Z4', 'X2X3Z6Z7', 'X2X5Z7Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'Z0Z1X2X4Z6Z8', 'X1Z2Z4Z5X6Z8'] : False\n",
+      "6 :: 86698: [[9,2, 2]] : 4 :['X0Z7', 'Z1Y2Y4Z6', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'X2X3Y5Y6', 'X1Z4Y5Y8', 'Z0X1Y2Y3Z4X7'] : False\n",
+      "6 :: 86704: [[9,2, 2]] : 4 :['X0Z7', 'X1Z4Z7Z8', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'Z0Z1Y2Z4X5Y7'] : False\n",
+      "6 :: 87116: [[9,2, 2]] : 24 :['X0Z6', 'X1X3Z7Z8', 'Z2X5Z7Z8', 'Z0X6Z7Z8', 'Z3Z5Z6X7', 'Z1X2Z3X4Z5Z8', 'Y1Z2Z4Z5Z6Y8'] : False\n",
+      "6 :: 87196: [[9,2, 2]] : 16 :['X0Z4', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Y2Z3Y5Z8', 'Y1Y2Z6X7', 'Y1Z2Y6X8', 'X1X2Y3Z4Y6Z7'] : False\n",
+      "6 :: 87214: [[9,2, 2]] : 4 :['X0Z8', 'X1X3Z5Z7', 'Y2Y4Z7Z8', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 87224: [[9,2, 2]] : 8 :['X0Z4', 'X2X3Z6Z7', 'Z0X1X4Z8', 'X2Z3X7Z8', 'Z1Z2Z3X8', 'Z1X2Z4X5Z6Z8', 'X1Z2Z5X6Z7Z8'] : False\n",
+      "6 :: 87225: [[9,2, 2]] : 8 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Z0X1X4Z8', 'Y2Y6Z7Z8', 'Z1Z2Z3X8', 'Z1Z3Z4X5Z6X7'] : False\n",
+      "6 :: 87631: [[9,2, 2]] : 64 :['X1Z8', 'Y2Y3Z4Z6', 'Y2Z3Y4Z7', 'Z3Y5Y6Z7', 'Z4Y5Z6Y7', 'X0Z2X3X5Z7Z8', 'Y0Z1X2Z3Z4Y8'] : False\n",
+      "6 :: 87726: [[9,2, 2]] : 16 :['X0Z7', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Y1Z2Z3Y4Z7Z8', 'Z0Y1Z4Z5Z6Y7'] : False\n",
+      "6 :: 87727: [[9,2, 2]] : 16 :['X0Z7', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'Y1Z2Z3Y4Z7Z8', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 87737: [[9,2, 2]] : 64 :['X0Z8', 'X1X2Z6Z7', 'X3Z6Z7Z8', 'X1X5Z7Z8', 'Z1Z2X6X7', 'Y1Z3Z4Z5Y6Z8', 'Z0Y1Y2Z3X4X8'] : False\n",
+      "6 :: 87773: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z4X5X6Z8', 'X1Z4Y5Y8', 'Z1X2Z3Z4X5Z7', 'Z0X1Y2Y3Z4X7'] : False\n",
+      "6 :: 87774: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'Y1Z4Z5Y6X7Z8', 'Z0Y1Z2Y3X6X8'] : False\n",
+      "6 :: 88110: [[9,2, 2]] : 16 :['X0Z7', 'X1Z4Z7Z8', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'Z1Y2Z3Y4Z5Z8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 88248: [[9,2, 2]] : 48 :['X0Z8', 'X1X2Z4Z8', 'X1X3Z6Z8', 'Z4X5Z6Z8', 'X1Z2Z3X4X6Z7', 'Y1Z3X4Z5Y7Z8', 'Z0Z1Z2X4Z6X8'] : False\n",
+      "6 :: 88272: [[9,2, 2]] : 8 :['X0Z8', 'Y2Y3Z5Z7', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'Z1Y4Z5Y6', 'Z1Z2X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 88315: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z6', 'X2X5Z7Z8', 'Y2X3Y5X6', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'Z0Z1X3X4Z7Z8'] : False\n",
+      "6 :: 88330: [[9,2, 2]] : 4 :['X0Z8', 'X1X3Z5Z7', 'Y2Y4Z7Z8', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'X2Z3Z4X5Z6Z8', 'Z0Z1Y2Z3Z4Y8'] : False\n",
+      "6 :: 88651: [[9,2, 2]] : 16 :['X0Z8', 'X1X2Z6Z7', 'X3Z6Z7Z8', 'Z1Z2X4Z5', 'X1X5Z7Z8', 'Y1Y3Y4Y7', 'Z0Y1Z4X6Z7Y8'] : False\n",
+      "6 :: 88668: [[9,2, 2]] : 8 :['X0Z5', 'X1X3Z6Z8', 'Z1Z2Z3X4', 'Z0Z2Y5Y7', 'Y1Y2X6X7', 'Z0Z3Y5Y8', 'Z0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 88699: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z7', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'Z0Z1X2X4Z6Z8'] : False\n",
+      "6 :: 88725: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'Z1X4Y5Y6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 88743: [[9,2, 2]] : 8 :['X0Z8', 'X2X3Z5Z6', 'Z2Z4X5Z8', 'Z3X4Z5X6', 'Z2Z3X7Z8', 'Z0X1X2X8', 'Y1X2Y4Z6Z7Z8'] : False\n",
+      "6 :: 88801: [[9,2, 2]] : 16 :['X0Z4', 'X2X3Z6Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Z2Z3X6X7', 'X1Z2Z4Z5X6Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 88872: [[9,2, 2]] : 4 :['X0Z8', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 88918: [[9,2, 2]] : 96 :['X0Z8', 'X1X2Z4Z8', 'X1X3Z6Z8', 'Z4X5Z6Z8', 'X1Z2Z3X4X6Z7', 'Y1Z3X4Z5Y7Z8', 'Z0Y1Z5Z6Z7Y8'] : False\n",
+      "6 :: 89090: [[9,2, 2]] : 24 :['Z0X1', 'X3X4Z7Z8', 'Z2X4Z5X6', 'Z2Y3Z5Y7', 'Z2Y4Z6Y8', 'Y2Y3X5X8', 'X0Z1Y2Y3Z4Z7'] : False\n",
+      "6 :: 89107: [[9,2, 2]] : 8 :['X0Z7', 'Z1X2Z4Z8', 'Y2X3Y4Z5', 'Z4X5X6Z8', 'Y1X3Y6X8', 'Z0Y4Y7X8', 'Z1Y3Y5Z6Z7Z8'] : False\n",
+      "6 :: 89411: [[9,2, 2]] : 16 :['X0Z8', 'Y2Y3Z5Z7', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 89450: [[9,2, 2]] : 4 :['X0Z8', 'X1X3Z5Z7', 'X1Z2X4Z8', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'X2Z3Z4X5Z6Z8', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 89452: [[9,2, 2]] : 16 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z5Z8', 'X4X5Z6Z7', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 89510: [[9,2, 2]] : 32 :['X0Z4', 'X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Z2Z3X6X7', 'Y1Z3Z4Z5X6Y8'] : False\n",
+      "6 :: 89562: [[9,2, 2]] : 64 :['X0Z5', 'X2X3Z7Z8', 'Z1Z2Z3X4', 'Z1Z2Y6Y7', 'Z1Z3Y6Y8', 'Z0X1Z4X5Z7Z8', 'Z1X2Z4Z5X6Z8'] : False\n",
+      "6 :: 89636: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z2X4Z5X6', 'Z3Y5Y6Z7', 'Y1Z2Y4Z6X7Z8', 'Z0Z1Z3X4Z7X8'] : False\n",
+      "6 :: 89640: [[9,2, 2]] : 32 :['X0Z1', 'Z3X4Z5Z8', 'X3X5Z6Z8', 'Z2X4Z6X7', 'Z2X3Z5X8', 'Z0X1X2X3Z4Z8', 'Z0Y1Z3Y6Z7Z8'] : False\n",
+      "6 :: 89850: [[9,2, 2]] : 192 :['X0X1', 'Z3Z4Z7Z8', 'Y3Y4Y5Y6', 'Z5Z6Z7Z8', 'X2X4X5X7', 'X2X3X5X8', 'Z0Z1Z2Z3Z5Z8'] : False\n",
+      "6 :: 89869: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z6', 'X1X3Z4Z7', 'X2X5Z7Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'Z0Z1Z2X4Z5X6'] : False\n",
+      "6 :: 89957: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 89959: [[9,2, 2]] : 16 :['X0Z8', 'X2X3Z5Z6', 'Z1Z2Y4Y5', 'Z1Z3X6Z8', 'Z2Z3X7Z8', 'X1Y3X5Y7', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 89976: [[9,2, 2]] : 8 :['X0Z7', 'X1Z4Z7Z8', 'X2X3Z5Z6', 'Z1X4Y5Y6', 'Z1Y2Y3X8', 'X2Y4Z6Y8', 'Z0Z1Y2Z4X5Y7'] : False\n",
+      "6 :: 89978: [[9,2, 2]] : 8 :['X0Z7', 'X1X2Z5Z7', 'X1X3Z6Z7', 'Z1X4Y5Y6', 'Z1Y2Y3X8', 'X2Y4Z6Y8', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 90139: [[9,2, 2]] : 4 :['X0Z2', 'X3Z5Z7Z8', 'Z3X4X5Z7', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'Z0Z1X2X4Z6Z7', 'Z0X1Y2Z3Z4Y7'] : False\n",
+      "6 :: 90145: [[9,2, 2]] : 8 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z5Z8', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 90308: [[9,2, 2]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'Y1Y2Z6X7', 'Z5X6Y7Y8', 'X1X2Y3Z4Y6Z7'] : False\n",
+      "6 :: 90349: [[9,2, 2]] : 8 :['X0Z2', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Z4Y5Y8', 'Z0Z1X2X3Z5Z7', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 90351: [[9,2, 2]] : 4 :['X0Z7', 'X1X2Z5Z7', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Y2Y4X5X8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 90582: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'Z0Y4Z7Y8', 'X1Z2X3X4Z7Z8', 'Y1X3Z5Z6Y7Z8'] : False\n",
+      "6 :: 90621: [[9,2, 2]] : 32 :['X3X4', 'X0X1Z6Z8', 'X0X2Z7Z8', 'Y3Z4Y5Z8', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'Z1X3Z5X6Z7Z8'] : False\n",
+      "6 :: 90626: [[9,2, 2]] : 8 :['X0Z8', 'Y3Y4Z6Z7', 'Y1Y2X5Z8', 'X3Z4X5Z7', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 90648: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1Y3Y5Z8', 'Z2Z3X4X5', 'Y1Z3X4Y6', 'Y4Y5X6X7', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 90691: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z6', 'X1X3Z4Z7', 'Y2Z5Y6Z8', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'Z0Z1X4X5Z6Z7'] : False\n",
+      "6 :: 90788: [[9,2, 2]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'Z0X3X4Z5', 'Y2Y3X5Z6', 'Y1Y2Z6X7', 'X3Y6Y7X8', 'X1Y3Z4Z5Y6Z8'] : False\n",
+      "6 :: 90799: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z7', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'Z0Z1X2X4Z6Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 90828: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Y2X3Y4Z5', 'Z4X5X6Z8', 'X1Z4Y5Y8', 'Z1Y3Y5Z6Z7Z8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 90921: [[9,2, 2]] : 8 :['Z0X1', 'X3X4Z7Z8', 'Z2X3X5Z6', 'Z2X4Z5X6', 'Z2Y3Z5Y7', 'Z2Y4Z6Y8', 'X0Z1Y2Y3Z4Z7'] : False\n",
+      "6 :: 90951: [[9,2, 2]] : 4 :['X0Z2', 'X4Z6Z7Z8', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'Z0Z1X2X3Z5Z7', 'Z0X1Y2Z3Z4Y7'] : False\n",
+      "6 :: 91029: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z5Z6', 'Z0X1X4Z8', 'Z2X3X6Z8', 'Y3Z6Y7Z8', 'Z1Z2Z3X8', 'Z1X3Z4X5Z7Z8'] : False\n",
+      "6 :: 91109: [[9,2, 2]] : 32 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'X1Z5X6Z8', 'Y1Z3Z4X5Y7Z8', 'Z0Z1Z2X5Z6X8'] : False\n",
+      "6 :: 91487: [[9,2, 2]] : 4 :['X0Z8', 'Z3X4Z7Z8', 'Y1Y2Y3Y4', 'Y1Y2X5Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0Y1Z2Z4Y6X8'] : False\n",
+      "6 :: 91491: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'X1Z2X3X4Z7Z8', 'Y1X3Z5Z6Y7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 91633: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z4X5X6Z8', 'X1Z4Y5Y8', 'Z2Z3X4X5Z6Z7', 'Z0X1Z3X4Z5X7'] : False\n",
+      "6 :: 91659: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z3X5Z7Z8', 'Y1Z2X5Y6', 'Y2Y3X6X7', 'Z0Z2Y4Z5Z6Y8'] : False\n",
+      "6 :: 91703: [[9,2, 2]] : 32 :['X0Z1', 'X3Z4Z6Z7', 'Z4X5Z7Z8', 'Z1X2Z3X6', 'Z2Y3Y5X7', 'Z2Y3X4Y8', 'Z0X1X2Y3Y4Z5'] : False\n",
+      "6 :: 91706: [[9,2, 2]] : 64 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z2X4Z7Z8', 'Z3X5Z7Z8', 'Y1Z4Z5Z6Y7Z8', 'Z0Y1Y2Y3X6Y8'] : False\n",
+      "6 :: 91718: [[9,2, 2]] : 4 :['X0Z8', 'Y3Y4Z5Z6', 'Y3Z4Y5Z7', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Y1Y2Z3X4Z7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 91848: [[9,2, 2]] : 8 :['X0Z7', 'Y1Y2Z3Z8', 'X1X3Z6Z8', 'Z4X5Z7Z8', 'Z0Z5X7Z8', 'X1Z2Z3Y4Z5Y6', 'Y1Z2Z5Z6Z7Y8'] : False\n",
+      "6 :: 91864: [[9,2, 2]] : 8 :['X0Z8', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z2Z5X7Z8', 'X1Y2Z4Y6Z7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 91889: [[9,2, 2]] : 16 :['X0Z8', 'Z2Z4X5Z8', 'Z3X4Z5X6', 'Z2Z3X7Z8', 'X1X2Z4Z5Z6Z7', 'Y1X3Y4Z5Z7Z8', 'Z0X1X3Z5Z6X8'] : False\n",
+      "6 :: 91948: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z7', 'X2X5Z7Z8', 'Y2X3Y5X6', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'Z0Z1X2X4Z6Z8'] : False\n",
+      "6 :: 91977: [[9,2, 2]] : 8 :['X0Z7', 'X1X3Z6Z8', 'Z2X3X4Z5', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'X2Y5Y6X8', 'Z0Y1Y2Z3Z5X7'] : False\n",
+      "6 :: 92001: [[9,2, 2]] : 16 :['X0Z8', 'X2X3Z4Z5', 'X1Y2Y4Z8', 'X1Y3Y5Z8', 'Y1Z3X4Y6', 'Z0Y1X7Y8', 'Y1Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 92015: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Z3X5Z7Z8', 'Y1Z3X4Y6', 'Y2Y3X6X7', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 92018: [[9,2, 2]] : 8 :['X0Z8', 'X2X3Z5Z6', 'Z1X4Z5Z8', 'Z2Z4X5Z8', 'X1Y3X5Y7', 'Z1Z2X6X7', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 92032: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Z3Y5Z6Z7Y8'] : False\n",
+      "6 :: 92089: [[9,2, 2]] : 4 :['X0Z8', 'X3Z4Z5Z8', 'Z3X5Z7Z8', 'Y1Y2X4X5', 'Z1Z3Y4Y6', 'Z2Z5X7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 92141: [[9,2, 2]] : 8 :['X0Z7', 'Z1X2Z4Z8', 'Y2X3Y4Z5', 'Z3Z4X6Z7', 'X2X3Y5Y6', 'X1Z5X6X8', 'Z0Y1Y3X4Z6X7'] : False\n",
+      "6 :: 92173: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z0Y4Z7Y8', 'X1Z2X3X4Z7Z8', 'Y1X3Z5Z6Y7Z8'] : False\n",
+      "6 :: 92200: [[9,2, 2]] : 8 :['X0Z7', 'X1Z4Z7Z8', 'X2X3Z5Z6', 'Z1Z2Z3X4', 'Z1Y2Y3X8', 'Y2Z3Z4X5Y6Z8', 'Z0Z1Y2Z4X5Y7'] : False\n",
+      "6 :: 92283: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'X4Z5Z6Z8', 'Z1Z4X6Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8', 'Y2X3Z4Y5Z7Z8'] : False\n",
+      "6 :: 92304: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'X4Z5Z6Z8', 'Z1Z4X6Z8', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'Y2X3Z4Y5Z7Z8'] : False\n",
+      "6 :: 92357: [[9,2, 2]] : 64 :['X0Z4', 'X3Z6Z7Z8', 'Y2Y5Z6Z8', 'Y1Y2Z3X7', 'Y1Y2Y6Y8', 'Z0X2X4Z5Z6Z7', 'X1X2Z3Z4X6Z8'] : False\n",
+      "6 :: 92358: [[9,2, 2]] : 16 :['X1Z8', 'X3X4Z6Z7', 'Z2X3X5Z7', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'X0Y2Y3Z4Z6Z8', 'Y0Z1X2Z3Z4Y8'] : False\n",
+      "6 :: 92508: [[9,2, 2]] : 8 :['X0Z4', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Y2Z3Y5Z8', 'Y1Y2Z6X7', 'Z5X6Y7Y8', 'X1X2Y3Z4Y6Z7'] : False\n",
+      "6 :: 92544: [[9,2, 2]] : 64 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'Z1Z2Z3Z4X6X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 92574: [[9,2, 2]] : 16 :['X0Z8', 'Z1X4Z6Z8', 'Y2Y3X4X5', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'X1Z2X3Z4Z5Z7', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 92625: [[9,2, 2]] : 4 :['X0Z8', 'Z1Z2Y4Y5', 'Z3X4Z5X6', 'Z2Z3X7Z8', 'X1Y3X5Y7', 'Z0X1X2X8', 'X1X2Z4Z5Z6Z7'] : False\n",
+      "6 :: 92681: [[9,2, 2]] : 16 :['X0Z7', 'X1Z4Z7Z8', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'X2Y4Z6Y8', 'Z0Z1Y2Z4X5Y7'] : False\n",
+      "6 :: 92683: [[9,2, 2]] : 16 :['X0Z4', 'X2Z5Z7Z8', 'X3Z6Z7Z8', 'Z0X4Z6Z8', 'Y1Y3Y5Y7', 'Y1Y5X6X8', 'X1Z2Z3Z4Y5Y6'] : False\n",
+      "6 :: 92763: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Z1Z2X7Z8', 'Z0Z1Z5Y6Z7Y8'] : False\n",
+      "6 :: 92830: [[9,2, 2]] : 8 :['X0Z8', 'Z1X4Z6Z8', 'Y2Y3X4X5', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Y4Y5X8', 'X1Z2X3Z4Z5Z7'] : False\n",
+      "6 :: 92873: [[9,2, 2]] : 32 :['X0Z4', 'X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z1Y2Z3X5Z7Y8'] : False\n",
+      "6 :: 92878: [[9,2, 2]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Y1Z2Y6X8', 'Z0X2X4Z5Z6Z7'] : False\n",
+      "6 :: 92893: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z3Z4X6Z7', 'Z2Y4X5Y6', 'X1Z5X6X8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 93058: [[9,2, 2]] : 16 :['X0Z7', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'Y1Z2Z3Y4Z7Z8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 93081: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Z3Y5Y6Z7', 'Z1Z4Z6X7', 'Z0Z1Z2X5Z6X8'] : False\n",
+      "6 :: 93119: [[9,2, 2]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Y1Y3X5X7', 'Z5X6Y7Y8', 'X1Z2Z4X5X6Z7'] : False\n",
+      "6 :: 93148: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z4X5X6Z8', 'Y1X2Y5X8', 'Z0Y4Y7X8', 'Z1X2Z3Z4X5Z7'] : False\n",
+      "6 :: 93302: [[9,2, 2]] : 8 :['X0Z4', 'X2X3Z6Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'Z1Z2Z3X8', 'X1Z3Z5Z6X7Z8'] : False\n",
+      "6 :: 93318: [[9,2, 2]] : 4 :['X0Z8', 'X4Z5Z6Z8', 'X1X2Y4Y5', 'Z1Z3X5X6', 'Z2Z4X6X7', 'Z0Y4Z7Y8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 93342: [[9,2, 2]] : 96 :['X0Z8', 'X3X4Z5Z6', 'Z3X5Z6Z8', 'Z4Z5X6Z8', 'X1Z2X3Z5Z7Z8', 'Y1X2Z3Z4Y7Z8', 'Z0Y1Z2Z5Z6Y8'] : False\n",
+      "6 :: 93396: [[9,2, 2]] : 4 :['X0Z8', 'X2X3Z4Z5', 'Y2Y4Z7Z8', 'X1Z5X6Z8', 'Z3Y5Y6Z7', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 93400: [[9,2, 2]] : 8 :['X0Z7', 'X1X2Z5Z7', 'X1X3Z6Z7', 'Z1Z2Z3X4', 'Z1Y2Y3X8', 'Y2Z3Z4X5Y6Z8', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 93410: [[9,2, 2]] : 4 :['X0Z8', 'Z3X4Z6Z8', 'Y3Z4Y5Z7', 'Y1Y2X4X5', 'Z1X3Z5X6', 'Z2Z5X7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 93433: [[9,2, 2]] : 8 :['X0Z8', 'X3Z4Z5Z8', 'Z3X5Z7Z8', 'Y1Y2X4X5', 'Z1Z3Y4Y6', 'Y1X2Z5Z6Y7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 93434: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'Y3Y5Z6Z8', 'Z2X4Z5X6', 'Z1Z4Z6X7', 'Z0Z1Z3X4Z7X8'] : False\n",
+      "6 :: 93486: [[9,2, 2]] : 8 :['X0Z8', 'Z3Y4Y5Z6', 'Z1Y4Z5Y6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'X1Z2X3Z5Z6Z7', 'X1X2Z3X4Z5Z8'] : False\n",
+      "6 :: 93525: [[9,2, 2]] : 4 :['X0Z8', 'X1X3Z5Z7', 'X1Y2Y4Z8', 'Z3X5Z7Z8', 'Y1Z2X5Y6', 'Z0Y1X7Y8', 'Y1Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 93526: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1X3Z5X6', 'Z2Z5X7Z8', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 93534: [[9,2, 2]] : 16 :['X0Z7', 'X1Z4Z7Z8', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'X2Y4Z6Y8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 93540: [[9,2, 2]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Z0Y1Y4Z8', 'Y2Z5Y6Z8', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 93578: [[9,2, 2]] : 4 :['X0Z8', 'Z3X4Z6Z8', 'Y3Z4Y5Z7', 'Y1Y2X4X5', 'Z1X3Z5X6', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 93628: [[9,2, 2]] : 4 :['X0Z8', 'X1X3Z5Z7', 'Y2Y4Z7Z8', 'Y3Y5Z6Z8', 'Z2X4Z5X6', 'Z1Z4Z6X7', 'Z0Z1Y2Z3Z4Y8'] : False\n",
+      "6 :: 93671: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z4X5X6Z8', 'X1Z4Y5Y8', 'Z1X2Z3Z4X5Z7', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 93681: [[9,2, 2]] : 32 :['X1Z8', 'X3X4Z5Z6', 'Z2Y3Y5Z6', 'Z2X3Z4X6', 'Z0Z1Y7Y8', 'X0Y2Y3Z4Z5Z7', 'Y0X2Z3Z4Y7Z8'] : False\n",
+      "6 :: 93683: [[9,2, 2]] : 32 :['X2Z7', 'X0X1Z6Z8', 'X0X3X4Z8', 'X0Y3Z4Y5', 'Z1Z2Y6Y7', 'Z0Z3Z4X8', 'X0Z1X3Z5X6Z7'] : False\n",
+      "6 :: 93690: [[9,2, 2]] : 32 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Z0Z1Y4Y5', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Z0Y1Z2Z3X4Y8'] : False\n",
+      "6 :: 93699: [[9,2, 2]] : 16 :['X0Z4', 'X2X3Z6Z7', 'Z2Y5Y6Z7', 'Z3Y5Z6Y7', 'Z1Z2Z3X8', 'Z0Z1X2X4Z6Z8', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 93705: [[9,2, 2]] : 32 :['X0Z4', 'X2X3Z6Z7', 'Z0X1X4Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Z1X2Z4X5Z6Z8', 'Y1Y2Z3Z5Z6X8'] : False\n",
+      "6 :: 93706: [[9,2, 2]] : 16 :['Z0Z7', 'Z1Z2Z5Z7', 'Z1Z3Z6Z7', 'Y2Y3Y5Y6', 'X1X2X3X8', 'Z2Z4Z6Z8', 'Y0Y1Y2X4Y5X7'] : False\n",
+      "6 :: 93747: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z6', 'X3X5Z6Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'Z0Z1X3X4Z7Z8'] : False\n",
+      "6 :: 93775: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'Y3Y4Z5Z6', 'Z3X5Z7Z8', 'Z1X3Z5X6', 'Z2Z5X7Z8', 'Z0Z1X2X3X4X8'] : False\n",
+      "6 :: 93814: [[9,2, 2]] : 32 :['X0Z4', 'X2X3Z6Z7', 'Z2Y5Y6Z7', 'Z3Y5Z6Y7', 'Z0Z1X2X4Z6Z8', 'X1Z4X5Z6Z7Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 93827: [[9,2, 2]] : 4 :['X0Z7', 'X3Z4Z5Z8', 'Z2Z3X4Z8', 'Z1X6Z7Z8', 'Y3Z6Z7Y8', 'Y1X2X5Y8', 'Z0Y2Z4Z6Y7Z8'] : False\n",
+      "6 :: 93901: [[9,2, 2]] : 16 :['X0Z8', 'X4Z5Z6Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'X1Z2X3Z5Z6Z7', 'Y1Y2Z3Z6X7Z8', 'Z0Y2Y3Z4Z6X8'] : False\n",
+      "6 :: 93910: [[9,2, 2]] : 16 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1Z2Z4Z5X6X7', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 93980: [[9,2, 2]] : 16 :['X0Z8', 'X1X2Z4Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X3X6Z7Z8', 'Y1X3Z4Z5Z6Y7', 'Z0Z1Z2Y3Z5Y8'] : False\n",
+      "6 :: 93982: [[9,2, 2]] : 16 :['X0Z8', 'X1X2Z4Z7', 'X1Z2X4Z8', 'X1Z5X6Z8', 'Z3Y5Y6Z7', 'Y1X3Z4Z5Z6Y7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 93983: [[9,2, 2]] : 8 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'Z2Z5X7Z8', 'Z1Z3Z4X5X6Z7', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 93996: [[9,2, 2]] : 8 :['X0Z4', 'X1X2Z4Z6', 'Z0Y1Y4Z8', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'Y1Z2Y3Z4Z7X8'] : False\n",
+      "6 :: 94020: [[9,2, 2]] : 8 :['X0Z8', 'X1X3Z4Z7', 'Z2Z4X5Z8', 'Z3X4Z5X6', 'Z2Z3X7Z8', 'Y1X2Y4Z6Z7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 94106: [[9,2, 2]] : 8 :['X0Z8', 'Y1Y2Z6Z7', 'Y3Y4Z5Z6', 'Y3Z4Y5Z7', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0Z1X2X3X4X8'] : False\n",
+      "6 :: 94108: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3Z4X6Z7', 'Z1X2X5X6', 'X1Z4Y5Y8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 94115: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'Z3Y4Y5Z6', 'Z1Y4Z5Y6', 'Z1Z2X7Z8', 'Y1X3X4Y7', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 94145: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z5Z8', 'Z3X5Z7Z8', 'Z1Z3Y4Y6', 'Z2Z5X7Z8', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 94150: [[9,2, 2]] : 8 :['X0Z7', 'X3Z4Z5Z8', 'Z2Z3X4Z8', 'Y1X2X3Y6', 'Z3X5X6Z7', 'Y3Z6Z7Y8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 94185: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z6Z8', 'Z3X4X5Z6', 'Z2Z4Z5X7', 'Z1X4Z5X6Z7Z8', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 94193: [[9,2, 2]] : 16 :['X0Z8', 'Z1X5Z7Z8', 'Y2Y3X4X5', 'Z2Z4X6Z8', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 94221: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z5Z6', 'Z0X1X4Z8', 'Y2Y6Z7Z8', 'Y3Z6Y7Z8', 'Z1Z2Z3X8', 'Z1X3Z4X5Z7Z8'] : False\n",
+      "6 :: 94233: [[9,2, 2]] : 16 :['X0Z4', 'X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'X1Z3Z4X6', 'Y1Y2Z6X7', 'Z1Z4X5Z6Z7X8'] : False\n",
+      "6 :: 94249: [[9,2, 2]] : 4 :['X0Z8', 'X4X5Z6Z7', 'Z1Z2Y4Y6', 'Z3Z5X7Z8', 'X1Y3X6Y7', 'Z0Y4Y5X8', 'X1X2Z3Z4Z5Z6'] : False\n",
+      "6 :: 94269: [[9,2, 2]] : 8 :['X0Z8', 'X2X3Z5Z6', 'Z1Z2Y4Y5', 'Z3X4Z5X6', 'Z3Z4X5X7', 'Y1X2Y4Z6Z7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 94298: [[9,2, 2]] : 16 :['X0Z8', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'Z2Z4Z5X7', 'X1Y2Z3Z5Y6Z7', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 94316: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z4Z7', 'Y3Y5Z6Z8', 'X3X6Z7Z8', 'Z2X4Z5X6', 'Z1Z4Z6X7', 'Z0Z1Z2Y3Z5Y8'] : False\n",
+      "6 :: 94340: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z4Z7', 'Y3Y5Z6Z8', 'X3X6Z7Z8', 'Z2X4Z5X6', 'Y1X3Z4Z5Z6Y7', 'Z0Z1Z2Y3Z5Y8'] : False\n",
+      "6 :: 94376: [[9,2, 2]] : 16 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Z0Y4Y7X8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 94423: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'Y1Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 94428: [[9,2, 2]] : 4 :['X0Z8', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'Z1Y4Z5Y6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 94475: [[9,2, 2]] : 64 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Z0Y1Z2Z3X4Y8'] : False\n",
+      "6 :: 94486: [[9,2, 2]] : 64 :['Z0X1', 'Z2X3X4Z5', 'X2Z3Z4X5', 'X2Z3X6Z8', 'Z2Z5Z6X7', 'Z3Z4Z6X8', 'X0Z1Y2Y3Z7Z8'] : False\n",
+      "6 :: 94507: [[9,2, 2]] : 4 :['X0Z8', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Y2X3Y4X6', 'Z3Y5Y6Z7', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 94508: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z6Z7', 'Z1X5Z7Z8', 'Z1Z2Y4Y6', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 94515: [[9,2, 2]] : 8 :['X0Z7', 'Y1Y2Z3Z8', 'Z4X5Z7Z8', 'X1Y3Z4Y6', 'Z0Z5X7Z8', 'X2Y5Y6X8', 'X1Z2X4Z5Z6Z8'] : False\n",
+      "6 :: 94531: [[9,2, 2]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Z0X4Z6Z8', 'Y2Z3Y5Z8', 'Y1Y2Z6X7', 'Z5X6Y7Y8', 'X1Y3Z4Z5Y6Z8'] : False\n",
+      "6 :: 94550: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'Y1X3Z4Z5Z6Y7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 94558: [[9,2, 2]] : 32 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Z3Y5Y6Z7', 'Y1Z3Z4X5Y7Z8', 'Z0Z1Z2X5Z6X8'] : False\n",
+      "6 :: 94562: [[9,2, 2]] : 4 :['X0Z8', 'X2X3Z4Z5', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 94591: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'X4Z5Z6Z8', 'Z1Z3X5X6', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'Y2X3Z4Y5Z7Z8'] : False\n",
+      "6 :: 94599: [[9,2, 2]] : 4 :['X0Z7', 'Z2Z3X4Z8', 'Z1Y3Z4Y5', 'Z1X6Z7Z8', 'Y1X2X3Y6', 'Y1X2X5Y8', 'Z0Y2Z4Z6Y7Z8'] : False\n",
+      "6 :: 94605: [[9,2, 2]] : 4 :['X0Z8', 'X2X3Z4Z5', 'Y2Y4Z7Z8', 'Y3Y5Z6Z8', 'X1Z5X6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 94610: [[9,2, 2]] : 8 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0Z1X2Y3Y5X8'] : False\n",
+      "6 :: 94625: [[9,2, 2]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'X3Z5Z6Z8', 'X1Z3Z4X6', 'Y1Y3X5X7', 'Y1Z2Y6X8', 'Z0Y2Z3X4Y5Z6'] : False\n",
+      "6 :: 94640: [[9,2, 2]] : 8 :['X0Z8', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'Y1Y2X5Z8', 'Z1X4Y5Y6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 94672: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X3X6Z7Z8', 'Z1Z4Z6X7', 'Z0Z1Z2Y3Z5Y8'] : False\n",
+      "6 :: 94691: [[9,2, 2]] : 4 :['X0Z7', 'X1X3Z6Z8', 'Z4X5Z7Z8', 'Y1Y2Z4X6', 'Z0Z5X7Z8', 'X2Y5Y6X8', 'Z1X2Z3X4Z5Z6'] : False\n",
+      "6 :: 94694: [[9,2, 2]] : 16 :['X0Z4', 'X3Z6Z7Z8', 'Z0X4Z6Z8', 'Y2Y5Z6Z8', 'Y1Y2Y6Y8', 'X1X2Z3Z4X6Z8', 'Y1Z2Z3Z5Y7Z8'] : False\n",
+      "6 :: 94699: [[9,2, 2]] : 16 :['Z0X1', 'Y2Y3Z7Z8', 'Y4Y5Z7Z8', 'X4Z5X6Z7', 'Z2Z5Z6X7', 'Z3Z4Z6X8', 'X0Z1Z2X3X4Z5'] : False\n",
+      "6 :: 94706: [[9,2, 2]] : 8 :['X0Z6', 'Y2Y3Z7Z8', 'X1X4Z5Z8', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'X1Z2X3Z4X5Z6', 'Z1Y2Z3Z5Z6Y8'] : False\n",
+      "6 :: 94813: [[9,2, 2]] : 4 :['X0Z7', 'X2Z4Z5Z8', 'X1X3Z6Z7', 'Z1Z2Z3X4', 'Y2Y3X5X6', 'X4Z5Z6X8', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 94814: [[9,2, 2]] : 4 :['X0Z7', 'X1X2Z5Z7', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'X2Y4Z6Y8', 'Z1X5X6X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 94835: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z4X5X6Z8', 'Y1X2Y5X8', 'Z0Y4Y7X8', 'Z1X2Z3Z4X5Z7'] : False\n",
+      "6 :: 94848: [[9,2, 2]] : 8 :['X0Z8', 'X4Z5Z6Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Z0Y4Z7Y8', 'X1Z2X3Z5Z6Z7', 'Y1Y2Z3Z6X7Z8'] : False\n",
+      "6 :: 94856: [[9,2, 2]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Z0X3X4Z5', 'Y2Z3Y5Z8', 'Y1Y3X5X7', 'X2X6X7X8', 'X1Y3Z4Z5Y6Z8'] : False\n",
+      "6 :: 94863: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z3Z4X6Z7', 'Z1X2X5X6', 'Y1X2Y5X8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 94886: [[9,2, 2]] : 8 :['X0Z8', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z0Y4Z7Y8', 'X1Z2X3Z5Z6Z7', 'Y1Y2Z3Z6X7Z8'] : False\n",
+      "6 :: 94895: [[9,2, 2]] : 4 :['X0Z8', 'Y3Y4Z5Z6', 'Z3X5Z7Z8', 'Y1Y2X4X5', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 94909: [[9,2, 2]] : 32 :['X0Z4', 'X2X3Z6Z7', 'Z0X1X4Z8', 'Z2Z3Y6Y7', 'Z1Z2Z3X8', 'Z1X2Z4X5Z6Z8', 'X1Z2Z5X6Z7Z8'] : False\n",
+      "6 :: 94951: [[9,2, 2]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'X3Z5Z6Z8', 'Z0X4Z6Z8', 'Y1Y3X5X7', 'Y1Z2Y6X8', 'X1Z2Z4X5X6Z7'] : False\n",
+      "6 :: 94977: [[9,2, 2]] : 8 :['X0Z8', 'X3Z6Z7Z8', 'Z2X5Z6Z8', 'Y3X4Z5Y6', 'Y1Y2Y6Y7', 'Z0Y1Y6X8', 'X1X2Z4Z5Z6Z7'] : False\n",
+      "6 :: 94991: [[9,2, 2]] : 4 :['X0Z7', 'X1Z4Z7Z8', 'X2Z4Z5Z8', 'Z1Z2Z3X4', 'Y2Y3X5X6', 'X4Z5Z6X8', 'Z0Y3Y4Y5Z6Y7'] : False\n",
+      "6 :: 95004: [[9,2, 2]] : 16 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Z0X1X4Z8', 'Z2Z3Y6Y7', 'Z1Z2Z3X8', 'Z1Z2Z4X5X6Z7'] : False\n",
+      "6 :: 95006: [[9,2, 2]] : 16 :['X0Z4', 'X2X3Z6Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Z2Z3X6X7', 'Z1Z2Z3X8', 'X1Z2Z4Z5X6Z8'] : False\n",
+      "6 :: 95007: [[9,2, 2]] : 16 :['X0Z4', 'X2X3Z6Z7', 'Z0Y1Y4Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 95018: [[9,2, 2]] : 32 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'Z1Z4Z6X7', 'Z0Y1Z2Y3X6X8'] : False\n",
+      "6 :: 95022: [[9,2, 2]] : 8 :['X0Z8', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'Y1Y2X5Z8', 'Z1X4Y5Y6', 'Z2Z4Z5X7', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 95030: [[9,2, 2]] : 8 :['X0Z5', 'X1X3Z6Z8', 'Z1Z2Z3X4', 'Z0Z1Y5Y6', 'Y1Y2X6X7', 'Z2Z3Y7Y8', 'Z0X2Z4X5Z6Z8'] : False\n",
+      "6 :: 95035: [[9,2, 2]] : 8 :['X0Z7', 'X1X3Z6Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1X4Z7X8', 'Z1X2Z3X4Z5Z6', 'Y1Y2Z3Z4X5Z7'] : False\n",
+      "6 :: 95038: [[9,2, 2]] : 16 :['X0Z8', 'Y1Y2Z6Z7', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1Z4X6Z8', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 95044: [[9,2, 2]] : 8 :['X0Z7', 'Z2Y3Y4Z5', 'Z1Y3Z4Y5', 'Z1X6Z7Z8', 'Y3Z6Z7Y8', 'X1Y2X4Y8', 'Z0X1Z2X3Z4X7'] : False\n",
+      "6 :: 95054: [[9,2, 2]] : 16 :['X0Z8', 'Y2Y3Z6Z7', 'X4X5Z6Z7', 'Z2Z4X6Z8', 'Y1X3Y5X6', 'Z3Z5X7Z8', 'Z0X1X2Z3Z7X8'] : False\n",
+      "6 :: 95103: [[9,2, 2]] : 96 :['Z0X1', 'Z2X3X4Z5', 'X2Z3Z4X5', 'Z2X3X6Z7', 'Z3Z4Z6X8', 'Y2Y4Y7Y8', 'X0Z1Y2Y3Z7Z8'] : False\n",
+      "6 :: 95104: [[9,2, 2]] : 96 :['Z0X1', 'X2X3Z5Z8', 'Z3Z4X5Z6', 'Z5X6Z7Z8', 'Z2Y4Y5X7', 'Z2Y4Y6X8', 'X0Z1X2X4Z5Z7'] : False\n",
+      "6 :: 95119: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z4X5X6Z8', 'X1Z4Y5Y8', 'Z0Y4Y7X8', 'Z1X2Z3Z4X5Z7'] : False\n",
+      "6 :: 95125: [[9,2, 2]] : 4 :['X0Z2', 'Z0Z1X2Z8', 'X3X4Z5Z6', 'Y3Y5Z6Z7', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'Y1Z2Z3Z4Y7Z8'] : False\n",
+      "6 :: 95145: [[9,2, 2]] : 192 :['X1Z8', 'X3X4Z6Z7', 'Z2X3X5Z7', 'X2Z4Z5X6', 'X2Z3Z5X7', 'X0Y2Y3Z4Z6Z8', 'Z0Z1X2Z3Z4X8'] : False\n",
+      "6 :: 95153: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z5Z6', 'Z0Z1Y4Y5', 'Z2X3X6Z8', 'Y3Z6Y7Z8', 'Z1Z2Z3X8', 'Z0X3X4Z5Z7Z8'] : False\n",
+      "6 :: 95155: [[9,2, 2]] : 16 :['X0Z2', 'Z0Z1X2Z8', 'X3Z5Z7Z8', 'X4Z6Z7Z8', 'Z4Z5X6Z8', 'X1Y3Y4X8', 'Y1Z2Z4X5Z6Y7'] : False\n",
+      "6 :: 95162: [[9,2, 2]] : 64 :['X1Z8', 'X3X4Z6Z7', 'X2Y3Y4X5', 'Z2Y3Z5Y6', 'Z2Y4Z5Y7', 'X0Y2Y3Z4Z6Z8', 'Y0Z1X2Z3Z4Y8'] : False\n",
+      "6 :: 95179: [[9,2, 2]] : 32 :['X0Z4', 'X3Z6Z7Z8', 'Z0X4Z6Z8', 'Y2Y5Z6Z8', 'Z3Y6X7Y8', 'X1Z3Z4Z5X6Z7', 'Y1Z2Z3Z5Y7Z8'] : False\n",
+      "6 :: 95201: [[9,2, 2]] : 8 :['Z0X1', 'Y2Y3Z4Z7', 'Z2X4Z5X6', 'Y5Y6Z7Z8', 'Z3Y5Z6Y7', 'Z2Y4Z6Y8', 'X0Z1Y2Z3Y4Z8'] : False\n",
+      "6 :: 95206: [[9,2, 2]] : 8 :['X0Z8', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Y1X2X5Y6', 'Y1X3X4Y7', 'Z0X1Z2X3Z4X8'] : False\n",
+      "6 :: 95213: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z5Z7', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'Y1X2X5Y6', 'Z0Y4Z7Y8', 'Y1Y2Z3Z6X7Z8'] : False\n",
+      "6 :: 95219: [[9,2, 2]] : 8 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Z0Z1Y4Y5', 'Y2Y6Z7Z8', 'Z1Z2Z3X8', 'Z0Z3X4Z5Z6X7'] : False\n",
+      "6 :: 95221: [[9,2, 2]] : 8 :['X0Z4', 'X2Z5Z7Z8', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'Y1Z2X3Y7', 'Y1Z2Y6X8', 'X1Y3Z4Z5Y6Z8'] : False\n",
+      "6 :: 95224: [[9,2, 2]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Z0X4Z6Z8', 'Z2Z3X5Z7', 'X1Z3Z4X6', 'X2X6X7X8', 'Y1Z2Z5Z6Y7Z8'] : False\n",
+      "6 :: 95241: [[9,2, 2]] : 16 :['X0Z4', 'X2X3Z6Z7', 'Z0Z1Y4Y5', 'X2Z3X7Z8', 'Z1Z2Z3X8', 'Z0X2X4Z5Z6Z8', 'X1Z2Z5X6Z7Z8'] : False\n",
+      "6 :: 95246: [[9,2, 2]] : 4 :['X0Z4', 'X2X3Z6Z7', 'Z0Y1Y4Z8', 'X2X5Z7Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'X1Z2Z4Z5X6Z8'] : False\n",
+      "6 :: 95247: [[9,2, 2]] : 4 :['X0Z7', 'Z1X2Z4Z8', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z4X5X6Z8', 'Z0Y4Y7X8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 95248: [[9,2, 2]] : 32 :['X0Z7', 'X3Z4Z7Z8', 'Z2Z3X4Z8', 'Z1X5Z6Z8', 'Z2Z5X6Z8', 'X2Z3Z5X8', 'Z0X1X2Y3Y5X7'] : False\n",
+      "6 :: 95270: [[9,2, 2]] : 16 :['X0Z7', 'Z1Y2Y3Z6', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1X4Z7X8', 'X1Z2X4Z5Z6Z8'] : False\n",
+      "6 :: 95280: [[9,2, 2]] : 16 :['X0Z8', 'X1X3Z4Z7', 'Z2Z4X5Z8', 'Z3X4Z5X6', 'Z2Z3X7Z8', 'Z0X1X2X8', 'Y1X2Y4Z6Z7Z8'] : False\n",
+      "6 :: 95283: [[9,2, 2]] : 32 :['X0Z4', 'X1X2Z4Z6', 'X1X3Z4Z7', 'Z0Y1Y4Z8', 'Z2Y5Y6Z7', 'Z3Y5Z6Y7', 'Z1Y2Z3X5Z7Y8'] : False\n",
+      "6 :: 95285: [[9,2, 2]] : 8 :['X0Z7', 'Y1Y2Z3Z8', 'Z4X5Z7Z8', 'X1Y3Z4Y6', 'Z0Z5X7Z8', 'Z1X4Z7X8', 'X1Z2X4Z5Z6Z8'] : False\n",
+      "6 :: 95289: [[9,2, 2]] : 96 :['X0Z3', 'X1X2Z5Z6', 'Z0X3Z7Z8', 'Z1Z2X5X6', 'Z3X4X7Z8', 'Z1Y4Y5Z6Z7Z8', 'Y1Z2Z3Y4Z6X8'] : False\n",
+      "6 :: 95293: [[9,2, 2]] : 12 :['Z0X1', 'Y2Y3Z4Z7', 'Z2X3X5Z6', 'Z2X4Z5X6', 'Z3X6X7Z8', 'Z2Y4Z6Y8', 'X0Z1Y2Z3Y4Z8'] : False\n",
+      "6 :: 95299: [[9,2, 2]] : 8 :['X0Z4', 'X2X3Z6Z7', 'Z0X4Z6Z8', 'Y2Z3Y5Z8', 'Y1Y3X5X7', 'Y1Z2Y6X8', 'X1Y3Z4Z5Y6Z8'] : False\n",
+      "6 :: 97806: [[9,2, 2]] : 8 :['X0Z7', 'Z1X3Z5Z6', 'Z2X4Z6Z8', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'X1Z4Y5Y8', 'Z0X1Y2Y3Z4X7'] : False\n",
+      "6 :: 98257: [[9,2, 2]] : 32 :['X0Z6', 'X1X2Z3Z7', 'X1Z2X3Z8', 'Z4X5Z6Z8', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Z1Z2X4Z6Z7X8'] : False\n",
+      "6 :: 98799: [[9,2, 2]] : 4 :['X0Z7', 'Z2X4Z6Z8', 'Y2X3Y4Z5', 'Z3Z4X6Z7', 'Z1X2X5X6', 'X1Z4Y5Y8', 'Z0X1Y2Y3Z4X7'] : False\n",
+      "6 :: 98934: [[9,2, 2]] : 8 :['X0Z7', 'X3Z4Z5Z8', 'Z2Z3X4Z8', 'Z1X6Z7Z8', 'X1X2Z3X8', 'Z4Y5Y6X8', 'Z0X1Z2Z5X7Z8'] : False\n",
+      "6 :: 98947: [[9,2, 2]] : 4 :['X0Z8', 'X3Z4Z5Z8', 'Z3X5Z7Z8', 'Y1Y2X4X5', 'Z1Z3Y4Y6', 'Y1X2Z5Z6Y7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 99196: [[9,2, 2]] : 4 :['X0Z4', 'X3Z5Z6Z8', 'Y2Z3Y5Z8', 'X1Z3Z4X6', 'Y1Z2X3Y7', 'X2X6X7X8', 'Z0X2X4Z5Z6Z7'] : False\n",
+      "6 :: 99447: [[9,2, 2]] : 32 :['X2Z7', 'X0X1Z7Z8', 'X3X4Z5Z6', 'X0Y3Y5Z6', 'X3Z4X6Z8', 'Z0Z3Z4X8', 'Z1Z2X3Z5X7Z8'] : False\n",
+      "6 :: 99453: [[9,2, 2]] : 4 :['X0Z7', 'Z1Y2Y4Z6', 'Z3Z4X6Z7', 'Z1X2X5X6', 'X1Z4Y5Y8', 'Y1X3Y6X8', 'Z0Y1Z2Y3X7Z8'] : False\n",
+      "6 :: 99596: [[9,2, 2]] : 4 :['X0Z8', 'X1X2X3Z8', 'Z1Z2X4Z5', 'X1X5Z7Z8', 'Y1Y3Y4Y7', 'Z1Z2X6X7', 'Z0Z3Z5Z6Z7X8'] : False\n",
+      "6 :: 99623: [[9,2, 2]] : 4 :['X0Z8', 'Z3X4Z7Z8', 'Y1Y2Y3Y4', 'X5Z6Z7Z8', 'Z2Z4Z5X7', 'X1Y2Z3Z5Y6Z7', 'Z0Z1X2Y3Y5X8'] : False\n",
+      "6 :: 99657: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z6Z7', 'X3Z6Z7Z8', 'Z1Z2X4Z5', 'Y2Y3Y4Y6', 'Z0Y3Z5Y8', 'Z2Z3Z4Y5Z6Y7'] : False\n",
+      "6 :: 99687: [[9,2, 2]] : 16 :['X0Z8', 'Y1Y2Z6Z7', 'Y3Y4Z6Z7', 'X5Z6Z7Z8', 'Z1X4Y5Y6', 'Z2Z4Z5X7', 'Z0Y1Z2X3X6Y8'] : False\n",
+      "6 :: 99693: [[9,2, 2]] : 128 :['X1Z8', 'X0X2Z5Z8', 'X0X3Z6Z8', 'X0X4Z7Z8', 'Z3Z4Y6Y7', 'Z0Z1Z5X8', 'Z2Z3X5X6Z7Z8'] : False\n",
+      "6 :: 99703: [[9,2, 2]] : 16 :['X0Z8', 'X3Z4Z6Z8', 'Z3X4Z7Z8', 'X5Z6Z7Z8', 'X1Y2Z3Z5Y6Z7', 'Y1X2Z4Z5Z6Y7', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 99819: [[9,2, 2]] : 4 :['X0Z8', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'X1Y2Z4Y6Z7Z8', 'Y1X2Z5Z6Y7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 99826: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'Z3X4Z7Z8', 'X3Z4X5Z7', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 99827: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'Z3X4Z7Z8', 'X3Z4X5Z7', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Z0Z1X2Y4Z5Y8'] : False\n",
+      "6 :: 99829: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Z1Z4X6Z8', 'Z0Y1X3Z4X7Y8'] : False\n",
+      "6 :: 99874: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z3Z6', 'Z3Z4X5Z8', 'Z1Y4Z5Y6', 'Z2Z4X6X7', 'Z0Y4Z7Y8', 'X1Z2X3X4Z7Z8'] : False\n",
+      "6 :: 99875: [[9,2, 2]] : 4 :['X0Z8', 'X1X2Z4Z7', 'Z3X5Z7Z8', 'Y2Y3Y4Y5', 'Y1Z2X5Y6', 'Z0Y1X7Y8', 'Y1Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 99891: [[9,2, 2]] : 4 :['X0Z8', 'Y3Y4Z6Z7', 'X5Z6Z7Z8', 'Z1Z3Z5X6', 'Z2Z4Z5X7', 'Y1Y2X3Z4Z7Z8', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 99977: [[9,2, 2]] : 4 :['X0Z8', 'X3Z4Z5Z8', 'X4X5Z6Z7', 'Z1Z3Y4Y6', 'Z2Z5X7Z8', 'Y1Y2Z3X4Z7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 99985: [[9,2, 2]] : 4 :['X0Z8', 'Z3X4Z7Z8', 'Y1Y2Y3Y4', 'X5Z6Z7Z8', 'Z2Z4Z5X7', 'X1Y2Z3Z5Y6Z7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 100000: [[9,2, 2]] : 4 :['X0Z8', 'Z3X4Z6Z8', 'Z3X5Z7Z8', 'Z1X3Z5X6', 'Z2Z5X7Z8', 'Y1Y2Y3Y4Z5Z7', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 100040: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1Z2X4Z8', 'X3X6Z7Z8', 'X1Y3X5Y6', 'Z1Z4Z6X7', 'Z0Z1Z2Y3Z5Y8'] : False\n",
+      "6 :: 100093: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z4Z7', 'X1Z2X4Z8', 'X3X6Z7Z8', 'X1Y3X5Y6', 'Y1X3Z4Z5Z6Y7', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 100204: [[9,2, 2]] : 4 :['X0Z8', 'Z1X5Z7Z8', 'Y2Y3X4X5', 'Z1Z2Y4Y6', 'X1Y3X6Y7', 'Z0Y4Y5X8', 'X1Z2X3Z4Z5Z7'] : False\n",
+      "6 :: 100266: [[9,2, 2]] : 8 :['X0Z3', 'X1X2Z5Z6', 'Z0X3Z7Z8', 'Z2Z4X6Z7', 'Z3X4X7Z8', 'Y2Y5Y7Y8', 'Z1Y4Y5Z6Z7Z8'] : False\n",
+      "6 :: 100307: [[9,2, 2]] : 4 :['X0Z8', 'Y3Y4Z6Z7', 'X5Z6Z7Z8', 'Z1X4Y5Y6', 'Z2Z4Z5X7', 'Y1Y2X3Z4Z7Z8', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 100326: [[9,2, 2]] : 4 :['X0Z8', 'X3Z6Z7Z8', 'X1X5Z7Z8', 'Y1Y3Y4Y7', 'Z1Z2X6X7', 'Z0Y3Z5Y8', 'Y1Y2X4Z5Z6Z7'] : False\n",
+      "6 :: 100342: [[9,2, 2]] : 16 :['X0Z7', 'X1X2Z5Z7', 'X1X3Z6Z7', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'Y1Z2Z3Y4Z7Z8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 100372: [[9,2, 2]] : 16 :['X0Z7', 'X1Z4Z7Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z2X4Y5Y8', 'Z1Y2Z3Y4Z5Z8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 100392: [[9,2, 2]] : 16 :['X0Z7', 'X1Z4Z7Z8', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Y2Y3X5X6', 'Y2Y4X5X8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 100395: [[9,2, 2]] : 4 :['X0Z8', 'Z3Z4X5Z8', 'X1X2Y4Y5', 'Z1Z4X6Z8', 'Y1X3X4Y7', 'Z0Y4Z7Y8', 'X1Z2X3Z5Z6Z7'] : False\n",
+      "6 :: 100396: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z5Z7', 'Z3Y4Y5Z6', 'Z1Z4X6Z8', 'Y1X2X5Y6', 'Z1Z2X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 100409: [[9,2, 2]] : 128 :['X2Z7', 'X0X1Z7Z8', 'X0X3Z5Z8', 'X0X4Z6Z8', 'Z3Z4X5X6', 'Z0Z5Z6X8', 'Z1Z2Z3X5X7Z8'] : False\n",
+      "6 :: 100415: [[9,2, 2]] : 4 :['X0Z8', 'X3Z4Z6Z8', 'Y1Y2Y3Y4', 'Y1Y2X5Z8', 'Z2Z4Z5X7', 'X1Y2Z3Z5Y6Z7', 'Z0Y1Z2Z4Y6X8'] : False\n",
+      "6 :: 100478: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z6Z7', 'Z1X5Z7Z8', 'Z1Z2Y4Y6', 'Y1X3Y5X6', 'Z0Y4Y5X8', 'X1X2Z4Z6X7Z8'] : False\n",
+      "6 :: 100489: [[9,2, 2]] : 32 :['X0Z8', 'X2X3Z4Z5', 'X1Y2Y4Z8', 'X1Y3Y5Z8', 'Y1Z2Z3Y6Z7Z8', 'Z1X2Z5Z6X7Z8', 'Z0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 100490: [[9,2, 2]] : 16 :['X0Z8', 'X1X2Z4Z7', 'X1X3Z5Z7', 'X1Z5X6Z8', 'Z2Z3X4X5Z6Z7', 'Y1Z2Y4Z6X7Z8', 'Z0Y1Z2Z3Z7Y8'] : False\n",
+      "6 :: 100545: [[9,2, 2]] : 4 :['X0Z7', 'X1Z4Z7Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'X2Y4Z6Y8', 'Z1X5X6X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 100610: [[9,2, 2]] : 8 :['X0Z4', 'X1X2Z5Z6', 'X1X3Z5Z7', 'Y1Z4Y5Z8', 'X2Z3X7Z8', 'Z1Z2Z3X8', 'Z0Z2X4Z5X6Z7'] : False\n",
+      "6 :: 100619: [[9,2, 2]] : 4 :['X0Z7', 'X1X2Z5Z7', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'Z0Y1Z2Z4Y5X7'] : False\n",
+      "6 :: 100620: [[9,2, 2]] : 4 :['X0Z8', 'Y2Y3Z6Z7', 'X4X5Z6Z7', 'Z1Z2Y4Y6', 'Z3Z5X7Z8', 'Y1X2Y4X7', 'Z0X1X2Z3Z7X8'] : False\n",
+      "6 :: 100629: [[9,2, 2]] : 4 :['X0Z7', 'X1X2Z5Z7', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'X4Z5Z6X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 100633: [[9,2, 2]] : 4 :['X0Z8', 'Y1Y2Z6Z7', 'Y3Y4Z5Z6', 'Y3Z4Y5Z7', 'Z1X3Z5X6', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 100680: [[9,2, 2]] : 4 :['X0Z7', 'Z1X3Z5Z6', 'Z1Y2Y4Z6', 'Z3X5Z7Z8', 'Z3Z4X6Z7', 'Y1X2Y5X8', 'Z0X1Y2Y3Z4X7'] : False\n",
+      "6 :: 100684: [[9,2, 2]] : 4 :['X0Z7', 'X1Z4Z7Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Y2Y4X5X8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 100789: [[9,2, 2]] : 16 :['X0Z8', 'X2X3Z4Z5', 'Z2X4Z7Z8', 'Z3X5Z7Z8', 'Z0Y1X7Y8', 'Z1Y2Z3Z4Y6Z8', 'Y1Z4Z5Z6Y7Z8'] : False\n",
+      "6 :: 100814: [[9,2, 2]] : 16 :['X3Z4', 'X0X1Z7Z8', 'X0X2Z4Z7', 'Y5Y6Z7Z8', 'Z0Z5X7Z8', 'Z1Z6Z7X8', 'Z2Z3X4X5Z6Z7'] : False\n",
+      "6 :: 100845: [[9,2, 2]] : 16 :['X0Z7', 'X1X3Z6Z8', 'Z4X5Z7Z8', 'Z3Z4X6Z8', 'Z0Z5X7Z8', 'Z1X2Z3X4Z5Z6', 'Y1Z2Z5Z6Z7Y8'] : False\n",
+      "6 :: 100859: [[9,2, 2]] : 4 :['X0Z3', 'Z0X3Z7Z8', 'X4Z5Z6Z8', 'Z2Z4X6Z7', 'Y1Y2Y5Y6', 'Z3Z5Z6X7', 'Z1Y2Z3Z4Z6Y8'] : False\n",
+      "6 :: 100882: [[9,2, 2]] : 8 :['X0Z8', 'X1X2Z3Z6', 'Y2Y3Z5Z7', 'X4Z5Z6Z8', 'Z1Z4X6Z8', 'Z0Y4Z7Y8', 'Z1Z2Z3Z4X5X7'] : False\n",
+      "6 :: 100887: [[9,2, 2]] : 4 :['X0Z8', 'X2X3Z4Z5', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'X3X6Z7Z8', 'Z1Z4Z6X7', 'Z0Z1Y2Z3Z4Y8'] : False\n",
+      "6 :: 100943: [[9,2, 2]] : 4 :['X0Z8', 'X3Z4Z5Z8', 'Z3X4Z6Z8', 'Y1Y2X4X5', 'Z2Z5X7Z8', 'X1Y2Z4Y6Z7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 100961: [[9,2, 2]] : 4 :['X0Z8', 'X1X3Z5Z7', 'X1Z2X4Z8', 'Y3Y5Z6Z8', 'Y2X3Y4X6', 'Z1Z4Z6X7', 'Z0Z1Y2Z3Z4Y8'] : False\n",
+      "6 :: 100995: [[9,2, 2]] : 4 :['X0Z8', 'X3Z4Z6Z8', 'Y1Y2Y3Y4', 'Y1Y2X5Z8', 'Z2Z4Z5X7', 'X1Y2Z3Z5Y6Z7', 'Z0X1Z2Y3Z5Y8'] : False\n",
+      "6 :: 100999: [[9,2, 2]] : 4 :['X0Z8', 'X3Z6Z7Z8', 'X2X5Z6Z8', 'Y1Y3Y4Y7', 'Z1Z2X6X7', 'Z0Y3Z5Y8', 'Y1Y2X4Z5Z6Z7'] : False\n",
+      "6 :: 101278: [[9,2, 2]] : 4 :['X0Z4', 'X1X3Z4Z7', 'X3X5Z6Z8', 'Y2Z5Y6Z8', 'Y3Z5Y7Z8', 'Z1Z2Z3X8', 'Z0Z1X2X4Z6Z8'] : False\n",
+      "6 :: 101337: [[9,2, 2]] : 8 :['X0Z8', 'Y3Y4Z5Z6', 'Z3X5Z7Z8', 'Y1Y2X4X5', 'Z1X3Z5X6', 'Z2Z5X7Z8', 'Z0Z1X2Y3Z6Y8'] : False\n",
+      "6 :: 101400: [[9,2, 2]] : 16 :['X0Z7', 'X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'Z0Z3X4Y5Y7Z8'] : False\n",
+      "6 :: 101401: [[9,2, 2]] : 8 :['X0Z7', 'X1Z4Z7Z8', 'X2Z4Z5Z8', 'X3Z4Z6Z8', 'Z1X4Y5Y6', 'Z1Y2Y3X8', 'Z0Z3X4Y5Y7Z8'] : False\n",
+      "6 :: 101464: [[9,2, 2]] : 8 :['X0Z4', 'X2X3Z6Z7', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'Z1Z2Z3X8', 'Z0X2X4Z5Z6Z8', 'X1Z3Z5Z6X7Z8'] : False\n",
+      "6 :: 101471: [[9,2, 2]] : 4 :['X0Z4', 'X1X2Z4Z6', 'X3X5Z6Z8', 'Z2Y5Y6Z7', 'Y3Z5Y7Z8', 'Z0Z1X3X4Z7Z8', 'Y1Z2Y3Z4Z7X8'] : False\n",
+      "6 :: 101504: [[9,2, 2]] : 8 :['X0Z7', 'Z1Y2Y4Z6', 'Z3Z4X6Z7', 'Z1X2X5X6', 'Y1X2Y5X8', 'Z0Y4Y7X8', 'X2X3Z4Z5Z6Z8'] : False\n",
+      "6 :: 101520: [[9,2, 2]] : 16 :['X2Z7', 'X0X1Z7Z8', 'X0Y3Y5Z6', 'Z3X4X5Z8', 'X0X3Z4X6', 'Z0Z3Z4X8', 'X0Z1Z2X3Z5X7'] : False\n",
+      "6 :: 101527: [[9,2, 2]] : 8 :['X0Z8', 'Y1Y2Z6Z7', 'X3Z4Z5Z8', 'Z3X5Z7Z8', 'Z1Z3Y4Y6', 'Z2Z5X7Z8', 'Z0X1Z2Y3Z7Y8'] : False\n",
+      "6 :: 101614: [[9,2, 2]] : 32 :['X0Z7', 'X1X2Z5Z7', 'X3Z4Z6Z8', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'Z1Z3Y5Y8', 'Z0Z1Z5Z6X7Z8'] : False\n",
+      "6 :: 101617: [[9,2, 2]] : 8 :['X0Z6', 'Y2Y3Z7Z8', 'X1X4Z5Z8', 'Z4X5Z6Z8', 'Z0Z5X6Z8', 'Z1Z3Z4X7', 'Y1Z2Z5Z6Z7Y8'] : False\n",
+      "6 :: 101631: [[9,2, 2]] : 32 :['X2Z7', 'X0X1Z7Z8', 'X3X4Z5Z6', 'Y3Y5Z6Z8', 'X3Z4X6Z8', 'Z0Z3Z4X8', 'Z1Z2X3Z5X7Z8'] : False\n",
+      "6 :: 101642: [[9,2, 2]] : 16 :['X0Z8', 'X1X2Z3Z6', 'X4Z5Z6Z8', 'Z3Z4X5Z8', 'Z1Z2X7Z8', 'Z0Y4Z7Y8', 'Y1Z2X3Y4X6Z7'] : False\n",
+      "6 :: 101661: [[9,2, 2]] : 32 :['X0Z7', 'X1Z4Z7Z8', 'X2X3Z5Z6', 'Z1Z2Z3X4', 'Z2Z3Y5Y6', 'Z1Y2Y3X8', 'Z0Z1Z2Y5Y7Z8'] : False\n",
+      "6 :: 101664: [[9,2, 2]] : 96 :['X0Z4', 'X2X3Z6Z7', 'Z0X1X4Z8', 'Y1Z4Y5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Y1Y2Z3Z5Z6X8'] : False\n",
+      "6 :: 101722: [[9,2, 2]] : 384 :['X3Z6', 'X0X1Z7Z8', 'X0X2Z6Z7', 'X0X4X5Z7', 'Z0Z4Z5X7', 'Z1Z4Z5X8', 'X0Z2Z3X4X6Z8'] : False\n",
+      "6 :: 1360: [[9,3, 2]] : 192 :['X0X1', 'X2X3', 'X2X7', 'X0Z4Z5Z6', 'Y2Z3Y4X6Y7Y8', 'Z0Z1X4X5X6X8'] : True\n",
+      "6 :: 1362: [[9,3, 2]] : 48 :['X0X1', 'X0X8', 'X0Y3Z5X6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 1612: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0X2Y4X5', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z4X6X7X8', 'Z2Z3Y4Z5Z6X7'] : False\n",
+      "6 :: 2086: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z0Z1Z4Z6', 'X0Z2Z3Y4X5Z8', 'X2Y4X5Y6X7X8'] : False\n",
+      "6 :: 2087: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'Z2Z3Z4Z7', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8'] : True\n",
+      "6 :: 2166: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Y4Y5X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Y4X5Z7'] : False\n",
+      "6 :: 2167: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Y4Y5X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Y4Z6X7'] : False\n",
+      "6 :: 2223: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8', 'X0Z2Z3Z4Y6Z7'] : False\n",
+      "6 :: 2247: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Z2Z3Y4X5Z8', 'Z0Z1Y2Z3X4Y5', 'Z2Z3Z5X6X7X8'] : False\n",
+      "6 :: 2259: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8', 'X0Z2Z3Y4Z5Z7'] : False\n",
+      "6 :: 2264: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1X2Z4Z5Z8', 'X0Y4Y5X6X7Y8', 'Y0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 2282: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1X2Z4Z5Z8', 'X0Y4Y5X6X7Y8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 2283: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8', 'X0Y2Z3Z4Z6X8'] : False\n",
+      "6 :: 2545: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z2Z3Y4Z6', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8'] : False\n",
+      "6 :: 2609: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'Z2Z3Y4Y5', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8'] : False\n",
+      "6 :: 2610: [[9,3, 2]] : 32 :['Z0Z1', 'X2X3', 'X4X5X6X7', 'Z2Z3Z6Z7', 'Z0Z2Z3Z4Z5Z8', 'X0X1X2X4X6X8'] : True\n",
+      "6 :: 2633: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X4Z5X6Z8', 'X0Z2Z3Y4X5Z8', 'Z0Z1X2Y5X7Y8'] : False\n",
+      "6 :: 2665: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Y4Y5X7X8', 'Z2Z3X4Y7', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 2853: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'X4X5', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X6X7X8', 'Y2Z3Y4Y5Y6X8'] : True\n",
+      "6 :: 2931: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'X4X5', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X6X7X8', 'Z2Z3Y4Z5Z6X7'] : True\n",
+      "6 :: 2963: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5', 'X0Z6Z7Z8', 'Z0Z1Z2Z3Z6X7', 'Y2Z3X4X5Y6X8'] : True\n",
+      "6 :: 2978: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5', 'X0Z6Z7Z8', 'Z0Z1Z2Z3Z4X6', 'Y2Z3Y4X5X7X8'] : True\n",
+      "6 :: 2981: [[9,3, 2]] : 128 :['X0X1', 'X6X8', 'X4Z7', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3Z6Z8'] : True\n",
+      "6 :: 3003: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'X4X5', 'Y2Z3Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X6X7X8'] : True\n",
+      "6 :: 3004: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'X5X6', 'Z4Z5Z6Z7', 'X0Z2Z3Y4X5Z8', 'Z0Z1X2X4X7X8'] : True\n",
+      "6 :: 3269: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X0X2X7Y8', 'Y2Z3Y4X6Y7Y8', 'Y0Z1X4X5X6Z8'] : False\n",
+      "6 :: 3304: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0X2Y4X5', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z4X6X7X8', 'Z0Z1Z2Z3Z6X7'] : False\n",
+      "6 :: 3311: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0X2Y5X6', 'X0Z2Z3Y4X5Z8', 'Y0Z1X4Z5X7X8'] : False\n",
+      "6 :: 3313: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0X2Y4X5', 'Y2Z3Y5X6', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z4X6X7X8'] : False\n",
+      "6 :: 3322: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8', 'Z0Z1Z2Z3X4Z7'] : False\n",
+      "6 :: 3324: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8', 'Z0Z1Z2Z3Z6X7'] : False\n",
+      "6 :: 3326: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3Y4Z5Z6'] : False\n",
+      "6 :: 3327: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8', 'Z0Z1Z2Z3X4Z5'] : False\n",
+      "6 :: 3329: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1Z2Z3Z5X6'] : False\n",
+      "6 :: 3332: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8', 'X0Z2Z3X4Y5Z6'] : False\n",
+      "6 :: 3390: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Y0Z1X2Y4X7X8', 'X0Z2Z3X4Y5Z7'] : False\n",
+      "6 :: 3493: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2X4Z7X8', 'X0Z2Z3Z5X7Z8'] : False\n",
+      "6 :: 3525: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8', 'X0Z2Z3Y4X5Z7'] : False\n",
+      "6 :: 3562: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3Z4Y6Z7'] : False\n",
+      "6 :: 3590: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8', 'X0Z2Z3Y4Z6X7'] : False\n",
+      "6 :: 3607: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z7X8', 'Z0Z1X2X4X5X6', 'Y0Z1Z4Z5Z6X7', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 3640: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8', 'Z0Z1Z2Z3X5Z7'] : False\n",
+      "6 :: 3731: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z7X8', 'Z0Z1X2X4X5X6', 'Y0Z1Z4Z5Z6X7', 'Y2Z3Y4Z5Y6Z8'] : False\n",
+      "6 :: 3741: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 3758: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Z2Z3Z5Y6'] : False\n",
+      "6 :: 3759: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X2Y4X6Y7', 'Z0Z1X4X5X6X7', 'X0X2Z4Z5Z6X8', 'X0Z2Z3Z5X7Z8'] : False\n",
+      "6 :: 3765: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Z4Z6X8', 'Z2Z3Y4Z5X6Z8', 'Z0Z1Y4X5Y6X7'] : False\n",
+      "6 :: 3788: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X5Y6Y7X8', 'Y2Z3Y4X6Y7Y8', 'Y0Z1X2Y4Z5Z7'] : True\n",
+      "6 :: 3836: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Y0Z1X2Y4X7X8', 'X0Z2Z3Y5Z6X7'] : False\n",
+      "6 :: 3839: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'X0Z2Z3Y4Z5X6'] : False\n",
+      "6 :: 3842: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'Z2Z3X4X5Y6X7'] : False\n",
+      "6 :: 3846: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'X0Y2Z3Z4Z5X8'] : False\n",
+      "6 :: 3848: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'Y4X6Y7X8', 'Z0Z1X4X5X6X7', 'X0Z2Z3Z5X7Z8'] : False\n",
+      "6 :: 3849: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Y0Z1X2Y4X7X8', 'Z0Z1Z2Z3X4Z7'] : False\n",
+      "6 :: 3851: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'Y0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 3856: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Y4X5X8', 'Z0Z1X4X5X6X7', 'X0Z2Z3Y5X7Z8'] : False\n",
+      "6 :: 3858: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'Y4X6Y7X8', 'Z0Z1X4X5X6X7', 'X0Z2Z3X4Y5Z8'] : False\n",
+      "6 :: 3860: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'Y0Z1Z2Z3Z4Z6'] : False\n",
+      "6 :: 3861: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'X0Z2Z3Z4Y6X7'] : False\n",
+      "6 :: 3866: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z2Z3Z4Y6', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8'] : True\n",
+      "6 :: 3872: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Z2Z3X5Y7', 'Y0Z1X2Y4X7X8'] : True\n",
+      "6 :: 3893: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z0Z1X4X6', 'X2X5X7X8', 'X0Z2Z3Y4X5Z8'] : True\n",
+      "6 :: 3935: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Z4Z6Y8', 'X0Z2Z3Y4X5Z8', 'Z0Z1Y2Z3Y6X7'] : True\n",
+      "6 :: 3965: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4Z5X6', 'X0Z2Z3Y4X5Z8', 'Z0Z1Z4Y5X7X8'] : True\n",
+      "6 :: 3968: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X2X4Y6Z7', 'Y2Z3Y4X6Y7Y8', 'Y0Z1Z4Y5Y7X8'] : False\n",
+      "6 :: 3988: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'Y0Z1X5Y7', 'Y2Z3Y4X6Y7Y8', 'X0X2X4X6Z7X8'] : False\n",
+      "6 :: 3990: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'Z0Z1X4X7', 'X2X5X6X8', 'Y2Z3Y4X6Y7Y8'] : True\n",
+      "6 :: 3999: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Z2Z3Y4X5Z8', 'Z0Z1Z2Z3Y5X6', 'Y2Z3X4Z5X7X8'] : False\n",
+      "6 :: 4049: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8', 'X0Z2Z3X4Y5Z6'] : False\n",
+      "6 :: 4056: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8', 'Y0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 4145: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X5Y6Y7Y8', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3Z4Y5Z6'] : False\n",
+      "6 :: 4183: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8', 'Z0Z1Z2Z3Z4Y6'] : False\n",
+      "6 :: 4188: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8', 'Z0Z1Z2Z3Y4Z5'] : False\n",
+      "6 :: 4189: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Y4Y5X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6', 'Y0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 4191: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Z2Z3Y4X5Z8', 'Y2Z3X4X5Y6X7', 'Z0Z1Z2Z3Z6X8'] : False\n",
+      "6 :: 4240: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'X0Z2Z3Y6', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8'] : True\n",
+      "6 :: 4245: [[9,3, 2]] : 64 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0Y6Z7Z8', 'Z2Z3Z4Z5X6X7', 'Y0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 4254: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X4X5X6X7', 'Z0Z1X2X8', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 4256: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'Z2Z3Y4X5', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8'] : True\n",
+      "6 :: 4259: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'Y0Z1X4Z7', 'Y2Z3Y4X6Y7Y8', 'X0X2X5X6Y7X8'] : True\n",
+      "6 :: 4272: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X2Z5X7Y8', 'Y2Z3Y4X6Y7Y8', 'Z0Z1X4Y5X6Z8'] : False\n",
+      "6 :: 4291: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z2Z3Y6', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8'] : False\n",
+      "6 :: 4321: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1X2Z8', 'X0Z2Z3Y4X5Z8', 'Z2Z3Z4X6X7X8'] : True\n",
+      "6 :: 4333: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'X0Z2Z3Y6', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 4336: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Y2Z3X8', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8'] : True\n",
+      "6 :: 4343: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z2Z3Y4Z5', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 4351: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'Y2Z3Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8'] : False\n",
+      "6 :: 4358: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Z4Z5Z8', 'Z2Z3Y4X5X6X7', 'Z0Z1Y2Z3Z4X8'] : True\n",
+      "6 :: 4360: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Z4Z5X8', 'X0Z2Z3Y4X5Z8', 'Z0Z1Y4Y5X6X7'] : True\n",
+      "6 :: 4372: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z2Z3Y5X6', 'Z0Z1Z4X6X7Y8'] : True\n",
+      "6 :: 4389: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z5Y6X7X8', 'Z2Z3Y4Z5X6Z8', 'Z0Z1X2X4Y5Z6'] : False\n",
+      "6 :: 4429: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0X2Z6X8', 'X0Z2Z3Y4X5Z8', 'Y0Z1X4X5Y6X7'] : False\n",
+      "6 :: 4438: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4X5Z7Z8', 'Z2Z3X5X6', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Z5Z6X8'] : True\n",
+      "6 :: 4439: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Z6X8', 'X0Z2Z3Y4X5Z8', 'X0X2X4X5Y6X7'] : False\n",
+      "6 :: 4440: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X5Y6Y7Y8', 'Z2Z3Z4Y6', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 4441: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z0Z1Z4Z8', 'X0Z2Z3Y4X5Z8', 'X0Y2Z3X6X7X8'] : False\n",
+      "6 :: 4444: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z0Z1Z6Y8', 'X0Z2Z3Y4X5Z8', 'X0Y2Z3Z4Y6X7'] : True\n",
+      "6 :: 4446: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y2Z3Z6X8', 'X0Z2Z3Y4X5Z8', 'Y0Z1Z4Y6X7Z8'] : True\n",
+      "6 :: 4448: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y2Z3Z4X8', 'Y0Z1X2Z4Z5Z8', 'X0Y4Y5X6X7Y8'] : True\n",
+      "6 :: 4460: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z4X5X7Y8', 'X0Z2Z3Y4X5Z8', 'Y0Z1Y2Z3X5X6'] : True\n",
+      "6 :: 4484: [[9,3, 2]] : 192 :['X0X1', 'X2X3', 'Z0Z1X2X4', 'X5X6X7X8', 'Y2Z3Y4X5', 'X0Z4Z5Z6Z7Z8'] : True\n",
+      "6 :: 4487: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Y0Z1Z4X5', 'X0Z4Z5Z6Z7Z8', 'X0X2Y4X6X7X8', 'Y2Z3Y4Y5Y6X8'] : False\n",
+      "6 :: 4546: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Y0Z1Z4X5', 'X0Z4Z5Z6Z7Z8', 'X0X2Y4X6X7X8', 'Z0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 4591: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X2X4Y5Y7', 'Y2Z3Y4X6Y7Y8', 'Z0Z1Z5X6Z7X8'] : True\n",
+      "6 :: 4636: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Y0Z1Z4X5', 'X0Z4Z5Z6Z7Z8', 'X0X2Y4X6X7X8', 'Z2Z3Y4Z5Z6X7'] : False\n",
+      "6 :: 4639: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z2Z3Z5Y6', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8'] : False\n",
+      "6 :: 4659: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Y0Z1Z4X5', 'X0Z4Z5Z6Z7Z8', 'X0X2Y4X6X7X8', 'Z0Z1Z2Z3Z5X6'] : False\n",
+      "6 :: 4692: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Y2Z3X6X8', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8'] : False\n",
+      "6 :: 4702: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2X4Y5Z6', 'Z2Z3Y4Z5X6Z8', 'Z0Z1Z5Y6X7X8'] : False\n",
+      "6 :: 4709: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z2Z3Y4X6', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8'] : False\n",
+      "6 :: 4733: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'X0Y2Z3X8', 'Z0Z1Z4X6X7Y8'] : True\n",
+      "6 :: 4745: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'Z0Z1Z2Z3Y5X6'] : True\n",
+      "6 :: 4747: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Y0Z1X2Y4X7X8', 'Y0Z1Z2Z3Z5Y7'] : True\n",
+      "6 :: 4776: [[9,3, 2]] : 32 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0Y4Z5Z7', 'Z2Z3Z4Z5X6X7', 'Y0Z1X2Y6X7Z8'] : False\n",
+      "6 :: 4777: [[9,3, 2]] : 16 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0Y2Z4Z7', 'Z2Z3Z4Z5X6X7', 'Y0Z1Z3Y4Z6Z8'] : False\n",
+      "6 :: 4782: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Y0Z1X2Y4X7X8', 'Z0Z1Z2Z3Z5X7'] : True\n",
+      "6 :: 4783: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Z0Z1Z4X6X7Y8', 'Y0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 4785: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Y5X7X8', 'X0Z2Z3Y4X5Z8', 'Y0Z1X2X4Z5X6'] : False\n",
+      "6 :: 4786: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X4Y6Z7X8', 'Y2Z3Y4X6Y7Y8', 'Y0Z1X2Z4Y5Y7'] : False\n",
+      "6 :: 4801: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z2Z3Y4X5', 'Y0Z1Z6X7', 'X0Z4Z5Z6Z7Z8', 'X0X2X4X5Y6X8'] : True\n",
+      "6 :: 4811: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X5Y6Y7Y8', 'Z2Z3Y5Z6', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 4812: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X4Z5X7Y8', 'Z2Z3Y4Z5X6Z8', 'Z0Z1Y2Z3Y4X5'] : False\n",
+      "6 :: 4823: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Z4Y6X7', 'X0Z2Z3Y4X5Z8', 'Z0Z1Y4X5Z6X8'] : False\n",
+      "6 :: 4827: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0X4Z5X6', 'X0Z2Z3Y4X5Z8', 'Y0Z1X2Y5X7X8'] : False\n",
+      "6 :: 4840: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1X2Z6', 'X0Z2Z3Y4X5Z8', 'X0X4X5Y6X7X8'] : False\n",
+      "6 :: 4857: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y2Z3Y5X6', 'X0Z2Z3Y4X5Z8', 'Y0Z1Z4Y5X7Y8'] : True\n",
+      "6 :: 4867: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'X2Y4Z5X6', 'Z2Z3Y5X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z4Y5X7X8'] : False\n",
+      "6 :: 4871: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Z2Z3X4Y5', 'Y0Z1X2Y4X7X8'] : True\n",
+      "6 :: 4894: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5Z8', 'Z2Z3Y6Z7', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8'] : True\n",
+      "6 :: 4919: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Y5X6', 'X0Z2Z3Y4X5Z8', 'X0X2X4Z5X7X8'] : False\n",
+      "6 :: 4923: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4X5X6', 'Y5Y6Z7Z8', 'Y2Z3Y7X8', 'Y0Z1X2Y4X7X8'] : True\n",
+      "6 :: 4926: [[9,3, 2]] : 32 :['X0X1', 'X6X8', 'X2X3X4X5', 'X2Z3Z4Z7', 'Y2Z5X6Y7', 'Z0Z1Z2Y4Z6Z8'] : False\n",
+      "6 :: 4950: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y4X5Y6X7', 'Z2Z3Y4Z5X6Z8', 'Z0Z1X2Z4Z6X8'] : False\n",
+      "6 :: 4968: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X2Y4X5Z8', 'Y2Z3Z6X8', 'Z0Z1Z4X6X7Y8'] : True\n",
+      "6 :: 4976: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z0Z1X4X5', 'X2X6X7X8', 'Z2Z3Y4Z5X6Z8'] : True\n",
+      "6 :: 9125: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'X4X5', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X6X7X8', 'X0Z2Z3Y4Z5X6'] : True\n",
+      "6 :: 9183: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'X4X5', 'Z2Z3X4Y6', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X6X7X8'] : True\n",
+      "6 :: 15533: [[9,3, 2]] : 384 :['X0X1', 'X0X8', 'X6X7', 'X2X3X4X5', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 15584: [[9,3, 2]] : 96 :['X0X1', 'X2X3', 'X0Z4', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3Y5X7Z8'] : False\n",
+      "6 :: 15627: [[9,3, 2]] : 1152 :['X0X1', 'X0X8', 'X2X3', 'X4X5X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : True\n",
+      "6 :: 15672: [[9,3, 2]] : 864 :['X0X1', 'X0X8', 'X0X2Y3Z4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 15681: [[9,3, 2]] : 24 :['X0X1', 'X0X6', 'Z2X3X4Z7', 'Y2X5X6Y7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y3Z6Z8'] : False\n",
+      "6 :: 15695: [[9,3, 2]] : 48 :['X0X1', 'X0X6', 'X0Y2X3Z7', 'Z2X4X5Y7', 'Z2Z3Z4Z5X6X8', 'Y0Z1Y3X4Z6Z8'] : False\n",
+      "6 :: 15737: [[9,3, 2]] : 96 :['X0X1', 'X0X8', 'X0Y2Z4X5', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 15772: [[9,3, 2]] : 384 :['X0X1', 'X0X8', 'X2X3X4X5', 'X2X3Z6Z7', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 15783: [[9,3, 2]] : 96 :['X0X1', 'X0X8', 'Z2X3Z5X6', 'Y2X4Y5X7', 'Y3Z4Y6Z7', 'Z0Z1Y2Z3X4Z8'] : True\n",
+      "6 :: 15835: [[9,3, 2]] : 288 :['X0X1', 'X0X8', 'X2X3X4X5', 'X2X3X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 15844: [[9,3, 2]] : 24 :['X0X1', 'X0X6', 'Y2Y3Z4Z7', 'X4Y5Y7X8', 'X2X3X4X5X6X7', 'Z0Z1X3Y4Z6Z8'] : False\n",
+      "6 :: 15846: [[9,3, 2]] : 48 :['X0X1', 'X0X6', 'Z2Z3Z4Z7', 'Z5X6Z7X8', 'X2X3X4X5X6X7', 'Z0Z1X2Y4Z6Z8'] : False\n",
+      "6 :: 15862: [[9,3, 2]] : 48 :['X0X1', 'X0X6', 'Z2Y3Y4Y7', 'X2Y5Z7X8', 'X2X3X4X5X6X7', 'Y0Z1Y4X5Z6Z8'] : False\n",
+      "6 :: 15865: [[9,3, 2]] : 192 :['X0X1', 'X0X6', 'Z0Z1Y2Z6', 'Z3X4Z7Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : True\n",
+      "6 :: 15901: [[9,3, 2]] : 96 :['X0X1', 'X0X8', 'X2Z3Z4X5', 'Y3Y4X6X7', 'Y2Y5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 15909: [[9,3, 2]] : 48 :['X0X1', 'X0X8', 'X2X3X4X5', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'Z0Z1Y2Z4Z7Z8'] : False\n",
+      "6 :: 15930: [[9,3, 2]] : 288 :['X0X1', 'X0X8', 'X3Z4Z5X6', 'X2Y4Y5X7', 'Z2Y3Y6Z7', 'Z0Z1Y2Z3X4Z8'] : True\n",
+      "6 :: 15996: [[9,3, 2]] : 384 :['X0X1', 'X0X8', 'X4X5', 'X2X3X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : True\n",
+      "6 :: 21428: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X0X2Z5X7', 'Y2Z3Y4X6Y7Y8', 'Y0Z1X4Y5X6X8'] : False\n",
+      "6 :: 21460: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0X2Y4X5', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3Y5X7Z8'] : False\n",
+      "6 :: 21592: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Y5Z6Y7X8', 'Z0Z1X2X4X5X6', 'Y0Z1Z4Z5Z6X7', 'Y2Z3Z4Y5Y6Z8'] : False\n",
+      "6 :: 21595: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8', 'Y0Z1Z2Z3Z4Z6'] : False\n",
+      "6 :: 21692: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z5X6X7Y8', 'X0Z2Z3Y4X5Z8', 'Y0Z1Y2Z3Z4Z5'] : True\n",
+      "6 :: 21696: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X2Y4X5Z8', 'Y2Z3Y4X6Y7Y8', 'Z0Z1Y2Z3X4Z7'] : False\n",
+      "6 :: 21713: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Z2Z3', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8'] : True\n",
+      "6 :: 21747: [[9,3, 2]] : 192 :['X0X1', 'X2X3', 'Z2Z3Y4X5', 'Y0Z1X4Z5', 'X0Z4Z5Z6Z7Z8', 'X0X2Y5X6X7X8'] : False\n",
+      "6 :: 21785: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z7', 'X0Z4Z5Z6', 'Y2Z3Y4X6Y7Y8', 'Z0Z1Z2Z3X5Z8'] : True\n",
+      "6 :: 21816: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z7', 'Z0Z1X4X5X6X7', 'X0X2Z4Z5Z6X8', 'X0Z2Z3Y5X6Z8'] : False\n",
+      "6 :: 21865: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'X6X7', 'Z4Z5Z6Z7', 'X0Z2Z3Y4X5Z8', 'Z0Z1X2X4X5X8'] : True\n",
+      "6 :: 21884: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X0X2Y4X5', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3X4Y6Z8'] : False\n",
+      "6 :: 21930: [[9,3, 2]] : 32 :['Z0Z1', 'Z2Z3', 'Z0Z4Z5Z6', 'Z2Z4Z7Z8', 'X2X3X4X6X7X8', 'X0X1X2X3X5X7'] : False\n",
+      "6 :: 21933: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Y4Z5X6Z8', 'X0X2Z4Y5X7Y8', 'Z0Z1Z2Z3Y5X7'] : False\n",
+      "6 :: 21935: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'X4Y5Y6Y8', 'Z0Z1X4X5X6X7', 'X0X2Z4Z5Z6X8', 'X0Z2Z3Y4Z5Z7'] : False\n",
+      "6 :: 21936: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Y5Z6Y7X8', 'Z0Z1X2X4X5X6', 'Y0Z1Z4Z5Z6X7', 'X0Y2Z3Z4Y6Z8'] : False\n",
+      "6 :: 21971: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y4Z5X7X8', 'X0Z2Z3Y4X5Z8', 'Z0Z1X2Z4Y5X6'] : True\n",
+      "6 :: 21972: [[9,3, 2]] : 64 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0X2X6Y7', 'Z2Z3Z4Z5X6X7', 'Y0Z1Y4Z5Z6Z8'] : False\n",
+      "6 :: 21992: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0X2Y5X7', 'Z2Z3Y4Z5X6Z8', 'Y0Z1X4Z5X6X8'] : False\n",
+      "6 :: 22020: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z2Z3Y4X5', 'Z0Z1Y4Z6', 'X0Z4Z5Z6Z7Z8', 'X2Z4X5Y6X7X8'] : True\n",
+      "6 :: 22023: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Y2Z3', 'Z4X6X7Y8', 'X0Z2Z3Y4X5Z8'] : True\n",
+      "6 :: 22032: [[9,3, 2]] : 384 :['X0X1', 'X6X7', 'X2X3X4X5', 'Z2Z3Z4Z5', 'Y2Z3X4Z6Z7X8', 'Z0Z1X2Y3Z4Z8'] : False\n",
+      "6 :: 22082: [[9,3, 2]] : 192 :['X0X1', 'X0X8', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'Z0Z1X2X3Y6Z8'] : False\n",
+      "6 :: 22811: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'X0X2Y4X5', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z4X6X7X8', 'Z0Z1Z2Z3X4Z5'] : False\n",
+      "6 :: 22812: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'X0X2Y4X5', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z4X6X7X8', 'Y0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 23090: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y4Z5X6X8', 'Z0Z1X4X5X6X7', 'Z2Z3X4Y5Z6Z8'] : False\n",
+      "6 :: 23186: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Y5Z6Y7X8', 'Z0Z1X2X4X5X6', 'Y0Z1Z4Z5Z6X7', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 23204: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Y4Y5X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6', 'Z0Z1Z2Z3X4Z7'] : False\n",
+      "6 :: 23334: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'X0X2X4Y5', 'Y2Z3Y4X6Y7Y8', 'Y0Z1Z5X6X7X8'] : False\n",
+      "6 :: 23343: [[9,3, 2]] : 128 :['Z0Z1', 'X2X3', 'X4X5X6X7', 'Z4Z5Z6Z7', 'Z0Z2Z3Z4Z5Z8', 'X0X1X2X4X6X8'] : False\n",
+      "6 :: 23345: [[9,3, 2]] : 32 :['X0X1', 'X6X8', 'X2X3X4X5', 'Z0Z1X4Z7', 'Y2Z3Z6Z8', 'Z2Z3Z4Z5X6X7'] : True\n",
+      "6 :: 23348: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z2Z3X4Y5', 'Z4X5X6Z8', 'Z0Z1X2Y4X7Y8'] : False\n",
+      "6 :: 34171: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0X1X7X8', 'Y4Z5Y6X8', 'Y0Y1Z2Z3X4X6'] : False\n",
+      "6 :: 34183: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0X1Y6X8', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 34186: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X1X2Y7X8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 34268: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y4Y5Y6X8', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 34295: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y2Y3Y7X8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 34296: [[9,3, 2]] : 4 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y4Z5Y7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 34307: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z3Z4Y6', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 34375: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z0Y3Z4X7', 'Z2Y3Z6X8', 'Z0Z1Z2Z3X4X6', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 34415: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y1Y2Y4Z6', 'Y0Y3Z4Y6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 34431: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z1Z3X4X5', 'Y2Y3Y5Z6', 'Z0Z1X2Z4Z5X8', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 34447: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y0Y1X2Z6', 'Z2Y3X4Y6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 34532: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8', 'X0Z1Y2Y4Z6Z7'] : False\n",
+      "6 :: 34537: [[9,3, 2]] : 4 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'X0X2Y4Z5X6Z7'] : False\n",
+      "6 :: 34540: [[9,3, 2]] : 2 :['X0X1X2X3', 'X1X2Y5X8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 34550: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8', 'Z0X1Z3Y4Z6X7'] : False\n",
+      "6 :: 34884: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X2Y6Z7X8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 34933: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1X2Z4Z5X6', 'Y2Z3Y4Z5X7X8', 'Y0X1Z2Z4Z6X7'] : False\n",
+      "6 :: 34938: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X4X5X7X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 34939: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Z0Y3Z4Z5', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 35023: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y1Y3Z5X7', 'X3Z6Z7Y8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8'] : False\n",
+      "6 :: 35059: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0X2Z4Z5', 'X2Z6Z7Y8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 35148: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X3X4Y6X8', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 35149: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X0X5Z7X8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 35180: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Z2X4Z5', 'Z1Y2Y5X8', 'X0X1X4X5X6X7', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 35212: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Z1Z6X7', 'X0X2Y6X8', 'Y2Z3Z4Z5X7Y8'] : False\n",
+      "6 :: 35220: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y0Y2Z4X6', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 35230: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8', 'Y0X1Z2Z4Y6Z7'] : False\n",
+      "6 :: 35248: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0X2X6Z7', 'X3Y4Z5Z8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8'] : False\n",
+      "6 :: 35250: [[9,3, 2]] : 64 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'X1X2Z5Z6', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 35264: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0X2Y7X8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 35303: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z1Z2Z5X7', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 35305: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0X1Z4Z5', 'Z0Z1X2X4X5X8', 'Y0X1Z2X4X6Z8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 35338: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5', 'Y2Z3Y4Z5', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 35341: [[9,3, 2]] : 32 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X1X3Y7X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 35361: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z1X4Z6', 'X0Y4Z5Z7', 'Z0Z1X2X6X7X8', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 35393: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X0Z1Y2Z5Z7Z8', 'Y1X2Z3Z4Z6Z7'] : False\n",
+      "6 :: 35406: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Y7Z8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 35436: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X1X2Z4Z6', 'Z0Z1X2X4X5X8', 'Y0Z1Z2Z3X4X6', 'X0Z1Y2Z5Z7Z8'] : False\n",
+      "6 :: 35460: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0X1Y7X8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 35579: [[9,3, 2]] : 4 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Z1Y2Y4Z5X6Z7'] : False\n",
+      "6 :: 35660: [[9,3, 2]] : 2 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X1Y4Z5Y7', 'Y1Y5Z6X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 35688: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Z2X4Z6', 'Z1Z2Z7Z8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8'] : False\n",
+      "6 :: 35827: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z2Z3X7', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 35848: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y2Z3Y4Z5', 'X0Z6Z7Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 35854: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1Y6Z7', 'Z4Z5X6Y7', 'Z2Z3Y4Z5Y6X8'] : False\n",
+      "6 :: 35953: [[9,3, 2]] : 2 :['X0Z2Y3Z6', 'Y4Y5Y7Y8', 'X0Y1Z4Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 35954: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z3Y7X8', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 35975: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y0Z4X5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 36002: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'Y1Z4X7Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 36003: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y4Z6Z7X8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 36004: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7', 'Y1X2Z3X4Z5Z6'] : False\n",
+      "6 :: 36005: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6', 'Y0X1Z2Z4X5Z6'] : False\n",
+      "6 :: 36006: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X3Y4Z5X8', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Y5Z6', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 36007: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X1Y6X7Z8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 36044: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6', 'Z0X1Z3Y4Z5X6'] : False\n",
+      "6 :: 36045: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7', 'Z0Y1Z2Z3Z5Z6'] : False\n",
+      "6 :: 36046: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X1Y2Z3Y4Z5Z7'] : False\n",
+      "6 :: 36047: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6', 'Z0X1Z3Z4Y5X6'] : False\n",
+      "6 :: 36053: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8', 'Y0X1Z3Y4X5Z6'] : False\n",
+      "6 :: 36058: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z3Y7Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 36069: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Y5X7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Y3X4X5Z7Z8'] : False\n",
+      "6 :: 36070: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Z4Z5Z6Z8', 'X1X5Y7Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 36074: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6', 'X0Z1Z2Z5X6Z7'] : False\n",
+      "6 :: 36075: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z4X5Y6Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 36078: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6', 'X0Y1Z2X4Z5Z7'] : False\n",
+      "6 :: 36089: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y6X7', 'X3Z6Z7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8'] : False\n",
+      "6 :: 36093: [[9,3, 2]] : 2 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'Y0Z5Y6X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 36094: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X3Y4Z7Y8', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z0Y1Z2Z3Z4Z7'] : False\n",
+      "6 :: 36097: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8', 'X0Y1Z2Z4X5Y6'] : False\n",
+      "6 :: 36098: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7', 'Z0X2Z3X4Y5Z6'] : False\n",
+      "6 :: 36983: [[9,3, 2]] : 64 :['X0X1X2X3', 'X4X5X6X7', 'X0X1X4X5', 'Y4Y5Y6Z7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 36993: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X2Z7Z8', 'X0Z1Z3Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 37011: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z1Z2Z4Z6', 'Z0Z2Z5X7', 'Z2Z3Y4Z5Y6X8'] : False\n",
+      "6 :: 37014: [[9,3, 2]] : 8 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y0Z4X6Z7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 37030: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0X1X5Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 37033: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z1Z4Z5', 'X0X1Z7Z8', 'Z0X1Z2Z4X6X7', 'X0Z1Z3Y4Z6X8'] : False\n",
+      "6 :: 37035: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'Y1Z3Z4Y5', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 37039: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X2Z6Z8', 'Z0X1Y2Z6', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 37195: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y2Y4Y5', 'X3X4X5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 37217: [[9,3, 2]] : 16 :['X0X1X2X3', 'Y4Y5Y6Y7', 'X0X2Y5Y6', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 37667: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8', 'Y0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 38153: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y3Y5Z8', 'X3Y4Y6Y8', 'Z1Z2Z4Z5X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 38455: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z2Y3X4Z6Z7X8', 'X1Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 38590: [[9,3, 2]] : 4 :['X0Z2Y3Z6', 'Y4Y5Y7Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z1Y2Z4Y5X6X7'] : False\n",
+      "6 :: 38905: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'X1Y2Z3Z6Z7X8', 'Y0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 38993: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Y3Y4X5Z7Z8', 'Z0X1Z2Z4Z5Z7'] : False\n",
+      "6 :: 39000: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8', 'Z0Y1Y2Z3Y4X6'] : False\n",
+      "6 :: 39031: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6', 'X0Y1Z2X4Z5Y7'] : False\n",
+      "6 :: 39071: [[9,3, 2]] : 16 :['X0X1X2X3', 'Y4Y5Y6Y7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8', 'Z0Z1Z2Z3Y5Y6'] : False\n",
+      "6 :: 39072: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7', 'X1Z2Z3X4Z5X6'] : False\n",
+      "6 :: 39076: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8', 'Z0Z1Y2Z3Y5Z6'] : False\n",
+      "6 :: 39162: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X1Z5X7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 39174: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Z3X4X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 39176: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z0Y1Y5Z7', 'Z1Z2Y4Y7', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 39198: [[9,3, 2]] : 288 :['X0X1X2X3', 'X0X1X4X5', 'X0Z2Z3X4', 'Y0Z1Y4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 39218: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X1Z4Z6Z8', 'Z0Y1X2Y6', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Y4Z7'] : False\n",
+      "6 :: 39289: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z2Z6X8', 'X3Z5Z7X8', 'Y1Z3Y6Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 39325: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z4X5Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 39337: [[9,3, 2]] : 24 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Y1X2Y4Z5X6', 'X0X1Z4Y5X7X8', 'Y0Y2X4Y6Z7Z8', 'X0Z1Y2Y4Z6X7'] : False\n",
+      "6 :: 39463: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X1Y2Z3X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 39469: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z7Z8', 'Y1Z3X6Z7', 'Z5Y6X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 39484: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Z2Z3X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 39493: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z1Y3Y6X7', 'Z4X6Y7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 39494: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Y1Z5Y7', 'Y1Y2Z4Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 43152: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z1Y2Y5Y6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 43218: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Y1Y2Z3X4Z5'] : False\n",
+      "6 :: 43232: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8', 'Z2Z3Z4Z5Z6X7', 'Z0X1Z2Z4Y6Z7'] : False\n",
+      "6 :: 43260: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z5Z6Y7Y8', 'X1X4Z5Y7', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 43266: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0X4Y7Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 43328: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1X6Y8', 'X0X2Y5Z6', 'Z4X5Y6Z8', 'Z2Z3X4Y6Z7X8'] : False\n",
+      "6 :: 43362: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0Z2Z4X5', 'X0X1Y5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 43363: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0X2Z7Z8', 'Y0Y1Z4Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 43364: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Z0Z1Y5Y6', 'X4Z5Z6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 43365: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Y2Z4Y7', 'Z0Z1X2Z4Z5X6', 'Y2Z3Y4Z5X7X8'] : False\n",
+      "6 :: 43375: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y0Z2X4X5', 'Z0Z3Z4Z6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 43395: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z2Z4X6', 'Z1Y2Y4X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 43413: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z1X2Z3X4Z5Z7'] : False\n",
+      "6 :: 43427: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y0Y2Z4X7', 'Y1Z3Y5Y8', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7'] : False\n",
+      "6 :: 43445: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z2Z4Z6', 'Z1Z2Z5Z7', 'Z0Z1X2X6X7X8', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 43514: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y4Y6Z7Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 43515: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Z1X2Y4Z5Z7'] : False\n",
+      "6 :: 43975: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y0Z3Y5Z7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 44342: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Y3X4X5Z7Z8', 'Y0Y1Z2Z3Z5Z7'] : False\n",
+      "6 :: 44531: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Y3Y4X5Z7Z8', 'X0X2Y4Z5X6Z7'] : False\n",
+      "6 :: 44541: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8', 'X1Y2Z3Y4Y5Y7'] : False\n",
+      "6 :: 44543: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z1Z2Z5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'Z0Y1X2Y4Z5Y6'] : False\n",
+      "6 :: 44580: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'X2Z3Z5X7', 'X1Z3Z4Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 44604: [[9,3, 2]] : 1 :['X0X1X2X3', 'X2Z4Z6Z8', 'X3Z5Y6Y8', 'X0Y4Y6Y7', 'Z0Z1X4X5X6X7', 'X0Z2Z3X4X5Z7'] : False\n",
+      "6 :: 44668: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X0Y4Y7X8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 44680: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X1Z4Z6Z8', 'X3Z4Y5Z7', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Y4Z7'] : False\n",
+      "6 :: 49143: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Y1X2Z3Z4Y5Z7'] : False\n",
+      "6 :: 49159: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Y1Y2Z3Y5X6'] : False\n",
+      "6 :: 49169: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X0X2Y5X6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 49173: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0X2X4Y8', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 49202: [[9,3, 2]] : 1 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y0Y4X5Z6', 'X0Y1Z5X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 49551: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X1Z3Z5X6', 'Y2X4Y5Y6', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 49571: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z2Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 49573: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Y2Z3X4X6', 'Y0Z1X6Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 50034: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y2X4X5', 'Z0Y3Y6X7', 'Z1Z2Z4Z5X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 50261: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X2X4', 'X0X1X4X5X6X7', 'X0Z2Z3Z5Z6X8', 'Z2Z3Z4X5Z7Z8', 'X0Z1Y2Z5Y6Z7'] : False\n",
+      "6 :: 50263: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z1X2X4', 'X0X1X4X5X6X7', 'X0Z2Z3Z5Z6X8', 'Z2Z3Z4X5Z7Z8', 'Z1Y2Z4Z5X6Z7'] : False\n",
+      "6 :: 50340: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y3Z6Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 50400: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Y5Z6Z7'] : False\n",
+      "6 :: 50428: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Z0Y1X2Z5Z6Z7'] : False\n",
+      "6 :: 50432: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Z0Z1X4X5X6X7', 'X0Y1Z3Z4Z7Z8', 'X0X2Z4Y5X6Z7'] : False\n",
+      "6 :: 50607: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z4Y5Y6Y7', 'Y0Z1Y5Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 50615: [[9,3, 2]] : 4 :['X0X1X2X3', 'X2Y5Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Z0X2Z3Y4Y5X6'] : False\n",
+      "6 :: 50639: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z1Z4Z5Z6X7', 'Z0Z3Z4X5Z7Z8', 'Z0Y1Z2Z3X4Z5'] : False\n",
+      "6 :: 50649: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8', 'Z0X1Z2X5Z6Z7'] : False\n",
+      "6 :: 50652: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z1Z4Z5Z6X7', 'Z0X1Z3Z5Z7Z8', 'Y0Z1Y2Z3X4Z6'] : False\n",
+      "6 :: 50654: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8', 'Y1X2Z3Z4Y5Z7'] : False\n",
+      "6 :: 50662: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8', 'Z1X2Z3Y5Z6Z7'] : False\n",
+      "6 :: 50978: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Y1Z3Z4Z5Z6X8', 'Y1X2Z3X5Z7Z8', 'Y0X1Z3Z5X6Z7'] : False\n",
+      "6 :: 50987: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0X1Z2Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z1Z3Y4Z6Z7', 'Y0Z1Y2Z3X4Y5'] : False\n",
+      "6 :: 51051: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Z1X2Y4', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 51559: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8', 'X0Y1Z3Y4Y5X6'] : False\n",
+      "6 :: 51560: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y1Z2Y3X4Z5X7'] : False\n",
+      "6 :: 51706: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0Z4Z7Z8', 'X1Z5Y6Z8', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 51786: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X4Z6Z7X8', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Y5Z6', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 51807: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0X1Z2Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z1Z3Y4Z6Z7', 'Y0Z1Y2Z3Z5X6'] : False\n",
+      "6 :: 51964: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X3Y5Y6Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 51965: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Y1Z3Z6Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 52011: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1Y6Z7', 'X3Z4Y6X8', 'X0Z1Z2Z4Z5X7', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 52028: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X4Z5Z7Y8', 'Z0Z1X2X4X5X8', 'Y0Z1Z2Z3X4X6', 'Y0X1Y2Z4X5Z6'] : False\n",
+      "6 :: 52029: [[9,3, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'X5Z6Z7Z8', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 52032: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1X6Y8', 'Y2Y3X7Y8', 'Z0Z1X2Z4Z5X8', 'Z2Z3X4Y6Z7X8'] : False\n",
+      "6 :: 52079: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X3Y4X6Y7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 52116: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X0Y1Z2Z8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 52146: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z2Z6Y7', 'X3Z5Z6X8', 'X0Z1Z2Z4Z5X7', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 52149: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Y1Z5Y7', 'Z1Z2Y6Z7', 'Z0Z1Z2Z3X4X8', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 52153: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z3X4Z7', 'Y0Y3Y4X8', 'Z0Y2Z6Y8', 'X0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 52160: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'X4Z5Y6Y8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 52185: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y3Y6Z7', 'X1Z6X7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 52205: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Y2Y4Y5', 'X1Y5Z6X8', 'Z0Z1X2Z4Z5X8', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 52211: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X0Y4Z5Y7', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 52787: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Y1Y2Z4', 'Z0Y3Y4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 52811: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y1X4X6', 'X0X1X5X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 52820: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Y1Y7X8', 'Z4Z5X7Y8', 'Z0X1Z2Z4X6X7', 'X0Z1Z3Y4Z6X8'] : False\n",
+      "6 :: 52833: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'Y1Z2X4Y6', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 52953: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Y3Z4Y7', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 52967: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z0Z2Y4Z6', 'Z1Z3Z4Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 52969: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z1Z4Z5Z6X7', 'Z0X1Z3Z5Z7Z8', 'Y0Y1Y2Z3Y6Z7'] : False\n",
+      "6 :: 53718: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X2X4', 'X0X1X4X5X6X7', 'X0Z2Z3Z5Z6X8', 'Z2Z3Z4X5Z7Z8', 'Y0X1Y2Z4Y5Y6'] : False\n",
+      "6 :: 53770: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'X1X5Z7X8', 'Z0Z1X4X5X6X7', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 53771: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'X0Z4Y6Y7', 'Y1Z2Y5Y8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 54931: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3Y4X5', 'X3Y4Y5Z6Z7Z8', 'X0Y1Z2X4Y6Z7'] : False\n",
+      "6 :: 55099: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X0Z2Y5X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 55167: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X5Y8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 55201: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X3Y5Z6Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 55524: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X2Z6Z8', 'X0Z4Z5Y7', 'X1X6Y7X8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 55670: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X1Z5Y6Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 55719: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X1Z2Z3X8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 57483: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'Z4Z7', 'Z0Z1X4X5X6X7', 'X0X2Z4Z5Z6X8', 'Y0Z1Z2Z3Y5Z8'] : True\n",
+      "6 :: 57484: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'X4X7', 'Z0Z1Z4Z5Z6Z7', 'X0X2X4X5X6X8', 'Z0Z1Z2Z3Z5Z8'] : True\n",
+      "6 :: 57505: [[9,3, 2]] : 1152 :['X0X1', 'X2X3', 'X4X5', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X6X7X8', 'Y0Z1Z2Z3Y4Z5'] : True\n",
+      "6 :: 57687: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Y4Y5', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3Y4X6Z8'] : True\n",
+      "6 :: 59732: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z1Z2Y4X6', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 59771: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Y2Z3Y6X8', 'Y4Z5Z6Y8', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 59780: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X2Z6Z8', 'Y0Y2Y6X8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 59784: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Z2X5Z6', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 59785: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Y0Y2Y4Z8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 59796: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Y1Y2Z4X5', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 59809: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0Z2Z3X6', 'X0Y4Z5Z8', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7'] : False\n",
+      "6 :: 59863: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7', 'X0Y1Y2X4Y5Y6'] : False\n",
+      "6 :: 59971: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X2Z6Z8', 'Z4X5Z7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 59972: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y4Y5Y6Y7', 'X2Y4Y6Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 59973: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y4Y5Y6Y7', 'X2Y5Y6Z8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 60061: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Z1Z6X7', 'Y2Z3Z4Z5', 'X0Z2Z3X4Y6X8'] : False\n",
+      "6 :: 60063: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y0Z1Z2Z3', 'X4Z6Z7Z8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 60089: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0X1X5X8', 'Z1Z3Z5Z6', 'Z0Z1Z2Z3X4X8', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 60090: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Z2Z4X6', 'Z0Z1Z4Z5Z6X7', 'Z2Z3Y4Z5Y6X8'] : False\n",
+      "6 :: 60091: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Z2Z4X6', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5Y6X8'] : False\n",
+      "6 :: 60095: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y2Y3Y4Z5', 'X1X2Y5Y8', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7'] : False\n",
+      "6 :: 60107: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y0Z2X5Z7', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 60112: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3Y6X8', 'Y2Z3Z4Z5', 'Z0Z1X2X4Z6X7'] : False\n",
+      "6 :: 60116: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z3X5X6', 'Y4Y5Y6Z7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8'] : False\n",
+      "6 :: 60120: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5', 'Z0Z1Z2Z3', 'Y4Y5Z6X8', 'Y0X1Z2X4X6X7', 'X0Y2Z3Z4Z7Z8'] : False\n",
+      "6 :: 60139: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y1Y2Z4Z7', 'Y0Y3Z4Z8', 'Z0Z1X2X4X6X7', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 60148: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z1X2Z3Y5X6Z7'] : False\n",
+      "6 :: 60155: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Z2X4X5', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 60217: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8', 'Z0Z1X2Y4Z5X7'] : False\n",
+      "6 :: 60222: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Y2Z4Y6', 'Z0Z3Z5X8', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 60223: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Y2Z4Y6', 'Y0Y3Y5X8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 60225: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X2X4Y6X8', 'Y4Z5X7Y8', 'Z0Z1Y2Z3Z6X7'] : False\n",
+      "6 :: 60228: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Z5Z7', 'Z0Z1Z4Y7', 'X1X2X7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 60229: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y4Z5Z6Z7', 'Z2Z3X4Y7', 'Z0Y1X5Y7', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 60267: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z0Z2Z4X7', 'Z0Z1Z5X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 60310: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8', 'Y0Y1X2X4Y5Z6'] : False\n",
+      "6 :: 60311: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6', 'Z0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 60312: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z4Y5Y7Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 60316: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6', 'Y0X1Z2Z4X5Z7'] : False\n",
+      "6 :: 60317: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Y5X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 60321: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y2Z4Z5', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 61193: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y2Y3Y6Z7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 61500: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7', 'Z0Y1Y2Z3Z4X5'] : False\n",
+      "6 :: 61678: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7', 'Y0X1Z2Z4X5Y6'] : False\n",
+      "6 :: 61679: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7', 'Y0Z1X2Y4Y5Z6'] : False\n",
+      "6 :: 61724: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Y0Y2Y6Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 61779: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'X1X5Z7X8', 'Z0Z1Z2Z3Z5X8', 'Z1Y3Z4Z6Z7Z8'] : False\n",
+      "6 :: 61788: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3Z4X5', 'Z0Y2Y6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 61789: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X0Y1Y2X5', 'X1X2Y6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 63012: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z5Z6Y7Y8', 'X0X1X5Z7', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 63028: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z1Z2Y6X8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 63046: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z0X1Z2Z4X6X7', 'Y0Z1Z2Z3X6X8', 'Y1Y2Z5Z6Z7Z8', 'Y0Y1Y2Z3Y4Z5'] : False\n",
+      "6 :: 63048: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8', 'Y0X1Z2Z4Y6X7'] : False\n",
+      "6 :: 63068: [[9,3, 2]] : 8 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X4X5Z6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 63168: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'X0X1Z5Z6', 'Y0Z2Z4Y6', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 63169: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'X1X2Z6Z8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 63387: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z1Y4Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 63600: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z1X2Z8', 'X0X1X4X5X6X7', 'X0Z4Z5Z6Z7X8', 'X0Z2Z3Y4Z5X6', 'Z0X1Z2Y4Z6Z7'] : False\n",
+      "6 :: 63730: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'X1X4Y6X8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 63750: [[9,3, 2]] : 1 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'Z0Z5Z6X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 63752: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Z3Z6Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 63761: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X1Z4Y6Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 63763: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Y3Y4Z5', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 63764: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4Z7Y8', 'Y2Z3Y4Y7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 63768: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3Y4Z5', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 63769: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z1Z3Z4Y5', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 63928: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'X2X4X5Z8', 'X3X6X7Z8', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Z4Z7Z8'] : False\n",
+      "6 :: 63935: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Y3Z4Z7', 'Y1Y2Z4Z8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 64042: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z1Z4Z5Z6X7', 'Z0X1Z3Z5Z7Z8', 'Y0Z1Y2Z3Z4Z7'] : False\n",
+      "6 :: 64050: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Z1Z5X8', 'Y1Y3Z4X5', 'Z0Z1X4X5X6X7', 'Y1Z3Y4Z6Z7Z8'] : False\n",
+      "6 :: 64052: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'X1Z4Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Y1Z2Y4Z5Z6'] : False\n",
+      "6 :: 64065: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1X2Z4X5Z7', 'Y0Y1Z2Z3Z6Z8', 'Z0X1Z2X4Z6X7'] : False\n",
+      "6 :: 64080: [[9,3, 2]] : 1 :['X0X1X2X3', 'X2Y6Y7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Z2Z3X4X5Z7', 'Y0X1Z3Y4X5Y6'] : False\n",
+      "6 :: 64346: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X0Y1Y2X5', 'X1X2Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 64565: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Y3X4X5Z7Z8', 'Y1Y3Y4Z5X6Z7'] : False\n",
+      "6 :: 64591: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7', 'Z0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 64634: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8', 'Y1Z3Z4Y5X6Z7'] : False\n",
+      "6 :: 64728: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Y1Z3Z4Z5Z6X8', 'Y0Z1Y2Z3Z7Z8', 'X1Z2Z3Y5Z6Z7'] : False\n",
+      "6 :: 64753: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X1Z2Z3Z5', 'X1Z4X6X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 64770: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z2Z4Z6', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 64782: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z1Y2Y4X7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 64839: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z2Y5Y7', 'Z1Z2X6Y8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 64994: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z2Z3Z4Z5', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 65038: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z2Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 65060: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X1X2Z5Y7', 'Y0Y2Y6Z7', 'Z0Z1Z2Z3X4X8', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 65079: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X1X2Y6Y8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 65108: [[9,3, 2]] : 24 :['X0X1X2X3', 'Y5Y6X7Z8', 'X1X2Y5X7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 65114: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X2Y5Y7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 65123: [[9,3, 2]] : 6 :['Z0Z1Z2Z3', 'X0X2X4X8', 'X1X3X4X5', 'Z0Z1Z4Z6', 'X0X3X6X7', 'Z1Z2Z5Z6Z7Z8'] : False\n",
+      "6 :: 65194: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X1X4Y7Z8', 'Z1Z2Z4Z5X7X8', 'Z0X2Z3Y4X5Z7', 'Y1Y2Z4X5Z6Z8'] : False\n",
+      "6 :: 65198: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0Y3X4Y7', 'Y1Y2Y7X8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 65211: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0X1Z2Z6', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Y1Z3Z4Z7Z8', 'Z0Y1Y2Z3Y4X5'] : False\n",
+      "6 :: 65220: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X1Z6X7X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 65304: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8', 'Y0X2Z3X4Z5Z7'] : False\n",
+      "6 :: 65306: [[9,3, 2]] : 2 :['X0X1X2X3', 'X2Y4Z7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6', 'Y0X1Z2Z4X5Z7'] : False\n",
+      "6 :: 65314: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7', 'Y0Y1X2Z4Z7Z8', 'X0Z2Z3Z4Y5Z7'] : False\n",
+      "6 :: 65482: [[9,3, 2]] : 2 :['X0X1X2X3', 'X2Y5Z7Z8', 'X0Z4Y6Z7', 'Z0Y3X7Y8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 65496: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Y0Y1Z4Z7', 'X3Z5Z7X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 65561: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Y3Z4X6X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1X3Z4Z5Z8'] : False\n",
+      "6 :: 66250: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z2Z3Y7X8', 'X2X4X6Y8', 'Z0Z1X7Y8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 66365: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X3Z4Z6X7', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 66386: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z2Y4Y6', 'X1Y5Z6X7', 'Y0Z3Y5Z8', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 70526: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z2X4X5', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 70556: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z1Y2Z4Z7', 'Y0Y3Z5Z8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8'] : False\n",
+      "6 :: 70562: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z1Z2Y6X7', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 70571: [[9,3, 2]] : 8 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X2X3Y6Y7', 'Z0Z3Z6Y8', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 70692: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0X2X6Y8', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 70694: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y3Y4X5X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 70708: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5Y6X8', 'Z0X1Y2Z4Y6Z7'] : False\n",
+      "6 :: 70710: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1Z4Z5Z6X7', 'Z2Z3Y4Z5Y6X8', 'Y0X1Y2Z4Y6Z7'] : False\n",
+      "6 :: 70765: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'X2Y5Y6Z8', 'X1X2Y4Y7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8'] : False\n",
+      "6 :: 70766: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z6Z8', 'Y2Z3X6X7', 'X2Y4X5Z7', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8'] : False\n",
+      "6 :: 70788: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z2Z3Z4Z7', 'X0X2Y4Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 70821: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y1Z3Z5Y7', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 70822: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z1Z3Z4X8', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 70826: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z1Z2Z4Z6', 'Z0Z3Z5X7', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 70875: [[9,3, 2]] : 48 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'X0X1Y5Z6', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 70897: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z1Z2Y4X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 70899: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Y3Y4X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 70949: [[9,3, 2]] : 48 :['X0X1X2X3', 'X0X1Z5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0Z2Z4X5Z7Z8', 'X0Y2Z3X4Y5X6'] : False\n",
+      "6 :: 70950: [[9,3, 2]] : 48 :['X0X1X2X3', 'X0X1Z5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0Z2Z4X5Z7Z8', 'Z0Y1Y2Z3Y5X6'] : False\n",
+      "6 :: 70960: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Y1Y3X4X8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 71021: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0X1Z2Z4X6X7', 'Y1Z3Z5Y6X7X8', 'X0Y1Y2Z4Z7Z8', 'Y1X2Z3Y4X6Z7'] : False\n",
+      "6 :: 71025: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y2Y4X5Z7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 71086: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Y0Z2Z6X7', 'X3X5X7Z8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 71168: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z2Y4Y6', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 71188: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z1Y3Y4Y7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 71203: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y1Y3Y4X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 71221: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7', 'X1Y2Z3Y4Y5X6'] : False\n",
+      "6 :: 71222: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Z3X4Y5Z7'] : False\n",
+      "6 :: 71242: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'X1X5Y6Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 71243: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Y0Y1Y4Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 71498: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0X1Z3Y5', 'X0Y4X5Y8', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 71516: [[9,3, 2]] : 1 :['X0X1X2X3', 'X1Y6Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8', 'Z0X1Y2Y5Z6Z7'] : False\n",
+      "6 :: 71545: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8', 'Z0Z1Z2Z3Y6Z7'] : False\n",
+      "6 :: 71570: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'X0Z1Z3Z4Z5Z7', 'Z0Y1Y2Z3Z6Z8', 'Y0Y1Z2Z3Y5X7'] : False\n",
+      "6 :: 71622: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8', 'Z0X1Y2X5Z6Z7'] : False\n",
+      "6 :: 71648: [[9,3, 2]] : 2 :['X0X1X2X3', 'X3Y4Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Z4Z5Z6', 'Z0X1Z3Z4Y5Z7'] : False\n",
+      "6 :: 72067: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z1X2X4', 'Z0X1Y2Z5', 'Z0Y3Z6X8', 'X0X1X4X5X6X7', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 72133: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0Y4Z5Z6', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5Y6X8'] : False\n",
+      "6 :: 72430: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8', 'Y1Z2Y4Y5X6Z7'] : False\n",
+      "6 :: 72494: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8', 'Y0Y2Z4Z5X6Z7'] : False\n",
+      "6 :: 72578: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z1Z2Z5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'X1Z2Z3Y4Y5Y6'] : False\n",
+      "6 :: 72585: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Y0Y1X2Z4Z7Z8', 'Y0X1Z3X4Y6Z7'] : False\n",
+      "6 :: 72587: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8', 'Z0X1Y2Z4Y5Y6'] : False\n",
+      "6 :: 72611: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7', 'Z0X1Z2Z4Y5Y6'] : False\n",
+      "6 :: 72612: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7', 'Y0X1Y2Y4Z5X6'] : False\n",
+      "6 :: 72661: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1Y6Z7', 'X3Z4Y6X8', 'Y0Y3Y5Y8', 'X0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 72683: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Z6X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 72687: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'X2X4Y7X8', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Z1Z2Y4Z7Z8'] : False\n",
+      "6 :: 72740: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z1Y3Y5Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 72756: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y3Z4Y6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 72762: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X1X5Z6X8', 'X3Y5Z7Y8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8'] : False\n",
+      "6 :: 72786: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y3Z4Y6', 'Y0Y3Y7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 72800: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X1Z4Z6Y7', 'Y1Y3X4Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 72946: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y1Z2Y4Y7', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 72967: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X2X4Y6X8', 'Z0Z1Y2Z3Z6X7', 'X0Y2Z3Z4Z5Z7'] : False\n",
+      "6 :: 73000: [[9,3, 2]] : 6 :['X0X1X2X3', 'X0X1X4X5', 'X0Y1Z2Z4Z6X7', 'Y0X1Z3Y4Y6X8', 'Z0X1Y2Z5Y7Z8', 'Z0Y1Y2Z3Y6Z7'] : False\n",
+      "6 :: 73014: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Y0Y1Z6X7', 'X0X2Y7Z8', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8'] : False\n",
+      "6 :: 73041: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Z1Z2Z5Y7', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 73129: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'X2X4Z7Y8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 73205: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Z1Z7X8', 'Z4Y6X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 73391: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z0Z1X2X4', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Y0X1Z2Z4X5Z8', 'Y2Y3Z4Z5Z6Z7'] : False\n",
+      "6 :: 73881: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y2X6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 73918: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X5Z8', 'Z4Y5X7Y8', 'Z0Z1Z2Z3Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 73932: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y1Y4Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 73981: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y2Z3Y4Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 73990: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y2X3Z5Z7', 'Z0Y1X3Z8', 'X0X1X2X3X4X5', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 74005: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Y2X6X8', 'Z1Y3Z5Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 74014: [[9,3, 2]] : 12 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8', 'X0Y2Z3Z4Z5Z6'] : False\n",
+      "6 :: 74120: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X2X6X7Z8', 'Y2Z3X5Y8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 74142: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0Y3X6Y7', 'Z1Y3X5Y8', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 74516: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y3X4X5', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 74550: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1Z2Z3', 'X4Y5Y6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 74553: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y0Y2Z4Y6', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 74583: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z2Z4Z6', 'Z0Z3Z5X7', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 74688: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y1Z3Y6Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 75159: [[9,3, 2]] : 8 :['X0Z3Z4X6', 'Y0Z1Y6Z7', 'Y1Y3X4Y7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 75419: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z3Z4Z6', 'Z1Z2Y4Y6', 'Z2Z3X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 75491: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X0Y4Y5X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 75504: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z5Z6Y7Y8', 'X0Z6Z7X8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 75596: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y2X4X6', 'Z0Y2X5X7', 'X2Z4Y7Z8', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 75598: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X2Y5Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 75599: [[9,3, 2]] : 36 :['X0X1X2X3', 'X0X1X4X5', 'X0Z1Z3Y4', 'Y0Z2X4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 75611: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z0X1Z2Z4X6X7', 'Y0Z1Z2Z3X6X8', 'Y1Y2Z5Z6Z7Z8', 'Y1X2Z3Y4X6Z7'] : False\n",
+      "6 :: 75658: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y2Y3Z4Y7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 75659: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y1X4Z5Y6', 'Z2Z4X5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 75661: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Y2Y3X6X8', 'Y0Y1X7X8', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 75808: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'X0Y1Z3Y4Y5X6'] : False\n",
+      "6 :: 75815: [[9,3, 2]] : 96 :['X0X1X2X3', 'X0X1X4X5', 'X0X1Z7Z8', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 75846: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Y0Y1Z2Z3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 75858: [[9,3, 2]] : 6 :['X0X1X2X3', 'X0X1X4X5', 'X0Y1Z2Z4Z6X7', 'Y0X1Z3Y4Y6X8', 'Z0X1Y2Z5Y7Z8', 'Y0Y1Y2Z3Z4Z5'] : False\n",
+      "6 :: 75957: [[9,3, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'X0Z4X5Z6', 'X1Y5X6Z7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 75959: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'X0Z2Z3X7', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 76034: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8', 'Z0Z1Z2Z3Y4Y6'] : False\n",
+      "6 :: 76041: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Y2X3Z4X7'] : False\n",
+      "6 :: 76096: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Y0X1Y2Y5', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 76143: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Y6X7', 'X0Z2Z3X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8'] : False\n",
+      "6 :: 76241: [[9,3, 2]] : 1 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X1Z3Z4Z6', 'X1Z2Z5X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 76318: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6', 'Y0X1Z3Z5X6Z7'] : False\n",
+      "6 :: 76319: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8', 'Y1Z2Z4Y5X6Z7'] : False\n",
+      "6 :: 76320: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8', 'X0Y1Y2Y5X6Z7'] : False\n",
+      "6 :: 76321: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7', 'Y0X1Y2X4Z5Z6'] : False\n",
+      "6 :: 76949: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X3Z4Z5X7', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 77141: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Y5Z6', 'Y1Y2Y4X5Z7Z8', 'Z0Y1X2Y5X6Z7'] : False\n",
+      "6 :: 77168: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8', 'Z0X1Z3Y4X5Z6'] : False\n",
+      "6 :: 77299: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z2Y3Y5Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 77302: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X2Z3Z5X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 77304: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y1X2Y5', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 77305: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z3X5Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 77315: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y1Y2Y5X6', 'X2Y4Z6X7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 77772: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X1X2X4X7', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 77775: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Y1Z3X4', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 77799: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z5Z6X7X8', 'Z1Z2X4Z5', 'Z0Y2Y5X6', 'Y0Z3X4Z6', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 77904: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'X1Y4X6Z8', 'Z0Z1X4X5X6X7', 'Y0Y1Y2Z3Z4Z5', 'Z1X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 77922: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y3Z5Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 78110: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8', 'X0Z2Z3Y5Z6Z7'] : False\n",
+      "6 :: 78111: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Z1Z2X4Z5Z6'] : False\n",
+      "6 :: 78124: [[9,3, 2]] : 1 :['X0X1X2X3', 'X3Y4Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Z4Z5Z6', 'Y0X1Z3Z4X6Z7'] : False\n",
+      "6 :: 78155: [[9,3, 2]] : 1 :['X0X1X2X3', 'X1Y6Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8', 'Z0X1Z2Y4Z6Z7'] : False\n",
+      "6 :: 78167: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8', 'Y0Z1Y2Z3Y4X5'] : False\n",
+      "6 :: 78195: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1X2Z4X5Z7', 'Y0Y1Z2Z3Z6Z8', 'Z0X1Z2Y4Z5Z6'] : False\n",
+      "6 :: 78857: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y0X2Z3Y7', 'Z4Z5Z6Z8', 'X0X3X6Y8', 'X0X1X2X3X4X5'] : False\n",
+      "6 :: 78869: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Y0X1Z2Y4', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 78874: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X2Z4Y5X7', 'Z1Y3X6Y8', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 78879: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Z2Z3Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 78920: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Y2Z3Y6Y7', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 78924: [[9,3, 2]] : 1 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'Z1Y5Z6X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 78932: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y4X5Z7Z8', 'Z0Z1X4X5X6X7', 'Y0Y1Y2Z3Z4Z5', 'Z1X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 79104: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Y3Z4X6X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1X2Z3Z5Z8'] : False\n",
+      "6 :: 79109: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X1X5Z7X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 79119: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Y0Z1Z6Y8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 79121: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y2Z5Y6', 'X0Z1Z2X4X7X8', 'X0Y1Z3Z4Z5X7', 'Z0X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 79144: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8', 'Y0Y2Z4Y5Z6Z7'] : False\n",
+      "6 :: 79156: [[9,3, 2]] : 2 :['X0X1X2X3', 'X2Y5Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Z1X2Z3Z4Y5X6'] : False\n",
+      "6 :: 79166: [[9,3, 2]] : 2 :['X0X1X2X3', 'X2Y5Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'X0Z2Z3Z4Y5Y7'] : False\n",
+      "6 :: 79349: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X0X1X6X8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 79481: [[9,3, 2]] : 12 :['X0X1X2X3', 'Z0X1Z2Y4', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z5X6', 'Z1Y3X4Z6Z7Z8'] : False\n",
+      "6 :: 79567: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X2Y5Y6Z8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 79568: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Y0Y2X5Z7', 'Z1Y3X5Z8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8'] : False\n",
+      "6 :: 79864: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Z1Y4Z5', 'X2Z4Y5X8', 'X0X1X4X5X6X7', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 79929: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y0X2Z3Y5', 'X0Y4Z5Z7', 'Z1Z2Z4Z5X7X8', 'Y1Y2Z4X5Z6Z8'] : False\n",
+      "6 :: 79975: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X3X5X6Z8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 80048: [[9,3, 2]] : 96 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y0Z1X6Z7', 'X0X2Y7X8', 'X0X1X2X3X4X5', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 80053: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2X5X7Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 80054: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X2Y5Z6Y8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 80189: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z4X5Y6Z8', 'Z0Z1Y6Z7', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 80526: [[9,3, 2]] : 48 :['X0X1', 'X0X6', 'Z4X5Y7Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Z1Z2Y3Z4Z6'] : False\n",
+      "6 :: 80632: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'Z1Y3Z4Z5', 'Z2Y3X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 80683: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z5Y6', 'X2Z4Y6X8', 'Y0Y1Z4Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 80712: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z0X1Y2Y4', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y1Z3Z6Z7Z8', 'Z0Y1Z2Z3X5Y6'] : False\n",
+      "6 :: 80741: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X2X4Y7Z8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 80785: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Y4Y6Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 80786: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Y0Y1Z6Y7', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 80787: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Z3Y4Y6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 80952: [[9,3, 2]] : 1 :['X0X1X2X3', 'X2Y4Z7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6', 'Y0X1Z3Y5X6Z7'] : False\n",
+      "6 :: 80996: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'X1Z4Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Z1Y3X4Z5Z6'] : False\n",
+      "6 :: 81003: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Z0X2Z3Z4X5Z7'] : False\n",
+      "6 :: 81326: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z1Z2Z7Z8', 'X4Y6Y7X8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 81539: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'Y2Z3Y6Y7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Z4Z7Z8'] : False\n",
+      "6 :: 81616: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y4Z6X7Y8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 81619: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Y2X4', 'X2Y4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 81621: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X0Z6Y7Z8', 'Y0Z1Y4Y6', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 82004: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X4Z7Y8', 'Z1Y2Z4Y7', 'X3Y5Y6Y8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 82005: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y1Y3Z4X7', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 82047: [[9,3, 2]] : 16 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y0Z1X4Z7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 82474: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8', 'X0Y1Z3Z5X6Z7'] : False\n",
+      "6 :: 82490: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Y2Z3Z4Z7Z8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 82583: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y1Y5Z6', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 82671: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8', 'X1Y2Z3Y4Z5Z7'] : False\n",
+      "6 :: 82674: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Y1Z2Z3Y5Z6', 'Z1Z3Z4X5Z7Z8', 'Y1Y2Z4Y5X6Z7'] : False\n",
+      "6 :: 82695: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z1Y3Y4Y5', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 83312: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z2Y4Z5', 'X0X1X4X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 83568: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Z0Z3Z4Z6', 'Z1Z3Z5Z7', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 83648: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X1Y2X3Z4', 'X0Z2Y4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 83689: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Z1Y2Y3Z5X7'] : False\n",
+      "6 :: 83696: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Y3X4X5Z7Z8', 'Y0Z1Y2Z3Z4X5'] : False\n",
+      "6 :: 83698: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8', 'X0Z1Z2X5Z6Z7'] : False\n",
+      "6 :: 85650: [[9,3, 2]] : 1 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y3Z4Z6Z7', 'Z2X3Z5Y7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 85747: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Y2Z3Y6X8', 'Z4Z5Y6Z7', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 85842: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X3X6X7Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 85893: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Y1Z3Z4Z5Z6X8', 'Y0Z1Y2Z3Z7Z8', 'X1Y2Z3Y4Z5Y7'] : False\n",
+      "6 :: 85894: [[9,3, 2]] : 24 :['X0X1X2X3', 'Z0Z1X2X4', 'X0Z1Z3Z4Z5X6', 'Z0Z2Z4Y5X7X8', 'X1Z2Z3Y6Z7Z8', 'Z0Y1Z2Z3Y5Z6'] : False\n",
+      "6 :: 85954: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3Y6X8', 'X0Z4Z5Z7', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 86033: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8', 'Y0Z1Y2Z3X4Y5'] : False\n",
+      "6 :: 86039: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Y3Z5X8', 'Z0Z1X4X5X6X7', 'X1X2Z4Z6Z7Z8', 'X0Z2Z3X4X5Z6'] : False\n",
+      "6 :: 86123: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Y1Z3Z4Z6Z7X8', 'Z0Z2Y4Z5Z6Z8', 'Z0Y1Y2Z3X5Z6'] : False\n",
+      "6 :: 86144: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Y1Y6Z7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 86162: [[9,3, 2]] : 1 :['X0X1X2X3', 'X2Y4Z7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6', 'Y0Z2Z4Y5X6Z7'] : False\n",
+      "6 :: 86179: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Y3Y5Y7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Y2X3Z4X7'] : False\n",
+      "6 :: 86202: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Z1X3Y4Z5X7'] : False\n",
+      "6 :: 86207: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Z0Z1Z4X5Z7Z8', 'Z0Y1Y2Z3Y5Z7'] : False\n",
+      "6 :: 86208: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8', 'Y0X1Z3X5Z6Z7'] : False\n",
+      "6 :: 86210: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z1Z4Z5Z6X7', 'Z0Z3Z4X5Z7Z8', 'Y0Y1Y2Z3Y5Z7'] : False\n",
+      "6 :: 86400: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z1Z3Z5X7', 'Y4Z5Y6X8', 'X1Z6Z7Y8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 86401: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z1Z2Z4Z6', 'Z0Y2Z5Y6', 'Y2Z3Y4Z5X7X8'] : False\n",
+      "6 :: 86449: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z1Y3Z5Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 86472: [[9,3, 2]] : 6 :['Y2Y3X4Y6', 'Y3Y5Y7Y8', 'X0Y1Y2Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 86612: [[9,3, 2]] : 2 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y1Z2Z3Y4X6X7'] : False\n",
+      "6 :: 86655: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Z4Z7Z8', 'Y1Z2Z4X5Z6Z7'] : False\n",
+      "6 :: 86657: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Y1X4X5', 'X0X1X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 86667: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0X1Z2Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Z2Z3Z4Z6Z7', 'X0Y1Z3X4Y5Z6'] : False\n",
+      "6 :: 86768: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y0Y2Y5Z7', 'Z1Z3Z6Z7', 'Z0Z1Z2Z3X4X8', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 86771: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y1Z3Z4Y5', 'Y0Y3X5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 86788: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0X1Z2Y4', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 86789: [[9,3, 2]] : 16 :['X0X1X2X3', 'Y4Z5Z6Z7', 'Y4X5Y6Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 86799: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1Y6Y7', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y3Z4X5Z7Z8'] : False\n",
+      "6 :: 86864: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y4Z5Z6Z7', 'X4Y5Z6Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 86888: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Y0Y2Z5X6', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 87071: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8', 'Z0Y1Y2Z3Y6Z7'] : False\n",
+      "6 :: 87247: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X3Z5Z7X8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 87385: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Y1X4Z5', 'X1Y5X6X7', 'X3Y4Y5X8', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 87451: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y0Z1Y5Z6', 'Y0Z2Z4Y7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 87457: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X2Y5Y6Y8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 87469: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7', 'Z0Y1X2Y4Y5Z6'] : False\n",
+      "6 :: 87655: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7', 'X0Y2Z3Y4Y5Z6'] : False\n",
+      "6 :: 87656: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7', 'X0Y1Z3Z4X5Y6'] : False\n",
+      "6 :: 87847: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Y1Y4X5', 'X1Z4X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 87873: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X3Z5Y6X8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 87937: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X1Y4Z6X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 87938: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X1Z4Z6X8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 87939: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X2Z4X6Y7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 87957: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Y0Y3X6Y7', 'Y5Y6X7Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8'] : False\n",
+      "6 :: 88338: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y3X5Y7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 88340: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X3Y5Y7Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 88443: [[9,3, 2]] : 64 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0X2X4Z7', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5Y6X8'] : False\n",
+      "6 :: 88541: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7', 'X0Y1Y2Z4X5Y6'] : False\n",
+      "6 :: 88601: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X3Y4Y6Y7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 88621: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0Y4Y5Z8', 'Z2Y3X7Z8', 'Z0Z1X2Z4Z5X8', 'Y2Z3X5Z6Z7X8'] : False\n",
+      "6 :: 88706: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Y1Z3Y7X8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 88707: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3Y6Z7', 'Z0Z1Y5Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 88757: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Y0Z2Z6X7', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 88791: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Y0Y1Z2Z3Y4Z7', 'X0Z2Z3X4Y5Z6'] : False\n",
+      "6 :: 88946: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3X4Z7', 'Z0Z1Z4Z5Z6X7', 'Z2Z3Y4Z5Y6X8'] : False\n",
+      "6 :: 89092: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X2Y4Z5X7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 89094: [[9,3, 2]] : 1 :['X0X1X2X3', 'X2Y4Z7Z8', 'Z1Y2X6Y8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 89128: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X2X5Y7Z8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 89169: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Z4X5Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 89170: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y1X3Z4Z7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 89171: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Y2Y3X4X6', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 89174: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Z3Y4Z7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 89221: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z1X2X7', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 89270: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y5Y6X7Z8', 'Y4Z5Y6Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 89505: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7', 'X0Y1Z2Z4Y5Z6'] : False\n",
+      "6 :: 89530: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z0Y1Y3Y5X6X7'] : False\n",
+      "6 :: 89539: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8', 'X0Z1Y2Z4Y5Y6'] : False\n",
+      "6 :: 89557: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Z3Y5Z6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0X2Y3Z4Z5X7'] : False\n",
+      "6 :: 89573: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3Z4Z6Z7', 'Z0Z1Y2Z3Y5Z6'] : False\n",
+      "6 :: 89590: [[9,3, 2]] : 1 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z0X1Y2Y3Y4X6'] : False\n",
+      "6 :: 89594: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7', 'Y0X1Y2Z4Y5Z6'] : False\n",
+      "6 :: 89595: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X2Z5Z7Z8', 'X0Y1Y2Y4X5Z7'] : False\n",
+      "6 :: 89618: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8', 'X1Y2Z3Z4Y5Z7'] : False\n",
+      "6 :: 89640: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X2Z5Z7Z8', 'Y1X2Z3Z4Y5Z7'] : False\n",
+      "6 :: 89673: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8', 'Y0X1Z2X4Y5Z7'] : False\n",
+      "6 :: 89702: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Z4Z7Z8', 'Y0Y1Z2Z3Y6Y7'] : False\n",
+      "6 :: 89716: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z1Z2Z7Z8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6', 'Y0Y1X2Y5X6Z7'] : False\n",
+      "6 :: 89728: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8', 'Z0Y1Y2Z3Y4Y5'] : False\n",
+      "6 :: 89761: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z3Y5X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 89775: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Y5Z6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 89786: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z2Y4Y6', 'X1Y5Z6X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 89895: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1X2Y6', 'Z2Y3X6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 90003: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8', 'Y0Y1Y2Z3Y5Y7'] : False\n",
+      "6 :: 90044: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X0Y4Z6Y7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 90059: [[9,3, 2]] : 2 :['X0Z2Y3Z6', 'Y4Y5Y7Y8', 'X3X5Y6Z7', 'X4Z6Y7X8', 'X0X1X2X3X4X5', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 90065: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z1Y2Z4Y5', 'Y0Z3Z4Z6', 'Z0Z1Z2Z3X4X8', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 90067: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z2Y4Z5', 'Z1Y2X4X8', 'X0Y5Z6X8', 'Y0X2Z3Z6Z7Y8'] : False\n",
+      "6 :: 90068: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z2Y4Y6', 'X1Y5Z6X7', 'Z1Z3X6Z8', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 90097: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'Z1Y3X5X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 90099: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z3X7', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 90110: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z1Y3X4Y8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 90135: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X0X6Z7X8', 'Y0Z1Y5Z8', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 90139: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0X1Z2Y8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 90144: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z2Y4Z6', 'X1Y5Y6X7', 'Z0Z3Y4Z8', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 90274: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0Y1Z3X7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'X0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 90516: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8', 'Y1Y3Y4Z5X6Z7'] : False\n",
+      "6 :: 90524: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8', 'Y0Z1X2Y4Z5Z7'] : False\n",
+      "6 :: 90580: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0Y4Y5Z8', 'Y1Z2Z4Z5', 'Z0X1Y2X8', 'Y2Z3X5Z6Z7X8'] : False\n",
+      "6 :: 90671: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z3Z4Z5', 'X3Y5Z7Y8', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Y5Z6'] : False\n",
+      "6 :: 90689: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Y5Z6X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 90693: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Z0Y2Z4X7', 'Z1Y3X5Y6', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8'] : False\n",
+      "6 :: 91111: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Y0Y3Z4X7', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 91128: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0X1X4X5', 'Z0Z2Z5Z6', 'Z0Z1Z2Z3X4X8', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 91185: [[9,3, 2]] : 64 :['X0X1X2X3', 'X0X1X4X5', 'X0X1X7X8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 91209: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Y0Y3X5Z7', 'Z5Z6X7Y8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8'] : False\n",
+      "6 :: 91256: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8', 'Z0X1Y2Y4Z5Z6'] : False\n",
+      "6 :: 91257: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8', 'X0Y1Z2Z4Y6Z7'] : False\n",
+      "6 :: 91495: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y1Y3Z4X5', 'Z1Z2Y5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z5Z7Z8'] : False\n",
+      "6 :: 91545: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Y4Z5Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 91546: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X0Z4X5X8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 91589: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X1Y4Y6X7', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 91594: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'X1Y2Y5X7', 'Z2Z4X5Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 91739: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X1Y4Y6X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 91758: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X3Z4Z5Z7', 'Z0Y3Z6Y7', 'Z0Z1Z5Z8', 'X0Z1Z2X4X7X8'] : False\n",
+      "6 :: 91896: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8', 'X0Y2Z3X4X5Z7'] : False\n",
+      "6 :: 91925: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y0Y1Z6Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 91949: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X2Z4Z6Z7', 'Z0Y1Z6X8', 'X0Z1Z2Z4Z5X7', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 91950: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X1X5Z6X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 91979: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X3Z4Y5Z7', 'Y1Y2X5Y7', 'Y0Z1Y5X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 91997: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y1Z3Y5X7', 'X0Z4X6Y8', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 92512: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Y1Y2Z3Z7Z8', 'Z1X2Z3Y5X6Z7'] : False\n",
+      "6 :: 92513: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Y1Y2Z3Z7Z8', 'Y1Z3Y4Y5X6Z7'] : False\n",
+      "6 :: 92555: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0X4Y6Z7', 'Y4Y5Z6Z8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8'] : False\n",
+      "6 :: 92566: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Y1Z3Z5X7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7'] : False\n",
+      "6 :: 92722: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0X2Y6Z8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 92726: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Y0Y2Z4Z6', 'Y1Y3Y4Y6', 'X0X2X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 92917: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8', 'X0Z1Z2Y4Z5Y6'] : False\n",
+      "6 :: 92947: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Y1Y2Z3Z4Z5', 'Z1X2Z3Z6Z7Z8', 'Z0Y1X2Z4Y6Z7'] : False\n",
+      "6 :: 92966: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8', 'Z0Z1Y2Z3Y4Y7'] : False\n",
+      "6 :: 93091: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y4Y5X6X8', 'Z1Z5Y6Y7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 93207: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8', 'Z0X2Z3Z5X6Z7'] : False\n",
+      "6 :: 93212: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'Z1Y2Z4Z5Z6Z7', 'Y0Y1Y2Z3Y4Z6'] : False\n",
+      "6 :: 93259: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y2Y3Z4Z5', 'X0X1X6X8', 'Y0Y3Y7Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 93286: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X2X6Y7Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 93287: [[9,3, 2]] : 2 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y0Y4X6Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 93303: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z0Z1X2Y5', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 93310: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Y3X5Y7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 93365: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3Y4Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 93369: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z2Y6Y7', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 93405: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z0Z3Y6X7', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 93708: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z2Y3X4Z6Z7X8', 'X0Y2Z3Y4Y5X6'] : False\n",
+      "6 :: 93738: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8', 'Z1Z2Y4Z5X6Z7'] : False\n",
+      "6 :: 93748: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7', 'Y0Z1X2Z4Z5Y6'] : False\n",
+      "6 :: 93828: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y1Y3Y6Z8', 'Z4X6Z7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 93850: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Z2Y7X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 93851: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Y5Z6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 93869: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y3Y5Z8', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 93885: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Y6Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 93964: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Y3Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Y1Y2Z3Y5X6'] : False\n",
+      "6 :: 93965: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z2Y3X4Z6Z7X8', 'Z0Y1Y2Z3X4X5'] : False\n",
+      "6 :: 94298: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Y2Z3Z4Z7Z8', 'Z0Y1X2Z4Y6Z7'] : False\n",
+      "6 :: 94300: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8', 'X0Y2Z3X5Z6Z7'] : False\n",
+      "6 :: 94301: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7', 'X0Y1Y2Z4Z5X6'] : False\n",
+      "6 :: 94457: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y1Z2X5Z6', 'Z0Z1X4X5X6X7', 'Y0Y1Y2Z3Z4Z5', 'Z1X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 94460: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Z0Z3Y6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 94679: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z4Y6X7Z8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 94897: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Y0Z2X4Y5', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 95006: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1X4Z6', 'Y2Z3Z7Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 96807: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z6Z8', 'Y0Z1X4Y6', 'X0X5Z6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 96889: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X2Z5Z6Z7', 'Y1Z3X4Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 97168: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Y1Z3Y4Z5Z6Z7', 'Y0Y1Y2Z3X4X6'] : False\n",
+      "6 :: 97194: [[9,3, 2]] : 2 :['X0Z3Z4X6', 'Y0Z1Y6Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Y0Z1Z2X3Z5Z8', 'X0Y1X4Z5Z6X7'] : False\n",
+      "6 :: 97197: [[9,3, 2]] : 2 :['X0Z2Y3Z6', 'Y4Y5Y7Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Y1Z4Y5X6X7'] : False\n",
+      "6 :: 97199: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Y0Y1X2Z4Z7Z8', 'X0X2X4Z5Y6Z7'] : False\n",
+      "6 :: 97206: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0Z1Y2Z3Z6Z7', 'Y0X1Z3Z4X5Z6'] : False\n",
+      "6 :: 97207: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0Z1Y2Z3Z6Z7', 'Y0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 97222: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Z1Y2X4Z6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 97232: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y2Z3X6Y7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 97260: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Y4Z6', 'X1Y5Z6Z7', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 97274: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X2Y5Z6Y8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 97279: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Y6Z7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 97282: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y2Z6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 97287: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y3Y5Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 97293: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X2Y5Y6Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 97305: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Y4Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 97315: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Y1X2Y6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 97319: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Y3Y4Z5', 'Z0Y1Z6X8', 'X0Z1Z2X4X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 97326: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y2X6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 97331: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'X3Z5Y7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 97338: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'X2X4Y6Z8', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Z1Z2Y4Z7Z8'] : False\n",
+      "6 :: 97340: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'X2X4Y5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 97343: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y2X6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 97377: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Y0Y3Y7Y8', 'Z0Z1X4X5X6X7', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 97511: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X1X2X5Z7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 97662: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X2X4Y6Z7', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 97663: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X1X4Z6X7', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 97717: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X2X4', 'Y0Y1X2Y5Z6X7', 'X1Y2Z3Z5Y6X8', 'Z0Y2Z4X5Y7Z8', 'Z0X1Z2X5Y6Z7'] : False\n",
+      "6 :: 97736: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Y1X4Z6', 'X1X5Y6X7', 'Z0Y2Y5Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 97740: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X2Y4Z5X7', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3Y4X5', 'X3Y4Y5Z6Z7Z8'] : False\n",
+      "6 :: 97755: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1Y4Z6', 'Z4X5Y6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 97756: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Y0Z3X5Z7', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 97758: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y1Y3X5Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 97784: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X0Y5Z6X8', 'Y0Y2Z5Y7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 97835: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y4Z5X6Z7', 'X0X4Y6Y8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 98157: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8', 'X1Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 98187: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Y3Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Y1Y2Z3X4Z5'] : False\n",
+      "6 :: 98282: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z2Y3X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 98295: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y2Y6X7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 98297: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X2Z4X5Z6', 'Z0Z1X4X5X6X7', 'Y0Y1Y2Z3Z4Z5', 'Z1X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 98394: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z1Y3X5X8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 98448: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8', 'Z0X1Z3Y4Z5Z6'] : False\n",
+      "6 :: 98653: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8', 'Y0X1Z2X4Z5Y6'] : False\n",
+      "6 :: 98676: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z1Z2Z7Z8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6', 'Y0Z2Y4Y5X6Z7'] : False\n",
+      "6 :: 98679: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8', 'Z0X1Z2Z4X6Z7'] : False\n",
+      "6 :: 98697: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Z0Y1Z2Z3Z5Z8', 'Z1X2Z3X4Z6Z7', 'X1Y2Z3Y4Z5Y6'] : False\n",
+      "6 :: 98739: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Z3Y5X7', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3Y4X5', 'X3Y4Y5Z6Z7Z8'] : False\n",
+      "6 :: 99033: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X2Y4Y5Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 99059: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y0X1Y2Z8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 99231: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z1Z3Y4Y5', 'Z0Z2X7Z8', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z5Z6Z7'] : False\n",
+      "6 :: 99322: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'Z0Y1Z4X5', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 99333: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Z2Z3Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 99699: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z1X2Y6', 'X0X4Z6X7', 'Y4Z5Y7Y8', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 99701: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X3Z4X5Y6', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 99707: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y4X5Z6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 99708: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2X4Y5Z6', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z0Y1Z2Z3Z4Z7'] : False\n",
+      "6 :: 99710: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1Y5Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 99716: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X4Y5Z6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 99745: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y2Z4X5', 'Z0Z1X4X5X6X7', 'Y0Y1Z2Z3Y5Z6', 'Z1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 99811: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y0Y2Y6Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 99812: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X3Y5Z7Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 100017: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Y1Z2Z3Y5Z6', 'Z1Z3Z4X5Z7Z8', 'X0Y1Y2Z4Z5X6'] : False\n",
+      "6 :: 100068: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0X1Z2X7', 'Z0Y1X5Y6', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 100072: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Y4Z7', 'Y1Y2X4Z8', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7'] : False\n",
+      "6 :: 100387: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8', 'X0Z1Y2Y4Y5Z6'] : False\n",
+      "6 :: 100451: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z2Z3X7Y8', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 100711: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8', 'X0Y1Y2Z4Z5X6'] : False\n",
+      "6 :: 100930: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Y1Z2Z3Y5Z6', 'Z1Z3Z4X5Z7Z8', 'X0Y2Z3Z4X5X6'] : False\n",
+      "6 :: 100986: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y2X5Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 101055: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y1Z3Y5X6', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 101074: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X3X6X7Z8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 101092: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z2Z5Z6', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 101267: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z1Y2Z4Y5', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 101772: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y2Y3Y5Z7', 'Z0Y1Y5Z8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 101808: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X3Y5Z6Z7', 'Z2Z3X6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 101822: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y3X4Z7', 'Y0Z3Y4X8', 'X0Z5Z6Y8', 'X0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 102041: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y3X6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 102059: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z4X5Z6Z7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 102078: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X1Y2Z3Y5', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 102090: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z1Y2Y4Z6', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 102116: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Z2X4Z6', 'Z0Y3Z7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 102117: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Y1Z2X5Z7', 'Z0Z3X5Z8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8'] : False\n",
+      "6 :: 102149: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y0Z3X5Z6', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 102155: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1X2Z3X8', 'Y0Y2Z6Y8', 'Z1Z2Z4Z5X7X8', 'Z0Y1X2Z4X5Z7'] : False\n",
+      "6 :: 102177: [[9,3, 2]] : 1 :['Y2Y3X4Y6', 'Y3Z4X6X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1Y2X3Z4Z5Z8'] : False\n",
+      "6 :: 102224: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z0Y2Y5Z6', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 102232: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Y1Z3Y4Y6', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 102262: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Z3Z4X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 102289: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y2X4Z5', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 102339: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y2X4Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 102340: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Y3X4X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 102407: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Z2Y3X7Y8', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Z1Z2Y4Z7Z8'] : False\n",
+      "6 :: 102582: [[9,3, 2]] : 12 :['X0X1X2X3', 'Z0Z1X2X4', 'X0Z1Z3Z4Z5X6', 'Z0Z2Z4Y5X7X8', 'X1Z2Z3Y6Z7Z8', 'Z0Y1Z2Z3Z5Y7'] : False\n",
+      "6 :: 102603: [[9,3, 2]] : 6 :['X0X1X2X3', 'X0X4X5X6', 'X1X4Z7Z8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Y4X5X7', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 102718: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1Y6Y7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 102772: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'X0Z1Y2Y6', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 102807: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y2Y3X6X7', 'Y1Z3X5Z6', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 102812: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Z5Y6Z7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 102887: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z1Y3Z5Y6', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 102894: [[9,3, 2]] : 6 :['X0X1X2X3', 'Z0Z1X2X4', 'Y0Y1X2Y5Z6X7', 'X1Y2Z3Z5Y6X8', 'Z0Y2Z4X5Y7Z8', 'Z2Z3Z4X5Y6Z7'] : False\n",
+      "6 :: 102946: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y5Y6X7Z8', 'X0Y1Y2X4', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 103203: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z3Z4Z5', 'Z0Y1Z6X7', 'X0Z1Z2X4X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 103251: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z2Z5Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 103252: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1Y5Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 103311: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X3Y4Y5Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 103388: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Y0Z2Z4Y6', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 103417: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Z2X4Z5', 'Z1Y2Y4X8', 'X0X1X4X5X6X7', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 103438: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z1X2X4', 'X0Y4Z5X8', 'X0X1X4X5X6X7', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 103505: [[9,3, 2]] : 48 :['X0X1', 'X0X6', 'Z2X3Z7Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y3X4Z6'] : False\n",
+      "6 :: 103506: [[9,3, 2]] : 96 :['X0X1', 'X0X6', 'X3Z4Z7Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Y3Y4Z6'] : False\n",
+      "6 :: 103559: [[9,3, 2]] : 6 :['Y2Y3X4Y6', 'X0Y3Z5Z8', 'X1Y2Z5X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 103587: [[9,3, 2]] : 1 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'X1X5Y6Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 103635: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'X0Z4Y5Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7'] : False\n",
+      "6 :: 103650: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Z0Y3Z4Y6', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 103652: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Z1Y2Z4Y6', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 103667: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0X1Z3Z4', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 103678: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0Z1Y2Z4', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5Y6X8'] : False\n",
+      "6 :: 103734: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Y1Y4Y7', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 103840: [[9,3, 2]] : 6 :['X0X1X2X3', 'Z2Z3Z5Z7', 'X0Y1Z2Z5', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 103856: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z3X5Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 103875: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y6X7', 'Y0Y2Z4X6', 'X3Y4X5Y6', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8'] : False\n",
+      "6 :: 104120: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Y3Z4Y5', 'Z1Y2Z4Z6', 'Z0Z1X4X5X6X7', 'Z1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 104126: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y2Y3Y4Z6', 'Y0Y1Y4X7', 'X2X4Y7Z8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 104213: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'X0Y1Z2Z4', 'Y1Z3Z6Z8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8'] : False\n",
+      "6 :: 104268: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y2Y7Z8', 'Y0Y1Z5Y8', 'X0Z1Z2Z4Z5X7', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 105747: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X2Y6Z7Z8', 'Z1Z3Z6Y7', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7'] : False\n",
+      "6 :: 105748: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y1Y2Y7Z8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 105793: [[9,3, 2]] : 6 :['X0X1X2X3', 'X0X4X5X6', 'X1X4Z7Y8', 'Z1Z2Z4Z5X7X8', 'Z0Y1X2Z4X5Z7', 'Y0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 105831: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y2Z3X4X5', 'X0X5Z7Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 105850: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0X1Y2Z6', 'X5Z6Y7X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 105871: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y3Z6Y7', 'Z0Y1Y6Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 106132: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Z0Y2Z4X5', 'Z0Z1Z2Z3Z5X8', 'Z1Y3Z4Z6Z7Z8'] : False\n",
+      "6 :: 106358: [[9,3, 2]] : 144 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4X7', 'X1X4X5X8', 'Y1Z2Y4Y5Z7Y8', 'Z0Z1Z2Z3Z6Z8'] : False\n",
+      "6 :: 106360: [[9,3, 2]] : 6 :['X0X1X2X3', 'Z2Z3X4X8', 'X0Y2Z3Z4', 'Z0Z1X4X5X6X7', 'Y0Y1Z2Z3Y5Z6', 'Z1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 106436: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'X0Z1Y2Y5', 'X3Y4Y6X7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 106464: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z1Z2Z6Y7', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 106506: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y0X1Z3X6', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 106698: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X8', 'X4X6X7X8', 'Y2Z3Z7Y8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 106706: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Z0Z1Z6X8', 'Y1Y2X5Y8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 106731: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y1Z3Z6Z8', 'Y0Y1Z5Z7', 'X3Z4Z7X8', 'X0X1X4X5X6X7'] : False\n",
+      "6 :: 106867: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X2Y6Z7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 106868: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z2Z3Y5Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 106922: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y1Y2X6X8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 106929: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y1Z2Z6X7', 'X2Y5Y6Y8', 'X0X1X4X5X6X7', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 107085: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'X1Z2Z3X6', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 107139: [[9,3, 2]] : 48 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X0X1Y6Y7', 'Y0Y1X5Y8', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 107280: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y1Y2Z4Z8', 'Y0Y3Y4Y8', 'X0Z5X6Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z5Z6Z7'] : False\n",
+      "6 :: 107292: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X0Y3Y5Z7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 107310: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3X5Z6', 'X3Y4Y5Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 107339: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X1Z2Z3X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 107361: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z4Y5X6X8', 'Z0Z1X2X4X5X8', 'Y0X1Z2X4X6Z8', 'X1Y2Z3X4Y6Z7'] : False\n",
+      "6 :: 107376: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y5X6Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 107446: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Z4X5Y6Z8', 'Z0Z1X4X5X6X7', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 107491: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Z2Y4Y5', 'Z1Z3X7X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 107525: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z2Z4Y7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 107532: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y3Y4X5', 'Z0Z1X4X5X6X7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X2Z5Z7Z8'] : False\n",
+      "6 :: 107536: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'X0Z1Z3Y5', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 107537: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Z1X3Z5X7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 107549: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y4X5Z7X8', 'Z0Z1X4Y8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 107681: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Y4Z5Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 107691: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0X1Y2Z5', 'Z0Y3Z6X8', 'Z1Z2Z4Z5X7X8', 'Y0X1Z2Y4Y7Z8'] : False\n",
+      "6 :: 107694: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4Z6Z7Y8', 'Z1Z3Y7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8'] : False\n",
+      "6 :: 107702: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'X0Z1Z3Y4', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 107711: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Z1Y5Z6Z7', 'Y4Z5X7X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 107729: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z0X1Y2Z5', 'Z5Z6Z7Z8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 107739: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y1Z2Z6Y7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 107824: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Y3Z4Y5', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 107836: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z1Z2Y4Y7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 107903: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X1Z4Y5Z7', 'Y0X3Y5Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 108027: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3Z7', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 108081: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z3Z5Z7', 'Z2Y3X7X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 108096: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0Y1Z5Y7', 'X1Y5X6Y8', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 108109: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Z2Z3X5X6', 'Z0Y1Y7Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 108184: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Y0Z2Z5Z6', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 108185: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y0Y1X2X4', 'Z0Z1X3X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 108192: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z3Z4X5', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 108216: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X2Y4Z5X7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 108227: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X3Y5Y6Z7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 108302: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y0Z1Y5Z7', 'X0Z4Z5Z8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 108310: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z0Z1Z4Z6', 'X2Z5Z6X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 108313: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0X1Y2Y4', 'Y4X5Y7X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y1Z3Z6Z7Z8'] : False\n",
+      "6 :: 108314: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X4Y5Z6X8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 108352: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z1Y3Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 108353: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z1Z3Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 108415: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0X1Y2Y4', 'Y4X6Y7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y1Z3Z6Z7Z8'] : False\n",
+      "6 :: 108431: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z3Z5Z6', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4Z6Z8', 'X0Y2Z3Y6Y7X8'] : False\n",
+      "6 :: 108486: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y2X5Z7', 'X2X6Y7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 108489: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Y1Z3Z7X8', 'X0X2Y7Z8', 'Y0Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 108534: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y1Z6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 108535: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'X2Z5Y7Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Z4Z7Z8'] : False\n",
+      "6 :: 108536: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z2Y3Y5Y7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 108578: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3Y6Z7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 108581: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X2Z5Y7Y8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 108582: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Y1X5Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 108585: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Y1Z5Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 108586: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z2Y3X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 108615: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1Y4Y7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 108629: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'X2Y5Z6Z8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 108685: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Y4X5X6', 'Z0Y1Z4X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 108697: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z3Y5Z6', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 108702: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X0Y4X7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 108705: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Z3Z5Y7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 108715: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X0Z5X6Z8', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3Y4X5', 'X3Y4Y5Z6Z7Z8'] : False\n",
+      "6 :: 108856: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z2Y3X7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 108859: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Y6Z7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 108864: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Z3Z4X6', 'Y1Z2Z5X8', 'Z0Z1X4X5X6X7', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 108866: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'X0Y5Y6Z7', 'Z0Y3X6Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8'] : False\n",
+      "6 :: 108971: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Y3Y4Y6', 'Z0Z1Y5Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 108986: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Y1Z3Z7X8', 'X0X1Z7Z8', 'Z0Y3Y4Z5Z6Y7'] : False\n",
+      "6 :: 109140: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y1Y6Y8', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 109150: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X0Y4Z6Y7', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 109156: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'X1Z4Z5Z8', 'Y1Z3X5Z7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 109188: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Y6Z7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 109242: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Y5Y6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 109270: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X4Y5Z6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 109275: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z1Y3X4Y8', 'Z0Z1Y6Z7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 109276: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y1Z2X6Y7', 'Z0Z3X5Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 109305: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Y6Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 109310: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Z2Y5Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 109314: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z1Y2Y5X6', 'Z1Y3X4Y7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 109474: [[9,3, 2]] : 16 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y0Z3Z4X5', 'X0X2Y4Z8', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7'] : False\n",
+      "6 :: 109476: [[9,3, 2]] : 16 :['X0X1X2X3', 'Z0Z2Z4Z8', 'Z0Z1Z4Z5', 'X0X2X4X8', 'X0X1X4X5X6X7', 'Z2Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 109483: [[9,3, 2]] : 2 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'Y1Z3X6Y7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 109502: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z0Z1Z6X7', 'Y1Y2Y7Z8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 109514: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0Z3Z4X5', 'Y2Y3Y5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 109535: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z1Z3Y5Z7', 'Y0Z2Y5Z8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 109546: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Z2Z4X6', 'Y0Y2Z5X8', 'X0X1X4X5X6X7', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 109565: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0X1Y2Y5', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 109607: [[9,3, 2]] : 2 :['X0Z2Y3Z6', 'Y4Y5Y7Y8', 'Y0Y2Y3Y5', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 109653: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y2Y7Z8', 'Y0Y1Z5Y8', 'X0Z1Z2Z4Z5X7', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 109713: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Z3Y5Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 109777: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1Z5Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y3Z4X5Z7Z8'] : False\n",
+      "6 :: 109786: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z1Z2X5Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 109820: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Y1Z4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 109826: [[9,3, 2]] : 1 :['X0X1X2X3', 'X2Y4Z7Z8', 'Z1Z2X5X8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 109844: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3Y5Z6', 'X3X6X7Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 109849: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Y1Y3X4Z5', 'Y0Y2Z5X8', 'X3Z4Z6X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 109852: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X0X5Z6Z8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 109864: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z2Y3X4Z7', 'Y0Y1Z6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 109921: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z3Y4X7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 109923: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Y1X2Z3Y5', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8'] : False\n",
+      "6 :: 109988: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Y1Y3Y6Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 110023: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y3X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Y3Z4X5Z7Z8'] : False\n",
+      "6 :: 110082: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Y4Z5Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 110097: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Y1Z2Y5Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 110121: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y4Y5Y6Z7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2X4Y5Z6', 'X0Y1Z3Y5Z7Z8'] : False\n",
+      "6 :: 110311: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Y0Z1Y4Y6', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 110313: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'X1X5Y7X8', 'Y1Y3Y6Y8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 110329: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y2Z3X5Z8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 110330: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y2Y3X5Y8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 110339: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'X2Z4X5Z6', 'Z0Z1X4X5X6X7', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 111069: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z1Z3Z5Z7', 'Z0Z3X4Y8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 111195: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X2Y4Z5X6', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 111209: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Z3X5Z7', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 111215: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y0X1Y5X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 111298: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Y1Z2Z5Y7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 111343: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z2Z6X8', 'X3Z4Z7X8', 'Y0Y3Z6Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 111378: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y3X4Z7', 'Y0Z3Y4X8', 'X0Z1Z2Z4Z5X7', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 111760: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X3Z4Y5Z8', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 111778: [[9,3, 2]] : 4 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'Y2Z3X6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 111779: [[9,3, 2]] : 2 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'Z2Y3X6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 111780: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y5Y6X7Z8', 'X0Z5Y6Y7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 111819: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X1Z2Z3Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 111881: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0X1Y2X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 111905: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y6X7', 'X4Z6Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8'] : False\n",
+      "6 :: 111912: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0Y2Z5X7', 'X3Z4Z6X7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 111951: [[9,3, 2]] : 1 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'X2Z3Y4X6', 'Z0Y4X7Y8', 'X0X1X2X3X4X5', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 112031: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X2Z4X5Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 112050: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z1Z3Y5Z7', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 112070: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3X5Z6', 'X3X6X7Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 112089: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X4Y5Z6X8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 112104: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X4Y5Z6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 112145: [[9,3, 2]] : 2 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'X0Z2Z3X4', 'X1Y2Y3X5', 'Z0Y4Z6Z7', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 112183: [[9,3, 2]] : 192 :['X0X1X2X3', 'X4X5X6X7', 'Z4Z5Y6Z7', 'Y0Z1Z2Z3', 'X4Z6Z7Z8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 112224: [[9,3, 2]] : 6 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X2Z3Y5X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 112238: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z4X5Z6Y8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 112331: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Z1Z2X5X6', 'Z0Z2X4X7', 'Z1Z3Y4Y7', 'X0X1Z4Z5X6X8'] : False\n",
+      "6 :: 112336: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y2Z3Y6X7', 'X0Y4Z5X8', 'X4Z6Z7Z8', 'Z0Z1X2X4X6X7'] : False\n",
+      "6 :: 112410: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X4Z5Z6Z7', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 112439: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3Y7', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 112523: [[9,3, 2]] : 2 :['X0Z2Y3Z6', 'Y4Y5Y7Y8', 'Z0Z2Z3Y5', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 112575: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y1Z3Y7X8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 112593: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X4Z7', 'Z2Z3X6Z7', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 112690: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'X2Z5Y7Y8', 'Z0Z1Z2Z3X4X8', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 112698: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z1Z2Z4', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 112701: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X0X2X4Z6', 'X1X3X5Z6', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 112710: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z6Z8', 'Z1Y2X4X6', 'Z0Y2X5X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 112721: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X0X1Z5Z6', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 112785: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z1Z6Z7', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 112794: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Z2X4Y5', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 112848: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Y2Y3Z6X7', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 112866: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X2Z6Z8', 'Y1Z2Y6X7', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 112873: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Y2Z3Y6X8', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8', 'Z2Z3Z4Z5X6Z7'] : False\n",
+      "6 :: 112891: [[9,3, 2]] : 2 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X4Y6X7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 112904: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z3Y6Z7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 112989: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0X2Z6Z7', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 113037: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Y0Z2X6Z7', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 113051: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Y1Y3Y5Y8', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 113075: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Y3Y4X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 113125: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z2Y3X4X6', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 113130: [[9,3, 2]] : 1 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Y1X2Z5Y7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 113154: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z2Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 113160: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y1Z2Z5Y8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 113198: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z1Z3Z5Z8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 113199: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z1Z2Z4Y6', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 113210: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X0X2Y4Z6', 'X4Z5Y6X8', 'X0X1X4X5X6X7', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 113284: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Y0Z3X4Z5', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8'] : False\n",
+      "6 :: 113294: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Z3Z5Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 113337: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Y1Y2Y5X6', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 113365: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X2Y5Z7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 113403: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y5Y6X7Z8', 'X1X2Z5X6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 113426: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X2Z6Z8', 'Y5X6Z7Y8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 113439: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y2Y3Y5Z6', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 113454: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Z2Y6X7', 'Z1Z3X6Y7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 113482: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y3X4Z5', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 113485: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y1X4Y5', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 113486: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Y2Z7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 113487: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2X5Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 113505: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z4Y6X7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 113515: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1X2Z3Y4', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 113518: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z4X5Z6Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 113527: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X2Z6X7Z8', 'Z0Z1Y6Y8', 'X0X1X2X3X4X5', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 113608: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X0Y1Z2X6', 'X1X5Z6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 113626: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y2Z3X4Z8', 'X0X5X6Z8', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 113632: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X0Z5Y6Z8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 113666: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X3X5Y7X8', 'Z0Z2Y5Z8', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7'] : False\n",
+      "6 :: 113680: [[9,3, 2]] : 6 :['X0X1X2X3', 'Z0X1Y2Y4', 'X0X4Y5X6', 'Y0Z1Z5X7', 'Z0Y3X5X8', 'Y0X1Z2Z6Z7Z8'] : False\n",
+      "6 :: 113681: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y4X5Z6X8', 'Z0Y2Y4Y7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 113702: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'X0Y1Y2Y6', 'X2X5Z6X7', 'Z2Z3Z4Z5Z6X8', 'X0Z1Z2Y4Z7Z8'] : False\n",
+      "6 :: 113869: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X0Z5Y6Z8', 'Z2Z3X6Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 114037: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z0X2Z3Z4', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 114053: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Z2Z5Y7', 'X1Z4Z6Y7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 114136: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X4Y5Z6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 114166: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z0Z1Y5Y7', 'X3Y4Y6Y8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 114222: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X0Y5X6Z7', 'Y0Z1X7Y8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 114227: [[9,3, 2]] : 6 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y3X4Y5', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 114246: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y2Y3X5X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 114341: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y1Z2Z4Z5', 'Z0X1Y2X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 114603: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Y4Z5X6', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 114604: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Y5Y6X7Z8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 114648: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1X5Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 114655: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y1Z3Y4Z7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 114740: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Y0Y3X4Y7', 'X0Z6X7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 114763: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z2Z6Y7', 'X3Y4Z6Y8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 114871: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Y0Y1Z5Y7', 'Y2Y3Z6Y7', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 114903: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z2Z4Z5', 'Z0Z3Z6Z7', 'Z0Z1X2X4X5X8', 'Y0X1Z2X4X6Z8'] : False\n",
+      "6 :: 114929: [[9,3, 2]] : 96 :['X0X1', 'X0X8', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 114961: [[9,3, 2]] : 32 :['X0X1X2X3', 'Y4Y5Y6Y7', 'Y0Z1Y2Z3', 'Y4X5Z6Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8'] : False\n",
+      "6 :: 114963: [[9,3, 2]] : 1 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'Z0X3Y5Y6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 114970: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z5Z6Y7Y8', 'X4X6Y7X8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 115016: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Y0X1Z2X5', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 115019: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4Z7Y8', 'Y1X2Z3Z5', 'X2Y4Z6X8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 115180: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y5Y6X7Z8', 'Y1Z2Z6X8', 'Y0Y3X5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8'] : False\n",
+      "6 :: 115185: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0X2Y4Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 115203: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8', 'Z0Y1Y2Z3Y5Y7'] : False\n",
+      "6 :: 115220: [[9,3, 2]] : 12 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y1Y2Y4X7', 'X3X5X6X8', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 115235: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0X2Y4Z5', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 115247: [[9,3, 2]] : 6 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y2X7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Y3Z4X5Z7Z8'] : False\n",
+      "6 :: 115250: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Y4X5Y6Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 115861: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Y2Y4Y6Z7'] : False\n",
+      "6 :: 115863: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8', 'Z0Z1X2Z3Z4Z7'] : False\n",
+      "6 :: 116019: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0X2Z3Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 116026: [[9,3, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Y5X6Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 116138: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 116168: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X1Y2Z3Z7', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 116173: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X3Y4Z5Z6', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 116182: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X0X6Z7X8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 116240: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'X0Z4X5Z7', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 116428: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z5Y6Z7Y8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 116430: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z3X4X5', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 116485: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X2Y4Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 116501: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y2Y4X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 116527: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0X4Y6X8', 'Y0Z1Z2Z3Z6X7', 'X0Y2Z3Z4Z5Z6'] : False\n",
+      "6 :: 116548: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y2Z3X6Z8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 116608: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z3Y5Z6', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 116699: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y2Z3X5X6', 'X0X2Y4Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 116705: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y6X7', 'Z1Y3Z4X7', 'X2Y4Y7Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 116747: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z3Y4Y7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 116781: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y2Y3X4X6', 'Z2Z3X5X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 116796: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Y1Z3Z7X8', 'Z1Y2Z7Z8', 'Z0Z3Z4Z5Z6Z7'] : False\n",
+      "6 :: 116837: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Y1Z3Y4Z6', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 116841: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y2Z4Y5', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 116855: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X2X4Y6X8', 'Y2Z3Z4Z5', 'Z0Z1Y2Z3Z6X7'] : False\n",
+      "6 :: 116934: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X4Y6Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 116964: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y1Z4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 116974: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z4X5Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 116991: [[9,3, 2]] : 64 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0X1X7X8', 'Y2Z3Z4Z5', 'Y0Y1Z2Z3X4X6'] : False\n",
+      "6 :: 117054: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X2X4Y6X8', 'Z0Z1Y2Z3Z6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 117104: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0X2X4Y6', 'Y0Z1Z6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 117126: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z2Y3X4X6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 117134: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'Y4Y5Z7X8', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Y2Z3Z4Z7Z8'] : False\n",
+      "6 :: 117135: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'Y2Z3X5Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Z4Z7Z8'] : False\n",
+      "6 :: 117137: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y1Z2X7', 'Z0Z1Z5Y6', 'Z0Z1Z2Z3X4X8', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 117140: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y0Z1Y2Y3', 'X0X1X7X8', 'X0X1X2X3X4X5', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 117141: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Y3X4X8', 'X0Z4Z6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 117142: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Y4Z5Z7', 'Z1Z2X4Y7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 117143: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y3X4Y5X8', 'Z1Z4Z6Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 117158: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3Z4X5', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 117166: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z1Z3Z5Z7', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Y5Z6', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 117167: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2Z5Z7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 117168: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y4Z6X7Y8', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 117176: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Y4Z7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 117177: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X0Y1Y2Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 117184: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Z1Y2Y4Y6', 'Y0Y1Z4Z8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 117226: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y1X2Y4', 'X0X4Z7X8', 'X0Z1Z2Z4Z5X7', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 117240: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z2Y5Y7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 117336: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X2Y4X5Z7', 'Z0Z3X5Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 117341: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y2Z3X4X8', 'Z0Z3Y6Z7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 117347: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Z1Z2X6', 'X1X4Z5Y6', 'Z0Z1X2Z4Z5X8', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 117431: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X0X4Z5X6', 'Y0Z1Y5X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 117479: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'X5Y6Y7X8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 117504: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z2Z3X4X5', 'Z0Z1X5X8', 'X4X6X7X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 117629: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'X0Y1Z2Z4', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 117667: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y3Z4X6', 'X4Y6Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 117668: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1X2Z5', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 117689: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y1Z3Y5Y6', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 117778: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X2X4Y6Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 117779: [[9,3, 2]] : 12 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z3X5Y8', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3X4Y5', 'X3X4X5Z6Z7Z8'] : False\n",
+      "6 :: 117796: [[9,3, 2]] : 6 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'Y2X3Y4Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 117797: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X2X4', 'X2X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 117899: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z2Z3Z5Y7', 'Z4X6Y7X8', 'Z1Z2X4Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 117945: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z4X5Z7Z8', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Y1Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 117955: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X1Z6X7X8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 117956: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X1Z5X7X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 117962: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0X1Z3X6', 'Y1Y2Z4Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 117978: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y2X5Z6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 117998: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Y4Y5Z6Y7', 'Y0Z3Y7X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 118004: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Y1Z4X5', 'X1Y4X6X7', 'Z1Z2Y5Z6', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 118023: [[9,3, 2]] : 6 :['X0X1X2X3', 'Y0Z2X4Z7', 'Z0Y2X6Y8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y2Y3Y4X5Z6Z8'] : False\n",
+      "6 :: 118053: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Z1Z6X7', 'Z0Z1X2Z4Z5X6', 'Y2Z3Y4Z5X7X8'] : False\n",
+      "6 :: 118061: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0X2Y5Z7', 'X4Z6Z7X8', 'Z0Z1Z2Z3X4X8', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 118067: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y0Z1Z6X7', 'X0Z2Z3X4Y6X8', 'X0Y2Z3Z4Z5Z7'] : False\n",
+      "6 :: 118098: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Z3Z4X6', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 118544: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X0Y1Z3X5', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 118562: [[9,3, 2]] : 12 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y3Y5Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 118651: [[9,3, 2]] : 24 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Y1X2Y4Z5X6', 'X0X1Z4Y5X7X8', 'Y0Y2X4Y6Z7Z8', 'Y0X1Z2Y4Z6X7'] : False\n",
+      "6 :: 118652: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Y3Y5Z8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 118816: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z1Y2X7Y8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 118817: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Y1Y2X4Y7', 'Z2Y3Y4Y8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8'] : False\n",
+      "6 :: 118847: [[9,3, 2]] : 144 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Y1X2Y4Z5X6', 'X0X1Z4Y5X7X8', 'Y0Y2X4Y6Z7Z8', 'Y0X1Z2X4Y5Z6'] : False\n",
+      "6 :: 118848: [[9,3, 2]] : 144 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Y1X2Y4Z5X6', 'X0X1Z4Y5X7X8', 'Y0Y2X4Y6Z7Z8', 'X0Z1Y2X4Y5Z6'] : False\n",
+      "6 :: 118880: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z1Z3Z4X6', 'Z0Z2Z5X6', 'Z4Z5X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 118883: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z3Y4Y5', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 119086: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Y3Y4Y5', 'X2Y6Y7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 119298: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y2Z3Y4Z5', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 119299: [[9,3, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Z4Z5Y6Z7', 'Z0Z1X2Z4Z5X8', 'X0Y2Z3X4X6Y8', 'Z0Z1Y2Z3Y5Z6'] : False\n",
+      "6 :: 119313: [[9,3, 2]] : 96 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X8', 'Z2Z3X4Z8', 'Y0Z1Z2Z3', 'Y1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 119362: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z3Y4X6', 'Y1Z2Y5X6', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 119368: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Y0Z1X5X6', 'Z0Y1X4X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 119381: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y2Z4Z5', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 119400: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y1Y2Z4Z6', 'Z0Y3Z5X8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 119447: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z1Y2Z4Y6', 'Y0Y2Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8'] : False\n",
+      "6 :: 119449: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Y1Y3Z7X8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 120282: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z1Y2Z4Z7', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5Y6X8'] : False\n",
+      "6 :: 120283: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y1Y2Z4Z7', 'Z0Z1Z4Z5Z6X7', 'Z2Z3Y4Z5Y6X8'] : False\n",
+      "6 :: 120289: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y1Y2Z4Z7', 'Z0Z1X2Z4Z5X6', 'Y2Z3Y4Z5X7X8'] : False\n",
+      "6 :: 120304: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X2Z4Y5Z8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 120312: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X3Y4X5Z6', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 120313: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X1Z2Z3X7', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 120314: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X0Y1Z2Y8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 120315: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X3X4Y5Y6', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 120317: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0Y5Z6X8', 'X1Y4Y7X8', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 120320: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X3Z5X6Z7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 120322: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y5Y6X7Z8', 'Y1Z2X5Z6', 'Y0Y3Z7X8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8'] : False\n",
+      "6 :: 120554: [[9,3, 2]] : 8 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Z0Z2X4Z7', 'Y1Z3X5Z7', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 120566: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X2Y6Z7X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 120581: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z3Y5X7', 'X1Y5Z6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 120596: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Y3X4X6', 'Z1Y3X5X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 120632: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y2Z3Y5Y7', 'X0Z4Y6Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 120769: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Y5Z6Y7', 'X3Z4Y5Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 120771: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Z0Z3Z4X7', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 120779: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Y0X1Z2Y5', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 120785: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Z4Y6Z7', 'Z0Y1Y6X8', 'Z2Z3X6Y8', 'X0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 120873: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0X1Z3Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 120876: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1X2Y6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 120886: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1X5Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 120887: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z2Y3Y4Z6', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 120888: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z2Y3Z4Z7', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 120895: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1X2Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 120898: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0X1Z2Z6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 120899: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y0Y2Y6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 120921: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'X0Y2Z3Z7', 'Z0Z1X2Z8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 120944: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z2Z3Y4Y7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 121023: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y0Y3Y5Z6', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z0Y1Z2Z3Z4Z7'] : False\n",
+      "6 :: 121088: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X3X5Y6Z7', 'X4Z6Y7X8', 'X0X1X2X3X4X5', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 121104: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Y2Y3X4X7', 'Y0Y1X6X7', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 121106: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Y0Y2Z4X6', 'Y0Y3Y5Z8', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7'] : False\n",
+      "6 :: 121122: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X1Z4Z5X7', 'X4X6Y7Z8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8'] : False\n",
+      "6 :: 121123: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X0X1Z6Z7', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 121300: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z2Z6X8', 'Z2Y3Y4Z7', 'X1X2X4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 121315: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y3Y4Z5', 'X1X2Y6X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 121354: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1Z6Z7', 'X3Z4Z6X8', 'Y1Z3Y7Z8', 'X0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 121473: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X0X1Y4Y5', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 121492: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'Z0Z3X4Y5', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 121542: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X0X1Z4Z5', 'Y2Y3X6X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 121543: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y3Y6Y7', 'Z1Z2X4Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 121592: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1Z4Z6', 'Z2Z3Y4Y6', 'Z0Z1X2X4X5X8', 'Z0X1Z2Z5Z7Z8'] : False\n",
+      "6 :: 121593: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y2Z4Z6', 'Y1Y3Y4Y6', 'Z0Z1X2X4X5X8', 'Y0Y1X2Z5Z7Z8'] : False\n",
+      "6 :: 121641: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0X4Z7X8', 'Y4Z5Z6Z8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 121644: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0X2Z6X7', 'Y0Z1Y6X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 121681: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0X4Z6X8', 'Y4Z5Z7Z8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 121798: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X3Z4Y5X6', 'Z2Y3X5X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 121819: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X0Z1Z2X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 121839: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X3Y4X5Z6', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 121847: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y1Y2X4Y5', 'Y0Y3Y5X8', 'X2Z4Y6X7', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 121852: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X0Y1Z2X6', 'Y4Y5X7Z8', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 121855: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y1Y4Y7', 'X2X4X7X8', 'X0Z1Z2Z4Z5X7', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 121889: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y0Y1Z5Y7', 'X3Z4Y7X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 121894: [[9,3, 2]] : 32 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Y4Z5Y6Z7', 'Y0Z1X2Y8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8'] : False\n",
+      "6 :: 121902: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'X0Z1Z2X6', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 121924: [[9,3, 2]] : 32 :['X0X1X2X3', 'Z0X1Y2Z6', 'Z0Z1Z2Z3', 'Y4Y5X7X8', 'Z0Z1X4X5X6X7', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 121928: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y0Z3Y5Z6', 'Z4X5Y6Z8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 121940: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z0X1Y2Y4', 'Y5X6Z7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Z2Z6Z7Z8'] : False\n",
+      "6 :: 121949: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0X1Y2Z4', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 121958: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Z1X2X4Z5', 'X0Y1X3Y5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 121964: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X4Z5Y6Y8', 'Z0Z1Y6Z7', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 121995: [[9,3, 2]] : 32 :['X0X1X2X3', 'Z0Z1X2X4', 'Y0Z1Z2Z3', 'X5X6X7X8', 'Y0Y1X2Y5Z6X7', 'Z0Y2Z4X5Y7Z8'] : False\n",
+      "6 :: 122001: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z4Y5Y6Y7', 'Y0Z3Y5Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 122030: [[9,3, 2]] : 384 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X8', 'X0Y1Z2Z8', 'Z4Z5Z6Z7', 'Y0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 122114: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Y1Z3Y5Y6', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 122138: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y4X6Z7Z8', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 122143: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Z0Y3X6X8', 'X4Y6Z7Y8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 122158: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z0Y1X2Z4', 'Z5Z6Y7Y8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 122203: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z3Y5X7', 'Y0Y1Y6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 122276: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z2Y3X7Z8', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 122299: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z1Z2Y5X7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 122300: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Z0Z2Y7X8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 122305: [[9,3, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Y4Z5Y6Z7', 'X1Y2Z3Y8', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8'] : False\n",
+      "6 :: 122409: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X0Y2Z3Z5', 'Z0Y3Z4Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 122434: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0Y3Z5Z8', 'X3Y4Y6Y8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 122622: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4Z7Y8', 'Y1Z3Y4X7', 'Z2Z3Y5Y6', 'Z5Z6X7X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 122648: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X3Y4Z5X6', 'X0X2Z7Z8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8'] : False\n",
+      "6 :: 122672: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Z2Y5X7', 'X2X4Z6Y7', 'Z0Z1X4X5X6X7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 122695: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y1Z3Y4Z6', 'Y0Z2Y5Z6', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 122701: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y2Y5X7', 'Z0Y1X6Y8', 'X0X1X4X5X6X7', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 122702: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z1Y2Y4Z7', 'X2X6X7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 122703: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Z1Z3Z4Y5', 'X3X4Z6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 122706: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y2Z3Z4Y5', 'X2X5X6X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 122707: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y1X4X6', 'Y0Z1X5X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 122712: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Y2X5Y6', 'Z0Z3Y7X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 122714: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y2Y3X4X6', 'Z2Z3X5X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 122731: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Y0Y1Y5Y6', 'Y4Y6X7X8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 122741: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0Z1Y4X5', 'X0Z4X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 122742: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Z1X2Y5X7', 'Z0Y5X6X8', 'X0X1X2X3X4X5', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 122773: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5', 'Z2Y3X4Y7', 'X0X1X6Z8', 'Z0Z1X2X4X6X7', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 122774: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5', 'X0X1Z6Z7', 'X1Z4Z5Y8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 122778: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5', 'Z2Y3X4Y7', 'X0X1Z6Y8', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7'] : False\n",
+      "6 :: 122804: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X4Z5Y6Z8', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Y1Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 122824: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0X1Z3X4', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 122826: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X4Y5Y6X8', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 122832: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y0Y1Z5Y7', 'X3Z4Y7X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 122844: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Z4X5Y6', 'X1Y4Z6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 122849: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Z0X1Z3X5', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 122850: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X0Y5X6Z7', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 122851: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0X1Z3X5', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 122897: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X4Z5Z6Z8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 122926: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z1Y3Y6Z7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 122958: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'Z0X3Y5Y7', 'X0X4Z7Y8', 'X0X1X2X3X4X5', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 122962: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X2Y4Y7X8', 'Z1Y2X6Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 122965: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Y2Z4X5', 'Z1Z2Y4X8', 'X0X1X4X5X6X7', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 123017: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y2Y3Y4Y7', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 123041: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1Z4Z5', 'X3Z6Z7Z8', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Y5Z6'] : False\n",
+      "6 :: 123647: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X2Y5Z6X8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 123654: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0X1Z2Z6', 'Z0Z3Y4Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 123655: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Y0Y1X2X6', 'X3X4X5X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 123656: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X0X5Y6Z7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 123657: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X0Y1Z2X4', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 123663: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X3Y4Y6Y8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 123667: [[9,3, 2]] : 8 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'Z0Z1X2X4', 'Y0Y1X3X5', 'X4X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 123744: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y2X4Z5Z6', 'X0Z3Y5Y6', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 123746: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X1Y2Z3Z6', 'Z4Z5Z7Z8', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8'] : False\n",
+      "6 :: 123757: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Y3Z6X8', 'X0Y6X7Y8', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7'] : False\n",
+      "6 :: 123758: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y1Y3Z6Z8', 'X1Z4Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 123785: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y1Z2X5', 'Y0Y3Z5Z6', 'Z0Z1Z2Z3X4X8', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 123862: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'X0Y5Z7X8', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 123885: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y0X2Z3Y4', 'X2Z5Y6X8', 'Y4X6Y7Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 123900: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Y3X7X8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 123903: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Y0Z1Y7X8', 'X0X5Y6Z8', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 123914: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0X4Y6X8', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5X7Y8'] : False\n",
+      "6 :: 123916: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z3Y4Y5', 'Y0Z1Y6X8', 'Z1Z2Z4Z5X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 123962: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Y1Y3Y4X6', 'Z1Z3X7X8', 'Z0Z1X4X5X6X7', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 123964: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y2Y6X7', 'Z2Y3X6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 123966: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Z0Y3Z6X7', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 123979: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Y0Z3Y5Z7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 124000: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y1Y3X5Z7', 'Z0Y2X5Z8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 124017: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'X1X4X5Y6', 'Z0Y1Z6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 124018: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Z3X5X6', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 124028: [[9,3, 2]] : 64 :['X0X1X2X3', 'X0X1X4X5', 'X0X1X6X7', 'Y0Y1X2X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 124044: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Y3X6Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 124065: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X3X4Z7Z8', 'Y0Z1Y4Y8', 'X0Z1Z2Z4Z5X7', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 124071: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X1Z4Z7Z8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 124072: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y0Y3Y5X6', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 124097: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1X6Y8', 'Z0Y1Y4Y7', 'Z0Z1X2Z4Z5X8', 'Z2Z3X4Y6Z7X8'] : False\n",
+      "6 :: 124098: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Y2X5X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 124108: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z3Z5Y7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 124113: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z1Z3Z4Y7', 'Y0Y2Z6Y7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 124128: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z0Y2Y4X6', 'Z1Y3Y5X6', 'X2X4X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 124130: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Y6Z7', 'X2X4Z6Y7', 'Z2Z3Y6Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 124163: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Y1Y3X4Z5', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 124171: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3Y6X8', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Y6Z7'] : False\n",
+      "6 :: 124172: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z4Z5Y7X8', 'X0Z6X7Z8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 124173: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X4Y6Y7X8', 'Y2Z3Z6Y8', 'Z0Z1X2X4X6X7', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 124197: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y4Y6X7Z8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 124201: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1Z4Y5Y6', 'X0X2Z7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 124207: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z3Z5Z7', 'X0Z4Z6Y7', 'X0Z1Z2X4X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 124210: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X1Z2Z3Z7', 'Z0Y1Z6Y7', 'Z1Z2Z4Z5X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 124223: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X0Z4Z5Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 124301: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y2Y3Y4Z5', 'Y1Z3X4X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 124349: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Z1X2Z4', 'Z0Z1X4X5X6X7', 'Y0Y1Z2Z3Y5Z6', 'Z1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 124357: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z0X1Y2Y4', 'Z5Y6X7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y1Z3Z6Z7Z8'] : False\n",
+      "6 :: 124368: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X0X1Y4Z5', 'X2X3Z4Y5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 124384: [[9,3, 2]] : 4 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'Y0Z5Z7X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 124406: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0Y1Z3X4', 'Z0Y3X5X8', 'Y2Z3X7Y8', 'Y0Z2Y4Z5Z6Z7'] : False\n",
+      "6 :: 124424: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z0Y3X4X5', 'Z1Y3X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 124425: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z0Z1Y4Y5', 'Z4Z5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 124440: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y2Y4Z7', 'Y1Z2X4X8', 'X0Z1Z2Z4Z5X7', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 124450: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'X1Z2Z3Y5', 'X4Y6Y7Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 124468: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X0Y4Y7X8', 'Y0Z1X6Y8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 124470: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Y0X2Z3Y8', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 124528: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'Y2Y3Y4Y5', 'Y0Y1Z6X8', 'Y0X1Z2X4X6X7', 'X0Y2Z3Z4Z7Z8'] : False\n",
+      "6 :: 124531: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Y3Y4Y6', 'X2Z5Y6Y8', 'X0X6Z7X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 124550: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Y1Y2Z4X5', 'Y0X1Z2X8', 'Y1Z3Z6Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 124589: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Z2Y7X8', 'X1Y4X6Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 124595: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1Z6Y7', 'Y0Y3Z5Z8', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 124598: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0X2Y4Z5', 'Z1Y2X4X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 124621: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y3Z4Y6', 'Z0Y1Z6Z7', 'Z2Z3X4Z8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 124644: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'X2Z4X5Z7', 'Z1Y3X5Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 124645: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y2Z3X4X8', 'Y0Z1Y5Z6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 124671: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X3Y4Y5X8', 'Y1Y3X5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 124677: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3Y4Z7', 'Z1Y3X5Z8', 'Z0Y2Y6Z8', 'Z1Z2Z4Z5X7X8'] : False\n",
+      "6 :: 124712: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1X4Z8', 'X1X2X5Y8', 'Y2Y3X6Z8', 'Y0Z2Y4Z5Z6Z7'] : False\n",
+      "6 :: 124766: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y0Y3X6Y7', 'Z1Y2X5Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 124769: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X0Z5Z6X8', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 124806: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X5Z6Z7Z8', 'Z0Z1Z4Z6', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 124863: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z2Z3X4Y6', 'X2Z6Y7Z8', 'Z0Z1X2X4X6X7', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 124910: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Z1X2Z3X6', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 124911: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Y0Z2X6Y7', 'X1Z4X5Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 124947: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Z0X1Z2Y5', 'Y1Z3X5Z6', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 124999: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y0Y1Z5Y7', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 125000: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'X1Z4Y5Y6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 125084: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X5Z6Z7Z8', 'Z0Y2X7Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 125094: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y1Y3Z4X5', 'X0X6Y7Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 125167: [[9,3, 2]] : 144 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1Z2Z3', 'X4Z6Z7Y8', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8'] : False\n",
+      "6 :: 125246: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'Y0Y1X2Z4', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 125248: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z2Z6X8', 'Y0X2Z3Z6', 'X2Z4Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 125254: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z1Z4Z5', 'Y2Y3Z7Z8', 'Z0X1Z2Z4X6X7', 'X0Z1Z3Y4Z6X8'] : False\n",
+      "6 :: 125288: [[9,3, 2]] : 2 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X0Z2X4Z5', 'X1Y2X3Y5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 125305: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Z2X5Y8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 125318: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X1Y4Z6X8', 'Y1Y3Y7Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 125390: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Y1Z3Y5Y8', 'Y4X5Z6Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 125408: [[9,3, 2]] : 32 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X5Y6Y7X8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y1Y2Z5Z6Z7Z8'] : False\n",
+      "6 :: 125443: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Z3Y5Z7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 125446: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y2Z4X6', 'Y1Z3Z5X6', 'Z0Z1X4X5X6X7', 'Z1X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 125447: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y3Y4Y7', 'X1Y4X6Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 125458: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y6X7', 'X4Y5X6Z7', 'X0X1Z5Y7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8'] : False\n",
+      "6 :: 125979: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Y2Y3Z6Z7'] : False\n",
+      "6 :: 125994: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Y2Y3X4X6'] : False\n",
+      "6 :: 126006: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y0Y1Y6Z7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 126007: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'X1Y4X5X8', 'Z1Z3Y5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8'] : False\n",
+      "6 :: 126038: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Y1Y2Z6Z8', 'Y0Y3Z5Z8', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 126039: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Y3X7X8', 'Y1Z2Z5Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 126124: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z3Z5Z7', 'Z2Y3X7X8', 'Y5Y6Y7Z8', 'X0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 126146: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Y0X1Z3Z4', 'Z0Z1Z2Z3Z5X8', 'Z1Y3Z4Z6Z7Z8'] : False\n",
+      "6 :: 126148: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z2Y3Z4Y6', 'Y0Z3X6Z7', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8'] : False\n",
+      "6 :: 126149: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'Y1Z2Z3Y5', 'Z3Y4Y6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 126329: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z2Z3Z5Z7', 'Y5X6Y7X8', 'Y0Z1Z5Z8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 126336: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X3Y4Z5Z8', 'Y0Y1X4Y8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 126345: [[9,3, 2]] : 2 :['X0X1X2X3', 'X2Z4Z6Z8', 'X3Z5Y6Y8', 'Z2Y3X7X8', 'Z0Z1X4X5X6X7', 'X0Z2Z3X4X5Z7'] : False\n",
+      "6 :: 126347: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Z1Z2Z4Y6', 'Z0Z3Y6Z7', 'Y1Y2Z5Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 126357: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Y2Y5X6', 'X3Z5Z6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 126358: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z0Z1Z4Y6', 'X2Z5Y6X8', 'X0X1X4X5X6X7', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 126385: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5', 'Z1Y2Z4Y6', 'Z0Z3Z5X8', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 126439: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Y1Y6X7', 'Y0Z3Y5Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 126517: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Z3X7Y8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 126570: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Y1Z2Y4Y5', 'X1X5Y6Z7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 126594: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Y0Z1X7Y8', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 126599: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Y0Y2Z4Z6', 'Y1Y3Y4Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 126600: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z1Y2Y4X5', 'Z0Z2Z4X8', 'Y0Z3Z6Y8', 'X0X1X4X5X6X7'] : False\n",
+      "6 :: 126606: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X3Y6Y7X8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 126625: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y3Z4X6', 'Z2Z3Y6Z7', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8'] : False\n",
+      "6 :: 126630: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Y3Y5Y7', 'X1X2Y6Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8'] : False\n",
+      "6 :: 126633: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Y3Z4Y5', 'Y1Z3Z5X8', 'Z0Z1X4X5X6X7', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 126635: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0X1Z5Z7', 'Z0Z2Y6Y7', 'Z0Z1Z2Z3X4X8', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 126647: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X3Y6Y7X8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 126652: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1X6Y8', 'X5Z6Z7Z8', 'Z2Z3X7Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 126660: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z0Y1Y5Y8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 126682: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1X4Z5', 'Y0Z2Y4X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 126683: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y2Z3Y4Y5', 'Y1Y3X5Z6', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 126687: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z1Z2Z7Z8', 'Y0Y1Y4Y7', 'Y2Y3Z6Y7', 'Y0Y2Y5Y8', 'X0X1X4X5X6X7'] : False\n",
+      "6 :: 126734: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Y2Y6Y7', 'Z0Y3X5Z8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 126746: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0Y1Y2Z4', 'Z0Z1Z4Z5Z6X7', 'Z2Z3Y4Z5Y6X8'] : False\n",
+      "6 :: 126748: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X2Z5X6Z7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 126989: [[9,3, 2]] : 2 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X1X3X4X6', 'X0X2X5X6', 'X1X5X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 127044: [[9,3, 2]] : 96 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X7', 'X0Y1Z3Z7', 'X0Z4Z5X8', 'Z0Z1Y2Z3Z6Z8'] : False\n",
+      "6 :: 127063: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y0X1Z2X5', 'Z4X5Z6Z8', 'Z0Z1X2Z4Z5X8', 'Y1Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 127066: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Y2Z3Z7', 'X0Z5Z6Y8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 127074: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y2Z3X4X6', 'Y0Z1Y4Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 127076: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Z1Z5X8', 'Z1Z2Y6Z7', 'X1Z4X6Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 127176: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Y2Y5Z7', 'X2Z4Z5Z8', 'Y2Z3X6Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 127194: [[9,3, 2]] : 288 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1Z2Z3', 'X4Z6Z7Z8', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X5Z6Z7'] : False\n",
+      "6 :: 127270: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z2Y4Y5', 'Z1Z3X7Z8', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z5Z6Z7'] : False\n",
+      "6 :: 127459: [[9,3, 2]] : 96 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'X0X2Y5Z6', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 127612: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y4Y5Y6Y7', 'Z1Z2Z4X7', 'Y0Y3Y6Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8'] : False\n",
+      "6 :: 127624: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y3Y6Z7', 'X3X5Y7Z8', 'Z0Z2Y5Y8', 'X0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 127651: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y4X6Y7Z8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 127653: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Y1Z3Y5Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 127788: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'X3Z4Z7X8', 'Y0Y3Y5Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 127797: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y1Y2Z4X6', 'Z0Z1Y6Z7', 'X4X5Z6Y7', 'Y0X1Z2Z4Z5Z8'] : False\n",
+      "6 :: 127837: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X2Z4Z5Z7', 'X0Z1Y2Y7', 'Z0Y1Z5X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 128345: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6', 'Z0Z1Y2X3Z4Z7'] : False\n",
+      "6 :: 128378: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0Y1Y2X4', 'X4Z5Y6X8', 'Z0Z1X2Z4Z5X6', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 128379: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y2Z3Y5Y7', 'X0Z4Y6Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 128389: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1X6Y8', 'Z4X5Z7Y8', 'Z0Z1X2Z4Z5X8', 'Z2Z3X4Y6Z7X8'] : False\n",
+      "6 :: 128390: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Y1Y4Y5', 'X3X4X5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 128395: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'Y2Y3X5X6', 'Z2Z3X4X7', 'X0Y4Y7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 128414: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Y1X2Y7', 'Z2Y3X7Z8', 'X3X5X6Y8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 128435: [[9,3, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X0X2Y6Z7', 'X1Z6Y7X8', 'X0X1X2X3X4X5', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 128436: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X1Z3Y5Z6', 'Y1X3Y4Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 128440: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Z0X1Y2Z5', 'X3Z4Z6X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 128444: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X4Z5Z6Z8', 'Y0Y1X2Z8', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 128452: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'Z0Y1X2X4', 'X1Y2Z3X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 128462: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Y4Z5X7', 'Y2Z3Y6X8', 'X4Z6Y7Y8', 'Z0Z1X2X4X6X7'] : False\n",
+      "6 :: 128485: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Y0Z1Z7X8', 'X0Z2Z3Y8', 'Z0Z1X2X4X6X7', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 128498: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z1Y3Z6Y7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 128501: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X4Z5Y7Y8', 'X3Y4Y6Z8', 'Z0Z1X4X5X6X7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 128502: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Y1Y3X5Z7', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 128503: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X4Z6Y7Y8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 128504: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1X6Y8', 'Y2Y3Y4Y7', 'Z0Z1X2Z4Z5X8', 'Z2Z3X4Y6Z7X8'] : False\n",
+      "6 :: 128505: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z1Y3X5X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 128506: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z1Y2Z6Z7', 'Y0Y3X4Y8', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8'] : False\n",
+      "6 :: 128507: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Y1Z2Y4Y5', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 128537: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Y3Y5Y6', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 128568: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z2X4Z6', 'Y1Z3X4Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 128569: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Y3Z4X6', 'X0Z4X7Z8', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7'] : False\n",
+      "6 :: 128570: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Z1Y2Y6', 'X4Z5Y6Y8', 'X0X1X4X5X6X7', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 128571: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Y6', 'X4Y5Z6X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 128615: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z3Z4X5', 'X4Y5Z6Z8', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 128616: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z3Z4X5', 'X0Y5Z6X7', 'X0Z1Z2X4X7X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 128619: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y2Z3X4X6', 'X2Y4Z5X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 128629: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z2Z6X8', 'X0Z2Z3X6', 'Z4X6Z7Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 128630: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X0Z2Z3Z4', 'X0Z5X6X8', 'Z0Z1X4X5X6X7', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 128644: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z5Z6X7X8', 'X0X2X4Z5', 'Y0Z1Y5X6', 'Z2Z3X5Y6', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 128647: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y3Y6Z8', 'Y4X6Z7Y8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8'] : False\n",
+      "6 :: 128656: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X2X4', 'Z0Z2Z4X5', 'Z0Z3Z7Z8', 'X0X1X4X5X6X7', 'X0Z2Z3Z5Z6X8'] : False\n",
+      "6 :: 128659: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0X1Y2Y4', 'Y1Y2X4Z7', 'Y4X5X6Y7', 'Z2Z3Z4Z5X6X8', 'X0Y1Z3Z6Z7Z8'] : False\n",
+      "6 :: 128670: [[9,3, 2]] : 4 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Z0X1Z2Y7', 'X0Z4Z7X8', 'Z1Z3X7Z8', 'Z0Z1X2X4X5X6'] : False\n",
+      "6 :: 128696: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X2Y4Z5Y7', 'Y0Z1Y6Y8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 128698: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X2Z6Z8', 'Y0X1Y2X4', 'Z1Y3Y4Z7', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8'] : False\n",
+      "6 :: 128699: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z1Y2X4Y5', 'Z0Z2Z5X8', 'Z1Y3X6Y7', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 128730: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Z3Y4Z5', 'Y0Y3X4X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 128738: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y2Y3Z4Y7', 'Y0Y3Z5Y8', 'X3Y4Y6Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 128844: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y1Y2X4Y5', 'Y0Z2X5Z6', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 128936: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Y2Y3X4X6', 'Z2Z3X5X7', 'X2Y6Z7X8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 128988: [[9,3, 2]] : 32 :['X0X1X2X3', 'X4X5X6X7', 'Y4Y5Y6Z7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Y6Z7X8', 'X0X1Y4Z5X6Z8'] : False\n",
+      "6 :: 129066: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Z1Z2Z5Y7', 'Z0Z3Z6Y7', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 129136: [[9,3, 2]] : 16 :['X0X1X2X3', 'Y5Y6X7Z8', 'Y0Y1Y2Z3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 129140: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z4Z5Y6Z7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 129152: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z2X5X6', 'Y0Z3Y4Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 129156: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z2Z3Z4Z5', 'Y0Z1Z6X8', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8'] : False\n",
+      "6 :: 129179: [[9,3, 2]] : 576 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1Z2Z3', 'Z4Z5Z6Z7', 'Z0Z1X2X4X5X8', 'Y0X1Z2X4X6Z8'] : False\n",
+      "6 :: 129184: [[9,3, 2]] : 32 :['X0X1X2X3', 'X4X5X6X7', 'Z4Z5Y6Z7', 'Z0Z1X2Z4Z5X8', 'X0Y2Z3X4X6Y8', 'Y0Y1X2Y4X5Y6'] : False\n",
+      "6 :: 129209: [[9,3, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'Y0Z3Y6X7', 'Z4X6Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 129272: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y1Y3X5Z7', 'Y2Z3Y6Y7', 'Y0Y1X4Z8', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 129277: [[9,3, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5', 'Z0Y1Z4Z5', 'Z2Z3Z7Z8', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8'] : False\n",
+      "6 :: 129719: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z4Z5Y6Z7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X5Z6', 'Y1Z3Y4X5Z7Z8'] : False\n",
+      "6 :: 129745: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X0Z2Z3X7', 'X4Y5Y6X8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 129748: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X4X5X6Z8', 'Z0Z1X7Z8', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 129762: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Y4Z8', 'Z2Z3X4X8', 'Y1Y3Z5Z8', 'X0X1X4X5X6X7', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 129763: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0X2Z3Y4', 'Y1Y2Z4Z6', 'Z1Z2Z4Z5X7X8', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 129764: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Y0X2Z3Y7', 'Z1Y2X7Z8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 129766: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0Y1Z3Z4', 'Z0Y3Z5X8', 'X0X1X4X5X6X7', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 129781: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Y2Y4Z7', 'Y1Y3Y4Z8', 'X3Z5Z6Y8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 129835: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1X2Y7', 'Z4Z5X6Y7', 'Y2Z3Y4Z5X7X8'] : False\n",
+      "6 :: 129869: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1X4X6', 'X2X4X7X8', 'Y2Z3Z4Z5Y6X8'] : False\n",
+      "6 :: 129949: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3Y6X8', 'Y4Z5Y6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 129952: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3Y6X8', 'Z4Z5X7X8', 'Z0Z1X2X6X7X8'] : False\n",
+      "6 :: 129957: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1Z2Z3', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'Y1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 129972: [[9,3, 2]] : 16 :['X0X1X2X3', 'Z0X1Y2Y4', 'Z0Z1Z2Z3', 'Y4Y5X7X8', 'Z0Z1X4X5X6X7', 'X0Y1Z3Z6Z7Z8'] : False\n",
+      "6 :: 129996: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X0Y1Z2X4', 'Y0Y3Y4Z6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 130005: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Y8', 'Z1Z2Y5Z7', 'X1Y4Y6Y7', 'Y0Z3Y5Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 130006: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z2Z6X8', 'Z1Y3Z7X8', 'X1Z5Y6Z8', 'Y1Y2Z4Y8', 'X0X1X4X5X6X7'] : False\n",
+      "6 :: 130008: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y0Y3Z5Z6', 'Y1Z3Z4Y6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 130013: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X4Z5Y7Y8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 130016: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Y5X6Z7Y8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 130038: [[9,3, 2]] : 8 :['X0X1X2X3', 'Z0Z2Z6X8', 'Y0Z1Y2Z3', 'Z5X6Z7Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 130046: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1Z2Z3', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Y1Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 130060: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Y3Z5X8', 'Z1Z3Z4Y6', 'Z0Z1X4X5X6X7', 'X1X2Z4Z6Z7Z8'] : False\n",
+      "6 :: 130235: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z0Z2Y4Z6', 'X1X4Z5Y6', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 130303: [[9,3, 2]] : 16 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'X0Z1Z2Y4', 'X5X6X7X8', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 130418: [[9,3, 2]] : 24 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1Z2Z3', 'X4Z6Z7Z8', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6'] : False\n",
+      "6 :: 130448: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y1Z2Z4Z7', 'Z0Y3Z6Z7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 130537: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z3Y4Y5', 'Y0Z2Z5Z6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 130553: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Y8', 'Z4Z5X6Y8', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 130555: [[9,3, 2]] : 16 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Y0X1Z2Z5', 'Z4Z5X7X8', 'Z0Z1X2X4X5X6', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 130566: [[9,3, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X7', 'X0Z4Z5X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 130625: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Y0Y1Z4Z6', 'X3Z5Z6X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 130651: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X0Y1Z2X5', 'Y1Z3Y4Z6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 131164: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6', 'Z0Z1Z2Z4X6Y7'] : False\n",
+      "6 :: 131203: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6', 'Z0Z1Z2X3Z4X7'] : False\n",
+      "6 :: 131221: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Y3Z4Y6Z7'] : False\n",
+      "6 :: 131257: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6', 'Z0Z1Z2Y3X4Z7'] : False\n",
+      "6 :: 131268: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6', 'Z0Z1Z3Z4Z6X7'] : False\n",
+      "6 :: 131368: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z2Z3Y4Y7', 'X0X4Y6Z7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 131403: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X4Y6Z7Y8', 'Z0Z1Z5Y6', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 131413: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z0Y3Z5Z7', 'X4Z5Y6Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 131583: [[9,3, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y3Y4Y5', 'Z2Y3X6X7', 'Z0Y2Y6Z8', 'Z0Y1X2Z4Z7X8'] : False\n",
+      "6 :: 131602: [[9,3, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3Y6X8', 'Z4Z5X6Y8', 'Z0Z1X2X4Z6X7'] : False\n",
+      "6 :: 131621: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X2Y4Y6Y8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 131622: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Y0Y1Z2Z3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 131623: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'X2Y5Y6Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 131633: [[9,3, 2]] : 8 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Z0Z1Y2Z3', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 131647: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z1Y4Y5', 'X2X4X5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 131648: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'X0Z1Z2X7', 'Z1Z3Z4Y6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 131669: [[9,3, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z4X5Z6Y8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 131684: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Y0X1Y2Z8', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 131689: [[9,3, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z0Y1Z4Z8', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 131732: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Y0Y1Z5Y6', 'X3Z4Y6X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 131733: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'X0Z4Z5Y6', 'Y0Z1X6Z7', 'X0X2Y7X8', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 131752: [[9,3, 2]] : 864 :['Z0Z1Z2Z3', 'X0X2X4X8', 'Y0Y1Y2Y3', 'Y1Y3Y4Y8', 'Y0Y1Y4Y5Y6Y7', 'Z1Z2Z5Z6Z7Z8'] : False\n",
+      "6 :: 131763: [[9,3, 2]] : 216 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Y1Z4Z8', 'Y0X2Z3Y4', 'Z0Z1X4X5X6X7', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 131943: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'X1Z4X5Z6', 'Y1Z2Z5Y7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 132168: [[9,3, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Y2Y3Y4Y5', 'Y0Y1X7X8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 132219: [[9,3, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'X5Z6Z7Z8', 'Z0Y3X7X8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 132583: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 132594: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8', 'Z0Z1X3Y4Z5Z7'] : False\n",
+      "6 :: 132617: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Z2Y3X4Z7'] : False\n",
+      "6 :: 132618: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8', 'Z0Z1X2Y3Z5Z7'] : False\n",
+      "6 :: 132643: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6', 'Z0Z1Z2Y3Z6X7'] : False\n",
+      "6 :: 132689: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Z2Z3Y6Z7'] : False\n",
+      "6 :: 132842: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Z3Z4Z6', 'X0Z5Y6Z8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 132873: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Z1Z3Z5Y7', 'Y2Y3X7Y8', 'X3Y5Y6Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 132885: [[9,3, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Z1Z2Y4Y6', 'Z0Z2Y5Y7', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 132922: [[9,3, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Z1Z2X4Z8', 'Z0Y2X5Y8', 'Y0Z3X6Z8', 'X0X1Y4Z5Z6Z7'] : False\n",
+      "6 :: 133719: [[9,3, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1Z2Z3', 'X4Z6Z7Z8', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7'] : False\n",
+      "6 :: 133869: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'X0Z2X3Z5Z6X7', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 133939: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1Y2X3Z4Z7'] : False\n",
+      "6 :: 134004: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'X0Z2Y3X4X6Z8', 'Z0Z1Z2X3Z4Y6'] : False\n",
+      "6 :: 134059: [[9,3, 2]] : 4 :['X0X1X2X3', 'X5X6Z7Z8', 'Y0Z3Z4X5', 'Y1Y3Y4X6', 'Z0Z1Y5Z6', 'Y0X1Z2Z4X7X8'] : False\n",
+      "6 :: 134109: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Z1Z3Z4Z5', 'Z2Z3X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 134120: [[9,3, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'X0Y1Z2X4', 'Y0X1Z3X6', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 134182: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Y1Z3Z7X8', 'Y0Y2Z4X5', 'X0Z1Z3Y5Z6Z8'] : False\n",
+      "6 :: 134211: [[9,3, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Y1Y3Z7X8', 'Y0Z1Y4Z8', 'Z0Z1X4X5X6X7'] : False\n",
+      "6 :: 134241: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1Z5Z7', 'Z0Z2Z4Y7', 'X3Z4Z5X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 134253: [[9,3, 2]] : 48 :['X0X1X2X3', 'X4X5X6X7', 'Y2Z3Y4Z6', 'X0Z5Z7Y8', 'Z2Y3Y5Y7', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 134914: [[9,3, 2]] : 64 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0Z1Y2Z3', 'X5Z6Z7X8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8'] : False\n",
+      "6 :: 135858: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8', 'Z0Z1Z2Z3Z4Z5'] : False\n",
+      "6 :: 135861: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6', 'Z0Z1X2Y3Y4Y7'] : False\n",
+      "6 :: 135938: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'X0Z2X3Z5Z6X7', 'Z0Z1X2X3Y6Z7'] : False\n",
+      "6 :: 135951: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7', 'Z0Z1X2Y3Z4Z6'] : False\n",
+      "6 :: 137461: [[9,3, 2]] : 96 :['X0X1X2X3', 'X4X5X6X7', 'Z4Z5Z6Z7', 'X0Y1Z2Z8', 'Z0Z1X2X4X5X8', 'Y0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 137485: [[9,3, 2]] : 96 :['X0X1X2X3', 'Z5Z6Y7Y8', 'X1Y2Z3X4', 'Y5Y6X7X8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8'] : False\n",
+      "6 :: 138659: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'X0Z2Y3X4X6Z8', 'Z0Z1X2Z4Z5Z6'] : False\n",
+      "6 :: 138668: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6', 'Z0Z1X3X4X6Y7'] : False\n",
+      "6 :: 142053: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'X0Z2Y3Y4Z5X8', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 142138: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8', 'Z0Z1Y2Y3X4Z6'] : False\n",
+      "6 :: 142162: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'X0Y2X3Z4Y6X8', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 153038: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6', 'Z0Z1X2Y3Z4Z6'] : False\n",
+      "6 :: 153050: [[9,3, 2]] : 24 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'X0Z2X3Z5Z6X7', 'Z0Z1Z2Y3Z4Z5'] : False\n",
+      "6 :: 153052: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6', 'Z0Z1Z2X3Y4X6'] : False\n",
+      "6 :: 153096: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'X0Z2Y3X4X6Z8', 'Z0Z1Z3Z4Y6Z7'] : False\n",
+      "6 :: 153100: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8', 'Z0Z1Z2X3Z4Z6'] : False\n",
+      "6 :: 153102: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'X0Z2X3Z5Z6X7', 'Z0Z1X3Y4Z5X6'] : False\n",
+      "6 :: 163595: [[9,3, 2]] : 576 :['Y0X1Y2Y3', 'Y5Y6Z7Z8', 'Z0Y1Z2Z3', 'Z5Z6X7X8', 'Z0Z1Y2Y4Y5Y6', 'X0X1Z2Z4X7X8'] : False\n",
+      "6 :: 165935: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'X0Z2Z3Y4Z5Z6'] : False\n",
+      "6 :: 165938: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'X0Z2Z3Z4Y6Z7'] : False\n",
+      "6 :: 166256: [[9,3, 2]] : 16 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'X0Z2Z3Y4Y5Y6'] : False\n",
+      "6 :: 170161: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'Y0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 170163: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'Z2Z3Z4Y5Y6Y7'] : False\n",
+      "6 :: 170184: [[9,3, 2]] : 64 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'Z0Z1Z2Z3Y4Z5'] : False\n",
+      "6 :: 170191: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'Z0Z1Z2Z3Z4Y6'] : False\n",
+      "6 :: 170192: [[9,3, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Y0Z1X2Z4X5Z8', 'Y0Z1Z2Z3Z5X6'] : False\n",
+      "6 :: 170872: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X2X4X6Z7'] : False\n",
+      "6 :: 170881: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X3X6Y7X8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 170882: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Y3X4X6', 'X2X3X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 170889: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y2Z3X4Z7'] : False\n",
+      "6 :: 170911: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'Z0Z1Z4Z5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 170916: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z7Z8', 'Z0Z1Y4Z5', 'X0Y2Z3Z4Z5Z6'] : True\n",
+      "6 :: 170921: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X7', 'X2X4Y6X8', 'Y3Z4Y7Z8', 'Z0Z1Z2Y3Z4Z5'] : False\n",
+      "6 :: 170938: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2Z3Z4Y6', 'Y2Z5Z6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 170945: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2X3Y6Y7', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 170955: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3Y7Y8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 170963: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z3X4Z7'] : False\n",
+      "6 :: 170964: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z4Z6Z7'] : False\n",
+      "6 :: 170965: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'X0Z4Z5Z6', 'X2X3X7X8', 'Y0Z1X2Y6Z7Z8'] : False\n",
+      "6 :: 170992: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X3Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1Y2Z3Z4Z5'] : False\n",
+      "6 :: 171001: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'Y2Y3X4X7', 'X0Z4Z5Z6Z7X8', 'Z0Z1X2Z4Z5Z8'] : False\n",
+      "6 :: 171003: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X2X3Y7Y8', 'Z0Z1X2Y7', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 171035: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'Z4Z5Z6Z8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2Z4Z6Z7'] : False\n",
+      "6 :: 171048: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Y3Z4Z5', 'X2X3X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 171053: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'X0Z2Z3X4X6X7', 'Z2Z3Z4Z5Z6Z8', 'Z0Z1X2Y4Z5Z7'] : False\n",
+      "6 :: 171054: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'X0Z2Z3X4X6X7', 'Z2Z3Z4Z5Z6Z8', 'Z0Z1Z2Z4Z6Z7'] : False\n",
+      "6 :: 171058: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 171097: [[9,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'X2X3Z6Z7', 'X2X4Z6Z8', 'Z0Z1Y2Z3Z4Z5', 'Y0Z1X2X6X7X8'] : False\n",
+      "6 :: 171105: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'X2X3X7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y4Z5Z7Z8'] : False\n",
+      "6 :: 171115: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0X2X6X8', 'X2X3X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 171116: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X2X3Z6Z7', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y4Z5Z8'] : False\n",
+      "6 :: 171159: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z3Z4Z6X7', 'Z2Y4Y6X8', 'Y2Y3Y7Z8', 'Z0Z1X2Z3Z5Z6'] : False\n",
+      "6 :: 171170: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 171171: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3Z4Z5X6X7', 'X0X2Y3Z4Z6Z7', 'Z0Z1X3X4X6Z8'] : False\n",
+      "6 :: 171172: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8', 'Z0Z1Y2X4Z5Y7'] : False\n",
+      "6 :: 171270: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8', 'Z0Z1Z3X4Z5X7'] : False\n",
+      "6 :: 171271: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8', 'Z0Z1X2X3X6Z7'] : False\n",
+      "6 :: 171272: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8', 'Z0Z1Y2Z3X4Z7'] : False\n",
+      "6 :: 171293: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1X2X3', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7'] : False\n",
+      "6 :: 171299: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z0Z1X3X4', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8'] : False\n",
+      "6 :: 171309: [[9,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'Z0Z1Y3Y4', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 171369: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z2Y3Z6X7'] : False\n",
+      "6 :: 171441: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z7X8', 'Z0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 171509: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Y0Z1Z4Y5X6Z8'] : False\n",
+      "6 :: 171529: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2Y4Y6Z7'] : False\n",
+      "6 :: 171617: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'X0Z2Z3X4X6X7', 'Y2Y3Z4Z5Z7X8', 'Z0Z1Z2Z4X6Z8'] : False\n",
+      "6 :: 171618: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z4Y7Z8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Y3Z5X6'] : False\n",
+      "6 :: 171628: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Z2X3Y4Z8', 'X0Z2Z3X4X6X7', 'Z0Z1X2Y3Z5Z7'] : False\n",
+      "6 :: 171639: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'Z4Z5Y6Y7', 'X0Z2X3Y4X6X8', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 171645: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z0Z1X2X7', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8'] : False\n",
+      "6 :: 171660: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1X2X3Y6Z8'] : False\n",
+      "6 :: 171662: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1Z2Y3X6Z8'] : False\n",
+      "6 :: 171677: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3X6X7', 'Y4Y5Z6Z7', 'X0Z2Z3Z4Z5X8', 'Z0Z1Z2Z4X6Z8'] : False\n",
+      "6 :: 172149: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X3Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1X2Z3Z5Z7'] : False\n",
+      "6 :: 172150: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X3Y7Y8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2X3Z4X7'] : False\n",
+      "6 :: 172152: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X2X4X6Z7', 'Y2Z3Y7X8', 'Z0Z1Y2X3Y4Z8'] : False\n",
+      "6 :: 172154: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z3Z4Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z3X4Z5X7'] : False\n",
+      "6 :: 172155: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4Z7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 172156: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'X0Z2Z3X4X6X7', 'Z0Z1X2Y3Y4X6'] : False\n",
+      "6 :: 172191: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z7X8', 'Z0Z1X2Z3Z5Z8'] : False\n",
+      "6 :: 172202: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 172203: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y3X4Z5Z7'] : False\n",
+      "6 :: 172207: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z3Z5Y6'] : False\n",
+      "6 :: 172258: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z3Z4X6'] : False\n",
+      "6 :: 172261: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'X2X4Z6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 172266: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'Z2X3Y4X8', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 172292: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3Y7Y8', 'Y0Z1X3Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172293: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3Y6Y7', 'Z0Z1Y6Z8', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 172294: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'Y0Z1X6Y7', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172331: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z3Z4X6'] : False\n",
+      "6 :: 172336: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2X3Z4Y6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Y3Z5X7'] : False\n",
+      "6 :: 172339: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y3X4Z5Z7'] : False\n",
+      "6 :: 172340: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2X3Z4X7'] : False\n",
+      "6 :: 172341: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2X3Z4Y6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Z3Z5Z7'] : False\n",
+      "6 :: 172345: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1Z3Y5Z6Z8'] : False\n",
+      "6 :: 172364: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3X4Z7'] : False\n",
+      "6 :: 172365: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2X3Z4Z6'] : False\n",
+      "6 :: 172370: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4Z7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z3X4Z7'] : False\n",
+      "6 :: 172371: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2X3Z4Z6'] : False\n",
+      "6 :: 172372: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Y3Y7Z8', 'X5X6Z7Y8', 'X0Z2Z4Z6Z7Z8', 'Y0Z1X2Z3Z5Z8'] : False\n",
+      "6 :: 172373: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4Z7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3X4Z5Y6'] : False\n",
+      "6 :: 172374: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Y3X4Y6'] : False\n",
+      "6 :: 172375: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y2Y3Y7Z8', 'X5X6Z7Y8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1X2Z3Z5Z6'] : False\n",
+      "6 :: 172377: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4X6Z7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X3X4X6Z8'] : False\n",
+      "6 :: 172379: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z3Z4Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y2Y3X4X6'] : False\n",
+      "6 :: 172384: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z3Z4Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z2Y3X4Z7'] : False\n",
+      "6 :: 172409: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1Z2Z4Z6X7'] : False\n",
+      "6 :: 172445: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z4Y7Z8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Z3Z5Z7'] : False\n",
+      "6 :: 172468: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Y3X4Z6'] : False\n",
+      "6 :: 172476: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z4Y7Z8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y4Y5X6Z7'] : False\n",
+      "6 :: 172513: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z3Z4X6Z7'] : False\n",
+      "6 :: 172514: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z3Z5Y6Z7'] : False\n",
+      "6 :: 172549: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z2Y3Z6X7'] : False\n",
+      "6 :: 172583: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Z4Y7'] : False\n",
+      "6 :: 172695: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 172703: [[9,3, 2]] : 24 :['X0X1', 'X2X3X4X5', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'Z0Z1X2Z3Z4Z6'] : False\n",
+      "6 :: 172731: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Y3Y4Y6'] : False\n",
+      "6 :: 172735: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X4Z5Z6'] : False\n",
+      "6 :: 172747: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z7X8', 'Z0Z1X2X4X6Z8'] : False\n",
+      "6 :: 172752: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z2Y3X4Z8'] : False\n",
+      "6 :: 172775: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y3Z4Z6Z7'] : False\n",
+      "6 :: 172781: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Y3Y6Z7'] : False\n",
+      "6 :: 172783: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Y0Z1Z6Y7', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172784: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'Z0Z1X3Z8', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z7X8'] : False\n",
+      "6 :: 172785: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z0Z1X3Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172786: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Y0Z1Y6Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172787: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'Y0Z1Y6Z8', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z7X8'] : False\n",
+      "6 :: 172806: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z0Z1X3Y7', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172807: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'Z0Z1X2Z8', 'X0Z2Z3X4X6X7', 'Y2Y3Z4Z5Z7X8'] : False\n",
+      "6 :: 172808: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z0Z1X2Z7', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 172824: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z0Z1Y6Z7', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172825: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z0Z1Y6Z7', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 172826: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y0Z1X5X6', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 172827: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1X5X7', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 172828: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1X4X6', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'X0Z2X3Z5Z6X7'] : False\n",
+      "6 :: 172857: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1X4Z6', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'X0Z2Y3X4X6Z8'] : False\n",
+      "6 :: 172860: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z0Z1X2Z7', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 172861: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z0Z1X4Z7', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8'] : False\n",
+      "6 :: 172862: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z0Z1X2Z7', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7'] : False\n",
+      "6 :: 172876: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y0Z1X5X7', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7'] : False\n",
+      "6 :: 172877: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z0Z1X4X7', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6'] : False\n",
+      "6 :: 172879: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z0Z1X2Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 172886: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X7', 'X2X4Y6X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1X3Z4Z5X6'] : False\n",
+      "6 :: 172934: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X4Z5Z7'] : False\n",
+      "6 :: 172935: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X2X3X6Z8'] : False\n",
+      "6 :: 172986: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'Y2Y3Z4Z5', 'X0Z4Z5Y6Y7X8', 'Z0Z1X2X4X6Z8'] : False\n",
+      "6 :: 173001: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'Z4Z5Z6Z8', 'X0Z2Z3X4X6X7', 'Z0Z1X2Z4Z5Z7'] : False\n",
+      "6 :: 173002: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'Y2Y3Y4Y5', 'X0Y4Y5Y6Y7X8', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 173007: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X7', 'X2X4Y6X8', 'Y3Z4Y7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 173009: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X2X3Y7Y8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X4X6Y7'] : False\n",
+      "6 :: 173063: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z0Z1Z3Y5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 173067: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z0Z1X4X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 173071: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3Y4Z5'] : False\n",
+      "6 :: 173181: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z0Z1X2Y8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 173183: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z0Z1Y3Y4', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 173185: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z0Z1Y2Y4', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 173273: [[9,3, 2]] : 4 :['X0X1', 'Z0Z1X6X7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y2Y3Z4X5Z6', 'X0Y2X3X4Z7Z8'] : False\n",
+      "6 :: 173480: [[9,3, 2]] : 4 :['X0X1', 'Z0Z1Y2Y4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y2Y3Z4X5Z6', 'X0Y2X3X4Z7Z8'] : False\n",
+      "6 :: 173482: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X7', 'X2X4Y6X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1Y2Z3Z4Z5'] : False\n",
+      "6 :: 173534: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3Z4Z5X6X7', 'X0X2Y3Z4Z6Z7', 'Z0Z1Y3Y5X6Z8'] : False\n",
+      "6 :: 173631: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Y4Z7'] : False\n",
+      "6 :: 173679: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X2X4Y6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y3Y4Z7Z8'] : False\n",
+      "6 :: 173711: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'Y0Z1X2Y8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 173720: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z2X3Y4X7'] : False\n",
+      "6 :: 173723: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y3Z4Z6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Y3Z5X7'] : False\n",
+      "6 :: 173733: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4Z7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Z5X6Z7'] : False\n",
+      "6 :: 173734: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Y6X8', 'Y4Z5Y7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 173808: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 173809: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Y3Y4Z7'] : False\n",
+      "6 :: 173858: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z3Z4Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y2Z3X4Y7'] : False\n",
+      "6 :: 173859: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Y6X8', 'Y4Z5Y7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 173860: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Y6X8', 'Y4Z5Y7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 173874: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1Y2Z3Z6X7'] : False\n",
+      "6 :: 173893: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 173896: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z2Y3X6Z8'] : False\n",
+      "6 :: 173909: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X3Y6Z7'] : False\n",
+      "6 :: 173930: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Y0Z1X3Y7', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 173939: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2Z3X4Y7'] : False\n",
+      "6 :: 173946: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 173996: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7Z8', 'Z0Z1Z2Z3Y4Z5'] : False\n",
+      "6 :: 174015: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X4Y6Y8', 'X0Z2Z3X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Z0Z1Z2X3Y4Z7'] : False\n",
+      "6 :: 174018: [[9,3, 2]] : 384 :['X0X1', 'X2X3X4X5', 'X2X3X6Z8', 'Z0Z1X2X3', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8'] : False\n",
+      "6 :: 174030: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Y2Z3Z4Z5', 'X0Z2Z3X4X6X7', 'Z0Z1Y3Y4Z7Z8'] : False\n",
+      "6 :: 174037: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'X2X3X6Z8', 'Z0Z1X4Z7', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 174051: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2X3Y4Y6'] : False\n",
+      "6 :: 174151: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Z2X3Y4Z8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Z3Z6Z7'] : False\n",
+      "6 :: 174154: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Y2Z3Y6Y8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2X3Z4Z7'] : False\n",
+      "6 :: 174155: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Z2X3Y4Z8', 'X0Z2Z3X4X6X7', 'Z0Z1X2Z4Z5Z7'] : False\n",
+      "6 :: 174170: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7Z8', 'Z0Z1X2Z3Z4Z6'] : False\n",
+      "6 :: 174178: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X3X4Z6X7', 'Z2Y3Y6X8', 'X0Y2Z3Z6Z7Z8', 'Y0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 174224: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'Z0Z1Z3Y5', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 174225: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'Z0Z1X2Y7', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 174226: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X7', 'Z3Y4Y6X8', 'Z0Z1Z2Z5', 'X0Y2Z3Z6Z7Z8'] : True\n",
+      "6 :: 174237: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X3Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1X3Z4Z5X6'] : False\n",
+      "6 :: 174238: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X7', 'X2X4Y6X8', 'Y3Z4Y7Z8', 'Z0Z1X2Z3Z5Z7'] : False\n",
+      "6 :: 174239: [[9,3, 2]] : 96 :['X0X1', 'X2X3X4X5', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'Z0Z1X2Z4Z5Z7'] : False\n",
+      "6 :: 174248: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Y4Z6'] : False\n",
+      "6 :: 174272: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Z5Y6Z7'] : False\n",
+      "6 :: 174312: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z0Z1X6Z8', 'Z2Z3Z4Z5X6X7', 'X0X2Y3Z4Z6Z7'] : False\n",
+      "6 :: 174313: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'Z0Z1Z2Y5', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 174316: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z0Z1Z3Y5', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8'] : False\n",
+      "6 :: 174318: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Z4X6Z7'] : False\n",
+      "6 :: 174320: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 174321: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X2Z6X7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y3Y4Z7Z8'] : False\n",
+      "6 :: 174340: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y3Z4Y6Y8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 174346: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y3Z4Y6Y8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X3X4Y6Y7'] : False\n",
+      "6 :: 174367: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y3Z4Y6Y8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z3X4Z7'] : False\n",
+      "6 :: 174368: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Y2Z3Z6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y3Y4Z7Z8'] : False\n",
+      "6 :: 174392: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Z4Z5Y6Z7'] : False\n",
+      "6 :: 174398: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1X2X4Y6Z7'] : False\n",
+      "6 :: 174399: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1X2X3Y6Y7'] : False\n",
+      "6 :: 174544: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2Z5X6X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Y0Z1Z3X5Z7Z8'] : False\n",
+      "6 :: 174545: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2Z5X6X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1Y2Y5Z6Z8'] : False\n",
+      "6 :: 174546: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2Z5X6X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1Z2Y3Z4Z8'] : False\n",
+      "6 :: 174548: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2Z5X6X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1Y4Y6X7Z8'] : False\n",
+      "6 :: 174549: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7X8', 'Z0Z1Z2X3Y4Z8'] : False\n",
+      "6 :: 174551: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7X8', 'Z0Z1X2X3X6Z8'] : False\n",
+      "6 :: 174552: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Z4Y5X6Z7'] : False\n",
+      "6 :: 174553: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Y2Y3Z4Z5'] : False\n",
+      "6 :: 174622: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1Z2Y3', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8'] : False\n",
+      "6 :: 174626: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1Z2Z3', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'X0Z2X3Z5Z6X7'] : False\n",
+      "6 :: 174638: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z0Z1Y3Z4', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6'] : False\n",
+      "6 :: 174639: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z0Z1Y2Y3', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 174640: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1Z2Z4', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'X0Z2Y3X4X6Z8'] : False\n",
+      "6 :: 174649: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Y2Z3Y6Y8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2Z4Z6Z7'] : False\n",
+      "6 :: 174656: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z0Z1Y3Y4', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 174690: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z7Z8', 'Z0Z1X6Z7', 'X0Y2Z3Z4Z5Z6'] : True\n",
+      "6 :: 174712: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Y3X4Z7'] : False\n",
+      "6 :: 174714: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z3Z4Z6Z7', 'Z2Z5Y6Y7', 'Z0Z1Z2Y3X4Z8'] : False\n",
+      "6 :: 174722: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z3Z4Z7Z8', 'Z0Z1Z3Y5', 'X0Y2Z3Z4Z5Z6'] : False\n",
+      "6 :: 174775: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4X6Z7', 'Z0Z1X6Z8', 'X0Y2Z3Z4Z5Z6'] : False\n",
+      "6 :: 174783: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Z5X7'] : False\n",
+      "6 :: 174789: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4X6Z7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y3Y5X6Z8'] : False\n",
+      "6 :: 174793: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'X0Z2Z3X4X6X7', 'Y0Z1X3Z4Z5Y7'] : False\n",
+      "6 :: 174796: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'Y2Z3Y6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 174844: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2X3Y4Z7'] : False\n",
+      "6 :: 174868: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3X4Z7'] : False\n",
+      "6 :: 174869: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Z4X6Z7'] : False\n",
+      "6 :: 174899: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4Z7Z8', 'Z0Z1X5Y6', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 174901: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'X0X2X3X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y4Z5Z7Z8'] : False\n",
+      "6 :: 174965: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Y3Z4Y7'] : False\n",
+      "6 :: 174972: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X2Z3Z4X7'] : False\n",
+      "6 :: 174975: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z3Z4Z6Z7', 'Z2Z5Y6Y7', 'Z0Z1Y3Y5X6Z8'] : False\n",
+      "6 :: 174984: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4X6Z7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 174997: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8', 'Z0Z1Z2Y3X4Y7'] : False\n",
+      "6 :: 175040: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2X3Y4Z7'] : False\n",
+      "6 :: 175046: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y4Z5Y7X8'] : False\n",
+      "6 :: 175055: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z5Z6X7', 'Y2Y5Y6X8', 'Z0Z1Y2Z4', 'Y2X3Z4Z6Z7Z8'] : True\n",
+      "6 :: 175066: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z3Z4X6', 'Y2Y4X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Z3Z5Z7'] : False\n",
+      "6 :: 175108: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Y3Y4Z5'] : False\n",
+      "6 :: 175135: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Y3Y4Z5'] : False\n",
+      "6 :: 175136: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1Z2Y3X4Z8'] : False\n",
+      "6 :: 175167: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8', 'Z0Z1Z3Z5X6Z7'] : False\n",
+      "6 :: 175168: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3Z4Z5X6X7', 'X0X2Y3Z4Z6Z7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 175176: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z3Y4Z6X7'] : False\n",
+      "6 :: 175227: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2X3Z4Y6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y3Z5X6Y7'] : False\n",
+      "6 :: 175243: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Y0Z1Z7Y8', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8'] : False\n",
+      "6 :: 175315: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z0Z1Z7Z8', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z6X8'] : False\n",
+      "6 :: 175356: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'Z0Z1Z2X3Y4Z8'] : False\n",
+      "6 :: 175367: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Y0Z1Z7Y8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 175392: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z4Y6Z7'] : False\n",
+      "6 :: 175403: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z3Z4X6Z8', 'Z2Z5X7Z8', 'Z0Z1Y2Y3Z6Z7'] : False\n",
+      "6 :: 175405: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z3Z4X6Z8', 'Z2Z5X7Z8', 'Z0Z1Y2Y3X4Z7'] : False\n",
+      "6 :: 175409: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z3Z4X6Z8', 'Z2Z5X7Z8', 'Z0Z1X2X3Y6Y7'] : False\n",
+      "6 :: 175422: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y3X4Z5Z7'] : False\n",
+      "6 :: 175425: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Y3Z4Z5', 'X2X3X6X7', 'Z0Z1Y2Z4Z6Z8'] : False\n",
+      "6 :: 175426: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Z4Y6Z7'] : False\n",
+      "6 :: 175567: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Y0Z1Z4Y5X7Z8'] : False\n",
+      "6 :: 175608: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y4X6Z7'] : False\n",
+      "6 :: 175635: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Z2Y3Z4Z6'] : False\n",
+      "6 :: 175637: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2X3Z5Z6'] : False\n",
+      "6 :: 175640: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'X0Y2Z3X4X6Z8', 'Z0Z1Z2Z4Y6Z7'] : False\n",
+      "6 :: 175641: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Y2Y3Z4Z5'] : False\n",
+      "6 :: 175642: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X4Y5X6Z7'] : False\n",
+      "6 :: 175643: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2X5X6Z7'] : False\n",
+      "6 :: 175644: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X5Z6Y7X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1Y2Y5Z6Z8'] : False\n",
+      "6 :: 175655: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Y2Z3Z4Z7'] : False\n",
+      "6 :: 175663: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2Z5X6Z8'] : False\n",
+      "6 :: 175717: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X3Z4Z5Y6'] : False\n",
+      "6 :: 175724: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Z4X5Y7'] : False\n",
+      "6 :: 175740: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Z3X5Z7'] : False\n",
+      "6 :: 175741: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Z3Z6X7'] : False\n",
+      "6 :: 175742: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4X5X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1Z3Z5X6Z8'] : False\n",
+      "6 :: 175743: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X2Y3Z5X6'] : False\n",
+      "6 :: 175844: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z4Y7Z8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z3Z5Y6Z7'] : False\n",
+      "6 :: 175879: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y3Z4Z6', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2Z5X6Z7'] : False\n",
+      "6 :: 175911: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1Y7X8', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8'] : False\n",
+      "6 :: 175999: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y0Z1Z6Y7', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7'] : False\n",
+      "6 :: 176004: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y0Z1Z6X7', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'X0X3Z4Z5X6Z8'] : False\n",
+      "6 :: 176009: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'Z0Z1Z3Z4', 'X0Z2Z3X4X6X7'] : True\n",
+      "6 :: 176077: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'X0Z2X3Z4Z7X8', 'Z0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 176080: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1Y2Y3Z6Z8'] : False\n",
+      "6 :: 176190: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z4Y7Z8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Y0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 176206: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z0Z1Y6Z7', 'Z2Z3Z4Z5X6X7', 'X0Y2Z3X4X6Z8'] : False\n",
+      "6 :: 176222: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2X3Z4Z6'] : False\n",
+      "6 :: 176326: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z4Z5X6Z7'] : False\n",
+      "6 :: 176327: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2X3Z4Y6'] : False\n",
+      "6 :: 176334: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Z5X6'] : False\n",
+      "6 :: 176396: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z0Z1X7X8', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8'] : False\n",
+      "6 :: 176397: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'Z2X3Y4X6', 'X0Z4Z5Y6Y7X8', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 176400: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Y2Z3Y6Y8', 'X0Z2Z3X4X6X7', 'Y0Z1Y4Z5Y6Y7'] : False\n",
+      "6 :: 176404: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'Z2X3Y4X6', 'X0Z4Z5Y6Y7X8', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 176406: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Y2Z3Z5', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Z4Y6X8'] : False\n",
+      "6 :: 176448: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Y6X7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y3Z4X5X6'] : False\n",
+      "6 :: 176463: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X2Z3Z4Z5', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2X5Z6Z8'] : False\n",
+      "6 :: 176471: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X2Z3Z4Z5', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2X3X6Z8'] : False\n",
+      "6 :: 176483: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3Y4Z5'] : False\n",
+      "6 :: 176486: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X4Z5Z6'] : False\n",
+      "6 :: 176522: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Y4Z7'] : False\n",
+      "6 :: 176539: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Z3Y6Z7'] : False\n",
+      "6 :: 176561: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Y3Z5X7'] : False\n",
+      "6 :: 176568: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Y2Z3Z7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2X3Y4Z8'] : False\n",
+      "6 :: 176571: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z5Z6X7'] : False\n",
+      "6 :: 176628: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2X3Y6Z7'] : False\n",
+      "6 :: 176629: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z0Z1X4Y6', 'Z2Z3Z4Z5X6X7', 'X0Y2Z3X4X6Z8'] : False\n",
+      "6 :: 176637: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3X6Z7'] : False\n",
+      "6 :: 176675: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z7X8', 'Z0Z1Y2Y5X6Z8'] : False\n",
+      "6 :: 176714: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z4Z5Y6Z7'] : False\n",
+      "6 :: 176791: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3X4Z6'] : False\n",
+      "6 :: 176865: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Y2Z3X6Z8'] : False\n",
+      "6 :: 176869: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Y3Y4Y5X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z4X6Y7Y8'] : False\n",
+      "6 :: 176871: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'Y0Z1Z7Y8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 176874: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3Y4Z5'] : False\n",
+      "6 :: 176880: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2X3Z5Y7'] : False\n",
+      "6 :: 176881: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'X0Z2X3Z4Z7X8', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 177020: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z5Z6X7', 'Y2Y5Y6X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1X2Y3Z4Z6'] : False\n",
+      "6 :: 177022: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3Y4Y7'] : False\n",
+      "6 :: 177025: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y2Z3X4Z8', 'Z0Z1Y6Y7', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 177080: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4Z7Z8', 'Z0Z1Y3Y5', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 177084: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'Y0Z1Y3Y6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 177125: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'Z0Z1Z3Y6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8'] : False\n",
+      "6 :: 177165: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z0Z1Y2Y7', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8'] : False\n",
+      "6 :: 177168: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'Z0Z1Z2Y7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 177207: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y0Z1Z3Y7', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 177223: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z0Z1Z2Z6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 177233: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'Y2X3Z4X6', 'X2Y3Z5X7', 'Z0Z1Z2Y4Z7Z8'] : False\n",
+      "6 :: 177243: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3X4X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y6X7Z8'] : False\n",
+      "6 :: 177251: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Z2Z3X4X5', 'Y2Y3X6X7', 'Y4Y5X6X8', 'Z0Z1Z2X3Y4X6'] : False\n",
+      "6 :: 177254: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'X0Z4Z5Z6', 'X0Z2X3Z4X7X8', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 177255: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Z2Z3X4X5', 'Y2Y3X6X7', 'Y4Y5X6X8', 'Z0Z1Y2Z3Z4Y6'] : False\n",
+      "6 :: 177267: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z3Z5Z8', 'X5Y6Y7Y8', 'X0Z2Z3X5X6X7', 'Z0Z1X2Z4Y5Z6'] : False\n",
+      "6 :: 177383: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z0Z1X5X7', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8'] : False\n",
+      "6 :: 177387: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3X4X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y3Z4Z8'] : False\n",
+      "6 :: 177389: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3X4X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z4Z6Z8'] : False\n",
+      "6 :: 177400: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4Y5Y6', 'Y2Z4Y7X8', 'X0Z2Z3X5X6X7', 'Z0Z1Z2Z5X6Z8'] : False\n",
+      "6 :: 177409: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Y3Z5X6'] : False\n",
+      "6 :: 177500: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'Z0Z1Y4Y5', 'X0Z2Z3X4X6X7'] : False\n",
+      "6 :: 177501: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X2X3X6Z8', 'Z0Z1X2Y7', 'Z2Z3Z4Z5X6X7'] : True\n",
+      "6 :: 177510: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'Y2Y3Y4Z6', 'Y5Z6X7X8', 'X2X3X4X5X6X7', 'Z0Z1Y2X3X6Z7'] : False\n",
+      "6 :: 177513: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'X2X6Y7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 177523: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X0Y5Z6X7', 'X4X5Y7Z8', 'Z2Z3X5X6X7X8', 'Z0Z1X2Z4Y5X6'] : False\n",
+      "6 :: 177524: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X0Y5Z6X7', 'X4X5Y7Z8', 'Z2Z3X5X6X7X8', 'Z0Z1Z4X5Y6Z7'] : False\n",
+      "6 :: 177526: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X0Y5Z6X7', 'X4X5Y7Z8', 'Z2Z3X5X6X7X8', 'Z0Z1X2Z4Y7X8'] : False\n",
+      "6 :: 177528: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X0Y5Z6X7', 'X4X5Y7Z8', 'Z2Z3X5X6X7X8', 'Z0Z1Z4Z6Y7X8'] : False\n",
+      "6 :: 177567: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Y5Z6X7', 'X4X5Y7Z8', 'Z2Z3X5X6X7X8', 'Z0Z1Y2Z3Z4Z5'] : False\n",
+      "6 :: 177571: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'X0Y2Y3X4Z7X8', 'Z0Z1X2Y4Z5Z8'] : False\n",
+      "6 :: 177585: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'Z2X3Y4X8', 'X0Z2Z3X4X6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 177598: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z0Z1Y2Z4', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7'] : False\n",
+      "6 :: 177601: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1Z7Y8', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7'] : False\n",
+      "6 :: 177609: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z3Z4X6', 'Y2Y4X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2Z5Z6X7'] : False\n",
+      "6 :: 177614: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z2X3Y4Z8'] : False\n",
+      "6 :: 177622: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'X0Z2X3Z4Z7X8', 'Z0Z1X2Y4Z5Z8'] : False\n",
+      "6 :: 177675: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y0Z1Y6Y8', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'X0Y2X3Z4Y6X8'] : False\n",
+      "6 :: 177684: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z0Z1Z7Z8', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'X0Z2X3Z5Z6X7'] : False\n",
+      "6 :: 177687: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z0Z1Y6Z8', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'X0Z2Y3Y4Z5X8'] : False\n",
+      "6 :: 177727: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y0Z1Z6Y8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'X0Z2Z3Z4Z5Y6'] : False\n",
+      "6 :: 177742: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y0Z1X6Y8', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'X0Z2Y3X4X6Z8'] : False\n",
+      "6 :: 177744: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z0Z1Z6Z7', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'X0Z2Y3X4X6Z8'] : False\n",
+      "6 :: 177767: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y3Z5Z6X7', 'Y2Y5Y6X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1X3Y4Z5X6'] : False\n",
+      "6 :: 177784: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'Y2Z3Y4Z5', 'X2X4X6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 177807: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X2Z3Z4Z6', 'Y2Z5Y6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 177826: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'X0Z2X3Z4', 'Z2X3Z5Z6X7X8', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 177828: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'Y0Z1Y7Z8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 177841: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z4X5', 'Z3Z4X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2Z5X6Z8'] : False\n",
+      "6 :: 177848: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'Y2X3Z4Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3X4Z5Z7'] : False\n",
+      "6 :: 177852: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2X3Z4X5', 'Z3Y4X6X7', 'X4Y5Z6X8', 'Z0Z1Y3Z5Z7Z8'] : False\n",
+      "6 :: 177854: [[9,3, 2]] : 48 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Y6X7', 'Z0Z1X2Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 177855: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X0Y2Y3X5', 'X2X3X6X7', 'X0X2Z5Z6Z7X8', 'Y0Z1Z4Y5X7Z8'] : False\n",
+      "6 :: 177858: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X0X2Y3Z4', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 177898: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z0Z1Y6Z8', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 177918: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 177929: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X0Y3Z4Z6', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 177957: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z0Z1Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'X0X2Z3Z5Z6X7'] : False\n",
+      "6 :: 177977: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y3Z5Z6X7', 'Y2Y5Y6X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1Z2Y3Z4Z5'] : False\n",
+      "6 :: 177986: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X7', 'X2X4Y6X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1X2Z3Z5Z7'] : False\n",
+      "6 :: 178040: [[9,3, 2]] : 24 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'X0Z4Z5Z6Z7Z8', 'Y0Z1Z2Y3X6Z8'] : False\n",
+      "6 :: 178048: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Y0Z1Z6Y8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 178050: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2X3Z4X6'] : False\n",
+      "6 :: 178052: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z4Z6', 'Z3Y4Y6X7', 'Y2Z5Y6X8', 'Z0Z1X2X3Z7Z8'] : False\n",
+      "6 :: 178056: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X0Y2Z3Z7', 'Z2Z3X4X6X7X8', 'Z0Z1Y2X3Y4Z8'] : False\n",
+      "6 :: 178058: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X0Y2Z3Z7', 'Z2Z3X4X6X7X8', 'Z0Z1Z2Z4X6Z8'] : False\n",
+      "6 :: 178059: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z3Y5Z6', 'X0Z5Y6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1X3Z4X5Z8'] : False\n",
+      "6 :: 178082: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'Z0Z1Z2Y5', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178089: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2Y4Z5Z8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2X3Z4Z7'] : False\n",
+      "6 :: 178090: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y3Y4Z5X6', 'Y2Z4Y5X7', 'X5Z6Y7X8', 'Z0Z1Y2Y5Z6Z8'] : False\n",
+      "6 :: 178093: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Y2Y3Y4Z6', 'Y5Z6X7X8', 'X2X3X4X5X6X7', 'Z0Z1Y2Z3X5X6'] : False\n",
+      "6 :: 178124: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X2X3X6Z8', 'Z0Z1X6X7', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8'] : False\n",
+      "6 :: 178128: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2X3Y4X6', 'Y3Z4X7X8', 'Z0Z1X3Y7', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178135: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X7', 'Z3Y4Y6X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Y3Z5X6'] : False\n",
+      "6 :: 178142: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 178145: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z6Z7', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 178146: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z4Z5X6Z7'] : False\n",
+      "6 :: 178161: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X3X6Y7X8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 178185: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0X2Y3Z4', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 178201: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y3Y4X6Z8'] : False\n",
+      "6 :: 178205: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X7', 'Z3Y4Y6X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z3Z5Y6Z7'] : False\n",
+      "6 :: 178208: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Y2Z3Z7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2Z4X6Z8'] : False\n",
+      "6 :: 178218: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y4Z5X6'] : False\n",
+      "6 :: 178225: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z4X5', 'Z3Z4X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X2Y3Z6Z8'] : False\n",
+      "6 :: 178226: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X2Y3Z4X5', 'Z2Y4X6X7', 'X0Z2Z4Z5Z6X8', 'Y0Z1Y3Z6Z7Y8'] : False\n",
+      "6 :: 178230: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X0Y2Z3X4', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y6X7Z8'] : False\n",
+      "6 :: 178245: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2X3Z6Z7', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 178251: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X2Y3Z4X7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 178257: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z0Z1Y2Y4', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178274: [[9,3, 2]] : 32 :['X0X1', 'X0Z6Z7Z8', 'Y2Y3Y4Z6', 'Y5Z6X7X8', 'X2X3X4X5X6X7', 'Z0Z1Y2Y3Z5Y7'] : False\n",
+      "6 :: 178284: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Y3Y4Y5X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Y2Y5Z6Z8'] : False\n",
+      "6 :: 178293: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X0Y2Y4X6', 'Z2Z3X4X6X7X8', 'Z0Z1Z2Y5Z7Z8'] : False\n",
+      "6 :: 178300: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z5Z6X7', 'Y2Y5Y6X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 178319: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'Y2Y3Y4Z6', 'Y5Z6X7X8', 'X2X3X4X5X6X7', 'Z0Z1X2Y5X6Z7'] : False\n",
+      "6 :: 178323: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'Z0Z1Y3Y4', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178325: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z0Z1X4Y6', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 178326: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y3Z4Y6Y8', 'Z0Z1Y6Y7', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 178345: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Y2Y4X6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2Y5Z7Z8'] : False\n",
+      "6 :: 178348: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Y2Z3Z7X8', 'X0Z2Z3X4X6X7', 'Y0Z1Y4Z5X7Z8'] : False\n",
+      "6 :: 178372: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z3Z4X6', 'Y2Y4X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z2X3Z5Y7'] : False\n",
+      "6 :: 178376: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z0Z1Y3Z4', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 178400: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Y3Y4Z7'] : False\n",
+      "6 :: 178402: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2Y4Z5X6', 'X0Z2Z3X4X6X7', 'Z0Z1Y3Y4Z7Z8'] : False\n",
+      "6 :: 178418: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z0Z1Z3X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8'] : False\n",
+      "6 :: 178419: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X7', 'Y2Z4X6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Z2Y5Z7Z8'] : False\n",
+      "6 :: 178442: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z0Z1X3Z8', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8'] : False\n",
+      "6 :: 178447: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y4Y5Y6X8', 'Y0Z1Z6Y8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178448: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z0Z1X2Z7', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 178449: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Z0Z1X6X8', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8'] : False\n",
+      "6 :: 178464: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2X3Y4Y6', 'Z0Z1Y6Z7', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178469: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'X0Y2Z3Y4Z5X8', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 178479: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Z3X4', 'Z0Z1Y3Y6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 178503: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Y3X4Z7'] : False\n",
+      "6 :: 178510: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'Z0Z1Y2Y4', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178526: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'X0Z3Z4X8', 'Y2Z5Z6X8', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 178528: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z3Z5Y6'] : False\n",
+      "6 :: 178530: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X7', 'Z3Y4Y6X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1X2Z3Z5Z7'] : False\n",
+      "6 :: 178534: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z4Y6Z7'] : False\n",
+      "6 :: 178539: [[9,3, 2]] : 96 :['X0X1', 'X2X3X4X5', 'Z2Y3Z4Z5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1Y2Z3Z6X7'] : False\n",
+      "6 :: 178544: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Z2Z3X4X5', 'Y2Y3X6X7', 'Y4Y5X6X8', 'Z0Z1Z2Y4Y5Y7'] : False\n",
+      "6 :: 178556: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Z5X6'] : False\n",
+      "6 :: 178558: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 178559: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X7', 'Z3Y4Y6X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z4Z5X6Z7'] : False\n",
+      "6 :: 178574: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y2Z3X4X6', 'X2Y4Z5X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 178582: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X0Z2Z3X8', 'X4X6X7X8', 'Z0Z1Z2Y5Z7Z8'] : False\n",
+      "6 :: 178618: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X3Z4Z5X6'] : False\n",
+      "6 :: 178652: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Y3Y4Y5X7', 'Z0Z1Y3Z8', 'X0X2Z5Z6Z7X8'] : True\n",
+      "6 :: 178660: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Y4Z5X6', 'Y3Z4Y5X7', 'X4Y6Z7X8', 'Z0Z1Z2Y3Z4Z8'] : False\n",
+      "6 :: 178661: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Y4Z5X6', 'Y3Z4Y5X7', 'X4Y6Z7X8', 'Z0Z1Y3Y5Z6Z8'] : False\n",
+      "6 :: 178664: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Y4Z5X6', 'Y3Z4Y5X7', 'X4Y6Z7X8', 'Z0Z1Y2Y5Z6Z8'] : False\n",
+      "6 :: 178665: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Y4Z5X6', 'Y3Z4Y5X7', 'X4Y6Z7X8', 'Z0Z1X3Z4X5Z8'] : False\n",
+      "6 :: 178667: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Z0Z1X6Z8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7X8'] : False\n",
+      "6 :: 178668: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z0Z1X6X7', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 178669: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y0Z1X7Z8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 178673: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z0Z1Y6Y7', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 178693: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X7', 'Z3Y4Y6X8', 'X0Y2Z3Z6Z7Z8', 'Y0Z1Y4Y5X6Z8'] : False\n",
+      "6 :: 178698: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z0Z1Z2Y5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178709: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Y2X3Y4Z6'] : False\n",
+      "6 :: 178711: [[9,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'Y2Z3Z4Z5', 'Z0Z1Z7Z8', 'X2Y3Z4Z6X7X8'] : False\n",
+      "6 :: 178713: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3X4Z6', 'X2Y6X7X8', 'X0X4Z7Z8', 'Z0Z1Z4Z5X6Z7'] : False\n",
+      "6 :: 178725: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'Y2Z3Z4Z5', 'X0Z4Z5Z6X7X8', 'Z0Z1Y3Y4Z7Z8'] : False\n",
+      "6 :: 178738: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z0Z1Y3Y7', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8'] : False\n",
+      "6 :: 178755: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Y5Y7Z8', 'Z0Z1Z7Y8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 178756: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'Z0Z1Z2Z6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8'] : False\n",
+      "6 :: 178772: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Y6X7', 'Z0Z1Z3Y7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 178797: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'X2X4Y6Y7', 'Y4Z5Z7X8', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 178816: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Z2Y3Y5', 'X3Z5X6X7', 'X4Y6Y7X8', 'Z0Z1Z4X5Y6Z8'] : True\n",
+      "6 :: 178824: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'Y2X3Y4Y6', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Y4Z7Z8'] : False\n",
+      "6 :: 178833: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z0Z1X6Y8', 'X0Y2Z3Z4Z5Z6', 'X0X2Y3Z4Z7Z8'] : False\n",
+      "6 :: 178857: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2X4Z6X7', 'Y2Z3Y6X8', 'X0X6Z7Y8', 'Y0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 178863: [[9,3, 2]] : 96 :['X0X1', 'X2X3X4X5', 'Z2Y3Z4Z5', 'Z0Z1Z7Z8', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z6X8'] : False\n",
+      "6 :: 178866: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X6', 'Y0Z1Y7Z8', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 178882: [[9,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'Z2Y3Z4Z5', 'Z0Z1X7X8', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8'] : False\n",
+      "6 :: 178898: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y3Z4Y7Z8', 'Z0Z1Z2Z5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 178901: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y4Z5Y6X8', 'Z0Z1Z2Z3', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8'] : False\n",
+      "6 :: 178904: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y3Z5Z6X7', 'Y2Y5Y6X8', 'Z0Z1X7X8', 'Y2X3Z4Z6Z7Z8'] : False\n",
+      "6 :: 178937: [[9,3, 2]] : 8 :['Z0Z1', 'X2X3X4X5', 'X2X6X7X8', 'Z3Z4Z7Z8', 'X0X1X3X7', 'Z0Z2Z3Z4Z5Z6'] : False\n",
+      "6 :: 178941: [[9,3, 2]] : 96 :['X0X1', 'X0Z6Z7Z8', 'Y2Y3Y4Z6', 'Y5Z6X7X8', 'Z0Z1Z5Y7', 'X2X3X4X5X6X7'] : False\n",
+      "6 :: 178961: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0X2Y3Z4', 'Z2X3Z5Z6', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 178970: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X2X6Y7Z8', 'Z0Z1Z4Z5', 'X0Z2Z3X4X6X7'] : False\n",
+      "6 :: 178980: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'Y4Z5X6Y7', 'Z0Z1Y3Y4X6Z8'] : False\n",
+      "6 :: 178988: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Y0Z1X5Y8', 'Z2Z3Z4Z5X6X8'] : True\n",
+      "6 :: 179054: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Y2Z3Y4Z5', 'X0Y6Y7X8', 'X0Z2Z3X4X6X7', 'Z0Z1X2X3Y6Z8'] : False\n",
+      "6 :: 179070: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z0Z1Y6Z7', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 179076: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y3Y4Z5X6', 'Y2Z4Y5X7', 'X5Z6Y7X8', 'Z0Z1Z3X5Z7Y8'] : False\n",
+      "6 :: 179093: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Y3Z4Z7Z8', 'Z0Z1X3X4', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 179098: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z0Z1Z5Y7', 'Z2Z3Z4Z5X6X8'] : True\n",
+      "6 :: 179121: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'Y4Z5X6Y7', 'Z0Z1X2X3X6Z8'] : False\n",
+      "6 :: 179187: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y2Y3Z4X5Z6', 'X0Y2X3X4Z7Z8', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 179192: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'Y4Z5X6Y7', 'Z0Z1Z2X3Y4Z8'] : False\n",
+      "6 :: 179202: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z0Z1X4Y7', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 179203: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Z3Z5Z8', 'X5Y6Y7Y8', 'Z0Z1Z4Y6', 'X0Z2Z3X5X6X7'] : False\n",
+      "6 :: 179206: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z0Z1Y6Z7', 'Z2Z3Z4Z5X6X7'] : True\n",
+      "6 :: 179210: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Y4Z6X7', 'Z3Z4Y6X8', 'Z0Z1X2Y6', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 179326: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'Z0Z1Z6Z8', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8'] : False\n",
+      "6 :: 179340: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3X4Z7', 'Z0Z1X6Z8', 'X0Y2Z3Z4Z5Z6'] : False\n",
+      "6 :: 179431: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'Y2X3Z4Z8', 'Z0Z1X5Z7', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 179440: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y2Y3Z4X5Z6', 'X0Y2X3X4Z7Z8', 'Z0Z1Z2Y3Y4Z5'] : False\n",
+      "6 :: 180598: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z4Z5Y6Z7'] : False\n",
+      "6 :: 180625: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2X4Y6Z7'] : False\n",
+      "6 :: 180628: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'X3X6Y7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 180629: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'X3X6Y7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 180631: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Z3Z4Y6'] : False\n",
+      "6 :: 180632: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y4Z5X6'] : False\n",
+      "6 :: 180671: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7X8', 'Z0Z1Y3Y4X6Z8'] : False\n",
+      "6 :: 180674: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Y2X3Y4Z6'] : False\n",
+      "6 :: 180675: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Z3Z4Y6Z7'] : False\n",
+      "6 :: 180700: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Z2Z4Z6Z7'] : False\n",
+      "6 :: 180701: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1X2Y3Y4Y6'] : False\n",
+      "6 :: 180717: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X2Z5X6X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1X3Z4X5Z8'] : False\n",
+      "6 :: 180737: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1X2Y3Z4X6'] : False\n",
+      "6 :: 180992: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Z3Y4X6Z7'] : False\n",
+      "6 :: 180993: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8', 'Z0Z1Y2X3Y4Z7'] : False\n",
+      "6 :: 180998: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Y3Z4X7X8', 'Y2Y5Y6X8', 'Z0Z1Z2Y5Z7Z8'] : False\n",
+      "6 :: 181021: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'X2Y3Z4X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 181024: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Y2Z3Z7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 181025: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Y2Z3Z7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Y4X6Z8'] : False\n",
+      "6 :: 181027: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Y2Z3Z7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2X4X6Z8'] : False\n",
+      "6 :: 181201: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Z3Y5X6', 'X2Z5X7X8', 'X3Z6Y7Y8', 'Z0Z1Y2Z3Z4Z7'] : True\n",
+      "6 :: 181215: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Y3Y5Z7'] : False\n",
+      "6 :: 181311: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4X5X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1Y2X3Y6Z8'] : False\n",
+      "6 :: 181386: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Y2X3Y5Z6'] : False\n",
+      "6 :: 181395: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X5Z6Y7X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 181441: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Y0Z1Y4Y5X6Z8'] : False\n",
+      "6 :: 181444: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Y3X5X6Z8'] : False\n",
+      "6 :: 181460: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X3Z4Y5Z6'] : False\n",
+      "6 :: 181478: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Z2X3X5Z7'] : False\n",
+      "6 :: 181549: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Y6Y7X8', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 181571: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'X0Y2Z3X4X6Z8', 'Z0Z1X2Y3Z4X6'] : False\n",
+      "6 :: 181572: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Y2X3Z5Y7'] : False\n",
+      "6 :: 181610: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Y3Z5Y7'] : False\n",
+      "6 :: 181615: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Z4Y6X8'] : False\n",
+      "6 :: 181622: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'X0Y2Z3X4X6Z8', 'Z0Z1X2X3Y6Z7'] : False\n",
+      "6 :: 181715: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4X5X8', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5Z6Z7', 'Z0Z1X2Y3Z6Z8'] : False\n",
+      "6 :: 181717: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'X0Y2Z3X4X6Z8', 'Z0Z1X2Z4Z5Y6'] : False\n",
+      "6 :: 181721: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'X0Y2Z3X4X6Z8', 'Z0Z1X2Z3Z4Y6'] : False\n",
+      "6 :: 181881: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2Y4X5X6'] : False\n",
+      "6 :: 181914: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 181928: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z4Y5Y7'] : False\n",
+      "6 :: 181931: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 181936: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Y3Z5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X2Z4Y5Z6'] : False\n",
+      "6 :: 181937: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Z2X3Z6X7'] : False\n",
+      "6 :: 181938: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2Z3Z4Z6'] : False\n",
+      "6 :: 181939: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X2Z3Y6Z7'] : False\n",
+      "6 :: 181940: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2X4Z5X6'] : False\n",
+      "6 :: 181959: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Y4Z5Y6X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 181962: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X3Z5X6Z8', 'X4Y6Z7Y8', 'X0Z2Z3X5X6X7', 'Y0Z1X2Z4Z5Y7'] : False\n",
+      "6 :: 181964: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X3Z5X6Z8', 'X4Y6Z7Y8', 'X0Z2Z3X5X6X7', 'Z0Z1X2Z4Y5Z6'] : False\n",
+      "6 :: 181970: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X3Z5X6Z8', 'X4Y6Z7Y8', 'X0Z2Z3X5X6X7', 'Z0Z1Z2Z3Z4Z6'] : False\n",
+      "6 :: 181988: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y3X4X6Z7'] : False\n",
+      "6 :: 182021: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y3Y4Z5Y7'] : False\n",
+      "6 :: 182022: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y3Z4X6'] : False\n",
+      "6 :: 182023: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z3X4Y5X6'] : False\n",
+      "6 :: 182025: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X2X5Z7X8', 'X0Z2Z3X5X6X7', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 182026: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X2X5Z7X8', 'X0Z2Z3X5X6X7', 'Z0Z1Z3Z5X6Z8'] : False\n",
+      "6 :: 182027: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X2X5Z7X8', 'X0Z2Z3X5X6X7', 'Y0Z1Y4Y5X6Z8'] : False\n",
+      "6 :: 182028: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X2X5Z7X8', 'X0Z2Z3X5X6X7', 'Z0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 182029: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X2X5Z7X8', 'X0Z2Z3X5X6X7', 'Z0Z1Z2Y3Z4Z8'] : False\n",
+      "6 :: 182030: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y3X4Z5X6'] : False\n",
+      "6 :: 182040: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z4Z6', 'Z3Y4Y6X7', 'Y3Z5Z6X8', 'Z0Z1X2X3Z7Z8'] : False\n",
+      "6 :: 182062: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X5X6'] : False\n",
+      "6 :: 182063: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'X2X5Z7X8', 'X0Z2Z3X5X6X7', 'Y0Z1X3Z4X7Z8'] : False\n",
+      "6 :: 182068: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2Y3Y5Y7'] : False\n",
+      "6 :: 182077: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y5X6Z7'] : False\n",
+      "6 :: 182078: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y3X5X6Z7'] : False\n",
+      "6 :: 182079: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2Z3Z5Y7'] : False\n",
+      "6 :: 182080: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2X3Y5X6'] : False\n",
+      "6 :: 182107: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z3Z4Z5Z6', 'Y3Y4Z7X8', 'X0Z2Z3X5X6X7', 'Z0Z1Z3Y5X6Z8'] : False\n",
+      "6 :: 182212: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3X4Z6', 'Y4Z5Y6X7', 'Z0Z1X2Y4Z5Z8'] : False\n",
+      "6 :: 182261: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1Y2Y3Z4Z5'] : False\n",
+      "6 :: 182274: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z0Z1Z2X5', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8'] : False\n",
+      "6 :: 182280: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z2Y3Z5Z8', 'Z0Z1Z4Y6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8'] : False\n",
+      "6 :: 182319: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Y3Y4Y5X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X2Y3Z6Z8'] : False\n",
+      "6 :: 182346: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Z4X6Z7'] : False\n",
+      "6 :: 182409: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z3Y5X6', 'X2Z5X7X8', 'X3Z6Y7Y8', 'Z0Z1X2Z4Y6X8'] : False\n",
+      "6 :: 182426: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4Z5Z6', 'Y3Y4Z7X8', 'X0Z2Z3X5X6X7', 'Z0Z1Z2X3X5Z8'] : False\n",
+      "6 :: 182452: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2X3Z4Y6'] : False\n",
+      "6 :: 182453: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Y3Y4Y5X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2X3X5Z8'] : False\n",
+      "6 :: 182458: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Y3Y4Y5X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Y4Y6X7Z8'] : False\n",
+      "6 :: 182505: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2Z5X6Z8', 'X0Y6Z7Y8', 'Y0Z1Z4Y7', 'X0Z2Z3X5X6X7'] : False\n",
+      "6 :: 182519: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2X3Y6Z7'] : False\n",
+      "6 :: 182534: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Y2Z3Y6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2X4Z6Y7'] : False\n",
+      "6 :: 182566: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3X4Y6', 'Y4Z5Z6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 182603: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1X2Y3X5Z7'] : False\n",
+      "6 :: 182631: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Y4Z5X6', 'Y3Z4Y5X7', 'X4Y6Z7X8', 'Z0Z1X2Y3Z6Z8'] : False\n",
+      "6 :: 182652: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z3Z4X5Z8', 'Y0Z1Y4Y5', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8'] : False\n",
+      "6 :: 182699: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y3Y4Z5X6', 'Y2Z4Y5X7', 'X5Z6Y7X8', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 182703: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Y3Y5Z6', 'X4Z5Y6X7', 'X3X6Y7X8', 'Y0Z1Z4Y5X7Z8'] : False\n",
+      "6 :: 182806: [[9,3, 2]] : 96 :['X0X1', 'X2X3X4X5', 'Y2Z3Y4Z5', 'X0Y6Y7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 182812: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X3Z5X6Z8', 'X4Y6Z7Y8', 'Y0Z1Z4Y7', 'X0Z2Z3X5X6X7'] : True\n",
+      "6 :: 182850: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Y3Y4Z5X6', 'Y2Z4Y5X7', 'X5Z6Y7X8', 'Z0Z1X2Y3Z7Y8'] : True\n",
+      "6 :: 182856: [[9,3, 2]] : 32 :['X0X1', 'X0X2X3X4', 'Z2Z3Z5Z6', 'Y5Y6X7X8', 'Y2Y3Z7Z8', 'Z0Z1Z4Y5X6Z7'] : False\n",
+      "6 :: 182978: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1Y2Y5X6Z7'] : False\n",
+      "6 :: 183037: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1Y2Y3Z4Z7'] : False\n",
+      "6 :: 183054: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1Z2X3Z5Z7'] : False\n",
+      "6 :: 183075: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1X2Z3Y5Z6'] : False\n",
+      "6 :: 183212: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1Z2X3Y5Y6'] : False\n",
+      "6 :: 183213: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1Y2X3X5Y6'] : False\n",
+      "6 :: 183224: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X0Y2Z3Z6', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y4Z5Z8'] : False\n",
+      "6 :: 183242: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0X2Y5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Y2Z3Z4Z7'] : False\n",
+      "6 :: 183247: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X2X3X5Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2Z3Z4Z6'] : False\n",
+      "6 :: 183267: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y3X6Z7'] : False\n",
+      "6 :: 183293: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X2X3Y5Y6', 'Z2Y3Y7X8', 'X0Z2Z3X5X6X7', 'Y0Z1Z4Y5X7Z8'] : False\n",
+      "6 :: 183300: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X0Y2Z3Z6', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 183307: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Z3X4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y3Z4X6'] : False\n",
+      "6 :: 183308: [[9,3, 2]] : 24 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Z3X4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Z3Y5X6'] : False\n",
+      "6 :: 183309: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Z3X4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2Y3Z4Y6'] : False\n",
+      "6 :: 183312: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z2Z3Y5Z6', 'X0Z5Y6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1Y2X3Y5Z8'] : False\n",
+      "6 :: 183635: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1X2Z4Y5Z6'] : False\n",
+      "6 :: 183641: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1X3Z4Z5Y6'] : False\n",
+      "6 :: 183650: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8', 'Z0Z1Z2Z3Z4Z6'] : False\n",
+      "6 :: 183694: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z4Y5Z6', 'Z0Z1Y3Z8', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8'] : False\n",
+      "6 :: 183712: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2Y4X5X6'] : False\n",
+      "6 :: 183738: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0X2Z5X6', 'Y2Z3Y5X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1X3Z4X5Z8'] : False\n",
+      "6 :: 183745: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0X2Z5X6', 'Y2Z3Y5X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1Z2Y3Z4Z8'] : False\n",
+      "6 :: 183747: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z3Z5X6', 'X0X2Y5X7', 'X5Z6Y7X8', 'Z0Z1Z4X6Y7Y8'] : True\n",
+      "6 :: 183757: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2X3Y5Z6', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 183761: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4Y5Y6', 'Y2Z4Y7X8', 'X0Z2Z3X5X6X7', 'Z0Z1Z2X3X5Z8'] : False\n",
+      "6 :: 183785: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z3Z4Y5Y6', 'Y2Z4Y7X8', 'X0Z2Z3X5X6X7', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 183809: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X0Y2Z3X4', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y3Z4Z8'] : False\n",
+      "6 :: 183811: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Z2Z3X4X5', 'Y2Y3X6X7', 'Y4Y5X6X8', 'Z0Z1X2Y4X6Z7'] : False\n",
+      "6 :: 183815: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z0Z1Z2Z4', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 183828: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'X0Z4Z5Z6', 'Y2Z4X7X8', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 183831: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'X3X6Y7Z8', 'Z0Z1Z2Z4', 'Z2Z3Z4Z5X6X7'] : False\n",
+      "6 :: 183833: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'Z0Z1Z3Y7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 183838: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 183852: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0X3Y5X6', 'Z2Y3Z5X7', 'X0Z2Z4Z5Z6X8', 'Y0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 183856: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Y6X7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 183857: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Y2Z4Y5', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1X2Y3Z6Z8'] : False\n",
+      "6 :: 183890: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y2Z4Z5X6', 'Z0Z1Z3X7', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8'] : False\n",
+      "6 :: 183935: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'Y0Z1Y4Y6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 183938: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'Z0Z1Z2X3', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 183942: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z0Z1Y3X5', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 183945: [[9,3, 2]] : 32 :['X0X1', 'X0X2X3X4', 'Z2Z3X5X6', 'X2X3Z5Z6', 'X0Z2Z4X5X7X8', 'Y0Z1Y3Y5Y7Y8'] : False\n",
+      "6 :: 183961: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Z3Y4X6'] : False\n",
+      "6 :: 183973: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0X2Z5X6', 'Y2Z3Y5X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1Y4Y6X7Z8'] : False\n",
+      "6 :: 183985: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Y2Z4Y5', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z2X3X6Z8'] : False\n",
+      "6 :: 184012: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z0Z1X3Y6', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 184017: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Y0Z1X4Y8', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 184019: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X3Y4X6Z7'] : False\n",
+      "6 :: 184046: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Z3X4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Y3Y5Y6'] : False\n",
+      "6 :: 184091: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Y0Z1Y4Y6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'X2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 184101: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'Y2X3X4Z6', 'Z2X5Y6X7', 'Z0Z1Y3Y6', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 184143: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X0X2Z5X6', 'Y2Z3Y5X7', 'Y2Z4Y6X8', 'Y0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 184150: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0X2Y5X6', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Z4Y6X8'] : False\n",
+      "6 :: 184151: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X0Z2X3Z6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2Y3Y4Y7'] : False\n",
+      "6 :: 184152: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2X3Y5Z6', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Y0Z1X3Z4X7Z8'] : False\n",
+      "6 :: 184153: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2X3Y5Z6', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1Z2Y3Z4Z8'] : False\n",
+      "6 :: 184181: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Y3Z5X6', 'X0X3Y5X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Z4X6Y7Y8'] : False\n",
+      "6 :: 184190: [[9,3, 2]] : 32 :['X0X1', 'X0X2X3X4', 'X2X3X5Z8', 'Z0Z1Z4Y6', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8'] : False\n",
+      "6 :: 184195: [[9,3, 2]] : 4 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Z3X4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2X3X5Y6'] : False\n",
+      "6 :: 184200: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Z3X4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Z2Z3Y5Y6'] : False\n",
+      "6 :: 184274: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z4X5', 'Z3Z4X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Y2X3Y6Z8'] : False\n",
+      "6 :: 184302: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'Z2Z3Y5Z6', 'X0Z5Y6X7', 'X2X6Y7X8', 'Y0Z1Z4Y5X7Z8'] : False\n",
+      "6 :: 184305: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X0Y2Z3Z7', 'Z2Z3X4X6X7X8', 'Z0Z1Y4Z5X7Y8'] : False\n",
+      "6 :: 184348: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X0Y2Y6X7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1X2Y3X6Z7'] : False\n",
+      "6 :: 184364: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Z2X3Z4X8', 'X0Z2Z3X4X6X7', 'Z0Z1Z3Y5Z7Z8'] : False\n",
+      "6 :: 184373: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y2Z3X4Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Z2Z4Z6Z7'] : False\n",
+      "6 :: 184381: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y2Z3X4Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2X3Y4Z7'] : False\n",
+      "6 :: 184383: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y2Z3X4Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2X3Y6Y7'] : False\n",
+      "6 :: 184385: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X2Z5X6Z8', 'X0Y6Z7Y8', 'X0Z2Z3X5X6X7', 'Z0Z1Z2Z3Z4Z6'] : False\n",
+      "6 :: 184387: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X2Z5X6Z8', 'X0Y6Z7Y8', 'X0Z2Z3X5X6X7', 'Y0Z1X2Z4Z5Y7'] : False\n",
+      "6 :: 184388: [[9,3, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X2Z5X6Z8', 'X0Y6Z7Y8', 'X0Z2Z3X5X6X7', 'Z0Z1X2Z4Y5Z6'] : False\n",
+      "6 :: 184404: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Y2Z3Z6X7', 'X2X4Y6X8', 'Z0Z1Y3Y4Z7Z8'] : False\n",
+      "6 :: 184406: [[9,3, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z4Z6', 'Z3Y4Y6X7', 'X3Z4Z5X8', 'Z0Z1Y2Y3Z7Z8'] : False\n",
+      "6 :: 184422: [[9,3, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X2Z3Z4Z5', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Z0Z1Y2X3Y6Z8'] : False\n",
+      "6 :: 184524: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X2X3Y6Y7', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 184546: [[9,3, 2]] : 16 :['X0X1', 'X0Z6Z7Z8', 'X4Z5Y7Y8', 'Z0Z1Z5Y7', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 184550: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z0Z1X2Y6', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 184612: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Y2Z3X4X6', 'X2Y4Z5X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 184728: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X6Z7'] : False\n",
+      "6 :: 184755: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X2X3X6Z8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 184759: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'X2X3X7X8', 'X0Z2Z3X4X6X7', 'Z0Z1X2Y6Y7Z8'] : False\n",
+      "6 :: 185048: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X2X3Y6Y7', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z4Z6Z8'] : False\n",
+      "6 :: 185072: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Y6Y7X8', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'Z0Z1Y3Z4Y6Z8'] : False\n",
+      "6 :: 185074: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X0Y6Y7X8', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7', 'Z0Z1X2X3Y6Z8'] : False\n",
+      "6 :: 185104: [[9,3, 2]] : 32 :['X0X1', 'X0X2X3X4', 'Z2Z3Z5Z6', 'Y5Y6X7X8', 'Y2Y3Z7Z8', 'Z0Z1X2Z4Y5X7'] : False\n",
+      "6 :: 185108: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X0Y6Y7X8', 'Z0Z1Y6Z8', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5Z6Z7'] : False\n",
+      "6 :: 185117: [[9,3, 2]] : 32 :['X0X1', 'X0X2X3X4', 'Z2Z3Z5Z6', 'Y5Y6X7X8', 'Y2Y3Z7Z8', 'Z0Z1X2Z4X5Y7'] : False\n",
+      "6 :: 185124: [[9,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'Y2Z3Y4Z5', 'X0Y6Y7X8', 'Z0Z1Y6Z8', 'X0Z2Z3X4X6X7'] : False\n",
+      "6 :: 185139: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z6Z7', 'Z4Z5Y6Y7', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 185145: [[9,3, 2]] : 128 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X2X3Z6Z7', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 185160: [[9,3, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Y6X7', 'Z2Z3X6X8', 'Z4Z5X7X8', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 185161: [[9,3, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'Z2Z3X4X5', 'Y2Y3X6X7', 'Y4Y5X6X8', 'Z0Z1Y2X3X4Y6'] : False\n",
+      "6 :: 185172: [[9,3, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Y2Z3Y5', 'X2Z5X6X7', 'X0Y6Y7X8', 'Z0Z1Z4X5Y6Z8'] : False\n",
+      "6 :: 185179: [[9,3, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z6Z7', 'Z4Z5Y6Y7', 'Z0Z1Z2Y4Z6Z8'] : False\n",
+      "6 :: 185197: [[9,3, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X0Y2Z3X4', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z4Z6Z8'] : False\n",
+      "6 :: 206622: [[9,3, 2]] : 1 :['Y0Y2Z6Y7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y0Z1Y2X3Z5Z8'] : False\n",
+      "6 :: 207025: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4X6Z8', 'Y0Z1Z2Z3Y4Y5', 'Z0Y1X2X4Z6Z7'] : False\n",
+      "6 :: 207484: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Y4X5Z8', 'Y0Y1Z2Z3X4X6', 'X0Z2Z3Z6Z7X8'] : False\n",
+      "6 :: 207489: [[9,3, 2]] : 1 :['Y0X2Y4Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'X0Y1X3Z5X6Z8'] : False\n",
+      "6 :: 207682: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Y1Y2Z3X4Z5', 'X1Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 208466: [[9,3, 2]] : 1 :['Z1Y5Z7X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Z0X2X3Z5X6Z8'] : False\n",
+      "6 :: 208978: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'Y0Z1Z2Z3Y4Y5', 'Y0Z1X2X4Z6Z7'] : False\n",
+      "6 :: 208995: [[9,3, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1Y2X3X4Z6X7', 'X0Y1Y2Z4Z5Z8'] : False\n",
+      "6 :: 209027: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'Y0Z1Z2Z3X4X5', 'X0X2Y4Z5Z6Z7'] : False\n",
+      "6 :: 209310: [[9,3, 2]] : 1 :['Y1Y3X6Y7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y0Z1Y2X3Z5Z8'] : False\n",
+      "6 :: 209532: [[9,3, 2]] : 1 :['Y0Z2Z7Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Y0Z1Z2X3Z5Z6', 'Z0Z2Z3X4Z5X7'] : False\n",
+      "6 :: 209533: [[9,3, 2]] : 1 :['Z3Y5Y7Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Y0Z1Z2X3Z5Z6', 'Z0Z2Z3X4Z5X7'] : False\n",
+      "6 :: 209684: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Y4X5Z8', 'X1Z2Z3Z4Z5X6', 'Z0Y1X2X4Z6Z7'] : False\n",
+      "6 :: 209696: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Y1Y2Z3Y5X6', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 209721: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4X6Z8', 'Y0Z1Z2Z3Y4Y5', 'X0Z1Z2Z4Z6Z7'] : False\n",
+      "6 :: 209750: [[9,3, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'Y0Z1Z2Z3Y4Y5', 'X0Y1Z2Y4Z6Z7'] : False\n",
+      "6 :: 209761: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Y1Y2Z3X4Z5', 'X0Y1Z3Y4Z7Z8'] : False\n",
+      "6 :: 209763: [[9,3, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'Y0Z1Z2Z3X4X5', 'Z0Y1Y4Z5Z6Z7'] : False\n",
+      "6 :: 209773: [[9,3, 2]] : 1 :['Y1Y4X5Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Z0X2X3Z5X6Z8'] : False\n",
+      "6 :: 209775: [[9,3, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'X1Z2Z3Z4X5X6', 'Y1Z2X4Z5Z6Z7'] : False\n",
+      "6 :: 217688: [[9,3, 2]] : 1 :['Y1Z3X7Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y0Z1Y2X3Z5Z8'] : False\n",
+      "6 :: 217731: [[9,3, 2]] : 1 :['Y3Y5X6X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'X0Y1X2Z5Z7Z8'] : False\n",
+      "6 :: 217732: [[9,3, 2]] : 1 :['Y1Z4X6Y7', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0X1Z2Z3Z4Z6', 'Y0Z1Y2Z5Z7Z8', 'Z0X1Z2Y3Z5X7'] : False\n",
+      "6 :: 217740: [[9,3, 2]] : 1 :['Y2Z4X6Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'X0Y1X2Z5Z7Z8'] : False\n",
+      "6 :: 218212: [[9,3, 2]] : 1 :['Y0Y3Z7X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y0Z1Y2X3Z5Z8'] : False\n",
+      "6 :: 218213: [[9,3, 2]] : 1 :['X1Z2Y5Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0Z1Y3Y5Z6Z8'] : False\n",
+      "6 :: 218214: [[9,3, 2]] : 1 :['Z0Z3Y6Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'X0Y1X2Z5Z7Z8'] : False\n",
+      "6 :: 537: [[9,4, 2]] : 864 :['X0X1', 'X0X8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5Z6Z7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 6527: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Z0Z1X2Z6Z7Y8'] : False\n",
+      "6 :: 10310: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Z0Z1Y2Z4Z7Z8'] : False\n",
+      "6 :: 11762: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X1Y4Z5X6Z7Z8'] : False\n",
+      "6 :: 11797: [[9,4, 2]] : 24 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3Z4Z5', 'Z0Z2Z4X6X7X8', 'Z0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 11799: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X4X6X7', 'Z0Z1Z2Z3Z7Z8', 'X0Z4Z5Z6Z7Y8'] : False\n",
+      "6 :: 11806: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 11807: [[9,4, 2]] : 8 :['Y0Z1Y2Y3', 'Y0Y2Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 11864: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1Z4Z5Z6X7', 'Z2Z3Y4Z5Y6X8'] : False\n",
+      "6 :: 11865: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1X2X6X7X8', 'Y2Z3Z4Z5Y6X8'] : False\n",
+      "6 :: 11868: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Z0Z1X2Z4Z5X6', 'Y2Z3Y4Z5X7X8'] : False\n",
+      "6 :: 11995: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X4X5X6', 'X1X2X4Z7', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 12017: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X6X7X8', 'Y0Z1X4Z6Z7Z8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 12096: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X2Z6Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7'] : False\n",
+      "6 :: 12146: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Z1X2Z3X4', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 12171: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4Z7Y8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 12177: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z7Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Z4Z6'] : False\n",
+      "6 :: 12178: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X2Y5Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X5Z6'] : False\n",
+      "6 :: 12179: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Y5Y6X7', 'Z0Z1X4X5X6X7', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 12184: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y3X6Y8', 'Z0Z1X2Z4Z5X8', 'Z1Z2Y4Z5Y6Z7'] : False\n",
+      "6 :: 12187: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X5', 'X4Z6Z7Y8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 12188: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Y5Y6X7X8', 'Z0Z1X4X5X6X7', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 12189: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Z6Z7X8', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Z5Z8'] : False\n",
+      "6 :: 12191: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X4Z5Y7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 12192: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y5Y6X7X8', 'Z0Z1X4X5X6X7', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 12194: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Y2X6X8', 'Z0Z1X2Z4Z5X8', 'Z0Y3X4Y6Z7Y8'] : False\n",
+      "6 :: 12195: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1X6Y8', 'Z0Z1X2Z4Z5X8', 'Z2Z3X4Y6Z7X8'] : False\n",
+      "6 :: 12208: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0Y6Z7Z8', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X4X6'] : False\n",
+      "6 :: 12209: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0Y4Y5Z8', 'Z0Z1X2Z4Z5X8', 'Y2Z3X5Z6Z7X8'] : False\n",
+      "6 :: 12244: [[9,4, 2]] : 2 :['X1Z2Y3X6', 'Y3Z5Y7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 12249: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'X1Z2Z3Y4Z6Z8'] : False\n",
+      "6 :: 12270: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 12271: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Z3Y4Z5Z6Z8'] : False\n",
+      "6 :: 12272: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0Z3X4Z6X7Z8'] : False\n",
+      "6 :: 12276: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Y0X1Z3Z6X7Z8'] : False\n",
+      "6 :: 12277: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Y1X2Z4Z7X8', 'Z0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 12299: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 12302: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 12333: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3X6Z7', 'Z1Z2Z4Z5X7X8', 'Z0X1Y2X5Z6Z8'] : False\n",
+      "6 :: 12681: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z5Z6Y7Y8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 12683: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Y5Z6', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 12725: [[9,4, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0X1Z4Z5Y6Z7', 'X1Y2Z3X4X6Y8'] : False\n",
+      "6 :: 12726: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3Z5Z7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 12728: [[9,4, 2]] : 32 :['X0X1X2X3', 'Y4Y5Y6Y7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 12729: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 12730: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 12764: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z1Z2Z7Z8', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 12772: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 12777: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Y1Y2X4Y5Z6Z7', 'Z0X2Z3X4X5Z8'] : False\n",
+      "6 :: 12788: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Y5Z6', 'Y1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 12798: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 12801: [[9,4, 2]] : 32 :['X0X1X2X3', 'Z5Z6X7X8', 'Z0Z1X2X4X5X6', 'Y0Z1Z2Z3X7X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 12813: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 12816: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Z5Z6Z7'] : False\n",
+      "6 :: 12830: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 12834: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 12835: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 12839: [[9,4, 2]] : 2 :['Y1Z2Y3Z5', 'Y0X1Z3Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 12843: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 12845: [[9,4, 2]] : 24 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 12848: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3Y4Z6', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 12853: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y5Y6X7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 12858: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Y1Y2Z3Z4Z5', 'Z1X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 12863: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Y4Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 12864: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'Z1Y5Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 12867: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z2Z6X8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'X0Z2Z3Z4Z7Z8'] : False\n",
+      "6 :: 12869: [[9,4, 2]] : 4 :['X0Z2Y3Z6', 'Y4Y5Y7Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 12872: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z0Y1Z2Z3Z4Z7'] : False\n",
+      "6 :: 12873: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4Z6Z8', 'X0Y2Z3Y6Y7X8'] : False\n",
+      "6 :: 12874: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 12877: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 12879: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'Y0Z5X6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 12893: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Y1Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 12894: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Y1Z2Z3Y5Z6', 'Z1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 12895: [[9,4, 2]] : 16 :['X0X1X2X3', 'Y4Z5Z6Z7', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 12896: [[9,4, 2]] : 16 :['X0X1X2X3', 'X5X6Z7Z8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 12897: [[9,4, 2]] : 16 :['Y0Y1X5X6', 'X3X4X7X8', 'X0X1X2X3X4X5', 'Z0Z2Z3Z4Z6X7', 'X1Z2X3Z5Z7Z8'] : False\n",
+      "6 :: 13354: [[9,4, 2]] : 4 :['X0X1X2X3', 'X2Z4Z6Z8', 'X3Z5Y6Y8', 'Z0Z1X4X5X6X7', 'X0Z2Z3X4X5Z7'] : False\n",
+      "6 :: 13355: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X1Z4Z6Z8', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Y4Z7'] : False\n",
+      "6 :: 13410: [[9,4, 2]] : 12 :['X0X1X2X3', 'Y1Y2Z4Z8', 'Y0Y3Y4Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z5Z6Z7'] : False\n",
+      "6 :: 13424: [[9,4, 2]] : 16 :['X0X1X2X3', 'Z0Z1Z2Z3', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8'] : False\n",
+      "6 :: 14217: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X2Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 14291: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y6X7', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z3X4Z7Z8'] : False\n",
+      "6 :: 14296: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 14307: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 14308: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z5Z6Z7'] : False\n",
+      "6 :: 16730: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3X4X6', 'Z0Z2Z4Z6X7X8', 'X0Z1Y2Z5Z7Z8'] : False\n",
+      "6 :: 16871: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y1Z2X4Z5', 'Z0Z1X4X5X6X7', 'Y0Z1Y4Z6Z7Z8'] : False\n",
+      "6 :: 17487: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Z0Z1Z2Z3Z5X8', 'Z1Y3Z4Z6Z7Z8'] : False\n",
+      "6 :: 17503: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Y3Z5X8', 'Z0Z1X4X5X6X7', 'X1X2Z4Z6Z7Z8'] : False\n",
+      "6 :: 17505: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Z1Z5X8', 'Z0Z1X4X5X6X7', 'Y1Z3Y4Z6Z7Z8'] : False\n",
+      "6 :: 17506: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3Y4Z6', 'Z1Z2Z4Z5X7X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 17516: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 17519: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Y3Z4X5Z7Z8'] : False\n",
+      "6 :: 17527: [[9,4, 2]] : 2 :['X0X1X2X3', 'X3Y4Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Z4Z5Z6'] : False\n",
+      "6 :: 17528: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0X2Z3Y4X5Z7', 'Y1Y2Z4X5Z6Z8'] : False\n",
+      "6 :: 17529: [[9,4, 2]] : 2 :['X0X1X2X3', 'X1Y6Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8'] : False\n",
+      "6 :: 17530: [[9,4, 2]] : 2 :['X0X1X2X3', 'X2Y4Z7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 17736: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4Z6Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8'] : False\n",
+      "6 :: 17758: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Y3X4X5Z7Z8'] : False\n",
+      "6 :: 17762: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3Y4X5', 'X3Y4Y5Z6Z7Z8'] : False\n",
+      "6 :: 17780: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Y0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 17783: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3Z5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8'] : False\n",
+      "6 :: 17833: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0X1Y2Y4', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y1Z3Z6Z7Z8'] : False\n",
+      "6 :: 17836: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X2X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Y0X1Z3X4Z6Z7'] : False\n",
+      "6 :: 17841: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X2Z8', 'X0X1X4X5X6X7', 'X0Z4Z5Z6Z7X8', 'X0Z2Z3Y4Z5X6'] : False\n",
+      "6 :: 17909: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X2X4', 'X0X1X4X5X6X7', 'X0Z2Z3Z5Z6X8', 'Z2Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 17927: [[9,4, 2]] : 32 :['X0X1X2X3', 'X4X5X6X7', 'X5Z6Z7Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 18382: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X4X5X6', 'X3X6Z7Z8', 'Z1Z2Z4Z5X7X8', 'Z0Z1Y2Z3X5Z6'] : False\n",
+      "6 :: 18391: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0X1Y2Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 19611: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X4X5Z7Z8'] : False\n",
+      "6 :: 19624: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Z1Z2Y4Z7Z8'] : False\n",
+      "6 :: 19634: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4Z6Z8', 'Y0Y1Z2Z3Y4Z7'] : False\n",
+      "6 :: 19637: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Z0Y1Z2Z3Z5Z8', 'Z1X2Z3X4Z6Z7'] : False\n",
+      "6 :: 19638: [[9,4, 2]] : 4 :['X0X1X2X3', 'X5Z6Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 21819: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2X4X6Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Y6Z7'] : False\n",
+      "6 :: 21835: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0X1Z2Z4X6X7', 'Y1Z3Z5Y6X7X8', 'X0Y1Y2Z4Z7Z8'] : False\n",
+      "6 :: 21839: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0X1Z2Z4X6X7', 'X0Z1Z3Y4Z6X8', 'Z0Y1Z4Z5Z7Z8'] : False\n",
+      "6 :: 21844: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0X1Z2Z4X6X7', 'X0Z1Z3Y4Z6X8', 'Z1Z3Z5X6Y7Z8'] : False\n",
+      "6 :: 21847: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1X2Z4X5Z7', 'Y0Y1Z2Z3Z6Z8'] : False\n",
+      "6 :: 21885: [[9,4, 2]] : 4 :['X0X1X2X3', 'X2Y6Y7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Z2Z3X4X5Z7'] : False\n",
+      "6 :: 21887: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Y1Z3Z4Z6Z7X8', 'Z0Z2Y4Z5Z6Z8'] : False\n",
+      "6 :: 21911: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'Z3Y5Z6Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 21948: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X1Y2Z3Z4Z6Z7'] : False\n",
+      "6 :: 21974: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'X0Y2Z3Z4Z5Z6', 'Z0Z1X2Z5Z7Z8'] : False\n",
+      "6 :: 21982: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z2Y4Z5', 'Z1Y2X4X8', 'Y0X2Z3Z6Z7Y8'] : False\n",
+      "6 :: 22523: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'X1X2Z4X5', 'Z0Z1X4X5X6X7', 'Y0Y2Y5Z6Z7Z8'] : False\n",
+      "6 :: 22558: [[9,4, 2]] : 2 :['X0X1X2X3', 'X2Y5Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 22565: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0X1Z2Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 22567: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Y1Z3Z4Z5Z6X8', 'Y0Z1Y2Z3Z7Z8'] : False\n",
+      "6 :: 22578: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'X0Z4X5Z8', 'Z0Z1X4X5X6X7', 'Z0X1Z2Y5Z6Z7'] : False\n",
+      "6 :: 22579: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y1Y3Z4X5', 'Z0Z1X4X5X6X7', 'X1X2Y5Z6Z7Y8'] : False\n",
+      "6 :: 22581: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z3Z4Z6', 'Z1Z2Z4Z5X7X8', 'Z0X1Z2Y4Z7Z8'] : False\n",
+      "6 :: 22599: [[9,4, 2]] : 2 :['X0Z2Y3Z6', 'X0Y1Z5Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 22602: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'X0Z1Z3Z4Z6X7', 'Y0X1Z2Y4Y7Z8'] : False\n",
+      "6 :: 22603: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Z0Z1X2Y4Z7X8', 'X0Z1Z3Z5Z6Z8'] : False\n",
+      "6 :: 22627: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0X1Z3X4Z6X7', 'Z1X2Z3X4Z7Z8'] : False\n",
+      "6 :: 22633: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'X0Y3Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 22671: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Y3Y4X5Z7Z8'] : False\n",
+      "6 :: 22674: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X1Z2Z3X5Z7Z8'] : False\n",
+      "6 :: 22682: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z2Z5Z6', 'Z0Z1X2Z4Z5X8', 'Z1Z3X4Z5Z7Z8'] : False\n",
+      "6 :: 22884: [[9,4, 2]] : 12 :['X0X1X2X3', 'X0X1X4X5', 'X0X2X4X6X7X8', 'Z0Z2Z4Z6Z7Z8', 'X0Z1Z3Z5Y7Z8'] : False\n",
+      "6 :: 22886: [[9,4, 2]] : 6 :['X0X1X2X3', 'X0X1X4X5', 'X0Y1Z2Z4Z6X7', 'Y0X1Z3Y4Y6X8', 'Z0X1Y2Z5Y7Z8'] : False\n",
+      "6 :: 22952: [[9,4, 2]] : 2 :['X0X1X2X3', 'X2Z4Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 23318: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Y2Z3Z4Z7Z8'] : False\n",
+      "6 :: 23367: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Y3Z4X5Z7Z8'] : False\n",
+      "6 :: 24784: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1X2Z3Z5Z6'] : False\n",
+      "6 :: 24785: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1Y4Y5X6Z8'] : False\n",
+      "6 :: 24791: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1X2X3Y6Z8'] : False\n",
+      "6 :: 25841: [[9,4, 2]] : 48 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1Y2Z3', 'Z0Z1X4X5X6X7', 'X1X2Z5Z6Z7Z8'] : False\n",
+      "6 :: 25844: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X6X7X8', 'Z0Z1Z2Z3X4Z6', 'X0Y4Z5Z6Z7Z8'] : False\n",
+      "6 :: 25848: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X6X7X8', 'Z0X1Z2Z4Z6Z7', 'X0Y1Z3Z5Z6Z8'] : False\n",
+      "6 :: 25909: [[9,4, 2]] : 24 :['X0X1X2X3', 'X0X1Z5Z6', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 25910: [[9,4, 2]] : 48 :['X0X1X2X3', 'X0X1X4X5', 'Z2Z3Z4Z5Z6X7', 'Z0Z1Y4Z5Y6X8', 'Y0Z1Y2Z3Y7Z8'] : False\n",
+      "6 :: 25911: [[9,4, 2]] : 48 :['X0X1X2X3', 'X0X1X4X5', 'X0X2X4X6X7X8', 'Z0Z1Z2Z3Z6Z7', 'Z0Z1Z4Z5Z6Z8'] : False\n",
+      "6 :: 25953: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X2X4', 'X0Z1Z3Z4Z5X6', 'Z0Z2Z4Y5X7X8', 'X1Z2Z3Y6Z7Z8'] : False\n",
+      "6 :: 25954: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X2X8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'X1Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 25984: [[9,4, 2]] : 4 :['X0X1X2X3', 'X2Y5Z7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 25991: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'Z4Z5Z6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 25992: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X1Z4Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 26000: [[9,4, 2]] : 2 :['X0X1X2X3', 'X3X5Z7Z8', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'X0Z1Z3Z4Z5Z6'] : False\n",
+      "6 :: 26001: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Y1Z2Z3Z4Z7', 'Z1X2Z3Z5Z6Z8'] : False\n",
+      "6 :: 26003: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'X0Y1Y2Z4Z5Z7', 'Y0X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 26065: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'Y3Y5Y7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 26067: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'Y1Z4Y7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8'] : False\n",
+      "6 :: 26507: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0Z1Y2Z3Z6Z7'] : False\n",
+      "6 :: 26539: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Y6Z7X8', 'X0X1Y4Z5X6Z8'] : False\n",
+      "6 :: 26541: [[9,4, 2]] : 2 :['X0Z3Z4X6', 'Y0Z1Y6Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 26586: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z1Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 26650: [[9,4, 2]] : 12 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Y1Z4Z8', 'Z0Z1X4X5X6X7', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 26947: [[9,4, 2]] : 36 :['X0X1X2X3', 'Z2Z3X4X8', 'X0Y2Z3Z4', 'Z0Z1X4X5X6X7', 'Y0Y3Z5Z6Z7Z8'] : False\n",
+      "6 :: 26953: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'Y2X4Y5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 26958: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X4X6X7', 'X0Z1Z2Z4Z7Z8', 'Z0Z3Z5Z6Z7Y8'] : False\n",
+      "6 :: 27097: [[9,4, 2]] : 12 :['X0X1X2X3', 'Z0X1Y2Y4', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Z2Z6Z7Z8'] : False\n",
+      "6 :: 27098: [[9,4, 2]] : 12 :['X0X1X2X3', 'Z0Z1X2X4', 'Y0Y1X2Y5Z6X7', 'X1Y2Z3Z5Y6X8', 'Z0Y2Z4X5Y7Z8'] : False\n",
+      "6 :: 27349: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Z0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 27352: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z4Z5', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Y1Z2Z6Z7Z8'] : False\n",
+      "6 :: 28402: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0X1Z2Z4X6X7', 'Y0Z1Z2Z3X6X8', 'Y1Y2Z5Z6Z7Z8'] : False\n",
+      "6 :: 28409: [[9,4, 2]] : 144 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Y2Z3Z4Z5', 'X2Y4Z5X6X7X8', 'X0Z2Z3Z6Z7Z8'] : False\n",
+      "6 :: 28416: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z1Y3X4Y8', 'Z0Z1X2Z4Z5X8', 'Y1Z2Z4Z5Z6Z7'] : False\n",
+      "6 :: 28445: [[9,4, 2]] : 8 :['X0X1X2X3', 'X1Z6Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 28447: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z1Z4Z5Z6X7', 'Z0Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 28450: [[9,4, 2]] : 2 :['X0X1X2X3', 'X1Y5Y7Z8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8'] : False\n",
+      "6 :: 28451: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Z0Z1Z4X5Z7Z8'] : False\n",
+      "6 :: 28452: [[9,4, 2]] : 2 :['X0X1X2X3', 'X2Y6Y7Z8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6'] : False\n",
+      "6 :: 28454: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0Y3Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 28599: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2Y6X8', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 28648: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'X1Y2Z3Z4Y5Z6', 'Y0X2Z3X5Z7Z8'] : False\n",
+      "6 :: 28658: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Y1Y2Z3Z4X5', 'Y1Y3Y5Z6Z7Z8'] : False\n",
+      "6 :: 28661: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z5X6Z8', 'Z1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 28662: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2Y4Z5X6', 'Y1X2Z3Y6Z7Y8'] : False\n",
+      "6 :: 28699: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X5Z6Z7', 'Z0X1Z3X4X5Z8'] : False\n",
+      "6 :: 28798: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0Z2Z3X6', 'Z0Z1X2Z4Z5X8', 'Z0Z1X4Y6Z7Z8'] : False\n",
+      "6 :: 29177: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1X2X6X7X8', 'Z0Z1Z4Z5Z6Z7', 'X0Z2Z3X4Z6Z8'] : False\n",
+      "6 :: 29275: [[9,4, 2]] : 32 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Y1Z2Z3X4X5', 'X0X1X5Z6Z7Z8'] : False\n",
+      "6 :: 30076: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 30082: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y2Z3X4X8', 'Z0Z1X2Z4Z5X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 30095: [[9,4, 2]] : 1 :['X0X1X2X3', 'X2Z5Y6Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 30096: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'X0Z1Z3Z4Z5Z7', 'Z0Y1Y2Z3Z6Z8'] : False\n",
+      "6 :: 30097: [[9,4, 2]] : 1 :['X0X1X2X3', 'X2Y5Z7Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8'] : False\n",
+      "6 :: 30132: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z1Z4Z5Z6X7', 'Z0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 30136: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Z6X7', 'Z1Y3X4X5Z7Z8'] : False\n",
+      "6 :: 30188: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z1Z2Z5Z7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8'] : False\n",
+      "6 :: 30197: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'Z1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 30198: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'X1X4Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 30777: [[9,4, 2]] : 48 :['X0X1X2X3', 'Z0Z1X2X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'X0X1Y4Z5Z6Z7'] : False\n",
+      "6 :: 30836: [[9,4, 2]] : 1 :['X0X1X2X3', 'X3Z5Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 30838: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X5Z7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6'] : False\n",
+      "6 :: 31091: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'Z0Z3Z4Z5Z6X7', 'Y0Z1X2Z5Z7Z8'] : False\n",
+      "6 :: 31439: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2X4Y5Z6', 'X0Y1Z3Y5Z7Z8'] : False\n",
+      "6 :: 31440: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Y2X4Z7', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y1Y2Y4Z6Z8'] : False\n",
+      "6 :: 31538: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Y1Y2Z3Z7Z8'] : False\n",
+      "6 :: 31540: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y1Y2Z7Z8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4Z5Z6'] : False\n",
+      "6 :: 31541: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X4Y6Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0Y2Z3Z4Z5X6'] : False\n",
+      "6 :: 32284: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Y0Y1X2Y4X5X7', 'Y2Z3Y4Y6Y7Z8'] : False\n",
+      "6 :: 32305: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z5X8', 'Z0Z1Z2Z3X4X8', 'X0Z2Z3Z6Z7Z8'] : False\n",
+      "6 :: 32308: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0X2Z3X5Z6X7', 'Y1Y2X4X5Y7Z8'] : False\n",
+      "6 :: 32312: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'X0Y1Z3Z4Z5X7', 'Z0X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 32503: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z1Y2Y4X5Z7Z8'] : False\n",
+      "6 :: 32823: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Y1Z3Z4Z5Z6X8', 'X0Y1Z3Y5Z7Z8'] : False\n",
+      "6 :: 33049: [[9,4, 2]] : 2 :['Z0Z3Y6X7', 'Z1Z4X6Y7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 33080: [[9,4, 2]] : 2 :['Z0Y3X4Z6', 'Z1X3Y4Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 33082: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 33084: [[9,4, 2]] : 2 :['Z0Z1Z6Y7', 'X2Y6Z7X8', 'X0X1X2X3X4X5', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z3Z5X6Z8'] : False\n",
+      "6 :: 33198: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Y0X1Z2Z4Z6X8', 'Y1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 33666: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Z4Z7Z8', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 33668: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y2Z3X6Z8', 'Z0Z1X2Z4Z5X8', 'Y0Z1X2X4Y6Z7'] : False\n",
+      "6 :: 33684: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1X2Z4X5Z7', 'Z0X1Z3Z6X7Y8'] : False\n",
+      "6 :: 33687: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Z1Z2Z3Y4X5', 'X1Z4X5Z6Z7Z8'] : False\n",
+      "6 :: 33821: [[9,4, 2]] : 2 :['X0Z2Y3Z6', 'Z0X2Z5Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 35148: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z3Z4Z5Z6', 'Y0Z1X2Z6Z7Z8'] : False\n",
+      "6 :: 35149: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 35150: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Y2Z3Z6Z7Z8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 35151: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0X2X3Z6Z7X8', 'Z0Z1X2Y6X7Z8'] : False\n",
+      "6 :: 35165: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1Z2Z5Y6Z8'] : False\n",
+      "6 :: 35176: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z4Z6Z7X8', 'Z0Z1Z3Z5Z6Z8'] : False\n",
+      "6 :: 35177: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1X3Z4Z5X6'] : False\n",
+      "6 :: 36165: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y1Y3Y4Z5', 'Z1Z2Z4Z5X7X8', 'Y0Y1X2Z6Y7Y8'] : False\n",
+      "6 :: 36170: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X7X8', 'X0Z1Z3X4Z7Z8', 'Z0Y2Z4Z5Z6Z7'] : True\n",
+      "6 :: 36171: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X7X8', 'X0Z1Z3X4Z7Z8', 'Y0Y3Z4Z5Z6X7'] : False\n",
+      "6 :: 36229: [[9,4, 2]] : 2 :['Z1X4Y5X7', 'X0Z1Y3Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 36230: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6', 'Z1X2Z3X4Z7Z8'] : False\n",
+      "6 :: 36231: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 36349: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z7', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y2Y3Y4X5Z6Z8'] : False\n",
+      "6 :: 36350: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X1Y2Z3X6Y7Z8'] : False\n",
+      "6 :: 36369: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X1X4Z7Z8', 'Z1Z2Z4Z5X7X8', 'Z0X1Z3Z6Y7X8'] : False\n",
+      "6 :: 36657: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X2X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 36661: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1X2Z4X5Z7', 'Y2Z3Y4X5Z6Z8'] : False\n",
+      "6 :: 36982: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y5X6Z7Y8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 36991: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Y3Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6'] : False\n",
+      "6 :: 36997: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z1X2Z3Z4Z7Z8'] : False\n",
+      "6 :: 37008: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Y0Z1X2Z4Z7Z8'] : False\n",
+      "6 :: 37010: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X5Z6', 'Y1Z3Y4X5Z7Z8'] : False\n",
+      "6 :: 37088: [[9,4, 2]] : 6 :['X0X1X2X3', 'Z0X1Y2Y4', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z1Y3X4Z6Z7Z8'] : False\n",
+      "6 :: 37095: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'X2Y6Z7Z8', 'Z0Z1X4X5X6X7', 'Y0X1Y2Z4Z5X6'] : False\n",
+      "6 :: 37682: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Y5Z6X8', 'Z0Z1X2Z4Z5X8', 'Y2Z3Y4X5Z7Z8'] : False\n",
+      "6 :: 37687: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'Y3Z5Y7Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 37688: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2Z4Z5X7', 'Y1Z3Z4Z6Z7X8', 'Z0Z1Y2Z3Z4Z8'] : False\n",
+      "6 :: 37799: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3X4Y5', 'X3X4X5Z6Z7Z8'] : False\n",
+      "6 :: 37800: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Z3Y4X5Z7Z8'] : False\n",
+      "6 :: 38160: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X0Z1Z3Y4Z7Z8'] : False\n",
+      "6 :: 38389: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X4X5X6', 'X0Z1Z2X4X7X8', 'X0Z1Z3X5Z7Z8', 'Y0Y3Z4Z5Z6X7'] : False\n",
+      "6 :: 38533: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'X0Z1Z2Z5', 'Z0Z1X4X5X6X7', 'Z0Y1Z4Z6Z7Z8'] : False\n",
+      "6 :: 38647: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z2Z4Z5', 'Z0X1Y2X8', 'Z1Y3X4Z6Z7Y8'] : False\n",
+      "6 :: 38651: [[9,4, 2]] : 32 :['X0X1X2X3', 'X4X5X6X7', 'Z4Z5Y6Z7', 'Z0Z1X2Z4Z5X8', 'X0Y2Z3X4X6Y8'] : False\n",
+      "6 :: 38675: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4Z6Z8', 'Y0Y1Z2Z3Y5Z7'] : False\n",
+      "6 :: 38676: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3Z5Z8', 'Z1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 38700: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z3Z6X7', 'Z1Z2Z4Z5X7X8', 'X0Y1Z2X4Y7Z8'] : False\n",
+      "6 :: 38759: [[9,4, 2]] : 4 :['X0Y1Z2X3', 'Z1Y2X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0X1Z5Z6Z7Z8'] : False\n",
+      "6 :: 38804: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Y4X6Y7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 39007: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0X1Z2Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z1Z3Y4Z6Z7'] : False\n",
+      "6 :: 39099: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z1Z2Y4X5Z6', 'Z0Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 39101: [[9,4, 2]] : 2 :['X0Z2Y3Z6', 'Y1X4Y5Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 39352: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'Y0X3Z5Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7'] : False\n",
+      "6 :: 39478: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z5Z6', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Z1Z2X4Z5Z7Z8'] : False\n",
+      "6 :: 39895: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Z0X1Y2Z4X6X8', 'X0X2Z5Z6Z7Z8'] : False\n",
+      "6 :: 39912: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z2Y3Z4Z5Z6Z7'] : False\n",
+      "6 :: 39925: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y1X2Z3Z4Z5Z6', 'Y0Z1Y2Z3Z7Z8'] : False\n",
+      "6 :: 39930: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X6X8', 'Y1Y2Z5Z6Z7Z8'] : False\n",
+      "6 :: 39932: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y4Z5Y6Z7', 'Z2Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 39937: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3Z4Z5', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y2Z3X5Y6Y7Z8'] : False\n",
+      "6 :: 39938: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'X0Z1Z2Z5Z6Z7'] : False\n",
+      "6 :: 39939: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 39992: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0Z4X5Z6', 'Z0Z1X2Z4Z5X8', 'Y2Z3X4Y5Z7Y8'] : False\n",
+      "6 :: 40143: [[9,4, 2]] : 2 :['X0Z2Z5Z7', 'Y3Z4Y6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6'] : False\n",
+      "6 :: 40149: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Y2Z3Y4Y5', 'X2Z4Y5Z6Z7Z8'] : False\n",
+      "6 :: 40334: [[9,4, 2]] : 16 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y1Y2Z5Z6Z7Z8'] : False\n",
+      "6 :: 40338: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Z4Z7Z8'] : False\n",
+      "6 :: 40422: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Y1Z3Z4Z5Z6X8', 'Y0X1Z3Z5Z7Z8'] : False\n",
+      "6 :: 40595: [[9,4, 2]] : 16 :['X0X1X2X3', 'Z0Z1X2X4', 'Y5Y6Z7Z8', 'X0Y1Z2Z4X5X6', 'Z0X1Z3Z4X7X8'] : False\n",
+      "6 :: 40603: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'X1Y4Z7Z8', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8'] : False\n",
+      "6 :: 40627: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Y1X2Z3Z4Z7Z8'] : False\n",
+      "6 :: 40913: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'X1Z2Z3Y4Z7Z8'] : False\n",
+      "6 :: 41279: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5X6', 'X0Y1Y2Y6Z7Z8'] : False\n",
+      "6 :: 41331: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z2Z3X4X8', 'Y2Z3X5Z8', 'Z0Z1X4X5X6X7', 'Z0Y3Z4Y5Z6Z7'] : False\n",
+      "6 :: 41338: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z2Y3Z4Z5', 'Z0Z1X4X5X6X7', 'Z0X2Z3Z6Z7Z8'] : False\n",
+      "6 :: 41869: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Y3Z4Y6Z8'] : False\n",
+      "6 :: 41888: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Z2Z3Z6Z8'] : False\n",
+      "6 :: 41889: [[9,4, 2]] : 48 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1Y2Z3Z4Z5'] : False\n",
+      "6 :: 42270: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'Z0Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 42274: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'X0Z2Z3Y4Z6Z7'] : False\n",
+      "6 :: 42282: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'X0Z2Z3Y5Z6Z7', 'Z0X1Z3Z4X5Z8'] : False\n",
+      "6 :: 42286: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'Z0Y1X2Z4Z6Z7'] : False\n",
+      "6 :: 42288: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z5X6Y7Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8'] : False\n",
+      "6 :: 42347: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z2Z3X4X8', 'Y2Y3Y5Z6', 'Z0Z1X4X5X6X7', 'Z0Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 42546: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'X0Y1Y2Z4Z5X6', 'Z0Z1X2Y6Z7Z8'] : False\n",
+      "6 :: 42881: [[9,4, 2]] : 8 :['X0X1X2X3', 'X5Y6Y7Y8', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8'] : False\n",
+      "6 :: 42897: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2Z4X5', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z3X4Z6Z7Z8'] : False\n",
+      "6 :: 42900: [[9,4, 2]] : 8 :['X0X1X2X3', 'Y0Y1Z6Z7', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0Z2Z4Z5X6Z8'] : False\n",
+      "6 :: 42903: [[9,4, 2]] : 1 :['Z0Z3X4Y6', 'Z1Y4X6Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 42906: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'X0Y1Z2X4Z5Z6', 'Z0X1Y2Y4Z7Z8'] : False\n",
+      "6 :: 42908: [[9,4, 2]] : 2 :['X0X2Y6Z8', 'X1Z6X7Y8', 'X0X1X2X3X4X5', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 42912: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z2Z4Y5Z6Z7', 'X0Z1Z3Y4Y5Z8'] : False\n",
+      "6 :: 42913: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4Y6Y7Z8', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3Y4Z6'] : False\n",
+      "6 :: 43008: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2X4Z6', 'X2Z4Z7Z8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 43037: [[9,4, 2]] : 12 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0X1Y2Z4', 'Z0Z1X4X5X6X7', 'X0X2Z5Z6Z7Z8'] : False\n",
+      "6 :: 43038: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'Y2Y3Y4Z5', 'Z0Z1X4X5X6X7', 'Y0Z1Y4Z6Z7Z8'] : False\n",
+      "6 :: 43597: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'Z1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 43599: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z5X6Z8', 'Z1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 43600: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'X0Z1Y2Z4Y5Z6', 'Z0Y1X2X5Z7Z8'] : False\n",
+      "6 :: 43651: [[9,4, 2]] : 1 :['X0X1X2X3', 'Y0Z2X4Z6', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 43756: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Y1X2Z4Y5Z6', 'X0Z1Y2X5Z7Z8'] : False\n",
+      "6 :: 43758: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z1Z2Y5Z6', 'Z0Z1Z2Z3X4X8', 'Y0Y2Z4X5Z7Z8'] : False\n",
+      "6 :: 43785: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z8', 'X1Z2Z3Z5Z6Z7'] : False\n",
+      "6 :: 43786: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Z2Y3Y4Y5Z7Z8'] : False\n",
+      "6 :: 43796: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y4Y5Y6Y7', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8'] : False\n",
+      "6 :: 43797: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Y2Z3Y5Z6', 'X0X2X4Y5Z7Z8'] : False\n",
+      "6 :: 43800: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z1Y2Z3Z4X8', 'Y1Y2Z5Z6Z7Z8'] : False\n",
+      "6 :: 43808: [[9,4, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Y1Z2Z3Z5Z6', 'X1Y2Z3Z4Z7Z8'] : False\n",
+      "6 :: 43810: [[9,4, 2]] : 1 :['Z4Z5Z6Z8', 'Z0Y5Y7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 43811: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Z0Y1Z4Z5Z7Z8'] : False\n",
+      "6 :: 43833: [[9,4, 2]] : 24 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X8', 'Z2Z3X4Z8', 'Y0Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 43843: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Z0Y2Z4Z5Z7Z8'] : False\n",
+      "6 :: 43886: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Y1X2X4Y5Z6', 'X1Y2Z3Y5Z7Z8'] : False\n",
+      "6 :: 43903: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5', 'Y0X1Z2X4X6X7', 'Z2Z3Z4Z5Z6X8', 'Z0Z1Y2Z3Z7Z8'] : False\n",
+      "6 :: 43909: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z2Z4Z5Y6Z7', 'Z1Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 43913: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Z4Z5X6', 'X0X1X4Y6Z7Y8'] : False\n",
+      "6 :: 43916: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Z0Z2Z4Z5X6Z8', 'X0Z2Z3Z5Z6Z7'] : False\n",
+      "6 :: 44108: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0Y1X4X8', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6Z8', 'X0Y2Z3Z4Z6Z7'] : False\n",
+      "6 :: 44111: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Y1Z2Z3Z5Z6', 'X0X1X4Z5Z7Z8'] : False\n",
+      "6 :: 44171: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4Y6Y7Y8', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Y4Z5Z6'] : False\n",
+      "6 :: 44173: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X5', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8', 'Y0Z1Y4Z5Z7Z8'] : False\n",
+      "6 :: 44178: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3X4Y5Z6', 'Y0Y1X2Y5Z7Z8'] : False\n",
+      "6 :: 44348: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0X1Z2Z6', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 44436: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0X1Z2X4', 'Z0Z1X4X5X6X7', 'Y1Z3Z4Z5Z6X8', 'Y1X2Z3X5Z7Z8'] : False\n",
+      "6 :: 44693: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'X2Z4Z5X6', 'Z0Z1X4X5X6X7', 'Y0X1Y2Y6Z7Z8'] : False\n",
+      "6 :: 44699: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1Z4X7', 'Z0X1Z2Z5Z7X8', 'Z0Z1Z2Z3Z6Z8'] : False\n",
+      "6 :: 44731: [[9,4, 2]] : 6 :['X0X1X2X3', 'Z0Z1X2X4', 'Z0X1Z2Y5Z6X7', 'X0Y1Z3Z5Y6X8', 'Y0Z1Z4X5Y7Z8'] : False\n",
+      "6 :: 45348: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y1Y4X7', 'Z1Z2Z4Z5X7X8', 'Z2Z3Z4Z6Y7Z8'] : False\n",
+      "6 :: 45376: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Z0Z1Y4Z7', 'Z1Z2Z4Z5X7X8', 'X1Z2Z3Z4Z6Z8'] : False\n",
+      "6 :: 46085: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Z3Z4X6Z8'] : False\n",
+      "6 :: 46086: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0X2X3Z6Z7X8', 'Z0Z1Y2Z4Y6Z8'] : False\n",
+      "6 :: 46087: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z3Z4Z5Z6', 'Z0Z1Y2Z4Z7Z8'] : False\n",
+      "6 :: 46088: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3Z6Z7X8', 'Z0Z1X2X4X6Z8'] : False\n",
+      "6 :: 46101: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 46102: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1Z4Z5Z6X7'] : False\n",
+      "6 :: 46109: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0X2X3Z6Z7X8', 'Z0Z1Y2Z3Z6Z8'] : False\n",
+      "6 :: 46273: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X2Z8', 'X0X1X4X5X6X7', 'X0Z4Z5Z6Z7X8', 'Y0Z1Z2Z3Y4Z5'] : False\n",
+      "6 :: 46297: [[9,4, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Z4X5Z7Y8', 'Z0Z1X2Z4Z5X8', 'X0Y2Z3X4Y5Z6'] : True\n",
+      "6 :: 46301: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y4X5Z6X8', 'Z0Z1X2Z4Z5X8', 'X0Y2Z3Y5Z7Z8'] : False\n",
+      "6 :: 46505: [[9,4, 2]] : 6 :['Z0Z1Z2Z3', 'Z0Z4Z5Z6', 'Z1Z4Z7Z8', 'X1X2X4X5X7X8', 'X0X1X2X3X6X7'] : False\n",
+      "6 :: 47265: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z2Z4Z5', 'Z0Z1Z2Z3X4X8', 'Y0X1Y2Z6Z7Z8'] : False\n",
+      "6 :: 47378: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y2Z4X5', 'Z0Z1X4X5X6X7', 'X1X2Y5Z6Z7Z8'] : False\n",
+      "6 :: 47699: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z3Y4Z7', 'Z1Z2Z4Z5X7X8', 'X0Y1Z2Z4Z6Z8'] : False\n",
+      "6 :: 47711: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'X0Z1Z2X6', 'Z0Z1X2Z4Z5X8', 'Z0Z3X4Y6Z7Z8'] : False\n",
+      "6 :: 48422: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z2Y4Z6', 'Z0Z1X2Z4Z5X8', 'X0Z1Z3Z4Z7Z8'] : False\n",
+      "6 :: 48610: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z1Y3X6X7', 'Z1Z2Z4Z5X7X8', 'Z0Y2X5Z6Y7Z8'] : False\n",
+      "6 :: 48759: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y4Y5Z6X7'] : False\n",
+      "6 :: 48760: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3Z6Z7X8', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 48761: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8', 'Z0Z1Y2Z4Z6Z8'] : False\n",
+      "6 :: 48762: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0X2X3Z6Z7X8', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 48763: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z2Z4Z6Z7Z8', 'Z0Z1Y3Z5Z6X7'] : False\n",
+      "6 :: 48764: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3Z6Z7X8', 'Z0Z1Z2Y4Z6Z8'] : False\n",
+      "6 :: 48837: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z2Y5Z6', 'Z0Z1X4X5X6X7', 'X0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 48839: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X8', 'Y1Z2Z5X6', 'Z0Z1X4X5X6X7', 'Y0Z1Z4Y6Z7Z8'] : False\n",
+      "6 :: 48840: [[9,4, 2]] : 2 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z2Z3X6X7', 'Z0Z1X4X5X6X7', 'X1X4Y5Z6Z7Z8'] : False\n",
+      "6 :: 48843: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X0Y1Z2X4', 'Z0Z1X2Z4Z5X8', 'Z0X1Z3Z6Z7Z8'] : False\n",
+      "6 :: 48887: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z2Z3X4X8', 'X2X6X7Y8', 'Z0Z1X4X5X6X7', 'Z1Y3Z4Y5Z6Z7'] : False\n",
+      "6 :: 48939: [[9,4, 2]] : 4 :['X0X1X2X3', 'Y0Z2Z4X8', 'X3Z5Z6X8', 'Z0Z1X4X5X6X7', 'Z1X2Z3Z4Z7Z8'] : False\n",
+      "6 :: 49035: [[9,4, 2]] : 6 :['X0X1X2X3', 'Z2Z3X4X8', 'Z1Y2Z4Z5', 'Z0Z1X4X5X6X7', 'Y0Y1X2Z6Z7Z8'] : False\n",
+      "6 :: 49076: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z2Z3X4X5', 'X2Z4Z7Z8', 'Z0Z1X4X5X6X7', 'Y0Z2Z4Z5Z6X8'] : False\n",
+      "6 :: 49090: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y3Z7Z8', 'Z0Z1X4X5X6X7', 'X0Y2Z3Z4Z5Z6'] : False\n",
+      "6 :: 49264: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z2X4X5', 'Z0Z1X2Z4Z5X8', 'Z1Z3X5Z6Z7Z8'] : False\n",
+      "6 :: 49265: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0X1Z5Z6', 'Z0Z1X2Z4Z5X8', 'X1Y2Z3Y4Z7Y8'] : False\n",
+      "6 :: 49275: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y2Z3X4X6', 'Z0Z1X2Z4Z5X8', 'Y0Z1X2Y6Z7Z8'] : False\n",
+      "6 :: 49276: [[9,4, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'X0X1Z6Z7', 'Z0Z1X2Z4Z5X8', 'Z0Y1Z2Z3X4Z8'] : False\n",
+      "6 :: 49290: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z2Z4X6', 'Z1Z3Y4Y6X7X8', 'Z0Y1Z4Z5Z7Z8'] : False\n",
+      "6 :: 49293: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z2X4X6', 'Z0Z1X2Z4Z5X8', 'X0Z1Z3Y6Z7Z8'] : False\n",
+      "6 :: 49973: [[9,4, 2]] : 12 :['X0X1X2X3', 'Y0Z2Z4X8', 'X0Z1Z3Y4', 'Z0Z1X4X5X6X7', 'X1X4Z5Z6Z7Y8'] : False\n",
+      "6 :: 50284: [[9,4, 2]] : 6 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Y2Y5Z6', 'Z0Z1X4X5X6X7', 'Y1X2Z3X5Z7Z8'] : False\n",
+      "6 :: 50319: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'X0Z2Z3X8', 'Z0Z1X2Z4Z5X8', 'X2Y4Z5Z6Z7Z8'] : True\n",
+      "6 :: 50320: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X4X5X6', 'Z0Y1X2Z4', 'Z1Z2Z4Z5X7X8', 'Y2Z3Y4Z6Z7Z8'] : False\n",
+      "6 :: 50429: [[9,4, 2]] : 12 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Y2Z5Z8', 'Z0Z1X4X5X6X7', 'Z1Y3Z4Z5Z6Z7'] : False\n",
+      "6 :: 50642: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3Z6Z7X8', 'Z0Z1Y4Z5Z6Z8'] : False\n",
+      "6 :: 50650: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Z4Y6'] : False\n",
+      "6 :: 50723: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Z0X1Z3Z8', 'Z0Z1X2Z4Z5X8', 'X0Y1Z2X4Z6Z7'] : False\n",
+      "6 :: 50836: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'X2Y4Z5Z8', 'Z0Z1X4Y8', 'X0Z2Z3Z6Z7X8'] : False\n",
+      "6 :: 51023: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z4Z6', 'X2Z5Z6X8', 'X0Z2Z3Y4Z7Z8'] : True\n",
+      "6 :: 51042: [[9,4, 2]] : 16 :['X0X1X2X3', 'Z2Z3X4X8', 'X1Z2Z3Z5', 'Z0Z1X4X5X6X7', 'Y0Z3Z4Z6Z7Z8'] : False\n",
+      "6 :: 51838: [[9,4, 2]] : 96 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Y5Y6Z7Z8', 'X0Y1Z2Z4X5X6', 'Z0X1Y2Y4X7X8'] : False\n",
+      "6 :: 51905: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Y3X5Z8', 'Z0Z1X4X5X6X7', 'Y2Z3Z4Y5Z6Z7'] : False\n",
+      "6 :: 51915: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Z2Z3Y5Z6', 'Z0Z1X2Z4Z5X8', 'Y0Z1X4Y5Z7Z8'] : False\n",
+      "6 :: 51930: [[9,4, 2]] : 48 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Y1X2Y4Z5X6', 'X0X1Z4Y5X7X8', 'Y0Y2X4Y6Z7Z8'] : False\n",
+      "6 :: 52024: [[9,4, 2]] : 12 :['X0X1X2X3', 'Z0Z2Z4Z8', 'Z1Y2Z5Y8', 'X0X1X4X5X6X7', 'Y2Y3Y4Z5Z6Z7'] : False\n",
+      "6 :: 52257: [[9,4, 2]] : 36 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z1Z3X4Y8', 'Z0Z1X4X5X6X7', 'X0X1Y4Z5Z6Z7'] : False\n",
+      "6 :: 52859: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5Z6Z7', 'X0X2X3X6X7X8', 'Z0Z1Z2Z4Z6Z8'] : False\n",
+      "6 :: 52862: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2X3Y4Z6'] : False\n",
+      "6 :: 52877: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3Z6X7'] : False\n",
+      "6 :: 52881: [[9,4, 2]] : 48 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3Y4Z5'] : False\n",
+      "6 :: 53209: [[9,4, 2]] : 48 :['X0X1X2X3', 'Z2Z3X4X5', 'Y2Y3X6X7', 'X0Z4Z5Z6Z7X8', 'Z0Z1Y4Z5X6Z8'] : False\n",
+      "6 :: 53309: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z2Y4Y5', 'Z0Z1X2Z4Z5X8', 'Z1Y3X5Z6Z7Y8'] : False\n",
+      "6 :: 53331: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z2Z5Z6', 'Z0Z1Z2Z3X4X8', 'X0Z1Y2Z4Z7Z8'] : False\n",
+      "6 :: 53413: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Z1Z2Y5Z6', 'Z0Z1X2Z4Z5X8', 'Y0Z3X4Y5Z7Z8'] : False\n",
+      "6 :: 53453: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Y6Z7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3X4X6Z8'] : False\n",
+      "6 :: 53456: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z2Z4Z8', 'Z1Y2Z5Y8', 'X0X1X4X5X6X7', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 53477: [[9,4, 2]] : 2 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1Y5Z6', 'Z0Z1X2Z4Z5X8', 'Z2Z3X4Y5Z7Z8'] : False\n",
+      "6 :: 53479: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z2Z4Z8', 'Z1Y2Z5Y8', 'X0X1X4X5X6X7', 'Z0Y1Z2Z3Z6Z7'] : False\n",
+      "6 :: 53557: [[9,4, 2]] : 1 :['X0X1X2X3', 'X4X5X6X7', 'Y1Z2Z4Z6', 'Z0Z1X2Z4Z5X8', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 53620: [[9,4, 2]] : 6 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2Z5Z6X7', 'Y0Z2X3X4Z7Z8'] : False\n",
+      "6 :: 55061: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y0Z1Y2X3Z5Z8'] : False\n",
+      "6 :: 55542: [[9,4, 2]] : 24 :['X0X1X2X3', 'Z0Z1X2Z8', 'Y2Z3X4Y8', 'X0X1X4X5X6X7', 'X0Z4Z5Z6Z7X8'] : False\n",
+      "6 :: 55617: [[9,4, 2]] : 4 :['X0X1X2X3', 'X4X5X6X7', 'Y2Z3Y4Z5', 'X0Z6Z7Y8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 55680: [[9,4, 2]] : 12 :['X0X1X2X3', 'Z2Z3X4X8', 'Y0Z1X2Z4', 'Z0Z1X4X5X6X7', 'Y1Y2Z5Z6Z7Z8'] : False\n",
+      "6 :: 55902: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Y0Z1Z4X7', 'Y0Z2Z5X8', 'Y2Y3Y4Z6Y7Z8'] : True\n",
+      "6 :: 56123: [[9,4, 2]] : 288 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1Z2Z3', 'X4Z6Z7Z8', 'Z0Z1X2Z4Z5X8'] : False\n",
+      "6 :: 56124: [[9,4, 2]] : 32 :['X0X1X2X3', 'X4X5X6X7', 'Z0Y1Z2Z3', 'Z0Z1X2Z4Z5X8', 'X0X1X4Z6Z7Z8'] : False\n",
+      "6 :: 56386: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0Z2Z3Z6Z7X8', 'Z0Z1Y2Y4X6Z8'] : False\n",
+      "6 :: 56394: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Z4Z6X7'] : False\n",
+      "6 :: 57081: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Z0X2X3Z5X6Z8'] : False\n",
+      "6 :: 57114: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'X0Y1X2Z5Z7Z8'] : False\n",
+      "6 :: 57130: [[9,4, 2]] : 6 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y1Z3Z4Z5X6Z8'] : False\n",
+      "6 :: 57352: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 57651: [[9,4, 2]] : 8 :['X0X1X2X3', 'X4X5X6X7', 'Y2Z3Y4Z6', 'X0Z5Z7Y8', 'Z0Z1X2Z4Z5X8'] : True\n",
+      "6 :: 58199: [[9,4, 2]] : 6 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z0Z2Z3Z4Z6X7', 'X1Z2X3Z5Z7Z8', 'Y0Y1Z3Z5Z6Z7'] : False\n",
+      "6 :: 58412: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0Z1Y3Y5Z6Z8'] : False\n",
+      "6 :: 58654: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'X0Y1X3Z5X6Z8'] : False\n",
+      "6 :: 58790: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0X1Z2Z3Z4Z6', 'Y0Z1Y2Z5Z7Z8', 'Z0X1Z2Y3Z5X7'] : False\n",
+      "6 :: 59148: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Y1Z2Z3Z5Z6Z8'] : False\n",
+      "6 :: 59267: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2X3Z4X6'] : False\n",
+      "6 :: 59269: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y2Z3X4Z6'] : False\n",
+      "6 :: 59812: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'X1X2Z3Z5X6Z8'] : False\n",
+      "6 :: 59820: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'Y0Y3Z4Z5X6Z8'] : False\n",
+      "6 :: 59871: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0Y2Z3Z5Z6Z8'] : False\n",
+      "6 :: 60080: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Y4Z5Y6X8'] : False\n",
+      "6 :: 60382: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Y1Y2Y3Z5Z6Z8'] : False\n",
+      "6 :: 60383: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'Z0Y2Y3Z5X6Z8'] : False\n",
+      "6 :: 60419: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'X1Z2Y3Z4Z5Z8'] : False\n",
+      "6 :: 61048: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'X0Y1Z2Y3Z5Z8'] : False\n",
+      "6 :: 61863: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Z0Y1X2Z4Z5Z8'] : False\n",
+      "6 :: 61877: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0X1Y2X4Z6Z8'] : False\n",
+      "6 :: 64751: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6', 'Y0Z1Y2Z5X6Z8'] : False\n",
+      "6 :: 64771: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0Z2X3X4Z6Z8'] : False\n",
+      "6 :: 64916: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'Z1Z2Y4Y5X6Z8'] : False\n",
+      "6 :: 64963: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'X2Z3X4Z5Z6Z8'] : False\n",
+      "6 :: 65227: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0Z2Z3Y5Z6Z8'] : False\n",
+      "6 :: 71866: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'X0X1Z2Z5X6Z8'] : False\n",
+      "6 :: 71906: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'X1X2Z4Z5X6Z8'] : False\n",
+      "6 :: 71907: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'Z0Z2Z4Z5X6Z8'] : False\n",
+      "6 :: 71908: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'X1X3Z4Z5X6Z8'] : False\n",
+      "6 :: 72018: [[9,4, 2]] : 3 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z2X3X4Z6Z7', 'Y1Y2Y3Z5Z6Z8'] : False\n",
+      "6 :: 72027: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Y0Z2Y3Z5Z6Z8'] : False\n",
+      "6 :: 72121: [[9,4, 2]] : 6 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z1Z2Z3X4X6', 'X0Y1Z2Z4Z5Z6', 'X1Y2X3Z4Z7Z8'] : False\n",
+      "6 :: 72199: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Y0X1Z2Y3Z5X7', 'X0Z1Z3Z6Z7Z8'] : False\n",
+      "6 :: 72233: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Z0Z2X3Z4Z5Z8'] : False\n",
+      "6 :: 72342: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Z0Y1X3Z4Z5Z8'] : False\n",
+      "6 :: 72575: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'Z0Z1X2X3X6Z8'] : False\n",
+      "6 :: 72578: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'Z0Z1Z2Z4X6Z8'] : False\n",
+      "6 :: 72585: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'Z0Z1X2Y3Z4Z8'] : False\n",
+      "6 :: 72983: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Z0X1Y2X3Z6Z8'] : False\n",
+      "6 :: 73314: [[9,4, 2]] : 4 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z2X3X4Z6Z7', 'X0X1Y3Z5Z6Z8'] : False\n",
+      "6 :: 73459: [[9,4, 2]] : 8 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0X1Y3X4Z6X7', 'Z1Y2Y3Y5Y7Z8'] : False\n",
+      "6 :: 73529: [[9,4, 2]] : 6 :['X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'X0Y1Z2X4Z7Z8', 'Z0Z1Y3X4Z5Y6', 'X0Z2X3Y4X6X8'] : False\n",
+      "6 :: 73606: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'X0Y1Y2Z3Z5Z8'] : False\n",
+      "6 :: 76896: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'Z0Z1Y2Z4Z6Z8'] : False\n",
+      "6 :: 76903: [[9,4, 2]] : 144 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1Z2Y3Z4Z5'] : False\n",
+      "6 :: 76904: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1Y3Z5Z6X7'] : False\n",
+      "6 :: 76905: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6X7X8', 'Y2X3Z4Z6Z7Z8', 'Z0Z1X2Z4Z5X6'] : False\n",
+      "6 :: 76907: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8', 'Z0Z1X2Y6X7Z8'] : False\n",
+      "6 :: 80185: [[9,4, 2]] : 12 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0X1Z2Z3Z4Z6', 'Y0Z1Y2Z5Z7Z8', 'Z0Z1Y3X4Z5X7'] : False\n",
+      "6 :: 80209: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'Y2Y3Y4Y5X6Z8'] : False\n",
+      "6 :: 87416: [[9,4, 2]] : 12 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Y0Z1Z2X3Z5Z6', 'Z1Y2Y3Z5Z7Z8'] : False\n",
+      "6 :: 87450: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Z0X1Y3Z4Z5Z8'] : False\n",
+      "6 :: 87458: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'X0Y1Y2Z4Z5Z8'] : False\n",
+      "6 :: 87463: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Y0Z1Z2Z3X4X6', 'X0Y1X2Z4Z7Z8'] : False\n",
+      "6 :: 87464: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2Z5Z6X7', 'Y0Z2Z3Z5Z7Z8'] : False\n",
+      "6 :: 87470: [[9,4, 2]] : 12 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z0Z2Z3Z4Z6X7', 'X1Z2X3Z5Z7Z8', 'X0Y2Z3Y4Z5X6'] : False\n",
+      "6 :: 87498: [[9,4, 2]] : 4 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Y0Z1Z2X3Z5Z6', 'Z0Z2Y3Z5Z7Z8'] : False\n",
+      "6 :: 87518: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z1Z2X3Z5Z7', 'Y0Y2Y3Y5Z6Z8'] : False\n",
+      "6 :: 87519: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Z1Z3Z4Z5Z6Z8'] : False\n",
+      "6 :: 87542: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z2X3X4Z6Z7', 'Z0X1Y3Z4Z5Z8'] : False\n",
+      "6 :: 87898: [[9,4, 2]] : 24 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'X0Z2X3Z5Z6X7', 'Z0Z1X3X4Z7Z8'] : False\n",
+      "6 :: 88204: [[9,4, 2]] : 3 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'Y0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 88559: [[9,4, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'X0Z2Z3Y4X5Z8'] : True\n",
+      "6 :: 88562: [[9,4, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X2Z4Z5Z6Z7X8', 'Z2Z3Y4Z5X6Z8'] : False\n",
+      "6 :: 88591: [[9,4, 2]] : 96 :['X0X1', 'X2X3', 'X0Z4Z5Z6Z7Z8', 'Z0Z1Z2Z3Z4X5', 'Y2Z3Y4X6X7X8'] : True\n",
+      "6 :: 88642: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6', 'Y0Z1Y3Z5X6Z8'] : False\n",
+      "6 :: 88764: [[9,4, 2]] : 6 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z1Z2Z3X4X6', 'X0Y1Z2Z4Z5Z6', 'Z0X2X3Z4Z7Z8'] : False\n",
+      "6 :: 89524: [[9,4, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1X4X5X6X7', 'X0X2Z4Z5Z6X8', 'Z2Z3Y4X6Y7Z8'] : True\n",
+      "6 :: 89543: [[9,4, 2]] : 36 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z0Z2Z3Z4Z6X7', 'X1Z2X3Z5Z7Z8', 'Y0Z1Z2Y3Z4Z5'] : False\n",
+      "6 :: 89556: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Y0Z1Z2Z3X4X6', 'X0Z1Z2X3Z7Z8'] : False\n",
+      "6 :: 89572: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Z0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 89579: [[9,4, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0X1Z2Z3Z4Z6', 'Y0Z1Y2Z5Z7Z8', 'Y0Z2Y3X4Z5X7'] : False\n",
+      "6 :: 89597: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 89613: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X0Z2Z3X4X6X7', 'Y0Z1X2Y6Y7Y8'] : True\n",
+      "6 :: 89614: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Z4Z5Z6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Y4Z7Z8'] : False\n",
+      "6 :: 89655: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Y6X7X8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 89697: [[9,4, 2]] : 16 :['X0X1', 'Y0Z1X3Y8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8'] : False\n",
+      "6 :: 89700: [[9,4, 2]] : 32 :['X0X1', 'Z0Z1X2X3', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Z3X4Z6Z7Z8'] : False\n",
+      "6 :: 89702: [[9,4, 2]] : 8 :['X0X1', 'Z0Z1Z2Z4', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8'] : False\n",
+      "6 :: 89703: [[9,4, 2]] : 8 :['X0X1', 'Z0Z1X2Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7'] : False\n",
+      "6 :: 89727: [[9,4, 2]] : 8 :['X0X1', 'Z0Z1Y2Z6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8'] : False\n",
+      "6 :: 89732: [[9,4, 2]] : 48 :['X0X1', 'X2X3X4X5', 'X2X3Z6Z7', 'Z0Z1Z2Z4X6Z8', 'Y0Z1Z3Z5X7Y8'] : True\n",
+      "6 :: 89761: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'X0Z4Z5Y6Y7X8', 'Z0Z1Y4Z5Z6Z8'] : False\n",
+      "6 :: 89801: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'X0Z4Z5Z6X7X8', 'Z0Z1Y2Y4Z7Z8'] : False\n",
+      "6 :: 89868: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 89871: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X2Z6Z7Z8', 'Z0Z1Z2Z3X4X6', 'Y0Z1Y4Z5X7X8'] : True\n",
+      "6 :: 89875: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'X0Z4Z5Y6Y7X8', 'Z0Z1Y2Y4X6Z8'] : True\n",
+      "6 :: 89897: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'X0Z4Z5Y6Y7X8', 'Z0Z1Y2Z4Y6Z8'] : True\n",
+      "6 :: 89929: [[9,4, 2]] : 96 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y6X7Z8'] : True\n",
+      "6 :: 89930: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3Z6Z8'] : True\n",
+      "6 :: 89942: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'X0Z4Z5Y6Y7X8', 'Z0Z1Y2X3Z4Z8'] : False\n",
+      "6 :: 89943: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Y2Z3Z6X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X4X6Y7Z8'] : False\n",
+      "6 :: 90008: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z3Z6Z7', 'X0Z4Z5Y6Y7X8', 'Z0Z1X2X4X6Z8'] : True\n",
+      "6 :: 90024: [[9,4, 2]] : 24 :['X0X1', 'X2X3X4X5', 'Z0Z1Y2Y4', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : False\n",
+      "6 :: 90056: [[9,4, 2]] : 48 :['X0X1', 'X0Z6Z7Z8', 'Z0Z1Y2Y6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8'] : False\n",
+      "6 :: 90093: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z0Z1X2Y6', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : True\n",
+      "6 :: 90111: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z0Z1X6Z8', 'Z2Z3Z4Z5X6X7', 'Y2Z3X4Z6Z7X8'] : False\n",
+      "6 :: 90112: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Z0Z1Y6Z8', 'X0Z2Z3X4X6X7', 'X2Z4Z5Z6Z7X8'] : True\n",
+      "6 :: 90117: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'Y0Z1Y7Z8', 'Z2Z3X4X6X7X8', 'X0Z4Z5Z6Z7Z8'] : True\n",
+      "6 :: 90253: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Y2X3X4Z6'] : False\n",
+      "6 :: 90269: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Y2Z4Z6Z7'] : False\n",
+      "6 :: 90272: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Y2Z3Y6Z7'] : False\n",
+      "6 :: 90282: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Z2X3Z4X5'] : False\n",
+      "6 :: 90285: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1X4Z6Z7X8'] : False\n",
+      "6 :: 90286: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Z3X4Z6Z7Z8', 'Z0Z1X2Z3Z4X5'] : False\n",
+      "6 :: 90310: [[9,4, 2]] : 12 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Z3X4Z6Z7Z8', 'Z0Z1X2Y3Z4X6'] : False\n",
+      "6 :: 90319: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y2Y3Z4X5Z6', 'Z0Z1X2Z4Z7Z8'] : False\n",
+      "6 :: 90328: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1Y2X4Z6Z8'] : False\n",
+      "6 :: 90344: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1X4Y5Y6Y8'] : False\n",
+      "6 :: 90353: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1Z2Z4X5Z8'] : False\n",
+      "6 :: 90354: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1Y2X5Z6Z8'] : False\n",
+      "6 :: 90356: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1Z5Z6X7Y8'] : False\n",
+      "6 :: 90357: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1X2Y5Z6Z8'] : False\n",
+      "6 :: 90358: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Y2Z4Z5Z6'] : False\n",
+      "6 :: 90360: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Z2Y4Z5Z6'] : False\n",
+      "6 :: 90364: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1X2X4X5Z8'] : False\n",
+      "6 :: 90376: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 90377: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1X2Y4Y6Y8'] : False\n",
+      "6 :: 90381: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Y2Y3Z4X5Z6', 'Z0Z1Y2X6Y7Z8'] : False\n",
+      "6 :: 90386: [[9,4, 2]] : 4 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1Y2X3Y4Z8'] : False\n",
+      "6 :: 90438: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0X2Z6X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y3Z4Z7Z8'] : False\n",
+      "6 :: 90455: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z8'] : False\n",
+      "6 :: 90457: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Z2Z3X4X6X7X8', 'Y0Z1X2Y6Y7Y8'] : True\n",
+      "6 :: 90460: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z4Z5Z6', 'Z2Z3X4X6X7X8', 'Z0Z1Y2Y4Z7Z8'] : False\n",
+      "6 :: 90536: [[9,4, 2]] : 48 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1Z2Y3Z4Z8'] : False\n",
+      "6 :: 90541: [[9,4, 2]] : 16 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1X3Z4X5Z8'] : False\n",
+      "6 :: 90548: [[9,4, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 90567: [[9,4, 2]] : 48 :['X0X1', 'X0Z6Z7Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Y3Y4Y6'] : False\n",
+      "6 :: 90570: [[9,4, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1Z3Z5X6Z8'] : False\n",
+      "6 :: 90571: [[9,4, 2]] : 24 :['X0X1', 'X0Z6Z7Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X4X6'] : False\n",
+      "6 :: 90572: [[9,4, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'Y2Z4Z5Z6Z7X8', 'Z0Z1Z4Z5X6Z8'] : False\n",
+      "6 :: 90574: [[9,4, 2]] : 8 :['X0X1', 'X0Z6Z7Z8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2X3X4Y6'] : False\n",
+      "6 :: 90593: [[9,4, 2]] : 32 :['X0X1', 'X0X2X3X4', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Z4Y5Z6X7'] : False\n",
+      "6 :: 90594: [[9,4, 2]] : 96 :['X0X1', 'X0X2X3X4', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Y2Z3Z4Z5'] : False\n",
+      "6 :: 90597: [[9,4, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0Z2Z4Z5Z6X8', 'Y0Z1Y4X5Z7Z8'] : False\n",
+      "6 :: 90598: [[9,4, 2]] : 4 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0X2Z5Z6Z7X8', 'Y0Z1Z4Y5X7Z8'] : False\n",
+      "6 :: 90819: [[9,4, 2]] : 48 :['X0X1', 'X0X2X3X4X5X6', 'X0Z2Z3X4X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Y2Z4Z5Y7'] : False\n",
+      "6 :: 90882: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0Z2Z3X4Z6Z7', 'Z0Z1Y4X5Z6Z8'] : False\n",
+      "6 :: 90914: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Y2X4X5Z6'] : False\n",
+      "6 :: 90915: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2X3Z6Z7Z8', 'Z0Z1Y2Y4Y5Z6'] : False\n",
+      "6 :: 90966: [[9,4, 2]] : 8 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0Z2Z4Z5Z6X8', 'Z0Z1Y2Y5Z7Z8'] : False\n",
+      "6 :: 90967: [[9,4, 2]] : 32 :['X0X1', 'X0X2X3X4', 'Z2Z3X5X6X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1X2Z4Y5X6'] : False\n",
+      "6 :: 90982: [[9,4, 2]] : 16 :['X0X1', 'X0X2X3X4X5X6', 'X0Z2Z3X4X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Z2Z4Z5X7'] : False\n",
+      "6 :: 90997: [[9,4, 2]] : 16 :['X0X1', 'X0X2X3X4X5X6', 'X0Z2Z3X4X7X8', 'X2X3Z5Z6Z7Z8', 'Z0Z1Y2X3Z4Y5'] : False\n",
+      "6 :: 91081: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z2Y3X4X6', 'Z0Y1X2Z3Z5Z8'] : False\n",
+      "6 :: 92786: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Y0Z1Z2Z3X4X6', 'X0Z2X3Z5Z7Z8'] : False\n",
+      "6 :: 93448: [[9,4, 2]] : 2 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Z1Y3Z5Z6Z8'] : False\n",
+      "6 :: 94321: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Y4Z8', 'Z1Z2Y4Z5Z6Z7'] : False\n",
+      "6 :: 94390: [[9,4, 2]] : 1 :['X0Z2Z5Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'X0Z1Z2X3Z6Z8'] : False\n",
+      "6 :: 94546: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0Z2Z4Z5X6Z8', 'Y0Y1Z2Z3Z6Z7'] : False\n",
+      "6 :: 94678: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z4X5Z8', 'Z0X1Y2Z5Z6Z7'] : False\n",
+      "6 :: 94679: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Y4Z8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 94845: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Z1X2Z3Z5Z7Z8'] : False\n",
+      "6 :: 94854: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z5X6', 'X0Y1Y2Z6Z7Z8'] : False\n",
+      "6 :: 94894: [[9,4, 2]] : 8 :['Y0Z1Y2Y3', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Z0X1Z2X3Z7Z8'] : False\n",
+      "6 :: 94951: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1X4Z6Z7Z8', 'Z0Z1Z2Z3X4X5'] : False\n",
+      "6 :: 94961: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z0X2X3Z5X6Z8'] : False\n",
+      "6 :: 94969: [[9,4, 2]] : 4 :['Y0Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0X1Z3Z4Z6Z7', 'Y0X1Z2Y3Z5Z8'] : False\n",
+      "6 :: 94971: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Z0Z1X2Z5Z7Z8'] : False\n",
+      "6 :: 94976: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Y1X2Z5Z7Z8'] : False\n",
+      "6 :: 94978: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6', 'Z2Z3Y4X6Y7Z8'] : False\n",
+      "6 :: 95052: [[9,4, 2]] : 2 :['Z3Y5Z6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6'] : False\n",
+      "6 :: 95058: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1Y2Z3Z5X6', 'Y1Y2Z4Z6Z7Z8'] : False\n",
+      "6 :: 95059: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z4X5Z8', 'Z1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 95061: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X6Z8', 'X0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 95067: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'X0X1Y3Z5Z7Z8'] : False\n",
+      "6 :: 95070: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Z4Z5Z6', 'Z2Z3Y4X5Z7Z8'] : False\n",
+      "6 :: 95073: [[9,4, 2]] : 4 :['Y0Z1Y2Y3', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 95078: [[9,4, 2]] : 1 :['X1Z4Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6'] : False\n",
+      "6 :: 95085: [[9,4, 2]] : 8 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Z0Z1Z2Z3Z4Z7', 'Y0Z1Y2Z3Z6Z8'] : False\n",
+      "6 :: 95088: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Z2Z3X4X5Z7', 'X0Y2Z3Y6X7Z8'] : False\n",
+      "6 :: 95097: [[9,4, 2]] : 2 :['Y0Y2X5X6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 95158: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8', 'Y2Z3Y5X6Z7Z8'] : False\n",
+      "6 :: 95165: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0X1Y2Y3Z5Z8'] : False\n",
+      "6 :: 95167: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z5Z6Z7', 'Z1Z2Z4Z5X6Z8'] : False\n",
+      "6 :: 95202: [[9,4, 2]] : 2 :['X1X3Y6X8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z3Z5X6Z8'] : False\n",
+      "6 :: 95206: [[9,4, 2]] : 2 :['Z0Z5Z6X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 95207: [[9,4, 2]] : 2 :['X0Z1Y3Z6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z3Z5X6Z8'] : False\n",
+      "6 :: 95210: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Y5Z6', 'Y0Y1X2Y4Z7Z8'] : False\n",
+      "6 :: 95211: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z5Z6Z7', 'Z0X1Y2Z4X5Z8'] : False\n",
+      "6 :: 95221: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z5Z6Z7', 'Y0Y1Z4Z5X6Z8'] : False\n",
+      "6 :: 95222: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Z0X1X2Z5Z7Z8'] : False\n",
+      "6 :: 95233: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'X0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 95240: [[9,4, 2]] : 1 :['Z0X2Z5Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 95242: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'X0X1Z4X5Z7Z8'] : False\n",
+      "6 :: 95244: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8', 'Z0Z1Y2Z3Z5Z7'] : False\n",
+      "6 :: 95245: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Y0X1Y2Z5Z7Z8'] : False\n",
+      "6 :: 95247: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8', 'Z2Y3Z4Y5Y6Y7'] : False\n",
+      "6 :: 95255: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Z1X2Y3Z5Z8'] : False\n",
+      "6 :: 95257: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'Z2Y3Z4Z5Z6Z7'] : False\n",
+      "6 :: 95268: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'X0Y2Z3X6Y7Z8'] : False\n",
+      "6 :: 95270: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y2Y3Z4Z5X6Z8'] : False\n",
+      "6 :: 95275: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Y4Z5Z6', 'Z2Y3X4Z5Z7Z8'] : False\n",
+      "6 :: 95276: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7', 'Y0Y1X4Z5Z6Z8'] : False\n",
+      "6 :: 95277: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y2Y4Y5X6Z8'] : False\n",
+      "6 :: 95281: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z0Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 95282: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Z1Z3Z4X5Z7Z8'] : False\n",
+      "6 :: 95284: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0X1Y2Y4Z5Z7', 'X0X1Y4X5Z6Z8'] : False\n",
+      "6 :: 95285: [[9,4, 2]] : 1 :['X0Z2Z5Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z1Z2Z3Z5Z6Z8'] : False\n",
+      "6 :: 95287: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'X1Z2Z3X4Z7Z8'] : False\n",
+      "6 :: 95289: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1X3Z4Z5Z8'] : False\n",
+      "6 :: 95290: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2X4Z5Z6', 'X1X2Z4Z5Z7Z8'] : False\n",
+      "6 :: 95291: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'X0Y1Y2Y5Z7Z8'] : False\n",
+      "6 :: 95292: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Z1Z2Z4X5Z7Z8'] : False\n",
+      "6 :: 95293: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1Z2Y3Z5X6Z8'] : False\n",
+      "6 :: 95294: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y1Z2Z4Z5Z6Z8'] : False\n",
+      "6 :: 95295: [[9,4, 2]] : 4 :['X1X5Y6Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 95297: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1X2Z3Z5Z8'] : False\n",
+      "6 :: 95301: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Z0Z1Z2Z3Z6Z7', 'Y0X1Z2Z5Z6Z8'] : False\n",
+      "6 :: 95596: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4X6Z8', 'Z0Z2Y4Z5Z6Z7'] : False\n",
+      "6 :: 95901: [[9,4, 2]] : 8 :['X0Y3Y5X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 95939: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y0X1X2Y3Z7Z8'] : False\n",
+      "6 :: 96373: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8', 'Y0Z1Y2Z3Z6Z7'] : False\n",
+      "6 :: 96442: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z3Y4X5Z8', 'Z1Z2Y4Z5Z6Z7'] : False\n",
+      "6 :: 96490: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'Z0X1Y2Z5Z6Z7'] : False\n",
+      "6 :: 96525: [[9,4, 2]] : 2 :['X0Z2Z5Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Y0Y1X2X3Z6Z8'] : False\n",
+      "6 :: 96603: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Y4Z8', 'Y0X1Z2X4Z6Z7'] : False\n",
+      "6 :: 96624: [[9,4, 2]] : 2 :['X0Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Y0Y2Z4Z5Z7Z8'] : False\n",
+      "6 :: 96679: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'X0Y1Z2X3Z7Z8'] : False\n",
+      "6 :: 96697: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8', 'Y0Z1X4Y6Z7Z8'] : False\n",
+      "6 :: 96702: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8', 'X1Z2Z3Z5Y7Z8'] : False\n",
+      "6 :: 96746: [[9,4, 2]] : 2 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z0Y3Z4Z5X6Z8'] : False\n",
+      "6 :: 96758: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0X1Y2Y4Y5Z6', 'Z0X1Z2X4Z7Z8'] : False\n",
+      "6 :: 96813: [[9,4, 2]] : 2 :['X1Z2Z5Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z2X3X4Z6Z7'] : False\n",
+      "6 :: 96826: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z1Z3Y4Y5X6Z8'] : False\n",
+      "6 :: 96850: [[9,4, 2]] : 2 :['Z0X2Z3Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 96852: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y2X3X4Z5Z7Z8'] : False\n",
+      "6 :: 96889: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Z2X3Z5Z6Z8'] : False\n",
+      "6 :: 96897: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8', 'Y0X1Y2X5Z7Z8'] : False\n",
+      "6 :: 96904: [[9,4, 2]] : 1 :['X0Y1Z5Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 96915: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Y1Y2X4X5Z7Z8'] : False\n",
+      "6 :: 96929: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Z1X2Y3X4Z7Z8'] : False\n",
+      "6 :: 96940: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8', 'Z0Z1Y4Z5Z6Z7'] : False\n",
+      "6 :: 96949: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y0Y1Y2Y5Z7Z8'] : False\n",
+      "6 :: 96950: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Y1X3Z5Z7Z8'] : False\n",
+      "6 :: 96955: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7', 'X0Y2Z3Z5X6Z8'] : False\n",
+      "6 :: 96974: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Y4X5Z8', 'X1Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 97007: [[9,4, 2]] : 1 :['X0Z2Z5Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z1Y2Z3Y5Z6Z8'] : False\n",
+      "6 :: 97010: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3X4X5Z6', 'Y0X1Z2Z4Z7Z8'] : False\n",
+      "6 :: 97017: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Z2Z3X4X5Z7', 'Y2Z3Y4X5Z6Z8'] : False\n",
+      "6 :: 97018: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'X0Z1Z2X4Z7Z8'] : False\n",
+      "6 :: 97024: [[9,4, 2]] : 1 :['X0Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Y0X1X3Z4Z7Z8'] : False\n",
+      "6 :: 97041: [[9,4, 2]] : 2 :['X0Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Z0X1Z2X3Z7Z8'] : False\n",
+      "6 :: 97064: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y2Y3Z5Z6Z8'] : False\n",
+      "6 :: 97065: [[9,4, 2]] : 1 :['X0Z2Z5Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'X0Y3X4Z5Z6Z8'] : False\n",
+      "6 :: 97069: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8', 'X0Z1Z2Z4Z5Z7'] : False\n",
+      "6 :: 97078: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Z2Z3X4X5Z7', 'Y0X1Z2Y4Z6Z8'] : False\n",
+      "6 :: 97087: [[9,4, 2]] : 2 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y0X1Y3X4Z7Z8'] : False\n",
+      "6 :: 97089: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z2Z3Y4Y5X6Z8'] : False\n",
+      "6 :: 97090: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X6Z8', 'Y0X1Y2X4Z6Z7'] : False\n",
+      "6 :: 97091: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'X0Z2Z3X5Z7Z8'] : False\n",
+      "6 :: 97110: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3Y4Z6', 'Y0X1Z3Y4Z7Z8'] : False\n",
+      "6 :: 97113: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8', 'Z0X1Z2Z5Z6Z7'] : False\n",
+      "6 :: 97114: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4X6Z8', 'Y2Y3Y4Z5Z6Z7'] : False\n",
+      "6 :: 97115: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8', 'Z1Z2Z4Z5Z6Z7'] : False\n",
+      "6 :: 97120: [[9,4, 2]] : 1 :['Y1X3Z5Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2Y3X4Y6'] : False\n",
+      "6 :: 97141: [[9,4, 2]] : 2 :['X0Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Y1Z2Y4Z5Z7Z8'] : False\n",
+      "6 :: 97143: [[9,4, 2]] : 1 :['Y0Z5X6Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 97144: [[9,4, 2]] : 1 :['X0Z2Z5Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0X1Y2X3Z6Z8'] : False\n",
+      "6 :: 97156: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8', 'Z0Z1Y4Z5Z6Z7'] : False\n",
+      "6 :: 97174: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z3Y4X5Z8', 'Y0X1Z2X4Z6Z7'] : False\n",
+      "6 :: 97181: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Z4X5Z7', 'Z1Z3Y4X5Z6Z8'] : False\n",
+      "6 :: 97185: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y2Y4Y5Z6Z8'] : False\n",
+      "6 :: 97189: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8', 'X0Z2Z3X4Z6Z7'] : False\n",
+      "6 :: 97196: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 97200: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1Y2Z4Z5X6Z8'] : False\n",
+      "6 :: 97201: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1X2X3Z5Z6Z8'] : False\n",
+      "6 :: 97203: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Z4Z6Z7', 'Z1Y2X4Z5Z6Z8'] : False\n",
+      "6 :: 97204: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'Z1Y2Z4Z5Z6Z7'] : False\n",
+      "6 :: 97205: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'Y0Y1X2Y5Z7Z8'] : False\n",
+      "6 :: 97206: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0X2Y3Z4Z5Z8'] : False\n",
+      "6 :: 97207: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4X6Z8', 'X0Y1Z3Z4Z6Z7'] : False\n",
+      "6 :: 97208: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'X0Y2Z3Y4Z5Z7'] : False\n",
+      "6 :: 97209: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z2Z3Y4Y5X6Z8'] : False\n",
+      "6 :: 97211: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4X6Z8', 'Z0Y1Z2Z3Z6Z7'] : False\n",
+      "6 :: 97212: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y1Z2Y3Y5Z7Z8'] : False\n",
+      "6 :: 97213: [[9,4, 2]] : 1 :['X0Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'X1Y2X3Y4Z7Z8'] : False\n",
+      "6 :: 97214: [[9,4, 2]] : 2 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Z1Y4Y5X6Z8'] : False\n",
+      "6 :: 97217: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0Z1Z2Z3X6Z8', 'Z0Y3Z4Z5Z6Z7'] : False\n",
+      "6 :: 97219: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Z4Z5Z6', 'Z1Z2Y4X5Z7Z8'] : False\n",
+      "6 :: 97221: [[9,4, 2]] : 2 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Z1Z2Z5Z6Z8'] : False\n",
+      "6 :: 97476: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Y4Z8', 'Z0X1Z3Y4Z6Z7'] : False\n",
+      "6 :: 97591: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z5Z6Z7', 'Y0X1Z2Z4Z5Z8'] : False\n",
+      "6 :: 97678: [[9,4, 2]] : 2 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Z0Z2X3X4Z7Z8'] : False\n",
+      "6 :: 97679: [[9,4, 2]] : 2 :['Y0Z3X4Y8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 97730: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'X0Z1Z3X5Z7Z8'] : False\n",
+      "6 :: 97731: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'X0Z1Z3Y4Z6Z7'] : False\n",
+      "6 :: 97767: [[9,4, 2]] : 8 :['X1Y3Y5X8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 97796: [[9,4, 2]] : 1 :['Z1Z5Y6X7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 97802: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3X4Z6', 'X0Y2Z3X4Z7Z8'] : False\n",
+      "6 :: 97831: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Y4Z8', 'Y1Z3X4Z5Z6Z7'] : False\n",
+      "6 :: 97835: [[9,4, 2]] : 1 :['X1Z3Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Y2Y3X4Y6'] : False\n",
+      "6 :: 97844: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y1X2Y3Z4Z5Z8'] : False\n",
+      "6 :: 97847: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8', 'Y0Z2X4X5Z7Z8'] : False\n",
+      "6 :: 97849: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z5Z6Z7', 'X0Y1Z2Z4Y5Z8'] : False\n",
+      "6 :: 97859: [[9,4, 2]] : 1 :['Z0X1Z5Y6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 97865: [[9,4, 2]] : 1 :['X0Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Z0Y1Y2Z4Z7Z8'] : False\n",
+      "6 :: 97866: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Z2Z3X4X5Z7', 'X0Z1Z2X4Z6Z8'] : False\n",
+      "6 :: 97867: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0X1Y3Z4Z5Z8'] : False\n",
+      "6 :: 97869: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y0Y2Z3Z5Z7Z8'] : False\n",
+      "6 :: 98040: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3Y4Z5Z6', 'X0Y1Y2Z4Z7Z8'] : False\n",
+      "6 :: 98043: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Z0Z1Z2Z3Y4Z5', 'Z0Y2X4Z6Z7Z8'] : False\n",
+      "6 :: 98048: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'X1Z2Y3Z4Z5Z8'] : False\n",
+      "6 :: 98050: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'Z2Z3Z4Z5X6Z8', 'Y0X1Y2Z4Z6Z7'] : False\n",
+      "6 :: 98052: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Y4Z8', 'Y2Z3Z4Y5Y6Y7'] : False\n",
+      "6 :: 98053: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X6', 'Z2Z3Y4X5Z6X8', 'Y0Z1Z4X5Z7Z8'] : False\n",
+      "6 :: 98055: [[9,4, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'X0Z2Z3X4X5Z7', 'Z2Z3Z4Y6X7Z8'] : False\n",
+      "6 :: 98057: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z5X6', 'Z2Z3X5Y6Y7Z8'] : False\n",
+      "6 :: 98169: [[9,4, 2]] : 1 :['X0Z2Z5Z7', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'X1Y3X4Z5Z6Z8'] : False\n",
+      "6 :: 98186: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'X0Y2Z5Y6Z7Z8'] : False\n",
+      "6 :: 98202: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0X1Z2Z4Z5Z8'] : False\n",
+      "6 :: 98214: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y3Z4Z5X6Z8'] : False\n",
+      "6 :: 98215: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y1Z2Z3Z5Z7Z8'] : False\n",
+      "6 :: 98220: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1Y2Z3Z5X6Z8'] : False\n",
+      "6 :: 98221: [[9,4, 2]] : 2 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z0Y1Z2X3Z5Z8'] : False\n",
+      "6 :: 98227: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Z3Y4Y5Z6Z8'] : False\n",
+      "6 :: 98228: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0Y1Z4Z5Z6Z8'] : False\n",
+      "6 :: 98229: [[9,4, 2]] : 1 :['Y0Y2Y3Z5', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z3Z5X6Z8'] : False\n",
+      "6 :: 98230: [[9,4, 2]] : 1 :['X0Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'X0Y2X3Y4Z7Z8'] : False\n",
+      "6 :: 98319: [[9,4, 2]] : 1 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2X4X5X8', 'X0Z2Z3Z4Z6Z8', 'Z0X1Z2Y4Z5Z7'] : False\n",
+      "6 :: 98376: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Y4X5Z8', 'X0Z1Z3Y4Z6Z7'] : False\n",
+      "6 :: 98388: [[9,4, 2]] : 2 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Y1Y2Z4Z5Z8'] : False\n",
+      "6 :: 98397: [[9,4, 2]] : 2 :['X0Y2Z5Z8', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Z0Z2X3X4Z6Z7'] : False\n",
+      "6 :: 98406: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Y0Y2Y3Y5Z7Z8'] : False\n",
+      "6 :: 98410: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Y4Y5Z6', 'Y0X1Z2Z4Z7Z8'] : False\n",
+      "6 :: 98412: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Y0X2Y3Z4Z5Z8'] : False\n",
+      "6 :: 98414: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'X0Z2Z3Z4Z5Z6', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 98415: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'X0Y1Z2Z4Z7Z8'] : False\n",
+      "6 :: 98416: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Z0X1Z2Z4Z5Z6', 'X0Y1Z3Z4Z7Z8'] : False\n",
+      "6 :: 98604: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Y4X5Z8', 'Z2Y3X4Z6Z7X8'] : False\n",
+      "6 :: 98605: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Y4X5Z8', 'Z0Y1Z2Z3Z6Z7'] : False\n",
+      "6 :: 98607: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'Y2Z3X4Z6Z7X8'] : False\n",
+      "6 :: 98632: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4X6Z8', 'Y2Z3X4Z6Z7X8'] : False\n",
+      "6 :: 98636: [[9,4, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'X0Z2Z3X4Z6Z8', 'Y0Y1Z4Z5Z6Z7'] : False\n",
+      "6 :: 98643: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Z4X5Z7', 'Y0Y1Y4X5Z6Z8'] : False\n",
+      "6 :: 98644: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0X1Z2Z4X5Z7', 'Y2Y3Y4X5Z6Z8'] : False\n",
+      "6 :: 98645: [[9,4, 2]] : 1 :['Y2Y3X4Y6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1Y2X3Z4Z5Z8'] : False\n",
+      "6 :: 98648: [[9,4, 2]] : 4 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Y4X5Z8', 'Y2Y3Y4Z5Z6Z7'] : False\n",
+      "6 :: 98649: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'Z2Z3Y4Z5Z6Z7'] : False\n",
+      "6 :: 98650: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'X0Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 98651: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Y4X5Z6', 'Y0Y1Z4X5Z7Z8'] : False\n",
+      "6 :: 98654: [[9,4, 2]] : 1 :['X0Z2Z4X6', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Z0Z1Z3Z5Y6X8', 'Z0Y1Y2Z3Z7Z8'] : False\n",
+      "6 :: 98655: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Z0X1Z2Y4X6Z8', 'X0Y2Z3Z6Z7X8'] : False\n",
+      "6 :: 98656: [[9,4, 2]] : 2 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0X1Z2Z4X6Z8', 'Y0Z1Z2Z3Z6Z7'] : False\n",
+      "6 :: 98657: [[9,4, 2]] : 1 :['X0Z2Y3Z6', 'X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z1X2Z3Z4Z5Z8'] : False\n",
+      "6 :: 98914: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Y0Z1X2Z4X5X8', 'Y0Z1Z2Z3Z6Z8', 'X0Y2Z3Y4Z5Z7'] : False\n",
+      "6 :: 98915: [[9,4, 2]] : 1 :['X1Z2Y3X6', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1X2Z4Z5Z8'] : False\n",
+      "6 :: 98916: [[9,4, 2]] : 1 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Y2Z4X5Z7', 'X0Y2Z3X4Z6Z8'] : False\n",
+      "6 :: 98918: [[9,4, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Y0Z1Z2Z3X4X6', 'Y0Z1Y2Z4Z7Z8'] : False\n",
+      "6 :: 6034: [[9,5, 2]] : 4 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 9375: [[9,5, 2]] : 48 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0X1Z2Z3Z4Z6', 'Y0Z1Y2Z5Z7Z8'] : False\n",
+      "6 :: 9556: [[9,5, 2]] : 4 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Y1Z2Y3Z5Z8'] : False\n",
+      "6 :: 9587: [[9,5, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1Z3Z5X6Z8'] : False\n",
+      "6 :: 9867: [[9,5, 2]] : 12 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z0Z2Z3Z4Z6X7', 'X1Z2X3Z5Z7Z8'] : False\n",
+      "6 :: 9875: [[9,5, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Z1Z2X3Z5Z8'] : False\n",
+      "6 :: 9952: [[9,5, 2]] : 8 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0X1Z3Z4Z6Z7', 'Z0Z2Z3Z5X6Z8'] : False\n",
+      "6 :: 10646: [[9,5, 2]] : 72 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Z0Z2Z3Z5Z6Z8'] : False\n",
+      "6 :: 11053: [[9,5, 2]] : 16 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0Z2Z3Z4Z5X6', 'Z1Z2X3Z6Z7Z8'] : False\n",
+      "6 :: 12043: [[9,5, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z1Z2Y3Z5X6Z8'] : False\n",
+      "6 :: 12046: [[9,5, 2]] : 4 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Z1Y3Z5Z6Z8'] : False\n",
+      "6 :: 12347: [[9,5, 2]] : 16 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0Z2Z3Z4Z5X6', 'Z0X1Z3Z6Z7Z8'] : False\n",
+      "6 :: 12351: [[9,5, 2]] : 4 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'X0Y1Y2Z3Z5Z8'] : False\n",
+      "6 :: 12388: [[9,5, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0X1Y3Z4Z5Z8'] : False\n",
+      "6 :: 13534: [[9,5, 2]] : 1 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Z2Z3Z5X6Z8'] : False\n",
+      "6 :: 13654: [[9,5, 2]] : 12 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0X1Z3Z4Z6Z7', 'Y0Z1Z2Z5Z6Z8'] : False\n",
+      "6 :: 13673: [[9,5, 2]] : 2 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7', 'Z0Y1X2Z3Z5Z8'] : False\n",
+      "6 :: 13697: [[9,5, 2]] : 12 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1Z3Z4Z6Z7', 'Y0Z2Y3Z5Z6Z8'] : False\n",
+      "6 :: 14806: [[9,5, 2]] : 36 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7', 'Z0Y3Y4Y5Z7Z8'] : False\n",
+      "6 :: 14959: [[9,5, 2]] : 72 :['X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0Z2Z3Z4Z5X6', 'Y0X1Z2Z6Z7Z8'] : False\n",
+      "6 :: 14986: [[9,5, 2]] : 1296 :['Y0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0Z1Z2Z3Z4Z5', 'Y0Y1Z2Z6Z7Z8'] : False\n",
+      "6 :: 15006: [[9,5, 2]] : 12 :['Y0Y2Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "6 :: 15025: [[9,5, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'X0X2Y4Z6Z7Z8'] : False\n",
+      "6 :: 15026: [[9,5, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0X1Z2Z6Z7Z8'] : False\n",
+      "6 :: 15027: [[9,5, 2]] : 2 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Y2X4Z6Z7Z8'] : False\n",
+      "6 :: 15028: [[9,5, 2]] : 2 :['X0Y3Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 15029: [[9,5, 2]] : 4 :['Y2X4Y5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 15030: [[9,5, 2]] : 4 :['X1Z3Z5Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Y0Z2Z3Z4Z6Z7'] : False\n",
+      "6 :: 15031: [[9,5, 2]] : 8 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y0Y2Y4Z6Z7Z8'] : False\n",
+      "6 :: 15066: [[9,5, 2]] : 4 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Y2Z3X4Z6Z7Z8'] : False\n",
+      "6 :: 15067: [[9,5, 2]] : 12 :['X0Y6Z7Y8', 'X0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'Z2Z3Z4Z5Z6X7'] : False\n",
+      "8 :: 960: [[8,1, 2]] : 46080 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1042: [[8,1, 2]] : 92160 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1096: [[8,1, 2]] : 73728 :['Z0Z6', 'Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1390: [[8,2, 2]] : 1536 :['Z0Z1', 'Z2Z7', 'Z3Z7', 'Z5Z6', 'Z0Z4Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 2100: [[8,2, 2]] : 3072 :['Z1Z7', 'Z2Z7', 'Z3Z7', 'Z5Z6', 'Z0Z4Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 2107: [[8,2, 2]] : 6144 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z6', 'Z4Z5', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 2915: [[8,2, 2]] : 23040 :['Z1Z7', 'Z2Z7', 'Z3Z7', 'Z4Z7', 'Z0Z5Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 3063: [[8,2, 2]] : 4608 :['Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 3066: [[8,2, 2]] : 4608 :['Z0Z1', 'Z2Z7', 'Z3Z7', 'Z4Z6', 'Z5Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 3723: [[8,2, 2]] : 12288 :['Z1Z2', 'Z3Z7', 'Z0Z4', 'Z5Z6', 'Z0Z1Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1067: [[8,3, 2]] : 1152 :['Z3Z7', 'Z4Z6', 'Z5Z6', 'Z0Z1Z2Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1080: [[8,3, 2]] : 768 :['Z1Z7', 'Z2Z7', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1090: [[8,3, 2]] : 1152 :['Z1Z7', 'Z2Z7', 'Z4Z5', 'Z0Z3Z6Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1507: [[8,3, 2]] : 256 :['Z3Z7', 'Z5Z6', 'Z0Z1Z2Z7', 'Z0Z1Z4Z6', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 1539: [[8,3, 2]] : 1024 :['Z0Z1', 'Z2Z7', 'Z0Z3Z6Z7', 'Z0Z4Z5Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1567: [[8,3, 2]] : 512 :['Z0Z1', 'Z2Z7', 'Z4Z5', 'Z0Z3Z6Z7', 'Y0X1X2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 1638: [[8,3, 2]] : 9216 :['Z0Z7', 'Z1Z7', 'Z2Z7', 'Z3Z4Z5Z6', 'Y0X1X2Y3Y4Y5Y6Y7'] : True\n",
+      "8 :: 4584: [[8,3, 2]] : 6144 :['Z0Z1', 'Z2Z7', 'Z3Z6', 'Z4Z5', 'X0X1Y2Y3X4X5Y6Y7'] : True\n",
+      "8 :: 4882: [[8,3, 2]] : 21504 :['Z0Z1Z6Z7', 'Z0Z2Z3Z6', 'Z2Z4Z6Z7', 'Z0Z2Z5Z7', 'X0X1X2X3X4X5X6X7'] : False\n",
+      "8 :: 6930: [[8,3, 2]] : 768 :['Z3Z7', 'Z0Z1Z2Z7', 'Z1Z4Z6Z7', 'Z0Z1Z5Z6', 'X0X1X2X3X4X5X6X7'] : False\n",
+      "8 :: 352: [[8,4, 2]] : 5760 :['Z0Z2', 'Z0Z1', 'Z0Z3Z4Z5Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 553: [[8,4, 2]] : 1536 :['X0X1', 'X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 752: [[8,4, 2]] : 256 :['Z0Z1Z4Z5', 'Z0Z1Z2Z3', 'Z0Z2Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 2066: [[8,4, 2]] : 3072 :['X4X5X6X7', 'X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 2623: [[8,4, 2]] : 1152 :['X0X4', 'X4X5X6X7', 'X0X1X2X3', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 3052: [[8,4, 2]] : 1536 :['X2X3', 'X0X1', 'X0X2X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 3061: [[8,4, 2]] : 768 :['X0X1', 'X2X3X4X5', 'X2X3X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 3062: [[8,4, 2]] : 192 :['X0X1', 'X0X2X3X4', 'X2X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 326: [[8,5, 2]] : 4608 :['X4X5X6X7', 'X0X1X2X3', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 472: [[8,5, 2]] : 5760 :['X0X1', 'X2X3X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 541: [[8,5, 2]] : 768 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z2Z3Z4Z5Z6Z7'] : True\n",
+      "8 :: 67: [[8,6, 2]] : 241920 :['Z0Z1Z2Z3Z4Z5Z6Z7', 'X0X1X2X3X4X5X6X7'] : True\n",
+      "8 :: 16578: [[9,2, 2]] : 1536 :['X0X1', 'X2Z8', 'X3Z8', 'X0X4Z6Z8', 'X0X5Z7Z8', 'Z4Z5Y6Y7', 'Z0Z1Z2Z3Z4X6Z7X8'] : False\n",
+      "8 :: 16583: [[9,2, 2]] : 384 :['X0X1', 'X2Z8', 'X3Z8', 'X0Y4Y6Z7', 'Z4X5X6Z8', 'X0X4Z5X7', 'Y0Z1Z2Z3Y4Z5Z6X8'] : False\n",
+      "8 :: 16646: [[9,2, 2]] : 768 :['X0X1', 'X2Z8', 'X3Z8', 'X5Z7', 'X0X4Z6Z8', 'Z4Z5Y6Y7', 'Z0Z1Z2Z3Z4X6Z7X8'] : True\n",
+      "8 :: 16729: [[9,2, 2]] : 3072 :['X0X1', 'X2Z8', 'X3Z8', 'X4Z6', 'X5Z7', 'Z4Z5Y6Y7', 'Z0Z1Z2Z3Z4X6Z7X8'] : True\n",
+      "8 :: 16732: [[9,2, 2]] : 3072 :['X0X1', 'X2Z8', 'X3Z8', 'X4Z6', 'X5Z7', 'X0Z4Z5Y6Y7Z8', 'Y0Z1Z2Z3Z4X6Z7Y8'] : True\n",
+      "8 :: 16804: [[9,2, 2]] : 2304 :['X0Z8', 'X1Z8', 'Z2X3', 'X4X5Z6Z7', 'Y4Y6Z7Z8', 'X4Z5X7Z8', 'Z0Z1X2Z3Y4Z5Z6Y8'] : False\n",
+      "8 :: 16808: [[9,2, 2]] : 2304 :['X0Z8', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z6Z8', 'X3Z4X6Z8', 'Z0Z1Z2Y3Z4Z5X7Y8'] : False\n",
+      "8 :: 25793: [[9,2, 2]] : 4608 :['X0Z8', 'X1Z8', 'X2Z8', 'X4X5Z6Z7', 'Z3Y4Y6Z7', 'Z3X4Z5X7', 'Z0Z1Z2Y3Y4Z5Z6X8'] : False\n",
+      "8 :: 25934: [[9,2, 2]] : 1536 :['X1Z8', 'X2Z8', 'X3Z8', 'X5Z7', 'X0X4Z6Z8', 'Z4Z5Y6Y7', 'Z0Z1Z2Z3Z4X6Z7X8'] : True\n",
+      "8 :: 25982: [[9,2, 2]] : 6144 :['X0Z8', 'X1Z8', 'X2Z8', 'X4Z7', 'X5Z6', 'Y3Z4Z5X6Y7Z8', 'Z0Z1Z2X3Z5Y6Z7Y8'] : True\n",
+      "8 :: 25996: [[9,2, 2]] : 3072 :['X1Z8', 'X2Z8', 'X3Z8', 'X0X4Z6Z8', 'X0X5Z7Z8', 'Z4Z5Y6Y7', 'Z0Z1Z2Z3Z4X6Z7X8'] : False\n",
+      "8 :: 26021: [[9,2, 2]] : 768 :['X1Z8', 'X2Z8', 'X3Z8', 'X0Y4Y6Z7', 'Z4X5X6Z8', 'X0X4Z5X7', 'Y0Z1Z2Z3Y4Z5Z6X8'] : False\n",
+      "8 :: 37481: [[9,2, 2]] : 46080 :['X0Z8', 'X1Z8', 'X2Z8', 'X3Z8', 'X4X5Z6Z7', 'Z4Z5Y6Y7', 'Z0Z1Z2Z3Z4X6Z7X8'] : False\n",
+      "8 :: 2565: [[9,3, 2]] : 64 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0X2Y3Z4', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 2966: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5', 'X0Z6Z7Z8', 'X0Y2Z3Z4Y6X8', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 2985: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5', 'X0Z6Z7Z8', 'Y2Z3Y6X8', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 3898: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z2Z3X4Y5', 'X0Z4Z5Z8', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 4363: [[9,3, 2]] : 256 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0X2X3X7', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 4378: [[9,3, 2]] : 128 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Z4Z6Z8', 'Z2Z3Y4Z5X6Z8', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 4383: [[9,3, 2]] : 256 :['X0X1', 'X6X8', 'X2X3X4X5', 'Y2Y3Z4Z5', 'X2X3X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 4428: [[9,3, 2]] : 64 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0Z2Z4X6', 'X0Z3Z5X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 4979: [[9,3, 2]] : 64 :['X0X1', 'X6X8', 'X2X3X4X5', 'X2Y3Z4X6', 'Y2X3Z5X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 8835: [[9,3, 2]] : 512 :['X0X1', 'X2X3', 'Z4Z5', 'X0Z6Z7Z8', 'X0Z2Z3Y4X5Z6', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 8843: [[9,3, 2]] : 1536 :['X0X1', 'X2X3', 'Z4Z5', 'X0Z6Z7Z8', 'Z2Z3Y4X5', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 12010: [[9,3, 2]] : 512 :['X0X1', 'X2X3', 'Z4Z5', 'Z6Z7', 'X0Z2Z3Y4X5Z8', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 15715: [[9,3, 2]] : 48 :['X0X1', 'X0X6', 'X0X2Y3Z4', 'Z3Y4X5X7', 'Y2X3Z5X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 15739: [[9,3, 2]] : 1536 :['X0X1', 'X2X3', 'X0Z5', 'Z4Z6', 'Y2Z3Y4X6Y7Y8', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 15763: [[9,3, 2]] : 192 :['X0X1', 'X0X8', 'X2X3X4X5', 'X0X2Z4Z5', 'X0Z2Z3X4X6X7', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 15796: [[9,3, 2]] : 96 :['X0X1', 'X0X7', 'X2X3X4X5', 'Z2Z3X4X6', 'X0Y2X3Z4Z6X8', 'Z0Z1Z2Y3Z4Z5Z7Z8'] : False\n",
+      "8 :: 15810: [[9,3, 2]] : 768 :['X0X1', 'X0X8', 'X2X3X4X5', 'X2X3Z6Z7', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 15858: [[9,3, 2]] : 192 :['X0X1', 'X0X8', 'X2X3X4X5', 'Y2Z3X4X6', 'X2Y4Z5X7', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 15918: [[9,3, 2]] : 1152 :['X0X1', 'X0X8', 'X2X3X4X5', 'Y2Z3Z4Z5', 'X0Z2Z3X4X6X7', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 15993: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'X0Z8', 'Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 15994: [[9,3, 2]] : 768 :['X0X1', 'X6X8', 'X0X7', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 16031: [[9,3, 2]] : 384 :['X0X1', 'X2X3', 'X0Z8', 'Z4Z5Z6Z7', 'Z2Z3Y4X5', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 16100: [[9,3, 2]] : 192 :['X0X1', 'X2X3', 'X0Z6', 'Z4Z5Z6Z7', 'X0Z2Z3Y4X5Z8', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 16131: [[9,3, 2]] : 2304 :['X0X1', 'X6X8', 'X6X7', 'X2X3X4X5', 'Z2Z3Z4Z5', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 16425: [[9,3, 2]] : 192 :['X0X1', 'X0X6', 'X2Z3Z4X5', 'Y3Y4X6X7', 'Y2Y5X6X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 16455: [[9,3, 2]] : 384 :['X0X1', 'X0X8', 'X2X3X4X5', 'X0X2X3X6', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 16638: [[9,3, 2]] : 768 :['X0X1', 'X0X8', 'X2X3X4X5', 'Y2Y3Z4Z5', 'X2X3X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 16812: [[9,3, 2]] : 96 :['X0X1', 'X0X6', 'Y2X3Y4X5', 'Z2Z4X6X7', 'Z3Z5X7X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 21542: [[9,3, 2]] : 512 :['X0X1', 'X6X8', 'X2X3X4X5', 'X0X2X3X6', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 21805: [[9,3, 2]] : 256 :['X0X1', 'X2X3', 'Z5Y7', 'X0Z4Z5Z6', 'Y2Z3Y4X6Y7Y8', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 21970: [[9,3, 2]] : 128 :['X0X1', 'X5X8', 'X2X3X4X5', 'X0X4Z6Z7', 'Z2Z3Y6Y7', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 21994: [[9,3, 2]] : 128 :['X0X1', 'X7X8', 'X2X3X4X5', 'X2Y3Y4Y6', 'X0Z2Z3X4X6X7', 'Z0Z1Z2Y3Z4Z5Z7Z8'] : False\n",
+      "8 :: 22015: [[9,3, 2]] : 32 :['X0X1', 'X5X8', 'X2X3X4X5', 'X0Z2Z4Z6', 'Z3Y4Y6X7', 'Z0Z1Y2Z3Z4Z5Z7Z8'] : False\n",
+      "8 :: 22031: [[9,3, 2]] : 768 :['X0X1', 'X6X8', 'X2X3X4X5', 'Y2Z3Z4Z5', 'X0Z2Z3X4X6X7', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 22037: [[9,3, 2]] : 768 :['X0X1', 'X7X8', 'X2X3X4X5', 'Z2Z3X4X6', 'Y2Z3Z4Z5', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 23431: [[9,3, 2]] : 1536 :['X0X1', 'X6X8', 'X0X6', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 172897: [[9,3, 2]] : 64 :['X0X1', 'X2X3X4X5', 'Z2Z3X6X8', 'Z4Z5X7X8', 'X0Z4Z5X6', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 178407: [[9,3, 2]] : 1152 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5', 'X0Y2Z4Z5', 'X0X6X7X8', 'Z0Z1Z2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 178727: [[9,3, 2]] : 384 :['X0X1', 'X2X3X4X5', 'Z2Z3X4X6', 'Y2Z3Z4Z5', 'X0X6X7X8', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 179028: [[9,3, 2]] : 64 :['X0X8', 'X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Y1Z3Z7X8', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 179041: [[9,3, 2]] : 48 :['X0X1', 'X2X3X4X5', 'X0Z2Z4Z6', 'Z3Y4Y6X7', 'Y2Y3X6X8', 'Z0Z1Y2Z3Z4Z5Z7Z8'] : False\n",
+      "8 :: 183460: [[9,3, 2]] : 256 :['X0X1', 'X2X3X4X5', 'X0Z6Z7Z8', 'X0X2X3Z6', 'X0Y2Z3Z4Z5Z7', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 185061: [[9,3, 2]] : 64 :['X5X8', 'X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X4', 'X0Y2Z3X6', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 185152: [[9,3, 2]] : 256 :['X0X1', 'X2X3X4X5', 'X0Z6Z7Z8', 'X0X2X3Z6', 'Y2Z3Z4Z5', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 597: [[9,4, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'X0Z2Z3Y4X5Z8', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 639: [[9,4, 2]] : 128 :['X0X1', 'X6X8', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 640: [[9,4, 2]] : 64 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Z2Z3Y4Z5X6Z8', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 690: [[9,4, 2]] : 192 :['X0X1', 'X2X3', 'Z2Z3Y4X5', 'X0Z4Z5Z6Z7Z8', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 752: [[9,4, 2]] : 64 :['X0X1', 'X2X3', 'X0Z4Z5Z6', 'Y2Z3Y4X6Y7Y8', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 6085: [[9,4, 2]] : 192 :['X0X1', 'X0X6', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 6530: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0X2X3Z6Z7X8', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 8925: [[9,4, 2]] : 384 :['X0X1', 'X2X3', 'Z4Z5Z6Z7', 'Y0Z1Z2Z3Z4Z8', 'Z0Z1X2X4X5X6X7X8'] : True\n",
+      "8 :: 9413: [[9,4, 2]] : 384 :['X0X1', 'X0X8', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 10323: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2X3Z4Z6X8', 'Z0Z1Z2Y3Z4Z5Z7Z8'] : False\n",
+      "8 :: 12521: [[9,4, 2]] : 64 :['X0X1X2X3', 'X0X1X4X5', 'X2X4Z6Z7', 'Z0Z1X2X6X7X8', 'Y0Z1Z2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 12682: [[9,4, 2]] : 64 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X4X5X8', 'Y0Z1Z2Z3X4X6', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 12817: [[9,4, 2]] : 48 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X4X5X8', 'Y0X1Z2X4X6Z8', 'Z0Y1Z2Z3Z4Z5Z6Z7'] : False\n",
+      "8 :: 13367: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'X0Y1Z3X5Z7X8', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 13403: [[9,4, 2]] : 8 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X4X7', 'Y1Z3Z7X8', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 15579: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0Y2Z3Z4Z5X8', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 15583: [[9,4, 2]] : 128 :['X0X1', 'X2X3X4X5', 'Z2Z3Z4Z5X6X7', 'X0Y2Y3Z4Z5X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 15609: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X4X6X7', 'X0X2Z4Z5X6X8', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 19800: [[9,4, 2]] : 32 :['X0X1X2X3', 'X0X4X5X6', 'X3X5Z7Z8', 'X0Z1Z2X4X7X8', 'Z0Y1Z2Z3Z4Z5Z6Z7'] : False\n",
+      "8 :: 21818: [[9,4, 2]] : 64 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 25862: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'X0X2Y4Z5Y6X8', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 25915: [[9,4, 2]] : 192 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y2Z3Z4Z5', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 39019: [[9,4, 2]] : 128 :['X0X1X2X3', 'X4X5X6X7', 'X0X1X4X5', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 46271: [[9,4, 2]] : 32 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2X7X8', 'X3X4Z7Z8', 'Z0Y1Z2Z3Z4Z5Z6Z7'] : False\n",
+      "8 :: 48996: [[9,4, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'X0Y1Z2X5', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 53814: [[9,4, 2]] : 1152 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X8', 'Y0Z1Z2Z3', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 54330: [[9,4, 2]] : 64 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2X8', 'Y0Z1Z2Z3X4X5', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 55645: [[9,4, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5', 'X0Z6Z7Z8', 'Y4Z5Y6X8', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 56166: [[9,4, 2]] : 48 :['X0X1X2X3', 'X4X5X6X7', 'X0Y1Z2X4', 'Y0X1Z3X8', 'Y0Z1X2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 58122: [[9,4, 2]] : 384 :['X0X1X2X3', 'X4X5X6X7', 'Y0Z1Z2Z3', 'Z0Z1X2X4X5X8', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 88596: [[9,4, 2]] : 64 :['X0X1', 'X2X3', 'X0Z4Z5Z6Z7Z8', 'X0Z2Z3Y4Z5X6', 'Z0Z1X2X4X5X6X7X8'] : False\n",
+      "8 :: 89465: [[9,4, 2]] : 2304 :['X0X1', 'X2X3', 'Z4Z5', 'Z0Z1X2X4X5X6X7X8', 'Y0Z1Z2Z3Z4Z6Z7Z8'] : True\n",
+      "8 :: 89527: [[9,4, 2]] : 128 :['X0X1', 'X6X8', 'X0X2X3X4X5X6', 'X0Z2Z3Z4Z5X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 89619: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3X6X7', 'X0Z2Z3Z4Z5X8', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 89623: [[9,4, 2]] : 384 :['X0X1', 'X2X3X4X5', 'Y2Y3Z4Z5', 'X0X2X3X6X7X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 89651: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Y2Z3X4X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 89673: [[9,4, 2]] : 128 :['X0X1', 'X2X3X4X5', 'X2X3X6X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 89742: [[9,4, 2]] : 64 :['X0X1', 'X2X3X4X5', 'X2X3Z6Z7', 'Z0Z1Y2Z3X4Z8', 'X0Z2Z3Z4Z5X6X7X8'] : False\n",
+      "8 :: 89774: [[9,4, 2]] : 16 :['X0X1', 'X2X3X4X5', 'X0X4Z6Z7', 'Z2Z3X4X6X7X8', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 89778: [[9,4, 2]] : 128 :['X0X1', 'X2X3X4X5', 'X2X3X7X8', 'X0Y2Y3Z4Z5X6', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 89791: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X4Y6Y7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Z3Z4Z5Y6Z8'] : False\n",
+      "8 :: 89795: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'Z2Z3X6X8', 'Z4Z5X7X8', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 89865: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Z3Z4Z5Z7Z8'] : False\n",
+      "8 :: 89879: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Z6Z7Z8', 'Z0Z1X2Y3Z4Z6', 'X0Z2Z3Z4Z5X6X7X8'] : False\n",
+      "8 :: 89911: [[9,4, 2]] : 96 :['X0X1', 'X2X3X4X5', 'X2Z6Z7Z8', 'Z0Z1Y3Z4', 'X0Z2Z3Z4Z5X6X7X8'] : True\n",
+      "8 :: 89982: [[9,4, 2]] : 96 :['X0X1', 'X2X3X4X5', 'X0X2Z4Z5', 'Z2Z3X4X6X7X8', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 90062: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X2Z4Z5X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 90198: [[9,4, 2]] : 24 :['X0X1', 'X2X3X4X5', 'Z2Z4Z6X7', 'Z3Y4Y6X8', 'Z0Z1Y2Z3Z4Z5Z7Z8'] : False\n",
+      "8 :: 90229: [[9,4, 2]] : 576 :['X0X1', 'X2X3X4X5', 'Y2Z3Z4Z5', 'Z2Z3X4X6X7X8', 'Z0Z1X2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 90290: [[9,4, 2]] : 1152 :['X0X1', 'X2X3X4X5', 'X0Z6Z7Z8', 'Y2Z3Z4Z5', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 90453: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z6Z7X8', 'X0Z2Z3X4X6X7', 'Z0Z1Y2Z3Z4Z5Z6Z8'] : False\n",
+      "8 :: 90465: [[9,4, 2]] : 8 :['X0X1', 'X2X3X4X5', 'X0Z2Z4Z6', 'Z2Z3X4X6X7X8', 'Z0Z1Y2Z3Z4Z5Z7Z8'] : False\n",
+      "8 :: 90551: [[9,4, 2]] : 32 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0Y2Y3Z5Z6X8', 'Z0Z1Y2Z3Z4Y5Z7Z8'] : False\n",
+      "8 :: 90553: [[9,4, 2]] : 192 :['X0X1', 'X0X6X7X8', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 90940: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2X3Z4Z6Z7Z8'] : False\n",
+      "8 :: 90942: [[9,4, 2]] : 128 :['X0X1', 'X2X3X4X5', 'X0Z6Z7Z8', 'X0Y2Z3Z4Z5Z6', 'Z0Z1Z2Z3X4X6X7X8'] : False\n",
+      "8 :: 90946: [[9,4, 2]] : 32 :['X0X1', 'X2X3X4X5', 'X0Z2Z3X6', 'Z4Z5X7X8', 'Z0Z1Y2X3Z4Z6Z7Z8'] : True\n",
+      "8 :: 90961: [[9,4, 2]] : 24 :['X0X1', 'X0X2X3X4', 'X0Z2Z3X5X6X7', 'X0Y2Z4Z5X6X8', 'Z0Z1Z2Y3Z4Z6Z7Z8'] : False\n",
+      "8 :: 90980: [[9,4, 2]] : 128 :['X0X1', 'X2X3X4X5', 'X0X2X3X8', 'Z2Z3Z4Z5X6X7', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 90983: [[9,4, 2]] : 32 :['X0X1', 'X0X2X3X4', 'X0Z5Z6X8', 'X0Z2Z3X5X6X7', 'Z0Z1Y2Z3Z4Z5Z7Z8'] : False\n",
+      "8 :: 2008: [[9,5, 2]] : 16 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 2011: [[9,5, 2]] : 96 :['X0X1X2X3', 'X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 3486: [[9,5, 2]] : 24 :['X0X1X2X3', 'Z2Z3X4X8', 'Z0Z1X4X5X6X7', 'Y0X1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 3701: [[9,5, 2]] : 576 :['X0X1X2X3', 'X0X1X4X5', 'Z0Z1Z2Z3X4X6X7X8', 'Y0Z1X2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 4251: [[9,5, 2]] : 16 :['X0X1X2X3', 'X0X4X5X6', 'Z1Z2Z4Z5X7X8', 'Z0Y1Z2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 4265: [[9,5, 2]] : 288 :['X0X1X2X3', 'Z0Z1X2X4', 'Y0Z1Z2Z3X5X6X7X8', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 5367: [[9,5, 2]] : 12 :['X0X1X2X3', 'Y0Z2Z4X8', 'Z0Z1X4X5X6X7', 'Z0Y1Z2Z3Z5Z6Z7Z8'] : False\n",
+      "8 :: 8643: [[9,5, 2]] : 8640 :['X0X1X2X3', 'Z0Z1Z2Z3', 'Y0Z1X2X4X5X6X7X8', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 8890: [[9,5, 2]] : 16 :['X0X1X2X3X4X5', 'X0X1X2X6X7X8', 'Z0Z1X2Z3Z4X6', 'Y0Z1Z2X3Z5Z6Z7Z8'] : False\n",
+      "8 :: 9151: [[9,5, 2]] : 12 :['X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'X0Y1Z2X3Z7Z8', 'Y0Z1Z2Z3Z4Z5Z6X8'] : False\n",
+      "8 :: 9886: [[9,5, 2]] : 8 :['X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'X0Y1Z2X4Z7Z8', 'Y0Z1Z2Z3Z4Z5Z6X8'] : False\n",
+      "8 :: 14941: [[9,5, 2]] : 48 :['X0X1', 'X2X3X4X5X6X7', 'Z0Z1Y2Z3X4Z8', 'X0Z2Z3Z4Z5Z6Z7X8'] : False\n",
+      "8 :: 14946: [[9,5, 2]] : 32 :['X0X1', 'X0X2X3X4X5X6', 'Z2Z3Z4Z5X7X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 14947: [[9,5, 2]] : 96 :['X0X1', 'X2X3X4X5X6X7', 'Z2Z3Z4Z5X6X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 14949: [[9,5, 2]] : 192 :['X0X1', 'X2X3X4X5', 'X0Z2Z3Z4Z5X6X7X8', 'Z0Z1Y2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 14989: [[9,5, 2]] : 48 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0X1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 15019: [[9,5, 2]] : 48 :['X0X1X2X3', 'Z0Z1X2X4X5X6', 'Y0Z1Z2Z3X7X8', 'X0Y1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 15022: [[9,5, 2]] : 96 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z2Z3X4X8', 'Y0Z1X2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 15023: [[9,5, 2]] : 16 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'X0X1Z4Z5X6X8', 'Y0Z1Z2Z3Z4Z6Z7Z8'] : False\n",
+      "8 :: 15060: [[9,5, 2]] : 4 :['Z0Z2Z7Z8', 'X0X1X2X3X4X5', 'Z0Z1X2X3X6X7', 'Y0Z1Z2Z3Z4Z5Z6X8'] : False\n",
+      "8 :: 15064: [[9,5, 2]] : 8 :['X0X1X2X3', 'Z0Z1X2X4X5X6', 'Y0X1Z2Z4X7X8', 'Z0Y1Z2Z3Z5Z6Z7Z8'] : False\n",
+      "8 :: 15081: [[9,5, 2]] : 8 :['X0X1X2X3', 'Z0Z1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3Y4Z6Z7Z8'] : False\n",
+      "8 :: 15087: [[9,5, 2]] : 32 :['X0X1X2X3', 'X0X1X4X5X6X7', 'X0Z4Z5Z6Z7X8', 'Z0Z1Z2Z3Y4Z5X6Z8'] : False\n",
+      "8 :: 15093: [[9,5, 2]] : 16 :['X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1X2Z4Z5X8', 'Y0Z1Z2Z3X4Z6Z7Z8'] : False\n",
+      "8 :: 15103: [[9,5, 2]] : 32 :['X0X1X2X3', 'Z0Z1X2X4X5X6', 'X0Z2Z3X4X7X8', 'Y0X1Z2Z4Z5Z6Z7Z8'] : False\n",
+      "8 :: 1219: [[9,6, 2]] : 144 :['X0X1X2X3X4X5', 'Z0Z1Z2Z3X4X6X7X8', 'Y0Z1X2Z4Z5Z6Z7Z8'] : False\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "special_codes_d2 = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['d'] ==2 and code['k'] > 0:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_d2 += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1078,
+   "id": "c6103f7b-fda1-4541-aabf-fc9c02faf4e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5121"
+      ]
+     },
+     "execution_count": 1078,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(special_codes_d2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1093,
+   "id": "68bceac5-b073-4d4c-a806-0ef3bae41ead",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 :: 17: [[5,1, 1]] : 3840 :['Z0Z4', 'Z1Z4', 'Z2Z4', 'Z3Z4'] : True\n",
+      "4 :: 6: [[5,1, 1]] : 192 :['Z0Z4', 'Z1Z4', 'X2X3', 'X0X1X3X4'] : True\n",
+      "4 :: 4: [[5,2, 1]] : 64 :['Z1Z4', 'X2X3', 'Z0Z2Z3Z4'] : True\n",
+      "4 :: 9: [[5,3, 1]] : 384 :['Z1Z4', 'Z0Z2Z3Z4'] : True\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "special_codes_n5 = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['n'] ==5 and code['k'] > 0 and code['is_css']==1:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_n5 += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1094,
+   "id": "db0672d5-aa29-4468-8efd-fafbfeb88a5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 :: 7: [[4,1, 1]] : 384 :['Z0Z3', 'Z1Z3', 'Z2Z3'] : True\n",
+      "4 :: 12: [[4,1, 1]] : 48 :['Z1Z3', 'Z2Z3', 'X0X1X2X3'] : True\n",
+      "4 :: 6: [[4,1, 2]] : 32 :['X0X1', 'X2X3', 'Z0Z1Z2Z3'] : True\n",
+      "4 :: 8: [[4,2, 1]] : 32 :['X2X3', 'Z0Z1Z2Z3'] : True\n",
+      "4 :: 9: [[4,2, 2]] : 144 :['Z0Z1Z2Z3', 'X0X1X2X3'] : True\n",
+      "4 :: 2: [[4,3, 1]] : 384 :['X0X1X2X3'] : True\n"
+     ]
+    }
+   ],
+   "source": [
+    "special_codes_n4 = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['n'] ==4 and code['k'] > 0 and code['is_css']==1:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_n4 += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1096,
+   "id": "842c3970-48c2-49f2-8189-cfb4e6cd1a87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 17: [[6,1, 2]] : 128 :['Z0Z1', 'Z2Z5', 'X3X4', 'Z0Z3Z4Z5', 'X0X1X2X5'] : True\n",
+      "4 :: 31: [[6,1, 2]] : 96 :['Z1Z5', 'Z2Z5', 'X3X4', 'Z0Z3Z4Z5', 'X0X1X2X5'] : True\n",
+      "4 :: 43: [[6,1, 2]] : 32 :['X0Z4', 'X1Z5', 'Y2Y3Z4Z5', 'Z0Z2X4Z5', 'Z1Z3Z4X5'] : True\n",
+      "4 :: 55: [[6,1, 2]] : 384 :['X0X1', 'X2X3', 'X4X5', 'Z0Z1Z2Z3', 'Z0Z1Z4Z5'] : True\n",
+      "4 :: 63: [[6,1, 2]] : 48 :['X0X1Z3Z4', 'X0X2Z3Z5', 'Z0X3Z4Z5', 'Z1Z3X4Z5', 'Z2Z3Z4X5'] : False\n",
+      "4 :: 69: [[6,1, 2]] : 32 :['X0Z5', 'X1X2Z3Z4', 'Y1Y3Z4Z5', 'X1Z2X4Z5', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 56: [[6,2, 2]] : 64 :['X0X1', 'Z3Z5', 'Z0Z1Z2Z4', 'X2Y3X4Y5'] : True\n",
+      "4 :: 111: [[6,2, 2]] : 96 :['X0X1X2Z5', 'X1X3Z4Z5', 'X0X1Z3X4', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 126: [[6,2, 2]] : 288 :['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5'] : True\n",
+      "4 :: 127: [[6,2, 2]] : 72 :['X1X2Z3Z4', 'X0Y1Y3Z4', 'X1Z2X4Z5', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 129: [[6,2, 2]] : 64 :['X0X1', 'Z0Z1Z2Z4', 'Z0Z1Z3Z5', 'Y2Y3Y4Y5'] : True\n",
+      "4 :: 131: [[6,2, 2]] : 16 :['X2Z5', 'X0X3Z4Z5', 'X1Z3X4Z5', 'Z0Z1Z2X5'] : True\n",
+      "4 :: 134: [[6,2, 2]] : 12 :['X0Y1Y3Z4', 'Z1X2X3Z5', 'X0X1Z2X4', 'Z0Z1Z2X5'] : False\n",
+      "4 :: 76: [[6,3, 2]] : 48 :['X0Z1X2Z4', 'Z0X1X3Z5', 'Z2Z3Y4Y5'] : True\n",
+      "6 :: 50: [[6,1, 2]] : 768 :['Z0Z5', 'Z1Z5', 'Z2Z5', 'Z3Z4', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 56: [[6,1, 2]] : 1152 :['Z0Z4', 'Z1Z5', 'Z2Z5', 'Z3Z4', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 38: [[6,2, 2]] : 288 :['Z1Z5', 'Z2Z5', 'Z0Z3Z4Z5', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 52: [[6,2, 2]] : 128 :['Z2Z5', 'Z3Z4', 'Z0Z1Z4Z5', 'X0X1X2X3X4X5'] : True\n",
+      "6 :: 82: [[6,2, 2]] : 384 :['Z0Z1', 'Z2Z4', 'Z3Z5', 'X0X1Y2Y3Y4Y5'] : True\n",
+      "6 :: 73: [[6,3, 2]] : 192 :['Z0Z1Z2Z4', 'Z0Z1Z3Z5', 'X0X1Y2Y3Y4Y5'] : True\n",
+      "6 :: 82: [[6,3, 2]] : 192 :['X0X1', 'X2X3X4X5', 'Z0Z1Z2Z3Z4Z5'] : True\n",
+      "6 :: 29: [[6,4, 2]] : 4320 :['Z0Z1Z2Z3Z4Z5', 'Y0Y1Y2Y3Y4Y5'] : True\n"
+     ]
+    }
+   ],
+   "source": [
+    "special_codes_n6 = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['n'] ==6 and code['k'] > 0 and code['d'] > 1:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_n6 += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1097,
+   "id": "e324188b-17de-4ac5-9fda-dbf58ec7dc83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 166: [[7,1, 3]] : 64 :['X0Z6', 'X1Z3', 'Z3X4Z5Z6', 'Z1X2Y3Y4', 'Z2Z4X5Z6', 'Z0Y2Z4Y6'] : False\n",
+      "4 :: 190: [[7,1, 3]] : 192 :['X0Z6', 'X1Z5', 'X2Z4', 'Z2X3X4Z5', 'Z1Y3Y5Z6', 'Z0Z3Z4X6'] : True\n",
+      "4 :: 226: [[7,1, 3]] : 1008 :['Z0Z1Z3Z6', 'Z0Z2Z3Z5', 'Y1Y2Y3Y4', 'Z3Z4Z5Z6', 'Y0Y1Y4Y5', 'Y0Y2Y4Y6'] : False\n",
+      "4 :: 227: [[7,1, 3]] : 42 :['Y0Y1Z2Z5', 'Y0Z1Y2Z6', 'Z0X1X3Z4', 'Z0X2Z3X4', 'X0Z2Z3X5', 'X0Z1Z4X6'] : False\n",
+      "4 :: 228: [[7,1, 3]] : 144 :['Y0Y1Z5Z6', 'Z0X1X2Z3', 'X0Z1Z2X3', 'X0Z1X4Z6', 'Z0Z3Z4X5', 'Z1Z2Z4X6'] : False\n",
+      "4 :: 257: [[7,1, 3]] : 32 :['X0Z6', 'X1X2Z4Z5', 'X1X3Z5Z6', 'Y1Z3Y4Z6', 'Y2Z3Y5Z6', 'Z0Z1Z2X6'] : False\n",
+      "6 :: 108: [[7,1, 3]] : 768 :['X0Z4', 'X1Z4', 'X2Z5', 'X3Z6', 'Z2Z3Y5Y6', 'Z0Z1Z2X4X5Z6'] : True\n",
+      "6 :: 115: [[7,1, 3]] : 576 :['X0Z6', 'X1Z6', 'X2X3Z4Z5', 'Y2Y4Z5Z6', 'X2Z3X5Z6', 'Z0Z1Y2Z3Z4Y6'] : False\n"
+     ]
+    }
+   ],
+   "source": [
+    "special_codes_n7 = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['n'] ==7 and code['k'] > 0 and code['d'] > 2:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_n7 += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1098,
+   "id": "c8a0240b-fcc2-4982-8092-8c6bf6901290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 :: 226: [[7,1, 3]] : 1008 :['Z0Z1Z3Z6', 'Z0Z2Z3Z5', 'Y1Y2Y3Y4', 'Z3Z4Z5Z6', 'Y0Y1Y4Y5', 'Y0Y2Y4Y6'] : False\n"
+     ]
+    }
+   ],
+   "source": [
+    "special_codes_n7_css = []\n",
+    "for code in sorted_codes:\n",
+    "    if code['n'] ==7 and code['k'] > 0 and code['d'] > 2 and code['is_css']==1:\n",
+    "        print(f\"{code['max_weight']} :: {code['index']}: [[{code['n']},{code['k']}, {code['d']}]] : {code['aut_group_size']} :{code['isotropic_generators']} : {is_planar(code)}\")\n",
+    "        special_codes_n7_css += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 704,
+   "id": "0c33575f-c876-436d-bb31-3577eaab1557",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G = make_stabilizer_graph(special_codes[1]['isotropic_generators'],special_codes[1]['n'])\n",
+    "nx.draw(G, with_labels=True, node_color='skyblue', node_size=1000, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)\n",
+    "\n",
+    "#pos = nx.planar_layout(G)\n",
+    "#nx.draw(G, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 701,
+   "id": "536b1ed9-63d1-44ff-b7bb-8c397e601c30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gen = ['X3X4X5X6', 'X1X2X5X6', 'X0X2X4X6', 'Z3Z4Z5Z6', 'Z1Z2Z5Z6', 'Z0Z2Z4Z6']\n",
+    "J = make_stabilizer_graph(gen,7)\n",
+    "\n",
+    "pos = nx.planar_layout(J)\n",
+    "nx.draw(J, with_labels=True, pos=pos, node_color='skyblue', node_size=50, font_size=12, font_color='black', font_weight='bold', edge_color='gray', arrows=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1070,
+   "id": "87097504-c671-4936-8808-4c717a87693e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from itertools import combinations\n",
+    "\n",
+    "def rank(matrix):\n",
+    "    \"\"\"Compute the rank of a matrix.\"\"\"\n",
+    "    return np.linalg.matrix_rank(matrix)\n",
+    "\n",
+    "def generate_all_matrices(m, n):\n",
+    "    \"\"\"Generate all possible m x n matrices over GF(2).\"\"\"\n",
+    "    num_matrices = 2 ** (m * n)\n",
+    "    matrices = []\n",
+    "    for i in range(num_matrices):\n",
+    "        matrix = np.array([list(map(int, bin(i)[2:].zfill(m * n)))]).reshape((m, n))\n",
+    "        matrices.append(matrix)\n",
+    "    return matrices\n",
+    "\n",
+    "def filter_matrices_by_rank(matrices, k):\n",
+    "    \"\"\"Filter matrices to include only those with rank k.\"\"\"\n",
+    "    result = []\n",
+    "    for matrix in matrices:\n",
+    "        if rank(matrix) == k:\n",
+    "            result.append(matrix)\n",
+    "    return result\n",
+    "\n",
+    "def generate_rank_k_matrices(m, n, k):\n",
+    "    \"\"\"Generate all rank k matrices of size m x n over GF(2).\"\"\"\n",
+    "    all_matrices = generate_all_matrices(m, n)\n",
+    "    rank_k_matrices = filter_matrices_by_rank(all_matrices, k)\n",
+    "    return rank_k_matrices\n",
+    "\n",
+    "def print_matrices(matrices):\n",
+    "    \"\"\"Print matrices in a readable format.\"\"\"\n",
+    "    for i, matrix in enumerate(matrices):\n",
+    "        print(f\"Matrix {i + 1}:\")\n",
+    "        print(matrix)\n",
+    "        print()\n",
+    "\n",
+    "# Example usage:\n",
+    "m, n, k = 3, 3, 3  # Dimensions and rank\n",
+    "#rank_k_matrices = generate_rank_k_matrices(m, n, k)\n",
+    "#print_matrices(rank_k_matrices)\n",
+    "\n",
+    "def np_weight(data, n, k):\n",
+    "    weights = []\n",
+    "    for j in range(n-k):\n",
+    "        weights += [int(np.sum(np.logical_or(data[j][0:n],data[j][n:2*n]).astype(int)))]\n",
+    "    return max(weights)\n",
+    "\n",
+    "def np_weight_list(data, n, k):\n",
+    "    weights = []\n",
+    "    for j in range(n-k):\n",
+    "        weights += [int(np.sum(np.logical_or(data[j][0:n],data[j][n:2*n]).astype(int)))]\n",
+    "    return sum(weights), max(weights)\n",
+    "\n",
+    "def find_smallest_weight(gen, n, k):\n",
+    "    gen = PauliList(gen)\n",
+    "    init_weight = np_weight(gen.matrix, n, k)\n",
+    "    weight = init_weight\n",
+    "    sym_matrix = gen.matrix.astype(int)\n",
+    "    t = n-k\n",
+    "    num_matrices = 2 ** (t*t)\n",
+    "    start = 2**(t*t - t)\n",
+    "    found_reduced = False\n",
+    "    mod_trans = np.eye(t, dtype=int)\n",
+    "    for i in range(start, num_matrices):\n",
+    "        trans = np.array([list(map(int, bin(i)[2:].zfill(t * t)))]).reshape((t, t))\n",
+    "        if np.linalg.matrix_rank(trans) == t:\n",
+    "            new_matrix = trans @ sym_matrix\n",
+    "            new_matrix = new_matrix % 2\n",
+    "            less_weight = np_weight(new_matrix.astype(bool), n, k)\n",
+    "            if less_weight < weight:\n",
+    "                found_reduced = True\n",
+    "                mod_trans = trans\n",
+    "                print(f\"Found a lower weight generating set: {less_weight} < {weight}\")\n",
+    "                weight = less_weight\n",
+    "    if found_reduced is True:\n",
+    "        print(f\" Initial weight : {init_weight}  -> Minimal Weight : {weight} with transform \\n {mod_trans}\")\n",
+    "        mod_matrix = mod_trans @ sym_matrix\n",
+    "        mod_matrix = mod_matrix %2\n",
+    "        print(PauliList(mod_matrix))\n",
+    "                \n",
+    "def find_smallest_weight_list(gen, n, k):\n",
+    "    gen = PauliList(gen)\n",
+    "    init_sum, init_weight_max = np_weight_list(gen.matrix, n, k)\n",
+    "    weight = init_weight_max\n",
+    "    weight_sum = init_sum\n",
+    "    sym_matrix = gen.matrix.astype(int)\n",
+    "    t = n-k\n",
+    "    num_matrices = 2 ** (t*t)\n",
+    "    start = 2**(t*t - t)\n",
+    "    found_reduced = False\n",
+    "    mod_trans = np.eye(t, dtype=int)\n",
+    "    for i in range(start, num_matrices):\n",
+    "        trans = np.array([list(map(int, bin(i)[2:].zfill(t * t)))]).reshape((t, t))\n",
+    "        if np.linalg.matrix_rank(trans) == t:\n",
+    "            new_matrix = trans @ sym_matrix\n",
+    "            new_matrix = new_matrix % 2\n",
+    "            less_sum, less_weight = np_weight_list(new_matrix.astype(bool), n, k)\n",
+    "            if less_weight < weight or (less_weight <= weight and less_sum < weight_sum):\n",
+    "                found_reduced = True\n",
+    "                mod_trans = trans\n",
+    "                print(f\"Found a lower weight generating set: max {less_weight} ? {weight} and sum {less_sum} : {weight_sum}\")\n",
+    "                weight = less_weight\n",
+    "                weight_sum = less_sum\n",
+    "    if found_reduced is True:\n",
+    "        print(f\" Initial weight : {init_weight}  -> Minimal Weight : {weight}: {weight_sum} with transform \\n {mod_trans}\")\n",
+    "        mod_matrix = mod_trans @ sym_matrix\n",
+    "        mod_matrix = mod_matrix %2\n",
+    "        print(PauliList(mod_matrix))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1065,
+   "id": "1c73e27a-962c-46f4-ae99-f6551c0ee341",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 1065,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = [1,2,3,4]\n",
+    "sum(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1032,
+   "id": "1b40b78d-7714-4479-bb3e-4eb3df09a999",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_trans = np.array([[1,0,0],[1,1,0],[1,0,1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1033,
+   "id": "736d4c0f-88cf-4ccb-bf28-95a2e553b9fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True,  True],\n",
+       "       [ True,  True,  True],\n",
+       "       [ True,  True,  True]])"
+      ]
+     },
+     "execution_count": 1033,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_mat = test_trans @ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 936,
+   "id": "444e793e-d08f-4c66-8195-2d75420e4740",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1]])"
+      ]
+     },
+     "execution_count": 936,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PauliList(['X3X4X5X6', 'X1X2X5X6', 'X0X2X4X6', 'Z3Z4Z5Z6', 'Z1Z2Z5Z6', 'Z0Z2Z4Z6']).matrix.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1073,
+   "id": "591dfcde-844b-4db8-9aff-60f6725cab31",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1073], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfind_smallest_weight_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mY0Y1Z5Z6\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ0X1X2Z3\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mX0Z1Z2X3\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mX0Z1X4Z6\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ0Z3Z4X5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ1Z2Z4X6\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m7\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[1070], line 95\u001b[0m, in \u001b[0;36mfind_smallest_weight_list\u001b[0;34m(gen, n, k)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(start, num_matrices):\n\u001b[1;32m     94\u001b[0m     trans \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mbin\u001b[39m(i)[\u001b[38;5;241m2\u001b[39m:]\u001b[38;5;241m.\u001b[39mzfill(t \u001b[38;5;241m*\u001b[39m t)))])\u001b[38;5;241m.\u001b[39mreshape((t, t))\n\u001b[0;32m---> 95\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatrix_rank\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrans\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m==\u001b[39m t:\n\u001b[1;32m     96\u001b[0m         new_matrix \u001b[38;5;241m=\u001b[39m trans \u001b[38;5;241m@\u001b[39m sym_matrix\n\u001b[1;32m     97\u001b[0m         new_matrix \u001b[38;5;241m=\u001b[39m new_matrix \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m2\u001b[39m\n",
+      "File \u001b[0;32m~/.venv/qiskit-qec-data/lib/python3.9/site-packages/numpy/linalg/_linalg.py:2086\u001b[0m, in \u001b[0;36mmatrix_rank\u001b[0;34m(A, tol, hermitian, rtol)\u001b[0m\n\u001b[1;32m   2084\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m   2085\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mall\u001b[39m(A \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m))\n\u001b[0;32m-> 2086\u001b[0m S \u001b[38;5;241m=\u001b[39m \u001b[43msvd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompute_uv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhermitian\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhermitian\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2088\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2089\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m rtol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/.venv/qiskit-qec-data/lib/python3.9/site-packages/numpy/linalg/_linalg.py:1812\u001b[0m, in \u001b[0;36msvd\u001b[0;34m(a, full_matrices, compute_uv, hermitian)\u001b[0m\n\u001b[1;32m   1808\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m errstate(call\u001b[38;5;241m=\u001b[39m_raise_linalgerror_svd_nonconvergence,\n\u001b[1;32m   1809\u001b[0m               invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcall\u001b[39m\u001b[38;5;124m'\u001b[39m, over\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m, divide\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   1810\u001b[0m               under\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   1811\u001b[0m     s \u001b[38;5;241m=\u001b[39m gufunc(a, signature\u001b[38;5;241m=\u001b[39msignature)\n\u001b[0;32m-> 1812\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_realType\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult_t\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1813\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m s\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "find_smallest_weight_list(['Y0Y1Z5Z6', 'Z0X1X2Z3', 'X0Z1Z2X3', 'X0Z1X4Z6', 'Z0Z3Z4X5', 'Z1Z2Z4X6'], 7, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1041,
+   "id": "f1260980-cafc-42f2-ad2e-c1781dcd5a73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 0 0 0 0 0 0 0]\n",
+      " [0 1 1 1 0 0 0 0]\n",
+      " [0 0 0 0 0 1 1 1]]\n",
+      "[1, 4, 4] 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_trans = np.array([[1,0,0],[1,1,0],[1,0,1]])\n",
+    "n=4\n",
+    "k=1\n",
+    "gen = ['X0', 'X1X2X3', 'Z1Z2Z3']\n",
+    "gen = PauliList(gen)\n",
+    "init_weight = np_weight(gen.matrix, n, k)\n",
+    "\n",
+    "new_mat = (test_trans @ gen.matrix.astype(int))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1040,
+   "id": "72da1866-5fe6-4f7f-b76e-e76dbf98eaed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 0, 0, 0, 0],\n",
+       "       [1, 0, 0, 0, 0, 1, 1, 1]])"
+      ]
+     },
+     "execution_count": 1040,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1043,
+   "id": "9aa8eb2e-5a45-4a44-a118-dba9ea53addc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 0 0 0 0 0 0 0]\n",
+      " [1 1 1 1 0 0 0 0]\n",
+      " [1 0 0 0 0 1 1 1]]\n",
+      "[1, 4, 4] 4\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1043,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_weight(new_mat.astype(bool),n,k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1045,
+   "id": "afaba72c-9cc2-4b3c-950a-fe417dfbfbf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 1, 1, 1, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 1, 1, 1]])"
+      ]
+     },
+     "execution_count": 1045,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gen.matrix.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1049,
+   "id": "69b66e3b-0a4a-47c7-891b-ac5e8cdb1659",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 0 0 0 0 0 0 0]\n",
+      " [0 1 1 1 0 0 0 0]\n",
+      " [0 0 0 0 0 1 1 1]]\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "max() arg is an empty sequence",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1049], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnp_weight\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatrix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[1048], line 48\u001b[0m, in \u001b[0;36mnp_weight\u001b[0;34m(data, n, k)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n\u001b[38;5;241m-\u001b[39mk):\n\u001b[1;32m     47\u001b[0m     weight \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mint\u001b[39m(np\u001b[38;5;241m.\u001b[39msum(np\u001b[38;5;241m.\u001b[39mlogical_or(sym_matrix[j][\u001b[38;5;241m0\u001b[39m:n],sym_matrix[j][n:\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mn])\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)))]\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(weight, \u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mmax\u001b[39m(weight)\n",
+      "\u001b[0;31mValueError\u001b[0m: max() arg is an empty sequence"
+     ]
+    }
+   ],
+   "source": [
+    "np_weight(gen.matrix, n, k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 960,
+   "id": "9d2c4462-ccdb-4ba1-9842-691ae3c4072f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True,  True, False])"
+      ]
+     },
+     "execution_count": 960,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sym_matrix[0][5:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 964,
+   "id": "859be59b-3a1c-4d4f-bea0-3adfacb27de1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 964,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(np.sum(np.logical_or(sym_matrix[0][0:5],sym_matrix[0][5:10]).astype(int)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 966,
+   "id": "d1a66cc0-4d80-4518-8c8d-76fa71c0f188",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight = []\n",
+    "for j in range(4):\n",
+    "    weight += [int(np.sum(np.logical_or(sym_matrix[j][0:5],sym_matrix[j][5:10]).astype(int)))]\n",
+    "print(max(weight))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 970,
+   "id": "d7c22f58-7457-4de9-82e8-84fddcc559a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0],\n",
+       "       [0, 1, 0],\n",
+       "       [0, 0, 1]])"
+      ]
+     },
+     "execution_count": 970,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.eye(k, dtype=int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1113,
+   "id": "b15cb571-832b-4b62-bdcb-fa036a60cf5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = ['YYZIZZZ','ZYYZIII','IZYYZII','ZIZYYII','ZIIIIXI','ZIIIIIX']\n",
+    "B = [item.lower() for item in A]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1114,
+   "id": "9ea3f0be-8b30-486a-91d8-2dd2ccdf6290",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['yyzizzz', 'zyyziii', 'izyyzii', 'zizyyii', 'ziiiixi', 'ziiiiix']"
+      ]
+     },
+     "execution_count": 1114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1130,
+   "id": "b36a5422-dfac-4d51-957c-d42fd1c8fdc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'yyzizzz' 'zyyziii' 'izyyzii' 'zizyyii' 'ziiiixi' 'ziiiiix' "
+     ]
+    }
+   ],
+   "source": [
+    "for item in B:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1132,
+   "id": "e11e6c54-fcd7-47b8-917f-3bfdd0482f3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = ['YYZIZ', 'ZYYZI', 'IZYYZ', 'ZIZYY']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1134,
+   "id": "c38248e6-66df-42c0-b46a-df2a4c87fbf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'yyziz' 'zyyzi' 'izyyz' 'zizyy' "
+     ]
+    }
+   ],
+   "source": [
+    "B = [item.lower() for item in A]\n",
+    "for item in B:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1135,
+   "id": "f74ee814-e488-4be0-b82d-0833c3e9dd2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'yyzizz' 'zyyzii' 'izyyzi' 'zizyyi' 'ziiiix' "
+     ]
+    }
+   ],
+   "source": [
+    "A = ['YYZIZZ','ZYYZII','IZYYZI','ZIZYYI','ZIIIIX']\n",
+    "B = [item.lower() for item in A]\n",
+    "for item in B:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1136,
+   "id": "1a7730a2-b110-4915-b26f-0e2d2c97e9a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'yyzizzzz' 'zyyziiiz' 'izyyziii' 'zizyyiii' 'ziiiixii' 'ziiiiixi' 'iziiiiix' "
+     ]
+    }
+   ],
+   "source": [
+    "A =['YYZIZZZZ','ZYYZIIIZ','IZYYZIII','ZIZYYIII','ZIIIIXII','ZIIIIIXI','IZIIIIIX']\n",
+    "B = [item.lower() for item in A]\n",
+    "for item in B:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1153,
+   "id": "22febd31-db61-409a-b744-68fae9e6d3c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes_813 = cb.all_small_codes(8, 1 ,d = 3, is_decomposable=False, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1154,
+   "id": "301f3910-0fea-4ea8-96a6-52a858f6364d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "157"
+      ]
+     },
+     "execution_count": 1154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(codes_813)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1156,
+   "id": "a83df8ab-4d94-467b-9092-5beed9accd8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['V4V5', '(4,5)', 'V0S7', 'H0H7^(0,7)', 'V2S3', 'H2H3^(2,3)', 'V1S6', 'H1H6^(1,6)']\n",
+      "aut_group_size       : 256\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 140\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z3', 'X4X5', 'Z2X3X4Z6', 'Z1Y4Z5Y6Z7', 'Z0Z3Z4Z5X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z1Z4Z5X6', 'Z3Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : f7d59a3a-c13d-495d-a70d-95605decc53e\n",
+      "weight_enumerator    : [1, 0, 4, 0, 14, 16, 36, 48, 9]\n",
+      "\n",
+      "aut_group_generators : ['V0S7', 'H0H7^(0,7)', 'V2S4', 'H2H4^(2,4)', 'V1S6', 'H1H6^(1,6)']\n",
+      "aut_group_size       : 64\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 152\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z4', 'X3Z5Z7', 'Z1Z3Y5Y6', 'Z0Z3Z4X7', 'Z2Y3X4Y5Z6']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z1Z5X6', 'Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : ad348e8c-ff32-4674-a1e2-5faf79f09ed4\n",
+      "weight_enumerator    : [1, 0, 3, 2, 9, 20, 41, 42, 10]\n",
+      "\n",
+      "aut_group_generators : ['V0S7', 'H0H7^(0,7)', 'V2S4', 'H2H4^(2,4)', 'V1S6', 'H1H6^(1,6)']\n",
+      "aut_group_size       : 64\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 154\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z4', 'X3Z5Z7', 'Z1Z3Y5Y6', 'Z2Y3X4Y5Z6', 'Z0Z2Z3Y4Y7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z3Z4X7', 'Z4Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 0e46de1a-2a45-4b34-9e72-054d8110aec8\n",
+      "weight_enumerator    : [1, 0, 3, 2, 5, 28, 41, 34, 14]\n",
+      "\n",
+      "aut_group_generators : ['V1S6', 'H1H6^(1,6)', 'V0S7', 'H0H7^(0,7)', 'V2S4', 'H2H4^(2,4)']\n",
+      "aut_group_size       : 64\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 164\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z4', 'Z2X3X4Z5', 'Y3Y5Z6Z7', 'Z1X3X6Z7', 'Z0Y3Z4Z5Y7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z3Z4X7', 'Z3Z4Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 86bb35f9-4bbe-4b1b-bb1c-6faf4f669134\n",
+      "weight_enumerator    : [1, 0, 3, 0, 15, 16, 37, 48, 8]\n",
+      "\n",
+      "aut_group_generators : ['V1S4', 'H1H4^(1,4)', 'V0S7', 'H0H7^(0,7)']\n",
+      "aut_group_size       : 16\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 181\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z4', 'X3Z4Z6', 'Y2Y5Z6', 'X2Z3X6', 'Z0X2X5X7', 'Z1X4Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z3Z5X6Z7', 'Z2Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : 23de1dd1-7563-4e51-a274-bbcf4fb78b86\n",
+      "weight_enumerator    : [1, 0, 2, 4, 8, 16, 46, 44, 7]\n",
+      "\n",
+      "aut_group_generators : ['V0S7', 'H0H7^(0,7)', 'V1S4', 'H1H4^(1,4)']\n",
+      "aut_group_size       : 16\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 259\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z4', 'X3Z4Z6', 'Y2Y5Z6', 'Z3Z5X6Z7', 'Z1X2X4X6', 'Z0X2X5X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z2X5Z6Z7', 'Z2Z4Z5']\n",
+      "n                    : 8\n",
+      "uuid                 : 00128804-a492-49c3-81e4-41415e9ca62a\n",
+      "weight_enumerator    : [1, 0, 2, 3, 9, 18, 44, 43, 8]\n",
+      "\n",
+      "aut_group_generators : ['V3S7', 'H3H7^(3,7)', 'V2S6', 'H2H6^(2,6)', 'V1S5', 'H1H5^(1,5)', 'V0S4', '(0,1)(4,5)']\n",
+      "aut_group_size       : 512\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 346\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z4', 'X1Z5', 'X2Z6', 'X3Z7', 'Z3Z6X7', 'Z0Z1Y4Y5', 'Z0Z2X4Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z2X6Z7', 'Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : 5aada1a9-a0f1-4f4d-923a-974f71338e7f\n",
+      "weight_enumerator    : [1, 0, 4, 4, 10, 8, 44, 52, 5]\n",
+      "\n",
+      "aut_group_generators : ['V1S7', 'H1H7^(1,7)', 'V0S7', '(0,1)', 'V3V4', '(3,4)', 'V2S5', 'H2H5^(2,5)']\n",
+      "aut_group_size       : 384\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 493\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'X2Z5', 'X3X4', 'Z2X5Z6Z7', 'Z3Z4Z5X6', 'Z0Z1X3Y6Y7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z3Z4Z5X7', 'Z3Z4Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 920cbad7-2530-4541-b9b7-be07bfd1303e\n",
+      "weight_enumerator    : [1, 0, 5, 0, 17, 8, 35, 56, 6]\n",
+      "\n",
+      "aut_group_generators : ['V2S6', 'H2H6^(2,6)', 'V1S6', '(1,2)', 'V0S7', 'H0H7^(0,7)', 'V3V4', '(3,4)']\n",
+      "aut_group_size       : 384\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 564\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z6', 'X3X4', 'Z3Z4X5', 'Z0X3Y5Y7', 'Z1Z2X3Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z3Z4X7', 'Z3Z4Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : b3d314a1-647e-47c0-9e8a-a3c835b99073\n",
+      "weight_enumerator    : [1, 0, 5, 2, 11, 12, 39, 50, 8]\n",
+      "\n",
+      "aut_group_generators : ['V1S7', 'H1H7^(1,7)', 'V0S7', '(0,1)', 'V2S3S4H5H6^(3,4)', '(2,3)(4,5)']\n",
+      "aut_group_size       : 192\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 595\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'Z2X4Z6', 'Z3X5Z6', 'Z4Z5X6', 'Y2Y3X6', 'Z0Z1X2Z3Z4X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1X7', 'Z2Z3Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : c415e6b1-1b37-4906-8dbf-546ccd51b06f\n",
+      "weight_enumerator    : [1, 0, 3, 4, 7, 16, 45, 44, 8]\n",
+      "\n",
+      "aut_group_generators : ['V1S7', 'V0S7', 'V3S6', 'V2S6', 'V4S5', 'H4H5^(4,5)', 'H1H7^(1,7)', 'H3H6^(3,6)', '(0,1)', '(2,3)']\n",
+      "aut_group_size       : 2304\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 631\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'X2Z6', 'X3Z6', 'X4Z5', 'Z0Z1Z4Y5Y7', 'Z2Z3Z4X5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z5X7', 'Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : baa2ddde-c0d7-4913-88c4-f4c3241a2d9a\n",
+      "weight_enumerator    : [1, 0, 7, 0, 15, 8, 33, 56, 8]\n",
+      "\n",
+      "aut_group_generators : ['V0S7', 'V1S6', 'H1H6^(1,6)', 'V2S4', 'H2H4^(2,4)', 'H3S4H5S6S7^(0,1)(6,7)']\n",
+      "aut_group_size       : 128\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 748\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z4', 'X3Z5Z7', 'Z3X5Z6', 'Z1Z2X3X4X6', 'Z0Z2Z3Y4Y7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z3Z4X7', 'Z4Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 5bdba388-f43a-4a5f-b496-7a8405bb41b3\n",
+      "weight_enumerator    : [1, 0, 3, 4, 7, 16, 45, 44, 8]\n",
+      "\n",
+      "aut_group_generators : ['V1S4', 'H1H4^(1,4)', 'V0S7', 'r2r3S4R5R6S7^(0,1)(2,6)(3,5)(4,7)']\n",
+      "aut_group_size       : 32\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 787\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z4', 'Y2Y5Z6', 'X2Z3X6', 'Z1Y3Y4Z6', 'Z0X2X5X7', 'Z2X3Z4X5Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z3Z5X6Z7', 'Z2Z3Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : 3493fc58-b780-4170-90de-b82ffc9c877c\n",
+      "weight_enumerator    : [1, 0, 2, 2, 6, 28, 42, 34, 13]\n",
+      "\n",
+      "aut_group_generators : ['V1S4', 'H1H4^(1,4)', 'S2S3S4H5H6^(2,3)', 'V0S7', 'H0H7^(0,7)']\n",
+      "aut_group_size       : 32\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 832\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z4', 'Z0Y5Y7', 'Z1Y3Y4Z6', 'Z2X5Z6Z7', 'Y2X3Z4Y5', 'Z3Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z1Z3X4', 'Z2Z3Z4']\n",
+      "n                    : 8\n",
+      "uuid                 : 251a1c8b-bc3b-4421-b3b1-84ad7b83dc34\n",
+      "weight_enumerator    : [1, 0, 2, 2, 14, 12, 42, 50, 5]\n",
+      "\n",
+      "aut_group_generators : ['V2S7', 'H2H7^(2,7)', 'V1S7', '(1,2)', 'V0S7', '(0,1)', 'V4S6', 'H4H6^(4,6)', 'V3S5', '(3,4)(5,6)']\n",
+      "aut_group_size       : 6144\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 855\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z7', 'X2Z7', 'X3Z5', 'X4Z6', 'Z3Z4Y5Y6', 'Z0Z1Z2Z3X5Z6X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z2X7', 'Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : b1beac76-46ec-47d6-9269-73a23db413d1\n",
+      "weight_enumerator    : [1, 0, 8, 0, 18, 0, 32, 64, 5]\n",
+      "\n",
+      "aut_group_generators : ['V1S6', 'H1H6^(1,6)', 'V0S7', 'H0H7^(0,7)', 'V2S4', 'H2H4^(2,4)']\n",
+      "aut_group_size       : 64\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1034\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z6', 'X2Z4', 'Z1Z5X6', 'Z2X3X4Z5', 'Y3Y5Z6Z7', 'Z0Z3Z4X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z3X5Z6', 'Z3Z4Z5']\n",
+      "n                    : 8\n",
+      "uuid                 : 63cd74ca-4abe-4456-9ce6-07d66830680f\n",
+      "weight_enumerator    : [1, 0, 3, 2, 13, 12, 41, 50, 6]\n",
+      "\n",
+      "aut_group_generators : ['V0S4', 'V1S5', 'H1H5^(1,5)', 'V3S7', 'H3H7^(3,7)', 'V2S6', '(2,3)(6,7)', '(0,1)(4,5)', '(0,2)(1,3)(4,6)(5,7)']\n",
+      "aut_group_size       : 2048\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1090\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z4', 'X1Z5', 'X2Z6', 'X3Z7', 'Z0Z1Y4Y5', 'Z2Z3Y6Y7', 'Z0Z2X4Z5X6Z7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z3Z6X7', 'Z4Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : a3da7246-dc61-4d39-b2ff-4bb937216758\n",
+      "weight_enumerator    : [1, 0, 4, 0, 14, 0, 84, 0, 25]\n",
+      "\n",
+      "aut_group_generators : ['V0S6', 'V2S5', 'H2H5^(2,5)', 'V1S7', 'H1H7^(1,7)', 'V3V4', '(3,4)', 'H4H5^(0,1)(2,3)(4,5)(6,7)', 'r4S5R6S7^(0,3,1,2)(4,7,5,6)']\n",
+      "aut_group_size       : 1024\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1094\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z6', 'X1Z7', 'X2Z5', 'X3X4', 'Z0Y3Z4Z5Y6', 'Z0Z2X5X6Z7', 'Z1Z3Z4Z6X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z3Z4X6Z7', 'Z3Z4Z5Z6']\n",
+      "n                    : 8\n",
+      "uuid                 : eebcc246-7e44-4e80-a401-8632fa112185\n",
+      "weight_enumerator    : [1, 0, 4, 0, 6, 32, 36, 32, 17]\n",
+      "\n",
+      "aut_group_generators : ['V1S4', 'H1H4^(1,4)', 'V0S7', 'H0H7^(0,7)']\n",
+      "aut_group_size       : 16\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1146\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1Z4', 'Y2Y5Z6', 'Z1Y3Y4Z6', 'Z3Z5X6Z7', 'X2Y3Z4Y6', 'Z0X2X5X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z2X5Z6Z7', 'Z2Z3Z4Z5']\n",
+      "n                    : 8\n",
+      "uuid                 : bf95209d-be8a-48d3-9915-4242ce15dcc2\n",
+      "weight_enumerator    : [1, 0, 2, 1, 11, 22, 40, 41, 10]\n",
+      "\n",
+      "aut_group_generators : ['S0H1S2S3S4H5H6H7^(1,6)(2,3)', '(0,2)(1,7)(3,4)(5,6)']\n",
+      "aut_group_size       : 8\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1157\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0X1Z6', 'Z3X5Z7', 'Z1Z4X6', 'Z2Z5X7', 'Y0Z1Y2Z7', 'Y3Y4Z5Z6', 'X1X2X4X5']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z3X4Z6', 'Z0Z2Z3Z4']\n",
+      "n                    : 8\n",
+      "uuid                 : 5a1036ba-8ce5-4135-9096-647da7a6bcfb\n",
+      "weight_enumerator    : [1, 0, 0, 4, 6, 24, 48, 36, 9]\n",
+      "\n",
+      "aut_group_generators : ['V0S7', 'H0H7^(0,7)']\n",
+      "aut_group_size       : 4\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 1247\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z7', 'X1X3Z5', 'Y2X3Y4', 'Z2X4Z6Z7', 'Z2Z3X5Z7', 'Z1Z3Z4X6', 'Z0Z5X6X7']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X3Z5Z6Z7', 'Z1Z2Z3']\n",
+      "n                    : 8\n",
+      "uuid                 : 4ef8ae2b-c63f-4e98-8ff9-ea54f70d280e\n",
+      "weight_enumerator    : [1, 0, 1, 2, 11, 20, 43, 42, 8]\n",
+      "\n",
+      "21\n"
+     ]
+    }
+   ],
+   "source": [
+    "count  = 0\n",
+    "for code in codes_813:\n",
+    "    if is_planar(code):\n",
+    "        count += 1\n",
+    "        print(code)\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1140,
+   "id": "663215c2-9a58-4992-af61-5a2113fc2381",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{aut_group_generators : ['V7', 'V6', '(6,7)', 'V0S5', 'H0H5^(0,5)', 'V1H2S3H4S5^(2,4)', 'H1H2V3V4S5^(3,4)', '(1,2)(3,4)'],\n",
+       "aut_group_size       : 768,\n",
+       "code_type            : StabSubSystemCode,\n",
+       "d                    : 3,\n",
+       "index                : 40,\n",
+       "is_css               : 0,\n",
+       "is_decomposable      : 1,\n",
+       "is_degenerate        : 1,\n",
+       "is_gf4linear         : 0,\n",
+       "is_subsystem         : 1,\n",
+       "isotropic_generators : ['X6', 'X7', 'X0Z5', 'X1X2Z3Z4', 'Y1Y3Z4Z5', 'X1Z2X4Z5', 'Z0Y1Z2Z3Y5'],\n",
+       "k                    : 1,\n",
+       "logical_ops          : ['Z0Z1Z2X5', 'Z1Z2Z3Z4Z5'],\n",
+       "n                    : 8,\n",
+       "uuid                 : f3888e35-a9a4-4938-976c-5a88c325acc6,\n",
+       "weight_enumerator    : [1, 2, 2, 2, 12, 38, 46, 22, 3],\n",
+       "}"
+      ]
+     },
+     "execution_count": 1140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "codes_813[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a6f002c-901a-4d0b-8431-9aa5be2f299e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'XIXZIIZI','IZXIXIZI','ZXZIIIIZ','IIZXZIIZ','IIIIIXII','ZIIIZIXZ','IZIZIIZX'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1150,
+   "id": "d12d1f87-4f2e-4840-93e3-9ed9392fb0d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'xixziizi' 'izxixizi' 'zxziiiiz' 'iizxziiz' 'iiiiixii' 'ziiizixz' 'iziziizx' "
+     ]
+    }
+   ],
+   "source": [
+    "A=['XIXZIIZI','IZXIXIZI','ZXZIIIIZ','IIZXZIIZ','IIIIIXII','ZIIIZIXZ','IZIZIIZX']\n",
+    "B = [item.lower() for item in A]\n",
+    "for item in B:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1144,
+   "id": "180087c7-5963-4752-89d1-050844a61fdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes_913 = cb.all_small_codes(9, 1 ,d = 3, info_only=True, is_decomposable=False, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1145,
+   "id": "d308c1bd-8885-4f9b-9776-231c22bf7bfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3411"
+      ]
+     },
+     "execution_count": 1145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(codes_913)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1147,
+   "id": "298d58e5-8b41-426e-a7da-6d466aeabac8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_codes = []\n",
+    "for code in codes_913:\n",
+    "    if code['aut_group_size'] == 384:\n",
+    "        f_codes += [code]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1148,
+   "id": "88540107-b1da-4331-bdb8-028c4216bd9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37"
+      ]
+     },
+     "execution_count": 1148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(f_codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1149,
+   "id": "837f72e3-f75b-4c04-b904-37f361de46c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{aut_group_generators : ['V0S8', 'H0H8^(0,8)', 'V3H4S6H7S8^(4,7)', 'V1S5', 'H1H5^(1,5)', 'H3H4V6V7S8^(6,7)', '(3,4)(6,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 525,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z5', 'X2Z5Z8', 'Z1Z2X5', 'X3X4Z6Z7', 'Y3Y6Z7Z8', 'X3Z4X7Z8', 'Z0Z2Y3Z4Z6Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z2Z3Z4X8', 'Z3Z4Z6Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : a3864250-ef00-459f-bf65-bc078e2c8b4c,\n",
+       " weight_enumerator    : [1, 0, 2, 6, 16, 10, 38, 106, 71, 6],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'H0H8^(0,8)', 'V1S6', 'H1H6^(1,6)', 'V2H3S4H5S7^(3,5)', 'H2H3V4V5S7^(4,5)', '(2,3)(4,5)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 1655,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z6', 'Z1X6Z8', 'X2X3Z4Z5', 'Y2Y4Z5Z7', 'X2Z3X5Z7', 'Z0Z6Z7X8', 'Y2Z3Z4Y7Z8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z3X7Z8', 'Z2Z3Z4Z5Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 4342ad38-8729-4abf-8bf0-5d239d86779b,\n",
+       " weight_enumerator    : [1, 0, 2, 4, 12, 20, 46, 92, 67, 12],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'V2S7', 'H2H7^(2,7)', '(3,4)(5,6)', 'H3H4H5H6^(3,5)(4,6)', '(0,1)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3391,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z7Z8', 'Y4Y6Z7Z8', 'Z2Z5Z6X7', 'Z0Z1Y3Z4Z5Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z3Z4X8', 'Z3Z4Z5Z6Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : c5dddea1-df04-40ae-bc73-68aa69cd3a6a,\n",
+       " weight_enumerator    : [1, 0, 4, 0, 22, 0, 100, 0, 129, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'V2S7', 'H2H7^(2,7)', '(3,4)(5,6)', 'H3H4H5H6^(3,5)(4,6)', '(0,1)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3396,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3X4Z5Z6', 'Y3Y5Z7Z8', 'Y4Y6Z7Z8', 'Z2X3Z6X7Z8', 'Z0Z1Z3Z4X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z5Z6X7', 'Z3Z4Z5Z6Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 1b1b22db-0fa7-44ec-b7fb-3ba34c2d117d,\n",
+       " weight_enumerator    : [1, 0, 4, 0, 18, 28, 28, 88, 77, 12],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S5', 'H3H5^(3,5)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3475,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'X4Z6Z8', 'Z2Z4Y6Y7', 'Z3Y4X5Y6Z7', 'Z0Z1Z3Z4Y5Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z4Z5X8', 'Z5Z6Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 46fc3725-ade3-4091-8730-c1678bb8feb5,\n",
+       " weight_enumerator    : [1, 0, 5, 3, 9, 21, 59, 77, 54, 27],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S5', 'H3H5^(3,5)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3476,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'X4Z6Z8', 'Z2Z4Y6Y7', 'Z3Y4X5Y6Z7', 'Z0Z1Z4Z5X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z6X7', 'Z5Z6Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : fc51d859-21fe-4556-94d8-f7b6112c21dd,\n",
+       " weight_enumerator    : [1, 0, 5, 3, 9, 29, 43, 77, 70, 19],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'V2S7', 'H2H7^(2,7)', 'V3S5', 'H3H5^(3,5)', '(0,1)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3479,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'X4Z6Z8', 'Z4X6Z7', 'Z2Z3X4X5X7', 'Z0Z1Z3Z4Y5Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z4Z5X8', 'Z5Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 9ddaf057-dd4b-4453-b708-297034098113,\n",
+       " weight_enumerator    : [1, 0, 5, 5, 13, 19, 35, 91, 74, 13],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S5', 'H3H5^(3,5)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3491,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'X4Z5X6', 'Z3Y5Y6Z7Z8', 'Z2Y4Z6Y7Z8', 'Z0Z1Z4Z6X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z4Z6X7', 'Z4Z5Z6Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 17cd23d0-dcbb-43b1-82e8-09d5ced3d676,\n",
+       " weight_enumerator    : [1, 0, 5, 2, 7, 34, 47, 70, 68, 22],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S5', 'H3H5^(3,5)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3497,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'X4Z5X6', 'Z2Z4Z6X7', 'Z3Y5Y6Z7Z8', 'Z0Z1Y4Z6Z7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z4Z6X8', 'Z4Z5Z6Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 8805bd0d-fb21-4ac3-9f69-81f95207fcc4,\n",
+       " weight_enumerator    : [1, 0, 5, 2, 9, 24, 59, 74, 54, 28],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S5', 'H3H5^(3,5)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3509,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'Z4X6Z7', 'Z3X4X5Z6', 'Z2X4X7Z8', 'Z0Z1Y4Z5Z6Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z4Z5X8', 'Z4Z5Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 6826b842-9b0f-49d1-97f2-26a0dcba57b1,\n",
+       " weight_enumerator    : [1, 0, 5, 2, 15, 18, 47, 86, 60, 22],\n",
+       " },\n",
+       " {aut_group_generators : ['V3S4', 'H3H4^(3,4)', 'V2S4', '(2,3)', 'V1S8', 'H1H8^(1,8)', 'V0S7', 'H0H7^(0,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3518,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z7', 'X1Z8', 'X2Z4', 'X3Z4', 'Z5X6Z8', 'Z0X5Y6Y7', 'Z1Z6Z7X8', 'Z2Z3X4X5Z6Z7'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z5X7Z8', 'Z4Z5Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : db2cbc96-7e46-4e15-a8cc-59aea974f55d,\n",
+       " weight_enumerator    : [1, 0, 5, 2, 13, 20, 51, 82, 58, 24],\n",
+       " },\n",
+       " {aut_group_generators : ['V2S7', 'H2H7^(2,7)', 'V1S7', '(1,2)', 'V3S5', 'H3H5^(3,5)', 'V0S8', 'H0H8^(0,8)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3520,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z7', 'X2Z7', 'X3Z5', 'Z4Z5X6', 'Z3X4X5Z8', 'Z1Z2X4Z6X7', 'Z0Y4Z6Z7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z4Z7X8', 'Z4Z5Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : b293cdde-af92-471b-93e7-bb9f6ba8ab53,\n",
+       " weight_enumerator    : [1, 0, 5, 2, 11, 22, 55, 78, 56, 26],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S6', 'H3H6^(3,6)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3528,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z6', 'X4X5Z6Z8', 'Z3X4Z5X6Z7', 'Z2Y4Z5Y7Z8', 'Z0Z1Y4Z6Z7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z4Z6X8', 'Z4Z5Z6Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 90aec9f5-50e4-4852-828d-70550d9f3104,\n",
+       " weight_enumerator    : [1, 0, 5, 0, 13, 26, 51, 76, 58, 26],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V2S7', 'H2H7^(2,7)', 'V3S6', 'H3H6^(3,6)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3550,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z6', 'X4X5Z6Z8', 'Z3Z5X6Z8', 'Z2Z4Z5X7', 'Z0Z1Y4Z6Z7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z4Z6X8', 'Z4Z5Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 071b2c83-09dd-4df0-b7ea-0304599963a6,\n",
+       " weight_enumerator    : [1, 0, 5, 0, 23, 0, 95, 0, 132, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['V3S4', 'H3H4^(3,4)', 'V2S4', '(2,3)', 'V0S7', 'H0H7^(0,7)', 'V1S8', 'H1H8^(1,8)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3571,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z7', 'X1Z8', 'X2Z4', 'X3Z4', 'Z5X6Z8', 'Z1X5X8', 'Z0X5Y6Y7', 'Z2Z3X4X5Z6Z7'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z1Z6Z7X8', 'Z4Z5Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 672c9a1e-824c-4de3-abc1-3383368ddbcd,\n",
+       " weight_enumerator    : [1, 0, 5, 4, 11, 24, 39, 84, 72, 16],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V2S5', 'H2H5^(2,5)', 'V3V4', '(3,4)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3576,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z5', 'X3X4', 'Z5X6Z7Z8', 'Z2X3Y5Y6', 'Y3Z4Z6Y7Z8', 'Z0Z1X3Y7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z3Z4Z6X7', 'Z3Z4Z5Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 8a5b4b90-0d18-49dd-9c44-80ed414df7d7,\n",
+       " weight_enumerator    : [1, 0, 5, 0, 17, 22, 43, 84, 62, 22],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S6', 'H3H6^(3,6)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3579,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z6', 'X4X5Z6Z8', 'Z2Z4Z5X7', 'Z3X4Z5X6Z7', 'Z0Z1Z4Z6X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z3Z5X6Z8', 'Z4Z5Z6'],\n",
+       " n                    : 9,\n",
+       " uuid                 : e7433204-784f-4649-9afb-6aa8beb77943,\n",
+       " weight_enumerator    : [1, 0, 5, 0, 15, 32, 31, 80, 76, 16],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V2S5', 'H2H5^(2,5)', 'V3V4', '(3,4)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3581,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z5', 'X3X4', 'Z5X6Z7Z8', 'Z2X3Y5Y6', 'Z3Z4Z6X7', 'Z0Z1X7X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2X5Z6', 'Z3Z4Z5'],\n",
+       " n                    : 9,\n",
+       " uuid                 : e9b42271-39bd-44db-acc3-4b6718a55a2e,\n",
+       " weight_enumerator    : [1, 0, 5, 0, 23, 0, 95, 0, 132, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S7', 'H3H7^(3,7)', 'V2S6', 'H2H6^(2,6)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3584,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z6', 'X3Z7', 'X4X5Z6Z7', 'Z3Z5Z6X7', 'Z2Y4Y6Z7Z8', 'Z0Z1Z4Z5X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z4X6Z7', 'Z4Z5Z6'],\n",
+       " n                    : 9,\n",
+       " uuid                 : d8b48a74-e5af-4bfe-a4a1-cfdf686f2303,\n",
+       " weight_enumerator    : [1, 0, 5, 0, 17, 22, 43, 84, 62, 22],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'V2S7', 'H2H7^(2,7)', '(3,4)(5,6)', 'H3H4H5H6^(3,5)(4,6)', '(0,1)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3728,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5Z8', 'X4Z6Z8', 'Z3Z4X5X6', 'Z2Z3Y5Z6Y7', 'Z0Z1Z4X5Z7X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z3Z4X8', 'Z5Z6Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : b7c98458-d040-41a5-b533-2b1dbf5262d2,\n",
+       " weight_enumerator    : [1, 0, 4, 6, 6, 22, 52, 82, 65, 18],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S7', 'V2S7', 'H2H7^(2,7)', 'V0S8', 'H0H8^(0,8)', '(3,4)(5,6)', 'r3r4R5R6^(3,5)(4,6)', '(1,2)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3754,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z7', 'X2Z7', 'X3Z5Z8', 'X4Z6Z8', 'Z3Z4Y5Y6', 'Z0Z3Z4X8', 'Z1Z2Z3X5Z6X7'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z1Z2X7', 'Z5Z6Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : e9478e30-1f55-4ff7-8616-612665031a39,\n",
+       " weight_enumerator    : [1, 0, 4, 4, 10, 24, 44, 84, 69, 16],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'V2S7', 'H2H7^(2,7)', '(3,4)(5,6)', 'H3H4H5H6^(3,5)(4,6)', '(0,1)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3757,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'Z3X5Z7', 'Z4X6Z7', 'X3X4Z5Z6', 'Z2X3Z6X7Z8', 'Z0Z1Y3Z4Z5Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z3Z4X8', 'Z3Z4Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : e9037210-f34a-4476-a168-c1ab482b9cd8,\n",
+       " weight_enumerator    : [1, 0, 4, 4, 6, 28, 52, 76, 65, 20],\n",
+       " },\n",
+       " {aut_group_generators : ['V2S7', 'H2H7^(2,7)', 'V1S7', '(1,2)', 'V0S8', 'H0H8^(0,8)', '(3,4)(5,6)', 'r3r4R5R6^(3,5)(4,6)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3898,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z7', 'X2Z7', 'Z3X5Z6', 'Y3X4Y5', 'Z4Z5X6', 'Z0Z3Z4X8', 'Z1Z2X3Z5X7Z8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z1Z2X7', 'Z3Z4Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 5841693e-1569-48cd-b01c-6c9ef9c3c374,\n",
+       " weight_enumerator    : [1, 0, 4, 4, 10, 24, 44, 84, 69, 16],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V2S7', 'H2H7^(2,7)', '(3,4)(5,6)', 'R3R4r5r6^(3,5)(4,6)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3910,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'Y3Y4Z5Z6', 'Z3Z4X5X6', 'Y3Z4Y5Z7Z8', 'Z2X3Z4Z6X7', 'Z0Z1Z5Z6X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z5Z6X7', 'Z3Z4Z5Z6Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 911daf1b-11c4-4536-99e8-48c4ab02c01f,\n",
+       " weight_enumerator    : [1, 0, 4, 0, 6, 40, 52, 64, 65, 24],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'V2S7', 'H2H7^(2,7)', '(3,4)(5,6)', 'R3R4r5r6^(3,5)(4,6)', '(0,1)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 3920,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'Y3Y4Z5Z6', 'Z3Z4X5X6', 'Z2Z5Z6X7', 'Y3Z4Y5Z7Z8', 'Z0Z1X3Z4Z6X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z5Z6X8', 'Z3Z4Z5Z6Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 70c14b87-d478-4107-b3e9-8d278709cf87,\n",
+       " weight_enumerator    : [1, 0, 4, 0, 10, 36, 44, 72, 69, 20],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S5', 'H3H5^(3,5)', 'V2S7', 'H2H7^(2,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 4139,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z7', 'X3Z5', 'Z5X6Z7Z8', 'Z3X4Y5Y6', 'Z2Y4Z6Y7Z8', 'Z0Z1Z4Z6X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z4Z6X7', 'Z4Z5Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : e960f732-1d9f-41b0-8d45-78a5844beb9d,\n",
+       " weight_enumerator    : [1, 0, 5, 0, 21, 18, 35, 92, 66, 18],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3V4', '(3,4)', 'V2S5', 'H2H5^(2,5)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 4149,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z5', 'X3X4', 'Z2X5Z6', 'Z5X6Z7Z8', 'Z3Z4Z6X7', 'Z0Z1X3Y7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z3Z4Z6X8', 'Z3Z4Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 925a38e8-b9ec-446f-b3b4-7f01a260d47a,\n",
+       " weight_enumerator    : [1, 0, 5, 2, 15, 26, 31, 86, 76, 14],\n",
+       " },\n",
+       " {aut_group_generators : ['V1S8', 'H1H8^(1,8)', 'V0S8', '(0,1)', 'V3S7', 'H3H7^(3,7)', 'V2S6', 'H2H6^(2,6)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 4154,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2Z6', 'X3Z7', 'Z3Z5X7', 'X4X5Z6Z7', 'Z2Y4Y6Z8', 'Z0Z1Z4Z5X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z4X6', 'Z4Z5Z6'],\n",
+       " n                    : 9,\n",
+       " uuid                 : d39f40b6-3f3f-469c-9753-201068320ee8,\n",
+       " weight_enumerator    : [1, 0, 5, 2, 17, 16, 43, 90, 62, 20],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'V2H4H6S7S8^(4,6)', '(0,1)', 'H2V3V4S5S6H7^(2,7)', '(3,4)(5,6)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 4372,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'X2X3Z5Z7', 'X2X4Z6Z7', 'X2Z3X5Z8', 'X2Z4X6Z8', 'Z2Z5Z6X7', 'Z0Z1Z2Z3Z4X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z4X6Z7', 'Z2Z3Z4Z5Z6'],\n",
+       " n                    : 9,\n",
+       " uuid                 : df741c82-aa6b-4ada-ac90-84a7aa5649f5,\n",
+       " weight_enumerator    : [1, 0, 3, 0, 21, 0, 105, 0, 126, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V1S8', 'H1H8^(1,8)', 'S2H4S5H7S8^(4,7)', '(0,1)', 'H3H4H6H7^(2,5)', '(3,4)(6,7)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 4389,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z8', 'Z2X3Z6Z8', 'Z2X4Z7Z8', 'X2Y3Y4X5', 'Z3Z5X6Z8', 'Z4Z5X7Z8', 'Z0Z1Y2Z5Z6Z7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z2Z3Z4Z5Z6Z7X8', 'Z2Z5Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 824aeaee-ce7a-484d-8b0a-01e0fc9fdc2c,\n",
+       " weight_enumerator    : [1, 0, 3, 0, 21, 0, 73, 96, 30, 32],\n",
+       " },\n",
+       " {aut_group_generators : ['S2S7', '(2,7)', 'S1S6', '(1,2)(4,5)(6,7)', 'S0S8', '(0,1)(3,4)(6,8)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 7419,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z8', 'Z1Z6', 'Z2Z7', 'Z3Z4Z6Z8', 'Z3Z5Z7Z8', 'X1X3X5X6', 'X2X3X4X7', 'X0X4X5X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X3X4X5', 'Z0Z1Z5'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 831075fd-e5d0-4c55-a221-d6c2d8147a03,\n",
+       " weight_enumerator    : [1, 0, 3, 0, 21, 0, 105, 0, 126, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['V2S7', 'H2H7^(2,7)', 'V1S6', '(1,2)(3,4)(6,7)', 'V0S8', 'r3R5S6S8^(0,1)(3,5)(6,8)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 7420,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z6', 'X2Z7', 'X3Z5Z6Z8', 'X4Z5Z7Z8', 'Z1Z4X5X6Z7', 'Z2Z3X5Z6X7', 'Z0Y3Z4Z7Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z2Z4Z6X7Z8', 'Z5Z6Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 3f02a48e-28bf-468c-8ab1-fc30c5948ef7,\n",
+       " weight_enumerator    : [1, 0, 3, 0, 15, 30, 37, 84, 72, 14],\n",
+       " },\n",
+       " {aut_group_generators : ['V2S4', 'H2H4^(2,4)', 'V1S7', 'H3H5H6^(1,2)(4,7)', 'V0S8', 'V3V5S6^(0,1)(7,8)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 7421,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z7', 'X2Z4', 'X5Z6Z7Z8', 'Z2X3X4X5', 'Z4Z5X6Z8', 'Z1Z3Z5X7', 'Z0Y3Y5X8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z3Z5Z6X8', 'Z3Z4Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 20b44c80-39f6-43a0-8cea-ab6293784d71,\n",
+       " weight_enumerator    : [1, 0, 3, 0, 21, 0, 105, 0, 126, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['V0S8', 'V2S7', 'H2H7^(2,7)', 'V1S6', '(1,2)(3,4)(6,7)', 'r3R5S6S8^(0,1)(3,5)(6,8)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 7917,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X0Z8', 'X1Z6', 'X2Z7', 'X3Z5Z6Z8', 'X4Z5Z7Z8', 'Z3Z4X5Z8', 'Z1Z2Z3Z4Y6Y7', 'Z0Z1Z4Z5Y6Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z3Z4Z5Z6Z7X8', 'Z6Z7Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : d1aed3be-8df4-4810-8f40-7bbb423b3127,\n",
+       " weight_enumerator    : [1, 0, 3, 0, 21, 0, 105, 0, 126, 0],\n",
+       " },\n",
+       " {aut_group_generators : ['S0S8', 'S2S7', '(2,7)', 'S1S6', '(1,2)(4,5)(6,7)', '(0,1)(3,4)(6,8)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 8816,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 1,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Z0Z8', 'Z1Z6', 'Z2Z7', 'X3X4X5', 'Z3Z4Z6Z8', 'Z3Z5Z7Z8', 'Y1Y2X3Y6Y7', 'Y0Y1X5Y6Y8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X2X5X7', 'Z0Z1Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 1feaf146-d4a9-457e-bacc-2970580d3725,\n",
+       " weight_enumerator    : [1, 0, 3, 1, 15, 27, 37, 87, 72, 13],\n",
+       " },\n",
+       " {aut_group_generators : ['(2,3)(6,7)', '(2,6)(3,7)', '(1,2)(5,6)', 'H0H1H2H3H4H5H6H7H8^(3,7)', '(0,1)(4,5)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 9709,\n",
+       " is_css               : 1,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['Y0Y1Y4Y5', 'Y0Y2Y4Y6', 'Y0Y3Y4Y7', 'X0X1X4X5', 'X0X2X4X6', 'X0X3X4X7', 'X0X1X2X3X8', 'Z1Z2Z3Z4Z8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['X3X7X8', 'Z0Z4Z8'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 9745d149-92cc-464d-bf3f-fe0e04756010,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 18, 16, 56, 96, 53, 16],\n",
+       " },\n",
+       " {aut_group_generators : ['R0H3H5S6S7^(3,5,4)(6,7,8)', 'V0V2S6^(3,4)(7,8)', 'V0H2H6S7S8^(2,6)(3,4)', 'V0V1V3V4S5S7S8^(3,4)(7,8)', 'r3H4S5S6H7R8^(1,2)(3,8)(4,7)(5,6)'],\n",
+       " aut_group_size       : 384,\n",
+       " code_type            : StabSubSystemCode,\n",
+       " d                    : 3,\n",
+       " index                : 10215,\n",
+       " is_css               : 0,\n",
+       " is_decomposable      : 0,\n",
+       " is_degenerate        : 0,\n",
+       " is_gf4linear         : 0,\n",
+       " is_subsystem         : 1,\n",
+       " isotropic_generators : ['X1Z5Z7Z8', 'X2Z6Z7Z8', 'X3Z5Z6Z8', 'X4Z5Z6Z7', 'Z1Z3Z4X5', 'Z2Y6X7Y8', 'X0Z2Z3Z4X6Z7', 'Y0Z1Z2Z4Y7Z8'],\n",
+       " k                    : 1,\n",
+       " logical_ops          : ['Z0Z1Z2Z4X7', 'Z0Z6Z7'],\n",
+       " n                    : 9,\n",
+       " uuid                 : 1876d407-c3a6-443a-9242-4352604c2b4c,\n",
+       " weight_enumerator    : [1, 0, 0, 0, 18, 0, 120, 0, 117, 0],\n",
+       " }]"
+      ]
+     },
+     "execution_count": 1149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1241,
+   "id": "ed8afa74-c87c-4902-866c-bacc649d1cd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = \"𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝐼\\\n",
+    "𝐼𝑍𝑍𝐼𝑍𝑍𝐼𝐼𝐼\\\n",
+    "𝐼𝐼𝐼𝑍𝑍𝐼𝑍𝑍𝐼\\\n",
+    "𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍\\\n",
+    "𝑋𝑋𝐼𝑋𝑋𝐼𝐼𝐼𝐼\\\n",
+    "𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼𝐼\\\n",
+    "𝐼𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼\\\n",
+    "𝐼𝐼𝐼𝐼𝑋𝑋𝐼𝑋𝑋\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1242,
+   "id": "a9b4a605-c5a8-41b2-b294-7ad6b970fdd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍𝐼𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍𝐼𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍𝑋𝑋𝐼𝑋𝑋𝐼𝐼𝐼𝐼𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼𝐼𝐼𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼𝐼𝐼𝐼𝐼𝑋𝑋𝐼𝑋𝑋\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1243,
+   "id": "fdfbb037-d0a3-40b9-889f-fd568de9805c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = ['𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝐼','𝐼𝑍𝑍𝐼𝑍𝑍𝐼𝐼𝐼','𝐼𝐼𝐼𝑍𝑍𝐼𝑍𝑍𝐼','𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍','𝑋𝑋𝐼𝑋𝑋𝐼𝐼𝐼𝐼','𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼𝐼','𝐼𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼','𝐼𝐼𝐼𝐼𝑋𝑋𝐼𝑋𝑋']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1244,
+   "id": "712b32f8-b402-4683-a1f5-2717bae7b321",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝐼',\n",
+       " '𝐼𝑍𝑍𝐼𝑍𝑍𝐼𝐼𝐼',\n",
+       " '𝐼𝐼𝐼𝑍𝑍𝐼𝑍𝑍𝐼',\n",
+       " '𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍',\n",
+       " '𝑋𝑋𝐼𝑋𝑋𝐼𝐼𝐼𝐼',\n",
+       " '𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼𝐼',\n",
+       " '𝐼𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼',\n",
+       " '𝐼𝐼𝐼𝐼𝑋𝑋𝐼𝑋𝑋']"
+      ]
+     },
+     "execution_count": 1244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1245,
+   "id": "48a2d9b8-e8ca-4143-a647-60c6f571d06a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝐼' '𝐼𝑍𝑍𝐼𝑍𝑍𝐼𝐼𝐼' '𝐼𝐼𝐼𝑍𝑍𝐼𝑍𝑍𝐼' '𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍' '𝑋𝑋𝐼𝑋𝑋𝐼𝐼𝐼𝐼' '𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼𝐼' '𝐼𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼' '𝐼𝐼𝐼𝐼𝑋𝑋𝐼𝑋𝑋' "
+     ]
+    }
+   ],
+   "source": [
+    "B = [item.lower() for item in A]\n",
+    "for item in B:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1246,
+   "id": "8b7087d1-95c1-4665-adb3-4237b87b7cf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['𝑍𝑍𝐼𝐼𝐼𝐼𝐼𝐼𝐼',\n",
+       " '𝐼𝑍𝑍𝐼𝑍𝑍𝐼𝐼𝐼',\n",
+       " '𝐼𝐼𝐼𝑍𝑍𝐼𝑍𝑍𝐼',\n",
+       " '𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑍𝑍',\n",
+       " '𝑋𝑋𝐼𝑋𝑋𝐼𝐼𝐼𝐼',\n",
+       " '𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼𝐼',\n",
+       " '𝐼𝐼𝐼𝑋𝐼𝐼𝑋𝐼𝐼',\n",
+       " '𝐼𝐼𝐼𝐼𝑋𝑋𝐼𝑋𝑋']"
+      ]
+     },
+     "execution_count": 1246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1252,
+   "id": "cb7a7d52-725d-4440-b782-aa98b930c47c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'𝑍'"
+      ]
+     },
+     "execution_count": 1252,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B[0][0].lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1254,
+   "id": "ea41b9bd-14e8-484c-a9b4-6fa3e3e2d15a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V=str(B[0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1259,
+   "id": "378bb985-0dcb-4270-b4df-1b114f91ee78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 1259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "V=='Z'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1260,
+   "id": "17266040-1f34-4c56-901c-0e8528d806c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'zziiiiiii' 'izzizziii' 'iiizzizzi' 'iiiiiiizz' 'xxixxiiii' 'iixiixiii' 'iiixiixii' 'iiiixxixx' "
+     ]
+    }
+   ],
+   "source": [
+    "A=['ZZIIIIIII','IZZIZZIII','IIIZZIZZI','IIIIIIIZZ','XXIXXIIII','IIXIIXIII','IIIXIIXII','IIIIXXIXX']\n",
+    "\n",
+    "B = [item.lower() for item in A]\n",
+    "for item in B:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8878c2f-2fef-43ba-965b-cdd6b9758ece",
+   "metadata": {},
+   "source": [
+    "## GF(4) Indecomposable Linear Codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1355,
+   "id": "1569f75e-0c4c-47ae-b162-ba30505ca5f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2,0,2]]\n",
+      "aut_group_generators : ['S0S1', '(0,1)', 'H0H1']\n",
+      "aut_group_size       : 12\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 1\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1', 'X0X1']\n",
+      "k                    : 0\n",
+      "logical_ops          : []\n",
+      "n                    : 2\n",
+      "uuid                 : 9e7112a5-868b-4832-bd42-2eaf5f1182de\n",
+      "weight_enumerator    : [1, 0, 3]\n",
+      "\n",
+      "[[4,2,2]]\n",
+      "aut_group_generators : ['(2,3)', '(1,2)', 'S0S1S2S3', '(0,1)', 'H0H1H2H3']\n",
+      "aut_group_size       : 144\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 9\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['X1X2', 'X1X3', 'Z0Z2', 'Z0Z3']\n",
+      "n                    : 4\n",
+      "uuid                 : 373b856e-a5af-4e9f-8524-997f1ccfe77e\n",
+      "weight_enumerator    : [1, 0, 0, 0, 3]\n",
+      "\n",
+      "[[5,1,3]]\n",
+      "aut_group_generators : ['H0V1V2S3S4^(1,2)', 'V0H1S2V3S4^(0,3)', 'H0V1V2S3S4^(0,1,3,2)', 'H0V1S2V3H4^(0,1,2,3)', 'H0V1V2S3S4^(3,4)']\n",
+      "aut_group_size       : 360\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 21\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0Y1Z2Z3', 'Y0Z1Y2Z4', 'X0Z2X3Z4', 'X0Z1Z3X4']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z2Z3X4', 'Z0Z1Z2Z3Z4']\n",
+      "n                    : 5\n",
+      "uuid                 : afef70ec-4dff-48ea-9361-3307ecc90878\n",
+      "weight_enumerator    : [1, 0, 0, 0, 15, 0]\n",
+      "\n",
+      "[[6,0,4]]\n",
+      "aut_group_generators : ['V0V1S2S3V4V5^(4,5)', 'R4r5^(2,3)(4,5)', 'V0V1S2V3V4S5^(2,5,3,4)', 'V0H1H2S3S4H5^(3,4)', '(1,2)(3,4)', 'H0V1H2H3V4H5^(3,5)', 'R4r5^(0,1)(4,5)']\n",
+      "aut_group_size       : 2160\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 4\n",
+      "index                : 19\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0Z1Z2Z5', 'Z0Y1Z3Z5', 'Z0Y2Z4Z5', 'Z1Y3Z4Z5', 'Z2Z3Y4Z5', 'X0Z3Z4Y5']\n",
+      "k                    : 0\n",
+      "logical_ops          : []\n",
+      "n                    : 6\n",
+      "uuid                 : c54f7a20-88d7-461e-830d-239f35fd5bc3\n",
+      "weight_enumerator    : [1, 0, 0, 0, 45, 0, 18]\n",
+      "\n",
+      "[[6,2,2]]\n",
+      "aut_group_generators : ['(4,5)', 'V0V1V2V3V4V5', '(2,3)', '(2,4)(3,5)', 'H0H1H2H3H4H5', '(0,1)', '(0,2)(1,3)']\n",
+      "aut_group_size       : 288\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 126\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['X1X3X4', 'X1X3X5', 'Z0Z2Z4', 'Z0Z2Z5']\n",
+      "n                    : 6\n",
+      "uuid                 : 309ff5ca-e6e5-4ff2-a9a2-c1acf9e68c2c\n",
+      "weight_enumerator    : [1, 0, 0, 0, 9, 0, 6]\n",
+      "\n",
+      "[[6,4,2]]\n",
+      "aut_group_generators : ['(4,5)', '(3,4)', '(2,3)', '(1,2)', 'S0S1S2S3S4S5', '(0,1)', 'V0V1V2V3V4V5']\n",
+      "aut_group_size       : 4320\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 29\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z2Z3Z4Z5', 'Y0Y1Y2Y3Y4Y5']\n",
+      "k                    : 4\n",
+      "logical_ops          : ['X1X2', 'X1X3', 'X1X4', 'X1X5', 'Z0Z2', 'Z0Z3', 'Z0Z4', 'Z0Z5']\n",
+      "n                    : 6\n",
+      "uuid                 : 7ba1bb8f-2185-44c5-af50-4ddcf3cf2226\n",
+      "weight_enumerator    : [1, 0, 0, 0, 0, 0, 3]\n",
+      "\n",
+      "[[7,1,3]]\n",
+      "aut_group_generators : ['(3,4)(5,6)', '(3,5)(4,6)', 'V0V1V2V3V4V5V6', '(1,2)(5,6)', '(1,3)(2,4)', 'H0H1H2H3H4H5H6', '(0,1)(4,5)']\n",
+      "aut_group_size       : 1008\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 226\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z3Z6', 'Z0Z2Z3Z5', 'Y1Y2Y3Y4', 'Z3Z4Z5Z6', 'Y0Y1Y4Y5', 'Y0Y2Y4Y6']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['X2X4X5', 'Z1Z3Z5']\n",
+      "n                    : 7\n",
+      "uuid                 : 69b11699-9064-4ca3-8b7a-c21e72d0756b\n",
+      "weight_enumerator    : [1, 0, 0, 0, 21, 0, 42, 0]\n",
+      "\n",
+      "[[7,3,2]]\n",
+      "aut_group_generators : ['R1r2^(1,2)', 'r0R1^(0,1)', 'H0V1H2H3S4S5V6^(4,5)', 'H0V1H2V3V4S5S6^(3,4)', 'H0V1H2V3V4S5S6^(5,6)', 'S0H1S2H3V4H5S6^(3,6,5,4)']\n",
+      "aut_group_size       : 432\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 499\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X3X4Z5Z6', 'Z3Z4Y5Y6', 'Y0X1Y2Y3Z4Z5', 'X0Z1X2Z4X5Z6']\n",
+      "k                    : 3\n",
+      "logical_ops          : ['Z1X4Z5', 'Z1X2', 'Z1Z3Z5X6', 'Z1Z3Z4', 'Z0Z2', 'Z0Z1Z5Z6']\n",
+      "n                    : 7\n",
+      "uuid                 : 81af7aa6-3c72-467f-bf8a-176e37111bae\n",
+      "weight_enumerator    : [1, 0, 0, 0, 3, 0, 12, 0]\n",
+      "\n",
+      "[[8,0,4]]\n",
+      "aut_group_generators : ['(4,5)(6,7)', '(4,6)(5,7)', '(2,3)(5,6)', '(2,4)(3,7)', 'V0V1V2V3V4V5V6V7', '(1,2)(6,7)', 'H0H1H2H3H4H5H6H7', '(0,1)(5,6)']\n",
+      "aut_group_size       : 8064\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 4\n",
+      "index                : 125\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0X1X2X3', 'Z0Z1Z5Z6', 'Z0Z2Z5Z7', 'Z0Z3Z6Z7', 'Z4Z5Z6Z7', 'X1X2X4X5', 'X1X3X4X6', 'X2X3X4X7']\n",
+      "k                    : 0\n",
+      "logical_ops          : []\n",
+      "n                    : 8\n",
+      "uuid                 : 66927953-9dab-4ae8-8ae8-77322912ab73\n",
+      "weight_enumerator    : [1, 0, 0, 0, 42, 0, 168, 0, 45]\n",
+      "\n",
+      "[[8,2,2]]\n",
+      "aut_group_generators : ['r1R2^(1,2)', 'S0V1S2V3V4H5H6V7^(5,6)', 'H0H1S2V3V4H5H6V7^(0,1)(5,6)', 'H0S1H2V3H4H5V6H7^(5,7)', 'R6r7^(4,5)(6,7)', 'H0S1H2H3V4V5H6H7^(6,7)', '(3,4)(5,6)']\n",
+      "aut_group_size       : 2160\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 4492\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y3Y4Z5Z6', 'Z3Y5Z6Z7', 'Z4Z5Y6Z7', 'X3Z4Z6Y7', 'Y0Z1Y2Z3Z4Z7', 'Z0X1Z2Y3Z5Z7']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z0Z1X2', 'Z0Z3Z4Z5Z6X7', 'Z0Z2', 'Z1Z3Z4Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 3637c14a-652f-487d-844c-b1986233476d\n",
+      "weight_enumerator    : [1, 0, 0, 0, 15, 0, 30, 0, 18]\n",
+      "\n",
+      "[[8,2,2]]\n",
+      "aut_group_generators : ['r0R1^(0,1)', 'V0S1V2V3S4S5V6V7^(6,7)', 'R6r7^(4,5)(6,7)', 'V0S1V2V3S4V5V6S7^(4,7,5,6)', 'H0V1H2H3V4V5H6H7^(6,7)', 'R6r7^(2,3)(6,7)', 'S0H1V2V3V4V5S6S7^(2,4)(3,5)(6,7)']\n",
+      "aut_group_size       : 288\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 4584\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X2X3Z4Z5', 'Z2Z3Y4Y5', 'Z4Z5Y6Z7', 'Y2Y3Z6X7', 'X0Z1Y2Z3Z4Z7', 'Z0Y1Z2Y4Z6Z7']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z0Z1X3Z4Z7', 'Z0Z1Z2Z4X5Z6Z7', 'Z0Z2Z3', 'Z1Z4Z5']\n",
+      "n                    : 8\n",
+      "uuid                 : 708cc1b7-fc90-4730-8caf-a162063923b6\n",
+      "weight_enumerator    : [1, 0, 0, 0, 9, 0, 42, 0, 12]\n",
+      "\n",
+      "[[8,2,2]]\n",
+      "aut_group_generators : ['(6,7)', '(4,5)', '(4,6)(5,7)', 'V0V1V2V3V4V5V6V7', '(2,3)', '(2,4)(3,5)', 'H0H1H2H3H4H5H6H7', '(0,1)', '(0,2)(1,3)']\n",
+      "aut_group_size       : 2304\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 4926\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3', 'Z0Z1Z4Z5', 'X0X1X4X5', 'Z0Z1Z6Z7', 'X0X1X6X7']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['X1X3X5X6', 'X1X3X5X7', 'Z0Z2Z4Z6', 'Z0Z2Z4Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 5120f73b-17f4-4b7f-af58-702c1f14aa41\n",
+      "weight_enumerator    : [1, 0, 0, 0, 18, 0, 24, 0, 21]\n",
+      "\n",
+      "[[8,2,3]]\n",
+      "aut_group_generators : ['V0V1V2V3H4S5S6H7^(4,7)(5,6)', 'V0S1V2H3H4H5S6S7^(1,7)(3,5)', 'V0S1H2H3H4V5V6S7^(1,7)(5,6)', '(2,3)(5,6)', 'R0r1R7^(1,7,4)', '(0,1)(4,7)', 'R4r5r6R7^(0,2)(1,3)(4,6)(5,7)']\n",
+      "aut_group_size       : 1728\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 4947\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0Y1Z4Z7', 'X2X3Z5Z6', 'Z2Z3Y5Y6', 'Z0Z1X4X7', 'Y0Z1Y2Z3Y4Z6', 'X0Z1X2Z3X5Z7']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z0Z1X3Z6', 'Z1Z5Z6X7', 'Z0Z1Z2Z3', 'Z0Z1Z4Z5Z6Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : dd2f05f4-c705-4de0-97d7-8478312821be\n",
+      "weight_enumerator    : [1, 0, 0, 0, 6, 0, 48, 0, 9]\n",
+      "\n",
+      "[[8,4,2]]\n",
+      "aut_group_generators : ['(2,3)', '(6,7)', '(5,6)', 'V0V1V2V3V4V5V6V7', '(4,5)', 'H0H1H2H3H4H5H6H7', '(0,1)', '(0,2)(1,3)']\n",
+      "aut_group_size       : 1152\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 2206\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3', 'X0X1X4X5X6X7', 'Z0Z1Z4Z5Z6Z7']\n",
+      "k                    : 4\n",
+      "logical_ops          : ['X1X3X4', 'X1X3X5', 'X1X3X6', 'X1X3X7', 'Z0Z2Z4', 'Z0Z2Z5', 'Z0Z2Z6', 'Z0Z2Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 0f5c1dea-450f-4f20-bfe4-f41c5ba94c3c\n",
+      "weight_enumerator    : [1, 0, 0, 0, 3, 0, 6, 0, 6]\n",
+      "\n",
+      "[[8,4,2]]\n",
+      "aut_group_generators : ['(6,7)', '(4,5)', 'V0V1S2S3S4S5S6S7^(4,6)(5,7)', '(2,3)', 'V0V1V2V3V4V5V6V7^(2,4)(3,5)', 'H0H1V2V3V4V5V6V7^(4,6)(5,7)', '(0,1)', 'V0V1V2V3V4V5S6S7^(0,2)(1,3)']\n",
+      "aut_group_size       : 1152\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 3041\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1X2X3X6X7', 'X0X1X2X3X4X5', 'Z0Z1Z2Z3Z4Z5', 'Y0Y1Z2Z3Z6Z7']\n",
+      "k                    : 4\n",
+      "logical_ops          : ['X1Z2X3Z4', 'Z0X3Z4X5', 'Z0Z4X6', 'Z0Z4X7', 'Z0Z1', 'Z4Z5', 'Z2Z4Z6', 'Z2Z4Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : 491bd914-f462-43df-8290-464f3f383695\n",
+      "weight_enumerator    : [1, 0, 0, 0, 0, 0, 12, 0, 3]\n",
+      "\n",
+      "[[8,6,2]]\n",
+      "aut_group_generators : ['(6,7)', '(5,6)', '(4,5)', '(3,4)', '(2,3)', '(1,2)', 'S0S1S2S3S4S5S6S7', '(0,1)', 'H0H1H2H3H4H5H6H7']\n",
+      "aut_group_size       : 241920\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 67\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z2Z3Z4Z5Z6Z7', 'X0X1X2X3X4X5X6X7']\n",
+      "k                    : 6\n",
+      "logical_ops          : ['X1X2', 'X1X3', 'X1X4', 'X1X5', 'X1X6', 'X1X7', 'Z0Z2', 'Z0Z3', 'Z0Z4', 'Z0Z5', 'Z0Z6', 'Z0Z7']\n",
+      "n                    : 8\n",
+      "uuid                 : d2812e9a-0222-40d3-8748-ff4fe98603cf\n",
+      "weight_enumerator    : [1, 0, 0, 0, 0, 0, 0, 0, 3]\n",
+      "\n",
+      "[[9,1,3]]\n",
+      "aut_group_generators : ['(3,4)(5,6)', 'r3r4R5R6^(3,5)(4,6)', 'R2r3R4r6^(4,6,5)', '(2,3)(4,5)', 'V0V1V2V3H4H5S6H7H8^(4,5)(7,8)', 'R1R3r5R6r7^(1,8,7)(4,6,5)', 'H0H1V2S3S4H5H6S7S8^(5,6)(7,8)', '(0,1)(7,8)']\n",
+      "aut_group_size       : 4320\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 9652\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0Y1Z7Z8', 'Y2Y3Z4Z5', 'Y2Z3Y4Z6', 'X2Z4X5Z6', 'X2Z3Z5X6', 'Z0Z1X7X8', 'X0Z1X2Z3Z4Z8', 'Z0Y2Z5Z6Y7Z8']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z1Z2Z3Z4Z5Z6X8', 'Z0Z1Z2Z3Z4Z5Z6Z7Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : 4b3e443f-1604-454c-93fe-843146759a98\n",
+      "weight_enumerator    : [1, 0, 0, 0, 18, 0, 120, 0, 117, 0]\n",
+      "\n",
+      "[[9,1,3]]\n",
+      "aut_group_generators : ['(2,3)(6,7)', 'r2r3R6R7^(2,6)(3,7)', 'V0V1V2H3S4S5S6H7S8^(3,7)', '(1,2)(5,6)', 'H0H1H2S3V4V5V6S7V8^(3,7)', '(0,1)(4,5)']\n",
+      "aut_group_size       : 1152\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 10201\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0X1Z4Z5', 'X0X2Z4Z6', 'X0X3Z4Z7', 'Z0Z1Y4Y5', 'Z0Z2Y4Y6', 'Z0Z3Y4Y7', 'Y0Y4Z5Z6Z7Z8', 'Y0Z1Z2Z3Z4Y8']\n",
+      "k                    : 1\n",
+      "logical_ops          : ['Z0Z1Z2Z3X8', 'Z0Z1Z2Z3Z4Z5Z6Z7Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : a6a4813f-fc38-40f2-bc3c-1969666065a8\n",
+      "weight_enumerator    : [1, 0, 0, 0, 18, 0, 120, 0, 117, 0]\n",
+      "\n",
+      "[[9,3,2]]\n",
+      "aut_group_generators : ['(7,8)', 'V0V1V2V3V4V5V6V7V8^(2,3)(4,5)', 'r4r5r6r7r8^(1,3,2)(4,5,6)', 'H0H1H2H3H4H5H6H7H8^(2,3)(4,5)', '(0,1)(2,3)']\n",
+      "aut_group_size       : 144\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 118847\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0X1X2X3', 'Z0Z1Z2Z3', 'Z0Y1X2Y4Z5X6', 'X0X1Z4Y5X7X8', 'Y0Y2X4Y6Z7Z8', 'Y0X1Z2X4Y5Z6']\n",
+      "k                    : 3\n",
+      "logical_ops          : ['Z0Z2Z4Z5X6', 'Z1Z2Z5X7', 'Z1Z2Z5X8', 'Z0Z1Z6', 'Z0Z1Z4Z5Z7', 'Z0Z1Z4Z5Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : f10da7b6-7258-4773-bd7b-868ad773322c\n",
+      "weight_enumerator    : [1, 0, 0, 0, 3, 0, 30, 0, 30, 0]\n",
+      "\n",
+      "[[9,3,2]]\n",
+      "aut_group_generators : ['(6,7)', '(5,6)', 'V0V1V2V3V4V5V6V7V8', '(1,3)(4,8)', '(1,4)(3,8)', 'H0H1H2H3H4H5H6H7H8', '(0,1)(2,3)']\n",
+      "aut_group_size       : 864\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 131752\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z1Z2Z3', 'X0X2X4X8', 'Y0Y1Y2Y3', 'Y1Y3Y4Y8', 'Y0Y1Y4Y5Y6Y7', 'Z1Z2Z5Z6Z7Z8']\n",
+      "k                    : 3\n",
+      "logical_ops          : ['X5X6', 'X5X7', 'X4X5X8', 'Z0Z2Z6', 'Z0Z2Z7', 'Z0Z1Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : ce19878f-5839-4365-9409-f23145f876ea\n",
+      "weight_enumerator    : [1, 0, 0, 0, 9, 0, 18, 0, 36, 0]\n",
+      "\n",
+      "[[9,3,2]]\n",
+      "aut_group_generators : ['(7,8)', 'V0S1V2V3V4V5V6H7H8^(2,3)', 'R0r1R3^(1,3,2)', 'r0R2r3^(0,2,1)', 'V0H1V2H3H4H5H6S7S8^(1,3)', '(5,6)', 'V0H1V2H3H4V5V6V7V8^(1,3)(5,7)(6,8)']\n",
+      "aut_group_size       : 576\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 163595\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0X1Y2Y3', 'Y5Y6Z7Z8', 'Z0Y1Z2Z3', 'Z5Z6X7X8', 'Z0Z1Y2Y4Y5Y6', 'X0X1Z2Z4X7X8']\n",
+      "k                    : 3\n",
+      "logical_ops          : ['Z0Z3Z5X6Z7', 'Z0Z2X4', 'Z2Z3Z5X8', 'Z5Z6', 'Z2Z3Z4', 'Z7Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : 97371997-73e6-4aea-94d7-956a4af695e9\n",
+      "weight_enumerator    : [1, 0, 0, 0, 6, 0, 24, 0, 33, 0]\n",
+      "\n",
+      "[[9,3,3]]\n",
+      "aut_group_generators : ['r3R4r7R8^(3,5,4)(6,8,7)', 'V0H1V2S3V4S5S6S7V8^(3,6)(4,8)(5,7)', 'r1R2r3r4r5r6R8^(1,2)(3,7,4,8,5,6)', 'R3R6r7r8^(1,8,2,3)(4,7,6,5)', 'R0R1R2R3R4R5R6R7R8', 'V0V1H2S3H4H5H6S7H8^(0,1)(4,5)(6,8)']\n",
+      "aut_group_size       : 1296\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 170235\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0X1X2Y3X4X5', 'Z0Z1X2X6Y7X8', 'Z0Z2Z3Z4Z6Y7', 'X1Z2Y3Z5Z7Z8', 'X0Z1Z2X3Z4Z5', 'X0X2X3X4X6Z7']\n",
+      "k                    : 3\n",
+      "logical_ops          : ['Z2Z4X6Z7', 'Z3Z4X5', 'Z0Z3X8', 'Z0Z1Z2Z3Z6', 'Z0Z1Z3Z5', 'Z2Z4Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : 321e22c2-60af-4aa1-b154-740c1caec781\n",
+      "weight_enumerator    : [1, 0, 0, 0, 0, 0, 36, 0, 27, 0]\n",
+      "\n",
+      "[[9,5,2]]\n",
+      "aut_group_generators : ['(7,8)', '(6,7)', '(5,6)', '(4,5)', 'H0H1H2H3V4V5V6V7V8^(1,2)', 'V0V1V2V3V4V5V6V7V8^(0,1)', 'V0V1V2V3V4V5V6V7V8^(2,3)', 'R0R1R2R3R4R5R6R7R8']\n",
+      "aut_group_size       : 8640\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 8643\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0X1X2X3', 'Z0Z1Z2Z3', 'Y0Z1X2X4X5X6X7X8', 'X0Y1Z2Z4Z5Z6Z7Z8']\n",
+      "k                    : 5\n",
+      "logical_ops          : ['Z0Z2X4', 'Z0Z2X5', 'Z0Z2X6', 'Z0Z2X7', 'Z0Z2X8', 'Z0Z1Z4', 'Z0Z1Z5', 'Z0Z1Z6', 'Z0Z1Z7', 'Z0Z1Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : cecb677a-dd59-4f92-8ee7-0b286b8a0aa8\n",
+      "weight_enumerator    : [1, 0, 0, 0, 3, 0, 0, 0, 12, 0]\n",
+      "\n",
+      "[[9,5,2]]\n",
+      "aut_group_generators : ['(7,8)', '(6,7)', '(4,5)', '(3,4)', 'H0V1V2V3V4V5S6S7S8^(1,2)', 'r0R1R6R7R8^(0,2,1)', 'V0H1V2V3V4V5V6V7V8^(3,6)(4,7)(5,8)']\n",
+      "aut_group_size       : 1296\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 14986\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 1\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Y0X1X2X3X4X5', 'Z0Z1X2X6X7X8', 'X0Z1Z2Z3Z4Z5', 'Y0Y1Z2Z6Z7Z8']\n",
+      "k                    : 5\n",
+      "logical_ops          : ['Z0Z1X4', 'Z0Z1X5', 'Z1Z3X6', 'Z1Z3X7', 'Z1Z3X8', 'Z3Z4', 'Z3Z5', 'Z2Z3Z6', 'Z2Z3Z7', 'Z2Z3Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : c9a29c3f-2763-4a93-b0c4-0a13ad27f13a\n",
+      "weight_enumerator    : [1, 0, 0, 0, 0, 0, 6, 0, 9, 0]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "gf4_codes = []\n",
+    "\n",
+    "for n in range(10):\n",
+    "    for k in range(n+1):\n",
+    "        codes = cb.all_small_codes(n, k, is_gf4linear=True, is_decomposable=False, info_only=True, list_only=True)\n",
+    "        gf4_codes += codes\n",
+    "        for code in codes:\n",
+    "            print(f\"[[{n},{k},{code['d']}]]\")\n",
+    "            print(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1334,
+   "id": "ff8cf7d0-aa7e-4913-b610-6510f4c025d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 1334,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(gf4_codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1358,
+   "id": "ab38bd41-61e8-4792-9b4b-7d015b414bad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = [[1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
+    "    [0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n",
+    "    [0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],\n",
+    "    [0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1],\n",
+    "    [0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1],\n",
+    "    [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1359,
+   "id": "767ff045-bb86-4df9-a850-949504c6a8a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B = np.array(A, dtype=bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1360,
+   "id": "62d745d5-38d5-4ccc-b4dc-d2a0bb77e24a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True, False, False, False,  True, False, False, False, False, False, False, False,  True,  True,  True,  True],\n",
+       "       [False, False, False,  True, False,  True, False, False,  True, False, False, False, False,  True, False, False],\n",
+       "       [False,  True, False, False,  True,  True, False,  True, False, False, False,  True,  True, False, False, False],\n",
+       "       [False, False, False,  True, False,  True,  True,  True, False,  True, False,  True,  True, False, False,  True],\n",
+       "       [False, False,  True,  True,  True, False,  True, False, False, False, False,  True, False,  True,  True,  True],\n",
+       "       [False, False, False, False, False, False,  True,  True, False, False,  True, False, False, False,  True, False]])"
+      ]
+     },
+     "execution_count": 1360,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1361,
+   "id": "7244b962-ee93-4f7f-811b-ee1b5368124a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "D = PauliList(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1362,
+   "id": "fb55ec42-78da-41ba-9af2-e7d49e0e379f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PauliList(['X0Y4Z5Z6Z7', 'Z0X3Y5', 'X1Z3Y4X5X7', 'Z1Y3Z4X5X6Y7', 'X2Y3X4Z5Y6Z7', 'Z2Y6X7'])"
+      ]
+     },
+     "execution_count": 1362,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1363,
+   "id": "ac1094b6-6ac7-43d9-a543-f5d613c0d475",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BasePauli.set_syntax(pauli_rep.PRODUCT_SYNTAX)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1364,
+   "id": "272ccb3c-23cc-4311-84a0-294a157f083b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PauliList(['XIIIYZZZ', 'ZIIXIYII', 'IXIZYXIX', 'IZIYZXXY', 'IIXYXZYZ', 'IIZIIIYX'])"
+      ]
+     },
+     "execution_count": 1364,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1369,
+   "id": "9f5f80d3-ad1e-4e20-a9e8-1f37aa7c1cf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C = ['XIIIYZZZ', 'ZIIXIYII', 'IXIZYXIX', 'IZIYZXXY', 'IIXYXZYZ', 'IIZIIIYX']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1370,
+   "id": "c8d1ece2-9df6-4d28-9aab-00718cd69c0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'xiiiyzzz' 'ziixiyii' 'ixizyxix' 'iziyzxxy' 'iixyxzyz' 'iiziiiyx' "
+     ]
+    }
+   ],
+   "source": [
+    "D = [item.lower() for item in C]\n",
+    "for item in D:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "296c93fe-6607-477a-a267-a7edb676f9bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T=[1 0 1 0 1 0 0 0 0|0 0 1 1 0 0 0 0 0]\n",
+    "[0 0 1 1 0 0 0 0 0|1 0 0 1 1 0 0 0 0]\n",
+    "[0 1 1 1 1 0 0 0 0|0 0 0 0 0 0 0 0 0]\n",
+    "[0 0 0 0 0 0 0 0 0|0 1 1 1 1 0 0 0 0]\n",
+    "[0 0 0 0 0 1 0 0 0|0 0 0 0 0 0 0 0 0]\n",
+    "[0 0 0 0 0 0 1 0 0|0 0 0 0 0 0 0 0 0]\n",
+    "[0 0 0 0 0 0 0 1 0|0 0 0 0 0 0 0 0 0]\n",
+    "[0 0 0 0 0 0 0 0 1|0 0 0 0 0 0 0 0 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1393,
+   "id": "b9817a1e-b4f1-4a7a-a206-2dbb2bbca0e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = [\"XIIZYZXXY\",\"ZIIIIXIII\",\"IXIZYIYIZ\",\"IZIIIIXII\",\"IIXZZIIIX\",\"IIZIYXIYI\",\"IIIXXXIZI\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1394,
+   "id": "76c5c750-0d3f-4b34-926a-11168beceecc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'xiizyzxxy' 'ziiiixiii' 'ixizyiyiz' 'iziiiixii' 'iixzziiix' 'iiziyxiyi' 'iiixxxizi' "
+     ]
+    }
+   ],
+   "source": [
+    "D = [item.lower() for item in A]\n",
+    "for item in D:\n",
+    "    print(f\"\\'{item}\\' \", end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1395,
+   "id": "071aa4c1-9dad-4704-b92a-3017dbeb1f74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codes = cb.all_small_codes(9, 2, d=3, info_only=True, list_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1396,
+   "id": "d00860d9-f89e-4e4b-9aec-2cbdd2b8e067",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4445"
+      ]
+     },
+     "execution_count": 1396,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(codes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1400,
+   "id": "862ce1ae-8dd8-4015-bb0a-697b957c44c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['H2V3H4S5S7^(2,4)', 'V0S8', 'H0H8^(0,8)', 'V1S6', 'H1H6^(1,6)']\n",
+      "aut_group_size       : 32\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 8842\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z8', 'X1Z6', 'X2X3Z4Z5', 'Y2Y4Z5Z7', 'Z2Z3X7Z8', 'Z1Y2X5X6Y7', 'Z0Z1X2Z4Y6Y8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z1Z2Z3Z4Z5X6', 'Z0Z2Z3Z4Z7X8', 'Z2Z3Z4Z6', 'Z2Z3Z4Z5Z8']\n",
+      "n                    : 9\n",
+      "uuid                 : 0b329b44-3fc7-4ba0-842c-9433c602205a\n",
+      "weight_enumerator    : [1, 0, 2, 0, 8, 4, 22, 56, 31, 4]\n",
+      "\n",
+      "aut_group_generators : ['V1S5', 'H1H5^(1,5)', 'V0S4', '(0,1)(2,3)(4,5)(6,7)']\n",
+      "aut_group_size       : 32\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 3\n",
+      "index                : 22515\n",
+      "is_css               : 0\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 1\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['X0Z4', 'X1Z5', 'Z0X2X4Z6', 'Z1X3X5Z7', 'Y2Y6Z7Z8', 'Y3Z6Y7Z8', 'Z2Z3Z4Z5X8']\n",
+      "k                    : 2\n",
+      "logical_ops          : ['Z2Z3Z4X7', 'Z2Z3Z5X6', 'Z3Z5Z7', 'Z2Z4Z6']\n",
+      "n                    : 9\n",
+      "uuid                 : b26f73c3-642c-4932-8693-aa8fb5658b4c\n",
+      "weight_enumerator    : [1, 0, 2, 0, 8, 4, 22, 56, 31, 4]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for code in codes:\n",
+    "    if code['aut_group_size'] == 32:\n",
+    "        if code['weight_enumerator'] == [1,0,2,0,8,4,22,56,31,4]:\n",
+    "            print(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1415,
+   "id": "b203846f-1d63-43ab-bf10-a9738264a182",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " [[(9, 3, 3)]]:170234             aut_group_size=324\n",
+      " [[(9, 3, 3)]]:170235             aut_group_size=1296\n"
+     ]
+    }
+   ],
+   "source": [
+    "codes = cb.all_small_codes(9, 3, d=3, info_only=True, is_decomposable=False, is_css=False)\n",
+    "for code in codes:\n",
+    "    if code['aut_group_size'] > 300:\n",
+    "        print(\n",
+    "            f\" [[{code['n'],code['k'],code['d']}]]:{code['index']} \\\n",
+    "            aut_group_size={code['aut_group_size']}\"\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1416,
+   "id": "b59d4fc2-21ab-47b6-94b3-b9f8c46c963d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "code = cb.small_code(5, 0, 4, info_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1417,
+   "id": "91c9e05f-6a5b-4f26-9466-c3259f39e613",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aut_group_generators : ['S1S2', '(1,2)', 'S0S4', '(0,1)(2,4)']\n",
+      "aut_group_size       : 32\n",
+      "code_type            : StabSubSystemCode\n",
+      "d                    : 2\n",
+      "index                : 4\n",
+      "is_css               : 1\n",
+      "is_decomposable      : 0\n",
+      "is_degenerate        : 0\n",
+      "is_gf4linear         : 0\n",
+      "is_subsystem         : 1\n",
+      "isotropic_generators : ['Z0Z4', 'Z1Z2', 'Z1Z3Z4', 'X1X2X3', 'X0X3X4']\n",
+      "k                    : 0\n",
+      "logical_ops          : []\n",
+      "n                    : 5\n",
+      "uuid                 : ee84af51-7ef1-4540-b635-a6377aa49596\n",
+      "weight_enumerator    : [1, 0, 2, 8, 13, 8]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(code)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69f76b2d-aff4-48fe-8921-1352888e7a78",
+   "metadata": {},
+   "source": [
+    "# Distribution of codes over distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "f6f464c3-378d-41e3-8c1b-f4089c891808",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "distro = {}\n",
+    "for n in range(10):\n",
+    "    for k in range(n+1):\n",
+    "        val = [len(cb.all_small_codes(n, k, d=d, info_only=True, list_only=True)) for d in range(1,5)]\n",
+    "        distro[f'[[{n},{k}]]']=val\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "62ffa5d6-1646-4fe4-b4b4-8c21192a10da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'[[0,0]]': [0, 0, 0, 0],\n",
+       " '[[1,0]]': [1, 0, 0, 0],\n",
+       " '[[1,1]]': [1, 0, 0, 0],\n",
+       " '[[2,0]]': [1, 1, 0, 0],\n",
+       " '[[2,1]]': [2, 0, 0, 0],\n",
+       " '[[2,2]]': [1, 0, 0, 0],\n",
+       " '[[3,0]]': [2, 1, 0, 0],\n",
+       " '[[3,1]]': [5, 0, 0, 0],\n",
+       " '[[3,2]]': [3, 0, 0, 0],\n",
+       " '[[3,3]]': [1, 0, 0, 0],\n",
+       " '[[4,0]]': [3, 3, 0, 0],\n",
+       " '[[4,1]]': [11, 2, 0, 0],\n",
+       " '[[4,2]]': [10, 1, 0, 0],\n",
+       " '[[4,3]]': [4, 0, 0, 0],\n",
+       " '[[4,4]]': [1, 0, 0, 0],\n",
+       " '[[5,0]]': [6, 4, 1, 0],\n",
+       " '[[5,1]]': [29, 6, 1, 0],\n",
+       " '[[5,2]]': [37, 3, 0, 0],\n",
+       " '[[5,3]]': [19, 0, 0, 0],\n",
+       " '[[5,4]]': [5, 0, 0, 0],\n",
+       " '[[5,5]]': [1, 0, 0, 0],\n",
+       " '[[6,0]]': [11, 13, 1, 1],\n",
+       " '[[6,1]]': [78, 35, 2, 0],\n",
+       " '[[6,2]]': [156, 29, 0, 0],\n",
+       " '[[6,3]]': [104, 5, 0, 0],\n",
+       " '[[6,4]]': [31, 1, 0, 0],\n",
+       " '[[6,5]]': [6, 0, 0, 0],\n",
+       " '[[6,6]]': [1, 0, 0, 0],\n",
+       " '[[7,0]]': [26, 29, 4, 0],\n",
+       " '[[7,1]]': [260, 169, 19, 0],\n",
+       " '[[7,2]]': [834, 241, 0, 0],\n",
+       " '[[7,3]]': [785, 67, 0, 0],\n",
+       " '[[7,4]]': [260, 7, 0, 0],\n",
+       " '[[7,5]]': [48, 0, 0, 0],\n",
+       " '[[7,6]]': [7, 0, 0, 0],\n",
+       " '[[7,7]]': [1, 0, 0, 0],\n",
+       " '[[8,0]]': [59, 107, 11, 5],\n",
+       " '[[8,1]]': [1023, 1170, 178, 0],\n",
+       " '[[8,2]]': [6266, 3724, 20, 0],\n",
+       " '[[8,3]]': [9304, 2117, 1, 0],\n",
+       " '[[8,4]]': [3699, 264, 0, 0],\n",
+       " '[[8,5]]': [603, 11, 0, 0],\n",
+       " '[[8,6]]': [70, 1, 0, 0],\n",
+       " '[[8,7]]': [8, 0, 0, 0],\n",
+       " '[[8,8]]': [1, 0, 0, 0],\n",
+       " '[[9,0]]': [182, 416, 69, 8],\n",
+       " '[[9,1]]': [5777, 10742, 3609, 0],\n",
+       " '[[9,2]]': [78567, 98027, 4445, 0],\n",
+       " '[[9,3]]': [222749, 130598, 222, 0],\n",
+       " '[[9,4]]': [122541, 24117, 0, 0],\n",
+       " '[[9,5]]': [17677, 768, 0, 0],\n",
+       " '[[9,6]]': [1331, 13, 0, 0],\n",
+       " '[[9,7]]': [99, 0, 0, 0],\n",
+       " '[[9,8]]': [9, 0, 0, 0],\n",
+       " '[[9,9]]': [1, 0, 0, 0]}"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "fb6f4a5d-86ed-4d78-80d4-2ef81c2cad6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distroind = {}\n",
+    "for n in range(10):\n",
+    "    for k in range(n+1):\n",
+    "        val = [len(cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)) for d in range(1,5)]\n",
+    "        distroind[f'[[{n},{k}]]']=val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "438b4276-c83c-4209-a67f-af630a781f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'[[0,0]]': [0, 0, 0, 0],\n",
+       " '[[1,0]]': [1, 0, 0, 0],\n",
+       " '[[1,1]]': [1, 0, 0, 0],\n",
+       " '[[2,0]]': [0, 1, 0, 0],\n",
+       " '[[2,1]]': [1, 0, 0, 0],\n",
+       " '[[2,2]]': [0, 0, 0, 0],\n",
+       " '[[3,0]]': [0, 1, 0, 0],\n",
+       " '[[3,1]]': [2, 0, 0, 0],\n",
+       " '[[3,2]]': [1, 0, 0, 0],\n",
+       " '[[3,3]]': [0, 0, 0, 0],\n",
+       " '[[4,0]]': [0, 2, 0, 0],\n",
+       " '[[4,1]]': [4, 2, 0, 0],\n",
+       " '[[4,2]]': [3, 1, 0, 0],\n",
+       " '[[4,3]]': [1, 0, 0, 0],\n",
+       " '[[4,4]]': [0, 0, 0, 0],\n",
+       " '[[5,0]]': [0, 3, 1, 0],\n",
+       " '[[5,1]]': [12, 4, 1, 0],\n",
+       " '[[5,2]]': [16, 2, 0, 0],\n",
+       " '[[5,3]]': [6, 0, 0, 0],\n",
+       " '[[5,4]]': [1, 0, 0, 0],\n",
+       " '[[5,5]]': [0, 0, 0, 0],\n",
+       " '[[6,0]]': [0, 9, 1, 1],\n",
+       " '[[6,1]]': [35, 27, 1, 0],\n",
+       " '[[6,2]]': [82, 25, 0, 0],\n",
+       " '[[6,3]]': [48, 5, 0, 0],\n",
+       " '[[6,4]]': [9, 1, 0, 0],\n",
+       " '[[6,5]]': [1, 0, 0, 0],\n",
+       " '[[6,6]]': [0, 0, 0, 0],\n",
+       " '[[7,0]]': [0, 22, 4, 0],\n",
+       " '[[7,1]]': [140, 128, 16, 0],\n",
+       " '[[7,2]]': [545, 209, 0, 0],\n",
+       " '[[7,3]]': [494, 62, 0, 0],\n",
+       " '[[7,4]]': [125, 6, 0, 0],\n",
+       " '[[7,5]]': [13, 0, 0, 0],\n",
+       " '[[7,6]]': [1, 0, 0, 0],\n",
+       " '[[7,7]]': [0, 0, 0, 0],\n",
+       " '[[8,0]]': [0, 85, 11, 5],\n",
+       " '[[8,1]]': [646, 964, 157, 0],\n",
+       " '[[8,2]]': [4858, 3450, 20, 0],\n",
+       " '[[8,3]]': [7373, 2043, 1, 0],\n",
+       " '[[8,4]]': [2579, 255, 0, 0],\n",
+       " '[[8,5]]': [295, 11, 0, 0],\n",
+       " '[[8,6]]': [18, 1, 0, 0],\n",
+       " '[[8,7]]': [1, 0, 0, 0],\n",
+       " '[[8,8]]': [0, 0, 0, 0],\n",
+       " '[[9,0]]': [0, 363, 69, 8],\n",
+       " '[[9,1]]': [4337, 9395, 3411, 0],\n",
+       " '[[9,2]]': [69122, 94048, 4425, 0],\n",
+       " '[[9,3]]': [202670, 128405, 221, 0],\n",
+       " '[[9,4]]': [107191, 23844, 0, 0],\n",
+       " '[[9,5]]': [13095, 757, 0, 0],\n",
+       " '[[9,6]]': [656, 12, 0, 0],\n",
+       " '[[9,7]]': [24, 0, 0, 0],\n",
+       " '[[9,8]]': [1, 0, 0, 0],\n",
+       " '[[9,9]]': [0, 0, 0, 0]}"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distroind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d1d4e021-70d4-4e29-bea7-f09e7744502e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distron = {}\n",
+    "for n in range(10):\n",
+    "    temp = [0]*4\n",
+    "    for k in range(n+1):\n",
+    "        val = [len(cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)) for d in range(1,5)]\n",
+    "        temp = [v+t for v,t in zip(val, temp)]\n",
+    "    distron[f'{n}']=temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "28a5f87a-8510-49a9-9b08-613a12ab487b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'0': [0, 0, 0, 0],\n",
+       " '1': [2, 0, 0, 0],\n",
+       " '2': [1, 1, 0, 0],\n",
+       " '3': [3, 1, 0, 0],\n",
+       " '4': [8, 5, 0, 0],\n",
+       " '5': [35, 9, 2, 0],\n",
+       " '6': [175, 67, 2, 1],\n",
+       " '7': [1318, 427, 20, 0],\n",
+       " '8': [15770, 6809, 189, 5],\n",
+       " '9': [397096, 256824, 8126, 8]}"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distron"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "84be227d-0220-40c5-9565-d61c8415d575",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del distron['0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "81ad0207-644a-429e-a476-4ceb6cf68f3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distronperc = {}\n",
+    "for distro in distron:\n",
+    "    total = sum(distron[distro])\n",
+    "    perc = [v/total for v in distron[distro]]\n",
+    "    distronperc[distro] = perc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "2b9a39c2-ca42-4e7a-830a-95e5d27c1f4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'1': [1.0, 0.0, 0.0, 0.0],\n",
+       " '2': [0.5, 0.5, 0.0, 0.0],\n",
+       " '3': [0.75, 0.25, 0.0, 0.0],\n",
+       " '4': [0.6153846153846154, 0.38461538461538464, 0.0, 0.0],\n",
+       " '5': [0.7608695652173914, 0.1956521739130435, 0.043478260869565216, 0.0],\n",
+       " '6': [0.7142857142857143,\n",
+       "  0.27346938775510204,\n",
+       "  0.00816326530612245,\n",
+       "  0.004081632653061225],\n",
+       " '7': [0.746742209631728, 0.24192634560906515, 0.0113314447592068, 0.0],\n",
+       " '8': [0.6924867167259474,\n",
+       "  0.2989944232204804,\n",
+       "  0.008299301804768806,\n",
+       "  0.00021955824880340755],\n",
+       " '9': [0.5997939745096321,\n",
+       "  0.38792001860875397,\n",
+       "  0.0122739232751407,\n",
+       "  1.2083606473187987e-05]}"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distronperc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "a11e7b30-9878-4a9b-9b00-b012c970caef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "2 : ['0.50000', '0.50000', '0.00000', '0.00000']\n",
+      "3 : ['0.75000', '0.25000', '0.00000', '0.00000']\n",
+      "4 : ['0.61538', '0.38462', '0.00000', '0.00000']\n",
+      "5 : ['0.76087', '0.19565', '0.04348', '0.00000']\n",
+      "6 : ['0.71429', '0.27347', '0.00816', '0.00408']\n",
+      "7 : ['0.74674', '0.24193', '0.01133', '0.00000']\n",
+      "8 : ['0.69249', '0.29899', '0.00830', '0.00022']\n",
+      "9 : ['0.59979', '0.38792', '0.01227', '0.00001']\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in distronperc:\n",
+    "    n = str(key)\n",
+    "    val = [f\"{num:.5f}\" for num in distronperc[key]]\n",
+    "    print(f\"{n} : {val}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "bbf26b8a-3568-4ed7-bc6a-22d0d9c375f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "5edf8fac-6104-4f17-a39b-6d9612a2e760",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = {int(key): distronperc[key] for key in distronperc.keys()}\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "num_bins = len(data[1])\n",
+    "bins = np.arange(num_bins + 1)  # Create bin edges from 0 to number of bins\n",
+    "\n",
+    "# Compute the midpoints of each bin for centering the bars\n",
+    "bin_centers = (bins[:-1] + bins[1:]) / 2\n",
+    "\n",
+    "# Create a figure and a 3x3 grid of subplots\n",
+    "fig, axs = plt.subplots(3, 3, figsize=(10, 10))\n",
+    "\n",
+    "# Flatten the axes array for easy indexing\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "# Plot each list as a histogram\n",
+    "for i, (key, percentages) in enumerate(data.items()):\n",
+    "    axs[i].bar(bin_centers, percentages, width=0.9, align='center')  # Center the bars on bin midpoints\n",
+    "    axs[i].set_title(f'{key} Qubit Codes')\n",
+    "    axs[i].set_ylim(0, 1)  # Set the y-axis to 1 since percentages sum to 1\n",
+    "    axs[i].set_xlabel('d')  # Label for the horizontal axis\n",
+    "    axs[i].set_ylabel('%')  # Label for the vertical axis\n",
+    "    axs[i].set_xticks(bin_centers)  # Set the ticks at the center of the bins\n",
+    "    axs[i].set_xticklabels(range(1, num_bins + 1))  # Use original integers as tick labels\n",
+    "\n",
+    "# Adjust layout for better spacing\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "b6ba4dc8-d573-4a05-9bf2-049f42b74574",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0,0]]: [0, 0, 0, 0] : 0\n",
+      "[[1,0]]: [1, 0, 0, 0] : 1\n",
+      "[[1,1]]: [1, 0, 0, 0] : 1\n",
+      "[[2,0]]: [0, 1, 0, 0] : 1\n",
+      "[[2,1]]: [1, 0, 0, 0] : 1\n",
+      "[[2,2]]: [0, 0, 0, 0] : 0\n",
+      "[[3,0]]: [0, 1, 0, 0] : 1\n",
+      "[[3,1]]: [2, 0, 0, 0] : 2\n",
+      "[[3,2]]: [1, 0, 0, 0] : 1\n",
+      "[[3,3]]: [0, 0, 0, 0] : 0\n",
+      "[[4,0]]: [0, 2, 0, 0] : 2\n",
+      "[[4,1]]: [4, 2, 0, 0] : 6\n",
+      "[[4,2]]: [3, 1, 0, 0] : 4\n",
+      "[[4,3]]: [1, 0, 0, 0] : 1\n",
+      "[[4,4]]: [0, 0, 0, 0] : 0\n",
+      "[[5,0]]: [0, 3, 1, 0] : 4\n",
+      "[[5,1]]: [12, 4, 1, 0] : 17\n",
+      "[[5,2]]: [16, 2, 0, 0] : 18\n",
+      "[[5,3]]: [6, 0, 0, 0] : 6\n",
+      "[[5,4]]: [1, 0, 0, 0] : 1\n",
+      "[[5,5]]: [0, 0, 0, 0] : 0\n",
+      "[[6,0]]: [0, 9, 1, 1] : 11\n",
+      "[[6,1]]: [35, 27, 1, 0] : 63\n",
+      "[[6,2]]: [82, 25, 0, 0] : 107\n",
+      "[[6,3]]: [48, 5, 0, 0] : 53\n",
+      "[[6,4]]: [9, 1, 0, 0] : 10\n",
+      "[[6,5]]: [1, 0, 0, 0] : 1\n",
+      "[[6,6]]: [0, 0, 0, 0] : 0\n",
+      "[[7,0]]: [0, 22, 4, 0] : 26\n",
+      "[[7,1]]: [140, 128, 16, 0] : 284\n",
+      "[[7,2]]: [545, 209, 0, 0] : 754\n",
+      "[[7,3]]: [494, 62, 0, 0] : 556\n",
+      "[[7,4]]: [125, 6, 0, 0] : 131\n",
+      "[[7,5]]: [13, 0, 0, 0] : 13\n",
+      "[[7,6]]: [1, 0, 0, 0] : 1\n",
+      "[[7,7]]: [0, 0, 0, 0] : 0\n",
+      "[[8,0]]: [0, 85, 11, 5] : 101\n",
+      "[[8,1]]: [646, 964, 157, 0] : 1767\n",
+      "[[8,2]]: [4858, 3450, 20, 0] : 8328\n",
+      "[[8,3]]: [7373, 2043, 1, 0] : 9417\n",
+      "[[8,4]]: [2579, 255, 0, 0] : 2834\n",
+      "[[8,5]]: [295, 11, 0, 0] : 306\n",
+      "[[8,6]]: [18, 1, 0, 0] : 19\n",
+      "[[8,7]]: [1, 0, 0, 0] : 1\n",
+      "[[8,8]]: [0, 0, 0, 0] : 0\n",
+      "[[9,0]]: [0, 363, 69, 8] : 440\n",
+      "[[9,1]]: [4337, 9395, 3411, 0] : 17143\n",
+      "[[9,2]]: [69122, 94048, 4425, 0] : 167595\n",
+      "[[9,3]]: [202670, 128405, 221, 0] : 331296\n",
+      "[[9,4]]: [107191, 23844, 0, 0] : 131035\n",
+      "[[9,5]]: [13095, 757, 0, 0] : 13852\n",
+      "[[9,6]]: [656, 12, 0, 0] : 668\n",
+      "[[9,7]]: [24, 0, 0, 0] : 24\n",
+      "[[9,8]]: [1, 0, 0, 0] : 1\n",
+      "[[9,9]]: [0, 0, 0, 0] : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "distroindperc = {}\n",
+    "temp = distroind\n",
+    "for distro in temp:\n",
+    "    print(f\"{distro}: {temp[distro]} : {sum(temp[distro])}\")\n",
+    "    total = sum(temp[distro])\n",
+    "    if total !=0:\n",
+    "        perc = [v/total for v in temp[distro]]\n",
+    "        distroindperc[distro] = perc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "16dfd467-8bea-44eb-bbc8-4c1030e3857b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'[[1,0]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[1,1]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[2,0]]': [0.0, 1.0, 0.0, 0.0],\n",
+       " '[[2,1]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[3,0]]': [0.0, 1.0, 0.0, 0.0],\n",
+       " '[[3,1]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[3,2]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[4,0]]': [0.0, 1.0, 0.0, 0.0],\n",
+       " '[[4,1]]': [0.6666666666666666, 0.3333333333333333, 0.0, 0.0],\n",
+       " '[[4,2]]': [0.75, 0.25, 0.0, 0.0],\n",
+       " '[[4,3]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[5,0]]': [0.0, 0.75, 0.25, 0.0],\n",
+       " '[[5,1]]': [0.7058823529411765,\n",
+       "  0.23529411764705882,\n",
+       "  0.058823529411764705,\n",
+       "  0.0],\n",
+       " '[[5,2]]': [0.8888888888888888, 0.1111111111111111, 0.0, 0.0],\n",
+       " '[[5,3]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[5,4]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[6,0]]': [0.0,\n",
+       "  0.8181818181818182,\n",
+       "  0.09090909090909091,\n",
+       "  0.09090909090909091],\n",
+       " '[[6,1]]': [0.5555555555555556,\n",
+       "  0.42857142857142855,\n",
+       "  0.015873015873015872,\n",
+       "  0.0],\n",
+       " '[[6,2]]': [0.7663551401869159, 0.2336448598130841, 0.0, 0.0],\n",
+       " '[[6,3]]': [0.9056603773584906, 0.09433962264150944, 0.0, 0.0],\n",
+       " '[[6,4]]': [0.9, 0.1, 0.0, 0.0],\n",
+       " '[[6,5]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[7,0]]': [0.0, 0.8461538461538461, 0.15384615384615385, 0.0],\n",
+       " '[[7,1]]': [0.49295774647887325,\n",
+       "  0.4507042253521127,\n",
+       "  0.056338028169014086,\n",
+       "  0.0],\n",
+       " '[[7,2]]': [0.7228116710875332, 0.27718832891246686, 0.0, 0.0],\n",
+       " '[[7,3]]': [0.8884892086330936, 0.11151079136690648, 0.0, 0.0],\n",
+       " '[[7,4]]': [0.9541984732824428, 0.04580152671755725, 0.0, 0.0],\n",
+       " '[[7,5]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[7,6]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[8,0]]': [0.0,\n",
+       "  0.8415841584158416,\n",
+       "  0.10891089108910891,\n",
+       "  0.04950495049504951],\n",
+       " '[[8,1]]': [0.3655913978494624, 0.545557441992077, 0.08885116015846067, 0.0],\n",
+       " '[[8,2]]': [0.5833333333333334,\n",
+       "  0.4142651296829971,\n",
+       "  0.0024015369836695487,\n",
+       "  0.0],\n",
+       " '[[8,3]]': [0.7829457364341085,\n",
+       "  0.216948072634597,\n",
+       "  0.00010619093129446746,\n",
+       "  0.0],\n",
+       " '[[8,4]]': [0.9100211714890614, 0.08997882851093861, 0.0, 0.0],\n",
+       " '[[8,5]]': [0.9640522875816994, 0.03594771241830065, 0.0, 0.0],\n",
+       " '[[8,6]]': [0.9473684210526315, 0.05263157894736842, 0.0, 0.0],\n",
+       " '[[8,7]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[9,0]]': [0.0, 0.825, 0.15681818181818183, 0.01818181818181818],\n",
+       " '[[9,1]]': [0.2529895584203465, 0.5480370996908359, 0.1989733418888176, 0.0],\n",
+       " '[[9,2]]': [0.4124347385065187,\n",
+       "  0.5611623258450431,\n",
+       "  0.026402935648438198,\n",
+       "  0.0],\n",
+       " '[[9,3]]': [0.6117490099488071,\n",
+       "  0.38758391287549504,\n",
+       "  0.0006670771756978654,\n",
+       "  0.0],\n",
+       " '[[9,4]]': [0.8180333498683557, 0.18196665013164423, 0.0, 0.0],\n",
+       " '[[9,5]]': [0.945350851862547, 0.05464914813745308, 0.0, 0.0],\n",
+       " '[[9,6]]': [0.9820359281437125, 0.017964071856287425, 0.0, 0.0],\n",
+       " '[[9,7]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[9,8]]': [1.0, 0.0, 0.0, 0.0]}"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distroindperc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "387f06ff-a09c-4eb3-84bf-c132e1010f5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1,0]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[1,1]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[2,0]] : ['0.00000', '1.00000', '0.00000', '0.00000']\n",
+      "[[2,1]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[3,0]] : ['0.00000', '1.00000', '0.00000', '0.00000']\n",
+      "[[3,1]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[3,2]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[4,0]] : ['0.00000', '1.00000', '0.00000', '0.00000']\n",
+      "[[4,1]] : ['0.66667', '0.33333', '0.00000', '0.00000']\n",
+      "[[4,2]] : ['0.75000', '0.25000', '0.00000', '0.00000']\n",
+      "[[4,3]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[5,0]] : ['0.00000', '0.75000', '0.25000', '0.00000']\n",
+      "[[5,1]] : ['0.70588', '0.23529', '0.05882', '0.00000']\n",
+      "[[5,2]] : ['0.88889', '0.11111', '0.00000', '0.00000']\n",
+      "[[5,3]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[5,4]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[6,0]] : ['0.00000', '0.81818', '0.09091', '0.09091']\n",
+      "[[6,1]] : ['0.55556', '0.42857', '0.01587', '0.00000']\n",
+      "[[6,2]] : ['0.76636', '0.23364', '0.00000', '0.00000']\n",
+      "[[6,3]] : ['0.90566', '0.09434', '0.00000', '0.00000']\n",
+      "[[6,4]] : ['0.90000', '0.10000', '0.00000', '0.00000']\n",
+      "[[6,5]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[7,0]] : ['0.00000', '0.84615', '0.15385', '0.00000']\n",
+      "[[7,1]] : ['0.49296', '0.45070', '0.05634', '0.00000']\n",
+      "[[7,2]] : ['0.72281', '0.27719', '0.00000', '0.00000']\n",
+      "[[7,3]] : ['0.88849', '0.11151', '0.00000', '0.00000']\n",
+      "[[7,4]] : ['0.95420', '0.04580', '0.00000', '0.00000']\n",
+      "[[7,5]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[7,6]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[8,0]] : ['0.00000', '0.84158', '0.10891', '0.04950']\n",
+      "[[8,1]] : ['0.36559', '0.54556', '0.08885', '0.00000']\n",
+      "[[8,2]] : ['0.58333', '0.41427', '0.00240', '0.00000']\n",
+      "[[8,3]] : ['0.78295', '0.21695', '0.00011', '0.00000']\n",
+      "[[8,4]] : ['0.91002', '0.08998', '0.00000', '0.00000']\n",
+      "[[8,5]] : ['0.96405', '0.03595', '0.00000', '0.00000']\n",
+      "[[8,6]] : ['0.94737', '0.05263', '0.00000', '0.00000']\n",
+      "[[8,7]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[9,0]] : ['0.00000', '0.82500', '0.15682', '0.01818']\n",
+      "[[9,1]] : ['0.25299', '0.54804', '0.19897', '0.00000']\n",
+      "[[9,2]] : ['0.41243', '0.56116', '0.02640', '0.00000']\n",
+      "[[9,3]] : ['0.61175', '0.38758', '0.00067', '0.00000']\n",
+      "[[9,4]] : ['0.81803', '0.18197', '0.00000', '0.00000']\n",
+      "[[9,5]] : ['0.94535', '0.05465', '0.00000', '0.00000']\n",
+      "[[9,6]] : ['0.98204', '0.01796', '0.00000', '0.00000']\n",
+      "[[9,7]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n",
+      "[[9,8]] : ['1.00000', '0.00000', '0.00000', '0.00000']\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in distroindperc:\n",
+    "    n = str(key)\n",
+    "    val = [f\"{num:.5f}\" for num in distroindperc[key]]\n",
+    "    print(f\"{n} : {val}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "5ec8484b-83e4-41e2-9043-12683802c552",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "a21bcfd5-dc3e-4f67-b6a4-51b733ca8924",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_n_codes(n):\n",
+    "    selection = {}\n",
+    "    for key in distroindperc:\n",
+    "        key_pair = ast.literal_eval(key)[0]\n",
+    "        if key_pair[0] == n:\n",
+    "            selection[key] = distroindperc[key]\n",
+    "    return selection\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "cf35c28d-55af-4b00-928a-3c44182b3a28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = extract_n_codes(9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "7ec95799-1f92-4e4f-851b-1b9b94256635",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "def plot_d_distro(data):\n",
+    "    num_bins = len(data[1])\n",
+    "    bins = np.arange(num_bins + 1)  # Create bin edges from 0 to number of bins\n",
+    "    \n",
+    "    # Compute the midpoints of each bin for centering the bars\n",
+    "    bin_centers = (bins[:-1] + bins[1:]) / 2\n",
+    "    \n",
+    "    # Create a figure and a 3x3 grid of subplots\n",
+    "    fig, axs = plt.subplots(3, 3, figsize=(10, 10))\n",
+    "    \n",
+    "    # Flatten the axes array for easy indexing\n",
+    "    axs = axs.flatten()\n",
+    "    \n",
+    "    # Plot each list as a histogram\n",
+    "    for i, (key, percentages) in enumerate(data.items()):\n",
+    "        axs[i].bar(bin_centers, percentages, width=0.9, align='center')  # Center the bars on bin midpoints\n",
+    "        axs[i].set_title(f'{key} Qubit Codes')\n",
+    "        axs[i].set_ylim(0, 1)  # Set the y-axis to 1 since percentages sum to 1\n",
+    "        axs[i].set_xlabel('d')  # Label for the horizontal axis\n",
+    "        axs[i].set_ylabel('%')  # Label for the vertical axis\n",
+    "        axs[i].set_xticks(bin_centers)  # Set the ticks at the center of the bins\n",
+    "        axs[i].set_xticklabels(range(1, num_bins + 1))  # Use original integers as tick labels\n",
+    "    \n",
+    "    # Adjust layout for better spacing\n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    # Show the plot\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "3f4a8cde-2d62-4995-91d3-6bd4d7782750",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 1]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a='[[2,1]]'\n",
+    "ast.literal_eval(a)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "a3c4e95f-5da7-47b9-83d7-5978251eae52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qo7LkY0W7NB+L89tvvykrK8sqH4vLlUtPIa1ozZo1086dO5WXl1fmJ0hcjYKCArVs2VIzZ860ub7wtEsvLy9t3rxZGzZsUEpKitasWaNly5bptttu0xdffMHlOajSyiMfDx8+rK5du8rf31+rVq0q8ZHc0iIfyw/5WDYc8YYCAwPVqVMny/XY69evV/369W3eLfFSbdq0UVpaWpFvr7777jt5e3uX+wfTjh07KiAgQEuWLCm2aX7nnXckyerRPYGBgUXu3piXl2fzSwPp4rd5l8rOztbRo0ctd6G0pbRN8d13360DBw5oy5YtV5zbsGFDFRQUFKkrPT1dp0+fVsOGDS3zbNWfkZFR5K7sjRs3VnZ2tqKjo20uDRo0sMx1d3dXTEyM5syZowMHDmjQoEF65513tH///lLtM+CIrjYfK9p1112npk2b6uOPPy72aEhJ81H6+wjI5S7PF8MwtH///nLNx5iYGJ07d+6KjySS/s69vXv3Wo3n5eXp4MGDV8zH8+fP6+DBg1ZjjRs31smTJ3X77bfbzMdLjx65urrq9ttv18yZM/XTTz9pypQp+vLLLy2nWwJVWVny8c8//1TXrl2Vm5urtWvX2nzCS3khH8nHyoLGG1aWLVumbdu2FbkexJb7779f6enpVo9eOHHihD744APFxMTYvP67LLy9vTVy5Ejt3btXY8eOLbI+JSVFixYtUkxMjNVN3Ro3bmy5ZrnQvHnzim3e582bZ3Wnx9dff10XLlzQnXfeWWxtPj4+kmQzoG0ZOXKkfHx8NGDAAKWnpxdZf+DAAcsju+666y5JF288cqnCbxt79OghSYqOjlb16tX16quvWt2B/fLtpIvPW9+yZYvWrl1bZN3p06d14cIFSRf/w3gpV1dXtWrVSpLK/XFxQGVXmny0h6SkJJ06dUqDBw8ukm87duzQtGnTdOONN1plWePGjfXtt98qLy/PMvb5558XuZNtoXfeecfqg+uKFSt09OjRf8xHb29vSSXPx8GDB6tu3boaMWKEzadjHD9+3PKc3+joaLm7u+u///2vVe4tWLBAmZmZlnxs27atgoODNXfuXKt9XbRoUZG6evfurSNHjth8Fu65c+csT4k4efJkkfVt2rSRRD7C+ZQmH3NycnTXXXfpyJEjWrVqVZFTnM1APpKPlQGnmjuxzZs3a9KkSeratatq1aqlb7/9VgsXLlT37t2tniEtXXwcwcSJE7VhwwbLswvvv/9+3XzzzYqPj9dPP/2koKAgzZkzR/n5+Zo4caIpNY8cOVI7d+7UtGnTtGXLFt13333y8vLS119/rXfffVc33HBDkednDxgwQIMHD9Z9992nO+64Qz/88IPWrl1b7F0W8/LydPvtt6t3797au3ev5syZo06dOtm8o3mhxo0bKyAgQHPnzpWvr698fHzUoUMHXXvttcXOX7Jkifr06aPrr79esbGxatGihfLy8vTNN9/ogw8+sDw7snXr1oqLi9O8efN0+vRpRUVFaevWrXr77bfVq1cvdenSRdLFa7mfeeYZJScn6+6779Zdd92l77//3uYdJZ999ll9+umnuvvuu9WvXz9FREQoJydHP/74o1asWKFDhw4pKChIAwYM0MmTJ3Xbbbepfv36+u233/Tqq6+qTZs2uv7660v4Tw1wPGXNR0mWDz+7d++WJC1evFhff/21JBV5zmp56Nu3r7Zv3245uvDwww8rMDBQaWlpeuuttxQcHKwVK1ZYPY5xwIABWrFihbp3767evXvrwIEDevfdd9W4cWOb71GzZk116tRJ8fHxSk9P16xZs9SkSRM99thjxdbl5eWl5s2ba9myZQoPD1fNmjXVokULtWjRwub8wMBArVy5UnfddZfatGmjRx55RBEREZKktLQ0vf/++4qMjJR0MfcSExM1ceJEde/eXT179rTkdrt27Sx3Ca5evbomT56sQYMG6bbbblOfPn108OBBLVy4sMilSY8++qiWL1+uwYMHa8OGDerYsaPy8/P1888/a/ny5Vq7dq3atm2rSZMmafPmzerRo4caNmyo48ePa86cOapfv77V3X6Bqqas+fjwww9r69at+s9//qM9e/ZYPbu7Ro0axV5OWBbkI/lYKRioEuLi4oyGDRsWGT948KAhyVi4cGGRdfv37ze6du1qBAUFGR4eHkazZs2M5ORkIzc3t8jcESNGGC4uLsaePXusxk+ePGn079/fqFWrluHt7W1ERUUZ27Zts1ljYS3Tp0+3Wb+Pj0+J9rWgoMBYtGiR0bFjR8PX19eQZEgyoqOjbdaen59vjBo1yggKCjK8vb2Nbt26Gfv37zcaNmxoxMXFWeYtXLjQkGRs2rTJGDhwoBEYGGjUqFHDePjhh40///zT6jWjoqKMqKgoq7FPPvnEaN68uVGtWrVif+eX27dvn/HYY48ZYWFhhru7u+Hr62t07NjRePXVV42//vrLMu/8+fPGxIkTjWuvvdaoXr26ERoaaiQmJlrNKdzXiRMnGnXr1jW8vLyMzp07G7t27Sqyr4ZhGGfOnDESExONJk2aGO7u7kZQUJBxyy23GC+99JKRl5dnGIZhrFixwujatatRu3Ztw93d3WjQoIExaNAg4+jRo1fcN6CysFc+FmaTrcUWScbQoUOLjCclJRmSjIyMjBLt76effmpER0cbAQEBlve74YYbjMzMTJvzZ8yYYVxzzTWGh4eH0bFjR2P79u1FMm7Dhg2GJOP99983EhMTjdq1axteXl5Gjx49jN9++83q9Wz9vr/55hsjIiLCcHd3NyQZSUlJV9yPP/74w3j66aeN8PBww9PT0/D29jYiIiKMKVOmFNmX1157zWjWrJlRvXp1IyQkxBgyZIhx6tSpIq85Z84c49prrzU8PDyMtm3bGps3b7aZ53l5eca0adOMG264wfDw8DACAwONiIgIY+LEiZb3Tk1NNe655x6jXr16hru7u1GvXj2jb9++xr59+664b0BlYY98bNiwYbHZaKsWwyAfL0c+OiYa7yoiLi7OCA0NNTIyMqz+ZSoMzldffdXIyMiwGYol0a5dO+P++++/qm0LCgqMjIwMIy0trUjjnZ2dbWRkZBgPPvhgiRvvy+Xl5RndunUzqlWrZqxevfqqXgNA1VWZ89EwDOPPP/80MjIyinywPHfunJGRkWE8++yzpfpgebn+/fsbkoz58+dfdY0AqibykXxExal8F6nhqh0+fFjBwcE2T+F48sknFRwcrE8//bTUr5uVlaUffvhBkyZNuqq6MjMzFRwcrJtuuqnIurFjxyo4OFhLly69qteWLp4i8+GHH6pNmzZ64IEHlJaWdtWvBaBqqqz5KF18CkNwcHCR8blz5yo4OFjTp0+/6teWpDfeeEN33323hgwZUq7PkwVQNZCP5CMqhothXHK1PRzWTz/9pD/++EPSxetjbr75ZkkXn6ldeE2hJLVq1apMzyi8GhcuXNDGjRstP4eHh1vumr1v3z79/vvvkqRq1apZXR8JAOWhMuejJG3atMlyQ8fQ0FDLXWEPHz5sdTfaqKgoVa9evcLrA1B1kY9AxaHxBgAAAADARHY91Xzz5s2KiYlRvXr15OLioo8//viK22zcuFE33XSTPDw81KRJkyJ3sAaAqoB8BIDikZEAHI1dG++cnBy1bt1as2fPLtH8gwcPqkePHurSpYt27typ4cOHa8CAATafRQwAjox8BIDikZEAHE2lOdXcxcVFK1eu/Mdn940aNUopKSnatWuXZezBBx/U6dOntWbNGpvb5ObmWj2ovaCgQCdPnlStWrXk4uJSbvUDcFyGYejMmTOqV6+eXF0r3z0nzcpHiYwEcGXOmpHkI4ArKU0+VvvHtZXMli1bFB0dbTXWrVs3DR8+vNhtkpOTNXHiRJMrA1AVHD58WPXr17d3GVflavJRIiMBlJyzZST5CKCkSpKPDtV4Hzt2TCEhIVZjISEhysrK0rlz5+Tl5VVkm8TERCUkJFh+zszMVIMGDXT48GH5+fmZXjOAyi8rK0uhoaHy9fW1dylX7WryUSIjAVyZs2Yk+QjgSkqTjw7VeF8NDw8PeXh4FBn38/MjNAFYccZTB8lIACXlbBlJPgIoqZLkY+W7UOcf1KlTR+np6VZj6enp8vPzK/ZoDgA4A/IRAIpHRgKwN4dqvCMjI5Wammo1tm7dOkVGRtqpIgCoHMhHACgeGQnA3uzaeGdnZ2vnzp3auXOnpIuPeti5c6d+//13SRevrYmNjbXMHzx4sH799VeNHDlSP//8s+bMmaPly5fr6aeftkf5AGAa8hEAikdGAnA0dm28t2/frhtvvFE33nijJCkhIUE33nijxo8fL0k6evSoJUAl6dprr1VKSorWrVun1q1ba8aMGXrzzTfVrVs3u9QPAGYhHwGgeGQkAEdTaZ7jXVGysrLk7++vzMxMbowBQBK5cCl+FwAuRy5cxO8BwOVKkwsOdY03AAAAAACOhsYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExUzd4FoHIKG51i7xJMc2hqD3uXAAAAAMCJcMQbAAAAAAAT2b3xnj17tsLCwuTp6akOHTpo69at/zh/1qxZatq0qby8vBQaGqqnn35af/31VwVVCwAVi4wEANvIRwCOxK6N97Jly5SQkKCkpCSlpaWpdevW6tatm44fP25z/pIlSzR69GglJSVpz549WrBggZYtW6YxY8ZUcOUAYD4yEgBsIx8BOBq7Nt4zZ87UY489pvj4eDVv3lxz586Vt7e33nrrLZvzv/nmG3Xs2FEPPfSQwsLC1LVrV/Xt2/eK33ACgCMiIwHANvIRgKOxW+Odl5enHTt2KDo6+u9iXF0VHR2tLVu22Nzmlltu0Y4dOywh+euvv2rVqlW66667in2f3NxcZWVlWS0AUNmRkQBgG/kIwBHZ7a7mJ06cUH5+vkJCQqzGQ0JC9PPPP9vc5qGHHtKJEyfUqVMnGYahCxcuaPDgwf94mlBycrImTpxYrrUDgNnISACwjXwE4IjsfnO10ti4caNeeOEFzZkzR2lpafroo4+UkpKi559/vthtEhMTlZmZaVkOHz5cgRUDQMUhIwHANvIRgL3Z7Yh3UFCQ3NzclJ6ebjWenp6uOnXq2Nxm3LhxevTRRzVgwABJUsuWLZWTk6OBAwdq7NixcnUt+j2Ch4eHPDw8yn8HAMBEZCQA2EY+AnBEdjvi7e7uroiICKWmplrGCgoKlJqaqsjISJvbnD17tkgwurm5SZIMwzCvWACoYGQkANhGPgJwRHY74i1JCQkJiouLU9u2bdW+fXvNmjVLOTk5io+PlyTFxsbqmmuuUXJysiQpJiZGM2fO1I033qgOHTpo//79GjdunGJiYizhCQBVBRkJALaRjwAcjV0b7z59+igjI0Pjx4/XsWPH1KZNG61Zs8Zys4zff//d6tvJ5557Ti4uLnruued05MgRBQcHKyYmRlOmTLHXLgCAachIALCNfATgaFwMJzu/JisrS/7+/srMzJSfn5+9y6m0wkan2LsE0xya2sPeJaCSIRf+xu8CwOXIhYv4PQC4XGlywaHuag4AAAAAgKOh8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJ7N54z549W2FhYfL09FSHDh20devWf5x/+vRpDR06VHXr1pWHh4fCw8O1atWqCqoWACoWGQkAtpGPABxJNXu++bJly5SQkKC5c+eqQ4cOmjVrlrp166a9e/eqdu3aRebn5eXpjjvuUO3atbVixQpdc801+u233xQQEFDxxQOAychIALCNfATgaFwMwzDs9eYdOnRQu3bt9Nprr0mSCgoKFBoaqieffFKjR48uMn/u3LmaPn26fv75Z1WvXr1E75Gbm6vc3FzLz1lZWQoNDVVmZqb8/PzKZ0eqoLDRKfYuwTSHpvawdwmoZLKysuTv71/pcoGMBFAZVMaMJB8BVAalyUe7nWqel5enHTt2KDo6+u9iXF0VHR2tLVu22Nzm008/VWRkpIYOHaqQkBC1aNFCL7zwgvLz84t9n+TkZPn7+1uW0NDQct8XAChvZCQA2EY+AnBEdmu8T5w4ofz8fIWEhFiNh4SE6NixYza3+fXXX7VixQrl5+dr1apVGjdunGbMmKHJkycX+z6JiYnKzMy0LIcPHy7X/QAAM5CRAGAb+QjAEdn1Gu/SKigoUO3atTVv3jy5ubkpIiJCR44c0fTp05WUlGRzGw8PD3l4eFRwpQBQ8chIALCNfARgb3ZrvIOCguTm5qb09HSr8fT0dNWpU8fmNnXr1lX16tXl5uZmGbv++ut17Ngx5eXlyd3d3dSaAaCikJEAYBv5CMAR2e1Uc3d3d0VERCg1NdUyVlBQoNTUVEVGRtrcpmPHjtq/f78KCgosY/v27VPdunUJTABVChkJALaRjwAckV2f452QkKD58+fr7bff1p49ezRkyBDl5OQoPj5ekhQbG6vExETL/CFDhujkyZMaNmyY9u3bp5SUFL3wwgsaOnSovXYBAExDRgKAbeQjAEdj12u8+/Tpo4yMDI0fP17Hjh1TmzZttGbNGsvNMn7//Xe5uv793UBoaKjWrl2rp59+Wq1atdI111yjYcOGadSoUfbaBQAwDRkJALaRjwAcjV2f420PlfFZlJURz/GGMyEX/sbvAsDlyIWL+D0AuJxDPMcbAAAAAABnQOMNAAAAAICJHOo53gAAAAAqLy5XBGzjiDcAAAAAACai8QYAAAAAwEScag4AQClxKiUAACgNjngDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYqMzP8T5x4oS+++475efnq127dqpbt2551AUADo98BADbyEcAzqZMjfeHH36o/v37Kzw8XOfPn9fevXs1e/ZsxcfHl1d9AOCQyEcAsI18BOCMSnWqeXZ2ttXPEydO1NatW7V161Z9//33+uCDDzR27NhyLRAAHAH5CAC2kY8AUMrGOyIiQp988onl52rVqun48eOWn9PT0+Xu7l5+1QGAgyAfAcA28hEASnmq+dq1azV06FAtWrRIs2fP1iuvvKI+ffooPz9fFy5ckKurqxYtWmRSqQBQeZGPAGAb+QgApWy8w8LClJKSovfff19RUVF66qmntH//fu3fv1/5+flq1qyZPD09zaoVACot8hEAbCMfAeAqHyfWt29fbdu2TT/88IM6d+6sgoICtWnThtAE4PTIRwCwjXwE4MxKfVfzVatWac+ePWrdurXefPNNbdq0SQ8//LDuvPNOTZo0SV5eXmbUCQCVHvkIALaRjwCcXamOeI8YMULx8fHatm2bBg0apOeff15RUVFKS0uTp6enbrzxRq1evdqsWgGg0iIfAcA28hEAJBfDMIySTq5Vq5a++OILRURE6OTJk7r55pu1b98+y/qffvpJgwYN0ldffWVKseUhKytL/v7+yszMlJ+fn73LqbTCRqfYuwTTHJraw94loJIpj1yoCvkokZElRUbCmZQ1F8hH50I+wpmUJhdKdcTbx8dHBw8elCQdPny4yDU5zZs3r/ShCQBmIB8BwDbyEQBK2XgnJycrNjZW9erVU1RUlJ5//nmz6gIAh0I+AoBt5CMAlPLmag8//LC6d++uX3/9Vdddd50CAgJMKgsAHAv5CAC2kY8AcBV3Na9Vq5Zq1aplRi0A4NDIRwCwjXwE4Oyu6jneAAAAAACgZGi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmqhSN9+zZsxUWFiZPT0916NBBW7duLdF2S5culYuLi3r16mVugQBgJ+QjANhGPgJwJNXsXcCyZcuUkJCguXPnqkOHDpo1a5a6deumvXv3qnbt2sVud+jQIT3zzDO69dZbK7BaAKg45CMA2OYo+Rg2OqVC3sceDk3tYe8SAIdi9yPeM2fO1GOPPab4+Hg1b95cc+fOlbe3t956661it8nPz9fDDz+siRMnqlGjRhVYLQBUHPIRAGwjHwE4Grs23nl5edqxY4eio6MtY66uroqOjtaWLVuK3W7SpEmqXbu2+vfvf8X3yM3NVVZWltUCAJVdReSjREYCcDzkIwBHZNfG+8SJE8rPz1dISIjVeEhIiI4dO2Zzm6+//loLFizQ/PnzS/QeycnJ8vf3tyyhoaFlrhsAzFYR+SiRkQAcD/kIwBHZ/VTz0jhz5oweffRRzZ8/X0FBQSXaJjExUZmZmZbl8OHDJlcJABXvavJRIiMBVH3kI4DKwK43VwsKCpKbm5vS09OtxtPT01WnTp0i8w8cOKBDhw4pJibGMlZQUCBJqlatmvbu3avGjRtbbePh4SEPDw8TqgcA81REPkpkJADHQz4CcER2PeLt7u6uiIgIpaamWsYKCgqUmpqqyMjIIvObNWumH3/8UTt37rQsPXv2VJcuXbRz505OAQJQZZCPAGAb+QjAEdn9cWIJCQmKi4tT27Zt1b59e82aNUs5OTmKj4+XJMXGxuqaa65RcnKyPD091aJFC6vtAwICJKnIOAA4OvIRAGwjHwE4Grs33n369FFGRobGjx+vY8eOqU2bNlqzZo3lhhm///67XF0d6lJ0ACgX5CMA2EY+AnA0LoZhGPYuoiJlZWXJ399fmZmZ8vPzs3c5lVbY6BR7l2CaQ1N72LsEVDLkwt/4XZQMGQlnQi5cdDW/B2fMCmfcZziv0uQCXwUCAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqpm7wIAAI4tbHSKvUswxaGpPexdAgAAqCI44g0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIkqReM9e/ZshYWFydPTUx06dNDWrVuLnTt//nzdeuutCgwMVGBgoKKjo/9xPgA4MvIRAGwjHwE4Ers33suWLVNCQoKSkpKUlpam1q1bq1u3bjp+/LjN+Rs3blTfvn21YcMGbdmyRaGhoeratauOHDlSwZUDgLnIRwCwjXwE4Gjs3njPnDlTjz32mOLj49W8eXPNnTtX3t7eeuutt2zOf++99/T444+rTZs2atasmd58800VFBQoNTW1gisHAHORjwBgG/kIwNHYtfHOy8vTjh07FB0dbRlzdXVVdHS0tmzZUqLXOHv2rM6fP6+aNWvaXJ+bm6usrCyrBQAqu4rIR4mMBOB4yEcAjqiaPd/8xIkTys/PV0hIiNV4SEiIfv755xK9xqhRo1SvXj2r8L1UcnKyJk6cWOZaAaAiVUQ+SmQkSi5sdIq9SzDNoak97F0CSoF8BOCI7H6qeVlMnTpVS5cu1cqVK+Xp6WlzTmJiojIzMy3L4cOHK7hKAKh4JclHiYwE4HzIRwD2YNcj3kFBQXJzc1N6errVeHp6uurUqfOP27700kuaOnWq1q9fr1atWhU7z8PDQx4eHuVSLwBUlIrIR4mMBOB4yEcAjsiuR7zd3d0VERFhdWOLwhtdREZGFrvdiy++qOeff15r1qxR27ZtK6JUAKhQ5CMA2EY+AnBEdj3iLUkJCQmKi4tT27Zt1b59e82aNUs5OTmKj4+XJMXGxuqaa65RcnKyJGnatGkaP368lixZorCwMB07dkySVKNGDdWoUcNu+wEA5Y18BADbyEcAjsbujXefPn2UkZGh8ePH69ixY2rTpo3WrFljuWHG77//LlfXvw/Mv/7668rLy9P9999v9TpJSUmaMGFCRZYOAKYiHwHANvIRgKOxe+MtSU888YSeeOIJm+s2btxo9fOhQ4fMLwgAKgnyEQBsIx8BOBKHvqs5AAAAAACVHY03AAAAAAAmovEGAAAAAMBEleIab6AyCBudYu8STHNoag97lwAAAAA4LY54AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE1WzdwEAAAAA4KjCRqfYuwTTHJraw94lVBkc8QYAAAAAwEQc8QacGN/QAgAAAOarFEe8Z8+erbCwMHl6eqpDhw7aunXrP87/4IMP1KxZM3l6eqply5ZatWpVBVUKABWLfAQA28hHAI7E7o33smXLlJCQoKSkJKWlpal169bq1q2bjh8/bnP+N998o759+6p///76/vvv1atXL/Xq1Uu7du2q4MoBwFzkIwDYRj4CcDQuhmEY9iygQ4cOateunV577TVJUkFBgUJDQ/Xkk09q9OjRReb36dNHOTk5+vzzzy1jN998s9q0aaO5c+cWmZ+bm6vc3FzLz5mZmWrQoIEOHz4sPz8/E/aoamiRtNbeJZhm18RuNsfZ56qluH22JSsrS6GhoTp9+rT8/f1NrKp0zM5HqXwysqr+Hf3T31BV3WeJvEBRlTEjyUf7Iyv+5oz7jItKlY+GHeXm5hpubm7GypUrrcZjY2ONnj172twmNDTUePnll63Gxo8fb7Rq1crm/KSkJEMSCwsLyxWXw4cPl0e0lYuKyEfDICNZWFhKvlSWjCQfWVhYKttSkny0683VTpw4ofz8fIWEhFiNh4SE6Oeff7a5zbFjx2zOP3bsmM35iYmJSkhIsPxcUFCgkydPqlatWnJxcSnjHpS/wm9NnOmIPPvMPtubYRg6c+aM6tWrZ+9SLCoiHyXHysjK/DdkFvaZfa4MKltGko9FVfa/ITM44z5LzrnflXmfS5OPVf6u5h4eHvLw8LAaCwgIsE8xpeDn51fp/rDMxj47h8q6z5Xl9MmK5ogZWVn/hszEPjuHyrzPzpiR5KNjcMZ9lpxzvyvrPpc0H+16c7WgoCC5ubkpPT3dajw9PV116tSxuU2dOnVKNR8AHBH5CAC2kY8AHJFdG293d3dFREQoNTXVMlZQUKDU1FRFRkba3CYyMtJqviStW7eu2PkA4IjIRwCwjXwE4Ijsfqp5QkKC4uLi1LZtW7Vv316zZs1STk6O4uPjJUmxsbG65pprlJycLEkaNmyYoqKiNGPGDPXo0UNLly7V9u3bNW/ePHvuRrnx8PBQUlJSkVObqjL22Tk44z6XFflozRn/hthn5+CM+1xW5KM1Z/wbcsZ9lpxzv6vMPl/x9msV4NVXXzUaNGhguLu7G+3btze+/fZby7qoqCgjLi7Oav7y5cuN8PBww93d3bjhhhuMlJSUCq4YACoG+QgAtpGPAByJ3Z/jDQAAAABAVWbXa7wBAAAAAKjqaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03pXE5s2bFRMTo3r16snFxUUff/yxvUsyVXJystq1aydfX1/Vrl1bvXr10t69e+1dlulef/11tWrVSn5+fvLz81NkZKRWr15t77IqzNSpU+Xi4qLhw4fbuxQ4EGfLR8k5M9LZ81EiI1F65CP56CyqQj7SeFcSOTk5at26tWbPnm3vUirEpk2bNHToUH377bdat26dzp8/r65duyonJ8fepZmqfv36mjp1qnbs2KHt27frtttu0z333KPdu3fbuzTTbdu2TW+88YZatWpl71LgYJwtHyXnzEhnzkeJjMTVIR/JR2dQZfLR3s8zQ1GSjJUrV9q7jAp1/PhxQ5KxadMme5dS4QIDA40333zT3mWY6syZM8Z1111nrFu3zoiKijKGDRtm75LgoJwxHw3DeTPSGfLRMMhIlA/ykXysiqpSPnLEG5VCZmamJKlmzZp2rqTi5Ofna+nSpcrJyVFkZKS9yzHV0KFD1aNHD0VHR9u7FMAhOVtGOlM+SmQkUBbkY9VWlfKxmr0LAAoKCjR8+HB17NhRLVq0sHc5pvvxxx8VGRmpv/76SzVq1NDKlSvVvHlze5dlmqVLlyotLU3btm2zdymAQ3KmjHS2fJTISKAsyEfy0ZHQeMPuhg4dql27dunrr7+2dykVomnTptq5c6cyMzO1YsUKxcXFadOmTVUyPA8fPqxhw4Zp3bp18vT0tHc5gENypox0pnyUyEigrMhH8tGRuBiGYdi7CFhzcXHRypUr1atXL3uXYronnnhCn3zyiTZv3qxrr73W3uXYRXR0tBo3bqw33njD3qWUu48//lj33nuv3NzcLGP5+flycXGRq6urcnNzrdYBV+JM+SiRkVU5HyUyEuWLfHQu5KPj5SNHvGEXhmHoySef1MqVK7Vx40anDMxCBQUFys3NtXcZprj99tv1448/Wo3Fx8erWbNmGjVqlMMFJlBRyMiLqnI+SmQkcDXIx4vIR8fLRxrvSiI7O1v79++3/Hzw4EHt3LlTNWvWVIMGDexYmTmGDh2qJUuW6JNPPpGvr6+OHTsmSfL395eXl5edqzNPYmKi7rzzTjVo0EBnzpzRkiVLtHHjRq1du9bepZnC19e3yDVXPj4+qlWrVpW/Fgvlx9nyUXLOjHS2fJTISJQd+Ug+VlVVMR9pvCuJ7du3q0uXLpafExISJElxcXFatGiRnaoyz+uvvy5J6ty5s9X4woUL1a9fv4ovqIIcP35csbGxOnr0qPz9/dWqVSutXbtWd9xxh71LAyotZ8tHyTkzknwESo98/Bv5iMqOa7wBAAAAADARz/EGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY038P917txZw4cPt3cZAFDpkI8AYBv5iJKi8QYAAAAAwEQ03gAAAAAAmIjGG04pJydHsbGxqlGjhurWrasZM2bYuyQAqBTIRwCwjXxEWdB4wyk9++yz2rRpkz755BN98cUX2rhxo9LS0uxdFgDYHfkIALaRjyiLavYuAKho2dnZWrBggd59913dfvvtkqS3335b9evXt3NlAGBf5CMA2EY+oqw44g2nc+DAAeXl5alDhw6WsZo1a6pp06Z2rAoA7I98BADbyEeUFY03AAAAAAAmovGG02ncuLGqV6+u7777zjJ26tQp7du3z45VAYD9kY8AYBv5iLLiGm84nRo1aqh///569tlnVatWLdWuXVtjx46VqyvfQwFwbuQjANhGPqKsaLzhlKZPn67s7GzFxMTI19dXI0aMUGZmpr3LAgC7Ix8BwDbyEWXhYhiGYe8iAAAAAACoqjg3AgAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhrvKqJfv35ycXGRi4uLWrRoYRk/dOiQZdzFxUUrVqywS30BAQGWGp544gnL+KxZs6zqO3HihF3qk6SNGzeW+HfUr18/hYWFmV9UOencubM6d+5s7zIAu6jM+Xj69GmrGl566SXLuuHDh1vGa9SoUeG1XWrRokVycXHR9u3brzjX0fImLCxM/fr1s3cZgF2Qj2VHPqKkaLyrkKCgIC1evFhTp04tsm7gwIFavHix2rdvbzW+Y8cOde/eXX5+fvL19VXXrl21c+fOEr3f5s2b1bNnT4WGhsrT01N16tRR9+7d9X//939F5s6bN0+LFy8uMt69e3ctXrxY9957b8l28v/7/PPP1b17d9WqVUuenp4KDw/Xs88+q5MnT5bqdcrL2bNnNWHCBG3cuLFU26Wnp+uZZ55Rs2bN5O3tLR8fH0VERGjy5Mk6ffq0KbUCzqii8/Fyjz32mFxcXHT33Xdbjfv4+Gjx4sV6+eWXi2zz6KOPavHixbr11ltL/D6GYWjx4sX617/+pYCAAHl7e6tly5aaPHmyzp49e1W1l9Uff/yhCRMmlPp3d+DAAQ0aNEiNGjWSp6en/Pz81LFjR73yyis6d+6cOcUCTqii87GwUbW1HDt2zDKPfCwe+eiYqtm7AJQfHx8fPfLIIzbXRUZGFlmXlpamTp06KTQ0VElJSSooKNCcOXMUFRWlrVu3qmnTpv/4fvv27ZOrq6sGDx6sOnXq6NSpU3r33Xf1r3/9SykpKerevbtlbu/evSVdDMpLNWvWTM2aNdP+/fu1cuXKEu3nM888oxkzZqh169YaNWqUatasqbS0NL366qtatmyZUlNTdd1115Xota7W/PnzVVBQYPn57NmzmjhxoiSV+JvMbdu26a677lJ2drYeeeQRRURESJK2b9+uqVOnavPmzfriiy/KvXbAGVV0Pl5q+/btWrRokTw9PYusq169uh555BEdOnRITz/9tNW6iIgIRUREaP369UpLS7vi++Tn5+uhhx7S8uXLdeutt2rChAny9vbWV199paSkJC1fvlzr169X7dq1S1z71bg8t/744w9NnDhRYWFhatOmTYleIyUlRQ888IA8PDwUGxurFi1aKC8vT19//bWeffZZ7d69W/PmzTOhesD52CsfJ02apGuvvdZqLCAgwPL/yUfbyEfHRePtxMaNGycvLy9t2bJFtWrVkiQ98sgjCg8P15gxY/Thhx/+4/YDBgzQgAEDrMYef/xxNWrUSLNmzbJqvMvL+++/rxkzZqhPnz5677335ObmZlnXr18/denSRQ888IC2b9+uatXM+/OuXr16mbY/ffq07r33Xrm5uen7779Xs2bNrNZPmTJF8+fPL9N7ALh6Zc3HQoZh6KmnnlJsbKxSU1PNLFkvvviili9frmeeeUbTp0+3jA8cOFC9e/dWr169FB8fr5SUFFPrcHd3L9P2Bw8e1IMPPqiGDRvqyy+/VN26dS3rhg4dqv3795u+DwCKV175eOedd6pt27ZmlmpBPqIy4FRzJ/bVV18pOjraEpqSVLduXUVFRenzzz9XdnZ2qV/T29tbwcHBpp0mPXHiRAUGBmrevHlWTbcktW/fXqNGjdIPP/ygjz76yDJe3PUpxV1nk5+frzFjxqhOnTry8fFRz549dfjwYas5l17jfejQIQUHB1vqKzxdasKECcXuxxtvvKEjR45o5syZRZpuSQoJCdFzzz1nNTZnzhzdcMMN8vDwUL169TR06FCbv+d58+apcePG8vLyUvv27fXVV1/ZrCE3N1dJSUlq0qSJPDw8FBoaqpEjRyo3N9dq3rp169SpUycFBASoRo0aatq0qcaMGVPsvgFVQXnl4+LFi7Vr1y5NmTLFrFIlSefOndP06dMVHh6u5OTkIutjYmIUFxenVatWaevWrZbx4rKquNw8e/asBg0apFq1asnPz0+xsbE6deqU1ZxLs3Xjxo1q166dJCk+Pt6Sj4sWLSp2X1588UVlZ2drwYIFVh8qCzVp0kTDhg2z/HzhwgU9//zzaty4sTw8PBQWFqYxY8YUyTLDMDR58mTVr19f3t7e6tKli3bv3m2zhtOnT2v48OEKDQ2Vh4eHmjRpomnTplmd6SRJS5cuVUREhHx9feXn56eWLVvqlVdeKXbfgKqgPD8/njlzRvn5+WaUaUE+ko+VBY23E8vNzZWXl1eRcW9vb+Xl5WnXrl0lep2srCydOHFCP//8s8aMGaNdu3bp9ttvL+9y9csvv2jv3r2655575OfnZ3NObGysJOmzzz676veZMmWKUlJSNGrUKD311FNat26doqOji71mJjg4WK+//rok6d5779XixYu1ePFi/fvf/y72PT799FN5eXnp/vvvL1FNEyZM0NChQ1WvXj3NmDFD9913n9544w117dpV58+ft8xbsGCBBg0apDp16ujFF19Ux44dbX5xUFBQoJ49e+qll15STEyMXn31VfXq1Usvv/yy+vTpY5m3e/du3X333crNzdWkSZM0Y8YM9ezZ0+Z1/EBVUh75eObMGY0aNcryRZ6Zvv76a506dUoPPfRQsWf7lEc+PvHEE9qzZ48mTJig2NhYvffee+rVq5cMw7A5//rrr9ekSZMk/X2taOE1lsX57LPP1KhRI91yyy0lqmnAgAEaP368brrpJr388suKiopScnKyHnzwQat548eP17hx49S6dWtNnz5djRo1UteuXZWTk2M17+zZs4qKitK7776r2NhY/fe//1XHjh2VmJiohIQEy7x169apb9++CgwM1LRp0zR16lR17tyZfESVV16fH7t06SI/Pz95e3urZ8+e+uWXX8q7VEnkI/lYeXCquRNr2rSpvv32W+Xn51uOHufl5em7776TJB05cqREr9O7d2+tXbtW0sVTaAYNGqRx48aVe70//fSTJKl169bFzgkLC5Ofn59l7tU4efKk9uzZI19fX0nSTTfdpN69e2v+/Pl66qmnisz38fHR/fffryFDhqhVq1bFXid1qT179ig8PLxEpxxlZGQoOTlZXbt21erVq+XqevH7smbNmumJJ57Qu+++q/j4eJ0/f15jxoxRmzZttGHDBstrN2/eXAMHDlRoaKjlNZcsWaL169dr06ZN6tSpk2W8RYsWGjx4sL755hvdcsstWrdunfLy8rR69WoFBQVdsVagqiiPfJw0aZK8vLyKXJtohpLkY+G6suSju7u7UlNTLZfbNGzYUCNHjtRnn32mnj17FpkfEhKiO++8U+PHj7d5rejlsrKydOTIEd1zzz0lqueHH37Q22+/rQEDBlguz3n88cdVu3ZtvfTSS9qwYYO6dOmijIwMvfjii+rRo4c+++wzubi4SJLGjh2rF154weo1Z86cqQMHDuj777+33C9k0KBBqlevnqZPn64RI0YoNDRUKSkp8vPz09q1a4ucgQVUZWXNR29vb8vlgX5+ftqxY4dmzpypW265RWlpaVafV8oD+Ug+VhYc8XZijz/+uPbt26f+/fvrp59+0q5duxQbG6ujR49KUonvijh16lR98cUXWrBggW6++Wbl5eXpwoUL5V7vmTNnJMnSEBfH19fXMvdqxMbGWr3H/fffr7p162rVqlVX/ZqXy8rKuuJ+FFq/fr3y8vI0fPhwS9MtXbxLsp+fn+Vanu3bt+v48eMaPHiwVUPfr18/+fv7W73mBx98oOuvv17NmjXTiRMnLMttt90mSdqwYYOkv29y8sknnxQ5hQioysqaj/v27dMrr7yi6dOny8PDw/R6S5KPhevKko8DBw60usfFkCFDVK1atXLLx6ysLElXzvlChe976ZEWSRoxYoQkWfKxMEeffPJJy4dK6eIjiS73wQcf6NZbb1VgYKBVPkZHRys/P1+bN2+WdDEfc3JytG7dutLtJODgypqPvXv31sKFCxUbG6tevXrp+eef19q1a/Xnn3+aclkO+Ug+VhY03k5s8ODBGjNmjJYsWaIbbrhBLVu21IEDBzRy5EhJKvFzEdu0aaM77rhD//nPf7Ru3Tpt3brVlGf+lTQUz5w5U6a7Ul5+R3QXFxc1adJEhw4duurXvJyfn1+Jw/23336TpCJ3CXV3d1ejRo0s6wv/9/L6q1evrkaNGlmN/fLLL9q9e7eCg4OtlvDwcEnS8ePHJUl9+vRRx44dNWDAAIWEhOjBBx/U8uXLacJR5ZU1H4cNG6ZbbrlF9913X0WUW6J8LFxXnvlYo0YN1a1bt9zysfAyotLko6urq5o0aWI1XqdOHQUEBFwxH4ODgxUYGGg19ssvv2jNmjVF8jE6OlrS3/n4+OOPKzw8XHfeeafq16+v//znP1qzZk0p9xhwPOX1+fFSnTp1UocOHbR+/fryLpd8JB8rDU41d3JTpkzRM888o927d8vf318tW7a03DirsAkrDXd3d/Xs2VNTp07VuXPnbF4DdLWaN28uSfrf//5X7JzffvtNWVlZVo3mpd/eXerSU6QqWrNmzbRz507l5eWV+Q6XV6OgoEAtW7bUzJkzba4vPM3Ly8tLmzdv1oYNG5SSkqI1a9Zo2bJluu222/TFF19w+hCqtKvNxy+//FJr1qzRRx99ZPWB68KFCzp37pwOHTqkmjVrFnuviqtxaT726tXL5pzC7Lz8izhbzL7ZUXH8/PxUr169El8jWqi4nL8aBQUFuuOOOyxNxOUK/9nXrl1bO3fu1Nq1a7V69WqtXr3achTv7bffLrd6gMqovD8/Shc/e+zdu7c8y5REPpKPlQdHvKHAwEB16tRJLVu2lHTxlJP69evbvNt2SZw7d06GYZTpdB1brrvuOjVt2lQff/xxsa/9zjvvSJIeeOABy1hgYKDNu38XfsN3uctv7mEYhvbv32+5i7ktpQ21mJgYnTt3rkSP3GjYsKEkFfmPUV5eng4ePGhZX/i/l9d//vx5HTx40GqscePGOnnypG6//XZFR0cXWS49uu7q6qrbb79dM2fO1E8//aQpU6boyy+/tJyODlRlV5OPv//+uyTp3//+t6699lrLcuTIEX355Ze69tpr9dZbb5VrnR07dlRAQICWLFlS7IfCkuZjXl6e5ZTRy12eL9nZ2Tp69Gi55uPdd9+tAwcOaMuWLVec27BhQxUUFBSpKz09XadPn75iPmZkZBS563Djxo2VnZ1tMxujo6PVoEEDy1x3d3fFxMRozpw5OnDggAYNGqR33nlH+/fvL9U+A46ovD8//vrrr5anxJQn8pF8rCxovGFl2bJl2rZtW5HriW0pPJ3kUqdPn9aHH36o0NDQMp2uU5ykpCSdOnVKgwcPLhKeO3bs0LRp03TjjTfqzjvvtIw3btxY3377rfLy8ixjn3/+eZE7fRd65513rBr7FStW6OjRo1aveTlvb29JKvFj1AYPHqy6detqxIgR2rdvX5H1x48f1+TJkyVJ0dHRcnd313//+1+rO2MuWLBAmZmZ6tGjhySpbdu2Cg4O1ty5c632ddGiRUXq6t27t44cOWLzWeHnzp2z3MXy5MmTRda3adNGkoo8igKo6kqaj7fddptWrlxZZAkODlbbtm21cuVKxcTElGtt3t7eGjlypPbu3auxY8cWWZ+SkqJFixYpJibG8iFZupiPhdfkFZo3b16xH07nzZtn9SSF119/XRcuXPjHfPTx8ZFU8nwcOXKkfHx8NGDAAKWnpxdZf+DAAcsjae666y5J0qxZs6zmFJ7NU5iP0dHRql69ul599VWrHL18O+liPm7ZssVy09BLnT592nIPkz///NNqnaurq1q1aiWJfITzKc3nx4yMjCJjq1at0o4dO9S9e/dyr418nGU1h3y0H041d2KbN2/WpEmT1LVrV9WqVUvffvutFi5cqO7du1s9A1C6+DiriRMnasOGDZbnDxZet9GhQwfVrl1bv//+uxYuXKg//vhDy5YtM6Xmvn37avv27Zajrw8//LACAwOVlpamt956S8HBwVqxYoXV4yIGDBigFStWqHv37urdu7cOHDigd999V40bN7b5HjVr1lSnTp0UHx+v9PR0zZo1S02aNNFjjz1WbF1eXl5q3ry5li1bpvDwcNWsWVMtWrRQixYtbM4PDAzUypUrddddd6lNmzZ65JFHFBERIUlKS0vT+++/r8jISEkXr7FJTEzUxIkT1b17d/Xs2VN79+7VnDlz1K5dO8tdMKtXr67Jkydr0KBBuu2229SnTx8dPHhQCxcuLHLq1KOPPqrly5dr8ODB2rBhgzp27Kj8/Hz9/PPPWr58udauXau2bdtq0qRJ2rx5s3r06KGGDRvq+PHjmjNnjurXr291N3SgqilLPjZo0MDqW/9Cw4cPV0hISLGnOpbVyJEjtXPnTk2bNk1btmzRfffdJy8vL3399dd69913dcMNNxR5PuyAAQM0ePBg3Xfffbrjjjv0ww8/aO3atcU+xSAvL0+33367evfubcmhTp062bxjb6HGjRsrICBAc+fOla+vr3x8fNShQwdde+21xc5fsmSJ+vTpo+uvv16xsbFq0aKF8vLy9M033+iDDz6w3EekdevWiouL07x583T69GlFRUVp69atevvtt9WrVy916dJF0sUcfeaZZ5ScnKy7775bd911l77//nubT2x49tln9emnn+ruu+9Wv379FBERoZycHP34449asWKFDh06pKCgIA0YMEAnT57Ubbfdpvr16+u3337Tq6++qjZt2uj6668v4T81wPGU9fPjLbfcohtvvFFt27aVv7+/5TNcaGio5XT18kY+ko+VgoEqIS4uzmjYsGGR8YMHDxqSjIULFxZZt3//fqNr165GUFCQ4eHhYTRr1sxITk42cnNzi8wdMWKE4eLiYuzZs8cy9tprrxmdOnUygoKCjGrVqhnBwcFGTEyMsXnz5mLrlGQMHTq0yHhSUpIhycjIyCjR/n766adGdHS0ERAQYEgyJBk33HCDkZmZaXP+jBkzjGuuucbw8PAwOnbsaGzfvt2IiooyoqKiLHM2bNhgSDLef/99IzEx0ahdu7bh5eVl9OjRw/jtt9+sXs/W7/ubb74xIiIiDHd3d0OSkZSUdMX9+OOPP4ynn37aCA8PNzw9PQ1vb28jIiLCmDJlSpF9ee2114xmzZoZ1atXN0JCQowhQ4YYp06dKvKac+bMMa699lrDw8PDaNu2rbF58+Yi+2oYhpGXl2dMmzbNuOGGGwwPDw8jMDDQiIiIMCZOnGh579TUVOOee+4x6tWrZ7i7uxv16tUz+vbta+zbt++K+wZUFvbIR1saNmxo9OjRw+a6wlqmT59us34fH59/fO1CBQUFxqJFi4yOHTsavr6+lnyMjo62WXt+fr4xatQoIygoyPD29ja6detm7N+/32jYsKERFxdnmbdw4UJDkrFp0yZj4MCBRmBgoFGjRg3j4YcfNv7880+r17SVN5988onRvHlzo1q1asX+zi+3b98+47HHHjPCwsIMd3d3w9fX1+jYsaPx6quvGn/99Zdl3vnz542JEyca1157rVG9enUjNDTUSExMtJpTuK8TJ0406tata3h5eRmdO3c2du3aVWRfDcMwzpw5YyQmJhpNmjQx3N3djaCgIOOWW24xXnrpJSMvL88wDMNYsWKF0bVrV6N27dqGu7u70aBBA2PQoEHG0aNHr7hvQGVhj3wcO3as0aZNG8Pf39+oXr260aBBA2PIkCHGsWPHbNZIPhZFPjomGu8qIi4uzggNDTUyMjKsmrHCsHr11VeNjIwMm8FSEu3atTPuv//+q67vzz//NDIyMoo03ufOnTMyMjKMZ599tlSN9+X69+9vSDLmz59/1TUCqJoqcz4WFBQYGRkZRlpaWpEPltnZ2UZGRobx4IMPlviD5eXy8vKMbt26GdWqVTNWr159Va8BoOoiH8lHVByu8a5CDh8+rODgYJunAD/55JMKDg7Wp59+WurXzcrK0g8//KBJkyZddW2NGjWyecOMuXPnKjg4WNOnT7/q15akN954Q3fffbeGDBlSrs/bBlA1VNZ8zMzMVHBwsG666aYi68aOHavg4GAtXbr0ql5bungJyocffqg2bdrogQceUFpa2lW/FoCqiXwkH1ExXAzjkqvo4bB++ukn/fHHH5IuPjfw5ptvliT99ddf+vrrry3zWrVqZcpNz65k06ZNlhtOhIaGWu6affjwYau7dUdFRal69eoVXh+Aqqsy5+OFCxe0ceNGy8/h4eGW68P37dtnuUN6tWrVLNdHAkB5IR+BimPXxnvz5s2aPn26duzYoaNHj2rlypVXvOnMxo0blZCQoN27dys0NFTPPfec5SYCAFBVkI8AUDwyEoCjseup5jk5OWrdurVmz55dovkHDx5Ujx491KVLF+3cuVPDhw/XgAEDbN7SHgAcGfkIAMUjIwE4mkpzqrmLi8sVv60cNWqUUlJStGvXLsvYgw8+qNOnT2vNmjUVUCUAVDzyEQCKR0YCcAQO9RzvLVu2KDo62mqsW7duGj58eLHb5ObmWj2ovaCgQCdPnlStWrXk4uJiVqkAHIhhGDpz5ozq1asnV1fHvOfk1eSjREYCuDJnzUjyEcCVlCYfHarxPnbsmEJCQqzGQkJClJWVpXPnzsnLy6vINsnJyZo4cWJFlQjAgR0+fFj169e3dxlX5WryUSIjAZScs2Uk+QigpEqSjw7VeF+NxMREJSQkWH7OzMxUgwYNdPjwYfn5+dmxMgCVRVZWlkJDQ+Xr62vvUiocGQngSpw1I8lHAFdSmnx0qMa7Tp06Sk9PtxpLT0+Xn59fsUdzPDw85OHhUWTcz8+P0ARgxZFPHbyafJTISAAl52wZST4CKKmS5KNDXagTGRmp1NRUq7F169YpMjLSThUBQOVAPgJA8chIAPZm18Y7OztbO3fu1M6dOyVdfNTDzp079fvvv0u6eIpPbGysZf7gwYP166+/auTIkfr55581Z84cLV++XE8//bQ9ygcA05CPAFA8MhKAo7Fr4719+3bdeOONuvHGGyVJCQkJuvHGGzV+/HhJ0tGjRy0BKknXXnutUlJStG7dOrVu3VozZszQm2++qW7dutmlfgAwC/kIAMUjIwE4mkrzHO+KkpWVJX9/f2VmZnJ9DgBJ5MKl+F0AuBy5cBG/BwCXK00uONQ13gAAAAAAOBoabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBE1exdAAAAAFAVhY1OsXcJpjk0tYe9SwAcCke8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATFTN3gUAABxb2OgUe5dgikNTe9i7BAAAUEXY/Yj37NmzFRYWJk9PT3Xo0EFbt279x/mzZs1S06ZN5eXlpdDQUD399NP666+/KqhaAKhYZCQA2EY+AnAkdm28ly1bpoSEBCUlJSktLU2tW7dWt27ddPz4cZvzlyxZotGjRyspKUl79uzRggULtGzZMo0ZM6aCKwcA85GRAGAb+QjA0di18Z45c6Yee+wxxcfHq3nz5po7d668vb311ltv2Zz/zTffqGPHjnrooYcUFhamrl27qm/fvv/4DWdubq6ysrKsFgBwBGQkANhGPgJwNHZrvPPy8rRjxw5FR0f/XYyrq6Kjo7Vlyxab29xyyy3asWOHJSR//fVXrVq1SnfddVex75OcnCx/f3/LEhoaWr47AgAmICMBwDbyEYAjstvN1U6cOKH8/HyFhIRYjYeEhOjnn3+2uc1DDz2kEydOqFOnTjIMQxcuXNDgwYP/8TShxMREJSQkWH7OysoiOAFUemQkANhGPgJwRHa/uVppbNy4US+88ILmzJmjtLQ0ffTRR0pJSdHzzz9f7DYeHh7y8/OzWgCgKiIjAcA28hGAvdntiHdQUJDc3NyUnp5uNZ6enq46derY3GbcuHF69NFHNWDAAElSy5YtlZOTo4EDB2rs2LFydXWo7xEAoFhkJADYRj4CcER2Sxl3d3dFREQoNTXVMlZQUKDU1FRFRkba3Obs2bNFgtHNzU2SZBiGecUCQAUjIwHANvIRgCOy2xFvSUpISFBcXJzatm2r9u3ba9asWcrJyVF8fLwkKTY2Vtdcc42Sk5MlSTExMZo5c6ZuvPFGdejQQfv379e4ceMUExNjCU8AqCrISACwjXwE4Gjs2nj36dNHGRkZGj9+vI4dO6Y2bdpozZo1lptl/P7771bfTj733HNycXHRc889pyNHjig4OFgxMTGaMmWKvXYBAExDRgKAbeQjAEfjYjjZ+TVZWVny9/dXZmYmN8kAIIlcuNTV/C7CRqeYXJV9HJraw94lAJUCGXkR+WiNjARKlwvcSQIAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmsnvjPXv2bIWFhcnT01MdOnTQ1q1b/3H+6dOnNXToUNWtW1ceHh4KDw/XqlWrKqhaAKhYZCQA2EY+AnAk1ez55suWLVNCQoLmzp2rDh06aNasWerWrZv27t2r2rVrF5mfl5enO+64Q7Vr19aKFSt0zTXX6LffflNAQICpdYaNTjH19e3p0NQe9i4BQDEcJSMBoKKRjwAcjV0b75kzZ+qxxx5TfHy8JGnu3LlKSUnRW2+9pdGjRxeZ/9Zbb+nkyZP65ptvVL16dUlSWFhYRZYMABWGjAQA28hHAI7Gbqea5+XlaceOHYqOjv67GFdXRUdHa8uWLTa3+fTTTxUZGamhQ4cqJCRELVq00AsvvKD8/Pxi3yc3N1dZWVlWCwBUdmQkANhGPgJwRHZrvE+cOKH8/HyFhIRYjYeEhOjYsWM2t/n111+1YsUK5efna9WqVRo3bpxmzJihyZMnF/s+ycnJ8vf3tyyhoaHluh8AYAYyEgBsIx8BOCK731ytNAoKClS7dm3NmzdPERER6tOnj8aOHau5c+cWu01iYqIyMzMty+HDhyuwYgCoOGQkANhGPgKwN7td4x0UFCQ3Nzelp6dbjaenp6tOnTo2t6lbt66qV68uNzc3y9j111+vY8eOKS8vT+7u7kW28fDwkIeHR/kWDwAmIyMBwDbyEYAjstsRb3d3d0VERCg1NdUyVlBQoNTUVEVGRtrcpmPHjtq/f78KCgosY/v27VPdunVtBiYAOCoyEgBsIx8BOCK7nmqekJCg+fPn6+2339aePXs0ZMgQ5eTkWO5QGRsbq8TERMv8IUOG6OTJkxo2bJj27dunlJQUvfDCCxo6dKi9dgEATENGAoBt5CMAR2PXx4n16dNHGRkZGj9+vI4dO6Y2bdpozZo1lptl/P7773J1/fu7gdDQUK1du1ZPP/20WrVqpWuuuUbDhg3TqFGj7LULAGAaMhIAbCMfATgaF8MwDHsXUZGysrLk7++vzMxM+fn5lWibsNEpJldlP4em9rB3CYDdXU0uVFVk5N/IR+AiMvIi8tEaGQmULhcc6q7mAAAAAAA4GhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACaqVtYXOHHihL777jvl5+erXbt2qlu3bnnUBQAOj3wEANvIRwDOpkyN94cffqj+/fsrPDxc58+f1969ezV79mzFx8eXV30A4JDIRwCwjXwE4IxKdap5dna21c8TJ07U1q1btXXrVn3//ff64IMPNHbs2HItEAAcAfkIALaRjwBQysY7IiJCn3zyieXnatWq6fjx45af09PT5e7uXn7VAYCDIB8BwDbyEQBKear52rVrNXToUC1atEizZ8/WK6+8oj59+ig/P18XLlyQq6urFi1aZFKpAFB5kY8AYBv5CAClbLzDwsKUkpKi999/X1FRUXrqqae0f/9+7d+/X/n5+WrWrJk8PT3NqhUAKi3yEQBsIx8B4CofJ9a3b19t27ZNP/zwgzp37qyCggK1adOG0ATg9MhHALCNfATgzEp9V/NVq1Zpz549at26td58801t2rRJDz/8sO68805NmjRJXl5eZtQJAJUe+QgAtpGPAJxdqY54jxgxQvHx8dq2bZsGDRqk559/XlFRUUpLS5Onp6duvPFGrV692qxaAaDSIh8BwDbyEQAkF8MwjJJOrlWrlr744gtFRETo5MmTuvnmm7Vv3z7L+p9++kmDBg3SV199ZUqx5SErK0v+/v7KzMyUn59fibYJG51iclX2c2hqD3uXANjd1eTC5apCPkpk5KXIR+CismYk+Vg1kZFA6XKhVEe8fXx8dPDgQUnS4cOHi1yT07x580ofmgBgBvIRAGwjHwGglI13cnKyYmNjVa9ePUVFRen55583qy4AcCjkIwDYRj4CQClvrvbwww+re/fu+vXXX3XdddcpICDApLIAwLGQjwBgG/kIAFdxV/NatWqpVq1aZtQCAA6NfAQA28hHAM7uqp7jDQAAAAAASobGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpvAAAAAABMVCka79mzZyssLEyenp7q0KGDtm7dWqLtli5dKhcXF/Xq1cvcAgHATshHALCNfATgSOzeeC9btkwJCQlKSkpSWlqaWrdurW7duun48eP/uN2hQ4f0zDPP6NZbb62gSgGgYpGPAGAb+QjA0di98Z45c6Yee+wxxcfHq3nz5po7d668vb311ltvFbtNfn6+Hn74YU2cOFGNGjX6x9fPzc1VVlaW1QIAjsDsfJTISACOiXwE4Gjs2njn5eVpx44dio6Otoy5uroqOjpaW7ZsKXa7SZMmqXbt2urfv/8V3yM5OVn+/v6WJTQ0tFxqBwAzVUQ+SmQkAMdDPgJwRHZtvE+cOKH8/HyFhIRYjYeEhOjYsWM2t/n666+1YMECzZ8/v0TvkZiYqMzMTMty+PDhMtcNAGariHyUyEgAjod8BOCIqtm7gNI4c+aMHn30Uc2fP19BQUEl2sbDw0MeHh4mVwYA9nU1+SiRkQCqPvIRQGVg18Y7KChIbm5uSk9PtxpPT09XnTp1isw/cOCADh06pJiYGMtYQUGBJKlatWrau3evGjdubG7RTiJsdIq9SzDNoak97F0CcEXkIwDYRj4CcER2PdXc3d1dERERSk1NtYwVFBQoNTVVkZGRReY3a9ZMP/74o3bu3GlZevbsqS5dumjnzp1cewOgyiAfAcA28hGAI7L7qeYJCQmKi4tT27Zt1b59e82aNUs5OTmKj4+XJMXGxuqaa65RcnKyPD091aJFC6vtAwICJKnIOAA4OvIRAGwjHwE4Grs33n369FFGRobGjx+vY8eOqU2bNlqzZo3lhhm///67XF3t/tQzAKhw5CMA2EY+AnA0LoZhGPYuoiJlZWXJ399fmZmZ8vPzK9E2zni9szPuM5zX1eRCVUVG/o2sAC4iIy8iH62RkUDpcoGvAgEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGCiStF4z549W2FhYfL09FSHDh20devWYufOnz9ft956qwIDAxUYGKjo6Oh/nA8Ajox8BADbyEcAjsTujfeyZcuUkJCgpKQkpaWlqXXr1urWrZuOHz9uc/7GjRvVt29fbdiwQVu2bFFoaKi6du2qI0eOVHDlAGAu8hEAbCMfATgaF8MwDHsW0KFDB7Vr106vvfaaJKmgoEChoaF68sknNXr06Ctun5+fr8DAQL322muKjY294vysrCz5+/srMzNTfn5+JaoxbHRKieY5okNTe9gcd8Z9hvO6mlyoCBWdjxIZeSmyArioMmYk+Wh/ZCRQulyw6xHvvLw87dixQ9HR0ZYxV1dXRUdHa8uWLSV6jbNnz+r8+fOqWbOmzfW5ubnKysqyWgCgsquIfJTISACOh3wE4Ijs2nifOHFC+fn5CgkJsRoPCQnRsWPHSvQao0aNUr169azC91LJycny9/e3LKGhoWWuGwDMVhH5KJGRABwP+QjAEdn9Gu+ymDp1qpYuXaqVK1fK09PT5pzExERlZmZalsOHD1dwlQBQ8UqSjxIZCcD5kI8A7KGaPd88KChIbm5uSk9PtxpPT09XnTp1/nHbl156SVOnTtX69evVqlWrYud5eHjIw8OjXOoFgIpSEfkokZEAHA/5CMAR2fWIt7u7uyIiIpSammoZKygoUGpqqiIjI4vd7sUXX9Tzzz+vNWvWqG3bthVRKgBUKPIRAGwjHwE4Irse8ZakhIQExcXFqW3btmrfvr1mzZqlnJwcxcfHS5JiY2N1zTXXKDk5WZI0bdo0jR8/XkuWLFFYWJjlWp4aNWqoRo0adtsPAChv5GPlxZ2KAfsiHwE4Grs33n369FFGRobGjx+vY8eOqU2bNlqzZo3lhhm///67XF3/PjD/+uuvKy8vT/fff7/V6yQlJWnChAkVWToAmIp8BADbyEcAjsbujbckPfHEE3riiSdsrtu4caPVz4cOHTK/IACoJMhHALCNfATgSBz6ruYAAAAAAFR2NN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABPReAMAAAAAYCIabwAAAAAATFTN3gUAAAAAqBrCRqfYuwTTHJraw94lwIFxxBsAAAAAABPReAMAAAAAYCIabwAAAAAATETjDQAAAACAiWi8AQAAAAAwEY03AAAAAAAmovEGAAAAAMBENN4AAAAAAJiIxhsAAAAAABNVisZ79uzZCgsLk6enpzp06KCtW7f+4/wPPvhAzZo1k6enp1q2bKlVq1ZVUKUAULHIRwCwjXwE4Ejs3ngvW7ZMCQkJSkpKUlpamlq3bq1u3brp+PHjNud/88036tu3r/r376/vv/9evXr1Uq9evbRr164KrhwAzEU+AoBt5CMAR+NiGIZhzwI6dOigdu3a6bXXXpMkFRQUKDQ0VE8++aRGjx5dZH6fPn2Uk5Ojzz//3DJ28803q02bNpo7d26R+bm5ucrNzbX8nJmZqQYNGujw4cPy8/MrUY0tktaWdrccxq6J3WyOO+M+w3llZWUpNDRUp0+flr+/v73LsTA7HyUy8p/8U1ZU1X2WyEgUVRkzkny0Pz5DAqXMR8OOcnNzDTc3N2PlypVW47GxsUbPnj1tbhMaGmq8/PLLVmPjx483WrVqZXN+UlKSIYmFhYXlisvhw4fLI9rKRUXko2GQkSwsLCVfKktGko8sLCyVbSlJPlaTHZ04cUL5+fkKCQmxGg8JCdHPP/9sc5tjx47ZnH/s2DGb8xMTE5WQkGD5uaCgQCdPnlStWrXk4uJSxj0of4XfmpTm21RHxz6zz/ZmGIbOnDmjevXq2bsUi4rIR8mxMrIy/w2ZhX1mnyuDypaR5GNRlf1vyAzOuM+Sc+53Zd7n0uSjXRvviuDh4SEPDw+rsYCAAPsUUwp+fn6V7g/LbOyzc6is+1xZTp+saI6YkZX1b8hM7LNzqMz77IwZST46BmfcZ8k597uy7nNJ89GuN1cLCgqSm5ub0tPTrcbT09NVp04dm9vUqVOnVPMBwBGRjwBgG/kIwBHZtfF2d3dXRESEUlNTLWMFBQVKTU1VZGSkzW0iIyOt5kvSunXrip0PAI6IfAQA28hHAI7I7qeaJyQkKC4uTm3btlX79u01a9Ys5eTkKD4+XpIUGxura665RsnJyZKkYcOGKSoqSjNmzFCPHj20dOlSbd++XfPmzbPnbpQbDw8PJSUlFTm1qSpjn52DM+5zWZGP1pzxb4h9dg7OuM9lRT5ac8a/IWfcZ8k597vK7PMVb79WAV599VWjQYMGhru7u9G+fXvj22+/tayLiooy4uLirOYvX77cCA8PN9zd3Y0bbrjBSElJqeCKAaBikI8AYBv5CMCR2P053gAAAAAAVGV2vcYbAAAAAICqjsYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE411JbN68WTExMapXr55cXFz08ccf27skUyUnJ6tdu3by9fVV7dq11atXL+3du9feZZnu9ddfV6tWreTn5yc/Pz9FRkZq9erV9i6rwkydOlUuLi4aPny4vUuBA3G2fJScMyOdPR8lMhKlRz6Sj86iKuQjjXclkZOTo9atW2v27Nn2LqVCbNq0SUOHDtW3336rdevW6fz58+ratatycnLsXZqp6tevr6lTp2rHjh3avn27brvtNt1zzz3avXu3vUsz3bZt2/TGG2+oVatW9i4FDsbZ8lFyzox05nyUyEhcHfKRfHQGVSYf7f08MxQlyVi5cqW9y6hQx48fNyQZmzZtsncpFS4wMNB488037V2Gqc6cOWNcd911xrp164yoqChj2LBh9i4JDsoZ89EwnDcjnSEfDYOMRPkgH8nHqqgq5SNHvFEpZGZmSpJq1qxp50oqTn5+vpYuXaqcnBxFRkbauxxTDR06VD169FB0dLS9SwEckrNlpDPlo0RGAmVBPlZtVSkfq9m7AKCgoEDDhw9Xx44d1aJFC3uXY7off/xRkZGR+uuvv1SjRg2tXLlSzZs3t3dZplm6dKnS0tK0bds2e5cCOCRnykhny0eJjATKgnwkHx0JjTfsbujQodq1a5e+/vpre5dSIZo2baqdO3cqMzNTK1asUFxcnDZt2lQlw/Pw4cMaNmyY1q1bJ09PT3uXAzgkZ8pIZ8pHiYwEyop8JB8diYthGIa9i4A1FxcXrVy5Ur169bJ3KaZ74okn9Mknn2jz5s269tpr7V2OXURHR6tx48Z644037F1Kufv444917733ys3NzTKWn58vFxcXubq6Kjc312odcCXOlI8SGVmV81EiI1G+yEfnQj46Xj5yxBt2YRiGnnzySa1cuVIbN250ysAsVFBQoNzcXHuXYYrbb79dP/74o9VYfHy8mjVrplGjRjlcYAIVhYy8qCrno0RGAleDfLyIfHS8fKTxriSys7O1f/9+y88HDx7Uzp07VbNmTTVo0MCOlZlj6NChWrJkiT755BP5+vrq2LFjkiR/f395eXnZuTrzJCYm6s4771SDBg105swZLVmyRBs3btTatWvtXZopfH19i1xz5ePjo1q1alX5a7FQfpwtHyXnzEhny0eJjETZkY/kY1VVFfORxruS2L59u7p06WL5OSEhQZIUFxenRYsW2akq87z++uuSpM6dO1uNL1y4UP369av4girI8ePHFRsbq6NHj8rf31+tWrXS2rVrdccdd9i7NKDScrZ8lJwzI8lHoPTIx7+Rj6jsuMYbAAAAAAAT8RxvAAAAAABMROMNAAAAAICJaLwBAAAAADARjTcAAAAAACai8QYAAAAAwEQ03gAAAAAAmIjGGwAAAAAAE9F4AwAAAABgIhpv4P/r3Lmzhg8fbu8yAKDSIR8BwDbyESVF4w0AAAAAgIlovAEAAAAAMBGNN5xSTk6OYmNjVaNGDdWtW1czZsywd0kAUCmQjwBgG/mIsqDxhlN69tlntWnTJn3yySf64osvtHHjRqWlpdm7LACwO/IRAGwjH1EW1exdAFDRsrOztWDBAr377ru6/fbbJUlvv/226tevb+fKAMC+yEcAsI18RFlxxBtO58CBA8rLy1OHDh0sYzVr1lTTpk3tWBUA2B/5CAC2kY8oKxpvAAAAAABMROMNp9O4cWNVr15d3333nWXs1KlT2rdvnx2rAgD7Ix8BwDbyEWXFNd5wOjVq1FD//v317LPPqlatWqpdu7bGjh0rV1e+hwLg3MhHALCNfERZ0XjDKU2fPl3Z2dmKiYmRr6+vRowYoczMTHuXBQB2Rz4CgG3kI8rCxTAMw95FAAAAAABQVXFuBAAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxBgAAAADARDTeAAAAAACYiMYbAAAAAAAT0XgDAAAAAGAiGm8AAAAAAExE4w0AAAAAgIlovAEAAAAAMBGNNwAAAAAAJqLxriL69esnFxcXubi4qEWLFpbxQ4cOWcZdXFy0YsWKCq/t9OnTVjW89NJLlnXDhw+3jNeoUaPCa7vUokWL5OLiou3bt19xbufOndW5c2fziyonYWFh6tevn73LAOyCfCw78hGomsjHsiMfUVI03lVIUFCQFi9erKlTpxZZN3DgQC1evFjt27e3Gt+xY4e6d+8uPz8/+fr6qmvXrtq5c2ep3nf9+vW67bbb5O/vL19fX0VERGjZsmWW9T4+Plq8eLFefvnlIts++uijWrx4sW699dYSv59hGFq8eLH+9a9/KSAgQN7e3mrZsqUmT56ss2fPlqr28vLHH39owoQJpf7dHThwQIMGDVKjRo3k6ekpPz8/dezYUa+88orOnTtnTrGAE6rofOzcubPVB8ZLl+rVq1vmkY/FIx+BimGPz487duzQ3XffrTp16qhGjRpq1aqV/vvf/yo/P98yh3wsHvnomKrZuwCUHx8fHz3yyCM210VGRhZZl5aWpk6dOik0NFRJSUkqKCjQnDlzFBUVpa1bt6pp06ZXfM+FCxeqf//+uuOOO/TCCy/Izc1Ne/fu1eHDhy1zqlevrkceeUSHDh3S008/bbV9RESEIiIitH79eqWlpV3x/fLz8/XQQw9p+fLluvXWWzVhwgR5e3vrq6++UlJSkpYvX67169erdu3aV3ytsvjiiy+sfv7jjz80ceJEhYWFqU2bNiV6jZSUFD3wwAPy8PBQbGysWrRooby8PH399dd69tlntXv3bs2bN8+E6gHnU9H5OHbsWA0YMMBqLCcnR4MHD1bXrl0tY+SjbeQjUHEqOh937NihW265Rdddd51GjRolb29vrV69WsOGDdOBAwf0yiuvSCIfi0M+Oi4abyc2btw4eXl5acuWLapVq5Yk6ZFHHlF4eLjGjBmjDz/88B+3P3TokIYOHaonn3zSEpJme/HFF7V8+XI988wzmj59umV84MCB6t27t3r16qX4+HilpKSYWoe7u3uZtj948KAefPBBNWzYUF9++aXq1q1rWTd06FDt37/f9H0AULyy5uMdd9xRZOzdd9+VJD388MPlX7DIRwAVo6z5+MYbb0iSNm/erJo1a0qSBg0apKioKC1atMiUz5TkIyoDTjV3Yl999ZWio6MtoSlJdevWVVRUlD7//HNlZ2f/4/Zz585Vfn6+Jk2aJEnKzs6WYRim1Xvu3DlNnz5d4eHhSk5OLrI+JiZGcXFxWrVqlbZu3WoZd3Fx0YQJE4rML+66lbNnz2rQoEGqVauW/Pz8FBsbq1OnTlnNufQanY0bN6pdu3aSpPj4eMvppIsWLSp2X1588UVlZ2drwYIFVqFZqEmTJho2bJjl5wsXLuj5559X48aN5eHhobCwMI0ZM0a5ublW2xmGocmTJ6t+/fry9vZWly5dtHv3bps1nD59WsOHD1doaKg8PDzUpEkTTZs2TQUFBVbzli5dqoiICPn6+srPz08tW7assC9aAHspaz7asmTJEvn4+Oiee+4pz1IlkY/kI1BxypqPWVlZ8vT0VEBAgNV43bp15eXlVe71ko/kY2VB4+3EcnNzbQact7e38vLytGvXrn/cfv369WrWrJlWrVql+vXry9fXV7Vq1dK4ceOK/MtXHr7++mudOnVKDz30kKpVs32yRmxsrCTps88+u+r3eeKJJ7Rnzx5NmDBBsbGxeu+999SrV69iv1S4/vrrLV8+FF4LVXgNUXE+++wzNWrUSLfcckuJahowYIDGjx+vm266SS+//LKioqKUnJysBx980Gre+PHjNW7cOLVu3VrTp09Xo0aN1LVrV+Xk5FjNO3v2rKKiovTuu+8qNjZW//3vf9WxY0clJiYqISHBMm/dunXq27evAgMDNW3aNE2dOlWdO3fW//3f/5WobsBRlTUfL5eRkaF169apV69e8vHxKa8yLchH8hGoKGXNx86dOysrK0uDBg3Snj179Ntvv2nu3Ln66KOPlJiYWO71ko/kY2XBqeZOrGnTpvr222+Vn58vNzc3SVJeXp6+++47SdKRI0f+cftffvlFbm5uio+P18iRI9W6dWt99NFHmjx5si5cuGDzW8Wy+OmnnyRJrVu3LnZO4brCuVfD3d1dqamplhsgNWzYUCNHjtRnn32mnj17FpkfEhKiO++8U+PHj7d5LdTlsrKydOTIkRIf9frhhx/09ttva8CAAZo/f74k6fHHH1ft2rX10ksvacOGDerSpYsyMjL04osvqkePHvrss8/k4uIi6eK1pi+88ILVa86cOVMHDhzQ999/r+uuu07SxdO86tWrp+nTp2vEiBEKDQ1VSkqK/Pz8tHbtWsvfCOAMypqPl1u2bJkuXLhg2mnm5CP5CFSUsubjY489pt27d+uNN97Qm2++KUlyc3PTa6+9psGDB5d7veQj+VhZcMTbiT3++OPat2+f+vfvr59++km7du1SbGysjh49KklXvCtidna2Tp06pYkTJ2rSpEm677779N5776l79+565ZVXdObMmXKtt/D1fH19i51TuK4s7z1w4ECruw4PGTJE1apV06pVq676NS+VlZUl6Z/341KF73vpN4mSNGLECEmyXMuzfv165eXl6cknn7SEpnTxkRuX++CDD3TrrbcqMDBQJ06csCzR0dHKz8/X5s2bJUkBAQHKycnRunXrSreTgIMraz5ebsmSJQoODrZ57Xd5IB/JR6CilDUf3dzc1LhxY3Xr1k1vv/22li1bppiYGD355JP6+OOPy71e8pF8rCxovJ3Y4MGDNWbMGC1ZskQ33HCDWrZsqQMHDmjkyJGSdMXnIhaeZtS3b1+r8b59++rcuXP6/vvvy7XekoRi4bqy3JWy8Bu8QjVq1FDdunV16NChq37NS/n5+Ukqebj/9ttvcnV1VZMmTazG69Spo4CAAP3222+WeVLR+oODgxUYGGg19ssvv2jNmjUKDg62WqKjoyVJx48fl3TxP67h4eG68847Vb9+ff3nP//RmjVrSrnHgOMpaz5e6tdff9WWLVvUp0+fYk9zLCvykXwEKkpZ83Hq1KmaNm2a3n//fcXGxqp3795auXKlOnXqpKFDh+rChQvlWi/5SD5WFjTeTm7KlClKT0/XV199pf/973/atm2b5frs8PDwf9y2Xr16ki6eKnOpwtC6/IYSZdW8eXNJ0v/+979i5xSua9So0RVf79JnRVYkPz8/1atXr9TXiF76LWRZFRQU6I477tC6detsLvfdd5+ki/8sd+7cqU8//VQ9e/bUhg0bdOeddyouLq7cagEqq7Lk46WWLFkiyby7mUvkI/kIVKyy5OOcOXN02223FWnQe/bsqT/++KPcGtVC5CP5WFnQeEOBgYHq1KmTWrZsKeniKSf169dXs2bN/nG7iIgISUWv5fnjjz8kXfymrDx17NhRAQEBWrJkSbGh984770iSHnjgActYYGCgTp8+bTUvLy/PckrU5X755Rern7Ozs3X06FGFhYUVW1tpQ+3uu+/WgQMHtGXLlivObdiwoQoKCorUlZ6ertOnT6thw4aWebbqz8jIKPIlSOPGjZWdna3o6GibS4MGDSxz3d3dFRMTozlz5ujAgQMaNGiQ3nnnHe3fv79U+ww4oqvNx0stWbJEjRs31s0332xWmeQj+QhUuKvNx/T0dJs5df78eUkq9yPe5CP5WFnQeMPKsmXLtG3bNg0fPlyurv/859GnTx9J0oIFCyxjBQUFWvj/2rv74KrqO3/gn4SHgEKiEAmIsXFdH1cBi5qidtAaoUrZstNuqbqFsj52WBcbq0JVKNo1dK0OO0rFZ91xKVS7PsyiUJYtuI60KIijrYXBh8I4JkCpCaZrosn5/cHP2JSL8nRycpPXa+b+cb/3nHs/B+Ib37nnnvvQQzFgwIC2Yn6gHHTQQXHdddfF+vXr44Ybbtjl8cWLF8fDDz8c48ePb/tHIGJnSHz8mZOP3XvvvbsN33vvvbct/CMi7r777vjoo4/i/PPP3+1sH1+l+C8Deneuu+66OPjgg+PSSy+Nurq6XR5/44032r5y4YILLoiIiLlz57bb5o477oiIiHHjxkVERFVVVfTq1SvuvPPOdlfQ/Mv9IiK+8Y1vxKpVq2Lp0qW7PPbee++1/aP3hz/8od1jhYWFMWzYsIiIXb6KArq6vcnHj7388svx+uuvx0UXXZTqbPJxbrtt5CN0rL3Jx2OPPTaWLVvW7r+hlpaW+NnPfhb9+/ePo48++oDOJh/ntttGPmbHVc27seeeey5uvvnmGDNmTAwcODB+9atfxUMPPRRf/vKX230HYETED37wg5g9e3b88pe/bPv+wa9+9atx7rnnRk1NTWzbti2GDx8eTz75ZDz//PNxzz33RFFR0QGf+brrrot169bFj370o1i1alV87Wtfi759+8bzzz8fjz76aPzN3/zNLt9/eOmll8aVV14ZX/va1+K8886LV155JZYuXRqlpaU5X6O5uTnOPffc+MY3vhHr16+Pn/zkJ3HWWWflvCLlx44++ug45JBDYv78+dG/f/84+OCDo7KyMo466qjdbr9gwYKYOHFinHDCCTFp0qQ46aSTorm5OV544YV47LHH2r4jcvjw4TF58uS4995747333ovRo0fH6tWr45FHHokJEybEOeecExE7zzD43ve+FzU1NfGVr3wlLrjggnj55Zfj2Wef3eVYr7322nj66afjK1/5Snz729+OkSNHRmNjY7z66qvx+OOPx9tvvx2lpaVx6aWXxvbt2+NLX/pSHHHEEfH73/8+7rzzzhgxYkSccMIJe/i3Bvlnf/PxY//xH/8REemeZv4x+SgfoSPsbz5Onz49/uEf/iEqKyvj8ssvj759+8ZPf/rTWLNmTfzwhz9sd4GyA0U+ysdOIaFLmDx5cvK5z31ul/W33noriYjkoYce2uWxjRs3JmPGjElKS0uToqKi5Pjjj09qamqSpqamXba95pprkoKCguT1119vt75jx45k2rRpyeDBg5PevXsnJ598cvLoo4/mnPHjWW677bac8x988MF7dKytra3Jww8/nJx55plJ//79k4hIIiKpqqrKOXtLS0ty/fXXJ6WlpclBBx2UjB07Ntm4cWPyuc99Lpk8eXLbdg899FASEcnKlSuTyy+/PDn00EOTfv36JRdffHHyhz/8od1zjh49Ohk9enS7taeeeio58cQTk549e+72z/wvbdiwIbnsssuSioqKpHfv3kn//v2TM888M7nzzjuTDz74oG27Dz/8MJk9e3Zy1FFHJb169UrKy8uTGTNmtNvm42OdPXt2MmTIkKRv377J2Wefnbz22mu7HGuS7Py7mzFjRvLXf/3XSe/evZPS0tLkjDPOSH784x8nzc3NSZIkyeOPP56MGTMmGTRoUNK7d+/kyCOPTK644ork3Xff/cxjg84iq3xsaWlJhg4dmnz+85//zBnl467kI6Qvq3xcsmRJMnr06KS0tLTt/x/nz5+fc0b5uCv5mJ8U7y5i8uTJSXl5ebJ169bkj3/8Y9v6x2F15513Jlu3bs0ZLHvitNNOS77+9a/v076tra3J1q1bk7Vr1+4SnO+//36ydevW5Jvf/OYeB+dfam5uTsaOHZv07NkzefbZZ/fpOYCuSz7KRyA3+Sgf6Tg+492FbN68OQ477LA466yzdnnsqquuisMOOyyefvrpvX7ehoaGeOWVV+Lmm2/ep7nq6+vjsMMOi89//vO7PHbDDTfEYYcdFgsXLtyn546I6NWrV/z85z+PESNGxN///d/H2rVr9/m5gK5JPspHIDf5KB/pGAVJ8mefoidv/fa3v227mni/fv3arp77wQcfxPPPP9+23bBhw/brOwr3xUcffRQrVqxou3/ssce2XfVww4YNsWnTpoiI6Nmz5y6fjwTYX/IRIDf5CB0n0+L93HPPxW233RZr1qyJd999N5544omYMGHCp+6zYsWKqK6ujt/85jdRXl4eN954Y9tFBAC6CvkIsHsyEsg3mZ5q3tjYGMOHD4958+bt0fZvvfVWjBs3Ls4555xYt25dXH311XHppZfmvKQ9QD6TjwC7JyOBfNNpTjUvKCj4zN9WXn/99bF48eJ47bXX2ta++c1vxnvvvRdLlizpgCkBOp58BNg9GQnkg7z6Hu9Vq1ZFVVVVu7WxY8fG1Vdfvdt9mpqa2n1Re2tra2zfvj0GDhwYBQUFaY0K5JEkSWLHjh1x+OGHR2Fhfl5zcl/yMUJGAp+tu2akfAQ+y97kY14V79ra2igrK2u3VlZWFg0NDfF///d/0bdv3132qampidmzZ3fUiEAe27x5cxxxxBFZj7FP9iUfI2QksOe6W0bKR2BP7Uk+5lXx3hczZsyI6urqtvv19fVx5JFHxubNm6O4uDjDyYDOoqGhIcrLy6N///5Zj9LhDkRGnjSra35G8rXZY7MeATqF7pqR8vHTyUjYu3zMq+I9ePDgqKura7dWV1cXxcXFu303p6ioKIqKinZZLy4uVryBdvL51MF9yceIA5ORhUUH7d2wecK/EdBed8tI+fjpZCR8Yk/yMa8+qDNq1KhYvnx5u7Vly5bFqFGjMpoIoHOQjwC7JyOBrGX6jvf7778fGzdubLv/1ltvxbp162LAgAFx5JFHxowZM+Kdd96Jf//3f4+IiCuvvDLuuuuuuO666+If//Ef43/+53/iZz/7WSxevDjVOSump/v8WXp7zrisRwByyJd8BMiCjATyTabveL/00ktxyimnxCmnnBIREdXV1XHKKafEzJkzIyLi3XffjU2bNrVtf9RRR8XixYtj2bJlMXz48Lj99tvj/vvvj7FjfcYE6FrkI8DuyUgg33Sa7/HuKA0NDVFSUhL19fV7/NkU73hD17YvudBVychPyEfYSUbuJB/bk5Gwd7mQV5/xBgAAgHyjeAMAAECKFG8AAABIkeINAAAAKVK8AQAAIEWKNwAAAKRI8QYAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAAClSvAEAACBFijcAAACkSPEGAACAFCneAAAAkCLFGwAAAFKkeAMAAECKFG8AAABIkeINAAAAKVK8AQAAIEWKNwAAAKRI8QYAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBRlXrznzZsXFRUV0adPn6isrIzVq1d/6vZz586N4447Lvr27Rvl5eXx3e9+Nz744IMOmhagY8lIgNzkI5BPMi3eixYtiurq6pg1a1asXbs2hg8fHmPHjo0tW7bk3H7BggUxffr0mDVrVrz++uvxwAMPxKJFi+L73/9+B08OkD4ZCZCbfATyTabF+4477ojLLrsspkyZEieeeGLMnz8/DjrooHjwwQdzbv/CCy/EmWeeGRdddFFUVFTEmDFj4sILL/zM33AC5CMZCZCbfATyTWbFu7m5OdasWRNVVVWfDFNYGFVVVbFq1aqc+5xxxhmxZs2atpB8880345lnnokLLrhgt6/T1NQUDQ0N7W4AnZ2MBMhNPgL5qGdWL7xt27ZoaWmJsrKydutlZWXxu9/9Luc+F110UWzbti3OOuusSJIkPvroo7jyyis/9TShmpqamD179gGdHSBtMhIgN/kI5KPML662N1asWBG33npr/OQnP4m1a9fGf/7nf8bixYvjlltu2e0+M2bMiPr6+rbb5s2bO3BigI4jIwFyk49A1jJ7x7u0tDR69OgRdXV17dbr6upi8ODBOfe56aab4lvf+lZceumlERFx8sknR2NjY1x++eVxww03RGHhrr9HKCoqiqKiogN/AAApkpEAuclHIB9l9o537969Y+TIkbF8+fK2tdbW1li+fHmMGjUq5z5/+tOfdgnGHj16REREkiTpDQvQwWQkQG7yEchHmb3jHRFRXV0dkydPjlNPPTVOP/30mDt3bjQ2NsaUKVMiImLSpEkxdOjQqKmpiYiI8ePHxx133BGnnHJKVFZWxsaNG+Omm26K8ePHt4UnQFchIwFyk49Avsm0eE+cODG2bt0aM2fOjNra2hgxYkQsWbKk7WIZmzZtavfbyRtvvDEKCgrixhtvjHfeeScOO+ywGD9+fPzLv/xLVocAkBoZCZCbfATyTUHSzc6vaWhoiJKSkqivr4/i4uI92qdi+uKUp8rO23PGZT0CZG5fcqGrkpGfkI+wk4zcST62JyNh73Ihr65qDgAAAPlG8QYAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAAClSvAEAACBFijcAAACkSPEGAACAFCneAAAAkCLFGwAAAFKkeAMAAECKFG8AAABIkeINAAAAKVK8AQAAIEWKNwAAAKRI8QYAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAACnKvHjPmzcvKioqok+fPlFZWRmrV6/+1O3fe++9mDp1agwZMiSKiori2GOPjWeeeaaDpgXoWDISIDf5COSTnlm++KJFi6K6ujrmz58flZWVMXfu3Bg7dmysX78+Bg0atMv2zc3Ncd5558WgQYPi8ccfj6FDh8bvf//7OOSQQzp+eICUyUiA3OQjkG8yLd533HFHXHbZZTFlypSIiJg/f34sXrw4HnzwwZg+ffou2z/44IOxffv2eOGFF6JXr14REVFRUfGpr9HU1BRNTU1t9xsaGg7cAQCkSEYC5CYfgXyT2anmzc3NsWbNmqiqqvpkmMLCqKqqilWrVuXc5+mnn45Ro0bF1KlTo6ysLE466aS49dZbo6WlZbevU1NTEyUlJW238vLyA34sAAeajATITT4C+Siz4r1t27ZoaWmJsrKydutlZWVRW1ubc58333wzHn/88WhpaYlnnnkmbrrpprj99tvjhz/84W5fZ8aMGVFfX99227x58wE9DoA0yEiA3OQjkI8yPdV8b7W2tsagQYPi3nvvjR49esTIkSPjnXfeidtuuy1mzZqVc5+ioqIoKirq4EkBOp6MBMhNPgJZy6x4l5aWRo8ePaKurq7del1dXQwePDjnPkOGDIlevXpFjx492tZOOOGEqK2tjebm5ujdu3eqMwN0FBkJkJt8BPJRZqea9+7dO0aOHBnLly9vW2ttbY3ly5fHqFGjcu5z5plnxsaNG6O1tbVtbcOGDTFkyBCBCXQpMhIgN/kI5KNMv8e7uro67rvvvnjkkUfi9ddfj+985zvR2NjYdoXKSZMmxYwZM9q2/853vhPbt2+PadOmxYYNG2Lx4sVx6623xtSpU7M6BIDUyEiA3OQjkG8y/Yz3xIkTY+vWrTFz5syora2NESNGxJIlS9oulrFp06YoLPzkdwPl5eWxdOnS+O53vxvDhg2LoUOHxrRp0+L666/P6hAAUiMjAXKTj0C+KUiSJMl6iI7U0NAQJSUlUV9fH8XFxXu0T8X0xSlPlZ2354zLegTI3L7kQlclIz8hH2EnGbmTfGxPRsLe5UKmp5oDAABAV6d4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAACnqub9PsG3btvj1r38dLS0tcdppp8WQIUMOxFwAeU8+AuQmH4HuZr+K989//vO45JJL4thjj40PP/ww1q9fH/PmzYspU6YcqPkA8pJ8BMhNPgLd0V6dav7++++3uz979uxYvXp1rF69Ol5++eV47LHH4oYbbjigAwLkA/kIkJt8BNjL4j1y5Mh46qmn2u737NkztmzZ0na/rq4uevfufeCmA8gT8hEgN/kIsJenmi9dujSmTp0aDz/8cMybNy/+7d/+LSZOnBgtLS3x0UcfRWFhYTz88MMpjQrQeclHgNzkI8BeFu+KiopYvHhx/PSnP43Ro0fHP//zP8fGjRtj48aN0dLSEscff3z06dMnrVkBOi35CJCbfATYx68Tu/DCC+PFF1+MV155Jc4+++xobW2NESNGCE2g25OPALnJR6A72+urmj/zzDPx+uuvx/Dhw+P++++PlStXxsUXXxznn39+3HzzzdG3b9805gTo9OQjQG7yEeju9uod72uuuSamTJkSL774YlxxxRVxyy23xOjRo2Pt2rXRp0+fOOWUU+LZZ59Na1aATks+AuQmHwEiCpIkSfZ044EDB8YvfvGLGDlyZGzfvj2+8IUvxIYNG9oe/+1vfxtXXHFF/O///m8qwx4IDQ0NUVJSEvX19VFcXLxH+1RMX5zyVNl5e864rEeAzO1LLvylrpCPETLyz8lH2Gl/M1I+dk0yEvYuF/bqHe+DDz443nrrrYiI2Lx58y6fyTnxxBM7fWgCpEE+AuQmHwH2snjX1NTEpEmT4vDDD4/Ro0fHLbfcktZcAHlFPgLkJh8B9vLiahdffHF8+ctfjjfffDOOOeaYOOSQQ1IaCyC/yEeA3OQjwD5c1XzgwIExcODANGYByGvyESA3+Qh0d/v0Pd4AAADAnlG8AQAAIEWKNwAAAKRI8QYAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBR1iuI9b968qKioiD59+kRlZWWsXr16j/ZbuHBhFBQUxIQJE9IdECAj8hEgN/kI5JPMi/eiRYuiuro6Zs2aFWvXro3hw4fH2LFjY8uWLZ+639tvvx3f+9734otf/GIHTQrQseQjQG7yEcg3mRfvO+64Iy677LKYMmVKnHjiiTF//vw46KCD4sEHH9ztPi0tLXHxxRfH7Nmz46/+6q86cFqAjiMfAXKTj0C+ybR4Nzc3x5o1a6KqqqptrbCwMKqqqmLVqlW73e/mm2+OQYMGxSWXXPKZr9HU1BQNDQ3tbgCdXUfkY4SMBPKPfATyUabFe9u2bdHS0hJlZWXt1svKyqK2tjbnPs8//3w88MADcd999+3Ra9TU1ERJSUnbrby8fL/nBkhbR+RjhIwE8o98BPJR5qea740dO3bEt771rbjvvvuitLR0j/aZMWNG1NfXt902b96c8pQAHW9f8jFCRgJdn3wEOoOeWb54aWlp9OjRI+rq6tqt19XVxeDBg3fZ/o033oi33347xo8f37bW2toaERE9e/aM9evXx9FHH91un6KioigqKkpheoD0dEQ+RshIIP/IRyAfZfqOd+/evWPkyJGxfPnytrXW1tZYvnx5jBo1apftjz/++Hj11Vdj3bp1bbe//du/jXPOOSfWrVvnFCCgy5CPALnJRyAfZfqOd0REdXV1TJ48OU499dQ4/fTTY+7cudHY2BhTpkyJiIhJkybF0KFDo6amJvr06RMnnXRSu/0POeSQiIhd1gHynXwEyE0+Avkm8+I9ceLE2Lp1a8ycOTNqa2tjxIgRsWTJkrYLZmzatCkKC/Pqo+gAB4R8BMhNPgL5piBJkiTrITpSQ0NDlJSURH19fRQXF+/RPhXTF6c8VXbenjMu6xEgc/uSC12VjPyEfISdZORO8rE9GQl7lwt+FQgAAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAAClSvAEAACBFijcAAACkSPEGAACAFCneAAAAkCLFGwAAAFKkeAMAAECKFG8AAABIkeINAAAAKVK8AQAAIEWKNwAAAKRI8QYAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAAClSvAEAACBFijcAAACkSPEGAACAFCneAAAAkCLFGwAAAFKkeAMAAECKOkXxnjdvXlRUVESfPn2isrIyVq9evdtt77vvvvjiF78Yhx56aBx66KFRVVX1qdsD5DP5CJCbfATySebFe9GiRVFdXR2zZs2KtWvXxvDhw2Ps2LGxZcuWnNuvWLEiLrzwwvjlL38Zq1ativLy8hgzZky88847HTw5QLrkI0Bu8hHINwVJkiRZDlBZWRmnnXZa3HXXXRER0draGuXl5XHVVVfF9OnTP3P/lpaWOPTQQ+Ouu+6KSZMm7fJ4U1NTNDU1td1vaGiI8vLyqK+vj+Li4j2asWL64j08mvzz9pxxWY8AmWtoaIiSkpK9yoWOkHY+RsjITyMfYafOmJHyMXsyEvYuHzN9x7u5uTnWrFkTVVVVbWuFhYVRVVUVq1at2qPn+NOf/hQffvhhDBgwIOfjNTU1UVJS0nYrLy8/ILMDpKkj8jFCRgL5Rz4C+SjT4r1t27ZoaWmJsrKydutlZWVRW1u7R89x/fXXx+GHH94ufP/cjBkzor6+vu22efPm/Z4bIG0dkY8RMhLIP/IRyEc9sx5gf8yZMycWLlwYK1asiD59+uTcpqioKIqKijp4MoBs7Uk+RshIoPuRj0AWMi3epaWl0aNHj6irq2u3XldXF4MHD/7UfX/84x/HnDlz4r//+79j2LBhaY4J0OHkI0Bu8hHIR5meat67d+8YOXJkLF++vG2ttbU1li9fHqNGjdrtfv/6r/8at9xySyxZsiROPfXUjhgVoEPJR4Dc5COQjzI/1by6ujomT54cp556apx++ukxd+7caGxsjClTpkRExKRJk2Lo0KFRU1MTERE/+tGPYubMmbFgwYKoqKho+yxPv379ol+/fpkdB8CBJh8BcpOPQL7JvHhPnDgxtm7dGjNnzoza2toYMWJELFmypO2CGZs2bYrCwk/emL/77rujubk5vv71r7d7nlmzZsUPfvCDjhwdIFXyESA3+Qjkm8y/x7uj7ct3UfoORujaOuN31GZFRn5CPsJOMnIn+diejIQ8+h5vAAAA6OoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAAClSvAEAACBFijcAAACkSPEGAACAFCneAAAAkCLFGwAAAFKkeAMAAECKFG8AAABIkeINAAAAKVK8AQAAIEWKNwAAAKRI8QYAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABSpHgDAABAihRvAAAASJHiDQAAAClSvAEAACBFijcAAACkqGfWA9A5VUxfnPUIqXl7zrisRwAAALqRTvGO97x586KioiL69OkTlZWVsXr16k/d/rHHHovjjz8++vTpEyeffHI888wzHTQpQMeSjwC5yUcgn2RevBctWhTV1dUxa9asWLt2bQwfPjzGjh0bW7Zsybn9Cy+8EBdeeGFccskl8fLLL8eECRNiwoQJ8dprr3Xw5ADpko8AuclHIN8UJEmSZDlAZWVlnHbaaXHXXXdFRERra2uUl5fHVVddFdOnT99l+4kTJ0ZjY2P813/9V9vaF77whRgxYkTMnz//M1+voaEhSkpKor6+PoqLi/doxu542nV3PGa6r33JhY7Q0fkYISP/nKyAnTpjRsrH7MlI2LtcyPQz3s3NzbFmzZqYMWNG21phYWFUVVXFqlWrcu6zatWqqK6ubrc2duzYePLJJ3Nu39TUFE1NTW336+vrI2LnH9Keam360x5vm2929+fQHY+Z7uvjn4mMfw/ZTkfkY4SM/DSyAnbqbBkpHzsHGQl7l4+ZFu9t27ZFS0tLlJWVtVsvKyuL3/3udzn3qa2tzbl9bW1tzu1rampi9uzZu6yXl5fv49RdS8ncrCfoeN3xmNkzO3bsiJKSkqzHiIiOyccIGflpZAW011kyUj52DjISPrEn+djlr2o+Y8aMdr/hbG1tje3bt8fAgQOjoKAgw8lya2hoiPLy8ti8eXOnOZ0rbY7ZMWctSZLYsWNHHH744VmP0uHyKSM7889QWhyzY+4MumtGysfOrTsec0T3PO7OfMx7k4+ZFu/S0tLo0aNH1NXVtVuvq6uLwYMH59xn8ODBe7V9UVFRFBUVtVs75JBD9n3oDlJcXNzpfrDS5pi7h856zJ3hXZw/1xH5GJGfGdlZf4bS5Ji7h858zJ0pI+Xj7nXmn6G0dMdjjuiex91Zj3lP8zHTq5r37t07Ro4cGcuXL29ba21tjeXLl8eoUaNy7jNq1Kh220dELFu2bLfbA+Qj+QiQm3wE8lHmp5pXV1fH5MmT49RTT43TTz895s6dG42NjTFlypSIiJg0aVIMHTo0ampqIiJi2rRpMXr06Lj99ttj3LhxsXDhwnjppZfi3nvvzfIwAA44+QiQm3wE8k3mxXvixImxdevWmDlzZtTW1saIESNiyZIlbRfA2LRpUxQWfvLG/BlnnBELFiyIG2+8Mb7//e/HMcccE08++WScdNJJWR3CAVVUVBSzZs3a5dSmrswxdw/d8Zj3l3xsrzv+DDnm7qE7HvP+ko/tdcefoe54zBHd87i7yjFn/j3eAAAA0JVl+hlvAAAA6OoUbwAAAEiR4g0AAAApUrwBAAAgRYp3J/Hcc8/F+PHj4/DDD4+CgoJ48sknsx4pVTU1NXHaaadF//79Y9CgQTFhwoRYv3591mOl7u67745hw4ZFcXFxFBcXx6hRo+LZZ5/NeqwOM2fOnCgoKIirr74661HII90tHyO6Z0Z293yMkJHsPfkoH7uLrpCPincn0djYGMOHD4958+ZlPUqHWLlyZUydOjV+9atfxbJly+LDDz+MMWPGRGNjY9ajpeqII46IOXPmxJo1a+Kll16KL33pS/HVr341fvOb32Q9WupefPHFuOeee2LYsGFZj0Ke6W75GNE9M7I752OEjGTfyEf52B10mXxM6HQiInniiSeyHqNDbdmyJYmIZOXKlVmP0uEOPfTQ5P777896jFTt2LEjOeaYY5Jly5Ylo0ePTqZNm5b1SOSp7piPSdJ9M7I75GOSyEgODPkoH7uirpSP3vGmU6ivr4+IiAEDBmQ8ScdpaWmJhQsXRmNjY4waNSrrcVI1derUGDduXFRVVWU9CuSl7paR3SkfI2Qk7A/52LV1pXzsmfUA0NraGldffXWceeaZcdJJJ2U9TupeffXVGDVqVHzwwQfRr1+/eOKJJ+LEE0/MeqzULFy4MNauXRsvvvhi1qNAXupOGdnd8jFCRsL+kI/yMZ8o3mRu6tSp8dprr8Xzzz+f9Sgd4rjjjot169ZFfX19PP744zF58uRYuXJllwzPzZs3x7Rp02LZsmXRp0+frMeBvNSdMrI75WOEjIT9JR/lYz4pSJIkyXoI2isoKIgnnngiJkyYkPUoqfunf/qneOqpp+K5556Lo446KutxMlFVVRVHH3103HPPPVmPcsA9+eST8Xd/93fRo0ePtrWWlpYoKCiIwsLCaGpqavcYfJbulI8RMrIr52OEjOTAko/di3zMv3z0jjeZSJIkrrrqqnjiiSdixYoV3TIwP9ba2hpNTU1Zj5GKc889N1599dV2a1OmTInjjz8+rr/++rwLTOgoMnKnrpyPETIS9oV83Ek+5l8+Kt6dxPvvvx8bN25su//WW2/FunXrYsCAAXHkkUdmOFk6pk6dGgsWLIinnnoq+vfvH7W1tRERUVJSEn379s14uvTMmDEjzj///DjyyCNjx44dsWDBglixYkUsXbo069FS0b9//10+c3XwwQfHwIEDu/xnsThwuls+RnTPjOxu+RghI9l/8lE+dlVdMR8V707ipZdeinPOOaftfnV1dURETJ48OR5++OGMpkrP3XffHRERZ599drv1hx56KL797W93/EAdZMuWLTFp0qR49913o6SkJIYNGxZLly6N8847L+vRoNPqbvkY0T0zUj7C3pOPn5CPdHY+4w0AAAAp8j3eAAAAkCLFGwAAAFKkeAMAAECKFG8AAABIkeINAAAAKVK8AQAAIEWKNwAAAKRI8QYAAIAUKd7w/5199tlx9dVXZz0GQKcjHwFyk4/sKcUbAAAAUqR4AwAAQIoUb7qlxsbGmDRpUvTr1y+GDBkSt99+e9YjAXQK8hEgN/nI/lC86ZauvfbaWLlyZTz11FPxi1/8IlasWBFr167NeiyAzMlHgNzkI/ujZ9YDQEd7//3344EHHohHH300zj333IiIeOSRR+KII47IeDKAbMlHgNzkI/vLO950O2+88UY0NzdHZWVl29qAAQPiuOOOy3AqgOzJR4Dc5CP7S/EGAACAFCnedDtHH3109OrVK37961+3rf3xj3+MDRs2ZDgVQPbkI0Bu8pH95TPedDv9+vWLSy65JK699toYOHBgDBo0KG644YYoLPR7KKB7k48AuclH9pfiTbd02223xfvvvx/jx4+P/v37xzXXXBP19fVZjwWQOfkIkJt8ZH8UJEmSZD0EAAAAdFXOjQAAAIAUKd4AAACQIsUbAAAAUqR4AwAAQIoUbwAAAEiR4g0AAAApUrwBAAAgRYo3AAAApEjxBgAAgBQp3gAAAJAixRsAAABS9P8AbKDQlFGbBS4AAAAASUVORK5CYII=",
       "text/plain": [
-       "<IPython.core.display.Math object>"
+       "<Figure size 1000x1000 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1261,334 +22685,475 @@
     }
    ],
    "source": [
-    "for d in range(5):\n",
-    "    results = np.zeros((9, 9), dtype=int)\n",
-    "    for n in range(10):\n",
-    "        num = 0\n",
-    "        for k in range(1, n):\n",
-    "            codes = cb.all_small_codes(\n",
-    "                n, k, d=d, is_decomposable=True, is_gf4linear=True, info_only=True, list_only=True\n",
-    "            )\n",
-    "            try:\n",
-    "                num = len(codes)\n",
-    "                results[n - 1][k - 1] = num\n",
-    "            except TypeError:\n",
-    "                pass\n",
-    "    print(f\"d={d}\")\n",
-    "    lprint(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8a07b30-12cc-417f-ac73-cb1adadb8c6c",
-   "metadata": {},
-   "source": [
-    "# Class of indecomposable stabilizer codes with n<= 4, k>=1 and d>=2"
+    "plot_d_distro(extract_n_codes(9))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
-   "id": "8219dd1d-497f-41c4-b425-cebf71ba8aff",
+   "execution_count": 84,
+   "id": "641c6c26-a5a9-49d9-802b-2380f69cdf39",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[4,1]]\n",
-      "[[4,2]]\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "all_codes = []\n",
-    "for n in range(5):\n",
-    "    for k in range(1, n):\n",
-    "        codes = cb.all_small_codes(n, k, d=2, is_decomposable=False, info_only=True, list_only=True)\n",
-    "        num = 0\n",
-    "        try:\n",
-    "            num = len(codes)\n",
-    "            if num > 0:\n",
-    "                print(f\"[[{n},{k}]]\")\n",
-    "                all_codes = all_codes + codes\n",
-    "        except TypeError:\n",
-    "            pass"
+    "plot_d_distro(extract_n_codes(8))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
-   "id": "6e90dcad-f148-4f88-b18c-390d92b06566",
+   "execution_count": 85,
+   "id": "a0085d4a-24fc-4966-8139-e812bf563729",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "[{aut_group_generators : ['V2V3', '(2,3)', 'V0V1', '(0,1)', '(0,2)(1,3)'],\n",
-       " aut_group_size       : 32,\n",
-       " code_type            : StabSubSystemCode,\n",
-       " d                    : 2,\n",
-       " index                : 6,\n",
-       " is_css               : 1,\n",
-       " is_decomposable      : 0,\n",
-       " is_degenerate        : 0,\n",
-       " is_gf4linear         : 0,\n",
-       " is_subsystem         : 1,\n",
-       " isotropic_generators : ['X0X1', 'X2X3', 'Z0Z1Z2Z3'],\n",
-       " k                    : 1,\n",
-       " logical_ops          : ['X1X3', 'Z2Z3'],\n",
-       " n                    : 4,\n",
-       " uuid                 : c49160c8-795e-4558-9978-08b491cdd091,\n",
-       " weight_enumerator    : [1, 0, 2, 0, 5],\n",
-       " },\n",
-       " {aut_group_generators : ['V0H1S2H3^(1,3)', 'H0S1S2V3^(1,2)', '(0,1)(2,3)'],\n",
-       " aut_group_size       : 24,\n",
-       " code_type            : StabSubSystemCode,\n",
-       " d                    : 2,\n",
-       " index                : 8,\n",
-       " is_css               : 0,\n",
-       " is_decomposable      : 0,\n",
-       " is_degenerate        : 0,\n",
-       " is_gf4linear         : 0,\n",
-       " is_subsystem         : 1,\n",
-       " isotropic_generators : ['Z0X2Z3', 'Y0X1Y2', 'Z1Z2X3'],\n",
-       " k                    : 1,\n",
-       " logical_ops          : ['X1Z3', 'Z0Z1'],\n",
-       " n                    : 4,\n",
-       " uuid                 : 51fc14fb-8309-4ff6-a51e-8801d0066f87,\n",
-       " weight_enumerator    : [1, 0, 0, 4, 3],\n",
-       " },\n",
-       " {aut_group_generators : ['(2,3)', '(1,2)', 'S0S1S2S3', '(0,1)', 'H0H1H2H3'],\n",
-       " aut_group_size       : 144,\n",
-       " code_type            : StabSubSystemCode,\n",
-       " d                    : 2,\n",
-       " index                : 9,\n",
-       " is_css               : 1,\n",
-       " is_decomposable      : 0,\n",
-       " is_degenerate        : 0,\n",
-       " is_gf4linear         : 1,\n",
-       " is_subsystem         : 1,\n",
-       " isotropic_generators : ['Z0Z1Z2Z3', 'X0X1X2X3'],\n",
-       " k                    : 2,\n",
-       " logical_ops          : ['X1X2', 'X1X3', 'Z0Z2', 'Z0Z3'],\n",
-       " n                    : 4,\n",
-       " uuid                 : 373b856e-a5af-4e9f-8524-997f1ccfe77e,\n",
-       " weight_enumerator    : [1, 0, 0, 0, 3],\n",
-       " }]"
+       "<Figure size 1000x1000 with 9 Axes>"
       ]
      },
-     "execution_count": 205,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "all_codes"
+    "plot_d_distro(extract_n_codes(7))"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "5077f3a1-b808-42e1-9e34-0a9cd409a5cb",
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "afe43db2-3177-4d5f-8915-cc0bf2cbb349",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# [[9,2,3]] Codes"
+    "date = distroindperc"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 345,
-   "id": "856fb89f-c8ea-4f63-96fe-4e4166432587",
+   "execution_count": 111,
+   "id": "320d65c9-4979-463e-9566-6b7dbc9a586b",
    "metadata": {},
    "outputs": [
     {
      "data": {
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DD4yKiop48MEHM5tlxowZcfLJJ0e/fv1i8ODBcd5558Xq1aszm+eb3/xmHHfccVFTUxM1NTUxcuTI+NGPfpTZPH9q5syZUVFREVdddVXWo7CLSqm3CM11hd7KUyk1p7eu0Vz5KaXeIjTXFXorT6XUnN66RnPlp5R6i9BcV+itPJVSc3rrGs2Vn1LqLUJzXaW58qO5zpVyc6XQm4XxbrJly5Y4/vjjY/bs2VmPEo8//nhMnDgxfvazn8XixYvj7bffjrPOOiu2bNmSyTwHH3xwzJw5M1asWBG/+MUv4oMf/GB89KMfjV//+teZzPPHnnrqqfjWt74Vxx13XNaj0AWl1FuE5naV3spXKTWnt12nufJUSr1FaG5X6a18lVJzett1mitPpdRbhOZ2ld7KVyk1p7ddp7nyVEq9RWiuKzRXnjTXuVJtrmR6K9DtIqKwYMGCrMdo9corrxQiovD4449nPUqr/fffv3DXXXdlOsOmTZsKhx9+eGHx4sWFUaNGFSZNmpTpPOyeUuutUNBcR/SWH6XWnN46prl8KLXeCgXNdURv+VFqzemtY5rLh1LrrVDQXEf0lh+l1pzeOqa5fCi13goFze2I5vJBc7sm6+ZKqTfPGO8BGhoaIiLigAMOyHiSiKamppg3b15s2bIlRo4cmeksEydOjHPOOSdGjx6d6Rzkj+ba0xvForeOaY5i0Vx7eqNY9NYxzVEsmmtPbxSL3jqmOYpFcx3THMWiufZKqbfeWQ9AcTU3N8dVV10V73vf++KYY47JbI6nn346Ro4cGW+99Vb07ds3FixYEEcffXRm88ybNy9WrlwZTz31VGYzkE+aa09vFIveOqY5ikVz7emNYtFbxzRHsWiuPb1RLHrrmOYoFs11THMUi+baK7XeLIzn3MSJE+OZZ56Jn/70p5nOceSRR8aqVauioaEhHnjggbjooovi8ccfzyTE9evXx6RJk2Lx4sVRXV2d/P7JN821pTeKSW/taY5i0lxbeqOY9Nae5igmzbWlN4pJb+1pjmLSXHuao5g011ZJ9pbZi7jnWJTIexpMnDixcPDBBxdefPHFrEdp58wzzyxcdtllmdz3ggULChFRqKysbL1FRKGioqJQWVlZeOeddzKZi91TKr0VCprriN7yp1Sa01vHNJcvpdJboaC5jugtf0qlOb11THP5Uiq9FQqa64je8qdUmtNbxzSXL6XSW6GguR3RXL5obtf4e+V2njGeQ4VCIa688spYsGBBLF26NA499NCsR2qnubk5tm7dmsl9n3nmmfH000+32TZhwoQ46qij4pprronKyspM5qJ8aW7H9EZ301vnNEd309yO6Y3uprfOaY7uprkd0xvdTW+d0xzdTXOd0xzdTXM7Voq9WRjvJps3b44XXnih9eOXXnopVq1aFQcccEAccsghSWeZOHFizJ07Nx566KHo169fbNiwISIi+vfvH3vvvXfSWSIipk6dGh/+8IfjkEMOiU2bNsXcuXNj6dKl8cgjjySfJSKiX79+7d7bYd99940BAwZk+p4P7LpS6i1Cc53RWz6UUnN665zmyl8p9Rahuc7oLR9KqTm9dU5z5a+UeovQXGf0lg+l1JzeOqe58ldKvUVobmc0V/4017lSaq4ke0v+HPWc+vGPf1yIiHa3iy66KPksHc0REYV77703+SyFQqFw8cUXF/7sz/6s0KdPn8KgQYMKZ555ZuHRRx/NZJYdGTVqVGHSpElZj8EuKqXeCgXNdZXeyk8pNae3rtNceSml3goFzXWV3spPKTWnt67TXHkppd4KBc11ld7KTyk1p7eu01x5KaXeCgXN7Q7NlRfNda7Um8u6t4pCoVDYydo5AAAAAAAAAJStXlkPAAAAAAAAAADFZGEcAAAAAAAAgFyzMA4AAAAAAABArlkYBwAAAAAAACDXLIwDAAAAAAAAkGsWxgEAAAAAAADINQvjAAAAAAAAAOSahXEAAAAAAAAAcs3COF12xhlnxFVXXZX1GNAj6A3S0hykozdIS3OQluYgHb1BWpqDdPTW/SyMAwAAAAAAAJBrFsYBAAAAAAAAyDUL43Rqy5YtMX78+Ojbt28MHTo0vva1r2U9EuSW3iAtzUE6eoO0NAdpaQ7S0RukpTlIR29pWBinU1dffXU8/vjj8dBDD8Wjjz4aS5cujZUrV2Y9FuSS3iAtzUE6eoO0NAdpaQ7S0RukpTlIR29p9M56AErX5s2b4+67747vfOc7ceaZZ0ZExLe//e04+OCDM54M8kdvkJbmIB29QVqag7Q0B+noDdLSHKSjt3Q8Y5wd+s1vfhPbtm2LESNGtG474IAD4sgjj8xwKsgnvUFamoN09AZpaQ7S0hykozdIS3OQjt7SsTAOAAAAAAAAQK5ZGGeHDjvssNhrr73i5z//eeu2P/zhD7FmzZoMp4J80hukpTlIR2+QluYgLc1BOnqDtDQH6egtHe8xzg717ds3Lrnkkrj66qtjwIABMXjw4LjuuuuiVy+/TwHdTW+QluYgHb1BWpqDtDQH6egN0tIcpKO3dCyM06lbb701Nm/eHGPHjo1+/frF5z//+WhoaMh6LMglvUFamoN09AZpaQ7S0hykozdIS3OQjt7SqCgUCoWshwAAAAAAAACAYvEcfAAAAAAAAAByzcI4AAAAAAAAALlmYRwAAAAAAACAXLMwDgAAAAAAAECuWRgHAAAAAAAAINcsjAMAAAAAAACQaxbGAQAAAAAAAMg1C+MAAAAAAAAA5JqFcQAAAAAAAAByzcI4AAAAAAAAALlmYRwAAAAAAACAXPt/yUSuOYC9XfQAAAAASUVORK5CYII=",
       "text/plain": [
-       "4445"
+       "<Figure size 2000x2000 with 81 Axes>"
       ]
      },
-     "execution_count": 345,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "codes = cb.all_small_codes(9, 2, d=3, info_only=True, list_only=True)\n",
-    "len(codes)"
+    "bins = np.arange(num_bins + 1)  # Create bin edges from 0 to number of bins\n",
+    "num_bins = len(data['[[1,0]]'])  \n",
+    "\n",
+    "# Compute the midpoints of each bin for centering the bars\n",
+    "bin_centers = (bins[:-1] + bins[1:]) / 2\n",
+    "\n",
+    "# Create a figure and a 9x9 grid of subplots\n",
+    "fig, axs = plt.subplots(9, 9, figsize=(20, 20))\n",
+    "\n",
+    "# Flatten the axes array for easy indexing\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "for cell in range(9*9):\n",
+    "    axs[cell].set_visible(False)\n",
+    "\n",
+    "# Plot each list as a histogram\n",
+    "for i, (key, percentages) in enumerate(data.items()):\n",
+    "    n, k = ast.literal_eval(key)[0]\n",
+    "    cell = 9*(n-1)+k\n",
+    "    axs[cell].set_visible(True)\n",
+    "    axs[cell].bar(bin_centers, percentages, width=0.9, align='center')  # Center the bars on bin midpoints\n",
+    "    axs[cell].set_title(f'{key}')\n",
+    "    axs[cell].set_ylim(0, 1)  # Set the y-axis to 1 since percentages sum to 1\n",
+    "    axs[cell].set_xlabel('d')  # Label for the horizontal axis\n",
+    "    axs[cell].set_ylabel('%')  # Label for the vertical axis\n",
+    "    axs[cell].set_xticks(bin_centers)  # Set the ticks at the center of the bins\n",
+    "    axs[cell].set_xticklabels(range(1, num_bins + 1))  # Use original integers as tick labels\n",
+    "\n",
+    "# Adjust layout for better spacing\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 346,
-   "id": "efecfa50-6325-48f7-81f7-be361efd800c",
+   "execution_count": 137,
+   "id": "7030b353-211b-4357-bb3a-2db41f38a24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distroindqec = {}\n",
+    "for n in range(10):\n",
+    "    for k in range(n+1):\n",
+    "        val = [len(cb.all_small_codes(n, k, d=d, info_only=True, list_only=True)) for d in range(1,5)]\n",
+    "        if sum(val)>0:\n",
+    "            distroindqec[f'[[{n},{k}]]']=val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "4819d5df-576f-47bf-afa0-77b4c487059d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4425"
+       "{'[[1,0]]': [1, 0, 0, 0],\n",
+       " '[[1,1]]': [1, 0, 0, 0],\n",
+       " '[[2,0]]': [1, 1, 0, 0],\n",
+       " '[[2,1]]': [2, 0, 0, 0],\n",
+       " '[[2,2]]': [1, 0, 0, 0],\n",
+       " '[[3,0]]': [2, 1, 0, 0],\n",
+       " '[[3,1]]': [5, 0, 0, 0],\n",
+       " '[[3,2]]': [3, 0, 0, 0],\n",
+       " '[[3,3]]': [1, 0, 0, 0],\n",
+       " '[[4,0]]': [3, 3, 0, 0],\n",
+       " '[[4,1]]': [11, 2, 0, 0],\n",
+       " '[[4,2]]': [10, 1, 0, 0],\n",
+       " '[[4,3]]': [4, 0, 0, 0],\n",
+       " '[[4,4]]': [1, 0, 0, 0],\n",
+       " '[[5,0]]': [6, 4, 1, 0],\n",
+       " '[[5,1]]': [29, 6, 1, 0],\n",
+       " '[[5,2]]': [37, 3, 0, 0],\n",
+       " '[[5,3]]': [19, 0, 0, 0],\n",
+       " '[[5,4]]': [5, 0, 0, 0],\n",
+       " '[[5,5]]': [1, 0, 0, 0],\n",
+       " '[[6,0]]': [11, 13, 1, 1],\n",
+       " '[[6,1]]': [78, 35, 2, 0],\n",
+       " '[[6,2]]': [156, 29, 0, 0],\n",
+       " '[[6,3]]': [104, 5, 0, 0],\n",
+       " '[[6,4]]': [31, 1, 0, 0],\n",
+       " '[[6,5]]': [6, 0, 0, 0],\n",
+       " '[[6,6]]': [1, 0, 0, 0],\n",
+       " '[[7,0]]': [26, 29, 4, 0],\n",
+       " '[[7,1]]': [260, 169, 19, 0],\n",
+       " '[[7,2]]': [834, 241, 0, 0],\n",
+       " '[[7,3]]': [785, 67, 0, 0],\n",
+       " '[[7,4]]': [260, 7, 0, 0],\n",
+       " '[[7,5]]': [48, 0, 0, 0],\n",
+       " '[[7,6]]': [7, 0, 0, 0],\n",
+       " '[[7,7]]': [1, 0, 0, 0],\n",
+       " '[[8,0]]': [59, 107, 11, 5],\n",
+       " '[[8,1]]': [1023, 1170, 178, 0],\n",
+       " '[[8,2]]': [6266, 3724, 20, 0],\n",
+       " '[[8,3]]': [9304, 2117, 1, 0],\n",
+       " '[[8,4]]': [3699, 264, 0, 0],\n",
+       " '[[8,5]]': [603, 11, 0, 0],\n",
+       " '[[8,6]]': [70, 1, 0, 0],\n",
+       " '[[8,7]]': [8, 0, 0, 0],\n",
+       " '[[8,8]]': [1, 0, 0, 0],\n",
+       " '[[9,0]]': [182, 416, 69, 8],\n",
+       " '[[9,1]]': [5777, 10742, 3609, 0],\n",
+       " '[[9,2]]': [78567, 98027, 4445, 0],\n",
+       " '[[9,3]]': [222749, 130598, 222, 0],\n",
+       " '[[9,4]]': [122541, 24117, 0, 0],\n",
+       " '[[9,5]]': [17677, 768, 0, 0],\n",
+       " '[[9,6]]': [1331, 13, 0, 0],\n",
+       " '[[9,7]]': [99, 0, 0, 0],\n",
+       " '[[9,8]]': [9, 0, 0, 0],\n",
+       " '[[9,9]]': [1, 0, 0, 0]}"
       ]
      },
-     "execution_count": 346,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "codes = cb.all_small_codes(9, 2, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
-    "len(codes)"
+    "distroindqec"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 347,
-   "id": "7fcab31a-6c08-44b0-9dfe-15ca107ae220",
+   "execution_count": 139,
+   "id": "5439d667-b97f-47b2-858d-7413f072f4bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distroindqecperc = {}\n",
+    "for distro in distroindqec:\n",
+    "    total = sum(distroindqec[distro])\n",
+    "    perc = [v/total for v in distroindqec[distro]]\n",
+    "    distroindqecperc[distro] = perc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "6dd03750-1ea8-438f-8ab7-3a1789cd0b1b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0"
+       "{'[[1,0]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[1,1]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[2,0]]': [0.5, 0.5, 0.0, 0.0],\n",
+       " '[[2,1]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[2,2]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[3,0]]': [0.6666666666666666, 0.3333333333333333, 0.0, 0.0],\n",
+       " '[[3,1]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[3,2]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[3,3]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[4,0]]': [0.5, 0.5, 0.0, 0.0],\n",
+       " '[[4,1]]': [0.8461538461538461, 0.15384615384615385, 0.0, 0.0],\n",
+       " '[[4,2]]': [0.9090909090909091, 0.09090909090909091, 0.0, 0.0],\n",
+       " '[[4,3]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[4,4]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[5,0]]': [0.5454545454545454,\n",
+       "  0.36363636363636365,\n",
+       "  0.09090909090909091,\n",
+       "  0.0],\n",
+       " '[[5,1]]': [0.8055555555555556,\n",
+       "  0.16666666666666666,\n",
+       "  0.027777777777777776,\n",
+       "  0.0],\n",
+       " '[[5,2]]': [0.925, 0.075, 0.0, 0.0],\n",
+       " '[[5,3]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[5,4]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[5,5]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[6,0]]': [0.4230769230769231,\n",
+       "  0.5,\n",
+       "  0.038461538461538464,\n",
+       "  0.038461538461538464],\n",
+       " '[[6,1]]': [0.6782608695652174,\n",
+       "  0.30434782608695654,\n",
+       "  0.017391304347826087,\n",
+       "  0.0],\n",
+       " '[[6,2]]': [0.8432432432432433, 0.15675675675675677, 0.0, 0.0],\n",
+       " '[[6,3]]': [0.9541284403669725, 0.045871559633027525, 0.0, 0.0],\n",
+       " '[[6,4]]': [0.96875, 0.03125, 0.0, 0.0],\n",
+       " '[[6,5]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[6,6]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[7,0]]': [0.4406779661016949, 0.4915254237288136, 0.06779661016949153, 0.0],\n",
+       " '[[7,1]]': [0.5803571428571429,\n",
+       "  0.37723214285714285,\n",
+       "  0.04241071428571429,\n",
+       "  0.0],\n",
+       " '[[7,2]]': [0.7758139534883721, 0.2241860465116279, 0.0, 0.0],\n",
+       " '[[7,3]]': [0.9213615023474179, 0.07863849765258216, 0.0, 0.0],\n",
+       " '[[7,4]]': [0.9737827715355806, 0.026217228464419477, 0.0, 0.0],\n",
+       " '[[7,5]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[7,6]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[7,7]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[8,0]]': [0.3241758241758242,\n",
+       "  0.5879120879120879,\n",
+       "  0.06043956043956044,\n",
+       "  0.027472527472527472],\n",
+       " '[[8,1]]': [0.4314635175031632, 0.4934626739772248, 0.07507380851961198, 0.0],\n",
+       " '[[8,2]]': [0.625974025974026,\n",
+       "  0.37202797202797205,\n",
+       "  0.001998001998001998,\n",
+       "  0.0],\n",
+       " '[[8,3]]': [0.8145683768166696,\n",
+       "  0.1853440728418841,\n",
+       "  8.755034144633164e-05,\n",
+       "  0.0],\n",
+       " '[[8,4]]': [0.9333838001514004, 0.06661619984859955, 0.0, 0.0],\n",
+       " '[[8,5]]': [0.9820846905537459, 0.017915309446254073, 0.0, 0.0],\n",
+       " '[[8,6]]': [0.9859154929577465, 0.014084507042253521, 0.0, 0.0],\n",
+       " '[[8,7]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[8,8]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[9,0]]': [0.2696296296296296,\n",
+       "  0.6162962962962963,\n",
+       "  0.10222222222222223,\n",
+       "  0.011851851851851851],\n",
+       " '[[9,1]]': [0.28701311605723373, 0.5336844197138315, 0.1793024642289348, 0.0],\n",
+       " '[[9,2]]': [0.43397831406492526,\n",
+       "  0.5414689652505814,\n",
+       "  0.024552720684493396,\n",
+       "  0.0],\n",
+       " '[[9,3]]': [0.6300014990001952,\n",
+       "  0.3693706178991937,\n",
+       "  0.0006278831006111961,\n",
+       "  0.0],\n",
+       " '[[9,4]]': [0.8355561919567974, 0.16444380804320255, 0.0, 0.0],\n",
+       " '[[9,5]]': [0.9583626999186772, 0.04163730008132285, 0.0, 0.0],\n",
+       " '[[9,6]]': [0.9903273809523809, 0.009672619047619048, 0.0, 0.0],\n",
+       " '[[9,7]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[9,8]]': [1.0, 0.0, 0.0, 0.0],\n",
+       " '[[9,9]]': [1.0, 0.0, 0.0, 0.0]}"
       ]
      },
-     "execution_count": 347,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "codes = cb.all_small_codes(9, 2, d=3, is_gf2linear=True, info_only=True, list_only=True)\n",
-    "len(codes)"
+    "distroindqecperc"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 348,
-   "id": "f7380bb6-d6be-4dcc-88aa-8b36d49e5786",
+   "execution_count": 145,
+   "id": "7b1843ba-3769-479f-bb20-682211588e02",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "750"
+       "<Figure size 2000x2000 with 100 Axes>"
       ]
      },
-     "execution_count": 348,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "codes = cb.all_small_codes(9, 2, d=3, is_degenerate=True, info_only=True, list_only=True)\n",
-    "len(codes)"
+    "data = distroindqecperc\n",
+    "bins = np.arange(num_bins + 1)  # Create bin edges from 0 to number of bins\n",
+    "num_bins = len(data['[[1,0]]'])  \n",
+    "\n",
+    "# Compute the midpoints of each bin for centering the bars\n",
+    "bin_centers = (bins[:-1] + bins[1:]) / 2\n",
+    "\n",
+    "# Create a figure and a 10x10 grid of subplots\n",
+    "fig, axs = plt.subplots(10, 10, figsize=(20, 20))\n",
+    "\n",
+    "# Flatten the axes array for easy indexing\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "for cell in range(10*10):\n",
+    "    axs[cell].set_visible(False)\n",
+    "\n",
+    "# Plot each list as a histogram\n",
+    "for i, (key, percentages) in enumerate(data.items()):\n",
+    "    n, k = ast.literal_eval(key)[0]\n",
+    "    cell = 10*(n-1)+k\n",
+    "    axs[cell].set_visible(True)\n",
+    "    axs[cell].bar(bin_centers, percentages, width=0.9, align='center')  # Center the bars on bin midpoints\n",
+    "    axs[cell].set_title(f'{key}')\n",
+    "    axs[cell].set_ylim(0, 1)  # Set the y-axis to 1 since percentages sum to 1\n",
+    "    axs[cell].set_xlabel('d')  # Label for the horizontal axis\n",
+    "    axs[cell].set_ylabel('%')  # Label for the vertical axis\n",
+    "    axs[cell].set_xticks(bin_centers)  # Set the ticks at the center of the bins\n",
+    "    axs[cell].set_xticklabels(range(1, num_bins + 1))  # Use original integers as tick labels\n",
+    "\n",
+    "# Adjust layout for better spacing\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 357,
-   "id": "dcdb8481-b1b2-4c11-bf9c-63707b5a431b",
+   "execution_count": 125,
+   "id": "7b0b5d27-2fdc-47d1-b973-1327491d7d11",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{1: 3178,\n",
-       " 2: 356,\n",
-       " 3: 4,\n",
-       " 4: 654,\n",
-       " 6: 13,\n",
-       " 8: 82,\n",
-       " 12: 10,\n",
-       " 16: 65,\n",
-       " 18: 1,\n",
-       " 24: 2,\n",
-       " 32: 25,\n",
-       " 36: 3,\n",
-       " 48: 3,\n",
-       " 64: 13,\n",
-       " 96: 4,\n",
-       " 128: 4,\n",
-       " 144: 1,\n",
-       " 192: 1,\n",
-       " 288: 1,\n",
-       " 384: 1,\n",
-       " 512: 1,\n",
-       " 768: 1,\n",
-       " 1152: 2}"
+       "{'[[4,1]]': [1.0, 0.0, 0.0],\n",
+       " '[[4,2]]': [1.0, 0.0, 0.0],\n",
+       " '[[5,1]]': [0.8, 0.2, 0.0],\n",
+       " '[[5,2]]': [1.0, 0.0, 0.0],\n",
+       " '[[6,1]]': [0.9642857142857143, 0.03571428571428571, 0.0],\n",
+       " '[[6,2]]': [1.0, 0.0, 0.0],\n",
+       " '[[6,3]]': [1.0, 0.0, 0.0],\n",
+       " '[[6,4]]': [1.0, 0.0, 0.0],\n",
+       " '[[7,1]]': [0.8888888888888888, 0.1111111111111111, 0.0],\n",
+       " '[[7,2]]': [1.0, 0.0, 0.0],\n",
+       " '[[7,3]]': [1.0, 0.0, 0.0],\n",
+       " '[[7,4]]': [1.0, 0.0, 0.0],\n",
+       " '[[8,1]]': [0.8599464763603925, 0.1400535236396075, 0.0],\n",
+       " '[[8,2]]': [0.9942363112391931, 0.005763688760806916, 0.0],\n",
+       " '[[8,3]]': [0.9995107632093934, 0.0004892367906066536, 0.0],\n",
+       " '[[8,4]]': [1.0, 0.0, 0.0],\n",
+       " '[[8,5]]': [1.0, 0.0, 0.0],\n",
+       " '[[8,6]]': [1.0, 0.0, 0.0],\n",
+       " '[[9,1]]': [0.7336404810245197, 0.26635951897548027, 0.0],\n",
+       " '[[9,2]]': [0.9550638246016675, 0.044936175398332535, 0.0],\n",
+       " '[[9,3]]': [0.9982818403744188, 0.0017181596255811421, 0.0],\n",
+       " '[[9,4]]': [1.0, 0.0, 0.0],\n",
+       " '[[9,5]]': [1.0, 0.0, 0.0],\n",
+       " '[[9,6]]': [1.0, 0.0, 0.0]}"
       ]
      },
-     "execution_count": 357,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "codes = cb.all_small_codes(9, 2, d=3, is_decomposable=False, info_only=True, list_only=True)\n",
-    "\n",
-    "aut_sizes = {}\n",
-    "for code in codes:\n",
-    "    size = code[\"aut_group_size\"]\n",
-    "    try:\n",
-    "        aut_sizes[size] += 1\n",
-    "    except:\n",
-    "        aut_sizes[size] = 1\n",
-    "\n",
-    "sorted_aut_sizes = {k: aut_sizes[k] for k in sorted(aut_sizes)}\n",
-    "sorted_aut_sizes"
+    "data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 358,
-   "id": "d7129203-204b-439e-84dc-a9a7dac9f35c",
+   "execution_count": 134,
+   "id": "6be36b4c-3220-46e6-b094-29827d0d82ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = {}\n",
+    "for n in range(10):\n",
+    "    temp = [0]*4\n",
+    "    k=0\n",
+    "    val = [len(cb.all_small_codes(n, k, d=d, is_decomposable=False, info_only=True, list_only=True)) for d in range(1,5)]\n",
+    "    temp = [v+t for v,t in zip(val, temp)]\n",
+    "    test[f'{n}']=temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "d69741bb-301c-48ec-8554-415ad4e96cd2",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aut_group_generators : ['V2S7', 'H2H7^(2,7)', 'V1S7', '(1,2)', 'V0S8', 'H0H8^(0,8)', 'R3r5R6^(4,6,5)', '(3,4)(5,6)']\n",
-      "aut_group_size       : 1152\n",
-      "code_type            : StabSubSystemCode\n",
-      "d                    : 3\n",
-      "index                : 15551\n",
-      "is_css               : 0\n",
-      "is_decomposable      : 0\n",
-      "is_degenerate        : 1\n",
-      "is_gf4linear         : 0\n",
-      "is_subsystem         : 1\n",
-      "isotropic_generators : ['X0Z8', 'X1Z7', 'X2Z7', 'X3X4Z5Z6', 'Z3Z4Y5Y6', 'Z0Z4X5Z6X8', 'Z1Z2Y3Y5Z6X7Z8']\n",
-      "k                    : 2\n",
-      "logical_ops          : ['Z1Z2X7', 'Z0Z3Z4X8', 'Z3Z4Z7', 'Z3Z4Z5Z6Z8']\n",
-      "n                    : 9\n",
-      "uuid                 : 3f463c93-83a8-462b-9e90-1cf4cdea9df7\n",
-      "weight_enumerator    : [1, 0, 4, 0, 6, 8, 12, 56, 41, 0]\n",
-      "\n",
-      "aut_group_generators : ['V2H3S6H7S8^(3,7)', 'V0H1V2H3S4H5S6H7^(1,5)(3,7)', 'R2r6R7^(3,7,6)', '(2,3)(6,7)', 'H0H1V2H3V4V5S6H7^(3,7)(4,5)', '(0,1)(4,5)', '(0,2)(1,3)(4,6)(5,7)']\n",
-      "aut_group_size       : 1152\n",
-      "code_type            : StabSubSystemCode\n",
-      "d                    : 3\n",
-      "index                : 80585\n",
-      "is_css               : 0\n",
-      "is_decomposable      : 0\n",
-      "is_degenerate        : 0\n",
-      "is_gf4linear         : 0\n",
-      "is_subsystem         : 1\n",
-      "isotropic_generators : ['X0X1Z4Z5', 'X2X3Z6Z7', 'Y0Y4Z5Z8', 'X0Z1X5Z8', 'Y2Y6Z7Z8', 'X2Z3X7Z8', 'Y0Z1Y2Z3Z4Z6X8']\n",
-      "k                    : 2\n",
-      "logical_ops          : ['Z3Z6X7', 'Z0Z1Z2Z3X8', 'Z0Z1Z2Z3Z4Z5Z6Z7', 'Z0Z1Z4Z5Z8']\n",
-      "n                    : 9\n",
-      "uuid                 : b5eea235-55bc-4185-bf91-7cd7476d0d1e\n",
-      "weight_enumerator    : [1, 0, 0, 0, 14, 0, 16, 64, 33, 0]\n",
-      "\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'0': [0, 0, 0, 0],\n",
+       " '1': [1, 0, 0, 0],\n",
+       " '2': [0, 1, 0, 0],\n",
+       " '3': [0, 1, 0, 0],\n",
+       " '4': [0, 2, 0, 0],\n",
+       " '5': [0, 3, 1, 0],\n",
+       " '6': [0, 9, 1, 1],\n",
+       " '7': [0, 22, 4, 0],\n",
+       " '8': [0, 85, 11, 5],\n",
+       " '9': [0, 363, 69, 8]}"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for code in codes:\n",
-    "    if code[\"aut_group_size\"] == 1152:\n",
-    "        print(code)"
+    "test"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd639568-dc7a-4984-9e56-9b0cc56c7d47",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/qiskit_qec/codes/codebase/__init__.py b/src/qiskit_qec/codes/codebase/__init__.py
index d1d26098..2a073269 100644
--- a/src/qiskit_qec/codes/codebase/__init__.py
+++ b/src/qiskit_qec/codes/codebase/__init__.py
@@ -140,8 +140,10 @@ def __init__(self, name: str, path: str, config_filename: str) -> None:
                                 codes_json_file = json_bytes.decode(
                                     "utf-8"
                                 )  # Decode bytes to string
-                                self.data[n][k] = json.loads(codes_json_file)
+                                # index needs to be converted to integer from string
+                                self.data[n][k] = {int(index): data for index, data in json.loads(codes_json_file).items()}
 
+ 
     @staticmethod
     def data2code(**record) -> Code:
         """_summary_
@@ -226,7 +228,7 @@ def _search(
                         codes.append(Properties(**code_data))
                     else:
                         codes.append(self.data2code(**code_data))
-                except KeyError:
+                except KeyError as e:
                     pass
                 return codes
 
diff --git a/src/qiskit_qec/codes/codebase/data/base/base_data/n_9/codes_n_9_k_1.json.gz b/src/qiskit_qec/codes/codebase/data/base/base_data/n_9/codes_n_9_k_1.json.gz
index d55e8109..89971724 100644
Binary files a/src/qiskit_qec/codes/codebase/data/base/base_data/n_9/codes_n_9_k_1.json.gz and b/src/qiskit_qec/codes/codebase/data/base/base_data/n_9/codes_n_9_k_1.json.gz differ
diff --git a/src/qiskit_qec/codes/codebase/data/base/config.ini b/src/qiskit_qec/codes/codebase/data/base/config.ini
index b78009e0..63eca363 100644
--- a/src/qiskit_qec/codes/codebase/data/base/config.ini
+++ b/src/qiskit_qec/codes/codebase/data/base/config.ini
@@ -10,7 +10,7 @@ fetch_method = memory
 n_min = 1
 n_max = 9
 k_min = 0
-k_max = 8
+k_max = 9
 index_min = 0
 index_max = 353569