obp_mpf_tutorial_L50.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reducing the Trotter error of Hamiltonian dynamics with <span style=\"color:black\"> multi-product formulas</span> and <span style=\"color:black\">operator backpropagation</span>\n",
    "\n",
    "In this notebook, you will learn how to use a  **Multi-Product Formula (MPF)** to achieve a lower Trotter error on our observable compared to the one incurred by the deepest Trotter circuit that we will actually execute.\n",
    "You will do so by working through the steps of a **Qiskit pattern**:\n",
    "\n",
    "- **Step 1: Map to quantum circuits**\n",
    "    - Initialize our problem's Hamiltonian\n",
    "    - <font color=\"blue\">**Use a MPF to generate the Trotterized time-evolution circuits**</font>\n",
    "- **Step 2: Optimize the problem**\n",
    "    - <font color=\"#4589FF\">**Slice the longest circuit**</font>\n",
    "    - <font color=\"#4589FF\">**Backpropagate slices from the longest circuit onto a Pauli observable**</font>\n",
    "    - <font color=\"#4589FF\">**Combine the remaining slices into a circuit with reduced depth**</font>\n",
    "    - Here we transpile our circuits for `ibm_torino`\n",
    "- **Step 3: Execute experiments**\n",
    "    - Use the `Estimator` primitive from qiskit runtime to run our experiment on `ibm_torino`\n",
    "- **Step 4: Reconstruct results**\n",
    "    - <font color=\"blue\">**Compute the MPF expectation value**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Map to quantum problem\n",
    "\n",
    "### 1a: Setting up our Hamiltonian\n",
    "\n",
    "We use the XXZ model on a line of 12 sites:\n",
    "\n",
    "$$\n",
    "\\hat{\\mathcal{H}}_{XXZ} = \\sum_{i=1}^{L-1} J_{i,(i+1)}\\left(X_i X_{(i+1)}+Y_i Y_{(i+1)}+ 2\\cdot Z_i Z_{(i+1)} \\right) \\, ,\n",
    "$$\n",
    "\n",
    "where $J_{i,(i+1)}$ is a random coefficient corresponding to edge $(i, i+1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=8315x59>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.transpiler import CouplingMap\n",
    "from rustworkx.visualization import graphviz_draw\n",
    "\n",
    "# L = length of our 1D spin chain\n",
    "L = 50\n",
    "\n",
    "# Generate the edge-list for this spin-chain\n",
    "elist = [(i-1,i) for i in range(1, L)]\n",
    "# Generate an edge-coloring so we can make hw-efficient circuits\n",
    "even_edges  = elist[::2] \n",
    "odd_edges = elist[1::2]\n",
    "\n",
    "# Instantiate a CouplingMap object\n",
    "coupling_map = CouplingMap(elist)\n",
    "graphviz_draw(coupling_map.graph, method=\"circo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate the hamiltonian we will evolve by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII'],\n",
      "              coeffs=[1.        +0.j, 0.52440675+0.j, 0.52440675+0.j, 1.0488135 +0.j,\n",
      " 0.60759468+0.j, 0.60759468+0.j, 1.21518937+0.j, 0.55138169+0.j,\n",
      " 0.55138169+0.j, 1.10276338+0.j, 0.52244159+0.j, 0.52244159+0.j,\n",
      " 1.04488318+0.j, 0.4618274 +0.j, 0.4618274 +0.j, 0.9236548 +0.j,\n",
      " 0.57294706+0.j, 0.57294706+0.j, 1.14589411+0.j, 0.46879361+0.j,\n",
      " 0.46879361+0.j, 0.93758721+0.j, 0.6958865 +0.j, 0.6958865 +0.j,\n",
      " 1.391773  +0.j, 0.73183138+0.j, 0.73183138+0.j, 1.46366276+0.j,\n",
      " 0.44172076+0.j, 0.44172076+0.j, 0.88344152+0.j, 0.64586252+0.j,\n",
      " 0.64586252+0.j, 1.29172504+0.j, 0.51444746+0.j, 0.51444746+0.j,\n",
      " 1.02889492+0.j, 0.53402228+0.j, 0.53402228+0.j, 1.06804456+0.j,\n",
      " 0.71279832+0.j, 0.71279832+0.j, 1.42559664+0.j, 0.28551803+0.j,\n",
      " 0.28551803+0.j, 0.57103606+0.j, 0.29356465+0.j, 0.29356465+0.j,\n",
      " 0.5871293 +0.j, 0.2601092 +0.j, 0.2601092 +0.j, 0.5202184 +0.j,\n",
      " 0.66630992+0.j, 0.66630992+0.j, 1.33261985+0.j, 0.63907838+0.j,\n",
      " 0.63907838+0.j, 1.27815675+0.j, 0.68500607+0.j, 0.68500607+0.j,\n",
      " 1.37001215+0.j, 0.73930917+0.j, 0.73930917+0.j, 1.47861834+0.j,\n",
      " 0.64957928+0.j, 0.64957928+0.j, 1.29915856+0.j, 0.48073968+0.j,\n",
      " 0.48073968+0.j, 0.96147936+0.j, 0.64026459+0.j, 0.64026459+0.j,\n",
      " 1.28052918+0.j, 0.30913721+0.j, 0.30913721+0.j, 0.61827443+0.j,\n",
      " 0.56996051+0.j, 0.56996051+0.j, 1.13992102+0.j, 0.32167664+0.j,\n",
      " 0.32167664+0.j, 0.64335329+0.j, 0.72233446+0.j, 0.72233446+0.j,\n",
      " 1.44466892+0.j, 0.51092416+0.j, 0.51092416+0.j, 1.02184832+0.j,\n",
      " 0.45733097+0.j, 0.45733097+0.j, 0.91466194+0.j, 0.38227781+0.j,\n",
      " 0.38227781+0.j, 0.76455561+0.j, 0.63711684+0.j, 0.63711684+0.j,\n",
      " 1.27423369+0.j, 0.47807517+0.j, 0.47807517+0.j, 0.95615033+0.j,\n",
      " 0.53421697+0.j, 0.53421697+0.j, 1.06843395+0.j, 0.2593949 +0.j,\n",
      " 0.2593949 +0.j, 0.5187898 +0.j, 0.55881775+0.j, 0.55881775+0.j,\n",
      " 1.1176355 +0.j, 0.55604786+0.j, 0.55604786+0.j, 1.11209572+0.j,\n",
      " 0.558467  +0.j, 0.558467  +0.j, 1.116934  +0.j, 0.72187404+0.j,\n",
      " 0.72187404+0.j, 1.44374808+0.j, 0.59091015+0.j, 0.59091015+0.j,\n",
      " 1.1818203 +0.j, 0.42975395+0.j, 0.42975395+0.j, 0.8595079 +0.j,\n",
      " 0.46851598+0.j, 0.46851598+0.j, 0.93703195+0.j, 0.5988156 +0.j,\n",
      " 0.5988156 +0.j, 1.1976312 +0.j, 0.28011274+0.j, 0.28011274+0.j,\n",
      " 0.56022547+0.j, 0.58338336+0.j, 0.58338336+0.j, 1.16676672+0.j,\n",
      " 0.58531893+0.j, 0.58531893+0.j, 1.17063787+0.j, 0.35519128+0.j,\n",
      " 0.35519128+0.j, 0.71038256+0.j, 0.31446315+0.j, 0.31446315+0.j,\n",
      " 0.6289263 +0.j, 0.40771418+0.j, 0.40771418+0.j, 0.81542835+0.j])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from qiskit.quantum_info import SparsePauliOp, Pauli\n",
    "\n",
    "# Genereate random coefficients for our XXZ Hamiltonian\n",
    "np.random.seed(0)\n",
    "Js = np.random.rand(L-1) + 0.5*np.ones(L-1)\n",
    "\n",
    "hamiltonian = SparsePauliOp(Pauli('I'*L))\n",
    "for i, edge in enumerate(even_edges + odd_edges):\n",
    "    hamiltonian += SparsePauliOp.from_sparse_list(\n",
    "    [(\"XX\", (edge), Js[i] /2),\n",
    "     (\"YY\", (edge), Js[i] /2),\n",
    "     (\"ZZ\", (edge), Js[i] )], \n",
    "    num_qubits=L)\n",
    "\n",
    "print(hamiltonian)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the observable we want to estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIIIIIZZIIIIIIIIIIIIIIIIIIIIIIII'],\n",
      "              coeffs=[1.+0.j])\n"
     ]
    }
   ],
   "source": [
    "observable = SparsePauliOp.from_sparse_list(\n",
    "    [(\"ZZ\", (L//2 - 1, L//2), 1.0)], num_qubits=L\n",
    ")\n",
    "print(observable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1b: Multi-Product Formulas\n",
    "\n",
    "MPFs reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.\n",
    "\n",
    "To make this more concrete, we define an MPF as:\n",
    "\n",
    "$$\n",
    "\\mu(t) = \\sum_{j} x_j \\rho^{k_j}_{j}\\left(\\frac{t}{k_j}\\right) + \\text{some remaining Trotter error} \\, ,\n",
    "$$\n",
    "\n",
    "where $x_j$ are our weighting coefficients, $\\rho^{k_j}_j$ is the density matrix corresponding to the pure state obtained by evolving the initial state with the product formula, $S^{k_j}$, involving $k_j$ Trotter steps, and $j$ indexes the number of PFs that make up the MPF.\n",
    "\n",
    "The key here is, that the remaining Trotter error is smaller than the Trotter error that one would obtain by simply using the largest $k_j$ value!\n",
    "\n",
    "You can view the usefulness of this from two perspectives:\n",
    "\n",
    "1. For a fixed budget of Trotter steps that you are able to execute, you can obtain results with a Trotter error that is smaller in total.\n",
    "2. Given some target number of Trotter steps that is too large to execute, you can use the MPF to find a shorter-depth circuit to run which results in a similar Trotter error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing our Trotter steps, $k_j$\n",
    "\n",
    "The choice of $k_j$ is up to the end-user.\n",
    "In principle, any values can be chosen but some $k_j$'s will lead to a larger noise amplification on real devices than other choices.\n",
    "Thus, it is important that one tries to find _\"good\"_ values of $k_j$.\n",
    "\n",
    "Here, we will simply pick some fixed values for $k_j$. The rule of thumb is that $t/k_{\\text{min}} \\lt 1$. In our case $0.4/2 = 0.2$, so we are well-within the MPF technique's limits. If you want to learn more about this and how to choose your other $k_j$ values, refer to the respective guide: [How to choose the Trotter steps for an MPF](https://qiskit.github.io/qiskit-addon-mpf/how_tos/choose_trotter_steps.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_time = 0.4\n",
    "mpf_trotter_steps = [2,3,6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting up the LSE\n",
    "\n",
    "Now that we have chosen our $k_j$s, we must first construct the LSE, $Ax=b$ as explained above.\n",
    "The matrix $A$ depends not only on $k_j$ but also our choice of PF, in particular its _order_.\n",
    "Additionally, one may take into account whether the PF is symmetric or not (see [Carrera Vazquez et al., 2023]), by setting `symmetric=True`.\n",
    "However, this is not required as shown by [Zhuk et al., 2023]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The static coefficients associated with the ansatze are: [ 0.5 -2.   2.5]\n"
     ]
    }
   ],
   "source": [
    "from qiskit_addon_mpf.static import setup_lse\n",
    "\n",
    "lse = setup_lse(mpf_trotter_steps, order=1, symmetric=True)\n",
    "mpf_coeffs = lse.solve()\n",
    "print(f\"The static coefficients associated with the ansatze are: {mpf_coeffs}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1c: Construct each of the Trotter circuits in our MPF decomposition\n",
    "\n",
    "At this point, we have found our expansion coefficients, $x$, and all that is left to do, is to generate the Trotterized quantum circuits.\n",
    "The [qiskit_addon_utils.problem_generators](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.problem_generators.html) module with a handy function do just that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are also going to add an extra circuit with 100 steps\n",
    "trotter_steps_total = mpf_trotter_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "from qiskit.synthesis import LieTrotter\n",
    "from qiskit_addon_utils.problem_generators import generate_time_evolution_circuit\n",
    "\n",
    "# Generate an initial state\n",
    "initial_state = QuantumCircuit(L)\n",
    "for i in range(L):\n",
    "    if i%2:\n",
    "        initial_state.x(i)\n",
    "\n",
    "trotter_circuits = [\n",
    "    initial_state.compose(\n",
    "        generate_time_evolution_circuit(\n",
    "            hamiltonian,\n",
    "            time=total_time,\n",
    "            synthesis=LieTrotter(reps=num_steps),\n",
    "        ) \n",
    "    )\n",
    "    for num_steps in trotter_steps_total]\n",
    "\n",
    "mpf_circuits = trotter_circuits[:3]\n",
    "ref_circuit = trotter_circuits[-1]\n",
    "\n",
    "fig = mpf_circuits[2].draw(\"mpl\", fold = -1, scale = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2a: Optimize the problem with OBP\n",
    "### Create circuit slices to backpropagate\n",
    "\n",
    "Remember, the ``backpropagate`` function will backpropagate entire circuit slices at a time, so the choice of how to slice can have an impact on how well backpropagation performs for a given problem. Here, we will group gates of the same type into slices using the [slice_by_gate_types](https://qiskit.github.io/qiskit-addon-utils/stubs/qiskit_addon_utils.slicing.slice_by_gate_types.html) function.\n",
    "\n",
    "For a more detailed discussion on circuit slicing, check out this [how-to guide](https://qiskit.github.io/qiskit-addon-utils/how_tos/create_circuit_slices.html) of the [qiskit-addon-utils](https://qiskit.github.io/qiskit-addon-utils/index.html) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Separated the circuit into 37 slices.\n"
     ]
    }
   ],
   "source": [
    "from qiskit_addon_utils.slicing import slice_by_gate_types, combine_slices\n",
    "\n",
    "slices = slice_by_gate_types(mpf_circuits[-1])\n",
    "print(f\"Separated the circuit into {len(slices)} slices.\")\n",
    "fig = combine_slices(slices, include_barriers=True).draw(\"mpl\", fold = -1, scale = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constrain how large the operator may grow during backpropagation\n",
    "\n",
    "During backpropagation, the number of terms in the operator will generally approach $4^N$, where $N$ is the number of qubits. The size of the operator can be bounded by specifying the ``operator_budget`` kwarg of the ``backpropagate`` function, which accepts an [OperatorBudget](https://qiskit.github.io/qiskit-addon-obp/stubs/qiskit_addon_obp.utils.simplify.OperatorBudget.html) instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_addon_obp.utils.simplify import OperatorBudget\n",
    "\n",
    "op_budget = OperatorBudget(max_paulis = None,\n",
    "                           max_qwc_groups= None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify your OBP error tolerance to optimize classical and quantum overhead\n",
    "\n",
    "As our operators are back-propagated, some terms in the decomposition will have small coefficients and can be truncated without incurring much error. By allowing some amount of error budget we can reduce both the overhead of performing OBP, while also reducing the size of the observable which we obtain in the end.\n",
    "\n",
    "Relevant parameters in this procedure are\n",
    "- `max_error_total`: The total amount of error you are willing to incur\n",
    "- `num_slices`: The number of slices over which to distribute the error budget\n",
    "- `p_norm`: The $p$-norm in which the error is measured\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_addon_obp.utils.truncating import setup_budget\n",
    "\n",
    "num_obp_slices = 10\n",
    "\n",
    "max_error_total = 0.05\n",
    "error_budget = setup_budget(max_error_total = max_error_total,\n",
    "                            num_slices=num_obp_slices,\n",
    "                            p_norm=2)\n",
    "\n",
    "error_budget.per_slice_budget = [0]*(num_obp_slices-1) + [max_error_total]\n",
    "# print(error_budget.per_slice_budget)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backpropagate slices from the circuit\n",
    "\n",
    "First, we will specify the Pauli-Z observable on qubit 0, and we will backpropagate slices from the time-evolution circuit until the terms in the observable can no longer be combined into 8 or fewer qubit-wise commuting Pauli groups.\n",
    "\n",
    "Below you will see that we backpropagated 7 slices but only used 6 of the 8 allotted Pauli groups. This implies that backpropagating one more slice would cause the number of Pauli groups to exceed 8. We can verify that this is the case by inspecting the returned metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Back-propagated 10 slices with 0.0486 accumulated error.\n",
      "New observable has 15 terms, which can be combined into 9 qwc groups.\n"
     ]
    }
   ],
   "source": [
    "from qiskit_addon_obp import backpropagate\n",
    "\n",
    "# Back-propagate slices onto the observable\n",
    "bp_obs, remaining_slices, metadata = backpropagate(observable, \n",
    "                                                   slices[-num_obp_slices:], \n",
    "                                                   operator_budget=op_budget,\n",
    "                                                   truncation_error_budget = error_budget)\n",
    "\n",
    "# Recombine the slices remaining after back-propagation\n",
    "bp_circuit = combine_slices(slices[:-num_obp_slices] + remaining_slices, include_barriers=False)\n",
    "\n",
    "print(f\"Back-propagated {metadata.num_backpropagated_slices} slices with {metadata.accumulated_error(0):.4f} accumulated error.\")\n",
    "print(f\"New observable has {len(bp_obs.paulis)} terms, which can be combined into {len(bp_obs.group_commuting(qubit_wise=True))} qwc groups.\")\n",
    "#print(f\"Note that back-propagating one more slice would result in {metadata.backpropagation_history[-1].num_paulis[0]} terms \"\n",
    "#      f\"across {metadata.backpropagation_history[-1].num_qwc_groups} groups.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can inspect OBP metadata to get more information about how the computation went\n",
    "This can be useful when determining OBP input parameters, such as the desired error budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from qiskit_addon_obp.utils.visualization import (\n",
    "    plot_accumulated_error,\n",
    "    plot_left_over_error_budget,\n",
    "    plot_num_qwc_groups,\n",
    "    plot_num_truncated_paulis,\n",
    "    plot_slice_errors,\n",
    "    plot_num_paulis,\n",
    "    plot_sum_paulis\n",
    ")\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n",
    "plot_slice_errors(metadata, axes[(0, 0)])\n",
    "plot_accumulated_error(metadata, axes[(1, 0)])\n",
    "plot_num_paulis(metadata, axes[(1, 1)])\n",
    "plot_num_truncated_paulis(metadata, axes[(0, 1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Before and after OBP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tXbIHtLm5mR07dtDd3c2iRYuuuV90cP2pOVXI3n3bsVmthMRPpvzA6UHbp6amArB3n33jsUguNTVV08bj5i54/ixcbDLy0uNbhXNweT6hM6MJnRUzaFvHuPRktPDnUvurnq8vNxGxKU04J6ub9LNwocnIrp+kCudATjcfltrfLfjqHbOJ8hY3jUZ2/eQR4RzI6WZfGRyt9g43Ld32d0HPN/rx8o+/I5wDOd3sL4PD1fDQHYlM9gI3L5yFggY/Xv7Rt4RzoM2NY2yAS9389RJ8UgWbPj+L6I2e7aa12/43Lb/Bj1f+89+EcyC7m5ma3GjFJQvQbdu2kZGRQVJSEjExzn+gWijKODKiucEIMsL2JEgbuDyrU+rzS8l88i2XZ0RYFW3/0otsbrZ5kZuV0fYvvSg3rnOzYor9Sy8yuRnn511u7phi/9KLbG4em+s9bj4/2f6lF5ncjFVudOGyq+DXrFnjqq77SHxoNQajH5a8YqxdPfgaDVi7eqg4NPBvgSPTeLGC9up6AsKC6bA0UX2ywOXjHU0oN/Ki3MiLciMvyo28KDeeiWcfw2SzERAeTEBoEJVHzxGxKJFKZ+WnejOGSzVUZxYSt/ZmSl10ZMFghJli+84nE2mrN+M2lBunGbeh3DjNuA3lxmnGbYwSN472gHIzAni9m0Hw6AVoR10TFYdyiF5uJiS+kfbaRkLiJ9N6aeCzEByZgPDxYLPRWmmB4b8OyykGf6PmfRl6Mu5CuZEX5UZelBt5GW1uAOVmBPB2N4Ph0QtQx5Vf9fmlmjMOqpy9SlLoQrmRF+VGXpQbeVFu5EW58UzUQfQKhUKhUCgUihHFre+Adnb1kJU3eFmHebMm4G80jNCIhsaePXtobW1l2bJltLW1UVZWhslk4vDhw6SkpPDWW29hMBhYtWoVn56/wPTAcOG+x8+YQmOh+DmjYN/7YfA3ap2GV6LcyItyIy/KjbwoN/LiSjeOPZ2WXPGz1GV149YFaFZeHYs2/GnQNkffuJvkOdev/RqzMpnAiSHUZBYQZorF1mPl4nufYLNaB+xPT0aUTZs29d0uLy/HbDYDkJCQAMDGjRv77q/KKmTvPduF+1763Lc4+Ei6pvGs/eAJt+0TUW4GR7m5jHJzGeVmcJSbyyg3lxlNbhwXH+1drS0j455Rj94DWvLhsb7boq8G9GT0EBUVNej9EROvv6j2FpQbeVFu5EW5kRflRl6UG89E7QEdAu29VUMu6qxnLRufVNoP0m3odPdIhk57j70yhd5a47LhcFOv3EjH4Sq7G0u7u0cydDp67NV2vMXNkWq7mzovcfPiWbjgJW6O9rqp9QI3nT3w4jnvcXN8hNy4ZAG6f/9+0tLSWLFiBW+//bZw7v7V07lveRwTwwIHbTdp8WymrVuiaUx6Ms546jS8XgjHaqGgUV8fU+9cTOLGVS7POKO+A36WBW9cgJ8M4Sg0Wdw8fRr2FMDxWjhXr68PWdw0dMLPvchNWo7dzbEayK3X14csbho7+z1vTunvRyY3r/W6yRl8e/6AyOKmuQt+dsp73Dx7Bl4tsJdLzvYCNz895T1/0549A6/m291k1ujrQxY3rd3weO/ftB+7+FhUH5tt+A++Sk9PZ/PmzTz55JNERkby0EMPXXH/zp07KSoqoqIhkDePxfd9f/KksWx9cC5bn75cKmt98nkiQ9oZU9tBwr7Kvu9PW7fkmmMU+lOwIgJAc6YtPEB4ngUJ91I+ZQm+3R3MP/Es49qqhLOO+cTddQsdDS3UZl+gs755wPbFiycw9Yj9r45oRst8uvzGcnJhKh1jwgmvzuLGM7/RPBcHUriZ8QXKp9yKT08XCzJ3Mq6lQjgrm5tuv0BOLNza6yabG8+8rnkuDmRwUzjjHi5NWYJPTzc3Ze4kuKVcOCudG0MgJ8wON6e58cwezXNxIIWb+H/hUvRSfK1dzDv5a4JbLgln5XMTwEnzVtrHTGRCTQ5zcl7TPBcHMrg5H7+OsujP2N1kphPcLH7kkGxuegz+nFi4lfaxNzCh5gxzcl7VPBcHUriZfjdlMbfha+0m6VQ645tKhLNa3fSfj0vc+PpzwqzPDUBsbCxbtmwRauuSPaDNzc3s2LGD7u5uwsLCrrnfMbhjOdW82e8ipJW3TuGDgyXERI6jpKIFgNTUVJLn3EDNqUL27rNvug2dGU3orMFrzKempgJozmjZqNtlhT8UwoyQAJZ+QXxDMNA3H5vVSkj8ZMoPnB60/Yb16zl4xL4pXDSjdT5ZdfBsDqTfOY9xfmnCOWndnIf44AA+84XvCedATjfZdfZ3p55fnUSQ0bPddFvh9+chPtifz3zhMeEcyOnmdB08kwPPr57rFW7+cB7iggP47D2PCudATjc5dfB0Djy/eo7XuIkNCmDZPd8VzoGcbs5Y4MnT8PzqGwn2AjdvnIepQf4suydVOAfa3fSfj6vc5Fpghw43WnHJAnTbtm1kZGSQlJRETMzgovuz549iNVjr80vJfPItTWPSk3GG0Rc2zhxaH0UZR5w3GoaMCPMmQNIEGDeE3wqp3CQMrQ+Z3CT1ugkawkkasrjx8zI3c73MzVe8yM0c5eYKZHJzY5j935xgL3HzoBe5MQ2DGxFcdhX8mjVrXNW115L40GoMRj8secVYu3rwNRqwdvVQcSjHaabxYgXt1fUEhAXTYWmi+qTYYl4hhnIjL8qNvCg38qLcyMtocePWY5jmzZrA0TfudtoG7AepOs6/EsFxWKuejNuw2QgIDyYgNIjKo+eIWJRIpbPyYL0Zw6UaqjMLiVt7M6UfuXjn8FUoN4NnlBsXo9w4zbgN5cZpxm0oN04zbsND3WjFrQtQf6NhwEPmr8bgb9R1kKqMh68OREddExWHcohebiYkvpH22kZC4ifTeqnWaSYgfDzYbLRWWmD4rysbFOVm8Ixy41qUG3lRbuRFuZEXT3WjFY8+iN7bcFyVV58vfnXj1VfyVTl7laTQhXIjL8qNvCg38qLcyMtocaMWoIrrMlAt29zcXEwmE3/961/p7u7m/vvv5+2332b+/PksXrzY3cMeFSg38qLcyItyIy/Kjby40o3HLEB7Ors0lcty7OHQmjH4u/iyLw/BWS1bx38Bvv7QVzn38XFqThUK9a3cDA3lRl6UG3lRbuRFuZEXLW4efvhhKirEz9z2mAWoJbeIvavFz9p0bDjWmnHXPpGYlckETgyh8cIlxk4Kwz9kHPUFpfjgQ8U/zwxbRg/Oatlacos49sAzwv0pN8rNcKHcOM8oN9ei3Cg3zjLKzbU4cwMQGRkp3J/HLEC9nZIPj41IRqEd5UZelBt5UW7kRbmRl9HkxiW14EcLlnZ7rdTX8909kuHhnYtwqBKKB67o5TFYOuxuXvMSN+8WwSeVUKTcSMcfe91cbHL3SIZOfYe9NvfufOkvoBXivV43FxrdPZKh09gJj2fCK3ne4eb9Yu9x09TlnW4KXezGJQvQ/fv3k5aWxoYNG/jd7343rH1PvXMxiRtXuTwjwtNnYG8JvJJvL/mmh0mLZzNt3RKXZ5xR0w4vnYOiFnhq4LNuB0UmN2k5dje78+0lRvUgi5vadnjxLFxsgacGr7g2IDK5efbMZTcna/T1IYsbS4f9eeMtbp7LhfdL7C8OTgx84sugyOKmvhNe7HXztDf8TTsDfyqB1wrgaLW+PmRx09gJLzj+pnmBm2dzLrv5p4e7ae7q97zR+TdNFB+bbfjX6+np6WzevJmXXnqJuLg41q5de8X9O3fupKhIfEMwwJjaDhL2VRJ31y10NLRQm32BzvqB3w4qWBEBoDnTFh4gPKYL09ZQGvNZDNZOFhx7mjEd9ZrnAzBt3ZJrjlC4muLFE5h65PJKSiSjZT7dhgAyFz5C25hJTKo8RuI58VJlcrq5k9KYZRh6ulhw4hnGtIuvQmVz02Pw5+SCVNrGTmJS5XESz70plAM53Xw6bTUlMbdj6Oli/ok0xraLr3Skc+Prz8mFdjc3VJ5g9rk3hHIgqZu4VZTGfA4faxcLTjzL2HbxVwjyuTGSuTCV1rER3FB5ktnn/iCUAzndXIxbQUnMHXY3mTsZ21qleT4gkZsFj9A6LpIbqjKZffb3QjmQ1E3sckqm3oGPrYebTu4kqLVS83xA/OcMaM6Izsfq68fJBam0jotkYtUpTGe1vYkYGxvLli1bhNq6ZA9oc3MzO3bsIDo6mosXL15zv+jg+lNzqpC9+7Zjs1oJiZ9M+YHBl+apqakAmjNaNh7bbHCwCvaX+fGTL/yncA4uzyd0ZjShs2Kctt+wfj0Hj6QDCGe0zqeiFc7Ww2ciF+Hnu0g4J6ubQ1XwlzI/fnLvfwjnQF43Z+rhs5HJ+PkmC+fkd/MD4RzI6aayDXIs8NlIM36+ZuGcrG4+qYJ9ZX785N5/F86BnG6q2uC0BT4buRA/34XCOZndfFjmx+P3il9QA3K7uS1yAUbfBcI5Wd38sxr+XGLk8S9sE86Bdjf95+MqN9VtkG2B2yLnY/SdL5zTiksWoNu2bSMjI4OkpCRiYpz/cLRQlHFkRDIi+PjAZyLgmM633MF+0Gzmk+LvNurNiBA51v6lF9ncLI3Q/1EVKDfKjRgRY+xfepHNzZIIOOIlbiaNgc8rN30oN65zc+skOCz+pvQ1yOTmhiG6EcVlV8GvWbPGVV33kfjQagxGPyx5xVi7evA1GrB29VBxaOBNJY5M48UK2qvrCQgLpsPSRPXJApePdzSh3MiLciMvyo28KDfyotx4Jp59DJPNRkB4MAGhQVQePUfEokQqnZWf6s0YLtVQnVlI3NqbKf0oc2TG248wU2zf+WQijJ8xhbUCb7Vf/RhuQ7lx+hhuQ7lx+hhuQ7lx+hhuQ7lx+hhuYxS5cfyc9WRkw6MXoB11TVQcyiF6uZmQ+EbaaxsJiZ9M66WBL2pwZALCx4PNRmulxS3nJhj8jZoPunXXwbh6UG7kRbmRF+VGXpQbeRltbsCz/AyERy9AHVd+1eeXas44qHL2KkmhC+VGXpQbeVFu5EW5kRflxjPx6AWobOzZs4fW1laWLVtGW1sbZWVlmEwmDh8+TEpKChkZGbS3t7N27VoKCwtZuFD8qkzF0FBu5EW5kRflRl6UG3lRbsRw6wK0s6uHrLzBz2ecN2sC/kbDCI1oaGzatKnvdnl5OWaz/UiWhIQEAO69996++yNvmETNqULhvsfPmEJjYZmm8YSZYjH4GzVlvBXlRl5c6cax98mSK37usHJzGeVGXpQbeVFuxHDrAjQrr45FG/40aJujb9xN8pwbrvl+zMpkAieGUJNZwNjICQTHRtBcXEVrlYW6059ety89Gb1ERUUNen9AXQd7V4uf5bb0uW9x8JF0TWNY+8ETbtknotw4R7m5PsPtxrFRX2tGubkW5Ua5cZZRbq5lNLtxhsd+BF/y4bG+26KvBPRkFNpRbuRFuZEX5UZelBt5UW48F49dgMrAxSZ47oy9EorVBr4+7h7R0HjxLJxtgE0zYP5Ed49maBQ32+vzVniJm13n7JWQNs6ABV7gJs0L3Tw4A8we7qakGZ45Y6+85Q1uXs6zV9t5MB6Sr/0gzaMo661pX94KPTYweLib3XmQZYEvT4fFk9w9mqFxqbem/SUvcfNqPmTVwYbpcLML3fi6otP9+/eTlpbG1KlTOXPmjHDu/tXTuW95HBPDAgdsM2nxbKatW6JpPHoyIryUZy/FWdgEp8RLjV/B1DsXk7hxlcszzqhsg7cv2ku97dZ5Dq9Mbl48Bwd63RwXL2d9BbK4qW6H//60102+vj5kcvPSucvPG73VkGRxU9MO/3PR7uZVL3CzKw8OVtrd6K3qIoubug74n0/hn17i5qVzcKDXzSfipcavQBY39R32v2n/rILXvOXfm0o43wQHKvT1IYubhs7L/968pvN5I4qPzTb8B1+lp6ezefNmdu3ahdlsZvHixVfcv3PnToqKiqhoCOTNY/F93588aSxbH5zL1qcvl8tan3yeyJB2xtR2kLDP/qybtm7JNUcoXE3BiggAzZm28ADheRbH3E7p1M/jY+th4bGn8O9qFs465hN31y10NLRQm32BzvqB88WLJzD1iH2VK5rRMp8eXyOn5n+b1rERRF36hBnn39M8F5DHTUn0MkpiP4+PzcrC40/j39kknJXNjdXXj1M3fZuWcZFElf+TGYV/1DwXkMdN6ZTbKI5bDjYrC44/Q2Bno3BWq5v+83GJGx8Dp+Z/m5ZxUUSW/5MEL3BTFLcCH1sPC46nEdjZIJyV2k3FERIK3tE8F5DHTdmUpVyMW4mPzcaCE2kEdtQLZ+V08/9oGTeZyIqjJBT8r+a5gERuJi+xuwHmn3iWMR0W4ax8bnzJuunbNAdNJqLyGDPz3xaeC0BsbCxbtmwRauuSj+Cbm5vZsWMH3d3dFBUVXbMAdQzuWE41b/a7CGnlrVP44GAJMZHjKKloASA1NZXkOTdQc6qQvfu2EzozmlCBCg2pqakAmjNaN+qWtkCIPwTf97imnGM+NquVkPjJlB84PWj7DevXc/CI/UIX0YzW+bR1w45s+NG/fBYfn88K52R1U9YC4/0h+L6faMrJ7eY2fHxuE87J7CbYCOPv+7GmnFY3/eej3Ihx2c2PNOVkdNPeDU9kw4/+ZSk+PkuFc7K6udQK4/wg5L7/1JST280SfHzE35WU380PNeXkdnMrPj63Cue04pIF6LZt28jIyCApKYmYGPFyXnv+6Py9+Pr8UjKffEvTePRkRIkeN7R8UcYR542GISPCGD/7gs1H5/4V2dxMUW76UG6UG1G8yU2gl7mZPHZoeeVGuRFhqG5EcdlFSGvWrHFV115L4kOrMRj9sOQVY+3qwddowNrVQ8WhHKeZxosVtFfXExAWTIelieqTOjfWKK6LciMvyo28KDfyotzIy2hx49ar4OfNmsDRN+522gbsB6k6zr8SwXFYq56M27DZCAgPJiA0iMqj54hYlEils/JgvRnDpRqqMwuJW3szpR9ljsx4e1FuBs8oNy5GuXGacRvKjdOM21BunGbchoe60YpbF6D+RsN1D5m/HgZ/o66DVGU8fHUgOuqaqDiUQ/RyMyHxjbTXNhISP5nWS7VOMwHh48Fmo7XSAsN/XdmgKDeDZ5Qb16LcyItyIy/Kjbx4qhutqHNAJcJxZV59fqnmjIMqZ6+SFLpQbuRFuZEX5UZelBt5GS1uPGYB2tPZpbn2KXhHvVR3sGfPHlpbW1m2bBltbW2UlZVhMpk4fPgwKSkpvPPOO5SVlZGSkkJVRSXTA8OF+1ZuhoZyIy/KjbwoN/Ki3MiLFje1tbUsXLhQuG+PWYBacotGbb1Ud7Bp06a+2+Xl5ZjNZgASEhIA2LBhQ9/9LYXl7L1HuRkplBt5UW7kRbmRF+VGXrS4CQwcuIjQ9fCYBai3E7MymcCJIdRkFhA6K4YxN4RSe/oCPj4+VHxy/WpSejJ6iIqKGvT+iIkeXuPOCcqNvCg38qLcyItyIy+e7CYyMlJTf2oBKgklHx7ruy36UYGejEI7yo28KDfyotzIi3IjL6PJjUtqwY8WTtbAQwfhe8egy+ru0QwNmw1+mgkflsFfytw9mqFzqg6+esB73PzslN3NPvE96dKS1evmsaPQ2ePu0QyN/m7+7AVuTve6edRL3Pwiy+7mAy9wc8YCXz0IW49Chxe4+WWvm/8rcfdohk6uw80R73DzRK+bDBe7cckCdP/+/aSlpTF37lwOHz48rH1PvXMxiRtXuTwjwn99Ctl18FG5/R9VPUxaPJtp68TLkOnNOKOizT6P2g74Y7G+PmRy89YFyLLY53S8Rl8fsripbIe/XrK7edeL3PytwvPdVLf3e97ofONBKjef2t38vQKOVOvrQxY3tR2Xnzfe4OaNC/Z/Z/5RAf+s0teHLG4snfBXL/r3ps9NJRyo0NeHLG4a+rtx8ZupPjbb8B8UlZ6ezubNm3nqqadYsmQJt912ZX3knTt3UlSkbWZjajtI2FdJ3F230NHQQm32BTrrmwdsX7AiAkBzpi08QHhM5ZGLKI5djs3HF/Pxp/Drbtc8H4Bp65Zcc4TC1RQvnsDUI5dXuSIZLfOx+vhyOmkzzUGTiS75O7HFfxXKgZxuKiLNFMWuxObjy8LjT2PsbtM8H5DNzRSiS/9ObNF+oRxI6ibCTFGcw80zGLtbNc8HxH/OgOaM6HxsPr5kJ32D5qBookv/QWzRX4RyIKebykkLuDhttde4OT33GzQFRzOl9ABxRR8K5UBON1WT5vPptNXYfPxYcOIZ/LtaNM8HJHGDD6eTvkFTcAxTyg4Sd/HPQjmQ1M0NN/Hp9Dux+fgx/8QzBHi8m6/TFDyVyWWHmHbxA6Gcg9jYWLZs2SLU1iV7QJubm9mxYwdz5sy57kJTdHD9qTlVyN5927FZrYTET6b8wOlB26empgJozmi98q2zB36dC1u/+AtNOcd8QmdGEzorxmn7DevXc/BIOoBwRut8bDbotEKAYQ0gXkpVfjc/15ST282dwJ3COfnd/ExTTqub/vNxvZvVwGrhnHIzkm5WAiuFc/K7+ammnNxuVgArhHPKzUi6WQ4sF85pxSUL0G3btpGRkUFSUhIxMc5/OFooyjgyIhlR/A3g46M/X59fSuaTb7k8I4KPDwQY9OeVG+VGFOXmMsqNciOKcnMZ5UZeN6K47Cr4NWvE30HTS+JDqzEY/bDkFWPt6sHXaMDa1UPFoRynmcaLFbRX1xMQFkyHpYnqkwUuH+9oQrmRF+VGXpQbeVFu5EW58Uw8+xgmm42A8GACQoOoPHqOiEWJVDorP9WbMVyqoTqzkLi1N1P6UebIjLcfYabYvgNyRRg/YwprBd5qv/ox3IZy4/Qx3MYocuP4OevJuAXlRijjFpQboYxbUG6EMrLh0QvQjromKg7lEL3cTEh8I+21jYTET6b1Uq3TTED4eLDZaK202Dc8jDAGf6PmPSaeVJlBuZGX0eYGPMePciMvyo28KDeeiUcvQB1XftXnix/ydvXVYlXOXiUpdKHcyItyIy/KjbwoN/Ki3Hgmbl2Adnb1kJU3+AGa82ZNwN84Arthh4E9e/bQ2trKsmXLaGtro6ysDJPJxOHDh0lJSeH999+noKCAlJQULLV1TA8MF+57/IwpNBZqOyE+zBSLwd+odRpeiXIjL6504/joSUt1EOXmMsqNvCg38qLciOHWBWhWXh2LNvxp0DZH37ib5DmeUft106ZNfbfLy8sxm80AJCQkAJCSktJ3/5mif7L3nu3CfS997lscfCRd03jWfvCEV7xNPxwoN/LiSjeOfVJ7V2vLKDd2lBt5UW7kRbkRw2M/go9ZmUzgxBBqMgsInjqJoKkRXPo4Gx9fnwFfGejJ6CUqKmrQ+yMmesaiWg/KjbwoN/Ki3MiLciMvyo3n4rEL0JIPj/XdFv2F0ZNRaEe5kRflRl6UG3lRbuRFufFcXFoL/s477+SZZ54Rzt2/ejr3LY9jYljggG1kqZcK9jrDKR/BX8qgtVtfH7LUs+22wiNH4At/hf+6oK8P5cY1bnps8N1eN296gZu/93PT4kVu3jivrw+Z3Pyj4rKb5i59fcjixmqDrUftbn5fqK8Pmdwc6HWzz0vcPNrr5rc6j92Uyc2hfm6avMDNY8fsbn7j4iNRXVoL/sUXXyQuLo677rrrivsdteArGgJ581h83/cnTxrL1gfnsvXpy9UK1iefJzKkXbp6qQC5pgepmXQTAHNPvUBYvfhfOa31bPvXG3dFPdv2wDBOLNxKj3EsoXV5JGXv0jwXkMnNA9RMmg/AnKxdTLDkCWelcxMQyknzo3QbxxJSl8+87Jc0zwXkcXN29gNUR9jd3Jj9CuF1Z4WzstWB7vAP4UTyo3QbxxFiyWdelqe7+TLVEQsAMJ3ezcTaXOGsfG6COWF+jG7/IEIsBczLelHzXEAmN1+iOmIhAKac15hYM/Ah61cjm5tOYxDHk79Ht38Q4+sLuenUC5rnAhK5SdxAdaR9n2fimd8wqTpLOCufm3GcSP4eXf7BjK8/z02ntF3fIE0t+O7ubjZs2HDN/Y7BHcup5s1+FyGtvHUKHxwsISZyHCUVLYC9hmnynBukrJd6oAJeOAcTAuCZH/wbgRou1tdaz7Z/vXFX1LPtscH3j8OJGvjW7bO4Z2Oa2ESQs5btoQp4/hyE+UPa9zcTqOE3XTY3Vhv8+3E4VgPfWjaTL3i4m08q4fmzEOoPz2z/OmNc6MbVdaCtNvj3E3CsGr5520zu+4pnu/lnlb2edYg/pG37mse7+cEJOFoND9+WQIqHuzlSBTt73TzzvYcY68FubDb4j5NwuAo2L53BFx/0bDfHquG5MxBshGce+wrjPNzND0/a/xZ8fWk892twoxWX14IPDxc/XmDPH52/3ytTvdTPRNq/hoIs9WwNPvBkMqTlwD06iybI5GZJpP1rKMjixtcHnuh184U4fX3I5ObWCPvXUJDKjdnu5r5p+vqQyc0tk+xfQ0EmN7/sdZPiBW4WT4I3vMSNjw/8fKHdzRen6+tDJjfJN8Aflg2tD5nc/KzXzf063Yji0bXgFQqFQqFQKBSeh8deBe+NJD60GoPRD0teMdauHnyNBqxdPVQcGnivjyPTeLGC9up6AsKC6bA0UX3SxbuHRxnKjbwoN/Ki3MiLciMvo8WNWxeg82ZN4OgbdzttA/aT/B0HsIrgqBagJ+M2bDYCwoMJCA2i8ug5IhYlUumsPFhvxnCphurMQuLW3kzpR5kjM95elJvBM8qNi1FunGbchnLjNOM2lBunGbfhoW604tYFqL/RIFzlyOBv1HWSv4yn/w9ER10TFYdyiF5uJiS+kfbaRkLiJ9N6qdZpJiB8PNhstFZa7LuIRxDlZvCMcuNalBt5UW7kRbmRF091oxX1EbxEOI6GqM8v1ZxxUOXsVZJCF8qNvCg38qLcyItyIy+jxY3HLEB7Ors0VSxwvIWuNWPwN2oemzeyZ88eWltbWbZsGW1tbZSVlWEymTh8+DApKSm8//77tLS08PnPf57ysktMDxQ/7UC5GRrKjbwoN/Ki3MiLciMvWtxUVVWxcOFC4b49ZgFqyS1i7+rtwu0d+z20ZjzpbXpXsmnTpr7b5eXlmM32Q3YTEhIASElJ6bu/Ma+UvfcoNyOFciMvyo28KDfyotzIixY3RqO2RbvHLEC9nZiVyQRODKEms4DgqZMIM8VRfiiHzvpm6vNKhi2jh6ioqEHvj5goto/XU1Fu5EW5kRflRl6UG3nxZDeRkdoO31YL0CHS1AUBvuCvoQrS9Sj58FjfbUtuEcV/PjZIa/2Zwei2QkfPkLqQCuVGXpQbeVFu5MXb3LR7kZvmLjD6QoAXuOmxjYwbX1d0un//ftLS0pg9ezbvvvvusPY99c7FJG5c5fKMCP/zKXzxI/jKAWjo1NfHpMWzmbZuicszzujsgW8cgoxSeFG8NPcVyOTm7V43D34M9R36+pDFTZcVNn9id5PuBW7eLbrsxuLhbrqt8HCvm+fFy6ZfgUxu/lgE//o3eOBjqG3X14dMbr75CewtgV95gZv3et18+WOo8QI3//ZPyCiBnWf09SGTm/eL4Yu9z5vqNn19yOKmxwb/9ondzXM63YjiY7MN/3X66enpbN68mV/84hdMnz6dL3/5y1fcv3PnToqKxDcEA4yp7SBhXyVxd91CR0MLtdkX6KxvHrB9wQp7rT+tmbbwAOEx5ZoepGbSTWCzkXTqBUIbzmueD8C0dUuuuYLtaooXT2Dqkbq+/xfJaJlPe0AYJ83fpds4jtC6PJKydwnlQFY3D1AzaT7YbMzNfokwi/hhvPK5CeWkeWuvm3ySsl8SyoGkbmY/QE2E3c2c7JeZYMnTPB8Q/zkDmjOi8+nwH88J86N0+wcRYslnXpZnuzk7+0tURywEm40bT+8mvE78FY9sbjr9gzlufpRu/2BCLAXMy3pRKAeSukn8EtWRdjemnNeYWCu+OpDOjTGIE8mP0eUfTIilkHlZLwjlQFY3G6iONIPNxuwzr3NDzeC12a83H5DDTZdxHMfNj9EVMJ6Q+vPMO5UulHMQGxvLli1bhNq65CP45uZmduzYwaxZs657v+jg+lNzqpC9+7Zjs1oJiZ9M+YHBBaempgJozmjZeHzGYn9lXdPhw8s//BZ+Gt5PdswndGY0obNinLbfsH49B4/YfxFEM1rmY7PBU6ehsBEeWDSLz2xME8qBnG7OWmBnLlR3+PDKD77p8W6ezoH8Bnhg0Uxu83A35+rhuVyobvdh9w82u9RN//m4ys0zZyCvHr6UPJNlX/FsN3kN8OwZqG7z4dV//7pHuwF4Jsf++7benMDnPNxNQYN9PtXtPrz2/a96vJtncyC3Hu43z+DzHu6msBGePg1V7T68tn0TRg9389wZOFMP/2qO544Hxd1oxSUL0G3btpGRkUFSUhIxMc5/OFooyjgyIhkRbgyDXUsgLQdNfwz6U59fSuaTb7k84wwfH/he0tD6kMnN7DB4yYvcPDZ3aH3I5CYxFF661XvcPDpnaH3I5GZWiPe4AdjqRW4SQrznbxpAqhe5mTH+shsti8/+yOTmkRuHvcvr4rKLkNasWeOqrvsYLfVSPRHlRl6UG3lRbuRFuZEX5cYzcclFSCNGv3qpjZ+W9/1XJGPw96M6sxC/sQFUZxaOzHhHE8qNvCg38qLcyItyIy/KjUfi0ccweXK91DBTbN8BuSKMnzGFtQJ7Pa5+DHeh3Dh/DHcxmtw4fs56Mu5AuRHLuAPlRizjDpQbsYxsePQC1JPrpRr8jZorLXhSZQblRl5GmxvwHD/KjbwoN/Ki3Hgmnv0RvEKhUCgUCoXC43DrO6CdXT1k5dUN2mberAn4G4dYWmCE2LNnD62trSxbtoy2tjbKysowmUwcPnyYlJQU3n33Xfz8/EhOTqaqopLpgeFC/YaZYjH4G+np7MKSK3Z+qiOjsKPcyItyIy/Kjby4yg1c/shWudGHciOGWxegWXl1LNrwp0HbHH3jbpLnXFv79erap4YxAXRYmuhsaKUm8/pXsenJaGHTpk19t8vLyzGbzQAkJCQAsH79+r77WwrL2XvPdqF+137wBBNvmoElt4i9q7Vl3IFyI5ZxB8qNWMYdKDdiGXcwmtzA5f2Fyo0+lBsxPHYP6NW1T12V0UtUVNSg90dMvHZR7S0oN/Ki3MiLciMvyo28KDeei9oDOkQKGqC9x92jGB6auuxVQ9xwIaBLKGiAtm53j2J4aOqyV3fyFjeFjd7nxqrcSEezl7k5r9xIi7e5yR0BNy5ZgO7fv5+0tDQWLVrEb3/7W+Hc/aunc9/yOCaGBQ7YZtLi2Uxbt0TTePRkRHglDzYegA/LoLpdXx9T71xM4sZVLs84o70bvn7QPp8d2fr6kMnNq/n2uey7BJWt+vqQxk1Pr5uD8IQXuHktHzZ+bHdT7gVuNh+yu/lllr4+ZHLzmwLY1Ps37VKLvj5kcdPZA9/odfNzL3Dz28LL/96UeIEbx/PmZ6f09SGTm9/3c1M8cIn5QZHFTZcVNn9i/zvw+Klh7foafGy24X9PJT09nc2bN/PLX/4SHx8f/uM//uOK+3fu3ElRUREVDYG8eSy+7/uTJ41l64Nz2fr05XJZ65PPExnSzpjaDhL2VQIwbd2Sa45QuJqCFREAmjNt4QHC8zw7+8tURywAaw/zTqUT0nhROOuYT9xdt9DR0EJt9gU666//m+sYl56MKB3+IZwwb6XbP4iw2nPMPf2y5rmAnG6Ssl4ktOGCcFY+N8GcND9Kl38woXXnSMr2dDdfojpiIVh7mJv9MmH14nuuZHPTaQziRPJjvW7ySMrepXkuIJGbxA1UR5rB2sOc068wwZIvnJXPzThOmB+jK2A8IXX5zMt+SfNcQB435xLXUxWZDDYrN57eTXid+LFBWn7OjrEBLnPT5TeW48mP0RUQQoilgHlZL2qeC8jjJm/W/VRGLQKbFVPOa0yszRXOyudmDCeSv0enDjcAsbGxbNmyRaitS/aANjc3s2PHDvz9/Zk2bdo19zsGdyynmjf7XYS08tYpfHCwhJjIcZRU2F/ipaamkjznBmpOFbJ333ZCZ0YTKnDod2pqKoDmjJbNumUt8FwuRI818J1138HHRzjaNx+b1UpI/GTKD5x2Oi49GS3sKYD3iuAXn0nEtClN81xkcnOp183ksQa2rPt/Hu/m9QJ4t9fNjRs9201FGzybA5FjDDyy7pse7+a3BfC/RfCzpbOY6yVuIsYYSF33sMe7+V0hvH0RfrZ0pse7qWqDZ3IgYowvj9z9DXxd5MYxNsClbn5fCP9zER5fmsC8r3i+m7QcmBjoy3fv/prHu/lDr5sfLUlgvgY3WnHJAnTbtm1kZGSQlJRETIx4hZg9f3T+Tkh9fimZT76laTx6MiJMGQdPJQ+tj6KMI84bDUNGhE0JYOkAU5i+vExuJo+DJ73IzcYEqOuAG73ATeQY2OFFbh5MgJoOmDtBX165cZ2bB2bYt0d5g5tJXubmyzOgqh3meYmbJ7zIzZdmQGU7zBc/HUoXLrsKfs2aNa7q2mtJfGg1BqMflrxirF09+BoNWLsGv8LJkWm8WEF7dT0BYcF0WJpGaMSjB+VGXpQbeVFu5GUgNxWHcpxmlBvXMlrcuPUYpnmzJnD0jbudtoFRUi/VZiMgPJiA0CAqj54jYlEilc7Kg/VmDJdqqM4sJG7tzZR+lDky4+1FuRk8o9y4GOXGacZtKDdOM25DuXGacRse6kYrbl2A+hsN1z1k/nqMhnqpHXVNVBzKIXq5mZD4RtprGwmJnyyUCQgfDzYbrZUjf1aPcjN4RrlxLcqNvCg38jKQm9ZLtU4zyo1r8VQ3WvHYg+i9EceVefX5pZozDqqcvUpS6EK5kRflRl6UG3lRbuRltLhRB9ErFAqFQqFQKEYUj3kHtKezS1PJLMceDq0Zg79R89i8kT179tDa2sqyZctoa2ujrKwMk8nE4cOHSUlJ4d1336W7u5tVq1bx6fkLTA8Uv1xOuRkayo28KDfyotzIi3IjL1rcFBcXs3DhQuG+PWYBasktYu/q7cLtHRuOtWbcuU8kZmUygRNDqMksIHjqJAxjArj43iea29us1iGPZdOmTX23y8vLMZvNACQkJACwfv36vvursgrZe49yI9JeuRl+lJvBM8qNHeXmSrT+rJWbkcNT3fT0aKtL7jEL0NFAyYfH+m6LvFrT2l4vUVFRg94fMVHsQjJPRrmRF+VGXpQbeVFu5MVT3URGRmrqT+0BHQI9Nvi/Ejhb7+6RDA9FTXC+0V6n19Ox9rrJrXf3SIaHoiYo9CI3fy6FMxZ3j2R4KG62u+nwIjc5yo10ONycrnP3SIaHkl437V7gxuaFbgpGwM2wLkD3799PWloaJpOJnJwc3n//fd55553hfAim3rmYxI2rXJ4RYecZ+HEmPHIESlv09TFp8WymrVvi8owzmrtgyxE4UgOPn9LXh6xuigcuqzsoMrl55CgcrbHPSQ8yufl1LvznSUg9Ap/qPCdZFjet3fbfMW9x83wu/OgkfPcIXGjU14csbtq67b9jR2vgR17g5oWzvW6O2hduepDFTXu/581/ntTXh0xuXjxnd5N6FPIb9PUhjZse+zyODcGNKD422/AdFJWens7mzZvZvXs3S5cupbi4GIA777zzinY7d+6kqEjb28RjajtI2FdJ3F230NHQQm32BTrrB15ZFKyIANCcaQsPEB7TucT1VEUm49PTxU2Zvya4WfzIBMd8AKatW3LNEQrXG5eejCidxiBOmLfSFRBCWG0uc0/vFs7K6CZv1v1URi3Cp6eLeafSGd9UrHk+IIubcZw0P0pnQAhhtWeZe/oV4azcbrpJOpVOSJP43wLZ3HT5jeWE+VE6A0O9xM2/Uhm1GHq6mZf9IiENn2qeD8jiZkyvmzBC686RlP2ycFZKNzO/SOXkm8HaTVLWLkIbzmueDzj/OTvGBrjMTbdfICfMj9ERGEZoXR5J2buEszK6yZ+ZQsXkW8DazdzsVwird15W/Or5gCRuDAEcT/5e7/Mmn6Tsl4SzALGxsWzZskWo7bDuAW1ubmbHjh2YzWby8/MxGo10dnZe0050cP2pOVXI3n3bsVmthMRPpvzA6UHbp6amAmjOaNl43NRlf8fgYpORXY9/VzgHl+cTOjOa0FkxQuPSk9HC/5XCiRr42h0mojalCedkdNPcBc+fhQuNRl7+ySPCOZDTzZ9L7a9Iv3rHbCZ7jRs/Xv6Jtr8FUrv5vPe4Od/oxys/+rZwDuR0s68MjlTDps8nEr3Rs920dNv/vSls9OOVH31LOAfa3DjGBrjUzV/K4HA1bPzcLGK8yc1/flM4B3K62V8G/6yGjZ+bqcmNVoZ1Abpt2zYyMjIwmUzExDj/QeqhKOPIiGRECDbC9+dB2sDlWZ1Sn19K5pNvuTwjwp3R9i+9yOQmyAjbk7zHzapo+5delBvlRgRvc7Niiv1LLzK5GecH27zIzfIp9i+9KDeuc3PHFPuXqxn2q+DXrFkz3F0OSOJDqzEY/bDkFWPt6sHXaMDa1UPFoYF/CxyZxosVtFfXExAWTIelieqT4m+ZK5yj3MiLciMvyo28KDfyotx4Jp59DJPNRkB4MAGhQVQePUfEokQqnZWf6s0YLtVQnVlI3NqbKf1I5w71IRBmiu07n0ykrd6M21BunGbchnLjNOM2lBunGbcxStw42gPKzQjg9W4GwaMXoB11TVQcyiF6uZmQ+EbaaxsJiZ9M66Vap5mA8PFgs9FaabGfoTDCGPyNmvdl6Mm4C+VGXpQbeVFu5GW0uQGUmxHA290MhkcvQB1XftXni199fvXVYlXOXiUpdKHcyItyIy/KjbwoN/Ki3Hgm6iB6hUKhUCgUCsWI4tZ3QDu7esjKG7x0wLxZE/A3GkZoRENjz549tLa2smzZMtra2igrK8NkMnH48GFSUlJ4/fXXCQ8PJzk5maqKSqYHhgv1G2aKxeBvpKezS7jMliOjsKPcyItyIy/Kjby4yg1c3jeo3OhDuRHDrQvQrLw6Fm3406Btjr5xN8lzrq39GrMymcCJIdRkFjA2cgIhM6Zw6eNsxkVNoOxvp67bl56MFjZt2tR3u7y8HLPZDEBCQgIADz/8cN/9LYXl7L1nu1C/az94gok3zcCSW8Te1doy7kC5Ecu4A+VGLOMOlBuxjDsYTW7g8gUuyo0+lBsxPHYPaMmHx/puW3KLKOu9eq3+3MAVb/Rk9BIVFTXo/RETr11UewvKjbwoN/Ki3MiLciMvyo3novaADoH2Hnt93iKd9axl43AVPHcGGq8tXuVxtPfAi17k5kivmwYvcNPRY6+dfNFL3BythmdzoN6L3HzqJW6OO9x0uHskQ6ezB17yJjc1djeWdnePZOh09sAuL3JzotdNnYvdDOsCdP/+/aSlpWEymcjJyeHtt98mJ0e8NMD9q6dz3/I4JoYFDthm0uLZTFu3RNO49GREeOo0vFYAR2uhoFFfH1PvXEzixlUuzzijvhN+egp+fx5+fEpfHzK5eeY0vNrrJq9eXx+yuGnohJ9m2d38ROcxdVK5yYFX8+FYLZyt19eHLG6auuDxU/CHC/Djk/r6kMnNs2d63dRAzuDb8wdEFjfN/dz8yAueN8+egd29brI93E1Ldz83p/T1IZObnbnwSq+bUwOf/DQosrhp7YbHM4f2vBHFx2YbvoOv0tPT2bx5M7t372bp0qUEBQXR3NzMnDlzrmi3c+dOioqKqGgI5M1j8X3fnzxpLFsfnMvWpy+Xy1qffJ7IkHbG1HaQsK8SgGnrllxzhMLVFKyIANCcaQsPEJ5vQcJ9lE+5FZ/uDhaceJZxbVXCWcd84u66hY6GFmqzL9BZ3zzouPRkROnyG8tJ83fpCJxAeHU2N555XfNcQCY391I+ZQk+3Z3Mz9xJUEu5cFY+N2M4ufC7dIwJ9w43M75A+ZRb8bV2Me/krwj2YDfdhkBOmLf2ujnNjWf2aJ4LyOOmcMa/cGnKUrubzOcJbi4TzsrnJqDXzUQm1OQwJ+c1zXMBidzEr+NS9GfwtXaTlPk845vFjxzS8nN2jA1wmZsegz8nzI/SPmYiE2rOMCfnVc1zAXncnI+/m7Lo2+xuTr3A+Cbxj/Klc+PrzwnzVtrH3sCEmlzm5OwWzgLExsayZcsWobbDuge0ubmZHTt2YDabyc/PH3AB6hjcsZxq3ux3EdLKW6fwwcESYiLHUVLRAkBqairJc26g5lQhe/dtJ3RmNKGznNeZT01NBdCc0bJZt7MHfnceEsYHcNsXxDcRA33zsVmthMRPpvzAaafj0pPRQmat/WPe9NVJBBnTNM9FJjddVvhdIcSP9+ezX3hMOAfKDYyMm+nBASy7x/PdZNXZa0Cnr57rFW5+XwjTggJYds9W4RzI6Sa7zv6O+/Or5xDs4W66e91MDfLnc/d8VzgH2tw4xga41M3pOngqB9JX3+gdbs5DzDh/Pn/PI8I5kNNNTh08mQPPrzIx3l/cjVaGdQG6bds2MjIyMJlMxMQ4l3w1e/7ovAZrfX4pmU++palfPRkR/A3w1ZlD66Mo44jzRsOQEWF+OMybAEE6T2yQyY3RFx5SbvpQblznZt4E73KzyYvcJPW6CfYCN36+sNGL3MydADd5k5uEofUhk5s5vW7G+7uk+z6G/Sr4NWvWDHeXo4bEh1ZjMPphySvG2tWDr9GAtatHKNN4sYL26noCwoLpsHjJTmiJUG7kRbmRF+VGXgZyU3Fo4Os2lJuRYbS4cesxTPNmTeDoG3c7bQP2w1Qd51+J4DisVU/GbdhsBIQHExAaROXRc0QsSqTSWXmw3ozhUg3VmYXErb2Z0o9cvHP4KpSbwTPKjYtRbpxm3IZy4zTjNpQbpxm34aFutOLWBai/0XDdQ+avh8HfqOswVVkPYL0eHXVNVBzKIXq5mZD4RtprGwmJnyyUCQgfDzYbrZUWGL7ryoRQbgbPKDeuRbmRF+VGXgZy03pp4Mu4lZuRwVPdaMVjD6L3RhxX5tXni1/dePXVfFXOXiUpdKHcyItyIy/KjbwoN/IyWtyoBajiujirZfvhhx+SnZ3Nww8/THFxMQsXLnT3kEcNyo28KDfyotzIi3IjL6504zEL0J7OLiy5RcLtHXs4tGYM/jovyfMynNWyXbduHevWrQOgs62dmlOFwn0rN0NDuZEX5UZelBt5UW7kRYubnp7BLzC8Go9ZgFpyi9i7WvysTceGY60Zd+4TiVmZTODEEGoyCwiKvgG/cYFcfO8Tze1tVuuwjstZLduAug7lRrC9cjP8KDeDZ5Sba1FutP+slZuRw1PdREZGaurPYxago4GSD4/13RZ5taa1vUI/yo28KDfyotzIi3IjL6PFzbDWgh9tWNrttbl/4/z8fI/g3SL4pApKBq4C5jFYOuz1bF/3Ejd/7HVT7AVu6nvd7Ml390iGhz8V290UeYGbxk74aSa8li/9BbRCvN/r5qLcxyEK0dRld/Oql7jZ2+vmQqO7RzJ0HG52e4mbjJKRcTOsC9D9+/eTlpaGyWQiJyeHAwcO8N577w3nQzD1zsUkblzl8owIz5yB90vglTzIsejrY9Li2Uxbt8TlGWfUtMOLZ+Fis70Elx5kcpOWA3/qdZNVp68PWdzUdcCL53rdDF6lbUCkcnOm100+nKzR14csbuo74AXH8yZbXx+yuXmvxP4P6YmBT3wZFFncNHT2c+MNz5scu5tX8+Fotb4+ZHHT2Akv9P5Ne8oL/r15rp+bf3q4m+YuSB/i80YUH5tt+Nbr6enpbN68md27d7N06VKmTZvG/v37+zaoOti5cydFRdreJh5T20HCvkri7rqFjoYWarMv0Fk/8FsOBSsiADRn2sIDhMd0YfpaSqNvw9DTxYLjTzOmQ3wV6pgPwLR1S645QuF649KTEaXbEEDmwlTaxtzApMrjJJ57UzgrpZtpayiN+azdzYk0xrSL/2sqm5segz8nF6TSNnaSV7j5dNqdlMQsw7fXzVhPduPrz8mFdjc3VJ5g9rk3hLNSuolbRcnUz9ndnHyWsW3irxDkc2PsdRPBDVUnmX32D8JZGd1cjFtJ8dTP49vTxfzMnYxrrdI8H3D+c3aMDXCpm8yFqbSOjeCGqkxmn/29cFZKN7ErKJl6B762buad3ElQa6Xm+YAcbqy+fpxckEprUBQTqzIx5f5OOAsQGxvLli1bhNoO6x7Q5uZmduzYgdlsJj8/n/fff5877rjjmnaig+tPzalC9u7bjs1qJSR+MuUHBl+ap6amAmjOaNl4bLPBxxXwUbkfP7n3h8I5uDyf0JnRhM6KERqXnowWLrVCrgWWRSXj55ssnJPZzV/L/Xj83h8I50BeN2cscLuXuDlQCfsveYebilY4bYHbo8z4+ZqFczK7+UuZHz+999+FcyCvm2wL3B61EKOv+PEwsro5WAn7yvz46b3iF9SANjeOsQEudVPZZv906vaoBRh9FwjnpHfzhW3COZDTTVUbnKqDZZHz8TfM15TVwrAuQLdt20ZGRgYmk4mYGOc/SD0UZRwZkYwIPj7w2Sj9H1WB/aDZzCffcnlGhMlj7V96UW6UGxF8fOC2SDiu8+N3kMtN5Fj7l16UG+VGBB8f+EwkHPMSNxFjYMUU/XnlxnVuJg3RjSjDfhX8mjVrhrvLAUl8aDUGox+WvGKsXT34Gg1Yu3qoODTwphJHpvFiBe3V9QSEBdNhaaL6pJdcrSIJyo28KDfyotzIi3IjL8qNZ+LZxzDZbASEBxMQGkTl0XNELEqk0ln5qd6M4VIN1ZmFxK29mdKPMkdmvP0IM8X2nU8m0lZvxm0oN04zbkO5cZpxG8qN04zbGCVuHO0B5WYE8Ho3g+DRC9COuiYqDuUQvdxMSHwj7bWNhMRPpvXSwJ+7OjIB4ePBZqO10uKWcxMM/kbN+zL0ZNyFciMvyo28KDfyMtrcAMrNCODtbgbDoxegjiu/6vNLNWccVDl7laTQhXIjL8qNvCg38qLcyIty45l49AJUNvbs2UNrayvLli2jra2NsrIyTCYThw8fJiUlhXfffZfCwkK++c1vUlRUxMKF4ldlKoaGciMvyo28KDfyotzIi3IjhlsXoJ1dPWTlDX5K+LxZE/A3GkZoRENj06ZNfbfLy8sxm+1HsiQkJACwfv36vvs7WtuoOVUo3Pf4GVNoLCzTNJ4wUywGf6OmjLei3MiLciMvyo28KDfy4ko3jj2dWkpuyurGrQvQrLw6Fm3406Btjr5xN8lzbrjm+zErkwmcGEJNZgHBUycxZlIYrZUWWi7VUHf60+v2pSejl6ioqEHvD6jrYO9q8bPclj73LQ4+kq5pDGs/eMIt+0SUG+coN9dHuVFuBkO5uT7Kzehx47j4SGtGxj2jHvsRfMmHx/pui74S0JNRaEe5kRflRl6UG3lRbuRFufFcPHYBKgNFzfDcGXtFB5vNfhitJ7PrHJyth40z4aYJ7h7N0ChphmfPQEUbWG3g6+FuXj4HufXwlRkwf6K7RzM0HG7KW73DzSt59ipVD86ABR7uprTFXg/+Uot3uNmdBzn18EA8LPRwN5da4JkzUOYlbl7Lt1ep+tJ0SL72Q06PorwVns6xu+mxgcHT3RRAdh1smA6LXOjGdzg7279/P2lpaZhMJnJycjh8+DBvv/22cP7+1dO5b3kcE8MCB2wzafFspq1bomlcejIivHjOXrauoBEyB9/KOiBT71xM4sZVLs84o7IN/vtTOFhl/wdVDzK5eeEcfNzr5oTO6hSyuKlu7+cmX18fMrl5qddNYRMcrdbXhyxuarzRTYXdzWHxUuNXIIubuo5eN5Xe8TftxXPwj143h8RLjV+BLG7qO+CtC3Y3u73gedPfzYEKfX3I4qahE/5riG5E8bHZhu/gq/T0dDZv3szu3btZunQpEyZM4M9//jMPPfTQFe127txJUVERFQ2BvHksvu/7kyeNZeuDc9n69OVyWeuTzxMZ0s6Y2g4S9tmfddPWLbnmCIWrKVgRAaA50xYeIDzf4pjPUTL18/jaull47Cn8u5qFs475xN11Cx0NLdRmX6CzfuB88eIJTD1iX+WKZrTMp8fXyKn536Z1bARR5Z8wo/A9zXMBedyUxCyjeOod+Nh6WHj8aQI6m4Szsrmx+vqROf87tI6NILL8nyQU/lHzXEAiN9GfpSR2Odh6WHD8GQI7G4WzMro5ddO3aRkXSWT5YRIK39U8F5DHTemU2yiOWwE2a6+bBuGsdG58DJya/21axkURWXGUhIL/1TwXkMdN2ZTPUBS3Amw2Fpx4lsAOi3BWXjeTiag4wkxPdzN5CUXTVoHNxvwTzzLGhW76z8c1bnzJmv9tmsdNIaLyKDPzxd9EBIiNjWXLli1CbYf1I/jm5mZ27NiB2WwmPz+fyMhIwsLCrmnnGNyxnGre7HcR0spbp/DBwRJiIsdRUtECQGpqKslzbqDmVCF7920ndGY0obOc15lPTU0F0JzRulG3pBlC/GH8fY9ryjnmY7NaCYmfTPmB04O237B+PQeP2DeFi2a0zqe1G3Zkw4//5bP4+HxWOCerm9IWCDZCyH0/0ZST281t+PjcJpyT382PNeXkdvMZfHw+I5yT382PNOVkdNPWDU9kw4//ZQk+PuLvfMnsJsgIoff9UFNOufEeN/3n43o3t+Ljc6twTivDugDdtm0bGRkZmEwmYmKcS76aPX90XoO1Pr+UzCff0tSvnowoMUFDyxdlHHHeaBgyIoz1sy+m9e5llc1N9Lih5ZUb5UYE5eZKZHIzRrm5AuVGuRFhqG5EGfaLkNasWTPcXY4aEh9ajcHohyWvGGtXD75GA9auHioO5TjNNF6soL26noCwYDosTVSfdL6YV4ij3MiLciMvyo28KDfyMlrcuPUq+HmzJnD0jbudtgH7QaqO869EcBzWqifjNmw2AsKDCQgNovLoOSIWJVLprDxYb8ZwqYbqzELi1t5M6UeZIzPeXpSbwTPKjYtRbpxm3IZy4zTjNpQbpxm34aFutOLWBai/0XDdQ+avh8HfqOsgVRkPXx2IjromKg7lEL3cTEh8I+21jYTET6b1Uq3TTED4eLDZaK202M+EGkGUm8Ezyo1rUW7kRbmRF+VGXjzVjVbUOaAS4bgyrz6/VHPGQZWzV0kKXSg38qLcyItyIy/KjbyMFjceswDt6ezSXPsUvKNeqjvYs2cPra2tLFu2jLa2NsrKyjCZTBw+fJiUlBT+/ve/M2bMGD772c8qNyOMciMvyo28KDfyotzIixY3WvGYBaglt2jU1kt1B5s2beq7XV5ejtlsBiAhIQGAVatWUVFhP3FXuRlZlBt5UW7kRbmRF+VGXrS40YrHLEC9nZiVyQRODKEms4AwUyzGsYHU55fg4+NLxT/PDFtGD1FRUdf9fmRk5LA9hswoN/Ki3MiLciMvyo28jCY3agEqCSUfHuu7LfpRgZ6MQjvKjbwoN/Ki3MiLciMvo8nNsNaCH21k1sBXD8D2Y9BtdfdohobNBj87BfvK4K+X3D2aoZNVB187CNuOQZcXuPn5Kbub/V7g5nSvm+95iZtfZtnd7Ctz92iGTk4dfLXXTWePu0czNPq7+bP4tRzSkmuxP28eO+odbp7Itrv5wAvcnO118+hR6PACN0/2uvm/Etc+1rAuQPfv309aWhomk4mcnBzy8/P53e9+N5wPwdQ7F5O4cZXLMyK8dRGyLLC/HE7V6etj0uLZTFsnXoZMb8YZFW32hWdNB/zvRX19yOTmzQt2J38thxM1+vqQxU1lu30e3ubmo3I4Vq2vD1ncVLfbXxTUdMA7F/X1IZWbT+0v3j4qhyMe7qa24/LftHd1vikkk5s3ep83f6uAT6r09SGLG0und7n5Q6+bv1fAAX3bIaVx09B5+W+aXjei+Nhsw3dQVHp6Ops3b2b37t0sXbqUiooK2tvbWbt27RXtdu7cSVGRtpmNqe0gYV8lcXfdQkdDC7XZF+isbx6wfcGKCADNmbbwAOExlUcuojh2BTYfH8zHn8Kvu13zfACmrVtyzREKV1O8eAJTj1xe5YpktMzH6mPgdNJmmoMmE136d2KL9gvlQFY3yRTHrsTm48PC409j7G7TPB+QxY0vp5MepjloSq+bvwjlQE43FZFmimJX9bp5BmN3q+b5gExuNtMcFM2U0n8QV7RPKAeSupm0kKJpq7H5+PY+bzzXjc3Hl+ykzTQHTWFK2UHiLv5ZKAdyuqmctICL01Zj8zEw/8QzBHS1aJ4PSOIGH04nbaYpOJrJZYeYdvEDoRzI6aZq0nw+nXYnNl8D84+nEdA1cN8DzQfEf86A5ow2N9+gKTiGyZc+Ydqn/yeUcxAbG8uWLVuE2g7rHtDm5mZ27NiB2WwmPz+f9vZ26uqufWtQdHD9qTlVyN5927FZrYTET6b8wOlB26empgJozmi98q29B57PhUe/+AtNOcd8QmdGEzorxmn7DevXc/BIOoBwRut8rDb7xwdj/O4E7hTOye/m55pycrtZDawWzsnv5meacjK6sdns8xnjtwoQf3dFuRlJNyuAFcI5+d38VFNObjfLgeXCOendpDyuKafVTf/5uN7NHcAdwjmtDOsCdNu2bWRkZGAymYiJcf5D0UNRxpERyYgSaABfH/35+vxSMp98y+UZEXx9YMwQfiOUG+VGFG9y46PcXIFyo9yIoNxciTe5EWXYH2LNmjXD3eWAJD60GoPRD0teMdauHnyNBqxdPVQcynGaabxYQXt1PQFhwXRYmqg+WTBi4x4NKDfyotzIi3IjL8qNvCg3nolnH8NksxEQHkxAaBCVR88RsSiRSmflp3ozhks1VGcWErf2Zko/yhyZ8fYjzBTbd0CuCONnTGGtwFvtVz+G21BunD6G21BunD6G21BunD6G21BunD6G2xhFbhw/Zz0Z2fDoBWhHXRMVh3KIXm4mJL6R9tpGQuIn03qp1mkmIHw82Gy0VlrsGx5GGIO/UfMeE0+qzKDcyItyIy/KjbwoN/Iy2tyAZ/kZCI9egDqu/KrPFz9I7OqrxaqcvUpS6EK5kRflRl6UG3lRbuRFufFM3LoA7ezqIStv8AM0582agL/RMEIjGhp79uyhtbWVZcuW0dbWRllZGSaTicOHD5OSksLrr7+On58fK1eupLzsEtMDw4X7Hj9jCo2F2k66DjPFYvA3ap2GV6LcyIsr3Tg+etJSHUS5uYxyIy/KjbwoN2K4dQGalVfHog1/GrTN0TfuJnnODSM0oqGxadOmvtvl5eWYzWYAEhISAHj44Yf77m/MK2XvPduF+1763Lc4+Ei6pvGs/eAJr3ibfjhQbuTFlW4c+6T2rtaWUW7sKDfyotzIi3Ijhsd+BB+zMpnAiSHUZBYQOisG/+CxtFZa6GpuG/DKNz0ZvURFRQ16f8REz1hU60G5kRflRl6UG3lRbuRFufFcPHYBWvLhsb7bom9F68kotKPcyItyIy/KjbwoN/Ki3HguLq0F/9prr3HmzBnh/P2rp3Pf8jgmhgUO2EaWeqlgr2Wb8jd73dS2bn19yFLPtscG3z0C9/4V/vtTfX3I5OZvl+CLf4O/lEGrF7l564K+PmRy8/d+blq8wM2jR+1u3vQCNwcq4Isf2d00d+nrQxY31n5u/lCorw+Z3Bz0MjePHbO7+b0XuDlUaXezzwvc2GzwvV43v3XxkagurQVfVFTExIkTWbx48RXtHLXgKxoCefNYfN/3J08ay9YH57L16cvVCtYnnycypF26eqkAuaYHqZl0EwBzT71AWL34M0lrPdv+tXldUc+2PTCMEwu30mMcS2hdHknZuzTPBWRy8wA1k+YDMCdrFxMsecJZ6dwEhHLS/CjdxrGE1OUzL/slzXMBedycnf0A1RF2Nzdmv0J43VnhrGx1oDsCQjhhfpRu4zhCLPnMy/J0N1+mOmIBAKbTu5lYmyuclc6NfzAnzI/R7R9EiKWAeVkvap4LyOTmS1RHLATAlPMaE2vEPyqWzU2nMYjjyd+j2z+I8fWF3HTqBc1zAYncJG6gOtK+zzPxzG+YVJ0lnJXPzThOJH+PLv9gxtef56ZT2q5vkKYWfFRUFEVFRdcsQB2DO5ZTzZv9LkJaeesUPjhYQkzkOEoqWgB7DdPkOTdIWS/1HxXw0jkI84e0f/83AjX8NLXWs+1fm9cV9Wx7bLD9GJyshX9bNosvbEwTmwhy1rI9UAEvONx8f7NHu7Ha4PvH4XgN/Nuymdzr4W4OVcDz5yDUH9K2f11TyTfZ6kBbbfDvx+FYDTx820xSvuLZbj6phF/nQkgApG37GmM93c0JOFYNmz+TwBc93M0/q+BXuTDeCGnfe4hxHuzGZoMfnIAj1fD1pTO4/0HPdnOkCnbmQrARnnn0KwRpuOBcRjf/cRIOV8HXlsazXoMbrbi8Fvwtt9winN/zR+fv98pUL/WzkfavoSBLPVuDDzy1CNJy4Atx+vqQyc1nIu1fQ0EWN74+sCPZ7ubeOH19yORmSaT9ayjI5OaJXjcp0/T1IZObWyPsX0NBKjdmu5svTtfXh0xubplk/xoKsrjx8YFf9Lq53wvcLJ4Eb3iRm58vtLtZr9ONKB5dC16hUCgUCoVC4Xl47FXw3kjiQ6sxGP2w5BVj7erB12jA2tUz6LEQjkzjxQraq+sJCAumw9JE9UkX7x4eZSg38qLcyItyIy/KjbyMFjduXYDOmzWBo2/c7bQN2E/ydxzAKoKjWoCejNuw2QgIDyYgNIjKo+eIWJRIpbPyYL0Zw6UaqjMLiVt7M6UfZY7MeHtRbgbPKDcuRrlxmnEbyo3TjNtQbpxm3IaHutGKWxeg/kaDcJUjg79R10n+Mp7+PxAddU1UHMohermZkPhG2msbCYmfTOulWqeZgPDxYLPRWmmx7yIeQZSbwTPKjWtRbuRFuZEX5UZePNWNVtRH8BLhOBqiPr9Uc8ZBlbNXSQpdKDfyotzIi3IjL8qNvIwWNx6zAO3p7NJUscDxFrrWjMFfw/kJXsyePXtobW1l2bJltLW1UVZWhslk4vDhw6SkpPDuu+/S3d3N3XffTd7Zc0wPDBfuW7kZGsqNvCg38qLcyItyIy9a3BQWFrJw4ULhvj1mAWrJLWLv6u3C7R37PbRmPOlteleyadOmvtvl5eWYzfZDdhMSEgBYv3593/3jmq3svUe5GSmUG3lRbuRFuZEX5UZetLiZMmWKpr49ZgHq7cSsTCZwYgg1mQUET53E2Khwyg+extfoN+ArNz0ZPURFRQ16f8REsX28nopyIy/KjbwoN/Ki3MiLJ7uJjNR2wLNagA6R5i4INICf79D6KfnwWN9t0V8YPZnB6LFBZ8+Qu5GG5i4IMIBRuZEO5UZelBt5aekGf1/lRkaUG+0M8Ud1Jfv37yctLQ2TyUROTg7vvvsuhw8fHs6HYOqdi0ncuMrlGRHe/hS++Dd44GNo6NTXx6TFs5m2bonLM87o7IFvHIK9pbBL595lmdy8c9Hu5sGPod7D3XRZYXOvmxfFy6ZfgUxu3i3qdfMPqO/Q14csbrqt8HCvm3QvcPNer5sH/gEWb3Dzid3N8+Il7a9AJjfvF0PKR3Y3Ne36+pDJzb/1uvmVF7jJKIYvfgRf/gdUt+nrQxY3PbbLbnaeGdaur8HHZhu+6/TT09PZvHkzu3fvZunSpfzlL39h4cKF3HbbbVe027lzJ0VF2lbpY2o7SNhXSdxdt9DR0EJt9gU665sHbF+wwl5PTmumLTxAeEy5pgepmXQT2GzMPfUCYQ3nNc8HYNq6JddcwXY1xYsnMPVIXd//i2S0zKc9IIyT5q10G8cSWpdHUvYuoRzI6uYBaibNt7vJ3kWYJV/zfEAWN6G9bsYRWpdPUvZLQjmQ382N2S8TbsnTPB8Q/zkDmjOi8+nwH89x86P0+AcRYslnXpZnuzk7+0tURyy0u8nZTXit+KpaNjed/sEcNz9Gt38QIZYC5mW9KJQDSd0kfonqSLsbU85rTKwVXx1I58YYxInkx+jyDybEUsi8rBeEciCrmw1UR9r3Rs7O2cMNNYPXZu+PbG66jOM4nvw9u5v688w7lS6UcxAbG8uWLVuE2g7rR/DNzc3s2LEDs9lMfn4+M2fOvO5CU3Rw/ak5VcjefduxWa2ExE+m/MDgglNTUwE0Z7RsPD5dB7/OhdpOH1754bc0fQzvmE/ozGhCZ8U4bb9h/XoOHrH/IohmtMzHZoMdp+F8IzywaBa3bUwTyoGcbnIt8Fwu1Lb78MoPHvZ4N0+dhoJG+FLyTJZ5uJuzvW5q2n149QebXeqm/3xc5ebp05DfCOvNM/ncVzzbzbl6ePYMVLf78Or3v+7xbp7JgbwGuN+cwOc93E1eg70+t93NVzV91CubG7C7OVsP95tncIeHuylosM+nqh1e277J492k5UBuPXxxYTwrHhR3o5VhXYBu27aNjIwMTCYTMTHOfyh6KMo4MiIZEeZOgJeX2mXp3QNan19K5pNvuTzjDB8f2J40tD5kcmMKg5eXeI+b73mRm9lhsMuL3DzmRW4SQ73LzaNzh9aHTG5mhVx2o3efoSxuALbOGVpeJjcJIfCSF7n57hDdiDLsFyGtWbNmuLsckNFSL9UTUW7kRbmRF+VGXpQbeVFuPJNhvQhpxOlXL7Xx0/K+/4pkDP5+VGcW4jc2gOrMwpEZ72hCuZEX5UZelBt5UW7kRbnxSDz6GCZPrpcaZortOyBXhPEzprBWYK/H1Y/hLpQb54/hLkaTG8fPWU/GHSg3Yhl3oNyIZdyBciOWkQ2PXoB6cr1Ug79Rc6UFT6rMoNzIy2hzA57jR7mRF+VGXpQbz8SzP4JXKBQKhUKhUHgcbn0HtLOrh6y8ukHbzJs1AX+jYYRGNDT27NlDa2sry5Yto62tjbKyMkwmE4cPHyYlJYV33nmH2tpa1q9fz4XC80wPDBfue/yMKTQWlmkaT5gpFoO/Ues0vBLlRl6UG3lRbuTFlW4cH9lqqaqj3FxGuRHDrQvQrLw6Fm3406Btjr5xN8lzrq392r/2aeisGKydXXQ1t9PZ2EpN5vWvYtOT0cKmTZv6bpeXl2M22w+mTUhIAGDDhg1991eeKmDvPduF+1763Lc4+Ii2A2HXfvCEW96mV26co9xcRrmxo9w4R7m5jCvdOPYX7l2tLaPc2FFuxPDYPaAy1EsdjKioqEHvj5h47aLaW1Bu5EW5kRflRl6UG3lRbjwXtQd0iFxogvYed49ieGjqgvwGt1wI6BK8yU1zl70SinIjH97m5tMmaOt29yiGB4cbq3IjHd7m5qIXuWnphnMj4GZYF6D79+8nLS0Nk8lETk4OR48e5ZlnnhHO3796Ovctj2NiWOCAbSYtns20dUs0jUtPRoTdefDgP2BfGdS06+tj6p2LSdy4yuUZZ7R3wzcOwlcO2Ety6kEmN6/lw1c+hg/LoKpNXx/SuOmBbxyCjR/DE9n6+pDJzesFl91UtOrrQxY3HV7m5reFdjf7LkG5h7vp7IHNvW5+kaWvD5nc/N7hpgxKW/T1IYubLits/sQ+n597gZs/nIcHD9j/ppV4g5ve583PTg1r19fgY7MN3+v29PR0Nm/ezO7du1m6dCmzZs3iz3/+M3fdddcV7Xbu3ElRUREVDYG8eSy+7/uTJ41l64Nz2fr05XJZ65PPExnSzpjaDhL2VQIwbd2Sa45QuJqCFREAmjNt4QHC8z07+wGqI+aDtYd5p9IJabwonHXMJ+6uW+hoaKE2+wKd9c0Dti9ePIGpR+wXbIlmtMynwz+EE+atdPsHEVZ7jrmnX9Y8F5DJzZepjlgA1h6Ssl4ktOGCcFY+N8GcMD9Kt38woXV5JGXv0jwXkNPN3OyXCasX33Mlm5tOYxAnkh+jyz+YkLp85mW/pHkuIJGbxA1UR5rBZmVO9stMsOQLZ+VzM44T5sfoChjvFW7OJW6gqtfNjadfJbzurHBWq5v+83GFmy6/sRxPfoyugBBCLAXMy3pR81xAJjfrqYpMBpsVU85rTKzNFc7K52YMJ5K/R2dACCGWQuZlvSA8F4DY2Fi2bNki1HZY94A2NzezY8cOzGYz+fn5WCwWbr311mvaOQZ3LKeaN/tdhLTy1il8cLCEmMhxlFTYX0akpqaSPOcGak4VsnffdkJnRhMqcOh3amoqgOaMlo26pS3w3BmYMtbAI+u+g4+PcLRvPjarlZD4yZQfGPxtxw3r13PwiH3DvmhG63xey4f3iuEXn0nkxk1pwjkZ3VxqgWfPwOSxBh5Z9/88300BvFcEP1s6i7kbvcNN1FgDqeu+6fFuXi+Ad4vgZ0tneryb8lZIOwORgb6k3v0wvh7u5jcF8E4R/GTpTG7ycDeVbfB0DkQE+vLdu7/uUjf95+MqN78tgP8tgh/fmsD8r3i2m6peNzcE+LL17q95vJvfF8L/XIQf3TqDBRrcaGVYF6Dbtm0jIyMDk8lETIy2yjAAe/7o/J2Q+vxSMp98S1O/ejIiRI+DpxcNrY+ijCPOGw1DRoSHZkJ9J9wYpi8vk5vJ4+Apb3KTAPUdMHeCvrxy4zo3GxOgzkvcRI2Fp5KH1odMbr6SALUdcJMXuIkY411uHkyAmg6YP1FfXiY3k8bAk17k5sszoKodFuh0I8qwXwW/Zs2a4e5y1JD40GoMRj8secVYu3rwNRqwdvVQcSjHaabxYgXt1fUEhAXTYWmi+uTQj5JQXEa5kRflRl6UG3lRbuRltLhx6zFM82ZN4OgbdzttA6OkXqrNRkB4MAGhQVQePUfEokQqnZUH680YLtVQnVlI3NqbKf0oc2TG24tyM3hGuXExyo3TjNtQbpxm3IZy4zTjNjzUjVbcugD1Nxque8j89RgN9VI76pqoOJRD9HIzIfGNtNc2EhI/mdZLtU4zAeHjwWajtdIy4ufBKDeDZ5Qb16LcyItyIy/Kjbx4qhuteOxB9N6I48q8+vxSzRkHVc5eJSl0odzIi3IjL8qNvCg38jJa3KiD6BUKhUKhUCgUI4rHvAPa09mlqWSWYw+H1ozB36h5bN7Inj17aG1tZdmyZbS1tVFWVobJZOLw4cOkpKSQkZFBe3s7K1as4NPzF5geGC7ct3IzNJQbeVFu5EW5kRflRl60uCkuLmbhwoXCfXvMAtSSW8Te1duF2zs2HGvNuGufSMzKZAInhlCTWcDYyAn4h4yjpbSans5uarPOD1tGlE2bNvXdLi8vx2w2A5CQkADAvffe23d/VVYhe+9RboaaEUW5uYxy4zyj3NhRbi6j3DjPKDd2tLjp6dFWX9ljFqDeTsmHx/pui75S05PRQ1RU1KD3R0wUu5DMU1Fu5EW5kRflRl6UG3nxZDeRkZGa+lN7QIeA1QYflsK5enePZHgobobzjfZasJ6Ow83ZenePZHhwuOnU9gJTSqw22OdFbkqUG2lxuOnwAjc2m70O/BmLu0cyPJS2eJebv5RBjpe4udQ6Mm6GdQG6f/9+0tLSMJlM5OTk8F//9V+8++67w/kQTL1zMYkbV7k8I8LOM/DDk/DIEfuTSQ+TFs9m2rolLs84o7kLthyBIzXwuM6jw2Ry86vcy26KBy6ROyiyuGnpts/jSA08fkpfHzK5eb7XzZYjcNHD3bR2X37e/NgLnjfpZ3vdHIYLjfr6kMVNW7/nzY+8wM0LZ+GHJ+xzKvRwN+39njf/6QVuXjpnf96kHoH8Bn19SOOmB75z2O7mhyeHtetr8LHZhu+gqPT0dDZv3szu3btZunQpJ0+exNfXly9/+ctXtNu5cydFRdreJh5T20HCvkri7rqFjoYWarMv0Fk/8L9eBSsiADRn2sIDhMd0LnEDVZFmfHq6uCnz1wQ3ix+Z4JgPwLR1S645QuFqihdPYOqRur7/F8lomU+nMYgT5q10BYQQVpvL3NO7hXIgq5v1VEUm49PTzbxTzzO+qVjzfEAWN+M4aX6UzoAQwmrPMvf0K0I5kNNN3qz7qYxahE9PN0lZ6YQ0iv8tkM1Nl99YTpgfpTMw1Evc/CuVUYuhp5uk7JcIbbigeT4gn5vQunMkZb8slANZ3XyRyqibwdrN3KxdhDWI7+3T6qb/fLRkROfT7RfIcfNjdAaGEVqXR1L2LqEcyOkmf2YKFZNvsbs5/QphFvHqQ9K5MQRyIvkxOgLDCK3LJyn7JaGcg9jYWLZs2SLUdlj3gDY3N7Njxw7MZjP5+fmMHTv2uptSRQfXn5pThezdtx2b1UpI/GTKD5wetH1qaiqA5oyWjceNnfDrs1DUZOTlx78rnIPL8wmdGU3orBin7TesX8/BI+kAwhmt88kohhO18LU7TEzelCack9FNUxf8OhcuNvnx8k8eEc6BpG5K4EQNfO3zsz3eTXOvmwtNfrzyY21/C2R080EpHKuGr35+NlO8yc2P/p9wDuR08+dSOFoNmz6XSMxGL3BzFs43+rH7R98SzoF2N/3n4yo3H5bC4WrY9LlZTPVwNy3d9udNYaMfu3/4TeEcyOlmXxn8swo2fW6mJjdaGdYF6LZt28jIyMBkMhET4/yHooeijCMjkhFhvD/8YB6kDVye1Sn1+aVkPvmWyzMirJlq/9KLTG6CjfDv3uQmxv6lF5ncBBnh+17kZnW0/Usvyo3r3KyKtn/pRTo3Sd7jZmW0/UsvMrkZ5wfbvcjNiin2L1cz7FfBr1mzZri7HJDEh1ZjMPphySvG2tWDr9GAtauHikMD/xY4Mo0XK2ivricgLJgOSxPVJ8XfMlc4R7mRF+VGXpQbeVFu5EW58Uw8+xgmm42A8GACQoOoPHqOiEWJVDorP9WbMVyqoTqzkLi1N1P6kc5d0EMgzBTbdz6ZCONnTGGtwFvtVz+G21BunD6G21BunD6G21BunD6G2xhFbhw/Zz0Zt6DcCGVkw6MXoB11TVQcyiF6uZmQ+EbaaxsJiZ9M66Vap5mA8PFgs9FaabGfoTDCGPyNmg+6ddfBuHpQbuRFuZEX5UZeRpsb8Bw/yo1n4tELUMeVX/X54lefX321WJWzV0kKXSg38qLcyItyIy/KjbwoN56JOoheoVAoFAqFQjGiuPUd0M6uHrLy6gZtM2/WBPyNhhEa0dDYs2cPra2tLFu2jLa2NsrKyjCZTBw+fJiUlBTef/99amtrueOOO6irqWV6YLhw3+NnTKGxsEzTeMJMsRj8jVqn4ZUoN/LiSjeOvU9aytMpN5dRbuRFuZEX5UYMty5As/LqWLThT4O2OfrG3STPubb2a8zKZAInhlCTWUBQ9A1MmDONin+ewQcfKv555rp96cloYdOmTX23y8vLMZvNACQkJACQkpLSd/+Zi9XsvWe7cN9Ln/sWBx9J1zSetR884ZZ9IsqNc5Sby7jSjWOj/t7V2jLKjR3lxo5yI5ZRbuwoN2J47B7Qkg+P9d225BZRsu+4SzJ6iYqKGvT+iInXLqq9BeVGXpQbeVFu5EW5kRflxnNRe0CHQHsPvHgOinTWs5aNI9X2+vaNne4eydDp8DI3R73Mza5zUNTk7pEMD8d73TR4gZvOXjcXvcVNjd1NvZe4edmL3JyogedyvMNNlxVezoNPvcTNyV43lnbXPs6wLkD3799PWloaJpOJnJwc/u///o/f/va3wvn7V0/nvuVxTAwLHLDNpMWzmbZuiaZx6cmI8PRpeDUfjtVAYaO+PqbeuZjEjatcnnFGfSc8ngm/Ow8/OaWvD5ncPJNjd3O0BvIb9PUhi5uGTnj8lN3Nj3UeUyeTm7QceCUfjtbCuXp9fcjipqnL/nzxFjfPnrG7OVYLufX6+pDFTXMX/KT3b9qPT+rrQzY3L/f+TTs9+KUTAyKLm5Zu+/Pm9xfgP73AzXNn7AvQYzVwysPdtPZ34+JjUX1stuE7+Co9PZ3Nmzeze/duli5dSltbGx9++CH/8R//cUW7nTt3UlRUREVDIG8ei+/7/uRJY9n64Fy2Pn25XNb65PNEhrQzpraDhH2VAExbt+SaIxSupmBFBIDmTFt4gPB88xPuo2LKrfh0d7Lg5LOMa60UzjrmE3fXLXQ0tFCbfYHO+oHfritePIGpR+y/2aIZLfPp8hvLyYXfpWPMBMKrs7jxzG80zwXkcVOQcC/lU5bg29PBTSd/RVBLuXBWPjdjOGneSkfgBMKrs7nxzOua5wISuZnxBcqjl+Lb08G8k78muOWScFarm/7zcYWbbkMgJ8xb6RgTzoSaHObkvKZ5LiCPm8IZ93BpyhJ8rd3My3ye4GbxY23kcxPACfOj3uMm/l+4FL0UX2s3SZnpjG8uEc7K5qbH4M8J86O0j5nIhJozzMl5VfNcQB435+PXURb9GbubrBcZ3yh+gZB0bnx73YydyISaXObk7BaeC0BsbCxbtmwRajuse0Cbm5vZsWMHZrOZ/Px84uLimDlz5jXtHIM7llPNm/0uQlp56xQ+OFhCTOQ4SipaAEhNTSV5zg3UnCpk777thM6MJlSgekZqaiqA5oyWjbqdPfCbQkgY78+yL2wTzgF987FZrYTET6b8wOlB229Yv56DR+wXuohmtM7nRA3szIUXVs8jyJgmnJPVzW8LYUZwAMvueUw4B3K6OVkDz+XCC6uTPN5Nl9X+vIkPCuD2ex4VzoF2N/3n4yo3mbX2d6fSV88h2Avc/LYQ4oL8+fw93xXOgcRucuD5VXMY7+/Zbrp73UwN8ueOe1KFcyCnm6w6eOY0PL/qRq9xEzPOn+X3iC2+HMjoJrvO/gnvr1eZCNHgRivDugDdtm0bGRkZmEwmYmLskhcsWCCc3/NH5zVY6/NLyXzyLU3j0pMRwd8AX581tD6KMo44bzQMGREWToSbJkCQztMaZHPzNS9ys8CL3Bh94WvXvi7VhExu5ofb3QR7iZuvepubcBjvry8vkxs/X3jIi9zMm6Dc9EcmN0m9bkJ0uhFl2K+CX7NmzXB3OWpIfGg1BqMflrxirF09+BoNWLt6qDiU4zTTeLGC9up6AsKC6bA0UX3S+WJeIY5yIy/KjbwoN/Ki3MjLaHHj1mOY5s2awNE37nbaBuwHqTrOvxLBcVirnozbsNkICA8mIDSIyqPniFiUSKWz8mC9GcOlGqozC4lbezOlH7l45/BVKDeDZ5QbF6PcOM24DeXGacZtKDdOM27DQ91oxa0LUH+j4bqHzF8Pg79R10GqMh6+OhAddU1UHMohermZkPhG2msbCYmfTOulWqeZgPDxYLPRWmmB4buuTAjlZvCMcuNalBt5UW7kRbmRF091oxWPPYjeG3FcmVefL37l6dVX81U5e5Wk0IVyIy/KjbwoN/Ki3MjLaHGjFqCK6zJQLdvc3FxMJhOHDh3Cx8eHW2+9lePHjzN9+nQWL17s7mGPCpQbeVFu5EW5kRflRl5c6cZjFqA9nV1YcsXP1nLs4dCaMfjrvJTVy3BWy9bxX4DpsXGc+/g4NacKhfpWboaGciMvyo28KDfyotzIixY3CQkJVFRUCPftMQtQS24Re1dvF27v2HCsNeOufSIxK5MJnBhC44VLjJsyEePYQJqKKulsbKUm8/pXsenJ6MFZLVtLbhHHHnhGuD/lRrkZLpQb5xnl5lqUG+XGWUa5uRZnbgAiIyOF+/OYBai3U/LhsRHJKLSj3MiLciMvyo28KDfyMprcDGst+NGGpQN+mgm/E/skQHreK4Z/VkFpi7tHMnQaOuFnp+C38h6Bpok/9bopGbjamsfQ2OvmN17i5v1eN8Ve4Kapy+7mdS9xk9HrpshL3Pz8FOzJl/7iZiEySuxuLja5eyRDp7nXzWte4uaDUrubC42ufZxhXYDu37+ftLQ0br/9dt577z3ef/993nnnneF8CKbeuZjEjatcnhEhLQfeK4GXzsEZi74+Ji2ezbR1S1yecUZNO6TnwqfN8OTgVb0GRCY3z+TAH4vh5Tx7WTE9yOKmrgPSz3qnm8yBTxUZFFnc1Pdzs8ML3KT1c3O8Rl8fsrhp6ITnHc+bbH19yOTm2Rx4txheyYdjHu6msdO7/qY53OzOh8PV+vqQxU1zF/y6dy3w1MDn3g8LPjbb8K3X09PT2bx5M7t27SI6Ohqj0b6J984777yi3c6dOykqEt8QDDCmtoOEfZXE3XULHQ0t1GZfoLN+4Je1BSsiADRn2sIDhMd0YfpaSqNvw9DTxYITTzOmXXwV6pgPwLR1S645QuFqihdPYOqRyyspkYyW+XQbAji5MJX2MTcwqfI4iefeFMqB3G58e7pYeCKNMe3iKx0Z3WQuTKVtzA3cUHmC2efeEMqBpG6mraE05rP49nSx4MSzjG0X/9dUq5v+89GSEZ1Pj68/Jxem0jZ2EjdUnmT2uT8I5UBON59OW01JzDJ8e7pZkLmTsa1VmucDsrgxcnLhd+1uqjKZffb3QjmQ1E3cKkqm3o7B2sNNJ59jnMe7SaVtXCQTKzMxnf2dUA7kdHMxbgXFU+/AYO1m3slfEdQqfiGObG6svn6cXJBKa1AUE6tOYcr9rVDOQWxsLFu2bBFqO6x7QJubm9mxYwfd3d0sWrSIiooKOjs7r2knOrj+1JwqZO++7disVkLiJ1N+YPCXTampqQCaM1o2Hltt8I8K+OiSHz+994fCObg8n9CZ0YTOinHafsP69Rw8kg4gnNE6n7IWyKmHz0cl4+ebLJyT0Y3NBn/vc/MD4RzI6eZSq/2d3Dsmm/HzNQvnZHXzjwr46yU/fnrvvwvnQLub/vNxtZvPT16I0XehcE5uN0Z+eq/4RRsgu5sFGH0XCOekd/MFz3dT0Qqn6uBzUfPxN8wXzsnq5kAl7Cvz42df+J5wDmR3cxP+hpuEc1oZ1gXotm3byMjIICkpiZgY5z8UPRRlHBmRjAi+PnB7lP6PEcF+0Gzmk2+5PCPClHH2L73I5MbHy9xMHmv/0otsbpZFwUnlBlBu9GZEUG6uRCY3kWNhlRe5uS1S/7YV8C43ogz7VfBr1qwZ7i4HJPGh1RiMfljyirF29eBrNGDt6qHi0MAbFxyZxosVtFfXExAWTIelieqTXrLrXhKUG3lRbuRFuZEX5UZelBvPxLOPYbLZCAgPJiA0iMqj54hYlEils/JTvRnDpRqqMwuJW3szpR9ljsx4+xFmiu07n0yE8TOmsFbgrfarH8NtKDdOH8NtjCI3jp+znoxbUG6EMm5BuRHKuAXlRigjGx69AO2oa6LiUA7Ry82ExDfSXttISPxkWi8N/BmFIxMQPh5sNlorLW45N8Hgb9R80K27DsbVg3IjL6PNDXiOH+VGXpQbeVFuPBOPXoA6rvyqzy/VnHFQ5exVkkIXyo28KDfyotzIi3IjL8qNZ+LRC1DZ2LNnD62trSxbtoy2tjbKysowmUwcPnyYlJQU/v73v1NZWcmdd95JcXExCxeKXzGrGBrKjbwoN/Ki3MiLciMvyo0Ybl2Adnb1kJU3+Cnh82ZNwN9oGKERDY1Nmzb13S4vL8dsth+Xk5CQAMCqVZcPwO1sa6fmlHgJpfEzptBYWKZpPGGmWAz+Rk0Zb0W5kRflRl6UG3lxpRvHnkFLrvh53crNZZQbMdy6AM3Kq2PRhj8N2uboG3eTPOeGa74fszKZwIkh1GQWEGaKJXDCeGqyztNZ30x9Xsl1+9KT0UtUVNSg9wfUdbB3tfhZbkuf+xYHH0nXNIa1Hzzhln0iyo1zlJvro9woN4Oh3Fyf4XbjuMBFa0a5uZbR7MYZHvsRfMmHx/pui74S0JNRaEe5kRflRl6UG3lRbuRFufFcPHYBKgPFzfBcLlS22i+e8/Fx94iGxst5kGuBh2ZC0gR3j2ZolLbAs2eg3Evc7M6DHAtsmgnzPNxNWQuk9bqx2uwFHTwZh5uvzID5E909mqFxqQWe8SI3r+XbKyE9OAMWeImbSy1e4qbA7ubL8WD2cDcVbfDUafvfNm9w83qBvRKSq934Dmdn+/fvJy0tjdtvv5333nuPt99+m5wc8Wr296+ezn3L45gYFjhgm0mLZzNt3RJN49KTEeGFc/BxBeQ32mXpYeqdi0ncuMp5wyFmnFHZBv91AQ5Wwa48fX3I5Cb9rL1sXUGj/uoUsripboe3HG50Xqgpk5sX+rk55uFuatrhvz61u3k5X18fUrk5Z3eT3wiHxUuNX4Esbiwdl583r3jB37QXe90UNMGhSn19yOKmvgPeOg8HK73DjeNvWmGTfU2gB1ncNHTCmxfsbl7W6UYUH5tt+A6+Sk9PZ/PmzezatYvo6GjmzZtHc3Mzc+bMuaLdzp07KSoqoqIhkDePxfd9f/KksWx9cC5bn75cLmt98nkiQ9oZU9tBwj77s27auiXXHKFwNQUrIgA0Z9rCA4TnWzz1cxRP/Ty+1m7Mx5/Gv7NJOOuYT9xdt9DR0EJt9gU665sHfqzFE5h6xL7KFc1omU+Pr5FT879Ny9hIoso/IaHwj5rnAhK5ibmdkql34GPrYeHxpwjwBjfjIokqP0xCwTua5wLyuCmJWUbx1OX42HpYcPwZAjsbhLOyubH6+pE5/zve4yb6sxTHrrC7OZFGYEe9cFY6Nz4GTs3/Ds1BUUSWH2Fmwf9qngvI46Z0ym0Uxa3sdfMsgR0W4axWN/3n4zo336Y5aAqRFUeYmf+25rmAPG7KpnyGi3Er8bHZmH/iWcZ0iL8jJZ8b397nzRQiKo4yK/9/hOcCEBsby5YtW4TaDutH8M3NzezYsYPu7m4WLVpEfn7+dRegjsEdy6nmzX4XIa28dQofHCwhJnIcJRUtAKSmppI85wZqThWyd992QmdGEypQdSY1NRVAc0brRt2iZgjxh9D7fqIp55iPzWolJH4y5QdOD9p+w/r1HDxi37AvmtE6n5Zu2JENP/mX2/DxuU04p9yMoJt1S/HxWSqck9VNcTOM94fQ+36kKafcjJAbI4Te95+acjK6ae2GJ7LhJ+uW4OMj/s6X/G5+qCmn1U3/+bjeza34+NwqnJPVTUkzBBkh7L7/0JST280t+PjcIpzTyrAuQLdt20ZGRgZJSUnExGgrTQiw54/Oa7DW55eS+eRbmvrVkxElNmho+aKMI84bDUNGhHF+EOqvf7+kcqPciDJVuelDuXGdm7HKzRUoN65zE6PcaGbYL0Jas2bNcHc5akh8aDUGox+WvGKsXT34Gg1Yu3qoODTwPlpHpvFiBe3V9QSEBdNhaaL6pPPFvEIc5UZelBt5UW7kRbmRl9Hixq1Xwc+bNYGjb9zttA3YD1J1nH8lguOwVj0Zt2GzERAeTEBoEJVHzxGxKJFKZ+XBejOGSzVUZxYSt/ZmSj/KHJnx9qLcDJ5RblyMcuM04zaUG6cZt6HcOM24DQ91oxW3LkD9jYbrHjJ/PQz+Rl0Hqcp4+OpAdNQ1UXEoh+jlZkLiG2mvbSQkfjKtl2qdZgLCx4PNRmulxX7u0Aii3AyeUW5ci3IjL8qNvCg38uKpbrSizgGVCMeVefX5pZozDqqcvUpS6EK5kRflRl6UG3lRbuRltLjxmAVoT2eX5tqn4B31Ut3Bnj17aG1tZdmyZbS1tVFWVobJZOLw4cOkpKSQkZFBe3s7ixYtwlJbx/TAcOG+lZuhodzIi3IjL8qNvCg38qLFTWNjIwsXLhTu22MWoJbcolFbL9UdbNq0qe92eXk5ZrMZgISEBADuvffevvvPFP2TvfcoNyOFciMvyo28KDfyotzIixY3FRXaTuH3mAWotxOzMpnAiSHUZBYwNnICQdE30FpRR0t5LXWnPx22jB6ioqIGvT9iotg+Xk9FuZEX5UZelBt5UW7kxZPdREZGaupPLUAloeTDY323RT8q0JNRaEe5kRflRl6UG3lRbuRlNLkZ1lrwo42sOvjaQfj+cei2uns0Q8Nmg59nwb4y+OiSu0czdE73utl+zDvc/LLXzV+9yM22Y9DlRW72e4GbMxbvcrMj2+5mn/i1HNJy1gJfPwjfOwadPe4ezdCw2eDJXjd/9gI35+rtbh47Bh0e7gbgqdN2Nx+42M2wLkD3799PWloat99+O++99x4HDhzgvffeG86HYOqdi0ncuMrlGRHeuACn6uAvl+z/1cOkxbOZtk68RJzejDMq2uAvZVDTAW9f1NeHTG7+0Otmfzkcr9HXhyxuKtvtv2Pe4ubNXjd/LYdj1fr6kMVNdbt94VnTAW/r/KRLJjdv9HNzuEpfH7K4qe24/Dftf3W+KSSTmz9cgMw6+KgcPvFwN5bOy3/T3vECN78/b3fzt3I4oG0bZB+yuKnvsC8+azrgnYvD2vU1+Nhsw3dQVHp6Ops3b2bXrl1ER0dzxx13sH//ftatW3dFu507d1JUpO23bkxtBwn7Kom76xY6Glqozb5AZ33zgO0LVkQAaM60hQcIj6k8cjHFcSuwAQuPP42xu03zfACmrVtyzREKV1O8eAJTj1xe5YpktMzH6mMge97DtIyLIrr0H8QW/UUoB7K6SaY4diU2H99eN62a5wOyuPHldNLDNAdNZkrpx8QV7RPKgXIzom7KDhB38UOhHMjppiLCTFHcKrubE89g7GrRPB+Qw43Nx5fspM00B01hctlBpl38s1AOJHUzaSFF01Zj8/Vl/vFnCHChm/7z0ZIRdoMP2fM20xwU0+vmA6EcyOmmctICLk67E6uvgQXHnyGga+C+B5oPyOPmdNJmmoJjmHzpENM+/T+hnIPY2Fi2bNki1HZY94A2NzezY8cOuru7WbRoEb/61a+44447rmknOrj+1JwqZO++7disVkLiJ1N+4PSg7VNTUwE0Z7Re+dbWDeln4dEv/lxTzjGf0JnRhM6Kcdp+w/r1HDySDiCc0Tofqw3ae2Cs32pgtXBOfjc/05ST1U1bD4zzWwWIv4JXbkbSzUpgpXBOVjft3fD8WXj0iz/VlJPbzQpghXBOejcprnXTfz6ud7McWC6ck9/N45pycru5A7h2DTdcDOsCdNu2bWRkZJCUlERMTAzJycnD2T0ARRlHRiQjyhg/8PXRn6/PLyXzybdcnhHB1wfGDuE3QrlxrZtxyk0fyo3r3AQqN30oN8qNKMqNdob9IdasWTPcXQ5I4kOrMRj9sOQVY+3qwddowNrVQ8WhHKeZxosVtFfXExAWTIelieqTBSM27tGAciMvyo28KDfyotzIi3LjmXj2MUw2GwHhwQSEBlF59BwRixKpdFZ+qjdjuFRDdWYhcWtvpvSjzJEZbz/CTLF9B+SKMH7GFNYKvNV+9WO4DeXG6WO4DeXG6WO4DeXG6WO4jVHkxvFz1pNxC8qNUEY2PHoB2lHXRMWhHKKXmwmJb6S9tpGQ+Mm0Xqp1mgkIHw82G62VFvuZECOMwd+oeY+JJ1VmUG7kRbmRF+VGXkabG/AcP8qNZ+LRC1DHlV/1+eKHVV19tViVs1dJCl0oN/Ki3MiLciMvyo28KDeeiVsXoJ1dPWTlDX6A5rxZE/A3GkZoRENjz549tLa2smzZMtra2igrK8NkMnH48GFSUlJ4//33qa2tZf369eSdPcf0wHDhvsfPmEJjYZmm8YSZYjH4G7VOwytRbuRFuZEX5UZeXOnG8ZGtlqo6ys1llBsx3LoAzcqrY9GGPw3a5ugbd5M8xzNqv27atKnvdnl5OWazGYCEhAQAUlJS+u4f12xl7z3bhfte+ty3OPhIuqbxrP3gCa94m344UG7kRbmRF+VGXlzpxrG/cO9qbRnlxo5yI4bHfgQfszKZwIkh1GQWEDx1EmGmOOrOXKStqp6azOtfxaYno5eoqKhB74+Y6BmLaj0oN/Ki3MiLciMvyo28KDeei8cuQEs+PNZ325JbRPGfjw3SWn9GoR3lRl6UG3lRbuRFuZEX5cZzcWkt+MOHD/P2228L5+9fPZ37lscxMSxwwDay1EsF+Nsl+OLf4K+X7JVd9CBLPdseG2w9Cvd9BP+js6a1TG7+fgn+9W/2Ot3e4ObRXjf/7QVu/lFx2U2rh7ux9nPzXxf09SGTmwO9bv5SBi1e4OaxY3Y3b5zX14dMbg5VXnbT3KWvD5ncbOt184dCfX3I5OaTSrjfS9zYbLC9183vdboRxaW14JOTk/nzn//MQw89dEU7Ry34ioZA3jwW3/f9yZPGsvXBuWx9+nK1gvXJ54kMaZeuXipArulBaibdBMDczHTCGsT/ymmtZ9u/brIr6tm2B4ZxYuFWeoxjCa3LIyl7l+a5gExuHqBm0nwA5mS9xARLvnBWOjcBoZw0P0q3cSyhdfkkZb+keS4gj5uzsx+gOsLu5saslwm3iF99KpubDv8QTiQ/SrdxHKGWfJKyPN3Nl6mOWACA6fRuJtbmCmflcxPMCfNjdPsHEWIpYF7Wi5rnAjK5+RLVEQsBMOW8xsSagQ9ZvxrZ6qd3GoM4nvw9uv2DGG8p5KasFzTPBSRyk7iB6kj7Ps/EnN8wqSZLOCufm3GcSP4eXf7BjLec56Ysbfu0pakFf/HiRcLCwq5p5xjcsZxq3ux3EdLKW6fwwcESYiLHUVLRAthrmCbPuUHKeql/vwQv5sGEAHj2B98iUMNPU2s92/51k11Rz7an91XPiVp4+LOzuG9jmthEkLOW7ccV8MJZCA2AZ7//MGM82I3j3YITtbD5szNJ8XA3Byog/RyE+kPa97+hqfyrjG62H4fjNfCN22byxa94tptDFfDrsxDiD89u+5rHu/n+cThWA9/4TAL/6uFuPqmEX+dCsD88+72HNJVKlK1+utUG/34CjlbD1z8zg/Ue7uZwFew8Y3eT9thXCNJwwblsbmy9bo5Uw9eWxrNBgxutuLQWvFb2/NH55l+Z6qUum2z/Ggqy1LM1+MBTiyAtB+6bpq8PmdzcFmn/GgqyuPHt5ybFC9x8JtL+NRRkcvNkst3NF73AzZJI+9dQkMnNjl43/zpdXx8yubk1wv41FGRy84TZ7ma9F7i5eZL9ayjI4sbHB37Z62ZDvPP2Q8Gja8ErFAqFQqFQKDwPj70K3htJfGg1BqMflrxirF09+BoNWLt6qDg08F4fR6bxYgXt1fUEhAXTYWmi+uTwHiUx2lFu5EW5kRflRl6UG3kZLW7cugCdN2sCR9+422kbsJ/k7ziAVQRHtQA9GbdhsxEQHkxAaBCVR88RsSiRSmflwXozhks1VGcWErf2Zko/yhyZ8fai3AyeUW5cjHLjNOM2lBunGbeh3DjNuA0PdaMVty5A/Y0G4SpHBn+jrpP8ZTz9fyA66pqoOJRD9HIzIfGNtNc2EhI/mdZLtU4zAeHjwWajtdJi30U8gig3g2eUG9ei3MiLciMvyo28eKobraiP4CXCcTREfX6p5oyDKmevkhS6UG7kRbmRF+VGXpQbeRktbjxmAdrT2YUlt0i4veMtdK0Zg7+G8xO8mD179tDa2sqyZctoa2ujrKwMk8nE4cOHSUlJISMjg3PnzvGv//qv1NXUMj0wXLhv5WZoKDfyotzIi3IjL8qNvGhx09DQwMKFC4X79pgFqCW3iL2rtwu3d+z30JrxpLfpXcmmTZv6bpeXl2M22w/ZTUhIAODee+/tu//MxWr23qPcjBTKjbwoN/Ki3MiLciMvWtxUVFRo6ttjFqDeTszKZAInhlCTWcDYyAkERd9A7elP6W5tpz6vZNgyeoiKihr0/oiJYvt4PRXlRl6UG3lRbuRFuZEXT3YTGantEGG1AB0iLd0Q4At+vkPrp+TDY323RT8q0JMZjB4bdFmH3I00tHaDvxe56VRurkEWN+p5cy0yuLGq5811UW6GH+VGO0P8UV3J/v37SUtL4/bbb+e9994jPz+f3/3ud8P5EEy9czGJG1e5PCPCOxch5SN48GNo7NTXx6TFs5m2bonLM87ossLmQ7C3BHbp3Lssk5t3iy67afACNw8fgowSeOmsvj5kcvPHXjcPfAz1Hfr6kMVNtxUe/sT+vHnRC9y8X3zZjcWL3KSLl7S/Apnc7C2GL/4NvvwPqG3X14dMbr7Z6+bXXuDm/0ouu6nxcDc9Nvi3f9rd/EqnG1F8bLbhu04/PT2dzZs3s2vXLqKjoxk3bhzt7e2sXbv2inY7d+6kqEjbKn1MbQcJ+yqJu+sWOhpaqM2+QGd984DtC1bYa5ZpzbSFBwiPKdf0IDWTbgKbjbmnXiCs4bzm+QBMW7fkmivYrqZ48QSmHqnr+3+RjJb5tAeEcdK8lW7jWELr8kjK3iWUA1ndPEDNpPl2N9m7CLPka54PyOImtNfNOEIt+SRlvSSUA/nd3Jj9MuGWPM3zATncdPiP54T5Ubr9gwix5DPPw92cnf0lqiMW2t3k7Ca8VnxVLZubTv9gjpsf63VTwLysF4VyIKmbxC9RHWm/wMN0+jUm1g58KPnVaHXTfz5aMsJujEGcSH6MLv9gQiyFzMt6QSgHsrrZQHWkfW/k7Jw93FAzeG32/sjmpss4juPJ36PLP5jx9ee56VS6UM5BbGwsW7ZsEWo7rB/BNzc3s2PHDrq7u1m0aBEFBQXU1dVd0050cP2pOVXI3n3bsVmthMRPpvzA4IJTU1MBNGe0bDw+XWd/hVDX4cMrP/yWprfeHfMJnRlN6KwYp+03rF/PwSP2XwTRjJb52GzwRDYUNsED5lks25gmlAM53eTUwa/OQk27D6/84GGPd7MjGwoa4UvmmXzuK57t5owFdp6Bmg4fXv3BZo938+RpyG+ADeaZfN7D3Zy1wLO9bnZ//+sYPdzN06fhXAPcvzCB5R7u5ly9vT53dQe8+v2HXOqm/3xc5ibHPqd/XTCDFR7uJq8BnsmB6jZ4ddsm/A3CUencwGU39y2IZ9WD4m60MqwL0G3btpGRkUFSUhIxMTEkJycPZ/cAFGUcGZGMCHMnwCtL7X8U9O77qM8vJfPJt1yecYaPD3x/3tD6kMnNnAnw8hLvcbPdi9zcGAYve9HzZlvS0PqQyc3sfm60LHD6I5Obx7zITWKol7mZO7Q+ZHIzK+TyvzdaFp/9kcUNwKNzhr3L6zLsFyGtWbNmuLtUKBQKhUKhUHgRHn0VfOJDqzEY/bDkFWPt6sHXaMDa1UPFoYH3xjgyjRcraK+uJyAsmA5LE9UnC0Zw5N6PciMvyo28KDfyotzIi3LjmXj0AhSbjYDwYAJCg6g8eo6IRYlUOis/1ZsxXKqhOrOQuLU3U/pR5siMtx9hpti+A3JFGD9jCmsF9npc/RhuQ7lx+hhuQ7lx+hhuQ7lx+hhuYxS5cfyc9WTcgnIjlJENj16AdtQ1UXEoh+jlZkLiG2mvbSQkfjKtl2qdZgLCx4PNRmulxb4jeoQx+Bs1V1rwpMoMyo28KDfyotzIy2hzA57jR7nxTDx6Aeo4eqA+v1RzxkGVs1dJCl0oN/Ki3MiLciMvyo28KDeeiVsXoJ1dPWTlXXtMU3/mzZqAv1HnZWUjzJ49e2htbWXZsmW0tbVRVlaGyWTi8OHDpKSkkJGRQXt7OytWrODT8xeYHhgu3Pf4GVNoLCzTNJ4wUywGf6PWaXglyo28KDfyotzIiyvdOD6y1VJVR7m5jHIjhlsXoFl5dSza8KdB2xx9426S51xb+/Xq2qf+IeNoKa2mp7Ob2qzrHwivJ6OFTZs29d0uLy/HbLYfTJuQkADAvffe23d/VVYhe+/ZLtz30ue+xcFHtB0Iu/aDJ9zyNr1y4xzl5jLKjR3lxjnKzWVc6caxv3Dvam0Z5caOciOGx34EL0O91MGIiooa9P6Iidcuqr0F5UZelBt5UW7kRbmRF+XGcxnWWvCjkYtN0N7j7lEMD81dUNjoln3YLsHb3BQ0eJGbZuVGVrzJTUu3ciMrDjdWL3FT5EVuWkfIzbAuQPfv309aWhq333477733Hq+99hpnzpwRzt+/ejr3LY9jYljggG0mLZ7NtHVLNI1LT0aEV/PhgY9hXxnUtOvrY+qdi0ncuMrlGWe0d8PXD8GDH8NT4mVsr0AmN68X2Oeyrwyq2vT1IY2bHvjGIXjwAOzwAje/6XXzYRlUturrQxY3HT2wudfNE9n6+pDJze8KL7sp93A3nf2eN7/0Aje/7+emrEVfH7K46bJeft78IktfHzK5eeO8fS3wYSmUeLib7n5ufq7TjSg+NtvwvTZMT09n8+bN7Nq1i+joaPz8/Jg4cSKLFy++ot3OnTspKiqioiGQN4/F931/8qSxbH1wLlufvlwua33yeSJD2hlT20HCvkoApq1bcs0VbFdTsCICQHOmLTxAeL5nZz9AdcR8sPaQdOoFQhs/Fc465hN31y10NLRQm32BzvrmAdsXL57A1CP2C7ZEM1rm0xEQwomFW+n2DyK07hxJ2S9rngvI5ObLVEcssLvJfonQevF9PdK58R/PCfNWuv2DCa3LIyl7l+a5gIRubFbmZO1iQr34wc+yuek0BnEi+TG6/IMJqctnXvZLmucCMrn5EtURC+1usl9hgiVPOCufm3GcMD9GV8B4QuvySfJwN+cSN1AVaQablRtPv0p43VnhrFY3/efjCjddfmM5nvwYXQEhhFgKmJf1oua5gExu1lMVmQw2K6ac15hYmyucldHNieTH6AwIIcRSyLysF4TnAhAbG8uWLVuE2g7rHtDm5mZ27NhBd3c3ixYtoru7m6KiomsWoI7BHcup5s1+FyGtvHUKHxwsISZyHCUV9pcRqampJM+5gZpThezdt53QmdGEChxenJqaCqA5o2WjbkkzPJcLU8YaSF33bXx8hKN987FZrYTET6b8wOBvbW1Yv56DR+wb9kUzWuezOw/eL4GfLU1k7sY04ZyMbspa4NkzEDXGwHfXfcvj3byaD38qhp8unUWSh7u51AJpZyByjC9b7/6mx7t5rQDeK4LHl878/+2deXyU1b3/38lkkrCErJIACQnEQIgQhARQ8LZoZROEn620haoV722xt/dK41LorV28q1CJYo3FqsXWVu29VGtLqlBqXUDDGpYQyAJm3/d9m5nfH5MJYUnmPE8mmfNMzvv1yssxOZ8z58ybSc7MPOd8meshbsLHePPY2s14G9zNa3nwTiH8dPEM5hncTUU7PHMWJvp78/jabw2rm/7zGS43v86DPxTCTxfHMf+bxnZT2esmzN+bJ9b+k+Hd/CYP9hbCTxbfSJIGN1px6QJ069atpKenk5iYSFSUXfKtt94qnN/zR+fvhDTklpC54y1N49KTESFqPOxcOLQ+CtOPOG/kgowI/zQTmrphToi+vExupoyDZzzIzT/OgMYuSPQAN5M9zM1DcdDQCXOVG0AuNw/GQV0nzAvTl5fJTcQYz3LzzTio7YT5HuAmfAz8zIPcPBAHNZ2QpNONKC7fBb969WpXdzlqUPVs5UW5kRflRl6UG3lRbuRltLhx6zFMc2eGcPSNtU7bwCipl2rQerbKzeAZ5WaYUW6cZtyGcuM04zaUG6cZt2FQN1px6wLU12y67iHz12M01Es1aj1b5WbwjHIzvCg38qLcyItyIy9GdaMVwx5E74moerbyotzIi3IjL8qNvCg38jJa3KiD6BUKhUKhUCgUI4ph3gG1dHVrKpnluIZDa8bka9Y8Nk9kz549tLW1sXTpUtrb2yktLSUhIYGMjAzWr19Peno6HR0dLFy4kPraOqb7hwr3rdwMDeVGXpQbeVFu5EW5kRctbpqamkhKShLu2zAL0PrsQvat2ibc3nHBsdaMu64TiVqxAP+wQGoy8xgbEcL4yBtoq6ijtbyWurPXP+BeT0aUTZs29d0uLy8nOTkZgLi4OAC+8pWv9P38XOFn7LtHuRlqRhTl5jLKjfOMcmNHubmMcuM8o9zY0eKmoqJCU9+GWYB6OsX7j/XdFn2lpiejh0mTJg368/AwsY1kRkW5kRflRl6UG3lRbuTFyG4iIiI09aeuAR0CVpu91nhug7tH4hqKW+DzZnudXqPjiW4ueZCbg6WQ0+DukbiGkla7my6Lu0cydGy9bs43uHskrqGszbPc/K3Mw9w0QadyIx0j5calC9CDBw+SmprK7bffzrvvvss777xDRkaGK++CqXctIv7BlSOWG4xd2fDkCfjXI/bSj3qYuGgW09YtGfaMM1q6YcsR+Kwa/v2Uvj5kcvPzbPjRCXjkiH3xpgdZ3LT22N1kVMNTOo91k8nNC9nww143BQZ309YDWzLsbn7qAW7SzsOTJ+3/3i416etDFjcdPfCvvW5+4gFufnHB/rzZkgEXje7GAo9kQEaNZ7jZ3evmkQzIa9TXhyxuOvu5+dFJl3Z9DV42m+sOikpLS2Pz5s289NJLREZGcunSJZKSkvjCF75wRbtdu3ZRWKjtbeIxtZ3EHagk5u5b6WxspfbMJboaBv7rlbc8HIC4A5UAQrm85eG0h/oJj+lC/EaqIpLxsvQw99TzTGgWPzLBMR+AaeuWXHOEwtUULQph6pG6vv8XyWiZT5d5PCeSH6PbL5Dg2mzmnH1FKAeyutlAVcSCXjcvMKG5SPN8QB43J5Mfo8svkODa88w5+7JQDuR0kzPz61ROWgiWHuaeTiOwSfx3gWxuus3jOJH8GF1+QQTXXmDO2V8K5UBWN1+jctIisPaQeHo3QY2XNM8HJHHjM5YTyY/T5R9EUF0OiWdeEsqBrG6+SuWkW8Daw5wzvyS4IV/zfED8cQY0Z0Tn0+Pjz/HkJ+jyD/YIN7kz1lMx+Va7m7MvE1wvXn1IOjcmf04seJxO/xCC6nJJPLNbKOcgOjqaLVu2CLV16TWgLS0tbN++nZ6eHhYuXIiPj891F5qig+tPzal89h3Yhs1qJTB2MuWfnB20fUpKCgD7DtgvPBbJpaSkaLrwuLHL/k5bYYsPLz/1qHAOLs8naEYkQTOjnLbfuGEDh46kAQhntM7nz0Vwoha+9aUEpmxKFc7J6Kap101Biw8vP/U94RzI6Sa9CI7Vwj99aRaRBnfT3A3PZ8PnzT688lNtvwukdFMMx2rgH++IJ8rgblq67Z/sfN7swys/+RfhHMjp5i8lcLQaHrpjJlMfNL6b57PhYrMPr/74n4VzoN1N//kMl5v3S+zvTnuKm59nQ36zD688+R28vISjUrrZX2L/NPTB22cQo8GNVly6AN26dSvp6ekkJiYSFeX8QdFDYfqREc0NRqAvPHkzpA5cntUpDbklZO54a9gzItw91f6lF5ncTPCFH97sOW5WT7V/6UUmNwFm+OFcD3ITZf/Si0xuxnuYm7si7V96kc3Nv3mQm5WR9i+9yObmB71utCw++yOTmxWR9q/hxuW74FevXu3qLgck/qFVmMw+1OcUYe224G02Ye22UHF44GeoI9NUUEFHdQN+wQF01jdTfVL8LXOFc5QbeVFu5EW5kRflRl6UG2Ni7GOYbDb8QgPwCxpP5dELhC+Mp9JZ+anejKmshurMfGLW3ELJBzqvgh4CwQnRfeeTiTDhximsEXir/er7cBvKjdP7cBvKjdP7cBvKjdP7cBujyI3jcdaTcQvKjVBGNgy9AO2sa6bicBaRy5IJjG2io7aJwNjJtJXVOs34hU4Am422ynr7GQojjMnXrPmgW3cdjKsH5UZelBt5UW7kZbS5AeP4UW6MiaEXoI6dXw254rvPr94tVuXsVZJCF8qNvCg38qLcyItyIy/KjTFRB9ErFAqFQqFQKEYUt74D2tVt4XRO3aBt5s4MwddsGqERDY09e/bQ1tbG0qVLaW9vp7S0lISEBDIyMli/fj1vv/02sbGxLFq0CEtXt6aSWRNunEJTfqmm8QQnRGPyNWudhkei3MiLciMvyo28DKcbxzWDWjPKjR3lRgy3LkBP59SxcOOfBm1z9I21LJh9/dqvUSsW4B8WSE1mHkEzoxg/JYysF/+EzTpwvUI9GVE2bdrUd7u8vJzk5GQA4uLiANi4cSMVFRWA/R/PvlXbhPu+7bnvcuh7aZrGs+a9p912nYhyMzjKzWWUm8soN4Oj3FxmON04NrhozSg3dpQbMQx9DWjx/mN9t0VfDejJ6GHSpEnX/X5ERMSw3adMKDfyotzIi3IjL8qNvCg3xkRdAzoEOi3wyxwo0lnPWjaOV9vrdDd3u3skQ8fj3NR4jpsuD3NzosZeBaWpy90jGTpdFng5Bwo9xE1mLTx/zl61zuh0W+GVHChsdvdIXIPDTYMHuSnwEDen60bGjUsXoAcPHiQ1NZXbb7+dd999l6NHj7Jz507h/NdXTefeZTGEBfsP2m7iollMW7dE09j0ZJzxzFn7H9Ij1ZDfpK+PqXctIv7BlcOecUZDF/z0FLyWD08N4Sg0WdzszLK7OVoDuY36+pDFTVMX/DTT7uYnHuAm9Vzv86YGLjTo60MWN83d9ufLrz3EzbPn4KXe5835Bn19yOKmpdvu5DcX4Scn9fcji5vnzsHuHDhaC2cH3zoxILK4ae2Bn570HDe7HG5q4JTB3bT3wI973fx4CG5E8LLZXHfwVVpaGps3b+all14iMjKSu+66i/fff5+77777ina7du2isLCQikZ/3jwW2/f9yRPH8tgDc3jsmculsjYsuEhEYAdjajuJO1DZ9/1p65Zcc4xCf/KWhwNozrSH+gnPN3fGeiom34qXpYv5J55lXFul81AvjvnE3H0rnY2t1J65RFfDwG87FC0KYeoR+79s0YyW+XT7jOVE0qN0jQkhtPoMN517TfNcHMjgJi/uXsqnLMbb0snNJ59nfGu5cFY+N2M4mfwYnf4hhNScZXbWHs1zcSCHm69QPmUJ3pYu5mY+T0BLmXBWNjc9Pv6cSHqMzjGhhNRkMTvrV5rn4kAKNzfeQ3nkP/S6eYGAFvFjbaRzY/LjRPLjvW7OMTvrVc1zcSCDm/wb/x9lU27D29pD4qk0JjQXC2e1uuk/n+Fz8xidY8IIqclmdtYrmufiQAo3sesoi/wHvKwWEk+/SGCT+Ef6srmxePtyYsHjdIwJI7j2PHPOviw8F4Do6Gi2bNki1Nal14C2tLSwfft2enp6WLhwIRkZGSxevPiado7BHcuq5s1+m5BWLJ7Ce4eKiYoYR3FFKwApKSksmH0DNafy2XfAftFt0IxIgpxU0EhJSQHQnNFyoW6nBX6dBzdO8OWOe7YK54C++disVgJjJ1P+ydlB22/csIFDR+wX7ItmtM7neDU8fx7SViUSYE4Vzsnopstif8cwNsCPL93zhHAOJHVTY3+V/eKqOR7kxpcv3fO4cA7kdHOixv7uVNrK2UzwNb6bX+dDzHhflt3zqHAO5HSTWWuvz/3CypsINLibbqv9783U8b4svydFOAfa3fSfz/C7SfAYN1HjvFlxj9jiy4GMbk7X2T/h/fnKWQRpcKMVly5At27dSnp6OomJiURFaSuxBrDnj2I1WBtyS8jc8ZamvvVknOFngm/HD62PwvQjzhu5ICNC8g1wcyUEDOG0Blnc+Jrg2zOH1odUbsJgXqhy40AmN0m9bib46u9DJjff8iA380LtX4Ee4MbsDf+k3FyBcjM8buaG2N0EDcGNCC7fBb969WpXdzlqiH9oFSazD/U5RVi7LXibTVi7LVQcznKaaSqooKO6Ab/gADrrm6k+KbaYV4ih3MiLciMvyo28KDfyMlrcuPUYprkzQzj6xlqnbcB+kKrj/CsRHIe16sm4DZsNv9AA/ILGU3n0AuEL46l0Vh6sN2Mqq6E6M5+YNbdQ8sEQdkPoQLkZPKPcDDPKjdOM21BunGbchnLjNOM2DOpGK25dgPqaTQMeMn81Jl+zroNUZTx8dSA665qpOJxF5LJkAmOb6KhtIjB2Mm1ltU4zfqETwGajrbIeXLevTAjlZvCMcjO8KDfyotzIi3IjL0Z1oxVDH0TvaTh25TXkiu88vXonX5WzV0kKXSg38qLcyItyIy/KjbyMFjdqAaq4LiK1bC9evMjDDz9MUVERSUlJ7h7yqEG5kRflRl6UG3lRbuRlON0YZgFq6erWVC7LcQ2H1ozJdwjbjD0IkVq2DrraO6g5lS/ct3IzNJQbeVFu5EW5kRflRl60uLFYLJr6NswCtD67kH2rtgm3d1xwrDXjrutEolYswD8skJrMPIJmRjHmhiBqz1ykp72L2tMXXZbRw0C1bB341XUqNy7I6EG5UW6cZZSba1FulBtnGeXmWpy50Vrf3jALUE+neP+xvtuir9T0ZBTaUW7kRbmRF+VGXpQbeRlNblxaC3600dgF/3kKfue6Fxhu5c9F8FkVlLa6eyRDp6kL/usU/Fb8kxqp2dfrpsQD3DR329287iFu0ovtbooHroRnGJq74b9O26u6eAJ/KbG7KfIANy29bl7zEDfv97op9BA3/93rRvKN50I43BQ0D+/9uHQBevDgQVJTU7n99tt59913+f3vf88777zjyrtg6l2LiH9w5bBnRHjmLPyxCF48D+fq9fUxcdEspq1bMuwZZ9R2wAvn4fMW2D54Va8BkcpNFrxTBLsvwNk6fX3I4qau07Pc7Ox189IFODXwqSKDIoubhk74ebbnuEnNgncK4Zc59vKvepDFTWMXvOBwc0ZfHzK5ebafm6PV+vqQxU1zNzzvQW6eOwdv97o5YnA3Ld1D/50mipfN5rr1elpaGps3b+all14iMjKSxsZGvL29ue+++65ot2vXLgoLtb1NPKa2k7gDlcTcfSudja3UnrlEV8PAL53ylocDaM60h/oJj+ni9LspjfwC3pYukk48w5gO8VWoYz4A09YtueYIhaspWhTC1COXV1IiGS3z6TH5cTIphY4xNzCx8jjxF94UyoGcbi5NX0NJ5BfwtnQz/0QqYzvEVzoyu7mh8iSzLvxOKAeSupm2mpKoL+Jt7bG7aRdf6cjmxmLy5WRSCu1jw+1uzv9WKAdyuvl82l0URy3FZO1h3snnGNtWpXk+IIkbbzMnkx6lfVw4N1RmMuv860I5kNRNzEqKp96BydrD3JO7GN9WqXk+IP44A5ozetyEVWWSkG1sNwUxKyiaegcmq4W5mT9nfGu55vmAHG6s3j6cTHqUtnERhFWdIiH7N0I5B9HR0WzZskWorUuvAW1paWH79u309PSwcOFCuru7r7srSnRw/ak5lc++A9uwWa0Exk6m/JPBl+YpKSkAmjNaLjy22uBgGXxSMYb/+MqPhHNweT5BMyIJmhnltP3GDRs4dCQNQDijdT7FLXCmHpZPWYDZe4FwTlY3fyuDjyt8+I+v/FA4B7K7ScLsLX7MhYxubL1uPqzw4T+//G/COZDTTWkrnKqD5VPmY/aeL5yT1c0H5fD3Mh/+88vimzZATjdlbZBZC8smz8PXNE84J7+brcI50O6m/3yUG+c43HxQZua/vvyEcA7kdXOyBpZNuRk/083COa24dAG6detW0tPTSUxMJCoqigULxBcxohSmHxmRjAjeXrB8CmTp/Pgd7AfNZu54a9gzIkSNt3/pRTY3y6bAWeUGkMuNlxfcOcW+oNaLTG6mjLN/6UU2N1+aDKd1XrYCcrmZPNb+pRflRrkRwSPdTHV5t9fg8l3wq1evdnWXAxL/0CpMZh/qc4qwdlvwNpuwdluoOJzlNNNUUEFHdQN+wQF01jdTfdJDruyWBOVGXpQbeVFu5EW5kRflxpgY+xgmmw2/0AD8gsZTefQC4QvjqXRWfqo3YyqroTozn5g1t1DyQebIjLcfwQnRfeeTiTDhximsEXir/er7cBvKjdP7cBvKjdP7cBvKjdP7cBujyI3jcdaTcQvKjVBGNgy9AO2sa6bicBaRy5IJjG2io7aJwNjJtJUNvOHEkfELnQA2G22V9W45N8Hka9Z80K27DsbVg3IjL8qNvCg38jLa3IBx/Cg3xsTQC1DHzq+G3BLNGQdVzl4lKXSh3MiLciMvyo28KDfyotwYE0MvQGVjz549tLW1sXTpUtrb2yktLSUhIYHs7GwSEhK4ePEiOTk5fPnLXyYjI4PJkyezZIlrz/BSXB/lRl6UG3lRbuRFuZEX5UYMty5Au7otnM4ZfNvY3Jkh+JpNIzSiobFp06a+2+Xl5SQnJwMQFxfX99+VK+2H4E4Oj+DCx8epOSVWDmbCjVNoyi/VNJ7ghGhMvmZNGU9FuZEX5UZelBt5UW7kZTjdOK7n1FJyU1Y3bl2Ans6pY+HGPw3a5ugba1kw+4Zrvh+1YgH+YYE0XSpj3JQwAqaGU344i66GFhpyiq/bl56MXiZNmjToz+uzCzl2/07h/m577rsc+l6apjGsee9pt1wnotw4R7m5PsqNcjMYys31UW5GjxvH5qN9q8TP6XWXG2cY9iP44v3HRiSj0I5yIy/KjbwoN/Ki3MiLcmNcDLsAlYHiFnguGyra7JvnvLzcPaKh8UoOnGuAh+JgToi7RzM0Slvh2XNQ7iFuXs21FzzYFAeJBndT1go7z3nO8+ZXeXC2Dh6Mg7ke4Cb1nL0SitVmL+hgZF7Lsx8O/kAszAtz92iGRkU7PHPWc9z8Os9eQez+WJhvcDeV7fCzs/a/O57g5jd5kFkH34iF5GF04+3Kzg4ePEhqaiq333477777Ln/5y1/4zW/E64h+fdV07l0WQ1iw/4BtJi6axbR12i7W1ZMR4cUL8FEF5DbZn0h6mHrXIuIfXDnsGWdUtsObl+CTStitczOgTG7SzsOHvW6Oi5cavwJZ3FR3eJabF/o9b44a3E1tB7x50XPcvHjB/rzJa4IM8TLwVyCLm/pOeKPXzS9z9PUhk5u07Mu/0w4b3E1DF7zR+zvtJU9w0/v3Jq8ZPq7Q14csbhr7uXlZpxtRvGw21x18lZaWxubNm3nppZeIjIxk8uTJ7N+/nyeffPKKdrt27aKwsJCKRn/ePBbb9/3JE8fy2ANzeOyZy+WyNiy4SERgB2NqO4k7UAnAtHVLrjlC4WrylocDaM60h/oJz7do6pcomnon3tZuko//DN+uZuGsYz4xd99KZ2MrtWcu0dXQMvB9LQph6hH7Klc0o2U+Fm8zp+Y9Quu4CCaVfUpc/jua5wISuYm6g6LoO/G29jD/+DP4dzUJZ+V086+0jpvEpPLPiMt7W/NcQCY3t/e6sTD/+E78uxqFs7K5sXr7kDnvkV43GcTl/UHzXEAeN8WRX6QwZrndzYln8e8Ur5cqnRsvE5nzt9A6bhIRFUeYkbtX81xAHjclU75AYcwKvLAy73gqYwzu5tS8R2gZP5mIiqPMyP0/zXMBmdz8A4UxK3vdPMuYTvF3pLS66T+f4XHj3etmCuEVx5iZ+7/CcwGIjo5my5YtQm1d+hF8S0sL27dvp6enh4ULF2IymZgxY8Y17RyDO5ZVzZv9NiGtWDyF9w4VExUxjuKKVgBSUlJYMPsGak7ls+/ANoJmRBIkUD0jJSUFQHNG64W6Bc0Q5OtH0L1Paco55mOzWgmMnUz5J2cHbb9xwwYOHbFfFC6a0Tqflm7YcRaeWvcPeHn9g3BObjcQdO9PNeVkdbP9DPz7utvw8rpNOCetmxYIMkPQvT/RlJPbzRK8vMTfXZHZTaAZgu/9kaacjG5ae+Dp0/Dv6xbj5bVYOCerm8IWmGCG4K94jpun1t2Kt9etwjmZ3QSYIeQrTzpv3A+tbvrPZ7jctPXA/5yGp9bdgrfXLcI5rbh0Abp161bS09NJTEwkKsouef78+cL5PX90XoO1IbeEzB1vaRqXnowoMQFDyxemH3HeyAUZEcab7Qs2vdfkKTfD6ybYz4PcjB9aXrlRbkQY5+NZbqI90I3e6yWVm+FzM3aIbkRx+Sak1atXu7rLUUP8Q6swmX2ozynC2m3B22zC2m2h4nCW00xTQQUd1Q34BQfQWd9M9Unni3mFOMqNvCg38qLcyItyIy+jxY1bd8HPnRnC0TfWOm0D9oNUHedfieA4rFVPxm3YbPiFBuAXNJ7KoxcIXxhPpbPyYL0ZU1kN1Zn5xKy5hZIPMkdmvL0oN4NnlJthRrlxmnEbyo3TjNtQbpxm3IZB3WjFrQtQX7PpuofMXw+Tr1nXQaoyHr46EJ11zVQcziJyWTKBsU101DYRGDuZtrJapxm/0Algs9FWWW8/22YEUW4Gzyg3w4tyIy/KjbwoN/JiVDdaUeeASoRjZ15DbonmjIMqZ6+SFLpQbuRFuZEX5UZelBt5GS1uDLMAtXR1a659Cp5RL9Ud7Nmzh7a2NpYuXUp7ezulpaUkJCSQkZHB+vXree2116ipqeHrX/86dTW1TPcPFe5buRkayo28KDfyotzIi3IjL1rcNDY2kpSUJNy3YRag9dmFmmufgmfUS3UHmzZt6rtdXl5OcnIyAHFxcQA8/PDDfT8/V1DNvnuUm5FCuZEX5UZelBt5UW7kRYubigptp/AbZgHq6UStWIB/WCA1mXmMjQgh7OYbqTtXQHdL+4A73/Rk9DBp0qRBfx4eJnYdr1FRbuRFuZEX5UZelBt5MbKbiIgITf2pBagkFO8/1ne7PruQUoHda3oyCu0oN/Ki3MiLciMvyo28jCY3Lq0FP9o4UwffOgT/dhx6rO4ezdCw2eyVDw6Uwodl7h7N0DlbB98+DD/wEDdP97r5wAPcnKuHbx/yHDfbz9jd/M0D3GT3utl2DLo9wM3Pet38tdTdoxk6FxrsbrZ6ipuzdjcHxPfZSEtOo/3vzfePQZfF3aMZOs9k2d28P8xuXLoAPXjwIKmpqSxfvpy9e/fy5z//mbffFq9bLcLUuxYR/+DKYc+I8LuLkFkHfy2DU+KlX69g4qJZTFsnXr5Pb8YZFe2wvxRqOuF/C/T1IZubk7V2N8dr9PUhi5vKDjhQZnfzfwX6+pDJzW8vwsne583Ran19yOKmptP+i7qmE/7vc319yOTmd5fsbg6WQ0aVvj5kcVPbefl32h8K9PUhk5vX8+1u/lYOhyv19SGLm/qufs8b8b1BVyCdm1r4oBw+Mbibhk7YXzK0540oXjab6w6KSktLY/PmzezYsYOIiIi+6wHuuuuuK9rt2rWLwkJt/+rG1HYSd6CSmLtvpbOxldozl+hqaBmwfd7ycADNmfZQP+ExlU+6haLoZdjwIun4zzD3tGueD8C0dUuuOULhaooWhTD1yOVVrkhGy3ysXibOzH2Y1nGTmVLyETGFB4RyIKmbiEUUxSzDhjdJx5/B3NOmeT4goZvSj4kp2C+UA1ndLKAoZmXv88bobrw5m/gwLeOnMKX0E2IK3hfKgZxuKiKSKYxZhc3Li6TjOzF3t2qeD8jhxublzZleN5NLDzGt4D2hHEjqJjyZgpiV2LxNzD++E7/ugfseaD4gk5vNtIyPZHLZYaZ9/hehHMjppnLifAqm3YV1BNz0n4+WjLAbvDgz92FaxkcyqexTpn+eLpRzEB0dzZYtW4TauvQa0JaWFrZv305PTw/BwcFYLBa6urquaSc6uP7UnMpn34Ft2KxWAmMnU/7J2UHbp6SkAGjOaN351tYDL56Hx7/6X5pyjvkEzYgkaGaU0/YbN2zg0JE0AOGM1vlYbNDeA+PNKwHxV4nyu/lPTTm53awAVgjnlJvhd2O12ecz3rwcWC6ck97N+v/QlJPbzTJgmXBOfjf/riknt5s7gTuFc6PdTf/5DL+bLwFfEs5pxaUL0K1bt5Kenk5iYiJRUc4fFD0Uph8ZkYwoY33A20t/viG3hMwdbw17RgSTF4wfwtFnyo1yI4onufFWbq5AuVFuRFBursST3Iji8l3wq1evdnWXAxL/0CpMZh/qc4qwdlvwNpuwdlsGPXbAkWkqqKCjugG/4AA665upPpk3YuMeDSg38qLcyItyIy/KjbwoN8bE2Mcw2Wz4hQbgFzSeyqMXCF8YT6Wz8lO9GVNZDdWZ+cSsuYUSNxxZEJwQ3XdArggTbpzCGoG32q++D7eh3Di9D7eh3Di9D7eh3Di9D7eh3Di9D7cxitw4Hmc9Gdkw9AK0s66ZisNZRC5LJjC2iY7aJgJjJ9NWVus04xc6AWw22irr7WdCjDAmX7Pma0yMVJlBuZEX5UZelBt5UW7kZbS5AWP5GQhDL0AdO78acsUPq7p6t1iVs1dJCl0oN/Ki3MiLciMvyo28KDfGxK0L0K5uC6dzBj9Ac+7MEHzNphEa0dDYs2cPbW1tLF26lPb2dkpLS0lISCA7O5uEhAQuXrxITk4Oq1ev5sSxY4TZxjA3YbZQ3xNunEJTvrbTlIMTojH5jsCVxAZAuZGX4XTj+OipPlv82Dfl5jLKjbwoN/Ki3Ijh1gXo6Zw6Fm7806Btjr6xlgWzjVH7ddOmTX23y8vLSU5OBiAuLq7vvytX2o83CmqB3y3/HsUmf6G+b3vuuxz6Xpqm8ax572mPeJveFSg38jKcbhzXSe1btU14PMrNZZQbeVFu5EW5EcOwH8FHrViAf1ggTZfKGDclDP+QCdScvkhXQwsNOcUuy+hl0qRJTtsECv6DMxrKjbwoN/Ki3MiLciMvyo1xMewCtHj/sRHJKLSj3MiLciMvyo28KDfyotwYl2GtBb93716ysgY+h+tqvr5qOvcuiyEseOBXA7LUSwX4sAy+9nf4Wxl09OjrQ5Z6thYbPH4UvvoB7NVZ01omNx9VwNd73bQb3I3VBk/0utFbb1wmN5/0ujlYZq+2oQeZ3Hz/mN3N/3qAm8P93LR6gJutx2D9B/DWJX19yOTm00rY8Hf4a6lnuNl23O7mjYv6+pDJzWdVl920dOvrQxY3Nhv8oNfN7/Jd2vU1uHQBmpOTwyOPPMLSpUtpamrqu+5BlI9PVHDr3HBq6jsGbFN15LzmcenJiPBeKVxshsoOyGrQ14e3yZvGS2X4Bo0f1owzKtvheA1caoEPyvX1IZWbEsjvdXNq8H1uAyKLmyoPc/OXXjdVHZA58CkpgyKLm5oOz3VzokZfH7K4qeuE47XweQv83QPcpJdAXjNUd8KRKn19yOKmvhOOV9vdfOgJboovu/nM4G4auuy/0z5vgQ8rXNbtdRnWWvC5ubm0tLQwe/aVu7t27dpFYWEhFY3+QGzf91csnsJ7h4qJihhHcUUrAM8++ywRgR2Mqe0kDvG6tM8++yyA5kx7qJ/wfGtCZzN22kpsXj68/nQqb1ivrXs/EI75iNamfePNN5nae1s0o2U+NrzwvelBzBOiqf30AI/u/VQoB0jq5ibGTluF1duHt3ak8n8e4qbOY9zchdXbh/99Zid/sAyfm/7zGS435oRvYg6Mof6zgzy695BQDuR0Uxsyi7HTV2P1NrN3Zyp/tHQKZ+V08wDmwGnUZ/yNR//wiVAO5HRTFzyTsbF3Y/X25Y/P7mSfwd34JNyPOXA6DUc+4NG3PxbKgdxuLCZf/vxcKu9ZBn4T7WrkcwOmhPsxB8bSePTvPPr2R0I5B9HR0WzZskWorZfN5tqTV7XUgj+WVS28C77mVL7mXV8g704xrfMZqZ3WqVnwqNhpEH0oN8qNrG5Gaj7KjbzzUW7knY9yI+989LjRiqFrwSsUCoVCoVAojIdhd8F7IvEPrcJk9qE+pwhrtwVvswlrt4WKwwNv5HJkmgoq6KhuwC84gM76ZqpP5o3gyD0f5UZelBt5UW7kRbmRl9Hixq0L0LkzQzj6xlqnbcB+kr/jrWcRHNUC9GTchs2GX2gAfkHjqTx6gfCF8VQ6Kw/WmzGV1VCdmU/Mmlso+SBzZMbbi3IzeEa5GWaUG6cZt6HcOM24DeXGacZtGNSNVty6APU1m4SrHJl8zbquyZDx9P+B6KxrpuJwFpHLkgmMbaKjtonA2Mm0lQ28VdiR8QudADYbbZX19nMURhDlZvCMcjO8KDfyotzIi3IjL0Z1oxX1EbxEfP7uYQAacks0ZxxUOXuVpNCFciMvyo28KDfyotzIy2hxY5gFqKWrm/rsQuH2jrfQtWZMvmbNY/NE9uzZQ1tbG0uXLqW9vZ3S0lISEhLIyMhg/fr1/PnPf6a2tpZ7772XS/kXme4fKty3cjM0lBt5UW7kRbmRF+VGXrS4KSwsJCkpSbhvwyxA67MLpTyqwFPZtGlT3+3y8vK+ogJxcXEArF+/vu/nlafy2HePcjNSKDfyotzIi3IjL8qNvGhx09OjrUSXYRagnk7UigX4hwVSk5nH+Mgb8DabaC2rw2eMLxWfnnNZRg+TJk0a9OfhYWLX8RoV5UZelBt5UW7kRbmRFyO7iYiI0NSfWoAOkfYeMHuDzxCLmhbvP9Z3W/SjAj2ZwbDaoNs65G6kQbmRF+VGXpQbeenosXtRbuRDudGOS2vBHzx4kNTUVJYvX87evXv55JNPePfdd115F0y9axHxD64c9owI7xTCvR/ANz+BJvFqglcwcdEspq1bMuwZZ3RbYfNh2FcMv8zR14dMbv5YCOv/Dt/8GBoN7qbHCg9/anfzks7rymVy86deNw98bK87rAeZ3Hyn181unWWmZXKzr8ju5v6PoUG80uMVyOjmRQ9wk14M9/a6qROv9HgFsrix2OCfP7O7ScvW14dMbt4rsbu57yOo9QA33+1184JON6K4tBRnWloamzdvZseOHURERPC1r32NgwcPsm7duivaOWrBa2FMbSdxByqJuftWOhtbqT1zia6GlgHb5y0PB9Cc0VL/NTvhfmomzgObjTmnXiS48aLm+QBMW7fkmh1sV1O0KISpR+r6/l8ko2U+HX7BnEx+jB7zWILqckg885JQDiR1M+t+asJ73Zx5ieD6XM3zAVncBPW6GUdQfS6Jp3cL5UBWN/dREz4fbDZuOvNLQuvFX/FoddN/PloyovPp9J3AieTH6fEdT2B9LnMN7ub8rG9QHZ5kd5P1CqG14is32dx0+QZwPPmJXjd5zD39C6EcSOom/htUR9g3eCSc/RVhtQMfSn410rkxj+fEgifo9g0gsD6fuadfFMqBnG4uxG+kKsJ+beSsrD3cUDN4bfb+yOam2zyO4wu+T7dvABMa8rn5lLgb0FYL3qUfwbe0tLB9+3Z6enoIDg7m+eef584777ymnejg+lNzKp99B7Zhs1oJjJ1M+SeDC05JSQHQnNFy4fHpOvh5NtR1evHyj76r6a13x3yCZkQSNDPKafuNGzZw6Ii93rhoRst8bDb4nzNwsQm+kTyTOx5MFcqBnG7O1sEuh5sfPmx4N0+fgbwm+EbyDL70TeO7eT4bajq9eOWHmzEPo5v+8xluNxuTZ3Cnwd1k10PqOajt8OKVH3zL8G52nIWcRvj6/DiWG9zN+V43NR3wyraH8DUJR6V087OzcKERvjr/RlYa3M2FBnvt9KoOeHXrJkO7Abub8w29bh4Qd6MVly5At27dSnp6OomJiURFOX9Q9FCYfmREMiLMDYFXbrP/w9N73UdDbgmZO94a9owzvLzg3+YOrQ+Z3MzxMDc/8DA3L/e60bLA6Y9yMzxuEoIvP288wc3WxKH1IZObWcGXnzdaFjj9kcnN9z3ITXwQ/NJD3AA8McflXV4Xl29CWr16tau7VCgUCoVCoVB4EIbeBR//0CpMZh/qc4qwdlvwNpuwdluoODzwtTGOTFNBBR3VDfgFB9BZ30z1ybwRHLnno9zIi3IjL8qNvCg38qLcGBNDL0Cx2fALDcAvaDyVRy8QvjCeSmflp3ozprIaqjPziVlzCyUfZI7MePsRnBDdd0CuCBNunMIagWs9rr4Pt6HcOL0PtzGK3DgeZz0Zt6DcCGXcgnIjlHELyo1QRjYMvQDtrGum4nAWkcuSCYxtoqO2icDYybSV1TrN+IVOAJuNtsp6+xXRI4zJ16y50oKRKjMoN/Iy2tyAcfwoN/Ki3MiLcmNMDL0AdRw90JBbojnjoMrZqySFLpQbeVFu5EW5kRflRl6UG2Pi1gVoV7eF0zl1g7aZOzMEX7PObWUjzJ49e2hra2Pp0qW0t7dTWlpKQkIC2dnZJCQkcPjwYby8vFi8eDGnTp5kbDssujlJqO8JN06hKb9U03iCE6Ix+Zr1TMXjUG7kZTjdOD560lIdRLm5jHIjL8qNvCg3Yrh1AXo6p46FG/80aJujb6xlwexra786ap82XSpj3JQwzGP9qTqWA14Di9GT0cKmTZv6bpeXl5OcbD+YNi4u7or/AgS3evG75d+j2vR/Qn3f9tx3OfS9NE3jWfPe0255m165cY5yc5nhdOO4Tmrfqm3C41FuLqPc2FFuxDLKjR3lRgzDfgTfv/bpcGb0MmnSJKdtAk3+IzCSkUe5kRflRl6UG3lRbuRFuTEuLq0FPxopbIFOi7tH4Rpauu2VkNxwHfaw4EluWnsgX7mREk9zU9QCHR7ipk25kRZPc1Os3GjGpQvQgwcPkpqayvLly9m7dy8ZGRns3btXOP/1VdO5d1kMYcEDvxqYuGgW09Yt0TQuPRkRfpUL938M+0vt5dH0MPWuRcQ/uHLYM87o6IFvH4YHPoZnxMvYXoFMbl7Ls89lfylUtevrQxo3Fvj2Ifjmx/bSgnqQyc1v8i8/byrb9PUhi5tOi/15882PYfsZfX3I5Oa3vW4OlEK5wd10WS7/TnvaA9y8cfHy77SyVn19yOKm2wqbe93892l9fcjk5q2Ll3+nlRjcTY8VNn9qd/OfOt2I4mWzuW6Nm5aWxubNm9mxYwcRERGsXLmS999/n4ceeuiKdrt27aKwsJCKRn/ePBbb9/3JE8fy2ANzeOyZy+WyNiy4SERgB2NqO4k7UAnAtHVLrtnBdjV5y8MBNGfaQ/2E55s9635qwueB1ULiqRcJavpcOOuYT8zdt9LZ2ErtmUt0NbQM2L5oUQhTj9g3bIlmtMyn0y+QE0mP0eM7nqC6CySe+aXmuYA8bs7Puo/q8Plgs5J4+hcENVwUzkrnxncCJ5If73WTQ+KZlzTPBWRy8w2qw5PAZmX26ZcIaRA/+Fmrm/7zGQ43Xb4BnEh+nG7fAALrcpl7ZrfmuYBEbuI3Uh2RbHdz5mVC6nOEs9K5MY/jRPITdPtNIKgul0SDu7kQv5GqXjc3nX2V0LrzwlnZ3HSbx3G8101gfR5zT/9C81xAJjcbqIpYADYrCVl7CKs9J5yVzo3PWI4veIJuv0ACG/KZe+pF4bkAREdHs2XLFqG2Lr0GtKWlhe3bt9PT00NwcDAFBQUEBwdf084xuGNZ1bzZbxPSisVTeO9QMVER4yiusL+MSElJYcHsG6g5lc++A9sImhFJkMCh3ykpKQCaM1ou1C1qgeeyYfIYE4+t+1e8vISjffOxWa0Exk6m/JPB39rauGEDh47YN7qIZrTO55c5sK8I/mttPHMeTBXOyeimpBWePQcRY7x5fO13De/mlRz4UzH8x20zmWtwN6WtkHoOIvy9eWztd/AeRjf95zNcbl7NgXeL4aklM5hncDdlrbDzHIT7e/P42s2Gd/OrXPhjEfx08Qzme4ibif7ePLH2W4Z3sycP3imEnyyOI+mbxnZT0W7/5DDM35vvr/1Hw7t5LQ/eLoQfL76R5AfE3WjFpQvQrVu3kp6eTmJiIlFR2irDAOz5o/N3QhpyS8jc8ZamfvVkRJg6HlIXDq2PwvQjzhu5ICPCt2farwOdE6IvL5ObyHGw04Pc/NNMaOqGuR7gZoqHufnHmdDYDfNC9eVlcjPZw9w8NAMaumB+mL68cjN8bjbFQX0nJHmAm4gx8IwHuXkwDuo6IVmnG1Fcvgt+9erVru5y1KDq2cqLciMvyo28KDfyotzIy2hx49ZjmObODOHoG2udtoFRUi/VoPVslZvBM8rNMKPcOM24DeXGacZtKDdOM27DoG604tYFqK/ZdN1D5q/HaKiXatR6tsrN4BnlZnhRbuRFuZEX5UZejOpGK4Y9iN4TUfVs5UW5kRflRl6UG3lRbuRltLhRB9ErFAqFQqFQKEYUw7wDaunq1lSj1XENh9aMydeseWyeyJ49e2hra2Pp0qW0t7dTWlpKQkICGRkZrF+/nnfeeYf8/Hy+853vcCn/ItP9xbcAKzdDQ7mRF+VGXpQbeVFu5EWLm8LCQpKSkoT7NswCtD67kH2rtgm3d1xwrDXjrutEolYswD8skJrMPAKmTmTMxGDaKutpLauh7uz1D7jXkxFl06ZNfbfLy8tJTk4GIC4uDoANGzb0/bzyVB777lFuhpoRRbm5jHLjPKPc2FFuLqPcOM8oN3a0uOnp6dHUt2EWoJ5O8f5jfbdFX6npyehh0qRJg/48PExsI5lRUW7kRbmRF+VGXpQbeTGym4iICE39qWtAh4DVBh+UQX6Tu0fiGkpboaDFXgvW6Nhs8Pdyz3FT1gYFzfYaykbHZoMPyyFPuZGOPjeN7h6Jayhrg8+b7XXhjY7DTW6Du0fiGso9zM1H5ZDT4O6RuIaKEXLj0gXowYMHSU1NZfny5ezdu5fc3Fxef/11V94FU+9aRPyDK4c9I8LPs+EHx+FfPrMv3vQwcdEspq1bMuwZZ7R0w79mwKdV8O+n9PUhm5ttx+C7n0HxwCVyB0UWN6099n9jn1bDUzqPdZPJzQvnYdtx+NfPoNDgbtp77PPwFDcv9rr5lwy4pPMFgixuOnrgkQz4rBp+6gFudl/odXPEA9xY7H9vPquGn3iAm1/mwNbj9jnpfWEti5vOfm5+dNKlXV+Dl83muoOi0tLS2Lx5Mzt27CAiIoKpU6fS0dHBmjVrrmi3a9cuCgu1vU08praTuAOVxNx9K52NrdSeuURXw8B/vfKWhwNozrSH+gmP6UL8N6iKSMLL0sPcU88zoVn8yATHfACmrVtyzREKV1O0KISpR+r6/l8ko2U+Xb4BnEh6jG6/CQTXZjPn7CtCOZDVzQaqIhaApYe5p9IIbBb/9yadG/N4TiQ/RrdfIMG155lz9mWhHMjpJmfm16mctBCsPcw9/QsCG8WvUdLqpv98tGRE59NtHseJ5Mfp8gskqO4CiWd+KZQDWd18jcpJi8DaQ+LplwhqvKh5PiCJG5+xdjf+QQTV5ZB45iWhHMjvZs6ZXxLckK95PiCLmzG9boI9w82Mr1I5+Raw9jD77CuE1Odqng/I4abH5M+JBU/Q6R9MUF0uiWd2C+UcREdHs2XLFqG2Lr0GtKWlhe3bt9PT00NwcDBVVVXU1dVd0050cP2pOZXPvgPbsFmtBMZOpvyTs4O2T0lJAdCc0XLhcX0nvJANha0+vPLUo8I5uDyfoBmRBM2Mctp+44YNHDqSBiCc0Tqfd4sgsxb+6UsJRG5KFc7J6Kaxy/4uaEGLD688pe3fm4xu/lQEJ2rgn+6YRZTB3TR1wfMONz/5V+EcaHfTfz7D5ebPvW4euiOeqQ8a201zN+w6B5+3+PDqT74rnAM53aQXwdEa+Mc7ZhreTUuvm4vNPrz6o3/Gy0s4KqebYjhaDZtun0mMJ7jJhotNPrz65MOGd/NeCWRUwYO3z2CaBjdacekCdOvWraSnp5OYmEhUlPMHRQ+F6UdGJCNCsB/8aB6kDlye1SkNuSVk7nhr2DMirJtq/9KLTG4CfeHJmz3Hzdqp9i+9yORmgoe5uXuq/UsvMrkJMHuWm9VT7V96kcnNeDP88Ga7Gy0LnP5I5SbK/qUX6dzM9Rw3qyLtX8ONy3fBr1692tVdDkj8Q6swmX2ozynC2m3B22zC2m2h4vDAvz0dmaaCCjqqG/ALDqCzvpnqk3kjNu7RgHIjL8qNvCg38qLcyItyY0yMfQyTzYZfaAB+QeOpPHqB8IXxVDorP9WbMZXVUJ2ZT8yaWyj5QOdV0EMgOCG673wyESbcOIU1Am+1X30fbkO5cXofbmMUuXE8znoybkG5Ecq4BeVGKOMWlBuhjGwYegHaWddMxeEsIpclExjbREdtE4Gxk2krq3Wa8QudADYbbZX19jMURhiTr1nzQbfuOhhXD8qNvIw2N2AcP8qNvCg38qLcGBNDL0AdO78acsV3n1+9W6zK2askhS6UG3lRbuRFuZEX5UZelBtjog6iVygUCoVCoVCMKG59B7Sr28LpnGuPaerP3Jkh+JpNIzSiobFnzx7a2tpYunQp7e3tlJaWkpCQQEZGBuvXr+fDDz8kJyeH1atXU1dTy3T/UOG+J9w4hab8Uk3jCU6IxuRr1joNj0S5kRflRl6UG3lRbuRlON04rufUUnJTVjduXYCezqlj4cY/Ddrm6BtrWTD72tqvUSsW4B8WSE1mHsEJ0QRMDafuXAHdLe0D7nzTk9HCpk2b+m6Xl5eTnJwMQFxcHAArV65k5Up7FYZzBdXsu2ebcN+3PfddDn0vTdN41rz3tFuuE1FunKPcXEa5saPcOEe5uYxyY2e0uXFsPtq3SltGxmtGDXsNaPH+Y323RV8J6MnoZdKkSYP+PDzs2kW1p6DcyItyIy/KjbwoN/Ki3BgXdQ3oEOiy2GvAFuusAy8bJ2ogLdte1cHodFngFeVGSrqt8EouFOmsAy8bmbX2imjNHuLmVQ9z8/Nse/Uto9NthV95kJvTdXY3jR7gpqfXTWGzu0fiGs6MkBuXLkAPHjxIamoqy5cvZ+/evfzqV7/i3Llzwvmvr5rOvctiCAv2H7DNxEWzmLZuiaZx6cmI8EyWfQGaUQUXm/T1MfWuRcQ/uHLYM85o7IKfZMKefHhK51FoMrnZmQW7c+BINeQ16utDFjdNXfDTXjc/9QA3qVmw+4K9RGJOg74+ZHHT0g0/Pgmv5dufP3qQyc2zWfCLC3CkBs436OtDJjc/OQm/9hA3z52DF3vdZA2+dWJAZHHT1mN/3vw63+5IDzK52ZV92c0Zg7tp74Ef9br5sU43onjZbK47+CotLY3NmzezY8cOIiIiCA8PJywsjEWLFl3RbteuXRQWFlLR6M+bx2L7vj954lgee2AOjz1zuVzWhgUXiQjsYExtJ3EHKgGYtm7JNUcoXE3e8nAAzZn2UD/h+ebOWE/F5FvxtnQy78RzjGurFM465hNz9610NrZSe+YSXQ0Dv7QtWhTC1CP2f9miGS3z6fYZy4nkx+jyDya0+iw3ndujeS4gkZu4e6mYshhvSxc3n9zF+NZy4ax8bsZwMukxOseEEFJzltlZxnaTF/cVyqcswdvSxdzM5wloKRPOyuamx8efE8mP0+kfQkhNFrOzfqV5LiCRmxu/THnkbXhbukjMfIEJLeLH2kjnxuRndzMmlJCac8zOelXzXEAeN/k33kNZ5D/gZelm7qk0JjQXCWdldhNcm82cs69ongtI5Cb2/1EW9QW8LN0knv4FgU0FwlmtbvrPZzjcWEy+nEh+nI4xYQTXnmfO2ZeF5wIQHR3Nli1bhNq69BrQlpYWtm/fTk9PD8HBwYSEhFBYWHjNAtQxuGNZ1bzZbxPSisVTeO9QMVER4yiusH92mpKSwoLZN1BzKp99B7YRNCOSIIGqMykpKQCaM1ou1O2wwJ48iAvw4857tgrngL752KxWAmMnU/7J2UHbb9ywgUNH7BeFi2a0zudItf2jxBdXzSHAnCqck9FNZ6+b2ABflt3zhHAO5HRztNr+kUjayjlM8DW+m9fyYFqAL8vveVw4B3K6OV4Nz2VD2srZBBrcTVevm+jxvqy451HhHEjqpsb+zuELK28iyEPcTB1vZuU93xPOgZxuTtTAs+fghZUJhnfTbbW7iRpnZuU9jwjnQLub/vMZLjcnayD1HPx8+SyCN4m70YpLF6Bbt24lPT2dxMREoqLskm+99Vbh/J4/Oq/B2pBbQuaOtzSNS09GBH8TfCd+aH0Uph9x3sgFGREW3QCHQyFA52kNMrnxM8HDHuRm4Q0wLxQm+OrLy+Zmswe5Sb4B5odCoAe48TXBtz3JTZjdTZByA8jlJsmD3Ji94Vszh9aHTG7m97oZ5GpIl+DyXfCrV692dZejhviHVmEy+1CfU4S124K32YS12zLosRCOTFNBBR3VDfgFB9BZ30z1SeeLeYU4yo28KDfyotzIi3IjL6PFjVuPYZo7M4Sjb6x12gbsB6k6zr8SwXFYq56M27DZ8AsNwC9oPJVHLxC+MJ5KZ+XBejOmshqqM/OJWXMLJR/ovOJeJ8rN4BnlZphRbpxm3IZy4zTjNpQbpxm3YVA3WnHrAtTXbLruIfPXw+Rr1nWQqoyHrw5EZ10zFYeziFyWTGBsEx21TQTGTqatrNZpxi90AthstFXWg+v2lQmh3AyeUW6GF+VGXpQbeVFu5MWobrRi2IPoPRHHzryGXPGdp1fv5qty9ipJoQvlRl6UG3lRbuRFuZGX0eJGLUAV18VZLdu33nqLadOm8cUvftHdQx11KDfyotzIi3IjL8qNvAynG8MsQC1d3ZpKZjmu4dCaMfnq3ALuYTirZfvggw9SUVEBKDcjjXIjL8qNvCg38qLcyIsWN1oxzAK0PruQfau2Cbd3XHCsNeOu60SiVizAPyyQmsw8xkaEEHjjFJoLK+luaR9w55uejB4GqmUbEREBKDeuyuhBuVFunGWUm2tRbpQbZxnl5lqcudGKYRagnk7x/mN9t+uzCykV2L2mJ6PQjnIjL8qNvCg38qLcyMtocuPSWvCjjaYu+K/T8OZFd4/ENaQXQUY1lLW6eyRDp7nb7uYNT3FTbHdT6kFufushbt4rsbsp8QA3Ld3wP6fht/nuHolreL8EPquC4oGrFBqGlm74nzPwG3mPddTE/l43RR7gprXH7ubXHuLmQKndTeEwu3HpAvTgwYOkpqayfPly9u7dyzvvvENGRoYr74Kpdy0i/sGVw54R4Wdn4Z1CSDsP2fX6+pi4aBbT1i0Z9owzajvg+fNwqRmeHryq14DI6uZsnb4+ZHFT32kvw3mpGbZ7gJudWXY3u8/DqYFPFRkUWdw0dMHz53rdnNHXh0xuUrPgD4Ww+4K9VKIeZHHT2AXPZ8PnLfC0B7h5Ngv+UAAv5djLv+pBFjfN3bDLQ91kVOnrQxY3Ld2w69zQ3IjiZbO57qCotLQ0Nm/ezI4dO4iIiKCxsZGkpCS+8IUvXNFu165dFBaKXxAMMKa2k7gDlcTcfSudja3UnrlEV8PAy/O85eEAmjPtoX7CY7o4fS2lkf+At6WLpBM7GdMhvtJxzAdg2rol1xyhcDVFi0KYeuRy/yIZLfPpMflxMulROsaEcUPlCWZdeEMoB7K6uZvSyC/gbe1m/vFUxnaI/zWV000KHWMnckPFCWZd+J1QDuR0c2n6Gkoiv4DJ2sO8E6mMbTeuG4vJl5NJj9I+diI3VJ5k1vnfCuVAUjfTVlMS9UVM1h5uPvkc49rE/5pK58bbl5NJKbSPCyesMpOE868L5UBON59Pu4viqKWYrD3MPbmL8W2VmucDsrgx258348IJqzpFQvZvhHIgqZuYlRRPvQNvaw83Z/6c8a3lmucD4o8zoDmjx01o9WluOvdroZyD6OhotmzZItTWpdeAtrS0sH37dnp6eggODiY8PPy6C03RwfWn5lQ++w5sw2a1Ehg7mfJPBn8rKCUlBUBzRsuFx1Yb/LUUPqkYw39+5UnhHFyeT9CMSIJmRjltv3HDBg4dSQMQzmidT1ELnK6DlZHJmL2ThXOyujlYCh9XmPjPL/+bcA7kdXOqDlZFJmH2ThLOye3GxyPcFLdAZh2snDIfX9N84ZzMbj6q8OG/viy+aQPkdFPSCidrYcWUefiZ5gnnZHRjs8Ffy+DDch/++8tbhXMgu5ub8TPdLJyT1c3BMvh7uQ///eUnhHOg3U3/+QyXm7I2OF4DK6bMxc+UKpzTiksXoFu3biU9PZ3ExESiopw/KHooTD8yIhkRvL1gRSSca9DfR0NuCZk73hr2jAhTx9u/9CKbm+WRkNWgvw/lRrkRIWq8/Usvys3wuYkcZ//Si0xuvLxg+RTI0nm5Fyg3w+lm2RQ46yFuJo+FtVNd3u01uHwX/OrVq13d5YDEP7QKk9mH+pwirN0WvM0mrN2WQY8dcGSaCiroqG7ALziAzvpmqk96yNXDkqDcyItyIy/KjbwoN/Ki3BgTYx/DZLPhFxqAX9B4Ko9eIHxhPJXOyk/1ZkxlNVRn5hOz5hZK3HBkQXBCdN/5ZCJMuHEKawTear/6PtyGcuP0PtyGcuP0PtyGcuP0PtyGcuP0PtzGKHLjeJz1ZGTD0AvQzrpmKg5nEbksmcDYJjpqmwiMnUxb2cBbax0Zv9AJYLPRVllvv4BjhDH5mjUfdOuug3H1oNzIi3IjL8qNvCg38jLa3ICx/AyEoRegjp1fDbklmjMOqpy9SlLoQrmRF+VGXpQbeVFu5EW5MSaGXoDKxp49e2hra2Pp0qW0t7dTWlpKQkICGRkZrF+/ng8//JAxY8bwxS9+0d1DHXUoN/Ki3MiLciMvyo28KDdiuHUB2tVt4XTO4Gdnzp0Zgq/ZNEIjGhqbNm3qu11eXk5ysv0oo7i4OABWrlxJRUUFAJaubuqzxc9CnXDjFJrySzWNJzghGpOvWVPGU1Fu5GU43TiufdKaUW7sKDfyotzIi3IjhlsXoKdz6li48U+Dtjn6xloWzL7hmu9HrViAf1ggNZl5BCdEMy4ihMoj57F09VB7+vo1/vRk9DJp0qTrfj8iIgKw/+PZt0r8nL3bnvsuh76XpmkMa9572i3XiSg3zlFuro+r3Tgu1NeaUW6uRblRbpxllJtrGc1unGHYj+CL9x/ruy36SkBPRqEd5UZelBt5UW7kRbmRF+XGuBh2ASoDJa3w3DmoaLdvnvPycveIhsavcu2HHP/jDLgp2N2jGRplrfDsOXtFB49wk2d3s+lGmBPi7tEMDU9z81oenPEQN+Vt9nrwZW32qkjeBnfz6zx7dbcH4uBmg7upaIedWVDa6hlufpNnr+52fyzMC3P3aIZGVTs8k2VfE3iCm9/mQ2YtfCMW5g+jG29Xdnbw4EFSU1NZvnw5e/fu5ejRo+zcuVM4//VV07l3WQxhwf4Dtpm4aBbT1i3RNC49GRHSzsOHFZDTaP8lp4epdy0i/sGVw55xRlU7/O4ifFwJv9C5GVAmNy+ch79XQG4TnBQvNX4Fsrip6YA3LsLHFR7i5sJlN0cN7qa2o/d5UwEveoCbF/s9b45W6+tDFjf1nZd/p73kAW7SsuHv5ZDXBIer9PUhi5uGLvjdJbub3Tn6+pDKzXn4oNz+vPmkQl8fsrhp6oLfXoSPKuElnW5E8bLZXHfwVVpaGps3b2bHjh1ERERw//338/7773P33Xdf0W7Xrl0UFhZS0ejPm8di+74/eeJYHntgDo89c7lc1oYFF4kI7GBMbSdxByoBmLZuyTVHKFxN3vJwAM2Z9lA/4fkWTr2T4ql34G3tIen4z/DrahbOOuYTc/etdDa2UnvmEl0NLQO2L1oUwtQj9lWuaEbLfCzeZjLnP0Lb2AgmlX9GXN7bmucC8rgpmnoHRVPvxNvaw/zjz+Df1SScldHNqXmP0DougknlGcTl/UHzXEAiN1F3UBTd6+bETvw7G4WzWt30n89wuLF6+5A57xFax00iojyDGQZ3Uxy1lMLo5XjbLMw/nop/p3htQenceJnInL/F7qbiCDNy92qeC0jkJvKLFMaswNtmYd7xZxnTKf6uh8xuwiuOMjP3/zTPBeRxUzLlCxRMW4GXzcr8E88ypsPYbk7Ne4SW8ZMJrzzOzJzfC88FIDo6mi1btgi1delH8C0tLWzfvp2enh6Cg4PJyMhg8eLF17RzDO5YVjVv9tuEtGLxFN47VExUxDiKK1oBSElJYcHsG6g5lc++A9sImhFJkECFhpSUFADNGa0X6l5qhmBfCL73KU05x3xsViuBsZMp/+TsoO03btjAoSP2jS6iGa3zaemG7Wfg39fdhpfXbcI5+d38VFNObjdL8PISfwUvq5vPmyHQF0Lu/YmmnFY3/eej3IjR5+YrP9KUk9nNU+sW4+117d+igZDVTUEzBJgh9CtPasrJ7eZWvL1uFc4pN8PvprUHnj4NT61bhLfXIuGcVly6AN26dSvp6ekkJiYSFaWtjBfAnj86r8HakFtC5o63NPWrJyPK9ICh5QvTjzhv5IKMCOPNEOyn/5o85Ua5EWWactOHcjP8bvRekyebmxjlpg/lZvjcjPMZmhtRXL4JafXq1a7uctQQ/9AqTGYf6nOKsHZb8DabsHZbqDic5TTTVFBBR3UDfsEBdNY3U33S+WJeIY5yIy/KjbwoN/Ki3MjLaHHj1l3wc2eGcPSNtU7bgP0gVcf5VyI4DmvVk3EbNht+oQH4BY2n8ugFwhfGU+msPFhvxlRWQ3VmPjFrbqHkg8yRGW8vys3gGeVmmFFunGbchnLjNOM2lBunGbdhUDdacesC1Ndsuu4h89fD5GvWdZCqjIevDkRnXTMVh7OIXJZMYGwTHbVNBMZOpq2s1mnGL3QC2Gy0Vdbbz7YZQZSbwTPKzfCi3MiLciMvyo28GNWNVtQ5oBLh2JnXkFuiOeOgytmrJIUulBt5UW7kRbmRF+VGXkaLG8MsQEdzvVR3sGfPHtra2li6dCnt7e2UlpaSkJBAdnY2CQkJXLx4kcrKSu68805Oncykp7KRJcliu+WUm6Gh3MiLciMvyo28KDfyosXN2bNnGTNmDF/84heF+jbMAnQ010t1B5s2beq7XV5eTnJyMgBxcXFX/BfAr7qd3/3j96g3vSPUt3IzNJQbeVFu5EW5kRflRl60uJkyZQoVFeIn8RtmAerpRK1YgH9YIE2Xyhg3JQxLeyetZXX4jPGl4tNzLsvoYdKkSU7bBJoGrl5ldJQbeVFu5EW5kRflRl6M7iYiIkK4P7UAlYTi/cdGJKPQjnIjL8qNvCg38qLcyMtocuPSWvCjjbN18O1D8OQJ6LG6ezRDw2azVz74ayl8pLOWrUycq4dvH4Yfeoib7Wfsbj4sc/dohk52r5t/O+4Zbn7W6+YDD3BzoQE2H4YfHIduT3Bz1u7mbx7gJqfR7mbbMeO7AdiZZXfz11J3j2To5HqYm9ReNwfE90DpwqUL0IMHD5Kamsry5cvZu3cvv//973nnHbHrNESZetci4h9cOewZEX57EU7Wwf5SOCVe+vUKJi6axbR14uX79GacUdEO75dCdSf8/pK+PmRy83o+nKy1uzleo68PWdxUdtjnUd0Jvy/Q14dUbi7a3Rwog6PV+vqQxU1N5+Xnzf8W6OtDNjcnauGvZZBRpa8PWdzUdsL+Erub//tcXx8yuflNnt3NwXI4XKmvD1nc1HXC+w43Bfr6kMpN/mU3H+t8A0cWNw2d8J7Djfi+LV142WyuOygqLS2NzZs3s2PHDiIiIvDx8cHb25v77rvvina7du2isFDbzMbUdhJ3oJKYu2+ls7GV2jOX6GpoGbB93vJwAM2Z9lA/4TGVTb6Voql3Al4kHX8Gc0+b5vkATFu35JojFK6maFEIU49cXuWKZLTMx+pl4szc79A6fhJTij8mpnC/UA7kdFMecQuFMcvAy4ukY57g5mFax09mSsknxBS8L5QDWd0spDBmBTYvb5KPP4O5u1XzfED8cQY0Z8TdePc+byYzueQQ0wreE8qBrG4WUBSzEquXN0nHn8HXwG5sXt6cSXyYloApTCo9zPTP/yKUAzndVIQnUzhtFVYvb+Yf34lf98B9DzQfkNHNp0z/PF0oB7K6Sep1YzK+G7w4M/dhWgIiiSj7jNhL+4RyDqKjo9myZYtQW5deA9rS0sL27dvp6ekhODgYi8WCxWK5pp3o4PpTcyqffQe2YbNaCYydTPknZwdtn5KSAqA5o3XnW0s37L4Aj3/1PzXlHPMJmhFJ0Mwop+03btjAoSNpAMIZrfOx2KCtBwLMK4AVwjnp3az3JDfLgeXCOfnd/IemnFY3/ecz/G6WAcuEc7K6ae2BF8/DEx7l5k7gTuGc/G7+XVNOVjet3TDB90vAl4Rzys1IurkDuEM4pxWXLkC3bt1Keno6iYmJREU5f1D0UJh+ZEQyoow3g7eX/nxDbgmZO94a9owIJi8IGMLRZ8qNciOKcnMZ2dyM87HPSS/KjXIjgskLJvjqzys38roRxeW74FevXu3qLgck/qFVmMw+1OcUYe224G02Ye22UHE4y2mmqaCCjuoG/IID6Kxvpvpk3oiNezSg3MiLciMvyo28KDfyotwYE2Mfw2Sz4RcagF/QeCqPXiB8YTyVzspP9WZMZTVUZ+YTs+YWSj7IHJnx9iM4IbrvgFwRJtw4hTUCb7VffR9uQ7lxeh9uYxS5cTzOejJuQbkRyrgF5UYo4xaUG6GMbBh6AdpZ10zF4SwilyUTGNtER20TgbGTaSurdZrxC50ANhttlfX28zpGGJOvWfM1JkaqzKDcyMtocwPG8aPcyItyIy/KjTEx9ALUsfOrIVf8sKqrd4tVOXuVpNCFciMvyo28KDfyotzIi3JjTNy6AO3qtnA6Z/ADNOfODMHXbBqhEQ2NPXv20NbWxtKlS2lvb6e0tJSEhAQyMjJYv349r732Gs3Nzaxfv56qikqm+4cK9z3hxik05Ws7sTc4IRqT7xCuwPcglBt5UW7kRbmRF+VGXobTjePj9Pps8aMsZXXj1gXo6Zw6Fm7806Btjr6xlgWzbxihEQ2NTZs29d0uLy8nOTkZgLi4OAAefvjhvp+35pez755twn3f9tx3OfS9NE3jWfPe0x7xNr0rUG7kRbmRF+VGXpQbeRlON45rP/et0paR0Y1hP4KPWrEA/7BAajLz+s7BarpYhnn8GCo+PeeyjF4mTZo06M/Dw4yxqNaDciMvyo28KDfyotzIi3JjXAy7AC3ef6zvtuhb0XoyCu0oN/Ki3MiLciMvyo28KDfGZVhrwf/lL3/hN7/5jXD+66umc++yGMKC/QdsI0u9VICPKuDrH8IH5dDRo68PWerZWmzwxFH46t/h7QJ9fcjk5pMK2PAh/K3M+G6sNvj+MbubvTprWsvk5nA/N+0e4GZrrxu99cZlcvNpJWz4u91Nmwe42XYcvvoB/O8lfX3I5OazKvvz5mCZveqOHmRxY7PBD3rdvOUBbo70c9PSra8Pmdz88ITdzZs63Yji0gVoTk4OjzzyCEuXLqWpqYkbbriBoqIi4fzHJyq4dW44NfUdA7apOnJe87j0ZET4SzHkN0FFO5yt19eHt8mbxktl+AaNH9aMMyrb4VgNXGq2P4n0IJOb9BLIa4LKDjg5+D63AZHFTVU7HKv2HDf7+rk5UaOvD1ncVHdcft78zQPcpBdDXrPdzTGDu6nr7HXTAn8r19eHTG72FdufN1Ud9gWPHqRyU21384EHPG/+3M/NpwZ309AFR3vd6P2dJsqw1oI3mUzMmDHjmna7du2isLCQikZ/ILbv+ysWT+G9Q8VERYyjuKIVgGeffZaIwA7G1HYSh3it7WeffRZAc6Y91E9orgDVYXMYO20lVi8ffrt9J29au4SzjvmI1qZ94803mdp7WzSjZT42vPCdvQlzwFRqPz3Ao3s/FcoBUrqpCZ3N2OmrsHr58Puf7WSvxdhuzDc9iHlCDPUZf+XRPxwSyoGsbm5i7LS7sHr78L87U3nb0imcldlNXcZBHv3DJ0I5kNRNyCzGTl+D1dvMH3bu5F2Du/G56ZuYJ0yjPuNvPPqHj4VyIKeb2l43FpOZPz67k31Gd5PwAObA6TQc/YBH3/5IKAdyuqkLnsnY2LVYTGb2PZfK+5aB30S7Gq1u+s9neNyAKeEBzIGxNB79O4++/aFQzkF0dDRbtmwRautls7n25FUtteCPZVUL74KvOZWvedcXyLtTTOt8RmpXYmoWPDpbU0S5UW6UG+VGuUG5UW60I+t89LjRiqFrwSsUCoVCoVAojIdhd8F7IvEPrcJk9qE+pwhrtwVvswlrt4WKw1lOM00FFXRUN+AXHEBnfTPVJ/NGcOSej3IjL8qNvCg38qLcyMtocePWBejcmSEcfWOt0zZgP8nf8dazCI5qAXoybsNmwy80AL+g8VQevUD4wngqnZUH682YymqozswnZs0tlHyQOTLj7UW5GTyj3Awzyo3TjNtQbpxm3IZy4zTjNgzqRituXYD6mk3CVY5MvmZd12TIePr/QHTWNVNxOIvIZckExjbRUdtEYOxk2spqnWb8QieAzUZbZb39HIURRLkZPKPcDC/KjbwoN/Ki3MiLUd1oRX0ELxGfv3sYgIbcEs0ZB1XOXiUpdKHcyItyIy/KjbwoN/IyWtwYZgFq6erWVLHA8Ra61ozJ16x5bJ7Inj17aGtrY+nSpbS3t1NaWkpCQgIZGRmsX7+e1157jZaWFr72ta9RXlrGdP9Q4b6Vm6Gh3MiLciMvyo28KDfyosVNVVUVSUlJwn0bZgFan10o5VEFnsqmTZv6bpeXl5OcnAxAXFwcAA8//HDfz5tySth3j3IzUig38qLcyItyIy/KjbxocWM2a1u0G2YB6ulErViAf1ggNZl5jI0IwWeML9ZuC92tHQPufNOT0cOkSZMG/Xl4mNh1vEZFuZEX5UZelBt5UW7kxchuIiIiNPWnFqBDpMMCZm8weQ2tn+L9x/pui35UoCczGFYb9FiH3I00KDfyotzIS4cFfLzAZ4iFmpUb1+NJbmzKzXUZTW5cWgv+4MGDpKamsnHjRl5//XX+/Oc/8/bbb7vyLph61yLiH1w57BkR3imEez+Ab34Mzd36+pi4aBbT1i0Z9owzuq3w8Kf2mrav5OjrQyY37/a6eeBjaBKvwnkFsrjpscJ3et380gPc/Lmo181H9rrDepDRzUs6r/mXyU16Mazvfd40iFd6vAKZ3PzzZ3Y3v9BZAlwmN38psbu5/2OoE6/0eAWyuLHYLrtJ8wA37/dzU+tBbl7IdmnX1+DSUpxpaWls3ryZ3bt3ExMTg7e3fX171113XdHOUQteC2NqO4k7UEnM3bfS2dhK7ZlLdDW0DNg+b3k4gOaMlvqv2Qn3UzNxHthszDn1IsGNFzXPB2DauiXX7GC7mqJFIUw9Utf3/yIZLfPp8AvmZPJj9JjHElSXQ+KZl4RyIKmbWfdTE97r5sxLBNfnap4PyOImqNfNOILqc0k8vVsoB7K6uY+a8Plgs3HTmV8SWi++qpbNTadvICeSH6PHdzyB9bnMNbib87O+QXW4fRPBTWdfJrRWfHUgm5su3wCOJz/R6yaPuad/IZQDSd3Ef4PqCLubhLO/IqxW/KNV6dyYx3NiwRN0+wYQWJ/P3NMvCuVATjcX4jdSFWG/NjI+6zUm1pwRzmp1038+WjKi8+k2j+P4gu/T7RvAhIZ8bj4l7ga01YJ36UfwLS0tbN++ncjISAoKCoiOjqar69q3OEQH15+aU/nsO7ANm9VKYOxkyj85O2j7lJQUAM0ZLRceZ9baXyHUd3nx8o++q+mtd8d8gmZEEjQzymn7jRs2cOiIvTavaEbLfGw2+K/TcLEZ7kueyZceTBXKgZxuTtfBz7OhttOLX/7wYcwGd/PfpyG/Gb6RPIM7v2lsN2frYFc21HZ48coPNxvezdNnILcJNiTNYLnB3WTVwbPZ9ndxXt72LXxNwlGp3XxtfhwrDe4mux5Sz0FNB7yy7SHDu9l+FnIa4avzb2SVwd2cr4edvW5e3fogfsPopv98hsvNz87C+UZYP+9G7npA3I1WXLoA3bp1K+np6SQmJhIV5fxB0UNh+pERyYgwLxRe/QdIzdJ/3UdDbgmZO94a9owzvLzgyZuH1odMbuaGwCu32d1oWeD0RyY3P7x5aH3I5GaOh7n5wdyh9SGTm9kh8GqvGy0LnP4oN8PjJiH48vPGE9xsSxxaHzK5mdXPjZbFZ39kcvP9IboRxeWbkFavXu3qLhUKhUKhUCgUHoShd8HHP7QKk9mH+pwirN0WvM0mrN2WQY8dcGSaCiroqG7ALziAzvpmqk/mjeDIPR/lRl6UG3lRbuRFuZEX5caYGHoBis2GX2gAfkHjqTx6gfCF8VQ6Kz/VmzGV1VCdmU/Mmlso+SBzZMbbj+CE6L4DckWYcOMU1ghc63H1fbgN5cbpfbgN5cbpfbgN5cbpfbgN5cbpfbiNUeTG8TjryciGoRegnXXNVBzOInJZMoGxTXTUNhEYO5m2slqnGb/QCWCz0VZZb7/qdoQx+Zo1V1owUmUG5UZelBt5UW7kRbmRl9HmBozlZyAMvQB1HD3QkFuiOeOgytmrJIUulBt5UW7kRbmRF+VGXpQbY+LWBWhXt4XTOXWDtpk7MwRfs85tZSPMnj17aGtrY+nSpbS3t1NaWkpCQgIZGRmsX7+et99+m9raWh544AFyzl9gun+oUL/BCdGYfM1YurqFqxw4Mgo7yo28KDfyotzIy3C5gcsf2So3+lBuxHDrAvR0Th0LN/5p0DZH31jLgtnX1n7tX/s0aGYUE6ZNou5cAe1VDdRkXv8iYj0ZLWzatKnvdnl5OcnJ9oNp4+LiANi4cWPfz8e1WNl3zzahfte89zRhN99IfXYh+1Zpy7gD5UYs4w6UG7GMO1BuxDLuYDS5gcvXFyo3+lBuxDDsR/Ay1EsdjEmTJg368/CwaxfVnoJyIy/KjbwoN/Ki3MiLcmNcXFoLfjRS3AJdFnePwjW09UBBs1uuwx4WSlqh04PcfK7cSIlyIy/tyo20KDfy0t4Dl0bAjUsXoAcPHiQ1NZWNGzfy+uuvs3fvXrKyxOvVfn3VdO5dFkNYsP+AbSYumsW0dUs0jUtPRoTX8uD+j+H9UnvpOj1MvWsR8Q+uHPaMMzos8K1DcN9HsFNc2RXI5OY3+fa57C+F6nZ9fcjiptMC3zoM938EzwxeQW5AZHLzej83VR7g5tuH7fPZ4QFufnfxspuKNn19yOKmq5+bp8VLc1+BTG7e6Oem3OBuuq3w7U/t8/kfD3Dz1iX77+f3S6G0VV8fsrjpscLmT+3z+e/TLu36GrxsNtetcdPS0ti8eTO7d+8mJiaG2bNn09LSwuzZs69ot2vXLgoLC6lo9OfNY7F93588cSyPPTCHx565XC5rw4KLRAR2MKa2k7gDlQBMW7fkmh1sV5O3PBxAc6Y91E94vtmz7qcmfB5YLSSe/gVBjZeEs475xNx9K52NrdSeuURXQ8ug49KTEaXTL5ATSY/R4zueoLoLJJ75pea5gDxuzs+6j+rw+WCz2t00XBTOSufGdwInkh/vdZND4pmXNM8FZHLzDarDk8BmZfaZXxJSnyuclc1Nl28AJ5Ifp9s3gMC6XOae2a15LiCRm/iNVEck97p5hZB68Z3B0rkxj7e78ZtAUH0uiaeN7eZC/Eaqet3cdPZVQuvOC2e1PM6OsQHD5qbbPI7jyU/Q7TeBwPo85p7+hea5gExuNlAVsQBsVhKy9hBWe044K50bn7EcX/AE3X6BBDbkM/fUi8JZgOjoaLZs2SLU1qXXgLa0tLB9+3YiIyMpKCjA19f3ugtQx+COZVXzZr9NSCsWT+G9Q8VERYyjuML+MiIlJYUFs2+g5lQ++w5sI2hGJEECB+SmpKQAaM5ouVi3sAWeOweTx5p4fN2/4OUlHO2bj81qJTB2MuWfDPz2iWNcejJaeOkCpJfAv98Wz80Ppmqei0xuilvg2XMQMcabx9d+F2+Du/nlBdhXAk8tnsk8g7spaYXUcxDu780Tax82vJuXc+DPxfDTxTOY7yFuJvp78/213za8m1dy4E/F8JPFM0j6prHdlLXCznMQ5ufN1rXfGjY3jrEBw+rmV7nwx0L48eI4kg3uprwNnsmCUD9vtq79R0xGd5MHfyyAJ2+9kYUPiLvRiksXoFu3biU9PZ3ExESiorRVUQDY80fnu88ackvI3PGWpn71ZESIHg/PLhpaH4XpR5w3ckFGhM3x0NoDN4foy8vkJmo8pHqQm2/HQ0sPzAvTl5fJTeQ4SF04tD5kcvOtmdDcDfOVG0AuN/80E5q6IckD3EweBzs9yM1DM6ChC5I9wM2ksR7mJg4aOmHhMO+Pcvku+NWrV7u6y1HDQPVsRTJX17NVuBblRl6UG3lRbuTFlfXTFa5ltLhx6zFMc2eGcPSNtU7bwCipl2rQerbKzeAZ5WaYUW6cZtyGcuM04zaUG6cZt2FQN1px6wLU12y67iHz12M01EsdqJ6tSMad9WyVm8Ezys3wotzIi3IjL0atn67cDJ5xd217LRj2IHpPRNWzlRflRl6UG3lRbuRFuZGX0eJGHUSvUCgUCoVCoRhRDPMOqKWrW1PJLMc1HFozJl+z5rF5Inv27KGtrY2lS5fS3t5OaWkpCQkJZGRksH79el577TVaWlr42te+RnlpGdP9Q4X7Vm6GhnIjL8qNvCg38qLcyIsWN1VVVSQlJQn3bZgFaH12IftWbRNu77jgWGvGndeJRK1YgH9YIDWZeYyNCCHs5hs599KfNbfvadVZlqkfmzZt6rtdXl5OcnIyAHFxcQA8/PDDfT9vyilh3z3KjUh75cb1KDeDZ5QbO8rNlWh9rJWbkcOobsxmbYt2wyxARwPF+4/13a7PLqTUyQ42re31MmnSpEF/Hh42zIeFSYByIy/KjbwoN/Ki3MiLUd1ERERo6k9dAzoEbDb4qBwuNbl7JK6hrA2KWuy1YI2OJ7op9CA3H1d4jpuKXjfdHuQmX7mRDpsNPqmAPA9xU9nugW4a3T0S11DZDgUt0DX4kb1DxqUL0IMHD5KamsrGjRt5/fXX+eSTT3j33XddeRdMvWsR8Q+uHPaMCD/Phu8fg+9m2Muk6WHiollMW7dk2DPOaOmGf/kMDlXBf5zS14dsbrYeh3/+DIoN7qatB/71MzhcBU+d0teHTG7SztufN9/5zP4HSA+yuGnvsT//D1fBUzrfdJDJze4Ldjf/8pn9D5AeZHHT0QP/0uvmpyf19SGTm5dy4IleN3pfvEnjxmKfx+Eq+LEHuPllzuW1wEWDu+m0wL9mwKdV8ONhPkbUy2Zz3UFRaWlpbN68md27dxMTE8Ptt9/OwYMHWbdu3RXtdu3aRWGh+AXBAGNqO4k7UEnM3bfS2dhK7ZlLdDUM/Bsyb3k4gOZMe6if8JjOx3+D6ogksPRw86mfM6G5WPN8AKatW3LNEQrXG5eejChdvgGcSHqMbr8JBNdmM+fsK8JZGd1ciN9IVUQyWHuYeyqNwCbxf2/SuTGP50TyY3T7BRJce4E5Z38pnJXTzQaqIhbY3Zz+BYGNn2ueD8jhpts8jhPJj9PlF0hQ3QUSzxjbTc7Mr1M5aSFYe5hz+iWCGy9qng9I4sZnrN2NfxBBdTkknnlJOCunm69ROWmR3c2ZlwlucF66+ur5gPPH2TE2YBjdjOFE8hO9bnJJPLNbOCunm69SOekWsPYw++yrhNTnaJ4PyOGmx+TP8QVP0OUfTGB9LnNPi7sBiI6OZsuWLUJtXXoNaEtLC9u3bycyMpKCggKef/557rzzzmvaiQ6uPzWn8tl3YBs2q5XA2MmUf3J20PYpKSkAmjNaLjyu77S/01bU6sMrT6UI5+DyfIJmRBI0M0poXHoyWninEDJr4Vt3JBC1KVU4J6Obhk543uHmp9r+vcno5t1COFEL/3RHPFMN7qaxC54/BwUtPrz6k38VzoGcbv5UBMdr4B/viCf6QWO7aeqCXefg8xYfXv3xd/HyEo5K6ebPRXCsBh66fSYxBnfT3A3PnYPPm3149UffGTY3jrEBw+pmXxEcrYFNt89gmge42XUOLjb78KsnNxveTXoxHKmGB5fOYPo3xd1oxaUL0K1bt5Kenk5iYiJRUc4fSD0Uph8ZkYwIwX7w43mQOnB5Vqc05JaQueOtYc+IcE+0/UsvMrkJ8jA366LtX3qRyU2gL/zIg9ysnWr/0otMbib0c6Plj2h/ZHJz91T7l15kchNghh/d7Dlu1ky1f+lFNjdP3uw5blZH2b+GG5fvgl+9erWruxyQ+IdWYTL7UJ9ThLXbgrfZhLXbQsXhgf+yOTJNBRV0VDfgFxxAZ30z1SfFP85QOEe5kRflRl6UG3lRbuRFuTEmxj6GyWbDLzQAv6DxVB69QPjCeCqdlZ/qzZjKaqjOzCdmzS2UDNORBYMRnBDddz6ZSFu9Gbeh3DjNuA3lxmnGbSg3TjNuY5S4cbQHlJsRwOPdDIKhF6Cddc1UHM4iclkygbFNdNQ2ERg7mbayWqcZv9AJYLPRVllvP0NhhDH5mjVfl6En4y6UG3lRbuRFuZGX0eYGUG5GAE93MxiGXoA6dn415JZozjiocvYqSaEL5UZelBt5UW7kRbmRF+XGmKiD6BUKhUKhUCgUI4pb3wHt6rZwOqdu0DZzZ4bgazaN0IiGxp49e2hra2Pp0qW0t7dTWlpKQkICGRkZrF+/ng8//JDKykq++MUvUlVRyXT/UOG+J9w4hab8Uk3jCU6IxuSrrTarp6LcyItyIy/KjbwoN/IynG4c13TWZ4ufbS2rG7cuQE/n1LFw458GbXP0jbUsmH1t7deoFQvwDwukJjOP4IRo/EMm0FxQQXt1IzWZ19/FpiejhU2bNvXdLi8vJzk5GYC4uDgAVq68XIGhNb+cffdsE+77tue+y6HvpWkaz5r3nnbLdSLKjXOUm8soN3aUG+coN5dRbuyMNjeOzUf7VmnLyHjNqGGvAS3ef6zvtugrAT0ZvUyaNGnQn4eHXbuo9hSUG3lRbuRFuZEX5UZelBvjoq4BHQJdFnglF0p01hqXjcxaePG8vS680em2wq9y9deBlw3lRl5O13mWmz0e5OZMHaSdt1eqMTo9VngtF4oGriBpKM72umnqcvdIhk6PFV7L8xw3WSPkxqUL0IMHD5KamsrGjRt5/fXXycjIYO/evcL5r6+azr3LYggL9h+wzcRFs5i2bommcenJiPBMFuy+ABlVcKlZXx9T71pE/IMrnTccYsYZjV3wo5Pwqzx4SudRaFK5OQsv9rrJa9TXhyxumrrgx71ufuoBblKz7G6OVEOuwd20dF9+3vzEA9w8dw7Set2cb9DXhyxuWnvgyROwJ8/+/NGDbG5euABHauBcvb4+ZHHT1gNPnvQcN89nwwvn7W7ODr6tZUBkcdPez82PdLoRxctmc93BV2lpaWzevJndu3cTExPD/Pnzef/993nooYeuaLdr1y4KCwupaPTnzWOxfd+fPHEsjz0wh8eeuVwua8OCi0QEdjCmtpO4A5UATFu35JojFK4mb3k4gOZMe6if8HxzZ3yVism34G3p4uYTzzG+rUI465hPzN230tnYJU8SkAAAJNxJREFUSu2ZS3Q1DPzyqWhRCFOP2P9li2a0zKfbZywnkx+j0z+YkJqzzM7ao3kuIJOb9VRMvhVvSxdzTz5PQGuZcFZGNyeSHqVrTAghNVnMzvqV5rmAPG7y4u6lfMpiu5vMFwhoET86RTY3PT7+nEh6nM4xIYTUnGN21qua5wIyufkK5VOW4GXpZm7mC0xoKRbOSufG5M+J5MfoHBPqGW5uvIfyyH+wuzn9IhOaxD86ls+NX6+bMIJrzzPn7Mua5wLyuMm/8f9RFvkFvCzdzDn9C4KaCoSzWt30n89wuLGYfDme/LguNwDR0dFs2bJFqK1LrwFtaWlh+/btREZGUlBQQEhICMHBwde0cwzuWFY1b/bbhLRi8RTeO1RMVMQ4iivsnwGlpKSwYPYN1JzKZ9+BbQTNiCRopvMipSkpKQCaM1ou1O2w2D9KvHGCL8vv+b5wDuibj81qJTB2MuWfnB20/cYNGzh0xH5RuGhG63wyquyv4l5cOYcJvqnCORnddFrs70rFBviy/J7HhXMgp5sjVfDz85C2cjaBHuJm+nhfVtzzqHAO5HRztNr+Dkjayps8ws2ePIgZb2blPSnCOZDTzfFqeC4bXlh5E0EGd9PV+7yJHm9m1T1if+AdSOmmxv6u7s+XzyJ4k/Hd7MmDyHFmVt/ziHAOtLvpP5/hcnOiBp49B88vn0WIBjdacekCdOvWraSnp5OYmEhUlPZK9nv+6Hz3WUNuCZk73tLUr56MCP4m+OdZQ+ujMP2I80YuyIhwy0T4tAom+OrLy+TGzwTfiR9aHzK5WTQRDldBoHIDyOVm4Q0wP9Rz3DzsQW6Se90EeYAbX09zE2Z3M8gVd4Mim5vNHuQmqddNiE43orh8F/zq1atd3eWoIf6hVZjMPtTnFGHttuBtNmHttlBxOMtppqmggo7qBvyCA+isb6b65NCPklBcRrmRF+VGXpQbeVFu5GW0uHHrMUxzZ4Zw9I21TtuA/SBVx/lXIjgOa9WTcRs2G36hAfgFjafy6AXCF8ZT6aw8WG/GVFZDdWY+MWtuoeQDnbshdKLcDJ5RboYZ5cZpxm0oN04zbkO5cZpxGwZ1oxW3LkB9zabrHjJ/PUy+Zl0Hqcp4+OpAdNY1U3E4i8hlyQTGNtFR20Rg7GTaymqdZvxCJ4DNRltlPbhuX5kQys3gGeVmeFFu5EW5kRflRl6M6kYrhj2I3hNx7MxryBXfFXz1br4qZ6+SFLpQbuRFuZEX5UZelBt5GS1u1AJUcV0GqmWbnZ1NQkICx44do7i4mPvuu4+zZ88yZswYvvjFL7p72KMC5UZelBt5UW7kRbmRl+F0Y5gFqKWrW1PJLMc1HFozJl+z5rF5Is5q2Tr+CxBxw0QufHycmlP5Qn0rN0NDuZEX5UZelBt5UW7kRYubKVOmUFEhfh66YRag9dmF7Fu1Tbi944JjrRl3XScStWIB/mGBNF0qY+zEYKw9PdRlFzIhJoLSv59yWUYPzmrZ1mcXcuz+ncL9KTfXz+hBuVFunGWUm2tRbpQbZxnl5lqcuQGIiIgQ7s8wC1BPp3j/set+v/nzgV9N6MkotKPcyItyIy/KjbwoN/Iymty4tBb8aKO5G/7nNLx1yd0jcQ1/KYGMaihvc/dIhk5LN/zPGXjTQ9y81+umzIPcvHHR3SNxDe/3uiltdfdIhk5LNzx9Bn7nIW7297op8QA3rT2w/Qz8VuyTZ+k5UGp3UzxwBUnD4HDzuoe4OdjrpmiY3bh0AXrw4EFSU1PZuHEjr7/+Orm5ubz++uuuvAum3rWI+AdXDntGhJ+dhT8UQtp5yK7X18fERbOYtm7JsGecUdsBu87BpWb7HyA9SOemANKyIcvgbuo7PcvNM1l2Ny+eh9MDnyoyKLK4aejyLDepWbC3AH5xHjIN7qapC3Zl2938jye4OQv/VwC/uGAvMaoHWdw0d9vLcHrK8+bZrMtujlTp60MWNy3d9jKcQ3neiOJls7nuoKi0tDQ2b97M7t27iYmJwd/fn46ODtasWXNFu127dlFYKH5BMMCY2k7iDlQSc/etdDa2UnvmEl0NAy/P85aHA2jOtIf6CY/pYuxaSqf8A96WLpJO7GRMR53m+QBMW7fkmiMUrqZoUQhTj1zuXySjZT49Jn9OJqXQMfYGbqg4wawLvxPKgaRupt9NaeQXMFm7mXc8lbEdNZrnA7K48SMzKYX2sRO5ofIEs84b282l6Wsoifyi3c2JZxnbLv7XVDY3FpMvJ5IepWPsRG6oPMms878VyoGkbqatpiTqi5isPdx88jnGtYn/NZXOjbcvJ5NTaB8bTlhVJgnZ4m+GyOjm82l3URy1FG9rDzef3MX4tkrN8wFZ3Jg5mfQo7ePCCa06zU3ZvxbKgaRuYlZSPPUOu5vMnzO+tVzzfED8cQY0Z7S5SaF9XASh1ae56Zy4G4Do6Gi2bNki1Nal14C2tLSwfft2IiMjKSgoICQkhLq6axdlooPrT82pfPYd2IbNaiUwdjLln5wdtH1KSgqA5oyWC4+tNvvHb4crx/BfX3lSOAeX5xM0I5KgmVFO22/csIFDR9IAhDNa51PYDKfqYNXqJHxNScI5ud2Y+K8v/5twDuR0U9QCJ2vhrkjPcHOgFD6uMPHfX/6BcA7kdrMqcj5+pvnCObnd+PDfXxbftAGyu5mHn2mecE5+N1uFcyCnm+IWOFELqyLn4mdKFc7J6MbW6+ajCh/++8tPCOdAu5v+8xkuNyWtcLwGVkbOxV+DG624dAG6detW0tPTSUxMJCrK+YOih8L0IyOSEcHbC+6KgguN+vtoyC0hc8dbw54RITrA/qUX5Wb43Ewdb//Si2xuVkZCdoP+PpQb5UYE5eZKZHITNd7+pReZ3Hh5wYpIONegvw+Z3ESOs38NNy7fBb969WpXdzkg8Q+twmT2oT6nCGu3BW+zCWu3hYrDWU4zTQUVdFQ34BccQGd9M9Un80Zs3KMB5UZelBt5UW7kRbmRF+XGmBj7GCabDb/QAPyCxlN59ALhC+OpdFZ+qjdjKquhOjOfmDW3UPJB5siMtx/BCdF955OJMOHGKawReKv96vtwG8qN0/twG8qN0/twG8qN0/twG8qN0/twG6PIjeNx1pORDUMvQDvrmqk4nEXksmQCY5voqG0iMHYybWUDb990ZPxCJ4DNRltlvf0CjhHG5GvWfNCtuw7G1YNyIy/KjbwoN/Ki3MjLaHMDxvIzEIZegDp2fjXklmjOOKhy9ipJoQvlRl6UG3lRbuRFuZEX5caYGHoBKht79uyhra2NpUuX0t7eTmlpKQkJCWRkZLB+/Xreeecd8vPz2bBhA42NjSQlie9mVgwN5UZelBt5UW7kRbmRF+VGDLcuQLu6LZzOGfzszLkzQ/A1m0ZoRENj06ZNfbfLy8tJTk4GIC4uDoANGzb0/by0qJiaU+JlEybcOIWm/FJN4wlOiMbka9aU8VSUG3lRbuRFuZGX4XTjuGawPlv8vG7l5jLKjRhuXYCezqlj4cY/Ddrm6BtrWTD7hmu+H7ViAf5hgdRk5hEwdSJjJgZTe/Zzeto6aMgpvm5fejJ6mTRp0qA/96vrZN8q8XP2bnvuuxz6XpqmMax572m3XCei3DhHubk+yo1yMxjKzfVxtRvHBhetGeXmWkazG2cY9iP44v3H+m6LvhLQk1FoR7mRF+VGXpQbeVFu5EW5MS6GXYDKQGkrPJcNFW32zXNeXu4e0dDYk2evm/6PcZAQ7O7RDI2yXjdlHuLmtV43m+LgJoO7KW+z1xoubfUMN7/JgzP18M0bYU6Iu0czNCra7fXgPcXN6/lwqha+GQeJBndT1Q47s+xVaqw2+8H0Rua3+fbKe/fdCDd7iJtiD3Hzu3zIrINvTId5YcN3P96u7OzgwYOkpqayceNGXn/9dX71q19x7tw54fzXV03n3mUxhAX7D9hm4qJZTFu3RNO49GREeOE8/L3cXm3ntHgZ+CuYetci4h9cOewZZ1S12//RfVQBL+rcDCibmw/KIacRToqXgb8CWdzUdMDvLsKHFfDieX19yOgmtwmOGtxNbQe83uvmFx7wvEnr5+ZItb4+ZHFT32lfgH5U6RluXjgPf+t182mVvj5kcdPQdfl5s9sDfqel9brJa4JPKvT1IYubpv5uclza9TV42WyuO/gqLS2NzZs3s3v3bmJiYvDy8iIsLIxFixZd0W7Xrl0UFhZS0ejPm8di+74/eeJYHntgDo89c7lc1oYFF4kI7GBMbSdxByoBmLZuyTVHKFxN3vJwAM2Z9lA/4fkWTl1GcfQdeFu6STr+M/y6moWzjvnE3H0rnY2t1J65RFdDy4DtixaFMPWIfZUrmtEyH4u3mcz5W2gbG86k8s+Iy3tb81xAHjdFU79EUfSdeFt6mH/8Gfy7xGtyyunmEdrGRhBRnsGMvD9ongvI5OYOiqKX4W3tddNpbDen5j1C67gIIiqOMCN3r+a5gERuom63u7FZmHc8lTGd9cJZ2dxYvUxkzt9C67hJhFccZWbu/2meC8jjpjhqKYXRy/GyWZh//FnGdIq/66HVTf/5DLubyuPMzPm95rmARG4iv0hhzAq8bFbmn3yOMe3ir6yldTN+MuEVxzS5AYiOjmbLli1CbV36EXxLSwvbt28nMjKSgoICkpKSKCwsvGYB6hjcsaxq3uy3CWnF4im8d6iYqIhxFFe0ApCSksKC2TdQcyqffQe2ETQjkiCBCg0pKSkAmjNaL9TNa4RQfz9C7n1KU84xH5vVSmDsZMo/OTto+40bNnDoiP2CfdGM1vk0d8PTp+E/192Gl9dtwjmZ3YT4+RF670805eR2swQvL/FX8LK6yW+CYF8/Qr9ifDct3fA/p+E/1i3G22uxcE5mN0G+EPaVH2nKye3mVry9bhXOyermYhME+kLYV57UlNPqpv98ht/NIry9FjkP9CK/m3/TlJPazVptbrTi0gXo1q1bSU9PJzExkagou+RbbxV/0u/5o/MarA25JWTueEvTuPRkRIkLHFq+MP2I80YuyIgQYIZQf/3XfSk3yo0oN04YWl4mN+N73ei97ku5UW5EiVVu+lBu5HUjiss3Ia1evdrVXY4a4h9ahcnsQ31OEdZuC95mE9ZuCxWHs5xmmgoq6KhuwC84gM76ZqpPOl/MK8RRbuRFuZEX5UZelBt5GS1u3LoLfu7MEI6+sdZpG7AfpOo4/0oEx2GtejJuw2bDLzQAv6DxVB69QPjCeCqdlQfrzZjKaqjOzCdmzS2UfJA5MuPtRbkZPKPcDDPKjdOM21BunGbchnLjNOM2DOpGK25dgPqaTdc9ZP56mHzNug5SlfHw1YHorGum4nAWkcuSCYxtoqO2icDYybSV1TrN+IVOAJuNtsp6+/kpI4hyM3hGuRlelBt5UW7kRbmRF6O60Yo6B1QiHDvzGnJLNGccVDl7laTQhXIjL8qNvCg38qLcyMtocWOYBailq1tz7VPwjHqp7mDPnj20tbWxdOlS2tvbKS0tJSEhgezsbBISEvjb3/5Gc3Mz69ev59DHH3OD11iS5tws1LdyMzSUG3lRbuRFuZEX5UZetLj59NNPiY2Nvebko4EwzAK0Prtw1NZLdQebNm3qu11eXk5ycjIAcXFxV/wXYHxDD79b/j3KTQMXEOiPcjM0lBt5UW7kRbmRF+VGXrS4iYmJoaJC/CR+wyxAPZ2oFQvwDwuk6VIZYycGg7cXbRV1eOFFxWfXryalJ6OHSZMmOW0TKPjLwIgoN/Ki3MiLciMvyo28GN1NRESEcH9qASoJxfuPjUhGoR3lRl6UG3lRbuRFuZGX0eTGpbXgRxvn6mHzYfjRSeixuns0Q8Nmgx1n4K+l+mvZysT5enj4MDx5wjPc/Oys3c3HHuDmQoNnuXmm182H5e4ezdDJabS7+bfjxncDsDPL7uaDMnePZOjk9rr5wXHo9gA3z/a6+ZsHuMlv8iw3z52zuzk4zG5cugA9ePAgqampbNy4kddff5133nmHjIwMV94FU+9aRPyDK4c9I8Jv8uFELbxfAqfEy/JewcRFs5i2Try0ot6MMyra4b0SqO6ENy/p60MqNxfheC3sL4UT4mV5r0AWN1UdnuXm1/l2N++XwtFqfX3I4qam87Kbtz7X14dMbn7T6+ZAGXxWpa8Padz0e978rwe4cTxv/loGhyv19SGLm7pO+Euvm997gJvX8i670fsmgSxuGjrhL8VDcyOKl83muoOi0tLS2Lx5M7t37yYmJoa8vDySkpL4whe+cEW7Xbt2UVgoviMNYExtJ3EHKom5+1Y6G1upPXOJroaWAdvnLQ8H0JxpD/UTHlPp5MUUT70TvCDp2DOYe9o0zwdg2rol1xyhcDVFi0KYeuTyKlcko2U+Vi8TZ+b+M63jI5hS8gkxBe8L5UBON2WTbqUoehk2Ly+Sj/3MA9x8h9bxk5lc8gnTCt4TyoGcbsojFlEYswKblxdJx5/Bt7tV83xAFjfevW6meIibhRTGrMTm5cX84zvx6x6474HmA/K5mVR6iOmf/0UoB3K6qYhYQEHMSmxe3sPupv98tGRE52Pz8uZM4sO0BEQyqfRTpn++TygHkroJT6Zg2irw8mbeiVT8upo1zwckcYMXZ+Z+h5aASCLKPiP20p+Fcg6io6PZsmWLUFuXXgPa0tLC9u3biYyMpKCggBkzZlx3oSk6uP7UnMpn34Ft2KxWAmMnU/7J2UHbp6SkAGjOaN351twNuy/AE+v/U1POMZ+gGZEEzYxy2n7jhg0cOpIGIJzROp8eK7T0QJDvcmC5cE65GUk3y4Blwjn53fyHppxyM5Ju/l1TTm43dwJ3CudGu5v+8xkuNxabfT5BvncAdwjnZHXT0g2/uABPrH9KU05uN7cDtwvntOLSBejWrVtJT08nMTGRqCjnD4oeCtOPjEhGlAAzmLz05xtyS8jc8dawZ0Tw8YYgX/155Ua5EUW5uYxyo9yI4kluTF6e5Wa8cqMZl++CX716tau7HJD4h1ZhMvtQn1OEtduCt9mEtdtCxeEsp5mmggo6qhvwCw6gs76Z6pN5Izbu0YByIy/KjbwoN/Ki3MiLcmNMjH0Mk82GX2gAfkHjqTx6gfCF8VQ6Kz/VmzGV1VCdmU/Mmlso+SBzZMbbj+CE6L4DckWYcOMU1gi81X71fbgN5cbpfbgN5cbpfbgN5cbpfbiNUeTG8TjrybgF5UYoIxuGXoB21jVTcTiLyGXJBMY20VHbRGDsZNrKap1m/EIngM1GW2W9/SyVEcbka9Z8jYmRKjMoN/Ki3MiLciMvo80NGMePcmNMDL0Adez8asgt0ZxxUOXsVZJCF8qNvCg38qLcyItyIy/KjTFx6wK0q9vC6ZzBD9CcOzMEX7NphEY0NPbs2UNbWxtLly6lvb2d0tJSEhISyMjIYP369Xz44YdUVlbyta99jTOnTjPdP1S47wk3TqEpv1TTeIITojH5mrVOwyNRbuRFuZEX5UZehtON4yPb+mzx4xKVm8soN2K4dQF6OqeOhRv/NGibo2+sZcHsG0ZoRENj06ZNfbfLy8tJTk4GIC4uDoCVKy8fgDuuxcq+e7YJ933bc9/l0PfSNI1nzXtPe8Tb9K5AuZEX5UZelBt5GU43jusL963SllFu7Cg3Yhj2I/ioFQvwDwukJjOP4IRoxkWEUPjeUQKmTqT076dcltHLpEmTBv15eJgxFtV6UG7kRbmRF+VGXpQbeVFujIthF6DF+4/13e7/VnTTxYGLl+rJKLSj3MiLciMvyo28KDfyotwYl2GtBX/06FF27twpnP/6quncuyyGsGD/AdvIUi8V4JMK2PAhfFAOHRZ9fchSz9Zig63H4Gt/h3e0VUntQyY3h/u76dHXhyxurDbY1uvm7QJ9fcjk5tNKu5u/lUO7J7g5bnezV2fdZJncfFYFGz+Ev5VBmwe4+UGvm//zADdHPMiNzQY/PGF387+X9PUhk5tj1XY3B8ug1QPcPNnr5i2dbkRx6QI0JyeHRx55hMWLFxMcHMy8efOYMWOGcP7jExXcOjecmvqOAdtUHTmveVx6MiLsK4a8Jqhoh7OD76UaEG+TN42XyvANGj+sGWdUtsORarjYDH/Vti+gD5nc/LnkspuTBndT1c/NAU9w0/u8qWyHEzX6+pDFTU0HHO11c1DnmycyudlXBLlNUNkBxwzupq4TjtZ4jps/FV92k1Glrw+Z3Byp8hw37/Y+b6o64HClvj5kcdPQdfnvjV43ogxrLfiMjAwWL158Tbtdu3ZRWFhIRaM/ENv3/RWLp/DeoWKiIsZRXNEKwLPPPktEYAdjajuJQ7xm8LPPPgugOdMe6ic0V4CqsDmMmbYKm5eJ327fyZvWLuGsYz6itWnfePNNpvbeFs1omY8NL3xnb8InIJraz/7Ko3sPCeUAKd1U97nx4fc/e4a9FmO7Mc/ehDkgmrqMgzz6h0+EciCnm5rQmxg7fTVWLx/+d+dO3rZ0CmdldONz04OYJ8R4lBuLtw9/2LmTdz3ETf2RD3j0Dx8J5UBSNyGzGBu7Bou3mT8+m8o+y8Bv1FyNVjf95zNcbkw3fRNz4DTqj/6dR9/+UCgHcrqpC4ln7PS7sZh82bdrJ+/3GNxNwgOYg6bTePRDHn3770I5B9HR0WzZskWorZfN5tqTV7XUgj+WVS28C77mVL7mXV8g704xrfMZqR2jqVnw6GxNEeVGuVFulBvlBs9xM1LzUW7knY8eN1oxdC14hUKhUCgUCoXxMOwueE8k/qFVmMw+1OcUYe224G02Ye22UHE4y2mmqaCCjuoG/IID6Kxvpvpk3giO3PNRbuRFuZEX5UZelBt5GS1u3LoAnTszhKNvrHXaBuwn+TveehbBUS1AT8Zt2Gz4hQbgFzSeyqMXCF8YT6Wz8mC9GVNZDdWZ+cSsuYWSDzJHZry9KDeDZ5SbYUa5cZpxG8qN04zbUG6cZtyGQd1oxa0LUF+zSbjKkcnXrOuaDBlP/x+IzrpmKg5nEbksmcDYJjpqmwiMnUxbWa3TjF/oBLDZaKust5+jMIIoN4NnlJvhRbmRF+VGXpQbeTGqG62oj+Al4vN3DwPQkFuiOeOgytmrJIUulBt5UW7kRbmRF+VGXkaLG8MsQC1d3VdULHCG4y10rRmTr1nz2DyRPXv20NbWxtKlS2lvb6e0tJSEhAQyMjJYv349+/fv59SpU2zcuJH62jqm+4cK963cDA3lRl6UG3lRbuRFuZEXLW6amppISkoS7tswC9D67EIpjyrwVDZt2tR3u7y8nOTkZADi4uIAWLduHevWrQPgXOFn7LtHuRkplBt5UW7kRbmRF+VGXrS4qaio0NS3YRagnk7UigX4hwVSk5nH+MgbGBsRQvnhLAKmTqT076dcltHDpEmTBv15eJjYdbxGRbmRF+VGXpQbeVFu5MXIbiIiIjT1pxagQ6TLAj7e4O01tH6K9x/ru93/o4KmiwPXwtKTGQybDSxWXVEpUW7kRbmRF+VGXjzNTY+HuTF5g0m5EcalteAPHjxIamoqGzdu5PXXX+f3v/8977zzjivvgql3LSL+wZXDnhHh3UL4ygfwzY+huVtfHxMXzWLauiXDnnFGtxUe/tRep/vVXH19yOTmz0V2Nw98DE3iVTivQBY3Pf3cvJKjrw+Z3Owrgns/gPs/hkYPcPOdz+xuXtJ5zb9MbtKLL7tp8AA3/+xBbt4vsbu57yOoF6/0eAWyuLHY4Lufwb5i+IXO8uwyudlfAvf+He7/COo8xM2fiyFNpxtRXFqKMy0tjc2bN7N7925iYmKoq6vD29ub++6774p2jlrwWhhT20ncgUpi7r6VzsZWas9coquhZcD2ecvDATRntNR/zU54gJqJN4PNxpxTLxLceFHzfACmrVtyzQ62qylaFMLUI3V9/y+S0TKfDr9gTiQ/hsU8lqC6HBLPvCSUA0ndzLqfmvB5YLMx+8xLhNSLr6rlcxPEyeTH6DGPI6g+l8TTu4VyILkb4KbTLxFaL76qls1Np28gJxY8To95HIH1ucw1uJvzs75Bdbh9E8FNZ18mtFb8L5Bsbrp8Azie/AQ9vuMJrM9j7ulfCOVAfjcJZ39FWO3Ah5JfjVY3/eejJSPsxjye4wueoMc3gMD6fOaeflEoB3K6uRD/Daoi7G7is15jYs0Z4axsbrrN4zi+4Pt0+wYwoSGfm0+JuwFtteBd+hF8S0sL27dvJzIykoKCAiIiIrBYLNe0Ex1cf2pO5bPvwDZsViuBsZMp/+TsoO1TUlIANGe0XHh8sgZeuAD1nV68/KPv4qPh/WTHfIJmRBI0M8pp+40bNnDoiL1usmhGy3xsNvjPU3CpBb6RNJM7H0wVyoGcbk7Vwc/PQV2nFy//8GHMBnfzX6fhYjNsSJrB8m8a283pOnj+HNR2wsv/thlfk3BUSjf/fRrym+Hr82ewwuBuztbBcw43275leDf/cwbymuCr8+NYZXA35+oh9RzUdMDL2x7Cbxjd9J/PcLl5+gzkNMJX593IXQZ3k10PO3vdvLr1QcO72X4GLjTC+nk3svoBcTdacekCdOvWraSnp5OYmEhUlPMHRQ+F6UdGJCPC/DD41W2QmoWmxWd/GnJLyNzx1rBnnOHlBT+aN7Q+ZHJzcwi8+g92N1oWn/2Ryc2TNw+tD5nczO3nRssCpz8yufnhzUPrQyY3czzMzb/NHVofMrm5KRhe7f17o2WB0x+Z3PzAg9wkeJibbUN0I4rLNyGtXr3a1V0qFAqFQqFQKDwIQ++Cj39oFSazD/U5RVi7LXibTVi7LVQcHvjaGEemqaCCjuoG/IID6Kxvpvpk3giO3PNRbuRFuZEX5UZelBt5UW6MiaEXoNhs+IUG4Bc0nsqjFwhfGE+ls/JTvRlTWQ3VmfnErLmFkg8yR2a8/QhOiO47IFeECTdOYY3AtR5X34fbUG6c3ofbUG6c3ofbUG6c3ofbGEVuHI+znoxbUG6EMrJh6AVoZ10zFYeziFyWTGBsEx21TQTGTqatrNZpxi90AthstFXW26+6HWFMvmbNlRaMVJlBuZEX5UZelBt5GW1uwDh+lBtjYugFqOPogYbcEs0ZB1XOXiUpdKHcyItyIy/KjbwoN/Ki3BgTty5Au7otnM6pG7TN3Jkh+Jp1bisbYfbs2UNbWxtLly6lvb2d0tJSEhISyM7OJiEhgb/97W9UV1fz8MMP8/HHHxMREcGSJa49RFZxfZQbeVFu5EW5kRflRl6UGzHcugA9nVPHwo1/GrTN0TfWsmD2tbVfHbVPmy6VMXZiMHh70VpSjaWrh9rT1z8QXk9GC5s2beq7XV5eTnJyMgBxcXFX/BfgK1/5ChUVFUO+TxlRbuRFuZEX5UZelBt5UW6Mi2E/gu9f+3Q4M3qZNGmS0zYREREjMJKRR7mRF+VGXpQbeVFu5EW5MS4urQU/Gilpha5riz0pJEC5kZdS5UZalBt5KWuDTuVGSpQb7bh0AXrw4EFSU1PZuHEjr7/+On/5y1/4zW9+I5z/+qrp3LsshrBg/wHbTFw0i2nrtF0roScjwq/z4L6PYH8Z1Ha4vHvDIZOb1/Ph/o/g/VJ7ebTRjkxufpvf+7wphap2l3dvOGRy88ZFuO9j+/Omss3l3RsOmdy8deny86ZcuZHKze8/v+ymtNXl3XssXjab684dSEtLY/PmzezevZuYmBjCw8PZv38/Tz755BXtdu3aRWFhIRWN/rx5LLbv+5MnjuWxB+bw2DOXy2VtWHCRiMAOxtR2EnegEoBp65Zcs4PtavKWhwNozrSH+gnPNzvhfmomzgOblcRTLxLUeEk460nI6Ob8rPupDre7mXN6N8EN+cJZT0JON/dRHT4fbFZmn/klIfW5wllPQko38RupjkjudfMKIfWjc2ew7G5uOvsqoXXnhbOehIxuLsRvpKrXTULWa4TVDnwAvqcTHR3Nli1bhNq69BrQlpYWtm/fTmRkJAUFBUyePJkZM2Zc084xuGNZ1bzZbxPSisVTeO9QMVER4yiusL+MSElJYcHsG6g5lc++A9sImhFJkMDhxSkpKQCaM1rO1ipohufOweSx3jy+9l/w9hKOehQyuilqgWezIHysN99f+8/KjURuilsg9RyE+3vz/bUPKzcSuSlphZ1ZMNHfm61rv63cSOSmtBWeyYIb/L3ZuvZbmJQbadyU9boJ9fNm29qHRq0brbh0Abp161bS09NJTEwkKsouef78+cL5PX90XgKrIbeEzB1vaRqXnowIMQHw3C0u79awyORm6nh4VrnpQyY3UePh2UUu79awyOQmcpxy0x+Z3ExRbq5AJjeTx0GqcqMZl++CX716tau7VCgUCoVCoVB4EC69BlQrWg6it3R1U59dKNy3o/ap1ozJ1yzcXmFHuZEX5UZelBt5UW7kRbnxHNy6AFUoFAqFQqFQjD7UOaAKhUKhUCgUihFFLUAVCoVCoVAoFCOKWoAqFAqFQqFQKEYUtQBVKBQKhUKhUIwoagGqUCgUCoVCoRhR1AJUoVAoFAqFQjGiqAWoQqFQKBQKhWJEUQtQhUKhUCgUCsWIohagCoVCoVAoFIoRRS1AFQqFQqFQKBQjilqAKhQKhUKhUChGFLUAVSgUCoVCoVCMKP8fj80hjW6fsOMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 847.13x842.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 613.019x842.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "long_fig = combine_slices(slices, include_barriers=False).draw(\"mpl\", fold = -1, scale = 0.2)\n",
    "short_fig = bp_circuit.draw(\"mpl\", fold=-1, scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2b: Transpile circuits into ISA Circuits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create PUBs (Primitives Unified Blocs) which specify our experiment\n",
    "Note that this time we will not be submitting the 100 Trotter step circuit. On real hardware this will not return useful results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pub_circuits = mpf_circuits + [bp_circuit] \n",
    "pub_observables = ([observable] * 3) + [bp_obs]\n",
    "pubs = list(zip(pub_circuits, pub_observables))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transpile PUBs (circuits and observables) to match the backend ISA (Instruction Set Architecture)\n",
    "By selecting `optimization_level=3`, the transpiler will choose a 1D chain of qubits which minimizes the noise affecting our circuit. Once we have converted our circuits into the format that the backend is prepared to accept, we will apply a complimentary transformation to our observables as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "\n",
    "# need to create a service that's not local\n",
    "service = QiskitRuntimeService(channel=\"ibm_quantum\", instance='ibm-q-internal/dev-services/middleware')\n",
    "backend = service.backend(\"ibm_torino\")\n",
    "\n",
    "# Transpile pubs (circuits and observables) to match ISA\n",
    "pass_manager = generate_preset_pass_manager(backend=backend, optimization_level=3)\n",
    "isa_circuits = [pass_manager.run(circuit) for circuit in pub_circuits]\n",
    "isa_observables = [obs.apply_layout(isa_circuit.layout) \n",
    "                                if isinstance(obs, SparsePauliOp) \n",
    "                                else [sub_obs.apply_layout(isa_circuit.layout) for sub_obs in obs]\n",
    "                                for (isa_circuit, obs) in zip(isa_circuits, pub_observables)]\n",
    "isa_pubs = zip(isa_circuits, isa_observables)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Execute quantum experiments on `ibm_torino`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configure ``EstimatorOptions``, to control the parameters of our hardware experiment\n",
    "For more information about the available execution options, see the [qiskit-ibm-runtime documentation](https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.options.EstimatorOptions). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "twirling_options = {'enable_gates' : True,\n",
    "                    'enable_measure':True,\n",
    "                    'num_randomizations':300,\n",
    "                    'shots_per_randomization':100,\n",
    "                    'strategy':'active'}\n",
    "\n",
    "zne_options = {'amplifier': 'gate_folding',\n",
    "               'noise_factors': [1, 2, 3],\n",
    "               'extrapolated_noise_factors': list(np.linspace(0,3, 31)),\n",
    "               'extrapolator': [\"exponential\", \"linear\", \"fallback\"]}\n",
    "\n",
    "meas_learning_options = {'num_randomizations': 512,\n",
    "                        'shots_per_randomization': 512}\n",
    "\n",
    "resilience_options = {'measure_mitigation': True,\n",
    "                      'zne_mitigation': True,\n",
    "                      'zne': zne_options,\n",
    "                      'measure_noise_learning': meas_learning_options}\n",
    "\n",
    "experimental_options = {'execution': {'fast_parametric_update': True}}\n",
    "\n",
    "estimator_options = {'resilience': resilience_options,\n",
    "                     'twirling': twirling_options,\n",
    "                     'experimental': experimental_options}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Execute experiments / load previous results from disk\n",
    "Here is where we would initiate our experiments through runtime, however, for the purposes of this demo, we will instead be loading previously run results from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from demo_utils import dill_dump, dill_load\n",
    "from qiskit_ibm_runtime import Estimator\n",
    "estimator = Estimator(backend, options = estimator_options)\n",
    "\n",
    "hw_results_save_path = f\"L{L}/{'_'.join([str(k) for k in mpf_trotter_steps])}/hw_results.pkl\"\n",
    "\n",
    "# # ========== Execute Experiments =================\n",
    "# hw_results = estimator.run(isa_pubs).result()\n",
    "# hw_results_dicts = [pub_result.data.__dict__ for pub_result in hw_results]\n",
    "\n",
    "# ========== Save Results to Disk =================\n",
    "# dill_dump([hw_results_dicts, estimator_options], hw_results_save_path, make_dirs=True)\n",
    "\n",
    "# ========== Load Results to Disk =================\n",
    "hw_results_dicts, estimator_options = dill_load(hw_results_save_path)\n",
    "\n",
    "\n",
    "# Re-organize expectation values\n",
    "hw_expvals = [pub_result_data['evs'].tolist() for pub_result_data in hw_results_dicts]\n",
    "mpf_expvals_hw = hw_expvals[:3]\n",
    "mpf_obp_expvals_hw = hw_expvals[:2] + [hw_expvals[3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Reconstruct MPF expectation values\n",
    "\n",
    "Now that we have obtained the results for our different Trotter circuits, we can reconstruct the MPF and MPF + OBP expectation values to compare them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpf_expval_hw     =     mpf_expvals_hw @ mpf_coeffs\n",
    "mpf_obp_expval_hw = mpf_obp_expvals_hw @ mpf_coeffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ideal:\t\t-0.5887\n",
      "––––––––––––––––––––––––––––––––––––––\n",
      "2 steps:\t-0.5434, |∆| = 0.0453\n",
      "3 steps:\t-0.5361, |∆| = 0.0527\n",
      "6 steps:\t-0.4161, |∆| = 0.1726\n",
      "6 steps + OBP:\t-0.6047, |∆| = 0.0159\n",
      "––––––––––––––––––––––––––––––––––––––\n",
      "MPF:\t\t-0.2399, |∆| = 0.3488\n",
      "MPF + OBP:\t-0.7112, |∆| = 0.1225\n"
     ]
    }
   ],
   "source": [
    "# This comes from a TN calculation,\n",
    "ref_expval = -0.5887371957802596 \n",
    "print(f\"Ideal:\\t\\t{ref_expval:.4f}\")\n",
    "print('––'*19)\n",
    "print(f\"{mpf_trotter_steps[0]} steps:\\t{mpf_expvals_hw[0]:.4f}, |∆| = {np.abs(ref_expval - mpf_expvals_hw[0]):.4f}\")\n",
    "print(f\"{mpf_trotter_steps[1]} steps:\\t{mpf_expvals_hw[1]:.4f}, |∆| = {np.abs(ref_expval - mpf_expvals_hw[1]):.4f}\")\n",
    "print(f\"{mpf_trotter_steps[2]} steps:\\t{mpf_expvals_hw[2]:.4f}, |∆| = {np.abs(ref_expval - mpf_expvals_hw[2]):.4f}\")\n",
    "print(f\"{mpf_trotter_steps[2]} steps + OBP:\\t{mpf_obp_expvals_hw[2]:.4f}, |∆| = {np.abs(ref_expval - mpf_obp_expvals_hw[2]):.4f}\")\n",
    "print('––'*19)\n",
    "print(f\"MPF:\\t\\t{mpf_expval_hw:.4f}, |∆| = {np.abs(ref_expval - mpf_expval_hw):.4f}\")\n",
    "print(f\"MPF + OBP:\\t{mpf_obp_expval_hw:.4f}, |∆| = {np.abs(ref_expval - mpf_obp_expval_hw):.4f}\")"
   ]
  }
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