From d913475c03b7e754909737d6ff1df937fcbc0b4c Mon Sep 17 00:00:00 2001
From: GitHub Action <52708150+marcpinet@users.noreply.github.com>
Date: Sun, 22 Sep 2024 23:40:39 +0200
Subject: [PATCH 1/2] feat: add str support for fit's metrics parameter

---
 .../mnist_loading_saved_model.ipynb           | 52 +++++------
 .../simple_cancer_binary.ipynb                | 91 ++++++++++---------
 .../simple_diabete_regression.ipynb           | 83 ++++++++---------
 .../simple_mnist_multiclass.ipynb             | 72 +++++++--------
 .../simple_cnn_classification_mnist.ipynb     | 78 ++++++++--------
 .../tic_tac_toe_alternative_dataset_shape.py  |  4 +-
 neuralnetlib/metrics.py                       | 29 ++++++
 neuralnetlib/model.py                         | 16 ++--
 8 files changed, 226 insertions(+), 199 deletions(-)

diff --git a/examples/classification-regression/mnist_loading_saved_model.ipynb b/examples/classification-regression/mnist_loading_saved_model.ipynb
index ce2c3d5..01fbeb1 100644
--- a/examples/classification-regression/mnist_loading_saved_model.ipynb
+++ b/examples/classification-regression/mnist_loading_saved_model.ipynb
@@ -21,8 +21,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:32:44.879695500Z",
-     "start_time": "2024-04-23T23:32:41.806868Z"
+     "end_time": "2024-09-22T20:58:52.408452600Z",
+     "start_time": "2024-09-22T20:58:45.258396800Z"
     }
    },
    "outputs": [],
@@ -47,8 +47,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:32:45.056739600Z",
-     "start_time": "2024-04-23T23:32:44.879695500Z"
+     "end_time": "2024-09-22T20:58:55.090640900Z",
+     "start_time": "2024-09-22T20:58:54.943027500Z"
     }
    },
    "outputs": [],
@@ -68,8 +68,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:32:45.166846Z",
-     "start_time": "2024-04-23T23:32:45.059739600Z"
+     "end_time": "2024-09-22T20:58:56.605497Z",
+     "start_time": "2024-09-22T20:58:56.511603700Z"
     }
    },
    "outputs": [],
@@ -92,8 +92,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:32:45.285935300Z",
-     "start_time": "2024-04-23T23:32:45.167845600Z"
+     "end_time": "2024-09-22T20:58:58.354481Z",
+     "start_time": "2024-09-22T20:58:58.201316400Z"
     }
    },
    "outputs": [],
@@ -113,8 +113,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:32:45.329886Z",
-     "start_time": "2024-04-23T23:32:45.288843800Z"
+     "end_time": "2024-09-22T20:59:00.067234500Z",
+     "start_time": "2024-09-22T20:59:00.052659300Z"
     }
    },
    "outputs": [],
@@ -134,8 +134,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:32:45.374527900Z",
-     "start_time": "2024-04-23T23:32:45.314964200Z"
+     "end_time": "2024-09-22T20:59:02.252551200Z",
+     "start_time": "2024-09-22T20:59:02.216863600Z"
     }
    },
    "outputs": [
@@ -143,7 +143,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Accuracy: 0.9738333333333333\n"
+      "Validation Accuracy: 0.9728333333333333\n"
      ]
     }
    ],
@@ -165,8 +165,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:32:45.444303500Z",
-     "start_time": "2024-04-23T23:32:45.375529400Z"
+     "end_time": "2024-09-22T20:59:05.493573700Z",
+     "start_time": "2024-09-22T20:59:05.445749600Z"
     }
    },
    "outputs": [
@@ -174,18 +174,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Accuracy: 0.9549\n",
+      "Test Accuracy: 0.9567\n",
       "Confusion Matrix:\n",
-      "[[ 958    0    3    0    0    3    7    2    4    3]\n",
-      " [   0 1117    1    6    0    1    1    2    6    1]\n",
-      " [   5    1  983   11    3    0    4   16    9    0]\n",
-      " [   2    0   10  959    0   13    1    7    8   10]\n",
-      " [   2    1    6    0  909    0    6    0    0   58]\n",
-      " [   9    1    0   20    0  838    8    2    3   11]\n",
-      " [  10    4    4    1    5    6  917    0   10    1]\n",
-      " [   1    8   10    6    0    0    0  982    0   21]\n",
-      " [   5    3    9    7    4    6    5    7  917   11]\n",
-      " [   3    5    3    5   10    4    2    7    1  969]]\n"
+      "[[ 963    0    1    2    2    1    4    1    3    3]\n",
+      " [   0 1119    2    3    0    1    1    2    6    1]\n",
+      " [   5    3  990    8    3    1    4   10    8    0]\n",
+      " [   1    2    5  966    1   19    1    6    4    5]\n",
+      " [   1    1    2    0  932    1    8    0    4   33]\n",
+      " [   7    0    1   15    2  852    5    1    6    3]\n",
+      " [   4    5    2    3    5   14  921    0    4    0]\n",
+      " [   0    9   17    5    6    0    0  969    2   20]\n",
+      " [   8    1    7   20    3   11    8    3  899   14]\n",
+      " [   3    2    1   10   18    9    0    8    2  956]]\n"
      ]
     }
    ],
diff --git a/examples/classification-regression/simple_cancer_binary.ipynb b/examples/classification-regression/simple_cancer_binary.ipynb
index 5621221..35f1a1d 100644
--- a/examples/classification-regression/simple_cancer_binary.ipynb
+++ b/examples/classification-regression/simple_cancer_binary.ipynb
@@ -21,8 +21,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:22.316823400Z",
-     "start_time": "2024-05-18T15:40:21.250682300Z"
+     "end_time": "2024-09-22T20:59:18.836461400Z",
+     "start_time": "2024-09-22T20:59:17.011617200Z"
     }
    },
    "outputs": [],
@@ -52,8 +52,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:22.332821700Z",
-     "start_time": "2024-05-18T15:40:22.318819700Z"
+     "end_time": "2024-09-22T20:59:18.865525500Z",
+     "start_time": "2024-09-22T20:59:18.832952600Z"
     }
    },
    "outputs": [],
@@ -74,8 +74,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:22.347820300Z",
-     "start_time": "2024-05-18T15:40:22.333821900Z"
+     "end_time": "2024-09-22T20:59:18.877592Z",
+     "start_time": "2024-09-22T20:59:18.863022600Z"
     }
    },
    "outputs": [],
@@ -98,8 +98,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:22.362820500Z",
-     "start_time": "2024-05-18T15:40:22.348820100Z"
+     "end_time": "2024-09-22T20:59:18.895612700Z",
+     "start_time": "2024-09-22T20:59:18.879095600Z"
     }
    },
    "outputs": [],
@@ -135,8 +135,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:22.377825Z",
-     "start_time": "2024-05-18T15:40:22.364821200Z"
+     "end_time": "2024-09-22T20:59:22.297364300Z",
+     "start_time": "2024-09-22T20:59:22.282347400Z"
     }
    },
    "outputs": [
@@ -188,8 +188,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:45.505483600Z",
-     "start_time": "2024-05-18T15:40:22.378824500Z"
+     "end_time": "2024-09-22T20:59:41.693163Z",
+     "start_time": "2024-09-22T20:59:24.562926Z"
     }
    },
    "outputs": [
@@ -197,33 +197,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[==============================] 100% Epoch 1/40 - loss: 0.6905 -  - 0.04s\n",
-      "[==============================] 100% Epoch 2/40 - loss: 0.6785 -  - 0.11s\n",
-      "[==============================] 100% Epoch 3/40 - loss: 0.6621 -  - 0.10s\n",
-      "[==============================] 100% Epoch 4/40 - loss: 0.6432 -  - 0.10s\n",
-      "[==============================] 100% Epoch 5/40 - loss: 0.6218 -  - 0.10s\n",
-      "[==============================] 100% Epoch 6/40 - loss: 0.5978 -  - 0.11s\n",
-      "[==============================] 100% Epoch 7/40 - loss: 0.5711 -  - 0.10s\n",
-      "[==============================] 100% Epoch 8/40 - loss: 0.5420 -  - 0.10s\n",
-      "[==============================] 100% Epoch 9/40 - loss: 0.5118 -  - 0.12s\n",
-      "[==============================] 100% Epoch 10/40 - loss: 0.4814 -  - 0.10s\n",
-      "[==============================] 100% Epoch 11/40 - loss: 0.4520 -  - 0.09s\n",
-      "[==============================] 100% Epoch 12/40 - loss: 0.4250 -  - 0.10s\n",
-      "[==============================] 100% Epoch 13/40 - loss: 0.4006 -  - 0.09s\n",
-      "[==============================] 100% Epoch 14/40 - loss: 0.3795 -  - 0.10s\n",
-      "[==============================] 100% Epoch 15/40 - loss: 0.3621 -  - 0.10s\n",
-      "[==============================] 100% Epoch 16/40 - loss: 0.3485 -  - 0.10s\n",
-      "[==============================] 100% Epoch 17/40 - loss: 0.3385 -  - 0.10s\n",
-      "[==============================] 100% Epoch 18/40 - loss: 0.3314 -  - 0.09s\n",
-      "[==============================] 100% Epoch 19/40 - loss: 0.3268 -  - 0.10s\n",
-      "[==============================] 100% Epoch 20/40 - loss: 0.3241 -  - 0.10s\n",
-      "[==============================] 100% Epoch 21/40 - loss: 0.3226 -  - 0.09s\n",
-      "[==============================] 100% Epoch 22/40 - loss: 0.3219 -  - 0.10s\n",
-      "[==============================] 100% Epoch 23/40 - loss: 0.3218 -  - 0.13s\n",
-      "[==============================] 100% Epoch 24/40 - loss: 0.3230 -  - 0.09s\n",
-      "[==============================] 100% Epoch 25/40 - loss: 0.3265 -  - 0.10s\n",
-      "[==============================] 100% Epoch 26/40 - loss: 0.3318 -  - 0.09s\n",
-      "Early stopping after 26 epochs.\n"
+      "[==============================] 100% Epoch 1/40 - loss: 0.6905 -  - 0.05s\n",
+      "[==============================] 100% Epoch 2/40 - loss: 0.6785 -  - 0.07s\n",
+      "[==============================] 100% Epoch 3/40 - loss: 0.6621 -  - 0.07s\n",
+      "[==============================] 100% Epoch 4/40 - loss: 0.6432 -  - 0.06s\n",
+      "[==============================] 100% Epoch 5/40 - loss: 0.6218 -  - 0.06s\n",
+      "[==============================] 100% Epoch 6/40 - loss: 0.5978 -  - 0.07s\n",
+      "[==============================] 100% Epoch 7/40 - loss: 0.5711 -  - 0.06s\n",
+      "[==============================] 100% Epoch 8/40 - loss: 0.5420 -  - 0.06s\n",
+      "[==============================] 100% Epoch 9/40 - loss: 0.5118 -  - 0.06s\n",
+      "[==============================] 100% Epoch 10/40 - loss: 0.4814 -  - 0.07s\n",
+      "[==============================] 100% Epoch 11/40 - loss: 0.4520 -  - 0.06s\n",
+      "[==============================] 100% Epoch 12/40 - loss: 0.4250 -  - 0.06s\n",
+      "[==============================] 100% Epoch 13/40 - loss: 0.4006 -  - 0.06s\n",
+      "[==============================] 100% Epoch 14/40 - loss: 0.3795 -  - 0.07s\n",
+      "[==============================] 100% Epoch 15/40 - loss: 0.3621 -  - 0.06s\n",
+      "[==============================] 100% Epoch 16/40 - loss: 0.3485 -  - 0.07s\n",
+      "[==============================] 100% Epoch 17/40 - loss: 0.3385 -  - 0.06s\n",
+      "[==============================] 100% Epoch 18/40 - loss: 0.3314 -  - 0.06s\n",
+      "[==============================] 100% Epoch 19/40 - loss: 0.3268 -  - 0.06s\n",
+      "[==============================] 100% Epoch 20/40 - loss: 0.3241 -  - 0.06s\n",
+      "[==============================] 100% Epoch 21/40 - loss: 0.3226 -  - 0.06s\n",
+      "[==============================] 100% Epoch 22/40 - loss: 0.3219 -  - 0.06s\n",
+      "[==============================] 100% Epoch 23/40 - loss: 0.3218 -  - 0.06s\n",
+      "[==============================] 100% Epoch 24/40 - loss: 0.3230 -  - 0.06s\n",
+      "[==============================] 100% Epoch 25/40 - loss: 0.3265 -  - 0.07s\n",
+      "[==============================] 100% Epoch 26/40 - loss: 0.3318 -  - 0.07s\n",
+      "\n",
+      "Early stopping after 26 epochs."
      ]
     }
    ],
@@ -250,8 +251,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:45.518524100Z",
-     "start_time": "2024-05-18T15:40:45.504481900Z"
+     "end_time": "2024-09-22T21:00:04.635660300Z",
+     "start_time": "2024-09-22T21:00:04.617649200Z"
     }
    },
    "outputs": [
@@ -280,8 +281,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:45.547522500Z",
-     "start_time": "2024-05-18T15:40:45.519524Z"
+     "end_time": "2024-09-22T21:00:07.348481800Z",
+     "start_time": "2024-09-22T21:00:07.333455600Z"
     }
    },
    "outputs": [],
@@ -301,8 +302,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-18T15:40:45.553523700Z",
-     "start_time": "2024-05-18T15:40:45.536524300Z"
+     "end_time": "2024-09-22T21:00:08.471323500Z",
+     "start_time": "2024-09-22T21:00:08.455806100Z"
     }
    },
    "outputs": [
diff --git a/examples/classification-regression/simple_diabete_regression.ipynb b/examples/classification-regression/simple_diabete_regression.ipynb
index 64ea18e..99a6208 100644
--- a/examples/classification-regression/simple_diabete_regression.ipynb
+++ b/examples/classification-regression/simple_diabete_regression.ipynb
@@ -18,11 +18,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
-    "is_executing": true,
     "ExecuteTime": {
-     "start_time": "2024-04-24T00:35:13.345636700Z"
+     "end_time": "2024-09-22T21:00:32.650134600Z",
+     "start_time": "2024-09-22T21:00:31.937963Z"
     }
    },
    "outputs": [],
@@ -48,9 +48,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
-    "is_executing": true
+    "ExecuteTime": {
+     "end_time": "2024-09-22T21:00:32.682667300Z",
+     "start_time": "2024-09-22T21:00:32.651134800Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -67,11 +70,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T00:35:13.897190200Z",
-     "start_time": "2024-04-24T00:35:13.895189700Z"
+     "end_time": "2024-09-22T21:00:32.697683900Z",
+     "start_time": "2024-09-22T21:00:32.683666100Z"
     }
    },
    "outputs": [],
@@ -93,11 +96,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T00:35:13.910701900Z",
-     "start_time": "2024-04-24T00:35:13.901704800Z"
+     "end_time": "2024-09-22T21:00:33.557163Z",
+     "start_time": "2024-09-22T21:00:33.550654500Z"
     }
    },
    "outputs": [],
@@ -129,11 +132,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T00:35:13.950703300Z",
-     "start_time": "2024-04-24T00:35:13.911705Z"
+     "end_time": "2024-09-22T21:00:35.641217800Z",
+     "start_time": "2024-09-22T21:00:35.628164600Z"
     }
    },
    "outputs": [
@@ -172,11 +175,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T00:35:14.078227700Z",
-     "start_time": "2024-04-24T00:35:13.921705400Z"
+     "end_time": "2024-09-22T21:00:38.623080800Z",
+     "start_time": "2024-09-22T21:00:38.589954900Z"
     }
    },
    "outputs": [
@@ -184,25 +187,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[==============================] 100% Epoch 1/10 - loss: 1.2716 -  - 0.01s\n",
-      "[==============================] 100% Epoch 2/10 - loss: 1.2699 -  - 0.01s\n",
-      "[==============================] 100% Epoch 3/10 - loss: 1.2680 -  - 0.01s\n",
-      "[==============================] 100% Epoch 4/10 - loss: 1.2659 -  - 0.01s\n",
-      "[==============================] 100% Epoch 5/10 - loss: 1.2636 -  - 0.01s\n",
-      "[==============================] 100% Epoch 6/10 - loss: 1.2612 -  - 0.01s\n",
-      "[==============================] 100% Epoch 7/10 - loss: 1.2587 -  - 0.01s\n",
-      "[==============================] 100% Epoch 8/10 - loss: 1.2560 -  - 0.01s\n",
-      "[==============================] 100% Epoch 9/10 - loss: 1.2531 -  - 0.01s\n",
-      "[==============================] 100% Epoch 10/10 - loss: 1.2501 -  - 0.01s\n"
+      "[==============================] 100% Epoch 1/10 - loss: 1.2716 -  - 0.00s\n",
+      "[==============================] 100% Epoch 2/10 - loss: 1.2699 -  - 0.00s\n",
+      "[==============================] 100% Epoch 3/10 - loss: 1.2680 -  - 0.00s\n",
+      "[==============================] 100% Epoch 4/10 - loss: 1.2659 -  - 0.00s\n",
+      "[==============================] 100% Epoch 5/10 - loss: 1.2636 -  - 0.00s\n",
+      "[==============================] 100% Epoch 6/10 - loss: 1.2612 -  - 0.00s\n",
+      "[==============================] 100% Epoch 7/10 - loss: 1.2587 -  - 0.00s\n",
+      "[==============================] 100% Epoch 8/10 - loss: 1.2560 -  - 0.00s\n",
+      "[==============================] 100% Epoch 9/10 - loss: 1.2531 -  - 0.00s\n",
+      "[==============================] 100% Epoch 10/10 - loss: 1.2501 -  - 0.00s\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": ""
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -218,11 +213,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T00:35:14.078227700Z",
-     "start_time": "2024-04-24T00:35:14.042229900Z"
+     "end_time": "2024-09-22T21:00:46.986919600Z",
+     "start_time": "2024-09-22T21:00:46.973904400Z"
     }
    },
    "outputs": [
@@ -248,11 +243,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T00:35:14.148266800Z",
-     "start_time": "2024-04-24T00:35:14.047231200Z"
+     "end_time": "2024-09-22T21:00:50.161630900Z",
+     "start_time": "2024-09-22T21:00:50.150315300Z"
     }
    },
    "outputs": [
@@ -278,11 +273,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T00:35:14.153268100Z",
-     "start_time": "2024-04-24T00:35:14.059228300Z"
+     "end_time": "2024-09-22T21:00:53.189977Z",
+     "start_time": "2024-09-22T21:00:53.178768100Z"
     }
    },
    "outputs": [
diff --git a/examples/classification-regression/simple_mnist_multiclass.ipynb b/examples/classification-regression/simple_mnist_multiclass.ipynb
index 8cfb668..7876dd9 100644
--- a/examples/classification-regression/simple_mnist_multiclass.ipynb
+++ b/examples/classification-regression/simple_mnist_multiclass.ipynb
@@ -21,8 +21,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:29:14.420006500Z",
-     "start_time": "2024-04-23T23:29:10.910211Z"
+     "end_time": "2024-09-22T21:23:17.470315300Z",
+     "start_time": "2024-09-22T21:23:15.274765600Z"
     }
    },
    "outputs": [],
@@ -52,8 +52,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:29:14.609051100Z",
-     "start_time": "2024-04-23T23:29:14.415004400Z"
+     "end_time": "2024-09-22T21:23:17.612787400Z",
+     "start_time": "2024-09-22T21:23:17.472315400Z"
     }
    },
    "outputs": [],
@@ -73,8 +73,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:29:14.698566500Z",
-     "start_time": "2024-04-23T23:29:14.594050100Z"
+     "end_time": "2024-09-22T21:23:17.702612600Z",
+     "start_time": "2024-09-22T21:23:17.609786900Z"
     }
    },
    "outputs": [],
@@ -97,8 +97,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:29:14.711565900Z",
-     "start_time": "2024-04-23T23:29:14.701566400Z"
+     "end_time": "2024-09-22T21:23:17.718270700Z",
+     "start_time": "2024-09-22T21:23:17.704611500Z"
     }
    },
    "outputs": [],
@@ -134,8 +134,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:29:14.741569600Z",
-     "start_time": "2024-04-23T23:29:14.705566800Z"
+     "end_time": "2024-09-22T21:23:17.763653100Z",
+     "start_time": "2024-09-22T21:23:17.719270900Z"
     }
    },
    "outputs": [
@@ -177,8 +177,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:30:26.688161400Z",
-     "start_time": "2024-04-23T23:29:14.734569600Z"
+     "end_time": "2024-09-22T21:23:28.493706600Z",
+     "start_time": "2024-09-22T21:23:17.734301400Z"
     }
    },
    "outputs": [
@@ -186,16 +186,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[==============================] 100% Epoch 1/10 - loss: 0.5726 - accuracy_score: 0.8099 - 8.15s\n",
-      "[==============================] 100% Epoch 2/10 - loss: 0.2319 - accuracy_score: 0.9333 - 7.96s\n",
-      "[==============================] 100% Epoch 3/10 - loss: 0.1948 - accuracy_score: 0.9432 - 7.10s\n",
-      "[==============================] 100% Epoch 4/10 - loss: 0.1726 - accuracy_score: 0.9502 - 7.08s\n",
-      "[==============================] 100% Epoch 5/10 - loss: 0.1587 - accuracy_score: 0.9530 - 6.98s\n",
-      "[==============================] 100% Epoch 6/10 - loss: 0.1487 - accuracy_score: 0.9563 - 7.23s\n",
-      "[==============================] 100% Epoch 7/10 - loss: 0.1386 - accuracy_score: 0.9587 - 6.78s\n",
-      "[==============================] 100% Epoch 8/10 - loss: 0.1349 - accuracy_score: 0.9603 - 6.91s\n",
-      "[==============================] 100% Epoch 9/10 - loss: 0.1320 - accuracy_score: 0.9609 - 6.93s\n",
-      "[==============================] 100% Epoch 10/10 - loss: 0.1222 - accuracy_score: 0.9635 - 6.81s\n"
+      "[==============================] 100% Epoch 1/10 - loss: 0.5703 - accuracy_score: 0.8109 - 1.10s\n",
+      "[==============================] 100% Epoch 2/10 - loss: 0.2287 - accuracy_score: 0.9336 - 1.05s\n",
+      "[==============================] 100% Epoch 3/10 - loss: 0.1950 - accuracy_score: 0.9437 - 1.13s\n",
+      "[==============================] 100% Epoch 4/10 - loss: 0.1791 - accuracy_score: 0.9468 - 1.02s\n",
+      "[==============================] 100% Epoch 5/10 - loss: 0.1600 - accuracy_score: 0.9525 - 1.12s\n",
+      "[==============================] 100% Epoch 6/10 - loss: 0.1469 - accuracy_score: 0.9567 - 1.01s\n",
+      "[==============================] 100% Epoch 7/10 - loss: 0.1398 - accuracy_score: 0.9582 - 1.10s\n",
+      "[==============================] 100% Epoch 8/10 - loss: 0.1337 - accuracy_score: 0.9601 - 1.03s\n",
+      "[==============================] 100% Epoch 9/10 - loss: 0.1292 - accuracy_score: 0.9620 - 1.12s\n",
+      "[==============================] 100% Epoch 10/10 - loss: 0.1243 - accuracy_score: 0.9631 - 1.02s\n"
      ]
     }
    ],
@@ -215,8 +215,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:30:26.752709800Z",
-     "start_time": "2024-04-23T23:30:26.683876200Z"
+     "end_time": "2024-09-22T21:23:28.536750900Z",
+     "start_time": "2024-09-22T21:23:28.490707700Z"
     }
    },
    "outputs": [
@@ -224,7 +224,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test loss: 0.17413642094878234\n"
+      "Test loss: 0.16901475773235153\n"
      ]
     }
    ],
@@ -245,8 +245,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:30:26.808706400Z",
-     "start_time": "2024-04-23T23:30:26.747485900Z"
+     "end_time": "2024-09-22T21:23:28.568699500Z",
+     "start_time": "2024-09-22T21:23:28.537750700Z"
     }
    },
    "outputs": [],
@@ -266,8 +266,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:30:26.814711Z",
-     "start_time": "2024-04-23T23:30:26.781708700Z"
+     "end_time": "2024-09-22T21:23:28.582991Z",
+     "start_time": "2024-09-22T21:23:28.567699400Z"
     }
    },
    "outputs": [
@@ -275,9 +275,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "accuracy: 0.9549\n",
-      "f1_score: 0.9548478204173041\n",
-      "recall_score 0.9543130769611624\n"
+      "accuracy: 0.9551\n",
+      "f1_score: 0.9549572674105582\n",
+      "recall_score 0.9543577978545592\n"
      ]
     }
    ],
@@ -299,8 +299,8 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:30:27.058485300Z",
-     "start_time": "2024-04-23T23:30:26.792708800Z"
+     "end_time": "2024-09-22T21:23:28.814879800Z",
+     "start_time": "2024-09-22T21:23:28.583991Z"
     }
    },
    "outputs": [
@@ -334,8 +334,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-23T23:30:27.226005600Z",
-     "start_time": "2024-04-23T23:30:27.057483700Z"
+     "end_time": "2024-09-22T21:23:28.867661200Z",
+     "start_time": "2024-09-22T21:23:28.815905Z"
     }
    },
    "outputs": [],
diff --git a/examples/cnn-classification/simple_cnn_classification_mnist.ipynb b/examples/cnn-classification/simple_cnn_classification_mnist.ipynb
index 5047135..5f31580 100644
--- a/examples/cnn-classification/simple_cnn_classification_mnist.ipynb
+++ b/examples/cnn-classification/simple_cnn_classification_mnist.ipynb
@@ -21,8 +21,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:02:19.112793Z",
-     "start_time": "2024-04-24T01:02:14.118578100Z"
+     "end_time": "2024-09-22T21:32:07.913450400Z",
+     "start_time": "2024-09-22T21:32:05.718419200Z"
     }
    },
    "outputs": [],
@@ -52,8 +52,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:02:19.299830Z",
-     "start_time": "2024-04-24T01:02:19.113792100Z"
+     "end_time": "2024-09-22T21:32:08.056161400Z",
+     "start_time": "2024-09-22T21:32:07.915452400Z"
     }
    },
    "outputs": [],
@@ -73,8 +73,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:02:19.414871400Z",
-     "start_time": "2024-04-24T01:02:19.301838100Z"
+     "end_time": "2024-09-22T21:32:08.147899800Z",
+     "start_time": "2024-09-22T21:32:08.053650300Z"
     }
    },
    "outputs": [],
@@ -97,16 +97,14 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:02:19.431387200Z",
-     "start_time": "2024-04-24T01:02:19.422377200Z"
+     "end_time": "2024-09-22T21:32:08.163408200Z",
+     "start_time": "2024-09-22T21:32:08.147899800Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "\"\\n    Side note: if you set the following:\\n    \\n        - filters to 8 and 16 (in this order)\\n        - padding of the Conv2D layers to 'same'\\n        - weights initialization to 'he'\\n    \\n    you'll get an accuracy of ~0.9975 which is actually pretty cool\\n\""
-      ]
+      "text/plain": "\"\\n    Side note: if you set the following:\\n    \\n        - filters to 8 and 16 (in this order)\\n        - padding of the Conv2D layers to 'same'\\n        - weights initialization to 'he'\\n    \\n    you'll get an accuracy of ~0.9975 which is actually pretty cool\\n\""
      },
      "execution_count": 4,
      "metadata": {},
@@ -150,8 +148,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:02:19.524909400Z",
-     "start_time": "2024-04-24T01:02:19.429385600Z"
+     "end_time": "2024-09-22T21:32:08.209470200Z",
+     "start_time": "2024-09-22T21:32:08.164406800Z"
     }
    },
    "outputs": [
@@ -198,8 +196,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:07:03.811531800Z",
-     "start_time": "2024-04-24T01:02:19.446385700Z"
+     "end_time": "2024-09-22T21:34:58.553485Z",
+     "start_time": "2024-09-22T21:32:08.179948200Z"
     }
    },
    "outputs": [
@@ -207,22 +205,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[==============================] 100% Epoch 1/10 - loss: 0.7200 - accuracy_score: 0.7635 - 33.31s - val_accuracy: 0.8955\n",
-      "[==============================] 100% Epoch 2/10 - loss: 0.3133 - accuracy_score: 0.9008 - 27.58s - val_accuracy: 0.9168\n",
-      "[==============================] 100% Epoch 3/10 - loss: 0.2532 - accuracy_score: 0.9204 - 24.93s - val_accuracy: 0.9295\n",
-      "[==============================] 100% Epoch 4/10 - loss: 0.2167 - accuracy_score: 0.9334 - 24.82s - val_accuracy: 0.9378\n",
-      "[==============================] 100% Epoch 5/10 - loss: 0.1920 - accuracy_score: 0.9416 - 24.69s - val_accuracy: 0.9419\n",
-      "[==============================] 100% Epoch 6/10 - loss: 0.1732 - accuracy_score: 0.9475 - 28.50s - val_accuracy: 0.9475\n",
-      "[==============================] 100% Epoch 7/10 - loss: 0.1574 - accuracy_score: 0.9524 - 28.95s - val_accuracy: 0.9501\n",
-      "[==============================] 100% Epoch 8/10 - loss: 0.1439 - accuracy_score: 0.9568 - 26.80s - val_accuracy: 0.9538\n",
-      "[==============================] 100% Epoch 9/10 - loss: 0.1328 - accuracy_score: 0.9597 - 24.41s - val_accuracy: 0.9572\n",
-      "[==============================] 100% Epoch 10/10 - loss: 0.1232 - accuracy_score: 0.9629 - 24.14s - val_accuracy: 0.9591\n"
+      "[==============================] 100% Epoch 1/10 - loss: 0.7200 - accuracy: 0.7635 - 15.83s - val_accuracy: 0.8955\n",
+      "[==============================] 100% Epoch 2/10 - loss: 0.3133 - accuracy: 0.9008 - 16.39s - val_accuracy: 0.9168\n",
+      "[==============================] 100% Epoch 3/10 - loss: 0.2532 - accuracy: 0.9204 - 16.10s - val_accuracy: 0.9295\n",
+      "[==============================] 100% Epoch 4/10 - loss: 0.2167 - accuracy: 0.9334 - 16.04s - val_accuracy: 0.9378\n",
+      "[==============================] 100% Epoch 5/10 - loss: 0.1920 - accuracy: 0.9416 - 15.89s - val_accuracy: 0.9419\n",
+      "[==============================] 100% Epoch 6/10 - loss: 0.1732 - accuracy: 0.9475 - 16.53s - val_accuracy: 0.9475\n",
+      "[==============================] 100% Epoch 7/10 - loss: 0.1574 - accuracy: 0.9524 - 15.98s - val_accuracy: 0.9501\n",
+      "[==============================] 100% Epoch 8/10 - loss: 0.1439 - accuracy: 0.9568 - 16.32s - val_accuracy: 0.9538\n",
+      "[==============================] 100% Epoch 9/10 - loss: 0.1328 - accuracy: 0.9597 - 16.38s - val_accuracy: 0.9572\n",
+      "[==============================] 100% Epoch 10/10 - loss: 0.1232 - accuracy: 0.9629 - 16.56s - val_accuracy: 0.9591\n"
      ]
     }
    ],
    "source": [
     "model.fit(x_train, y_train, epochs=10, batch_size=128, metrics=[\n",
-    "            accuracy_score], random_state=42, validation_data=(x_test, y_test))"
+    "            \"accuracy\"], random_state=42, validation_data=(x_test, y_test))"
    ]
   },
   {
@@ -237,8 +235,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:07:05.325462900Z",
-     "start_time": "2024-04-24T01:07:03.803531300Z"
+     "end_time": "2024-09-22T21:34:59.411359900Z",
+     "start_time": "2024-09-22T21:34:58.555484800Z"
     }
    },
    "outputs": [
@@ -246,7 +244,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test loss: 0.13425407882793627\n"
+      "Test loss: 0.1342540788279363\n"
      ]
     }
    ],
@@ -267,8 +265,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:07:06.835112400Z",
-     "start_time": "2024-04-24T01:07:05.320464300Z"
+     "end_time": "2024-09-22T21:35:00.252551Z",
+     "start_time": "2024-09-22T21:34:59.410359Z"
     }
    },
    "outputs": [],
@@ -288,8 +286,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:07:06.850112500Z",
-     "start_time": "2024-04-24T01:07:06.834116500Z"
+     "end_time": "2024-09-22T21:35:00.267930600Z",
+     "start_time": "2024-09-22T21:35:00.254057900Z"
     }
    },
    "outputs": [
@@ -321,17 +319,15 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:07:07.155680200Z",
-     "start_time": "2024-04-24T01:07:06.853113300Z"
+     "end_time": "2024-09-22T21:35:00.500759500Z",
+     "start_time": "2024-09-22T21:35:00.269931600Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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U48aN9eijj6pGjRravHmzNm7c6Lp4smnTppKkQYMGqXPnzipXrpySk5NLLMeibvFesmSJDh48eNHVo7xt6DNmzCjwLta7d+/W8uXLNWjQII/fDwkJUefOnTVnzhz97W9/U7t27fTQQw/pb3/7m7Zs2aI77rhDTqdTq1atUrt27TRgwADX+Vu2bJkmTpyouLg4JSQk6KabblJycrKee+453X333Ro0aJBOnDih1NRU1atXTz/99JPrcTt16qTg4GDdeeedSklJ0bFjx/T3v/9dsbGx2rNnT6Hnp6i3H9mwYYPWr1+v559/3ucXxgKwN+amwuemyZMna/r06brlllsUHh6u9PR0t+/ffffdriI0MzNT7dq106hRowr8hIrTp09r7ty56tixY4E3Tu7evbumTJmiffv26aqrrtKIESM0btw43XbbbbrnnnsUEhKiH374QXFxca5LkJo2barU1FSNHz9eV111lWJjY9W+fXt16tRJtWvX1mOPPaahQ4eqXLlyeu+99xQTE6MdO3a4HrNly5aKjo5Wnz59NGjQIDkcDs2cOdPrucbb2498//33evjhh1WlShXdfvvtlrfMW7ZsabmZfYGKssU1b5vuDz/8cNF+ffr0MREREQV+/5133jFNmzY1YWFhJioqyjRs2NAMGzbM7N6929Xn3LlzZsyYMaZGjRomLCzMtG3b1mzYsMHEx8dfdIt3nq+++sp07NjRREVFmYiICNOoUSMzdepU1/fPnj1rBg4caGJiYozD4bBsS/ZljsYUfYt3cnKyqVChgjl48GCBfaZOnWokmcWLFxfY54033jCSzBdffFFgn7S0NCPJLFiwwBhz/ty89tpr5tprrzXBwcEmJibGdOnSxaxZs8Z1zObNm03r1q1NWFiYZUv70qVLTYMGDUxwcLC55pprTHp6uset35988olp1KiRCQ0NNXXq1DGvvPKKee+99yxbt31x+5Hnn3/eSDLr16/3+hgA9sDcVPJzU95tNQr6uvB39sKFC40k89ZbbxU43ty5c40k8+677xbYJzMz00gyU6ZMccXee+89c+ONN5qQkBATHR1t2rRpYz7//HPX9//44w/TtWtXExUVZSS5zR1r1qwxN910kwkODja1a9c2EydO9Hj7kdWrV5ubb77ZhIWFmbi4ODNs2DCzZMkSy8/zUm4/kve4BX3NmDHjosdfyGFMMe7hgIDQs2dPZWVl6fvvv/d3KgAASJKGDRum2bNna+vWrQoJCfF3Ope9Un1rFb5jjFFmZqZleRsAAH9avny5/vKXv1DElRJW5AAAAGyqVHetAgAAwHco5AAAAGyKQg4AAMCmKOQAAABsKqB2rTqdTu3evVtRUVHctBUyxujo0aOKi4uzfDYiAJQG5iXkF2hzU0AVcrt371atWrX8nQYCzM6dO1WzZk1/pwGgDGJeQkECZW7yfyl5gaioKH+ngADE6wKAv/D7BwUJlNdGQBVyLFvDE14XAPyF3z8oSKC8NgKqkAMAAID3KOQAAABsikIOAADApijkAAAAbIpCDgAAwKYo5AAAAGyKQg4AAMCmKOQAAABsikIOAADApijkAAAAbIpCDgAAwKbK+zsBAABQMp599llLLCwszBJr1KiRJdajR49Cx09NTbXEvvnmG0ts5syZhY6F4mFFDgAAwKYo5AAAAGyKQg4AAMCmKOQAAABsymGMMf5OIk9OTo4qVark7zQQYLKzs1WxYkV/pwGgDLLbvPTBBx+4tb3ZsOBr27Zts8Q6dOjg1t6xY0dppVNiAmVuYkUOAADApijkAAAAbIpCDgAAwKYo5AAAAGyKT3YAAMCG8m9skIq/uWHz5s2W2JIlS9zadevWtfS58847LbHExERLrHfv3m7tl19+uagpogCsyAEAANgUhRwAAIBNUcgBAADYFNfIAQAQ4Jo1a2aJ3X333YUet3HjRkuse/fultiBAwcssWPHjrm1g4ODLX2+/fZbS+yGG26wxKpUqXLRPFF8rMgBAADYFIUcAACATVHIAQAA2BSFHAAAgE2V+c0Onm6e+Pjjj1tiu3fvtsROnTrl1s7IyLD0+eOPPyyxrVu3FiVFAEAZV6NGDUvM4XBYYvk3N3Tu3NnSZ8+ePcXK4f/+7/8sseuvv96rY//9738X6zFROFbkAAAAbIpCDgAAwKYo5AAAAGyKQg4AAMCmHMYY4+8k8uTk5KhSpUql+pi//fabJVanTh2fjX/06FFLzNOdtgPBrl27LLFXX33Vrf3jjz+WVjou2dnZqlixYqk/LgD4Y17yVnx8vCWWf845dOiQzx5v3bp1lliDBg28OrZDhw5u7eXLl/skJ38KlLmJFTkAAACbopADAACwKQo5AAAAm6KQAwAAsKky/8kOnj7FoVGjRpbYpk2bLLHrrrvOrd2kSRNLn7Zt21piN998syW2c+dOt3atWrUsfbx19uxZt/b+/fstfTzdJdyTHTt2uLX9sdkBAGD1+++/l+j4Q4cOdWvXq1fPq+O+++47r2LwDVbkAAAAbIpCDgAAwKYo5AAAAGyqzF8j98UXX3gV82Tx4sWF9omOjrbEGjdubImtWbPGrd28eXOvcvDk1KlTbu3//ve/lj6ervmrXLmyJbZt27Zi5wEAsIdu3bpZYmPHjnVrBwcHW/rs27fPEnvhhRcssRMnTlxCdrgYVuQAAABsikIOAADApijkAAAAbIpCDgAAwKbK/GaHknb48GFLbPny5YUe5+2GC2/ce++9lpinTRj/+c9/LLEPPvjAZ3kAAAJTs2bNLDFPmxvy8zRHrFixwic5wTusyAEAANgUhRwAAIBNUcgBAADYFIUcAACATbHZ4TIUGxvr1p4+fbqlT1CQtYbPfxdvSTp06JDvEgMA+N38+fMtsU6dOhV63D//+U9LbOTIkb5ICZeAFTkAAACbopADAACwKQo5AAAAm6KQAwAAsCk2O1yG+vfv79aOiYmx9PH0iRO//vprieUEACh9NWrUsMRatmxpiYWEhFhiBw4ccGuPHz/e0ufYsWOXkB18gRU5AAAAm6KQAwAAsCkKOQAAAJviGjmbu/XWWy2x559/vtDj7rrrLktsw4YNvkgJABAg5s6da4lVqVLFq2PT09Pd2tu2bfNJTvAtVuQAAABsikIOAADApijkAAAAbIpCDgAAwKbY7GBzSUlJlliFChXc2l988YWlzzfffFNiOQEA/KN79+5u7SZNmnh1XGZmpiU2atQoX6SEEsaKHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYFJsdbCQsLMwSu+OOOyyx3Nxct7anC1bPnDnju8QAAKXO0yc0DB8+3K2df/NbQX7++WdL7NixY8XKC6WLFTkAAACbopADAACwKQo5AAAAm6KQAwAAsCk2O9jI0KFDLbEbb7zRElu8eLFb++uvvy6xnAAA/vF///d/lljz5s0LPW7+/PmWGJ/iYF+syAEAANgUhRwAAIBNUcgBAADYlMMYY/ydRJ6cnBxVqlTJ32kEhK5du1pinq5rOH78uCWW/ybB3377rc/y8ofs7GxVrFjR32kAKIMCeV46deqUJebNDYBr1qxpie3Zs8cnOZUlgTI3sSIHAABgUxRyAAAANkUhBwAAYFMUcgAAADbFDYEDRJUqVdzaf/vb3yx9ypUrZ4ktWrTIErP75gYAQMmpXLmyJXbmzBmfjZ+dnV3o+J42ZXi7qeSKK65waz/zzDPeJ5fPuXPn3NrPPfecpc+JEyeKPX5pYEUOAADApijkAAAAbIpCDgAAwKYo5AAAAGyKzQ5+4GnTwuLFi93aCQkJlj7btm2zxP7yl7/4LjEAwGVv/fr1JTr+nDlzLLH8nxxRrVo1S5/777+/xHLy1h9//GGJvfTSS37IxHusyAEAANgUhRwAAIBNUcgBAADYFIUcAACATbHZwQ8SExMtsaZNmxZ6nKe7V3vaAAEAuPx5+mSfP/3pT37IxN19993ns7HOnj1riTmdzkKP++STTyyxH3/8sdDjVq1a5V1iAYQVOQAAAJuikAMAALApCjkAAACb4hq5EhYfH2+JLV26tNDjhg4daol9+umnPskJAGB/99xzjyU2bNgwt3aFChWKPX79+vXd2pdyw9733nvPrZ2VleXVcXPnzrXENm/eXOw8LkesyAEAANgUhRwAAIBNUcgBAADYFIUcAACATbHZoYQ98cQTlljt2rULPW7FihWWmDHGJzkBAC5Pr776aomN3atXrxIbG8XHihwAAIBNUcgBAADYFIUcAACATVHIAQAA2BSbHXyoVatWltjAgQP9kAkAACgLWJEDAACwKQo5AAAAm6KQAwAAsCkKOQAAAJtis4MP3XbbbZZYZGSkV8du27bNrX3s2DGf5AQAAC5frMgBAADYFIUcAACATVHIAQAA2BTXyPnBunXrLLHbb7/drX3o0KHSSgcAANgUK3IAAAA2RSEHAABgUxRyAAAANkUhBwAAYFMOY4zxdxJ5cnJyVKlSJX+ngQCTnZ2tihUr+jsNAGUQ8xIKEihzEytyAAAANkUhBwAAYFMUcgAAADYVUIVcAF2uhwDC6wKAv/D7BwUJlNdGQBVyR48e9XcKCEC8LgD4C79/UJBAeW0E1K5Vp9Op3bt3KyoqSg6Hw9/pwM+MMTp69Kji4uIUFBRQf3MAKCOYl5BfoM1NAVXIAQAAwHv+LyUBAABQLLYr5OrUqaNHHnnE1c7MzJTD4VBmZqbfcsovf464NI888ojq1Knj7zQAoEDMTWVP27Zt1bZtW3+nUbRCLi0tTQ6Hw/UVGhqqevXqacCAAdq7d29J5VgiFi1apNGjR/s7DY9eeuklde/eXdWqVZPD4fBpnkeOHFFoaKgcDoc2bdpU7HGmT5+utLQ0n+VVkj744AM9+OCDuvrqq+VwOALiPx4A32FuKh1Op1OvvvqqEhISFBoaqkaNGmn27Nk+GXvTpk2un92RI0eKPc6ECRM0f/58n+RUGt59911dd911Cg0N1dVXX62pU6cWeYxirciNHTtWM2fO1LRp09SyZUulpqbqlltu0YkTJ4oz3CVp3bq1Tp48qdatWxfpuEWLFmnMmDEllNWlGTlypH744QfdeOONPh97zpw5cjgcql69ujIyMoo9jp0KudTUVC1YsEC1atVSdHS0v9MBUEKYm0rWiBEj9Nxzz6ljx46aOnWqateurV69eulf//rXJY+dnp6u6tWrS5I++uijYo9jp0Lu7bffVt++fVW/fn1NnTpVt9xyiwYNGqRXXnmlSOOUL86Dd+nSRc2aNZMk9e3bV1WqVNHEiRO1YMECPfDAAx6POX78uCIiIorzcBcVFBSk0NBQn4/rT9u3b1edOnV04MABxcTE+HTs9PR0JSUlKT4+XrNmzdL48eN9On4gmjlzpq688koFBQWpQYMG/k4HQAlhbio5//vf//TGG2+of//+mjZtmqTz57hNmzYaOnSo7rvvPpUrV65YYxtjNGvWLPXq1Uvbt29XRkaG+vbt68v0A87Jkyc1YsQIde3a1VW4Pv7443I6nRo3bpyeeOIJrxcefHKNXPv27SWdL0Ck89c0RUZGatu2bUpKSlJUVJR69+4t6fzS7OTJk1W/fn2FhoaqWrVqSklJ0eHDh93GNMZo/PjxqlmzpsLDw9WuXTtt3LjR8tgFXYfw3XffKSkpSdHR0YqIiFCjRo00ZcoUV35vvvmmJLktx+fxdY6StG3bNm3bts2r81lS14Pt2LFDq1atUnJyspKTk7V9+3Z9/fXXHvump6erRYsWCg8PV3R0tFq3bq2lS5e68tu4caNWrFjhOnd5b1eOHj3a4xb9vLc+srKyXLEFCxaoa9euiouLU0hIiBITEzVu3DidO3eu0OeyZ88ebd68WWfOnCm0b61atQJiiziA0sXc5Lu5acGCBTpz5oyeeuopV8zhcOjJJ5/Url279M033xQ6RkFWr16trKws19y0cuVK7dq1y9LP6XRqypQpatiwoUJDQxUTE6M77rhDP/74oyuf48eP6/3333edu7xrAgu61trTnDVjxgy1b99esbGxCgkJ0fXXX6/U1FSvnsuOHTu0efPmQvstX75cBw8edDufktS/f38dP35c//73v716PKmYK3L55b0IqlSp4oqdPXtWnTt3VqtWrfT6668rPDxckpSSkqK0tDQ9+uijGjRokLZv365p06Zp7dq1Wr16tSpUqCBJevHFFzV+/HglJSUpKSlJP/30kzp16qTc3NxC8/n888/VrVs31ahRQ4MHD1b16tW1adMmffrppxo8eLBSUlK0e/duff7555o5c6bl+JLI8fbbb5ckt0KmtM2ePVsRERHq1q2bwsLClJiYqIyMDLVs2dKt35gxYzR69Gi1bNlSY8eOVXBwsL777jt9+eWX6tSpkyZPnqyBAwcqMjJSI0aMkCRVq1atyPmkpaUpMjJSzzzzjCIjI/Xll1/qxRdfVE5Ojl577bWLHvvCCy/o/fffd61eAkB+zE2+m5vWrl2riIgIXXfddW7xFi1auL7fqlWrQs+BJxkZGUpMTFTz5s3VoEEDhYeHa/bs2Ro6dKhbv8cee0xpaWnq0qWL+vbtq7Nnz2rVqlX69ttv1axZM82cOVN9+/ZVixYt9MQTT0iSEhMTi5xPamqq6tevr+7du6t8+fJauHChnnrqKTmdTvXv3/+ixz788MNasWJFoZ/6sHbtWklyrSDnadq0qYKCgrR27Vo9+OCD3iVsimDGjBlGklm2bJnZv3+/2blzp/nXv/5lqlSpYsLCwsyuXbuMMcb06dPHSDLPP/+82/GrVq0ykkxGRoZbfPHixW7xffv2meDgYNO1a1fjdDpd/YYPH24kmT59+rhiy5cvN5LM8uXLjTHGnD171iQkJJj4+Hhz+PBht8e5cKz+/fsbT0+/JHI0xpj4+HgTHx9vebyL2b9/v5FkRo0aVaTjCtKwYUPTu3dvV3v48OGmatWq5syZM67Yli1bTFBQkLn77rvNuXPn3I6/8HnWr1/ftGnTxvIYo0aN8nhe814727dvd8VOnDhh6ZeSkmLCw8PNqVOnXLE+ffpYzl3ea+zC8bxRUN4A7Iu5qeTnpq5du5q6deta4sePH/d4Tr2Vm5trqlSpYkaMGOGK9erVy9xwww1u/b788ksjyQwaNMgyxoXPMyIiwvIcjfE8jxjjec7yNDd17tzZ8vzbtGljmU/atGnj8eeXX//+/U25cuU8fi8mJsYkJycXOkaeYr3f1KFDB8XExKhWrVpKTk5WZGSk5s2bpyuvvNKt35NPPunWnjNnjipVqqSOHTvqwIEDrq+mTZsqMjJSy5cvlyQtW7ZMubm5GjhwoNuS55AhQwrNbe3atdq+fbuGDBmiK664wu173tyVu6RyzMrK8utq3Pr16/Wf//zH7TqRBx54QAcOHNCSJUtcsfnz58vpdOrFF1+0vB3p67uah4WFuf599OhRHThwQLfddptOnDhR6NJ0WlqajDGsxgFwYW4qubnp5MmTCgkJscTzrgM8efJkoWN48tlnn+ngwYOWuWndunVubwfPnTtXDodDo0aNsoxRknNTdna2Dhw4oDZt2ui3335Tdnb2RY/NzMz06jNYT548qeDgYI/fCw0NLdL5LNZbq2+++abq1aun8uXLq1q1arrmmmssk3758uVVs2ZNt9iWLVuUnZ2t2NhYj+Pu27dPkvT7779Lkq6++mq378fExBR68V/eUnpxL2ovjRz9IT09XREREapbt662bt0q6fyLpU6dOsrIyFDXrl0lnT9/QUFBuv7660s8p40bN2rkyJH68ssvlZOT4/a9wv6zAEB+zE0lNzeFhYXp9OnTlvipU6dc3y+O9PR0JSQkKCQkxDU3JSYmKjw8XBkZGZowYYKk8+cvLi5OlStXLuYz8N7q1as1atQoffPNN5Ydz9nZ2apUqdIlP0ZYWFiBb8efOnWqSOezWIVcixYtLO/r5hcSEmL5D+R0OhUbG1vgbS98vUOzOOyQY1EZYzR79mwdP37cY4G2b98+HTt2TJGRkZf8WAX9ZZR/A8ORI0fUpk0bVaxYUWPHjlViYqJCQ0P1008/6bnnnpPT6bzkXACULcxNJadGjRpavny5jDFuv+f37NkjSYqLiyvymDk5OVq4cKFOnTplKTwladasWXrppZd8suLm7dy0bds23X777br22ms1ceJE1apVS8HBwVq0aJEmTZrks7mpRo0aOnfunPbt2+dWnOfm5urgwYNFOp8+2ezgrcTERC1btky33nrrRavN+Ph4Sef/Aqlbt64rvn//fsvuHE+PIUkbNmxQhw4dCuxX0A+1NHIsbStWrNCuXbs0duxYy4Wqhw8f1hNPPKH58+frwQcfVGJiopxOp3755Rc1bty4wDELOn95f/EdOXLE7e2DvL8S82RmZurgwYP6+OOP3e6zlLe7DABKC3NT4Ro3bqx//OMf2rRpk9uCwHfffef6flF9/PHHOnXqlFJTU1W1alW37/36668aOXKkVq9erVatWikxMVFLlizRoUOHLroqd7G5ydONhvPPTQsXLtTp06f1ySefqHbt2q543lvXvpJ3vn788UclJSW54j/++KOcTmeRzmep3pOhZ8+eOnfunMaNG2f53tmzZ10nuUOHDqpQoYKmTp3q9l7z5MmTC32MJk2aKCEhQZMnT7b80C4cK+++Qfn7lFSORbn9iK/lva06dOhQ9ejRw+3r8ccf19VXX+36K++uu+5SUFCQxo4da/nLI//58/SfIu+X1cqVK12xvO3gF8q739CFY+bm5mr69OlePaei3H4EAC6GuanwuelPf/qTKlSo4PY72hijt956S1deeaXl7gfeSE9PV926ddWvXz/L3PTss88qMjLSNTfde++9MsZ4vFmyt3NTdna21q9f74rt2bNH8+bNc+vnaW7Kzs7WjBkzvHpO3t5+pH379qpcubLltiapqakKDw93Xe7kjVJdkWvTpo1SUlL08ssv6+eff1anTp1UoUIFbdmyRXPmzNGUKVPUo0cPxcTE6Nlnn9XLL7+sbt26KSkpSWvXrtVnn31mqdrzCwoKUmpqqu688041btxYjz76qGrUqKHNmzdr48aNrgv7mzZtKkkaNGiQOnfurHLlyik5ObnEcizK7Udmzpyp33//3fXe/MqVK1037n3ooYdcf3FlZmaqXbt2GjVqVIEf6XL69GnNnTtXHTt2LPDmlN27d9eUKVO0b98+XXXVVRoxYoTGjRun2267Tffcc49CQkL0ww8/KC4uTi+//LLr/KWmpmr8+PG66qqrFBsbq/bt26tTp06qXbu2HnvsMQ0dOlTlypXTe++9p5iYGO3YscP1mC1btlR0dLT69OmjQYMGyeFwaObMmV5dJCoV7fYjK1eudBWW+/fv1/Hjx13ns3Xr1kW+8zqAywtzU+FzU82aNTVkyBC99tprOnPmjJo3b6758+dr1apVysjIcLsZcN4tUmbMmFHgZ7vu3r1by5cv16BBgzx+PyQkRJ07d9acOXP0t7/9Te3atdNDDz2kv/3tb9qyZYvuuOMOOZ1OrVq1Su3atdOAAQNc52/ZsmWaOHGi4uLilJCQoJtuuknJycl67rnndPfdd2vQoEE6ceKEUlNTVa9ePf3000+ux+3UqZOCg4N15513KiUlRceOHdPf//53xcbGut5Gvhhvbz8SFhamcePGqX///rrvvvvUuXNnrVq1Sunp6XrppZeKdi2g1/tbzf/b4v3DDz9ctF+fPn1MREREgd9/5513TNOmTU1YWJiJiooyDRs2NMOGDTO7d+929Tl37pwZM2aMqVGjhgkLCzNt27Y1GzZsMPHx8Rfd4p3nq6++Mh07djRRUVEmIiLCNGrUyEydOtX1/bNnz5qBAweamJgY43A4LNuFfZmjMUW7/Uje9mVPXxc+z4ULFxpJ5q233ipwrLlz5xpJ5t133y2wT2ZmppFkpkyZ4oq999575sYbbzQhISEmOjratGnTxnz++eeu7//xxx+ma9euJioqykhy24K9Zs0ac9NNN5ng4GBTu3ZtM3HiRI+3H1m9erW5+eabTVhYmImLizPDhg0zS5YssTzPS739SN72ck9fvrq1CwD/YW4qnbnp3LlzZsKECSY+Pt4EBweb+vXrm/T0dEu/qVOnGklm8eLFBY71xhtvGEnmiy++KLBPWlqakWQWLFhgjDl/bl577TVz7bXXmuDgYBMTE2O6dOli1qxZ4zpm8+bNpnXr1iYsLMxyu5WlS5eaBg0amODgYHPNNdeY9PR0j7cf+eSTT0yjRo1MaGioqVOnjnnllVfMe++9Z5lzLuX2I3neeecdc80115jg4GCTmJhoJk2a5HY7FW84jPFyCQQBZ9iwYZo9e7a2bt3qcVs4AAClrWfPnsrKytL333/v71TKhFJ9axW+tXz5cv3lL3+hiAMABARjjDIzM5Wenu7vVMoMVuQAAABsik8SBwAAsCkKOQAAAJuikAMAALApCjkAAACbCqhdq06nU7t371ZUVJRPPlsN9maM0dGjRxUXF2f5bEQAKA3MS8gv0OamgCrkdu/erVq1avk7DQSYnTt3qmbNmv5OA0AZxLyEggTK3OT/UvICUVFR/k4BAYjXBQB/4fcPChIor42AKuRYtoYnvC4A+Au/f1CQQHltBFQhBwAAAO9RyAEAANgUhRwAAIBNUcgBAADYFIUcAACATVHIAQAA2BSFHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYFIUcAACATVHIAQAA2BSFHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYVHl/J4BLU69ePUts8+bNbu3Bgwdb+kydOrXEcgIA2EtERIQl9tprr7m1U1JSLH3WrFljid13332W2O+//34J2eFiWJEDAACwKQo5AAAAm6KQAwAAsCkKOQAAAJtis4PN3XjjjZaY0+l0a+/atau00gEA2FCNGjUssccff9ytnX9ukaSmTZtaYt26dbPE3nzzzUvIDhfDihwAAIBNUcgBAADYFIUcAACATXGNnM01btzYEjt+/Lhbe968eaWUDQAg0MXExFhi77//vh8ygS+wIgcAAGBTFHIAAAA2RSEHAABgUxRyAAAANsVmBxtp0KCBJTZgwABLbObMmaWRDgAgwA0aNMgSu+uuuyyxFi1a+OwxW7dubYkFBbmvG61bt87SZ+XKlT7LoSxhRQ4AAMCmKOQAAABsikIOAADApijkAAAAbIrNDjZy7bXXWmIRERGW2AcffFAa6QAAAtykSZMsMafTWaKPec899xQa+/333y197r//fktszZo1vkvsMsWKHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYFJsdbGTYsGGWmKcLRn/88cfSSAcAEGAWLVrk1s7/iQq+dvDgQUvs2LFjllh8fLxbOyEhwdLn+++/t8TKlSt3CdmVDazIAQAA2BSFHAAAgE1RyAEAANgU18gFqDp16lhizZo1s8T++9//WmLHjx8viZQAAAGkTZs2ltg111zj1vZ089/i3hD4rbfessSWLl1qiWVnZ1ti7du3d2uPGDHCq8d88sknLbHU1FSvji0rWJEDAACwKQo5AAAAm6KQAwAAsCkKOQAAAJtis0OA8nQRqyf79+8v4UwAAP7maQPcv/71L0usatWqxRrf083l586d69YeM2aMpc+JEyeKNf4TTzxh6RMTE2OJvfrqq5ZYaGioW3vatGmWPmfOnPEqr8sBK3IAAAA2RSEHAABgUxRyAAAANkUhBwAAYFNsdghQDRs29KqfpwtBAQCXl/LlrdN1cTc2rFixwhJLTk62xA4cOFCs8T3Jv9nh5ZdftvSZOHGiJRYeHm6J5Z/3PvnkE0ufbdu2FTVF22JFDgAAwKYo5AAAAGyKQg4AAMCmKOQAAABsis0OAeLmm292az/66KOWPmvXrrXEPv/88xLLCQBgbz/++KMl9uc//9kS8+XGBm942qDQu3dvS6x58+alkY6tsSIHAABgUxRyAAAANkUhBwAAYFNcIxcgOnTo4NauXLmypc/ixYstsVOnTpVYTgCAwBUUVPhazE033VQKmRSdw+GwxDw9H2+e4+jRoy2xhx56qFh52RErcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgU2x2CBA33HCDW9sYY+nz0UcflVY6AIAA0q9fP0vM6XT6IRPfuPPOOy2xG2+80RLz9BzzxzxtdihLWJEDAACwKQo5AAAAm6KQAwAAsCkKOQAAAJtis4MfVK9e3RK77bbb3Nq//vqrpc+8efNKLCcAQODytDkgUMXExFhi119/vVt7+PDhxR5///79bu0zZ84Ue6zLAStyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTbHbwg0ceecQSi42NdWt/9tlnpZQNAAC+M2LECEusf//+xRorKyvLEuvTp49be8eOHcUa+3LBihwAAIBNUcgBAADYFIUcAACATXGNnB/Ex8cX2ufw4cOlkAkAAMW3aNEiS+yaa67x2fi//PKLJfbVV1/5bPzLAStyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTbHbwg27duhXaZ+HChaWQCQDADhwOhyUWFFT4WkyXLl28Gv+dd96xxOLi4go9zlMOTqfTq8f0xp133umzsS5XrMgBAADYFIUcAACATVHIAQAA2BSFHAAAgE2x2aGEtWrVyhKrXr26HzIBANhVamqqJfbqq68Wetynn35qiXm7GaG4mxaKe9xbb71VrOPKOlbkAAAAbIpCDgAAwKYo5AAAAGyKQg4AAMCm2OxQwu6++25LrFy5cpbY2rVr3dorV64ssZwAAPby8ccfW2JDhw61xGJiYkojnYvav3+/JbZp0ya39hNPPGHps2fPnhLL6XLGihwAAIBNUcgBAADYFIUcAACATXGNnA+Fh4dbYklJSV4d+9FHH7m1z50755OcAAD29/vvv1tiycnJlthdd93l1h48eHBJpVSgl156yRJ78803Sz2PsoIVOQAAAJuikAMAALApCjkAAACbopADAACwKTY7+NCZM2csscOHD1tin3zyiSU2ZcqUEskJAHB58nTj+PyxpUuXWvp4uhnvnXfeaYnln6veeecdSx+Hw2GJ/fLLL9ZkUWJYkQMAALApCjkAAACbopADAACwKQo5AAAAm3IYY4y/k8iTk5OjSpUq+TsNBJjs7GxVrFjR32kAKIOYl1CQQJmbWJEDAACwKQo5AAAAm6KQAwAAsCkKOQAAAJuikAMAALApCjkAAACbopADAACwKQo5AAAAm6KQAwAAsCkKOQAAAJuikAMAALApCjkAAACbopADAACwKQo5AAAAm6KQAwAAsCkKOQAAAJuikAMAALCpgCrkjDH+TgEBiNcFAH/h9w8KEiivjYAq5I4ePervFBCAeF0A8Bd+/6AggfLacJhAKSklOZ1O7d69W1FRUXI4HP5OB35mjNHRo0cVFxenoKCA+psDQBnBvIT8Am1uCqhCDgAAAN7zfykJAACAYqGQAwAAsCnbFXJ16tTRI4884mpnZmbK4XAoMzPTbznllz9HXJq2bduqbdu2/k4DAArE3FT2PPLII6pTp46/0yhaIZeWliaHw+H6Cg0NVb169TRgwADt3bu3pHIsEYsWLdLo0aP9nUahMjIy5HA4FBkZ6ZPxNm3a5PrZHTlypNjjTJgwQfPnz/dJTqXpq6++cr1+Dxw44O90APgAc1PpeOmll9S9e3dVq1ZNDofDp3keOXJEoaGhcjgc2rRpU7HHmT59utLS0nyWV0n64IMP9OCDD+rqq6+Ww+Eo9oJFsVbkxo4dq5kzZ2ratGlq2bKlUlNTdcstt+jEiRPFSuJStG7dWidPnlTr1q2LdNyiRYs0ZsyYEsrKN44dO6Zhw4YpIiLCZ2Omp6erevXqkqSPPvqo2OPYsZBzOp0aOHCgT88ngMDB3FSyRo4cqR9++EE33nijz8eeM2eOHA6HqlevroyMjGKPY6dCLjU1VQsWLFCtWrUUHR1d7HGKVch16dJFDz74oPr27au0tDQNGTJE27dv14IFCwo85vjx48VO8mKCgoIUGhoaEFuAfW38+PGKiorSXXfd5ZPxjDGaNWuWevXqpaSkpEv6z2JH77zzjnbu3Km+ffv6OxUAJYC5qWRt375de/bsUXp6us/HTk9PV1JSkh544AHNmjXL5+MHopkzZyo7O1tffvml4uLiij2OT15h7du3l3T+hyydf984MjJS27ZtU1JSkqKiotS7d29J51dFJk+erPr16ys0NFTVqlVTSkqKDh8+7DamMUbjx49XzZo1FR4ernbt2mnjxo2Wxy7oOoTvvvtOSUlJio6OVkREhBo1aqQpU6a48nvzzTclyW05Po+vc5Skbdu2adu2bd6eUm3ZskWTJk3SxIkTVb58ea+Pu5jVq1crKytLycnJSk5O1sqVK7Vr1y5LP6fTqSlTpqhhw4YKDQ1VTEyM7rjjDv3444+Szp+z48eP6/3333edu7zrLgq6ZmD06NGWezDNmDFD7du3V2xsrEJCQnT99dcrNTXVq+eyY8cObd682evnfujQIY0cOVJjx47VFVdc4fVxAOyLucm3c1NJXQ+2Y8cOrVq1yjU3bd++XV9//bXHvunp6WrRooXCw8MVHR2t1q1ba+nSpa78Nm7cqBUrVrjOXd7blZ7mIOn/vS2flZXlii1YsEBdu3ZVXFycQkJClJiYqHHjxuncuXOFPpc9e/Zo8+bNOnPmTKF9a9Wq5ZNC3ycVQt6LoEqVKq7Y2bNn1blzZ7Vq1Uqvv/66wsPDJUkpKSlKS0vTo48+qkGDBmn79u2aNm2a1q5dq9WrV6tChQqSpBdffFHjx49XUlKSkpKS9NNPP6lTp07Kzc0tNJ/PP/9c3bp1U40aNTR48GBVr15dmzZt0qeffqrBgwcrJSVFu3fv1ueff66ZM2daji+JHG+//XZJcnuxXMyQIUPUrl07JSUl6cMPP/TqmMJkZGQoMTFRzZs3V4MGDRQeHq7Zs2dr6NChbv0ee+wxpaWlqUuXLurbt6/Onj2rVatW6dtvv1WzZs00c+ZM9e3bVy1atNATTzwhSUpMTCxyPqmpqapfv766d++u8uXLa+HChXrqqafkdDrVv3//ix778MMPa8WKFV5/RMpf/vIXVa9eXSkpKRo3blyRcwVgP8xNvp+bSsLs2bMVERGhbt26KSwsTImJicrIyFDLli3d+o0ZM0ajR49Wy5YtNXbsWAUHB+u7777Tl19+qU6dOmny5MkaOHCgIiMjNWLECElStWrVipxPWlqaIiMj9cwzzygyMlJffvmlXnzxReXk5Oi111676LEvvPCC3n//fW3fvr30NkKYIpgxY4aRZJYtW2b2799vdu7caf71r3+ZKlWqmLCwMLNr1y5jjDF9+vQxkszzzz/vdvyqVauMJJORkeEWX7x4sVt83759Jjg42HTt2tU4nU5Xv+HDhxtJpk+fPq7Y8uXLjSSzfPlyY4wxZ8+eNQkJCSY+Pt4cPnzY7XEuHKt///7G09MviRyNMSY+Pt7Ex8dbHs+TTz/91JQvX95s3LjRGHP+fEZERHh1bEFyc3NNlSpVzIgRI1yxXr16mRtuuMGt35dffmkkmUGDBlnGuPB5RkREWJ5jXq6enueoUaMs5/vEiROWfp07dzZ169Z1i7Vp08a0adPGEvP25btu3TpTrlw5s2TJErdc9u/f79XxAAIbc1PpzE159u/fbySZUaNGFem4gjRs2ND07t3b1R4+fLipWrWqOXPmjCu2ZcsWExQUZO6++25z7tw5t+MvfJ7169e3zBfGeJ6DjPl/r53t27e7Yp7mppSUFBMeHm5OnTrlinma7/JeYxeO542C8vZGsdb0OnTooJiYGNWqVUvJycmKjIzUvHnzdOWVV7r1e/LJJ93ac+bMUaVKldSxY0cdOHDA9dW0aVNFRkZq+fLlkqRly5YpNzdXAwcOdFsKHTJkSKG5rV27Vtu3b9eQIUMsb6F58/EqJZVjVlaWV3/x5Obm6umnn1a/fv10/fXXF9rfW5999pkOHjyoBx54wBV74IEHtG7dOrcl97lz58rhcGjUqFGWMXz98TRhYWGuf2dnZ+vAgQNq06aNfvvtN2VnZ1/02MzMTK9X4wYNGqQuXbqoU6dOl5QvgMDG3FRyc1NJWb9+vf7zn/9Y5qYDBw5oyZIlrtj8+fPldDr14osvWt6OLMm56ejRozpw4IBuu+02nThxotBLetLS0mSMKdXbkhTrrdU333xT9erVU/ny5VWtWjVdc801lhNbvnx51axZ0y22ZcsWZWdnKzY21uO4+/btkyT9/vvvkqSrr77a7fsxMTGF7uzIW0pv0KCB90+olHO8mEmTJunAgQM+37WUnp6uhIQEhYSEaOvWrZLOvx0aHh6ujIwMTZgwQdL58xcXF6fKlSv79PE9Wb16tUaNGqVvvvnGsqssOztblSpVuuTH+OCDD/T1119rw4YNlzwWgMDG3FRyc1NJSU9PV0REhOrWreuam0JDQ1WnTh1lZGSoa9euks6fv6CgIJ8ucBRk48aNGjlypL788kvl5OS4fa+wRQZ/KFYh16JFCzVr1uyifUJCQiz/gZxOp2JjYwvcLRkTE1OcdHzKnzlmZ2dr/Pjxeuqpp5STk+N6AR07dkzGGGVlZSk8PLzA/8gFycnJ0cKFC3Xq1CnLf25JmjVrll566SWf/FVT0Bj5LxLdtm2bbr/9dl177bWaOHGiatWqpeDgYC1atEiTJk2S0+m85FwkaejQobrvvvsUHBzs+qsz7/55O3fuVG5u7iXtFgIQOJib7MUYo9mzZ+v48eMeC7R9+/bp2LFjPrmPqrdz05EjR9SmTRtVrFhRY8eOVWJiokJDQ/XTTz/pueee89nc5Eu+2Q7ppcTERC1btky33nqr29JlfvHx8ZLO/wVSt25dV3z//v2W3TmeHkOSNmzYoA4dOhTYr6AfamnkWJDDhw/r2LFjevXVV/Xqq69avp+QkKA//elPRb5/28cff6xTp04pNTVVVatWdfver7/+qpEjR2r16tVq1aqVEhMTtWTJEh06dOiiq3IFnb/o6GiPNxrO+ysxz8KFC3X69Gl98sknql27tiue9/aAr+zcuVOzZs3yuJ29SZMmuuGGG/Tzzz/79DEB2Atzk3+sWLFCu3bt0tixY3Xddde5fe/w4cN64oknNH/+fD344INKTEyU0+nUL7/8osaNGxc45sXmJul8oXbhW9v556bMzEwdPHhQH3/8sds9APN2PgeiUr3BTc+ePXXu3DmPuwbPnj3rKgA6dOigChUqaOrUqW7XQU2ePLnQx2jSpIkSEhI0efJkS0Fx4Vh5N4XN36ekcvRmi3dsbKzmzZtn+WrXrp1CQ0M1b948vfDCCxcdw5P09HTVrVtX/fr1U48ePdy+nn32WUVGRrr+yrv33ntljPH41m7+8+epYEtMTFR2drbWr1/viu3Zs0fz5s1z61euXDnLmNnZ2ZoxY4ZXz8nb2494Op/333+/JOmf//ynJk2a5NXjAbh8MTd5f2ssX8p7W3Xo0KGWuenxxx/X1Vdf7Zqb7rrrLgUFBWns2LGWVTFv5yZJWrlypSuWdxutC3mam3JzczV9+nSvnlNRbj/iK6W6ItemTRulpKTo5Zdf1s8//6xOnTqpQoUK2rJli+bMmaMpU6aoR48eiomJ0bPPPquXX35Z3bp1U1JSktauXavPPvvMsqKUX1BQkFJTU3XnnXeqcePGevTRR1WjRg1t3rxZGzdudF082bRpU0nnL4Tv3LmzypUrp+Tk5BLL0Zst3uHh4R5v/jt//nx9//33lu/lbUOfMWNGgZ+ft3v3bi1fvlyDBg3y+P2QkBB17txZc+bM0d/+9je1a9dODz30kP72t79py5YtuuOOO+R0OrVq1Sq1a9dOAwYMcJ2/ZcuWaeLEiYqLi1NCQoJuuukmJScn67nnntPdd9+tQYMG6cSJE0pNTVW9evX0008/uR63U6dOCg4O1p133qmUlBQdO3ZMf//73xUbG6s9e/YUeI7yeHv7EU/nM28FrkuXLoW+ngBc/pibvLv9yMyZM/X777+7rmleuXKlxo8fL0l66KGHXKuBmZmZateunUaNGlXgx3idPn1ac+fOVceOHRUaGuqxT/fu3TVlyhTt27dPV111lUaMGKFx48bptttu0z333KOQkBD98MMPiouL08svv+w6f6mpqRo/fryuuuoqxcbGqn379urUqZNq166txx57TEOHDlW5cuX03nvvKSYmRjt27HA9ZsuWLRUdHa0+ffpo0KBBcjgcmjlzpteb64py+5GVK1e6Csv9+/fr+PHjrvPZunVr7z8VpChbXPO26f7www8X7VfY7TLeeecd07RpUxMWFmaioqJMw4YNzbBhw8zu3btdfc6dO2fGjBljatSoYcLCwkzbtm3Nhg0bTHx8/EW3eOf56quvTMeOHU1UVJSJiIgwjRo1MlOnTnV9/+zZs2bgwIEmJibGOBwOy7ZkX+ZoTPG2eOcp6HxOnTrVSDKLFy8u8Ng33njDSDJffPFFgX3S0tKMJLNgwQJjzPlz89prr5lrr73WBAcHm5iYGNOlSxezZs0a1zGbN282rVu3NmFhYZYt7UuXLjUNGjQwwcHB5pprrjHp6eket35/8sknplGjRiY0NNTUqVPHvPLKK+a9996zbN2+1NuP5MftR4DLC3NT6cxNeb93PX1d+DwXLlxoJJm33nqrwLHmzp1rJJl33323wD6ZmZlGkpkyZYor9t5775kbb7zRhISEmOjoaNOmTRvz+eefu77/xx9/mK5du5qoqCgjyW3uWLNmjbnppptMcHCwqV27tpk4caLH24+sXr3a3HzzzSYsLMzExcWZYcOGmSVLllie56XefiRvLvL0VZRbuziM8bLMRMDp2bOnsrKy9P333/s7FQAAJEnDhg3T7NmztXXrVoWEhPg7ncteqb61Ct8xxigzM7NEPvMOAIDiWr58uf7yl79QxJUSVuQAAABsqlR3rQIAAMB3KOQAAABsikIOAADApijkAAAAbCqgdq06nU7t3r1bUVFRPvncT9ibMUZHjx5VXFyc5bMRAaA0MC8hv0CbmwKqkNu9e7dq1arl7zQQYHbu3KmaNWv6Ow0AZRDzEgoSKHOT/0vJC0RFRfk7BQQgXhcA/IXfPyhIoLw2AqqQY9kanvC6AOAv/P5BQQLltRFQhRwAAAC8RyEHAABgUxRyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgUxRyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgUxRyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgU+X9ncDlpEmTJpbYxx9/bInVqVOnFLK5uE6dOllimzZtssR27txZGukAAC4Td955pyX2ySefuLUHDBhg6fPWW29ZYufOnfNdYpcpVuQAAABsikIOAADApijkAAAAbIpr5Hyoc+fOllhISIgfMimcp2sY/vznP1tiycnJpZEOAMCGqlSpYolNnz690OOmTZtmib333nuW2MmTJ4uXWBnCihwAAIBNUcgBAADYFIUcAACATVHIAQAA2BSbHS5B+fLupy8pKclPmRTdmjVrLLFnnnnGEouIiHBrHz9+vMRyAgDYS+vWrS2xmjVrFnrc7NmzLbFTp075JKeyhhU5AAAAm6KQAwAAsCkKOQAAAJuikAMAALApNjtcgnbt2rm1b7nlFkufV199tbTSKZLo6GhL7Prrr7fEwsPD3dpsdgCAssnTJxWNGDGiWGPNnDnTEjPGFGusso4VOQAAAJuikAMAALApCjkAAACbopADAACwKTY7eKlBgwaWWP47U2/bts3SZ8KECSWW06X405/+5O8UAAA20rBhQ0usadOmXh179uxZt/Znn33mk5zAihwAAIBtUcgBAADYFIUcAACATXGNnJdGjhxpiUVERLi177jjDkufY8eOlVhO3qpcubIl1qZNG0vM6XSWRjoAABu69957i33s0qVLfZgJLsSKHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYFJsdPOjRo4cllpSUZIlt3brVrf3jjz+WWE6XYsSIEZaYp40NmZmZltiRI0dKICMAgN20bt3aq365ubmWmKd5CL7BihwAAIBNUcgBAADYFIUcAACATVHIAQAA2BSbHTy47777LLHw8HBLbPr06aWRTpHVqVPHrd27d29Ln3Pnzlli48ePt8TOnDnjs7wAAPbRsmXLi7YLcvz4cUvs559/9kVK8IAVOQAAAJuikAMAALApCjkAAACbopADAACwqTK/2aFSpUqW2M033+zVsampqb5OxyeeeOIJt3bVqlUtfTZt2mSJLV++vMRyAgDYS/PmzYt1XKDOjZcrVuQAAABsikIOAADApijkAAAAbKrMXyMXEhJiiV155ZWW2OzZs0sjHZ9ITEwstM+GDRtKIRMAgF01a9as0D5HjhyxxLhGrnSxIgcAAGBTFHIAAAA2RSEHAABgUxRyAAAANlXmNzscPXrUEvv5558tsUaNGllilStXdmsfOnTIZ3l5KzY21hLr0aNHocd99dVXJZEOAMCGWrVqZYn16tWr0OOys7MtsV27dvkkJ3iHFTkAAACbopADAACwKQo5AAAAm6KQAwAAsKkyv9nh5MmTlti2bdsssXvvvdcS+/e//+3Wnjhxos/yatCggSVWt25dS6xOnTqWmDGm0PGdTmex8gIAXH6qVKliiQUFFb7W8/nnn5dEOigCVuQAAABsikIOAADApijkAAAAbIpCDgAAwKbK/GYHT0aNGmWJORwOS6xr165u7dmzZ/sshwMHDlhinjYxVK1atVjjp6WlFes4AMDlx5tPBDpy5Igl9vbbb5dANigKVuQAAABsikIOAADApijkAAAAbMphvLl7bCnJyclRpUqV/J2G1xo3buzWvuqqq3w29kcffeRVv/fff98S6927d6HHlS9vn8sjs7OzVbFiRX+nAaAMstu85I2aNWtaYr///rsllv+GwBs2bLD0adiwoe8Ss5lAmZtYkQMAALApCjkAAACbopADAACwKQo5AAAAm7LPFe8B6Oeff75ouzT89ttvxTquQYMGlpinC1kBAJeXli1bWmL5NzZ4Mn/+/BLIBpeKFTkAAACbopADAACwKQo5AAAAm6KQAwAAsCk2O9icw+HwKpYfGxsAoGyqUqWKV/0OHDjg1p4yZUpJpINLxIocAACATVHIAQAA2BSFHAAAgE1RyAEAANgUmx1szhjjVQwAAEnq3LmzV/127Njh1s7Ozi6JdHCJWJEDAACwKQo5AAAAm6KQAwAAsCmukbO50NDQQvucPHmyFDIBAASaChUqWGKJiYleHXvq1Cm39pkzZ3ySE3yLFTkAAACbopADAACwKQo5AAAAm6KQAwAAsCk2O9jco48+aokdOXLErT1u3LhSygYAEEicTqcl9uOPP1piDRo0sMS2bt1aIjnBt1iRAwAAsCkKOQAAAJuikAMAALApCjkAAACbYrODzf3www+W2MSJE93ay5cvL610AAAB5Ny5c5bYiBEjLDFjjCW2Zs2aEskJvsWKHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYlMN4usLRT3JyclSpUiV/p4EAk52drYoVK/o7DQBlEPMSChIocxMrcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgUxRyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgUxRyAAAANkUhBwAAYFMBVcgZY/ydAgIQrwsA/sLvHxQkUF4bAVXIHT161N8pIADxugDgL/z+QUEC5bXhMIFSUkpyOp3avXu3oqKi5HA4/J0O/MwYo6NHjyouLk5BQQH1NweAMoJ5CfkF2twUUIUcAAAAvOf/UhIAAADFQiEHAABgU7Yr5OrUqaNHHnnE1c7MzJTD4VBmZqbfcsovf464NG3btlXbtm39nQYAFIi5qewJlLmpSIVcWlqaHA6H6ys0NFT16tXTgAEDtHfv3pLKsUQsWrRIo0eP9ncahcrIyJDD4VBkZKRPxtu0aZPrZ3fkyJFijzNhwgTNnz/fJzmVpq+++sr1+j1w4IC/0wHgA8xNpWPr1q3q0aOHoqOjFR4erlatWmn58uU+Gbsszk179+7Vo48+qtjYWIWFhalJkyaaM2dOkccp1orc2LFjNXPmTE2bNk0tW7ZUamqqbrnlFp04caI4w12S1q1b6+TJk2rdunWRjlu0aJHGjBlTQln5xrFjxzRs2DBFRET4bMz09HRVr15dkvTRRx8Vexw7/WfJ43Q6NXDgQJ+eTwCBg7mp5OzcuVO33HKLvvrqKw0dOlQvv/yyjh07pk6dOmnlypWXPH5Zm5tycnLUqlUrzZ07VykpKXr99dcVFRWlnj17atasWUUaq1iFXJcuXfTggw+qb9++SktL05AhQ7R9+3YtWLCgwGOOHz9enIcqVFBQkEJDQwNiC7CvjR8/XlFRUbrrrrt8Mp4xRrNmzVKvXr2UlJSkjIwMn4xrF++884527typvn37+jsVACWAuank/PWvf9WRI0e0YsUKDR8+XIMHD9bXX3+tGjVq6Omnn76kscvi3PT2229r69atmj9/vsaNG6f+/ftr+fLlat68uf7v//5Pubm5Xo/lk1dY+/btJUnbt2+XJD3yyCOKjIzUtm3blJSUpKioKPXu3VvS+VWRyZMnq379+goNDVW1atWUkpKiw4cPu41pjNH48eNVs2ZNhYeHq127dtq4caPlsQu6DuG7775TUlKSoqOjFRERoUaNGmnKlCmu/N58801JcluOz+PrHCVp27Zt2rZtm7enVFu2bNGkSZM0ceJElS9f3uvjLmb16tXKyspScnKykpOTtXLlSu3atcvSz+l0asqUKWrYsKFCQ0MVExOjO+64Qz/++KOk8+fs+PHjev/9913nLu+6i0ceeUR16tSxjDl69GjLPZhmzJih9u3bKzY2ViEhIbr++uuVmprq1XPZsWOHNm/e7PVzP3TokEaOHKmxY8fqiiuu8Po4APbF3OS7uWnVqlW68cYbdc0117hi4eHh6t69u3766Sdt2bKl0DEKUhbnplWrVikmJsb1GpXOF/89e/bUH3/8oRUrVnj1eJLkkwoh70VQpUoVV+zs2bPq3LmzWrVqpddff13h4eGSpJSUFKWlpenRRx/VoEGDtH37dk2bNk1r167V6tWrVaFCBUnSiy++qPHjxyspKUlJSUn66aef1KlTJ6+q1M8//1zdunVTjRo1NHjwYFWvXl2bNm3Sp59+qsGDByslJUW7d+/W559/rpkzZ1qOL4kcb7/9dklSVlaWV+d0yJAhateunZKSkvThhx96dUxhMjIylJiYqObNm6tBgwYKDw/X7NmzNXToULd+jz32mNLS0tSlSxf17dtXZ8+e1apVq/Ttt9+qWbNmmjlzpvr27asWLVroiSeekCQlJiYWOZ/U1FTVr19f3bt3V/ny5bVw4UI99dRTcjqd6t+//0WPffjhh7VixQqvPyLlL3/5i6pXr66UlBSNGzeuyLkCsB/mJt/NTadPn1Z0dLQlnnf+1qxZo6uvvrrQc+BJWZybTp8+rbCwMEv8wvPZsWNH7xI2RTBjxgwjySxbtszs37/f7Ny50/zrX/8yVapUMWFhYWbXrl3GGGP69OljJJnnn3/e7fhVq1YZSSYjI8MtvnjxYrf4vn37THBwsOnatatxOp2ufsOHDzeSTJ8+fVyx5cuXG0lm+fLlxhhjzp49axISEkx8fLw5fPiw2+NcOFb//v2Np6dfEjkaY0x8fLyJj4+3PJ4nn376qSlfvrzZuHGjMeb8+YyIiPDq2ILk5uaaKlWqmBEjRrhivXr1MjfccINbvy+//NJIMoMGDbKMceHzjIiIsDzHvFw9Pc9Ro0ZZzveJEycs/Tp37mzq1q3rFmvTpo1p06aNJebty3fdunWmXLlyZsmSJW657N+/36vjAQQ25qaSn5vuvPNOc8UVV5icnBy3+C233GIkmddff73QMTwpq3PTwIEDTVBQkMnKynKLJycnG0lmwIABhY6Rp1hvrXbo0EExMTGqVauWkpOTFRkZqXnz5unKK6906/fkk0+6tefMmaNKlSqpY8eOOnDggOuradOmioyMdO1+WbZsmXJzczVw4EC3Jc8hQ4YUmtvatWu1fft2DRkyxPIWmjcfr1JSOWZlZXm1Gpebm6unn35a/fr10/XXX19of2999tlnOnjwoB544AFX7IEHHtC6devcltznzp0rh8OhUaNGWcbw9cfTXPjXSHZ2tg4cOKA2bdrot99+U3Z29kWPzczM9Ho1btCgQerSpYs6dep0SfkCCGzMTSU3Nz355JM6cuSI7r//fq1du1b//e9/NWTIENfbmidPnix0DE/K6tzUt29flStXTj179tTXX3+tbdu26eWXX9a8efMkFe18Fuut1TfffFP16tVT+fLlVa1aNV1zzTWWCzrLly+vmjVrusW2bNmi7OxsxcbGehx33759kqTff/9dkizLtDExMR6Xdi+Ut5TeoEED759QKed4MZMmTdKBAwd8vmspPT1dCQkJCgkJ0datWyWdX3IODw9XRkaGJkyYIOn8+YuLi1PlypV9+vierF69WqNGjdI333xj2VWWnZ2tSpUqXfJjfPDBB/r666+1YcOGSx4LQGBjbiq5ualLly6aOnWqnn/+eTVp0kSSdNVVV+mll17SsGHDin2LrLI6NzVq1EizZs1Sv379dOutt0qSqlevrsmTJ+vJJ58s0vksViHXokULNWvW7KJ9QkJCLP+BnE6nYmNjC9yREhMTU5x0fMqfOWZnZ2v8+PF66qmnlJOTo5ycHEnnb0NijFFWVpbCw8ML/I9ckJycHC1cuFCnTp3yeA3DrFmz9NJLL/nkr5qCxjh37pxbe9u2bbr99tt17bXXauLEiapVq5aCg4O1aNEiTZo0SU6n85JzkaShQ4fqvvvuU3BwsOuvzrx7FO3cuVO5ubmKi4vzyWMB8C/mppI1YMAAPfroo1q/fr2Cg4PVuHFjvfvuu5KkevXqFXm8sjw3SVKPHj3UvXt3rVu3TufOnVOTJk1cm2OKcj59sx3SS4mJiVq2bJluvfVWjxf55YmPj5d0/i+QunXruuL79++37M7x9BiStGHDBnXo0KHAfgX9UEsjx4IcPnxYx44d06uvvqpXX33V8v2EhAT96U9/KvI9cj7++GOdOnVKqampqlq1qtv3fv31V40cOVKrV69Wq1atlJiYqCVLlujQoUMX/cunoPMXHR3t8WaOeX8l5lm4cKFOnz6tTz75RLVr13bFfXVzyTw7d+7UrFmzPN6Xp0mTJrrhhhv0888/+/QxAdgLc5P3IiIidMstt7jay5YtU1hYmGtVqSjK8tyUJzg4WM2bN3e1ly1bJkkXfY3kV6o3uOnZs6fOnTvncdfg2bNnXSe5Q4cOqlChgqZOner2XvPkyZMLfYwmTZooISFBkydPtvzQLhwr76aw+fuUVI7ebPGOjY3VvHnzLF/t2rVTaGio5s2bpxdeeOGiY3iSnp6uunXrql+/furRo4fb17PPPqvIyEjXX3n33nuvjDEe39rNf/48/adITExUdna21q9f74rt2bPH9b5/nnLlylnGzM7O1owZM7x6Tt5u8fZ0Pu+//35J0j//+U9NmjTJq8cDcPlibvL+1lgX+vrrr/Xxxx/rscceK9bbjWV5bvJky5Yteuutt9StW7eirXB6vS3C/L+dQT/88MNF+11sl2VKSoqRZLp06WImTZpkpk2bZgYPHmzi4uLMnDlzXP1eeOEFI8kkJSWZadOmmccee8zExcWZqlWrXnRnkDHnd/FUqFDBxMfHm9GjR5u3337bPP3006ZTp06uPh9++KGRZB566CGTnp5uZs+eXWI5GlO0Xavens+8n8eMGTMKPPZ///ufCQoKMkOGDCmwz7333muqVKlicnNzjTHGPPTQQ67nP2XKFDNp0iRzzz33mKlTp7qOSUpKMhEREeaNN94ws2fPNt9++60xxpgDBw6YiIgIU7duXTN58mQzYcIEU6tWLdOkSRO3nTybN282wcHBpmHDhmbatGnmr3/9q0lMTDQ33HCDkWS2b9/u6nupu1bzY9cqcHlhbir5uSkrK8u0aNHCjB8/3vzjH/8wTz/9tAkLCzM33nijZScrc5N3c9N1111nXnzxRfOPf/zDjBgxwlSuXNnEx8e7dll7q9QLOWOMeeedd0zTpk1NWFiYiYqKMg0bNjTDhg0zu3fvdvU5d+6cGTNmjKlRo4YJCwszbdu2NRs2bDDx8fGF/mcxxpivvvrKdOzY0URFRZmIiAjTqFEjtx/22bNnzcCBA01MTIxxOByWE+/LHI0pmUJu6tSpRpJZvHhxgce+8cYbRpL54osvCuyTlpZmJJkFCxYYY86fm9dee81ce+21Jjg42MTExJguXbqYNWvWuI7ZvHmzad26tQkLC7NsaV+6dKlp0KCBCQ4ONtdcc41JT0/3uMX7k08+MY0aNTKhoaGmTp065pVXXjHvvfcehRyAImFuKvm56dChQ+ZPf/qTqV69ugkODjYJCQnmueeesxRxxjA3eTs3JScnm1q1apng4GATFxdn+vXrZ/bu3evVsRdyGOPlPRwQcHr27KmsrCx9//33/k4FAABJzE2lrVQ3O8B3jDHKzMxUenq6v1MBAEASc5M/sCIHAABgU6W6axUAAAC+QyEHAABgUxRyAAAANkUhBwAAYFMBtWvV6XRq9+7dioqK8slnq8HejDE6evSo4uLiLJ+NCAClgXkJ+QXa3BRQhdzu3btVq1Ytf6eBALNz507VrFnT32kAKIOYl1CQQJmb/F9KXiAqKsrfKSAA8boA4C/8/kFBAuW1EVCFHMvW8ITXBQB/4fcPChIor42AKuQAAADgPQo5AAAAm6KQAwAAsCkKOQAAAJuikAMAALApCjkAAACbopADAACwKQo5AAAAm6KQAwAAsCkKOQAAAJuikAMAALApCjkAAACbopADAACwKQo5AAAAm6KQAwAAsCkKOQAAAJuikAMAALApCjkAAACbopADAACwKQo5AAAAm6KQAwAAsKny/k7AzmJjY93aH374oaXP119/bYm98847llhWVpbP8vKlSpUqubVbt25t6bN48WJL7MyZMyWWEwAAOI8VOQAAAJuikAMAALApCjkAAACb4ho5L0VHR1tiGzdudGvnv55Mkvbu3WuJ2eV6OElas2aNWzsmJsbSp2nTppbY1q1bfZcYAMBnKlasaIm9/PLLlliDBg3c2h06dLD04Xpo/2NFDgAAwKYo5AAAAGyKQg4AAMCmKOQAAABsis0OHlStWtUS++CDDyyxypUru7WnT59u6TNw4EDfJVbCRo4caYklJCS4tVNSUix92NgAAIGpd+/elthLL71kidWqVavQsTxtkjh48GDxEoPPsCIHAABgUxRyAAAANkUhBwAAYFMUcgAAADblMMYYfyeRJycnx+OnC5S2Tp06WWKfffZZocdVr17dEtu/f79PcvK1+vXrW2L/+c9/LLF58+a5tR955BFLn6NHj/osL0+ys7M9XmQLACUtUOYlb9WsWdOtvXbtWkufKlWqWGLelAKeNv0NGDDAEjt06FChY10OAmVuYkUOAADApijkAAAAbIpCDgAAwKYo5AAAAGyqzH+yQ2xsrCV27733enXsY4895ta208aGZcuWeXVs/s0OJb2xAQBQfM8++6xbO/8nEF2K+++/3xK74447LDFPnxwxdepUt3Zubq7P8irrWJEDAACwKQo5AAAAm6KQAwAAsKkyf43cG2+8YYk9+OCDltiaNWsssTlz5pRITr522223WWLVqlWzxNLS0iyx9PT0kkgJAHCJ4uPjLbFHH3200OPWr19vie3du9cS69ChQ6FjebpZcv7r9CQpIyPDrf3HH38UOja8w4ocAACATVHIAQAA2BSFHAAAgE1RyAEAANhUmd/sYIyxxJxOpyW2e/duSywQbmgYFhZmiQ0fPtyt/dRTT1n6eHref/7zn32XGACgRDVu3NgSi4qKcmuvWrXK0qdNmzaWWGhoqCX2wAMPuLXzzy2SlJiYaIlVr17dEluwYIFbu0uXLpY+hw4dssRQOFbkAAAAbIpCDgAAwKYo5AAAAGyKQg4AAMCmyvxmB2917drVElu6dKlb+8iRI5Y+qampPsvB0wWqbdu2tcRuvvnmQsf66KOPfJESAMBPQkJCLLH8G9kmTZrk1VinTp2yxGbMmOHWvu+++yx96tat69X4J06ccGsHwmbBywUrcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgU2V+s8OUKVMssXbt2llicXFxlljr1q3d2g6Hw9Kne/ful5CdO0/je/qEhvx+++03S8zTHboBAPaR/5MXPPG0UW/+/PnFerxmzZoV6zhJ+vbbb93ax44dK/ZYcMeKHAAAgE1RyAEAANgUhRwAAIBNlflr5NasWWOJNWrUyBJr3LixJXbHHXe4tYcOHWrps3//fkvs/fffL0KG/8/MmTMtsXXr1hV63Ndff22Jbdu2rVg5AAACw+zZsy2x/NdlN2/e3NLn2muvtcQaNmxoid19991u7ejoaEsfTzfC99Tv8ccfd2t7ms9++eUXSwyFY0UOAADApijkAAAAbIpCDgAAwKYo5AAAAGzKYby5o2wpycnJUaVKlfydRsCqW7euJbZ161ZL7Oeff3Zrd+7c2dLH0yaMQJWdna2KFSv6Ow0AZVAgz0uVK1e2xPLPCZ5yL+7N5ZctW2aJ9e/f3xL79NNPLbGrr77arf33v//d0qdfv36F5hBIAmVuYkUOAADApijkAAAAbIpCDgAAwKYo5AAAAGyqzH+yg528+OKLlpinC1Sfe+45t7adNjYAALxz6NAhS6xnz55u7Y8++sjSx9vNG1OnTnVr559bJOnUqVOW2Mcff2yJPf/8825tT5vwEhMTLTE+hahwrMgBAADYFIUcAACATVHIAQAA2BSFHAAAgE2x2SFA3XfffZbYww8/bIkdPXrUEjt48GCJ5AQACGz5P32hR48elj69evWyxI4cOWKJ5d9g52ljgyfjxo2zxK677jq3dvfu3Qt9PEnq06ePV49ZlrEiBwAAYFMUcgAAADZFIQcAAGBTXCMXoLp06eJVv08//dQS++mnn3ydDgDAhvJfM1dQzJdOnjxpiX3wwQdubU/XyLVr184Sq1y5siXm6UbIZRkrcgAAADZFIQcAAGBTFHIAAAA2RSEHAABgU2x2CFCeNjscP37cEnvjjTdKIx0AAIrtww8/dGt72uxw//33W2IDBgywxMaOHeu7xC4DrMgBAADYFIUcAACATVHIAQAA2BSFHAAAgE05jDHG30nkycnJUaVKlfydhl/069fPrT19+nRLn3379lli1atXL7GcAkV2drYqVqzo7zQAlEFleV4qSY0bN7bEVq9ebYmFhoZaYtddd51b+7///a/P8iqKQJmbWJEDAACwKQo5AAAAm6KQAwAAsCkKOQAAAJvikx0CRP7NDp72oPz73//2aqyoqCi3dnR0tKXPjh07ipAdAAC+8/PPP1tiL774oiX22muvWWITJkxwaz/00EOWPidPnix+cjbDihwAAIBNUcgBAADYFIUcAACATXGNnI2cO3fOEuvdu7cl9vTTT7u1N27caOnTp08f3yUGAMAl+uc//2mJpaSkWGL33HOPW3vs2LGWPuvXr/ddYgGOFTkAAACbopADAACwKQo5AAAAm6KQAwAAsCmH8XTnWT/JyclRpUqV/J2GX+S/OWLDhg0tfRwOhyXm6cf37rvvurXHjRtn6bNz584iZug/2dnZqlixor/TAFAGleV5KRDUrl3bEsvKynJrz54929LH00ZAXwuUuYkVOQAAAJuikAMAALApCjkAAACbopADAACwKT7ZIUAMGDDAre3pTtUrV660xFJTUy2xw4cPu7Vzc3MvMTsAAErfjh07LLFly5a5tbt3727pc/3111tiv/zyi+8SCyCsyAEAANgUhRwAAIBNUcgBAADYFIUcAACATbHZIUB89dVXbu327dv7KRMAAAJXjx493Nrr1q2z9LnqqqssMTY7AAAAIKBQyAEAANgUhRwAAIBNcY0cAACwjZycHLd2QkKCnzIJDKzIAQAA2BSFHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYFIUcAACATVHIAQAA2BSFHAAAgE0FVCFnjPF3CghAvC4A+Au/f1CQQHltBFQhd/ToUX+ngADE6wKAv/D7BwUJlNeGwwRKSSnJ6XRq9+7dioqKksPh8Hc68DNjjI4ePaq4uDgFBQXU3xwAygjmJeQXaHNTQBVyAAAA8J7/S0kAAAAUi+0KuTp16uiRRx5xtTMzM+VwOJSZmem3nPLLnyMuTdu2bdW2bVt/pwEABWJuKnsCZW4qUiGXlpYmh8Ph+goNDVW9evU0YMAA7d27t6RyLBGLFi3S6NGj/Z2GR3v27NETTzyhhIQEhYWFKTExUc8884wOHjx4yWNv2rTJ9bM7cuRIsceZMGGC5s+ff8n5lIYLX7MXfv31r3/1d2oAfIC5qXRs3bpVPXr0UHR0tMLDw9WqVSstX77cJ2OXxblp7969evTRRxUbG6uwsDA1adJEc+bMKfI45Yvz4GPHjlVCQoJOnTqlr776SqmpqVq0aJE2bNig8PDw4gxZbK1bt9bJkycVHBxcpOMWLVqkN998M+D+wxw7dky33HKLjh8/rqeeekq1atXSunXrNG3aNC1fvlxr1qy5pIsr09PTVb16dR0+fFgfffSR+vbtW6xxJkyYoB49euiuu+4qdi6lqWPHjnr44YfdYjfeeKOfsgFQEpibSs7OnTt1yy23qFy5cho6dKgiIiI0Y8YMderUSV988YVat259SeOXtbkpJydHrVq10t69ezV48GBVr15dH374oXr27KmMjAz16tXL67GKVch16dJFzZo1kyT17dtXVapU0cSJE7VgwQI98MADHo85fvy4IiIiivNwFxUUFKTQ0FCfj+svn3zyiX7//Xd9+umn6tq1qyteuXJljR07VuvWrSt2AWKM0axZs9SrVy9t375dGRkZxf7PYjf16tXTgw8+6O80AJQg5qaS89e//lVHjhzRhg0bdM0110iSHn/8cV177bV6+umntWbNmmKPXRbnprfffltbt27VF198ofbt20uSnnzySd188836v//7P/Xo0cPrPwJ8co1cXhLbt2+XJD3yyCOKjIzUtm3blJSUpKioKPXu3VvS+a3ckydPVv369RUaGqpq1aopJSVFhw8fdhvTGKPx48erZs2aCg8PV7t27bRx40bLYxd0HcJ3332npKQkRUdHKyIiQo0aNdKUKVNc+b355puS3N92y+PrHCVp27Zt2rZtW6HnMicnR5JUrVo1t3iNGjUkSWFhYYWOUZDVq1crKytLycnJSk5O1sqVK7Vr1y5LP6fTqSlTpqhhw4YKDQ1VTEyM7rjjDv3444+Szp+z48eP6/3333edu7zrLh555BHVqVPHMubo0aMtW/dnzJih9u3bKzY2ViEhIbr++uuVmprq1XPZsWOHNm/eXKTnf/LkSZ06dapIxwCwL+Ym381Nq1at0o033ugq4iQpPDxc3bt3108//aQtW7YUOkZByuLctGrVKsXExLheo9L54r9nz576448/tGLFCq8eTyrmilx+eS+CKlWquGJnz55V586d1apVK73++uuuZe2UlBSlpaXp0Ucf1aBBg7R9+3ZNmzZNa9eu1erVq1WhQgVJ0osvvqjx48crKSlJSUlJ+umnn9SpUyfl5uYWms/nn3+ubt26qUaNGq4ly02bNunTTz/V4MGDlZKSot27d+vzzz/XzJkzLceXRI633367JCkrK+uiubdu3VpBQUEaPHiw3njjDdWsWVPr16/XSy+9pLvuukvXXnttoc+/IBkZGUpMTFTz5s3VoEEDhYeHa/bs2Ro6dKhbv8cee0xpaWnq0qWL+vbtq7Nnz2rVqlX69ttv1axZM82cOVN9+/ZVixYt9MQTT0iSEhMTi5xPamqq6tevr+7du6t8+fJauHChnnrqKTmdTvXv3/+ixz788MNasWKF13fWTktL0/Tp02WM0XXXXaeRI0cWaekagP0wN/lubjp9+rSio6Mt8bzzt2bNGl199dWFngNPyuLcdPr0aY8LMxeez44dO3qXsCmCGTNmGElm2bJlZv/+/Wbnzp3mX//6l6lSpYoJCwszu3btMsYY06dPHyPJPP/8827Hr1q1ykgyGRkZbvHFixe7xfft22eCg4NN165djdPpdPUbPny4kWT69Onjii1fvtxIMsuXLzfGGHP27FmTkJBg4uPjzeHDh90e58Kx+vfvbzw9/ZLI0Rhj4uPjTXx8vOXxPPnHP/5hrrjiCiPJ9dWnTx9z5swZr473JDc311SpUsWMGDHCFevVq5e54YYb3Pp9+eWXRpIZNGiQZYwLn2dERITlORpz/mfv6XmOGjXKcr5PnDhh6de5c2dTt25dt1ibNm1MmzZtLDFvX74tW7Y0kydPNgsWLDCpqammQYMGRpKZPn26V8cDCGzMTSU/N915553miiuuMDk5OW7xW265xUgyr7/+eqFjeFJW56aBAweaoKAgk5WV5RZPTk42ksyAAQMKHSNPsd5a7dChg2JiYlSrVi0lJycrMjJS8+bN05VXXunW78knn3Rrz5kzR5UqVVLHjh114MAB11fTpk0VGRnp2v2ybNky5ebmauDAgW5LnkOGDCk0t7Vr12r79u0aMmSIrrjiCrfveXNX7pLKMSsrq9C/ePJceeWVatGihSZPnqx58+bpmWeeUUZGhp5//nmvjvfks88+08GDB92uE3nggQe0bt06tyX3uXPnyuFwaNSoUZYxfH1X8wv/GsnOztaBAwfUpk0b/fbbb8rOzr7osZmZmV6vxq1evVqDBw9W9+7d1a9fP61Zs0YNGjTQ8OHDdfLkyUt6DgACB3NTyc1NTz75pI4cOaL7779fa9eu1X//+18NGTLE9bZmcX+XltW5qW/fvipXrpx69uypr7/+Wtu2bdPLL7+sefPmSSra+SzWW6tvvvmm6tWrp/Lly6tatWq65pprLDspy5cvr5o1a7rFtmzZouzsbMXGxnocd9++fZKk33//XZIsy7QxMTEel3YvlLeU3qBBA++fUCnneDGrV69Wt27dXEvFknTXXXepYsWKGjNmjP785z/r+uuvL/K46enpSkhIUEhIiLZu3Srp/JJzeHi4MjIyNGHCBEnnz19cXJwqV65c7OfgrdWrV2vUqFH65ptvdOLECbfvZWdnq1KlSiXyuMHBwRowYICrqGvVqlWJPA6A0sXcVHJzU5cuXTR16lQ9//zzatKkiSTpqquu0ksvvaRhw4YpMjKyWOOW1bmpUaNGmjVrlvr166dbb71VklS9enVNnjxZTz75ZJHOZ7EKuRYtWriKjIKEhIRY/gM5nU7FxsYqIyPD4zExMTHFScen/J3j22+/rWrVqlnOb/fu3TV69Gh9/fXXRS7kcnJytHDhQp06dcrjNQyzZs3SSy+95JO/agoa49y5c27tbdu26fbbb9e1116riRMnqlatWgoODtaiRYs0adIkOZ3OS87lYmrVqiVJOnToUIk+DoDSw9xUsgYMGKBHH31U69evV3BwsBo3bqx3331X0vk7AxRVWZ+bevTooe7du2vdunU6d+6cmjRp4tocU5Tz6ZPNDt5KTEzUsmXLdOutt15092V8fLyk83+B1K1b1xXfv3+/ZXeOp8eQpA0bNqhDhw4F9ivoh1oaOV7M3r17LS8sSTpz5oyk8xfqFtXHH3+sU6dOKTU1VVWrVnX73q+//qqRI0dq9erVatWqlRITE7VkyRIdOnToon/5FHT+oqOjPd7MMe+vxDwLFy7U6dOn9cknn6h27dquuK9uLlmY3377TVJg/IIG4F/MTd6LiIjQLbfc4movW7ZMYWFhrlWlomBuOv8OUfPmzV3tZcuWSdJFXyP5lepHdPXs2VPnzp3TuHHjLN87e/as6yR36NBBFSpU0NSpU93ea548eXKhj9GkSRMlJCRo8uTJlh/ahWPl3Tcof5+SytHbLd716tXT3r17LVvWZ8+eLal4N7FNT09X3bp11a9fP/Xo0cPt69lnn1VkZKTrr7x7771XxhiNGTPGMk7+8+fpP0ViYqKys7O1fv16V2zPnj2u9/3zlCtXzjJmdna2ZsyY4dVz8naL9/79+y2xo0ePavLkyapataqaNm3q1eMBuHwxNxU+N3ny9ddf6+OPP9Zjjz1WrLcby/Lc5MmWLVv01ltvqVu3bkVb4fR6W4T5fzuDfvjhh4v269Onj4mIiPD4vZSUFCPJdOnSxUyaNMlMmzbNDB482MTFxZk5c+a4+r3wwgtGkklKSjLTpk0zjz32mImLizNVq1a96M4gY87v4qlQoYKJj483o0ePNm+//bZ5+umnTadOnVx9PvzwQyPJPPTQQyY9Pd3Mnj27xHI0xvudQZs3bzYREREmMjLSvPDCC+att94yDzzwgJFkOnbs6NY37+cxY8aMAsf73//+Z4KCgsyQIUMK7HPvvfeaKlWqmNzcXGOMMQ899JDr+U+ZMsVMmjTJ3HPPPWbq1KmuY5KSkkxERIR54403zOzZs823335rjDHmwIEDJiIiwtStW9dMnjzZTJgwwdSqVcs0adLEbSfP5s2bTXBwsGnYsKGZNm2a+etf/2oSExPNDTfcYCSZ7du3u/peys6gUaNGmRtuuMGMHDnSvPPOO2bMmDEmPj7eOBwOk56eXujxAAIfc1PJz01ZWVmmRYsWZvz48eYf//iHefrpp01YWJi58cYbLTtZmZu8K62uu+468+KLL5p//OMfZsSIEaZy5comPj7etcvaW6VeyBljzDvvvGOaNm1qwsLCTFRUlGnYsKEZNmyY2b17t6vPuXPnzJgxY0yNGjVMWFiYadu2rdmwYYOJj48v9D+LMcZ89dVXpmPHjiYqKspERESYRo0auf2wz549awYOHGhiYmKMw+GwnHhf5mhM0W4/snnzZtOjRw9Tq1Yt13/6Z5991hw/ftyt39SpU40ks3jx4gLHeuONN4wk88UXXxTYJy0tzUgyCxYscJ2b1157zVx77bUmODjYxMTEmC5dupg1a9a45di6dWsTFhZm2dK+dOlS06BBAxMcHGyuueYak56e7nGL9yeffGIaNWpkQkNDTZ06dcwrr7xi3nvvPZ/+Z1m6dKnp2LGjqV69uqlQoYK54oorTKdOnS56PgDYC3NTyc9Nhw4dMn/6059M9erVTXBwsElISDDPPfecpYgzhrnJ20IuOTnZ1KpVywQHB5u4uDjTr18/s3fvXq+OvZDDGC/v4YCA07NnT2VlZen777/3dyoAAEhibiptpbrZAb5jjFFmZqbS09P9nQoAAJKYm/yBFTkAAACbKtVdqwAAAPAdCjkAAACbopADAACwKQo5AAAAmwqoXatOp1O7d+9WVFSUTz5bDfZmjNHRo0cVFxdn+WxEACgNzEvIL9DmpoAq5Hbv3u36MHMgz86dO1WzZk1/pwGgDGJeQkECZW7yfyl5gaioKH+ngADE6wKAv/D7BwUJlNdGQBVyLFvDE14XAPyF3z8oSKC8NgKqkAMAAID3KOQAAABsikIOAADApijkAAAAbIpCDgAAwKYo5AAAAGyKQg4AAMCmKOQAAABsikIOAADApijkAAAAbIpCDgAAwKYo5AAAAGyKQg4AAMCmKOQAAABsikIOAADApijkAAAAbKq8vxOA70VHR7u1a9euXeyxfv/9d7f2008/bemzYcMGS+y///2vJbZu3bpi5wEAAKxYkQMAALApCjkAAACbopADAACwKQo5AAAAm2Kzg4107drVEuvevbsl1rZtW7f2VVddVezHzL9pIT4+3tInJCTEq7HKlStX7DwAAIAVK3IAAAA2RSEHAABgUxRyAAAANsU1cn6QmJhoifXv39+t/fjjj1v6hIWFWWIOh8N3iXlQr169Eh0fAAAUHytyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTbHbwg5o1a1pigwcP9kMm7jZv3myJbdy40Q+ZAAD8Lf/N5KtWrWrpc/fdd1ti+W9KL0lOp9Ot/dZbb1n6rF692hLbunVrYWmWeazIAQAA2BSFHAAAgE1RyAEAANgUhRwAAIBNsdnBS54u8sy/QcHThZqLFy+2xE6fPm2JZWdnu7WPHz9u6RMREWGJLV261BLbsGGDW/u7776z9Fm7dq0ldvLkSUvMUx4AAPtq0KCBJTZgwABL7J577nFre5oHi+umm26yxM6ePWuJ/frrr5bYV1995db2tFkwNzf3ErKzF1bkAAAAbIpCDgAAwKYo5AAAAGyKQg4AAMCm2OzggbebCm644Qa3tqc7XHvy7bffWmJNmjRxa2dlZVn61K5d2xLbtWuXJZb/DtoAgLKhUaNGbu3+/ftb+tx///2WWMWKFQsd+3//+58ltmrVKkts+/btltiwYcPc2mvWrLH0adGihSVWuXJlSywpKcmtvW7dOksfT58ccbliRQ4AAMCmKOQAAABsikIOAADApsr8NXLBwcGW2KxZsyyx/NfDSdKECRPc2suWLSt2Hp6uictvx44dxR4fAHB5efvtty2x/Ndqe3sT3y+++MIS+89//uPWHj58uKXPqVOnvBq/ZcuWbu0nn3zS0ue9996zxBo3bmyJ7d2716395ptvWvrMnTvXEtu/f39hadoSK3IAAAA2RSEHAABgUxRyAAAANkUhBwAAYFNlbrNDZGSkW/uFF16w9OnWrZslduDAAUvs9ddfd2ufOHHiErMDAJR1oaGhllj+G+pKUt++fS0xh8Ph1vZ0gX9qaqol9tprr1lix48fv2ieRVGlShW3drly5Sx9Ro8ebYktXrzYEouPj/dZXpcDVuQAAABsikIOAADApijkAAAAbIpCDgAAwKbK3GaHu+66y639/PPPW/p4+gSF2267zRLLzs72WV4AAEhS27ZtLbGhQ4daYvk3NkjS//73P7f2vffea+nz/fffFz+5fDxtWqhVq5Yl9s9//tOtvWjRIkuf6Ohorx4z//OeOXOmpc+RI0e8GutywIocAACATVHIAQAA2BSFHAAAgE1RyAEAANhUmdvs0LJly0L7rF271hLbtWtXSaQDAIAbTxsIzp0759WxZ8+edWvfdNNNlj49evSwxK699tpCxz558qQldt1113kVy//pSNWqVSv08Qqyd+9et/b48eMtfc6cOVPs8e2GFTkAAACbopADAACwKQo5AAAAm3IYY4y/k8iTk5OjSpUqlehj7Nu3z61dpUoVS5/Tp09bYq+88ooltmDBArf2zz//fGnJwaPs7GxVrFjR32kAKINKY17KLywszBKbNWuWJdahQwdLLDw83K0dFGRdr/F22s9/XZ6na/d8yel0WmLz5s2zxAYNGuTW3rNnT4nldDGBMjexIgcAAGBTFHIAAAA2RSEHAABgUxRyAAAANlXmNjvkf7qeLq70Vv5j33rrLUufb7/91hKrXbu2JbZ161a39saNG73KoX79+pbYN99849a2+82MA+WCUgBljz82O3jriiuusMSef/55t/att95q6XPw4EFLbMeOHZZYSEiIW/uGG26w9GnRokVhaXrN0xw6fPhwS+zIkSM+e8xLEShzEytyAAAANkUhBwAAYFMUcgAAADZFIQcAAGBTZW6zw2uvvebWfuaZZ0r08fxh//79bu3MzExLn+Tk5FLK5tIFygWlAMqeQN7sUNr++c9/WmIPPvigV8cePXrUre1p7k1LS7PE8n+6RCAJlLmJFTkAAACbopADAACwKQo5AAAAm6KQAwAAsKkyt9mhXLlybu0bb7zR0mfWrFmWWPny5S2xWrVqubWDggKzLvb0Ix49erQlNn78+FLIpugC5YJSAGVPWd7sMGzYMLe2pznC09zoSe/evd3as2fPLn5iASJQ5qbArDwAAABQKAo5AAAAm6KQAwAAsCnv3ty+jOS/ueCPP/5o6VOvXj2vxrr99tvd2hUqVLD08XQtWvPmzb0a31ccDocl1rRp01LNAQAQuPr27WuJjRw50q3t7fVwGzdutMQ+/vjj4iWGQrEiBwAAYFMUcgAAADZFIQcAAGBTFHIAAAA2VeY2O/jSF198UWifxo0bW2KeNjucPXvWrT1jxgxLn7///e+W2JAhQyyxXr16FZoXAKBsatGihSX2xhtvWGKRkZGFjnXs2DFLrF+/fpbY6dOnvcwORcWKHAAAgE1RyAEAANgUhRwAAIBNUcgBAADYFJsdStjSpUstsZdeeskSy3/H7Mcff9zS56qrrrLE2rZtW6y8du3aVazjAAD2duedd1piUVFRhR53/PhxS6x79+6W2OrVq4uXGIqFFTkAAACbopADAACwKQo5AAAAm6KQAwAAsCmHMcb4O4k8OTk5qlSpkr/T8KmwsDBL7L333rPEevbs6bPHPHfunFv73//+t6XPgw8+aIl5upA1EGRnZ6tixYr+TgNAGWT3ecnTJoYDBw5YYhUqVCh0rHfeeccS8/QpDmVFoMxNrMgBAADYFIUcAACATVHIAQAA2BQ3BC5hJ0+etMSGDBliiUVGRrq1mzVrZukTGxtriWVlZVliM2fOdGuPHj364kkCAC4L+eeSX375xdLHm+vhJGn9+vVubU9zF/yPFTkAAACbopADAACwKQo5AAAAm6KQAwAAsCk2O/jB3r17LbE777zTrf3QQw9Z+tx8882W2JgxYyyxffv2XUJ2AAC7at++vVu7Zs2alj7efg7A008/7dY+depU8RNDiWFFDgAAwKYo5AAAAGyKQg4AAMCmKOQAAABsymG8veqxFOTk5KhSpUr+TgMBJjs7WxUrVvR3GgDKILvNS+vWrXNrN2zY0KvjXnvtNUvsueee80lOl6tAmZtYkQMAALApCjkAAACbopADAACwKQo5AAAAm+KTHQAAuExUrlzZre1wOCx9PH36z+TJk0sqJZQwVuQAAABsikIOAADApijkAAAAbIpr5AAAuExMnDjxom1JGjdunCW2Z8+eEssJJYsVOQAAAJuikAMAALApCjkAAACbopADAACwKYcxxvg7iTw5OTmqVKmSv9NAgMnOzlbFihX9nQaAMoh5CQUJlLmJFTkAAACbopADAACwKQo5AAAAmwqoQi6ALtdDAOF1AcBf+P2DggTKayOgCrmjR4/6OwUEIF4XAPyF3z8oSKC8NgJq16rT6dTu3bsVFRUlh8Ph73TgZ8YYHT16VHFxcQoKCqi/OQCUEcxLyC/Q5qaAKuQAAADgPf+XkgAAACgWCjkAAACbopADAACwKQo5AAAAm6KQAwAAsCkKOQAAAJuikAMAALCp/w/JbckuKugxOwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x1000 with 10 Axes>"
-      ]
+      "text/plain": "<Figure size 1000x1000 with 10 Axes>",
+      "image/png": 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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -358,8 +354,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-24T01:07:07.318255900Z",
-     "start_time": "2024-04-24T01:07:07.154681500Z"
+     "end_time": "2024-09-22T21:35:00.609076100Z",
+     "start_time": "2024-09-22T21:35:00.501759300Z"
     }
    },
    "outputs": [],
diff --git a/examples/real-life-applications/tic_tac_toe_alternative_dataset_shape.py b/examples/real-life-applications/tic_tac_toe_alternative_dataset_shape.py
index 623e475..0670ece 100644
--- a/examples/real-life-applications/tic_tac_toe_alternative_dataset_shape.py
+++ b/examples/real-life-applications/tic_tac_toe_alternative_dataset_shape.py
@@ -10,6 +10,7 @@
 from neuralnetlib.metrics import accuracy_score
 from neuralnetlib.model import Model
 from neuralnetlib.optimizers import Adam
+from neuralnetlib.callbacks import EarlyStopping
 
 
 def main():
@@ -72,7 +73,8 @@ def main():
     model.compile(loss_function=BinaryCrossentropy(), optimizer=Adam(learning_rate=0.001))
 
     # 7. Train the model
-    model.fit(x_train, y_train, epochs=500, batch_size=32, metrics=[accuracy_score], random_state=42)
+    early_stopping = EarlyStopping(patience=5, min_delta=0.001, restore_best_weights=True)
+    model.fit(x_train, y_train, epochs=500, batch_size=32, metrics=[accuracy_score], random_state=42, callbacks=[early_stopping])
 
     # 8. Evaluate the model
     loss = model.evaluate(x_test, y_test)
diff --git a/neuralnetlib/metrics.py b/neuralnetlib/metrics.py
index 86969c3..06548c0 100644
--- a/neuralnetlib/metrics.py
+++ b/neuralnetlib/metrics.py
@@ -2,6 +2,35 @@
 
 from neuralnetlib.preprocessing import apply_threshold
 
+class Metric:
+    def __init__(self, name):
+        if isinstance(name, str):
+            self.function = self._get_function_by_name(name)
+            self.name = self._get_function_by_name(name).__name__.split("_score")[0]
+        elif callable(name):
+            self.function = name
+            self.name = name.__name__.split("_score")[0]
+        
+    def _get_function_by_name(self, name: str):
+        if name in ['accuracy', 'accuracy_score', 'accuracy-score', 'acc']:
+            return accuracy_score
+        elif name in ['f1', 'f1_score', 'f1-score']:
+            return f1_score
+        elif name in ['recall', 'recall_score', 'recall-score', 'sensitivity', 'rec']:
+            return recall_score
+        elif name in ['precision', 'precision_score', 'precision-score', 'positive-predictive-value']:
+            return precision_score
+        else:
+            raise ValueError(f"Metric {name} is not supported.")
+    
+    def __call__(self, y_pred: np.ndarray, y_true: np.ndarray, threshold: float = 0.5) -> float:
+        return self.function(y_pred, y_true, threshold)
+        
+    def from_name(self, name: str):
+        return Metric(name)
+    
+    def __name__(self):
+        return self.function.__name__
 
 def accuracy_score(y_pred: np.ndarray, y_true: np.ndarray, threshold: float = 0.5) -> float:
     if y_pred.ndim == 1 or y_pred.shape[1] == 1:  # Binary classification
diff --git a/neuralnetlib/model.py b/neuralnetlib/model.py
index bbffc26..4253b7f 100644
--- a/neuralnetlib/model.py
+++ b/neuralnetlib/model.py
@@ -12,6 +12,7 @@
 from neuralnetlib.optimizers import Optimizer
 from neuralnetlib.preprocessing import PCA
 from neuralnetlib.utils import shuffle, progress_bar, is_interactive, is_display_available
+from neuralnetlib.metrics import Metric
 
 
 class Model:
@@ -121,7 +122,7 @@ def fit(self, x_train: np.ndarray, y_train: np.ndarray, epochs: int, batch_size:
             epochs: Number of epochs to train the model
             batch_size: Number of samples per gradient update
             verbose: Whether to print training progress
-            metrics: List of metric functions to evaluate the model
+            metrics: List of metric to evaluate the model
             random_state: Random seed for shuffling the data
             validation_data: Tuple of validation data and labels
             callbacks: List of callback objects (e.g., EarlyStopping)
@@ -142,6 +143,9 @@ def fit(self, x_train: np.ndarray, y_train: np.ndarray, epochs: int, batch_size:
             x_test = np.array(x_test)
             y_test = np.array(y_test)
             
+        if metrics is not None:
+            metrics = [Metric(m) for m in metrics]
+            
         # Adapt the TextVectorization layer if it exists
         for layer in self.layers:
             if isinstance(layer, TextVectorization):
@@ -179,7 +183,7 @@ def fit(self, x_train: np.ndarray, y_train: np.ndarray, epochs: int, batch_size:
                             for metric in metrics:
                                 metric_value = metric(
                                     np.vstack(predictions_list), np.vstack(y_true_list))
-                                metrics_str += f'{metric.__name__}: {metric_value:.4f} - '
+                                metrics_str += f'{metric.name}: {metric_value:.4f} - '
                         progress_bar(j / batch_size + 1, num_batches,
                                      message=f'Epoch {i + 1}/{epochs} - loss: {error / (j / batch_size + 1):.4f} - {metrics_str[:-3]} - {time.time() - start_time:.2f}s')
 
@@ -195,7 +199,7 @@ def fit(self, x_train: np.ndarray, y_train: np.ndarray, epochs: int, batch_size:
                         for metric in metrics:
                             metric_value = metric(
                                 np.vstack(predictions_list), np.vstack(y_true_list))
-                            metrics_str += f'{metric.__name__}: {metric_value:.4f} - '
+                            metrics_str += f'{metric.name}: {metric_value:.4f} - '
                     progress_bar(1, 1,
                                  message=f'Epoch {i + 1}/{epochs} - loss: {error:.4f} - {metrics_str[:-3]} - {time.time() - start_time:.2f}s')
 
@@ -209,14 +213,14 @@ def fit(self, x_train: np.ndarray, y_train: np.ndarray, epochs: int, batch_size:
                         val_metrics.append(metric(val_predictions, y_test))
                     if verbose:
                         val_metrics_str = ' - '.join(
-                            f'{metric.__name__}: {val_metric:.4f}' for metric, val_metric in zip(metrics, val_metrics))
+                            f'val_{metric.name}: {val_metric:.4f}' for metric, val_metric in zip(metrics, val_metrics))
                         print(f' - {val_metrics_str}', end='')
 
             if callbacks:
                 metrics_values = {}
                 if metrics is not None:
                     for metric in metrics:
-                        metrics_values[metric.__name__] = metric(
+                        metrics_values[metric.name] = metric(
                             np.vstack(predictions_list), np.vstack(y_true_list))
 
                 callback_monitor_metrics = set(
@@ -344,4 +348,4 @@ def __update_plot(self, epoch, x_train, y_train, random_state):
         ax.set_title(f"Decision Boundary (Epoch {epoch + 1})")
 
         fig.canvas.draw()
-        plt.pause(0.1)
+        plt.pause(0.1)
\ No newline at end of file

From 4fae3bcda0189716ec17ecf0ad6d42a78eb060cd Mon Sep 17 00:00:00 2001
From: GitHub Action <52708150+marcpinet@users.noreply.github.com>
Date: Sun, 22 Sep 2024 23:40:57 +0200
Subject: [PATCH 2/2] ci: bump version to 2.7.1

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index b8e8edb..ca6d1ca 100644
--- a/setup.py
+++ b/setup.py
@@ -2,7 +2,7 @@
 
 setup(
     name='neuralnetlib',
-    version='2.7.0',
+    version='2.7.1',
     author='Marc Pinet',
     description='A simple convolutional neural network library with only numpy as dependency',
     long_description=open('README.md', encoding="utf-8").read(),