diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 1c2d223184315363de42ad2c12d6f77987f81718..4c511427f9c7d253d4fc0644105a73d099c92d07 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -5,7 +5,7 @@
    "id": "7edf7168",
    "metadata": {},
    "source": [
-    "# TD2: Deep learning"
+    "# TD2: Deep learning (Elouan BISSON)"
    ]
   },
   {
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "330a42f5",
    "metadata": {},
    "outputs": [
@@ -43,15 +43,15 @@
      "text": [
       "Requirement already satisfied: torch in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (2.5.1)\n",
       "Requirement already satisfied: torchvision in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (0.20.1)\n",
-      "Requirement already satisfied: jinja2 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (2.11.3)\n",
-      "Requirement already satisfied: fsspec in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (2022.2.0)\n",
-      "Requirement already satisfied: filelock in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (3.6.0)\n",
-      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (4.12.2)\n",
       "Requirement already satisfied: networkx in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (2.7.1)\n",
       "Requirement already satisfied: sympy==1.13.1 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (1.13.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (4.12.2)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (2.11.3)\n",
+      "Requirement already satisfied: filelock in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (3.6.0)\n",
+      "Requirement already satisfied: fsspec in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torch) (2022.2.0)\n",
       "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from sympy==1.13.1->torch) (1.2.1)\n",
-      "Requirement already satisfied: numpy in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torchvision) (1.22.4)\n",
       "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torchvision) (9.0.1)\n",
+      "Requirement already satisfied: numpy in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from torchvision) (1.22.4)\n",
       "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\eloua\\nouveau dossier\\lib\\site-packages (from jinja2->torch) (2.0.1)\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "b1950f0a",
    "metadata": {},
    "outputs": [
@@ -80,34 +80,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[ 1.0305e+00,  2.1928e-01, -5.8484e-01, -4.2694e-01,  9.4086e-02,\n",
-      "         -1.5717e-01, -6.9468e-01,  3.6041e-01, -8.0000e-02, -1.1150e+00],\n",
-      "        [-8.1360e-01,  5.7366e-01, -3.9370e-01, -2.1028e-01, -8.5509e-01,\n",
-      "          4.4009e-01, -3.4995e-01,  1.0338e+00,  1.9398e-01, -1.0608e+00],\n",
-      "        [ 1.2515e+00, -1.0458e+00, -7.0494e-01,  8.1538e-01,  1.4329e-01,\n",
-      "         -1.0582e+00,  1.4210e+00,  3.0752e-01,  2.0237e+00,  1.9417e-01],\n",
-      "        [-8.8335e-01,  2.1310e+00,  1.3559e+00,  1.1505e+00, -9.8069e-02,\n",
-      "         -2.9167e-01, -3.7658e-01,  5.4823e-01,  5.6016e-01, -1.1355e+00],\n",
-      "        [-1.8523e+00,  8.4509e-01,  4.9774e-01,  5.5219e-01,  1.8432e+00,\n",
-      "         -1.2231e+00, -7.4602e-01,  6.2106e-01,  2.1055e-01, -7.6698e-01],\n",
-      "        [-1.9545e+00,  9.4290e-01, -1.8900e-03, -1.5746e+00,  2.8379e-01,\n",
-      "          1.0765e-01, -4.5590e-01, -1.1182e+00,  5.4219e-01, -1.4630e+00],\n",
-      "        [ 1.3065e-01,  7.4744e-01, -1.2504e+00, -1.2077e-01, -2.1310e+00,\n",
-      "          9.1643e-01, -1.3295e+00,  1.4490e+00,  5.5368e-02,  9.4062e-01],\n",
-      "        [-1.1280e+00, -5.4262e-01, -9.2099e-01,  7.0206e-01,  1.8870e-01,\n",
-      "          1.4340e+00,  6.1384e-01,  2.1229e-01,  1.3686e+00,  4.4983e-02],\n",
-      "        [ 8.1227e-01, -3.4309e-02,  3.0000e-01, -2.1976e+00,  1.5052e+00,\n",
-      "          1.0231e+00,  2.3562e-02, -3.9516e-01, -1.1958e+00, -1.1450e+00],\n",
-      "        [-5.3217e-01, -9.4708e-01,  3.5960e-01,  3.1978e-01,  6.5693e-01,\n",
-      "          1.2438e+00, -3.4286e-01, -8.4294e-01,  8.6192e-02, -1.6249e+00],\n",
-      "        [-1.6462e+00, -2.8996e-01, -1.7270e-01,  3.9132e-01,  2.0564e-01,\n",
-      "          6.2459e-02,  6.9755e-01,  4.8864e-01,  2.0693e+00,  1.8460e+00],\n",
-      "        [ 4.8269e-01,  1.6141e+00,  6.9728e-01,  5.2733e-01,  7.5356e-01,\n",
-      "          1.0753e+00, -8.0632e-02,  1.7839e+00,  1.2324e-01, -1.1968e+00],\n",
-      "        [-1.3453e+00,  3.9696e-01, -6.9182e-01, -1.1625e+00,  8.5186e-01,\n",
-      "         -9.5603e-01,  2.0826e+00, -5.8598e-01, -4.8563e-01,  2.8007e-01],\n",
-      "        [-5.3753e-02,  8.5545e-01,  1.3453e+00,  2.2817e-01, -1.0763e+00,\n",
-      "          3.7051e-01, -7.3824e-01, -1.7190e+00,  1.2376e+00, -2.4513e-01]])\n",
+      "tensor([[-0.8354,  0.6986, -0.9240,  0.5699, -1.3718,  0.3678,  0.6918, -0.2276,\n",
+      "          1.2251,  0.6346],\n",
+      "        [-0.3056,  0.1768, -0.0742,  0.7742, -3.0012,  0.1911, -1.1961,  1.1823,\n",
+      "         -1.1420, -1.2182],\n",
+      "        [-0.2875,  0.4598, -0.9344, -1.3919, -0.3196,  0.0736,  1.2608, -0.3892,\n",
+      "         -0.0871,  0.5752],\n",
+      "        [ 0.8058, -0.0086, -1.1543,  0.6956,  0.2965, -1.2332, -0.3041, -1.0828,\n",
+      "          1.3678, -1.3688],\n",
+      "        [ 2.5530, -0.1216, -0.1476, -0.7269,  0.3183,  0.3691, -0.9889,  0.4616,\n",
+      "         -1.0836,  0.8698],\n",
+      "        [-0.0149,  0.5666, -0.8890, -1.5123,  0.2192, -0.3229, -0.8430,  1.9568,\n",
+      "          0.2830, -0.0110],\n",
+      "        [-0.6957, -0.7729,  0.6842,  0.3273, -0.3956, -1.4114,  1.0842, -1.6491,\n",
+      "         -0.4592,  0.6916],\n",
+      "        [ 2.4517,  2.2761,  1.2229,  0.7897, -0.8707, -0.8146,  0.1773,  0.6482,\n",
+      "          1.1529, -1.3428],\n",
+      "        [ 1.7447,  0.0232,  0.1639,  0.1289,  0.1756,  1.2685, -1.4111, -0.0483,\n",
+      "         -0.6080,  2.5827],\n",
+      "        [-0.7786, -0.0350, -0.2533,  1.4422,  0.6408, -0.6341, -0.4195,  0.1505,\n",
+      "          0.9281, -1.5950],\n",
+      "        [-1.3845, -0.8500, -1.3187, -0.9713, -0.1946, -1.5168,  0.1416, -1.9680,\n",
+      "          0.5069, -1.4657],\n",
+      "        [ 0.6479,  1.2840,  0.1235, -1.0165,  0.6948,  0.2286,  1.2465,  0.5508,\n",
+      "          1.4938, -0.5830],\n",
+      "        [-0.1754,  1.2461, -0.9551, -0.8802, -0.2196,  0.3452,  1.5842, -1.1822,\n",
+      "         -1.5783,  1.5120],\n",
+      "        [-2.0858,  0.1850,  0.6595, -0.1538, -1.9726, -1.4741,  0.3529, -0.2569,\n",
+      "         -0.0316, -1.0380]])\n",
       "AlexNet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 45,
    "id": "6e18f2fd",
    "metadata": {},
    "outputs": [
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 46,
    "id": "462666a2",
    "metadata": {},
    "outputs": [
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "317bf070",
    "metadata": {},
    "outputs": [
@@ -356,30 +356,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "4b53f229",
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[0;32m     16\u001b[0m model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m---> 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data, target \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;66;03m# Move tensors to GPU if CUDA is available\u001b[39;00m\n\u001b[0;32m     19\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m train_on_gpu:\n\u001b[0;32m     20\u001b[0m         data, target \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcuda(), target\u001b[38;5;241m.\u001b[39mcuda()\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:701\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    699\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    700\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 701\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    702\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    703\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    704\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    705\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    707\u001b[0m ):\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:757\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    755\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    756\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 757\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    758\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    759\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torchvision\\datasets\\cifar.py:119\u001b[0m, in \u001b[0;36mCIFAR10.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    116\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img)\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 119\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    122\u001b[0m     target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform(target)\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torchvision\\transforms\\transforms.py:277\u001b[0m, in \u001b[0;36mNormalize.forward\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, tensor: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    270\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    271\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    272\u001b[0m \u001b[38;5;124;03m        tensor (Tensor): Tensor image to be normalized.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[38;5;124;03m        Tensor: Normalized Tensor image.\u001b[39;00m\n\u001b[0;32m    276\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torchvision\\transforms\\functional.py:350\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tensor, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg should be Tensor Image. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tensor)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 350\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF_t\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torchvision\\transforms\\_functional_tensor.py:917\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    912\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    913\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtensor\u001b[38;5;241m.\u001b[39msize()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    914\u001b[0m     )\n\u001b[0;32m    916\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n\u001b[1;32m--> 917\u001b[0m     tensor \u001b[38;5;241m=\u001b[39m \u001b[43mtensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    919\u001b[0m dtype \u001b[38;5;241m=\u001b[39m tensor\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m    920\u001b[0m mean \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(mean, dtype\u001b[38;5;241m=\u001b[39mdtype, device\u001b[38;5;241m=\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 42.796791 \tValidation Loss: 36.322623\n",
+      "Validation loss decreased (inf --> 36.322623).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 33.357780 \tValidation Loss: 31.255297\n",
+      "Validation loss decreased (36.322623 --> 31.255297).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 29.678899 \tValidation Loss: 28.711525\n",
+      "Validation loss decreased (31.255297 --> 28.711525).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 27.450259 \tValidation Loss: 26.429781\n",
+      "Validation loss decreased (28.711525 --> 26.429781).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 25.614821 \tValidation Loss: 25.854407\n",
+      "Validation loss decreased (26.429781 --> 25.854407).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 24.085401 \tValidation Loss: 24.179177\n",
+      "Validation loss decreased (25.854407 --> 24.179177).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 22.817846 \tValidation Loss: 23.455681\n",
+      "Validation loss decreased (24.179177 --> 23.455681).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 21.797026 \tValidation Loss: 24.118613\n",
+      "Epoch: 8 \tTraining Loss: 20.884398 \tValidation Loss: 22.210912\n",
+      "Validation loss decreased (23.455681 --> 22.210912).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 20.100546 \tValidation Loss: 22.356711\n",
+      "Epoch: 10 \tTraining Loss: 19.340222 \tValidation Loss: 21.803377\n",
+      "Validation loss decreased (22.210912 --> 21.803377).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 18.627567 \tValidation Loss: 21.786751\n",
+      "Validation loss decreased (21.803377 --> 21.786751).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 17.961525 \tValidation Loss: 21.016253\n",
+      "Validation loss decreased (21.786751 --> 21.016253).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 17.340272 \tValidation Loss: 21.490398\n",
+      "Epoch: 14 \tTraining Loss: 16.757335 \tValidation Loss: 21.692361\n"
      ]
     }
    ],
@@ -389,7 +399,7 @@
     "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
     "optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n",
     "\n",
-    "n_epochs = 30  # number of epochs to train the model\n",
+    "n_epochs = 15  # number of epochs to train the model\n",
     "train_loss_list = []  # list to store loss to visualize\n",
     "valid_loss_min = np.Inf  # track change in validation loss\n",
     "\n",
@@ -442,7 +452,7 @@
     "        )\n",
     "    )\n",
     "\n",
-    "    # Save model if validation loss has decreased\n",
+    "    # Save model if validation loss has decreased \n",
     "    if valid_loss <= valid_loss_min:\n",
     "        print(\n",
     "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n",
@@ -458,7 +468,7 @@
    "id": "13e1df74",
    "metadata": {},
    "source": [
-    "Does overfit occur? If so, do an early stopping."
+    "Does overfit occur? If so, do an early stopping. Passage à num_epoch = 15 du fait d'overfitting."
    ]
   },
   {
@@ -469,7 +479,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -508,7 +518,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\eloua\\AppData\\Local\\Temp\\ipykernel_8492\\3291884398.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "C:\\Users\\eloua\\AppData\\Local\\Temp\\ipykernel_15020\\3291884398.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
       "  model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n"
      ]
     },
@@ -516,20 +526,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 21.756315\n",
+      "Test Loss: 20.684050\n",
       "\n",
-      "Test Accuracy of airplane: 73% (731/1000)\n",
-      "Test Accuracy of automobile: 78% (788/1000)\n",
-      "Test Accuracy of  bird: 53% (532/1000)\n",
-      "Test Accuracy of   cat: 46% (468/1000)\n",
-      "Test Accuracy of  deer: 55% (551/1000)\n",
-      "Test Accuracy of   dog: 41% (412/1000)\n",
-      "Test Accuracy of  frog: 71% (719/1000)\n",
-      "Test Accuracy of horse: 64% (648/1000)\n",
-      "Test Accuracy of  ship: 74% (744/1000)\n",
-      "Test Accuracy of truck: 66% (661/1000)\n",
+      "Test Accuracy of airplane: 64% (647/1000)\n",
+      "Test Accuracy of automobile: 73% (735/1000)\n",
+      "Test Accuracy of  bird: 55% (553/1000)\n",
+      "Test Accuracy of   cat: 50% (500/1000)\n",
+      "Test Accuracy of  deer: 59% (593/1000)\n",
+      "Test Accuracy of   dog: 44% (448/1000)\n",
+      "Test Accuracy of  frog: 69% (693/1000)\n",
+      "Test Accuracy of horse: 68% (684/1000)\n",
+      "Test Accuracy of  ship: 78% (782/1000)\n",
+      "Test Accuracy of truck: 73% (734/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 62% (6254/10000)\n"
+      "Test Accuracy (Overall): 63% (6369/10000)\n"
      ]
     }
    ],
@@ -623,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 28,
    "id": "67033ac0",
    "metadata": {},
    "outputs": [
@@ -642,6 +652,17 @@
       "  (dropout): Dropout(p=0.4, inplace=False)\n",
       ")\n"
      ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'train_on_gpu' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [28]\u001b[0m, in \u001b[0;36m<cell line: 50>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28mprint\u001b[39m(model1)\n\u001b[0;32m     49\u001b[0m \u001b[38;5;66;03m# move tensors to GPU if CUDA is available\u001b[39;00m\n\u001b[1;32m---> 50\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtrain_on_gpu\u001b[49m:\n\u001b[0;32m     51\u001b[0m     model1\u001b[38;5;241m.\u001b[39mcuda()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'train_on_gpu' is not defined"
+     ]
     }
    ],
    "source": [
@@ -706,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 17,
    "id": "95629205",
    "metadata": {},
    "outputs": [
@@ -714,52 +735,50 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 44.197613 \tValidation Loss: 40.215276\n",
-      "Validation loss decreased (inf --> 40.215276).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 38.467062 \tValidation Loss: 34.526591\n",
-      "Validation loss decreased (40.215276 --> 34.526591).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 33.661273 \tValidation Loss: 30.167926\n",
-      "Validation loss decreased (34.526591 --> 30.167926).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 30.869085 \tValidation Loss: 28.323083\n",
-      "Validation loss decreased (30.167926 --> 28.323083).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 28.970111 \tValidation Loss: 26.628171\n",
-      "Validation loss decreased (28.323083 --> 26.628171).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 27.452613 \tValidation Loss: 26.199421\n",
-      "Validation loss decreased (26.628171 --> 26.199421).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 25.914476 \tValidation Loss: 23.764141\n",
-      "Validation loss decreased (26.199421 --> 23.764141).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 24.249988 \tValidation Loss: 22.064054\n",
-      "Validation loss decreased (23.764141 --> 22.064054).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 22.873817 \tValidation Loss: 22.064090\n",
-      "Epoch: 9 \tTraining Loss: 21.494869 \tValidation Loss: 20.166262\n",
-      "Validation loss decreased (22.064054 --> 20.166262).  Saving model ...\n",
-      "Epoch: 10 \tTraining Loss: 20.376597 \tValidation Loss: 19.364416\n",
-      "Validation loss decreased (20.166262 --> 19.364416).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 19.276140 \tValidation Loss: 18.517585\n",
-      "Validation loss decreased (19.364416 --> 18.517585).  Saving model ...\n",
-      "Epoch: 12 \tTraining Loss: 18.297486 \tValidation Loss: 18.442464\n",
-      "Validation loss decreased (18.517585 --> 18.442464).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 17.443514 \tValidation Loss: 17.434145\n",
-      "Validation loss decreased (18.442464 --> 17.434145).  Saving model ...\n",
-      "Epoch: 14 \tTraining Loss: 16.590350 \tValidation Loss: 17.478173\n",
-      "Epoch: 15 \tTraining Loss: 15.774860 \tValidation Loss: 17.659180\n",
-      "Epoch: 16 \tTraining Loss: 14.950333 \tValidation Loss: 16.470274\n",
-      "Validation loss decreased (17.434145 --> 16.470274).  Saving model ...\n",
-      "Epoch: 17 \tTraining Loss: 14.307792 \tValidation Loss: 16.560596\n",
-      "Epoch: 18 \tTraining Loss: 13.616580 \tValidation Loss: 16.897359\n",
-      "Epoch: 19 \tTraining Loss: 12.822283 \tValidation Loss: 16.807405\n",
-      "Epoch: 20 \tTraining Loss: 12.265942 \tValidation Loss: 16.145765\n",
-      "Validation loss decreased (16.470274 --> 16.145765).  Saving model ...\n",
-      "Epoch: 21 \tTraining Loss: 11.622514 \tValidation Loss: 15.571411\n",
-      "Validation loss decreased (16.145765 --> 15.571411).  Saving model ...\n",
-      "Epoch: 22 \tTraining Loss: 10.992820 \tValidation Loss: 15.829255\n",
-      "Epoch: 23 \tTraining Loss: 10.491268 \tValidation Loss: 15.969115\n",
-      "Epoch: 24 \tTraining Loss: 9.982615 \tValidation Loss: 15.879691\n",
-      "Epoch: 25 \tTraining Loss: 9.484194 \tValidation Loss: 16.662651\n",
-      "Epoch: 26 \tTraining Loss: 8.875920 \tValidation Loss: 16.290887\n",
-      "Epoch: 27 \tTraining Loss: 8.446034 \tValidation Loss: 16.507190\n",
-      "Epoch: 28 \tTraining Loss: 7.938818 \tValidation Loss: 17.239120\n",
-      "Epoch: 29 \tTraining Loss: 7.549957 \tValidation Loss: 17.333169\n"
+      "Epoch: 0 \tTraining Loss: 45.781030 \tValidation Loss: 43.486057\n",
+      "Validation loss decreased (inf --> 43.486057).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 40.008026 \tValidation Loss: 36.381664\n",
+      "Validation loss decreased (43.486057 --> 36.381664).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 34.670090 \tValidation Loss: 31.741343\n",
+      "Validation loss decreased (36.381664 --> 31.741343).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 32.052780 \tValidation Loss: 30.128892\n",
+      "Validation loss decreased (31.741343 --> 30.128892).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 30.411507 \tValidation Loss: 28.104287\n",
+      "Validation loss decreased (30.128892 --> 28.104287).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 28.592401 \tValidation Loss: 26.537134\n",
+      "Validation loss decreased (28.104287 --> 26.537134).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 26.778925 \tValidation Loss: 24.482508\n",
+      "Validation loss decreased (26.537134 --> 24.482508).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 25.032638 \tValidation Loss: 23.202029\n",
+      "Validation loss decreased (24.482508 --> 23.202029).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 23.598022 \tValidation Loss: 22.966552\n",
+      "Validation loss decreased (23.202029 --> 22.966552).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 22.151841 \tValidation Loss: 20.537834\n",
+      "Validation loss decreased (22.966552 --> 20.537834).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 20.996790 \tValidation Loss: 20.152655\n",
+      "Validation loss decreased (20.537834 --> 20.152655).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 19.967560 \tValidation Loss: 19.665188\n",
+      "Validation loss decreased (20.152655 --> 19.665188).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 18.941463 \tValidation Loss: 18.266540\n",
+      "Validation loss decreased (19.665188 --> 18.266540).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 17.996472 \tValidation Loss: 17.686217\n",
+      "Validation loss decreased (18.266540 --> 17.686217).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 17.214306 \tValidation Loss: 17.209811\n",
+      "Validation loss decreased (17.686217 --> 17.209811).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 16.345385 \tValidation Loss: 16.916167\n",
+      "Validation loss decreased (17.209811 --> 16.916167).  Saving model ...\n",
+      "Epoch: 16 \tTraining Loss: 15.609089 \tValidation Loss: 16.564478\n",
+      "Validation loss decreased (16.916167 --> 16.564478).  Saving model ...\n",
+      "Epoch: 17 \tTraining Loss: 14.852868 \tValidation Loss: 17.794168\n",
+      "Epoch: 18 \tTraining Loss: 14.104541 \tValidation Loss: 16.003444\n",
+      "Validation loss decreased (16.564478 --> 16.003444).  Saving model ...\n",
+      "Epoch: 19 \tTraining Loss: 13.426449 \tValidation Loss: 15.940674\n",
+      "Validation loss decreased (16.003444 --> 15.940674).  Saving model ...\n",
+      "Epoch: 20 \tTraining Loss: 12.804823 \tValidation Loss: 16.247604\n",
+      "Epoch: 21 \tTraining Loss: 12.117133 \tValidation Loss: 16.469703\n",
+      "Epoch: 22 \tTraining Loss: 11.521068 \tValidation Loss: 16.257805\n",
+      "Epoch: 23 \tTraining Loss: 10.977840 \tValidation Loss: 17.273661\n",
+      "Epoch: 24 \tTraining Loss: 10.397445 \tValidation Loss: 16.175424\n"
      ]
     }
    ],
@@ -769,7 +788,7 @@
     "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
     "optimizer = optim.SGD(model1.parameters(), lr=0.01)  # specify optimizer\n",
     "\n",
-    "n_epochs = 30  # number of epochs to train the model\n",
+    "n_epochs = 25  # number of epochs to train the model\n",
     "train_loss_list = []  # list to store loss to visualize\n",
     "valid_loss_min = np.Inf  # track change in validation loss\n",
     "\n",
@@ -834,15 +853,23 @@
     "        valid_loss_min = valid_loss"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a64c9aa2",
+   "metadata": {},
+   "source": [
+    "Pour num_poch = 30, on observe toujours un overfitting à partir de l'époque 25 --> num_epoch = 25"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 18,
    "id": "b8383f00",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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bHp5NxaadYZckInJYCoI21rt7Jg9++QR2723khodms2WXTisVkcSmIIiBo/K789drj2d17Q6+/tgcGvZpjGMRSVwKghiZMDiXOy4byfSVddz2wkLcdY2BiCSmWJ4+mvIuP76QdZt28ru3l1Oc3YVvnTkk7JJERP6JgiDGbj1zCGvrd3LXW8soyu7MpccVhl2SiMinKAhizMy447KRVG7ezQ+eXUCfHp0ZPygn7LJERD6hPoI46Nghjb9MOp6SnK7c/GgZK6q3hV2SiMgnFARxktU5gweuP4GOHdK5/sHZ1GzbE3ZJIiKAgiCuirK78MD1pdRtb+D6B2exrl4XnIlI+BQEcTaysCd/unYMa+p2ct7vp/Ly/A1hlyQiKU5BEILTj+7Na986mUF53fjmE3P5/jPz2bFnX9hliUiKUhCEpDinC898bTzfOH0Qz35YwYV/eJ9F63WjOhGJPwVBiDLS0/j+547h8RvHsbOhkUv/NI37pq6iSSOdiUgcKQgSwIRBubx+68mcdnRvfvnqEq5/SGcViUj8KAgSRK+uHbln0vH84pLhzFxVx7n/M4V3P64OuywRSQEKggRiZkw6sT8v3XISOV07cf2Ds/nFKx+xZ19j2KWJSBJTECSgo/t058VbJvKl8f25//3VXHr3dFZUbw+7LBFJUjELAjPLNLNZZjbfzBab2c+C+dlm9paZLQ+ee8WqhvYsMyOdn188nHu/VErlll1c+If3eXZORdhliUgSiuURwR7gDHcfBYwGzjGzE4EfAZPdfQgwOZiWgzj72Hxev/UURhVl8b1n5vPTFxext1ED3YhI24lZEHjE/vaMjODhwMXAw8H8h4FLYlVDsuiTlcljXxnHV04awMMz1vDF+2ZSu11nFYlI24hpH4GZpZvZPKAaeMvdZwL57l4JEDz3Psi6N5lZmZmV1dTUxLLMdqFDehr/fsGx/O6q0cxft5mL/vA+Cyo2h12WiCSBmAaBuze6+2igEBhrZsNbse497l7q7qV5eXkxq7G9ueS4fjz39QmYGZ//ywyeU7+BiHxGcTlryN03A+8C5wBVZlYAEDzrZPlWGt4vi5dumcjxxb347jPzuf2lxeo3EJEj1qIgMLOuZpYWvD7KzC4ys4zDrJNnZj2D152Bs4ClwEvAdcHbrgNePMLaU1pOt048+pWx3DBxAA9NL+fa+2ZSp34DETkCLT0imAJkmlk/Imf6fBl46DDrFADvmNkCYDaRPoJXgDuAs81sOXB2MC1HoEN6Gv9x4bHcdeUo5q3bzEV/nKYb14lIq5n74W9wZmYfuvsYM/sm0Nndf2Nmc939uNiXCKWlpV5WVhaPj2q3FlZs4eZHy6jb0cAdl4/g0uMKwy5JREJmZnPcvfRw72vpEYGZ2Xjgi8CrwTwNfJ9ARhRm8dI3T2J0UU/+7an5/Pzlj9infgMRaYGWBsG3gR8DL7j7YjMbCLwTs6rkiOR268RjN47j+gklPDBtNZPun6V+AxE5rBY1DX1qhUincTd33xqbkv6ZmoZa79k5Fdz2wkLyunXir5OOZ3i/rLBLEpE4a9OmITP7m5n1MLOuwEfAx2b2/c9apMTO548v5NmvjafJncv/PJ0X5up6AxE5sJY2DR0bHAFcArwGFAOTYlWUtI2RhT15Wf0GInIYLQ2CjOC6gUuAF919L5H7BkmCa95vcO39ut5ARD6tpUHwV6Ac6ApMMbP+QNz6COSzyUhP4/aLhnHnFaP4cO1mLvzD+yys0PUGIhLRoiBw99+7ez93Py+4q+ga4PQY1yZt7PLjC3nuaxMA+PxfpvP8h+o3EJGWdxZnmdld++8GamZ3Ejk6kHZmRGEWL3/zJI4r7sl3ntZ9ikSk5U1DDwDbgCuDx1bgwVgVJbEVuU/RuE/dp0jjG4ikrpYGwSB3/6m7rwoePwMGxrIwia2M5vcp0vgGIimrpUGwy8xO2j9hZhOBXbEpSeLpsjGFnxrfQOMii6SelgbB14C7zazczMqBPwI3x6wqiavo8Q2+98x8bnthIbv3NoZdlojESUvPGpofDEI/EhgZ3HX0jJhWJnG1f3yDm08dyN9mruWKv8xgXf3OsMsSkTho1Qhl7r416h5D34lBPRKiDulp/Pjcodwz6XjK63ZwwR/e5x9Lq8IuS0Ri7LMMVWltVoUklH8Z1odXvnkS/Xp25oaHyvi/byylsUkXkoskq88SBPrNkMT653Tl+X+dwFWlRdz9zkom3a9TTEWS1SGDwMy2mdnWAzy2AX3jVKOEJDMjnV9/fiS/+fxI5qzZxPm/n0pZeX3YZYlIGztkELh7d3fvcYBHd3fXCGUp4srSIp7/1wlkZqRz9T0fcN/UVbR2HAsRSVyfpWnokMysyMzeMbMlZrbYzG4N5t9uZuvNbF7wOC9WNUjbGdY3i5duOYkzjunNL19dwjf+9iHbdu8NuywRaQMxCwJgH/Bddx8KnAh8w8yODZb91t1HB4/XYliDtKGszhn8ddLx/PjcY3hjcRUX/3EaSzfqJrQi7V3MgsDdK939w+D1NmAJ0C9WnyfxYWbcfOog/nbjOLbt2ccld0/jyVlr1VQk0o7F8ojgE2ZWAhwHzAxm3WJmC8zsATPrdZB1btp/t9Oampp4lCmtMG5gDq9+6ySOK+rFj55fyDX3fsCqmu1hlyUiR6DVg9e3+gPMugHvAb9y9+fNLB+oJXL66S+AAne/4VDb0OD1iaupyXmqbB3/+doS9uxr4punD+bmUwfRsUNc/sYQkUNo08HrP0MRGcBzwOPu/jyAu1e5e6O7NwH3AmNjWYPEVlqacc3YYiZ/51TOPjafO99axvm/n8qcNTrNVKS9iOVZQwbcDyxx97ui5hdEve1SYFGsapD46d0jk7u/MIb7rytlx559XP7nGfzk7wvZqjOLRBJeLK8FmAhMAhaa2bxg3m3ANWY2mkjTUDm6i2lSOXNoPicOzOGut5bx4LTVvLm4ip9fPIzPDetD5G8DEUk0Me8jaAvqI2ifFlRs5kfPLeSjyq2cfWw+P794GAVZncMuSyRlJEQfgaS2kYU9eemWidx23jFMXV7DWXe+x0PTVusGdiIJRkEgMdUhPY2bThnEW/92KqUl2dz+8kdc9ufpLKnUhWgiiUJBIHFRlN2Fh758Av9z9Wgq6ndy4R/e5zf/u1QjoYkkAAWBxI2ZcfHofrz9nVO55Lh+/OndlZzzuylMX1kbdmkiKU1BIHHXq2tH/vuKUTx+4zgc+MK9M/nBs/PZvLMh7NJEUpKCQEIzcXAub3z7FL526iCe+3A9Z931Hq8s2KD7FonEmYJAQpWZkc6Pzj2Gl26ZSN+enbnlb3O58eEyNmzeFXZpIilDQSAJYVjfLJ7/+gR+cv5Qpq+s4+y7dKqpSLwoCCRhdEhP48aTB/Lmv53yyamml/95usY8EIkxBYEknOhTTdfW7+SC30dONdV9i0RiQ0EgCWn/qaaTv3MqF4+OnGp68q/f4e53VrB9z76wyxNJKrrXkLQLi9Zv4bdvLWPy0mqyu3bk5lMGMml8f7p0jOV9E0Xat5bea0hBIO3KvHWb+e1by3hvWQ253TrytVMHce2J/cnMSA+7NJGEoyCQpFZWXs9v317GtBV19O7eiW+cPpirxxbRqYMCQWQ/BYGkhA9W1XHXW8uYtbqegqxMvnH6YK4sLdJQmSIoCCSFuDvTV9Zx55sf8+HazfTr2ZlvnTmYy8YUkpGuQJDUpSCQlOPuTFley11vfsz8ii3069mZG04awFUnFNGtkzqVJfUoCCRluTv/WFrNX99bxazyenpkduCLJ/bn+gkl5PfIDLs8kbhREIgAc9du4r6pq3l9USXpaZFrE7568kCO7tM97NJEYi70IDCzIuARoA/QBNzj7v9jZtnAU0AJkcHrr3T3TYfaloJAPqu1dTu5//1VPF1Wwa69jZx6VB43nTKQCYNyMLOwyxOJiUQIggKgwN0/NLPuwBzgEuB6oN7d7zCzHwG93P2Hh9qWgkDayqYdDTw+cw0PTV9D7fY9HFvQg5tOGcj5IwvUsSxJJ/Qg+KcPMnsR+GPwOM3dK4OweNfdjz7UugoCaWu79zby4rz13Dt1NSuqt1OQlckNEwdw9dgiumdmhF2eSJtIqCAwsxJgCjAcWOvuPaOWbXL3XgdY5ybgJoDi4uLj16xZE/M6JfU0NTnvLqvmnimr+GBVPd07deCaccVcP6GEvj07h12eyGeSMEFgZt2A94BfufvzZra5JUEQTUcEEg8LKjZz79TVvLawEgMuGFnAjScPZHi/rLBLEzkiCREEZpYBvAK84e53BfM+Rk1DksAqNu3kwWnlPDlrLTsaGpkwKIevnjKQ047KU8eytCuhB4FF/sc8TKRj+NtR8/8vUBfVWZzt7j841LYUBBKGLbv28sSstTw0rZyNW3dzVH43bjx5IBeP7qt7Gkm7kAhBcBIwFVhI5PRRgNuAmcDTQDGwFrjC3esPtS0FgYSpYV8TryzYwD1TVrF04zbyunfi+gklfHFcMT27dAy7PJGDCj0I2pKCQBKBuzNtRR33TF3FlGU1dM5I58rSQiaNL2Fw725hlyfyTxQEIjG0dONW7pu6mhfnrWdvozNxcA6TTuzPWUPz6aDrESRBKAhE4qB2+x6emr2Ov81cy/rNuyjIyuQLY4u5amwRvbvrvkYSLgWBSBw1NkVudPfIjHKmLq8lI904Z3gBk07szwklvXS2kYSipUGge/OKtIH0NOPsY/M5+9h8VtVs5/GZa3mmbB0vz9/AMX26c+2J/bn0uH501e2wJQHpiEAkRnY1NPLS/PU8MmMNizdspVunDlw+ph9fGNdfdz+VuFDTkEiCcHfmrtvMozPW8OqCShoamzg6vzsXjirggpF9KcntGnaJkqQUBCIJqG77Hl5dWMnL8zcwuzxy9/WRhVlcNKov548soCBL9zeStqMgEElwGzbv4tUFlbw0fwML128BYGxJNheOKuDcEQXkdusUcoXS3ikIRNqR1bU7eGX+Bl6av4Hl1dtJTzMmDMrhwlF9+dywPmR11q2xpfUUBCLt1NKNW3l5/gZenl/J2vqddExP46xje3NFaRGnDMkjPU2nokrLKAhE2jl3Z0HFFv4+bz0vzttA/Y4G+vTI5PLj+3HF8UXqZJbDUhCIJJGGfU38Y2kVT5dV8O7H1TQ5jB2QzZWlRZw3og9dOur6BPlnCgKRJFW1dTfPfVjBM2UVrK7dQdeO6Vw4qi9XlBYyplhXMcv/pyAQSXLuTtmaTTw9ex2vLqxkZ0MjA/O6cmVpEZeN6ad7HYmCQCSV7Nizj1cXVvJM2Tpml28izSJNR+cOL+Cc4X3I76FQSEUKApEUtbJmO3+fu57XF21kRfV2AMYU9/wkFIqyu4RcocSLgkBEWFG9jdcXbuT1RRv5qHIrACP6ZXHO8D6cO7wPA/M0oE4yUxCIyKesrdvJ64sqeX3RRuat2wzA0fndI6Ewog9H53dXR3OSURCIyEFVbtnF/y6KHCnMLq/HHQbkduX8EQWcN6KAoQUKhWQQehCY2QPABUC1uw8P5t0OfBWoCd52m7u/drhtKQhEYqdm2x7e/Ggjry/cyIxVdTQ2OQNyu3LeiD6cP6KvQqEdS4QgOAXYDjzSLAi2u/t/t2ZbCgKR+Kjbvoc3Flfx2sLKfwqF80YUcGxBD4VCOxL6CGXuPsXMSmK1fRFpezndOvGFccV8YVzxp0LhL++t4u53VlKS04XzRxYoFJJMTPsIgiB4pdkRwfXAVqAM+K67bzrIujcBNwEUFxcfv2bNmpjVKSKHdqAjhZKcLpwX9CkM66tQSEShNw0FRZTw6SDIB2oBB34BFLj7DYfbjpqGRBLHgUKhOLsL547ow/kjChjRL0uhkCASMghauqw5BYFIYqrf0cCbizfy2qKNTF9Ry74mp7BXZ84bUcC5w/swuqinQiFEofcRHIiZFbh7ZTB5KbAonp8vIm0ru2tHrh5bzNVji9m8s4E3P6ri9YWVPDhtNfdMWUXfrEzODZqPjivqSZrGUkhIsTxr6AngNCAXqAJ+GkyPJtI0VA7cHBUMB6UjApH2ZcvOvby9pIrXF1UyZVktDY1N9OmRyTnD+3D2sfmcUJJNxw5pYZeZ9BKiaaitKAhE2q+tu/fyjyXVvLawkneX1dCwr4nunTpwylF5nDm0N6cd3Zvsrh3DLjMpKQhEJOHsbNjHtBV1TF5SxeSl1dRs20OawZjiXpwxtDdnHpPPUfnd1K/QRhQEIpLQmpqcxRu28vaSKv6xtJqF67cAUNirM2ce05szh+YzbmA2nTqkh1xp+6UgEJF2ZeOW3bzzcTWTl1Tx/opadu9tokvHdE4anMspR+VxypA8inN0C+3WUBCISLu1e28jM1bW8faSKt79uIb1m3cB0D+nCycPyeXkIXmMH5RDj8yMkCtNbAoCEUkK7s7q2h1MXV7L1OU1zFhZx46GRtLTjOOKenLykDxOPiqXUYU9SdfpqZ+iIBCRpNSwr4m5azd9EgwL1m/BHXpkduCk4GjhtKPzKMjqHHapoVMQiEhK2LSjgWkra5myrIapy2up3LIbgGF9e3Dm0HzOHprP8H6peS8kBYGIpBx3Z0X1diYvjXQ6z1mziSaH/B6dOHNoPmcN7c2EQblkZqTGmUgKAhFJeXXb9/DOxzVMXlLFlGU17GhopHNGOicNyeXsofmcfkxv8rp3CrvMmFEQiIhE2bOvkQ9W1fP2R1W8vaSKyi27MYPRRT05a2g+Jw/JZVjfrKTqcFYQiIgchLvzUeVW3v6omslLq1hQEbmYrUdmB8YPymHi4FwmDs5lYG7Xdt23oCAQEWmh6m27mbGyjmkrapm2ou6T6xb69MhkwuAcJg6KBEOfrMyQK20dBYGIyBFwd9bU7WTaylqmr6hj+spaNu3cC8CgvK5MHJzLhEG5jB+YQ1aXxL6gTUEgItIGmpqcJRu3fnK0MGt1Pbv2NmIGR+d3Z+yAbE4oyWbsgGzyeyTWEYOCQEQkBvZf0DZzdT2zVtfz4dpN7GxoBCK3wDihJJuxJdmcMCCbkpwuofYxJOQIZSIi7V3HDmmMG5jDuIE5AOxtbOKjDVuZtbqeWeX1TF5SxbNzKgDI694pEgolvRg7IIdj+nRPyFHadEQgItKGmpqclTXbmbm6ntnl9cxeXc+G4GrnrM4ZjBuQzYRBOYwflBvzsRd0RCAiEoK0NGNIfneG5Hfn2hP7A1CxaSezVtfzwao6pq+s482PqgDI7daRcQNzIsEwMIcBIZ2uqiAQEYmxwl5dKOzVhcvGFAKwrn4nM1bWMWNV5KykVxdEhm7v0yOT8YNyIo+BORRlx2f8hZgFgZk9AFwAVLv78GBeNvAUUEJk8Por3X1TrGoQEUlERdldKMruwpUnFH1ym+0ZwdHClGU1vDB3PRAZre03l49kwuDcmNYTyyOCh4A/Ao9EzfsRMNnd7zCzHwXTP4xhDSIiCc3MGJjXjYF53fjiuP64O8urtzN9RS0zVtXF5SK2mHYWm1kJ8ErUEcHHwGnuXmlmBcC77n704bajzmIRkdZraWdxWjyKiZLv7pUAwXPvg73RzG4yszIzK6upqYlbgSIiqSbeQdBi7n6Pu5e6e2leXl7Y5YiIJK14B0FV0CRE8Fwd588XEZFm4h0ELwHXBa+vA16M8+eLiEgzMQsCM3sCmAEcbWYVZvYV4A7gbDNbDpwdTIuISIhidvqou19zkEVnxuozRUSk9RK2s1hEROJDQSAikuLaxd1HzawGWHOEq+cCtW1YTnuTyvuvfU9dqbz/0fve390Pe/59uwiCz8LMylpyZV2ySuX9176n5r5Dau//key7moZERFKcgkBEJMWlQhDcE3YBIUvl/de+p65U3v9W73vS9xGIiMihpcIRgYiIHIKCQEQkxSV1EJjZOWb2sZmtCEZESxlmVm5mC81snpkl/ag+ZvaAmVWb2aKoedlm9paZLQ+ee4VZY6wcZN9vN7P1wfc/z8zOC7PGWDGzIjN7x8yWmNliM7s1mJ8q3/3B9r9V33/S9hGYWTqwjMjN7SqA2cA17v5RqIXFiZmVA6XunhIX1ZjZKcB24JGoEfF+A9RHDY3ay92TbmjUg+z77cB2d//vMGuLteB29gXu/qGZdQfmAJcA15Ma3/3B9v9KWvH9J/MRwVhghbuvcvcG4Eng4pBrkhhx9ylAfbPZFwMPB68fJvIfJOkcZN9TgrtXuvuHwettwBKgH6nz3R9s/1slmYOgH7AuarqCI/gBtWMOvGlmc8zsprCLCUmLh0ZNUreY2YKg6Sgpm0aiBWOkHwfMJAW/+2b7D634/pM5COwA85KzHezAJrr7GOBc4BtB84Gkjj8Dg4DRQCVwZ6jVxJiZdQOeA77t7lvDrifeDrD/rfr+kzkIKoCiqOlCYENItcSdu28InquBF4g0laWalB0a1d2r3L3R3ZuAe0ni79/MMoj8Enzc3Z8PZqfMd3+g/W/t95/MQTAbGGJmA8ysI3A1kaEyk56ZdQ06jjCzrsC/AIsOvVZSStmhUff/EgxcSpJ+/2ZmwP3AEne/K2pRSnz3B9v/1n7/SXvWEEBwytTvgHTgAXf/VbgVxYeZDSRyFACRUej+luz7HgyNehqRW/BWAT8F/g48DRQDa4Er3D3pOlUPsu+nEWkWcKAcuHl/m3kyMbOTgKnAQqApmH0bkXbyVPjuD7b/19CK7z+pg0BERA4vmZuGRESkBRQEIiIpTkEgIpLiFAQiIilOQSAikuIUBCKAmTVG3alxXlverdbMSqLvDCqSaDqEXYBIgtjl7qPDLkIkDDoiEDmEYFyHX5vZrOAxOJjf38wmBzf1mmxmxcH8fDN7wczmB48JwabSzeze4J7xb5pZ59B2SqQZBYFIROdmTUNXRS3b6u5jgT8SuVKd4PUj7j4SeBz4fTD/98B77j4KGAMsDuYPAe5292HAZuDymO6NSCvoymIRwMy2u3u3A8wvB85w91XBzb02unuOmdUSGRBkbzC/0t1zzawGKHT3PVHbKAHecvchwfQPgQx3/2Ucdk3ksHREIHJ4fpDXB3vPgeyJet2I+uckgSgIRA7vqqjnGcHr6UTuaAvwReD94PVk4OsQGS7VzHrEq0iRI6W/SkQiOpvZvKjp/3X3/aeQdjKzmUT+cLommPct4AEz+z5QA3w5mH8rcI+ZfYXIX/5fJzIwiEjCUh+ByCEEfQSl7l4bdi0isaKmIRGRFKcjAhGRFKcjAhGRFKcgEBFJcQoCEZEUpyAQEUlxCgIRkRT3/wDk6F0lVVyVJgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -880,10 +907,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "ef623c26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'model1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 12>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m     os\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemp.p\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m size\n\u001b[1;32m---> 12\u001b[0m print_size_of_model(\u001b[43mmodel1\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfp32\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'model1' is not defined"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -896,7 +935,7 @@
     "    return size\n",
     "\n",
     "\n",
-    "#print_size_of_model(model1, \"fp32\")"
+    "print_size_of_model(model1, \"fp32\")"
    ]
   },
   {
@@ -1185,7 +1224,7 @@
     },
     {
      "data": {
-      "image/png": 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uRqjYdcngFHSPojXpWIDyYmSPcD7Jmwe1FS2RGbhct/zg06c8e9ogcWbY3zONg9KsYiLNA5Jm5ikyzhFrHHEeSSkxjyecbzPL5YEYAsYq4cDXLXXjubze0NSOulFDUmqW0LiZDy/BO8+7h4kQ0YQqldJfIpTjO4OQpYr7OHMtVRrN+IN6LIvu3Ow+JOOPUkKg/Ejp7LmM0fuirCq7hEzW2sUOggQkGQ0dMBhRdCJlT54EjJizt/x7Ht8xRsyAM+BY9k/eEbZYYjbY7G3k7LGsUarYcqSXJODvfS+z3OIzuWq5DIzRo9lYuwT5i2dADSrleDElmLOhlSqPzRl6ib1KZlg5uL7a8OmHl1xfVhB7+ru37G9fEeaAoHhcmGc1ivHE6dgjrsE5r55vnPBOj8ZpmjBVh8PQVBVVXXN5taWpEkw7hknANUpGiJEYAkkSF00grQzvDjlmNpYYhbBUjcqRmPIJo4mKtYUkshRXl3UpSUTKKEGp8pRYcUmUCokDWJyY6Gmr9WYF0qWUWeOsT0gVzjnwTmEsDQRIRsM5Z96TR1zSdkq9VXfAGTbRhxW9gFiK+hTD008kKdct4RFAej4GyuNcYc7vXp77jYyuxIHFoRZvLXh35iiSzj5QjxaT4ZpzHOSc59OPnvLRB2saO5KmA3Hs2b/9OafDnmRrYpjoh4kQEyKO8XBDmANztPjKIcZjrKduAEnM80jtHMZWeO/p2gqTBsbTpBCKtVTVwBBn5pBXOympde0hrhreHfReqhNQL7gkVVgweqxak7DGLaU3g8mxsyVJzHFnqU1rAmKMwWPAlurR2cdaU5hROS7MxNiSaKYU8823efPngqExpOw9z4iEvvO3Pb5zic9lYyxONlMql/2kPEFHlGKI+fwuQDbZK0nK2a09x4Y5kykwgnmML5pvXsfypzGPvJxdMuHymoXwIvlmWAMuG2gUPZadNWzXLT/49BkvntUwH/XonQce3r7i4e6eiCfGPeNpz/E0MAVHSolpOGqGKIKranA1zlX4ekOYB3b372jbnqvLS4I9cZp3hHYFrs1HYiRMA33f57sb6LpLqtUKiZGNOSGrC256h0kGsRqoKzpQvJ8jxW/eI0mZ2V1uVcokFKNZ+1LvL4dyYaYnPYvLyWcykGjyGa3e0CApYkiIcWDqJdYWk5nzGZorjkokEdK3m+J3z5rzp02iyHp85CPLnwGrsU22rlI2I9d9z8SGEgnroW9ydq17M39gtVYKwFL+KPjVshOXqDq7xseYIuBtPopztuhKfd5arrcdP/rBS64vPXP/QBp2OIlMpz3Hu1f0h3smMZz2txx3d/RTYjYXeGeJYSSOJ5qqxcaI8ZFuVeFMYppHpnGmroXxtKPfnajbLe00aWrgGuLwwOl0IgQhxsDYH1i3FU+ef0zdrXCuYlXPNNun3PaOfoyQzpRa9UCCcQpCxRRze0EJ2rNvNHYxVCVIuMWQ1U5DPlIsGJfJzecY2jmrHhE1Uk1CK32PHKumFJEouRSYE8sMFZEmUgzvyRDzh1drV5IXsHgtUOJrEs1Rc6yqDJVshEha0HkNnM3ZuPJ9M3lHkWudj6EYPY1KkmMW+ADswgk8/5m3hm5qNdvlOFfjXbcNn330hMuNZT49MPcHXBqJYeD+7Vd8/uM/Yb9/wDQXnIaB/nQiUWGqmeQ9cQ6EKeHsBClSuxqLYR52hHnEW8fYH7FSM08BF3qmGBmPd8x49rdfMPY9yVRY50lhpq8cKQbWmw3t5hLjjqwu4Nn6Ke/wHE9Tvs+l9CfntVFaDdY+umdka8txMsZo5SUfqSIJm86l1oXqpla7bPZSBTeANyC5aqNMnEIsFkQCEJf1swaSM9j3BmhLzkSNNjVJ/hBmgYRNfo7kpCHXPNHjxJiElYjJWBc5/ihGIVScG5WyrZYDvcShegIs6bdWHB6BwKUSwOMPbZbERv8pOCx17fnw2QVPrmvCsGPsD7hwJKbA3ds3/Pzzn/Pl257T4Ui3SlB3ehTZRhfEGEKYkaSVlKZd0dRrvIPT6QRA09bM08w4JXArwjQzzTtOuwemKNx+/Yb+tMNXNQZoKs/shdVqjavWpPSArQ7M08jmSeTF9iXvbMfuOKqhiR6ThRH0jXpuAWThXPUQwRKx2DOJ1qDULiktGMsKLMez1ujTOfk0+VDPtzklQ8rHjDLiR11XV2GNwzqLM9W3mtZ3hG8MEa8QijlnV+VDFoNfqP/nz8P5IFbPmYxmX+X7S1LyOKsWFlA23/XlmLYlUJfzTTamGGLu7iv1w/y98jCoR9iuGp486TDxxNgfMXEmTUdeffElX3zxBe9uHjjGhiPXyHyikR7rKmxV49pLwjhQ100GqKFuOtrGM04DIh5EO/JIAYwlhZF5OhHixDDODMPAnBzTLBz7A0YSYZpo647m4sjVs88wJjL3O477Hf3pgesPJp5cfY+UGnanIfegZPaNVcy4QGHL2ZBKEpIrSdZQkeP6AmeZM+G31PT1NKPEM4szWNaxREOiWXvKGT8iSJp1baJyJZGEe5TQ/gMNkVw5yZnycszmn6ZzdaTgfqV+qxduc5HdgXHZyf8STYpz5lbOFvMIcC7vZbIxl+8aWMgWJt8wbUIic+NM9rD65M265eOPrlh3ifk4aNIlE1988QX//k/+ksMo7E6B25sH9vs9zy4d3//4BVXlcL4Bb2hcgxPL6XjA+jVN0+TwQGGaaei1FOc7wjzQ90fGfkdMCecrXRznSDEy9CdiskBNtd5yHIWbm9e8fP6M9eYajgeODw+E+S95geNi+ynDbIlJDTCJoRTiyoZcEAtHNkzLnCIxBkrrmy3xkC0l1XNcWUquGbT8hiUoZyBqsimZVpdmrbkvUJ6gjLIIEuB9GSKop19qn8uHXUhcLKU6YoYIyufKyYXxy8l6hnbOcdy5zkredNngJWS7PO9EiyGQX8godf8xhUn/NN/I/IwxNLXjkw+uuNx65uGoWXo8cXx4xy+++IpDn/jLH/85P/78K/pRMLbig+s1z58+4crOBDyVOVFVNfMwkWa4fvYM5w0xBJq6wVhhMhZfdRhrmYMgHJiGPQlHEK1LpzBggHmK3B4nxBqO8YTYt9zfvuP23Rt+5/f/kG69ISYYh4Hbr3/CtbVcbz5hnDVT1fbepEXWEs8t9iNnjzgLkwRiDHijEIJBkwqx7tEvlRMIMIkC9ZT2gtIQXuh2ul4+J7TqhBQiy20E4jKZ+X0ZYunTLYBqRuULyaA0oC+O0tilGqK+7UxUPZ/FJf8tcWZaDFBSVAJraWgXm08KfYOY2b/O2swk5pHRFxaL0uetNXhn+ejFFU+uKq2EhIQJR8LxHa9+/je8uzvyf/+X/x/+1Z/+nKZ5wnj8miiJYfiA39sd8GJxjaftnrJuK/oobD/8iMunz5jDxDyPSBgZx4m6aVHepqPB0R9fMw8HpdKHxBzgdDgwTRNvHwZe3QWiC1j2vHr3ju9/+BwxHd3f/pwf/OAz6qYhJKE/nnBvPufJJyuuN0+5P4zL51bPWO4B+RQooLXFOajyMWks2kxvHuEQvwTmiinpiVmqO4KlLHDBLPXI9/jKazKkv70wtYyYX+EPf52eFYkLdvgNBobkWKEYI4UBbM+9DcXUirtDn2tz8XyJ5/Rja+ad1LAXQyQtwahmcxqfkLSP4nFiZpZypO7k2js+ennFxx+sQVQRwRCReeB03PP65oE//fFP+a//+Mf86Hf/Bc3mJV/8+N8wS+Lu/i1fffk18/UVF9cdHzwXGjPSXV/jmw3GJLw1uKbhFCJVXeG8IyUhCnhleuFszfHwwJQOHE+R+/sbjocj94fI3SkyhKOSfW/gy7d3/MEPjrSNYbOquL7eYo1gfMs8zRzvvmL7tGVqao7DTOmpiSh+b79hWIKR3BBfteo5U1QKmhit4f0SDSznmBij9fviVM780MdF2lIifVzyAGMKt0DOm+MfbIiilCbMuS+keL5z2e5MIdAynGQsKWdcmZJUuGlGtMFGpOxIe4ZkRHshjDMk6zM4bvKHRU+PZPSIE1G+nX1sibnrzGhf7rPrLS+frUECcZqQOGuCMh958+Yr/vwv/4p/9W//GF+tqNIOI54X3/8tdjevOE3vkOjo+5EPnh+x4zsO05btkxpbdcpWTwZJM2Ic3lcoMSMxDSMhRMDRrtbEOHH75pa3t3t2uyO3+8DtGJliovYtvnKYODGFwJ/9zZdcbRo2HXTNx4SUSFLh/CXzMDMeXnO5+pg5WKY56L0vGbK1lLarYi7GfKOPElDMUaVHbGa8Zzez1KE1Gy+kkVi8LuQ2jiLVkp0RaWldXVo14NzU9g82RHTz2BK/ZgjGWq23PNp8aLeY1l6tiZCbjEwGTJMhp2QzJK1VGuOw1mWGDedMDoNJekSoYenRLSKkoDdojgphFCetchpqhMYari9WfPzhBc5MxCCZuNDD+MBweMef/PGf8K//+EvGuMXakf7hjuHN50QMYz+wbVd89r3P8By4WlmG0HD54gestk+p2o6UInYaGUdL29bUtScmYR5Hah8Zh0TVbuiPO3zdcXX1lK/fzYTYM4tjCoGmWnH95CPqtuLw8JrmdMtuSvzk52/YtpanV2s2240ez/t7nK8wR8+2eeB6/ZR3DwoYpxIXZyZOISMsyUL2csa57DXV4BaEzJX7v6z6o9Cp8DfJaWvC8SgUQ0hoqfGbdYWz8f7DDdEYoM7ZvdL1jdH2Qq3bqi+UpJBBxv0R6/XDSFIPSd6RTnLfi9bcrHVY585ObQl8DcZENWSJC6wQ83uI0Y2QSkzCeaN0Tc2zJxs+ernGpgPTacTUHfPUk4YDbn7g9asveHcPf/hP/3u8uD3wF3/xZ7x7+3MqEzFJuFhd8ge/+zt88tE1K39B1665eP49NhdPqesV1jlijBijx16uPTDPM95aJB6pTGS2gvE1w87SVfD9T1/yuYGZAzdDIMWeMN2AeK2wOHBGuDv0vLrZ89ntHU3j8e2WlIT+eFDvu/NsriuebLfcH2fFeiFjnAnnvLJlHmGAKg1jM60sA+P5+FyY7GQmTi7blTUpSYcUtrxhgdk0TrcUXmSJHx9ls+/BECkYoRqSyVmscuAsNsMpEDHmUQOOLcFufGQsLlPkvcI9jqXwnjgTXbOpZRKs/jxKuXmFUZMrMEl1XrxzNHXFdt3x8tmG6wuPTPccd7cY1+KMYzwdkGEH4w339yN/9Ef/Hahavnp7y0Uz89Ofd9ze3PPk8oIPn1/yydOG7arj+nLLavuUbnNF3axyVmi0vuwtiHr4lAKzt4RxYrSeruuIYcJbQ9u1HHcDlxc1L59f453l5iQcTgf2DzeaEIhwQJgi+Ch8fXPgzbsdL15cE/s7XLUmGhjHPudmjsunDU3VcbOfGeegpN+Ftl9jrFswv+LPNDbPcXe21HN5T/+OUpQsUk5I9XlJwKS09L2IsSydjOVYXsq7vwq8+c6kB8GZuGRiBTQu0AC5n6WU+fTiykWxeC79dwY+BQSHsYYoSZuz8u+kwlYwKBhqchKUHhXwRBvCxRnMI42StvY8veq4WIFMDxxvvmI4Dfj1FbWrGI73mHDg+OZLrJlZrQxjmLjuhH/0vWtebAG+x/X1FfuHO1ZtxfOXH7PdrOi2z6jbTW4PyKWtTDwI06glLknKqzSw2awYBsFXuQne3iHGkeLI1dUVQz/w2bMN+5Pn9f2e4zAQovYur5uaJ5sVJMMvvnzLDz+7pl3DNAeabkswlpHIOI5YV3F5/YyPn6652Rn2x8g8Fzw3V6hMhmtM5gtmCboz7JVIMWNp1oCzqgIh2r9TsuHi5IpGEMu6n1EVpNDN9Mz/FSHir1Frlojkum6MhU+jjVIWiLbk1Db39iYK72YhMaDN3ZaAkaTeMWaSpzEEsRjjMqdRHXyW+VnAUzXGXMzPjfzWQuU9Ty/VE246IU339Lu3HO6+ZhoDnWtxvmGeZ5iOvHn1U45Hw1V9xXr1FMTgHay7hrpZY6zw/PkTrq+esF03tOsLuvUWXIWkQJhH4jwwj4GYTMYFI77uqJ3D1yumOSGuoQozdbNie3XNfrdj/3BDsxrZ7/a8jBNXF1ueXbZ89XbHOE90tef6Ys2Tqy2GRAo9b9685uOPPXMUUhxpt8/1KB/3hHEkhYnrZy/5+OkVh9WW1/c9x3HWe2lyi7w59+uYnMDoiZWygJOGRDY3YuUmQcrpVmI/A+CstopIIsZZr0uU2GutW+C6ohP0ngxRd05WoSDFpBmUsWgUGCkIvl5oRHIgqzT27CVFvZtelvZhJJlzbKhVmSVmIWVcUbO+QrkiowEqBGpxdUVXe7arhsuLhraOxPHAfLyhv/+a8XDDaQj4zUuqOAOG6bRDUsfq4inEARNPrNZbsOpp66x+dXmxZbOqqKqa1eYKV7eEeWAajsxzYBqPTGNPioKrPG1dUbc1MTnmqQcjNF1LCA0xJSrvWK9qhqOjrhzXT54gYUcd4Gq14ulFw/400NYtq82aVVtB6okz7O92HLcrXNUS5xnXrMCvGPoTb19/pW2qkrh8ZmmrhqeXDbJ3THNm15R7twRB557uYEvFTCAKyZ6brIqzWYQDyKwna0BUS0dkzt16pXVUk9PCgH+vMWJMRU7NQCZilopIoZinnKCdEwzAKCwQHwHi5ULLPrHo862U+owac1GWfQwFlBvhcuOUd46urdmsGxofMeFEGA7M/ZF57BlOdzzsJprrE816gyERxp6LZ7+Fq9dMw4EwHTBVwGLxXjl7m80Fq9ZBnFg9/YCqrQnTwHH/gKs6xESmeWYcT6QY6PwWW60RLLZucMlz8+ZrpmFH0zY4XyOiZb26qZmmkadPLxkPG+a7d0SxdE1D16ypmpZu1VFXjnG/Z5Qj/emB27fC1bOXCJ5qHDDi6E9H7t/dQphp2hVV3dGsL+iaxIuLituDYxyzAFT+Q0HmvBY5Bi8FBpJkxVpZuiZFIBlZFDukiDRlfMf5msokXKk9l27BUlB8X60CijdXSxB77vxXIDmlHNSmsBybhZ5fLqxsqAI8m/waJgMBWjSRhUWSklm6Sb8BlKKxTlNZrjc1XWvpGkdbRYgH5tOO4XhHf7hlntVwjkeh39+xvbzCEHCuoV5/hKs3yN2XnA6vmYcjURzGrVlfPGe7qbDxSLO6olmtCXPQo7becBhPHHZ75kmIscYaR20qxhCYzUBtHO32gmv/fb76/Gc87PZcbDSM0LBKqCoLRF58+BmH/R1TmDRMMSqQ6QSYZtJ0TxwfIA7s7ie891TNBt/sSTFxOh4Yxom7+wd29+9YrTdEDHUMVO2a603LzhjlMxboK2/qlB2W6O4mnz3McyBK1Kzb+UdJRwa/SRk4z+EWKmssGCRaEsqNlBQy7PO+AG0MyZ4NMYv4KUhjtAiuHixj7RkQLZ/PLrSh4rbNYpSFR5cyHFB093RXFdqYxqal9VFQWd6YIl0VadkT9geGw47T/o7+pA1PTV0x9nt29yfq7gueffgpxITvnuPqNWIMru4w1QVxeMUUeurWs1nVND6RcKy3l4zjRDAVd8eBN2/ueTgGjkPkOASOh4kkico9kOKAIeAtXFxu+OjjD3n50cc0zhP6B6qmRUQYhgdSjISQqLuGp89e8vbNlwsMFoY9QY4konrrFKidx4ow9kd8s2IaDoh4Tqee4/FAU10ynA4cH95l2ttzEhV4T9vUCJZhCtpGyjnhSNlNLhBPRipUScwupcOzIppk8SmTOw1L3blUZBSum4eeaZ6om4ambt6fIZbqxkLHF7Og78a6TH5MC760SFVkEqz2v2YVreJNl9fX+E/tW/srzsQISyxPFMEZwVnDRQdPugN+OjL0J/rjjtPxyDiMTOMREKKHEGfub17j2yeEEKmrjphqqnZDmE5gLM431O0FsT/gjNA2Fm8S9eUzknEEt+bVm7e8evWa+yN8+fbA/e09u/s7jscdVVUDhjANYKBbbdhsOn76i3c8v97yW997wWcfPWez3eKZmfpLxv6IM4l57rm8fsJxd8M0DzibkDAyJNEGJrFYW+G8xTpHCloEiCEQ5cjxeGToj4RVzdQf2N2/xjnVOsTXiNSqVlm6+R5Vr4rjKPFfYS8Za3ECMSt5FEh8IX4X1KOEZVKwjLJOGRFa4v1vf3znEl+K8xJ0ltYAiq2g4LagbFxDIceVsLjQxjPib8iVElO2phpl2ZZJ+2G1WyycM2YB6x3rzvKk3kF/4jhOTKcbjrs3HPcnxhBzMA3WXhBS5NT3DEOvKmLWYHyLq2qm8cA8nRSAbjf46URdN9Qe6qrBVg2T7Xhz+8DPPv+Krx8Cf/2zV7z+6guODzdIHBlPR+1gM4b+dFLpOGvZXFzx6fd+QD+MvH79isPv/oAffPKCysLTDz7ldDpwOvyCFCJ1vWK97pjudqRpJMWRlEF+7xps09G2LcbVjP0eiQkxkaF/4HTq6ccJQ2Tsd0gacb4GY7i0gt1Ui+5hETE1UnQdzVnUVNWmNO7OILSRgPYWnQ/XwnZUOC2eVTO06x5B+5ibdoWrq4W5834MEcHKxKIxYysy/xqJkpnSuUknN9xLFgqy8givN0XbRkHUJaEyLPo3ZhF2EsTEbIiqpACw7RwvLmasOILZEOWOMEemyXIchCnAqrUcbl8zjjP3dw+ECGEe1VvbjrruSGlmnibwK9r1iuHwNdZ1XD37GO8srtlyvxuY65afff45n78+8O//5C/46vOfkOYRYx1GhBAm0pSwxnDqTwxDzxxHbu++5v7mKz757IdcX2yY9q8J4++zqQ1PLldcP33J4eEtYd4xTz3r7SX3t18Ro2pie9dQry41kbOCrVa07Ya26YCAWEff9xxPE2EOkGamYU9/PJBEWT6EiU2YMNvvUbkttnKMGIZAJn7oSoUQsnKErkB6NOpDY3khz5NgThFiUJim8jluLGZSqi1gvcFLpYlPel/JiggSZrAqn1HoQYYsK5KzImPORfAkSjNS3T+V+l2qPcZq430pcsLCSwSzVBjKXBdNlrTrzpDoB21WMvOBeHrN6eEV97cP3O8HrG9pzMjh/h33uwMPu4C1HcMw46qOFGeca5ingTD1Wu+OM9iWJy9estl0+LohRMOhH/n5L/6WP//Ln/I3n7/i8x//MQ/37xARVt2WqlplNKEgA4FxPhLijEueh93M8Jc7Xjy5ZPrwI37c1Xxw3TL3K55cPWV79ZyhPxLjTLdasdlecTxAjAnvW3xVq75QSljncXWLpULSxCwwBeHYzxqTjSfi5DF+zfF4AnOnDO4UWaeEbH7IIGvmqDNdtBswcxmlZLm50Sx3+qmDMQuJVpJADIQwYwx4PN5kEfnSRQnEfISnxx2F78MQgaUhPiXRHWPio+PfZFWA8/AfsvBR6ZM1xmbVgAKKakVk8bJGd+RCFcundPl3YefMwTJFhxfBzYF5OHK4f8XN61uOs+fjTz4h9V8xnE7c72/pg8G4Dl9vNUs3Na5qGYcebKWSHKbmycVLLi7XmHCPc47TCO92Iz/721/w47/8MV999SXjcUcIMzEGDNoPLWIIMZFioO8fmLMkSYwJ29SEaHn77i1VZRXIn55i5QKH0NQ13hmG04AzwubiguP+DueUxeNsBSSsA+uanN06fL1h3O+ZAxxOA+tGGIYjm9CArZHZIvud1vC9x1c3rL3Hdp8xm5YJGJNkJQZdN82Gy1rn9TEsx6rNvE5JPl+T5axFWWrQ2bFIcS+SOaSPRmr8QwzRGJPjDotIpuln4UmfY0PFpMjZNDiSNuSIgD33RiwXKSq3UdRwS/XF5ePAWLscf7qrDEU2o/ZGP5prwHkV3gxC223pGst+NzL3R3Z3D/ShZnWh0sJq6E7hk6alCRvCaGibmtVmo2C7cRhXE4whuRV3D/cc9tqZF7PSljGGOUyc+qOumrWkELIRRrzzhBhwTuO8cZ457W44dSsO03OSWzMFwbtEu7pgGGfGcWK1WnH19EP604CvHHW7wriKFHqsd8pzDJOqelnPGKAfJjZVVqCIgXmcmeZAmO/ZPdxxuF1zullxffVjti8+Zn7ynzGkDSGey3Wg7KckojF0Ob9Ee5gtBiMq0lRXFd55FiDRiJKTM6K3SEE/gmzEvEdDrKpa66RiIekgGW9AGeFmkbJIGWcqHDSzcBhLUVzZMykLOBZ4xmQxcWvObZLGZDHy7EWXGqatgQriHTb0GNviu0tc0+rwmTDw8LDn/v6EVODngLUVMUxZmyYfL75i2z2haRTmMSS6doXxNad+Rz9MiN1yf/9Af9wzjiPGWEKIxDjj/Jz7iCFJzCKdiUBAUBY52TsOM5iqY46JcTiR1lfMc6BuGlarNX1vSann6YsXPNyfmOeBGAe8gXnsSWGkaVqadss8PIBVbFAkEQJMITEMA3Z1ScpNW+MwsRMwMiPBE0PPii1p+99lFr8YjzVnRHDJfwUksxqtFM3wfGzbDLuVLNpYnCvVlnNiSTHHX5E5f7sYybf9gsnSFjZ3gzmnczmsxVnBGe0oizEwx0iIWa6tpCqSs+YohJAIQSWEJQYkzjoaIc0gIX/oCJkVnmvweAd15fBOqOINdbpFJDHNgb4/8vBwTwo9p/2Ru9s9+9PMFBPzNOoxY6vcvCQ437DaXNBmABjXUNcNxlUEccRkcb7jB7/9Bzz56PeYQmQO4yIF532DczUhzEzzmCcv6bpoAmoY55lhGpljwNYbnG9IAofDkXGatP3WGHxd0a1WyuqeerabGkcghhlXVbiqxvhGfZNzqGiy5XhUtYl+Sgxj4mF/IswTu/2e0+nAPA9MU89wOhKSEKRjfvgZPrx5JE6gQqApU76SmPzcrN4hKjRfKmOJwiH4JitcIKtKFKC75NdyDr/+4YZYCAdo4Cx5joY999Rakz1kJg1GKVDN49lv5GxZA9uwKBMEkDkLYOavbMjGCN6V/lr9gJVR452Hiel04Lh/YLfbqzecR06nnofdgX5MYCzDcNIxXFnjOolohaLKeoDWK9zgPda3CNqx13YNh/1bPvvRb/Py09/D+45pnll313z2/d/DWkcISrsXUWlnZ90CyMeozUfO+QUuaddXYD19f2AceoytscbSNCu6i5cY3+F8zfXzj7C+I6aZenVB1ayWxK1aXRKToR8GNRCBQw/3u5HD7k435lE35HA80PdH7t99zSQV05SQw0/xNi2qbppYlBk2nMuqCCmLwQfR1ockOu4ioPNWykg2fQ7MCUJKSNRSYZhVz+fbHt+9r7koS2WqpMmzTErcZ0TpQmK1ZGdi3hMp91EYsiSGunpZqEX6KCMcMgd7WTjvsjCn0T6VGAL96KjlghjvGPqBKVpstdHmdBsZT/0iamklcTr2dOuLRf0LHNbl4qJr9frjgKtWiNWasm6owGl3S7/7mmcffMTQDxx2Nzx/+QH96Z6h3y+xUqFbVVWNyeJIBhVWr51D0kxVN9SVJhBhThzlSNspkSHFmXa1Vd5BGLEmcfX8Ofv7e5BpUfwP84SvV8z9QY09CfMs9EFYV5Dmifv7QFM7utaDs9i6pV5viGHEv/hdja9NIFCr0eSQ7nG7sGRFXp9j+xhhRnC+4CUK55Tks3j3hcktskigfDuK+OuQHiQbA0tLfLbSDMtIpoTlWqn+jqiWNKiBLgyaMn3gMUCqCVDRALRWa9oBwaRCkhWqEpe4BlN12PaaujvRnm5pak/TVDRdy3brMaFFrCZaLk9UnMdT1vhrwTXqfcdRlVR9wzxOxDlptchanr94yevXb/ny85+CjDhnefP1z+n7HXORZStHkSTAUdetwjlhABSKWW+2tK0nTSclGqzU203B0PiOaeypahXvrNotkgLzcEvTtfSHHc5ajK2pug0iiTj3xCjMURgRag9N4/DOECLMx0nvozF4dnR2ZKgm5qefQXvBPPb0yWk8icGW9lKjcW6KQbWwc69RTEJKM41JeFdhjRAxWYg+6+tIUbtVY1WQ+9sxRP3N7/KQYoAxf51LN5JZvEUESJLqn1ir4xhKa0FKsVSVdOnk0YuncxxhkaU3BVHKWQg6AFGnaxq8EzwTpCNj/4772y857B+wxhHEcv38BR+8fEZbg4kT7WpL026Y46yKr/Oo4pchMPZH4jxjq1bFhVyD6a45DSN9P+TBiIa2rZj6B8J0YByPxBgyiG+Wo3gJ0I2o3nWKNHXFZr1iu71A4oRErYJIioitieLBtWCbPGd51upE3VJ1F9R1Q9V0LDS8GBlPd8QYmILqd08RpqQE3JBgiolhVnB/mA1DNEQscRwIwy1UF5pEEpdQSIpoUs6kVXhJk8+YuaUhCrMqiFLkOs/ezixh2KI7meE8Y7/dJ/4afc0x144LQpQyLmfPcUUqAwyzWkB264IyaRYamdgMphamduHXmEICyQg3Zw0XhDnCOCcuVl6rAGFiGkbmVAL5mt3pyJPnP+A0OA7Da37x6h0iU4Y+InE+Ir7FGkuIkTD1OGO0Sy4IxnrinJhnVdja7XcY59ist6zWW/b7e4VpJGsSZrLHkkKKMGcFWW8t3lU64NsqmTOlRAgz/SHQrde6gfFUdUN/vGcKCZt6XEo0dYt1J6q6QWLCtRfEqSdOAzHMeuKIaKt4qU37liQTYzS4KeF8IgUH4rHVmni8wX5QgfE4U7BQBauDhMV4yjjkkpSUTRaiGnpl3bJMeXExthzDOUnJd+W9Hc1CrkWaRy9rNDtSQzJoyS8tnk4gC0RmUNSYzK5Wb3qm9+viPR6pIJKWH5X1zbKAxFgmuav5JuNwVsU5Y4gkY6ivrmgvTqyvR677mWr7jKryxDiTwkzdbAghsb97SxSh69Y0YknJaFO/eNbbS24e9szThEGomo6LyyfsdvfMh1uIWjWSKLCIYCYd/WWUJ9lUSqo1eUZdiJF+mNluLjFeA/8UAyk1uGpNmN5x2L1jGgaabsPm+hnr1RNieiAkYdh9RdO0hDgS4kyKuR6/INGRtoLaO2WNY9idAk4CV6vEk+sVpEhlZ6rVGjMpBz7mIFFPrUyQWAKwHDiJlm1TiszzCPhlsKY1pQWkxI/ftJ1fBd/8GmpgmWn9KHtNZWwEvxTAokfTN4Y02gwXLKe6QM6elwGPYhfMMGbKjTUak0oGWwVyX8cETJmNM0OaOPUHVquWaDxNt+H66TNSmDkMWuOO80Qcj9jrjzgddzzcvyPGkdUn/5h6tSGIJ9ka5kiMyvNbrTbAa6q6xtUVTbtilSLzNBJjZJ6nZbFs9oCgIG9T1XRtR9t2FAWt1WaLmBpF7i3jONG0a83mjWOeIUiFjAl5ONB2G0CYT3fM4wFLIobpEZlAT5nGaewd5p62gmFKeKPiAsMcGacZZ2ea9RWeCecTY6ioPJg8d1is46xeXBQ5HhuWMqSmqCNxq4pFWmRh4CPnpqqUsmzdtz++Mw0MzCLvpk03hU5EzpYeqYHlbjGzcBZZMmzLo1SniJJLgQ5iZn2r91S7Lfo4ZqnA1F7woqB5iCPjacfuYafZYf2Cfpyw9Zrr68i4u6XqOiyRGEcdOJ4S4zhwf/clYbZ88KnTioqtcdbysHvNNEW2l0/4xavXiFhSFCyOpmkIMWDEMMqQr11JpFXd4q3KBRvAWKdgdj+w6VqFnMYet1mRjCUah7c1/TBqW4BtCXOv2GiamMYdEu65uHxGmCec88xTr3rbMajnNXoWNV51r4dhoq001KgTSFIS8e4QGPuBanVJ8M8IY56tfBYWWTikizzMslL6szKNSzP7iDMzuu9MqcVqj1HMHU0Z3P5V6cp3ZmgXIJ0cx531mvVD2LILynOsztso2mjluJWy25YXzhlX0jFeoBSJZRflNzZWTXgKgel0oJE3HB7e8ub1V9ze7/F1h68sU5h4eDjQrbdUTcfmssOORmPEGNUPZ+nhOCt+djrcs7l6iXhPCCPet1RNohonNpuVtmNawzgNWX4NQgxIilinsZKvao0P43wG+n1Ds9qyaiqqpqGuW0CH4SjADqvtBmcNjkTVrEg4xtORY3+kqh1V9UQby+aZGPY434JxWL+hqU509kTnYVVr+HI4qXLDPCeCEWqv6mshOb7++pbLF39Lvf0EX6/YNNcMzjFMwhQiMZxHi5zlZQqGnAkM+ZgtvUvJhmUiKZnwXCa7F0fyK+b9fHcamClJRT4StC02d4PlbOtsaFDSlPwPNcTEuTC+GLWF3FaaZd4Xguaiv5g0WXJZPMikPTH27B5u2D0cqdYv8E2NtY7j4YCkCusGTA3d+gLfao9Lilr5CfNETFrfvb3b8fLTRIwTcQ7EPOEpzBOn05HT8cQwDAx9zxwCOgFKaJoOEMa5YJY1IUz6eUThDpOPrSAJ4zy+qvGVU5zQW+Yx5Yxd3VlKerSaZkXlLyEOhJDyBnKE6LDeKXxiA8wTtYPLFbQNDMGw6xOXXab8B0AilfELa+rw8MBm9wvq9TW2fZIB6RzHZb+hGG72ailLuqRiXNqbblCWvE+SCTEZ/Xj0vLOb+vYY8TvBN6UiUgiuZV6ctYtHPhusUe3EIqR0hnvIx7NZ0PokLBdpTJmg7jDO5RdXbYGYtIU1hERtA7XT+us8jvhmS9ttsLYmhMi7dw9Y57m7fU0I2pjVth1huCXGkXnumcaTNn9Va7rNJb/42z8jhqjHjzVQGDVYqqZlfXFF1TQLOlDVNV27YrXa4H1DXemR3tQd3uuGKOU/QXDWcxomhnHEGcM8j8zjCUmRkFQ/+7i/Z54HUkocDzccHl4zxxnxDYLFtRvEOMI8kmTC2UTlIrUTNi34yjDMcJi00a14XItQ+aSaonWL766o1k9J1RUPfWSYQqbjqRtwVo97Z0o1rBy7JfzKU7WsMvSjpCV5XLynPJah+XYjhO/MR1RoxpVRWyXD5bFUXOkzefQ7gE3lQC+Ey1w4LzmZOQ/3UfinzG62qqSfslh47iIcoyWwwtlK67fVQMpTQQ+HE113wXh6IIaJw37H9dojJnB6eMPWN6RpoD/es33yfdruLT/9yb/j5s0N3/vRH/Di4+8jpqZuO1w1YpynW19S+XeEKdCttzR1w2F/r5BPmHn57CN8VbPf73RK6dgv/b2gcs1V3XCxWdG0LVNIOBcI04ngHW3b4hAqZ+j3bxmnyLGPzDGR4kznRqZ1zeH2C2IMJG9oahX77HwiOUPTCAHHMMMYhSmq6oVYRzIe4yx112Kqlqr2mPYZfewYYiSloud6PtmyS8yQTK6a5UlVtqQBudcoxIDKU5eWAllKvIJKXsf3KsIkiWwnlAhCp82XlkN5FAuej9VIucDHvbTmUaLCYsRFFuPxEb807eRAeQrClBo614EEjK0w1hKmgSQVMc6MQ2J39xqJwtOLK/rTLclOrLZPiWFiHEcuq5oYJyQpAeKvf/ynbC6vqeoVySa8s1TW0NYOX1XUdUPbdAzzTNs0bDZbVqs183REJOHdNb6q2O93TPOEAZpmQ9u2tE1H3bRUdUO3bjFpoO46vIUw7rXeTcNhv+d+f2J/GqmbGutrqrpbkgOJM9Y3zOPIPM06qcFqfD4mOA1JZ1mLwVpP5SoihoAnJk8IwsPNa64OX5HqzwjRPwqVlhuuw5QeebLCxjmHW0UPxyDiFkgul8oUTcki7yLo0f7eDBEW6TNDqSIUxPkMxRSE/uzSswHaAgaWONEux/UyF3kxvBJTZnwum7Ag9KNwGC0NShaw1oJM2KqlioG2bXCclHFyZUi2QULg0L9j++wTrOswtmIajnTbp2wurtnvjnzxxSu+91tvePb8A8KseOiqrXg4GJDIerOhH3qsNVxebBmHA7uHG75+/SUhBOqm49n1c7abLeNUE0JgtVJZY2sd6/WGulJVCu89NowYYB5GTNNw3N1wOI3MNMTYY6yj7tY0bcc0nhjHAe/13scEce7xLtPwsvdSjqFkup46Cp1RGBmHgTQJm8pxfPtT1tt/BOYDBI3diwa2MQab68ZqazkBKYMVyZWyjDWazEWMIgv9Vb2oW1YN9574iNn+KH2thfyqxlMCWQU8FwAUk1XAHov7ZJ6ieTwa6lH3GJyDTsmZsqgpR0mM0wgpcOtatuuWqulI7AhRiGHkdDxyub7mePOOqt5wOBzohyeZ+2jojwcunj1nnh4YT1vttru85smUePP6S/7tv/43/A//R/8TMMrOEYShP6KC5SMyj1Q28Ornn3O/e2B30lJfjJHTMHA87Hjx7ANevPwEbI01Ol1gvVljSJgYSDNULZqcSYA4EfqBm3fvePV2R7u95Or5J1gmVm1DXdccHu6JMWK9IcQRZy3TGBCJ1JWGSSFo1cNabcOQrFXulraLWU+O4EnJQ5jx1cyYnNaRpRi05CkiaYHrtHVASS0Ub5dPQGu1cSxltd8yDiMCZXSxXQLGf6AhiiRSmLKpZ0HN3LEVg/IHydy2DPYtYGhKgthsbFJii2J4SqgtpNqi7iX6efUGFRxRtH6dRBjnyBQMKQrTOFI1LQ/3r9isLrBhz8PtDbcPJ0y14ZMXHbWZGPsTdzc3tNvnOBsZju9YXX/KxfVLpiD85Z/9W9rtc+0BcZ5pGrURaR6Y+wPTaU9jIg/3b7m7u2OKEW/d0rMiovPy3t6+JYSZDz78lKrdYIDKK3eztglvI6HfYeIIMlM5z9vbO378xT37Y+BZiHzw8ffYbJ7R2SMmBYbjPd43WFTkKcWBcVa8tqksKcIwwhSF2nucq6msxRmDRIOpcsInemynMCDGMiWboblSldB7H7Nop5VcusgUPsNZfOsbw8Rz37mkiC/qEPkk/FVgNvwaxFiRhKpj6uiGlKI24UghUBa9KCjgtZIhUCJsFGLUjFm/lLd2Zm9YTJ5nZ/IuQxRa0FZHR+UbqqojmYqJBld56qrmdNghKXLZCXdf/4zjfg8p8uLZhqE/YfwaxDKPEw+3XyvhII54Z9hePsUaePHiQ+7efcVf/+TPmabAfn9gGkdimDncvyNMI13dYDAKw/gKa70mbJlhXvsaZz27w4E3X30BadT+k8pReYOrdG4fEpmnPSkOvL19y7/80y/52as9D8eecR6Zhz21i3SbS9I8EqcDiUCRFR6HniSeFFUoKQTYDzmjdy5T+R9luEanORT/I2nGpwP1cqznnqBicEVOMK9TKjF6ht8U7cjto/k5ktsLYsZZXRne+f/HFL8bfIPBWZflh0siv0DZ+QN6yk/SUikp3V2KcUYMSSwxGSUcLACpAYrS6BljVNLtOQhwxuCsDimfYqWDrtNMGCdeXndMp1uads2T64YPP3rGujNMw4gRR7u6RuaR/uGGeZopI2C7ruNie8nT5y+Q2PN/+T/9H/jX/9//B/PUM5xODMcDXbfm008+43q7ZdVtuVhf0NYrnGuwxmOMZdWsWLcbat/ibMUw9ty8+ZLQ74nTABKREJiHgSgVznccT8L/60/e8NPXBw7DSOsVgK49yHiPkyNV3VJVDaSIpBnnaw7HQJgTU3KEpO2h/az3zefprK7AN8ZogcDopqkrj/cNCUeImQ9QYvski+6knmZKy4upJEGyEHHJXXvntYZFHSKvn7UW4/4OxveNx6+RrORg1ZaMV4/gUnoreb8IGg+ZclCbZTR1gXTOg3oeKYvmpOc8vqWMTpMs3qnYpEWPlyk4HB1dU/PiekV/OuC7a55eb3hlKx3SXSa+mwrrHXE4cNrfMg4vWF8+x/mWuopcXl6yu7qkaTt+8eOf8ef/u/8t//1/8V9wfX3JxcUVv/8HfwST8MXf/g3r1SXbi4SwJ4Qdsxtxfs2Ti6c4Y5imSUt8EpmOPXE4EMMVISRMCliZsCbx6vWOn3z+jle3pxxzC13l2HQtba1qYWl4WJTI5nCLscpYn0NFTDqAcw5qLBhtKKtcQHA4I1RGYZcoZpm77L2lvviA5LZK+xdHGQxZ2FTlbhdEQ3IINUtaEpJFHUIDe0qoFcUiS9+RySod3+73vpNHVDxIOWnlbTXgi1Ca6jPd3+Q4pvS5nlOX8kopqzC4BXtajDA/p8A46hGLweb4NFd1BvHQfsD10yfsj3v64Lm6uuTmbsccK+Yw47zN4HilMVKKnHY7jvt7wqjKDxbo1hu2m0sur59Q+Ybdsednv3jDv/o3f8q713f86Ee/w+biktXmku3FU7abJ3SbK3CeKIm66ri8eka3vmK9vqRt1kr9b1YwDoTDLfubNwynA6SB8fiWP/3Jl3x9t6NAWLUzdI2lrg1NBd4Ewjwwnm6p6yrL8ikR9TiBrxzOKhc0BJ03XXtDY1VUtbIafVun9y5Ei7eJ9XpN1V4y2AtC0v6b0spRTjmzOJlMULalXJfjSXP+eTkX1R6VDTTPiWEaGUYd9zGO07fa1nf2iI7MIczGoqmJLEFpGTqTaQ7EhEoTCyxjb8uuI8eAyGKwdglwz4V3Zfk6kk1L4GwKbcJUpPYF8KdIErquZfdww6vXD4gIbeO52mzxdYupVshxz3B4y2F3z+r+GS8+hnnYEcNI42suL665XLdsVy0Xm6dM08Tu4YZjf6JtHG27oltf0mwuaceAPZ2Yg06132yvuLx+znTqGd2Jqm5J44nKBrwRbl5/xdubO/7oj/6Qi/WW+7uvuDtoe2qBtyoHq9rS1RXeKpgtKZGmGesi7cUV4+41Y1CB03mcWG9WHHZ7DHC5rjAp4G3EGot3jimLapvK63qkmfbiOWHzIwL1khWrikOiTKECe+ZPaoapWXFKClSLNujr0E91IkkSMSp5WWIOI6pKY/731U6qFxJzrHGOIWwhTy4Z8ZxTezIEUNy6ZAxKshEupRSW8auSmwZyr8R5skA8s3iyERqTjwbXsNlc0DUNfT/y+uYrDrsThol19xLbPqXeXGAkMJ5uOdy9YYqOED2H/R3ef07VdDTtFev1BU+ePOXjD59xe/JcXH9AEDgeHiBG2nbLenvN5dUz+n6mafdYV9E0G66uP+Dy8iknt8f7SvWtvcOlgbZN7G7v6VYrLp8/J8mJL98cOE6BxrssrC6sKs+69XStw8rI8TjRrTpc03Ha3xFoMdWW+XTP6TRx2898+sk1F0+umQN8/Hv/Ofdf/Dn97U8RVzHMkQSMs9C6iLOOVeNot9eM9pIpKqRTPHKS3IWYmVVI0hbZLB4gKZ57ltP5xCseMeZ5OItuIgkjHu9qrHtvamDKWBELVqy2DS5NJ9qwrm7ZLgC91iLJQWEZg2BQxdii8hBzll2yMfUO7lGC8rinWfLOK2wfa73Ox0sTx+ORt6/eAonLqy1PPvvnbD/7Q1y6Z/f5f8X+9hWn/Ynm6gMebr+gXdXU3pL2losnlm51Qdc1vHh+xeWN46PPfkhMM/0UOe1mKt/RdmtW6y3dtudqjmzffk16uKFbbVmv1pgwY1JSvR8jmWUN27bDtS2r1hLvd3xxuyOIsDJKwvLOsGkMTW2pzKyTTKdRR2VIIoSJvj/inGUOhmlKJGA4HsE6motrnv3g99k+/4yv//3/kYeHB1I6KfgdIcxQVZGLdY3fvGCYJ0Kcc2L5OCwySwJSMF1buKRON00JpxbBVTljytizTveSM9hGhRDelyGSIZUF1M7Vj5JFF46FHpulDGSXuuRCVcxaemqAKRtXaRlgSVDKMS5y1sGBtNwk7xUecN7gLdy+eQNxYLNZ89k//Z/y7Ef/HG8j8eGO4XBLCInu4gXD6Y6pP3B5/QGn/TuMX3F/+wZjLev1JR++eMaTrxOrpuLqyXO+/Os/IyZP1VRLqc9YR+Urnl4/R0KkW22om5a5avFuVAKvczjU2z29WHGYDHG/4+bdA2+PYyYY6GnggIvO4h3KcxxPOAzjccc4zZyGWftHwsQwBMag9zAkg5VE7E9I6Ln44IdMd/8R+7/8rzlOJ7wly4QYOh/ZXnZQXWCtoy49GRl2M1anxhpTnEJJXPRhjOQqljoEa87rX5LSzNc5O5YYCClqO8X7MMSz3IdZenhNKnQwuwCikr2VCh3m2BBDGYubrxCReN5FfGM/PoJsshEWF5tr0KY0hGMQsTRVS5r21Daw+egzPvpn/3Ne/Og/QSToYB9bcXH9Gcbd8PXP/4IUBd+1nPZvOWwampVhfzhinGe7veaic1yvYOrvmU4PXG0v2VxccTj1VL7B+xabgHmmAtqmo7IVznicz3NfYsCI4L1BYuDyYkt4+45XP/lrvtyfGGKidhpzV8awWdVs1xW117sQ54R3wnGIDAGm5Kms4JJRsc0Mhw1joPGW4agzBeuXP2T74e/wcgp8ffv/ZjwNNK32oD+5aLj6+B9D+wLnW/wjMXbFbV3GBdURxHQuvVJWr4RbZS2Mjs1Qmf/Hz9V1zjJdmPSeJtiXYrekREwqE6e96nkU9QLhaECrkaFgokFKw3nOgksrgX6mM5FWckkPa/O43QVEQIga9JbnG0MQeJiEyfwjnv3B/wz78YH2+e/QXL0kyYwkIVJRdddU3Qpufoa1FXXb4quWFEaG/kgSGMfIcfXAqtuw3Wz43d/+LX5x07NtHP/xP/8fM/U9D/cPDFNgmpUZbTNryFvPqttQVRW18zjrIPMZnQs0FZgIV9eeL7868PnNQ/Ym6v0wwosLx6r1NN4y9nekGDiNkdOsupPeuRyrJeaoNK2YY3NDxAk8vP4pL//xf8rFi+/hrOfdmxteff5jIOIZubzc0lx9D2mul6KBxn1CUSsq1lbsL6upZCen37RoB59OEMt1aIkati1GiLK5jaHMbH4vhqjvFdTQCga4YH8FY1RDlKUWrWMuClHyG7PZpGTMwnkIjctaOCxzSky5ERTcKuWf60VNyTCmC+TZP+HiaWEIJ84ipgYbj8T+LdM44qxnnpXBbETod7eEOWCrjoebN3Tra66ffMITDtwfD/zhP/snzKcT/+q/+r8xipCajtMUmOaZZCuc9bR1lxv7LTFElSKptALT1g1VBUkmfvH6gZ++2/MwTlhjqJ2hUfoM153DE5AI0zgwTTNzcszJIgm61jONMyYqrb/yel+qytB1evwf3/6M0/1rusuPaa9e8Olv/0cc7l4zTie2rWW93WCbC4JTYFtvsFucQlpQifOam6UHCVLMyaZhMcKz+kZe/0XKOMf+mZSS0ns6mkENSsqI24zrGXJLgEGPW1HZCSSPrn1EYPhl/RNNvlTjRocFCYhTEgGPs+ZirBkeknP/cIhA1tvRkV122YEpCk4m/PRaZYjrVlnbswoKpeToh5mqNcSpJwQLVLSbp6wnw/debFnbjn//Z/+Sd+9eM7mK2VeY1Qpjq5xkeGyrpb4waUP6ar2hqhwfPHlG7eB0/45h/yU/eXPk7WFYoKqr1lJ7Ybuu2XSepnGIjDo2I1impIoQJJgmXdgYE9bq71kH65XH+aQa5eOew9vPqbYfYaqW5z/4ffa3r3j7t/+e1arDd5d5XJ0s7Q5L7aAsU3Eyy1c2woIL5xVRWlhCcj3b5JmMKlafJ+MsiYv9VYWVXyNrNoVZkzsajMMa/2gXqDaKedwAlXeZSJm5HBfZ3OLCUwr631k4fKltPt6bIngry0DJMiDJZJcpObHh0c2zxlIRsWGvTVbzxDRrX4igEz1t1apaKom6s+zufs71s2dcXl4T373l9PYrbBiJw57ZNsSqZjjuMM2Gqt1QuQpb10iIBAyr9RWrdcuzywtWVcX+/oZ3f/OnPLz6govNJa/u9zSV57L1ND5Re8fzy4a69XibGIeeIQiJSnmIpsI4bWqPUYhBq011bbmoVfy0gNzEwHj7t8yf/MfYqsa1K77/n/yXVN4j+19g7AUGv+B/qahtWDCSe9MLTY/SWJ/XNZZwi6wGlw00S0HXvnBSVUhBWTmcn/O+iLFLDXhh2KDNTibkczvlwLfM6I25Nywbb05wdJggS8alLylnLyvw2HGWWEzIfRWUKneeVlqmpC/JTqZBSQJr8Mky7d+xv3/DYXeLMZ1OlLdKcVpvVpzGgKsMPvbcv/mcyycv+Pj7TxnuX7F7N+BTwIUZ6wyzNYsQlRiHqzuadpM/i2N9ccmTiw0X64bKOvb3b5hXjj7OfP/5M27ub1g1ltZHSJZnVx1Pr7Z0LVhG+t4RkiVQ6f98pQ1aXpCQEOMxNlBXgjGW46BsnsrpMR0efkbob3H2BWINvun49J/9l8zHG6xLHM2avARqICJZSEsRjhI+GWPOar0iZy9ntA9a16jwE7V4oeuXR2hkdEUKU/vb7fC7AtqJOI25vJMNr3ASzSMyLEYpRI88lP4+lLZFHhtb+Y8CC2WwPMPWmhChbI+YtFe4METEPDbE/O6iU1NTyoTN4YEw3GsAHg3TNBKNZU6B9eaCYUycxp7ryxX96Yirag4PN0ynHaED+/wJzcNOXysJEsFUDcY4nLVUztE2HRIjpmqom47NZsOm9cSQmOY9owmMEqhDz5POYGwkiVB5y5NNTWVnrPQY1OtZ31DZKvMJ9XMuimLeYyUyRbNMARuCoWvAWyGeviYdvsBffKzOQEB8jb/8EBFhEMHGX6J0pVxJNsUfnI2wiKSWtT3faVQuS4SYDDJHnFE81GUFiCJlVwz4vRiiahAOAIu8sDWixpATB/1LBzuWQLeQIvRycgvlsvPOwGkpnBurIEAsPLmMJyY5cxZtjlFMUgzOW5aqjAFsSkSxOm7DwHp7ze7dL3De4ILRnhJqhjlyeHjLxcWa42mmrmsuWsdx947d3deYuGeeAusPPsW/eYWLCYfD1B22XeGtp+426rVEqOuOpm6prMf6hoebLxnDLbcPb/jr2xvqIxhJBJ2tycdXDbVJEE6I06mudWPY73V4jqu08SsGQ0hoF54BSQbjjGbRolqV3lu8E1KY2P/4/8qzy08xm4+zpuG5zn9GLUwGXFJOaIUiluQyYH2edYOOWDKlT8kQMjqSRDdnQCUJnXVg6px5p6wgsXQh/b2P78xHhJhVF8hvZDLS7jLnrqKqGpz1OFdhnRIOFCpQrRVjqgyMK12oWKGgrOKQIETLLOh/Z9A2JJavKPrvOQrjnBjnxJQ7/JTCLouaVeMtrmlpNhes1g3WJMI8EsLE7uEBQ1pep1tdYF3Lw/0dD3df0zQrvn7zFc2TD3jyyT+m2z7HtavMO2xpuzVt09G0K7YX19S+ovYVzjmmYeLtV3/JEO/5+Rdfcj+O7HrtY7EifHDR8mTb4r2jcgYSxGhYtQ1dnbK2zqxl1bx5UzKU6V8qHK+3r/aGeU44pxDMuL9n/1f/Z+i/1jFxqMczUgywMJ5AVXxt7lHWWDDEuCQn6ngMzukgdlUkU8OyPEJCslNRAVZtZCtjTPQAfV89KybrFJYPULLabPGSjdNAbqjK3zPk26Dgbcp4YaE0UG6CLJ0tiLAEycVbSnG55H7hkqzk15OkNPaUSvUGLDM+3OHbK5p2neMXVUiYBJJJGF8zjhMX7RrvPbvdDmcddV2z6p5w9aznF/df8PKHv4PZvKXa3TJME6ZqadcbrMB6fUG36mgqx7prkXnmq8//jJPc8PWbVzzcPdBVTonECWxMbH2kqot8nyUGZSQ5m1g1wv4QsDGBqYkh4IxnilHjX+eYZ42vvRPGINQOrDMkUxFSzfzwivkX/0/qz/4HuPpqAaIXTyjlFCnEVQMmZtnBjG8iWJsyYcWox8yQmp6G6EQBK4txqz7SnE9Bt6xffF+GaChUf50nEosBkDMtSXmojDbKu5IyZW2cR0BMvgnlaNAPXFB8FTAqVRr9sPaRURubZ/yVTBly2SmwNPTkm1zbE54erMp0uLrBuxOKhSqEMw4DtdERYXe3bxjHievra6z3dJfPuPrI8Oqvf0JMkY8//hHbq0v2p55pnhW+SsL1xRUXF2vtyNvf8PXuC+76L9g9vOLm63d03rIfQ96VBpLQjzE3ySvCEKL25nijGKHq2xhwkRi9bmVHlqOxxJQyNcto3Jp5hGJqpuA4DYH29m+oVtfUH/xTku0QozNRkoEyJrfE5CUL1omv2WhFT0BnhJDLrEbOyITW+nP8vkyHKI1vGqYVfCjG91RZEUGpPTkelOyllKWRv5kSxhpscsRMtNQdIZQDojAYdKSqRntqZPJol5Ys+hw/FG0BvXE2e1/1gjEmGs+StZM9cM2Es4ZoHJurF4zjwO7hgPMTZhRCmBmjcLHdMBwfOJx2OGd5uBvpjzs++N4ldTfStWuO856bsKNroJpGum5LmFTFtasjh3ef8/DwFb/42Z9y8fFz9jdf8dd/9lcMaiUkSTxZ13x8tWZdW5ARZwFUxCkZi8MwzYEUIiZCSJk+ZTQBdMYScmVFuZkZmM+cQ42hE3XlmaNjmmaau5/QXLzEbX9AsromVtC5KKY4EU09SDHHiZainViElRZ+S1lrYx5VUSQPdTp7zwXuyU5lfm9HczGefBEFrCzg6JIUibJCMqNoSR7ImZ8aSfmJpvZm6Zlg2Y0JzeZS7nlwefc+bj1AFNQ16DT51gte9ZIZU0VtJs1o64Z4usL51+BqvO2RFBlHS9uBlYn+OOcLdkSpOB11KvxHH33MV6/f8Ppv/pZme+RYX/Pm7Vta/47j3VvieKL525bd8YG7d2+YxoH1m68Yhz2DgDGOtvZcrGpMirTrNc+ut3g7gkz0o4Cp8M6RJOJEm81i0ASgbiziDQTd1tY5CIEoqpxb+ke8dYh4MBpztlcfYmvPPA3Mb/+Y1jr85mOiqZFcVSqk5aL+pYlLRGwqtrZMA3sEbqiPSKX3PJdyM2tf20Bgzi/q7RkSei+GKMgiY2Fznm/y0WDkfIULOy1fvSCqzZNEx5mh3hGjb5/y+e5MQQgLKL68MYaUTzSn49YQam8JIU9osmd12c4NWBmo7QWdBWcrJAZcvaZePcH7rzNAqy/eVMLQPxCkwtUNCY+kiVdffc6Hb37BB5/9IT/8rd/mz/7yr/j8b/6K9upjjrPhr3/2V9zdfsU0HLOwpVLzLYnXu1timPBWuN7UtJVKSm0vr3n54gM2q5r++IbDaVIam9VwxuIgBlKMSrVDmMcZ67XvI8WolZZxLFscrMaWZdKAYPA2UTcV2w9+F8KJNL4j3v0VDSP19hOCWZOsJSRhntVjFK6APLY6QatjlIIFZy9nKDGWGqNF3YVYYsESKZn3r7DC72qIemFxkbM17txOeh7kk4NeOf9MikaiSUDMzBrNmpNxeGPUK2IWdTGEhfVhTOZ/i1nYHSUsaLx6VJ1mkGWSEZr5CxocbbNBa9MGX19Qt9vcaqnx1boxEGfGyWIrn6fQG9W4SYFXP/9zmtWWTz78hD/4vd/n3/27/4YvfvxvGKdI3x+VLCowzEPODCNJAjZXkawxnKbIqm54+fQpP/rh9+jaimk+cRgnfNXmzzjnKkdAUmIOKvYpyZGC1t8rL0iKmMrjvYFkgZThkojNnVIpicrrDbeY8S3dy9+F9DGMd8Rhj/O31J2QXKPwliUjFtm75XUrBpbiOY5MeZ0WNbAsJK+xY+bqZ3WHAteEpAJS762vWYPQAOKVTZONS4qLXp6npT/VkFbmgc0AuGQCJh6MiZBZ1/oR7LJ7ZPGsmsTYTFUJSTvcKq+6i7Uru1X5fLXV1zESsMMXWPMSqjUpGTAOX3nqpsN7T9NGxlnoZ0MyijnWtsI6j3WG/e6W3e3X7N79Ldvrl/yTf/rP+Orrr9kfdgyHd4zDCS2xTDijahSRuMRSTd1wfbHBGeHjly948fQSXxlCGnj37ksES910TNOAcwJp0tbcGBmDtuDGFLEpkJLBG6vzaCRiJaqGeO4PccYiUVX/nVPDdRbi8EA6vaZ9+tuweY6EXmcEhkFjT9GuywJxFD1wMhqi0WIO1nM4VCAbiuHmfLnoEhVBQWOz+HseCCklef0HG6KAxEQyemxINpgo2o5obMmLM2htivRwPBtpDtyVxxhzk74GvUVV1BgoA8Mx5GFCxUjTUqxva6cN5MQ8xUBf3xOwfqUGIQEjgRRUv6+pG5w3VHWFnCLDbIjJgHcLccPkTdbULUkM43DitH/H5dMf8oMf/oj7+zvmSWftzfNAnyeJKk0+4XJWX3lLW1d8+vEHfPzySW6Mj9zdvOH+9parJ08w1iqO6C1pmhkjhFmYZsMYBGMCKWpIog5QmMKMNerRndH7r/rfWjHxlY7EFSJVZXEywnRLtX5B8FeECGOMmDkTEfJxrHH6YxkZNcaY48TlJC5L+ctxn5Gs9hEzR9WDtXirIvKPCbb/IEPM+RYpHwmYlBs7yTFXATazxk02qpK1mAxAqXpAyZWzkNOyG7VjLGXBc28dlXd6TBgl0lYOnaQpQmW9jrOVQGUnkjhcSjiZMa7WUl8WWTJGm+LXl89o391gGRQPswZrtWMwhYj20Hia1ZZmfcU8j9y/+SntxUf87u/+AV999RVzFIwRxv6ENcIcI9M00FSeOQQqa3h6/ZTf+73f4dn1Rj11OHJ/8xW3714pTGMtMY6kMGPKQHCJnIbIECwR8ArMIcYwR0NlCrFEqG0RHVCjidFjrcV5p9gfARP31P45jY2k6Z66WmPdiig6eaGo8hbzKutQvGMGgXOIlZSMXLhi+ehejJbiQfVnSmQpY9Fc1sB8D4YIZql9knISYtDids6os2/TeqkhizdmYgOK91mXcaiUSIQl00rFUNFxamXweEqqt+gQnAPvNTGQJLT5iPYkvIw4lKLvLETRIedx7knBgK1o2ksur19ws/4F6+7INCeGmLPGGBFvclkKdvueSb7WUqGr2b/9Kevnv8V/9i/+c/7ir/6Kn/10S394YBxO7B7uOJ0OOsRIDJUTPvn0M66fPqVyiam/I/S3xNhTN54wByQOhKQQiaImOrZDYz1t7Qwx6QwUq3Vl38gix5J0SbSaEhTzK7Oym8rTdSu8hcZburYiGAsmgEm0VcUYdXZfUflKi5qXup0ivFTq/tGU72dHkb2ODmg3y7x3ciypxjhTZiz+qsd3T1YyZ7DskCS53T2Vzq3sCckpvshZM8Vk+n/OyrQNUbOr3EygO9SAc+fKpO76iJhE21o2rR79IehA8cYLNk54ArU5aG+1cxjfYhDieAA6YohU7YZudcHF5XP2D0fG0MMgDEFl1KwR5mlinBNiJrY4NhfXzPGSmzc/Y+gf8N1zfvDxU65Wv0c/9IQEx/0DfT9otx/ak3yxaqlc4rS70VpyVK5m11SMCCbNGk6TsorLjIjB1yvaMBJiYgq64J6opc14roIkEarKUbmKYBJ1XVNVjUp8OKibhma1xthA23jEVQSzwSSPBF0lTRDtUghYMFwe6WOV7Jcc66dHteGSaVP8oiaeKavGSkq65vbbMcRfzxAfv5mUUl05YmOGa3IGnTK8k6nwenykPMtZqzMmacKChIxZWcRWGkeqRjHaP1v6Wwytt1QeaMCZRJUGJPVUZsCKYOMBCEQDadZZJMk4Tqc9K9fhXEfdbWhXK5ohMIeZIcIcEjYIIU1gE86DJPVMKWbVhsPXjPsbxjkgxxNmhrpa4TvLRdfBs63qAs09N2++4tXbX9B4Q9d1tLVnHh0pTtQ+gcw4WyNimcPMPAWMEZyplEs4T3qf0A1S2Rw7Z28pGJyr1eBa1f+pWmWDO6eGU1ceE4405gjeMWVwMJpqqYgoqG2zGoOciRRGmU8pCXPQSavW2Py3Qeyj7PmRWiyYLDNtwHh1RMhSt/6HG+LizLLgzqMdU6rECwGyFHtjUXMosZhqqQgGojJIFqF3CickAG5xq86KsmasxVlDVTlW1YyJPSYeMemElRM2DBn+mDASqJsL+tMBEZimnjCcmOp7Vttr2m7N5uolITmm+RYzTCp4iVaKwjxjbYWzws3rL+mPB54+e0LbNXRVQkKP9DvSmJgD+GatRX3niSHys5/+jK/evOE0DPzR7/4WQ3/AUFG3Tb4HkkkEotOgRFsCTKbWWaMzkacp5gqGIgcJCDHRVBVdWyvBwlrqRmdRV05weaazy8L3zhok9lTumhBHjGtZ2cBoVFkCY5mjjhKZQiQEHYnhvWUMRZIYvHeLPHFhQZVEpySQmFw713HePG6Y+2V2/q9viMsbZt+Uja0Eu6oMlo2sGGJ+qBdcfKXGmCaRSrmukC5RQq3JzVYqHeSonDaGr2qHM0GnaqYBk24x8YQJR0hzRsNKDTxvAN8gwz3WzKTQI+YFzeqS7VXi6oPf4vkJfvIX/w23b99QNQ34inEY8E7o9zcKRTnP27eJ7XYNFzWVTVSVJ4QTYQ6YVC2Z8jiPzNMR4gRJE6DKw8PNW548fUZV1aprmNntIQkJ1fI2BtIcCHEEFC3AqIdxzlA5iwuWtvELsdg5T+U93nu8h8opM7oUCoxxxKmnjTvWbsXKQqBmU3nEOProccESvd6vGGa8icxzQMRTeUeFZZwDIShMlQ3gnIA8SjYL5mhtmW4vuejx7Yb4HWlgpSSUXWwBPJPKTIQ5ZID3jEXZJT4sUYbkeCRpcpByfRPJGXWuSEvM3w+6wM7QVo62NjQuYGKPlRErM1Z0KpRJIxJHJA6QlABhrdNSpHNgPf1px/7hln7QCe/OWdaryB/8s/+Uj77/21niI7Ber/BWGIceZ4U4HTk+3HA87LjfD1jXUNeVLr6zWBOp6hqMxXnLxXbNqqmQGJmnicpZUpqysoTSuEISIpK7cCrEeGzV4JsaX3kN9FNQaNhqFO2spfFaJJjnbCwxYFGCrtMxD0rnColx1CkJQ38iTQdcOlBxYOUn1k2krqCrYN0aVo3BOUNda/gwThPeqsRdEqOoSK6IGavKDcbVmd7nMg5sMvmh1JslIyCaCXzb4zuTHmJIOGfPBhXLBPosR5KNvmRaxtrcrKNfKcYzM9g6lcclO9lcM9XfzxTXZJiD/k5b22UipgkTxBEovbWAbZE8tR0007VisSEhpiaEHcfdDeMo9ONEmAKV7wjTAOmOz374Q+Yw8ubVlwxDT900bFYNMQYebt7g2jXtZkNbVzgLU8zznFttokKEMlB7s1mz7hrudjoOw7sVXdsyDkearlWBzDGRJGBMTRJHyCSBpqqpKpjMSMlSwWZyg8kdgTDP6pWNqTLkmnQzJB0SOc+BOXuxeZzohwnnOyqfsExaRTJC62ASQxBLXRksnl5WrCtDiIYhBEJCjQ9zFmACNfoU8pfkpjWrLaZLFn7OrN+LIRaPqG/4yy9qcNarxIbJqZUxmcFROrjOTAwhY58IyRb2brbQ0jyFUSJoRMtdB/V8T1coDikGlTqpFSayFkwFrkJEtaRjxlglzXq01TXj3DONE8Y2hGlgGgbmZDgcdzRNy2pzwTxPiLEc+0GrFb5CQmSaIErDKaxIVOAbKh+Y51E9Fo5ZoG5a1psN3cOBeZ6IYaKuKobjzDwowjBGR5wtTSUk47C+Yg4Tla+pmgrrjgtJroiddt4iEvAWTJVLnkYllWOuVdtKl9VYCHFmHE466aDvca5GRPCNEpatDUTjSanCgY5Qw+GTYTjNDHNgTho3SyntZSTEGoE0k8KEiNE2Busp7Jwig2wekZ/fkyGSs1g1ApMNLd+pPBWKs/srEM/iEctxfTbsUoL+xi4zJuOKNntIQYKAJG6ZIFquG+gQTJJMDvBQ6qAh6TAfUZb3GAR8i9gBX18wTQnrIiHNzGHSzr5hoHI1dWW5vNwyzTPGeuZp5NT3JHE03QV1u2ZKjjRAXa/wdsBXDbaqSEHVFzhpLNw0Na5yiESmeaaqdD5fmE9gKipXMyePmRPRJJqmUXa0WKUu5Dhb+2+UWBqCJhgikpnXCYlTHgypUxVMNghrKqY+4MxMVUWcm4ADKUZaMbhKELdmtgZjEtYm5lmbsfpZGIIy10MGzfVy9Loq77BoyVXHxnmM87q+kluEUSnjGHXOjvffbm7f2RBNqT5KqQdn3tovecjFIBeEnsXhZZPM5b+c/lPA8EJqL4YqlEZtyQjugzhlqVhLnRJWNNbDOlKYSHjmpNleTIYQhBgtVXdNTPeE055TPyII+/u3dJsrjsd72lpw6Uic9sz9xDQLxtasVl0eJHnPaafg7Pr6OdY7utWa1WpFGHekMDOMPeQ5zNY6KqMQ0/F4YrNpNFHpR4yZcQxEVswB5thTLyMsJgXiQ8g3UOEVMcI4zVRtboy3aCtGrt+rbLDVafN5DK+hZZqSfl7nwVVYH7HTTGVqLRgYYQyJPkTuepWU1pwkJ5hZfhgBY63ea1TtIwoY41GZkizSL2eOQRGQByEj4u/DENVrLaBnzngfsyoKqWExNNCEpASG5yRLsap8Q0uZ0Fg1dJtjxLPGgD5/Dolj2QDNmkt7wstImmdc1ohOKSKuZp4GHQdRb/IA26BaMUOPYOialnnqsWNPCpHd/b3CIRJIcWIeZ6ZwJOwdzlc6L3mKmL6nu0q07ZrLiy0pnlTT2zn60w5SwjuPpEBlE62vGIYj3kd87SHVpDhh5xMuRSI1wxipTieQSIojWMM89gvwUNhJcU4MVjIDHdzKY7yQ0kRKTuWYRRQCM0Z1w23NPAdcP6mEoLGIa4gmYdJEcieCtMRY0TnHnIwe9yIEURTDoTVxkUgKk5Y0o8rcKQ8ylkBflWtFsnY24DzeWuz7SlZMNjxlx6h+oRqRyTXlbKwiGeHJ38wuXVm+Vo/qIrCUJc7OozIKJunPr7t40pTliw39bLgzDa66YM1AZXM2bxxYgwSdueLEEWNkGidSmpiiIUrNZtshcSLEiXTcsz+N7PdHPnj5lPVmS0iGGGBOAQnCNAXEVrgYueo6Li4uuNyumacTziScd8ynUQXSnSf5SJgGNqsWbYBPxElrylXbQbRgZmQaSTHiMczjQStR4YSQmKaZJIXtkqtUAmHSYsA4K963XXsskRQGxjiTZpfHXXji3GOdw7qaGHRMLi5g3Yjg8clApcoYtTWkmOgnYZoSc5Y2aRxZNDQwh0CMhSVQVuV8dBccsdSgS9uvnqXvC0dcDNJkISbJLJrSZ2yWwFrfO0M2j0iRItomasn4YTZGSItxJpyyTcrvIMrvQ12/E80a+9nyQIdxG9Y8aFlPZpIYsI65PzCOE0mUfg+JcUqsLp7Q1J7T7h0kwzgHTn1gfwqsjzObzrO5uML6FXWIzEEwvqHpLtheP+f6yTNWXhhPd7R1g/eWaTySQsBbh3Wa2BgC61XDw8OOGIXaG6b+RLVZ022vqesT7PakNNF6yxwj85SwMjOHyKhOjUrZx5mUrLS4OfcLOxcxNmo2m1QFI4ZZNWlcgccmrRkYncwappHJZdKJiKrx+pYpCadJqXFzUuy3zGdJIowxkdAJrediWeajFuxWHjGnKIx71U9/b81TZwpQof9kr4bWiZaYLwsgGSP5mDhD2VpEL5VJZbCY7OViSrmRPHvSrDqWJOFJmqUBZEpRjJGTMTj7BAN08UF3Y74R3ldUKTANI13tMK7J7Y0V97fvkOSo2jWxnwjjoBxHXzGEoJuladmsPNapKLyvV7RdjaSRGDy1V3WH0+mAEQ3Gh5So6obD4Y62reiHgbv9QNPWNKs103BiGk951EVF0zQM40xtE0RlrJAip0lwzudBmKUJHqpKN72zwqoSVivVm3He4qqOedZZh1NIVMkxR0vsI34eSGKpavV6zhq89VjfQXKEaJmiIhVV5ahrPUmGOSrgLpqULBYgLJ5Os2RVfijCrcpV1SJFWnim702ESZZyT7HDxatlz2eKseSzevFq2VULOb5JWrpafKUxOdhNJKJmXKnEJ9lYc6aeMvZj0NLUfnK45gprE026x6RInAMSR5wEPDpAfA4zNkXmpEebX11C6pmGV1iTWK02bC6ecPP2ax7u77m4foKVQO0szjYYhDAPCDoTrz8dWW8uWLUNlU8cH96RcMxTzzyNGFdx9/BAPwqrtSOJx/oK77X0FkIkidHJWcMMacQgDFGIxqv+dZJMSihDyBUwr7yw7Sp8ZcBagkgWpq900E+rc2jA5FnSOrsZRkxbE6IQklHEIgpBVN3QiLZ/tk3NNE0cBh3wVPTPy/qz6KGX4oauttHu/0eHsFnCsvdX4uNcNioRXelHLnCNQq5ZpN1YJW6GuFxw4dJp7qLe0+JY9LgxLGU+yMmNLaGnYotRIAp1BU2lEuRzckS/BhlIcVRgWRRcFqMN4TEcidORaQiYJDjX4Ksa52C12XD14vvMceDm9gFnK8I0MIeRaWxxfkTy5rBVy3pzxdMPPmW9vcYxc3h4xdifCHPieNwheI6HBw77ga6pqSzM00Rdt/SnPcZaxTQD3B8TnVPPNwRDxGuygyEoKzbX6jUsatqWtjZ0bYU1garS7DbM2p4wTT2CUNWNhilofJnCTDRCDJZ5njHThNgBZ0aSq8AZhX3ELN7LOy0aqx68po+Yc9NVscKYExRNWh71tGDOjKtvT5q/OzG2JB9kAxSJyg9ZelTMogRhCpqeu+p4fC2p0MtnUgqKyGMXXZ1FCCiLduoHOidGy0Aa42gqw7qeabzgzArjDYwHvSG+oe5q5vGIr2raVeJ4fMtqfUGzWuO9475Zs71eYZ3jdHuPMZZ2taLtWpgq+uMeYaJqWuaQqMRzvVqxvbgkhZ79w2vm4UgKE/2xJ+EY+weGQZOTrvE0tafylrrZcOx7xDZMwfNw7HnYR8xKp8sbIyrC6YU5JmKmUTXGIgb6KdG2unmdt1nLJ1I3HXVXE4OqWKSQGE6a/Fha6tUGMi80hUQMM/M0Yf1Mmk8gNTMNyWoRIIxJgWyTeYbZURir4HZZ3/Kaxuj3rbGQs/qlgeRxHfp9GOIS5JX8NqP9kkWPCktEjDJmjCELO56zJ4uoczTnJvpyvblzHLE6btMUEDXXWhepRRG8MzSV5XJVsWk966aiUe4RJjm8a5hchXMVYPF1ha0aYrpjtb2k8g4nE3GGw+6eEB0Yj/UWSYmqamnallO/J1mPTUJlDX7V8eKjT7h68oz+cMc8HrEoATckwbUb+t0dp/6IiLDuOpwTLq6uAGEYZpXEE8PN/YH9aSTEyDhr3JdSQqLBV1p2q7zDOoVCYgJbOcZpYlXXpBhpqoo5jIRZExSJitJar1KBIQYYJ4Qjla8w3hJlZhRRVbGqo65rsLVCX2KZgjDLvIw6K2oey7QpNEC01mCMy6ONBbEpI8EmiyKUbsxvP5J/PUPMLtgukErhY2cWNrmby1hym53GDTmtN0uckHdHiXgzE5jyutZiXbXEjansQPQ4rxxsGsP1Cq5WgaZRUXdHRwyGlGZVdKiUthTCxDRMhCBU7QbfnyBFptx5Z0hUzYbN5RMedsq2abqWFHXS6XEyXF+sEISLi0ucr7l5+5rK6whdUiSKJdkV+4d77m/eEsNI23UkGWjblqZdcdzfEFKkqlwGeiNGRPmKXokjZFkPrStrbJZkpjKJIEJVV1TWMIdAiJbKG/XUs8bo+poJl2cECoZx0lBFJOKTwdTavSc5MZI04SRmmRg1sEoMEU06E5JJDI8Nk0wq0ezblnVM5zUmrxf8agGmX8MQ1bhiNiBVmAcnytou9WVy9aVQycsQIKQQK7ORZmMmQz/k2cLkj5FVds5KXwKNNzzphCfNyKaFpul0wKLVxnJnwKYqA+tOAeloiFT4dss0HghS0bZbUpoIcWT79CNOQwAJHHZ7kkAIgXa9ISVDmAL9aWS2ewCSWOqmIYpw3x/B1RhXc/vuHcPxnq521PUG6xtOx57Ly2vqumGuPV4cBksIM01X04wTroLaCaeoFaXGqeQIwTBOOnnUOzQpiYl2VWkXnzVYb7QOTmIaZpyt1DWIMM+jRuy+Y8xKtgbHPJ9wbqVJRZxV7N4dIM8U1OKdgs9m6dZU8kpxbkuvci5guLx+S5tchvIs5wPvV/nF756skFP4bEjWlCaob1ZTVO9fTcpIyaYFyngEHhmhdTk2PE88KvGotU7fB+1P+XBjeNoOtHWkatb4ZoPxDTrxaMRWne7OFJmw+OQQU+OmRIiBfhTai4+I8wkRsM5h2DENJ+qqVnUtK6w2F3SbDuNqRALjPNOsPfvdPYfTmOllQt1ekJLhdDrgHFxdrOkai3M1h8OBpq25ePIcawLToJOfvFPdw2kemYPgvPAwCNiaulLhd1vVMIK1iconnBO89fTDTIiJy3WDGCXJGqf3yZpICDO+brTh31lSmJE0EWJiPw1sNhusqYnR5KmyCazF2JqEXRS8YjpXuxYGjTXEpBm/GuX5FIRS+z4nJoUrKzw60t+HISpwmWlcmfhosjsueI6ewvKNuW7GoZl0vpAiN+JdAcN1gkDKokylnJJE1cfWTcWmgafNzEUTaJqVahLWSqeKiqLmmMVkHFPAVrhKG7PatYLanbS4qqLfv2E4Caf9LVF0YFDC4VzNauVYbS6oGp2oNc6RzbrBe8/D/TtiGqjbNc36kt3DgePpntVmy5OLay42Dc7l6tM88uyDj3RgT5ixruL/196f/UiSZWme2O9uIqKLmbl7rLl1Ld09VcWeGbJJgAQIEuAL+UCCL3zi/0tgnjjdJGamh6yurs49ItzDbVVVEbnb4cM5ohbFYWYzK32AekgFAhFwD1NTFbly7znf+ZY07mitIlJ4eF54PAlj8Bx2kSlA8o5hSDQ34FxliB7vFEMdx0AIyjH0bmIY9yzrmVoXE6VFXG+UvKhqThJD3Kkhgi/k9cJ8ORHCLUkUfai1EcqKH4o1uc76USVAt9bM3zBsICEbKryF/ejpZaWae11uhln8YPV8ooV4HbP5SPCO4HTS0XEqnra6ZsMX5Vo7evvor7NG54Rq81MHV5FVNxzLO2FIni/vdnz59sDtzjOEhqMTYyKkAaTRqkZ8uauFsUEP0Sum1VYInTjuaFTiLlDywrR/y2W+4NKBMGTCOrMuZ1LypOmGu7df8Pz4nnlRZ4tpt8cHIReh9ob4ldqf6L0yjQcOuz03xyOH44gAz48fuXn7lt3hiKDH/s3bL1gvs3rRlFfH2DE59lPA9co47HW36ZHeClN6Bfo9wn4aaLlQ64qPNxzikdPLC7VAcFVra6lId0pb65ktFnfaHehtVUlAXokpKoG4V1rNuFpxcatPr/sB25RM60N1UPthN/wKcv8A0JErxvL657/nbP7DQyG9aiGCU1JsFUGuOGAyDqEe2Z5O9Jjh41YLqq/aBnA6EYLX43qzskvBc7vzfHkT+ewYOBwDw7THDxMpDTiEXi6U+Zlem96kNKjDgZg21DkIDWK3SWNX/l1I4OuVniQkbt9+RXCeZVm5uXvD/u5HhGHk/ftvyDmz30WOt3fUfCHuDgQJ0DN5OatE9RC5e3PL28/eAYXn+0ficMOwOzAe9qTgYRqV9xgycRiRfjYvRzF80jFFR4pCiIl51kZvHBPrgoHaMITAlKKFLmam4xuWtbPMJ9R+RGMrRDouTXg/0EtmyZlpUHN4HLRWyHlh6HvERXDpqkNqValf/dqUcK0Ft5rfyasBQvgBaL2tt83bcmPg6GbzyRaiKdp6VoYFgSYGXnf/SmTAPry3uTHa/epcpV3BUHfdMRUwDQKD73y+h6+PcBxnYne0EmjDhBfbHepKns+Uy5neVSLqQ7ACORBjtPpHiOJppdEa5Fyu1WxeslLcfSOMRw5vhCHPzPOZy+mB+//+nvsPHzjsd9y9vWPc3bAuJ3LRUdVx58nLiojw46+/4usffck8X1jXlTAeOBzfME07pbCJBnzXvEKHEEcFqptGCSev33s3qj94TJHyUhm8alQW56i9X4/CNCSESllndoe3pGFPXha8Q8X9adCoj9ZoLSO9qka6dnwIpK0U6kKeZ+I049KKG3RM1zY3Nyu3lKj8A/dYL0p2MajDlNFswvzSfujs8Z+Gbv4RC1GollDZDRsU94PjFq0dw9a6O3ctZsXsRHq/Oqmga8eh9UfA4zjEzrtwYuoFLzf4dEeaDqrzEKHmzDqfWM/PtKLHSwpWv/ioJY5pYAIT0i84rzEWgtO5MZkQB8KwZ3SBeam4HnBxzzAduTw+0kslmkn73ZvPTMutQUG1ZN7c3vDVj37E7d1nvH33loePH5nnzO3nP+XNW7UXyctZoZQAvVYu5xP333/HeLjhzZtb7j7eE33lzRH2qeu8OASqCCVXhggxqiNENyPO3jtpiHTrJtb5RIwHGwgoKSLXTIxqNq8aEr0/zZqQgI4PQ9QZeu9qlxJNY67NsNkCovoTBJ2D4whmE3g1bUIlp2xhQAKNzbpErt7bn2wh6k7nEOJrw+La1fF+e2JfF6GDXlTQ1DsSknZVjh8QH7YjGW4m+Hxa2fGEFKhhJB0TcRgQadR1Ja8ry+WZupzUxi043Wl8ZIij2vqysYRXcNochZCIw4FlVkZ1bZqvAoF8uScvJ3ZTJKTANO3I6xP73YEQPbvjkTTs+O79twgQTYUHHvdyuo7RhsMb9odbfF85P79H8Ny9+YxaV/WliTsOt29orZGC52dfHVmXgHdqzLmdJErkraTkiLZDrqbNblIJ4k2a62mlEGMnDjtqnlX11zrzsjKOfqvANU9mUNNPHxy1NGJoEBMMB0h7uot61HqVlvbWuQal2Dpy24jXkBBvEFx3KgS72pf8gGhzPbA/5dEsLiLmfLVpEbau9yob3HQK0kCaGdVb4Ja30Z5x2DTcOnI7Ob7ezRzkESkXskuEkInrTFxXWm/knKm16NM77jRx0zlyzZAjcTgqFczp78EnOgXBsb/5jMt8VoeqXJWWNR25lI805+kEfBxxPpKmCXd6JqTEtNfF83j/kculstvvKcsF5yOtLDwuMzdv33Fz9wXQceWJx/uPpN2Bw80XXM7PPH74Net6wTtPqZ3T6cQ5wN1+gPqs+hhxdCK5NnXQ6pUpBaJZrCwZo7dFhGZifE/yiZpnPTEMP3Uh4aSTcyalgRh0wNBkG8s6JfmGRO9em8+0o4c9vekxrAsyGKVP5/XiA9KayhI2HDPoSLfqCU73+v4/lLM2w3F+d2z4P3LEp+/vNADQBZzZD29yAa0dGkhVuMeYndIrznKavYfbw8jtcWQXYdcv7PoDri96jIZEChGcUEq2o0frMxeSEhpq0bFhU5yvS2d/+7nCOj7QS0FEGMYj67LQW8EHjVfTnW1CRKMuggs4n3BpD0tmvz/w9PjAuPsMcDw9PjDPLxx2R6bB431HWtSMkSYcDiPr/Mz5shDiRAiJ09N35HUGnB65TTMt7u7uaK1wvjzgu8e5iCCUAj445lxJXsy7ulOL5i2r1VxUuCToruVbA1fUGHML0RSUEylOFwGecUjgFCdcloILKlt1WjdAXZQs67UW33TovVlYTy9a63cxH0dlYvtu5ZXfRri6TK7elmiTIj8Aw//ohSh0Wis2lus4CWx6+80YSH+5vP5S2dwTDFOyzOMff37DP/vqjl1qlNM9+fnRJh3KDB6HqFpecVb/JTUyd+U6tqvziV6zuT8ow0Zu3ql2JSulKoREqytlveBdoIkZYtrEwDmn+mofECLBR3Ip7G8/Jxct3nvvNMs97rUgNN6+fQvOcz5fmOcLHz58y2BwCNGzLpl1vpB2N0w3IyUv+LzoZ6iFmDwp3FGWTluh9UyMesLk0plSUN8YJyx5O7a95v9VrTuVr9loBVwYqVW/r3fqAuuC/4Hdi061QhwIcdTmbS2EuMKhGzzTbG7s7HTVnRHR86u3ZvIOwTAmWjV80CuRdiNBB+/VU7srUeL3sbP/4IUIINKMquVxNKvBlAWj/0NjEzup5YWj9UYIAec8h/3An331hp9+eWSfhPX0PXV+D/VCrU07W/GI67Rypq2RMB2JcTLXMcg5qz758ox0OBwmhuGAc42Wz9Q44n2kewtYXC/EIdGaUMqiInQ2F4J6bYTER+IwEpxQ1gvHu3eczxfieMPtu895//4DPmqO3fn5mc9//OeMuzfUuhLjXt0nAsQUKMuF45uvwEcuL4/UUghRVXy7YU9vmR6F6O8onAhB7eyeT43TUhmjYxp1582lEaN5U7dqYDP4GKnm8Sy9g58otTKOA2kIZl5lcHMY8THiQyCkyOb62kqlLC/E6YiEA52mfUDvSN+mXWiqg/cEw3691+K7m7aFBpt1zFX64fToFhFzfvtUgLb8cKJiVhhOJY1b+LSXom2+c9A83QyUdvuRz9/e8uPPb3h7HIhkzo8fyS/vqXmlu6hPYBiIYcAFj/RGa8Xkkis1r8zziZenj5TZqPnRE1LCBRVxSauEEK7ki1YzPqkwvJQX/btNh5sOBB+JMeHo9LLg05798ZaX50dS1JT688sjx7dfMUy/oJTGbpyQllkuT6Rxx24/4SjgAtI6uS3cvPkaH0ZKzfg4kEI0F4lCtUDxVgq1rGz9aWmObx8rpXRupsBu1BNhKY29YX/0RCmNcdhCd1Ryq1EXmvjQRIgxspFpQ/TXzMIQkqIWdvSKAdrSC71lcAmISlBumjW4sUyjc3TnbLOxU89GvJtMBKdHsOtiCyZcmVe/j4Xzhx3NG0kB2IivfmPL0BF9LIyd4SxpQHj35sDPvnrHF28ONkFolGVmOT9Ytl+EEHESCcFZQKEC4CEOhDQC6HFcq4rEJz0mle2ktHofBoVwvE4hcqs4nxiGgcv5iZhGWl5pVWfSIg0X1NwyxoFaF3qBMO6I4cXGmY7L6cLx7o7bt59z//0HXAgqJfCBvFy4nE/c3d3gRTUzb778CcEnzqcnlmVlWWZ1JKsZH7wai7pGb1X9gqTTxPH9k2av3EzCbqdM7Nq0YWgmv9jcu2rvDOLpojUjXufOuEBr4N3reE0EM8sUA2RMh+xsNxWQWvBthbQzloB6UHa1CrtCdQ5z+tqcZa1Z1d+jf+C2Md/WK2wEik/lBrateO+94VCOGCNDjPhN7C2a61bNxeEwRd0FbwfGWKnrok9bWdQag6DFuhvALThUT6HrPKgSL45m26YO/MPuQL90ei+0XqltwIUdvRbVEqdRxUytEdPIOr8QQsKFyHx6VCgnjZQmdlxBTAlHYZkvrJeLptv3xhA9908P5LwwjXumaUfwnmLSVICSzzw/Ft59/gXvvvgRwzjx/PFbau20qrXZ5fwR7yK5d50OOW3mpMGa4flUeb5UxiQcd54xgrKZhMMY6a3ipKmXdvS4ruE53UasISZqezX3TDEwJA2lxAXFD4eRECIQNA4jRlzUJNRSM1MvahgvjiaBVhVm20RW2JC2WSftLfgRkesk2ckPSIHXHXBb2r8bS/wD5aSOm31kNw4cpoHdlBiHxBCD0cE7YuHdYp3VNEaOe29yRHOVlY701aYvmsvXW6HWfLVpi7ZAnU8ayrNcFPvzevz1VrW+KQveD7SW6WFg2L/RpqpqIn1ezuAcMU3kvCB0lQcMO5bzE9Irw6BEiFa66oOlIa3iENK4B3nk9HyPNLXLq62yP95ymWdubm8ZxPHVl2853twSvOfy8kFpUm7QcWIujLtbel1xqqWldWFdhctLobdC6Y7eK8cRpnE7ynQ+v98NnE5VZ9wSrmMzBZgDOcPowVlciKNTe2dMA35QXmdHj/YUlCDsvLPBBEpC9h7vhRSEGAO+qAd3s1pRmyWD3Iwxb52MDSjcNY9FOx1l5W8ykuB+PyPxD1qIMXh+/Pkdt4eRw5QYouZ5OGls3DapxXZFhUjSeCQN6lzfRYgx6W7oROsNp/4sJWctnltDvDnzI7S60NaB1pohkWgtI5VaMpfzC8OQdIyVbgnDaAs8Aha5ZTdIeiOEyLQ/apZer3iEmBJhmKh5S07Vo6fkmWm6I/nA6XJPmiZqWSi1sbu543J+BALzkjldOsOug6u0BuJHpHWknvBO8OOe6iOSM+sy01rh5eVEqZnjNJAvWvfd7hXcl20q1QoBFZpZHArBb6E6SvIQUZ6iD0LvG1QkxOQYh8EMDlAssTebSA0MKRGiapq7Tb96V0PSGNRdDHGKEdoidLYItwhdDWWy+yW269mwIzihmg+TbBEln2Ihphj48t2B/ZRIruH6qsqz3pC20EqGXpUqDrjxDWE8KlWrNaOVN/BO//xyATNRl65cOizPL4RA64V6fiR2zWMpZdExX1X2SZOmrvzDDlwkTrcaI9sKG9dRMUNvuxmk4cAlPyAta+MStZuOQVNVEf2cIU2IFPLyzPHNG54fviMFzYTpISFdI3HXNQOeh4dHhjGx2w3EsKNLBrRTjsNotWJWY3nR7zyNsN/tKKUgdN7dRBDbhdrGYm8kp4n1relC0YbDU2pDfFKssINHMdbg9XOW2ghBHcO8NW9dlGic84wjkvx0derQWbHuph00Oi4EnVLpKtMdzqxFlP232cK8entr9jNmWf0DPPH3jPn+oIUYguMwRQIZKTO9LfSe8aJuVL3pTfEuEtIBv3tLGCeQRu+VkhdCSozDgXS8ZTrcKbu4N87PHzk9fKCVBfVEVFmm+AZxoeHI60V3iibU1mhlIRolvndlmyAGsodOm1WUJaWzLKu69nfFEeO4g1qp4pniDa1eaFVIaaK1zrqqai/PJ8J4QxwScZjwfqauhdYa+5u3PL7/DV0CD+dHWl348usfcXOzJwRPCDuqT8znM/Pp+boAt4dug42ci9zeJHrNfP9QuNk7QhemQe2EQ1CcVB9CHSaEkFhqNqaRgc/oru+a1bwOal3NF0ibHzFv6zENigOaNbR3w5UPoLihgzjhEXqZ2cILrpJf2zDUK5vXRYr+P87Kj+343uh+n2QhqgpgppYZqcu1m+rOIT7hjDwZ4kiYbiAmdR2QqhycqKSD/fGGYRjxIVDWFXGew9sf8dmPV/LyzOn7X9CyzlYFTy42Vmpq+wHgfcI7T5OuifSyUcy0Y2u1XZnG63qxuXKntRd8HPFhoJQn0rBXIfnzmVKFOOxw6wyt0EpR4kIutHymj7fgOvnyxDoG9oc74rSj5srNzS23d+8Yd3vt8nuj1sr5cmKdT+A6wUzwsd2pVFGD+RSQ3vjuoVAK7LozeYAnl84wOMaip0lpQm9qvhSjalWwEyjYYuit00IjxahsqVp1FNM7aZwUmHYwTDu2kHb9R+itQBA6kcZEp9C6Pvh4bXK2CLqAzaOv/AJ04TlPNRMmZ1CfA7Mr/AQLERFqWaFm3Xm8Qibb+29HagiRHia623ywm473emWa3jDtdoQ4Ijgu9x94vn9guv2K23efE6fEeHPm/rd/h/MTcf9GIYNyUj+badAL3w3l95rArnWggbjSaTXTykpeZ1zQRb9ezkrVTwf9vGHUEd+sjVAYRvK80poaXkqbVeTfMq531cM4IUbPfLlwzJn98XPyclJ8sKoxJgIlz+o1s8ykNDCMjlYyeS307shFR3TTNHKZM8+nwrKK2gSnQEpaj22KyBgcFU9rjlw6yet1rq2h4QTeumdlyrTaNJlBDG/0qsnurTKkEWjQM97vFQ8WPWmQqgrMWsjlhU3LrStNHxhxOlFr3RnG2Ixxw+uYcfPDsaWzMXU+yUIUQNyAS8rQ9iEQ00SI5vbklejQa6GJ1/ooBlqBJvpUCmpo5JpH/Ej3R1pYeP/deyQMLOcn0nAkHb7g1z//O/xwQWpmt9uxO97g0kjOq9r1ev0dnk1qoETT1gqlFGrdcC1PvpxoRSNtQxzIeSWERO2N+fKA815t4LqQhoHgIq0uSK9KNrVjTlWEnvP8wnp5wYWB54d7HJ00TeQ1I7Jy9+5ral4YxondNFHyQu6F1jy1KU66293wfLpw/ziTYsB52I2BaYAYnAYrScN7GBJXY9PSOr528OaWYce9GKis2XzumnVTm5rhex+oVYkQKU04pw+tzpQ1Wg1RB7Pkdf7cJAINH+I/mIs474loyn0ToYgDFw3Y7loxGOlh668/GfvG4UnTLSlGQhy024yDCeqrwhDrWV2k4kQcJzydXoRWssE3zzzefw8uEoYb4nigux1rm/nw3XfK2nh+oS0X3n/3DcIHUoSvfvzP+eztT9nf3rGuZ+iNp4fvjTZVaK3QWqX1pkePkTOdU5KDj4PapyE69/WeteqkpuaZECd6q4RhYNp/yeX0kXldGaadzRUa89N7ShWkrQwBlvMDadzjnKOWzOQnfIDD4TM8lZubAykmLpcLy+VEK5tbmEdC5OVl4eH5TAo6RovRMU0qHegdclFfQYem01d1rqJ3zZjREYJqfbrY5MPkvj5ogxhTRERn88Mw4vxAqyvDOOAtRkS6ohM1C30coWYcA8F71lyuQLnBCdqYBIcTDbHU3697MoC4fh1IvEoJ5NM1K857ht2dUoss701JkZmatQAvRQjDHbvDAe86+fJMKTaeO5+RkDi/PJF27/jyp19qtzvuuHnzGct8RvBczvd89x//G371i78jhImbmxvS/ksOn6+kQ2SY3nL32U9Yl4Wnx++JvZCXF/JyZtodFMMMJuwSsTm4YYQ1EzyUUjQxwEeGaW8CsM7u5g7phcvlmd46u/2RZV5wMSLrhTyvOh70gbIqRhmc4AdtDlpplFxIhz2twuX8RF4vOpe3G7KWTs6Np8sCwJgSaylMQ2Q3BZoItamRksIijhAgRAOwu9C3SAprVtR9wV1Z1NtCqLVpXrWoeD+lESeVlhfCbm81q0pvpXVqWWh1pbJD48s8pZuliMFIDl3wYotTzba4Tlm2WbN24e3aMbvfsyX+gTkrOgpzmx+KpU62Wqil0ZvDxyM3b96x20XW0wPLeiJfZloP5JJZzyeqDHz+xc+4+/xragN3mfEhUWvncnnmt7/69zx+1N3m4fuP+DSRa+cyz6QXTQZ9+uZvdbwnmeB31/kngA+qcW5lNTWbJ6+LNlJpoAtcnn9Lrwtpd0uMgx2zkRAnLs+PLOdnpYx1BYP3hxukZGqptNYpreiuVAebcwdabdReWZeV0+kZJxpdO42jNkRrVt/BUmgNaqmMSYnBXXQK5Zxjyera0LvgojloOIjB6jCveKBzUU3W+Yf1l4ExxOC1megdp8JoeisaAuRAk2bVN1H9x/VI721lTJmZxCbyN39pNk8b6Z3Su5ZkQQcWKm3eDuKu7BsdvcDvXYb/iGal1Irvneo0g7n3SqvlihMe794yTSNlPbGcnjmfHrlcCrqJOx7v7wnTO4Zpx7IUxEdD37XGVPuPTty/46svf8Yw/ZLlcuHp4QM+7liXBScX3n/3S9r8xI///F+QxgnpnbrOlFpIUY+jOB7oIiwv93iXEJ/oUsnnR7x37G8+p9aiUFFRHmFeZpbLmWHYkddM7Qpet3EgDXvSyzOtF5b5zJo7tTZaUY/oOT8T0sA0Tkr4HUarkVfmy1kXGAHpjpxXjcQIiVxMKThEctFAytazYnlmBRecygacJT91E6OpTOhVkrGx5HvvDEOi906umWnYs8V90BuEoI5oKdJLp66rAtkpEP3CMHRi9CzlKk5GpG6TPiVEtILEqHN+DEekX08ih6N5bYK6ub99koUoItRScCGou1Rr9K4YYWuw2+/ZH/YIleVy4TLPvDw9czovvPnqz/C983j/EQb49S/+A9+9/7fgd7z57B27/Qi9sJweaLWzu/1MGwgZeHj4FZfTiZqVOHD39h27m8/4+PQ93/36F3z5078kDiOXl/dMN+9IN3eEMNCjo60zLiZ6E8M59bmcbr6k5gutVnK+kMaDwjXzEyEOHO6+onz4LcHD7viWsuxZ64lLXQlxz90XX9F6Zz4/c345KbnWN9bzibwsDHlHSAMOT2+r3jSn9nLLqm5kY4o47yirZuXhInldmSZvUxTVnGzGVMEbeGzMBxUr+uu0Y4snE7hGpcUUKLXRazNfH/DjpJICW1Vp2lHXC61mel3JqzBMB3ZpxwlLCuv9uqcpdliBCqIsqe7EkBQdZwiiD4o4StcpWBjGT7cQWy3Qtt2wGwtDcD6xv7llmAbm5xObfDQOB26nN6zLwv2Hb/n+u19zWj7wcnrkV3//CwgT0+0X/MVf/UvevftM1W0+Aok4HhjvvkA+fEdzDQmJ0oVlXpgOn3H35V8wn+95frwnBM1b1ibFRuxOO/sQB3rPBB807HuLbBXh/PI9ZV3MZqSw202UosBw3O1Y587p3Pj47bfksnKeC728MB1+ynjzjsPnf85trhr8uD5wevzI9x++51JXxCV1SnNCSo4UozGtC9ErKN2sEx6i03CeXqnVUyoMyY5TMWs9Y8s4M7DaloZ37nUkKLYQZQtk1O9a8kqKe60vW8OlhPPRSBQQholeFyMwK5Fj3H3NkITclLPjnbej1o5pkrm9YdZ5OuHpG1t80zA5bc76D4R2f9xCRKg1a7F6HWgrKdb7xDhNV7KBK43SPNXvaK1x//2vWXPWLo/OdPOWz34KL0+PfPMf/zvWfOav/uavKeuF+XzGj/Dx4YmXlydydUzHPa0LeV1ZYySNd7z9+p/jPu64vLxn+c1v+MlfHk3Qoxa+V9jAcLC8nEwFt6eXheX0kRgibnfDcnni9t1XOvnAMZ9PvDw80SUwn77hm1/9PV//s79mv4PqO/PLhe8/nJn2O3wMxLgjTe/40Z99zu27d3z49hvOl5Uq2oy4Td/dddEF54kucloWhgjOdXKuBh47Q1WaTSjUM9EFcL3ZuM0MsJogzpx2r/UYOlzAXY9rh54IMQx0qXrMoikMFWPo1EItMOxU4BXbTMBQEf86LHBBibHSX49h4TUUchsVbnCS847wn1hqf7BmpdX+g0wUdXYQgRCjJVIpyKkd7Ud++fO/V7/A8z2np4/MS+Pw9qcc7r5muv0JH9+/5/njR16+/4bvvtlTS+P7j/fMa+V8fuG3v/yPfHGz4+4//8/ZHe8orfJyOpF2d0yHG3Y37zg/vefl9Ej49a959/UTx9u3tldoBok69Re6NIuRhVb1OBY/IWXh+PZox16hiXbVwzggkni+/zUijdPTR+4++4Lpq685PT3x9Ju/5fLkePPuK6Y3gdoyue25+/zPGfafc//9Nzw/PVHyihT1AE9DIjq1vit1JXglGJTa1cdms2pxTkMjvWNtQhBdvHh1ZRVBGU9OTypNGei26LTxkK1uk454Y+zIlrMMG6lVuo5mtRB0KpDCIzWT4g636HTFbRFiYmZafrOtbgRlTduitFqwtythNnh3Xcx//ELEiJlwvRCq6VCtQ22NGJUps66FZS3kdea8aoNwfvqe2maeHh74KYndfkdvTckDz9/zm1/9gvv7J76/v6fmTGmZwzTw5U9+xvHLP+Pw9kf0msnLzLyseHeiLAvdJ2qF56dHfv63/5bkMtPQ6WWllIvGtFUI6ZaY7hCn+uzWxUJoRkpZ8SESx1vOLw/cff5npGnPb//233J6vKeL8Oazz7l5+yMulxN5PWsutNvh4o5xf0cYEpeXJx7u7+llJfmBMUaWy6zCpxjoVWhOmUzON539to7zumiCaUS0+fR2fVViSmhKhjC8sHedQ29yVtjIBRYmKVDr6/x466ydeNM36wzaO3Nsc7zuanasBq8qPc82037dc3UNqs4d/yqcanZ8KysLhZ+8ugd/koUoIpTeX2e6vSEGVLZaabXSo2eeF56eXzjPK09PJ56fH7g83fPy+D3zPLO0E+9+8p/x03/2M6JveLOt+9u//VueTxec0+MrBdjv9gyHN5RcmM8L0/5AGgOX0yP373/D8fYWl27xw4nL5cT33/4S2szXXx4YfQWXKLWQ5xfWtZCOP2J68xPWyzNpOiqPsWfSMOFc5Pn+Nywv31OqsK4zT/ffUvKiO2lKzOdHPvzmFzzfv+d8WSnPC09PZ377q19wvLlj3B9UI9NnMHrcNDguc2Ndm8Ewgeqd7jLOM3jHkDSSotRqqU2v1PuNQOC95mRH/2p21BrXbvS6G9lCbmayNA5Rl4jJOcTpeK63TlS9ATEkahFyWQl5ZXBBBwXR2amnkJJm7fWrD6KYKL+1Tm/6fb3xG2UT2bcCzSPyiZKnBKgNe4I2F3kdoYWuA/zLeebD+/f88le/5je//Hvu37+n18zp5cz77+95ejohLvHf/5v/G+eX/wzXC34ISNWFVFonRc95ydwdBr57/54QDoQwqrKv39J7pywnyvrC80MhpGjp7jCvhfunJ+aXe3701VvK+gG8Y74slLzQP76n/Yf/mpsv/orPf/aWXs8Mwx6f9sznj4zjSDnccP8f/x3P9++prdM6jNMB7xIlZ3qZ6aKL6uZmUFMo35lf3vPwfcfHQV3/x4AyzjXQMlfdoTRqAyNldMYYECfEoDnNVTY3hS2kuyu30auD2tWGErUoWW2uLOJNqLZ50ajftR7P3si22Bi06UgzmsQCIaWR1gOtLPTggEb02iHX9aJKSpP16nHrzbvSUYuGQ3qPyTaCunr0hndJQfNPJRUQgdJEgdCumcque5xrpEHIuVDziV/83X/L/+P//m95fPjIm7vpyuzQ9HrHnFe++81/pPdn1gJffPYlzxd1KRiiYlHedUptxDSQXOP7X/89yVU8Chm1mgkpcn78Hucjw+jozrOsmelww8PTd4Dw9Y+/oi5PvDw/kpeVUjLz5YXvvvueMB25ffsG6dGgCyFOb1g//JY4Htm/8Tw9PZHXTBwq+fJAXs+Mo2e/H3HOE6c961JIu4lxN7LkJxwV52BZq86QWzP3NFX1RK92v96bWbrY/xtgtwsqBXAerF50Uq+QTIqRLQlVzZAsvUt099sWKMb7tJwRJSW0xjCIwTZ6opVaiMNgo1qnp1PQ2AtHJHp1ZcuZqwzEO2fkFnO0NZJsQLFJb0ZQztnvdgF+YEv4SRbisqpOJIRgMRpNL5bPOvnwmeeHX/H9d7/iMq/E+JZpdLRe2I3DlYld64X7e4Ulzi/PPJ9eyM3YNCZH7V047CaON2/xLLw8fAdBO8VhnEjjyJwLZX1kTAPDOBBa5vT8DK3x65//LWnY89kX77h5m/nu17/gcr4w50ye7/m7/+a/4r/4X/3vCWNiPt3z9PgBHybOL4/Ml5nz6cKyFOa1E/eO/e3XHMnMpwd6/4bWhZfTGXpnccI4jtzeHXh6OrPf7UgBalF5bCnZPCO3VIUNejHgwaYXtVWGYdDThqa6YOfwQa6k0+jV2AqTaPZacV3z8HqrjP015ak1robw6neumc69V7r462TMe6+SAbeB5p5aVvxQGUdNJ5iX9YpPCrqbb3Xl1d0DJSMDhm/q/rmFRH2ShdhFOC0L3jv1XzEU3TtH6p1cGzE5psM7fEicX75h2u2YRp3luuDZTwP7w0htjXNROnsMwt3dkdveOc+zWg07PZ6SF8q6cNgnzudnfv3d98y5EuPANO3J64nbQ+QwBW5v7xjHQZkt+5FaMr/55d8T018z7d6Rpu+ZBFzeEcLAw/tf8vT0xNsvdjw//IY03PL88dc8fPNzLucLz0/PdEmENPL8eM/5cuGLLz9XildzzJdC8rC72RP8oAsrjtzdKPA/pIFxl1gumdYg0hmiY86NJo4UX0mj3uusrjbB1Ub0Xke7XsdliB6Hral7bDBTThEoVa5GSq01pJu0tut/19YYnAqdSq2ElPBeWUrKyKn4qE4QIaSriYLIQG0NIYEb8K6omMo4jAhXK84rycG+j5IcsAfKdrFPNVnpvVOLJrW3tmkQhN2UuDke2I9qniluxzrPlMtMb5VcVs2Ac5B2g/58d7jclfq0i1xOZy7nlWkc6D1QCkQXFAQuF7oc+Pj4yC+/vQfnOOx2pBhxdKa0J4in5sKbN2+gdbw7cL6c+Pbbj9Qm/Mt/9a+J0y2pO8Rnht0trf2SD7/+f+H6RVOfauP0dK9d/3Km5Jk4Hvjsyy9I0XN61Acrjkca2kDspmjh4besy5m8qh5E+YmZadKItFr15qQgyCCcV0Ugotm06JGm17mWRojOMvLcNR0kxsCWquVxhDTSaiVFjU5ba1WIpglE2/V6UiWfuUh0UXRjCCr21+bXmf+ONSAIIoWYGq5nvBsJHlUFNkd3KnbT1RisYRV9aGzagw06BDECuft0NDARZVyI6BWrrWhbHgKtdy7zwpiUDia16BEghfv7Czk3RpMFiPHlvHfsDoMW9NExTYnLpVA7DMPE/nBkjCPBJ/J64fl8oSNMMYJU5mVmb8d9JHJ+fqb3hS8+/wJezpxeTjyfOuIDL0+PCBGfDtweNLO5lkaen7g8/ZaSC2v5wHJ6MM1L4+buVuEWH5mOX5DzzOPH7/jLv/nXvDs/M19OTIMGA80y4MNADEK1ncaJipm81+OwmT+coE2HrpetA9a/C4bNhahJCaC7jWfD7vQdEKXoxzQxCNS6sIHJtTZk9EA1TqYGI7mgi6HXRgv6kNMr3u/ZtMkOaKXQ+mLv3VESrQrNut8068F+djuqdQq0zbmvltZOjCxhn/t3vH73zOX3LUaMOo4mtQuex9PK83kFN6p4vMy8e7dnjCuX54+0fKGsC601am2U+cI6v+CDkjT3+1u+/vHXfPnlHbvRU3tk/+YnvP3qJ6q9NWqXdqKVJc+INFqrXOYLTy/PLHmmri/0tuBc5+H5QnMT+y//hunNV3QCrXkao2KfdWadZ7yPrMvM48dfk88nzqdHwnRDGI6spfF4f8/88sRu2pPnC//u//lfc7z7MV/8+C/Z3X7G/uaOLgvn0yOCsqydeYLXKmooNUTSkFTVhtcoWvFqeGrTKTE3f282HxrI2MFrveVCsBQtzapWs8zGMEyM42B2NJ1WTQ7bVUKqzPF2XSrBiA/q4pYsqUrxPxcSw+4OfKS1VT1vaqblC851xSd7MzGaWrZIr9TaqLVrtFqD3BxVbCrU2nVx/q7XH55O6vSI9s4rtha0jtBZbyRGDdqOvvPnf/ZjfvaTG371K8f9/YX39y/Uvmfc71QLHT2+d25ubliWyM2bO47HIyF8y3fvX/jmV79k/eyNMoVrZ1kz87oQnCOGwDgkBEeugO8kp9SnlBLeBW1k2pkP379we7NHJChtqgVV7o0Tj/ff8ObLn2k66XPm/Pyi2FxdWNZMySslr8znj9y8uSGkgfPje379719wPiFS2R3uGHZveH74wPn5kePNDXGI5GWm1owUzTrecJdtsFGqElhrtwlH8Djh2gD0VkgxEVxAJOC8BjWC7pKtbWybwjgMTHGlLOoetq6QgiM6fZ9aOj2NSBBwCR9GXf/e03sjr4sxc4Rpd2uCtPXV/cupb2IAmm90381uWhuU3jq5zGrgGgcbUQrOBd0nf49M4B+1EHWUZFrgEOhNbUWGQUd8XeDzr3/E3/yX/4qf/Ogtvc6k9ICThVaFx8uJYXfkePuWcV/NdEk47Ec8gen2La0uLPPKb377yN/efyBFnb2uRUmtw+BxLrBl/q2lQRTG0RukoJbKwxA5PZ74b//Nf0V+/Bk//dFbnIfLyyNOCmm4ZT498fj+G1xMfPPrX/N8UiHWMAZiDKQUSaPgY6JLNIB4RNpKSlGpbCKEOHK4+YxeVbe8OxyZdnvycqG1TiuV2mwXEkf0wqV1XAsMzpFsQmKeSvgutOZwHmLy1xQoXSAq7cULa9YH0znHflLZau+dnDt+8HgcKSki2avDDQOOgncj3gVCjEj30IXWK046JQ3EYSJ4G9G5iAtiSFAnoN6IVxmpyUT8NtnrHXolOK9iN4lqoSy/O530DzuanW6zwQe92UYBiyESQqDUwuUy88VXf87/8f/yf+XHf/HXdB8prRPSSPSOMTnW+czlUgmjOrFCQPxIk4BIYjq+4fbtkbu7Eec6lyVzyVlnp97TuoLDc+7aYbdmnjimYfFqzD4kdZB9eviWb37zC5blTKuZ09N3rJdHXSR54f79fwCEn/7zv+BwTBxvBvaT6nJKKcRhhw8j58tZ9dU1E6cdaXdgf3NDa5lpfwBRFWBKiXWdiXHHME4qDUAbx9p01hvN46cUmBf9HsFrA9hwlh4qqoZDFC5DF2AtVevzIeJdsOOwEmPgsB9JyavZpiggHT0MyavZlHOkOOhCxk628QafDvg4qdpPBB9HwnCrS8QFuk90P6ha0wWkrbS20mqmlqzisxBJUZOxorkAq/OaXOUKn2YhitUqXhdhrdWA18iaC6fzC/M8q55lvAU/8HxZeD5ncq3Ma2WcRnKZOZ0e6VVdW+N4oJPItfP8/MKaHdPuhjdvD9wdRmP7OLpE5upZi2okYhwYpz3TGIibEVO07JWmsRrH446lnHl4fibPLyzzC/PpkYeP75kvj1eA++Nv/z378cif/eVfcjxMOOeoDZ5eVnIbmGskV3BhYrr5EhducH5i/+ZHNNHFsX/zBqLDDzvSsGNeFnzcE6c9eI+gnozNqGpD2GIkPJfcKd0j3WkdK4G1Wgfc9QgXYxQ1m+sv80wa9tYDRHLNjGMkRUs3dZE0qreP816PTJfUfm86kHYHxGkwUEzJKGQRQXdd5wddJBbJ1rvFj8RJv0838msr5hupIi56NcmCEKTiKTZZ+d1L6w8/mp2n1Y5zJqJunVJWhQrqQry7IcXIfKrcPzzyza9+S66ieXBN8LVqfVMyTy8zn391RxgnfG88P9/T60wp2VSCI8OQiHFFGspGQacRMWriZ3SFEDvDoE8jLlG7PhzT/oY0VLo0LsuszYR0Xh7eU9rMzX4iRJ2bt7by9P2vcUl3sFmEp5czL5fO8RaSCGupsBZ6q0y7IyRPXxsxHfn+w7e8+eLHTPt3zOdnvAghqBd2CAPBrTTX6N6zOcd4F2x3DBYMCWMKRrTXWjHXRiwrUwgq92QDjVEHL9fxYaC3Fei4FBnGgZwLPqpDRgyBRqDlhi8LpTV82nO4eashQU5nw0Gg5pMGBoVAbMmwSgXW2Zja3uP8iPeL8Q4Vx3R9m2dv6IAyoOgqI/Cfkn3TaqO7ZsxgR22Z1CIiwhg9h8OeUgsf3r/n1z//OadzYX/7BS1nfHxWLz/ncdJ4enhi3N0QQyAN6hf9+Hxini+IePIys646ZfE2Z01eqWfRe6JvJFdIo0IRaYjgPWvplNJIcWAw563bm0aTyv2Hb6n5xG5nOBowDDvidKSURrm8MIwHDl3rshguIIUYNSQyDhMPj/cs85k3/msVvc8nzudnugT2xxtyVu9wfXA6kUG1M8GrklB0puwi+NY0Matr/EQ0R4bateaS3sm1ktpKcFEFVd5Zg+iZ55P6YaO0qGppCIwjy7oyoMf0MIwmsS2E1ZJFQ2R/+znDMFGWlVozrTsNV19P+pAON3QidJUthKCMbb95HRllzHmzk5FXdvjmILsRYzZr6z96IQrKzlDem84OpXVSCAxD4t3xLQiczye+/ea33H98oNbAzd2XXO4/EJxnrhkfDxyOI2sPeN95eXnGe8/NceLuOFGXM98/PvPwdNJJgtGjBu/ZTZGU4rXeTEEtTZwN9xEhLxfECa0spDQwjSMpCsvlhZJPeNSLGjR7xeEIw57D8Zbn548sywkf9tzcDtzc3OjMOwTWlgnAzc2RdVl5+f63Gr0RHNI6L48faDWzritd1KZvjGbJZhZuzig1zqt8tBQd+227YqldpZpOR3LR8LjedaJSRa47kBd1vS0lE2Kg5kL0wUZ6QdWBa8F5TynCOGkqVlkXLqdnHL+h18bNu69J+x0uOC5PH2i54v2ojHzRkkJZNUZHbIXetd7rrepacF5zXarOxX0Ipr/RubTzG5b4CRYiIpRcEITeM61lnEB2nmUJeAfjkDjsPS9PL7z/7beE8YZcGstaSIPnfOmARoqV88LL0xOlzJxfXvjy6x9xPA7sbm4ZzqsaWnp9Andj5PZmYhq1ZozB4+jQi3ajwGbQlAaP1Ib3BSeNIalb2Hy6h151B00RNazU7q/lBXa3vP3sx5xfnnh+OVGyEIc9u8OR1tT16nJ+UrZR0PI6LydaSAjarD3cf39t6pw41iDcHLWR6q1frXxr1eyU2gQftAlp5l9dm2AGakZG9eASeE9tGW/+/L3JdZjQqhlr9oZ3UY3s0dqv1qyelQXGNCizRkQfmssjITiG+o5xd6tancu9QjPxhtLV5EkfcofUoiPO3nFhwIlasyjJIikcVFZKy+pBlAY1BTV+5CdZiNI7eZ2NRpT1l9tscYiBx15IwXF7HPnNL3/D6f6Bw5cjH777hjAkXIxUgSkmfIgMfsVJ5zyfaHXl4eO3fLxXBV5ZG9OYGJLjuN9xOE5Ia8SQLOasmu4k4mvXMRaaPSxdoRvHaAL/hby+UGsmBs9u2uk8VCx3zmtuYC06TXj75c+4ebPwcP+B8+lCq2osMJSV+fxsJqTBtL7QUKuRUiq1bjCNMgpXJeRxc5xg0wObLFMTVSu1ORNPqVQ1SFSXBkQNbZxJM4pobWgC9tqaaVKcdc2J2rXd8CHQRGMwvHfEZDG3KKNpi59o0qi9EfLCYuwoFwZ8TIZk+Ku5kjo3VJpppMUnc9ioOAnEoKJ9MRjIhwCKDCn+2H83fPMHz5rXy4taEvOq2NrSo3pvrMuFh3zhu9/8nHV+oXyEmzdv6TXzdFGH2FoyIcB/8T/9K8Iw8dtff8NvfvMdL+eFXGZ2Q+QvfnbHuB+Q1pSOlKZrVERvhZohpaT0pxYoWZ2xHJWeXxC/x4dILSvL6QEnBYcwjAPDeFSRlWjcRe2dtmRCLKx+ZskaGfbZF1/z9rPO08Mja53ZHd8ivbAuC7nokZ5L1c0gRqvdtD6tXUghkoJwWao2U0FzmnX46o0HYGC2qDGoipvEXBwECRBG9aFeS2NdK8PgGYakLPMmpDgQnKOWQkyJvC6McUcMEfDUPOtCMQ0yiHbEDlpRw9M+iJ0+Wt96d6SSLKcPcELrRetCNAZYc7v9VZuihF4NRHI+GG6orVcpjVLKp1mI0Om1IsbAcF51FGFjE6MY2eXlgfXxA046P/tnX/Dh/p5vv/3Iec7s9zv+6q//gv/F//w/53B8By7yz/9l5fT8kcfHR1qrHPaBdT7z8rJwOb8oz84nUlL/lVZm5pYZh0kJFDWbtfF6ZaOsuYHMtDwjpTAMkMJASsPVXb9VZwtX7fVyWcBzPYbnU9JMvd2ByUFtgZoP9F6opVJaJvhEb43LfCbFxGCBjbV1Fgvy2QXPkjNxv2MLTe+oRkWFRvpn0TsVMqExv006rQmtCGEMxK7almaaZW9TEelNd8P5TKMTg2e5nEnTgBNhGiarOVVIn4YBcQEXlPrVe7drJ6+21Gk0wPo1v8VtTYmoN3nv4KkEH8jrQhJv9aAuXi19gvFH61XE9kcvRIcjRRV3O2dWtWK5a04Q476ty5neKn/zP/tf8r/5P/xv+X//u3/D3/3dz5mmA//ir/8nfPHl5zbr3Km008P0xZF3n/9Egc8y8/z4HSKPhKiSy94aLa8QEmX1+L4SgjBMO/LqNTmpn5iXhY/P6nW4GwRpjckpJT+ZV0/w6gVYuwYGqeXHqyWI9KrNRq9cTh/Vk3o44qIyforzDMlmya1pIQ6sq5p/+uCRog+sHsNaD7bOVW3n0S7ZXXcTbbg2Uqs4oTuFTTSGQ7XIKTrWXEE0Nb47T+vGbEoGtscB6KyXWadf3pOGqDzGWlkFdscd3sdrYPumjwEdPrg0aZfbqwLaDjVjd/GKiTonmqcYB1pZtYmSZvhiAhy9rldWzycjxuKUTaRTAot4dSb0MdvbXNSJi7Tjz/7qv+T2zY/41//6f82/+uu/QfyOHnbMlyfTH2ecxY41s/DwIZKGW/aHhVKFNB0RvIqmzvc0P1Gj4zh52wl11zhdFrp4Hp4Xnp5nllwYoud27Ny+GZh2k5mZD7ig8WVKTA3ghGEweZoow7jlGQkBJNBr4fnlt8Q0ImHCO2suWlBaVbcclFbI2cInt+7YZsvOqW66i9L7u3kOxqi2cUoKUKVyl0YQfbjppifuHRFPCNaUlUpMOgUJwSFdu+OUojLmo2fNmlrgvRIXehgZxolcMufnB/Y3b/AS6E13Kz9EwjDihyMu7HDi8KIBoFJV64I1W60X+15dmeQhGoittLBNarr9gwjyqeAb2GhKuiuqBMEBr6EuJWfSOPLFj37K1z/7mbb+PuHTAXFRa7nW9GbXpuY9YmzjUmhNGPcT0+6GUhoiuvM4GunNntM88/TUOIw71vXC6XTmu49nni+Zy5yZl2wsncgUG+8OjWmcCOOBaFEZPo1QVqtxVSccbHTlN8mm14w6PLpTuc68vDCMuiCaaGEfvaN76N2pRbBvivE5cObMBQbbdBWgCxYla7FdHmVeaxOiGdbe9Nnb5EU7cYWxohdyyWqo5NW0tFqDsI1f0qANWKsWl+sCspyRLkyHW2rJ5PnMOB1oeabHQK8RN4y4MOnSUFIQThwbnu36xldsV7wwhBEXCnWdVY1oDaXyEaLKBfj9xIc/GL5pG51HYEund9dJoUArHG8/43/3f/o/89nnn+NokHaAUNcFyqxPu3hksxKuHdfVfVTMWy+mHdOU8X6kirJqjrvPuVtPgGOInv3NkTdvMjkvlLpyOleiF26OieOYOEwaSh5iMi/riZAmbQa6Egikd0KIpo5TC+TWVUmHaJKUmKZ3w8OiH8jlhNhidM7jvRbmSgDQa1XKFtWheu+2dcyi5FeHpQe4TmkQnO6W0rtNlsQGF8YzbOpHqNzGRqmNcQiIFIV+AOdVV+Icah/oHaWs0BqNQJlnisBuukGk0lolDloP4hw+DhBGlYRu5gn2cPRuOubr9/RXBCCkHaVk3dm9ZsIUEUjayOlm9ckAbQ2VccaX0w/kTNVnBt6ieuFpf6uzS9GZpXOrgqBd546tFlyc9OmqWVVlveOCo3eHS4E07ghhIjlHjIn9YcAfjnQ8OWfGlMiXe/7qn/+UL97dcf9wIpeCR+3Vask4Ii5q172lj66XFw1N7JXenBXemkUMQhNNQO0ihO4sxUCAqAvIFd0ZWsNdBUMqFvI+ErtOgnwoYJYj0oVuWFo3I/SNWmW1Da1Bih68Yosxeh0TirCuhTXrjVfrD20cSYopqhWyJsGGgJm5azB5jApIO+9oXe1HPI5hmGh1peVAS4PWcj4hLui0RIzrTzceo3prN9GSZpMIOK92LyVn9Tkv2lD54PEh4Lrhnp+qWdE3U48TzVg230Gxgls6UoWcF2X51oxDFWz6aKu+VqRr59UKEtSRtFMJliec0qBOWXEiDDtjdShkMcQ9w+6Wy/mFcn7A1UR6+yN8PLLbn8lZA7pPz/cEHPhBP58fGIY966odcvTGUPGK7SFqfkn06hHd9DM5oj4gPlrXt2XEqHOERsoOOlZzjhDiD6hRijU6gzl08enO6B1I0MAk73XC1DqGa+oUJSVtakRERVyzkAb1lolBR4BdNFa3Fi2ZWm9qkdc6PiTVWHtnXtaOYYjkrIQV7805rWvz462fKE3ANQsHsq4eRRiUGa4wkHRVrPRqnMWgCz5XbXC8VxC/Ofu5T3Y0A6aEMSGP7SJuK/T1Cbk6traCPqIjPmijoNu9XvAuovG0KeBCUueFNNkkpBOGkWnagYMheoZhz7Q/UnJmXc6sUoj7Lwm+kaYD++PKw8cP1HwhRYUPujh67cSgeo9eskXlFsPMHEjV7+SaETrdtZsVdLFuoytEF0bwjhAcraheI8RETP4qQPdOrZWr1Wjb93Z4LUW8JzqdEHW7bjpSE6JTjDYNUf1oumNeKqV1YlcPRR/Qh0g0prfaqM05c95oHRd08rId55sv0DiNrKvCXcGr/2Pvhe7EgjcNz+wq99jWom+F0hrNDUQ0Oq61RinVmjB9EDcTpq1G1fXiP92sWY8VPXZ6V6wLp/R1j1i9o79WbcmUceHjhAbyDMQ4UnO2otoCBbvQlpm1OvouEYZCiIGUJsZpB8A0DQzjgS6Q1wslF0h33N3c4qRQ6y2lfkuMjpTgeNyTS9M6LelCqcb8cS7QJdNbMzmnp/f6Opi3haK0KF0UVRbtdKve5Bgjwaldh04MBtIwkpLmtji/4ktWSMNbJFh3NgEy5V7AfMc19bMF9ccBdXUAPRmXtbMWbWQ2/0HQe9GyTiuka0kRbRKjEBEa7B00qIhobJ8YmEZPLqt23T6Y9sgaT6BYcNPobdvxSZlcTRAKtXcbczowu2gfguGQ6g7XbQLTmuC9kD4V+0YAnDKgRSo4USGUOOsy9cn2BnZ7a92buVql8UgrK6Gu9KxZKs5vnDwhRGGckvm5QEg7QpoIHsZxj+AoeeHl5YnTIrx9+wW7/UCtq9WGmoN8c3dLPxxZ1sy6FM2aa1i0hccVywjBdnAzTPfb2M1ZLWMdaAgDoQl1udiuI3SacvbY9LqOcTwQk+6ky6qgOThqLgpOm5ZEM1g8IWlH6c32w1dnEytncJCQV91xrjdAtI4U0RFdbaoj8aYl2XL4QJu/lALBBXyM1wT73hohTgwArkFf8OFAiN4aN3/FbmMEfKCKw0m0krHReqGL1qxbUBOb0ZLtgt7KFez+frodEZ04dJuZOnMQcG7zf/JsQmt1MrU5rmi9gt/j0pkwHun9ovEIomMlccGsMRzLPDMME2mYGMeR4APiAnm98PDwng/3z9zevuV4c0crF2optLLgnON4fIcLX7KuC2NZWZeFUmG+XOi94NZCK4Fq/tet6/4dQmDzanFobeR8tIamEYdEXq1B8aZWEzVWql0DvmNSaz59YBcbcykrqLfOUi+k6JVd4zENcTNjdF2A14UYAstaWHKzP8OOSD2+daFAiFsnq0ejdEU2QkC/UxdKX7Rx0PMHkU7rKyEGTUwIgZgSKe3NBqW9EkuiTkci+j3Fp2tp0yrkmpHeSR6kVs2ktjoZ1H6EH7qUfZqFCJoipV9cd4duFyjgMX8VEzGpjYVexeCj3riQSHGgxxVpDvF6032ayMtCv5yYbm65uXvHbjdY0KBnuZx4enrg4eGFcdzz7u073QGKUtaVK7hnPB6oecWFiVhWfFzxa6a2xlj39Drj5awMmpgo63KlXekXsXGW9/SqpFo2rNMFhIJD3bl6x+AhbzbAA+PuqA+erizoQgHKctFgn2jlotemRNOeOr7rwrnSrbxahLTSzbLYghk7lN50Z+tbDSjXWi4Y2UJLdt0InMW64Tbq/qu800dNc41xII17lu5Z5jO9m69jiPZY6sSr1qayiJBY6qoqxa4BQt53Rh/xMVHWVa9BgK1m6xt74pMsRLtJbpsabB2506KXrsSC0exxpVdtGgwI9UHtesUpZb5kZTw7Ot41DjefcXz7JdNhotWCeGj5xMPH9zw9PbHbH/niqx+TAqzrs0ZqlFXjHXaf4eNAkydSGCAMNCK1eYYxk0slxAUnmWGaqKvuFNvOhTTEjhUJis/p/VKv7KtlcO9XGKbWbo2KphOM047W1BEBWZDUQTKIpQ7YYtlcbXWX8nQHAXXZ8l7/tjfNxGs2ErR+kFJFYR/bdeIPFrU0qx/xmnbgfuD+r3urdekGu4So+pQ06enVRFNURZA+M8/ququmSnoNlkslpUGnMR66jfuwefYQ1PhAWkGCv6o++T2xkP8I+AZTZ5nizBaht0G+c47bww3TOCJS7AYLreksUpQ1SYgToTc6K9Pulml/vBpkXi4vtLay2020lnk5vSAt8+Of/iUh7pjnEy1Uzf+rKyVn3PCWMB6hZ9Jw1CPfXfCl4UMhph0xzHgnpGlP6Gi+c7fmpWaDoKDT8L3hnN7c3kyzW1+tmgEQE5NtcR9xII47XN3CjzS5GKcajuCjtimi5uYKtzgQr0wWOnSvRFubNqnbll1j+++N8Ww6TfyGuzjAd70vWzMpAR+DdfqWJh+iLkDncD6q13cY6TjiEDkcbpCW6evCcjmxNh29uphwweNqZr08Me3vGMZJQyS7DjgCYrhiZF3Pqs9xTuffn0zXbE9klY40XmeJ6NPrvOO433N395aYBk2nl2g41BYmKPiwIyRHz0+UUjhfVtJlJQTTNow7gj/wtM7Ml0VlndMN7z985Pnxgbu7HW/vDtQyk9dnvB8ZhhuLegj4MOKlE4LWMzEkqluIW3Jn2tG6ThRqWWzysTkWFHXOsgZBXKT2rKQEwwO2+kx9wgWcyk190OmN8+oZrc2KJxcN2xEZrEYTatEYOXrH4fFdNDBHFKPuvV1dWkMwyr1Tr+wYdAIDDuflCv146/o1ccpOLbtvPmp4+GCG6oLXOI+gNaBzHifdFhE6Wu0CIdjx7+hExmFP9NDzjNSsUxUXtLaWhvSVJvqdRPS7hzhQe6PXTzhZYWNh4K6EUkQR90jgsN+x201aEDejEFk07NUxyqnhUKuZ5fxELpmcD6SUGKeR0UPJK2tuRukS5o+/4fTyCHRub35GXhdqOZPXzP7N15QekFaIKbxS7K2+9JYPqMbuqlf24um1UNNoiL+zYzFQ82rAsFDrQpNtfOfo1Y7MJnTUy9E5Txz2xEEzBkMaqXlBXERCZtgdGaeD7axN5+04BfWrTnbU+Cjiuio618uq40QjZeC9TjvYdjYVk7ntnlgcrd8o+U7p+gpkqzm+c1v+30RvheDjP+AoSqvgOqUo3przijiIUYcCtTtNw4qRFAekVWpe9GFxGkXcW1Fy77jX9NXaCDHpw/67S8R/RDqpPsbXiwHObDM6N8cjX335OdOYkJZtFNTxLlmnVk2W2G32FxHvaQitaHhiGnf07nh8OHG+vLAuM/P5hVYrLkQON7c4jwbulBkfDzQZWeYL4xhZV037bNIsFMeyP2wxaldXCC6QeqEVVbyFGCl51Z0heE1natXYxvo9dSJk2Nh2EgZPGnfENCrAjLdgb02PStORcX9rSaczvSxq/VGh9aDDJucha9Oi/BslRMSoR1ztGvcbUKZM2MitYHPuLSEbatfxIPYACl7j3XrABcxLXF3LtqZls4zrrVLrzOVpxntnUK9KZbvNspFE64U4aCPT24qz47f3jfBb8V19kUjRlILu1bv7j12IRru58u8QFYCXVri7ueGnP/4Jn332BSKdZTkrfT1sN3Hzfi70XpEOKY4aUyvqLjVfTlwuM4K6cc3LSutCK4VgNYYi/918/0AYKJdZ8/a8TVFipFj2i6YfaB3ogtVEMZlJ0oRMldZeaNURfKJ2y79zAlien3c6xkJnxNWG/ylqGmhKO5xL1Fz0yHWJXit1nandaGVNYzt8SISw4NxA8jvbdTO9alPQxZjjSRnVAgSJJvYKNK9CfG+jUp1jVx25Bm1atsbEOac/ZxqbUpUqVlthF3dsFigOb0alibI2Hj9+5PDmC6ZRk2dbmSnLgg9Ro367ozuN3HVBiSPdR1xK19xC751BYuqA21r/dP6IYl9eF7bWBa2pB/ZuGKHD6XQm+korWcdNTrEuetMPItuTq/zDXHTUlPPKfHrAucC4v8OFhIi6CCg8kXAhMe0OKsaxSC78wOVyYjftuFxmvPPsZbQaTv/xQQO6cVqYJ1GRkkgnTnuSWbL1vhBcuhIeRBwpep1BO0GamSqJqu6CV8ESLuDCQK1CXrPeHBytrdSc6U13ijSNujhiIqZJAeBekFm70OqqLv4WCQG8b1aVBtWveK+BpGabLOLQ9FKtwZ0TTR6wEWHwg02RLGrC/nzb3bcds/eOr4VWzoThHRI8pUPyiWnU+X/rzcLDA3GcoOnUKKaR1pUw67w2QJ6unj3B08TR10xzGMPjEyxEZ3XSZsqod1oNekpZ+fa73zIvZ968uWGIivSH4Gzg32ilvNYbpTAvZ5bzM+syU5pOHkI0sNisRBRg1iJ8HPYg3m6+LsKX5xfEeUqtnF5eePPunUoubfdq3Rn7R4VG0SjwzerC3jrDtKd3r4zpVmmiTcarH6EyaEru9CpmlKRYqTOGdLeblNdMGHZ0F4njkTRCL5U1Z3qr+BBI0xtSmvR0yDPNrEB8qdRruLjTRs9hVoBKTNXGpepo0jBOHSAGm98r1zMENd70IdJbp7R29cgJmy70uhtWxFWCK8TpS8bDHWEYcXHQ+jd2xmlHboJLO1waDXf1V+qeXm8tTluH2ispmbjKD7jAp6OBaVGs0sAt6UiPaceaV0pVIsF+TMTj3kaBmyOUJ1toYm2FnAtrnlnWmWU+UVZNKIjjAR8mSl6ptVDzqrzB1kAyh+ONftnWWVb1SDweP+d8eqa1wjjsyOuK9521aAkQozcDoYBPe6IEfG80V2mlMOzvEBKlNfpywadIJJr0Qelp1RgmGrZjdZmPdPEqPqqrEj18oKyFYdgzHI+K2YVEbTN5ecGJI6RI8F6t+nplGPfgPHVdWKTSpBO9Jav6qIV+q9eGrzV10/XScbId5/q5nHgDsvUoV0lppKwZXQ3O3lPRDE0CKLrjxwHvPbc3d7q7eU/NF3rOOBGGYaSFpK62EhV6woGz64AlYpnQPlYhDaPWiLjftyH+4Tiiapo3cNTwrK5e0SEElnVmnV84HCZi2tm8MdCdanLrqmnxa86UNZtvjFDKSoiD1hRdyKVosqYPNtesfPHVz7i5eUurJ5BCKZlhuOH56YFSFz7/4mulebVCSo5S1Biod0eTrjmztUNIOiOmQkj44BmPkVJnel1BIl0iTVR11pvKVbcSR4RrvegFesm0fKauE94HkrnJBlPRgSONt4S4Q+rMZrLuvDYUw/5WT40ueD8gCZwM9JrxQXclcStdKqA+id45giiDnZYN0XA4SVpCbaxucUSf8D7brqrz744aiVILnQwhIE3/LqQdrWv6aGue7qarTqVWazq6QFNysXfqy4OlT0kXdbm4du3YVOWTzZq3l/5Cb3dFnBFBvdqrjckZ765dMSgt1D1lvrDMK2vWHavYLqn9osoxyWouqaThwHSc+Gd/8dekccfD40duDzp0SsOeh2/fU2tmdziy2+35+OE7bm5vyFlMf6yPT+8a8UAYtMvxaAxFr/TaSMOO/e07ynpBesO5UbUjXQhNTY+Ch2bYoROMj9cRVyjri4qOfCAMe0KaqK0p48R5NPRmQKJCSuI71AsuDniXVKIbB3t4BzMvEAWffaB7gW5ZyNiOSEMk0mvQBw2Hk4BQbcFt9SB4P9Bqse7XAiWlm7qu0IOq+0KrLNVTG0YK2VucXOcKSm4iHK0b9HdLJ1zTrRzJPre+bHz6yQBtbCyEs7pElWSY1iPFxM1+ZLcbNX7VXFCLfavahGVZOJ1euCwL83whzxdEIA57Hc/1juRFRT8pcHP7hh//7F9QSufh43f4GFiCjhDnywt5uceFkTfvvuLx8YFluXBzd0epWS+y1SbNJiTODcSkJlBu0CMFUb30dPycWiuX5++hTcRhoqwvOH8G1/FFqKsRPJzJPqXqcdhW6vyID4k2vNCCJ+yO5FrxXUi+EmLEO62RdQdLePe6aHwIxGlSIktNtKBHaQjqXe261nTOqV2Jc4MyYXzANVUeIkYrM0IFXk2dxKSkzvDSToXWkF4UgnHR7P0MokIM+9vqbbHBxDY6DobTmnEozXDYzdRJm9ne+nW8G8MnSicF2/mcsqrVD/sHCzR4pmlkmPSpjnEk56IuX63SzdR9WWYupxfWNdsOciSmUcHkrsfPbn/kcLzh5vYdz48v5FpIaWBIO3KplPyCiMenowrfnef7D+/Z7/dm7SaUqiE0PSiAXVsnDqri06jXho+j6i481LKSdu84ph1tnZFyYb0kNWlKT6yXBVxTz8KuZYq3TBh6Bhno6xM1RIpNG8Kwp7aV4tCOPeoosOERv0dCQ0Rpdc4lwhj02E4jriSbb2uESCDRWyGmQceAaF529EH5jLXa1KdYHQvOMDwXlHsooE4NtUEvuF5BlOTsybr7NnRIIZ2NVyWIZZvpnLvrH+qUUVkchu5pI9R7p9HYDHO00f1EyVOwuc73K+9QO9tGq0KPGuXlwmjwReP08sCvfv7fcX5+1PHQkMhFKD1A2OHCQEiDFbSd0Y8cDjcMxj98elkULkk7hMDzy0ytF46HRAiJONww7d9wejlfKf2lVnLOyhwOnrU0dlFVZ6VmQlBH1HVdEFS8H2LUG1UrDY+LEyGOiAu0Mpv++oQLF5OwqshLRFEJ7zQ8HBqtnMgXlDWdV935fCAZfBVdoDmnl98NdlQ2Paa3kw+H+IHeKlIX9TgE8IvOtqMKz3x3SEjGDsrGilfoyUc1VNUyJNCb4EzW0VrGUfVB8uY20dUDe8P8NOJOm9G+TcZ+ME1z8ArnbTqmDSC3dbuNwbXG/IQ0MIfYiE5ZKLUVNBfY411kMO1wk866zDx8/C0ffvt33H/3a4Y4cvPlXxKnO8QfSdMIKGXKh8huGhnHgdrg/uHFCAWJGBKX+WJp8zpTbdXTWmW3P1Bb5+HhnryuegOlm+eLYoLLkgkHnXZ40Tmt815joWtHtvFZGPFJCF0xxdY7cdgbCD6aJ8yocMhWb+mGZTuPHtfOCb2vSOk4qTinP6eTJEcNI92ZTlmS6oq3BYnQ6DgXlXSMyhnEmSuXHyB6egy6E9dZ87PxOD9AWWh2b5w1S86pKrIahW1zF3OiNDLvwFHp0indmdlmwdHN2EmMZxBsp9zGvJsTROBq1K5gpdbFBvXVpvim9E/YrOhx5MyQabHCXl279ruBabcH56g5c5lfeHr8wHx6ouUzpXfyWnCD4oKlKJEgpkDAc75knp7Or2KeoHXLpZ31GHWOahmAULm9u8X5yMPHD7w8fCQOgRAT67pyOr8wDpMJ9wtrGQkdfLOjqIIPI2nEfK6VYeLDQIgqkpdWkRbw4pQp7jwpTbSqZlS1N635envdMVCavxqzC64uipoQtQOtDemz8lR6w20WI+JwLulSFLFd0rTW0TBRSTo+dM7mx4U0gG8r4qICzt7bg1CAhHiHE5VPyJZg0Du9J6QqyVZpYQmf1Cfcs7GpbRt22PhSaz5E7ZAVVU9XSHnbDZ2z8SDaYW8cg09mwgQOb/a7tWm4jDcIIsXA7fHIOO6vc8WyrizzC2U5Kw6Y4hWHjDGqiVBVRVn2xepPrDETSikIZpZu7GM1MYqUCkLicn5mvjxr53x8Q22N1rJFjgXa5jwBHHY6Jw22k4WYcHHCpYZvVXmRcSQINFnwPtF9tqmEI/mAeE9oortjmekqIzJjKj3CvLc5uumZuxRauVDcBEXorJsyRVkyIep/e29S0U09rLrkKiAm8MJ8FnNPOKKlKHS6iwQXtU5wQiuZJs1krgqMByNWKLDerJlQ0nD0HsKBQqJa962IzGuXrZM1/RgNRxBdcK9N9KtasItoudbNiCBhw4pPshCxwbX+o0+SspNTjOz2O2Ia9GlvjfXywuX0RC0raqbptIu7zovVDH7rxoLz9mcK0CoU0JSEiSPnhdrEQhMdz09PtLqgM1PP/nDL5XzGOchrxbmVnlekN4o0Spw0+Um0YFf/Ha01q6jpaG8NsXEV0okhKRDcCi5FoqgdsY+JkAdNbEKNqKykN4ikIW5gu1q0hnAG36jNpLVRTaU2QbwaL9lkxWn6Z63KKXQ4vBSIgZii2XwkUhSkFZ2YBMMQzYGBmo3ipbEVaVA7vqWp3FMwkgfNSq5AtpwURP0dnWnWrzxIa15s4KTmA85pqj3WlBhkVLsSn6chmIb7kzUrGzApbJR6BXc7u93EYXfQtr111rzw+Piey9OD2l6ImRV5D12TR53hUCoW75qXIjoewhxJNTNFnWqLQQtj0JiJ8+lks9POtNtTW+fl6YnD8YD0TlkLZZ0VwgrqyfL2dqKWjDhLvEra6bXmKN0ulihXseSV0tXSo1uLGKYDrhSdSPgJwkwtK05UMCYIvusItPVKUYmS7RwBpCprmg7qZEjvTT1t+hYZ0U05qJIAhw7tHYledA7uo4qUWkscxjsGv+gNTQkRXVj+ByGNPgRCUrTA5YUQgOr06MZGtn7ShW9HuNiC2yAc3fScwYLNSL26LkIXXEgmmAMvStqMfqOBWTf/aRaiM2Mgc2i1emaMgbd3O9KQtDMtK+eXe14ePpDns42mNvVPtyNXrLGwgo2uSQWbyaU0cwnoLPNZj2LxxDHRWuV0erFkJq1nhru3XJ5fkFZZ10wuBVlnPaKa1pcxD+z3e0ZfcCHRxbGeLyzLYoyhC9M0KTAfB8Yxcj690PF4P9IpRD/QYwAyXjwhKXRBM1VjF5rbDEALiFCYQIKNRTeYo+N75XVSmwwWQ/UyXrOOaXZM2892UdKuayqAnwZVAAoJ9SrQaZJLjmLdbTfYSsTjvOJ5tUMPDdeKEhWCozJZ+jx4PN0Zl8DrhrERg5Xca42HMUuu/ANbqM4rYcT7HXHQ0eEnY98AVui2K7rknefuZs+bmxtCVK+9ZblwOT2wzifTb2wmTToe7LngvB1jrdPLTOhF1YGiMsSYIq0LJV90p0ojMer8d16eFDYKqkD7/PMf0Wvn6fmBNI74Wqg5I1JZ15nl5WKegGdwga9//Dm9zKS0t537Xhsnp9OJ4B1tiIwxkYaBeV5xHU2Cdx6PeuQQHdRuHbFeaBc6WF3rfCR2Bb1XiSbFs4iLannKzkEXvO+2W3rrQrUpaVQ7u60RQG2MNyLsUnTas0sDx5SNFKHdrbSgeKGPRn5QkJoQkZJ1KzBttvOe5qNVXbZjy5YFjXExtTFVooOiF8FqQ5zparrYPVQa2DjuiMOoD1H9ZM2KPiE61tNyOkTH3VFFQ7hEq5r/MV8uV1NxJxok6Z0O+iVM1lmJkjHXWbEycbiYCHFPb511XSmt49NEcpr9kZezJchX6uIJcQdMCDO5zDSEZZkpWSO9Wi6sZWbNF7yLtFo57Ef2h5GXlwwOzi+PfLx/ZDcdAJVRhjWypsKYlNVdSqG0AawfCClSiwbj9Jrt4KqI5VdL6+q26gVfC95FSptsDFahV92p42CLarMk2QT0tjU5eE32FDvixRKqOqUJMWpddrOz2W6riH1GRZdVuupcNz5p0CkMzh5+Jcq6sCM2bGZmMbd2NMsVrpHrKWSApxJ9XQMUh1T+geq2XUj4zVvbfaJmZRMNObdVDLCfBm5uj7iYaK2zlsKynMl5VUggJLqc7EGr0GaFdzaVJh1xnkqEVhjcSl2wobtFaDXh9PJs0lGd0LReEeeJw8J3v/25dtm5US+PVjcqy/lyOrEuMxj1K1wiu/2en/75T5FeKM2r+eZ8Yj6dOB4PzDXr3DUEFq+m8SlFMg3nlYktTmWkTqB6R6tFQdvWNMh7U+KJ6koGyYiL5Batt9KjdyO1OkyUhuJtyqQWU0nqQ6/psBusgEk4BemB0nTsFkOnGx/SuaYNDkoHk9rU29oaGOciYiafIahZvGvtWjMqsL05eCgvsveORRdd583SK65VneO7tFFOjair/4/yIz9hzoo9KyDCkDxfvLvjePMGnDJvLucT8+lZsaY06JPQdc4K3cw1xWwogoG4jo26lGuhrycank6gy6o0rLIq3clKg9pUX328ecP59MTl9ELrbft0pKS7XquZVovqL6QzDBPv33/L/uaW3S6yXIoeoSkyzyfWXJjPL+wON+oLU8zS1zl2xyPOJSQFG+3pkRScsm50tqszXzFyLU53HFwj+lVrM7RB6i0TXCSE4XrzdM7bdEflB7jchpFsUxdbjCLmddN1cXmvWpVAoLX1aqDpvYekMgT12gMIxtmsBnKrc4Q4LR+6mbt7UxWKAduboai63+pUSPXpG/ymobkxRGOZK3h/3eX/2IUooD43Vp989u6GL774jHE8cFk1xbMsT0Blt7+h1pWTE7XsGCaIo7oX1IoQDG9zRgLQI793ZxcVWldOoloVdnBaLGtTFzTsMAReTo/kdTF9tO42Ja9M0+cmmIr4VhR4D4HeMu+/+YYf//RHtLLQiOYssTJfXjRWdl1ZWkbqqvpsgcvlQhfH/nAgpaAYnk2ZNjGT9wkJiVJXeqlIVUuQ1jWYMYVGcSrk6q3ojiYbMUD3mg0E/qF7lq5DhVHcJjHFNCeiLOuO7uK+NhqKr/iQ6F2NpzzqBuKMcqZ6Y8NsW6PXrrCWtKteXbFDJbl4Qw64jur8tREVP11hOYeyhpxXeztvU5ctheGPXoi6yepTOU6Jd2/esTu8oXQzT/cjcbwllsDaZopcyP4tfXek4lmb2JxZt34T8+Fcu5I+EV3s0lXB17rC6LIJhERHjM4LcRhZloWSZ2UfixbdWqMV5ssZrB6Srk9+q51+0diHcZoYBkftSu8fxol1Wai56HtJ0Tm6iO6EwPPDI6U0bu6OFAd+r24Wm15ZzIxojDtC0HDydS3gE0402VMBL9hkts5qLZ1ObDPcLa3htSS6AseiiwlnvjvBxP49GB3PE7ygzAyVM8SopvfSVVTmY6AVS4eIe5zf0S3ReTuOt1eXTkA3oGDdO4YVavbBq5+i0ycSwkCXoB6LLlydKT7JQgRoTR+VWIVc4emUOV8Wnp/PdOlc5pnT8zOXeeV8qSzyhhaqWehmOt1wJWFj8GgnaJUxFgWLA6cBOQrTcG3/veiF6rWquN5t7mN6N5VJ0pkvz0y7Gx3+e/TCe2i1s84zDx/fM06T+dUIKY1cLi/0VimrgFQQHf3hMr07cDM5L3gPwzgAYiIjhycaoCx6RHqdLXcHy6qp9ttO001QhKsEp8bnr0SCH9bjRsUy+E7JToouaESxUvMB1qL5hTFWuhSrBZ3V6lEXonQbyzpj/ICn6s68kRu8e+Wamg1fdRo8HjzQdbZ9xYB9wgnqBAxsdtU4BeI37Pl3L8N/RLOyGP1+WTvl598yfnPPvKzMq9rWrlmLdRGFKDbczHlPQPl3zobhrW3tyjanVfgkxkSpWlc6U9LRgbDZ4VmN0lWHojWcsIndt4snwJo1S0XJC0FZ21FHU+u6kHOm1UYcB3Vg7Z2AszwRpZF56arRFSh5xnnP+eUZH97Ryplo8k5NyhpVpegEMb9xF8w9rEGpauGH65QqqGt/t51bt+5tRst1Z9J6tKMrUeUAsDln1NqowGWpOL8npZEunhi2IYJNwGyOrsbYBh2JzudxEY+zfCG53gv9NdosebPvu9Z60uwUsp3bvQr6ndtYq9vPy6fbEXuHZdFOSuic1xc2RzC/qerMQxkLFN8y8vqVlWF0qw0O0NPJvprtXF1vhsMRXMR5cIOF+hhGpc4H/to56omlDYyYE4X66ywM0441ZwWZnfImm3RyLdBVlzKs41WQ5HCWk2zePiLKkBaQrobzy0XNi4YhUtbV6lJhPyXrOpMCwa4ZcK/Xr9TXHcc5R6eq3hteJxwoNCMG2WyYLTgNF/JbXWrmnhpHxbpWOo6URpZ51Stq+dV4TxodQ2l2rKsxlnRznECbkgFFF64TtO20wtk0bCuhzK/IpAB+O7UE4uDtu7MVs2zUsU+yELdjp/dusRbm33zt4BSB0jZxo0RhFnVcQVARaA3AEji9dplssJAt1E2b651KOnXH2vQY3naPak+xo4o1PqZn3vh08+XCbrdnubyYuah2dJu1nrTG0mbGMahYqG+lgrklBMvW44rd0kpmnZ9J8R3ny8z+uCdnHWHtB6G2VYO47Ttszq3O+2uyFKK7OrUQpoQzsZNY6SHyits2dGdzRgxxtkMKmL9ioDShtEBwFfEmW43p+nNIZAmLamn6Ntrb9j2uuvBu6kkltThUhOUQS1xVDoAGg2oL9IPywWuD5Fy4HsbO1k1rnwjQds7hoic0tKjeVryzi2pbdbcPxXbc2mYoog3Hq6xQu+Rat44YW7FKMnWolrbLK3yhGXO6/ykKoZYc3WpENSy3o80p1ao1zclLaWDNq42jvN72bRG3hjSdhW/WwmAThaZEghh0Z/EuXo/5db0wuB15zfQunE4z8XbCU2l9pleVqV73F79lpthR1bXu7j0YicDgZLGWwJszBrYwZHMSw64Tys9E5/fLWhmjM4N5M0v1XrP1zGdns7Lr1igpxtsIUpG+eUZwfRhEGrLFcOj/fO2Qm5jzHGh3HYLxQPt11xewLv1374i/R+D3/2shGsvIYx/Odryw6Z29LQbzC/ReXWG3I9GpDngTvLfWXj+ccK2RFMG3Ctwpvuh8IsZBjZRs8XjbGXUXcTgLY1Ndh+62zj5DLll5fKbbuOKx9vscYo1PNKsN9wPZrHkTOt0dnNe5cRdRN4oGy5yNptZ5epkRUX/FViulVkoptNqvp4Zcd7jwei1aNxeM15uoR6N9F8QwQ/MVErXy04dYY8aeTjO9b6693iQcA8FHvNs8gAbdifFWY1bz+l6JXlncXTY2vtBFrVfUGEqFboJ203r7Xh1scWp1d+2q7btta+V3vf7gZqVXc0FARToKW1gN4PSp0A+51S+K4G5NhlY6ldoELB5MWcCwjYyUBuZ/sBjkSrTQgt3jukYobJu9Yfx24Z36BG4FqAH6vYl2tq5fa3V1qjAWNGrAuSV+6sZju0vQ2tSFSJdXqlNvnfkyawctBQTmuREQ9jvLQGmC8xhzZnvPDXo3G5ZWCX6wa8Lrwyga0ojbmhSrj8VBUHKCzqJ1h1/nmSZ7dSaza+VDsvxEU1RaGeWuOhO9T616g2NezQ2823Zjdz38tiMdtO6trRHM+CDGZJxKzP3NHnTjUX6ShWjtL8qUwY4GIfStTtRaoLR+3Xn0cNbif8On2hWht53HcpNxHU20xHYL00GIWX5gO1oTc8D3Vo9iR59DqfUb3mg7MULYnG3tMykGxvVz+uCv9ZYG2zQ8/voZgh3JmC7Y2dxUkYJZ17xENgH881kTF8YEXhpSIQZHrtvnNagEvWGtFXzcYJxND9Ov313Pbbk2APr7Xx22HNoY1lrJpTOlqE4QaCJCGgbVaYsaBvioDmQaqpsR8bSGkXC3sErj4xsOeDWRt4snhmNKybiognykGxlW268f/vMJvW+MitS3D2uXQLb8Ka25gk0aMHJEl9ci1TuPD/poqWB8E+XY7+jOOi7VmDjUbrdtjYg43QmbwQtbubTRlJyG5IhNB9zWKNna3Bab8woNBauluj1EYrWtKvu2s8SpxZzbwsat1nXotxYoueh3c9BxeHE8Pq8c9gPTqAZItXmbsggiisPh1Wid683HJiuvDZ+iFNtDY5/INYOBbFxnP1tLpZTGfhrUbQxQPnVHWtb3vSLjBrO4ZGiG1qt6qvnrRgFY07LhtWaBYkIsaRXt7xJa9/cf3Bqt5+XahX+ChWiMJb2QbA+o7TDbUMnrQtieDOk6pQjO0/12JHp89LSq56MZH+jiMgzqCuhah6ZfXnHEZjjlP4RI9X101w3E4Oh+e4L1mFHlW8NbspMTh7cRlGahYFaEmjiAdfi6IyiUE/ymapPrYnUolidkUlRa/lZpPL7M3MiONAT6UrePShW1mnMExRxbNYAa+8y2GK3Eeb0mji2U3DndsTeUQUM54eW8cndzIHidosQQVW8cgplbbTnKuuA8yvYmBvTgMJGUnXLebImVUaQkDmeb81XHQmEYJqumNXHBm7JPb2Xniub/sQsR2543F/wu6r+ijUWzC+KvW7tJ8MF3PV36hpNZB7V9ULZubIOAYNPJtl71Ahhzw5vvTBfdfTdIQIt7uQ7oCYNicb3jujI/QgwqVMcM08WrfBOxKYQ+WOIMNQGzTLHi2x6WED0iCsdo1eZtsqLTEhWwmfKuCy+nmf1O7VRaez3utoJeoaaKa1kzlK0B6NemajOeej2V9Of8D7zS/bXbnueV0irRqat/SoPm1rDD+ddU1opdPweJzjQmlrYlSZn40+5PbxrZ67aBAbAZ+7etKalqQKon0+sGsYHy21Tuj16IGxMkeJvrdmtSNp2zYPYTGERj4mrnDTg0qaK8YpCqgdFjVv/bBES2nUvfDIW2ncGs4EyUA7p41U0/2nFgITzXHeSVjqTO/Vwvlu4o4PpG2zIyqPMKmGtpfm0gYBve/3A/Ni1Ht2oeVfG12q7vP6/mjiViHo9mCr+9izTTniRwr6WCQ/AxXhGJayqUQx8Cg9G2740IOa+suTIeJmM+aU51KU2JylZrmzwe+orvI855aquGEWLHq7t+RmeMeO+2e6NTGrtClGqRumJmT7bvaE1+3Rr/+IUo22LEWQdXDZDectm23dddtQ66K/frQvZBJyvdtnntPLefi3YMYGA02hw5d21iNjxxayKuCZm2ZLzbnAZ0RAdi1ry6qIKZBnX7pc4IFtuC3nZBJ9Csc8fec5vdbrUmtkCj0ao6TYFiu+j2WOkO3rUzDVb79o0Y63XC0VxAuqaxbnWfWD236ae18xT1ujbpRRPse2rUhnORnOu1+++9XkMcay1qWu+dXVYhuGaVX9MQSecIYotww3YFNte3bXzaW9WTxPnr/FvpeoXgR9Wfey19ukXqfjLHWL3ouugar5iYM6xLjNKuw4CtudAL5tCQbLrWbDhHspptaa9PzgZbbN2udtI/pEW56zjJIQSDe5w4NFUTq3mMh4dDnMO7QPI6etqiz2LU8WFvahuM97qzelUhKkZqlh0/3JGsKfLbn6MbQDegvtuIUk2IdLfwztFrw6fXWkSds9Du30edO28PhJFn9bU1LfrAaJMHImZAummqU1Q5be/UWtgQUIdQcyavM62qzNaZaEu6HsHqSrHgZaczculIa9d32D5HN8pa7z8g6dpfuw75ciLEgeQHa1+7NSrXs/qPX4hgO9V21G9HKNsWYRgVP4yBcK//7zYHtiZH5ZvdUkRtFGg4V+ubkF5hAnXC6j94P/1Lj7Nga8XXmkEiKvaJEJQ3uNEya1OW8tY8BtMqby4EIVr/76ze/QHlyYngXFcbjW032CYHgu1ccl2gG+W/93ZdxFJNl4LYnNYgGL8ZiFpjwqvV7xYlh3vtmrXS8NeHFBdU82zF9rqsrw8MXmWvIhqeHrPS1Vyg2PeS2q+Gns6xFa52CmoZdTVcsjpdrHHayqYuuiPWsjIMm0k+Vk787vEe/COCw7eLrceT0uhFtOvbnn4ngB2l20V8/Rk9cpxsQdqWJecCgj7NfZtJ2jhLGxIbtm8Y1bb76rlhTBWBpkekwjiKbXmsyLYdIJiVR2+VtTfrXK0oFwNeTUgk5oIqTggGg3jRyDYkIt1baHe/anTkChpr2HcIfntuEJuJ+evN1YNRXS003lajy7ieMDhv/jRaO4ZgRBCTi26Mo42W5Twsa7Z0Bb1WPghDitSUSOYPiYgxyRsO1dFsOJds4vxuJvN2Ha4LzxojxB4KXaGo33i9NpH68W1k6X/3rviHM7TFfiGd4Gy9iVy7WHWK0t2ki1NmjFNWNaI3Qox4oA+yBRja5KUUNXuPQR0hdFStYeJbEa2zWoUDZLsQIiY50JuLGD3J2U61qQ+dRrP1qt7czgeakRC8CXx632AU/Z2Isa/te2+NTxdew26uNaQB3c0+m9+YQnZTtqkDYnNkuf4MaOfpXUANCXQ3UkMtY1z7bXZ7bcvYpAMbfUu67uAphCsFy6MuXiEG8GKnVkVEfShVhlyuu/AGu1wJv3qXCITrWlDWk24E+kgoxlzySsorweKO47Wh+d2vf4RRp/zD7tCORAx66VcA1NwJXLcvYDfY/nw7d9217uo677Qb2x3m9bx1lnJdTGKLZmsIunv9mu56XNqRda0a9Ox33qsFSUcxNQyO6q/lhDq3vhoIbP9WK454fU/n9CHYVKGqTHTXxXYNx3y9crqTXCcPGDHhlXHTpdJ6wQf/DyJErCFmGwzqpqClin61DTIJuCCMyVJUa8WLqDqwq3Wd2AnTjRASo/ET7fs7xB5SYXONcOaho4vWKkc77Vo31CI4e5tGLQZFdYV9/LaxfJKFKNum3K9Pi+7k3hambk/bKG9LG7Vx4+uuaXWV2FGPLcze2rWz1J3MgOu+sYoF8Rio3W3b96//dlu7Y93ztcbRP+9df4c2EhZT5oxOJRuhFoJP+vubeVOj+OEVrxN1FVMqnK7CbSLzeqHclbavu5izIt+WkYC7hrUISn417JANZnbXtxRbjFjnqvR9bCKln3HbmR2e1jo1a36z7lzqPNHKaqlX+t9dIJdMjJHoO8HGi1rKWL2NY5OVOveDul82pGGrX/U6a0OYEVNIqmmuEjA+zULkFS5R6pIxZTY279ZsuO3YsM5QXlv3fm1CNizK9gk7Jr2Y+s+pVYbiZBYq6ERrLAPNN23tNYrNjjgFt/21qwvOsZkBYQ+DN2ZNM7H45ukj8gpbeCd27HW63dwQdJE7Y6C0rjERYVM02bG9YWvbXFaPsA38BpW7Wn0qjc1PGyt1tvpv25G394FrJX4tzrZabbvepaq2W3olDhObejKvM72tSK+0ulDzSskVF7XGH3xmCJ55qfY5DS764bHqXplROoI10yUnml/oTQFo8Jv6fRuD6JMtxO1BMOjBbQXr1gzY3ynm5O1C285lZXmz+lA77g0r1OWjpuCW32Lcue3Ld14XY4xbWoHtgAaEb8W0R7SJdFqDSd+kn/76oGy7ubPSQkPDHQpRKNMnBE347G2TJ2zXQbvU7bmzL2gE1W3xmHc2dtRfpyMb3KId89bUsA0ATMWnl1WsPrbH3XiYG4Dd+yt7W2s2m+agbhkKc2nTkPPMOj9T8wKtaqRIzpaAqgyfQCF5z0xQVHZ7CLa6QG8+V+bOtYHBatHtnuk9VofYSJPfl9Ssrz/4aH7tmsUYIhjMoQ1EcAbNmEhHbNcM1kg45/8BtOPD9h5db2JVOzrvvbm4blJLvWHBgha9HcW1qc5kSzcQp6GVfpvhSmezjLvuvHDlTG6SSrZ60m2HkS5ccR6xnZrQrl3jtuu/Hp1cd8OtfnObU9b2XhvB97rrqvu+lgHlusBaq0ja4CEPXq7zfQRTAW2ljn0At20KsBEVWquQourA80IrGqBU80yZL8q+kQ0m6rSSEapRxbSEuRKErTRw1lyZTSLONULXBtBZYr1zqisHtYvWe/l6zf7ohajzxNenYTtmN7xI74NcgwEdr45UONsZrW7cLph2Z2I/74yYsDVDja1fc2zD/mDC/O3yWHaeYX/KowtIU/UcbLWTTVm2Un+rbWw1qQOCJw5JGdXWCGxNj6bOu63j4FqKbDXTNn9lwxD1rfURdYYPWkNhD6OTbl41uiNJF91ZxOhwsrFdrAaT7Vj01+si8voZu3SC7aI5V4WVBGpeWOcXyjpT1sx6ebbUBqHVQguO2gqtZSQW3OYAK6/fd9MYKf1RgfguDu8qEoJNhNy1MXQ+IXiTdog1rr97Ibofirj/Uy/n3AfgF/9//8CfXn96/Q9ffyYiX/x//+EftBD/9PrT63+s1x+kWfnT60+v/7Fef1qIf3r9k3j9aSH+6fVP4vWnhfin1z+J158W4p9e/yRef1qIf3r9k3j9aSH+6fVP4vWnhfin1z+J158W4p9e/yRe/x837gyKudT8CgAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1383,7 +1422,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1485,12 +1524,39 @@
     "Execute the following code in order to display some images of the dataset."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1974256a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "# Décompresser un fichier zip\n",
+    "with zipfile.ZipFile(\"hymenoptera_data.zip\", \"r\") as zip_ref:\n",
+    "    zip_ref.extractall(\"hymenoptera_data\")\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "be2d31f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -1582,7 +1648,90 @@
    "execution_count": null,
    "id": "572d824c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:224: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.6171 Acc: 0.6270\n",
+      "val Loss: 0.2437 Acc: 0.9281\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.4701 Acc: 0.8115\n",
+      "val Loss: 0.2546 Acc: 0.9216\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.3459 Acc: 0.8607\n",
+      "val Loss: 0.1909 Acc: 0.9477\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5491 Acc: 0.8197\n",
+      "val Loss: 0.2381 Acc: 0.9085\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.8079 Acc: 0.6844\n",
+      "val Loss: 0.1819 Acc: 0.9477\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.4815 Acc: 0.7910\n",
+      "val Loss: 0.1871 Acc: 0.9477\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.3744 Acc: 0.8525\n",
+      "val Loss: 0.2118 Acc: 0.9281\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3467 Acc: 0.8484\n",
+      "val Loss: 0.1828 Acc: 0.9542\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.3355 Acc: 0.8566\n",
+      "val Loss: 0.2043 Acc: 0.9412\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3242 Acc: 0.8852\n",
+      "val Loss: 0.1854 Acc: 0.9477\n",
+      "\n",
+      "Training complete in 4m 32s\n",
+      "Best val Acc: 0.954248\n"
+     ]
+    }
+   ],
    "source": [
     "import copy\n",
     "import os\n",
@@ -1780,6 +1929,777 @@
     "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2e98db28",
+   "metadata": {},
+   "source": [
+    "# Code study\n",
+    "\n",
+    "1. Chargement et pré-traitement des données \n",
+    "\n",
+    "Augmentation des données, normalisation et dataloaders (images regroupées par lots), exploite les ressources CPU pour un traitement plus rapide.\n",
+    "\n",
+    "2. Visualisation des données \n",
+    "\n",
+    "Affichage d'un batch d'images avec les titres grâce à imshow().\n",
+    "\n",
+    "3. Modèle pré-entrainé\n",
+    "\n",
+    "Le modèle ResNet18 est chargé et toutes les couches sont gelées sauf la dernière, pour ne pas êtres mises à jour pendant l'entrainement. La dernière couche entièrement connectée est remplacée par une couche adaptée à un tâche binaire.\n",
+    "\n",
+    "4. Entraînement\n",
+    "\n",
+    "Suivi des pertes et de la précision et ordonnancement de l'apprentissage avec StepLR().\n",
+    "\n",
+    "5. Analyse des temps d'époque\n",
+    "\n",
+    "Avec la liste epoch_time\n",
+    "\n",
+    "6. Résultats\n",
+    "\n",
+    "Le modèle modifié est maintenant capable de prédire entre deux classes sur le dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d94c7063",
+   "metadata": {},
+   "source": [
+    "# Eval_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c8cca00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:224: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.6171 Acc: 0.6667\n",
+      "val Loss: 0.2521 Acc: 0.9281\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.5148 Acc: 0.8000\n",
+      "val Loss: 0.2399 Acc: 0.9150\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.6058 Acc: 0.7128\n",
+      "val Loss: 0.2086 Acc: 0.9542\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5841 Acc: 0.7590\n",
+      "val Loss: 0.2383 Acc: 0.9150\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.5329 Acc: 0.7897\n",
+      "val Loss: 0.4906 Acc: 0.8301\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.4559 Acc: 0.8154\n",
+      "val Loss: 0.2582 Acc: 0.9020\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.4018 Acc: 0.8462\n",
+      "val Loss: 0.1968 Acc: 0.9477\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3840 Acc: 0.8462\n",
+      "val Loss: 0.1914 Acc: 0.9542\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.2703 Acc: 0.8769\n",
+      "val Loss: 0.1829 Acc: 0.9477\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3401 Acc: 0.8256\n",
+      "val Loss: 0.2067 Acc: 0.9346\n",
+      "\n",
+      "Training complete in 4m 16s\n",
+      "Best val Acc: 0.954248\n",
+      "\n",
+      "Evaluating on the test set:\n",
+      "Test Loss: 0.1966 Acc: 0.8776\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torchvision\n",
+    "from torch.optim import lr_scheduler\n",
+    "from torchvision import datasets, transforms\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from torch.utils.data import Subset, DataLoader\n",
+    "\n",
+    "# Data augmentation and normalization\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.RandomResizedCrop(224),\n",
+    "            transforms.RandomHorizontalFlip(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"val\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "data_dir = \"hymenoptera_data\"\n",
+    "\n",
+    "# Load the train and validation datasets\n",
+    "train_full_dataset = datasets.ImageFolder(os.path.join(data_dir, \"train\"), data_transforms[\"train\"])\n",
+    "val_dataset = datasets.ImageFolder(os.path.join(data_dir, \"val\"), data_transforms[\"val\"])\n",
+    "\n",
+    "# Split the train dataset into 80% train and 20% test\n",
+    "train_idx, test_idx = train_test_split(\n",
+    "    list(range(len(train_full_dataset))), test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "train_dataset = Subset(train_full_dataset, train_idx)\n",
+    "test_dataset = Subset(train_full_dataset, test_idx)\n",
+    "\n",
+    "# Data loaders\n",
+    "dataloaders = {\n",
+    "    \"train\": DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4),\n",
+    "    \"val\": DataLoader(val_dataset, batch_size=4, shuffle=True, num_workers=4),\n",
+    "    \"test\": DataLoader(test_dataset, batch_size=4, shuffle=True, num_workers=4),\n",
+    "}\n",
+    "\n",
+    "dataset_sizes = {\n",
+    "    \"train\": len(train_dataset),\n",
+    "    \"val\": len(val_dataset),\n",
+    "    \"test\": len(test_dataset),\n",
+    "}\n",
+    "class_names = train_full_dataset.classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "\n",
+    "# Training function\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "                model.train()\n",
+    "            else:\n",
+    "                model.eval()\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                with torch.set_grad_enabled(phase == \"train\"):\n",
+    "                    outputs = model(inputs)\n",
+    "                    _, preds = torch.max(outputs, 1)\n",
+    "                    loss = criterion(outputs, labels)\n",
+    "\n",
+    "                    if phase == \"train\":\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "\n",
+    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+    "\n",
+    "            if phase == \"val\" and epoch_acc > best_acc:\n",
+    "                best_acc = epoch_acc\n",
+    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\"Training complete in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60))\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "# Evaluation function\n",
+    "def eval_model(model, dataloader, criterion):\n",
+    "    model.eval()\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    test_loss = running_loss / len(dataloader.dataset)\n",
+    "    test_acc = running_corrects.double() / len(dataloader.dataset)\n",
+    "\n",
+    "    print(\"Test Loss: {:.4f} Acc: {:.4f}\".format(test_loss, test_acc))\n",
+    "\n",
+    "\n",
+    "# Load the pretrained ResNet18 model\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Linear(num_ftrs, 2)\n",
+    "model = model.to(device)\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "\n",
+    "# Train the model\n",
+    "model = train_model(model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10)\n",
+    "\n",
+    "# Evaluate the model on the test set\n",
+    "print(\"\\nEvaluating on the test set:\")\n",
+    "eval_model(model, dataloaders[\"test\"], criterion)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cb7aa9b",
+   "metadata": {},
+   "source": [
+    "# Eval_model + Relu / Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fc817b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\eloua\\Nouveau dossier\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:224: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.8119 Acc: 0.4769\n",
+      "val Loss: 0.4899 Acc: 0.8105\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.6963 Acc: 0.6513\n",
+      "val Loss: 0.3688 Acc: 0.9020\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.5643 Acc: 0.6974\n",
+      "val Loss: 0.2934 Acc: 0.9477\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5703 Acc: 0.6564\n",
+      "val Loss: 0.2555 Acc: 0.9412\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.5628 Acc: 0.7077\n",
+      "val Loss: 0.2474 Acc: 0.9542\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.6060 Acc: 0.6564\n",
+      "val Loss: 0.2893 Acc: 0.9150\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.5332 Acc: 0.7077\n",
+      "val Loss: 0.2803 Acc: 0.9412\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.5144 Acc: 0.7282\n",
+      "val Loss: 0.2626 Acc: 0.9477\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.5456 Acc: 0.6821\n",
+      "val Loss: 0.2787 Acc: 0.9216\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.4765 Acc: 0.7333\n",
+      "val Loss: 0.2820 Acc: 0.9150\n",
+      "\n",
+      "Training complete in 4m 18s\n",
+      "Best val Acc: 0.954248\n",
+      "\n",
+      "Evaluating on the test set:\n",
+      "Test Loss: 0.2798 Acc: 0.9184\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torchvision\n",
+    "from torch.optim import lr_scheduler\n",
+    "from torchvision import datasets, transforms\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from torch.utils.data import Subset, DataLoader\n",
+    "\n",
+    "# Data augmentation and normalization\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.RandomResizedCrop(224),\n",
+    "            transforms.RandomHorizontalFlip(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"val\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "data_dir = \"hymenoptera_data\"\n",
+    "\n",
+    "# Load the train and validation datasets\n",
+    "train_full_dataset = datasets.ImageFolder(os.path.join(data_dir, \"train\"), data_transforms[\"train\"])\n",
+    "val_dataset = datasets.ImageFolder(os.path.join(data_dir, \"val\"), data_transforms[\"val\"])\n",
+    "\n",
+    "# Split the train dataset into 80% train and 20% test\n",
+    "train_idx, test_idx = train_test_split(\n",
+    "    list(range(len(train_full_dataset))), test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "train_dataset = Subset(train_full_dataset, train_idx)\n",
+    "test_dataset = Subset(train_full_dataset, test_idx)\n",
+    "\n",
+    "# Data loaders\n",
+    "dataloaders = {\n",
+    "    \"train\": DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4),\n",
+    "    \"val\": DataLoader(val_dataset, batch_size=4, shuffle=True, num_workers=4),\n",
+    "    \"test\": DataLoader(test_dataset, batch_size=4, shuffle=True, num_workers=4),\n",
+    "}\n",
+    "\n",
+    "dataset_sizes = {\n",
+    "    \"train\": len(train_dataset),\n",
+    "    \"val\": len(val_dataset),\n",
+    "    \"test\": len(test_dataset),\n",
+    "}\n",
+    "class_names = train_full_dataset.classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "\n",
+    "# Training function\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "                model.train()\n",
+    "            else:\n",
+    "                model.eval()\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                with torch.set_grad_enabled(phase == \"train\"):\n",
+    "                    outputs = model(inputs)\n",
+    "                    _, preds = torch.max(outputs, 1)\n",
+    "                    loss = criterion(outputs, labels)\n",
+    "\n",
+    "                    if phase == \"train\":\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "\n",
+    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+    "\n",
+    "            if phase == \"val\" and epoch_acc > best_acc:\n",
+    "                best_acc = epoch_acc\n",
+    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\"Training complete in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60))\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "# Evaluation function\n",
+    "def eval_model(model, dataloader, criterion):\n",
+    "    model.eval()\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    test_loss = running_loss / len(dataloader.dataset)\n",
+    "    test_acc = running_corrects.double() / len(dataloader.dataset)\n",
+    "\n",
+    "    print(\"Test Loss: {:.4f} Acc: {:.4f}\".format(test_loss, test_acc))\n",
+    "\n",
+    "\n",
+    "# Load the pretrained ResNet18 model\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the final classification layer with two layers\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Sequential(\n",
+    "    nn.Linear(num_ftrs, 512),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Dropout(0.5),\n",
+    "    nn.Linear(512, 2),\n",
+    "    nn.Dropout(0.5),\n",
+    ")\n",
+    "model = model.to(device)\n",
+    "\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Observe that only the parameters of the final layers are being optimized\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "\n",
+    "# Train the model\n",
+    "model0 = train_model(model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10)\n",
+    "\n",
+    "# Evaluate the model on the test set\n",
+    "print(\"\\nEvaluating on the test set:\")\n",
+    "eval_model(model0, dataloaders[\"test\"], criterion)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af76b636",
+   "metadata": {},
+   "source": [
+    "On remarque que la précision augmente légèrement par rapport à celle du modèle sans Relu et Dropout, avec pourtant une perte plus élevée sur le set de test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb251ac",
+   "metadata": {},
+   "source": [
+    "## Quantized model (post)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "752e2412",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size comparison:\n",
+      "model1:  FP32 (original)  \t Size (KB): 45831.61\n",
+      "model1:  INT8 (post-training quantized)  \t Size (KB): 45043.622\n",
+      "\n",
+      "Evaluating on the test set:\n",
+      "Test Loss: 0.2777 Acc: 0.8980\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.quantization\n",
+    "\n",
+    "# Apply dynamic quantization\n",
+    "quantized_model = torch.quantization.quantize_dynamic(\n",
+    "    model0, {torch.nn.Linear}, dtype=torch.qint8\n",
+    ")\n",
+    "\n",
+    "# Save and measure size\n",
+    "print(\"Size comparison:\")\n",
+    "print_size_of_model(model0, label=\"FP32 (original)\")\n",
+    "print_size_of_model(quantized_model, label=\"INT8 (post-training quantized)\")\n",
+    "\n",
+    "\n",
+    "print(\"\\nEvaluating on the test set:\")\n",
+    "eval_model(quantized_model, dataloaders[\"test\"], criterion)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88eb86eb",
+   "metadata": {},
+   "source": [
+    "La précision a baissé."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3550744b",
+   "metadata": {},
+   "source": [
+    "## Quantize model (aware)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "463428dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QConfig: QConfig(activation=functools.partial(<class 'torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize'>, observer=<class 'torch.ao.quantization.observer.MovingAverageMinMaxObserver'>, quant_min=0, quant_max=255, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize'>, observer=<class 'torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver'>, quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){})\n",
+      "Epoch 1/5\n",
+      "----------\n",
+      "train Loss: 0.5453 Acc: 0.6974\n",
+      "val Loss: 0.2446 Acc: 0.9542\n",
+      "\n",
+      "Epoch 2/5\n",
+      "----------\n",
+      "train Loss: 0.5025 Acc: 0.7538\n",
+      "val Loss: 0.2577 Acc: 0.9216\n",
+      "\n",
+      "Epoch 3/5\n",
+      "----------\n",
+      "train Loss: 0.4725 Acc: 0.7436\n",
+      "val Loss: 0.1987 Acc: 0.9542\n",
+      "\n",
+      "Epoch 4/5\n",
+      "----------\n",
+      "train Loss: 0.5717 Acc: 0.7231\n",
+      "val Loss: 0.2588 Acc: 0.9085\n",
+      "\n",
+      "Epoch 5/5\n",
+      "----------\n",
+      "train Loss: 0.7991 Acc: 0.6051\n",
+      "val Loss: 0.2952 Acc: 0.8889\n",
+      "\n",
+      "Training complete in 6m 47s\n",
+      "Best val Acc: 0.954248\n"
+     ]
+    },
+    {
+     "ename": "NotImplementedError",
+     "evalue": "Could not run 'quantized::conv2d_relu.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::conv2d_relu.new' is only available for these backends: [Meta, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n\nMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\MetaFallbackKernel.cpp:23 [backend fallback]\nQuantizedCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\quantized\\cpu\\qconv.cpp:1972 [kernel]\nBackendSelect: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\nPython: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:153 [backend fallback]\nFuncTorchDynamicLayerBackMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:497 [backend fallback]\nFunctionalize: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:349 [backend fallback]\nNamed: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\nConjugate: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\nNegative: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\NegateFallback.cpp:18 [backend fallback]\nZeroTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\nADInplaceOrView: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:96 [backend fallback]\nAutogradOther: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:63 [backend fallback]\nAutogradCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:67 [backend fallback]\nAutogradCUDA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:75 [backend fallback]\nAutogradXLA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:79 [backend fallback]\nAutogradMPS: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:87 [backend fallback]\nAutogradXPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:71 [backend fallback]\nAutogradHPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:100 [backend fallback]\nAutogradLazy: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:83 [backend fallback]\nAutogradMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:91 [backend fallback]\nTracer: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\torch\\csrc\\autograd\\TraceTypeManual.cpp:294 [backend fallback]\nAutocastCPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:321 [backend fallback]\nAutocastXPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:463 [backend fallback]\nAutocastMPS: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:209 [backend fallback]\nAutocastCUDA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:165 [backend fallback]\nFuncTorchBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:731 [backend fallback]\nBatchedNestedTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:758 [backend fallback]\nFuncTorchVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:27 [backend fallback]\nBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\nVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\nFuncTorchGradWrapper: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:207 [backend fallback]\nPythonTLSSnapshot: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:161 [backend fallback]\nFuncTorchDynamicLayerFrontMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:493 [backend fallback]\nPreDispatch: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:165 [backend fallback]\nPythonDispatcher: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:157 [backend fallback]\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [33]\u001b[0m, in \u001b[0;36m<cell line: 55>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     52\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m accuracy\u001b[38;5;241m.\u001b[39mitem(), avg_loss\n\u001b[0;32m     54\u001b[0m \u001b[38;5;66;03m# Évaluer la précision\u001b[39;00m\n\u001b[1;32m---> 55\u001b[0m test_accuracy, test_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate_model_accuracy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_int8\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloaders\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrécision sur le set de test après QAT : \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_accuracy \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Input \u001b[1;32mIn [33]\u001b[0m, in \u001b[0;36mevaluate_model_accuracy\u001b[1;34m(model, dataloader, criterion, device)\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m inputs, labels \u001b[38;5;129;01min\u001b[39;00m dataloader:\n\u001b[0;32m     43\u001b[0m     inputs, labels \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mto(device), labels\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m---> 44\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     45\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(outputs, labels)\n\u001b[0;32m     46\u001b[0m     _, preds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(outputs, \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torchvision\\models\\resnet.py:268\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_forward_impl\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    267\u001b[0m     \u001b[38;5;66;03m# See note [TorchScript super()]\u001b[39;00m\n\u001b[1;32m--> 268\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    269\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn1(x)\n\u001b[0;32m    270\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(x)\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\ao\\nn\\intrinsic\\quantized\\modules\\conv_relu.py:152\u001b[0m, in \u001b[0;36mConvReLU2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    148\u001b[0m     _reversed_padding_repeated_twice \u001b[38;5;241m=\u001b[39m _reverse_repeat_padding(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding)\n\u001b[0;32m    149\u001b[0m     \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mpad(\n\u001b[0;32m    150\u001b[0m         \u001b[38;5;28minput\u001b[39m, _reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode\n\u001b[0;32m    151\u001b[0m     )\n\u001b[1;32m--> 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantized\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d_relu\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    153\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_packed_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mzero_point\u001b[49m\n\u001b[0;32m    154\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\Nouveau dossier\\lib\\site-packages\\torch\\_ops.py:1116\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_torchbind_op_overload \u001b[38;5;129;01mand\u001b[39;00m _must_dispatch_in_python(args, kwargs):\n\u001b[0;32m   1115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _call_overload_packet_from_python(\u001b[38;5;28mself\u001b[39m, args, kwargs)\n\u001b[1;32m-> 1116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(kwargs \u001b[38;5;129;01mor\u001b[39;00m {}))\n",
+      "\u001b[1;31mNotImplementedError\u001b[0m: Could not run 'quantized::conv2d_relu.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::conv2d_relu.new' is only available for these backends: [Meta, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n\nMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\MetaFallbackKernel.cpp:23 [backend fallback]\nQuantizedCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\quantized\\cpu\\qconv.cpp:1972 [kernel]\nBackendSelect: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\nPython: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:153 [backend fallback]\nFuncTorchDynamicLayerBackMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:497 [backend fallback]\nFunctionalize: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:349 [backend fallback]\nNamed: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\nConjugate: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\nNegative: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\NegateFallback.cpp:18 [backend fallback]\nZeroTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\nADInplaceOrView: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:96 [backend fallback]\nAutogradOther: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:63 [backend fallback]\nAutogradCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:67 [backend fallback]\nAutogradCUDA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:75 [backend fallback]\nAutogradXLA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:79 [backend fallback]\nAutogradMPS: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:87 [backend fallback]\nAutogradXPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:71 [backend fallback]\nAutogradHPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:100 [backend fallback]\nAutogradLazy: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:83 [backend fallback]\nAutogradMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:91 [backend fallback]\nTracer: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\torch\\csrc\\autograd\\TraceTypeManual.cpp:294 [backend fallback]\nAutocastCPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:321 [backend fallback]\nAutocastXPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:463 [backend fallback]\nAutocastMPS: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:209 [backend fallback]\nAutocastCUDA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:165 [backend fallback]\nFuncTorchBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:731 [backend fallback]\nBatchedNestedTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:758 [backend fallback]\nFuncTorchVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:27 [backend fallback]\nBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\nVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\nFuncTorchGradWrapper: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:207 [backend fallback]\nPythonTLSSnapshot: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:161 [backend fallback]\nFuncTorchDynamicLayerFrontMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:493 [backend fallback]\nPreDispatch: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:165 [backend fallback]\nPythonDispatcher: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:157 [backend fallback]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.optim.lr_scheduler import StepLR\n",
+    "import torchvision\n",
+    "from torchvision import datasets, transforms\n",
+    "import torch.ao.quantization as quantization\n",
+    "\n",
+    "# Préparer la quantization aware training\n",
+    "model.qconfig = quantization.get_default_qat_qconfig(\"fbgemm\")  # Utiliser backend \"fbgemm\"\n",
+    "print(\"QConfig:\", model.qconfig)\n",
+    "\n",
+    "quantization.fuse_modules(model, [[\"conv1\", \"bn1\", \"relu\"]] +\n",
+    "    [[\"layer1.0.conv1\", \"layer1.0.bn1\", \"layer1.0.relu\"],\n",
+    "     [\"layer1.0.conv2\", \"layer1.0.bn2\"]], inplace=True)\n",
+    "\n",
+    "# Préparer pour QAT\n",
+    "model_prepared = quantization.prepare_qat(model.train())\n",
+    "\n",
+    "# Optimizer et scheduler\n",
+    "optimizer = optim.SGD(model_prepared.parameters(), lr=0.001, momentum=0.9)\n",
+    "scheduler = StepLR(optimizer, step_size=7, gamma=0.1)\n",
+    "\n",
+    "# Critère de perte\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Entraîner le modèle préparé\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model_trained = train_model(model_prepared, criterion, optimizer, scheduler, num_epochs=5)\n",
+    "\n",
+    "# Convertir en modèle quantifié\n",
+    "model_int8 = quantization.convert(model_trained.eval())\n",
+    "\n",
+    "# Évaluer le modèle\n",
+    "def evaluate_model_accuracy(model, dataloader, criterion, device):\n",
+    "    model.eval()\n",
+    "    correct = 0\n",
+    "    total = 0\n",
+    "    running_loss = 0.0\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs, labels = inputs.to(device), labels.to(device)\n",
+    "            outputs = model(inputs)\n",
+    "            loss = criterion(outputs, labels)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "            correct += torch.sum(preds == labels.data)\n",
+    "            total += labels.size(0)\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "    accuracy = correct.double() / total\n",
+    "    avg_loss = running_loss / total\n",
+    "    return accuracy.item(), avg_loss\n",
+    "\n",
+    "# Évaluer la précision\n",
+    "test_accuracy, test_loss = evaluate_model_accuracy(model_int8, dataloaders[\"test\"], criterion, device)\n",
+    "print(f\"Précision sur le set de test après QAT : {test_accuracy * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f266f4a",
+   "metadata": {},
+   "source": [
+    "La Quantization Aware n'a pas pu être réalisée du fait d'un erreur que je n'ai pas réussi à résoudre. L'erreur est la suivante :\n",
+    "\n",
+    "\"NotImplementedError: Could not run 'quantized::conv2d_relu.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build).\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "04a263f0",
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