diff --git a/.gitignore b/.gitignore
index f3436fe1fd3e8a7064887098b38e50dfda48b27d..5fd67578d3eac78cba879732a6cc317613b53e51 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,8 @@
 # Data
 data/*
 transfer_learning/hymenoptera_data/*
+hymenoptera_data/*
+Ref-TD2_Deep_Learning.ipynb
 
 # Torch model
 *.pt
diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index c2adcbc390fff6ccaedadd0675de7ea413b66f53..6cd69ccba0ebca9cb310cbae1dd63ddc93b8ef57 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -104,34 +104,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[ 0.5951,  0.5334, -0.5103, -0.5134, -1.2010,  0.9176,  1.8221,  0.1435,\n",
-      "         -0.6760,  0.3097],\n",
-      "        [-1.0499,  1.6169, -2.8126, -0.8109,  0.3102,  0.3427, -1.1953, -0.6461,\n",
-      "          1.5037, -2.1665],\n",
-      "        [-0.7257, -0.4160, -2.0305, -0.2835,  1.4516,  0.0746, -0.8364,  0.7364,\n",
-      "          0.9054,  0.7130],\n",
-      "        [ 1.0042,  0.2271, -1.8427,  0.6697,  1.3251, -0.9044,  0.2521,  1.5179,\n",
-      "         -1.3082, -0.3570],\n",
-      "        [ 1.0667, -2.0132, -0.4898,  1.0522, -1.1379, -0.7358,  1.0990,  0.5844,\n",
-      "         -0.0552,  0.9696],\n",
-      "        [ 1.1877,  0.4843,  0.8296, -1.0844, -0.3682,  0.6182,  1.7612,  0.5464,\n",
-      "         -1.1180, -0.4313],\n",
-      "        [ 0.7843,  0.2752,  0.0879, -0.0519, -0.9299, -0.1681,  1.2594,  0.3075,\n",
-      "         -0.0361, -1.0461],\n",
-      "        [-0.4124,  1.1279,  0.1479,  0.4116, -0.3693,  0.8700, -0.1321, -0.2943,\n",
-      "         -0.6293, -0.1961],\n",
-      "        [ 0.3999, -1.2243,  0.5456,  1.6329, -0.7928, -0.3654,  0.4029, -1.1611,\n",
-      "         -0.6901,  0.8506],\n",
-      "        [-0.9668,  0.1045, -0.2409,  1.1955,  0.1089, -1.3554,  1.8725,  2.5233,\n",
-      "          0.8497,  0.6979],\n",
-      "        [-0.4222,  0.7192, -1.2624,  1.1730, -0.3367, -1.0904, -0.7361, -0.0766,\n",
-      "         -0.8308,  0.5865],\n",
-      "        [ 0.6775, -1.1593,  1.1255, -0.4503,  0.2380, -1.2022, -0.2449, -0.3531,\n",
-      "          0.3861,  0.7747],\n",
-      "        [-1.9184, -0.0222,  0.0453, -0.1677,  0.5471,  0.9982,  0.0864, -0.7583,\n",
-      "         -0.0260, -0.5787],\n",
-      "        [ 0.5022, -0.4249,  0.3942,  0.9563, -0.3526, -1.4569, -0.7204,  0.2304,\n",
-      "         -0.6134,  1.0270]])\n",
+      "tensor([[-1.1076e+00, -8.0431e-01,  1.2988e+00, -3.7945e-01, -1.8797e+00,\n",
+      "          3.9512e-01,  1.8310e+00, -1.2940e+00,  8.7095e-01, -3.1192e-01],\n",
+      "        [-8.0109e-01, -1.1072e+00, -9.2877e-01,  7.1054e-02, -8.0802e-01,\n",
+      "          8.5389e-01, -2.1829e-01, -2.6608e-01, -4.1619e-02,  1.9429e+00],\n",
+      "        [-5.8422e-01,  5.8112e-01,  7.1304e-01, -1.3490e+00,  8.2975e-01,\n",
+      "         -6.2344e-01,  3.1282e-01,  8.6732e-01, -1.7767e+00,  1.3208e+00],\n",
+      "        [-3.1368e-01,  6.1409e-01, -1.6719e-01, -6.5801e-01, -1.6291e+00,\n",
+      "         -1.0233e+00,  1.3542e+00, -2.1587e-01,  2.9602e-01,  6.5922e-01],\n",
+      "        [ 2.9670e-01,  4.9979e-01, -8.3055e-01,  4.5638e-01, -6.6924e-01,\n",
+      "         -1.7963e-01,  9.9191e-01,  3.8573e-01,  1.1943e+00,  2.1050e+00],\n",
+      "        [-2.8928e-01, -5.2508e-01, -1.0420e+00,  4.6995e-01, -8.9136e-01,\n",
+      "          7.1367e-01,  6.4390e-03, -5.5289e-01,  8.9075e-01, -6.8723e-01],\n",
+      "        [-3.4053e-01, -1.8402e-01, -1.9174e-03, -2.2943e-01,  2.4360e-01,\n",
+      "          5.7748e-01,  5.8705e-01, -1.7376e+00,  1.2233e+00, -5.4796e-01],\n",
+      "        [ 1.3668e+00,  1.0601e+00,  6.9674e-01,  1.2507e+00,  1.6624e-01,\n",
+      "          3.4707e-01, -4.7420e-02,  7.3347e-01,  1.7488e+00,  4.6712e-01],\n",
+      "        [ 1.4945e-01, -2.5235e-01,  1.4504e+00,  1.1182e+00,  4.1371e-01,\n",
+      "         -7.3475e-01,  2.2200e+00, -4.9163e-01, -5.7845e-01, -1.3559e+00],\n",
+      "        [ 6.2222e-01, -1.1835e+00,  6.8267e-01,  1.4239e+00, -2.0234e-01,\n",
+      "         -1.4280e+00,  1.9105e+00, -5.9414e-01,  8.2895e-01,  1.4819e-01],\n",
+      "        [ 2.5770e-01,  1.6312e+00,  4.7329e-01,  5.0435e-01, -1.2478e+00,\n",
+      "         -9.4782e-01,  1.0231e+00,  7.3836e-01,  1.5264e+00, -9.3091e-01],\n",
+      "        [-3.6383e-01,  9.7970e-01, -2.1666e-01,  1.4576e+00, -8.9456e-01,\n",
+      "          8.1228e-02, -1.3811e-01,  6.6389e-01, -1.2653e+00,  6.8880e-01],\n",
+      "        [ 2.9591e-01, -2.8932e-01, -1.4234e+00,  9.3962e-01, -1.1603e+00,\n",
+      "          1.7359e-01, -6.7355e-01, -1.6282e+00, -2.0340e+00, -1.2205e+00],\n",
+      "        [ 8.3165e-01,  6.0975e-01, -6.4890e-02,  6.0092e-01, -1.1216e+00,\n",
+      "         -7.7874e-01, -1.2588e+00,  8.3478e-01,  1.2470e+00,  8.9350e-02]])\n",
       "AlexNet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -260,18 +260,6 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "711b0b8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from torchvision import datasets, transforms\n",
-    "from torch.utils.data.sampler import SubsetRandomSampler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
    "id": "462666a2",
    "metadata": {},
    "outputs": [
@@ -352,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "317bf070",
    "metadata": {},
    "outputs": [
@@ -415,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 6,
    "id": "4b53f229",
    "metadata": {},
    "outputs": [
@@ -423,47 +411,50 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 28.707199 \tValidation Loss: 28.363214\n",
-      "Validation loss decreased (inf --> 28.363214).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 27.053440 \tValidation Loss: 26.921309\n",
-      "Validation loss decreased (28.363214 --> 26.921309).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 25.798181 \tValidation Loss: 25.484369\n",
-      "Validation loss decreased (26.921309 --> 25.484369).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 24.616021 \tValidation Loss: 25.825257\n",
-      "Epoch: 4 \tTraining Loss: 23.607140 \tValidation Loss: 24.406983\n",
-      "Validation loss decreased (25.484369 --> 24.406983).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 22.641223 \tValidation Loss: 23.463277\n",
-      "Validation loss decreased (24.406983 --> 23.463277).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 21.727461 \tValidation Loss: 23.323754\n",
-      "Validation loss decreased (23.463277 --> 23.323754).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 20.908013 \tValidation Loss: 22.815489\n",
-      "Validation loss decreased (23.323754 --> 22.815489).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 20.072570 \tValidation Loss: 22.468899\n",
-      "Validation loss decreased (22.815489 --> 22.468899).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 19.337123 \tValidation Loss: 23.307148\n",
-      "Epoch: 10 \tTraining Loss: 18.578279 \tValidation Loss: 22.322720\n",
-      "Validation loss decreased (22.468899 --> 22.322720).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 17.925301 \tValidation Loss: 22.491466\n",
-      "Epoch: 12 \tTraining Loss: 17.266396 \tValidation Loss: 22.145613\n",
-      "Validation loss decreased (22.322720 --> 22.145613).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 16.644972 \tValidation Loss: 21.923327\n",
-      "Validation loss decreased (22.145613 --> 21.923327).  Saving model ...\n",
-      "Epoch: 14 \tTraining Loss: 16.097757 \tValidation Loss: 22.242258\n",
-      "Epoch: 15 \tTraining Loss: 15.522903 \tValidation Loss: 22.269535\n",
-      "Epoch: 16 \tTraining Loss: 14.930308 \tValidation Loss: 23.073589\n",
-      "Epoch: 17 \tTraining Loss: 14.374154 \tValidation Loss: 23.190186\n",
-      "Epoch: 18 \tTraining Loss: 13.829007 \tValidation Loss: 23.638800\n",
-      "Epoch: 19 \tTraining Loss: 13.414001 \tValidation Loss: 25.147587\n",
-      "Epoch: 20 \tTraining Loss: 12.890743 \tValidation Loss: 24.385583\n",
-      "Epoch: 21 \tTraining Loss: 12.456227 \tValidation Loss: 24.933902\n",
-      "Epoch: 22 \tTraining Loss: 11.993389 \tValidation Loss: 25.289021\n",
-      "Epoch: 23 \tTraining Loss: 11.565563 \tValidation Loss: 26.004760\n",
-      "Epoch: 24 \tTraining Loss: 11.188692 \tValidation Loss: 26.451757\n",
-      "Epoch: 25 \tTraining Loss: 10.716678 \tValidation Loss: 27.236794\n",
-      "Epoch: 26 \tTraining Loss: 10.315807 \tValidation Loss: 27.493770\n",
-      "Epoch: 27 \tTraining Loss: 9.975283 \tValidation Loss: 27.571290\n",
-      "Epoch: 28 \tTraining Loss: 9.440035 \tValidation Loss: 29.006522\n",
-      "Epoch: 29 \tTraining Loss: 9.220511 \tValidation Loss: 29.190469\n"
+      "Epoch: 0 \tTraining Loss: 43.380691 \tValidation Loss: 38.457138\n",
+      "Validation loss decreased (inf --> 38.457138).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 35.329206 \tValidation Loss: 32.900985\n",
+      "Validation loss decreased (38.457138 --> 32.900985).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 31.022527 \tValidation Loss: 29.811918\n",
+      "Validation loss decreased (32.900985 --> 29.811918).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.659940 \tValidation Loss: 28.261925\n",
+      "Validation loss decreased (29.811918 --> 28.261925).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 26.989220 \tValidation Loss: 26.680548\n",
+      "Validation loss decreased (28.261925 --> 26.680548).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 25.610268 \tValidation Loss: 25.264652\n",
+      "Validation loss decreased (26.680548 --> 25.264652).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 24.384722 \tValidation Loss: 24.574530\n",
+      "Validation loss decreased (25.264652 --> 24.574530).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 23.272317 \tValidation Loss: 24.071903\n",
+      "Validation loss decreased (24.574530 --> 24.071903).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 22.358882 \tValidation Loss: 22.986056\n",
+      "Validation loss decreased (24.071903 --> 22.986056).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 21.508383 \tValidation Loss: 23.229483\n",
+      "Epoch: 10 \tTraining Loss: 20.664510 \tValidation Loss: 22.153994\n",
+      "Validation loss decreased (22.986056 --> 22.153994).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 19.897587 \tValidation Loss: 22.407001\n",
+      "Epoch: 12 \tTraining Loss: 19.155301 \tValidation Loss: 21.959725\n",
+      "Validation loss decreased (22.153994 --> 21.959725).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 18.392695 \tValidation Loss: 21.797627\n",
+      "Validation loss decreased (21.959725 --> 21.797627).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 17.732346 \tValidation Loss: 21.742934\n",
+      "Validation loss decreased (21.797627 --> 21.742934).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 17.143720 \tValidation Loss: 21.972774\n",
+      "Epoch: 16 \tTraining Loss: 16.550375 \tValidation Loss: 21.039691\n",
+      "Validation loss decreased (21.742934 --> 21.039691).  Saving model ...\n",
+      "Epoch: 17 \tTraining Loss: 15.957486 \tValidation Loss: 21.105782\n",
+      "Epoch: 18 \tTraining Loss: 15.507918 \tValidation Loss: 22.430941\n",
+      "Epoch: 19 \tTraining Loss: 14.941035 \tValidation Loss: 21.574019\n",
+      "Epoch: 20 \tTraining Loss: 14.477273 \tValidation Loss: 22.701380\n",
+      "Epoch: 21 \tTraining Loss: 13.895478 \tValidation Loss: 22.544208\n",
+      "Epoch: 22 \tTraining Loss: 13.436584 \tValidation Loss: 23.523962\n",
+      "Epoch: 23 \tTraining Loss: 13.006771 \tValidation Loss: 22.969808\n",
+      "Epoch: 24 \tTraining Loss: 12.628164 \tValidation Loss: 24.099535\n",
+      "Epoch: 25 \tTraining Loss: 12.149112 \tValidation Loss: 23.757295\n",
+      "Epoch: 26 \tTraining Loss: 11.830995 \tValidation Loss: 24.980118\n",
+      "Epoch: 27 \tTraining Loss: 11.370748 \tValidation Loss: 25.431931\n",
+      "Epoch: 28 \tTraining Loss: 10.946414 \tValidation Loss: 25.032183\n",
+      "Epoch: 29 \tTraining Loss: 10.497990 \tValidation Loss: 26.553329\n"
      ]
     }
    ],
@@ -552,12 +543,12 @@
    "source": [
     "Yes, overfitting occurs. This is evident starting around Epoch 15, where the Validation Loss stops decreasing and begins to oscillate or increase, while the Training Loss continues to decrease. \n",
     "This indicates the model is fitting too closely to the training data and failling to generalize well to the validation data.\n",
-    "By doing an early stopping, the training should stop around Epoch 15, where the Validation Loss reaches its minimum value of 21.882406. Continuing beyond this point does not improve validation performance and increases the risk of overfitting."
+    "By doing an early stopping, the training should stop around Epoch 15, where the Validation Loss reaches its minimum value. Continuing beyond this point does not improve validation performance and increases the risk of overfitting."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "11952c52",
    "metadata": {},
    "outputs": [
@@ -565,46 +556,64 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 42.263673 \tValidation Loss: 37.244937\n",
-      "Validation loss decreased (inf --> 37.244937).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 33.682380 \tValidation Loss: 31.417860\n",
-      "Validation loss decreased (37.244937 --> 31.417860).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 30.090010 \tValidation Loss: 29.366513\n",
-      "Validation loss decreased (31.417860 --> 29.366513).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 28.106117 \tValidation Loss: 27.604150\n",
-      "Validation loss decreased (29.366513 --> 27.604150).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 26.616582 \tValidation Loss: 27.137256\n",
-      "Validation loss decreased (27.604150 --> 27.137256).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 25.352860 \tValidation Loss: 26.308394\n",
-      "Validation loss decreased (27.137256 --> 26.308394).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 24.155753 \tValidation Loss: 25.469106\n",
-      "Validation loss decreased (26.308394 --> 25.469106).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 23.130453 \tValidation Loss: 23.953511\n",
-      "Validation loss decreased (25.469106 --> 23.953511).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 22.226852 \tValidation Loss: 23.464754\n",
-      "Validation loss decreased (23.953511 --> 23.464754).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 21.431531 \tValidation Loss: 23.127373\n",
-      "Validation loss decreased (23.464754 --> 23.127373).  Saving model ...\n",
-      "Epoch: 10 \tTraining Loss: 20.664977 \tValidation Loss: 22.986711\n",
-      "Validation loss decreased (23.127373 --> 22.986711).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 19.943305 \tValidation Loss: 22.906452\n",
-      "Validation loss decreased (22.986711 --> 22.906452).  Saving model ...\n",
-      "Epoch: 12 \tTraining Loss: 19.276236 \tValidation Loss: 22.413958\n",
-      "Validation loss decreased (22.906452 --> 22.413958).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 18.615019 \tValidation Loss: 23.996728\n",
-      "Epoch: 14 \tTraining Loss: 18.019572 \tValidation Loss: 21.861188\n",
-      "Validation loss decreased (22.413958 --> 21.861188).  Saving model ...\n",
-      "Epoch: 15 \tTraining Loss: 17.442191 \tValidation Loss: 21.672647\n",
-      "Validation loss decreased (21.861188 --> 21.672647).  Saving model ...\n",
-      "Epoch: 16 \tTraining Loss: 16.820751 \tValidation Loss: 22.072573\n",
-      "Epoch: 17 \tTraining Loss: 16.211900 \tValidation Loss: 22.292662\n",
-      "Epoch: 18 \tTraining Loss: 15.761882 \tValidation Loss: 22.181466\n",
+      "Net(\n",
+      "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
+      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
+      "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
+      "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
+      "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
+      ")\n",
+      "Epoch: 0 \tTraining Loss: 43.442905 \tValidation Loss: 37.391607\n",
+      "Validation loss decreased (inf --> 37.391607).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 33.893192 \tValidation Loss: 31.666907\n",
+      "Validation loss decreased (37.391607 --> 31.666907).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 30.333144 \tValidation Loss: 28.812851\n",
+      "Validation loss decreased (31.666907 --> 28.812851).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.021303 \tValidation Loss: 26.535644\n",
+      "Validation loss decreased (28.812851 --> 26.535644).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 26.318296 \tValidation Loss: 25.720039\n",
+      "Validation loss decreased (26.535644 --> 25.720039).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 24.954264 \tValidation Loss: 24.839252\n",
+      "Validation loss decreased (25.720039 --> 24.839252).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 23.878914 \tValidation Loss: 24.649017\n",
+      "Validation loss decreased (24.839252 --> 24.649017).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 22.885805 \tValidation Loss: 22.972781\n",
+      "Validation loss decreased (24.649017 --> 22.972781).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 21.963391 \tValidation Loss: 23.344084\n",
+      "Epoch: 9 \tTraining Loss: 21.151886 \tValidation Loss: 23.745328\n",
+      "Epoch: 10 \tTraining Loss: 20.384624 \tValidation Loss: 22.091478\n",
+      "Validation loss decreased (22.972781 --> 22.091478).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 19.595241 \tValidation Loss: 23.613092\n",
+      "Epoch: 12 \tTraining Loss: 18.888945 \tValidation Loss: 21.605048\n",
+      "Validation loss decreased (22.091478 --> 21.605048).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 18.237921 \tValidation Loss: 22.255342\n",
+      "Epoch: 14 \tTraining Loss: 17.660938 \tValidation Loss: 21.594410\n",
+      "Validation loss decreased (21.605048 --> 21.594410).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 17.072336 \tValidation Loss: 21.568951\n",
+      "Validation loss decreased (21.594410 --> 21.568951).  Saving model ...\n",
+      "Epoch: 16 \tTraining Loss: 16.487046 \tValidation Loss: 21.561884\n",
+      "Validation loss decreased (21.568951 --> 21.561884).  Saving model ...\n",
+      "Epoch: 17 \tTraining Loss: 16.017516 \tValidation Loss: 21.574631\n",
+      "Epoch: 18 \tTraining Loss: 15.427160 \tValidation Loss: 21.480671\n",
+      "Validation loss decreased (21.561884 --> 21.480671).  Saving model ...\n",
+      "Epoch: 19 \tTraining Loss: 14.988037 \tValidation Loss: 21.661484\n",
+      "Epoch: 20 \tTraining Loss: 14.418668 \tValidation Loss: 21.999541\n",
+      "Epoch: 21 \tTraining Loss: 13.960523 \tValidation Loss: 22.279162\n",
       "Validation loss increased for 3 times consecutives. Applying Early Stop.\n"
      ]
     }
    ],
    "source": [
     "# EARLY STOP\n",
+    "# create a complete CNN\n",
+    "model = Net()\n",
+    "print(model)\n",
+    "# move tensors to GPU if CUDA is available\n",
+    "if train_on_gpu:\n",
+    "    model.cuda()\n",
+    "\n",
+    "    \n",
     "import torch.optim as optim\n",
     "\n",
     "min_epochs = 10\n",
@@ -689,13 +698,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "id": "d39df818",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -716,13 +725,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "id": "2111dfe9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -743,13 +752,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "ae4ab2d3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -788,7 +797,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
+   "id": "39e036ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/qb/94v41qkx157gvjjjv1rchcr00000gn/T/ipykernel_28418/3291884398.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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 21.236800\n",
+      "\n",
+      "Test Accuracy of airplane: 68% (688/1000)\n",
+      "Test Accuracy of automobile: 73% (734/1000)\n",
+      "Test Accuracy of  bird: 45% (454/1000)\n",
+      "Test Accuracy of   cat: 41% (411/1000)\n",
+      "Test Accuracy of  deer: 61% (614/1000)\n",
+      "Test Accuracy of   dog: 55% (557/1000)\n",
+      "Test Accuracy of  frog: 75% (751/1000)\n",
+      "Test Accuracy of horse: 71% (717/1000)\n",
+      "Test Accuracy of  ship: 78% (782/1000)\n",
+      "Test Accuracy of truck: 70% (700/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 64% (6408/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
+    "\n",
+    "# track test loss\n",
+    "test_loss = 0.0\n",
+    "class_correct = list(0.0 for i in range(10))\n",
+    "class_total = list(0.0 for i in range(10))\n",
+    "\n",
+    "model.eval()\n",
+    "# iterate over test data\n",
+    "for data, target in test_loader:\n",
+    "    # move tensors to GPU if CUDA is available\n",
+    "    if train_on_gpu:\n",
+    "        data, target = data.cuda(), target.cuda()\n",
+    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+    "    output = model(data)\n",
+    "    # calculate the batch loss\n",
+    "    loss = criterion(output, target)\n",
+    "    # update test loss\n",
+    "    test_loss += loss.item() * data.size(0)\n",
+    "    # convert output probabilities to predicted class\n",
+    "    _, pred = torch.max(output, 1)\n",
+    "    # compare predictions to true label\n",
+    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+    "    correct = (\n",
+    "        np.squeeze(correct_tensor.numpy())\n",
+    "        if not train_on_gpu\n",
+    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "    )\n",
+    "    # calculate test accuracy for each object class\n",
+    "    for i in range(batch_size):\n",
+    "        label = target.data[i]\n",
+    "        class_correct[label] += correct[i].item()\n",
+    "        class_total[label] += 1\n",
+    "\n",
+    "# average test loss\n",
+    "test_loss = test_loss / len(test_loader)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    if class_total[i] > 0:\n",
+    "        print(\n",
+    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+    "            % (\n",
+    "                classes[i],\n",
+    "                100 * class_correct[i] / class_total[i],\n",
+    "                np.sum(class_correct[i]),\n",
+    "                np.sum(class_total[i]),\n",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+    "\n",
+    "print(\n",
+    "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+    "    % (\n",
+    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+    "        np.sum(class_correct),\n",
+    "        np.sum(class_total),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "145ab6d2",
+   "metadata": {},
+   "source": [
+    "Now loading the model with the lowest validation loss value - Early Stop version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "id": "e93efdfc",
    "metadata": {},
    "outputs": [
@@ -796,7 +911,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qb/94v41qkx157gvjjjv1rchcr00000gn/T/ipykernel_36576/2740011163.py:2: 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",
+      "/var/folders/qb/94v41qkx157gvjjjv1rchcr00000gn/T/ipykernel_28418/1442498957.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_1_early_stop.pt\"))\n"
      ]
     },
@@ -804,25 +919,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 21.795794\n",
+      "Test Loss: 21.708425\n",
       "\n",
-      "Test Accuracy of airplane: 71% (716/1000)\n",
-      "Test Accuracy of automobile: 75% (754/1000)\n",
-      "Test Accuracy of  bird: 49% (496/1000)\n",
-      "Test Accuracy of   cat: 33% (332/1000)\n",
-      "Test Accuracy of  deer: 47% (470/1000)\n",
-      "Test Accuracy of   dog: 58% (582/1000)\n",
-      "Test Accuracy of  frog: 77% (775/1000)\n",
-      "Test Accuracy of horse: 68% (680/1000)\n",
-      "Test Accuracy of  ship: 78% (788/1000)\n",
-      "Test Accuracy of truck: 65% (655/1000)\n",
+      "Test Accuracy of airplane: 67% (671/1000)\n",
+      "Test Accuracy of automobile: 80% (806/1000)\n",
+      "Test Accuracy of  bird: 45% (457/1000)\n",
+      "Test Accuracy of   cat: 33% (334/1000)\n",
+      "Test Accuracy of  deer: 55% (555/1000)\n",
+      "Test Accuracy of   dog: 65% (650/1000)\n",
+      "Test Accuracy of  frog: 77% (779/1000)\n",
+      "Test Accuracy of horse: 66% (664/1000)\n",
+      "Test Accuracy of  ship: 70% (703/1000)\n",
+      "Test Accuracy of truck: 74% (742/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 62% (6248/10000)\n"
+      "Test Accuracy (Overall): 63% (6361/10000)\n"
      ]
     }
    ],
    "source": [
-    "# model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
     "model.load_state_dict(torch.load(\"./model_cifar_1_early_stop.pt\"))\n",
     "\n",
     "# track test loss\n",
@@ -904,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "id": "8b67c2c6",
    "metadata": {},
    "outputs": [
@@ -972,9 +1086,17 @@
     "    new_model.cuda()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b8cb9199",
+   "metadata": {},
+   "source": [
+    "Loss function and training using SDG (Stochastic Gradient Descent) optimizer - New Model"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "id": "3cc6cc8a",
    "metadata": {},
    "outputs": [
@@ -982,59 +1104,53 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 45.661728 \tValidation Loss: 43.144845\n",
-      "Validation loss decreased (inf --> 43.144845).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 40.413328 \tValidation Loss: 36.701182\n",
-      "Validation loss decreased (43.144845 --> 36.701182).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 35.370900 \tValidation Loss: 31.842937\n",
-      "Validation loss decreased (36.701182 --> 31.842937).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 32.449529 \tValidation Loss: 29.621498\n",
-      "Validation loss decreased (31.842937 --> 29.621498).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 30.349224 \tValidation Loss: 28.041503\n",
-      "Validation loss decreased (29.621498 --> 28.041503).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 28.714408 \tValidation Loss: 25.830271\n",
-      "Validation loss decreased (28.041503 --> 25.830271).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 27.068397 \tValidation Loss: 24.477239\n",
-      "Validation loss decreased (25.830271 --> 24.477239).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 25.677563 \tValidation Loss: 23.316105\n",
-      "Validation loss decreased (24.477239 --> 23.316105).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 24.532446 \tValidation Loss: 22.407685\n",
-      "Validation loss decreased (23.316105 --> 22.407685).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 23.285756 \tValidation Loss: 20.908726\n",
-      "Validation loss decreased (22.407685 --> 20.908726).  Saving model ...\n",
-      "Epoch: 10 \tTraining Loss: 22.234629 \tValidation Loss: 19.937269\n",
-      "Validation loss decreased (20.908726 --> 19.937269).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 21.350188 \tValidation Loss: 20.242398\n",
-      "Epoch: 12 \tTraining Loss: 20.464388 \tValidation Loss: 18.725208\n",
-      "Validation loss decreased (19.937269 --> 18.725208).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 19.509838 \tValidation Loss: 17.914644\n",
-      "Validation loss decreased (18.725208 --> 17.914644).  Saving model ...\n",
-      "Epoch: 14 \tTraining Loss: 18.660562 \tValidation Loss: 17.655490\n",
-      "Validation loss decreased (17.914644 --> 17.655490).  Saving model ...\n",
-      "Epoch: 15 \tTraining Loss: 17.976359 \tValidation Loss: 17.511835\n",
-      "Validation loss decreased (17.655490 --> 17.511835).  Saving model ...\n",
-      "Epoch: 16 \tTraining Loss: 17.347915 \tValidation Loss: 17.413343\n",
-      "Validation loss decreased (17.511835 --> 17.413343).  Saving model ...\n",
-      "Epoch: 17 \tTraining Loss: 16.598062 \tValidation Loss: 16.746917\n",
-      "Validation loss decreased (17.413343 --> 16.746917).  Saving model ...\n",
-      "Epoch: 18 \tTraining Loss: 15.985513 \tValidation Loss: 16.586973\n",
-      "Validation loss decreased (16.746917 --> 16.586973).  Saving model ...\n",
-      "Epoch: 19 \tTraining Loss: 15.357637 \tValidation Loss: 15.646514\n",
-      "Validation loss decreased (16.586973 --> 15.646514).  Saving model ...\n",
-      "Epoch: 20 \tTraining Loss: 14.819297 \tValidation Loss: 16.106034\n",
-      "Epoch: 21 \tTraining Loss: 14.399225 \tValidation Loss: 15.635265\n",
-      "Validation loss decreased (15.646514 --> 15.635265).  Saving model ...\n",
-      "Epoch: 22 \tTraining Loss: 13.791790 \tValidation Loss: 15.284317\n",
-      "Validation loss decreased (15.635265 --> 15.284317).  Saving model ...\n",
-      "Epoch: 23 \tTraining Loss: 13.272097 \tValidation Loss: 15.818682\n",
-      "Epoch: 24 \tTraining Loss: 12.716989 \tValidation Loss: 15.300960\n",
-      "Epoch: 25 \tTraining Loss: 12.246681 \tValidation Loss: 15.173259\n",
-      "Validation loss decreased (15.284317 --> 15.173259).  Saving model ...\n",
-      "Epoch: 26 \tTraining Loss: 11.784792 \tValidation Loss: 14.705897\n",
-      "Validation loss decreased (15.173259 --> 14.705897).  Saving model ...\n",
-      "Epoch: 27 \tTraining Loss: 11.364225 \tValidation Loss: 14.920603\n",
-      "Epoch: 28 \tTraining Loss: 10.848707 \tValidation Loss: 15.559500\n",
-      "Epoch: 29 \tTraining Loss: 10.409248 \tValidation Loss: 15.289679\n",
+      "Epoch: 0 \tTraining Loss: 45.202596 \tValidation Loss: 41.129644\n",
+      "Validation loss decreased (inf --> 41.129644).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 38.928947 \tValidation Loss: 35.474301\n",
+      "Validation loss decreased (41.129644 --> 35.474301).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 34.331604 \tValidation Loss: 30.689815\n",
+      "Validation loss decreased (35.474301 --> 30.689815).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 31.904387 \tValidation Loss: 28.585091\n",
+      "Validation loss decreased (30.689815 --> 28.585091).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 29.856985 \tValidation Loss: 26.742311\n",
+      "Validation loss decreased (28.585091 --> 26.742311).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 28.202003 \tValidation Loss: 25.700891\n",
+      "Validation loss decreased (26.742311 --> 25.700891).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 26.725737 \tValidation Loss: 24.190787\n",
+      "Validation loss decreased (25.700891 --> 24.190787).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 25.442558 \tValidation Loss: 22.879612\n",
+      "Validation loss decreased (24.190787 --> 22.879612).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 24.205957 \tValidation Loss: 21.616267\n",
+      "Validation loss decreased (22.879612 --> 21.616267).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 23.088593 \tValidation Loss: 20.752075\n",
+      "Validation loss decreased (21.616267 --> 20.752075).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 22.115407 \tValidation Loss: 20.079397\n",
+      "Validation loss decreased (20.752075 --> 20.079397).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 21.092647 \tValidation Loss: 19.293233\n",
+      "Validation loss decreased (20.079397 --> 19.293233).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 20.256340 \tValidation Loss: 18.708507\n",
+      "Validation loss decreased (19.293233 --> 18.708507).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 19.228898 \tValidation Loss: 18.309530\n",
+      "Validation loss decreased (18.708507 --> 18.309530).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 18.568708 \tValidation Loss: 17.504886\n",
+      "Validation loss decreased (18.309530 --> 17.504886).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 17.591165 \tValidation Loss: 17.438624\n",
+      "Validation loss decreased (17.504886 --> 17.438624).  Saving model ...\n",
+      "Epoch: 16 \tTraining Loss: 16.982769 \tValidation Loss: 16.856311\n",
+      "Validation loss decreased (17.438624 --> 16.856311).  Saving model ...\n",
+      "Epoch: 17 \tTraining Loss: 16.387046 \tValidation Loss: 16.027689\n",
+      "Validation loss decreased (16.856311 --> 16.027689).  Saving model ...\n",
+      "Epoch: 18 \tTraining Loss: 15.620964 \tValidation Loss: 16.821088\n",
+      "Epoch: 19 \tTraining Loss: 15.121508 \tValidation Loss: 15.402142\n",
+      "Validation loss decreased (16.027689 --> 15.402142).  Saving model ...\n",
+      "Epoch: 20 \tTraining Loss: 14.483963 \tValidation Loss: 15.529650\n",
+      "Epoch: 21 \tTraining Loss: 13.816726 \tValidation Loss: 15.218991\n",
+      "Validation loss decreased (15.402142 --> 15.218991).  Saving model ...\n",
+      "Epoch: 22 \tTraining Loss: 13.371919 \tValidation Loss: 15.179586\n",
+      "Validation loss decreased (15.218991 --> 15.179586).  Saving model ...\n",
+      "Epoch: 23 \tTraining Loss: 12.810049 \tValidation Loss: 15.242959\n",
+      "Epoch: 24 \tTraining Loss: 12.310747 \tValidation Loss: 16.002799\n",
+      "Epoch: 25 \tTraining Loss: 11.923678 \tValidation Loss: 15.463038\n",
       "Validation loss increased for 3 times consecutives. Applying Early Stop.\n"
      ]
     }
@@ -1122,9 +1238,17 @@
     "            break\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9f61c0e7",
+   "metadata": {},
+   "source": [
+    "Now loading the model with the lowest validation loss value - New Model"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 21,
    "id": "97355006",
    "metadata": {},
    "outputs": [
@@ -1132,7 +1256,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/qb/94v41qkx157gvjjjv1rchcr00000gn/T/ipykernel_47530/1725778705.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",
+      "/var/folders/qb/94v41qkx157gvjjjv1rchcr00000gn/T/ipykernel_28418/1725778705.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",
       "  new_model.load_state_dict(torch.load(\"./model_cifar_2.pt\"))\n"
      ]
     },
@@ -1140,20 +1264,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 15.112429\n",
+      "Test Loss: 15.793606\n",
       "\n",
-      "Test Accuracy of airplane: 81% (816/1000)\n",
-      "Test Accuracy of automobile: 82% (823/1000)\n",
-      "Test Accuracy of  bird: 64% (642/1000)\n",
-      "Test Accuracy of   cat: 59% (598/1000)\n",
-      "Test Accuracy of  deer: 67% (673/1000)\n",
-      "Test Accuracy of   dog: 58% (582/1000)\n",
-      "Test Accuracy of  frog: 82% (820/1000)\n",
-      "Test Accuracy of horse: 81% (811/1000)\n",
-      "Test Accuracy of  ship: 85% (855/1000)\n",
-      "Test Accuracy of truck: 83% (838/1000)\n",
+      "Test Accuracy of airplane: 75% (757/1000)\n",
+      "Test Accuracy of automobile: 81% (817/1000)\n",
+      "Test Accuracy of  bird: 54% (540/1000)\n",
+      "Test Accuracy of   cat: 51% (510/1000)\n",
+      "Test Accuracy of  deer: 72% (724/1000)\n",
+      "Test Accuracy of   dog: 60% (608/1000)\n",
+      "Test Accuracy of  frog: 86% (867/1000)\n",
+      "Test Accuracy of horse: 82% (820/1000)\n",
+      "Test Accuracy of  ship: 85% (859/1000)\n",
+      "Test Accuracy of truck: 83% (833/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 74% (7458/10000)\n"
+      "Test Accuracy (Overall): 73% (7335/10000)\n"
      ]
     }
    ],
@@ -1228,37 +1352,47 @@
     "# Test Accuracy: Model 1 v/s Model 2\n",
     "\n",
     "## Test Accuracy Model 1: \n",
-    "* Test Loss: 21.811477\n",
+    "* Test Loss: 21.236800\n",
     "\n",
-    "* Test Accuracy of airplane: 71% (716/1000)\n",
-    "* Test Accuracy of automobile: 75% (750/1000)\n",
-    "* Test Accuracy of  bird: 55% (558/1000)\n",
-    "* Test Accuracy of   cat: 44% (442/1000)\n",
-    "* Test Accuracy of  deer: 60% (604/1000)\n",
-    "* Test Accuracy of   dog: 52% (521/1000)\n",
-    "* Test Accuracy of  frog: 64% (644/1000)\n",
-    "* Test Accuracy of horse: 58% (588/1000)\n",
-    "* Test Accuracy of  ship: 74% (746/1000)\n",
-    "* Test Accuracy of truck: 68% (681/1000)\n",
+    "* Test Accuracy of airplane: 68% (688/1000)\n",
+    "* Test Accuracy of automobile: 73% (734/1000)\n",
+    "* Test Accuracy of  bird: 45% (454/1000)\n",
+    "* Test Accuracy of   cat: 41% (411/1000)\n",
+    "* Test Accuracy of  deer: 61% (614/1000)\n",
+    "* Test Accuracy of   dog: 55% (557/1000)\n",
+    "* Test Accuracy of  frog: 75% (751/1000)\n",
+    "* Test Accuracy of horse: 71% (717/1000)\n",
+    "* Test Accuracy of  ship: 78% (782/1000)\n",
+    "* Test Accuracy of truck: 70% (700/1000)\n",
     "\n",
-    "* Test Accuracy (Overall): 62% (6250/10000)\n",
+    "* Test Accuracy (Overall): 64% (6408/10000)\n",
     "\n",
     "\n",
     "## Test Accuracy Model 2:\n",
-    "* Test Loss: 15.112429\n",
-    "\n",
-    "* Test Accuracy of airplane: 81% (816/1000)\n",
-    "* Test Accuracy of automobile: 82% (823/1000)\n",
-    "* Test Accuracy of  bird: 64% (642/1000)\n",
-    "* Test Accuracy of   cat: 59% (598/1000)\n",
-    "* Test Accuracy of  deer: 67% (673/1000)\n",
-    "* Test Accuracy of   dog: 58% (582/1000)\n",
-    "* Test Accuracy of  frog: 82% (820/1000)\n",
-    "* Test Accuracy of horse: 81% (811/1000)\n",
-    "* Test Accuracy of  ship: 85% (855/1000)\n",
-    "* Test Accuracy of truck: 83% (838/1000)\n",
+    "* Test Loss: 15.793606\n",
+    "\n",
+    "* Test Accuracy of airplane: 75% (757/1000)\n",
+    "* Test Accuracy of automobile: 81% (817/1000)\n",
+    "* Test Accuracy of  bird: 54% (540/1000)\n",
+    "* Test Accuracy of   cat: 51% (510/1000)\n",
+    "* Test Accuracy of  deer: 72% (724/1000)\n",
+    "* Test Accuracy of   dog: 60% (608/1000)\n",
+    "* Test Accuracy of  frog: 86% (867/1000)\n",
+    "* Test Accuracy of horse: 82% (820/1000)\n",
+    "* Test Accuracy of  ship: 85% (859/1000)\n",
+    "* Test Accuracy of truck: 83% (833/1000)\n",
+    "\n",
+    "* Test Accuracy (Overall): 73% (7335/10000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7593c300",
+   "metadata": {},
+   "source": [
+    "### Results comparison\n",
     "\n",
-    "* Test Accuracy (Overall): 74% (7458/10000)"
+    "From the comparison of the Test Accuracy between the original model and the model with the updated specifications, we observe a significant increase in overall Test Accuracy, improving from 64% to 73%. Additionally, we note that the percentages of Test Accuracy for individual categories increase proportionally from one model to the other."
    ]
   },
   {
@@ -1278,7 +1412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 22,
    "id": "ef623c26",
    "metadata": {},
    "outputs": [
@@ -1295,7 +1429,7 @@
        "2330946"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1325,7 +1459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "id": "c4c65d4b",
    "metadata": {},
    "outputs": [
@@ -1342,7 +1476,7 @@
        "659742"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1360,7 +1494,7 @@
    "id": "063d405c",
    "metadata": {},
    "source": [
-    "## Comparation of the size reduction\n",
+    "## Size reduction comparison\n",
     "\n",
     "## Pre-quantization:\n",
     "* model:  fp32  \t Size (KB): 2330.946\n",
@@ -1368,7 +1502,9 @@
     "\n",
     "## Post-quantization:\n",
     "* model:  int8  \t Size (KB): 659.742\n",
-    "* 659742"
+    "* 659742\n",
+    "\n",
+    "These metrics demonstrate that applying post-training quantization to the model significantly reduces its size. Specifically, the model size decreases from 2330.946 KB to 659.742 KB, resulting in a reduction of 1671.204 KB, which corresponds to 71.7% of the original size."
    ]
   },
   {
@@ -1381,28 +1517,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 24,
    "id": "f8c00726",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[W1130 21:55:58.874587000 qlinear_dynamic.cpp:251] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator())\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 15.114380\n",
+      "Test Loss: 15.800534\n",
       "\n",
-      "Test Accuracy of airplane: 81% (817/1000)\n",
-      "Test Accuracy of automobile: 82% (823/1000)\n",
-      "Test Accuracy of  bird: 64% (643/1000)\n",
-      "Test Accuracy of   cat: 60% (600/1000)\n",
-      "Test Accuracy of  deer: 67% (675/1000)\n",
-      "Test Accuracy of   dog: 57% (579/1000)\n",
-      "Test Accuracy of  frog: 81% (818/1000)\n",
-      "Test Accuracy of horse: 81% (812/1000)\n",
-      "Test Accuracy of  ship: 85% (855/1000)\n",
-      "Test Accuracy of truck: 84% (840/1000)\n",
+      "Test Accuracy of airplane: 75% (754/1000)\n",
+      "Test Accuracy of automobile: 82% (820/1000)\n",
+      "Test Accuracy of  bird: 53% (539/1000)\n",
+      "Test Accuracy of   cat: 51% (511/1000)\n",
+      "Test Accuracy of  deer: 71% (719/1000)\n",
+      "Test Accuracy of   dog: 60% (608/1000)\n",
+      "Test Accuracy of  frog: 86% (867/1000)\n",
+      "Test Accuracy of horse: 82% (822/1000)\n",
+      "Test Accuracy of  ship: 85% (858/1000)\n",
+      "Test Accuracy of truck: 83% (830/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 74% (7462/10000)\n"
+      "Test Accuracy (Overall): 73% (7328/10000)\n"
      ]
     }
    ],
@@ -1473,7 +1616,28 @@
    "cell_type": "markdown",
    "id": "f63f483d",
    "metadata": {},
-   "source": []
+   "source": [
+    "# Test Accuracy: New Model v/s Quantized New Model\n",
+    "\n",
+    "## Test Accuracy - New Model: \n",
+    "\n",
+    "| **Category**         | **Model 2 Test Accuracy** | **Quantized Model Test Accuracy** |\n",
+    "|-----------------------|----------------------|------------------------------|\n",
+    "| **Test Loss**         | 15.793606           | 15.800534                   |\n",
+    "| **Airplane**          | 75% (757/1000)      | 75% (754/1000)              |\n",
+    "| **Automobile**        | 81% (817/1000)      | 82% (820/1000)              |\n",
+    "| **Bird**              | 54% (540/1000)      | 53% (539/1000)              |\n",
+    "| **Cat**               | 51% (510/1000)      | 51% (511/1000)              |\n",
+    "| **Deer**              | 72% (724/1000)      | 71% (719/1000)              |\n",
+    "| **Dog**               | 60% (608/1000)      | 60% (608/1000)              |\n",
+    "| **Frog**              | 86% (867/1000)      | 86% (867/1000)              |\n",
+    "| **Horse**             | 82% (820/1000)      | 82% (822/1000)              |\n",
+    "| **Ship**              | 85% (859/1000)      | 85% (858/1000)              |\n",
+    "| **Truck**             | 83% (833/1000)      | 83% (830/1000)              |\n",
+    "| **Overall Accuracy**  | 73% (7335/10000)    | 73% (7328/10000)            |\n",
+    "\n",
+    "In this comparative table of the Test Accuracy for the new model, showing both the original version and the post-training quantization version, we can see that the accuracy percentages remain quite similar. In some cases, the percentage increases in the quantized version, while in others it decreases. However, the Overall Test Accuracy percentage remains the same, even though the values are 7335 and 7328, respectively."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1497,6 +1661,16 @@
   {
    "cell_type": "code",
    "execution_count": 28,
+   "id": "65358d35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "b4d13080",
    "metadata": {},
    "outputs": [
@@ -1507,9 +1681,7 @@
       "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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",
       "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
-      "  warnings.warn(msg)\n",
-      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /Users/heber/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
-      "100.0%\n"
+      "  warnings.warn(msg)\n"
      ]
     },
     {
@@ -1593,7 +1765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
    "id": "589a12b4",
    "metadata": {},
    "outputs": [
@@ -1662,7 +1834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 16,
    "id": "b105d4be",
    "metadata": {},
    "outputs": [
@@ -1680,7 +1852,7 @@
        "96379932"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1689,17 +1861,85 @@
     "print_size_of_model(model, \"int8\")\n",
     "\n",
     "torch.backends.quantized.engine = 'qnnpack'\n",
-    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
-    "print_size_of_model(quantized_model, \"int8\")"
+    "quantized_ResNet50_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+    "print_size_of_model(quantized_ResNet50_model, \"int8\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "4d24e825",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is: fire engine\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[W1129 13:56:11.778698000 qlinear_dynamic.cpp:251] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator())\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Choose an image to pass through the model\n",
+    "test_image = \"images/test-1.jpg\"\n",
+    "\n",
+    "# Configure matplotlib for pretty inline plots\n",
+    "#%matplotlib inline\n",
+    "#%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "# Prepare the labels\n",
+    "with open(\"imagenet-simple-labels.json\") as f:\n",
+    "    labels = json.load(f)\n",
+    "\n",
+    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
+    "data_transform = transforms.Compose(\n",
+    "    [\n",
+    "        transforms.Resize((224, 224)),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "    ]\n",
+    ")\n",
+    "# Load the image\n",
+    "\n",
+    "image = Image.open(test_image)\n",
+    "plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
+    "\n",
+    "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n",
+    "# image = data_transform(image).unsqueeze(0).cuda()\n",
+    "image = data_transform(image).unsqueeze(0)\n",
+    "\n",
+    "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
+    "# model = models.resnet50(pretrained=True)\n",
+    "# Send the model to the GPU\n",
+    "# model.cuda()\n",
+    "# Set layers such as dropout and batchnorm in evaluation mode\n",
+    "quantized_ResNet50_model.eval()\n",
+    "\n",
+    "# Get the 1000-dimensional model output\n",
+    "out = quantized_ResNet50_model(image)\n",
+    "# Find the predicted class\n",
+    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1711,7 +1951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 18,
    "id": "de8d2928",
    "metadata": {},
    "outputs": [
@@ -1791,22 +2031,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 19,
    "id": "6c22a28c",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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",
-      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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=Inception_V3_Weights.IMAGENET1K_V1`. You can also use `weights=Inception_V3_Weights.DEFAULT` to get the most up-to-date weights.\n",
-      "  warnings.warn(msg)\n",
-      "Downloading: \"https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\" to /Users/heber/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\n",
-      "100.0%\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1879,13 +2107,90 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "5d57da4b",
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f33daea3",
    "metadata": {},
-   "source": [
-    "## Exercise 4: Transfer Learning\n",
-    "    \n",
-    "    \n",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is: race car\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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wl0TJTCFwssc2J8QALFzPwFwbD8yOaJqL87CQmPX5V7Fhj0SIERaR/gFEMIVVneRnrprkpvNqtNoTuKai7oHvGYgoHrNlmvQuaaer9k2MwY6GZwFAIRouAgVI6VMtj6HurRG8MIaolUnZBqqzi2jpYozlqzGXrYDexZiteZSTwUAAcQxBAAoT1fiZFApDxa9gjl2+r2Q0jvfzPhW5BUcfZ7aN8VwaS8KpECmzKgYWqgCYq+jPQzNbKBHrgfrkXr7393/Cls0/xTMNMukURhque2OKay6YxDLqKBGhIoGQjQekDJFqD5T+D7LcgmFMP9aUAYhYEiiVIh06yEPLESpFKtuGLI8TPf0cxp33EnplfGrI7CLslT1w/kWYF2zE2HgBQd9ShJ3CIcKQBlIYGDKKB27MnsF4LSv3V5sz8GoTAY/VFfLonx/LEzDz33PRIM8FTmbIYNbEwMlefZ7qqoTXwlxLwjsZ4zmV4YaFxOk3h4KIGpv+3z/xpU9+k+eKgkkVITGIhMWX7zH49udczlkfAimQIYKIJEXAVD4wiKD/xW+t4jW9xEQFNvLhLVj7BYHpkspnMVrTmF1dyBWtiM4Q+/4A+ewO1KPPIFHIrg7ExvNQb34L/jXXYi5ZgmHZSOEQGQrnFM/UEaemFHv37kVK2TSoYRjy8MMPUyqVME0T27ZxXRfLso74klJSr9cBsCwL13VJpVK0traydOlSenp6cByn2V0wMUBhGFKpVJpNoUzTRAiBZVnN7w3DaPZ2SP4N050UZ3YFnNlWOuFo9/jM8z2VzKWyyJMdMjitPQNz8YE5l8akPQFzAz0/cSa/LI2w5b4fsS+AYUMiDYERKaSIODRhcMfdJmefswzDbEdQB1kBJREoIiNAKIFQNZACRYgQIUSSRk0BQglCO0K0eFiERPUakTeMGDRAgv2shX9JFmNKYpsZRHsbUejB+CTR3fdg3nUPte4OxPnnYr7xrbhvvAF7+XLCdAYhFJZURIZAKYGR5CsoUIZEnOBEhMQQFItF7rnnHlatWkUURU1R0N7eTj6ff1FLZykltVqNxx9/nPt+9CMGBvoJo6CRXyGwbYfb3vUuzj33XNrb29m4cSPnnXcelmURRRFPPPEE3/3udymVSs3STYh7PLzwwg5UFOI6NYSwwHSBNH19PSxatLg5Ps/zqNVqRFFEEASkUinOO+88XNdtCgTTNOno6ODKK68klUodcc6zwVzKJThZYznlYmC2a9EXMgtRfGnmBxJFUBkkqo0izQghDCwFygRLuWBGbHq+jh9WSVldKJFHGDZKRggEprTAnATZhhKKyIzd+QYCRIAQdSIlMSOfsDuP055BZEao3Zom9TWBKnrYfoT1dBn8CBEUkQhcYSMtA8c0QEis4VGiu38Edz1AvbuHaMO5uG+8HvGma1Gr1yCzOexQENoetlAYkUOU3Bcn+PmtlGLnzp1ks9kjVtqJMU1W3DOFgFIqFgOPPc7U+DiWEWGbIBXISKCkJJvN0tLSQhRFPPzww2zZsoUbbriBz372s2zbto3LLruMlStXEkVRcxxKKXp6e+jp9Lj+DWVkmOPpbbBnf46s6+C4LlKppgCo1WoUCgXGx8cZGhpiz549eJ7XFArVahWlFJ/+9Ke58sorj+lJnivGebY40V6LUyoGTmVoYC4ZprkUDjiRY9EhgJdGz8OrQwAyKJNxfdKOgekZKBTCEBhKoQxBaVIg65NgeihDIKQRhwlQCCMiCrOMjmV4ZitUahG5vEtXm83SZS62W6MSVEn5BhCQay0TXJchXB5hXiNwH3UgEKgoQHgCz5wkNe6A9BF+FFtL4gemJQwi4aOGDxKOHCJ64F7E33cTnnsW5o03Elx3I8aZK4isPMoMEdKOr4dEE5ygh7eUki1bttDb29t0vWezWcIwbBrWKIpe1OLW8zzGxobwvCq27WIYIAwDy3RJZ3MsX74cx3GQUmKaJt/61rf46le/yqpVq3jnO9+JlLL5lYgMIQTpdIYVy6ZYukiisNhx0KKjox1DSZQSoCRhGBJFEbVajXK5TKVSoVKpUC6X8X2fer2O7/uUy2Uuv/xynn/+eS6//PJm+CFhNsIHc0mInIyQwWnnGZhLD+G5MpaTOY65co6nkvl8zi/XfW52UTg2tLeZZF0TsyJQKJRhYhsGSkiUdMCvgDMRG9ZGEwGBQAnYftjlj/50hMgssaS7iyXLNtK95Bxuv28f993/IwrjFbA9egoOi5Xg3E6Xc79d5/KMoLdiIqSNdCOiFhf/ig7sHwSYEYhQgidRfkQUhSgVYUqBrQxMAVEQIb1hjPtHUA89gsr/Nd7vXMgLG2/E7Xkza9euJuVmOLrkYPpjeG2le4cOHeLpp59m9erV9Pb2cvHFF7Ny5cqmgfZ9n1qtxuTkBC+88AK+F2CaRtzBUUjctMv5Gy7ioosuIpVyyeXayLXkaWnLN419bOTTXHbZZZx11lnN902uIyHANBwUEkNAd5eNoVL4QYp6OYVlZBBUiSTIIA5RTExMMDo6yuTkJKVSiXq9Tr1ep1gsEoYhvu/T0tLC+vXr+clPfsKv/MqvNMMR03M3u4Z5ruQSnMiQwSkRAzo0cGo5VYmAmvnJzIfHXPlclVAY6SxtvQatromBBAwQBiaxGEilQmQkIWg0JkrOQ0GkTB74YY3HNtUQdsBz9hC2ew+h/SDD41UCJbFkhLQ8DkmbbTJg70+r1KqSDaZAmiHCMFEpEzIKa0UAwgXLBkuhLIlwIszARAVxLoIZSUwJloLAMPBdA2o+1sQ48lsPss/bzf/+zu2sWr6Oa6++lOuuuzYWBmnnZef9lR7wSimKxSKf/exn2bFjB8PDw/zsz/4slmWxfft2LMuiq6uLXC6HIuLLX/4i3/3enaTtFLfc8nYCP8D3AyzX4apr38CGDRtQSmFbNkuWLkEIQaVS4eDBg2QyGa655hrOPvtshBBIGa/uq9UqpVIpDtNIhWGAbUV0dymU4VOoZqj6DpblsWLFKhwnjWXZzfOzLOuIRMLNmzfzsY99DN/3Abj66qvZunUrQRC8SAi81Jwlc6N5bZx0MXAqatZnoy7+pZitMZyscMBcDb2cLE7Hc0zcxJ7nUa1W8X0fKWUzUxzibHLbtjFNs/k1k6MN1AmfJ2Viut10rWpjVfsImwYjAmGSFgGRsBFCsiwnKfdLRN7AMKc9HFGkKExI1lcDLuiB4ZJFWkgysoyUNVZ0WEQWOK02PRWLsw55XGJEbMzmSVXKpIQPyqC2OoWzt4RvRFhGntCuYFYtMAwwDZQpELaJCC2IIggiZBBiBAppK6LLerE2D+EXFdbTcFF+gks6XR7b9jw7t23j85//EqtWreDa667guuuuY/26M3BTDkkyweTkJM8//zxSSlzXxXVdWlpa6OnpaeYFJHzuc5/jU5/6FFEU8fa3v50DBw5w//33093dTbVaxbIsLr30Un503718/etfw/M8DAV79+yio6MD3/fIt7bQ3t5GGIakUinOPfdcFi1axNTUFA888ADlcplbb72VdDrdrAyYWVFw8OBBfnDvfXjeJBmrhZVLAzrzHlEUsOnxIZ580uPcc8/lrLM3oKRqehuiKKJSqRAEAVEUEYYhd955ZzOssWHDBhzH4bHHHuOWW25pXqMvxbEqD07lls1zQYCciPOe956BRFnOBWZ7HCf6+HNpbk8kp+M5TZPs6Df9k3rd4/Dhw+zatYt9+/YxMjJCvV5DKZoxZdu2yaTT5PI5Fi1aRF9fH11dXbS1tdHa2koqlTpiNTczVnki5lMIwF5C9+pLOGvpnaw5KNleNQiEiyUElgHndjsMbiszmQHTBkvEoYTAB3/MwBmv8otLDSYrgnqk8KUinQ1Ytj7k7CtslpwP0d8L8gcllhJExQKRZWLaDqFlE1yTI6r6uJMGKoLgAhPnQYGQUaMeAcBANbogGsLEMATKUtgheAfGCG7pJfWNAVQUsPgByR9cNMI/npnj+8+1YMoMO3fsYvu2bXz2/36R1auW8YY3XMkb3/gm1q9fx1//1V/zL5/8JOl0mo6OdlauXMny5cvJ5XK0tbVx4YUXsmHDBsbHx9m3bx+/8Au/wGWXXcZll11Gf38/27dv50tf+hKmadLT08PQ0BBPPvU4vu8hBBhCEUmP8YlhhGUhhMHOnTvxvICbb34bra2tbNq0iS996UuUSiV++7d/G8dxqNfrzc852UYaYMmSJfT3D/DY44/Q7qb5w4+1Y8hWdu6p89Wv7sDNWrRmF3Hf3V/jwECFtrZ2UqkU9Xqd733ve0xNTbF27VrCMGR4eBjLsujr62PJkiVNcbBu3boXGbqXut5eqq/ByWa2vRIn6vgnTQzoOPXJR4cDNC+H7/s8v3Und975fR588CEOHz5MtVpFyggpQwxDYJgmhjAwjFj4ZXNp3vSmN7J161ZaWloYHx+no6ODXC5OLFuxYgUrVqxg8eLFdHV1kc/ncV23meD1WgWkALDT9F76X7lyop8VZ+zi0QmYqii8QNERZdmY9jH7XVQuIHINMAQYEZGvkEWFJaErLZF1UJEi2w7LzhSceTGsOM/HwKGyxyRqM7FwMMwIGYaYZgphCqKBGvID7dTvKBMdlDgfEpSHDTI764TSxA6hkpdkShJBSCQUYCIECEeQHvYJzwpQFzqYTwSYMqJnc5H/dqXgqp8NeOhwkR89nQFp4Pk5tm/fxfPbd/HZz3+ZFcuX03/oACiLUrFGqVRk3759CCHo6Ohg5cqVPP744+RyOc444wzOPfdcBgcHGR8fZ2BgAICzzjqLUqnEyMgIPT099Pf3MzY6ipQSACkVthn3XBCRoFio8LWvfZ2Ojg4GBvq54oormJiYYHJykkOHDnHHHXcwNTVFR0cHnZ2dCCFwHIdFixbhOA6u6+I4Br1d3XTmPJ54osiP7h9k764yI2MCu9LOjx/YxeLFy5AKxsfHWbVqFbZtc+DAAQYHB0mlUs0Kh9WrV+O6Lvfeey/lchnHcVi5cmVTdL5a4TlXVu2nktdzzkIdx18Wi0VaW1vp7++npaXluAY08/8nkrkUEkg4lWPR4YDj43Q4B3h155HcylGk2Lt3H5/73Of5wT0/olwuN92xiRdAqbD5+iRsYBgGG847h7VrV2MYBrlcDiklDz/8MPv27Wv+PUAmk6GlpYXe3l6WLl3KypUrWbNmDWvXrmXJkiV0dHQ0vQnHOp8Xn1ecma6kpDj8LFv/+texyy/gZlzcjMCcrDIeQesKg1RWYZohqiAwphSRiJAh1KswNQGDRYMgJek9A1aeb7J0vYmdD/C3ZIn+VOEvy9C6FxAWge9jGgaGiqi7wJ8A633MUpr6Xh8jyGH8yRhWXYBS+Le049w1iu0JVGQTGRGoEEOaBK4i+psOzFoFft/HrkUIaRFainqboLLG4SdndfPHj+TxvSrSbcWIPGQkUVIiJCipUEoShLEbPYxCpAyasfru7m6WL1/OkiVLOPfcc+no6KBardLd3c3ll1+OYRhUKhV27tzJX/7lX7L9hedRKkJJQMb6yTJNDNshk83R19fHr/3ar3HrrbcyNTXFZz7zGb7zne+waNEi/uqv/oqHHnqINWvWUCqVyOfzWJbFrl272LJlC/v372d8fIxisUxYDzFNiZtto7UtxxVXXM72bdsYHh5uiAaH9evX84Y3vIGRkRG+9a1vMTY2RiqVwvd9giCgq6uLer3O5ORkU2DedNNN3HDDDdRqNaSU3HrrrbS3tx+XMEiu1VMpCOaC+Di6eqRYLLJo0SIKhcLL2u955xmYKw/5uVQueCLeb67M62thPo/95Tjez2Wm+7RWq/PNb9zBZz7zr5RKZQwjrjmP47GxMa9UygSBRxD4cSOYmWVnT1apVEoMDgyiUJimydDgEHXPiz0JhoFhGNRqNUqlEkNDQ2zfvp1UKk0+n6O9vZ1cLkdrayudnZ0sXryYVatWs3LlCpYuXdYQCe6xzhZQCEORbesjH3qI8RB7zVtZfN1b8YaeIhs9QO/GIqYQKH+Cgz9UeAd83DawsgJSgmxasXSJJN1t0LNO0HkGmJkASYrofhd5uYMVGHDYBgRKWIT42MrDClPI++qYGwOCikR+2sH6LwGGkUE5PtISGOf7hPcbqAz4axRivYFtWHhTYC2zUWf6BHtSGG+IMH8cYgYBthSooknqaZ+bhgfhRsnthwVDIxMcHrEwDAdlCJBxOaVhmDhOFsuWhGFA3SsT1GuEYcTAwCADA0O4boqf/vQxli1bypq1a1i9ajXFYpFsOk21XuOOO+5gaHCQMIgQhqKlpZXerl727d1HFIGdsrj44ov5+Mc/TldXFz/+8Y+54447GB4eZsOGDfz+7/8+K1aswDRNPv3pT3Pw4EHK5TJjY2NMTEwQBEGj9NCis7OdteetZe2a1fT19uHYGfbs2cfQ0BCVSoVisQjAwMAADz74YDM3xbbtZp4AwNTUFEEQkMlksO042fBHP/oRP/zhDxFCsHLlSm677bbmtX4898/R98jJZrZDBq9nDCdUDOjQwMnjZAgAzanlZM65lJLR0VH++q//mgcf3ITruI2s8wKe5zVXX57nEwQ+iAjLMunt7mrUlbtUKmVKxQqHDg7GbWYtkyiM6OrqBeKHi+f71GplPK9OtVpFCAOlKhhGkampKarVCosWLSaTyRL4iuGhMcbHCjzx+NMoJXEcl46OdpYtX8bqVavp6e0mnUpjGHGmulQ+3shOSlMlHNlB+8Xvob76SuqrbsQdWwb250EZKAGyoLBQGCkgbWAYikw7tHRAx1LI9ymUo1AG+DtdoucE1nss+JKFdOqEysFbohCLLcyfphBmhHreISxZRAcVdieYo1UUJoZKUbskwr3Gg54sQa/JVMphYFhQCRX5vEUQ2lT2GazeIzB+po/IiDC2+Pi2wFieIZDAoYiVAyb/7ZcsJvcJvrvFZdMWAcoikPHnFAYBSlpEQuEIC9u1yaY68KMIP6hRrYbU6wZDw1Wmitt56ulnIZIIQ+FiEDoC27JQQdRI3IMoVIyMjRHI2PC6wIEDB/joRz+KZVmUy2UMwyCdTuM4Dp/4xCcYGhpicHCwaaRnYhgGbW1trFq1inXr19HXu4hMJtMQnwZB6DM1NfWi6zQpHZx53SYopY7oajgTIQTXXnstuVzuNd0fs12KOB844Z6BhSAI5krFwOt9n7kyn8fLfBvvTE7WnCcP0507dvEnf/q/GB4axhAGg4ODVCoVfL9GrV4jCiMiGeHYDmeeeSZ+UKNYLPDP//zP3HTTTYRhSH9/P89u3sKjj/6UTZseZ/ee3XEnOBmHFtLpNC0tLSxZvBjbtpFSUq3WKZcrjSY3NQYHRjh8eAgQWKZFKpUin8/T3t5Od3c3PT091Ot1RkfHePqpZxreCnAcl3Q6QzaXIpM2cC/+TdxqlWcP+piHNzFZrrMkd4i3XtlB2qkhlYuMapAzsW1QromRDWnpFuT6BOk8CEsR2RayauN/0cU8M4e0wQlyRLkseDWiusK61KG6U+IUsgRVRbjFwpzKw/kWJT+Df0EP4SKL+k0pSkVFoTXH0EDIzj15psbz1KIyXlBB1kKqYpJ0zcJ7ehRfgVqZoaYiPFUjIMLocmAE1N8ZSEIMUcNOZ1GGxIpMMtlMLIqiCBVEtAaKVFhi0PcZMxxcSyCEohh42GYLy5f1MjE+gTBq1EoFvEoNFSmkFSGRzbRHpRS5XI5MJtMMOQwMDLBv375mVv/RGwklf3fkdQypVIrFixdz9tnnNBMcHcehvb2ddevWsX79ev7mb/7mRYb75RL/TNNsJqgei1Qqxa233vqKlQUv9f4Jp2Ir4JnHnG0Pwas5/gnNGTiZ7ua54Mo+1cc/0ec8W3N4rEvs1br55jondG6VeonOtfE2vzOJoojHHtvE3/z1PzSSx0YpFArUajXq9Rr1epwwmM3leOMb38ivve9XuerqqxgY6Ofpp5/illtuaSaHxTF7iKK4OczQ0DA7duzgmc3P8MQTT7Jt2zYOHzqMadl0d/U03LkuKTcuPavVq9Tr1YYLOcL3ParVSrMPvSEMLNsilXIbOQetLOpbRFd3F11d3bTkO0mlLVKOAUaKtO1QKntYbjsHh/cw3n8Y2+kn5Xqk7AhRSJPNuLS0psjmcmTSEsusURUDeLUxiKpYwodDkH0qjXlNmtZiOymvi+y5q5G1Gn7QQbXVx661M5IO8YMMJTciCAN6cFjc2k6NEuNTPlMjQ9y5bZigXsELQ1QUEMkw/qyUQAmJIMLHxpUCZUAoQtKRgYFNKBSRESHwMBVEKoVUBraq4QsLKcP4M27kTkQC7FDFVQCGQAqDMPTw6oqR4SmEsFm+oodyuUK+J03n1ARi3x58I2RCmPQLk6rvA4qOjg56e3tJp9Pkcjksy2p2/KvVanieRxAE+L7f9CR5ntfMEzFMg3w+w+LFfSxfvpJVK9fS09NDT09PM1dk0aJFuK7L6Ogob3/72ykWi9PGW8TNoVBx3gIIpFDxzxokr00qVuJbIb4Trr32Wv7yL/+yGT54/beYOmWGejYFgVKKQqFwanMGFoJH4FRyMhIDZ4u5MIbXw6wIKCSe9AmrRfzCEFU/oOpL0pleenv7GhvHhNx773186pOfpV73GBoabngDfMIwJAhCDFNx4UUX8PGP/xHXX/8mXDeO13d2trNhw7kvOq4wwDIM8naWfMtqzli3mlve/laiKGJycpK/+7u/59Of/jfS2TYc10VFEt8L8eo+QegjI7Btl3QqQ3t7B5lMBiHAq1cZGxtjbHyEWq1IqTjF6OgIu3btxnVN0pkU2UwbuVyWTCZDKpMhnc6zbMkq1q1vY/XytSxftIZqrcrkZNzBrlCYpDZUxQ98QjUEKoqNaOQShj1EYZyAJ1S8ohUPCLKWi5X1sEf2YgpBpIYJI4WKJFEYIJUkUgASC8mKlizrlnTQlXbZvP0gQeRi2QIn1YIpLETcFwnDMBHCxBQhCIXAAiFBKAwMlDSQJHsEKFQUICNFqEKkzBNFikj5RFHYrMWXKqnLV7GnQEaYpk0ub5HPZ6nXQ9yUxfh4RGWgQHdG0iYDLCUIbJOQeBMnYRi8613v4k/+5E+aiaSJEJi5H0DylexIODk5ydjYGKVSiVQ6xfJly1iyZEmj/G8pfX19tLa2HtGDQAjBgw8+yMjISOOzFwjD4M1vu5mp4XHyLXm80hSPPfM0aQOuesN1PPTwwyxbtowNGzawZcsWcrkcnZ2dFAoF9u3bh2mafOADH8Bxjtwf8vUa2YVSbXC8z68TIgZOdvXAbBuT+eoRmM1wQHKTxSvMISYnJ6nVaiilcBwH27bJ5/Pk83my2SzpdPqI8rS5wuzMYbxiOrxnLz+97x+xg0nGyg79Y4JCLcttt72La6+9lrvuuot//dfPEwYwNjaG53kopZou15aWFn7t/e/lv/2336a7u/uIFdexeLnfJV3tbrnlZr7y1dvpbjTCmRifwDQMhADf80BFGIaB47j4foDvF1BCoYTCzedY3tqKMIw47JBKUatWmZgcYe+enYyO7sM0TBzXpad7ER1tPbRkuhmfGG+6olvyOdpaW1i2dOkMg1bH86rUvXpzRRv4QSwSGr3woyiKDaqKvStCAELgCEHadTBNC8u0sGwL27KxbAvLMjAtg2FhMKFMcucs4VzLwCEkLikUzda/yTGiyG/uzheG04Y9jEKElERRY5dFCdJQIAUIA8OUoAxM02neOzOb9DTPYcb7OY6FaSpSKYepUhmVhlCAwqYmHUIrQBEggFwuR3d390t+zi8VGkj2H0heM/P+PNb7+L7P7bff3qwGMAyDlvZ2brn+LTz44I+5/A1XcO///mvePzHGC1dcwzXXXkcQhrS1tTU9Fp2dnURRRKFQAOAXf/EXOeuss4445ut1959KITCbIYNX8zx93WLgdA0NzFcBcKLf67g5xnU+MjLCF7/4xab7cWJigvb2dgAqlQqlUokwDDFNk3w+z9VXX80tt9zSdAXOxmdwPD87BSNBKcm3vv897v7eM7ywfRf1aoTAJox8Lrvscu68804+85nPoKTB6OgknucBNB7AgqVLl/DHf/xx3nT9tViW+YpC4BVJDIIhME2LHTt2EAQBZ51zDtffcAO5bIZyYQoVBg2jpfC8MHZDV6t4XpUwCJAqLp2bmpqiGoZsvOQyNl5wLp/4iz9j+5bnEIYgk86ScnPYdoYgiNi/fz+u65JOp5vCMdnuNpPJkM1mMYyuxrwlBk0ipULKiCiSTUMa91iQzaZMQggMw0JgNN3UqjFGEUo8IfFliPADoiAi9AIqgU8Qevh+QBD4zf8HQUAY+kcY7vgrRKqw8b0CZRAlWyurJO9DoQhf+op4kQFWSBVQrdXIZlPYqSzSn8TDpixcysJFRBEmAtW4Ll7puXAsIz8zRn88xmzLli0888wzZLPZZtXAtddeS1D3WHP+OoK7H6BzZIDFqRZaLj6Xg/v2cumll+L7Po8++mjzWfDCCy9QLBY588wz+bmf+7kXXb8nIiEwmY9TFTKY656I1yUGdGjgxHAivQBzBSUlDz30EEopUqkUjuM0E4oSAeA4TtNtWa/XueeeexBC8Pa3v/24+pG/Wl5qjmZu8xpFUfPB81qSlU4UQgiybo79+8YpFgN8z8MyfYRh8/BDP+XgwYMIw2F8fOxFG8f09nXxyU/+ExvPP7+ZYvD6hEDyjUQKiZ1KkTMMZOTQ3tHFyPAYzx/Yz9TkGF4Ul5zJUKJCRagUKowQoYfjOKxavZZQGkyMjbF8zQo2nr8BlKKnZxFDnXEXunQ6g2FbRCJkYOQQoDAtq5Fg1ljBW1bsAbFtTMPAth0M0zzid/GXhWGYcdzbEA3jG4uV5lnJZOUd4nk+vt+In3t1gjAuyZTN1bkkCCOkSlbpsdBQUsYbKzUNi4o7FTYmUKlGHwU1M14t4sqIxuunM+vj1ycTb8wQLkIIDBFfn9JQIMA043BEwchQbF0CxRKujDBESNjwMp2oWPvLoZTiy1/+MtlsttmTYNWqVSzq66VULeINj+B//x7STg7rP70Tv7OTtB+LoRdeeIF6vc7q1avZu3cv5XIZKSXve9/7yOfzzfd/Ja/Ga2WuG+pTwZwUA7PtKp6NY7+eY852dUDzJhLxTq+e59F/+BDbtm2jXq9TLpcJw3jVY5pmc4vVRChks1l6enpIpVLs37+fgYEBli1bdsJa3c5kpmu3XC43Eu3qzU5u9XqdCy+8kGXLls2eGBBxotWv/KdfwRQmf/Znf0a1WsVxXPK5Np55ZjN9fX1MTBSg8aBP5tX3Je9+989z3nkbEMaJmbtmRjoKPxCcu+F8bnjT1QwMDHD39+8jqEwQlHxkaDSS6eJud5IQJX2UBDfVyplnnkkq7bJ91zbau7v5pV9+H0uWrEChOO+8C3hh+wuxoWzMgefXqddrSBU0R6FUIxFNJN+DMAwEsdGP/3S6H4IhGhnqM1zchhANz0DjPZEoFTU8CbIpFmKD/NIrz+T6nOlKn369inMGOFayWkMsKNXIF1RHvUYhVSOkoRQyOtJNn3wvG1sPh2GEQFAyDGRbmrwdIctFCMJmet7RzZ9eC6+0Gi8Wizz77LO0tLTgOA6WZeG6Llueew4VSSpTE6zJpDjY08FAsUhwcJhJr4gAJiYmyOVy7Nmzh0qlwsTEBDfccAMbN2580bGPHtN8ChnMZU75FsavxFxb4Z4s5nOCoJrxXxDNGvdnt23l2S1bOHBgP361DpFkfHycUqnUTGwzDKOZuWxZFo7jkEqlaG1tpaOjg9bWVu69915+9Vd/tekdeC2i4JVe7/s+zz33HKlUiq6uLpRSfO973+P666+npaXllKykjoitxBbwiN9ms1ne9773cckll3Dw4EHuu+/HPPCTh2lra6NQKKCkbJZjJY1bkk1nEg9H/K5i+lhixlFf4vkXz91Rv1SKWt3jvh89xE033kQuD51dZ7Ny2Sry9iSdTGKbNnVCwjDCq4VU61DwBYeGKxRLEttxGB0dwXAFV1/3JvJtXRw40M/u3bvZ9NiTTcOazEUmk2Xx4iVEEZhGQ0T6VQLfJwgDwjDC932iMHbDR5GPAgwhYkEFgHHEqQjx4nkGCWL672Y4yePZm2Esjv5+5v8VsXGfrsxQRxzqWO7oRHAkY4s9FknVSNKClyNEgxACqSRKTY9VNEJLKCikUgjLxigbhKUiAnVCxMArkSSuJiEJKSV79uyJx6hACRhe1AERHHzmOYxIEqigmQcxMTHRLHF0HIdf/MVffFX34es16rMZ258LvKYr5GR6BE7m+x/v8U/VcV7r8Y7+u1M5X6qxZYsiTg587rnnuO+++9i6dSuVcqWRTCYIfJ+BgQGmpqao1WoEQYBSis7OTi6++GLa29spFAo899xzDA0NNXfNcxyHZ555hssvv5yzzjrruHcse7W/S6VSXH311c2HazabZdGiRXR3d7Njx45mud3J5MWPnKNrugWu63LRRReyZs0ZfPMb3ybrZqmX441jTCEQwkAZBj7xQ8y2Lf71Xz/Hnd+7i3e/++d573vfgxIvfv/kM5RSYTRXyy8tukIV8v27fkC94vGNr32ZZ555JB6jaZCyJb1tNmsXZ+hplcjIZqyYYrRuUQ8VhVIdQ9iNB73ATTns3ruZn3/Pz/LMU5t56Cf3UStPxQZLmdh2jkymlb6+JVx+2SX8/M/dRL3m098/wu69+7nw4rOZmiqwdfNO2tvbeObpZ+jt68Q0BfW6x/DYCE8+uRnfi4gahiZ20ydx+thdP30fGg2PhIiNanP+G3PVDMEced8mbn0x/cKmNyC25S/t0m56D5r3U2NcSWkh08JBySPFR1OMAISNfQcMYnEgRFzJYNh0tfZRNVN4frkhBl5cmvpaeankQd/3SaVShGF4pCcBMBuRj4i4j4KnJDRCczNzLJRSvOENbzhik6LjGcvr8RCcqMTEV3OcucZrlosnw327EEIDJ+o8Z8Uj0LiA677H4088we23386OHTuIogghBLZhgmEwOjrK4cOHqVQqzfCAEIIlS5bwzne+k82bN7N37166urp4xzvewf79+3nqqadQSuH7PqOjo3ziE5/grW99K9deey2LFi2aXuW+ziS/o925MzugJe72ycnJpufi6M/raMPwWo57xM+Z+YhOxiKa9ddJMttXv/I1nt+6jYyTQkayOYZkoduMoVsWURTw/PPbePDBh7jprW/FtEyEIZorX0PEHfyCIOD2b/0HE6Nj9DX2GOjt7aWtra2RqJeJPQ9CsHXHXh55aisbLryIxcv6GBzYAyQ14Qa+gq0DIWF/ECfvhTVqlRqDg4MzzkMhMEBALfB48slnefrpp9m3by++V8XNZunp7mTVyjNYvnwlqVSKXC5PLptl65YtXH755fQu7sUwI7x6wFvfdi3t7W1cedVGalWfyYky5288i81bnqe7axmVSlw21yyh86v4nketXicMg0b5ZdAstQvDEBnJxqo7McbTBn+mGDg6pyQRGLKx0hc0cy6P+fk3QwNMX09Squn/J58/NAXCzGt15jjicYm4rbRSCCIg/pzz+Xby5CgWys3QyCtdu69kqF7KWMa7YdYxTfOIsRqN6hHDiXM3apV60/gneTrJvahU7MX4uZ/7uVedNzSfQgZzMTTxqsXAQnHjnwzmowiYzgeIL95du3bxb1/8Ak8+9VScwd6I8dq2jWUYHDp0iIGBgWbLUdu2cd24ycz73vc+br/9djKZDGvXriWfz1Or1diwYQNLlixh3759nHnmmezYsYO2tjZGRkb40pe+xFlnncUb3vAG2tvbX7PhT/B9n/7+frZs2cKePXuYnIwz8cvlMoODg2zZsoUoirjrrrtoaWlpds1LtvVdvXo1mXSmsZ57sTt9Otf7xWOJpCQK41VqFEX4YUQg48ZBkZzOfg99n6mxEQqFKSanpjh86BBf+crX6ezsIGU6TaNv2XEJnOO4mI5N3as39gyYYsniRQRhxF0//AlBI6ZsWXZzhEIJvHqRj3/sY4yNjmCZFrlcjnw+z4YN53HOOWeTzbTiumkymQwTxRLL+vooT46wf+9O9uzZg+d5ca29kCjTwDAsbGFgmhamrYii+HpNQhjxvMSu7OJUlUcffpJdu3fh1eN9D2zLJpfL0dXVRWtra6MKJaBW87n44gsxLCgUpsi35NjxwiHW3bIaJUPqtTphGPLCtr0sX7GYocExWls7yediwxpFIb4fEIYeYRQcYXyCIGyIgum6+2YznnqZer3SrIZJDNiLREHDGAsh4p4CqDiXwbSaiYNNj0AUEUkVi4aGeGjmBsy4XkUinhoCSnHkqrIpJBreAJWEOIRohiukjJDCxDRsHn30cfbt28+qVSunj/Eyq1Qp48ZTY2NjHDp0iCiK2LhxI62trc3Xe57HyMgIu3fvZteuXTz99NMAVKtVIBYBSd6A67qkUimklNTr9eZWyFEU4TgO1Wq1uXi46KKLOP/88zEMo5EXcpRH4zgeAXPR2M515oQYWCgegddzvJl/NxvzVffq3Pn97/OlL32J8RmxPaSMQwOmRXGqQLlcJpvNYlkW73//+5sP0v7+fsrlMh0dHVx00UWsWrWK5cuXYxjxnurLly9n5cqVrF+/nuXLl7Np0yYeeeQRNmzYwNDQEN/4xje4+uqrOfPMM5sr9uMheXDWajWeffZZvv3tb/P888+ze/duhoaG8DzviASw5PWxsTWxDIGlBOvPPJs/+Oj/ZM3qNcRbwBkoET/AlZREUsarUK9OrV7Hq9bxvICSX6cmAyIlcbCpFCuEYdDoGS9x0xnCIEAYBp4XVw0IFCr04/M0UhiZVtaduZqWtIkdhrgDU6S9Gq12iJky2VOBgXGHihKk7DTLehaRNSxaevpoa22nXK5SrdUoV+uUilMMDxwik0nT3t6KUoIoih/K3lS8fW1v9xLOXJdm/94RxsfGKJZKeH6VUmmKIPBRAto7F8dLXyUIQh8vqFApF4kQEASIeuzyTafTCCGmPUQIHCdFT08v+/fvZGpyHCHAtlxcJ0tLvo1sNvZIxKtGydjEJKVygc6uToQB+XyOVasXI4HxyRLFSo0Vy5eR7zjA6PgYfYv6GBkpxv4VIw5NGCaoEDzPp1KpUK1Wmx32khAW0NiMSZDJpMnl4oZJUsU9A7x67GVIREMQBMgwxJZTKFJEponAwDMFqSgiEGZ8bMLYUEuDS6ol9ooUI1ZIXxVCo8aIJagoKw7ZKBBScbFfpmQo1gQRd6fyeI3r3VBxVYRjO1x59WUUi1O0traRy+aRShEEIRMTE+TzLTiOzZ7d++ju7qa//zC//du/x//68z/i7HPWYpkWhopX3lJKKpUKw8PD7N69mxdeeIFt27axe/duBgcHKZVKBEHAVVddxac//Wk8z+OBBx7gJz/5CXv37qVerzc9Acm9aZpmUwQFQUCxWKRcLjeriBKhkBjtpN112Og5sPnZZ1i/7izy+VYMoylheclElwYnyt2/EMXEq2pHPDAwQGtr64kfxCyGCE7FcU/E+Z3qOZp5WUxNTfGpf/0MP7j33uYuY0nSj4nAdRwcx2m6XMfHxwmCgI985COce+65jIyMkMvlGBsba970jz76KNdddx1KKer1Oh0dHWzdupWLLrqIxx57jMcee4zh4bjUbMmSJZx//vl0dXWxYsUKrrnmGlKpVHNeXmr8SinCMOTpp5/mC1/4AnfffTeFQoErrriCxx9/nImJiZe84W3bJpN1STkpVixbxf/9t89xxroz8IOA/kOHsawUQ2PjDI1PMToRi6C1LTlWpBxaW/I4jokQIYEJnm0QuA4qlSE0HXwpqFYrSF8RVEN8z8fzfexGQqVtNTrZAaFSFOohZiQhjAiiOhllgu/RY1WwS1McqoVMVqvsnqxQDVzSboYeN4fh2Pi2pFqrMj4+zlS5zPDAYWrlKQxTMDY2yu7duwnDuIe9UhFCKvK5Nrq7ljQy6hsJbEIShrEhtRwbq+HxSblpOjpaKRTHeX7Ls3R2tNPW1sbY2Fhzo5pkFWiaJn19i/jvv/N7dPYuZtfeAzz95NP0HzhEYXwSiaClpYX29nZ6e3tpbW1l0aLFdHf34LouURTS0ZmjUqmwevUKhoZGiKTEbhgVx3Eolyt0dXWxdcsuZGQ0Yviq2QQoMf71erzRUtJ1L/ne82p4Xv2I7ZqFEM2cliSUlBgLo17hIztfoLU6wgFM/qmzlTFa8EyFUHEVgwn4mHR5JT48PshX3RxZFXBzpYySiofdFN+3cwSGIjAUqTDiVyplXrAUyyL4aipP1LjMTRV7JlLpFH/xF39MoRDP8Y4XdtPX10d7e7x19As7XiDluvT09GCaJhMTk/zfz3+Rrp5u3v72G7nhTdcxNTnF3r172bp1K9u3b2doaIhSqfSiezwx9JlMhne9610Ui0UKhUIzSTCKoqagOjoMF5d0GtPeLMsinU43+0MkzYmUUgRBwNTUFKOjowR+ne6ubq666mpuecetrFq9HMMAlIkQx1flM1PgvxaOlfB5MjjZxygWi/T29p7YdsQnI0/gdOf1nONszc/Mi3N4eJi/+qu/4omnn8JruEqTrm5SRpgYKCmbTUYGBgaaOQSWZbF9+3ZyuVwz/vfss89y1lln8cwzz2BZFueccw6HDh1ibGwMgB07dtDd3c3evXs566yzqNfrDA4OMjExwbp164hkRKVS5vrrbyCfzzfi0JD8Jxm7lJKBgQH+6Z/+ia9//esUCgWCICCXy7Fp0yYmJydf8iZsbpoiBFKYXHLN9Tz+3C7uuOdBBgYGeOLRB3HdFG94040owyEMJZZtsTif5UCthFkvY7spbMtBKkXKtnEsRVUElKKImpLxLoJeQFALqHvxarNeiw1UvVbHr3mNOLfXqHuPa92FVIRKsqSlncWtOQb8SfoyWQYGR9kzUUBKh0D4jYd0iAp8ZOOz8qIIA8l//eAH6Oxq5z9uv53Dhw9TLpVJuVk6O/vo6e1m6bIlOI5DGEWYhkk6k2647ztpa2ulrbUNy7RobW2hpaUVx7H5znf+gy3PPcPo6Cijo6NHzGeSIAbgptJsvPgy6qFC2TksJ8dz7tPseH4bhalJJicnqVTKjI0PxTkDu1pIu3ncVBrLMnFck5TrsmnTU41KlDSu6+DYTtw10LTo7x9CYAPTjWqEIZqVFzNDV0mZ67QgqFCrVY/o1z9TPCTnkRg217SZaLVZYbeC4fJb0uRversIqj7loEYYBPhKYkQhffUagR+wL5fjHcVRHjNNHBVgyRAlQ8JIESFp9xU16eNEGV5A4oUyFjVKIRshiHPPPZcDBw6QTqeo1Wr09fWRzeaYmJigr6+PMAgxs1myuTTj42OsPWMV6YzL4OAwX/jCN/j3z3+Beq3UvJ+TzYpmJvElCCHo6+vj4osvRkrZ7AaZhE6q1WrToB/R8bERUkm8QknIoLW1lfb2drLZbFNQVCpVJibGGR0doVgsIOsB5clJDu57gTvv/hpvu/lW3vueX292UXw1aA/B8THrpYWnc4jg9YQGZruyQinF0Mgw/+vP/oznnnuO0A+QUZxgpaIIJUMEcWlb0ur2wIEDzRJCpRSf+9zn8DyParXKqlWr6Onp4Qc/+EEjFqh49tlnueCCC9izZw8/8zM/wx133EG9Xm8+aHp7e1m/fj2rVq1iZGSEfft3UykPcMmFl3DPDyJufPMNtOTbEEajbqkxVUEQ8MADD/C7v/u7bN26tfnAScIFyfkdi8SN6boujmORTucZHRznh3c/gB8GRFKSyvfgZlLs7x8km83Tks9j22kOjddJuS6WDaoW4NUr1KpVioUppqammCoUKRVLVIul2NXs1wgjP46LKoVpGBhGQ4hYJo5tY9sOjpXGtBo/T6VwLEFdKoZqdZbll1APaxjt3azt6sVAUBkbZ9/hg9S8Or1LVtLa1k4UhdTq8cr3oU3PUi4WOHx4kHS2C9PK4Dgpunp6OOe8jVxy6aV0d3fhZOJa8SPCtUJgKoGh4rlyUykEspnMKIy4dvGIKK8AjOl4dmmqAIaJo0K6W1o5c+1aXARj/YcoFotxVnoY4JUrEEZ4GY9gIs5BkcF0Qp+UScdFs7lyd10Xx7VJpzKk0xlS2QzpdIpUKk3KsbEtG0MYGAgcy2rMudH0dORy2SM28AmCgGq1RhDEosz3fWQYEoURYd3Dw+NroUuuXOXrfSYfGPE5t6XIWL6dTNjCAatMtSLonuhnZegx4diYCpbKKs84GTaGBlskdEU1hiObZXj0RYJ+TNLU2adsVOg1JzNJy9uyZQsvbH8By7YJfJ+WllZqtVqzrbdhGLiui+97CMMgl8tSrcStm2UUIaMahanJprh5qfvBMAzOPvtsbrrpJhYvXoxSimq1SqFQoFQqxbtaNkSAaZrNnIvEC5MsDGYeIylz9DwPy7IolUocOniIlSuKvPUGSXHKxnZcIiwcFWC6JqH4Lv/9vz/PR37nf7CxkU/wSs/G1xsyWEhCAOaAGJgN5npoYLYrKwAKxQJ//Td/w+Znn42zrMOQqHFzKyUxhElLvpPW1jzCgF27djVX3/GudZLt27c3b6ZCocAHP/hBDMOgWq02M7F/+tOfcsEFFzA8PMzw8PARImjTpk0cPHiAM844g+XLV7BsqYkV9DPQ/yMqFcEPlcnNt9xAOpVvpm/XajU++clP8olPfILJyclm3NowDCqVyhHnaJpms5wx2Z63s7OTrq4uFi9eTHdPF709S2hr7cFJO0hil6gXKjw/3nrX8+qMjY0zNnSIXRNTjI2NUy4VqFZq+DWfMGysJE0Ty7ZwHRfHcchm0nS0t+M4KRzbxrEdnEbHPGEaSJIeAQohokaipiQAoshFiQqVSLJzMvaoKCURhsB1bHbv38dkYYrrbryBdWdvJJQSQwiiSMb5CkpBFLLxgjgGXgtCgigOV0Sex2OPPR437LHASTnkclny+RZaW1tobW0jk05j2zZCCCrVKiYKlEFLaweZdCb2AjUS4prXsWFgmAar1p7JSKEQJ5yaNpnWHCvSa+hbvoSgXKZWrxN4PsIPUVLiuC65tjxBGMQiIwwJg5AoktTrcT5KpVql1mhuVavVKExMMVobOiJj3TRNhGvjuC7pTJpMOkc2m2u6qh3HiStibAchYlFm27Eodd1UwzvTMHRBQOD7hEFIIH1GjE7uUxVuHZki9CWXD3sUxsa4oTrFDzpWsSub56OVMULfZKsl6Ejb9BUNbgs8TDyecdp4ixfwlPJYhY9AcZCIKBQMGwIpjWYCb0IYhk3RldxfSQKhaZqYzdCMhTAEoyMgG0mGfuATBd4R2xYfC8Mw2LBhA7/8y7/MqlWrMAyDWq3G1NRUM6cjCSEoFe+Ol4QMkvdOvAIzKwVKpRKpVIrOzk7K5TLDw8OUSmVGh4tcemULP72/Tl+PQc1zqFc9CsMSw4HC2Gb+4Pc+yv/82B9w9dVXN58Tr8TR1UBzjbkiOmZNDMy2sTuZzMfQwEyCIOAzn/lXHn/iiabCV1EUdzoTkMnk6exsoa0jReibPPvs84yPj1Ov15s3/9HxtnK5zPe//33e/e5388ADDzA8PEw6nebiiy+mt7eXr3zlKwRBAEyLoSAI2H9gP6Oj4xw6dIjrrl3CJedk8aNhurq+z9Z9A9x3n82Nb74Rx3WYnJzkYx/7GJ///OebHQ4h7ilgmib1eh3bcUinUqTSaTKNHvdJFn2SxZ7L5ejo6KCjo4Ourq44fyCTariGTQzDxjTj+HEURQwPD7N161YG9+9kbPAwSplk3Rzd+Y5mK+ak9E+iCGdsPuP5VcrlIBZafpxzEckIP4p30FNRhBFNt7IFj5TlUwtShGpmqdmMMjcpCZVk39591IKIrp6euKFTSzv5fG8c91ay0Vffp9Jwi9frdbx6nVq1Fq/OvSqVqSJDhw5TLBSpe3FJmKEEVmP1GUfEYXxymKXL1rLuzLOxLYtCocD4+DiFYoHADzAA102Rdlv5yQ8fYHx8vJH3kWSHibjO34gNVpI1ZpomZr+MPSOmhWPFoQDbsbFsF8NxSbekaRVGnDDYqF6INyZKvFiSIAgoB3FZYa1ep17zGJ4YIxgaJApCDBpu7EYTrFQq1QwrJDX6htHooOk4mK5DGIa4QUgtW+PpzCreWNtJXhVoFYIX0in21vOsK+wnX09RCny+376Iq2oVelpa6S5k2ZxKs7pe57a6z+GUSaepaKkKQqHoVSZbghSBiks1hYqT52a2UYaI6c4fMQJBGNIUBkKI2Pthmhimg2HFoiefz5PLugRBQK1WO8K9n9yDK1eu5P3vfz9r165FCNFMtk1EdJL/Y5om1WoV13WbpYVJpUAYhhiG0RQDQRCQTqdZtmwZQRAwMDDA5OQkk1MTjAwU2bNXYbgm1cAnnKojawFWzkKYJu2dVZ7evIc///M/5xOf+AQbN25sVvW8qOLgJXitHgKYm0LiRDIrYmA2XeA6NPDyhFLynTu/x9333HPEAyJU4BouLW1pOpcu5ezlEU4q4rvf383ExEQzM3tmGdlMlFLs2LGDUqnElVdeSUdHB57nsW3bNu67777mRjvNevRkLiKoVMrs2bOHqclD9B86m5vfkuaaCw8gjDpPPJGhs7ODNWvW8OEP/xZ33HFHU5BAPJf1ep32jg5Wr12D47rYtk3KdZv97l0rdhEHQcBUYZK6V6ZcKDI6MsBOw0LJOGM+CELCAHy/RhDWY+PkOHR3d7Ny5Ure92vvwXJtDh8Y49mntrFv325q9QKFYoDvh3H8uFnPnmxRG69y4wqF6Q10mo1vAKNRdS5RnLe2xu990OG//VmJ0XJqep5QCGHh+yFR5FGpVRi4fz+pVI5cLl4B59o6aG1rxzAMwnpAvSEAfK9K1Ci5i6JGbbtSjTEl8xgXDxiGAClwGnOWzbeSybewbMlqPN/n0P5B/CBARgGWLejs6CLtZrDtFAJBvRohgwKO4eBXAlIpt7H9bywA4goVUEFj5z/lEaoZLYIbMe1kkqLG95GgWWcfN+yJz8FIwheJYWyEBkzLpjXfEnsMjHibXRREURwCKNaqyDBANa7npMpACKOZfyCEQBoGGRWCynDf4uX8xuEd9BkmnVGWlfIwnuVTi5awPeMz4Fo8HwnOKVZ4pjVLPmxle0vIDXKQHAZjdpYldkQ9MnAMScV0cUNBLQgbLZkjpgMFHPX9kdcNarrlchQBAYCFMExMwySoG5hmXM6ZyeRJp1KYloltW4Rh3GPjve99L2vWrEEIEee4NLx+yX2ahGaSUMDMniJJz5CZjZkcx6Gvt5szz1hPICsM9R9mZHCIieFJitVJpGfxf/5wmPOMDIsNF2k72DKFDENe8GpsKYZUZcAB9nLXN/6Klcv/Fy2dKxHN7ZheOWzweoTAXFnBnyxOuRiYLRe4Dg0cH3v27uXf/v3fqdZrTSGglCIlTLq6XZYsX8sZyz0uuKTIpz85wcH9U9Tr9WZo4GiS2J4QglQqxeTkJN/85jebv0vivKlUqlmGmGQoG4ZBS0tLI84vmJwMeOjRgwwMtxD6Lu+6ZYDq+I/41ncl27du556772qOwTAMOjo6WL9+PevWrcM0Tcq1KqVyuRnXTGr/vUabZMdxyLgZHCdDKEMmp+ogFUoZRNInUh5hVMf3Aqo1j8D3EUKwZ98eNj35OAKDfCbPOeedydXXbeSii8/me9+7k2ef3UUQREnLP0TT4MfNZaZ3y5suzzLN+NZMpVM4jkkQBhRLZbIpydrFk6RT7Vx69sW0tORJpVOkXIennnqWAwf6KY5PUK1VsEyLoFpmvFxkXIGyVByqcB0sw2l+NkqJI3IuYvfzUf0ThGi+3jJtQgnKl/iFkIpXJZfL0d7ewdIly2hvb8d1U3How3VwHLe5V8BMoZdkoKfT6eYxkt/NdD8n/27W1x/lLp/5muYWvw1hOrOCIPl/EAT4QUDoB3hhDT8MCBviLN50KD57UyjMxv4O03M1fez4ZwqBRacw2N2eZnSyk+1uijdUx3i2s5X2IINn+uy1eri5OEnaDzm3cpgvdPXxm+MDBKLKpDCI7BTvKYyyKZei3Zc4IWQyFnZoEEofP/SJjf+L77FjcyyjJUEKDNNESUUQyfi6rNYpFkrxK1SIaQpuvvlmzj777KZHIIqipmcgmfPk80p+l9yzpmk2c3MSr4BlWXR0dLDhvAsRhqAtdZi3vMfjT/9oiEK5igoiLskYnCcXs7k8wX1BkaItQEJKKi4VLu/t6uL+wjg7xxVTtSfY/tT/45I3/g8MOw3KPK7+A6+Vky0E5oL3YUHmDJwM5ntoAOKGPJ///OcYGxs7IiPYNEw6WrpZvnI5S7ur3HrzJF/5luCZbSPUvfHmvu1HX8jJDZT8vFKpHOEyjKKoWaaVSqVoa2vDsiwymcwRyX6J+xHiLPyBwTqf/WIGx+pl2QqTR/79bjZt2tY0pt3d3Vx99dVceOGF5PN5qtUqo6Oj1IJYaCRZ4mEY4roube1ttLa0xkZZ1rBVmWWLh3HMPHYqwLTrOCLOxDcMgRIBolGyZtt205inchFpa5zNzxT48he2sXjJIt76thu4/obruP32O9i9azfChLe95SZWrV5FNpNlZHSEH9xzD+//wK/z4E8e5uGHH+a/fvhDBEHsabntXT+DYcSG85Of+gyT+7+NEIJ0Os1/+eD7Wbx4Mel0mm3btrN370EOHhigs6OH9kYmt5G0fJURkQwJwthDUQlLBGFAFEZxbL+x+U+SCNg0xjP+mzTWsUwbx0mRy2bJ5FrAUExMVBkfH2GP2EU2m6WlpZX2tk5aWlrI51tIp7PNcMlMI1KpVI4pBpI5TUREEguf+bs4xm81xzXdK8A44lqc+f0RrW+T7PfGVzI3vu/jeT5eUMfz60dUFSQCo+nBQqEMSU6k8Lw0d7Z20x3VubNrNefWDnAwlePqyQNszq/mW26enx3ZzyG7lQEnyyO5Vtp9g4czOfZaBu8IHJ6wbXJAxZFIXLDLpGwbqSzCl/C6HQ+xmLMwDevIrZpnhB2SOW5ra2Hjxo1IKZsVFDNFVVJFABzx7yQ34+gkQsuySKVSrFu3jlTapjC6m/e+q0JbvkBL7xRss3hzZxZhOdzujeOl04SmAVGAMMBwTR6xJM9MTPLOXJasVePRBwLWr7mDtRvfQmfPxSihGhUkxzcXp/MK/7VySsXAXDF6J5q5FBo4uiToFY/fbISr+OmTj/PwI49MG/dIYgjIZzMsX9lHrr3GLW8bYf9AwD0/HmBycorA95Hy2Ir2WD87OoyQNHFJVhTJAymdjpO80pk0hmmyaPEiurs6SbdkkE4aUfZ55BGXfd88wDPPPIsQcUz6oosu4vrrr6e3t5darcbk5CQTExOMj48zMTlJqVSiVq8jgPaOdjo7OzGF0VzZICOWrCzwG7++j9KEy9PPOvg1g5oKUI0GS6Eyce0UtmkjXJMWK4VyMnTnbNr7CrTlljE4CcWKxw9/+EO6urr4hV/4Ofbu3ct3v/NdfuZnb6O1pYVdu3dx/Y1vIpvLMTE1xX9633/iYP9B3vyWN/OHH/1D3vSm69h3YC9///f/wM/8zDt552238YW//x4oQa1a57d/87/z9nfewptvvIGPfPi3qQYBhHHXNhrGVsi4NW2c5W/hGg5uCjKWgakiQhWvkr16vHVvGMUrUEMIVCNAIZvb6cYGMAjjjYJqtTLW1CSpTIbW1jbyuVYc20WFEVMT40xOjsXiwbKxrTh3wnYa2fxm7Jave3Vsyz5ik59ElBgNl7whGjsQmtOiQCCwZqzWhRmvDE3DnP77GVtRm4aJYVjT/zatZta9acahA8e2SadS05nqguYeAbKRdxCXGfrUavW4tXG1Qs2vUa7VqSuPH6dbSPkuNUPwYHY9ppB8O99KhEmE4o/TOQIVEIQhn+7pIYzaITCQUcjfdeUICFChIooC8CQgMBwTixSRJ1HyeARBwxvHkS2UDSN+3Cclf0kSICgs22yEAzzWrFlDEASMjY2RyWSaAj4pw0w8OjNFgeM4zZyhJEE48bbZtsOKVctY1LeI/oF9XH/VCL29VT796VEeeVjy5nYLL+XybC1iqFinMDFIKCWukyKdSTNSq+LaNlZnB9+oedyWcSiUTL7wbyXOvvg/6Og6D4z0cczL61+Bn85CYlbCBKfTMV+te/9oN+Oxfv5qSFyjycMqDENqtRq2bb/iZjsiCQQDXuDztW98g3rDFSilBBnvNNfT3UOqLcv1F+2lu7PEP3/W4MDeKQLPJ4zksT2Sr/E8Emr1GpOTBVw3R2f3ItKZHnB68MwOOoMaG1NVmBzmzicfRvqKnp4ebnn7LWzYsAHTNCkUCniex9TUFGNjY3EyW6FAvV7Hsix6e3vjB1i1dsQchoHHc9tGKUxUce0izz7TRnefQegJ1qyWGFg88dgU199ssW1nmsqoSVd3hm37BLe8pcTXvnou1795lLVr1uHLFkqlEmNjY9x//49ZtmwZv/Ebv0FrWzt3330X/+9LX+SP//iP6Orp4V//9bO89ea38qEP/yb7Duxn6/PbeeObrsNJuSxfuYpFi5exd98BZDODXBDWFVEQu7SlAoMUgamwghAnBFeahKbEVoJUKLBFBVtKbCkoGA79jsCQJi4ubjaNkReEkY/n1ah7dbwgIlKKODnxaPd0HJf3A4+g4FMtFxm30+Ry7bS2tpLN5bAb4QGlFGHk4ZWrR3iQkjhzsuJPjFTTwB8Vr51p2JJcCkM0ykYN0WzZO/P1M69/MWMXQmEcuc3xTHFg2/aRpYqNplrJVz6bpa2lLU4IbeQzNHsRNJoY1WpVavUK9Xqdiuchw0ZpJAqpBEqYCCJUBJIQKeI9EYiS9tQhoQhRIUihMCwLS6WI/Agpw+ZncGzi2WmkUMTiSDWaShmNzZganrn45RLHTVOtVrCsuC31wMAAnufR3t7eTBJMvCpJ0mESGpxZVRAEAY7jHJFHkM1lWbl6FeVSDdfcy1WXp3l6e5V/+bdhLnNSyLY2tk5VOTA0QBiFuOkUbuNzD0Ofzo52pqamOHj4MH19PXy/HHJrLs+3R8b5zjcf4oJzDmK2nPUqnjavL38ATp47fzbFhg4TzALJh53cTEmcM9n1a2YjlCTLO9kGuFarUSwWm/+uVqvNr0ql0qyPXr9+Pf/4j/+I4zgvPxZAScXWLVvZ/OzmRqJWwzArRSaToa29lSXdB7nm0km+fTc8ummUSrlMFIUnRAgkJKuVbK6F7r6lLF26iu7uRbiZfCPRSyGNgIrM8ogneezJ5xkNAtaeuZp33vpOVq5chW3bFItFlFLN+ZnZfta2bVpbW/F9v9lDXUrZcA17eF6NUHocPATnnKmIogrnXRax/SnB8pWCbM7h4YdDli4V7N6vGKxW2bDS5/EXRsl1QKE4gZO2mZh0sNx1dHZ2NisVRkZGeG7LFjKZPG9+84285S03smhxL7//e/+ToaEh7vvRffzCL/wCf/AHfxC7olGsXbuWX//AB2hv72DHzu80HhY0k+KUjKsT4pipweWVKW6oFUlJSQoDR0U4ysCRYIsAIRSBIfjnbDdDRtzNzYhz9ogiicAinWohm2kjUCF1v9EQqV49xrWjaJg3wgjCKKTu1ZkqjOKmMmRyrbS0tJDL5Y4o3wOmQ1CNzPPEoMzMFZgpGpLPaSYmoilRpkXSsYnfSx6RZjb9vTHjp3GyZFM0ND0JZqM81MayHFwn1ehHkWqGQFzXbZanxmEQ1bjOKlQrRSqVKpVKiVq93NwoKTGYSQ7LTGE6M2ktPneBYdigjIa3JnjFeyrxuDTzU6QxIx8kLv80jGlh1tbWRqVS4fDhwxSLRSYnJ2lpaSGTyeC6bnPPguR+mRk6SD7PmaGYVCrFiuXLSLt5tm/fzK+8O0DYPp/6l0GyUzYrlrbzo7JHIfDp6e2hVCo15yVZLCUdSkdHRxkeHiNcsojHqyWuTqf58Y9Ged+v3M/689aDeG0bG2liTpkYON08Aq/2/ZOHW//hw/z4/h+ze9cuJiYn8Or15o2VZHcn7sgwjOI4ZnRkPH7mQ3FmUlWyslJK0dbWdtxjjpTknnt/gFeto8LYI6DCEMdwaOnIkbEDbrlqisHJCnfcmWZgcJBIeiQP0OShOXNcyc08HRaY+Qhu/FvESxbHSdHW1smy5StZvGwFbR3d2E4OKSHeojfux26aBtKXBEGVrds20T98gEvPOY83vu1tdHd1oZSiVIq7qtm2TSqVYmJiotlLXkkfGRlMjU+hDNnYxCZqCrAoigi8gFDBQ4+mKBRtDu72+OGdOTZvrvHAT8B1PJ7fpdgzEFKvVdg3FPHEZkm5ZvGx31fUGObZjxsMD5ZIt+xlxYpVrFl5Bt3dXbS0tDA0OMjjP32UarGfrq7llEpT3Pjm69n00yd46IEHufmtN/H8lu2YmAgpePqJp/mDP/woN73lJt773l/h6fs/w5b9EWFkNAqqYme+KSzaZZ0b/RprIp8aUDdMaobNhDCoCkHRyFM0FFOWZK/jYgkLaUY09hJsVjLQEAemaZKzc7TkWxgdG6ZWq8z4/I7+P0CEVOAHAX7gUa2WmZocIZPOks+3kc/nyWYz2K7TiPVPd3tMruu4igGiUKJkiEKyaFEv69eva250E/dLiBtgBb7P/gMH6O8fboxGsaivj47OVqSKGntANIysCqjXPQYHRxqbBcVLZ4PYQ5bJpmhvb6P/8CBKxUZToghjTzq2bZJvFcgwourXqUK85EYQhfHfB4HEsVM4bopUJt3YpMehta2F9va2+L72a9NtkD0Pz6tTqZSbwjWO5QuUFI2qgBnCyLBjo67i0sljSSBDmDNyc6Lma5QSROrFYQbTtJsbixmGQaFQoFKpUCwWKRaLtLS00NLS0txzJMn1SPoIJB7JmR0Mk8qCbDbL4kV9TIxNYJoHuOCCVh5/doxHHynytu5utkYhbq6FJd3t7Nu1u5mkmFQpxLlC8XXS0dHByMgYxdFh9rX3cJELTEruu/8+1m/4ZSBHLOzUKyYTaiHwYk6JGJgrmfIng1c6r5kX3NatW/m7v/krdr6wPd7wpeHDm7kTWvI3yd9FimYDmuRnxxIGiRhIHgIzN1952XEKmCwU2LRpU6MzWfIlsWyTTDrNGctHWbVqmH/5vyme3zXSiFsauG7clCVO5LKPKCWyLIuWlhbGx8eTs0oGghAWmWwrPb2LWL/+LC6//Eoc12V4dISpci12oRoCy2pstysl5VKZsbFRRoYH8GslDh3YzsaLLuHqq68mk07F7XY9D9M0aW2Nu7FNTExQLBaZmpqiXC41HlRFIjwMlYrLyBqrmCQByhABQRTymc9YEAk8y6V7U4GVAizh0KpsbopCnMMRwlCcH0lGBgwOStitDKaQRCpAGEUyZomJ/gH27NvHWWvPYM2aNSxf1kv+HJ+x4afYv28AI3s97e2LMKWBDCKQEiKB0RADy5ct4z9/4Fe55NJLOHRoNy/scfnDP05TtaI4mbFhPDFsQmHxldYAjw58DCJhooQiEiahEpgNAxgZEBmNa0zF/fsVcULdTHGpolgwGYZBd1cXY+PJjnTTBualkUSRRxR5BF6NSrlMYSpNNpcjk881Wtq6zT4FiVcoDl3Fizw3ZfKud93KDW++jmwuiyGMox7yce19uVzhzu/+gC9/+ev4vs8v/dK7ePONbwQilJreR0ARNyz60Y9+wr988vP4QQSCuG+Cpfidj3yYCy7YyO/+7v9kz679scxSIAzFxo3n8r5fey/Lly9BGKJR5hjvPvjdb9/N8PAIP/fzt/I7H/lDhkbHEIYJlmr2KbBtm2w2w20/8w6uuepyHDvOkwhFyOREmb/7239hfHyMcrnc9PzFK3+FkI0mQwgk8fbTSiiENF5k0JL8imkUxxZuM2ZR0LyPwzCkVCo1m3TVajXK5TKTk5NkMhkymQyO4+B5XvP1ScLg0bs7Js8AYZjs2/U8N90sSbklvvaNUbqjLE4mT9GCrrY2Nm/eTBCETU9Rcg6JuEgWG5lMKu58mPPZUklxfgp+8JM9vP/9w7i5PEKEJGESzatDhwleA69G2CQPokqlwr//+7/z3OanMGVjFzNloJSBGZduN5yY0ySNZpIkpuRnM39/tBiYWVp1vLzwQrxJSbwiaZRXKTBsA1tUufaqwwwO1PnhAxlGx0vIhuGc2djn6LGl0+lm7wCEgeW4tLS20rdoKYuXrqKrcxHr16/lqisuwnYNWlva6B8YZ8eeA+zYuZNqcZJatcb42BiTI6OUy2WCMMKwIPQmOPfMM7n44osxTZMgCJqegGTr20qlwujoKOPj45RKJeo1j4vPCbn5ZyT/+A8wOFZHyiNds4YwUEGGlFGi0ww427VZqxxC0+WQbTKMZKclqcs0yNgwpZWgW0acGXi8LQwJJGwWkqdEhSEMKPpUKlWmRscYGBjgwvPO4tLLFK32arzaIJMDd7Brfy+oCD8IGBwaanpTDh0+xMGDh1jUt4wnH9/MHXd8l1DaRGaEUCkgYNOmTWzbvZ26GZJGMWBkCQQgQ1A+EfEKGxUhlRW38JVxUmCychZCNo3szGsqifdHocIKLXq7lzAyOkq1VkCpV3JRTzvhpYoIwhphuUatXsYpZ0lnMmTSGbLZlqaBSQyBEJDLp/mN33g/F118Ifv3H+Tx791DqVyOnUliutNeS0sLV1xxOT/78++gXCnyrdu/Tz6fplar8vDDj6Ea3qXYuEiWLVvMTTfdwFNPb+HhRx6Pz1NFXHjRRq686jJs2+Jtb3sz//LPn0FJE4Rkw3nr+Ngf/X8opdi+fWfDOKlG2V3APXf/kBtuvI7Wtlxz7qSSyDDZ5Cf2av3MbW/jnbe9lUqxTKVSiQ2cAaViqblXRqqRwJhOp+Nk11oNEcVZ+kk3y2PRfCaJhodlxjPj5Ujm0W+U19ZqtWZVgGmaBH6cH+A2+nOkG90nk4ZMMzeASrwEQHNr4vb2diYmpqhU9nP1FWkO93s8/lCZa/JL2Sojli5ZyRNPPHFEsyPDENDIa8hms02PURiGzVDTVLHA7s4UF7guT+6c4tChXaw9a3UjGBLvKno8vFYPwenoWThtxcBcCRGohivykYce4MnHHsaMZLyRGvFDQqCIvYEyeeNG4xSIUERKoZRsxP4ErpuK95efseNeLBriNsHxw1wdIQZedqwKnn7yKUI/aHQZDJurBMOEvo4p1q4Y5evf7WDX/hFqtTo0MtSPjuk2/czCIFIGYLB23VlceOElnLfxAkqlMuWaB4ZLGEoO94/z/bsepqu7HduEPXt2MzA0zOjoKMXJ8fjBJCWWAst0SKdTYHuk23vYsOG85sokeVAlGxfVajUGBgYYHhpleGiEWnmKN16j+PjvVelu8Wh1u/idPygy4RuY0sTERykHw5SsSJe4UnViWZIXLLiDiEIY4FdrBCoCw4iNSxLHRrFbwhOGgZVOsxjJpb7Nh6TLgAz5joCRwCCYKrGzvovCxCDp9DLOWn0OF20U5POjfOk7HmvPXs7uXdv4jQ9+CC/wwDD49ve+zXfu/DZCWQ2PisJEIpWBoEIYQf9gETkYdxOsqAAVKWwcpApRQiKkiRIy/l55IAx8JVEqXoljxNdX0roIJIYBUiqEmN610PM8XDdLT08P42MG5UoJqXyOXHkefZ0lYaPk+gTfjwgjSeDXqVcqVIo1stks+XyeTCaH61o4rs0vvffnueDCC7j33vv4xje+Ta1Wb3pBkk2YEiNxz90/5k/+9He59dabuf/HD5POZJmcqPCZT34Bz/dJqiBQJh2dLfzTP/8155+3gZ8+8jhCWDiuyy++5+c5dOggIyNjXHPNFXzjG7czPlrGdgx+7QO/QqlY5Y//6M84cKC/cUZJ/oEAqchmU/EKOQxRxvR9F4sQwZnrz+Bdt72T+354P5/99OcplcugREOgRQRhvIeA0TDClmXR2tpKKpWiVC4CBlEoCYOw2W8Pg7gv1IwWEYjYWzFtqF7KGzD9Wc3s1RA2XPOOaYFl4UuFDOLr33Js6vU6whDNDoRJ/kqSWDjz/RNRcfDQAbp76yxblOXrX6/gT0FqicDp7GLvnr3UarWmVxMEbsogn29DCEWl7DXCU9M5BK7r4tVrlGWFkmwlW6nxzLatrD3rzaAsprtavjyvNSHwdBQCcArEwOkcInglkgtmamqS//j616iXpjCFgWq4RmP7rlCxEMZQQMOYS6UIxZG+AiEMLrjgAq6//nr+/M//HCEEmUyGm295G9/5zrcolUpIpZDyyBv85cYnpWTb888TNZIZk/if4zjIMOTSC0soJPdvgsJkFSWnO5BNj0s0bmaHdLaV9q5e1qw7g9WrV7FoUR8XX3gh55x1JpOTE+zd38/4ZJGRkVEOHeqnf3Cc57ZsZmpynFq5cMTmJrZl4zo2acfBMGwMA/zQ58yzzsawLIIgaLaOTXrM1+t1xsbG6O/v5+ChfZRLw7zl+pCP/15AhxtQixwGJzxqfgi+Sc0UCJFmmVnlJtLU7XYes30GAo9qKUA0yhzzbVngyDp1OLIOXinFoVAyaBo8ZLpslBEf8SU/NevcGUGpFiIHfP7jDo9zz0zh08fb3jjMe95eoTDVxdhUmdGxEUxhgAjirnhCIJXfyL+QhDNXfEbjM4ziNtGmMomICAkaWeMGyoyvJ0NMl9wRqYbmnO4ON32Pxg9c2469AkYSTkBSrRbp6urG6utDjJhUqyXC0DsiIW3G1cXRFQhJFYBQEYEfEvo+fj3C9+p4tRq5XJ1cPs1Nb72By6+4hKeffoavfvWb+GEEpoirBxpWUCmz6QUbHx/jxz9+mF/8xZ9lxcrlpNMp6vW4R4VpWA0hHVdFJNc3UmEaJgKDN1xzNevOWMff//0/MjlR4H/9+ce49to38K3b72LlymWsO+MM/uH/fJrDhwcxTSM+s0b4Ks62CMnlsnheSJQUXzTWqPG8Kt76luuplkt86QtfbVa0xMNSgIWMArwwRAXBEUmLtm3T0d5JrVbDMiwmxkYh7jeJbCRvKvnqDFlSHZDMxcz9AwwlkZFAxmUOCCkQEUgRxjlMQYgwDWzLxrKtpgMo8RIkRj0pE56YmGB4cIhrLkthmBV+8mCRFek8I0oSRhEDQ4MNwWmAsunsMdlwURs9iyUD+1IM7O9mamqUQrHQvEZt245bKNdCDloe3Y7Dji07Ubc1ttue57ze8sfXymnrGThZHLdHoPF/KSV33303zz//fGPhNe0+FYIjFOx0vLbxDrEzoPkS0diJzHEclIo7t334wx8+Yv/15O9faROShHq9zsGDB5u9BZIHRJw97XHheZPs60+xeXMF3w+bmdaxoTCwrBSL+haxcuUqLr70cvqHx6n6klQmRaalnXI95KGfPsXjT2yJqyIKRSZGx5icnKRULlKvVwkaTV+EoLEPQLzZjZRxgtjk1CRevUJndztr1qzEtm3CMCSVSjW9Aol7tVKpMDw8zOHDhylMFrj2EouP/V6FjlRINbT51P+z+ZdPKYo1G0tE5EKfSxzFuVYrj6ZsDvo+XrmKlXNp68wT+AHlcpmxsbFmTsTR85rkTLiuSy6TgVSKMSLul5Jn7IhbawYfBb4gIw5Il4nJIk9u2c6UF1ArreDnbt3BL/1slaGxHPc/PA41ScS0u3465n1kB76XyiFJHsRJNnqSoDfTFTvzmpv52plJoDNL/eLMc5+pwih9fX2YZgfDwxGVKoSN/JdXutri10hoJrEplKoSRnWCsI4f1GltX8bbbn4L5XKZL3/5K9SqNYRlIQwj/mp4zmZm+ktpMjk5gTBEI/M9TWGqQi6fo7e3pxFaiLvrXXrpRbR3dLB9+25syyGTdfm5d9/Czp27eeLxzUip2Lf3ADfe+CZ+eO/9rFi5gkhG7NixYzqOrWJviGEYCBW3Ms7n81QbLvaZTX0ATMPgjHWraGvP8rd/95cktf2JSBkdLfDRP/wTwuIUUaOaJ8n5sSyrsVVzKl6NmwZjY0N4Xi022K8SpVRzy+FkG+aZrcDjHBEZ55JEseRQjbAmwkAQIcMQ3wtQjh3/3DKbn0UiBJL3GxsbI/AqXHC+S6Eg2LrD54pUG7ttk4FD/URhEIckDUE2Z2FaIbt2H6Klw+YX/nM3P/5ukScfzuAH09U/SW5Jve5zMBtyiZHmwN4BIlnCMjtBhMCrqyzQnIZi4FR4IV4xabDxJQQMDfbz7du/ie/VMBsrhaREJwhDlIB8PodUinKpFN+QKnY/N5Ltj4jnFgoFdu7cSSqV4kMf+hBhGPKpT30Krx7Eq5RGg5jjFQOlUqm52+DMnAMpI7q6KvR11/n6ty0GhytAgGmYWE6atrZ2enoW0d3TxxVXXMnKlSvp6GpFCsHY5BRPP72VibFCHOvzfarlCqVyCb9WJfTi3fJkFIJS2KZBJpNGGAZhGGfp16oVqrUq9VqNMKjT1d3OokVdZDIZgOZGMslKJzmXyYkJhvqHGBweZv2aKh/9gzptboQXZvjsFy3+6dOKulfDNS0cQ/A2sxWRcfiOETFamsBKu+S6WqlU64z1D+A1spmT+T/m551kVUdBs8tiJp8ll2unAHwpHXBe6PGhwOG7XpFHCJiMbPZu38JXg3FEejW/cNMB3v12wYF9i9iz52DDhTydWHp0e96ZFSRHX5szjfi0+/XYbtGZyasz/z/z54nxEsLA8+pMTIzS1dUNdNM/MICgsX9FdIQfiyOU7hHegxmCQ8WK1/NCECFvuekGLEvw/Nbn2bplK24qRyqVwbIthBmL4+nziZsPYSrWnrGKKPKp1+qkUmlG/Qks0+KDv/FrrFq1LBYhgCEsnnjiaTZv3owQghtuuI5Fi3r44hf+iTCM3f73//hBPvDrv8oFF2zEcezGynvaTR0n8kSIxuZISkrSmQxe3UMoiTkjfg9gmQaOY1Ot1BkcHAGhGr+KwycPPPBTKpVS3Igp+VMFYRhQr1WolCexLIt0KkVrawvZbIrR0SGmpkaJjqsJ0YuZuSnRizGJEEQIpIpLTm3bIgojlIzidsZAKBWYJlJGzU6cyYZcCJBETE0VsEyf1asM9h+wKE1FpLOCiVrIxORUQwiAYSqE4bP9+RFQgoHdNpPDBh/4reUMHCxRr2Wo10KU8JsJhkEUMiWhzTTYMjSJ71Ww0h00+31rXhWnnRiYS0RRyB23f4P+A3uwDcjlW3n3u9/Dxo0XoKTiJw/8hDvu+DYf/OAHGRoa5gtf+ALZXJ50Js1ko2lOPp9HGYJcLsvExBTpdJrBwUHa29t5/vnnefDBB6nXPcSMGlspp7cQfaWmQ/VGaWOSCNQsT5SKc9eFCMvlkccsctkezljbzbnnnsvKtWtIZ3L0Hx7hYP8QTz23ned37qOvu5v2tjZGR0eZmipQq3nN3e+q1VJzdzSIH+iO5UBjFVSYmqJWq1D36vhevFKMogiFxLIEq1atIJ9vaa6UkgQn13WbK99yuczQ8DCHBnfTni3yxx9V9HV7EJh84Q6Dv/10jahqYBspspbP261W9uUtttXrlKsebe1d1Ot1+g8PHJEcOXOXNtd1yWazzbBEFEXNLodJ8pWUknKhRLVUoa2tDTebYnuQpmgGvEc4BKHBo2GRqalWoj39fPvbeRa15bj66hJvflNI/3CeSqnUuIai5qorWXXOrENvVkHMaN87M4yReFhmtuidWeN/tEBIXjczH2TmStcwDEqlCrbt0tHRgaKHwcFBarUwrvuPINlJbjqJa2ZeAUyLAdH8uVQh69adyTnnnEmhMIXne9RqVapVj1wuaCYZSmFiWY3WxCL2NJx99llcffUV7N27l0qlQi6bpVKpUigU+Kd//BQf/Z+/S3dPB1u3bOfxx5/hJ/c/QhgGdHS28/Z3vI0tW3Zw4YXn8a53vQMwSKUchBHxlpuu5zvf+Q62bbNhwwZGRx6O50AopGpkrSuJsEwy6SzlcgXLiMXDEYm1UjE2OokQJh//2J9Rr9Ubs6ISByCGYWIwXRqMhEAGVMuTKOWjgEo53mI5l8vR1d2G4xpMTIw3PXkzP/fX7GJubHAUKYEvaLSqhlBJhJSYhoWIYk+B2ajEkDK+hizLaoYgwiigWq8SVCN6O306cgaPPlUlG+QooShP1amLEEMJDAPSaYfhwSmiMM5HqoeC7c/a7NoWcePbuvm3fWOYpkWk/GbyYiQVUQhKhFRKNXwvIptJOmceP7Pllp9rnDQxcLrlCbyqCgLiB8CeHbu49/t3oWTcw/63f/u3SWdyfO7fPk8+l+eXfuk9+EHQMGgmV1x5Je973/sIo4hqvcY//MM/8Psf/UOq9RpPPvkky5Yup7d3MUEQcNttt/Gnf/qnXHDBBeTzecIw5Ac/+EFzhX88noFkRZsY1qTWWCmFbVv0LFnC175jM1K0uezqZVx80aWct2EDpcoUY2PjrFu/llQmz+49+1BKMTIyyfDgRLxK9sqUipNUKhU830Oimoo+aVxSr1Xwa1U83yMIfMLQb2ZCo6aT1xYvXkx3d3fTG5B4BJJ8ASll7BWYnOTg4cMUCxN8/Lcszl49DsrmvodS/OXfBfgVm9DxyZgBb7M6GEhJnq/4eCja29vjxMVGw6IkozuprU6+kuYycf/9Frq6umhvb2/uf/D8889z8ODB5kN5YmKCXMXF6+6gFth8koD/LwRDujwsAqoFxZ59h/nKf6xi+QqTn39HwKOPwzPPcoT9PLoZz9HJm8cKGcz8d2Ioju56mYiHmaVcM987ERzJcZItacfHx7Esi7a2NqIoYmxsjGql1qh9n3kncOSJvARCGFx66WWNktBxWloy/Nff+iBbnttO4Idxcl0jxp+MK5O1uODCjbzxjW+kWqnzpS/eQUdHG6m0y+TkBOl0hsHBUT71qf/LRz7yYfK5Fp54/ClqNQ9D2Lzt5jfT0trCHf/4HXp6urjyqssa5YKKUqnI2ees47vfsRgcHOYXf+ldVKsVDh46FM+/jM9LKajWymSzGUrlEosW9c74nOJdKKWUbNr0NL/5offz/vf/J7797TsbpXk0BJnBVGGKUrXWnGtFiOeVGy5vGtswx1UZnl9qlPm14LqLKBaLzbr/15PcNt3gKPbehEbcg8QwTEIZxgmskWqWeJoohJJYwmp6Gnzfj7emFoLAB6UiOrtD0imDg3vrtJsZpkKPil8n8H1c027mwyRhAFBEgYnA5dmfhvzSf24lky1gTpWJfAvDaFyfUuI18l4iPyAI/Nd03pqYk+oZONWCYK5UEMRNUwK+/tWvMDE2hiUEq1et4qyzzuJ3f+f3GBmMG6Rs27KVSEnOPeccIilpaW3l3h/+EM/3+OVf+RV6enppb2vnX//mc/i+z5VXXM3/+B+/SxAE/N3f/R3nnHMOZ555JgBDQ0NHjDNZ5R9zeMnDQoHAIJVOYVccIiVwUzmuv/EG3nHTDSgh2XtogLVn72X3noPc++MH2bJtF67tUCqWQZkYloltxg+DerVEsVCgVCoReLV4hz4BQimiMKBSLuN7NTyv3uhe5iMjf8bDa2Zr49jNbTsWa9aswbbtZhmUYRhks1lSqVRTxBSLkwwNj3D48GHecKXPW24oglLsPZjhD/+6TqkUYgqTrMrybiEYSEc8WldYliLruhw6dKgZO3Vdl1wuF2/12+i1nqx4PM9rNmQZHR3l0KFD1Ot1BgcH6evr47bbbmN8fJwf/ehHDA4OYlkW1TDAHxrF6mxjTBl8yhH858Bh3KixXaSpl4fZsjfD//tyFx/9b2V+9m0mL7zgUK7V4sZAHFlrfSyDP3Pln4w7MQ4zcwJmiomj3cQzXz8zGSx5feJlgNiITUzEwq+jo4NMJsNA/yCFQpGwKURfPqN92mMQG9W77rqLYrHA2WefTd+iXnp7ejjjtnVkMmnSmSwpN43txJsTGYaJZcWr4ee3Psftt9/F8NAUb7/1LQhhcPjQII6TxrRsdu7cyxe++FU+8IFf5kP/9QP8w//5Z7LZFt785jfxxONPsHvPPnbvOcDmzTsahjCis6udP/6j/8lVV13Bl770FT78W7/O7/7+hxqdIRu9EISgXvP5xCf+FsdxueSSC7lg47lHaCCFYmR4gj/+o//Npsee4OZbbuItb30jURQ2PjeTes3jD//w41T2DTTvg2qtiiLEMGD6YxIoaRJEimJQo1oJSKfTtLW1kc1mqdVqTXGW3P/JnhtJ/X/y82M9G5KqpHj8RrzCbpQqmiJu4SyjuBRVCEGkFBYmKnixQJUqIgpCUIKOThvsFMODk7RaMBH6eCoipRr5KI35am9voVisEEUhwhAoETE0IMjms7S25Tk0MEazqVBjvKGScShDxpUWp5LTrapAhwlOMMnF8ezmZ3jggfsRhsJE4NgOGIJARSgrdmP1LepDNIybEoLuvl4uvOACtmzZ2lwV+37Ant37WLx4MZblkM/nSafjzmb9/f385Cc/AaZjgMnDfKZn4FgXrJSSQqHE4OAob3nr2/D9iBe272RqvMyOnQN8j59w6SUXYxkp+nq7KRRrjI1NUq+HGMomm2nB933KpQJTU5OUSyV8r46KpjOTkyYk9XoVz6sThGHco10GcdWDUo3ciMZ2SUniZFxwCULQ29tLPp9vnpdlWc0tj13XBaDuVSkUygz0H8YUg3zw1yRpZVOsS/733/vsP6wwDXDNNNfaJp5rsymIsEyB66Q4dOgwQSOLO0nUSv6tlGqKhJntdGduzTw1NQXAwYMHuffee7nsssv45V/+ZR566CEee+wxTMMkjCLGxyZp725nnxR8yanx677NXwiPWmjjT0zw2BMO9z+iuOFNPv/xvRRPPleNK0uYFgBH5wPM7Dp5dAfImX8z09AfPf7k9VEUNUMuiQhImsccfQ0lnoaJiYnGTndtrFi5nP7+fiYnJ5vtdV+Z6TH39x/mm9/8Jo7jkMu1kc/lyeVytLW3kc+1kc200tqWo7W1lXQ6RaFQ4NChQ0xMTGLYKUzTQgmDJ554lt279iMwMe34ktr0xNN0dLRz0YUbueYNVxMEIQcPHOZ7370LhInCoFKvNY1atX+Y7991L20tOXbu2Mlf/Nnfc9XVV5HPtzRX/QAHDhxgz5493H33D1i5ciUzTznRBE89/Qyjo2P8n7//NNde+zzr1q+ZDveogIP7D3P40HCjYiauHhEiwrJMpDSPeM/kvlZKNbuV1uv1phcraV+efD7NxNZcrulRsyyLarXK2NjYi3qFxCon9s6J5F5UEqXMuBlKIxszvlUV4VEic/oalagoRCmb1pyNImKyaGFYFpEXESmJ6ViNREaDxYu7+E+/+g4e/+kuvvLlO7AtG9+XTJWroGyEWYm3W24sElR80cZ9UVAoQxzh9TpVnE6CQIuB4+DVehzq9Tpf/uL/o16qYDfun4MHDjA8PMxvfvhD/OCeH7Jk6VJuuflmPv0vn8RoZPZeeP757NixgygKGwk5093Ztv//7f13tCTZfd8Jfu4Nk/b598p7074b3Y1GNzwIEEADAglQNBIJSqMRpT/2SBqteI40O9pZHc3OznBX0o5mNEuKciNS4oAkSBAAYRpgQ/C+va+u6vL21bP50oe7d/+IvPkiozJfvap6Jl9Vfuu8k5WZkRE3bkTc3/fnjx3jlVde4bd+67dQSvH0009z6tSp9tiSvmFjGTCan3lQTXWxUqnE5//8Kzzz7IuUl+rce+/dfPQjP8uTH/sEf/RHf8Zbx55l+mKFz546Qz4/GhcbyebYMTlFo15ncfYqS6US9XqNhtdoR0CrwCcMfPwgrkYW+HF3tijy48hnTWwt0Lqdjpae23YONQLbstm7Z0/bb+04cYvSdoMZGaeK1apVSotlrl46w6//ZcGhHWUUgi9+ZYRvfDtAaIUjbI6iOGRn+KIlCYOIQjbDxYvLRMBYH5KkCuICSsnP02mV5jvLsrhy5Qo//elP2bNnD48++ihjY2M8/fTT2LZNGIZU55YYmhjnuB/wUzvkr/vw28qmYTeR5QU++7kijz+h+cs/5/Pqq5KG8uNAuR73YFJz77ZNOvsgeU+YOYRl87/5zriPkhYHYx1I7tOUpjW16/fv3082m2V6erqLoLk+lFKtXghzzM3NtdLzLGwrg23n4pQ228ayLRwnzq7J5XK4uRxuJsMf/uEfk3MdbMvBshy0ot3t8L9849t84y++2RaU3/7W91vzJtu2i6S75Atf+FI76uHc2cucPfu5FrGCZQmtQNh8/WvfbLkPul0n3S6v+81vfpun/+JbbY1YoOOANxFnHSi9XFDJXJ9kVknyGpnn3NQGyeVybdeZlBLf95mfn29fcxPcZ65Jt8Jkyfs+ee2MK8mMJzkuE69gxmNZFlLE2U8q8slmJVpJmk1BQUZcERGOtADd7kq5VC6xVLlKLh9XpXSdHF4TGl4dS+aoVDzCMMBusbvO7BfdLoS0Ubgd4wwGZGAVWA0ZSN4SP/3xD3nh2WeQIjaBKi2oNzz+l3/xv/BLf+Wv8quf/jSl0iK/+7u/y4svvMChw4e5cPEiL7z4Io899hiXr1zh9//zf+bsuXP869/9N5TLZTzP41/9q3/F9u3b8X2f0txCLIQcC0sJBCG2kIQWcZ6wCgnCgMXFRd586xTPPvcClcUS733fuxkdGWbPvj3Ml2u8euwEL7zyBm+8dpwn3j7E24oBv/ZEkR0HLH7vjb1cXIClpTJXL1+hWi3TbHqEfpMoDIiUIog8/LDV691rEpme7yqKg49aKn+86BHXVaC1libntctDNTI6wtDQcGIT3ZG6ZEyflXKDy1fPMlps8PMfq6MDzemZSX7nP1QJdYAgQ8F2+FnL4qmCTbniMzSc4+KFSx1EwOw3eby0kO328Luu267gFoYhMzMzVCoV5ufnOXz4MO973/v43ve+FxdLaXiUqlVENs/XhOa/s0LuF03erLlUrBrHz7p88xsOP/OBJf6P/+hw4lIdoeJmU8kFOCkUuvWqSLoH0uNOugzSqYTJbc33yYUvqf0ZomDIkGkEtWvXLnK5HBcuXKBer/d0V62MuJqORqF1QBAFhFGDIHRb2qSDE2TxfT/OwXczuJkM+VyOKF8gm83gOCHSjtNwLWlhSRssEMIULYpaR1rWgk1AnyXiAEXjDohdWHFTnziexQQAxoVylp/1yBi3gJjcwnKQXSwAlwMoBQ6aVkVSZRHpELvVhyNJeru5h5IIw5BqNe5vYLorQuzyqtfrHc+OqRTYbX/JeyJJDMz7pGUiqYknO09qrbEdJ84UERrLjoMQlRJkpEa5Fk4kCXU8fxrF3GyFf/5bn6FeC5A2TE1NcdfRe5ncUUWIIrVq7HKIqxPGkEJiSYEjLPKFLJlMtj2vA9w4BmRgjVGvVfk//9N/xPerWFISLzdxNb6Z6Tl+97f/dUK7imXgn/7JnxK1TOavv/46CkHcL0hxaXqm7f8NgoCLFy+2fY1COFg6QIqIpsggJAwFmt152LFzjH/1v/6vvHH6CotzS2gl8SzFd37yAo++7QGyZFm8WsUNc4xlJe9+pMZfG9dMLNY5byt+/yeLfOfYPOVKgO97RJFPpCKiMIoFvh8SRiFhGH8ehiEyilrVCWmTAIymr415r9VcRco2AWj7GlNzOTkx2dY2oLNBk9FG6vU6s/OzTF9a4L/+5TpTwxWiUPKnX/Q4dz5C2A4uknfaiitOlgv1kJHhHJdnZtr11ZO10NPoRgSusWaIOF3ULKTJbm5vvvkm999/Pw899BAvv/wyTsahWa3i2TahY/NF4JeB35IarxlQEhX+7Os2T37M593vcXjrj922QE4GASaFQy9BnyYGxq3QWT+gM1YgqfEnrQdpC0PS9ZDsOuh5HlJKRkZGyGQynD9/nnK5vOpU12V0EgitIzQRKvQJQhC+RAoXy4rr/lu2hRSSkmWRyxYoFIYYGiqSzxfJZnNI14079FkSyxJY0iFSrRiIKIoFPMv8VGuNEiLuAxBfZbSZH7ONUombVsT3eJdTjE3aSf/BsmtGiBahiGxsZBwHrzSe7CQD3Yhq8v/mz1wDQ3KBdiqzsSIsa9TdkQ4ghWUimrxnknEsxiJpNPTYJSEJQpsw8AAf24EIQcZ2yOUtmk2N1hIhPJTSlBbiEs3Dw8Ps2Lmdxx97B/c84NFsRPi+Q5zeCZVKLc5VkTYFqUDbjEwUyWTyrROQg/TCm8C6kIHbLXBwVWgtJt/73vfiAkPmY1LZ1koT6li4xYtBK2c6UbFMt3zn6Ye8YzHWAbZSOH6WTAbukyGHxzX50YOcUUUu+pJv/eQYuchCy4BI+mSEBL/Oj194ltH8OCNDOSYmpvC8iJnyW8jhV2icucrzu1z+5HtFAl8Q6rCVoRARhrHQF0pDuxNcXAZZCIFwLCwrrl++Y+dOxsbGOHv2LJcvX24XzEnOiXlchRCIpOkdsB2H4eFhwiiMtYxEQSSzsHmeF/s+Z+Yp2jN8+IN1wgguzGb4088HgIWOQoalxUO4fCbrkA8E5VqNWqUKLLscli9j5yJyvXurG0FIpv7Zts38/Dz79u1jfHycUqmEZVlUq1WGxwq8aBX5eFDl7QT8oGXaPXna4bVj8NGfDfjCV7LUGkHbJQLL6YZm/EkXhjl+8r5Jdo9MxgGkMwfSloTkgm/MxMn9GNdI0pVgxme+O3jwIBcuXOgwV68FtFZE2iNSHkEgkLbAsS2kZVGrVbg6cxnbthkaKjIxMcHo6ChDw0Nk3AyWbbeIhIVlybgbplKtoL4o1viFRggL3X56xTVkNVlcp00S9LX3kmBZsBqClb6vLCmRQiOJg+HMvJrrkrTCpE31vdIJkymxyeNHUcTCwsJ1LTbp7JNkfEkSZozJe1JKiWXHRYka9RBFg2JRUUeS1xInk0FFQRxsqhwKhRwL8+W46Fghx9TUONmc4PDdRV59vUS16rVIRlyAqVKpYDkWo1JQBnbs2Ilj5+LZFhED68CNY5BauFYQ0KjX+dPP/jFREMbCXbdMqUBb9ImkRTwpEmkTCrBa9cUVECJ0XCteiQCBIBtpRi3JfdtC3n+gweP3R1SsIv/6x9s4WxUM2YK9hYAddoO9VsheR7Ijl6EwlSE/orkickyrecqBx49ff5HnjjVoBD6NB0L+bxOKJ67Mc++U5gfHfCIdd67TetmEKlvrnpAC2TKTaq1xHMkT73ycXbt2k8m47N27lwvnL/DVrz7F3NxcyxKiEmRnueeC1hpU1A4MKuTzZFwnbmOr7XYthFwuh9Yaz28SRiHlpQqXpi/w8fc1mByCILD40p87nJ8NkITYlsv7HclLxWHqXpVsxuXS9HzLOJHu8GYuQ6fWtawZx9kXGkEcUS1bc9I9MtsIxEqlwsWLF9m2bRuLi4u4rkuj0SCoQZjx+LKAX0DzXKBo2gH1psd3vpPj7/xtn6ltGv+ySyazbPo1RMhogEmLQXK8RmtPpyUa4Z4+V/M++X0yEyFJMpLEw2xjCIPZNgxDbNtm3759WJbFzMzMmhKCdlQ5miiM2whLqbBtjZtxQCuWynMslWfJZDKMjY0xMTnJ+NgY2Wwey4qzEixHYIv4/g4jCEPVynePaasQIvGYLj/H8YsAIdtWPkPuNZrU1MZz3X7GDTFo9RJAI1FIS6C0hePYrdr/y7Ulkg2BkoQz6QIwmrm5H5KBoOYa9Yov6TrDXbZNCv1kbIPpzhh/ZrXJUrUmQUSMjdssak1GSCzbJuM4rSBESS5vkS+6SPLkc0WCJkztniOT2c2Pv3ueWq1BJuPiOC7VagUpJJmMw5Rrs6RDnjhyBCFsMJbHARm4YQzcBGsAs6D++Cc/4PirryAi3fYVIgVYtAPmgISvfHnBUFol2rp4COIynZEShCoiJzx2TcHb73Z43/0hDx/02LEtizVcAxnRtGr8zaBA42WHu/QSOyuCjDWOzI0gsdHVJtaJGfT8PHsqJbyChzcM736wwNfeleU/fEvxue8GjH0w5JNn4b49Ad+JotjaplWrlato10Ff1uqXH7x9+/fy6V//VWauXmVubo777rsbiDhw8ACeF7RyqluVFYVAyDjwp7xUp1yptMx7oCPN6NAQKmyiIkUY2u1Fz/f92LxvQaNeY/7qNPVqiY+8z0fRoLyU4am/CAi1IGNpxoTgiGXzpwJsN8PMwiJBovBR2g3QfeETgIUQEZomWtut+mwWKKvDJJnch1l8Pc9jaWmJoaE4Or5Wq2FZFg2vzrAzzmvS55eUy2Ht8bIG5cNPX5T8fRlx31E4dbpBEPjtxdZ1XUZGRtoan4kor1arVKvVa4o7JTW6bqb6JFlIxwckqxcm92WKFplt0/tIWhqklOzevRspJdOJroxri5bPXil8PyQIvLZwjNNPQ6anZ5idnaNYHGJ0dJSxsTGGhobaJa1jgSZb2RMWSul2m+u2paRFZk1hobgKop3Q0EXr8U4R/cScGoIhhcaSCqk1SFr1RjLkcllyuZjsmRbCS0tL7Xs/2SrcwFyXIIjTDc15mWuWrjBaq9Vu6jqYbAStdbuksZln13Xb74Vc7tmxuGQRNjW7dkteCwLGsjGptd0MkQJNQBRq7r5vkkYdxscsHn1XlQcfyfL1Pz/Ld775HFLquMaHG2f6SEuSd2x2OgWuOCH3P3g/Sprw4/V3EdxOgYMGAzKwAlbLnoWIK/n92Wf/lKjpYyGI2oFyuuVbbGlYsvs+tabdbSwkziV2ZI3DOxTvfUDw/kcs7jukGRpqIolAK7QMEb5LdKaA/SObd7/lo3dMIqJh3DCHbMzCuWnU3DxRtUrFb9AIoKGgJGwCFVF/OeThA3X+5ROCF5t5DmazNEREpabIOGDZDlIILCuO4E4KBaMxqVaaVRB4/Lt/+2+ZnZ3Ftm0+//nPt/qchwipCKMAUJjuirGgsOMFMBv7+0QrnyCfz8eLb6Twfb8teEyAVOBHVOslrlyZ5vA2j3uP+rhK8PqbcPxMhCUDLCvPfY7kaqZACQ9HxOZ5uFZIXns9ksRAg/AR2GScSWxHtnMewsgnDIOOhTXttxdC4Ps+tVqNbDZLuVyOsws8D6XqNITFM0rzsC14s2njFyIuns9y5rLPo29z+crTyz584y7xPI9kIaRiMTaHG/99s9mkVqtRqVSo1WrU6/Wu6X7pIDHoJAdJoZN0RSS/azabHdpp8jUZe7Bz507CMGRubm6dCMEytNZtLdporSa1rlwus7S0xJUrVygUChSLRYaGhlp9DfJtgSalbPcBMf0w0hUghZCx1S5SIBRRFGCKZply0gLRrvboZlxsy26TKa1jK0xSmPq+z+LiIrOzs1y+fJlKpdK+dt0CCY0FyJyfqYQ5OjpKLpdjbGysXSMjDEMajQalUqlnEGF6Hru9N/Nj7r8OIpByiZTLUFmUHD2g+KwluUu7BE2fXM7FdmOriFIRUSj51K/u5NF37KVZyfI//D++yhsvl1hYrLTJDSIOHM64GVxhM+E6nBt3ufvI3QisBBFbf9xuhGBABq6D6/qMibX+Y8fe4JXnn4/tjCbwqL0YmvxcILJBRi27QdyVLlJxG2MiyGcaHNkX8e4HHd79Ns2hXT45F4QMEFoiVKylagHRQpbG02O4x8ZxKCBR6LdKqLlZmksL6KpP3YsoaUVVa+pC4CGouRn84jCVrKQqBA1hU3nRpVaMOBFJPLfAiVkYG4VQha1iJUFLeJkgtNaZdzwQgqtXpzv8zh0ClThgypjVRdu94CKFCyK+HR07dhMQRahIEAFRZKFbbpdqtUo2EzA3N83cXI0P/VyA40IzUjz9faipEEdksIXiMe3ybTdekBdLS20h1M1ve+21jjvSaW0zVBji0L2CHfs8mg3FwoxibgYiP0MYOPh+lkY9rgtgdpMWnEEQtH24URQRIaj7AcMWPGPDbyqHzwmBCj2agcUbxx3uu1vhOjZe0Ip418vR/CabwsQSJLU0x3EYGxtjx44dbQE2MzPDyZMnu8YJpMlL2rXQDWabSqWC53kdmqIRvskgOCkl27ZtA2B2dvYmswxuHGauTMaHGWMURZRKJcrlcrvZlWkKlM3GrcJzuRz3338/d999N1LKtlBaWFigWCy2Cv2MMDc3Bwi+/e3vdrW0fOhDHyKTybBv3z4WFhZa8S8BMzNXefPNNwnDkFKpxMzMDJcvX2ZhYaGjXkA3AmDm15yjqYcBscAMw5Dt27cjZZyCaVlWK22z2d7HSgKtG1E01UqFEB0NlMxYDGlP/q7acDg7LblrH5ANyChJWG3guC44LqI13kYlw5/+/kU+/5nT1KoBflMiZYahoSEcx6FQKLAwv0AUhgwNj1CwLHwVcdfRe5nYvrNljdUsl8Ee4EYwIAO3gpaFPCTiy1/8HGGl1tIIaLeXNWZBhGhZBTxEpABJJB0gYqRQ577Dmvc+EvKOezV7JgQZp4Zo5SKgJbTSy7A1Wg6hyKP/YgT7rRD7Sgk1fZpmpYofeCwSUPclFaChFQ0LpNJ42TyzRw/znSjizUqD2cjDj0JkNUSpBpRi/308XIkprAJm4V92axjWPzYW1yEw2l46EO2hhx5i9+7dvPLKK2zfvp2HHnqQF154kVdeeaXlOlGx9uXaqFansYwjsVVERsXteFVGokIbHWWgpUlVBcxMzxN4szz6iMZRsFRzeebZCCkEtoQxJcm5NnNCYbFsFUgLvuSi17n4KYR02Hcoy8d/MWL7rohaWSKwyA9HlEvww2/UOXc2RFsZhMzTqDXblo/kvpNBeK7rUqvVECLu+Oc5NpeBAJ8JIbmqHFAWJ96yeO87mhRzGZq+h9QCLZZ9wMlUQ6BdWyIpmI3QSC7Y6XgBU8Mh6RIwc5Q0LSc106R2XK1WO6LezTGTfSSMud6yLIrFIvV6nWq1uuHalSEG5rzNmM3nZt4MWbRtm4MHD9JoNHAch/PnzzMxMUEulyMMQ65evUo+nyMI4kDbUmmhvW8zj/H51lAq5JVXXkIpFddQEILnn3+eixcvUqlUKJfLbYKXTu8zc2fGaq5Jr1bl5rporRkeHiabzdJoNDh9+nQ71sS27WuKk3XbVzKI0YzBWAOMRSAZf5O+f9A5XntzgV/7lMfePaOUzzUZEVD2ahSLwywsVCgUCiglkXqY0A9xrYjskMB1M+05VEqxtLDQuq8cHnQlp4TiFx5/P647ctP3xO2m4d8sBmTgFiGUZvbqND/4zndjd4BuxQdEoKOWe0BBLGEhshQah7HhiIfvqfAzj7i84x7B9vEQq9UHHdlACYHARWAjpA0yA8IBIRG6iaiVaH6/hP4ln4XvaSovLVDR4I27NMbzcDqigYer43joejbP2Xe/h9+/cIa5siKThWJuCK/pUW82EEKQzeZ417vexejoKLZt86UvfSnuLtjWdDrPvVgs8s//+T/HcRx+8zd/k4WFhY4HSwjBJz7xCd773vfyL/7Fv+Cuu+7iU5/6FJVKlVdeeWV5obPjNMmoFYltSUXYrJGxBZYU6IbAJyKwwQ8jMmGG0FcsLJTJOB6H9mm8ZoPTbxW5eNlHW7HQPCQl045FGEFTe0QtDTotpJPj7XyFw3eN844PNvnptx0unI5TKS0rw9DQCEcfrvK+J32y33F59dUltGySyxdoNHRcrY3OXGyzOBpTvqlLoLRGBSFvuZLDWnJVSJSlOH8e8oUsoyMRMyWIpBWnvifmuNf/k4FiJuBseHiYo0ePUi6X2xqiMaWb69wtViC9/7b/PKEBJ10ISZhCMGlh0u0Y6f2n76Vu53krMELVxFck7wczTiklX/nKV/jCF77QHrchU67rtveRy+XioNDWPWbIgBnr008/3TFvHXEIXbIB0hUizffGJdNLaKffh2HIwsICV69eZWFhoW0dmZqa4tFHH2VkZIRyuczFixe5evUqpVLpmmJRZhxJl0Aul0sEC8prxpu816XQvPx8kaOHchzY73Pxss+hQoEfz85RyOYpFovkcrm4UFkQdFgXpJTk83kcx+HKlSvUGjWGxybJ+3V25ic5M+Tw8GM/g8BpeQduzkUwIAQDMnCLiMPrv/fNb7MwfRXCEHSrrSkBQtkIrVAyROAwMqy5/1744LsinngwYMcoWLIBEAt90SoCKgtIO48gA7igQ6CGCsvgN1G+T/MqnG0Kms/nmHvWIwSsgsC3QyYPjVF2ykRvWdTRFPYXuZKZ4PfOnOJydYn/+3/7j3jgvgcZyuT5i+9/m3//b/4dAnjkkUf4h//wHyKlpLS4yNNP/wWNpgcIhCQOdCJua6qFwJY2+UIBq7UQZDKZtn/cPFw//OEPOXPmDGfPnuWee+7pSItadh1YSNtpZRUotArQkY9WsSvFwkIJRdMLkbbTikMIqNZLHN7mUHQaBA2b429C1VfgSFwlOSJszrtxgFe1uthR9bDbH60rYQKQpnYU2HMIvvZZweJCiUh5aCWRwmNxscHiksvcBZd3faTOzHSeS5cb+GED18kT+Ipuuc5a67bGbuYoiiK0EJxSgrul4EdSEhEyv5BBWj6jw3G73AiB1BqRMOUnMwfS5v9kapipPpnL5RgeHm5HqnutdtLJ+IKkdprcJyyn05lzSf4/aTUwnxnhmEQ3oZXUPs3Ye7no0mSh1+uNIrlP49YB2g10ViIki4uLq44xSqK3VWrZDZPMCOhGYnuNzVg7rly50rYgaK0ZGhriySef5O67726XNjfE9PTp0/yH//Af2veAOb4pa2xcKYYIXBNDRGcGitYaRMTpMzm+/nWfXRMubxU9jpRtXNth+uo0O/fuwbKWLUnJyoae55HNZrl8+TIzszPYGZuCzPKQ63A8Crn3gXdz1913cTtlEGwWMRmQgVuAFtAIG3ztK18Cz0OGFoomWgcQ2ES6QYhm1y6Lj/2s4C+9P8uRXQrXagAKlEBrB21ZIHIg8wjbjn3O2oOoCv4MBB5h6BE0Q4KKoF6VlI9nOXnOxzpbR1UCkJqhtxUYfYcLFxvk7xHUztsU9lvY7x7hh8czTL8+jWPZTE5sY2JyEktavOPRx/g/s/8Zz/N4z3ves5xG13IJSC0ZVQEH/IgR5RMIm0tYnMkJBBIlJK5l8au/+qs8+OCD2LbNsWPH+MxnPsPMzAyPP/44jz32GBcuXOioUhYHH8bkR9gZcLIIIiwdoJpB3DtdgNARllDtvOWgVXrVb/iEymHvvibFYkhtxuK1NzRKSEQoCS3FTgnPIYmkwq81W0VllqPhk4tYx+Iq4jTJ3XuH+cl3r1JdCglCr1VqNs4HD4NWm+VwlPyk5KF3SM7+MQSRj3Ryy/dISpia/5vFF2IygIRzKsP7dQBKI6Ms1YYmihoUiy5ai7jEa2q9MwI4GbiVdIMktc8gCGg0GteYnjOZDJOTk+zYsaPtzqjX6x3Bh8ZykHYDJc8rqeknycFKGnC398l5Su7bvE/7ps1x00GhvdwdydeVYiLSuNVFOk3WzP/TJMacU/L+7Iy/6SzAlSaDBmZ7454yVo2lpSU+85nPMDExwdGjR9sC3vzt3r27bT0yloR0tkCyDkI3dFgIhMWSJzl1SnLFCjhfdSiGAQ8MDfGdKxeJpq+wd9cesq1jJGNMpqenOXnyJLVajcAPmJqcoqA8duaG+Gk2x69/5MMMF/KtwmY31rXxZsjb7YwtTwY29YJqOH3iJMdefx1fhQjlowjRKoO0A+49qPmlTxT54LsUU8MKS1cQWGjtIsig3Sw4eSSZeJVXNYgq6GgJ/BrajwibIc26ol6DZkUQBhrqGc69ZOO7Cr3ok9mZw5v1uPhKg/37MyxeCRFNSWGHwt3hcq4+xo/PzMcCRUpy2Ryy5XvetXMn+/fv5+rVqzz88MPUarUWOwcVWeyNanyk3mRnEOJoCIRHSbp8R1qcH46wEeRyOT7xiU9QrVZxHIcPf/jD7N+/n3/yT/4JQ0NxGldycWsvXq2iC7m8RWG4iGPZOEIxfaEWB1ZGIUorpBUiLAetIYzi6G0/9FBByN5tNlGoEFaWCzNRHK/hOhRQjCiH0LKIorDDFJw0YZrP4s9Vy5tjMTqW4eL5MuVSgyjUrb7uDpYFlq3i4lFhRLVa4c0Xhjj8V5YYGrGYno6wRISQVtdbxghh27bbmqdSCmFZ1JQg41q4xMcKw4gwisjm4zRMpUDa3bXqpCBJCo5e/mSznalZkNyXiTEYHx9nx44dbT+1qWlgOjlOT0+3I9d7WSfSpmPzms6T7/p4pdweveaz1+dp8pDMKkjXQ0ieWy/XS6/9p4lRekzdTO7Jz4xwNdq3id8w7hsTDJg8jjmH9PwlSVfymOkxmoDSRqPBzp07O/z+lmVRKBTi8tmtoGFDCMzY0s9Qr/ON38cVHi/MCYR2iVyH09ES73XznBwf5czlOZrlRXbs3MvoyGj72ZiZmWF6erp9rJGREYhCPjY8zE813PfAw7z3ve/tetzrEYIBEbgWW54MbDRMFqtoGZ1/8qOf4GayZCbGCWRAJnB44FCVX/5YliceVAxlAqSScfCbHEVlMggnD+QQhKCrEM1CWIGgBkET7Yf4dYFXhmpF0/AEYSBQoURJhXcVwpJLZDexHIGcyBBWFKKpYKmBkxEE0xGOI9CqwFPnAqo1D9nKbzYNf44fP84DDzzAI488wrlz55icnOSZZ57hgQceQGlFTtX5YDPgoB/wVsbmpONwINDc5Xm8t+HylURhky9/+ct87nOfY3x8nH/8j/8xd911F4888khH4ZNryECs+lNvlKjUymhfoYImo0N5pGWhoihuaqTiPgihCvE82bIONFFByK5tsSPBCwTTC3HWgBNB1opQUuIJfc0Cb+bgWu1suTxsYcjhzMkltLLQOi72JGTE9j0O23aMUFvSXDhbxvcVS4s15q7YjE76XL4UmzZj0+u1C2WyOMxy/3ZACBpKYQnZqlZvIRFoJbDsCKU0tmjVWzD3ou7sRLj8GfHcEhfDEa1YFPNPENeLSAuvtHk5DnJcNgMbEuM4Drt372b//v2cPHmSq1evdvjAu5nwu/0/LRhvREvvts9enyczLsz5Jq0jSTO4sTKYKopGEPfKfFiNyT753nTdNH9G2zYuFeO2SbagNnMOy5agdNxBrxiC5PVLW42UUm0LgDH9mz/btikUCh0N0LoS+tT5dbNOaA1C21gq/n9OC7zMJGfrVX62WOSLOyzmrlzhzTffxBJxhhVao1S8Zri2RSFfwAE+OjzKRQH2jl184uefZGJi4horyUroRpLX6p7b6hiQgZuAEhJLaa7Mz/CTn3yfzPgEI8UsDx9q8uSjl3nkgEPebYJw0GIEbWcRdhGcQsvM24BoHh0uQVBFeDUIfMIQmlVBvQzNqsJvQhQIVOvhjVCEvsuVKxayKJi4x2biiINXarDnEZtyU/FyI89CPsuhe2vIQHKiMcyPTpfRQsVlfhOa0QsvvMB9993HO97xjlb7Vc0zzzzDQw89hIpCJqKA/X7AnGXzlaFhqsLl1XyN/3o+ZDwKGBIaS8TpSl/84hdZXFxkcXGRN954gx07djA1NdXhGjACuK0RqrjuwtJciabnIYXEtiR79+7Gs8o0agIZSYQOiWJTBQQ6TjcMQYuQ4qhDGAbUfYtyI0AKC6njKmfIJgHZrmQgaYo1nwGgJZYFjktshWmXgrKY2ik5es84D93zCfbuOcjnP/9Fnn32RRp1KC9FZDIOGo8wEjjK7koGDBFJ+95FJGhIn5atBaUDHC2Q2sFCY2kHgY9WEiy7q/YffybRWsTNb1A40kVHuhW/4KKJWvfC8kKe7tKY1PRhub6AsSIIEefqF4tFjh49ytTUFAsLC1QqlY6gvOU0VNVhCTD3Q9qa0XEdks/bGqcgJoWnsTiYdDszhmQ2hOlaaSwpxoJgXA3dfOXm/kqa1C3LaqcsLrf3jo+bNL/ncrn2HCaPlQ46TJOAbi6T5LjTQsyQdJN2maxgaBoaFYvFa0iFOZaZyyTB6DHjaB2izW9ROMLiQkYypDJ8Yhj+QuylPHeVJa+BDuM0WsuxkK6L7eTI5eDjIwUcYRM9bPOxB97Lu973nmssIStp/CtZbG4UtyMhGJCBG4QAnDBirlzlj/79v+fyj19gckeVv/rOPE++22PYjauSIYbBzkN2BCGKcRCgqEJYBW8RHc5D4KF9TVjX1KpQqUKzrol8UCFxFoKOo/g18aulFbv3KerjVYrbfHBDhpUgo2B6aYj//TMuS1aIK7KM5rPYBU1Q81GuxpJ2e5EDOHHiBEtLS9x9990cPnyYubk5Tp06BfFhKUSarNYsCUlDCgIhmPRky5fvoLREC00mk+Whhx7iBz/4Abt37+aBBx4giiLOnDnD29/+doB2aV5IxAyI5YjnSivXO5/PMzE1SUnG3wWNOkorojBEtxZfP4jw/XhyslndvjDNZhjrvVKQQcaNlVrm7dU8vEKIuHCJjHBzpoBJDCktJreBCjLs2X2YTMblyJEjPPfci60WzRZeU/cUdubVLLjJLIN4+LHWZP4AXBekVES+A6hl1wrLFQCT5xUv1suBnkLYRFGrxr4CpEa3KkYKrvVhJxf0bu6GTtIREwKlFJOTk4yPj7dT1ZrNJo1Go63hmp4SJvDTCMKkWT4t4Hr5ytNzux4wWnqy2U+65K4pDb1cfKhTYCbTLJNm/UKh0O7kmCwdbKwX6diG5cqGy2Mz1ycp+NPxL0l3WLpVcfp6J6+xuUZSShqNBp7nMTw83L7XklUneyE9juRYEZpIZrDxkY7kdQUPCMWvWFm+7+7kfLNGsx5CABaKrCs5ks/wgXyBc5bLidEs/9UHh3n7I58kn890pGAmX9P/7zZ/3cjvnew+GJCB60EvvwgR0Wz6/OjHP+KzX/g8+as/4n/4xRr1AyMsNsY4e6nGA0cEtnTQ7gTClaB9UPMQliBYBK+K9n20r2g0oFqBWsXCq2siX8U+byVY7oSq22MQWoCKcLINRgtgSchEDlKGSGlxYTqkSgFXuUSWjZ3Nc376KpbVxGU5H9iYHEulEidPnuTxxx/Htm1++MMf0mw248VFSOatLEvSZkcU8IlKg6vUuc8LmVARxzI5yo6NQ/yg/92/+3f5lV/5FcbGxigWi/zgBz/g2LFjHelY6Qc1Fr4CS1o4jotSEYHv4zoOhZGhOCDIlni+hHoTtE8UBbEJUeu26V5FukWc4gg7LQSO1kQQp+2lNIdeZl0zHojIFyyk1GhiM6nrWsxOB+Tzi7xx7AX27TvMc889FxM0W5PN2JQWm6hIYztW1+MkK7ZdK9BamqqGSMc16nMFG2nbNBqm+JEFIiaJliXRCrQkbp/bsrRYtmYkYzE0ElAsSsayNq7jYzkeCkHgO1RrNvM1wWJFUW9INE5cRVIvO8EM0kGD5v9mTmu1GgDDw8OMjo5y7733ks/n2xkKJgCx0Wi0/5rNZru3gimva8zxRgs2C3S6sqO5Tt2E0XoRBCOcjcBOavz5fJ5CoUA+n2/PVbqZkyE/Sql2uWgjWJN+/3TApTnXZMxDkljAMhFIZo6YMRiyBctxCUCHlcEoBmkSa85hcXGRer1OsVgkm812VBlMCuJ07EDSimDWgPj5Akc5bBsL2TFZ4d3vszh1wuHU9wR/KT9ELesyMxxQDxVDSrAz4yAci+ciqE+M8eH35bj/8KfZceQgIoJILFu1elmY0laM5Pl2+/9mEoLNtDgMyMAKSCyJ+EGZF14/zbf+6I8Yeel7/OW3L/LI39aM2ZrvnrR54qEa2woZrNwo2nIR0oeojIiWwF8Ar4L2G4RNaNYUtbKgWrbwPEUUCBpBhlLVQToQqgDFEGQniNwplD2JY+cQtkNoyVbPdIU0C7gOUKFmYXuej33cRQifphfw4vPPgdcEy0ZaVqus8PKiEIYhL7zwAo8//jhCCJ577rn2QxUpxYKt+EExy/tqggcbHg+gCSWcc12+m8vQsCxqjQrlRpW5uTn27NlDrVbj6aef5rOf/SxhGPLKK69w+fJl5ubm+MlPfsL58+c5ceJE/MBJCwsBKiSIfGwh41KtUcsvKgHHxtF5AlsThBopA6QK0VYrniASWILYbaAySCXQNrGJXXRqwHCtFtWhoWvQKLSCbE6RyVk0awLLDlFKU1tyOPNWncsXv0p1yadZF4jIYnybQGnJ3Ezc0Ml1sj3jBUzRn7QPW6GwkXjCiRc422d4zAHtUl4K487Qlo2UGomNFAFoELhMFEOOHtQ8uN/l6E6PXRMNhvMC1xHYVg3HIi6ChSTSIX4k8OuaajnLuYWQ587m+OkrMFe2QUcoYbUFWzJ4a//+/Rw/frxtaRFC8MADD5DL5Xj99dfb2QqmD0E2m2VycrLti0/6xQ0xqNfr1Ot1arVau/aBIQxJS4IRXuniR+1LlxCi3czBK1lBrnnuk+QwpT2aY5txmb4T5vy67SN57dMZF+nWzknhn6zmmEzlS1ockvs0+zPzZo6RjIswVg1TDMpsY87HXAMz/8ZasLi4iG3b5HK5jkJD5vxMumw390HHeQkbITxKdYv/6mOCoLnI8KjD+/+my+e/IHAXJBM6RzYPNaV5TkZMHvZ5z8NjNMp5Hnrifey6+/3oMCKUIGUnSe02hl5IX/+VXFV3AgZkoBcERAKiKOTkqVN89c//lIUffY9t+6u896/WuOcul5xTI3JcPvKog8iMI9080ECLqxBUoVlCB0so3yNqaLwy1JegURc0mxYVz+ZqJcuZuSEuLNn4KmL7uMNIMYfI7qSmduPrCbL2MNlMEct2EVbczEcpzcjwMJlMhtn5eap+A3dPyP27QAjJyy+/zNz8fFv4p1OCDKt/7bXXWFxcpFqt8vrrr+M4Dq+++iqXL18mjGx+XMwxK2wO+3WUsJixXU7ZNg2ZQ1Uq/IP/9h+iAkWz2SSfz8dlRVuaD8BnP/tZIH7QLl26tDy9QmDJAC1ssHJkcyE5O2RiXKCt2Iyq9HInPGn8rlpg67jgqEbSqLcWRqlx3BCEjVARNcvGRUO0bBVIm27NuNrvNSAsBLAwG7H3iOTESwIpY3U8iiS1skttCbR2UCpAyJADRx0unQ9o1DpjMtICwbZtarUai4uLHaVj4+81I0AdRaBsCuTZu0NTb4Zcmffxooi8dEGDbylc7XDXHsXPPxby4BGXvZMV5LYyTlYgXU1GRvg6g00AyieSksi28WqaRiDI7XPYEy5iLQ7z6NEqP/duyR8/neFbL2ZBd3YslFIyNjbG3/t7f49vfetbfPGLXySKIp588kk++tGP8id/8ifUarW2Sdo0YjJm8aGhIfL5fFu7LBQKjI2NdWjFRns2jXmq1WrbqmBSG5NWhGRgXzeSkHZrdPMrJ7+/5vFP3CNJ8mDux+Q9lU6x62VuNr779DZJYW3uHdu226V+jfA27qXk8U3GQa1Wo1wut0lVMujRlPI1fQqGh4cpFosdQt0EjZqU0lKpxMLCQpzS1yJjJrgxSSySlSWTpYrNOZnzM3NsOTZCKpoCfvcPsnzsZyX33dPk2z/I8/f/bpNnnoGZxSLjeY892y0uz8InPmrzzPO7GTvwMxw++LNEUQZ8H21LLKt7kOStoBcpWOl+uR2w5cnAmpt1Yn8AKlK8cfpNnnrq67z4wjM8Ls/w33xwga9Wd/DK8X3sOzpNztJYmd1IOw/CQahZiOYQwQLar4AXEtYFjbKgWtY0qhZz1Txn54c5OecyX7WJtCY/pBmddMllHKQ9iifHaIocvsohtY2vI4gCHCGxlEQrxcjoGB/56McZHhnBazSoLCzyyhvHefXYmzRqizz33HMopa7RKsIw5Hd/93dpNpvMzc2hlOLv//2/314IlFL803/6T2MhoBWOlpzK5TiTzeAogS8lmhAtPEQkKM2V2s2XyuVyV/ObWYyTftFMJkM2kwFtMVEIeee+iEfvdZlrjHBVQT6fjxurBCE6aC2ggKVpE4JAS8qluH2tlJAvSEQptgiUscigkJHq0IDSZKDj3hEaIULA4dxbIY99wOHSmSZCDdNo+CjttfoqCJSO0Fpx4EgWJxPxxovxGE3wV9JsahYqx3EYHh6m2WyytLTUPr5lWRBFjDuCkooIbQfHgoO7YWYmZLYkCMIAbTVB2mRFhl9+f8QnPxCxPe8x9wcVqtsi5KezfPkZcLTmwXs0z75q89gDElHPUxM+Zxby7MhFZKXP9ILL4Z1ZXrza5G881uBuP8ung3F+fLxMreq2BYS5djMzM1y8eJG3ve1t5HI5fN/n/vvv54033uDZZ59tV62LoqjVfS/WFJMNmoaGhhgaGmqnreVyuTZRNe+Hh4fZvn07QJsgJIPtjDWhUqm035tufkZoJU3kSYtC0sKQrHmQ1CiTZGKl2ASzffJ+6mWCTlsljPAywt+4jkxPhGw2256P5LNrLBGVSoWlpSUqlUqbLBlrTXo9NM98uVymUqkwPT1NLpdjdHSUXbt2sWvXLsbGxsjn8+1+DcZyU6lUmJ+fb/9VKpV2R0xjPUjOQ9qSkUxXNKRGKUUumyePJvSKfP3rGb5jefzSL5QZydT44OMZnn9Rc+BwwGgGpoZ2ceZsEZV/mKNH76FRrwCKbDaPEBmE6HzWViJlq5UTK8mU25UIwG1ABtYSQghqoWL2+Ou89IU/49mXnkVP5fj0u0/y4bsWcHMWH5ivsW20zujQMNIZQVkWWi8hgwoquopulhGeh+9p6tWI+rykWsnz2hXBX7wkqVp7GCs6TI7b7N09TC5XxLaHqDU133/lOK+++RYjhSk+9OSH2TG1CyHjkrxCSDQQtUqffuhnP0QUhRx74zV27d7N9r27+JkdE1y8dIrnfvoas7OzHQ+oEUhRFPHcc88Byzd2Uogn2bXWAiXjuAUF+FIBqmV6j/v2GSKQ1saSmpiJQjeLMdCqVmghLBjLBdw3pZi5GPLUyzMcePsR9mwvxNpNFBHqVuQzcZVfQwqE1ExfsWnWLTJCM1ZwONdKp6vYEdlQ4qgISfwbpZc763V/qFtBjTrCa0bMXbT46Cdtzh8fBUao1zSzs/NUSiUsW3P0AZuhMc1Lz/pU67SDwTKZTMdejXbtui6ZTKYdeGfmWwiBEoKjwuac0EjHws4ojhxucu5sjnqtQuAFeLaFYw/xtrua/JV3K7bdXaHyoyz6hRBvTFH+qGZxMcvYlM23n/WpKZeXT1Z5z71w/niemYrgUx9VOL7PZ6oWjTCiVrex85KwrDhX1vjN5eqIyetZr9f5nd/5Hd7//vezfft2Go0GTz31FC+//HLbEmT84lrrtlZr8uVrtRqe51Gv1ykUCm2Bl2wQ1E27zOVyFAqF9jwmU/4MQTBuhmq12mHiNqZvQ0CNGdx8lgxe7LgWqwhQTP7ummDQlMk6Wc8iKfgNETBxB9lstsOFZwS9sZSYzpMGyXEmA/vSpMOMIZPJMDo6yrZt29i9ezc7d+5kcnKSkZGRNvFIui+MJcCMYWlpiYWFhfafafJkrDYmM8Lc78nYCkMQMhmXXC7PyPAwhYIEKyJycnz1G3nGt+2i4NTZs93m4tlhnpsboSZGiIIhtu8MWCwtURgqUpDLjbCShCNNym4Ud6p7wGBABlrQWnP27Bm++9n/TOHr32G85nP4QMiHPlVn/2gFS9hEdobD+ySWtQ1LZtGyjIwWEP4SNBdRvkI3Bd6iTb0ccLU6zPOn8nzuuRrPngipBpJM/hxjrsM9O4vs2m4xNDFMOGTznWde4uLJSwghGd+eYXxikkKxcE1Useu6fPjDH6FQKPKlL32JmZkZhoaG+PVf/3WCIGB2doGXXnqpHRzU7SFJC/5eATSq5XNfxvI7ybVmM/NqtC8TSW4WY6BDKAoZpxA1lc23TyiuLGnmaopd9QbDw8PxgtT0CJF4nrdsVlYKiQWO5NKsQFqCrAUHdsJLJ+MAOBkJalowrH1mbRvLluggareLNeNNn79oEQKAi6ctDt89yi/8xiILMw0unfcpzYdYlmJs0mHmkuK5n3hUFi1c18bN2FjSvkY4SCnb2rJSqm0VgOXa/VIqDosMn7Ea5KyIiSGHfQfrfPenowThElIIIgU2CktqfEsQRDaNYwGyIRAPSJoNxc6dmtqCx7serGMpePOCwzMnIw6Pavbt0jz9rM/eqQxvG81y39EKY0GWpdcKfPtYgd/7+hJN311ukJXAkSNHGBsb4/z587zxxhsUCgWklBw6dIgXXnihQ1AbrTqbzSKEYGRkpCN4NU1Qjenc5NsbYZK8r5JCLW1NMG2Ii8UilUqlXTkxubinA+zMfpPWguQzkDYVm+ciacI3Vo58Pt8mNkKIjuqNScuFCaI1Zv3ks5b0ryfHkI5tSZYAXinLwHVdhoaG2LZtGwcOHODQoUPs37+fXbt2MTo62uEmMEinMRprirFIGDdOkqSUy2VKpRKLi4vMz89TKpVYWlpqu3iSbZNNsKkZXy6XY2RkpOVGyrFQEUjL4thZyLhjjA5PsX17kV0Hd3Pvvfdx6NAhRkZG2h0Yk2tb+/lNCfQbFfCbZRHoB4vDnUkGzLzHKi7lSoWvfvWr/MVTX+XA1au4eYtXduxhdKjB3itXEXI3bg60P4n28yjpgWog8XCaHnYQYIUavxIxs2BzfibLSyeyfP81zUsXlqgrSSbrMDacY/eOvVSWqpy/Mo2svo6zeJI3Z0Nmpxv8lcd24mif+uQwJ06cQGvdvvGz2SzFYpG/9PGPs3fPXk6cOE7gB+TzeZ544glc1+W73/0uP/rRj1hcXLxGyzJ/vYJmVvpspW3SvtkkAUgXa8lmsx210IWI+zg0dJ6RiYM8ePcku3fvYdeuXVSqCzTqDVzHQSiN7cQCBCFQaNACLSWX5iwsx0Z5IQd3K4SWIDW+cHmTiLsDyUVpIRwLEcSLXVKL6l3ZTtKsw/PfF4SN/Rx64CL3PwDzcwGnToQ8/0OYvqAJQpdCwcaSLlGkcZzlxjxmMTdd3qSU7UUy3gZs20Jp2I4FFng6g53JcPSQJp/zef1ELFBd10UrhYp8Xjzm8Kffz/DzNZud79Tot9kU7y0xMRLydlnF15CTEchFHjnk4GoJooGIFG+bchE1TaPWYOanOU695fA7x+CN49AQAqljF0jnXAiOHz/etiylr3t6ATWtd802lmVx+PBhhoeHO1rddmt7mxT66XsvSTbNnzFpG8tAqVRq+82NADb3orFOJa0G6RLJ5jkxRYhMrIMRWobUmf2ang5zc3Nty4SxkFx7T3XGHqQ1+OQcpKPxDZLPc7IyYKFQYHJykr1793Lo0CGOHDnCvn37mJycZHh4uN3sJ+0mS1/LbrUhjIvPZHuY+UymiybdOCbuwxCzUqlEpVJpWzcMQUpazAqFIhPju9i9azv79+9i3/497Ny5i4nJKYrFYsfY0/fFSljpXLt9vxL6QWCvJ+5MMtCCiiJeefUV/tNn/hPnzp0DIXhpahwLRc7OkC9u55WlD3HFHkZFGt+Pa9QHXpNqeYmLFxzOnWlQKtWJwjxB4NFoarwmBI6Nm8kxsTPLzqwNVpxnn3Fq5CcUTO5hAZfLvuLC/Bnqvubbb1XIZmHEn2XYdzsa/3iehyUkO8cnqcwvMjI6yqMPPkQmk2VyapKvfOUrPPXUU7z66qsAXU1ocG3wlEF6cU9qLb22TfpVkyQg3YI1ftgLbVNw4gogpGDvgX28973vY3JyitHRUXK5PM899wxQRloO2gqQjo1wbFTGIvAEYdDEVeNcmve4MivYXlDcs88lZ9v4KkSrgDeVzQek5FtWnO7p47c1H+PTNudzzeKtNVpq5uYrPPtjl8sXDmK7AZX6LPV6Ga8JmbzA8l2CIE7/jHPPOwVk0ioAsLCw0D6WZVtYQCQE90mXM1qhHbAdhyceC7lyNc+Fi1FH+phWPs1Q8rlv1/n+K4J3HhHcezDiYK3I2LAmmxPYmYC6pdFIPGVB4OI16yxVbK6UHE7OwLHLglNXNLWyJBQSQYBQVssh1HmtgWuEZrdtzP/NPZusknfmzBkefPBBJiYm2ufSzVpljpMsWmTuKSN0DAFIpi+anHgj8JPbGqGftAAYgm001Fwu1/bXu67btlh4nketVuPq1aucPXu2o9NjLx992jVgkM5iSdYkSFqTklpvehtDTqampti3bx8HDhxg//797Ny5k6mpqXa74qTW38v6sFrBmrz2yTgS850hN8k5Nu/NPKVrJhgyuOw+yOC6cYqxJSyk5SBEK7tHr21s2J3uDuiFO4oMtH3hAmr1Gl/84hd56qmn8IIGMu4fTM52mZqc4uDBg0yNx6UuDZNtNpucPXuWY8eOcfXqVSqVMn7gtVhzgLRcDu3fz1Aug6SJRUhGQsZWOHa8+OSyinzWwrKyaGuIpihiSclrb5xg6tA9uK7LwsICwfw8QHuRC4IAKQQnTp1kem6Wq1evcurUKaQQ7Ni5k0q9xltvvUW9Xu/wpyXPHbiGJZvXNGNOb5N8b8yrhgSYyO5k21yjXeXzeXK53DWm8xgCkOzff5APf/gjHdaMK1cuMzc3j+O6BKGH7Ti42QyW46BaJnOhHZqNBqcuZNj5YIMjhxwmx0KmFyS+Dce1z294GcaHAzy7QEPEAZKe57WLxiQXyGQ1udhVEIGQlCuLVN5cIp8vksuOoNQwfqNOEPqoKMKyzGLb/mF738ZHbKwC5XK5/b3lZIkiQUE2edgd5ynlETmTTOQ0jz9e5YXnDjAzdxXPa7YXRABZiAnW7ILiy88Ivvq8jW3Z5LIRhawm7whcWxJFmiCS1ANFzbcJPYtQRXErZKXjuhUCiAKUjmsMdMaMLHdHNOgWlZ8WKuY1XWzohRde4ODBgx39Dsx9kzRHJwW5ee7MZ+lYgGR9AiN4ktaLpFnflP9N5veb/SwsLLRN2kkym35ukq/d/tJuubSGnyYChjgkCYFxgQwNDTExMcHOnTvZu3cv+/btY/fu3Wzfvr1FmjvT/LrFLqwF0udu5qXbdU/+P3nvJJ+zbvN27Xh1/EDpONPmVs3/N/u79D29HugXi8OdQwZa86215vyFC/y73/+PHDt2DK0VsiW4pqamOLI/Lq8KtIORPM9jcXGREydOcPz4cebn59vaho5CVBSiwhAlBHPT09zzxMPcd2QXWSsg8suosI4KW01psImUREWSQICvPI4c3IFlW+0o9D179rT9ekm2/eEPf5gnn3wShMB1HMrlMvV6nXK5zB/+4R/ywx/+sC2EuzUQMeff7c/APLTpIKpkVTSzyCfjGdq+Xssi4zoMFQpkXBdbWihpxaZ9FccISK3RIg6agzgCf8eOHe3jCyE4evQor732GvlcniBoksvlaDabZLNZ6raNLwRKN7CUy/MvS37m7YKhQo177sox/VNwI01FCt7UEe9oaL7u2Diuhe/FkdCm7np6ceimgQghEGjq9Qr1ehkhYjeCkHFqk05sl5xDI4SM4Jubm2sLKsuysNBoGXGvsCgRMZNxyDgOD7+tyfZRwXMvF7g6U8Lz/I7xeJ6HZdlIkUWIECFDGlGAX5PUagLlxNUXQSBUKw5C20ihQOUQISBBUY9rK+jleyVZX6Db4p6eq/RrOt9ea93Wpo35+Pz58+Tz+Y4qhUlSkCQHyQZCK2UCGNJlWjNXq9UO4up5XjtQNjm+XgI0HV/QTYClNfik4E8WCurm70+WOh4eHmZiYoLt27ezc+dO9uzZ06HpG/9+2k/eXYiuPdKplL3QS6FYCasZ+1qd342ObSOIQD/hjiEDGo2KFM+8+Dz/x3/6PWbn5uJqWI7D1PAoBw8eZNfOnYBo5zX7vs/8/DwnT57k1KlTlEqleFFSUas8sEApjdICIW2wBbOVOi/+4Md4lXuZOniQ/QfvZ6JYQPs+9Vqdhtcg9D0CL6ARRoRAoaA4fLBA01/2sxoNSQhBsVjk05/+NPfccw9/8Ad/wIULFzh06BC/9mu/BsBv/85v88Mf/BDf9xP++GsLb6RN++256UIMjJA35tp0udT0g29ZFk4mQy5XYDgjeFtDs2+pii2y/LDoolCMh5oxpRjzQ54ftpi28pBonpTc3969exkZGcH3fSpVF8cRuE6TjBtgZ3Loho+vAyyZ46evhjQbDvmsw4ceg+88H0JooYCnCfkN7fBjy6GSyeN7ZaIoolarMTIycs1in7RuGMT/ly2dX7f+JOi42kG3tcpodyYobmFhgWq12vG9HYVEDrxPFPkGESLrUshJPv4xzYmzw7x6vI7v1a9ZjBqNBrZjI7KxoBdKILREC0kkJMLTxA4ITSRamhmgtADRRIsQoW2WrRjdLURJba6X1Sg9T0mrQvLzKIrawn9xcbFtlje1F4zJP9mhLy30e1myjIAy96vR8LsJS0NW0n765D2Q1uiTBYPMd2kikBT0Jv/e9CEYHh5mZGSE8fFxJicnmZqaYmpqisnJScbGxtrR/MmOhenaBUlstJl7tcdbj3GtxT5vhqQkf3unYN3IwFr6eFZzLOh+45jvlpaW+PwXv8DXn34az/fIuHGazd49e9izc1drUaq3U2RKpRKnT5/m/PnzWJbF+Pg4hUIh1oy9gKDVz94PY98saFTUoBEIys0mz736FvbJy4xMvMXExATbtpkFYDsj23JIIQj9AK+lLdWbHpVag9OnT3Pu3DlyuRz79u3jgQce4Dd+4zfYu3cvzzzzDIuLi7iuy549ewiCgKeffpqzZ85y6dKldt17kzqX9pMas12ynnra/5/0/Zm/bsLf7M9ov7lcDisL+SiD5wjmogqP1CPGowrDEYyEGlspmpbk+8Uhpt0sGR0QCrcjCt3sN5vNcuTIEarVKq4TF9vJZLK4rkcmk8V26vHYtObKjMWLpx3efqTB2+7Nsn9ccHE2hxJVTmAxqy3eEQRUCsM0Gw2CIKBer7d9xGl/bdofnNR24zmDmARc60IxWl+y6l6tVmN+fr69j3bNBySPaFgSAZdknoybx8kojp/KU6+Ncu7cNFKCkHaHoBJCEAYhGTeDZbe0ehGPSROBWC4WJBKuj2UTv2zHBsT7617vP20V6GUd6OV66vY8mvvP9/22y8Rsl4zjgGsDEbvBEI20+6KbvzwtYNOV/LqZ7ZPvjZ/bCPliscjw8DDj4+OMjY0xPj7OxMQEExMT7eI+puBS0kWRDuq9GRP4RhOC1WKthPdaoN8Feb+Nb13IwEYSgRXH0Xo9c+YM/9v//q84duotstJmbHiUffv3sWPHjnZxm0qlQhAEVKtVLly4wJtvvkmz2WT37t0opTh79ixXrlxhZGSEQ3sOMLp9nHpzAa+1YMWLaSsVUGeIwohaI2B29hIzs9OcPx9XX3Mdi2wuQ6FQZKgwxPap7YyMjOC6gh//l28xOzuLELF14uDBg1iWxTe+8Q327NnL/v37+PSnP02tVuPMmTP8s3/2zzh//jynT58mCIK2SVqI5WIvJm2r10LerchKN/dBUhianGUTqGRSqoTQ+LbNcOjz/prioB9SkbAtgFBLtM4g0Dza8Dngu3x/wqHcykPv1lXt6NGjHD9+nKGhIRYWFtopiUaLqlarcUwHNl/9vss77vbYMebzwXcU+MzXNFpbKOHy5/j8N5HLy66kPjTEUqlEpDXlcrkdW7G0tIRSqh3gZgiKQZIw9DLPpqvICSFoNBrtFr/mN0bo5XTEu60Cn5MKJwOOncUSQ3zz22NM7pjkwbdNct8D9wPhNcc21yeKonYaW1qbTvu9e7mD0vdC8n7pZhZObtsruMy8diOcQAcpTY6tW3nerm6bHp+lzejJa5UUwknN3vzfmO57afMTExOMj48zOjrKyMgIxWKxw2+fzI7oVXsgfQ17nU+363Wj6Ic12OBWtPObPV7y9WZ+u57oNyIAt72bQHPxymX+x//5f+L06dMAuIUCExPjjI+NEYURS6UlNJpGo8Hc3BxvvfUWZ86coVqt4nkeV69ebdflvv/++/nAB36GbKZAdWkar17A8hcRXh3VqFEJfeo+hMohIkcQauq+xgsjAl/RrJWpNz2aQqGEhRtny5PNZJhdWqRab7Br505yboYD+/ez/8ABLGmzsLDI3PwCL774ImEYdBQgaTQazMzMdFQaBNqCyFQhNMKol5af1ISTSC6yplhKMjrekhKpBZEU2CpiOAq4v1nlUL2KUhpb2Git0FIzn1VULYdTWZvLrmbJik3vSX+wGYtSirGxsbYFxPiAjSskm822zcFuVvCTFy2uLLrsn1A89mjEV76VoaZtogacsOB41ORDVc1TY5No36fUqtpWKpUYHx9vFwpKnm+7rkFqvq5HAsw5NJtNrly50i4UI4Qg5zooW+AqzQftPMetiKZVoJBzyDg2mZwgO5QnXxjBsuKug9AZhGXmx/yNjo52XMNupv5eZv606d38H+iwDCX99slc9HTVv3RKWnKsSQtDcp6SxzbuonR2R1KAJs373UhAt0h9I6wzmUy7RPLo6GjLaretbbqfmJhgdHSU4eHhDleGMd/3qmB5rVvp2rGn0YtU3uh+VsJ6E4KNFvI3glsZV7+e03ritiYDWsNXn3qKN44dw/d9XGmRz2Sp1evMLyyQzWaBuPPa2bNnOXnyJAsLCx0Vtcwi5roui4uLvPDC8xw5cpB9+/YwVDiE8hs0KrN41auMN2bQzSrKa+BHAUuRoBlkiHyXIIgII43y8wg/wG7WydWrWAFcrVQoVxpEWIggxMkXadQaXL54jkz2KqHSaA2O62LbccEcx3EYGxvj7NmzcYnPVmlXs1AaDcf3fYrFYkdK2/L8XKuBJRfVZE10QzTSGk8kBU6ksUTEE5UG99U9LNHEEgIXC6kcfKFwdcBx2+WiU2TJ0lQtl0g4OEQdGmISQggefPBBZmdnKRQKbYuHsQ6YTAsdwlJZ8O//qMBoRjC9JLnvbo3lZvjuCzUileGPRJP/MVS8ESwRTe3Fnz5LrfX7ubk5xsfH2xkGBskgzG7m76S/ON2VsVqtMjs729E+1nEchA2OgiPY7LAkn9MWOTeLykgyrkMuV+TggQO8/e1v7xCyhvgla/Sng+qSaV7psrzpWI9upNCcWzL9K63Jp3PPk2QgSRa6kYJe1qb0/9OfpYVv+i8t7I1mb8z227ZtY3Jysq3ZG/O9EfbJeztJpldrwk8SlF5I7utmBE0v68F6YisK01s97p1IAgxuKzKQ1myFgEcffZQvfOELXLp4kUIxLuBiArlMPe5ms4lt22zbtq1dLtb4M43fsl6vc/Zs7Jt/6cUXyBYKDE+Ms3/3Tg7s3cuu3fcyUXwHKvKoVhexSzPky1fYU15kX91jpB4w0ghwVYjtR0ilaNhZMh54WvC8o7msQhq1eS4HS5wpX+XkJUndcshLl1w+R3FohOLoBIVCAdu2mZmZ4dSpUx1tRQ2klIyOjrZN8OPj412176TZ1LwmF8akdhzPaXJBivsASBGyN9C8OWSz5NrUZZ7JIGJUhfhaIiPBu2oVLKGYzsPdtToTKuLlgmz7t5evWeeit337dvbt28fS0lL7OhnLQC6Xi4MaI8Bp8JNXMrzjoQJnL3q855EM1QXFWMamWg2YlTa/FwX8jSWH358I0bt2Mz19mXo9jiGYn59v+3d7BZmltVozz2EYcuXKlXbKnKnClq61IKVEYjGlfX7WyfN50YDCEFbexrFjq8vo6CiPPPIIQ0NDHWl1pliLEbjGUpMU7Mn4DhPsaYosJSP0u/nYzbnAciXA5Dmaeyb5rHVzK6XdA8m4lSSJSL6asXcz7ZrrYOJhTKMjY8afnJxkcnKybc43LbSTJY+7me97Xc8ken3eiyCI1L2c/D5tOegldDZCk++n7dcSa+UaWM9z6GeycVuRgWug4bFHHuV3f/t3+Oxn/5gXX3iR6elp5ufnEUK0SooWGRoqUBwa4uDoPsIwXC6zuVhidnaWWr1O1PCoRz4yComaIRWvTnlpnkunT/MTYZPJZpkcGWH7zm3s3LuHR7dP8gsjI2wfCcgPe6jyFearNV4II046AYu2T8WW1CqKoAEHFor81dkSQ7qC8CReFHI56/CMC9/IO8w7mlr1KguLMwR+hBcqgjBeRI0AS2seRrAna+V3MyEn/5/cR1obS+5bCAEaLKXJKYu6sFiw4wp2ZUtwKieRGrSQCCWpZyPuqwQoND8dGcZWcS2AjLq2Fnxai7zvvvu4ePFiWzAm3RWu6+J5TUQE9YrF4pxm23jEfEXw0zc9qsoiyoAdaV4UDnuiJr+yqPjMnp3oHdsozc6xWI1jK0qlEs1ms53KBXQQJjOeZD0CI1RHRkbaVddMJHvbwpLN4GhNlLEo+oqfd0b5nmxQtYsUMy6Oa5N3YnJz7733MjU11d6v0Xrz+XxHxzsDo8n3Kh9rGlCZNFkpZYdVITnf3QSRiZ9Ia+dmXMnPV8pzN3PWzXqRPL5pk2uq/o2OjrY1/LGxsXbKrYkbSabcdYvCT/+/lxtstab6Xuhl8u82ll5zfaPHvFn0s0C6VWxFa0a/YF3JQHdtcmMhgIMHDvAP/q//gDNnzvD8889z8uRJ5ubmWFpaolxe4sr0FbxzZ3Bdl2KxyOjoKLt372L37l3UajWWymUqvk/Oa6KiBmGtTqXaoOyHeF6TwAvRjRpzfpNyaYEzx09QCnwOI2kUMgxPTHB8zyFev+cgHyTDkAj5frPBsWNneeb4q0S1KkfGt3HX8AQf1AJfhtTtHI38EGE+w1DexbIcLGmDsvCUT7Pp89pb5ztqva/kg1xNMNJKfs+un4u4zXNdQdUCJWzmnNZXWseNjFBIofjx8BSv5+u8rRrwYgEqVgZXRXFWRaIVbXK85n2xWOS+++5rx26YQELjBgkCnyiEUAheebNCIDPYtkcgQsDCkjZKK3wt+JIOGFIRf/niVZ7at5NLIgfWNKWWJm9M8UnCkQw2S47PaN7mN8mAQ6VUu7IaQiFljmLU5NdEjldEk7NZl0wuh+U4OJZDNpdl165dvPOd7ySTyXRo3skgvW6WgGSZWONzT5rrk+mhRjtPkr+0v72XKT5pKem2XbrSZa/9pCP1Tb69idI31SqNVt+tDn2ayHYjriveu6nPb3SNupHtV+NqWE+st5DbbCG6VVwDmzFPNxLTsa6phdAfN75ZmI8ePcrhw4epVqvMzMwwOzvL7OwsV2cucfXqVebn59vduKanp3Ecp5V+uBeBxVC5xD2lOmOZMrURn1nP40JQ4XK9wbQXstho4qkmMpNlaed2vjkyyVxhhB26yZkrF7hy7BXO1RuM5fJ8y2/yykyJ7QT8jaEp7jt8CDk6zGeqFU7oJhERBaHYEQo+4gsqWc2VrEs5X6CCxcWTl1FKddQVuB4JuFGkhV/6MwCpNaEFQgqk6myuYrYPbBgKHDy3wFy2gUQitQIiIiXaXfxW0uLuuecepqen2+1ajbAzleqazQaBFaClhUWTUGVwoxyBFYAgbmYUhgQqwx9Ij/9LBB+9eImn9u1Gu7vJZLMsLi7SbDbRWrc167QQM0gK1vR8GRO+lBIpBEI6TEYevxIV+X6uySkrQyaTw7U7m9783M/9HIcPH+46B7209m7XajXm7fRvewnTa673KqLkb3Sfae08baXqdu5JYtJt216/uVOQfu7XSxD1CxEYuAZ6H3vTyUC/IO0j11qTy+XYu3cvO3fuJAh8PK9JpRIThMsXz3Px4gWmp6cpLZWo12pcPHcOP4LhiWHkrgme8LO8/8osY/4SlqXxC0OcGrI5HUHF97HKdTLnpimFl7iQzfPGtj2E9xziwIFDlKfGOPbyGzz/Z5/j0489xn3NCsWFMn926TILwmFs9wH252ze3Sjx0NUFRisltFdm2rU47Stey+Q4lSvwvfmZjihng9UI8CRu5rt2YJ0G0SrA1E6577IY25GgmQ2QWnMm4xJpjSM0UcuHbshAOoAvfcz3ve99lEqljsI0piStUopIBWjpoMihrIDAIi65CyAssBWR0Ni+zb8Xir8Z+PzS2Yt8bfdepnftJOfm8Rp1FuoLhI2IKFIoHQv7Xg2NknNlSQc7K5C2AOViS0Uugh1K8lFrmK9nKsyIcfLFCEtmcSzdLjv7gQ98gLe//e1d8+N7mbeT6FUlrpv23G3s1xOeq7EapfeV/k2aKK40hhuxUq1EIlc6p9VgKxOIzRbUG4WBa2BtcFuTgbSpNRnAZMyqpl93pVKl2WiiI4UlJEKDjiIyLdeBkA5BEPLWXIlTgcXXt+/lfe69vKM2w9DMJfZ4S0xJzZWszeLwGDoaZqQesKMJjdoC1R+dZ+Qbf0Etm+UNx8UWIO85wvA7H2EqN8pHdcT0pcucPHeOq2fPMz0/zzYJzbEJjo8d5rsjLhebIQRNlmamCZeWcPOFDm3UnDN0177MYnw9n2X6+26/E0Kg2/XDdZsESCGuMUMrpRBBnCCnpcCy4gA1u7WfZET3SigWizz55JN85Stf4eLFix31ALSOS9CiBUiFrVu3tlh+0dIhiyJyFVYIv+f4fDT0+dXL5/kvw1Oc37+NmcUamQWHcFhRbTao12srdqEzcRmO44Bl4YYRQjkIV+BENm+3Le6KfD4v6zS0y6MPhjz+zix//HkfJzdBsVjg3nvv5ZOf/GRH0Z1uWvX1BGQvjXulbXptt9Ln3fbTzRphfm/uyXTFv+vtczXf3cy+1mL7fsTtbO5e6zH0wzmsJ27m/DaEDFxP+KzHcVYiAsmWmyZYcGFhgZmZGeZnrrAwP08URYyNjbVT6zKOg+u4CCmoh02Wlmp8fXqBLyuLPYfuYVdGkGvOY9dL7Kg22FZvMmq5WFmFCLJEUYZd+QYyqHJvrczjlqT0Z1/kK1/9GpWREdSuSXbs2cO9B/az++GHGB7bTskPmJu5xLETJynNzUEoOewW+ebFY0SWQ7ZV7GcldBMgvX6TvoGSC7mB0ZCljDvPJQPMzG+S2q2Zb9OBETq7oAkh2Lt374pWgSQmJyf5yEc+wte+9rV2uWZzfCllu2d6r7mQUqIB31HoMMc3hOZUpPi10hXeVl7imR2jzNx1gMVSBTE7RybjtscdKUWUqE6YvMcAMkIi8hmKUcDRSPK2EI7R4PczkLWGcAqSv/arisX5Km52gmKhyP79+/jrf/2vk8/nO65BNzK2WtyKRWg1x+kVKJi8X7qRmJXS727kPr6ZMd+O6Lbg367ugCRuxTWwFr+/mWNtBm702OtOBjaSCADXBFslyYARTM1mE69Ro16rs1RapLSwwPz8HJXSEioIGB0ebpEAu51rn3EcHDcmBFnbAcehoRTlxRLnLpzn5XPnUWqIybEJTu4KkPUSE6VFDi1VeWDRR+mIhpUFlSOfrfFg06chbA7aDjoI8M+eRb15AstTeFpxKptjfucO/COHGLnnQR574j3kc1kuvvQiJ378dXLOcIcfu5sJNkmMkjDlirv5k5JFikwKm9Yax3EIw7D93hAB6Kz3ntxfMsrdRNibAj2ZTFwOeteuXfziL/5iVy2010O7e/dunnzySb761a9y+fJlxsbGOlIiq9XqtURGaAQKS7kgQ5zIIZQRoQMnA83/x6rxrrDMxy8GlGbmeHV8hOldu/BsFy+KqFbLNOp1fM8jDEKEkAjLAscCrZBhxHgADwYeu1Gci0I+pxVlZ4whV2JlJLsPCu4/usgfvzJKpjjJrr07+Vt/628xNTV1jQDvZo1ZyerT7f/dtrmeSb3b73tp/en36f2lrVUruQlWGlO3Y67WbbHScdYSG01IbsQXfDviVojAnTxvK0HoVcxMuVxmZGSEK1euMDw8fOMHuc4CsBZIC7fk+6RbwPM86vUa9coS5aUlSouL1Frd/0yfe8dx2hYB895ENSdri0spUa0Fz/d9rs7NcqrVzyAMQ0Zd2BHW+NnT0+yoLTKqPDKRRHgRp3XIm7aNL7JU7CwlV+DpiCACNwS3HjKjAk5KxYUwpClDhooFZGaIs+evYg8XcKXoEMRmrpNzknw13xtzvFKqnY9utHkzZ+YczXuzX1NNL33c5LHStf4/9alPMTExQS6X66jfbqq8GVfCSte2G6anp3n66ae5cOEClUqFhYWFVoZIuW0h6CAF2iGjAiKpibRASUkuW6dRl3iBi1QRlg55MKzxmMywRwvqUjLvZpjOZahlXSqWooFG+IqcFzEUBEx4AVNRRF02eTlyOaEldSdCZvM4WUGGDO98zOPxB1227XT5g89lyUwc5e/8xt/h8KGDHdfGvPZ6ZtJZDd3ed0Ovfa6GEFxvm14Wp9Xm6V/vWDezzWqPt1ZY7zVuI60AG7X/G8FaBETeaRYB81oul9m5cydLS0sryu/bkgyk/0wFN1PFrVatUCktsLi4SLVSQQVxe2FTxSwt9M3/DZKLnBLLLWxl6xw9z+PqwiJnTp/myvRlojBkm4rY3awwXl9krF4i2whxIk020gTapmJlCBC4UUhBaPJaYWtNEEY0wwjL8yjXFf8kaHJ+aIxR20Lb13ZdS2qUWuu2H9qY1GE559tUmDPXJ5nHnjR/JwsPJVPYummsUkoKhQKTk5Ps27ePo0eP8vM///MUi8VVXb8b/X5paYlvfvObvPXWW3FtiFKJSqXSJgRtkiFAa8UDh7I0mw3KTfjVvwQffk/E8yclT31Z8OKbAaEX4amIQFu4SrBDh+wnYlRajCMYVxKpBFVLU7I180pzWUdcEBkq0iYnmuSKmqP7clyemaASNXjPww1+8+/ZfPVLBb77fJ7d+x/kb/3tv8H+PYegR6/2tL89+dmNkoG0yb6bBanb87kaUtDru9VudyvbrBZrtfas5ZhuFJuh0fYbGbjV8aykcKw1NnPu0ue5WjKwYTEDBuvxwFzvRrmmUhoQqRDfbxJGAQKFQBCGQUdDmW6v1xy79dcaCCamzpWa+w4f4O59+7haKXPl8iVenFOI3BDWrgPkwwB7qcLUYpm9jTpZQmwsEHGJZM+KqFkWdcuh4lpEWvBypcrFi+cZkhbKFlgtv72BEejJHG6TYx4EAblcrr2dIUnd3ARpZpmsKy+lZNu2be0Obst1GXazb98+9u3bx65duxgZGekodtTtWOlreCMw4xkaGuLjH/844+PjPPvss+0iS6Z5TKVSaR0bEJLT55q86xHFb/6tgPGRBn/yVJZv/dDiA++T/MpfE/zJl3O8/FNJTdUJtWA6tLkkCliqjhU5RLZPhI0tBZaCUOZQroUrA45ORbz73Q4/936HK3MW/+J3lrC05vCBUT7/ZyV+/PxOHnjsQf7mX/81Jid3o4S5X3pr3r2E+PWE70rbrpRWmMZqzPrXO/6N/K7bNhuN1bgp1hObacruJwIAa6PNb+R8brZF4FaOv2HZBBsVO9ANxrduor5d16VYKBIEAdKyqVerNJpN/KaHalVmS+eQJ7vqdavx380vaoSnlJKd4xPsGBun2mgwv7BAqVZGZ0do7B9GB3UOzy9QrFVxrbiHQlZYZMgSSDsmG9Lhu0szuE4RbWlc2+2IjzBaftK8n/TTW5ZF0LKApP36aYtCchtj9jdV8Pbt28c/+kf/qF310FhTklYC8/8gCDrmIzlPt2oqNmM3JOOhhx6iWCzyzDPPMD8/33HNK5UKXtPH0gpPZXn+ZcW/+UPNR39mnPmSQjoBO4bgQ/c2cb0sZ19TfPojRRbnPL78E4FQWVwrQz0IyYocmYxPPhsxUhAUhuDylSa//Ik8Dx+SPPauGl/7fsC//LcBdT/uaPe1px3GCvfyiU99nE986hPkcwWEFFj0JgCrEe7dBPz1yML1iMGNXovk970WovXWqNdyXdkIK6ZBvwjefhmHwVq4BG7ldzeLrU7gbuvUQvNQG6FlPpNSkrEdMpkCw0N1yrUqlWqVaqVCYyl2HdRqNRqNuG59svGKET7JMqrJHHQTXJckA6bKmmVZ2K3Katu3bycMFIu1Cq8GEafH9iB3QC702FOp8lClyaSl8TIRPnneimz+S6NBvljsOEYyGLCb6dfcKOY8HMdpf58OUhMirqFfKBSYmJhg+/bt7Ny5k71797J7927Gx8cpFosdLgKgo01yct/Ja3C9a5Qe9/UWZTPvydTCvXv3Mjo6yksvvcQbb7zRzjKILQQ16jUfqeoshhF//kOLb/9I8vaHQ/7aL1oMj2r++9+2+PEPmuzYIXjv4wEP3RNS+n9rHn5IcOCAw7/8/wo++SmfIUfjSMFH3m9zejbiN//7EV48Mc+Hfsbi2GmHi+czDGcEYeSQz+XYu+8gv/rpX+SRR57AtvIIcW2q3Wo1/V6afLcyvOl9dHMhrDbCf7VuhJWwWcpAGv0yDug/QdwvWKt5uVPmdy3Oc0NiBtoHW4Vp8GbQK3DQvDevHS1ZgxDfDwgCn0arQ2G9XserVahUyiwtLbGwsMjSUtx0pl5Zol6r4Qd+KlUxRCnTiEWgVWfPdiNoTdyB7TjYJjMh41CwHITt0ETghxGRVri2ZBeCQ0EAUYNKpHj+3BVe8X0cabfPL915zsxr+vhax8Vt7r777vb5G6E/PDzM8PBwu9nL2NgYIyMj7S6ISbLQ7Vp2u57dSEEvpIVcksB104ST19NYa8y1MNUIa7Ualy5d4vnnn+fcuXNxk59mg2qjhl+O8MI6Cg2Wj1AOUlq4wiMKHO46KPjf/l8ejcWI7z6jeeIJi5xj8T/9/xqMjI9z194QSwje9kCDg4fgH/+WQrsOf/fXBdPnIr75E5tXTuRpqgwjo6O884kn+OAHP8y2bbvIZXO4GY3juB3ZD4Y09iJCKxGB672mr9FqBfhqBftqfp+Oe7gZrNWasV5r0Ero9QxshqDqd+F4q6butbIq3MoxNxKrcaP0VcyAwXq5CrqZKbsJMSPcjDndcmwyKkM2zLU05xE8b5xms0mj0aBSqVCtVlkqlVhamGFhfq5FEJaoVCo0mx5B2ERFUVypLhKoaJkMJK0HZnxm0TeBiu32wK4bEwXbJrItztgWZwCvGVGan6fsNck5LkGk25p4t05vSZJg3msdV1385Cc/ydDQ0DXBkcl68WZ8phRvt5rw5jW94KfJyI1ev2596NMEodvvktfflCi2LIv9+/fj+z4nT54kDEIcYUNeoz2HRqNBFAAESBkRCglEnLpo89/9E4vtexRTUy6vnND82dMahyKPTYIVKnwpuTTt8NmvBrz4RpEHjkj+n/+zx0xlHBwoFAoc3bePRx55hAMHDhBFAdXqAlBE6QxKxamaJovDBKeu1Gynl0Wgm4BdjWn/etfjRp/TlYTszRKOm8VGmvqTWMuYmLXEViAAa7mfjTzffpjbgZtgBSQXpmQbWaCjLa8xsZvmN1EUtVukBkHA2NhYq+59k2p1XxylvrTEwsIC8/PzzM7OsrAwR6VSptFsgA6ROmoLrXRFvbR/1iz8URQRNpuEUdQOzLNbAjtrO0xNbSObzVGuVAmioC30km4LA0MwTOc30/VtcnKSUqlEvV5vV/trH6vV4jXZ5tXEASSJQ3LM5hyuJ4jSlhrzWfpamfnpRk6McDR1D2DZ6pFs1mMsA6ZcMcTdBHfs2MGFCxeYn5/H87x2Yx+TctpRtMiz+OGbGnnCRguFrQRSKobGmpy9GOEFEhWCUAGRGCaymrx0wkY4LtmsYsfkTu666y4OHTrE9u3bKRaL7S57JqgxOdfmGnRLW+12T/e632/l+1vd/nrjWw/0k6k/iX4QDlsRA7fA5mPDyUAvQbAW6Gau7qWpJIVUUrM0JuekwFgmBn7bnVCtVilXyiwtlSiVFimVSpQXFqhVKni+RxR1L1/bGllCuArzZVzjX0gsIeKSyEKgBSAl+aEiE2GI54cEYYhWGq1pC5JMJtPq957BdVuuCNtGyuWub4uLi23hYwR+khgkS+ua75Pbd2sVmyYH0FmDIG3aT5OC9P2QFo7J75LkLlld0gh1oE3sCoVCW/M29Q1MY6pSqUS5XKZarXZcawBLQFOAEDYWMk4dtQSLNYGQVSwskC7ShoLjUcznGJ/axYF9e7n76BH2H9jP1NRU29ViMhvStSqS3fjSVpBeFoCV7vfVkIH0QtnNlJ/8/Gaez40Q0v2o+a/m+41AP4xhNeilHNzKvu4ki8B6nPOmWAbSgWtrgeQClhTy3VwIvSLcYVmQGWFoOgMmK+mZmgVeItagVqtRqVTaOe4mzz0u6tNKbWuPldZ7BaJLtz4NFqJNBpS4NpocBILOWvaxMJUIoRBSoPVy21qgQ/gkBXmaDKQ1VUMYkgIsSRLM+7QwS46rly8wbTlIB0T2Eo7mOpnPk5UNc7kcw8PDTExM0Gg04toStVq79PTS0lKbENTr9fY2xmqQDAg1hMlYWcbGxti2bRs7duxgx44dTE5OtgV/LpfrIE7JuIC0daWbCyY5X+nz7Xa/r6W2v9Gm/F7H2kyNfzUL62YLgTT6bTzXw1oJsTstNiCJ9SI/m+YmWK/4AYNuJKCbj9m8T1sJDBEAOgIGDTlIWg2S5MAIHkMOqtVqK76g2W54s6ylKaR17VysRAba22kBWNcIj5gnRMuCR7hdtUIjiMzn6SZOyeC2biQg/ZrWcNPBh2kLQfIapOMdls+lU3gmPzdIVlM0nydTLtPXyZSjbjabbZeCaYlsXEaGUOTzeQqFQlvQm4ZKSS2/W+Bferwrjb+bALyeUFwpA6Af0K8m/NWgXxb82xkDl8CtYz3O/baOGVitSyJJBpJCJWn+TlbhM9H4Rus2aXtJq4EJQmy7FFrWgkqlQr1eb0XCK4Rq+cqlAAFStDTt1h9CgGwJGiN4iN0JsCyIOiwhLWuDALTutAR0O/dewii573YWRosspAV/UjgmyUFSQCavyUr17pM1HtJCNC1cDelIn0ty215MOr2/bsdIkzAz7l6V/1Yj2FfaZrWC9GYF7kYJ6n4gBLebsNiq57PW496MeeiHuV9vd8gNkYH1GERSO18PJPfbSxgk0S1vvxtJSH5vWVbbjWCsBvl8vq2NGlJQq9XatfOTNfQ9r97ar0DKZVP+sslZIqWVMj3T+lv25ZuxCWHIAonPrhV2yTnpJkiTv03OWTchnxTuyX0mzebpMZjfd/tLZlqkCUY3K0Ry3N2EeHqM3e6PXrgZAd9ru+t9dz2rwGahH8Z0M+tPPyziN4qtOGaDXmPfim6BjTzO9bARcRGbahnYaCJwo1pXmhSYz8w2Ui638TVuhaT2bF5zuRxDQ0P4vs/ExETbdVCpVCiVSiwuxgGI1WqVIKi3o+bTvvtewX3prID4s+WAvG4CNCnEk9qz+Sz5/2TqW1KAdxP2yX2m4xBWyhboJeTTBKGboO9lgk/fA2mL0fWE9Urvu22/kkVqvYT8zeyz360KK6FfFucBemO9rtGdfu3X+/w33TKQ3Pd6LjY3uu80UUlq0kmfdNJ9YMiBEX6GGBhrQRiGFItFhoeHO9wIhhQYYrCwsNB2J2itOwSqCeZLkgLzZ3zZyYZLyWBIcw7JFLakkE7OlflLprolhXmapHTzn6eFenK7bsK/l4nezH83l0H6Gq/GJH8z98X1zPhJQtXNAnEzxHc9ycNGCvY7fRHvhdtlXjZC+N/proGNwA1bBtZDm19vIrBa9BIUaU1PiM7o+GREfFIAA21fuykyY2oZGDdCMqDNRLybaPeFhYW2xcAEKEatWgTJ13SAoyEspmxw2lRuyIRJw0sSibT2n9bMVxLkSb95N5dEUrPv5TbopvWnz6GXS6PXdUx/t1YEoNf26f9vxP19s2R3o9APC+tm4XY89/Uy22+WO2CzjrcSNsI1kMRNkYF+ENxrjZUW77TwT3++0jbJIkcmEC9Z5Mi4EUzEe7d0RdOad2lpiXq9ju/77cj3ZCVDt9X3wHXdjv+b75L/T1oT0jnwaS2/W8zBasz13YhIr+9XSwSSn/d63ws3GoV/K2RgNZ+v9vvVoJ8WsQHuDAzcAeuPvrYMrBdWG/m/EUgfP20N6ZUul/5L/j4Zf5DMSjDCORlrYP48z2v/mVx4kxeftBIYcpH8Swr9pPsgaQHoVndgJQKQnJ+0+yT9+fWIQre5vh556HV9um3Ty29/s9jse3KA/sWdILzW20W8EcdZ7Rg2G5s1lpsmA+thIUgKy35bfNNCaqUxJkkC0CH8jYAz2QlGKCe3Mab+dLdEYz1IFkAy++oWtGeIQlLA9woi7CaUV6u9p3+X/LyXIF5NZH96m17H7LVNmsTd7D3Vb/fiABuPfhIWG4X1NNkPCEAnNts9AjdJBtZTWPc7EUhaBXp91ssysNI+e70m/5JEwbzvte+0hg/dUwzTY0mTnpWE+mo0/17nfD3Tfy/0IgirOUavz9YS/XbvDjDArWCjLAIDbP583JJlwGA9LATrte9bRTch3u2zdN59tziE9G/S6MYW026IbmQgPd5ugjxtDUh+nh5jMiAyve9ux7gR3/lKwrzXfbDS/tfyfum3e2+AtcVmL779hG5zsV6WgLXe981gs4+fRL+M5ZZiBtbTrN/PLoMkugmzdPW85P+TZKGXIO9GHrrhRm+ibv74btvciEZ9ve2vdw43eqzVbDfAnYt+WVj7Hest/Lvtb7OvzWYfP42NcJVcT1lM4pYDCDeCEPQruo2tmxadJALdfNnmu26fr4TVXuTracyr0bpXs59e+73ZY6/mWOtxf/TzPTfAALeKtDtyo443wLXYiLnZMDJgDraeMQQGW2GR7jXG1ZxHL3JhvrtRsnC946W3uRnhuxp3x2rHcCPbrSdu9gHth7HfDhgIj7XFRlgBeu13M69lP99H6z22myF8a5pauF6kYKu4DK6HlYhC+rtugr/b/9dqPm5E6+9GCG722mzl65nGej/g/TJX/bzIDrC5wn89j3Uj6IcxpLFRROlmLT9rRgbWW1D3KxFIxwJcb7s0ep3XzZjj1wO36r+/mZiBAQYY4Oaw0S6AjT7WVsdmuGZWu8auedGhmzVl38i+12v/N4NuY7qRC77W57HW0fRrsb9+uVZbHYNFdwCDle6FjbQC9BP6dXwbRdBu9Thr7iaA9V38+9VCsJVxs/M5uA4DDLB52Azh168Ct9+xmcGaGxpA2Ovgd1pQoSEqqw2quxWsdRDhzcxjr2JHAwwwwNriRlLEtvIxbxT9Or6NnLu1sjysa2+C9XYZ9GNQYZqorJQhsBLWs3jOeltuVjOGAQYYYBnXWxM2Q7PsZ0Hbr9gMwrZWx103MjBwGfQPNsqSMiACAwxw8+gXIdcv49iq2CxCcKvYkK6Fd2odgl6EaDPH2c20v9HH3OjjDzBAv6DXs9Av/v9+JQL9Oq4ktrr1Zt3JwEpV99brGOt1nJtFv43rZtMg1/OYGzWOAQZYT6zmPu8nzbGfhWw/j81gI4ssrbcLZ8MsA7BxJuqBEFk9NsNSsNLxN2scAwywVuhHIdaPY7pdcLvUdNgQMpDERvn5+zWeoN9JSz8J54F7YYB+wlYQqFvJ9J/EVhijwWYEWW7EcTaUDGyEy2Ajj3Or6Fdi0M1asNEWhOsRgY3OyBjg9sZqF9t+EVpb1fSfxFYZJ2ysO2CzjrkplgHYOGEyEAi3js12JaQxIAIDrAe2knCCrTferY5+CfJcL2w4GUhiI10GBv0qJLbCGA22gvn+Rh+ifhn3AGuD20VQDs5jc7GWefz9fEzYRDKwkS4Dg34sUtQN3W6CfhtzrzFulZiDJFbrduj12wHWHmuxEG4FAdSP2Qdrhdtl3LejS6AbNt0yABu3oG4FIrCV0U9E4EYwIAL9i60qUG4Ud8p5bhXc7i6BbthUMpDEZlXJ2yoL+lZyI6RxO/v41+MB3mpzsV6L2GYvjmuF653HnXKe/YzNHHu/zFtfkIGNNuUnLRJbUchu9TGn0W/uhdVivYhAvywON4KtOOa1wu1s6u+F2+F8NtM03w9ugTT6ggwksZGm/K0oVG9HbEUisF7oh0VhgBvH4LptTQwsAsvoOzIAmyOktyox2Kpuj9Xieg/M7Xa+A/QX+m3B3izcLvOw2eexWZkCq0FfkgHozDbY6ADDrUoMYGtkIqwWqyECN5IxMMAAcOMLcT8u3OuJ2+18N9skv9nHXy36lgzA5kT/b2UicKdhQAQGuFn064I8wPpgs6/3Zh9/NehrMgCbbwa/HcjBZs/hZmOQOnhnYCssuP2I22ne+uVc+mUcN4K+JwOweabvblkOG3n89cLt5EpYLW6mIuGtPNC3+3yuNdZq8dyKi/BG4Hael34xw/fLOG4WW4IMJLFZhYNuFyIwwOowIAIbj622eA7QX+iH+6cfxpDGatejLUcGDDZbOG/28dcad6K1YD3Rj4vCALc/7pT7rl+08NtpvrckGdhs8/1mH3+jMCAIN4fbaYEYoH9xJ91n3c61XwhAv1+H1Y5vS5KBJDZbEG/28Tcag/4OAwyw+eh3AbQe6Kdz7qexXA+3vZugFza6+VGv42/mGNYbg4qBAwywMdhKQmet0U/n3k9jWS/cVmRgQAQGGGCAAW4f9JMQ7qex3AjuGDdBN2itO8zZm+U+6EYObmeSMLAYDDDAjWGrCpi1Rj+W6U2v4bc7bksyYNBP5YX7aSwbjet1LBxggNsZd5JAWS36KSCw38awWbityQD0l+m+n8YywAADDLBZ6Geh289jW0/c9mQgjX4RyIO0vRiDroQDbGXcqYLjRtDvc5QcX7+PdT1xR5GBfqsPMDCfXx+reTgHczXAeuBOFgw3i15z1m9zOSAA1+KOIgNJbDYR6IZ+HNMAAwwwwI1gqwjXrTLOjcIdSwbS6DdBPHAjrB438lAP5vDOxkAArC22wnz2a7Biv2FABug/9wF0bzs8IAi3jkE7462PwUK+OdgqLgDYeiWD+wEDMpBCPxCBbhgQgQEGGGCzsZWE6lYaaz9gQAaug34lBwbdLAgDrD3WemG5E67TYDHemthq122rjbdfMSADK6Af3QdJrNaVYL4boH8wWMAG6BdsRZ/6wA2w9hiQgVWi34hANwyIwAADDHAz2IrCdCuOuZ8xIAM3ia3kwx+QhAEGuHNxPaG5FYTqVhjjVseADNwEehGBfrYeDFwKAwxwe2MlgbmVhOlWylq4nTAgA2uEfiYC3TAgAgMMcPvhdhGYt8t5bCUMyMA6YStH+Q+IwgAD9C9uN0F5u53PVsWADKwDtnLBoJWIwKCp0AADrD9WIxy3qgDdipkLdwoGZGADsFWIwEoYEIEBBtg43M4C8nY+t62MARnYRGxlV0I3DLowDjDA9XGnCMM75TxvFwzIwCZhtQ/KVhGi1yMCA8vCAHcCVvtc306C8nbJYrjTMSADfYTbzVJgMCACA9xJuBMF4J14zrcbBmSgz3E7xBusBqtZTG7H8x5ga2Ag7JYxmIvbEwMy0MdYTXEj89lWxmqJwO3mWhlgc3GjQu1OE4ID8/+dhQEZ2GK43YjAanGji8+dMi8D3BoGQm1lDObnzsGADNwGGBQJuhY3u4jdyXO2FTEQVreOQfnfAWBABrY8BkTgWgyIwJ2FgdC6NQzmbwAYkIHbFlrrnjEHA6HXHb3m7GYwmOfuWMs5HmD1MHM+mPsBemFABu4wDAoDbQwGi+4AG43boVXxAJuHARm4AzFwLWwMBovvABuNwT03wM1iQAYGaGNgNRhggP7FQPMfYD3Rl2QgeVMPhNDGYWAxGGCA/sZA4A+wXlgVGTA3YLlcvq1uxkGQ19piPefS3Hcbeb0245hbCf0+P1rrvh1bErfTmtpvGASsQqVSAa5/n62KDJid3Xvvvbc4rAEGGGCAAQYYYKNRqVQYGRnp+b3Qq6BNSikuX77M0NDQlmDaAwwwwAADDDBAbBGoVCrs2rULKWXP7VZFBgYYYIABBhhggNsXvWnCAAMMMMAAAwxwR2BABgYYYIABBhjgDseADAwwwAADDDDAHY4BGRhggAEGGGCAOxwDMjDAAAMMMMAAdzgGZGCAAQYYYIAB7nAMyMAAAwwwwAAD3OH4/wOt5jWGpXWkQQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Choose an image to pass through the model\n",
+    "test_image = \"images/test-4.jpg\"\n",
+    "\n",
+    "# Configure matplotlib for pretty inline plots\n",
+    "#%matplotlib inline\n",
+    "#%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "# Prepare the labels\n",
+    "with open(\"imagenet-simple-labels.json\") as f:\n",
+    "    labels = json.load(f)\n",
+    "\n",
+    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
+    "# data_transform = transforms.Compose(\n",
+    "#     [\n",
+    "#         transforms.Resize((224, 224)),\n",
+    "#         transforms.ToTensor(),\n",
+    "#         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "#     ]\n",
+    "# )\n",
+    "\n",
+    "data_transform = transforms.Compose([\n",
+    "    transforms.Resize(299),\n",
+    "    transforms.CenterCrop(299),\n",
+    "    transforms.ToTensor(),\n",
+    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "])\n",
+    "# Load the image\n",
+    "\n",
+    "image = Image.open(test_image)\n",
+    "plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
+    "\n",
+    "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n",
+    "# image = data_transform(image).unsqueeze(0).cuda()\n",
+    "image = data_transform(image).unsqueeze(0)\n",
+    "\n",
+    "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
+    "# model = models.resnet50(pretrained=True)\n",
+    "model_inception = models.mobilenet_v2(pretrained=True)\n",
+    "# Send the model to the GPU\n",
+    "# model.cuda()\n",
+    "# Set layers such as dropout and batchnorm in evaluation mode\n",
+    "model_inception.eval()\n",
+    "\n",
+    "# Get the 1000-dimensional model output\n",
+    "out = model_inception(image)\n",
+    "# Find the predicted class\n",
+    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d57da4b",
+   "metadata": {},
+   "source": [
+    "## Exercise 4: Transfer Learning\n",
+    "    \n",
+    "    \n",
     "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n",
     "Download and unzip in your working directory the dataset available at the address :\n",
     "    \n",
@@ -1896,13 +2201,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 24,
    "id": "be2d31f5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1999,7 +2304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 25,
    "id": "572d824c",
    "metadata": {},
    "outputs": [
@@ -2008,9 +2313,21 @@
      "output_type": "stream",
      "text": [
       "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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",
-      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /Users/heber/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
-      "100.0%\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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"
      ]
@@ -2019,58 +2336,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10\n",
-      "----------\n",
-      "train Loss: 0.5703 Acc: 0.6844\n",
-      "val Loss: 0.2192 Acc: 0.9085\n",
+      "train Loss: 0.6658 Acc: 0.6393\n",
+      "val Loss: 0.2281 Acc: 0.9216\n",
       "\n",
       "Epoch 2/10\n",
       "----------\n",
-      "train Loss: 0.5315 Acc: 0.7623\n",
-      "val Loss: 0.1639 Acc: 0.9673\n",
+      "train Loss: 0.5210 Acc: 0.7664\n",
+      "val Loss: 0.2044 Acc: 0.9346\n",
       "\n",
       "Epoch 3/10\n",
       "----------\n",
-      "train Loss: 0.3543 Acc: 0.8361\n",
-      "val Loss: 0.1767 Acc: 0.9216\n",
+      "train Loss: 0.6092 Acc: 0.7418\n",
+      "val Loss: 0.2203 Acc: 0.9085\n",
       "\n",
       "Epoch 4/10\n",
       "----------\n",
-      "train Loss: 0.4044 Acc: 0.8402\n",
-      "val Loss: 0.1485 Acc: 0.9608\n",
+      "train Loss: 0.4670 Acc: 0.7787\n",
+      "val Loss: 0.2910 Acc: 0.8824\n",
       "\n",
       "Epoch 5/10\n",
       "----------\n",
-      "train Loss: 0.5096 Acc: 0.8033\n",
-      "val Loss: 0.1614 Acc: 0.9608\n",
+      "train Loss: 0.3328 Acc: 0.8811\n",
+      "val Loss: 0.3162 Acc: 0.8889\n",
       "\n",
       "Epoch 6/10\n",
       "----------\n",
-      "train Loss: 0.3941 Acc: 0.8115\n",
-      "val Loss: 0.2661 Acc: 0.9020\n",
+      "train Loss: 0.4381 Acc: 0.8197\n",
+      "val Loss: 0.3590 Acc: 0.8758\n",
       "\n",
       "Epoch 7/10\n",
       "----------\n",
-      "train Loss: 0.2987 Acc: 0.8402\n",
-      "val Loss: 0.2089 Acc: 0.9412\n",
+      "train Loss: 0.4280 Acc: 0.7992\n",
+      "val Loss: 0.1870 Acc: 0.9346\n",
       "\n",
       "Epoch 8/10\n",
       "----------\n",
-      "train Loss: 0.3720 Acc: 0.8197\n",
-      "val Loss: 0.1883 Acc: 0.9477\n",
+      "train Loss: 0.4074 Acc: 0.8197\n",
+      "val Loss: 0.2123 Acc: 0.9412\n",
       "\n",
       "Epoch 9/10\n",
       "----------\n",
-      "train Loss: 0.3857 Acc: 0.8320\n",
-      "val Loss: 0.1744 Acc: 0.9608\n",
+      "train Loss: 0.4329 Acc: 0.8402\n",
+      "val Loss: 0.2643 Acc: 0.9020\n",
       "\n",
       "Epoch 10/10\n",
       "----------\n",
-      "train Loss: 0.3512 Acc: 0.8484\n",
-      "val Loss: 0.1883 Acc: 0.9477\n",
+      "train Loss: 0.3617 Acc: 0.8402\n",
+      "val Loss: 0.1934 Acc: 0.9412\n",
       "\n",
-      "Training complete in 8m 45s\n",
-      "Best val Acc: 0.967320\n"
+      "Training complete in 8m 41s\n",
+      "Best val Acc: 0.941176\n"
      ]
     }
    ],
@@ -2271,6 +2586,1521 @@
     "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1cf2ad3b",
+   "metadata": {},
+   "source": [
+    "### 1. Modifying the code and adding an \"eval_model\"\n",
+    "This function evaluates the trained model on a separate test dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf24a914",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\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",
+    "\n",
+    "# Data augmentation and normalization for training and validation\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",
+    "    \"test\": transforms.Compose(  # Test set transforms\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",
+    "# Create datasets and loaders\n",
+    "image_datasets = {\n",
+    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+    "    for x in [\"train\", \"val\", \"test\"]  # Assuming \"test\" folder exists\n",
+    "}\n",
+    "dataloaders = {\n",
+    "    x: torch.utils.data.DataLoader(\n",
+    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
+    "    )\n",
+    "    for x in [\"train\", \"val\", \"test\"]\n",
+    "}\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}\n",
+    "class_names = image_datasets[\"train\"].classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "\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(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "def eval_model(model, criterion, dataloader, dataset_size):\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",
+    "    loss = running_loss / dataset_size\n",
+    "    acc = running_corrects.double() / dataset_size\n",
+    "    print(f\"Test Loss: {loss:.4f} Acc: {acc:.4f}\")\n",
+    "    return loss, acc\n",
+    "\n",
+    "\n",
+    "# Load pre-trained 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",
+    "test_loss, test_acc = eval_model(model, criterion, dataloaders[\"test\"], dataset_sizes[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c46944cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tamaños de los datasets:\n",
+      "{'train': 244, 'val': 153}\n",
+      "Clases disponibles: ['ants', 'bees']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data augmentation and normalization for training\n",
+    "# Just normalization for validation\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.RandomResizedCrop(\n",
+    "                224\n",
+    "            ),  # ImageNet models were trained on 224x224 images\n",
+    "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+    "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+    "            transforms.Normalize(\n",
+    "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+    "            ),  # ImageNet models expect this norm\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",
+    "# Create train and validation datasets and loaders\n",
+    "image_datasets = {\n",
+    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataloaders = {\n",
+    "    x: torch.utils.data.DataLoader(\n",
+    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
+    "    )\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+    "class_names = image_datasets[\"train\"].classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "print(\"Tamaños de los datasets:\")\n",
+    "print(dataset_sizes)\n",
+    "print(\"Clases disponibles:\", class_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3d5a1a3",
+   "metadata": {},
+   "source": [
+    "#### Preparing Test Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "26f642cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Datasets sizes:\n",
+      "{'train': 244, 'val': 153, 'test': 49}\n",
+      "Available classes: ['ants', 'bees']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "from torchvision import datasets, transforms\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Data directory\n",
+    "data_dir = \"hymenoptera_data_test\"  # Cambia esto por el path de tu dataset\n",
+    "\n",
+    "# Data augmentation and normalization for training\n",
+    "# Just normalization for validation\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose([\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",
+    "    \"val\": transforms.Compose([\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",
+    "# Create train and validation datasets\n",
+    "train_dataset = datasets.ImageFolder(\n",
+    "    os.path.join(data_dir, \"train\"), \n",
+    "    transform=data_transforms[\"train\"]\n",
+    ")\n",
+    "val_dataset = datasets.ImageFolder(\n",
+    "    os.path.join(data_dir, \"val\"), \n",
+    "    transform=data_transforms[\"val\"]\n",
+    ")\n",
+    "\n",
+    "# Split train dataset into train and test\n",
+    "full_dataset = datasets.ImageFolder(\n",
+    "    os.path.join(data_dir, \"train\"), \n",
+    "    transform=data_transforms[\"val\"]\n",
+    ")\n",
+    "train_indices, test_indices = train_test_split(\n",
+    "    range(len(full_dataset)), test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# Create sampler for test\n",
+    "test_sampler = torch.utils.data.SubsetRandomSampler(test_indices)\n",
+    "\n",
+    "# Create DataLoaders\n",
+    "train_dataloader = torch.utils.data.DataLoader(\n",
+    "    train_dataset, batch_size=32, shuffle=True, num_workers=4\n",
+    ")\n",
+    "val_dataloader = torch.utils.data.DataLoader(\n",
+    "    val_dataset, batch_size=32, shuffle=False, num_workers=4\n",
+    ")\n",
+    "test_dataloader = torch.utils.data.DataLoader(\n",
+    "    full_dataset, sampler=test_sampler, batch_size=32, num_workers=4\n",
+    ")\n",
+    "\n",
+    "# Create dictionary of dataloaders\n",
+    "dataloaders = {\n",
+    "    \"train\": train_dataloader,\n",
+    "    \"val\": val_dataloader,\n",
+    "    \"test\": test_dataloader\n",
+    "}\n",
+    "\n",
+    "# Calculate datasets sizes\n",
+    "dataset_sizes = {\n",
+    "    \"train\": len(train_dataset),\n",
+    "    \"val\": len(val_dataset),\n",
+    "    \"test\": len(test_indices)\n",
+    "}\n",
+    "\n",
+    "class_names = train_dataset.classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "print(\"Datasets sizes:\")\n",
+    "print(dataset_sizes)\n",
+    "print(\"Available classes:\", class_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78cd0248",
+   "metadata": {},
+   "source": [
+    "#### Function `eval_model`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "472141ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def eval_model(model, criterion, dataloader, dataset_size):\n",
+    "    model.eval()  # Establecer el modelo en modo evaluación\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    # No necesitamos calcular gradientes durante la evaluación\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            # Forward\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            # Estadísticas\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    total_loss = running_loss / dataset_size\n",
+    "    total_acc = running_corrects.double() / dataset_size\n",
+    "    # total_loss = running_loss / len(dataloader.dataset)\n",
+    "    # total_acc = running_corrects.double() / len(dataloader.dataset)\n",
+    "\n",
+    "    print(f\"Test Loss: {total_loss:.4f} Acc: {total_acc:.4f}\")\n",
+    "    return total_loss, total_acc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc92f36a",
+   "metadata": {},
+   "source": [
+    "#### Execution of `eval_model`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "72cb713c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.6398 Acc: 0.6434\n",
+      "val Loss: 0.6859 Acc: 0.5686\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.5735 Acc: 0.6721\n",
+      "val Loss: 0.5224 Acc: 0.7451\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.4626 Acc: 0.7623\n",
+      "val Loss: 0.3472 Acc: 0.8693\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.3386 Acc: 0.8648\n",
+      "val Loss: 0.2688 Acc: 0.9150\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.2913 Acc: 0.9016\n",
+      "val Loss: 0.2371 Acc: 0.9281\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.2587 Acc: 0.9139\n",
+      "val Loss: 0.2227 Acc: 0.9412\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.2460 Acc: 0.9221\n",
+      "val Loss: 0.2206 Acc: 0.9412\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.2607 Acc: 0.8934\n",
+      "val Loss: 0.2185 Acc: 0.9412\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.2393 Acc: 0.9344\n",
+      "val Loss: 0.2175 Acc: 0.9412\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.2587 Acc: 0.8934\n",
+      "val Loss: 0.2195 Acc: 0.9281\n",
+      "\n",
+      "Training complete in 9m 36s\n",
+      "Best val Acc: 0.941176\n",
+      "Test Loss: 0.1926 Acc: 0.9592\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\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",
+    "\n",
+    "\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "\n",
+    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        epoch_start = time.time()\n",
+    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Each epoch has a training and validation phase\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "                model.train()  # Set model to training mode\n",
+    "            else:\n",
+    "                model.eval()  # Set model to evaluate mode\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Iterate over data.\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                # zero the parameter gradients\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                # Forward\n",
+    "                # Track history if only in training phase\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",
+    "                    # backward + optimize only if in training phase\n",
+    "                    if phase == \"train\":\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                # Statistics\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",
+    "            # Deep copy the model\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",
+    "        # Add the epoch time\n",
+    "        t_epoch = time.time() - epoch_start\n",
+    "        epoch_time.append(t_epoch)\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # Load best model weights\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model, epoch_time\n",
+    "\n",
+    "# Download a pre-trained ResNet18 model and freeze its weights\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the final fully connected layer\n",
+    "# Parameters of newly constructed modules have requires_grad=True by default\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Linear(num_ftrs, 2)\n",
+    "# Send the model to the GPU\n",
+    "model = model.to(device)\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Observe that only the parameters of the final layer 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",
+    "model, epoch_time = train_model(\n",
+    "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")\n",
+    "\n",
+    "# Evaluate the model on the test set\n",
+    "test_loss, test_acc = eval_model(model, criterion, dataloaders[\"test\"], dataset_sizes[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d75b55bf",
+   "metadata": {},
+   "source": [
+    "From the results obtained during the training, validation, and testing stages, the following conclusions can be drawn:\n",
+    "1.\tThe accuracy values remain fairly consistent between the training and validation stages, with a peak value of 97.39% on the validation set and 93.88% on the test set. This indicates that the model has generalized well, although a slight performance drop is observed when evaluating on unseen data.\n",
+    "\n",
+    "2.\tThe accuracy on the test set is slightly lower than that on the validation set (97.39%). This could be due to the test set not being fully representative of the original data or the model being more closely tailored to the validation data.\n",
+    "\n",
+    "3.\tOverall, the model has proven to be effective for the problem addressed, based on the results. However, there is always room for improvement, such as fine-tuning the hyperparameters or implementing data augmentation techniques to increase the diversity of the training set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf0cb503",
+   "metadata": {},
+   "source": [
+    "### 2. Modifying the Classification Layer\n",
+    "Replacement of the current classification layer with a two-layer architecture using ReLU and Dropout."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e394104d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\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",
+    "\n",
+    "# Data augmentation and normalization for training and validation\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",
+    "image_datasets = {\n",
+    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataloaders = {\n",
+    "    x: torch.utils.data.DataLoader(\n",
+    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
+    "    )\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+    "class_names = image_datasets[\"train\"].classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "\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(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "# Modify the classification layer\n",
+    "class ModifiedResNet18(nn.Module):\n",
+    "    def __init__(self, pretrained_model, num_classes):\n",
+    "        super(ModifiedResNet18, self).__init__()\n",
+    "        self.features = nn.Sequential(*list(pretrained_model.children())[:-1])\n",
+    "        num_ftrs = pretrained_model.fc.in_features\n",
+    "        self.classifier = nn.Sequential(\n",
+    "            nn.Dropout(0.5),  # Dropout before the first layer\n",
+    "            nn.Linear(num_ftrs, 256),  # Fully connected layer 1\n",
+    "            nn.ReLU(),  # Activation\n",
+    "            nn.Dropout(0.5),  # Dropout after activation\n",
+    "            nn.Linear(256, num_classes),  # Fully connected layer 2\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.features(x)\n",
+    "        x = torch.flatten(x, 1)\n",
+    "        x = self.classifier(x)\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "# Load pre-trained ResNet18 model\n",
+    "pretrained_model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in pretrained_model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the classifier with the modified version\n",
+    "model = ModifiedResNet18(pretrained_model, num_classes=2)\n",
+    "model = model.to(device)\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer_conv = optim.SGD(model.classifier.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 with the modified classification layer\n",
+    "model = train_model(model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "88af5dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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",
+      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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": [
+      "ResNet(\n",
+      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  (relu): ReLU(inplace=True)\n",
+      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+      "  (layer1): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer2): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer3): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer4): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+      "  (fc): Sequential(\n",
+      "    (0): Linear(in_features=512, out_features=512, bias=True)\n",
+      "    (1): ReLU()\n",
+      "    (2): Dropout(p=0.5, inplace=False)\n",
+      "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torchvision import models\n",
+    "\n",
+    "# Load ResNet18 model\n",
+    "model_resnet18_v2 = models.resnet18(pretrained=True)\n",
+    "\n",
+    "# Replace the current clasification layer with a set of two layers and Dropout \n",
+    "model_resnet18_v2.fc = nn.Sequential(\n",
+    "    nn.Linear(model_resnet18_v2.fc.in_features, 512),  # First layer fully connected\n",
+    "    nn.ReLU(),  # ReLU activation\n",
+    "    nn.Dropout(0.5),  # Dropout mechanism with 50% probability to avoid overfitting\n",
+    "    nn.Linear(512, 2)  # Output layer for binary classification\n",
+    ")\n",
+    "\n",
+    "print(model_resnet18_v2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "9b4ee699",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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: 0.7168 Acc: 0.5451\n",
+      "val Loss: 0.6617 Acc: 0.5817\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.7063 Acc: 0.5533\n",
+      "val Loss: 0.6538 Acc: 0.5882\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.7055 Acc: 0.5328\n",
+      "val Loss: 0.6505 Acc: 0.5752\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.7081 Acc: 0.5574\n",
+      "val Loss: 0.6525 Acc: 0.5686\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.7315 Acc: 0.5246\n",
+      "val Loss: 0.6512 Acc: 0.5752\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.7270 Acc: 0.4877\n",
+      "val Loss: 0.6498 Acc: 0.5817\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.7240 Acc: 0.5369\n",
+      "val Loss: 0.6512 Acc: 0.5817\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.7248 Acc: 0.5328\n",
+      "val Loss: 0.6516 Acc: 0.5817\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.7267 Acc: 0.5410\n",
+      "val Loss: 0.6488 Acc: 0.5752\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.7110 Acc: 0.5451\n",
+      "val Loss: 0.6516 Acc: 0.5621\n",
+      "\n",
+      "Training complete in 9m 30s\n",
+      "Best val Acc: 0.588235\n",
+      "Test Loss: 0.8294 Acc: 0.4286\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\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",
+    "\n",
+    "\n",
+    "model = model_resnet18_v2\n",
+    "\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "\n",
+    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        epoch_start = time.time()\n",
+    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Each epoch has a training and validation phase\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "                model.train()  # Set model to training mode\n",
+    "            else:\n",
+    "                model.eval()  # Set model to evaluate mode\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Iterate over data.\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                # zero the parameter gradients\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                # Forward\n",
+    "                # Track history if only in training phase\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",
+    "                    # backward + optimize only if in training phase\n",
+    "                    if phase == \"train\":\n",
+    "                        optimizer.step()\n",
+    "                        loss.backward()\n",
+    "\n",
+    "                # Statistics\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",
+    "            # Deep copy the model\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",
+    "        # Add the epoch time\n",
+    "        t_epoch = time.time() - epoch_start\n",
+    "        epoch_time.append(t_epoch)\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # Load best model weights\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model, epoch_time\n",
+    "\n",
+    "# Download a pre-trained ResNet18 model and freeze its weights\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the final fully connected layer\n",
+    "# Parameters of newly constructed modules have requires_grad=True by default\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Linear(num_ftrs, 2)\n",
+    "# Send the model to the GPU\n",
+    "model = model.to(device)\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Observe that only the parameters of the final layer 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",
+    "model, epoch_time = train_model(\n",
+    "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")\n",
+    "\n",
+    "# Evaluate the model on the test set\n",
+    "test_loss, test_acc = eval_model(model, criterion, dataloaders[\"test\"], dataset_sizes[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b47707e4",
+   "metadata": {},
+   "source": [
+    "After analyzing the results from the training, validation, and testing stages, we can draw the following conclusions:\n",
+    "\n",
+    "1.\tWe observe that the accuracy values for both the validation and test sets have significantly decreased compared to the previously tested, unmodified ResNet18 model. The best accuracy value in the validation set dropped from 97.39% to 47.06%, while in the test set it decreased from 93.88% to 44.90%.\n",
+    "\n",
+    "2.\tDespite the drop in accuracy, there is consistency between the results of the validation and test sets. Both show a similar decrease in precision.\n",
+    "\n",
+    "3.\tThe decrease in accuracy values may be due to the changes in the classification layer. By replacing the original layer with two layers that include ReLU and Dropout, the model’s ability to learn effectively may have been affected.\n",
+    "\n",
+    "4.\tThe ReLU activation in the intermediate layer may generate intermediate outputs that are not ideal for the classification task. Additionally, excessive Dropout could have reduced the model’s ability to retain important information during training, which might explain the drop in precision.\n",
+    "\n",
+    "5.\tTo improve the results, it could be beneficial to adjust some hyperparameters. Specifically, adjusting the learning rate or reducing the Dropout rate (or even considering removing it) might help the model learn more effectively and prevent overfitting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "016bbe5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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",
+      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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": [
+      "ResNet(\n",
+      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  (relu): ReLU(inplace=True)\n",
+      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+      "  (layer1): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer2): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer3): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer4): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+      "  (fc): Sequential(\n",
+      "    (0): Linear(in_features=512, out_features=512, bias=True)\n",
+      "    (1): ReLU()\n",
+      "    (2): Dropout(p=0.3, inplace=False)\n",
+      "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torchvision import models\n",
+    "\n",
+    "# Load ResNet18 model\n",
+    "model_resnet18_v3 = models.resnet18(pretrained=True)\n",
+    "\n",
+    "# Freeze the earlier layers for fine-tuning\n",
+    "for param in model_resnet18_v3.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the current clasification layer with a set of two layers and Dropout \n",
+    "model_resnet18_v3.fc = nn.Sequential(\n",
+    "    nn.Linear(model_resnet18_v3.fc.in_features, 512),  # First layer fully connected\n",
+    "    nn.ReLU(),  # ReLU activation\n",
+    "    nn.Dropout(0.3),  # Dropout mechanism with 50% probability to avoid overfitting\n",
+    "    nn.Linear(512, 2)  # Output layer for binary classification\n",
+    ")\n",
+    "\n",
+    "print(model_resnet18_v3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "45c95636",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/heber/.pyenv/versions/3.11.7/lib/python3.11/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: 0.8100 Acc: 0.4631\n",
+      "val Loss: 0.7539 Acc: 0.5294\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.7961 Acc: 0.4672\n",
+      "val Loss: 0.7584 Acc: 0.5229\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.8012 Acc: 0.5082\n",
+      "val Loss: 0.7597 Acc: 0.5163\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.7766 Acc: 0.4959\n",
+      "val Loss: 0.7604 Acc: 0.5229\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.7757 Acc: 0.4959\n",
+      "val Loss: 0.7635 Acc: 0.5163\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.7969 Acc: 0.4959\n",
+      "val Loss: 0.7617 Acc: 0.5359\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.8020 Acc: 0.4795\n",
+      "val Loss: 0.7592 Acc: 0.5229\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.8018 Acc: 0.4795\n",
+      "val Loss: 0.7622 Acc: 0.5163\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.7860 Acc: 0.5164\n",
+      "val Loss: 0.7622 Acc: 0.5163\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.7817 Acc: 0.4959\n",
+      "val Loss: 0.7630 Acc: 0.5033\n",
+      "\n",
+      "Training complete in 9m 29s\n",
+      "Best val Acc: 0.535948\n",
+      "Test Loss: 0.8345 Acc: 0.4490\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\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",
+    "\n",
+    "\n",
+    "model = model_resnet18_v2\n",
+    "\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "\n",
+    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        epoch_start = time.time()\n",
+    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Each epoch has a training and validation phase\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "                model.train()  # Set model to training mode\n",
+    "            else:\n",
+    "                model.eval()  # Set model to evaluate mode\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Iterate over data.\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                # zero the parameter gradients\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                # Forward\n",
+    "                # Track history if only in training phase\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",
+    "                    # backward + optimize only if in training phase\n",
+    "                    if phase == \"train\":\n",
+    "                        optimizer.step()\n",
+    "                        loss.backward()\n",
+    "\n",
+    "                # Statistics\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",
+    "            # Deep copy the model\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",
+    "        # Add the epoch time\n",
+    "        t_epoch = time.time() - epoch_start\n",
+    "        epoch_time.append(t_epoch)\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # Load best model weights\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model, epoch_time\n",
+    "\n",
+    "# Download a pre-trained ResNet18 model and freeze its weights\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the final fully connected layer\n",
+    "# Parameters of newly constructed modules have requires_grad=True by default\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Linear(num_ftrs, 2)\n",
+    "# Send the model to the GPU\n",
+    "model = model.to(device)\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Observe that only the parameters of the final layer are being optimized\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.0001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "model, epoch_time = train_model(\n",
+    "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")\n",
+    "\n",
+    "# Evaluate the model on the test set\n",
+    "test_loss, test_acc = eval_model(model, criterion, dataloaders[\"test\"], dataset_sizes[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b0edba6",
+   "metadata": {},
+   "source": [
+    "### 3. Applying Quantization "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3ddb1cc",
+   "metadata": {},
+   "source": [
+    "#### a. Post-Training Quantization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "ee792290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  fp32  \t Size (KB): 44780.42\n",
+      "model:  int8  \t Size (KB): 44778.106\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "44778106"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print_size_of_model(model, \"fp32\")\n",
+    "\n",
+    "import torch.quantization\n",
+    "\n",
+    "torch.backends.quantized.engine = 'qnnpack'\n",
+    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+    "print_size_of_model(quantized_model, \"int8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14e5a685",
+   "metadata": {},
+   "source": [
+    "It can be observed that after applying post-training quantization, the model size does not change significantly compared to the original size.\n",
+    "\n",
+    "This may be because ResNet18 is a relatively small model, with a moderate number of parameters compared to larger architectures. As a result, the reduction in model size is limited.\n",
+    "\n",
+    "Additionally, post-training quantization is typically applied to layers that have a large number of parameters, such as Linear and Conv2d layers. However, in models like ResNet18, many of the layers are small or have fewer parameters, which can reduce the impact of quantization on the overall model size."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1891c5df",
+   "metadata": {},
+   "source": [
+    "#### b. Quantization-Aware Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "598ab36a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "04a263f0",
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