diff --git a/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints/stagewise_analysis.ipynb b/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints/stagewise_analysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2a982f57e6efb90bb5c2e90d017eac7b4f739b19
--- /dev/null
+++ b/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints/stagewise_analysis.ipynb
@@ -0,0 +1,2243 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/datadrive2/samyak/parity-nn/\")\n",
+    "from utils_competition import *\n",
+    "sys.path.append(\"/datadrive2/samyak/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints\")\n",
+    "from torch.utils.data import TensorDataset, DataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "width=20\n",
+    "n=20\n",
+    "k=3\n",
+    "device='cuda'\n",
+    "model_path = \"/datadrive2/samyak/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def parity(n, k, n_samples, seed=42):\n",
+    "    'Data generation'\n",
+    "\n",
+    "    random.seed(seed)\n",
+    "    samples = torch.Tensor([[random.choice([-1, 1]) for j in range(n)] for i in range(n_samples)])\n",
+    "    targets = torch.prod(samples[:, :k], dim=1) # parity hidden in first k bits\n",
+    "    return samples, targets\n",
+    "\n",
+    "\n",
+    "\n",
+    "def data_preparation(seed_id=42, test_samples=100, test_batchsize=100, return_raw=False):\n",
+    "    # Data generation - different seed each time\n",
+    "    N=1000\n",
+    "    _data = parity(n, k, N, seed=seed_id*17)\n",
+    "    train_dataset = TensorDataset(_data[0], _data[1])\n",
+    "    train_dataloader = DataLoader(train_dataset, batch_size=100, shuffle=True)\n",
+    "\n",
+    "    data = parity(n, k, test_samples, seed=2001) # constant test samples\n",
+    "    test_dataset = TensorDataset(data[0], data[1])\n",
+    "    test_dataloader = DataLoader(test_dataset, batch_size=test_batchsize, shuffle=True)\n",
+    "\n",
+    "    if (return_raw):\n",
+    "        return train_dataloader, test_dataloader, _data[0][:100], data[0]\n",
+    "    return train_dataloader, test_dataloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def acc_calc(dataloader, model, device='cuda'):\n",
+    "    model.eval()\n",
+    "    acc, total = 0, 0\n",
+    "    iter_counter=0\n",
+    "    for id, (x_batch, y_batch) in enumerate(dataloader):\n",
+    "        iter_counter+=1\n",
+    "        x_batch, y_batch = x_batch.to(device), y_batch.to(device)\n",
+    "        pred = model(x_batch)\n",
+    "        acc += (torch.sign(torch.squeeze(pred)) == y_batch).sum().item()\n",
+    "        total += x_batch.shape[0]\n",
+    "    \n",
+    "    return acc / total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def get_svd(arr):\n",
+    "    U, S, V =  np.linalg.svd(arr)\n",
+    "    return U, S, V"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/4008625498.py:3: 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_path+'/model_299.pt'))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "FF1(\n",
+       "  (linear1): Linear(in_features=20, out_features=20, bias=True)\n",
+       "  (activation): ReLU()\n",
+       "  (linear2): Linear(in_features=20, out_features=1, bias=False)\n",
+       ")"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = FF1(input_dim=n, width=width)\n",
+    "model = model.to(device)\n",
+    "model.load_state_dict(torch.load(model_path+'/model_299.pt'))\n",
+    "model.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "singular_values_final [3.6981118  2.057817   1.3466041  1.2434828  1.0755663  0.95081556\n",
+      " 0.9194288  0.7707997  0.6697056  0.5568011  0.5009025  0.37642404\n",
+      " 0.34557137 0.28535122 0.2435944  0.16020165 0.11774121 0.06673991\n",
+      " 0.02046109 0.0126453 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "\n",
+    "\n",
+    "left_svd_final, singular_values_final, right_svd_final = get_svd(weight1)\n",
+    "print(\"singular_values_final\",singular_values_final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2834330161.py:16: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "####################   Loss   ############################\n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_all = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "for epoch in range(300):\n",
+    "    model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "    model.eval()\n",
+    "    weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "    bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "    weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "    weight1_imp = []\n",
+    "    bias1_imp = []\n",
+    "    weight2_imp = []\n",
+    "    for i in range(width):\n",
+    "        if i in important_indices_set:\n",
+    "            weight1_imp.append(weight1[i])\n",
+    "            bias1_imp.append(bias[i])\n",
+    "            weight2_imp.append(weight2[0][i])\n",
+    "    lst_proj = []\n",
+    "    for i in range(len(weight1)):\n",
+    "        proj = np.dot(weight1[i], right_svd_final[1])/np.linalg.norm(weight1[i])\n",
+    "        lst_proj.append(proj)\n",
+    "    lst_proj_all.append(lst_proj)\n",
+    "    loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "    lst_loss.append(loss)\n",
+    "    \n",
+    "# for i in range(len(weight1_imp)):\n",
+    "#     print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(bias1_imp)):\n",
+    "#     print(\"b{}\".format(i),bias1_imp[i])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(weight2_imp)):\n",
+    "#     print(\"w{}\".format(i),weight2_imp[i])\n",
+    "\n",
+    "\n",
+    "print(\"####################   Loss   ############################\\n\", loss)\n",
+    "lst_proj_all = np.abs(np.array(lst_proj_all))\n",
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch- start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in range(20):\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/364829839.py:16: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "####################   Loss   ############################\n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_all = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "for epoch in range(300):\n",
+    "    model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "    model.eval()\n",
+    "    weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "    bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "    weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "    weight1_imp = []\n",
+    "    bias1_imp = []\n",
+    "    weight2_imp = []\n",
+    "    for i in range(width):\n",
+    "        if i in important_indices_set:\n",
+    "            weight1_imp.append(weight1[i])\n",
+    "            bias1_imp.append(bias[i])\n",
+    "            weight2_imp.append(weight2[0][i])\n",
+    "    lst_proj = []\n",
+    "    for i in range(len(weight1)):\n",
+    "        proj = np.dot(weight1[i], right_svd_final[0])/np.linalg.norm(weight1[i])\n",
+    "        lst_proj.append(proj)\n",
+    "    lst_proj_all.append(lst_proj)\n",
+    "    loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "    lst_loss.append(loss)\n",
+    "    \n",
+    "# for i in range(len(weight1_imp)):\n",
+    "#     print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(bias1_imp)):\n",
+    "#     print(\"b{}\".format(i),bias1_imp[i])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(weight2_imp)):\n",
+    "#     print(\"w{}\".format(i),weight2_imp[i])\n",
+    "\n",
+    "\n",
+    "print(\"####################   Loss   ############################\\n\", loss)\n",
+    "lst_proj_all = np.abs(np.array(lst_proj_all))\n",
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch- start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in range(20):\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "lst_indices = []\n",
+    "for i in range(20):\n",
+    "    if max(lst_proj_all[start_epoch:end_epoch,i])>0.5:\n",
+    "        lst_indices.append(i)\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch-start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in lst_indices:\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n",
+    "plt.grid(True, which = 'minor')\n",
+    "\n",
+    "plt.minorticks_on()\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst_indices2 = lst_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VRXR0NEBNQOIuiqJQUVGBv7+/R3JGPKHVFjohISE171lzEcHIlcb336tTKAD//a8aiAA8/DAMG6aOlJw5owYnH34I99+vvq4oaB95RC3UFRysFgprIOn1MiZOxHngAOu/+orr7r3Xo13qiGSOH68GI4sXi2BEIGhjJEkiJiaGTp06NWnpsMPhYPPmzUycONEjSbWe0GptHb1e36IRkWpEMHIlkZOjBh2gjo5cd13t10eOVHMsfvYzWLUKHnhALQ729tv0/fJLNIsXq3uqfPMNXLTZktsYDFSGhbW8H1cAWePHM+g//0Hatk0t9HZx7RSBQNAmaLXaJv2h1Wq1OJ1O/Pz8WhyMeErL13TqQySwXikoihqI5OWplT9ff73udmFh6mqUl19Wt6/+8EN0w4fTb/Fi9fUPP2x01Y2g5VSGh19YilxVUE4gEAg6KiIYaY+Ulqr7tjSlRMxHH6lBhtGo7vXS0MZwGo1afn3FCggNRTp9GgDXM89cGFkRtDrVq2qoDgQFAoGggyKCkfaEoqi/kvv0UZNLJ09WN5BrjJMnYf589fGf/qSOjLjDjBmwbx/yDTdw+qabkP/wh+Z7FzQZ+dZb1cBw925ISfG2HYFAIGg1RDDSXkhLUwuK3XUXZGerxzZtUjenu+MOSE6u+zyHQ80BqahQp1eamgwZH49r6VKOPvRQ/Ut4Ba1DVJRaKA7EVI1AIOjQNPmvy+bNm5k9ezadO3dGkiSWLVvW6DkbN25kxIgRGI1GevXqxaefftoMqz5MTg7SkiXEbt6MtGwZrFwJGzaoNTsOHFC3iE9NVYMIl6tp2k4nvPuumjD6449qsbCXXlKDjwcfVPM6qhNKH3sMzp+vdbrm1VfVpNTQUPj0UxFQtDfuuku9F1M1AoGgA9Pk1TTl5eUMHTqUn//859x6662Ntj979izXX389jz76KJ9//jnr1q3jF7/4BTExMUyfPr1Zpr2Oy6UOna9cqeZV7NmDDnCrlmhoKEybBjNnqlVPG1qbvW8fPPKIeg9wzTVq7kf//urz//xHnX75/e/hhx/U5NLPPlOPPfUUYceOoXnrLbXtP/8JLdjnROAlbr0VHn9cDWqTk9UpOoFAIOhgNDkYmTlzJjNnznS7/Ycffkj37t155513AOjfvz9bt27lL3/5S/sKRvLzYc0aNfhYtUqtRHoRytCh5CsK4QEBaGw2qKysfauoUO+LitRfudW/dIcPVwOTmTPVKRdAW1GB5pln4G9/U8uzh4TAW2+pyaOXjmwMHqzWDtm8GZ59Vh2Nee01dB9+yFWyjCTL6hJdUWSsfRIRoU6vrV6tTtU8/7y3HQkEAoHHafU6I9u3b2fqJUtBp0+fzlNPPVXvOTabDZvNVvPcYrEAatGV6lv185bgjo6UlITm1VeRdu6stRusEhyMMnUq8syZKNOm4QgPZ1tjO0g6nUh79iCtWoW0ejWavXth/3719sc/ogQHI02ezHU//YQ2Lw8A+Y47cL3zjjqC4nLVP80zbhxs2oT03Xdon38eKTkZf0Du1k09vwXvVVu+38LT5TrS7bejW70a5csvcT77bJv78aRWR/bUkfvmi546ct980VNzddxtLylKU9aHXnKyJPHtt99y880319umT58+PPTQQyxYsKDm2IoVK7j++uuxWq34+/tfds7LL7/MK6+8ctnxRYsWYWqs/LiH0Dgc9P/f/+i1fHnNsZL4eHJGjiRnxAiK+vZF0bUsljMUF9Np/36i9u2j04EDGEpLa16zRkZy8NFHyR05ssm6kstF17Vridqzh5P33ENJjx4t8inwLvqyMmY8+CAap5P1779PqSiAJhAI2glWq5U5c+ZQUlKC2Wyut51PVmBdsGAB86uXoqKOjMTFxTFt2jTMZjMOh4OkxkYh3KBenRMn0N13H9LBgwC4HnsM+emnMXXpQnege1O0GmPOHPXe5cK5Zw/yqlUkZ2bS4623uKq+zercwDFjhkfeI2hB31pJ54r0tGgRrFjBtdnZyL/6VZv68aRWR/bUkfvmi546ct980VNzdapnNhqj1YOR6OhocnJyah3LycnBbDbXOSoCYDQaMdZRlEuv119WE98TZWlrdBQF/vUvePJJNccjIgL+8x+0N9yAuwWBm+1Jr4cJE3CMGcOZFSvoGxLi2b55AI+/3x7givF0zz2wYgXab75B++qr6iqqNvbjSa2O7Kkj982TWr6m40mtjuypqTrutm31YGTcuHGsWLGi1rGkpCTGjRvX2pduGoWF6sqVpUvV5wkJ6kZyMTHe9SUQANx4o1o19+RJtfrupTsqCzo2igJOGzgrwFEJDis4K9XXtEbQVd20BtD5qfdiGb+gHdHkYKSsrIzTVeXBQV26e+DAAcLCwujatSsLFiwgMzOTzz77DIBHH32Uv//97zzzzDP8/Oc/Z/369Xz11Vf8+OOPnutFC5E2bVJrdmRmqiMUb7wB8+aJf8wC38Fshlmz4Ntv1ZVYIhhpPygKuBxq8OCsCiSshWAtuHArz6/1XFeeT4KlAN2Jp6rOqQCamN6n0aPTGZjpAt1x3UWnK1VbSSgX/LmBDrje5UR7pGW/YX1NR3i6iAe+hy5Nz1P0BE3u5Z49e5g8eXLN8+rcjgceeIBPP/2U8+fPk56eXvN69+7d+fHHH5k3bx5//etf6dKlC//61798Y1lvVZKqdulS9R9knz7wxRcwYoS3nQkEl3PXXReCkddfb9JUjaAVUBSwZEH2Ycg+jPb8QSalHUSX9krtUQxnBShyk6QlwARQ10IESQN6kzoCAuCyq9dz2Wq3kx1IdgcGgCbWWqzPkw5AtjXSsn3pCE8X0cTPqSdpcjAyadIkGlqAU1d11UmTJrF///6mXqp1sVrRTppEn9271ecPPwzvvQeBgV61JRDUyw03gMmk7lOzdy9c5VaZPZ9BURQcLgdOxdngd0hD5zsVJ3aXHZvLht1ub5ZOc6isKMaafxJn7hFcuUdx5p7AkX8SZ2UJLgmcSDglcEoS9goJmyRhlyQcRrAb/bFXP5ckHFo9Rp0//noT/oYg/I3B+BuDMfmH4W8Kx98UicEvjAOHkrl6/HX4+4egN5rRGkxIehNo9XUHoopyITCpCk4cleVs3riOiRMnVc3dSxfOrdGQ3ApsHU4nGzdsZNLkSehbsJLQ13SEp4sIbKAIZyvjk6tp2gSTCQYNwn7sGJqPP0Z3zz3ediQQNExAgBqQfPWVOjrSxsGIoigU24opLC0k15qr3ipyax7nWfMod5TjkB04ZWfNrea54qzRevmLl9FJOvRaPTqNDr3mwr1eo0cjabC77GrgIdtqAhD5kl9uQVIQ237axtjYsYyKHkWXwC5Ibo4YWR1WThWf4tiZdRwt3MiR7xdS6iij1FmOxVmJRbZjwYVFUrDXpRlhomr8oplUqjdbHtiAuhYdbPyi5qGEdOE90qrvk4SErMgoKCiKgoysPlcUZEV97HK50K77Twt8XsDlcqFd/VGH0/GkVnv29Mn0Txgc6eZGqh7myg1GANe777Lx6quZLKqTCtoLd92lBiNffaVW5W2lqRpFUThXeo49OXvYl7uP/Tn7ySzNxLnE2fjJbuJUnDidzdeTkChVSlmZtpKVaSsBiAmIYVT0KMbEjGF09GiiA6JRFIUcaw4nC09yovAEJ4tOklyUTLolHaU6b0IDlF5yAc2FK9UcUkAnadBptOg0BnQ6IzpJh06jQytpqbRWEhYchlFrRK/VY9AYMGgvumkM6DQ6bC4bFc4KrE4rFY4KKpwVF547K6hwVNQK3gAUFByyA4fsgCa+bQ5Xy4tweVrL13Q8qdVePcm0o2maDkVAABWRkd52IRC4z8yZ6lRierpa+t9Dq9JkReZM8Rn25exjb85e9ubsJbcit862YX5hRPpH0snU6bJbkCGo5o9z9WhH9U2v0YMMSWuSmDx1Mmio+eNaPXrikB04XA5kRcagNWDUGjFqjTWPL7632qx8/MPHaLtr2ZO7h8N5hzlffp7lZ5az/IxarDA2MJZSeykWe921DiKdTvrYHYRLQXQyRxHsF4zZGIrZFIHZFIU5KAZzUCzmkG6YAqLQaOpf5O9wOFixYgWzZs7ySI2JH378gWkzpiFppdrvkevCewZq8UkJCY2kQZIkNGhqHstOmQ0bNzB58mT0uhZ6cjrYsKHlWr6mIzxdIMI/otnXaSlXdjAiELQ3/P3hppvg88/VqZoWBCPljnLWp69nbdpa9ubupcRWUut1nUbH4IjBjIwaydDwoaTuTeWOWXcQ4BfQ7Gs6HA78Nf6E+YW1+A+2UWukh74Hs4aof/ytDisHcg+wK3sXu7N3c7TgKJllmQBoJS3dg7vTN6wv/bSB9Nm7mL5F5wjXGHHOeocfMwKZNavlQYQn0UgaDFpDiwtVhWpC6RzQ2SMBkie0fE1HePINRDAiELQ37rpLDUa+/hrefbdJS9AdLgdbM7ey4uwKNmZspNJVWfOav86foZFDGRk1kpFRIxkcMRi/qhUbDoeDkgMlGLQGT/fGY5j0Jq6OvZqrY68GoMxexpGCIwQbgukZ0hODRg97P4WVz6iJnmE94M7/oYT3hYwVDYsLBIJWRQQjAkF7Y9o0dSfnrCzYsgWuvbbB5rIisyd7DyvOrmBN2ppaIyDx5nhmdZ/F+Njx9A/vr06ldBACDYGMjVF3wsZRAd8/BQc+V5/3vR5u/gD8Q1q0iaRAIPAMIhgRCNobRiPceit88olaF6eeYKTEVsLqitX8/bu/k23Nrjke6R/JjO4zuL7H9QwIG+D26pN2S2EKLL4fcg6rNTqmvAhXPymKGgoEPoQIRgSC9sg996jByDffwPvvq5WDL+H5bc/zk+0nAAL1gSR0S2BWj1mMihqFtoFEzA7FyZWw9FdgK4GASLj9E+g+0duuBALBJbSrYMThcNTcqp+3VM8TOp7U8jUd4clHPY0fj65TJ6TcXJyrVqHMmFHr5fyKfLaf3w7AH8b8gYT4BIxadfNJ2SUju5q2hK/dvd+5x9BuegNNsrrkV44dhevWT8Acc9m0TLvrm/DkUR3hqXV13G0vKW1VwrAZJCYmkpiYiMvlIjk5mUWLFmEytaTIkEDQcRj8z3/SY8UKMiZNYt9TT9V6badtJ99XfE8XbRceDXrUOwa9QEBlNv2ylxJbtBMJBQWJlMhpHO18F4qmXf32Egg6BFarlTlz5lBSUoLZbK63nU8HI9VYLBaCg4PJz8/HbDbjcDhISkoiISGhxUuePKHjSS1f0xGefNeTtH07umuvRQkMxJmZqS77reKRtY+wN3cvM/1m8vLsl9td35qsZc1Bu/XPSAe/QFLUjVjk/jfhmvg7iOjdJp7a82epPXrqyH3zRU/N1bFYLERERDQajLSrnwp6vb7Wm3Dpc0/p+oKWr+l4Ukt48pDONddAt25IaWno16yBqkrCedY89uXuA2CgYWD77JubGB0lGDe8hHbfp+pyXYDe0+G659DEDKUpKaq+1jdPanVkTx25b57U8paOu23bVTAiEAguQpLg7rvhzTfVVTVVwUhSWhIKCkMihhDiDPGux9bCbkWz8U2mHvsArVwVhMRfA9e9AF3HeNebF1AUBYetEntFRdXNir2igoqyUsrSUzizZyd6gwGtVotGp0Oj0aLRadFodWi0WjQaTaNbCzidTmzFhRScS0fXgo3bfE1HeLpAcKco9Ea/Zl+rJYhgRCBoz1QHIz/+CBYLmM2sTl0NQELXBEjxsr/WQJZhyS/QnvxRfdp5BJopL0KPSa22V09boygKLruNwswMbGVllJcUYS0uovySW2V5GXarFUdlJUoD27//uHWdx7x9vmJJh9TxpFZ79XTPq3+mc59+HrleUxHBiEDQnhk6FPr1gxMnYNkycm6bzv7c/QBM7TqVvSl7vWywFdj6Dpz8EUVrYHfXXzH8nhfRGHy3MuylOO12yooKKSvMp6ywQL0VFVbdVz8uxOWwc/abz5qkLUkaDP7+6P39Mfqb0Bn9KCm1EBocgiK7qlZSOXG5XMguJ7LLVXNrHAW7zY7BaODizQObjq/pCE/VaLTeW/IvghGBoD0jSWrNkZdegi++IOkqDQoKwzsNJ8oU5W13nufUWlj/OgCuGW9xPiuM4T48GuJ0OMhPO0v2mVNkn0km+8wpCjIzwM11A0ZTAAEhoQSEhqn3ISGYgkOrHofiFxiEwd+E0WTC4OePzmisVcSuZvM+D+y74yktX9MRnnwDEYwIBO2d6mAkKYnVp4MBmB4/3cumWoHCs7DkYUCBkQ+iDLsXsnxnTxlX1dz8sc3ryUtNIedMMnlpZ3E5nZe11ekNBIaF176FhhMYFkZgaDh+5mA279jBDbNv9Ok/IAKBpxDBiEDQ3undG0aOJPvsIQ4UHUVCYmrXqd525VnsVlh8H1QWQ+xImPkWeKEogSLLlBbkU3Q+i6LzmRRlq/fF2VmU5OYgu1xkXHKOX5CZ6J69ie7Zp+q+N6bgkAbL8DscDjTaxr+eZVnB5pSxOV3YnDKVDvXe5lCPlVXaOVok4XcyD0nSICsgKwqyouCSFRQF9d6NvrtcTg7lSlTuz0Trhrf2oiM8XWBy30jCA43NvlZLEMGIQNARuPtu1iSlAqhTNAFRHqkC6RMoCvzwlLq3jCkC7vwf6IxtssGdy+kg7fABTu3czvlTJyjOOY+rgetKWh0xvfoQ06sP0b36EN2zD8Gdolq8/09JhYPknFJOZl+4JeeWUmx15z3Qwon9Lbr+xVqfnznaAXU8qdV+PS19/GoRjAgEghZw112sLvkHANNDOtjS1l0fw6HFIGnhjk8hOLZVL+ew20g7uJ/knT+RsncXNmt5rdc1Wh0hUdGExHQmNLozoTGxhMZ0JjCiE5t37OT6669v0tSKwyVTUuGg2Gqn2Oqg2Oog11LBujQNSz/bR3JuGedLKhvV0WkkjDoNRr0Wo06Dn16LQSthLSslNDQYrUaDVpLQSBIaDWgkCa1GQpIkNG7ESoqskJuXS6fITkjunNBOdISnC5j9vDclKIIRgaADkBWq5VAvE5KskLC9AMZ525GHSNsOqxeojxP+AN2vaZXLyE4Hp3b+RMqenaTs243DduGPf0BIKL3HXE33YVcRFhuHOSKyzlUHDoej3hGQYqudfelF7E0r4kimhYJyG8VWByVWB6W2y3NKVDRAfs2zzsF+9IkOom90EH2j1Ptosx9+VcGHTnt5ibcLyYtjPZhQOcJDCZW+oSM8+QYiGBEIOgBJaUkAjEy2Epn0Hcx/wcuOPIDlPHz9AMhOGHgrjJvr8Us4KitJ+tcHnN22iZSLlrcGhUfSe8zV9Bkzns59+iFp3K/lqigKZ/LK2JuqBh9704s4nVvW4DmSpP4qDTHpCfHXY/bToZTmkTB6IANiQ+gTFUSwv+/+IREIWooIRgSCDkB1obPpe0ph71k4dQri471rqiU47WogUpYDnQbATX/3eEEzm7Wcb998hcwTxwAwd4qiz5jx9Bk7nuiefZqU55FbWsmKg5l8fULDSwc2UlxxeS5Hj8gARnYNZXjXUGKC/Qg26Qk1GdTgw1+PVlPHktzRcT79a1Yg8BQiGBEI2jmZZZkczj+MRtIwNWAYsFYtD79ggbetNZ/Vv4eMnWAMhrsWgiHAo/JWSwlL/vgiuWfPYDCZiBg7idse+gWGJhRPyy6pZNWR86w4ks3u1MKq0iEawIFRp2FoXAgju4UysmsoI7qFEhbgvrbT7kJ2gMsho9MqbuUMKIqC7FRw2F047TJOu4sKqw1bkYas5GJAg8sh43LKyE4Zp0PG5VTU5676q7dWI7tkLGcMHFiTgaaOKSF38TUd4ekCfcfEEBgqElgbxeFw1Nyqn7dUzxM6ntTyNR3hyfc9rTyzEoARnUYQMnsCfLcWZdEiHL/5jUf8NMdTS3SkI1+j2/0xAM6bPkAxd61z5UxzPZUVFbLsTy9TmJmBv9nMDb95nv0nT+F0OhsdDckqrmD1sVxWHc1hX3pxrdeGxprppi1iTsJoBncJxaCr/cXfmE+HzUXa4QJO780j41gRihzEv9f+BIBGK6HRSmh1GjQ69V6r1SArCs6Lgo+6K8IH8MOOw429LW5iZFdyagfU8aRW+/UU1SMIY2DdAUtz/725215SFDdLAXqBxMREEhMTcblcJCcns2jRIkwmk7dtCQQ+xT9K/0GmK5Mb/W/katcgZjzwAFqHgw1/+QuW7t29ba9JSIqThKO/xd9RyMnomzgRc5tH9R1lpWSu/xFnWSla/wBir5uFITikwXPKHLAnX2Jfvoa0strBSvcghWHhMkPDFJrzg1KRobJAS0WWnoocHYrLQ1NRkoKkBUmrIGkuutcAmurHFx2T1HMEVzbmHnZ0AZ79HFitVubMmUNJSQlms7nedj4djFRjsVgIDg4mPz8fs9mMw+EgKSmJhISEFmcZe0LHk1q+piM8+banc2XnuHH5jWgkDWtuWUOYXxjaO+9Es2wZjvnzWTFxYrvqm3R0Cbplv0IJ6ITzif1qPREPeSrMOse3b7xMeVEB5sgoblnwCsGdourUcbpktp4p4Ju9maw/mYfDpX5NShKM6hbKjIFRJAzoRLT5wg6n7vpRFIXc1FJO7c4lZX8+lWUXfjkGRfjRa2Qk8cPC2LV/K5MmTUaDFtlVNZ1SNa3icinIThlJI6HTa9AZtegMGvQG9b56OL69f77bQkd4al0di8VCREREo8FIu5qm0ev1td6ES597StcXtHxNx5NawpPndNafWw/AqOhRRAVV7UXzs5/BsmXoliyBa65pP31TFNj1IQDS6EfQ+wd6zFNuagpLXn+BCksJYbFx3P78qwSFRVymk1Fs4+u951iy9xy5pbaa14Z2Cea2kV2YMSiaTkF1b7Gef66M0rN6Dq87X5W7IeO0uXDaXepjuwuHzUV5sY2yogva/kF6eo2Mos/oKKK6m5EkSV0mfAgCgvyv6M93W+p4Uqsje2qqjrtt21UwIhAIalOziubivWiuvx4CA5HS0gg9eVJ93h5I2wZZ+0HnB1f93GOyWcnHWfqnl7GVl9Mpvie3PfcHTObgmtfLbU525Er871+72JNWXHM8LMDALcNjueOqLvSLrvsXnb3Syek9uRzdkkluWingx54TaY160hu19BgWSe/RUcT1C21xsqNA0N4RwYhA0E5Jt6RzvPA4WknLlK5TLrzg7w833wwLF9Jl82aYP99rHpvE9kT1fug9EBDRcFs3ST9ykGVvvYrDVknnvgO49XcvYTRdWJmz7XQ+T3yxj8JyLVCMRoJJfTtx51VduK5f1GVJqNXkplk4ujWLU7tycNjU+iQarYQh3EF87y4Y/fTqtIlRi06vVe8NGnQGLQY/LVE9gtEbvLddu0Dga4hgRCBop6xJWwPA6OjRhPmF1X7xnntg4UI6//QTOJ3g67UqCs7AyaodeMc+7hHJnJTTLP3Ty7gcDroOHsbNv30evZ86xaIoCh9vSeFPK08gKxBuVHjwmt7cMaob0cF1T8PYK5wk787h2NYs8tJLa44Hd/Jn4IRYel4VzoYta5k4q7eoDSIQNBERjAgE7ZRVZ1cBl0zRVJOQgBIejl9BAc4tW2DatDZ210R2/ANQoPd0iOzTYjmX08Gqf7yHy+Gg+7CR3Pib59BV1RAptzl5Zskhfjx0HoBbhnfman06N1/bo1YQIcsKBZllnD9dwvnTxaQeKcBZPQqik+g5vBMDJ3Smc5+QmjwPgUDQPEQwIhC0Q86WnOVk0Ul0kq72FE01ej3K9OlIixYh+XowYi2EA5+rjz1U8n33d0vIT0/FP8jMjLnzawKRs/nl/Op/e0jOKUOnkXhp9gDuGtmZlSvTcdpd5J6tCj7OFJN9pgR7pauWbmi0iQETOtN3bDT+ge4XMRMIBA0jghGBoB2yJlWdohnTeQwhfiF1tlHGjoVFi5B27WpDZ81g76fgsELUYOg+scVyBefS2bH0SwAmP/SrmmTVtcdymLf4AKU2J52CjPzj3hEM7mRm/+o0creb+HTNdmRX7UoHej8tMT2CiekVTGzfMKJ7mJtUJl4gELiHCEYEgnZIdb7I9G51TNFUIY8ZgxaQdu8GWYYmbPbWZjjtsOuf6uNxc1u8/4wsu1j90d9wOZ30GDGKfldPRJYV3lt3ir+tOwXAVd1C+fvdwyk4UsjCvx+lotQBaAEFU7CBzr1CiOkVTEyvEMJjA9G0cCt4gUDQOCIYEQjaGYWVhSQXJQMwKW5S/Q0HDcJpMKArKlI3zuvbt20MNoWj30LpeQiMhkEtr7Z6YPWPnE8+gcHfn6m/mIulwslTi/ez4WQeAA+M68YDPaLZ8P4hCrPKAQiO9EcTVcj02yYSFh0oRj4EAi/ggz+VBAJBQxzIPQBAj+AehPqF1t9Qr6ekZ0/18Y4drW+sqSgKbH9ffTzml6BrWQ5GSW4OW774LwATf/ZzLNoAbkzcyoaTeRh1Gt6c2o9RGS5WJqqBiNGkY8Kdvbn99yMIiHNijvATgYhA4CXEyIhA0M6oDkaGdxreaNvCvn0JP35cDUYeeKCVnTWR1C2QfRj0Jhj5UIukFEUh6eO/47TZ6DJgEL2umcIdH+0krcBKjyB/fh0Vyfml6eTLChqNxOBJXbjq+nj8AvRiFYxA4AOIYEQgaGfsz90PuBeMFPWpWia7c2drWmoe1UXOhs0BU1jDbRvh6KZ1pB3aj05vIOGRX/P0ksMcy7IwESPj87RkZajTNN2HRnD1rb0IiRIbbgoEvoQIRgSCdoTNZeNowVHAzWCkOk/k0CEoL4eAgIZPaCsKTkHyKkCCMY+1SKq8uIiNn30MwLg75rDwuJUVh7OZYNcxpkKDExfhXQKZcHsvuvRrWdAjEAhah3YVjDgcjppb9fOW6nlCx5NavqYjPPmWp4O5B3HIDsL9won2i27wWg6Hg8rwcOTOndFkZeHcuRPlmms87qk5OmqRM5B7T8cV3A2aoVutteHTj7CVlxMZ34OcuFG8+9URujk0jKtQC5iNvbk7gybHotHUXZjsSv0stXdPHblvvuipuTrutpcURVEab+YdEhMTSUxMxOVykZyczKJFizCZxPCq4Mplc+Vm1lSuYYB+AHMC5rh1zqg336Tz9u0cvf9+Tt96ays7bByDs5SEI/PQKXa29v49BYH9mq1Vln6W7K1rQZIwXnsLf8vohNGp4eFyI3qXRECcndBBtsaF2hmaykpMp0+jLStH0euQdToUvR6l6l7W6VB0ehS9DkWrBSSQoOo/IIFycbKuSNwVAC5/f9B5dozCarUyZ84cSkpKMJvr3nASfHxkZO7cucydOxeLxUJwcDDTpk3DbDbjcDhISkoiISGhRXtAeErHk1q+piM8+Zan1RtXQxbMGDSDWf1nuaUTccMNsH07/S0W+sxq+JzmeGqqTtr/fo1OsSNHD2XMHfOa/YewrLiI/y5dCMDAGTfzanY3nK5KHnKZ0LtkwrsEcNO88ej0DS8abC+fJcf585Rv3ET5xo1U7Nql7jkkEHiQLgv/h9/QoXW+1tzPt8VicaudTwcjl6LX62u9CZc+95SuL2j5mo4ntYSn5unIisyhgkMAXBVzldvX0Iwbp97v3IlGp2vRr+AW981po3tekurn6l+jMTR/Oe+OrxbiqqwgJCaW/1j7kFls4Sb8MZfLGPx1zPzVEPxNRrf1WtI3xenEumcvoZs2U2F3IEdHo4uMQBcZiTY0FKk5BecUBdepU5Rv2kzphg3Yjh+v9bIhPh5Dz54odjtKZSWy3YZiu+SxzYazshKtRgOKglKlS/WAePVj3x0gF7QhOp2u0X8DTf134m7bdhWMCARXMqklqZTYSvDT+tEv3P2pDWXECNBq4fx5OHcO4uJa0WXDSEeX4ucsQQmKQRp4c7N10o8c5Njm9QCc7Xc9O1MtDFH09ClRX5/6YH+CI/094Lh+FIeD8h07KV2zmtKktbiKi4kEclesqN1Qp0MXHo4uMlK9RYSr/z8UagcFaqiAoijIlTa6b97MuYt/VWo0+A8fTtB1kwmcfB3GHt0b9ehwOFixYgWzZs3yyGiNJ7R8TUd48g1EMCIQtBP25e4DYFDEIPSaJnypmEwwdCjs26fWG/FWMKIoaHd9CIA86hG02uZ/Me754VsAymP7szBVS5gsMbPCgIzM8Gld6T400iOWL0Wx2ynfvh3L6jWUrluHXFJS85omNJSSLl2IMgfhyi/AmZeHq7AQnE6cOTk4c3KadC09IPn7EzhhAoHXXUfgtRPRhYnVQIKOiQhGBIJ2QlPqi1zG2LFqMLJzJ9xxh4eduUnmPqTco7gkPfKw+9E2U6assIDUA2pg9q00FJ0CD0tByA4HnXuHMPamHp7zTNUIyLZtWFaspHT9euTS0prXtBERBCVMxTx9OvqhQ1m5Zg3DL/oFqjgcOAvUwMSZl4czNw9XUSGKLKsCknSh6qskUZ1gKisKh0pKuOaJuRgDAz3aH4HAFxHBiEDQTmhK5dXLGDMGPvjAu2Xh96ml2rNCRhHtH9JsmWNbNqAoMrn+MRTpQvmlnxlyHPibDUz7xUA02pbvcqEoCpWHD1Oy/HssK1aoIxxV6CIjCZo2jaDp0zCNHImkVcOqupYwSno9+uho9NHRTbq+w+GgfMUKNEb3c14EgvaMCEYEgnZAfkU+6aXpSEgM7VR3tnuDjB2r3u/dq9b0aOu5Y1sZHFkCQFr4JJr2p/kCiqJwdONaAA4H9GGSpCE4x4EkwfSHBxIQ3LI/3vaMDEq+/x7L8u+xp6bWHNeGh2OeORPzzBn4Dx/evIRUgUBQLyIYEQjaAdWjIr1Ce2E21L9Wv15694bQUCgqUquxjhzpWYONcWwZ2MtQQrtTENj83YPPnzpJYdY5nJKOYmNvZpWoq3HG3tyT2L4NbBrYAK6SEoJ37ODcl4up3L+/5rjk50fQ1KkE33QjAePGIXm4/oJAILiA+NclELQDavJFIpsxRQNqPsLYsbBypTpV09bByL7/ASAPuxeKm7+0+OgmdVTkrKkHN9mDkBSJroPCGJ7QtclaiqJQsnQp2X98g6jycioBNBoCxo4l+KYbCZwyFW2gj5TPFwg6OCIYEQjaATXBSFQzgxFQ80aqg5G5cz3kzA3ykiFjB0ha5MF3wZZ9zZJx2G2c3LYFgHDdIAKdElp/mcn39UHSNC3AceTkcv7FFyjftBkAW1QUne+7l9DZN6KP6tQsfwKBoPk0a+IzMTGR+Ph4/Pz8GDNmDLt27Wqw/XvvvUffvn3x9/cnLi6OefPmUVlZ2SzDAsGVRoWzguMFasGrZiWvVlOdN9LWSaz7P1Pv+0yHoOZmi8DpXduxWctxaIKIkbqh1WsIH16B0eR+/ouiKJR8/wMpN95I+abNSHo94fPnkfbUk4Q+8IAIRAQCL9HkYGTx4sXMnz+fl156iX379jF06FCmT59Obm5une0XLVrE7373O1566SWOHz/Ov//9bxYvXszvf//7FpsXCK4EjuQfwak46eTfic4BnZsvNHq0en/6NBQUeMZcYzjtcPBL9fHw+1okdXTTOgD8DANRJImEh/tjCJbdt1JYSOaTT5H19NPIJSX4DRxI96VLCH3oIRAJqQKBV2nyv8B3332XRx55hIceeogBAwbw4YcfYjKZ+OSTT+psv23bNsaPH8+cOXOIj49n2rRp3HPPPY2OpggEApWLp2iklmxoFhoKfauSR3fu9IAzN0heBeV5EBgNvac1W8aSn0vaoQMAaA0DCLg6kq4D3S8AVrp2LSk3zKZ0zRrQ6Yj49RPEf/kFxt69m+1JIBB4jiYFI3a7nb179zJ16tQLAhoNU6dOZfv27XWec/XVV7N3796a4CMlJaWmNK1AIGicFhU7u5S2nqrZryauMuwe0DY/RW3bNz8ACpKuC3sCA7ntdvfK4btKSsh69lnOPfFrXIWFGHv3Jn7xl0TOnYvkw6WxBYIrjSZ9O+Tn5+NyuYiKiqp1PCoqihMnTtR5zpw5c8jPz2fChAkoioLT6eTRRx9tcJrGZrNhs13Y9rt61z+Hw1Fzq37eEjyl40ktX9MRnrzrSVbkmmW9g8IGNUm7Lj+aUaPQ/ve/yNu342qhVqNYstCdXosEOAbfDc38t5ufUVqzD02Of3/8h4di0jfuyZ6eTuZDP8eVmwsaDSEPPUT4448hGQy1zrlSPksdzVNH7psvemqujrvtJUVxf7vGrKwsYmNj2bZtG+OqdgIFeOaZZ9i0aRM76xj63bhxI3fffTevvfYaY8aM4fTp0zz55JM88sgjvPDCC3Ve5+WXX+aVV1657PiiRYswmUzu2hUI2j3Zrmz+Xvp3DBh4Lvg5tFJzi6irmFNSmDx/Pg6TiRULF7ZqrkSf7O/of34J+YH9+Kl383LEnBUS2ZuKqCz8GkXS83HcA9w/QMOgsIa/tiSHg7jED/A7fx57RATZd95JZbemL/8VCAQtw2q1MmfOHEpKSjCb66+R1KRgxG63YzKZ+Oabb7j55ptrjj/wwAMUFxfz3XffXXbONddcw9ixY3n77bdrji1cuJBf/vKXlJWVoanjy7CukZG4uDjy8/Mxm804HA6SkpJISEho8W6GntDxpJav6QhP3vX0Xep3/HH3HxkdNZoPp3zYcj9OJ7qICCSrFcfBg9C/f/O1GkKR0SVehVSSjvPGD1AG39lkHXuFk+XvHSTn1Le47Ec5EdCPPfHT+OmZa9FrNQ1q5b78CpYlS9CGhRH39VfoOtW/SuZK+Sx1NE8duW++6Km5OhaLhYiIiEaDkSZN0xgMBkaOHMm6detqghFZllm3bh1PPPFEnedYrdbLAg5t1V4O9cVBRqMRYx17Muj1+lpvwqXPm4undDyp5Ws6ntQSntzXOVRwCIAR0SOarVnLj14PV10Fmzej37sXhgxpvlZDpGyEknQwBqMbdMtl5ecb03E5ZdZ+coSCzGJcjmQAjpr7ctOwWEx+tb8bLtUq+e47LEuWgCQR+86f8Y+N9Wzf2kjHk1od2VNH7psntbyl427bJo/Rzp8/n48//pj//ve/HD9+nMcee4zy8nIeeughAO6//34WLFhQ03727Nn84x//4Msvv+Ts2bMkJSXxwgsvMHv27JqgRCAQ1I1Hk1eraYsk1qqKqwy+HQxNm1pVFIWNC09w7kQRKGdAcVCiDybLGMNtI7o0eK7t9GnOv6xO8UY8MZeAi6aTBQKB79Lk9Pa77rqLvLw8XnzxRbKzsxk2bBirVq2qSWpNT0+vNRLy/PPPI0kSzz//PJmZmURGRjJ79mxef/11z/VCIOiA5FnzyCzLRCNpGBrZjM3x6qM6GGmt5b3WQjj+vfp4xP1NPn3/mnRO7MhG0kgEms9iK4FjgX3pHRXEoNj6h3nl8nLOPfkUSkUFAVdfTcSjjza3BwKBoI1p1lq7J554ot5pmY0bN9a+gE7HSy+9xEsvvdScSwkEVywH8g8A0Ce0DwF6D+6RMmaMen/4MJSVQWCg57QBDn8NLhtED4bOw5p0anmxjd0/nAXgqpmhbFl4AgU4EdiXuSO61FtnRVEUzr/8CvYzZ9B16kTnt99CEiOvAkG7QZQdFAh8lOolvR6dogHo3Bni4kCWYc8ez2orCuyrKv8+4oEmn77r+xScDpnoHsE4Ko4AkOHXhXJ9IDcPr7/6bPHXX2P5/nvQaol99x104eHNsi8QCLyDCEYEAh+lemTE48EItF7eSNZ+yDkCWqOaL9IECrPKOb7tPADjbulRU1vkWFA/xveMICbYv87zbCdOkPOaOu3bad5TmK66qgUdEAgE3kAEIwKBD2JTbCQXqatIWjUY8XTeSPWoyIAbwT+0SaduX3YGRYEewyNxVKZhycvFoTWSYurObSPrXhGjqagk+ze/RbHbCZw0ibCf/7ylPRAIBF5ABCMCgQ9yznkOl+IiJiCG6IDm73RbL9V5Izt2qFMrnsBeDkeWqI+bmLiamVxE6qF8JI3EuJt71myKd8LUE6OfkekDL38PFEUhaskSHOnp6Dt3pvOf3kASG94JBO0S8S9XIPBB0lxpAAzrNKx1LjBiBOh0kJ0N6eme0Tz2HdgsEBoP3Sa4fZqiKGxbchqAgRM64x+kcGrnNgCOB/Zj5qAYTIbLc+1LFn1B0OHDoNMR+95f0IaEeKIXAoHAC4hgRCDwQdKdaoDQKlM0AP7+MGyY+thTeSPVtUWG39ekMvOn9+aSm1aKzqhl1A3dObl9C067jWJDKDnGTtw24vIpmopDh8j/858BiPjtb/FvYvE2gUDgW4hgRCDwMVyyiwxnBgAjOo1ovQtVT9V4Im+kMAXSt4GkgWFz3D7N5ZTZ8V0KACOmdcVkNnBkQxIARwP60jnEn7E9aq+McRUXk/nUPHA6KR08mOA597Tcv0Ag8CoiGBEIfIzTJaexYSNQH0ivkF6tdyFPrqg5slS9734tmOtfgnspR7dkYsmrwGQ2MHRKHPnpqZw/dRJF0nAisC83D49Fo7lQW0RRFLJ+/xyOrCz0cXHk3HZbvbVHBAJB+6FZRc+8haNqC3Jvb4ncmlq+piM8tb3W3uy9AAwMG4jskpFdcuv4GTkSPaDs24ezrAzq2A/KXS3dkaVIgLP/TSgN9P9iHXuFk90/qgXORszsiqRVOLh2FQBnTd2w6kzMHhxV65pF//2MsvXrQa8n4s0/IWdk+MxnwBc/Sx3ZU0fumy96aq6Ou+2btGtvW5OYmEhiYiIul4vk5GQWLVqEydS0fS4EgvbG4vLFHHYcZorfFCb7TW69CykKM+6/H2NpKZveeoviPn2aJRNYmcWU479DRsuqwe/j0LlX0bUk2UDpGSO6ABdRE6woipPUbxch220sj5qFEtmV3wxx1bT3S0sn7sMPkWSZnJtvpmTc2Gb5FQgEbYfVamXOnDme3bW3rZk7dy5z587FYrEQHBzMtGnTMJvNXt8SuTW1fE1HeGp7rfe/fR8ccOuYWxkX2/yN3tzxo50wAVauZIJOhzxrVrO0NJvfUh/0nEzCjXe65enqUdeyZO0BQOa6OYOJHxLOyW1bSLHbqDAEke4fxwuT+jNrbFdAzRPJ+Mt7OGWZwBkz6PmHV3A6nT71GfDFz1JH9tSR++aLnpqrY7FY3Grn08HIpVy6dbGvba3sSS1f0/GklvBUP+mWdHIqctCgYVjUsNbv29VXw8qVaLdvRztvXtO1FAWOfweAZvDtaNz0ezApE5dDJqZXML1GRCFJEsc2rQXgkKkvAX4Gbh/VFb1ejyLLZD//As7sbAzdutH5tVfRGgw1uSK+9hnwlc9Sa+h4UsvXdDyp1ZE9NVXH3bYigVUg8CGWnV4GQE9dT/x1dZc/9yiTJqn3Gzc2r/hZ7jHIPwlaA/Srf2TlYhylGpJ35ABw9a29kCSJouwsMo4eQkGtLXLP6DjMfuqXWOF//kPZpk1IBgOxf30Prac39hMIBF5HBCMCgY/glJ18d1odZRhpGNk2Fx09GkwmyMuDo0ebfn71KppeCeAX7NYpJSeNKAr0HB5JdA/1nCPr1wCQ7h9HhdHMQ+O7A2Ddt4/cd/8CQNRzz+HXr1/TPQoEAp9HBCMCgY+wLWsbuRW5hBhD6Kdvoz+6BgOMH68+3rChaecqChytCkYG3erWKVmniqnM0yFpJMbe3BMAl9PJkY3qFM3RoAFcPySGziH+OIuKyJw3H1wuzDfcQMiddzTNn0AgaDeIYEQg8BGWJKv7ulwffz06qQ3TuSZXrdhpajBy/qBa7EznD31mNNpcURR2LU8FoP/4aEKi1JVxKft3Yy0pxqr156ypG49c0wNFlsl65lmcOTkYuncn5pWXRT0RgaADI4IRgcAHyK/IZ/O5zQDc3PPmtr14dTCyaRPITahpUj0q0mcaGBvP4zh/upjc1FLQKIyY0bXm+OF1qwE4HtiXsb06MSg2mIJ//ZvyLVuQjEZi33sPTUCA+74EAkG7QwQjAoEP8P2Z73EqToZEDqFnSM+2vfjIkRAYCIWFcOiQe+coChz5Vn080L0pmgNr1RL3AbEOTGYDAJb8PFIP7APgaFB/HrmmB9Y9e8j7618BiH7hefz6Nq/+iUAgaD+IYEQg8DKKorD0lDrKcGsv9/6wexS9Hq65Rn3s7lTNuT1Qkg76AOg9rdHmxblWzh7KByAw/kJFxqMb16IoMuf8OtMptgvXdA0i63cLwOUi+KYbCb7ttiZ3RyAQtD9EMCIQeJn9uftJtaTir/NnRvfGcy9ahabmjVRP0fSdCYbGqyIfWn8OFIgbEIo+UJ0KkmUXhzeoq2iqR0UKP/oIx7lz6KKjiXrhRZEnIhBcIYhgRCDwMtWjIjPiZxCg91JuRHUwsnkzuFwNt5VlOLpMfezGKprKcgfHt58HYMh1sTXH0w8doDQ/j0qNkeJO/ZlprqDgk08AiHru92gDRZ6IQHClIIIRgcCLlNnLWJOmjg7c2tsLUzTVDB8OwcFQUgL79zfcNmMHlGaB0Qy9pjYqfWxrFk6bi/DYQDr3Cak5fqgqcfVkYB/uH9+DwldfBYeDwMmTCZrauK5AIOg4iGBEIPAiK1NXUuGsoHtwd4ZGDvWeEa0WJk5UHzc2VVNd6Kzf9aCrf6dfAJdL5tCGcwAMnRJXM+1iLSnm9J6dAJwOHcgtuQex7tmD5O9P9PPPiekZgeAKQwQjAoEX+faUuiLltt63ef8PsDt5I7ILjqlVYt1ZRXNmXy7lxTb8zQb6jIqqOX58ywYU2UW2sRPXD+tB+V/fBSDyibnoY2PrkxMIBB2UdrVRnsPhqLlVP2+pnid0PKnlazrCU+tpnSo+xeH8w+gkHTO6zrjs/Dbv24QJ6AFlyxacVqu6yuYSLVfKZvTluSh+ITi7jocGtBVF4UBSOgADr4lBxoXD4UBRFPavVadojgX15509y3AVF2Po3Zuge+5x26+vfQbE57t96ghPravjbntJUZqzO1bbkJiYSGJiIi6Xi+TkZBYtWoTJ1HjmvkDQHvjR+iPb7dsZoB/AnIA53rYDsszM++/HUFbG5jffpKhv38uaDE3/D/EFG0gLv5YDXR9uUM5WqCVvpwk0CjGTytEa1a+aitzzZK79AbukJyf6Gn616mMA0h97jMr4bp7vl0Ag8BpWq5U5c+ZQUlKC2Wyut51PByPVWCwWgoODyc/Px2w243A4SEpKIiEhoUVbIntKx5NavqYjPLWOlt1lZ/q30ymxl/D+pPcZ33m8xz01R0d7++1oli/H9eqryM8+W0tr7ZqVzD75G6SKIpz3fIPSY1KDWms+PkbqoQL6jY9m4t29a3Q+een32NJPcyywH/MyjqJNT8V82210evmlVu9fe9ARntpWR3hqXR2LxUJERESjwUi7mqbR6/W13oRLn3tK1xe0fE3Hk1rCE6w7t44SewlRpiiuibsGrUbbap6apDNlCixfjnbzZrTPP1/rpYjSY0gVRWCKQNdrMmjr//ooybOSergAgOFTu9Vcv7K8jIpzZ9EAg50utOmpaMPCiH76t2ib2Vdf+wyIz3f71PGkVkf21FQdd9uKBFaBwAtU1xa5qddNdQYiXqM6ifWnn8Bur/VSbJG6+oUBNzYYiAAcrCpy1nVgOGExF+qFHN60AY3solgXzNT9aqJs1LPPoA0J8VgXBAJB+0MEIwJBG5NZlsmO8zsAuKXXLV52cwkDB0JEBFitsGvXheMuOzEle6vaNLyKxmZ1cHybWuRs2JS4muOKorBz9SoAupeUo7XbMI0Zg/nGGz3bB4FA0O4QwYhA0MYsO70MBYUxMWPoEtTF23Zqo9HApEnq44uW+EopGzC4rCgBnaDb1Q1KHK0qchbWOYAu/UNrjmedPI5ckIWMhqvOnkLS64l+6SXvL2kWCAReRwQjAkEb4pJdLDu9DPDSpnjuUD1Vs3FjzSHNsWUAyP1vggamlVwumcNVRc6GTY2rFWhs/E7ViCouR++SCX/kEYw9unvUukAgaJ+0qwRWgaC9s+P8DrLLszEbzEzpNsXbduqmOhjZtg1sNtAoSMkrAVAG3NzgqSn78igrsuEfpKf3RUXOrJYSzh/YiQT0zi1A37Ur4b/6ZSt1QCAQtDdEMCIQtCFLTi0B4IYeN2DUNlxK3Wv06wfR0ZCdDTt2QFQ5kr2MCn0Yui6j6j1NURQOrFWLnA2e1AWd/sIIyuH1a5BkF2arjZAKG5HP/R6N0Uf7LxAI2hwxTSMQtBGFlYVsyFDzMLy6KV5jSFLtvJEz6wDIDh4GUv1fGefPlJCbVopWp2HQxAsl3WXZxd7VKwCIzy+hMjIS/3HjWsu9QCBoh4hgRCBoIxafXIxTdjIgfAB9wy6vbupTXLxPzRk1gMoNGtzgKQfXZQDQd0wU/kGGmuOpB/dRUZiHJENMcRllQ4eKpFWBQFALMU0jELQBZfYyFh5bCMBDAx/yshs3qA5Gju+EQiOKpCU/qH+9zcuKbKQcyANgyEXLeQEOrP4RgK4FJWgVhdKhQ1rHs0AgaLeIkRGBoA348uSXWOwW4s3xJHRL8LadxunVC2JjIU4GQIkdiVNb/75QqYfzQYGo7mbCOwfWHC/JzeHsAbU+SXx+CbrevXF06tS63gUCQbtDBCMCQStjdVj57OhnAPxyyC99q+JqfUiSOjrSQx08Vbpf22DzswfzAeg+NKLW8UNrV4KiYKiEALsD88wZreNXIBC0a0QwIhC0Ml8nf02RrYi4oDhmdp/pbTvuM+naC8FIj8n1NrNXOjl3shCA7kMia447HQ4Ob0gCYEB2DgBBM0QwIhAILqdd5Yw4HI6aW/Xzlup5QseTWr6mIzy1TKvSWcl/jvwHgIcGPITiUnC4Gr6Wr/RNGhCOLkNCqVSw+3cH8uvUSj2cj+xUMEf4ERihr2lzcttmKiwlONETXVKGfsAAdcnw4cMd8jNwJX6+O4KO8NS6Ou62lxRFUZrsqo1ITEwkMTERl8tFcnIyixYtwmSqf95aIPA1ttu282PFj4RIIcwzz0MrtYMpmip6Zy9nwPlv4LiDbf2fIW/48DrbFR7yw5qpJzDeTkh/W83xc0nLqczLwVyiMCE1hbxZsyi6dmJb2RcIBD6A1Wplzpw5lJSUYDab623n08FINRaLheDgYPLz8zGbzTgcDpKSkkhISGjRlsie0vGklq/pCE/N17K5bNy4/EbyKvL4/ajfc3vv29vUU0t1tP+7EU36NvixAsfEX7Ni4sTLtGSXwv+e24Gt3MkN/zeYzr1DAMhPT2XR7+chI3Hd0VRMTifd1qyGiIgO+xm40j7fHUVHeGpdHYvFQkRERKPBSLuaptHr9bXehEufe0rXF7R8TceTWleKp6VnlpJXkUeUKYrb+t6GXts0fa/2zVYG53arj8840UpbYOLEy7SyUouxlTsxmnR06RuOVqumoR3dsAaACiUIk9OJcehQTF271gzZduTPQEfumye1fE3Hk1od2VNTddxtKxJYBYJWwOFy8O8j/wbg54N+jkFraOQMHyPtJ5AdEBgLRQrSvn3orNbLmp09pK6i6TboQiBis1o5tmUjAD3yigAIuX5Wm9gWCATtExGMCAStwPcp33O+/DwR/hG+Xfq9PqqqrtJnKvTogeRyEX7s2GXNUquCkfghF5b0Ht+yAUdlBeWGYEacT0WRJIKmi1U0AoGgfkQwIhB4GKfs5ONDHwPw4MAH8dP5edlRM0ipCkZ6XldTjTXi8OFaTYqyyynOsaLRSnQdGA5UbZa3Rq246qzwQwL8Ro5EHyUKnQkEgvoRwYhA4GFWpa7iXNk5wvzCuKPPHd6203RKMiHvBCBB94kwZQoAsVu3QkVFTbPqKZrYPiEY/dX0s8wTRyk4l46i0zMsU93BN1RM0QgEgkYQwYhA4EFkRebfR9VckfsH3I9J3w6XoqdsVO9jR4ApDG65BaVrV/wLCtD8/e81zS5M0VwodHZgjbo7b05APP2LzqFIGoKmTWsz6wKBoH0ighGBwIMcdhwmrTSNYGMwd/e729t2mseZ9ep9ddVVPz9cL78MgObNNyE/n4pSO9lnSgCIH6JO0ZQXF3Fq5zYA9PlqvRH/MaPRhYe3nXeBQNAuEcGIQOAhZEVmU+UmAO7rfx8B+gAvO2oGsnxhZKTnhRLwypw5lMTHI1ks8PrrpB0pQFEgvEsg5nB/AI5sSEJ2OVEiuzH+3AlArKIRCATuIYIRgcBDbMjYQK6cS6A+kDn953jbTvPIOQLWfNAHQJfRF45rNBx98EH1cWIiZ7enAdC9ahWNLLs4uHYlAClSZ3pYzqNotZgT2sEOxQKBwOuIYEQg8ACKovDxEXUFzT197yHIEORlR82keoomfgLoatdGyRs2DDkhAacM6SfVKZrqXXqzTh6nND8Pnb+JkIxsAPyvvhptSEibWRcIBO2XZgUjiYmJxMfH4+fnx5gxY9i1a1eD7YuLi5k7dy4xMTEYjUb69OnDihUrmmVYIPBFVp5dSXJxMgYMzOnbTkdFoPaS3jpwvf46mbHDcUp6AkwSkV3VoCtl/x4A7DF9uTbzECBW0QgEAvdpcjCyePFi5s+fz0svvcS+ffsYOnQo06dPJzc3t872drudhIQEUlNT+eabbzh58iQff/wxsbGxLTYvEPgC69LW8dxPzwEwzjiOYGOwlx01E0cFpG1XH1+UL1KLYcM4m/AgAPGZu5GqDp/dp5aOTy0z0rU0F1mnJ6hqSbBAIBA0RpP3pnn33Xd55JFHeOihhwD48MMP+fHHH/nkk0/43e9+d1n7Tz75hMLCQrZt21ZToz4+Pr5lrgUCH2FFygp+v/X3uBQXCV0TmFAywduWmk/aNnDZIKgzRPSps4miKKSGDIAyF923L4EVA7GMGUV+RhpIEhFpmQAEXDMBbVA7naoSCARtTpOCEbvdzt69e1mwYEHNMY1Gw9SpU9m+fXud5yxfvpxx48Yxd+5cvvvuOyIjI5kzZw7PPvssWm3d26nbbDZstgtbkVssFkDdNbD6Vv28JXhKx5NavqYjPNXPsjPLeHXnqygo3ND9BhaMWMCGdRt85n1qqo7m9Dq0gNx9Ei6ns06t7LPFlJe50OEkNms/ytNPc+rtPwIgR3Rj4vYDAJhnzqjzuh3tM9AaOsJT2+oIT62r4257SVEUxV3RrKwsYmNj2bZtG+PGjas5/swzz7Bp0yZ27tx52Tn9+vUjNTWVn/3sZzz++OOcPn2axx9/nP/7v//jpZdeqvM6L7/8Mq+88splxxctWoTJ1A6LSAk6HDtsO/ih4gcARhlGMdt/NhqpfeeDTzr+HMGVGezp9hiZYePqbFNyykDpaSOm8Ap+9v49GEpL+fTe2ymwV5AW2I/HfvoRp07P2RefRzEa27gHAoHA17BarcyZM4eSkhLMZnO97Zo8TdNUZFmmU6dO/POf/0Sr1TJy5EgyMzN5++236w1GFixYwPz582ueWywW4uLimDZtGmazGYfDQVJSEgkJCS3aEtlTOp7U8jUd4elyPjv+GT/sVwOROX3n8JsRv0GSJJ97n5qkU5aLfn8GAENveZKhARG1Xq7W0leEAlZGzxyKNvRFHL97lpKKctBqiMxVd+gNnDSJmbfc0qp986SWr+kIT22rIzy1rk71zEZjNCkYiYiIQKvVkpOTU+t4Tk4O0dHRdZ4TExODXq+vNSXTv39/srOzsdvtGAyXb61uNBox1vGrSq/X13oTLn3eXDyl40ktX9PxpFZ79aQoCh8e+pAPDnwAwCODH+HXw3+NJEm12vna++SWTsZP6n30EPQhMXU2cVZIFGZakSToMbQT2lG/Jm3hf3FqNSg6fyacU3f1DZ99Q6PXa6+fgbbU8aRWR/bUkfvmSS1v6bjbtknjygaDgZEjR7Ju3bqaY7Iss27dulrTNhczfvx4Tp8+jSzLNceSk5OJiYmpMxARCHwRRVF4b997NYHIr4f/mv8b8X+XBSLtlur6IvUs6QWozFV/u0T3DMY/yABGIykzpgKQJ5uJrihGNvoReO3EVrcrEAg6Fk2e5J4/fz4ff/wx//3vfzl+/DiPPfYY5eXlNatr7r///loJro899hiFhYU8+eSTJCcn8+OPP/LHP/6RuXPneq4XAkErIisyf9r1Jz458gkAT1/1NL8c8ksvu/IgigJnquuL1LOkF6ioCkbiq6quKorC2VK1+JnWof76CRg9Co2fXyuaFQgEHZEm54zcdddd5OXl8eKLL5Kdnc2wYcNYtWoVUVFRAKSnp6PRXIhx4uLiWL16NfPmzWPIkCHExsby5JNP8uyzz3quFwJBK/L27rdZdGIRAC+MfYE7+97pZUceJvc4lGWDzg/ixtbZxF7hxFagTrVWl4AvOJeOJT8XRaOlb/Z5AIL61L0kWCAQCBqiWQmsTzzxBE888USdr23cuPGyY+PGjWPHjh3NuZRA4FWWnlrKwuMLAXh1/Kvc3Otm7xpqDaqrrnYbD/q6RzXOnSgCRSK4kz+h0eoGgClVhc5yTbFMyd8KQMDmTfD0b1vfs0Ag6FC077WIAkErcjDvIK/teA2AucPmdsxABNyaokk9XAhAt8FhNcfOVpWAL3aY8HM50LhcGFesgKKi1vMqEAg6JCIYEQjqIM+ax7wN83DIDqZ0ndKxckQuxmmDVHVUo77kVdklk3G0KhgZFA5AZVkZmSfV1TNRlnIAArRaJFmGOkZHBQKBoCFEMCIQXILdZWfexnnkVeTRM7gnr094vd0XNKuXjJ3grIDAKOg0oM4mWaeKsVmdaPQyUd3VokWph/ahyDIlxjCG5KUBENC/n3pCUlKbWBcIBB2HDvoNKxA0D0VR+OPOP3Iw7yBBhiD+dt3fCNAHeNtW67H3U/W+xySoZ5ny0S1ZAPhHO9Fo1TY1G+MZOtOvsCoYmT1bPWHt2lazKxAIOiYiGBEILuLr5K9ZcmoJEhJvTXyLruau3rbUehxdBkeWgKSBMb+qs0l5iY2U/XkABHRV95iQZRdnD+wFQGfXolNk9J07o7/1VtBq4dQpSEtrky4IBIKOgQhGBIIq9ubs5Y2dbwDw5IgnmRDbjnfgbYyyXPhhnvp4wnyIHVlns2Nbs5BlhajuZgxmtXBh9ulTVJRasGsM9CjMB8B09TikkBAYPVo9UYyOCASCJiCCEYEAyLHmMH/jfJyKk+nx0/n5oJ9721LroSjw/ZNQUQhRg+Haumv+uFwyRzdnAjBw4oUS8WcPqKto0vzjGFlwBoCAsVUVmBMS1HuRNyIQCJqACEYEVzwOxcFvN/+WwspC+oT24Q9X/6HjlHmvi4NfwMkVoNHDLR+Cru5tGc4eyKe8xI5/kJ7uQy9snFddXyRHH018kRqsBIwdo744VS0Pz7p1cNEWEAKBQNAQrb5rrydxOBw1t+rnLdXzhI4ntXxNp6N7stvtLLcu56jjKMGGYN655h306Jul62vvU506JefQrXwGCXBd+zvk8L5Qz3UObVR38e07LhoZFwDFebnknj2DAgSX2wAw9OqFEhysXmfkSHSBgUj5+Tj27IHhw1ulb57U8jUd4altdYSn1tVxt72kKIrSZFdtRGJiIomJibhcLpKTk1m0aBEmk8nbtgQdBIfiYIttC+sr1yMh8WDAg/TU9/S2rdZDkbn69FtElh2jMKAXW3o/ryav1oGjVEPO1gBAIXpSOTp/9WvCcuYEuTu3kG3sRFy+QkLKDorGjyfvxtk154557TWi9+zh6AMPcPqWW9qiZwKBwEexWq3MmTOHkpISzGZzve18emRk7ty5zJ07F4vFQnBwMNOmTcNsNuNwOEhKSiIhIaFFWyJ7SseTWr6m0xE9ZZZl8vWpr1mespxiWzEATw59kvsH3u81T22ho9nzb7QHjqHo/Am6/3NmhdUfeG396jQ5nCd+SATTbptYoxXgUEdDUv27cXvJRgAG3H0XAZMm1ZyrOXMG9uyhf2YmfWbNapW+eVLL13SEp7bVEZ5aV8disbjVzqeDkUvR6/W13oRLn3tK1xe0fE3Hk1re8CQrMj9l/sSXJ79ky7ktKKi/9KNN0YxURnLfgPs67Puk1+vRW9Jh3csASAl/QB/Vr9729gonp3blAjBkclyNB8Xl4tyxQwCUaEIxF2SDVkvQuHFoL/Y5fTr85jdotm5F43JBHbv4duTPZUfumye1fE3Hk1od2VNTddxt266CEYGgqZTYSlh2ehmLTy4mozSj5vjVna/m7r53My5qHKtXre7YCauyC759VK202v1aGPWLBpuf3JmNw+YiJMpEl36hNccr8rJxVFZSrjUxuLIEAP9Bg9AGBtYWGDAAYmLg/HnYtg2uq7vMvEAgEFQjghFBh6TCWcE7e95h2ell2Fzq1EKQPoibet3EXX3vIj44HvBMopmvo9nxdzi3C4xmuPkD0NS/iE5RFA5vUlfIDLo2tlaQVp6ZDkCqf1futKqBnWnc2MtFJEldVfO//6lLfEUwIhAIGkEEI4IOR0FFAb9e/2sO5x8GoG9oX+7udzezus/CpL+yEqCDKjLQHPyT+mTmmxDcpcH2WcnFFJ0vR2fU0m9cTK3XqoORNP+udD3+FXBRfZFLqQ5G1q6FN95oWScEAkGHRwQjgg5FSkkKj699nMyyTIKNwbx5zZtc3fnqjj0NUx8uOyPTPkKSHdB3Fgy9p9FTDm86B0Df0VEY/S98PRRnZ+Ess+BCQ7jJjFRYgGQ04j98WN1C1fVG9u6FggIID29pbwQCQQdGFD0TdBj2ZO/hvhX3kVmWSZfALvxv5v8YHzv+ygxEAM2WdwiuSEcxhcPsv9a7EV41ZUU2Ug6o5d0HXVt7BKV6L5osvxhu1VaVgB85Ao3RWLdY585q7oiiwIYNLeyJQCDo6IhgRNAh+CHlB36Z9EssdgtDIofw+fWf0z24u7dteY+sA2i2vQeAa8bbENip0VOObs1EkRViegUT0aV2UuqZ6l16Td0YnHMKAFN9UzTViNLwAoHATUQwImjXKIrCRwc/YsGWBThkBwndEvj3tH8T5hfmbWvew2mHZY8jKS4yQ0aj9L+x0VNcTpljW7IAGHzJqIi9wkrWiWPq40690B3eD0BAXcmrF1M9VSM2zRMIBI0gghFBu8WluPjDzj/w9wN/B+DBgQ/y52v/jJ/u8roWVxRb3oHcoyimcA51ca+QW8qBPKwWO/5mAz2GR9Z6LfXQfpBdFOuCuTHaiFxaisZsxm/AgIZFr70WdDpISVFvAoFAUA8iGBG0S0rtpXxW/hnfpXyHRtLw3Jjn+M1Vv0FTT3nzK4bsw7DlzwC4pr+JXV9/+eWLOVK1nHfghM5odbXfw2PbtgKQEhDPBMtZAEyjRyFptQ2LBgXB2KrREzE6IhAIGuAK/+YWtEfSLGk8nPQwZ5xn8NP68bfJf+Pufnd725b3cTlg2eMgO6H/bJT+N7l1WkFmGVmnipE0EgOvia31mtNuJ2XfLrVdaA8Cjx0AGljSeynVUzUib0QgEDSACEYE7QZFUVh+Zjl3fH8Hp0tOEyQF8e+Ef3Nt3LXetuYbbH0Psg+Bfyhc/26jq2eqqS5y1mNoBIGhtVfHpB7aj2K3UaoNpGunECoPHADcyBeppjqJdf16cLncO0cgEFxxiDojgnZBmb2MV3e8yoqzKwAY0WkEUyqn0D+sv5ed+Qg5x2DTm+rjmW+pq2fcqC5rq3Bycmc2AIMmXV4QLXmHOkVzOqAHU8rTUGw2dJGRGHr0cM/XqFHqdE1hIezfD0OHuneeQCC4omhXwYjD4ai5VT9vqZ4ndDyp5Ws6vuDpcP5hntv2HOfKzqGVtPxq8K+4t8+9rF+7XrxPALIT7bLH0MgO5N7TcfW7Gdz8d3Lsp0ycNhch0f506h5Qq63T4eDU7p0AnAvuTc8sdXmv/5gxOJ1Ot/ujvfZaND/8gGv1ahxVSa/t+v1uZR3hqW11hKfW1XG3vaQoitJkV21EYmIiiYmJuFwukpOTWbRoESbTlVXO+0pGVmS22LawrnIdMjIhUgh3BtxJV11Xb1vzKXpnf8+A819j15rY0P8NKvWhjZ+EWo8se1MArgoNIQMqCexW+0ujPDOd85tWU6Y1sWfIvbyy7u/4Z2SQfcftWK66ym1/3X/4gSH/+hd5gwez7dVXm9Q3gUDQvrFarcyZM4eSkhLM5voT6n06GKnGYrEQHBxMfn4+ZrMZh8NBUlISCQkJLdoS2VM6ntSy2+2sSVrD9GnTO1zfmqKTZ83j+e3PsztH/TU+res0nhv9HEGGIK95aiutJunkJ6P71yQklx3n7L+jDLmQyNuYztmD+ST96zhGk445fxiN3lh7dczaf77Psc3rOWgeRMLt9zDyqXuQFIVuSWvQR0e736ETJ9APGYJiMFCRmUnSTz+13/e7DXSEp7bVEZ5aV8disRAREdFoMNKupmn0en2tN+HS557SbWstRVE4WXSS5WeWsyJlBQ6bg0EVg+ht6u0VP62t1ZjOpoxNPP/T8xTbivHX+bNg9AJu7nVznWXd291noLxATTBtYOdctz3JLvjxSXDZoVcCuhH31pm0Wp/OkY1qkbOBE2MxBdauzeJyOjmzV11Fc9rUk5esGdgVBX23bpji4hr1XotBgyA2FikzE8Pu3e71rQn42megI/fNk1q+puNJrY7sqak67rZtV8FIRyPXmsuKlBUsT1nOqaJTtV57fP3jfDbzM2ICY+o5u2Phkl1sydzClye+5KesnwDoH9afNye+2f7Lupdmw5GlcPhryNoHvafD3YtA28J/fjs+gHO7wWh2a++Zi8k5a+H86RI0WokhdSSuZhw9hK28DKvGn4CuvfA/shU74D9mdNN9SpK6xPe//0VauxYmTmy6hkAg6NCIYKSNqXBWsD59PcvPLGfH+R3IigyAQWNgUtwkpsRN4c/b/ky2NZtHkh7h0xmfEuEf4WXXrUdhZSFLTy3l65Nfk1Wu/lKXkLh3wL08NeIpDFqDlx02k4piOL4cDn8DZzcDF82GnloNa56DmW82Xz//NKx/TX087TUIjm24/SUcWJcOQO9RUQSEXL7ZXfJONSA8E9CdSf2iqPiHmshqGjOmeX4TEuC//0Wzfr0IRgQCwWWIYKSVsbvsnCw8yeH8wxzMO8jGjI1Yndaa10d0GsENPW9gWrdpBBuDcTgc5B/KZ6FrIWmWNH6V9Cs+mf4JwcZg73XCwyiKwqG8Q3x54ktWpa7CIauJk8HGYG7pdQt39rmTOHMTpwJ8AUcFnYt2of36SzizVp0+qSZuDAy+A3R+sPwJ2PkhRPaFq37e9OtUWmDpI+CshB6TYYR7Jd+rsRRUcGZfHgBDp1z+PssuF6d3bQfgdEBPHonUYj99BkWS8B/djJERgClTAJAOHMBQUtI8DYFA0GERwYgHkRWZsyVnOVFygsN5hzmSf4QTRSdwyrWXQXYJ7MKNPW/khh431PlHN1gTzD+u/Qe/WPsLkouSeXzd43yc8DEmffteSVThrGCvbS8LVy3kRNGJmuMDwwdyd7+7mRE/wzf3lXHaIHUrWAugoqjem86SxSjHhUCTyP4w5A4YdBuExl84XpatjmqseBrCe0H3JowUWAth4W3qdI9fMNz4tyZNzwAc3nAORVaI7RtKZFzQZa9nHDtMRamFCo0fReY4+mYcJQ+wxcSgDQlp0rVqiI6GwYPh8GEiDh+Ge+5pno5AIOiQiGCkBSiKQkpJClvObeGnzJ/YX7If24+2y9qFGkMZFDGIwRGDGRMzhuGdhteZjHkxXYO68lHCRzy06iEO5R3i/zb8H4lTEjFqLx9S92UUReFowVGWnlrKyrMrKXOUQYU6LTWj+wzu7ns3gyMHe9tm/VgL4b+zIedIo00lwGqIwHjVz9AOvQuiBtbd8JrfQt5JNYdk8X3wyHoI79m4l/J8+OxmyDkM/mFw/zIIadoyZ3uFk2Nb1emwYVPrHn06VTVFk2LqzrhekVQkfQJAWWMb4zXG1Klw+DCdqqq4CgQCQTUiGGkiVoeV3dm72ZK5hS3nttTkOVTjp/VjQPiAmuBjUMQgYgNjGw0+6qJPaB/+MfUf/GLNL9h5fidPb3qadye9i07j+//biiuL+SHlB5aeXlorOTdUE8p9Q+7j9r63E+rnXj0Mr1FRBJ/dpAYifiEQM1RdCXPxzRRW89hhCCFp50lmTb4BbUMZ5JIEN74PhWchcw8sugt+sRb8Q+o/pzRb9ZJ3AgI6wQPLoVPTq88e+ykLe6WL0GgT3QaGX/a6LLs4VTNF04NH4wIo/7ManJQNaWHQmJAAf/kLkQcPqkVOBAKBoArf/6vmZRRFIc2SxtbMrWzJ3MKe7D3Y5Qu5AAaNgVHRoxgXPY6KUxU8cP0D+Bv9PXb9IZFD+Pt1f+extY+xIWMDL/70Iq9NeM0nd6d1yS52nN/B0lNL2ZCxoSYXxKAxMLXbVG7sfiO5e3O5YcANHlv21mpUlsD/blX3ejFFwIM/Qqd+DZ/jcIB0quE21ej91RU1H0+GglPwzUMw5+u6V9iUnFNHZwpTwBwL9y+HiF5N7pLskjm0/hyg5opImssD5MwTx7CWFFOpMXLOP5ax549iczgw9OyJPSqqydesxcSJKHo9prw8HKdPQ0tHWgQCQYdBBCMXoSgK2eXZHC04yrGCYxwrOMbRgqMU24prtYsNjGVC7AQmdpnIqOhR+Ov8cTgcrEhZ0SqjFqNjRvPOpHd4asNTfJ/yPSa9iefGPNes0ZbW4ttT3/LBwQ/ILs+uOdY/rD+39L6FWd1n1STnrpBWeNGlm9hK4fM71LwM/7CqUYhGApHmEBQF93wBn8yAM+th9e9h1lu12xSehc9uhOJ0dUrmge9r5580gZQD+ZQWVuIXqKfvmLqLliXvuDBFE9/JjH7rV9iAgGkJzbpmLQICUMaNQ9q8Gc26dSIYEQgENVzRwUh2eTbH7Mc4e/AsJ4pOcKzgGEW2osva6TQ6RkaN5JrYa7imyzV0N3dv80BgUtwkXp/wOgu2LGDxycUEGYJ4csSTbeqhPk4UnuClbS+hoBBkCOKGHjdwS69b6B/eDjexs5er0yYZO9UE0fuX1Z/74QlihsItH8FX98Guj9QVNsOqVscUnILPb4PSLAjrqQZFwZfXBHGXA2vV5byDro1FZ9Be9roiy5zatQ1Qp2imxwVQ/qm6UV7gtGmQnNzsa9dcY8oU2LxZrTfy61+3WE8gEHQMruhg5FfrfkWGNQOOXjimk3T0Cu3FwPCBDAgfwMDwgfQO7e0T9S6u73E95Y5yXt3xKv86/C8KKgr4/Zjfe3UFiqIovLX7LRQUpnadyp8m/qn1k2yddqRzuwkvPYGUGQ3+geqSWb2/el99c6PKaS0cFfDF3ZD2k1pI7L5larDQ2gy4Ea57Ada/CiueRgqJJ6jiHLr//RbKcyGyH9z/HQQ1oQT7JZw/U0LOWQsancTga+sOaLKST1BeVIhdYyTDvwtTipNRHA4MPXpg6NnTM8HI1Knw0ktIGzeC0wm6K/orSCAQVHFFfxMMjhiMw+pgbPexDIoYxIDwAfQJ6+PTK1bu7Hsndpedt/e8zbenv+V44XHevfZdr9XlWJ+xnt3ZuzFqjTw96unWe+9KzsGpJDi9FlI2orOXMQHg9B/rP0drVKdXBtwMA2+BsAYquTor4Zv71QJlhkC4dynEjvBwJxrgmt9UrbD5Cu2SnzPe4URylUH0YDUoCmhZ4buDVaMifUdHYzLXHVgn16yi6YbBoCd6/zasgHnGdI+NBCojRmAPDMRgscDu3TBunEd0BQJB++aKDkZeHfcqK4tXMmvMLN9PqLyIewfcS6/QXjy7+VlOFJ7grh/u4rUJr3Fd1+va1IfdZeedPe8AcP+A++kc2Nlz4i6HOlVyag2cWgu5R2u9rJgiKHPpCTRqkZyVajDhqADFdZGGDc4fVG/rXoHOw9WgZOAttZbESrIT7ZKH1LwNvQl+9g3EjfJcX9yhZoVNClLmHoyA3HkEmvuWqqt1WoAlv4KUA1VFzupZzqvIck0wctrUg2tjTVR8p07RBE2f0aLr10KrJX/wYDpv3w5JSSIYEQgEQDsLRhwOR82t+nlLcDqdHtG5WKOlWu7qjIwYyaIZi3h267MczD/Ikxue5IH+DzB36Fx0Gp3H/DTkaeHxhWSUZhDhF8ED/R5o9FrueJIy96DZ8QHS2Q1IttKa4woSSuxVKL2mIvecgiO8P+vXrrt8B0nZqQYlzkqwlyGlbkFzbBlS2lakrP2QtR+SXkTuPBJlwE04e0xjVGoimpK9KDp/XHctQul8lboypom0/D3Xwu3/RfPVfeRaIfiOL9HrApvl5WIfh9afQ1GgS/9QzJHGOv1ln06mrCAfl9ZAun8c88pOo9jt6OPj0XSP9+jnO3fYMDpv3468ejWuBQtapOUpT76kIzy1rY7w1Lo67raXFMV3F/wnJiaSmJiIy+UiOTmZRYsWYTK17yqknsaluFhduZptNjXxsLuuO3ea7iRIc3llTU9SLpfzruVdbNi4xf8WRhpHtkjP6ChhQNZXdC3cUnPMpgsiN2gwOeah5JoH4dA1v08Gh4XOJXvoXLSTiLITSNT+2LskPTt7zCPPPKjZ1/BFZAec3xCI4pKIuMqKX6Srznb5+3dSfPwQyQG9WN0pgYUnPiH8xDEKrptMwfTpHvVkys4m4dFHkTUaVi5ciFP8mxYIOixWq5U5c+ZQUlKC2Wyut51PByPVWCwWgoODyc/Px2w243A4SEpKuvxXcRPxlI4ntZqrk5SexCs7XsHqtBLhH8FrY14j/0B+q/Xtjd1v8PWpr+kX2o+FMxa6Vfekzr65HGj2/AvNlrdqRkLkIXcjj/g5SsxQ0Fy+6qNeLXcpy0Fz4gekY98iZexElrQ4bvsUbd+WTUd4+zNQl87SjzZSctKP0BgTty8YUWfuh6Io/Hf+o1jyclnRaTra6F78bfHvUOx24r75GmPfvh7v2+z589GkpOBcsgRl9uwWafnS+91Rvk/ag6eO3Ddf9NRcHYvFQkRERKPBSLuaptHr9bXehEufe0rXF7SaqjOr5yz6R/Rn/sb5nC4+zdxNc0kwJjBTN9PjfTtVdIolp5cA8MzoZzAampa0WtO3lE2w8hm1qihAzDCY9Wc0caNwdx1Ms97v0C4w7lEY9yiOwgzWr1vLdX1ntPvPwKXILpmyNDVZdXhCVwyGuhNXc1JOY8nLRdHqSfOPY4ErHcVux9CtGwEDB9YKYDzVN2XqVPjnP9Ft2AC33toiLV95vz2t40mtjuypI/fNk1re0nG3re+V8RQ0m+7B3fl81udc3+N6XIqLVZWrmLd5HsWVxR67hqIovL37bWRFJqFbAqOim5HoWXIOvrpfLeaVdwJM4TD7r+oeLW2dOBoUTaUhrG2v2Uak7M/HVanBP0hPn1H1LwtO3qEmqmYGdcep0TPi7D4AgmbMaLV6OkrVLr4kJbWKvkAgaF+IYKSDYdKbeGPCGywYtQAtWjZnbub2729nT/Yej+hvydzC9vPb0Wv0zBs5r2kn28vpk/0dug/HwbHvQNLA6F/Cr/fCyAfrnZIRNB2XU2bfKnU578CJndHq6/6nrihKzSqaw/pumHHgv28noC7pbS2UyZPVOjAnT0JGRqtdRyAQtA9EMNIBkSSJO3rfwaOBj9ItqBs51hweXvMw/zj4D1xy3QmM7uCQHby9+21AXV4cF+RGbZPiDNj9L/j8DnR/6Uv/80uQnBXQ9Wr41WaY9XaLl64KLufA2nSKcyrQGGQGToypt11e2lmKs8+DVk+aqRv3uDJQbDb0Xbti7NcKJfCrCQmBUVWjYGJ0RCC44hHBSAcmRhfD5zM+58aeNyIrMh8c+IBHkh4hpzynWXrfnPqGVEsqYX5h/HLwL+tuJLsgfSesfQU+uBreGwQ//gZOrUFyVlJu6ITz5o/goRVqQS+BxyktrGTPilQAgvvZMJrqn7M9vnUjAMXhPXBo9FyTdQgA83TPFTqrl4Sq/W5EMCIQXPGIYKSDY9KbeH3C6/xxwh/x1/mzO3s3d3x/B5vPbW6SjlW28tHhjwB4YvgTBBoCazc4sx6W/gre7gWfTIOt76qFyiQNdB0HU1/G8cgW1g54G2XgbWqRL0GrsPWrUzjtMtE9zZg6O+ttV1lexqG1KwHYru2F0Wkj/MhuAIJacYqmhupgZO1akOXWv55AIPBZ2tVqGkHzmd1zNoMjBvPM5mc4Xnicuevmcv+A+3lqxFPotY1nO6+vXI/FbqFPaB9u7XXR6ofCFFi1AJJXXTjmFwy9pkKfGeq9qSpB1OEA6ayHeya4mNTD+aQcyEPSSEy4sxc7DmTW2/bgmhXYKyrw6xTLaWMcN1tOgM2GPi4Ov7bYUXfsWAgIgPx8OHgQhg9v/WsKBAKfRAQjVxDxwfEsnLWQd/e+y+fHP+ezY5+xJ2cP9w24jyhTFJ1Mnehk6oS/zr/WeSklKeyy7wLg6VFPo9VowW5VRz9++ptadl2jU5NQB94KcWNAKz5abY3T7mLLYnUzu6HXdSGscwAcqLutw25j38rlABT0mAA5EjPyjwFgnj6tbXalNhhg8mT44QdYs0YEIwLBFYz4i3GFYdAa+N3o3zEmegwvbHuBYwXHWLCldknuIH1QTWASaYokuTAZGZlrY69lbPQYOLYcVv8eSqpWQfSYBDPfgsi+bd8hQQ37Vqdhya8kINjAqBu6A/XXMzy2aR3WkmKCIiL5zh6N0VlGXPJ+wMN70TRGQoIajCQlwbPPtt11BQKBT9GsnJHExETi4+Px8/NjzJgx7Nq1y63zvvzySyRJ4uabb27OZQUeZHLXyXwz+xvu6HMHo6JHEW+OrxkRKXWUcqbkDNvPb2f5meWcKDqBBg1Pxd8M/7sFvrpPDUSC4+DO/6m7yopAxKsU51rZt1pdyjvhzj4Y/Or/nSG7XOz+fikA8ZOuJ73Izri8k2hslehjY/EbNLBNPAMX8ka2boWKira7rkAg8CmaPDKyePFi5s+fz4cffsiYMWN47733mD59OidPnqRTp071npeamspvf/tbrrnmmhYZFniO6IBoXhz3Ys1zRVEoc5SRa82tdcuxZND16B56LrpL3YhOa4TxT8KEeWAQ+4p4G0VR2LI4GZdTJq5/KD1HRDbYPnnHVkpysvEPMnMmdCCQwg3FxwE1cbVNpmiq6dcPYmMhMxO2bIFp09ru2gKBwGdo8sjIu+++yyOPPMJDDz3EgAED+PDDDzGZTHzyySf1nuNyufjZz37GK6+8Qo8ePVpkWNB6SJJEkCGInoFdGOeUuOn8GR45vIbntnzC/ed/QpKd0GcmzN0B1z0nAhEfIeVAHulHC9HoJCbe3bfBYEJRFHZ99w0AAxKu5+Pt5zC4HPRLrVrSO6MNp2hAXVUllvgKBFc8TRoZsdvt7N27lwUXbfut0WiYOnUq27dvr/e8P/zhD3Tq1ImHH36YLVu21NtO4CUqSyBjF6Rvh7TtkLlXTUqtQgLKjFH43fQXdAOu955PwWXYK51s/eoUACOmdSMkquEAMe3gPvLSzqI3+rHLrz+F5ee5peIsWlsl+s6d8RvkhV2LExLg009FMCIQXME0KRjJz8/H5XIRFRVV63hUVBQnTpyo85ytW7fy73//mwMHDrh9HZvNhs124Y+hxWIB1F0Dq2/Vz1uCp3Q8qdWmOtmH0K58GilrH9IlyY5KQCRK3FiUuLHYY0ay7lA2CfGTUVrgqyO/397ytOuHs5QV2QgKMzJkSuda59Sls2PZ1wDEj7+Op3erxe9+ZlWDmYCEqTidddcladW+XXsteoCDB3GcOweXfL80SctTnryoIzy1rY7w1Lo67raXFEWpP+X+ErKysoiNjWXbtm2MGzeu5vgzzzzDpk2b2LlzZ632paWlDBkyhA8++ICZM2cC8OCDD1JcXMyyZcvqvc7LL7/MK6+8ctnxRYsWYTKJqQFPEFWyn6tSP0Anq0FfmaEThYF9KQjsQ0FAH8qN0aIwmY/jKNWQ85MJFInwEVb8oxou9V+Zn8O5NctBo2HfkHv4qcRMP3877yx5GY3dTvrcx6ns2rWN3Ndm0lNPEZyayp5588i89lqveBAIBJ7HarUyZ84cSkpKMJvN9bZr0shIREQEWq2WnJza5cRzcnKIjr58V9AzZ86QmprK7Nmza47JVZUWdTodJ0+epGfPnpedt2DBAubPn1/z3GKxEBcXx7Rp0zCbzTgcDpKSkkhISGjRlsie0vGkVlvoaHZ/jObAX5EUGbn7tbhueB+juTMxQF27mPha34QnNffjh/cPg1JCt8FhTH/o8sTwS3V++MufAIgZMZ5theqXwp96KWjsdnQxMUz+1a/qzTdp7b5pNm+Gd99lREEBQ2fNapGWpzx5S0d4alsd4al1dapnNhqjScGIwWBg5MiRrFu3rmZ5rizLrFu3jieeeOKy9v369ePw4cO1jj3//POUlpby17/+lbi4ujdaMxqNGI3Gy47r9fpab8Klz5uLp3Q8qdUqOrJLrZa6Sy3rzoj70Vz/Lho3KrC2micf0Wpvnk7uzOb8qRJ0eg0T7+rb4PX0ej2WnGxS9qojl2v1A1EUuGlYZ8I3/4sy1L1oDAZDizw1hct0ZsyAd99Fs24dGp2uSaNyvvYZaG+fJW9p+ZqOJ7U6sqem6rjbtslLe+fPn88DDzzAVVddxejRo3nvvfcoLy/noYceAuD+++8nNjaWN954Az8/PwZdkhAXEhICcNlxQStjK4MlD18o2z71FXV5rpiKaTMKMsvY8tUpeo3sxKCJsc3WKS2s5Kdv1DyPkbPiMUf4N3IG7P5+CQDB/YazPlvCoNMwf7CZsj9sBCDkzjua7ccjTJgARqO6xPfECejf37t+BAJBm9LkYOSuu+4iLy+PF198kezsbIYNG8aqVatqklrT09PRaMT+ez6FJQsW3QXZh0DnB7d8BANv9rarK4r8c6V895cDVJY7yDxZhH+gnp4j6q/LUx9Oh4tVHx2motRBRFwgw6c2nuNRWpDP8S0bAVilUfeceejqePzXfI9VljGNHYvR20vu/f3hmmvUTfPWrBHBiEBwhdGscvBPPPFEndMyABs3bmzw3E8//bQ5lxQ0l+zD8NXPoDQLAiLhni+hy1XednVFkZdeynd/3Y+t3InRpMNmdbL202MEd/InoktQk7S2fJlMblopxgAdM381GK2+8cB//8rlyC4nhi692VNhJsSk57HxXcl9Xa03Ejrnnmb1y+MkJKjBSFISPPmkt90IBII2RAxhdGCiSg6g++wGNRCJ6Au/WCsCkTYmN83Cd++pgUhUdzP3/mEccf1DcdplfvzgEFaL3W2to1syOfbTeSQJpj080K3pGZetkqMb1Poda3Rqmfcnp/RG2rwBV0EBuk6dCLruuuZ1ztNUFz/buBHs7r8vAoGg/SM2ymuP2Eoh6wBYC6CiUL23Fl30vBCdtYAxRalq/ZDu18Kdn4F/iLedX1HkpFr4/m8HsFmdRPcwM/vXwzD465j2i0F886c9lORVsOqfh7npqeFodQ3/Lsg+W8Lmqh15x9zUg64Dwt3yUJJ8DIetEsI6c5ho4sNN/GxMN7Lufx6AkLvuRNL5yNfA0KEQGQl5ebBjB0yc6G1HAoGgjfCRbyFBoygKpG2D/Qvh2DJwWBtsXp2WKg+7F83s98DNFTMCz5B9toTv/3YQe4WTmJ7B3PDroTWb1/kF6Jn1+BCWvLmH86dL2PxlMpN+Vn8Zd6vFzqqPjiA7FXoMi2TE9G5ueXBUVlKcfASAdYZBIEk8O6Mf8ulkKvbtA52OkDu8nLh6MRoNTJkCX36pTtWIYEQguGIQwYivU5IJBxfB/s+h6OyF48FxENwF/MPAFAqm8KrH4WAKw2kIZv2eE0y+/gG3l+4KPEPOWQsrPziCvdJFTK9gbnhi6GW76IbFBJDw8EB+/OAQx7ZmEdElkMGTulym5XLJrP74COXFNkKjTUx5oL/bG9kd27wO2WbDGRDKcb/ujOwWyoxB0WS/rC7tDkqYir6BzS29QkLChWDk1Ve97UYgELQRIhjxQTSyA+nYMjj8JZxZD4paKA5DIAy6FYbfB11GNbgsV3E4qDhc0DaGBTXYirSsSDyCw+aic+8Qrp875LJApJr4wRGMu6Un25eeYctXpwiNNtGlX1itNtuXnCHrVDF6Py0zHx2Mwd+9f7KOykr2LFeX8271H4QiaXju+v7IZWWUfP89AKH3+Eji6sVU543s3g1FRRAa6l0/AoGgTRDBSFtQlgdZ++D8QbAWguwE2QEu50WPHSC70DptTE/dju5g+YXzu02A4ffCgBvBEOC9fgga5PzpEvJ3+6O4XMT2DeH6x4eiN2obPGd4QlcKMstI3pnDqo+PcMfvrsIUoo5knd6Ty8H1GQBMfWAAodHu/7/fu+I7youLqDAEcTSwP9cPiWFE11AK/7cQxWrF0KsnplGjmt/Z1iIuDvr1U2uNrF0LvjSNJBAIWg0RjHiaimJIP6IGH1n7IXM/WM65fboGMABKUAzSsJ/BsDkQfnnJfIF3kGUFa4mdsuJKygptlBVVUlZso6zQRtqRfBSXpAYic4eiNzQciABIksTke/tRnFNBbqqFHz84zE3zhmC3aNi0tqqw2Yxu9Bge6bZHq6WE3cvVZbubg0ej1el4dno/FEWh6IsvAHVUxN3pnjZn9mw1GFm8WAQjAsEVgghGPEHeSbSb32FK8kb0+3PqaCBBRG/oPBzMsaDRqQmlGi1o9FWPdaDR4VQkdp7MYvQd89Eb/dq8K4LalBZWcnzbec4dL6S0sJLyEjuKXP/eksYIJ9N/OcCtQKQanV7LrEcH8/Ubuyk6X866T09QcNYfl0Om64AwRt/YtIJkO5Z+ib2iAktAFMkBvXloTFe6hpso37ETe0oKGpOJ4JtuapJmm3LPPfD22/DDD2CxQAObawkEgo5BuwpGHA5Hza36eUv1WqRjK0Wz9c9odn2ERnYSWHVYCe2OEjMUJWYYSsxwlOghYHSvuJXD4SA/KwmHS4YW9K8jb2Xd2p5kWSHjWCHHf8om42ghl+5rLWnAFGwkMMRAQIiRwFAjASFGTKF6TpzbgyLJTfZlCNCQ8MgAvn/vIBnHigANgWFGJt3fB5fLiavhDXlrKM45z8E1KwFYFzQafx08Mj4Oh8NBwecLAQicfQOy0YjcBI9t+hkYOBBd375IJ0/i/OYblPvu876nNtQRntpWR3hqXR1320uKculXre+QmJhIYmIiLpeL5ORkFi1ahMlk8rYtUBS6FG1nYOaX+DmLATgfPJyzEVMpNnXHoQts+HyBT+KskCg/p8d6To+r8kLdD2OYE1OsA12AjM5fQWNUWm1LH2umjsJD/qBR6DTOisEsN+n87K3rKEtPIdMUx9KoG7i5m4vJnRV0JSV0/9ObSLJM6lNPYY+5fJdtX6LP4sX0/+ILcoYPZ8dLL3nbjkAgaCZWq5U5c+ZQUlKCuYFRTp8ORqqxWCwEBweTn5+P2Wz27pbIOUfRrn4WTcYOQB0FcU37I/ZukzrEds9XmidbpZ0fvtiAqbIz544X14yCGAN09B0TRb+rowmJci8A9pSncycL2HtwN7NumdIknZwzp1j80jOAxBedb8cvqgvz+pYxc3oClo8/pugfH+I3YgRd/vtpkz21+Wfg1Cn0AweiaLU409KgjiXIvva59MXPd0f21JH75ouemqtjsViIiIhoNBhpV9M0l25d3KZbIlcUw8Y3YNfHoLhA5w8Tf4s07gl0ej+UqqGo9r7dc1toeduT0+4i40QRZw/kcfZQPpVlJgooBiC2TwgDrulMz2Gd3Nr3xVOeLqZL33AOnZGbpKMoCtu+UqdhTpn7kG+M4K/T+kDGPnRA6TfqMt/we3/WIm9t9hkYMACuugppzx70y5bB3Lne99TGOp7U6sieOnLfPKnlLR1327arYMQryLJadCzpJbDmq8cG3ATTXoeQOO96E7hNZZmD1MP5nD2YT/qxApz2C9MfGr3MoGviGDSxS5OWz/oSqQf2knH0EIpGx9bgUQyLC2HmoChWZkD5+g048/LQRkQQNHWqt626zz33wJ498MUXDQYjAoGg/SOCkfqQZTjxPWx8E3KPqsci+sDMt6DnZO96E7hFWVElZ/blkXIgj/Oni2slogaGGuk+NJKug0I4cGo7Y2/o4bFfMm2NLLvY/Pl/ADgQNJAyXRDPX3+hUmvJ4sUAhNxxO5LB4DWfTeauu+C3v4WffoK0NOjmXhl8gUDQ/hDByKXIMpz8ETb+CXLUfT0wmmHi0zDmUdC1oy/zK5iMY4X8+I9DuBwXRkDCYwPpPiyCHkMjiYgLRJIkHA4HB8940agHOLZ5A/kZabh0fuwOHsHMQdFcFR+Gw+HAkJNDxf+3d9/xTdXrA8c/SZo0TdN0pYOWFlr2VkBKGaJMWRcRlOEVryj+VFAUrxsZjgtexV23oFdliAoqSysbKZuydwtt6Uxpm7bpyDi/PwKVIqMjbZL2+3698mpzcvKc53wJ5OGc79i9G+Ry/O+5x9mpVk94OPTrZ1/Fd+lSeO45Z2ckCEIdEcXIJZIEx1fD5vmQeci+TeUDPR+F2MfAS0xL7S5Sj/1ViARF+tAmJpSoLnp0ei9np+Zw5vIy/rzYVyTB5yasSi+eu6Ntxeu+O+wdrX0G9EfZpIlTcqyVCRPsxciSJaIYEYQGTBQjkoTsxBrY9hZkHrRvU/lAz0eg52OgCbj++wWXknr8Aqs/shcizTvruePhjig8atYR1VEkSeJIupG1hzPYdjqXkZ2b8FDf6k1kdi371/5KUa6BEpWOg7pOTIptRnO9vd+LrbgY3d59gIuuQ1MVY8bAtGlw4AAcPWrv2CoIQoPTeIsRSUJ2ch39TszGI/GsfZtKCzH/B7HTRBHihtKOX2BNnL0QadYpkDumOK8QsdkkEtPyWXsog3VHMkm9UFLx2oHUfErNVqb1b1WrY5QUFbLr5+UAbNN1R6NR88RlMQtXr0ZRVoayeTM0PXvW6lhOExgIQ4bYZ2NdskSs5CsIDVTjLUZsFhS/v4hfSQqSyhtZj/+DXo+LIsRNnT+Rx+q4g1jMNpp1DGTow51qPDS3pqw2idMF8Mrq48QfzSbTWFrxmlop57bWwQTrPPlfwjne+v0kcrmMx25rWePj7VzxPWXFxeSr9ZzQtuKF/i3x97b3aZJsNvK/WwyA7z33IJM79+pQrUyc+Fcx8sor112tWhAE99R4ixGFEmu/50nauYaoiQtQ+rr2jJTCtZ0/mcequANYzDYiOwRyx/91rNdCJCmniCW7Uvhp33lyiz2AFAC0nh70bxvM0I6h9GsThEZl/+sWolPz5m8n+O+6EyhkMv6vX/UXQizIziJx3a8AbPaNIczfm0mxzSteL9q8GXNSElZPT3SjR9f6HJ3qH/8AjQbOnIHdu6FHD2dnJAiCgzXeYgSQOt3DsVQtUZpAZ6ci1FDG6QLWfnwYS7l9Ubmhj3TEQ1n1RepqqtxiI/5oFt/tPMf2M7kV2zUKiSGdwxneKYw+rfSor5LL1NtbYrNJLIg/yby1x5HLZEy5tWp9SIw52aQePcTB9b9htVhI1zQlxSuC9+5oU+lYF75cCEBBTAxyrZsvT+DtbS9Ili61Xx0RxYggNDiNuhgR3FvZBUVFIRLRPoChj3aq80Ik9YKJJbtS+H5PGoaiMsB+1+D2NsHc0y0M0+k9jBzR8apzlpw9uJ/V77+Jh0pFk/AInvD2YX2GjIU/ZUCZiSmDOlbaX5IkLqSnkXbsMGnHjpB27DCFhpy/XpfJ2eLXky4RfozsHFaxveTgQUx79oCHB/m9e9dRS9SziRPtxcjSpfDWW6Co+4JTEIT6I4oRwS1lninAsMcLyWojop0/wx6pu0LEapNYfyyL73amsOVUTsXkaUE+nozrHsH4HhE09ddgNptZk3T1GGcT97LyrdewXlw2oCjXPptv/4uvG79YydtLdEREReHXJJzME8f4cvVyTAX5leLIFQpColviHdmaecc9yVEF8eGwdsjlf/WjyL14VcRn2FAsfr4OawenGjIE/P0hMxM2b4b+/W/8HkEQ3EajLkYs5WW4wTqBwmXyMos5+mcGhzenIVllhLfxY9ijnfFQ1U0hcqG4nEf+t4d9ydlY5ParHX1b6ZnYI5KB7UNQKm7cNyV5/x5+XvA6VrOZFt17css/xnDhfCq5aecwpKVy9nQSsuJ8pGIjKYcPkHL4QMV7FUolTVq1oWm7joS0bk+upgm704pYmJhOpqqIwe1DiIn+6zZjeUoKhfHxAPjdfz+cPu3gFnESlQrGjoXPP7ffqhHFiCA0KG5VjJjN5orHpee1sX35YlITtnE8QEebmN4VIw4kSSI1r4TE1AISU/M5k1OMj9qDIB9PAr1VBPl4oteq0Gs9CdKqCNR6IpesDsnJUedWlTjn80vYczaP6CBvOobpKqYPd2ZOV2Mpt5K038DxhEwyzxgrtnsGWuj/QCskmQ3zZTOtOiqn0xl5/OfDJbRK380tZiO2Trcz+l+TiAq5uPKkzYrZZr1urLOJe1n17nxsFgstusdwx7QZKDyUBEf/NYpGkiQWrD7Myi2J+JfnMSJChrfVSOyQ4eRqw9idWsTPyRfY92MWpeaMivd5qxQ8PbBlpeMZFi4Cmw1N797Io6Lg9Ola/7ndqJ3qK47s7rvx+PxzpB9+wPLOO5gv/n11p79zIifXiyNyqts4Vd1fJrnwpYG4uDji4uKwWq2cPHmSxYsXo9FUbTn3G5GsVs7+shRricn+3CeA7IhuHPWM4myxnCJz9YYPeikkVArwkIFSDh5y++8ecqnStiA1tPKVaK6171+fbBKcK4LDeXKO5MnIMP11jsFqiW56G930EkEuMlFpuVFOcaoSU7oSyXIpVwl1sBXvpuWog611MsrTWlZK8uFjmE4fQWMtqfSayj+QkJ798PS/cafn4vPnyNj6B9hseEdEEdq7/zWH2EoS/JoiZ326/fXmWonzxWCWKp+gt4dEK51EC51ExwCJAM+/XpMXFxM9bz5ys5nUKQ9R0rLmw4ZdktXKkIceQp2Xx84XXyRTdGQVBJdnMpmYOHEiBQUF6HS6a+7n0sXIJUajEV9fXwwGAzqdDrPZTHx8PIMGDarV4mavrdjH+S1riDYcwlMqByBHFcguv+6k+kTTPsyXmyJ8aRvqQ0m5lZyiMgxF5Rgqftp/N1ur34QqDzldI3yJjQ4kNjqAjuE6lAq5w87tUpxet97OznNGNpzIYdPJHC4U/1WlymXQvomO0zlFlF52ZaFLU19Gdg5leKdQ9FpPh+d0vTg2q8SJHZkc355JTkpRxXafAE/axIbSpmcI3n43zkmSJNYczsLTQ87tbYJQyK991edSHFNeLvvX/sqhjX8gWeyfh1KVD7EjRxEUFMiWbxdSWlSIXOFBzF330G3EXcgv60h5eazUg/tZ8/6b2KwWWvaIZchjM1B4XP9CpCRJvPHbSb7881zFtgBvJTHNA4iJ8qdHVAAtg7yveQXrwsefcOGjj/Bs146my5ZisVgc8ud25bnV9WfgeuTPPIPivfew3X03pV995RI5OTqOyKl+44ic6jaO0WhEr9ffsBhxq9s0SqWyUiNc+by6juRa2KPtgadXZ/qWHaVNbiJB5bkMz/4NvVcUvWIm0rJ7x2v+4w/2L5CCEjOZ+Sb+2LiZW3r2woqMMouNcovtsp9WSsqtHEk38udpA9mFZexIzmNHch7vrLdfcu8RZf/SKSuEEgtoNNU7N0mSyCgo5Ui6kYOpF4g/Kuffu7ZVKpZ81B70ax3EwHYh9GsdhL+3iqIyC78dzmRl4nn+PG3gQFoBB9IKmLfuJL1b6hnZKQSLpfbtfcn14mxbeYoD61MBkCtkRHUJokOfMJq29Ud2lYLiarEkSeK11cf4clsyAFF6b6b0jeauruFXHWpbasgm/uN3ObN7B5JkL8pyVHqsHfox+4mJaDX2yw/RN3cn/vM4zuzZQcLyxSTv280dj80gsGlEpXgpB/ax9oO3sFkttI7ty7BpT9+wELlk5ogOtA7Rsmf/QSaP6EvbML/rfv4usZWWUrB0KQCBDz2ISqWqeJ+j/twcGavGce69F957D/mqVSjLylwjpzqK48hYDTmnhnxujozlrDhV3detihFHe7hvFJ089zB51GAiAsdQUlTI3lUr2bf2FwznkvnlrdcJbt6C2LETaNE95qpfCjKZDD+NCm+ljBNa6Brpd8PGlySJJEMx208b2H4ml4SkXPJNZjaeyGHjiRzAg3cObyTQW0WU3tv+CPImWu9NdJCWyAANKoWcs7nFHE43ciS9gKPpRg6fLyDPdPn9OTkg0SxQw4C2IQxsF8wtUQF/63Sp9fRgTLemjOnWlOzCUlYdyODnxPMcSCtgy8kctpy05/ThqS10CPO9+NDRPkxHE191lb4sq6Igx8ShTWkA9BgZRcdbw/Hyqd4qyTabxKxfDrNs+2n6524n2HIB6byVQ4k2jsvBT61Aq5Ij2axIVitWi4XSosKK95/zimCf700MH9KX5+6oPErF28+fUf9+iWNbN7Jh0adknjnFN88/Qe9x99Ft+CgAilKTWbtsITarlTa9bmXYtKcrXT25EZlMxl03h6POOEDLYG2V27Zg5UqsFy6gDA9HN2RIlY/ndrp3h5Yt4fRpZL/+Cr4NZLSQIDRyjboYub1NECVnJEJ1agC8tD70GW//Ytm7eiX71v5K9tkz/PzWa7SO6c2Qx55Epa59hwqZTEaLIC0tgrTcF9scm03iWKaRhDO5bD2Vw/7kHIxmGbnF5eQWl7PnXN4V7wdPD3mlWyuXKOQyWgVradfEB1leKlNGVv1/1wDBPmom94licp8oknKK+DkxnV8PnCfJYOJ8finn80v5/WhWxf7+GiUdwnxpH6YjJiqA29sEV/oCr46dvyRjs0pEtg/gluFR1X6/1Sbxwk8HWbEriX9krSasNPNv+0hlUHjlRpmcnJCO/K5oS4Faz6ujOjIxJvKqx5DJZLS/tT8RHTvz+6cfcDZxL1u+Xcjp3TtoFdObzG3rQZJo27sfQ6fOqFYhUlOS1UruokUABNx/P7IqXoVxSzKZfSXfV19FvmwZPPywszMSBMEBGvC/WjXn5aOjz/hJdBt+J3tWrWDPrys4ufNPcs+nMurfL+HfJNyhx5PLZRVXHO7vGcGaNWu4dcBgzheUk2QoJjmnmGRDEcmGYpJyiikss1BqtqFWymkbqqNDmI6O4farFa1DfFArFfY5L9akVOt/11eKDtLy1KDWTLstih9+WUNEp54czyrmaIaRo+lGTmUXkWcys+20gW2nDXy2JYl2TXTMGNSage2Cq3XcnJRCTu22Fzk9R1d/enSL1ca/lx9g9b6zFYWIp8abAZMfQe2jQ5LJ2XE2j58PZHL2Qik2mRyZXE6/NsEkpBaRZvZC6+nBonu7cmvroBsezydAz13Pz+Hwxng2/e9z0k8cJf3EUQDa9Lq13goRgML16zGfS0Hu64vfmLvq5ZhOdbEYkf3+O8rx452djSAIDiCKkevw8tHRd8L9tOjWg1/enkduWgrfvTiDodOepkW3uu3Jr/X0oGO4Fx3DK1+GliSJ3OJyjCVmIgM0eFRhngtH0HhATFQAfVqHVGwrNVs5mVXI0XQjB88X8EtiOscyjEz53x66NPXlqUGt6dc6qEpFScIK+3wYzboGccZcjne5pWItlxsxW21MX7qf+AMpjMpaQ5OLhcjYma8R2uKvVWyju8CEf0hsOpnDJ5vOsDP5AsuTrIAXYb5qFj3QgzahPlVuE5lMRqf+g2nW6SZ+++Q9Ug4fwCeqFYMeeaLeChFJkiqmfvefMB65t3e9HNep2rWDm25ClphIWEICiIJEENyeKEaqIKx1O/45711+fXse6SePsfK/r9Dr7nvpede4el8NVSaTodd6otd63njnOqZWKujc1I/OTf0YDzwzuA2fbU3iqz/PciCtgH8t2k33Zv7MGNyaXi30V41htUls2pJC6rE8bDKYeTqV/KQUfDw9GN01nIkxkbQNvXYP7DKLjSe/P8DmI2kXC5GMqxYil8hkMm5vE8ztbYLZn5LHF1uSOHc+nc+mxBAWULM1XHRBwYyd+Rp5mRls2bkLubz+xmyX7NtHyYEDyJRKAu69t96O63QTJ0JiIpEbNjg7E0EQHMCN1xWvX1r/AO6Z/R+6DB4OwPbl3/HzgtcpMxU7OTPX4e+t4rk72rL1udt5qE8Unh5y9pzLY+LnO5nw2Q72Xuz7Yigq44e9aTy+ZD/dXo1n0/JTAOxTWsiXS/hrlBSWWfhfwjnueHcrYz7ezk/70ig1Wysdr9wKjy3ef7EQWX3DQuRKN0f68+64zkxpayPIp3bFnUwmw0dftatAjnRp6nffO0fhEXTj20sNxn33ISkUBJw4AceOOTsbQRBqSVwZqQaFh5KBDz5KaHRL/vjyI87s2cl3L85g1L9fQhfSxNnpuQy91pOZI9oz5dZoPtp4miW7UklIso8aCvRUkJuwuWLftuUKQq0eWBUw5O5WzO/ShCCtJwlJuXy38xy/H8li77k89p7L45VVRxnTtSkTYyLRaxR8dlzO2fxM7sxeTeilQuSlV6tUiDQEZUlJFF28MhDwwANOzqaehYYiDR2KbNUq5F9/DQsWODsjQRBqQRQjNdDx9kHoI5vzy4L/kJdxnu9eeppBDz/u7LQqSTt6mOydW9iQeQ61txZPLw0qLy9UV/z09NYSENa0Tv5HH6JTM3dURx7u14IPN5xm+Z5Ucu1TQ9Ap3JfbWunx3WSgzFROr+FRdO/71wia3i319G6pJ9tYyvd7UlmyK5Xz+SV8uS2ZL7clo9eqKDBauTN7DaEllxUiLVs7/Dxc1YVFXwGg7d8fz+ho5ybjBLZ//Qv5qlXIv/sO5s8HB83rIAhC/RPFSA2FtmjFP+e9w6p33yD16CHWvP9f1MFN2JafTVjrtoREt8I3OKT+L9unpbJl8SKS9u4C4PCZEzd8T1jrdox46jl8Aq7er6O2wv28mHdXJx7u04zvVm/iwTv708Rfy8GNaWzNT8dLp6LLgKsPpQ3WqZnWvxWP3taSzSezWbwzhQ3HsykwFjMq64orIo2oELEYDBT8/DMAgQ9OdnI2ziENHUqpry/qrCxYswZGjXJ2SoIg1JAoRmpB4+vH2JmvseW7hexd/TOl2RnsW/Mz+9bYvyTUWh9ColsS2qI1IS1aEtqiFVr/wDopUEwF+WxfvpiD69ch2WzI5HJ8olrRrsvNWMpKKS8xUV5SYv9ZWkK5yURZSQnF+RdIP3mMb59/khHTnyWiQ2eH53ZJU38vOgVI6LWelJda2LPGPkNqjxFRKD2v3+lTIZfRv20I/duGkJxh4Oc3XsFamoFKo2HMS680qkIE4MK33yKVl+PVpQteXbs6Ox3nUCpJvf12Wq1cCQsXimJEENyYKEZqSa5QcNukKbTrN5Dff1xOiFZDdvIZcs4lU1pUyLmD+zl3cH/F/pEduzByxguovWs2cuNK5rJS9q35hV0/L6e8xL6oW4vuPYm95152JB4kZtiw684Im5+ZwS9v/4ecc8ksf20mt078F91GjK7zKzqJ8SmUFJrxDfaiXe+q97cxpJ5j44LXsWakI1equPO52TRp2aYOM3U95WnnyfvmWwACHpxc71ffXEnKgAH2YmT1asjMhNBQZ6ckCEINiGLEQQLCmqJr0YbbL375W8xmDClnyUo6ReaZ02SdOYkhLYWUwwdY/spLjHnpFTS6mk9lLdlsHN26kW3LvqEo1wBASHRL+t33IBHtO9mXbU48eMM4fqFNmPDqm/zxeRxHt25k87cLyTh1giGPTkfl5ZgVkq9kMpaz/w/7+jM9R7VAUcW5Uk4kbOO3j9/FXFaKNlCP3y19CW3RuK6ISFYr6c89h624GK+uXfEZMMDZKTlVUUQEtp49ke/YAd98A8884+yUBEGoAbcqRsxmc8Xj0vPaxnNEnGvFCoxsTmBkc9rfNgiAnHPJrHxjDtlnz7B09nOMfn4O2oDAG8a5UvrJY2z6+nMM5+y3OXwCg+g17p+07tkHmVxe/TaSKxjw8OMER7diy7cLObnzT3JSzzJ8+nMEhEc4vL33rDmLpcxKUKSWyE5+N4xrs1pJWP4de1etAKBp+04M/L/pbNu50+U/A46Ok/fFl5Ts3YvM25vg11/DYrOB7e/LAjgyH0fGqpOc7rsPzx07kL78Esv06fYp452QU6NpbxfJqSGfmyvmVNM4Vd1fJkmSdOPdnCMuLo64uDisVisnT55k8eLFaDR187/1+lJuzCd9wxospmI8vH0IHzAMpfbak3pdzmaxcOHgHvKPHwJArlTi3+FmfNt0QK5wTF1ZasgiY+t6rCXFyDyUhPS8FW2k40ZqWIplZG71BkmGvocJdaD1uvtbS0vJ/HM9JVnpAPi160xgl1vqfbI5V+B5/jyRH8Yhs9nIvOdujN26OTsll+BhMjHkgQfwKCtjy/z55LVt6+yUBEG4yGQyMXHiRAoKCtDprv1d59LFyCVGoxFfX18MBgM6nQ6z2Ux8fDyDBg2q1ZLIjopT3VjGnGxWzJtNQXYm3v4BjH5+DgHhEdeNk3nmJPGfvk9e+nkA2t/an17jJ13zVk9tzs1UkM+6uAWkHT0MQJchIyjyD2HwkCG1bu+lb26lJENJRDt/hj7W8br7ZyefYfW7b1CYm4PSU82AKdNo3bN3RSx3/gxUN46tpITUceMxJyfjPWgQoQveumFfEXc5N0fEUj/yCPJvvsH2wANYP/3UKTk1pvZ2hZwa8rm5Yk41jWM0GtHr9TcsRtzqNo1SqazUCFc+d1Tcuo4VGBbO+Ff+yw+vzSQ3LYUfX5vJmBdfISS65d/iWMxmdvy4hF0rf0CSbHj7+TP4/54guustDsvnSr76IO6e+Trblv6P3b/8yIHfVqHWh2C8uQuhzWt+lST7bCElGUqQQa8xLa+b15HN64n//EOsZjN+oU0Y9fRL6COb/20/d/0MVDdO5rz5mJOT8QgKIuyVuXioVPWejyNjOTon+ZQp8M03yJcvR/7++6CtfgdxVz03kVP9xHFkrIacU3XjVHVftypGGhKtfwDj5sznx//MJivpFN+/8iJ3PT+H4MtmD80+m8S6uLfJSTkLQNve/eg/+RG8tFVfzK2m5AoFt977AE1atmHtR+9QashiyYsz6DFqDDGjx1Xpy9BcZiX9dD5px/NIO34BQ1oRAC27B6Nv+tc52GxWyoqLKSkspLTIyNEtGzkQvwaA6G49GDp1hsNGH7mjos2byVu8GIAm8+bh4e/v5IxcUJ8+0LIlnD4NP/wA//qXszMSBKEaRDHiRF4+Ou5++XVWvDGX88eP8MN/XmbEk88j2WzsWrmcXSuWYbNa8fLRMXDKVFrH9K73HH2CO+AX/iC5qWuwlSex46dl7P99PR1uu5fmXW5GF6hGp/dC6anAarGRddZYUXxkJRuxWe13ASWpDGv5SWTyNArOq1k8cxGlRYX2AqS4CK68WyiT0evuifQcXf+LEdYlyWJB5lH1v3aWCxdIf2kmAP6T7kPbp/4/A25BJoPJk+HFF+1zjohiRBDciihGnMxTo2HMi3P55e15nE3cyy8LXsdDq+NM/gUAWvXoxcCHHkPj61fvuZ3YkcHGb09gtahQakZhU57CbNpIWZGBfaveI/H39ii9+iGTe+Hlo8RcbsNS9leHVEmyofbKQi47TkH2EWyWcgBSj1z9eCovDV4+Pnj7BxIz+m6ib67arSh3IJnN5H65EMPHH6Nu25ag6U+giY29br8PSZLInDULq8GAZ6uWBM+YUY8Zu6FJk2DmTNi6FU6ehNaNa9i3ILgzUYxg7xxYlpqK+Xw65vSLj4y/frcVGNHExKAbNgyf229D7u3t0OMrPdWM+vdM1nzwJqd2bqc8/wKeGm8GTH6Etn1uq/dJrWw2iR0rzrA/PgWAZp0CKdGl0LXTaAqyBnB0849knfkTW/lRyi3JeKhvxSS1RyaTodYqCWpqxlJ2lKykXRRkXKiI6x8WDgEhdO0Zi7efH15aH7x8dKi1Pqi1PiiqccXAnZQeP076iy9SdtS+umzJgQOkTH4QzS23EPTUk2iuMYNq4YoVFP2xHpRKwt58E7laXZ9pu5/wcLjjDvvU8F99Bf/5j7MzEgShihrmv/5VlPHkU0Tv2EHSc8/fcN+iDRso2rABmVqN9vbb0A0divbWWx32BeGhVDJi+nNsWfI1J48c5u6nnsM/OMQhsaujzGTm9y+PknIkF4Duw5pz85CmrF13lsgOAShvCqHrkOdJP3mcPz7/kJyUs5hNvxHaJJXITreQcngHpxL+Wg9H7a2lTe9+dOjXn8DIKNauXUuH2wY6rHOXK5PKyzF8+hmGTz8FiwW5ry/BT8+g7PRp8pcsxbR7N+cm3ot3374ETZ+OV8cOFe9VGgzkfBgHQPCT01GL4apV8+CDfxUjr7wCDbTAFYSGplH/TbUVGvEoLgZA7u2NMizM/ggPw6NJk4rnMqWKwvV/YFyzBvO5FArXrqNw7Trk3t74DByAbtgwVLfU/paCXKGg97j7KFizBq1/QK3jVVd+lonVHx0kP8uEh1JO//vb0ap7yFUnrQlr3ZZ7573L3tUrSfhhCZlnjpJ55igAMrmcqJu60aHfAKK7xeBxsfBwxERA7qLk8BEyXnyRspMnAfAZNJDQWbPwCAoCIPCBBzB8/An5P/1E8datFG/dis+ggegffxx5RAShS5chlZSg6dGDANH/oepGjAC9HjIy4LffYPhwZ2ckCEIVNOpiRP/MM2zdto3b77kHdWDgdff16tiBoCeeoPTIUYxr1mBcuxZLRgYFP/9Cwc+/INfp0HftirV3b5T6uln9ti6lHMnlty+OUF5iQevvybBHOxMUef1ROwoPD3qMGkvrnn3YuuRrinINtO7Zm7a9++Ht1zhHfMjMZgzvvkf+V1+B1YrC35/QWS/jc8cdlW63KZs0ockrcwl86EEMcXEU/PIrhfF/UPjHejzbtsErNRW5jw9h8+chU1x/EUHhMioV3HcfvPOOvSOrKEYEwS006mLEs21bypOSUFxnIpbLyWQyvDp2wKtjB4L//TQliQfshcm6dVgNBgI2beLcHUMJmPwAAZPuR6F1bN+SuiBJEgfWp7L9x9NIEoRG+zL0kU5odFWfx8IvJJSRTz5Xh1m6h5LEA0S+9z75OTkA6IYNJWTmTDwCrn2VSxUZSdgbbxA4ZQo5H8ZRuG4dZceOAxD00osow8LqJfcGZfJkezHyyy+QkwMXr0YJguC6Gs6YyXomk8vRdL2Z0Jkv0WrzJkLfe4+yJqHYioowvP8BZwYNInfRV9hKS52d6jUVXihlw9fH+PMHeyHSrlcT7nzq5moVIgKUJCaS9tRTnJ80Cc+cHBSBgYR/8D7hb7993ULkcp4tW9L03XeI+ulHfEaMwDBoED7if/U107Ej9OgBFgt8+62zsxEEoQpqVIzExcXRvHlz1Go1MTEx7Nq165r7fv755/Tt2xd/f3/8/f0ZOHDgdfd3RzKFAm3/2zn3xBOEvPlfVM2aYc3LI/uNNzgzeAh5S5chuUh/ifJSC8e2Z7Dynf3876XtHN+RiUwGfe5pxe33tUWhFPVpVUgWC8Z16zg7bjxnx0+gcO06kCSMXW8m8ueV6AYNqlFcdfv2hMz7DxcGNu7VeGtt8mT7zy+//PscNoIguJxqf/MsW7aMGTNmMHv2bPbt20eXLl0YMmQI2dnZV91/06ZNTJgwgY0bN5KQkEBERASDBw/m/PnztU7e5cjl+NxxB9GrV9Hk9dfwCGuCJTubzDlzODNsOAU//4xkvf7CcHXBZrVx7nAuv395hEXPbGPD/45x/kQeSBDWyo9/PHkzXfpH1PsQYndkLSwkd+Eizgwewvknn6LkwAFkSiW+o0cT8cNyMseNQ+F79fWChHo0fjyo1XDkCOze7exsBEG4gWr3GXn77beZMmUKDzzwAACffPIJq1evZuHChTz//N+HyH733XeVnn/xxRf8+OOPrF+/nkmTJtUwbdcm8/DAb8wYdCNHkr/sewyffoo5NZX0557H8MmnBD70IL4jRyKrxvoi1SVJEuUFchJ+SuL03hxKjOUVr/mFaGgTE0rrHiHo9F51lkNDUp6ayoX/fUPBjz9iM5kAUPj74z9hAv4TxuMRFGQfLXTmjJMzFQDw9YWxY+23aRYutN+2EQTBZVWrGCkvL2fv3r288MILFdvkcjkDBw4kISGhSjFMJhNms5mA69xLLysro6ysrOK50WgE7ENDLz0uPa8NR8W5ZiyZDJ/x4/D+x0gKliwlb+FCypOTyXhpJtnvvY/fpEn4jh1TaRK12uZUdKGU03tyOLk7i/xMb7KxX4FSaz1o0TWIVj1CCIrUVlwFqcpx3Ka96yCOZDZjWLCAgsVLKi73q1q0wPe+f+IzfDhytRoJXPJz6Y7t7chYsvHj8fj2W6R167Bc53jueG4ip4Z9bq6YU03jVHV/mSRV/YZqeno64eHhbN++ndjY2Irtzz77LJs3b2bnzp03jPHYY4/x22+/ceTIEdTXmDBszpw5zJ0792/bFy9ejEajqWq6LkdWVobfzp34b92Gx8UCy+qlJj+2F/m9e2GtwUqjADYzmDKVmM57UJ53WX0pl/AKtqAJM6MOsiIT3UGqRW4yEfbtd2guXu0obt2avD59MLVuZV8LRXBpipISht97LzKbjd++/JLSGwzfFwTB8UwmExMnTqSgoADddUau1uvQ3vnz57N06VI2bdp0zUIE4IUXXmDGZetwGI3Gir4mOp0Os9lMfHw8gwYNqtVMno6KU61Yo0cjlZdTuGoVeYsWwdlzBG7YgH77dnSjR6O9dyIbjxy5YRyr2UbK0Quc2p1NypEL2CwXa0oZNGnpS/TNgSRfOMCQYfV4bvUUpz5yKk9KIuPxJzCnpCDz8iJk/ny0/W93ak7OiuPWOb3xBiQmMsDLC2nYsDrNSbS3e8YROdVtnEt3Nm6kWsWIXq9HoVCQlZVVaXtWVhahoaHXfe9bb73F/Pnz+eOPP+jcufN19/X09MTT0/Nv25VKZaVGuPJ5TTkqTpVjKZUEjhtHwNixFK5fT+5nn1N6+DAFS5ZQ8P33hNx8E/K+fVFe5SpQWYmFfevOcWTrecpMlortgeHetO4RSqtbQvAJUGM2mzm35kD9n1s9xnFkrMvjFG3dyvmnZmArKkIZFkbTjz9C3aaNU3NyhTiOjFVvOfXuDYmJeOzaBRMm1EtOjbq93TiOI2M15JyqG6eq+1brwr1KpaJbt26sX7++YpvNZmP9+vWVbttc6b///S+vvvoq69ato3v37tU5ZIMmUyjQDR5M8+XfE/nVIrx79QKrFd89e0kbP4HSY8cq9rVZbRzalMa3Lyew77dzlJksePt5cvPgSMbN7MH4l2PoOqQZPgFiMbWakiSJC19/Ter/PYKtqAivbt1o/sPyahUigovp3dv+c/t25+YhCMJ1Vfs2zYwZM7j//vvp3r07PXr04N1336W4uLhidM2kSZMIDw9n3rx5ALzxxhvMmjWLxYsX07x5czIzMwHQarVoa9hHoqGRyWR49+yJd8+eGHft4uwT0yElhbPjxhP80ksUtLudhJ/OkJdpH8XhH6ohdnQLmnXSI5eLvguOIJnNZMydS8EPPwLgO+YumsyeXacjnoR60KuX/ee+fVBSAl5i9JgguKJqFyPjxo0jJyeHWbNmkZmZyU033cS6desICbGvMJuSkoJc/tcFl48//pjy8nLGjh1bKc7s2bOZM2dO7bJvgLxuvplz05+g88ZNZO89zb4fc8gLOASAWqukx4go2vcNQ6EQvVEdRVFUxPmHplC6bx/I5YQ89yz+kyaJeVcagshICAuD9HT7fCO33ursjARBuIoadWCdNm0a06ZNu+prmzZtqvT87NmzNTlEo2aWe3Oq1+OckGcBMmQ2M82LE+nz+F3oOjR1dnpuwZKXh+GDDyk/n4Zco7E/vDR//a7RIPfWYAUiP/yQ0rx85Fot4e+8jbZvX2enLziKTGa/VbN8uf1WjShGBMElNeqF8lyNzWpj/++pZG7xJsOaDchoHuVB09/eRZV+ivT7vkeaOxffkSOcnapLM+3dy/mn/43l4i3BG1ECyshIIj7+CM8WLeo2OaH+9er1VzEiCIJLEsWIi8jLLOaPRUfJPlcIyAhu7kOfu1vTpIUvlgfbc/7fz2DasYP0Z57BtGcPIS++gPwqI44aM8lmI/ezz8n54AOwWlE1a0bA5MlI5eXYTCb7o8T+UzKZsBWbsBQXkwXc9O47eOr1zj4FoS5c6jeyfbt94jpx+00QXI4oRpxMskkc3JhGwsozWM02VF4KvFsVMeqhPqgudp700OuJ/PILDHFxGD7+hPxlyyg9dIjwtxegat7cuSfgIiw5OaQ/9xzF2+0zAetGjiR09mwUWu/rvs9sNnNwzRqxnkxDdtNN9nVqcnPh5EkQo6MEweWIYgSw2SRKi8yUFpspNZkpLTJTZrJQWmymrNiM1WKjadsAwtv4O3T0itFQwvqvj5F+Kh+AyPYB9Bnfki0J6//WeVKmUBD0xBN43dyV9GefpfToUZLuGkPoyy/je+eoRt3Zsnj7ds4/+xxWgwGZlxehM2fie9foRt0mwmVUKvvaNFu22K+OiGJEEFxOoy5G1n58mPOntHyxbhvcYFL8fb+loNGpaNU9hFY9Qghu5lPjLztJkjj2Zwbblp/CXGbFw1NB7zEt6dA3DIvFct33avv2IWrFT6Q/8yym3bvJeOEFirduJXTuHBQ+PjXKx21ZreS+/wF5X3wBkoRnq1aEv/M2ni1bOjszwdX06mUvRv78Ey5OQyAIguto1MWIpdyKZP6roFCqFag1StRaJZ4aD9TeSjy9ldgsNpIO5GAylnNgQyoHNqTiG+RFqx4htL4lBP/Q698KuFxxQRkbvz3OuUO5gH3q9gH3t8M3qOpr7ihDQ4n8ahG5n39BzgcfYFyzhpIDBwhf8BZeN91U5TjuzJyZSdPPPifv4mgtv3vusfejuc4yA0Ijdnm/EUEQXE6jLkb6jGvFli1bGDy0P96+Xtedu6OfpQ2pRy9wcncWyYk5FOSUsGf1WfasPktQpA8tuuoxZXiQtD8HuVwBkv0KiCQBkoQElBaZ2bP2LGXFFuQeMnr+owVdBkbU6NaPTKFA/8j/4d0zhvP/fgZzWhpn7/0nQY9PI3DKlJo3iouyFhRQcugwpYcOUnLwEMW7dqEpLkbm7U3Yq6+gu8a6I4IAwKUZoo8dgwsX4DqrhguCUP8adTHiH6pBqbXh5aO64SRiCg85zTvrad5ZT3mpheQDBk7uyiL12AVyUgrJSSkEvPgj8fgNjxsU6cOAf7UjMKz2M9B63XQTUSt+InPuKxhXrSLn3fco3p5A8Ouv1Tq2s9jKyyk7cYKSAwftxceBg5RfZb6a0qZNafPZp2iio+s/ScG96PX2viInTkBCAgwf7uyMBEG4jFsVI2azueJx6Xlt49UkjkwB0V0Die4aSElhOUn7DZw9aCAnOxd/f3/kCjky7NO8I7s4klAmQ3ZxRd3O/cORK+RXPW6NclKrCfrP66hje5Lz+n8w7dpFypixaEeMoDgiAvLysGRnY80xYMnJwZLz1++2ggI0t/ZF/+yzKK+x2GF9tbdktZL32efkLVyIVFr6t9eVERF4duyIunMnPNq2Y0tmBm1CQ2uVl6POzZGxXC1OQ8lJ0bMn8hMnsG7dim3wYIfnJNrbPeOInOo2TlX3l0mSdIOum84TFxdHXFwcVquVkydPsnjxYjRXWclW+IvSYKDJkqWo09Kq9T6bSoVhyGDye/UCef1PNe+RX0DosqVokpIBsGo0lEZEUBrRlJKISEojmmLzrnrfHEG4UmR8PDfHxZHTsSPbX3PfK4eC4E5MJhMTJ06koKAAnU53zf1cuhi5xGg04uvri8FgQKfTYTabiY+PZ9CgQbVaEtlRcRwZyxFxJLOZnPffJ2/JUlQ+WjyCglAEBV/8qccjKBiP4CAUQUFgsWB4awGliYkAeLZvT9CsWag7tK+3cyveuJGsl2dhKyhAptEQPHMm2hHDrztayZXa21VzasjnVqNYx46h7NIFycsLi8EAF9/TIM6tEebUkM/NFXOqaRyj0Yher79hMeJWt2mUSmWlRrjyuaPiukKsWsVRKgl++mn2tGvHsGHDbhhHu/g78r9fTvaCBZQdPUraxIkE3PdPgp54AvllVyMcfW62sjKy33yLvG+/BUDdoYN9Irdmzaody1E5OYKr5dSQz61asTp2BH9/ZHl5KI8ehe7d6yQn0d7uGceRsRpyTtWNU9V9xdKvAjK5HP/x42ixZrV9VIrNxoWv/8eZESMpXL++To5ZlpTE2XHjKwqRgAceoPmSxdUqRAShWuRyMcRXEFyUKEaECh5BQYS/vYCIzz9D2bQplowM0qZOI2P6kygNBsccRJIwrlhJ8pixlB0/jiIggIjPPiXkuWeRXZz+XhDqzKVi5M8/nZuHIAiVuNVtGqF+aPv2JfrXXzB89DG5ixZRvGEDURs2cG7Z93j37oV3r154x8SguM79vytJVivm1FRCly4lO/EAAJrYnoS98QbK4OC6OhVBqExcGREElySKEeGq5F5eBD89A92IEWTOn49p507MKSnkp6SQv2QpKBR4depkL0x698Krc2dkSiVSeTnl585RdiaJsjOnKT+TRFlSEuXJyUhlZegALq6zEzjlIWROGLkjNGI9eoBCAWlpkJoKERHOzkgQBEQxItyAuk1rwj/7lHU//kQff3/Kdu6kePt2ys+epSQxkZLERAwffYTc2xsPvZ7ytDSwWq8aS6ZSYQoNpeXrr6O7pftV9xGEOqXRwM03w5499ls148c7OyNBEBDFiFBFNi812v634z/EPlmUOT2d4u3bKfrzT0wJO7Dm51NeXAyA3NsbVYsWeLZogWeLaFTR9p+EhLD2t9/odFMXZ56K0Nj16mUvRrZvF8WIILgIUYwINaIMC8Nv7Fj8xo5FstkoPXYMm9GIKjoaj+Dgq84R4ohZCQWh1nr3hvffF/1GBMGFiGJEqDWZXI5Xhw7OTkMQquZSJ9bERCgqAk9Pp6YjCIIY2isIQmPTtKm946rVCrt3OzsbQRAQxYggCI1R7972n+JWjSC4BFGMCILQ+IjJzwTBpYhiRBCExudSMZKQADabc3MRBMG9OrCazeaKx6XntY3niDiOjOVqcURO7ptTQz63Wsdq3x4PjQZZfj6Ww4cdkpPLnFsjyakhn5sr5lTTOFXdXyZJklTtrOpJXFwccXFxWK1WTp48yeLFi9FoNM5OSxCEBqDXyy8TdOgQiY89xrnBg52djiA0SCaTiYkTJ1JQUIDuOkuIuHQxconRaMTX1xeDwYBOp8NsNhMfH8+gQYNqtSSyo+I4MparxRE5uW9ODfncHBFLPmsWivnzsfzzn6weO7ZBnVtjyKkhn5sr5lTTOEajEb1ef8NixK1u0yiVykqNcOVzR8V1hViuFseRsURO7hnHkbFcIqe+fWH+fBQ7d8LYsQ3r3OoojiNjuVocR8ZqyDlVN05V9xUdWAVBaJxiYwGQnTqFqqDAyckIQuMmihFBEBonf39o3x6AgBMnnJyMIDRuohgRBKHxujjEN+D4cScnIgiNmyhGBEFovC7OxBq0fz8kJzs5GUFovEQxIghC49WnDwB+ycko27SBli3h0Ufhp58gP9+5uQlCIyKKEUEQGq+WLbF++CG57doheXjAmTPwyScwZgwEBto7uc6aBVu3ggMmshIE4epEMSIIQqNme/hhts2bhyUrC375BR5/HNq2tU8Tv2MHvPoq3HorNGsmFtYThDoiihFBEAQAHx8YORLefx+OHYOUFPjySxg/HvR6yMiA/v1h8WJnZyoIDY4oRgRBEK4mIgImT4YlS+DsWbjzTigrg3vvhTlzwPUnrxYEtyGKEUEQhBvx9oYff4Rnn7U/nzsXJk6E0lLn5iUIDYRbTQcvCILgNHI5vPEGtG4NjzwCS5far5isXAkhIVWPU1ICRiMUFUFhof3npcfF53KTCa2PT12diSC4HFGMCIIgVMeDD0J0tH3EzY4dEBMDq1ZBmzZX31+S4OhR+PVXewfZHTtueItHAfRTq+1DjcWKwkIj4FbFiNlsrnhcel7beI6I48hYrhZH5OS+OTXkc3N6Tn36wNateNx5J7LTp5F69cL69dcgl9vjmM3I/vwT2apVyFetQpaU9LcQklZr7zTr7Q1abaXnUlISHvv2IY0cieW775BGjaq/c6vjWK4WR+RUt3Gqur9Mkly3F1ZcXBxxcXFYrVZOnjzJ4sWL0Wg0zk5LEAQBAKXRSI833kB/5AiSXM7pO+9EbTAQsncvquLiiv2sSiU5nTuT2aMHWV27UhoYaL/tcw1ys5luCxYQtmMHklzO/qlTSR0woD5OSRAcymQyMXHiRAoKCtDpdNfcz6WLkUuMRiO+vr4YDAZ0Oh1ms5n4+HgGDRpUqyWRHRXHkbFcLY7IyX1zasjn5lI5lZejmDoV+ddfV9os6fVIw4ZhGzECaeBA0GqrldMf69YxbOVKPL75BgDrm29imz69Wqk1yPZ2cByRU93GMRqN6PX6GxYjbnWbRqlUVmqEK587Kq4rxHK1OI6MJXJyzziOjNWgclIqYdEirB07YvzsM3T/+AeK0aOR9eyJTKGo8ZBFSaFA+uILCA6GBQtQPPMMioICeOUVkMmqmWIDau86iuPIWA05p+rGqeq+blWMCIIguCSZDNv06Wxp1Yphw4ahcNAXETIZvPmmfWr6F1+E116DCxfggw+ue5tHENyN+DQLgiC4MpkMXngBPv7Y/vtHH8F994m1coQGRRQjgiAI7uCRR+xT0Xt42H+OHg0mk7OzEgSHELdpBEEQ3MX48eDra5/jZPVqCAsDPz/QaOxDhDWaSr/LvbzodP488nXr7HObWK1gsdh/Xv6w2W54aIXNxi2ZmSi++qpWt4hcLY7I6TLz5tkn9XMCUYwIgiC4k6FD4fffYdQoe/+RgoJr7qoAoh10WDkQ1gDjODKW2+f0zDMOOGLNiGJEEATB3fTpY19VODnZfqvGZILi4r/9bi0s5PSxY7Rs3RqFpycoFFd/yOU3HKFjtVo5fPgwHTt2RKFQ1Dh1V4sjcrpMVFSNj1NbohgRBEFwR97e0LHjdXexmc0cX7OGaAeM8LGZzZxds4b2tYzlanFETq5BdGAVBEEQBMGpalSMxMXF0bx5c9RqNTExMezateu6+y9fvpy2bduiVqvp1KkTa9asqVGygiAIgiA0PNUuRpYtW8aMGTOYPXs2+/bto0uXLgwZMoTs7Oyr7r99+3YmTJjAgw8+yP79+7nzzju58847OXz4cK2TFwRBEATB/VW7GHn77beZMmUKDzzwAO3bt+eTTz5Bo9GwcOHCq+7/3nvvcccdd/DMM8/Qrl07Xn31Vbp27cqHH35Y6+QFQRAEQXB/1erAWl5ezt69e3nhhRcqtsnlcgYOHEhCQsJV35OQkMCMGTMqbRsyZAgrV6685nHKysooKyureG40GgH7Qj2XHpee14ZY7lnk1JBzasjn5oo5NeRzc8WcGvK5uWJONY1T1f2rtWpveno64eHhbN++ndjY2Irtzz77LJs3b2bnzp1/e49KpeLrr79mwoQJFds++ugj5s6dS1ZW1lWPM2fOHObOnfu37YsXL0aj0VQ1XUEQBEEQnMhkMjFx4kT3XLX3hRdeqHQ1xWg0EhERweDBg9HpdE5fErkuY7laHJGT++bUkM/NFXNqyOfmijk15HNzxZxqGufSnY0bqVYxotfrUSgUf7uikZWVRWho6FXfExoaWq39ATw9PfH09Pzb9iuXLna1pZUdGcvV4jgylsjJPeM4MlZDzqkhn5sjY7laHEfGasg5VTdOVfetVgdWlUpFt27dWL9+fcU2m83G+vXrK922uVxsbGyl/QHi4+Ovub8gCIIgCI1LtW/TzJgxg/vvv5/u3bvTo0cP3n33XYqLi3nggQcAmDRpEuHh4cybNw+A6dOn069fPxYsWMDw4cNZunQpe/bs4bPPPnPsmQiCIAiC4JaqXYyMGzeOnJwcZs2aRWZmJjfddBPr1q0jJCQEgJSUFOSXrQzYq1cvFi9ezMyZM3nxxRdp1aoVK1eupOMNpjEWBEEQBKFxqFEH1mnTpjFt2rSrvrZp06a/bbv77ru5++67a3IoQRAEQRAaOLE2jSAIgiAITuWSQ3uvdGkqlMsnPzOZTBiNxloPVXJEHFfMqSGfm8ipfuOInOo3jsipfuOInOo2zqXv7RtNaeYWxUhhYSEAERERTs5EEARBEITqKiwsxNfX95qvV2sGVmex2Wykp6fj4+ODTCYD4JZbbmH37t21intpMrXU1NTrzgxXVY7IyRXjOCpWQ29vR8YS7V2/sUR7128s0d71G8uZ7S1JEoWFhYSFhVUa3HIlt7gyIpfLadq0aaVtCoXCIR9AAJ1O55BYjsrJ1eI4OlZDbW9HxhLtXb+xRHvXbyzR3vUby9ntfb0rIpe4bQfWqVOnOjuFv3FUTq4Wx9GxHKUht5No7/qNJdq7fmOJ9q7fWK7Y3ldyi9s0dcVoNOLr63vDBXwExxDtXb9Ee9cv0d71S7R3/arr9nbbKyOO4OnpyezZs6+6Do7geKK965do7/ol2rt+ifauX3Xd3o36yoggCIIgCM7XqK+MCIIgCILgfKIYEQRBEATBqUQxIgiCIAiCU4liRBAEQRAEp2rUxUhcXBzNmzdHrVYTExPDrl27nJ1Sg7BlyxZGjhxJWFgYMpmMlStXVnpdkiRmzZpFkyZN8PLyYuDAgZw6dco5yTYA8+bN45ZbbsHHx4fg4GDuvPNOTpw4UWmf0tJSpk6dSmBgIFqtljFjxpCVleWkjN3bxx9/TOfOnSsmf4qNjWXt2rUVr4u2rjvz589HJpPx5JNPVmwT7e1Yc+bMQSaTVXq0bdu24vW6au9GW4wsW7aMGTNmMHv2bPbt20eXLl0YMmQI2dnZzk7N7RUXF9OlSxfi4uKu+vp///tf3n//fT755BN27tyJt7c3Q4YMobS0tJ4zbRg2b97M1KlT2bFjB/Hx8ZjNZgYPHkxxcXHFPk899RS//vory5cvZ/PmzaSnp3PXXXc5MWv31bRpU+bPn8/evXvZs2cP/fv3Z9SoURw5cgQQbV1Xdu/ezaeffkrnzp0rbRft7XgdOnQgIyOj4rFt27aK1+qsvaVGqkePHtLUqVMrnlutViksLEyaN2+eE7NqeABpxYoVFc9tNpsUGhoqvfnmmxXb8vPzJU9PT2nJkiVOyLDhyc7OlgBp8+bNkiTZ21epVErLly+v2OfYsWMSICUkJDgrzQbF399f+uKLL0Rb15HCwkKpVatWUnx8vNSvXz9p+vTpkiSJz3ZdmD17ttSlS5ervlaX7d0or4yUl5ezd+9eBg4cWLFNLpczcOBAEhISnJhZw5ecnExmZmaltvf19SUmJka0vYMUFBQAEBAQAMDevXsxm82V2rxt27ZERkaKNq8lq9XK0qVLKS4uJjY2VrR1HZk6dSrDhw+v1K4gPtt15dSpU4SFhREdHc29995LSkoKULft7RYL5TmawWDAarUSEhJSaXtISAjHjx93UlaNQ2ZmJsBV2/7Sa0LN2Ww2nnzySXr37k3Hjh0Be5urVCr8/Pwq7SvavOYOHTpEbGwspaWlaLVaVqxYQfv27UlMTBRt7WBLly5l3759V111Vny2HS8mJoavvvqKNm3akJGRwdy5c+nbty+HDx+u0/ZulMWIIDRUU6dO5fDhw5Xu8QqO16ZNGxITEykoKOCHH37g/vvvZ/Pmzc5Oq8FJTU1l+vTpxMfHo1arnZ1OozB06NCK3zt37kxMTAzNmjXj+++/x8vLq86O2yhv0+j1ehQKxd96AGdlZREaGuqkrBqHS+0r2t7xpk2bxqpVq9i4cSNNmzat2B4aGkp5eTn5+fmV9hdtXnMqlYqWLVvSrVs35s2bR5cuXXjvvfdEWzvY3r17yc7OpmvXrnh4eODh4cHmzZt5//338fDwICQkRLR3HfPz86N169acPn26Tj/fjbIYUalUdOvWjfXr11dss9lsrF+/ntjYWCdm1vBFRUURGhpaqe2NRiM7d+4UbV9DkiQxbdo0VqxYwYYNG4iKiqr0erdu3VAqlZXa/MSJE6SkpIg2dxCbzUZZWZloawcbMGAAhw4dIjExseLRvXt37r333orfRXvXraKiIs6cOUOTJk3q9vNdq+6vbmzp0qWSp6en9NVXX0lHjx6VHn74YcnPz0/KzMx0dmpur7CwUNq/f7+0f/9+CZDefvttaf/+/dK5c+ckSZKk+fPnS35+ftLPP/8sHTx4UBo1apQUFRUllZSUODlz9/Too49Kvr6+0qZNm6SMjIyKh8lkqtjnkUcekSIjI6UNGzZIe/bskWJjY6XY2FgnZu2+nn/+eWnz5s1ScnKydPDgQen555+XZDKZ9Pvvv0uSJNq6rl0+mkaSRHs72tNPPy1t2rRJSk5Olv78809p4MCBkl6vl7KzsyVJqrv2brTFiCRJ0gcffCBFRkZKKpVK6tGjh7Rjxw5np9QgbNy4UQL+9rj//vslSbIP73355ZelkJAQydPTUxowYIB04sQJ5ybtxq7W1oC0aNGiin1KSkqkxx57TPL395c0Go00evRoKSMjw3lJu7HJkydLzZo1k1QqlRQUFCQNGDCgohCRJNHWde3KYkS0t2ONGzdOatKkiaRSqaTw8HBp3Lhx0unTpyter6v2lkmSJNXu2oogCIIgCELNNco+I4IgCIIguA5RjAiCIAiC4FSiGBEEQRAEwalEMSIIgiAIglOJYkQQBEEQBKcSxYggCIIgCE4lihFBEARBEJxKFCOCIAiCIDiVKEYEQRAEQXAqUYwIgiAIguBUohgRBEEQBMGpRDEiCIIgCIJT/T8ek0XwajcVMwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "lst_indices3 = []\n",
+    "for i in range(20):\n",
+    "    if max(lst_proj_all[start_epoch:end_epoch,i])>0.8:\n",
+    "        lst_indices3.append(i)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch-start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in lst_indices3:\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n",
+    "# from matplotlib.ticker import AutoMinorLocator\n",
+    "# plt.xaxis.set_minor_locator(AutoMinorLocator(2))\n",
+    "# plt.yaxis.set_minor_locator(AutoMinorLocator(2))\n",
+    "plt.grid(True, which = 'minor')\n",
+    "\n",
+    "plt.minorticks_on()\n",
+    "plt.grid()\n",
+    "# plt.grid(True, 'minor', color='#ddddee')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lst_indices2 [0, 3, 4, 5, 6, 7, 11, 12]\n",
+      "lst_indices3 [0, 3, 5, 6, 11, 12]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"lst_indices2\", lst_indices2)\n",
+    "print(\"lst_indices3\", lst_indices3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "lst_indices3 = []\n",
+    "for i in range(20):\n",
+    "    if max(lst_proj_all[start_epoch:end_epoch,i])>0.8:\n",
+    "        lst_indices3.append(i)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch-start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "# for i in range(len(lst_indices3)):\n",
+    "#     lst_indices.remove(lst_indices3[i])\n",
+    "for i in lst_indices:\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n",
+    "plt.grid(True, which = 'minor')\n",
+    "plt.minorticks_on()\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2421044613.py:16: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "####################   Loss   ############################\n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YbFx0+5ejHY4gCKdJJCOCIPRLhq6z+vlnAJi84DoSMjKjHJEgCKdLJCOCIPRLuz58n4byUlxxXmbecGu0wxEE4QyIZEQQhH4nHGjlk3+/CMDsm2/DFRMb5YgEQTgTIhkRBKHf2bLsLYK+ZhKzcphw+YJohyMIwhkSyYggCP2KGgmz/f1lAMy59Q4Umy3KEQmCcKZEMiIIQr+y9+PVBP0+vKlp5M+YHe1wBEHoBSIZEQSh3zBNk63L3wZg8lXXIitKlCMSBKE3iGREEIR+o3jHVurLSnC43Yy/9MpohyMIQi8RyYggCP3G5mVvATDukitxemKiG4wgCL1GJCOCIPQLdSVFFO/chiTJTFlwbbTDEQShF4lkRBCEfmHL8ncAGD7jAuLTMqIcjSAIvUkkI4Ig9HmB5ib2rvsIgKlX3xDlaARB6G0iGREEoc/bvmI5uqqSMXwEWSNGRTscQRB6mUhGBEHo07RIhB0rlwMw9errkSQpyhEJgtDbRDIiCEKftveT1QSam4hLTmXEzDnRDkcQhLNAJCOCIPRZ1iBnVsPVyQvEIGeCMFCJZEQQhD6rZNcO6kqKsDtdYpAzQRjARDIiCEKftWXZmwCMu/QKXDGxUY5GEISzRSQjgiD0SfVlpRzZvgUkiSlXXRftcARBOItEMiIIQp/UPiHe8GkzScjIjHI0giCcTSIZEQShzwn4milY+yFgdecVBGFgE8mIIAh9zs6V76KpEdKHDid71NhohyMIwlkmkhFBEPoUTVXZvmIZIAY5E4TzhUhGBEHoU/Z/upbWpkZik5IZccGF0Q5HEIRzQCQjgiD0GaZpsqWt4eqk+deg2GxRjkgQhHNBJCOCIPQZ5Xv3UFt0GJvDyYTLr4p2OIIgnCMiGREEoc/Y+q419PuYiy7BHRsX5WgEQThXRDIiCEKf4KutoXDTBsCah0YQhPOHSEYEQegTtr2/FNM0yBs/iZTcQdEORxCEc0gkI4IgRJ0aCrHrw/cBmLJADP0uCOebHiUjTzzxBNOnTycuLo60tDSuv/569u/ff8rrXn31VUaNGoXL5WL8+PEsX778tAMWBGHgKfj4Q8KtrSSkZzJ08rRohyMIwjnWo2RkzZo13H///WzYsIGVK1eiqipXXnklra2tJ7zm008/5bbbbuOrX/0q27Zt4/rrr+f6669n9+7dZxy8IAj9n2mabH13CQCTr7oGSRYFtoJwvulRJ/733nuv0/6zzz5LWloaW7ZsYe7cuV1e84c//IGrrrqK7373uwA8/vjjrFy5kqeeeoq//vWvpxm2IAgDRfGu7TSUl2J3uRk774pohyMIQhSc0YhCzc3NACQlJZ3wnPXr1/Pwww93OjZ//nzeeuutE14TDocJh8Md+z6fDwBVVVFV9Qwi7qz9uXrzOYUTE/f73Oov93vLsrcAGDP3UmS7vc/HeyL95X4PFOJ+n1une7+7e/5pJyOGYfDQQw8xZ84cxo0bd8LzqqqqSE9P73QsPT2dqqqqE17zxBNP8Nhjjx13fMWKFXg8ntMN+YRWrlzZ688pnJi43+dWX77fEV8zJdu3ANDoiBkQ7cn68v0eiMT9Prd6er8DgUC3zjvtZOT+++9n9+7drFu37nSf4oQeeeSRTqUpPp+P3NxcrrzySrxeb6+9jqqqrFy5kiuuuAK73d5rzyt0Tdzvc6s/3O81zz1NCTB40lSu++Jt0Q7njPSH+z2QiPt9bp3u/W6v2TiV00pGHnjgAZYuXcratWvJyck56bkZGRlUV1d3OlZdXU1GRsYJr3E6nTidzuOO2+32s/JLd7aeV+iauN/nVl+93+FAgL0ffwhYs/P2xRhPR1+93wOVuN/nVk/vd3fP7VGzddM0eeCBB3jzzTf58MMPGTJkyCmvmTVrFqtWrep0bOXKlcyaNasnLy0IwgCzZ/VKIsEgSdm5DBo/KdrhCIIQRT0qGbn//vtZvHgxb7/9NnFxcR3tPuLj43G73QDcddddZGdn88QTTwDw4IMPcvHFF/Pkk09y9dVX8/LLL7N582b+/ve/9/JbEQShvzAMnW3vLQVgyoJrkSQpyhEJghBNPSoZ+ctf/kJzczPz5s0jMzOzY3nllVc6zikpKaGysrJjf/bs2SxevJi///3vTJw4kddee4233nrrpI1eBUEY2I5s20JTdSXOmBjGXHRptMMRBCHKelQyYprmKc9ZvXr1ccduueUWbrnllp68lCAIA1j77LzjL52P3eWKcjSCIESbGOpQEIRzqq60mJJd25Ekmcnzr4l2OIIg9AEiGREE4Zza9p419Pvw6RfgTU2LcjSCIPQFIhkRBOGcCbb4KVj7ESBm5xUE4SiRjAiCcM7sWvU+WiRM6qAhZI8eG+1wBEHoI0QyIgjCOREOtLKtreHqlAXXie68giB0EMmIIAjnxJrnn6GlsYGE9ExGzbk42uEIgtCHiGREEISzrmjHVnZ9uAIkifnffBCbwxHtkARB6ENEMiIIwlkVDgRY8bf/BWDyVdeQM1oMeCgIQmciGREE4axa+8L/4a+vJT49g4u+eHe0wxEEoQ8SyYggCGdN8c7t7Fz1HgDz73tQjLYqCEKXRDIiCMJZEQkGeP9vfwBg0vxryB0zPsoRCYLQV4lkRBCEs2LNC/+Hv66W+LR0LrpdVM8IgnBiIhkRBKHXFe/czs4PjlbPOFzuKEckCEJfJpIRQRB6VSQYYMXf/wjApPlXkzt2QpQjEgShrxPJiCAIvWrti8/iq63Bm5rORbd/OdrhCILQD4hkRBCEXlOyewc7Vi4HYP59/ymqZwRB6BaRjAiC0CsioSDv/9Wqnpl4xULyxk2MckSCIPQXIhkRBKFXfLz4WXy11XhT05h7x5ejHY4gCP2ISEYEQThjdSVFbH9/GQBXfuM/cbg9UY5IEIT+RCQjgiCcsU3vvA7AiJlzGDR+UnSDEQSh3xHJiCAIZ8RXW8O+T9cCMOP6W6IcjSAI/ZFIRgRBOCObl72JoevkjZtI+tDh0Q5HEIR+SCQjgiCctqDfx64PVwAwfdHNUY5GEIT+SiQjgiCctm3vLUULh0kbMky0FREE4bSJZEQQhNOihkJse28JADMW3YwkSVGOSBCE/kokI4IgnJZdH60g1OInIT2T/Jmzox2OIAj9mEhGBEHoMV3T2Lz0TQCmXXsjsqxEOSJBEPozkYwIgtBj+z9di7+uFk98AmMvviza4QiC0M+JZEQQhB4xTbNjkLMpC67D5nBEOSJBEPo7kYwIgtAjR7Ztpq60GIfbzcQrF0Y7HEEQBgCRjAiC0CMb334VgAmXL8AVExvlaARBGAhEMiIIQreV7yugfF8Bis3G1IWLoh2OIAgDhEhGBEHoto3vvAbA6IsuJTYpOcrRCIIwUIhkRBCEbqkrLebwlo0gSUy/7sZohyMIwgAikhFBELpl85I3AMifPoukrJwoRyMIwkAikhFBEE7JV1fD3nWrAZi+6KboBiMIwoAjkhFBEE5py7K3MXSd3DHjyRw+MtrhCIIwwIhkRBCEkwq2+Nm16n3AmhBPEASht4lkRBCEk9rx/jLUcIjUQUMYNHFKtMMRBGEAEsmIIAgnpEUibHt/KQDTr70RSZKiHJEgCAORLdoBCILQdxV8/BGB5ibiklMZMeuiaIczoJmmSWVlJbt372b//v0YhoHH48HtduPxeDptt69lWUZV1TNeNE2L9ts/LZqmUVBQEO0wBoy77rqL7OzsqLy2SEYEQeiSaRhsXvomAFMWXodiEx8XZ0NNTQ27d+9m9+7dNDQ0dHqssbExSlH1H7quRzuEAcMwjKi9tvh0EQShS4e3baKxogyH28P4S+dHO5wBpaGhoSMBqamp6Thus9kYOXIkY8eOJSYmhmAwSCAQ6Fgfux0MBjFNE7vdftLFZrN165z+RtM01qxZw8UXX9wv4++L4uPjo/ba4icoCEKXNi+xSkUmXrEAp8cT5Wj6vkgkQigUory8HFVVCYVChEIhgsFgp+2mpiYqKys7rpNlmeHDhzNu3DhGjhyJ0+mM4rvoP1RVxeVykZycjN1uj3Y4whkSyYggCMepLNxP2d7dyIrC5AXXRjucqDMMA7/fT3Nz8wmXUCgEwN69e0/5fJIkMWTIEMaNG8fo0aNxu91n+y0IQp8mkhFBEI7TXioyas7FxCWlRDma6KmtrWX79u3s3LkTv99/yvNlWSYuLg63243L5cLlch237Xa7GTJkCLGxsefgHQhC/yCSEUEQOmmqruLgZ58CMO3a829CvEAgwO7du9m+fTsVFRUdx2VZxuv1Eh8f3+Xi8XhYtWoVCxcuFNUGgtBDIhkRBKGTrcvfxjQNBk+cQmre4GiHc05omkZhYSHbt2/nwIEDHb0KZFkmPz+fiRMnMmLEiJM2lFRV9VyFKwgDjkhGBEHoEGzxs+ujFQBMu2bgl4rous7q1avZsmULgUCg43hGRgaTJk1i3LhxojpFEM4BkYwIgtBhx4rlaOEwqYOHkjd+YrTDOatUVeXVV1/lwIEDAMTGxjJ+/HgmTZpEenp6lKMThPOLSEYEQQDahn5/bwkA06+5YUAP/R4KhXjppZcoLi7GZrNx3XXXMXbsWBRFiXZognBeEsmIIAgA7F23mkBzE7HJKQN66PeWlhZeeOEFqqqqcDqd3HbbbQwePDjaYQnCeU0kI4IgWEO/L3kDgKkLBu7Q742NjTz//PM0NDQQExPDnXfeSWZmZrTDEoTz3sD8xBEEoUcOb9tMQ/vQ75ddFe1wzoqamhqef/55/H4/8fHx3HXXXSQnJ0c7LEEQEMmIIAjA5qVWqciEy68akEO/l5WV8eKLLxIMBklNTeVLX/oSXq832mEJgtBGJCOCcJ6rKjxAWYE19PuUBddFO5xed+jQIV5++WVUVSU7O5s77rgDzwBMuAShP5N7esHatWu59tprycrKQpIk3nrrrZOev3r1aiRJOm6pqqo63ZgFQehFm5YeM/R78sAa+r2goIDFixejqipDhw7lrrvuEomIIPRBPS4ZaW1tZeLEidxzzz3ceGP3B0Xav39/p2LRtLS0nr60IAi9rLmmioMbPgFg2jU3RDma3tHY2MiePXsoKCjoGM59zJgx3HjjjWKqeUHoo3r8l7lgwQIWLFjQ4xdKS0sjISGhx9cJgnD27PjgPUzTYNCEyaQOGhLtcE5bQ0MDBQUF7Nmzh8rKyo7jkiQxffp0rrrqKmS5xwXBgiCcI+fs34RJkyYRDocZN24cjz76KHPmzDnhueFwmHA43LHv8/kAa8TE3pz/of25xJwS54a43+fWqe63aZocaCsVGT33sn73c2loaGDv3r3s3buX6urqjuOSJDFo0CBGjRrFyJEjiY2NRdd1dF0/q/GI3+9zS9zvc+t073d3z5dM0zR7HFX7xZLEm2++yfXXX3/Cc/bv38/q1auZNm0a4XCYp59+mueff57PPvuMKVOmdHnNo48+ymOPPXbc8cWLF4v6XkHoJeGmBkqXvw6yzNCbvoRsd0Q7pG6rq6ujtLS007G4uDgSEhKIj48Xs+YKQh8RCAS4/fbbaW5uPmkPtrOejHTl4osvJi8vj+eff77Lx7sqGcnNzaWurq5Xu+OpqsrKlSu54oorxIfXOSDu97l1qvu96e1XWf/qYgZPnMp13/1xFCI8PXV1dTzzzDNomsagQYMYO3YsI0aMICYmJqpx9fbvt6mq6I2N1lLfgN7YgN7QYO03NKA3NGLqOpLTgex0ITmdSC4nksNaW8ccoNhAUzFVFVPTMDUN2rfb1mhnt9TobDAMg7LycnKys0UVXC9J/OpXsefmdPnY6f5++3w+UlJSTpmMRKU114wZM1i3bt0JH3c6nTidzuOO2+32s/IldraeV+iauN/n1onu9+EtGwHInzm73/w8dF1nyZIlaJrGsGHDuPPOO/vcHDrd/f02TRO9ro5IaSmR4hLU0hIixSVESkpQS0vRm5rOfrD9XALQEu0gBpCkW27Gbj9527Gefn5399yoJCPbt28XQzALQhT56mqpPlwIksSwqTOiHU63ffzxx1RUVOByuVi0aFGfS0RORPf5CO3eTXDnLkJ79xIpLiZSUoIZCJz8QkVBSUzElpSEkpR0dJ2chJKYhGS3YYTDmKEwZjiEEQpjhkIY4RBmOIIZDmFqOpLdjmSzITnsYLO17duttd2OpMjQT+5lO13XOXDgACNGjBATHPYSWxS/l3ucjLS0tFBYWNixf+TIEbZv305SUhJ5eXk88sgjlJeX89xzzwHw+9//niFDhjB27FhCoRBPP/00H374IStWrOi9dyEIQo8UbtoAQPbI0cQkJEY5mu4pLy9nzZo1AFx99dV9dgRVIxwmWFBAcOcugrt2Etq5i0hRUdcnSxL2zEzsg/Jw5A3CkZeLPS8PR14etrQ0lPh4JFEF0SVVVWlYvpykhQv7TcmecGI9TkY2b97MJZdc0rH/8MMPA3D33Xfz7LPPUllZSUlJScfjkUiE73znO5SXl+PxeJgwYQIffPBBp+cQBOHcOrR5PQDDp8+KciTdo6oqb7zxBqZpMnbsWMaPHx/tkDBNE62mlnDhQcIHDxLcf4C8jRs5/KMfg6Ydd749Jwf3hPG4xo7DMWwojrxB2HOykR39p+GwIJwtPU5G5s2bx8navD777LOd9r/3ve/xve99r8eBCYJwdgT9PkoLdgP9Jxn54IMPqK+vJzY2lquvvvqcv77R2kpwzx7CB63EI1xYSPhgIUZzc6fzXG1rJSkJ9/jxuCaMt9bjx2NL7B8lUIIQDWI4QkE4zxzeugnTMEjNG0xCeka0wzmlw4cP89lnnwGwaNGic9a93zRNglu20PT6G/jef7/r9h2yjGPQIJz5+diGDWWPz8+su76EOy+v37RnEYS+QCQjgnCeKdxkVdEM6welIsFgsGP+q2nTppGfn3/WX1OtqqL5rbdpevMN1OKjVc62zExco0bhzM/HmT8cZ34+jiFDkNt6/qmqSsvy5djb5u0SBKH7RDIiCOcRNRyiaMc2APJn9P1k5N1338Xn85GYmMiVV1551l7HiERo+fBDml5/g9ZPPgHDAED2eIhbuICEG2/CPXmSSDIE4SwRyYggnEeKdm5Di4Txpqb1+bloCgoK2LlzJ5IkccMNN+Do5YaeWmMjgY2baP30U/zvvYd+TPsPz7RpxN90E975VyKLUZ8F4awTyYggnEcKNx7tRdOX/8v3+/0sWbIEgDlz5pCXl3fGz6m3tBDYvJnAhs9o/ewzwvv2wTGN8W3p6cTfcD0JN9yAY9CgM349QRC6TyQjgnCeMHS9Y9TV4dMviHI0J2aaJkuWLCEYDJKens68efNO+7kCW7fRsmYNgQ0bCO7eDZ+bLM+ZPxzPzAuIvXguMbNnI4nBswQhKkQyIgjnibK9uwm1tuCO85I9cky0wzmhbdu2ceDAARRF4cYbb8Rm6/nHlGma1P3vU9T9+c+djtvz8oiZORPPBTOJmTEDW2pqb4UtCMIZEMmIIJwn2kddHTp1BnIfLQHQNI2PPvoIgEsuuYT09PQeP4dpmtT+9rfU/+NpAOIWXEXsRXOJmTkDe3Z2r8YrCELvEMmIIJwHTNPsSEb6ci+aHTt24Pf7iYuL44ILel6VZJomNb/6FQ3/sqajSH/kByTdfXdvhykIQi8TyYggnAdqjhzCX1+L3ekib/ykaIfTJcMw+OSTTwCYPXt2j6tnTMOg6vHHaXrpZQAyfvZTEm+7rdfjFASh94lkRBDOAwfbetEMnjQFu8MZ5Wi6VlBQQENDA263mylTpvToWlPXqfzZz2h+7XWQJDIf/zkJN998liIVBKG3iWREEM4D7aOu5vfRUVdN0+Tjjz8GYObMmTid3U+YTE2j8kc/ovntd0CWyXril8QvWnS2QhUE4SwQyYggDHBNVRXUl5UgKwpDJk+PdjhdKiwspLq6GrvdzowZM7p9namqVHz/+/iWvwuKQvav/wfvwoVnMVJBEM4GkYwIwgB3aLM1yVzu2Am4YmOjHE3X2ktFpk2b1u2J8MxIhPLvfAf/yg/Abif7t0/iveKKsxmmIAhniUhGBGGAO7TFSkaGT+ubA52VlJRQUlKCoijMmtW9aiQjEqH8Px+kZfVqJLud7D/+gbhLLjnLkQqCcLaIZEQQBjAtGKDq4H4Ahk2fGeVoutZeKjJx4kS8Xu8pzzc1jfJvP2wlIk4nOX/6E7EXzjnbYQqCcBaJZEQQBrDWsmIAMoaPIC4pJcrRHK+qqoqDBw8iSRJz5pw6oTANg8of/ZiWVauQHA5y//ZXYk5jPJJo0A2dgBYgpIWQJAm7bMcm27DJNhRJQZGUTvMFmaZJUAvii/jwR/wdy7H7hmlgV+w4FScO2YFDOWaRHdgVO7IkE9EjRPQIYT3ceduwtlVDjeKdOT26rnMoeIhD2w+h9NFB/PqbW0fcSmZsZlReWyQjgjCAtZYVAdbEeH3RunXrABgzZgzJycknPdc0Taqf+BXNb79tNVb9/e/7RCKiGRp76/eyoWIDq1tX897q9whoAYJakFa1lVa1tWP/VGyyDbtsR5EUQloIzdTOwTvo39YWrI12CAPGvNx5IhkRBKF3hQOtBKorgL45MV5DQwN79uwB4MILLzzl+XV/+jONzz8PQNYTvyTu0ui0EdENnX2N+9hUuYmNVRvZWrOVVrX16AkVJ79elmQM0+jyMc3Q0IzOCYhNsuF1eolzxBFnj7PWbYsiKUQMq5RD1dVO2+0lH6Zp4lAcVulJW4lJ+3b72i7bz/S2nHOGYVBUVMTgwYORZTna4QwIKe7olZ6KZEQQBqii7VvBMEjMyiY5Ozfa4Rznk08+wTRNhg8fTmbmyf8ba3juOeqeegqA9B//mPjrrjsXIaIbOrXBWspbyimoL2Bj1Ua2VG/BH/F3Oi/OEcfUtKk465zMnDiTOFccMbYYYuzW4rF7OrYdsgMAwzTQTK0jAVENtWNbN3Vcios4Rxxum7tT9Y1gUVWV5dXLWTh1IXZ7/0umhM5EMiIIA5AWibBz5XIAhk3te6Uifr+f7du3A3DRRRed9Nymt96i+pdPAJDyn98i6c47ejWWumAdxb5iKloqKG8pp6KlomO7qrWqy6qSGHsMU9OnMiNjBtMzpjMycSSGbrB8+XIWDuvel6MiKSgoOJW+OSKuIJxLIhkRhAFGU1Xe+e0vqTy4D8lmY/Tcvtfldf369ei6Tm5uLoMGDTrhef5Vq6j80Y8BSLr7blK++c0zet3GUCMF9QXsqd/D7rrd7KnfQ02g5qTX2CQbGTEZDI4fzLT0aczImMHo5NHY5M4fn4beddWLIAinJpIRQRhAdE1j2R/+myPbNmNzOEi/6EoSM7OjHVYnwWCQzZs3AycvFWndsIHyh74Nuk78DTeQ9v3v9ai6wjANttVsY0ftDvbU7WFP/R7KW8qPO09CIis2i5zYHLJis8iKzSI7NrtjnepORZFFbw1BOJtEMiIIA4Sh6yx/6kkKN21Asdu55uEfsrvk+C/faNu4cSORSIT09HTy8/O7PCe4cydl/3E/pqoSd8UVZD7+c6RuNlIs9hXzduHbLDm8hKrWquMeH+wdzJjkMYxNHsvYlLGMThqNx969UV8FQTg7RDIiCAOAaRi8/5ffc2D9x8iKjese/iG54yb2uWQkEonw2WfWiLAXXnhhlyUd4cJCSr/2dYxAAM+sC8h68jdItpN/VLVEWni/6H3ePvQ222q2dRyPc8RxQeYFjEsZx9jksYxOHo3XceqB1QRBOLdEMiII/ZxpGKz8x1MUfPwRkixzzUPfY+iU6ahq3xvIauvWrQQCARITExkzZsxxj6vl5ZR89V705mZcEyeQ+9RTyA5Hl89lmAYbqzbyduHbfFD8ASE9BFhdZ+dkzWHR8EXMy50nGogKQj8gkhFB6MdM0+TDZ//Org9XIEkyC7/1/8ifMTvaYXWptraWtWutAarmzJlz3KiZWkMDJV+9F626GsfwYeT97W/IMTHHPY+qq7x64FWe3fMsla2VHceHxg9l0fBFXDP0GtI8aWf3zQiC0KtEMiII/ZRpmqx54f/Y/v5SkCTmf/NBRs2eG+2wutTQ0MBzzz1HIBAgMzOTiRMndnpcb2ml9OvfIFJUhC0rk7ynn0ZJSOh0jmEaLD+ynKe2PdXREDXOEcfCIQtZNGwR41LGifE4BKGfEsmIIPRTn/77BbYsfROAK+69n7EXXxbliLrW1NTEv/71L/x+P6mpqdx5552dxuEwIhHKvvUAod27URITyXv6GewZGR2Pm6bJx+Uf84etf+BA4wHAGinyGxO+wQ35N4hqGEEYAEQyIgj90PrXX2LDG68AcMmXv8GEy6+KckRd8/v9PPfcczQ3N5OUlMRdd91FzDFVL6auU/G97xNYvwHJ4yH373/DOXRIx+Pba7bzuy2/Y2vNVgBi7bHcM+4e7hh9h+gBIwgDiEhGBKEfMU2TdS/9i41vvwbA3DvvYcqCa6McVddaW1t57rnnaGhoICEhgbvvvpu4uLiOx03TpPoXv8D/3ntgt5P71P/iHj8egMLGQv6w7Q+sLl0NgFNxcvuo27ln3D0kuBLO/ZsRBOGsEsmIIPQTpmGw6p9/Y8eKZQDMveMrTL/2xihH1bVgMMjzzz9PbW0tcXFx3H333cTHx3c6p+6pP9G4+CWQJNL/+5ccHO7hsx1/47Oqz9hctRkTE1mSuWH4Ddw38T4yYjJO8GqCIPR3IhkRhH7A0HXe/8vvKfj4I5AkLv/qfzDxigXRDqtLoVCIF154gaqqKmJiYrj77rtJTEzsdE7tC8+z8bU/sXuGxMHLR7Cz8ecE3w12OueKQVfwwOQHGBo/9FyG3zt0FfyV0FzWtpRCc7m13VINjlhwJ4A78ejiSTq67UoANQiB+ralrm3dcMyxejB0cMSA3WOtO7Y9YG/bt7W1qTFNwDxmm6P7/ZCs64ysLEResxMUMUJur5hyF8TnROWlRTIiCH2cpqos+8P/ULhpPZIss+D+hxl94bxoh9WlSCTC4sWLKS8vx+12c9ddd5GScnRa8o/LPublj//ElpbdtN7T9vFjHgINEp2JTM+YzszMmVyQeQF53rxz/wa08NHkoan0mO0SCDWBJHdekDq2FeCiumpshT+AliowxVw1Z5MCjAI4fpBd4XQNv0IkI4IgHE8NhXj7yV9QvHObNcT7Qz9g+LSZ0Q6rS6qq8tJLL1FSUoLT6eRLX/oS6enpHY+/c+gdfrzux5iY4JbwGDam581hZuZMZmTMID8xH1nq3pDvPaZrVulCSzW01Fhrf9XRbV+5lXy0VHO6pQUykNTpgB3isyE+1/qAb19i00ENQLCxbWmySjw69tsWhwc8yW1LilVy0rGfbO3LNoi0Ws8XCYDaau13bAdADwNtXZ47uj4f0wW6n3aH1g2DkuJi8gYNQunmVAHCKcSmRu2lRTIiCH1UqLWFN3/1GBUH9mJ3ulj03R8zaPykaIfVJcMwePXVVzly5AgOh4M777yTrKysjseXHFrSkYjM3WVwo+sC5v7sz9jtZ6FbrhqE4k/h8GooWmeVbLTW0e0kw+ayEoiE3GPWeVYCgGmVeHQsR/c1TWXr9h1MufhabMmDISYVxJfkWWOoKjuXLyfnqoUox3QVF/onkYwIQh8U8DXz2i9+Qm3RYZwxMdz4g0fJGjE62mGdUEFBAQcOHMBms3H77beTm5vb8diSQ0v40bofYWJyxVaD/ygdwdCX/4Js73qY9x4zdKjcYSUfhz+Cks/aSgM+R5KtBCE2zSqdiE0/uh2XeTTpiEk5rdICU1WpLHJgZk8B8eUoCD0ikhFB6GP89XW89l8/pqGiDE98Ajf98OekDe67jTgNw2DNmjWANfnd4MGDOx47NhG5fKvBV1dC9iuPn3C+mW4L+2H363DoQziy1qrWOJY3G4ZeAkMvhrTRVsLhSQZZNHQUhL5IJCOC0IcE/T5eefT7NNdUE5ucwi0//i+SsqLToKy79uzZQ21tLS6XiwsuuKDj+NLDS/nxJ1bVzJUHPdy9vhn5oZmUe95DOrwCCQVJkpEkW8caSUaSFGTJgd2eiN2RhMOeiN2ehN0ej2QYsPVfsPpX0Frb8VqG00tk6AzCueMIpeURdkA4XE048ilSw0ZsPi92mxdb+2KPw2Y7eszpTEOWxUiughAtIhkRhD7CNE1W/uMpmmuqiU9L59afPoE3tW9P+HZsqcisWbNwuVwALDu8jB+t+yGZNp0b7ImMHlpF7a9MYB0UrzvNV5Owa2CPqDiGmyhSOqo3ibCiEdabge0Q2g4lp/HMkgOvdxzx8VOsxTsFpzN6jfkE4XwjkhFB6CP2rFnFwc8+RVYUrv32I30+EQHYvXs3dXV1uFwuZg6OQW88xIryN9hy+Gl+kqGTaDOBStRk63xv3AS88dYkeaZpYJoapmmAqWO2LxgYRhg10kBEbUQN16AZAcBEtYFqsxEAQAdqrRUgSTacznSczgyczgxczgycznRMU0PV/Gia7+ii+o451oxhhGlu3kpz89aO9+Z25R1NTuKnEBs7AkkS1TyCcDaIZEQQ+oCm6io+/OffAJh9yx2kDx0e1XhM0yAQOExz8zaam7fi9+9BN0JWdQptPUTUELWNLUycJBFPC7u3/pmmWBsORWJOrHWKpMk495jER0Yz/MGncbnST/yin1d7AFY9Bvs2Y0iguj2o0+4gMnYBqhRGU304HMltCUgmDkcy0ml0DTZNk2CwuCMZaW7eSkvrAYKhEoKhEqqq3wJAlh243YNwuwfhcQ/C7Rlsrd2DcLkye/y6giAcJZIRQYgyQ9d596knUUNBskeNYfqim874OUOhCkpLF+Nyf8i+fR/hcme0fWm3LY50nM7UjnYSmuanuXk7zT4r+fD5dqBpvlO+jjsG3FidZpuxIwFNmkTQZzBnj4vgaway4mbYsr9i724i0lIDq5+ALf8CUwdJRp78JZzzHsHp7f0vfUmS8HgG4/EMJjPTGl6/4360Jyi+7eh6C62tB2ltPdjFczhwuXJwue0U7H0fuy0GWXGjKG4U2VrLihtF9qAobqstjCMFpyMFmy0BqZ+O9SEIvUUkI4IQZRvfepWKA3txuD0suP87yKfZ48M0derr11BWvpj6+jWAgc0GNbX7T3iN3Z6ETYklGCrl8+NwyJIDr5lIfGOQ+MpybKqOiQQS6JLEEmk+ftPNqByFffENbGzYRb0mM1PJ40cFeyh6U0dSZVLHN2Df/EuQvgZZk072BmDb87DixxBqto6NXAiX/QzSRp3WPTldNlscyckXkZx8UVtoOqFQOYFAMcFgMYFgMcFAkbUOlmKaEYLBw9hsUFd34vvdFUmy4bAn43Ck4HC0r1NO0KC2c9JimhqGGcEwwhjGseuj26apn+5t6NNM08TtbmD7jhdFMtdLRo18nNjYEVF5bZGMCEIUVRbu59PXFgNw2Ve/SXxaN0sPij6B+kLIv5KwU6ai4t+UV7xCOFzZcUpCwiyqqrIZPXoImlZn9S4JVxMO1xAOV2OaEVS1AVVtAMDtyCBeT8Tb2Ep80QFim+uQzYqjr5k0FAbNhrzZbGtNpfiDT6hJquENtYCG8gbAxp2j7+S7079L1fe+hx5ZhjNFIWl4E2x7wVpypsPUL0P+ldYYH+3qCmHJg0cbt2ZMgKt+BYPnnP7N7UWSpOB25+F25wEXdXrMSlSq8PsL2bRpBePG5QNhdD2EbgTQ9RCGHkA3Quh6AF1rJaI2EInUoWnNmKZGOFJNOFIdlffWnyk28PmORDuMAUPXW6P22iIZEYQoiYSCLP/f32AaBiNnXdT9+Wa2/Atz6UM0JCiUH3JRl+zEbPvH0G5PJDPzJrKzvojdnkNZ6XJycxZi/9wgXKZhoNVsI1T6EVrVZjxHduBs2N35dTwpMHQeDLvEGrMjPhsAXddZ9ucn+CT9E6o91RCGYfHD+NnsnzE5bTKtGzbQvGQZSBKZT72AlBSCjf+AgrehbJO1AGROtJ430AA7X7EGKrN74JIfwcz7rG+afsBKVLKx2dLQtCayso6/3ydiGGEikXoikbpj1tZiGCq6ZqKpOlrEQFN19La1plprQ5MxDTsYNkzDjqFba1O3Yxo2DN2GhIJsk1EUCVmRUWwyik3qOKbYZGRF/nyhS59nGAYVFRVkZWUhi5Fue4UZyTr1SWdJ//hrF4S+zjRB18HW/T+pNc89Q1NVJbHJKVx+7/1IkkRzUOX/1h2hrDGI0y7jUGRibH689hJilRLc/g249APYZ6Ug245OxJbQrJJdGSLVnosiuSHJRD32+zASgIptULoBSjcilW7EHmyg01em4oRBs2DYpVaSkD7uuOHMNUPjVx/8itdiX0OXdRyyg29M/AZfGfsV7IodIxym6tHHAEi87Yu4J02yLsy7APy/hK3Pwb4l1oip7Uu7mDS44Jsw9oY+lYiYhklDZSu++hC6amDoBrpmoGsmumZgtK3ViEbzAQefqUeQJRnTNK2KLwNMzI5Jc03DRNMMtEhbkhHR0VUDNRKDrrpRI1loEYNwQMM0+u+suudGLjXRDmEAGZLvgij1aO87f/GC0Nc0NkJBAezZA4cPQ3Mz+Hydl2OPqSoMGgTjxsH48UfXI0eCs3P9f+GmDexc9R6SzeTyb9xFWC/mvc8OsmRrAabRRLqnjqzYSrJclcQ7/UcvjDu6GVDdbK+bSYIxnusCn5FR8y6wDcq3wQc/w5Y2jol6Ksr//Q6qd4GhdX5/NhdkT4XcGTBkLuTNArv7hLdjd91uHv30UfY37gcZRrhH8OT8JxkcP7jjnPq//4NIURFKagqp3/525yeIS4eLvwszvw7v/gB2vITVTkWy1q01Vu+ZVY9B4hBwJ1glJXZ32+LpvHbEQMIgSB4OycPAGUdv0DWD2hI/FQebqDzUTGVhE+GAduoLAXCy43BZr8TR8YweG+44B+44O+7YtnXbvt1pQ5ZBkiVrkSRkWWqbSNg6ZhomumpYpSkRvdPaKmkx0NX+167EMExKS0vIzc1DlvtZsU4f5Y7rpSkaToNIRgShsdFKOPbsOZp8FBRAZeWpr/284mJrWbbs6DFFgREjaL1oOIcva6E1xaSptYrx96godpOi+q9RVG/lGbePPP4pTVMiFPHi97lpbkmk0TaBOvs01h52U9tilY48ST6zBn2TL6aVMt//Oq7i1Ug1uxl87BPFZULuzKNLxniwnfrDxx/x8+ftf2bxvsUYpoFDdzCtdRp//OIfcR6TZIUPH6H+738HIONHP0KJ6yI5OPC+1TbE33ZvJ98Jlz8GjcVQ+IG1lG+GxiPQePzlJxWbfjQxSR5+dPFmgzP2hJdFQhrVh31UFDZRWdhE9REfmmp0OsfmkEnKjEGxt1dzWItss6o5FEUCGUpKihkydAiKTUHCmg4HJGuqG8nquSNJoNhlbHYFm0PG5mhb2zuvXTF2XLF2FJuoguiKqqosX36QuQvzu10tJvRdIhkR+g5dhyVLrC/y3FyYONFaBg3q/WnODx6El16Cl1+GvXtPfF5uLowdCyNGQGIieL3WEh9//LbNBgcOwO7d1rJrF+zejdnURNmIcgqvD2I4rS8We8zRl9AMmRY1loAaS0JsKrkpGcS4c4iJHUGsYxAxK/4bZf9KkBRY9CeYdBsAEc3gg73VvLyplI8P1rK+2M/64gTi3fdxw7gfcXN8Ac4DyxkyexG2wbOtGWh7cB8N0+CdQ+/w+y2/pz5UD8Cw8DBGVY1i0RWLOiUipmFQ9eijmKpKzNyLiJs/v/OTmSZ8+r+w8qeACUnD4NrfWyUyYE1OlzMV5n3fakNSvQfUQNsS/NzSdizsg4YjVkPe1hpoqbaW4k+OfzOOOKtkJjYD4tKJuLIpbhpOYXkaxSVO9M8VDLgcYTK9VWR5DpNp20OKuQvFCIESD84EcMVbJTeuo9u6PY4d4UNMGD4JmyIffd/tvZTM9ioXE3TVKqnSIxBRIdS23XFcBbvLem6n11p3bHuPbtvdWFmObP1sRa8SoZ8SyYgQfcEgPPccPPmklSR8Xnz80cSkfRk7FtwnrlLoUmkpvPKKlYRs3dr5sfakY+xYGDPGWo8ebSUZPZGRAXPnduyGQzXs3fEQ9a2fAWAvjWPf7ngOmUPYbJ9GuZxBSHdxdWyIH989l8zcY3qYBBth8Retdh42F9zyLIxc0PGwwyazcHwmC8dnUt4U5NXNpby6uYzypiDPbqriWZLIibmDS4sGMdmUGZ/dytCUmG4Vae+q3cXP1/+CfY17APBIGUySbiKxvI6UOBfTpk3rdH7DP/9JYONGJJeLjJ/+tHNXS12Dd78Hm5+x9qffC1f+wvqy7YonCYZc1PVjJxJqhvpDbUvhMcshiPgh4ketC1NcnkphcCjF4aloHE2mYuUashwFZDkKyHTsJVEps77XI21Lu7APmku7DEEBpsBpDUffe9oTk2MW2WaVgNlcYHNabYNszrb9tuNK9IrnT5dimkyvqkJ57RWRhPWWyx+FlPyovLRIRoQzo2nWF/yvf21Va8ybB/Pnw5VXQs4pJnirq4M//QmeesraBkhIgC99yWqLsWOHVV3S3Axr11pLO1mGvDwYMsRaBg/uvJ2ZaZ1TVwevvmolIB9/fPR6RYHLL4fbboPrrrNKPc70VqgqgaZGWpsaaWlqoLFpNX5pMcgBTEPBd2A0mzcm81HCbCpdmWDCsJpSfr7yr8wp3gH/5YRFi6z3P3sCvPxFqNkDzni4/RWrcekJZCe4eejyEXzr0nzWFdbxyqYSVhZUU9YKz20o4bkN1jdkrNPGuGwvE3MSGJ8Tz8ScBHIS3VQ2h9hd3szm0hI+qP4ntVhdbE3dQbjuMvwNc3gfGzLZzE51saPcz9RBiUiSRGDbNmp++zsA0n/wAxzH/tzDfnjtHji4ApDgqiesRqpnyDRNmqoDGLrZ1hvEgewZixw3DmW41WtEVqz2EiXbyyncVMGRfQE09ehzeN1+8pMKGO5aT7K7GikmBWKSwTPfKqnxtO+nWPuKA0I+CDVCsMlKgEJt62ATRqCR2vIiUlNT2xK+Y0sq2tbt+7LNej7FDrLdWh+7LdvaSn+a217HZyVC7duhZjCOeTNH74w1UNznxxaJdHFqPycDWQDNUQ5kIJnzUNReWiQj57OGBiuRyMy0EoielDSoKjz/PDzxBBQWHj3+739bC1glDO2Jydy50F6vW1hoJSD//KdVKgJWVcy3vw1f/SrEHlO/H4lY1Sg7dnRe6uqgqMhaPvro+PicTisZKiqiUxn8RRdZCcjNN0Nq7zQbN02Tz954hQ1vvIyuacg2g+zZVSSPtj4lmxriWLJxPju08TRmWEmPx6Hw4GX5fGXweBzDm+E51Uq8/v1vWPkq3JsIHs1qB3HnG5AxrluxKLLExSNSuXhEKlVNrTz12iqU1CHsrvCzp6KZlrDGhsMNbDjc0HGNwyYT0VTsSZ/iTPkASQkDoDZPIV27kYm5eTjTm/hwXy31ZgzryiKs++t6RqbHcduEVCY98QhOXce7cAEJX7j1aDC+Clh8K1TtApsbbnoaRl9zRvdaU3UObKxm+welNFb2fEyEuGQXw6emkT8tnZTcWCRp0RnFcyxdVdmwfDkLFy5EPtttGEwTtBBoYTANa980ul4MzTpPC1lVQVoItEjnfb3/ZSu6rrNr927GjxuHoog5g3pF4qCovbRIRs5Hug7PPAM//CHUW20BiImBq6+Gm26ChQs7JwTHCoWsJOJXv4KStvLo5GQrkbjwQvjwQ3j/fdi0yfpyLSiA3/0OnE6UCy9kht+PbdOmo/XnU6fCd79rvW5X3WIdjqNVM+1ME6qqrB4uR44cXYqKrHVpKYTDcOiQdf6UKVYC8oUvWNUxvUhTVVb+7Y8UfGwlRJ7MCDmXVuGJbcU0Jd4rupQ3C69Gl23gAFmCq8dn8sOrR5MZ35b8ff/78L3vWVVHLz8O0kdWIhJ2w7feg+ShpxVbcoyD6akmCxeOwm63o+kGB2ta2FnWxM6yZnaWNbOvqhHdeYCYvKXITquTZLpzOF8d/W2uHTWbWKcNVVX54x//yLVOPyNnXcE2fyzv7Khgf7WfR1f6cU36OpcOPsh9/3EX2e3/+VftghdvBX8FxKRaJTvZU0/7Pgf9EXavLWfX6jKCfqtEQLHL2J0Khm5iaIa17qIrbGyik+FT0xg+NZ20wXEDY7ROSTray+g8ZagqxVXLGTtlIYpowNrviWTkfPPZZ/DAA7B5s7U/ahQEAlZi0V6q4XJZJRo33QTXXmtVnQQC8Pe/W9UxFW2jcqanw//7f3DffUeTl4svhsces0pdVq2CFSus5KS0FHnVKjpmFlm40EpCLr645/W9kmSV5mRmwpwuRujUNCgrs5KT7GzIPzt1oEG/j7d/8wvK9u+mLi2NxAs8DMtYgSIZ1AcTeXr3nRxozGdISgxzhidz4fBUZg1NJt7TxQdnSzUc/C3EtJXyVBnwQg24noef/ey0YwyZIQqbCqkN11LVWkVFSwWVwUqqXFWEMyuJia9BbyvST3Qm8uCUB7l++PUoxwxJ/9lnn+H3+/F6vdxy2Uxus9n40dVjeP4vb/BKYSul3nSWp4xj+dNbuWBoEr+eVEvuB9+ESAukjIQ7Xj3t/7gaKlvZsaqU/Ruq0DWrh0tsopMJl+Qy5qIsnO7OH2GmaVpJiW5i6AaGYeLy2JFE109B6NN6nIysXbuWX//612zZsoXKykrefPNNrr/++pNes3r1ah5++GH27NlDbm4uP/7xj/nyl798miELaJrVjiI5ufvX1NTAI4/A//2fte/1wuOPw3/8h9V+YvNmeP11aykshLfftha73WoHsmOH9RxgVX98//tWlcqJqnaSkuCWW6zFNGH/fvTlyzm0fj1DfvQj7O2DYZ0NNpvVbmTw4F57SmvI70oCwSKCgSIaandTvPdD3JOCTLgohCLv6zh3a80MKrmfr16ax5zhKeQkek72xLB9Mbz/iNUOQLbBhQ9DURr87evw6KPWWCU33tjDeE3+Z/P/8HLzy7D85Oc6FSc3j7iZb078JvHO+E6PNTc3s2bNGgAuvfRSbG2lV46De7nsn7/gUlWl/OGf8U7cSN7bXcnQ4lfJLP8nSAbaoIuwffEFq9dJd+M2TNSITnWRjx0flFK8u77jsbRBcUy6PI+hU1JR2nurfI4kSSg2qW3MNFF0Lwj9RY+TkdbWViZOnMg999zDjd34gDxy5AhXX3019913Hy+++CKrVq3i3nvvJTMzk/mf7/4ndM00reqOVavggw9gzRprkK28PJg1C2bPtpaJE4+2y2inafCXv8BPfmIlMABf/rJVzZJ+zDwo06dbyxNPWF1SX3vNSkwKCmDlSuucwYOthObuu48bxOukJAlGjcIYNoy9w4YxZOzYM7kb54yuB6itXUlV1Vs0Nm3AMDrXq3syjm5HdDuqlENWztf5zrxbujcIU1OpNebGoVXWfuYkWPSUNf4HwM498Ic/wF13WaU748d3O/a/7PgLLx94GYB4RzyZsZlkxGSQGZPZsbTvp7hTOpWEHOv9999HVVXy8vKY2FZVpvt8lH/7YVBVvPPnM/prX+CK5jJa3M8Tu93qMfOaPpffV9zPDwuDLBgXjyRJ+OqCHNhYRV1ZC2pYP7qEjtmO6J3n65Ng6MRUJl6eS+aw+IFRxSIIwnF6nIwsWLCABQsWnPrENn/9618ZMmQITz75JACjR49m3bp1/O53v+tfyYhpWl/m7YNafX4pKzs6HLiiHF0fu22zWaUZeXnWMmjQ0XVOjtU+ol1p6dHkY9Uqq43E55WUWMsrr1j7breVULQnJ06nVRWyc6f1+JQpVsPRWSfulYEkwYQJ1vLzn8O+fbB8uVUlcvPNxyc7A4xp6jQ2bqCy6k1qa1d0mjhKkhxoWhKFNTEUhfOoDqRSH0pjzsjJ3DN3BunxJykBOZZhWN1cP3jUqspQnHDJIzDrW52HQf/Nb6zE8MMPrV42mzZ1qzTszYNv8pcdfwFgkXsRP7vhZ6c1KFRhYSEFBQVIksTChQuRJAnTNKn80Y9Qy8txDMok6wtjkf51LRR9THsro8PjHuSPh+ZR1hjk289vZVVSAlNNB43F/pO+3rHsToVRszOZeGkO8andvK+CIPRbZ73NyPr167n88ss7HZs/fz4PPfTQCa8Jh8OEw+GOfZ/PB1gj7qlqV93ZTk/7c3X5nJEI0scfIy1bhrxmDRQVIfm7/2HaU2ZbOwgzNxepoQHpc+NtmC4X5oUXYl56KcZll8HQoUhbtyKtX4+0YYO1NDUd3wUWMJOSMB5/HOOee6ykqCf3cNgw+Na3ju6fwf0/6f2OspbW/dRUv01N7VIikaOzXbhcuaSlXUezfiH/9Vop2/3W2BgyJrdMyeLxS/PJjLeOdet9NRxCWfYQcsl6AIzcC9Cv/h0k54NhHt9d88UXsc2ejXTkCMYtt6AvW3bS+W8+rfiUx9Zbc8N8edSXGV41/LTut6ZpLF9u1e9Mnz6d5ORkVFWl6YXnMXYvJWt2CO/gOqQVWzquMQZdiDHjG2QNnc9fdtWz6r0izIogNl+YRsKYQNaIeAaPS8bpsWFzKtidCnanjN3Rtu2y1opd7igF6Yu/L13py7/fA5G43+fW6d7v7p4vmaZ5fPPzbpIk6ZRtRkaMGMFXvvIVHnnkkY5jy5cv5+qrryYQCODuos3Bo48+ymOPPXbc8cWLF+PxnL3/khw+H+lbtpC+aRNp27Zhb+92eoxwfDzBlBQCqakE0tIIpqYSTE0lkJKCYbMhGwaSYSDpeue1YSBpGk6fD3dtLZ7aWty1tbjr6vDU1qJEOlcBmLJM4/Dh1E6cSN2ECTSMHInhOMnARIZBbHk5Sfv2Wcv+/bhraym7+GIK7rwTtaeDdw14QRSlBEU5gmLbhaIcHfrdNN1o6mRUdSqaPpg3Dkt8XGMlAJJpMNFWx7XjE0lxd7/KQNHD5NcsZXj1chRTRZOdFGTdypGUy9rHDD+huKIi5v7gB9hCIQ5dcw277723y/MqtUr+0fIPIkSYaJ/IzZ6bT7tao6qqisrKSmw2G2PGjCEpXMrgkvfJaVyPzXV0qHS/K4vSxDmUJs6iOZBGoMJOsNKGoR59Tz67wVZFZ69Dw+k2uTLHINYOdhkcsolNsrbbF5sMThlO0CxEEIR+JBAIcPvtt9Pc3Iz3JN9DfbI3zSOPPMLDDz/cse/z+cjNzeXKK6886ZvpKTUSYcM//8mFjY3Y3nvPKl0wjn7QmunpmAsXYlx1Febo0ZCXh+zxEAPEnPhpe8wwTYy6OqSSEqvKx+HAvPBC4hISiANOr2On1YgxW5LI7sVYz4SqqqxcuZIrrrjinM4lYZoGgcBh/P4d+Pzb8fm2EwgUcmzjBEmyk5w0j7S0RSQlzcXQTA5t38ajH5SyORgPpsnIQCEPXTGKyxfc2ZMXR9rzGsqHP0dqm4/FGDIPc+FvGZ2Qx+juPk9WFtx6K8OWLmXQokWYd9/d6eHK1kp+v+L3RIgwLX0af5r3JzA4rfvd3NzMX//6V5yEuWMM5Fb/Fql6l/WgC3TDCTPuwhx/K2FlJM1baqnZVIOvLtTxHB6vg+HTUsmfnkZSdgzv7anmF+/up9oX5qVD3WtYmhrrIDPBRVa8m8x4F5nxLrLa1wkukmMcfaoNSbR+v89X4n6fW6d7v9trNk7lrCcjGRkZVFdXdzpWXV2N1+vtslQEwOl0dpr3op3dbu+9XzrTxDZ9Opfu2NH5+MSJVnfWa69FmjYNSZY5J/+gZWVZywUXnItXi6oz+Tmapklj0waqq5ega61IkgKSjCQpSMht+0rbl5RMIHAYn28Hmnb8H4TbnUe8dwoJiTNIS70KQ7VzeNsmNr/0Rw5s38aShIsp8gxGNnWuDW7k/913K7ljJ3Q/2LIt8N73oWyTtZ8wCK78L+TR1yL39Ev0llusnjWPPort/vutGYHbfld8ER8PrnmQumAdwxOG84dL/4DH4ekoHu3R/TZNti75Bwu15YyXDmLfGWk7LOMvddDiz8X75FIOH9I48HIV1UeOVtPYnApDJ6UwcmYGOaOSOjXivW5yLpeOyeQvqwvZdKSRkKYTUnXCmkFI1QmpBmHNWrerbYlQ2xJhZ1nXH2YORSbeY8frsuF12/G67G1rG/Fue8ex7EQ3Q1NiyEpwo5yDLr69+jklnJK43+dWT+93d88968nIrFmzOuqe261cuZJZJ2tEeS5IEubQoRh79iBddhnyddfBNddYDUqFPkdVfVRWvU55+UsEAod6fL0su/F6JxAfP5l472Ti4yfhcKQQamnh4KZP2fj87yneuQ1d04hIdpamL6DcnY1dMvnl5ZncdOljyCfocXIcXyWsegx2vGTt22Ng7nfggvtPPB9Ld/zkJ1YX6zfftLr6bt5MJD2Fhz56iMKmQtLcafzl8r/gdZxG6WGwCXb+m/D6v3FlU9uIuiaYicNoKvZS9WEt1QlT8c3/GmW/2ovZNriYJEHumCRGzsxgyMRU7M4T36NYp43vzh910jBM0ySiG/hDGpVNISqag1Q0tS3NoY7tGn+YiG5Q6w9T6w+f9DnbOWwyg5M9DEmJYUhKLENTYxiaEsOQlBiS+lgpiyCcb3qcjLS0tFB4zPDfR44cYfv27SQlJZGXl8cjjzxCeXk5zz33HAD33XcfTz31FN/73ve45557+PDDD/n3v//NsmOnWI8S/Te/4f1bb2X+TTed/eGbhdPi8+2krHwx1dVLMAyrGkBRYshIvw5PzDBMUwfTwDQNTHRM0wBTxzR1TAycznTi46cQGzMSWT76Mw76fXz8+r/Y9t5S1NDRtkHOrCEsS7yc8oCNWKeNZ+6exsyh3RzPRQ3C+qfg49+B2tYLZ+LtcNlPwZt58muxvojXV6zn9YOvY5gGU9KnMC19GiMSR1hdb2UZ/vUvazLB3bsxbrien/xqLpuqNuGxefjT5X8iIybjlK9zzAtC8aew7XnY8xZoQZxA0HRxKPZawsymdG0lfmcGLdOzMRQHFFulLWmD4hgxI4P86el4vL03yZokSThtCs5YhZRYJ+Nz4rs8T9UNavxhmgMqvpCKL6jSHFTxhTR8wfZjGk2BCCUNAYrrA0Q0gwPVLRyobgE6l9YqsoTbruCyK7jsMm67gtth7bvblliXjZRYJymxDlLjnKTGOkmNc5IS6yShq4HsBEHoth4nI5s3b+aSSy7p2G9v23H33Xfz7LPPUllZSUn7MOHAkCFDWLZsGd/+9rf5wx/+QE5ODk8//XTf6Nabm4u+a1e0oxA+R9eDVFcvoaz8Rfz+3R3HY2NGkp19BxkZi7DZTjBc/SkEW/xsWfoW2957h0hbA+XknDxGzZ5Lwtjp/Oe7ZRyubiHRY+e5e2ae8MuwE38V7HoNNv4Nmtp+93NmwIJfdWsI9KAWZOnhpbxY8CKHmo+W+nxQ8gEAcfY4JqdPZlr6NKamT2X0m69jnzmLP+aVsLzkfWySjd/N+x2jkk5e6tChuQy2vwTbX8RsKKJKHUmNehmV5nhKQkOJaMlI1W2lBKmDOy6LS3YxcmYGI2akk5jRm62mes6uyGQnuMlO6N5w6LphUtEU5FBtC0fqWjuWw7WtlDcF0Q2TlrBGS1g7zXgkkmIc2DSF/yv9DKddwaHI2BUJh03Grsg4FBmHTcamSOiGSUQzUXWjY4noJqp2dN/tUIh320lwO0jw2In32Dvvu+24HQqKJKHIErIsdWwrbduybLWMCqk64WOqwsKatR9q21d145Tvsa/RNJ3ttRLajkpsNjHAXW+4KD+VpJjozODc42Rk3rx5nKwDzrPPPtvlNdu2bevpSwnnmUiknpLSf1Je/mJHGw9JcpCetoDs7NuJj5962kXpodYWtix7m63L3yYSDACQOmgIs2+5g2HTZlLWGOSOpz+jpCFAutfJC1+dSX563EmCbYV9y2DHy3D4I2tCMgBvNlz+GIy/+ZTD3Fe1VvHyvpd57eBrNIetAek8Ng835N9AijuFLdVb2FazDb/qZ23ZWtaWWV223TY3I56azY5IEQA/a5jG7OzZJ78BatCKd9sLcHg1YFKv5rHa/99URToPly8B9oifuNZy0kalk7twFmmDE4hPc/fbqgxFlshN8pCb5GHeyM6PhVSd5qBKSNUJqjrBiLVub8vSvt8cVKlrCVPXEqHWH2pbh2kOqqi6SbUvDEiUB8Q0sueOwvOF4h/K3vLGf8zuP8mIIPS2cLia4pKnKS9/CcOwSivcrjyys79IZubNOBw9GPb+888daO1IQsIBq+okJW8ws2++neHTL0CSZQ5W+7nzmc+o9oXJS/Lw4r0zyU3qogu5YUDRx1YCsvcda8CydjnTYcIXYNLt4Dh5qcGO2h28WPAiK4pXdMwLkx2bzR2j7+D64dcT57CSoHvH34tmaOxv2M/m6s1srt7M1uqt+CI+dlAEwH+8Wc31bz8NQ66xBkY7lmmS0HoY+d3vQsEb1nDzgGo62Kx8h+2N0zEMCZtTAY+fZrWGmJYyZq1/l4QRuWT/8hc4z9K8Pn2Jq6165vNUw6RB1ahTNeoiGjGqhkfT8aoaSZpOuqrTrGk0hFQaWlWaWsO0tIZxyAo2U0IxQTFBNk0UEyQTFMNEMiHOrhDvsJHgtJHosJHstJPistZORcEmSwTaEqDmQISmgEpTUKUpEKEhoNIYiNAcVAmrhtUbzzDRTRPDsHrndcVuk1EUCUWRUWwSkiyDIoEMZj+duyeiRnDYo/PlORA1GNErIRPJiBA1wWAZxSV/p6LiVUzT6rURFzeeIYP/g5SUy5FOMf7GybQ2NbJj5btsffdtwq1tSUjuIGbdfBv5M2YjyTKmabKpqIGvP7eZxoDKiPRYXvjqTNK8xzQyNU2o2gl73oSd/wZf+dHHEgbBxC9aSUjysONiME2T6kA1h5sPc7jpMIebD7O7bjd7G/Z2nDMtfRp3jrmTeTnzuhyS3SbbGJsylrEpY7l77N0YpsHBxoNsqd6Cy+bihk2rgD/DHXfAJ58cnd24dj/K0oe5uHjd0SeLz6Uk/T7W7J6Ar9qqjhgyKYVEZRfvlx9Etulc/OmH5N1/D8n3fAXpJIOr9XemaVIeVtnbEmRva4jyUIQ6VaM+olHflnw0anrPntSjgMdD6NRnHkO3Fj0MrSC3QprDTobTaoMSkAwCbp2AwyAYB0HDDtiBU8x3BFb9THteIkOon5ZqCedOYuIZNLA/QwP300boswKBI5RXPENV1VuYpvWlGB8/lSGDHyAp6aLTrgowTZPKg/vY9t5SDmz4BEO3njs5J49ZN9/GiJlzkGSZwho/S3ZUsmxXJYU1VunGxJx4nv3KDBJjHKBFrBKQ/e9ai6/s6Iu44mHsDTDhi5B3gdUryzSpaqlkf+N+Djcf5lDTIY40H+Fw82Fa2xuyHsMu21k4ZCF3jrmz++082siSzMikkYxMaqtr+P11sP+ANW3AddfBp2ug4J+w/ilkQ0OX7EhjriM08k7WbUrj4NoaQCMmwcG0YX4c7z3G20OGQGwsYxoamPSvZwdcaUirprOvNURBa5CClhB7W4IUtAbxaaf+L1AGkuw2Uhw2kuw2Eu0K8TaFBJuNBLtCgk0h3q6QaLMRI5lsXvcxF86diykrREwT1TBRj1lHDJOgYVAT0agKR6gIq1S1LxEV3YSqiLV9MhLgVmSckoQkYbUPwVpLbWtZAgUJmywRp8jE2RRiFYU4m0ysohBrk4lrW3tkud9VwWmaxo7t25k4aVLHBI7CmRns7sGcY71M/ASFXmOaOroeQNNb0bUAut6CrgesY1oLkYgfp+tNNm/ZAVhfBEmJcxg8+H4SEmac9oehGgmz75M1bH9/GTVHjjYAzcwfyZSFixhxwRyK6oP870eHWLazkv3VR4f1dygyC8dn8F9X5RJ76G3YvwwOfgCRY4b+t3tg2KUw/ha04ZdTHKxmb8Ne9m1+kn2N+9jXsK+jzcfnKZJCblwuQ+OHMixhGEPihzAraxYp7pTTeq/Hsdng3/+GmTNALoI/TgO39UVm5M9nle1KMlKvZOOzRYQDNUgS5Kf7yFn7V/R3Svn4gpm0xsYSK8tc++tf4zyLIxyfK6ZpssMfZEltE+/VNnMo2HXXX5sE+R4Xo2PdDHI5SHXYSHbYSLHbSHHYSbFbCYfSzd9LVVUpMzRGeFynNe6FbprURTQqwyrVERUJ8Cjy0UWW8SgKHkXGLUv9Lnnobaqq4tQCLExLEOOMDAAiGRHOWEPjeg4V/g8+/85Tntv+mZGSfCmDB/8H8fGTT/t1m2uq2L5iObs/WkmoxUoeFLudUXMuZvL8awjFZ7J0ZyXf/t9P2Vt5dOAsuyIxNz+Vq0fGcbmxDu/BP8EfPgXzmGL5mDQYuQBzxAI+dTtZVbGOfUX/5uC2XxDSjy+It0k2hiQMYXjCcIbGD+1IPvLi8rArvftBWXWkme0rS2ioaMXmULDLKrbrv489XIUtHMYe1rCNnYccO4ziDYc52Gh1xU9wtDJi+zPEfrSfiN3OJ5dfTk1SIrIss+i22/pEIqIZGrvrdlPRUsH0jOmkelK7dZ1pmuxsCfJOTRNLapooCXWeXiHDYWd0rIvRMW7GxLoYE+tmuMeJQz51VWBEj9AQakA1VGLsMcTaY3Eop9dOQTd0IkYEl+I6LplQJIl0p510p/hiFc4/IhkRTltr6yEKD/03dXWrOh2XJAVFiUFRPChKLDbFg2KLQZbcVFUFmTnzeyQm9mAk02OEWls4vHUT+z9dy+Ftmzvqx72p6Uy8YgEZ0y9m1ZEW/vJuBTtK93dcZ5MlLsxP4eqxqVxp30X8vt/ByhWdJ6ZLHQ0jF8Coq4mkj2NZ0bs8V/B3CpsKO8XgtrkZlTSKkYkjGZ08mlFJoxieMPy0v6C6wzRNSgsa2Pp+MeUHmro4YyidJg7YAlAC2FDQGHr4HbJLP0I2DbRRo/h45gzqwmEcDgdf+MIXGDbs+DYvZ5upGuitEWpqKiko3UVx5WHq6mpwRxw4DTufOVZiT/OQP3wMc8ZeTE5cTufr2xKQJW0JSPExCYhblrk82cu1aQnMSYgl2XH0o649uTjYUE99qJ76YD2N4UYaQ400hBpoCjd12u6qqs0m24i1xxJjj+lYPIqHxtZGln+0nJAeIqgFOy0hLUTEsGK0y3aSXEkku5OttSuZJHfbum0/xhGD2+bGrbhx2Vwdi10WyYow8IhkROixSKSeI0f+l/KKxZimjiQpZGfdzqBBX8duT0aWux7NUlVViouXExvb7RlZAAg0N1G4eQMHP/uUkt07MPSjJRiDJkxmxGULOWDL5n93VPLJ/26kbXBQZAlmD0vhmgmZzE+sIHH/S/DhGxBqOvrkGRNgwq0w6mpIGkpTqIlX9r/CS5/8P+pD9YDV3fa6YdcxNX0qo5JGkefNQz6DxrU9YegGh7bWsnVFMXWlVvsWWYYRw/zkh1/C8FejmS7U5Aloo7+Aumkf6vIVqDYXvrET0SpLyS1bjSvciGfGDMwv3Mob+/fj8/mIjY3ljjvuIDPz1AOydcU0TLT6IGp5C5GKFtTyFoxg2zgdJ+j9r5om4ZCG3BrBfkweOIpERtHFmCw1wG4IvVPIGk8BdfEOGhJjKI93sdNhUKbqSGDNHSRLzPC6mOCKkCFV09S4m7KKGp6kknK9ioZQA/XBevxqz2ffViQFh+IgqFm9vTRDoyncRFO46fiTK48/dNx9MFSqA9VUB6pPffLn2CRbR2IiS9ZAIgYGhmlgmmanbRPz+G3Mjmvaj/dnP17842iHMGC8sPAFJqZOjMpri2RE6DZdD1NW9ixHiv6MrltfjCkplzF82PeJiend/6x9dbUUblrPwc8+pXxfgTWyapvknDyGTJ9NffZkVpWG+dHSasJaQ8fjk3ITWDQpi2sGGaQWvgYbXoaGw0efPC4LJtxiNUJNHwNAia+E5zb8F28Xvt1RDZPmSePO0Xdy04ibTm+I9dNhmlBfiFa5n32bm9m2y4svYHUVtkkhxrpXMDHmHeL89W03Ix3m/xLG3WSNa3L1ZMzK1VQsfQdf4xpMSSLuqvmk3PNVarxxvPzSS4RCIZKTk7nzzjtJTEzsXliGiVYbIFJuJR2R8hbUilbMSA97nADHNpFTJah3SjQ6JBo6FhlNhrxWgyGtBnmtBi5DYViLm2EtQHkITthnpb0UwwXkti3QqPgodVZR6qiixFlFhasWvzeMHGcnyWOVRCQ6E0lwJZDkSiLBaa0TXYkkOBPwOrxIkoRu6AS0AK1qa8fSorbQqrbiC/nYsmML0yZOI9YZi9tmlWh4bB6rhKNt36E4aA43dyRHDaGGjhKahlBDx35ADRDSQh2lLEbb34BmarSoLbSoLV3eAUHoj0QyIpySaZpU1yzl0KHfEApZPUviYscyPP8RkhJ7PseQaRgEfM20NjXS2tRIoG3dvjRWllN9uHPVSPrQ4QyfPpvQoIl8UBrhdzsqad5e1PH40NQYrp+UzXUTsxhslsPHT8AHrx4djMweA6OvhYlfxBx8ET6tlbKWMkqPvMe7R97lo9KPOv5DHJU0irvG3MVVg6/q9fYex4m0QvlWKP2M0OEd1BxpoKIlj4LgFQQNq8TCJfmYELOU8Z53cdnCkJAHSZMhawrMfsDq4dN+bzWNcqcLv9eaaTi1tYWEH/yAg/X1vP7cc+i6Tm5uLrfddhuek7QRMVWdcImfSJGP8JFmIiU+zMjxvU8ku4w9MwZ7diyOrFgUr6PTYG+mabKsYi+v1qqEJGvUXM2ox+fUCcfEoLs82G0ekCQ000Q3QTNNJCDeppBot5Eky2S2homprcFdV09CY4hBQQ9Z4Vjkjv/sTUzJ2pIkCVmSkSUZRZJxhu0k6l4SA14mBEZ0jt+pYEt1Y4t3IrlsyC4Fyakgu2xILgXZaUNy6URcfiS7VRrmlGRcUhzJxFndWmwS2ECza8QYDi6OuxibooAJum7S0qriD6rUhjRagj4CYQ3TMJEMJ5KZSYKRRaJhkm9aY5BIholkmiCB5JCsIj7JKsnQ0dBQ0VHR0JAwcJgKDlPCbkjYkbEZEjZTwmZIKAbIOqCaSJqJpAKagaSaoLUd06zt/kjXdRRFjL7aW7z+bOheM61eJ5IRAcOIEInUEQ7XEI5UEwnXEg5XE47UEglXEwiWEAwWAeB0ZjBs6MNkZNzQo3FATNNky9I3KXrndZ56+RnMUw2uI0lkjxxN/ow5eEdPZWVJiB9tKePQxqNJSrrXyXUTs1g0KZuxWV6k2n2w+luw+w3ApFaR2Z83nfKcyZTFJlEWqKG84C+UffajLovqL8q+iLvH3s2MjNPv2XNKzeVQsh6taDN1B0uorpKpiQyjWs2nWZ/S6dRYVwuTR1UzerILe9pdkPhTa4TXE0zYZ0QilD/0bVrXb8Mx4ko86ZOIxGVR9tutNCshZkrDcaXFMnnqDGxNBoasIbusjwAjqBEu9hE50ky4yEekzA965y8oySFjz4zFkR1rJR/ZsdhSPUjK8fdKMzReO7SKXxTVUmsfB6kg603MdezjZ2MvZlTSqNO4x1Ypli/iY03pGv5d/QmJrkSyYrPIjskmKzaLzNhMnErn7olGWEerDaDWBNBqgta6NoBWH8QM66hlLahlvVPKMI4E6rfvOO64HUhqW3pfz0unBgoZmY56WeGMldSFSRh66vPOBpGMnKcMQ6W8fDElJU8TClec8nxF8TAo7+vk5d2LonRvPpB2pmny6b9fYMMbr3Q67o7zEpOQiCchkZj2JT6BmKRkUkeM5ZPyML/fUsa6dTs6xnFy2WWuGpvBzVNzmTUs2ZoSvmoXvPprKHgbgAZZ5q9DJvGq2YRmVkPZe13GleJOISc2h1FJo/jiqC8yLOEsNeLUNTjwHv51L7NjbwqVkdHUaZdgcHypS3ySTPqwZPLGpTJ8WhqK0r2ET63xUfn405jqNGLnf9F62bbH7ECu0TaXTyU0v3yg4zrZY0P22NHqg8e185DjHDiHeHEOiccxOB57ugfpFCN1hrQQbxW+w28K91LuvhzTng6mwSR7KX+cPI0RCfO69X5Oxuvwcu2wa7l22LUnPc9UddTaIEZQw4zoSIqMLdmF6lZoTXTgb3YRaQpjtkRANdBNqzoK08Q0rJFTZQNsJthNExsgYY3nIWMVikif226/O0f3zzypNbE6whuS9TNt3zbatjVMNGhbTFQTIljjmoTbjhlY17Y/l9kWlilJgInZC3GeeyaGYSDL7T8B4UxNd0WvlEkkI+cZ0zSpq1vJwcL/7ijtAJAkO05HKg5nGk5nGg6HtXY60nA4U/HGTcDh6Pn/daZp8umrL3YkIsmTprPonm/gTU5B+dxARaZpsrWkiX9tLmXpii2dJi2bMTiJm6fmsGB8BnGuti/xim2w5tfW2CBAUJJ4YfgMnjGbaNWtNhVD44eS580jJzaHnLicjnVWbBZuW8+Sqh7zV8HW54hseomtlRewvfUO9GNaTLhcBumD40jPTyN9sJe0wV5cMd2vFtJ9YYK76ghsryFS2oIcMx1iAEzCyTIHqWCfvxi36WBGUGLI5deiN4bRG0JoDSGMVhUjoGEErPtsS3bhGByPc0g8ziFelKTju5+2M02TxnAj1a1WI8zq1mrKWsr4d/FWymNuQouxhqbPVFr445h8LkqZ0uXz9IaOhrRVAdSqVtTqVsIVrRiNIaQT/NPspHPbla5JJ9juGckhI8fYkT12a+2yqnBMzWhbTGjf1q19UzUwwxpmxLAGMcMaWr7jt6PT+zqNOM3Prfuz/jfHX5+VmhC97v0iGTmP+Hw7OXjwlzQ1bwLAbk9m6NCHSEu9Crs94YyGX+9KRyLy+ssAXHTHV6g0bcQmJXdKRJoCEd7cVs7LG0s7DUiWk+jmpik53DQlh7zktj8SNQh72iZ8K1wJgI7EOyMv4inJR03Y6sowOmk035n2HWZmzuzV93RKpmmN3rrpGYy9y9nbejGftfyYoGE1FM0c7GL8ZcNIH+IlLvnEX/YApm6i+8PoTWH0xjBaU6htHUZvCqHVdi7NaA2UUZYlsTV0hNZWa6AvUzbQSldTenAP3DGM3ItnkupKRpIkjLCO3hhC90ewp8dY7T3ahLQQJb4jlPvLqWipoLy1nKrWqo7koyZQg2qomNjRHDlo9kGorpGEE78DkoxL0vnBkEy+ljex24OGdev2GiZaTcCqSirxEapsRa8JIOnHf6tKQDMGDZhYTV5Ngm3bhiJhc9lwuGy4Y+x47IqVoJhgN8Bugl03sRkmsm4iayYyYPfYkF3W0tG2pG1t2mFbwQ6mXTgDh9fdUeok2U7/78rUDIygdvwSUDGDmpW0GCYYZscag87HTNMq0WpbpM+tka1RXPtb4YKuGxw6dIhhw4Z1uwRROLljPwPONZGMnAeCwTIOHf4N1dVLAJBlJ3m5X2XQoK9js51kZtoz9OmrizsSkYu/9FUmzr+GyuXLAStR2XikgZc3lbJsVyWRtqG5nTaZqydkcuu0XGYMTkKWJWt49gPvw+7XrZln2yaoMyWZT0Zfzm9tAQ76iwDIisniW1O+xcIhC89Z91siAag7ACXrYfM/oW4/JeGJfOr/b+q1wQBkpziZPCwBZ30QY2URrZJEq0zbF4HUVtZ/9AvCaFXRfeFT/tcXUmsoCh1hR2IIf5J2tJNJPGxXtlMWW0Z4SBjmZsHOH8BOq6tyblyuVWIUl0O6J536wnrKW8o7lrpg3XGvZUgeNMcgNMd4tMRBaPZB6PYskDoX7S5Ki+fnw3N6ZfAuI6ITKfUTKfbhP9SEVuJHUTvfFAkr0TiCwWF0Dret1UQnqRmxDEmNJSfRTU6imxEJHrIT3cQ6e/+jT1VVmqpVHEPie21EUMkmo8Q5UOLEZHCfp6oqFct3M+nKPDEC6wAgkpF+wjAihMPVhEIVhEIVaJoPmy0Ouz0Bm83bto7Hbvciy1YBtKr6KC7+C6Vlz2IYEUAiI+N6hg19GJcr66zGa5WIvARYici0a25AVVVaVHh6XRGvbi3ncO3RwaRGZcRx+8w8Fk3KJt5tB0OHorVWArL3HQg2Hn3y+Fz2jbyUJ80GNtRZjQXjHHF8ffzXuW30bcc1YOw1wSYr6ajdB7X7raVuPzSVdJzSoOXwScujlIQmEivD2DgbQ+LsKAEN9jegnfjZu6ZIKPFObAlOlAQnSqIL2WtnV+EuNhZspMmlQ1s+mZycTHZ+Nq/4XqEgWADAFMcUBrljKV+/nNI0B1UpTgJagP2N+9nfuP8kLwwueyIx8bOJuCdSL+fRbHY9G3GSXWF8rIdxcW6uSPZyQUJsT99lJ2pVK7WfltN6uBlnfQj5mEIPBQhgUoDOLnQOotMSZyc+M4YRGV5GpMcxJyOOYamxuB2il4Ug9BciGelDTNPE59uOz7eTULiiLfGoJByqIBypobsVvLLsxm6PR9NaOsYDSUy4gOH5j+CNG3cW34Hl01dfZP1rbYnInfcw7Zob0A2T36w4yNNbFHTTakDpcShcNzGLL87IY2JOPJIahJI1cGAFFLwFLccMCBWbjjpmEavTh/BK3RY+q/oIsEayvG3UbXx9wteJd8Z/PpQzV7ENNj0DB1dCS9UJT2uwjWdn5CaONEwgy64wL04mvr2XSUADm4x7dBKeiakoiS6rCN0021oUHi1Ox7B+D2SXDVuCEznOgSRLaI2NBLdvp3HrNt6rqabC7QY7uMJhxk+YwKS5c/mk5ROe2PQEIT1EsiuZn8/6OY3bGlm4cCH256vgf94gcvP1lD39JGX+Mkp8JZT6S6kJ1JDsTiYrJhvTmUuJnsaOgIMt/jDqsTPAArkuB+Nj3YyLczMu1loynfZe6X2kB1UKXt2Pt6ARBWhv0VOLwU50dqPTmOggfrCXcTkJXJUdz4MZcXhd4r9iQejvRDLSB4RClVRVvUVl1esEAkdOeJ4sO3A6M3G5srDbEtA0P6rWjKY2W2vNh9XCPEg4bI0U6fEMJ3/4D0hOnndaXxihlhYK1q5C13UyhuWTPmQYDveJGzl9+urizonItTfSEtZ48KVtrNpXA0iMz/Zy24xBXDchndj63XD4aVi1Gko/A/2YOUVcCTBmEVX5l/J6qJTXD75Jba3VM0aWZOYPns9/Tv7P44YJP2NqEPa8CZuehvItHYcN04kROxLDOxbdMwKfPoT6xiSa62RMn0GSDMO9x1QNyRKuEYl4JqbiGpOE3M2qAVPXCR88iG/9doLbthPcvp1IcTFN8fGsu+hCWmNjsakqMw8f4cKfP0YkL43H1j/GymKrDc2crDn814X/RbwtnuXbrGoxHn8c3nwTx2tvMfT7P2LotLmohklpKMLG5hY+a2rlzUY/5XUqVt2QVd+T53JwabKXS5PimBEfQ4L9+PegmyZlwTAHA1Y7lSynnSynnXib0q3fOVXTWb+0kKSN1SQa1vkfo1LgVVBy4xg0JIHxOQnckuXFc8yw7qapY5oauh4CjLaB8cy240bbMRNFcaEoHqTPVScFdIPKcITKsEpAt6p+JEBum/n289sOWSLBbiOxbaZeZ9u8NoahoeuthEKNSHIVra37URTZigMD2uKx4rL6tCiKu23KhFhsthgUJQZZDPMunMdEMhIluh6itnYFlVVv0NCwjvZ/PWXZTVLSHNzuPFyuLFzOLFyutgTEnnzyBo+mgab50bRmVLUJ09SIi5uALPf8x9zS2MCWZW+xY+W7qKHg0QckieTsXCsxGZZP5rARpAwags1ub0tEFgMwty0RKWsMcO+/NrOvyo/TJnN3Ti3fn1SGcuSv8NHHEPrcbLfeHBg2D2PUNWzweHjl4Bus2fQoetskdsmuZG7Mv5FbRtxCZuzpDWPeTm9VCe1tQK0JYEZ0TH8TZs0RjMYaTF3B5G5MvoFhS8A03Ji6BGGg/phw2xYcR5MQ59B43BNTcY9LQelm7xitsZHWdeto+Wg1LevWYfh8nR4vzclh46wL0BQFr93OzQsXkjttGjvqdvK9JbdQ2VqJTbLx4JQHuWvsXciSjKqqGEBxKEJpeg6Hf/EbjpRXcXh7IUc0DyXByHEjVMhAhtPO+Dg3cxPjuCA+hsFuJzE2hYhhsK81yMHWMAdaQxwMWMuhQJhQF2M9uGWZbJedTKedLKfDSlJcdtIcdrw2BacJWzeXk/xJNVNCEgoSZRjsHZfAwutGcpvXBVglRQHDoFnTKWkNUtNSxoGKpVQ27QbTGvhLxkTGQMJEalvLbQ1u/HipJ4UGKZ1GKdXaNhPwc2Y9B5yEiaGFGLOFWPzWdkwr/966BDsqDiLYibStO+8r6NjQkdHbtjVskoxDceCQHThsTsKSl1bi8Ute/MTRYsbgw4PPdOM3XDSbTjRTRsFElkyUjvtgokjWtoKBTTJwSQYuGTyyiUs2ccvglsElS7hlCZcs9Uo35HPJMHRKnBXUH1yBfIKxd4SeWZA7mfSY9Ki8tmSaZp/v3OXz+YiPj6e5uRmvt/eG5VZVleXLl1vF2OegAZRVDbONisrXqa5e2lGFApCQMJPMzBtJS70Km+3M6tzPRFN1FZuXvM7u1R+gq9bkISl5g0lIz6Tq8EFa6o9v2CgrNhIzs6gvs9pOzL3zHqZfeyNbSxr5+nObqWuJkBJj4++przOlqvNYIzjjYchFMOwSGHoJgbgMXj34Gv/e/29K/EfbYkxLn8YXRn6By/IuO6NRUfXmMMGCeoK76wgfae5xt0DdNAmbEDGssRxsXidxWTEkDIrDkeTGNTwBJf7UbVZM0yR84CAta9bQsno1we3b4ZiB4CJOhbqhifhH5lCRkU9Nk5XspOemc83115CRkME/9/yTP2//M7qpkxuXy//M/R/GpVjVcHtbgjx5pJL3apvReqEKJcmu0Kzpnx8HrYNTlhjmdiJLEhXhCA1qzwfichkmCU478Q4bTlnCr+k0azo+TT9rA4S6zCBJ1OEm2DbWhpXKdLVWsdNKLK3EYJ6rxtGCcA49k9rE1ePmdfnY6X5fdvf7W5SMnEWGEcbv34vPt51m3w6am7d2DKcO4HJlk5lxE5mZN+B250UxUqgrLWbj26+x75M1HaOjZo4Yxczrb2XolOkdJTKtTY1UHTpA1aGDVBVa61CL/2gicsdXmH7tjby9vZzvvraTiGYwOtHkGeP7ZFUdwZAUyLsAedilMPQSyJrUMaLortpdPLLsCxT7igGItcdy3bDruHXkrWc0IJlWFyS4p57gnjoiJZ1HXrXbynCaW5BoRZZCSGlDkIbNRs6dQGNThINbayk52IjWloCYNpncMUkMn5pG/oQUnG7rT0g3dKoD1Tg8Mif6H800TVo/+ZSWD1fhX70araLzjGplaQqbhhlsGS5zMAsUWphR4yWzLRE54D3AG8ob/Hnpnztdd/XQq/nxzB8T64hlT0uQ3xZVsay2rcRJknBKEoPcTtIcChUl5Rz2JgBWY9AvZCbx7cEZOCSJomCYI8EwxcEIRcEwRW3rRk3vSC5iFZl8j4sRMS7yPc62tYs8t6NTF96gblAZVqkIR6gIq1SGVIqDYT6raKKhJYxHkQnZJFpsEuG2tjUhWaJK1ahSu27mq5gaHlrxECDeJpPkScemuNt7s2KYbQN7HbNvAIk2mUwHpNsN0u0q6UqYFKWVVKkFDy1oeiumqWENUdZWKSN13gYJWbJjs+lIskZQiqMFDy2mG7/pxG/YaYgYbNpTwOD8EYRNg0BEI6CqhDSNoKYT1HVCukHIMNDahr3XTGuQMg0TrS1+HdAlCaem4o4EcYdacQVbcba24gy04g614g4FcYcDKLqGIcmYkoQhy5iS3LaWMNq2DVlBtdnRbA5Uux3V1r440Gx2VLsd7WxPeyD0C674szNGcHeIZKSXmKZJMFiCz7eDZt82fL4d+P17Mc1Ip/Nk2U162gIyM28iIWFGr4/t0ROmaVJ5cD8b336NQ5s3dBwfPHEKM66/hZzR446rFopJSGTY1JkMmzqz4zmaa6qpOnQApyeGQROm8NsV+/njh9aw7ZcnVvGHwCPESGHMjAl8lHg7c2+8F/mYzFozNP6x8x/8beffiFXdLDDmcsXw+cwcMguPN84aw8E0T1pFZYQ1dF8Ewx9B90fQfSp6c5hwYRNqVecp4B1ZNtz6B7gbn8cmV4M7CabcBdO+ghE/iKIddWx7r4Sqw0erkPLGJjFyZgaDx6fgcNswTIMDjQfYdGQTG6s2sqV6C/6IH4fsYETiCEYnj2ZM8hhGJ48mPyEf80gJVT9/nMDGjR3PqdttHBjmYN2gEFuHSdTHS6R5Mrlu2HV8kSz2f7QfLahhyibNw5qJxEVICibRGG7EMA08Ng8/nPlDrht2Hbtbgvx2/xHerbNiloCrU7xMLNrH/Llz+WN5PW9UN2J4E5AMgxtXvct3Lp7B0FGTOuJJd9qZ2UVPmGZVozQUIdlhI8PRvcaqbkVmqMfJUI8T3TB5fWsZK949wHdaFWa0feyEYmyk35iPc3QSPs3Ar+n4dB2fqhMyDBxaNf7qVwjWL8WDHydhUpMvZciQb+H1TjhlDGdTsMWPvaIcKg+jVlYQrCzHVlHGyKpK0DXsus7ZKt+UJBlnTAyumFjsLheyIiHbFBTFhqwoyDZrrdhsyIoNpe1Y+37HYx3nKUhyvxtmBN0w2Lt3L6NHj0aRRUlVbxg1eHzUXlskI2cgGCynoXEdDQ2f0Ni4HlVtOO4cuz0Jr3ci8d6JeL2TiI+fgs3WdRfJc8E0DCoO7OPgpvUUblpPc3VbDxFJYsSM2cy4/hbShw7vdI2mG2iGict+/P/8kiSRkJ5BQnoGwYjOt17axrJd1n/834hZy/cCf0ORgAsfRrvw/9Hy/gedri/xlfDoRz8lvsjGo777mBIYjWzKcAh87KOj5YQstQ0iZQ0kJbtt1uBPbclHV5O4dZDBOTQB9wg37rpnUXb9GUwd7A6Y9W246Duokof96yvZ/sEGmmutNjKyTWLkjAwmXZ5HQqabg40HebVoFRsrN7K5ejO+SOd2HRISESPC7vrd7K7fDYAzYnLrJ7Bwo45igO6wUTA9lXezatmZZ6LbdGKlRC7MuJB5mfPIj8unuamZ999/Hy2s4fV6+cIXvkB2dnbH6+iGTlO4iRh7DPuDBnfvOsyK+qMlPhcnxnJdWiKxErxQEcd/byvsqOa4OjWe//faC4x+4qewYipceXmnie26Em+3Ed9Fw9Xu+KSwjl8s3cuIqiC/w4UHCUOWiL8kl+x5uR2Tz6U4ZFIcNsLhWvyRPVRXL6Gq+h3cGLixZoceMviBc56EBFv81Bw5RM2RQ9SXldBQWU5jZQUhv+/UF2NVYTpcLuwuN3an01q7nNgcTmx2O4rdgc3uwOZo325bOxw4PR6cMbG4YuNwx8a1bcfidHuQxJcvqqpShZ0p56iaXTi7RDLSA6raTGPjho4EJBgs7vS4JDmIixtDvHeSlYDET8Llyj17k651k/7/27vv+LiqO+H/n9umF5WRRl2W3I17xRACCTWQLIRQQvpu2j6B/SVhd/OQRpLN7joJm4SE8IR0kmwIpAGhYxwwoRncu1xlyVYvo+kzt/3+uKOGZbCNbdnWeft1XvfOnTszZ45kzXfOPed7DJ2WrZvZ8+rL7Fn7CumB2NB9quZi+nlvZ8nV76O0unbU43TT4jcvH+DOZ3aRyBqEvRrRkJtoyEN50EM05KY86NwO+zS+/cRONh0cQJMs/kv9OTeYzzkrzF77E6g/DwpjUACsvMFzzz5B77oWvpL4CC57+I+JGvU5GULTOmbaAMMCy8ZK6lhJHcgwFsmloIRcyEFtKFGUVhXAOy2EvO3X8NwKyMawbdCnvZfc8i+Tc1Ww7+lutqw+RDbp1M/tU5n99mpmXljBtvQmvrvvf3n++eeJ5WKjXs+n+lgYXciSiiUsrVjK9JLpdCQ72Na3jR0929H/9nfO/9MuSuIWuqry7LmNNDfUo+KlylKpO6QODxpshlcK/wbV1dVxww03EAgMf8fWLZsHO2P8tq2XrcmDZMYYOLq6P8nq/sJ4JFcQbHhnSZD/21jJvKAPov8M3/0OrFsHDz4I1157hN+c47enK8mKx3ewY2c3t+FlYWGirlofpPT66ailHnK5dhKxrcQT20gUSj7fNep5nCDkXwiFTv43tlSsn679e+ksBB+d+/cS7+484vmBklKKK6sprqyiuLKaUHmUjTuauOTyK/AFgmgeN4oqPiQF4WiIYOQN5HKdxBNbiQ9soK//JeLxLYwc8ShJCqHQfEqKz6ek5HxCoTlDCcfGm2kY7F37CrtffZl9618jn0kP3ef2+2lcuJSpS5Yzad5CNI/nsMe/tLeHr/91G7s6hwfZDmR0BjL6qGMAU5A5D5VLkHknCpfKL1NrF5Gs+AryohuQMkHkfQOYik2oX6PrD9vIbu9jmlkEFDlPUqoRWlCJd14ZWtnoWQ5W3iysoaIPbzPOirNK0IUccgIP2a1gWzaxrjRdBxJ0tyRIPb+V3O93k8tJ5Kz/IkeYvO3H6gT+3gq0Dr1OsNTDvItrMab28FTbA/zfp5+iLzvc2+VVvSwsX8jiisUsrVjKzNKZaK+bjlkbqiXaZzLrnj+TemEn8WCQ9RfMY19tNaYFwSOM63S73bhcLlwuF263m8bGRi666CJ6TJuXegZ4pjfO6r4Erdn8EcfcSjhjOkKqQlBVCCoyVm83X140m/NKR+RgKSuDz3/eme77la/A1VfDCViG3bYtuga6+cXzm1m1bS8XKzn+v6iBoqbpdafRpqtI5RbtrQdJ7NiGrveP8SwSPl8jodBcams+elKCEEPX6TvUSk/rAae0NNPdvI9k/+E9mwDhaAXRSZMpq2+guKrGCT4qqg77f6PrOjvauwmWRsQ3dUE4RiIYKRgMPBLxrcQTW0gktpLPdx92ns83hZKS8ygpeRvFRUtPajr149XXdojH77qDzn17ho75i0uYsvhcpixdTu2sOYctUjeoLZbhvx7fwWObnUstxT6NL1xQxrsqE3THs3TGs3TFdaxOhVCfm+qUh7A18rlcYF7OAEAz0Nwy6vmnEsQmgRuNLq2P9DSZJe+8EFdV8Ig9SLJLQXYpUFQI9GwburZjx9sZOJSk67U8XR023d0uumN+dGNkfSRg2tjPq0i4fSpFUR+RJQrrfc/xhQPfoO1vw6sYF7uLuWzSZVwx6Qrmlc87LPgYycpm6f3pz+j+2c9oKytj9zsuojNamCZnQSQSYdmyZVRXVw8FH263G00bHofRms3zYGc/v4ql+NwrO+g5wqyUIlVhSdjPR6pKmRnwElIVAoqMPKINndHvO1gSGmMK67/+K/zoR7BjB9x3H3z4w0d8X0dimhni8U3EYq/R27+Wvth6FNIsC8CywpJAo+ZemcCIMbuSpOL3TyUYPGeoBPwzTthlzMHxTN0t++lpaaantYWelmb62w8NDdIeRZIoqaoh2jCZ8obJRBsmUzapEY9//Ga3CcJEMaGDkZbWn+LxPskrr6wgrx8eeICM3z+ZUHAOxcXnUlx8Hh7PW8ttcTLZts3WZ1fyt3t/gpHL4fEHmP3Oy5i69Dwqp0wbdZ3Z1i3SW7rJbOp2xgwENDb2p3hyfy8xy2QmMhc2Wnxa+TnFq5/CtIvRzCUUW0tpsOZjM/yt0CLHIc9WDrpakdUiVFcU1dLQDBWXoaKZKm5Tw21q5G2dNcEt7Knq4NNX/gsLS2cc/RvMJWDzA3Q89zSvti6jU59G3j58qphKjoi2nzJtD2G1G/fUpbgX/APucBi3T8Xt05DcFtsGtvBqx6s80rKKPc3DgZtP9XFx3cVc2XglyyqXvWEAond1kdm0iezmzXQ/vZJdHg97Lr+M1IhLK9OnT2fZsmU0NDSMGXAlDZNHu2Pc19bHq/HUYfeDk/9jpt/De6PFfLCylGLXW/yvGw7DF74AX/wifP3rcOON4Hrj9U/y+V4GBtYRi60lNrCORGJrYRaKY7BvRTJdKLofxfDjCpXiLo6gaWFUNYSqhXG7ygkGZ+H3T0c5Qan7bdtmoLODzv176Ny3h859u+ncv5dcauz2dPv9RGonEambRFldPZG6BsrrG8bsJRQE4eSb0MFIb+8qVHU7eR1GBh7B4GyCodkEAzNRlPFbUvlYZJIJVv70LnaveQmAutlzueIztxIsjYw6z+jPklrTTuq1DqzU6CmUM4AZIxdW3wcpbibDh7EGL6cUDKgx1gS28EJwM5t8u8jLOkfrQzM+xIrF3zv6NWQ6t8FrvyC38RFe6b2GrZlP4Xw8gyLpRAJ9lBfFKSvNUF5uUlymIftC4H4bVC+C4noMy2B773Ze7XiVNdvWsKFrAzkzN/QSmqxxQfUFXNl4JW+veTte1XtYNaxMhuz27WQ2bSazaROZzZsx2tsxFIUdM2fStGQxZqHHyePxsHDhQpYsWUJxcfHQcyQMk73pHHvTWVb3JXhlIEVrNn/ERP9zg15uqizlmvIiio9zEOmRWDf/M11r7qJ7ThLzyXdh11Zj2Qb2YLEMLFvHtg0sM0s213bYc8RyRezva0SNTWVBfBYN8SpkW8PdGKb4ummoJSf+w32wx6Nz3+43DTwUVaW0pp5IXT2RuklEap39QPEbJxAUBOHUmtDBSFXlB9jc3cjSZddTXDTntA08rLyJ3p4ifzCBfihJ/mASK5VHqwrgqgsxYPew8sF7iPW1Iysqb3v/h1n87vcO9YTYlk1uT4zky21kd/YNrTNi+FVWe2FdT4IIMjWyyVLaKLNtTLukEIAoQ4HITs9+1gS38GpgK/vcB0ECt+JmZsks5kTmUB+qx6240RTNySJZyCapKRqarCHbMpte2sT7F77/zROXGTnY8Qi89gvsAy+xJ3s+LyRWkLacefAzlpQw7/IpFFd4MSWTrJklZ+RIm1n6jRw5M0fGyLCj/XnWrF/D2s61pPTRH1alnlKWVi5leeVyLq6/mJDL6WUxEwkyzVvJtxwgf+AA+oEWcrt3k921C4zhAM4GDtbWsmnxYlJup1ehPBJh2fLlzJkzB1ehp6E1m+eJ7hhP9AywJpZ6wzxrEU1lis/N4rCf6ytKmO4/8R/m+Xwvh9ru59DB35H7l8Hnb4b+5jd9rNs7hdbEFP62K0qoZyrnZaNcj4pSGIgruRTCVzbgX1rhrED8Ftm2TbKvl459u+ncu5uOvU4Akk0mDjtXUVXK6huINk6hvGEK0cYpRGrrxCBSQTgDTOhgJBq9Gl3XCIcWopwmSX9s3SLfnhwKOvIHExhd6THXyMvtjpHbHUMGLgt9mFRwgODMSooqazC6MyhBF6n1XaReacfoGZ6B0l3i4re5FA+n4pgpUDH5uPI4N6gPEpCypKsX8ljjEn4f3013XyclRpg+dYCElmZq8VQWlC7jw5GPMzsym8lFk9/wMsZIuq6zXz7y2juA84G4/jdOSXUzYER5PnE7LbkFAKjFFm0L1/EgK4mvjpM1sthHuYBg0BVkacVSllYsZVnlMhpCDWQ3bCC1cg3JA/9N34ED5FtaMPvGHsgIoJRF8M6dR2bWLF60TA70Ornhw+Ewl19+OTNnzgRgZyrLE20dPNE9wJbk2LN/NElidsDLFZEQF5eGaCikXT9ZEsmdtLbeS2fnw4VVnMGlRah6PI53w0Gk89+O/Kn/gySpTpFVZElDklT60hZ/eM2m+ZkUF1oKX0TFNSIzhVYTwDe3DN/8cpTQ8S93bxoGbbt20Lpty1DPRyp2+EBXWXECj4rJJzbwsCybfMYglzbIpXVyGYNcyiCfMdBzJpZpY1mWszVtLMvZ2qaNrhv0N7v5e2I3iqwU8qVJzhYKW2koj5okSThphiTndiF4G9y3TBtTtzAMC9OwsHRnaxp2YWthWzYjc2iPSqhtD7+nofqa1uh9a/h9nIkMPcCvnn1pvKtx1rj6cwuITjpxWc6PxYQORsabbduYfVnyrQnyLQlyLXH09hRj5dyWgxqu6iCumgBaTZBkpp/NDzyKO+mi1F1NUCsmIBVhN2Xob9p92ONNxeI1NcGPchLNhc/aAGn+QXmJf1KeZIrWy6Fz3s1PSiL8uf0FEgefAMDr8bJk8vlc1XgVM0pmjHn54i3Ts7DzUVj/a9j/vFNfW2WD8TFe6383lqVgyQbrqp5mQ/UzWOmxB3VKSHhUD27FjVtx41E91ARrWFaxjKWVS5lRPANFVrAti+Rzz9Hy0684adjHoEQiuOrrcdXV4aqvwzWpAe/cOZglJaxevZo1a9ZgWRaKonD++eez7Lzz2ZbV+cbeNp7sGaA5kx/7eYF3lIa4oaKEy0pDeJSTnS/CoqfnGdraf0sstmboaDA4m9qajxGNXomsrodvnQdPPQ7v/BKcf/7QeQNpnV89sZPw2m7eZ6v4RowVUst9+OaVOTOgIsf/exHv7qJ503r2b1xLy9ZN5DOjgzdJlonU1hNtnErFZKeU1tajHseMFduyifdm6e9I0deeor8jTX97ivRAnlxaJ5899jT2o7nY0Xrk1Z2FE01CN9/qz0wYZI9jUCqCkVPIyhpOb0drnHyLE4BYqcPHWsh+bSjocFUHcNUEUELO+Aojn2fj04/x4h/+1xmkGghy2Uf+hcpZi53nPDBAbtch9E4D21KJEeN+LP5sqmRM55vXUmkHN6jPcWVgD96ySbxa8x5+KMVZ3f4yVsK5iFATqOGmGTdxzdRrhi5fnHAdW5wekM1/gGxs6PAW/+X8vf167HQpAAdDu/h74x8Y8HYzs2Qm51Wdx3lV51EVqBoKPjyKB1VW33ghQV1n4JFH6f35z8ntdgasSi4XwUsvxT11Kq5JTvCh1dWjBEbP6LBtm82bN7PyvvtIJpPosoJ7zgKkmXP5cd7kE682Da38Opa3FQW4OlrElZEiSo9x8Kll5Uml95FM7hxVdL0PWXYjyx5k2VXYHy6S5MLn38n2HU70KUkKZWWXU1v7McKhhcNttXw5fPzj8ItfwGc+A+vWkUPiNy82c/CZZj6ka3hxPvj1oEbxoij++eVD+WCOlZHPc3DHVpo3rWP/xvX0HWoddb83FKZ+znwqp86gYvIUyiY1ormObaCrZVoMdGecgKM9RV97mv6OFLGONIb+5gsSqW4Ft1ctDHhWcXtVNLeCrMjIioSkSCiyhKzISIqErEiAxZ49e5g6dSqyLDsdE3aht8Jm6LazsjCF4uSstwvHsJx+PtsGWZZQVAlFlVE02dkO7TvHh3tThn8OQ7sjemJkRUIp1N0pI/clJFl6s9x3px1DN3juuee46KKLUE/weKqJyl80fqkpxE/wJLDyJkZXGr3TKUZnCr0zjRnLHX6yIuGqCuCqCzqlNoRS7D7sj7xpGGxb/Qwv//n+ocXq6mbP410330qgpBR69+LtuB9v0/0c6MvyHfsDvMoyupEBmYia5iNV/dwwy8vkhvOxSz/Gs32buWvDXexpf2LodZZXLueDMz/I26rf5nQ1n2CqkUJe90vYdB+0bxw6PuCZykouYUfPZEo6nHVoMmqCzVOfoWZBkNtqbmV55XJKvaXH/JpWNkvsz3+m7xe/RG9zBmHKgQDFN91EyUc/ghqJjPk4wzDo7e2ls7OT1evWsy6Zpb28np6ZUTp9IQyAzoExHwuwNOznH8qLeE9ZEVH30X2Lt22L2MA64gMbSCabSKZ2kkrtxbbHHiBsmmlMMz3mfQCyDKoaprr6JmqqP4jHUzX2id/6Fjz4INbmLTxyx708YDXyjwmZd+FccslEvdRdOxVXXei4AhDbtjm0cxsbnnqMfetexcgP/1+QZJmqaTOYNG8RDfMXUT6p8agzjFqWTbwnQ1+b09PR1+aU/s4U1hFW11NUmaKoj5JKH8WVfoor/ARK3Hh8Gm6fisuroqjH3mOl6zpdj29n0ZX1Is/IKaDrOqrfJlTmFe19FhDByFtgGxZ6V9oJPDrS6INBR392zDEeAEqRuxB4hJxtVQDpDf7w2ZbFzpee56U//I5Yp5OkIVhaxvLrbmL2skVI2x+Cv9wPB19lwPbxI+Ma7jWvQEdFlmwunlrKjcsm8Y4Z5WiFSwLberfxPy/8O2s71wJOMq9/mPwPfGDGB2gsajxxDWQa0Lvb6QHp2IzSvpnLm19G2eJcwkjZETZ6r2Nd/0zkjhokZEpwviWqs5Jcfu1Ubq1+D/Jxrt9jxuP03/d7+n7zm6FxIEppKSUf+QjFH7gJJejkiLFtm2QySWdn51Dp6OxkRzrP/pJyDpRU0F0/77C06X5ZxrBtciOu08/SXPxDJMy1taXUHcPg01RqLx0dD9LR8fCYs1ZUNUjAP4NAYAaBwHQCgZm43VFsW8e0clhmFsvKFUoey8qR19Ns3LiVSy7+HB7Pm/RuRSK8fPt3+Z/1MRYPTOK/UVGRMDSJ0ndPpnrJ8Q1INfJ5dr64mvVPPkJ3876h44GS0kLwsZDKqbPJphRiXWladqTZvHoHqVgO2xoxBqLQizB003LGTcS6MphH6OlQXTIllX6KK/3OtsIJPkIRL/IJGFwrCMKJI4KRo2CbFkZvFr0jNaqnw+jNHHEJetmvoUV9qFEfWoUfLepDK/ch+472G7LN3rVrePGB39LT6qSd94WLWPbuf2BuvYza9L/wvQ+CmUe3Fe6zLudO60b6TecD8IKpEb5y1SymVwwnZetIdfDD9T/kkX2PAKBJGu9U38NF+nvw7PCxZ2uaXeYWzDEGu40aGFdgWiYZI0PWzKIbcUrV3VQFW6lxtxM12wjEm5HM4bETMpCzQmxXbmBjbjnxvgokhle5NbwZgn4/sqEhtXtYf3c/66WXhx4/lDq9MADQ7dPwhVx4Qy5n6wGl+yDygSbsHRuwtq1HzmewZA25bjqBG94P5y7nQCLOhtXr6I31Ehvoo3+gl1w+iy4rtBVFaC6t4EDDfNLu0eMgSlMG4aRN0iXRFVYYnBfjzVnMa84xf2+OsoQFdPCoLOEvchEs9hAo8RAscRMo9hAodoMkYeRMcrleUvlnyFhPYco7h1/I8iPlF+DRphEIzqC45BxKog34Qq6j7pUwdJPUQJZcXMW23rjrdXdngm89sZO+9gq+UDmJ2sJPRDunhMqrpx7VgFTTNBkYGMDn8+HxeEj09rBp5eNsfuZJMoV1XBTVRfWscylvOA/DKCHeneXFv6RJx9cf1Xs6EkWVKa70UVLlBB0lVQFKKv2ESj0nZEaPIAgnnwhGbDCTeexUFnMghzmQxxjIOfuxHGY8jzmQG3NQKYDkUdEqfE6wEfU7wUfUhxI4vhkFtm3TsmUTLzzwGzr27ALA7fWyZNEkFgR249p0M6zPF86FVcFr+O/sNexLOq83tTzAl6+ayUXTy4eeM6Wn+MWWX/Drbb8hbznd41N7F7O0+SqC+RIOEAeObuGv1wvIPSz1Pc4s39N4SEECpxTkbDftcpR9yhxa0peQ7KtDKuQIkQBLMpFt58NPzXjJZCxgjMtZY8oedsTGwpJrsErKsS68CFPNYKhpTCWD2bIb+6DzoW9KElnNTcblpqu0gubSCg4VlWGOSIuuGRYNHQbRmEFWk9hR52ZfdLiXpqFLZ+khgwUx8HlUlDKNtDtPsi+HZdkk+3Ik+3Kwd/hSjiTrBCo3E5r0CoHKLUiyCTLYlkyqYzYDzeeSbJ+HbY78/WkD2lA0mWAhsAmWeNA8qjPjI22QTemFGSAGuZQ+YlxEgF+ufhFvUMNf5AREsk+h37ZoyebYEUtzoD/JP+LmYtkZJ2NmE6S2Pkey6nJ2PamRzxjks6bTG2HpZK0UWSNB1kyQMZySNZLYWEhIuNM6cuce5GzSCR/lIKp7PoprNl2tXrpa88DoQZ7eoEZRuY9w1EdRuZdgiQe50JM3OPNkKBYdnImiSIQjXkJloqdDEM50EzoY6f/VdhbsL6bnlTf/Zia5lOGejmihp6PChxw8+m+rbybe3cXTP72LA5s3AKCqMouq0iz2vIyn1wBnFilmSSOvVL6bH3YuY81BG5Ap9bv4/KXTeP+SWtTCH/FMJsevX76P37T+kgQxACrjk1nefDXlqXr8JS7s2gRZd4KsnSVrZ8hYaTJWmrSVJm2mSJoJslYWWxrdBTTNiHF1fj9v0ztQCtek0kqIdqmOzlwdseQM+vJTGDCjDCYog8EAxMCSdGzZQELClhSKy/xEasJE68JEaoIgWxiGUSg6emGb6ewk3bSTVHMziVSatMdL1ush51bRXTKGamPKEjFfkF5/iKTbS8YVJeNyky4EHxnNQ/YI2UYjJpxjqoQUhZhqs7lGZ1fN8LmlqsJ1ZUV8qCbC1MDYM0gsyybR30df9y7i/XtJpfaTyzdj0IqkHURShoMtKzsZOfNOVP0dRLylVMyT0ZYo5NIGib4sid4sib4sqYEcpm4R60wT6zzyGJFRJAAbxZaQUs66PrmOFB5JIipDnSTxDtlC0iwsOcE+DNqMPIdkHXP+DOzWvViHmrAlE1s2MJUMljL2LCEAbBtbgqxPhYYZyFkdTyqCzzwHX8CHN6DhDWr4Qm6KCkFHUdRHuNyH2zuh/xQJwoQ3of8C9Pb1EbRVZyS7V0YJu3GX+lCLPChhd6G4UIo8KCHXEbt8bdsmlzbQPArKcUzVtG2bLU89zHO/+zV6XkeRLOYVtbM00opf1UGSyVafx9+KF/JgJs7a/DqM9EMQfIjgTJAtF4rl5p4tbn6xyY1mOiWu9RHzOquOhjIRzjt4NeeXvR3tgjyvaqt4rO9BctbR9UL4NT8ziqbwnrzEhQe3ERnYO3znpAvg3M/gmXIpxfEERnc3iW3dpF9LQX8h8Ro2OU83aX8Lpnb4h2mfBXtbgJbD7hpbRQCbACmXh95AmD5/iF5/iD5/iJgviHUUAyBloNSlUu12UevR0C2bzYk0q/MGMJzczK/InFcU4IaKEi6PhHC97rlt26Kv70W6u58ild5HOr1v9LpGMuAZDsnc7goqoldTUXENgcDY6+a8nmlYJPtzowIUI2fi9jvp7d0+FY9/eJvvTrPjsd2U9+Txj8gHYmHRIyVok/vZK/fTKccwRwaaR9Gh53Z5CfrCeFUPen8nyYNN2KkYkp7H9ofRJs0ggY3p0Uh7BjDd62iYO5clS5ZQXl7+5i8gCMKEM6GDkTWePfS4e0mRc8ZExEBJKJSXlxM1o0S1KNFglHK1HB8a2JCO5wtTBVPDI/jbU+RSBrIsESz1EC53vvWN3AZL3EPdzoBzjaV3D/G1D/H0I6s5UFhRrMo7wBVVu3B5Smkp+STPuYp5ydzLdmkj+eRfnZNeN8nFkvNk5DwZDs9K6TF9XKXdyLWLrmHL8jX8uvmb7IkNr8MytXgqcyJzCLlChFwhwu7w0H7IHSKkBQmbJoFtD8OrP0NJHHJeU9Joj5zHjvA7aNWLSDy9h4E/rEPOhPAn69D0MKBhY5H1dpHxt+AJK7gtCIaimKY5oudjuIwkA6phoOTzKKZJRnHRWVRKe0U17ZEK2v0hMkeY8RNSZWb5vdR73ZS51ELRiGjOfsSlsiOZ4bvNnawZSLFxRNNpksSikI8LioNcUBxgQciPNkYgmst10tb+J9ra/kA2e/Cw+12uCD5vAz5fAz7fJHy+Bry+Bvy+RiTp2GYqKapMuMxLuOzI+Txs2yazq5/9f24i3JWlAWccda+UoFXpp03tp4sYBqPb2ev2ECoK4/F4cLvdzra5Gc9jj+ExTdxf+hKeqirC4TClJSV07Wliw1OPsu+V18C2kYBwWTnzLr2S2e+4FF8oTDqdZuPGjaxdu5a+vj5ee+01XnvtNerr65kzZw7FxcWEw2FCodBQplpBECauCR2MXHj1JaxatYqpZWV0d3fT2dmJruu0t7fT3t5+2PmSrSBZCpKtDm1lS0FSVKSgCrZEIgNtByRolhgacmk78/g9Pg1VzmMZKUwjQ96IkTVi2K5GqJFRtSB73W6+5J1Pi6+FNt9TGMrwlE7L8GMkzqHOWsDHJy+nOmiTyMdI5AdI5BMk9DipfIKkniStp8gbOpXZCix9gPuaf4qNzTSmMZ3pTm4O2U1teychqxmfncZnJ/HbKfx2Ct/gljTaiA+vFF5eYy5r7Xkku/xIHWlUAxTTRzA9F80oDJiVbYoaYMryEmoaZlJaWoqiKDz++ONceeWVo6biWZkM2R07yGzeTGrrNpIbNpDv7GRfdT3bG6ewdfIMds44hzb/4SskqxJM9nmY5fcwM+Blpt/DrICXKrd2xMtnG+Jpbtnewup+JwKRgDlBL28rcoKPpUV+/MrYwYJtm/T2/Z22Q/fT0/s3bNtJuKSqQaLRqykKL3SCDu8kNO3UZDK0TZuBjW3sW7WTVCxGXMowoKY5qKTJyMlRi9mBs27OpEmTaGhooKGhgbKyssPbyrbh4Yfh+eehuJjc7/6XbatXsfKpx+hvPzR0Wv3cBcy//N00LlyMPCIw9Pl8nHfeeZx77rns37+f1157jaamJg4cOMCBAwdGvZTX6yUUCg0FJ+Fw2Al8SkuJRCK43eOX+0AQhFNjQgcjNTU1lJWVDX04WpZFLBaj7VAbOzbs48D+Q6T1ASzVGShpSya2YgJvcN38DSQHd9RCQSEr++n19NLj6aHH00PMFWNErzqWHsJIzMZIzKYqG2a+2kGp3MPODY+w8/UvUODBjaew4J2N7WQmHZE500uGBeY6FrOZEo6cJ2OQaavsNWaxwTyPLnU2qh0iqHvwp1UsY/SHmOqSOeft1Sy4pO6wBDq6roNpkt2+neSOnWS2biG7ZSvZPXtoK4mws34yOydNZsdNn2RXXSP663IHSMAMv4dFIT8Lwz7mBX1M8blxH2VOip2pDN/Z18HjPc571iSJD1eV8v/VR6l4kzwg2Ww7be1/or3tD6Om3obDi6iuej/l5e9CUU5CdtoxpFIptm/fzsGWVnpau+iL9ZMZHPT7+k4GGzRNo76+fij4qKioQH6zNpMkuPtuUsuWsm7fTjZ+8kPohhMYu7xezrnoEuZfdhUlVTVv+DSyLDN58mQmT55MPB5n/fr1tLa2MjAwQDweJ5/Pk8lkyGQydHZ2jvkcwWCQSCRCJBIZClAikQihUOjN34cgCGeECR2M0L2TULoFYgfAX0Im42L3izG2/b2fTCKAl+kEXTJTlpZRVu/DV6LiDkoYpk42kyGXGiCXGiCbSpBLJzCTPdjJbqx0L3ZmAAuwkbFsibztJWt7aJct9ir9dPn76QnESXgOnxGimRGS/edgJM7BytYwv1Tiwto0RaTIZAJkMgq6oaO6VUzVJCNniFkxus1u0lKanJIjJ+fIK3lUWeW8qvO4tP4S5hg5fNt+j2fv40NTbi1XAKN0JpYvguWLYPsiWN5SLF8ZXQOl7N3t40CTjpFzxhXIOLOZB0cZSBIESz0UV/iJNoSY/fZqPAENs6eH9Fpnobl8s7PNNTczZd8+NvqD7KxvpGnSZHZe9l6aPjmZhD9wWDuUaAoLQ34WhXwsDvmZH/IRPI61Ww5kctyxv4M/d/ZjF97DdRXF/NukCuq8wwGTbdvoei/pdDPp9H6nZJxtKrV36F2rapjKivdSVXXjUY/5eKsymQzbN25l6+YtNHe0jjnVGlvF8gSY3lBFVbSMUChEU1MT1157LR7PsS24l+jrYe3al9j8nosxbBsMndKqGua/6x+YdcFFuLzHvqhkKBTioosuGq6ubZPNZonH40PByeC2v7+f3t5eUqkUiUSCRCLB/v2j1zXSNI3a2loaGhpobGyksrJSBCeCcIaa0MGI8sS/8Y7WV+jc8ms2pa9ib/Z8rEKTBNR+5pSvZVbVbqdPYXcKcnHIDkA27uy/wQJtFtASirKzrIEd/hCb9T62p9pIv242goTE1OKpzCyeR8uhKC9sC2LpYSQJrppTyS3vmEJxKENTfxO7+nexv28/u/p30RxvxrIPT3LiU33MKJnBjJIZzCydyTuiywjvegr+fjt0bB4+sWIuLPk48pzrcbmGU5+nYjl2r+lgx7PthVkbzjdul0ehKOqjuMJf2PoIhWU8yQ6slmby+9eSe2Y/7b9wVrm1Csu594XCNNU1squugd3veDdNH2ukp/jwLKpuWeKcgJcFQR/zQz4Whfw0eI88UymmG6QK6dftwVL4gB78qWQsi18d7OF37b0MJuO8qizMFxoqmew2SSQ20Ny5kWSqaSj4MM3kYa81qKhoKdVV76es7AoU5eRcOrB1EzOex+jJkG6Ps2vvbpq69nEg14E14vctYgWpMyNYtpcXbJXm8iK+eO18FtWXDJ2j6zoHDx5EOcIlp7EMdHXy2l//xNZnV2IWxvBUJFKcu3E7jdfUIV18ORzD870RSZLwer14vV6i0eiY52QyGXp6eujp6aG3t3dov6+vD13X2bdvH/v27WPVqlWjLj81NjYSiURO2Ey3s5lt23AGru9iGwaYJrZhYIuf84mhKOP2f2bCBiOWZbMnsYhNfdfSlZ86dLxS285c/6M0utcgWxYcPi5xNMVFxhNmvy9IUzjKDp+fneRpynSSNjJgtUGi0K2vgGxBlVzGjNp5TCmeQl2wgRe2u/jT0zmyheEh8xsspk/eS4/5Fz65ehexXGzMly7xlDCzZKYTfJTOYGbJTGqDtcjJbmjbALtWwR//pRA4AYobZl8LSz4B1YuGMoqahkXz5h52vNROy7beoSyXqktm8sJyps3yUmJ1oTfvJbdvH/kX9pHbv4+ettHjanrCxeyqa2DXRVewq76R3ZMm0xMMH1ZvGZjm97Ag5GN+0MeCkI8Zfs9hs1Rer083eLQrxl86+3llIPUmP5jRLggrfLpoP9X5NcS2bWR1chdjZ6yT8Hiqhwedep2t3z/1yKnU34BlWfT29nLw4EFaW1ppaz2EbVhoKGgoqJaCasiohoSal1B1GRWZTjlGi9yDMWKmS5HlJ2KWY1kR2mw3f8Jku0/m366YzrcX16K8hVwb/e2HWPPQH9nx92exCh9M1TPO4dz3vZ/6XXuRrr8efvxj2L0bfvc7OEWzYrxeL7W1tdTW1o46bpomPT09NDc3s2/fPpqbm8lms+zcuZOdO50LmIFAgMbGRoqKinC5XGiahkvT0FTV2VcUXKqKqqr4PB68Xi+SJI9Y22VUYhNn33JWyi2kh3VuFxaaMXJ51NgA+f37MfI6VjqFlUphpdNY6TR2YWulM9iWCdbg462hfbCxLee2bRjYho6t62AY2LqBreuF484+pjn68XahfpY1XDfTxC58aGMYzr5pgq47W+vN1+o5XU0D9n7py+NdjbPGpPt/j3f+/HF57QkbjEjAa/F3MGBYyKrE1EVlzD2/mPLyKZB7B+QShRIHPQMuP3FVY5+RYn++n72ZLval2tgXb6Yt2YaNAeahUQm/ZEuiZECjNO6iJO4i6zLZU53kYKCb1pZneGJ7G7nOALburI0ie1rxRB9hr6eFvcNjBFEkhUmhSUwrmcb04ulML5nO9OLplPnKIN3nBB4tG+CV3zr78UOj32xxAyz5OMz/IPicb865jEH77hgt2/vY/VoH2dTwIMdSd4La/C7Kml/B+vt+UpkMgx/9liTRVVzKgcpqDsxcwMG6BlrqJnGgpIzYGIuZScAUn5t5QR/n+Nwkt27kk5dcRNFRXjZImSZP98T5S2c/z/bFGbnciEsaseT6qNcsfFBgMUVu573Wb5keWwcxJ33YILe7knB4AcHAOfj8Dfi8DXi99W+p1yOdTnPo0CEOHjzIwdaDHDx4kFz+aJO4Aa8fumJ5GLBK2WOW0GR7yANVYQ+NZQEW1BVx1wWNhL3Hvy7HQFcHL9z/W5pe+rvzoQbUzZnP8mvfT82s2c5JcxfAb38Ln/40PPMMLFwIDzwwanXfk83O59G7ujE6OzA6O9E7OqG7m7pUipp0mvMyGboNnTZFoc3jodvvJ5lMsnnz5jd/8gLZNPFmMocVTyaLL5Me2tdeN+trpEaOfna6IAjDJm4wIkvUXfAz8vY6VKUIt6ecQ+koPW1luNzluF1lEChhdfoAz3ZsYF+8lZ5s75s+r6pLTOrwMas5RFFSQ7YlWqJpmmbqeKvKaNDqSSXD7N+3kGSsEgBNS1M7aT2lZXuRJD9B11KmFU9jWvE0ppdMZ3LRZNyDH5ADB2HXU/DSz53Ao795rHcHZdOhaiHMuQ4a30E2a3Jw3QEObtxK+4EMfUmVkR/hrlyMyo41VHa8gi/ThSlJtETK2TftHPZX19HSOIXWqhpaQiVkj9BNP9jjMSfoZV7Qx9yAl3MCXvyFcR66rvP4ptwRZ6oMylsWz/UleLCznyd74mRGfHOb5ZN5d7HN5eEMxeZB0plmMulm0pkDZDIHMM3X9ZgUHirLXkKhuYRD8wiF5xMOzcftHvvSwNGyLIuenh5aW1uHSm/v4b8jii1TZocot8L4rQC9SAxgEpd0kpikMclIBnlMDMkALLKyBzNcQ2VFJY3lAd5RFqAx4qexzI/vTVb9NZMp8nv3kG5qouT5v9Pb3IyiqM6PW5KQJAnLttlxsJmN+5swLKcnpLZmEovfdhFVM85BCYexUikkX2Fl3g99CBYsgOuug5074aKL4Nvfhs9//rA1e96MbdtYySRWPI4Zj2MOxDHjA1iJxNC+2R9zgo6uTozOLswx2vX1/MDUQjFlmZ5IhO6yMnJuN4aqYqgqpqpgqCq6qg3tG6qK7nJhKQqpQIBU4PDxSyOpuj5mwOLNZnDn8rg0DdXtRhssHg+q14vL60X1+ZB9XlBUJ2+RJDurGcpSIcuss48kIakakqoiaRqSpg7to6rOfYVjFB4nyZLzXGPtD56vKE5XvKaN3h/Z+3OG0HWDlSuf5tJLL0MTq/aeELLf/+YnnSSSPeZIuNNLPB4nHA4zMDBAKHTipkt+4YVv0Zk3qeQQlbRRxSGK6SNlwgtJlReSGklr9H/QsGJRodpENYsy1aZUtShVLHyyjGooyHmwLQlskDU3nnAYjzeEKmsk8j4e2LaIlfumYtkSmmzy3lkHef+8DgJubcTy7xqgQDaPlUjhatuPr70Jf18z7nz/Ye8jJ3vJuEJk3GHS3jCZQBhdcRPrryLWWcZAfxWpfCUjM6ECeMxOQrldSNI+YkUGrfXTOBBtpLm4kv2eYnJHyIXhkqDBqzHF52Kqz8tUv49pfg8NHhXV6iOf6yKX6yKX7yKX63Ru57vIZjtJJDqGusNBQrc1DhLlgF1Ni13JAauSPVYtSYYHSEbtds7jBZbzd6o5NGadhsmFyyyT8Hkn4Q9MIxyaj98/DVke/oOlmxabD8Z4cU8vnfEsAY9K0K0ScKsEPBpBVaFYtwhmTLwpAyVlkEik6Eh005XupTffS795eM4OgJDlpdwOU26F8VlBWvHSU+RCi6rUVLkplQ20fNYp2QxqLoOSz6Jk08iZDFI2g8utoYSLUMIh5FBoaF8Jh1FCIeRgEDubJbdvH7nde8jt2UNuz25ye/ZgtB0+LX2kAa+LLTXlxH1OgFuSzDCzrYdwZoxZYpo29JqSx42EhHTgAFJ3t5NjpLwcaf588HqQFBUsEyuXw87msHM5Zz+Xw8plsXN57GwWK5M5rksDksuFGo2iRaOo0ShqeTlyMIDs9SH7CsXvQ/Z6h25LXh+S5nxgS4oCsuysCDz4Qa04+4ZpkiwMlE0kk4dvC/t5fezVk4/6PUgSqqoe8br8yOPOwoD2qP2RZZAsy0NFUZRRt9+sSJI0tH+msW2b7u7usaemC8fl8ssvp6ysbMz7dF0fMzXDmznaz+8JHU5ucF3DFn30bBbVziFZ7chaG0qgnZDVwRLtIDPVHia74gTkI3XRWqBaMOrqQx7LTNDV7+OZAxfxTMuFZAxn+uei8o1cP+0hynx99HfDyBBD1S1K+3VK+/KU9uVxFa5NWCbkczIDqouYVyNZopCMyOhBGdttYlsDpDqr6Tu4iI6+RcQ1L2m3RLpaIu2WyQYy5ANJsh6djCqRUoqIqZeSUceeGeEy89SkD1KfbqE23Up19hA12UOU613IsgWyDRLYCsRljQ2KAaqNrYKtMrRvqCpxNciAGqJbnczBdB2t1NMi1dFBJeZgkDD4B8W2KTW6OV9/geXGi0zS9yPpIBsaihlENl1gaLiUEnyeKG53FLerHJerHLerFCmrQsa5jm+l0+ixtfT0P0NfWxc9bV0ku3uxYgMEcinenk+jWiaG6sZS3UiqB1X1kAuGSIQCtAX8xP0e+vwaMa9y2LdHxbSJJHXKknkiiRxFsQRmLoVNDo0cLiNLWWHswJGYhXJMJAne4HuEWlaG1thIu21T1zAJWZIwTJMt3W3s7O/EBjRZYUGkksb6MNakFOZADGtgADM2gBmLOWMSdB2zpwezp2f0C/gKvzOpFLz44rHW3nkLLhdyOIQSCqMEg8P7ISfoUqNR1Gg5WkUFajSKUlR00j50XECJz0fJEQbSDsrlckOze15f4vE4XV1duN3uUUn9zBGDQ50ZW28toHk9y7KwzuBxH29VPH5862oJh7vwwgvH7bUndDDyvn3bqe3tp6WihH1+H2lXBENyg2uSU4A08EihALjyWby5NL5sCl++UHIpfHqSiJWl1s4TsbL4zRSqkaXFDtFm+KjKJfi39ANU5XqZnGsl2JGGTSAVxsJJFmBKYIFkgJGVSRlF9Fhu0paHtOUhj4u8ppHXNFIeLz1FJXQXlXCovIH2slp6ikuIl6qkqo/0LccHHD6TRTZNaro6aGhrHSqNh1qp7OlEGfMDT+b1vSyOI/86VZMCUkA7cPTX8R0jk2fkGF5Irw/YM+rISDZgKgqWLDtFkXHJCkWDt70y8UCIrMfDQChMPBQiHg6RCAaxjnApyZ9MUtrTS6Snh9LeXopiMeQ36Vwc+TEhaRpyIFD4Fu93yhj7tmE4wcHgZYyBgcIljQHsdHooEFEiEdxTpgyXqVNwT56MUlSErutsevxxFl95JYe2b+GZn9/NQL+Ty2P68gt4x8c+hb+oeMw627aNnc06r1sIUOx8bmjwJKaJ3dSE/YMfQl8vtqph33gD0vlvQ/J4kNwuZI8HyeVG9riR3G4ktwfZ7ULy+pwen2Ocbnw6cLvduN1uIpHIYfcd6ZujZVmHZRweq0P69cekoTFR0qgy8pht20PByJGKaZpveN7gfWca0zTZtGkT8+bNO6YZY8KRlZSUvPlJJ8mEDUZMy2TXyz9g3+Qkvck8/iT4bIXyeB3lsQZMrZL+ojL6whFi4VIyHh+2LJN3eci7PAwE39oPTbasQiptG8kGybacLTaWJKFrby1FtmoYlA70U5wYIJxMDJdUgmLLpESWKNFUSt0aVfksbtN0FjqzLagohWjJiNH9hVH/lkleN0jk8iR1nVROJ2MY2IaJYlkoloGmG7gMHc0w0Apbl6GjvIU/djlZxVA0LFkFRUNWXGiyii3LZN0uUl4Paa+btM9D2uNy9r1uMh7XUa1RMxZFkih2uShRFIptmyLTIqrIBAIBqKxyeiYGr+2PuNYv+/3Ot/xAECUYQA4GkQMB51gwiHwCsona+TxmIgGyjFo8djAxyMxmeOrHd9L04moAgqVlXPKJz9C4cMkbPk6SJCSvF9nrRauoGPukK66AD38YPvIReOwx+H//zzl+993H/J7OZoOXQY6la1t4c4NT1+fOnSva9iwwYYMRSZKo6ell4R4Ljw6hlEwgY+Eym1Gt/ci2jWJaQ996LUki5fUx4A8yEHhdKRxLe7yF4iHj9pDxeEi7vWTcHvKvW3/jWD8k3bZzFUg1bWTdRtUtghmbUMaiSLdpLHYzs1RmWkAnmkkSTg+AnkaJhlFm1qKUFKOWlqIUFRGzbLYmMmxOZngmlcGwbFRZQpUkNGl4q0gSmiyhSLA/nWN9PM2B7OHjCjyyxJyAj5kBT2HdF2cNmIhLJVjYhrAx0mmefuIJLr74Evb1pHlxTw8v7elm88EBLMtGxqRczjFN1al0qxQpEl7bRrMMTNsgj4EuGeiY5CSDDHls6eiHPMnIqLKCoigoqoKqaSiagtfrJRKJUFZWRllZGZFIhKKiotP2OrrkcqGWHt7DNSjZ10v77iYONm3nwDNPYOVyIEksvOI9nP/+D+PynMBMsSUl8Ne/wne+A1/+shOQXHwxXHvtiXsNQRDOehM2GJElmWUDJZTsHVxd9Y2v3Mu2TSiXoMiMI6dt5D6QvB6kQJisp4g2KcymtJteNcCA209RRRmXnTeDxVPLUEtKscJhMoqCPhjc2IPJuuyhfcuyiXVl6GmJk2hLM9CaJN6ahLzF66+Uax6FhnkRpi6NUjuzBEUd+4OzM6ezJZlhSyLNlrY4W3Z10jpGQHEspvrcLAj5WBD0USur5PqyNLXF6T7QT78kEZckmiUn4JMlCUUGWZKwbYsNh/x84ycb6E3mKJIyROUU53vTRNU0FinsQmKvPNA1GGe8QUwgyzJhf5CQP0Q4UCiD+8EQ/uIg7jI/inbkQYNnMj2fo2vfXtp376R9dxNte5pI9o4e3xGpm8Rln/4XKqdMPzmVkGW47TZIJuG//gtuuQXe+U4oKjo5rycIwllnwgYjAOd88CZa1qyitlRDzbQjJVuQzQSyaiEpICs2kmo722ApUt0y7JolHAzMZlW8muf3p3hlXy/p/HAQM6+2iM9dPJWLph8+wvv1HfS6ZbM3kWHzth7at/Vh7o7jjo8eICsBtkuGqAdvtZ/i2gAV9UFKyv0MWCY7dIMXuvrpzRv06oWSN+jTDdpyOj362ANu6z0u5gS9zA548ReCJMO20a3CtnB78FjUpVEnKxDLs689wZatHdzVFqcv9caBjYqJX8oTkHIEpDxFUpYL5TRBd6owUMYx2IIeWyMih/H7/Xj8XrxhH54iP96SAG6Pe+iavdvtJhgMEggETtsejJMlk0yw4YlH2Lf+VboP7B9KUjZIkmQitXVEJ0+jO5vnuk/fjMd7CtbN+cpX4I9/hF274P/+X/jJT07+awqCcFaY0MFIuO9e5hbtdD4JXUAJzrX/0qkQnQXRc6D8HLr9k/l7l48X9vTywvM9dCVywPA6GaV+F+dPifC+RTW8feroFNRJw6Qzr9OVN+jM6exN59jbmyLVNEBof5qGjjxuY3jtPEOG1ohKe4lKe7FCR7FKX0AuzOIwgQFoHYDWo3uPg0nH5gZ9zAl4hwKQsKZiWTZtAxl6k3liWZ1YWmcg42xzaZ14Jk88o9Of1nmyK8lA5vBZAC7Z4pxShelFEsWKjpVLYeaSkElh5dJY1pGDFc1WiFghytUiqiqrqJ0+ibJzqlFLvWdlL8ZblUnEWffYQ2x48hHymczQcX9RMZVTp1MxZTpVU6cTnTwVl8c7NKBSUU/Rf3OPB372M7jwQvjpT+EDH3D2BUEQ3sSEDkas+gvozaqUzno7SuVc9MhM9lHFjm6dHR1xdu5JsPOFOJ3xPaMe51ZlljaUcO7kUhpqQ5hBjeZMnpX5HP+77QBdeX0oAEmbzkDVaMxkSrvO1DadWb3GqMsuWa9MrjFAeGYRs2aV8ja/i17doKfQy9FT6PXoyTvHevIGacukRFOdQaiDxTV4W6FUUylzaUz1u/ErCj3JHE0dCZq29fDXjgQ7OxPs7kyM6tU5EhWTMinHZCVHg9+mzKWj2RmMfJKMnnGyziYgc4THu2yVgO0hYHsI2h4iahE1tTVUzKzFM6UYtUwEH28kHR9g3aMPsuGpx9CzTiuX1U1i0bvfS+2sOQQjp1Gehbe/HT71KScY+dSnYNMmJ0gRBEF4AxM6GPn7lH/jLy2vQn8Nu5qS7O1uQzcPT6olAfXRANXVQTzlXpIhlaaczjPZJHbL2AuruXSbhk6dKe0609p1ApnRs0nclT7q5pQwd0GUaH3IyZJ4BHnDIpbJ05/S6U/niaXzxLMGhmlj5C2MjI1h6ehmHtOyaTMtDlg26ZzB7q4kuzoT9CTH7qGIygrTfW6qNZVyWaKILC4riWkkyJgJElaC5MgwIw/ZPIzMzuKyVcK2j6DtIWB7ncBD8hIOhigqKcIXCaKWeCGs8vL2tZz/vstwud/abKGJID0Q47VH/sKmpx9HzzktXjapkeXvez9TFp/rJO86HX372/DII87lmv/8T6cIgiC8geMKRu6++27uuOMOOjo6mDdvHnfddRdLly4d89x7772Xf/zHfxx1zO12k81mxzz/VFrx+A6auhRoGc5Y6XMrlJX6cIVcZPwK7S5I+RR2qjI7AewcDAxntQgqMpN9HiZ7XdSkbIpbMqh7U+Rbktjm8EwPRZMpnRwi3BjCVecjr8kksgZPtvWR3N9FImsQzxokcwYDGZ3+VL4QeOgkc0deC+NoSRLUl/iYE/GxRLapSqVR+uOk033051L055PEpDQD0thTcD22RggfYcVPWAtS5A1S7AtTHCrCF/IjezWUgIZS4kEt9aCE3IcFWLqukzlgvmHgJUAq1u8EISsfx8g5v2vlDZNZft0HmLxo6enTC3IkRUXO9N5rr3UCkxtugLlzx7tWgiCcxo45GHnggQe49dZbueeee1i2bBl33nknl19+OU1NTZQfYSXPUChEU1PT0O3T5Y/pXHMnC7x9ZFwBYl4/XYEAsWCQbp9N3KtiKE7zKECpJFOKTNiEkpRJUb9BYMDAFTdQUjHcKRN33h41Jyem2OxRTPZrJq2qhdmRgo52eOnY6ypJUOTVKPa5KPJphLwamiI7U3IV2ZmCi4Vq59HMLLKZRTVyhJQ8vmwGI5EgkU6Qbs6yT4J9g0/8uun5mqxS6iumrKiU8kg50cpyKmqrCJaFkTSRWOhkSvb3sfaRP7Np5ZMYhcX1oo1TWX7dTTQuXHLa/L85Ku99rxOM/OUv8MlPwksvgUhMJQjCERxzMPK9732PT37yk0O9Hffccw+PPfYYv/zlL7ntttvGfIwkSVQcKXHSODLMJH5bx5PrpzjXT0Ns9P05XORMD6bhQTPdeE0XAcON23QjWy5kS0MaMe/UwKZVtdinmexTLWLKcM+IJIFPUwh6nPVPgh6NoEcl5NEKt1UCLhm/auPXIKCBXwWvCl7FRpNsDENH152SySRIDCRIDMRJ9CVIpBJkcqN7mwycHKV9Iw9KICMRcPkJh0IUlZcQiZYRjUYpLy8/rfNrnK2Sfb28+tc/seWZpzB053JaxZRpLL/uJhrmLz6zgpCR7roLVq2CV1+FH/0IPvvZ8a6RIAinqWMKRvL5POvWreOLX/zi0DFZlrnkkkt4+eWXj/i4ZDJJfX09lmWxcOFC/vu//5tzzjnniOfncjlyueFLIYNrDwx+EJ8oanYRcjKBZmeQyWCpaUwli6lmsGUDN3ncSh6U4bUPMoweqClJLlTVg+rx4vK6qNcUJssyquL0WAyVQhZGwzDI5XLk83nyuTy5uLOfy+WIWRaxt/ieFFvGZ7vw4cZnu/HbHgKql6LyEoonlVEys4pwdckRP+BM0xy1lsaJNPizO9Frc5ypEr09rHv0QbY9txKz0CYVU6ez7L03UjdnPpIkYbzBcvVvZtzbu6wMacUK1M98BvvLX8a48kqYNGl86nIKjHt7TzCivU+t423voz3/mFbtbWtro7q6mpdeeonly5cPHf/CF77A6tWrWbNmzWGPefnll9m9ezdz585lYGCA//mf/+H5559n27Zt1NTUjPk6X//61/nGN75x2PH77rsPn2/sRd2OR9crXvL9r4vHJBvJZSFpOqYrg6VmMJUstprHlPNY5J3BoifxP4CMjIaMaitOQUFDQbVlVAq3bQWf7caLC7fswq1ouFQXsqZgajamaqNrNqmgQSpgcFjWNGHc6Kkkse2bGNi7c2j1Wk9ZlJLZC/FWVJ+5PSFjsSzO/+pXiWzbRufChbzy1a+ecUvVC4Jw/NLpNB/4wAfedNXekx6MvJ6u68ycOZObbrqJb37zm2OeM1bPSG1tLT09PW/4Zo5V254+1rzyGue//Vz8IQ9un4qiyUf1YWDbNul0mmQySSqVIplMDi069fpi5U3MtI6V1pGyFmpWQsnYqEkbJWmjWTKaraIVgg55ZMpRWUIpcaOUep2BoZHCANFiN7Jfg6Os7+lA13VWrlzJpZdeOmHWkrAti2R/H7GONmIdbXTs2U3TS89jmU6PR9WMWSx7743UzJpzwn+Op017NzWhLl6MlMth/PrX2DfdNH51OYlOm/aeIER7n1rH297xeJxIJPKmwcgxXaaJRCIoikJnZ+eo452dnUc9JkTTNBYsWMCePXuOeM5ghs2xHnsif+kq64twN5mU14WP63k1RSPk8mP5TKyggRnLYfZnMfpzGP1ZzD5n386CM1L0CK+hSKglHtQST2E2ihe1zItW6kUp9iApZ0awcbRO9M/xdGDk83Q176Wv7RD97YeItbfR336I/o72ocGoI9XOmsPy626i9pyTP8tk3Nt79my4/Xb48pdR//Vf4corYYxVb88W497eE4xo71PrWNv7aM89pmDE5XKxaNEiVq1axTXXXAM4y2OvWrWKW2655aiewzRNtmzZwpVXXnksL31S9P96B4sOlND52hokTS4UZXhfLWwVGTtvYuVM7JyJlTWwcya2fvQr0cr+wrTXYrcTbIwIPJSQS0x3PcPo2SyHdu3g0I6ttG7fSseeJswjjO+QFYVweQXFlVUUV1YxZfFyambNPsU1Hmf//u9w//2wZQvceiv85jfjXSNBEE4jxzyb5tZbb+WjH/0oixcvZunSpdx5552kUqmh2TUf+chHqK6uZsWKFQD8x3/8B+eeey5TpkwhFotxxx13cODAAT7xiU+c2HdyHIaCCdPGNk3srAkc+1gQSZOR3ApK2I1a7EYp9qAWe4aCD6XYg+wS0xrPZLl0mkNN2zi4fSsHd2ylc9+ew9aE8YWLiNTWU1xZ7ZSqKoorqgiVRU9dSvbTlabBz38O554Lv/2tkyr+iivGu1aCIJwmjvkv5I033kh3dze33347HR0dzJ8/nyeffJJoNApAS0vLqKmh/f39fPKTn6Sjo4Pi4mIWLVrESy+9xKxZs07cuzhOJR+fxdNPPM0lF70TxZaxdWu4GBa2Xuj9MGwkt4zkVpE9irN1K4V9BUkRU2HPNpZl0rFnN82b1tG8aT0de3Zj26N7woKlZdTMmk3NzNnUzppNUUXVGTN+Z1wsXepM773zTvj0p2HbNggExrtWgiCcBo7r69ott9xyxMsyzz333Kjb3//+9/n+979/PC9z0kkuZ+aJEnaLa44Cyb5emjetp3nTeg5s3kA2NTrVf1G0cij4qJk5m3B5dJxqegb7z/+Ehx6C5mb48pfhBz8Y7xoJgnAamOB9x8JEpedzDHR2EOtop23XDpo3rqO7pXnUOW6/n/rZ85k0fxH1cxcQipSNT2XPJn6/s4jeZZc5SdHe/34YMTNPEISJSQQjwlnHtm0MPY+Ry5Ho7SHW2U6so70wvbad/s52kr09hz9QkqhonMKk+YuYNG8RlVOmIYsU5ifepZfCxz4G994LH/84bNgAY8yeEwRh4pjQwUiyvw8zm3EGIorLNKcFI58nl06RTSbJJONkk0myyQTZRJxsytnPJBJkkwn0XBYjn8fI59DzTvAxePtouLw+iiuriNTWUz9vIfVz5uMLhU/yOxQA+O534YknYMcO+O//hjGSHAqCMHFM6GDkqf/3fQ7t2MqP/vK/uLw+PIEgnkCgsA3iLey7/QFUTUNRNWRVRdE0FFVFUQvbwm1ZUZEVpVBG7o8ukiQhSTKS7GyRcG4f4+BHyzQxdR3D0LEMw9nXdczB24Yx9JqDdZVVZ9855tTZskzMwuMtw8DQdSxDd44VtgCyrCDJ8vB7kWWkwlZWFCzLQs9myWcz6NksejZDfsQ2l07RvWMHK1v3oGcz5NJpcukU+UyaXDpNPp064vTY4+UJBCmuqKKoorJQqiiKOvveYEgMOB0vJSXOZZobbnCCkeuugzlzxrtWgiCMkwkdjFgjPvjymTT5TJp4d+cbPOLkGw5O3vhD0rbsw2Z3nCkGdm174xMkCY8/8LrAMDgiWAzhCQTQPB40lxvV7Xa2Ljeqy4Xmdo6pLheyLC6znLauuw6uucYZ0PqJT4iVfQVhApvQwcj1X1vBY48+yjsvfDtGNutcDkglhi8NJAv7qSSmPtxTYBk6hm44vQcjjpumiW2aWKOKgWVaRx042LYFNhx1jv5BkoSqak4vjVbowSn0Vgz2klimUairAWOtAjDiOUb1ACkqSBK2NfieLCzLxLas4fdpmUhIuLxeNI8Hl8dX2HrQPM4x1eWm5eAhZs6Zgy8YxOXz4/b5cHudrcvnw+3z4/J4kcTKwWc/SYK774Znn3VW9v3hD+Hznx/vWgmCMA4mdDACIMky3mAIraT0pL6ObVlOsGJbUOjVsG17qIfDtm0YXM/GOorARZJQNG04eDjGb5SDl2aswqWcwUs3J5Ou6zz++OMsvvJKMZVacFRVwR13wKc+5Uz1vfpqaGwc71oJgnCKTfhg5FSRZBn1NPq2L8uKkxXWJWYxCOPsE5+A++6D555zkqE9/bRY2VcQJpjT59NREISJSZLgZz8DjweeecaZ8isIwoQighFBEMbflCnwH//h7N96K7S3j299BEE4pUQwIgjC6eHzn4dFiyAWg3/5l/GujSAIp5AIRgRBOD2oqrOyr6LAn/8Mv/71eNdIEIRTRAQjgiCcPubPh69+1dn/1Kec3COCIJz1RDAiCMLp5atfhfe+F/J5JynagQPjXSNBEE4yEYwIgnB6kWX47W+dXpLubnjPeyCRGO9aCYJwEolgRBCE04/fD3/9K0SjsGULfOADYJrjXStBEE4SEYwIgnB6qq2Fhx8GtxsefRS++MXxrpEgCCeJCEYEQTh9LVsGv/qVs3/HHSIhmiCcpUQwIgjC6e2mm0bPsHnhhfGtjyAIJ5wIRgRBOP19/etw3XWg685Mm/37x7tGgiCcQCIYEQTh9CfLThK0RYugp8eZYROPj3etBEE4QUQwIgjCmcHncwa0VlbCtm3O5Rsxw0YQzgoiGBEE4cxRXe1M+fV44PHH4bOfBdse71oJgvAWiWBEEIQzy+LF8JvfgCTB3XfDN7853jUSBOEtEsGIIAhnnuuvhx/+0Nn/2tfgxz8e3/oIgvCWiGBEEIQz0y23wO23O/s33wx/+MP41kcQhOMmghFBEM5cX/86fOYzzriRD30Inn56vGskCMJxEMGIIAhnLklyLtfceKOTg+Taa+HVV8e7VoIgHCMRjAiCcGZTFGdA66WXQioFV14JO3aMd60EQTgGIhgRBOHM53LBX/4CS5dCby9cdhm0to53rQRBOEoiGBEE4ewQCMBjj8GMGXDwoBOQ9PSMd60EQTgKIhgRBOHsEYnAU09BTQ3s3AlXXQXJ5HjXShCENyGCEUEQzi51dc6smtJSZzDrxz8+3jUSBOFNiGBEEISzz8yZ8OijzgJ7f/gDvPTSeNdIEIQ3IIIRQRDOTueeC//0T87+v/+7WMNGEE5jIhgRBOHs9Y1vgNfr9Iw8/PB410YQhCMQwYggCGevqiq49VZn/7bbwDDGtz6CIIxJBCOCIJzdvvAFZ5ZNUxP84hfjXRtBEMYgghFBEM5uoZCzsi84WzHVVxBOOyIYEQTh7PepT8HkydDZCd/97njXRhCE1xHBiCAIZz+XC1ascPbvuMMJSgRBOG2IYEQQhInhuuuctWtSKWeWjSAIpw0RjAiCMDFIEnznO87+T3/qDGgVBOG0IIIRQRAmjgsvhHe/G0wTvvSl8a6NIAgFIhgRBGFi+da3nDTxf/kLvPzyeNdGEAREMCIIwkRzzjnwj//o7Is08YJwWhDBiCAIE89gmvgXXxRp4gXhNCCCEUEQJp7qavj85519kSZeEMadCEYEQZiYRqaJ//73ob9fXLIRhHGijncFBEEQxkU4DF/9Knz2s05g8oUvgN8PtbWHl5oamDXL2RcE4YQTwYggCBPXP/8zPPssPP889PU5CdF27nTKWJYvhw98AG68EcrKTm1dBeEsJoIRQRAmLpcLHnzQ2U+l4NAhaG0du+zY4UwFfvll+Nzn4LLL4IMfhKuvhkDg6F7PNKGrCzIZ0PXRJZ8f3jdNWLAAKipO2lsXhNOJCEYEQRDAuUQzbZpTxtLeDg88APfdB6+9Bk884RSfD66+GumGG5AMAzo64OBBaG6G/fudMrjf0uIEG0dDUZwEbR//OLzrXaCKP9fC2Uv8dguCIByNykqnR+Rzn4Ndu5yg5He/gz174Pe/R/3973mPJCG92SBYWQaPx+mV0bSxSz7vXCp6+GGnVFbCxz4G//RPMGXKKXizgnBqiWBEEAThWE2bBl//Onzta7B2Ldx3H/b99yN1dGDLMlJNDUyaBA0NThm5X1Xl9Hq8mW3b4Je/hN/8xumVWbHCKRddBJ/4BFx7rZMrRRDOAiIYEQRBOF6SBEuWwJIlGCtW8Lff/Y53vv/9aH7/W3/uc86B737XCUD++lf4xS/gqafgueecUlQE55473Juiqk55/b4sg2U505Zte/T+4O0zkGJZzG9tRXn4Yec9Cm/dF78IkyePy0sfVzBy9913c8cdd9DR0cG8efO46667WLp06RHP/+Mf/8hXv/pVmpubmTp1Kt/+9re58sorj7vSgiAIpx1FIRuJOJdfTiSXC667ziktLXDvvU6PyYED8OSTJ/a1ziAyUD/elTjbfOITZ04w8sADD3Drrbdyzz33sGzZMu68804uv/xympqaKC8vP+z8l156iZtuuokVK1bw7ne/m/vuu49rrrmG9evXM3v27BPyJgRBECaEujq4/Xb4ylec6cgHDjgDYg3DKWPtm6bTcyBJw9vBMvL2GcY0TZqampg+fTrK0Vz2Et7cOObROeZg5Hvf+x6f/OQn+cfCQlP33HMPjz32GL/85S+57bbbDjv/Bz/4AVdccQX//u//DsA3v/lNVq5cyY9+9CPuueeet1h9QRCECUiWnbEjE5il6+x+/HGmXnkliqaNd3WEt+iYgpF8Ps+6dev44he/OHRMlmUuueQSXj7CUtwvv/wyt95666hjl19+OQ899NARXyeXy5HL5YZux+NxAHRdRz/aaXFHYfC5TuRzCkcm2vvUEu19aon2PrVEe59ax9veR3v+MQUjPT09mKZJNBoddTwajbLzCBkLOzo6xjy/o6PjiK+zYsUKvvGNbxx2/Omnn8bn8x1LlY/KypUrT/hzCkcm2vvUEu19aon2PrVEe59ax9re6XT6qM47LWfTfPGLXxzVmxKPx6mtreWyyy4jFAqdsNfRdZ2VK1dy6aWXooluvpNOtPepJdr71BLtfWqJ9j61jre9B69svJljCkYikQiKotDZ2TnqeGdnJxVHSFtcUVFxTOcDuN1u3G73Ycc1TTspv3Qn63mFsYn2PrVEe59aor1PLdHep9axtvfRnntMk7NdLheLFi1i1apVQ8csy2LVqlUsX758zMcsX7581PngdPMc6XxBEARBECaWY75Mc+utt/LRj36UxYsXs3TpUu68805SqdTQ7JqPfOQjVFdXs2LFCgA++9nPcuGFF/Ld736Xq666ivvvv5+1a9fy05/+9MS+E0EQBEEQzkjHHIzceOONdHd3c/vtt9PR0cH8+fN58sknhwaptrS0II/Ihnfeeedx33338ZWvfIUvfelLTJ06lYceekjkGBEEQRAEATjOAay33HILt9xyy5j3Pffcc4cdu/7667n++uuP56UEQRAEQTjLiYT+giAIgiCMKxGMCIIgCIIwrkQwIgiCIAjCuBLBiCAIgiAI40oEI4IgCIIgjKvTMh3869m2DRx9Wtmjpes66XSaeDwuMvidAqK9Ty3R3qeWaO9TS7T3qXW87T34uT34OX4kZ0QwkkgkAKitrR3nmgiCIAiCcKwSiQThcPiI90v2m4UrpwHLsmhrayMYDCJJ0gl73sEF+FpbW0/oAnzC2ER7n1qivU8t0d6nlmjvU+t429u2bRKJBFVVVaMSor7eGdEzIssyNTU1J+35Q6GQ+GU+hUR7n1qivU8t0d6nlmjvU+t42vuNekQGiQGsgiAIgiCMKxGMCIIgCIIwriZ0MOJ2u/na176G2+0e76pMCKK9Ty3R3qeWaO9TS7T3qXWy2/uMGMAqCIIgCMLZa0L3jAiCIAiCMP5EMCIIgiAIwrgSwYggCIIgCONKBCOCIAiCIIyrCR2M3H333UyaNAmPx8OyZct49dVXx7tKZ4Xnn3+e97znPVRVVSFJEg899NCo+23b5vbbb6eyshKv18sll1zC7t27x6eyZ4EVK1awZMkSgsEg5eXlXHPNNTQ1NY06J5vNcvPNN1NaWkogEOB973sfnZ2d41TjM9uPf/xj5s6dO5T8afny5TzxxBND94u2Pnm+9a1vIUkSn/vc54aOifY+sb7+9a8jSdKoMmPGjKH7T1Z7T9hg5IEHHuDWW2/la1/7GuvXr2fevHlcfvnldHV1jXfVznipVIp58+Zx9913j3n/d77zHX74wx9yzz33sGbNGvx+P5dffjnZbPYU1/TssHr1am6++WZeeeUVVq5cia7rXHbZZaRSqaFzPv/5z/PII4/wxz/+kdWrV9PW1sa11147jrU+c9XU1PCtb32LdevWsXbtWt75zndy9dVXs23bNkC09cny2muv8ZOf/IS5c+eOOi7a+8Q755xzaG9vHyovvPDC0H0nrb3tCWrp0qX2zTffPHTbNE27qqrKXrFixTjW6uwD2A8++ODQbcuy7IqKCvuOO+4YOhaLxWy3223//ve/H4cann26urpswF69erVt2077appm//GPfxw6Z8eOHTZgv/zyy+NVzbNKcXGx/fOf/1y09UmSSCTsqVOn2itXrrQvvPBC+7Of/axt2+J3+2T42te+Zs+bN2/M+05me0/InpF8Ps+6deu45JJLho7Jsswll1zCyy+/PI41O/vt37+fjo6OUW0fDodZtmyZaPsTZGBgAICSkhIA1q1bh67ro9p8xowZ1NXViTZ/i0zT5P777yeVSrF8+XLR1ifJzTffzFVXXTWqXUH8bp8su3fvpqqqisbGRj74wQ/S0tICnNz2PiMWyjvRenp6ME2TaDQ66ng0GmXnzp3jVKuJoaOjA2DMth+8Tzh+lmXxuc99jvPPP5/Zs2cDTpu7XC6KiopGnSva/Pht2bKF5cuXk81mCQQCPPjgg8yaNYuNGzeKtj7B7r//ftavX89rr7122H3id/vEW7ZsGffeey/Tp0+nvb2db3zjG1xwwQVs3br1pLb3hAxGBOFsdfPNN7N169ZR13iFE2/69Ols3LiRgYEB/vSnP/HRj36U1atXj3e1zjqtra189rOfZeXKlXg8nvGuzoTwrne9a2h/7ty5LFu2jPr6ev7whz/g9XpP2utOyMs0kUgERVEOGwHc2dlJRUXFONVqYhhsX9H2J94tt9zCo48+yrPPPktNTc3Q8YqKCvL5PLFYbNT5os2Pn8vlYsqUKSxatIgVK1Ywb948fvCDH4i2PsHWrVtHV1cXCxcuRFVVVFVl9erV/PCHP0RVVaLRqGjvk6yoqIhp06axZ8+ek/r7PSGDEZfLxaJFi1i1atXQMcuyWLVqFcuXLx/Hmp39GhoaqKioGNX28XicNWvWiLY/TrZtc8stt/Dggw/yt7/9jYaGhlH3L1q0CE3TRrV5U1MTLS0tos1PEMuyyOVyoq1PsIsvvpgtW7awcePGobJ48WI++MEPDu2L9j65kskke/fupbKy8uT+fr+l4a9nsPvvv992u932vffea2/fvt3+1Kc+ZRcVFdkdHR3jXbUzXiKRsDds2GBv2LDBBuzvfe979oYNG+wDBw7Ytm3b3/rWt+yioiL74Ycftjdv3mxfffXVdkNDg53JZMa55mem//N//o8dDoft5557zm5vbx8q6XR66Jx//ud/tuvq6uy//e1v9tq1a+3ly5fby5cvH8dan7luu+02e/Xq1fb+/fvtzZs327fddpstSZL99NNP27Yt2vpkGzmbxrZFe59o//qv/2o/99xz9v79++0XX3zRvuSSS+xIJGJ3dXXZtn3y2nvCBiO2bdt33XWXXVdXZ7tcLnvp0qX2K6+8Mt5VOis8++yzNnBY+ehHP2rbtjO996tf/aodjUZtt9ttX3zxxXZTU9P4VvoMNlZbA/avfvWroXMymYz9mc98xi4uLrZ9Pp/93ve+125vbx+/Sp/B/umf/smur6+3XS6XXVZWZl988cVDgYhti7Y+2V4fjIj2PrFuvPFGu7Ky0na5XHZ1dbV944032nv27Bm6/2S1t2Tbtv3W+lYEQRAEQRCO34QcMyIIgiAIwulDBCOCIAiCIIwrEYwIgiAIgjCuRDAiCIIgCMK4EsGIIAiCIAjjSgQjgiAIgiCMKxGMCIIgCIIwrkQwIgiCIAjCuBLBiCAIgiAI40oEI4IgCIIgjCsRjAiCIAiCMK5EMCIIgiAIwrj6/wFAJIjCUwX13gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_all = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "for epoch in range(300):\n",
+    "    model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "    model.eval()\n",
+    "    weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "    bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "    weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "    weight1_imp = []\n",
+    "    bias1_imp = []\n",
+    "    weight2_imp = []\n",
+    "    for i in range(width):\n",
+    "        if i in important_indices_set:\n",
+    "            weight1_imp.append(weight1[i])\n",
+    "            bias1_imp.append(bias[i])\n",
+    "            weight2_imp.append(weight2[0][i])\n",
+    "    lst_proj = []\n",
+    "    for i in range(len(weight1)):\n",
+    "        proj = np.linalg.norm(weight1[i])\n",
+    "        lst_proj.append(proj)\n",
+    "    lst_proj_all.append(lst_proj)\n",
+    "    loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "    lst_loss.append(loss)\n",
+    "    \n",
+    "# for i in range(len(weight1_imp)):\n",
+    "#     print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(bias1_imp)):\n",
+    "#     print(\"b{}\".format(i),bias1_imp[i])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(weight2_imp)):\n",
+    "#     print(\"w{}\".format(i),weight2_imp[i])\n",
+    "\n",
+    "\n",
+    "print(\"####################   Loss   ############################\\n\", loss)\n",
+    "lst_proj_all = np.abs(np.array(lst_proj_all))\n",
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch- start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in range(20):\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2421044613.py:16: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "####################   Loss   ############################\n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_all = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "for epoch in range(300):\n",
+    "    model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "    model.eval()\n",
+    "    weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "    bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "    weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "    weight1_imp = []\n",
+    "    bias1_imp = []\n",
+    "    weight2_imp = []\n",
+    "    for i in range(width):\n",
+    "        if i in important_indices_set:\n",
+    "            weight1_imp.append(weight1[i])\n",
+    "            bias1_imp.append(bias[i])\n",
+    "            weight2_imp.append(weight2[0][i])\n",
+    "    lst_proj = []\n",
+    "    for i in range(len(weight1)):\n",
+    "        proj = np.linalg.norm(weight1[i])\n",
+    "        lst_proj.append(proj)\n",
+    "    lst_proj_all.append(lst_proj)\n",
+    "    loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "    lst_loss.append(loss)\n",
+    "    \n",
+    "# for i in range(len(weight1_imp)):\n",
+    "#     print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(bias1_imp)):\n",
+    "#     print(\"b{}\".format(i),bias1_imp[i])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(weight2_imp)):\n",
+    "#     print(\"w{}\".format(i),weight2_imp[i])\n",
+    "\n",
+    "\n",
+    "print(\"####################   Loss   ############################\\n\", loss)\n",
+    "lst_proj_all = np.abs(np.array(lst_proj_all))\n",
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch- start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in range(20):\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2934221896.py:16: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "####################   Loss   ############################\n",
+      " 0.025476610660552977\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "for epoch in range(300):\n",
+    "    model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "    model.eval()\n",
+    "    weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "    bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "    weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "    weight1_imp = []\n",
+    "    bias1_imp = []\n",
+    "    weight2_imp = []\n",
+    "    for i in range(width):\n",
+    "        if i in important_indices_set:\n",
+    "            weight1_imp.append(weight1[i])\n",
+    "            bias1_imp.append(bias[i])\n",
+    "            weight2_imp.append(weight2[0][i])\n",
+    "    lst_proj = []\n",
+    "    for i in range(len(weight1_imp)):\n",
+    "        proj = np.dot(weight1_imp[i], right_svd_final[1])/np.linalg.norm(weight1_imp[i])\n",
+    "        lst_proj.append(proj)\n",
+    "    lst_proj_6.append(lst_proj)\n",
+    "    loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "    lst_loss.append(loss)\n",
+    "    \n",
+    "# for i in range(len(weight1_imp)):\n",
+    "#     print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(bias1_imp)):\n",
+    "#     print(\"b{}\".format(i),bias1_imp[i])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(weight2_imp)):\n",
+    "#     print(\"w{}\".format(i),weight2_imp[i])\n",
+    "\n",
+    "lst_proj_6 = np.abs(np.array(lst_proj_6))\n",
+    "print(\"####################   Loss   ############################\\n\", loss)\n",
+    "\n",
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch- start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in range(6):\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_6[start_epoch:end_epoch, i], color=color_lst[i], label=i)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2446321885.py:16: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "####################   Loss   ############################\n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "for epoch in range(300):\n",
+    "    model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "    model.eval()\n",
+    "    weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "    bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "    weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "    weight1_imp = []\n",
+    "    bias1_imp = []\n",
+    "    weight2_imp = []\n",
+    "    for i in range(width):\n",
+    "        if i in important_indices_set:\n",
+    "            weight1_imp.append(weight1[i])\n",
+    "            bias1_imp.append(bias[i])\n",
+    "            weight2_imp.append(weight2[0][i])\n",
+    "    lst_proj = []\n",
+    "    for i in range(len(weight1_imp)):\n",
+    "        proj = np.dot(weight1_imp[i], right_svd_final[0])/np.linalg.norm(weight1_imp[i])\n",
+    "        lst_proj.append(proj)\n",
+    "    lst_proj_6.append(lst_proj)\n",
+    "    loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "    lst_loss.append(loss)\n",
+    "    \n",
+    "# for i in range(len(weight1_imp)):\n",
+    "#     print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(bias1_imp)):\n",
+    "#     print(\"b{}\".format(i),bias1_imp[i])\n",
+    "# print(\"#############################\")\n",
+    "# for i in range(len(weight2_imp)):\n",
+    "#     print(\"w{}\".format(i),weight2_imp[i])\n",
+    "\n",
+    "lst_proj_6 = np.abs(np.array(lst_proj_6))\n",
+    "print(\"####################   Loss   ############################\\n\", loss)\n",
+    "\n",
+    "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n",
+    "start_epoch = 0\n",
+    "end_epoch = 50\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot([i for i in range(end_epoch- start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n",
+    "for i in range(6):\n",
+    "    plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_6[start_epoch:end_epoch, i], color=color_lst[i], label=i)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 0.36537448 0.3580904 0.04556298\n",
+      "w1 0.72418284 0.69748586 -0.78825355\n",
+      "w2 0.34797204 0.42660952 0.5363234\n",
+      "w3 0.33605936 0.24907304 -0.06005018\n",
+      "w4 0.38028616 0.11175094 -0.13124968\n",
+      "w5 -0.34890136 -0.27284747 -0.2611591\n",
+      "#############################\n",
+      "b0 0.12267428\n",
+      "b1 -0.8747314\n",
+      "b2 0.3733876\n",
+      "b3 -0.13120407\n",
+      "b4 0.21289438\n",
+      "b5 0.10772384\n",
+      "#############################\n",
+      "w0 0.5685848\n",
+      "w1 -1.256696\n",
+      "w2 -0.5483628\n",
+      "w3 0.5944816\n",
+      "w4 0.63369596\n",
+      "w5 0.60463965\n",
+      "################# Loss ############# \n",
+      " 0.8777030944824219\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/4157714226.py:17: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 20\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in range(width):\n",
+    "    if i in important_indices_set:\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 1.0061741 0.95482004 -0.45426163\n",
+      "w1 1.4641773 1.4704639 -1.4941789\n",
+      "w2 0.935344 0.94653237 0.9586796\n",
+      "w3 0.83078367 0.6868357 -0.22606844\n",
+      "w4 0.8800349 0.70537746 -0.50904787\n",
+      "w5 -0.41551253 -0.33124772 0.02181336\n",
+      "#############################\n",
+      "b0 0.46015227\n",
+      "b1 -1.530311\n",
+      "b2 0.9009224\n",
+      "b3 0.17700297\n",
+      "b4 0.51167285\n",
+      "b5 0.034514215\n",
+      "#############################\n",
+      "w0 1.2700663\n",
+      "w1 -2.730364\n",
+      "w2 -1.5298215\n",
+      "w3 0.8574937\n",
+      "w4 1.0062855\n",
+      "w5 0.45464066\n",
+      "################# Loss ############# \n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2594646103.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/2594646103.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[-6:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in range(width):\n",
+    "    if i in important_indices_set:\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 0.0853401 -0.16724034 0.12309753\n",
+      "w1 0.08548605 0.14597915 0.0693136\n",
+      "w2 -0.23844586 -0.10693528 0.37807927\n",
+      "w3 -0.14546578 -0.22376855 -0.6339036\n",
+      "w4 0.33106035 -0.2731311 -0.1632673\n",
+      "w5 -0.21233243 -0.08899325 -0.45256308\n",
+      "w6 0.60019016 0.37528753 -0.2121918\n",
+      "w7 0.59348345 0.4699195 -0.2736816\n",
+      "w8 0.045255914 -0.09446194 -0.027584856\n",
+      "w9 -0.13971551 -0.26737675 0.38807774\n",
+      "w10 -0.05824005 -0.23724952 0.44288102\n",
+      "w11 -0.25162622 0.10441463 -0.44112182\n",
+      "w12 -0.2764275 0.09530643 -0.33410606\n",
+      "w13 0.47695023 -0.74833834 -0.56568223\n",
+      "w14 -0.41551253 -0.33124772 0.02181336\n",
+      "w15 0.83078367 0.6868357 -0.22606844\n",
+      "w16 0.8800349 0.70537746 -0.50904787\n",
+      "w17 1.0061741 0.95482004 -0.45426163\n",
+      "w18 0.935344 0.94653237 0.9586796\n",
+      "w19 1.4641773 1.4704639 -1.4941789\n",
+      "#############################\n",
+      "b0 -0.1362047\n",
+      "b1 -0.04841186\n",
+      "b2 0.07253106\n",
+      "b3 -0.05862399\n",
+      "b4 0.06417704\n",
+      "b5 -0.0049577104\n",
+      "b6 0.21860224\n",
+      "b7 0.28144687\n",
+      "b8 -0.0039949925\n",
+      "b9 -0.27605534\n",
+      "b10 0.31877872\n",
+      "b11 -0.45604855\n",
+      "b12 -0.108413234\n",
+      "b13 0.20883371\n",
+      "b14 0.034514215\n",
+      "b15 0.17700297\n",
+      "b16 0.51167285\n",
+      "b17 0.46015227\n",
+      "b18 0.9009224\n",
+      "b19 -1.530311\n",
+      "#############################\n",
+      "w0 -0.1627666\n",
+      "w1 0.23271199\n",
+      "w2 0.29851052\n",
+      "w3 -0.60590136\n",
+      "w4 -0.37646684\n",
+      "w5 -0.4429593\n",
+      "w6 0.60445035\n",
+      "w7 0.6306787\n",
+      "w8 0.36392713\n",
+      "w9 0.5134147\n",
+      "w10 0.6221878\n",
+      "w11 -0.5241894\n",
+      "w12 -0.52441424\n",
+      "w13 -0.7579778\n",
+      "w14 0.45464066\n",
+      "w15 0.8574937\n",
+      "w16 1.0062855\n",
+      "w17 1.2700663\n",
+      "w18 -1.5298215\n",
+      "w19 -2.730364\n",
+      "################# Loss ############# \n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/4037432759.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/4037432759.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in important_indices_set:\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 -0.107883126 0.040169857 -0.11615411\n",
+      "w1 0.08774963 0.013394084 -0.10910419\n",
+      "w2 0.007123738 -0.15046093 -0.18074623\n",
+      "w3 -0.0016741039 0.11995243 -0.18403849\n",
+      "w4 0.023994112 -0.046816267 0.15968044\n",
+      "w5 -0.05126393 -0.07847294 0.1835921\n",
+      "w6 0.009406718 0.14414209 -0.16902886\n",
+      "w7 -0.016056353 0.22120051 0.08077506\n",
+      "w8 -0.1288202 0.12856407 0.11841627\n",
+      "w9 0.0738332 0.1677561 -0.07198258\n",
+      "w10 0.011759831 -0.11463951 0.037830845\n",
+      "w11 0.21035793 0.15067026 -0.097498186\n",
+      "w12 0.101176046 0.089925796 -0.13245405\n",
+      "w13 0.19882815 -0.057257425 0.09838746\n",
+      "w14 -0.04307008 -0.043903563 -0.20065355\n",
+      "w15 -0.20839128 0.19867295 0.17002161\n",
+      "w16 0.08121782 0.18568039 -0.046018317\n",
+      "w17 0.21181068 0.14200282 0.21229532\n",
+      "w18 0.01867821 0.027920755 -0.17578797\n",
+      "w19 -0.15129054 -0.2205965 -0.17956872\n",
+      "#############################\n",
+      "b0 -0.07473675\n",
+      "b1 0.21401024\n",
+      "b2 -0.15813878\n",
+      "b3 0.14299408\n",
+      "b4 0.12570158\n",
+      "b5 -0.043140188\n",
+      "b6 -0.12705092\n",
+      "b7 -0.035042387\n",
+      "b8 0.009239239\n",
+      "b9 0.1898648\n",
+      "b10 -0.10557266\n",
+      "b11 -0.16975006\n",
+      "b12 0.20550534\n",
+      "b13 0.20679818\n",
+      "b14 -0.08150263\n",
+      "b15 0.091441914\n",
+      "b16 0.016621593\n",
+      "b17 0.16980113\n",
+      "b18 -0.06069698\n",
+      "b19 0.02139931\n",
+      "#############################\n",
+      "w0 -0.080114946\n",
+      "w1 -0.040012747\n",
+      "w2 -0.14903118\n",
+      "w3 0.011971986\n",
+      "w4 0.08056405\n",
+      "w5 -0.1704595\n",
+      "w6 -0.15878995\n",
+      "w7 0.08312065\n",
+      "w8 0.014666002\n",
+      "w9 0.18979256\n",
+      "w10 0.011460803\n",
+      "w11 -0.20436287\n",
+      "w12 0.10721659\n",
+      "w13 -0.2030058\n",
+      "w14 -0.095409796\n",
+      "w15 0.11091796\n",
+      "w16 -0.13806377\n",
+      "w17 -0.16617216\n",
+      "w18 0.0488277\n",
+      "w19 0.11109016\n",
+      "################# Loss ############# \n",
+      " 0.9785285949707031\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/3507984063.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/3507984063.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 0\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 0\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in important_indices_set:\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 0.59348345 0.4699195 -0.2736816\n",
+      "w1 -0.2764275 0.09530643 -0.33410606\n",
+      "w2 -0.13971551 -0.26737675 0.38807774\n",
+      "w3 1.0061741 0.95482004 -0.45426163\n",
+      "w4 -0.23844586 -0.10693528 0.37807927\n",
+      "w5 1.4641773 1.4704639 -1.4941789\n",
+      "w6 0.60019016 0.37528753 -0.2121918\n",
+      "w7 0.935344 0.94653237 0.9586796\n",
+      "w8 -0.21233243 -0.08899325 -0.45256308\n",
+      "w9 -0.05824005 -0.23724952 0.44288102\n",
+      "w10 -0.25162622 0.10441463 -0.44112182\n",
+      "w11 0.83078367 0.6868357 -0.22606844\n",
+      "w12 0.8800349 0.70537746 -0.50904787\n",
+      "w13 0.08548605 0.14597915 0.0693136\n",
+      "w14 0.47695023 -0.74833834 -0.56568223\n",
+      "w15 -0.14546578 -0.22376855 -0.6339036\n",
+      "w16 0.045255914 -0.09446194 -0.027584856\n",
+      "w17 0.0853401 -0.16724034 0.12309753\n",
+      "w18 -0.41551253 -0.33124772 0.02181336\n",
+      "w19 0.33106035 -0.2731311 -0.1632673\n",
+      "#############################\n",
+      "b0 0.28144687\n",
+      "b1 -0.108413234\n",
+      "b2 -0.27605534\n",
+      "b3 0.46015227\n",
+      "b4 0.07253106\n",
+      "b5 -1.530311\n",
+      "b6 0.21860224\n",
+      "b7 0.9009224\n",
+      "b8 -0.0049577104\n",
+      "b9 0.31877872\n",
+      "b10 -0.45604855\n",
+      "b11 0.17700297\n",
+      "b12 0.51167285\n",
+      "b13 -0.04841186\n",
+      "b14 0.20883371\n",
+      "b15 -0.05862399\n",
+      "b16 -0.0039949925\n",
+      "b17 -0.1362047\n",
+      "b18 0.034514215\n",
+      "b19 0.06417704\n",
+      "#############################\n",
+      "w0 0.6306787\n",
+      "w1 -0.52441424\n",
+      "w2 0.5134147\n",
+      "w3 1.2700663\n",
+      "w4 0.29851052\n",
+      "w5 -2.730364\n",
+      "w6 0.60445035\n",
+      "w7 -1.5298215\n",
+      "w8 -0.4429593\n",
+      "w9 0.6221878\n",
+      "w10 -0.5241894\n",
+      "w11 0.8574937\n",
+      "w12 1.0062855\n",
+      "w13 0.23271199\n",
+      "w14 -0.7579778\n",
+      "w15 -0.60590136\n",
+      "w16 0.36392713\n",
+      "w17 -0.1627666\n",
+      "w18 0.45464066\n",
+      "w19 -0.37646684\n",
+      "################# Loss ############# \n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/9398109.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/9398109.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in range(width):\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 -0.0016741039 0.11995243 -0.18403849\n",
+      "w1 0.08121782 0.18568039 -0.046018317\n",
+      "w2 0.011759831 -0.11463951 0.037830845\n",
+      "w3 0.101176046 0.089925796 -0.13245405\n",
+      "w4 -0.20839128 0.19867295 0.17002161\n",
+      "w5 0.21035793 0.15067026 -0.097498186\n",
+      "w6 0.08774963 0.013394084 -0.10910419\n",
+      "w7 0.21181068 0.14200282 0.21229532\n",
+      "w8 -0.04307008 -0.043903563 -0.20065355\n",
+      "w9 0.023994112 -0.046816267 0.15968044\n",
+      "w10 0.009406718 0.14414209 -0.16902886\n",
+      "w11 -0.016056353 0.22120051 0.08077506\n",
+      "w12 0.0738332 0.1677561 -0.07198258\n",
+      "w13 -0.1288202 0.12856407 0.11841627\n",
+      "w14 0.007123738 -0.15046093 -0.18074623\n",
+      "w15 -0.107883126 0.040169857 -0.11615411\n",
+      "w16 0.01867821 0.027920755 -0.17578797\n",
+      "w17 -0.05126393 -0.07847294 0.1835921\n",
+      "w18 -0.15129054 -0.2205965 -0.17956872\n",
+      "w19 0.19882815 -0.057257425 0.09838746\n",
+      "#############################\n",
+      "b0 0.14299408\n",
+      "b1 0.016621593\n",
+      "b2 -0.10557266\n",
+      "b3 0.20550534\n",
+      "b4 0.091441914\n",
+      "b5 -0.16975006\n",
+      "b6 0.21401024\n",
+      "b7 0.16980113\n",
+      "b8 -0.08150263\n",
+      "b9 0.12570158\n",
+      "b10 -0.12705092\n",
+      "b11 -0.035042387\n",
+      "b12 0.1898648\n",
+      "b13 0.009239239\n",
+      "b14 -0.15813878\n",
+      "b15 -0.07473675\n",
+      "b16 -0.06069698\n",
+      "b17 -0.043140188\n",
+      "b18 0.02139931\n",
+      "b19 0.20679818\n",
+      "#############################\n",
+      "w0 0.011971986\n",
+      "w1 -0.13806377\n",
+      "w2 0.011460803\n",
+      "w3 0.10721659\n",
+      "w4 0.11091796\n",
+      "w5 -0.20436287\n",
+      "w6 -0.040012747\n",
+      "w7 -0.16617216\n",
+      "w8 -0.095409796\n",
+      "w9 0.08056405\n",
+      "w10 -0.15878995\n",
+      "w11 0.08312065\n",
+      "w12 0.18979256\n",
+      "w13 0.014666002\n",
+      "w14 -0.14903118\n",
+      "w15 -0.080114946\n",
+      "w16 0.0488277\n",
+      "w17 -0.1704595\n",
+      "w18 0.11109016\n",
+      "w19 -0.2030058\n",
+      "################# Loss ############# \n",
+      " 0.9785286712646485\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2799166105.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/2799166105.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 0\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 0\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in range(width):\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 -0.16456097 -0.08612313 0.059961807\n",
+      "w1 0.1673276 -0.036041696 0.023660751\n",
+      "w2 -0.20878048 -0.16157073 -0.11527601\n",
+      "w3 0.0675532 0.12275387 -0.02822306\n",
+      "w4 -0.22305404 0.041852944 -0.0376688\n",
+      "w5 -0.05627843 -0.2130074 -0.0040191393\n",
+      "w6 0.10580827 -0.21449457 -0.13253324\n",
+      "w7 -0.016171614 -0.2008708 -0.10600689\n",
+      "w8 -0.19307058 -0.034989875 0.0028917235\n",
+      "w9 0.062418614 0.107444264 0.07896874\n",
+      "w10 -0.1535088 -0.12983987 0.15650986\n",
+      "w11 0.0063309884 -0.19377987 0.11076986\n",
+      "w12 0.00035796352 0.11511805 -0.21624838\n",
+      "w13 0.1716231 0.081107356 -0.07467566\n",
+      "w14 0.17823304 0.03641184 0.18550968\n",
+      "w15 0.05152009 0.043912094 -0.16602568\n",
+      "w16 0.017572867 0.15484096 0.20149516\n",
+      "w17 0.1253047 -0.13456187 0.20102637\n",
+      "w18 0.17653087 0.12097925 0.2097914\n",
+      "w19 0.1992181 0.07397333 0.22353442\n",
+      "#############################\n",
+      "b0 0.14299408\n",
+      "b1 0.016621593\n",
+      "b2 -0.10557266\n",
+      "b3 0.20550534\n",
+      "b4 0.091441914\n",
+      "b5 -0.16975006\n",
+      "b6 0.21401024\n",
+      "b7 0.16980113\n",
+      "b8 -0.08150263\n",
+      "b9 0.12570158\n",
+      "b10 -0.12705092\n",
+      "b11 -0.035042387\n",
+      "b12 0.1898648\n",
+      "b13 0.009239239\n",
+      "b14 -0.15813878\n",
+      "b15 -0.07473675\n",
+      "b16 -0.06069698\n",
+      "b17 -0.043140188\n",
+      "b18 0.02139931\n",
+      "b19 0.20679818\n",
+      "#############################\n",
+      "w0 0.011971986\n",
+      "w1 -0.13806377\n",
+      "w2 0.011460803\n",
+      "w3 0.10721659\n",
+      "w4 0.11091796\n",
+      "w5 -0.20436287\n",
+      "w6 -0.040012747\n",
+      "w7 -0.16617216\n",
+      "w8 -0.095409796\n",
+      "w9 0.08056405\n",
+      "w10 -0.15878995\n",
+      "w11 0.08312065\n",
+      "w12 0.18979256\n",
+      "w13 0.014666002\n",
+      "w14 -0.14903118\n",
+      "w15 -0.080114946\n",
+      "w16 0.0488277\n",
+      "w17 -0.1704595\n",
+      "w18 0.11109016\n",
+      "w19 -0.2030058\n",
+      "################# Loss ############# \n",
+      " 0.9785286712646485\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/3343835734.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/3343835734.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 0\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 0\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in range(width):\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][3], weight1_imp[i][4], weight1_imp[i][5])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 -0.12177175 0.012367001 0.0672816\n",
+      "w1 -0.1064364 0.043942913 -0.1648784\n",
+      "w2 -0.37269515 -0.15441549 -0.24142677\n",
+      "w3 -0.008919735 0.044151243 0.05095814\n",
+      "w4 -0.25237677 0.09243578 0.016891096\n",
+      "w5 0.008098835 0.008452178 -0.017598232\n",
+      "w6 0.14204125 0.007200663 -0.12847616\n",
+      "w7 -0.005139193 0.006273578 -0.0006887438\n",
+      "w8 -0.13342802 0.072076134 0.067445934\n",
+      "w9 0.027164377 -0.10233683 0.423036\n",
+      "w10 -0.10458674 -0.04557202 0.19910717\n",
+      "w11 0.054744028 0.039117094 0.025234343\n",
+      "w12 -0.04351923 -0.039850097 -0.11124437\n",
+      "w13 0.20971815 0.20566352 -0.058098353\n",
+      "w14 0.07472449 -0.049919307 0.07452662\n",
+      "w15 0.01910459 0.07758677 -0.20731401\n",
+      "w16 0.07741324 0.121971756 -0.008562315\n",
+      "w17 0.13863195 -0.18164772 0.13693254\n",
+      "w18 0.2916657 0.23547813 -0.24633749\n",
+      "w19 0.1466668 0.20596148 0.18489817\n",
+      "#############################\n",
+      "b0 0.28144687\n",
+      "b1 -0.108413234\n",
+      "b2 -0.27605534\n",
+      "b3 0.46015227\n",
+      "b4 0.07253106\n",
+      "b5 -1.530311\n",
+      "b6 0.21860224\n",
+      "b7 0.9009224\n",
+      "b8 -0.0049577104\n",
+      "b9 0.31877872\n",
+      "b10 -0.45604855\n",
+      "b11 0.17700297\n",
+      "b12 0.51167285\n",
+      "b13 -0.04841186\n",
+      "b14 0.20883371\n",
+      "b15 -0.05862399\n",
+      "b16 -0.0039949925\n",
+      "b17 -0.1362047\n",
+      "b18 0.034514215\n",
+      "b19 0.06417704\n",
+      "#############################\n",
+      "w0 0.6306787\n",
+      "w1 -0.52441424\n",
+      "w2 0.5134147\n",
+      "w3 1.2700663\n",
+      "w4 0.29851052\n",
+      "w5 -2.730364\n",
+      "w6 0.60445035\n",
+      "w7 -1.5298215\n",
+      "w8 -0.4429593\n",
+      "w9 0.6221878\n",
+      "w10 -0.5241894\n",
+      "w11 0.8574937\n",
+      "w12 1.0062855\n",
+      "w13 0.23271199\n",
+      "w14 -0.7579778\n",
+      "w15 -0.60590136\n",
+      "w16 0.36392713\n",
+      "w17 -0.1627666\n",
+      "w18 0.45464066\n",
+      "w19 -0.37646684\n",
+      "################# Loss ############# \n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/1508651472.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/1508651472.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in range(width):\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][3], weight1_imp[i][4], weight1_imp[i][5])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2496271550.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_path+'/model_{}.pt'.format(299)))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.83078367,  0.6868357 , -0.22606844,  0.05474403,  0.03911709,\n",
+       "        0.02523434, -0.19140272,  0.06310938, -0.04532412, -0.02466479,\n",
+       "        0.10296442,  0.26652837,  0.10855434,  0.10202617,  0.11761178,\n",
+       "       -0.13377447,  0.1275938 , -0.05882618,  0.00789413,  0.08073723],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "\n",
+    "weight1[11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 0.0853401 -0.16724034 0.12309753\n",
+      "w1 0.08548605 0.14597915 0.0693136\n",
+      "w2 -0.23844586 -0.10693528 0.37807927\n",
+      "w3 -0.14546578 -0.22376855 -0.6339036\n",
+      "w4 0.33106035 -0.2731311 -0.1632673\n",
+      "w5 -0.21233243 -0.08899325 -0.45256308\n",
+      "w6 0.60019016 0.37528753 -0.2121918\n",
+      "w7 0.59348345 0.4699195 -0.2736816\n",
+      "w8 0.045255914 -0.09446194 -0.027584856\n",
+      "w9 -0.13971551 -0.26737675 0.38807774\n",
+      "w10 -0.05824005 -0.23724952 0.44288102\n",
+      "w11 -0.25162622 0.10441463 -0.44112182\n",
+      "w12 -0.2764275 0.09530643 -0.33410606\n",
+      "w13 0.47695023 -0.74833834 -0.56568223\n",
+      "w14 -0.41551253 -0.33124772 0.02181336\n",
+      "w15 0.83078367 0.6868357 -0.22606844\n",
+      "w16 0.8800349 0.70537746 -0.50904787\n",
+      "w17 1.0061741 0.95482004 -0.45426163\n",
+      "w18 0.935344 0.94653237 0.9586796\n",
+      "w19 1.4641773 1.4704639 -1.4941789\n",
+      "#############################\n",
+      "b0 -0.1362047\n",
+      "b1 -0.04841186\n",
+      "b2 0.07253106\n",
+      "b3 -0.05862399\n",
+      "b4 0.06417704\n",
+      "b5 -0.0049577104\n",
+      "b6 0.21860224\n",
+      "b7 0.28144687\n",
+      "b8 -0.0039949925\n",
+      "b9 -0.27605534\n",
+      "b10 0.31877872\n",
+      "b11 -0.45604855\n",
+      "b12 -0.108413234\n",
+      "b13 0.20883371\n",
+      "b14 0.034514215\n",
+      "b15 0.17700297\n",
+      "b16 0.51167285\n",
+      "b17 0.46015227\n",
+      "b18 0.9009224\n",
+      "b19 -1.530311\n",
+      "#############################\n",
+      "w0 -0.1627666\n",
+      "w1 0.23271199\n",
+      "w2 0.29851052\n",
+      "w3 -0.60590136\n",
+      "w4 -0.37646684\n",
+      "w5 -0.4429593\n",
+      "w6 0.60445035\n",
+      "w7 0.6306787\n",
+      "w8 0.36392713\n",
+      "w9 0.5134147\n",
+      "w10 0.6221878\n",
+      "w11 -0.5241894\n",
+      "w12 -0.52441424\n",
+      "w13 -0.7579778\n",
+      "w14 0.45464066\n",
+      "w15 0.8574937\n",
+      "w16 1.0062855\n",
+      "w17 1.2700663\n",
+      "w18 -1.5298215\n",
+      "w19 -2.730364\n",
+      "################# Loss ############# \n",
+      " 0.02547661304473877\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/4037432759.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+      "/tmp/ipykernel_2812799/4037432759.py:23: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "lst = []\n",
+    "for i in range(width):\n",
+    "    neuron = weight1[i]\n",
+    "    # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n",
+    "    proj = np.linalg.norm(neuron)\n",
+    "    lst.append(proj)\n",
+    "indices = np.argsort(np.abs(np.array(lst)))\n",
+    "# print(indices)\n",
+    "# print(np.sort(np.abs(np.array(lst))))\n",
+    "important_indices_set = indices[:]\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 299\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in important_indices_set:\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w0 -0.0016741039 0.11995243 -0.18403849\n",
+      "w1 0.08121782 0.18568039 -0.046018317\n",
+      "w2 0.011759831 -0.11463951 0.037830845\n",
+      "w3 0.101176046 0.089925796 -0.13245405\n",
+      "w4 -0.20839128 0.19867295 0.17002161\n",
+      "w5 0.21035793 0.15067026 -0.097498186\n",
+      "w6 0.08774963 0.013394084 -0.10910419\n",
+      "w7 0.21181068 0.14200282 0.21229532\n",
+      "w8 -0.04307008 -0.043903563 -0.20065355\n",
+      "w9 0.023994112 -0.046816267 0.15968044\n",
+      "w10 0.009406718 0.14414209 -0.16902886\n",
+      "w11 -0.016056353 0.22120051 0.08077506\n",
+      "w12 0.0738332 0.1677561 -0.07198258\n",
+      "w13 -0.1288202 0.12856407 0.11841627\n",
+      "w14 0.007123738 -0.15046093 -0.18074623\n",
+      "w15 -0.107883126 0.040169857 -0.11615411\n",
+      "w16 0.01867821 0.027920755 -0.17578797\n",
+      "w17 -0.05126393 -0.07847294 0.1835921\n",
+      "w18 -0.15129054 -0.2205965 -0.17956872\n",
+      "w19 0.19882815 -0.057257425 0.09838746\n",
+      "#############################\n",
+      "b0 0.14299408\n",
+      "b1 0.016621593\n",
+      "b2 -0.10557266\n",
+      "b3 0.20550534\n",
+      "b4 0.091441914\n",
+      "b5 -0.16975006\n",
+      "b6 0.21401024\n",
+      "b7 0.16980113\n",
+      "b8 -0.08150263\n",
+      "b9 0.12570158\n",
+      "b10 -0.12705092\n",
+      "b11 -0.035042387\n",
+      "b12 0.1898648\n",
+      "b13 0.009239239\n",
+      "b14 -0.15813878\n",
+      "b15 -0.07473675\n",
+      "b16 -0.06069698\n",
+      "b17 -0.043140188\n",
+      "b18 0.02139931\n",
+      "b19 0.20679818\n",
+      "#############################\n",
+      "w0 0.011971986\n",
+      "w1 -0.13806377\n",
+      "w2 0.011460803\n",
+      "w3 0.10721659\n",
+      "w4 0.11091796\n",
+      "w5 -0.20436287\n",
+      "w6 -0.040012747\n",
+      "w7 -0.16617216\n",
+      "w8 -0.095409796\n",
+      "w9 0.08056405\n",
+      "w10 -0.15878995\n",
+      "w11 0.08312065\n",
+      "w12 0.18979256\n",
+      "w13 0.014666002\n",
+      "w14 -0.14903118\n",
+      "w15 -0.080114946\n",
+      "w16 0.0488277\n",
+      "w17 -0.1704595\n",
+      "w18 0.11109016\n",
+      "w19 -0.2030058\n",
+      "################# Loss ############# \n",
+      " 0.9785285949707031\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/2693479869.py:7: 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_path+'/model_{}.pt'.format(epoch)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "lst_proj_6 = []\n",
+    "loss_fn = MyHingeLoss()\n",
+    "lst_loss = []\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "# for epoch in range(300):\n",
+    "epoch = 0\n",
+    "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n",
+    "model.eval()\n",
+    "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "weight1_imp = []\n",
+    "bias1_imp = []\n",
+    "weight2_imp = []\n",
+    "for i in range(width):\n",
+    "    if i in important_indices_set:\n",
+    "        weight1_imp.append(weight1[i])\n",
+    "        bias1_imp.append(bias[i])\n",
+    "        weight2_imp.append(weight2[0][i])\n",
+    "for i in range(len(weight1_imp)):\n",
+    "    print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(bias1_imp)):\n",
+    "    print(\"b{}\".format(i),bias1_imp[i])\n",
+    "print(\"#############################\")\n",
+    "for i in range(len(weight2_imp)):\n",
+    "    print(\"w{}\".format(i),weight2_imp[i])\n",
+    "loss = loss_calc(test_dataloader, model, loss_fn)\n",
+    "print(\"################# Loss ############# \\n\", loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class n_neuron(torch.nn.Module):\n",
+    "    def __init__(self, input_dim=20, width=1,act='relu'):\n",
+    "        super(n_neuron, self).__init__()\n",
+    "        self.linear1 = torch.nn.Linear(n, width)\n",
+    "        self.activation = torch.nn.ReLU()\n",
+    "        self.linear2 = torch.nn.Linear(width, 1, bias=False)\n",
+    "\n",
+    "    def forward(self, x,return_first=False):\n",
+    "        x = self.linear1(x)\n",
+    "        x = self.activation(x)\n",
+    "        x = self.linear2(x)\n",
+    "        return x\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2812799/1294201671.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",
+      "  model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[((0, 1, 2, 3, 4, 5), 0.71)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for epoch in range(1):\n",
+    "    model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n",
+    "    model.eval()\n",
+    "    weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n",
+    "    bias = model.linear1.bias.clone().detach().cpu().numpy()\n",
+    "    weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n",
+    "    weight1_imp = []\n",
+    "    bias1_imp = []\n",
+    "    weight2_imp = []\n",
+    "    for i in range(width):\n",
+    "        if i in important_indices_set:\n",
+    "            weight1_imp.append(weight1[i])\n",
+    "            bias1_imp.append(bias[i])\n",
+    "            weight2_imp.append(weight2[0][i])\n",
+    "\n",
+    "\n",
+    "train_dataloader, test_dataloader = data_preparation()\n",
+    "dataloader = test_dataloader\n",
+    "width_value = 6\n",
+    "model_test = n_neuron(width=width_value)\n",
+    "idx_lst = [0,1,2,5]\n",
+    "indices_init = [0,1,2,3,4,5]\n",
+    "planting_sparsity = 6\n",
+    "lst_value = []\n",
+    "for comb in itertools.combinations(list(indices_init), planting_sparsity):\n",
+    "    with torch.no_grad():\n",
+    "        counter=-1\n",
+    "        for idx in comb:\n",
+    "            counter+=1\n",
+    "            model_test.linear1.weight[counter] = torch.nn.Parameter(torch.Tensor(weight1_imp[idx]).clone())\n",
+    "            model_test.linear1.bias[counter] = torch.nn.Parameter(torch.Tensor(bias1_imp).clone()[idx])\n",
+    "            model_test.linear2.weight[0][counter] = torch.nn.Parameter(torch.Tensor(weight2_imp).clone()[idx])\n",
+    "    model_test.cuda()\n",
+    "    # print(model_test)\n",
+    "    acc = acc_calc(dataloader, model_test, device='cuda')\n",
+    "    value = (comb, acc)\n",
+    "    lst_value.append(value)\n",
+    "\n",
+    "print(lst_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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