Kraskov_tests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c908945f",
   "metadata": {},
   "source": [
    "## Kraskov method "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2b57c3",
   "metadata": {},
   "source": [
    "In Kraskov, the MI $I(X;T)$ between $X$ = input and $T$ = activations is approximated as $H(T)$, the entropy of T : <br>  $$\n",
    "I(T; X) = H(T) - H(T|X)  \\\\\n",
    "= H(T) - H(Z)  \\\\\n",
    "= H(T) - c\n",
    "$$\n",
    "where $c$ is an unknown constant. <br>\n",
    "But $H(T)$ has to be computed using the Kraskov estimator:\n",
    "$$\n",
    "\\frac{d}{P} \\left(\\sum_{i} \\log(r_i + \\epsilon)\\right) + \\frac{d}{2} \\log(\\pi) - \\log \\Gamma\\left(\\frac{d}{2} + 1\\right) + \\psi(P) - \\psi(k)\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3729b78",
   "metadata": {},
   "source": [
    "Paper:<br>\n",
    "*\"Here we focused for simplicity only on the compression\n",
    "phenomenon in the mutual information between the input and hidden layer activity, leaving aside the\n",
    "information with respect to the output (as this is not relevant to the compression phenomenon)\"* <br>\n",
    "*\"Compression would correspond to decreasing entropy over the\n",
    "course of training, while a lack of compression would correspond to increasing entropy\"*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb2aa00",
   "metadata": {},
   "source": [
    "## Importing stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae0f44d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "import torch\n",
    "import torchaudio\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset\n",
    "from torchsummary import summary\n",
    "\n",
    "from utils import load\n",
    "from dataloader import FMA2D_spec\n",
    "from architectures import SimpleCNN2, ResNet\n",
    "from simplebinmi import bin_calc_information2\n",
    "\n",
    "import kde\n",
    "import keras.backend as K\n",
    "\n",
    "from Kraskov import kraskov_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "150d2ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef1ca87",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db4019aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR='C:/Users/loren/OneDrive/Bureau/Information theory/information_bottleneck-master/information_bottleneck-master/fma_small'\n",
    "\n",
    "# download data first from these links:\n",
    "# curl -O https://os.unil.cloud.switch.ch/fma/fma_metadata.zip\n",
    "# curl -O https://os.unil.cloud.switch.ch/fma/fma_small.zip\n",
    "\n",
    "tracks = load('C:/Users/loren/OneDrive/Bureau/Information theory/information_bottleneck-master/information_bottleneck-master/fma_metadata/tracks.csv')\n",
    "subset = tracks.index[tracks['set', 'subset'] <= 'small']\n",
    "\n",
    "tracks = tracks.loc[subset][:1000]\n",
    "train = tracks.index[tracks['set', 'split'] == 'training']\n",
    "val = tracks.index[tracks['set', 'split'] == 'validation']\n",
    "test = tracks.index[tracks['set', 'split'] == 'test']\n",
    "\n",
    "labels_onehot = LabelBinarizer().fit_transform(tracks['track', 'genre_top'])\n",
    "labels_onehot = pd.DataFrame(labels_onehot, index=tracks.index)\n",
    "\n",
    "NUM_LABELS = 8\n",
    "labelixs = {}\n",
    "y = np.argmax(labels_onehot.to_numpy(), axis=1)  # Add .to_numpy() here\n",
    "for i in range(NUM_LABELS):\n",
    "    labelixs[i] = y == i\n",
    "\n",
    "labelprobs = np.mean(y, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2392b4f",
   "metadata": {},
   "source": [
    "## Kraskov method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e76f1380",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data\n",
    "BATCH = 256\n",
    "EPOCHS = 100\n",
    "labels_onehot_np = np.array(labels_onehot)\n",
    "\n",
    "NUM_LABELS = 8\n",
    "labelixs = {}\n",
    "y = np.argmax(labels_onehot_np, axis=1)\n",
    "for i in range(NUM_LABELS):\n",
    "    labelixs[i] = y == i\n",
    "\n",
    "augment_prob = 0.8\n",
    "\n",
    "\n",
    "# create a training dataset and dataloader\n",
    "dataset_train = FMA2D_spec(DATA_DIR, train, labels_onehot, transforms=False)\n",
    "dataloader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH, shuffle=True)\n",
    "\n",
    "# create a validation dataset and dataloader\n",
    "dataset_valid = FMA2D_spec(DATA_DIR, val, labels_onehot, transforms=False)\n",
    "val_dataloader = torch.utils.data.DataLoader(dataset_valid, batch_size=BATCH, shuffle=True)\n",
    "\n",
    "# define the loss function and the optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "# Lee 2017\n",
    "# SGD optimizer\n",
    "#optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, nesterov=True)\n",
    "#scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.2, patience=5)\n",
    "\n",
    "from utils import plot_spectrogram\n",
    "for spec, label, ixs in dataloader:\n",
    "    #print(spec.size())\n",
    "    #print(len(label))\n",
    "    #print(ixs)\n",
    "    #plot_spectrogram(spec[0])\n",
    "    input_size = spec.size()[2]\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c80adf1",
   "metadata": {},
   "source": [
    "### Plotting MI I(X;T) for different Convolutional blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "410696da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,   768 subsamples] Training loss: 2.011\n",
      "[EPOCH 1, 0.0208 min] Validation Loss: 1.9408 | Validation Accuracy: 0.3038 | Training Accuracy: 0.2234\n",
      "[2,  1536 subsamples] Training loss: 1.941\n",
      "[EPOCH 2, 0.0514 min] Validation Loss: 1.9139 | Validation Accuracy: 0.3165 | Training Accuracy: 0.2549\n",
      "[3,  2304 subsamples] Training loss: 1.890\n",
      "[EPOCH 3, 0.0816 min] Validation Loss: 1.8892 | Validation Accuracy: 0.4177 | Training Accuracy: 0.3114\n",
      "[4,  3072 subsamples] Training loss: 1.843\n",
      "[EPOCH 4, 0.1121 min] Validation Loss: 1.8601 | Validation Accuracy: 0.4430 | Training Accuracy: 0.3771\n",
      "[5,  3840 subsamples] Training loss: 1.790\n",
      "[EPOCH 5, 0.1423 min] Validation Loss: 1.8141 | Validation Accuracy: 0.3671 | Training Accuracy: 0.4205\n",
      "[6,  4608 subsamples] Training loss: 1.746\n",
      "[EPOCH 6, 0.1729 min] Validation Loss: 1.7652 | Validation Accuracy: 0.3924 | Training Accuracy: 0.4323\n",
      "[7,  5376 subsamples] Training loss: 1.698\n",
      "[EPOCH 7, 0.2040 min] Validation Loss: 1.7120 | Validation Accuracy: 0.4304 | Training Accuracy: 0.4520\n",
      "[8,  6144 subsamples] Training loss: 1.668\n",
      "[EPOCH 8, 0.2364 min] Validation Loss: 1.7043 | Validation Accuracy: 0.4177 | Training Accuracy: 0.4481\n",
      "[9,  6912 subsamples] Training loss: 1.652\n",
      "[EPOCH 9, 0.2694 min] Validation Loss: 1.6556 | Validation Accuracy: 0.4304 | Training Accuracy: 0.4560\n",
      "[10,  7680 subsamples] Training loss: 1.625\n",
      "[EPOCH 10, 0.3004 min] Validation Loss: 1.6396 | Validation Accuracy: 0.4684 | Training Accuracy: 0.4783\n",
      "[11,  8448 subsamples] Training loss: 1.590\n",
      "[EPOCH 11, 0.3316 min] Validation Loss: 1.6333 | Validation Accuracy: 0.4557 | Training Accuracy: 0.4888\n",
      "[12,  9216 subsamples] Training loss: 1.571\n",
      "[EPOCH 12, 0.3630 min] Validation Loss: 1.6184 | Validation Accuracy: 0.4430 | Training Accuracy: 0.4823\n",
      "[13,  9984 subsamples] Training loss: 1.551\n",
      "[EPOCH 13, 0.3940 min] Validation Loss: 1.5931 | Validation Accuracy: 0.4557 | Training Accuracy: 0.4980\n",
      "[14, 10752 subsamples] Training loss: 1.534\n",
      "[EPOCH 14, 0.4252 min] Validation Loss: 1.6130 | Validation Accuracy: 0.4557 | Training Accuracy: 0.4888\n",
      "[15, 11520 subsamples] Training loss: 1.509\n",
      "[EPOCH 15, 0.4567 min] Validation Loss: 1.5947 | Validation Accuracy: 0.4810 | Training Accuracy: 0.4980\n",
      "[16, 12288 subsamples] Training loss: 1.489\n",
      "[EPOCH 16, 0.4932 min] Validation Loss: 1.5965 | Validation Accuracy: 0.4430 | Training Accuracy: 0.5099\n",
      "[17, 13056 subsamples] Training loss: 1.479\n",
      "[EPOCH 17, 0.5285 min] Validation Loss: 1.6031 | Validation Accuracy: 0.4430 | Training Accuracy: 0.5046\n",
      "[18, 13824 subsamples] Training loss: 1.463\n",
      "[EPOCH 18, 0.5627 min] Validation Loss: 1.5813 | Validation Accuracy: 0.4557 | Training Accuracy: 0.5164\n",
      "[19, 14592 subsamples] Training loss: 1.440\n",
      "[EPOCH 19, 0.5953 min] Validation Loss: 1.5952 | Validation Accuracy: 0.4304 | Training Accuracy: 0.5125\n",
      "[20, 15360 subsamples] Training loss: 1.441\n",
      "[EPOCH 20, 0.6272 min] Validation Loss: 1.5855 | Validation Accuracy: 0.4051 | Training Accuracy: 0.5283\n",
      "[21, 16128 subsamples] Training loss: 1.439\n",
      "[EPOCH 21, 0.6597 min] Validation Loss: 1.5801 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5099\n",
      "[22, 16896 subsamples] Training loss: 1.417\n",
      "[EPOCH 22, 0.6953 min] Validation Loss: 1.5412 | Validation Accuracy: 0.4557 | Training Accuracy: 0.5283\n",
      "[23, 17664 subsamples] Training loss: 1.382\n",
      "[EPOCH 23, 0.7272 min] Validation Loss: 1.5349 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5335\n",
      "[24, 18432 subsamples] Training loss: 1.364\n",
      "[EPOCH 24, 0.7625 min] Validation Loss: 1.5384 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5361\n",
      "[25, 19200 subsamples] Training loss: 1.354\n",
      "[EPOCH 25, 0.7978 min] Validation Loss: 1.5349 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5453\n",
      "[26, 19968 subsamples] Training loss: 1.346\n",
      "[EPOCH 26, 0.8314 min] Validation Loss: 1.5468 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5453\n",
      "[27, 20736 subsamples] Training loss: 1.326\n",
      "[EPOCH 27, 0.8668 min] Validation Loss: 1.5135 | Validation Accuracy: 0.5316 | Training Accuracy: 0.5506\n",
      "[28, 21504 subsamples] Training loss: 1.314\n",
      "[EPOCH 28, 0.9006 min] Validation Loss: 1.5647 | Validation Accuracy: 0.4684 | Training Accuracy: 0.5532\n",
      "[29, 22272 subsamples] Training loss: 1.317\n",
      "[EPOCH 29, 0.9335 min] Validation Loss: 1.4966 | Validation Accuracy: 0.5063 | Training Accuracy: 0.5532\n",
      "[30, 23040 subsamples] Training loss: 1.292\n",
      "[EPOCH 30, 0.9676 min] Validation Loss: 1.5418 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5572\n",
      "[31, 23808 subsamples] Training loss: 1.285\n",
      "[EPOCH 31, 1.0043 min] Validation Loss: 1.4996 | Validation Accuracy: 0.5063 | Training Accuracy: 0.5532\n",
      "[32, 24576 subsamples] Training loss: 1.284\n",
      "[EPOCH 32, 1.0398 min] Validation Loss: 1.5384 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5532\n",
      "[33, 25344 subsamples] Training loss: 1.267\n",
      "[EPOCH 33, 1.0740 min] Validation Loss: 1.4991 | Validation Accuracy: 0.5316 | Training Accuracy: 0.5598\n",
      "[34, 26112 subsamples] Training loss: 1.259\n",
      "[EPOCH 34, 1.1069 min] Validation Loss: 1.5029 | Validation Accuracy: 0.5316 | Training Accuracy: 0.5782\n",
      "[35, 26880 subsamples] Training loss: 1.245\n",
      "[EPOCH 35, 1.1407 min] Validation Loss: 1.5400 | Validation Accuracy: 0.5316 | Training Accuracy: 0.5782\n",
      "[36, 27648 subsamples] Training loss: 1.234\n",
      "[EPOCH 36, 1.1763 min] Validation Loss: 1.5155 | Validation Accuracy: 0.5443 | Training Accuracy: 0.5795\n",
      "[37, 28416 subsamples] Training loss: 1.216\n",
      "[EPOCH 37, 1.2096 min] Validation Loss: 1.4947 | Validation Accuracy: 0.5316 | Training Accuracy: 0.5821\n",
      "[38, 29184 subsamples] Training loss: 1.205\n",
      "[EPOCH 38, 1.2415 min] Validation Loss: 1.5716 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5861\n",
      "[39, 29952 subsamples] Training loss: 1.207\n",
      "[EPOCH 39, 1.2742 min] Validation Loss: 1.5207 | Validation Accuracy: 0.5443 | Training Accuracy: 0.5900\n",
      "[40, 30720 subsamples] Training loss: 1.203\n",
      "[EPOCH 40, 1.3114 min] Validation Loss: 1.6255 | Validation Accuracy: 0.4304 | Training Accuracy: 0.5716\n",
      "[41, 31488 subsamples] Training loss: 1.223\n",
      "[EPOCH 41, 1.3488 min] Validation Loss: 1.4898 | Validation Accuracy: 0.5443 | Training Accuracy: 0.5848\n",
      "[42, 32256 subsamples] Training loss: 1.190\n",
      "[EPOCH 42, 1.3822 min] Validation Loss: 1.5332 | Validation Accuracy: 0.4810 | Training Accuracy: 0.5795\n",
      "[43, 33024 subsamples] Training loss: 1.190\n",
      "[EPOCH 43, 1.4149 min] Validation Loss: 1.5434 | Validation Accuracy: 0.5316 | Training Accuracy: 0.5926\n",
      "[44, 33792 subsamples] Training loss: 1.164\n",
      "[EPOCH 44, 1.4477 min] Validation Loss: 1.4950 | Validation Accuracy: 0.5316 | Training Accuracy: 0.5940\n",
      "[45, 34560 subsamples] Training loss: 1.164\n",
      "[EPOCH 45, 1.4802 min] Validation Loss: 1.5366 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6005\n",
      "[46, 35328 subsamples] Training loss: 1.151\n",
      "[EPOCH 46, 1.5128 min] Validation Loss: 1.5109 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6071\n",
      "[47, 36096 subsamples] Training loss: 1.128\n",
      "[EPOCH 47, 1.5480 min] Validation Loss: 1.5053 | Validation Accuracy: 0.5063 | Training Accuracy: 0.6110\n",
      "[48, 36864 subsamples] Training loss: 1.129\n",
      "[EPOCH 48, 1.5831 min] Validation Loss: 1.4880 | Validation Accuracy: 0.5570 | Training Accuracy: 0.6005\n",
      "[49, 37632 subsamples] Training loss: 1.120\n",
      "[EPOCH 49, 1.6190 min] Validation Loss: 1.5471 | Validation Accuracy: 0.5063 | Training Accuracy: 0.6110\n",
      "[50, 38400 subsamples] Training loss: 1.097\n",
      "[EPOCH 50, 1.6520 min] Validation Loss: 1.4866 | Validation Accuracy: 0.5316 | Training Accuracy: 0.6202\n",
      "[51, 39168 subsamples] Training loss: 1.104\n",
      "[EPOCH 51, 1.6849 min] Validation Loss: 1.5298 | Validation Accuracy: 0.4684 | Training Accuracy: 0.6163\n",
      "[52, 39936 subsamples] Training loss: 1.086\n",
      "[EPOCH 52, 1.7178 min] Validation Loss: 1.4974 | Validation Accuracy: 0.5063 | Training Accuracy: 0.6268\n",
      "[53, 40704 subsamples] Training loss: 1.075\n",
      "[EPOCH 53, 1.7506 min] Validation Loss: 1.4926 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6163\n",
      "[54, 41472 subsamples] Training loss: 1.066\n",
      "[EPOCH 54, 1.7832 min] Validation Loss: 1.4850 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6268\n",
      "[55, 42240 subsamples] Training loss: 1.053\n",
      "[EPOCH 55, 1.8167 min] Validation Loss: 1.5211 | Validation Accuracy: 0.4937 | Training Accuracy: 0.6321\n",
      "[56, 43008 subsamples] Training loss: 1.051\n",
      "[EPOCH 56, 1.8510 min] Validation Loss: 1.5131 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6439\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[57, 43776 subsamples] Training loss: 1.047\n",
      "[EPOCH 57, 1.8859 min] Validation Loss: 1.4878 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6465\n",
      "[58, 44544 subsamples] Training loss: 1.039\n",
      "[EPOCH 58, 1.9184 min] Validation Loss: 1.5015 | Validation Accuracy: 0.5316 | Training Accuracy: 0.6452\n",
      "[59, 45312 subsamples] Training loss: 1.015\n",
      "[EPOCH 59, 1.9517 min] Validation Loss: 1.4807 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6439\n",
      "[60, 46080 subsamples] Training loss: 1.021\n",
      "[EPOCH 60, 1.9848 min] Validation Loss: 1.5004 | Validation Accuracy: 0.4810 | Training Accuracy: 0.6478\n",
      "[61, 46848 subsamples] Training loss: 1.004\n",
      "[EPOCH 61, 2.0174 min] Validation Loss: 1.5060 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6610\n",
      "[62, 47616 subsamples] Training loss: 1.004\n",
      "[EPOCH 62, 2.0502 min] Validation Loss: 1.5559 | Validation Accuracy: 0.4430 | Training Accuracy: 0.6491\n",
      "[63, 48384 subsamples] Training loss: 0.997\n",
      "[EPOCH 63, 2.0845 min] Validation Loss: 1.4797 | Validation Accuracy: 0.5316 | Training Accuracy: 0.6728\n",
      "[64, 49152 subsamples] Training loss: 0.979\n",
      "[EPOCH 64, 2.1192 min] Validation Loss: 1.5234 | Validation Accuracy: 0.4684 | Training Accuracy: 0.6610\n",
      "[65, 49920 subsamples] Training loss: 0.985\n",
      "[EPOCH 65, 2.1543 min] Validation Loss: 1.4711 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6715\n",
      "[66, 50688 subsamples] Training loss: 0.973\n",
      "[EPOCH 66, 2.1873 min] Validation Loss: 1.5137 | Validation Accuracy: 0.4304 | Training Accuracy: 0.6754\n",
      "[67, 51456 subsamples] Training loss: 0.950\n",
      "[EPOCH 67, 2.2198 min] Validation Loss: 1.4554 | Validation Accuracy: 0.5316 | Training Accuracy: 0.6833\n",
      "[68, 52224 subsamples] Training loss: 0.947\n",
      "[EPOCH 68, 2.2532 min] Validation Loss: 1.5143 | Validation Accuracy: 0.5063 | Training Accuracy: 0.6689\n",
      "[69, 52992 subsamples] Training loss: 0.938\n",
      "[EPOCH 69, 2.2868 min] Validation Loss: 1.4438 | Validation Accuracy: 0.5443 | Training Accuracy: 0.6873\n",
      "[70, 53760 subsamples] Training loss: 0.919\n",
      "[EPOCH 70, 2.3206 min] Validation Loss: 1.5111 | Validation Accuracy: 0.5063 | Training Accuracy: 0.6951\n",
      "[71, 54528 subsamples] Training loss: 0.913\n",
      "[EPOCH 71, 2.3565 min] Validation Loss: 1.4584 | Validation Accuracy: 0.5316 | Training Accuracy: 0.6899\n",
      "[72, 55296 subsamples] Training loss: 0.898\n",
      "[EPOCH 72, 2.3926 min] Validation Loss: 1.4731 | Validation Accuracy: 0.5190 | Training Accuracy: 0.7030\n",
      "[73, 56064 subsamples] Training loss: 0.900\n",
      "[EPOCH 73, 2.4284 min] Validation Loss: 1.4836 | Validation Accuracy: 0.5190 | Training Accuracy: 0.6991\n",
      "[74, 56832 subsamples] Training loss: 0.885\n",
      "[EPOCH 74, 2.4626 min] Validation Loss: 1.4346 | Validation Accuracy: 0.5316 | Training Accuracy: 0.7162\n",
      "[75, 57600 subsamples] Training loss: 0.885\n",
      "[EPOCH 75, 2.4967 min] Validation Loss: 1.4899 | Validation Accuracy: 0.5190 | Training Accuracy: 0.7043\n",
      "[76, 58368 subsamples] Training loss: 0.867\n",
      "[EPOCH 76, 2.5308 min] Validation Loss: 1.4495 | Validation Accuracy: 0.5063 | Training Accuracy: 0.7162\n",
      "[77, 59136 subsamples] Training loss: 0.872\n",
      "[EPOCH 77, 2.5653 min] Validation Loss: 1.5541 | Validation Accuracy: 0.4684 | Training Accuracy: 0.7043\n",
      "[78, 59904 subsamples] Training loss: 0.870\n",
      "[EPOCH 78, 2.5998 min] Validation Loss: 1.4732 | Validation Accuracy: 0.4810 | Training Accuracy: 0.7057\n",
      "[79, 60672 subsamples] Training loss: 0.841\n",
      "[EPOCH 79, 2.6372 min] Validation Loss: 1.5888 | Validation Accuracy: 0.4304 | Training Accuracy: 0.7267\n",
      "[80, 61440 subsamples] Training loss: 0.865\n",
      "[EPOCH 80, 2.6734 min] Validation Loss: 1.4709 | Validation Accuracy: 0.4684 | Training Accuracy: 0.7148\n",
      "[81, 62208 subsamples] Training loss: 0.824\n",
      "[EPOCH 81, 2.7084 min] Validation Loss: 1.5232 | Validation Accuracy: 0.4557 | Training Accuracy: 0.7227\n",
      "[82, 62976 subsamples] Training loss: 0.824\n",
      "[EPOCH 82, 2.7426 min] Validation Loss: 1.4971 | Validation Accuracy: 0.4557 | Training Accuracy: 0.7359\n",
      "[83, 63744 subsamples] Training loss: 0.830\n",
      "[EPOCH 83, 2.7789 min] Validation Loss: 1.4716 | Validation Accuracy: 0.5190 | Training Accuracy: 0.7162\n",
      "[84, 64512 subsamples] Training loss: 0.814\n",
      "[EPOCH 84, 2.8128 min] Validation Loss: 1.5449 | Validation Accuracy: 0.4684 | Training Accuracy: 0.7464\n",
      "[85, 65280 subsamples] Training loss: 0.803\n",
      "[EPOCH 85, 2.8482 min] Validation Loss: 1.4080 | Validation Accuracy: 0.5316 | Training Accuracy: 0.7451\n",
      "[86, 66048 subsamples] Training loss: 0.796\n",
      "[EPOCH 86, 2.8842 min] Validation Loss: 1.5569 | Validation Accuracy: 0.4810 | Training Accuracy: 0.7385\n",
      "[87, 66816 subsamples] Training loss: 0.784\n",
      "[EPOCH 87, 2.9221 min] Validation Loss: 1.4323 | Validation Accuracy: 0.5316 | Training Accuracy: 0.7490\n",
      "[88, 67584 subsamples] Training loss: 0.784\n",
      "[EPOCH 88, 2.9584 min] Validation Loss: 1.4856 | Validation Accuracy: 0.4557 | Training Accuracy: 0.7451\n",
      "[89, 68352 subsamples] Training loss: 0.755\n",
      "[EPOCH 89, 2.9937 min] Validation Loss: 1.5059 | Validation Accuracy: 0.4937 | Training Accuracy: 0.7792\n",
      "[90, 69120 subsamples] Training loss: 0.758\n",
      "[EPOCH 90, 3.0304 min] Validation Loss: 1.4449 | Validation Accuracy: 0.5443 | Training Accuracy: 0.7635\n",
      "[91, 69888 subsamples] Training loss: 0.741\n",
      "[EPOCH 91, 3.0644 min] Validation Loss: 1.4893 | Validation Accuracy: 0.4937 | Training Accuracy: 0.7569\n",
      "[92, 70656 subsamples] Training loss: 0.723\n",
      "[EPOCH 92, 3.0990 min] Validation Loss: 1.4397 | Validation Accuracy: 0.5443 | Training Accuracy: 0.7792\n",
      "[93, 71424 subsamples] Training loss: 0.723\n",
      "[EPOCH 93, 3.1325 min] Validation Loss: 1.5091 | Validation Accuracy: 0.4937 | Training Accuracy: 0.7753\n",
      "[94, 72192 subsamples] Training loss: 0.736\n",
      "[EPOCH 94, 3.1673 min] Validation Loss: 1.4674 | Validation Accuracy: 0.4937 | Training Accuracy: 0.7648\n",
      "[95, 72960 subsamples] Training loss: 0.720\n",
      "[EPOCH 95, 3.2029 min] Validation Loss: 1.4515 | Validation Accuracy: 0.4937 | Training Accuracy: 0.7806\n",
      "[96, 73728 subsamples] Training loss: 0.711\n",
      "[EPOCH 96, 3.2371 min] Validation Loss: 1.5267 | Validation Accuracy: 0.4937 | Training Accuracy: 0.7819\n",
      "[97, 74496 subsamples] Training loss: 0.695\n",
      "[EPOCH 97, 3.2705 min] Validation Loss: 1.4230 | Validation Accuracy: 0.5570 | Training Accuracy: 0.7871\n",
      "[98, 75264 subsamples] Training loss: 0.668\n",
      "[EPOCH 98, 3.3041 min] Validation Loss: 1.4860 | Validation Accuracy: 0.5316 | Training Accuracy: 0.7989\n",
      "[99, 76032 subsamples] Training loss: 0.655\n",
      "[EPOCH 99, 3.3379 min] Validation Loss: 1.5390 | Validation Accuracy: 0.4430 | Training Accuracy: 0.8108\n",
      "[100, 76800 subsamples] Training loss: 0.651\n",
      "[EPOCH 100, 3.3717 min] Validation Loss: 1.4610 | Validation Accuracy: 0.5316 | Training Accuracy: 0.8042\n"
     ]
    },
    {
     "data": {
      "image/png": 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Dc+fOpUqVKmzevDnXz1tvvUVMTAxr164F1N5mmzdvNg8/y0uPHj04deqUubt0XkJCQgA4fPiwxfKVK1cWuuymwD1n0lOAn3/+2eJ5Ub7/HBwczMO4pkyZQsOGDWndunWhylOrVi2aN2/O7Nmz873Bn1Nhrl/u1rFjxzh06JDFst9//x1XV1ceeeQRADp37kxERESu7+zffvsNjUZjno+9R48epKWlWWTcLy5BQUGMHDmSrl27yrWDKBLJ7i5EMdu0aRMXLlzItbxnz55MnDiRrl270rFjR95++23s7OyYMWMGR48eZeHCheYv5ObNm9O7d2/q16+Pp6cnx48fZ968eeaA9PDhw4wcOZKnnnqKatWqYWdnx6ZNmzh8+DBjx44tsIw1a9akZcuWTJw4kaioKH755ReL9atWrWLGjBn069ePypUroygKy5cvJz4+nq5duxZ4fKPRaO6Od7tGjRpZXHDExcXx+OOP89JLL5GQkMAnn3yCg4MD48aNA9Su85MmTeK5556jd+/evPLKK6Snp/P1118THx/Pl19+aT6WKeNt8+bNGTt2LFWrVuXKlSusXLmSn3/+2WIcWUF++uknNm3aRK9evQgKCiItLc3cJbK4xyEKIYS4P9auXUt0dDRfffWV+eZvTnXr1uWHH35g5syZ9O7dmwkTJrB27VratWvH+++/T7169YiPj2fdunWMGTOGmjVrMnr0aBYvXkzfvn0ZO3YszZo149atW2zdupXevXvTsWNH/Pz86NKlCxMnTsTT05Pg4GA2btzI8uXLC132mjVrUqVKFcaOHYuiKJQrV46///6bsLCwXNsW5ftvxIgRTJo0if379/Prr78WqT6HDBnCK6+8QnR0NK1ataJGjRoW6wu6finI6dOn87x+CAwMtOjOHxAQwGOPPcann36Kv78/8+fPJywsjK+++sr8Om+++Sa//fYbvXr1YsKECQQHB7N69WpmzJjBq6++am4YeeaZZ5g9ezbDhw/n5MmTdOzYEaPRyL///kutWrXMeQwKIyEhgY4dO/Lss89Ss2ZNXF1d2bt3L+vWreOJJ54o9HGEkOzuQhQTU9bw/H7Onz+vKIqibN++XenUqZPi7OysODo6Ki1atDBnVTcZO3as0qRJE8XT01Oxt7dXKleurLz55pvKtWvXFEVRlCtXriihoaFKzZo1FWdnZ8XFxUWpX7++8u233yqZmZmFKu8vv/yiAIqjo6OSkJBgse7EiRPKM888o1SpUkVxdHRU3N3dlWbNmilz5swp8Lh3yu4OKKdPn1YUJTuT67x585TXX39d8fHxUezt7ZW2bdsq+/bty3XcP//8U2nevLni4OCgODs7K507d1Z27tyZa7uIiAjlqaeeUry8vBQ7OzslKChICQ0NVdLS0hRFyT8L/+2ZeHfv3q08/vjjSnBwsGJvb694eXkp7du3V1auXFmo+hVCCGF9/fr1U+zs7O6Y9XzgwIGKjY2NEhsbqyiKokRFRSlDhgxR/Pz8FFtbWyUgIEB5+umnlStXrpj3uXnzpvLGG28oQUFBiq2trVK+fHmlV69eyokTJ8zbxMTEKP3791fKlSunuLu7K88//7x59pTbs7s7OzvnWbaIiAila9euiqurq+Lp6ak89dRTSmRkpAIon3zySa5t7/T9l1OHDh2UcuXKKampqYWpRrOEhATF0dEx32zoBV2/5Keg7O4ffPCBedvg4GClV69eytKlS5U6deoodnZ2SkhIiDJlypRcx7148aLy7LPPKl5eXoqtra1So0YN5euvv7aYLUZRFOXWrVvKxx9/rFSrVk2xs7NTvLy8lE6dOim7du0ybwMor732Wq7XCA4OVl588UVFURQlLS1NGT58uFK/fn3Fzc1NcXR0VGrUqKF88skn5qzzQhSGRlFy9B8VQogSsmXLFjp27MiSJUvMyXuEEEIIcX/FxcURHBzMqFGjmDRpkrWLU2QhISHUrVuXVatWWbsoQtw30t1dCCGEEEKIB9ylS5c4d+4cX3/9NVqtljfeeMPaRRJC5EMSxwkhhBBCCPGA+/XXX+nQoQPHjh1jwYIFVKhQwdpFEkLkQ7q7CyGEEEIIIYQQpYS0pAshhBBCCCGEEKWEBOlCCCGEEEIIIUQpIUG6EEIIIYQQQghRSjx02d2NRiPR0dG4urqi0WisXRwhhBACRVFISkoiICAArVbunxcH+b4XQghRmhTlu/6hC9Kjo6OpWLGitYshhBBC5BIVFUVgYKC1i/FAkO97IYQQpVFhvusfuiDd1dUVUCvHzc3tno+n1+tZv3493bp1w9bW9p6P9zCQOrs7Um93R+qt6KTO7s691FtiYiIVK1Y0f0eJe1ec3/dyTtwdqbeikzq7O1JvRSd1dndK6rv+oQvSTV3e3Nzcii1Id3Jyws3NTT7ghSR1dnek3u6O1FvRSZ3dneKoN+mWXXyK8/tezom7I/VWdFJnd0fqreikzu5OSX3Xy8A3IYQQQgghhBCilJAgXQghhBBCCCGEKCUkSBdCCCGEEEIIIUqJh25MuhBCFJaiKGRmZmIwGKxdlBKl1+uxsbEhLS3toXvv9+JO9abT6bCxsZEx56WMwWBAr9cXuJ2cE3dH6q3oiqvO5H+OEGWbBOlCCJGHjIwMYmJiSE1NtXZRSpyiKPj5+REVFSUXeEVQUL05OTnh7++PnZ2dFUonbpecnMylS5dQFKXAbeWcuDtSb0VXnHUm/3OEKLskSBdCiNsYjUbOnz+PTqcjICAAOzu7h+oC02g0kpycjIuLC1qtjIoqrPzqTVEUMjIyuHr1KufPn6datWpSr1ZmMBi4dOkSTk5O+Pj4FHh+yzlxd6Teiq446kz+5whR9kmQLoQQt8nIyMBoNFKxYkWcnJysXZwSZzQaycjIwMHBQS7siuBO9ebo6IitrS0XL140byOsR6/XoygKPj4+ODo6Fri9nBN3R+qt6IqrzuR/jhBlm/zHFEKIfMhFpShO8nkqfR6mHjLi4SP/c4Qou+TsFUIIIYQQQgghSgkJ0oUQQgghhBBCiFJCgnQhhBAWOnXqxLhx48zPQ0JCmDp16h330Wg0/Pnnn/f82sV1nDv59NNPadiw4X19DSFKqw4dOjB69Gjz8wft/BZCiAeBBOlCCPGA6NOnD126dMlz3e7du9FoNBw4cKDIx927dy8vv/zyvRbPQn6BckxMDD169CjW1xLiQSDntxBCPDwkSC8OhZhjVQgh7rehQ4eyadMmLl68mGvdrFmzaNiwIY888kiRj+vj41NiWe79/Pywt7cvkdcSoiyR87vsysjIsHYRhBBljFWD9IkTJ9K0aVNcXV0pX748/fr14+TJkwXut3XrVho3boyDgwOVK1fmp59+KoHS5uHQImz+146aMcus8/pCiBKjKAqpGZlW+VEKeSOwd+/elC9fnjlz5lgsT01NZfHixQwdOpTr16/zzDPPEBgYiJOTE/Xq1WPhwoV3PO7t3WFPnz5Nu3btcHBwoHbt2oSFheXa57333qN69eo4OTlRuXJlPvroI/R6PQBz5sxh/PjxHDp0CI1Gg0ajMZf59u6wR44coVOnTjg6OuLl5cXLL79McnKyeX1oaCj9+vXjm2++wd/fHy8vL1577TXzaxWG0WhkwoQJBAYGYm9vT8OGDVm3bp15fUZGBiNHjsTf3x8HBwdCQkKYOHGief2nn35KUFAQjo6O1KpVizfeeKPQry1Kh8Kc37cyDHJ+Zyns+a3T6fD09CzR8/vs2bP07dsXX19fXFxcaNq0KRs2bLDYJj09nXfffZeKFStib29PtWrVmDlzpnn9sWPH6NWrF25ubri6utK2bVvOnj0L5B4uANCvXz9CQ0Mt6vTzzz8nNDQUd3d3XnrppQLrzWTlypV07NgRJycnvL29eeKJJwCYMGEC9erVy/V+GzduzMcff5xvfQghiihiJSx6Dq6esmoxrDpP+tatW3nttddo2rQpmZmZfPDBB3Tr1o2IiAicnZ3z3Of8+fP07NmTl156ifnz57Nz505GjBiBj48PTz75ZMm+gcx0NHERlHORlnQhHnS39AZqf/yPVV47YkJ3nOwK/ndtY2PDoEGDmDNnDh9//LF5eqklS5aQkZHBc889R2pqKo0bN+a9997Dzc2N1atX88ILL1C5cmWaN29e4GsYjUaeeOIJvL292bNnD4mJibkuWAFcXV2ZM2cOAQEBHDlyhJdeeglXV1feffddBgwYwNGjR1m3bp354tnd3T3XMVJTU3n00Udp0aIFe/fuJS4ujmHDhjFy5EiLQGXz5s34+/uzefNmzpw5w4ABA2jYsKH5wrgg3333HZMnT+bnn3+mUaNGzJo1i8cee4xjx45RrVo1pk2bxsqVK/njjz8ICgoiKiqKqKgoAJYuXcq3337LokWLqFWrFmfPnuXMmTOFel1Resj5rSru83v9+vUkJSURGBiY6xj36/xOTk6mZ8+efP755zg4ODB37lz69OnDyZMnCQoKAmDQoEHs3r2badOm0aBBA86fP8+1a9cAuHz5Mu3ataNDhw5s2rQJNzc3du7cSWZmZoH1l9PXX3/NRx99xIcfflioegNYvXo1/fv356233mL+/PlkZmayevVqAIYMGcL48ePZu3cvTZs2BeDw4cMcPHiQJUuWFKlsQoh8JMfBn69CRjJc2guhq8G7mlWKYtUgPWdLBcDs2bMpX748+/fvp127dnnu89NPPxEUFGS+61urVi327dvHN998U/JBemATADxTz6MYDYBtyb6+EELcZsiQIXz99dds2bKFjh07AmpX2CeeeAJPT088PT15++23zduPGjWKdevWsWTJkkJdxG/YsIHjx49z4cIF84X3F198kWucac4L05CQEN566y0WL17Mu+++i6OjIy4uLtjY2ODn55fvay1YsIBbt27x22+/mW/c/vDDD/Tp04evvvoKX19fADw9Pfnhhx/Q6XTUrFmTXr16sXHjxkIH6d988w3vvfceAwcOBOCrr75i8+bNTJ06lenTpxMZGUm1atVo06YNGo2G4OBg876RkZH4+fnRpUsXdDodHh4e5noXoriVxfPbyckJR0fHXK91v87vBg0a0KBBA/Pzzz//nBUrVrBy5UpGjhzJqVOn+OOPPwgLCzOP8a9cubJ5++nTp+Pu7s6iRYuwtVWv66pXr15g3d2uU6dOFn+LguoN4P/+7/8YMGAA48aNw83NDa1Wa34vgYGBdO/endmzZ5uD9NmzZ9O+fXuL8gsh7sGWiWqADpB8Beb0zgrUq5Z4UawapN8uISEBgHLlyuW7ze7du+nWrZvFsu7duzNz5kz0er35H6pJeno66enp5ueJiYkA6PX6InWHzJNHFWxsnbDRp3IrNgICcndDErmZ6v2e6/8hI/V2d+6m3vR6PYqiYDQaMRqNANjrNBz9tOt9KWNB7HUaczkKUr16dVq1asXMmTNp3749Z8+eZfv27axbtw6j0YjBYOCrr77ijz/+4PLly+b/kU5OTubXMHW/NdVBzscREREEBQUREBBgXme6+M9ZX0uXLmXatGmcOXOG5ORkMjMzcXNzy/Uaeb0v03EiIiJo0KABjo6O5u1atmyJ0Wjk+PHj+Pj4oCgKtWvXRqPJriM/Pz+OHj2ab53lfO3ExESio6PNxzVp1aoVhw8fxmg0MmjQILp3706NGjXo3r07vXr1Mn8PPfnkk0ydOpXKlSvTvXt3OnTowFNPPZXru8j0eoqioNfr0el0FuvkvLYuR1sdERO657veaDSSlJiEq5srWm3xjhR0tNUVvFGWmjVr0qpVK2bNmkXHjh3N5/f69esBMBgMfPnllyxevNji/M6vd+Ltjh8/TlBQkEXLd8uWLXNtt3TpUqZOnZrr/C6K48eP06BBA4uytW7dGqPRyMmTJ81Bep06dSzOF39/f44cOZLvcVNSUhg/fjyrVq0iOjqazMxMbt26RWRkJADh4eHodDrat2+f5/7h4eG0bds2z3O4KJo0aZJrWUH1Fh4eztChQ/M95ksvvcSQIUOYMmUKOp2OBQsWMHny5HsqpxAiy9VTsH+u+vjp32DLlxAXAXOzAnWvKiVanFITpCuKwpgxY2jTpg1169bNd7vY2FjzP24TX19fMjMzuXbtGv7+/hbrJk6cyPjx43MdZ/369cWSKKW1fRDe+hOc2DifSK+8/+GLvOU1zk0UTOrt7hSl3kwtQMnJyaUi4U9SWtG2f+aZZ3j33Xf54osv+Pnnn6lYsSJNmzYlMTGR7777jmnTpvHFF19Qu3ZtnJ2dGTduHKmpqeabmAaDQX3dpCRADVDS0tJITEzk1q1b5uDWXL6s7W7dukViYiJ79+7l2WefZezYsXz22We4ubmxfPlyfvjhB/N+6enpGAwGi+OYmI6T1zamx6by6vV6NBqNxTZ6vZ6MjIw8j337a99+vLy2qVq1KgcPHmTDhg1s3bqVAQMG0KFDB+bOnYu7uzv//vsvmzdvZuvWrbz99ttMmzaN1atX57rIz8jI4NatW2zbti1X19nU1NR8/54PihkzZvD1118TExNDnTp1mDp1Km3bts13+wULFjBp0iROnz6Nu7s7jz76KN988w1eXl7FXjaNRnPHLudGo5FMOx1OdjbFHqQX1dChQxk5ciTTp09n9uzZBAcH07lzZwAmT57Mt99+y9SpU6lXrx7Ozs6MHj260P/H8hofb+pWb7Jnzx4GDhzI+PHj6d69u7nVuajBoqIouY6d12vefh7lvCGXl3feeYd//vmHb775hqpVq+Lo6Ej//v3NdZBXq35OBa3XarW56imvm2y33xgpTL0V9Np9+vTB3t6eFStWYG9vT3p6esn3IhXiQbXhE1AMUKMn1O4LQa3UAP3qCZjbB0JXQbmS67VSaoL0kSNHcvjwYXbs2FHgtrf/Uzf9s8zrn/24ceMYM2aM+XliYiIVK1akW7duRb7rm6ewf+G/E9T1TKduz573fryHgF6vJywsjK5du97zneqHidTb3bmbektLSyMqKgoXFxccHBzucwmL36BBgxg3bhyrVq1i8eLFDBs2zDzme+/evfTt29fcVdRoNHLhwgVq1qxp/p9oarVydXVFo9Gg1WpxcHDAzc2NRo0acenSJZKTkwkICADUHk6gXmC6ublx6NAhgoODmTBhgrlMM2bMQKPRmF/D1dUVIM//w6bjNGjQgEWLFqHT6cwXvDt27ECr1dKoUSPc3NywtbXFxsbG4jh2dna5luVkb2+PTqfDzc0NNzc3AgICCA8Pt+jSu3//fpo2bWo+hpubG6GhoYSGhjJw4EB69uxJZmYm5cqVw83NjYEDBzJgwACGDRtGs2bNuHjxYq5M22lpaTg6OpqTcuWU3w2FB8XixYsZPXo0M2bMoHXr1vz888/06NHD3DPjdjt27GDQoEF8++239OnTh8uXLzN8+HCGDRvGihUrrPAOSo+nn36aN954g99//525c+fy0ksvma9/tm/fTt++fXn++ecB9fw+ffo0tWrVKtSxa9euTWRkJNHR0bnOb5OdO3cSHBzMBx98YF52e8Z5Ozs7882+O73W3LlzSUlJMZ/fO3fuRKvV3lX3cpPt27cTGhrK448/Dqhj1C9cuGBeX69ePYxGI1u3bs1zSrv69eszd+7cPHtngpoNPyYmxvzcYDBw9OjRAoe5FKbe6tevz6ZNm/INvG1sbHjxxReZPXs29vb2DBw4sMQy8wvxQLuwE06uAY0OumQ17rr4wIt/q13eb5yFqycfviB91KhRrFy5km3btuWZXCQnPz8/YmNjLZbFxcVhY2OT5911e3v7PKf7sLW1LZZAJ7NiU/gPbGLC0UjgVCTF9Td42Ei93Z2i1JvBYDAHp9ZuNbsbbm5uDBgwgA8//JCEhAQGDx5sfh/VqlVj2bJl7NmzB09PT6ZMmUJsbCy1atUyb2O64DfVQc7H3bp1o0aNGoSGhjJ58mQSExP56KOPAMz1Va1aNSIjI/njjz9o2rQpq1evNmd0Nh2vUqVKnD9/nsOHDxMYGIirq6v5f7XpOC+88ALjx49n8ODBfPrpp1y9epU33niDF154wdxrypQdPuffyVT+/P52t69/5513+OSTT6hatSoNGzZk9uzZhIeHs2DBArRaLd9++y3+/v40bNgQrVbLsmXL8PPzo1y5cvz2228YDAaaN2+Og4MDixcvxtHRkUqVKuV6fa1Wi0ajyfOz+KCf01OmTGHo0KEMGzYMgKlTp/LPP//w448/WmTKN9mzZw8hISG8/vrrgPp5eeWVV5g0aVKJlrs0cnFxYcCAAbz//vskJCRYZBWvWrUqy5YtY9euXbnO78Lo0qULNWrUYNCgQebzO2dQaXqNyMhIFi1aZD6/b79xEhISwvnz5wkPD8fd3R17e/tcrcTPPfccn3zyCS+++KL5/B41ahQvvPBCrh6TRVG1alWWL19Onz590Gg0fPTRRxYt7yEhIbz44osMGTLEnDju4sWLxMXF8fTTTzNy5Ei+//57Bg4cyLhx43B3d2fPnj00a9aMGjVq0KlTJ8aMGcPq1aupUqUK3377LfHx8YUqV0H19sknn9C5c2cCAwMZNGgQRqORtWvXmsesAwwbNsz899y5c+dd15MQIovRCOuz8kU0fhF8ctwkdCmvBuqxR6Ba7pt695NVg3RFURg1ahQrVqxgy5YtVKpUqcB9WrZsyd9//22xbP369TRp0sQqFzlKQGP1wdUIyEgBu8KN+xJCiPtp6NChzJw5k27dulm0VH700UecP3+e7t274+TkxMsvv0y/fv3MOUEKotVqWbFiBUOHDqVZs2aEhIQwbdo0Hn30UfM2ffv25c0332TkyJGkp6fTq1cvPvroIz799FPzNk8++STLly+nY8eOxMfHM3v2bItgA8DJyYl//vmHN954g6ZNm+Lk5MSTTz7JlClT7qlubvf666+TmJjIW2+9RVxcHLVr12blypVUq6ZmdHVxceGrr77i9OnT6HQ6mjZtypo1a9BqtXh4ePDll18yZswYDAYDtWvX5q+//rovXbLLqoyMDPbv38/YsWMtlnfr1o1du3bluU+rVq344IMPWLNmDT169CAuLo6lS5fSq1evfF+nKDlo8so7cSd55WmwpsGDBzNz5ky6du1KYGCguUwffPAB586dM5/fL730En379iUhIcGi3Le/j5zPly1bxksvvWQ+v6dOnUrPnj3NddWnTx9Gjx5tPr979uzJhx9+yPjx483HePzxx1m2bBmdO3cmPj6emTNnms9v03EcHBxYu3Ytb775pvn8fuKJJ5g8ebJF7oq8ymo6Tl4mT57MsGHDaNWqFd7e3rz77rskJiZaHGf69Ol88MEHjBgxguvXrxMUFMTYsWMxGo14enqyYcMG3n33Xdq3b49Op6Nhw4bmvBWhoaGEh4czaNAgbGxsGD16NB06dLhjnQKFqrd27dqxePFiJkyYwNSpU3Fzc6Nt27YWx6lSpQqtWrXi+vXrNG3a9I6fxzvlwXjQSN6eopM6U2mOLcMm+gCKnTOZrd+G2+vDoRyEtDcvv5d6K8o+GqWwE3TeByNGjOD333/nr7/+okaNGubl7u7u5juu48aN4/Lly/z222+AOgVb3bp1eeWVV3jppZfYvXs3w4cPZ+HChYUal5OYmIi7uzsJCQnF0t1dr9eTOakajvqbMHgtBLe652M+6PR6PWvWrKFnz54PfOtRcZJ6uzt3U29paWmcP3+eSpUqlcnu7vfKNObclF1YFE5B9Xanz1VxfzeVJtHR0VSoUIGdO3fSqlX2d+QXX3zB3LlzOXnyZJ77LV26lMGDB5OWlkZmZiaPPfYYS5cuzfc8/vTTT/PMQfP777/n6hJsyjtRsWJF7Ozs7uHdCVGyFEWhWbNmhIaG8tprr91x24yMDKKiooiNjS3yFHJCPAy0Rj2djr+Hc8Y1jvs/wSm/fvf19VJTU3n22WcL9V1v1Zb0H3/8EYAOHTpYLM/ZohITE2POyAlql7c1a9bw5ptvMn36dAICApg2bZpVE2fcdKqMY8J+uLRPgnQhhBAiD3nlk8kvcVhERASvv/46H3/8Md27dycmJoZ33nmH4cOHM3PmzDz3KUoOmqLmnVAUhaSkJHOeBlE4Um9Fd6c6i4uLY/78+cTExDB8+PACL/LvlAfjQSN5e4pO6gy0/85Ad+gaiosfVZ//lqqF6BF9L/VWlPwzVu/uXpA5c+bkWta+fXsOHDhwH0p0d246VyEgYT9c3mftogghhBClire3NzqdLs98MvmNPZ44cSKtW7fmnXfeAdSEWs7OzrRt25bPP/8810wuULQcNEXNO2HqUnx7/gNxZ1JvRXenOvP398fb25tffvmlUENq7pQH40H1ML3X4vLQ1tmtm7BDHT6n6fQhts4eRdr9buqtKNuXisRxZd1Np6x58y7tt25BhBBCiFLGzs6Oxo0bExYWZs64Deq0iH379s1zn9TUVGxsLC9RTGNqrThKTwirks++EMVo2zeQFg/l60DDZ61dmlzktmYxiHeqhKLRQuIlSIoteAchhBDiITJmzBh+/fVXZs2axfHjx3nzzTeJjIxk+PDhgNpVfdCgQebt+/Tpw/Lly/nxxx85d+4cO3fu5PXXX6dZs2bmqcGEEEKIu3LzAvz3i/q46wTQlr7EitKSXgwMOgfwrgFXj8Pl/VAz/+yzQgghxMNmwIABXL9+nQkTJhATE0PdunVZs2YNwcHBQO78M6GhoSQlJfHDDz/w1ltv4eHhQadOnfjqq6+s9RaEEEI8KDZ+BoYMqNwBqna2dmnyJEF6MVECHkFz9biaPE6CdCGEEMLCiBEjGDFiRJ7r8so/M2rUKEaNGnWfSyWEEOKhcn47HF0KaKDrZ1BKk1pKd/diYqyQNV+6JI8TQgghhBBCiNLl4HyY/4T6uMEz4F/fuuW5A2lJLyZKgClIPwhGQ6kc2yCEEEIIIYQQDxWDHv55P3sces3e0PNr65apABKkFwNFAXxqgq0zZCTBtVNQvpa1iyWEEEIIIYQQDx+DHq6egOhwCF8AkbvV5R3eh3bvQCmfFrJ0l66UOxGbyOC5+zl2U6O2nAc0VFdclqnYhBAPjg4dOjB69OhCb3/hwgU0Gg3h4eH3rUwAW7ZsQaPREB8ff19fR4gHWWk9v4UQ4q7cioff+sHEQPipDawcqQbodi4w8Hfo8F6pD9BBgvR78ufBaHacuc5fF7XoDUYIbKquuLDDugUTQjyUNBrNHX9CQ0Pv6rjLly/ns88+K/T2FStWNGfwFkIUDzm/hRAPjBNr4KsQOLmucNuvGA5T66tjyo3GO2+7fw6c2wyZaWDvBiFtodUoeHlrmUruLd3d78GIjlX4Y18kcSl6Fu29xJCqXWDnVDi9XsalCyFKXExMjPnx4sWL+fjjjzl58qR5maOjo8X2er0eW1vbAo9brly5IpVDp9Ph5+dXpH2EEHcm57f1ZWRkYGdnZ+1iCFH2HV4Et27Cps+gevc7Z1hPjIFDC9XHf70GB36DXpPBr17ubY0G2DdLfdzja2g6rEy0muelbJa6lHBzsOWNTlUB+H7zWRJ8GoODO6Rely7vQjxoFAUyUqzzoyiFKqKfn5/5x93dHY1GY36elpaGh4cHf/zxBx06dMDBwYH58+dz/fp1nnnmGQIDA3FycqJevXosXLjQ4ri3d4cNCQnhiy++YMiQIbi6uhIUFMQvv/xiXn97d1hTt/SNGzfSpEkTnJycaNWqlUWAAfD5559Tvnx5XF1dGTZsGGPHjqVhw4ZF+jMtW7aMOnXqYG9vT0hICJMnT7ZYP2PGDKpVq4aDgwO+vr7079/fvG7p0qXUq1cPR0dHvLy86NKlCykpKUV6fVFGFeb81qfK+U3Jnd8Gg4GhQ4dSqVIlHB0dqVGjBt99912u7WbNmmU+5/39/Rk5cqR5XXx8PC+//DK+vr44ODhQt25dVq1aBcCnn36a6/WnTp1KSEiI+XloaCj9+vVj4sSJBAQEUL16dQDmz59PkyZNcHV1xc/Pj2effZa4uDiLYx07doxevXrh5uaGq6srbdu25ezZs2zbtg1bW1tiY2Mttn/rrbdo165dvvUhxAMl5rD6+8pRiPrvztueXq/+dvFT839F/Qs/t4MN43P//zyzEeIvqvFYo+fLbIAO0pJ+z55uXIEfN0QQm6pn+raLvF+1CxxdBqfWQcVm1i6eEKK46FPhiwDrvPb70WDnXCyHeu+995g8eTKzZ8/G3t6etLQ0GjduzHvvvYebmxurV6/mxRdfZN26dXTq1Cnf40yePJnPPvuM999/n6VLl/Lqq6/Srl07atasme8+H3zwAZMnT8bHx4fhw4czZMgQdu7cCcCCBQv4v//7P2bMmEHr1q1ZtGgRkydPplKlSoV+b/v37+fpp5/m008/ZcCAAezatYsRI0bg5eVFaGgo+/bt4/XXX2fevHm0atWKGzdusH37dkBtpXzmmWeYNGkSjz/+OElJSWzfvh2lkAGUKOMKOL+1gMf9eu0SPr9feOEFKleuTPPmzfM9TnGd36bz627Ob6PRSGBgIH/88Qfe3t7s2rWLl19+GX9/f55++mkAfvzxR8aMGcOXX35Jjx49SEhIMP9PMRqN9OjRg6SkJObPn0+VKlWIiIhApytaL8eNGzfi5uZGWFiY+f9BRkYGn332GTVq1CAuLo4333yT0NBQ1qxZA8Dly5dp164dHTp0YNOmTbi5ubFz504yMzNp164dlStXZt68ebzzzjsAZGZmMn/+fL788ssilU2IMiktAW6ez36+91cIyv//kTlIbzJEDbz/eR8i/oQdU9R8YLX7Wh4LoOHzYOdU3CUvURKk3yMbnZa+wUZ+PqFjzs4LvNyrI95Hl8Gpf6Dzx9YunhBCWBg9ejRPPPGExbK3337b/HjUqFGsXbuWv/76645Bes+ePRkxYgSgBgbffvstW7ZsueNF/P/93//Rvn17AMaOHUuvXr1IS0vDwcGB77//nqFDhzJ48GAAPv74Y9avX09ycnKh39uUKVPo3LkzH330EQDVq1cnIiKCr7/+mtDQUCIjI3F2dqZ37964uroSHBxMo0aNADVIz8zM5IknniA4OBiAevXy6EonRClWmPN73bp1LFmy5I5BenGe3wDTp08v8vlta2vL+PHjzc8rVarErl27+OOPP8xB+ueff85bb73FG2+8Yd6uaVM1P9CGDRv477//OH78uLkFvHLlyvm+Xn6cnZ359ddfLbq5DxkyxPy4cuXKTJs2jWbNmpGcnIyLiwvTp0/H3d2dRYsWmYccmMoAMHToUGbPnm0O0levXk1qaqr5fQnxQIs9qv62cYTMW2rA/ehEcPbOvW1mOpzboj6u3g3cK8DTc2HjZ7D9G1j7HlTuCA5ucPOiZUBfxkmQXgxqeSi0ruLFzrPXmXQmkEkardp9Iz4SPIKsXTwhRHGwdVJbvKz12sWkSZMmFs8NBgNffvklixcv5vLly6Snp5Oeno69vf0dj1O/fn3zY1O329u7e95pH39/fwDi4uIICgri5MmT5qDApFmzZmzatKlQ7wvg+PHj9O3b12JZ69atmTp1KgaDga5duxIcHEzlypV59NFHefTRR3n88cdxcnKiQYMGdO7cmXr16tG9e3e6detG//798fT0LPTrizKsgPPbaDSSmJSEm6sr2uLuPmmF89vZ+c4t98V5fnt4eNz1+f3TTz/x66+/cvHiRW7dukVGRoa5i3pcXBzR0dF07tw5z33Dw8MJDAy0CI7vRr169XKNQz948CCffvop4eHh3LhxA2NWIqvIyEhq165NeHg4bdu2zTcnQGhoKB9++CF79uyhRYsWzJo1i6effhpnZ2fzsYR4YMUeUX9X7gApceoQ4YPzoM2bubeN3A0ZyeBcHvwaZC9v97bac/nmedj8f9DjK9g/G1DUoN27akm8k/uq7HbUL0U0Ghj3aHW0GvgjIpUkn0fUFaf+sW7BhBDFR6NRu6Ra4+dOCVWK6PaL88mTJ/Ptt9/y7rvvsmnTJsLDw+nWrRsZGRl3PM7tF58ajabAi8uc+2iy3lPOfTS3vc+idjVXFOWOx3B1deXAgQMsXLgQf39/Pv74Yxo0aEB8fDw6nY6wsDDWrl1L7dq1+f7776lRowbnz5+//WXEg6gw57et0wNxfnfv3r1MnN9//PEHb775JkOGDGH9+vWEh4czePBgc9lvT5R3u4LWa7XaXGXQ6/W5tru9TlNSUujWrRsuLi7Mnz+fvXv3smLFCoBCl618+fL06dOH2bNnExcXx5o1ayxa54V4oMVmjUf3rw9NhqqP981Sk77d7lRWy3i1bpbjy20dofcU9fF/v0Dkv2pCOVCTxT0AJEgvJjX8XBnQtCIAS5KypiWRIF0IUcpt376dvn378vzzz9OgQQMqV67MmTNnSrwcNWrU4L//LJPH7Nu3r0jHqF27Njt2WE6BuWvXLqpXr24eh2pjY0OXLl2YNGkShw8f5sKFC+bWPI1GQ+vWrRk/fjwHDx7Ezs7OfPEtRFmU1/l9+vTpEi/H3Zzf27dvp1WrVowYMYJGjRpRtWpVzp49a17v6upKSEgIGzduzHP/+vXrc+nSJU6dOpXneh8fH2JjYy0C9cLM/X7ixAmuXbvGl19+Sdu2balZs2auXgb169dn+/bteQb9JsOGDWPRokX8/PPPVKlShdatWxf42kI8EExJ4/zqQ90nwMFD7X18ZkPubU9nxVLVu+VeV6UT1O0PihEW9FcTd7tVgOqP3reilyQJ0ovR291q4O5oy+/xtdUF57epmVuFEKKUqlq1KmFhYezatYvjx4/zyiuv5Mo6XBJGjRrFzJkzmTt3LqdPn+bzzz/n8OHDuVrf7uStt95i48aNfPbZZ5w6dYq5c+fyww8/mMfkrlq1imnTphEeHs7Fixf57bffMBqN1KhRg3///ZcvvviCffv2ERkZyfLly7l69Sq1atW6X29ZiPuutJzfr732WpHP76pVq7Jv3z7++ecfTp06xUcffcTevXsttvn000+ZPHky06ZN4/Tp0xw4cIDvv/8egPbt29OuXTuefPJJwsLCOH/+PGvXrmXdOnVe5g4dOnD16lUmTZrE2bNnmT59OmvXri3wvQQFBWFnZ8f333/PuXPnWLlyZa555keOHEliYiIDBw5k3759nD59mnnz5llkvO/evTvu7u58/vnn5rH6QjzwMtPh6nH1sX99tUW80fPqc1PSN5PrZ+H6GdDaqF3j89L9C7B3h/RE9XnjUNA9GKO5JUgvRl4u9rz3aE3OKBWIUsqDIR3ObbV2sYQQIl8fffQRjzzyCN27d6dDhw74+fnlGtddEp577jnGjRvH22+/zSOPPML58+cJDQ3FwcGh0Md45JFH+OOPP1i0aBF169bl448/ZsKECYSGhgLg4eHB8uXL6dSpE7Vq1eKnn35i4cKF1KlTBzc3N7Zt20bPnj2pXr06H374IZMnT6ZHjx736R0Lcf/ldX7369evxMtxN+f38OHDeeKJJxgwYADNmzfn+vXruca1v/jii0ydOpUZM2ZQp04devfubdFTYNmyZTRt2pRnnnmG2rVr8+6772IwqF1qa9WqxYwZM5g+fToNGjTgv//+s0iylx8fHx/mzJnDkiVLqF27Nl9++SXffPONxTZeXl5s2rSJ5ORk2rdvT+PGjfnf//5nMSRAq9USGhqKwWBg0KBBhapHIcq8uONgzFRbz93VHsjmJG+nw9TA3OR0mPo7qKU6pVpeXH2hS1aibq0NPPLgnEsa5SGbXyYxMRF3d3cSEhJwc3O75+Pp9XrWrFlDz549sbW1xWhUePzHXfSL+Y7BNv+oH5bHvi+Gkj84bq8zUThSb3fnbuotLS2N8+fPU6lSpSIFiQ8Ko9FIYmIibm5uxZ8kqwi6du2Kn58f8+bNs1oZiqKgervT56q4v5vEneu0qOd4aTknypo71VtZO7/vh5deeokrV66wcuVK87Li/Kw9TN9lco1UdFapswPzYOVICGkLoauyl897As5uBO8a6nKX8jDvcTi7Cbp9Dq1G5X9MoxG2TwbPYKh//2dIuJd6K8p3/YPRH6AU0Wo1/F+/ukya/giD+Yf04+uw7220THYghBDCQmpqKj/99BPdu3dHp9OxcOFCNmzYQFhYmLWLJoS4R6mpqfzyyy9yfmdJSEhg7969LFiwgL/++svaxRGi5JiTxjWwXN5rMszpBddOwtw+8OxiuJCVY6ZaHuPRc9Jqof07xV9WK5PI8T6oW8Gdas26k6w4YH8rjvRLB61dJCGEKNU0Gg1r1qyhbdu2NG7cmL///ptly5bRpUsXaxdNCHGP5Py21LdvXx577DFeeeUVunbtau3iCFFyciaNy6lcJXjxb3ANgKsn4Of2YMgAj2DwvrdpFMsqaUm/T954tC7/HWpAJ+VfjmxcSJPBja1dJCGEKLUcHR3ZsCGPzK5CiDJPzm9LW7ZssXYRhCh5RiNcOao+9q+fe71XFbWr+5xekBSjLqvevVinqSxLpCX9PnFzsMW5QT8APC6uIzEt/2k4hBBCCCGEEOKBdeMcZCSDjQN4Vct7G68q8OIqcPFTn9fqU3LlK2UkSL+PGnd9Bj02VCWK5f9stnZxhBBF9JDl1RT3mXyeSh/5m4gHmXy+RakSe0j97VvnztOkeVeF4dvV7u+V2pVM2UohCdLvIxtnT276tgQg/sBybqZkWLlEQojCMGXrTE1NtXJJxIPE9HmSzMPWp9PpAMjIkO9l8eCS/zmiVIk9ov6+fTx6XlzKP9QBOsiY9PvOu2l/WLWdjsq//LT1LON61rJ2kYQQBdDpdHh4eBAXFweAk5MTmodoTJTRaCQjI4O0tDSZbqoI8qs3RVFITU0lLi4ODw8Pc4AorMfGxgYnJyeuXr2Kra1tgZ9zOSfujtRb0RVHncn/HFEqmZPG1bNuOcoICdLvM23NXiirRtNAe47Ru/cxtE0lyrs92HNVCvEg8PNTx0OZAvWHiaIo3Lp1C0dHx4fq5sS9KqjePDw8zJ8rYV0ajQZ/f3/Onz/PxYsXC9xezom7I/VWdMVZZ/I/R1hN7BH4fQCUrw2PfqmONc9v+jWRJwnS7zcXHwhuCRd30dH4Lz9sbsSEvnWtXSohRAFMF/Hly5dHr3+4Ej/q9Xq2bdtGu3btpJtkEdyp3mxtbaU1q5Sxs7OjWrVqheryLufE3ZF6K7riqjP5nyOKXcwhiD0KfnXV4FuXz+fTaICVoyDxsvrz41ZoPBhSroJGq+4rCiRBegnQ1OoLF3fRXbeX5//rycvtKhPo6WTtYgkhCkGn0z10Fzo6nY7MzEwcHBzkwroIpN7KHq1Wi4NDwb3b5G97d6Teik7qTJRKhkz4rR/cuqE+19mrwXqdJ6Dla5bTpO2bBdEHwd4NApvC2Y3w38/qOu/qYCcxUGHIAKGSUKs3AE21J3E3xPNXeLSVCySEEEIIIYQQhXD1uBqga23B3h0M6XB5P6z/AP75AEwzCSTGwMYJ6uPOH8Pzy2DAfHCvqC4LbGqd8pdBEqSXBPdACHgELQpddftYezTG2iUSQgghhBBCiIJd2qf+Dm4FYy/CqAPQ+RN12Z7psP5DNVD/ZxykJ0KFxtBkiNrCXqsPvPYvPDUHuk6w2lsoayRILym1+gDQQ7eXo5cTibwuUzsJIYQQQgghSjlTkB7YRA28vapA2zHQ+1t1+e4fYOEzcGyFOu6897egzTFU0M4Z6jwOTuVKvuxllATpJaXWYwC00h7DjWRpTRdCCCGEEEKUfpdNQfpt3dWbDIFek9XHp9aqv5u/Khnci4EE6SXFuyr41MIGAx20h1hzNNbaJRJCCCGEEEIINTlc8tXcy9MS4epJ9XGFJrnXNx0GPb9RH7sHQcdx96+MDxEJ0ktS9e4AdNAd4lBUPJfjb1m5QEIIIYQQQoiH3spRMLk6XNxtuTz6AKCAR5A6tXRemr0Er/0Hr2wFe9f7XtSHgQTpJalqFwA62x5Bg5F10pouhBBCCCGEsKbYo3Dod1CMcOA3y3WX9qq/82pFz8mnhow5L0YSpJekis3BzgV3YwJ1NBdYe0TGpQshhBBCCCGsaNuk7McnV0NmRvbzS/vV34EFBOmiWEmQXpJs7KByBwDaaw+z7+JNYhPSrFsmIYQQQgghxMMp7jhE/KU+tneDtAQ4v1V9rij5J40T95UE6SWtamcAejsdBeCfY9LlXQghhBBCCFHydDuzsrPX7gv1n1YfH/tT/R0fCSlXQWsLfvWtUr6HlQTpJS1rXHoN/QncSGaNdHkXQgghhBBCFKe0BFj5OlzYme8mrrcuozG1ord7F2r3Ux+fWAUGffZ4dL+6YOtwf8srLEiQXtI8gsC7BlqMtNYe478LN7ialG7tUgkhhBBCCCEeFHt/hQNzYUkopCfnuUn1K3+hQYGavdVAPLgVOPtAWrza5f1y1nj0gpLGiWJn1SB927Zt9OnTh4CAADQaDX/++WeB+yxYsIAGDRrg5OSEv78/gwcP5vr16/e/sMUpqzX9cdfjKApsPH7FygUSQgghhBBCPDBOrFZ/p8TBru9zr792mgo3/1Uft39P/a3VQa0+6uNjf8IlGY9uLVYN0lNSUmjQoAE//PBDobbfsWMHgwYNYujQoRw7dowlS5awd+9ehg0bdp9LWsyyxqW3UsIBhbAICdKFEEIIIYQQxSAxJrsVHGDXNHWZSWYGurVvoUHBWL0H+OcYb56zy3vMIfWxZHYvcTbWfPEePXrQo0ePQm+/Z88eQkJCeP311wGoVKkSr7zyCpMmTSpgz1ImuDXYOOKSEUcNTRQ7zuhIzcjEyc6qfw4hhBBCCCFEWXdqrfq7QmPQ6ODSf7DlC3jsezVj+9p30EbuQq91gI4fW7baBrcGJy9Izeqp7OgJ5SqX9Dt46JWpqLBVq1Z88MEHrFmzhh49ehAXF8fSpUvp1atXvvukp6eTnp495jsxMREAvV6PXq+/5zKZjlG0Y+nQBbdGe3YDjzlH8HVyEJuPx9Kttu89l6csuLs6E1Jvd0fqreikzu7OvdSb1LUQQohiY+rqXrMXBLeBWd3g4HxoMQLOb4f9c1DQsD9kBI29q1nuq7NRu7zvn6M+r9AYNJoSLb4og0H6ggULGDBgAGlpaWRmZvLYY4/x/fd5jLPIMnHiRMaPH59r+fr163Fyciq2soWFhRVp+0rpftQHOrKPr3mUuRsOknnBWGzlKQuKWmdCJfV2d6Teik7q7O7cTb2lpqbeh5IIIYQoU+IjIeawGlznFRhf2gfGTAhqkf8x0pPg/Db1cc3e4FNDDbqP/w1Lh8LVEwAYO33MlZtV8j5G7X45gnTp6m4NZSpIj4iI4PXXX+fjjz+me/fuxMTE8M477zB8+HBmzpyZ5z7jxo1jzJgx5ueJiYlUrFiRbt264ebmds9l0uv1hIWF0bVrV2xtbQu/440a8ON8ahpP48wtTiW70a17e2x0D37C/buus4ec1NvdkXorOqmzu3Mv9Wbq5SWEEOIhpSiw6DmIPQx9voPGoZbrb5yDWY+CYoDhO8C3Tt7HObMBDBlQrgp4V1eXdRkPJ9dC3DH1eYNnMLYYCWvX5n2MkLbZXd4rStI4ayhTQfrEiRNp3bo177zzDgD169fH2dmZtm3b8vnnn+Pv759rH3t7e+zt7XMtt7W1LdaLzyIfz7cmlKuC9sZZejseYfGtZhyKTqZFZa9iK1NpV9x/g4eF1NvdkXorOqmzu3M39Sb1LIQQD7mYcDVAB9j8BdTtD/Yu2es3jAdj1tCosE/g+aV5H8fc1b1ndmu8VxVoMhT++1nN1N57KnCHLuw6G+g/Cy4fgCqd7+FNibtVppptU1NT0Woti6zT6QBQFMUaRbo3dR4H4HlndXoDyfIuhBBCCCHEQ+jg/OzHyVcsp02L2gsRfwIa0NrAmTA4tyX3MQx6OL1efVyzt+W6bp/BkzPh+WVg61BweSp3gLZjZDy6lVg1SE9OTiY8PJzw8HAAzp8/T3h4OJGRkYDaVX3QoEHm7fv06cPy5cv58ccfOXfuHDt37uT111+nWbNmBAQEWOMt3Ju6TwJQJ/Vf3EghLOJK2bzZIIQQQgghhLg7+jQ4skR9bOrmvmsaJMWq3eDXf6gua/Sc2iIOsP4jMN6Wz+riTkhLACfv3HOb29hDvf7g4H7f3oYoPlYN0vft20ejRo1o1KgRAGPGjKFRo0Z8/PHHAMTExJgDdoDQ0FCmTJnCDz/8QN26dXnqqaeoUaMGy5cvt0r575lvbfCphdaop6ftfiJvpHLqSrK1SyWEEEIIIYQoKSdWqcG1e0XoNUUNsPWparf3E6sgag/YOELHD6D9e2DvpnaNP/LHbcfJ6upe41HQ6kr+fYhiY9UgvUOHDiiKkutnzpw5AMyZM4ctW7ZY7DNq1CiOHTtGamoq0dHRzJ8/nwoVKpR84YtLPbU1/Xnn/wAIi4i1ZmmEEEIIIYQQJcnU1b3hs2pw3e3zrOXzYO176uNWI8EtAJy9oM2b6rKNn4H+lvrYaFSTw0Huru6izClTY9IfSHWeAKB2WjheJMi4dCGEEA+kGTNmUKlSJRwcHGjcuDHbt2/Pd9vQ0FA0Gk2unzp18slmLIQQZVV8VPb48obPqr+DWqiBtmKExMvg7AOt38jep8Wr4BYIiZdg8Qswpzd8FQIJUWDrpI4nF2WaBOnW5lUFAh5Bi5Eeuv84dCmBuMQ0a5dKCCGEKDaLFy9m9OjRfPDBBxw8eJC2bdvSo0cPiyFtOX333XfExMSYf6KioihXrhxPPfVUCZdcCCGKUVKs2vodHZ697NBCQFGnPfMMyV7eZbyaJA6gw1iwd81eZ+sInT9SH58JgwvbIT0BdPZqMG/reJ/fiLjfytQUbA+suk9C9AGecfyP+cld2XrqKk81qWjtUgkhhBDFYsqUKQwdOpRhw4YBMHXqVP755x9+/PFHJk6cmGt7d3d33N2zkxv9+eef3Lx5k8GDB5dYmYUQotitHKVmX98+GZoOVceYm7q6N3rBclvvqtDvR7h6Ah4JzX2sek+rc6enXIWARuqPT03QyZSeDwIJ0kuDuk/A+g+pk3kMf66zRYJ0IYQQD4iMjAz279/P2LFjLZZ369aNXbt2FeoYM2fOpEuXLgQHB+e7TXp6Ounp6ebniYmJAOj1evR6/V2UPJtp/3s9zsNG6q3opM7uTlmoN030AWxM06OhwN5fUQ4tQpORjGLvSma1R+H28td6HGoBRiV7jvSc2rxj+dxI3tvloSzUWWl0L/VWlH0kSC8N3AIguBVc3Elv3W7+OO1HpsGIjU5GIwghhCjbrl27hsFgwNfX12K5r68vsbEFJ0uNiYlh7dq1/P7773fcbuLEiYwfPz7X8vXr1+Pk5FS0QucjLCysWI7zsJF6Kzqps7tTmuut+dkp+AFRnq2J9GpL/ai5uKbHAHDRpQmHwrZYpVyluc5Ks7upt9TU1EJvK0F6aVH3Cbi4k362e/jfrd4cuhRP4+By1i6VEEIIUSw0Go3Fc0VRci3Ly5w5c/Dw8KBfv3533G7cuHGMGTPG/DwxMZGKFSvSrVs33Nzc7qrMJnq9nrCwMLp27YqtrXQlLSypt6KTOrs7pb7eYg5hezAcRaPFb8Bk/LyqguF1DP/9jCZqDxW6f0UF98ASLVKpr7NS6l7qzdTDqzAkSC8taveDNe9SRzlHRc0Vtp68KkG6EEKIMs/b2xudTper1TwuLi5X6/rtFEVh1qxZvPDCC9jZ2d1xW3t7e+zt7XMtt7W1LbYL0OI81sNE6q3opM7ujtXr7epJWDEcglpCl0/BJuv/1s4pAGjq9sfWr5a6zNYW2qk3Fq3Zd9bqdVZG3U29FWV76U9dWjh7q13egW7a/Ww5ddXKBRJCCCHunZ2dHY0bN87VNTAsLIxWrVrdcd+tW7dy5swZhg4dej+LKIQQ9y71Bvw+AKIPwJ7pMLcPJF2BmMNwcjWggXbvFHgYIUCC9NKlZi8Auur2c/hSAteS0wvYQQghhCj9xowZw6+//sqsWbM4fvw4b775JpGRkQwfPhxQu6oPGjQo134zZ86kefPm1K1bt6SLLIQQhWfQwx+D4OZ5cKsA9u4QtQd+6QBrsgLzuk+CT3WrFlOUHdLdvTSp0RPWjaWp9iSeJLL99FUeb1Sy41OEEEKI4jZgwACuX7/OhAkTiImJoW7duqxZs8acrT0mJibXnOkJCQksW7aM7777zhpFFkKIwls3Vp2r3M4Fnluizle+6Fm4dhKSopFWdFFUEqSXJp7B4FsP3ZUjdNKGs/VkTQnShRBCPBBGjBjBiBEj8lw3Z86cXMvc3d2LlAlXCCGsYu+v6g8aeOJ/4FtHXT5sAyx/GU6thQYDoXxNqxZTlC0SpJc2NXvBlSN00+1j3OkuGI0KWm3B2W+FEEIIIYQQJejGeVj7nvq488dQs2f2Ogc3GPg7xB0Dn1rWKZ8os2RMemmTdXK30x4mNSWJI5cTrFwgIYQQQgghRC6HF4MxE0LaQps3c6/XasGvHuikXVQUjQTppY1ffXCviKMmgzbao2w5KVnehRBCCCGEKHFGA/w9GhY9B5m3JXRWFDiyRH3c6HnQSM9XUXwkSC9tNBo1gRzQTbuPvw9HE3VDxuQJIYQQQghRojZ8Cvtnw4lV2QG5SfRBuH4GbBzNMzQJUVwkSC+Nsk70LroDnItLpOM3W3h/xREux9+ycsGEEEIIIYR4CIQvhF3Tsp/v+kFtPTcxBe01eoC9a8mWTTzwJEgvjYJbgYM75TRJDA66QqZR4fd/I+n49Rbm77lo7dIJIYQQQgjx4Ir6D/5+XX3cfLg6tdrV43Bmo7rMaICjy9TH9Z+2ThnFA02C9NJIZwvVHwXgoyrnWTK8JS0re5FhMPLV2hNkZBqtXEAhhBBCCCEeQAmX1DHohgyo0Qu6T4RHXlTX7f5e/X1+GyRfAUdPqNLZemUVDywJ0kurrHHpHF9J0yB3Fgxrjo+rPUnpmew5d926ZRNCCCGEEOJBoigQsRJmdoeUOChfB574Rc3Q3mI4aHRwbgvEHM7u6l67H9jYWbPU4gElQXppVbULOHhAfCQcXY5Wq6FLrfIAhEVcsW7ZhBBCCCGEKAtu3YSNn8HNOwwZvX4WFvSHP16AxEvgEQzPLAR7F3W9RxDU7qs+3j4Zjv+tPpau7uI+kSC9tLJ3gVYj1cdbvwKjgW61/QA1SFdyJq4QQgghhBBC5LZjKmz/BpYOsUz8ZhK+EGa0hDMbQGcH7d6BEXvAM9hyO9N1ecSfkJ4I7hWhYov7XXrxkJIgvTRr9oo61uX6aTiylJZVvHCy0xGbmMaRywnWLp0QQgghhBClmynZ2+V9cGyF5br4SFg9BgzpUKUTvLobOn0Idk65j1OhMQS3zn5e90m1K7wQ94F8skozBzdoNUp9vPVLHLQK7av7ANLlXQghhBBCiDtKugJXjmQ/3zgeMtPVx4oCa94FfSoEt4Hnl4N31Tsfr+XI7MfS1V3cRxKkl3bNXgYnL7hxDo78Qbc6voAE6UIIIYQQQtzRuc3qb59a4OIHNy/A3pnqshOr4NRa0NpC7ymg0RR8vOqPQpMhaiOab537VmwhJEgv7exdoVXWPI1bJ9GxWjl0Wg0nYpOIvJ5q3bIJIYQQQghRWpm6utfoAR3fVx9vm6ROs7bmXfV56zfAp0bhjqfVQu9vodvnxV9WIXKQIL0saPYSOHnDzfN4nF5Os5ByAKyPiLVywYQQQgghhCiFjMbslvSqnaHhc+BTU832/r/OkBQNniHQ7m2rFlOIvEiQXhbYOat3+QDWf8hTQUmAdHkXQgghhBAPOaMR7f5ZeKacsVx+5QikXAU7FwhsBjob6DpBXZec1dDVazLYOpZseYUoBAnSy4pmL6lZJW/dpO/hV6mkiWHvhRvcSMmwdsmEEEIIIYSwjkO/o1v3Li3OToZb8dnLTV3dQ9qCjZ36uFo39TlAnSegapcSLaoQhSVBellh6wjPLwPfeuhSr/KHw0QCuMqmE3HWLpkQQgghhBAlz5AJ274BwM6QgnbnlOx1Zzepv6t0yl6m0cCTM6Hb/0Gf70qwoEIUjQTpZYmjJ7ywAryr46NcY77tFyzfupfENL21SyaEEEIIIUTJOvIH3DyPYuMAgHbfr2oG9/RkiNyjblO1s+U+rr7QaqQ61bEQpZQE6WWNiw8M+otMtyBCtFd4Pf4rhsz6j9SMTGuXTAghhBBCiJJhyIRtXwNgbPsuV11qozFkwMbP4OJOMOrBIwjKVbZyQYUoOgnSyyK3AGxC/8Kos6eF9jjlLoXxyrz9pGcarF0yIYQQQggh7r+jy+DGOXAsh7HJEI5VGIiCBo4uhZ1ZXdmrdC7c/OdClDISpJdV5SqjbTUKgA9sF7LndCyjfj+I3mC0csGEEEIIIYS4j4wGcys6rUaCnQsJTiEo9Z5Sl13cqf7OOR5diDJEgvSyrM1ocC5PsCaWUNsNrI+4QufJW/ll21niUyXruxBCCCGEeAAdXQ7XT6v5mpq9bF5saP8+6OzVJxodVGpnpQIKcW8kSC/L7F2h04cAvOvwJ0EOaUTeSOWLNSdo/sVG3l16iGvJ6VYupBBCCCGEEMUkMRq2fqk+bvmaej1s4h4ILV5VH1dsBo4eJV48IYqDjbULIO5Ro+fhv1+wvXKUTU32sLT8KH7bfZGImET+2HeJA5Hx/P5Sc8q7Oli7pEIIIYQQQmQzZMKptbB/DlyJgPK1IKAhBDRSf9wqZI8pN+jh359hy0TISAbHctDsldzH7DAOnL3VOdGFKKMkSC/rtDro9jnM64fNgVkMHPEyA5q2Yd/Fm7y+8CBn4pJ55pc9LHypBeXdJFAvSzYev4JRga61fa1dFCGEEEKI4pOWCLu+hwO/QXJs9vKkaDi7Mfu5k7carPvXh5NrIS5CXV6hiTrPeV7TqNk6QFbeJiHKKunu/iCo0hGqPwrGTNjyJRqNhqYh5Vj0cgsC3B04ezWFgb/s4UpimrVLKgopJT2T4fP3M2LBfpLTZXo9IYQQQjxAlg2FbZPUAN3JG1qPhhdXQa8p0OgF8K2njilPvQZnwmD7ZDVAdywHj30PQ8PAr66134UQ9420pD8oOoyDU+sg4k9I/AzcAgj2cmbRyy155n97OHdNDdSXDm+Jl4u9tUsrCnD+Wgp6gwJATPwtqvm6FrCHEEIIIUQZcHYznF4PWlvo9yPU7gs2duq6Sm2zt9PfgivHIPogxISDsw+0eh2cylml2EKUJGlJf1AENISglmpr+r5Z5sVBXk4serkFFTwcOX8thS/WnMhz97ikNM5fSymhwoqCXLie/beITpAeEEIIIYR4ABiNEPaR+rjpUKj/VHaAfjtbRwhsAs1egr7TocunEqCLh4ZVg/Rt27bRp08fAgIC0Gg0/PnnnwXuk56ezgcffEBwcDD29vZUqVKFWbNmFbjfQ6H5cPX3vtmgzw7sKpZzYvpzj6DRwLIDl9h/8YbFbhevp9Bl8lY6T97C1lNXS7LEIh8XctwwiU24ZcWSCCGEEEIUk8OLIfYI2LtBu3etXRohSi2rBukpKSk0aNCAH374odD7PP3002zcuJGZM2dy8uRJFi5cSM2aNe9jKcuQmr3BvaI6fufoUotVDSt6MKBJRQA++vMYBqPalTpNb+C13w+QmJaJUYGRvx/g7NXkEi+6sHT+Wqr5cXS8tKQLIYQQoozT34JNn6uP244BZy/rlkeIUsyqY9J79OhBjx49Cr39unXr2Lp1K+fOnaNcObW7S0hIyH0qXRmks4Gmw2DDJ/DvT9DwuexpK4B3utdgzZEYImISWfDvRQa1DOH/Vh/n6OVEPJ1sCfJy5lBUPC/N3ceKEa1xd7LN9RKZBiPrjsXyV3g0vev707dhhZJ8hw+NnN3dY6W7uxBCCCHKuj0/QuIltUHJ1PtTCJGnMpU4buXKlTRp0oRJkyYxb948nJ2deeyxx/jss89wdHTMc5/09HTS09PNzxMTEwHQ6/Xo9fp7LpPpGMVxrGJR/1lstnyJJvYImee2oQS1Mq9ys9cypktVPl11gm/+OUlmpoF5ey4C8PWTdakT4MYTP/3LuWspvPb7fv73fCNsdGpni5upGSw9cJl5e6KIyQoat5yMo1I5R2r5Fy2pWamrs1IoZ3f3y/GpFp9XqbeikXorOqmzu3Mv9SZ1LYR4oKVcgx3fqo87faSONxdC5KtMBennzp1jx44dODg4sGLFCq5du8aIESO4ceNGvuPSJ06cyPjx43MtX79+PU5OTsVWtrCwsGI71r1q4N6CkOubiVs5gb2VX7dY565ABScdl1MzmbBaTSLXtYKRlDN7+e8MPB8M3x3VsePMdbp9HYZRgZsZkJqZ3SLvYqPgbgeXU+GV2bt4q74B2xwDJ84lwrZYLU19FOp4KvmWszTVWWmSlgnXU7JPzTOXr7FmzRrzc6m3uyP1VnRSZ3fnbuotNTW14I2EEKIsSrgMi5+H9ETwbwD1nrJ2iYQo9cpUkG40GtFoNCxYsAB3d3cApkyZQv/+/Zk+fXqerenjxo1jzJgx5ueJiYlUrFiRbt264ebmds9l0uv1hIWF0bVrV2xtc3cPt4qrleGXNvgnHqBn63pqt6IcAurFM+B//wHQLMSTaaGNzS3mAEG1rzBq0SGiUjQW+9X0dWFQy2Aeq+9HcnomvX7YTUxKBsdtqjD20RoAbDgex49/HCYj08jB69C7nh8f9qqJl3N25s5SWWelyNHLibB3DxoNKAokG23p2bO71NtdknrLW0amETubvNOSSJ3dnXupN1MvLyGEeKBE7oHFL0BKHDh6Qu+poJXJpYQoSJkK0v39/alQoYI5QAeoVasWiqJw6dIlqlWrlmsfe3t77O1zzwtua2tbrBefxX28exJQDyq1R3N+K7br34eBv1v8Q2xexYc3Oldjz7nrTHumEY4OlvXTp2Egbk72RN1IpYKHIwEejvh7OODmkP3+XJzgyyfr89Jv+5i16yJdavsTdTOVscsOY1Sgpp8rp64ksepILDvPXueDXrV5vFEFdNrswL9U1VkpEpWgDs+o5edGREwiyemZpBnAIauupN7ujtRbtpk7zvPV2hPMHdKMllXyT9wjdXZ37qbepJ6FEGVOyjW127qdc97r982GNe+AUQ++dWHgAvAMKdEiClFWlalbWa1btyY6Oprk5Ozs46dOnUKr1RIYGGjFkpVCXSeAzg5OrYVd3+Va/WbX6ix+pSW+bg557t6+ug/PtwimY83y1PBztQjQzS9R25eBTSuiKPDKvH28u1QN0Ps3DmTVqDb8+Vpravm7cTNVz9tLDtFy4kY+WxXBsehElPx7wT/0TOPRawe44e6o1rskjxPFafOJODIMRnaeuWbtogghhCiLrhyDqfXg166QmZ57/YF5sGq0GqDX7gdD10uALkQRWDVIT05OJjw8nPDwcADOnz9PeHg4kZGRgNpVfdCgQebtn332Wby8vBg8eDARERFs27aNd955hyFDhuSbOO6hFdAQekxSH2+cAOe33ZeX+bB3bYLKOZGYlgnAK+0q83X/+tjotNQP9GDlyNa8+2gNPJxsiUtKZ+aO8/T7cQ9Tjui4kZJxX8pU1pmC9Erezvi7qzdRoiVIF8Uo6maqxW8hhBCi0IxGWPUm6FMh7hjsmGq5PvkqrP9Qfdz6DXhqTv6t7UKIPFk1SN+3bx+NGjWiUaNGAIwZM4ZGjRrx8ccfAxATE2MO2AFcXFwICwsjPj6eJk2a8Nxzz9GnTx+mTZtmlfKXeo1DocGzoBhh6RBIjCn2l3Cxt+GHZxvRsKIHn/SpzbietdDkmPbNVqdlRIeq/Pd+F/43qAm96vtjb6MlMkXDgn+jir08D4LzWdOvhXhlB+mxCbesWSTxADEYFS7fVD9PUTckSBdCCFFEB3+DqH9BmzVqdvtkuH42e/36DyEtHvzqQaePLaYDFkIUjlXHpHfo0AHlDv2e58yZk2tZzZo1JeNwYWk00GsyxB6GK0dh6WB48W/QFe/Yx/qBHvz5Wus7bmNno6VrbV+61vZl6d6LvL3sKMsOXmZ01xpotQ/nP+9dZ6+x88w1Rnepjm2OxH2mlvQQbyf83NUeItHx0pIuikdMwi0yjer/3aibcvNHCCFEPlKuqRnZy1XOXpZ8FcI+UR93nQBnNsLZjbB6DLzwJ1zYDocXARo1SZyuTKW/EqLUKFNj0sVdsHOCp38DezeI3A1hH1u7RHSv44ujTuFyfBo7zz68Y2I/+vMo0zefZWV4tHlZQqqem6nqfMkhXs4EmFvSJUgXxSPqRnZgfjUpnTS9wYqlEUIIUSolx8GPrWBaI/jrNTVgB1j/QXYrebNXoNc3YOMA57ZA+AK1GzxAkyEQ2MRapReizJMg/WHgVQX6zVAf75kBR5ZatTgOtjoae6steYv33t8u71tPXeWxH3Zw+krSfX2dokpOz+RcVov5phNx5uUXsrq6l3e1x9neBj/zmHRp8RTF4/Yu7pekNV0IIUROiqIG5slX1OcH58P3jWHd+3B4MWor+XdqK3m5ytDubXW7v0bC9TPg4gudrd8oJERZJkH6w6JWH2iTdXdz5Si4EmHV4rQobwRg/bEr3LyPCeSmbTzN4UsJLPg3suCNS1BEjgz3205dJSNTrQ9TkB7irSZY8c/q7i4t6aK43J4sTpLHCSGEsLD3Vzi9HnT20O9H8K2ntp7vma6ubzoUAhtnb9/qDfCuAWRd2HT/Ahw9SrjQQjxYJEh/mHT6CCp3ULNxLn4e0hKsVpSKLlDb35UMg5EVBy/fl9e4kZLBwcibABzI+l1aHL2cXfdJ6Znsu3ADgPOmzO5eWUG6h9qSHiNBerG4cC2FL9eeIClNb+2i3HdxSWkYjblzfkTe3pJ+n5PHRUQn0uf7HWw5GVfwxkIIIawr7kR2ZvauE6Dhs/DyFnXGIHs38KykXk/mZGMHfaaqQX2NXlD3yZIutRAPHAnSHyZaHTw5C9wrwo2zsGK4Oo2GlTzVuAIAf+yLumMCwbu19VQcphglIjqxVI29PRqtBum6rKR5G7O6vJuSxgV7OwGYs7snp2eSlDXNnbh7n/59jJ+2nmXurgvWLsp9tf30VZr930a+Wnci1zpTd3c/N/Wzdb+7u689GsORywks2Xfpvr6OEEKIe5SZDsuHQWYaVOkMzV9Rl+ts1MfvnIURu/NuJQ9uBW+fUvMgSTZ3Ie6ZBOkPG2cveHou6Ozg5BrYYL0xQ32ypmM7EZvE4Ut316qvKArHohNIz8wdgG86cdX8ONOocOSy9XoO3M7Ukv5EI/VGhWlc+vnragBlakl3srPB3VHNxm/NLu+nriRxMasrflmVkp7JrjPXATh0l5+3suLfc2rPjG2ncydmNGV0b1XVK+v5/W1Jv5Kofm4vxcvYdyGEsBqjAfbNggVPQezRvLfZ/H8QewScvNRcRrcH2zZ2YOuY/2s4ekg2dyGKiQTpD6MKjeGx79XHu76H3TOsUgx3R1t61PUDYNFdJJBLStMzfP5+ek3bwZuLwy3WZRqMbM3qXmvKkH7gYuno8n4rw8CZuGQAXu1QBVudhvPXUjh3NTnH9GvO5u3Nc6Un5h+kp+kNrD8WS0Jq8XfjvpqUTt8fdvLkj7vRG6zX8+Je7Tx7nYys8h8rRTds7ofzWTdUzsQlmfMdgPrZu5qUDkCrKt6AZbb3u5GmN/DaggP8nk/ehyuJ6utdlgR1QghhHZf3w6+d1czrp9fD8pfAcNv1Qswh9ZoQoM80cPUr+XIKIcwkSH9YNRgInbPmufznfTi63CrFeLppRQBWhl9myb6oPLukJ6TqSc2w7Op9Ji6JvtN38s8xNfPomiOxnInLzuC+/+JNEtMy8XCy5fmWwUDe49I//usonSZvMQcut8vINBY6ML2VYeCXbWeJLqDF8HhsIkYFfFztqezjQovKaovmsgOXSLiVPf2aSUFBenhUPL2mbefleft5d9mhQpW1KHadvcYtvYFryemlqjdCUW3M0bMiOiGN68l5/80fBKabPXqDYr4hBHApq9Xc1cGGuhXcgHtvSd9y8iqrj8Tww6bTea43taRfSy77071dSUwr8PwWQohSIz0J/h4N/+sM0QfVMeUO7hAXAbunZ29nNKjbKUao8zjU6m2tEgshskiQ/jBr8yY0fQlQYMUrcGFHiRehRSUv6gS4kZJh4J2lh2n+xUb+b3UEi/6L5J0lh+g0eQsNJqyn3qfr6f39dj5deYyft56l7w87OXc1BT83BxoFeQDw6/bz5uOauo93qO5D05ByAByIjLcY+349OZ35ey5y7moKS/fnHi+bnJ5Jp8lbeOyHnWQWIlCftuk0X6w5wbC5++64vakVt26AGiR1qlkewJyB3s/NAUc7nXl7v6wM77cnj0vPNPD1Pyd4YsZOzl5Vg7LNJ66SnJ577PrxmEQ+WHHEHDAVxZ5z1/N8XJYYFdhySg3SbbLyAJTlGw53oiiKOUgHiIhJND82JY2r6OlEoKea9yA+VX9PifSOXI4H4EpSOoY8EtXF5bgBVpYD3PRMA4/9sINe07aTksc5JoQQpUp6EszvD/tnAwrUHwgj90H3ier6LV/CzYvq432zIPqAGsQ/+qXViiyEyCZB+sNMo4EeX0HN3mDIgEXPQlJsiRZBq9Xw+7AWvPdoTSp4OJJwS8//tp9n7PIjLNl/iXNZwafBqHD0ciJzdl1g4toTpGQYaF6pHKteb8MHPWsBsPzgZXOLuClI71TLl3oV3LHRarialG6RJGvdsVhzYrm/wnNnmP/7UDSXbt7ieEwi205fzbU+p/RMA39kddmPiElk7u6L+W5rCg7rVnBXy5gVpMdndVUPyUoaZxJgbknPDnaS0vQ8Pn0X0zefxahA34YBBJVzIsNgZOvJ3GUd//cxFvwbydhlh+/4PvKy62zOIP1GkfcvDS4mw40UPa4ONnSr4wvAsejEAvYqm64mp5OSkd1iHZHjfZqSxgWVc8LF3gZPJzXfwb0kjzPlkzAYlVw9UtIzDdzIMcXi5TIcpP93/gZXEtO5map/YD8799uMGTOoVKkSDg4ONG7cmO3bt99x+/T0dD744AOCg4Oxt7enSpUqzJo1q4RKK0QZlp6sjj2P2qO2nL/4NzzxM7j6qtnag9tA5i1Y84563bdxgrpf54+lm7sQpYQE6Q87rQ6e/BX8G6pTsoWVfCI5dydbXu1QhW3vdmRWaBO61valReVyjOhQhVmhTQj/uCu7xnbi+2caEdoqhOaVyvFG52osGNYcbxd7Ggd70rCiBxmZRubtvkDUjVROxyWj02poX80HB1sddbJarQ9GxZtfd82RGPPjE7FJHI+xvPDOOU5+cQFj5v85doXrKRnmVtop608Sk5B3QHL0svo6piA92MuZquVdzOsr5RiPDuDnnnsatmX7LxERk4inky0/Pf8I3w1sZB7fvz7C8kbL5fhb5uB688mrbDpx5Y7vJafo+FtcvJ7dHXrfhRu5uv9fjr9F7++3M2fn+dt3LzWO3lD/1XWoUZ6GFT0AOPKAJo+7cM2y+3pETPb7NCWNq1jOMeu3ekMo6i6nYVMUxSLp4+2f+duD9tI2Ll1RlEL1kgG1W7/J0Qe0F8b9tHjxYkaPHs0HH3zAwYMHadu2LT169CAyMu9cBgBPP/00GzduZObMmZw8eZKFCxdSs2bNEiy1EGVQRgr8/jRE7gZ7d3jhT6jULnu9RgO9p4DWFk7/A7/1hfRECGgETYZYrdhCCEsSpAs1U2fvbwENHF4MF3dZpRg6rYZONX3536AmLHq5Je8+WpNONX3xcLIjwMORPg0C+PSxOix+pSVvdq2OjU79+Go0Gl5uVxmAeXsusjor+G4c7Il7VkthoyBPIDt53LXkdHZntRDXD1SD5T9zzNd+IjaRQ1HxZMXcbDwex7U7jGFesEdtOR/RoQqPBHmQkmFgwt8RubZLzzRw6oo6dt4UpAN0zmpNB8vx6AABHmpAlTO7+8pD0QCM6lSNR+v6A9C1ttpCvOlEnEUgbXpfpuneJvwdkWc2/LzkrCNPJ1tSMwy5uonP232Ro5cT+XLdCYtW09Lk6E31vXepVd5c73l1d49PzSjzWexNXd29XewBtSXdNMwjMkdLOqjd3iE7eDdJSc8koRB/yovXU815FCD3DARXEm8L0ktZS/qv289T7cO17LtQcA+RnPO8S0t60U2ZMoWhQ4cybNgwatWqxdSpU6lYsSI//vhjntuvW7eOrVu3smbNGrp06UJISAjNmjWjVatWJVxyIcqQjBT4fQBc3Kl2XR+0Aio8kns7nxrQ+g318dUToNFC76lqw40QolSQeRKEqsIj0PhF2D9H7f708tYyNY1G9zp+VCznSNSNW0zdcArI7kYO0CjIgzm74GBW8rh/srq61w90Z0SHKgyff4C/wqN579GaaLUac8t59zp+RCekcSgqnhUHLvNS1s2AnM7EJfHv+RvotBqeaR5Ej3r+9P5+B2uPxrLpxBU61fQ1b3sqNplMo4Knk625G7uprD9vOwdYZnaHHC3pWePJo26mciBSvYHQu75/jvfoibeLHdeSM/j33A3aVPNGURRWZAXp7/esxU9bz3Lheiqzdlzg1Q5VCqxXU1f3VlW8ueCewrpjsew5d51Hsm56GI0Kf2fdMEjTG5m76wJvdq1e4HFz2nrqKltOxvFMsyCq+7oWad/CiLyRSuwtDTqthg7Vy0PWjZfL8be4mZKBp7MdoLaqDpr1Hydiklg7ui1VfFzucNTSy5TZvUut8iw7cInEtEyiE9Ko4OFobjEPzArSAz3VG0C3t6QP/e0Ah6N01G+WSMNgr3xf6/BtNzpuz5sQd1sOhNLWkr7yUDSKot7YapKVuyIvUTdSzXkfAI5FS0t6UWRkZLB//37Gjh1rsbxbt27s2pX3TeGVK1fSpEkTJk2axLx583B2duaxxx7js88+w9Ex7ymg0tPTSU/PvjGUmKjeTNHr9ej19zbzhWn/ez3Ow0bqrejuus70qegWP4v24g4Ue1cMzyxBKV8f8jtOyzewObIUTfwFDE1fwuhTJ/9tywD5rBWd1NnduZd6K8o+ZScKE/dfp4/h2J9w5aiaaKTZS9YuUaHptBqGtq7Ep39HkKZXW5Fztk6bgspj0Ymk6Q2sPqy2tves50+HGuVxc7AhNjGNPefVANQU2A5oWpHL8bc4FBXP4n1RDGtbCc1t84bO3xNpfj1/d0f83R0Z2qYSv2w7x8d/HaNlZW9zIrij0dnj0XMep3GwGmBfT8mgpp9loGrK7p6SbiAtE1YfVruzt6jsRXm37EBfp9XQpZYvi/ZGsT4iljbVvDkWnciZuGTsbbQ81SQQTydbxvxxiO83nebxRhXMNwDyoiiKOVFcqype+LnZZwXpNxjRQd3mQORNi9bRubsv8Er7yjjZFe5fS5rewBuLDhKfqmfOrgs83qgCb3apbu6GXRw2ZXVTbhLsYe5ZEezlxMXrqRyNTqBtNR9AzSVg6rr9z7FYRnSoWmxlKEmmlvRqvq5U8XHhRGwSEdGJBLg7WIxJh+xgPeeY9KgbqeyPjAc0fL7mBEuGt8r1mTc5nGP4COSegcCUqNBGqyHTqJSqudIzMo2cjFV7tZy/dufeE1uzkg6aPjen45JJ0xtwsJVWp8K4du0aBoMBX19fi+W+vr7ExuadB+XcuXPs2LEDBwcHVqxYwbVr1xgxYgQ3btzId1z6xIkTGT9+fK7l69evx8mpeP6nhIWFFctxHjZSb0VXlDrTGdNpfvZbfJIj0Gsd2B08mpuHrsChNXfcz8X/VXydwzmf3hTjmjtvW1bIZ63opM7uzt3UW2pq4YcXSpAusjl7QeePYPVbsOkzdRoOZ29rl6rQnmpSkW83nCbhlp5AT0eLcd6Bno54u9hzLTmdLSfjzMFnr3r+ONjq6FnPn0V7o/jz4GWuJWcQn6onwN2BttV8SMnI5LNVEZyJS+ZgVLw54Ad12rVlB9TM8M+1CDYvf6NzNVZlJZ77+p+TfNynNpDdxbpOQHZXdwAbnZbfhjQnLimN4Nu6uzvZ2eDuaEvCLT03M2DVEfWi9rEGAbnqoFudrCD92BXGP1aH5QfUmw1davvi5mBLv4YVmL/nIgci4/ly7XGmDmyUb31G3kjlcvwtbHUamoR4Ut5N7T5tGpduq9PyV7jait6vYQAHo+K5eD2VRf9FMaRNpXyPm9Pfh6KJT9Vjb6MlPdPI8gOX+ftQNC+2DOH9nrXQavMODotic9bUazlv2tSt4M7F66kcuZwdpJuGEABsOXE1V5BuMCp8tioCH1d7XuuYO4CPSbjFK/P280yzIJ5pFpRrvaIoZBoVbHX3d5SRKeCs5O1E7QA3c5DeONjTnFCuQtYQiopZLemXckzDlrNb976L8aw6HEOfPD5rkJ00rqafKydik3Jlb7+SNSa9doAbhy8llKqW9FNXksjIGhZSUJBuGo/+dJOKzNxxnhspGZyMTaJBVn4DUTi33+xRFCXfG0BGoxGNRsOCBQtwd1f/X06ZMoX+/fszffr0PFvTx40bx5gxY8zPExMTqVixIt26dcPNze2eyq7X6wkLC6Nr167Y2tre07EeJlJvRVfkOtPfQrfkebTJESh2zmieWULLwGZFes0ad1nW0kQ+a0UndXZ37qXeTD28CkOCdGGp8WC1y3vsEdjwKfT9wdolKjRnextCW4Xw3cbT9Krvb3Hxp9FoeCTIg/URV5j0z0lzV3dTi22/RhVYtDeKtUdizRfsTzWpiE6rwc3Blp51/Vl+8DJ/7I2yCNL/PhxNUlomQeWcaFvV26Isnz9elyFz9jFr53naVfemQ43y5unX6lWwDNJBDWRqk/eFpL+7Awm39JyI13DySjK2Og096vrn2q5VFW+c7HTEJqZxMCreHHg+0agCoGbT//SxOvSdvpM/w6NJTs+kf+NAOtYsj72NZaugaTx6w4oeONnZUL28K55OttxM1XPkcgL1Kribk+/1a1SBppXK8cGKo/y6/RwvtAwuVDA6L2ss/xtdqtG6ijffrD/J9tPX+HXHeaqWd2FgHsFuUSSm6fnvgjrEoVMNH/PyehXcWX04xpwATFEUVh3KTiS4P/ImCbf0uDtm//PdffY6c3ZdAGBg04p4ZY35Nlm2/xKHLyWQnH4uzyB9yf5LvLv0ML+80Jhude5P9lxFUcyJ/kK8nKnt78ZyLhMRk2BuRfdzczC3AOdMHGcKmEwBaTl7hRvpGiauOU6XWr4W0wJC1owLWT1Dutfx40RsUh5j0tXnjwR5cvhSArGJaWQajOZ8EsXNaFSYueM8Lat4WeR8yEvOhHcXr6fmGzCmZxrYdfYaAO2r+7Dn3HW2n77GsehECdILydvbG51Ol6vVPC4uLlfruom/vz8VKlQwB+gAtWrVQlEULl26RLVq1XLtY29vj729fa7ltra2xXYBWpzHephIvRXdHevMoFfHkkcfhPCFELkLbJ3RPL8cm6AWJVvQUkY+a0UndXZ37qbeirK9JI4TlrQ66PmN+vjgPDj+t3XLU0SmrO9vdsk9LvqRYDW4Nk3r1qtedpDbLKQcAe4OJKVnsvfCTTQaeKpJoHn9000rAmrLb2pG9hzJprnNn20elKvVt1NNX15sqbauv73kEDEJtzgea0oaV7RWHVOX9y0x6inbvrqPuet2Tg62OtpXV4PR8X9HcC05nXLOdrSrnh2g1g/0YHh7dTz6huNxDJ9/gOZfbOSLNcfJyMxOOGcaj96ysjomWavV0KySOm53z7nr7DxzjespGXg529G6qjdPPhKIt4s90QlprAzPbpXOT3hUPIcvJWCn0zKgSUUaVPRg3tDmvPeomr35q3UniE+9+0R0UTdS+XrdSTKNCr6OCsFe2d1d62b1ZDBl2j8QGc/l+Fs42+kI8XLCYFTYcfqaxfFyTtOX11R0pvo6dzWFhNTcY45MCfwW/pd/Nut7dSUxnVt6A1oNBHqqLemgduU3z5FeLrsF0tSinpJh4GaqnjS9gZ1ZAemL1QwEuDsQnZDGz9vO5nqts1eTSc0w4GSno2019QZV7jHpakt6nQA3bLQaDEbF3Lqen4X/RTJ60UHS9IVLbpjTP8di+b81x3l1wX5zsrz85EwceEtvyJXkzmTfhZukZhjwcbWnToCbuRfMURmXXmh2dnY0btw4V9fAsLCwfBPBtW7dmujoaJKTk83LTp06hVarJTAwMM99hHjgGfSw/BWYGAg/tYGVo8wBOs8vhYc8QBfiQSJBusgtqAW0eE19/OcIuJ77Ar200mo1tK7qnedY0Ua3tXr1zBGka7UaHmtYwfy8TVVvAj2zg7rmlcoR7OVESoaBHzad4bsNpxn4y24ORcVjq9PwVOO8LxrH9axFTT9XriVnMGjmf2RkGnF1sDGPCS4s/6xgKj5DvRGQX/djwDwP+KGs8cJ96vvnatV+79GahL3ZjuHtq+DrZk98qp5ftp1j7LLDKIqCoijszhoS0LJKdg+BFlkB+55zN8yt9D3rqcd3sNUxpE0IAD9tPYvReOcg6bfdFwA1+V3OVulhbStRrbwLN1P1TAk7ZbFPeqaBGVvOsO5oDHkxGhWW7b/EgJ9303bSZnNLfVMfy2m2TDdJIm+kkpCqNye/617Hz5wlf3OObt9pegPrjma3AppaVnOu3581cwDAoUvxFuszDUbz32P3uet3FYAWxoWspHGBnk7Y2Wip7a++z6gbt4jImmIw53h/B1sd5V3Vur90M5V/z98gTW/E182eYBcY+6h6s+unrWdzZWY3vZ+6Ae5UyOo2fyUxzeLvbmpJD/BwxN9DvdF0py7v205d5f0VR/gzPJp/juU9VvlO9mX9DaJu3DI/zs+Ry/EWz89dS85zO1P3/3bVfNBoNObPzjGZhq1IxowZw6+//sqsWbM4fvw4b775JpGRkQwfPhxQu6oPGjTIvP2zzz6Ll5cXgwcPJiIigm3btvHOO+8wZMiQfBPHCVEm6G/B1VMFb5eX8N/h8CLITFOzt4e0hVavw7AwCJaZD4R4kEiQLvLWdTxUbKHOnfnHIMi4u3mUS5P6gR7mecwb5OjqbvJ4o+wgfWBTy+7KGo2Gp5uorekztpzl2w2nzK2pA5sG5er6bOJgq+P7ZxrhYKvldJwaBNQJcMt3HGZ+/HMkiHO01ZoDybx0quFrnm4N4PFH8r6BUM3XlbE9arJrbGe+G9gQnVbD8oOX+TbsFGevpnA1KR07Gy2NgjzM+5iC9H0XbrD+mDrfet+G2TcMnm8RjKu9Dafjkvlp29l8W8KvJ6ebu5e/0DLYYp2tTsv4x+oAMH/PRXMm7eT0TIbM2cukdSd5dcEBc3f8nL765wRvLTnEv+dvoNGoN1u+ebIunQMsbxh4ONmZW5QPXVLHXYN686NjDXXs+paTV80B56YTcSSlZ2L6s+267bUPRsaTnqMXQvhtCdVOXkkyjwdP0xvNORGKmylpnGmGAA8nO/MsAuuzgt6Knpaf++wu77fYfEINSDtU90ajgUfr+NKsUjnS9EYmrjlusZ+pJbp+oDs+LvZoNZBpVCymKjQF6b5u9uZW+8vxef8viUtM483F4ZgawP89X/C0aLfLWe8rckypeLv0TIM5aZwpd8Xt88ubmJLGdcgaLmFqST8em2Qx1aG4swEDBjB16lQmTJhAw4YN2bZtG2vWrCE4WD3/Y2JiLOZMd3FxISwsjPj4eJo0acJzzz1Hnz59mDZtmrXeghD37sQa+KEZTG8Kh5cUbV+DHrZn9XTs/DG8dxFCV0G3z8C3TvGXVQhhVRKki7zpbOGpOeDso2Z7X/0WFNB9tLRztNOZu//mbEU3qeHnyrPNg+hSyzfPIPjpJhUJcHfA28We3vX9+bxfXTaMaceEvnf+cqzm68pHvWubn9cNuPNY2byYWtJBna7tTtnT3Z1saVFZ7ZZe2duZBoF3fj2dVkPfhhX44vG6AEzbdIaP/jwKQJNgT4teCTV8XfHImi89OT2TCh6OFmP03RxszQn0Jq07SePPN/Dcr3uYt/sCSWnZXcAX74siw2CkXgV3GuYxrrdVVW961ffHqMAnfx3jalI6A3/Zzc4zanCrKDB68UGLednXH4vl563qNHajOlVl53udmD+sOX0bBpBX/jnT32HmjvNcS07Hw8mW1lW9aRJSDmc7HdeS083zYZu6uj/bLAitRk00ljNJmqnXga1OfaHbg/QDkZbPTeO+i5tp+rVKObr2mz7zpinEbr85ZUoeF3UzNUersdp7QqPR8Emf2mg1sOpwDP/muLlwKGtMd/2KHtjotJR3zZoqMKvL+60MA4lp6tCQ8m4OVPBQXzevlnSDUeGNReFcT8nA1V79bP9bxBsZGZlGiy7sqw/HkJ6Zd4+FU7HJ6A0KHk62tMnKJWHqhZBTdPwtTl1JRqvB3KU/uJwTLvY2ZGQaOXs179Z3kbcRI0Zw4cIF0tPT2b9/P+3atTOvmzNnDlu2bLHYvmbNmoSFhZGamkpUVBSTJ0+WVnRRNt28oM5fvugZSMi6GbVvZtGOcWgRxEeq12XNXwWtXMIL8SCTM1zkz80f+s8CjRYO/Q57f7V2ie7Zx71rM7RNpVyttyZfPF6PX19sgp1N7lPDx9WeXeM6s/eDzvzw7CM83yKYquVdC9Uq/myzIHMX9Y45sowXln+OqdL61Cs46dgLLUIAeKV95UK32g9oGsSoTmrWcnNX98qWc2RrtRqaV8qeT7pPg4BcY/HHdK3O292qU9PPFYNRYeeZ63z01zHaTtrMjC1nSEzTsyBr2rpBLYPzLd8HPWvhaKtj38WbdP12K0cvJ+LlbMeil1tQ2ceZK4npvLtU7Z4feT2Vt5YcAmBom0q81a0GAR53vpg3JRYztZT2qOuHnY0WOxstrbMCty0n40hI1ZszxL/QMph6gR5qHeVoTd+d1f39yaxeC4ei4i3GRB/I6npdK6v7uek175aiKPy09axFF3zI3ZIOmLu8m9w+1MIUtG8/fZUL11Ox1Wks/u51AtzNifA+WXmMTIORjEwjx7NuYNTPqkfTdH6mID0uSf3taKvD1d7G3CX+9m7zAN9vOs3uc9dxstMxd2gzNBr1poLpGDntPHMtzwD+eEwiGZlGPJxs8XNTEy2aegbc7nBWV/d6FdyplFVXeWV4N91MaRTkiYeTHaCeA6Y6PXa58FlahRAPIX0abPkKpjeHU+tAawvNXgE0ELlbDbpvZ9CjNepzLTO3ord6HeyKb5pSIUTpJEG6uLNK7aDTR+rjNW/DX69BepJ1y3QPmoSU46PetQs9j3deitpV3bTPtIEN+e/9zuYAsCgq+zhjo9XgYqPQplrB+z9a148z/9eDAU2Llh19TNfqFt3+W1X1yrVNixwBXF7TwNnZaBnZqRrrRrdjy9sdeL9nTSr7OBOfqmfSupO0+GIjl+Nv4eFke8ex9QEejozqrN40iE9Vp9VbMrwlLSp7MW1gI+x0WjYcv8L/tp/j1QX7SUrLpHGwJ2N71CzUe709w37OsphupGw+GcfaozFkGIzU8HWlpp8braqo79/U5T01I9Pccj60TSVsdRqup2RYzD1+IFIN0kd2rIqtTsP5aynmgPpO8hvXv/fCTb5ce4LXFx0k4Vb2xZypy3bOIL3WbUF6zsRxoE5PCJh7KTQNKYerg+X58Xa3Gng42XIiNokF/0aapy9zd7Q1J+Qz3UiKTVDftykRm6+bPRqNhkAP03RvlkH67rPX+W7jaQD+7/G6PBLkSU0/tcz/3dbl/XL8LQbN+o8XZv3HzRTLoRQHs+q4UUUP8xAM0xSEtzuaY5aFOwfppu7/PhbL62SNS5fkcUKIfJ3eADNawJYv1DHkldrBq7ug5yQIaaNuc+S2Lu+Kgm7xQHoceRXN4cXZy48sUVvjnbyh6dASewtCCOuRIF0UrPVoaPsWoIGD89WMolH/WbtUZY5Go6F8jrHlReHv7shvg5swqo4B+zxa+fNyN9NcaTQavnqyPv0aBtCtti8NslqNc+pYozx2NloaVvSglr/rHY8X4u3My+2qEPZme74d0IAQLydSs8ZmD2hSMc8EfzkNbVOJDjV8aFPVm2WvtqKyjzp+uG4Fd3Mw/sWaExyLTqScsx0/PNuo0POQ55yiy8fVnuaVsm8+mMYfH4yKNyef69tIDfyyg/RrKIrCvgs30RsUKng4UrW8i7mV9WBW4H4tOd08LVqbat40CVZ7IuScj/x2x2MS6fP9Djp8s8VimIDJtqyW+IxMI6uzxtMbjYq5y3Ylrxwt6QHZQbqdTouvq+Vn8PYx6qYx+Tl5OtvxVjd1Jt3J60+aW6jrB7qbb1r5u6tBeEzWOHTTeHTTZz6/lvRvN5xCUeCpxoE83kjtiWAarvHvbVn0Vx+OxmBUyMg0suWUZf2Z6rtRkCePP6LeaNp8Mi5XMA/Z06/lDNIjr6diyHFTxGBUzL0l2te4LUjPGiphGg4hhBCAOhbr8n5Y9BwseBJungcXP3hyJgxaCT5ZM8/Uf1r9fWSp5f6n1qE9vxUbYwY2f78G68ZBZjps+1pd32oU2DkjhHjwSZAuCqbVqklKQleDe0X1bu6s7rB7urVL9lBpGuKJXwn0cLOz0TJ1YCN+GdQkz0A/xNuZTW+157ehzQrdq0Cn1fB4o0A2jGnP1/3rM7h1CCM6Vi1wP3sbHXMGN2P+sOb43naDY3DrEDpltXhrNPDdwIbmQLEwyjnbmZOZ9arnb5Fsz9/dkZp+rihKdiBm6jXQJLgcdjotMQlpXLiemj1VXRUvNBqNeYx9eNY4dFNX92rlXXB3tDXfANiSR5d3g1Htxt73h50cuZxA5I1UNuXRZXv76ex9l+6PAiA2MY30TCM2Wo25dRzUINwla5x3oKdjruEJt49R71jTMiA1ebZZELX93UhMyzS3fNfPke/A1JIeE28ZpJv+bqa6jo6/ZR4KkJimN2fFf71z9rzXphsmtyfYMyX4A9gQYVkvpt4MDSt6UNPPjVr+bugNCquOWM4EkKY3cOqK2huoXqA7AR6O2Om0ZBiMFnkGjsckkpSeiYu9jTkoNzFleI+ITixwFgMhxEMgPQn2zYaf28H/OsGJVaDRqTPljNwL9fpDzu/LWo+Bzg7iIiBWzQGDosCWLwGId8zqBbdnhtpV/sY5cPKCpsNK+I0JIaxFgnRReCGt4dWdUH8AKEb45/0HYpy6KLpATyfcHHLP014QG52Wp5pU5JM+dXB3LPr+OWk0Gr55qgH9Gwcy5ekGtK2Wd3B5J/0bB+LtYsfzLXLnKOiQo0W5aYineUo+RzudOeP9rrPXzOP3TS3sDbPWhUepwacpaVzjYDXBnqkr/e6zllOxRd1I5Zlf9vDl2hNkGIx4Z80YEBZxxaJc8akZHM7qrq3VqMc/dzXZ3H2+Yjkni5srWq2Gmn6u5nW383d3MN+gqODhSJWs3gq302k15iSJmVmBab0KHub1fubu7qYx6Vnd3bOmeDNNwZamN3I9q3V75+lrGIwKlX2cLcrWLCvvwem4ZHO2+IvXU8wt4KCO6zclhrueo7dCg6ybJE9kDdtYceCSxfs4GZuE3qDg6WRLBQ9HdFqNeQhAzuRxey+orfiNgz0tbuAAVPVxwd5GS3J6pnn+eSHEAyz5KkTuyf65uAsOzFOT6v6vE0yqAqtGQ+xh+H/27js8qjp7/Ph7WnqvtAChN+ldQaQpqNjr2nXVxbq460++uq6FXVz7rr2jYsFeUQkiSFOkCUjvJKSQhPRkMpmZ3x937p2akEwmmQk5r+fJk8nUT24mkHPP+ZxjCIdTLoVbV8JZ/4aIOO/ni0yA3tOVy1s/Uj7vWQK5m7Gboljb817qLlqgzD8/fkC5fdztEO7732chxMlHgnTRNBHxcOGrMPFe5etv/wZbPgrumkS7lRQdxpOXDNHKpJvqr9P6sP6BadoYLldnuJQ4zxra2e228Y7Z8T/8kc9Wx0z0cY4gXd0isO1oGRarTcukq13we6fF0Ck+AnOdTQvwd+WVc8GLq1l3sJjoMAOPXzSYV68ZAcCKXceodRnvtnpvEXa70mn/dMde6U83Zmud3bsnewfiAx0l79183GY06LUs+Bn9UhusjhjZPcmtZ8GQDB+Z9DJ1T7p7Jj3c6JzJrnZ418ab9XEvsU+KDtNOLKj70tUs+vieyaTFhlNhrtPK4dUsei9HtQLALEdX/42HSzjkEnyrHeAHdXaW6memKD9/133papA+2qVRospo0Gvrk33pQpzkzOXw4hilglD9eGsGfHW7kqjI2QBWMyT1hOnzYM4OuOi1E49F00rePwWbTcui20bcQK0pDnu/c+CmpZA2QPkY/ecW/kaFEKFEgnThnzP+z9Gh1A6f3wq7vgv2ioQIqOHdEslIiiQhysTZHiP71IZ6P+8+hs0OmSnRWql9Zko08ZEmbSTY744gfni3BECpADjdkaVfsesY24+WccVrv1BYUUv/jnF8f/dELh2VwdAuCaTGhlNurnMr+1ZL3Sf0TuHiERmA0iBt/zHvzu6q60/N5JIRXbhmXHff32vXRHQ6OGdw/Y38VHNn9KNDXARDMhLo4LIFQR0TmF9qxmazu+xJD9fu47ov3W63a0G6555vQJsioHZyV4P0WUM6MaW/cvyW7lCqDDY5qhWGuYzzS4+L0Jo0LnT0FQBn0zjXUv3MFOXkhRqk2+127eSAryAdYGBn2ZcuRLuw81uoKgJTFCT1cH50n6Bkty96A+7YCHdsUPaMR3s3XPWp95kQHgdl2bDsUTi6EUxR2Mbe5rxP+gCYvRZuXQ3hDfeAEUKcXCRIF/7R6eCsx2Dw5WC3wkfXwsHVwV6VEAFjMuj56rbTWPLXiSRFh7ndNqRLApEuTe/ULDooQbhacv3Br4cx1yld0HukOLP16r70b7fmcsVrv1BcWcvgLvF88OcxWtm3Xq9jqiMYVUve7XY7K/co494m9EllSv804iNN5JbWaLPcM30E6d1TonnikiE+KwZA6ar+w90T3Tr31yctLoLlf5/E538Z75Z1T4sNR6eDWqtSzl6gdXd3BvLqvvSc48r88dzSGsKNerexfip1Lb8eKGZvQQU7cssw6nWcNagDU/unA7B0ez52u925H92x1UCljo57beUBPlinjDpybRrnenzAOcLuQGElhRW1hBn1bsG8K7U6YVuOZNKFOKmpHdhPvQvu3OT8uO4bOPNfyn7z5J7ue84bwxSh7E0HWPW08nnkDcocdE8yE12Idkd+64X/9Ho47wXod45S6vXZn6FG/mAVJ4/E6DDSYr078ocZ9W4ZVs958mrzuC83HwVgWNcEt4Ztp/ZKwWTQcazcTGm1hWFdE1h40xhtFrdq2gBHMLpDCUb3F1aSU1KtvH73JCJMBs4domT5CyuUfd7dk5ve+Tc2wkSf9MZnaSJMBq8GdCaDnlTHPvq80hqvcndwz6SvcHRnH9cz2WeXf/X47swr1zLhp/VOISEqjFN7pRBh0nO0tIY/jpZpQfqwjES355gxqAO3TOwBwP99vpWP1h/Rmsa5dvdXu+EfdOxrV0vdh3ZJINzoewLBIJcO72ojPCHESabiGOz7Sbl8yiWBf/7BLs9pjFBmoAshBBKki+YyGOHC15TSr7IcWPKPYK9IiFYx3iV77pmBVsuua63KXnJ1P7oqJtyoPWZU90TeuWG0z0Z843umEGkykFtaw7acMlY6ysNHd08iMkwJHtWSd5WvTHprUfel7z1WTqVj1J66Dx1wm5W+fJej1L2P74Z/yTHh9HZk/tUxeOc6yvEjTAatUeDLK/ZRYa4jKsxAn3T3SgGdTsd9M/rxpzFdsdvh3k+2UGezu3X2B8hMdYxhK67CYrWx7oDSR6C+UneAvh1iSYgy0SstRvtehRAnmT8+U6oFOw1XsuWB1n2CMqINlCx6bHrgX0MI0SZJkC6aLywKZj2vXN74NuxbFtz1CNEKpvRPw2TQMap7IqkugSg4O4yr1M7urv51/ik8PGsgC64fTWw9nfIjTAYm9lH2VWdtz3OWuvdOcb5Wl3itjD3MoKdTQuPH0AWa2uH99yNKRU1MuJFox/g3cGbS9xaUa9nqST7msqvUExlWm50wg55pA51/wE5zlLyre9VP6Rzvc2SgTqfj0fMGuTW8c20aB5AeG0GESY/VZif7eDXrDir74Ec1EKRHmAxs+sc0PrplnDbiTghxklFL3dUmb4GmN8DZTynd4Cf+vWVeQwjRJkmQLgKj+6mORnLAV3cq3VCFOIn1SotlyV9P57VrRnrdlhQdpnVS1+u8g3aArslRXDu+u1sQ68u0AUqW5btteVo3+NNcgnSdTsfFI5Tu9hlJkV7jwlqT2jxPLT93bRoH0DlBOSYHi6qwWO10TYry2Y1eNaaHM0g+vW+qW7XBGf3S3LaADuvqfSJEpdfreOLiwZzpCPJP7Znsdbu6TeCX/UUcKa5Gr4PhHnvcPTXUCV8I0cYV74fs30Cnh4EXttzr9D9H6QYfVf9JQSFE+yNBugicqf+EhG5QegSyHnReb7OCpTp46xKihWSmRHvtI1ep+9L7dohrVqZ1cr809DplZnhVrZWUmDD6d3Cfu3vlmK7MGtKJu6f28ft1AkEtd9/u6Hie7rGfX82kq07v0/DIN9dy83MGu3fYT40Nd+vmPuwEAbXRoOelP43gmztO48bTMr1uV4P0j9YfAWBgp/h6KxyEEO3A1k+Uz5mnSxm6EKLVSY2eCJywaDjveXj7XFj/JuzJUhrJmR0jiqJSICkTQ0J3updEgm06IH8Ei5PT5H5pfLn5qNah3V9J0WGM7J6kjQQ7rVeKV9O2uAgT/7tiWLNeJxDUcnd1L366RyY9JtxIfKSJ0moL4OxyX5+02AguHtGFQ0WVWhM9V1MHpLPRx/i1+uj1OreGca7UfenqOLdR3SWrJUS7ZbfDlo+Uyy1V6i6EEA2QIF0EVuZEGPVn+O01JaPuqqoQqgrRZ//GEMC6Mg2mSqM5cXKaNaQT/TvGBaSR2/QB6VqQrjZMC0VqubvKtbO7qnNCJKXVFsIMerfRdfV58pIh9d42Y1BHns3aQ8+0GNJ8vFZTZHp0xR+dWX/5fHvQvXt3brjhBq677jq6du0a7OUI0bpyf4eiPUrH9X7nBHs1Qoh2SMrdReDN+A9c/x3ctAxu3wB/3wf/7yDc8jNc8jbWMbMB0K9+BnI2BHetQrQQnU5Hn/RYTD6amTWVOhcc3Pejhxq13F3lK3BWS95HZyYRFda888SZKdF8d/cE3rlhdLOeB5yz0lXtPZN+zz338OWXX9KjRw+mTZvGhx9+iNlsDvayhGgdasO4PmdBRFzD9xVCiBYgQboIPL0Buo2HLiMgpRdEp0BkInQcAgPPxzb1EbITxqKzW+GzW2S/uhAn0D0lmscvGsxTlwzxmZ0OFZ5r8yx3Bxjj2Gd+vku39ebomRrj1V3fH91TnA3seqZGkxzT/Odsy+644w42bNjAhg0bGDBgAHfeeScdO3bk9ttvZ+PGjcFenhAtJ/8P2PSucllK3YUQQSJBugiKLRnXYI9JV8rJlj4c7OUIEfIuHZXBRY4u7qEqzKgnxSW49XVC4fpTM/n572doHelDRWpMuNbgr6H56O3NkCFD+O9//0tOTg7//Oc/ef311xk1ahRDhgzhzTffxG63B3uJQpzYru+VpMCBnxu+X8EOeHuW0k+n03DoNa111hcku/PLeX7ZHmos1mAvRYhWU1Zj4cXleymqCO3qMAnSRVBYjDFYz35W+eLXl078H6cQok1wLXn37O4OYNDr6NrA2LVg0el02rz5MZkn3ivfXlgsFj766CNmzZrFPffcw8iRI3n99de59NJLuf/++/nTn/4U7CUKUb/jB+H9y+GDy2DLh0pj209uhPI87/sW7FRurypUKv+u/gyMvqd3nCwe+24nTy7ZzTdbcoO9FCFazbtrD/H497t47LudwV5Kg6RxnAgae69pMOI62LAAvpgNt/2qdIgXQrRZHeIj2JpTCnjPSQ91D80ayKo9x7zGvbVHGzdu5K233uKDDz7AYDBw9dVX88wzz9CvXz/tPtOnT2fixIlBXKUQDpYa2L8czOXO6wp3w5r/QV0N6I3Q4wzYuxS2fQK7f4DT7lbGxgJYa2HpQ1B5DDoMhqu/ULbpneT2H6sA4EBhRZBXIkTrOVRUCcDqvYXY7fYGR8EGkwTpIrim/wv2/qh0gv/9Axh1U7BXJIRohk6OTHp8pIkIkyHIq2maoRkJ2nz79m7UqFFMmzaNl156ifPPPx+TyXtc5oABA7j88suDsDohHAr3KCf6N78H1cd936f7BJj5JKT1g6Ob4Nt7lKa1yx71vm+HU+CaLyHq5N/yYrPZySlRegIdKZbeQKL9yC9TytyPltZwqKjKq3FsqAhqkP7zzz/zxBNPsGHDBnJzc/n88885//zzG/XY1atXc/rppzNo0CA2b97cousULSg8BsbdDt//P/j1FRhxA+hlF4YQbVUHxxg2X03jRNuxf/9+unXr1uB9oqOjeeutt1ppRUK4KM2Gr+6Afcuc18V1hpTezq8NYXDKpXDKxaBmyjoNgxuXKo3hdnwFtjrn/RO6wtSH20WADpBfXoPFqvSUyD5eFeTVCNF68stqtMtr9hVJkO5LZWUlQ4YM4frrr+eiiy5q9ONKS0u55pprmDJlCvn5+S24QtEqhl4Jy+YppWn7l0GvqcFekRDCTz1Slf/suiWH5n96onEKCgrIy8tjzJgxbtf/+uuvGAwGRo4cGaSViXbv0BpYdLWyd1ynh97TYcT10HuaMl3mRPR6GHGt8tGOuWbPjxyXTLpoP9yD9EKuHNM1iKupX1CD9BkzZjBjxowmP+6WW27hyiuvxGAw8MUXXzR4X7PZ7DbbtaysDFCa4Vgslia/tif1OQLxXO2F1zEzRKIfciWG317BtuZFrN1OD+LqQpe81/wjx63pmnPMJvZM5ImLBjGqe2K7O+bNOW6hdqxuu+027r33Xq8gPScnh//85z/8+uuvQVqZaLfsdlj/Bnz3/5QMePopcOnbkNwz2Ctrk44UO7Pnx8rN1FisbW6LkhBNZa6zcrzK+f/t2n1FIbsvvc3tSX/rrbfYt28fCxcuZN68eSe8//z583n4Ye8RX0uWLCEqKnAdhrOysgL2XO2F6zGLMvdiKjr0+3/kp89epyKiUxBXFtrkveYfOW5N5+8xCwN+Pwq/B3Y5bYY/x62qKrTKTbdv387w4cO9rh82bBjbt28PwopEu/Pb60rWXFVVpDSHAxh4IZz3AoSF3qSIxjhRUNAakw2PeJS4Zx+voldabMu/sBD1sNns6PX1/16c6PbGKHDsRw8z6jHodBRV1rI7v4K+HULvvd+mgvQ9e/Zw3333sXLlSozGxi197ty5zJkzR/u6rKyMjIwMpk+fTlxcXLPXZLFYyMrKYtq0aT4b6whv9R0zu+VHdHu+Z1LUbmxnSQM5T/Je848ct6aTY+af5hw3tcorVISHh5Ofn0+PHj3crs/NzW30/79C+G3nt0qDNy86mPpPOPVu5z7zINtbUMHlr65lxqCOPHr+oBPe//1fD/PYdzt46/rRjOjm3UH+vs+3sWybgQmTLSS34L+/ns3ijhRXS5Duh/IaCxe+uIbhXRP5z8WDg72cNuut1Qd4Oms3C28cwxAfDVzv+GATm48c57u7JhIT7v//QWqpe4e4CLolR7FyTyFr9hVKkN4cVquVK6+8kocffpg+ffo0+nHh4eGEh3s3MDKZTAH94zPQz9ceeB2zcbNhz/cYtnyIYeqD7WL8iT/kveYfOW5NJ8fMP/4ct1A7ztOmTWPu3Ll8+eWXxMfHA1BSUsL//d//MW3atCCvTpzUyvPgy9uVywMvhAyXLRcZo6Gzd4VHMD35wy4KK2p595dDXD46g4Gd4hu8/9Id+ZTV1PHrgSKfQfpPu45xvFbHtqNlnB7XcpUCaibdoNdhtdm9MuuicX47WMyeggoOFlUy74JBmAzS/Ngf32/Lo7ymjp93H/MK0q02O99vy8VitbM1u5RxPZP9fh21s3t6XDjje6awck8ha/cVcf2pmc1ZfotoM0F6eXk569evZ9OmTdx+u/KPt81mw263YzQaWbJkCZMnTw7yKkWzZE6EtAFQsB02vgun3hnsFQkhRLv01FNPMXHiRLp168awYcMA2Lx5M+np6bz77rtBXp04adls8MVsqC5WxqFd8DIYQ3dSxNbsUr7/I0/7+pms3bx+7agGH6Nm8spr6nzerl5fUG72eXugZDv2pA/pEs/GwyVue9RF4+3KU2bMW6x2DhZW0js99DKybUG2o3mhr5NF+WXOSQRHjlcxDv+D9DzH719aXATjHcH+L/uLsNrsGJpZSh9obeZ0T1xcHFu3bmXz5s3ax6233krfvn3ZvHmzV3Mb0QbpdDD2L8rlda+CuTy46xFCiHaqc+fObNmyhccff5wBAwYwYsQI/vvf/7J161YyMjKCvTxxslr3Kuz7EYwRcOHrIR2gAzydtQuAsT2S0Otg6Y4CNh2uZ167g5rJq/ARpJvrrFowot6vJdTW2ch1BCvje6YAMivdX7vznX+r7sqXv1v9YbHayC11BOk+3oeuJ5CymzmJoMCl3H1gpzhiw42U1dSx/WhobTmDIGfSKyoq2Lt3r/b1gQMH2Lx5M0lJSXTt2pW5c+eSk5PDO++8g16vZ9Ag970+aWlpREREeF0v2rBTLoGf/g2lR+Czm+Gy92RuuhBCBEF0dDQ333xzsJch2ov87ZD1oHJ5+jxI6xfc9ZzAhkPH+WnXMQx6HY9dOJjnf9rLJxuyeTprN+/e6DtxZLHaKKpUgu/yGu+JDq7Z9fwWzKQfLanGbocIk56hjtJiKXf3z648Z2C+O68cZFt6k+WW1GBzNEvMLvF+H7qOCMxuZsWHWsmSHheO0aBnTI8klu4oYM2+Qk7p0vBWldYW1Ohn/fr1DBs2TCulmzNnDsOGDePBB5V/pHNzczl8+HAwlyhamykSLn0XDOGwazH86N2ZXwghROvYvn0733//PV999ZXbhxABk7cVvpkDb0wHq1mZez4q9JvHqln0i4d3oXtKNHdN6Y1Rr2PlnkJ+3V/k8zHHys1a53Zf5e6u2fUCl1nOgaYG5F0So+iarOx7l3L3pquz2th7rEL7WjLp/nE9QXS0pIY6q839dpf3ZnNPJuVpQXoEAOMclSRr9vn+nQ0mvzLpR44cQafT0aVLFwDWrVvH+++/z4ABA5p01n3SpEnYG5gzsWDBggYf/9BDD/HQQw81+vVEG5ExCs57Hj77M6x+FlL7wdArgr0qIYRoN/bv388FF1zA1q1b0el02v/V6tgoq9UazOWJk8G+n+Cnf0H2b87r0gYqo9VCpHN7fdbsK2T13iJMBh13TOkFQEZSFJeNyuC9Xw/z1JLdLLplrNeYtXyXwLvc7B2kuwbuLbknXS0pzkiMpEtiJABlNXWUVluIjwytJpah7FBxFbV1zoByd35FA/cW9XENwq02O7mlNWQkOZsmugbmzd2WUaA1jlOCdHVf+m8Hi6mtsxFmDJ3qXb+C9CuvvJKbb76Zq6++mry8PKZNm8bAgQNZuHAheXl5WiZcCL8NvhSO7YSVT8HXd0JSD+gqfQeEEKI13HXXXWRmZrJ06VJ69OjBunXrKCoq4p577uHJJ58M9vJEW2a3w+r/KpVydhvojdDvbBh5A3Sf2Ogtbl9syqFPeiwDOjV/nK6njYeP8/PuY/Xe/v02pVncFaO70iXRGUzcPrkXH2/IZt3BYlbtLWRC71S3x7kF6T4y6a4l8PXtSc/ans8fR0u1r3XomNI/jUGdG1+qqwY9GUlRRIUZSYkJo7CiliPFVcS7PM/W7FKKq2o5vU9qfU/VYrZml3LkeBUzT+nY7OeqMNfx5eYczhzYgZSYpvc5WH+wmOLKWqYP7OB2/W5HqXun+AiOltZwsKiSGouVCJNBu09tnY1PNmQzbUA6qbEnfu19xyrYdLiEC4d1bvZM8Jb2084CIkwGn93WbTY7n2zIZlRmEpkp0Q0+j2d2/MjxKrcgPdslMM8vr8FcZyXcaMAf+R6Z9L7psSRGmTheZWFLdgkjuyd5PebNVQeoqq3jvKGd3dbV0vwK0rdt28bo0aMB+Oijjxg0aBCrV69myZIl3HrrrRKki8A44wE4tgt2fgPvXgBnzoMR14f8GXYhhGjr1q5dy7Jly0hNTUWv16PX6znttNOYP38+d955J5s2bQr2EkVbVFsFX90O2z5Vvh52FUx+EGLTm/Q023JKuXvRZvp1iOX7uycGdIl2u52b39lAYUXDmexwo57bzujldl3H+EiuHN2VBWsO8uG6Iz6CdOdz+tyT7pJdP1ZhxmazuwVq+WU13PzuejyLUN9Ze5Cf7z2D6EbOj1YzlxmOEwxdEqMorKgl+3iVFuxbrDaufvNXymvqWP3/JtMhPqJRzx0IpVUW/vT6L5TV1PH6NSOZOqBp7w9Pi347wqPfbGdPfgUPzRrY5MffunADhRW1/PS3SW4Bp1rePq5nCst25nO8ysLeggq3EyYLfznEI99s57ON2Xx86ziv6gpXtXU2rntrHUeKq6m2WLl6bLcmr7W1lFZZ+PM76wkz6tn84HSvDPSK3ce499MtTOidUm+PBpVndjy7uBp6utzuEsTb7ZBzvJoeqTFNXnN5jYXKWqUKLD1OOWGi1+sYk5nM93/ksfHwcZ9B+jtrD3KwqIrRmcmtGqT7ldO3WCza7PGlS5cya9YsAPr160dubm7gVifaN70eLngFuk8ASyV881dYeCGUZgd7ZUIIcVKzWq3ExCh/BKWkpHD06FEAunXrxq5du4K5NBGqbFbY/IEy59yXsqPw5nQlQNcb4eynYNbzTQ7QAfYUKMHRwaLKBrdN+qO02qIF6H8a05Wrxvr+eOmq4Vo2ztWE3soe1wOFlV63uWbSK05Q7m6x2jleVet2+75jFdjtkBBl0tbRMT6Cospa3l57sNHfo9ohOyNJKXVXS95dg6Ut2SWUVFmw2uwcKvL+XlrS66v2U+Y4Fk9l7cZma97P+GiJ8n35831YrDYKK5Sfw6q9hW637XGUt/frEEsfx+i13R770n/eo1RkrD90nJ/3uD/e00frj2g/g+eX7aHGErrbirJLqqiz2amqtWqd2V2pv6M5jejGrgbhydFhbl+DMvFA3UfuvN2/knf1JFlshJGoMOcJrc6O939RRa3Pxx2vUk6oJUa17lYQv4L0gQMH8vLLL7Ny5UqysrI466yzADh69CjJyf7PrhPCS3gMXPMVnDlfGcmybxm8OB7WvQZ1LTtDVAgh2qtBgwaxZcsWAMaMGcPjjz/O6tWreeSRR+jRo0eQVydC0i8vwhe3wpe3+779+/uUJnHRqXDt10pzOD8r49RApsZi04K5QFGfOyUmnH9dcArzzvf9Mbmf75MLaqbNV4OrPI9yd88TDJ7Z9TyP5nFq2e8pneO1ddx7Vl8AXlmxnzIf2Xlfsl0ax9W35rUujbRastO8p6IKM2+uOgCAQa9jR26Z2yx6f6gnO/wZa1dW7Tymv3g0F1Mz6X06xNK3Q6zbdaAE+L8dKNa+fmrJrnpPKtVYrDy/TJl4ZdDryC8z896vods8u8DlWPoem6Zcd6KKFNf7qmXzrnvUj5bUYLdDpMnAEHUSgZ9NDgs8St1VavDteVIMlD3y6u9VQlSYX6/rL7+C9P/85z+88sorTJo0iSuuuIIhQ4YA8NVXX2ll8EIEjF4P42bDraug80gwl8Liv8H/hinBuqXlOqAKIUR79MADD2CzKQ2R5s2bx6FDh5gwYQKLFy/mf//7X5BXJ0KOzQa/va5c3vcjlOe7315TCru+Uy7/6WPoNr5ZL+f6R3qgu6A792tH+vV4NStdXlNHaZV70Owa2Fhtdmos7l2sPWenF3gElUc8gmuAWUM60ysthtJqC2+sPHDC9VXV1mmZYTU4V8veXY+ra7frluw07+mVn/dTWWtlUOc4bTvB01m7sTYjm17i+DkUlDf9+yh1CdLX7i/SsvrmOqtWLdE33SWT7jKSbUt2KZW1VuIijESFGdiSXUrWdo/fDYf3fz1MXlkNneIj+Oe5AwB4afleKn1UXIQC16oQXyek1OvKaurcmut5qq61aoH8eEendbeRa9p7PpKuDZwAa4w8lxnprtTg+3iV90mu0mqLtr0koS1k0idNmkRhYSGFhYW8+eab2vU333wzL7/8csAWJ4SblN5www8w4wmI7QhlOUqw/t8hsHtJsFcnhBAnjTPPPJMLL7wQgB49erB9+3YKCwspKChg8uTJQV6dCDn7lsHxg8pluw3++MztZt3Or8FaC6n9oePQZr+c6x/pntnmZj+3x37tplIbsYF3MJHvsVbPzLlnx3fP+3uWqYOSdf3r1D6A0uDqeKXvkl3P54iLMGqd3NXnU4OjGouV9YeOa4/JK22dIL2grIZ3HGX790zvy00TMomPNLG3oIKvfs/x+3nVDGlhRW2DAaMvrpUaxZW1WqZ8/7FKrDY7cRFG0uPCtUy6a4f3tfuU8vZTe6Vw3fjugHLCwbN8v6q2jheXK1n0O6b05orRXemWrPQJaMo2htbk+nvnK7Ptel1xA+9JNQiPDTcyqHOc12O1SQRJUdoJsGw/O7yrlRRpce4N/NTgu8RHJl29LjbciMnQup3f/Xq16upqzGYziYmJABw6dIhnn32WXbt2kZaWFtAFCuHGYIQxN8Odm5X9bHFdoCIPPr4Oju0O9uqEEKLNq6urw2g0sm3bNrfrk5KSGmx6JNoxNYse4+h+veUjt5v1fzgaxQ2+JCDNX13La/0pYW7wuZuZSQdnptszePE8oeBZqn+icvf6TiDMGNSB/h3jKDfX8erK/Q2uTXsOlwZY6vNlH6/Cbrez8fBxt2C2tcrdX1y+jxqLjRHdEpnUJ5W4CBM3T1S21zy7dA8Wa9MCbFWJS4b0WCPKr125ZtLBWWGg7j3v2yEWnU5HnzQlSM8pqdZ+jup9x/dM5uaJPYgNN7Izr5zF29z7d7295hCFFbV0TYri4hFdMBn03D21N9C0bQytyfX3znOPuN1u104GQcMl71p1SFKU9j4sKDdr+/G138fEyAa3kjRuzfWVuysn1Up8ZNLV7HpCdOuPJvQrSD/vvPN45513ACgpKWHMmDE89dRTnH/++bz00ksBXaAQPpkilP1sd25yNpb7+Fqlc6wQQgi/GY1GunXrJrPQReMcPwS7v1cuX7IAdAY4uhGK9gEQUVuM7uAq5fZBFzf75SxWm1ujKs9sc3M5Z4j738XZVzBRVVunNYaLi1CaVnk2j1NvjzQomVbPExCuo9Nc6fU67pmmZNMXrD7IsQaCal+BfqeESHQ6ZY//sQqzth89xtEtPr8VMuk5JdW879iDfc/0PtoJwevGdyc5OoxDRVV8ttG/xsGuGdKmvl/KPIJ0NTu+y1HWrpa5x0eZtDLq3fkVbtUI43qmkBAVxk0TlBMOz7iU75fXWHjlZ+V35e6pvbVsres2BnWPfihx3QKR7RE0H6swY3Y5yVPUQCbd+fsWSUKUSXvPqUG+60klX9symiK/3nJ3dU+6d5CuvncSIlt3Pzr4GaRv3LiRCRMmAPDJJ5+Qnp7OoUOHeOedd2SvmmhdxjC46A2IToOC7fDd34O9IiGEaPMeeOAB5s6dS3Fx8YnvLNq3DQsAO2SeDt3GQc8zlOu3fgxA55Jf0WGHjLGQ2PyRUrklNbhWCwc8SK8nEG6KDB/d0tWAOyrMQKcEdd+6R7m7I0jvEKU+xvm91Vis2nOoz+9qSv80hmQkUG2xcup/ljHgwe8Z8OD3DHl4iVtwe8RHyXyYUU9HR+BypLhaywDPGKRURuQ3Yi/34aIqJj7+k/a66mt/tP6Iz/t/uiGbIQ8v0e57xhPLqbXaGNcjWdubDBAdbuQvk5R5XP/7cS91PrLpJVW1nPP8Gr497B3W2Gx2t2y4rxMOC385xOQnl3O4yDv4Ux/b2fEz+3V/MXVWm1smXdWng7PDu1qNkBYbTs9UZWzbDad1JyHKxL5jlQz8p/J9j5y3lJIqCz1TozlvaGftuVy3Mby+8sTbGFrCU0t2cdazP3tVE4Bnubt7Jt3z66KGMukuQbhOp3NOGnD8Hqrv1y6JUdp79niVxed0hBNxZtLdy92dmfRar8Z+Wia9lfejg59BelVVFbGxyhtxyZIlXHjhhej1esaOHcuhQ4cCukAhTig2HS5+A3R62LQQNr8f7BUJIUSb9r///Y+VK1fSqVMn+vbty/Dhw90+RDt1cDUc3ez8us4MG5XKSkbdpHw+5RLl85aPwG6nS/Fa5evBlwRkCZ5Zu0AG6Tabs0w3EJl017W6ZvFiHZn0co9ydzXwSI+0uz0GlEwzKEF+UrR3Vk+n03HfWf0w6HXU1tmoqrVSVWultNrCvxfv0BqQ+Sp3B6XcGJQM8e9HSgC4YFhnbR0nGnX3VNYuDhdXaa+rvvbbaw76vP+CNQcprbZo96212jAZdFq3eldXje1GVJiBnJJqDvoYo7Z6bxG78iv47Zj3VoqyGssJT+p8siGb/YWVXiPW1McDjOmRRFyEkXJzHduOljk7u6c7g/S+6crYyl155Vo1wvieyVpVQGyEibunKGXsNRblZ6RmnO+b0R+D3n396jaGikZsY2gJH60/ws68crcO9SrXKo/CCjPVtc7KK8/f0fpGm4F7OTu4/O443qfZ2vs1ktgIkxYse75GY6hrrq/cvc5m9wr+1Ux6Yit3dgcwnvgu3nr16sUXX3zBBRdcwA8//MBf//pXAAoKCoiLiwvoAoVolMyJMGku/PQv+GYOdDhF+RBCCNFk559/frCXIELNgZXw9jnK5SFXwLRHYP8KqCpUmrn2nanc1u9sMEZC8T502z4mofogdr0R3YALArIM9Y96g16H1WYnL4B70gsrzNTW2dDroGOC9wz0xtLKco97l+WnxYVrM5o9u7mrmXVnkO465spZpl5fb4hxPZPZ8MBULfi32+HqN3/lUFEVb689yOxJvZyZdI+TEBmJUaw7UMxnG7Ops9nJSIpkeDel91SNxUZZdR3x9WQTd+WV89XvRwF498bRdE+OprTawjnPrWJ7bhnHK2tJdDmxUFplYdvRUgC+uv1ULQCKizD5fI0Ik4E+6bFsPlLCrrwKeqXFut2uBswVddSbCVX5er+oAZ+vEVxl1cqxTIwKY0yPZLK25/PjjnwtW+wapLvOSlf39KtjxVTXnZrJzFM6upWDR4UZSI5xz+6CcxvDTe+sZ8Hqg9xwaiapsd73awl1Vpu2bcJzD7jFaqOoUrlN/T3MPl5Fb8f371mOXljZUCbd2RgO3H93Ks11Wqm8Ni4wMYqSqlKOFFfTr0PjY06bza519/cM0iNMesKMemrrbJRUWYiNcL4Hj2tBeutn0v0K0h988EGuvPJK/vrXvzJ58mTGjRsHKFn1YcOGBXSBQjTahHvg0BrY/xO8dTZcugB6ShdiIYRoqn/+85/BXoIIJTYbLHnA+fXvH8DOxRCZoHw94nqlsStAeCz0nQF/fIbh+3sBsPc4A120e7DiL/WP+oGd4tiSXRrQ8WBqMNIxPrJZnZzVsly1EZtOp3NrWqXGkZ4NwdTgOt1RiV5UacZitWEy6H2WqfuSEBXmNs/57qm9+eui33llxX6uGtvNLTPpa83qPurxPVKIMBmIjzRRWm0hv7ym3iD92aW7sduVzO+E3qnK8wG902LYU1DBrweKOGtQR+3+vx4owm6HnqnRDO6S0OD3o+qrBun55ZxNR7fb1LFnFpuOqlorYS5JT8/A2/P94jqSzld3b7XUOz7SxPieSpD+wTpl73xKTLhbVYMapP9xtEyrXHAt3VelxTX+BJC6jeH3IyW8vGIf/zhnQKMf2xxFlbVaBYJn+fqxcjN2O5gMOnqmxrAzr5wjbkG6s+qjqtbauEy6I0jXyt2Lq3xOIuiSGMnWnNIm70s/XlWLxWpHp8PrRIdOpyMxykR+mZmSKgsZSc7bSrRy9zayJ/3iiy/m8OHDrF+/nh9++EG7fsqUKTzzzDMBW5wQTaI3wMVvQtdxyiz1hRfDb284b7fblUY2R9YFb41CCCFEW7PtU8jdDGGxcOVH0GGw8v9sySGlUdzwa9zvP/hSAHS1yjgqWwAaxqnUP+pHdlP+ki4oN3uNtPL7uYsbFwifSKeESPQujdjAmRVvsNzd8XVKhB2jXofdjpbNVIPrLk0sw3dtQPb0kt3amDfP5/HMrI/vlaytV1m/75Mh23JK+W5bHjod/NXRvE57DkcW2XXmuuvXvgLY+mj7vV3mkKvU/eHg3aTMc1a95/561y7kvhqHqSdS4iKM2nrVoL5vhxi3+/Z2lLuXVluos9np4tKR3F86nbMp4Lu/HGq1cXgNzUHXqkJiI5yzy10CefX+g7vEA/XvSS+tsmi/A+qef+dWkWrfkwj87PCu7qFPjg73eQIuUZuV7v7+UYP0YGTS/T5N2KFDB4YNG8bRo0fJyVFmF44ePZp+/foFbHFCNFlUElzzJQy+HOxW+HYOfHEbfDEbnhkEzw2HN6bBpveCvVIhhAhZer0eg8FQ74c4SeVsgG2fKZlzlaUGfnxEuXzaXdDnTLh5Ocx8EuK7wvg7IM49s0nPKRCplErX6cOx9z4rYEtU/3Af2jUBvQ6sNnuD5bT+PHdz9qMDmAx6Osa7N49zlrtHaOW0rvtfbTY7FbVqd3dntk99nL8N7VwbkKkzt1Niwokwuf8eez7vuB7JjvUq66gvOHw6Sxl/O2tIJ7fSb1C6moN3kK7u1/YsBW9IX5dSclc1FqvbPnXPIF0NusKMep/fh2tG1tcILrW7e3yUiT7pMST7yJyrosKMWtAKzpMUzTWhdwqjuidSW2fj+Z/2BOQ5T8T1OHlmrV0bsGlBs+tsc8d7dWiG8m9Afd3d1fslR4cR7ejqrp4gO3K8ymW/uuu4QO+mjI1RoO1H971dwNnh3ff7p81k0m02G4888gjx8fF069aNrl27kpCQwKOPPorN5t8MQyECxhgOF7wMkx2leZsXwub3oCwbcOzjWvpPqCkN2hKFECKUff7553z22Wfax6JFi7jvvvvo2LEjr776arCXJ1pC2VFlq9gn18OiP0FNmXL9uleh9DDEdoKxtynX6Q0w+s/w160w7WHv5zKGwUBlD3pu/AgIiw7YMtWy7+7JUaQ49vEWBGhfeiA6u6s6JzpL3sE9sHFm0p1BYUVtnVYGH2l0BhNakO4yqqqp1AZk6vP7qhRwva5narRWkq1m0gt8jHXbePg4y3YWYNDruMvREM3V2B5J6HSwt6BC2w98rNys7SEf26PxQWwfR9b6YFGlNkMblOd2LaQorvAMspRj3DtNebzne8U9SPe1J13NpJvQ6XRuJxb6egTp4B64N6VSoCE6nY57pisN9Rb9dsTvEWRNke/y884+Xu2219+1AZtnN/Y6q42jJcrPemiGmkn3HaSrvxtdXDPljoC8pMrCjlzl3yDX92YXH00ZG/X91DMjXVXfrPQ21939/vvv5/nnn+exxx5j06ZNbNy4kX//+98899xz/OMf/wj0GoVoOp0OJv4dLn1XGQtz6l1w1Wdw3yFI7g2Vx2DF48FepRBChKTzzjvP7ePiiy/mX//6F48//jhfffVVsJcnWsJP/4I6R3Zq12J4fSpkr4eVTyrXTb4fwpoQvE55EOuk+9nW+cqALbHGYtXKvzMSo7Q/uD1Lsa02O0u352v7gj0dr6zlp50FXk3G1EC4ix+BsCfPmc4nKndXS91NBh0mPaRpmXRHuftx/8rdwX2OuuvaXKXFRmAyKIkM1+CyvmMM8PQSJYt+4bDO9EiN8bo9ISqMgZ2U5l5q9vyX/crn/h3jfHapr09qTDgJUSZsdiUwV3lm1j2ztmrgrY5KKzfXub0vjriVuze8Jx3cj02fDt5BumsJfFMqBU5kbI9kTuuVgsVq57llLZ9Nd927X2GucwteXQNe5/tcOY65pTVYbXbCjHr6d1R+9oUVZp/TAXydeIoON2rvi7WO90qGjyD+SHHVCScOuMo7QZCeUG+5e/C6u/sVpL/99tu8/vrr/OUvf2Hw4MEMGTKE2bNn89prr7FgwYIAL1GIZhgwC679SulC22sKRMTDWY8pt/36MhzbHdz1CSFEGzJmzBiWLl0a7GWIQMv/wzm+dMYTSrf2wl3w+hSl6ixtoNLRvSkiE7Gd+ldqTYGb+qMGqjHhRhKiTNof3HkeAeSHvx3mpnfW89h3O30+zwNfbuP6Bb9pHclVgcyka2W7xUoW0jWwiXGU9rqWu6sBu3pbuku5e4W5Tsvo+btfXm1ABtA9xbuywaDXaaXarmXa6fWUu+8tqGDV3kKMeh13+siiq9SyeTVIX+MymqwpdDqdW/d01e78Crf71Vfu3iUhkugwpcTfbb/1icrdHT+XOC1IV9at1zmz867UjuM9U6PrDQj9NWe6cqLl0405Xk0HA81rW4BL5to14PXcI65+7pIQqW3ZMNfZqHQZ0eb5nJ6/b54l7a4nldQTaJW1Vp89BOqT38hyd+9MehsL0ouLi33uPe/Xrx/Fxd6z9IQIKb2nQp+zwFYHP8yFJpyJE0KI9qq6uprnnnuOLl26+PX4F198kczMTCIiIhgxYgQrV66s977Lly9Hp9N5fezc6TvoEs2U9U+w26D/LBhzM9y8AjLGOG+f/ohS4h5krplunU7nUhLuXsK8wdGhfJmPbHmd1caKXccA+Glngdv1uY7ApLl70l2f48jxKkqrLdrIrdTYcG1PeplrJt2sBAdqlt31BIQaSCZEmdzGQzWFTqfj2cuGct347lw1tqvP+zxy3iDuntqb6QM7aNdpmXSPcvc/HCPUhmQkNHhSY7zHvvS1+wod1zc9y6yWl+9yC9KVy/GRynHzDtKd3bmdVQEuo+1cMukl1Ra394vdbtcy6XGO4949JZp55w/iiYuH+PxZnDWoA7ed0ZPHLhrc5O/vRIZ3TaRjfARWm91nA71A8vx5u+4Bd93frQbN5TV1lFZZyFZ/R5OiiAozEunofeCreVx9PSC6eAbtLiemIkwGrcqkKWX/amVAh3rL3dUg3fn+qbFYqbEov7cJ0W2k3H3IkCE8//zzXtc///zzDB4c+DelEAF35r9Bb4K9S2H3Dye+vxBCtCOJiYkkJSVpH4mJicTGxvLmm2/yxBNPNPn5Fi1axN13383999/Ppk2bmDBhAjNmzODw4cMNPm7Xrl3k5uZqH71715+xE37avxz2ZoHeCFMfUq6LTYdrv1Z6u5z5b+g1NZgr1Hhm3tSgy3Os1h5HdjWnpJrDHn/Ib80p1TLYa/YVaUGZa5luWgBmUbtmGNWgMDHKRITJ4HNPuhqwq7epaygoMwesoV1mSjQPzRpIWqzvQOXUXincPbUPBr1zDvuJjrFn8zRPozKTMOh1HC6u4reDxRwsqsKg1zE6M6nBx/niq8P7Lsfl0d2V5/Pc/6x2d0+MdlZeqPvj7Xa71jUflG0S5S7VDVW1VqyODe9quTvAVWO7cdEI3ycrTQY9fz+zH6O6N/37awxnNUHFCe7ZPOrPWy09d82ku1aFRIcbtWZ67s3elMA6OUa5rdDHvvT6xgp6Be2ekwj86PDe+HJ35++kmlU36HXEhvs1tbxZ/HrFxx9/nLPPPpulS5cybtw4dDoda9as4ciRIyxevDjQaxQi8JJ7wrjZsPq/8P19EJMKnYYre9mFEKKde+aZZ9C5/Huo1+tJTU1lzJgxJCYmNvn5nn76aW688UZuuukmAJ599ll++OEHXnrpJebPn1/v49LS0khISGjUa5jNZsxmZ7amrExpOmSxWLBYmlcaqj6+uc8Tcuw2jD88gA6wDr8eW1xX0L5HPYy7W7no5/cd6ON2qFAJTDrHh2OxWEhxZLdyS6q117Da7OwpcAZxK3cX0GmkM6BatduZPS8oN7Mrt5SeqdEcOFbmeO4IrNY6rN7VuU3SIVZZ29GSGg4XKetJi1XWHeEoSqioqdPWXVKhBBHRjsxjUpTyOa+0moPq950Q0ervQXUdBeVmasy1WgC/M1fJpPdKjWpwTeF6GNw5jk1HSnnie6USZlCnOCIMTX9f9ExWgrldeeVYLMr4rpwSJdAb1S2erB0FFFaY3Z632NH5PzZMT6ojYMw5XonFYqG02qIF5SaDDovVzrHSKiINShBY5AjmTQYdBqxYLMFvjt0rNYoVu2FHbmmL/rumlrsPz4hn6c5jHC6q0O6njTOLMmKxWOicGEFRZS0HjpVzqFDptN8xTnmvJ0WbyD5eTUFpFRaLc3uA3W7Xtq90jA1zW0OneGdpeWpMGAZsbse+c3wEG4CDxyoafQzUEwtJUQafj4kLU/LWxyud759jZcr64iON1NU5T94059+1pjzGryD99NNPZ/fu3bzwwgvs3LkTu93OhRdeyM0338xDDz3EhAkT/HlaIVrXxL/D74vg+AF4bTJ0HAIjrodBFyp714UQop267rrrAvZctbW1bNiwgfvuu8/t+unTp7NmzZoGHzts2DBqamoYMGAADzzwAGeccUa9950/fz4PP+zdaXzJkiVERTW/fBkgKysrIM/TWozWKjqV/EZOwhisBu8MUpfi1YzI34pFH8lS81BqWyjREqjj9tsuPaCnLPcAixfv5/BxHWBgb84xLUl0rBpqLM4/bz9btY3Ygi3a119vV55Dhx07Ol7/+mcmdLCzNl95rvC6ioAknGx2MOgMWG3w6U8blNesKWPx4sUU1gAYKams0V7rF8frV5cVQyfYt3UDYCSnuIJVm3YAemqP57J4cU6z19YUVjvoUL6Pj7/6jjhH/LT5gAHQcfzAHywu3tbgc6TalGO+7uBxx9fH/TrGlRYAI0dLa/jsq8XkVStfx5vsHD+0EzBwKL/Y7blzi5R1bt/0GxXFyjp+3bKLzmU7OFKhPD7WZMegs1Ni1fHt0uV0c8SSRyuV28P1Nr777rsmr7cl1BQo75Nfth9ksW5/QJ7T8/fTYoOSauV3KLo6DzCwafdhFi8+iNkK5TXKbb+vXcEuIxiqleO6dO1GthbrAR2FB3eyuGIH1krltuW/bKD2gHMrQVmt8nuqw87va5fzh0ttd26J8j0CRGP2eq/UFCnPuXbLLjIqdpzw+7PaoKhCeR9sW7eKgz4q1/eXARjJKSzVXm9PqbIOk63W5/vVn3/Xqqoan/33O3ffqVMn/vWvf7ld9/vvv/P222/z5ptv+vu0QrSe8Fi47hv4+Qn44wvI/R2+uVv5CI+HuE7K7Ne+M2HUTZJlF0K0G2+99RYxMTFccsklbtd//PHHVFVVce211zb6uQoLC7FaraSnp7tdn56eTl5ens/HqKPeRowYgdls5t1332XKlCksX76ciRMn+nzM3LlzmTNnjvZ1WVkZGRkZTJ8+nbi45jUvs1gsZGVlMW3aNEym1t+b6C/D13eiP/w+Q8JzsF7yrvv/Y5XHML72NwD0p/+NqeMvC/jrB/q4vXJwLVDO9FNHMKVfGj3zynl551qqCGPmTOUETtb2Ati8mTCjnto6G4fNEcyYcTo6nQ5znY17f1sG2DhncEe+3pJHRVQnZs4cws6le2D/AYb16crMmQOavVaA/+5ZxcGiKsrCUoBiBvXMYObMgRyvquXRTcuptemYduZZmAx6clYdgP17yMzoCORw/plnMP/3lVRbddRFJQPFTBwxkJmjMwKytqZ47I8VFJSbGTTqNAZ1jqOqto67f1kGwFWzprjNDvclcX8RS97aoH39p2mjOK2Xf53P/7trBfnlZjKHjacuvwK2beeUbilMm9CDl3b8Rq0unJkzJ2n3n7vhR8DKzGmTiNx1jGVHdxGd3JGZM4fw/R/5sPV3enZIwGyxUZJXzsBho5nYW9lHv+5gMWxZT2pcNDNnnubXegOta04Z7+37hSJruPae91d9v5+Hi6vg11WEG/VcNHkEX761AbMxhpkzT+NQURWsW0VUmIELzp2GTqdju3EPm1YeILZDd6qKCwAz504ez+Au8aw0/8Efx3PolNmXmZN6aK+x6XAJbFhHh/hIZp3j/m/6gKJKXtqxGoBTenRk5kz3rdSVG3JYkvMH+thUZs4cccLvM7e0BvuvP2My6Lj43Bno9d5/z+8tqOC/f6yhVmdi5swzAfhuWx5s30KX1ERmzhx9wuPWGGqFV2O0foG9EKEkpTdc+KrS8X3z+7DhLSjaC+ZSOFYKx3bAvmVgqVLGuAkhRDvw2GOP8fLLL3tdn5aWxs0339ykIF2l8zjRabfbva5T9e3bl759+2pfjxs3jiNHjvDkk0/WG6SHh4cTHu69l9hkMgUssA7kc7W4qmL441MA9Hu+R79lIYy8QbnNbofFf4XKAkgbgGH87Rha8PsK1HHLduxhzUyNw2Qy0TlJSXker7Jg0+kJNxrYV6hkqqYPSGfpjnwKK2o5dNxM7/RYNhwpwlxnIyUmnOtOzeTrLXn8eqAYg8HI0VKlLLpbSkzAfsYZSVEcLKpic7ZSGt4xIRKTyURijLMJX61NR1SEiapade+zEvAmxkQQFWagqtaqPb57ANfWFB3iIygoN1NUVYfJZOJQfiV2O6TEhNEhwbtTvKfRPVK1kyYmg46xPVMxmfxrRNinQyz55Wb2F1az1/Gz7tchjvQEpVrmeFUtBoMRvV6Huc5KlaOreGpsFJ0cay0or8VkMpHr6BXQNSmaIkdZfLnZph1jdXtyfFRYyPze9+uUgE4HxZUWSs3Ke7m5PH8/i6uVY5YeF0FmqnKCM7ukBoPBSGGVUvbdIS6CsDDlvdotRfk9PFBUpTWcy0xTfkdTHXvAS2rq3F4jt1zZo56RFOV1bLumxKLTKf9MdUv2fs93d4z7yympadTPpahK2S6SFhtBeLjvE0qp8cr7p7ymDp3egNGgp7xWKbFPjA73+Tr+/LvWlPtLkC4EQFQSjL9d+agpg/JcKMuBfT/Bmv9B1oMQ0wGGBD7TIIQQoebQoUNkZmZ6Xd+tW7cTNnvzlJKSgsFg8MqaFxQUeGXXGzJ27FgWLlzYpNdu1zYtBKsZTFHKiebv/w+6nQapfWD9m7D7ezCEwYWvgSmwo6JaQmm1RWuupnaUTogyacHfsXIzXRKjtM7fp3SOp6TKwqq9hazZV0Tv9Fi38V+DuyQQHWbgeJWFnXnlAWvO5kptcKUGimmOgMVk0BNh0lNjsVFeU0dCVJjWzC4mwgi1OLrXR3CgsFJ7fCBGw/lDaTRXSr5jj7barO1ETeNUESYDI7omsnZ/EcO6JhIZ5v+kgL7psazcU8iu/HJn87oOsVp3bptd6dKeFB2mNY3T65SGfNo0AMf34TotQG0Q5zonW+vsHhkaATpAZJiBbo6TP7vzyknp1fwg3ZOzMVw4HeIj0OtQfscqzNptaS6jzNTGb+pUhegwg/bzUKssPBv6NfT7Fm400CEugtzSGp8jB9XHHCiq5LT/LDvh96N2aK9v/BpAgsvPuLTaQnJMuNY4Tv1eWptf3d2FOKlFxEFqX+g5GaY/CmNvU67/craSVRdCiJNcWloaW7Zs8br+999/Jzm5aWWqYWFhjBgxwmv/XlZWFuPHj2/082zatImOHTs26bXbLZtNCcRB6c7eYxLUVcNnNykz0X+4X7lt6kPQYVCwVtkk6h/1ydFhRDs6LbuPYVOCB3UkV58OsYxzjPla4xj79YsjSB/XMxmTQa91GF+zr7DeTtPN4RmAuI5/Usd3qfPR1bnXMS5dpD27zHdOCNzamkI7xqUex7iRQTrArKGdlM9DOjVrLVqH9/xy7YRM3/RYTAY9UQYl0FbHfbmOX9PrdW4j2Ox2u9u0AF9zssscQXp8CAXpAL19jKILJLVpXHpcBCaDno7x6tzyKpfxa873svo+V4PhjKQorUpKzfSrlQqq/Y4Gc92SfZ94Gt4tEZ0OhmZ4NyrtGB9Bp/gI7HaluuZEH4WO98PgLgn1fs9Gg16brKC+b9RxbIkn2M7RUpqUSb/wwgsbvL2kpKQ5axEiNE2fp2TW//gMFl0N130LnYYGe1VCCNFiLr/8cu68805iY2O18vIVK1Zw1113cfnllzf5+ebMmcPVV1/NyJEjGTduHK+++iqHDx/m1ltvBZT95Dk5ObzzzjuA0v29e/fuDBw4kNraWhYuXMinn37Kp59+Grhv8mS2f5nSFDU8HgZfCn3OgpfGKb1XXp+qBOw9zoAxfwn2ShtN7QTtOUM5PTaCI8XV5JeZqa2zsf+Y8sd/3/RYLTv2y/5iKsx1bDqiZPrUGd3je6bw065j/LSrgGOOMt3AZtLdg2rXTF5suJFj5WZtDFuF6wg2R+zVIT7C7bERfpaIN1cHj/niuxo5fs3V5aMymNQ3td451Y2lzkrffLiESkeFQe/0GMBOjAmqrMq4r97pzqy4GoCr2d/aOhslVRa3bG6O4yRNia9MekRoFR73TY8la3u+drIk0ArK3QPxjKRIckqqOXK8yucos04JkVp5OriPTFNHsHlm0k90oufZy4byj7MHuP0OqIwGPd//dSL7Cho/hs5k0NO/Y8O9SRKjwiivqdPeA2qwHqyTNE1618XHN9zxOj4+nmuuuaZZCxIi5Oj1cMHLUHkMDq5U/sAZeQNM/BvEpAV7dUIIEXDz5s3j0KFDTJkyBaNR+VPBZrNxzTXX8O9//7vJz3fZZZdRVFTEI488Qm5uLoMGDWLx4sV069YNgNzcXLcy+traWv72t7+Rk5NDZGQkAwcO5Ntvv2XmzJmB+QZPFvl/wMfXQc8pSsZc7yiQ/O0N5fPQKyAsWvk493/w0dVK6XtkEpz/kvP+bYBamqzOX1Y5s6M1HCispM5mJzbcSMf4CNJiw4kJN1JabeHdtYewWO10ToikqyPQd2balQx7TLhRC+gCwTPgT3fLpKuz0uvcPrvOY/aVrQwG7Rg7ysTVOeV9O8TU+xhPOp1Oy8g2hxKQowXoXZOiiApTRoHFmqCgxpm11TKhjhnY4UalDPt4lYW8shqtx0FGUiQ785Sfu+ucbLW6IdQy6Wo1wa68lgnS1aoU9YRKRmIUv1DsOBnmHaSHGfV0jIvgqCMD73pyKjlaOTHiOifdarNrWxX6dvAdpJsMep8BuiouwsSwrk0fB9qQxCgTh4t9ZNKj2kAm/a233mqpdQgR2ozhcPl78MkNsHcprHtF2e837jYYf4dSIi+EECeJsLAwFi1axLx589i8eTORkZGccsopWlDtj9mzZzN79myfty1YsMDt63vvvZd7773X79dqF2xW+PJ2KNytfNRWKIF4Wbay3xxg5I3O+w+YBaNvhvVvwXkvKNNL2pBsl9JkV2qwkFdWo5X/9k6PQafTYTToGJOZxI87C3h5xT5ACczVUtwBHeOIjzRpGdMuiZH1NjP0h+ta9Trcmnyp5e7qXvRys6PcPcKIOqTJtdw9WPvRwZmBziutodQR4IKz7Lo1RYUZ6ZoUpXQgxz0TG2OyAzota6uVu7sE2elxERyvsrAtpxRznQ29TskEq4FYqO9JB2c1we78igYbcPpLLXdXf+7qe+9IcZXbfnVXXZKinEG6ywmlFEcmvbjSjM1mR6/XcaS4CnOdjXCjXjthFgriHe8Bz0x6sPakh1b9hhChLCIervoU9i+HpQ/D0Y3w8+Ow/Qvl+oSuwV6hEEIEVO/evendu3ewlyF8Wf+m8v+QKQrqamCTY8RaVArYbZA5UWkS52rG4zDtETAFZ29zc2h7xr2y00qwUFBmxqRXM7zOwG1cz2R+3FmgBVxqqTuAXq9jXI9kvv9DaWoY6EA4McpEdJiBylorqbHhGFxGP6l7z73K3cOdQbprJtGzgqA1qesoKDezu0A5xp3iI4iLCE7w0ic91iVId2bzYxzLce5JV8vdnZnQ9LgIduaVs94xs71jfCQmg76ePenKzyTUMumZKdEY9ToqzHUcLa0JeK8CX+XuAEeOV2lbHtI9ti1kJEax7kCx4/7O3yN1P7drQz/Xk2kGH+PQgiXR4z3g6/3TmtpOnZMQoaLHJPjzMrj0XYjrrGQwXp8GeduCvTIhhAiIiy++mMcee8zr+ieeeMJrdroIgvI8+PER5fK0R+CCV0Gnh43vwKpnlOtH3eT9OJ2uTQbo4NIN2muft7PcfZePfa7je6a43X9cz+R6vw50SblOp9MCFs+gRi13L/Msd4/wXe7uuRe/NaXHKusorqxlW44yDq5PPWXKrcE1MHc9IRPriKULK5XgqtRHJlQ9qbP+kBJQOicFOLKo1c5Mepm2Jz20gvQwo54eqco4ud0BLnm32+0+y90Bt3J3z94Crr+XrpddT4CoJ092N3E6QGvxrKbQurtHSyZdiLZDp1NKBzuPgIUXKfPU35oBl78PmROCvTohhGiWFStW8M9//tPr+rPOOosnn3wyCCsSbn74PzCXQafhSo8UvUHJnn9+C2BXRob2PfH+/ZKqWvLKaujXIfBbtuqsNn47eJw6m+/bS6ssrN1fhE3tNnUCWifuevZ555XVYHOM0err8sd/P8d4ruNVFjJTor32Rbtm1gPZ2V3VJTGKnXnlPoJ0j3J3R5Du2t1dDY4huHvSXUfdrdqjdMrvG8QAyzUw9y53986ku3bnVoPLfY4Gg+pJFC2LWum9Jz0uMvTCpT7psezOr2B3fjln9HP2R9qZV0ZabARJfnYkLzfXuYwMdC93zymp1u6X6jF5wPX96fleTY4Oo6TKojX0c+3KH0rUkwnHqyzYbPa2tSddCOEhvjPc8B18cCUcXgMLL1T2BA69ItgrE0IIv1VUVBAW5v2HiclkoqysLAgrEpq9P8K2T5XM+TnPKAE6wJDLlM/f/z84/V4wnDj7c8cHm1i1t5Bv7jiNgZ0abg7cVO/+coiHv97OWV30zPJx+z0f/87SHflNek51/7ArNTOac7yaWqtyRsA1y6vX6xjbI5nvtuUxtof3+MBeaTGkxoZzrNzcIvtj1ef0zDzGRDjL3c11Vm3trpn0tLhwrWt213pGVbUGddTdkeJq1u5XmuwFMwuqBulGvU7LKIMzk+61J90lk57mo0wbnIFYubkOi9WGyaDXtkiEWrk7KAHuN+S6jWFbtaeQq9/8lVN7prDwpjF+PW+BI1MeG2EkKkx5L6bGhGsnaUA5np6TBtT3p+uIRFVyTDj7jlVqDf1cxySGkkSXPenl5joc5/zaRnd3IYQPkYlw9efK/NkdX8MXt8Kh1TDziTZbViiEaN8GDRrEokWLePDBB92u//DDDxkwYECQViWw1MC39yiXx9zqPQ50yGXKyLVGNJIqq7Gwem8hdjv8dqA44EH6Xsd4pG3HvddirrOycs8xAIZ1TcDUyC7z0wakE2Z0v6+aoTY7Aojk6DC3Bm0Af53WB6NBz21n9PR6Tp1Ox6PnDWTlnkIm9klt1Dqa4soxGeSX1fCnse59a+JcururWXRAC4wAIkwG7j2zH2U1lqDNSFepo+7ULGt9XblbQ9/0WO6Y3IsO8RGEG53BopZJr1TLlb0zofWVacdFmrQTIiVVFlJjw0O23B3c58WDUqb+xA87sdvh9+wSv59X3XPuepz0eh1dEiO18Ya+xugNy0jgitEZDPMx1zzFZQyb65jEUCt3d+1LoL53Ik2GoI0+lCBdiEAwRcAlb8PPT8Dyx5QGPjkb4ZIF3o17hBAixP3jH//goosuYt++fUyePBmAH3/8kffff59PPvkkyKtrx/YuVeafx6TDGf/n+z6N7PS8bn+xlilS514HkprNzKl0BD3xzkBn0+ESzHU2UmLC+ewv45vVnTo63EhsuJFyR9m4rz/8+6TH8twVw+p9jrMGdeSsQS3T7b5XWiwv/Gm41/VqWXtFTZ3WNC4m3OjVSOsvk7xPLARDuksTO51OqUAIFp1Oxz3T+3pdr+1J18rdfXd3d6WWchv0OuIjTVqAlhBl0sa8hWomHWBPfgVWm51lOwv4PVvpF1BeU0dplYV4P7qSq53dfTWGU4Nrz2oEUGaXz79wsM/nVMewFVWYtTGJMeFGOjUwYi0YElz2pAe7sztI4zghAkdvgEn3wTVfQHQaFPwBr06CfcuCvTIhhGiSWbNm8cUXX7B3715mz57NPffcQ05ODsuWLaN79+7BXl77tecH5fPACyC8eVkodTY4OLNxgaSWttrRse5gsc/XHu8yDq050lzGQQUzw9sU6p5010y6a6l7qHHdH989OTpo2cWGxDgOX3lNHeY6q9b4y727e/17qdVg/niVxa26IRR/LhlJUUSY9JjrbBwsquTprN1ut6s9HJoqv9x9/Jrz9ZyVHOke+9FPJNmRSS+srHVp7hgT8NFxzeXa3T3Ynd1BgnQhAq/HJLh1FXSfAJZK+Og6KNwb7FUJIUSTnH322axevZrKykr27t3LhRdeyN13382IESOCvbT2yW6HPVnK5d7Tm/10a/YVapd355Vjb2QDt8ZSM+kAv+x3D9LXOl57fE/vPeL+cB1VFmoltPVxdne3aGPYYsJDLxhUuQa3rt3VQ0mkUdmnDsr7Tyt3d+nOnRzjHIUXZtS7zaJPcNmTrO5Hjwk3YjSEXrhk0Ovonaa81/+7dA87csuIDTfS07FHX52G0FQFPsrdwf1kRocmZsCTY5yZdLWzeyieTHPt7u7rvdPaQu9dJ8TJIDZdmZ2eMQbMpfDB5VBT2rzntNfTIlcIIVrIsmXLuOqqq+jUqRPPP/88M2fOZP369cFeVvuUtxXKc5W56N1ObdZTFVWY2en4Y9mg11FuriPP0TAqUNSSY4C1LkF6VW0dmw6XAN7j0fzlmuXt2yE0A0hPauO4CnOdVqofihlblWtgFmpduVV6HVpX80NFVdQ59nO47kk36HWkOoLGLgmR6F22F7hmUp370UP3Z6KekPrq96MA3DghU+st4W8mvd5yd5emir7K3RuSEu3ck+5rTGKoUPekm+ts5DqOg2TShTgZGcPhsoXKLPWiPfDJjWCz+vdcxw9ifG4o4/b+R8mmCCFEC8nOzmbevHn06NGDK664gsTERCwWC59++inz5s1j2LD69/aKFqSWuveYpPRBaQY1s92vQyw9UpTM264AzluurbNp878B9h6r5Fi5ErQrY9nsdE6IDNjIM9egoXcI/vHvi6/GcbEh2KBMleZyIiTUunK7UoP0vceUPgsRJr1Xab5aFeA5e941k+ocvxa6PxPXE1IJUSZuOC1T+506Ulxd38MapJa7N7QtoOnl7o5MemWttrUmFE/0xIQbtUqMg4XK/vuEIP78JUgXoiXFpCmz042RsDcLfny46c9RZ4aPr0NXfpS08j/QHfw58OsUQghg5syZDBgwgO3bt/Pcc89x9OhRnnvuuWAvSwDsXgLAp+UDWPjLoWY91dr9Srn5uJ7JXl2iA0Hdz2nQ6+gcZXe8prIPfY1LqXug9qR2cAQUHeMjQrITty+uc9LVrG1MCGdt28qWgmRHkL7PMV3A14xrNUuckeh+ksjZOMyilbuHcpDuekLqlok9iYswacG0r0z6oaJK/vzOejYfKan3OdVyd+9MuvNYNb3cXTmuuaXVHHaU4YfiiR6dTqdl0w8WKusM1ox0kCBdiJbXaSic/4JyefV/4bv7oLYJZUg/3A9HN2lf6n95PrDrE0IIhyVLlnDTTTfx8MMPc/bZZ2MwhF5zqHapsgiyfwPgyf3d+OdXf2iZHn84G7elaBmtXXmB6/CulronRZnoE+8I0h3B+Vr1tXsFZj86QL+OcQCM6p4UsOdsaer+c6vNzjHH8Qrl0mrlBIiRlJgwMlOiT/yAINGCdEcm3Vdn9v6O98vgLu5jB50juGopq66r9/GhYnDneKLCDHSMj+Da8d0AZ1m6rz3p7649RNb2fP728e9Ybd5VmTabnfwy3+Xu8ZEmMpIiiTDp6ZbctJ9/iqO7e43Fht3ue0xiqFBP1OxXM+nttbv7zz//zLnnnkunTp3Q6XR88cUXDd7/s88+Y9q0aaSmphIXF8e4ceP44YcfWmexQjTHoItgkmNczq8vwSsT4Mi6Ez9u26fw22sAWM98HDs69Pt/grxtLbhYIUR7tXLlSsrLyxk5ciRjxozh+eef59ixY8Feltj3I2CnKKY3uSRjtdn57497/HqqvNIa9h+rRK+D0ZlJWlY0kJl0tWlccnQYvR1B+pp9RZRWWdiWo/RnGdcjMPvRAcZkJvHtnacx/8JTAvacLS0qzKA1MMstUUqTQ7lxXITJwLd3TuCr20/DFIKN1FRq1nZvA5n0287oxVe3n8rFIzLcrlf3pB93aRwXypUZyTHh/HD3RL66/TSiwpT3jppJzz5e7dUMUt0Pvreggq9+z/F6vuKqWupsdnQ6SPUoadfpdHx663i+v2tik09cxEU6y8ghtCsx1PeAeqKx3WbSKysrGTJkCM8/37jM4M8//8y0adNYvHgxGzZs4IwzzuDcc89l06ZNJ36wEME26f/Bnz6B2I5QtBfePBM+uwU+uxkWnAPPjYAXxsDXd8GWj+HQGvjqTuWxp83BNvIGchJGK1+vlWy6ECLwxo0bx2uvvUZubi633HILH374IZ07d8Zms5GVlUV5eeBHdYlG2K0kJDZHjNGu+mJzDnv8CKzVUvdBneOJjzRpXZb3FJT7zK75Qx2/lhQTRs84Owa9jkNFVXy2KRubHXqkRDe5ZLYhOp2OgZ3iiQ7hINeTTqfTgvKjJUr2MpT3pIOSpe2UEJg+Ai1FzaSrjb98decOM+oZ3CXBaya9s7u7RduTHsqZdFB+Jq4BdceECPQ6pfmZ2gdC5Xoi7tmle7BY3RsSq1n05Ohwnydi0uIi6O5HFYVOp9NOnkBodnZXeTaKa7fd3WfMmMG8efO48MILG3X/Z599lnvvvZdRo0bRu3dv/v3vf9O7d2++/vrrFl6pEAHSexrMXgtDrlC6tW/5ELYsgoMrlcD92E7YsAA+uwnemgG1FdDtNDjjfgD2pc9Qnmfrx1DqfRZUCCECISoqihtuuIFVq1axdetW7rnnHh577DHS0tKYNWtWsJfXvtissHcpAFmWwYDSHMtuV/7Qbqo1e5Vy83GO8Wddk6IIN+qpsdj8HtvkyTWTHmGAUzor5cUvLt/n9trtnRaklyqZ9FDu7t5WqI3jVE3pzp3oEqQ796S3rZ+JyaCnY7yjeZzLvvSSqlryy9TssIlDRVV8vumo22Odpe6BL0VPjnYd4Re6QXqiR3l7MLu7t613ngebzUZ5eTlJSfXvQTKbzZjNzjNJZWVlAFgsFiwWS7PXoD5HIJ6rvWj3x8wYA+c8h67/+egOrIDoFOyxHSGmA9RWoDu8Ft3hNejytkBMB+rOexlsdiwWCyVRPbBmjMNwZC3WtS9im/JQsL+bkNfu329+kGPmn+Yct1A+1n379uXxxx9n/vz5fP3117z55pvBXlL7kv0b1JRgj0jgm+IuAMw7fxC3vb+Rb7fmMvtoqTZ26UTsdrvbfnRwzFtOj2FbThm78sv9ypR5KnQE6WrANC4zic1HSrXMXqBGr7V1alCuBkehXO7eViR5ZD49g66GJLiUu5e1gXL3+nRJjCSnpJojxdWMULaqsztfKf/vnBDJDadl8ug323l++X7+1s/5uPx6ZqQHgnsmPXTHJHqWtwezu3ub/tfgqaeeorKykksvvbTe+8yfP5+HH/buqL1kyRKioqJ8PMI/WVlZAXuu9kKOGcBYqAWOA5Q7r+swFkNqDXadHtvKjW6P+M00jrGsxbbuDZZUnUKdIbRLz0KFvN+aTo6Zf/w5blVVgclgtiSDwcD555/P+eefH+yltC+OUveqjElUbIUwg57pA9I5d3Anvvr9KM9k7eb1a0c16qmOFFeTU1KNUa9jVPdE7fo+abFsyyljd145Zw7s0OwlFzn2cyZHh0EljO2RxEs/H9BuH9uj7TR4a0lqAGixKtsMQr3cvS1I9sykRzYhkx7tnUkP9XJ3XzKSovj1QDHZLpn03dp88hj+NKYrr/68j9zSGtbk61Bro9QZ6U2dg94Yro3iQnlMYnyU50keyaQ32QcffMBDDz3El19+SVpaWr33mzt3LnPmzNG+LisrIyMjg+nTpxMXF9fsdVgsFrKyspg2bRomU9v7RQ4GOWb+UY/b4Ivuwf7WN5gKdzPDshh78mAwhGE3RWHvOxOiU4O91JAi77emk2Pmn+YcN7XKSwgve5TRaweSTgWgZ1oMRoOeu6b25pstR1m6o4DNR0oYmpHg9VCrzc6W7BJq65S9pyv3KPvRh3VN0BpNgXMc0q4T7HEvKKshIsxwwuxiUaWz3J1KGN41gTCDnlqrjX4dYrW5ye2d58i1UO7u3la4Zmyhad251axprdWmBayhPIKtPtoYNpdZ6VqQ3iGWCJOB2yf35h9fbCMrR88FB4oxGY38cVT5f6hlyt2Vn0unEB+T6BqU63TB/fm3yX8NFi1axI033sjHH3/M1KlTG7xveHg44eHebzaTyRTQPz4D/XztgRwz/5jCwtGNvwO+ugP9ji9hx5fOG7cught+AH3odl4NFnm/NZ0cM//4c9zkOAufdn4L+dsAHb8ahgEF9E1XSkV7psZw4fAufLIhm6eW7OLdG8d4PfzJJbt4ybEP3NU4j3Lzvo3o8L7vWAXnPreK7snRfHvnaQ3OOHfNpJtROoMP75bAL/uLpdTdhece9FCek95WeGbSm5IJjQozaCeT1HnebTOT7r0nfVee8rut/q5fNjKDl5fvJaekhqveXO/2+JYpd1disVDOooP79oj4SJNXc8HW1Ob+Nfjggw+44YYb+OCDDzj77LODvRwhgmPon6CqCEqOQJ0Z6mpg13eQvQ62fgRDLg/2CoUQQjRH3jb49M/K5TG3sKVY+ZOtj0tn5Lum9OaLTTms3FPIugPFjM50lpHnl9Xw5iqlxDwzJRr1b834SBOXjOji9lLqc+4/VkltnY0wo/eJ3meydlNVa2V7bhmHi6sanJWs7UmPCSPXcd2dU3pj1O/juvHdG30ITnaeQbqUuzdfVJiRSJOBaosVaFp3bp1OR0KUiYJyM2ZH9UlbaxwHLrPSHUG63W53KXdXftfDjHoePrc/D366kYioGNRzbskx4Uzpnx7wNc0Y1IGfdx/j+lO7B/y5A8m1UVwwS90hyEF6RUUFe/fu1b4+cOAAmzdvJikpia5duzJ37lxycnJ45513ACVAv+aaa/jvf//L2LFjycvLAyAyMpL4+MY1TRHipKA3wGl/db9u1TOw9CHIehD6nQ3hoX22UgghBPDFbGXf+YQ5MPoWMBihshA+uAIslZB5Okyfx67n1gLOTBgof4xfNiqD9349zJNLdrHo5rFahvvFn/ZirrMxslsiH986rsHMd6f4CGLCjVSY6zhQWOk1ImlHbhnfbMnVvl6zr6jeIN1ut2sj2JKjnUH6+J4pkkX3EBNu8vi67QWEoSg5Jozs40qpd1O7cydGhVHgMrqsTWbSHeXuR0tqqLPaKK6q5XiVBb0OeqU5m7ad3ieV/zfEysyZp7Z4NVf3lGg+uHlsi75GILgG5k3ZKtESgloTu379eoYNG8awYcMAmDNnDsOGDePBBx8EIDc3l8OHD2v3f+WVV6irq+O2226jY8eO2sddd90VlPULEVLGzoakHlCRDz8/EezVCCGEOJGyXNj8PlQVwg//B69MhP0rYNHVUHpY+Tf9kgVYMLD/WCXgPb7o9sm9CDPqWXegmNWO8WrZx6t4f53y99M90/s2GKCDkkHs4yij97Uv/Zms3QBahl3tEO9LVa2VGouShfQsPRbuvDPpEqQHgmvPg6ZmQz0Ds1DeP12ftNhwwox6rDY7uaU17M5TOrt3T44mwmQI8upCm2u5ezA7u0OQg/RJkyZht9u9PhYsWADAggULWL58uXb/5cuXN3h/Ido1Yzic9Zhyee2LULi34fsLIYQIrh1fA3aI7wqRSVDwB7wzCw6vgfA4uOJDiEriUFEltVYb0WEGOie4T/ToGB/Jn8Z0BZQ96Ha7neeX7cVitXNqr+RGzyRXs+d7PIL0LdklLNmej14H/zx3AABr9xVht9t9Po86Iz3CpCcqTAKChrg2igsz6CWACpAUl5NDTW3G5xqkG/W6Nvke1ut1dElw7kvf5VHqLurn2t092OXu0l1KiJNJnzOh95lgs8D390E9f0QJIYQIAdu/UD6PuQXu2ADDr1W+1unh4jchtS8AuxyZsN7pseh9NDL6y6SeRJoMbD5SwpurD/LxhmwA5kzr2+ilqH/Aqw2mVE87sujnD+3MxSO6EG7UU1hhZm9Bhc/nKdRK3cNPmMFv71wbxUnTuMBRO7zHRRgxGpoW6rgGZnGRpjb7Hu7i2JeeXVzN7jxnZ3fRsHCjQTsx09StEoEmQboQJ5uz5oPeBHuz4PUp8NZMeHsWfHyd0ohICCFE8JXnw6E1yuUBsyAqCWb9D2b/Ajcvh97TtLvucplx7EtabATXOhqyPfrNdqw2O2f0TWVEt0Sf9/fFV4f39QeLWb7rGAa9jrum9ibcaGBUd6U5XX0l72omPSVGSt1PJNZlT7qUugeOWu6e6Md2C9fArC3uR1dlJPrKpPv+90O4U0/UJLbnPelCiBaQ3BPG36FcztkAh1bDgRXwx+fwxjTY/mXDjxdCCOG3I8VV3PvJ7+wtaHjmODu+AuzQeQQkdHVen9YfOg5xu6uWCWugXPWWiT3cGo81JYsOzizboeIqblzwGzcu+I27F20G4JIRXbRGcWr5/Jp9hT6fRxu/JrPQT8g1ey5BeuCovRD8yYS6BmZteW692uH9cHGVtoWlr5S7N4q65SEhyD012u67TwhRv8kPQOZEqK1USt9tVti0EPb/BB9dAxPvhUlzZZ66EEIE2Ks/7+ej9dmEGfXMO/+U+u+onjAdcP4Jn1PNbnt2XXeVGB3GTRMyeXbpHmYM6sApXZo29SYlJpwuiZFkH6/mx50F2vVhRj23T+6lfT3eEaT/sr8Yq83uNUe4qFLJpEvTuBNzDcyls3vg9EhVTih1T45q8mM9y93bqi6OTPq6A8VU1loxGXR0T6l/bKJw6p4czR9Hy/x6/wSS/IsgxMlIb4CeZ7hfN+B8ZTzbLy/Az49DwXa46HUwRfp8CiGEEE2nZpiPuYxx8lJxTKlyAqXUvQE1FisHi5TO7ifKhN0xuTendI5nbI/GNYvztPDGMaw7UOx23YBOcXRJdP6xekrneGLCjZRWW9iRW8agzu4nAwolk95orp3DZUZ64Ezqk8Y7N4zmlM5NH8/s2jiuLQfp6hi23NIaAHqmxmBq4v789urR8wdx+egMTg3yyEgJ0oVoLwxGOOvf0GEQfH0X7PwGfnxUuU4IIUSz5ZfVsM8xKk3dm+3Tjq/AboNOwyCxe4PPubegAptdCR5SYxsOfA16HVP6pzd12ZruKdEnzLYZDXpGZyaxbGcBa/cVeQXpsie98Vyz51LuHjh6vY6JfVL9eqxriXxbHL+mUsvdVdLZvfGSosOY0Nu/908gySkVIdqboVfCpe8ol395EQ7/Etz1CCHESWKtSzM1tezbJz9K3fukx4ZMp+nxDexLL1K7u0uQfkJue9Kl3D0kuO5Jb8uN4xKjTES7jI9raKuMCE0SpAvRHvWdAUOuBOzw5W1gqQ72ioQQos1zDVrVsm8vlYVwcKVyecB5J3zOXSHY9EltHrfuQDEWq83tNjWTnhwt5e4nYjLoiXTMRpdy99DglkmPbLsnTnQ6nVs2XTLpbY8E6UK0V2f9G2I6QNFe+OlfwV6NEEK0ea5jycpr6jDXWb3vtPMbpdS94xBIyjzhc+7JV+aRh9KM4/4d4kiIMlFZa2VLdonbbYVqkC6Z9EZRs+kyJz00JJwkmXTArZdEKJ3kE40jQboQ7VVkIpz7rHJ57Qtw5LegLkcIIdqyI8VVZB+vxqjXaR3Piz1L3u12ZdIGaKXuVpudCnNdvc+7Ky/0Mul6vY5xjuZ0riX+NpudYke5e4o0jmsUdS+67EkPDSaDXtt60Jb3pANkJCmNgSNNBq3bu2g7JEgXoj3rOwMGX65kdRZdBW+dDa+eAS+Mhfcvg/ztwV6hEEK0CWqwOjQjQRs/5tU8bsfXkP0bGCNhyBUA3LpwA+Pm/8jeggqv5zxWbianRNmO1Cc9pgVX33TOfenOIL2k2oLNrlxO9GNGdXuklrlLuXvoiHdk09tyd3dwdnjvkx6DXh8a/SxE40mQLkR7d9Z8pey9Ig8OrYKjG+HYDtj9Pbx8GvxwP5jLg71KIYQIaep+9PE9k7XxY2770q0WWPqQcnn87RDXkTqrjRW7j1FeU8czWbu9nvPVn/cBMKRLvNte2VCg7ktff+g4NRalrL/I8f3GR5oIM8qfmI1x8fDODOgYx9geScFeinC4YnRXhnSJZ3jXhGAvpVmm9k+nX4dYrhzTNdhLEX6Q2hoh2ruoJLjhezi8FowRYIoCgwk2vKVkfdY+D9s+g3OeVjLvQggh3Njtdi2jPLZnMpuOlAAemfT1b0HxPohOhVPvAuBQcRW1dUrjtW+35jL7aCkDOykjzQrKanhn7SEA7p7Wp5W+k8brmRpDamw4x8rNbDpcwrieyVpHe9mP3nhXj+vO1eO6B3sZwsVtZ/TitjN6BXsZzdY1OYrv754Y7GUIP8lpTiGE0rxo6JUw6ELoexb0mgKXLYQrP1Zm+JYfVcrhc7cEe6VCCBFy9h2rpKDcTJhRz/Cuic5yd8f+bGpKYcVjyuVJ90G4sr98d557lZJrNv2Fn/ZirrMxolsik/yc+dySdDqdVvK+1lFFoM1Il87uQgjRLBKkCyHq12c6zP4F+swAWx188Reoa2D2rxBCtENqkDqyWyIRJoNW7q7NSl/1LFQVQXJvGH6t9jh1vNro7knodbB0RwGbDh8n+3gV7687DMA90/uEzHx0T5770mVGuhBCBIYE6UKIhpkiYdZzEJUM+dvg58eDvSIhhAgpapCqBq1qkFpUUQul2fDLi8odpz2sbCdy2O0I0qcPTOei4V0AeDprN88v24vFamd8z2TG90xprW+jydS1bT5SQqW5TsavCSFEgEiQLoQ4sZhUOPsp5fLKpyFno/O2OjPs+wmqioOzNiGECCKbzc7a/UqQPs4RtKrl3kUVZlj7ItTVQNfx0Hem22PV8Wp90mO5c0pvjHodK/cU8tH6I4CSRQ9lGUlRdEmMpM5m57eDxVrjuGQpdxdCiGaRIF0I0TgDL4CBF4LdqpS9F+2DHx+BZwbCu+fDG9MkUBdCtDs78sooqbIQFWZgcBel6ZuWSa+shdzflTsOvwZcytZrLFYOFlUB0LdDLBlJUVw2KgMAmx0m9U1lRLfQ7/jt3Jde5NyTLpl0IYRoFgnShRCNN/NJpTPxsZ3w3HBY+RRUHlNuK9qrNJerMzf8HEIIcRLZml0KwPCuiZgMyp9V2p70ilrl30aAFPes+P5jlVhtduIjTaTFKve/fXIvwh2jy+aEYEd3X9SS9zX7irQ96UmSSRdCiGaRIF0I0XjRyXDuf51fd58Al7wNt/wM4XFwaDV8eRvY7cFboxBCtKLjVRYA0uKcgana3b264jhU5Dmu7On2OHU/et/0WK0xXMf4SD64eSwLbxzD4C4JLbzywFDnpW87WsqBQqUyQPakCyFE88icdCFE0/Q7G25apowQSnXJ9Fz6Drx3MWz9GBIzYfL9wVujEEK0krIaJUiPj3Q2hFOD1E7Wo8pfWtGpEJng9ji1s3ufDjFu1w/vmthyi20B6XER9EyNZt+xSgode9Kl3F0IIZpHMulCiKbrMsI9QAfoeQac86xy+efHYdUzYLO1+tKEEKI1lVUrQXpchDNIjwozEmky0EOXq1yR3MvrcXtcMultnWcHemkcJ4QQzSNBuhAicIZfDRP+plxe+pCSWa8oCOqShBCiJZVWe2fSQcmm99DXH6RrmfSTIkhP1i4b9DqvYyGEEKJpJEgXQgTW5AfgnGfAGAH7foSXxsOepcFelRBCtIiymjoA4ryC9HAydep+dPcgvdJcx5HiauDkCNLH9HAG6UnRYej1ugbuLYQQ4kQkSBdCBJZOByNvgJuXQ9pApfv7exfBV3fIiDYhxEmnvkx6SnQYPXRHlS88gvQ9BRUApMaGkxjd9vdvJ0WH0b9jHOBsmieEEMJ/EqQLIVpGWn/48zIYfbPy9cZ34PmRsGmhdH8Xoh168cUXyczMJCIighEjRrBy5cpGPW716tUYjUaGDh3asgv0U7m2J929F29ytMmZSU/p7Xbb7ryTZz+6Si15T4mR/ehCCNFcEqQLIVqOKQJmPgHXfw+p/aGqSBnR9tZMKDkc7NUJIVrJokWLuPvuu7n//vvZtGkTEyZMYMaMGRw+3PC/A6WlpVxzzTVMmTKllVbadGom3bPcvVt4OTG6GmzoIbG7220n03501YXDO5MUHcb0genBXooQQrR5EqQLIVpet3Fw60qY9giYouDwGnhtCmSvD/bKhBCt4Omnn+bGG2/kpptuon///jz77LNkZGTw0ksvNfi4W265hSuvvJJx48a10kqbxm63+xzBBtAdpWlckakDGN2zy9qMdI/xa23ZwE7xbHhgKteM6x7spQghRJsnc9KFEK3DYIJT74IB58OHf4L8rUpG/fwX4ZSLg706IUQLqa2tZcOGDdx3331u10+fPp01a9bU+7i33nqLffv2sXDhQubNm3fC1zGbzZjNZu3rsrIyACwWCxaLxc/Voz2H62dVVW0dFquyfSfS6H57B4tSJZCt70yCx+N2OcrdeyRHNnttoay+4ybqJ8fMP3Lcmk6OmX+ac9ya8hgJ0oUQrSuxG9zwPXx6E+z+Dj69EfK2Qr9zIKUXRCYGe4VCiAAqLCzEarWSnu5eBp2enk5eXp7Px+zZs4f77ruPlStXYjQ27k+V+fPn8/DDD3tdv2TJEqKiopq+cB+ysrLcvi4xAxjRY2fF0iXoXJqadzykVArtMidzePFi7fpKCxSUK9/T/s1rOLo1IEsLaZ7HTZyYHDP/yHFrOjlm/vHnuFVVVTX6vhKkCyFaX3gMXP4eZD0Ia5+H1c8qHwDRqZA+CHqeAT2nQPpA3P7yFUK0STqP32O73e51HYDVauXKK6/k4Ycfpk+fPo1+/rlz5zJnzhzt67KyMjIyMpg+fTpxcXH+Lxwl+5GVlcW0adMwmZxl7bvzy2HjWuKjwjj77DPcHlP9zgIogwO6DP42c6Z2/bqDxbB+PV0SIrjw3InNWleoq++4ifrJMfOPHLemk2Pmn+YcN7XCqzEkSBdCBIfeAGf+CzoOhU3vQOFeKD+qjGzb/5PykfUgxHSA/ucopfIJXYO9aiFEE6WkpGAwGLyy5gUFBV7ZdYDy8nLWr1/Ppk2buP322wGw2WzY7XaMRiNLlixh8uTJXo8LDw8nPNy7s7jJZArYH6Cez1WljEgnPtL7NXTlBwHYZk5DbzBicMwO31+ozEfv2yGu3fxhHMifQXshx8w/ctyaTo6Zf/w5bk25vwTpQojgGnyJ8gFgLofCPXBkHez7EQ6ugoo8+O112PA2DL0SJtyjlMwLIdqEsLAwRowYQVZWFhdccIF2fVZWFuedd57X/ePi4ti61b0G/MUXX2TZsmV88sknZGZmtviaG6u0yndnd+pqMZQeAmCfrQMlVbUkO0aTqZ3de59End2FEEIElgTpQojQER4LnYcrH2NvhTozHFgJa/4HB1bAxrdh83sw4W9wxtxgr1YI0Uhz5szh6quvZuTIkYwbN45XX32Vw4cPc+uttwJKqXpOTg7vvPMOer2eQYMGuT0+LS2NiIgIr+uDrb7O7pQcQme3Uk04eSRRVOkM0v84qpQ7nkyd3YUQQgSWBOlCiNBlDIfeU5WPQ2thxWOwf7nyuetYZd+6ECLkXXbZZRQVFfHII4+Qm5vLoEGDWLx4Md26KVUxubm5J5yZHorK1BnpER5BeuEeALL1nQAdhRVm+qTHUmGuY0t2KQCjuie15lKFEEK0ITInXQjRNnQbB9d8CaNvVr7+9h6w1AR3TUKIRps9ezYHDx7EbDazYcMGJk50Nk1bsGABy5cvr/exDz30EJs3b275RTZRabWyKd2r3L1oLwAFYUofjaKKWgB+O1CM1Wana1IUXRID03FeCCHEyUeCdCFE2zL5AaWZXPE+WPV0sFcjhGjH1HL3uEiPwsQiJZNeGqUG6cr89jX7CgEY3zO5lVYohBCiLZIgXQjRtkTEw4zHlMurntHKSjUVBWC3t/66hBDtTml1PXvSi/YBUBmjNLkrqlQy6Wv2FQEwToJ0IYQQDZAgXQjR9gw4H3pNA2stfPNXpez990Xw+lR4sjd8fB3YrMFepRDiJFfvnnRHuXtdYg8ACitqOV5Zy/ZcpWmcBOlCCCEaIkG6EKLt0eng7CfBGAEHVyqB+ec3Q/Zvyu3bv1BmrAshRAvymUmvKYOKfAB0Kb0Bpdz91wNF2O3QOy2GtNiIVl+rEEKItiOoQfrPP//MueeeS6dOndDpdHzxxRcnfMyKFSsYMWIEERER9OjRg5dffrnlFyqECD2J3eH0e5XL5jKI7QRn3A9nP6Vct/Z5Zb66EEK0kLIaH43jHFl0otOIT1Ay5sWVtax1lLrLfnQhhBAnEtQRbJWVlQwZMoTrr7+eiy666IT3P3DgADNnzuTPf/4zCxcuZPXq1cyePZvU1NRGPV4IcZIZf5eyRz06DfrOBIPjn7Tq47BsHiz+O7qYzsFdoxDipOUsd3f5c0oN0pN7abPRiyprZT+6EEKIRgtqkD5jxgxmzJjR6Pu//PLLdO3alWeffRaA/v37s379ep588kkJ0oVojwxGGHWT9/UT/gbFB2Dzexg+v5Hh0UMxfPMDGMMgPBZOuQQ6Dm799QohTiplvsrdHU3jSO5JckwYANnHq7BY7eh0MCZTgnQhhBANC2qQ3lRr165l+vTpbtedeeaZvPHGG1gsFkwmk9djzGYzZrNZ+7qsTGnaYrFYsFgszV6T+hyBeK72Qo6Zf+S4NdFZT2A4fgj9oVVk1K6G46udt635H7bM07GNmY29x2Rlj7vQyHvNP805bnKs2x6rzU652Ue5e/lR5XN8F1KilUy6xapMnBjQMY7E6LBWXacQQoi2p00F6Xl5eaSnp7tdl56eTl1dHYWFhXTs2NHrMfPnz+fhhx/2un7JkiVERUUFbG1ZWVkBe672Qo6Zf+S4NZ4h4Vo62/sSVleBzm5Fb7cSW5NDp5Lf0B9Ygf7ACkoju7Iu8y6qwlODvdyQI+81//hz3KqqqlpgJaIlldc4T6y4dXcvV5rGEduBuEgjRr2OOpsSpMt+dCGEEI3RpoJ0AJ1HxsvumIfseb1q7ty5zJkzR/u6rKyMjIwMpk+fTlxcXLPXY7FYyMrKYtq0aT4z+cKbHDP/yHHzj8Vyjtdxqys5jP63V9BvWkh89WGmFrxC3bXfKfvbhbzX/NSc46ZWeYm2Q+3sHmkyEGZ06cNbkad8jumATqcjKTqMgnKlom98z5TWXqYQQog2qE0F6R06dCAvL8/tuoKCAoxGI8nJvs9Oh4eHEx4e7nW9yWQK6B+fgX6+9kCOmX/kuPnH7bil9oSZj8Opd8HrU9EV7sb02Q1w1adgkGOrkveaf/w5bnKc256yaqXU3W0/OkC54++U2A4AJMeEU1BuxqDXMSozqTWXKIQQoo1qU3PSx40b51VGuGTJEkaOHCl/4Aghmi6+M1y5CEzRcGAFfDsH7HawWWHXd/DBlbDoKucf3UII4aBm0uMiXfIdNitUHlMuO4L0FEfzuCFd4okJb1O5ESGEEEES1P8tKioq2Lt3r/b1gQMH2Lx5M0lJSXTt2pW5c+eSk5PDO++8A8Ctt97K888/z5w5c/jzn//M2rVreeONN/jggw+C9S0IIdq6joPh4jfhwytg4ztQXQI5G6Asx3mfI+vgkgXQbXywVimECDFlNT46u1ceA7sNdHqIVvpcpMdFAFLqLoQQovGCmklfv349w4YNY9iwYQDMmTOHYcOG8eCDDwKQm5vL4cOHtftnZmayePFili9fztChQ3n00Uf53//+J+PXhBDN0/csOHO+cnnHV0qAHpkEY2+DtAFQkQ8LzoE1zyuZdiFEu+ecke7aNC5X+RydBnoDALee3pObTsvkhtMyW3uJQggh2qigZtInTZqkNX7zZcGCBV7XnX766WzcuLEFVyWEaJfG3gq1FUrW/JRLoP+5YIqA2kr4+i7Y+jEsuR/ytsIFL8vYNiHauVJfM9K1zu7OSTS90mJ44JwBrbk0IYQQbZxsjhJCCNXEv3lfFxYNF74GGWPg+/tgy4fQawoMvrT11yeECBlqubv7jHRHJj2mQxBWJIQQ4mTRphrHCSFEUOh0MPrPcPp9ytff3weVRcFdkxAiqJyN41yC9ArnjHQhhBDCXxKkCyFEY516l7JHvapIKX0XQrRb6gi2uAiXokSP8WtCCCGEPyRIF0KIxjKGwaznAB38/gHsWxbsFQkhgsT3nnQJ0oUQQjSfBOlCCNEUXUbC6JuVy1/frTSWE0K0Oz73pFc4gnTZky6EEKIZJEgXQoimmvIPiOsCJYfggyuU0WwHfobq48FemRCilZT6HMGmZtLTfTxCCCGEaBzp7i6EEE0VHgvnPA3vXwoHVigfql7TYPqjkNY/eOsTQrQ4dU+6Vu5us0JFgXI5tmOQViWEEOJkIJl0IYTwR58z4aYf4Yz7od85kNBNuX5vFrw0XimFV/9gF0KcVOx2O2Vad3dHvqOqCOxWQAfRacFbnBBCiDZPMulCCOGvLiOVD1XRPlj6T9jxNWx4C7Z+DOe/BANmBW+NQoiAM9fZqLXaAJdMujojPToFDPLnlRBCCP9JJl0Iq5j7iAAAJm9JREFUIQIluSdcthCu/w46DYfaCvj8FsjfHuyVCSECSN2PrtdBdJgjIC+XGelCCCECQ4J0IYQItG7j4aal0HMyWKpg0VVQUxrsVQkhAsRZ6m5Cr9cpV0pndyGEEAEiQboQQrQEvQEufF3pAl+8D76YDXZ7sFclhAiAhju7S5AuhBCieSRIF0KIlhKdDJe+A4Yw2PkNrPlfsFckhAgAdUZ6fKQE6UIIIQJPgnQhhGhJXUbAWY8pl5c+BD/9G8qOBnVJQojmUcevaZ3dwRmkx8iMdCGEEM0jQboQQrS0kTfA0D+B3QYr/gPPDIJFV8OBlcFemRDCD2q5u1smXd2TLjPShRBCNJME6UII0dJ0Opj1HFz0BnQdr8xS3vEVvH0OLH1Y9qoL0caU+dyTLt3dhRBCBIYE6UII0Rr0BjjlYrjhO/jLWhh+rXL9qqdhyQMSqAvRhnhl0m02l+7uUu4uhBCieSRIF0KI1pY+AGb9D2Y+qXy99nn47l73QN1uV/7wF0KEHLVxXJwapFcXg03Zpy5BuhBCiOYynvguQgghWsToP4PBBF/fDetehZIjYIqAwr3K2LaIBLj6c0jrF+yVCiFclFZ7BOlq07ioZDCGBWlVQgghThYSpAshRDCNuE4Z0fblbbD7O/fbLFXw9rlw3beQ2icoyxNCeNO6u0c4/owql6ZxQgghAkeCdCGECLahVyp/3O/+HuIzILkXxHWCL2dD3lZnoJ7SK9grFULgI5Mu+9GFEEIEkATpQggRCnqeoXy4uvpLJUAv+EPpBP+njyGus9KETm+EsOjgrFWIdk7dk641jivPVT5LZ3chhBABIEG6EEKEquhkuOZLJUA/thNePs399t7T4dJ3lX3sQohWU+o5gk3GrwkhhAgg6e4uhBChLCYVrv0aMsZ637ZnibKXXca3CdFqbDY7FWZlT3q8V7m7BOlCCCGaTzLpQggR6mLS4MYflMs2mzLq6dBqeO9i2PaJsof9jLnBXaMQ7UR5TZ12Xiwu0rNxnATpQgghmk+CdCGEaEv0etCHKfvXz3kGvroDVjymBOqDL4GKY7BrMexfDuExkNAVErpBUg/oNFx5vBDCb+p+9AiTnnCjQblSyt2FEEIEkATpQgjRVg2/Bgr3wJr/KZ3g178JR34Bu833/U+/TzLuQjST1350u126uwshhAgoSakIIURbNvVh6HcOWGvh8BolQO84FCbNVT6G/gkyxij3XfOckmn3tOJxeH4UHD/UqksXoi3KPl4NuOxHrz6u/P6BZNKFEEIEhGTShRCiLdPr4cJXYeVTEJUM/c9VStxd2e3w6iTI3Qyrn4Uz/+W8LXs9/PRvwA6/vQbT57Xe2oVoY+x2O6/8vA+A03qnKFeq+9EjE8EYHqSVCSGEOJlIJl0IIdq6sGiY8iCMu807QAfQ6WDyP5TLv70OZUeVy9Y6+PpuwNEF6/dFynVCCJ+W7y5k0+ESIkx6/jKpp3KlOiNdOrsLIYQIEAnShRCiPeg1RRnjVlejZN0Bfn0Z8rcqGcDIJKgsgH3LgrtOIUKU3Q7P/rgXgGvHdSctNkK5oUJtGif70YUQQgSGBOlCCNEe6HQw+QHl8oa34dAaR5k7MO0RGHypcvn394OzPiFC3JZiHdtzy4kOM3DL6T2dN5TmKJ/jugRnYUIIIU46EqQLIUR7kTkBekwCmwXeOR8slUp2fehVMOQK5T47F0N1SRAXKUTosdrsLD6i/Ml042mZJEWHOW8sy1Y+x3cOwsqEEEKcjCRIF0KI9uQMRzbdaga9UZm1rtdDxyGQNgCsZvQ7vgjqEoUINd9uzSOvWkdchJEbJ/Rwv7FUDdIlky6EECIwJEgXQoj2JGMU9J2pXB53O6QPUC7rdFo2XbflwyAtTpzMXnzxRTIzM4mIiGDEiBGsXLmy3vuuWrWKU089leTkZCIjI+nXrx/PPPNMK67Wqc5q47mflI7uN53W3Tl6TaWVu0smXQghRGDICDYhhGhvLngZDq6GPme6Xz/4Ulj6T/Q564mJyw3O2sRJadGiRdx99928+OKLnHrqqbzyyivMmDGD7du307Wr90SC6Ohobr/9dgYPHkx0dDSrVq3illtuITo6mptvvrlV175sZwEHi6qINtq5ZqyP6QlaJj2jVdclhBDi5CWZdCGEaG8i4qHfTNAb3K+P7QC9pgKQUbzK+3E1pbDmeXhuJLx7IZgrWmGx4mTw9NNPc+ONN3LTTTfRv39/nn32WTIyMnjppZd83n/YsGFcccUVDBw4kO7du3PVVVdx5plnNph9bynTBqTzxjXDuSjTRnS4R26jphRqy5XLsiddCCFEgEgmXQghhNOQK2DPEroWrUS/7mWISoLwWDi4Eja/D7WOwLxoD3xwOfzpYzBFBnfNIqTV1tayYcMG7rvvPrfrp0+fzpo1axr1HJs2bWLNmjXMmzev3vuYzWbMZrP2dVlZGQAWiwWLxeLHyp3GdY+nYo/d+3mKDmIC7JGJ1OnCoJmvc7JRj1dzj397IsfMP3Lcmk6OmX+ac9ya8hgJ0oUQQjj1nYk9Ip6ImhLIesD79tT+MPgSWPm0Erh/dC1cthCMYd73FQIoLCzEarWSnu4+Rzw9PZ28vLwGH9ulSxeOHTtGXV0dDz30EDfddFO9950/fz4PP/yw1/VLliwhKirKv8V7yMrKcvs6rfR3xgFlxLJ88eKAvMbJyPO4iROTY+YfOW5NJ8fMP/4ct6qqqkbfV4J0IYQQTqYIrBe9xZEfnqdbWjz62gqoKVFK4UfdBJmnK03mMsbAwotgzw/w+c1w0Rve5fNCuNDpdG5f2+12r+s8rVy5koqKCn755Rfuu+8+evXqxRVXXOHzvnPnzmXOnDna12VlZWRkZDB9+nTi4uKatXaLxUJWVhbTpk3DZHI2jtNvyIf9ENulPzNnzmzWa5yM6jtuon5yzPwjx63p5Jj5pznHTa3wagwJ0oUQQrixd5/IlowKusycib6+/4C6nwaXvaeUvP/xOdjtMOs5iGheMCROPikpKRgMBq+seUFBgVd23VNmZiYAp5xyCvn5+Tz00EP1Bunh4eGEh4d7XW8ymQL2B6jXc1Uq35M+IaP+3xUR0J9BeyHHzD9y3JpOjpl//DluTbl/0BvHNWUkC8B7773HkCFDiIqKomPHjlx//fUUFRW10mqFEEJoek+Fi98EnQG2fwEvnwZH1gV7VSLEhIWFMWLECK/SwKysLMaPH9/o57Hb7W57zkOCzEgXQgjRAoIapKsjWe6//342bdrEhAkTmDFjBocPH/Z5/1WrVnHNNddw44038scff/Dxxx/z22+/NbhHTQghRAsaMAuuXwzxXaHkELx5Fqx4HGzWYK9MhJA5c+bw+uuv8+abb7Jjxw7++te/cvjwYW699VZAKVW/5pprtPu/8MILfP311+zZs4c9e/bw1ltv8eSTT3LVVVcF61vwTZuRLkG6EEKIwAlqubvrSBaAZ599lh9++IGXXnqJ+fPne93/l19+oXv37tx5552AUgZ3yy238Pjjj7fquoUQQrjoOhb+sgq+vQe2fgw//Uv5PP5OGHyZNJUTXHbZZRQVFfHII4+Qm5vLoEGDWLx4Md26dQMgNzfX7QS9zWZj7ty5HDhwAKPRSM+ePXnssce45ZZbgvUt+FZ6RPksmXQhhBABFLQg3Z+RLOPHj+f+++9n8eLFzJgxg4KCAj755BPOPvvsel+nJUeyqM/j+lmcmBwz/8hx848ct6bz65gZomDWS+gyz8CwZC66wt3w1e3Yl83DNuYv2EZcD6bAdNgOVa01lqWtmj17NrNnz/Z524IFC9y+vuOOO7jjjjtaYVXNYLNB2VHlssxIF0IIEUBBC9L9Gckyfvx43nvvPS677DJqamqoq6tj1qxZPPfcc/W+TmuMZAEZX+APOWb+kePmHzluTeffMYvB2OdxuhX+RM9jPxBZkYfhx39SvuYN1vW4m+qwlICvM9S09FgWESIqC8BmAZ0eYjsGezVCCCFOIkHv7t6UkSzbt2/nzjvv5MEHH+TMM88kNzeXv//979x666288cYbPh/TkiNZQMYX+EOOmX/kuPlHjlvTBeaYXQR1Zuq2fYJh+TwSKg8z7eB8rBctwJ4xJpDLDRmtNZZFhAi1aVxsRzDIvy1CCCECJ2hBuj8jWebPn8+pp57K3//+dwAGDx5MdHQ0EyZMYN68eXTs6H0muzVGsrTE87UHcsz8I8fNP3Lcmq7Zx8xkglHXQe8p8MEV6PK3Ylx4Ppz9JPQ4A2x1SoO58BiI6xSoZQddS49lESFCDdLjpNRdCCFEYAWtu7s/I1mqqqrQ692XbDAYACUDL4QQIgQlZMCNP0D/WUp58Nd3wX8Hw3PD4YVR8HR/2CPbEUQbI+PXhBBCtJCgjmBr6kiWc889l88++4yXXnqJ/fv3s3r1au68805Gjx5Np04nTxZGCCFOOmHRcMnbMOn/ICJeaSIXHgemaOX2pQ8pjbiEaCvKHOPXpGmcEEKIAAvqnvSmjmS57rrrKC8v5/nnn+eee+4hISGByZMn85///CdY34IQQojG0uth0v9TPlRVxfDfIZC/DXZ8CQMvCN76hGgKbfxaRnDXIYQQ4qQT9MZxTRnJAm1kLIsQQojGiUqCsbNhxWOw/DGlJF5vCPaqhDixUkcmXfakCyGECLCglrsLIYQQjP2LUgJ/bCds+yzYqxGicWRPuhBCiBYiQboQQojgikyA8Y4KqRWPgbUuqMsR4oTqzMqcdJAgXQghRMBJkC6EECL4xtwKkUlQtBe2fgR2OxTugQ0LYMvHytdChAq1aZwxAqKSg7sWIYQQJ52g70kXQgghCI+FU++Cpf+EH+5Xur1X5DtvL8uG0/4atOUJ4cZ1P7pOF9y1CCGEOOlIJl0IIURoGP1niE6D6mIlQDeEQ8ehym1LH4LfPwzm6oRwkv3oQgghWpBk0oUQQoSGsGj400dw4GfoPBI6jwBTBCx5ANY8B1/eBtGp0GtKsFcq2rsyCdKFEEK0HMmkCyGECB2dhill791PVQJ0gKmPwKCLwVYHH10DRzcHdYlCSCZdCCFES5IgXQghRGjT6+H8FyFzItRWwHuXQMmRYK9KtGcyI10IIUQLkiBdCCFE6DOGw2ULIW2gMvrqwyugtrLhxxzbBV/cBvtXtM4aRfshmXQhhBAtSIJ0IYQQbUNEPFz5obIvPW8rfHYz2Gy+77vre3htCmxeqGTeJVAXgaSOYJMgXQghRAuQIF0IIUTbkdAVLnsPDGGw8xv4aZ777XY7rHoGPrgcasuVwN5qhg+ugOz1wVmzOLnUlIK5TLks5e5CCCFagHR3F0II0bZ0HQOznoPPb4GVT0F1iRKMY4eCnbD7O+V+I66H6Y/Ch3+CAytg4UVw3bfQYVAwVy/aOnU/ekQChMcEdSlCCCFOThKkCyGEaHuGXK7sOV/1NKx/w/02nQFmPg6jblK+vvx9ePcCyF6nfL7yQ2W8mxD+KN6vfE7ICO46hBBCnLQkSBdCCNE2Tf6HUv5esAN0etDpQG+A/udBxijn/cJjlPnrC86F/K3w2mQYfJnj8RJoiSY6ukn53HFIcNchhBDipCVBuhBCiLZJr4eR1zfuvpGJcM2X8MP/wZYPYcsi+OMLGDcbTr/POZNdiBM5ulH53GlYcNchhBDipCWN44QQQrQP0clw4Stw83LodprSUG7VM/DJ9WCtC/bqRFtgtzsz6Z2GB3ctQgghTloSpAshhGhfOg2D676BS98BQzjsWgxf36UEYEI0pOQgVB9XpgukSwNCIYQQLUOCdCGEEO2PTgcDzoNL3lL2s29eCEsfCvaqRIjTqVn09EFgDAvuYoQQQpy0JEgXQgjRfvU7WxnnBrD6WVjzXFCXI0KbLtcRpHeWUnchhBAtR4J0IYQQ7duwq2Dqw8rlJQ/A/uVBXY4IXbrczcoF2Y8uhBCiBUmQLoQQQpx6F4y4Trn8/f+BzRrU5YgQZLehy92iXJZMuhBCiBYkQboQQgih08GUf0JEAhT8AZsWBntFIsTE1hxFZ6kEUzSk9An2coQQQpzEJEgXQgghAKKSYNJ9yuVl88BcHtz1iJCSUHVAudBpKOgNQV2LEEKIk5sE6UIIIYRq5I2Q1BMqC5QZ6kI4JFbtVy50GhbchQghhDjpSZAuhBBCqIxhMP1R5fKa56HkcHDXI0KGM5MuQboQQoiWJUG6EEII4arvTOg+Aaxm+PGRYK9GhAJrLXHVjhM20jROCCFEC5MgXQghhHCl08H0eYAOtn4Mq/8LdnuwVyWCqWA7Bnsd9shESMwM9mqEEEKc5CRIF0IIITx1GgoT5iiXsx6ExX+XsWztmP7oJgDsHYcqJ3GEEEKIFiRBuhBCCOHLlAfhzH8rl397DRZdDbVVwV2TCApd7mYA7B1lP7oQQoiWJ0G6EEIIUZ9xt8ElC8AQDru+hVcmKvvUD66Cutpgr060El2uSyZdCCGEaGHGYC9ACCGECGkDL4CYDvDhFVC0B1Y+pXyYoiFzApz/kjJjXZycaivh2E4A7NLZXQghRCuQIF0IIYQ4kW7j4I6NsPsH2LcM9v8Elccgez1EJAR7daIl5W5BZ7dRbUrEGNsx2KsRQgjRDkiQLoQQQjRGVBIMvUL5sNkgfxuU5YBedo6d1DqPoO76LDav+J6RwV6LEEKIdkGCdCGEEKKp9HroOFj5ECc3Yxj2TsMoiM8N9kqEEEK0E3L6XwghhBBCCCGECBESpAshhBBCCCGEECFCgnQhhBBCCCGEECJESJAuhBBCCCGEEEKECAnShRBCCCGEEEKIECFBuhBCCCGEEEIIESKCHqS/+OKLZGZmEhERwYgRI1i5cmWD9zebzdx///1069aN8PBwevbsyZtvvtlKqxVCCCGEEEIIIVpOUIP0RYsWcffdd3P//fezadMmJkyYwIwZMzh8+HC9j7n00kv58ccfeeONN9i1axcffPAB/fr1a8VVCyGEEKKpmnJS/rPPPmPatGmkpqYSFxfHuHHj+OGHH1pxtUIIIUTwBDVIf/rpp7nxxhu56aab6N+/P88++ywZGRm89NJLPu///fffs2LFChYvXszUqVPp3r07o0ePZvz48a28ciGEEEI0VlNPyv/8889MmzaNxYsXs2HDBs444wzOPfdcNm3a1MorF0IIIVqfMVgvXFtby4YNG7jvvvvcrp8+fTpr1qzx+ZivvvqKkSNH8vjjj/Puu+8SHR3NrFmzePTRR4mMjPT5GLPZjNls1r4uKysDwGKxYLFYmv19qM8RiOdqL+SY+UeOm3/kuDWdHDP/NOe4nezH2vWkPMCzzz7LDz/8wEsvvcT8+fO97v/ss8+6ff3vf/+bL7/8kq+//pphw4a1xpKFEEKIoAlakF5YWIjVaiU9Pd3t+vT0dPLy8nw+Zv/+/axatYqIiAg+//xzCgsLmT17NsXFxfXuS58/fz4PP/yw1/VLliwhKiqq+d+IQ1ZWVsCeq72QY+YfOW7+kePWdHLM/OPPcauqqmqBlYQGf07Ke7LZbJSXl5OUlFTvfVrypLycuPKPHLemk2PmHzluTSfHzD+tdUI+aEG6SqfTuX1tt9u9rlPZbDZ0Oh3vvfce8fHxgHJ2/uKLL+aFF17wmU2fO3cuc+bM0b4uKysjIyOD6dOnExcX1+z1WywWsrKymDZtGiaTqdnP1x7IMfOPHDf/yHFrOjlm/mnOcVMDypORPyflPT311FNUVlZy6aWX1nuf1jgpLyeu/CPHrenkmPlHjlvTyTHzT0ufkA9akJ6SkoLBYPD6D7qgoMDrP3JVx44d6dy5sxagA/Tv3x+73U52dja9e/f2ekx4eDjh4eFe15tMpoD+8Rno52sP5Jj5R46bf+S4NZ0cM//4c9zaw3Fuykl5Vx988AEPPfQQX375JWlpafXeryVPysuJK//IcWs6OWb+kePWdHLM/NNaJ+SDFqSHhYUxYsQIsrKyuOCCC7Trs7KyOO+883w+5tRTT+Xjjz+moqKCmJgYAHbv3o1er6dLly6tsm4hhBBCNJ4/J+VVixYt4sYbb+Tjjz9m6tSpDd63NU7Ky4kr/8hxazo5Zv6R49Z0csz809In5INa7j5nzhyuvvpqRo4cybhx43j11Vc5fPgwt956K6CcFc/JyeGdd94B4Morr+TRRx/l+uuv5+GHH6awsJC///3v3HDDDfU2jvNkt9uBwJUWWiwWqqqqKCsrkzd4I8kx848cN//IcWs6OWb+ac5xU/9PUv+POpn4c1IelAz6DTfcwAcffMDZZ5/d5NcN5P/38jvhHzluTSfHzD9y3JpOjpl/Wu3/enuQvfDCC/Zu3brZw8LC7MOHD7evWLFCu+3aa6+1n3766W7337Fjh33q1Kn2yMhIe5cuXexz5syxV1VVNfr1jhw5YgfkQz7kQz7kQz5C7uPIkSOB+u81pHz44Yd2k8lkf+ONN+zbt2+333333fbo6Gj7wYMH7Xa73X7ffffZr776au3+77//vt1oNNpfeOEFe25urvZRUlLS6NeU/+/lQz7kQz7kIxQ/GvN/vc5uPwlP2zfAZrNx9OhRYmNjG7UX7kTUPW9HjhwJSCO69kCOmX/kuPlHjlvTyTHzT3OOm91up7y8nE6dOqHX61tohcH14osv8vjjj5Obm8ugQYN45plnmDhxIgDXXXcdBw8eZPny5QBMmjSJFStWeD3Htddey4IFCxr1eoH8/15+J/wjx63p5Jj5R45b08kx809r/V/f7oL0QCsrKyM+Pp7S0lJ5gzeSHDP/yHHzjxy3ppNj5h85bicv+dn6R45b0/3/9u4+pury/+P463B3PJyYQ1EP6FRcFt6kGVhTKVObeZPNsjuHivWHw4QwV2lpaS7TP5q5tqLpzH+00Zg3I00LzCxthQNRvElreVMmI6epZGLK+/tH2+f3O0FxOJjnIzwf29ngui7wOi+plxfn5kNm4SG35iOz8Nyo3Frnr+sBAAAAALgJcUgHAAAAAMAlOKS3kNfr1cKFCxu97AsaR2bhIbfwkFvzkVl4yK314u82POTWfGQWHnJrPjILz43KjdekAwAAAADgEjySDgAAAACAS3BIBwAAAADAJTikAwAAAADgEhzSAQAAAABwCQ7pLfDee+8pNTVV7dq1U3p6ur766qtIb8k1li5dqsGDByshIUGdO3fWxIkTdeTIkaA1ZqZFixYpJSVFPp9P999/vw4ePBihHbvT0qVL5fF4NHv2bGeM3Bp36tQpTZkyRR07dlR8fLzuvPNOlZeXO/PkFuzq1atasGCBUlNT5fP51KtXLy1evFj19fXOGjKTvvzyS02YMEEpKSnyeDzatGlT0HwoGdXV1SkvL09JSUny+/16+OGH9fPPP9/Ae4GWoOv/HX3fcnR96Oj65qPvm+bKrjeEpbCw0GJjY23VqlV26NAhy8/PN7/fbydOnIj01lzhwQcftDVr1tiBAwessrLSxo8fb927d7fa2lpnzbJlyywhIcHWr19vVVVV9uSTT1pycrJduHAhgjt3j7KyMuvZs6cNGDDA8vPznXFya+js2bPWo0cPmz59un377bd27NgxKy0ttR9++MFZQ27B3njjDevYsaNt3rzZjh07ZkVFRXbLLbfYihUrnDVkZvbJJ5/Y/Pnzbf369SbJNm7cGDQfSkY5OTnWtWtXKykpsYqKChsxYoQNHDjQrl69eoPvDZqLrm8afd8ydH3o6Prw0PdNc2PXc0gP09133205OTlBY2lpaTZv3rwI7cjdampqTJLt3LnTzMzq6+stEAjYsmXLnDWXL1+29u3b2/vvvx+pbbrGxYsXrXfv3lZSUmLDhw93ipvcGjd37lzLzMz8x3lya2j8+PH2zDPPBI09+uijNmXKFDMjs8b8vbhDyei3336z2NhYKywsdNacOnXKoqKibNu2bTds7wgPXd989H3o6PrmoevDQ983j1u6nqe7h+HKlSsqLy/X6NGjg8ZHjx6tr7/+OkK7crfz589Lkjp06CBJOnbsmKqrq4My9Hq9Gj58OBlKmjVrlsaPH68HHnggaJzcGldcXKyMjAw9/vjj6ty5swYNGqRVq1Y58+TWUGZmprZv366jR49Kkvbt26ddu3Zp3LhxksgsFKFkVF5erj///DNoTUpKivr370+OLkfXh4e+Dx1d3zx0fXjo+5aJVNfHtGzbbdOZM2d07do1denSJWi8S5cuqq6ujtCu3MvMNGfOHGVmZqp///6S5OTUWIYnTpy44Xt0k8LCQlVUVGjPnj0N5sitcT/++KMKCgo0Z84cvfLKKyorK9Nzzz0nr9eradOmkVsj5s6dq/PnzystLU3R0dG6du2alixZosmTJ0viZy0UoWRUXV2tuLg4JSYmNlhDX7gbXd989H3o6Prmo+vDQ9+3TKS6nkN6C3g8nqDPzazBGKTc3Fzt379fu3btajBHhsF++ukn5efn67PPPlO7du3+cR25Bauvr1dGRobefPNNSdKgQYN08OBBFRQUaNq0ac46cvs/H330kdauXasPP/xQ/fr1U2VlpWbPnq2UlBRlZ2c768isaeFkRI43D/4bCB19Hxq6Pjx0fXjo++vjRnc9T3cPQ1JSkqKjoxv8ZqSmpqbBb1naury8PBUXF2vHjh3q1q2bMx4IBCSJDP+mvLxcNTU1Sk9PV0xMjGJiYrRz50698847iomJcbIht2DJycnq27dv0FifPn108uRJSfy8NebFF1/UvHnz9NRTT+mOO+7Q1KlT9fzzz2vp0qWSyCwUoWQUCAR05coVnTt37h/XwJ3o+uah70NH14eHrg8Pfd8ykep6DulhiIuLU3p6ukpKSoLGS0pKNHTo0Ajtyl3MTLm5udqwYYM+//xzpaamBs2npqYqEAgEZXjlyhXt3LmzTWc4atQoVVVVqbKy0rllZGQoKytLlZWV6tWrF7k1YtiwYQ0u+XP06FH16NFDEj9vjbl06ZKiooIrIDo62rkkC5k1LZSM0tPTFRsbG7Tm9OnTOnDgADm6HF0fGvq++ej68ND14aHvWyZiXR/W283BuSzL6tWr7dChQzZ79mzz+/12/PjxSG/NFWbOnGnt27e3L774wk6fPu3cLl265KxZtmyZtW/f3jZs2GBVVVU2efLkNnW5h1D9/3d8NSO3xpSVlVlMTIwtWbLEvv/+e1u3bp3Fx8fb2rVrnTXkFiw7O9u6du3qXJJlw4YNlpSUZC+99JKzhsz+evflvXv32t69e02SLV++3Pbu3etcgiuUjHJycqxbt25WWlpqFRUVNnLkSC7BdpOg65tG318fdH3T6Prw0PdNc2PXc0hvgXfffdd69OhhcXFxdtdddzmXG8Ffly9o7LZmzRpnTX19vS1cuNACgYB5vV677777rKqqKnKbdqm/Fze5Ne7jjz+2/v37m9frtbS0NFu5cmXQPLkFu3DhguXn51v37t2tXbt21qtXL5s/f77V1dU5a8jMbMeOHY3+vyw7O9vMQsvojz/+sNzcXOvQoYP5fD576KGH7OTJkxG4NwgHXf/v6Pvrg64PDV3ffPR909zY9R4zs/AegwcAAAAAANcTr0kHAAAAAMAlOKQDAAAAAOASHNIBAAAAAHAJDukAAAAAALgEh3QAAAAAAFyCQzoAAAAAAC7BIR0AAAAAAJfgkA4AAAAAgEtwSAfwn/N4PNq0aVOktwEAAP5D9D1wfXBIB1q56dOny+PxNLiNGTMm0lsDAADXCX0PtB4xkd4AgP/emDFjtGbNmqAxr9cbod0AAID/An0PtA48kg60AV6vV4FAIOiWmJgo6a+nphUUFGjs2LHy+XxKTU1VUVFR0NdXVVVp5MiR8vl86tixo2bMmKHa2tqgNR988IH69esnr9er5ORk5ebmBs2fOXNGjzzyiOLj49W7d28VFxc7c+fOnVNWVpY6deokn8+n3r17N/hHBgAA+Hf0PdA6cEgHoFdffVWTJk3Svn37NGXKFE2ePFmHDx+WJF26dEljxoxRYmKi9uzZo6KiIpWWlgaVckFBgWbNmqUZM2aoqqpKxcXFuvXWW4P+jNdff11PPPGE9u/fr3HjxikrK0tnz551/vxDhw5p69atOnz4sAoKCpSUlHTjAgAAoA2g74GbhAFo1bKzsy06Otr8fn/QbfHixWZmJslycnKCvuaee+6xmTNnmpnZypUrLTEx0Wpra535LVu2WFRUlFVXV5uZWUpKis2fP/8f9yDJFixY4HxeW1trHo/Htm7damZmEyZMsKeffvr63GEAANog+h5oPXhNOtAGjBgxQgUFBUFjHTp0cD4eMmRI0NyQIUNUWVkpSTp8+LAGDhwov9/vzA8bNkz19fU6cuSIPB6PfvnlF40aNepf9zBgwADnY7/fr4SEBNXU1EiSZs6cqUmTJqmiokKjR4/WxIkTNXTo0LDuKwAAbRV9D7QOHNKBNsDv9zd4OlpTPB6PJMnMnI8bW+Pz+UL6frGxsQ2+tr6+XpI0duxYnThxQlu2bFFpaalGjRqlWbNm6a233mrWngEAaMvoe6B14DXpAPTNN980+DwtLU2S1LdvX1VWVur333935nfv3q2oqCjddtttSkhIUM+ePbV9+/YW7aFTp06aPn261q5dqxUrVmjlypUt+n4AACAYfQ/cHHgkHWgD6urqVF1dHTQWExPjvFlLUVGRMjIylJmZqXXr1qmsrEyrV6+WJGVlZWnhwoXKzs7WokWL9OuvvyovL09Tp05Vly5dJEmLFi1STk6OOnfurLFjx+rixYvavXu38vLyQtrfa6+9pvT0dPXr1091dXXavHmz+vTpcx0TAACg9aPvgdaBQzrQBmzbtk3JyclBY7fffru+++47SX+9E2thYaGeffZZBQIBrVu3Tn379pUkxcfH69NPP1V+fr4GDx6s+Ph4TZo0ScuXL3e+V3Z2ti5fvqy3335bL7zwgpKSkvTYY4+FvL+4uDi9/PLLOn78uHw+n+69914VFhZeh3sOAEDbQd8DrYPHzCzSmwAQOR6PRxs3btTEiRMjvRUAAPAfoe+BmwevSQcAAAAAwCU4pAMAAAAA4BI83R0AAAAAAJfgkXQAAAAAAFyCQzoAAAAAAC7BIR0AAAAAAJfgkA4AAAAAgEtwSAcAAAAAwCU4pAMAAAAA4BIc0gEAAAAAcAkO6QAAAAAAuMT/ACcg71WrD8IQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "p_dropout = 0.3\n",
    "#model = ResNet(FN=64, p_dropout=p_dropout)\n",
    "#added a condition to allow to specify ReLU or tanh\n",
    "model = SimpleCNN2(activation = \"tanh\")\n",
    "model.to(device)\n",
    "\n",
    "\n",
    "#summary(model, (1, 128, 1290))\n",
    "\n",
    "# Adam optimizer01\n",
    "lr = 0.01\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "\n",
    "timestamp = time.strftime(\"apr%d_t%H%M\", time.gmtime())\n",
    "model_name = f\"{model.name}_B{BATCH}_E{EPOCHS}_LR{lr}_pD{p_dropout}_A{augment_prob}_{timestamp}\"\n",
    "\n",
    "i = 0\n",
    "running_loss = 0.0\n",
    "best_val_loss = float('inf')  # initialize the best validation loss\n",
    "\n",
    "# train the model\n",
    "acc_tr = []\n",
    "acc_val = []\n",
    "loss_tr = []\n",
    "loss_val = []\n",
    "\n",
    "mi_array_a1 = []\n",
    "mi_array_a2 = []\n",
    "mi_array_a3 = []\n",
    "mi_array_a4 = []\n",
    "\n",
    "activity = np.zeros((1000, 4, 10304))\n",
    "activity2 = np.zeros((1000, 16, 2576))\n",
    "activity3 = np.zeros((1000, 32, 648))\n",
    "activity4 = np.zeros((1000, 64, 164))\n",
    "\n",
    "t0 = time.time()\n",
    "prev_a = 0\n",
    "\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    # evaluate the model on the training dataset\n",
    "    train_correct = 0\n",
    "    train_total = 0\n",
    "    for spectrogram, label, ixs in dataloader:\n",
    "        model.train()\n",
    "        label = label.to(device)\n",
    "        train_label = torch.argmax(label, dim=1)\n",
    "\n",
    "        # forward pass\n",
    "        spectrogram = spectrogram.squeeze(0)\n",
    "        spectrogram = spectrogram.unsqueeze(1)\n",
    "\n",
    "        spectrogram = spectrogram.to(device)\n",
    "        output, a1, a2,a3 ,a4 = model(spectrogram)\n",
    "        activity[ixs] = a1.cpu().detach().numpy()\n",
    "        activity2[ixs] = a2.cpu().detach().numpy()\n",
    "        activity3[ixs] = a3.cpu().detach().numpy()\n",
    "        activity4[ixs] = a4.cpu().detach().numpy()\n",
    "                \n",
    "        loss = loss_fn(output, label)\n",
    "\n",
    "\n",
    "        # backward pass\n",
    "        optimizer.zero_grad()\n",
    "        model.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Update the learning rate\n",
    "        # scheduler.step(loss)\n",
    "\n",
    "        _, train_predicted = torch.max(output.data, 1)\n",
    "        train_total += train_label.size(0)\n",
    "        train_correct += (train_predicted == train_label).sum().item()\n",
    "        # print statistics\n",
    "        i += 1\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    loss = running_loss / len(dataloader)\n",
    "    loss_tr.append(loss)\n",
    "    print('[%d, %5d subsamples] Training loss: %.3f' % (epoch + 1, i * BATCH, loss))\n",
    "    running_loss = 0\n",
    "    # evaluate the model on the validation dataset\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for val_spectrogram, val_label, ixs in val_dataloader:\n",
    "            val_label = val_label.to(device)\n",
    "            val_label = torch.argmax(val_label, dim=1)\n",
    "\n",
    "            val_spectrogram = val_spectrogram.squeeze(0)\n",
    "            val_spectrogram = val_spectrogram.unsqueeze(1)\n",
    "            val_spectrogram = val_spectrogram.to(device)\n",
    "            val_output, a1, a2, _, _ = model(val_spectrogram)\n",
    "            val_loss += loss_fn(val_output, val_label).item()\n",
    "            _, val_predicted = torch.max(val_output.data, 1)\n",
    "            val_total += val_label.size(0)\n",
    "            val_correct += (val_predicted == val_label).sum().item()\n",
    "\n",
    "    loss = val_loss / len(val_dataloader)\n",
    "    loss_val.append(loss)\n",
    "    val_acc = val_correct / val_total\n",
    "    tr_acc = train_correct / train_total\n",
    "    acc_tr.append(tr_acc)\n",
    "    acc_val.append(val_acc)\n",
    "    t1 = time.time()\n",
    "    t = (t1 - t0) / 60\n",
    "    # Save the model if the validation loss is the best seen so far\n",
    "    if loss < best_val_loss:\n",
    "        best_val_loss = loss\n",
    "        best_val_acc = val_acc\n",
    "        best_tr_acc = tr_acc\n",
    "        best_state_dict = model.state_dict()\n",
    "    print(\n",
    "        '[EPOCH {}, {:.4f} min] Validation Loss: {:.4f} | Validation Accuracy: {:.4f} | Training Accuracy: {:.4f}'.format(epoch+1, t, loss,\n",
    "                                                                                                                val_acc,\n",
    "                                                                                                                tr_acc))\n",
    "    # Compute the MI using kraskov_MI2 for activations a1 and a2\n",
    "    # NOTE: we are computing the MI wrt to the first (0th) feature map, could do for others aswell\n",
    "    mi_a1 = kraskov_entropy(activity[:, 0, :], k=2)\n",
    "    mi_a2 = kraskov_entropy(activity2[:, 0, :], k=2)\n",
    "    mi_a3 = kraskov_entropy(activity3[:, 0, :], k=2)\n",
    "    mi_a4 = kraskov_entropy(activity4[:, 0, :], k=2)\n",
    "    \n",
    "    mi_array_a1.append(mi_a1)\n",
    "    mi_array_a2.append(mi_a2)\n",
    "    mi_array_a3.append(mi_a3)\n",
    "    mi_array_a4.append(mi_a4)\n",
    "\n",
    "\n",
    "mi_array_a1 = np.array(mi_array_a1)\n",
    "mi_array_a2 = np.array(mi_array_a2)\n",
    "mi_array_a3 = np.array(mi_array_a3)\n",
    "mi_array_a4 = np.array(mi_array_a4)\n",
    "\n",
    "np.save(timestamp + '_MI_a1', mi_array_a1)\n",
    "np.save(timestamp + '_MI_a2', mi_array_a2)\n",
    "np.save(timestamp + '_MI_a3', mi_array_a3)\n",
    "np.save(timestamp + '_MI_a4', mi_array_a4)\n",
    "\n",
    "\n",
    "#plotting\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(loss_val, label='Validation loss')\n",
    "plt.plot(loss_tr, label='Training loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title('Loss vs Epochs')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(acc_val, label='Validation accuracy')\n",
    "plt.plot(acc_tr, label='Training accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title('Accuracy vs Epochs')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "t = np.arange(len(mi_array_a1))\n",
    "plt.plot(t, mi_array_a1, label='Kraskov MI Conv1')\n",
    "plt.plot(t, mi_array_a2, label='Kraskov MI Conv2')\n",
    "plt.plot(t, mi_array_a3, label='Kraskov MI Conv3')\n",
    "plt.plot(t, mi_array_a4, label='Kraskov MI Conv4')\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MI/Entropy(T)')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "fig.suptitle('MI/Entropy(T) vs Epochs for Conv Blocks')\n",
    "\n",
    "axs[0, 0].plot(t, mi_array_a1, label='Kraskov MI Conv1', color='blue')\n",
    "axs[0, 0].set_title('Conv1')\n",
    "axs[0, 0].grid()\n",
    "axs[0, 0].set(ylabel='MI/Entropy(T)')\n",
    "\n",
    "axs[0, 1].plot(t, mi_array_a2, label='Kraskov MI Conv2', color='orange')\n",
    "axs[0, 1].set_title('Conv2')\n",
    "axs[0, 1].grid()\n",
    "\n",
    "axs[1, 0].plot(t, mi_array_a3, label='Kraskov MI Conv3', color='green')\n",
    "axs[1, 0].set_title('Conv3')\n",
    "axs[1, 0].grid()\n",
    "axs[1, 0].set(xlabel='Epochs', ylabel='MI/Entropy(T)')\n",
    "\n",
    "axs[1, 1].plot(t, mi_array_a4, label='Kraskov MI Conv4', color='red')\n",
    "axs[1, 1].set_title('Conv4')\n",
    "axs[1, 1].grid()\n",
    "axs[1, 1].set(xlabel='Epochs')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "torch.save(best_state_dict, model_name + f'_VAL{best_val_acc}_TRAIN{best_tr_acc}.pt')\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09583873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4, 10304)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6402becc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 10304)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = activity[:, 0, :].shape\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49a4f99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 16, 2576)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "31d26ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 32, 648)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d18f670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 64, 164)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3266066a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 64, 164)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b479954",
   "metadata": {},
   "source": [
    "## Plotting MI for different feature maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2b1913d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Create a directory to save the plots\n",
    "plot_dir = \"Kraskov_plots\"\n",
    "os.makedirs(plot_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "63bc2a28",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,   768 subsamples] Training loss: 2.072\n",
      "[EPOCH 1, 0.0211 min] Validation Loss: 2.0212 | Validation Accuracy: 0.3418 | Training Accuracy: 0.1721\n",
      "[2,  1536 subsamples] Training loss: 1.995\n",
      "[EPOCH 2, 0.0673 min] Validation Loss: 1.9667 | Validation Accuracy: 0.3671 | Training Accuracy: 0.2326\n",
      "[3,  2304 subsamples] Training loss: 1.945\n",
      "[EPOCH 3, 0.1150 min] Validation Loss: 1.9464 | Validation Accuracy: 0.3418 | Training Accuracy: 0.2957\n",
      "[4,  3072 subsamples] Training loss: 1.894\n",
      "[EPOCH 4, 0.1628 min] Validation Loss: 1.9189 | Validation Accuracy: 0.3924 | Training Accuracy: 0.3233\n",
      "[5,  3840 subsamples] Training loss: 1.847\n",
      "[EPOCH 5, 0.2089 min] Validation Loss: 1.8916 | Validation Accuracy: 0.3291 | Training Accuracy: 0.3706\n",
      "[6,  4608 subsamples] Training loss: 1.806\n",
      "[EPOCH 6, 0.2579 min] Validation Loss: 1.8537 | Validation Accuracy: 0.3418 | Training Accuracy: 0.3890\n",
      "[7,  5376 subsamples] Training loss: 1.767\n",
      "[EPOCH 7, 0.3047 min] Validation Loss: 1.8267 | Validation Accuracy: 0.3797 | Training Accuracy: 0.4047\n",
      "[8,  6144 subsamples] Training loss: 1.730\n",
      "[EPOCH 8, 0.3520 min] Validation Loss: 1.8067 | Validation Accuracy: 0.4051 | Training Accuracy: 0.4152\n",
      "[9,  6912 subsamples] Training loss: 1.696\n",
      "[EPOCH 9, 0.4019 min] Validation Loss: 1.7562 | Validation Accuracy: 0.4430 | Training Accuracy: 0.4284\n",
      "[10,  7680 subsamples] Training loss: 1.672\n",
      "[EPOCH 10, 0.4517 min] Validation Loss: 1.7507 | Validation Accuracy: 0.4810 | Training Accuracy: 0.4363\n",
      "[11,  8448 subsamples] Training loss: 1.643\n",
      "[EPOCH 11, 0.4994 min] Validation Loss: 1.7073 | Validation Accuracy: 0.4557 | Training Accuracy: 0.4402\n",
      "[12,  9216 subsamples] Training loss: 1.620\n",
      "[EPOCH 12, 0.5489 min] Validation Loss: 1.7186 | Validation Accuracy: 0.4430 | Training Accuracy: 0.4547\n",
      "[13,  9984 subsamples] Training loss: 1.599\n",
      "[EPOCH 13, 0.5981 min] Validation Loss: 1.6889 | Validation Accuracy: 0.4304 | Training Accuracy: 0.4796\n",
      "[14, 10752 subsamples] Training loss: 1.580\n",
      "[EPOCH 14, 0.6480 min] Validation Loss: 1.7073 | Validation Accuracy: 0.4177 | Training Accuracy: 0.4849\n",
      "[15, 11520 subsamples] Training loss: 1.571\n",
      "[EPOCH 15, 0.7015 min] Validation Loss: 1.6865 | Validation Accuracy: 0.4304 | Training Accuracy: 0.4704\n",
      "[16, 12288 subsamples] Training loss: 1.547\n",
      "[EPOCH 16, 0.7512 min] Validation Loss: 1.6249 | Validation Accuracy: 0.4557 | Training Accuracy: 0.4823\n",
      "[17, 13056 subsamples] Training loss: 1.527\n",
      "[EPOCH 17, 0.8021 min] Validation Loss: 1.7696 | Validation Accuracy: 0.3924 | Training Accuracy: 0.4836\n",
      "[18, 13824 subsamples] Training loss: 1.517\n",
      "[EPOCH 18, 0.8516 min] Validation Loss: 1.6566 | Validation Accuracy: 0.4177 | Training Accuracy: 0.4809\n",
      "[19, 14592 subsamples] Training loss: 1.498\n",
      "[EPOCH 19, 0.9017 min] Validation Loss: 1.6441 | Validation Accuracy: 0.4051 | Training Accuracy: 0.4941\n",
      "[20, 15360 subsamples] Training loss: 1.473\n",
      "[EPOCH 20, 0.9519 min] Validation Loss: 1.6770 | Validation Accuracy: 0.4430 | Training Accuracy: 0.4941\n",
      "[21, 16128 subsamples] Training loss: 1.462\n",
      "[EPOCH 21, 1.0008 min] Validation Loss: 1.6280 | Validation Accuracy: 0.3924 | Training Accuracy: 0.5099\n",
      "[22, 16896 subsamples] Training loss: 1.450\n",
      "[EPOCH 22, 1.0487 min] Validation Loss: 1.6371 | Validation Accuracy: 0.3924 | Training Accuracy: 0.5046\n",
      "[23, 17664 subsamples] Training loss: 1.422\n",
      "[EPOCH 23, 1.0967 min] Validation Loss: 1.6768 | Validation Accuracy: 0.4304 | Training Accuracy: 0.5112\n",
      "[24, 18432 subsamples] Training loss: 1.419\n",
      "[EPOCH 24, 1.1461 min] Validation Loss: 1.5843 | Validation Accuracy: 0.4177 | Training Accuracy: 0.5099\n",
      "[25, 19200 subsamples] Training loss: 1.395\n",
      "[EPOCH 25, 1.2008 min] Validation Loss: 1.6775 | Validation Accuracy: 0.4177 | Training Accuracy: 0.5177\n",
      "[26, 19968 subsamples] Training loss: 1.391\n",
      "[EPOCH 26, 1.2582 min] Validation Loss: 1.5748 | Validation Accuracy: 0.4304 | Training Accuracy: 0.5204\n",
      "[27, 20736 subsamples] Training loss: 1.366\n",
      "[EPOCH 27, 1.3084 min] Validation Loss: 1.6520 | Validation Accuracy: 0.4177 | Training Accuracy: 0.5322\n",
      "[28, 21504 subsamples] Training loss: 1.355\n",
      "[EPOCH 28, 1.3575 min] Validation Loss: 1.5497 | Validation Accuracy: 0.4304 | Training Accuracy: 0.5414\n",
      "[29, 22272 subsamples] Training loss: 1.348\n",
      "[EPOCH 29, 1.4077 min] Validation Loss: 1.6065 | Validation Accuracy: 0.4177 | Training Accuracy: 0.5466\n",
      "[30, 23040 subsamples] Training loss: 1.327\n",
      "[EPOCH 30, 1.4609 min] Validation Loss: 1.5478 | Validation Accuracy: 0.4557 | Training Accuracy: 0.5466\n",
      "[31, 23808 subsamples] Training loss: 1.317\n",
      "[EPOCH 31, 1.5125 min] Validation Loss: 1.6076 | Validation Accuracy: 0.4177 | Training Accuracy: 0.5519\n",
      "[32, 24576 subsamples] Training loss: 1.310\n",
      "[EPOCH 32, 1.5613 min] Validation Loss: 1.6021 | Validation Accuracy: 0.4051 | Training Accuracy: 0.5361\n",
      "[33, 25344 subsamples] Training loss: 1.294\n",
      "[EPOCH 33, 1.6111 min] Validation Loss: 1.5815 | Validation Accuracy: 0.4557 | Training Accuracy: 0.5519\n",
      "[34, 26112 subsamples] Training loss: 1.298\n",
      "[EPOCH 34, 1.6599 min] Validation Loss: 1.6711 | Validation Accuracy: 0.3924 | Training Accuracy: 0.5598\n",
      "[35, 26880 subsamples] Training loss: 1.282\n",
      "[EPOCH 35, 1.7110 min] Validation Loss: 1.5819 | Validation Accuracy: 0.4304 | Training Accuracy: 0.5519\n",
      "[36, 27648 subsamples] Training loss: 1.272\n",
      "[EPOCH 36, 1.7658 min] Validation Loss: 1.6128 | Validation Accuracy: 0.4177 | Training Accuracy: 0.5558\n",
      "[37, 28416 subsamples] Training loss: 1.259\n",
      "[EPOCH 37, 1.8187 min] Validation Loss: 1.5950 | Validation Accuracy: 0.4430 | Training Accuracy: 0.5572\n",
      "[38, 29184 subsamples] Training loss: 1.250\n",
      "[EPOCH 38, 1.8696 min] Validation Loss: 1.6012 | Validation Accuracy: 0.4051 | Training Accuracy: 0.5664\n",
      "[39, 29952 subsamples] Training loss: 1.237\n",
      "[EPOCH 39, 1.9274 min] Validation Loss: 1.6038 | Validation Accuracy: 0.4557 | Training Accuracy: 0.5729\n",
      "[40, 30720 subsamples] Training loss: 1.230\n",
      "[EPOCH 40, 1.9808 min] Validation Loss: 1.6068 | Validation Accuracy: 0.4557 | Training Accuracy: 0.5664\n",
      "[41, 31488 subsamples] Training loss: 1.230\n",
      "[EPOCH 41, 2.0339 min] Validation Loss: 1.6413 | Validation Accuracy: 0.4051 | Training Accuracy: 0.5742\n",
      "[42, 32256 subsamples] Training loss: 1.212\n",
      "[EPOCH 42, 2.1002 min] Validation Loss: 1.5849 | Validation Accuracy: 0.4684 | Training Accuracy: 0.5677\n",
      "[43, 33024 subsamples] Training loss: 1.202\n",
      "[EPOCH 43, 2.1541 min] Validation Loss: 1.6157 | Validation Accuracy: 0.4177 | Training Accuracy: 0.5848\n",
      "[44, 33792 subsamples] Training loss: 1.183\n",
      "[EPOCH 44, 2.2049 min] Validation Loss: 1.5428 | Validation Accuracy: 0.4937 | Training Accuracy: 0.5913\n",
      "[45, 34560 subsamples] Training loss: 1.176\n",
      "[EPOCH 45, 2.2580 min] Validation Loss: 1.6557 | Validation Accuracy: 0.3797 | Training Accuracy: 0.5861\n",
      "[46, 35328 subsamples] Training loss: 1.173\n",
      "[EPOCH 46, 2.3106 min] Validation Loss: 1.5768 | Validation Accuracy: 0.4937 | Training Accuracy: 0.5940\n",
      "[47, 36096 subsamples] Training loss: 1.176\n",
      "[EPOCH 47, 2.3602 min] Validation Loss: 1.7478 | Validation Accuracy: 0.3671 | Training Accuracy: 0.5874\n",
      "[48, 36864 subsamples] Training loss: 1.174\n",
      "[EPOCH 48, 2.4110 min] Validation Loss: 1.5041 | Validation Accuracy: 0.5190 | Training Accuracy: 0.5887\n",
      "[49, 37632 subsamples] Training loss: 1.170\n",
      "[EPOCH 49, 2.4634 min] Validation Loss: 1.6170 | Validation Accuracy: 0.4051 | Training Accuracy: 0.5913\n",
      "[50, 38400 subsamples] Training loss: 1.152\n",
      "[EPOCH 50, 2.5147 min] Validation Loss: 1.5925 | Validation Accuracy: 0.4430 | Training Accuracy: 0.6018\n",
      "[51, 39168 subsamples] Training loss: 1.130\n",
      "[EPOCH 51, 2.5665 min] Validation Loss: 1.5273 | Validation Accuracy: 0.5063 | Training Accuracy: 0.6176\n",
      "[52, 39936 subsamples] Training loss: 1.119\n",
      "[EPOCH 52, 2.6158 min] Validation Loss: 1.6347 | Validation Accuracy: 0.4177 | Training Accuracy: 0.6176\n",
      "[53, 40704 subsamples] Training loss: 1.098\n",
      "[EPOCH 53, 2.6654 min] Validation Loss: 1.5478 | Validation Accuracy: 0.4810 | Training Accuracy: 0.6321\n",
      "[54, 41472 subsamples] Training loss: 1.098\n",
      "[EPOCH 54, 2.7164 min] Validation Loss: 1.6073 | Validation Accuracy: 0.4177 | Training Accuracy: 0.6268\n",
      "[55, 42240 subsamples] Training loss: 1.091\n",
      "[EPOCH 55, 2.7685 min] Validation Loss: 1.5675 | Validation Accuracy: 0.4810 | Training Accuracy: 0.6347\n",
      "[56, 43008 subsamples] Training loss: 1.089\n",
      "[EPOCH 56, 2.8215 min] Validation Loss: 1.5482 | Validation Accuracy: 0.4810 | Training Accuracy: 0.6334\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[57, 43776 subsamples] Training loss: 1.072\n",
      "[EPOCH 57, 2.8720 min] Validation Loss: 1.6058 | Validation Accuracy: 0.4177 | Training Accuracy: 0.6452\n",
      "[58, 44544 subsamples] Training loss: 1.063\n",
      "[EPOCH 58, 2.9221 min] Validation Loss: 1.6098 | Validation Accuracy: 0.4304 | Training Accuracy: 0.6229\n",
      "[59, 45312 subsamples] Training loss: 1.069\n",
      "[EPOCH 59, 2.9739 min] Validation Loss: 1.5307 | Validation Accuracy: 0.4557 | Training Accuracy: 0.6334\n",
      "[60, 46080 subsamples] Training loss: 1.043\n",
      "[EPOCH 60, 3.0261 min] Validation Loss: 1.5833 | Validation Accuracy: 0.4304 | Training Accuracy: 0.6426\n",
      "[61, 46848 subsamples] Training loss: 1.033\n",
      "[EPOCH 61, 3.0817 min] Validation Loss: 1.5787 | Validation Accuracy: 0.4684 | Training Accuracy: 0.6491\n",
      "[62, 47616 subsamples] Training loss: 1.028\n",
      "[EPOCH 62, 3.1348 min] Validation Loss: 1.5402 | Validation Accuracy: 0.4557 | Training Accuracy: 0.6570\n",
      "[63, 48384 subsamples] Training loss: 1.037\n",
      "[EPOCH 63, 3.1857 min] Validation Loss: 1.6145 | Validation Accuracy: 0.3924 | Training Accuracy: 0.6373\n",
      "[64, 49152 subsamples] Training loss: 1.019\n",
      "[EPOCH 64, 3.2398 min] Validation Loss: 1.5286 | Validation Accuracy: 0.4557 | Training Accuracy: 0.6623\n",
      "[65, 49920 subsamples] Training loss: 1.004\n",
      "[EPOCH 65, 3.2914 min] Validation Loss: 1.6161 | Validation Accuracy: 0.3924 | Training Accuracy: 0.6597\n",
      "[66, 50688 subsamples] Training loss: 1.009\n",
      "[EPOCH 66, 3.3512 min] Validation Loss: 1.5022 | Validation Accuracy: 0.4937 | Training Accuracy: 0.6610\n",
      "[67, 51456 subsamples] Training loss: 0.994\n",
      "[EPOCH 67, 3.4080 min] Validation Loss: 1.6174 | Validation Accuracy: 0.4304 | Training Accuracy: 0.6689\n",
      "[68, 52224 subsamples] Training loss: 0.983\n",
      "[EPOCH 68, 3.4619 min] Validation Loss: 1.6079 | Validation Accuracy: 0.3924 | Training Accuracy: 0.6689\n",
      "[69, 52992 subsamples] Training loss: 0.973\n",
      "[EPOCH 69, 3.5120 min] Validation Loss: 1.6209 | Validation Accuracy: 0.4430 | Training Accuracy: 0.6899\n",
      "[70, 53760 subsamples] Training loss: 0.952\n",
      "[EPOCH 70, 3.5657 min] Validation Loss: 1.5741 | Validation Accuracy: 0.4304 | Training Accuracy: 0.6859\n",
      "[71, 54528 subsamples] Training loss: 0.946\n",
      "[EPOCH 71, 3.6203 min] Validation Loss: 1.5909 | Validation Accuracy: 0.4304 | Training Accuracy: 0.6912\n",
      "[72, 55296 subsamples] Training loss: 0.971\n",
      "[EPOCH 72, 3.6723 min] Validation Loss: 1.5685 | Validation Accuracy: 0.4051 | Training Accuracy: 0.6478\n",
      "[73, 56064 subsamples] Training loss: 0.941\n",
      "[EPOCH 73, 3.7230 min] Validation Loss: 1.6130 | Validation Accuracy: 0.4051 | Training Accuracy: 0.6873\n",
      "[74, 56832 subsamples] Training loss: 0.929\n",
      "[EPOCH 74, 3.7724 min] Validation Loss: 1.5870 | Validation Accuracy: 0.4177 | Training Accuracy: 0.7004\n",
      "[75, 57600 subsamples] Training loss: 0.912\n",
      "[EPOCH 75, 3.8220 min] Validation Loss: 1.5752 | Validation Accuracy: 0.4177 | Training Accuracy: 0.6899\n",
      "[76, 58368 subsamples] Training loss: 0.892\n",
      "[EPOCH 76, 3.8765 min] Validation Loss: 1.6461 | Validation Accuracy: 0.4177 | Training Accuracy: 0.7109\n",
      "[77, 59136 subsamples] Training loss: 0.898\n",
      "[EPOCH 77, 3.9297 min] Validation Loss: 1.5432 | Validation Accuracy: 0.4557 | Training Accuracy: 0.7135\n",
      "[78, 59904 subsamples] Training loss: 0.878\n",
      "[EPOCH 78, 3.9799 min] Validation Loss: 1.5579 | Validation Accuracy: 0.4430 | Training Accuracy: 0.7227\n",
      "[79, 60672 subsamples] Training loss: 0.870\n",
      "[EPOCH 79, 4.0290 min] Validation Loss: 1.5228 | Validation Accuracy: 0.4430 | Training Accuracy: 0.7188\n",
      "[80, 61440 subsamples] Training loss: 0.848\n",
      "[EPOCH 80, 4.0778 min] Validation Loss: 1.6432 | Validation Accuracy: 0.4177 | Training Accuracy: 0.7332\n",
      "[81, 62208 subsamples] Training loss: 0.857\n",
      "[EPOCH 81, 4.1292 min] Validation Loss: 1.5233 | Validation Accuracy: 0.4684 | Training Accuracy: 0.7280\n",
      "[82, 62976 subsamples] Training loss: 0.852\n",
      "[EPOCH 82, 4.1805 min] Validation Loss: 1.6646 | Validation Accuracy: 0.4051 | Training Accuracy: 0.7122\n",
      "[83, 63744 subsamples] Training loss: 0.855\n",
      "[EPOCH 83, 4.2301 min] Validation Loss: 1.5925 | Validation Accuracy: 0.4304 | Training Accuracy: 0.7162\n",
      "[84, 64512 subsamples] Training loss: 0.833\n",
      "[EPOCH 84, 4.2804 min] Validation Loss: 1.6221 | Validation Accuracy: 0.4430 | Training Accuracy: 0.7162\n",
      "[85, 65280 subsamples] Training loss: 0.829\n",
      "[EPOCH 85, 4.3367 min] Validation Loss: 1.5747 | Validation Accuracy: 0.4051 | Training Accuracy: 0.7254\n",
      "[86, 66048 subsamples] Training loss: 0.814\n",
      "[EPOCH 86, 4.3944 min] Validation Loss: 1.6328 | Validation Accuracy: 0.4051 | Training Accuracy: 0.7240\n",
      "[87, 66816 subsamples] Training loss: 0.795\n",
      "[EPOCH 87, 4.4496 min] Validation Loss: 1.6079 | Validation Accuracy: 0.3924 | Training Accuracy: 0.7398\n",
      "[88, 67584 subsamples] Training loss: 0.786\n",
      "[EPOCH 88, 4.4999 min] Validation Loss: 1.5663 | Validation Accuracy: 0.4430 | Training Accuracy: 0.7477\n",
      "[89, 68352 subsamples] Training loss: 0.768\n",
      "[EPOCH 89, 4.5511 min] Validation Loss: 1.5929 | Validation Accuracy: 0.3924 | Training Accuracy: 0.7490\n",
      "[90, 69120 subsamples] Training loss: 0.760\n",
      "[EPOCH 90, 4.6019 min] Validation Loss: 1.6174 | Validation Accuracy: 0.4304 | Training Accuracy: 0.7543\n",
      "[91, 69888 subsamples] Training loss: 0.772\n",
      "[EPOCH 91, 4.6548 min] Validation Loss: 1.6991 | Validation Accuracy: 0.3924 | Training Accuracy: 0.7516\n",
      "[92, 70656 subsamples] Training loss: 0.757\n",
      "[EPOCH 92, 4.7100 min] Validation Loss: 1.5692 | Validation Accuracy: 0.4430 | Training Accuracy: 0.7595\n",
      "[93, 71424 subsamples] Training loss: 0.777\n",
      "[EPOCH 93, 4.7615 min] Validation Loss: 1.6967 | Validation Accuracy: 0.3924 | Training Accuracy: 0.7464\n",
      "[94, 72192 subsamples] Training loss: 0.736\n",
      "[EPOCH 94, 4.8100 min] Validation Loss: 1.6038 | Validation Accuracy: 0.4304 | Training Accuracy: 0.7727\n",
      "[95, 72960 subsamples] Training loss: 0.737\n",
      "[EPOCH 95, 4.8590 min] Validation Loss: 1.5821 | Validation Accuracy: 0.4304 | Training Accuracy: 0.7635\n",
      "[96, 73728 subsamples] Training loss: 0.727\n",
      "[EPOCH 96, 4.9104 min] Validation Loss: 1.7306 | Validation Accuracy: 0.4177 | Training Accuracy: 0.7766\n",
      "[97, 74496 subsamples] Training loss: 0.707\n",
      "[EPOCH 97, 4.9633 min] Validation Loss: 1.5426 | Validation Accuracy: 0.4937 | Training Accuracy: 0.7714\n",
      "[98, 75264 subsamples] Training loss: 0.733\n",
      "[EPOCH 98, 5.0134 min] Validation Loss: 1.7835 | Validation Accuracy: 0.4051 | Training Accuracy: 0.7635\n",
      "[99, 76032 subsamples] Training loss: 0.739\n",
      "[EPOCH 99, 5.0627 min] Validation Loss: 1.6723 | Validation Accuracy: 0.4557 | Training Accuracy: 0.7700\n",
      "[100, 76800 subsamples] Training loss: 0.702\n",
      "[EPOCH 100, 5.1125 min] Validation Loss: 1.6958 | Validation Accuracy: 0.4051 | Training Accuracy: 0.7806\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 100\n",
    "p_dropout = 0.3\n",
    "#model = ResNet(FN=64, p_dropout=p_dropout)\n",
    "#added a condition to allow to specify ReLU or tanh\n",
    "model = SimpleCNN2(activation = \"tanh\")\n",
    "model.to(device)\n",
    "\n",
    "\n",
    "#summary(model, (1, 128, 1290))\n",
    "\n",
    "# Adam optimizer01\n",
    "lr = 0.01\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "\n",
    "timestamp = time.strftime(\"apr%d_t%H%M\", time.gmtime())\n",
    "model_name = f\"{model.name}_B{BATCH}_E{EPOCHS}_LR{lr}_pD{p_dropout}_A{augment_prob}_{timestamp}\"\n",
    "\n",
    "i = 0\n",
    "running_loss = 0.0\n",
    "best_val_loss = float('inf')  # initialize the best validation loss\n",
    "\n",
    "# train the model\n",
    "acc_tr = []\n",
    "acc_val = []\n",
    "loss_tr = []\n",
    "loss_val = []\n",
    "\n",
    "mi_array_a1_fm1 = []\n",
    "mi_array_a1_fm2 = []\n",
    "mi_array_a1_fm3 = []\n",
    "mi_array_a1_fm4 = []\n",
    "\n",
    "\n",
    "activity = np.zeros((1000, 4, 10304))\n",
    "activity2 = np.zeros((1000, 16, 2576))\n",
    "activity3 = np.zeros((1000, 32, 648))\n",
    "activity4 = np.zeros((1000, 64, 164))\n",
    "\n",
    "t0 = time.time()\n",
    "prev_a = 0\n",
    "\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    # evaluate the model on the training dataset\n",
    "    train_correct = 0\n",
    "    train_total = 0\n",
    "    for spectrogram, label, ixs in dataloader:\n",
    "        model.train()\n",
    "        label = label.to(device)\n",
    "        train_label = torch.argmax(label, dim=1)\n",
    "\n",
    "        # forward pass\n",
    "        spectrogram = spectrogram.squeeze(0)\n",
    "        spectrogram = spectrogram.unsqueeze(1)\n",
    "\n",
    "        spectrogram = spectrogram.to(device)\n",
    "        output, a1, a2,a3 ,a4 = model(spectrogram)\n",
    "        activity[ixs] = a1.cpu().detach().numpy()\n",
    "        activity2[ixs] = a2.cpu().detach().numpy()\n",
    "        activity3[ixs] = a3.cpu().detach().numpy()\n",
    "        activity4[ixs] = a4.cpu().detach().numpy()\n",
    "\n",
    "        loss = loss_fn(output, label)\n",
    "\n",
    "\n",
    "        # backward pass\n",
    "        optimizer.zero_grad()\n",
    "        model.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Update the learning rate\n",
    "        # scheduler.step(loss)\n",
    "\n",
    "        _, train_predicted = torch.max(output.data, 1)\n",
    "        train_total += train_label.size(0)\n",
    "        train_correct += (train_predicted == train_label).sum().item()\n",
    "        # print statistics\n",
    "        i += 1\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    loss = running_loss / len(dataloader)\n",
    "    loss_tr.append(loss)\n",
    "    print('[%d, %5d subsamples] Training loss: %.3f' % (epoch + 1, i * BATCH, loss))\n",
    "    running_loss = 0\n",
    "    # evaluate the model on the validation dataset\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for val_spectrogram, val_label, ixs in val_dataloader:\n",
    "            val_label = val_label.to(device)\n",
    "            val_label = torch.argmax(val_label, dim=1)\n",
    "\n",
    "            val_spectrogram = val_spectrogram.squeeze(0)\n",
    "            val_spectrogram = val_spectrogram.unsqueeze(1)\n",
    "            val_spectrogram = val_spectrogram.to(device)\n",
    "            val_output, a1, a2, _, _ = model(val_spectrogram)\n",
    "            val_loss += loss_fn(val_output, val_label).item()\n",
    "            _, val_predicted = torch.max(val_output.data, 1)\n",
    "            val_total += val_label.size(0)\n",
    "            val_correct += (val_predicted == val_label).sum().item()\n",
    "\n",
    "    loss = val_loss / len(val_dataloader)\n",
    "    loss_val.append(loss)\n",
    "    val_acc = val_correct / val_total\n",
    "    tr_acc = train_correct / train_total\n",
    "    acc_tr.append(tr_acc)\n",
    "    acc_val.append(val_acc)\n",
    "    t1 = time.time()\n",
    "    t = (t1 - t0) / 60\n",
    "    # Save the model if the validation loss is the best seen so far\n",
    "    if loss < best_val_loss:\n",
    "        best_val_loss = loss\n",
    "        best_val_acc = val_acc\n",
    "        best_tr_acc = tr_acc\n",
    "        best_state_dict = model.state_dict()\n",
    "    print(\n",
    "        '[EPOCH {}, {:.4f} min] Validation Loss: {:.4f} | Validation Accuracy: {:.4f} | Training Accuracy: {:.4f}'.format(epoch+1, t, loss,\n",
    "                                                                                                                val_acc,\n",
    "                                                                                                                tr_acc))\n",
    "    # Compute the MI using kraskov_MI2 for activations a1 and a2\n",
    "    # NOTE: we are computing the MI wrt to the first (0th) feature map, could do for others aswell\n",
    "    mi_a1_fm1 = kraskov_entropy(activity[:, 0, :], k=2)\n",
    "    mi_a1_fm2 = kraskov_entropy(activity[:, 1, :], k=2)\n",
    "    mi_a1_fm3 = kraskov_entropy(activity[:, 2, :], k=2)\n",
    "    mi_a1_fm4 = kraskov_entropy(activity[:, 3, :], k=2)\n",
    "\n",
    "    mi_array_a1_fm1.append(mi_a1_fm1)\n",
    "    mi_array_a1_fm2.append(mi_a1_fm2)\n",
    "    mi_array_a1_fm3.append(mi_a1_fm3)\n",
    "    mi_array_a1_fm4.append(mi_a1_fm4)\n",
    "\n",
    "\n",
    "\n",
    "mi_array_a1_fm1 = np.array(mi_array_a1_fm1)\n",
    "mi_array_a1_fm2 = np.array(mi_array_a1_fm2)\n",
    "mi_array_a1_fm3 = np.array(mi_array_a1_fm3)\n",
    "mi_array_a1_fm4 = np.array(mi_array_a1_fm4)\n",
    "\n",
    "np.save(timestamp + '_MI_a1_fm1', mi_array_a1_fm1)\n",
    "np.save(timestamp + '_MI_a1_fm2', mi_array_a1_fm2)\n",
    "np.save(timestamp + '_MI_a1_fm3', mi_array_a1_fm3)\n",
    "np.save(timestamp + '_MI_a1_fm4', mi_array_a1_fm4)\n",
    "\n",
    "\n",
    "num_samples = 1000\n",
    "\n",
    "# Save the combined loss and accuracy plot\n",
    "combined_plot_filename = os.path.join(plot_dir, f'combined_plot_epochs{EPOCHS}_samples{num_samples}.png')\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(loss_val, label='Validation loss')\n",
    "plt.plot(loss_tr, label='Training loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title('Loss vs Epochs')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(acc_val, label='Validation accuracy')\n",
    "plt.plot(acc_tr, label='Training accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title('Accuracy vs Epochs')\n",
    "\n",
    "plt.savefig(combined_plot_filename)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Save the MI plot\n",
    "mi_plot_filename = os.path.join(plot_dir, f'mi_plot_epochs{EPOCHS}_samples{num_samples}.png')\n",
    "t = np.arange(len(mi_array_a1_fm1))\n",
    "plt.plot(t, mi_array_a1_fm1, label='Kraskov MI Conv1 - FM1')\n",
    "plt.plot(t, mi_array_a1_fm2, label='Kraskov MI Conv1 - FM2')\n",
    "plt.plot(t, mi_array_a1_fm3, label='Kraskov MI Conv1 - FM3')\n",
    "plt.plot(t, mi_array_a1_fm4, label='Kraskov MI Conv1 - FM4')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MI/Entropy(T)')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.savefig(mi_plot_filename)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "torch.save(best_state_dict, model_name + f'_VAL{best_val_acc}_TRAIN{best_tr_acc}.pt')\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1858f915",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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