tutorials/notebooks/DL-gwas-gcp-example/1-d10-run-first.ipynb

{
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  {
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   "id": "ddee0923-844f-42c8-9273-7e32d7178628",
   "metadata": {
    "tags": [
     "skip"
    ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: tensorboard-plugin-wit, libclang, flatbuffers, werkzeug, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, protobuf, numpy, keras, google-pasta, gast, astunparse, absl-py, opt-einsum, markdown, h5py, google-auth-oauthlib, tensorboard, tensorflow\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "yellowbrick 1.3 requires numpy<1.20,>=1.16.0, but you have numpy 1.24.1 which is incompatible.\n",
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      "ml-metadata 0.30.0 requires absl-py<0.13,>=0.9, but you have absl-py 1.3.0 which is incompatible.\n",
      "kserve 0.8.0 requires numpy~=1.19.2, but you have numpy 1.24.1 which is incompatible.\n",
      "kfp 1.7.post0+41.gb3589b6c6 requires absl-py<=0.11,>=0.9, but you have absl-py 1.3.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed absl-py-1.3.0 astunparse-1.6.3 flatbuffers-23.1.4 gast-0.4.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 h5py-3.7.0 keras-2.11.0 libclang-14.0.6 markdown-3.4.1 numpy-1.24.1 opt-einsum-3.3.0 protobuf-3.19.6 tensorboard-2.11.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.11.0 tensorflow-estimator-2.11.0 tensorflow-io-gcs-filesystem-0.29.0 werkzeug-2.2.2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# mark in Kale as skip\n",
    "! pip3 install --upgrade pip\n",
    "! pip3 install pendulum==2.1.2\n",
    "! pip3 install pandas==1.4.4\n",
    "! pip3 install matplotlib==3.3.4\n",
    "! pip3 install --upgrade tensorflow==2.11.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7352040d-d4ca-41af-81ca-8df30e7de6cb",
   "metadata": {
    "tags": [
     "skip"
    ]
   },
   "outputs": [],
   "source": [
    "# mark in Kale as skip\n",
    "import os\n",
    "\n",
    "if not os.getenv(\"IS_TESTING\"):\n",
    "    # Automatically restart kernel after installs\n",
    "    import IPython\n",
    "\n",
    "    app = IPython.Application.instance()\n",
    "    app.kernel.do_shutdown(True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "619de0db-3878-4254-93a4-4404553d0ecf",
   "metadata": {
    "tags": [
     "imports"
    ]
   },
   "outputs": [],
   "source": [
    "\n",
    "# mark in katib as imports\n",
    "import pendulum\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv1D, Dropout, BatchNormalization, Lambda\n",
    "from tensorflow.keras.regularizers import l1,l2, L1L2\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42103b5f-1c53-4047-99ba-690e5bb7d398",
   "metadata": {
    "tags": [
     "skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2023-01-051310'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set time in the pipeline parameters and in Katib (as string and the only value) to the output of this\n",
    "pendulum.now(tz='America/New_York').__str__()[:16].replace('T','').replace(':','').replace('_','-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd604bf6-0d00-4178-9deb-28465c915c29",
   "metadata": {
    "tags": [
     "pipeline-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# mark in Kayle as pipeline parameters\n",
    "data_file_to_run = \"IMP_height.txt\"\n",
    "\n",
    "experiment_description = \"Soy Height GWAS\"\n",
    "\n",
    "learning_rate = 0.001 # 0.0001957\n",
    "conv_1_dropout_rate = 0.50 # Dropout rate for first convolutional layer\n",
    "conv_1_kernel_l1 = 0.2 # L1 and l2 regularization for the first conv1d layer's weights\n",
    "conv_1_kernel_l2 = 0.6\n",
    "conv_1_bias_l2 = 0.6 # L1 and l2 regularization for the first conv1d layer's bias\n",
    "conv_1_activity_l2 = 0.00001 # L1 and l2 activity  regularization for the first conv1d layer\n",
    "\n",
    "conv_x_kernel_l1 = 0.3\n",
    "conv_x_kernel_l2 = 0.3\n",
    "conv_x_bias_l2 = 0.6\n",
    "conv_x_activity_l2 = 0.0001\n",
    "\n",
    "dense_x_kernel_l1 = 0.3\n",
    "dense_x_kernel_l2 = 0.6\n",
    "dense_x_bias_l2 = 0.0001\n",
    "dense_x_activity_l2 = 0.0001\n",
    "\n",
    "dense_out_kernel_l1 = 0.1\n",
    "dense_out_kernel_l2 = 0.6\n",
    "dense_out_bias_l2 = 0.6\n",
    "dense_out_activity_l2 = 0.00001\n",
    "\n",
    "conv_initializer = 'TruncatedNormal'  #    # 'TruncatedNormal' 'glorot_uniform'  \"GlorotNormal\",  \"HeNormal\" 'random_normal'  \n",
    "dese_initializer = \"GlorotNormal\" # 'TruncatedNormal' # \"GlorotUniform\"\n",
    "\n",
    "dropout_rate = 0.151\n",
    "num_dense_layers = 3\n",
    "num_dense_units = 20\n",
    "\n",
    "conv_activation = \"elu\" # \"linear\"\n",
    "activation = \"elu\"\n",
    "loss = 'huber_loss' # 'mean_squared_error' 'mean_absolute_error'\n",
    "\n",
    "final_activation_scale_factor = 3.5\n",
    "\n",
    "batch_size = 30\n",
    "epochs = 2\n",
    "\n",
    "time = '2023-01-051310'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e06747ae-77b6-4524-b803-fc0d87512fbb",
   "metadata": {
    "tags": [
     "block:preprocessing"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing successful\n"
     ]
    }
   ],
   "source": [
    "# mark in Kayle as pipeline step: \"preprocessing\": Depends on none\n",
    "\n",
    "ht = pd.read_csv(data_file_to_run, sep = '\\t')\n",
    "ht_pd = ht_relevant_cols = ht.drop(columns = ['strain', 'height', 'folds'])\n",
    "phenotypes_norm = ht_pd.pop(\"norm_phe\")\n",
    "for col in ht_pd.columns:\n",
    "    ht_pd[col] = ht_pd[col].astype('category')\n",
    "ohe_height_genotypes = pd.get_dummies(ht_pd)\n",
    "\n",
    "def train_test_splitting(row, split_ratio):\n",
    "    string_of_row = \"\".join([str(l) for l in list(row.values)])\n",
    "    return (abs(hash(string_of_row)) % 10) / 10 < split_ratio\n",
    "belongs_in_train_set_index =\\\n",
    "    np.array([train_test_splitting(ohe_height_genotypes.loc[i],0.7)\n",
    "     for i in np.arange(ht_pd.shape[0])])\n",
    "\n",
    "train_ohe_height_genotypes = ohe_height_genotypes[belongs_in_train_set_index]\n",
    "val_ohe_height_genotypes = ohe_height_genotypes[~belongs_in_train_set_index]\n",
    "\n",
    "train_phenotypes_norm = phenotypes_norm[belongs_in_train_set_index]\n",
    "val_phenotypes_norm = phenotypes_norm[~belongs_in_train_set_index]\n",
    "\n",
    "# Make sure the number of rows in test and train add up to the original rows\n",
    "assert train_ohe_height_genotypes.shape[0] + val_ohe_height_genotypes.shape[0] == ht_pd.shape[0]\n",
    "\n",
    "# Data as a numpy array...\n",
    "train_ohe_height_genotypes_np = train_ohe_height_genotypes.values\n",
    "val_ohe_height_genotypes_np = val_ohe_height_genotypes.values\n",
    "\n",
    "train_phenotypes_norm_np = train_phenotypes_norm.values\n",
    "val_phenotypes_norm_np = val_phenotypes_norm.values\n",
    "\n",
    "# Reshape to fit the conv1D network. \n",
    "train_np_ohe_reshaped_for_conv_1_d =\\\n",
    "    train_ohe_height_genotypes_np.reshape((train_ohe_height_genotypes_np.shape[0],\n",
    "                                          train_ohe_height_genotypes_np.shape[1], 1))\n",
    "val_np_ohe_reshaped_for_conv_1_d =\\\n",
    "    val_ohe_height_genotypes_np.reshape((val_ohe_height_genotypes_np.shape[0],\n",
    "                                        val_ohe_height_genotypes_np.shape[1],1))\n",
    "\n",
    "np.save('train_data_ready', train_np_ohe_reshaped_for_conv_1_d)\n",
    "np.save('val_data_ready', val_np_ohe_reshaped_for_conv_1_d)\n",
    "np.save('train_labels_ready', train_phenotypes_norm_np)\n",
    "np.save('val_labels_ready',val_phenotypes_norm_np)\n",
    "\n",
    "# Since the data was reshaped for the convolutional 1D neural network, we are also saving the \n",
    "# non-reshaped data to be used for calculating saliency.\n",
    "\n",
    "np.save(\"val_snps_for_saliency\", val_ohe_height_genotypes_np)\n",
    "\n",
    "print(\"Preprocessing successful\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dd01e8b2-78ba-4315-9e12-9153b1c57ce5",
   "metadata": {
    "tags": [
     "block:train",
     "prev:preprocessing"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      " 37/120 [========>.....................] - ETA: 1:07 - loss: 569.6301 - mean_absolute_error: 2.0097"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [41], line 132\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[38;5;66;03m# qa_data_model = Model(inputs = inputs, outputs = outputs)\u001b[39;00m\n\u001b[1;32m    126\u001b[0m our_data_model\u001b[38;5;241m.\u001b[39mcompile(loss\u001b[38;5;241m=\u001b[39mloss,\n\u001b[1;32m    127\u001b[0m               optimizer\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39moptimizers\u001b[38;5;241m.\u001b[39mAdam(learning_rate\u001b[38;5;241m=\u001b[39mlearning_rate),\n\u001b[1;32m    128\u001b[0m               metrics\u001b[38;5;241m=\u001b[39m[tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mmetrics\u001b[38;5;241m.\u001b[39mMeanAbsoluteError()],\n\u001b[1;32m    129\u001b[0m               jit_compile\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    131\u001b[0m history \u001b[38;5;241m=\u001b[39m\\\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mour_data_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtrain_snps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[43m                       \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtrain_phenotypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    135\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    136\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mval_snps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_phenotypes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    137\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[43m                       \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    139\u001b[0m \u001b[38;5;66;03m# Requirement 6: save and log your artifact. \u001b[39;00m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;66;03m# I'm adding a random number to the file name as an\u001b[39;00m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;66;03m# extra layer of safety nets against race conditions\u001b[39;00m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;66;03m# / file name conflicts\u001b[39;00m\n\u001b[1;32m    143\u001b[0m \n\u001b[1;32m    144\u001b[0m \u001b[38;5;66;03m# tn = str(int(np.random.random() * 10 ** 12))\u001b[39;00m\n\u001b[1;32m    145\u001b[0m model_folder \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-model\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/engine/training.py:1650\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1642\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1643\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1644\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1647\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1648\u001b[0m ):\n\u001b[1;32m   1649\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1650\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1651\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1652\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:880\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    877\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    879\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 880\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    882\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    883\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:919\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    916\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    917\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    918\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 919\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ALLOW_DYNAMIC_VARIABLE_CREATION:\n\u001b[1;32m    921\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    922\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:134\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    132\u001b[0m   (concrete_function,\n\u001b[1;32m    133\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 134\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    135\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1745\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1741\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1743\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1744\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1745\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1746\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1747\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m     args,\n\u001b[1;32m   1749\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1750\u001b[0m     executing_eagerly)\n\u001b[1;32m   1751\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:378\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    377\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 378\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    384\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    385\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    386\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m    387\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    390\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m    391\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     53\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     55\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# mark in Kayle as pipeline step \"train\": depends on \"data-preprocessing\"\n",
    "nb_classes = 3\n",
    "\n",
    "data_files = ['./train_data_ready.npy',\n",
    "              \"./val_data_ready.npy\",\n",
    "              \"./train_labels_ready.npy\",\n",
    "              \"./val_labels_ready.npy\"]\n",
    "# artifact_bucket_root_name = artifacts_bucket.split('/')[-1]\n",
    "# print(artifact_bucket_root_name)\n",
    "# storage_client = storage.Client()\n",
    "# bucket = storage_client.get_bucket(artifact_bucket_root_name)\n",
    "\n",
    "ht_np_train = np.load('train_data_ready.npy', allow_pickle=True)\n",
    "ht_np_val = np.load('val_data_ready.npy', allow_pickle=True)\n",
    "train_labels_np = np.load('train_labels_ready.npy', allow_pickle=True)\n",
    "val_labels_np = np.load('val_labels_ready.npy', allow_pickle=True)\n",
    "\n",
    "train_snps = ht_np_train\n",
    "train_phenotypes = train_labels_np\n",
    "val_snps = ht_np_val\n",
    "val_phenotypes = val_labels_np\n",
    "\n",
    "# print(\"min\")\n",
    "# print(val_phenotypes.min())\n",
    "# print(\"max\")\n",
    "# print(val_phenotypes.max())\n",
    "\n",
    "inputs =\\\n",
    "    tf.keras.layers.Input(\n",
    "        shape=(train_snps.shape[1], \n",
    "               train_snps.shape[2])) # train_snps.shape[1] ,nb_classes))\n",
    "\n",
    "x = Conv1D(10,\n",
    "           nb_classes,\n",
    "           padding='same',\n",
    "           activation = conv_activation,\n",
    "           kernel_initializer = conv_initializer,\n",
    "           kernel_regularizer=tf.keras.regularizers.L1L2(l1=conv_1_kernel_l1, l2=conv_1_kernel_l2),\n",
    "           bias_regularizer=tf.keras.regularizers.L2(conv_1_bias_l2),\n",
    "           activity_regularizer=tf.keras.regularizers.L2(conv_1_activity_l2)\n",
    "          )(inputs)\n",
    "\n",
    "           # kernel_initializer = conv_initializer ,\n",
    "           # kernel_regularizer=\"l2\", bias_regularizer = \"l2\")\n",
    "x = Dropout(conv_1_dropout_rate)(x)\n",
    "\n",
    "x = Conv1D(10,\n",
    "           20,\n",
    "           padding='same',\n",
    "           activation = conv_activation,\n",
    "           kernel_initializer = conv_initializer,\n",
    "           kernel_regularizer=tf.keras.regularizers.L1L2(l1=conv_x_kernel_l1, l2=conv_x_kernel_l2),\n",
    "           bias_regularizer=tf.keras.regularizers.L2(conv_x_bias_l2),\n",
    "           activity_regularizer=tf.keras.regularizers.L2(conv_x_activity_l2)\n",
    "           # kernel_initializer = 'TruncatedNormal',\n",
    "           # kernel_regularizer=\"l2\",\n",
    "           # bias_regularizer=\"l2\"\n",
    "          )(x) # Leaving l1 l2 on head layer only to see if this prevents everything from zeroing out.\n",
    "\n",
    "x = Dropout(dropout_rate)(x)\n",
    "\n",
    "\n",
    "shortcut = Conv1D(10,\n",
    "                  4,\n",
    "                  padding='same',\n",
    "                  activation = conv_activation,\n",
    "                  kernel_initializer = conv_initializer,\n",
    "                  kernel_regularizer=tf.keras.regularizers.L1L2(l1=conv_x_kernel_l1, l2=conv_x_kernel_l2),\n",
    "                  bias_regularizer=tf.keras.regularizers.L2(conv_x_bias_l2),\n",
    "                  activity_regularizer=tf.keras.regularizers.L2(conv_x_activity_l2))(inputs)\n",
    "shortcut = Dropout(dropout_rate)(shortcut)\n",
    "x = tf.keras.layers.Add()([shortcut,x])\n",
    "\n",
    "x = Conv1D(10,\n",
    "           4,\n",
    "           padding='same',\n",
    "           activation = conv_activation,\n",
    "           kernel_initializer = conv_initializer,\n",
    "           kernel_regularizer=tf.keras.regularizers.L1L2(l1=conv_x_kernel_l1, l2=conv_x_kernel_l2),\n",
    "           bias_regularizer=tf.keras.regularizers.L2(conv_x_bias_l2),\n",
    "           activity_regularizer=tf.keras.regularizers.L2(conv_x_activity_l2)\n",
    "           # kernel_initializer = 'TruncatedNormal', \n",
    "           # kernel_regularizer = \"l2\",\n",
    "           # bias_regularizer = \"l2\"\n",
    "          )(x)\n",
    "\n",
    "# x = Dropout(dropout_rate)(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "# x = Dropout(dropout_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "\n",
    "if num_dense_layers > 0:\n",
    "    y = x\n",
    "    for i in np.arange(num_dense_layers):\n",
    "        y = Dense(num_dense_units, \n",
    "                  activation,\n",
    "                  kernel_initializer=dese_initializer,\n",
    "                  kernel_regularizer=tf.keras.regularizers.L1L2(l1=dense_x_kernel_l1, l2=dense_x_kernel_l2),\n",
    "                  bias_regularizer=tf.keras.regularizers.L2(dense_x_bias_l2),\n",
    "                  activity_regularizer=tf.keras.regularizers.L2(dense_x_activity_l2)\n",
    "                  )(y)\n",
    "        # y = Dropout(dropout_rate)(y)\n",
    "        y = BatchNormalization()(y)\n",
    "        \n",
    "    x = tf.keras.layers.Concatenate(axis=1)([x,y])\n",
    "    x = BatchNormalization()(x)\n",
    "\n",
    "outputs_unscaled = Dense(1,\n",
    "                activation=\"softsign\",\n",
    "                kernel_initializer=dese_initializer,\n",
    "                kernel_regularizer=tf.keras.regularizers.L1L2(l1=dense_out_kernel_l1, l2=dense_out_kernel_l2),\n",
    "                bias_regularizer=tf.keras.regularizers.L2(dense_out_bias_l2),\n",
    "                activity_regularizer=tf.keras.regularizers.L2(dense_out_activity_l2),\n",
    "                # bias_regularizer = \"l2\",\n",
    "                # kernel_initializer = 'TruncatedNormal',\n",
    "                name = 'out')(x) # Should have no activation\n",
    "# Softsign coerces the output to the range {-1,1}. The labels are norm scaled, \n",
    "# where the range {-2,2} or {-3,3} encompasses most values. We multiply by a scalar \n",
    "# and the range will terminate at +/- said scalar. No telling which one is optimal,\n",
    "# so we'll le the the tuner figure out what: \n",
    "outputs = Lambda(lambda x: x * final_activation_scale_factor)(outputs_unscaled) \n",
    "\n",
    "our_data_model = Model(inputs = inputs, outputs = outputs)\n",
    "# qa_data_model = Model(inputs = inputs, outputs = outputs)\n",
    "our_data_model.compile(loss=loss,\n",
    "              optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),\n",
    "              metrics=[tf.keras.metrics.MeanAbsoluteError()],\n",
    "              jit_compile=True)\n",
    "\n",
    "history =\\\n",
    "    our_data_model.fit(x = train_snps,\n",
    "                       y = train_phenotypes,\n",
    "                       batch_size=batch_size,\n",
    "                       epochs=epochs,\n",
    "                       validation_data=(val_snps, val_phenotypes),\n",
    "                       shuffle= True,\n",
    "                       use_multiprocessing=True)\n",
    "# Requirement 6: save and log your artifact. \n",
    "# I'm adding a random number to the file name as an\n",
    "# extra layer of safety nets against race conditions\n",
    "# / file name conflicts\n",
    "\n",
    "# tn = str(int(np.random.random() * 10 ** 12))\n",
    "model_folder = f\"a-{time}-model\"\n",
    "our_data_model.save(model_folder)\n",
    "\n",
    "history_df = pd.DataFrame(history.history)\n",
    "\n",
    "history_df[[\"mean_absolute_error\", \"val_mean_absolute_error\"]].plot()\n",
    "plt.savefig(f'{model_folder}-history.png')\n",
    "\n",
    "print(model_folder)\n",
    "\n",
    "val_mean_absolute_error = float(history_df['val_mean_absolute_error'].values.min())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "74954dbc-9133-40d5-9f23-400a39807724",
   "metadata": {
    "tags": [
     "block:saliency_observed",
     "prev:preprocessing"
    ]
   },
   "outputs": [],
   "source": [
    "# mark in Kayle as pipeline step: \"saliency - known\": depends on \"data-preprocessing\"\n",
    "\n",
    "# Calculate observed p values: (Depends on data preprocessing)\n",
    "\n",
    "val_snps_s = np.load(\"val_snps_for_saliency.npy\", allow_pickle=True)\n",
    "val_phenotypes_k = np.load('val_labels_ready.npy', allow_pickle=True)\n",
    "\n",
    "p_values = []\n",
    "for i in np.arange(int(val_snps_s.shape[1] / 3)):\n",
    "    column_index_lower_bound = 3 * i\n",
    "    column_index_upper_bound = 3 * i + 3\n",
    "    data = val_snps_s[:,column_index_lower_bound:column_index_upper_bound]\n",
    "    data_reshaped = np.argmax(data, axis=1)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(data_reshaped, val_phenotypes_k)\n",
    "    p_values.append(p_value)\n",
    "p_values_observed_np = np.array(p_values)\n",
    "np.save('p_values_observed_np', \n",
    "        p_values_observed_np, \n",
    "        allow_pickle=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "464ffbb4-eb9e-438e-ac1e-292a2e45a99f",
   "metadata": {
    "tags": [
     "block:manhattan_observed",
     "prev:saliency_observed"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HjhyBtbU1pk2bJp8nEAgwY8YMldso+11oep4pIzMzU2lp86RJk+TnpIeHB+bOnStfFhQUhBo1ashzTcLDw5GTkyPPKerUqZM8efby5cuQSCQK+ShisRjbt2/H2LFjwXEcAMjDtmUTaPv27YvLly9j6NChCA8Px7Jly9C/f3/Url0b+/bt0/l9lyUuLg5hYWF4/fXX4e7uLp/fvHlz9O3bF4cOHSq3TdnvaebMmQAgX1cWotq7d69C+Ks0O3bsQFBQEAIDAxXOn169egFAufOne/fuaNKkicK8Xr16oWbNmti+fbt8XlpaGo4fP46xY8dqfSyZ/bNmzVI4zocffqj0PRDmpVolzgKAh4cH+vTpg23btiE3NxcSiQSjRo1Suf6BAwewePFihIWFKcQ1ZRef0tStW7fcPDc3N6SlpVW4rpubGwDpj8/Z2RmANNY9f/58XL58uVy+SkZGBlxcXNS8U/Xcu3cP//vf/3Dq1KlyN+CyAqhOnTrl3q+bmxtu376t0bHat2+PxYsXg+M42NvbIygoqMLE1WfPnqFRo0YQCBR1dFBQkHy5oUhKSkJubi4aN25cbllQUBB4nkdMTAyCg4Pl8+vVq6fx/r29vbF27VqsWbMGT548wdGjR7F06VJ89dVX8Pb2xltvvSV/P8psCAwMVEjW7Nu3L7y9vbF161b07t0bPM/j77//xrBhw+Dk5KSRTe3atVNaeu3m5qaQQP7kyRMwxtCoUSOl+1GXLPvs2TN4e3vD3t5eYX7Dhg2Vrm9rayvPOSltj7Lfj6Y4OTkpLfNetGgR3n//fQDSz7M0HMehU6dOOHfuHHiex8WLF+Hp6Sm3u1OnTli1ahUAyMVKaZFy7NgxJCUloV27doiIiJDP79mzJ/7++28sXbpU4bxu27Ytdu3ahcLCQoSHh2P37t346aefMGrUKISFhZW7acvIzs5WeG9CobDc5ydD3fkVFBSEo0ePlksAL/udN2jQAAKBQF4tOHbsWPz2229466238Nlnn6F3794YOXIkRo0aJX9/T548wYMHD1TaVTY5WNnvysrKCq+++iq2bduGgoICiEQi7Nq1C0VFRQoiRdNjPXv2DAKBQOGBUtVnQ5ifaidSAGDChAmYNm0a4uPjMWDAAJU3zPPnz2Po0KHo1q0b1qxZA29vb1hbW2PTpk1Kk+BUPVnLnpa1WTcyMhK9e/dGYGAgli9fDl9fX9jY2ODQoUP46aefVD65aEJ6ejq6d+8OZ2dnLFq0CA0aNICtrS1CQ0Mxd+7ccvvW5n0po2bNmujTp4/O9loipT1RmsJxHAICAhAQEIBBgwahUaNG2Lp1K9566y2t9iMUCjFhwgRs2LABa9aswcWLFxEbG6vg7TIUPM+D4zgcPnxY6XlgyAZsmnimtCUwMBDh4eEoKipSEFRlPUZl6dKlC/bv3487d+6Uq8zq1KkTPvnkE7x8+RIXLlyAj4+PggdO5i0ZM2aM0n2fPXsWPXv2LDffxsYGbdu2Rdu2bREQEICpU6dix44dmD9/vtL9/PDDD1i4cKH8tZ+fn1HbDZR9ULGzs8O5c+dw+vRpHDx4EEeOHMH27dvRq1cvHDt2DEKhEDzPo1mzZli+fLnSffr6+pbbpzLGjRuH9evX4/Dhwxg+fDj+/fdfBAYGokWLFvJ1tD0WUTmoliJlxIgReOedd3DlyhUFF2JZdu7cCVtbWxw9elShh8qmTZuMbuP+/ftRUFCAffv2KXhdlLnXlXl11M0/c+YMUlJSsGvXLnTr1k0+v6JqG1Pi5+eH27dvg+d5hadOWShKVqWj6j1qg4eHB+zt7ZX2+Hj48CEEAoHBL3D169eHm5sb4uLiAJS8n0ePHsnd0zIePXokXy5j8uTJ+PHHH7F//34cPnwYHh4e6N+/v0FtBKRPz4wx1KtXDwEBAVpt6+fnh9OnTyM3N1fBm1Lau6At2n7fgwcPxpUrV7B7926VokEZpfulXLx4USEU0Lp1a4hEIpw5cwZXr17FwIED5ctycnKwd+9ejB07VqmHdtasWdi6datSkVIamZdLdn4oY/LkyQoeHHXCufT5VZaHDx+iZs2a5cronzx5ouDZiIiIAM/zChVcAoEAvXv3Ru/evbF8+XJ8++23+OKLL3D69Gl5+Ds8PBy9e/fW67farVs3eHt7Y/v27ejSpQtOnTqFL774QmEdTY/l5+cHnucRGRmp4D0xdo8fQjeqXU4KIH36W7t2LRYsWIAhQ4aoXE8oFILjOIWy3OjoaJO0UZc9VZb2VmRkZCgVSA4ODkhPT1c6H0C5Zcr2XVhYiDVr1uhrtsEYOHAg4uPjFUSkWCzGypUr4ejoiO7duwOA/Oan7P1rilAoRL9+/bB3716FJ9GEhARs27YNXbp0kYfgtOXq1avIyckpN//atWtISUmRXyTbtGkDT09PrFu3TiGsePjwYTx48ACDBg1S2L558+Zo3rw5fvvtN+zcuRPjxo1T6P1jKEaOHAmhUIiFCxeW85wxxpCSkqJy2/79+6OoqAgbNmyQz+N5HqtXr9bZHm2/7/feew+1atXC7Nmz8fjx43LLVXkD27RpA1tbW2zduhUvX75U8KSIRCK0atUKq1evRk5OjoJQ2L17N3JycjBjxgyMGjWq3DR48GDs3LlT/h2fPn1aqQ2yvAl1IYj69eujT58+8qlsCXRpvL29ERISgs2bNyt8dnfv3sWxY8cUhJaMst+TrAPugAEDAEgbFZYlJCQEAOTvb8yYMXj58qXCOSAjLy9P6W9DGQKBAKNGjcL+/fuxZcsWiMVihVCPNseS2f/LL78orLNixQqNbCFMS7X0pADS1ugVMWjQICxfvhyvvPIKJkyYgMTERKxevRoNGzbUOB9DV/r16wcbGxsMGTIE77zzDrKzs7FhwwZ4enqWe7pq3bo11q5di8WLF6Nhw4bw9PREr169EBISAqFQiKVLlyIjIwMikQi9evVCp06d4ObmhilTpmDWrFngOA5btmzROHxjCt5++22sX78er7/+Om7evAl/f3/8999/uHjxIlasWCHPvbCzs0OTJk2wfft2BAQEwN3dHU2bNtW6tfvixYvlPR+mT58OKysrrF+/HgUFBXr1hNmyZQu2bt2KESNGoHXr1rCxscGDBw/w+++/w9bWVt5TwtraGkuXLsXUqVPRvXt3jB8/HgkJCfj555/h7++P2bNnl9v35MmT5T0ijBHqAaRPp4sXL8a8efMQHR2N4cOHw8nJCVFRUdi9ezfefvtthT4VpRk+fDjatWuHjz/+GBEREQgMDMS+ffvkNzddnqy1/b7d3d2xe/duDBkyBC1atMC4cePQtm1bWFtbIyYmBjt27ABQPkdMFno5f/48RCIRWrdurbC8U6dO+PHHHwEo5qNs3boVNWrUUNoiHwCGDh2KDRs24ODBgxg5ciRmzpyJ3NxcjBgxAoGBgSgsLMSlS5ewfft2+Pv7K+0Poivff/89BgwYgI4dO+LNN99EXl4eVq5cCRcXF6W9ZqKiojB06FC88soruHz5Mv766y9MmDBBHmJZtGgRzp07h0GDBsHPzw+JiYlYs2YN6tSpI/9MJk2ahH///RfvvvsuTp8+jc6dO0MikeDhw4f4999/cfToUY2HpRg7dixWrlyJ+fPno1mzZvL8NBmaHiskJATjx4/HmjVrkJGRgU6dOuHkyZN6efgII2KWmiITU7oEWR3KSpA3btzIGjVqxEQiEQsMDGSbNm2Sl+iVBgCbMWOG0n1OmTJF/lq2bdmyPZmNpfsV7Nu3jzVv3pzZ2toyf39/tnTpUvb777+XWy8+Pp4NGjSIOTk5MQAKpbwbNmxg9evXZ0KhUKEc+eLFi6xDhw7Mzs6O+fj4yEsfS6/DmLQ0uGzfDMbKl5uqQtlnqoyyJciMMZaQkMCmTp3KatasyWxsbFizZs2UltheunSJtW7dmtnY2FRYQqiqBJkxaRlv//79maOjI7O3t2c9e/Zkly5dUlhH03NJxu3bt9knn3zCWrVqxdzd3ZmVlRXz9vZmo0ePZqGhoeXW3759O2vZsiUTiUTM3d2dvfbaa+zFixdK9x0XF8eEQiELCAjQyBZN7Ff1fe/cuZN16dKFOTg4MAcHBxYYGMhmzJjBHj16JF9H2TmRlJTEJkyYwJycnJiLiwt7/fXX2cWLFxkA9s8//yhs6+DgUO64yn5r2nzfMuLi4tgnn3zCmjRpwuzs7JhIJGL169dnkydPZufOnVO6zbx58xgA1qlTp3LLdu3axQAwJycneSl6QkICs7KyUuglUpbc3Fxmb2/PRowYwRhj7PDhw+yNN95ggYGBzNHRkdnY2LCGDRuymTNnsoSEhArfl7acOHGCde7cmdnZ2TFnZ2c2ZMgQdv/+fYV1ZJ/5/fv32ahRo5iTkxNzc3Nj77//vkJ5/MmTJ9mwYcOYj48Ps7GxYT4+Pmz8+PHs8ePHCvsrLCxkS5cuZcHBwUwkEjE3NzfWunVrtnDhQpaRkSFfT9U1VAbP88zX15cBYIsXL1a6jqbHysvLY7NmzWI1atRgDg4ObMiQISwmJoZKkC0QjjELenwmCEJjkpOT4e3tja+++gpffvmluc3RmD179mDEiBG4cOGC2hAFQRBEtcxJIYiqwB9//AGJRIJJkyaZ2xSVlG4tD0h70qxcuRLOzs5o1aqVmawiCKKyUG1zUgiisnLq1Cncv38f33zzDYYPH15uvBxLYubMmcjLy0PHjh1RUFCAXbt24dKlS/j22291KuMmCKJ6QeEegqhk9OjRA5cuXULnzp3x119/oXbt2uY2SSXbtm3Djz/+iIiICOTn56Nhw4Z477335I3UCIIg1EEihSAIgiAIi4RyUgiCIAiCsEhIpBAEQRAEYZFU+cRZnucRGxsLJycng7RQJwiCIKoujDFkZWXBx8en3ACnhiQ/Px+FhYV678fGxkZh5PSqRpUXKbGxsTSwFEEQBKEVMTExqFOnjlH2nZ+fj3p+johPlFS8cgV4eXkhKiqqygqVKi9SZO3TY2JidB5/hSAIgqgeZGZmwtfXV37vMAaFhYWIT5Tg2U1/ODvp7q3JzOLh1zoahYWFJFIqK7IQj7OzM4kUgiAIQiNMkR7g6MTB0Un34/Co+ikMlDhLEARBEIRFUuU9KQRBEARhiUgYD4kencokjDecMRYKeVIIgiAIwgzwYHpP2nDu3DkMGTIEPj4+4DgOe/bskS8rKirC3Llz0axZMzg4OMDHxweTJ09GbGysgd+1dpBIIQiCIAgzwBvgnzbk5OSgRYsWWL16dbllubm5CA0NxZdffonQ0FDs2rULjx49wtChQw31dnWCwj0EQRAEUQ0YMGAABgwYoHSZi4sLjh8/rjBv1apVaNeuHZ4/f466deuawsRykEghCIIgCDMgYQwSPYbPk22bmZmpMF8kEkEkEullGwBkZGSA4zi4urrqvS9doXAPQRAEQZgBQ+Wk+Pr6wsXFRT599913etuWn5+PuXPnYvz48WZt30GeFIIgCIKoxJRtVqqvF6WoqAhjxowBYwxr167V1zy9IJFCEARBEGaAB4NEywqdstsDhm1WKhMoz549w6lTp8zeBJVEioFhkjiwnD+B/AMAywWs6oGzfw2wHQKOo4+bIAiCkKJLGXHZ7Q2JTKA8efIEp0+fRo0aNQy6f12gu6YBYUV3wFKnACwPQPHAUUV3wTLmAnmHAbfV4Dhrs9pIEARBVE+ys7MREREhfx0VFYWwsDC4u7vD29sbo0aNQmhoKA4cOACJRIL4+HgAgLu7O2xsbMxiM4kUA8FYEVjau1LviULtevHfhWeBnN8Ax/fMYR5BEARhYRiqukdTbty4gZ49e8pff/TRRwCAKVOmYMGCBdi3bx8AICQkRGG706dPo0ePHjrbqQ8kUgxFwSmAT1KzAgPL/RNwmEZhH4IgCAI8oGU7tvLba0OPHj3A1AgbdcvMBZUgGwhWGIYKNR+fAkjiTGEOQRAEQVR66JHeUHAa6j3yohAEQRAAJHpW9+izbWWBPCkGgrPpDECsbg1AWBcQeJnKJIIgCMKCkTD9p6oOiRRDYdMRsAoAIFSxAgPnMA0cx5nSKoIgCMJC4Q0wVXVIpBgIjuPAua0HhN6yOcX/F4sW+6mA3RhzmEYQBEEQlRKzipRz585hyJAh8PHxAcdx2LNnj3xZUVER5s6di2bNmsHBwQE+Pj6YPHkyYmNjzWdwBXDC2uBqHgTn/C1g0xmwbgHYDgfnvgMC53nkRSEIgiDk8OAg0WPiUfXvKWYVKTk5OWjRogVWr15dbllubi5CQ0Px5ZdfIjQ0FLt27cKjR48wdOhQM1iqOYzPRJ7kOTIl8ciUpCEXDBJq4EYQBEGUgWf6T1Uds5aaDBgwAAMGDFC6zMXFBcePH1eYt2rVKrRr1w7Pnz9H3bp1TWGiVhQVXEFm6mSA5UMWLZSIn6Ag9y/YOy+AneNb5jWQIAiCICoRlaoeNiMjAxzHwdXVVeU6BQUFKCgokL/OzMw0gWUAz6chM3WKgkCRIm2Pn5u5AFbWQbAWdTaJPQRBEIRlIwvb6LN9VafSJM7m5+dj7ty5GD9+vNpRGb/77ju4uLjIJ19fX5PYV5D7r5KW+KURIi97g0lsIQiCICwfffJR9BU4lYVKIVJkIzMyxrB27Vq1686bNw8ZGRnyKSYmxjQ2FlwA1DbWkRSvQxAEQRCEJlh8uEcmUJ49e4ZTp06p9aIAgEgkgkgkMpF1pdEkg6kaZDkRBEEQGsEzDjzT3Ruiz7aVBYv2pMgEypMnT3DixAnUqFHD3CapxMqmPdR/nEJYi9qZyhyCIAjCwqFwT8WY1ZOSnZ2NiIgI+euoqCiEhYXB3d0d3t7eGDVqFEJDQ3HgwAFIJBLEx8cDANzd3WFjY2Mus5Viaz8OeVkrABRCucdEAlsHqu4hCIIgpEgggEQPX4HEgLZYKmb1pNy4cQMtW7ZEy5YtAQAfffQRWrZsia+++govX77Evn378OLFC4SEhMDb21s+Xbp0yZxmK0Ug9ICT+3pIdV/p1vjSv+0cP4SNbW9zmEYQBEEQlRKzelJ69OgBxlTnaahbZonY2PaBq+dJ5OdsQmH+MTBWBCub1rBzmAprUSdzm0cQBEFYEEzPnBRWDXJSLD5xtrIhtKoPB5ev4eDytblNIQiCICwY6pNSMRadOEsQBEEQRPWFPCkEQRAEYQYkTAAJ0yNxtnJlROgEiRSCIAiCMAM8OPB6BDT4atB7i8I9BEEQBEFYJORJIQiCIAgzQImzFUMihSAIgiDMgP45KRTuIQiCIAiCMAvkSSEIgiAIMyBNnNVjgEEK9xAEQRAEYQx4PcfuqQ7VPSRSCIIgCMIMUE5KxVBOCkEQBEEQFgl5UgiCIAjCDPAQUDO3CiCRQhAEQRBmQMI4SPQYyVifbSsLFO4hCIIgCMIiIU8KQRAEQZgBiZ7VPRIK9xAEQRAEYQx4JgCvR3UPT9U9BEEQBEEQ5oE8KQRBEARhBijcUzEkUgiCIAjCDPDQr0KHN5wpFguFewiCIAiCsEjIk0IQBEEQZkD/Zm5V389AIoUgCIIgzID+Y/eQSCEIgiAIwgjw4MBDn5wU6jhLEARBEARhFsiTQhAEQRBmgMI9FUMihSAIgiDMgP59Uqq+SKn675AgCIIgiEoJeVIIgiAIwgzwjAOvTzM3PbatLJBIIQiCIAgzwOsZ7qkOfVKq/jskCIIgCKJSQp4UgiAIgjADPBOA16NCR59tKwskUgiCIAjCDEjAQaJHQzZ9tq0sVH0ZRhAEQRAEzp07hyFDhsDHxwccx2HPnj0Kyxlj+Oqrr+Dt7Q07Ozv06dMHT548MY+xxZBIIQiCIAgzIAv36DNpQ05ODlq0aIHVq1crXb5s2TL88ssvWLduHa5evQoHBwf0798f+fn5hni7OkHhHoIgCIIwAxLoF7KRaLn+gAEDMGDAAKXLGGNYsWIF/ve//2HYsGEAgD///BO1atXCnj17MG7cOJ3t1AfypBAEQRCEGTCUJyUzM1NhKigo0NqWqKgoxMfHo0+fPvJ5Li4uaN++PS5fvmyw96wtJFIIgiAIohLj6+sLFxcX+fTdd99pvY/4+HgAQK1atRTm16pVS77MHFC4hyAIgiDMgKEGGIyJiYGzs7N8vkgk0ts2S4E8KQRBEARhBhg48HpMrDifxdnZWWHSRaR4eXkBABISEhTmJyQkyJeZAxIpBEEQBFHNqVevHry8vHDy5En5vMzMTFy9ehUdO3Y0m10U7iEIgiAIM2CocI+mZGdnIyIiQv46KioKYWFhcHd3R926dfHhhx9i8eLFaNSoEerVq4cvv/wSPj4+GD58uM426guJFIIgCIIwA6YeBfnGjRvo2bOn/PVHH30EAJgyZQr++OMPfPrpp8jJycHbb7+N9PR0dOnSBUeOHIGtra3ONuoLiRSCIAiCqAb06NEDjDGVyzmOw6JFi7Bo0SITWqUes+akVMYWvQRBEARhCCQQ6D1Vdcz6Ditji16CIAiCMASycI8+U1XHrOGeytiilyAIgiAI02CxviJdW/QWFBSUaxFMEARBEJYGD4HeU1XHYt+hri16v/vuO4X2wL6+vka1kyAIgiB0QcI4vaeqjsWKFF2ZN28eMjIy5FNMTIy5TSIIgiCIclBOSsVYrEjRtUWvSCQq1yKYIAiCIIjKh8WKFEtt0UsQBEEQhoAxAXg9JqZHt9rKglmreypji16CIAiCMAQScJBA95CNPttWFswqUipji16CIAiCIEyDWUVKZWzRSxAEQRCGgGfaj79TdvuqDo3dQxAEQRBmQJZbos/2VZ2q/w4JgiAIgqiUkCeFIAiCIMwADw68Hsmv+mxbWSCRQhAEQRBmQN+usdRxliAIgiAIwkyQJ4UgCIIgzAAlzlYMiRSCIAiCMAM89Bt/h3JSCIIgCIIwCkzPxFlWDURK1fcVEQRBEARRKSFPCkEQBEGYAZ7pGe6h6p7y5OXlITc3V/762bNnWLFiBY4dO2ZQwwiCIAiiKqPPCMj6Jt0aA2PoA63f4bBhw/Dnn38CANLT09G+fXv8+OOPGDZsGNauXauzIQRBEARBVF6MoQ+0FimhoaHo2rUrAOC///5DrVq18OzZM/z555/45ZdfdDKCIAiCIKobsnCPPpMlYQx9oHVOSm5uLpycnAAAx44dw8iRIyEQCNChQwc8e/ZMJyMIgiAIorpR1driG0MfaO1JadiwIfbs2YOYmBgcPXoU/fr1AwAkJibC2dlZJyMIgiAIgqjcGEMfaC1SvvrqK8yZMwf+/v5o164dOnbsCECqmlq2bKmTEQRBEARR3ahq4R5j6AOtwz2jRo1Cly5dEBcXhxYtWsjn9+7dGyNGjNDJCIIgCIKoblS1EmRj6AOd6pe8vLzg5OSE48ePIy8vDwDQtm1bBAYG6mQEQRAEQRCVH0PrA61FSkpKCnr37o2AgAAMHDgQcXFxAIA333wTH3/8sU5GEARBEER1o6qFe4yhD7QWKbNnz4a1tTWeP38Oe3t7+fyxY8fiyJEjOhlBEARBENWNqiZSjKEPtM5JOXbsGI4ePYo6deoozG/UqBGVIBMEQRCEhjDoV0bMDGeKQTCGPtDak5KTk6OgkGSkpqZCJBLpZARBEARBEJUbY+gDrUVK165d5W1vAYDjOPA8j2XLlqFnz546GUEQBEEQ1Y2qFu4xhj7QOtyzbNky9O7dGzdu3EBhYSE+/fRT3Lt3D6mpqbh48aJORhAEQRBEdaOqlSAbQx9o7Ulp2rQpHj9+jC5dumDYsGHIycnByJEjcevWLTRo0EAnIwiCIAiCqNwYQx9o7UkBABcXF3zxxRc6HZAgCIIgiKrnSQEMrw+0Finnzp1Tu7xbt246G0MQBEEQ1YWqJlKMoQ+0Fik9evQoN4/jSj4oiUSitREEQRAEQVRujKEPtM5JSUtLU5gSExNx5MgRtG3bFseOHdPaAIIgCIKojjDG6T1ZEsbQB1p7UlxcXMrN69u3L2xsbPDRRx/h5s2bOhlCEARBENUJHpxezdz02dYYGEMf6DTAoDJq1aqFR48eGWp3BEEQBEFUAfTRB1p7Um7fvq3wmjGGuLg4LFmyBCEhIToZQRAEQRDVjaqWOGsMfaC1SAkJCQHHcWBMcdSADh064Pfff9fJCIIgCIKobuibV2JpOSnG0Adai5SoqCiF1wKBAB4eHrC1tdXJAIIgCIKojlQ1T4ox9IHWIsXPz0/ngxEEQRAEUTUxhj7QSKT88ssvGu9w1qxZOhtDEARBENWFqhDuMbY+0Eik/PTTTxrtjOM4EikEQRAEoQFMz3CPJYgUY+sDjURK2TgTQRAEQRCEsfWBwfqkEARBEAShOQwAY3pMWh5PIpHgyy+/RL169WBnZ4cGDRrg66+/LleNY0noNAryixcvsG/fPjx//hyFhYUKy5YvX24QwwiCIAiiKsODA2fCjrNLly7F2rVrsXnzZgQHB+PGjRuYOnUqXFxcDJaqYWh9oLVIOXnyJIYOHYr69evj4cOHaNq0KaKjo8EYQ6tWrbQ2gCAIgiAI43Pp0iUMGzYMgwYNAgD4+/vj77//xrVr1wyyf2PoA63DPfPmzcOcOXNw584d2NraYufOnYiJiUH37t0xevRonYwgCIIgCF3JFRcgLi8dueICc5uiFYYaYDAzM1NhKihQ/jl06tQJJ0+exOPHjwEA4eHhuHDhAgYMGGCQ92MMfaC1J+XBgwf4+++/pRtbWSEvLw+Ojo5YtGgRhg0bhvfee08nQwiCIAhCGx6kv8SCO7vxKDMB0gwNDvUda+KrZsMR4m75Pb14xoEzQDM3X19fhfnz58/HggULyq3/2WefITMzE4GBgRAKhZBIJPjmm2/w2muv6WxDaYyhD7T2pDg4OMjjTN7e3oiMjJQvS05O1toAdVTGJB+CIAjC+ISmROG1i+sUBAoAPM1OxtTLv+FswkOz2mdKYmJikJGRIZ/mzZundL1///0XW7duxbZt2xAaGorNmzfjhx9+wObNmw1ihzH0gdaelA4dOuDChQsICgrCwIED8fHHH+POnTvYtWsXOnTooJMRqjBFkg9BEARR+Zh9cxt4+StFbwQDMDf0X1wZ8JWJrdIOWZWOPtsDgLOzM5ydnStc/5NPPsFnn32GcePGAQCaNWuGZ8+e4bvvvsOUKVN0N6QYY+gDrUXK8uXLkZ2dDQBYuHAhsrOzsX37djRq1MjglT3GTvIhCIIgKh/XkiORUZSHsuKkNPl8EQ69DMfA2i1MZ5iWmLrjbG5uLgQCxQCKUCgEz/MqttAOY+gDrUVK/fr15X87ODhg3bp1Oh1YEzp16oRff/0Vjx8/RkBAgDzJR92bLSgoUEgayszMNJp9BEEQhOm5nvIU6gSKFIaryZEkUkoxZMgQfPPNN6hbty6Cg4Nx69YtLF++HG+88YbONpTGGPpA65yUt956C2fOnNH7wJogc0sFBgbC2toaLVu2xIcffqg2yee7776Di4uLfCqbUEQQBEFUbuyEIg3XszGyJZWLlStXYtSoUZg+fTqCgoIwZ84cvPPOO/j6668Nsn9j6AOtRUpSUhJeeeUV+Pr64pNPPkF4eLhBDSqNLkk+8+bNU0ggiomJMZp9BEEQhOkZ4dtag7U4jPZra3Rb9IEvHrtHn0kbnJycsGLFCjx79gx5eXmIjIzE4sWLYWNjGDFnDH2gtUjZu3cv4uLi8OWXX+L69eto1aoVgoOD8e233yI6Olpvg0pTOsmnWbNmmDRpEmbPno3vvvtO5TYikUieRKRpMhFBEARReXATOaBtDX+obgzP0MDRAw2capnQKu3RqyW+nkm3xsAY+kCnsXvc3Nzw9ttv48yZM3j27Blef/11bNmyBQ0bNtTJCFUYO8mHIAiCMAypBTlY+/Achp5Yg95HVuCdS1txJv6x0VpGrG47GfUdPZUu87R1xh8dpxnluIR6DK0PdBq7R0ZRURFu3LiBq1evIjo6GrVqGVa1GjvJhyAIgtCfxxkJmHJhMzIL88EXezcS8jNxLiECw+u2wDethkHA6Z4gqgwboTV2dpuJE/H38HvkOSTnZ8PVxg6v1euEIXVaQshZ/vi5Um+IPomzBjTGwBhKH+gkUk6fPo1t27Zh586d4HkeI0eOxIEDB9CrVy+djFDFypUr8eWXX2L69OlITEyEj48P3nnnHXz1lWXXvhMEQVQXxDyPdy5vQ1ZRiUABAEnxHXTP83AEu3pjYoP2Bj82x3Ho690Ufb2bGnzfpsDU1T2mwND6QGuRUrt2baSmpuKVV17Br7/+iiFDhkAk0izTWltkST4rVqwwyv4J3eAZw8XESOyMDkNsbgY8bB0xvG4L9PQOgJXA8p9eCIIwHKfjHyE+T3WrBw7AHxFXMKF+O4N7UwjLwhj6QGuRsmDBAowePRqurq56HZionBRKxJh5ZQdOxz8BBw6suB318dhHaFmjDjZ2fg2O1sYRrQRBWB43kp/BihNAzJTnCjIAL3PTkZSfhVp2VMhQGgbVqb+abm9JGEMfaP3YO23aNBIo1Zhld0/gdHwEUEqgyMbNuJXyEp/e2GNW+wiCMC1chU3VtFuvOmGoUZAtBWPoA/LNExqTXVSAbZE3UKLfuTL/M5yIfYS43AzTG0cQhFloW9NPpRcFkF4d6ti7wcPW0XRGEVUGEimExlxPflZ8MVKl3jkAHPY8M16DP4IgLIvuXgGobe8KoYp8EwZgaqOO4CgfpTzMAFMVh0QKoTEvctKhyXgZDzMSTWANQRCWgJVAgHUdJ8DVxl4hpCMTLaP8WmF8vTbmMs+y0TfUY2HhHmOgdeJsQUEBxGIxHBwcjGEPYcH4ObprtB4lxxFE9aKhswcO9JmBndGhOPTiHnLEBWjk7Ilx9dugk0d98qKoQN+usZbWJ8UY+kBjT0pSUhIGDBgAR0dHODs7o0OHDoiIiDCYIYTl06ZmXZUu3RI49PFpbBJ7CIKwHFxt7PBmQGfs7PU2jvSbiZUdxqKzZwMSKNUAY+oDjUXK3LlzERYWhkWLFuGHH35Aeno6pk2jtsPVCXsrG4z2b6l2HT8HN7StWddEFhEEQVReqkp1jzH1gcbhnuPHj+OPP/5A//79AQCDBw9GUFAQCgoKjNbMjbA8PmveF/fT43E7LVZhPgfAxcYOazuNpScngiAITdA3r8RCRIox9YHGnpTY2Fi0aNFC/rpRo0YQiUSIi4vTywCicmFvZYNt3adgYcuBaOziCQcrG3jbOePdwC442PddNHT2MLeJBEEQhAkxpj7QKnFWKBSWe22sES4Jy8VGaIXx9VtjfP3W5jaFIAii0lKVEmeNpQ80FimMMQQEBCi48rOzs9GyZUsISo3XkpqaqrdRBEEQBFHlqSJ98Y2pDzQWKZs2bdJ65wRBEARBVG2MqQ80FilTpkwxmhEEQRAEUd3Qt0LHUqp7jKkPtG7mRhAEQRCEgbCQkI2lQiKFIAjCBNxMeoktD0NxM+kFbARC9PFthImNW8LX0dXcphGExUIihSAIwsisun0JP4Sdg5DjICmueIi+fw1/PLiBjb1Go4uPv3kNNABZhQXY8/QezsZGQczzCPHwxrhGLeBl72Ru0yyWqhLuMSYkUgiCIIzImZdP8UPYOQCQCxTZ3zyTYNrpnbj46ntwt7U3l4l6cyclHpOOb0dGYT4AaQTjXFwUVt2+jJ+6DMaQekHmNdBSqSLVPcaERkEmCIIwIhvvX1M55hUDkC8pwo6IO6Y1yoBkFOZj4vHtyCwqULjn8oxBzHh8cGE/7qbEm9NEC4YzwFS10ciT8tFHH2m8w+XLl+tsDEEQRFXjakKMggelLAzA1YTneKdpe9MZZUB2Rd5FZmG+yod6Dhw2PriBn7oMNqldhGkwtj7QSKTcunVL4XVoaCjEYjEaN5aOdvv48WMIhUK0bk0dSAmCIEpT1Z91T7+MVLtcwnicfGGYEXGrHFUg3GNsfaCRSDl9+rT87+XLl8PJyQmbN2+Gm5sbACAtLQ1Tp05F165ddTKCIAhCU/LFRXiYlgzGGALdPWBnZW1uk9TS3qsuLsZFq/SmcAA6eFXekcMLJZIK75VFPG8SWyodVUCkGFsfaJ04++OPP+LYsWNyAwDAzc0NixcvRr9+/fDxxx/rZAhBEIQ6CiUSrLh1EZsfhCK7qBAA4GBtg4mBIfi4VReIhJZZB/BWk3Y4FxuldBkHwM7KGqMbNDetUQYkpKYPrie+kIswmRaTpeEIOA4taniZyTrClBhDH2idOJuZmYmkpKRy85OSkpCVlaW1AQRBEBUh4Xm8e2oP1ty+KhcoAJBTVIgNd67jzeO7ILbQp/VuPvXwScvuAKCQQCvgONgIrbCh56tws7Uzl3l681pAiNQhwANMDEAMQMyBFQFMAkh4hteD2pjZSguFcfpPFoQx9IHWImXEiBGYOnUqdu3ahRcvXuDFixfYuXMn3nzzTYwcOVInIwiCINRx/HkETsZEginxb/NgOB8bjUPRj8xgmWbMaNYRewZOxrB6wfBzckMjlxp4N7gDTg2bhs7e/uY2Ty98nVwxul5zQILim6bsxskBPFDb1gW9atc3o4WWi2wUZH0mS8IY+kBr/+i6deswZ84cTJgwAUVFRdKdWFnhzTffxPfff6+TEQRBEOrY9ihcoRFaWQQch20PwzG0vuX24wip6YOQLj7mNsPgJOflYOeTu1CeIswhNjsTWx+GY2owFVZUdYyhD7QWKfb29lizZg2+//57REZKs7obNGgABwcHnQwgCIKoiOjMNLVlvDxjeJaVZkKLCBn/Pr4DCa/+kX7TvZskUpRRBRJnS2MMfaBzM7e4uDjExcWhUaNGcHBwALM0vxNBEFUGd1u7Ckt53USVN6+jMvMgNVFtnTUD8CwrHQUSsclsqjRUsZwUGYbUB1qLlJSUFPTu3RsBAQEYOHAg4uLiAABvvvkmVfYQBGEUXm3YVO1yDsCoRurXIYyDSGgFrgIJyQGw4qjBeVXHGPpA67Nm9uzZsLa2xvPnz2FvXzLWxNixY3HkyBGdjCAIQj3p+Xn4LfwGZp44gNknD2H34/vIF1efJ9ORDYNR18lVaXt5IcehtqMzRjVqZgbLiN51G0LCVFdWCTkOvXwbQCggkVIWjuk/WRLG0Ada56QcO3YMR48eRZ06dRTmN2rUCM+ePdPJCIIgVHPyWSRmHNuPAokYHDhwHLD7yX0suXIOWwaPQoB7TXObaHQcrG3w78DxeO/0XoQmxkIATlo8whia1qiFtb2Gw9lGZG4zqyV96zZEPWc3PM9KV5o3xDOGd5u3M4NllYAqlpNiDH2gtUjJyclRUEgyUlNTIRLRRYIgDMmj1CS8c3QvJDxffD1j8gtTcl4OJuz/F2fGvwVHGxuz2mkKvBycsHvwRNxJjseV+BgwxtDeyxctPLzNbVq1xkogwF+vjMHEI/8iKjMNQo6TnqJMWnX1fbcBaOfla24zLRN980osLCfFGPpAa5HStWtX/Pnnn/j6668BABzHged5LFu2DD179tTJCIIglLPx9k0wpqw7CCBhDMl5udjz5D4mBoeY2jSz0aymF5rVpA6mlkQdJxecePVNnHgegRPPI5AvFqNJDU+MCWiGmnZU+VldMIY+0FqkLFu2DL1798aNGzdQWFiITz/9FPfu3UNqaiouXryokxEEQSjnWFSE1IXOAJQN+xf3zToeHVmtRAphmVgJBHjFPwCv+AeY25TKQxUL9xhDH2idydS0aVM8fvwYXbp0wbBhw5CTk4ORI0fi1q1baNCggU5GEAShnAKJpIxAKdXRs3h+Xqk28QRBVCKYASYLwhj6QKcRuVxcXPDFF1/odECCIDSnsXsNhMUlQHo1Kh1/LvW3hcWljcndxAQsu3Qed5MSwTOGxjVqYmbbDujsWxecksofgiBMi6H1gdaelIYNG2LBggV48uSJwYwgCEI5Qe6eAJhUiPAoPzGGqPR08FW8mSJjDAvPnsKQ7X/hfMwzpOXnIaMgH9diX2DS3v8w88gBSCx0gEGCUEkV86QYQx9oLVJmzJiBgwcPonHjxmjbti1+/vlnxMfHG8wggiBKwQCO56T/y14r/M8hKTsH6fl55rTS6Gy/fwd/3L6lcvnBiMfYcOuGCS0iCANQxTrOGkMf6NTM7fr163j48CEGDhyI1atXw9fXF/369cOff/6plzEEQShiIxRK/yh+YpJFNOT/F8+3EghNa5gJ4RnDiquXKlxv3c3rKJJITGARQRDKMIY+0LkFYEBAABYuXIjHjx/j/PnzSEpKwtSpU3XdXaUgOTcXV17E4FZcLF0MCZPgJrKTek1UPTBxUqGSnld1PSnPMtKRkJNToWs7oyAfEWmppjGKIAxAVes4K8OQ+kCnxFkZ165dw7Zt27B9+3ZkZmZi9OjR+uzOYknKycGis6dxOOKJPPbvbmeHd9u0w5stW1HCHmE0Djx+qHbwNhm/3rqOxT37Gt8gM1Akq3DSAF5Ne3bCePCM4WjEE2y5HY5Hycmws7bCoEYBmNyiJWo7O5vbPMulipUgl8ZQ+kBrkfL48WNs3boVf//9N6KiotCrVy8sXboUI0eOhKOjo05GWDJpeXkY9e/fiM3KUkhOTM3Lw7fnzyIhOxtfdOtuRguVE5+dha23b+PIkyfIExehmWctTGwRgk6+viSqKhHPMzI0Wi82K9vIlpiPui4uEAo4pS3XSyPgODRwczeRVYQMnjHMOXoEex49gIDjwDOGtHzg91uh2HrnNv4c8SpaefuY20zCBBhDH2gtUgIDA9G2bVvMmDED48aNQ61atXQ6cGVhQ+gNxGZlqbxAbrx1E+OaNkMDd8u5OIbGxmLy7p3IF4vlwiohOxtHIyPwRstW+KJbdxIqlYQiWcWKuq+LA/xdXExijzmwtbKGi7UIqQX50hnKPgsmHcjO1srapLYRwNbb4djz6AEAKDzISRhDvliMt/btwaU3p9F3Uw0whj7QSqRIJBKsX78eo0aNgpubm94Ht3QYY/j7zm21T3BCjsOO+3fxWZduJrRMNXlFRXhz754SgVJsuoRJy0F+vxWKprVqYXhgkHkNJTRCCEAClG+TIqP4++1Up67JbDIHYsaXfAalP4tSP00rEt4mhzGGjbduyr+WsvCMIT0/H4eePMHIoCamNs/i4aBfXoklnfHG0gdaJc4KhULMnDkT6enpBjOgIl6+fImJEyeiRo0asLOzQ7NmzXDjhmlKDQskYmQUFKhdhwGIzcoyiT2asO/RQ2QU5IPnGcCXSq4q7k7KAdh486aZrSQ0xdvRueTqXzZ+Xervjr5VV6RkFxYiq6BQ8YKsJJbv6VD1ws2WTnp+Pp5nZKhNjbASCHAz9qXJbKpUmKEE2Vj3VGPpA53a4j99+tSgRqgiLS0NnTt3hrW1NQ4fPoz79+/jxx9/NJkXx0ZoBZFQvbOJA+Bqa2sSezTh+suXECjJ+pY/ePLAvcRE5IuLTG5bdYQxplejNV9nV3lflJKdQkG4cAywt666rvR8sVj+N8dKBgYoOzVyr2EW+6ozQoFmN0kBp3MhKWFAjH1PNYY+0DonZfHixZgzZw6+/vprtG7dGg4OiiNcOhswk3vp0qXw9fXFpk2b5PPq1atnsP1XhIDjMDwwEP/dv6cy5CNhDMMaW07opEgigSzKU/byIXfJMoCzKEdh1eNOfAI2XL+O4xGRKJJIUM/dDZNbtsS45s1gLdS8p4mzSKRQglz6NJRHNyw4w98QuNnawt3OHql5udIZZcM9xSd2Vz9/s9hXnXGyESGwZk08Sk5WeRqKeR6dfH1NalelwcTVPca+pxpDH2gtbwcOHIjw8HAMHToUderUgZubG9zc3ODq6mpwD8e+ffvQpk0bjB49Gp6enmjZsiU2bNigdpuCggJkZmYqTPrwTpu2sLWyglBJvFvAcejpXw+tvL31OoYhEXKcyvQFFM8XQPqETxiH4xEReHXrNhx5/ASFEgkYgKjUNCw8eQrv7NmrVY+dlt7e4CCQt8Uv7TkADwggQAsvb4MnQidkZ+NeQiISs81fNSQUCDCxeQvFp/Ey3iSR0JryrMwAx3F4u3VblfdKIcehtpMz+jZoaFK7Kg0Gaotf9p5XoCJNQZd7qjYYQx9o7Uk5ffq0TgfShadPn2Lt2rX46KOP8Pnnn+P69euYNWsWbGxsMGXKFKXbfPfdd1i4cKHBbPB3dcM/o8Zi1uGDiEpPg4Dj5Df4oY0D8U2vPhZVKeNkI6rQR8IAJOXmwrcKV4SYi8z8fHx44BB4xpSmj5yLisbm0Ft4q20bjfY3OrgpVly+VCx2uHJPTjxjeKNlK4PYDgDhcXH4/twFXHkeI5/Xxa8uPuneDcG1PA12HGW8zMjEqcinKBCLEeBRE138/SAo/m2907otzj17htsJ8QrhM5ko/6FffziLLCfsWp0Y1jgQT1JSsPbGNQg5aam47BrkZmeHTcNHwEpA4R5j4lvGUzV//nwsWLCg3Hq63FO1wRj6gGMW/EhtY2ODNm3a4NKlkpbYs2bNwvXr13H58mWl2xQUFCioyMzMTPj6+iIjI0OvUBRjDNdevsCD5CSIhFbo7l8PPk5OOu/PWPx86RJWXb1aYR7Ejffeg5udnYmsqj5sDr2Fr0+dVuuF9XFywrm339JY3J58+hTvHdgHxpg87Ci7GUxqEYIFPXoaRChff/ECk7f/B0mZPBoBx8FaKMS2caPRwghew/wiMb44egL77kvLWLniXhu1nZ3w05CBaFVb2mMjr6gIG2/dxJbwMCTl5oID0N3fH++1aY+2tWsb3C5CO8Lj47D1zm08SEqCvbU1BjRqhJFBwdKQZSUiMzMTLi4uet8zNDmG/zffQKBHTiOfn4/oL75ATEyMgq0ikQgiJZ+7LvdUc6NTx9nz589j/fr1ePr0KXbs2IHatWtjy5YtqFevHrp06WIw47y9vdGkiWLZWlBQEHbu3KlyG1Vfjr5wHIf2dXzRvo5lx1YHNm6MX65cUblcwHFoV6cOCRQjcS8hEQJOfeOx2KwsZBcWwknD87R3/fo4+Nok/BF2C8ciIlDES9DUsxamhISgT/0GBhEojDF8fuR4OYECSL01RRIJ/nf0BPa/PknvY5Xlw/0HcSoyqiR6U3z8uKxsTN6+E7snT0CjmjVgZ22N99t1wPS27ZFdWAiRUAiRlV5NswkD0sLLGy28LCf0XSkwUE6Ks7OzRoJKl3uqthhaH2jtg9u5cyf69+8POzs7hIaGyr0WGRkZ+Pbbb7U2QB2dO3fGo0ePFOY9fvwYfn5+Bj1OVaJRjRoYGBAgd5OXRjZnVocOpjWqGmEtFGgkGqy1dH83qlED3/Tug+vvvIuw92bgr1dHoW+DhgYLNd6KjUNUWppKDxzPGB4kJeF+QqJBjifjdlw8TkQ8VXpcmThae+WawnwBx8FZJKqyAkXC8zj66Ane2L4LfdZvwpgt/2BraDhyC6kij9APY99TjaEPtBYpixcvxrp167BhwwZYlyp77Ny5M0JDQ3UyQhWzZ8/GlStX8O233yIiIgLbtm3Dr7/+ihkzZhj0OFWN7/v3x6CAAADSC7osHuxoY4NVgwejPWXaG41eDepDzKseP0bAcWjvWwe2FlYy/CwtXaP1nhu4B8K++w/V5itIGMOhh4+rzYCeBWIx3vlvL97ffQAXo5/jWVo6wl7GYcGxUxi26S8kVJLhD7IKCrD1Zjj+d/g4Fh07jfNPo/Uqxa+yGChxVlOMfU81hj7Q+lHk0aNH6NatfHdVFxcXgzdxadu2LXbv3o158+Zh0aJFqFevHlasWIHXXnvNoMepathaW2PFoEH4sFMnHI2IQHZhIRq6u6N/w4YWd3OsavSoVw8N3N0RnZamNOTDM4Z327Uzg2XqcbLVLPTkbOCeQOn5+RVWmol5HnlFRbAWCpFbWIQzEU+RlpcPH2cndKnvp1VJt6Xz8/nLOB/1DACkDRlRfB/igJj0DHy49yD+njjWfAZqwMknkZi99xDyi8QQFgvQLTfD0NijJn4bOwJeTtR0T4a+Ixlru62x76nG0AdaixQvLy9ERETA399fYf6FCxdQv359nYxQx+DBgzF48GCD77c64O/mhnfatjW3GdUKoUCATa+OxOT//kN0WjqExUmgsrDMgt690K2ev1b7ZIwhOScXEp6Hh6OD/MJvSLr41YWjjQ2yCwtVruNmZ4e2dQyboFpHgzi6k40N7K2t8fvVm/j53GXkFRXJe/7UsLfDgld6o39gI4PaZQ7yioqw7Va4VJwU33zk7WAYIOEYbryIxf2ERDQxcqWVrtyNS8CMnfvlAr20VzEiOQVT/9mJ/W9OomofM2LMe6ox9IHWImXatGn44IMP8Pvvv4PjOMTGxuLy5cuYM2cOvvzyS52MIIiqRG0XZxx5fQqOPYnA8YgI5IvFCKhZE+OaN4OPltUCe+88wK+XruNJcgoAoKaDPSa1CcGbHdvAxoAeBFtra7zfqQOWnDmncp0PO3cyuNfi1WbBWH35qsrlQo7DmBbN8OeNMCw5UWKb7AEyJScPM3cewPqxw9CzoeEfkkzJw8Qk5BQUlRMocoq77V57/sJiRcqy0+fVNr6MSE7F6Yin6BtAfVMA6NzaXmF7C8IY+kBrkfLZZ5+B53n07t0bubm56NatG0QiEebMmYOZM2fqZARBVDWshUIMCmyMQYGNdd7Hz2cvYfWFqwpx5+TsXPx05hKOP4rAP1PGwsaAyaNvtmmNQrEEv1y6DAnPQygQQMLzsBYK8VGXznitZQuDHUuGr6sLZnRqj1WXygsVAcfBy8kJk1qGYOCvf6jdz5IT59CjQT2L6lmkLaxUjoG6btEFYsvMzwl7EYvL0TFqR70TADj+KIJEigwTd5w1NsbQB1pf4TiOwxdffIFPPvkEERERyM7ORpMmTeDoSHFGgjAUjxKTywmU0tyNS8Qb23Zhy6TRBrsxcxyH6R3bY3xIcxx6+BhJOTnwcnLEgMYBcDHi+FS17BzASQAmgEK7e55nGBIQgLCXscgvUn9jjkpJw8PEZATV8jCancamrquL2kaMMqGSX2R5VT4JWdmY9Nd/FQ7LywPIKxKrX6kaYeqcFGNjDH2g82OYjY0NmjRpgszMTJw4cQKNGzdGUBC1pSYIQ7D91h0IOIBXcxG69vwl/gu7i9Etmxn02G52dkbxmigjIikFC46ckl5sxVAQKRwH/HrpBka3bKrRvp6lplVqkZJZwYjrgPTjKbRAT8rfN2+jQCIp+f5UiRUGeDjam8oswkwYUh9onb00ZswYrFq1CgCQl5eHtm3bYsyYMWjevLlBG8IQRHXmSVIy1FQyy/n2+FnjG2NEtoXelo/yzAEQMOnEoWTE4+vPXmi0r8TsHOMZagIcbGwqXEfAcXAUVbyeqdl3T9otWJPwg5+rq1FtqVSYuATZ2BhDH2gtUs6dO4euXbsCAHbv3g2e55Geno5ffvkFixcv1skIgiAUKXcjkl2QeChcnHIKi5CSk2ta4wzI1egYuRgr/fAt/5sB8ZlZGu2rhkPlfkKv5eSIEB8vpY0YZfCMWWQlU2pOnuINs+wNlJX8X4tKkEtgJSEfXSZLEynG0Adai5SMjAy4u7sDAI4cOYJXX30V9vb2GDRoEJ48eaKTEQRBKPJKYECJKOGLL0gygcKXmpjU61JZyczLB6A2OgCJRAOXEoDmPl6GMcqMzOrWSWXfGAHHYWBQAOrXcDexVRUjKzWWecWU3jyLo0Ehtal1flXFGPpAa5Hi6+uLy5cvIycnB0eOHEG/fv0AAGlpabA1YnIdQVQnGnnUKHlRfNHnIP3ByvJLZUlzNsLK2x7e1tq6wmRRngE9GtZT6WEQcBy6N/CHr6uLUWw0JV3q++GHYQNgZy39Tq0FAgiL3/crgY2wZHB/c5qnErEsT4YHOAkgkAAQA1wRgCJAIAY4HhAJBfBytryBWc1GFQv3GEMfaH11+/DDD/Haa6/B0dERfn5+6NGjBwCpm6dZM8Mm8BFEdWVP+F0FcQKoKEvlAf8aria1zZDUcXHG89R0tetYCQT4ZlBfjNu8HS8zMsuN0FzbxRnfDOprZEtNx5DgQPRqVB+H7j9GVGoqHG1EeCWokUV6UGQIOA68RPq9yM5T2RNw6fNWyKiJmwJVrATZGPpAa5Eyffp0tGvXDjExMejbty8ExZ0D69evTzkpBFGK7PwC3IlNAM8Ymnh7ws1e85GnTzyIBKBeoMj+P/0wEq+2qpwPCL0a1celqOdq1+nSwA8ejg7Y/eYE/B16G//euouknBx4ODhgdEhTTGjd3ODt+s2Ng40NRodoVtVkbgrFYkgkTOrZ46T3TWU+Lw7S8uPzEdHo2tDfpDYSpsEY+kAnP3GbNm3Qpk0bMMbAilt+Dxo0SCcDCKKqUSgW48fjF7D9xh3ki6U9IawEAgxrEYR5r3SHowbj5IgZk4dzKgqHrD1/rdKKFGdbUcnToIo32qle3eJ1bfFOp3Z4p5PljX1UnUnOyVF4oq+oa8/RB09IpBRT1fqkAIbXBzr53jZu3IimTZvC1tYWtra2aNq0KX777TedjSCIqoKE5zHj7/3482qYXKAA0sTC3WH38frmnSjQoJlVg5pS135FF3wGIFnL6h6eZ7gY8QzfHjqDhftPYsfNO8gtNE+DsD1h9yEoVfmBMn8LGBD2PM4sthGaEZeh3cjMltjnhTAchtYHWntSvvrqKyxfvhwzZ85Ex44dAQCXL1/G7Nmz8fz5cyxatEhnYwiisnP2SRTOR0QrXcYzhruxCdgb/gBj2qj3fHSr749LkepbjAPSxVac5s8aCZnZeGfLbjxKSJYP8ia+zmPp4XP4aewgdG3kr/G+DMGTpJSSdvAcStqqFk8MwOPEylu9VB2wsSo1npOqWE8pWvpSdU9VxRj6QGuRsnbtWmzYsAHjx4+Xzxs6dCiaN2+OmTNnkkghqjX/3bwLIceVDLJWJjGOEwDbb9yuUKTciU2U3qS5ir0prf00G5m4SCLBm5t3IjolDYDiCLW5RYWYvnUv/nt3Ahp7ma5rq72NDYBclaWrHMr3jGGM4WF8El6mZcLVzhYt/XyMMjI0oRmNa3nAwcoKOUXiEpGp7KQt/m77BVlenxezUcUSZ42hD7QWKUVFRWjTpk25+a1bt4ZYTGMyENWbF+mZUoHCpCWXgOL1mkmAZynpFe6nSCKRXu81eDKd06eLRradfvgUkUmpSpcxBjCO4feLN7H01Vc02p8hGBgcgF8vXFeo2CnLgOAA+d9hz2OxaN8pPIxPks/zdHLAR/26YGjLJka1lVCOjVAIdzt75ORnltTHyyg1zAEAeNrbV/qme4akquWkGEMfaP34MWnSJKxdu7bc/F9//RWvvfaaTkYQxIvUDPxy/BI+2X4Ii/aexI2oFyqbWlkyHo720uu0EoEiIzevEAfDH6rdj4+rs3R7VixUlMEAF5EIjTxramTb8QcR8p4bypDwDEfvmbYh4/i2LeAgslHaA0XIcajhaI8RIcEAgNsv4jFl4w48SkhSWC8xKwef7TyKHdfvmMRmQpHk7By8TM8sOe8lJee/QqdkMZCena9RTla1oor0SAGMow808qR89NFH8r85jsNvv/2GY8eOoUOHDgCAq1ev4vnz55g8ebJORhDVm3Wnr2LliUsQcJzUU8xx+OfabbSv74uVrw3RqBrGUhge0gQXnzwvabZW5t4rC2t8vusougb4w9lOeensiJAm2Hw5VLpN6YtR6SdTBrzesbXGtuUVFpWEoVRQIBaD5xkEAsOMrFwRtZwdsfzVAfh091Gk5eaV5MnwPHxcnbH+teFwKv7+lxw8gyI13WeXHj6LwS0CYWdjbRLbCSlZ+YXyG6YsrQh8KaFSCjHP4+zjKPQLppBPVcHY+kAjkXLr1i2F161bSy+MkZHSXg41a9ZEzZo1ce/ePZ2MIKovu27ewy8nLgFAqTwO6f/Xo15g7o4jWD1pmLnM05r+TQLwP8ExFIl51WEaDiiS8NgX9gATO7ZUukqglwf6BTXEsQcR0hmlq16KsbexxpjWmpceN/Bwx5lHT1UKFQ6AXw1XkwmUuy/i8eORC7j6NKZYnAIebvZo7V8bA5s1RvdG9eS5JjGp6QiLUV/lk1tYhFMPIjGoRaAJrCdkeDo5VDhid2mSsir3QJAGpQrkpBhbH2gkUk6fPq3TzglCHTzPsPb0FdXLGZPmUSSmoIFnDZXrWRI2VkKlHhRlRCSmqF2+fPQgfLrrMA7dfVxumZPIBr9NHokaWgx7P7pNM2y4cF3tOq+1D9F4f/oQ+uwl3vhtZ7kxX5LScnAiMwKT27dSSIaNTk7TaL9RGq5HGA4HkQ16NK6HUw+jNFrfw8nByBZVHqpCToqx9YFeKfEvXrzAixeaDaNOEGWJTknDy7RMtesIOA5nHj41kUWGQdNKEztr9WEJK6EAy0cPwunZb2JUq6ZoUccLXRr6YcHg3jjz8TS0qKNdKWcdNxd82r87AJTLAeE4Du3r+WJMm+Za7VMXGGP4ctdxiHm+XMIszxjEEh7zdx9XyEmKS9dgJGQGpFXiEaErM18O6a3RevY21ugeUM/I1hCWgKH0gdYihed5LFq0CC4uLvDz84Ofnx9cXV3x9ddfg+c1G62UIAAgv3QCXankOkggH+FXwHEoqGTNnzydHDVy42paOuzt6ozFw/pi+7Tx+G3SSIxr2xwOZcpyNeX1Tq2wcvwQBPt4yufVdLTHrF4dsX7ScMWeF0bidkw8opLSVFb08IzhUXwyHsQmyuflFhaVb/ZWaiRoGbWrwCCDlZFaTo7o4F+nwvU+G9AdIuvKOyCmwdEnadYCk2eNoQ+0Plu++OILbNy4EUuWLEHnzp0BABcuXMCCBQuQn5+Pb775RidDiOoFYwy3o4tzDMrebEq1SReDR4CXZtUrlkJtN2c8S06XPgIoC/0Uv7+4CrxIxqJPUEP0CWqIjLx8FIolcHewM2mfEU1KsAHgeWoGmtSuBQDSihDZeSGBwsCLgLSfDATle6oQpmH71du49uSF4jDdpeAAfDWkF0ZX0B+oulEVwj2lMYY+0FqkbN68Gb/99huGDh0qn9e8eXPUrl0b06dPJ5FiZJIys/E0MRUiKysE+9aCtdD4T77GYO3Jq1h9/LL06lWqXFehrQKTDvveqq5mHgdLoW09X1x+8rykx4mS6hyOB1Jz88xgXQkuKiqLjI2znWbVWs6lqro8nBxKvCdQkvLDAEig1SCOhO7kFRbhwqNoZOTmw9PFESuPSZPfBbx0ZG5WWqgwwE4oxNAQ6mNT1TGGPtBapKSmpiIwsHz2fGBgIFJTlTeKIvQnISMb3+45hdP3nsrd5O6Odni7d3u81jkEnJr+F5bG85R0rDl+WfqCV+yGXhpZM7ODYQ8wqUsrk9l3PfIF/jx3E9ciX4CBoV0DX0zq2hLtG9bVaPthLQPxy7GLJT1OSn81pW6yXi6Ohja9UtCxQV04imyQXVCoch0XO1u0qVcSPujQoG6FAy0CwN0XCejbjMpbjQVjDFvO38KqY5eQWyAd74kBQKlnJQ7ly4/zeQkuP3mG3sENTWVq5aAKVPeUxhj6QGsfb4sWLbBq1apy81etWoUWLVroZAShnuSsHExY+TdO34sEzzP5iZ2alYcle87g5yMXzW2iVuy6flde5lrae6KsUSUHYN/NByaz7c9zoZi6bgfOPYxCTkEhcguKcP5hFN5cvxO/n7mh0T6c7e3gaGNTMhwNKzUVz7MWCvFK88ZGfCeWi8jaCjN6d1S7zsw+HRXyY3zcnOHt7FThvveF3tfbPkvhUWwS9t98gBN3niA7v8Dc5gAANp29iWX7z8oFijZkWch7sCiqWE6KMfSB1p6UZcuWYdCgQThx4oTCAEIxMTE4dOiQTkYQ6tl4+jqSMrPB8yXeBQDyO/nGk9cxsm1T1K3paiYLteNZcrpC0qS6il0G0+Vu3Hz6Asv2nwUg7b4qQ/b38oPn0aZ+bTSvq76q5vt9Z5GbVwj5uH9K3twH/TubLdxiCUzu3BL5YjFWn7wMiYSHUCCAhOdhJRRgVt9OGN+h/AWttptzhVU+qTnmDaEZgsj4FPxv+1HcjUmQzxNZCTGxWyvMfKWT2cYpys4vwOqjl3Te3se1YpFJVG6MoQ+0Findu3fH48ePsXr1ajx8KG3tPXLkSEyfPh0+Pj46GUGoRsLz2Hn1DnhZYiknbXolQxZOWHvsCr6bYLoxV5Izc3As/AkycvNRp4YL+jRvqHGnTydbEQScABJWqkeGCjgoDoRnLDJy8/H+pr1qFZNQwGHbxXC1IiUjNx97btyTfi8SlE8iLM6r6N64epdhchyHd3q0w9h2zXDkzmMkZeXA08kR/ZsFwNVeuXir7e6CW89iFQRkWWpW8h4cL1IyMHn1duSUCYUViCX4/dR1pGfnYcGYvmax7eTdSKWVdsoenBQoXqaL96WqU9USZ42hD3SqBfPx8aEEWRMhDTmIpb99JRcArjiZ4/LjZyaxR8LzWL7/PP46dwsMDEJOADHP45udNvjfqF4Y3Dqown0MDGmMndfvSl9U0PiMASZJDv5u92lpe281tkh4hrDol2r3czcmQdq6XXbxkQDgiqtPSonMm1EvUc/T3YDvoHLiam+Hce2lXpMiiQSZuQXILxTD1qb8pWl46ybYe1N1OEfAcRjVtqnRbDUFG05eQ05BoVIhxgDsvHYXk7q1QgMv0zc3TM3OhYDjlJeO81BezcZKlr9INU81m0VTxXJSAMPrAypYt3DsbKzlTyoqc2M5ICU7F4ViMWysSr7S+y8ScDj0ETJz81G7hguGtQ1GLVf9kjWX7z+PP8+Gyl+Li70hOQWFmLf1CBxENujZtIHafbRv4ItW/j4Ii45F8YDBKt8bx4CaRh41NSUrF0fCHlUomICKG7XJRKNCFUqxYJFVLEGASpXobGxSsnKx4cRV7L56D3mFRRBwHHo3a4i3+7ZH49oe8vWa1qkFZ5EImfkFSsu6HUTWmNApxKS2G5IiiQQHQh+o9RQJBRz2hz7AhwM1G/nakNRydVLZ24aDtKpHqVApDlPXJ1FeniooUgwNiRQLR+ZF0OSWViTmYWMF5BeK8dlfh3DqbqT8psoYw5ojlzFzYCe82budTrYkZ+Vg6/lbKpdzAH45dBE9guurvQlzHIfVrw/DnK0Hcenhc/mNXGk/EQ5oYOSL26PYJOmNoZS3QxkcB/RoUl/tvpr61oKASTVK2d3IK5h4IKSCvJbqQlJmNiau+AeJmdnymzPPGE7djcDZ+0+x7p2RaNNAWuWz4/IdZOcUC5QyJa5gQHZ2IRLSs+GiIlxk6eQWFKFQg8aFSZnmGfumZ5P6cBDZlAtFyZANLKik4h42VkK0b+hrZAuJqoh5MrCqKIwxnAx/ghnrd2Pi8r+xcPtxRMarH59FEyoUKAywFgpgL5LmhCz89zjO3JO2kpfwPCTF7cd5xvDzwYvYe023gZ5O3YmQVhepNgMR8SmISkyrcF/Odrb49a1X0b6hr+L7K/tkwQMDWhp3wDihbEC9ss3kytgl4DiM7ag+Q132oCkXXkxajskV5xTJKnyiEqlcHwC+33tWQaDIkPDS9vif/XUYkuKcpO0Xw6XfAw9wYsVJIAGsBBx2XrljjrdhEOxF1hBp0PHX09k8eTd2NtaYO7S79EXxdyAoArgiKPxmuFKTjLd7tzNbwq8lo1D5p+NU1aGzxkDcfhaHrvPW4KNNB3DhQTTuPI/Hrst3MXLJn5ixbjfEaoaYrwg3h+IGVWpOSHtrG3AchxcpGTgU+lClWxYA1h27olZsqCIzt0CjEXKz8jQvNZw1oDME4KQ/ttIfEQ8IOaCehxu6V+C90Jdmdb3kYTUFoVKmzG/hqL4VVlC9SMmQChXZRUSW8Cy7uTJAwAGRCSRS0rLzcDz8icrwBs8YEjOycemhNN/qZamcBoXS7uJ5Ep7hWSUeYNBaKMTQNk1KRLMSJDzD0Dbma4o2om0w2vnVhlACCFiJGBGIofL6NL5Tc7zTu70JraxEaFJiXNFUxdFJpGRkZODRo0d49OgRMjIyDG1TpePRyyRMWfGPNPFSCRceRuOTPw7qvH+JmCnePFHq7+LXecU9CM7ci6zQ9fIyNRORCdp7eOrUdFEbLwekIREfd81LDVv4eWPZxIGwsRJCwAFWHAcrjgMHwL+mOza88yqshMbV0vYiG4zv3AIcp+gBkcEB6BlUH8PbBle4LzsbawVBUu6pkgEQQ6Mn5qrO8+S0Cs8noYDDk/hkAICDrfqW9wKOg3MlL+ue1rsdnGxFKoXKuE4tzJpw/cepG7gZoZg8LhcqRYC1hENArRqo7+GGYa2CsHfOZHwxojflYFUjDK0PtLr6//bbb2jSpAnc3d3RpEkThb83btyotzGWjoTnkV8oVhidFQAW/XMcFVXJnroTgdQs3WLJEolE0dNQVqjwQJFEOjO/UFxuhFtl5BeKK1ynLL2aNlDb0lwo4NA1sB48nLVLzu3fIgCnvnobc4Z0x6CWgRjRrilWvTEMu+ZMgpeJeiu8P6AT+jSTdsO0EkhFkhUn9fC08vfBd69pVt5dz9MNNgJB+QecUt4VACjU4fOvathWMAo0IPWmyEaLHtQqUK2XgWcMA1pW7gZ53m7O+GvmOLTwUyzXtLexxvR+HTBveE8zWSa9/v12/JryhcWCnJcwPH2RgpjYdBy48gA/7TmPzNx8k9pZmahK4R5j6QONE2e///57LFiwALNmzUL//v1Rq5Z04K+EhAQcO3YMH3zwAdLS0jBnzhydjbFUHsQk4JvtJ3H3mbS5klDAoXMTfyyY0BcSnsfd5wkaZbb+ceomPhrWTevjS5Nni9Qmpsl0SUPvGhU+nVoJBDo1frOxssL8MX0w50+pV6i0VhMKODiIbPDJsO5a71fC87h0Pxqnbj7B0/gU2NlYw8nKBo28asLH3Vnr/emCtVCIHycPxvXIF/j9xDU8fJ4EK6EAnQL98b8xvWCj4cit6Tn5EIt5Rc9J6f+LWXPoMvq3bAw/TzdDvYVKRyPvmvBydUJ8BQ3aejSVhvsm92iFfdfvI79IXC6cKRRwaOzjgW5NKn//GT8PN2yeMQZPE1MRGZ8CkbUV2tSvI885MxdRCanIVuYtLnNuSyv2pDMvPYzG+7/uwR+zxmoUKq526BuysRCRYkx9oLFIWbVqFTZt2oQxY8YozA8KCkKPHj3QokULfPLJJ1VOpJwOj8Ds3/YrzJPwDOfuRqHf/37DoHaaJ3XGp6m/GKuirKtU2U/dwUbqCu8SWA8ezg5IycpVmpciFHDoHxIAFwfd3OL9WgRg7TQb/HLoIu6/SCy2D+gWVA8fD+0GPw/tbrpiCY/P/jiIE2ER8h4MmbkF2Ho6FP9duI3177+KZv6mqYTJyM3Hl5uPICE9Wz5v35V7OHTtAeZP6Ish7SvOBSgoKuUhURI2YsXzGQPWH72KbyeZrgGfpSEQcHi3Xwcs+Pe48uUch0GtAuHtJhWqvjVcsXH6KMz+4wDi07MgFAjAihPCW9evgx8mD6pSyZn1Pd0tqmy3qKK8urKCnAMkEobwqDhcfvQMnYP8jWgdYU6MqQ80FimJiYlo1kz1MNvNmjVDcnKy1gZYMvmFRfjot/0qy1LFEh57r93XqL8GANT1cNXNENl4PWqO4VQcr7cSCrBk4gC8++tucDyv4FURCjjUcnHCx0O19+aUpnOgPzoH+iMmJR2ZufnwcnVGDSfdeplsO3MLJ8MiAEBBVEl4hvxCMWat34uji97S2JOhK1m5+ej7xa8oEpe/EIslPL786yjcnezQuYIn9RrO9uC44p4RZQSK7H/Z7EM3HuCbif2rdbx+ZIemSM3OxarD0nbrXHGjGQnP0KtZA3w1po/C+k3reuHI/97AhQfRuBeTAGsrIboG+SOwtqcZrK9e+Hm4Sc9tZU/vxWHn0ue3rDeQwAo4EvqIRIoyqognxZj6QOMrf9u2bbFkyRJs3LgRVlaKm0kkEixduhRt27bVyQhL5bdj18qPYlua0j/YUk8PqpjQvaVOdtRydUJ6Tn7J+Vi6P0TxVaFBrZIOlG0b+mLrB+Ow4fg1nLwTAZ4x2IusMaJdU0zr2w7ujoZpjuZbwxXQo/ElzzNsPROq8nfGM4a07DycDI/AgDb6lSHn5hdi35X72HvlHlKzcuHl5oRXOzfDK20aw0ooxIRl29Q/KTLg2x2ncXC+epFiLRTC1d4WaVklcXhV/VIYD+QWFMLBVnWeT3XgrT7tMKRNE+y9fg8vUzPhYi/CwFaBKoWHUCBA9+D66B5s3KovQhF7kTU6B/rjwoPokpmym2ypn07Z850XA2nZucY3sBJStlRbl+0tAWPqA63CPf3794eXlxe6deumEHM6d+4cbGxscOzYMZ2MsFQOXHtQ8Vkgu8PykH6aykQNA/w8XHUWByM6NMXSnadLBFPpu3rxxWFER8V24IG1PfHj64NRUCRGTkEhnO1sjV4loy0pWTkKoRVlWAkFCI+K00ukpGTm4M2fduB5cf8WBiA5Iwe3o+Kw8+IdTO7dGjHJFWehv0zKUNmyvTSu9nZIy8qvcEwiAIiITUGL+tV3zKv41Cz8feYWDl17gOz8QtSp6YrR3ZpbVJjDnBRJJIhJTAfHcfD1cDX7b3jJpIF4ZdFvyM4rlHpOmPrnM3l5uBIPJVF1MKY+0FikNG/eHI8fP8Zff/2FK1eu4OlTabMwLy8vLF68GBMmTICzs2mSHE1Fdl6ZrPSyzSCFUKjaYOJS80qLCR7oFqT7U9+w9sH492I4niWWL9kUcBya+3ujZ3FlSllE1lYQGTlUoguMMfx16pYGK0LvhLv/bT6CF8npitqu2AV2NzoeP+w6q/G+7j9PQKuGtVUuZ4whOr5Ur44KTE/KUC/SqjKPYhIxbcV/yC01Vk1kbDK+++cUjlx/iNUzR2o8aGVVo0giwe9Hr+Pv07eQkSO9DtVwtsfEXq0wqU9rk+XepGbm4sDV+3iWmAZHWxH6tQ7AssmDMHPV7hIvctlBNMvAAOTm0+CCSqki4R5j6gOt7l5OTk5477338N577+l0sMqGrbU1svOK5E8MgOJvkYmlM+Tjs0gAVjzyLZPNl0j/33XhNua8qn3lCyB1s/4+cwwWbT+O03ci5RcHoYDDwDaB+HxUL7M/YWnLmv2XsPn4jZIieBUXOTHPo0PjujofJzohFVcePle5nGcM8SlZGvtdb0a8UCtSCsWSkguHBl+JUyVt4a4vPM/w8a/7kVNQqNBYUPZX+NM4rDtwGbNH6pc/VRmR8Dzm/Lof529HldyDGJCSnoufd13AoxeJ+HbqQKPnMu04G47v/z0DnjEIOA4MDH+euClfXtapqwoOGiTdVlOq0ijIxtIHBnvELioqQlxcHOrW1f2GYmm0buSLo9ceKSQ9loOVCBL5fY5XWAwOQF6+tL+KrhcWN0c7/PTmUMSlZuLOs3gIOA4t6/ughplaZOtDalYuNh27UTLEu4qbuVDAoXYNF3Ru4q/zsW5Fxla4jqziRpPQXlEFY6vYWJUaa0ndPosvLk4VNCirqlx+8AwvU1SPisszhp0X7uC9wZ0qDK9VNU7eeoJzt6NKHn7KLD9y7TGa1PXCpD6tjWbD6bAIfPfPKflreVJ7mXO6dMWaqnNdKOCqdam9WqqIJ6Ui9NEHBnv8vn//PurVq/w9CkoztXcbAKofsjkUezrVnCilnzYS0vR37Xu7O6NfywD0CWlUKQUKAJwIfQK+ePRk8CgJo5VOQmbSfi6/vDNML9e2xpJQk4sFB/hWUKFVbvgDVfssPjFszdz7wlzcjY6r8HvNyS9ETFK6aQyyIDYeuaYoUMqcQxyAn/47h32XdBuDqyIYY1h34HL5kcmLhUjZ62FFT/MSnmFUF9WVH0TVRx99UKliBEuWLAHHcfjwww9NcrzAup7ykccroqIkSQ7AhbtRBrGrNI9jknD6VgRCH7+QD8Rm6aRl50LACeS5PFzxYGXyKoHieYV5Epy/rd9n1qphnQrXUWi8purLlgmnCjxhZ8Ijy3cGZkomXupF8a+mCaJSgVLxL0tdh9mqytPYFIUwj9KLCwMW/nkM6Tl5Bj9+Qlo2nrxMViw1Zkr/VJyh4usc3iEYrRqoDpFWe5RdHzSdqgEa+1FbtWqldnlenuF/LKW5fv061q9fj+bNmxv1OMpQe5mUeUG5ip/aCwzYCv3O0zh8t/UkHsUkyed5uDjg/ZFdMLij+QYg0wQvd2e5oCotEDglkZS/TtzEhN4tdfam1PV0Rdem9XDpfrTSTrwCjkP35vVwKuypNPlZ5vpSSD6S/sfx0kHx1HH/eQJshAIU8mWaRgAKrzkA0wa0r7ZdODs28cPq/ZfUruPp6gi/WtUvTCDhmdQ7oS5cyEkXr9l7EZ9P6KNiJd3IK9QuyVXeCbtMXLymswMm926NiT1bVeteQOqoKjkpxtQHGouU+/fvY9y4cSpdNnFxcXj8+LHOhqgjOzsbr732GjZs2IDFixcb5Rj6oslPsHFdD4Mc6150PKb9sAOSMqGFpIwczN90FAWFYrza3fRiTlP6tGyEJf+cQn6BuMJUkKSMHLxMzkRdT1edj7doUn+8/fN/eBKbLG9GJetu27pRHQxq2wSnbz2VurfLXmxLCRQOkHqA1GAtFEpzj2SlmVyp3fBM/l77tgrApN7GyymwdIL9vBDSwAd3ouJUDuMwxYRVLJYEB2n/Rk3u69cexhj8+F5uThBZWyl2T64ADsWFA7x0lG9vdyf0atEIvZo3qLZCvDphTH2gsUhp2rQp2rdvrzJzNywsDBs2bNDJiIqYMWMGBg0ahD59+phcpATUqYnHL9R0ymOlbkIqvCmy3NA2Ab4GsWnFjnOQSHhpMltpn2zxVe2n/85hYIcg2FlovoODrQ0+erUbvt12quKVARUtLjXH1dEOWz4djyM3HmHflXtIzsyBj7szRnZuhp4tGmLWyt0KHg55Qi9K5smWN/RRH57pHOyP3w5flW/DGMB4BqGk1L4AeNjbQyzhYV2NR0P+YdpgvPPzTkTGpchFo1DAQcIzjO7aHB0C6yLiRRLqeLrCtpqWIquFodyDiiGwE1ljaMcm2HXhTnkBqeypgkGeVyYofh2XkoWtJ0Lx96lb+OatAejXtnIP/Gg0qkjirDH1gcYipXPnznj06JHK5U5OTujWzfDlgv/88w9CQ0Nx/fp1jdYvKChAQUGB/HVmpuoKAk1YNKU/xn2zVXq3UfZoI7u5cSUvFSIFxcv7tGqklx0SnsfJG0/w17EbuPc8sfjuV3xs+bGkiimvoAinwyIwsH2QXsc0JqO7tcDLpAz8efym2vVqONvDp6aL3scTWVthWMdgDOsYrDD/4fNEXLn3HJwA0lyS4lQJha9a9qXyQEqmerdl8/reaOrvhQfPE6RuezFTWr6+/VQYLt2Nxr+LplRboVLD2QF/z3sNp8IicOTGI2Tk5KOupytcbUU4du0Rdp0MBwDY21pjZLfmeHdYp2qRaGxnbY2cgiIwWeKsmpBPkF8to9gwfWhnXHsYg5jkdIUScQ5S0Q0BV3KtKyPAS8PzDF9sOIxGdWqinrce7amrKFUl3GNMfaCxL/Xnn3/GihUrVC5v0KABTp8+rZMRqoiJicEHH3yArVu3wtZWs34S3333HVxcXOSTr69+3osL4VGAWMWZIP2tyv8u3U9FBgfpvLaNdbdDLOExd81+fL7+IB5EKxcoCjYBSDRAJZGxmTWiK3w9XCBQ4dfmALzWu5VRe8DsOncHxcPFlCsfV6D4u63Icc1xHJa/OxT1vNwBnkGg6nsCEJOYjmWaepMMDGMM8alZiE3OKF+RZEKsrYTo36Yxfnp3KH7/eAwcrazx19GbCudvbn4Rth0PxfTlO7UKQVRW+rVpDI6rQKAU896QjkaxwcXBFps/HYfXerWCY+kyecYgkAAQM3DF16GKWgwxxrD9VLhR7CQsA2PqA4sO+N68eROJiYlo1aoVrKysYGVlhbNnz+KXX36BlZUVJJLymZbz5s1DRkaGfIqJ0T1mK+F5bD95CwJWXH0iZtJgMc+AIgaBmIHJ8gyUVXLwJX8XVtBfQx1bjtzA2bBIAMX9ClTc+GSveZ7Bwc7y+28IBBxWzBgOV0dbBaEii2H3aR2AiUbsBQEAz+JTi78r6YcqFyoSxf9l4rOJf8VPrjVdHLD189cQVNujZJ8qOHT5gdQDZiIYY9h15jZGfvY7hszZgGGfbsSgj3/FpoPXINbjHDUED6IT8M/JW0qX8YzhTmQs9py/Y2KrTM/EPq1gJcvFURMOGNKxiVG9E84Otpj9ajec/OFdHF/2Nt54pS24Yj0ruyZy4jIhZyUwABfvRBnNzkqNPpU9eoaKTF0tqytad0m6ffu20vkcx8HW1hZ169aFSGSYAdN69+6NO3cUL0pTp05FYGAg5s6dC6GwvJtcJBIZ7PjJ6TlIyZQOjCXziJRtjS/kOEhQkrfAiv+XIbtB1fPS7WIilvD4+0So/DqgrPikNLL54qLyNxyeZ7hw+yl2n7mNZ3FpcHa0xSsdAjG4SzAc7cwzyF09L3f8N38K9ly8i8PXHiIrrwD1vNwxqltzdDdB0p2Tva08YVb2IcuEClfawcABzRp5o46GI1lbC4VIyah4ULWCIjHyC8Umyx/66Z+z+Pt4qMK81MxcrN11AXciYvH9zKFmS1bdff6OPCdFGQzAf6fDMbZXS9MaZmLq+9TAz+8Px5x1+5FXUFxpU+pHzwGY0r8NZo3oahJ7rIVC1HB2wIxhnbH9+C25TWWL19RRWdojmBpzhXuMVS1rDH2gtUgJCQlRW05mbW2NsWPHYv369RqHaFTh5OSEpk0VB85zcHBAjRo1ys03BkINwgwcOHi6OiCxeKC8ct4NDqjl5oT2Qbp14o1PzURqpuLNrqLbtoDjkJCapTBPLOHxxdqDOHXzScmNIBG4HxWPrUdvYv1nYwyS+6ELro52eL1/W7ze3/SjaPdrG4DToU+kL3hIk59L6buSfB/gYWQC7kTGolkDzQYE1FRgmWpIgzuRseUEigzGgPPhT3Hs6iMM6GieXKZn8akqBYqMl0kZJrLGvHRs4odjy97G4WsPcfFONBLSs+DqYIcuzethVLdmsLEyfRfeIrGkRDRpSRs9wt2EYTFmtawx9IHWV8fdu3ejUaNG+PXXXxEWFoawsDD8+uuvaNy4MbZt24aNGzfi1KlT+N///qftri2OGs72qO9TQ20poITn8f6IznBztCuXWyEQcLASCvH1G6/o7BFQla+hDo5DuXDPHwevyW/GpW8EjAFJadn4ZOU+k4YdLIWeLRvCr5ZbybAGpZIAFbpqQir0Pl25X+OwSNvAii/Mbk52Jkuc3XXmttrmaAKOw3+nwkxiizJcHGwrPN8rQxjTUDjY2mBUt+b4acZQbPviNcwY0hGJ8RlY+c95HL5436B9lzTB2kqo8xAF4/tUbe+Xzpgh3FO6WtbQGEMfaH3GffPNN/j555/Rv39/+bxmzZqhTp06+PLLL3Ht2jU4ODjg448/xg8//KDt7ivkzJkzBt+nKjiOw+sD2uKrjUeULhcKOPh5uWNA+yC0buyLX/dfxqGrD1EkloDjgK7N6uPtIR0QWNdTZxu83J3hXcMZcaXHOZGdmCqu5xKeoXfrAPnrIrEE/xwPVRk6lvAMj58n4XZELFo0Mn1nyPyCIhy69AD7z91FUno2PN2cMKx7U7zSMQgiI4/bYm0lxA8zhmLMl5tVJyOXIjkjB+fCnqJXm4qrtWaO6irNOVGzzusD2mllrz48fZmi1lPBM4aouFST2VOWfu0CcSo0QuVyoYDDgA6WW7FmLGKTMvDud/9KB8IsxdI/T+K7GYPRsbny3hSGhuM4tGxUB5fvRiuEniq6T747rKPRqpAqPQYqQS5bxaoq7UHballtMYY+0PoOcOfOHfj5+ZWb7+fnJ88fCQkJQVxcnLa7tkiSU7OleShClMSFi/+3Fgqx/P2hEAg4eLk74asp/fDp+F5Iy8qFk73IIHkeAgGHif1b4/ttSjKjlSSnCDgOPVs1RH2fkhyYZ3GpyMjOV3scoYDDzYcvTC5S0rPz8N53OxD5oqTRWlJaNu5GxmHnqXCsmTsajvbGzZep71MDLRr64HZEbIUXDCuhAPej4jUSKTWcHfDJhJ5Ypuy7A9CvXWO81s90Dd0c7EXyz1gV9mYc8LBHSAM0rFMTUbHlxZRAwMFOZI3xfdR3tqxqpGflYuy8zUqrmnLzi/DRT3uwaf4EBBYndKdn5WH/ubu4fDsaEgmP5o18MKJXc/h4VBzKfRaXim1HbuLk1cfILxTD38cdo/uEYFDXYFgJBeB5hohnxR2uy157eFmZT8nMGs72mDuhF3q31q/9QlXGUDkpZatY58+fjwULFijMk1XLHj9+XO9UDFUYQx9oHe4JDAzEkiVLUFhYKJ9XVFSEJUuWIDAwEADw8uVL1KpV+ZXzs/g0rP73AgQ8wBVBmrMgG1tGDBTkinHtnmL1kK2NFbxrOBs0EXV0zxCM6CYdoEuZu14oFMjn92jZEAvfeEVhuaa/AXOEe77ZeAzRsSnFx4fC/4+fJ2HpnydNYsdbQzpo1DOOMQYrq4p/NkcvPcBrX/yJH/84Da4QsBEIYGMlhL3IGoF+nlj14Uh8+/YgA1iuOX3aBKh9jwIBh/7tA01nUBmsrIRY89EoNC/O+REIOHkSby03J6z7ZAy8azibzT5zsGjDUbVl1xKeYePeKwCA8McvMeLjjVj97wXcfBCDsMcv8dfhG3j1k99x6OJ9tce5cf85Jv5vC/aduYOsnAIUFUnw5HkSvt14HJ/8tAdisQShj2KQkp6jOHwFk4ZIhUWAoAAQFDAIixiEBQx1nJzQ0gye2epITEyMQlXrvHnzyq2jS7WsthhDH2jtSVm9ejWGDh2KOnXqyDOD79y5A4lEggMHDgAAnj59iunTp2u7a4tj75nbEBQnmXIoU+0B6UPDjhO3MLKncVvQCwQcPp/cB690CMSuM7fxNDYFjvYi9GjZEFZCAeJTM2Fva4PerQMUPCgy/L3d4ewgQmZOgeICmYpn0otdSIBpLyhxyZk4FxqpUkTxPMPxq4/wwbjuqOlq3BGfOzb1x1dT++Hr34+pXU/CM3Rqpt69vnHPFfy665L8oVIAQJLPg+M4NA30wc+fjDRLA7cBHYPwx8FrSEzLKu+p4KSeitG9Q0xuV2ncne2xYe5YPIhOwOV70RBLeDTxr4WOTf0hFAjAGMOVO9HYeSIcj54lwtbGGr3bBWBk7+bwdHcyq+2GJievQNqnqQLO3YpESno2PvxhN/ILihQeNmSN2L7+9SjqebsjqL5Xue3zC4vw2S/7UVgkUQw/FP9/6XY0th25iRpujgAUG7gBJXlcsm6zsmX3n8Zj5pId+PnTV+FoJ6oWjfi0xkDhHmdnZzg7qxfwulTLaosx9IHWIqVTp06IiorC1q1b5b34R48ejQkTJsDJSXqRmDRpkra7tUgiX6iP4TMGRMeaJobPcRxaN/ZFax2y5K2thBjTuyU27r8ifZIufvop2wn15y1nsfj9Qajr7WYwu9UR/uRlhb9Pnme4GxmHHq0bGt2eoV2bIie/EMu3nVG6XCjgEORfC80aeKvcR3RsKn7dJR04r6zXgjGG0Acx2HPmDkb3CTGQ1ZpjJ7LG2k9HY/bPexAVmwKhUCBPCK7hYo8fZw2Hl4Xc6IP8ayGoTE8axhiW/XESu07dVihV3nzgGrYfC8Uvc19Fs4aaVV5pioTn8fRFCgoKxajr7QZnB+O4yZVx9IrqDp6lYQzYc+Yu8goKVXrKOA7452goFr43sNyyE1cfIyunQGnrBAYAPPDHvmtYNH1gueUqu80Wt0+JeJ6Cwe//Co4DurRsgNeHtUOwmt9PdYNjxU3x9NheU0xRLWsMfaBTVqKTkxPeffddXTatVNjZWsvHFFGFsRM7DcWbQ9rj0fNEnL/1VNoxkpW/sETGJOGdRf9gy7eTULP4qcmYcBoNy6jZQGuGYlyflkhJz8HmQ9flN0LZOeDn5Y7vZw1TW2K35/Ttint9nAgzi0gBgNoeLvhn0WRcf/Ac1+4/g4RnaN7QB91a1IeVhbfnP3D+HnadkvZhKP358jxDfoEYH/+4B/t+nmaQcX4YY9h98jb+2HsFianS9gLWVgL06xSEWRO6w8XJTu9jVMTFW5EarWdrY4WbD56rDeVJeIZLt6OVLjt57bFSgVL675zcQtTxcIGLo21JfpuqRPNShsh+44wBl8Ke4lJ4FL7/aBg6tTBNsi9hegytD3S6w0ZGRmLFihV48OABACA4OBizZs1CgwYNDGaYJdCjdUOcuv5E5XKhgEPvtgEql1sSVlZCfD9zKNbvvIQ/915Tuo6EZ8jIycf2o7cwY5zxG0WFNK5doQgUCgUGfzpWB8dxeH90V/TvEIg9Z+8gKi4VTvYi9G3XGD1aNqjwRh4dV3Gvj5j4NEOarDUCAYf2wX5oH1w+wc2S2XbopsrEX54xZGTn48SVxxjcLbj8Clry63+XsGnPFYV5RWIehy/cx53Hsdi4aAKcjOxVyc4trLCSDwAGdm6CqJcpFe5PVUO1u49j5X/LD1PmHOYALN9yGjPHdMPiUiFRBbPkSWWylv6KRkt4Bo5jmL/mEA6sfKfSPOAZFQOFe3TFGNWyhtYHWifOHj16FE2aNMG1a9fQvHlzNG/eHFeuXEFwcDCOHz+ukxGWSq82jVDH01VpsirHAQKBABNeMV11RmnEYglOXH6Ij5bsxJTPtmDuj3twITRSYTCwsggFAsQnZartlcHzDAfP3TOGyeWo5e6Enm0aqewhI+A4DOgYBHdne5PYA0ifoBljaOTrgU8m9sKaT0Zh6Ywh6NM2QCNPg72tTYW9PmhEX+3JySvE05cpar0FQgGHsEcv9D7W0xfJ5QSKDJ5neJGYjq0Hb+h9nIqoVcNJOjaYqtOpWAzMHNsNzRv6qO3FJBRwSsV+bGKGYq4aY4AsB6/UBADXbz+Di50In0/tK624Kz0eiGy4DhUCpfTus3IKcOaG6oe/6oSsukefyZIwhj7QWsp+9tlnmD17NpYsWVJu/ty5c9G3b1+dDLFEbKytsOazUZj1wy5Ex6YqxPDtbW2w5P0haFCnpsntysrJx4ff7cT9yHgIBBx4niEyJgnnbkSiYwt/LPl4GGyslX+1aZm5FT7pZ+SoL1c2JJ9P7YOXSel4GJ0of0qWeVeCG3hhzqSeJrHjclgUth24jtAHLwAGNA3wwfhBrdGjrXblk73aNpK6z8tSfHcVchy6myC/pqqhWciPUxuK05QFaw6pXc7zDLtPhuOd0Z0NcjxVDOnWFEcvP1ScWSYW8/rgtnCws8GIXs3x12HVwknCM4ztV76hWnZeQcluSw0YWBZZ54UvVx7Azp+nYUDHIJwJfYKvVh0q6Zki8woI1X8mVkIBnr6o2PNDVD6MoQ+09qQ8ePAAb775Zrn5b7zxBu7fV1/mVhnxquGMf76Zgp8/HoFXezXHkG5N8fnUvjj08zto39Q87vLvfj2Gh1EJAEqy92XC48rtZ1jz93mV23rXdFbrSQEATxPko8iwFVmjuZ8XrCUcWPEAjryYRyMvdyyc9opJ+nZs3nMVHy3dhdAHL8DzDDxjuPskFvOW71P7WSqjR+uG8Pd2V/yMJQyCQkBYCKCA4cSZ+/h69WEkV4KRqi0Fe1sbNKrrAXWnroTn0TpIv/brKek5ePIsqUI3ekZ2vtE7vrZp4oturRoovudiISAA4O/lhkmDpc0AfTxc8NW0/hBwnMK5J/t7ypB2SvNAXiakg2Os3Jhj8iqdUhPHAxIJw96TtyGysYKdtXXJylqELRhjVOkjgxlgsiCMoQ+0FikeHh4ICwsrNz8sLAyenrp3VrVkBAIOHZvXw5yJvTDv9T4Y3qOZyQaEK0tcUgZOX3uiMqzDGMPuE7eRnVugdPmQ7k3VelIEHIcRvSouqS4oLMLV8GjsOXEbS389jtfnbsHrn27Byj/P4EV8ukbvhTGG+T8fxO5j4WBFPKyKAKtCwKoIeP48Fe8v/BdpmRUP0qcPDyLjsW77BQBQ+Exlf2/Zdw037z3XeH9WVkKs+mwUGtaVjoAs5AFhmXuZhGc4cu4+3vx8K1LSc/R8B9WHiQPblE2VkHuoBAIO7i726NVOv8Zhl8OiNOqXYyUUqPRWGgqO4/Dt+4Mxrl8riKyF8tCLkOPQs3UjrP9irEI/pgGdm+D3BRPQt0MgnB1s4WBng7bBfvjp4xGYPrqL0mNs/O8yZKOAy0M7DNJ+UDI7ZP8zgBczhD6Q/h5SM3LL3yyL96XuQ5TwDN1bV638RV2pauEeY+gDrX9l06ZNw9tvv42nT5+iU6dOAICLFy9i6dKl+Oijj3QygtCc0PsxFa5TWCTGvYg4tG/uX25Z04beGNAlCEculG/XLhRw8PVyw6t9WqjcN88zbN51BVv330BuXmG55RHPk/DvoVDMnzUQfTqpbwx2+9FLnLlWHJsuY4xEwpCcnoN/D4XinXHKL7CGYOfxMLXVOEIBhx1Hb6F1sOYDRHq4OWLzwtdw7mYE5n2/D0B5FzoDkJSajfX/XMDn7/Yvtw+iPP07BeLx80RsPXADQh5gkuIwBMcgshPi+w9Uhzk1Jb+wSBp2VLcSA1oF+Rp9hG5A2j7gw9d64K0RHXH7SSwkPI/Gfp4qe8IE1auFhe8O0GjfYrEEkc+TpSEeWbpVsUApnW5Skmci/T/qubTtQv06NaWfvywXpbjRJWMArFXnmXUKqYf6ZgiTWyRmTpw1NMbQB1r/or/88ks4OTnhxx9/lHe18/HxwYIFCzBr1iydjCA0p6J8EhmqPC0cx+F/b/dHbU9X/H34JnKKhYZQwKFPh8b4aFJPOKhpQ79800nsOhpe4XEX/HIIDet6wL9O+eZyMg6duSfNqRGXryRgAHgJw/5Td4wqUu5FxJX/TOUXXQYeDJevP8Wx8w/Qq1NjjUcs5jgOF65XXEJ65Nx9fDS1F7m/NYDjOHRpXh//7Q9VqFThGFCUJ8bKP8/gly9HQaRHYnIDXw9FJ4AydckBrw9rr/MxtIHnGW7ceYYn0YmwsbJCx9b1Dda0TsKzkoaORQCsUSJIlNw8ueKoTkZGHjKz8xHc0AuO9iKp17bU+gIGMDEDK5tnznFo7O+JhdPL92ohqgbG0AdaixSO4zB79mzMnj0bWVnSAa9kTVqqMzm5BTh+4SGiX6TAztYaPToEoHF9ww8N0Dyg4nJcoVCAQDXHFgoEeGtkR0wc3AYPniagSCxBQ18PuLuor6KJfpGiVqCUhgPw35FbmPOW6pE2k9Ny5AJF6XMXA9IyjBvuKffkzQBOwkp6QHAcxGIJFv5yCAdP38X380Zo/LR+427FYSKxhEdqRi58PF20Nb3aUVgkxuc/7oNEwpQ0ygPuPY7DH7uu6iVqQwJro66XG14kpJWElmT/F8dDQhrXRqsm+uW+aMKDiHh8uXw/4hIzIBBwYAxYsekUenYIwOczXoG9niNCi2ysFLwkMo9IacGh6nd56Ow9jBvUGrMn9cDX649KQw+lVuFkQ4gIACZLrOUZJg1qU61Gsq4IQ43dYykYQx9onZNSGicnJxIoAE5cfIghb63FD78ex+6jYdi69zre+HQLPlr8H3JU5Iboin/tGmjVpI7K5FeBgEPfToFw06Bs19bGGi0D66BdU78KBQoAHDxzr8KkWxkSnuFqeLTadayLq6VUVRNI1zFug7FurRuUlAyXEigcV7pSRPr/jTvP8es/FzXed35BkUbr0UVbM85cfYLM7HyVY0zxjGHX0TAUiXUfg4TjOCx6fxBsbaxhJVCM+wgA1HSxx8IZxvcExMSlYeaC7UhIlo5uy/NM/r7PXnuCecv2GGSsLUHpYYwlxepEjUCRzQstFuCDujXFx1N6Kd23bCgRgUQ6cQzIzDbs9bDSU8USZ0tjKH2g0SNhy5YtNS61Cw0N1cugysbNu8+xYMUB+W9bLClxQ1+//Qz/+3EffvpytEGPOX/GQLy3YDvikjPlFyrZt9PAtyY+mmKcst2k1CytfhPqerYAUi9GsfdcKRwAsZhHUZEE1tbGESvDejfH1oM3UFAgBi/mpR0y1Zzq/x26hWljO2vUiKpOLTdkZKkf7dPe1tok3UsrCy/j07HnaBiuhz8DALRuVhcjXglBHW83PHqaAKFQAIlEeVMyQNqDIzElC7VruepsQ2D9Wtj0zUT8secqTlx+CHGRBCIrK3Ro4Y9ZU3qilgkGOdy65xoKC8VKf0PSENBzhD94gRA9PTr2tjbIyS0EGIOAB1jxY2tFV/uE5Cz536P7tcTOg6F4Hpde4fG8ParXAJHVAWPrA41EyvDhw7XecXXhjx2XwanomsrzDNfCn+FBRByCGhpuvApPdyf88d1E7D11BwfO3EVaZi483Z0wvHdzDOoebLT8BjcXh+KTsWKpIhRwaBWs/gIqEFTcGJ8xhrz8QlhbG+dGXtPNESvmvYqPl+5CTmEBit0oKtcvEksQFZOMwAblB2ory6hXWuLeKvUiZUivZtqaXGU5dekRFi4/IM1HKr45P32ejP8OhuKr2YMqFCgybAwgaP183PHWyI5ISchE6J0YiAvFuHA5AqFhzzFhWFtMHtXBaImzjDEcO/+gJFeq7BMzBwiEHI6df6C3SLG1sUZOToG8xwnHA0yg7tFBuqSsSK/h4qCRSGnZpI5e9lZFLC1koy3G1gcaiZT58+cb1YjKSlZOPkLvqa+2EQoFOH35sUFFCgA4Odhi4pC2mDikrU7bFxQU4dCpu9h3/DaSkrPg5uKAgb2bYkjf5nB0UJ44O6B7E2w/eFOj/Ut4hlGvlG8eJR1npQiJyVm4eD1SGghXIwrsbK1hryaR1xA0D6iNrctex4i311f8CAkgK1uzZne9Ogbgn4M38CgqUelyT3dHvD3WeEnBpoIxhruPYnH28mPk54vhV8cd/Xs0gbMWHqLnsalYuPyANKxRar5MrCz66SBeHVz+fFI0RHq+GGLcqaSULLw7bxsys/IU5ufmFeK3fy4iNSMHH01TnW+lD0ViSUkPlmJNpnAjY9LP/JmeDdEYYygqLFLIS5HOLz6Imt9lUJmcN7/aNRD2UP2Aoc4OIqOXbVc6mKwcSo/tzYyx9QGdMXqQn19xzgEHIE+D9UxJVk4+PvzqXzx+WtLlNSMrH2v/PIs9R8Kw+tvxqOle/kIf4O+Jfl0CcfziQ5W/DVkH3I/f7K2QOJyWnoNtu69h//Hb0jFJZE+harKihAIOg3s21biiRh/kx6jIbckYGtTVrHzS2kqIlV+NwdJfj+HU5ccKF/BOLevhf9NfgZ2t5l4vsYTH1dAoRMckw87WGp3bNkQtM7vPs7Lz8fmSPQi79wLC4s+Q53ms3XIOn07vh1d6aDaOzq7DYQDU++iuhkZL/ygrbEudjPl5hRCLeb3Dg1t2XUVmVp7Karpdh8Pw6oCW8FNTvaYrNtZWsLe1Rm5uUYk4UVJldOdBLOKTMuGl4zmQkpaDrKwCeTWdzEnKMVTYNbZsYv7gXk2x58TtEuMUGtBJQ6gTh+n2QEVUb0ik6IGrsz3s7WyU9guRIeF5+NVxN6FVFfPzb6cQEZ0EQFGIMwYkJGXi6xUH8fOisUq3/d/0V+DiZIfdx8MhFpe43oVCAZzsRWgf4o/RA1qiSSnPUXJqNt759C8kpWSBlfXWyzzLSjwq7m4OmDzSNKWezo62xW51eVlP+ZUYg5WVAO5aPKk72ovw9YdDMHNSFsIevABjQLPGPlpX89y6G4NFyw8gOTW7uNKDYcWGk3ilZ1N8/G5fswzWxhjD50v24vaDlwCgEIopKpLgm58Po6abI9q08KtwX9duRaktr5fwDEkpWRAyaQNUAADPg+OlIQrZt8UAXL0VhS7tdB96QCLhcejUPbX2CAUcDp+5h3cndtP5OKrgeYaCfLFqgVI8j+cZdh2+hemTu+t0HAnPKwoUBSOY9EGi9O+y+G8BA+KTMhVWb9LIG8P6NMO+Y7elCbiC4p0yBo4D6tWtiZH9K/CEVUOqWnWPMSCRogfW1kIM7dMM/x4MVZkkam0lRP9uTUxsmWrS0nNw4twDlfZKeIbQOzGIjkmBv2/5p0QrKyHeHtsZjf08cO9JPBwdRLC1tkL0s2QUFolRx90ZNVwcFLZZ8esJJCVnSa9xKC5JlF38GKQXRIUnL2kW8vdzh8PdVXFfxkIoFICThRpkF2dAfqGVXajr6Fgq7FnDCf26BOm07eOnCfh44Q5IikVh6e/uyOl7KCgowsJPhuq0b3249ygOYWrCnQIBh807LmskUjS51lpZCYGCYu8CkwoUoNxwNpj37W588cEAvNKzqQZ7LU9eflGFlVk8Y0hKMc6wBpdvRoKXVdpU4Nk7eva+ziKlppsj7GytpZ7eUmko8gZtpX+Xst+DhMnLoctSz9sdAknxAh6QfascGCCuOJeoWqJvhU41ECnG96NXcV4f1RF1fdzKJdHJXn/6Tj+jD+muDY8iEzRqCHf3UWy5eTzP8NtfFzB88hos+ekI9h8Iw9//XsWmbRdx+sIjXLwWiT//vYwx037FgWNS129yajbOXX5S0oOhDNIyRQauiIEr5KVTEQPHM7i5mEagANLSUxcnW+nFtax7CQB4XvqEKDH9VWHzv5chkfBKk7MZYzh18REiiz1jpuTc1SfyEI8yeJ4h7N4LjXJ4Wgb7qk0HEnAcOraqBysrofQhXU2ZLAAsW30MGZl5Kpaqp2weijIYD9iKjPOMd+L8Q3BMffKqDE1CzqoQCgVoFlS7ZEap04tD8VM+D+k5X8pjJeEZQsokwCalZGH1pjNKj8MAPH+Zir/3XNPZVqL6opdIuXjxIgoKqnfdu5ODLdYunoDRA1spDIbXNMAHy/83CgM0jMlrQnZOAf7ZdQ0T392I/qN+wtDXVuGT+Ttwt9jdrgkCDfM7lFUurPztFP7cfhkFBcVJfWUfYSGLmDAsW30UobefIzomRT54GVfqSU3+gmfgigcvExRPHA8IxMC9h+WFkjHJLxBLL84SgBMXCxKegZNIbRQwIDYhw8Q2FeHC1Qi15dxCoQDHzz8woVVS8vOLNMkz1qhfTE03B7V9P3jGMKhnU3w5S9qjpGzzsLKIJRIcOXNPA+sUYYxh7uJdFY4/A658hYuhePI0oUQwqNPEDPCsqV8fitEDyoRglHyopXsZCQUc/OvUQMumilVFh07eVfuF8DzD7sNhBuntUpWQhyv1mCwVQ+kDvX5lAwYMQFhYGOrXr6+3IZUZZ0dbzHq9J959rStS03NgK7KGqwbN1LQhJTUb78/9G7GlBu/LLxDjWmg0roVGo3WIHxbPGw57e/WNwZo08oaNtRCFReobXpW9CMXGp2PngTI17hXcobbtuoqhr4SUj5vKcj9kT2pM6hKWFSRL4+QM85ftw7Z1b8HHy1X9gQyEWCyRWsAzuV1yk4VSw3gTe1KycwqUelDKkqmj10Af6tWtodCeXhmODiK4adAo8MSZB9K264yXisRizxvjpJ89x3G4/zgOE0d1QHxCBtZuPqt2fwKBANEx2le/3H0Yi+jnKSWPb8qqzxgDx3EQaCTRpMInMysfQqFAZeWcjKOn7+HZ89SSmIu6MmcOGDOkjUY2KOPB4zgsXLpfGubkUBJ+VXU4DnBzdcCSecPL9cWIiU2t8HgZmXnIzStUO+xGtaMKh3sMpQ/08qSQKlbExtoKXh4uBhcoAPDdisOIT0hXnMmkT/rgGW6GRuOT+Tsq3I+jgwhD+7VQ2XxHIODQrUMjeJfJvTh+5j640n3ONGiXcj0sGk6ONoqPYih+ApDfiJjUta1EyDAJw4r1Jyp8T4bCVmQt9+zIChTkPWcl0snBwbTdYV2c7CoMK/y/veuOj6Jo/9/ZS++hJfTeu3SRJigi9q7Yu4KKHfVVsYLKqyIK8loAFQRRQUSkCAjSew+hdwKEkJ5c2Z3fHzuzO9vujgAS+O33w5G73dlpOzPPM08bSum/xsiJuKJ7M0RFRjiSakkiuP7K1qotSRBk5xTg0NFTIAqF5Gfjg/c/VU+RJgGKTdsOYVvmEXw9YXFY9SvLSeWLV+wAoDNJFvA1j1JbDzgRAVnBT9NW47YHxuK6O0eh/20j8diz32Hx0h2O6b8crzJfvO22EhXh2rV9yhZnR5YVvDHsN5SU+ED8VD/1mLvEUvVU5Do1KiKtciIa1KmMJ+7pge8+vR81qqbqVaEU+QUl8EhSyIBekkTOi4F3ecbFdgqyiLPFH7gj5gLAoSOnsHr9Pv0CJ+qmMbBl62G8+vavePu1G4K67T5+b3ccOJKDVev3aS7D/G+jemkYMsh6Ku/J3CL1qHZefhgRBikFdu/NNhjlcUhcms7F9qb8CFE9WFat3XtOI86K6NG5IWbP3aKWL9aF/aUUaFzn7J/HFAyRkR5c3bslfpu9Iagt0VW9zp5aMVwkxEdjyKCr8PYnMyERYlBJSRJB3ZqVcM8tob2zxk9aypht9bdd3xMK7N2XjTHjFgU/AJBBlhX06NLotNoDACdOFOgeLxo/Yux3LnS4ooezIbQsK3j17V+wcs1ew/XtO7Lw+vvT8ch93XH3bZ0N9zZvO4STp4r0cqjaJ5Rzy1o1CKBQRJ5BMLnV6/fh+Il8bf55AhRUpqAS0aQ4HhD8b/gAR+ns/EUZmDR1JXbtOa5KvBxOPgZUNdFlnRqGZFhduDDjjJiUsWPHIi3t3120/z/CYnMSRES4dMUufD1hMR5/sKdjftFREfjwtZuwYt1e/PHXZhw9noeKqfHod3kL9Ohsv5BUrpCo8iaUqm7EoWRwFEhNicPO3cf0OltiPdCguy+VUQFWrNmDbl0ahijwzFE5NUGvop2RLwFOHC+w3jjHuPfWzvhn5S7knCq0ZVQevbtbyF39uUKfbk1QMTUOE6auwNpN6nkuiQkxuL5va9xzc6eQh+B5fQHMnr9V62+n0UChqrSOnyjQ0lGJ2vrPEgK0aFwNrZpVt+QTCrW5Rxsfrw78eLdODVExiCv69D/WWxgUEV9NWIzLOjdEnVpqees27McX3yxU6w9onjVm9aPaXFU9065NndNuH8c/y3daxjihUM+uYqBQkLk7C21b1rI8P37SUoz7Yak+f6leZ7v3QQjBPTf/O+EELihcBMHcnHC2+IMzYlLuuuuuM67AxQZKKTZsOoBpv6/Hjp1ZiImOQPfLGuO6/m1QqWLZjNwMRqx2qhETfvltLQbc1hmJCc5eRR6PhK4d6qNrh/ph1eHKXs3w9ff/6EWLLsQOlOWm/pfg+PF8w4KvnwMf5i6QUmTuzPpXmJRDh0+xMqHtMMWqA8DBQzkIBOTT2hEGZAVzF2zBxB+Xo7DQi5SUODz2UE9c2qkBKzcH2zKOQPJIaNOqpmWcVExNwNgPB+Dzbxfi7+U7NIlFWuUkPHDHpejf+/yG1m/bohbatqiFomIvSr0BJCfFhhWALzevGBN/WgG/L/SBgARAaYlfs9EglAIBgEbAuFATAsjAgBs6hn2eiIjr+7XBuB+X6W64FsZavfT8Y8GjzX43eXnwgijFx6PmoE/PZsjOKcR3k5apGUcIRJ9AdfkVuocIf6tXNqpkTwcrVu8OK92OXVYmZffeExj3w1LWDKrXi78PxpR4JIKArCAhLhpvPn8tGjcIfZTE/zdczHFSzhZ/4Kp7ziIopRjz9UL89MtqwzkjByYvx8/T1uCj925D8zLs7k43lLTPL2P1un24vHuT0y7LCWmVk9D90oZYvETVp6vSFBtGhar/VaqQiLtv6YQVa/bg97mbrGnCpR+E/CuqHgDYf+CkrpvnxYt/KQBJNcoMF3n5xbj7wa8Mbrh5+SV49c1fkFY5ETVqVMDa9fv18ghB755N8dxTVxqOA6hcMRFvvXgdTuUV4/DRU4iJjkS92pXPyfkx+/ZnY+nynfB6/ahTuzK6dW0U1jtQZIr8vGJAoahYMbhk58jRXDz9wkRk56jqDSKdpg2gAtV1XSHqoXgSmCG2ysR/+Mls/DKx3mmrFw4dOoWYSA9K/QHVkNtmo9u3V/OgAf28vgBy84rtbzIVDgGwafNBbNqsx5lRRAmpAkBWDcoBwHzKFQXFzFkb8fC93ZGYeHohDk7lFiE7uzCsOWgXPmHGrA3weAhkkxE5AUAC6pcKleJw5eXNUa92JfS8tLFri+KizHBHzlnEgr8z8NMvqwEYo2/y82qGvPkzfvr+CcTGOIvAKaU4djwfPm8AaWlJACH4eORsa8CzEPCG4fZ5unjjxWtxzerP1NgMfEHl1JsKcgcKfP7BHfB4JHRuX08N8c2fYckByh7lcn6HxlGKXt0bn/W2mJFfUIL9B7K1305dTShBcYkvpJcGx0NPjHOME3LsRAGOnTCqjyilWLAoA1nH8vDpR3dZJBKpyXFhecuUBSUlPrz3we9YunyXevgjIZBlBUlJsXh9yLVo366u7XOHj5zCN+MXY/E/mZo6qnWrmnjovu5o2cL+QLm33v8Np04VqTtxotpYkBAMV9NG6cjYeUyNWaNAk5QQBRbmMi+/BEuW70LPbuGPnZ27juGFVybD65dViYYEXSXJ6ukhBHeHsLPJOVXkqN4011MEAUBlAB5Anx+wlQgREMgKxey/NuPWG08v3PypXAcGygYVUxMw/vslWL5yFwIBBS2aVce2nVkqg8KlKGKbCEAJRXG+F0/c1+O06hUM2ScLsHdvNiKjPGjWpBqiLhamJ4jqPuznL3JcJG/63OLEiXws+icTBYWlqJqegh7dGiPWRtc+5ZdVmsGnGYpCUVBQivkLM3BNv9a25Sz4OwPfT1yGfftVYhkbE4kWLWogv6BUXdwig7sIiqhbO7zzZU4HkREe3H5TB0yYtIzpygGqUGafolkZIik+GmmVVFG0xyNhyOB+eGP4DPW+QpmIkqrxzSVYw28LqFWjAmpVP/vno5ixbMVuKEpoPpBSiiXLduKqK0JHM83IPILsMkQlVRSKLdsOY/GSTPh9Mpav3AW/T0aDBlVwTb/WqHwOzuuhlOLNd6ZpUh1F2NYXFJTilTd+xhef3oNGDY0i+0OHc/DkM9+juMhrsJfZvOUQnn1xEt5/+xZ07GB0QdyeeRSZO7NMFUBIg+z6daogY3uWpk4Ihc1bD50WkzL+hyXwczf0gCqHpx6iE+OAAhCCn35ZhRcH93PMRz2PycZeJoTdDcBsvmQSlEERsWT5ztNmUiqkxmuBlINVJiUxBkPfmQ6fP6CpGPcfyIaf9wdTVfBI0oD+Cs/WcVsnTxZi5BfzsGTpTm1dTUiIxu23dsJdt5+7k6j/LVzM6p6zBTfibBDIsoKRn8/D7Xd/idFjF2LSjyvwwYhZuPmOzzH3ry2GtD5fADt2HQvqdiVJBJu22IcR/+nnVXjn/RmG3XxJqR+r1u4FFHYUhp+CyEpwYytKUbtmRTRueG70vzf0bwuPcPgYAQyBhTyE4Pr+bQ2RSHt2bYx3X70elSsmAIQJsFlwNHUXbNMWShHhkTB0yPXnpB1mFBaVhiWoIoQ4i/IF7N59DENe/7nM9SEE+OCjWRj+0R/4Z8kOLFuxCz9MWo477vkSf87ZFDqD08S2jCNYvXafbdA4SimoQvHDj1Y7i8/HzEeRiUEBVCZHoRTD/zvLIFUEVENwA+1l7q5atF/biL8UR7Ny9XShQCmKisIPJFVY5MWylbt0wR5UAiAFqPYhVGXK587fqp9SbIOU5DgkcTWJoS1hVISQ8NsI4NSp8KUiHCnJcejcob52oKCd+z8oRWmRH15fwDAmZJlqrtGEvS8iZECghhQoLfLhVG4RzgR5+SUYNPgHLFu207CuFhZ68c24xRj5+dwzyt/FhQGXSQmC0WMX4Lff16mLNKUIsMW2pMSPYR/+gaXLd56Vco4fz8fYr/4GYMN7aAIKdfITEF28ak7MtkadLqlTJqPBcFAhNR5PPdobgHWXJ0kEtWtWxJ024vDuXRph6rePY9SwO9CrWxM9rLnIqCgUkBX1E1Dwzis3oH6dyme1/qWlfhw+cgqnThkX0OrVUh2eMIJSirQqwSUZGRlHMPDp75GXW8YAa1QV+/u8foBSKIwxVWQFiqzgwxGzsHGT85k5ZcHCRRlBQ9zLCsXS5TvhE4jziRP5WLl6j2M0XEqBU6eKsHL1HsN1cfcr7gR1RgU6s0IBMMLI1S6qUWsQRh0ACEHrljWd75tQUFDinB1lUYcD6sdfGsDRo6cc85JlBSVFPuM8Zf72IWclFQk+greRUiQnxwo/KbZuPYyxYxdi5Mi5+G3GOkdG7dH7uyM6KkJnFqnxb+tmNeH3B+w3XWzOUjEUgvi+GDM3c9bGUK0Nip9+XoXjJ/IdXe9nzNyA3XuOn1EZ5x1CXJoyfy5yuEyKA7JPFmD6jHWOY4AQ4Jtxi7VJnJNTCI8UXB2jKNR24fxzzqbgMmBqXNy49EJbHFgaUFUjFBmmsaCiUJw4kY9jx/Isu91guPHaS/Duf25Eg3o6AxEbE4mbrr0Eoz66y9FeQ5IIWreoicu7NTa2hzIpkVeB5KXweCk8fmD4+zMwbfraoCHhw0VubjE+HTkHN940EvfcMxY33zIKTz/9Pdau3QcA6NCuLiqkhj4rKD4uGpd2dj5hl1KK4R/NVHfaZ7CAEBOxMr//SaG8R0KAUoqDB05ix44sFBaWMmIWvL6KQg1nxRw+mhuyHEkiOHTYGI20Xds6zvMKOuMqBnUDgBZNqxnSBbNj8ngI+vQK/2DPlJQ4RETYLIcKVY1BZfU8KSgUkkwxaND3yHA4tsHnD0CWFebSy9oRoJrkwQmacbbZW8kObL6nV0rEiRP5KCgoxbPPTcJTT3+PqT+vwu8z12PkyLm45dZR+Ptv65EJ9epUxqgRA9BI9LihQGJiDAY/0QfRUZ6g70hbkyj0c324bRBbm+bM2+zY1nAwc9bGkMdBzJ5zZmWcb1zMwdzOFlybFAf8s9QoYjSDUjW41KHDp1CzRgV8O/4f9ayXIMxGYkIMeve0LpwHD51ik12xnMVAI0x2/UzpyxdztYpqPQlUQpKelhy0bZRS/D5jPaZMWYGsrDwAQMWKCbj55g645daOQXfUe/eewIzf1mLTpoOQJAk3X9UGPXo0QZMmVREdZoTPQwcFokUpU/1QmE8cLiz0YtSoecjMPIqXX+pfZulQbm4xBg6cgGPH8w2L3raMI3jp5cl47dXrcPnlzfDyc/0w5I2fg/IWzzzZJ6inQkbGERzk7RPDjJ9O1XkdqZFGiZve1Wv2aEH4Thd//bUF301YgsPM5ToiwoM69SuH5KkSEqIRLzCg4RgPKwq1hEGvXbMiOrSri7Xr90FhUhKKoOYoIITgistb4LuJy4xu8NaEAIC7b+98Wl5hsTFR6NWjKebNF877UZj0hGfNPhRAcaEXzzz9Pb744j40NKlWY6IjkZwUi7z8EvVZWWVQFNHF2KmdQrMoGLMqzgtRqgNgwZytmD93KxITY1HADkYUj27wlvrxzju/IbVCPFq3MroSN6qfhv+NvBe7957A4SOnEB8fjVbNayAy0oNly4JIiTlxZAyVmYGmAKAQHA2DiXVCICCHPOpBURQcO5Ff5jJcXBhwJSkOKCwoDcvVlO9CF/6doRJAjcAICygTy119ZUvExFgJeWxsJBRZ1k4ZFXfO4mmv2mJgs8vm9yIiPLi8p3MkTEopRo6cg5Ej52gMCqAaqH311UK89+5vjruX339fj4cf+hp//LEB+/ZlY8+e4/h9xjq8/NJkbNhwwLFMM9KqJOsLnEz1hRiwpVRz527BypXhxXWww7jx/+gMiiAmVWQFlAIj/vsnSkp86NS+Hj794E7Ur2dVMVWulIihr16PK3sHj+y6Zdsh7bu209RWbgFBGALtnTrQMwJVnL5s6Q5MmbwCv/6yGkd4jJcQ+OmnlRj2/u8agwKoBGH3rmNBd62SRHDt1W0MDGy9ulVQNT04Q+zxSOhqE+PmtZeuQT2uyhNev0rgKEhAgeRTP0Sm6N61EbZsOahuBELYZF1xeTPcN+CyoPWyQ2pCDFM3qvmbGRQtfg6rrxxQ8Phj4/DkE+M19aEsK1i+fBcq8+B6zKOHApokJhQ3aJacGeZ7QDHUC6xenEEx1Bec6aH45utFjuXVr1sZ3bs2Qrs2tTXGrmWLGiEZYMIYFHMqLmlRAmXf5ns8EmJjg296JElCyjnydPvXQM/C5yKHK0lxQPXqqSFVIIQQpKcl42ROoSreZZEhKfcKYBwGUSg8niDHkfGIkub8YRAsqKBON1Q88UgvJAQJ4rZx4wH8PmO97T1KgUWLtqNHz0z06GGMsZKRcRiffjIbgLo487ooikrs33zjZ/ww8UlUCuNU1g4d6qqbQibOp7zwIOcJ/TZjPToHUbM4oaTEhzlzNjO7DuOCStn/pSU+LFiQgf79W6N1y5r45osHcPxEATJ3HkXAL6Nq1RQ0bpgeliTn4AGjlEhlKIi9JEUBWrWqgc1bD2tSu4gIDyiVQWUlqKifyMCbb/wKSVKj8n7x+V/o0aMJXny5v63nGcAY0f8ttM9SppAAKBLRPT8YJImgerVU3GkK4y5JBA/d3x3vDv/dNk9CgJuuvwQpKVZCkpIchzEj78X4Cf9g0pQVWh9JsqpOEXk7EqDI3HQI6ZUTEeGRVNswAlDzCs0YiBuuueS0JUxZWbn4ZepqFm6FGqxHNAbFcEFH5vajeOjBr/HZqHsw9I1fsXfvCfW0cRYRV7MtoQD8bG3wiJwZRXx8DIqKvRoTKMsKoiM9SE2Nx7HjTFogK4bAbk71sWNUtmw5hJISn+PYMKP/Va3xw4/LoSgOgfZMDK0do0JPU00rBxQsW7YDW7ccBpEI2rSqhVWr9+g2KWxTw8tWiIya1VNVe71zZIN3ruF694SGy6Q44LJLGyIhIRqFhfaGZ5JEcGnnBkjldgxsxwSoaxOEXQQFQGXF8QyMxYsynONyyNQgItbEwapuR2VuZApCgJq1KqJCclxQNcDvM9YbAs3Ztev3GessTMovP7MAdQHZVirg98qYMP4fPP/C1Q4t0ZGcFIsr+7TA3Nmb9LYEWWQUhWLv3hMh87XDyZOFzADVek/j9STg4CHjiblVKieiSuXTjxCclpak520qR6yDJAHVqqXi0w/vQs6pQmzfkQVJImjRrDpuvHGkcwFMPcYhSj/++ScTBQWl+HDEHbaL9lxRf2/ezRMCyBTRER5UqZaCg4dUZisqKgJ9+7TAww/YBw3r3asZCgu9GD12PvwBGR6PBEVRDc2vv/YSPPZwL8emREZ6sH9/NiPk6nELElNVmAntyZOFmDdni7oZAHSVgw0qVghtW2TG7NmbtPIIVKZNg/gi7UCAvNxiPP3UdygsUGPiKLJ6mjNl7vWcGZcAQKZqPBRCecggDBzcC1WrpWDJ8p3w+WTUr1sZV1zeHPHx0di56ximTluNjev2ITvL/liGcEj0sWN5qBOmIXrFigl487XrMfTd6aAK1RgFj4domxTbMtm44sPr+/H/ICY2Cl27NUK1IMbpO3dm4T+vTEV2dgE8EWpkv4CsgER71HPFAorqAi5uNCjw5ai/cGBfNp57vt+FyaiI0veyPn+Rw2VSHBAVFYEXBl+Ft977DYAx9okkESTER+PxR9UFODU1HqnJ8cjNsXe541MnxiFybGGBg6skL5KXzYNXAapIWlzQKXD4YA7eHjoNV1zZAi8NudaWUdm3LzuohEhRqBanRcTatfvsGRQBs2dtxL33dUPlMIj7s09fiYXztiBAlZCSFAAhRb9OiIuL0upslzvvy/Wr9uKtw78gOTkOva9ogRYta5Rp0WvSuJrKrIqaQqoaXWoH1kkEycnxeP+tmyFJBJUqJuKyLnqfSQSQnciOzSF82i2FYt26fdi86SBatbaet3L4yCnmIaNoUU8BXaIECfB7Axg54i6UlPrh9fqRnpYccvd9/bVt0btXUyxYtB3HjuUhKTEGvXo0RZUQXlCAqlbl70eUoFjaJivIPVkEREqOFFmSCJo1q470MpwI7aQuI1SoUYgxavbmIoBGRMzt4id/c2b26KFTmPbTKhzYrzLLtWpXRHrFRHS+tCEaNkjDqy9eg8ce/daeSXHqNFNdNqzfHzaTAgBduzTEN18+iOkz1mHZCjWYW/Nm1dGvb0u8OuQnY7EKNcwzfv37CUtAKTB29Hz06t0ML7zc32K7lp1dgOefnYiSYh8AGJgg4leASAnEr1hsX3jTZ/2+AfXrV8ENN7YPu20uLhy4TEoQ9OjeBB/ERePbCf9ge+ZRAOpCeFnXRnjs4Z6oVjVFS1vsIHERsW2rvTdAMKhiUwBCbBJ1N23ccQL6rnre3C1o3qIGrr3uEkt+8Qmhxb32h8KF5tgVheL339biwYd7hkwbHR3JCCbsF1iqL3qSh+Dy0/DUEJGcHAfJIcAeBEKxZ/cx7Nl9DJIkYeaM9ejYuT7efPumsI2BOS65pA7S0pJw7ES+FldDYnY3vAYSgA6tajkaOKenp+Awd3ElpvdOg9Mjj0fCX39ttWVSEhNiQGVV+mbWXlBAZVwiVMYuNQxPJxEJCTG4rn+b03oGANLSkkE2H1JVnvwiNe0u2Q2JqhJJauOFQwiBJBE89oiz5CYYzFJObsuhIziDEgwW+xIKTRXI8eMPywzP7N1zAv95ZSquu6Ednn5WPZX8dDzw7OAPnP7ztWpWxNMDr8DTA6/QrokeXmrF9H4y95Ao6ft7YQb8fhlD37nZkOa36WtRUuyzj9GjUFCvzrLb2r8AGPP5Xxcmk2KSsJbp+YscruFsCHRoXxdjRt2LH79/HF+NuR+//vQU3nr9BgODAggLCFtgiV/RPyzOhVOoeic1kAYKo8GgYmVQRBAC/DJ1lS1h7tmzadC1VpIILu9tZQhat64d1oT4e6HV3dEJlkWXtY8EZNVwUlY/8MsoKfSWaZEuKfEF9dIyFa2VsWbVHnz639mnXZ4kEbzx+g2IiYyEBAopoGi7TG2HSYEF87bg/Xen2+bx5JO9jcG8BHePUGSSRza2Q5dLG0BxkFbwulVKTThtxuxMkBgfbWBIVfssfXybVTvET0F8ikVdVa1qCj764I4ynY0FAH2uaGHMk9dJNNAJOozsb1reFzOctZMK2GHG9LVYuXwXAKBx46rOZYQxxjt1qhcyTTgw2JqYjPiNCamuzlAoqKxgyaLt2LPbGNtkwfytQY22w7G7CAQU7Nl1LIzaly8QIKSLcdDP+W7AvwCXSQkT6WnJaFA/DclJsbb369arrA44ZvgHCIusokaudNqdtm5dC8GCU/GBDBl6sKsgoBQ4eDAHJSU+y72+fVuhQoUEW1WQJBHEx0fj2mutEpibbg4v9LZllxUEqSlqf2iLEKVWvTMAUOCnH5fjvx/8ERbDISI2NgpRUTauqIwYOk1yRaH4a+4WZJ+wtwEIhqZNq+HLL+9HalKcvpDYFLRkUSZ2MAmdiC5dGqJZM6Y24rtURjRDtV6SgHQHj5vDB3NsvTFE5Jws1Owq/g0sWpChq0Qo1eLDWIg4hWZcLskUUqkC4pVBfOrfG69pa3Cx5QEYw0Xz5jWQmBQrcKuMWdIkHzhziuDgDRMK37BAj7fcEmQOMumMU7mJiTGoVevsHJWxfPlOjZF0VD9yGz2qf3jcm9ksyNvG9fvx2otTkHUkL6xyQ/Xbd98tCa8BLi4ouExKCAQCMhbN34bXXpiMJx74GkNfmYoVS3daOP8bb2yvGtsJelkOLk5fvXQnAgGrtXyjRlUNhFpfbIyyQCPXHXoBtnOhjo+PxiefDtCM2DweSfMoqFgxAf/9+C7bE2xbtaqJOnWCL3KSRFCvfpWQ9eLod3Vr3fMYCGpvAQBzZ2/Cti2Hw84fUNvXu08LQyh/KMxLIIT4nlKK1avK5vocFxuJU/ykWScZNYAfv19q+/zHHw/A5b2aqo/zYFkAYqIjglZblimuvrq17b0jh3NVo8QgUBSK7OzTZ8zKivz8EtXQnNntGEa1MBeIaV4RMGYloEBSVMkXAPw9fyvuv/0LXHnZe7jysvdw142f4Q8HbzYRMpfasXLFDYZ6Daa5aaonEDS+EE9nO2tDjMMD+7ORfSIfS/7ejkp2RsGiekyso/DdQ89cXcSxZNF2tdpOEl3BiYDANAUo8PdfW/HHb+vwwlM/YM3K3c79KiAUg0KgMuEXHISQCGX+XORwbVKCoKiwFEOenYTtW49oUt9dmVlYuigTrdvVxvv/vVM7jTM1OUbfXdiAAMg9VYxl/+xA917GOCbNmrFImlRILIqYNSsx9eweSgEEITaSRNCocVXbmCwAUL16BYwb/yjWrNmD9ev3gyoULVrWRJcuDRwX2jUrd+PIvpO29zgUheK6G9o53g8EZCz/Zwcyth6GJElo1KwqKlZMQE5OoXq4X4gJ5/FI+POPDWje0v5kXScMuPtS/LM4E8XFXuZ1Ed7EJgSGMPCngw/em2F8n3agwLq1e21vRUVF4D+v34CHH+mJpUt3orTEh5o1K2LJggzM/zsDTh5Rd9zZGTVr2R/ImJAYE5ZbaDAX9rONihUTcPx4PiRFUEMpVDPspYzCafdsVC+UUmRuO4wnH/gKO3cYRf4njufj0w/+wIqlO/DOB7c71mPf3hPIz9clSLw8Qikon/w+CkRJMBjQ8vrIFHfd0wUTJ6q2JcZNDIVEJEZTQjPHZigBBQNuGgVKqcqLRBA1D6KeVE2ZRJDyYJL8wE5AUzXm5RZjZ+ZRNAmiDpNlBetW78X+vScQExuJzl0boZKNEXzGlkPGPrBUWP3j1MpTJ4swcsSfalJF9UykZT0okEu6FIrjh3Lxzn9+wVXXtEH7TvUuCG8f1wU5NFwmJQhGDJuJzAzV2NVMPzeu3Y93XvsF73ykLnyTJiwNye1LEsG2LYcsTMol7eoiPT0Zx7WAY8bnCAAiEVCvrC/eoscP37mwBV1RVGIVqi4dO9ZHx471Q9QaOJldgDdfmap69xCiGvGaF2pC0PeqlujSxT6WSWbGEbz58k84eaJAjSFBKRSFonrtimjYIB07th0BQhgnyrJSpiiWVaumYORnd+OD4TOxI0NQr4QgGJQC9RuknXZ5uaeKsH7tfvVHsEFBgJLi4Oqx9PQU3HxzB5SU+DBk8ERs23xIff8eCfDA8A5atqiBhx/p6ZhXj55N8NWXCxzvSxJBk6bVbAnTuUL/69piwreL1cizAGAOVAYAUM+sEo1ORRAA+TnFyA9yVtKKJTvx+7Q1uNbBuFIRpQKaPQoASdLqBQDwKmqcE87Ls7gdBMBtt3dCh071MOXHFVi+fBcUWUFSfDR8RX54vQFAchhuQQk+BZWpgd8lfjWSCyIIkpPj0KFzfcyfu0WvtwMTXup1ZrhXr9iN99+chsKCUm1J+UyajX7XtsGg567SgrxRSnHiWIHKKGkSHKIzzTbqOjMsTWVMhi4mMya4sm8LzJuzBRZQqo0XCqC4yIslizKxeEEGLuvZBK+9fSMiwjwixEX5RblW9wwbNgwdOnRAYmIiqlSpghtuuAGZmZn/StnHsnKxZOF2tvO0m/QUK5buxPZth+H1+sNSQ1BKbVUwkkTwxtCbEB0dYVRLsHu1alfEsGG3GiPS+lVRn+QNwOOV4fHL8PhkSF4ZndrXxWWncUR9KMyasR4Bv6yupSwaqBEETRqm44WXrrHdvRzPysOLT32PHGbfQWVF29Ef3n8S+3ccgxTGDl+SiG1gsHBQp25ljBn7ADp1rAeJMKlNkOifkkRQu04lNG9xelIbQPXMCMvqnqqeS6FQWurH809+h22bD4EbkUp+GaQ0AFLqBynxg5QGsG3dfuTnORPq9KopuPqaNkGPg7n5lg6YNmUlvh0zHzN+Xo38ME58PhNcf2M7VKueqjGuVtUB9L4UGBRiFnUThOzzH7+zV60BaoyhaC551FQ8jOjKgkssmJrJr6gfRQ2CxzcOzZvXwNvv3oLZc1/C44/2QuGpEvh8AX282XW+yOybo9I6MBwEAAlQ5J8sQs0aFULa3xBCULNmBdt7P3y7GK8+9yMKWRh6TVgVkPHn9HUY8Z4erM9wqCBgPI8oDAaFJzO/Ou3sHwFVq6Zg8HNX4aVXrkXdepWtr9fGq0hh72rpou347uvFIWpSDkDPwuciR7lmUhYtWoSBAwdixYoVmDdvHvx+P6688koUFZ3ZEeDhYMPa/YBmqWg37dRV8Ydx/yDgZ3YmIXSElAJt29exvde4SVV8+b8H0bF9PUTIKvMRQwgu79kUI0fdi/Vr9hlLpxSST7EMWEKBNUt24n+fzzvNFjtj3eq9+k6TslD2Pln/BBQcO5zrGEBu2tRVKC3yWWNFsL8+b0DtNptdsghFoeh9RYszaktiYox+2nJAsS+TUsTGRuG1N24ok8jYcMBjiEWkTp3QNjy//bwaO3dkCcRa/SJBdcuV2HeqULw48Dvk5TozFk8/2xf9r20LwlQFXL0XHx+Nyy5tgGH/+QVffjoXP09cji/++yfu6P8xfjK5x55NxMVF464BXZCUEG0IUseh24YIHcmJITGNphCvKpgRdGxsFPpzBk7wxlIlJeEZu37/rUoUTxzPx8RvF+ObMQsN9VXFnDZrBKVAQJ1Lkl/9EJ8M4g0Y3bIpVcWkimJYa3ZlHkV61RTH+efxEFx6WUNUtIkGvWxxJiZ8tUiX5ohlsHIXzN6Et17+CbKsIGPrYU0Npq07MlXnEq9qyJ6yplDzhHogY4AiLjIC3096AtdedwkIIfhk1D2oJNrKURrUu4VSdd6cjiH/+QCh9Iw/FzvKtbpn9myjC+j48eNRpUoVrF27Ft27dz+nZSsKP3AjuLx+26ZDiIuPRuUqSThxPD9IeoqU1Hi066C7AcqyAm+pHzGxUVAUBd+OXoBVizIheSQQCsjeABbO2oRTx/NRoUqyKTuqMSWWRU8i+OWHFbjp1k6oHOJslXCgnXmj6KJVTV8PABKgBJksc//YCCDIQs8XSAWqCsNG/C1JBI2bVEMnB3VSOFg4dwsWzduihpwHI+x+2ag6oRTRkR58+c1DqGpyMw8XjZpURWJCDAoKS2HoMBGsu4b857qQ+U2dtEJlqjgR4YuzKU8CYN/u43j2sXEY9e3DhsMAOSIiPBj8fD8MuKcr/lm8HcVFPlSvkYoDu09g4rf6zjPA1BuBgIKvP/8LcXFRuOamsxuHYs2KXXjvP7+giMUYIszOwhGMMBOTmrGs8UvMeOCRnti65RB2bD4Mqqj9TZTwy1i9Yg/iYv/GxHH/MPotPMMOBaVaoD+BKWCxa8wMvDajeLvN1xj2783Gq69fjxcGT0QgIBvsYSQPQXJKPAY+3ddSX0opvh49X6ufuRxN+wJVMvHwHV/gtnu7sYeJ4T4UCvhk1R4myDlcanriaJvD1W0NGqYZmNCEhBh89+MT+POPjfhp8gqcPF4AxY6rFVBc7MOuzKNoYRMzyMWFg3ItSTEjLy8PAFChgr3YEgC8Xi/y8/MNn7KgWZhifp9fBiEEN9zSgct81RuGv+qUf3/EHerR9ftPYsRb03Fdt/dxY8/huKX3B3j24W+xlB2pzkWWfLHZtG4/tm48AG2J4oRK2JVpO05AExl/E8T+AACKCr2YN3MDfvpuKRb8uQmlNi7LAFC3fmXDTtZQFtR6NGjobLtRXOgNvrsyRNIVrgu7xc6XNsSwEXeE9qCwAaUUnw6biWGv/wo5oBjqr4rvFUh85+qT4S/2oUIZQqtzREVH4JY7O7HCxYoYf191dSvUsTnMUEQgICOPHV6nMyhBtpAADh/IwR/T1gbNt3KVJNx0S0fcfd9l6NS5AX6etDxo+u++WmTrmVZWzJq+Dq8OnqQxKBrsmF2TakCXKjins0OlysEj4MbGRuGFl/ur5YSQ6tmhqKAEP3yzWA1AJh5mKYCrNiBDixptd26XBmqMCUNgmn+UIuCX0axFDXw+9gF079lEmyPRMZG49rpLMOarB1Elzdr2wwdzcHD/SQuDAkA/DFHWP4f352DalBWsfzhzRQ0HDRIupWR1E8ElPXXrVjKulYb2qte8xda1KDo6Ejfc1B6TfhqEJwb2CYs3lcNQI59XKGfhc5GjXEtSRCiKgsGDB6Nr165o0cJZ5D9s2DC89dZbZ1xe7br8hFZ7jp/fI6BYvigTyxZsUyczMd7nO6iXX78eDRtXxc6MI3jxsQnw+QKaS2BhoReZ263xMjgUhSLr0Ck96qwg5rNTxfNrq1kQKGu1KX7+YTm+G7sQPm8AkodAkVUVx6ODr8TVN7UzpF0yf5sjTeTSFF+JD6WlPsTEWAPTRUZFQDYzQJQtfrxNAqNCZQACIa5SOQFvv3+rQw1C49cfV2LWtHX2GgEutoYonSBYsjADva9qVeYy77ynK45l5eHP3zeIfCoAdWfw8BOX47a7uoTM5/ixPIHhZd3ECajDuKSUYtb0dbjt7kvDquualbvhDSEWzz1VhG2bDqLVJXXCyjMYDuzLxsjhM60SpmBzTQQhVvWPeIyyQxZ33R/8ZOQD+7Lx7MPjLGNC3fXzXw6gFAVmeyBDIDhj2wiva5BYPSIdd1Y4AxFsDtWrXwVPD74K3bo2RkmJF60vqYOqNZw3dIcP5Vg3VYBR3cWbB5Vx27vruD5XWf3NW111DiuqVIX3A4DmLWvgznu7Yuync9W5L3ohiVAoTp0sdKw3ADRrWSMkDxkZ6SmT4fu/iTNV2bjqnnKEgQMHYsuWLViyJHjAnldeeQXPPfec9js/Px81a9Y87fJKS/1GImoHQuAv8WPoC5MhSUSNLOshqvqAqKfJXtajMW4b0AVNmlUHpRTvv/YLvF6/LpINtQByNYtEQKlAZYVFxCwm5n9LHEL1T5+8El9/ptusKMwAraTEh5HDZiIqJgJ9WKyN9av3Iv9UMSgx2QDwhZwZDmasP4Dru72PWnUq4bLLm6JKegraX9oAldOSUbtuJWzfdsTYQrYDgwJLmHN9lwgQiaB/ELdmSinyThWBUiClQrzFhkQOKPjpu6U26hZqOG/EAAlB7TrCgSQRPPdyf/S/vi3mzNyIw4dyEBHpwaWXNcSl3RojJcyw894SlXnQCRanWoLKg78LwsYHITh5GrFOLNIMO1CKNct3ISe7EA0ap6NG7bIHBvt96mqrqRdvg9lzzFQHlVgK741SNYKdIR0M8wQE6Nq9Mfpf1zZovT4b/ocWAJFYGCi7i0b4ubs6l/IQqqmnzhUpIQCKCkpRWurD2P/Owdzf1yPAz74hwKW9muKZV69Bcop1vB0/mqczYbzLHeaEtq5QqJ5FRAICsuPaxVVA3C+p/43tMJhJqSYlxUIiTFLMn+cDnF1KSDQGzcw6cgr/zN+GooJSVKtZAd37NEeTZtWwM/MoZBvjYkkiuPLqVkiwORSzXMEkXS3T8xc5LggmZdCgQZg5cyYWL16MGjWCq2Gio6MRHW3VxZcFqg6ZOrrcIqB6vEBR1DgfgOoSF1C0sXPFlS202AQb1+7DETHgUDhcMF80WDkQjOOCLZkUMJ7kylBS7MXXo/4KWuS3o+ajV9+W8HgkzGGBsIwMhmKNhcAWmAN7TmDSHvXEYiIR9O7XCrcO6IJ3X/tFqJxIZKC7HwpiZ75wVaqUhGtvsjIplFLM+W0dpn63FIfYoWzp1VJw092X4tpbO2heVLt3ZuFUTpFVR2WzW+TVIQpFnsNhkaeLxk2qoVHjqpg/axOmTliCzxZk4DMA6dVTcdOALoa62qFKerK+MwXRXzo1httXLzOS4CGQFAUDB3yJmNhIXNqzKa68rq0aUdUG1R28PgDozJxCMeXbf7TLrdrVwfNDb0B6kJNtnbBy6U7j4GX5q+1QAI8pFglDZGwkAiUmiY8oVTLPUYb7H+6Ju+6/LKgR9OGDOdi0bj8AgJiYEY2QBzHObd+5PtYKkksJ6vyjmshEwOnY0Ig2OE5JAAx9bjI2rtlrsEehFFi+aDsO7DmBUd89gtg447qoeTPxMqhpTojqKqFfCZFAA/w8neB143fFKMi9+7bE1k0HdWmS5qDAiyLo068lAFXdOWrYH5g9Yx0kQkAk9RTm0R/9iXuf6IXsEwU4ebJQ8xbkzWjYpCoee0o/b8jFhYtybZNCKcWgQYMwbdo0LFiwAHXr1v3Xyo6OVvk3QmH0AuEEVlaYuJPrX9mH6Ql5kJ7P3p+p5bl35zGj0R8HNf3Wrusn56o7HWbIF+yodAYCWNyZKaUY8uR3ujeSA05mF2DbpoMAVPdhc504QTEsZjagCsWCPzdh9q9r0LZDXUOFiaAKI7LqTUB8ARBfgHk4qN87tK+jeuSY2jH6o1n45J0ZOHxADzCXdTQXoz+chf8OnQ7ukukX28qrqdiE3uf1Yn+XLsiAoijw+wM43VD8Znwzah4+euNX7N+jn1mSdeQURn84Cx++/qtqpO2A+IQY1G1YxRjCX3NTdZCmyRTeYh92bT+KLesP4KtP5+DBGz/Dnp1ZtmW0bFML1Wqkgpi9Q0S7A9MzWzYcwOD7v0ZOGaLT6udcQWeCWBskPr61d0UBv4wYiWDIa9ciPsFIaDXbEcEbRZujFOjVpzkGPNAtJKE/JJ78rbDKUWpUt9jYmERESLj34e54+oV++kXKBS9Us+0gFIAvoHvmhAM+/4OlpxTxcVFYv2qP7fk3ikxxaH82Zv+23nKvCQsiqdZREaSbivZbX9c4s6r+Pl2bHVHy1vuqlqhaLcXWvszjIahQMR792OGonw//A7N/WwvIFIpfgeyVAZmipMCLsR/NxqNP9ML9j/RAtRqpSEiMQf2GaXjmpX74+It7ERvqTLTyAG28nsHnIke5ZlIGDhyIH374AZMmTUJiYiKysrKQlZWFkhLnWBBnC4f2n9QGAIE6kYnMPvwQNMFWwCK2Y79zThRg64YDAFTDLzuCp+0ozPdk1QCP8MMFmdtsOFEGKYCUCsbw9hmbD2H75kOhHwY0/Xppsc8Y30E2MWwhoCgUa5ftRt3alZAUH23bTkLZQXzUevDa7GlrMenrRYb0m9ftx4wpq9R2mvscwF9/bMTKf3YAAGrVqaSGguebY24LE2LhP7jnBG649D1c0/Ed3NrrA3z72TzdgJWhtMSHJfO34c9f12L9yt22zEbG5oOYOmGpta5s0V/4xyYMuutLTJ+0QotTofedgjVLdyL/eCGIad/q5H6paTlEHpgChfkleHXg9/DZHHJJCMHz/7kOHo9kdGPlxMgGiqwgL7cIv04MbnBrh5Zta+tMg81p3hKlKpNa4leNmQMKfEU+vPfiVFX0GzAZk4rxg/iHUnS5rCFeeuP6sOpkIGgak0OYDZA69ySfDMkbAPH6AVkGZAWTpj+Nex7sjvRqKYiNiXSQRkB1MQYgBTgz7gfx+QG/HHwuUQSXvBCCgDfg6H7Ms/jTxpC6Vt3KaNW2dtD1y/zRmBgOjZlxqj9FdHQEOnVtqF2KjYvCiDH3oUGjdACqaobHC6pVpzJGjL4PScmxyDpyCn9OX6saGdtlT4GPXp+G2jUrYsJPAzFtzgsYM/4RXHNDO0RFXxBKgjM7XDDI/LyYUK7f5JgxYwAAPXv2NFwfN24c7r///nNa9tHDp5h6xTFMpMBcBMmIAmP/+yc++/4xdOjaULUtsbM4F9VK3P1ZEQahsDtU9fAkqLqHAOhvUpP8fBoHcKVVSwEA+Hx+Xe3F9AqGMs2Lk2WxUlNP+2G5LlKWTGJzgRm0w/f/+xtXXtcWldOSAQAzf14Nj0dyPItEkgh+/2kVOndvjMSkWCQnxSLnZCGgEH136qgno5oqy8sidBbklWDqhKVYOHszPp3wCCpUSsCv3y/D92MWGjyiKqUlYfCb16P9pfqC/NuPK61lCHp/AmDP9iyMzZyFiV8uxHtj7kWj5tVRUuzFm09PxKY1+0A9RJeKE6IaK1ObYWkWzYtFMmPExX9tQ5/+rS33W7apjU/G3o/xXy7EWnYOjirtguP4VmSKP6evxcPPXClUgWLd8t34/aeV2LvjGGLjotDtyhbof0t7jWm+5qZ2WDB7s5axpbbsHdi9ovxTxSAKVMUWt2Xi7utscBJCULdBGt4cflvY3mDNWtVEckqcbovE+1IBiF9mthjQ6ktkGUkV45HK2rRt40GU5JWqYfMB49w0jW9RA0QA0ICitsXm/WlzjxuZmlXOlEKRlaCnCIM6x4hp37EeNq3ao5ZkVsM6gRvL8vrKVK+/2W4NQIOGaVrEWo7KVZLw2TcPYvvWw9iwdh9AgRZtaqFF65oaA7tkQYa994rAJMk+BW8/PxlDP7kTXXo2CVJpFxcqyjWTcqZi9jNBbFwkWyA4oyLcpGBGYeFxsnt3qWL+ymlJ6NOvFf76c5NuLyKoPahfNp4rI+lKFX3BY3+YUa8TvY2Lj8bVNxqZlK0bD+oGfYA980UpPB4J9dkux8dtANhuSVtgRRsS3g6TCoKClUXUdlD+PDMw1p4LAQJg3swNuOuhHgCAPTuygh6WpigUe9mx7Yf3n0TOsXx9EeUMgt3OU2BQ7PLMPl6AL4b/gYZNq2LC5/MtabKP5eP1gT9g+P/uR+sOqmpy1dId1jIEAiVeLiosxWtPfIfxfzyLz96dgS3MRkLtU7UfKWdQ7OoeQqVBJIL1K3fbMikA0LhZdQz77G6cOlmI3NxifPvZPKxassM2LUdhfinkgAxPhAeUUrz/0k/4Z95WQ5p9uxdg2vfLMHTkAKxctB2zflkDEpBVhus0zDMAGGyhqMzsVwBVXcjGRFR0BG6+reNpicIjIjy4++Hu+GLEbD1/xXjOk7mqBSeLsHJxJjp1b4y//tioPuOV1TaxgH6coQymm1Xbwl6q6dwdynbLlMIq92Zj2VvqD/7+mTTDDEVRMP7zvwTVDQUzogkKwuumaFNbZbQ8krWdsoKc4/YMEiEETVvUQFOHcA+H92ebpDu6xMYwdxQFI9+dgU7dGwW17yqXOFOVjavu+f+L+o2qQrOTM+tn+URxsCOBojBxsPoJlPpVtQmAp1+5Bp27NVLTavp3Cshq5FbNJc28IzFZ3fM4BVoS6LejoiIw4n/3I9kUQr6Ye3GInkXmugOQfTKKCtXD1pKS1DyIQAT0AvXdHGQhAqRBX0rsd2WhVC4CCCE4djhX+x1nE6TMDC6+37/nODNo5jp3VvkyLA6KrGDZwgx8N9o5/gylFF8wOyRKqeo5I5ZjNjgW81coCgpKMG3iciyeu8XoAcbA41JYOjWMplCFhnUSbmrFBNStXwUVKllPwzYjLj4aHkaQv/lkroVB4XUrzC/Biw99g6nj/kFRfglIQAYJOKg6wnktVD1sE14/4PWD+GXwSML+Yh/++59fcEPnt/H2sxOxZe2+sDY8LVrVQCR/MWy8G2yvTCAS0YyJT54oYFI6Ni9lHhUWQfNQM2K2WRS6SlmcT/y+AkNsFQ8h6N67KQpyS0IyqKU2cUf+/HmNGlyOwriAcIhrmdmWhlJDkap0SVHVVwGZ/VVV007xl0LB7zPZzlFq35eU4tTxfMz6JXhsoPII7YTzM/icDs7nUTNlhcukOCAuPhodu6rMBNdxE0VRP6JEAcaJK05mTaIgKxh891gU5BUjKjoCDw3sjR69m+pMhqw4ntGhi1Sto5Eb9VJZEQzbKHpe0UyThIjgJzZrTI8dWD02rNoLAKjbME1tEyPyejqjoaKTYWHQaJ1hnkYMAEkCw9WtT/OghpCSRNDjSjWWDvdg0BZRzuzZBZwKk9DTEIT+wN4TOHLgJEq4PY8JofaqS/7aauWfOFPI629uP78fwtYmKjJ84WlcXGhmMJ2pBf2+AH79YZlz+ZT1m6DS4Dtya1qHwvjcEkLD8yMBtDkqJPf7ZCybn4EXHvgao4fNDMqoBPwBvPLYBMilfhDmShzKOJQqFNs2HEBRYSlq1alknAsBTthZg0K8F0cQgmatVEmDxyNBIgQRTHrUtmM9PPhkb90mRJRqBmTApzJw8MsoKSy1ZP3dmAX6c2JdAUMfa3laGBXBAJpXF9CYK75GVnc4lTsUqvEYL3ZlC+BrwQ+jrdJNF0acz6Nmyopyre453xj40tXYumG/YxwJblMHYfcFGIkQ/35g73F8MnQa/KUBrF6yAzymhRoiGpbn1PwESQUgTFKjztog3SFAgybVbOtbsVKCapzJs5Httk8quCFe+0sbYN70tSxvFs5afN6j2gNQE4EIBZ2Bo1YbFRNkWUEv5pIIAFfdcAmmfrcUBXnFFl28JBHExkWj/80dAAAt2tZGXHw0iou8WrlUk/Qwl27tCARYY27YVj50Sw/uy0aVqsl6vnbSMQNh0Rpg731lJpjmocBVVU42GOzZE0dyQ9adY3fGEQNTYZffySw1ovPCPzexuBcOajTzX55Oe0a4ZmcHY+4viCrQ0Nzl75NXom6jdFx9SwfD9VPZhZg6bjF+n7JS27kTQkBZGIGQZzdRikWzNsHDY6KIdeOhCAjR7WccwI27uaSLF9uybS28N+puHDuahzm/r0fWkVwkJceiV9+WaNGmFk5yVQrzUIccUCUtWv3YePfJePe5SXh1xB2aSiT3VLHxXWiLmXOAMWpgZKAfHOUEAvQNEZ/GCfmninQGBcGZewIgN7sQy+ZvRaceTTTpXrnHv6zuOZ9HzZQVriQlCNKrp2LU94+hvem8mEpVkvDcmzegQeOq+sUgokjIFIpPwbJ527D6H13HL3psmJ8zq01UWw7hIEMHREZFONoctOlYz+ipEwRtO6lnDLW4pBYzP2HPyHp9NNG2QbJkgsMkNNBYhxDiYOV2v6I56jXUJUOJSbH46H/3oxIL9e2JkNRFHkByajw++PI+VKysHqYWHROJW+/vaqkToWo8G+JTxdL6gYPBJ73ZrdsJMbFRyDvFjDANgheq2jkpinZ4HXfr5BKe7KO5pvrCmI8oUaGmdHaqPP5dofD5wj9wze+XhbrZ9IsCyCxU/u7tR4Jn5rQLBtS+Fz1E7HVhtsy/o6TOzNQQ4Odx/xikKSeycjHo9s8xfeJylUER8jJLZTRi6Zf1T0D18Pnsrd8w5avF1vSyUAdbyZ0utXjnkztxxTWtERsXBUkiqF6rIp54vh/e+/weREVHomadSnj4qSvwn2G34ukh12geUhUqJyAi0qNLTwLCuDC4CwNL5mzF3b0/1FS5hpOkuaqKjU3HWaCtQWK/mBht8btC0aVH2U5kzxajLTuBUjZ/1Xq8/fQkDLj8Q/w5dXWZyvzXQc/C5wwQzlEz5xuuJCUEqteqiPc+vwfHj+bi8MEcxMZFoWHTavB4JBTmFmN35lH1cD3z4mPY4RPDHwvsJqJp90mhLjbULxt3y6a4Ky++cyPibaIslhR5sWDGOtPC7WxoJ7Ey9u86brhFAFXlAZWBIDbNNKSl1FY6YSY4+sFreluIRHDldW0waMg1ludr16uC8dOfwYrFO7BxzV5QULRoUxuX9mqCSJNK444HuyE3pwi/TVqhETsqeI+Iu0bKPRcc+qZt5/pYu3SntbFaBioT2Kh5NeTlCDtV7n2ixdcROs70Dr2FXpCoCBCJQBEOddTSKArg8eixLTgBjPToZ8YQ4SlWHgFQ30HKBgDHDp/CX9PXYf+uYzhy4CROZBdqdpzaDprofySJoH4TlVHfk3k0pNTFCZxRoQFWBoU6xvm4EcZsOJIN0a2ZAurEkQiOHMzBzq2H4S31Iz4hBuM+m4vck0XqWVlOjA4hRps0w32n79Rqe6RQUC65o+ydcXd+AqxYkIFnX78efa9ti68/nIU924/im+EzMfvHFbj/2b7o1LMJcnMKsXnVXvj9Mhq1qI4adStDkiT0u6kdfp+8Un3HvNEOXZ5zvADP3T0WY6c/o18UVdSGSMYwjVM+V2WAe+tw6Ysm9RU6RFbvR0VF2lcG6phbtWg7vKUB1GuSjjad62uSnsioCO3YDYPUTHjHrNKGPHNPFmLk0OkoLvLi5hBHIVwsMJ9RF05Q03CPmjnfcJmUMFGlagqqmE7FveK6tvhu9AL4vAF2arLNxAZ0UQmfbTxdEGIoio5VBoVqbpk0wBY7j2kRlxUc3HUcuALwef3Yve0IZFlBnUbp+P3HFSjMZ3ppWVaJnEhU+ILMiGDALyMqKkJdMMTzOVhAOSoxbx2zS6QJhFLNQ0riRFeSVM8JUxdRwY20aauaeO3D2zS3Yzt4Ijzo1K0RatetBEkiSKtRwdblVJIkPPnS1diyajf2ZGQZ5Yd8ceb9HJBBIzxqGpnZHxECBUC/Wzvggad6487eH+knQ5tVOISgY7dGiI2LRmRkBOITYtSdK3MdthA7QZ0n9gf1BUAjPew4BIHgsnFBAwHVaE6sP38fVL9uxo13W88LopRi0ugF+OELo06fAgCzYyKGiyoUheK6OzsDAE6dKFTLDNe7wqzykRXDa1EUGfBQdYzLNuovp3wUY1/y6cZVm8/cNlpvhsf67ixQmB7FzIRw2D5jX1VCKeBXLHWkFJg5cQUO783G+qU7Dc/s25GFoU9MQFKFeBTllxoMn1t3qofn378FT7x0NVYtysTxQzlh7a737zyObev3G+pgaRszMhcZBFHQRX0y4CG6CticExuDVaunIsYmqFppiQ8j35iGv2dt1CS1ikKRVj0FQ0bciSata6Jjt0ZYyLymNDDnApX5DM6wjh85F31vaocEh0jL5QFn6+we89Evb775JoYOHRr02XCPmjnfcJmUM0BSShxefv9mvDPoB3WwSLpkQRt2ZhN4bZdFjbEPggxUkb8BqKomCjBxs2l9+HPqKkBRMP27pRpTEhnlAYmIYARO3Q7xo+gNZcuqt0VicqzmHdO4ZQ09tovm7sm8ZLS4LjSowphQCirL6NqnJfJOFSMuPhrdrmgOiQDffDpX9YxgzYhLiMadD/fAzfdeGtSd0O8L4Kev/saMH5Yjn8W2qJSejJsf6Ibr7u5ieTb3ZCH2bGOHOCrEdnHWrvkDhn4HVJ5lye8b0OOK5njshasw5oNZNg0lSEiMxlOvXQsAiIj04Lo7O2HK14tN0jabRZ3VQcsKjFHxSIhNioXPF0CFSgmIiPDg6P5sjUExL+CIcIrrA1x5wyW2B87NmrLKwqBodQjIqjutQMT5eLjqxkvQ9fKmatVFVZSZ6bVVFSkAkdQHFcDMJEiAeuxEQGY2SyS4JFKQXpqTGedPENgxKmaGL5gUkmr/Wa8R4lhH/t3MoIjIzykyzlVKsXH5bjx2zacYNv4hdO7RCDMmrgjJoHBM+Xox2nauj/XLd1nfj+YF59xnhKWDh2iEUlSlcQ/FB562hqanlOL9Zydh7ZKdmuCMP3viaB6GPPA1Rv08EJdd0RyfvT0DJcXMJtBOHRuEUQn4ZSyavRn9b+vomOa8w2l+nM7zAA4ePIikpCTtcigpyukcNXO+4TIpZ4ADu45hxItT9N0tl5A4SkfUP9rOhMd5EAepEKIagCpW5Z4QfKm1Ec/ySX7yaJ5KcATbBF+pAkRQw2pNhMXOjJQU/aC+uPhoVKySiOyj+cxjgeoicEL000yD7aDZDv+1D2+ziOsv798a+XnFOLz/JBISY1C1ZgUc2HkMG5fvRuWqKahRr7IlOzkg452nfsCaf3YYFsbsrDyMHTYT+3cdw9Nv3WgoaxuL+ksUBRTE0O/mnYwdAQGA4iIvXn90HCTOJHKJC2tj09Y1MeSD21FZOKfkjoe6Y9OqPdi26ZDOVHKKyYgOYc/b9Rn8AQQKSzBj3dvwRHiwY8shPH37aOvui8fa4WNKYBQ8HgnXD+iMhwb3tfalrODHLxdqZZrHLlEoUzESrZx6DdNw492Xovc1rbU+JkRYbM2MNzfqNIiKACiyPic0aYiiXdPUCyC2dTP2V3AmJCSDItY72P2gTIpNJajwJRjhD0aoRGmlQUpLUVJYisG3fAFPTIQ+rsKgebk5hXj5w9vw4DWf6KoTHjvIJEFxBIXK0Hs8xjWFoWnL6uhxVUvLY9s3HsTqxfbxdxSFwu+X8dPXi/H8+7fg5eG3YujTP6hq2tNgUABA8kg4cTQvVCvOLygcJW9hPw8gKSnJwKQ4JqcUTz31FKZNm4a///77Xz1qpqxwDWfLiDWLM/HEdZ+ipIjFwZAVJqUIwqAQqJ4w7BLhk5zrpnmcAR4jgV/jzA/7zY36RCJMCIEW0ZW7C5tjuyhCXQDrDpH9NqtM2nRqYMyXUsMZQsEMX1nl4JGIoz1BUnIcmraqiaP7T+LJaz/FwOtH4tUHvsYjV43AM7eMQsaG/Yb0i2ZtwurFmXByKZ09dTW2rNlnuJadlQdidrNEmMSLgcoKZJ8Mv9evx+go1T/bV+1BaZHR1TMmNgrDv3oAjzzXF8kVEgSmBFp/UZG4K4puSMrGRaA0gGuavYp3nvwOG5bvZvYNArEU1HQkoKhnxPC4IQEF381+Ho8+389WFbZn+1GcPJ4flEhyI2P4ZaRVSsAXU55En2vbaO9zzZIdOMQOldQ7K8SYYONbG7faM0IC/sfOHsSSHw2HNlvraIZNPBDtuqyodQ02x4O5VNucgXRa9QwSVTbgZbHjw+yEtOqpqFazIkb+8LgqdRTqaJHQ8Vgpdow0hR4Xha85bB3bvnY/dmyxHsOxcOaGoJGAFVnB339shCwr6NyzCW554LIyEXJFpkgNI9bP/yecz6NmygqXSSkDCvKK8e7TP0Dh1vtOi4fZfVhLx54LqBFmiV8G4QGpIBBOou5kteBQfAENtlAqpgWW6zzNZYvieei3oKjh3Wf/tBLP3/4FHuw9HNvX7tUWLiKUQYigNgkIq4hZSiPLiATw+ZvT8M6T32H029Oxa6tx8Vo6dwuGPjYeh/YYDXV3bjmMlwaMxbZ1+7RrMyevsB6GJ8DjkTBrijEcfa0GVdTqmOt2uqJW4TkCXafM++WLt3+zPBIVHYmb7+2Kd8fcq15QKPRwnTB5uDjXbdlfWzFp9F+sXHZRCxxoZCB5sDRCqaYOs4O3xBdcOsDrIcsggQByjuZi7s+r1SinAI7sz8bQxybo9TYzJ5RxyWZGgwqMFofZ2JyarivQ2mipn5mwhoCZUQyakHm8sMMJdMmXXT24SsLSttOonKUOJHQ9Kd8FaReCJr/z0Z4AgJTUONRtUEXPw8ygybJ1XAr3eamEqsbARDiQ0uOR8MePKyxlF+QVO24wOAJ+GT42xh4a3BfVaxtjrTi+AzGNRGwlOeUJ2vpxBp/TwZgxY5CXl4eePXuiatWq2mfKlCnnqIVnDlfdUwb8NW2dNoEsBnXiQi8exBUIGJkLs6uoSLA4+DWFGpM5iaVlG0ZBuycDxKPWlxN4LblxYTqYeRQjF2ZoRnFECM+v1w3CIkHUPAOM8HJbFUoBvwyiKPD6ZMyeshKyosAjSfj9+2XofeMlePb9WwEAnw+dBmqzE6QKhQwFY96ZgVHTngYAHNpzwv78I62pCg6amJ2W7esaPAUooEokQB0lPBbwNgVhEjezc2/sUK9pNYHgUqghI4k+TsSXbMeoUApvkZeVbRpHdlBUrjNY8LkadSsLxo92eSiGcRXwBfDJK1Px3adzMPy7RzHzR/WdqnZHAHh4CtEehXe4iZAa+l0kOGaVE2HSJs0oGNAGCqVAIKAybRGekCoAA3iTCasLASRCIMsKatSuiGo1K2DbugMoPFWk1jqYBxAgvFvVUNxunppZCUM+wcoIxUtRyuYE60eP5Fham871UadhGr77dA5+HD1fVRV6PHr0WV6mqHoDn/LUMD6D9bYsK9i+8aDlelr1Cnwhc3w2ISlWM7glhKBB02o4si/bmEisi02/3f5wd6RULOeSlHAY5VDPn07yMynrPMFlUsqA7RsPGHflItiE4TRcXWDUyU7EAwtFghdKOgKBsDqtcnYiavPzlLLFk2VkMRKkiI3y4OSxfPZTzUc7XNDMSPGdu7Yec3WTUL6w0HHPBP53/vR1qJSegmaX1EFudqFj26lCsWvrYezfmYXaDdMRnxijndJsB0KI1aLfvKiKdj/m2CdOTKDT+1YL1Yiot9SvRbo1pyUEmtifytwlFSHfn0Hkz4PfhRHiHgpFjXqVHG+nVExAnUZp2Ls9C5YxYWJQRJzKLsQr930FypksChAP0YPzGdyAeP1Z3oQaCbmIYNIc0dhbqKPhEM5geZjApV9UVlC1TiUEAjKqVE1BQlwU1v2zA4e3H1WN4UU1Lh9HhFjpA5fmUOhnfon14vMJJgZNbKPddUKgWyYHaaesxnohiqLO2QiPMODVZ5pfUgdvj74HMycux4+f/8Vuqf3K26RLKcz141VRgjIHIiJtzgy68qZ2mPK/v+3bzxj25m1rwu+TtdOM7xt8BRb/ucmQ3MA0CYiOicQdj/bAHUxa5OLChqvuKQPycorsRY2mxUib7NzoS7AdsDxjB5PYXLWpcIjpIO6iHECYPYvTLj0iQkJpkVeNGyGWzxdXLv7VxPfsozhII0LtACnw2/gl1h2SA/j5Pb2ubRv8aHpK0bN/G8O1/ezAQS22CBUC0pkZhHCInEK1YF5qIDj9fJO8HCPDlXuyEGPf+Q23tHndoK4gUN8JNUtSxLqItkWAsGsXt73BcWD3iaD3u1zezH5MmMepYNStBGRkZ+WhuLCUU12NSBOF6meLyFwsDcTGRLB8YLVjUoIwXDxfrR6KroIRmSgx7L5p7Nq+UkUB/AGQgIxje44je382ti7fhZXzt8Hv9et1NdVFZNad1A6aPRlXl8hqWQjIVkNpPk+cXqd2nzrahqhl6muL6u4c0MpEQFHVyv4APB4Jk0b9ZVSd+QNGmxlb5pTqjJgWciH4BG9rCoSZl1MEf6kfN9xzqbH+vK5sPq2ctxUDLn0bqxZmAACq1aqEpFTjWWSAdY19cdgtmLz4Fdz5WK/wJaTnE+I4LevnIofLpJQBp47nG1c9vnj4hQWEUlWMLMYDCbZbtmV6YLNzDDY4Q0xKQtRFUhF0mcKiX6VKovGacEgijxcCwHRuD5wnTBgTqLTEh9wcZymKiOTUeADANXd0QnxiDCSb6K+SR0JajVT0NEXdnfDxHL0+AVPcDVFlonBGDM7vixNJMxgxEkNy55zIx+AbR2LGd0vVg9ZsnrMldJwp0K6biC/0S6FQUmR/rANHlz7N9PqL5WuEURHqoteNENUwWH//Qh4WIqygbsN0fDVzsOoVxNtnl5Y3jlLdowxgTIkM4vWBlHpBSr3WMcaZAc7cKQoioEoIYR7zwjhQBJWqgWmg4lhn9fEHDPOGKAqIP6AyTEJ9VCaY22vIOlPsD+iSPJZvVJR+mrMt+BrD2+VAgInpu3jmGCjF1rX7sGphBnKz8/XEvK2cUXBaX4T+UfO1eX9ifal68jsA7N1+FG8+9A3u7DAUj181AtO//hs1a1dEcmoc83qz5lGUX4K3Hh+v2aN9/OMTtnOeM4tPDb0eva9ri9gwDiAtN1DOwucih8uklAHZR0/pOwkuEqdQv3t9OpEzLzh2u1UOp900XwjERUkksuZFPRgURbWN8fmAUi+Il31KvSCKgtjYaGExtKm7uEhyIsOr5fUZ22ArFdB3wOL95ApxWlh7WxAgvWYFNGxZHQBQoUoSPvjuUVROTwHAwuIzb4E6DdPw4YRHLQGk9mUe1etHYDT05X2j1ZHq7sJi/bW0wft5z7bD2vev35+JE0fzdOlUOGNCJOC8viK4EXVYoKhWS1f3+Lx+zJ26Ci/e9jke7Pkehtw1Ggd2ZKHZJbXVPuS7fp5/kLbSgIJErlYzSEWokI9O8G99tCdq1quCvje3F56Bzghx8PDzYoBEmY1dTkRDQRtrFAGvH6WFXtUby+cXYgzZjVFqYv4E1SxnmAIyUOo1fgIBQJGN0lK/X51rPp+1LLE9ARm+Er8qHeTriGU8CPYh4eyiefnih83rE1l5AhNmfo46Mg3afc5oAfpaZLe7l2VEx0Ri15ZDePbmz7B60XZQWVb7SVZwaOdRFJ4scFwrqKJG6Z3E1FLVa1fCV388h6ZtaxmSV66ajP98djeuvq2Tc3+4uGDh2qSUAYpCmSGsJMQWoPquy+cHSEAlLhLbVTvtOjjx599Z0DXNoDIQYNFlPerCJgnSDAJ7vb4dxHK0hgjP+QNo2bEO9myzugxqaXm9ePk8X07QuGhZktS6KVT97uTCyHT2/xs6Tf0e4TAcKfDwS/0NAdrqNkrHN3NewNolO5Cx/gCIRNCmc3207FDXIubNOZ6P7Kw8neAB0A4X5G3gxMjcX+ZgpyEYFAD4Y+JydOjZFPmnirD4jw2MQWFlcH0+t03iZbHbFgZFUfS4J1pdqU7MeJ84qNtiE6JRke1mC/KKMeTO0diTcUSjpVkHTmLjsp2IiYsGIZKWFVUQFiPkLTFJacQxIvyOjY9Gl96qxKZhixq6sS6X1nFw41iAjXFeGWKcQ7y/7Mo014dCYBJZ/4WtCRDem5afqV80g17GGNgxEDb2X1oaQuCJ9KBRq5omezfOEPj1dYRDDLJnRsCBkWNtSOUSUyc4GPVrMEmStAjWmkG+oq0PdZtUxaevTIW32CT1ogqoX4HM1whenmmNojKwdlEGCvNLkJAUi2q1K+HjSU8g50QBsg7lICEpFjXrVb4wVDs2OFsRZy9muExKGZBcIR6lBcxwMxjzQQHj4TYUWmArnkZ0K+a7H07ANIkMBRS2c6GCMRzfYYYC5UxV8ImcuX6/cTEWiWUEN4a0WWwt6Vm9eURdceHRiDF0gg3oDJnHYzDKS0yJwxOvX4eufa1nS3g8Ejr2aIKOPZoEbde0bxdBkQPC+yB6/3qEM0jsCAknaoDKlIaBnZtUj4Yj+7MhixIb3g+BABAZKTAeQnn8u6hSIkQ/QoFCrT8Vxo2dESN7Lzc/2I1lp+CFW0bhALPN4YSQv75STkQ8HmZXzfMLTlyPHcxB9fpVcHj3cbW+ERHqGOZ1l1TidfNDvbV65GYXICYuEiWFXiMjpCjCmUQCA0ah7r7NY5JCJ9ZlgeV926TRjER5Gqf5Jsxhyy0T82tjkCwHZLTqVBd1m1TFrCkrNZsrRXMRN809Ls2QJOvxGCEI11dvTQvO2HHYeSlpZcDYVjuGHsDebUewe/NBaB1oZvJNkhftmghZwR8/LMXtT/bBmkUZ+Prd33BgxzFQSiFJBJd0b4yHXrsedcQDXy8UhPG+Qj5/kcNlUsqAhi1q4Ni+E2w3roUctU9stvInUBkVUddtZ99ChR0GoKtWzAsGIcZnIiLsrf7DwI6NB/QFyDL4OSG0WSzNxEOssyLrTInIXInPi4yKqb8+/P7RM1585v28WlDvEIGYsIp4TESOM3WWNskqcxECkdFqmuiYKD0/gy8n0TwxDFAUlaibbV4UBfBECAytZHrGGGFWV7soaNqmNgDgv89NwoGdWSaJjKnNnBkmRN8Vi+kdbHEuu7Ilpoyaw5hmn/EmI15TP5+LxIRo/DxmAU4cOcXGqSkjTni1/jIRX7sNgWwK20+pQV3liYqEbDc3RSmBKA0wjG2TysuOIIj5mCVihBjvAc6qOkoxa8ISfP7ni6jToDJ+/2E58k8VIa+4FJq7uuSxfw8ioxrEoJzjxJHc8BhuzohIpvEGhC2J+ufPjdAkQraB7gQGRVRpiX0Giokj58Bb7MOPn801PK4oFGv+3o51/+zA2xMeRbvuwTcsLi48uDYpZUDTNkwnGg4XrEkaRJsE0zNiwDb+gZHYWDwt+MfnV+1BuKeJaBMTbh15NbTotnYEjNVblCxo94Lkb2CqTPec3K4Fu5XfJ/wTVt2DodAczExkGrj0KhDQg6L5/PZtIkRluoJA8kia1Kd2ozRUqZ7KyuQfgeBpEgdqvO5oD6TYvxvxHv/Lrvn9MvZnHsWCX9cEV4mY26upMKixDBv8PHquI4/O8/cW+TDmPz/jxOEcVp5NP1qkbuI4s7nGwW0++PgPBLQxJJd67ZvN8+F2IRZbFDGtYvwr5mGaDtaNBLuuKMa/NijIK8aD3d7B6Nd+xsGMw8g7lqerWfmzvG3meoRtoyQ8E+yeKDHh3mtm1VwYKC5kEZgNNlbCC5HFcetcP78NgyJCkRW8++i3qkTwQoKhr8v4ucjhMikhoCgK1izchvcf/xaDr/0v3nn4a0SznXLYg4Qv8KIhIN+laEaoijENh0hADM9T3e1VrAul6kJdXIL7n70Cdz55uTEvBxAee8OcRCSevA5mkbs5vdgvhFgJYIi6iMg9GZ7nTzCkVkkS2qH9ZwQ3evb77TPRpD3OiwMhBBEREq65pysA9fTlAU9foZbHI2XZSU8URSeATkQgWKA+no/gjcXTNGxeHTN/MJ1yKjIzYpt4O8V3zdOL48D0kf1hEC6xznxMOI0BTrB4GWJcIdu8oTKW/N2Z6kdFNQI3HOdMYkDWmWVDnyi6caiWJ6y/OcMrEnQbtRuA4ASeMVmyLyBeNI7bUIyIXR9x1RI3FmaSnMhoB3sWrS42kr4y0EOPBCuDYi5XFhgUO+Jrx5iLayb7XVrsw6IZ60+/kucTLpMSEq66Jwh8Xj/effQbrJ6/DZJHgiIrkDwEy2ZvEk4E5ouSHYUXQKGLSLXF3/QbEBYG9oDBZoUCkvDd7BFkGrDj35uBlLQUm7yhp6dqOZRSJKfGIS+70F4lIMv6oXy8zeKC4zRpPJLQcJv2hkDtRulhpzVDlhX89Pk8FGQX6P2vMLG+YbdLdSYgHAM8WciDPUMIEBMXide/fABVa+khvK+8tSO2rd6DOZOWMbE5tRIxWQluV2FgsABACaueaTUqoEJaMlbO3Wp8N/xkYZ63mUGhwtgSCbcT4Qh2uKQ5LQdnCoikDw+zfZVZVWqXrx1zZYY/AEgmKaE5poqZOQswZoGrWXk/eAQ7Ga0e0CUoooTOIE0JxmQJzJjhuvjdJGkQn+XtF+vC62BrA6IefXH0wClnSSYfAxabFPZfGOMvMioCJw7nBmc4AJ0hMreFwzxf7CRJLN3OzQfR947OIetWbsCE5mf0/EUOV5ISBN++PwNrFqjBhLh3gCILCxsHD3DGoe3YmMujGABNTAMYdgLadXGXZo6JIIiyDfk4LIC5x/OsafguUQv0FEBUBEHzDvXsO0IkXPy7KPo178zNz9oh1O4YAAhw6+O9ne8D8JX6UVrss1ynlOK/z3yP7z+aCS8/9I/v0M11DWV8LNr9cHdOr1eTWBACPPbGDfh+6eto27Wh5fGOzKvFlhDx9yiezSS+L1HCJu7Sw9hBDXznJlBKcSo7nz0D4zsT6yK+Dz7ueDqnna2FeQoThudCjAEuIbK7bifpcUonEl6zUbIIWdYZFAAG2zHDXwf1K+9jMxOstTcIzH1s+G2T1twWMQ8n9SDD0T0noEX+tStXtNUR5zz3HApj/D313i04tj9EoEZzXsGYH5FBMbeftaM0REwgFxceXEmKAwrzijHr+yWwPeuAUpWDFY3UFGHxMgcLC8iC14pNXoBR5Cw+Z94hUqoSSO7GKubhBPPuylw/SuEr9mHZzPVWKYMIM8NkZlxs3WChLoYUNiJ16NIZfk9QYaXVroSsA9mo27S6Jdu5k5dj/LDfcIqF8I9PisV1D/fC3S9cDUmSsGHJDiyctkaou0DgNXfq8BZbx7gxfj8gEbTs1ATX33eZ4+OG+tvtfPl10R3Tyf7ETnJg+h4R6cHT79+KDj2boTCvGAGfELeHZ6mw9vv9RqmQyMhyg1zed2bJw5m4fWrvnOpeYHb3xTpxOEl3zM+LEj/+nF06URJkqQeMUhcF0DyrPKZ0vHN5FgHmUSZJ+o7ZrrpOEgQRoicaJ8qSybBYSyswtEHskOLjolBU5DNJg01wUkHbGe+za1HREXhtzP3IPpwTfvuc1g9zGl4nOygKNi3bETyPcgbXBTk0XCbFAVtW7obfZ7ODEz1vzIuA066PS1bEOCBmgsOv8b9OO15xwbATcYu7DUAgNHyHpRgXFvOEFz1FzPU0lyPuvoMusIrV1VckHISo/WNino7tz8bAy9/HS6PvR88bOwAAfKU+PN7zXRzlofRZ3YryivHjx7Owcs4mjJo3BH9OXKqp6LR05pgyYtwZc7s4zDtVMxRqOaHVjPSaFQzpNQNchVEuM+MXrD/tXG5Z+ladG+DSvi1x+Y3tkJishhAnZgJs3mlzcGaEEz/+XoKNN/MYDqX2MYO3k8f8MecrCaogW8kEdS5T3HGLzL/TWOZtcLrHwaUs3FVaHFdiLCFC9E0Gv09hZf5kUxqnOvCPHYMi/hXzDwZKUXSyQI/BpMU2UhCfFI2ifK++bnHwOcznlLkOioL4pFiMmfsyCnIK8OY9XwavQyiYGSTe5+J9cUxIEk4cPInDe46jer0qZ1b2v4UwpVJBn7/I4TIpDti2Zo/1ojhhZNkaSMtppyYOJHFhchL98+/mBVjMy25wm9UF/JqHnX4snsRsR3zFhY4zSlwCZE4TLoNiXkQtdab2jJ32OMUHT4xD47Z1kFIlCbc2fh6y31m1tGfLQXz77nQc2XPCGOVVsVlYNQIZRv2DYMWcTXj6wzsBqEfMr5izEdvX7oUkSbikZ1O0vqwxpAgJCneD9gnqKb7wi4ttOIyKRJCQmoBGrWuhWfu6uO3J3oiMtE7n+MQY1GteHXu2HjYSVbEPnNorxtYxMwhmlYZTfmaIbSPE6jESGWkch5yBlSTrfHCyYwgmXRHz1oiuycbI/IwsqxsM83zh9TYzgibJghb/RyT44vN8Pppd4c3t4ePYzh5MXC9CMSri5kSMwcRQdNKP6KQ4ePOLrc/yOgSgSkF5yAOW5z3PXgUCisH9PrKuYaHGm/guxQ2f2Kf8t916wvpm5oR/8NhbN9u33cUFB5dJcUDW/mzAHEBJNCYkRBeViwHIOCi17tQNKgfFtMuiuujbDPMkFfMzi3ydJCvi7lS8pqUT8jQzY3aRYMMhpmLdRSM8se3iwu2kwyfA/978BRuXZTozKAKmf70AtRvX0OvP03NiIUpUxEXfXH6YjNip4/koLfZif+ZRvH3fl8jJylUlGJRi6qg5qNmoKnrd2A7zp662eg8pilXCZsegmqFQREVIeO/7x1k2CjYuycTxQzlIrpiAtj2aIjJKzffWx3vjg6e+08ev2c1XlJ7wOpmlJOJ1J8mS+F7EvuQMPWdKKFXbbLBrYs/xdyQ+K44ZkWlyGgeivYmZoPFrovcKbxefx2JZfHxyho3nwW0zeJu008WF/MT8xblvZsrNBDeYNCXYb7tn7PIKw43Yb7btEKU4fFmUFUtcnKL8Ynz+8mT4fab+cqqT+d3wcS/GTeHqwCA2NmJ+cyYuvXCYFK52PZPnL3K4TIoDPB6+6JgmlVmMzBdacfFUmNujRHQWR1wwzTsCjaGxkZyYF1JzfnyBNOjpYRSf+/3qBcLyFHfIFKDqfyrYQWhiHajCDkkUd3B27bFZELlND2ELOz/mnYjMkJlBsHgkACtmb7TkbQtKIZcEsGfTfm13rB0tL0ixCGMMqKKodfN4QP1+NZ3Ho16zY/occPJoLobc/Il6RgyFLsUBcDDzKHKz85FcMR55R09ZF2m2kze/tlDMUXIF9cDFlXM34YuXfsTxQye1e4mp8Xj4zVvQ9+6u6HH9Jdi99RB+HjVHzxcQ+kQBjYrUNSv8fStUiHRr7AvDezXv3M1jQmSE+PvVDDCNbaL8lGBJYuoS4aYogRQJbTAJBPecYvWhlNkAUBjnLZ8XYp7B4g2x9lDZdBJzhMc+fTCYmR3AngkzjwcxvXldcZLMUGodZzZQZNmoLgUE5s/5uU1LMrF15W5jWVwSZpYA+QPQ1lfOJBo2VcHraDk9nOVfwg3lLwSczjhxev4ih+vd44BqdSsbGBKqKGxhNu26OYSdORXFuOY05kEly6CyAgr1MC0LzAuPDah5MTXQQDX0OSVqfagsq1myBY9SBYRb9LEytBoqbJFRFO3QMSqUQ9liQtkzZiNjyk9Q5v3CpAjEkIcxT8edAbseakpSxnRRWWFtpaaFj7WRqowkgbrYUe6tw4gnv09DMAqgFJFREZj1/RKUFnpBWT2J8KGUouBkEeJNhx6K/URZYDdDX4RAn1s7Ye2CrXjzri8MDAoAFJwqwieDJ2DWd/+AEIL7X77GkC/VO0sdd6WlrM0KiEK1dohePlToO4s4nv81j1GRkIhnyoinDoOPU/WkbcrnEB9vJqaOyrI29hwZf7HufJxqeajzwa6HeT1sz5oS6gAIzJzD/bBhqLOij0ObuDda3/gDxrnDnxXzZL/FeUkDgbCNLalYp3DaRSm2rdqNgF9W3xF/34qibVAoXzd8fmu+fOOiKPq74xsrhWr14XMTdmNAUUDDOSrExQUDl0lxgHYgFl8QS72wRFs1QZ2EAPjunC2MVCS84qLP0hOwbAVmSFuEtcytuyptsmpBrKAt6JRSUOG0VwJoCz/PTmVuCJvwfKHUpT+UCV/4AkF9fp1YAaxvqO6ay8vl9ff71X6QZVA/83QQNkhU6E8ajEHh3WOW8lgSUdaXxkVLY354cRRWF2oRPp/qQSUeFicu9JxQymrA9chICfN/WgGqMCLL3yNLw585sue4heExEHx2aq7GQNjVjV1LSo3HFbd3wmfPTwSCjMnPX5wIb4kPnggP4pNjtb7UF36BIPC6mtQBOuEWGBTO4PBEBkmeiXBIEiRTfBF1pNk1jep1Y8TK4mFHqVFCLtbXrLISyuTP8jK0scJvicTRfI8zOdop4LB9NzQgG9M75GXbZpvrWp0YQw9F0ZzlVJWLrOdHqWEzQWV9YwBA2zSEDXGtCEetQAj83gCTzLJ+8unrgnYqtFhnMxRFl5BAWCvMjJy4ebBIYhUUnCoKv53nFaa5crqfskTYu8DgMikOWPr7en3X6PXZLiIcVBEYAkVV84gTDVSceCbJAfgiLExAFmOFyuw58w6JQ+ZMCAwDlgqLFaCK5CmLw6GfFmpd+MX6aPVlRFdbGLw+lVh52eFwVNFUBij16rFXOFNiyBu2xCGUrpmanndOZ5OWCgTFwLwIfRUkT7FMqigqo+bz6xE8fX74S3zqokipuns07/I4cTAt9JzwW8rni7j5vbN0nggJH894DscP5uDYweyg65QiK5g2dj4AoMf17dlFRX+GByszlaEXq+jh+8WYIaKUQnzWpi8ffO06m36mRgIEqo9NlSpqBEc84VbrM3N9WR9Ts3s9TwcIc8La7+Y5o9dLYCrEe6bf/BoB9LztmBFWVwORNr1nKsugXp861zQir5dHef/wZ2XFUB9NEuT3q/OwpBS0uETN7zQih0VEsvOVHA9VtEdiSpyF8TXMSbsxb07DoR1XYJqvTtJNoorIpo3967TqfN5wJgxKOGvXRQCXSXFAYV4xDIGZANtFh7JgaIYdOz93RySIMpeQ8O88E1gHG1+EfT6DyJbqBatMhBYlFTrR5aoZUz05YdLEpOKZGSI4UyQyP2LdKGVES1goxG6Rhf7g9dYWFgBU0a/xB+0Ii6EBCgQSECyh/a7a8C64EaPQnqBZipKhgG16f6kPsnhEgTlfjXgIO0ROSDTRAtX7XRSFC+J+QoDoaA9GTH8W1etVwY71e0N3C6WY/b16/tHV93YzMpwcRNB9KOY6CvY8FFZRvKYGZX3l94MWl4AWFqmfkhLUbpiOPrd0FJqqzgmB9dCva8y6kKcgjTL2MXTJoT+gEnaHXbomRTQd6aCNRT4GRRUJvydIGvXxbEe4qa72FPpYm/dUkAqZytHayt+/ORgb7xseaZrPTeG31jIe/t4OYhscQClFbHwUXhr9gNq9FIA5qJ0DYhOiUZhTpKclpg0b+y4ypIb6CP1BGaOq2T9RWNcr+xZg5rd/h5HOxYUAl0lxQPV6VWAbmE2co2JoertEpgVII3JijApx4Q0EjIsLpYDXB1pSajynBzBGglRMNjOGqlDjbkY8M4UKacQ2BfS8LWonS1ND3BOZPPPuSrwWkIXqsPYEAsxWQ+Do+OIcCiKD6LTroFQlCOI7cvK8CLI4Ugrj7pCXJdoT8B2vYRcEndHjhIXn4fODen2MaPlAS0ogeb1o3qYWAl51d31o93Hj6+ZMhemd5Z0sAAAU5RdBOycllP0Lrz8R7wtEXhy/XPVVUqoTWK0PFLxxyyeY+/0/ghpJZ0ZoQJe4GSQpImSFSag4Y071HChTQ4ptNttqiBDVJ7y//H7j+w0I80WWdeZMZHJFSZxQT8MzbBwTQjSGzsCQcNsMlg+VFaPdDk/HmXQz4ysy27xMM8SxKLbPvDaZ5kedxlVx2TVtcelVrQSmAcHtswDcMfgqozTLnFwrkuXJNm3EVg3KmUGe1qSOcwIhKMwrCZ2uPICrD8/kc5HDZVIc0OPG9qodBQfbyVh2WHYwECITAZSFHZth0dMXT+2+mB9XAfkD+gTmO1xA2O3ZqwiM7Qhy3wy7HVmoZ8Q2iAsWJ8xiGvEv10fz0POCTYeehQOjQqmwGxaYAFMbRZsZw2LKD2EzN43vbkMtBqZ22PazuMiKRN5JksQNDhljEfDL2LBoG17oNxx/fLMQcYnRet7+AKjPr0oyGIPDmWKPxwM5IOPjJ8bZM2TmEPiAcWxrHmY2Y5rX0+e39LUGSdIkbJqkjzIGRVBnWphssRyfX5csUgBUYYI5TqwFuwQueTFUwsZbi5dtHuOsPy2ZmPvOrr0iI8aYIP7+tDI5eHu47YZsZVA0RtvSIFO5CmPezJJfrgYzvzNZsH0TcqTCmPP7/FgyfQ1rC2CUgpq7TJX09bqpk7WvOOwiaLM03EhdM1ZXeP/CeaPB55Q/oKu22DsNwUuVH1DlzD8XOVwmxQGFOabTdw0EFvaLMWNkDIRYVAUBxr8G9z7G/JjTADpRZV4gtLhE3UuKu3RxB8ee1VxvxTzDgUZ8bAxLw2ByqHk3aGH2HCAQNMa1lQAAMERJREFUMl0yBJP+X1+wlUAASkkplOISKMUlgohcsWdO+H1GmLhnj+WwRs7I+FSCTwIB24XZ0CbRYNMMkYBqRBba4nw6kAPq86Oe/x4VqySrefj8RuLEq8XGS8O2tTBv0lJk7Tuu19dkaGghelom0Meh2F5AH7/BPGHE9BAYE0BTFVG/n79k++fM3mvsr7ZRsCN8jEGzrZWNGlAkvtp8M8e0Eec1Zx6YYT31+411ENOKaj0Otqkw2Fg4McJc2qT1M2caTOVo54RBJ/zsNgGMjBVnjsW2Cwxk1v4TuLPBYJ3BYsSQcm9E09yCQtG1f2vM/3Gpsa18/NisgbxMzSi71MvaoFjTU5P3I69/gHvwsfyYRPLCYVLomX8ucrhxUhywbeUu9Ys4oQIBIDIC6q7M5iFFASAJJ8xC3ekRk9TTNPm0y4piCDRGAXv1AyF6bAZCdLMETvQ0GwLhN6Ug5tD0djDUTdh9SpK+oEqSwSOJ1wMAqM8HwtIaSuAME2f2mDGvUHu9D0z1I8IzatUYYRP6hghxOigjeFyd7sQkalIxricX6yDYKFBAbT9gL5aWZT2uinhdaAcRdvmUXaf8uTLA45GwY91e4zuwAwWSU+MxcvB4VfXgEG2WyopxfJjyoIJdkOGN2UmfLHUQ5hCHyTiYctWFrIBE6PFtnFx8KYV6srEsG0P/ixAlKsQ0xlhMDiIwOyQyEpoXCk8rUes74u1h9eMMChGi4lLA5IGkaOsCl9ASojn/q/WA0T1f+woAATXWkTbvzPWhugu3tkbY9Ym4xhBiLylVFOQdy4Mc0PuWBmTAQzUGggKgfJ1jzFVKagLWLtwKS8wnCoOxM6VUlSKxepDISH1jw70B2YnhvO+5TQ8koqm3uVE1b6duZkaNsWtcXNBwJSkOIOZFDVAnCbcrcRKz2RBDc1wCg4RDe4xN0EBAF7uGYSQm2oxwUa3B5gUwiNRZ45zzsxAoYafFYiwYGS7oDJFAfCHs4izgwe78fmNMA7udgdb30O0AAkYGhXsgmWNnUMPz1PhuTB9DmwKChIrDhqHQCJpo2Mn/Cu/FQuTsdt6nCTmgYOeG/YhPiA6ZduHkZVD8pt2pWlH9fZvbqyWhoD6frm7k18V+DZfREvrZ4oXl108Mp16f7m5urhN/RpZVN3F+OYyytWfF+SAyrYGANSKw3w+LPIaPLYHRBWA08DWHwIfKCHLjXpEhJsJfW5jVpnZtM9fPZEBMTe891DuTRVdqXl+zXZzJJuLPbxdCZupoJ+8lTZ3LGEMAGoPC3fe1dyXrkh7RVkdkULQ6Cq7qBIDf678w3JBdm5SQcJkUB7Tt2QxE2ykIBAdQ9f9isDARwuQyEEtTGoNo2Uww7YiXeZExfeeLpjj5DW6w5rKcwJkSQFe5CLtLg4eFuNiJiw//7dAGdSclBHoSF3lhN6Xtsnk/UzYpRcNGs+2O+NfUfu15akpvhl3duVjfvPiaGVAbYm+rKqLUIn0J+YwJEZEelBTaGAjKCjT39EBAX8jMnhasHto1m/ZoY1n0BguTgba0R3z/4nuxI5hB1GCqZ5vRONX2/QhlU0VBlRoVdfUaJ4Bim81qJV6O16fNHa6KtIwtzqgL9wz2HrJqAG2YiyytIT8tvU28pCBqNQODzOvPyzEzTOK8s1tLzC70ZobOAX5fADHx0YY+syBgo/oVGA5DmVRQ0bJ7vD+0eEQ85AEb79QfYOuIIA0vzzCt/WX6XORwmRQHXHnPZYiOjbJ4t2gTj+/8FBtjMrvJbV5ERI8buwEnLjZcZSPcM9hYcKJkhg3BMexW7KDZbDgYBop1AjR1k4XwiG3kdebpuI5eE+WaiIvYNzLTMbO8je6MApNkJhhmA0IxABd/NyY3SMe+40SeGTpq71tUC9gt+iwf445PCc8Oxa5OhtsUTTvWh+yXje0OBKAZONrJF0QCxZ/h9RZD2IsElz9HiBAzxegSb64b98yifr/eDjsDULs2CgbktoaaZsaUG6SLnjKUgnq9oCUlQGkp4PUia9dh03u1Yc742BEJNZfaMOJr6UcxT9FTSIiyqhp1mvrZxFDw79Rntm+h2q7ZdkzY5cc//gBsmQynua0okCTBPoa/hzCYUkKAw7uy9GdNjIqRwTfNR16Gouj9xPteqD9h/a7Zn1AKiyEp20AW2R2Q6OKCg8ukOCC5YiJufvIKS9RRHrZbMzb0+bQ0XAIBQDcu4xMQsC5KfKEz77zFRcW8e+LXRI8Tu8XS7GbICYudGJoRJMXnMzE+wiLOyjbseGRZVQV4WXResZoKcyEWFiAtAJxZEmLeuVE9D4AAihBiW5QSAdbF1InR0/6yD1/4AYu6xiBCFb8zdZwWkZbVQ3u3DkwKlWUofj+oz6d+LEaWRuJjsNWwIST82oGMQ0I99YCBFLqNEv/N+5eIjIidxEfsK7P3j5aZ+pf6/RrjqfUsU+NZduJie6gekdnCZJqJlvndCO2C+L4URqBKS0FLSlTjclmNiMw/FtG4yJxQm2vmtD4e2dmG2AsE1cJwMwkK5QavnEE2tUOTDPC2W9qqE2lLf7HrBpuWgAxKhSjBdnU3MWUeD0HVOpWNrufiewoCQgiy9grG2YAuhRITUsqcrYTjDzSmAxAD+ZnrrI3RgAyVGdfjT3GVD6Vqm4ovBCZFZNTK9DnfDTj3cJkUBxQXlGDqxzONQb80Dj4Agyks32mxw9FoQNb1+DwOgJi5sPgargHq4m8XaE1kNjiY1Z0qgjZPZEXPyyyeDgRUBoLv6gMB9aPZ2wh1Mi3Ylp0tZ3oE3b0WaI5S1b5AUdT+4Bb8ALhnhLZAB4y6bJ3gsoUnEIBSWqpLMmRdIiGGn7clMHwhEGc0I2raAs7aQUXCTKm1vTxLRZcMEaGfbNNqdkKsb9l74btsxaQGMOzwhTwNdZFl7N92WP3OGWfK3z1/hliYH0MeNsRW40E44RLGksgUUBZDiLs9a15swrjWjg4QI7mKRMm8QLOgciqdEsateOQCV7OyOlvax/tYs5oWygCMbud2EpRwYGZ4hLVBm7u8r5n3icks1lFNycc74Uat2nyk2ngxq5QUr1frY6ooUChPqxjK05gFoT+MdaD4ae9nKpNirhvvX7v5pRVBAVl4lxw+v1EVTLXioMXt0TYugorM3Mfidz53TdNa34NQzJ2w2LGu5QZnxKDYvMeLEC6T4oC/pyyHt8SrTQCNeGjiaztiSKEE2GFxoLokgeoLjKjTF6Ua+lpEdUmGDRFRFzL1YECjPYtiFIMCoIrMrtsQUEqNdh+BAPNCEgyG+YJI7BdXxeez5snTcCLBGBVbjwTzMxojJSzOpj5TuDQG0HejXKpky6Dwd2VHrKETZk4MZUUrXwvEJT6jKFB86vs1xw3h0h4D4VRMbRHrFWDhzxUKpdQLhRlVGvqG7855PgIxlTwSIqOEE7AN+av/EUJ0GydKDaomOxUXVRQopaWqqoQKWYnVpwKR5vUVGD0AwiGbqpcGNWdmkV4J75+p+AxqQL9fD+7H1H9UHC88b1viK0DoS1siZ9cvakOE+4rWJio+o7DMDCoeJsEy5KDXS3yeB7YzR2PlaVXjVbWPFVlW31NJqR7PSWEG2WxMAdDPEOPvyDQGtTGtKEhIikZ8UhyqN0gzjjk+bgLO3miUUsTGs7g9dmkMjIhQF+6uRPV3bWDUhb4y9Ju2YAr9R43jbOeGvbZ1dXFhwXVBdsD+7YcREeFRT/RUrBERHcEZFIUCkkDyeXhxj8AX8onIjyinfPIpADEes05lWXf91S6yicrXNEVhREG4z21QwHb85nYIbsFEUtPqi6QCKASKcI0w92aFERXNRZG1V4urEKS7KKXMkZX95rtworpv60TCyFjoBE7YbWntMLbZcWHj7osASKS6cze6fRolDVSi6sIvEfU9BQIgvI81ZgkC4VZAIiMNZfO6G+yBCAAiaWXxftXE/BEe/brDjqlZpwaIiI7EnHELhfKEXmHjiRCBEfN4dLd0wU1UF6MLMW6oLpmgVGEutESXmFFVHSeZvZ4Eg3JCqNpfIvGiUPOi7KBAIQaH4d2x8WqQLLD+1iQmXN2mCP0qdAGrhLHjAjKoRxKGDJsfAAzj3wlso0DYGV3auGLtIMJGQUtP1eFNqeqaTCWitU/x+QDC6kPYOANncBXVHZfqqjrK1aswesOIr573hcIkeESStPFJtTmrqAwOYxjzSkox4qExqNm0BnucyX+EMUgDMkiER+snkaGL8BB4Ij1qLB8nKFztJcxtWVGdFKgCSnm/snWQevSQDrxdhldhYmh4F0gSZK9VtV3uoCgAgvRXWM9f3HAlKQ6IiYs27v4cOPhgICzKpWbCaF68ONhEI4AWP0InBFS1Z/AH2O6WagSRitIDviNUBAkLz5sKTJZBjE9txNzG3Qz1M9dT8TRTXk9LOxzsSux27Ibf/LKit4NfF9Rghv5ihMXQfnNEVAEKpfqBjdpFe3WWWm/u1cJ3qVTdGRMC85kiWp1Yew2uqEK9DMHkqE4AbONwBImhwt+v7A/g7ldu0Ntv024xP028zuvG6qq9IxvvEW2Xzg0SeV8wBsU8GYwqF3P9jf1FhbFKWf/oEhgT4yOqLyhUN3StLmLK4ARNfT8BPVCbsKvXp4xdP4rzghNQ/k6pdeyKYPcNzLcYGVhRADmgrRPa/FUEKRXrA8Xv11WhwlgXv+udRHUpKje6Zd5xij+gql5NNmpzJ/yNSe/9AnBmXRyvVGXYlVKv5k0FhUmMS73IO56Hdr2bO49Dsf9E6Yn2V2iLWGeRadXaCqNht6XPnaPjlisITGaZPxc5XCbFAZfd0EHYEQhcf1DOVZ18VFsYGMFyOvWTg08+di4JX5gUygzpOEFmaUWXUcpPITbsbnk2+iS3TFZR589rzye/efCzBZN6hcXJvIPhBJlSyyKqi3EV0IAf1M8NCbVOBVX46cjW/jUfE091xbOWXu8Tu8lLbBkSXm+reka/rpdNBYmJIAoX8+F1kGXNONbILLLvzPYi6C6IwtiPZgQC2L/1ENJqVVKFEgpnjBStjzR7HoFpBKWaeszwHs0xMCDUT+yfAJMUOoxnaiNBsLeBAWOSAvo9Pm+E5zVmRrADU3fiPAlVmQ6904ydCD0d5IBO+LUUjEhyqUUIAkt9XvCxp4e7NzFKpiy0kc7VvOJYEeqrjX/T3KKsL2xdibXvJgZc7HcAhOpjiVKqu0NbYv8AxfklqmRDqK+hTmxdol4fs73T1Tjt+7a21s1QgKBuM0d6NiQT7M2EsaHNv3AItBL8fbq4MHBBMClffPEF6tSpg5iYGHTq1AmrVq0652XGJkQbFi19dyP8DpUJtxMJ+NUPJ3ywrGMaATTsErirrpglNzxUK2GdsIqQO4XBQyfkhOW3tZ2bebVVd1JU9GyhFIoswxiymjFN4qJGjRIbGgjYSua1djhVVSC4op2IgUmggGbcKKvMnl3QI1WyYhXvGw/CM5ap95HODBmIgeitwg1m+fuxkYwEfSeyohtEim1lXhBxSbE4mHkEstevjxleFvfOcspfUfQ0DgGh9HcqjEkTkywyw4os60TXdDSBzqgIxJf3ITeuhSnYG0+jGUXrIdT1PtKDK9qpCQ3pjI2DwpgfjQkQ8rZ7L9Tv03OnVCekZnWDLTPMn1McCbN2zUyAmaE7ADWSrEnNolVKY0R0FZQ2FwHdDsjnheGoBhsofjOzGR6xzztRABBhfTIz8qIRNYG+ngYC6mWzREpR9JgxPDthcxIK3mJfWOnOG8T3XdbPRY5yz6RMmTIFzz33HN58802sW7cOrVu3Rt++fXH8+PFzWu6M0XP0hRYwrHsGCYXpOeOuURCHKwqoz6vuHEXDPTPEddYpmiA7XM0wSCk12mWIBMKsgrCrs3nSGxZSewIPQF90WDpqfk4jmiY1F6XGIG1i3YIuQOaFD9p7opwZMh8r7/fBcTILzI3zQszaolBr3uZdtOVRNobsVDoik2lTLwPDwc4pEQl03/t6YMn0lTD0CdX+YwRRDl4/E4io/zcTU6qYrnPJH0vvEKuH2r1fhwVWDNZlKFssl6c1x9MRiZihCg79q3m+sTZwjzRBGqQxYGIkWp6fxqja5G34rV7TpqfWDucxeVovjT/D+1pQRxnrQ3U1WTj5mfIOBckjYcea3VD8smrPIpZPRVsU45pqsAEzlw3okkmNCUfY3XNox5HwEp4vuBFnQ6LcMykff/wxHnnkETzwwANo1qwZvvzyS8TFxeHbb789p+Wum78ZSoDttFiALFFkTvj5PYqwyw2Hq5Vl3UtBWLyJRFQmgxOJYLZ7AmHVL1Fwt1qDNAX6V8vBf8JzVJZRIT1FvxGWobAY/Au6+yXPlwbPR7NBEIN3aTcVWFYioe5UJPyGxRBQZB4/QbH0k309FCgaYRKMO60prVcYAeJnzwSzZ7Be1hkqw3NEZW5tF2x2LTI6Alfc0x0n9mc7lCH2VXhi76ZdGlmlZ6xuGoExEz6N2RLHmyDRArQ5YmynmfAJhEubS1abF+3QS7Ec8bcdE+nkWixKfQx1UBkVRfQac2KWFPtxarceGGal7WYB0NU0TLVlHr+OmxvxfSh6Ztrt0ydmkdFGA/CQoBSeCI8qIAlwKTA3KBYZFOIwDij/B0ufAnqAwNMwFlXKuWGptk6dwediR7lmUnw+H9auXYs+ffpo1yRJQp8+fbB8+XLbZ7xeL/Lz8w2fssBAW/muX9CfW/S/FgM+diMIaCAAxe8HoQoefPt2fPL3UEw78U04jxoXawfCYnNRK1Nhf6nfr+1IH3j7Vlx+56UhCtYys1YyzB2XAeIizwmUmJ8Tt0bZHZv3JFbPzrjVPj+2gza916Aw7BCN7tunBUWXcmnEnP1v6Av+IcDrkwfDE+FB084NjfUBZxCFytsQaTPTcEnvFnh/5hA0v7QxJI/JQ4zC2neC5IZSxaGrBMKpeQmptgiKjSpTyFAzILepeOj3aJFEhEdgLWkpN7S1sZMKNa7spHOhdr0Cs+TIVLDrVqNqll5xYshOYy6w+1c92FP7GZZ3IyFo37e1Nv44o0cVdRNARRdkw+sxMbx276IsIEDdlrXPLA8X5x3lmknJzs6GLMtIS0szXE9LS0NWVpbtM8OGDUNycrL2qVmzZpnKvqR3K+NiDei7FLaQcs8IXX8uSFtO49C1bjd1xO0vXofmXRohISUeV9zbPcxaGncjht1JsAVRk37oaSSPhI792mLIhEH4PW88Hh52Jxp2qh+kHTaLpCGeCjVGrQy3LYadtHDdXH/LjtK409WlNNZdsmPZpwUj8aGKorEUhl20Xdh+S1aUGXWqY4t7D4HbeGhSJYomHetjxPw30PmadgCALtd1sNbJrhg5oBqYCl4yXDVGAwG8MfVZxCfF4f1Zr+CqB3shIioidJ78hqgGs2sb+6t7wgjMp+YSbuA2Da7xtmUGG1dmFVpYc9GBCQtShjG9TfsdPNucGW/OWQv1sR2udkxMaLdpY14hxiOAR4YPQIe+rXUGJUQRd75yI/rc3U09v0fMyy6GUbCynRjj00S7Pq0QJUqDyiN4/5T1c4Z9dCGgXDMpZcErr7yCvLw87XPw4MEy5XPtk32d1zZxp8YJPluQJI+ED+b9hyUMvTh6IiTc9uL1hmsvfvskGrWrFz6jA3AaJjwjDGBiCSVlAJEI+j3cG6lpKQCA6Ngo3Pb8tRi97H3UbVnL4Vn7nadt+WG3QVygAC2cuYXA810jX3hDMQI2Jrpi2pDVdBwIxvKEiLJqEab+4OUCDgGvFOO5MQCgKPh8+bsYu+5DTNo3Gp8tfRetujXVHklIiUdy5SRjHhohtLGDUVj+sn5adrX6VRCXEAsAiI2PweDRj2DKoS/Re0A3Y52D9YMjU2zqIz5fNFdjE1EW0wVFaCbC4F59WnAYJ5ZknDEJMv7YteQqSWFVW9sIMYmTIUaImMasZgq5VlCdINqWaax7zSZVERMXg7emvYgH37sDFaumqswKgZEJAZCYGo/HRtyD+4beitj4GNz/9u3O1SCCCs+WYTOtKzaqK/67SacGjsXEJMTgua8ec65HeYHY92X9XOQo10xKpUqV4PF4cOzYMcP1Y8eOIT093faZ6OhoJCUlGT5lQY2GVfHKD8/AEyFBitC7yRMhgRCCJz+9H11v7KBJWyIiPeh1R1fMyJ+ASy5viS9WD0NqegqCMQfRcVF485cX0ahdfcN1Qgi+WDUMr016BqnpqWHWmAoT2nzNRm8OaHXvdnNnPPnpA7a5/m/DR2jcsYFjOwx6a1vG5HQmEedOzAsScM/QW/HkJ/cL0i1hoTMzAk4gvI5WT6Qr7u2Oxh3qo0qtSmjauSHemvYCJu3/Are/dB3S66cFeY32hCkq2oPPlr2D92YOwV2v3mStc5ho1L4eGrVvgLota6FS9Qq2ab7NHGmtk506zgHPffW45VpiagKe+OR+JFZIgN5nwfILcc9mYU2vWwXJFRMheRyYYAfpjIcFuotLikXdVrWDlIszWMTFNodqt5mwGtO3vbw5xm37BO//8QqaX9qYXQ1H8qFufqQICdFxUY5pGl5SB9NzxuHNX18yGj7b1lUs26aPqQIiAR/NfxMAEBEZgdtfvB4T932BHw+MwS/Hvsbv+d/h683/xRtTn8Pw2a9h8uGxuOXZazSJy03PXI0nPrkPsQkxammsuJQqSXh9ynO4/eUb9Lo4MesGJpvq/cH6u12flhix4E08NuIexCXFGh5v3bMZ/rf+Q1SpWSlIX7i4UEBoOXck79SpEzp27IhRo0YBUA2hatWqhUGDBmHIkCEhn8/Pz0dycjLy8vLKxLAczDyMGaPnYO28TaCUom2vFrhu4FWo0zy0GolSiu2rdmH7qt04tPMwcrPykH34JCpWq4C2l7fA5QO6IT4pLmQ+AX8A00b9iXkTFqHgVCFqNKyKqx/pgxaXNcb4N6YgY8UOFOUXI+9EgXoqbhA079YETTo0QO7xPKRUSkLvu7uj4SX1QtahMK8Ynz46FttW7oDHI6FR+/rodUdXdLm2PUqLvXjn9o+xdu7G8HkSG5vIqNgo+EpMLoOEYODIB3DDoH4A1Pc/atDXmPnlvDALUvHWby+hxWVN8cpV72LH6t3a9YrVK+D5rx5Hh6vahsyjpMSLN6//AOv/2hw0Xfur2uCVH55GUoVE7dqf387HmGfHo6SgVG+aJKHXXV3x9+SlUGyidNZpXhOj132AyMjQIuv92w7g4RbPh0wnIiomEq/88Awuu6mTY5qsfccx5Kp3cXjH0dPKOxSueKAHXvpmEA5mHsawASOxc93eoOlj4qNx56s3IjI6CiX5JajRqCouu6kTomKisGjqcox5bhxOHj4VNA/JI0GxsS8JF8TDI6Fa7yVXSUK/h3pj5cy1yD6Ug4iYCLTo0gS3v3IDGps2IYW5RfCV+lGcX4y3bv4I+7YesmYIQJIIOl/XHq9OfAa+Uj+G3f0ZVs9er5UfHRuFB96/Czc/0197ZvfGfXj92uE4cehkmdpYsVoqRiwcihoNq5XpeRElRaVYNWs98rLzUaVmJbTv2xoRkRGglGL04HGYPurPkHnUaV4TtZvXxLZlmSguKEH1hlVx7eNXos893RERqaok/b4Ati3fAW+xF7WaVkd6nSpnVO8zpRmnU0bvxAGIIDYMaJgIUB/mF0w8p3U93yj3TMqUKVNw3333YezYsejYsSM+/fRT/PTTT9i+fbvFVsUO/8aAK084sjsLOVm5qFY/Dd4SH2aMnoOTR3JQo1E19Ly9K2o1qX5Oy1/62yqMf30KThzKRlR0JDr1b4f4lHj4Sryo3awmut3cCZsXZyD7cA5S0pKRVrsyco/nIblSEppd2giHdhzF35OXojC3CFXrpaHP3d2RVDHRUs7Rvccw+5sF2L5qFyKjI9D1ho644t4e2LhoC4bdNQoFuYWIiPCgY7+2eH7cQCQIzCClFL5SHyKiIuDxeCx5h4Osfcfxv5e+w5YlmfCV+FCxWip63n4pbn3hesTERTs+t+mfDBzMOISaTaqjVfdmAFTGa86EhZg8bDpKCkqQXi8Ng0Y9iEaX1HfMxwkzxs7BD29NRUlBKSpUTcWdr9yIy27qhN0b9iFrzzFsXrIdlFK07tkcV9zTI+zjHnas3Y35E5egpKAYrXu1QI/bumDx1OWYPHw6CnOLUSE9GXVa1EJkVARqNamOFX+uw7q5m7TnI6MiUKt5DXS7qRPuGHKjpd8z1+zGzrV7EBkdgXZXtkZedj5WzlyLiKgINLykHtr0ahG0rrIsY+vSTJzKykWlGhVRUlCCMc+Ox6kTeUhMTcDAzx5C+ytbYe3cjfjrh8U4dSwPabUro+8DvRCXGIM1czfCE+FBQnI8co7l4vjBbGxatA352fmIS4zFVQ9ejusH9UN0XBTG/Wcy/hg7F95SP5IrJODpsY+iS//2p/2uOPKy83Fs3wnEJMagVuPg81NRFOTnFCI2PhrRsc7jbM/m/diwYAsoBYryCrBlSSYOZh5FUV4xiATUb1MX9791O1KqJGP9/M1QZAVNOzdEk44NHfM82zi86yjmjFuIY/tPIKlCIrrf1gUEwNG9x5FcOQkN29bVVND/Jv5VJiXhrjNnUgonXdT0rdwzKQDw+eef46OPPkJWVhbatGmDzz77DJ06Oe/+RPx/Y1JcuHDhwkXZ4TIp5QsXxAGDgwYNwqBBg853NVy4cOHChYuzBqoooKTsKsj/D3FSLggmxYULFy5cuLjoYPZcKtPzFzfKtXePCxcuXLhw4eL/L1xJigsXLly4cHE+oFA1VlBZ8f9AkuIyKS5cuHDhwsX5AKUAzsCuxGVSXLhw4cKFCxfnAlShoGcgSbkAnHPPGK5NigsXLly4cOGiXMKVpLhw4cKFCxfnA1TBmal7XBdkFy5cuHDhwsU5gKvuCQ1X3ePChQsXLly4KJe46CUpnNPMz88/zzVx4cKFCxflHZxW/BtSigD1npHKJgD/WaxN+cRFz6QUFBQAAGrWDH1qsQsXLly4cAGotCM5Ofmc5B0VFYX09HQsyZp1xnmlp6cjKqrs5/+Ud1wQBwyeCRRFwZEjR5CYmBj2ia92yM/PR82aNXHw4MGL9iCnfxtun559uH16buD269lHee1TSikKCgpQrVo1SNK5s4goLS2Fz+c743yioqIQExNzFmpUPnHRS1IkSUKNGjXOWn5JSUnlakJdDHD79OzD7dNzA7dfzz7KY5+eKwmKiJiYmIuauThbcA1nXbhw4cKFCxflEi6T4sKFCxcuXLgol3CZlDARHR2NN998E9HR0ee7KhcN3D49+3D79NzA7dezD7dPXYSDi95w1oULFy5cuHBxYcKVpLhw4cKFCxcuyiVcJsWFCxcuXLhwUS7hMikuXLhw4cKFi3IJl0lx4cKFCxcuXJRLuExKGPjiiy9Qp04dxMTEoFOnTli1atX5rlK5weLFi3HttdeiWrVqIIRg+vTphvuUUrzxxhuoWrUqYmNj0adPH+zcudOQJicnBwMGDEBSUhJSUlLw0EMPobCw0JBm06ZN6NatG2JiYlCzZk18+OGH57pp5w3Dhg1Dhw4dkJiYiCpVquCGG25AZmamIU1paSkGDhyIihUrIiEhATfffDOOHTtmSHPgwAH0798fcXFxqFKlCl588UUEAgFDmr///huXXHIJoqOj0aBBA4wfP/5cN++8YMyYMWjVqpUWOKxLly74888/tftuf545hg8fDkIIBg8erF1z+9XFGYO6CIrJkyfTqKgo+u2339KtW7fSRx55hKakpNBjx46d76qVC8yaNYu+9tpr9Ndff6UA6LRp0wz3hw8fTpOTk+n06dPpxo0b6XXXXUfr1q1LS0pKtDRXXXUVbd26NV2xYgX9559/aIMGDeidd96p3c/Ly6NpaWl0wIABdMuWLfTHH3+ksbGxdOzYsf9WM/9V9O3bl44bN45u2bKFbtiwgV599dW0Vq1atLCwUEvz+OOP05o1a9L58+fTNWvW0M6dO9NLL71Uux8IBGiLFi1onz596Pr16+msWbNopUqV6CuvvKKl2bNnD42Li6PPPfcc3bZtGx01ahT1eDx09uzZ/2p7/w3MmDGD/vHHH3THjh00MzOTvvrqqzQyMpJu2bKFUur255li1apVtE6dOrRVq1b0mWee0a67/eriTOEyKSHQsWNHOnDgQO23LMu0WrVqdNiwYeexVuUTZiZFURSanp5OP/roI+1abm4ujY6Opj/++COllNJt27ZRAHT16tVamj///JMSQujhw4cppZSOHj2apqamUq/Xq6V5+eWXaePGjc9xi8oHjh8/TgHQRYsWUUrVPoyMjKRTp07V0mRkZFAAdPny5ZRSlXmUJIlmZWVpacaMGUOTkpK0fnzppZdo8+bNDWXdfvvttG/fvue6SeUCqamp9Ouvv3b78wxRUFBAGzZsSOfNm0d79OihMSluv7o4G3DVPUHg8/mwdu1a9OnTR7smSRL69OmD5cuXn8eaXRjYu3cvsrKyDP2XnJyMTp06af23fPlypKSkoH379lqaPn36QJIkrFy5UkvTvXt3w0mfffv2RWZmJk6dOvUvteb8IS8vDwBQoUIFAMDatWvh9/sN/dqkSRPUqlXL0K8tW7ZEWlqalqZv377Iz8/H1q1btTRiHjzNxT62ZVnG5MmTUVRUhC5durj9eYYYOHAg+vfvb2m7268uzgYu+gMGzwTZ2dmQZdkwgQAgLS0N27dvP0+1unCQlZUFALb9x+9lZWWhSpUqhvsRERGoUKGCIU3dunUtefB7qamp56T+5QGKomDw4MHo2rUrWrRoAUBtc1RUFFJSUgxpzf1q1+/8XrA0+fn5KCkpQWxs7Llo0nnD5s2b0aVLF5SWliIhIQHTpk1Ds2bNsGHDBrc/y4jJkydj3bp1WL16teWeO05dnA24TIoLF+UYAwcOxJYtW7BkyZLzXZULHo0bN8aGDRuQl5eHn3/+Gffddx8WLVp0vqt1weLgwYN45plnMG/ePPc0XxfnDK66JwgqVaoEj8djsUY/duwY0tPTz1OtLhzwPgrWf+np6Th+/LjhfiAQQE5OjiGNXR5iGRcjBg0ahJkzZ2LhwoWoUaOGdj09PR0+nw+5ubmG9OZ+DdVnTmmSkpIuyt1pVFQUGjRogHbt2mHYsGFo3bo1Ro4c6fZnGbF27VocP34cl1xyCSIiIhAREYFFixbhs88+Q0REBNLS0tx+dXHGcJmUIIiKikK7du0wf/587ZqiKJg/fz66dOlyHmt2YaBu3bpIT0839F9+fj5Wrlyp9V+XLl2Qm5uLtWvXamkWLFgARVHQqVMnLc3ixYvh9/u1NPPmzUPjxo0vSlUPpRSDBg3CtGnTsGDBAouqq127doiMjDT0a2ZmJg4cOGDo182bNxsYwHnz5iEpKQnNmjXT0oh58DT/X8a2oijwer1uf5YRvXv3xubNm7Fhwwbt0759ewwYMED77varizPG+bbcLe+YPHkyjY6OpuPHj6fbtm2jjz76KE1JSTFYo/9/RkFBAV2/fj1dv349BUA//vhjun79erp//35KqeqCnJKSQn/77Te6adMmev3119u6ILdt25auXLmSLlmyhDZs2NDggpybm0vT0tLoPffcQ7ds2UInT55M4+LiLloX5CeeeIImJyfTv//+mx49elT7FBcXa2kef/xxWqtWLbpgwQK6Zs0a2qVLF9qlSxftPnftvPLKK+mGDRvo7NmzaeXKlW1dO1988UWakZFBv/jii4vWtXPIkCF00aJFdO/evXTTpk10yJAhlBBC586dSyl1+/NsQfTuodTtVxdnDpdJCQOjRo2itWrVolFRUbRjx450xYoV57tK5QYLFy6kACyf++67j1KquiG//vrrNC0tjUZHR9PevXvTzMxMQx4nT56kd955J01ISKBJSUn0gQceoAUFBYY0GzdupJdddhmNjo6m1atXp8OHD/+3mvivw64/AdBx48ZpaUpKSuiTTz5JU1NTaVxcHL3xxhvp0aNHDfns27eP9uvXj8bGxtJKlSrR559/nvr9fkOahQsX0jZt2tCoqChar149QxkXEx588EFau3ZtGhUVRStXrkx79+6tMSiUuv15tmBmUtx+dXGmIJRSen5kOC5cuHDhwoULF85wbVJcuHDhwoULF+USLpPiwoULFy5cuCiXcJkUFy5cuHDhwkW5hMukuHDhwoULFy7KJVwmxYULFy5cuHBRLuEyKS5cuHDhwoWLcgmXSXHhwoULFy5clEu4TIoLFxcQxo8fbzhVdujQoWjTps1ZLePvv/8GIcRy5ooLFy5c/NtwmRQXLs4hDh48iAcffBDVqlVDVFQUateujWeeeQYnT548K/m/8MILlnNN/g1s3LgR1113HapUqYKYmBjUqVMHt99+u3YGy759+0AIQZUqVVBQUGB4tk2bNhg6dKj2u2fPniCEgBCCmJgYNGvWDKNHj/43m+PChYtyCpdJceHiHGHPnj1o3749du7ciR9//BG7du3Cl19+qR1QmZOT4/isz+cLq4yEhARUrFjxbFU5LJw4cQK9e/dGhQoVMGfOHGRkZGDcuHGoVq0aioqKDGkLCgowYsSIkHk+8sgjOHr0KLZt24bbbrsNAwcOxI8//niumuDChYsLBC6T4sLFOcLAgQMRFRWFuXPnokePHqhVqxb69euHv/76C4cPH8Zrr72mpa1Tpw7eeecd3HvvvUhKSsKjjz4KQFXv1KpVC3FxcbjxxhstEhizuuf+++/HDTfcgBEjRqBq1aqoWLEiBg4caDhB+vvvv0f79u2RmJiI9PR03HXXXYZTaENh6dKlyMvLw9dff422bduibt266NWrFz755BPLic1PPfUUPv7445D5x8XFIT09HfXq1cPQoUPRsGFDzJgxI+w6uXDh4uKEy6S4cHEOkJOTgzlz5uDJJ59EbGys4V56ejoGDBiAKVOmQDw6a8SIEWjdujXWr1+P119/HStXrsRDDz2EQYMGYcOGDejVqxfefffdkGUvXLgQu3fvxsKFCzFhwgSMHz8e48eP1+77/X6888472LhxI6ZPn459+/bh/vvvD7tt6enpCAQCmDZtGkId/XXnnXeiQYMGePvtt8POHwBiY2PDlia5cOHi4oXLpLhwcQ6wc+dOUErRtGlT2/tNmzbFqVOncOLECe3a5Zdfjueffx7169dH/fr1MXLkSFx11VV46aWX0KhRIzz99NPo27dvyLJTU1Px+eefo0mTJrjmmmvQv39/g93Kgw8+iH79+qFevXro3LkzPvvsM/z5558oLCwMq22dO3fGq6++irvuuguVKlVCv3798NFHH+HYsWOWtIQQDB8+HP/73/+we/fukHnLsowffvgBmzZtwuWXXx5WfVy4cHHxwmVSXLg4hzidQ8bbt29v+J2RkYFOnToZrnXp0iVkPs2bN4fH49F+V61a1aBuWbt2La699lrUqlULiYmJ6NGjBwDgwIEDYdf1vffeQ1ZWFr788ks0b94cX375JZo0aYLNmzdb0vbt2xeXXXYZXn/9dcf8Ro8ejYSEBMTGxuKRRx7Bs88+iyeeeCLs+rhw4eLihMukuHBxDtCgQQMQQpCRkWF7PyMjA6mpqahcubJ2LT4+/qyUHRkZafhNCIGiKACAoqIi9O3bF0lJSZg4cSJWr16NadOmAQjfWJejYsWKuPXWWzFixAhkZGSgWrVqjkayw4cPx5QpU7B+/Xrb+wMGDMCGDRuwd+9eFBUV4eOPP4YkucuTCxf/3+GuAi5cnANUrFgRV1xxBUaPHo2SkhLDvaysLEycOBG33347CCGOeTRt2hQrV640XFuxYsUZ1Wv79u04efIkhg8fjm7duqFJkyanZTTrhKioKNSvX9/i3cPRsWNH3HTTTRgyZIjt/eTkZDRo0ADVq1d3mRMXLlxocFcDFy7OET7//HN4vV707dsXixcvxsGDBzF79mxcccUVqF69Ot57772gzz/99NOYPXs2RowYgZ07d+Lzzz/H7Nmzz6hOtWrVQlRUFEaNGoU9e/ZgxowZeOedd04rj5kzZ+Luu+/GzJkzsWPHDmRmZmLEiBGYNWsWrr/+esfn3nvvPSxYsACZmZln1AYXLlz8/4HLpLhwcY7QsGFDrFmzBvXq1cNtt92G+vXr49FHH0WvXr2wfPlyVKhQIejznTt3xldffYWRI0eidevWmDt3Lv7zn/+cUZ0qV66M8ePHY+rUqWjWrBmGDx8eVhwTEc2aNUNcXByef/55tGnTBp07d8ZPP/2Er7/+Gvfcc4/jc40aNcKDDz6I0tLSM2qDCxcu/v+A0NOx7HPhwoULFy5cuPiX4EpSXLhw4cKFCxflEi6T4sKFCxcuXLgol3CZFBcuXLhw4cJFuYTLpLhw4cKFCxcuyiVcJsWFCxcuXLhwUS7hMikuXLhw4cKFi3IJl0lx4cKFCxcuXJRLuEyKCxcuXLhw4aJcwmVSXLhw4cKFCxflEi6T4sKFCxcuXLgol3CZFBcuXLhw4cJFuYTLpLhw4cKFCxcuyiX+DzzStsmLnx6mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# mark in Kayle as pipeline step: Pipeline step: manhattan_known: Depends on: saliency_known \n",
    "model_folder = f\"a-{time}-model\"\n",
    "p_values_observed_np = np.load('p_values_observed_np.npy',\n",
    "                               allow_pickle=True)\n",
    "\n",
    "neg_log = -1 * np.log10(p_values_observed_np)\n",
    "slug = np.arange(p_values_observed_np.shape[0])\n",
    "\n",
    "pt = plt.scatter(x = slug, y = neg_log, c=neg_log)\n",
    "plt.title(f\"Manhattan Plot for {experiment_description} - observed\")\n",
    "plt.xlabel(\"Ordinal SNP\")\n",
    "plt.ylabel(\"- log10 observed P values\")\n",
    "cbar = plt.colorbar(pt)\n",
    "cbar.set_label(\"- log10 observed P values\")\n",
    "plt.savefig(f\"{model_folder}-manhattan-observed\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f18fae63-9e34-4d5b-b5d7-76e08e94142c",
   "metadata": {
    "tags": [
     "block:saliency_predicted",
     "prev:train"
    ]
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'val_snps_for_saliency.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m val_snps_s \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mval_snps_for_saliency.npy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_pickle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m model_folder \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-model\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_saved_model\u001b[39m(final_activation_scale_factor: \u001b[38;5;28mfloat\u001b[39m, model_folder: \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/lib/npyio.py:405\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[1;32m    403\u001b[0m     own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    404\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 405\u001b[0m     fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    406\u001b[0m     own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    408\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'val_snps_for_saliency.npy'"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "val_snps_s = np.load(\"val_snps_for_saliency.npy\", allow_pickle=True)\n",
    "model_folder = f\"a-{time}-model\"\n",
    "\n",
    "def get_saved_model(final_activation_scale_factor: float, model_folder: str):\n",
    "    final_model = tf.keras.models.load_model(model_folder)\n",
    "    for layer in final_model.layers:\n",
    "        layer.trainable=False\n",
    "    return final_model\n",
    "    # print(layer.weights)\n",
    "\n",
    "final_model = get_saved_model(final_activation_scale_factor=final_activation_scale_factor, model_folder = model_folder)\n",
    "val_phenotypes_p = final_model.predict(val_snps_s).flatten()\n",
    "\n",
    "p_values_p = []\n",
    "for i in np.arange(int(val_snps_s.shape[1] / 3)):\n",
    "    column_index_lower_bound = 3 * i\n",
    "    column_index_upper_bound = 3 * i + 3\n",
    "    data = val_snps_s[:,column_index_lower_bound:column_index_upper_bound]\n",
    "    data_reshaped = np.argmax(data, axis=1)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(data_reshaped, val_phenotypes_p)\n",
    "    p_values_p.append(p_value)\n",
    "p_values_predicted_np = np.array(p_values_p)\n",
    "np.save('p_values_predicted_np', \n",
    "        p_values_predicted_np, \n",
    "        allow_pickle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "043678ab-c0ae-45d7-8fdc-259d3138adba",
   "metadata": {
    "tags": [
     "block:manttan_predcted",
     "prev:saliency_predicted"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# mark in Kayle as pipeline step: Pipeline step: manhattan_known: Depends on: saliency_known \n",
    "model_folder = f\"a-{time}-model\"\n",
    "p_values_predicted_np = np.load('p_values_predicted_np.npy',\n",
    "                               allow_pickle=True)\n",
    "\n",
    "neg_log = -1 * np.log10(p_values_predicted_np)\n",
    "slug = np.arange(p_values_predicted_np.shape[0])\n",
    "\n",
    "pt = plt.scatter(x = slug, y = neg_log, c=neg_log)\n",
    "plt.title(f\"Manhattan Plot for {experiment_description} - predicted\")\n",
    "plt.xlabel(\"Ordinal SNP\")\n",
    "plt.ylabel(\"- log10 predicted P values\")\n",
    "cbar = plt.colorbar(pt)\n",
    "cbar.set_label(\"- log10 predicted P values\")\n",
    "plt.savefig(f\"{model_folder}-manhattan-predicted\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bae392ab-a377-49a1-afaf-b5330892d89a",
   "metadata": {
    "tags": [
     "block:qq_plot",
     "prev:saliency_observed",
     "prev:saliency_predicted"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# mark in Kayle as pipeline step: qq plot\n",
    "\n",
    "model_folder = f\"a-{time}-model\"\n",
    "\n",
    "p_values_observed_np = np.load('p_values_observed_np.npy',\n",
    "                               allow_pickle=True)\n",
    "\n",
    "neg_log_o = -1 * np.log10(p_values_observed_np)\n",
    "\n",
    "\n",
    "p_values_predicted_np = np.load('p_values_predicted_np.npy',\n",
    "                               allow_pickle=True)\n",
    "\n",
    "neg_log_p = -1 * np.log10(p_values_predicted_np)\n",
    "\n",
    "pt = plt.scatter(x = neg_log_o, y = neg_log_p, c= neg_log_p - neg_log_o)\n",
    "plt.title(f\"QQ Plot for {experiment_description}\")\n",
    "plt.xlabel(\"- log10 observed P values\")\n",
    "plt.ylabel(\"- log10 predicted P values\")\n",
    "cbar = plt.colorbar(pt)\n",
    "cbar.set_label(\"- log10 predicted P values - log10 observed\")\n",
    "plt.savefig(f\"{model_folder}-manhattan-predicted\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be03ced0-99f2-43fd-b29e-407d0f85212c",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "## There is a second notebook to run after you run this pipeline. Please find the model folder for the best model that Katib found.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4b240cfb-b264-49dc-ae2c-73b10ef8a69d",
   "metadata": {
    "tags": [
     "pipeline-metrics"
    ]
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'val_mean_absolute_error' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [39], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mval_mean_absolute_error\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'val_mean_absolute_error' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "print(val_mean_absolute_error)\n"
   ]
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "python3",
   "name": "tf2-gpu.2-10.m100",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-10:m100"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "kubeflow_notebook": {
   "autosnapshot": true,
   "deploy_config": {},
   "docker_image": "",
   "experiment": {
    "id": "new",
    "name": "gwas-7-d"
   },
   "experiment_name": "gwas-10-d",
   "katib_metadata": {
    "algorithm": {
     "algorithmName": "bayesianoptimization",
     "algorithmSettings": [
      {
       "name": "random_state",
       "value": "10"
      },
      {
       "name": "acq_optimizer",
       "value": "auto"
      },
      {
       "name": "acq_func",
       "value": "gp_hedge"
      },
      {
       "name": "base_estimator",
       "value": "GP"
      }
     ]
    },
    "maxFailedTrialCount": 10,
    "maxTrialCount": 40,
    "objective": {
     "additionalMetricNames": [],
     "goal": 0.05,
     "objectiveMetricName": "val-mean-absolute-error",
     "type": "minimize"
    },
    "parallelTrialCount": 2,
    "parameters": [
     {
      "feasibleSpace": {
       "list": [
        "IMP_height.txt"
       ]
      },
      "name": "data_file_to_run",
      "parameterType": "categorical"
     },
     {
      "feasibleSpace": {
       "list": [
        "GWAS on soy height"
       ]
      },
      "name": "experiment_description",
      "parameterType": "categorical"
     },
     {
      "feasibleSpace": {
       "max": "0.3",
       "min": "0.00001",
       "step": "0.00001"
      },
      "name": "learning_rate",
      "parameterType": "double"
     },
     {
      "feasibleSpace": {
       "max": ".98",
       "min": "0.0001",
       "step": "0.0001"
      },
      "name": "conv_1_dropout_rate",
      "parameterType": "double"
     },
     {
      "feasibleSpace": {
       "max": "0.95",
       "min": "0.0000001",
       "step": "0.0000001"
      },
      "name": "conv_1_kernel_l1",
      "parameterType": "double"
     },
     {
      "feasibleSpace": {
       "max": "0.95",
       "min": "0.0000001",
       "step": "0.0000001"
      },
      "name": "conv_1_kernel_l2",
      "parameterType": "double"
     },
     {
      "feasibleSpace": {
       "max": "0.95",
       "min": "0.0000001",
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