Model_1_Accuracy_96%.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd6c3144-e1df-463b-ac17-5910088f7483",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1dee3c9-6481-4fb0-89e6-825bcf5fb905",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_directory = 'chest/train/'\n",
    "dataset = []\n",
    "labels = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35af39bc-0d70-4186-9855-cd34bdff0d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_scan=os.listdir(image_directory+'NORMAL/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1ce2b68-f394-41f4-bdbb-922b192b9ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,image_name in enumerate(normal_scan):\n",
    "    image = cv2.imread(image_directory +'NORMAL/'+image_name)\n",
    "    image = Image.fromarray(image , 'RGB')\n",
    "    image = image.resize((300,300))\n",
    "    dataset.append(np.array(image))\n",
    "    labels.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87cbe5a5-3bea-4d45-ac18-a3810e867530",
   "metadata": {},
   "outputs": [],
   "source": [
    "pneumonia_scan=os.listdir(image_directory+'PNEUMONIA/')\n",
    "for i,image_name in enumerate(pneumonia_scan):\n",
    "    image = cv2.imread(image_directory +'PNEUMONIA/'+image_name)\n",
    "    image = Image.fromarray(image , 'RGB')\n",
    "    image = image.resize((300,300))\n",
    "    dataset.append(np.array(image))\n",
    "    labels.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "898412bb-43b4-4193-9e3e-86ff02313e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2682,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = np.array(dataset)\n",
    "labels = np.array(labels)\n",
    "dataset.shape\n",
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "28024fe6-b3bb-48bf-90c8-b51f98abf80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset= dataset/255\n",
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "796b785e-90f3-4f60-aa44-f492e4844661",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "222b80dc-f047-48d3-91fc-ecb78d4f4cdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(dataset,labels,test_size =0.2 , random_state=42 ,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e3fce20-832b-4873-a1d4-c91f48f6086f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2145\n",
      "537\n"
     ]
    }
   ],
   "source": [
    "print(Y_train.size)\n",
    "print(Y_test.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "185ce636-07f7-4b34-bb18-e477dac98886",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model= Sequential()\n",
    "model.add(Conv2D(64,(3,3), input_shape=(300,300,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "\n",
    "model.add(Conv2D(32,(3,3), kernel_initializer=\"he_uniform\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "\n",
    "model.add(Conv2D(32,(3,3) , kernel_initializer=\"he_uniform\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation(\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ae63a0d-8c45-43fb-bac5-575f58f479c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'binary_crossentropy',\n",
    "             optimizer = 'adam',\n",
    "             metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e91fc2fd-a2cf-49db-ad7c-ef2cf7fb01ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 298, 298, 64)      1792      \n",
      "                                                                 \n",
      " activation (Activation)     (None, 298, 298, 64)      0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 149, 149, 64)      0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 147, 147, 32)      18464     \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 147, 147, 32)      0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 73, 73, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 71, 71, 32)        9248      \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 71, 71, 32)        0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 35, 35, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 39200)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                1254432   \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 32)                0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 32)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1283969 (4.90 MB)\n",
      "Trainable params: 1283969 (4.90 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5672d950-30c6-4535-ac5e-9a43db12dda5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "34/34 [==============================] - 191s 6s/step - loss: 0.7304 - accuracy: 0.5413 - val_loss: 0.6471 - val_accuracy: 0.4935\n",
      "Epoch 2/50\n",
      "34/34 [==============================] - 178s 5s/step - loss: 0.6051 - accuracy: 0.6900 - val_loss: 0.4961 - val_accuracy: 0.8976\n",
      "Epoch 3/50\n",
      "34/34 [==============================] - 171s 5s/step - loss: 0.5246 - accuracy: 0.7939 - val_loss: 0.4365 - val_accuracy: 0.9032\n",
      "Epoch 4/50\n",
      "34/34 [==============================] - 171s 5s/step - loss: 0.4975 - accuracy: 0.8145 - val_loss: 0.4671 - val_accuracy: 0.7970\n",
      "Epoch 5/50\n",
      "34/34 [==============================] - 169s 5s/step - loss: 0.4969 - accuracy: 0.8042 - val_loss: 0.4227 - val_accuracy: 0.8771\n",
      "Epoch 6/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.4772 - accuracy: 0.8242 - val_loss: 0.4042 - val_accuracy: 0.9255\n",
      "Epoch 7/50\n",
      "34/34 [==============================] - 169s 5s/step - loss: 0.4459 - accuracy: 0.8452 - val_loss: 0.4157 - val_accuracy: 0.9274\n",
      "Epoch 8/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.4286 - accuracy: 0.8769 - val_loss: 0.3913 - val_accuracy: 0.9348\n",
      "Epoch 9/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.4122 - accuracy: 0.8848 - val_loss: 0.3748 - val_accuracy: 0.9460\n",
      "Epoch 10/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.4080 - accuracy: 0.8816 - val_loss: 0.3611 - val_accuracy: 0.9460\n",
      "Epoch 11/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.3836 - accuracy: 0.8960 - val_loss: 0.3582 - val_accuracy: 0.9534\n",
      "Epoch 12/50\n",
      "34/34 [==============================] - 169s 5s/step - loss: 0.3716 - accuracy: 0.8946 - val_loss: 0.3747 - val_accuracy: 0.9404\n",
      "Epoch 13/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.3617 - accuracy: 0.9012 - val_loss: 0.3465 - val_accuracy: 0.9590\n",
      "Epoch 14/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.3457 - accuracy: 0.9133 - val_loss: 0.3449 - val_accuracy: 0.9665\n",
      "Epoch 15/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.3363 - accuracy: 0.9226 - val_loss: 0.3422 - val_accuracy: 0.9553\n",
      "Epoch 16/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.3158 - accuracy: 0.9361 - val_loss: 0.3316 - val_accuracy: 0.9628\n",
      "Epoch 17/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.3054 - accuracy: 0.9389 - val_loss: 0.3771 - val_accuracy: 0.9516\n",
      "Epoch 18/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2859 - accuracy: 0.9459 - val_loss: 0.3260 - val_accuracy: 0.9609\n",
      "Epoch 19/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2811 - accuracy: 0.9483 - val_loss: 0.3738 - val_accuracy: 0.9460\n",
      "Epoch 20/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2689 - accuracy: 0.9557 - val_loss: 0.2921 - val_accuracy: 0.9553\n",
      "Epoch 21/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2677 - accuracy: 0.9524 - val_loss: 0.3314 - val_accuracy: 0.9590\n",
      "Epoch 22/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2621 - accuracy: 0.9515 - val_loss: 0.3171 - val_accuracy: 0.9572\n",
      "Epoch 23/50\n",
      "34/34 [==============================] - 169s 5s/step - loss: 0.2554 - accuracy: 0.9529 - val_loss: 0.2891 - val_accuracy: 0.9609\n",
      "Epoch 24/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2457 - accuracy: 0.9562 - val_loss: 0.3088 - val_accuracy: 0.9572\n",
      "Epoch 25/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2385 - accuracy: 0.9585 - val_loss: 0.3873 - val_accuracy: 0.9348\n",
      "Epoch 26/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2442 - accuracy: 0.9534 - val_loss: 0.3470 - val_accuracy: 0.9590\n",
      "Epoch 27/50\n",
      "34/34 [==============================] - 170s 5s/step - loss: 0.2357 - accuracy: 0.9552 - val_loss: 0.3376 - val_accuracy: 0.9665\n",
      "Epoch 28/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2337 - accuracy: 0.9534 - val_loss: 0.3037 - val_accuracy: 0.9683\n",
      "Epoch 29/50\n",
      "34/34 [==============================] - 169s 5s/step - loss: 0.2218 - accuracy: 0.9622 - val_loss: 0.2793 - val_accuracy: 0.9683\n",
      "Epoch 30/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2196 - accuracy: 0.9590 - val_loss: 0.2992 - val_accuracy: 0.9628\n",
      "Epoch 31/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2160 - accuracy: 0.9604 - val_loss: 0.3256 - val_accuracy: 0.9628\n",
      "Epoch 32/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2122 - accuracy: 0.9604 - val_loss: 0.2873 - val_accuracy: 0.9683\n",
      "Epoch 33/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2002 - accuracy: 0.9660 - val_loss: 0.2954 - val_accuracy: 0.9721\n",
      "Epoch 34/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1964 - accuracy: 0.9669 - val_loss: 0.3007 - val_accuracy: 0.9534\n",
      "Epoch 35/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1983 - accuracy: 0.9627 - val_loss: 0.2701 - val_accuracy: 0.9665\n",
      "Epoch 36/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.2145 - accuracy: 0.9510 - val_loss: 0.3306 - val_accuracy: 0.9553\n",
      "Epoch 37/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1895 - accuracy: 0.9664 - val_loss: 0.3081 - val_accuracy: 0.9609\n",
      "Epoch 38/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1960 - accuracy: 0.9604 - val_loss: 0.2735 - val_accuracy: 0.9665\n",
      "Epoch 39/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1841 - accuracy: 0.9548 - val_loss: 0.2243 - val_accuracy: 0.9702\n",
      "Epoch 40/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1775 - accuracy: 0.9538 - val_loss: 0.1847 - val_accuracy: 0.9590\n",
      "Epoch 41/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1453 - accuracy: 0.9632 - val_loss: 0.2021 - val_accuracy: 0.9609\n",
      "Epoch 42/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1777 - accuracy: 0.9487 - val_loss: 0.1464 - val_accuracy: 0.9665\n",
      "Epoch 43/50\n",
      "34/34 [==============================] - 167s 5s/step - loss: 0.1535 - accuracy: 0.9483 - val_loss: 0.2074 - val_accuracy: 0.9646\n",
      "Epoch 44/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1404 - accuracy: 0.9576 - val_loss: 0.1902 - val_accuracy: 0.9628\n",
      "Epoch 45/50\n",
      "34/34 [==============================] - 168s 5s/step - loss: 0.1396 - accuracy: 0.9566 - val_loss: 0.3050 - val_accuracy: 0.9534\n",
      "Epoch 46/50\n",
      "34/34 [==============================] - 167s 5s/step - loss: 0.1265 - accuracy: 0.9627 - val_loss: 0.2594 - val_accuracy: 0.9628\n",
      "Epoch 47/50\n",
      "34/34 [==============================] - 167s 5s/step - loss: 0.1390 - accuracy: 0.9538 - val_loss: 0.1303 - val_accuracy: 0.9609\n",
      "Epoch 48/50\n",
      "34/34 [==============================] - 167s 5s/step - loss: 0.1294 - accuracy: 0.9576 - val_loss: 0.1956 - val_accuracy: 0.9628\n",
      "Epoch 49/50\n",
      "34/34 [==============================] - 167s 5s/step - loss: 0.1104 - accuracy: 0.9618 - val_loss: 0.2105 - val_accuracy: 0.9646\n",
      "Epoch 50/50\n",
      "34/34 [==============================] - 167s 5s/step - loss: 0.1188 - accuracy: 0.9594 - val_loss: 0.1306 - val_accuracy: 0.9683\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,\n",
    "                    Y_train,\n",
    "                    batch_size=64,\n",
    "                    verbose=1,\n",
    "                    epochs=50,\n",
    "                    validation_data=(X_test,Y_test),\n",
    "                    shuffle=True\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58d6de78-510e-43a5-8c27-c173e59932c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: CNN_bal.model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: CNN_bal.model\\assets\n"
     ]
    }
   ],
   "source": [
    "tf.keras.models.save_model(\n",
    "    model,\n",
    "    \"CNN_bal.model\",\n",
    "    overwrite=True,\n",
    "    include_optimizer=True\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "66b3b781-e96a-4795-be9d-42245f68060d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "resnet_bal = load_model(\"resnet_b_model.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "804d69d2-cc0c-41e5-bdea-282790b3745d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "CNN_model = load_model(\"CNN_bal.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bae2822b-cabf-451d-994a-474753cc67d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 10s 560ms/step - loss: 0.1306 - accuracy: 0.9683\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.13062962889671326, 0.968342661857605]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNN_model.evaluate(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6ff138ea-05de-4716-89df-0d04348a68f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 8s 447ms/step\n"
     ]
    }
   ],
   "source": [
    "test_values1 = CNN_model.predict(X_test)\n",
    "y_preds1 = test_values1.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f3ed5787-0144-4013-b88e-befd3c048197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 48s 3s/step\n"
     ]
    }
   ],
   "source": [
    "test_values2 = resnet_bal.predict(X_test)\n",
    "y_preds2 = test_values2.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "99430ad6-0747-4191-aa6e-5c36b8dd5c1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[258   7]\n",
      " [ 10 262]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_preds1 = (test_values1>= 0.5).astype(int)\n",
    "cm=confusion_matrix(Y_test,y_preds1)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e824e5c4-b6ca-430b-b4c8-adc72c70cbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds1 = test_values1.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "031918c5-973f-4ed5-a1eb-089f65ebccdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr1,tpr1,thresholds1 = roc_curve(Y_test,y_preds1)\n",
    "fpr2,tpr2,thresholds2 = roc_curve(Y_test,y_preds2)\n",
    "plt.figure(2)\n",
    "plt.plot([0,1],[0,1],'y--')\n",
    "plt.plot(fpr1,tpr1,marker='.')\n",
    "plt.plot(fpr2,tpr2,marker='*')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0ff8570-2470-420c-be73-cbaf4ee3adc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['accuracy'])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy of the model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "24db8402-da1a-41ca-a2f6-4871776d0d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['loss'])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss of the model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "55541339-9346-44e7-997c-f6baedbc1cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests=[]\n",
    "sample=os.listdir('pam/')\n",
    "for i,image_name in enumerate(sample):\n",
    "    image = cv2.imread('pam/'+image_name)\n",
    "    image = Image.fromarray(image , 'RGB')\n",
    "    image = image.resize((300,300))\n",
    "    tests.append(np.array(image))\n",
    "tests=np.array(tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c1ed939d-e9b8-4f95-a146-6d4e70e495d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21ee2018890>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Iz/VHjqty5w0yQOdvJItIak9q9A9/T2pnZ2dYnqGVNd17O/vx4c//OH4tV/7tfi8z/u8+OZv/ua4cuVKK5TNvGcHgJJR8JQajXKWnuO7CuNp0PSegNHfY2TAH++HhI7vMULp4pmiEOeB5/Zd6fjjQKZ3uI9Iv5VucQBzhXVQOD09jaOjo2aJufjIOQyliSKilfahIaLRcIDj9SwVRUNM792NjPOiJGtZqs6JvMsicx+fDIBKRHllP/zHU4+lfWgs03/8HnWG+6F8ZRt1zGVwY2Mj9vb24oEHHoiHH344XvziF8eLXvSiJsqazWatjcGqx/cqciylC4xMqPdqU2bcqYdsK+vl6lOVzX2X5LnrA//m3Brb6lta6DBmOu3jUpJD3mffXBYpczs7O/HlX/7lRfkj3dMgdRGP8fT0NP7v//2/8cQTT8R8Po/Xvva1cf/99y+keDKlcQUqPVPKuXt7MyXOvGaBVAmEHAxZT8YbByxd83az7TS2JW8w65ODWdYWlcMUqCsL69L/VFKNn+amNA/IKJKGIOL89A4pquawaBx9nEvePo0pDbQreknxXen5TCm94s87jzMD4c5EVk7WV46RG1kHVa7Iy9pRMnD8m+Og1BYjXzfW3iYZ5r29vbjvvvuaI7S06ZZjlYFoRLQMvH7ceaIuuu6VxqlE3MyfnTCRAYQ7KNQVOhj+PHlV+uE7mS529S+7nzllJXnI6J5O98kTWoWqqopPf/rT8eSTT8bHPvax+Lqv+7r4wi/8wvjMZz4Th4eHCwMYsaiwroBZLteFI2JxM3AJ5FiWnsu8G587KimKCwfLy/qU8YAg5fxkeSyDIOWeqZ7P5qpoAJg6cXDWffJqNpvF0dFR7OzsxPb2dvT7/dbZbeprVbW3BuiIqJLyOair36zfwcQjDC8rc05YNu/7nJe/z2sRi4taOO4uF1kb+K7LrfrKdxhNenqPxtadwBKJtwIoP1HC20CeClz6/X686EUviuvXrzfHqWleyYFIffCy3AmhrpQMevY3eee8p6xzg7k7hSXDz0Ub1Bc/9YapOI6xeMDFIKyXDjzlPiP1hVkFH1f+PZvN4hOf+ERaltM9HUl1LZ12qqoqxuNxfOQjH4mf//mfj8/93M+N1772tfHFX/zFrb0zJL+WeY/0nPWOnvXoJ7vnBsKva1KVHiDrl1DQ8EaUF2YoLy3PjelOf5bpDQfELE1KQ6V2ZtEF/1dbdJ3esCuH2iseigfizdHRURwdHcXx8XFzThv3u1DpuOx4Pp/HZDJpPHa12VNXPr76P0vZ+KGbmfxQLkqKzTKyNCTJn/O5AX+W7SxFFd52l2dfwcfys3q76uGPDjfm5D/TvOqnZCUiGnD6vM/7vHjooYdiOBzGfD5vFkBo7Le2tmI4HDYHzvKAZ87feIRDwBUftCip3++3Vgt7ujc79YJ/U45Uv6j0nmTY73n2o2QfdDCAAxOnHjQ27pC7M1+S45Lzd3p6Gvv7++k9p3s6kupS2IjF1FlExMHBQfz7f//v43u+53vijW98Y3z84x+P//2///fCpKLXU1XtZda65lFI1j73OkvzB3qGK/Vo+Gkg/CgUKo5HNzT0ElhtghUwUgEJHuqbl00j4V6/lC3ifOWcSHyU9xcRzWKGbKm7e3qqWwdYsuzpdNpsLZAX7ZEFAVZzU5yYn8/PV21lCun9J/+49F1t95M3dI2Ay3KdWI/GxT1aH186L6VyWB/Hgv3k+GayQTD0iCMDIv7vP+Sroh72j21Um+nY7O7uxtWrV+PKlSvN4cJyPKQvVVU1S83FMwHhbDZbOIXBHT46jOKxFnPQ0KsfpY3wJQdNc6haxce0dcS5PaAMeGRJXmaAI33MojeOm/Pby3d9cCqBVpdTVqJ7GqS6aGNjozk9nYw8OTmJT37yk/GBD3wgXvOa18RXf/VXx4c//OF43/ve15zxVoqMMuMvAVbKiCvFPF2i8uQp0eORwLnyapKXiydkEDMFyOqgt+p7ovgO20rvVV5mBhaumG5seByRFNf5Sf6Ip/rfPVh6ja5EUnKttOJKP+crvU591kPerMbYzzlz0KI8cBzFE57CEbF40gZXlbGekmL7WDugeBRVyjR4X0reL+t1AHJg8rY6kKpcyqIDHBe6ZNG6G2Dy49q1aw1AVVW1sOFX4zwcDhtjPx6PYzQaxdHRUSstrXqy6EHt9KxGFk24LBPw+Yz+VnRLIOKz7qjQ4VN71WbfJC/Aoi4w+qIjQABk29354Lv+/jIQKtmujP5EglRVVbG/vx+//du/HQcHBwvKMp1O4z/9p/8U9913XxwcHMTVq1ebe11Kl11XfaXns3t6JxuozHhIcAVUjF5oKN3o6zkKvPZ7kDzPnhlyghS9Leb12X7V5ZuM2R7yJDOW6q9/goT987pp7BgR0RB6GZq/0mZN9r+kcFRQ8oQ8daBwkKYXm9VRAqWMlwQGT1XyXXcG2PYSOFG2fB+MyyqBLGu780vlMGXqcyT6nRk1RUe7u7sxGAyaeVBu+N3Y2Gi+MyY5VuSsH0aZmS6p71kaNyOmy1VG9rz2IVLvjo+PG4fZ9YptUnvp+NB5o1wxTUinKptz9fLd3mR2RvxR37uIZa9CfyJBKiLiD//wD+M//sf/2HhUzpBf+7Vfi93d3aiqKj772c8ubMyNaE9kl9J0JaNbArVluV33vmSkdU6Z0ly6l5Ha7cIlT83ne0Sl+TOlDLz/NNRZikfK5rz050sgpfp4kod707rGEzlkhHiCekSkY6g+6GN29JAJMuInUyNueDMiMGZ7pjKQKymxP5uBFoGKZTI1yXdLKW7Wz/Y6SGXPueyXwIXvEAD8mQygyAPNLSl6oFOnaFpn752ensZoNIrJZNKkvB10shS+ohaeOsH7bE/miJYcHo6H+nhyctJKVdNh9Hr9f26+72qLR1Hsp4+dp/7Z5+z9y6Y/sSD1sY99LP7gD/4gIhZX1FRVFU899VT8q3/1ryLijPFMC1KYsuiHf7sXn5F7Zk7yhrKcu+oQSCkyIWhkBo9l0fgqutGEa0mQ2TZ+ZFFEXqr9+hwA+820pnv2mUI47/2e+s56I85P5lA9AgafU/HjctReRVNsA+fWMhliGVR6H0OVpbFgn1im99PJZTGTTTeKzkcHLh9/f871gIZT/SqlAB1UHcT0vJww7oNierskEwJ+faFZfTo9PW3kVV8OUAT17LPPxsHBQfOpGKZcKRelCIaAQd7JeaG86ZovZCAYu/NFp0btYH+cn5mDy3a43Pj8oz5xQzB0GdB4cC4yc6IyWibPq9CfSJDKOp8JOU8V9wH38F/KFLGozPpqJ9NfvE9A0H16ULxG5WR6bz6fN5O8u7u7Le+GCkNwms/bZ53p/snJSdMmKhPbFREtcPG5MypERKRzZHVdt7y6zGNb5nW7QtGA6e/t7e2Wwc0iCR8TT4lsbW01QEV+CHh9bOq6bubqShGNjIvuc2xKYO38c4NJciPp1GUgHFDZbpbpJyxk0UAGXmy/t4npSNcrf4fj5MZR80zb29tRVeefXa/rulndqfbrxPzRaNScKKGUn4NO5hxxIZCncH1OkWDjes5vQqk/npKez+cxm82a+TWVKzvDMWRkz3rcoZNzSodZ81RsM0FY/Ne8sOusj3nGC5eHTOeX0Z9IkHLKlIoKUVImj6xKHp3nZH1lEPPTLNPTaxnQsC0y+qPRaOHEBj9/TII+mUxa/VNUplQn20lvzL3ykteve1w1RD75AhER+0jhz4w2r4u4oEPzEFn/ucyWqzLFZ4KeUn7ZN7eY+vOVldncmOQh45//zkAq84LJL1KXwpfk23mdecUESNaTGSsnl5OI83MsZcC5XN/nSLI+80eyJuch4jylyahK17TnSu2IiFbdXo8cF1+er7EXyLJ/qpM8UPbCHYbMjrBvk8mk0SvypBThSX9la1S3nnUZ1dQB7Qbf83aVskE+n+081P8XBSbSCwKknBwUeD0DDv3P5xhd8DrfjWgLcFaWiBPoXYZKHiP/39g4/9ifPCNffeR1uXB5O5m+Inh59CZiVOXt5ju658rs/C7xSUaJh3tGtDe9yuv01Y1st+pnmkZpUPIp6494p7ZwhZXXwbSjylimtJmcuNJ7GZmHS+88q8tBqgScPjYsszRJnjkZBCcaS3duSu1x4+d90P8EHd8DSZmmDnCsadAdGFkHgTJzYjlG/i555PpdVVULQPk8o7O6Pt+KktXjY83Nv9Ih7SVkG9yBFDk/Mn0tOTw3Qy9IkIpYTEVkSpx5t26o3FiXKBtMB77sWsTinJbP7fizDgzeDnpcBMbMG9KPT7R6nl3X2AaClJS/5HXzb+c32y4w4TE19B71HtNJXo+PGdtfAnm+w02eMhhK65F/bsQzYPH+81l/r+QQkecOSrzm5fkYlLxflyGV6WnVrD+UXQIUHZ8sVZjpVMlRykBLdXN+J2JxcRDT3CWgcaegpPe873M7qov3S/3l3A/5yzR0JsNdc0WUITpYWlTCyLD0TtbWkv3LHCm3VZncZ/SCBakSURAyD5I/PinPdyLOw2R6KkoBMc/riuZKHhEtQMnapf/pOcrwMvLiCjitiHMFVtv1WwLNaIWLKOil0ZNTH5nOIbiTPCXmAkyA4tJy9cnrpyEl/7O+qk38TELmCHCssj0yBE6WrfulxQBZKqXLI81AYZW/yUve5/9uSCibBDLOK2X1qV+sT0dQeV0+0U+irOgZ8l71swzNpWlxhN4tgRfBKnMsaNh93Fkn+alTyDPeZQ6C91kyqzk7Bz22x6cSXM84XlyooTnT7e3tZm9hRCzoC0E3W36f2STyrvTsqrQGqRXJGc6d4r1er0kTMerIjACVgYqTAYTXLSLAleZD1NZsdZorKdugMmmMCRAUVvcomd+m8WB6UW3IFMC9S5YlxdVpAu6tOg8190ZA8+cddLh6kt/tEaldmjzPvF2f2C4Bb8lrJzhkYF4CLvFZ/XR58L6LR760OANmPcv7NMxZn9i30uZXggGNvcsK+6QxkeNEY0seSfYJYNRZpnuzKMN5TZmlzvk1vecbcnU9k3HnuWyK+jufz5u51UyPaGPUB4+KyHtf7j8cDpul+AJEj24zZ4fj5XLszzo/L0JrkPpjyoQmop324CStSNfcuFPQsrLco6bgZ3NdvJ4JinvlVDgqkrdP4EpjxdSe6hBIMNrQb5bL8/B8pZza51RSXvFP6b3BYNA6qyzzyNwjl9JxpWJmfPi+ojW1n/yk8RcfdUKCVl/pR+cFsh7xO3Ng3KBdBKDqun0MD8GdZbI/7uB4mS5rTCVTBjyS0A/BKWuL3iOQ+qKHUrtEWpiglXAaa322hfzY2tpqjZ/3j2BIHvFZnaAScT6fpr/FD/0m6Gh1nWc2MufEjy5ipJ/pv+sux4PgRYfTx1fnGU4mk9bCoZLTovpKmREfs1U2QJdoDVKgzGjwHhUyE2aRDxqVkcrJa278KMSumPzfQZRtoEfoBoH1uReeRXiZd0tyRdPyYNblz3cZYpbHlUjkBz1g/qayMtVIQ+L8Vjv1rHL1WqLMfSLkC49TomFWmZ4CpLF2oGL/PbJxHtGI86vNlC29zyjBZZVGLZNb/s1x9EjJZVagnQFaZmgzeSCfaOCYOva9bOyjAI+OJL9S7e/oh2BPp41bVlSn7xvy8XNAcAdL9TnA+3PaJ+m6lC0MciL/fUxYjpwrLZP3dtBGRMSCjHXVfyPgJFqDVOSLKJypNNB8psR89zj9nQwE/f2sfCqqty0zdl525rkxQnQhk4AyxeZKSQUkdZ2wsGpfZRw4n5PxS2U7yPG0ETd6vBZxbmxlWHTuH8tn2ogORgYKdB7EY6aBsvHp8kodBDLQWCZXXrY/n9VfAj79EKRYls9RkiddxtTbyXLFSz6jfYC87jKte5q/pCPjACngoMyXHA1+50rvsh9ZOaTSClznix9IrOsuSyXe8YcpOgfjkvNXVe159Kxsr9ufY5v4exm9YEGqxFgKXBdIZMqdAZgLpQvLMqDSxH/pGRE9bwdUtc+FTu9lYMJyPNJieazHlV1KpfcVqbhgdwE+jUrGdyc3fsrp6x1fYEGq6/Oji5QK5ccSff5CvGPfVA5PMuCiE9ah9mZGmOPkSu1zAJ5q8zL5no+dz105gNCosR79z8ODfY7G2+//i2hoS+8T7D19rHQrFwI5H/Vbaditra3W96U8mvKPHJIndJh89SB5za0SWaSm57JFOBmwKAqkrDg4ZpG6y4HaTf1k3T5NQd571LyMbhagIl7AILWMSkCSPRfRTptIeHiUiXt3EecCylSCns28FRG9Gv2vsD/zUL0sKpmE2o8LorJRYb0MElOC6m8G1LyX8VDvSME1t9PVNxol0cnJSUwmkyY9M5/PYzgcdiqvxltzX5qwFth5qkPjzHZzjH0pPPnsaSC2QRFalvZzWaBBcQPjY+NRCMvJHA/qAdvHY7bcy/dUuAMoy2ebWIcbYE/B6bmqqhYiKUbEnNORnm1vbzenTfhpChoj1pk5j71er0lnq+1Mt/o3qgjwzA6Qx+Ideaz+1/XZqRmSR8qZ2xG2U2VxQYT4ylQ47ZP+Jog7eFFO3WlQ+1aJElehNUh1EA01c8y8J69EAkRlp8ep/x3M3GDyPfdwaETcQ6WQ8x33yj3VQWV2gXMhU96aX0qlh0W+UbGpCF4mn+PnBfSOlDLrv36rDTSU+sSJpzZ4WG4WyXKJrgCSZYoP9KA9MnJng4ZCdTIqYBkerTlYZJGXOxeqI1uB6bIhkKORJ188iuLHAH2VWGbUvezM4VPUQQCdz89WtMk5oDPCOrRhm23w+Ssaa4HL9vZ2TKfTGI1GrbHSWPADiFm7xXNF2zpVQkDR7/cXeEieExiGw2FrbssNPQFO0ZTkid9VK2UIPALmKfGlT/5k15xP3jfdF10k4uqiFyRIuQHgNf2t35m3nXmZHn34O/rNHeO8piXQ7q3zWQeRkvHxZ3mNRpreagY0zhcCLp9Tf+k9654DMd/N+iOgqus63Q1PsHLPWsaNy515jd/q0T3vr4+fjJYOICaI8R0Hqmx89B5BXmPBcSGfSm30ax6xUNY4fr5Xi06TyzXlm+k9X7UXsegcZU4Y5cLltGQQ+/1+M37+PvXYHScCVya//X4/hsNh66OF6kdmB1wXSTyk1bMiIjpF5IV0no4TdZHy7XIdEa05sZJjnO1nUxvlbAm4BFrUZU/9uRy6MyVed71zEXpBglTEYu69dM3vO7DRaPv/vlydwk6vSYKQKUGpPfROXan8XY+iJPgU6CxCZF1Z//0+jZKDpBspjzgcMMW/LHJ0b9QdBClzlnrTaj0Hx8yY0ltW+tENsBsej5Dcw6ThUJuUjuRRT84L8tkdAV3zqJEOBA2Pl+veMEFaZcjQKbJhPVnairx1PrM/JceL5TLi83pk5JnW83r5DtvMLQKZjnibvE9sg887sZ/OczoInqJmO8jjrA9suz/H+Ss6wi7rGlMduktwc3nz8c2c5BLPS/ZkFXrBgtSq5Ix2D9SfVUQUsSicIhoUNyZOLvgeyZQUQ0RQYgTlRpZGtSQ8WWTA6x72Z/NcBBq+7/MzBBiPLrJTMjKD6gsW6rqO2Wy2cKwS77ujolPQFelw7o9jxiiF9fqKKKZHGYGq7R5RuaNTAi9PDelZ8l/tYX+za1m7VLZPnHvUwrp03yPskqy6LLhT4xuCyQfyU+/wb/ZL5csp8P1Dmb5SNt1QKz3o81uZU+Z2xPWJMp71STZG9VJG+KyDduaIaE5xMpk0+6M8K0R+UU44jsvshrc/A64uWoPUCkQh12BJODhA9LD5bnaN5brSU5l5SKqe8XPiXODdGyU4SaHcW9zY2GjmXEhucLih16MFb4N+q/0EZhoj8kXXs4UkdBB88YcAiIqm34xcT0/PPhu+vb3dWtrONCP5KUUW3xlNZaCtOnyuiWOSpZQiomk7wdnTOzKgWYqMUSSNF6MHpXOyPWMujz6XkfHV5/ZcJjVW+p/80Hv+ORfqC8f+9PS02aTrxNMlVLYvLmJbxQPthSNPHYyoe57Kd0fAncbMkWX2InM4CRRstyJu8YKLJbI5Pzob7hRJro+OjlpfJna++6pUfvaH7ZasurzyGb63KkBFrEGqIQpmZkD4nH5nm+K40kvGL8svu7JLeGlE3Aj6/143DSeFjKBEoePqrMxLpFdJA9Lr9ZocvKfk+JtRpZMLccT5nhKdLuF9YTs4YczPji+LSCPODJoUk1GVR13il1Iizj+1Wf87+Kgv5Gdd163FJwQlvce9XQSLzHirDo1l6TQDlSv+cHGAR9Wl+uhI0aB3zT9wroXnw9HDp4HVdZ/XIT9ZJw2kronnDpyqh/3T4oxMlzyiI+iRJBcsn7rOtjivVL6fGqF+UBZctrk1gm31fkZESzak+1rd6ONG2XcnJZM/H9cSALker0prkAK5oc4iEwKZe1CeOmOZEe3zxihwnh7kfRlJ1eEbWxldcee9jJAWH7D9Luyu8A6yTBsI1Dy6U/8ykMuORKEi86u6ivx8/owCTkXWb62udGWhF86x5dyK+OxpUPJmPj//PlWWKqXX64Au4jVFNeINQdLHlkaKURwjA4E1+ZwtAc5k3LcvaMwIHqpT8kje8V13UFyHaPTIlxLIM0WXGU8RjwyS3Pphy25AXa8dDDKHlfV6ZMZnvX2UDy/L9c5/6Ey6odcznE9yO8V+aAFRtjm41FfKFHWa7WcfnRdeRwnESrQGqQ5axkz3Bjio8o58clPK5+W7d+j3Xcld+SPap0PwtPAsNeT1KCpy5eKz9KDd+LJNfD4iP3lA7RVAKTKih58JNNONbgzYT/LdVy3SK1Q7PS2ndrthFUC70SsBqiuug7Abp2ycS04TwUhty97LxjK7zzYzwsqMsjsdlANvr8rM7unDggR2l3v/n3NH0jEugtB4lkCEf3OM9LfKcF76WHTpdMYL1kPQJzmIZ0Cg9qlM/+Co+Onp2uPj4yai4j2XB+eNO2x6PkvtZTKX6WrJzmS0BimjTEFWYagDjgbQN9cpvZMJQpaqKnl6BAkqpwyMdtXTaGb9yIwQwYVt0DOZt+TXWB7nM2jgBaTMqRMIM1IZ3CTLVAk9b2+PSMY84vyAUucVf6jQblDZf7adY8U+M1qUceHnK9hPv6brpTQSDUqpvK6yMoBnWeq/y5H/LaPtAO+AwbHMxt3BgWCg5yVXkiF3PPg/+0SjzPaybsqt88gdPPa3xGuWJZ5kG+WdrxmYsT+cJxUY8X/9MIJyx8jbmlGXA+v86HpvVXASrUFqCfmAZqBFT4P5YRlQClnmvbhhURk03t4mj7oIUP1+v7Wjnc9lQEivjUrvCqh6uGrPy3cl9Hk7GZdSG90gecrMI4/MkBBMWL7arlSQ0m2z2Sw2Njaa0yiyPpE3DmpcrMHlyGwXfzOaooFxh8H5yX54m2iMyT/KF4GA0RzHclnaiWVJdhxI5vN5s7Gd12h02VaNSWaoHUwoh/w0jE4IIU+yZdweqZE8PZ21h/LkjiX7JefE7QXlQDKoFDL7yBWH+t91hTaCDhXTg4qe1F6St2tVZ7w0PqVnLlJ+Ri9okFrmaYoEGhR2/p15EbrvhoppJaZ8IhaXFdd13eyfYblqk69wi4gmMvH+ZEqi+gQkHgX4s1VVtSaaPeVDMNM95r7VZ31yg5Sl29wD1hhQ6ZznrgjcL6Wx4Nlt+jCe9k9xMQGNPwFUBkTp1IhobRRmpKxxUh9UHlM0Pn/ikXKJOC4qwwHbo2GCI6N8L5c89dRQ1gYRU6eeGizJJNvXpYcZ8Sw+yQcXHxG0HGC5rF7XMxnKiBEw26+/nSi/dGa9ffyhrjPNzHQmf1SGtlnwVBAtdNJSc4KgO82ibBEO7Y3u0XnJnKqbAaiIFzhIrUpupLLrIhoZKQCVQvnzzLvKDIN7fi6QEeeT/lJWn89h+ygwjFZ0j3t9POpjXfSk2Ra2042tDKSfJEEPlP3jbyeBhXLsWn2XOQwkGggZttlsFuPxOMbjcfT7/WaOjOPrRsiXNwsEBTaDwaAlA+S9+CN+yACRh6enp60VjD52Ln9qL+cbNC6sy//WO76MmZGHxtgdNfKZsqMowZck6z1fANDr9RoeKKrKVmoyNcjIcXd3twFc3x7isuOrM3nNoyLvI/WY0Raj54hIVwz6fBFT4T7PyWf1t/SF88wOKJRFfhOKOsvVkZQRJ/ZfdfncbqZjXWDEdy4CWvc0SGWh5q2izGNnGziQGWhl0Qk9ODeuDlxugFmOG3hPFdFD5HWVxRVmJL3HCMv3WLH/GUgQaLiKz4Ezog2ANMBsl5RKhkJGqQTE5Jc7FFJonhHHL79yKbPK4G950kpdcp5MH0BklOv8ZUTFebKI9jJpzq2wPB9HXSPY8BtI+mE6Uu2ikaUH78a8i680Pj42ukZ9oLPFH8mYg4aDpyJCX3GpMks6I/lR5Mm6WA/fJTiS1643dEJ8/k78Y3vo1LIMtxmqW1smyO/SAgbWpfozoPcxYR/0DOdiyRufquDvjKhLw+Ewjo6Ois+K7mmQ2traaj7QdbPkhm7ZcxlglZ7nczQyjIT8ef2deTkiGnH3jnWfv0vKoh9f2u7GLSJa/9Mbc+F1D1secvZ1US/XgTcDbKZEGMn4GNLoZOBI8FVUFhGttpbqp+H1uQo/aYKG38eOqT8fe/KdfGM/PO1Z6iN5ynHnNoWSl0vvP1va7u3w6JGgzuv8m+mlUvQskswzdcx2d+3RU11yDEr6R15S5jJQcAdIIOXleATkNsHtEMdfIOWZFD5H2fS2MeLjeLrOZNcduJ1Hfq/LLkrmHnjggfiDP/iD9BnSPQ1Sw+Ew3Yi3KrlAuJK70i6rx73+iPM5CQmihNxzzrpPgypP2g0Q2yWh47Jt9x6pIMyF8xn32n1zIev1qIn9979lhH1y3NvnAKJ3s2XxDqBSXq5cyrzp0phFRLPHShPOs9ms9Uly8YQT6mqLp8tU9+npaXOKO9udecmZFyt5yYBG8uKpJ/KOhozj4XIt+XFZ9PoYHVC+aWjd6aHMc++U94dAxbm9bN5V464oyo8kosEnuaPIU+05nowqSJyLXTXi94iG16mnzMCQz64bnupzJ1F1u82QHurzHOQJeUNZIT8yMGNf+eO8pK3T736/Hy95yUv+5IPU4eHhLS1/FfDTQBCIItqLJDhA+gw0hc8jEj2bCQU9VQksPX7+qC1c4qv/aeSpcO5F0VipH4PBoDWv4GAjPnCJOftFjzCLAt3r5DXfsOzAyLQRrzsvaaB7vfbnEvQdKn1Cnt4rP/+hvkvxuehCE9Uy7uJlZjg1HkwP8iQAvUtiukopypK3y8/LO8gQCLjIhWCpd3kkjkdMmYPlIKqxzqJjL1fjrEiJ73QBlI8v/3cHj311J5E8n8/PP6zoOqr2UNboyHHsq6rqnDv1djANW4peqdMqR06Tf6pG4810cEaq21PQ6p//7eDPPrF9fO/o6Cj+23/7b2n9Tvc0SN1OKoEHBdEVxVM1et/Lo7JzoCPa8wAsX/l4ASG9UQcWVzpvq54h0PrEtTzjjY2N5qNxqteNsdq+tbXVRCMkKrHaxfQaiWXSKxcIOE+cX94uRjI0IL1erznPj/NTLFsGUYfOsv0cM/FSCzq6DEPmvZLo9HAhAqNxgjLLowzIkfIl+QLGyWTSilRI/DaXgJq8zyK++XzenIknABbPPb1GB0gkfggYtZhlPp+3ACojlud6FxHNBw+5P41AwTkbOgMESUYzXFlInXFZ9PZINl3fPLVLB9PbGxELstfr9RZOQffoT/WWynOeZePscufllMbV+76M7mmQWqWDyygzEhTYTMg1UPSmfTDkkWvVFcs+Pj6OwWCw4NExj07wKuWOXel9DscNo0dEjFzc29bqLE+3ydDpbL3S5kAtSMiiIa/bo0HykAskBBzz+Twmk0mrHzylXOVkS/dpFOkpc95MfNCBte4pZ/MlVGCNCSfOu9rR5a1mz2lsCLrugNAw6RpTuZQNeuEqT9d5SoGecSdKv1Wvxovj605JFpGwz/rNfimy5QpWkfehxFttNxDo8n4GbA7cWRrV577cSXTnKOM3QYpRlC8iyaIV1+uMl4xo3PnUffYpkzvKsoORO+7O12wsVqV7GqRuB2VRgK571OIGOQM7N5AR7eNmOJj0hLIIww0Af1SXlIL/eznMc8vg+f4m/daqNX5FV0rHHL+eUf8dqNhmj4DUDj84loA1mUxaixZkuDj/Rd7QGNPYqn00quKHnmd6NOMh+0jjzcOFyUcHLMpFRm5U3KBlzlpWRwlc+Zx7yeR71r7MILtX73NuGSh1GVqmP31/V1e9bswjzs549ANRnb9ucD1lR6LukOe0BSwr6ze/bqzryi5kERifyxxAj5T0vzuUBCOOe4kyXntbSjLcBaDLaA1SN0gUQBqKTFFKcyuZd+Mg5YCjvz237zlwXXOi8qos/0on7/vS0+Pj41bU4l68ohqlQFzo3SPV8xHtSXt58Jys5hJv3tO+EAGu0kEyaNnKOzdqAkXO0WnDrzsKmTF1L7OqzjY+K+XFMXQPnnKTOUEqk46EAzrBOIsIHGSzdrhXzTSmp3r4jqeVVIaDesnDzu5ljgDnLh0UvK9ucCXf/G4SqRQJZGk8XVeb2D8CA51XT4fSUfJP5LjD4OQOJflc6pc7zc4zEiNw8tTTkCVnwOXD/+4Cs4zWIHUByrwWkqf3JEh8xz0OGmClYRSlHB8ft1aYsQ2cpJfwyEBHtL1vgqRPtLqnGLEofN6XzOtTPYzMRJmXx7ZHRPPZAEUKMiT8HAE/W+DKzihLP7PZrEkPeTqQKTOPCKnAnEth+z1C8jGV8RZAZt4uDY2TyxBBgnXwGc4pSh44Ll63y4nucZwVzWocCOIlGciiTo0rwYxjyDk2d86UOtZhtKyHbdB1EeX6+Pg4xuNxK83n8k/eOJ/1v/istnP5u94niFDfOO+s5zS3y/H0VbCM6jMgV3ZD9Xh5lF391vhmjg2PRGNK3AFY5Nfp2Pj4XAScRGuQugSSInHnuisgjQbnekR6jqdR0GAQnCjMNPZMi0S0vVkXbi6ZppEuebvuvVFpVS/TfF42IyzyhidG6DmPdhxQvX56qGqzDOtsNmsiK0ZXep6rBWm8PLIVz1iX5/cd1OV00KPOVkxl8kTjwXZpvN0QqM/OBzoFukbjSbCkI1TXZx+RnM1mCzKl9/gs5S0DkJJHLblimWw7V4hyvCkrWZSnsuXcjMfjODw8TNOvbJM7IC7H1DeBp/fTdY31MG0q+edzDiTuEEv/KXvuOGZ9kk5pntWjNZd7/8ac9COLFCVj5NUyGV9HUjdA9JYyo+dKlnmJVE4fBAcrN/7+DgGOnhuFi0JVMuBO9ATZLy7aYHlc6URAcuMyn89by7VJngJUfczFK9Xm9TBd6m3z/S1UEncUZKy2trZid3e3aRMV2Mc9iyQHg8GCQaAyZ/N/rshu2N3IsK8eQXvbMhl148H7NN7LZNXTwpQ1lumRU9ZO9jfTJeqC7uuYL0bBkh1Pc7FOlSMZ02o+OnxO7IvS2GxbxPk8o+qQTLM88pXlZjrvezzdpvB3lz2iLnvf6KT6mNLRcTnxcStlT6inmV0sZQrYt2W0BikQlT67VwIrCpQz3j1VeuwUHk+FCAiyiIsCxXqYCmHdNFYOTpmBKgkPhc6ByCMoKicXNKhv2jRLEGL53m4npuF8Yp8GUgZZk+VqE6NQPUtj57yNKBtHT2vwx8titM0oiGPlgMCxo5PD52g4MuOdyXXWR44B21Ayivo/S2tm/eczBHGCu/hEgMrmoeq6boFGVj9XhjoPsnZ5f0R6X+2QbtIZ4FiU+kvZ9D6L6AT62HXN/7Bfrjt1fZ5d8EhHY07ZW2brMieE9zIbUrq+jNYgtSJ1GW8+47/d+/EB1W8Jj0crTCeIXCGzOjLh1bMl5cjANuuzK6O8Sl8+HxELRkaA4XMDMijeB58bo/IKALlayxWLbSH40UunMSgZVb0/n5+fLScw4GZa56X+Frg56LrxdEOmPrMdvV6v9WkK1sHn+Lyn4Lx/7h27I1UyiowQnXcEdPbNQZTPq71KzfJ9N64lJ4z98RO/S3OJPgaeFfF0FrcrZE6g3mObCAJMafu4q43qr2dbPML19lNXVBedQo/CnQ+rAonzLgO2VezmMlqD1IpUMvr67d47ja5HF55uYURFoGLUwpx45iVzPkll0zDSE6VRduOSKZDa6JPABAulPTzdRcXUe9xIqfKobOQTPTs3eOqfgEabTFm/pyGUl1cqyI+TYv0OfFV1vgRfz/ohsg4wBG6fOPdowvlMQGaZ4hcB1ttAeWUfS6kXymdmAJ0/lB1+dsWjMMqPZIXlu26ovdzcTXnkc4rM/dBjRQ0yzHVdN/vWXDY4HiyXsuf8FO/p5CiyItBn8kw9d11k+eoz54/cQVNZnIvUPTpOs9ksRqNRc419Vrme1RC/mDqX3Ol9gTVT5Z4i7wKqVcHrBQ9SmYeYecTuXUW0Ack9OpapqIhzPAQB7ikiadDdiLJsRiF+0KYbXbXZy9L7vhBAZfAdLfemoZQh5iQwJ5dpNGQ4XPlYn0deDqgedSqykOdMI+nARh5oYYCMDY2d5+BpxFge5xrcQ/WxVFnkMWWC7WX73TOmfNKD9zSw5IaRjqfN2D4HeK/bdUB8p0HL3lWZWbTtvNRJ+Q7MLsM0fn6ih2RNG7497eX9Zt/5THZP/dDGYuqHQHU8HrfGlGMXcXbiBQHDgTqzG5lMifyDjyI5hOPxOCaTSTMHpvFRu8lb76uuZQ6Q+u6OiZdXSoevSsu/rHZB+qEf+qEFz+tLvuRLmvuTySS++7u/O+6///7Y29uLN7/5zfH0009fdjNumJx5znBncibc/h4XKkQsLvWW4Y2IRsEoyCWv1P/2HDnTE27Y3HvzVWfu4bHfEkidPDEYDJq6Od9D4FCf1C/3JrkcnPtYBEJcOu685ibizOCwHgEk0x/6P1uWz/7qb0/T6Ige8tyBmoaUbZSRdXDmWJfG3vvmyu+nMmTOicuiZIOr2BSt6gQR/a90XBY9ez8pFyRGyZlBFv8YXeknG2/Nd+pYII6BR1yUI/Wf/XZZ0N9Z9C1+aBVpxnvPZqh/WTsoq+Iv05Zqq8rwcRUISbalR5RxgqHe8xW+7B/L528Sx/eiYFSiWxJJ/bk/9+fil3/5l88rQZTwvd/7vfFLv/RL8Qu/8Atx7dq1eOyxx+JNb3pT/Pqv//qtaMpNEb0wNwSZoZfh9jL8f07Csmzep5DRsy9FU66sfDYzDJ52Yb8IRtmcgN7j6ib1iakSCT0jKCmMz7d4lJJFJLxGT1AeHZUvm3uQAqr/9HRprLkowx0EjYkoizB03ede1I6svRxDGm6fs+r1eq2I1QHNZYN95HXW6ZEsnR5693yXDlBWNn+6ogE3zhHnRpP889Qn+cGxYYqKbaezkO0lKo1T5oQSzPQMnQud/+jlswzy242+2xStfHV7U4p+JEM8o9DHkOWT/5wqcJDJ5M352AVgN0q3BKQ2NzfjoYceWrj+/PPPx8/8zM/Ee97znvjGb/zGiIh497vfHS9/+cvjgx/8YHzN13zNrWjOTVMWTYky78KNf1Ye3ycIeApRikfAySIcKlbWTl2TALrx8rawjCwdQUBi+o8Gm6dGCKRkRHweytvhPC3xjcrlESPBMzPKIoIxU2Qlo8o8vXvZpXkhBw8nj9yyMrreVxlubLN6PUogf/QsoxufuyFvKB/eT3daHDz4jqfrvBz+XUpxugx4xKJPVchhcb4ygmJf3IFgW1wXVZfS4s57l8cuEs8UFWbRDMvk/wSobB6yBKBuP0p2T+/59QywbpZuCUj9/u//fjz88MMxHA7jVa96Vbzzne+Ml770pfHkk0/G8fFxPProo82zX/IlXxIvfelL44knniiC1HQ6bR0vs7+/fyua3aIury8iX7IsAafQZIaYXi5PQaDxVlnKS9P79+hDZXmExIjFAYTtqOu6lZ5wQXODxPQbFd6XdOswTx5SyrQZ+dqlHJyw9ffUd39Pz/pKxrquWxP9ekfpID7vqQuWqwl7N+hsI/eeedTEMjnelBlvt7eD5DLlaUM+R/DQO+6c+PyC2s9UF8vkGKpclwfKo6jX6y3MqVB+KbPeXtXn+3Qy4GV73KhzDAjgjCjIG/1QLnmfwEzeZPrIPnYBw3Q6bWQ3kwk6tZPJpFkoQTvli6GyukpOeDZ2Jb6UHOQbpUsHqUceeSR+9md/Nv7sn/2z8alPfSre8Y53xF/8i38xfvu3fzueeuqp6Pf7cf369dY7Dz74YDz11FPFMt/5znfGO97xjstuaidR6UoeaUR7FZcrK4XZN7m6UlFR+R6fVf2+X4fKRQXk/+4lZnMA/M25BrZVu+wJohGLK8g0/6Qd7r6XiYaISuw8cH6J3IN1D5tzWqSqqlpzWDzJQMfuqJ2aY6BMREQrBUugcs+aMkBvnDLCqLYEWrpGQHXe8D3yge2ikfd2uMxkRoXOjOsBAZ0AJ+OmVWisi6CX1UuHgQ6dAwBTYow4dNQWI1+twJNDx/1dmS5wXHjdx83T8f1+v3U+Z13XLdBQVOfRSOao6F3JpmwJZUqOIDcuUy84F+UOA3mvvjh4OUjJMXP+8P5FrnfRpYPUG97whubvL//yL49HHnkkPv/zPz/+3b/7d7G9vX1DZb797W+Pxx9/vPl/f38/XvKSl9x0W2+Gskgroj2nxJw1PZqIxbkYGUkKoJSbRodecOa50AvODAeNmJ7Pfph6pMCzXJ6gQeU/Pj5uol8/ydwNdV3XrbPH1FbVzY2rVDamFzkmbmiyvinCUd36ezqdtvZ1ZZPvHHuVlc0/uSKynNLmUo6xys/mXMgPRgYah2zcdZ/ty7xdXnd+MoLO+K4yfN7HQcKdIxpJtlf3vM3UGYEfQUJnX8rxY0Ste11GlL9d7rRIhjzW+Xs+TzUcDpu2cfGC2sSUIHlZ6rOe1XwXF1T4GYtdYMDxkLOVRXoqxxdvcJGTymP7nZ8XBSWnW74E/fr16/HFX/zF8ZGPfCRe85rXxGw2i+eee64VTT399NPpHJZIK8juBip5OjSsvMZ0D8HLB889cTcQqpNpArbHQS+bc4qIVhs8QvSog6CiZzgPJSMqgJJAK4IqgZ/31/vH/zNDmj2ra54Syk6jkOdNHuu65s5oJAaDQTElJn66t02+ZnMojCoyZ4ZApPo8Wua7njp2Och4pTF2WeT7NMb8u8R/tTUDMUU5fJ91+8qyUj3kPY0qyxJ4u0Pmc0LqEx0xyYKDonjA9vA9Pq/VrXrG28pISG3zMv067xF4Pd3p51FSRp0n2fjTiRQvCUyuz9m7Ge9ulC59CbrT4eFhfPSjH40Xv/jF8cpXvjK2trbi/e9/f3P/937v9+LjH/94vOpVr7rVTekkZ3Tpfhfz3QuNWExT+X0nV+6uNpYAgHXzfxcs9/yzEx9KnjXr47wDF0lIQUpCTWPTxU9/d5ngq51cJu0RCtNQTGvKK9VSeX5/iHWzDG+veOKpKQJX1zirrFKk5eU6QPnzXndGJWdAZTNd7WV6ff4/I1Ffuq1r2fJypvcysMlSyJ7m8rQ2ry/jDXmSpcepA3xH9TMFTjkjX9zxEWXzRxFtwBMPqOeus6wvc/Aym0XHKGuD60GJZ3z2ZgDr0iOpv/t3/2688Y1vjM///M+PP/zDP4wf/MEfjI2NjXjrW98a165di+/4ju+Ixx9/PO677764evVqfM/3fE+86lWvuitW9nGwSoNTMijZQGSRQMlQlSKfDBzd0+F9AkbWRveoVAbz3WwjjRXnX7KoiykHBwEqVpbqpIH1qML553VnxqWu69YnB9RHBxXVn/FL12azWWrs9G6mgJ52dIfADUpJhvh3lsZVXSWis+HtznjnRBBkapNjyPJZJuWxBAqcXyulsNkWjqXkjF8OkLPFCDMrw+tw4HE9ZnupY3rW+aH+8MxK8sp54gCyitPGiM/76HNkWX+pX94Gr2+ZLfPMjv99M3TpIPXJT34y3vrWt8Yf/dEfxed+7ufG137t18YHP/jB+NzP/dyIiPj//r//L3q9Xrz5zW+O6XQar3vd6+Kf//N/ftnNuCPUNSgeEVCB3BPMDmqNOF8JpdMlSkAn5WGqUSRFY/jOHy9TCqiz7iKilQ6TQtR13XxQTm3xPSuqn32l1+tzbZ6ayDzATOn1ruabtPiBkZ+v+suMtvquPk4mkxgMBs11efol54Vt5pyWrxosec0OZs4/lp95xiKXN++nl6t6fY5Fz/k8VQZSnJPyOT03zpSBzNFjOXVdN+CkOrgAgbxRXSqHS9DdaaKTUspA+FxUFg1xDDnuvi9Pz3BFKQFPCy9YDjfcU24cQPyYKvFI72WpTLdNpXFwZ6vkWJXk9Ubo0kHq3/ybf9N5fzgcxrve9a5417veddlV3zJa5m1GLOZxec1TFRowpaJ83iATHk6SR5x/2kICw42XpfQZoyb99pWCLFdl8MgggSgXOsir5akNDq40DN43/eiIItWdRQgqx8dFbXelYErEAZ1AyLy9AyTr9GX0m5ubMRwOF3isOviboJNFkxlwOqh7yonkUZ2DS3b0FttAGXbgYxSXybqX48RnffLdj6Air0opP6bRHLD5rs8Z8nMxAjwCpMpWuzyK8kiQEYTfEwl0JpNJJ78IEhHtbS7iG9/VD+fyaCs4RnS41G72j2Os9lMXfL7Qy/Y2XSa94M/u66KM2fRcMkWlweeEIwWIQpjNWVGx3CgxxKcA+0IAL49KFnG+ykk/BCEqHleL8Z6iMRltgRv3Q/Hrt3rfFY0KKH4RtP0Yl8wwqi1dzgS9Wt/IqTL0N8dDKwFV9vHxceu4p7qum/PrfI6OYMS2MULhGJUUnNfckJCP7iHTUJXSXlkdrCszSnwnuy6i7GaOjEfxbBtlVnNW/v0l1cH+0NhmqW8CGA8HJtBJD7xfPFaJfKZD48BCcNza2orj4+MW7ym7BFlGNNQJHw/qlq5zc73z2W0XgVbyks3ziTfeBtbrPxmt8ozTCx6kVomSujzGLiXVux4qs0z3fPWbXng2aRuxuHvdgUvluzfZ9Y4bORpon7eJOD8rjUrjSu/XnIcyYj5v4Z5mNg5uMPg/+eTA7l6wKw4NEPmseTv2h0t4vW3Z+MtIeoTNen2M+AyNDSPCUh9KfMu8fnegnHxcM9nlb7VNhi/b1M26CBJ8X8/LwZA+MMr3fvv8ofql93l6PvuvH9eZLIIt8YP1yZEZDAYLjlrmeGWA5WPDesSvTF9c51xXnDI7UHJmMpnzui6DXvAgtSr54PnktRskCldEeyKd80UsU+X4BD2v8R6FzZWUZbun5IaNiyVoMPi5BCo+jeXp6dmnL7JPxmcCnxGB1Pf4ZAriAJaVnwE42+VKmoEBDSyNJ0+SUDTJBQBdxjsbYwcE8rrEL/ZjmTFw2eQ19TEzWJlzsAz02F+BCkHKF9FkY1OKELRZNeIcpPr9fgyHw1a7MsfA2y1Hoa7P57j4PGVZY+HRDMtaZqg3Ns4OY+axTNm4+HipDDlxBHf2y9vg9qVLF11H/Nkuhy4Dx8umNUhdIpUGKZswdSWlILrQOBhxU2km0BT8bF4jazc9VQEG51s8jaFrSi34Yo/ME6dB9wgoAxs3VFV1tpPflZzLezNjy43GKp/p0dICE9WtcdG72uSrZ7QZ2cvXpxwyfstIVlXVmiAnOdg70Zj6ddbDiFgG14+1ogFcBlwZGHj94gHnnwiKmUFnv91RiIhmewPr1okm+kiifyiRfMrkyVffedpRP6XVpwQLEcFP+iRd4jWRxsN5Q8dIji37Il5QBnzcHJC8bo9Yfe7JnQaWR2d3Gd0MgK1BagUqGQs3rPQ4Is5Pf/dnsvmBiFgAKBpQnjuWve/pErXboy/9yHtkffQo5flRQXzimErqhocGj3NXupd5n1QSKiT7q7q5vNyBjf3khxBFvV6vZcx4340NjZPK1vyKgNkV//T07Ds+WlXoqVr1Xxs+OaHuRqFE7oW7R82+8hnxTjzNVha6oczq4/OSVd1TdO2ff2FUxYjFZSibg2UURTo5OYnxeBzT6bQ5xmo4HDZjzHLc2VAb5Ph4e9lvz5z4vQxYCNCz2Sz6/X7s7Ow0dfOL0mwvZVY2gGlC15uI9gIhl0vd05xgyZGVnGoloKdiXSZc7m8V3dMglU2MXpQy7/ki7/n7bhSkZFm6QEpJgXGD4l4u73kE5eV7Tp6REpeR657qkPHkwg83UgI0vTedTlv885OsPbXF8vSc6tXRM843PSNFYtTnfadRckPMCML5Tn7Qw2XEQe+W3qSXoSXw7ryw/9pwLAPsHqt7xVmU4J6u2iUDmm3+5ViwzZIbGisfJ5cbvUPDnKWHsvSep6m9v5rD0teUfawp//wMDBdcUPZ9rNVPAQSXqDuPOX58nzJHZ9LHXPLGMyKzqJKUgQf7QKe1BPrkJ6+57LIfBEK/l5XlY+2OY9Yn8rKL7mmQWsXjvAi5kuia6uIArfKsA1A26BQSXpcAnZ6eNsfyeF0uSCVB83fdaLuweQqMRo3v0BD5xwh9bMQDTnh7u3g9i/4izqMblitgJcngkF/sNyfg1R+mUT3N6v1wB4MGU89x6buMh5dDw8aVoARZPl8CKt2jTGRLptleeue6T4fIAYNOl3vhMrr03vUenZnMaBKwWJfu8TPwnvaiDGmMeagxn/GIx/vNqIUZBec36yOR14yiRPP5vElJylFU6pi89BWoKtezKIwKs7Z5NsMBoWQDMqfCo1DxyMGo1J6MuCK2i+5pkNKy1NtBDgKkkveQgRhBw5XUjQmfd2PN9yMWTyGIWDx7LyIWjCXLcXAoGQ+PsCjQfN9BWm3iiixdy57NQMwjgswR8P/da/Po0I0fx0KURVpucPk3x5beNp9x4M68aj5XkgEnPePn+AnMGUVxDH2cMlnOxikiWpHtMjAq1ZP1jaeYZDKZjRfTWwRqn3vyd7O0MufveI/OJJ1MtsHlmpmKfr/fAKr3K5MFH0fyy+0Pr3nKrkQOSu5YZMCVARvb10Wbm5tx/fr1+NSnPrX82aVP3MW0vb29cIrwrSIK9SrPMucvD849nxJARbQnvUtenHvu+s0yskNB3TjrHut0w+0RQrbfSe8qzcI0Csm9X3rnTMl4Wkft4IS8nveJZ3rPPAtuPB63Fm94BKO6/Yw5B0X3fglG8jzplZciHxonrhjM5M0dJbWFPCudasF2q898jkZJbWC0RJ5maWSmK9keN1x0kjKwoqPEMXZgYV3ZnJD6wM+yKFJ33dE4cZyzv9V+tsUdJZXL+SPyQhvWdSrE8fFxjMfjFn/YPxHTiC4PHLtspan3jVEVHUqCqqdGKduZQ74MoBxwd3d348//+T//Jx+knnvuuVtafsb4rkHIPJyItsKTJGBSRB4ppN/uxepZVw4Jmc/NyJukMXLjIeNQVVXzuRAeYUPFUCqCk6ucG6qqqomSdOadG2X3wkRSDL/HtIDz1lfu+ZJnfgp+OBzG1tZWHBwctDaG0rg7v8U/bnam8VY5BBmVyfaS1zRuaoPm4Bycs3Fiu/mOp4I43nRCPHohrzmeXjf7nqWffeyYosvazjpVBtOwSodpXopRDeVdwKMy1DaNzXw+b76BxpSeRwaql/NrPoY+vpRBkfSD+uz9raqzBRt7e3stZ4syQt7K4ZvNZq261E4CqvrFzfQeEXGsXD7VJ/af53GyLJdvlwcntf2P/uiP4n3ve1/6jNM9DVK3g0rM5n0ONEFA13yFGPfZlDxEXRdo6FSDLiOReeteHo1FZjBksLlgQLvkdU0nhLMts9msWXJNY0KD50LsRptAJyVjJEalcqXLIjDNTbBfvV4vrly50tzTiefsv3669j65smfpTt4jMHn/KTcObln94rkbTs6BKApmvzwy1X0Zc839cAFCVxSsshh90ZDRI+e4epl1XbecDf1wcU5JVtV+yZzz3oGGxyJ5BCr+ySnxKFptZX/oPLjs+ti5s6b2bG9vx3w+b2SSkZ14y/a5c+UgK32g3rMd4i/7xOg901PynD+Zs7kstXhRWoPUDVLJ4/GfbNBpPCTcFLxsTofCpv9pMJhu0jPu5biwuuJnG3fdo2bEwvb7gbeZd6XnM1BWBMa+sq5SJJDxhfV65CPjPRgMmmNqCMRsJ1NDPlfAsWU07OPVNR78cWPs75EPmYywTPWVxtP5kdWvCIBRhhvqLMrLohGObUY+zhkg+ze3fIy9LI4VP6Qp3vFLuXSMMtAUAFCeGYHR6WBWg5EogcYdU/3u9XrNVo+I6IzenG8Ri+la13fPMJRAhcDC591WZDbO+1Qa8xulNUgl5ODjBpcA4QOyDKT0DEFFQpHNH3m6jOTXWRffzQxrJky+yTZi8dwzBw56c95XGtgSCJMXmVI6nzwKdPL+8jqv+Wka/C2P3Bej+HNsYwY0DtilMWP5/E0eRix+Vdnf93L0N/+no5KNrQMr6ynJvBszAsUqxosRg55hCsyBsTQOIo9YVR7HlqDrc2iZPGaRaMkhywDDI33yg3sSuUiEY8O+OSj7WGRjlAFLVn72bOndbGxLv2+G1iBltEwBMqaXAMANlhs2/ZZw+gbVLIURsfgV3lI/6OFRaNxzUvsyD5htYUTC9mk+hfccwFgWvT9FNm5YMoDL+pwBAo0N/yfvVa/mmjY2NlqnUETEQjtdJjim9JY52e5jnslURHlBAetSGpZjVyrP61f73PnwCXFvk/OUsi5wyTxuAg/77sZd/eJkPh0NOmue7hJRPtVOpsEEOqenZ+dMas6V81OUP/KbQOMRHa+rjwR/5y3TvnpXc206kUJpP85jZzKu/nCujmNBXrIc6hgdY8mfy6CPbYn/Lht+/2ZoDVI3QZk35UaMRiEDAhoPVxLOQ+mHe2c4Yao6KHS6xvoyb09LYlmPDEVVtdNwXCQhI69v4mQ5bVdq7l2RoXB+ubeockpRVJYiEW+56i+ivUqKY5Iti1e9VNhSdCVHQ+2Uw8GVhJnn7Q6D98EjvIynLjdehsjTpC4T3kZ/VnLqE/Qq20ExAw9vk6dQdW86nS6Uq/Sd+EwgohxRL9ww+2pgRszeBradVHKCqM+eumaq0Z2w+XzezDsPh8PWnK/6wKiU17Oxkkz5F6XpENOJZVbEZZPvevmZLt4KWoPUClTyEPyZzPPPnotop/PciIlk0GlQ9S7LULkCjyzSojL70SgOgK7gHhlob5oEnkaapHZwboAAR9DzSIzvS8F5zhrBR19mdc+fKSd3Hgio5K0bEPGTnvyyORcanswbp1NCkMvAxb1xT/e5PGQy5oY0I/fW/X+RjxO3Ivj77B/Bnd56XddFY0qHiv1h+bzuoOK6of+56lALKTIZEf/puLgDJ/DU+57iLM331HXd1K0VjOIT50uZ9iRQO59oQ3SdP76x3R0P/mT8pq44mDtIXjZwrUFqRSopsf52jy3zbv29zPOkEDDioAJlaSNFCO75E7Co+CTP0UvQ3fMtCa0DDZ/xqIgKr9Vkes8nd6mU4oOiSzd+BC6fyHaFYvt5jW2m00GA5GS+80i8yMDZDS7f8ahO/VUZfN/39NC4lowLr7mMZo4XPexsrpDGTPzwdjp4sS3km675vjt3zDKHSWVR9vmT1e+RiWSMURkX6pAnbJs7UqU5VX+fVMqu6GgwbpNgvQRl6o6eU1/VBy5Dl0NAEC5F2J4+zDI+mR28bFqDlBEFrQuYSkaO79LQrVJPZkC8bBlHKoV7zN52CrenljKAilg8VNejKyoNjQLLjGgvPZaga/+Gcu/0Qt2j5kGwekbt4NyC2sxnNVdW2nicjWnGUzdA2XL3Eu997NzouuHxZzKQclB1WVrWDt4ryWaXIdJvOiZeXgaYGWXpaI8+vF/kkxttl2HKLdvNvsrRcQeF9ep6FuW5M6ay2FbnM9vh47m5uRn9fr/ZE0WQYDtZRzYO7gT53i2mr93Rztqa9aFEF3l2Ga1B6oJEIXRP1BWJz1OZMkVntOHekIxvZnQcIFhnRL681I0vo7VMiaXIVXWequISVU9tcf7IjftsNovJZNKKovQOUxL03Ohp6oTouj7fz0JPWv9zQQT5o/QkJ5qztup5T3USLGm83Gjwfx9PXSOv2Uc6A5lB8jLYP77nk++Z09UFIG5cXXZdzggOLmcir9NTaB5Zer9d1n2MVAf5Sr64Pmhfkp7zkzpc17KNzj73xfapvZzL1Xsu33pHc8T9fr+piyejZ3LpQEIw1T1+sZfj6Q6T9yFzuMnjkkxk11jnqrQGqZugbGI18xwzYyAvhkLmxkxG1vdXcAJdwpIZGxrgzMNlXRRUN4oerVBo9a7a6SlD5dQnk0lMJpOYTqdR14vfGtL7vj9DK7LUbh3OKaDwUy3EF+X2t7e3Wxs4yU+enOFGTr/dGPh3s/isLwRRuT5HKIOhPtFB8HK9LI6/g5SPrY9ZRpljw/GlIWL7/XkRnYtlYMg63NCyPjkfWURCuZO8iOf8mKf3S//P52cn+PPT8AQqX3mqPlJPWG52fBHrd6ByYFd/h8Nh6/SUrB9ckcrnOG4ZcJP4DsnT7iqD9Zdk4LJpDVI3QR7ql7wE98JpWNzDowAwipGAaZ6D9UuIslU62dmGinxK3rCu+RFJUoQslVPXdattJycnMZlMGpARIDES4xyUNgPznhTR+VNKM9LAzOfzODo6iuFwGMPhMPr9fmtOK6L9rR5XOCqoDKHARH8LJDMj7AZX1xThqH4+z7/puWYGPgNVByf+z/kjRoBd5ZacH/WB77NO97AzI8j/x+NxazEDD42mbHOBAu8LjNRed+qyOSQCvOrkGY8R0TrtpOTMEBzUNh187UArvjlfCezqq0BWCyi2trZamQfVr36zLR6tuW3x8WDUmc0NUhZpr/jcRaOji9AapArUZQS6nhF5VNIVbTFCogB4mEwvKVMWrXIjeerO65SiygAzYqHCysi50vGn1+s1H6ebTqfNMmK9z3Z7jl9lC9B0fTAYtNpfijaoPDSyOlB2Op22PFSPXnznfmmMpbC93tlJATJIiow9qil5nFlU4Pd8AYAbGPeayRtvvztQbqTcQerylL0eb48f1aN7no7ydjiYs0xGSeI/289xId+zhRTsn8qUgZfcM7Ki05TV79FUtkgi66vaRzBQfySPaos+jMnshOr1NLmPq/+wnQRf/aYDmckD+eD6uAyouqL6Eq1ByijzYDPvLbueeRy6zvcyheH/rIc5dAcoGmOfx3AhYvvcADjQcP9TRCxEQaVjnebzeZPWE9iw394HkVIu9DSXGU2mFQl27IvqJ4j4+wRnLkdWWzPvkYfWik/ZJ2P4rjs42bhk4MJ3M+OXGXa2n9d84QvrzMDYy8oMkq7Jg8/aw3FWWfrfdSaTbzpG+p2BhZ6VPrAsym2mcwShbJ+Tfvw7aAQWlctIm+Ppxj0DbF3XAiECf5ez4TrDe84bd8jcLmRAkjlAfo9/u7zcDK1B6gYpAzPeKw1Q5k3Rk+K79Mpp3OkZuoB0gZSeYWrGlZwARQ+W6QhPsVVV1SyE0Ke8mUfP5sWyn5K3S6WhF8i5L4KI7olfnsJQPdzAK+PCKC/jL8dQxiOb+1H9dCB8LsJBKavD++xGnVQqk2NNPrlBKwEp++vlE6S6HCKXawKA7pfScvqfWw8cyPgsoyPez7YEsH16j84VnUTqFxdEePsz/XSQ8rpLgO7lOZVAysfV5UvvuNOSgWBJF7r+v0xag1RCq3oAbjg52P6MK6uELzPerpgsS0qRzQt5m70Obx+9RF13w+0AJcOs9kWcfxPn+Pi4mYdS+UxhMkoSkKkfbIPzlnM4DtIEAvec1baM5x61KSryNKbIPU32y5+LWFwM4dEQx5lRQZb2ISB3OURd5EBDI65rPkfngMR32R43dp5iZl3ss8pg1CBS6li8zBYQEZT0TrZIhX+70eW4SNZ5Cjz7q3s+F6rntBDH09psD8esrs8jJjoQfM/lzsfTx55g6YAqXnqb+JtA6xt9Wf/tpDVIXRJR4DND4oavFA1wwpSbU7uASL959IrXH9HecMgzv0rtzjYIagWe7k+n0zg6OorRaNTydKXkPv+jZeieR6dSq8+e5uQeKd9oSu+c5RBoteJPq7mo/L1er5m36vV6zfeHdF9t8tPeCVI+tnzX3xEP/Yw5lsNx6/KkVwGp7BnyyOcsnBjpav+ZrskR8Ghb5VLO6Hjp/mw2a5wj35bARQSUBY2H98dP+CBA6XzMTJdo2DU+Dpo+h+Njp2s+5u7EOo99vkupQjp26i8Pn+V2jMzJ4ypWjU/GG+9nKU3vjp2Xo+dcflaV0RKtQcrIIyG/JyqlHPgcja+U1T05F2aSe7+8LqObAZgLn7c1A7mqqlqGWeUTDNjX8Xgc4/G4WSSR5dVpuPxYHLaDys/UHSM6tdXnLBiBMK0no8JreoZAKF7K2HK8+v1+0wYaRI9qnOccb7abIO78L417BoBehoOav+8ec+aFeyTqEbj64svsNU5a+MK+cjWlfhwMtCdIdXLxjwOW2sw+0UngyjkuBVdf1Eb/JI1nJVQenRb13d+h05bJYJYCLGVBGKmoLGYbON4qjzIr/njUQ2Bn26qqan1Q0mUs+3Gd83axDVmUdyNgtQapGyQOWkTZW3Dvxo0Xf9MAlTbX8l2+R2Pu9XmahETj4MvSqVwUXKX3lOKTYWDfqGQEI9VJb6zkFOgdKr689uFw2FrYobL0N6NQHw/3ZuVhcj8VeZWlSJzPzlMqM6+TH5QHBxQaWvHTDXWXE5MBHHmQ9SVzatwwcZsBiZ685jX1mXSfvyGgRcTC8VgR0cikG1eVT/5lhpQA5RE5+0SHSe/JePOrzEy7sk7qRyYDrq8+diVe+hh69JKli3mPOslnHcQc9MlrdygcZHxcsjJuBJSc1iB1k5RFLrzHv90IuUededCKRpZ576u2z+uR4fHNkgQ4etUnJyfN8nIda8Q2eArJJ9T1jJ/q7JEBQZftoefoHr2ekXKqrsxrZj/FXxosevA8wNYdDeeZ6pPx4k/J0+SYOA+6xpbPsk+ZEfRnMofG32UfKSc0orpPvqq/DrKMODge/Mw5wcLHTm30+T5fEUqe6n8CVgYCzi8dIUS+MLIvgU4m6w4qLrdOPhZuN/hcyWboN9vI91wP/PlloOTt9Dr9uZsBqzVI/TGtYvD5rA/MKkBR13UrRedGi6kLXefmPKYbMpAr/S2w4fMyAjonLKIdkVC5ZIS0MEJzSm743ctlPl33XfHcsPA5lSGSt80z/tyDVL/Ia91nnt/Tj4oiqZw8AcN54oaaxChIPOa4exTpfMgiMP64d+6G1j3czMstGRy+Q6Dw+TiXVf+ApMrxOQyBl0faHiU40Gvc3Zlxh6tklDXOiswJOt4XjYXLr4Mu08d0KriQY9lCJ5/bKTkRPu4OMqyf/XXQ5zU+x7rUd5cJB+cMeG4WkDJag5RRVzTk111h/HlXlGwAM4Mkj7UEXFJ09/r1PuvJPPiI8/PBBoPBQhpJ5atsrWqaTCZxdHTUGHBuvJUCaPUTDa97rWqX6qBSM9fPPSkCRb5DUIo4Sxtx/sM/a6D3ptNp9Pv9JsUZcb4YgN/aiVg8ZcP5mI0R5SDzdAlyq3jImTyxbgEux97f1z1GqO5k6R4X1VA2aRxFvvqU93mKtxtQbkjVfS4g8KiW403yiFn9IPi5l09wIW8yIKiqqon0BNS6r/1U4oMcIRHLYmo6cxTIe7bV9yhmUSP55lkPPefTAa6Lbpco/xxDlskxyCLZy6I1SC2hVZhNw+CpBF1zLzxLL2Ur1iLaH3Hz5d8EIHrpMuh6hgqo+geDQbNYQn3gEnQJvw6F1dl72XlpbKdI97M0BI+cyTxqtlt8UmQjw6GjbBxAxEvvB71PergaA6Z41LadnZ3muoCPxs7TUnQg3EFgv/wavVnxzQ2yUxYl0Tv2yIbjTOL8DdvnqS3V4cDF6MHHge3R0T7ehlKEoLEWT7LUGPtP/klW2A5mBdRmzXux3f6JDOmlZJ8RHnmierkIqCsSd4eCbdD77gTpfwGnnis5U0yjKoItRf96x8fBoz228XbQGqQukVyR6LlQ2bPrLMNTgu75ZN43Q3m/5warqqomkiBYCggizlN/AiaeIEGl01d59bw8LSqx2sz69TdXYfmzbAeNI71JV2QaNypYZrjIWxoztlNRJFMmWo3GsVBE4ykyj5IZodJD5Zi5fHg7SbzuRoSRpD/r/9Oh8FQoy3bg5PtuSL2+kgPCVY/u2GSyQ6fAHb2u59zYZ5GFQCqLYLkox5d3e51eB//nD6OxzD54P5wXzlPJXPaRQzqqPn4ZTzKgokzeLqBag1QHZV5jRPe8lYMKFY5ClilxxPlcEZU1E/zMeHXdo4JqHorHo/hy9vl83kRQWiDRlQdnOkL3MmPDeRkaaebvnXdUHoJx1mfyV3NPfJcA5gaEhoxjoE2aOnFdKZ+S4+BOBcfWZcPbQDly+fFnvA0ssySjmWFmBFwCyhJ16UTWbgchefd63peRuxyV9IDXOIZu6DOjTF54KpB1Uqb0w0hF/WNb3Vl0mdV1dx6yjIzzlg6m2wunbEogS4WWxpf1rgpQJdm+KK1B6o+pZBRWpQwcSgZEAuKK4ApFQfX3/R1XFhdI9m0wGDRzUZlSKyIajUbNCRKuQBmAZsbCvUDd53OKcvhZe92nIYjITxXIxq2uzzc3Z9EA+erPOP8izpRc8208HX6ZrLiBZaRFHlAOdN29ZLZflBkaf4ZtUVnu3HjUwXopQ1k04ryqqqqVFhWP2W9eUypVCyO4sEF1EsicP5RFyrOnY30eV++rrYzaM96rjYyUyUPqL0GDMuQyxja7DGTE8ii/TI1m4855X7/nAFK6xrJLNsmdmxL4XYTWIHUJVPL8OSeUvVNS2ohoGXh/n4dNUuk4d0XB5xxSr3d2EjjTTnqG6TSdJEFlVh8VWWj+iicQqO9MybGvOuqGkRlTctzAyTkon6dxQHXjqfkGX6GlPvOLvz5nQB7QM1Xf/DBZvccPITJlJvKNnl1OB42tGzH3xj0Nq7KcGL2QF/pbZWRevHvPHG8aPk9dZpEM69M4SzaVPpYuEZw4j+l95EIjd0qkS3VdtzZnZyCiuR6CLOeaKAvKRkj32M6IiMlk0un0Ok/d2eB1P4FCf7PPvpKSPCIgE3SrqmpSg13847iXwOcyACmjNUhdAmXev3v1WXRFT08pN99QSm/SPVo3Nl5+ZkD0bSUJfl3XrVVu0+k0RqNRHB4eNkKvFAejKEYqg8Eg5vN580HDiLZycCFBadVVxDlQZsCYGTUqL5+lwmYGmfxXOzjhTWdBBkp9J0C7gfdIhsDvxpWOhBukjIeMFsgfNwwZILhDRBni3yyD7dE1N1z8zYhJzxPgI6IFpnxP0ZOngCUT7lBR3vk/wScz9KWIRwtiuDE9IhaOIOLpEmojHTQHYY9WS9EH+e78zsZPOkAdpgz6FAXHivKsT4CwjV2y5XKw7NnLojVIrUgXGQAXWAcXGRp67hIa96IJEBR6epmrtFfl7+zstM4Gk6L1emdHrIxGoxiNRq3l5Zwn8FRDRDRn4s1ms1ab3JD7/iZ6oFlajuWId1pmzg/diUc0IHVdN3NINF5sC40VDafavL29veAFa5GIf0xSZXMPli/h1nMEEQcvN2TiTRbRiShPXi6NsZ5je7PIhmORkTsXmUfNtBgNmgOU2qMIhvIj2eSWDI25886dAQckAn6WnaBT4UafwKUyNE+7t7fX2r8Ysfi14MyJ4bip3XzOoyw6vmqTfwvK+yzd0I8/42BTkgm+5/+XwGmZvfT+ddEapP6Y3IPze6WQXffdW+p6P/Oq5B25J0bPnB5+xOL8AMknjRWlKRWhNBrPVJtOp62FEu4t6XmV70rEFEkpGmC7abSo6OSP2u7llcaAXqdHZCL1rQQWqqcECuSLgzbTMO6kuOEnILAe8slTxiXPthRBUQbcsydPS3wtRQYaG++zfsiDrO98jmPle3tOTk6aZeKeTWAkxLbKYeGqwSyKyYDBV+2JmAoXnZ6eHZasTILP3WZOAces63o23lk//BrfzcZ1GaBk8sFnMnAqlZm9fyN0T4PUcDhsLY2+FeRGcxlgZe/78zQ+fIYAVBI+ekkOFHw2oj13xRV98loFWhsbGzGbzWI0GjXLzR0kl/WpqqomjegpPRoqT8e4sS8ZENXBFYmMJjMF1XVfvUZjx98O6g50TLkwCiSvVR+P1HEF9/Hl/Aj522UQPALzuTqXF45dF0i5vDso+AR9VVVNfzOgYr3ePrbfIyX37iOiVT7f04kppahZ/MnGgTIiudCBuWy3xpy8kWPHiJvynPWVfWb92RyZt9PHU7zKAMDlmX1x/eqSjwyIfAz8/jLSQQIbGxtxeHi49Pl7GqS+4Au+IJ5++ul49tlnb3ldXYPggpUJZiZgej4DKRENuxtLGkU+mwldv9+P4XAYVVU1IKRDQCOileZTPTQU7oWzbLVpMBhEVVXN2X4y5ErN6YdzDEzTOHC5V+opKxlHgT6BgukW3edp3SzXx1fXxQvxim2kAc4iFW629PF0coDJDICnnFh3BkqZc5GlU33+wkHKiXMwTI/KWXRDyHJdRziuMrhMw3o0o7QVea02MA2seV134hx83eHQ2NC5U/9ms1nzPFd2yqljeYriMl30zASvk1cenWYOrOtMNuZOvn+K5TkILwOqrumGLrp69Wr8qT/1p+LBBx+M97///Uufv6dBigec3krqUlo+E7GYLsy8Rub89T9PjKYQMlrg311pBBem4XAYg8Ggtcybp55PJpPY399vnbZARVE5NJY0NARSeUlVVbUU25XeQYunToiPTCfxmgBJq+wEVEwL6T2f09P8mcpT+3SqhK5pcYSMonvUHAuPhAhUHH+foxBfVQfnMggCpeiDv9lHygXBwNNR7IMb0MxholEXCHNFGct2Y8f+cjEL5V0g46vU+DcXt4hfs9msuSaZ1hYL35hMHWEE48a+1zvbtC254FhSdk9OTmIymcTOzk4LaCWrPG6Lbde4C4wJ1pmORMRCpJo5VnrOn+VzHBel+n0ukKduOM/47EUiKJFWzq4SRUXc4yD1iU984paAFAdStAyo/L6X4d6Ie0f+MTaWw/STFIF5+MwgMaIYDofNSd5SCC1DPz4+jsPDw2a5bMS5kDPlxTZnCuSLIra2tpqPsnUZVDfs9GhpvEoRotrMNIt7qIyw2B5OzPsn78l7Gg6PpAikNLqZ8pauuQPAfnsb9A776XNn5Cllx+VK7wrsaegISHqO7fRjeTw96u3LNnmTd75wgf0WMd2otmiPle+n05FZcsZ8Plf1bW5uxmw2a8kqz4nMPjlPfYs4c5aHw2FTttrk6UeCk5fhUYzuUZZ9rtXliWDDfWeSpexIKpdt9k9/E/CYyuczpZ+MZG8++clPpved7mmQ8v0ql00l0BCVgIuCJWF3gXNhlAfIM+L0LEkCwrSXynCwijjP/1K4ZZhPT0+bY48U9eg5GnhvMz0/tokGiMt6nTcyQH4ALL06KaWMDI236mXkQU9XbaOxkYJz7xXLI59oOBxwPEJg5JcBZ/aNri5ZoYedzSVk8pCVJ+qK7N04lcqlIfOIN5O7DGDcQcn4qeckByxTzzpYRpQdFDpbkiMClq/Kc/mnI8jjvzgWjHrpEPg8KJ/LAIHliSeebSE4850skvXxcLnNxt2dpax9Wf2UgxIwkZQ+XZXuaZC62yjzIDLg0P/6LeGmEZDgZZtCachYhgSRdXIPlMoeDofR6/Vah8YKTAhK7rF7G0QEFdbd7/cXPEQpsS/TdZBi+x0o+b8mYKfTadMWgVuWLmMkSOJy8RKgkKj8KjdTYudZVmYWIWZzZTQsWepQ190oOdAShH0eiX979FZK79GwURbVHv3mfBLry0CKTgf56lsN2BYac72r1NvJyUkzZ0qgIsA4DxhF0kFjn1XGyclJ60vOGfgRHDKQKo0X+cjxIw94PdNbBz6Xh8zxIundjLL2XiatQQqUGeOLkqds3MPwAVVEISXWrnoJvoytlJcGRuXpefdge71e7O7uNoqtPLOWzAqkNOmt+ukNitgHb4cbJhk0rfajEVHdDlIyYFwezjkF9/7ID0ZK3KCsEyP0vn772MiQESBoXP0IJI2Lk6d3SorrXmwWebiT4KCYOUIZn5w4bpIxGmCWT8OoMfKv6GaOmcpyj171lr7uS15wcQ1lnXLm91QvU156ZjKZNO2Wk6aFNIq2BNoCH/ZJcujRv0ibfTW3rD2DlHNGVGo/+ev2p+RIZdEl7UDmDJQcFS7V5/j7nrVMNm8lMJHWIPXH1AVQXekJGk39zgDKvbWsfAkxhS4zVnzW34toRxM7OzsRcf49Ji2gODw8bNJ8usdVSRHnXqOnjLLraiuVcmtrK4bDYfOZea3GY9+y9JF4pJQAN/PyN71h8YmLT7is2Q0evWhuDOYYi1dqo56hkaWHynlFNwy8XkqXOc3n89ZRQG6kM3KgpTGl0eEyecpZBn7kHcEri1S7ysgiF85XcUzZXpbtgOU6or5NJpOFuU+ltDkfpXsERY0RHTCRp2/Vrtls1lpZmO2z8mwHHR9PnXvKjalnyrEcDbcZPrfrjpM7fHQMJBt6jmPvPxzjkg29WTBbg9SKtEqUVVKaiPb8VMS5VySPzoVW76huByjdd3Dk3MtwOGwmhuv6bBn1cDiM09PTGI/HMZ1Om+8ycXVbRKS5+yw9xPr5+Q+2V2kWLRPWIg55sSyDKU/nu+rTJHXGB3rRV65caZaR0yj7fBodCZGU1I+pUh00Ljp41lNeTjIAHllw/DJ54vtuuDMZo/PiBk91imcEDhoeN4i67hFzRh6FeXsdFEseviIROiLiNXWHWwVU1vHxcWspumT4+Pi4NV7UP73L9HgpPSfiqQ/cf8ixq6rztDXL9PEXv/mcdJkrA7Mx1DWuRqRzy3Fxmcn6mTlP2T3+9nG8rGhrDVKXSJnX7AorxaD34p4jyT30zAC6oRMQKIqSYdTE8WQyifF43ERRBB9O2vs8iwuc6pKiez6eYM3ogwrpYOSbQj3lRM+bCkgvVX3e3Nxs0otMb/E59YmAqbrVDs17uZJ2GdosoiqBe8TiggL1iddKjoqPSQZ4np7L5pWyCCEzXnzeqcsoZU6V+Oz9jjhfoKPnPY1J+dH/1C/qE6NKyYHe3dnZWYhQM+PLcaSjo+fkCPELA3qP+lTiX+Z0uaPqf5famT2XyR3HuNT3rrHuAvDLojVIXSJlnkem9Az3Od+xihEq1cnnlGobDAYtj1RRnI4+4nlpBClGBeyH6vDFD1wq7YItkOVKKueL2kcjIsqWLjNlSGNcAlWulvS9daqTRouG0ecoaASzcXZQycYoMyK+QIZRsd4lj/1Z3u9qn/jdJaslo5P1kfVnz/uzqt8jDS/fo0AHHcqX/nfg9WiQ48hoPfv4Z1YX++sRYcR5ClVzXjydX/riPHNS3SybKWr2jbriOudj5nVk45L1t+S8ZM94GZdF3d+nTuhXf/VX441vfGM8/PDDUVVV/OIv/mLrfl3X8QM/8APx4he/OLa3t+PRRx+N3//9328988wzz8Tb3va2uHr1aly/fj2+4zu+Y+WNXXeKfJBWHWT+z+tSGM7hMAUY0VZWzt24MooYVQgYNIGr8k9OTpo0n+ffVbYDl9eh0ysGg0GTxqjr8/SdpzR8DsBz5Wwvfxx01D/1id6rK4ye1cnsqr/f77fSRQRvb7uvdMxAlLKRRUL+jIOI2kBeUA5oEAlcHln7/1mbMzl0Y5fJLPvG/70vJbDm/5RlyoFHPYymfFy8DPaR8ynsC9NnEdF8xFK/x+Nx61MrWR8kV76AIWuTFmIona5+OUBkzqiPW5Zh0XNZdMn9UB5tlsaoC5hcp5ZRBlo3QxcGqaOjo/iKr/iKeNe73pXe/yf/5J/ET/7kT8ZP//RPx4c+9KHY3d2N173udTGZTJpn3va2t8WHP/zheN/73hf/4T/8h/jVX/3V+K7v+q4b78UlUwYyGXVFO13K6vumIs68OZ1BpufUDl9p454u04dVdZb/3t7ejojzlIkM7ng8jvF43AIiGjCt4JJxVxs3Nzdje3s7rly50oCTGygCG42q2siNlRHRpCW1P0kgoue08IL1OQDSOAtYeHoHFVl86Pf7zY/K3t3dbepSG8RLtpm8p8HyzZvZCjY3TiWPtuS1ixzY2Q560tmkt8rsmj/1NmSUATJ/3MDR4/dIjxkFvc+DkDXHojL0vy8H97qyFKp0jDI0m83i8PCwBVSqn84VeUO+cRxUv5wf6i+f4Y/rIvlE2SKPyDvnZzaGWcSZ9cfnyLLx6qJV7OZF6cLpvje84Q3xhje8Ib1X13X8xE/8RPzDf/gP4y/9pb8UERH/+l//63jwwQfjF3/xF+Mtb3lL/O7v/m68973vjd/4jd+Ir/zKr4yIiJ/6qZ+Kb/qmb4of//Efj4cffvgmunNnyL1YUinXLePMSVsaIwmHoiAaFQeGiPOTn6Xgw+Ew5vOzAzB3d3ebsrQUV0DEdJpHaMfHx43RdkVU/WqPz6t5ilHPDQaD1AirfVphyI2YTPuQh/xbfWekqpSnRyY8f44pvuFw2LSTyqkyBVaeRmKqkkaDY+XpnZJHzX45gPi8G98TQGderBtYls9nCGJVtbgFwclTYy6X2XhRVmSgHTS16GFra6sVFQioMp1QHXQauJqP48BUX13XC+lvybw7JiIufvHsB3lB2XUHQc9S3jJek4cegdJp4z1d90jLnVrxgJGndI/y2ZUevsyIqUSXOif1sY99LJ566ql49NFHm2vXrl2LRx55JJ544ol4y1veEk888URcv369AaiIiEcffTR6vV586EMfim/91m9dKFcHlor29/cvs9k3TKUQ2L1fNypSYKa2skipK5XiQiIPUeDX6/Wa1XQCCoGTfjIBY0rCvXU3mqV0CBWYbVc52SZfj3pklLj0W2kTlsUfvl9VVSt9o/LcSJJ3nKjPzojj3674bgzd4GfPuwxl4+EOTFY+6yFo+JiUUnr8v4vkSJSeZT8ZUZXAsMQTjYHkhEAmI6z3uubofL9Qv99vDDnBhIt1VC71hnzzOSC1l/KYPRMRrfKYri7pOHUhS0EyQ5KlBJeNJ+vT31y05O+XphluNV0qSD311FMREfHggw+2rj/44IPNvaeeeioeeOCBdiM2N+O+++5rnnF65zvfGe94xzsus6krU+aFZl5n13v+fMTiF0xdUGmcsmt6NktncAktl1BL4flBQ08H+bxQZhgzvrB/NOiuzEz/cT7ByyBoESQJgGxbr9drltpTsQkSVHaCGcti+oweJUGbjobPG/mcosuNy0PGQ3/Wr2XAwn52jU1XnX7Pf2eOV9Zujl/WT5K3iWPATbEs26M0b1PGX7WJRphgJlnj1gN9NYCy6iBLGeXYZ85IyYlhGzMZWDaGTplOld6hvmSp4VKdpTJuBV14TupO0Nvf/vZ4/vnnm59PfOITt6wuBw1dc/IByVID7gWzPObLM2+bQsPyMiGSEilKYZqBJ1gov8/UgE/Oak5nMBi0NtFmPw6eel95fM4t8Xgm9lkRkqJlbjBmqodKynI5v+TpOPI581KZNiHAaOm6FoVwbsBXNvIex8NToyXZyWSLbeKPy1Pm2JC3/kzJ4cicH2+vP+MynRlST3N1zY+xjZyXylY9ejl0Cugc+TxZRPskBQKU/hdQKS2eORgZUGvMfB7MnQe3E9l0QDaOtAOs16Mb7wvLznQ2+8nasoy8zdm9G6VLjaQeeuihiIh4+umn48UvfnFz/emnn45XvOIVzTOf/vSnW++dnJzEM88807zvNBgMms8r3O3kQilhoYB6Gk2k5auumJ6Co9DSO9vY2Gj2aHCDoxRTpz1LEV2wt7e3mzkcXdd3oVSPDICe8QiCxtsjIs4pRERrkQbnCMQfpSurqmpWJZ6ensbOzk4DUKpP/5+enn0tdTqdLuyTopGlYXODobmwK1eutBaQsE8sxyfWxSenzNCI3OngGGTveKRScogUEbrxcfBjGVm7uiInL5PXCVgiRqh8z+eOsiOIHDB9TLL6JR8qh8/yK9R6vtfrNeMunSDY+epBj/S8L1kfSwDPZ7ucD87J8R5TspkDxLoZVVIvS05K6X+Ve6siqUsFqZe97GXx0EMPxfvf//4GlPb39+NDH/pQ/K2/9bciIuJVr3pVPPfcc/Hkk0/GK1/5yoiI+MAHPhDz+TweeeSRy2zOHSGCh3ueInp2upf9Ty+RkQ6Bj8It7z/i/GBNlaHoKVsVFRHNZDFz3HVdN/NYMsacUJYx5c5+AShBcDabtVKMUiRfVq95Ms756PBbRVmnp6dxdHTULIPXirxer9e0TYZFkSX77suH1V71iXMREdH6BIOPo4hGRCDnq/syUMoMVfZcKerouu7y5G2hccquZ23UNU+bsQ4aSC4K4nMEYKXKSkCvlB/nCAU4kkk6QlyyzpWiBBc31L4/jhGWntPnbnwOSSTZchAhj5XVoJ5kUZnqZx2UWfWT73tGpMtZKAHMsijuZiKtm6ELg9Th4WF85CMfaf7/2Mc+Fr/1W78V9913X7z0pS+Nv/N3/k786I/+aPyZP/Nn4mUve1n8o3/0j+Lhhx+Ob/mWb4mIiJe//OXx+te/Pr7zO78zfvqnfzqOj4/jsccei7e85S137cq+TFkj8jkCeje6l3k8TB05sBGQXPmZH6d3V1Xny2YlxPq8gH5rwQSX8zK9x+W5LEP9kOIypaffvC9g0+oq7mtSWSq/ruumHJ5+oY8OeqTD+ypfIDObzZoUkfrFFGA2ptyjpDqY9uP4sS00Roo+KQ+Kxry+bFEDZWkZSFHelnm8lMlVoiQaODd2DsSkkh7Q8JEnNHo0uj7PKKBTVM3z5GhAPaoiUPvCIR8TP4Ek4hy4JLtqu1bJMlphPyWX2lKhPrBtvoRc+kTiqko9w1W4y2yRyxjHzd/PUrCZvJVkjvVk9y4DxC4MUr/5m78Z3/AN39D8//jjj0dExLd927fFz/7sz8b3fd/3xdHRUXzXd31XPPfcc/G1X/u18d73vrflkf78z/98PPbYY/HqV786er1evPnNb46f/MmfvOnOXDZxYJdRaTDcO6T3l6UEmI6gAmSeEA2AUn1uCAk4PGGCbfH5Ip8Lo6JFLK54U+QkMBIgygAwqsrqUHvcgNAYZSkwKS/TdAJWAZyvCBRx3iJLo2SRSbYnheDl49lFbjBKxmcVA9Eln37PPWm1w691lUN+qd8+/xQRC9ezlJz/7frgzpxHipITRmKZnDoPGY2w3xlw82R9AZADTfY+o6mSE0Ii390hyq658+G88zaxjszZ8Db49WVySCqVfyN0YZD6+q//+s7Kq6qKH/7hH44f/uEfLj5z3333xXve856LVn1X0ypgJlAoHYWkciIWF1O4l+ghutJdDmpUyiyC4kZVz0nTYHMejUZcaTp+3ZRRklJ4pWhAZdDbJHjR4HBlHQFB7xOQqVAEfXnyWTqMhoX8JYj63i+SG+9SpONy4jLgf3OcVY7LR9YXJz6/zICUyhY/3aCyzMxjJ8g7/zK+632eLO7ymRlllkdHSlEYx7T0iXRvO+d06WQu4x+jLtdbr5M/2dg6jzNA9/cy8OT17P/MAdb/mYNzO2h9dt9NkCuLrpVAhyBFw0mDn61m8jppeOlJ6ttTMqr67hGjnfl83qy2Y11+dIuvbGO71VaVORqNWgsM1GZFVFS+7KBZ7x8BixFQXddN6sfrobfsi0QEbgLybC6AY0R+MQUrAMwMgi+CUTv4HOfkMrlxvpQi7UwmmH5yL3/V6C7z1PUewYn7lvS/6naDqDEXyEuWvN0OXDKIktV+vx+j0SiNKpRBoHMV0T6Tj3IoPeA8F/vNdBvHcDQaxcnJSWxvb7dOJeEcHdNnBF7PTjgwkR/ZilS1zdPKGxsbzSIpgkiWMizJWxYlLQO220lrkLpBKnmq+k2PsKS0+skmXOUJesQhohAp1cfrUkLN9TBlQWPNxQuqgykWPeuRE49WimifXK12MHLTNd/NrutUTvGIUY8UMSJaE+dc/UdAEh8ODw9baU3yU3V4+9z50LwbjWsWiWVREgGJXnUmP15WllJy8PB3u8ol0JXqZdkZIEheve4M0HjMV8S5wc/Kpj6w/8w6aNUmgcMdGoJ4Xdet6N5lKZuDIymVTMeFx7v5XkJGcOJDtlI3Sws7AJVS7r61wU+WoIPjTlIpgqJs6rfPU3FsHVxvNa1BqoO6jElEbhRKxosRAn8yY03l8MnmiPZ8ioSWH/jzHwEH562UAmGqjCsHdTyMwI+LFXT+H8t0b8y978yTo4HxCIEpP87h+ZE2moNS1Mj0ju5zpZZ4SePCfpPHHD8pLlcysh8ySL78XnLgqRKXDc7XeCopi9wyWfQ5H/La/85AwcE5a4Nf0xFTngIUL8S3rlRdtiiA+9H02Zlr1641B1FT9mlYySOPMLhyVTLhjgB1SOSOFt/Xqj86npQltU98Iu/IC/LfAUr/C3C9XeJDaY6NutcVIWXOY6a7/l7292WC2BqkLkClcDlicVUfiQaZnj6FQteys8xYthssGQh+zjwTEC0fpwfGVUwR7ZPZGTloj5K+Q8VFETLwirYo2DLYWXSidjpgZN47Acb56n2lA8B5CBmqzEvUcUk0evSGXVk5Ge9ePWWFQEZjzUikFIVl3m8GKiWPne/48zSg5HMGjl4v5YT3MmPvKadS23ys1RbKPxfDcL7JU2NdG9W5FD0DhQyg2D6mNzU/Rb2m3BCUHQyXURdQUG5YpoBT8pgBfwZYbKenDLN2XSb4rEprkLpkyrwOVyzP3VOQqFg8S07P8W8HP9UvRaLCC3iULuGnOtgGApTaMJ1Om+hJc0+ck1IZPDyWRj9LW7iw+4cd+V7JCyfP/L42sWYGkJEbvU9GmV1RizsNvMYfzmexvxlAcexEJc87o5KDROoCva5+cszEM3csHMTcIGY6kdXtjgmf9yjV09KSe6a32ZcMSNleJ++fG3dfSOGn5VPOs3K8f2qL/vd2uWOQOSAl3i67R4Di+N0JUHJag9QNEKObZYoesQhSvqRWJEWjwCqNR1KKy6MeKoZ7hjS8nCvSfe55kjIIlHhUEYHDhdnn0zY2Nlrn6Yl32mBLHmZHJnk6Uv9zQYd4pUhC5WoRCesvGQn1xVMy8op9RaOPq8bOU3aKIjP5yYB6mbEpAYz407XqkOW7R54RnQwHU66y9DQt2+8pQC+Hf3sUo2iNACk59422aqtkUHI8Go2a8dF9OSU0zBm5cyj9cpmfTCYLUZ5+0+FUGZ66zuSA0bnqz4CCERrnzfhO5oiyX6zf9TQbO9Iyx+myaA1SKxIVrMsDzO5JaZhColGT8lDIIs6P4s9W/FEhfdJabSTgUIGYyyaoCCiOj49jMpnE4eFhHB0dterVPIMLsRZT8COIpV3zAirdoyKrvXo3A0XP9xOgCFI6rYD8UIRF45oZFZ+TUvt5YK/edWBysGKb2Re2iXIk47Xs1ArKAimLwNzgZHOFy6Ib73NGajejWz2va4x63NBWVbUw9u5saZxns1lrzpSOxHw+b30hV2PAkzJYrjsT3j+tDiR4isbjcaM/kmvJE8eeH/bM+Opg4Q6AeCteakWuy7HrnfdlGbh4mtTb6GVlMnaZtAapDlo1UtKzpb9l3PlRP4KGnsuE1g0mjaH+ppcnwdIpEizLBT+LxPTxt8lkEgcHBw1ICvCkHJ6WYPpMZVFhyRP3sGVItFFSBkzHIDkQ0NBIIXd2dlpKq3mmiHMwpifNCE1tpvfJuUHySLx3Q+c85vjQoSBAqn6Xsy4vlpEGxzaTH5bHyJrGy48jyuSFcuzzFnQsPDNQVe1TULzNBC323+WD9Xv/Ne9YWrigw4e1HcKXyzN1Sb673KlOnoSica7rull5yIOZOf5KQVbV2VFdPFvT+SoQkz65Y0y91wpejpOuEyjFc7Wd0wElKsnUKmB0mVHWGqQ6aFlKhORhM6Md3fccugsehUkKQCXSOxloUak495Utt/Ul3BLy0WgUBwcHMRqNmvP2GKVR4Hk94nziVvfcU1XbXXnYH52NxpWEjP7qum5FMrqm8lzZZSwIMFwowvaTz+pD5jW707Ls/8yzpXy4oc6o5HHzf4+GMuPi41dyjLzOrE/sA6MkGnaPIAXSes7niNyBIm8iotlGMJvNmjIiouVQONFRiYgWOGQ8Vbt9310m97QPjG58HyLLo45SNhwQeZ28oZOqdjngeCo84zeB1yO4DFxKTtPtoDVIXRIRkEQesbinnAlGZqz4TCaAVBzOe0Wce39qjzbDMiqIiIXFEXynBFBsGyMgAqWnrPSOKyX7oLP/mILROwQo8phAwLEgPwj+epZRlYOXRx26no0vgTj70fNej48vy86cpAyg3Fi65+1llpwjlpm929UHfybrDw2s+MY+ZPIgUuRA4+988ZR5BtRKRWbjnfGAxlztyHigPsu5UraEP56m5vyaE8cp03+/nvG6S97Y5qycjHd8h2VdVsRUonsapDIFu131OmWDquf8pAZ6RowGGFW4AtHLpJIy/cT5JaYzmK/f2tpqgZS8v6OjoyaCkuL4vhIpeQZSfshsxLlhYfvlPXIuRmVzZRZPCOAXhlWGwIZzWJQH/VbkRbDN5rmosEwBOgi4MmvM1F8HJTdqXAizzFByMUT2TAZQ/LtkaPi3gzHL7TJwXl6pP5RRplX5nDsKkj030r1er/VRTz3r6e7SnIzSbYp0/DxL9k1pPaYe2S6XKdYxm81ie3t7Ya6R46X9X1ptS375XBTfz3jtfHIHlrbHMyA+jl0OUZdDs4qjQ/L5xy66p0FqOBy2BvlGKAOcZc9nXqUGkELg91gfvfvMw5dCCEQ88smEQgaTeXDN00ScCYa+r7SxsdHsfTo8PIxnn3124QQJtYPHx0jJXUEi2kChVJ0mr5m2Ky1OcE+TAKuojHzU9WyCme3lmPHTI85rzmNFnIOEwF088SjO69M1b5fq0jwNy+J9yYdvQegi1k05zOaPljl3BGOPJFQ+28tyySOfey1FSZxTYfmMwtUnbdoWcY6FqSuNmcaBwEV5kh5wTOlkkScegamfvlAoImIymbS+7Es7IJ5tbp5970z1uxNFHksfJKs8kYU884iNTgLL9HSiKOMD27xK1EWeZfdkF1784hfHRz/60fRd0j0NUpubm01++k5SJtQ0fpwjcs/MoyeCG6MUzh+5cOh5em/yEmmwIs7z+oqeDg8PYzQaLQinzwfICEhZ/IihiEidhel0Gjs7O60oT32WskjZaEx4vqFHouQZ5wH4vSj9zjzkbBGB+pwplTYzV1XVOupHz4ofaqM7J27YxDvd85SPOzsqM6PMALINKouOUBYBOC+8TjpTpWdYFqNL8aiu6+bcRZabGT93ylRuv9+P7e3thQjdDankxvvl2xf0IyeFG9wpEw5ybDtXqqqv4/F44ZxOL3NjYyP6/X4jW76vkDrIE1Qi2ieiEIyqqmr0QDzh4gs6CqVN9uyL00Wd+lIZW1tbcf369ZWev6dBajKZLA0rbzd5BMUl4pofUjQT0Z6E9sUH7oXR0HE5q8/9aCUTU2KMPDY2NuKZZ56J/f39mEwm6aorn9ClsdCy7gx46bmpHbPZrJWuY6QkxfUFDSqLyhhxvt+Fc2bibbYPTc/4Igv1lfV4G1m2jJeIZfM7ReST80U/5DcjILap9D/7TBnjPbY9ey+TPQdtr9/b7XJB2SSf6RTIoSBQ0ujztIgsCtO46/p83v5ERybHardHQWrTdDptrV7lylXJMYGQe/eUzvZyBXgCH+ldRLSWwxOoWO4qJL5pjtkzKARVOmW8vkqqzeu8DHtb13WMx+P4vd/7vZWev6dBiqmsy6Ab8RI0+P63fjtIUWD0jg8+r/k8E8vXvSy14R4Xyzw5OWkWSHDuhz+eJ6dnpr4Q/FgXU3v+DassumAE454dQVb/cx8N28P5Jyks9+zIgPgii4yfDgQcPwIdjbEfjeNt1LOldKGDBKMDJ3+X1z2ll3nJ2fusO2sLn3dQY6qNskMv3g0meSoDyoiasqLy9OFOLazhAbRZ+tjTnXRYKHPimeaKNF5Me7OvdAbcgSQgK3NBh4A8FdD4qsfMaeDfGYB6/Wx3ZqN8PL0Op1JbLkrizapZsHsapC6TbhageI1l+n3Ox/CavCqVUfLyI9oesIRckZQUnN4e69Cn2Pm1XI8QXMB9/iaLEASUzhsvL+J8ubeey+qk4mXgTOBVOQQZtTlLE7rT4OVmPHZP3pVaYMzJdvZNdbAt7J9HzCVjQp67gXGjmaVfL2Jclj1Loy0wUn8ZxbBt7L/K8PHOIjvyeTabxWQyiePj42bfIedpRIpOHFTIF676jDjfsO38zgCf+iVi3wRSTPe7Taiq8xW3jPa5pSRz4Aig7JefqOHtLvXLHeVVyMfrRt5dhdYgtSKVhDQTOv3OjGlVVa25E5bPvRR6ll6eG+WqqppNuxJsGnsprkDo5OSkdYoE61NUSs9XhteVkXuKpBi+EESAwG/3SBGZ2pPhZ2rE+UK+MvLzuQHOARAAfG5OPFEbyQsu1KCx1LsaO4675tSY1pSR4GIUtYHAFdE+6Z0gqd+eUsvkTuVTPkogVXLI3Ni4HHrZ+uGBrXQ4slQmo1CNGaMr71umb+L1ZDJpjiWSHgyHw0YfFJ1Rj9ROfjbeIxSOu7dNeqUfRkkebUVEI9eal2U/RH5WptqhlLZoY2Oj9aFGzi9n8lAi6h6BjMSMBYHd+3k7aA1SN0iuUCUPiV6UhMLnLwhQHsF0ednD4bCZRHbvyRX9+Pg4Dg8PY39/v/Hw+Ll3Vwg3KD7PpGdUJ9upa+IBDctsNmvqVvRHL1zCzwUQEe2PLDp4SMmZ7mO/yWsupVffCYoEGY+u+NsjYfFDvHRAoVJ7dEFA0TV3TErefEYO9Cw3ezYDM73DcdEPnSGPoDLvWuPIcthH/a3yKDfeXy1BJ9grOyDQ2t3djb29vdZJKkw9q01yxJh14IIbOlmSVeoWsxtso/RBYChZF3iS9CzlUeMhUPIzM6WbylwIjJlBycaRdoapa5abEeX7TtALHqS6PEsneiAlr1N/lwwchc2Ne8S5gfHJcM7zSECHw2Hz+Q0aGgc3nsPny8n9XUYH/X4/JpPJwhyT2u+rkjIPmOBQ1+cfoWM0pvkFpsFKBjLz6Ou6br7pQ156OlNGYzAYLKx4yqIE5v0ZuWbGk+9yxWLpWBuOKw19V9TMtmZt57iXDIpfdwPkMu1jkKVvs+dlOGmUM+OZ1Zk5fTSwvCZQozM1mUyiqqrY3d1dmMtlJE6wZX11XbcyEFxU4Se7sN8ipt5V52QyWVgM4u+oTarXZUd2wb9gQL0g0GUpdJcf132PBFWvO5JZ3beKXvAgtQpRUByodM3/liDS42PkxFy5z5+QMgCIOPusRL/fb83tsH16fjabxWg0ivF4vPDRNHmC2tPkHrXAUG1wD1rPUHiZBnRjq2cUafhcmPeR6RG2zw2qxsKX8jvwiAjULJ+ePY0t5UC/M36rH5yPydrclb6jfOmHssT+0tg4ZW13bzkzzqTMSfC2sf3+v6f7aGxL+sQo0A20R2G+v0rPKpoVIHmkrGezMWZZ5I3X5eDnPFMZdOy0Z5BbLkS6zjSzyqCjR8fJ+acys3kpl0+XAx9f8iEDpuz3raI1SN0gZd60e8huJGTE9X+pTN0vRSm9Xq/5ImjEoselZ+fzs/P4dJIEDaiU15WC9UdEej9Lk9Gg0ghn+0v4Lj1SeaBKf6hupgx93k3Pk++ZN0iDpPMDmdrzJejepwysOG6ZsS95mKVUXBYtuYFh//RcZih1n+0opXRK78sZYVuyvmd9YT9dR7L+EzDZR48AVK7Su5xvZPqtruvWNoyIaN13HmfEtrhsO6B0yQdTksPhsLWNwkHW9z+RV5p7YsRMQOc4Z2NdkgEHrNK9O0VrkLoJKnnC3D8UsXjyQSkN6BEYBZbpjI2NjdjZ2WmllHq9syNj6LnPZrN4/vnnW3uh6FnRK9VyUBo4lc2FDuofU1M0Skw3ERg4N6e9MjqeRqkVHny7tbXVbJLU6ehMGfoeD/c+ff4j4hwcHXTVZgKj88fnx9xIlKKhzAFw+fG//X++Lx447/lsycteZnBosDXunDz3tmTRk5fjz3qbHVD5N8tneVqwsLW1FePxOOq6buSFUYoyBHrX54W4yIZ6w/+Z6uaSd85dleb0yJe6rpvUOdPSPl78pA+vOy8IoD7eGV/VfvWFPx4Vkw/LgPx20BqkboCy9ICIAsiQO2IxzZH9ree4+s8NwdWrVxtAoqcoIT85OYnJZBLPPPNM6/MVFGym/ra2thoFkiArStO+IldGB8/5fN7MY2lxBZVanzDQog15wlrswAUO7mkTEPUMFZXnucn792/tyLD4aQK654suVD75y7Eg+f3MaHmqqARKlIXs7EDW48CYzWdFnAOOA4zKofHTOHPeoysyJH9pALsithJwu5OnaIiRscZbpzoQtKkv2kvF6Ekf8Nze3m4+4SE5JZCr/+S3ZETlSa79o54OtpQHHT6wt7fXlMNVhtJ7RYgc67quG/lVFOZzV+54eRScyfIqDsydBqo1SBm5N9xF9JTd69Z9UWYA3Tv19xRRsJzNzc3Y3d1dAC6eYTYajWJ/fz+m02lELJ7fFhGNokv4pfDqjxScJzVnhtH5wFRav9+P4XDYStexDfTueDq1FFV1y2DIEPqKv/l83vqWj360mpA8ZR/cm3WDxxO3S+OWzaVlsiOjy7kv3uNYMpqh7DhIudGhLGZzgk4OqJ5OZX0Z8DoQsm9ZNJdFY+RNZuh9rlTjJidHMq+T+8kr7pMiUJ2cnMT29nbs7OzE5uZmDIfD1uId1csoUt8786OOeMSV94vgKsdQZw9ub28v6LvGTAuW1NdszOhgeVtdV1U+HQmWxXa4Y10a89sJWC94kOoCo2X3MpDhD1Mcft0NCD1IggCNoBSKxyrRs6uqKqbTabNQguDiO//dWA+HwyYtSFCTAeEJFmov26p9KlzM4f2V4eeJETzeyJ+Vtypjw7SjR5rkld7V39wo6WMVEc3J60zpkRyMfG4oe75kLJgOLcmbj31JJgn2Lo8OClkUJZIsOUCRZ95mj65o2LoMWCka5Kq5Ujt5jdkGRoqSMd2X3ESc74+jnMix6fV6TXTiG7Y55q6/kjUed5S1W9G9HCp+c8pXd/qGcI6R2iOecfETP6yY2RcBPseMNoq/OUYuP7c7onrBg9RlkHvm/sN5piydpTJcCSLOFxwIpHSIrJRCALW5udkAlBZK6H33rrO2DwaDhTO/6OGrDcqXywiofgJU5vHTuES0T3uQp0s+EYxoNGksqDwELbWZx9vQOLn3yPZw4p2eJYFDz7N95GWJPN2bRQ4uT13ve5TiC0VEy+Yd+HdmGLv6VLpPUGNbu/rt1/w5L1OyR4Ck3vl19lUp8YjzVXfcXpBFqeyD95VtcyPuEep0Om2dlEFAIfC5Dmc8ypyT7Bny1IHLozD/+07TGqRugjwlkpEEgca75JU6qDkYbG1txc7OTqNkjGJ6vV4cHR3FwcFBExHRGxZx8QDr1iGXVIytra2mHCqw7imvzwUQEe1vZqmtEefpBqbryCPyqa7PUo5KFzJlQr4xOqHBquv26fGKBj1X7+NEIyfg4jWd0cez+mTQuOhCZbos8G+OMeVJP+QT/3fDxLK9X7zmYOYg5e9kVJJ3N+hqtxtEN34lXjnASGfoqCil7OClunliigP/fD5voo/5fN44f4rAuWE9YjFyFzH60f9MtQqgGJ0JpCTbkiHxi9EgnUZ3ftkW6hAXFbHPlAHy08u622gNUkvIhYKURU4EACoEhczL9UiFAspjegaDQbMRdT6ft+ZfptNpfOYzn2kASgKpNINHcK7U/PaNlFfvcBJ3Y2MjhsNh7O7utlJ72hkvI8g0HhVURsCXevNoF85D+DyBAILLd2kQvN++ATRzEHwynGlA8o1pSjoAHuGsKkNuQEpA43JTAjWSR0Q0nlkdrEv3PVrtIho9vZ/N14mfPu+l9xlVCAAI6M5jpvbkMEnmWK5AhytFNWer7RkEDc5TSUY8jU/+04HgewIoyurp6WlMJpMYDAZx9erVlhOmZzwVSP6R1G//IoTrA8HWHVfqG5/3CNzv3y5ag9QK5OFyZmwkqJ6ykkD7nA6NJ5ezRrSXm6ucwWAQOzs70e/3Y39/P05PT2NnZ6eZ43n22Webj6e5EKrdTLG553t6etqsGNS78uikuDzlggs1/Bw0ervkxebmZhOxufdJIBAA06MlyOl5bdjU+W30XMlH8Z8gSUOpPrqnyfd5LfNmS2ncbCWfU5fCOxCrnV3eL+WOoM0yu6ItRlc0nP5OFhXpGsHUeVVy+tzwlwBfvwlMdOgkN94nzQepXZQBpf4Gg0EDdIPBIB2LUn+p/1qgIWeJZz4qmjo8PIzd3d3W4iRmATjn5tFpabwk/77gyXnb5YC4I3enaQ1SK1JJST2NQKMsoREg8X8CkL+nv+mdyZDKS5OCbmycfVl0f3+/Zby4xyUTRrY5m5TVte3t7WYDoqI8vUMjQsFn6o2Riq+WUx/1HvuryJFgp3fUPrZBBorplbqu048UMpJTu5XaJNhoDNUfGjU35By7VWSpFFnpty9aoFHJZNHLYPvYl8v2gr2+Uv28R+J77H8WNWncmOrWYiKthsv6zTIUSbA8j7bouHjkRJ2hQ8gInKtiuaiDKyBPT0+b1aeK7uhQqAxuzShlZHzfFkGYslQavyxiuptoDVIrUqY0+t+BKru3zDOkweZPxPkmRC6Y0IkT8/k8jo6OGk9QbZWh9jZ7ipLP0wPmHJHy/vTmVK6Al/0nWEiBPZXmfY5YXDmY8YrpIrVd7ZfCEaCZqtR9tlPkEYoDt+oqAUTJGLtRKRnfLELP+u/RTde72XMXIbZ51XczgHHZ97KY6tPzHCf2kT8yzsPhsBlnjxq9bc4LOSCqk3OePsdIB8v746nfbEGE+iDZ0hwwT23PHCnyi+W4U+jZm1Ucpq4IOqvb311W9mXQGqSWkAQwm3+KaCuge7sywoySaOz1vgyvBE31qmwtO9dS016v1+yzmEwmcXBw0Mw7sc1+vh49R/YvYvF0Zf3NdJWnGnjd012c9KenpzaoXW6Q2H6lL/UO57N4SrY82mwZMMFXOX6uGGRqg1FidtKCeFICKXraziMaLDe6rIeUOTcOGJSpLqOw7H5mTMWLLmBdZrjcgcva4DzyekSZg6NIajabLex1Ink0zn4y7ay5qqo6X5BE3SU/nE/6m3pMu+GLKvSJmr29vYWILWLxg6k+7hsbG43eixddabyMpxll/LsRB2cNUncBZUBFpSQAZPtAItpLnTNvs9frxd7eXgwGg0bQ+/1+DAaDmM1mcXBwEAcHB63UA+dNVLeAQkrJ5eZss1Yd1XXdTCzzk91U0GwymVEYwYt1KN2hH35SnH3Q3JRSLzIamttSXzTJ7SCgctTezNkQj/mNL7+n/wWMbkj9HZcFV1hvh8tDFsWRKCtu2NyTzkAkAxK+n6WBL0IeXWcg5J67v++gnhlrjr++lTabzRZATuMqJ4RgzGiJOqP04Xw+b1avZryKiIW9fA6Adb14lJfe1ccbmd6XXlBnM5nwNB0dQJeHzOHJZJbvXRbI3CytQeoGiSBCAXOPTUQhoOK4kZfgcIGFPjkgBVD6bTQaxeHhYWOgeXyRFJDEBQf8jo5SJorW2O6Dg4PWR9l8Tk33qIC6rmhIE8ecW5HXq7Skf7hNnqwiJAGreC0AjYjWXBeVTKu2nPeZUfSx4jiyv3xH/OS8o97Pog/97+WLZzQ6mfHI5Cqbg/C6SmBJyiJEj/bIX9bh95gOzvrvdXTVqTbLYWG/1Xc6LV2ndAioeDq65En6pbLruo7xeNwaEzmKlAePjiLa0TsX6lDn9LwOntXpFwI8d5oIUnTi3LlwR48gL+BTG32OK3OI7gZag9QNUMnwSEDcmDCd4LlqkZSGwjKfn+3fGAwGTXpAzyktMZlMmhVLnqLi/iSRL+mW4mrnPY2iFFhAwfYpuuHEM5V1Op3GeDxuTr5Qu/QsIyk3LHrWl+BnqwKrqmoWdjBlKg9YfWZ/vC4aAJ5OoL5zfFU2+8OxFRFIOMYlQ0DvnnLlMlJ6v5SGzLxnL4vy2AVCeo/tFWUrIVk/9SMr1+vjb6areYyWdEJlKtqWw8M0s2RD12nU5WAoEur1zg9BVqSvthAo6VBSrpXG43PaiK+5Y9Y3m82aL/dyLLTYSDLtekY7o/P+tB0li7zcaeV4sj93G73gQaqk+Ms8DBq5knHgb5brnmYpDO/1eo0B5gfX9P90Om1y8e4xUrkp3K4A2pCbrYLTogmuepLCMe+ucvl5eEVHSnOoXr7j/HODrL7wOldncYwEKnpHvx04qPAqI4uSNA5czOFRED3dzMDrvWWGn9e9vyXZLN33SCTjKftTolJK0OdzWKfI53GoC+RH1h4HPtbl7/mYaUwUQXMzt2ctnHeKXgRSKlPAw/H1BRWUaUYxzmcBKB1FttedHpXtjmAWaevdknNUaif76VFXVk5XCvBWpQdf8CDllKVpMoX2a1nY3fV8RPtDZ/QWeW84HEbEucLqWR19xE20eo9toNBRGHu9XrM5WEvZ/RPyVVW1vvwrMPCPLQqUjo6Omvbww4ZUdHquBLpsoUJpfLhqsaqqZjFJVVWttCI9XfGB6Rca9NJ+Jgc5vUeAYruz+SKnTDbYnyzKXkaZ3PJeBjql9nnbM6NOMKXzwf/dGfD32DZ3eLzdisL029vNsZS8sS+UAeqBfvw0dJVDp0vAwU/YO6+8DyQ5hmofF/wIVDM5ZBrP5wk9ss9sEvngfPVxuKjcXeS5G6U1SN0ElYAnM1wCI0UhEeeHwxKk3IvXMUhSIG001FwP53sizg0Ec/ZMZ1TVWapOJ0aoPa5oAoNer9dEWfpb7RcYaWPi4eFha6c/UyBUXM4hZQBBMHOwUXkEDy5EoEfo3qiMhCu1jAN57xGT2seUKJ/tMg6Uha732D8vb1kURENfikzFH3rsLLPr/awe8jdrE8eoy2CqDPXT5204pnxP9WfzM1wwJJIeEnh4jyCg93i0koBPG9+5mEllCHA0x0VQUVqOdRGsVK9HqJnjQufTP/fhkaDKZWTqDgivX2TBjDsUmZN0s7QGqQuSDy7Dcc+Be3RE4aBiuzevn+FwGNvb2w0QRZwrz2QyiaOjoyb/TECIiAZ8BDaKXDY3N5tJWr2THWRJAy5gkhGt67M5p6Ojo+ZHOXhXgsyjVXuVHtze3m7aqB/11yfLlYIUvxU90RNldEaDp+c9N0+AdmDkWHrZamu2QEVjTFlhffxR3SUPvEvxOV7+N71vH1fvuztWND5uNEXeb447HasMFDNZYx/okMn4e/tUB/dIRZydGiHZ4qIirQL11J6PB6N6ja9kX6k51c9MBxdGyFGU0SfgaIESP6WTbZVQ+5jpcKCKOE8jRrRP92eUpeezqIs64ccwrUJZZHaZ9IIHqVU83NJ7HGSCkojeUZaCYtQlgZKAbWxstISZkdh8Pm8WTHC+iN6M2qH7Sutx7oaKTi9TBllHxOh4GE0iHx4eNuDEhRFZCoH1qF80SDIkKl99pGetv5nWpEPAiJVjwr5FnC+G4MfrxPdsPAhKGjMZEE93dVHmmGTy5EbewSOLRhxUWCcNJAHBows9TyBxIPM6SoYp86g1Dp4G59++es354PrBSJcLJbRYRiDENnIxhEeW5KU7NhnACuzm83mzZUPU6/VagCLg03FFbKs7Z3o+i87JB/KVKwzZVqW3I2Ihqsui0szh8XHKqCQfl0UveJCKWD4I2fNUuMwT1fVs4l0RikdOJM0ZedpOSqmzwXSf9VB5FWEoIvLl2hROenz83o0U+/j4OI6OjuLZZ5+NyWTSOkWac2FSnoyvTGmpvVqVxPSnrnvkqjI8elV/SoaT/c2+VsznvC/ZWGeK6WPp0Q2vZcZa77oxyXjg/eK4M4r1d/0ZGb0swnLySMfvld7L2uzPesTG64xIXI/kTKkvBDOfY41YvsfMnUmOicuKnEXpH2WGjhgdAOmFp/4cqFgP6/U++t9sv0fmXlZ2tp+PczbWpesXtaOr0hqkbpBKEZgbAb/vylwCul6v13hoEnIJlw5V9clXeWksk996Ygow85ZUr57jl08FjPociB/gqTy9lJQpr8xDZ93yMLPUmoPdfD5vgaeeFQ/IZ91TfTR4bBOfccDoAqXS+Gay4ZQZ60wmSpTdK4GYl+0gxWfZhwyQHESzsr3+LtDOaBm//JpkWrrH/XeulzToktusnRyDTE90T86iO4CeMtbzdOjokHn5zivaFbbBx0/XGDF6P1aR57uJ1iB1Qco87Ax89LenhmQ4aTTpcUnQ9Ylp9x7r+iwvzkhKyiYF0TWdWs49UFwl6OXqDDHOP83nZ+eZTSaTGI/HzbJ3ggfz3BGL0Qb7S97oWd+7RV74JLA+v60yHNg9TadUEAGd82H0Lj3l5/3QNZcHGRz3vDPZycrkdTdMqxDHQWWUgITPZWBQqrcEWP43x4/X3WB7Wd6GDDS9Dz6m7qTpPrMQzt/sZAeOYbagge1gVkO6RjmkbvNTNrIf/PAhy9YYsm1Zqo8bkLkJmAuBGKkxkuwCqAykuxyLW0lrkAKtMgiZ8hCsslQOFzawHr7L1TdK9enbUb7iR8vPPXXBVT4CKH1aQ+3QB9d4TJOURV8oVT2z2SzG43GztFzvDAaDODo6WvDeaCQjzpfqctOu7jEVw1SGlHk+nzfgqoM4OXdFYHADqnmtrmhWCsvNmjzoM1sK7KkblksjGZF/1oG/Mzlx6gLHrFyf8CdvnNyx4FxPVj/7Q0Ne6kdm5FYlbzfrYjqN5bPvWhXb6/WadJxIc7pcbk7HUUQnjE6TSHVq4RIdxu3t7Vb2Q3pHnaNjxOOa6ODImZR+jkajluxKbskj1aml7npW4NUVwZfG605HW/c0SF2/fj0ODg7SjWcXpcwg+URv9lzmFdNQ+WoxppxINBpcFq49QBHnZ3op1cey6rpu9jQJcLa3t9OJU//AIFfMUQEEUKPRqOGHds5rPkqKxZRbxPkiBW0GVn08M1D9JZ+5ymg2mzUgRf7QyMjo6F0aW7VLbaTXSQN0enq68OVhNyjisXvYmbPiKRafp/DowuWJHjOfy6IzkcsTnQb12WWa7WTbM6PECMDbLWPKPmcG0AGR77NO997dCeF9yQDlgoZa0ZKcEa3Gkz5QP5gGVDt9jkflabM69U8R1Ww2i729vVZ/1ZbstHYdFq1x8vGkDJL0HS3ynMeH0T54+91JcdnkOLGtHCOOxUWirY2Njbh27Vq84hWviA984ANLn7+nQcq/RnkzdJFQlgPqRoApNxpMX5ruxo3lapEDPb2I8xSAvLJMUHZ2dpo5KBo8jwAFhPTUIs4/H6Ajl6S8Msxarq5JX7VDc1ScYNb/IkZYnmrTfRpk9dMnlwlGbhCpsNlcWLagg1GVL6t2o76xsdH0NZMDOhQ0OKWozmXF7/n7mSeceb4cA5cDB18aZudpSb8crBxIs3tqQ5byywAoe4b1UCZcl+RouRPEd7J6XU/ccanrugEoOk/MFkjGlJlQG7iSj+3NVtyKmAYnULnTy2tZFkD3fWwzudQKVudJacwuSnV99pHJj3zkIys9f0+DlIzFrSZXOF4j6JQEqcuDdoMkAFG47sLG1IKDT6/Xa4448tSEK4efHBHRBihFSvSgPP+fRXOqQ39nEYFAhh4r61BOXfl+T0VlE9HcMOygT2OdeexU9syDdaOePcP/eZ188SjDxzWL3EldHmsXqHj7szZnHjzrLZHzx6PGUj8chLMy9bfuSzYizjen6sfbQFBUHXqHfaZjSSfL26Z2cAM9dYPyziicX7HOHA93Grp472NG55HtdCeO71wk4rlVVNdnUwmf+tSnVnr+ngaprvTHraDMMDhYuRC44SkJJZWSIMV0lbw5emTigcCJEZLuMz2ldvrhsPpK6Hg8btIYnJxWfQImlaFrmaJUVdVSZPKCXq+ICi4QHI1GqaJTyZXK0QpHgVbGZwKb1+2A6+Or5ziOHDuCLg2Iv+cGhPW6o8O+8v9MbkpUAil3IkqeNev0MjInjHrgUUvWdgdv/9/Bx/vk/dS7TNPx8+3utFBXWC8dh6qqmgiK5+95WerfZDJpLaCgM+tj6P1R1JU5vfrt6VnW7XXpHY8ipSekTPa7HKAbJerGMrqnQepWRlEUdid6biIJhdJjap8Ehs/xmiskAUT5a35vSQbZ52EGg0Hs7u42cysENPVHZehk9YjzuR+dWj6dTheUzjfrqv8CBEVd9Fy5UZb7nUpL4B1MM69R8wdaNDKfn590zmuqX/W5gWPqi0BMD5Tt5HvifclAd42ze/2ehtJzmeHV375k2sG+BCqURTdqqjMzGizfTzxwYrkOzhGxIEcElQzY3OlT3wQqapdH4tQXjoE+Zsjl4py/cYNNY1/XdbN53ueByCvyZzKZNFkB1ce9j3V9vhiDGQS1i2Pp4+rj4Gl9Zkg41mwj+8Bpigz8KL9dzswqlL3fRfc0SN0pomfm3jANdWZspBBuoGSEtYxVQs15N1d+laOz+DgJzG/mcN5JACVwmkwmMRqNWvuu2M4snaLrWkWndjp413XdLIslyOh9Pkvjqygom49z71ht9ZMBspy63nNF0zhqtRR5y6/90mvmvq7M29SkNldt6l169SRX3Iyf6gfrzcopgU5Wj0dUpXcIluStp1tZB8dVwM/6KWN8z3WEYEmjWlXtA4ZVFr89RmdEbZKuaf6Fc0uMsDgWJycnDfCoXZz34ikSKuvo6ChOT0/j2rVrzTwznUw6lNKRzc3NxukSLwgUdJ44l6xy1HYdD+WgyiyHR4CehbgbaA1SF6RSSkEgELHoodCbp2F37z5i8ZPRjMw4H6VrWsnHNJd+mGbQgZg650tRkyIoGkQJOxWbfZLxFimSG4/HLQVSOfqb0ZanFMhf1kfDpnfFV7WNGyj5o7bScJGn/PF5iWzhA+fkKAtsg8ZJZbJvmbFhtJCRGwwa76wd7EMWhej/zCMvzWPwGqM59iUDLpIAxAEqA1POFbHd4ikX04j3dD4EFh6dEvD4t86OzCJWyoPkkTzmHBYdLrVXzuB4PG7t76Oz6k6T7AUjVzpIHmHxfMuIaDmczvssiuJzJWC6aPRzmbTabkHQr/7qr8Yb3/jGePjhh6OqqvjFX/zF1v2/8Tf+Rsv4VFUVr3/961vPPPPMM/G2t70trl69GtevX4/v+I7viMPDw5vqyM3SKoPAgY1Y9Gg97ZLl0On1Zx4wj0tyz5SpM6Uttre3myWsAgJO7PqJE3VdN9HTeDxuVuuxf1RA54kbSBE3s7p3zOs8U60ECB7t6EcRIucEOEcn8CJ/u8Y0A0nvv7crU/KMJzQopYgrKzOL9MiDTO5KP/5uBlolIHO5dB5kvMh+srqyslmO80I/Gms6YgQJUTYPlPGG/RawaSsG2ySgEai47tImOI8YgfEwaMopx5v33ba48+A6yHvev5JeZzzx8bhT4CS6cCR1dHQUX/EVXxF/82/+zXjTm96UPvP6178+3v3udzf/a/5D9La3vS0+9alPxfve9744Pj6Ob//2b4/v+q7vive85z0Xbc5dQZnXSMHKjFlp4PkpDxrJiPYnn3u9s2OTdnZ2mjQCT3dWG7hJt67r5oDY0WjUOlldnptIAl3qk+e8Fa0RQBh1iAQo/j6jHl6nt8kFEjQUNBKe+sv47z8lA8t+yGnIlgpn4+q8U3t8XoZ9dV5732hQM+enNF+gMmiY+Df7Q0OZRWdeR/Z+Vx0ZZfNkPnb0+t0JyubkJAeeqvbnGElwMzYPbp7Pz7ZeeASWyayn8SKi2fQ7Go3iypUrrXkyT6szjU7AJk/IN9VJ/nk2wcffHQhmKJwnpGXjeKvowiD1hje8Id7whjd0PjMYDOKhhx5K7/3u7/5uvPe9743f+I3fiK/8yq+MiIif+qmfim/6pm+KH//xH4+HH374ok265eRehgZckQoFWwsXOOB8j+/Sm4o4Awul7gQ6Mhj6yq3+39nZaZ5V3QIZHQ47GAwagOIc1P7+frMHSeDiipIZSipFxKJxuHbtWmv5uvZq+ZL5qqqayW1+pn0+P99cyc8eMDoT73xeh8aByiml1zX/YCIVmOPhnrAbFd3XMU0EqQzcKQvZ3A3B0uXG03qZUVZZNOIkyY1HNxxfzh/pOvtKckOVeeM0dq4HWT/Jg0x/6AAwupJh5vMEqbo+O/JLsuORnkcwEe15Xcmb5DXrs56RY+jjOZ/P4/DwcOEcTa7e1XMqQ/LlKX7yXpt3JRdMNZb2ZlJGHHSd5z6edyKquiVzUr/yK78SDzzwQLzoRS+Kb/zGb4wf/dEfjfvvvz8iIp544om4fv16A1AREY8++mj0er340Ic+FN/6rd+6UJ7mTkT7+/u3otlLyaMhCRHnnah8vCZQkjBkS6qlBDR2EjYuhNjb22sETQKq32qLlqSfnJw0809K8/EU8IhoKaO3n6c0CNRo1GQstre34/T0tKlXbVP5XLLrkYzPB3GpcBbtRLQ3y6ofniLRNc4j0NC7F+1jLJ6wrMzbPz4+blZtZZEM5UdtZPt534nGxKOWiGgZVo/G+CwNIfvp0breYb+zKMkjiIi2o0CwyKJqN+IlfjkQ0GGiE6HrHG93GJkadofM+afsj69c5YIG9Wc2my1E2O7M6O+Dg4PY29trHeDMlLL0hg4heeeObsT5fCj7or99QRFTjL5w4k4A0Cp06SD1+te/Pt70pjfFy172svjoRz8a3//93x9veMMb4oknnoiNjY146qmn4oEHHmg3YnMz7rvvvnjqqafSMt/5znfGO97xjstu6oUoMxISQO7tcWX2yVKRBJlGk5GBynAD1Ov14sqVK3F4eLgQWUg5BVK9Xi/G43EcHBw0HyYUWOpru56fzlKXNM7z+by1UZirrFSOgFar/tRu9peroshTpSt9mTF5oPIizhcW6Cup6hM9X46XJt4ZGakf5IOPNw2Je7SluREaIF4Tj9jvLL3Cd/hDI6kfn/gXj92b94UwbBOJPPLnKZvst6c7Pbrhu7q+imF0gCXIsEzNU3G/HJ2g+XzeSher/bzvDoBOR1EmQv1VXzN+EjT1rOrU2Zk6skzlUQbUbp4zyLMBReS3OyKZM6U+Ug4IemwrdeZOg9elg9Rb3vKW5u8v+7Iviy//8i+PP/2n/3T8yq/8Srz61a++oTLf/va3x+OPP978v7+/Hy95yUtuuq2rUMm71W96Sq50NAI+mZ+lHCIW88yMZug1+4om1aEl7NprMRqNYn9/v9mkK4Pu80/+WXQRoz/WRwPkiybo4dGTU/sz756gQN6pDJXtkZKeYdRaGh+Bn6+GIv/dcDOS4X2XA0bKGr8sLSegzOrL+EKjyDKysSIfs6iK/fG+ZOV4P5262pHd93Kcl36f48x+Mc3n9eg+U2AaBy2MkExyM3rEuQFnmxXxbG1ttRY+UBfYF8ocsy5c9s3FP9Jj1R9xDj50QpwfBEnuqRSxLOq2O2L+dxd1AVaXw3OzdMuXoH/hF35hfM7nfE585CMfiVe/+tXx0EMPxac//enWMycnJ/HMM88U57F0IvjtposMHDcCumJKKXxVH70oEdMEIgocj0ty0OBy9Pn87INsiqKYLlTEQiF1RfdowtvmESDBVv3ihwz5rOae6A2X9g3xXDN5994mHsir+j2FSDCnB8r/vWzxOPP4M681M9qZAfA6OAbuLTtAlcrlGHZFKBmIZ/dXlf1V+ubPZ23LUqWuR9QbBwk9T310x0YZBgI/jzdiOjF7T/M/zkPKk6eRCVSMZgRUivg85ScZpv6QJ66DngGhrKsMn7dVW5iVuBvploPUJz/5yfijP/qjePGLXxwREa961aviueeeiyeffDJe+cpXRkTEBz7wgZjP5/HII4/c6ubcMNFou0BEROtbLgQkCdvW1tbCoa6eanPDWtfnn6muqqrxBLnfh3MH2gclgHr++efj2WefbbwsKYwWLTDKYTnsb5aCcO9QRsE9Nv34Z7KrqmpWAqr/nGwWMcLL0mHima+GcsUlkLunzPEkGKj/4gv3YrEf2V4oBxYHDvdiOeb6W+3OIkcfI7XLZcfrzVKSHgmwD6Wo0Z+nAS4R9SeLoPjjfPFrLIdpK7WFkQmjmoho7WUUKPlcjq4zVa8UnfRcRF7ToYqIJsXnWQfxazKZNJ+Hcd5yHtJBiv0iX7PISLK4ubnZnLhBMPfxWyXKvt10YZA6PDxsnV77sY99LH7rt34r7rvvvrjvvvviHe94R7z5zW+Ohx56KD760Y/G933f98UXfdEXxete97qIiHj5y18er3/96+M7v/M746d/+qfj+Pg4HnvssXjLW95yV67si8i9Vv6WYI/H4+b7TTKeEWcAIJCS4EtwWL4iEv8svH+1lrlx/Vy9ejV2d3fj+Pi4SfF99rOfjclk0mp/BoRqD38rLUFjyzQZgY+ASuOuPVsEEY9q+IkMKQs9PvcA1Q4dbSPgVr38am8WpagefrU1ImJnZ6e5RwMp8gjN5cMjEAcPXsvASv/TyLojkzlHLJOg7AsEsufZDo9MMh5k82uliCurh8bajSCNJ9OzblC70liMhPSMHLmIaEUtbJMflsz0H8FzY+PsO1XMTOgdyrfkVnWrLuryfD5vNvju7e2lc8ElORGpHHdCaUv0PrMYHrV6ZOqOSGmMbxddGKR+8zd/M77hG76h+V9zRd/2bd8W/+Jf/Iv4X//rf8XP/dzPxXPPPRcPP/xwvPa1r40f+ZEfaaXrfv7nfz4ee+yxePWrXx29Xi/e/OY3x0/+5E9eQncuTpmXlt0XySvLJoUjovWtJ302g4AlQ0cPSUZCqbyINmjwo4WKrOghaqn5yclJHB0dxf7+fuzv7zffYyKg+SILKQO9Of34xDmjQk4YyxhIkQU2WkXI5eY6ksmXArtHS37TeIgfvmmXPFO/mIZ0MOB3qDjXx2fZH91jqkg/GQiyLjc4bvgZzZEH7iFT3jySUHv121dqep3ku+Qi4zvLzaKgDHBIpQiQ5XrZJf4xqs1AS/3wFC2Xk3OzPOuinPnclPRuPB5Hr9drUurMcvhKXuo19yMy+6CMh45nEhFcJWOSeUaOapfzlnLK1b78tIiPhcspadVrXddvhi4MUl//9V/f2ZD//J//89Iy7rvvvrt6426mWJmx8+clACcnJ63DLH0ZKI2ciEbP0wMUfuXQZVj1EcK6rmM8Hsf+/n4cHBw0EZTKpmGQcBNwGSXoWe0BopHKIglFf+71uidPg6I6tdGR5bO9vtBCIMl0I4GfwOH8Vpsyb5Fl8LorMcvMQISGn387b1iH18d++fuZIfFyPXpiP7I2+LMOAHw/K8/vl/Qnu5+1ldEQ28gxouy6kxXRjpYlK/oUDfcPibeuI4y0fRx9oRD1n6DvzkBELICUvjjNuSxPKfd655+ZdxKgZbLINojv7K87iF12vSS3t4PWZ/ctoUw5S94tjYiWrM5ms9YHCLP3WY4rhJRPCsYTJZTukld3dHQUR0dHrY8VZpOhbqhcYKl0rjQUaDcmmSH2+QoZFCqKG6SI89SM9h9xwYWWmzsgOXDQiMjbZQSnPrJedzicZyWg4vizvOy695fAk5XP65xXcyPqwMtyeH0ZyGXkIFPqzzLyd0uy5PxzJ9H1gwad/PD3HICY5XCw8mX43mce7kqw1Ri54XcHTHZiOp0utINOisrk3kq1waMr7wPbTr3zaNN5fjfRGqQuQFQK/Z8ZyIhzxdLkqNJ+XQouD4zCzNTZbDZrpQEEUBFnG5611NxTFQQGtpVGwj13eqkR7S+D+kZUgoD6pes+6esA7Aqr+zwRnnVtbGw0qWM3nJ6CY0Tqiu+edzbWPsY8RYNGjuW4wXVg8HsZYHS1S2Ph45iBVKmMrmjKve6MJ86frPzSu119z8ooAZR+04njPQKHzwvxZHDNaTI9pgwF50IlOzLw0r+tra3mpHNvM/VJGQDnoeam5Hz56SUR7U/jMIWtMkp6wL2Gui5nb5mTVhqTrM5bTS94kMqUMbte8nBpxPWbqTjlnWXMhsPhwjsSegJHxPlqPX3jiTnovb29qKqqObxyf38/Dg8PF75WvLGx0ToCySdXpSi+Oo+gQy+RURyNBxdakE8lb1ZtGwwGTV+U9lM7eeag8vLchOwTvb56UgaF46e2+N4zLtPleLAvTDHKs1XE10U0KllaSv+vYrTVtux+Rll04oBKmSu9m4FE9uxFIrJSPaWItRQhsi/eVq9bDhYzE84PAZDPU0UsHr8VcbboRhGNdMmjHMkkN7Hr/ePj45hMJg1QEdgoj4PBIA4ODhpH1dOL5I+nDKXPvCZ59nkt53kmm5lc3Sp6wYNUF7lx1TUOGsElAzZ5M/re0nw+b4DKowmSIomIaH0R9OTkJAaDQVy5ciUmk0kcHR3FZz/72Xj22WdbR0exTKYfItp7I7jHiNET++fXpNjcB6VnVZZA0Tcj8wvCNPZqi5/F5tGReMrTsKWAzkePaqqqiuFw2LzjIMfxcP55FOZ7WErRQ5dnelEqRWGUQa87m2twefWUsAOOA7w7HG4M2ZbMgNH4i7LI1nnH6IL9kzzTcLsh96hIP/oSAD+E2OudraDltghGalx1qMzH7u5u80kOXRdPBD7UFfaZUwNyTCPa0aCiPn1rygHJHU+BsMuD89TnrETLnKbbSWuQ6qCugcoARsrAFBf/1kQpvRoe3yIPTwsDIqI54l+pvq2trWa59HQ6jcPDwzg4OGgJJNuuiI7n7EmJ1D4pf3Zem+9kF/GEBW52dI+bqSmPRERMyfC+IraMn/Q25fn6HJWf6aZyCdYEbF9pR15GtDdmUul9nsjHwB0X/7sUHXVRBgS8VgLHLGLKqBSZeLopq5dprywCKrWZ5bFe10MCozuNlGMHWwEZ62b6XPzZ2NhoFjzRMZF+u4NAGVRmwxcJEZz0LtspoJpOpwtpQdqVwWDQZEbU3pJzkOmifrg0n+B/N4GTaA1SHVRS9C7v2BU78zxlHLk0tdfrtU7+liBNJpPWKciKROR56fBdD/slsB4J8Z7aIMDMDLCvMBN5ntv3gBGcSJk3zLL1rgDPgS+ivYqKy/nZ9qy/PkbiAdvUNc7L0myZXHiUUYrO9f6NGohSH/1e6ZnSvQyAIvKNwV1l8j1vd+n/jN9yCMRXT8k5aDllkYTAQilvrshlFsKdvYj2R0qln1tbW020I9nkKlUHT+kZv3TAPYmM2DyaWuZEy464brP+u53WILWEMkV0I1P629NU7vEwgtDeIxcuRVCcp9nY2IjDw8OYTqetvQ8Sdv8/i2wIlPr8tupkuxklMIoRKFGZVbZAS/VEnBtrrkTie4xw9L5HNeSbwIWeaja/w77rnnvD/g7HTvfcK8289Ew2XG6ytnkd3pZl5XXdy8CQVAKtUnvccy+1xaObEkgt65+XlxlayQPHlbpEQGOkzevSJW5v4F48/zSM72PihuHd3d1muwjlXP1XOd5G6SLnh2knFK3pHufT3Ilz2xNRnv90WVtlfP29W0lrkFqRNGievvKUH6/7viM+E9H2sjXgTJ3Jq2IKot/vx3g8bj6/wf1JvuqOdUS0hdR32FdV1VIQrVyq63YKTl4mv2DKurmCUWVmBp7zObruG4xFrmz0CmlMPUqjQXXj5QbMF1JwrLlsnZGje/olgGJZGXh6tLIsouoCMAdlykDmxXvb3YDR4WEZ4p3KyyJuyVfWPl8dqufZfo94M2dPZfnCAbbT66Ts6DmVQWBhKp7AJdASueMnPSVYcHx9A7aOT+IpLXJGKTeKsDQXrAUPmV6rLqUs/brbri5wymTpdtIapBLywXKvMLvOdx2gXMD1/SEKiqKp3d3dZgUbd8/3+/0YDodRVVWzJ0obhnWqeWkCXOCh+jjx6gqbvSfg5FJ6gVddn58soT4zleng7ntPqGCeG+cqR65EZJRG/rIfepena7A9XGihaJRzgWqP6vFxzuagnO/8mxGkKAOEUlRRIo+WWG4GBCLOsZSeIWXPlQDJ+cJ5nayODFwcsCRbdK5K0TDna5iu4/vUScmk3j86OmqdTOGLctQegoCWtYuuXr3afFeNjm0pkpQcz+fzGI1Gsb293coQ8PxOnspOXniZLstafUtdjIjWKsS7kdYgZeSeKL34UurOUz1dHklWju77V3n1aYDhcNgsx9YKIt2TomQA5QZR/aF36Ckt54PAcXt7u9mfRHCQx0kDq/J5MoauMUohcDMVox+lWKhMIh8XEo0VI0WmfBjtKmKlZ60fjyTIW/7v98hHAm8mb1nKRn3M3il5suQ/nytFXmorF8z486W6WHYWpZFHrlP8OzPcnl3wjANBOANPzR2xbEZ+dFS2trZa2zYkV/pYqDILPOhY5XB+yPlSVVXs7u4upCGZbZCe87tRVVU1n5mXUyrSPj3uj/SNvNIHj9Y0p+VZAZ8OKDled5LWIGXkSlUauK6B9LkWVy43PlKY7e3tJjrRactMsSl3rq/rOvBkyh3R/iaU6pKX5+0mgGgPkzw31cN9FfQmBQD0Mtl3tYVpDPaf9fN35jVzE20W8XiUkKWMsvmsDPgyw87rJZDRvdL9DOz8PdbRVY+Xq99ZdMM6IxYPso3IPyOT1dOVTch0KetDBqoOSrpOeWC56gPJgYlGmrqgLQ1sF6Mjpbf9aCK1hbxVezkvy+PM/DnyhGk5fRzR+SN5z74O4O3gdV4TL7g95KLg1CUXl01rkDJyMMnu+99dwFZKcehZ/VbEwvREXZ8vUKjreiGKYt3cIR9x7mGxDwQKpghVhoRfde7s7MT29nbrfYIu6xdQed8j2vMVVDAHHe63khIJsDkmKoPX6LWXgCTz6CNi4eT0rDzW7WXIOyafPDLlO27YSmBAGewCKL+WAYc/TwBY1YP2dnodWRtZdun5rvLd4cqMvPeJ5GApORXRWNORoQ5W1Vlktb293YpuMseB7aKjGhGtiF7PunOpOieTSeO00lmTI8itGcxsuFPiekgnVDalS1ZoD7tomUNzM7QGqRsghu2MZpTeciV14ypipLW9vd1EN9wzpChmNBrF4eFh89l41SfB1bLXiMXPhOtQTR65orZLKXR/MBg0E7/9fr+1aol9oQfGFJrq1HMEHr3H+TrxSkonheXBtmonldMj1Kpq77qX95t5u+QN5904b8e+qq1dcypq30XlKAMHGr7MSHhE4hFYNkdBysDTwSQDELYt6wPb2gV6vM90Uwmoeb3kfLnT4JGwDDgXHCiyGg6HzWZ5jTm/2VbXdXMgcxbx0wZERJOm1jU5YNqMzwVDGQ8jzvZHanNvlh7UcU5a7OSAzkVP/lka1ptlOzjedwOtQWpFyiIjCqCimWyuhO94iK+o5vr1641XJfDRqRNK8R0dHTU72pUOZDmqX6kLN86+ysf3eFy/fr05luX4+Dim02njOQogBAZcDcUVifQimSIUD7j/yXlI/uiHH47zVXVMc0oxtTRYcw10HFRetqFTzoB75uSTp3o8EuqK1lyWMloWLd1IFCUgKEV2dBK4nYHfR2LZmRNWagvL96hjVQ+d5dAIOxhyWXdXBOaRvFbTqQzyjHWr7PF43Dhwkj06feKlQFGgpEVRk8mktciC7ZY8z+dnR6npTD/JJvdLcl6K7eVqPi68yhyJzMG4m8BJdE+D1O7uboxGo1sWZpZIHgjDeBpiGsWI8pyGjKMWRgigdKaYAGM2mzUnTyhXztMU9MMJWBpnKqrmtZTW06o9pRYIZIyIItqrBLVxN+J8fkgAwj0mIo+kaLwUARLg5Lm60en1erG9vd1yBJhqdGPoHja9aPGbc3YOgiyHkWIWOTmvS8Tn+Kxfd5nJoht3Qtw4e1u8H3zX+0dnIgMsUskTL0VHjKS6jGMGdFww4fNOAiFfXaq/Nd4ae4GTZJNt8bGcz+fNBw/n83mzAk/lUCe4EEj/67BkOgPOC6b0jo6OGudva2srDg8Po67rZkz8sGOOm7dbddFp9GjTZexW2dWqqmJvby++9Eu/ND70oQ8tff6eBqlsOefNkhu2DGwyb5sC4OV5WRI0Gfvt7e0mPKenpMhgMpm0Tp7gBCyFdGtrKyaTSUS0zwWjpylSG/TBRCmuKw3TcyrPhV33S5/QULny7OS56jonlbmwggaJgMkFGgLbzICq/Z6+U3ks1w1sNpYsOxtf/l8CFN1zgFH7MsDKDLjLqdfpY+39yqIYApi3JyszA6ouWuale12+eGNV4M34WOqrZEf65ysd+TzlShtvpcd0UCOiSbGpTZxjVlpfQEpHkHoyHo+beWGlubkfUQuqHKSzBUPeh0xPdT2zbxndDKCdnp7Gs88+u9Kz9zRIZaf3XjYxMuI199gjuj/vnRkoCZoWTHAuSl6sNu76ic0ZmGrvBNvIJa/qh1IFWrFEQKDx9LkNj644l0OvWyTlINDQ09N1eaYEHXq7VDK9IzCjl5gBFefOaLyY8usyYqXIJhsD3l/FYHdFD6RSWZSFDGxL0Zobq0ymCBLeZtZFHpaMXaktXm8G+NkYdYG27tHpElGONLaKurR4gikyl3f25fT0tEnbac8jHUHfEiL5lGOo9njkxvnkqqqa9KAyHRlIedSYjYU7JgS1LsfqVtHJyUk8/fTTKz17T4PU7diAlimORwjugdJbzRRQ//f7/WbFUF2fL3BgJDGZTGI0GrUOlcwE0b0iKRrLU9uU3uMyVrWfqS/3HEv95LwcAUnt1eSuiPNPSm/6YgpXdqYLBdaqR33jggjxxw2B2kdecqWgyKNK/z9zWrJVe11E2cnqz4xHZvhL4FQCqQyUSxkAr89lX397ViMzfnrf21UCs4wyoPJIQs4SjbdHECzL0/PHx8et5ePqK9N5uifZ1R5HyVyJh71er+WUyqkk8GkeNuL8gOm9vb1GR7jYycHT5505X6Yo0d8pAVUmJ5dFJycnsb+/v9Kz9zRI3S6iYSp5pDTu7lVluf66rhuQ4oGx8sqUDjg8PIzxeNysPKJBkNByIYOOY2FdGUAp5aCyuECC/VYEJ+PO1X/8LAc33hJI6HFGnK/i4yo9bvrNJsCzuTV3FgiSaoN4r34qaqNRizhPzRBYfTEH66VDwJ9Mod2ZyOSqRKXynLK0N6911VHyvMVTj5rI6yyS8hSw7vkcngOcOwRd/S/1h3JCB6sE4JqfpUO3ubnZRPZ8V7JVVVUjQwKt4+Pj5ptSkmfqIctgpKZVhWqnFnHwBAytOoyIJp2vtmmuin0QSPMDqcxouMz5SSi3Mnq6UbqnQepWoXxEnpvlAFIJ9QznMRSSi6i8+ltfnu31eo1Rl5BV1dm3bp5//vlG0EqKLAMg4KHHpfIGg0Hs7OzE7u5uo8x6TnNCw+GwlZsXKLlXpm/eUKmkjOyHlITArbTKdDpt8WQwGCxETZy8J19L/PRVe3qee2CYLqEzoUUs4i//d/IFFU70dEXulWZAm5EDJCM63WcklxlVr2+VdmSRF1OrlD2NG2WUmQTyzPlB8GI/dN1BzKOgjHdcOOH3HbyztKBOe1G7M1ugcrmXjwuHdJ9AkQEnV+1lgKIPIh4fHzcLJVSmnEV3ItQG6TKv04FVf6hLDmTuiGR0UTt80QjtngapW01UsswYMUrR/5nhygZEe5L4gTOG8Kenp835fFxuTgPOiVf95t4qKY5SikxXUFAjzk915vxVRLRWd0Wcn04wnU4bL48r8QiMEecK6ftL1HaCsqclec0n/MkLtZ+pPPWNBpT35Ikqt89Pgni6tks51Q4aIP5mGtMNK0FjWbRTMpLZs6X/M7l0UCqVG9E+SJXv0/PXfeed+MT3CWbeRjoZes7TyCrX02sOquQxnZ9e73yVKsc8W0SkcjOd1x4o1SO58nY6n3q9s5WqR0dHjUyyzxHRfJ+KWRaCHh0rH1OXMf3vzgzJ/xe/7iStQapAXYrTBUrLjI0UlYe1+mKAiLMUwuHhYRPluOK7IeE1GV5FR9zTwfA+m9wVUHGRBb3Zuq6bFCC/GEzBZ+rT25YZwCwyoVIxYopozzXo2QyEItoGlf1kHdlGXhoiH0P+rWd9IYbqzuZ6CLRZuaIsPcMySsCSPV8aj5K8dgEgDZ3zkmDkZWVymwFuplOMBMQblkfD7vOQWX/oAEnuXQf5LnXHHRrpME8l5x4myogDquqjDHud+s7Uzs5OI5fu/JE8xen9oDxQ5v05H48uh+1W0hqkbpBKIFZSwoj2N4oU3dDAcRPgZDKJw8PDZn6HhpPGj8pKIeT+CqUk/Lw/PevePgVZHqHuCZR0KGvWPimX3qeAM5XD55neYUSmiIdeNw1SXZ8v5eXxSVT2bG6O73vakM/RMaCTwWd9lSF/CDQ0vpmX7/LispZFXg6MfIbGK4vASteyMl2m+Rwj3K4ynR+lfmbtUz265gbdwV/9Z50eQeiaH5BM8BJpkYTvpWJqT3Lop6doHCQnrqtccCG9YX9Ud0Q0c2bql2wG+bRK5EN5c11UfzKn4k7QCxKkuqIdp8w7zLyOzPuj8Y9of7KAn+SQ0Emwp9NpHB0dxWg0agRf3n7EuVHw4064qVDpRIGKlEu5bPemuCGXuXUaF50MrSXxnDNSG7iknSnDzGA66GZ5coKx6tCzjATVPiosU6IOymofT5rg8ne2l4rqUZ/PWThxn5anTuids41eV8los5zMSJXmzmhYb8QoOeiqbKZts+ezyFL3vT3sB68z3a1IQlEMo2kHyoj259pVPoGCEboyERojptzpfEnGOLepvYqSM/aTJ3noXckiwZ46NZ/PWyCleujAiWivBIqU/cyh9sUT4nNJpm83eL0gQeqilKVssmcyIfDJc4HEcDiMiGiOHyLQjEaj2N/fX1A2pSNUH1dZ6RmlEZVK5KcE3FB5hKEfzWv1er0G4LShmHu56Olp1R+jC9VPsImIBkzUJ6ZGuCpSSi3iCkG9m3nK3ENFw8/2igf88QhLZbockGcOfFR8LolnWTTalB1PBfnfDkYkb7ffI+BRZviMLypwA0s+uKH3606sPzNwy6KrUrvozHBTuGRMMqDyGRFFnB//JFnTqjxGPb1er7XhXfrlKb2I83HXiTE8OT3T57pun60pfaPjJv1jKl59ZTuyeVDyS4uWeLyS+ked47sXcehvFa1BqkAcIPdkCQ4eNjtQUUl0TUJOISSgjMfjmEwmaRpDCiRhi2ifLafd6ZwEprel8tyg9Hq9RuDdK5xOp81CCZHK9vw7Db76xwgoSwNKQVm2e/aMRLa2tloAJf5xPopg4X2VsVE6lHzxSIfywPdpAHxyPwObkucpGcnkzSMrf49ld9WVGZusXF9yXqrTx0Q8Jog7cDmwePmSSXco/F3WzfESD6mbnhbX+Ph+IzpeXEzD1B7L0SZ41uvt5PmZ0kcBoK/mY1Q/GAxiNBq10oKSKU+Tc+uGeEN+MFKUw5npVebUdP1/u2kNUkbLPAi/v8qz+lvCJTCQoNL71yGyTKe50aZiSUh1BiA/6650oNJ+brDVJkY99Pp0sK2fdhGxuARcyu+GzJWBSu0GhNEQ75PnVFK9W1VVK0+vd6jkIkY+3Jfl/KUT4ukmjqun5FS3kxtXl5MMFLqecwfDr9M5uCh1tcejPDkpGR+yvnhWoque0hjwPR9ftYtzQg52GRj6u4zGdV86ov7qXUU2IuqfyuQnbzhGioikS/1+v9nQS8Dhdg7qnOrLnJDM2XA56Rqv0r3bTS9IkMoUPfuf19zTdUPPcnk9AyntTtd5fAKpqjr7Kqc+aqgIgfW74ZXQ9vv92NnZabVRgqy5r4j2/i4pHU8rj4hm1d5kMmkO1GT7VQZXQjlw6JoTwUz9kUL6Z0yYgvMfla374/G45T2ybl1XfZ6KdMoiWL/nzorL1I1EQf68R2QEUF3X/6uAkjsO+u0LELyNrMvveYo066P+d/1x8HAnwessGc4MyDwqZLkEGIIQQUzy5+dK8vRxOoxqo+uRz5OSF3V9/gkQybrO8tRhspxn80iKabuSY5RFTZzTY8Tm72bl3Ql6QYJURpknous00BRKToh7FECl0T0JoTbUSgHotR0cHDQLJiTEBBY3XFImpg9FEmh5ajLKUgptBtT8mBZGaF+GFl0oIlPOnmk8let8kSLLWEjJuFtfSib+iB96j2lEjok22jJdSmPDsdQeFubrq6pqfW6BHqsAzh0PgpIvGy4pr49HScZKThOBVc9lRtaJgOARicrxSCRztLI5C5bDSNhBYFUHcFnklVEW3WYAx7Qe04FcQEGdJs/0fTbymUcfeTuU2fAUshY9TCaTZsM6tytIB/Su9jXqkzzeRjpiBC7vJ8eW86Nc8CGdVj+ziLQks7eT1iBVIFduz627kjMa4vv6W7/1xVsZZRpMreobj8eNIc6MkOrUEUc65kjlUzgVuSmNoM90aHOvwHE+n8f+/n4cHh42QOK79qlcWhUnxdLZgvTsOAelOgS6s9ms5QBI2X1lYUQ7IpOhoVHl3Fxp5Rr3qGTREI0vx5cknmpursux8eXG/lwp6ikBFg1P5pnzfQc1/813fE4nI58zy4wZeco07TLyd718ev1ZH92BoTx5WsxX6PE5Bz2Vqfkn1cPFMBoLtk16rbq1UOP555+Pa9eutfooZ206nbZAVkB1eHjYtEdgls2Hytki7z3F7VEVI0WWWXIm7iStQapA9MqykJnAlXmoepa/e73zo5B4JpcERqk1XxLKyXi1aXt7u1nGzn06eoZRgYwm0xH8VL3A8fDwsDnhQoady9WZ52e/uYhD77FvFHJGQCqTH6FjGRHtz32Lv76yUP1UfUyHkI8CNNVPnrE+/Z8tBOA9euqUDY4/jUkXODl5VMCyZQBd3uhEsBz3rNVmT8u6nLt3LaPrpLaoHue7/vZT8kU+f8T6+b+n5jy686XvnhJXmVrd5ylqjlPE2XmWdK7EY+p+Vgbnb/Vbm/OvXLmyMK+qeulsbW9vtz7P42AufnLRCmWCNkrOm9L2lMXMrvnY3YnoifSCBqlM0H2QMi/U55rcEPkPlUXRi0JsCVFVVc0KOkYfbowknLu7u80yc5GiBbaTKRtFPRHnxv/4+DhGo1EcHBy0lr86KLIsN/TyFpkKYd8Z3XA+y4kpRI9MWZ7awNSHRzER58uLfT+Ze+00UG6wHVTcGRBlYELZyeZIMuqKVjxCcUPo7c4cKE9tZqDpvM6cNa/X3/VIh/3I2uiREMvL+uHvZPxzvYyI1rfHCDjckqFoRs+o3GzsmTkgfz0lq4VISsuznWoT28pPepQcX+kgQa8LeOjMsQymHDPZudP0ggapElEQupRy2WBmCstvwMigSnCyT0vLW2IZWsnnk/5UEJYrUnkSyuPj4xiPx808GFMrJaOu/DbTKDqBQl8qdaOReefZknPf2OptULt8HqoLLBjh6VopLVgqr0SZV+/kMtIVyZCYRnJ+ulx23VvVC/Z+l1KV2fPZ3JmnDzkW3s9MzrIxVV26xra4c6l36ISoPqbv6BhlfcjS7eSRp/scFAlSEdHMTamNAqRs+4S+5OtLx9mXLE3rY9blzNDZ9IxAaTxuN61BakXyiInpMzd+vMZISXNIvV6vmZOh5665KJ/HcSXzM//cO1V5Il3X6qT5fB6j0SiOjo6aNF9Ee+I1y7Xz/ng8bik2NyRyr5XelbcnhWW6haDnURv7pOe1JF7jwvEh/91gRJx9cltbAJgizIwWx75EmRHLoiD9pmx4v0oRkbfBow7W5wbdnajM2PDYK68rqz9rH41nVxTqUZnzwp2SUtYiAy5vH8umgafuaqsHnRbyhNcYyUrWItofX3Vec/NsRMRoNIqI9idrnI/ctC4HlY4c51cVifkYuFxwDNwB8aOe7qYoKmINUhciHzwaUhc2GWNuHNX3o7SKTZOydV03px27wVE9ur67uxt7e3tpSiRblabVRpwLOjo6ik9/+tPNLnZ6UTytXEbeD87s9XrNAgj1T58M4PlmihjVPu7yn81mTbu4SIJEgKFiSdHFv2wnfxZ9RMTCEmIHZjoXHAOWQ0Cl0SR59OipFkZz4q33XeWQB0zf8l06Eex/Bjy8zrL5XAY0XvYyY9YFev53KWIhnzkm+vv09LQYibB+j5KoK85DblHgylbVRUDj3KXKkT5Lzwkw+jacPguvb8mJ19IvLZxSWwROniYnb9QOzT+x/+qXr/JzML+bwEn0ggapVQeEA0nAIDiUjKKURgsmtMrOoyOl3jipy+P/tXT96tWr6aeuOZEfEY2X5pt7x+NxEznRa8tSAs4DzkHx66Xsv1J/ihSdlOrj3i2CudrE+SE3SgJItYl9Zp1cBOGH67oRZ1nuwZMH/n8WqbiclOQsW7DA/jrQ0XnIogmOkUfCjE59PDyCIz/YllJkl/XH52/UfjoUemeZzJGnpTFWm5haIzCpHu+vtmLo6DDxhPX5XC/7X1VV4yhxvNUGtmc+n7eOZJIe+HwUz/5T+xjtZA6I5puldxzHqqpSu1NVVWthhniUOX2s73bTCxqkIlabc+Cg6396L/RuqdzuudEr8gURbkhpcCSomnTN2sdNtaqLkYbO3js6Omqd5adIyY1RKR3TNVdBj1Xl6ou/nuZjH2kIyC96euSTjLDKdM9Y9bmHq9/sU1daL0v3qGz2VXzL3ncA4DuZrDkvS+1x2SQvltXv0Qb7ELG4qMLBqTT2Wds9EtS4OLBn/WNkRPnMIl7KVAZI2fiIlCFg+1nGfH6+h8+jrdJ8ovOQy9+10Gg8HjeRvZ731aoCZUaL5JV+0xY5z/gO9Upt4pjpGV/FeacAKmINUp1UEupSuqNkVCLaqTKthKMCc54log1Sw+Gw9dFCV9qsHm6G5ekRo9Go6CV7dJKlAzw1RqKnSwCS0HsaSobVQcsVkIaGykNDRJ5k46Lr/vHFrB8lA5oBB8kBlTz0sjMHiO8vK8f/duAq9YfPd5W1LIrMyHnOevzvrj5xXLP0XTbWNLCltpaiQDl5BGyWQ6dLUT8/kUGnhXJFPkrWCVra7iFdpe6xbUzV02HKUq+Zc5nxiqnh0hjeLbQGqQK5NyjqMg5dxo+fhJjP583mXb0rgfXJ5aqqYmdnpwVS8kY5l6NnJfACCwGUjluazWaNgCvaquuzky2oCGy/94cTz04EKU/Xcfm5vFPWkQEO+axJaPe49aOl8QJqV1oZF54I4BuDszFnHzzyyp7n2PGeX9dYlqIdr9/Bk3KSGWY37v6uA0gJ6LwtLC+TAfY1A6esLHdeMiBlhoIAl4FV9oxHIR5h0Enjhm1GKH56g95hhCL5I3ipD+LXfH7+dWvOT0pnmXXo9c6+4FtyjimX6hPnnyTnAljxlyex+Fj5WN5JWoNUgdxYUgj5jG8C9fkOgoEAgcKjZwVYnsvf3t6OnZ2dloFXek5lqE2MEmToNAc1Go2aUyH8aBSemq53GYl59KJ2EEh46rPuu7fn0YzKY6pCfSL/3GByMpoT28ypq93cd6bylePXe1xIQQPmjoYbHDdC5A/Lcw++ZKwvYgy8DB8/gl02N+ZGybc5+HN+fVnbvH10frJorRT9ELR6vV5rzxvTf5wfzUBZv7vu6306eixb0Y/6oyPD6IBJ/jgOHhVKrk5OTuLg4KC1mIe2QSCltrAMpQw158w0IYFZ72nRR8RZ1kbng2bOCevpGpvbRWuQukGi4WV+OjNsAil6Ny4EAhAJzMbG2Xl8L3rRixrPn+lAGlH96Js3EdGsJOIxSwQ0rrzTxkGd16fUnASbQiqDx6/lyvhzFSDnE6h4AgV+gpvU6/Wa+TemFsWnjY2NGI1GDU8UHQ2Hw6Yu1V1VVbPEWJPUmqgWqcyS0fRxzuaS6CH7PZVBoyU+doGWO0QZ0YmSnGXtpuHpqieL8vgcf69C4g2BMgPx7D0vg8+Th3ICNY4ZgGX105FzIy0Z1Ofa+b03b6fuce8Vx4TtETAx4zIajWJ3d7dxFPU+9Y6ZAe+D+OAkB81PU9dnd3wBC3XRZbnktNwuWoPUisQBzIyPBEEG2SMDARkXCqhM7Uanx7u5uRl7e3sxGAwWFCQL9xVJaIJXJ0nosFoZRX4iRO+qffqcAD8Nr/ZGRAOcIvVJ5UZEq57Ma2U+X6TUBg1NxPnHDdlPX92V8YbRnUfDAhp64JxDcI/S21QyFE6Zt+7RL5/piioYeXh0Jp6X+LGqQemKLvh/qZ0OdmqXv0dd4fXMSSiNhz/HMjjOHh0SvLr4oPvccE5HS/8rkhfRifHN5tQjXVOkpA2++p4UF0hQ5nkqhjs9Wd/0juzCeDxugZbu+0Kqu43WILUi0UhkaR4qUckwMx+sKEWhOs/7UuQlT869bnppMrTapMsl4JqDylI5rtyKqNzz1nP6mwCr+hlFsh71l4ZDURQNGiMf5xt5qvv0mDkOHnVR0em98hqve7ThHjG9dlLJiVhm+DkOpXe8jgyglxldPl8CwCx6yUCU80JdfXMwycBzlWuZjnk7CQbZ2Hj0ldWXOQDSLS3flo4xMlGd/OSOZIaLE6S30n22U5/GoR4R/KhrzuuS85DpO1cYZs+VeHi7IyenFzxIZYoRkXuMmcFkGdlgKhXAeR+BEvPJVB59U2Z7e7sFigIsRjYCQH2fSuG8FktQ6DKjo/IZ2VFRNYfFVXjz+byVrhP4MBqRUnKOqt/vN/l1znM5SGUAROPA8wcZGYkv7sEzknKA51yOnnfvlArsYJoZCZWfLeMt1ePPeVnZM5lBKdEyQCvpAdsQsXjwrr/vTpBHqFl05e3I6s3q0LXSlo6sjGzxRZYdoa4pwtnd3W3NiTKFyLMkpevSc0b03mc9pxW/2mZC541j405rNk4OPFqkxPII7tRTH4MS3U7guqdBahVPsouWKSbnl2issrQTwcsNoueTJbgSeO5Or6qzuaW9vb1m/oUHTdZ13WykFUBUVdVEUPqarjYGU3DFJ73rm2Z5bqD6yO9Z8SNuFPy6rpujmrgiieDBBQpSWvKU4ykP1hdh+EQ2y5TnGnH+uQT97QtKyDfWnaU6MuPr844XjSJczrLooCvKyAxRRllUKC/dy/etA6JsfqIU8WRRjztD7At5m3nyWcTAKJ784JyrZJLpOi4i8LlSto+gQH3f2dlpdE9zOnrPv/mm+nQWJ/ciSX5Vh3RbX0XQvCkXZbBfmlOVDJM35AkXA2k6wceHekbbpfscq8ukzCZ10T0NUreSslA484T0rC9M4CDT23cwi4hG8OfzeVy/fj2uXr0ag8GgiYwioom6Tk5OGuXjEUWTyaQ5LFZHr9AjzCIeeYRqO/cy+dl2TGFUVfuwUC5fZ/oxoj0Hpbp8Pkzv85gk8pVGLYvgmIIT2CuFoiiTp1wwxUfj1TUvxfEqgQh/8zl/3o0g66LsdMlm9o63vfSee868vyrgrmK4JC+ZIWJk5Qbe9Y3ev4+J6xbnWySjGmP9rfrpbPjCiixaGQwGsbe3t5CepyxWVdWcYynHUe3g6S9q/8nJSbNRV9HU9vZ2oz96Xgc4Uw655cL7ob95+ovbA9oo8Y6Lt+4WuqdBytNGt5M46J5CimgrNw9dZcqKhns+P9u0e+XKlWbydDKZNEpKw67jleq6bqX49vf3W8euEDiz6E79kNJRUdlON9TuGcprJGhw3sh5pjqoGOon/6eiyNBQ0SMW9xmxn2qP0ozufWdOBw0j2+k88H6sChB8ryRXJcoApIv8Pvu1LHIptbvrOt/XdRnFLhDX81kkp+ey1ZBd7Y44T/lJpij/zIT4Apsspak6uLhB+kldVtt5Hh8dMe7fk95vb2+3trLoMzz6PwMhto+grvMGtXhKP1ycxKiRc8bUq8uOnpw2NjZiOBzGwcHB0mfznXj3CPH8t8umZcbEn+Ug0wBWVbWwmi0zgBsbG7G3t9ecks55Kno4OupIAK1PU4/H49beB6YDKOhsh+8H8X5QgAka7rEybch+Z9+XYpqEizGyVXu6rh+l92QouGk5i3rEK38uM7iZw8H0zzJZWBaBLCMatGVAlYGP/83ow5/tAupVvWgauxJ4OpCVAC0jtq3LgDLaztpFR4gHvnodrMtX4YmYZaAeElxVPvWQ9xXhc/XfyclJc0ySdJhyr7r9CDDPzqitSsszHe82x3npcpHx+jKp3+/HS17ykpWevacjqctkIFMJy8rPBjqLGiRQ2kOhd+SpUYD0WXl5QkwnSEBlnDkpq3kozUFRgFUHoyaVp/YyVeYT0Go/+0yvjSnAiPYSXPeY9ZsgFXG+L8qjMBom34PGFA+B3CflxXuWyXa7cnvfM/nIZKKk1CUwzKgU1XWRO1LuSLDczAD5/8siKnc4Sm3JfrrI+ZK1UTKf1VUqM5PdjB++XF11Ze1XuVrkwH15/BHQuLzxJAv2i+nz8Xgce3t7rZS7rxKUrnBuWz+KnrocCNW9zIm5VVRVZyfprEL3NEgdHR1danldBop/08umIHqkQUNJYdPKPnlRp6ensbOzE1evXo3JZNJsqpVR56KBqqoacNIO+PF4HOPxeEEAI873c8gj09yXUmBsL3fzsz6fM+AXRlW2eMKyxB+lORlxcTf9cDhsFFIREnmtPqktbozlmRK49Fu887QJAZvvraKYnEdhWwjgmWzxN58pGdNSGSJ3qrL28KcEQt5utkP9XSXC8zSdA0u2IMMjYG+XOx7OP9Xriwj4dxZ5SUf8+Cym0xiNOT8ionEsJe9cBMWN8nKwmHJzZ1Jt1Wfmd3d3WylqziXped1T/VrVyyiKIMl3yVfqpuvHrQCquq7j8PAwfvM3f3Ol5y+U7nvnO98ZX/VVXxVXrlyJBx54IL7lW74lfu/3fq/1zGQyie/+7u+O+++/P/b29uLNb35zPP30061nPv7xj8c3f/M3x87OTjzwwAPx9/7e30u/2nk3kUApYvEjeQzdMy9NSqBQX0JKYb9+/XrLA5IC9Xpn53ZduXIltre3m096aLOuNuhFLH6SXeUwTSGDLWWld8aPAVZV1aw0YhqkqqrWOWJKfVBhtJBDeXlRr9drDstV2TwShqlHN9wl757Al3mOBFyPoMSzriiDaSZe93ZkVIpqKD+rlCPZK0UCeteNT6lsL8P75gBAY9ZVh0eqXl7GC5HPi2btyRymkuEt8Vc6KFDhwhyClt5nys35ogzI9vZ2ozuqIyKaLIeiJ8mroh05mNxwX9d1s5le+kEHl+ftVdX5B0B9ky77lMmwxst53DW+d4ouFEn91//6X+O7v/u746u+6qvi5OQkvv/7vz9e+9rXxu/8zu/E7u5uRER87/d+b/zSL/1S/MIv/EJcu3YtHnvssXjTm94Uv/7rvx4RZ8L4zd/8zfHQQw/Ff//v/z0+9alPxV//6389tra24sd+7Mcuv4eXQD7YNBielopoGwxGUvTUJMwy3FtbW7G/v7/wkUClFTT/ppw1FU2AIsHiXiTtcyKo8JPqWfSlPum0C6Y+dAQRIzy9zyXqek59Vzpva2urla8XuBAE1TaVS9JzVEhuGtZ1pUc5Jtm8mcaT82c+/+HPZt7mKtEPnZjsOsvM0jJqP4nOE9tMfrEe5yufLUWFDjJZ1OPpNNXtTob4p9/uxJQiQZJnLSKilQWIOF9UpfJkeP04oIho0vFsL3lKGRNo0TkaDAZR13WzElfRPdtIp4gOreyA6pdejUaj5gQYbtkgkPrYuiOdRUjZmFG+XJbuBroQSL33ve9t/f+zP/uz8cADD8STTz4ZX/d1XxfPP/98/MzP/Ey85z3viW/8xm+MiIh3v/vd8fKXvzw++MEPxtd8zdfEf/kv/yV+53d+J375l385HnzwwXjFK14RP/IjPxJ//+///fihH/qhW7oYYhVypSiFu+6Z0ivxZ6hUXr4M93g8Xpjsreu6iToYoWj+SWUzOopoL4hQeT5PRYPgm4kVHfFjaxR+Bye9o/I0zySQIpi796b/6cHSEPrENL15trmu6xYg+dJkr7M0ri4HDhqsz40x3+VvB6RSZLiKrDmguaEv9YVGMQNAl1HW4/Vnbcna7HpEvi+L9rKIOAPjrnLYX48yOJck54vyUuJtVZ0fLcZ5H+mMUvUCI46VjwHLZkSnbIkczIhogaycV8l4FvkwJeg8cbkuyWLmiNwJuqnVfc8//3xERNx3330REfHkk0/G8fFxPProo80zX/IlXxIvfelL44knnoiIiCeeeCK+7Mu+LB588MHmmde97nWxv78fH/7wh2+mOTdFy4yWG6GSsmbGis/RwMor4hl1BChFWb3e+V6oyWQSBwcHcXR01Pq8B6M5robjCiQCDFN/vieK80QUahoZzvNwxd1wOGxOy1Aqj+VzjoKbdAlGDlI+nydDwLw7y2TbdN2BkkpYMpz+nI9pl3J3/fhzGWX3SkZjmeyWysvanPWRhsx/l/pRMn6eKtezXHTgepL1M4uu3PBmPyIuNvAUWlfaknLEciLOnTumzZ18nktyrnIUWXF/lWcDVEY235uN6f/f3rnFWHYV57/O6Zm+d4+xzXjsAI4hBOTYkISAM0IhSFi2iRVB4IEAIiZCIMgYhUsQMiLcosQRkZIoEX/yhvMAJEHCQbEIirnYiGDuIHNJLGyRmCQeW9iMp6+ne7r3/2H4dv/217X2OT2Mp3uYXdLROWfvtdeqdauvqlattUueAe+HUpq2sXMm6JQDJzY3N+NNb3pTPPe5z40rrrgiIiKOHj0a4+Pjcd555zXSXnTRRXH06NE6DQFK93UvI+0DEh0/fvxU2d4x+YRt0zDoTtDHLQFG4xAIlAcBitaMTpJYXl6O48ePR1VVtZuB0UkS+r6mQ+GsNSDWTxPB3Yu0zqhdUuALjNQ+Ai2m5QZI1c0Bk22cuf80QX1vHBefGcThoFVaf2JbsY4lxYP95ekzDbyU3scQy3PrzHmmgPc8HVRKZWbk1mfm/nH3Uam+DjjK368zvbehW34UrA4sLEf7kvisu/rYhlwrFp/uVi4JbK4Z6igzznO2Vwl06QIkTwpw4rFGTMu55SdoME0byGTjM1OQRh1Dp5tOGaSOHDkS3/nOd+ILX/jC6eQnpZtvvjne+973PublRLSH1+q+vmmZKA0tA20+lVbFQAm5wwRCEVuLxxL8Bw4ciIitEykEUNKwBNz9fr9+w6d44wIzB392Mob410Ktv19KvCvNxMREfQySAEHE+rNstZUmE607ujRo3VHD1LfqzkNqpcGy7/hKECoKKtP9+i7U5SrNtHfXTlVvtwCZr48jBxVed4AqWUx+nYLfFQmlU19R+Ip/Cjn2f4lP5efKm1ssLoCzehKUfE+bu7PoGnNroM31ynUiF8S+7hOxta7lx4exTLndNa/27dsXc3NztddDfPmhyvrmeirbyzfRazy6J8DnUa/X27bmrLmrcS9lWgEdHE8cP1k77QadkrvvxhtvjNtuuy0+97nPxROe8IT6+qFDh2JtbS2OHTvWSP/ggw/GoUOH6jQe7af/SuN00003xaOPPlp/fvjDH54K20MpAyP/TU0rm5gaAJxImdvCo4Fkwehbe6bW19djaWkpFhYWYnFxMVZXV+uB5MeuSPviZkHe870UtEw0MWjVcHKOj483ogw9RJ0bHGlJadJQs6TWqnw4adzNQt+7Jr8WmOXOVLtNTk7Wrz2Qq9QX5L2/WIZrkuwzCjDmQ0HAaz5u3LXJOlLjdSsk017dWiopV7znVonn5yAsoUsljHOgjR8nf56auQMmLRwHt6yeJevEwTLzMNBi1Id7mcbGmm9zVnqCgm/OlRKq+mnTvZ7jWOALTCO21qgjTiqhipB1cNG85LozPTVsU/afW4alPmpr7zNNOwKpqqrixhtvjFtvvTU++9nPxmWXXda4/6xnPSv2798fn/nMZ+pr99xzT9x///1x+PDhiIg4fPhwfPvb346HHnqoTnP77bfH/Px8XH755Wm5ExMTMT8/3/icbip1kP+mNs5nPbBAaX2Ca+JLgNIXLfBSwEFVVfV5fAIolcF9Tn6sCyeHvgkU7iqiy47CfGxsLKanp2N2djZmZma2rVOJF+4FidjSMB0EOYkZWkvriNYZ2yYTyGoHApXy9k28bQCTCR6/xtB81dGVD34TZLLxVOKJ5CCWCZZM+/VnSjwN05CpoZNfdx/57+y/8+OWgPPEOeE8lfgptY3PWQIkN/HSJS3y+rNNfD1InxMnTsTU1FQjupVrYO62doVErwSRAko3qNIzcpfyxr0abEtan6yH91s27nYTqHbk7jty5Eh85CMfiU984hMxNzdXryEdOHAgpqam4sCBA/Ga17wm3vKWt8T5558f8/Pz8cY3vjEOHz4cv/7rvx4REddcc01cfvnl8apXvSre//73x9GjR+Od73xnHDlypPFCvb1GbRPaXQ/+nAtDvkqeoOGuLr2obGlpqbYeOLlFBB/XxLkGwLUbH6yu6XLw+0D2Ac3J73uv/BiaEiCq3MyaUBsL9J0Xutr0jIQF3Xqsu/ef97G7d0YRgq6Zt032NqWIQtXXf0jMr82KGfasX8/+uzvTeS1ZPLxeKo9t53x5e2X9l+Xj5TtwukLhbl9G1nH8uFtU44zjUFa+3hYdEbUHxJWGrL35Jm5Zab4/jEoU25vjJlNIXFnJxuOwcXqmaUcg9cEPfjAiIp7//Oc3rn/oQx+KV7/61RER8Vd/9VfR7/fjpS99aQwGg7j22mvj//2//1enHRsbi9tuuy3e8IY3xOHDh2NmZiZuuOGGeN/73vfT1eQxpDatMSIPMSa5wNVpDXwhYa/Xq4MJZEkIpLSLnBOOAEStTOCmCZWtPWSLwQQWuThkqTHvXm/r1dbkRWlppelZXxfxswx13cFSwoMWVRbtp+f4yhO6KWlpcVIT5Jx3gp4LYab3vqdF7eWUrAofQ+wv/6bwcIBoA6hR7nsaF1YOumwjd+FpTGX5Z1Yf291Bi+ncqvCxyTQ8gsvX2FgXt0AcgJXOxx4/DnwbGxv1PsOqquqTXrhPiqQ2lNtcddMp5tyaw/pTedB/fRM8KVe4vlvqY7bFblpRETsEqVGYnZycjA984APxgQ98oJjm0ksvjU9+8pM7KXpXKNN6qDH5YNEzFMru0tHaicKoGZU3PT3dMM3pkyYvDD5Q3ktLS7UfnH5qLqY6r+5u0+75bA2CrjBd41oRgZN5c23KBa27vGRJu1uEbgtuBNarOLJNlAJarQWobyS8XFgNc4WxDg6mKrs0oQlcqk+Wh5frgOd5qr0zsHMXWEno63rJZVRaRKfWzncSsf6aJ84b+XFesnoSkHwuet0z8PM0BBq3RFQvHtulcT82NhYTExMN60nruFoL9nI0pgeDQSwuLjb6TKChshRMJatJh0fv378/ZmZm6v2SagMCm+rDIAlvR1fC2D/6VrCIQG63ASriLD+777GgzDriPRf0+k0t38GMwkauPkX+cHKPj4/XJ0qsrq7Go48+Wh9lTxPe/8sF0e/3twUfkC+6wKTlRWytPc3Pzzf4ydZM6AKkj15t5kKeL57jRJB1Q2uQR9UohFgLy9rgqImjyU8h7dqtyiHIKp3vy8qsoqqqGs+zvnxW6V04lKwGadoi31OTAaDz5eSatfNCgC4BR/afz2bUZhn6mp2DrgMglSvy0gb8dIMRlDyKVdfdwqWLzoGKgQg+32WZy6ugcTwYDOoDk9k+c3Nz9QZd5kHQ8jEqHhQgNTExUYOjrivYSh+ukbF8H8OlPmT/7QUrKqIDqZTaJqprhy4g6W6gEJbAlKXCwUSLZ2xsrH71++Li4rb30qh8CW8+x93orIv7oAlOCinXxKJ7Tdaab+rVh4NYZVCIaxJywjC9hIkHdbAt9c4eWlEEPtXRAUGChxOTwocCKzs+J6L5sjyvO4Wb7rOe7uoST9kagoRgZrGwzdh3mUVEV66X4by4xZaN+bZrbqF5GzkIqJ3bQNavu6DN+CpZoy54S3XhOKZy5vM6YgsQNW+q6uT73OhN0LhnfSOijoxdXl5u7J0igAhofExpC4o8LVQefSzTnUdFyMfAMHd0W/ueaeDqQMrIJ7VTNplKE0j/lSf381AYRzQtgrW1tVheXo7BYNB43gWAC4iqqur9UgKXiO2v3CYwKVxWgKK8GCadrTeU2oYaG4GTglb1p7bqkYcRUe+8F6jTZck2dEGUCU3vGwKMA05muZSsGebtgtvLZhuKMv5LCpHn6ZTxwOsOcp6e/Uilg/mPQt5e7AcJWfLGPi+VxXZzgM2EbsT2oAIfg8OsBV9fZV0Ymq45zf2Oqivn/tzcXGxsbNRBUKyL7+djXTT2VadMMXbFkX0uosVGYM36LavzblEHUqCsM7JJnK0RuPbredG/7e96IShsbm7WlhSFKPOj1iTeNPjo0lNaCXcB1+zsbExPT287hYGaqyYhAzaGkerI9Tk/dsYnEN18FIy9Xq+OiNJztDj03xfOmT/58D5kXUvXSho8JzPda9m6FsGgJPAp4NiWnofzy+dLwEzKAKxEJcujtM5UAgSSFCGO9ZJbztuD/e9Wg89HBy0KYwfEUhAD+5P1crev3PfcKyUvh7s3Z2ZmYnV1tU5L4JYXw0FC5XoouitpBK+2sdI2Ptime4k6kDLKNO8McNwVwQHJiaTBI62r12vufKd/OyLqV3AsLS3VWqfK0bdPTA1UP3RSgQUqZ2JiImZmZupFWGqx/J2BlyYo3/XE9vF1ml6vV4fPyyJUfnKXbG5u1ovD3p5VdfKVBe7+oAtR7ZOdKEFBxXZ24ePKhQtY9+F7KDD7xgW4j6m2cjy9CxrVXdapiOlKAM3x7FsC+C3SeMrAKJsf2e9Mk5dg9rU9lekWBC1bH2sEfj/dQ3OBr/8h39nxQV4PB0mNN67FRmwFBpEfBhxwHu3bty+mp6djeXm5HsOanxybPKFGfcYTMdhfUkj5jLd7Rmoj9YnqnilvbKdRrenTSR1IjUCZNs/1DGmHEds1c2qLeoZuLC6+SmBrI6xrhD6IuNeIk52uMUUG8T1O4lv56jdPnKDmqMmXAZHXV+HyWuxVPXU/4mQEqLRJWpVeBxEncMRJwcDov4itY324xqe2lnBnH1IbdWvFSXXnmoSIlp3Ssr5tebYR+7206E1NvETDBFAGcqW6ZR4E5kOwLvFEPsiLA08G+L5e6TxRGSDQuZUuHtxqUR6ZBcWN8R7ook3kXAcSqGgsymWtw5cVDaj7OiWF5MrF2tpazM3N1ZYV82fwhHgUSLNvKUfYZ057yZrqQGoIcaC49heRW05KL4HP08gFIJwE0tC0w9wnvJ/FlS3mR2yPdOr1ejE5ORlzc3P1HiwFcRAEs+NZdM8tQtc6GfigicT3XBHouFaWnZUmweQL1c5Pv99vuFfo/nGBI0GVTVg+lwlgTuQ2kKAgd2uAlPFWEgaZoHaN3QV+lgfLdcGuNsjcSM6719vJ24+KgCsyJcHI8dzv97dFhnJthvl5vyi9g5wLa/LIj/be0X2r62xDPcvQdHkwlIaKacRWJO3GxkYsLCzU+6mkcAroONckAwaDQczOzjbqrjoQCN0Nznbg3HI5lCmee4E6kGohdhgnT5bOJz6flYZDgemWQ1VtbfiTW40Dm4PJNXpfQNV6ktx6fsIDQUEg1aZVMa2ASQJDkUc8hZmTgq8EIUBleUc0I6JUF040bwu1pwd+uAXh60Xkc5iFkP1m/mw7F+YEkZLg1HcGYq6IOA2zAl0IZdYDwVBtzvQOMqU5wDIza43pWF5m4fmcimgqLbrGce/1Lo0HkltNnEu+jipidB6FvqwpD/DRmNY+RgGa5g5d2ByTtBw3NjbqeUaXuxRcfaiAZX04TLnJxkdpbJ0p6kDKKBPWDlTZ/Wzy6ZqfUydNTemkpSnqh0EL7l4kyLigjNjaLDw1NVUHR3Dgki+BoUcBOtGCqaqtQ2z1ChXxvbm5WZ8pqLUT+fDpSvSd7gJKRvipH3xjcWZ9SDBQeLoQcwuHGn0m5N1K4seFp48Z17aVJnNzeXnZveyZYYJDPLmwyYDSrw8DRKYp5eX3Su2WlUE+XLvnfR8HGSAPq5MDZBtvHDu8JqVTa61y4zn4qf46l3N6erpeg/J+ydpZJ9RQ4ZTlRRkREQ2rKsvP16KzNhvWHmeKOpAqUAY66lANEhIHsFxquqYQb5nsVVU19h6NjY3FYDBovI1TG361qVV5cHCRV/EzOTkZMzMz9X6siKhdeQIDDUpZWNTkXHuX20V1UwjtwsJCDAaD+uWDGtyy3HSqhgdE+B4R3ZNlJotMeREclcaPZYo4Cf46mFN9JMHAiaf1EmrNXNciT1zHcqHjkzkTBszDNW+6Gt0aoiBxBSgDTx+DLogyAU1Bz7ZQenfvKl9q8ZmSRM29JNic92z9KQNT8iEeBRLZOlKpjKzNeD0LGuK3SNbQ8vJy3df79++P2dnZ+u3Zyp8yQ/9nZmZSgBH4iG9dE0hReaN7UdcyS1dKAt18dAt6H7b135mmDqSMXEhHlNejnDIholPNJaA1cGVdyKzX+6I0AB0IqTVxAGtAjo2NxdzcXMzMzNRrTLpOgMkWmgmmBDMKWFkra2tr8cgjj8RgMGgIi42NjXrPiHhXHjxdIiLqhWPmqclG9wmDIUSKkqQ/3V0jrkFn617SfEvWia/F8X62ruLCzn9zzSCzvD1/9lHGm/qUykUbKLDumebsJy6w7fTtCoeDqyt07kbkWHPeXGnJQtC9TfSbShbBSnnquvelt72DXDaeOId0TYqn5roCg3T0mfOoPPQmhOXl5QYfPI9SY1RAvLq6Wiug6jfNL55mo7zovuz3+/WSQsT2F0eyvfxkkt2kDqRayAc0wSojAo/S6eWAEVtnz+m38llbW4vjx4/XVpOsFwk2av8UsCqTwREEyBMnTtRv79WE0es2IqIxgfxNuhKoAo61tbXatcdJoDpPTU1FRNSAy/U0Xy+TFUcXKK0fanIMEiF4qzzlyzLVby5QBIrZukrmFmV7+zNu9fi4yawz8utpM2DI1rMyBYrCRn3A/PnbwYn18/wEMgINjmvy6nVnm5esIX37qQgcg2yXjByYXaiTHJjdrexCmRGBBHbdY7/Im6Dr09PTMTU1tS0UXe0id+DExEQcOHCgnjMqQ14BKqpyn2s+MiqY61J+2gz5ZwBWyfJ00NL93aQOpIxciLgLyDW+0n+Z9NKWODCoJQkICF56XoenOlhqIip/vSCRPAg8Ipoak/LLXl1PASPX5MrKSsOS4gvhxJeX5VopwYP7TFwIsI7Ki31BLdQtTQpWujRVP4K8yubzfKmd+GHQBoHKrQGOHeYxbJJnFoj3YUnb138Pxc/48jZygNJzbeBF3lg3twx1TWl9D5O73RxE29rL282vsS50axHcffM3LUSSW4qcv/6hEslo3omJiUa0HuvMNpuammp4WTTuHPB7vV4NiP6eKrUtlS2vS0n5crnl4LXb1IHUCNQ28UXesQKZycnJiIhtg1UWkwImXMuJ2BKcrhlrMGrPBd/DRSDgK6spVDQRqN35wJWvXRsPM4tCvGQuROXpG3Gz+67V6ZuCkQJWVqYDnVwfJIIRtVrWmUKV1wiELlBLQEVy0OI15pM9o3bytvH6uBXmlIGcC9ssrVspEc0DYjPr0Hn3fNray4Uj8ykJTQdMksalKyMukB1sVY4rCR5l6vNB41Ov1ZDrW4odAZLzbnNzK9iIb6wuKUG02Hxdk54Gt0K9LXnd+8z7areBqgOpESmb1Lyu39RMdEaeTO3s5ASd08e9ERx07vbQNR1YyZMjNDF9sDqIcPJ6GDvDypeXlxtuE2rD+nb3Cjc6UssT7wJIrq/RBddmoTAv1skDIdgfjLziRkcvRy4TL9cncWmSqzxP46BbysPTuYXj+al9ea1kiWRCvm08s9wMqFiXrL2otbuVkgnOYWBOoUtrlumyPDQ2uaHXXbYaG1yrlLVOBUZlcO5QIVVaBf/I2qEnxd14qpPO0ZRHhcDn7cXAIt9UzOUG38rhip6DcEnJ2AvUgVSBMg2L1/VbH65F6f/s7GwNIgSpiKg1p5WVlfr1HCJNDK0VMaCg1+vF/Px8zM7O1oNWE42BCxqo0uK4oVjEASq/+PLycqyurtZrPIzu04QXAHOiSgOku1D11SKygIC+d3fDuVBeX1+vJzVdmuwX5aGJrVMuBoNBTE1NNQSI8iTASjBRix02UUsWkWvnriAw/SjCILMU2Ea+blUCqoxX/ifIu2D28t1F5Plm0XrklW2cgUbpf8R2K9j5dGtR9whCvD4sQMDLcctd+Wj/nzbpRkTMz8/XUbly5WmORkQdPDQYDGpAk5JIQCbxMGaucdG6ouvbT9wQv25RK11JedlNOqtBigPvsSDXbl3TV0e7Fi4hPjMz05gI1Myqqqr3GekaXX6qnwIEpN1NTEzUu865s13PMehibW2t4ZpTWr5YTYN7cXGxBkv5vV1z7PVOrmUJtHyScuKKFwpTHfukHfe93tZberWpkXmqTWh5KmhDdaDlqfJdO2e/sK00kX1zpAfAtFkMPk7cEiIYZdaJ2omk9bA2C8kthzYgK5HagxGj5CurH++Td28PWgqep68L+dxh+owf58EtACpBGVBxK0e/36+Vlojm2569DG9TleUK6Pr6eiwtLcXY2Fg9TsfHxxtj04FUb0iYnJys8/J1VUW1rqysxNTUVGMpYX19vVZUVS/JHZbJBREJpAAAXj5JREFUsUhlKuvDbGyNMq6GEcfdKHRWg5SE2mON+pykEogS3tmajAaLQs9pCXHdRGfb+eIzv8fHx+uDVsfHx+uoIQrSiO3BCwIrBmsQZDVRGbWnfU+cwEpLjYs73Enu0tS1TBNVG/JQXBH71F2GVVXFyspKw7LKhDfL41oYgYptQuvM+8KDZ0pEwdZGmaXhAj4DqMx6YvSk0zCB4oLI1+ZKYK80TOdpsnKppJWsSubr4M22ccugpAB4eepLBjpojLE96H73diLvAgi64BSlyj2OtNwzi1W8TE5O1l4Hrxs9E3rhoeoifulmpYuUMsD73oNHMmXgdJJk5L59++q3arfRWQ1Sl1xySTzyyCO1eX0miANBvynENNi0Z0GDlRNIx6fwrL6I7cJLlsbU1FQNOnIreNm6F9E8bZraI0PN5WZQSDmtQroSmEe2HkHBwT1NHmWnfEoRSBFb4OqTixNW13m0lAsz8UWFglGF7LvMUsrycgHqY6LtmVLazDpoo0wQR4wGjjsRNM43y3SrxS3GYeWVLJOsT7JxRh69HG8f/+3zNiuDQttBhOXyOkFCz2t+K3+5/Wj9s0wd7SVlVAFLKpNzkIfT0gKMKAd48L/z33addDqsqIiTryy58MIL43GPe1x84xvfGJq+fGjZWUAHDx6s3WE/LWWdVRJgFLYECg2GsbGxejFUWhGtg/Hx8RgMBg2NSfl62fv374/5+fmYm5ur9zz5JCZIUYgTrBgaGxH1qRHHjx+v3wBMH7Xz5C4wtZXKyz7OIy2wkrWSaXsET6XPfO3uXvL3aHl/lT6ZEHGB0WZZlQQa7zuv2ZgrpcsETxvg7VSwtFlQDipubYxKbrVorYUnmDhP2W/2Fa9lAOT1oeLkfZq5MUvk7nxZV9zQu7m52VAkyY/qn633+jqevB9ra2sR0QQljfXMWholyIYK52NJU1NTcejQoXjqU586Uvqz2pL66le/elrzKwFUpj35BCBQyS23b9++WFpa2jZQIiJWVlYaoeciaXFyq8n3rD1Lyl+WVcTW8SweGCHepJ1Jmzt+/HgcP3688YbQ1dXVGoT4TQGoyZytNXgdpU3KalR7KcKO/nJ9y03Ca5qYuu6be6WZOiizTIIktVK3wrIgh0z79jHBie9WAsGD9zxggOUTnEpKUgYMJetC16mE+LP8TffhKBp1BsqjCDznxZUEWf0Z0bLIXIgR0Tj/krxrbHB8sw6MIGTfuCubgUBcR1LdZEmpHClKUiLpZaHiy5NolCfBTMAXEbUsUFt4G5a2vrhizXGSuVmz36dKDz30UDz00ENx1113jZT+rAap3SQfCHKl6Uyu2dnZ6Pf79TEmGoxaJ1paWmqc1RextX4SsRXgMDMz01jc1UCemJhonCYR0QyjlkXHnei9Xi/W1tZiYWGh1vQYSTQ9Pd0IkRUQaoLy3Uy02jS4ucGQg3lqaqohnDXRHPhUB7pONMlFPJNPbSYtXJNd7ecnaeh5Pad2Evlkza6xjDZBX/rdJvwJ0Eo7TIv350tE4Musj6w+o1KbMsfvLI1AwwWsxjzHDQWrnpWSQh68v6joZLz53joHzIhmZGLE1otBFcgjXqUIcv1I61I8Mixby9ZncnIypqamYnFxsb7f7/drcJLLP/MscG8ko3y5sTlTnjJr9FTHw+mmDqRGoEwj98kR0bRaNJDkwmA0m9aBuPbCAasw1ZmZmYjYAh/tuZDg9iNQmJah6BFRh5QrFJZrOi6wCUJKr3bgBPAB7IvZ4o9uNrrpWKZ454KvrC1piyXat29frK6u1iBJ1yH7i5YZwUz8OmWT2a95el7PAjG8zVzA834GhtR+lcYtBdUxA0oXPHTPlgSSW0oOnsPaog1oPW+CqFuuPPaH9Xe+dc1d6Zk7W4CosSOlzV14+s09dpnrjuNa/e8WDS04V/6UXgEU7mnRHNZSAee9B76Qd6Vxi9HTuDzbC0DVgdQQ8gGtycfJwQmm3eZ0D3BC9fv92tXH+wRAHVIp15gmBwWSu6nEG9dfNIkEinIRUCixTrJKeESQuyP02wWy/O7uehGYil8CkbeNa3OyuiSw6OrLhDsnFF17/IgyQZsBkpODScl6cE1+FKKwKz3j7tAsj53y5MKtjUrgVAJR5pnlnQFpljazMDkGHGSzumZzkr/l4qb7Lms7ktKqnJJiwE2+mpt875TScR5rj6XmrQDF12M5nyVDxJsrIRnw+Fxg3XcboCI6kCqSd+IoHdzr9Wo3nEx+PUuXGw9pdbDTmV9y5VHoSuOjgOeElRUlkOr1Tm721QZd+fl90IoPaXVyUWRalguLiLIWF7H10sOI7YENrqE6yHGLgf67S4ztoOcEaGoz/qb7yJ8fZbG8JIjYHk6uDGT3s99eLvPyemfPEsx8rJSsnAyo2qwmWuLkQcqMt0HGp8+pUjqt4bQpHm1C1S0sKlwcY76B3nnI+M7ahWkcpBh4pblLPiVL+JYAuuxKATWqQwn8MrDleHC3qNdnN6gDqZ9QSYP2iU3BnE32ycnJ2j2ltaKI5uGPCpiga0oAMjU1Ve9Ij4ht76GZmJiIwWDQcB3IYvK34FZVFYuLi/XaF60l1ZMWiga+fN+yCGlJeYScBIfqqDbUfwoGTUSGptMFwdOkBdi+qdZDePWtlz0SkCYmJra5AAlG7kL09SxO3MzqErG+HEeZi5Jg4df5nVkkriSw/kxLoKcS5GDsCgqJrsqMRgVdkls8Gfhl7cB7siQ4bukqE+9qf5bngSmsewZUfO+SK4wighrLzNbQNLa19kwQojIWEfXY1/hfX19vLBFo7nBNlwof+XQ3H62vrI0zb0Gpr88EdSDVQhk4+XWRwESCsaqqOhpvZWUl5ubmIuLk2tDS0lKdRoNtfHw8Hve4xzX2UDFIgeXyxPSIqENWBVoS5MeOHYulpaWaRz9yRZPQ89e9wWDQWNuSgNOBtspPi8ayGAlOq6urdZ6aFLKQCFQEWwoBRiWqzmojChqF6NPN5+G47DcGmBBcdE3E9athE1XCZ5hGP8z6agNE3uf4KQFfZrnyPtvQXbB8vtQGFJT+TAmUmIeXy/q6wCSQqN6yTPydZcrbLXTnxU/b4BidnJys3eTMQ2OCZVKpIB8cazoGrd/v1+58thujB7l9ot/v1++sIlBzLYvP6hlaRPrvgMVxkLXTXqAOpFrIJ3nEljnsFtTGxkYdLq61FG66lfDioicX9mdnZ2NycrIhNDi4aTXRmlJ6hbwLFFdWVmJpaalxJiBDvwlSLI8gI4CjcNeaG6MDNWk1cSQcqqpqgDEnMYFIVpP44EQRACudhA6tRlqpak/tCaMFpb7IBC6tFLln6Apygcm0GcA5KLpWzbHjz2aAQ6HuY9MtBl5vA7gSH84T+R4GhpmFSQvHLT931+lb93ifdeQc5Bh3nmhNSiFTe/kccMtTc62qtt4ZlVnzBBuOa38n3ObmyfMxlVZKlfMkL4by1tq0IvtYPw+jZyg8z/nTPY5LVxY4RrOgn92iDqRGIJ+g2SdiKyovYsvKWV9frwXt5uZm45XwynP//v0xPT1d5yNAk8VAASCrg9F5tHR0YK1ciplg0G8KbYKgu4gcrBmxmLmW3KpzQUDXjFs9XIeSZcKJT8BnPixb/8V/FjDB+rOfXcCLD8/fx0fJ2vYxlKXNQFB5uGXi+fG6px0GULScs3y9TG8vb6fMcnOA8vI80EZ5Or++VuKgyOACn5ssNxO8bX3FdWB3Mes7259Ii4ZjTiAkJUoyg5vpZfG4AsW28AhZei/YJ2xH8uAKDu+5ezNTQM4kdSA1hHyg+ATiPVkZMtcjon47rq4JPGgJTExM1BoRgUMuRAcMTRxNAFkrciWurKzUbr2I5voZBaLyd8Hu1qKAketQzENtwJB48ugba7kvRcEeXEeja4KapvjjOpPauGQhsb5uFfEZn7glwVX6n42ZUdK6YGf6EkhlmnAGSiUhxWvuFmqrzzCLKwMqt/wyS87LcUGbtZHSOQApH1duMj6Z1ssiv5pfWlNidJ0UGN/47q5mkUCKex39qDMHVrUjlSYHk2weZ32WtafPjbYxuRtA1YFUgTIzd5jZq/WoiOZiZcRWGKoWPyX4dToFT0vgN8OulYfKoiY1GAxiYWGh3g8lc18g4a/v4KZdWi5aR6uqquHW0GSl+83bhK4IpVceDJrQpOdkpkXX6/XqiSwBIK0zohngQIvL15Y4+SRM3EVDV5lr4G41iFxTZR87IGRA51qxk9Jla0gcH0qjPN3tl7UFyTX0THg5X255liwQ8ZZZLlldR7meKYYU1hrzFO6st6x0X8/UvPBjtliODozWm6o1F1WexqbmJ70nVC6592liYqJ203OuccyKJ3fzuXuX17J5kG134bOZ5bRXqAOpEWjYJNNnenq6oZVxY61cfSsrK/Wi7IEDB+pBqoErS0wTLqL5jhgNILkVBU4LCwvbghF4qoN4pY9agz9zN6gMfaanp2N6erphFbJtTpw4UfNEcN3c3KxfKUA3hoCKwkQfHh/DPWMCB3czqixuMKYLU+ncQqH2rfbR/wy4BHTkt6Tl+7jJgKM0npwfr1NWLt2RBD72f9seIf/NfLN6ZZp4RP5SRB+DWXnD2iUDZu9PjQu55xR8RKEtQPIy2c/e7hFbgRT9fr/xktKI2Hb6g7cbx7nWoxX0MDs723DrM9JQkX8nTpyIycnJOhqQPMvT4O+gorXl7juCWQbMe406kCpQSVN2TYWDmQDDhU5NGp06HhExOzsbU1NT9ZoNrScNVgZXRGytVUVEDWyDwSCOHz8eKysrjY2I/jJEaX2eLyemXn2tBV0BlCwiWoC+sZbrSuKRUXgCBG5S1HN0cTAf1ZOh8LpGS9UtOPfPexsSuCK2BxiwbdwlKB7bhGqmzbatffn6Q2bRcJzpPgHQBayPTdaXGj7TeZ1KlmTJWvP/roQwTWbV8TdduQQVb4c2S87XbWTtONBzm4fqTSuQeUnhFIioPWj1Ky0VOQeLjY2Nev1Y750j6El+aB4LpAhiPgf0ySxgWlveBm3rUJ7PblAHUi1UsqBcCEmw8vRiDVRp88vLy3VEHs185cFgCp+Emkj0s/OE5YitdSc/Wihia9D7Gz0JUgIzuQH9vyauANXfzOtuSrpJCMJqL7cQGDpOFwxDun3dQW1EYeVuQ/LE53g/0/pdQLM8Vx7cknAhl2nnera0HpTx6VaIC5UMkDim2oI/ShZaxu+wPJyfYdaa8i3VlRY0PRXKTwqUePBAAKXjywzpss2OC2Kd9VtpFfUnbwXdyOTVw/M9CEoh6T63I7bmtwAw4qR7cGVlpdHWBEBXtkqKlD+XjaFMkehAahepTSvmfQctCnu5zTjRJRjkC9c5eNywm607uDAjiPR6J8PYFRyhiUBgdC3b18cEOtxf4kBIgPINxRT+5FltQsDKBKPKFnnou+o/bE0o6yf2kYNVlpZWCOuVATkps2ZEvr5Yep68OG8Zn3yuDaS8XqW8yUsmlDJ+RxWCbUCXCcIsXyolVAKGCc8SmGtM+boeN7eX+HUlREDFeay08jh4+cqTa6mrq6t1AAXHiyJoOY488rZUX6fMgir1UYl2C6AiOpAamajhZ4NObjENCFoJ/X6/cdyQTkinO8IPq9Qg4jl80tiWlpZieXm51oRkbQiM/L8LTbk9FAihtRyWpY/+a8Lqv/hUWdLM2E60ggg44sGtL1onyku8S5Aqb1ogJTAQuJMPlpMJJfKvayxH6ZVXyZ/vigbbg2V6GZ4HlR6ma9N6OfYyyoDB65WVkfHHvhgFeEvX2Z+0hLy9Mn64Gbdk+WleCmA4pn08iQgmrKPGs4IpmE78rK2tNdzdUgY5L3u9Xr0EIH7kxmfgk2QD3Yk+LthOmXJCl6nLMNaLvz3NblEHUkZZx3DAZ4NZrj5G9tF9sbGxUb/9VicjaKC6a6ltDUXRgYPBIDY3N+uNtFVVbdvgK6HoloImivhllJusJ98x7xaTLEaCC10uyo9gRsHpL3/LJgKtPQqYkkXnmiIFnbsAS/2duQqZp0c76vksWIB1cjcuwcqppMVn1o4Dcia0yGOpPNWtrT+cX19zawPNrEyvZ0nDZ79SuHr70tKRQM7c53R9c22VvPE5Bh94nr3eyddqzM3Nxfr6en0qhNeDgEt3pZQwneAvi0oRtuRZ62Ga58qXAO3jlpv3pSh7ezMNx8leAqoOpKKpfZQETQm41IkSqny7KCPcNFAUKME9QAQkCn3uk1pZWam1LkX88LwwuQKXlpZqDYybEBXJp/dTEXgpAFQ2gYvWlVwcBGE+p7bhx/dPqV4U7pqwGXDrOU4Yd63SiiMP2V4Z9l/pGvvBrRi5WLNxUYryYh944EobZVaRj8vMktF/By32d0Q09tM5ryK3Xr1/1UYZZdZcBrYOOgQbLzNzkbO+bdcpmLXRnuNWypY+vsZLRUX8HThwoF4b5tsN1F76lueCMqDXO+m+X11dbeydOnbsWM27LLO5ubmYnJxsBGSIxA/HpdqR/UvgZaRgBvwcA7sJVh1I/YSGCQul0SSJaGq9erGfTwa5tbgXYjAYNF6H4ZFEbqVok640NWmCPHWCAoPCX5F0AlLu35AQFjgqko6vG5mcnGxYV+7PzzRqAlxE8xXXuk+QdzcqwcEnB4GMeXrghX6znZXOgYy/2X8EOL/vwj+z0Nw9qL7zhf625zMhnz2bhVWrf0rg5dYZ+9RDnVlXB1gHBh8Png/BsMRXZjmqTXjf82aZmYXLNtJc5PmUSu/7prJ9TCpjY+PkkWjiT5YQA4YIeOyXqjoZVcu9gPI0+IG0ESeVM23J0Hiikpa1J/uFSp2DN8kBajepA6kRKRNotIAo/KmJysLRxHY/s/KLaEY4SegqAkiBErrOQc/1DuXL6CIBlYeIC6Bk2cidwAVhLui2ucI4yFU/BzTxzWucPO5izECKoM40bsV5PnqWZfrH+1p88X+pDUiZYMj6mdcyIep5ZtdLz3md2qysjNosoAyImL8LuFLbOqg4j7yfAU4bSGX1zSwDgQk9CK40Oj+epxSPycnJGnRkoXodGGFIMOSZfnTxsyzuu6R71omubpWj4C1aUg5iTrtpPZE6kCpQacJENK0HDW5p7Bq0GqB+0rF84SyDApGWgKJ/VlZW6k2AEVuHWGqwcSLwqCNt5tVmWpYh9x2DJ/SaEZ2cQQ2O7UABlgn70pE0bDvXpjOQykKBvc0IuMqPQFbS7EvCmUBUqiPrlllMKjcDI0/nfLGNeM/HSEl4ZKCbAb1bOE6ZVs5nS+3YBjTOnysBzheVEHedeR7OF60fUWa5UXGkckhr3qNjvT1kdQqodHameww2Nzdrq0ntq7wFaidOnIiZmZn6dT8cQ+vr6/UxStzwzjGbAQ/nk7dVZlVlRNA709SBVEI+gRnBpgGj/RIaANxIy+dlSWmwRzQPnaUlwGOH9LLC5eXlxvtmVAYnhvigRhgRxcFHQKL1pE28nLBuSRAEKYDd2nCftq67JpdprO7+Yxni3/NVuzJoRe3DOgl4Xcg6cHjd1K4M+Vce/t8FY7Z+1Qa8BCJpz3RFkciDt4cT68i+yPrKf5NHB6yMp8xS0u9sXGZjlv3IZzMFgmsvVA55GoS3A+uj48Q0B7lGS561dsqy/fSVubm5ej8T24rKq4Ox1si0NqVtKixX54AywEnPex0pczjXyGdGvt6n9LtJHUidItGS4qkStHb0EkO+kyYiGou2PM5fi6Krq6u1Ntbr9ba9JkAb/VS+86QBOTU1Vd9T9ND09HT9WxrZ5ORkTE9PNzR01s9dXAz28MHPyVjykVNI8nnXlqkdEixEam/tP6OLRc8OBoMayDMXSWnyOuBkYM/02eTOymgjpStF/1G4sV3UVsM04WydQQKyxKO7Z5UX75Mfb1umZ3/7GMgCToblR8DK3MOZFUVgESjpvuYlPQhSEJQ+YuskFW227fV6dRCEAOaCCy6IRx99tFbINJf90GjdHwwGdfCFjgLzV+KortxX5dF5EVGnV7tENEPQqeyyX7xv9wp1INVCbjGINLAkTAQyLqCllS0sLKRuClo+3KhLC4rl6XkNKvrROeglCHR4rfiTsNYg56sCskAICnuBlbsjNUlVf2qVtHjc6nDrgsEEbH8KR2qGEVuh/jxWyPuJlqwsVtfOXUBnwDQMpFzoqK5Kx75rI1o3bsXpOtugZK1kFpCvt2RWqj/PfKSQeVp/y7Sepys34zOzwLwepXb35znu/blM8aBAd8CXUsh1VY1lKpuKxtNRZ/SkTE1N1eDE9SbKDfHMM/oUeDE3N9cIoGBbT01NNY5gY/+yL11J5LFoBPjMsnXX4G5SB1IjEicEhY+sKPd/93pbp4BT25FAj2ieLaawVF9/ygYPj1vRh24Ngd7k5GTtNtDglcVGS4pn47GejOhjVJwLAG8bgppPAIKh8iJvbD+6OtzNSW2QxyfxQ6vK1wcywCAoOEhk63D6TfDTR4DC+qlskurH9mF7evu6BZKl8X4Uj9k6oKfJnmW+fo0KCl9XkVkzpXxZh0xRYZ2831gvJx+XGjOZMHblUc/6HJCgzyxPKVGa+9PT0xGxtWUhA2DxQBf4YDCItbW1WpGUPOC4YsQsFY9s7LBt2yx+52u3wUnUgZSRC5RMGLiFwfBVChtFyHFyUOC5Jsh9UBxMHCwSytmgVLlyM05NTTVcA/poHUqDnQOe9cxAShOx1F7U/DOtVjxSyFPzIw+0SghK7trJBKv3UWldiM/7c/q4i0a8qt+UF4FZgsPXUFxYuVXggsW1Y7ax85O1hdeVZSm/TBhlIFJq66y/M9dilreXzzyGUdZ3JevJQZDp9WGf9nq92mpioAL3UbGevMaoWp1crog/H0ebm1tvB9ZH7nytEY+NjdVBUFREPYCnJKucP7ZLBpzMYy8A1TkPUpmg13VRNrBdU4+IxuuatZ/BXTMSZBz0vV6vfuWG1q/03Pj4eAMUNje3XprmPn3tg5IFJbcA9z45+BAwKRjpk/e6Z1ZXyWqRJak00rjdkiIpnSynTHBlWrELUg+acIHEySh+uH4lC4mWb2nSUlhme7z4zRMRuOjN9ud9tpW7ErM2IOCx3ExhyEDCtW/nn8CbgQTHWNav3mbeD84zeXNe1J6+3kgrnv8zBcfbSv/lplPIt8K/VQ69F+pTnoK+sbFRezF4ojktH7n3aRXp9IqJiYmIaAaV0IortS/lha5zu4oDtKf1IBrOqd0ArXMepEqUTWiRBrwmIjVtvZ4jO3ySncwjidbX1+s1KGpj3LnOc/kiouEvl7Y/PT3diNaTq0/uP7440AUNgckFFck1VpEEBQGQ1iOtAg/BV77UTiUAuN5EQKNwId9sk0xQitwnzz73NnBBPcpkzYRiVudSezNya5h1ofu+3qbrvrCufFl38uP1KIEF28zPz2Nf8Lo/x/YtueJ0ja5zku7TbeZHk7GumYLD6zy5QQESEVGf0ydFhuNTigkVCq1raW4KjKj0KOiH9eSpNZrHPArN32bgigLnEe95H7gyQ4AqKY67QR1IDSFqdxzYrjVGbIUbKzqn1+s1JouEpsx4vaxsaWkpFhYW6gNqI7YmJF0BGmx6/YUmjLQ1vo7aTzrnTnbxxYFdepMoBzKJ6w+qd6b5O3C40HLtkEKPawPML2JLeHugAsumgC1ZLaxbJqSofTp5v/C6ns2e83TZRmQHZNWB/ebj0Ovj3/68W0esJ9f5XPv3+mZgnCk4IheY3OqgfDS+OR75ElDmRRDmOCLAMxSbwKy55IECUjYjmgEHsm6kbMkacleaW7pTU1Nx4MCBOHbsWA2yAiO2PddZ6TbW/iu2sSuppQ2+bBfvA0+XgdluUwdSIO/ENk1X1wUCmlwMpNBgpJtI4KQw1pWVlVhYWIjl5eV67xLdPRHbo9omJiaiqqrGIbHj4+P1y9McmLRZlz5s3++UDeBhYJWti1AwsB7M033wBBoK3Uzw0YJ1ACr1JXny/hSVLIWMvN4kCirXTDMLzHnxfqDG7OW6dut1cIGmvFzZyvgoWRr6dmHGumXrf/4c+c+UBfEgfhk16mOOIMPr3t9Zn2X9ITB3QNeaEjf8qizWQVYWx0BVVbXyKpe+LDTWS3nKYpInhPPX3Zjsa40Z8e3rZ64Yqg28/VxpPN2gVeqPjM5qkJIW9NM24CjabpbOLQcusmpyZcJRoLa8vFy/dmMwGDQ0Ik4Cd0fRghIQyb3oIMW9WL7W4fXihCwBdvZMZikRZMi7Tw5+u7YuYVHqMxdwGTC5dZLVyTVv77PSOPCJ3Ea0doYBprc1hZfz7DxkIJC1T1Z+JswzIOQ179+szryftWcGVh7pWWrjTIhmwJeNceejlEZEz4a7lfWsW6Oizc3NOqBJb9T2A2GZh/Y6cTO3b+moqmobcDngODh5AIUD0WMBSE6qy9TUVDz66KPD0+8k85tvvjme/exnx9zcXBw8eDBe/OIXxz333NNI8/znP3/boH/961/fSHP//ffH9ddfH9PT03Hw4MF429ve1jjQclRShNpjSdnkj9h+vI9cAgILtwIIaNpJvri4WFtROglZ5j8nCkPYVZ7K4UeuRGlsSsMgDUYquYUh8gnHiefCX8BKK0qDkL53TjrWh22jsrMP82afZ9q9p2e6rIyIJkCy333SjiowXdNl+2VjNquz583n2S5MkwmiElFQubXL+ihNVk4m4Lwts/Zhnb0sbxOuA3l9s4NwfS2rNJaURza3OW64xsU3Ykt5lNLJ57OzObUUwLP3fHwqrYIndBIG9zpy+YEAqTw8UCkD5LYxTD4eC+r1TrovL7300pHS78iSuvPOO+PIkSPx7Gc/O06cOBHveMc74pprronvfe97MTMzU6d77WtfG+973/vq/9ovEHGyAa+//vo4dOhQfPGLX4wHHnggfu/3fi/2798ff/Znf7YTdkaaiD8NZVq6C0VNnqqqak1JYaqaMJoY2lzb7/fj0UcfjR//+MexuLhYuyt0rlfE1r4paW/y0RN8ZCkJmLgeJU0lE4j0f2fWBOufrZfom5sSvc3UN/LX+30XILQ03Gpwzc/Bh9ZEJoiYV9tv5cfd+YqIZJ+T/yyPUe7reltwhz/nGj6FFf+X+CCIcO1Da39cx3RB3ibQyF+bNu6ClX2WlcPfqpvWrzgmuBZDq4HjhxGV9ExkPAiAPJBHbnW9Mp7HmI2PjzeCGuSG5x4nhZFrvuq1Hq7IiA++tdcBSfxprqtOtPR83PR6vYZySIuPQRaix0q+qk31XqxhtCOQ+tSnPtX4f8stt8TBgwfj61//ejzvec+rr09PT8ehQ4fSPP7t3/4tvve978WnP/3puOiii+KXf/mX40/+5E/i7W9/e7znPe+pI+JGoeXl5Z2wPxJlg0XX3YpgCPnm5mZt2XEdgqc89Hq9mJ6ejsXFxXj44YdjZWWl4RLUwBM46WiTqqoarj0dmaJJoig++q/99HUKQ9XPXzzYFiQQ0dzEnIGMBDwnuMLlWU5E80w73Rum3bpGrLZnBKC+BeoEscxaUD4EUoKAp+X/YeTtSQHD//671CZcoymlK/1Xf3j/cJE+cy2V5gJpFK2bz2bRhnTvUWBmoMcx3ev1tlnoDOTwc/Y80CYi6jdmk0+WKwBX2oitdeaNjY3GqzN0j0E/Kk/7ncbGTh6dtLa2Vr83ystUWSWXHz03vs0jC8kXcLGvXNEr9dvpBqrNzZNvF//+978/Uvqfylcmf+L555/fuP7hD384LrzwwrjiiivipptuaoDJXXfdFVdeeWVcdNFF9bVrr702jh8/Ht/97nfTcgaDQRw/frzxOV2UuSX8/rD0mkwU+nS7SLPSGtrCwkKsrKw0hAO1O3elaP3J36ZLNx8nrtLQxUf3lwMW3Sl8hlZJBhJsBxcSjJKjoM00uZ26fzKeSn2TaersF1EJnN1dkvFVmsgOQl5/gkepXVkGLQKvT8afj6Vh9aF1JQHpQQnU5t3qZb9k5Gkz8rHBNskW9yOicdiqhLH3s0cAUiGkK97r4IDO9tjYOPlW3cXFxXo+y8KipaNX5eh8vqo66XVRoBOVKHczyosioJS3JCIa5dGKVmi7jwufP66ktFlOpxuoOG6H0SkHTmxubsab3vSmeO5znxtXXHFFff0Vr3hFXHrppXHJJZfE3XffHW9/+9vjnnvuiY9//OMREXH06NEGQEVE/f/o0aNpWTfffHO8973vPVVWT4k4SUpuCO90aeQcFJo0Grirq6uxsLDQiHDTAJGLj5NYQMSNujzOiOtLXAsSyGR7Z/w7uxaRBx+wDSjc3BLRZ5jAatP0xEvJ4iKvbb8zy8w1dRdUGRANIwdEv8c2a7NYnf8SDw44WR3beM/63oGzjSda0W4FZ/PF8+G3r2+VKOOLfSd3oLcP/3u0W8avf2gV8b/WqDTH/WQKgg8BTtbU5OTkNrcX+VSYul63Q0DiBmICM9d8fXxk9WxTDPcCnTJIHTlyJL7zne/EF77whcb1173udfXvK6+8Mi6++OJ4wQteEPfdd1885SlPOaWybrrppnjLW95S/z9+/Hg88YlPPDXGR6A2jdono4SNgEOb7pjPxMRErXWtrKzE0tLSNo1aglLaq4Ig+AqN8fHx+hRzTQjfE0WLyLUmB5wMgNxVWRIwTJu5Z7J2VJ68x/ydNw8gcWFB/jPFIeO9JIwi8gCONgFfytfJ65jVNUuT5eMCheDOPHwdrwQ8WRk+VrztMxemkwv1DJzd/eyRnJnSkQVlcA72eluvznDXuwQ7+eEal/LPLE+2AxVJAQLnmOakn6epfpCFNTExEXNzc40DqNkPEVt7qcSjv9Fbyi3f1Mt9h8yLwOwAnCmie4VOCaRuvPHGuO222+Lzn/98POEJT2hNe9VVV0VExL333htPecpT4tChQ/GVr3ylkebBBx+MiCiuY0lQnwkqadyiTFuL2NJwNDGonc/MzNRugYWFhXoB1S0wCXBNMr6UUIERWtvyqD2FocsdQPcHwTTTQjNBlKXVNU0+F4S+KJ21K9M7mFHz9PZnHTx/9gfrOYo1VFobY/0z5cTbqJTG2y5zcbQBVMkqoWDNgkhYJtcrmGemOTMPaf+8rtdbZK5WVxRcYfEx5f2a9S3LjtgOviIeZaWxyQg8Pcu1I+UtNx4DCJi/8mMdmWZsbKyOzK2qqgYpzRVZTmtra/Xc132dErO6upq65+R63djYaJzoT7AZHx+vD6VVWufR27VNYRumdJ1p2hFIVVUVb3zjG+PWW2+NO+64Iy677LKhz3zrW9+KiIiLL744IiIOHz4cf/qnfxoPPfRQHDx4MCIibr/99pifn4/LL798h+yfPnINlUTB5ffkYuOZXcqDEUUrKytx7NixOHbsWGMg6b4GoK7LhTc+Ph7z8/O1313A5a4FHZ/ikVnim6cHaNJQcCj4wNeMlDZia81CAkC8M0CBvnwX1hTSDm5sa6bPtGnvNxfEJSsq0yyzPGmdOE/ZfbZViYZN+hKw8dlsjPrv7Plh7cj7PCElAzUBF8eOg41bXSQpMm3POxi45eN1pQCPaB4Qq/EsJdDX9BjezUhajnVaQ3yWAChPiV7bMTs7W89Z8SLAF/D0+/363VMPPvhgLC8vN9ZxxZ/qoWOZ1L6ah3xZqSsZbiXyuisSHA+jul/PBO0IpI4cORIf+chH4hOf+ETMzc3Va0gHDhyIqampuO++++IjH/lI/NZv/VZccMEFcffdd8eb3/zmeN7znhfPeMYzIiLimmuuicsvvzxe9apXxfvf//44evRovPOd74wjR46cMWtpp8ROJ3GCSVNiEISARGtR2rSrTqdZTq3VQYcuvMzFx/dGUZBxjcX5Jv+kkruLC9DZc06ZxjyqlkYBlUUgZQvwFIou3H0hPOPLeWfZrAsVFueLbZ4tQnta76usnLY24ifj1S03afYl4eSg5Pl52S7weJ9zhQAUsT1KlHOL/elrnaRMQciUKwUZbG5upq/AoRdDUXE8nkzkUZDsRw8+WF5ebuyXpEuQAKRrs7OzsbCwsC2iV20i8JSrj+NLIMW0HuhBxYNA5q7YbCzsBaDaEUh98IMfjIiTG3ZJH/rQh+LVr351jI+Px6c//en467/+61haWoonPvGJ8dKXvjTe+c531mnHxsbitttuize84Q1x+PDhmJmZiRtuuKGxr2ovUZsQLy22u9tKxx+trKzUg8bzlzVA19309HTtFpDFxjJpVbmW58I74z+rp39nGlUpfxf6JR48fam9XSt3gMissJKVUOKXz5aebxPYw+qTpfFvb89RBIOXk/Ge9Ru/RYwMa6vTsH50wZ3VtfRpC7BpE6Sj9lmvt/2N0pyncvsSACYnJ2ugU4BEpnxwv1lVnYziW1lZqecm5YNbSeJtenp62xmCbEvt0/Lx4S5f3yivPLzNlK+X51bUXqAdu/va6IlPfGLceeedQ/O59NJL45Of/OROij4jlE2qzHXGwcZIOj4bsaXVa/e4NDnXHjkgGcU3PT1d+9llNRGgGPnn9XABTJ7a6s7fpUXkYSDFMocJtrb7WT4uNDOrhmDmZTgosI1KQq+NL7btsOdceA4DtGHkwEbKgM77kXxkQQv+cUuFVhyFW6mNMkDya86bl1cq33l3PjRnRG6hsZ4a94qk1WvcI7a8H96uDHdfW1uLpaWlev04YisCjySrbN++fTE5OVmfOiNr10Po6Q0QwKp89mPmLSBlVtReAaSMzuqz+x4LGqY1ihjyqvByPqvrEVub8rLIOZrmY2NjMT09HbOzs/WCqkcNCRT1rihG8rEODoClurl2J1L4qw9kr+cwEFJevO6CxYVg6VrpmQwc+UyJX28PL78kLJk2AwjymtVJpHuZkB2FnAcXWpmCwWczAdW2Fud9yjLpmhsGwNT+28Zptsbi9S7NV2+LiC2goMtOip8Oj5V7TnPUo2UjonF8WabAnThxIpaXl+uTdsbGTr4Ne2Zmpnb3K28prgqQUvkMQorYinz0NTKVJ95ojWXBTeTV52X2vReoA6kCZRqd/su1JmuG75aJiG3vbsrO5IvYvr9Ix/nPzs7WJ51n2p0fGuvgKF5ZVrY25SRhl7kMRJlryIVtBjj6plslEzqc9FICXACzPJ4P6GXxm7ywDmwf57fN6hlmcTGvNmHPurRZOiWAYZ4ECZKPs6xfM2uF5TN/Ckm6mZlH1q5OHkTj4yJTnLz+HEOuAPB5ta+DsNLprQQOvky7sbFRbwFZW1uLwWAQy8vLDbddRNQg9+Mf/7j2iMzMzNRbUbw+XMOenZ2tlwXYJgIVvlKHcoQRvVrfJhj5HCwFr+xFy6oDqQJJmJWCJaQNCSxcw9GA0evg9awmCrXDqjrp5jv//PNjfn6+sRDKI45kTfF18MqD322WCX9nws81r5KA5fOcBMyHfGWC1/P0fHjfBXPWN8yLk5tETbUERtmH+e+EhoFZyWpxIZu1lwsUfg/ra95z5SmiGYXHvOlycrBwBcPB2JUNfWusEEjdumcdfPE/a0elzdqernpaHpxDnAt0sXN8SQmVNcRz8zY2NmoAE2hNTEzE2tpa4w29ehGiyhAIrq6uNsDJg3FY38x1KSAdRt4/mQK029SBVAtl2jvvcfe3A4IGlayoiO1apZ7r9/sxNzcXMzMzjYVWPUOXg/7rQyBoc4dlwk7/swlfEmreLuLR1xRKAtEFaQmUnD9SVh55YntQmLrgJF/eLyWQymjUNFndvMw2PtiHpag3zzvr07Y+UrkRzfHqwEdrx+tGBUpE0CuBh6yVrHz+Z51cySq1aTb+CXgEp0zwC5RYZr9/MoRc4eWMzuv1tl6GKFnBMzwJbL7uPDExESsrK9vKypQG78usTfSdhdWLvF32EnUgBcoESNtgEKCw0909oTO9OLGZnzSsubm5bRaSBi73RalsHm5bck9QkHg9M4FYyicDCpILpJLwI5UETZswz7RqF0wOLiWQy6zMNsB0HjLK0pRASenahOioNKpQyfhzXjPenTcHptL4GpamxIu7p3Qvc8mWxuYwwGKdaLF4Hej1kMdEYNLr9ertHwIkrnfJStOzOv2c9wTMBHi+Z4kuUO67pOLllmAmi7K670WrKaMOpH5CmTDxjnUhrrUhpSFV1cnXVGiDHqMANSE2Nzdjamoq5ufnY3Z2dluIeVVV9eBmFJ+uc5OteCJoZus+tCZoZWQHl3q7lAZ0liazRNydo/QuzAhAEc0TJbIJlglXCQbnW23Hujv/XpYL2FL0VAZ+/j9zaWZjpwSYJYBxKilXmcWQgXkG/MyX+ZSsIqZjnl5elo8EPcerC9VMGRp2LRPMbWlYD/LO+mstSelXVlbq90BFbI0Xvqlg//799Zl9Pia09URtIpf/2tpaPX4jog5LZ0QfQbWk8HCeZRZ5Jvd2kzqQGoGkOVEISSviTndtAtQ17ZeI2HptvKiqTq5Dzc7OxgUXXFDvGhcp/czMTOOV8j6ZNGEiciHAAevCQUIqm6Al4cM0ri1LqJQsqZLWy3Zm2ux4Fy/T86V71IGKz1PjpOJALVWT2AV7Vje2i677OoJTVo+srb0MDxdnfuw/18Ad9DNwLPGRUQZqDmhZnhlIZ+lc+crqW7KWqIRFRLEvqNDQCiJwsgxf92V07/T0dPR6Jz0sP/rRj+p6ra+vx+LiYlRVVW/SHx8fj5mZmVhfX4+FhYUGICnN8ePH6437OjppMBjUJ0/Q/cmgLc0djn+tg/FtBTvt792iDqRayE1oRuPwHTI06ykYlpeX6+NPfG2g3+/HgQMH4sCBAw3tSu49aV2cDDxpwgU2eSaRJwJIFsmkuuk5z4dtkpVfElCZ+yELSHGtdRggUHixTbK1OQfuTABlFgPTultoJwKZ/cg6+DMOHuSZdS1pud4vbhF4maW+bKMMVLPxkrmbS9amrzHyXsRWoEa21lWKRGUbZGM9Iyk5ek7XFP2nAApuyme/yGU/NzdXvzJDgCELi8clsU04xubm5mJlZaVxAoaCLvQM66Q2kAzxjboCKAdqnxN7wXJyOqdBKhuwmeDyjpNG5O47HnK5vr4ey8vLDTOcZvvk5GTMz8/XG3ZVprQmWVacmBJyOsLFXSGsEyeA1yk7xp/fWRuVtC63HDJeMpdCJjCyts/KyohreeSF1pHyLOVTApkMoL3sUj18/LilxnLJF8dNG39t7ULwcWDz8ezPkY82l102vrI2KfGX1cfJFZ7MusnGcwnM/bfXn/WlYNe8c0VICqQsGr2GQ9tOFDzV7/drkJL1pbVmnnS+vr7ecA3ytR++lUDywSMRvV3ZJspPgJe1d9v/M03nNEhF5Iu6vF6aSAwT5wDQxFhfX4/V1dV64MldqAHJDbuc8DzmiEKR1hCF/zCgdRATv9mkpZvDtVtvgzarQb9dY3Mes+tt1AYWnJzsh1EEeomHzCLJxgvLcasta5+MB7Z9W92z/8NAipFhbeBU4oX/M+ur5IpjGaWxULJuMouyxLe3eaktSnxk63IO7P5yRCoavV6vtq70XwEVOhxWUX+rq6u121/znetKss60nMD+6/e33klFxVaWlK5rvSuzmDJyK9v7frfpnAepNmoTPDoM1zen9vv9eqMfN/Aqn/Hx8Thx4kTMz8/Xa00aiH7MkawzgRuPX4rYcmNkARJtEzfT5IcJ8zYhSc2tBAL6ZK4yPttWDvllOW5F+TNuzYgPleeaeKac+OTldd93tRNAaVMifLG+VP+MvA7uZnPLw9vJ6+IKUQZS3oaZ8CvxWgIPB8asHUv9na1pedALjxvStcyKzQ5pJbi5Atnv92NycrLOa3FxMXq9ky4/eVwmJibqczr5+hvR9PR0w1Uu5VcnVNBbIEtMLketc3l/qAy/3jaedhuoOpAycg1JHcvrWjOihsSX+ekMLj4ry2j//v0xNzdXR/DI/B4fH4+5ubk6WpChq/wIqDiR3E8vPlzwlAZj5h7Q/zbh4kDBcn2C6JvCsg0cfRJl4EOA9+CGLD0nJ4MHMkGTrYtl2qV+ZxF/0rBZj+y38snq7mnZ9xmRd/LIsejpKIA9L68n7/lpE+SRz7S5LEsau/L3umbz05UE590pAzEHVOdjfHy8BgLWiy88ZB5SOqVg9nq9OvBBaeVF0b4o1nFlZSVmZ2frV3WIV564HrElJ/gGBXphfB3V21LkANkmL840nfMgRa0109gimqea6xwuWkG8d+LEiRgMBrG6urrtCJR+/+SOcpn5enb//v0xMzNTCyAKOG7a1XfEdksk0zhdEGR1zyazC7oSkLQJgtJ9F2IZmDgIZJqyg8mwNZCM52wtyX36GaDwt4Of8+3v3SIvEduPxnFeWWbJLTVMIFO75xE+3tZsg5LFJzcT293HW6n8YdZ5Wx38ef/ta7TirQSCbURXMeUDXfOKlvM5p7T9/tZ5gOPj43HgwIE6kEKApAg/BjtovMjdpwCNtbW12uLS6znkJhRx47DKdwXFQa5EewWgIjqQ2kYlTZBamwCKprMm7okTJ2p3HwWsgElRgQIchqQqnfhgxF9JQGaApN/uKmA6/S5ZHp52lOslQBGpvWh1lkArs9L0DK2TEm+eB4VvNgFLwQEsW4qGv0/IXboUcCIXDN5W3NDp7ZcJi0xwZ+Mhe87bYBQlhH3l7tVM2cmUD9abfDhldSq1A/PwCFvyJWWirSz/XfIEtAG/5IK3o9x6ejvvYDCIxcXFetlA3hk9qwAqBVgIcBg+TpDi5mDJErWZ5p1kita+vB9K1uRuUwdSQ4hWlkiDwEFKE0Haj57lwJuZmYmIrY14PIiWE1ID3o8/EnHSqxzy7CG3mSBpG4gZmLE9SlZY9uwo1kjE9le/u7XgVhMB1vl0ftq0dmq/mYXgmnVJs6fmnrW7a/VM61F0wyzDrP5Zfb1sb4sMUPmcfrvXYFibMo8sXRtIkt9h1513Kg0+7rJ+G5ZX1rYcK4yUJQhEbLfkZAkJaHSup+Y5zwZkmDq9NgIqKkaqswI3uHezBDxZezhg7RWw6kCqhVw46rc0HoKBSOd48boG8P79+2Nqaqq+z5ei6b8LggyoSgOMg4whqaK2AZuRpykN8uxaBnLOu/Ptz1Ngqx1c6DtI+fWMzzbh40pCBjpt/0uAzLJdQGfXMpBycMtoFDAuARjbhcqVj0l/tqQklMDvVC2lYfUk4GaHCGcgVSo3s4hEPOmF40xjVJaKQEfzXW82WF9fj5WVlXrtWvOep8jwLbyKAGTdGArP8/hkYWWA67Iim4fennuBOpAqkGvRIvqmudgoF5Si+qpqK1jC3XoazDz+ROShpSrTT7wgn0zPII+SNj5s0nv5bSDTRrIOfDI77yVBRaDO+oNaLfPLhDnL4Gs9MmstE2wq29+qnK0zlQDcXYUi9lUJQHWvDajalJesbQlCVKpKa3VsI6UZtrZBXrKxWwryIJ+8t5OxpzmYtQt5UL6cz+4yFBBw/CnidnPz5Cs0BEBePwGXXHczMzNRVVUsLS3F4uJiTE1N1V4Vha1zT5YCNwaDQc0LN+/y+KUTJ07E1NRU41xPja/MDbnXrKaMOpAqkHcYtXlpOQxf1YDkgqoGDQ+XFMgpz4gtYOJmYJYp7YmTlOHp+k/fc6axql5tGlUJ2DLhoEGfRXkxL/IzDOQoVErCRfk4QHj+pUnnpz6QR+YTEbUSQRdLKd9h9Su9/db7qeRO5Tqo0vK5jNwaagNT/lY6WvGjUNu483TZs211yYjWk1vWblHRkpS3wetbstD4W/8FOtzfpPz5bif2p8BK75ZaXl6OpaWlGlh4yLSe0TKBgrHW1tbqoIyIaET/qUzJEg85L7XhXgWoiA6kWsk1aw0+Dk4KyKqqYjAY1IdLauBMTk7G5ORkLYCnp6frd724f18TiZ9MO6ZlxfBT8uIgoXslwPE1IV33NqFV49e9HHcHZf76zGJSeq+388D/no7ClZOQQO5pOWG5PiWBx/ydRgX4USywDPBcA872nDmAKR3zo5bt+TkIuvXEfi1RW785tYW+UwnMXHQlq8gVMX1nAOXCnWX5tgIpLGwnvl5eZWhjr/JkcIUUXSmqCrTifkiuUcuaktWmY5Y4Fk+cOFF/xN/ExERjG0w2ZlRXX+Pda4DVgVSBsgklgNIg9MmsEFIGTuzbt68+iFYDTSZ6ZjF5pJgLcAqMiO0vn+OzJe3QNfVRACbLJxM+BG0HMZYlPig0h5EDr1NJC26zbjLytvBrpfRs8zbXVEnIuyCNKAeJiErrJs5bmwXFcsgflaGs/5yfzILK6jysL6TMeB9k5ZSsnKxcKWKsB91g5C1TziK2nzKuPLiXkX3iAUz0xvT7/VheXo7V1dX6KDStJzF8XM9rr5Yst6qqGuWxXeWRGWZFeRuWFIDdpLMapIZpdDuhNqEikquu3+83fM96luAk0qnGAikPKedAp4DTPa+vA5RrhO6LZn5OFGAlwcP7owr6tj5x96gmreqQCaZR+jkD0xJglOrCMtyq07VMCDp/bqGU+PL6ubKQ5eHXve+8LbJ8svL17RZuZt1mPJX6LSs/U4ZK/JR49zpmVoBbfg4YWX1ORZkRmPvRSCWQojtufHw8lpaWGm/1LdVX1hatuQw02Y9tMiADozMFTpKlq6urQ9Oe1SA1OTkZg8FgpMXbjIYJXtdQNFAiol4wjdhaa6B1pcGmgSU/NCN5NLgyt5l+u5WkfHkKM/mlxeWao9IMAy/yonTk2Qe381tqV9bVAbek6ZfcNhkPLuSzNYfMfco0bcCU8er5O18ZYGVC0fudbcM6Oq8ZmGZpMtc0y/V+a3M187vNesraZxTKgEPj2dOVFIKqar5MUMT5yXWjYeDF57P2c4DigbPiQyDEteTp6elYWlpqKJnsN5Wl/YWcgzpP0MGIkcLON/utNI5PVZ6OSv3+yRc7Hjp0KO67776h6c9qkNKJ4o8VcXc9TXo/RVwCXH5pTmS9sFD7Hsi7tCfPK2JrsHDvhO5p1zm1NX9Xlb5LApk0CliVBFAmOPnbtTuu85QEIPPJhLjzpeslEHU+RQSBrEwvh0qLWzE++VU+A2Fc6HmdsvZinhn4ElTYlk6jgCTbJdubx3IJeiWB7u2i53wd1tvT+fRyvJ6Z0pWBlp71k809Xx+7rG8mxFkPnoiutlSZel73T5w4EePj4/V7qCLytVHR5ORk/V4q5a1x6eOFLnS2a6bcnCnrSdTrndyOc955542U/qwGKXbY6SIfnBQ6sohci5FFo6gbaU6K6Juenq5PmtBg0UCl/1r5MWhCg1YDUAClZ0QuBJxcKPn17JpbYW1aMydCBkzMYxQXUqZJs1x+uwskey7jmZOb4Cltl0qAC9CMl1K7eBtL4yZx7dGFuLeT8s2oBBYqQ+2v+jvQUoA5D3RFUXiXwN01/KyNHIgcoDJFprQOx3HvruNMoXDXvPeV86ZrCqCgtaa5ykOgFbige+KBR2Up7FzjTIfHykvk84oh59qLpfwZTOHrvFSa1D4lBeCxps3NzVhYWIi77757pPRnNUg91mZp2wClYNWEYASPBtTU1FTjtR4c0NSYMi2a2iwHsRMHseenemTPlK4NG6wUdJ4/AciFnbebysomE3lykPJ6SVi4lkvLNcufv3lUk69l7FQRGtZ+vraVKUWeX0nDZxuUynVh5OV6tGOmFGR18+jJbAySZ1pRp9Km2Xz0eoo8IKJkAWURkg6UzgN/U6FUHgIrBTrwzchuBW1sbMTU1FTtztO7pOS1obdFadyS9nbgkUfZOKFCR7Aq1ft0U5ssy+isBqnHikoaLE15dqw6VNYV16FkQWV5acDRunCg0rf7t8lnG7gME14lIVQCDhdy/mwJpNraNQPTzC3BZ3zSSUEoad/8nwFVqd4lRWinE5nCeliQjAv4rA4ZP86bg4QL2Yy3rLysbUpKTgk0M2FaenYYtYEJFSiBkOebjfusXZh3ifcSuGdbVQhQKlPBExLYckXSzU9XIYGLsoD1bFM6VK7GdtY2e406kCoQBWSv19t2zp4Aidq69nUolFSnTJQixahVUivLhIFAipof0/pAzAT8MMosOgeDNuvI+XeQKllJ/D8sje5nh7x6HuoP8pSt5+g66+NAUhLKw9ozq3cbSA0Tlp4fBVIbjdLOpTFZKn9U5YWWVdYebQCQCXfm2fY80w67lo1vD5ziGKESoOfVhjwpgssDyosbxPlqHv+oP6Sc6mAAAddgMIjZ2dmGZeJzj+DmlgvbMFO6H0vaSRkdSCXESZ+55CgguTCqQ2N7vS1Xn0x55UuSZeaDhQCjAc5jWTJy1xZdK6yX19Pr64JnmADM1lIyarMEKMBKgK500v58Pa/Efyms3dsrO+utxG+Jd/2n8HTA87bw+mbluTbvmrnny2ey9RsqGBxvXJujVu5jjnOh1BZZIIfz0gbEJT6zNvL29g3CDrSusPC656VrdNkxT75PqtfbeqmpQElbTrRenc1zjUWuVTqw6LpcrJubm43j1+jerKqqcUBtpkhmCsmZBKmdUAdSRpnA8M7loFXkXlVtBUP0+/06YMIHvVyBvsjPNSj5swWIDG3PXvCW8U6+SwOPgmYnFpe3CSeAt11W3ihCyq0cAlNpEmVCjDxxwvp6hb8+xAWdl1GqI4kCsWQRttXBn81C6rP/3o4eTMI+03WeVlJSTpQ+s66cj6ztsuOosvq3KQiZIHXw9np4fgR5AgzzZ950zVM5yupCHrjBl/PQ100lO5if+k2Ax7ZX8IQim/fv3x8rKyu18ry2trZtP6YrFrtpRe2UOpACuVbBicbXt3OQCVQ8Sk/7orLJpHI0QH2diVpaycXnQsAtJ+brdfE6l/5n+6xUdnYQbpaPA0fJMivllQmEtgXXzLrINP7sXsk6yQSXT+xMsHt/t4FWVqbXQWPPrZQSQLFOBHrvk9KpEsOsupJC5//9NwW232M7lerlz/mcdH69jUglC21jY2PbGCdAZ8CkvHjaQ8RWFJ97QwiQkiNsU57XyW0mUg4ZVKH+E4jKS0OF2PkujW228V4ArQ6kfkLDLAoeox+xfdATSPQCM26o886nJcLwVE0MrkFlwq1NQ6eQKbm7Mo2d9ygQfB0qKzcrP2urUrkZWHCijyrQnReSh0uzndqEq9ctE65Zndr4ceDJns/yy4Sk/jsQqv34ySK/Sv9LwMNrpBIgZHXItHcH16x+5DMDrTby50tjLhsLzlfWdmwz76deb+v8PY5peUb8LbtZX2h9StcdqOnykyzJvC573XJy6kAqIZ+0GiDUViKaGhTP0hofH69PPFd++i0tR3kTpDRYI7bWuOhidB6Zt08+TpJhgs/zayunjUYR1m2CSNcoXDNBUfqd/ffyXKiUwDaiuY+qjeeMXICM+uwwwM0EogMntW9GhlGj5lpiBj7Zvawv/Lm2NN7+VKKyMTNMwdL1NuvAf/s6jMrJhDaDUugOzerHSF21r0BF+XrEnwCH21p42r76gLKjqqptHh13uVO2ZMFLGUi1zYPdpg6kCuSDhEeSeOBEr3dyg54i+yYmJupB62siHAi6pwVSP2W5JOjcBcff2WBz4SDKgiuYNnN3ZMJ9GFBQoJb4da3frUfx60KCabO6uys1s+DawMrTZho+260EMmpPF/ql/EjeHgQf54HPODGduw09bDnrL1+LyoDFn+W4zQRl1j60KLxuPlZL5fp1Anf2vPMbsT1y0pU/WsBtdSbvejuv1pk515QnI/mcT28jWsYklU3F2tsu27+1F+mcBqk2we7paDq7+0tmtvzY/mZMajTc8a178h33er3G6eiubfokcS3TIxGVRxY8MKzePBKKZVAbHaVtRyG1HQVvJowyUG4rr8SDCx+uBVDr9cN628rPys2sgCxdFgyicgmy1PxFwwStBFQpEETkChUtHOXD/23gWlKeRhGGmiOaSyULp63ePl40PzOrMEvv99V+/lp29YNH4blcUR/ICqI1p9fHM8pPad06Ux9pg7DC11358LGrayULjen2Ilid0yAVURY2JVPYF0mZfmxsLCYnJ2N8fLwRrcOy6O5zYb+2trZNsycPDo6ZteETQ7yRB05ej/7LXATeTlkwRWbNkAfnlxNP5Wehzt5+bYIz47XUvw5KGe+8zzSlskrjZScAynwyTTyLLmOe7FsuvGfjJeuLjK/Mcs9c0G11K7Wn7pWskey/ty/rIJArufBGEcKZRVpVVR1lpzmi9zupPeh5YPnkW0AVsRWc4coBZYfK0TurpLgonYNgxMmXIPo5ogRqffYqKDmd8yA1jBiJk7lH2On9fr8GKN0rCXwPaZd2xOAMByyRC3QHCV7nt1MbwJUEffYM+Srdp+Dl5HWALLlNMr5Z95Ilkj3jvGZ1y7Ryb3fXwDMQzspy4ZzxOCoAZPXKfnvfZv2sZ/y5zKXKPsuo1I+lcofVk2PD512p/Kz+bWHqbiU6D3StZWH8LKPEt55leLq3C8ea7unIJLkClbe+qfByXau0pj2KVbtXqAOpFqIwYXSftBkHLYZ9UoMpCSjeo0uG7gCfEKWwcFGbG4T3M83aBbADIcm1/IwyYFJbkYdM+JdArgR6wygDpYxKAM1ysrbj909b/rD6nEoIt9evpPyUeM/qm/GclZsJf2/XbHwxWs3zabO8SkoNyy9ZEW386JsWZxZtmdUzU0h4Pp/LA1c69a31LC4/OLh5+d4/pXbaq9SBVOQauw8QAoe/qkPE1z5TQ2J+2eDnIqquUVMtaauZIPfBnU06anQZ4A0Tpl5v5uFtybbyCcS2zQBM+VBQlTT/kuAdxrsElisATsyXPGbCxwVNJrQzwZH1v1/P7rsVkCkFmeDKwKaNXKHwcZrVlW1WslCyMZv1Q9YOJT6zOZ2Vl7WX183zoGWZtUXGP6P9GAmsgwB4mrrkh+rM8bG2trbN1efl6DrXprJ3z5XcxnuNznmQcoES0TSfGeAQEY0FUg1M7kgfFjrug1vpdY0WmjRJvkyN77/hIFV0oB/RzzKZPovqy04N97YptR/TirKJkEUU+rNtmnoGdM7LMA3ReaRbpY0yS6IklHUvEyQeSVfKaxgffC4bZ228nSqpLxyw1Y7Z+pX/9/p7v3FM83kqEko7ShBJmxKRCWpacJmFQrB2wCPAZEoqlVd5Xeiek1uPbTo5OVnnf/z48W0Knc8zH3duNWbph/3eTTrnQSpiuxVB7ZPaDS0QP7x03759sby8vC1yzMvhM3zdPEPWs4FN8nUun9wl4UhwzMgFdWaBZd9Mr29qhwTGTIv1MjKt2i2BtvYgLx4Y4sKRz7S1jZPnW3re65JZDTsBvQx8SpSlY1tmvLcpHew3/qcXIHOF8flMGcjq7+Dh/Zm1q1MGqKW2LilGbtmz33kaDC0qRftxvpNP5qElAn+r79raWlRV8ww+grYUYlpUbAfypfmhNS3yzboNs1B3izqQ+gm5oPTFSO9ItwZ4tlemaRLguNfB7++ER1Ip4ML/Zz70TGArTQYYLN8nB5/1fPx51zRLdXbN19fKSgpBqa2c3E3Txsup0DBhqG8X/q58uKBqo5Jy4PeyPs74zOpDHl3h4D3y5FYK03qfDRtTqlu2xpnlUwJ8V4Ky8pk3Px7Fp7w0jnzdWvdpzfCgaSqq+i9FQNF9vd52xVU8aoxQiZbM0bJC1q57DZhI5zRIZQO6pGUP+6/Jku31yfKlFUX3XmZdcOC6duqgWbIQ2oQ2n9mJlt6mdVN4lcrOBIHfy5SErOxhvPm9tnJLgs3z2enELmnxwzTYEniV0oky4eu8nCrwOl9eh6x9StYa8/A5w2tZO2Vjd5R+2Wn/MW/fxMs37VIxyMCfPEdsWXdufQls6JnhQdPM0+c9wY1A5UDrbbFXweqcBilSScvMBhc7lIPKNSQODn5rncndDxyoJHcdcBBmRO040+i8ng5obW2UCfmsrTJ+PILPyyZfWV4OvqWymG8GBqX/PgbormybvGyTEgi3AWOJz5JF4M+V+mQUKo31Em/OSxvg8NmSAubp28rxNV7lw9MaOPbdasjGhANnKX22diX+dNqM8vN5TOvH5wKtOX/nE/uE9zJl2AGJbeZv+G1TJPYidSCVEDvTd3BHbNe85erTJjwBld64KXNe+aysrDQ0qCzAQsRIHp+EbZFoFAaZ9ZVZXCXK0pYGe0lDV1sOW/fx5zMBNozn0v1S/7EOJYHkVBKq2f9RrD/2mfq6TZhTKOteBgTKaxTK2oVKmcoddcyU0vr1TBkUcbwM23xKJVD5umvR8/E2G9ZebgVxv6SASQEREc2Xc7JdVHa/32+80buqTp5CIVCiQtrr9eqz/6isyvXH9SYfJ1qPyjY5+/zda8DVgZSRTxiFiIo46LUTXIOEEYGMAqR2MxgMGmkjYlta8lBa+BcvGdBQM/P1myyqr03oMJqQbZC5C4YJ4TYro/QM07YJvVI5bfn6bz+hYRSL0TXwUtnDhCvrlvV3W75tID4MoFw4jQJAXkaJJ6/PMAUlU3Sy9m5TXBygR7XgKPQ5BobVUWmpVCigwV9YmClzbnlxzjrvkiMEGsoWeWjW19cbconKVzYOHbT2GnUg1UIc7D5gpcHQ1eeRPBHRsMKobZUmAAMwIrafdOGT0H/TyqLZr092aKXXl/99UrVZGlm9hgGXUyZ8HFSzfEYVLCUeMkuhVB8H6BKYZZYby8pAOxMYWT7q44gtjd7XHUrltSkIJaBqU2y8/iVrJCuzJBh5tBH7iJaS8+Z8UaBn5bs1wfxKfcbnVE/2mRRUuuaZ3pUCbuYVzwx2IP/yzHC9i/2uMeHbULKxxPZhnVmvvUKjx9z+DFJJCJWu+cQsHWtCrYWakZviEWVNOPsoPZ9Vej5HgOL9rI5tmroP9NInK6PN2miraxsPbXz777b8M+HkINcGLMPyz/hvA+9h7ZJdH5auVNaofI9KO1FK2NYlwcl0pf72qNtSmdkcKoG/tyn58Gs+dty9SCWzNKY43jzoiSAlnquqivHx8TSy1Q+/zrwfe9VKGoU6S8poVOEhHzI1oIjt76bRgKNPOGJrsZcf7jJ3s75NgJI3D533emWWwqiCNLM2fCKXtGX9p1vE8yjVa5iW2/Zsdt01blcy2oSm8zBMAGV5uMBrs148ffa8jz3nK7P22gSx389+Z7ySfKuBl8XfdLXpXrbpm3VyV1gpsMFDwHmPbZfl4UqYh5tzPYptxLWqrA7e5rSA1R6UBcqTIEW+WIbXic+7PGsbe3uJOpAyKmkcdD1wDcoj1ihEFPRAXzHTllwiBEJd0+Dz/VC6r8VaDu4SX5mQdu2UlEUckkoCze/zegk8sjzbaBhIloDT+dX9kpIyKj+jAq3n6UqA90lJwHhdfFwNWwdqo1EEWAZAEe1t7cK9tFbirjoHel3LXmdCPtxtlo2NDEAz4MnqrYAp1pmbc6XQ+mZcfcg/wY33Nzc3Y2JiovF2X12ne0/jxtsra0evy16lDqSMqL26htrrnQyWWF9fj3379tUvN+SzIg3ssbGxGAwGsb6+3ogAzAbGMM0mE2bUKrmORX5K0X3DSGUxQGMUYa102cZbT6PfJVDjf7ZPqf1GJffru0bLcka1HoaVN4zcsioFuVDjdi2Z/I7aX0qbCeqdaNpUqpwHt2K83s5LqY/9mUxJbBPCWSQtLTi/nkUCqgxtI9GBr+JbEb0K+6bLXyeYsx04zgROExMTdTqB24EDB+p1Kb4xQUBMsKKc4TKDfrtVuJepA6mfEIWwBkvmF47YspB0j2Hi/jwHc8QWeO3fvz+dMD75+J+WUpsga9PaWVfmzzw9bLdkhWVCNWvTUQDIBVlGJUsvqxfTuUbplmVEFK2WUtuWLMGS1VqyONiG2TpGVpdRBEuWh+rJfNrAs2S5tKV3wcs8WL82y4XXvD6l9m/rM/3PTnpRmzgoZ9ZUWzuw7/1tCeTfrRyn0nyRwte2Fcb/c2xRmfHy9zpQdSD1ExpmvTBdVVWN9Shd1z4HURZd5JopB6ELt2yRlJOnFMFHaluf8v/Z5G6zeJy3jM9RtOE28CndKwki5ycDJH+eCkUbn1l9sv+uPJT6n2PL71G7Jq/ZugnTe/0z8v4p9X+pjhmYD2sPL7f0XFuaUamkIOgePSSlcZG5B/U725/mddB/ggNDyFWG11/XS8EhjP7js5nieKpAtNdAqwOphDLtw60L12j6/X799syIrcGk87aYd3bulgZxBjK0wpSXLLFsIPugdsoEemYxlLRLF54Z6DKvLG2bRcLr2XNtFgLrlFkCWZm+lcDzI78RzZPTvS5sOxdew0CqrX7koyRIhwmXNnAp9RvvZd+lMgkAowJViTfyOEod2qyUiPwdULzf9mzWPyXrhzJEY0bjrG2Tb0Rzf6Lki5YPeBg1ecqCRHwuD1NY9yKdlSB1upA+0zrUwXqFhnzLg8EgBoNB9Ptb74SRz1kmvV7bLCBaXl6u/7MM+Yt9z5KsMQJhxPZDajVIuTjrk0iC10FSA9n3VnhbuCam8v1/aZBnoMGJSMsxExr+vE+0rGyPmHS+S5PVT5L2Z0p8OI1qBTjg6zuLCHP3nIOUHzhLkG2zWrK2zNope9Z5KtVJ152y9alhSovfy9ov47tkVeij9vP7JdcYldaNjY06ajeiGUglINL1tbW1RgCFniUfBLQTJ07E6upqrK2t1SeiLy4uNtaUNjY26sMB1tbWGm5MvdZ+bW2tEVnsn9K8P10ydhQaVtZZCVILCwunJR9G7Kizl5aW0rTf/va3T0uZHXXUUUcdbdHCwkIcOHCgeL9XnUnIPE20ubkZ99xzT1x++eXxwx/+MObn53ebpTNCx48fjyc+8YnnVJ0junqfS/U+F+sccW7Wu6qqWFhYiEsuuaQ1gOestKT6/X783M/9XEREzM/PnzOdKjoX6xzR1ftconOxzhHnXr3bLCjROX0sUkcdddRRR3ubOpDqqKOOOupoz9JZC1ITExPx7ne/u7Ez+2edzsU6R3T1PpfqfS7WOeLcrfcodFYGTnTUUUcddXRu0FlrSXXUUUcddfSzTx1IddRRRx11tGepA6mOOuqoo472LHUg1VFHHXXU0Z6lsxKkPvCBD8TP//zPx+TkZFx11VXxla98ZbdZOq30nve8Z9uZak9/+tPr+6urq3HkyJG44IILYnZ2Nl760pfGgw8+uIsc75w+//nPx2//9m/HJZdcEr1eL/75n/+5cb+qqnjXu94VF198cUxNTcXVV18d3//+9xtpHnnkkXjlK18Z8/Pzcd5558VrXvOaWFxcPIO12DkNq/erX/3qbX1/3XXXNdKcbfW++eab49nPfnbMzc3FwYMH48UvfnHcc889jTSjjOn7778/rr/++pieno6DBw/G2972tsb7mfYajVLv5z//+dv6+/Wvf30jzdlW79NNZx1I/eM//mO85S1viXe/+93xjW98I575zGfGtddeGw899NBus3Za6Zd+6ZfigQceqD9f+MIX6ntvfvOb41/+5V/iYx/7WNx5553xf//3f/GSl7xkF7ndOS0tLcUzn/nM+MAHPpDef//73x9/8zd/E3/3d38XX/7yl2NmZiauvfbaWF1drdO88pWvjO9+97tx++23x2233Raf//zn43Wve92ZqsIp0bB6R0Rcd911jb7/6Ec/2rh/ttX7zjvvjCNHjsSXvvSluP3222N9fT2uueaaxjmZw8b0xsZGXH/99bG2thZf/OIX4+///u/jlltuiXe96127UaWRaJR6R0S89rWvbfT3+9///vre2Vjv007VWUbPec5zqiNHjtT/NzY2qksuuaS6+eabd5Gr00vvfve7q2c+85npvWPHjlX79++vPvaxj9XX/uM//qOKiOquu+46QxyeXoqI6tZbb63/b25uVocOHar+4i/+or527NixamJiovroRz9aVVVVfe9736siovrqV79ap/nXf/3XqtfrVf/7v/97xnj/acjrXVVVdcMNN1QvetGLis/8LNT7oYceqiKiuvPOO6uqGm1Mf/KTn6z6/X519OjROs0HP/jBan5+vhoMBme2AqdIXu+qqqrf/M3frP7wD/+w+MzPQr1/WjqrLKm1tbX4+te/HldffXV9rd/vx9VXXx133XXXLnJ2+un73/9+XHLJJfHkJz85XvnKV8b9998fERFf//rXY319vdEGT3/60+NJT3rSz0wb/OAHP4ijR4826njgwIG46qqr6jreddddcd5558Wv/dqv1Wmuvvrq6Pf78eUvf/mM83w66Y477oiDBw/G0572tHjDG94QDz/8cH3vZ6Hejz76aEREnH/++REx2pi+66674sorr4yLLrqoTnPttdfG8ePH47vf/e4Z5P7Uyest+vCHPxwXXnhhXHHFFXHTTTfF8vJyfe9nod4/LZ1VB8z+6Ec/io2NjUaHRURcdNFF8Z//+Z+7xNXpp6uuuipuueWWeNrTnhYPPPBAvPe9743f+I3fiO985ztx9OjRGB8fj/POO6/xzEUXXRRHjx7dHYZPM6keWT/r3tGjR+PgwYON+/v27Yvzzz//rG6H6667Ll7ykpfEZZddFvfdd1+84x3viBe+8IVx1113xdjY2Flf783NzXjTm94Uz33uc+OKK66IiBhpTB89ejQdD7q31ymrd0TEK17xirj00kvjkksuibvvvjve/va3xz333BMf//jHI+Lsr/fpoLMKpM4VeuELX1j/fsYznhFXXXVVXHrppfFP//RPMTU1tYucdfRY0+/+7u/Wv6+88sp4xjOeEU95ylPijjvuiBe84AW7yNnpoSNHjsR3vvOdxhrruUClenMt8corr4yLL744XvCCF8R9990XT3nKU840m3uSzip334UXXhhjY2Pbon4efPDBOHTo0C5x9djTeeedF7/4i78Y9957bxw6dCjW1tbi2LFjjTQ/S22gerT186FDh7YFy5w4cSIeeeSRn5l2iIh48pOfHBdeeGHce++9EXF21/vGG2+M2267LT73uc/FE57whPr6KGP60KFD6XjQvb1MpXpndNVVV0VENPr7bK336aKzCqTGx8fjWc96VnzmM5+pr21ubsZnPvOZOHz48C5y9tjS4uJi3HfffXHxxRfHs571rNi/f3+jDe655564//77f2ba4LLLLotDhw416nj8+PH48pe/XNfx8OHDcezYsfj6179ep/nsZz8bm5ub9UT/WaD/+Z//iYcffjguvvjiiDg7611VVdx4441x6623xmc/+9m47LLLGvdHGdOHDx+Ob3/72w2Avv3222N+fj4uv/zyM1ORHdKwemf0rW99KyKi0d9nW71PO+125MZO6R/+4R+qiYmJ6pZbbqm+973vVa973euq8847rxH9crbTW9/61uqOO+6ofvCDH1T//u//Xl199dXVhRdeWD300ENVVVXV61//+upJT3pS9dnPfrb62te+Vh0+fLg6fPjwLnO9M1pYWKi++c1vVt/85jeriKj+8i//svrmN79Z/fd//3dVVVX153/+59V5551XfeITn6juvvvu6kUvelF12WWXVSsrK3Ue1113XfUrv/Ir1Ze//OXqC1/4QvXUpz61evnLX75bVRqJ2uq9sLBQ/dEf/VF11113VT/4wQ+qT3/609Wv/uqvVk996lOr1dXVOo+zrd5veMMbqgMHDlR33HFH9cADD9Sf5eXlOs2wMX3ixInqiiuuqK655prqW9/6VvWpT32qevzjH1/ddNNNu1GlkWhYve+9997qfe97X/W1r32t+sEPflB94hOfqJ785CdXz3ve8+o8zsZ6n24660Cqqqrqb//2b6snPelJ1fj4ePWc5zyn+tKXvrTbLJ1WetnLXlZdfPHF1fj4ePVzP/dz1cte9rLq3nvvre+vrKxUf/AHf1A97nGPq6anp6vf+Z3fqR544IFd5Hjn9LnPfa6KiG2fG264oaqqk2Hof/zHf1xddNFF1cTERPWCF7yguueeexp5PPzww9XLX/7yanZ2tpqfn69+//d/v1pYWNiF2oxObfVeXl6urrnmmurxj398tX///urSSy+tXvva125TwM62emf1jYjqQx/6UJ1mlDH9X//1X9ULX/jCampqqrrwwgurt771rdX6+voZrs3oNKze999/f/W85z2vOv/886uJiYnqF37hF6q3ve1t1aOPPtrI52yr9+mm7lUdHXXUUUcd7Vk6q9akOuqoo446OreoA6mOOuqoo472LHUg1VFHHXXU0Z6lDqQ66qijjjras9SBVEcdddRRR3uWOpDqqKOOOupoz1IHUh111FFHHe1Z6kCqo4466qijPUsdSHXUUUcddbRnqQOpjjrqqKOO9ix1INVRRx111NGepQ6kOuqoo4462rP0/wFlBs+zfJVvnAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "63dcecde-0673-49a0-afaf-9cc44ac4cdf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 298, 298, 64)      1792      \n",
      "                                                                 \n",
      " activation (Activation)     (None, 298, 298, 64)      0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 149, 149, 64)      0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 147, 147, 32)      18464     \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 147, 147, 32)      0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 73, 73, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 71, 71, 32)        9248      \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 71, 71, 32)        0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 35, 35, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 39200)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                1254432   \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 32)                0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 32)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1283969 (4.90 MB)\n",
      "Trainable params: 1283969 (4.90 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "CNN_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a648be8e-1b58-4c55-ab11-d0c12628526a",
   "metadata": {},
   "outputs": [],
   "source": [
    "difference= []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fe6afada-6591-40b1-b1c3-75508d30576c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(Y_test):\n",
    "    difference.append((j-y_preds1[i])**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "414824db-ba83-4724-890c-67c9d76f5fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = np.array(difference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a184e10f-2b10-4890-a0fc-27e65ce1bc15",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = diff.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0a1ed0d1-841c-40ce-8f15-7ed6d37f4322",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "rmse = math.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b07f3eea-8a7c-4089-9d74-7d28ef943ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean square error =  0.15616943805591527\n",
      "mean square error = 0.024388893382700356\n"
     ]
    }
   ],
   "source": [
    "print(\"root mean square error = \",rmse)\n",
    "print(\"mean square error =\",mse)"
   ]
  }
 ],
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