cat-vs-dogv1-ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:05.126789Z",
     "iopub.status.busy": "2021-02-25T12:48:05.125784Z",
     "iopub.status.idle": "2021-02-25T12:48:05.134526Z",
     "shell.execute_reply": "2021-02-25T12:48:05.132921Z"
    },
    "papermill": {
     "duration": 0.052169,
     "end_time": "2021-02-25T12:48:05.134996",
     "exception": false,
     "start_time": "2021-02-25T12:48:05.082827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/dogs-vs-cats/test1.zip\n",
      "/kaggle/input/dogs-vs-cats/train.zip\n",
      "/kaggle/input/dogs-vs-cats/sampleSubmission.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032077,
     "end_time": "2021-02-25T12:48:05.204227",
     "exception": false,
     "start_time": "2021-02-25T12:48:05.172150",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:05.257048Z",
     "iopub.status.busy": "2021-02-25T12:48:05.256228Z",
     "iopub.status.idle": "2021-02-25T12:48:11.472607Z",
     "shell.execute_reply": "2021-02-25T12:48:11.471019Z"
    },
    "papermill": {
     "duration": 6.245539,
     "end_time": "2021-02-25T12:48:11.472784",
     "exception": false,
     "start_time": "2021-02-25T12:48:05.227245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.preprocessing.image import load_img\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.022493,
     "end_time": "2021-02-25T12:48:11.518484",
     "exception": false,
     "start_time": "2021-02-25T12:48:11.495991",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## extracting files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:11.569240Z",
     "iopub.status.busy": "2021-02-25T12:48:11.568532Z",
     "iopub.status.idle": "2021-02-25T12:48:31.032601Z",
     "shell.execute_reply": "2021-02-25T12:48:31.034103Z"
    },
    "papermill": {
     "duration": 19.493136,
     "end_time": "2021-02-25T12:48:31.034356",
     "exception": false,
     "start_time": "2021-02-25T12:48:11.541220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.11 s, sys: 2.56 s, total: 10.7 s\n",
      "Wall time: 19.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import zipfile\n",
    "with zipfile.ZipFile(\"../input/dogs-vs-cats/train.zip\",'r') as z:\n",
    "    z.extractall(\".\")\n",
    "with zipfile.ZipFile(\"../input/dogs-vs-cats/test1.zip\",'r') as z:\n",
    "    z.extractall(\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:31.111143Z",
     "iopub.status.busy": "2021-02-25T12:48:31.110229Z",
     "iopub.status.idle": "2021-02-25T12:48:31.184496Z",
     "shell.execute_reply": "2021-02-25T12:48:31.183593Z"
    },
    "papermill": {
     "duration": 0.115474,
     "end_time": "2021-02-25T12:48:31.184827",
     "exception": false,
     "start_time": "2021-02-25T12:48:31.069353",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "filenames = os.listdir('/kaggle/working/train')\n",
    "categories = []\n",
    "for filename in filenames:\n",
    "    category = filename.split('.')[0]\n",
    "    if category == 'dog':\n",
    "        categories.append(str(1))\n",
    "    else:\n",
    "        categories.append(str(0))\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'filename': filenames,\n",
    "    'category': categories\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:31.265638Z",
     "iopub.status.busy": "2021-02-25T12:48:31.264658Z",
     "iopub.status.idle": "2021-02-25T12:48:31.281903Z",
     "shell.execute_reply": "2021-02-25T12:48:31.282524Z"
    },
    "papermill": {
     "duration": 0.06575,
     "end_time": "2021-02-25T12:48:31.282724",
     "exception": false,
     "start_time": "2021-02-25T12:48:31.216974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(        filename category\n",
       " 0    cat.607.jpg        0\n",
       " 1   dog.6400.jpg        1\n",
       " 2  dog.10509.jpg        1\n",
       " 3   dog.7843.jpg        1\n",
       " 4   cat.9702.jpg        0,\n",
       "             filename category\n",
       " 24995   cat.5212.jpg        0\n",
       " 24996   cat.8014.jpg        0\n",
       " 24997  cat.10660.jpg        0\n",
       " 24998   cat.3661.jpg        0\n",
       " 24999   dog.6895.jpg        1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() , df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:31.377047Z",
     "iopub.status.busy": "2021-02-25T12:48:31.376090Z",
     "iopub.status.idle": "2021-02-25T12:48:31.574454Z",
     "shell.execute_reply": "2021-02-25T12:48:31.575584Z"
    },
    "papermill": {
     "duration": 0.248686,
     "end_time": "2021-02-25T12:48:31.575841",
     "exception": false,
     "start_time": "2021-02-25T12:48:31.327155",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12500\n",
       "1    12500\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['category'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.026628,
     "end_time": "2021-02-25T12:48:31.626134",
     "exception": false,
     "start_time": "2021-02-25T12:48:31.599506",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## visualizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:31.697583Z",
     "iopub.status.busy": "2021-02-25T12:48:31.696699Z",
     "iopub.status.idle": "2021-02-25T12:48:32.050369Z",
     "shell.execute_reply": "2021-02-25T12:48:32.050878Z"
    },
    "papermill": {
     "duration": 0.394104,
     "end_time": "2021-02-25T12:48:32.051064",
     "exception": false,
     "start_time": "2021-02-25T12:48:31.656960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7dcd931750>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAD8CAYAAAD+D4bnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz92bMtW3beh/1ml93qdnv62zdVtxqgAKIRAVKiTFFkiHLogZZpKcKhB0fwye/ms5/0BzgcNh2yww92qDMpNiERBEDCEJsiCgCBqkJ1tz33tPvsfjXZzsYPMzP3OvueUyiCLPoy4s6Idc7aa2XmyrVy5phjfOMb3xAhBL4YX4wvxhfji/Hjh/z/9wl8Mb4YX4wvxr8N4wtj+cX4Ynwxvhg/wfjCWH4xvhhfjC/GTzC+MJZfjC/GF+OL8ROML4zlF+OL8cX4YvwE4wtj+cX4Ynwxvhg/wfipGUshxF8SQvxQCPGBEOKv/7Q+54vxxfhifDH+TQzx0+BZCiEU8CPgLwAPgW8B/1kI4Xv/2j/si/HF+GJ8Mf4NjJ+WZ/lLwAchhI9CCC3wXwP/yU/ps74YX4wvxhfjpz70T+m4d4EHW38/BH75ZRvv7S7CK3duxD9e4OgKIQDR/w/b3vDwfPjfD28FcM7Rtg3GGJIkwYeAFIL+MPGjQiAQGA4phBgf3jmcD3jvcM7jnCP4gDEa11maqiazgQkSLQJeCWrvCJ0jFwolJR6HCP2aJAUMnx1ABACJQNClGpEqpJZIo0ArglQIZNzYe/CO1nY8PTujReARKKmRAaSEIMCkhoP5bv8dAwhou46Hjx7TdhYhFAiFDB4lAkJJnPf99xdXP3+4uhjD7/Sycf39QPyaQgikUjhrx+OKq4t69fwFY7jWCNFfo6vP2D7Wc5GRuDqikuLq72vnRgiEEOL1JB5fbO0rhCD0nykAKSUBCN7H7yQFQsjx8M5ZQojbDftqrQn9cbuuG1+XSsbfRkqUVPHYwQPgnR+P4X187oMn+PD8cyWRQqKUGr9V13VordHa0HUdAMZotNZsyhLnHEopjDY450iShKZp0FrTdl2cq8Nn9t9TKTV+pjEGay0hxPtBaUWapnjnMElC23ZMJ1OSNKEqS6RUOOdomgYhBGmakmUZ6/Uaay1JkiClZL1eI6VEa03dNEgpSdOUoijQSrFer8nzDCkETdPQti3Oe6RSEOL177qOtmvJ85xhVrVti3OOLEsBgXMOIaCuG5IkAQFSSEIItG2L1hohJN47VuvyJIRweH1O/rSM5Yvug+fuNiHEXwP+GsC92zf4zf/m/wwEvA84Z3HO9RdfxwmEjjc68QcK2xO+f26tp+nCODkB3n//fUIIvPfeexhj4jFNvODWWqy14+SQMk5ArTVGJzRNy6osOb+8pK4bLs4vKZdrMmUwQuPLBvXhA956esnbUnHpKj44O6XoDO/uHJJMFGt/QeanCA8SD1IQtKQS0GYZen+P2d17+NfuoA4nCFFhVUuYTZE7N5DpFGE9omthc8nm/JT/y//wt/hHH36Ey+dMzIwpGi0cZiKZHMz4K7/8H9A2JRhP0IGqa/g//V//K374wQOaLsE6TRIsuAqVJMg0JSkKEJLgAR8ILsQFB4f3Hu/9+Nu/7P94I/nRSCRJwmw24/LyEuvccN3Hx2AYBkMyTJzhtcEYDJ8/bOP6cxmMyvZnDq9PJhlaixeeq3Px2l9eLrFdPH6SJONxkiTBOTfOwTRN+5uyBSBNDVmWYRKFEILLy3OcdeRZgZQSYwyTyYSyLLHWUpbleFdM51OklMymU6SQGK2xXcfOYkG53pAkCZvNZjR+SZLQdd04151zTKdTlFLs7e0C0DQNFxcX3L59m/Pzc5qm4caNG+zs7nD37m1+93d/l9VyzWw2Z3d3j/V6zWKxy9HREUU+parXo8FO0xRrLW3bMplMxus6my24vFjRdTVBWCaTlNdff42qqnj11df54Q/f5y/9xf+Ie6/c4bd/+7dZzHeQUvHtb3+H1157jVdffZ2mafjDP/gOf+rnfoZPPvmY+/fvM99Z8Nbbb/Pt734HHwKLnR2klPyFv/AX+N53v8dmdcmf/dV/h8Vswh/+wR/wT//ZN/n+jz7kq1//WXwQVJsVjx8/pixLbty4wWKxoGkaHj16xMXFBXfu3OHWrVtsNhsuLy95+PAh8/mct956C+89ZVly//596rrhZ3/m55FK8V//93/n/ouM2k/LWD4EXtn6+x7weHuDEMLfAP4GwDe++qUgRPS+hPDR85ECY1K0MXF7D+Kad+m9x4eACAEfPEIKtI43mLUWIQSz2YzHjx+z2Wy4ceNGvwpVzxmAwUgOj7jaB5RWaKMJ0W6QpAmtMbRNx2JvQX5wgNyZ4H7/h1w+vcDWDrV2TCYL0AllV9LmGmnBeIkJEtu2+ETTzTPsa7cofuFr2NfvIGQCtsXXHtf1i4UPOKEgTdFZBjKgqhVvvPYKv/Xd72CkQTtBlhQYAqqB9YPH/INnf5PV8hyVCNrQUsynHBaG9uYeZ+cVy1WN0AancrwUWO8xCKSQCNl/WQnBB1xgNGzD77V1Dftr9rxXNgzXG0gpJfSL2vb7gydzfXwmWuiN2HWD+KLHi85r+H/bqEop+89+/jttf49tox6/uyAu6J6u68iLjCQxbDYrnL1aDJRShBBI05S2bRFCYK2Nc4noDGitR4+26z0bpdRoZCeTCev1Os5DpbDWjr9n/D/+Hrdv3+bRo0fs7u7inOPw8JDHjx9HT7DraNs2eoAT6LpufAyOyGazIQRHkhrKsiTP89GBkFJirWW5XDKdzKP3rCTOgxByXHTSNCVN06voTUpu37nD+dk5dV2zv7/Pzs4Ov/Zrv8ZX3vsqN2/d4uTkGIDXXnu9jwBbXn/jDS4uL5lOp0wmU/b29ujaiulkgtqaJ4v5nLLcIHXCyckJN2/epCgKTk9PaZqGo6MjDg8Pefvtt3tDWFNVFev1mtdff52LiwvK3ts+Pz8nSRKscxwdP2Vvb++F8wF+esbyW8A7Qog3gEfA/wb4z1+6dYx1xj8lILRGKIUfb5K4itN7I4SA8ALRTxoRBMHF58aYMRQa3P2zszOyLEMpiQ+WEKI3OUyaYbJeeTsSpCTNUrTRtOs1VVXRtC3NZsMrd+9R7MxZyYb61pyH52d0q4ZqkvM0OBIFidYoLVDBAJ4OQZ1KxO6UyZdfJ/2Vn6O8teB4ljKVCcWmRKwq/OUaWzUkc4F1kk4oXGoQOHSR8dabr7OXpZT1hjyTKO/AWdYna8q6pMGD8GRFAtJTL08odMI7t/epdhyPHh9zVnmqoEFJLAElJUKKaDsEIAJBguwhhB/nUQ5j25gNixkwXq/htcFADvsOv/n22N5/MJTjdLlmHLdfu+7pbm83/D8shj/OWA7bb4fWMZSVSKnoug7vHRAXc+/DuNi2bUtRFDjnMCaGxVLKMezTKs63ru3Isgy8ZzKZcHF2jhCCLMtI0xSIi/4Q0g/fZzDGVVUxm81IkgQhYqgppeTmzZtsNhuKouDGjRt88skneBc4ODjk9PSM27fvcHZ2HsNsFa/3cNzBmLZtS9M04/Var9es12uMEXS2oSiiB1rXNQDz+ZyTkxOeHT8lSRL2dnd5/OgxzjkePHhICPE6vPnmW0jhaZqGW7dusbu7wyf370cvvOuYzWZjuP/KK6/w9Mkj1usNWsbfYrPZcPPWTaROObu4ZD6fc+vWLeq6Zmdnh+PjY/b29rh58yYAN27cwFrL6ekpb7/9NlJK8jyn6zrKssR7z40bNxBCUDU1zncvnQ8/FWMZQrBCiP898GuAAv7vIYQ/etn2AoFUugfy4nOl1BV25F3EjLZwrjFs1jreVCEgpScEN17sYf+iKMYfend3F9NDPQNWMhjJ4RHxKkHwngD44OmsRWlN3dT86P33Kdcl9157FWRHVZ3TyTUbtabE8ejTJ/yieptfefUei7ZCdp4OT5NJxI1DZu+9iXnnNdw0A5UwERkEgVMSA7TLNYoE7QTSCYLShKBQSYEqCu7cusW7d+9wdHyC6xqa9QWh87SbikQITApKg7QtJpEEF3qfyDKfzth57QbHjeLhecW6rhDGYIMnEEDG0DZiigIpnjc+LzKW20bxulc3GKUX7bt93Jfh0dcN5fa13/6cAUp5mYHd3nfbu9x+fZxvW/uO+PWWVxqhGonoccMkSWmbbjxmXdfj/J3P53Rdh7WWznakpBitEULQti1t244YY9M01HVN13WsVqvnFpHBWBsTIYCui/N7s9mMoX5RFDRNA8DOzg6z2ZQkSeKxRMQQB+NqreXg4JBnR8fs7e9ycnLMfD5nuVwipaQoihGSiJ/nmM9nhGCh9SPckCTJCDtcXFzw9Ogxd+7c4eT0FO89s9kMYzRlWY6f623D48ePWSwWo8d8+/Zt2rZlOp9HI+6j4V8sFuRFTp7E+9wYg9GG2nZMJhMOdndQSpFlGScnJ0wmE+bzOXmeU5YlxhiklNy9e3fEJ2ezGZvNhrIsmc/nTKdTmrYhlynO/hs2lv3F/R+B//En2jgi3kQTFYFzKdVzhmsAc308+HOTfrhRhJRoLcYwYzvMHlZqIQTGmNELGG4GrfVw3vFzgkcoRddZWmsRSqKMobUdVVPz5OlTvHXcurHDKpc82zN880cfc7HqKLuao4c/5NbejF9MCgQClyWIOztMf+YteP029WKKNIbcavILQZdJ8ALpQDQdiVS0z56hbk9QJsfWFrSDrCBLC37xa1/nH/za32dTV7iuw7Ue4QPCgZWegCBVhq5qSbMESSBNFdK3GA3JdIdituDo9ITjy0sEks4DfWIqANY6hPysAdy6xn9s+Dt4OwFGDHL7ONtYpxBxgRq2uR72D9vLLahkeG3YdkhksPV54zTbMnrXPdVhnrwImx1eU+rKI5VS0LYNWqseN/e0bfsclj4YgmGumcQ8h5vuLBbcvHmTRw8fsllvxnB7OI/BWEbj4cdziR5gPN+2bUdPc/DyBoPatt34+YIYUjdNi+0hg/V6heqTKENCZPjOgwOhlKKua7K0oOtiwqZuKubzfPyNh/NN0xQpJUmSkBgzzo1bt27RNPFcvHeUZUnXdaRpymQyZTqbUTU1VV0zmc16rNhwsTxHShnxYS2vfhclybShrOLiMp/Padt2PN/htxp+l+HaDvjvarWiKIrekMeEl1GKplphetjvReNzUcETMUGBlAplNEobhFIIqRBKo/TVQygZvR9i9nf7MUys4SINuKXWmvl8TgiB6XRKnuekaXqVPLrmUXjvcS56q5uqjJkpETPFWZZjkoR7r7zCz//sz/JzX/sa7/3Sn+KNP/NLXC4KHsnA6W7Bd7s133z4MRugKQzdTsbsS69h3rhNs1NQGoWTCmEVspbIWqCdwtUtvm3omg3l8pSTBx/TLc9Jg0P6gBUa0Hz17S8xNRrfltRtSW1rWmtp6o5NWbOpGsqqJQSFFAajDAqJdA4VHHmo2dUtX3n9Jl965SYpLYn04DoI0aMWL8ATf9y4HhrHa3uVuHnZPte3/ePnS+hvPP+cVzu8t/369j7bxvxFof/177B9rCuME6QUzxnlto0GaPvGHM5xSDbO53OAmHXtjdwAMTlradpmjICUUkynU6bT6RjOD4mmJEl6jy8d4aOmzyK/9957TCYTiqKgKAqEFGOm1/swhs1SxsxyzHB3zx1XKUWe5+PvY60dz0EpTZZlzKazCB+Mv5EbHZLpdIoxhul0ymq1QgjBJ5/c55NPPuGNN97g0wcP+Ae//uukadonxy64OD/n/v37Iy45eKyXl5eI/r7u+kjRb8FnAFmWjdc2SZLnron3nps3b5JlWX8sMXqrWZZxcXExJoi9s7xx7x77s/lL593nwlgixGgYpTJIbRBSEYQEIfvXFEJJhIwPqSRSqf65unr0q4uUcvRqgBG7zLIMKdWWUXT9its8nxlXirKu2JQbdGLIizyG485y8+ZN3n7nbXZ2dyjriuPLc1brkqksmJBhnaKd5vxgecIjX7M8yEnfuos+3CEYE+k+QiF0SshSfJGgkxQRFN4H0nnBxlf8o2/+I/7hb/8a68tn+HqF9JZWJOQ7+ywmM37+a19Ha4HONGQJKs9QWYFIC7xM2dSOsglUlaNtA21rsc6ipEB2a3Rzjm4uee/1W3zl7ddIZQAf6TTWBYT6yQKP67jg9ZBWiC0az7X9hvHjwubrGKXvs8IDtgU8B6O8zBBeP+aLkksvGoO3IWVkSnSdfW7uAD2WeUXnGbDz4cZP0xQp+vPrcUnbddy/f5+2bcnSFO898/mcuq5ZLpdcXl6OmfDtKEkIQZ7nI51noOLM53OKomC1WrFerxEIqqoaQ05rLbu7O1xeLkmSpE9y+DEBEkIY74X1es1qtaJpGrqu66EFid/ybgdnIwS2vO9IKVqvN6MHt16v+KVf+iV+4Rd+gYcPHnDz5k3+/J//89y4cYNHjx7x0ccf8yu/8ivcvXePJ0+eMJvNgMBqtcJoExONLuKce7u7TCYT7n9ynyePn4we9QBhfOc738E5R57nOOf48MMP+eijj3jrrbdwzlEUxZjsOTg4wDkXaU1JwvnREfSMhxeNn1oY/i8zBDznEcJAjQtbN15E3V6WPRVCRD5jn1UfwOqBBmKtxfShQTxGeM6YbuNYcaVxnJ6e4rynmBR0ncUkSZw0WrMuN3z/+ITl+oRnomS9Kjl0KeeXDhYFPvUcLS85Upb9e7vkb98jZClSG1KvQGlEmtBOcjppSBqHWUmE0ZSu5df+8T/iX/zgQ3buvMndD7/PzxQJ02QPKzWgMDrlvS9/mX/23d/l6fERrVPoZEIqU4TuSBJN2zZ457hYVRStIk8VWaYo8gyNRfmOqrpkc6F485U7dEHyvQ8f0XiJUALnfvLqrm0D+ULs7yX7/MsOIQTuBVSmbU/yRR7uy873+niRBzoY5RD8OC8jTpZjjO7hBh8pZ8ZQVdVncM6yLMcQ0fnoKSoTF3SSBNPP08lkwtnZ2ejpDdhg9F7tCG0M5zDc7EIIPvjgg9EoxgSUZ71eA1DXFWl6i9lsTllW3Lp1h4cPH/LKK/d4+vQxk8lk/M7e+9HDDSGMIXPbtjhr8cGPC0fEbOOCMHh7QgjOL84xxvDo0WMODg5HSlLTtrz9xmt479nZ2eF3f//32d/bY3d3l7Pzc7TWFEXByckpJycnvPXGayitcPYq6vvB939AkhVMpjGplOc5q9WK5XLJ7u4u5+fnI6QxJHdOT0/Z29vDWsvx8TGPHj3ia1/72pj9P336FLe6YH9//6Vz5nNhLHmB93A9HIok4IEIPLweJ6+UGghIDR6P0KC1QdYKGkkQgrrr2Fns0DlPkRick3ip8D39s3MOTyRpd9ayrhraxjI1GdoHjAikRYIqDE+fPeOtoEiTAiES9o0izQVnhwuq+w/ZPTiAAOXa8UOV8PbhAeV8gskNSIvxgsRrhO1J0VkCqkJ0nqZs+IPf+T6/8Rt/QHr3NuuLkt//g3/BYar50pfeI58dYJs1Ihcc3NzhT731BpcPHiDSAqc75CQlEYbORhJuUIo0mRJwBKMgUYhEY7QmtJK0E9h1jc6W/JmvvgnNmu999JTgC5AZbbDj9XhZgmf4/7rhGm7sMVnHldHcJpRvE9W3zbPoXxvnwPh3ZCvE/Ye9A5FOI6Kn40AEFalQA781BLyL+yip0DJByvozSaEhzBte71zbFxB4ggejE5RUtGVHh4VOYWQy4uGDJzokHjabDYvFIlKBrEcpia07vOwLJEIgTRKEDLRdTZqmY2JCCNGHzvK58D5+1440S3o+pKCqGpbLFda23Li5jxAOFQRGKFKT0DUNTVXHsLY3pueXlwglKbJ8TIIO5z6fz3tidwZCsl5vKCY5XRPQQlAkKa1OSE1GkuQIqcdzvLi4IMsynHN8/etfH42UQECQGGX49MkDRICvf+1r5GnGo08/ZbNasl5d8uypQOAIeKwPrMqGs8sNy01DXsx4+513kVJz//6HtG3L2dkZd+7cYXd3l6dPn+K95+joiLZt+cq7X2F1viHLZpwvL1lVLcV0xvnJMe+8co/jx09Qm3NSDbKrXmqmPh/Gki3Gep8Rf85Y8vyNM27X53iFlDExIAAJQgls52LIQMCkCZ211G3Lpqyg8zGJpBRCK4IArQwueKxzWO+4WC5J04xJkmC7mtRoahxJkVHWNVmW48oWrXJSZRG5Y3I4x00k+XzCvb1bhPkuR23HZVNzqAXpYkK5WUVIQID0AVxABoVNFC7XJJOCNClI04RsOqfNUu6+/gof/PB7vHHjEKUzlA50GpLJlK+/93V+9AffY9W0XNqGlWvwDtrW4lz8QSaTgukkx2QSiKu7UAlC59ABzlMvN3TzJT/73ttcrBs+eVJFPt21BM91z+t6+Hw9lH4u6XPdOA7bvszD29onJvqGfbe33/7sq/+9D4P97D8ieoYQIp9UqM/wdq9/j1j1ITBGE7zD263zDx5jUrq2QwlJ1V5xGOGKSD+E0qPX5QVKKJSQNHWN1qrnBoe4uAXParXEOT96jMN5bP+W3jus8+zuLLg4X7JY7NB1MUN8fnFK01QoBYeLfaaTCd4FnHWI/tzSNMU6R7Neo2SgKHY5Ozsbvd8B4x+SRlJrEFA3FalW5EmC7BedeFqC0J+j9548z3n65GmPbQaWyyXee9555x1W5xeslhs+/OBDJBKjNJfn5zx58oS9/V3qquTuV95js172EaJjta7YVA1N68jygul0hu0s8/mCo6PIj9zf3x/PfYAV3nrrLXTQpEmOtY7zyyWL3T0Od3c4fvAJrqqQdcNrB4cslxfoFyQVh/G5wCxj1YakJ3zBMIn7R3wuhy377a7wS9E/h6sqnKG8KU1TdnZ2CCFweXnJ2dkZddvSOUfnXTSO9oo8raSMfMqmIc9zlNIkJmb3hlDitKdF1HVNWdZMihmL6QLfWVJjwFuyVHHv3k2KieHZ0ROyNEEEj5KCztno6SAJCLxUWJVTiYSQpdx87SZ3X9lnMZPcvLXgO+9/j67I+NHjh7hqiQieoBJkvsvizltku3dZ1oGmi+fkBEijMUajlADvsHVJaDuMiJ5G1wTaTuNDiguG9ablxs27WOe4e+8eWZYi1WdD5Zd5/5+5pluv/yRh8TC2w/fthNuLXr/u1T5n8HrjOljLMePOVRLw+v7P4aLPVQUJpIzJlED0/tbrS0Jo2ZQXtN2KvEjGYw1e4MCP1D1VaGBkDAmRIZPddR3L5bLHQ7sRExySWENovE3yH5I+y9WKECIBfsgy7+wsaJqWLM24ceMGvi/7y/Ocs7Mz8jzn6Oho9JybtmW1WrHZbMbPWa/XNE3DZDKJ3mUaEzqTyaS/D9x4rl0XM92y//2apiHtq7estXz7299GCDFyPo+Pj9Fas1gsUEqx2WwQQnDv3j2yPGM2m3Gwvx9/r/4a1XU9JqiKokAI+s9u2dnZ4datW4QQyLKM1WoFwL179/oJ6FA6IKTnYH+Xm4eHJMaws9gjBMnN2/fYXewzlQZjXw49fS6MJUKgtUEpjZAaIVU0jtsPhnrc7YeKDxn/fw7kVxKtI5ayWCxiDWzbcn5+Tm0tne+5lP2K6HykLYUgKDfVOCkiJhMxJaUUZ2dnHB8f07Yddd0yKWbMpnNmxZS9+QINBNugpcf7iqZZcnT0BGTkLE7nMyB6PsF7fIj80SBzSCZgDIu9CV9+5y47Obx6a5+8SMkOD/nBowd05QqpFGmxgyh2Cfk+u3ffppMZrYs30qau6LwjLTIOD3a5d/smd2/dZGc6wXcdtm1xHbhOEKwgUSmJ0nzwg+9TLs+5eTDnrddvI0P3QqN33fi9yBBuY8s/aSJl24sc/t82mNuljS8K/weMcXyIYc5cLaJDaDv8/SLv+LMJpTg3lNIIAc61JJlEKseXvvw6X/v6uzRNPSY3huTH4BEOhlMgSNNk/E26rhsLKJyN2OM2l3LIak+n0/E3HL6DNpo8zymKHCFigqksSzabDRcXl+zu7pKkMYnTWYtzMfw/ODhgMplQ1/V4X+zv71NV1Uij6bpupBMNmODTo6ejt13XNUKKsYwz8qQlJjGjE7FYxLLFg4MDfvZnf5Ysy/j+93+A84693T2ePn3K/fv30X2N+enpKUdHRzRNy3Q6HROy2sQM/HQ6YW9vj729vUgi7+1BdGjUmP2uqmosRqmqKs4DHSjrJcF3zGcTvHPgAlmScXmxxuiCquoQHmb55KXT83NhLGP2WSO17jPaihhPbz2k/KwBlRKkYqi2EfL5TOQwMbcnf1035JMJ+WxKUhSoJIn4plQopSk3Jc465rM5SiqKIkfpaDAhEofPzy948ODTnuS+R5akzIoJN3Z3KYyi2axwtmK9OuP07CmPnzzm5OQEYUxcGNIEazuss7jgQMRKmVTlGG1IteTuwS5p23BrNuOXf/FP8/Gnjzk6X3H05AkEQVAJwUzoVM7h3dcx2YzOgdYJk8UOk/kCnRg2mzXnZycsZlN+5utfY3//gM2mptmU2LLElmsSb5kaQbs8JaPlzm7OwUSQiealHuKLjOb2dn/cay/zNF+GhX7Gc9zaBp7Pho9G8VqGXGuNHLiS4op+cv3zBw8OrspmfZ/oAE+SGrIsoW42LBaTKN4iwohZDiNJIp4opewX34Sus9jeMA6E6EgLisIWQ1XJIBpR1/VIcn+ueKJ3GKqqpm07Pvro4zGLPJ1MEKKvrReMHu75+Tmr1Yq2aa9+D3lVXSWEGM95Z2dnJHhvQxJ1VdM2zbiwDdVGiUmIQhSxIiYS2KOX/frrr1MUBY8ePeTP/Oqf4fadO3z3u9/h3Xff5c033+To6Ij3P/iA2WzGpCjQWjOdTsfr8fDhQ548icb61q3b5Hk+Oj9aay4uLsbvA7GayBgTjboQeOG5cfsGxSTn9OQZSkT5mizNuXvvNVabkvsPH3O22RCSfwt4lh76cHrLqyROCNm/NnqCwyMMoXrcdjtkk+KqwsC5WDBf1zXFpGA2X5DnE0ySkaQZSZZTFBNA0rYWKTVpmpGkcQJE7CtOjul0iveei4sLZrMpIVjaukLiUXikc+RGc3l2ynJ5jnMdJ5dr/unvfIvaehpro5qQgOAtMjhksGjnkK3DVjXBOnznWCQTlg9OSBvFpx8/oe0kH95/zKbq8F5ikpTFfMEbr7/Bu+++S15MQGg6J7lclTx48Jj7nzwiS9NIQRGSu6+/hdMJnoBvNiTSo+kQtiK0K1x1gbIlvrpE2nJkDFyn42z/1tue2nZt/UCrgc+G4sM+LzOsL9p22H67PHU7bB6GcxFeGcjiIfgxpB3PPUTlmu3jXa+Y2Sa6i63zcM5RbjYYY/jmN3+H7373D5EyjJnjoYoGoCxLQgh9pU08hyHxAbBarXqmRvIcJWcI2/M8H2ltw3eGWKMtBKMntV6vo7GZTFitV6PhztKsZ4N0oyH3wY9Vbt6HURlo8CoHI7f9mbdu3hojtrwoRkM14KomMaz66p9hgRjKJJ1zPHr0iLt377Kzs4OUUbPhjTfe4PDwBpvNhsQYvvSlL43HHKCBp0+eYozh4OAApRR/9Eff5eLignVfuVTXNY8ePeLDD2OiZ71e89FHH/FHf/RH3L59eyyIODk7xQePkgqFQAvJYj5HaQVS4qSAImUdrubs9fG5MJYQpdVcoA9L+9BUCIKQI98yZkW2H9seZjSaV4IYV1JXy+Ulq9UKrRSvvfoaQcZ6aC/oa82jQR4mzt7uHlrpvtKnD9dkNJpFv/JdXFzQdS3O1dT1ks3qnExJXr19i1mes1kuaZsWISWXteXv/P3f5L/7m3+b82WcPEIrmnKJbzeErkR1JWKz5OLijLUPyGyHf/fP/cf8/Nd/hfOjNbYV/OZv/VN+45/8C771B9+jrhtCUzJLJW+/eY8vv/k6WZpRNZZnx5ecniwpq4ad3R3u3HsVlaR878OPEcWUL//8LzLd30fnCU6BkwGZaFRiCELw5OlTnh4fY7cMDFx5g/+yGOT1fbfHy1gQ29Ur2+EzMKrvvEgYY/t4SsoxYhi8q4GKo6TCbylWXccth0c0FldZ6BAifimlQQpD8CI+ghjVegbDMwhEDN6htRap5MjnHYzTUF0CjKTwxWIx/kZDxdmwAIUQ68tln9gsyw1ZlvHs2TO0NtieB2qt5fzivPeIBWUZM71np2cIIcbQNc9zqqrqK2omI9ezKAogetfrzTrWubcd9Nhg/E0sfgjPe06mELGabsjix0jswfidHj9+MnKdy7JktVrx5S9/OVbU9HDXgwcPODk54e69uxwcHJAkZkyUXV7G+/n4+BnPnj3jq1/9KoeHhxwdHY35hqqqOD8/x3vPpmx48OgJP/zh+9y5fZciy5kUBWfHx/z6b/wmUgvQgk4K0tnspXP5c5MNH0KmISSAcJUlHW6m8HLwNYbiAdnbf7eFdT19eoQxhsO+YL52DikFSkRcS4aYhayqGoJgPltQ9zWi2wmDyC+LE/Ts7BzvLVJZ8kzRVBvqzQotgM4ROo/JEpyrOVnWNKHlb/9Pv86njx7wX/zVv8q9V15D+cDq/BmzIsHZJbZe8enxI3AN1XSXHzy+RGrBppWgM7LFAWs553e/+z537t7mzRs70NQkdc29G7vMJgX3z05wTpMmKcVkQZEbnh6dcO+1uxyfnvK93/iH/OKf+RUO33kXt66wtaOrKnQqqOuEjW95+vSST45LVp3CCTcamcGAXCU+eP432hrDPttG6I+fBOI5iTbghZ8z/H+9tvs5TzUEnPcIL4ibhfHa+XBV2riNhw7H3ubfxoPH+RETM5H64p3AO4WSGVJ4grj67MErG0JD7/2IowkHUjFik7PZjLqusNZSVdGYeRd1F7uuG2u0r3vuUsmx1rsoCozJKMuS9Xo9ereiLxOOHjhMphO89+zu7iKV5uz8vK9dr0eMbz6fs16vx/B7+A6JD+RZRtdU5EWxVfGj8c7hnWexu0e5jiWKSY+77u/vo5Tk8PCQk5OTPhlkx4z10dFTqqqi6zmRRkcsdj6fc/v2babT6ZhIEkKws7NDmqZcnJ9TlhXvvPMOeZ5zfHzMkydPRiz2tddeG8n2y/WGEDTOWnACowx1WVLVG7yv+d4Pv80rd+9w8uQJH7//0Uun5+fDWI5AfKwPv8b5uLoBXna/hdCLfkb1obh9GMNl52x046VkNpvhpAQBXoAg1pu3TUtZluwsdoC+pM1ahIsXCXFVPaG0pipLAo7Oljx8dMLDTz7g6OSC1XlNbRUSRQiCura0wtAGz7Jq+Rff/i7aWv7zv/KfcnjrNkdPn/L4/BmL2ZxNeckHzx6wc3DIza/8DMcPSj56/ymVc9x87Q38ZIp2M7xW/NF3v8P0rVvcLAzd+RLRrJEQqRXJDnmSYEJLkqRUTcejo2PynT1Onx3zf/t//Xfs7ezwyq1bfPXdrzFb3EKIlpMHG+4fnfHJkwtOak0gQ3Xdc6FhvCTPG8wfRxe6jjP+2GlApHMN+w77b2tZDu9dH88TtgGtcNaMFSZSRewu8iyj8O4QeVxPGF3Xt/TeRSUsISAIvAfvJdYKvIsSfi50KOPHhOAQPg/HHs4vMWbEN4W48karqhoTRLbzdF01ktG3w2PoYYbOUuN4880brFc1m01JmqbUdTkK8w5eqtaa2WxO21lOTk5Z7O1iTJRV67oO32tkDhjf4MleXFywWCx4+vQp+4eHPDs+Jk1ixl4pNXJBB7rUgF/mec56vebi4gKlNE+fPuXk5CTqSpYb1us1lxfnvPb6q0ileP3111ktl6RZOhpF+rzDfDbv1YROaNtYXVPX0bjv7x+MIsbD+WxjxI8fR1XIs/Mlt269Qm5SnAvUTcXq4oKnjx+zuzvD+Qbb1uRCjwvWi8bnw1j2tjGMEV8fBtFP0B/jlQQfejJRDIUCMuJxRE8wyxK0Vj12FaI8k3a9aEf8XAF0bYNzlsX+Pm3bYaTAeocfEjBCkWpDpjWzIkUpz5Mn9zl6uiRTgs3ykmq9IhEa7S3Weapa0HUemRYkQSNELMn69re/R3n+X/Fz3/g5TlYrzusKk2eoLOH1997logocXzzh61/+ZWyyx4PHD6nLM/Zv30L4Cft54MY8gbLi7Pgpq+Ul333/Y85Xl0gTFdZVYmirlvPVCus0taspgmexOOCjoyX3jy/4+NEzfve7HzIrpiSJpKzXbNqW1ms6Cpzz5PJKp/GPKyN84fW55lkOz19mQG13RYLv17/PqKwHrvYdQ+hxcR328RA8IsRwnBBwnQUhMNrgvcPZ7rnz2j7X50oXffQc5SA+7SHi6Yq8mBJCoGkrtFIx8QKIAIkxaKXw/VzGB8pmg9KCvb0FAYm1LZ1tEQLSNMOYpNe3jHS19WZDluY0bU1dN2gdo5skTZAyhvKbcoPWCTs7O3g/Q5/FKG1TlhzuKtIsZ7lasyk3TKYTCLCzs+Dxk6dUZUUxycbSxEFceLVasVgsYkR2eIjWBkIgMVH3UmrFdD6Li0MatRyUiOrsEHj85DHeO+7evcf9+5/yZ//Mn2W5WvHNf/bPOT+/4Gtf+yqnZ2fUdc3H9z/izbfeGmGKwQjneRard1wsPnE2erIgesPqwLak2nBW15Sbkt3d/YgrWxeTOEZz69ZtFjt7tJsSb1tWZ+ecPXuC9A2pCuSTguryHNE6ihck/Ybx+TCW0Gf3YvXFdij2Mu9lGK43goOx9GgcAaEEKpEkuSLLDV3XIAFvO7S4IlsPq/5mvSLPM4QSeByZD0jf0QRH19+MhdZMlWCvUOwvDJdnn7KXQTKdMc9yVixxriYP0JmcTklWIkE0DuFjGKeNgSTl02fnnPz2P+0z9wl6vs/Xf/FPc2v/K/z6b/3PbFrL0WnLu+++y+7dXV6fv8of/fD7bJpLbu3e4Gu37zFv1nRmwg8+fcS3P/2Yja+YzwznqzWrqiZJUuqyJXOePIDfbFgUB2TpPq1fQSKpvKVaX8YkWhCEkBCCQNP0k0ON+Nr1a/HHeY3b/ECpFGx5pP4alhkPCHHZE1cL6Hb2m2goY1XrAM3QL3jxOIOB0zKQaEjNgFfGNVfKWJ5X1zWJuTLaQ+JmMOTbWo5GGoIbSh376jApUUZjenqOkKAQ+K5DBnA9dmekonMeGcAoRUeD9wGlQYcoObYpLZNpTlU1CAyEmFBqGg9BIaVBa49SdjQkwIgp6p46E2vAN5jE0LYNt2/fYra7R9V2VE3DwY2bHB0dcevWjNVqSVOXGKMIvZ7mIOS7XC5HUY+2bWNCynoOdvZAOERIMWmCCw4lFc41MaLzLSE42rbm4uKUvMj54IP3uXHjNjs7ewhhaJqWX/6Vf4ednQU//NEP+PD+J7zzzjsYY7i8vCTLEtIkoesaYKsLQhcIQUHQ7Ozs0jQlUgZyEbC2ZV5MyN56l2cnZ0wnExIZolxhcGRG4FyFpKErN2zOHlKEFm08OI8vNzjvcVLgf4wz8LlI8ETn8bOtBq7ef7kqNlyFx1JKGMF+hVZX6tMCEeX49VUp2nAhBiC4KIoxI+mFAJUgtIl0JgGubUiV4JXbN9mZ5tH7bB11WVPkRcyuSRW76nhPsJbQWYJtEMGiCGTG9KFRxrJssDLBypRX3nyXP/sf/CW++4MPkEmOTgtskFyuS6QyTGZzvvbeV7iVz/jSq68zm0zAJHQIjo+OuXdwi/duv8pNmTMXkBHw1lG3jnXjEDphb38HLVqmqsGIa7jcC0YIV8mU4fGThtQvPNi/hhE1Nnu8GYHqn19/SKGQUqOUQUqNSVKEUBgTF4Oui8UI2/Ng/IwtuCHKm11hnAOeOYTGQ6i9zafcTkBtq+IM1TBJEjPIq9W6J6MPohRiTBAN4W3XtTRNPWKgEbMU2K4bRTCaph3PS2vVJ22KsU49Yovx9YODAwBOT09Hrc2BrjQkfAY9hW3MdeifE88hYpLAVgHIsBDSczS7CBXUNV/60pdYrVZ89NFHfZifIAScnJzwyiuv8Oabb3J4eBALKnroo6kb2qZBhBCjAtshQuBgf59HDx7wox/+kCxNabzAC4MTkqbpSI1Bi0CqJW+/+SpGBDbnz1g9e8zZ4wc8/fQjQltjIhI3ZsN9CGOZ6cvG58azvA7qX6+oeFHotm04r278gHVXytaEiKV0zQalNdJoCH40ioMC9N7e3sjdklLihML3hxTBI/H4rqJdL8lVoF4vaVYruhDoipy9vT0Sk6ClwkiBC32jKmvxbY0QMJ8V5GlKU7egNJaEZQtf/fJ7/C//6v+Wjx48RhULMqtQPoYHUqecnF9wePMGb77+BoumxW4qNs5SnZ3wO9/6J8z3Dvilr34Vhebo4RF/95u/xfc+ecTRpqZuPaeuYmcx4Uv7O8xD4Pz8iPP1GVYk/U006Ek+r/8IV43cBizvJwnFt98f8cZ/qfA9vOR5/5cPYx+e7c95bjsRaFodSyR7HM77Xsy5T+h0feXWdnXMkEDZVtu37RVuO2Syh7k49glyz3M6h2NsNpvnVHCiQIXsxTYiJzBSg1yPO+Z4J3oPWEfPD4fva8qH+d60LVkXQ/boCbe9F5hT15vRiA2UqBELBJbL5UjFGb73QBOyNiZfzs/PWSwWnJ+fx9pzX5NlWW88o8LXQJ4ffgffR3lpmrK7u8vp6TmzWUwYFfmUp0+f8vrrr7Ner9Fa8uzZM957772YbV9vuLi44OBwn6ZpODs9pq4qnLW0TUNTVzjX8fDhQ7y3HO7vc3Z2xv7+TS5XK9abku9+9zu8du8uB/fuELqKD3/wPm25gqZmU5UYBHMT27s0VR2r6bwHISOc4p/n2F4fnxNj+bzBe5FRfNHYXtGvkgGx+14IUbpNG93TMjZs1mt81xFUwNoImA8hR55fiZmGEOiCwAvZ3+RR57G6POfppx9zfvQEW1WE1iFNRjCealPHKgkpMVrgLHTOo70D5cmShGmWYLSisYA0ZJM5B/de4y/9lf+MMhienK3Rkx2muhjbZ1St5fxyyd/+O3+X/9V//B/x7mv3+J1/9A+5X625PHnCH/zgj3jn3bd5pW25UUx46+6r/Ny7b/PhJw9oygqpUzbNmtOLJWW54tVXbvG1t2/xwZMjrNe9OoujbV+uEP0nuqJbi1sIsU/SH78TXDeOn/2byEzoDZ8gMik+k2Qi0tCqOuLEqi9YGBJ1WZpR183YmEuIKFhx3QjCVYg+PN/eZuiZ01RXor+DYd0uXyzLcuQsdl2f7EnSvkFZOSqm7+zs0rVX8m8xc263Sh9jw7Q0i/O6rKpRwSdKtUUDPZRfNk0zllJGGt2SLMtommb8f+BqDkm8QaV9+M7ee1bLC/Z39zg7PWNnd0ZT1+O1cc6hcjXSmcqy7DUyUxrfxOTOzTssFgtOT8/Y29vhwYMH3L17l7aN1+f8/LxXTOo4Pz9nU5Yxyeo6NusVlxcXnBwfY7uW977yZZQWnJ6eUVmPDbFtzKt376B8R7M6Q9ga4xrKcol2DQepIlhPkSQ0VYNOFB6BDRLrQ6TJ2SvJvReNz0UYDp+tJX4REfpFIfjw/IpzN4TkAqWifuVQOtW2LaFvbTtM4LIsmU6n43FGqbi+IkgpgRQe12x49MkHrM6eIV1H6DqE9yipQShaG8M6nSRMphMEcSUy3qMkZFmCAGzbARKkwRQz/txf/MvcefNLfP/DB8h8QeMVMp0y3z1k9+AQj2DVK1r/7u/+Difnz/i97/4Lfuv3vsk3f/Q9LnXg+0eP+a3f+xa/90ff5smzp+wtphzuLtAyeijaJD2rACZ5wnvvvsnNGwcjAbgoiuhRjb/rgCdeVcQMBuA65/H6NbueyBmM5jaNaNszey78ZUjiDAmhMF7Hbe7kwH/UAyk9xB5CskcuhzNre/GUdVmy2mxYlRvWZcmmqqi7lqaLPXAGebKB8gM8p5w/hGbbBnMbwkjTFIQYM9fDMQbPdPBSB22BYZ9BtXvQjBy8QKki3SkQlcUHYFZKgVRgXTf+HrFNhBm7Og6dNIfQeL1ej9ds6ENV1/WY8d5sNmPN+gAZDGpJUkbKj3OOvd3dXrA44qyuD9tHPqrSKK1YrzdMJhPu3LmNkpKdnR3eeecdyrKi6zoePXoYsWMVQ/xBIHgyKbhz5w4hxN9mUhSkxuBsR7nZsFpekiWGNElItEZLRZ4m1HXD5fkZGs+b925y52BOffkMmiWFchzMcvZyQxos2tZo3zFJFLnRGCXRKqreX/X6enkE9DnxLK96qLzIIMLLEzzXkw7x72HfeJw0S8fJIeQVWX0g4g46l9vHFIDWAls3JDLw8aNPOXnyEOEsk6JAOmJddZKjkwyhJUmWkiwM55drvO/QUpMlhk4piumUIknxNuCExkx3eOfrP88v/+qf5fd/8D5WJVjvaYPHKINQGmtbbNeSGo1yikmR8r0Pv0exO0EZj1uCDymXTcUf3f+Ix58+Yi4MXtRcbtZICUYGsjzBCMfx06cUqWK2s0M+maEvbK9DaBBCUtcttsfyvH++3nq71G6bcrP9m72ITjRWwojnNS5fiJcKgRTxukUPX24Z3S3FHedi869+ARhfF4O3GV/3XuD9kPgTo8pQpOZ4tDZ0fffCoQtilmUxzO2rWgY60vB8IHsLEWGWARN01pJkZsT7hsThsO025hkzy4wk8AHLTNNBezVGPUkSb89Ibm8QMhBCxCUHD+xLX/oKT588Y72uRoWfrquxNnq0JlEURTFeN9MLwiilxnB8UBHfhgw2m2j0lssleZ5T1w3z6Yw01SPGCaqvZ/djFdF0OmF/f5+yrCnLksVihzt37vA7//x3ubi44C//5b/M++//iI8+ep/1Zs0rr8QmsF1nOTk54fadWxwcHPD44QYlBd468jTFOxtVmpTEaIUQgSJPOX96TrM65/bBDrnosNSgHAZPbWsMluA9XduiAoSuw7lIsQtS4gKxdt6HiHer61DU1ficGMsXN6X6cWP75nz+Jo2hCr13GcOdvvud96OxHOS0BuWT7WNGcrwn+A4tPOXlGUeP7uO7mjxNcFlOUzmCFAit8b1BVnmKDVA9q/EiEIKjKHL0zozUJPjOgfAkJiMrpvyF//AvcnRyxsMnTyjmhygBqTbIfuJ2zRrbNtzc32OaHXJjf8r//Gt/k5lOSZXhcLFP6WK2MnQWbOB8WbJcX9AE2NnbQSQJwtUssoC3nmena775nQ95uOyw1o+LVJZlaGWw1tN1dlQDH7zL0evZMnLXPcXrNKHBsAzPr4/tChzRG76IfAy8W4i7BbZtq77GkRRCjomf0BvFICJ1zHkXRVL6hFbw/X4E5osFq1Xs2pll2VhhMgjojsnGntg9KARtE9iH6pvtsBWigRsadA0e5uCJDec9VPhIGcP5qmx7eltGWW7GsNt5i3UtMgiUSplMisjcELFc8vJyySuvvDIaWh8s0+mUgSM6VOgMHuXe3t4IFwzq4UNr2NlshtaayWQytp5IkgTX999Zry/Z21/0RtL2x/BIKTg5OQEZ58NmE1WLtNYslysuLy/5+Z//Ofb3D/jwww+o64Zf/dVf5fj4GUdHR3zyySfs7+8xKYrRsZnPphRFRrlpmc9nHB5GYd7EGNquJnjPXqFJdnJuzAx2c0ZiK7RxMbmqPEEKqlU07PGaDZTEeJ2UFBgjkS4Q3I+3Of9KxlII8QmwAhxgQwi/IITYA/4b4HXgE+B/HUI4/wmO9S/9/nUP9LqnM9CRvIv1wZv1GriapMaYkX6xnUjy3qMIWNvgmpL7H/2I5fkxvq/qsS4QKXsx/BYCgtY4KblcLalci+zxn7zIWLuAkAEdYvWHloqf+8bP8fobb3DaWfb291mWHUrnZHlOCI6msgilSIuMncmMb3zlHf7+3/ubnD55TCMS9pIJhMDB4R4bndDoGtoWn1vwM7yv0DKQJRJaxzQxGKXxMue80qzqButtTzDWMaGQphgDxrjnjOX277ydOb6+YD3v3T9vLMMLjOX1CpyYfb7+nn/uGnvvMVpDiJhwcL4XpRJ9cQK9kY11+NtlkXGaMuK0SZKOGLVzjtVqRVlWhOD7hl9xjiTaPJcMGZNFIYyeZTRMVwvH9nGFEGNv7ShHdhWGDhnkpFfh9z5ygQfPFALOx6RQnkeD7lyHUkXESzeb0VgPnuvgHHRty3SyO7aKmEwmY9JJKfVcDfsAxwz11kNJ7+HhIWdnZxT5ZEz+ZFkWr0MvdBxC6JWBpgThx3YXEbbRPPj007EPkHOOp0+f8vY7b49G+eHDB9y5c4csixqbu7u7PPr0Y6qy5OzkFIIE75j12HJTlSACkyxF1UtUCqmvSGSHUo4mtHSuI5GCdVnRdrFlsZACGyVNIYg4/0M0qEoqpEo+a3i2xr8Oz/LfDyGcbP3914HfDCH8l0KIv97//X/48Yf4bPh9PegWW/+Or20ZuG2PJobRz4dNWZZxcb7E2diHJgqlFn13vqFKp7/hhCB4h8Tx+OF97n/8AafHz6hWG2wHZVlxsVwDit3FjEVRYIPHx5Z7KKNwnWA2nWJUxsp5NnWD7ByLYsruzh7TIk7cG7t7/IxW/N53PiD4WIfsXaDIc5rQMp/u8KU37vDhBz/in/3j32Zim1iTngoWkyksK9JUAZrKVdF7zgpSoakvLmmbDYlr2Cw3OGkwYYou9vDrBmfrvipC9s2eBIJeTqxXkBmElZ3z+OB77uNVmeBzPMhri9V1WbXtsb3/eO0B1zf+Gqgu21ncAQ/UIU7bzsYQazvpMuDNoa/Q8t6PmfPBmBhjKPse8H4rIROpM25MdAwKPMH5kXM4MAKGcLWu62hEhSBsCVRsY5ADPj4sHFJKqrIiMCRm3FgGKEQ+UoWUihVtXefGzzs8PODs7HQ0xE3bkCSRKD5glMZIlr7l8PCQnZ1dxj4zWcZ5X+I4tKmYTKaU5QYEY8XO0CJ2d3d3lH1bXq4QAbLcIOQu95JbEevzsdG8MQk3b97i+PSox2kT8iJKyNV1ze3bt7k4P4+dGo1hPpuNEd5yueTVV1/l+OSYyWTCer3hRz/6Ea/eucHu7g7eCbIs5+OP/5C93V1293do6xJnW6Z07OzNWJ08hW6DwrFZXhACWB8IPjCZzvBe463FdR3eOqSSJEIhRaB1MQyP9N6XJyJ/GmH4fwL8uf75/xP4Lf4YYynEVaVOGNWF+jd6QWDpx1e3RlQUifwWCcKDdOAlPmhccDivUSZBpwYvHUFZQq3w1pIW0IoVIstwnUYLhbcNSnY09Tlnx4/46INvc3r0gK5aIQm0naUlUMuooJ0WKWmWxpuTwGZzibaBoAIoi0ha9lpLaS3p3g6z/ZvovRu8f3HO7rf/kD/1C7/IvZ0dJr/0M/zh999nVXqCmtJZT1CaG3d2mc01f+9v/7d05RlNGxBJwrloKC2YNkEH3VNiJMKkmNKTO4U3GYiCdZmw9h4hNWrTMpGavcxwVEHowEpQWoNSKCGQIaBVIAhP52PPlb4JMfSK4z64vk4KCFGVXkhB8AFrXR/Ox7BySGQAV/jlYES5vgReHz0K2Sd8vA9Y+sy9AJSI7UDE1vbhWtfH3iBrpdCD6xoCTV1ju2gcs1TjhO/rviHPJ9y5fZvlckW5WY09aLbxzKF2O01Tzk4bgg9ju9Vhsdjm7Q3e6bAYNG3MAg/6kSEQKUadI08LLi7OY0vZIJFBcXZ8gWtjn55gQUuDQiGCwDYxWZWahMkkx4eORBmqeh1bVSQpENjb2+sNbSBNc87PLpktCqyzBBEIIlDWJSY1JGnCxfKCs4szFvNF5H62DeV6RSoVSaIoEsPpZkPdlAQh8EHRdYHTk3M2l2tc0yE6yyc/+hFf++pXyUTgxm7G6vIJN/bf5mF5wd7unLqONefSZOhswvzGHczOgmR+SLWswafIDlLbIC4fotozpqlggsZvPMbX2BA4vVjTOtN7/5Y00ajOsypXgIiq9Fr39iEgvMcoTdm2NO5K6f5F4181Gx6AfyCE+D0hxF/rX7sZQngS52N4Atz4kxz0KjcqBmv6/GvD6/CC968SPVpdtcyEQFVuRhX0OJGvuISRXhIIONq64vvf+Q5PHj2iWq+pq4p26ADpHEpIsiQlnwxVFBrbObrOkWZ5FEAQ0NqOzjmkNiz2D1jsH6KLKVZq/vE3v8Vv/vY/YdO0TIqCb/zs17h1cxdBhVENr9/b5+7NBf/j//C3OH3ylHZT413kB7bW0XkfM75N29NRoleok9ijOstyXPCoJKXuLGXT0LQdiEBiFIJI9rVdF1uMbv2u27jO8Hu+CCmJOOPwXqx0ih7cFR9xUNNu28gFtD1e7L0fjeaLvM94/G3u4jWB563HOAeEGK3vNqtieGxn9mWvheqc73tsX5HOm7rhwYOHPH36lLq/7p+JXvrkT+iz8XDVxnUIzYeaZaAPs+P3TNKEopj0IXf83CyLaucRW41ekdZRqd+YpBfn9WO2WvUebsQlM0LwFHnO2dlp1GPVanxv4BQPNd/7+/tMp1MODg6hx8iH0Hs+n4/fbeBZCiFo2mYU/0iSZGw92/Xw1MXFBVIEvO+4vDxnNp+S5RlnF+d8+SvvUXcdnQ88fPKMpgvIJGe+s0+S5ZR1RZ7nZEms4JlkGUVSIENAuBbXrphkgkQ5UgWJlNimoa1r6qqk61qqsqZuIgZsTMpiscNsNhuve5omY8TS2g7rHCHQd4g1CJ0idPpSu/Sv6ln+agjhsRDiBvDrQogf/KQ79sb1rwG8eu/utfeIldt9OB1BHj4Tm4feLQnDEyG2hDSuPFbZc+xiSFEiQsZ8ZxKTPUGDiH1RBj6gVIGz42ccP3mCCpHuU61KlNQEp8B6MqVI+h++rmt0mqK0YbbYwQNV01JWFUEIZJIxme8y2T0kJDnB5MikQJmMP/z+hxxdlvzFv/Dvsre34Ctvv8KNvZyLs2PeuDPnN/7e3+J3fvs3yYJE6AItwxjaSSljV8AekxqSDTpL8bID31E28dyyIqdqGzrXQRu9NCViiwOCAx/GJEk0mf2/4rNKQ5+BTLaI69crfbbxy+EYQ5j74wjAV9f4BXXgfwwzYnh+XbZtm1S/bUCHEBsYidZt246/6XYGfyx2gOcSX3meU1duFMUYOiIOxi/P8zHLPHyXpqlxLooBT6dTpDCRkdBn0Qfcs65r2q7tVdEHbFdiXcdiPiNLs5HdYUwsfUT4HhaIGGSeT8ZET57ntG3LcrlECo31Lffu3mO9XkXoyCRRvahtWczm7O7tYvoMvghuFOeImfcrhXLvPakRlOsLFvOCo6eP+cH7P+Qr732V199+i2dHJ/zOt36Xyd4Nbty6ycOnp3zwyQOUFLz15huRUaAl01STIpmplEViqN0aHVa8dm+Gr0vK5TnlaolrG+pUkmhD18U5t7OzCwjapqXxXT8fA1meYUxKVTe4JuL0PtCX3UbWROsknXu5//iv5FmGEB73/z8D/hbwS8CREOJ2P2FvA89esu/fCCH8QgjhFw4P9j7z/nM0omEfIbYeMLo/W3+PIT2MrpFSkq6zEAJlGSt5TJrifEBKjRC9lwFIGdisLvngB98jNC0GibAggsQ2ltB5pAvkSpGIWEkRvTWJThKUTqg7R1l3dB5skHQqQaQTnEpphcFKw6pxWGFI5zucLkv+1v/n7/DxD39ALhveuJ3z2k3NP/i7/w9++9f+e7rLE2RjSTF4BE1nWZcVZR0rP1rraDpL01k650EJdBahh/nOjDTP+vA5NpPvuga8QwmPliCCx7sOIUL/G/ZpaZ7Xh3zRo7+WIx5oRw+yG72ewXhu05C2eYzXM+kvOv7LDOSPmZuf2S8aGTUmJrS+6jM/fIdtbckB75biKoOdZbFHzGw2GyGGsiyxfSJn8EyHyGXwZJ1zoxCwlLIng9djaB/D915fMstHb3S7/HG77Ww83obZbELbNVFtqNdezfO0r+zxrFYRQjA9JhuViWLd+3Q6paortNIcP3vWIxiBtmlQUuKd5+bNm9jOcnl5iZSS1WqFc3Zssta2Hc+OnvViwAqlHOdnz2jqNZvNEqkCUku8CFgCQUm++o1fIJ/t8fjoBJMU/OIv/jJGKZpyzSwzdJsl7eoSUdWcPPyEB+9/m2b1EFc/4/L0PsuzE7qyRYUUJRRaJz1v1Y7c6c2mpK5qggdtDEJonIsQUFYU5MWELC9IkizCdMaQ9Y+XjT+xZymEmAAyhLDqn/+HwP8R+DvAfwH8l/3/f/tPcHS4jmZdfylseZvDe+HKG0JEL+nKs4D5YsF6vWF/PwEhsS72P8bHfZUQCBm4OD/m4vgZhTGs1itsZxH0iu1eoUVU2U6Mjp6jUuR5waZpWW5KNnWLDYKgM5CSbLaHyKaQ5Igkx0lNkhVIY0AKEqOZ4Pn1v/s3+eTde/zsN97hH//2/8Q//o1fRzSeXEG7qTE6xUuPdx7vHJ11SKWZZBmmbWmbaKRC6HFB4dnZ28U6R9u1eOh7bge0FBSpoQuSIA1SK/pOR8ROiIylgi+5/qOHOxgaa+2WYPBg8CRSPu8djgKxWwZx+7jbY8iKXzecL0oYbT/fJtEPxxkoPFcGzNMK+5z3OCQ+hizvkCByTcN8PueVV15BSsnp6en4PdI0pWs91SZitINE27ZHu60gH4Lvs8Wq12uMyRzvw2iQvLd9hU1M9iRb5yOEwPnIwjBGU9cV8/ki7q8EXdfSts34vZfLJYQVb7zxJqvVmv39PaAnqacpzrbs7eywKUs2qxU3b97k+PiYxWIR+ZZ5zmw65eJyOcq9VVWNcy3TuaWsSl57/XUCgUf3f8Bif8H5+RlSws0bB3RdQ/CWDz74EUmiydKMx4+fUNcN3/jaV1hMC6jXVN6SC8/F0UOoVjz44fc5O3vE+dmnJImlrVd01QYjU7ogaWuLtTVnZxcYk1zV+fsodhI9xoDzcd6nWYaJXfjwCFQAoTzWO1If0MLRhZ9OueNN4G/1k1gD/+8Qwt8XQnwL+G+FEP874FPgP/1JDjZmo0Wfxun/uUr0EHM4vWUM0Jc19gcIgL/CKgMRi4hZzoh9zedzqrJEJRHcRSqsjXiTFgrwkfxarmiqNRKJs108loqCGr5xaCmwLiBFzLiZJKVznsvlmrp1tC7gRdSzzLMpspiRTBcEnUKv2q0lKCwmBAwKuz5lffKEv/eH/1+++Q9zLs4fk/rYp9p5Il1mSHZIBQHatmO9KdnZ3aUopni/7ukumk216W9CRWJSdhbzqGTTlzW2riXVkqZsEUbGUHxMlAxtO65dn60xLEJDZ7/Bsxxu5quw/XlJtm2j9+MM8YuebxPdP0MTu1Y9NITI2y0jtrcdun9uV+0Mxm1oxCVlrAATxMUxkrRXPHnyeNSe9L7vl9OXSm5/1+168uF4Ay9UK00IdvS+l5dL2rZB6xSTGLrWj8miiPc2ZHlGkkRjnyYJJslpuxYpBXVdkSQpIXjyPMN5S9s2BB81LbvOja1VpFSs1ysODg7YrCu6NmLGm/WayWTCs2fPYl/0pkH3+pwhwGw6JUuicY6loZYQourR7s4ODx8/YrlcsbOzx2QyYVoUrC7O2VvsELqGannGpJhw8ewRx4/uI7uSbn2BKCTHn35IaEuay2NWTzfIZs3xo2Oq+hxNRyb7Dq5pBt7gGktZVwhVby1MsmdqQJ4XZFnfq8d61psN2hhC28Wii3AVqXoPRity4WhfOCPj+BMbyxDCR8DPvuD1U+DP/8sdq78RxFVk/Rxm2ZOMgR6j7P/v/77CMqMHGcZNr7wXpSS655YFAai+O2QAIWLrAWwMT+t6jdESjUIohdSGID3eE3UNtUSL2MhW9R0kvQsIqSJY7SqazsVmaPmEpJihsgKhNUKCUQFNi2waUmFwVc3pw484+vgH+GbN8eYMLeL+zsdSt6BiSZZ1PhKvpcT5QNU05E2LyDMCgs46fF2B88gQqDYb1m6FEIpMawySsipJJGRGsw4Voscvg/cEGeKCFUbFz88Yu8HYDFjf9TYPw3ZXYcDzxu9lRvLHz5HPdnHc9k63Pc5R3OIaWX7bM9uuxLluZAdcMsuyXuiiomtbTk9PY7+YPoEz6AlsK/TYntu5fextHiT05ZJKIr2kKArOz8+pqqqnsDmyJMNJyWazoW2bKHDbxtB5+L2lkrRtFJgYFrmmqamqDXmeMhDSZ/Pd6EXbMMICg9dZVVWsuikSbNtx++atSCz3AZNoujYWbTRNQ5JFbcmyLEmT6CV3nWcyKZAytoH48OOP2d/Z486de6xXJcFaZlmGLTesjp+i2g1CBiahZhoqFvOM0/s/xGx2KUTDwY0Fq6efQrBQL9mZCookpyw7bN2wPK9pO4sIlqaxeBy+s4QQK/HaxmLM0G44UNcNUnR4KVHG0PRJvNa66DRIiTEJWV6ghEc4jfwx2fDPTQWP9x4p5FYmE4ZQLohhq+09tqLvrb+3kxLDY/BA265jsymjUVYSUMggkGiGmF5piXcd02mBbD1CSaRJ0JnAdT5K0VuPlrEWVvb4VmdtbETvPUEokjwnnUzJiikqyRA6QSqN1pJUBVRoSZXHr844enif5vQpqW9RQhK8pm08TQt+KNVLepJ9GwjeYUzW170bXIjepzIJLmxYnp1jbYdE9lUtksSkFGmGSg2pkKy7NYkWpEbRBhcrloIjeBVlxRHPXYPrHuEQsg6ej3PbvMvPGsrr4fS2Af6xM+Ml3uRwjO2s9HZlzbYBHcLlYYyG9iUJrAG7HJJ3k0nBWVU9l7gaCOWDsk+a52xWzdhSdkgeDfuMRPGuI0n0+HlDAlKb6P3WdU1wcZmKRlhdZb+3egF57zBGRd0ALcakTdNItIldSfMiG7c/PDwYr0+UbovJq8mkoCpXaBWx2qJvGVGWJefn56PQiJQS3261x5CD8E2k0H3wwQcsVyvu3DzEeUlVtuRJxi/+3Dc4Oz7mD775j7m5Gyt02pOHvLIwKCloK8ssCUiTQLfm8myF7RpSo7FNjXewXm5oGgehAN8QRCDNJYu9BNtMYhWUUAgtKXoxZoi4cGM7HCC1xvnIJOl6DQelNNokdLZjVVZUtqVrX+5bfk6M5XAzDLfZ4E3G94QQ+CBGwuhwyww1IYOxRNCLQVyVzzlnGcyq9/Fi00/kIBTBC0RQowvv8bRNhVQgtSTNMpK8pek8ddXQdDbqJWqFVJLOB9abEovAipiA0SZB51Mm8wX5ZIZP88hjFEBwCB+QrsE3NadP7rN8+pAMh603JCanqhy5mVN3FhscXnqEiSVl8SZVo1zW8F3LsuT09JTlcomRNpKrlSbJYntdJRUqgA6BSZqBcehCo9KC48uSIOWYHZRXej6fMW7bj+3w27mIhQ4X4so4bVO5PutZ/qQG80VGc8hKv8zYbdezb2+3/Zq1/oXnMxi6zWaDFDynDbl9LkPZoO+J3/v7+2NmfTvrPxjN+Lmhp051PH78mOVyxXS6oK66qJCuEkIPX2RZStc1vVG+6mYZQlTKMSYjS7Mx/AZP29UoLUkSg9KRcmSd4/Liknv3XuH09HT0iquqQviATjRVWWG07nVCJUWek+hY+XX/k0+Y7+xw42AvGm0fhT1Mr+p19+5dzPEJxuQEJ1gv1xilqfuWyztFRoZDtiXzaYpRKUIEfD7B25aLs/Oo+eo6jAJnK+r6krYVrJYbvE9BKAIJqA4XGhyCyXRK7grapuu5lQHvozKTkn3bCwJlUyODoOtqpFJRb9PWBBGLTE5Xa84dP5Zn+bkwloGAH01f79HIflL0uFfMzvZbCCD4aBjHm9fH+FsKCH3HPRFrt6W3fdjqCCKgQnTFvHAgFB6PdQ6JQjiFtwpFRp4l1KkjMSVKBtqmRIqAUYo8yVA6pwlp5MJ7SyJbBGDyhJBpTJYSdII3GV4laOcQtsUEgeoqLp49pL44QXUNdsubCRLKtsR66EK8+EoqjJAYDQhJZ1uqusZ2lgC0TUddtySJQSuJMpBlsef5kECQqaGxHSbRzNScmZbkpiTTGZfrilZ62mBxQeCkwgeB9BbwyOBQeKyPnmToHHiHsw7vo6EdbvChE+ZwPfur+kKj9CcJyQdlqOuh+baXGFfQgQ4lezi8N6o9hiOEwBuBdX0zMkGPCXm8sAjR49qNxFmPCrEuGdfXHkuJ9B6sYzLbYXd/l93dXZ4+fUxZrlGdBOEwCSRJvI7eSQgCZ6MoBkGzu3PA3t4uy+WKqmqYG4FGxFAxBGa7c+q2YbVek5mMhICyDh865jsp512LF5a6uUpQ2cYjgqGtLFjBydMTvA+Uy4puY3n1lVc5Ojpilk5pbM3Fcsnh4QFlXZLOJpEC5Buc8BT7U+a0SJPx6bMjbt7Y405qEB4QniJTzHPBWjb40ye8/+BD2pNHLLTm/T/45/i2IVWSVEqyRCK6CtvG+9Vai+1atNSkaUHbNpSbDedna2znaFtPohQqUf0cEwhpMElCmiWk2YSqbqmdYN2BUvE9B2gNwUYOcds0KBEoEshzjU5yhE5og6BsOm4rw6cnl9R1zXc+vXzhvPtcGMtBJSYQRnK06MPiMN5kgwDX0DVc9rdh71dG4LNPiF+Bn6PUmlYEYlZMhhAVGmQgyFh5QpBIkeBaCV4RvEQojVGGLE0jiTt40iTBd4Gm7siNpPWCidZA5Cs2TUu+OyFkOVZGoqsNgpSImyZIaGrOnz2lXl0inCfYKPbQdo7gHR4BUmKdjUISQ8bexcqg4ANlWVNXDUNrViU1eV7E7DNVL18mY/mjkpg8iRlUI1GpIXQeYaEwKfl+xiyvONmULFtP6T1OSjyD4jiI4MBZQtdg64bOBqztzxmBFIogtkyj/KwRFPxkDcxelODZ9uZeZGA/u080mCLu1Gf3+317oxjZEiDwiDAkt3xMD/aLsSTggMbFzo5ZmiKliEo1xEqmWzdu841vfIMHDx9y/5NPMEayM5titKRaS5p6g/MCJQUTM2d3Z8ZivsAY1YezsFjMUK/comlbciti0s9biskEk6YcnTxjuV4TgLzISURASMkiy5C+xVqP1AbpXZzfzqOFZNNEzc75bIZzLiY4leTh40d0TctivmBVd+g04XK9AhFYbVacnZ1y5+4tEIHzy1MgoIA8TcmTlEQqtITV8TGUa97/g9/l2ZMnFMTkU5GlSOfoNiVaSoxJEcFiO8+kmOKdZ71eIxAYkyHoQGjqumRTWZzX1HVkd1jnsC4yAgZZQWMMPgS6tkMbw2J3yrqKikJV3eDbBuUtuI6gFVmSYBSowpAkCmUSahfYLDdcVi07h7eYFTneft7DcBEz2364mXxUJmdbaGHIcA87iJffcINnIYWIykDi6mYbKCFAX3kSvaBBbGPg2aVpiu/rlKWI3slkOkMJg/Carg1s2hafaISRuBATLk4a0DlBpkiVE2SGxOFti0k0vm55dvSE5fFTEukIrqW1Fu8FjQ1417fcVbHJmkQQvMU1HTZEFQCjDRLI8ywqB7mAEAFBrIhRiSTL0n7RkCRpStTzrBBS0jSB0AWKPCNNcpQxSJPglMZvanzrEciIlQaJDOCdoG0dXedpbKCxHuv6lR76CpaBj/gi1fV/henxAuP4stB83Kf/5/p2z8EBQNIFvO+BhyB6by+uvdGuKiol6IxGEiiKlJ3dHTbLJbatEQruP/qI5fKEdlORJob5bEJq5ky0Rk4L9LxgUuQUecYiLUgSg1Sipy9ZQnBMZEeWpYg8RbpY3dM5C0JSVhW3D3a5sT/H9gmq2Fqiod0sKYwiGE1AoFG0XSBXAmlb6mpJ123woWU2myMklOuK6XSGlXC0PGYxm1BtShaLOWma8PjhA27v7dOu1njnuH3rJqfPTlG2QQvwp+c8aRuM8Dxua7AtIjimkwKZJT3coMizFClABD8q8rumY2k3o/p8lmes1muUVHRdS1k1WBuTmE4I6i7WzWdZSlAKGwLSh9iRQGsQjqZzMeoLfYdYLXAuRp1JluK1YrazwNmW4C2t81SbhvOyZll1nK0qni4/pZjOmS32XzoPPx/GkmHyDrSh3sP0kfc4eIriylRG7zO8qOx9UNGOHqPwV/qLkS5ypZEohYzGNPSerI1Gc8DibBv5as46sizn1Vf3uLhYc3x8QZJOcS56pi2eLgRcAG8KWjQeQ5bOCDIlNxYVPL6tWJ4dc3L0BNk1dL4BH0sVu6CwPmZJ27rCNy1FnsWQ0McEjJSCNM2QSvfn6XslFUfwsfFZmqWkKnrAiJihT/Ns7MncOQchkBdZ74lGXNe7jlRLMq1pbIt3HttbDO9jGWdrPbYn2tsQIsdUSqTwV3HAuIhdVxnaTvr85OM6TjrMlR9nKK8+77PHGOeaEIgA0gVMpEcQgoi47UC5CHHBqB1RX7RrWG42TCY5aaZwnWWzXBF8oAoduVTcWMy5e+uQw90d8sTQ7c0wUiC8A+8RtsJ0HcIyimDMZgWp8pQXJyACk907qNBhbSSpz7MkYmtVxyTVKK0JwdFJTddW3D3Yi6V7fcxVVTVZqqhX54SqZKLBhYZ5oblcr9nbLWi6Bh8aFns5OoAQHmxHWW3Ym04otOH88hIjBBcPn2B8IPRUNHCUmyWLSY50HXjLbJJjfMCLqABvtKHrLM5assTgfUeiVa8pq5jMMuqmYbkpCYhRQT70i3RtHSpNmacpVRW5plUvmyeFpCO2ETbCx6Sq8ygRkzhGBkxqUFlMIlXB0zhL18Xy4MZ6zpYlx5drvMlI5ofUXcd07xBrP+eY5TAirhRX+ViG1E/u8f3BWIaXOpYjDYnhpqCv2IjagqvV6jkQP4goDgv0dcp9VlUEVN9IqusiN+vwcIfF/i0+fvjPwJboLKdqShxDuGyIDTgTknRGkAnOSxLXooXn5OkTnj34lFBtUN7ibYUUsWTMCoMXBtMnsxCyh18DRtLTPjSeKAbhiCT61re44HpsLlbl4D1KCpI0jb+gjx6pEoK0KGJdcj5BS816tcK6jjwxtJ0lk9AqSfCO2jqcAN/XczsHzkusFzjHuIAZrXplIocgAv/De1dG7aqj47/qeJGhfFGm/Pq21zmgAzQ+QDeB2LgqIHB+0MaUKB9wXYORniyAXS6ZTXNu7O/RFgkHe7vs7yyYTwqKNCM1mkxrNAEnDApwbez1otNZX0UmybRhb3cXKSHRhtDXWxsFy+Vp7Bte1wTbVxy5lkQFDJHNoZKC5WqJVJImOJTWkepmLYkSyLZmRwS+/spdVuWGtqmYCY90DcI7prOMdVlSVh2pThC+ZZYluLbDbjYkLjCfTtgso5JR09UYk5MaDc5i24rgYyO+poGAQxcT2qaFELUiAYSUpMaQmJjwquqOi8t1X/UTmQFNsySE0PNAJXkxI8g+keUFqG6shGqdR7oo1RdCn7QVAa0T6tbSdh6dRlX1pmmoyppnl5d0PnC5LmlsIJ3t0Okpp8sNqo6qSV5qTi7OXzrvPkfGUozJgYEQPURfYcSTnh/PeRqIEZOSfamW97FVp+1a0jR5jkYyJgMYsuZuTBzkec5aa2xTUVUlXdPStJbVesPtV2/SAKv1mpnWdLbDi0CaTuiCxAZJZhKkNrHCxmhM6Lg8ecbRw/vYckNXrhHOkpiomNM5Rxdi9j+C3YJEp1FOTKs4kXs8N/jYLmHk8gGZUbEVq4JEQ6IMRscwxfffGTxZlqJ1xH3of7tikrPZuDhJg6VIFEprVNkSui7Wk5e9DFkQOB9imBSisvTu3i5FYWjqyNmr6wbnupHI7Hu+n1Qp3r3YO/yJZsdWFv06XWh4vv3eNj66vThucx2VkngVaVeDspIaNCYhwjBCMA8xsz7NCw52Zty9ecDuLGcxyRA+euTzxZSmawjeM80URkSlrJBo2qom0RKpNJ1MmZiczrYUacJkvoNzNorldoLWW4xdkmhFcC242FccGzU8re/oqr4G37uYTBOSvJgwnU0JCDKlY6tfpTAKKiFiG14pWLYNKkArogq58D5W/dgGH1xMhqxLMpNyePMWGkEhDXVX4WWLV56WgNaBGk+SKKRWYDQyy7DOo1WsnoEIF3RtFJ+p+/O2LlA37VjZ5L3H2Sg3N4w0S2maWBEljcH0VVeiiypY1gXarqZ2LUYnWNcRQhVhi7phXVZY79mUFRdlxbKqOD1f46Xm9mtvsL93ix98+l2C1EjhWezOUTpFqH8Lujs+N3oKkBASBlzxpSHc1usReIqviujNxLIwMRrCtFdKiZuH547StS3eWvI8p5gUnFxccHl5SVlWOBdo2o7GeoI2VHQUSmGrCiE1UvtYuaMN0miUiZJxUlrazQXPHn5Cs1kiOovwsQooeI8NvZI3Hik8SqrYF0TElqmEiMk4ZwnOIdyAtRIVWUQPsLmA1pI8URTZBE+sA4/qQxMCYNKkT3gJCCp6Ud6jhSBLIiaXopjIBOED9drS2RrtW7ROqFpLsA4ds3FkRnP78IDpLGO5WsblyjushSyNYrZ5FgnZm+oqvLkqFFCjIXuRkvr2tj92urzAkAqepxQNNJ9hMR0+0/oWj0UbRfAOrftoRCqSNGc6mXI3mzPLcm7dPGCWZyQqkCqB72p8Z0mVpF1dYn0X522iKaZT8IHGV+zszXsMVbHsNGVV0QVJluS0RE89mc4RXUfb1phUoPqvrFSO62JbWaM1xmhWy1VMVjob+a1A13TUVU2eFSR5HvvUEMgEmCylMAadGJb5hrqzNF0XNVZ9QKWa1nnyNCdRmtt7exRpjvSC4CIWuelKZCPx3sVkWJ8Ia11cULwg9iyXAbMwKBWwXSy9TXtuppKx/NC6EO8ZEelPsaImkKQFeZGPalRSJmw266hHm0aqXFuvsc5jjO7pWBqJ6qv7AlIrCpNgpebsckUVJCHNmOQFlUjI57vcff1tZDbh9r1XuLhcRiWlyYRiMiVNX5wJh8+dsbxOLRl4eyHib38s5nUltwa9wYRegCAd5fOV0leejRgEHOQohjC099yUZRRx7XUQvYeybliWVcRMkBihCDbQ1C1l51G5AhGQ0iFkXLE3x0/YXJ4SWgvWkihFcHGCIAQmSZGAFDI25JIRu2373tCh14BUQqGCRY9qNyLmaoPHhQhyX7iGLoty/kIpIEcbHeW8ug5kLAVLsiJSMwgokSKJnqANAqFSpkVBkWienTzhmW1ZVpvYotXHTPFiVpAlhpOnD3n2bFAaDbE/kVTcuHEj6j3mOZtyw8f3H/XVSMO1eR533H79uRnxkuTO8N7Amdzu4RRn0ouFOIZw3HsPwZMb0AI0Aa0Es8Kwv7vglTu3uHl4wHw2ZSZSMp3QtQ2X56cR4+s8iQBTpIDn4nRJVqQYnaBEADxI0JnB9GpDSZKSqTRGMt6glMA7i5QxLlIKJtOCrlpxuVmRGMN0MsVkGVZ1lOuSuqxomob5YgEip2naSLRuO7yDpu7Q6w0eQZomsfWzgC5YvBPkk4JCSNrOkm42lKWhMBIL7Cx2YyWSMmRJSrWp8ajovHhFmqRxQTEaLaLhjH2iOtouoLWJeGXSf8e+3QS9kLQSmsSkpJMM52PFUWctSbzDo7fcr/1d06GFQgWFs5a6qyJUEicpdRNVjlSekZqM1EiCszSuo3UOkWbMDwuM9ZRdw87BAcXRKSqd8J0fvk9VNfz5/8Wf59Gn98FZ3nz1DiJJePjw05dal8+FsQw930ooCV7GLLhQI8UDrkLyn+RYQxWJEFEu3toYYi8WC549e7YVgsftwsC5c45EylGvb5D+j7XZOYvFLh99cp9lWdKaDBsCmdQ0LoAMSCFZLObMpjlIR9PVHB09pX78AGyLtx3SR482hIBQCmM0ymhUcARn8d7ibeSXdtbjepJtkPFiTXosMAY5Aa0UShuECH3XPwh9Bz6lNUKAdw5jNJM8G3sQhV5vUvdVRcHHzH+KwgvVr7Y5r9zZZVU13H9yyo8+fsjFukYGT6YVtw8PEDis0j2t8coo5VmK6ZVpys2G4H0fIdBfz+0eOi8W1fhJxlUVjHzu7yFo2OZjXldUl0hy79k/OGR/Z8b+zoxXbh2yO82ZZgYlAhJHUB7nKyaFJLiE1XKJNJrWdjRt7A+UZYZpkaO0if2WvKdsakyaUTpLpyUm0YRVCbZBSwitp6w6tFFkaYJvGzofhR28iELYLoQ+eZcymQhsZ0mSFIlEm4w0zamblqppqeuGuqr7iEzStinNpMfdncNIzzSbRSWlRDNLdkiKjso2NG1LXa7YWeySpglV3dA5i5AKoQyTRJE5TVPVcU55jwygVYb1EevNkoSimI6Z7RBAChW5uM5hiRBY0B22b8ExMCiGQoK6LqN4SNNFmh2MdepDZ0mBxHcWT6CuLXW7QgSLdw0BKBY7WAT3nxyxbiy1a9nvHHuHt/ng4wdcrjd457k4O+XuzUM0jgcfvU+y2OMXf+EX+Nbvf/+Fc+1zYSyFEGNlzUB3CUKOF33kxl3V6sRnImbEY5ercOVKQuTW9ds75wjEVqRN0+BDiMIZPQdRhJhN62yLNoJ8tkuaF73nKRBKk04mnC+XfO+HP+LOvVd5crliU1YUQlCXFU60NEGQzRfUdQmdom1rlidPodqQqsj11ELGTnMy1phL1VeTOEfwnqaJ6kBDe90g4sIhYsYErRRSxMSEIJLxg/WRbC/AaIMPEjOEPkaTZllUcrcORN+pUMaM7wAF2K4hTVJMmsZ+yg4SJQghJy9m7O7f4s7dV3n45Bknp6dIIZgmGq0SaiE4v7jk4vKStmt7bz7iuQgZxR6URLjh0lzJscU6aTW+7vtKlwA9fh1Gcjn9Pj70ogmi/w59Ln5I6Ek5JAmHIlYPApSgX0AFRZaxt5jzM3dvcOPGPokSGBlYTDJ0cCjbxHrhEGiDRxmDcy3WN+SThLppUOZKrSibFPgeQ41thQOJibBHbAo2iVGD8lRdRZIY8iLDOdlXZkkUBus6VqUlS3LyPEMGcN6RJQanPL5z5JMpIkSYJiCo674YwugRwyzrmsvVklTFslhnHV0ImMxivYvFFdrQ+C4upn1jsQsE06knzQqk1rSdhRAZI1gPvl8Qgx+5KVlaoGSMzJq2jWr0/X3cWYvRkkQZhIgVMk1Z03YOkyQUeR6pciIq0K9X65i0QXBxdjI2UQvQa2r6vqAgJnmb1rJcXlBVG0KwUX/hZEknFPl8j0w5VLBcrDa0/pimbbl75w478wXrzZJXbh3w9NGnHB0f8/gH7/Pn/ty/91I79fkwlmwnW2KC53mqyUAqugrXAtDfGb1RkwgvwXqsdaMhEERZLm+jZ+HsoGauIl6jNF1X0nU12ngwCeg9Fvu38EJghSKdTVm2ge9/cJ9ksstXv/oNzCf3+fCDH7G/N8U7SyINbeNYnl6wf/sWQgbWx8/Q6xUqBFRvmL33KNN3JwwW2X+t2jqsl1R2uOkj11IA1rYILwh9QsL3pGMpAgqJ9R5jFIpokKQC7y1t61FODc03oFff6boOoROcj+RqYwxJVuCVoh1oW1JErqSckGhNJiXz2wWv7scGWIPmY9u2LJuWx0+ecpwnrMuS5aakdQ5lNK13kS8aPJkUPd0qaowOGejhyg7X3RJwoodIBChkvJ5dzztVeSxVDT7CGT3eqYXEu9iXJ2iNFaDwpEqRK8XUKPamObf2djnYXXD7xgFz0+JcFwVWbGCzqZhMJngpqV2EdVTTYVxUp5qneSxVDC2Kq8UuCkG5Xmsy8oSnkwnr1QZ8IE9yurqhrhu00hR57FXeNk0Ut2g6JpMJiRG4ZonRhlznEWtXks5FfmBLwLUNkzTDdR3rzYa2hzsa1+GbGmsbhLQE31KdWUIWKTdWtFx0keieFEVsDSIcQkmauiVNM9q6xU/66CDPEbKh7Syus0gtyVSGG2X5otI4QtCFqFUpCFyuVhijoiHs533jXOQ9C0XbtTjrSZOI27uuRRtNpiWHiylSCjbrJWIeNRI6u0LphNZ3GJ2ilEZJQV3VNA2cX67w2rBqLEmekmUzqrqjPGso8glpMaH1K24c3qRtOubzyCcty5IHR0c8O7vksrV4pfn44eOX2qnPhbGESBmKsfZVkDn8DS+v3Pjjjxv7wkBPv9Gq569FqoVvW5yNILMQEqUMUsKdN9/hzfd+lqen/4wPP31MVTsWu/v88p/998gXu1zWJR9++EO8SgjaoE2KCR1tU+O7ltX6nNXZMaGtx2TDoKM4hJ/b/aebJjYOe47yErZ6T0uJ9II2xD4iIURg38ghSTKaRIyJhmw7WzwYybHtLw7rr3C/eB4tbRU1KSMxP4tUlH5BMn0FxdC5bzBS+0Ixu3ePL73yCq33rMoS6z2NdSzXa9ZlSVeX2L4uV/SeT1UNGpuBum95sfEQOktiNF7GiisRYiNTkxrwgbprohHTEqkjpCBkZOgF5SJJHs90knHr5k3uHO6zOy3YmeYURjIvMrxt434hqvsMbWLTNB2vDcSui7L3+q/qye0ozwZ9z3spSNKkr5H3ZJnp+2tHPHxQUB/mYdM0lD0mPpvNRrGKKLQRaWJaR0+xaRs2VTm2rZVS4jpH13SRVzhcE6VQUhJ6YWrvPWma9219Y2Kw2myoqiqStfuwtq0tUsTPa4Onaxpmsxmib+c8VDMFcdVD3Tvbl7vGtiQQZQSFMgjvacoWevjHd7HbQNu1aBWFt0UIuK4iSIeRgrpcIYi0t/39A9JU0e3txHYtzvH4yVPOLy6haqj7fkOXyyUqmbGzf4CZTMjrmp2DGzw7PuPV127z7OiU1nZcHp3wzpffGWG47es7nU55+vQpeZayf7DP5fl278Xnx+fGWMKQ9Y7h1ZABD2GrTO1PMMZEkYhSWfP5nPV6xXw+i7y6PlsewpXeoHOgzJSf+dP/PvnOHT58/0OKYsobb72NSlIeHR1xsL9DaztIJ5iJQDjJdJrSqYAtV3SbJYUKYMB2cQUVfUbe9ViNVj0W432fyfekiY79V8JVuCNF5KzJHmWwzjMpJrEigWhssBYp+/DauYh19Qa37Tqgl6kzhsQkBN+xLqMWYNaT1oWUpGmKHgxD/7lKir6GtyH26AbZN7dKU8M0TWnqmqppEF2L60s2EwGT2Qy9t4cRHakJSKWpm4a6aamblkDkXzZNx7qq+fDokmdnJ3TegZIEFZkRqTZoIWmrGoFFaRmFJULEDKeTAmMUi9mMnd0d9qY5e9OM+WxCnmhEsKRaYpsK41pCcLjg2dSxlUiaJLFfk49ZXCWjun5b18zyfOzBM0ixhXAlBRejIhe9Rq05ODjEmJT1as2kSGiajhDijTmIagwGd3d3lyyLv/9oTPv+QPB8R0ptDGkelYS8sBEq8p66bbEucojrpo7QEvF3c22HMRrrPE1d4YInTVNwHtt1GKUpsow8yxE9nUoQ8LaL2ff+WF4JRNDItqGqymgou4a2KrFdi9EKKRLKdTkm0VwTW3Rsy/fpLPbsMcbE3ucEuqYBGRf5pq740fs/xClNFSJRfTKboma75Drj9OyctW15enyG94HUNXTOcWtnwSRb8KOPPqSuO4QxvPvel3jw6QMO793g5s2bhBDGzpYhhNEeLBaLXm1JEz7/5Y4vMJR9WH6VgPmTHdr14aoUEmMMu7u7PHnyiLt378TqmBBGlZhIL4qTtAkCNdnnK3/qV/jy138hhiECPvzkI2azCXt7c6y3+KRg7+aC7nxFoiUY8G1FQmylK2XAJkn0APosvLdu9CLkCD30jay0HjPgA8dM9h4D3keysZQkWUZbR+zSCRtxTh/pUm1rkb3wbMDjrI8ljypKr9kew1V9JrnclAQBJjHIPlSPWVGNVpK6KkdvMl4ugRSRMZAkhqyvlApBomRCmmmatsM6SVZMUMbgmw10FW1TY5wjSQ2piDCA9zCbJxzs7DDdPeRydZOz5QWX6xVt29NxQiB0jjzV3Jzl7O7uMJlMyIxmOpuwu1ggRSwBTZOEJDjS4FFaYrRESU3X1fhUoKSkLKNGpXcJWkm0Ngw15UUe27Wen5+PcMHgVQ+iv4M025CoaruG0AT29vaZTuds1htA0HUxajF90zHvI+k6TdNRem1QOBrkz/BRwX/AYCeTCVKpmPjpYRIlJPjYX1NIge9ir3OhJJuyHNtRBGK300RJvBN0dUtpu+hdubgAHuweYNuGtuuicpR02KaOxtb2ql0h8oHbLmKcIjgkkBiF9ALX1ZRtTWJmtH00VG5KSlMSAKkUWZrRdg4nDWXrOatX2K7F2RiRBe/ZrFYsV5c4oXBJjnWW5Wod8WdpUEazt3/AdLbLs6MjtJTk0xmV8yyXa2yAG3dvsy4rdGK4cfMmQge+9a1vIaXka1/7GicnJ/8/6v4k1rY0y+/Dfl+3m3PO7V8b70VkREZmZCWrMllZ7ESZLhTsqQCCA4vUSIAJ0wMZmkryRAYEAhq4mRA2QMOErIElc2bBMCyAMgV2RVbRrCYrsyqrMqN//bvtaXbzdR6sb+9zXzKiMl2kifBJvHz33bjtOXuvb63/+jfknLl//z4Am82GRdugUuSt+1+er/jVKJYgSxpmOYVgkAhRW2W5iN/48FvUk+nfUmD3m1bFNCIhigcjrtEfffQpKUdMloshTRcnBrIA7srUJFWstxYNVQqsry/p+p72oBHJW1szYrhz/y3W4SkqDmiX8Xkg5kAae1F/KDEXUEpcuaciFZBUSAWoLO4oOXgZO7U4OOcc0RmMUuKOlDSL2pWLWhYgxlXzNnsy4chKle6yLMKMAa1F7hgzKQWRTU4GuQpCDMXnz6DKBW+1dC1CA9GzFHW3NWKc2tR4HaDgjMmBRhfMbTEv6Gq3JA2W3PdkLznNTdvCKNSXECJj31Mpzd3DJadHS3wS4nWKicpYDAqnNMvWcXx8iFaKyloWbUMIIxqKfVgiBwVRlSIZ6OKIMYq6XhHiCK7FYHElNtfZaVMv0sQcFcu2KSbBnspVwowo5PfZmq10fiFE2naBc5UUYh/IZRF1eHhEXTcz73CaGqYsoMnQt21bKcSjF0s/mDHLwY8liAyatkWhGLtecupLhzsV2zB6chRjXj+IC1FKwo81SuODx4eAcRYNjENHXTfkKOFmSmk6K/4Cq0XDzc2aMQitZxgGIamUfUDKApVM2eeXV32R4QqWHjc7Ys6sVockU+O7LWsX+ez5C54/f0ZMSbJvmoq3Htynag4Zg6JqW5bLFTfrNZWW57Rql5Kb1R6gqgraLe3qgLptWR4doZsFpw8fslyu6LYd3dBhneXZi6f0fc92u52TLXPOEsrmPffv32fodqjkaevqS0vUV6RYyjJhUtPIDrN0mSCdpwTlvPlZt4jIUIrCLdXPtDbIeb8qkgveS7HQEJN/c5zK0p3lrMSCq9TwkBL9OKCtxlWO1eESWzu2/YCtHK4y+I1HmYzVslSgalA+gHGy4c+lmys/ZFM3s39e5ewsGQQwSpP0NJ2LU5IxkkQZYmIYPdYIjCDddzkgQsTYClCFdiQ3Zz8M8r4YsNbhR1/IvY66qVFafq/9+C/jaB+6ma0gXoFxjpKw1rLbWZIaULp8v5zQykpGUdXIFtPVHLZLdLNEY3AOlk2D0gbbDwzeM/QjqdtR5ZHB+7L5hnqxoGkaVu0CqzRx9LS1jKpGaypnCb4nDmIKOxYNvHYtXjmaZkWOgTx0ZKMYUexGT8yOrDRtm0ukbCJloVJVlag4ktHle1QFzzazK/oU7zt14WdnZxhrcLYq0R41xkgWTE6ZbteVZaJ0dcvl8o1COV2Dfd9jlJbrpzQEXdfJossY6qamKUa/2lmsKhrpKEuYEAJt09BUNdvtlhhGGa+VAmNI1mC0XG9KKwyZMHSo0jX6YcC4mjAO7DbXHB4eo0kkP+CD0NBykpF+HPqZr+qHxG43sNvJ4aeKpMzHjDaOjg61G7m+WbNtD3hyfsVmK/eg8SNuiKzuO77+6D3aceBPf/c7vPvOY54/f8EPfvBDPn/6lJjh7r173NysCQnO7j3kwdExVzc30s0PRc3jI6vliquLSypjefniOYeHhzx69Ij1es1qtWK32/HDH/4QrTW/+qu/yuuXL/Ddjsr9/y4K91/bI6e8F+pKheT2NpxbhXD+nPxTxZNpozr996kAppmsLuOPlg2zKk7Xt0xiRSdeLq5CV5o2dtvthqatsZWlXS0klzkHrFWslg3dcAMIR861C7Fn68T52pdljoZi2irjdZ5iVZWBnIlGTDEyIidUTm42smiuta2kYIWA7MLLQqyoflKG5CPW6jI2UorcpJAxJb5Al4znRE7iEK9UMR3JmVjGOKE6eXEeyiVfO4RSxDPJBwbVy4FS8DiFFgc8JdZcVVUx1B1N1Uj11xodI0PX42MqFltLjo6PyLs1fRjZDQP9OOCHQQwcYmZRNzitsbr81lm2yTdXV5AlC90a0SGbpsEPkeut4GvG6HIjjahscdqhTWblJC42p7FcB4qcZMHV1FIku26griuWy+Wc/X10dDTH3BpjaBcLYox4H9Da0jQGax1aGfp+ACSgbIrZvZ1hvlwu90R5ZKs/FcpMee2UwlhZjg3DMMf0okRE0dQ1/a4jxchmvSHGyPHREZWVEX8YRznsgbaqxbg6hJlSNfY7ySRXkINIbo1SxDCikfeTAqlMGUOI9F4iGrz3dLuBzbYr14bCOg1ZEzIoDNevr+jHwGa7ZeeuoKqpFg1913F0dMzjR2/x3e/9Mu+8/Tbnr1/xzW99iwdnZ1hb8dFHn7JaHbI6POTi4pIf/ejHvPf++7z37rvYwXO/XXFweMiL1+ccHx6z225L3nji9PSYzb371IsGpRTX19ecn58zlCz4scSFVFXN0eqAdrH40hr11SiWOZPixJ+auJVM/KA9c/+nOssveryxCCr8vMmUI+dM2zY0TUXX7ci5LZ8DWhu0FpWOUnLiKiIgJrfBDwxDR91UaGNwVYWrHP2wY+y3uMkcNwYx50CREyjTUOl95zB1kqpwBKeFAVk4fSaVZUoQDz9VjGtNubkioI2FLKOf1VpwOePQWhGDpxsCqSyJJlxt9HIoVHUt37d0JJO5xdwpWTOPgDklEnGmbU2jlzGFvZhkURGVBwXRB2IohRgj1JQgQoMQtoxhLF9b0Ye+6HsDftCYgwOW7ZKqOmbwI/XQMwTPUDovcibHiC3cxapyeD/gfcQ4KWxGKepKXOH7cSBN2TjOCEE+elTwVCgodKu+37HdblmtVvMyYgoqm7J2JC55CjlT8/unWN3lcskwjOX6EbNkpTRDPzAMI1VVl+VOELeiGBmGge12O+OgE2vBOUf0gW63I5UOdLFYEIoO3FVuPtCU1mJwUlynjBWl2eHBQelmt/ixL9eTR5dlISmCyugiDd5uNozDILhqiZS9vjzf83mVgRTZbdesN1symqg015ueTTcwhMToA/2oyEGI7K6YYPgEMXhuxgjaYQ5OOF0Yjo4POT46ZLfd8uDBPb79C9/i1371V1Fkfu/7HXdPDjk+POLmeiMeBz6gU+bq4pJvfP196roRSWa2nF9d8gc/+kO++Qsf4Iyltg6D4v6du5wdHvPi/OUtpoFEZ0yYZdM0XF5e8sE3P+Dq/JLv/97/+0try88slkqpvwP8O8DLnPMvlfedAv9X4F3gY+DfzTlflv/2nwB/XaoM/2HO+b/9Wd9jLh63ljtvdpaU8Tj9y5/zL32N/Tgt1ffNDrSqapqmYbfborVc7Kgpl6UUaemNMHKuE1MoJ2wWXz2r55Fod+3pdhuUHwoR2UJlGUIk5MDBYkVMIyH4EkoVZ9pNKJZT1lpS9JAyxoijj4zXxeez4GZoBXaPM0onKf5+uir+fkCjncS3pojKsqjRhauno6T11a7CxTjLQbOCfhBcBwSQd9Ywel/kn7dhjX38rfcj5CBu5DGhg+jVc/KorEh+gKTQrSHkHs80TooO2TqxuatUwKoAuUIrwUNd0+LGEqmQFU1V0dYNzmmUwKc4V5PbReFXSp66VgplDXVtaeoaqzRh9GL+lYXuooDD1YLr2LFcLIT4nCRimCyqozBTrcwcRDYteCbf02EYuLq+xllH3QjmGELCWscYR0BhrZujHYZhmPH229SuqfBO13QsnWaSFr2oreTADUHysau6pnIOrRTddofKmaZqSCEyjD1a6Vmqa61l9CNqGOatvsqKzXaLAtqmkfTDLJrzYRgZxpH79x9Kx58jRsnmejsEhpjY9IGb3cC2GxljKsbaAqMobXFNC8YRssIuVqKXR/O1R0ek0PHBB+9x5+yUFDzHR0senq3oux13jxsOGoPVGt/3WG1o6pqmqrl/9x53796jH0f63Y7hasuTz58wBs/zp8957+vvoVEcLFek0TP0OxZtQx/DTD9cl7hf2GPPL1685PWrC7rhX82i7b8A/hbwX956338M/Hc55/9cKfUfl3//R0qpPwX8NeAXgbeAv6eU+iDnPyaMF8EaU7VX6mQSvPEpby54vlwvrEA1JJVIykNO5ChFTg7TjEFhtSWMYmvmrKgglJE/WcnIrg0ktd82DuXk1ch4saxbWmVIJlJXmsFHUq2xRVljtMIsGrKBPGYiSRAGq/d6ZiWdX8yJHANVcVySDqOa4zqNk62qOKRESbXLCa0ttnYykoeAripsu0AXT74QQlFaZOrKMgyRod/J81KWJ1prLLZQp4TOFFMi+BEfNcoYeh8wKc9YpVZKslvK7xG9vF7OOWylywWoUaX4S2MSqIwGowkh0fcjmYAyYvCw3np8HLAqMYy9LCuUFjuynLDWYJzCNoZFU5NixGjpdkETRjFOrmwrk4LS1K7GaluujYSrG5pmIQ5LStPHSNYaa2tiLt6fiyU5Jdn6Kl34nBbnzHyzBQTv6IdBjE9qiVRW4oYsB01d46oKbcUgOBHRFkw2b2DkEw1pyvdp25Zls6BxDZvtlq7rcW2DT8yu/30IYCva5hBtFMGPglOnxJgiprLUWrrkIdZs1xtCjvgkXp3CPZaFXVNZvBcBhKivBGvsBk/uMvniivUYcFbRD1eMeWTjMze9YjMYtoPjZrMjpYC1muBqMZGOCTsEVsuayhiWleHBySGPHj/m7O4p680NxrQoal6+esVqdUxMil0nfGPtKrK1ZGvQznJwfCik+LaiH7bl2g5c7q6xi4pf/No3uL6+pm1brq6uuN5u+fTTT+l2O/70977Di1cvsFpxcnTKgwf3+eTTTwmjHFzL1ZKjwwP+4Pf/sJhYf/HjZxbLnPM/UEq9+1Pv/svAr5W3/8/Afw/8R+X9/3XOeQA+Ukr9GPjzwK//zO8zv5He/Pcb/9oXyS80SZBSg9GGqOWmzKkoB1JkSkA7OT7h8uKCs7M7UphsNc0bzNri0thK+y74RtM0WGPIWhL5jDIQpauyzuG9Yew7aueorSOkXDwyJcJ0CkybNOvJCl6oFNRVjUE4kZPMUmvR3DpXgYrFlivMXYi1ImXUXhQou66jLY4zk3RQOpQ401SmURKm8Kx9ZOzEIBDa0KQyomxT95ia4Gd2flkmz89hHOfFy21ttpq7/ST0JgRrjEmhXaJCE5KM5LWVbXzf90CiriucNVhnysgqXb0iE6O4SsUY8OMg7tlORAUpZcGlSmfcNAWn80E6ssrR9x0OV7rFZk5gzDmzLNeW954x5LlrnxdwxrBYLAtOKTBEKJSwqrjxe+/neI3Rj/N1Ok8TKc0YqDjZy8a71sXiTIGz4tq0WizIWuz5pgxzmThScUuyxEI+TynK51ZOyOFK6GIKRV1LUJg1ot+ePCGn20xbQ20cMWu6QaahkDKLtqLrR16dX3IzGEZadkNi1/WE4tZ0eHzAwd27XF1dsb65ZtE0nJ2cYBR865sfcP/ePVEpLVtR+VR1SVsMuKri+mbNq1evWCyWsjzsOjbbDVVd0bYtfd9jrSkm2KK/f/T4Edvtlpwjp6cnXFy8JkbP+fqG0Q8kEl3fc7Ba0XVdSRcI8315cnJSmhfF6mBJu2i/tEb9STHL+znnZ6VYPVNKTeSkR8A/vfVxn5f3/YxHpoCFM6Hyp3vHjPqX3/kFXwaYgfNQcMrJ3UeVYrlarfj4k0/44Ft6Hin1GyYPWhzaS3c7Fu+9tm3ISkD34AUc18rgB4+OWYLLULR1TeVquZB8oN915BCpCodxslszhRrlnKN1lhxFPWOMEZOCqmb0vph5yM1n3X5cmwrc1J147yUHuqnnrJIQwhvb6+lGVYXjOB0203+f1A1TYqPWpnAyTck9SRIJwFSoIsaAMXK62DK+55hJSKZQDAENDDEQcsaYCmUNlRU26vSaTT+ztaZs7eVGNmUxopRIP3vhMEtn5pwUUCXk+RwTwzgKLot0Yq6Sg6Xve8lBKlLNpmkQuXFGKVP8NmUrnlIqUSOZxXIlhOlheOP6EoVOIY3bal4oAnNw2BRjEmMUJoR5M3Fyev5DCLPxLTrjfRAJ4HJBzjCOA8Y5qpI9E8NkQiKdLDnhrCMmP4/1ci2LZykp0scOaw0pBoa+JxboR57bEtyW5YCvqorRR7p+YBw9fWcZhgTUQGa93tAHSGSOz45wDh6+9YBf+bN/nmEY+OzTz1i0LceHh2zWax4+eMjp6Snrmxs++oM/IKTE2ekxi8WCo8Mj/Oh5+vQJw27H6mAhxHFl6LqOly9f8qMf/YiTkxPef/991uu1GAcbK0kGMQiuX16T9XpN3/c8fPiQppHky4ODA8Zx5LPPPuPZs2f8pb/0l2Yq2PHxsbAWyDNe/0WPf90Lni8qZ1+4lVFK/Q3gbwC8/fajfXeY9y40f5LHbZMGYww5iJtQBlSWC3SibIQQcdXEyTTzaaO1qBam7mwcxzdGJ5GjCaXEWQPZUNkGagm30ihSCJIgGSJGQbNs5YIMoVCTMs4Jfqi1Qecko9wE8qdJe5uLOYOmaVtS2psXT91IKhd3Xdd479l1Emk63QTTn+nGnn6PSW45Fd5JArZXMgViDKImKbEWksE+qZ4i0iqqYmGX8ONAjobalThXAAqPVTtS4W9ORiIpS5eelSLlVL4fc072MHQkH6lr4RBaK3EOWYnsUw45g3IOrURWp42wDsy8tIvkbGYD6Ol3d85ibTvTeaalWM4Qg3RbTdPKyN73s1w0pTRju20rn2+tFYedwggA5sNpMtew1mI188fM8SUhiDa9LORAFUMMtadtKei6nVCy6hpnih9pEWykhBRCLweu9yP9MGBdxbJuaZwjeDH7GIeObrcjBll6+GGUrxGjuPQbhSuu7f0g3W4eAtgGbRJ+2DJ0HR4Jwnvna29x98EZ3/nOL/Lu2+/xkx//BKLn/fffZ+wHzl+fU9UVVeXYbDecnJ5ycnpCTpGXL1/y7OkT2qbmgw++wdFqNUf/bvsNT58+ZRxHHjx4INzkMuWsViucc7x6eU7TyB7i5uaG5XIxL3IXi1bYDP2WxaIlxsj5+Tl93/Pq1SsODg7mjnXb9RzfOS0TzRc/vnxA/+MfL5RSD8tN9xB4Wd7/OfD2rY97DHyhMj3n/Ldzzn825/xn756dFc5LiYsQD4X57anc3l7U3B6/bxfW2++fDH+BIiHcg+m2YDTa6DkHef+5++15CEHI2reULRPGtN5sGQbJDamqBusqjDgqkEOkNlYCrA4PODxYYY1sJeqqEsC6rmjbhsqJZ9+0KZ8K80QvSYX0OxXAyTT3drHr+37+dy7j3XTCTjfl9DXFdUjPb3/RczgX5KljKzhmCJ4QPH6UQPqpuGk97Z80VeleSNI3ulIwcs7UdUVVV0VdRFGzVDOH0VqxrQMJPnOVbPn7vqfvJ2zMYp1wIUfv5y2nc46qqmibhnYhrj1KZfq+K1vqzHa7oeu2TBlBU4JgLKYZE89WKVO24PtraPr609vOuZL/nQoXcw8N3f5Y8VOt5o3s9FppreePWy6XrFYrQJZ2rq5oFpI3r41cH9ZIprczlkW7YPIVkOtmn2E+TQ5t07Bq2yJZlQWp90Iqr6wFpBkQ7mSeoRdSZhwGSInaVbR1S0qKEGC5OGS1WnKwbLlzesA333+Hv/hv/1m+891f4N2vP+at+2f0uxtWi4q7Z8e8evUc7wesFY37erMuk5GmKxDEyekpp2enPH78CB/8DBW9evWKGCWPHShdojAEbm5u+PVf/6dUtZshldVqycnJMZA4OzvFOTvDEuM48uzZM5qm4Vvf+hYgh9mrV6/49V//dT7++CMePnyA/oJU0unxJy2W/w3w75e3/33g/3br/X9NKVUrpd4Dvgn8xs/zBXW5MRVqduJ5s2juO8/bf6bHv7QZn/9/z1ULYd+p1eWJz7pkXpcjeoqQnSg1PngUEyk7zSNVPwz0/UhUFoxD2QptrCQ1loRGlJiHmXJqkxOVNdTOUjmREpLEpMDafee6L5b7G3UysLj9u4v/Xz+PE0PRmadb3dMkU5xG7On3v/01bh86Oef561hjqJzFWY01Shzcs0SipujJU0FU8vrZQn6WrtuKsYPRZWFXtr9ZrPImOaYxskSx1rJoW5wr0bsKXGVL4cx4P5TDTgpuU26gnPIsF5x4c13fs91siFF00YJvBXyJFzFGFXbCbsYeZeOvBYu+dVhNX3M6PKbidPv5k0kgzrSj6fWYF2LlUBqH4Y2ucuJLTgUO2IsEjKittrsdQ3mNm6qmqSqBQEqxBUqcrhF8dZhki4WzaY3o95dLlouW2jkUma7bkoLHapGSDn1PLJDRdrMhjOK8LtZ4aRZTBN9TG8X900O+/Y13+PYH7/L+u49wVlFVhq5bk9NI323YbW8Y+h2HRyu0kaXg6enpXNw//+wzum6Hs5a7d+4KLFKaEqVUMeo2PH36dKZzdV3H1dUVH330EYeHB2w2a1zlqGqh8v3kw59wfHIskSEpFo9XNXeijx49om0Fl+z7nouLCymkT59ilOJwufzSGvXzUIf+K2SZc0cp9TnwnwL/OfB3lVJ/HfgU+J+UQvYDpdTfBX4IBOA/+Fmb8OkmnTDDCWOccESxCnuTRvTzPKR7KhzNMtKmW36KbdPQd7IVZdqk33rkgt1QDDbkxBEqkSyFLMcnJzx9PpK0JSiNsU7oEiSMEmwKI+NqTpK4KL9fKt6Sah5twy0MMhXMcEqiHMeRxXLJrutReu9aJMsMPRsDSEKep7ZCcJ86RGAe86ZCkKaFzq0x3RgzdzziyaiEAoQpm3ItG3SjihVcUSOlVMKyJGkylSWEMWreLqYUZ4MHcjkctWiyhdwvhQIyMYn0z1qJT61rR4wKV9k9+T1IwRHeXE0uevohS/HRRs+dVts2+9e0PJ8zrBGF1C2Fy5dDSyzyvI+AdHnmVhc+FUJjDKvVipSSRI+ENL8m02sEsNvt5ufeajUX1Gk6mA5CpWQBo63bY9UFNyUjo3fKdNutwBdFz97Uwg4A2Zg7a4v5ioacCD4SvKfbbem7jqHfiTnKEEk50FQ1OUM/DGIr1/Vs1muUcUzKsMoabO0YxowzCW0tj+6dcO/eKcva8Mn6it36hn68oHFil/jsyWfcOTvh4PCAYQx89ulnKG04u3uPGBPL1YrDwwNuri9YLBY457i5WVNVhu1uS9u2HB4eslgs5mLnnGO9XvPOO++wLIXNGEVKmmfPnvKTn/wYpeDRo0fYJNfLo0ePuLq6KtZsAlXJomtPIwohcv7iJV9/790vrSk/zzb83/uS//Q//pKP/5vA3/xZX/enH7O5K1K7UhYKzrSU+Xlq5TxC38L0jDHiOl3wnWnUpYyu6hay9tNfKxRuljGmJNVlYukmqqri/sOH/OTJR+SqwrQV4NFVRRg6oc4YIWerlMTEV8tWMnmhCtVNQ2UNw9CXeAHpaKOIwhlGwZwE0xKD10litx9r6xlrnPBW2I+MEyY2QRIzCbp0erdv0olwPcENMXicljwYYRkACZTRRMCaEoExdcfGYCZqSpQFic4F9qgq8fNEnlfjxOVnDAHrJJJA8tKnLjiRkmiatZHoDQHwtwyMVEXJJN3aKNG/hScpm3ONIgkxnimuo8AoxDJlJMCILLA8F7cljFPnmPKbz+HUyTZNQ13XdF1XrhE748JTpzh1prZAKCmM8nfBmafN/3SdDcNAGsV6rdXilwlS+IIvk0WShkLlXLbDoodvmxbrNMlL1xxCYNHWgMUryLESI7/kMbpmuWjYrjd0vSeMI+ubNVVT4mhjIidP8gFjHdokjBpZNlBpUCazOX/G0XFLd3ONTomx27HUVkLSomdzc8W9B4+IMUg+T1Xx6PFjxhCl0GVJc3RVVawTI69fvaJpKly9RCnBIh89ejRDF8MwcHp6OsNTXbdjt9vx8uULNpsNv/RLf6rY7Rn6PlDXjidPPme1OmC1Ws3qqc1mw9e+9rV5G+7HkXG3xRfnpy96/EnH8H/tD8VEMxFun+QL61vU9J+t3rn9tShjvTV27jClCUrzCCRb6aJq+KlHTqIqyuTZ4FW6sn3Hdnb3Llgrki7nMFWFcpYxBSKZpCCVLV3bNMUSn9lIQytVUu/6/Y2Q92TlGNOcRum9OCPtqT/7tyca0bww8H7eaL9B+YF56zqNklNXOS0l4DakIQeYpO8Fghdw3RR4QLBfKWw5SRdmrS6ju8FNRUvt5ZZKiQFz3/dyGKX98+msKyOYuH7vMedcfr7CQ1wIvrdcLiVnRsuiZyr6b2KFgnkaK3Sbtm1ZLpccHKzEusxHMd9O02ueCV5oZinKv+3kLVmgjan4WWtnfuSiaNgXi8XsiTkVVokxFtzxNpQyjeO3JwChKnliFrrZtIG//boZtQ9k2+vVhZ0wTRx1Xc/wg0JgoKHv6fsOlUVVk8viUFgGgWby8vS+WLXJ/eOsw1nFwcpxdrzkzsmSs+MljYXu5orj1ZKTw0OOVgfcuXNCjgE/9iiVGfoOpeD582c0bTNr4V+9esWLFy9IKXH3jlD42rbh9OyUtx69xTvvvM3x8fHMEMhZLNVOT0959erV3CAIm0UOnsePH8+d6FAgj+vra37wgx/w2WefzVOA956vf/3r3NzcSApmVWGN4WCxxPf7hMmffnx15I65ZLRMlUssH5kG5DcKZi4xA2pyUJfHlLM9EdRzLma+ygKiBU854FNiO3TcNZqo1YzxyTfTiI1+hDhAjqWTELK6cxUxRWpreXjnhNZEQjfiTu6TNVR1R2qv0HrEKE2lW7QeCSmRjcJUDoIpDv2GsfPid6k1fRoJMZGBISWMtsSsUNqVH00corVxTOdcCKncLBatEyEmjLP4lCUuI8mSJIYSm0oum1vxx6ydK8C/YJBTmFtdV2Sr8UMndAoFMQjxPIZAVfiUGTBO46yCLNroZEQXbbSbSe4pCSOhqmrhoEaJgzBVhbIVylTYymFVLAuPVhZpuy0hZuqmonKOYejRlWPX96QUcQUTHf1I5QxVJaB+CEIb8lFSLmsMJkjnGWOeNe5QDmalqQp3bxwGtrsNWimaZonSil0pimLIO3FxFcMoHp9aG6HeTOoqK3HDGWa6kI9iehLlnWQtXSJGzbryVDBC6UzlwDSmwYeRlAIhSMeurJGNvXLUlaOuatraSd77rsOWA1o2y4EIZK0J5Weafs/dbstu09N1PVXTYLVFqUgcxvney0HEHAujuXNyTIyJfvQEIFjN8dGS51eaEHuyamkODhgvrjk6OqM5OMaHRLM4EChgu8XUNReXF3zrF75JVRlCHGgaJxi/sTTtkqpq0VoOl4uLC5qmoe97fvd3f5evf/3rHBwccHV1BWicqzg4OERrzfX1muPjI16/PielxOvXrzk6Opo709VqhbWWTz75hIODA46Pj1FKcbBaYZCl2Jc9vhLFcr/w3metpJzELGGiUPDTUamT2+WttlABOguvXeQviOWaEaJ6ki6yG8WkoVm2ZYyc1kHFbSgnSAGSLxk/EuEpnYpls73BacP902MWLhK6HpVqUvLEnMEEgu+xSmNVg1eJkDwKTdaamBMpgc9Jvnc2JCAg9vygZDy1irpqUSlLsZ5+84kvghR6H4RaUzetdMuFglNVVbFmM8RUonTLttOYkl+UpxFaF+xYvrRRUsC9nuKE5RR3VmRoE2Qy6cv92FO5GucsuUAn2pi9Tp7i7Zg6tEsYWxefR422FcbV0oWTMCh8L3ZmTd0Q46Sdh7ZuCSkTUsQZQ7toxYg4Bsa+Yzf0EoPRtFhXlY5UM4yefhhnGEJUWxZiIpYlACTGUTreiY+ZcoSkynUieOqM63oZ6ZQWvqc23DIJlmsmBxmpp4VWSvI6ytksgbIpi+u9tpaEQquIJmM0xBDodhs54ExpDbIcHvWiwVjJJe+HyLJpWS2XVMYwDiOh0Ihu1ms5JDL4EBnHAWLAh4CPmaZtMMbSDR0piy+mreR1HL0nRBEL7G42dHXL8dEJy3bFZhzYxMjv/va/IDcVDx7dRxlLSBldtSjXYOolm+6a65sNi6Zm0bR8/tlnHBweCsSgEq4y4oE6dGIMXDX0wzjjws+fP+f4+JhXr17NuPoEkZydnQEIL9XagmluqKoJHrF88MF7gCx0FosF19fXfP/73+f4+JgPPviAm5sblNYcHx3Py58venwliiXcogXdUpNMfEegXF0//yh+++s650jBMpboTV9MTpfLpdCVprEGyFn8E1OcNop738xpaRSC4I3HpyfEnNjs1iQCrnas14Fh56liYtEonFFEJfJFP06E4bIsKBirMZqQpTjMixkKZJACClkwiYWbl266wAwCL2TGoUNrI7G3xpGSmSkXY1nuTI7bKSWSks5zIn87Z2+N5Gnuoqos+d/m1lKiaRpxt0aw0YxwMlEUkwkZt6tKTBlCiCILrBxZS+CYOOjsM9IVgm9qreXGDkH4kjqXdErhwYYYGUOalxhd6QA1UqAWi4VII8t2ePKJnLarE97rnBPMtJ+MJsJMIp8WLnP8x3g7DykU/mYxxr3FklBQxldZtAi+WKF1gAzBF8/SaRk3jmw2G3munS0QgiYFWfItmoa6cIL73U7+u7WMKRGDJ6WK1ohaTZWlWcriCeCDRDhrV1OPNd12O5O3K2uJZDGTSYGYM+2ipm4kBdI6xzB6xD2oYhigXTYorcSgBPElaJqGtqnYbXc01lIlSD7Q7/rZGergYMWHH35EXdc0dc3oR46PjlAFltjcXFPXAiH1fU9OmdpVVE6s7vq+5+TkhHEcOTg44J133pmhqckE4/d+7/f47ne/O79+4ziyWq3Erq5t5+s+hMB2u+X58+csFotZ0KG15t69exil2A5fzrP8yhRL2BfM23//cfSgn/cxYUc5C/9QszcYSDGiSvsvmSNSkMIoOT1KiDLM9mVRsKTFohFczSqu1ufs/A2LqsJWDcEsyXELSZHCiLZiYjEMI23dMhEElBLzCoA4ZZ1o4flVlRPcLIjz0Zx1XuhVpV7KjVsp+q4jZTCVOH+L11YsS62xFP5MTrokO5ZtTdGOayVbRWMsHi/FS+05ezFGrN6740zOO9ZaQlSEIFCFYEwTJ1UuL2tM0V5r2kWLdTXGVYQknZUiYVQWgn8SEnzK+25WuuWI0oL3WqOorBTPmEQjP7ne64JLp9KNT9vm6eaalDw5Z/qhJwUvTIkUC0ndlsNUMFIhzeqCw414H2ZGQdM04nJkrJhzKMFOTcG4+35AK0mSnJaLIo3Mc0a8mwyFUyaFWLp7iezwfpgLcdNULBZtWeCJuS7ek6xEHOckuPHQd+QsxiveD2hrqKxhHTzeCwvAh1HEA0mKCMmjVJRpQytcpUtXnjC2oqqsQCRVzdgP9H6gkpMBh+KoXaCywg6eQIIoUEBOidcvX1FZx/GDI3zfMfQ9L16+oGlaHj+6z4vrGx48uEsMgfXNjaSsjmLk7L1nHEcODw+RXKO9yGKz2fDs2TM2mw2Hh4f0fc/du3fZbrclImJv4tu2LbvdTsxExpGqqnj48OGsBFqtVpxfXvLhRx/NneoXPb5SxRJ4s4tT0k2VqvAn+3qFeDtZ+evNBqXKDezD3L1KcVCyPU1B+Fk5F3ebPcl9KnRVLdvcx48e8cknTxn8lsXSsTo6Bt+RkiyrUvJATU5qLj5jP1LV0mmactOrKCNuCMgWuWzwpXksud5kqqqMkSWELQaPqxyLRQsotEacaEr6ncj2ZDnjKif+mcqUqAYhnKvyHE03LzmVZdheBz14X7oXoSpNHc+kOe+6qWhB3w+klOn7Uag0SuNTZAijJBGuDrAlWdMYKxBLDNJF50RVWZJPszwRlaUjLYW6riui74uph0bbTNf3xBCpnCvmvXuy/cR1vH19VVXFdruVTquqyFk4j32/DxUDGd0GLyPhtBGfSOSuULSaqmIYR4xGTFzSHjdP0TNEsW9TZHTBKYehE+J3jsJZzRlrFVVVQxL2RfBCmD84WAnlaZBlhy20p5wzxCgLyHLwb7fbshyVpZUfh3nBSBK7vWDE8KV2hnEc8H5EK4kesc6RUaLTr2vGMaBUQGvJ/wGhjDWtuCst6oqsNFZbxusNN9Zz8eo1m03P4v2G1xdCC7q5uSEFjx966qrmYLVEK83du3dYLZes12tev3gl13u5I28r8dbrNSklDg8Fm9xut6SUeOuttzg5OeH8/HyWoQ7DwA9+8APeeecdTk5O5iVnVVVvcDkn1yjvPdfrGy6ur3ny/PmX1pKvzDb8px8/rSi5vTmc/vsXd5p7XmbOSTTciHFtXTdY6yCBURI3kIoCRQrGiNGJGHo5eWd6TZw3s6mMUArFarHk3/0rf4W/+Bd+hboFbSFpi64OwSwZE2gnvpZaW4x2+FEUCYtFi7V6Vr+IOa0Y8sYgyxZR/KSCK2ZyihilJA52JoNDLIVMFTrJ0O+IQRYeKXnqykpsrhjKzJ6GtbOFpCz/zimKg43WMwl5ImI3ZZtvrZk3jdZahmGYi6a1Zi6eU8bMXlXT0jQtFCrOvIklYzWEYVdyX+RnSVFI5Lvdlr7bMQ4DSmXqylEZVbDNBMkz9JKnA2CswTgHap9EOPEap25j4kmmJN1qRsyShaJkqGon0cJWCrXVir54Q45DBzkRw4gfe5zRjGOHHwb6bsfQ7QjjSBhHSFFgBhQqJ4a+Z+g6tIJl28wZ8PIrBzmMkOTEoduhgdWipW0aKmvnA61tGqzWEgNcfAb8ODCOQ6FDyYTUNDWLtpGFkZGDkSw/08FqgTWa1XLJ8eEhdWVZtDLmO2OoKzcrspZtS/Se5EfunJ0C0/Y+MfYdeE+NIu46Xj55RhhGzo5PuLm6pqlqLi8uMMZwdXXF6D337t7jYHlACJ6PP/qYq4tLXr94KeF9Qa6NcRi4uLjg5cuXfPjhh3OY26tXr2aGwNnZ2ZyyOV2L2+2Wn/zkJ6zXYspRVVWZBBfcv3//jcNzMmJeLpe8evWaZrHg7r2veAaPdE9//Ij90/LGn0c/nkE26mnqDDVt29D1kbZu2FyvOTs5ERVN4QCOY08I/fTZwN4lxlUVw6YTyaG1RO/5xttv8/DBHf7wo4/58UdPyMZhmyOUj4Rtz2bcYCorRPvye1gnYfHOOfRCSNJj79mvQmScjIVaI/hecTAPHuWkuIuyYu8oI0oSKUJGaxlFCxheFVNcrVVJ7VNCLlcSOmVdU+zOJlnklEQpN0YumJsp0bspJZbLpeBAu6siUxTeoNYWY0rmtpFM7DFF6kVLXcn2PcWAHwass1hlySqTkmfoIrE49Bglr5n3IyFFKmfIKUCQjtp7IfYbI4oo52pMuWlSTDMhfqKZTM5RQiLfCZUHISij1MxZTMW+TsbAAT+KdrqualIUvHDswSzEFAWQZMAko2PMohCb6D0xRAncKgdh1xUebVHmLBcNIVgWi4bKOUYs1rRlMhH8dh+WVs1dqqGYQhccHDJaSLGsSjdKFB/QobKonAjeMHQ7UhSMN3hP27iy4DPFx9IzhlAkvBaUxvnI7vqak8NDKmeEglSob1knamsIwXNzeUltHWcnp1yu19zsXlEvlnvVlpPMqaqqeP36guPDIw6WK+7dvUsMifPdBckHLs7PefXqFXVdF4GCnU1JtNacnp7y4sULqqqi67qZ+/rpp5/SNA3vvvsu6/UkrRTY6Ic//CFnZ2ezY1POmaOjI6pKtuntwaH4xn7J4ytRLPfd4Jc/NJSMnf3H3sYzv/RRukPKWCTJfDdYa7m6uuRd9TWmkzjnILkiMcwa0SnlcNrCbbdbDg6WchN4j79Zc3Sy4Jvvv81Hnz6hTwpTrwh9x6BAWYq5QRKnHC1FJMUS/WCqmXydUiwFK5ebTJL1rJGN/oyjFVMLVQqqgvnfVdFVxyB4jyqLj4kkLYdOpHINbeH/TV3zbSMNMUXWDJMTDhMnU6I5Us7zaT9rzDMSMJYyOctSy1rhMjqzN4aYeIeSqqhROeGcJkVIQRQn08JDa81g5LUwWjEOPcPQzV1ju5BxuBtGoX2VaBKthWM5STinv2+bWmitGPu+6Ns1VSU3ZN/3RRUVadsaa4TOM2Fo49iX5yLP9mhN05LzfnmX076Qg9CXZOSXPHHhQlb7g7hwUyGLkXC1YPQjQy8HiymQVBg91kpukFWq5G9LwNlysWCxXLBeryW5MQQcmcqJeXPwEhdhFwvGsUerJZv1Gj9I5IVC46yQ2HMehHcahDu7ahdcX14xbra4xYLd0NEulqKo8iN9t+NmvWV7s2bsR148ew625uz0DFPcs4C5u3/x7Dm77oaTI5GaHh4c8qM/+BH9TmCBjz59OlvmAXOULQhE8s/+2T/jl37pl+YC2nUddS2O9AcHB7MhxkRk3+12PH36lM1mw6/8yq/MhfTOnTv84R/+IU1dozOonybd3Hp8RYrlz+4sJeT9//uvO0knbemIZExsWa/XdCUOdRyj4E0pllEmzsVSqckGTOyiyFDVtWRop8z2+pKqyVSV5vziFddrx9nRI7KtGXImxp461ORE4Tv6uXg7Z8tWOZdlRrHc0gqFBjuN8EWzXlcYu0+unAjcEjkhN6UojTShLDeAkioopN7KOaq6pnYSr1vXe8MH7ydNdFmIoebu0RdJZYyJuq2h4IdiiDECE4PBFLegvZ5a4g4su3HEj566qbBacDRTaFLOWLHOKTeEVhROZJi180ZrktIYZ1BOo7Qt238t289UIi2c4K7GTJ1imnXe6/V6NiJRChYlO2fSxE/GGJPhBYg3wdSN7ErM7HTATFimtU5I7KnkcudiflK5cohVokcfR1ms2HZmJgiGLq9fjFHw2yCvm1aQQqAPgbZtOD4+Ev/OfkRNrBFjyCmy224ISYyLUVBXFWEY6LuOHIvcNgWGvnTLSPEOI1jjCCFJbpIVL1ilDcvlgSylQuB0eYDvelFjKVFyoUSb3g0dV1cXvHx+TtUu0WrAGKFuxXHg6uaGXd9xsFrMePDBcsXFxQVvP36LnBJNXXPx+pLnz54DMnFtNhuOj4/n1+Pq6oo/+qM/IoTA5eUlJycnOOd4+PDhDJvlnN/IaN9sNnzyySccHx9zdHTElNm+Xq958uQJDx8+ZBw9IYKp/v8gsGyqg/m2zVB5D0ruI5g4hvIZX+QQosrHTFSgiatprSxJtJYteBglQZAyEg5+FH8/oeySEV6bKVtLhZygungmdmXbl+PI+fkr7PEBL1684ONP13zrGxX3Tpa4dsGwu8BlcXSRU1AWNboUmjAMKDLOyKInaw3WopUuY10Qh3et0FpMLXzwMuIWC7LbtmC73QZVljzGyI0qI6mRjqWuS8B9jbt1IEiRKxv3Iu9LScLErNF4mH0hE3nuuG9LLGNMUAjakpEt5G9QNFXNaiUSt9EPZGQEdNaW2AODDwmDLCeUUkwWdVrL2B2DFxjBVeXwVOV5yvhxEAmmEx5pThGrQeuKSQbrKlf07LqY7ka6bmRS/uQc2O264qvZYLTEaphyrXVdj0ZRV+K5mVMqueep0M1EyVMVmkpO4gEgVnIysrZtw3JZrNLGoYS6laVdBlSWIoT83lXB5FxVFSpUsYmzQjGCLEshrVAp4cOIc7rcQYmmbhnHgcnmL2WZGgQjTeSqgiS5TnEU+pkwQyRDaNGuJMY37UTsMAbRpRsnI3vuWbYL/OjpfEBZw/nVJQenlqNDSwie6D277ZZ33n5HHPjHke12y3JRF+oZ9OVw1UqV36HmcLWi7zpubm5YLJfCd855VulMNK/tdsurV6/IWXJ1NpvNbKq8WCwIIfDBBx+I5Vzfs9lIoNvV1RU//OEP+eY3v8nDBw95/vzlvOP4osdXo1hmIRVnZBQOCJ0nkYT3SILRoOI+kkEpBYX7Nn8ZQBmFztJ5kXPJyM74nMrnOtpljY8jVSV65KpypOiJXi4osiaNGV0JJjXGHUPe4fseA4zbnjxK9ravNyi9ZH214/mTF1xdXLHd3cEfPsTURzAkuu0zamup6sI1LJJJUpbxygcMoLJCpYwtsavaTfSfEaNFOzsOwhsNE7ygzfwnKyVjbMiSfRJLl2hatLbEqMhJY3RVNvPSSQqvcurEDCnJVl2JLo7GaAaVyQaCElOSZd0Kbz9kQhkvB9+jjGIcPdY4XO1kiVaMiytlBX9Se/u56YAYy+FltRTJru9lNHWWVLbQ2hTZXqpoaqGRDMNISvu0xEZLwR9yLInKxXdNQ8qRqpZCpq2i2404VcwpduJvOnV0RkfxdHS2OCspKpfxYyCGwBgDOe9HS1H0BGLybLY3s4lHiAMuiTdkVTvZPOsiIY0jPiW0aUlZOIJN5SBK0YghEItTVhg9Rmv8MJKtlQnEGdnQKvkdU/AcF0dwYwzj6Mm1xdYaHxPa1nN4mdHSCIyDJxFIWsvrm2O5/gAiXbeR5z5HunGk1y03G9nAr6qEST3JGq46z7ObDZ+vr0EZ7p0ecnzniKwMl5fXnB4dsrvZcHR8wtX1Bb3vOaxXVHZJyJptP/Dq4pxsE/XS0NQN3dNLVrWjdxIEp6uKo5NjwhjIIbG+Ws8pja9evWK32/Fn/syfmelfy+WSk5MTnj179sYiaIJZLi8vaZqGly9f8uitB7Q2sDj6V3Ad+jfxyDkXEFwItUklyRYpOu6YJAPGZDMveqaCKWqIUjKVcBT3PM3JaDVIYZ2yu+2Uvy10GmfkopP0Qhkvs2R/FuONKUgqFcKyp+u28nO4Ctcu+PV/+ts8e/GKIWpJV1SGqmnZbbdk7RhTJmSorZHvlyW3xo+efhiEh1iKCFDwyDjb509kdigWblnoNGhDmAwxyiZ66Pdu2aaMSRMVqe87cTKvpVDe9vy8vS1OxRxDaU3d1LQ5sSub78pWZbyWcb9yYmoxY51ZVERKKRaLKeY1vEGMhzcXddPPIOFqjkkPn2caU0LFXNQ3e+/G/fOlZjMMay2Vq7BlzJ0oP7eTNZ1zBOcYt918DY7jOH/MpLZRSpGC+GVOxiSTR+j0+06LjqZpaAulZ8Iup45f6zdNmKfgssnDcjJxTilhlTgsTTSYqUGYsN7bPNDbqZAT1NC2rXx9LcuxKTDND6F4BwSBkYoAwzknbkXOlcOycGLbttCH/FxowhjYDVucs8Ru4Hgl8t8QI1c3a3nNlwtOT095/Pgxr84vMXbLMHhyVlycn7Pe3vD48dtsNjd0uw2vX7/mxfMnXF2e897X3haTk0roYAcHBzx/9YrTkxNO797lk48/oakacXiqHE3T8Pz5c955553Zv7VpmjmX/fz8nEmQMEFKTdPMBjLvvvvuDK/dufNgxuG/6PGVKJaAyP6UFh23yrOLjTWORMZEhUr7rfgE8E832U8vfabNc7yFe+VboGddVQxjL0YWfiwjumCEk0TOFC7WYrGY8bmu6+YXxViHtof8+JMn/P1/+OvsxoiyNUlVZC3+eouDSAgbfNcRsqIxTmRrvhj36uKIlETqqcvmuZhsihGIKYoQJdZYEy5jrUMZI9rjKBLA2eLOC44YU6SyDteIhpiUMKUQadJcIOfD5xbjQBdfyuwVupeRUVrg0qzpvZFwint6lRQa5pt8Mi+p63YuQtNWegLgJ1OOkMOs5JHCIwTrlIS0L58f6foyLlcVVVXPxS7EIJ6VzmKsxRqLt8WMBC/wC8xc1b3JhyqmH1PWjkgfrRUeqLjD60LczqgkzIkYR1QZpauqggxD35OR68hZS+XcDJVMxe62K9Fto5O+76mMbLunbmjyLJ2wUq01w9BzO+30tulvXdczvWazmdIb6/ke0UYTe5H0tm0rHGYlGmvxxQx0u44QIjDgfYBZRmvJWFTWs/dAt+u4vO642XYEIxxXrbUwDoqBh9KW5XLFxeUVTd2yXm+IIXBwcMjl5RUvXzxj2dYMo+fTTz8lRc3VxQ5V3IU2mw1/9OGHPH70tjx3MaGjZrlccnZ2NgskNpsNDx8+ZBgGfvM3fxOlFN/97neFL1t8WttWdhbT5x0dHbHe7CAmrq4uv7RGfTWKZYEojTagLEnJBjdPlCKdUUGUASkLgfp2wZzHcqXQZo9ZTuPulBs+fUxd1SxXKzYv1jKqpEgKY/k8GUedrcrFIzZZm/Wa9XrNdrvbO3NXlk9fvuYf/OPf4I9+8gljqjg9OWV1fIZ2LUkr2oMTfOzx8ZyoRBuulNiUpVtxAtOWW5WfecI1UwoF09P4Mc4dYwEa9sRdLV6SSilc0SVrpYkpydZdK5rKUVeVdDklGmLqTKbHRLoWSZkUvKauUZutdDfWCT6XIrm4iE8X6oQ8y9u6jIJys+W8N7ydFiPT+DppfWMMxITgdkqgAJEmSqBYKORtVRZixkh3m8rXFnkkQm5PCqfEuT4oGZ3rQkKeXIPIEkTWdf1MAUoxzUa+zlWslkuiHwneM/ZFqoiS57VI+ionB+DY72bV0OSU47S8NpXR+CyKo+kxXbdT5zt7k+q9PdxEnp7+nrt+LRjn9PxPUr8YIzc3N6KrtlYWHSGgtFw3q9WKwWgMsN1cS+HWlRD9i1lMXddlozwINBEjKfhyPSYUkdrWEBAKkYPLmw1DSByeHnJwcIAr1oIvX77EGMPp2V1ubtbS1cVAKsYh1kqyQFM37HY7Pv3kMx4/fosUFS9evAKt8SHw8e//PrbwIg9Xh/hu4N7dezSLhmfPnnH37t0596jve37v934PrTVd181E/RgjBwcHc4MwDMMs4a2rmn438vTpS77s8dUolghp3CixZctFy1d6RsgKpQsVA4R0DMQyusx2WLc6IwBywezSvvtUMAdgTad6XTlUJTePVhQ7eotCInAvLy+5vLycTUNXqyU5Z3wIvHx1wXrbETEYV3N69z7Hp3fQtiGmIGTgxTF68MQ4grKEUDra3Vb0uYiELRWOl0aKoJZZXAKpYsQUFYuYyGq5kH2UZUlM+DIahuCJIZbO2JbMZaGmVEUDrjGFFK/nDn0in+8jNFShvNy2qBMVT0qZrOWGH2OQpEsm7XQWt6dCi5FOw71BBZks5KbxMRZHHqWYjXsnjXpKau7MrJXJo67q2Qpt4sZNRUV4nJEwjPJnWlZZi0qZxslI7ZVmu+mEuF9XMw1lu90yDB2QCKEV2lM2c6G3RlOtlnRdV7pm0e8nLc5MQjTfx5ikGIlay1Io5vmQmjbnU+c9/T1mMXyZuu5pspnoa0JrSnMcBzA/j7cPHz96bLOU33XsMcpB1gy7LVVVEepaLNMKLpqzBKVNSQGTE5L3nqHrMFoRdU1CeLqTjZ02loTBZ42Z70lhA4zjSNsuZ0z86Sef4pzj8eNHhe2QWK0OeOvhAx4+uEdd2bJDgGX7lKfPn7EbB46Pj7n38C2MEcpXu2h5/vw5N9sb3n///fm1ma6xycIvRvkZJn35y5cv+a3f+i2+973vld/XFxxzQBmH0v/mAsv+RA/BoEa0UcUiqkj8psW4rLjf+PPTDMvJzUWbW4VRS8C9WFtF6iqLLCwnFq3obBfLRTG3zSXEXm60vt/NSXEhBNpFzcosZq5eDJJvrbXh3t37GPMjTu/e5/Hjt1kuViKxRGziqA+oVomwPmfXD6h+gw7C56TgrikFYvRCIVGCnzrn5lExhBFj9u7aMjjq+caDPd5plAYtqh9bcMu6ciwXC5q6Kqf68EZHM3UoU8GUkzhgrCUXSaDP4h6upZxz2/hj+mOMoa4a9vwGCsYmY9yE8d12B5+9ImPAaTv/LEoxj6vTIaG1LkmTcp0Ab/ApQ/Dl61GMR8QYom1b+q4jlElkMs5oq7pQlmDshD9plaYt7uNj1xM1hQep50Ol28kY3DYtxmj6vmO3FWhg0iaLMXBPih5dOca+p/Pxjd9p+r2mQ75tW+ry3E8FflJBTaYwwLzhnl7zCau7fagppRlKx1nXDb7b0e12AEVTL1DTMAzloBW8MiVJL50K+PSzzZEbShP9QOh3oo13jnZ1BJd9IXivaIoDVj0fkIrtdsu9e/c4Ojou97G43h8dnZCip+8HqqkRioLr+iDRxW3O1E3NOHi22y2Xr895+uQpMUfee++9Ak0MsxxysVhweHg4R3dMphyvX7/m6uqKjz/+mPv3789+lvcfPuDTT57w6O3H8M9/5wvr1FeiWAq/bBAcT7cSGE8GrUptFGyEW2mHX2i2kRJJTXbe0kXKTanph46c6/lzqkrGBFUGdtmsDoxDN+c3p2I0IFI/i2RR9zPutt1sODo45OToiMZV3Ltzh3t37+KsIcaEM0YiSKmw7SGEgbDpsTnT7XboLAYK5SnAaIVzcsEKXlkMMaA8F3nOoZ7+XyskkW+WoEmXbo3ovGtr57Aqo8vIWFcMg5qNWKebrq7r2WD24OCAum4Yh04AfzU5z8vWfqYZFeqKdbKtn3TR01Imxv3Y+dM481Qs5tcyZQbfY5OdlyRiauwJIdEuhKSeY5wVJJIjPpnzTkuyvSosJTFmqG4tYrqCpcUQ0NnPIzNFIz9tWKeiu2ga4XjajPcjvry24xBYX1+X5YuoiISnaGmqim63486dM1arlWRbpwRGpJjTkui2u9HUaY9l7J8w4Kk7mrrL/aJnmJU9U1GYqGETlmxM2YB7Tz/07HY7wtjh+56cfFEF1cRy0Fmr6Lphhmf2vgilM1YF4CqS3JwNu27AJwiYQoWquL664vziEltJdvjr15J1U9US4dHvepQykD1DP5BSoKpq7ty5y2q14OZ6zXL5mrfsQ252O56/fs3V1XWx7EvEEHn8+DHaiut5XdccHx+LI1bpFvcQF2y3W16/fk1Kiffee4/JaX2CHJSCtm1Yr9dfWqW+MsUSJf56kLFTdKqxKPSM4d2mCU0372TDJeYBisnPUUbyiS4keAVTx4K06846bm6uqSuHHwbEZUhO8+WynTu26YI1RtyAZJnQ4/3I8eERMQScEUWFMC4ks8QULC3oCqUiaCugebkhieJxidIYq2c6j3wvWWRMXYi1jlB4jMbIRRtCKrhVWXqVC1xrVVyEdHlbwsSC94xDXziY+/zx6WadFi3TsqSuq3KI9MQUC3SR8IOXGzYL/pqtfM+6toyjSAStrUqxnNgLZnYXv52RPS3Npi4qhghqipGdfrZ9mqIU3KJcUjLepxQZhjBnjk8FeepYYS95nInqUVRQaYxlHNyPogpo6nq28RrKAToVJldZgvcsFs3sZj4MHpcmsrtY5llrS+aNKI5cVRPGOBfJ249p2SUSxSD801vLvAnD3F+T+9F7gk0mEv30XIUQ6YLEDufyPIgdIAUzF7d0XbLIrRW+7dSdTjCIsCoaYVFkTd/1EDKkkaVd0BwcwG5LRnOzvsG+eM7l1Q0HB4e89dZDdp0YqpycNISYaJoFTz5/QoqJpnFcXV3TNhVGW7Q29N3A5cUVu+2W9W7HuttxfHTE1WZD33Us6pa79+5Kc1NMTuq6Zrfb8f3vf5/T01Pef/99Xrx4MR8wq9UKpRSnp6csl8tZRWWt5enTpzx9+ow/++f+POvtzZdWqa9GsVRgLIQx0A8BmyPWVThqtLbzyD3dBNPoMXte3upWpBPd+z1mNWVd74teLmB33dS8ePGShw/ulQusxZi9b+RYzBkmoP12fodYQbVCXXj6hOAH/DgIxmdFweHHAZUiOEcYB3TWorUOgdEPOGSz6kOkTN8SvFXoHJPyKJW/Y8zz751QoETZpFJJxdS6QAmyLMopkrNofveyxmIkEbxYmt0a26bOZsKAp+c8BqGYlA+TJYWifE+FcrZ03HLxDb2XSSFnJDFRtsjT97gdreC9n2/625Sb27SgWVF0izI2vS5TQfxpZoSzdk4Fve2NKMsKkZUapVkerEqXKt1313UlG0czjv1Mq0kpopSdl15g8SFgrKE1MnLK9JHmZYJgrDvati0a+YF+iHRd98bBMXWX0/OgjRxst0f0qfhPJiCQWa2Wb+TGA3NXJeogjyoE/ClRICPiAj9afBRf1xwjlOd3oowZa6kQzuwwiJ1bSpGALuYgUbDxGKm04cXLV7w6v+Tk2PLgwUOaRvDk16/Psbbm1atXnJyccXh0TNcNaG25uT5nuwVrFKcnx9LJpszy8ICDgw6U4urqis+fP+NPfec7TL6TdVVz8fK1XANGz6/r06dP50wqCaprCSHw6NEjtNZl8SRjftNIF7nZbLi6umK72/Lk6eecnJx+aZn6ShRLMbdd4NwoNvhhwKiMTxGNxVU1aKEQTYoeSMScUE5uEIlFkPhWsi6GFIgpr9NYBymJIWrUA8p4jo6WQGa5XIluW2UUkUgiqkwgU1uHQeMHTx4ywzgydhGy4/j0mBdXr/jo80/px1AWNYZsDD4G8YU0CZd6lrUmeI06OGLdXeODWKO1lRF97xjEtIBpwyzjToxiguvLVtJUkoBIwU2VUtiUhZgMGDQYVwD1El8awqxLGqP4RWqlUDHMtCptDAYhhZPBGU30I2MYSEmCy1LwhTQPw9ij6gafMy5X2GpF1/fUzoISY1dnHX70+DhKMmM14VHF+s0qUpaFAkoI+joJib3f7ZAY17qA9hE/hLl43C7oU3GY6DViwiEKI1e8MXMKxCTUIu0U3bAjpUjMtUCWShNSwlRNIfwLRLDpB2pncHWDspY0HcJIfjdI1+qyodIZ70WXv1iusK7CVjUYyxgTISUqFUk6QQ5UxhKi0JkyUzfsUDmQwsCQPXVVE7KnsiKBVTkSgxxM00E3TQRTVOweC9UYn0h+YLteC7vAh3JYKKbbX+mGmBJxTBgVSaUTHn2EouLy0dFnUDHgcoQQSMFwvYVdE7la9zR1xcN37hB15Gp9CTjuVw2DD5zdOeXszolY4GXPwwcnWNNjdKRuBrbbJ1xdB4xes92uuLjaMaDYes/L80u+FRPLZsHLFy948fw53/72twUyGoUh0bYtVVVxcnIyNwaT6/mPP/qM48NjTo7P2N5cYo1cg5VreLXrGTyc3nlI1/W8/Xj1pXXq54nC/TvAvwO8zDn/Unnf/wr4nwGvyof9L3PO/4/y3/4T4K8DEfgPc87/7c/6HnLBO6as32n5UFUFCI8JTbqliSxZ4EWpk5n+LQFThSFUfn5NRpZF3u9drq0zGGu4uLggf/39mZcpJ6iM8tN+KYTIUGgUkgoIJ6dn1KuWH/7Tf8TTZ88xpp07K0XRpCvxtLQKSMUvsq7ZGjPfHHuqnCrY4D5ETGtbYg2mzsKUPHI102dSEjxXpWkTFiQGAsn7mZ7DoVicGWswJlGX0S2Mkg804WOq0CmAGbuFKbrWUFUGck/WhqppuLq6ou99caRODFGUMKqQ53UledHee6GvlBvcGLFEk2xnMZkw1pQpQDwtq6oq3da+KEqMw347v9/c7/PPRdcO04ZQFUiiqhwxClNAG4EQpuLqnJPOPwstKsV9wFrOmdF7xkJ3attWpIW3MsSdSGnEzIMsnGHj0NYRY6brZcmg/K7ggaZkfJdD0NUzA6C1maap5/RH2Yr3tG1DJJTM8D3JfnLluX2IpJSKuYRETSTv2e027LZbYQr4cY4ZUfl2Nx5mvNRZI1lJOZExktKSskwl3uMTtK7h8nrN9c2a6uCIqqoLL7QhJcV6s6GumyIeyYToef78Kd12xzc+eA+jPPfun/DsyWc8e/aEse8EK4+Oi6srPv7kU/q+58WLl6SUePLkyaz3XhYfzPv37zPFIk9wyfPnz2f990cffkZKmV/9t/8CKoykMJBTYrlYYI3lzp271E3NsL0mhvHLa9TPUcf+C+BvAf/lT73/f5dz/l/ffodS6k8Bfw34ReAt4O8ppT7IP0d2uIw5uQROlZO94FNiCybYVZmtmDtMmUaZbMnMdFqWj9FZxkWjHd4nmlrG7Ek3enVxUwpOJhPE07I45MjPlQilaOQsbf/yYEW7WvFHH3/EP/n136AfPO3qaD7VUgjSgRmL0bl8v0DlDIyiMjJGbtzBj6g8ZR9PRfE2zKBmHM6HWJZc8htKNyxUnqjTxMQvhTOjc8JpORSckwKVc8b3HREKLmbQWjo+MYXwTBnp05i152MmtBb+aSyLN7Hx98TZOSmJ+kSJVFSjZhWSQQwyhK8pP0+MYLVi9CP9OGDKAdA0zbz5v73YSEmmh6lQ3nZyn7bQkzuQaWyJ1khsu10hxgtdJieLdpbdTjTE3vvZp/N2jLB8P1ueDykkY8HBUzFE0eVQHIJnGDxoTZWAGIljYPSBXdcL39EP5QDSuLpBG8mGIkZyFGzX1NWs1sqZeZk2jp4YS7BfZo5X2GOrwxtwxGKxwPs4G4RMh0LImaT3ccrGWKwzZC8hblM6okhMM/ggWfcZSEInimMgoKm15tnTF6w3W+4cndAPMtofrw64uemKjVpdvr8IBtq24d7dO3g/oJyYlNy5c5dnz57Rtgv8GOeFlVKKb3zjGzNEcXh4OJtkVFXF4eHhG8KG6b6ZDIM/++wzFouWuqp5/foVxwcrOj9wcnqHxcGh7AVUJnkPOdFtt19ao36e3PB/oJR692d9XHn8ZeC/zjkPwEdKqR8Dfx749T/+e4geF5UwRuIFzBT4VDhbqpialpYBEL/DqXswWrA5pap5c56KfNGYiqpqGYeeGCGrNBupaq15/fo1ZycnOGcJueRJq73kMIQAWlFZibtNSnF+dcnvfv/3+KMff4K1DYdHJ5ycngBS1LXWGGVJMYjKIwdU6X7rumWsarbXOypdTH+NLl1tIiWFnC8JpeyM3Ym8LTPFJkiRyFCyubWZjDX0vNxSuuCK5fcxWpOT6I4LdbUcRHvHoxA8fb9Xl0w3VfDism6dJYziAnO7qzFWQ5LukSxZPZRCpPTeRm7a3IfgyURcJQmK49ij8pt0oQmjnrDKCXOasL6p+3TOzZnfzjlMbTHast6soRD9u67DWcuw6xiGnpCCRIwUA2SrNbW1IhMs0seqqoq6SxcLNcjF79IawYhjlO44JVCuxrpKnKC1Zdd1bLa7MjKOmNiV56yiH0QFZmwxPEmZye0p5ilNU+89OPtB3Kask/RI9tELE6vBGMkh77quFP2G7XbLbrdDKVEFSZZRmA+cYejJ1LgJ048BbTS+TGmykPNoV8u9GrOYljQLhpB5eX7OydkJ99+6jzby81xdr9luena7nhAyb7/9CGcsOSVOTo7KtNGjl47tZlvuVYVWloODJc3S8umzmzm6uCmpi1OuVAiB3W7H69ev+fTTT3nvvfe4f/8+V1dXhBDKhltxdnbGanUsz09MxJSp2yWfPXlKTE95/+vv8+Enn1IvW1xbS0rplzz+VZzS/xdKqd9VSv0dpdRJed8j4LNbH/N5ed+/9FBK/Q2l1D9XSv3z1+cXaCRAK4yyKNFAZYwQgpNgTrK9mUOemTwgxZ0mloIgJ7JksJSxGdnoxWKkmpOYswrOU3N5ecknn3xalBdq7jQ1qnx9UajUTUNVN2x2W56+eM6270nZUDVL3v7auxweHAjFwhkqK6R2cTEJ5CzjMUZzenaXo9M7oAyBTB884RZnbu6cYU4TFJK6RmIIhJMZvJeuunxeDOLHaYxQhKpCZ2nran67md82Rds9FSxLu6glW6gST0ylmUnQU8cyG26oPcdT62kbLuqgmCb+pny8dabEVchzarTCmiIvhOLXyLwBnsjlsJfx3V4GLVersvneb7sna7WZbxkjMSd8DOz6jq7vhUtZNsar5RKrtUheS4xIjJPmWqhXkBiHDkWe4zdISezpcqaylsoaWeaJvxcHxyccn5xhy0h4c33N5uamLP/8TBEKcU8ZEqu8Zj4kqqalXSyl6KJkqz3BQDGRmXwymZ+X2zLVyTBXKTXnmldVPR8stngLTI3AJJ+cdOgTY0QBTdNyeHhIXVVMmFHKkgiArbjcbFj3Pd/89i/wy7/yPY5O7nD/4WOWq0OaZsHjR2/z1/7qX+Nb3/ym0LJ2O/7oD39EVVnqRpz3J0el1eqA5bK4HIXI3bt3OT09ZbVacXx8PNPbJos1gKurK05PT9/wYj05OWG5XHJ5eSl4tlHU1hRBg+bV+QUXVzd8+NHHhBBYtQ39bsvZ2Smnp//6M3j+D8B/Vu7o/wz43wD/U24zkfePn+aPyztz/tvA3wb4lT/9SzmWLmPiyMXoQYPWTuhD04qidJLydzFoIKOsIatIppgl6GLRnwrH0BRjVr3f8DrnWC6XdLue3/4X/4J33nmEhJLmebscET9GZysimaubK16cvyYquNl1xGQ5O77Dw4dvoYxI75ytMEoxjCNOO5IRsnMekhCXK0e7OEC7CpUF9/JB7LhAxi7Zxg7ld5i6uwIv5DTRwovxR4mmVQXPA9q6FgOMtqZpakIJ/ZooRTlJqP1kHLCP0I2lODHjdjA5pzPTk6p6P+6CeHOGEGaT2xi95HjXJU0yBFLMYqSrpLBLUZfuWytVut48j9xTMbyduDj5R4YYWS6X8yg+FfSJXuODx0cRIfhOKGmVs0Q17cIyy6YVm7UywqksTuZaie7+oBgvpBjIJSwtzdchEt1aqGuoYmjiA5WL+LHn5uqaoevwfSffv3I466TrqxLtYok2jqpQtKaRWgj6kWEcqesaHyM+RLyXeARUkVsWHuok25vgionukzM0jZDZN8Ub0ijFOAzSfSvh2poi7VVRDsZQohaUsWy6gRjFmSt0A6DwKYNxJFez2ayplytO7pySSKRsOD0949d+9X/EzdUajWa3WfPDH/wQP+zKPkKgnjh6Tk/v8ODBfXIM3FyvOTw8pq4buuE13t/w8OFDrq6u+Cf/5J/w9ttvc3BwwGazmQ1DTk9P58N1IphXVcWPfvQj7t27J/zWebch49Xr80v6vqNpW87Pz2mbmqOjFV/72ntisvMljz9Rscw5v5jeVkr9H4H/e/nn58Dbtz70MfD05/iCBD+ScsAYCo4mJ3Yu/EhtyyY1izVaSpNBb9EKZ3GjDkmkYmRklFXSdbpKs1o1KAVjDPgh0LQtS5948fxlCUCKOKuJseivvccojWscMSZeX5xzfnVN0HC9ueHjTz7FR027OMC5Wo6K8v1zFC/DylqCkYK9221JMVHXLY7MYnXAdnNFIBGzXLjTQmMeAed0w0krrDBGNqOT2iJFj4TNG+qqLjceOGdoWyHUL10jqo8cadsDrq+vMUbTNNNiIZabotBcijns1GmM44AyYk7R7XZUTTtz+ULwUoi1dNXClRQrN3H+yXNhH8ehdD+SXnmbCgZyYEwd1m1d9NRxTjJTVySSEw43mfNOxdU4LfZniC3cbrsRZx3r5FoJcq3sIZ+EMlr8I8sCxFjLcrlAFWPdSRmScp6D9IzS4kegJfObHEl+ZNhuSGNHDj06j5AzaQwEV82dm7WW1cEB2li6fpiLHUozDiMi86RwYAUL1MaRs0TUatIsG81ZRuzbWvFx9PPS0d4a24P3AplMHEojEtLo93DMMA60iwOMiWQvC5HovVDfjANX4xM8P7+gT4nzq0t6EiFl3vna1/n6Nz7AIrLc//3f+luSNW8td86OUc5ijIgHpte4XSzou5Gqqlm0K5S65Orqiqqq+PDDD3n48CHb7ZYHDx7MJhiTtHOKlpiupQ8//JCnT5/inOPs7IzN1RVaS+he0y6oF0uOz+4y9juGoefenVN88Py//sE/+tfvZ6mUephzflb++VeA3ytv/zfA/0Up9b9FFjzfBH7j5/uak542E+M0WigUJdPFFvVGOdklNGoKMCsFQ2kiJWiLvTGFaJsz1mnB6pR0H32/4+b6mvV6zfHxcelehnkrD8zk3OvrazabLe1ywXro+f0//BE/+fhjtG6p6hZXlBMhlO44aypb4awla9ist2y2G+4dnHDv/gPG7Zqnn3/C9c05epLn5f0mfOK8Gasxys3dpc5laaURPJIZthXdfMEEK2vQWcbGphLidlVIxcM4FJXR3jR40lmDROcKZclQqamD9NTWUZVivBuGma6h1J4oLbungr1lwZuhWEsacTvPiD0dScjhGEXT1LiqIqZ9dzgtXqZxf8Irjda4qpp/3mkJNJuKFI5m8L4wJ+T5STHOI3QcPWM/0I27cmi08nv2u7JVlsycFMR5Sbr0VmSTfV+4fcKXjTFirKNplxy0tRwyOZDDgIojtZYxPuWMdk6Ma6t67gpTHlDazIfBMI5kMnVbllw+0C6l41wWS7faWfqdmNze1tdPiymAg4MVu13P9fU1KRTfgCwqsG7XEUOgqWtMWV55P4orlZZDF/ZE+KFwGQcvcAPG0YfEdhxJzuFT5OzuXb73vT/Hd77zXd56+BiN5g9+9/t470Vl42Tqsdbw7Pkzcg587e1Hwh0dPXsDajPDKjFG3n777Xk5Ny3/Xr58iVKKhw8fzjzLiTkQo0ggJ19PZzVWG+7ef8BuDMSMOMoHz+HqkG674dmrV7y63P6ruQ4ppf4r4NeAO0qpz4H/FPg1pdQvyyXIx8D/HCDn/AOl1N8FfggE4D/4eTbhIHiV0iX2NAV88etTiuLIHYXiYvV8Gk43Y8oFtyRjdKkcSd6fYyBpyUSRiyETlXgkBu8ZhpGmaWiK/LGuRGqpsmJykOn7gb7v5eI1hp/8/g/4jd/8Ta5v1hwd3uXs7C5kGbvFmSXidEVdVaQQ2XUbrq8v8cOIbzzPnr/koHEslivyK8oBsFfS3Ja2Ga1Ruly0MZJjnEnaALlEUGht58ybqQurKrEHs9ZIuBclZyfvMdwJJXFuAvvH0ilmUgRtXAHXfcGXnEAUo5+x1KqWblh+bhk3SZmYAn0fyrJLFfGBJkbZtKtitxZi8WbMGVe1c7c4EbAnzfMkU60L2D914lPRnHAskAnEx0D0AZUidVOTRk8KsRQTwaEr3JwjLg7p9V6DXrbu0XuWiwVVVRO88CInYrtWGluJV6pRYsC7Wd+wvbnCdztysYyrazlIt36clVOTWYqzVvruUhxuQw/r9YacM03TloIoGPF2u0WT55yi26yF6ecOJYd8HEdSiMXHUra+wzgQBknyjGoQ7NoH2tqyaCoh4hfvgbquWBRqWNePZJWJPrLxI66qObl3l+989zv8W3/pf8h3v/tnqF3Nj3/8Yz758cdcX1xwcnJCHHe8fPaEsGwJKtH3HTGOdN2O9fqG9XrNol3OWvIQ0hv531dXVwBcXl6y3W558eIFWmseP35M13UsFosZz5xkj9vtVvwr65qmbnjx/AWmWbBYrhjHkaZZik49DJy/PuemT7SrQ+AZX/T4ebbh/94XvPv/9Md8/N8E/ubP+rpvPJTclFqLrhltaVqhRyglnaXStrSfmUws2+Is9lxJ8M6MYNCiSigLoOK+o5XCKoOuxK1HdLMRpQ2Lg0Ni8Ly+uuLO6TE5ifFB0oYxBkJCtM858JOPf8I//kf/iM8/eUllFrz18C7vvP0IU1WElEnI6FS5DCbSba7ZXr5k3G4xWIYwcLEeub72VIsapSImB4wWe/8YPCQJHquMENBjEkqJsoakRxLFnDYmcizxsK4Wd/KqwlSScqiNol0syEBVN/R9V1QnFoI4IlmlUUqTYsa1DSgnLushMvpAiuDqBY0pz2/hEq4ODghBilmltcjo0ggZMYLI0sHH4NE5Y10luxFEvG1cLREP2MKDFKcnWw4CZw1C1YbmllGGRjruEIN0akWVlLPwbqcDThktctMwys9gDagAJlFZTUoGZR2xD9Q1My1JazHslAWhJ+cRV3w8h13PZrMtTjqNRCqj5X8KnIpl9BYKlEhQM0lpQoJsHNFrxpjx3Yh1CRMS1mUWy2WhykjnXLsKVMY5WWq4koWujHiFhgh1ZahcNftnVlUtUIqtiDkyDhL+VVlLXwyDYyyRvIMXzmRM1DlgVCIbRMmjhYSflUKHJN6oWpGVJhtD0hBU5KZb87X33uUv/9W/yi9895dBG370g+/zR3/4R3S7HSrDxatXdNsblqsGdGLXbbHqgJwkhrnbDozdQPQed+hAG9GZp8jp6Qld1/Ho0SO+8f43ePr0KcM4Ym2F1pa+67m+vKCpnKQzLhZ8+umn8yG+XC7xo4eYeH35kk8/+4zD4yPO7t4leGlIrq6vOD0+oV4c8Nadg1KQfvSFZeoroeABRUwQS772RBUB5m1nVpMzeioLyUSII973+DCUbbgQL5VSSLK3xhlbeHBGEgZSxiERr2SFsY52abm8vOD58xccrhbUzuD9yOgTISScqRjTmlcvPuMf/8O/zx/84IdoVty/+zbf/MbXWCxbBi9WVUIzgpQ9/faKq8vnjDeX6CS/yxhGDk/PsDlw3FqefFwxbjcoxMhXlCygEezLuQrQKO0IKaCMwlCcgpAbsqlqlu0Coy11VeOtLcUpoIxFfCIljjVTss+1RhUaknEGow3DGAotRRHCIMePtmTtqBft3PkYo0ripWildfAYNE5polKYgpkaDW3VzPI8+Vlu8UTVnlRe1zVtU5PGXcnl0TgjXUZ1C3MzxpCMpjI15eWWZVTK9MMg3TdQa0trNc2yIQZDDJ7Nbsc4DlTFREE6sVygE1M6zjDfbEop6WqVYeiESpRTprIVY7/veGOMaAI6enwIaIT8b6sKHzO7IWKTplm0GG1QVqCNFBKN1Wx3O5lKyibXZKF2heRpGkfdTFhtIgwesVDImARGZZQxWO0KhCV82zAGUpTllfhxjoyD6Ni3u91MLRpDRI87Wfw5h60dpmnQpfAqqxnDKDzRrFHGMWYRH2AUf/F/8Bf5t/7CXyRrx9//7/8BP/nJH5ScmzUKOD06wlWKB/cf8eD+GRevzzH1McP4OSeHDY8fvE1tXFGkRWLOPH/9iqcvXnB+ccXh4SEnJ6d0fS8OTiljtOXk+BS/jNSV8KifPXnC8xcvePe99zg9PiEWo+hxGHnx4lxI/csWpQKrpSFFQ06Kb3zzm1xerME44cFOapYveHwliqWCuXOY/kwX8/wHhcQ7KlSWt1VWaAqfMQvZOzOFbxmsNkUiJt8nR3G81oAq2N+irvEhsLu+pksB3nmEVg4dexbaEpVi2O24efWKp598xrgbIBvu3XuLb//Sn+bk7gNiBmUEFyFHLIl+s+Hq9Qt2N1clt1wTYmC8uaGtamwO2GgLzqoISX4eY0XPnrIsb1CqmCJLVVDFOSgnCeBSlRILq+WSylYzrScVTqrEChhCKDpjEjFSaEgCfoaYaOuWIQQJd9OaKmXh/2mREk7BT7ti8XVbky9dXipUF3CuKibH4IoySDRYbx6Ck0zt9uuuSqeb814PPo4S0Oaqin3sgYSVTQdr3/c0lSyrdrsdAbBWCtnQ93TdTnJnzD5GYxrfp5F/Gv+nRcnMLy3STYuM/pQiGotKpmmb8vzs2HU9w+jxMTKOgayEWD6GQNztaA7P5gXa5Ca03Yq/5HK5lN/DShJkygGjWxmVsxhix5hp24bddpwhpOl5ijHOKaYT5SvdgnUmt6zbptlaS/ePUbgiEbXGSjzJ4IW7jJn18RmhDvmUhSlS1fyL3/4dfvzjj/nhH/wIPw503Y7jk2PunJ5S1Q33797l9PSEtqm5e/eGX/+N36Fpah4/fkxdV1Tl+SZn+n7g1ctXoOD4+Jjvfe97rNdb+n7g7t275AzPn78oE4Rn6OX3fvX6FV3fsdvtODu7Q2stN+s11ok/pjGKszvH9P0WUBwcHGK040c/+gPu33uLs9MTrtbrmR71RY+vRLHMSDi8UhOwL3I0UTBIoZF2RJVRqxjDojC6whonKjOloLh3GwoBury6KUmKoxDWM1YpKq0JxjBst4zrGwyJ3eUF0WiMlXjWm9cbLs+v2XVrVq7i7OiEtlnyC9/+Jd56+z20U6Qs1JqcAk5l0tizvXxFv7nCEMjWMo4B7yNGVZyenBCHjnGQEy3hGJPC5AQetFMl0pYiE2Mml+viOoSBpqqxxlLZGldXuJKNs2pbRu8JQXwRq8ri/TjjvRMmm4u6ZghB/AyNuL7ElNDGUrcy7ky649vF4/ZBlkqmuZ0J3HJzkRNj9FhtqOsWH/cE9+lmXS6Xc/HQSlG1++iJ2zc4av98TNzE24Ya03JkMpVQKs8xAkoLsXsc+jLi21L4O7zfL0UmzuJtsw5bsPSQJCNKDiHR0CulsXWFtrIJDxPMILw1CZwbPV3fY12FMooco8AqCH1N4oFHwbdjJIwj235ksWyxqhiwaPm6ORdICuZrQRcBQIxRtPDW3eqModvt2Gw28++22+3eyPURruVI29RUrpaDLsN2u6Preq6vb9hsNgz9iPcZnzKd99z4iG5X/P4f/pj/53/3D8lIfG7bLnnr4TscHBywWi5om5qbdcfl5VoUbMDp6Rm///u/z4O7x5ydLGdbvHYhWem7bkfTNJyd3eP4+JhxDHz++ed0u55PPvmUX/7l7/H06VPBZdOIMoZ2seLg8Iirq6t56XN2eiKSx5sNdXMISGzwOEq08/n5E54/f8WL569LFs+a25aCP/34ShRLgCnwyhjBh+Ytd5622gqyLriL8OyERiPYnFagtJGxMYOetuGymiQnv+9Y45SNIt6G2+sr1peXHC9rjB8hJPx2zegjYR1YZEVV1SRfsXA1ddUSokJph3JSJBUZTSKOHZvLV2xvLkhjDzkQDLPXY1UpDldLtjkQaIhohqRQ2pBCJGYx162sgyJPzDHirMNZKzhf6fbapqWpmzkCwxnZFltn57zq8/PXjOPAMPaYKOxMkfTVpIKB5YJPutpi61oOF0a0sUVLv3dcmvwS35gAciYWt5tUNsOT61E98f6cxdV2Lkrymu+7HGtt4YBKTrvwPWWRJwmcFdZMOTR75YwvxPwpfjaUYm70Pm5kDKNsRrWabdZA6FyTNdpkxvHTrkgxRhaHx9Kteo+75faOEqqTRDJ4QgJdsOM4BnQOKJNJaOp2QdsuJE63FPcYAn2J3jVKs9tsaaqaHII4hjeWWFyBfIgo7XDOsNtugIR2ej44KNBCJs/81ylre9L4T5LOiWY0HYDKOhaLFYeHRwId+MDgQ7HbEy9RpuZDKXxUdGPi+HTFg7fe5vym45OPP+HO2V2sW/Ds+Wt+8pNPCmOluNAXj09rDRfX12w2Nwzvvy1uBmViuTg/5/DEsFqu6PoLPv30U66urkkpc3FxQVO3/Lk/9+dIKXN2dsbz5y9ZLA+4OH/N4Ef6PpXffaByls3NNU9vbnj48C2u12uUsizskmHoefbsOV0xew5xBBKNNYRpDP2Cx1eiWEqHksk5zqPYtKWdr8sid1Q5iqKiSPOM1nOXkVHEVKw1SkuWUyJFLzzOFFBasMpJV64VrK+vCP2Ok/unnCwbLIk4DiTtWSbH2Edudp4aWNU1tbNcXF3wKAaqpGQpkSIpDgzba/rNNckPBD+QYyBUgg3lkAkM9L146alckbWjjwqNwmkLKTOGRMriPDR12/MWvDwhTdPQLmRDaZyTztIVo9c46anFsHW325Bz2mfPkCTGo3hkauPE0zFlcha+ZoiZpm3JMc1j3uw5WQqKUuILqlEMWQ62plkIXShljKtI0aP0NFrvqT3TxnmiA8kYLJ2bmvBYVQwxlDATtEGMLkbZaFsjS66JVjJ1WUYpdrvtzOm0RhdqlGywJ4PjiY40KYQmqpIuneg0Jm92W5QCbY0Y8xbVS9u2E4aET4l+9PgQsbZC6cwYBpStaJaaqlkyhEgo/Fk/jvSdZM+7csgoYH1zgyHT1g5rF+Qc8UXmGn1AK8Fzx9GjKnktKAUzs5eBbjYbfBn1p0NloolNBPapC7fKsVqtCrcZxtHTdbIEmiJEfAj4kFGuJqDZ+cT6+SvOPn3Co7ff4cc//pDXr19jKzEmSSEUmpBF+4RSsuSb/D+btuHi4oLLyyOGYc3l5SUPHj6eMWznHOv1Kz755BOqquH46JhPPvmE7bbj6OiIuq558PABN5trjk5POb+4ZNd1OKPZbbf4cWDoO66vrjg+PZaFHHB1dS3eBhHadsH9+w+Kubfm9OSIw6Mj+Hv/7Avr1FeiWEpex16utHfd0fNIp1CQJHheJ4lrEHwrz1rdlBVJy1ZTJckglw5H5IEU7iQqM/pAN3S8Pn/FMGwJY8/9OydUVqP8iFKI2kPLUqRW0FjNonJYDV23JUWP1ZbGarJP3Gyu2V5fkPyAynnOzM5WMLYQJIjel1yVq10HpuJivUOvhD5ikYVGQlQaE47mKifJhVVFVTUldVKI2K6qRStsNDHIiChjl5yYxhpUEtelKSlRZUO9XOKMwceIiuIyr4wWwnOQhEtrpVhPnaW8XvkN7E8Z2Z6mEGaBgPeTw5EhRelSJlfuqbOburkJv2RSJxlLTF4WL5UppiPlZ0yqkP0dtROCeU6ZruiLp/TGiYOrlDjgO2tYtA3ej/Slu5p4fbedxice321NfC7FarrRJ79EWzmygn4tSYpZGVztMNaStEWPAgFk7UhKMfr9AqHrOvquRxVCfN/3jMMgERiVFYzVSta5j4GmXbBcHaHQ+CCKmpmu5dwML00/94Td5pzmTnPqmCdqmStxG6fHhzhT4IYoxT6EHbttx24YCl3JkJF7bDeMDBGCgt/94R/gh5EwiClvzJ7Vakl9cCCEfQXOGqwRbHy76zDGsljUs5R1tTylH0RRc3p6QjcmPnvylOVyyfX1NcfHx6SUePbsGRcXVywWC37xF3+RDz/8mJ988jG/+It/CltVdOevObx7l8PDAz758CVtU/Ptb33Adug5uXPAbtdx7+49VqsDLs4vxXzHaA4OJC7GOst6/VU3/6Wk8pUT8LbOVQoiUuiKTnviyaW0d76elggBkbZpEoL0RCTHOqKU8DK7kBh6z67bcXlzhbGGTOD4+ICURnQWD8moJou1jFMKC8JDax2vhw0xdJjsICQ2ly+5uXhJHDusVkRj8BFCkmRKrbRgXqVgicmF4fTuA65/63dorcc1rXROWW6sxsnW1BhTFiXF8NbY2TwCVSgdWonkM2jGYSjPTxA7rQJdTHhwTAGlHFkpnK3IKoDOBATKkNO9+CSW7zmlB942Ct5bollxcjdi+2W18DGNViUqdUssEQYTYfq2ndi+0xQ8VRtLpfcfG1MilYmjbhq0Exd1P0rioHFOVGAhMGYYhp4U0/5rF123WNcxc1EFf42zhHIqJJO9GTDHyt4ulJPptLV2NvY9PDrGx4mTOzCEILHFSQ7mTdfT7XacNA22qskhkkqDcHB3iXWOp0+eQMoEEuvrK2Icsc6QkPgDVahyOSV06S5BFlR+HOl2O1nqlPycmKJo0svzOBH8pwTPKeZZK0kcaBdLsYwrOG/OMA4jfgxM0R1DVnRDkIC+asH1pqPfblk1jmEcULHDVQID1ZWlbWtOT45ZLdrC3Q1sthuury8wWrwZmlpTOcfJyQlHR0c8fXHB+etzTs/u8ujRI25uNhwcHJS3JSFSRCIbVodHfPTJJ7zz+BFN03LnzhmQOT464q2H92nqinB1SVVZhsHQdT3Pnr3i/v23uLh4RdPUYsRtHTfrHdt/FdehfzOPPBNxgXkk2p+SCYOXfWpZduSc0Qa0zkwKElFrqCKjEg9GJk8Ko8gxCVCvNYHIzW7NGD3a6qIgMRL1kIN494lMBqXFwVuTqZ3h8KDlxasOP+5wecGw29CtryAMaBLjKC4tQ0j4lKlcKqCCZLyMQ08KEtAUAYxl14+cLA8EP4wBpyBE0bQOwyDehlbPI7DxUgitqwS/ReFqhTKKmMXz0FpDVTn6XuShqjwnxlbFsi7usUmnRdIWIiFJREU/DOgUZ2LwdJjdXr5YaxnGyejDkows4XIGleQ1qetWtMfkuWBOk8RPb2anbWsulJ6psxbjCDm8QggSbFWoMuOwJ6ebMoUohdB4tDh7+1HylZRSZerIpWP2M/l5yrkeR8E4J8MGf+tgUIDSmqoRHiJKlbeZrdh8iMX93jL4QTbhMeOqepZmOucYhoGu61guJYFxzgZXEaNrOVQNYmySM123Q6uKnMWl6nai4+TLOZlnCFbpJaTtlnuUjJwCPUybdAkoi+x2nTAOjKPvz0UNU14DrTXJB3o/MoyBZB2Hx6f46yus88QYWFQVWrg8QnJXjpQTIYxos2C5WLJaragqx2//1r/Al/jpUOz1Hjx4INLFvuPw6JCUEldXV9R1Cyg++OADXr58RdeJGUo/DGx6sb7TxnL33j3RtKMYx4Enn39OUzm20RMyXF1uuLpa8+L5K7797W+XonvFYlmTqkQ/jHz25MvV2V+RYlnGbqWLhlhhnQD7KQq+pmV/WC5aVbh6FPcbXZY6Gq0cKCGqKFKhG+liSyX45TgqeaJ3W1xTEaKY11bGUVlDGgIR5GbQCoxCaXBWU1vNqm0I4zX9bs3YtayvLiTGNothhIRaRYxz5KRQSgqcL5jVzc2GVbuiso6IUH826w3x9Hj2c9Ta4seAa0rmj/fYbLCFfjMMPfXQyMhILpiv3BDkxG63oXIVVV2xVwWJVkhrJU7oMZHySN0WxQelwBQNcAgRV7bKU0G7nUUzFT5FcT/Pwnmb3hfKwVA5R2XFHKSuG2KK9F23t79DwrV0LJjlVIiVous7CMXRyMg232eRBAotSi7hrgSYBe/xXsLm5uiGCbsL4tgkrk0ypbhKOqJxGKUzQu27Mu8ZvXhPGmtmnboqRbJpWparA4wx3Nys6bqBYfQYJ5ETY4isNxtSFIL0crHgyGlWBythAZfPkwTSTNMu6PuR9WaNc4qcHEMfiEG6bqMdPo+IajOBLVHE5fcWWpeoo7puR991sqwZ+hmzrJsaW5IxtZFc+SHEEluSsEyHc421nVz3Tg7dMWuyH2RZ5ypJ2EQzjCPNosFYh7amwC+6sFym7yMTn9Lw5MkTnjz9nLffuoexhuCl+K1vblA7T9/1DMOIcyJnzBm6bpAIj7bl3r37vD5/jVYFbklJJMsHS377d36Hw2XLO4/f4urinOvrEbda0MbAJ598jB/B2rp0qwu6bsfRccvX3v0av/kbv/vV34ZLOSs08qkZjCXsVRm0MiQqQs7k4viiyrYz3SKRKhQuazKxbMJFqZPjSMyRnDTBZ1TvWV/eMKSMqVs2Ly44sEcs8godpaOrzACpZ9CZ0SVGLy/8Qivuti1pt2F78ZpNpfFDR99vyUSG5BlzJJOwSmznfO/xKrEJiZgNm12kXw8c1TWN1RwdWD6/2nLjrzmqHDpHibNNiRRESxuixN8aI7LHlDNxGEjDSFu6LH9zTR4HctySxzUpVWh3iIqB2lb4mLBGaDIaJ8Tvusb7wBgjtpHkvAS0i5btdiNjft5rsEHGvr7vZ29EYsKWMVhrTVMLmO69x4+e0UsAW2UrOQqVoWmX+7A5K76LMSf6JPEgi4LXZS00spTHchNbqARby8XiTpHwcRSSPR5lA3mIhC4x7kSwMAwdE11VWE2ZMfYoJV3zEEfGKGNqVVfobBiCGD7LYZDIWZFQLBcrgUbqBVlbNv3AOHi6zRaMwTYNY0z0XU+/3XHULjirKg6rigd3j9BaFmimrlFZcXW9RllHpmI7JLQe6cMW5zNOG1S20DlisvisQTmiTzSrFoUq5hgBnaHvdygSMciCMQRNTJp+HHBO42rFwVGLNU40/EaT4iiGwBm69YAyht7viIwY42lqTT9uMdWS4bLDmEhbKwwB7wcRYyTNkDRNjOQkS9icgphV1IbVoiIrTww9YgLsOD07IgQvWVWF7+vHnu2mg6xZLidFjeDv2+2GzfaGDz/6MV/72tc4OGy5vL7BWI0zmtfn5/iUCdpSHZ1ifETlhNEJ52oW7QG6ben7wG4zcnx8wL37ZxydNDR15uHZfXbXX/ExPCNdiAJQk0Hsm0x6VZQhQjwv/9a34wOmLzaZBMdZG+59Txg7Rj8ydB39ZiDnxMFqSRwD6+sb3l4eYK1EcyojJhDTpnNaOBmjJfWvbdA58/z5cw5qhzaiuU45FopGJIZclEbFpIBEiLFkdovzi140HB0uODk95cc//pCuGzhuGjRi0dWUSIgYIiFntElYzewKvdlsiDFycHAwd3kxRGKQm15wuMRyucAHkbalrIghFdqVaM+jFlNUlSV+VyzTrIROlU5ywpJv031mfNnv88enxc+0LZ/cgHKK86g4bZKnzfpk1hCCRxlo6kZGUCWWZTnGoq4A73OJOhC+o0wfkb7bkUKQnz/F0jlOKZCUMT0WR/eClWrHbisk+xxjca6KjAXjm/6tKFtjLU7+h8sWlMYa6LutbJ1TLPzUEtVRsqSayrJoapzVLBcty4MDzs8vBGcNQcwwui0xZ3IccFqK6DB6VN6xaGoiEWMbxrChXhxyeHjIrqRjjoWnKRPTUPDWnrqqyTETtkIfml6L5XI5mw9XTkb6XK6DkDLjdsN2s5Fs9L4jZ4TVMHquNh1Xmx3ZNCxWB7iqme/frMTJ32pPSuIsPwfNFXesie9dVRUPHz7EGMOu2+EHYYcEH6AYiiwWy3maubm5IefM5eUl6/UaYwzn5+ccHR1hnr5GaxELrFarkioA282WfhDLttOTuzg74f+KRCBlQ0wjMYz89m/9hN/lR3zj3e8UfvcXP74ixTITcpj+8YUPow3kvbOMKssDuWemTSqFdpREFhnk5BuHHj/0+DAyei/8OCdLEp8VcRw5fHAoaouUCnmYfaGcDD20bPdqZ1m2NZv1NX1/j3YhuKHkW4f5Z5ot1FIi5j13b9pMeu/prnqscYQEvY9F6SL0oWCLd6KKMy3mdpRAzpmbmxsxC2iaN5IOrbUEH4tmuAIt8jjpLuXCnZzLXeUwhWo1xRwYYNm29AVfnOhDE3dvonhMGvU3SOqzLNLMnD6jFePQvyElvB0LAZJbFJOoTeqqFkoMGYyVdV1M+GEkazN/n6HvCX6EooUOZVHY7QZCSGXzKxr7YRiKr6f8TmIyvY9XyNNhdotraa3FWU3jDM4qlm1NU1lG7/H9jpAy3a6j60f6wRO6jhADKFi2NdkHrMlYDcEPfPbkCSkl6qbF+y3rm2uM0tSVg+Q5O1qQmyXD2DEOA6r3LGpRYIU4cHQqbkCu+IQCJQ0y03fStXk/klOk73uGQZQ8TWtLsVwVz0xResUIrq4JXj4vhcA4DOy2W0IQKMlWLYyWPo9c7zzn/cBpfcjZwaloyQsR31iHc3uDm8o5VosFTSMGJzLiakbvWa/XHK5qDlYHPF9fzddA3/ecn1/w+eefcXh0zIMHD8Q1KUlsxmq14vr6mr7vxduTzMmJRN9+/vln3NzciBvUOPLO1x7T9z3f/90fYY3l/v37/NGP/xBrFca2dN2aDz/8iN1OjKHv3bmmWezNWH768ZUolgBZTZI3+MKKGWXMLpoWctJFSnaLHA1YI9vznCV4LEVfjGdFM2uVBithYAPFzDZFDpcLCY43pSDrstyZ8FTEZsuoTFNZDpYtN5c7Jjv/6e+U5HeIKYnLS8iEGAg57AtnjLNj9Zg8d++eUbUNfciMERqjxRi4GO6i9CzhVIq50E5u2NM2d6LiODt158U9nRGtXYl1zWW7ncScYZIkWkuIWWZUwJQboCpuTBNdaNqMAzOGp63EBUzvn3BEYCZ2G713QJ/4jLcdcibH9dY1qEI5ETPgWOhfmXGQkTpELxEa8kLPN7h01GK+YbQiKjHDGMe9m1OIYf65QM883b7vCaVTtlOnVVyonBJIxWpx7/ejmOD2/ciuF631boiMSdF1O4LveP/rX2PZVMR+oNYGP/ZsUgRXUVWObSfSvDHI8k4rTVMZnF3g65o2H3H5+gXb7ZamXoDW1M5hrKRVaqUZJ8x3ahaUbK9zFtnglE1jrWWxkK5ysWjloE3gfaRta/la442IF4ae3XYtufFNi7YVUTl2fuS6T4zJEJI4t99c35DKAlCUVQCCt2ujZqisrmvquhEc1DjIgjFbK3uJm5sb7t69S4iBq6s16/Watm05ODhgvV6XvPrAyckJr1+/pu/7wpGt+PrXv/7/Ye5PYm1Ls/w+7Pd1uznNbV8fLyIjIxtWZVbDoinaAGlYtmHYMGQQHliQB7INE6YHMgQBGkjiyIBAQAObhkYGytDAAiTIBCTAgiGalmSJMimSZVWjarKqMjOajHj9bU+7m6/zYH373BtZGcW0aQhxgJcZ777bnHvO3utb67/+DcM48Bu/8Rs8f/5eUSgJRLTdbvnRj37M5Zs1WllOTpagRjKJpm0wFvb7kbEXI+zL6wueP3/+lTXqa1Isi5+NAlUY9BNONo3jE3/v7iN3dBzIGA13/oklwyaJpVnOueCiRSmTInVVCfA/jsRhpDKGHANYRUI6K/mpSMelFTFHDNBWjtoawiiUElfpQviV3yOX8S3EKCN5LlK5UixzzgebsSkAHm3ZDYFt72lbh2kqEvI9Elm01lrhSrc2LVruez7e6ej94WcNw4CNmaYVriY+FepIWdh4ca1WZJyROFiB7Ut6ZukA+xLHMAWCTYdDCAGndNGyp8OmfKIYTZK63Thg9B3j4acNC7SW93IYSs6MylRO6EdD3ws/siwwmPThCjFgKBCOQv4uvM0k29Yp16aM7DEkYpJrYgqpUwgNaRxHMuCcWAYaLd1XXTsxwFCGrDW7fc++H9nuO65uVmJM0h7RhczNasv65opvfvQBx0dH5Hak3+6Jo2dWzRhVZtd3hQ4VqWaNjKlaYZsaFSPeNPjg2fWJHIW/aetawrxCEGMNOLz+Q9+TYoAMfS+xLH4cygFnadqao+WSo6Ojg0pJUlBVoR2JgivG0p17jzGatp2RtGPTj1xuel5fbxiyop7PCTGIcqb3EgVRNVijiWlA6btpQWmRLSs01lS4qpZFVJZDNZcDE2C9XhfFTjocoDlnFosF6/WaV69ecXx8jLWW/X5P1+3Z7fZ8/MmPGMeR42OBKLwXB/VhGLi9XdE0M5pKMPjZrKEf1jx7/oRZc0pdfca8XTAMHbvdBlckmT/r8fUolhkMdzebLpZqh38EyZS+p+xBLDKYgo6UnrDMKMYZMZCSF60yEksLwkl0TovHZfDcXl0Shp7FrIUUSEmVpMR8KJTy0yhgcaZ2mlltUSnQdR1VbcrW3pcLOJWxvGBnJNCyvEILp211e4s+XkItRatqZqxWe65XW5bVCWPMYuabJY1cpyTdasiH7m4ahycKydS1iaSuxMUU6o38DpMPpozbqCQgv5YL14cghsEg1nZKkQoGOaUiTiP5fRWPImGK6mVfuH7Tc4JSMIN4lBpjDsV2ynW+rxdXpUVJJDrfoVSWjbqdIhl2xDJqpuLtqJWM0BMlK4RARJzPTdnchxAY9qMwLYwwDJKapISBmEveekzsu4GmkdcoZMWYNDkmjuZzPnv5lpv1jvWuo54tqNtjYsrcDvDm6pbLt9fMnGV5coatK+EbOsewH/AlKtiHQMlMoW5m9ONIQkyes3W8u9lycXHBbnXD8awmojG2omlajLW4yhIT5GE8QB2TnJN7ME/0EW2Kx6kTr05rpkKWi6OVGOF23cDN9Q277QprNO1iAdbhs8hx9z6z2XuGlJnNhGWhrCMFz7jfUhnNydlDZvVSlmElG8sUXLWyDu8Dfojsu04YLlqEFyDTUrfvWCzmDOPA0fHpYQpbr0WfPnWYk7jBWst2uxFj6uJlOZ/Puby84Pz8nJvbK8mUUi373Y4YFzx9+pTl0Ud0+4HN7SXf/s53+P3f/SF17Rj6Hd1+85Vl6utRLMkQxQWcDPqup7/3GZGERExMi6AJq5T3PgtxXYnPYcoyvkmBkhWodB4ZkI5TpcTq8oqzo2NJg4ujeHboIvYpBVaXDlNlGVmd0RwtZqQgjjHzhdz0k5diCNMIXkj0iD2amHzcFZAQAsrqQ7H0tx3rfc8wJnJbDC2ULgdBwQL5k6mHcNdlTFEbMSTh9tVgnRN3oRBEt40ueUS6cAr3SKOriqu6aNmV0eIjyv1On8PofIBACj415TZPOuT7nzfhv/c361OxnfibtlBOZOwX/DBGz9B1bEv36P1AzCL7HMeRFAO1c+W9PkiuCH7El4RLY6QDTzGXQi+0KFnKTcomea+0NnTjgLJgETeoqMQs5fVnL7lZ7xhi5vTBY5r5ktVmz2a3Z9VHXt3u6YbA2cNjXLsAo0jBY1xNPa8IY0Bj6PpBjDG0JVuH1o6QNLf7Pev1lp+8fMft7Q2LyjCrwVbtQdIq73kGZZjP5zJNpUTf7cWncrwjzqeUqBqJnq2KY1NGHONTkg2zQAmeoZDuFZlZUzObzdh6GEJm23v2PpKNRWWhGekcIUbmlaIfAvvVNRurqR4ecX52zmK+QCN+mzKGt2htGX3g9mbFbrsrMcrjHRxjDbutjNj73R7j3MHYd1rqTBxVY4RGtF5vCvYuhsgffPA+x8dHXF1ds1wK9NDtOtq5Zrtbc3K64LNPXvP6zQV+hF/55e+zPGoZ+h3nJydU+uu+4IkJ33XCtZu4HXAoCEpPIDwHjpsCUUCUG1GwsQBqEG5lIShnrchRHTwtMepAbSAltus133//Q+rKoWMmmli+PAu3U01KItGvayVUlaP5DFIsWdMBrb8sNQtxcuRWol9WmTxt2YGjoyNuLt+hvcWcFFWHssQMq+2ex0vJelFliSJGE7LwgTsFzP1oCABjqqLxlp9tirRNevdJIjqZIkxdp2TokBUxCIc1R4EOhvI+HBImy/OZurUJp50yt+/b+0+cxGnJk8v4O8nc7tugAWU8FJwtRSHWxzAcNvzGGMZ+YAyJuqmZHJCkhRbjDZJMBVVdYyjuVMWpKmVIQTbk3hfeK7I4DEGc7o02jAFyHzk6mmOc5XrXsd7u6McApuLk8WNUPePzixtevb1g34+shkjnA04Ztv3Ibgg8f/KEPPQM+wFFxBqHDp6UK7COkA3J1oSkeXt1yyc/ecnNak2/68k+UBkDxuGaFleLJdw4jlhnQVdYWxPCvde+KNwEYpDfOY8jzhl2ux3X15p2VpfrQZex3dOPqZDpPceLBScnR2ArehImK7pxxeXNimGE2axh1jgygZRE/+0aS9d79utblBlpmppuv+fB+SOsNcxmcx6cPRAqULo7YMmCqU9mH2x3bLcDdVXRzGZUpVs0xrBarXDOcXNzw3e+8x1ub28ZR8/JyQk3t9fkLBLivu/55JOPubgQKOT58+f88R/9l2hjODt/xM31hhdfXBKjXAf7fcfJyYzVzZ7j+Yy2cl9Zp74WxdL7kXevX99Fdd5LqrNlEZLU5FV5lyF9N77pQsiWwLNUFgKTbvhucSQjdQyBmBL73Y71asXTf+IpOmdywT1NZSF46UILGqAVOGfRYUTnzGIxw2iND77oprMkCvpw+BNDhix4XsxRRJghslqt+PSzz2ic4ei4JabEydkZn/zkHdlotrs91ze3nLYO11jQAjNMj/vF5b788KCGUVnYA2V5IcYENSmrQruR0SuMg0RRZMvQi+TLjwGVQZe0wr5gvl9268mHpZIYgtype6aFwn2aUUoJ19YYWx0K+/T+TdjrpF7JKRw6xpwS/b4nJwlFmzUNuWzA5Xey4kLuPVrJNBAPjkGCWXofDp2kNQ7Jd4rEKNSu7SgqlSm7RZVry4cBZQf67pZ36w0hQzNb4D3sL2+5vH3Bu6tb9mMgZU2PxqfMwmlut3t+8Mc/pNaRB8u58E6VjKZGGeq2JSjLF28u+eLNF3z+5prXVyuyccwWR2QVsJXgyQpJaTTGoEoHpbSQ5HPmkD+jtcIaScXMKZKiBjS2RODmHOm6jpTlgDPFnavrOrZdpOslr+bs7Iz5rKFPoIp3p4+ZYYw417CYzyVbO0eyFoOTmCKWjK3k5/+lv/QX+fhHH7O63XBzdc1yfsRmteP8/AGL2bzE2kb6oZfrrCwGpyUtStHUNeg7C78YI7uddKNv377lt3/7d9Da8l/78/91trs1WmuOj49LQd3Qzmqur6549uwpVa1YzGsWiwVj30EWs+yUR9abNe3MsFi0/Oov/dKXokl++vG1KJZOwSM9kvxIGDIZcdwOaHxJFExKLOetsRhr0FY6TqVVkVcFIpkBS/KeHAKVkjvIahj8yL7f0fU9m+tr9vs9b1+9ZlFrjo4bso2olDFB/qRcFwK8J5AIxjLoll5Dtp5WbzmqNatuQwiniCmtJniFHxUpCI8vhEBOCqccwYMfI0Mdqc+PWc5q5q0jhJ4n1jAf9xhV453jzW4kGINpDNqPWDXirGUIDlWsxnzKmKTxg4cUysY2E50kBKoMcQjomKhqRe9HqsqK5DNklJfgMGs0fYpkDMoKKD+EUeKCs2yKk6uwTopdGEeBNYpgwNWGGMTdexgToxceaMoKH4rCJ1iJSi2elpM6S2sNSYGSjKRxvBVcM/Ri+qESkQgWhjwy5kCrLOw9vRmlW7eOpCQMTBlD6AeGPhKwhAjDMJIyRMCPga7rDws5nQGlGXIkGItXFX3U7MbEuO+5ud0QsiIrWASRk767eHkw1Q1RSPPKGcmmdhWDVfz+yxterX7Ag5NjjmZzjhZzHpyfY+05F283/P6PfswfffIFu8Gz2+0hjDw+XXK0hLpx5KGnMQo1bpmZBHFkPyZ03YCtcXVL7IQBoBEyt+jABxbLBq3EPSgZ2X47a9Aa2lqMhbWV59/vdjBAHAaOnxzD0pJyxOwCVZfo95l1slwOgaOZIimNl/wJUgqEJLBXIKFyYmEVS2vw2y3niyNCm+j6gU8//RG/81/+v6lqx/F8Xg5Fz8nJAq802TVk19AFz/LolPVmxzDu+eKLF4ecndubFdY6OBND4hQTarzh/Khmux9ELx8T7ayhsgrfbaHf8f7jb/Dg4WOePnnG76x/n7ZJGLMX3u6w4/u/8Cucn5/xw598zunJ6VfWqa9FsVRAo0WzrIwBIzkcMWVilrE7oiWVbRwZeiGcq3Laivu3BJolpbFK0WiNthoDDMOey7eveP32FS9evWS93bOYzXn84AG/8t3v0s4b7niaCmIWDprKh4xplCYpTVSm+DwqGme43EsS4hSXELyMs8ZYQhDtcUhB6D9GumRtDOcPH3B+vKDb3OD7yLx21E4oK0kp9mPgerNj2TpqI5YgSgs3kqLCzLnkfWtL5Sw6yeIjZosuYH70gW63l67dmhJwJpEPGUS3mw1tU+NzKWITF1Ly46grx77bY2KgKj6YOZWllTakOIqEshUp4ziOhYojKYxaKbbbDbGQ1yVv587oWVgMmXHsJI6j3x8wzakTNTIyEHPEIYYbIIzaXJrufdeRx5EchCs5pijYZIyEmAhRsFgfJaJB/B+LAYmtCUmx7jwXqz0Xt1v6KFhlyEI2X3WyYfbjIPG3ZIyTDhejcLWlLWmJu8Gz3VzyyWcvGHpPbQ3HR0K0vl733HaedR/QTvxDXcpsS9zDrHagobKaymrapiKXTjvGgC5TRM6ZUKzevB9FGoqEujVNXSI4WrwPKOR9nM9nxWgkHhgIYfBUzlG3NRERL6icGQfP1fWGy+sVGIUPgX4Y8MEwxbhMB7ewCCzfeP4eN5dXXF1cMpuNPHz8lOPjE2bzltH3/OSLT3kxjjx/9pRZ84uHCeRmdUtKG/o+Mp+fsNls6buOd2/fELxnuTwmp8RyscAozWK+IMXA6vaa0+Mj5keKZ8+ecnl9K6qkFJi1Dfvthvc/+JB+8Pydv/Of8b3vfY/r64uSdw/n52csl0tevHjF7/3gZ2fvTI+fJ93xfeDfBJ4ggNmv55z/daXUGfB/AT5EEh7/6ZzzTfmafwX4K8hh/s/nnP/2n/pDJp5ksVBTUFxOXLEeMyhdk7NsYX1xrXaN5LBkBLvLMaF8AB9Q3hNWW7r9lu3mluH2BrvtMJs9p7M5/+1/8r9DW1fMmxZnLEQxz8CoQk6flAlfei3QSMyDs5a2afCrdeEMyjMXuo3Be+lgMrmkJnpSiUoIITCW1ML1teiP67YVJVBOwgyIidx5Lm7X1OczlosZPkFVMsgNgrsmH4kqkbRDaVMWPHeYZp7wxBBo2wVD2RY75dBGi0Y7JdrZjBQlzCylUNQrImWUBEJTEImM1VoghZzxvqcquK4xilnd0FY1OiNZ7KF0XllckEJUZRsrdKyJ9iSb2UROCmsqyGI3J8sIUURN/NC+xLrmpPAxFYqWdNc6ZQxTdrbHF0d97yM6G/oxs+sTMSmycnhlCX2mD4HVZs/FzYb9EBiTImtDTBljxEzaD3u0hrqy4jqvFa6SxZbwVyXKgqJYUlo6nf0wsusSq+0OEoQMybpiESYZ4DNXY/JI1/dEq6msQSELO6MUKUpUrC26fqc1QSnGYWC92dB14oA+n7cY67CuljTOdilOOklw46pE3N7eXJdMoUQkM18ucMbhh5FhP5A72I2J282W16/fopU4Pd3BKPlAyYtRroymbfn2t7/Ntz76NuMY+bt/7x8SEjx58oSmbnjvvWc0s4rt1SXLecvY7xmHLfvNFj94Tk4fcnJ0JNZ8KtG2Nd/+9kcoNI8fP8VozTe/+RFffP7isJPYbPe8f/aQbrXhb//f/za//Cu/woOzU9a3N1irsK7m448/4dXrNwzDwOeff86zZ8/47LPPWC6XnJ6e8pOf/ISPP/mUzW7/jz2GB+BfzDn/llJqCfymUuo/BP4XwH+cc/7XlFL/MvAvA/+SUup7wD8DfB/JDv+PlFLfzX9KJK4sFEDZstAxhqyyGPVS4mwTKCWxrpWxRTkghVLC5w1KJVzMoBM4S46OWaiomhZd79CDZeccmwhzW9G4GlVOZ2dkGTTRizL5rmAyUeGLOsYYnLHM2hkp3Rb54R0wnEoHIJEGhTQeBQucrLGqsgkchoGmbjg9qzFOiirIqD3mzNV2h7ORtnUsnOFYe1ROKJVKpozE/47ekxEKkLGy5c4mYcoWfL/v0M5i24bWGoiQo3Rn3o/QUdQ+jpQEFxNeqzx/q8XUIux31I3ckDEWLmY12Z3JaS2qGYv3lnH0xOhFj/1TKi2hEY0Hr0znLDmFEr0rD5Ep2i8tz8ZR/CMTWTrGEMlJqFYqCh0oK0XQFEOLzBAyY+oZQmaIgHagHbdB0XU96+2OzXZHPyaUNsSSTW+tA+VRKtE0FU0rRUibYjxthVlgyKgoQoiMKhOSYrY4wlYS8xG9R4cRIngUx8sFzayV7t2AySMhRbZ9z9m8OdjloTJhHEr35lApEMeBzWpdHNzl/jDOifO9kuRKnyJVUXvFsiiJUQQa4+gPBhu+FMA4SnEOETof2YZMn0oksjUH6a5sr2XCmfBqVbLinXMsl0t+5Vd+hZg0/6+/9/cBODs/Y3KQeu+9xxzNGlIU85LLdxfMl0vef/4NlFasbq5JYeDd29ecnJyijS1Ya+A/+U/+Y37tz/4aJydL1us1zWzG7XrNy5cvsUbx+uULHj95zPrmiqqeHezmJrnnzc0N5+fnByettm0PBPeTs5YYInD7M+vUzxOF+5oSpJtz3iil/hB4D/jLwD9ZPu3/DPynwL9UPv7v5JwH4FOl1I+BvwD8/T/1BwkpUG7QlMgKtEll+10I58QDRb2QeWRCLnQj+bcAWW5apTPaZCqnsE4oF9ZqwqZj2O5pqxqVE9ZZKY4g23OQrR1FWVTMPWR5orBawtDaujksO6ytkPjUSE4ynk7UiNEPchEXPpwfRy4vLzk/WfLs2TPaynC1k/S5vfcSWKbEn3Mkcbne07Y17vykaOiFhK/QAgEoKfCRovuOq4FlOgAA4GZJREFUo0gzK7max3Fkv98RSJw4I/SjIOPv1A2FIDLDFATvbdqW2lr84DHKyZIqeLr9jqHvJI7UyJjddSPku6zvXDpjZyUvZvTjQWwwUZxyzoQulBEuFRmqRBjHuC9WYvXh5pwCuMZxwIexkP4TOZRlHhJeJ8spLWN4Tgw+su1HxqQZsxYcMzuGPrEbd1wMsbw+gmGp4qlpnCydrBF+YjtrZNxW+UDPUtM1k2RE1jmJQkybksmT0YXcXjmBI5o0SpwslqQNMWdx/BllaRV94OpW3NL1zEiuT5IET+9HnLFYFDkGdrutXE8hMIZAO5uBlve3ns2FTVeWoN2+YzSKoUhcZaEmnaKyAmX5fhCSeoBBGd5sb7na7HBW8GqhqeUD5KS0Euehwh6JIfD69Ru+8+1f4PGTxzx/75bHjx/x8uUrRj9yenYsLuXDmsotAMnjee/Zc169foP3nvVmRdu2uMrSzD5kGGQB2HcdV5dvUSTGoWcxb4lhJCsjCa1tI9OoAp0Ts1mDNoZn73/Aiy9eHJz0p2XR0dER7733Ho8fP+b169cyATq5zr/q8f8VZqmU+hD4NeAfAo9LISXn/Fop9ah82nvAP7j3ZS/Kx376e/1V4K8CPH94KlvrJJwxpRTKlIuQVFIgSs7OgVdZ3NOVwSBpiDknkopkDSoGUIloFbnSUFtoLNSGeN3Rb3ao5RJTyQ0tW/UD6wi0EoX5RNFJUaSBVqJ1K2tZFJMIIWhLAmEIEQrxOQT5GqU1qXhphhAIWnN9c8OrVzXffP6EylU0VZJ87NK9ZiXbzoic7m+u1szbGU1TUVuNznJzhiAGsMpWRGQzXBuJVpAYiJJVEwK60DSqQgbXVuNqR1VX9F0nEQrrNU1dEwahcCgrUR0pKQYNGjHGHdSenBXL5bIUwUhwkm095ef0fjiYVsToBQO9px8HkeelQqfy41i6TgUMB49K6YLGO4pMlnRBsjrAAUZbwoRzp8QQE31M+JDYB0XvM9thYD/uGCLEpOhCYkuhRaGEBmM0WmVq51jOZ1SVQ6mIdRZri31b8OiSj0TpqITDK6+pFFAp4lgh9WsruLJNskFWWkljkDMkj9a5OEsJh/J6u6etl+x9ZL3raM+OMUm23zNTEWKm7/bs97uyDRcOpTYVytSMQZQvjbUSODbIwswVoxFzYJtYtJHUT50tcYiga9a+481my203UFUNmKpkY002iRLrARQT4sRmu+Xy8oL9fseDEiNiraXrOt68eUNMnnbWoHwnDk/eUkc4PXvEF68uePPmks1+x8MHD2jahpPjJWTYdz05Rr754Qes1xtmbYUisl5lPvzmR9wUSMEajdaJcdjz9OlTFoslr99cUNXNwctgYox88MEHXFxc8Nu//Tv8hb/wT3B8dMR+CDK1fsXj5y6WSqkF8O8C/0LOef3TcrX7n/ozPvYnynXO+deBXwf41W9/kHNMh2I5JT2iJLsjK4XK4aDuIALaQi6FpXDGyIqgFUkrdFagxfreK0WyGqoK3dQY27Ner3nw8BzjTOkiS7wq0qkmZEk7eVqqwu2UzkW6y6aqCx45YG1VFheqpFfIrzylMcZ4t7DQChaLBU+fPOHk5IQ47Bm6jkobcgioGmIoF6MGP3rWfuTVxYrZ0yectS05dJjy2sQUqYyW7OiY0CoeOJi50HKmIh+DdFIacNYBEklQVQ41yQ2TxHKk4vRjFSQNOkdy8IShL4a4UDlZWDnrina7kM79JGHMBz7klA1UN7Vo+DFEFSEUfX2hjtR1hVIa7+8Mgidll/ee1XZPzpLfbZTEcQQMnR/xGQYf2Y2BARh9ZLPr2ex7Op/wESLS/cUMsQgbrFE4q6mMZtY2zNumOKwDSrKRgi8iCHSBMHIZbjKqCA5ClAKqjGEKbZObQnBwWVpOERBSVItus6i8LLEyDCmy6gOL3vPuZs2zJ0+EXeBjYRjogxGK956QE7ZphSrUtNiqQbta1FVlqRiHXjLEgy9Nh+S/01Tibp8tKUlHfLPv+fzqhpt9YNk2tE0NFYzjUDpLOWS0kS57eo9jTAyDiDW22y273Y7RewY/wEVicTRn5pLgwWgurle8eXOJj/DZi9fEFNgPIx+8/4zKKTabLbvtnlevXnN9fcOf+3N/ntlsxnIxRyn44tUbzk6OZeETRlLwVMYQQ+C3fut32HY9v/y9X6BtW/b7PfP5nLquef36NZ9++ikxRj755BNOjo/ZvXolAoevePxcxVIp5ZBC+W/lnP+98uG3Sqmnpat8CrwrH38BvH/vy58DX20/DJDF8drEhDbiG6iyjFYwSeDKfyfZWksWdzEonGRyANmgSKisyVmXDlRjtBiW1nWDsVvW2w3GFWJ0jHKBZ9k4A8R81+SoEjmaps2skvTFtrhO37l+y8WSIl9Sq9gSL2DS9HfHyckJjx8/5mjeUJtjsnY8efiAz1+8hpixWjpTg0ZlGdtuNgMv2h7TLljalmQCrhblUkzitCQGE6I0MiUHWqJh9UG6KVI/6QaVymWs9cQguJqI8TK2LLy0EgMPoxR1ZWkqS+wCgx8Zuj2gcMdHwr0rvDulNLYSd3RlFAqHq5qDImnSmB+cvoeRfugI40AMiZClG3bOkSL0nWTBeO9BWVHdjAGrZboYc6QLilHBqg/c7Dq6ILSVfhhl0aOEmJ8LtKONwkVfkgcdlXM4a2gqh7OyUTVak7MselJMh+uhXITyd5TkPxXmRs6Z5EOZTMQlX2uBVaIyIjbI4nUqDpkl9rlsl5VpSMGzC57tmLhc7bjZdrTWUjUNMWvGvj9kBon/pmDAddPSzhe4usXVLTOTD7r+g0y1yE1zTjhj0Y2BkDEhEYNmFxI3+56tDyjnUMrK8hSRGYrz1F46VEyxYjOF8uXJObFYiF/parUmxYirHdvtln234/HjY263O7bbazab4owUlMBVOtOFG548fUS33+GMZrmY8ezpYxbzOdEP3FwJ9PDHf/THXO09v/S973F+fs6rLz7j/OwUawyrm1v6cWQ2n7Pdbjk5OTkQ2+u6Pph1aK3puo5HDx8yq434pX7F4+fZhivg3wD+MOf8N+79078P/M+Bf638///13sf/baXU30AWPN8BfuNP+xmiVIxlFM8lfAlynFL+MjrLBZrlH8pFqVA6FUWPVDmbNCkpdNLkVLSLWCoctXJUqkI5y+12LbQT5GcbLeMyInwpvzzyvbUqhgCCmepiLjFrW+q6FrJvUU9MxXIayQ95LcaQnCywjDVsNxt+/OMfU+nEclYTIzTWSSjX6NFNjTNOlEc6YrVmDJHXtx1ZXfON8xn1TGO0EXd1NXU98hqaSpZQE07TNA2msri6RjtbXkdQSR26PqMlCyWnhB8GKmsF8C4Z2eSE1QpbAqj6lBiHjpjEkCGlVOzYzCEjyHsvz9E4lJlwL7mxnW0OHbC1ijorSIndbnf4+NqvD4Fb04Y/JI0f5ToJ2pCVYjN4tj6wGgduuh2bMTEmXTBPhdaOXBRNWmu0kaK4NFV5LlZoXQUfJ8nvqkjEXLK6S9JhJpfcJunsRHVmyHlywYqgkUTSIrmVohtlsaTi4RLTIkDj8O4pRdKisBljoo+Z3SCb+uP3npUO3pORA6dtWta7HVobZvMFy+Nj2tmCpBT94On2t8UYQ7qt3W4r76MxB5FHykm8KL0hRUs/eG63O7xSuLbFZFtMa3LxixRYxXtKRynYd4qGly9e8Lf+1t/iD//oR3z++Ru6bn9436qqYraYMV8e0Q2BhObs4WNQjncXl6wv3hJCTyTw8vVrjislRPbFkvl8xuPHj9HacH11y9zMOD09gRmsViu+9eFzLl4bxr7j8aNHRODMC02sHwZOj49YLBacnZ1xdnZG27YH96+x+IGeLOccHx0BP/6Zdern6Sz/IvDPAr+nlPqd8rG/hhTJv6mU+ivA58D/RApf/gOl1N8EfoBs0v+5P20TTimGMSdyCBhny4iiZSwpBVOuSu64PEpW6LmM4tIZlgXRBDzeG/610lgUTmma2tJ1O4a+F9pQiZ3N0/cuGJpCNuwgOT9KJ7QWknulE/PasKgN69VIPyRc1aBUQOWB4Dt8yiSlUEFBKeZSnFPJvzacHh+TfU8kMVvMcFVFtgZV6jZFtqi1IxvHJnvS9TW1ijQscU2F1hFlMrrS+BzIQ5IMHqVQWHIWTBVrcRisbUlJEvlikAz24AOmdFw+BukklEIbuZu1MWWbnTAq47SitloWZMoy7Dt8jFjrsM5KdxklD6mqhKA++BHr3CHj2xdbOWMseeJV9j3dekPMmVjwqoTAIcPoC+fWCmk8ZsbYiXY5RHZjpAtRguKUIimBVkz5U1lH09bUlZhy1M7SMB4mAOkWi1RUgSldYMy+GLFksi64ehnBp0KnEGWUJGpmjBGzaK1skckqssrFXq0kAiTFNMqoMkHlDDl5jAZyph89Q4hcXN3w5NHDgukjmOryFGVqhqRoFwsePXnC8uiYpmnZ78VybrvdFWXVWDBNWYJZW6KJcyLHvkxsDTEadp3INX2hd0niZsZHD0ooZMYaEQ0U+apzotW2Crrtjs8+/piLy2vi2LNoK2GraEh+5OLthRT3xTHnD56CdoSc8Smy3W/puj2XVxvWqSdEuXeOj4+AzGa9IufI1cUt82ZOVB3nZ8c0zlFXM77xjQ85Ojrhsy/eShpszqiUaeuaX/re91mtbvnB7/8B33j+Pj/84Q+pXYUfRpytePb0/S/ZC/704+fZhv9dfjYOCfDf/Yqv+evAX/9Hfe/pkYAhR+oQxF0naoLOEKW4maRJGrBlCYOMBFCiIxRIMUoFP4KoEllnslWiVjGyvbNGUVea3XbNZr2ibdqC/whdCZDCmzRWWZKGqBRa5/IniGO2ScyrzHGjeOEDvVcoV6Gi6JRT7AhK4bVBh8IZLNJL8Y4sKYjaYOoG12hsU5F1FlqKgcn8IqPwCZTTeOsZk+LN5Qo3ZqpH5xzPHUp5qkqBTjSqlu9rNEolvB9Q2pB9QI0i5xRcs9BwsozwWdohlBEy9q7fobRkazvqCfEowWZekh+Vom3n4sKeYLPZMlvMcZVhDCNWT8FjGqL4dQYv6ZG6+Ej6EAXX6jvybk/0AZ8VIYPPhqA0Q8h0PkKJZLjeDKz6gX3KIllUgrfp7MoUMmD0iLWGpqpoa0flLE3thCaWEir3UhynpaJSsriZxtUoy7M0FcRJCaAyIRVD5MLI0DmhMdgJwyv5T1rdGYiIu/tYxvhyyGtVsHddus+MyhFTFpp9P7Dd94wps+975rVj0dYk7dgPCWUSR6cPOTo55vj4DAU4NFUGv+uIKdP1Q/HZHEXt5iUvaj6T+OLEgDWa7AMxOcao2Ow9PoFTiTH16BTIRqAwlEwXwRqST2UKkMOidmIHB9DWhhzdgUKntMaHwNB5rIP1zQ038wsePn7K+dkxSmvevLsgY9htNuz6Ae/fkhKSchmDMB9SJoXE6vqW5YnhydkcP448efycTz99TVXd8PTx+7x49QU6jyzahjgGPv7hj3j79q1MQNZxvDzi9vaWo/mCFDIvXl5ycXHxlXXqa6HgSTkzxoxXsvlUWjh8ICXQTOBh5nARoqdERw7bOTKHC1BrTTYCmGdrCFofsBWtNeMwFs7VQ4lsLW2oFN7J8Bcx9TCCWx0MNUokQ11VnB4dodVb7gLDghgFW4MpW9s7L8t8GEM3643QVU6W3Fzf0JeturVa8DStSVnGN1V0v8pAqzNOQTcOvF7fYGzifXvKUitMF2kaIfNX1lG5EgOrNNbKtjQGL6odJR3QVDBTjHTDgJ66CSWvSd/3dH1PVY3SAZYXSTBS4fZFLSOfrSvCOAgfM1qsFiu8sfeoLPEMYzGw0LbGJ/l7iJmUpxA6x1i1pKwZYmDvR7bdwLYb6caRbpDArn4MbP1IEB5MccMR7b010kG6SrrIylmMFqf7yhlUzuX55xKxe4cvT1ZxwGFjj77Lsv9Zj8kYQinhXAplRzDB+4+pG81JeKBay3gfk9iVFdKaQDYxFMd+xe16w2a3px89be0E/y5uUNZanpyd0MzawpGd+I6WEMUUOceJ3yrTljGOqnIizYySymm1Y4yw6ju2/cAwjBgUVglxXlIa5flNJtATH3ZSW02uTWoywikQ0MENv1xTKSecs+IHenXJbLGknS85OloIbKUUKgRQma7fc3u7YbGYY4249y+P5iwWSxaLlm13g6ssXdez22/ZbtfUzciz5knx5FxwdnrK0PdcXV0d4kymZY+1luVySYyRq6srMeP5isfXoliCpvOZRoELieqw3Cn7G0HR79rbaRKf+BqlIRQOdUF/VOkEDl81RRkYccI25hDJgFGHz9VaFTK85EweCmcpWnLTyEVQWcvJcoFVWfiJOWEri0klOydlTCwRDk4cfyazgOPjI54/f07bWPZbh0RFtNiSsphyRCmLMloOjOKH6YrLj20q9tHzerdmPjRYU2FGGX1p60KhEYxJjCoMpq7QrkKTCX4k+B7vB0IcyTGVsCuwRvA7UISY6PYdWne07ax4QcqbIDG0g5C7dxtmbSuRsrsdOcOsFu6b+GSOOJMYQ6QfPJE9Y8xEJZQfHxIpa6J27LqBbhxZ7/Zsu4Fu9HRjIKEYfChWbIqohTJGjmIWbKFqRJU1m1VYW+ShOZOil6KhSlaQFRcqHycRwT1/zlI8D4Yt5feVnlEVfLF8pBzWKosrlcDcqjhlRabmUb6ndI5ZT5xdhPqmZWkknwMhiTWdsxplLUMcuF5vGUucbi4beGs02lYsFjOyAh+E79rHQBg949AzdCWh0TrJoyruTdZWjGMEpYkBotVEa9iGnsv1Bm0MlTZlCVXs+ArHcnpUVXXgL8YoNLmIMBCm+9o4eziAJ0gEhO/cVJZ+v+Pi7WvOHiSWxyecnhzJ6xcTA0JyH8bMarVFEdEP1CGv/eHjU07C7ECSPzqa0/fHAt3kQF1X9ENHSkloVE3zJf/PoyOJ2z0/P6euG16/fkPb1hRa+Z94fD2KpTbsgmJhlUTQpiT0nSwnUc6Fy1PwOwoPUpWxkZJTDZT/jqXU5bJ4CaX4gLECjGujubm5oe97louqMPdLcSRJdvUBsAfuFXBdOGrOGk4XM2qr2Q4dpmpo5jUmOuHS5SQXipWsk5Sm7lUzm83E2l4lcXcGCQgjY6zo2qeipHKSohATlkQ2AksEY9jmxM0wMtMamxPVIBESdVNhs2jVxS7N0LQLjNUMY884dozjXoodkvOsFAQv3cjkBJQRes04eEa/le6ywMEpgx88gY6QM1c31zw4OweE+nQ77IvE05EIDOMWHxLaikXZmKDzievVhs12T0gwpEw/jKScGaIA9ELQVyJob2ox44gRbQyzpqKqZJttlaJxMmZrZOmgyk2OEXGBURlyKFxaUMp8yU3pvmXcwZB4OpxLuZT38I5ZoPJ0qHPAFI2R6OZJzz99foyT2qxswKcnku98JifnKICERmvLzWbPat9zPgaSgdoKIXwMElRWt60U5Qzdbs+4l4C+nCLj4PElOlghz2304UAmD1E8PXuvuPGBi/UGHzJNXZd0SemUUfpLr8+kr56YDSkmeh/YdF3pmOU9goQxMt1Eo7FWAvmoHL0PrG6uROKoNUcnJzw4P6HShndRMfoOP3ZcXa8h+wNbpW4cPvTsdh0xJla3az757AvOzx/z/gdPqWrDfNFycnrE2clp8bGdyPjjYfH6ne9855BW+vDhg6//GI42dMnisQxjwDqFrkROJqe3KAyI4ZCNQ4qCMcbpSp0AcvEvPIzFeYp7AME4ZVlRV/WhsxRT3OmG4Z7nHkzKk4P9mdaHjWrtHGfzOcfzlu0m4CqDcRJFMAxRKDdZyOhlwVpuQNmaxxCZnywJY8ebt29Yb1aEEKmr0lVrVZLxZKGgcsIaCGQiUkQGFNebHUfK0CiDzZG03dHGSOUWEhGhFL4oiVyJHBUPTqET9YX02/eDmCIrJQ7wrkK7mradl4AvL12iku22MeDDQDcOhBgx1tL3opt0TqJtd53HJw/O4tUSrxMhgPeKISRuNh2X11v6QYLkUhSHIDFsNiRTFl1lvFNKo02mNhWVleC4RTl0chQIRKlQlByqYLHl3VfTeFz6wlwMSsrhdz8WA7jDMQ+Lv2nKuTem52k5I4e2kc2MHJRlCz59LKaEmcxQyBLtgOKgs84ZXUZ1W0ZfpTUJx3YYWe32bPoB3ViM0sVxPKMqQzNvi+WdYL/7/U7kpykduMPC9FDEKNxMV1XkDEY17H3kuve83e247UeSrWi0xSoDKpbFzp0x7sSrnLpwsZDTDDESthuJi9BaKGyCZOG90ETkIJGFapWE5bHd3PLujSKlyIOHjzg/PcFieffuDesgBtVDv6Xrd4Q4Yp3AA7PZTJ6HVjzuO7q+4+hoxtX1NScnx7x8+YqP//iHfOujb/Hw4UM2m00xI65pmoaf/OQnXFxcsFwu+eijj3j79s1XlqmvRbHMSpNsy7YfmLUSi2sCYKxQN5LQjsWQV+zZshKaiVwFmsO2uUTgipeldJUo2U5OY7stOEoIxeWlnR+wpmmUV+puxAKKVloIvrZgNpW1LNuKk8WcV5uVaLLDSOo6YgzUTU0AQt/fdYlqCnGqCLEoaqqK+WJOXVdiphE9RluMthKRKn2VLH6UovCeUVkoSb1P7MbE6XzGkDNxHMmqp94ZojM0tRR3PwxkFLE4oUtxUvTdSBg9KWaMdmV01FhbE5VmvpyJlDEkhsEfnMWNC7imIY2BZCNtZbBK8+7ikphHkmlY9ZGL1Y7bYWDISaIVRpHnhSRjbUKjtCvjahCljzFiolLwZ1vVuKoSJQ2pbOMNTitUiqgkCytUOozH00KtgMAHHDgnSRnMqC8VxvtJkwcjkly8Qe/hlROtCTj4qwqb9yCaFamn0kVDLsIBef/N4dpK5MLAUGQthTNliEm2/spYUQEnsS28uFnzzQ+eEbL8FHGYSpjgyigcgExVV8KXHR05OsRjxBCNqMJ8FNd4ZeSazCGz7QI7FBddR0eiUQaTlSxc1ZRQ8OXXYMLfD/+tYD+MJSAw0NYVs7ZG7NwELyZnchwxJaBtWoeZrNlv11wrxaxtmM2WHJ8uUToWXbhMht7vWa92tLNbmsrx8KylbUWK+sE33sOHgLEZ6+D161e8efOGpsTgNk3D7e3tl8LQbm9vqaqqmCcHFov5V9apr0WxRGlMu2R7teWscQJ4J4rccYoOUwg+mYtdVWaKaJjcw+VRcCOZcphkjMaI6YEuoVmTlO7m+obTk7M7CVqJJdBGlUWAnPbiu6gPHD3BfQyzynF+ckx+eY0PI1pl2T6HCMNIP8TS29ydylNOjjWSm71bb1itbum7TignzmHripSU6OULvmXKPauzwpUbTyfRg697z6UdOW4bWqVlCzqMhDESQyUxsD5QzyRaQjoCI6YZWFksGdEpT1jd8fExpq0KnUIRfKRuxPbM+1DIzZkmJgmPWt+irCy++qh5t9ry8nrP9V44kGOJeRAA2mBtGWcLXmc0zOsjcYd3Flc5lNZlGSKbaq01JgeIkUpnLJkUgxjSGvG1TEpc9CWYrPy4XA5lBFfLWZ6H4S4dFO5G8QNOXfi892logm1/OdhKkcu4XfiUCF4s9WRCv6VhppTUA7tgwjWUwAamvEQRiuOW+De+ubpms+uwyVJrfXgtxZ9gPFz/xmjaWQsxopMnBXk+EyZKkuPXh9K5Dpl+THTWcjt4squw1sk1lhKx0McU97b63B0UU2eutcbnRMgygajJhSlrrAZrSjcSI9qV2GUdhUlQLPhWtzdorXj05Cnz5QmL5YyHQShT240sl0JK3Nxsmc9qKnPL6ekJVW2YLU9Q2nB9sxYcFwmZ8/2A9/7Ar5yI6FMdcE4ahHEcaGcNX/X4ehRLNNXsiLy+ZhhHagcmSUiXtFD3MaIo5gkqYmyxxxew6J4sUTaLSRXlRAGU5E3VRelQo5Ti+uaa5+P7GCOn37QQyGWzbrQWvCPfbcmmG8ka6WwePTjHmM/Y7/eiHx5HnFIM40CKimzujD8mM44QPNvtFiojjtHlZFbSBIlPZS7F2eZyk01lN2MwVKZ4VqqRPkS+uL3leqj5cOkwWlQuMYlLESVHuhkGmtkcVze4qmbWGirbUtmJeK1KZKmhbRuCiex2Hev1mt1uLy5CQV4f8U1scRha55hpzWZzS+UsXUrsvWfjA5uQCIjNmy4FgRzFv1NlaqdoG8mJSaWCTL6XmUkRpArGmET6qiI2KSr5ICnI+4/hcJBaKwdvQoxGUoaYNVmARHl/1aRdj0Vjrb+UL5QnrPqnNuFTVwV3ZhW6UI9SyiXCt2CRh+8jj5zSBKFJp1kq8UR7s6Z4t2ZQWl6Hxmp86Hh7ccnJN54RomyUbVWRjTpEIackXpTWGFzliNYxGINGiLshRHrv8SERUw9KY70Wh6tuy2oYMLbBVRVOVoFopUiTYGP6He6N4ZPWX/wzxbdVG6GD7VXGLVpxKYKDCfF0b81nLVXd0I9e8NkxsFmvZHrUmcXimAePzrHW8uaVpu939P2Gy6tbbm5g3O8J0XN8vCD1CescIQxsdgOffPICa1s+fO8D5rPZgYD+4MED3n//fd6+fVs4oo5xHFitVszns6+sUl+LYpkV2LqB2Zy+2zNLGh1AWbEi0zoBQW4UpctNc7flJsv3mMbmCf9JpauEArxT7PlVFK200Wy3WzEwKBhlLhGrOWXpeLUhE/H3TlR5KKGiGM3pyRF17Vj5AW1qjLZYpSSzRdnD5x+2AAqGYeDm5hq1mKFz5tmTJ9zsNkI7yTLGjV7yoY21spSIkkhILj9bG5xx7IYRHyPKwDD0nKgRzUziXMtopzKMXY8fPH03oFzNfL5ksVjgbFWwNUNOmaaZYadCVdyJpvQ97wPDIEFXbdtyfm6o3YyYMsdHS4ahIyiN3+/pxoDPGVVVtEYzsxJvWzmN0xM3MWILRqd0wuumHFZZuH0l84gMOZaCkz2Nq3FKk8e7QywjCaCpFECDQsVISEJNArlBs9LoouNvdEm2LFZxORf+xDSew4FDeO+KPVB0dOl6Jeq14IzkkpkkF6VWUCoEoIiKsmXWRaGl5WDPor6KxSzaGHEQCjHiFZASL1+/5tsfPBOH9uIaJVAFxGI8Y4xGJ8Fwlb6jn6WcDzk9MSaGUZRAda7QVcV2tSKiqKsK4ywmieGI1nK4TAtP4Ev/f6BVKVnQiuEz+LGYoPSGeV2TY8LWFTPXEL1nDLJ3EFcmR9d7FrMZXbk3kgFtDIvZCUfHR6SYuXj3hhA8NcLeuL6+wRhYLFqyyRwdLamqGm0GvvnND3n16oKmrdhs1hyfHPPhN79JPwz8F7/5W3zwwfucnp6y3+9wToQqjx89/Mo69bUolgpNbx1x1rILFqMyywgmlC2mBpUtk/hf5Wl+gcN2svxvRhYsOQfIhpzEJFZoGwalHM70aB1QOrHpdgwpUmeFShqVFDYptEHGdqXwMZBCpnK1aLUTBOuJynJSt7TthtlJzeVlR+3n1KFCJY+2kdEMmLiQG0JL1QpppA8d17fXVDmxqCqUDxxVNbU1+GwZsyVq2STa7HGMKA0+VaDKGJWCpDgSiVkkoSYZXm0N70bPB/NT6pg5HxPLkKkzRJ0Y04DxgSEE0n5LVogM0mqMc4ypx1ZOXs00SKEIPYaIcYbCbabrOlarNbO2Z9a2xZ0ccDP2fgvO4WaK1HmOlhXH7m5suyNqW6KSTbdCo5JojcdxkKzu8g6raXzNmUzNGBTRiBCATLGuE4qMo7gaKdBYnL4bsw9c2YJtqgwpeOnGtD5sSe9zKo3m4O4k2GDCOl26KVk6oSJaZ1QqGJybOk91KLTaWiLmkGJKzmjBitBaTIuTSqg0KymeZSGVEn3IZAPvVh2fvV3x3Q/fQ3UbTB84XZ4Sh8SgE8laWXz2gYqErzSpFzcu5RQai0kKkyfjjEmuaBnWnjqJqcao9iibyAFycoAp085d0zDBFVNnKbC8QyW576yqsa6CBNFD1oZxiLgYMAosMgH0g0djWNQVYxioVcKTuF7d4lPi/fc0y8URy7M52Twia83QzzGmJ25XbFdw9W5Pu7CMCw85M5855rNz5vOKsyNLjCNj3nOx3vL5izdo7TjqB2zTwm5L7TQnR0cs59VX1qmvRbHMCpJWVLOWODTs+zVtO5nLBsnjtppip/flrnIabiYcxRTwvGzGAcFbCmHYFNyrrmtc5ei3A7v9nraZH7oOygkqmcYSEdG2M1kYFb6Y1kakYwoW7YynDx/y+duPSSoIFqgElFdM2eay3VYgbuQx4keJIF00DZdXN6y3O5Qy8txjCTgrxOlUFgjayM9WSjqJmIqEziCjTk74BH038sWrV8y0pz4Wqy5DLuawMkINGbr9npATrq4EGzRSMJu2oalrap3xMTJ0YzGZ1VgjiZDGSGyuqxu0c3RjByUobbvv6PoBVEVVyWtY1+5QJIHD6CaMhIgx4tOolLy2EsolyxitZVPqg3Tb96dipRWVke99cHaaOJLlhnbOHqIY5HLJBT6Lf2KpMxXMw/V5r62clhny73efl3IuSZJ3v9tPF11dvC7LFwgdrRTNnPJB7aOMgphl0akU2jmMgjh2RJP5wR/9EefHM44rCEpLJ2oUfS//no3BFR9Q+X0y1lkqDJLKqBAkXZFiJqSMj4Hdbo9t2gL5yPOSZZQ6UKSMNl8qkIfXpfxOOaUSICeYpEoBMozeY7SirmbM5hXfeP6cfvB88eIlxmZqVzOGuzC72jnG4FlfXXFpK5y21LXowXNWXF3dMHjP7OgY7zuubjfobSDmwNn5CXUlHN8nD87Z79YsF0uGJM5SZ6cnjGMkp4h1EyYvUSMff/rZV9apr0WxhEzWClPV0Mzo92vGKB6KSil0FOwyKyU4EBw6yzvlTdmrlZGzfFLBCIuyouBgppgBSAphT9d1Iit07o54bnTxtJxYLLLrnJzcJ2WCcZbaWp6dn9OoT8TFmjs1hkY+H2Qxk1KiqSo0QpAH2ZRGwFQ1xjlM0mAtPkixlsWEPiwRbKE5TbSn+5vJKcMbLOtuz9bvmJNYnp2hlditmarCj2Mx6pVta9f1KKMYvSfEyGw+5+z0BNO2hJDI2eBcC2iUcUAliwelhVw+eHbdIGa7w8Cu67ldb9jrGtrFgc5VWqpytiWMsYBkeVsrxWQaGmL0pBJxq40u78HdUiPlIFicUuIUbjQpyyiqSjGTZYuMv1pPZ+qEEQIqlfFdDorp0lKlIOfCf1RaH0Z0VWSJRqtSxOXdUdociuSEe94vKkorDjYJU3dJMY4BKBLeXPppozjYq2ky2SsxYen2fPzZp3z/ow9YVpbRj5gsnd/gPanY31lXYUKkrmuU1rgKjPXAQEbiPIY0gtHs+p5+DNiFmBVril1itgdIwrlpqVUMRPTEIEmHw4csjABtNEYZCImYA10/iBWgc/SDZtv14oOpNe2sIWbE+MWKJNLkDEYs77a3N9y4igcPn9DM5pw/PEM7S8gBv9mjyGz7Pd5vicnT9z3vPXvK0bxi3+9JMbLdd1xer3j19pqz88csjo9YzGZYxE2/rh3f+OhbvLu44B/HSOO/kkcGgjKYZsFohfe1cILxGS2ncEpizDppa3KhUEyjWi4X3qSYkAIicRFisyZFzpSsmlk74+Z2x+3tLWcn52LRpQQ/UrksitQd/M6hq9SHYLW6qWj2lvfOzjltGrZdIGvBzXJW6GRQyCm7XM5Zzmc8e/KQWVVxtDyico4E1PM5NvgDF5MY5WZEo7nLv7mjusjFWlXuS9tJgJAjUWW8km5m48VwuJo1pByISniauXQeMSVxHUqGFKMEYJXgr7A8wlUVIWSads5ycUxEM5t5tru9RDJsOnZ7MSYxtqIbRpSWDXo/9FT1rISF3S0Epsf98ThGyf7RWvJ8xNJUlWZMiq21unyuKJkm0vfkpSgHyV2hMvWdDn3qLOX1kptba8eUsX4XolawwENRTneHMXeKHPkadcCZp9jln+Zp3v+Ty4b+YPx7j44jXyasD22kdMbyfKbpaPQBazQ//PRTHhwvOGkX7HZbrDNUTTnss4Ighih9X0LWylJHKTG9UEZTRclFSlqxvVkXtojGao3JoJLkCh3sWfPdBn8qmHlaOSp5PbOGylnpSskY58hRZLZ9OVCdhldv3snvpCU50+ii11cSX6HJkBO6Mmx2HVfv3qJQPHr6HqaqcbXl6OSIG59JQWAV3++4uFyRQqS2DnWeWMxnNG2LrVusa+iHSBg6Hj9/jrMVY9dzenrGoyeP6HxgcXzylTXq61UsMyhbQ71gv1/Th8xiyuFJCoy6+2QoFxBf2lRK/IT8XYwOikGBViSpsAi9wtK0DVobbm5uGJ721M5RTxIvLd9FgHr5Mq04xO+K76DGVo5lU3PetjxaLthurki1K4B4wbBU5hvP3+cbHzwXW7B5y5uXL9nvdqi2wboZJMmSUWTa2uFTuutUpk7n8JvdHRF/MkNdnmMgobRF65qgNLqd0x4tSGlkHzpUkI57HEcy+UuE7KaqmWIChn6UvJ0QiSFSVY2oapyicqIOGcdeOoO6pWlmbHpPU1fMZzP2e0l+NEqYDEKtmQoJpFQiJoLw6GpnBJNNUXw9rSg+QApHLrk42hixwgO0zlRlvFVT+6gKXFFerRgktkLraUN9N4FMzlXTZXT33yJsmEx2c57G8PtjtlxP07/ff9zXmOfynA5jPvkw0k4XtC68x1hC4mThJtOHXAdOKFtkxjHy4t0FHzx+ghtHXFIYE1GVxVnxtxzjva399NyKaMM5i3UwjBofDL0f0ROHWGl0LhT0JO10LmYr0nFPsIjwl6fXRSmxmDNKUgZULhQ8JdQcP4q+3xlZsM0XDUorQhypXAU+M/QDXTfinBWMXBmO5zNWuz2XF++YLxccnz/AOc2Dh+c09YI3r18SY+D07BEpdOQc2O8GbtUti3bGbDlncXzKYtkxm815+eI1lYHry3ecnz8kHx3xxz/8EW8u3/HRRx99ZY36WhRLlTM6ZbSuCCESTUtUHTsfOXYGnySZT+V73Laci5qHe8fdl4mzwGFLd5CXQRH4Tz6PNbvdjqEfGKua1DTl5rHFluvwjQ5fO43o2hm017TWsjSGD84f8NnLd2CEtpSDojYVuqp5eP6AZ4+fcHt1RfaB5XxO7Sq5yIwBhXhFFjmeVoJXKiOb75ziITqjPCEg4X1gSnScbuAYvWzNrQHlJGIhJPaDZ95aalPJ9sBnyRAvWd6UV1ArgR+sModiNuGH4sQj8RUpi5RyGEe898zbVtzbk5jyVgZmtcE0lnnr0PhS4wXfE0jByWZXlzA3Z++iMpSiqtzBjVsCwSAT73WL0gXqou6JxchZik7JYlL6IHc9FI90t+nWBw+Bctjc635zVliri1Q1l9iQO6OMCROcRu/paycs9ksE9pwQ6EEOKgWF/ynGwqGIHoyVY1E61VDMpAUWsHVFyoFq0bDa7+iDp3YGhSQiTvp0YxxNqwmdwEziYD6w3W3JKJrZTMyXDRhtGaLgntZYdM7FtcqJ5FjJtt+U02DKydJZRCKFPoCxlqyFeG7UBFFEjNI4VzOOnv1ebBG18ZjaonQkpwA6YW2NtaL6ERPpgb7vqJqWWVOx3nV88dmnhBBYHB2jjGK+aHjy5DHXl5p+v2PWVhBHbm63pJSpm1tOTcX8yFA7x6PzUxZNjdaOxlVsdz2vXr+m63rOz87u8VX/5OPrUSyBCi1WXQGUa6Ga0fktXfRYHalpICfBMbVCJe4u8ulIn8ad+9+4jDoT1ueK5nniVxlj2Wxu2O62zNqWoR/QdVUoKuKzqbQUkMNaVgl2pKPBVJZKK87ahmdnp9RWsSeStRF3duUIMbHbbWlczYOTU6zKLJqGTOTi5pJs4ezkiMpqvvXN90kps9nuGH0UN/JhkBuREndROqw4eU5qcxgtZZlUOioljMwExCK3JGesUiTrJMFQKTFXKHxSa21RByXqyqGtwVrFfL4kxhJ928xwlWbfR3zo8WMPKXG0mLHb7nAqc7Jo2QXPLmxR2eOMwxQRQUq5kOxF4eK0ASddjRTnSNPWQkTX0tWkFIGEsXfb4wMTQk3MnMPIUZY9d8mDxkxjdyqNp7x2BlOUPnek9PsYsIzZU/d4B/iIAiwcPmfqzCed+d2/fXnJAxljRa6YU7rnGyqvvxR70WEbZUuKogZtiYXCFtFo5RhSYr3fcXq8FHyzTDu5sAOsNtR1I+87WsLRBlugpAxKxtTBW7b9SNO2RWIsNmjiD1uev5qQ30M7Lq9VKr9j8b3M8R738t59mZWimYl7ejcE+n5F0pHlwqF1FBqTkwVSU9eF8QGjDwX/1CxmDdvdnncvX0CMnJ8/QDmojucQPVc+0O33VNbSe7j6yRu23chzFA9z4mR5JBZ9as719S3JF8jJe05OTzFa7tOvenwtiqVYVWa6MQCOZnlKUom46dmHgbYBh3SfWefCgbzHe1Q/VTCnjx24jUVxURYzUxegS4e53+9Zr9Y8PH9wl06Yknh35AxK47SRjksJUG+sxeWKnBy1tcxN5uHRksYZdiRMVYn2KMjS5fNPP8Nl+PO/8qss5w3X15fkQmnxIXB1eYEl89/7b/03OT8/4/Xbt/gQub5Z8er1W9bbLRdXN3S+LGZSLs45CopB8ZQ8qKNonsmKyTEmlehbncXuLeiIrhzHJyesbm6KMUXhlYYopg4x0Mwrjk+WHB+f0PcjIWZxZVKpeG6KGXLT1hiViWPH8aKlyYp98FytV4TQo3N90N9rfRdmNRWlu5F1FPytskw6/wmvPJhSlMNvYgJM3fCUoYQqYgJVCNBlNPch3BVFphybSXEjz0EKTSryOnUoZAopCLnIZeVz73TkcnDpw+/zJf4hdzlCyoggISNKMvE5KIdUkiTOWOStigi5SASTuK/r4uguMmDP1fUN7x8vUUrh/VAMKyoyJV2ywBOZTNNUaHssi8EUOTpaMpu1XF51hByp6hplLSpFTNSyvZ8KuVKYeyqn6fUyWktsSoyMg/imOieiiEl9lWKWPCXZjonc1o/s9z1VlWlbQ1VNh6kmhUwioStLZSZ+J7S1o3YV+13P5cvXLFzN/MEJylnc+Rmta3j96i1dt6Nqj4hKcbPtya9e0w89zYeOet4ydntqa9iFjnEY6IeBlBN/9pf/DG/fvv3KOvW1KJbkTNgP1FWNaRpSGnBNS+wMu75nWTlcDFhlDmRzXQDyA0Su7sYo+Z5ToTQoI8TmXArnFG9qzHBg9b989YqnT55QW8ssVWVTem/bXnAr4XiWbSeW4MVQI9jM2dERp8dHXO/28maXBMppJPvsk09gGPil7/0CTVNTzSpmx3Ns47B5xObAo4fnKODB8ZLNbseDb32Dp48fkbVhP3heXVzy6aef8vr160P3Ir/+3RJBhrJiEgEoNMFPEkWwShQedTtHo+j7nrHrqKwT4rct22Gtmc1rFosZVWVo2mNAs9n1DEOinVXM5y1RRdq6LmocSyz8OdGKS4SxLtjeNC4D99x+4K5jE5x2GPpC85LOUqYJ2Sbfd6OaEJhpSXNY/pXvN0kkQwlfUwWy0NxNISISU4fno+MdBiyd5V10yWTOIoVEvv90wE7mvdP7cZ8+NHVftpaONJeCPBHGJ9FEVTlGotB08qQ4FyNkmC724olhDDerlUACWjbyKQSSTjhTQcoSoFYexhp0ipIVlSJVXaGUONgbK+R4bbT8xCSv0UGYoTg4ucvrnZn06LnAB85J1pR0+gIlCA6uRVlTeJqyudP0fmS7D9iqxZU4Xq3swXhEO3kfUxB8NyXBQZvKMfaeNy9ecpI7Hj58irOGo6MjYlS8efuGrt9zfPaI9eaWm5tb/NCxqB08OGfRzljMFxwdnzFbbKhmR1y8e4PTiuYfJ4Pnv4pHyIlUQ13XRKUYA9R1i12cMuxuydHSEaiNxeWMy1EKUdTSNegSRAVQTmBprHJ5gzM6gU6aCstoZziTqIsdfrI1f/zygve+7ambzKIfME5hW/GXNPdHe4SWkZVoma2a09UJv5QR5+GDJS9ubtEGkq7YJwPsMFZiDn588ZLNH4z86vd/iY8ePURnaFzF8cKSUk9tGm7XK5ydU9eGGDLg8EOgdRXfev6Q5w9P+OTTE/7ohz+i6weRxkVJR8xaUxnJjwkIQdcYRSKB1iRlRaNtDdo2NHWFq2/EYgshW9fOYk0tNCtdkUJm7ANNW1E1FdUYZOusFLNZQ50U9axmN+5RDtIg3Dp8RpmGXM8YMNS2kiJTNP6TLFUbxTh6jLFIDEYgebHpU4X0rVFoVaSPMZYYhIwrskZhEFDYAxPeJ69JDKX4K4NR8rkxixwx5SzxESmJZFLJNlkjzvlZZF0YNyVkTmRske5Np7UySiACdW/rzhSlLIe2MQYVlWTa2GnxMunEpQsTPmMZQ7UWzqVV6HwHR6hYOk5bs+kCmyD3TuuKrV9MJJOIhoIpGqyT7zklZpIKLonCZkkFiCgiioAYTGsKxJFSkaGaQwcfYxTal4rlYAClRciQlZUAt5QISXKIJghDo0gabFOT8AwD+E6DqTAaKpNQLssSkZZ27oRPHCIxjDgULiuOHix5/fI11xeJppkzX57gWsexPsLnwOXFBeN+z1F9RFSazfqSq8s1KSTee+8p86rCuIoHT86xbSvJBimwXC6+sk59LYqlNpp63qCSaHxThjEpFrMFnanYDYm2NpicMKhDF3E/K2dqKWV0U3ddYZ6IP4WYroX7RVY4I9Sb2WLBDz79MT95+YazWcNZpbDBoVOU7z11sWWLq405mFxUpsG6EVMHXFvx5PED7A8/wyjoMkQjaiJxt5bO4/XVO/j930UDz84f0iws+30POtD7PT7A5c0Nicxu37HvesYQWCxa2lq25L/8/e9xdnbG//M//c9ISRYe1io5eChpe0oWQ1PUb4Yi9SvyNRSualgslnS7HTmLQ1PKkhYZQsDfiCt3284YfaANAWsqjo6WkuuSM3W9IKrMkEeMMygfcMoya1raekanxFUoZxiDR5fOVmziBDc0WmO12Ic56w4oijwK509rmtoR+oHJI1KXMfCnvSinrBvrHJNfZAglSU5eCJTiS9ZsosZRB7rOnanGRLOS7i8n4XEqOGSeGyuWYUJMTzKGlr4+Td1dhlhC7LIuz7MwEEpT+yVo6X7HXUCKQqmh/E6GRGKz73l6fsTYC16ntZbrIEsSKYfXyeDtSAwi962ripwSs7qhrWv2SpEn+EJpdBKBRpoSVUumkT6oQzhAWhJ/ktFG3JYywjs1qC+9liklXNOIGm3wxKiIAbr9QDWX+GGrZL/glbyGCY02gsWbnJm5mvOTEyqj+Z3Xl7x++YrHzzRHZxV1U/Hw0QOsMVy+fcvQ9TTVjNwe0+09290blLM8VnB21uKMZbFo+DPf/Ta1019/P0tVNmfBB2IEtGYIAj6nas7Nbo12GhMyVuciBZNOSed7ozLlYpvuhwk3KlQfjC7GBMWmzWgqZzk5XrLf93zx4gXffHLOqtZQG1TtvoSrTQC+5IALbmiNE8qRd8ybhmePHlE5zRg9MWtMXZHjXV8q3QGsNiuub654eHzCZrPGM7Der8gooo/SBSbwoyhSTk/P+OijbzAM12x2e96+veDo+IRf+MVf4Hd/7w9IOcmFGtJB3+6cJQXI0RfqiVzY6GL/X0bF2XxO0zQM3VYWIlm8H402VMaK8UOKhYPZUzdQuRqqjDFLdBfZ9nu0Fl1xHAM5yXbbGsOw65jPanLWhyxuVd4xrSQpMKdIJKN1fSChy8KqkMqVKc7imboRLXsqdKsQg2xxCztCK9HDS1eTS5GTbfIkmUXLqkIUUZrJOk3UORCjKgevpq7tgWQeyxZclmqgyvZ32v0JlUYKqS5uSpRYkak4xwMvfVos3YXlHbwh1d3nTP8/WcToUoxk/M5cXl/xzWcP5ACxlpQzIXkURviV3tP3A9bqwyhcVeJwPo4js7Zm0dSSuJiCZKgjS5VU8tHFKOTOJ96Ue8KHwOQRaq0l5nSw/5swz4lhoI0tS8pETpNPQ8aHSOMsxlWgwCdJWzU5yGSV0uGA9/1IlzxfvHlLColZ07Lf7bl8+xatNCfnj9CN4+T8GIzi3dt3hP2euj2i7zfcrG5QRpbJtmpo6pau66ldRdd17Pf7r6xTP08U7vvAvwk8QaCSX885/+tKqf8t8L8CplL813LO/0H5mn8F+CsIn/Wfzzn/7T/tZ+Sc8aMnek3CkLXBo/DKQXtE1410PmCMUEwUwtvTWQzchIYxmbzqMgrwZeKb1kJRsgZbsDFrDLUzzNqaqtK8eP2Sq/U3eXg8pxo9ehjubUX1l56zMTKCqZipbUVtBuZ1w4OTYx6dn/HZ5QblLGjpsij50XeuQ4G3797x/PFTHr//AZuw5+//vb/Dyekp7z15D50QvpxVbDZ7To41z54+ZxwXbPd7lHZcXl/z3e98lz/4wR8RvJclgdbFV1FGO2MMqHCHpWmN1nJhV5V4atZVxXK5ZOx3B0dprbXorEtQVAz+QOoXY42xmGzkglvJOCa8SMPQBeIYIAWhzMSIqXUZtaVTD95LuiZivqDKqCyLmrKwMhx4fYefRSZlIzihnRZGoXQ6EtRmlIUxHcwxtJb3O0/4pJYu0OgyKUR90JOLRWphUShdXJvk59ROnKbK3XG4HmJOhVxtDnQidXi/OTAqflpbPWF/d7xT4Z5OBWqidFlrKRDiAVoiCyxxdXPDxfUN58sZ/SQI0BZ0Ltk4Qhvq+4gP/iDv2263aK2Zt0tOjxZsbjuCUXKAlDtJZUVMhT85+V8eHNCn55EPyx5VwtemDbo8zSLZjUJ/i8g0KRCXFMuQEt5HbO2wzkgePIgJSgxET4EyHBiHD6J6a2uBdob9jstXL2mcoz06Zj5vMU3NqDPvPn4BWVG3R8xj4OLilmEcUUrx5OkTVIaqsmjrqJr2K+vUz9NZBuBfzDn/llJqCfymUuo/LP/2f8g5/+/uf7JS6nvAPwN8H8kN/4+UUt/N/6g43IJB5SSLmKwUQ0qoZk6q9nR+hQtycRslxdIeKIeFs0Yxby03hCxppkUP5KIWyHIf4owAurOm5vT0iC9eX/Li7TuePDhltqiE7OvcYVS7Hz+gteSjEKByFY2rmFeek8WMD5495vOLa6yJ+NRjtSxbppEtZnHC2e63rLdrdn3H1X7FT9684Ys3b/nJi9cYNE8ePubB2QPxjwwRayuOj56gb2+YL47xf/gDlC3Syyw+hoMf0CVadHqe0+Y/BOky2qbGOUfTNOSUsBrm8zmbVSWmEvc0z34cJY8nC39yGOQGrapGtp0pUlWO1ik6BsZdXygkkTiKU3dtrejalSpO7EgmkHI0lZPY3oL/KX3nZq7K+5wRuo9WsiChcB4n6pS1SjpBMlC+NmesUZhG3MClDhZ1iNIFaswHQwyyaNO11qXD1UyS2HH0yCg80dCmfJ07nbvRuiSfxIMpR4xS0MT2r/AO8+R7WTb69+Qx09itmGg56XDgqzIuyTJT9pdaibymG0aublc8Oj8tP88QU6LbdaRhKPZzgXEcUBpOTk4IIdD3HbPZjGwVbWVwGhJROKhZYlGYYBsoRiNFA18OCmc0zlVMhh8T82SiU03sA11G+GmaOBRPDbHATaTIcjGnrSuszhidDooie4ByRMVkXEWMI33XidGyUexX17z7HE6fPGX54BFKGY7Pz7Bec/nyDbvdmqPjh1R1zXZ7zeefv6RyjpOTI4yGul0cDoGf9fh5onBfUxJ8cs4bpdQfAu/9KV/yl4F/J+c8AJ8qpX4M/AXg73/1z5i2bFLiYhzJRHxMNLWjTwmGkcZVGJ+orC3ehAmVNMQgdlxG7gpR2YhjkOCWSTqFVL4mi7diVf60TcXxyRGfvXzHF2/e8N1vfZN2vaGpLcMwIEYM7oCPTSTjnIUYb6wEN9V+YFFVPDk7oVKZpCMpD+Rk7qkZyrIhK/b7nSwaNGx2W2JOzOdLRj8SxsBus+Unn/2ERw8eMQwjP/zhj/now2fEAO8uL8loYoiHk1xphXUWCok55kQKER2EPI5SkhPuNfO5OEJLIZUFgXMOnyJhHGVcNQj7oHL4cZAx0jrCKLzMumlRCHeVDG1u2OzW+HHAaEtdWeZ1w9W4RSV534zRBC/fX4xhJWpB5VTwRcHNJm/HSU4IqSRfKrIKh99Vbshw+DfZTMvBprIuN/ZdETb34IdJ05CTcDGnPG9TCQYaQiD4sSRkyoJiHIfDdTClBGqtJSlxHFEo6pKymKIUEMHxFN6PZC2BcDFFgU5Kx5zLAaWViFtD8AfTlIzgpLkc2BOHcaIZh5i43W7Z7HsWbX3ws6wrWzo2IfnHGJk3M2azGavVislQJmSodcYS8MmW5yV0JY2icrbwdAeBRbIUTJEOF1x8uonRxBAod4jc4BPrQE3donSSKovoQhlNyoptNxBiws9nnJ8dQeqFL2oqtDK4qi6ZTuKg340jVV3jFMQwYqxhd/nucC3ZkzPJyTo/p9KOly8/Zxz2uHrOAhj6LTfXt/R9R9vMcZWiqv/xOsvDQyn1IfBrwD8E/iLwv1FK/c+A/wLpPm+QQvoP7n3ZC35GcVVK/VXgrwI8fvKYpCTFUKuEU2IpRUqM/Y7tbkXoOo4aS+0sPmaqYqibs+AfWcuLfzh2UxKrKjW9SRq0FFKjM2iNNTJWNY3j+OSIMUU23cDnb99xMntK3/cH8nrf93IK3+PPZa3ITl5GlxxtVTGvKz56/pT3HpzwZjugrJNxVGsMpmx3lWRoB88XL19wfnqK7zscGTPRVIqze4qZdxdvuLq+woeR/9H/4L/P5c0VtrKMgy+k9RFTSfd34BvqMkJaMcg1xhCDjE9TkHxKsi3WTn7H+XzOahwk18RLVK7WmRQCoWRw3+F0tWw5EQ9NnIDySkP0nhwSRLBKSdA9mYwUq3o2Q02JmEkWHsZZKmcPRrmTvC5n4XuKBDKVBYx0P1UlTkNdJwufum5QSotBcU6y3MiSvKhV6Qa1UISsLthe9mglxsGirBGSvmx7zaGbnMZhrcScV4HE1SIdr1GqdJNJiozSOCt/J8sCzlnDED0+jAX7vrNxk+8veKXO8RBdIrr3SJ7ezzL5m0m1lUUB9u7qhpsnW+q6IuVI1gFnTTGPucNjq7o6FHpjJM52HAO1BZ0jtdVyL4ZwwB1LdA9Wyz2adNHSqwmzkNflS4q38nuVe/0Qfhe8RwGmTHuq0Ii0cQQ/sO9HEgpXO44WFa6y6ASqsBzkPQ/4FGlrxxAyurgzOWdpUmJ7dcmbmDn/UFOfnJEsnDw4IeTA5bu3dLstbXtM7SpubrZ0+z3L+TUhOeaz/z/ESiilFsC/C/wLOee1Uur/CPyryPHxrwL/e+B/eXiFvvzIf+IDOf868OsAf+YXfzELgC/zsVWRiGfYbVi9ecmwvkLlxHbvhNYCVFrI0XpybIlBOFoFq8wlrEqkrancsMXseTrpkFGtcoaTk2OMc2z6nhfvLvnOszP63t0D/TV+wgXVtJuE7CxKJWyqaJqao9mM9x4+4M99/xf5f/znv4Exml4J6dbWFSEnxhJyX1vLm7ev+QOjaGY1y8qhohCWTbmwjLEklbGV5u3FG37y2eecPXxQqC+Kjz/+RFxanJHlA6IhzqVoGgRH1NqQsoRIaW0KnlQwMSUF9PT0lN16LQQSpUp3Jhha9ApvDOiA1vaAe6liaWeUlSgIa8WdRlnMEHBGUztZ9ExYmyJDkoKAVsLLBKZUtynQK6ZEToEUS6pflpvGOcPoPTEGjJGfJ2wAITArVdyesihpJjnnJHFVipLlHlFq2raImsYXypFRCpIUnFSWPdpolBFXnun3n2I4ZGEkWVGpGFAkcnGHvzOHUK4uLAAZ92KcZJh3RHetRCFmbTFhKddrBmII+JKpLYYjGltZ+t2O1WbLo4fn1NbSbTf4oSeOxeCiQDLOOuBOHgqyAHQ6o1KgdlNxNKCEWRLKoWpUKq+tKu/9dKunw/eL6afNQ5A7rXSeVptCui9xKaU6DD4IRq8lE/7Fm3ecny05Oz6m1hYTo3iqKsjBk6JHk7GukQymGLHBs6xrZrbiarNj9fIN526GmTXknFgczcnpIZcp0223zJuWfr/n7fU1q9Wex883PHv27Ctr4M9VLJVSDimU/1bO+d8rxe7tvX//PwH/t/LXF8D79778OfDqT/v+gtholEryYqhEN+y4uXjN6t0r1H5P5VpuN1saZ3CzilGL9M0UwBsl20GN4CxFygGk4l59Z7oxnYJ3EkjDbN4yX85Z7Xa8u77h3cUlp61sQXe73WELPo3jSgknLRnhqUln5FjUNWfLJb/2ve/x8ecvebtao12FsWKuMZGjtdGknOiHnpvbW95rHvLw+Jjb1RofZZlSOSMMkZiJeMIw8urVG+ZHR9yub1lv13z66afUVT3R/aSwI85JkyvThH/dt3FLMZF0oXREkak1TUM7a9mub++WEfJmk3LAjwPaOpSVtMhUxh1nKlKW7G05fKqDy7szBmLAj5Czg6wZRzF/jWRq53BNXU5Y+TlW2QKlZFIy0vEZdegk0fJ3wQsT1pWlSi54pQarTRE2ZUJZMoQg/E9jbPGW1Iwx3MEYZZttrUgtvQ+H7juTJbfJTougVDo1J1v5KPZj1piCcYpD1pRfJL+tJpWvO2B59+SUh+VPwYhTGXWn98JofYiKzjnhWicc45yxVUU3juz2HbpxglEOPWOfC14dpaMsIoxJd5+zuKa2dYUz4qU5jL5MeRLJohF5sSpMAxAYYCrgFDxZGy2HuBKC+7QFhzvqFQVaObzmhdZmrMhyU8w4V9OPievtHq0dZ4ulHKwhEv0I0aNzIqTAto/UKTAjU2vF6axlmQ1j3HF9s2LVXnD8/CHVrMG5hYQTKsur4QuGYeBoeUZTVax3G16+fMlQoqF/1uPn2YYr4N8A/jDn/DfuffxpwTMB/sfA75f//veBf1sp9TeQBc93gN/4R/2cVHhcWmfi0LO5vGJ7dYWOGasdCcW+79n3NYtaE6MiGUPO8obmjJhxqFhOK11MTO/oJDkrlEnoKNGeVrdYBa0LnLSOhyczPnt1xZgyr65u+ODZIxplGHxgu9vhXEVdl0WA0WSdSLona4tSLVorqioxT5knDxR/9vu/xG/+/h8TYscweMIgi4e5Vag00raWFDLD7h2KYx4/fQTa0g8S+DRGSfmrXA2qwlSGpDwxjXT9jt/+nd9iHAeauibcy0onC6XKxFygCYnPVc4SlUjqrLNFrQHeDxIihWbWNuw2FOxYXF/ICpVFyxxDOFz4OlrINXXrhMKTIikIzuaqzFFVcZPF7HgMwv9EKVL0VJVDvCoFe+z7npiShJgZ+T20KuTwQquZVFQ5ZynC3Ln4KK1KFEbZzmZNKtjytDyS5qbkypfOctLcKy3mIJMqiKxx1tD1Hlu6sYnKNJmAaG0L5ivdlxQBg7F3UsopYVIb2fL7QTpqcVmSDPuUJAQu54SraihUr/vbcuBAz1FKMQQxS0kpQVJYLClotqs9udMQevwwEmNi9MIKaGctzhr23Z7JxERrS+Vq2rrFmoqQZOkkU7KMysZqWSjdey5q4rTmoqgrklLh/E7GI5PvpTl8XSzmzgcPh1JxYyyHqxM/AkaNHzLXNzvymDk/XtI6QySDURilUWNmMasxwWK0YhxGNiEyxkDUYlK2ub1ktIGThw9ZLE6xdcXR+RmZzIuffErvR5x1HC2W3G53vHn5jyd3/IvAPwv8nlLqd8rH/hrwP1VK/Vn5VfkM+F+XN/QPlFJ/E/gBskn/5/5Rm3A5uRLZZmLwrG6v6G5uYO9xVGjXkFTEj3vW3Y55A5V2WGUPFAOFJqhcLlQl6o+kCjYi28+UFVkbLCMJByxRSdHqkSMbeXJs+PzlgHYV17uR2yFShURjFGkvcsCmaqCSEUlXmaivQZ2S0xJFhTaBuh1ZVIrvf/+XePL+9/mD7nP+4X/+G/Q3W2YKXLrlo4+O+d73PuTm9pbPPntBP7zm5MG3+fZ3f5GcKj7+5MegR/phR84WzZzFfMF7Hz7mev2W3/m932I/bMqoJGR7iVZQaGWxSsvoku+yxked8SoRxyA6YVVhtWIYeqrCHa3biqZt6HZbrLbkZPE+UWtX8ogyMQyC+xqJVCW1gCUHTfRaoi7yiKkdQ+iISklQmLYY59AlV0kRUTqI+YPJGGcwJmJscVAykz2ZXCdq2spmJdiXki20L8T7GP3d+KfL55ebOpOpSsd1MOhVUoiFWqQP3E0Jr5OvqyvHZDistT44rgvGKBQqKQKehFj/uUoWVX6USNgpEjfGRGXFgFrUMVqSSovLSYoBVdRIkzE05KJNF6xWZJkaZyzJQPSJ2swI+5Fxl+idp1k6nNYkH0hIdLE2iuVyxnwxY7ffEUJiHAKVa+S9ywatKmLQGN2gGEvH7KGyAvnnO2elyU1JFRrc5BfqDku3O5MX8QhJKApVS9/ZzimtD99vGAZSlA6YqGjdDKsV622PdRVp2YhU18phnjXYKF1yLOFsN2jG7BlrwTi74RZ/m/Ee9LOa0wePaJUhqsQzHXj38if0+xWzquVsec7Nzc3/78Uy5/x3+dk45H/wp3zNXwf++j/qe08PBajsGfue3fqSy4tX7Dcrchipm1bCjaJnHHqIA7VOtPYYi8JpeRNsFpyIfBdYdgBKC51I/lPGuEmJoI3GWk1dO548ekj63c8Jw8BOB25ubzlezqibihg9+82Kk+Vc8o5jJ84sIaDSCLaDHEEHUW+MhifffMIvvfc9fkknZu4h/+Dv/C261cd86xca/qn/4Xc5P624fDfyZ3/1e/y9f3DFze0Npw/OOD55xLDacXo+Y9dfE5NHa8eDR08YUuA3fvs3uV3dFuPUhCTwGYwu1KToJR6jYEqTN2U/DKR5VeyzZBPtvYSSBZAMbqWZLxb0fSecv5yKAgjpjhRQ4hMEUzV0XUcyjpB0Gak0wctoP1G9JuqLQBkNadzLTZKSmHgcmAb+sEw5qFnK+zZhZBPccDC6KMqo++4+UhBFryxjJwXjkwzp6UYXBVJdXOf94Xkc1CauwgcZk0UplBiGcFANSXGYxugJb6V0w7kQsyXDJ6WMs+5QSKrKsd/vywLEHP745OV1LJm4E03JGH1Qykx0Nl1+59FPxS3Q9xFdF523cYxjoG1bHjx4eIAAQojUlQgHMpnZrGU2n9EPsrTJxRlKa0XWoHPBTe+R5IGDaGOi1jlj8MGTCq3LKF140JmkBQ5JhXdZUDEREFvBtJWiKIYyIXqcq/Hec3l5yTi2HB/NmM+b8rOdqHsmo+4si9xcpqukNFFlurFn1b/FWS20ubpl1jbUjx+T48ibl579MDBvW07Pl19Zp74WCh7RCg+sbt6xunnLfrfCj3ucAgjEOLIbxCEketmEH80aLIpKJyqV0S5jCpD9ZdVDYTRMN5vcNbIcMkKStsbQVBXnZ6c0DrarG3TvuFlteP9ZMQE2muh74rDDzCpUzOhoqIqmFTfKRr4xZNUyPznmwem30O6cM2b85X/qn+bD92pev/iP+HO/avkz387k8YLHj2o2V4mXX1TsNzvWl6+o85zHx494/OiMZvEcbUe6Yc/bq1v+3m/+IdfXKyrn5IbXdyPNtFtNWRQUzonSRUVPTJFhGMh5KYYKiqLMSYcuwRnBGlGG2XzJZr2RTa4SypWPQS76ouiJUagltnVQ0iAPCwpxLKata5oq0vd7YvDkWEkRUCWjZdokl3E2xjJmu7tFxPQcpz9SLO/MKSQSWQpkKhp5Kbh33pKTmcYUBzJdH02J4BXPxztK2PQ1OU9mv/nQRcpCSTOZAovqpSx5UkChcLZGNVUxMPEoramcpGeKfZqQ3cXPOheXcCWb/3sH/c8y5Mg5o+xkbJyJQ7zrdnXJbWLiPGratuX09BTnLJvNFu9lMVZVlfyelePk+Iizk47bi7Ww7JLATaDFhSkmrCpGIWpSEWViGMuhFyWFVCWcM2SjhbWgkuCRKYucWQk7QU2JADLzTUnWSHSLuK0LpzOU92fDehNoasPRco5zlUAqRkOUAz+V18AHL9SlkohR6YxWifXVG0iRJ88/oJkvMMrx4PFTUIaLN6/ouvWBS/2zHl+PYpkTm9sLbq9e0+1WhHGPs5naOlIciXEg5Egy4gC+2nVc3G5waBwGh8ZmyarB3eE7qAk8P0DRBYzRkhhpknDZlKJtKpZtzYPjY15dbHDLucgVnfDQKq0Ywsh6sy5vlkHh0GkJSjZ8o4JgKuzyhGbxBLt4j6ETw48nTx/y+OlfYrceaaof0LjXxN0GujmjMXzjwxkff/GSqzcdJ6fPOD39kJwdwxC4fndLN+z43T/8Qy4v1zjX4kOgbRaMw4BWBl3GNCElezKRnIOQtYPHOUMssQ6mSAMPOULFpNUnwRV3+566maH3A2EM2KI1Dn0HgLO6qGHyIW/b1tJFOGugcoQeIFNbS20MFMu3XJZtlbMoJfigtZYchXd4p8e+0xRPj6mTtOoOu7xvkybr8ozET5jCBwwHio614pIDirqWYhxjwPtMzqIOm9zPU5pyuDMxpEMHKd2mLabLEqHb9z3WuQOpXUw8hCJTOY3W7tB5BS+8WOccwzCwWMyIUQ4y8SJVWGeYzDrsFNQH5ByFT4qYz1hjwSr6bqCpG1xZ2MzmM85OZtxcvyVFoXw55yQRYJC/T111CAGTAs5a2rbB6q0sM6sMoWcIXiAnbYR5kin0Oznc+q7DWCvEcWMIOR3iSkzJvpocnpISuAxjDuT26T0XsUg8CA2m3PdJYQUN0Q9sN3vqSojklXMC4ZRkz1A6U4eVydJodFQ4Lcuf/dCzvXnHum1QwGxxRHV8RlYO52refv4Jt6vVV5apr0WxTCmyvn7HdnVNij1WSViTQrweh3Eg5kzMmawNgcxqN3DcRpa1FhA+yQ2jTSZlobZMnct08x0SdI3wD3UUiZbRisYaFm3De48f8Pr1xxhzzNPn32C2PKWxEZ1G0tiz2ncc9QNUNdpriMdE5Yk2kxtHtTymOXuGcsfEYMFkWrdD2xtiWjFbJIwRdYSravK4oHU1s+MNg3rL7QD64gjbHpGqB3Trjk8/v+Ld5TtuN5HGVeSUcbYWDElZVBIqkMoy8tiagiVFoYAgS51kZawzetK2Sya5F/JkuXgVPoNO0C6O6HcbUgqMMZCCp7JW4iqMGC2kmOjHnqZuQDuqysEo41hMmbZyVMaUC3pSwSSs0YfimZIU8RTigXUAdxLTqRhOHZ8pfJNQTCmmhYJQcnTpOhVFZYlSCleUQlNHHCOHkXk6NLpuX37eFBQmdLGM2JvFccRYjXWySNJGFDR1Ux8oScbAGCN+HAAt7u9WCe6sNaQohSGOOAsxDKJAqQzDEIpJh6GuZWkmC6B0SK2ctPJaK/zgyRGGvud8eUrTtsIvtZaz83O839PtVvTdyM3NTVm0TA7zctCN44hLnsrWQnPSkt3jyhLfGk0fPNo46sqJO1UKhPKeSSZSIcgX1Y+wEu5G6hhDKdAFIkrFialAPCDYs3WWbCdKW0apVHjBGrIlhpGu61mthAKVZyJTVOoutrrvhR+cyzUPGWdEPlvpzDB2XL99yTiOPPvGR8yWLcujY1lijoqYXgI/+pl16mtRLIMf2a9XpHEgJy8bUq0ZB08YQzFFU9JdJhhiZuV73pkNc1eLy7ZWKKtQPmNLF2nvjXoUqhAAWoMR9YC8GYamrljOGj54+ojf/71PmbVLzh6/xze/+4ukYY/vVmRj2W+3vN70DPWCo2qG5gg701Rzh10uMbNTMHMZ/fNA5Qa0eYH3F4T4Dm0yxi5ReSBrD3qJsjWzxZpf/OUGrUdefv5jfvcPd9jqjJANt+s9/RCFI+dvych2WidH68wBo3FGAquy3iMsUCFDK2uoMCgtip+qrtEqli2s6PKV1sQkOl3tGroxspjPIEf6fie4ZoqoBMaLSoOUMLXwO2MKJaRMujJXtPOVtQellNGKylqcVuQ0CtZsSnFDo2sH2h2uiwMRXE+b5XT4+NRMTmO2MRywvaqqhPMXZXIQs2DEGsyZg5JqwtumsDNxDipdnZZOR+mMzuUmz7EUchkVVeliBa+cojAqnFVYUxU7tIm2Jtefs4YYxcnJWFMwy3wo8Ewj9DSe5giIzZ5Sd68ByGs7eI9RQi43xU7wdrVivV0wXy5wFvx4zXq9pqpq5vMFSglGX1WVLGeSxzQtikwYR7IuKqtUXKGm5xX8gaVgjSkqInX3HhkFSXw/p4/dP+jIEjcR700LdhJpkA9eA9OBF3MkRjHaNkbURrKq1ZJGqhTWtGLekjPjKIVca0McJRk0KyGrEyPztqFtFdt9T7+9ZditcVWNqxccHZ3Q0OKaJfydv/sz69TXoliSIfQ9cRA6TV05uq5jHEb8ECj8azH8NYKh7EPkar3lZDHHzlucAZsNNsiYMOFS0wB+WBBMFCKtUVbylOtgSaoW16DHj2grx74fCDioFpyendPWHzJ0O3b7Hd04YusGMz9n1j5CLwzUhmQaol5AMui8Q5tbyJek+MekuMPYAVsvIZ/hRw90KKsxczirKv4bv/YtHh8N/Pi85/PPt1zdXDIOidrVONsQRk9trsFaoT8njdYNIWkiFmUqxB08l8WFwxqHihlT8Jx915HzCUrfjbkhBjSOqm4YY8Y6xWpzg60cR0cLEhE/jlSVJXovZgxK+KopivnBRMkxRuOsxVQOnTTOl+yWnBmHAWNkBE4hQkxYZ/FJ4jOUMbhGiuVPj9/3u8wJ37uTQspFNEUEy6JFHzazIQSGoedgslHUM7KVzYSywBF8ryHnLIbIJS5Ym+qg6DnYk3FH5bHWokj0/V42vkYoRJRO+EAKV4CR8dM5Ry4HWs7QNBVVJRtlH+UazpSDsGz9RW9d+K8JtLGsuw0gnZYPkmWzv11Rv1IcLWpOFy11U9N1HdZa6lqWI7PZDIVmtVqxMJm2kUI2Dj22qQ+FGyivl8UictlxHAtxvRDm1STcKJOc1nfJoVE4qmaCfYxlGMWpHUTUoMv0Y+ydu1dM07JyxBeJc+UczprieCQChO1uQ44tGrFbdE5iXHyOqGyIKtOPnuRlU2+NoTKZsduyunyLcRWN0igczbzixBx/ZZn6WhTLFCXJzxlL9JE+SoiWUZasxR6f6FG5BGepLF6P/cjl7YqZ01htsV7TmgqTS6FUk8sLwr2bqM8TadYgpOpQMcQdjbMcz2c8e3TO5ztwzYJNH6iaBts2qEXN6dlTjo2RjPMsKXWqNSStCLkmeC2yRb0n+8/J/hO8+gJbNZh6DmpBCB2BDbYOYAaU2THfZpo4wz5qeFCf8MGjFSHDx5+/5M27S2KsydGQB0/VaKpmztVqz3p3S8hONL1YXDVDJcM4JlxlZcGjsnQKhR7U9wPVTIxaTbnZUZrBezbbLSFEdvueYRx58Ge+wVll2W02aDLddgchCPVlwrwKId1YhassqnL4OJKjQnv5PFfcihQc0jJj4W0KR1IX3CofOsmpywMOhrrionR3+E0yQQBXll7TyHoX8zClL8oIO3EjlZLnL5icO4y50+JBa4VzokfOmSL3nIrmRJGRDtNZSwh3xRpAGxliUswFm5uEEFL4xtGXziwXtY5kgCsrpsQisCmYbNlMo8RfUqEIo2e/27GcyQ0evMdWRf1TiX56t9+Lu1MxTpmw17qq2e06GcOPZuU1zIcCXzkNxhVDYnkddc4klamqiWg/PaYlTTl49R3FKMZYsHJxD0oxorCSAFmcz6f/nrrUlESXnnWx2gtewvyslesmTiF7mf8Pc38Salu67flhv6+cc65i732KiLhx4xavfs9Zpy1nwy0jqSG3hAw26tgGJ043ZNRxQ5I7MogECWy5JQzpVmIQciIQThILQRonQpCpLETq1fmq++69UZw4cc7ZxSpm8VVujO+ba5374r58yjQmFvcSJ3acvfcq5hzfGP/xL272lnmZxSHLurU4N8f9XMDYjr6zlBSI84Qz8v5+9epzppD48Dtw9+w52imGP6YifiOKZVOYiDZAsKacBFgvzfKJWI1PBWRfQmCcR0xeuBkcndngZmrMplkvfHHGkU1xVjKO1TAXMNUqqnYknXNshp6f/5nv8wf/7Q9YYuTlhx/Rd47iZBM4YynK4swgywKb0KYQUCRVRNNNIS8HptMPUOX3sJsRY+8oyRFSoSiP626xJlDCiaweMaHHLx0f7GHnCs/2N2ST+eTjj/jx55Zpsjy7+xCdM+dxRvstn7665/GcwG55/fbIF68fyATIN3Ur25Nr7o7WSjLOO0+OCYUHxCAkF8mziUEWDcfzxBwWPn/1lo8/esZ3v/OxQAApYpQijiOh5lErJdngvevonfBep9rNZSxaKZy1eG/JueqCjcV2HcUpydDRcvOEJNp1vU4ALbogrREaKwsAVnyzYY6ria82q3tMrt1NqnQnKXSXzfqyLO9JWFtRlhte5JvUrklrvfoFLNWR6jqywtWfI9ZvumLkaiXEKzI5irpI1yXVZhikoIS4LpC0q8VSqVpodE3XbPitfF7H0xkK7Lc7Kf6ZFauLMTFsbijjmRgrNcs5VqVSfb8639F1A4/HM/fv3nEej3RbSTi1WrwMcqqCgIovdl0nzIFKXH8PGvDdOlY3uKRFb4BwX51xlV1QX1tVxoUQLx28ld2EqVSndjCKSkz+foqJh4cTRmtub27wxrOEgO86rHeMIchiMkrnrYtjJhNDxFsDRjOdjrz+/FOUynQ7xzBsfmqd+mYUS8RpKFX2v1KKmJQoSFVmTjOJQCIypUAgE/RCKmc4w5t3ng/2W+KcCHoWkNqJFb/Y6ms5YZIR/pXfonUCNQkfzDms26GWiZstfPhyy7fvHJ/c9GwNWNdju0HoMTmTSbiUic4z9Q7LTCkLPgZsTqT5gePxd4n8Ottnf4DDAwdy2pLKFud3WPMhlA5VOrwZCDcnxn4mbgL6XNieEvMhcucsL37uezw8nrA+U5g4jyO7W8PLO3j15sDd8xu+eut4PDzj88/f8uMvP8fQQdqxLBuMzgxDwfiFvsvVj7EjlUyKBmcU5IW4jBgNMQceTgcezjN/8OOZ73zvBd3g8WrCqcRoCoecCEF8JVPOuJQoaUQrj7Ke2W1ZkmbRGe8yVhfOoTCNM7l3aJVw1rDkTEQUF5mCjkVMe5VaDWZ1znSmdTcZTJYIVyuLCq1dVcAoQCCBXMThNGXFtIBWvdBKchYWhdYoEpvBoSolpvNSBMIiDk0FiYlNdTmVYhCz6Zhw2uC0OIQPvpND2OXVP7LlPEkBDGw2mzpiL6thh654nLaW0+mEhMuBzgFywmhLSBFtPDkZQqV1OTtwXGbejIUPbl6yd569zSxlZsozyUaSghwVzvZY6+mGju1uQ9f1pAgqW2wZiMnw9rjj1bt7Xn11JJdMUImoDV5bOm3wCN0pK4niLUpgBuncyuok25gWgCzGqrQ0ZTHlaNBYynHFk8WdvurE9fWBAIa0+p2uvgzaULQl1qx3gvjfnk8RqyNd7ykpoBXsjEAfuYeYF6YlYr1BOY+Y31jO48Lh1Wsc0D/fUZ5dCv9PPr4RxbIZB8Q51g2WGCCEKj+Th2A3zfcyZekWxmXh/uGR+9tHzH5H7wxqCaAl28VWLplojbNkj1cMDahmuKZKv6oh6Icf8qf+lONb3/kubthQjCFpJTptJdyzrCW3ZBN7KB2aBZW+Iiz/gKfHf0DOv8ft7Qlbc7ZLHMnK4f0OUYYFVKpOSUWj2KLNhq7PlBIIYUTtIpudWKENt5Y3b99Bgu2+5+XLZ3RDx3keOR/f8OL2GT/3M99n1xm2uwNffJF4/eURYqHbbPGm1O6yq7ENMnblHMXgN0ditfJalkgumhgzT4cnzuPEy7stOoFmW7mWivN5YRrDSn3xk8VvLaaaEOdY1pHJKEVYAtM4Ue62FAohJTH/0C3yQ4m8kivSs5FUTrhglxq3LnOab6IxQudplKJYx/sWMjYvkzwvrpyqSMQccc7TlCiqXovi5K3WcfL6/8DaAbWuV7iYgkVaY+v1ZFaSfSOTW2MrabspiVqksabvesFXw4ixhnkOGO0AwzIHlLIsS+TweObxPJJCYrO/E5EBLWNI7M72uz0xRKwW4vnNzQ23tzcYY5nHQI4iMphD4PHhK756eORweOJmv2e32xJzDZmrxct78Uhth8H1e9G4n6Xet63haZhlG8vhUjDneaoHnMghi4or/htjlCx5c8kzAimYggFHdjuhzVntZPnT92I6UztPVZdkpcEpxgCamQQRjPVMc8IaTd957t++YSgLg+9+ap36RhTLAogbhtAOmhJBtpGqsv4TRRVCFAunHCTEyGvDw+HAp6++FJdkb9dIAu+DcLxMwbgqMSNDtfxSlXOJMRjrGIaBcQ48u91z96zweDjwch6xm1I5KAbVgqeMYFVmmiEvUF4zj3+f4/T/od/8AcMwo5OHaUdUM8oYbFdQOpHSWB3ExRxXRpkBcoffOjInwvQO7yKUwBIPmCFw88IRRoNEK1gSnud3W+Y5s9tvMGrm5a3CmYGXu54/dIkffnqmThxs+p6bmxt0MCxxEllhMcIRDJVzaTzbzQ0he77iiTmOPBweePHsQ9liKo22jn4YKFhSmchRRl+h25haAFJN6jN1RHU4J2O5MiKJtKZy9oxGGSPdf7mA/Nck7GtVj3WWGEFroY3kVNab1HvHWCZSWGR8RALYetciYcUX0zrZkrf4VVUPQRmbM6530skhn3PDMVsxFjWNaNxTEjaALJUEN2sKojb652rGkfIlT3zVvCtR8LQsm5REd2610LTevb1nGgOlmpCEEMBanu33vLi9Y6jhY0tYOJ9HtCo8vXvg+W7H4yJk+1jHfOnfZZQ33jMugdO8cBxPxJS42+1k9EViJEqUiBBVbZ+ul2hSwy6cV/mMJL8HY8hJ7uNrTidV9SRuTLJszTmRWzgcTd4qhba9P40Nsd1u1yZKVRVaLoWUxfIwLBMlR7yzdJ24lIUQq0mHWoULOU1ohOvrnCEukfHpiS9qw/B1j29EsVzB5aZmKJdTQf7XuoHMeDqjraPEaj2lNVMIvLp/x36/Y/AW6x1oUTbkEjBOoXWleZQMOlXwXMYJrS1aW/pOsekXcko82y28/uz3+fCDDW52BBLFiCqm9wPFDxgsZRop5TNi/i1C/k222zd4DWr0oAYIiuIyxisUkRxOoJxsC5lRWcLTtLLETCXMG0zfibZdG3q35Xh4oKsONcsycRwPhGVhv3V0LnK7F1wwTJZnW81hZymhcDyMPB5GOnPH0Hd43wGKaZ7xxqCUaJxNJQovi3Qw5/OCtR39xnM4HZjmW1ylxiltsL6jV0703ouQ0a8z2SWL56Kmcs6JyCDnyuMzQua8wrtE/va+6qYpedoN0jBMuXnlIG0b5+bwY61GaU/KkVI0VsJHsMZREGK3sYqcA2Ea6fuu8izndbnUFDHee1JqBhBNsSOFQQjeW6h0mtY9pRgrTiiHhfc9IQRiiuTSuKSOGIQgb4wcCO01WtOTk4KimKZAToacAmEJWOvxQ4fvO57d7rndbHAh471hKQmjPfN04qtXX6FuF5QRk+XHh0fJVPKdCBUwpGIp1jClwMPhEdc5truNQCJF7PVSUqQi+vUEVx1jqp+1XmEMwbDzuuShkuovXGdTP8t5JZ9rbWr3f6GKKSXu6IkrxkaM76m6lmWpJiiGGBeOpzMpeWyjWJWy2vhpOZUpyM/VnYNiqqFx4TxO9N4xx5nDu3c/tUp9Q4olQK68v4Xm/BLCQtPElpKJIZBjgCJLIK0tISWsMhyXyBfv7nm22zLsdpgMIUScUihdpChl0VTpLBepUoqC2NVbmygl0HtPyYmPbwOPD5+ijreik7WRSCAoBbYnaU9YIufpkTn8PtvbN+xvI1bdQv6AFAMxPOF8Et2ryuR4JpeItkN9PqF21BptFpSJhEX+jt1YSIYSE3qzxy2GGA/0m4XnHz5Da8W7119hdMFaxTQ9ME+Fm70m24LTEf29Hms/5O/9wx/hXEEjqZSddxTl6XojStqqaY4xssyBZYHD4Si8Nq94Oj3wdL7jZhCDCOM6UJJVPRiL9YowL6K5rlZgfT9wHI/1gs2i8NCWcRwJMQqR2xZIdZFTi1/rJuCCXV3/WSlFDOWKdJ5WnXaMQSI4woK1Ii8EQ04wTwtkOSSL1pSsoBaueZ5lTC6FUprax1JKquOzwAXtWsxFsmwEWw/r+N0WEG2ZseKX1tEC7lYDCvS6kKIWhdRI+bbjPAbm88g0zbx8/pK8K4ynEQqM40xnDL3RxGlmGRdOSjHmBaU8OZ1wRtI0Y5Lp7Hg8YbWh7zeEWAhZo91AoDDnyBQXtvutjLAqQ6XoGJuITfBRoYqcm+tSFXlU2pjWkiYZUkKtRihI3ERKkksvczdJSYeck/y9sMQ14KyQyVkiea+7SmB1pm+drLYOlCGmzLwEzNChjcF5yY9yTq4N8UalSp0tuWjKEnFWYZTIITtjMJ3/qRXqG1Es5VRdKr6T1lNK5GftNJcckc55CVAqRUZia4hhJubM2+OJT9++pdtt8Lc3lKJIqZBjJgXpMrRr3nxVTV3dp63rKFl8/VSOfLz3xNMr5q/+CR99b4/xkawmUsk47VA4jmXh9btX/PhHP+Zf+B/8eR7fTJgXGqUfUOaI20g3qlQWD77a2ZZ5oXBZYJSsKBzxPkiandFoPaCMo2hHWTTe3BKNAnuPNUJl8V2P3VjEx1PGiZQi5TSj7cxpOfELv/ht/snvfk6Oik23wyDuQVqJx6Eztt7ged3yorwsJEj4wRJL4On0hM49xEjvBCcy3mE7hRozocbilpKxrkNr6aSky4oUbeic4zSN8nmnxJKW6qAjOue2Af6jBHS1FsRGCheitqlLBHPZYhtFmiMqi3O6dz05qxq9K7G23lmskxEwKrNG+l604GbtdMThu2BMjbwoZS2MbROvrrrK5qRj9QV7jVGUMt578epU4vmpda5E6mZOLK9xngNPjwfG81nMIQo8u71lcB2HpyfGFLBY0jJxXBJpTGAsudOYoWezvxUGZ0yo3mKtl8VIKoQ5cJ4DpyWhu8Sr+7e8evsWv+15+eEHKCtkcJslQiMn4c4WqNzHRtW6bLzbe++cQxfJH0JRFVotzwhKjpX6FFc3fzlIPdnW15/rhGEt5PxHJorrrl8OoGqQohTncaKQ2W0GTC3MMQlHU5zxK12tMmWcs0Ci6wwhRvEf/enS8G9GsaSetteGCW0Ea+NJqvSMkjNhSoCR9t8YQoGCYgK+eHig327YbwY2FT+KKaFiVQJpJTQYqWLSXWoByK2NqBRRzqF6y60rfP4Hv8aHH/0sbhPI+oQloJV0faZEnt04PvkL/xK3m1/h009/nd32FWXz+/TDAeWfkZZbwX10qSNvpPlDgoTRy3JDzETEOMWijIxxISRKmNHFCIVJ+RUb2uxuaRktvnNorykpUPyJxD364Ynj/AbXFWK29H5HmEGnKBCGkoKnCiS1oI0Y7Sqj2Gw8x+mE7RTWWw6nE4O1qJTwvqPzcoKXUrBBNr9immAr+blbaTUgmTCDtYyLbFwxCqOF65lSFs1HTRGUS+KSbnhNSgeu/CWpyp2a1V2ld5tNTymCeecSACOmxN5XGopILKUDNCs/U5qeSz7PtQKl4Y5yqEhGuPc1UyhmKSy5rIVe0fwi5XU0tZAxoppZalfViuSyBLSBZVn48vWBw+HMfrtn6DpCzUQaOkfuO/LSMXSOzeAFf0aik5cM49OB3hZePLtpmAm+Ewd/X2385liIOTIej3z++ivuDwe+893v4jpZnEmuE5SUxXtAK1wnpiyoSwZV67apGv92f7btdoPPrLFgRD1mtK7+r1LgUhKOavNIbT6YJaWKZ5u1SAKrGYrgu6kmIci1bJ0nZ1hiJBdpHhp1q2nZKZFYQuUHF/q+w3lxpkrNIPqnPL4RxbIgTjmFvBKOtZFw+nmemeYJSlqD7QXaVJVPmUlobGfJ3vIwjbx+fOST+QNe7HcoI6arGTFZ0LrIggcFytSbs0UCSLdXjMjv7p494/D6U54OrxleOrR7Ah3E9TxZNibzXA+Mj58T1MDz2wWjvqLvz1gbyVNCxYHEkziVq1i3WXLTSMZFHU2SwAG65PpMMqgZ52ULr2LB6ERRDpQDfFV4CBdOd1bMiOOM9hm/XdjenXj3+Yi2kDK8e3tkaxO3OyvqJduhlSfGmfN55Hw+sb/ZshRNeXwkl0XS/nRmDjOxiPRvXiLOe7yTC7Hvh5o/E8SNpjRHJ7N2akpruq7ncLKi8QdYvUjBZHmd6Mv2uRWrdnOsGv86qZcihSbnSPOALEXSJksqKDIlBmEBOCchYVl4sRKXC0u4LBAa7hlDWotYjBHr1Oqg3TqbxjUUUrs4+MzzssICkpeja2G+JIO24ttI1a0xkN97WYbM88LNrlQ8VbMZPJuu48XdnqeHDdNyBoqYZ2wtxyUxxciX7+55dtPzycvnKDLb3ZbNZsPNbk/vHE9PR8JyABTvnh55PB15/uFLbp7fMo+jdJJakYJwavu+uuBnCYNo78HFDemiolK1a2wFrXXgomeX5ZqQWfIqDGiQWyPLWys/b46RGPN7eVE/aaxSSibEhFJ2PdzmZaruUIX9dqDrPNTAuDWdFeGKLkEWQrEZBnNJYP26xzeiWLaxSimha+Sc61iemOaxgrvixCw0ho4YYZkDiYzxDuMtIYvZ6v3hwP3jE9+6vWFjjUQs1HgJTaG0DZ0S4F9QIwGWtVYSyKUd2m+xw4Yffv5jug/ucPsRbRZMseQIMRR2/pbNs5ll+QGbLtAPAVf2qOkDSnpGCTek/iy8znp6kpHiNEaGzR1dv8Nk1/RFFJ0ouoAOKBWAALp202Unz09ZcrHEpKRLTnK4pKzplaUYy7DfomxAW0uMQrPSRrFxPU4bShbfyePxzJuv3hFyZHsjmSf700DUgZgDSzSAY5xmBueJSXTitlhaNIehk+eS5aK1RgjvKLnQp/NIX3OAcuVRppxqlKoVIxTZdb1HSbmmqDS8j9qNFwreC/RA7Wxk1Jb30RiNQorbMss4l4u4syvdFkh2vSlzbpk4UjzFysxACdWwQVdfyoz3HfPciqo8v7aAED9GaJBroxnlnFkm2bbmKoG01qydWvv+73zn26ia2+Sdpe898zySw8Km6xh6R0iKRFWreM9yPnJ/PuA3Gz741ocY73Aa9jc33Gy3OG2lKMxCWE+l8PD0xGa/58OPP1rD/VquT04ShDH0PWjFOM9V011qkc8VMru41SelMEnXADcNNXZYqQZpyOsTG8CyNj4KUffIEs4Kbl1YC3I7dEspAmVUQr3WmpiUFL1Q2Gy6utxJHA5HSo7csMd6MQPX9bA1pnKDnUObuvDLliml96aWn3x8I4pl23ZJUZcxJiwyQmi8YC1ZrwVNKVAEluWAsYbObMhxkrCrDPN54vh4YDqe0F0v3pNAUYm0BII1mCKGpuSKXWpNNoWiC9kotqZjeRd5tvmEV/fvmN4ZNp1HuRllFigaCkQ94vozg5slqL0YOAvpVetE0SeUE/pJjhMQUNow35/57Adv2fiRF8++y93HM3SPqGwxxaKCr1tGJSC4zoQyoYnoLM7spWwIJWJdBD1ifcTqhMkHOqXQh57Cjmm+R9kA+YklGnK6ZSkblBHVzGFcOJwmbnZ77nZ3KGNJc+B8fOI0R5GKKXEk31YsyWDQWVNiYsKKr2dReCvb4KIKvqqnUl3czNMiRT0oVKqshdqJKCNgvC1yYzjvaQbD0t05tKq2cCVhlBTDvAoZxDjDmq52f+KvKS7lUOIiYx2SE1OKOKA36opzFmt1jbeojuRWjC8k6FCc1COgVKpuRsJAsNYxTaHio5LuKGwLWVr1fY9SiuPxgNJSNEqWWUYrzTwLidpoVaNfIx99uBOTDRPIaL56d8Iqy93NHSUXzlOR6yhPpHzmML2lt3C73/Ld5ztMCGz6nq6GyKWYGceJ4ziTCkzTxHQ6cvfRh5DOcnBZQ1hmchZ6mjY1FiMV4XGamlZJxvkeq6WopdwiSDSlXLjRl4J2OYBAUiLJQmXXSnjOvpPPImbxqHTKQS4s84L1ojZDKTrrMNV+TzBKwT69l2aj7zzGKOI8EZcgIXTTmazA9z0ZhXGebujlwEo173wJDPoS4vZ1j29EsYTaTSgZTySQ3lypMgw5tRzp6kEYF4wBowuKRFoWlnlBpUzvnOCgIa0OKUqL20nOmVikS9WNO1cu1CUhqVsUke2+I3JLf3zO+SHw0bdfICmJAWc9nbckp1ApoaKHmClzIRXxf9RmwXhNmjuM8xjrKWmipMD+ds8n39ZMR3h6+ILtswHtLKoMEB3ns6Q0CjjfgcuQZ0rpCcuenPZos6UwYbcZ0zvQE0mMJMVihw1vvnrNvBT6oWPAMRRJ0FvCzHyG6Xji+PBIzrDZbOlcT6Rwd3uH+rFmWRK6jpXjPNMZhyuF/XaLUYbj+Uw2HaZ3eOPJcaKzMhrf3OzY73e8uj9WD0TpHMiKoduwpFFysq10qLlkSq6u21qMctvhWBo4hbjImOpW07Duiweir18TKV7rVJu5SsNRCxoxThK+4DwvayfTFkmXDsbSNOiygJCI4pxlMdn8B8ZxIsZY3XzS2gU1P82u86Qsi0yjLdQNfruuS8nCPyTTdT1aJ4yq5sLOkENkXGacra5KOQnFpzN88MEtkNA5Ec9Hbva3PLu9q13pjMowLQuPTweS1jw8PIiH66bHGpHqUhI5R0IKeN+hjeTBp1TE0cpAJBGFronTwq4oJaIqp7bFZ7Quv31OF45qZRrkatJcCktYRCFUlJDhjYMCOcrBk+skaCuvMgXpCjvviPOCMhIzklXBbcSr0lrLPI0cDwfsbiOZ9ErhnGeOibwsmPozMnJ95RjX3Kave3wjimWpI4nWpgK8VM7fQoplxTDr3xYSaw2bss7UjVk1aC0JYx3aiH3VFALDpquAswDB60O1pYuERRmVgAQGkgp0d4bBbLmLn/Dq9e+we5G5fX5DUbPIzjpHzhO6OMgeoz0hZMZzZAyFUCpHrTM4q9jtd3TdDlXOdB28+HhDyQvz+Sh+i1OP0QM5ak6PB5TKGAvOF3Y3G7zZE8dbzk+J8TyjbKaYEeUcG2dQaqBER0kwTpF/8tu/y+/97peosicnS9EG1w0SWO80D09PvP3qKwyaoRvIKA7jxGmZyUoxpiQndR2FQ4ycp5FOG+Yl4G3AOU9UhhgDfS/wRKlZ0/3Qc3Ozx7uv8M6yu9mDFs/GRu9RXNxpdMWO5fMX7Khtm5uG2xg5rFIqa3Frah5QxBiuloQXvuYaw1D/3Mbspptuo3JTkMh1WVVEVgNt811WrC5X+7S2lRXBglpdh5SS/xbCjBj+Wkxp+nNkTK1sj5QgpoAxHQpZVt3cbETBkzXn48QcItP4SHIdzmk2nWWOslBpKp/GPdzvb4Vb2Lq8SiPCyns5TmeePb8jp0hKepUad13HPC+EsGCMFX18XbKkatlm63ZcRuxCzrJLskajlUW31NCVN8mKazYetaryx1wqC0LLz1RJzGlSSugsB2P7HOW6SHUslzjd7XZDSgK/GW3E5k2B1QrnO0JMPJ1n9ntPyYpBW6xVdF1HoRDDjFbQmQJGGDE/7fGNKJZXdLr1BpimUS5kZdfTvtGHUjWN7YcOKByPByETK6F8pJzIWoGznJaZW3VTrZlEylKqk7bEo4gJvyKjlCwz0OKyXFSHx/NM9zxNI6l0PB4eQQecVUzLjHEBhaNkR0mOd/cTn3954PPX99wfThxH2XB33vHyxS0ff+s5L190fPLJln5I9DsjtvdkVBZzD6sLu70WnKosQq5mg9YD1u7wbia4BUwWWEA5ShpQZiAtisc3jv/mv/k9/vE//pwc94TZg4aFyFQW+u2W3jv0IWDI3N7corThNE/cjyOv7x+YYuQcIt+5eU4piUQRX0NrUMZynmfCErnZ7rBejFlzKRjXE+KCrlulTd/z4nbPtCQ2vWecXU3nE1OMmDIphoqXSbmUhyhgGp4tN1iNdQ3CXW22YK2Qved/WfG31apPKcZxvPDztF4dwK/5nNfeme3PMaUVgyxFcMImoBAeXyZl2fi3JZNgY6rioOLEnrOQvUFI9iCJlDmD1nWTXhJKp6pmoSpeOm72ntgb5mkhxll4nq4ZhsgIe3Nzg1OGruvJCcZl5jxKJzqOI8fjkeP5xGevXqGtZbsdmOKCKsKDlU5eCk1rKnKM0nWRUVaqXjP31abS36xggFK06/1cJ0VWrqSq4zPEkNZIk1wEjnBdR0qFUBshObykUDZ61TAM1Xw4kbPFOYeyBnINOaNwHkfxDK1WgaCYEjBFknJMy4Gus0Cm7xxOicw3hUTW3XoNfd3jTxKF2wP/JdDVv/+fllL+XaXUc+D/AfwMku74Py+l3Nfv+XeAvwwk4N8spfwX/7Tfk3NmiSJhnOeZcZxq5yhu1XKCXUjrkuuRmKZR6AnWMk8Ld7vnmJKwvsNvNpzDQqjUFVNHi0xcTYHFlFRsiWq6iwDO9oZiBDDubOCX/twdfnPPb/3W30aZRz788JbzwyPz6UTKHude8MWr1/zmP/mcL14feDwtxKIJOWN0luiH8sCmf8WzO88v/OJLfvlX7vjO9z3b3R2dA61GKAvKaLbPYIulqBp5YE/k5YAygeFG0+012ioCE9rOpFSYJsWrz478+j/6kt/8jS+Yly05b/Cux9LjbS/LLCvZ0TkEyYIBcXFaRp7mma8en8A7PvzOtxlcHXXrRZ0ozCFQYmZjO85qwhdF13lCTNJtlwIqk+dA5wy73jPPT4TpzPHpAbfxpHRDSuLT6PpBbrRCnSRYb7DmStOKlxSrXKeRKrWTW7IaayRiihitqwmwdFJtVJf6Izf1T0bMXv+3NrILgVy66pylqDtnJRHTS6xECEkSHSucY504iqM02hRsxeFjWmpHu6wYnuSty4qxuaNnZNlhjCbHRAwJqy19b0XXX5RE1pZZyNbFYHRH3w8MtufZsxd0toMiXpzTJOYrc5AsK6ziw49fEtICJZFioV8LUV7fdyH9V+mxypgi14rV1ClQ8pa0M6gApSRhbqjq92DaZ3jJeM+1qyx1LLfGSidZ6vKzFArpakOeq2ggMY5SeHe7HU2EoMVuZ2UlSP6TQRvLNM/0Q4+yPW8fT6BtFXEIrzeMJ+bphHea3vaMleP5z1wsgRn4F0spR6WUA/4rpdR/DvxPgf93KeXfV0r928C/DfxbSqk/BfzrwJ9GcsP/tlLql8ofE4e7UjfyhePUqBkrOTkJniJGB4JjhbAQY6hvaqLve3a7G3qr+KVf+e9h0sx8fBAjDBTe1CQ+5yimrk/bjacgZwXaCcY53JH0gN84Njag1GtiOfDxz70QpYKZyUTuvzrx+3/wh7x+DY9Hw/2TAbOnsK2tSESrBVUsMRgOs2U+KQ737/jD33/HL/zCDb/4K9/i7i6y3SqsFav8rnekEtCuAwSbgYwqI8sCBY/OjiUZlqB4/dUTrz57y+//zltefXkkxgHKQMkeoSdJpokuhZBC7azlQrXeE0MkLgtYxe75DXcffoDWkMOjUHzqwuK0TGxtT5xmtrdbSh31mlGEVmpNgzTGSLEcPOeT5jye0CWzTGc0UihziRVHTCsbQCgk0qFJUUxVNgeg6G1Xw7BK9Q5oOmw5TK21K3e3FUQpUrKJHoZhHbWbpLFt2sdxfI9gLppzcYOv8Xe1CFAXUVYmFNVkexfvzGlquKWYYmw2Qz3wJYu8PQelZSnm/QatoyykkG52nEaKNni7oeRZMHhrMEUxhlw7UMNm2LLd7ulNJz6wSMFyxnKq7AXlLEtJ7J/f0g0dLAsm14CzFDBaUjgVslQsudTmMGPqFGaMIadYzauV0Pask0C/JO13e8/buHzxIvW0o01YJxLyFlOspPckGeW1i2+ihr7vpJNGpoWYAp3vsM7UFNJCWBZK3ZzPIaBTYrPZSFNkHPubOw7nE/tNx8PjA7ps2PZiNB7mM8Oux9TX/dMef5Io3AIc67+6+v8C/KvA/7h+/a8Dfwf4t+rX/5NSygz8QCn1e8BfAv7uT/sdbcyWk1xwpOY65Kxc6NMiTi3OWcnqKBKJ0MxUjTUY5VhC4Ge//7P8wq/8Mm9ffcqb+cQUIt55Weo4RzYC/GdValymoShDxmD8gNnsMN0trt+CTRBek+NIive8/I4nZ0MMRUDw5QMOp8KPPn3NuBgZhUtfT11Nbx1FJYzxWDwlOiwWoubtF088fvUVP/y9E7tb2O4dfe/Z7TYMG8922zNsvJBti4E8o8qZuHjmKXMezzLqHxOvXo08PXSU9IzF1AswgkHJaOZ8xZsMsUTCoug2N2A6MIYSI9rqGgKlMA4okU3v62EDS0zkXIhtHFW6JisKqJ+yOMU4I7w3mwpWzXQGcpjx1vDRy2cc5hFvL9ZcTXoqeuJCKbVL0G1ZIEWq4V6yvTZ4L9I08Za0tXv0QsEpYrDRYm+vUx2B97rM1lGBkJ43m81aPJuqqRljSDGXImfWMLG2iKoFWRW0KfXnB5x3aOOrLDWtHZccCGrF6uVwEAzWGcmk0XXiV0UC3VRRtNyipvOHnu3ujpvdc0hCa9r1HTkt5DgTU8J3nvv7t2Rd2G57sko4L9SqZmISQ5SRtySclc5Ma9n4FyRio+RYGwD5zGIMxCUJW0FrdDPdrWofeZ2u3s9NvlzHAWlBUaVIeqQVt+QQIzlGwKzu6fY9aU2p/jeGwTuBAErGOAsYUJKVPi8B5xUpTnjn0EpJUY0Lr7868b1vf8TdzQ1njbxutEBCP+XxJ8IslSjg/xHwC8B/VEr5r5VSH5VSvqgX3BdKqQ/rX/8E+HtX3/5p/dof9/Mrt1Je5LIsLIt8cCmWNbKzlCg3pND0KsFYMsBDCMRSWMYDb9/dE3Lmg4+/zdPDW87LxN3NTe30hF9ZFGQD2WiStmTb4Te3+O1zlN9KPo4J5HRgCV+AfUUx79DmhE4ZbwdiXui3me/9zLd4/Q6efvORTg+k7ElxRis5kedcE+50oegoY3827IYbUrQ8vlm4fyhgEqWcsO4BVK4KEYV11aA4L5R4hLwlhg0Zx7QEQk6k7ChlADz0Hdo4ii6IsVGk73cMXUfJYksVkkFjiXPkdJqIObBGuSrIeUSVLJnlqhbUyrTKqpAKNWuqmSlALAVlLK7roOSV4Nx7y91+i9/uKUYRXn9ByYlKrSMraheokPDoUnmxCqWbQ/nFsDfGTLqKzG0b7ra8CSGspHC50ew6VjfM8lI4r00g1Krm6fuecRzr9wsueMnJAWNVdeNONIMNa3WNqABjLoYcrZuSkX1hWRZ2O4+1jnkOdSOuMZU4P50nSueFSif28RUClFG2VKd0hccaw93dx9zsXtJ3W0wBo2TkVyUxTxOd7wglMS0TympCDiyjsBasMags/poiC5SMINGqS1HEOGEXuGa00mQcteNUgkEXZNMj+GyTLQPI8vXakJuqmKx6HRoundYQOjlE5cBM0GhgWmOtUMZCEI280pqoq6AEgRBSKcxLJCtJokwhk8JMsprdMLAsmS+/+opxOrPbbgR/zXXj/lMef6JiWUfov6CUugP+M6XUn/njat/X/Yg/8peU+ivAXwG4u9nStJ6xJvBJJ5CrPVs9dbB1wZPWN7eQqzN0IEdNWgJLjDw8PvH973/C8w8+4PDVVywx4rQTILsS0TOKbBx62DLcfYDu78DuicWA+oqk7on5Ncm/RvEaa54kaGwxOLMnjye2+8AUFI9PT0zzzG7vyMlQosghtUoYI+MAJaNdxqpCSROiWZZxicETdCEnhbKVu6YU07JQpvbGFrZ2Sww982Qkt8AWQpnJZgJ1wtqBqLra4Ukynk4AiZDmKiPzxGyIRXGeE8sSQWW0Bcl91ihVnemrPR7GYJwlLYkglarKwxIlL5KlEyPLIpnUmkKTITqjudlv2dzcMCWRP87ThFEe4wXjzEVuGxEmmHVk/roc577v6nUQ1qVCG79lpJaOqWThOF5nhl93jFLkTP3+y1i+LMuFYlQLcoN9pLtsypUap2FafEl1Bq+qFu8dObd4BYGQhmGQ361Fu+29+F4uNWArxoCzHb3rK4cUiVPReqW4aK1JGZZQcG5g6PbkbMhZy3KmCJOkJHH3SSXz9v6ecZrodj3KKFRWhBRIcaGwiJuR88JXVGIFJ+4+0pkvcaakWKWQzaRXrw2i2NjBvMxXGKV8NikJ7uicE2VP5UmilER2IObCcKFuOe/RVVrZroG23FmW9nMTtm/+k+UKCxVfgEw1tapxwIN3xLhwODyyGQaUKYzzgu96keeWVVT2tY//TtvwUsqDUurvAP8K8KVS6uPaVX4MvK5/7VPgu1ff9h3g86/5WX8N+GsAn3zreRF/ujZitNQ8teapbKolmjaZogIhzeLqHDISl13wtqfzO/78n/1zbHaOlE68+OgDxuPE4ZxQJTEQSH6DHTxu08N2A/0GZTcU7WULWAyFe0L+MVk9oN2RlGZyoWJHCwQ55bWDlM988IHlcOw4PD3g3A30IDXGSSdZhCBr66ibgsIqMR4FmHVAmwwm4ruWT1lNjLVFYaq2tkdpj9IOjCaUhV4riilM4YRyC515SUmKEhbAYp0HXZjmWSz9i3DumhTUORmTtFJ01uKHgWwquTpRFU5FYgacxilPXBbGeYGoxPXcVV1xkgu/95KoJ2qdmqro7KrRbuwFKmVMJgRxtheC+Sz32urVnOuoW0n6SsxAFBqlVXUAry7nUPHTi+SyGfu2G/kim2sFWrAy6Qg9OYuksWnOc06oCErLdHJt6uB9f9WJgTOGnBIlixZaoWruUCaXRbh9qcidrIz4gSrxsrRG423NqClCz0klQiqkJK19SYpcDJ3bst09R9WiElOgxIJxwhIYz4EQZ+4PT7x7eIfrPNaZRle9KGiUpFfOJTOHiRjkve66Hu8ty5Lpu46iHNmmmotTx7ta6HL9HCWnvfo2FPkktDb0vRP3pZgIQfDtnKUpEAhC4DZvRb+eYyGT1kNOazE3Rsk23VpD73vBVhXr75MIDskwD7FKTTOkGBi6DVo5UtKMc2C323AaJzIjH32ww1rh5v4zF0ul1AdAqIVyAP5l4D8A/ibwvwL+/frP/2f9lr8J/MdKqf8QWfD8IvD3/2m/p40p7aJO6dJVKmXQccAoCfhKdiSqBas6YGAcAyUVLI7B93z7w5fcPetQNhBCZvfBt9FRozceeoPe36E3W1Q3gOso1lVBfkSXR3SMRPNDlPoRSgWUSXjzDJ0NcESbR8pyj3Ejmo4PP3L8C//977H1r/gnv/uO83ii+D1T6EnaYcsDnetQdORooDiM7WRz13mUKoRlxJVCLgpHM/mAQMKoDu8GsgGSElKzrwFkS8bYLZgbBi/b1SWOotoAhKmiSUWsrJzforWj6yLGauIok97hNLPd3THlGWo3orQhVZzOoCvYn1B19BxDRCnPphvEzCQjMQ7TQmctznnoN6ScOefMUgJLzoAmzInd7YasE7Y3mDoel1AzXUrdFicJrwo1dEu22yO+89IBA1aJnZjrBI5Zc3H0xXT2WlXSlocNC23X3MqfbFnWRrEsCbEqSfTDRWp3Kb6y2CgpCe6rNUYpnNYiyK88QlMH181GE5ZIwVGCRmW93uC6JKZ5oesGcop4qyXNNChiBm06ocdFKEGx273EKk+IEeOgmCRKnbkQp4llShzKkcfxCd0Zdtt+pWCdx7nyQZUcilUmSL44PY3jgnOeECcpaLnRu9oSR2E6S14a5qtl2aVVVeZIORb/TwiLuDcpY6VY50yJ7T2Xpsc7h9OFEBLKWIyydcrQoodSmpgzccmUFDGmMiGcJWVwvtKTktAPlZLM+Lgk8YvFklG4biBmQ9GK45ixD2d23cU/9Z+pWAIfA3+94pYa+BullL+llPq7wN9QSv1l4EfA/wyglPIbSqm/AfwmEIF/44/bhAMVKI7Mk1Bg5OfkulEVInIqsn2W97+TnGnVif0+J7QJKD3z87/8EbfPPNBRlKWoid0zw9BtsVh018NWgfEUekg7SrEURrQ/kNMXpPKGnF+hrVAYcikY5SlawqVIgaIz2g/Cj4uB3a3j535hwPnn/N7vveHdQyKWSW56tUOrHqMdqRKwc1qwvWHJM6VExM1FXq90Lmq9ccU2WDptZy2uLkUy4FyH0oYlRrHgUQanK/G8cySV0ckyTwGSRueJZT7KSKcKJQa8lYumvrksMaK8XDjOKJbKodR1CaeKwjqLioWYAtps8M5SimXoOqwW12qtDMqI4ay1kyzqpgVValZ5ygx9V3NphFJj9SUKt2GMLRyr8R6VvloIwXvjMryv7/5J16Dr7/vJLrM53DTc8noJ1LrSpuNuz62ZfKSS19/RCnv788VYwhKWs6iilBKH+prxUwhoUxg2Yt2W1EJW1aRX1WWalnbQast2t2O7HTjPc9XAwzLNEntse+YxQTHMy8Q8j/RDL/S6eZLpxkv3DM0RyawkfaMvlJ0W9BZjQFR1WeRGqn4O63ZbCqXTMmqHpWaxV19N+TOUIhzYgmjhnVXM87Je2ymJbV1BoSo0197vNiU0fbe8txNdHaOX0A5F0bcPXV8XSLkGooX1sGvTzbIIFfHt23eUfc/t7e1PrVN/km34rwJ/8Wu+/hb4l37K9/xV4K/+03729SMsaeWbXZj/Beuka4hxJpcFHZPktBQrI6lJ7Lea8fSOFy+2/KX/0Yfs9pqSdhS9wW9Glnwg+QgMmLJB60mwquwBLz2TnsnhU0L8TZT9srb9A9JvGgqamDsZG43Fmhu0W2B8Ajvhe8PLDzd4s+Vmd8cXXxz44Q/fcDwdWfJeJnGNxFGUJJighcxCygtG75CaaasxLYiVmyEG2VQabXEbU2kVwktFSereMieGjVinzUtgOs+SGBgy++G22hAaUoBliozLSCqJ/X5D0WLKcJ4mdFcVLiv9RqhVWlE7EUuuS4xSsbs5zDgvGmTrDFYjJN8qi9PG0jnHvATm00haAtvdjhwipniUERck5WW8zlmB8tU0N4oDVZaAq1Jk2+ucXbvBn3Qoui5uP1kM299tBfa6cDb1TsPIdOVqWqcqnhjXrfo0TXjv18K4HXqWaVxzYtr3t+fQNsQxCaxijSNGGcuFN5pAJaxR5BLIKpKKBm0wzpOihPjFObLte/qhJxfBODGa47sTZIXTjmgTccloVQhxwjpN3/u6gBK1Tr1P6/vpr7ppsVKLMbHdbgElY3Mpq7OS+I427Ffij4V5EMmlmgMbjXP9lTGFrlZ0i0TDlEwpQhoXPFc8MIUSJmxncVyXiI1m9deWdg0blgIbmc4nQi543+NcC1UTIhQxVrVdXg/V8/nMMAzvwTHH8yhd7095fCMUPDm3YCOzXlhNRSE3KBQdiWkhLQlXPFb3ON1BPmNNwNszf+bP/gy7/ZHT8Z5N92cgP8N0J6xeKFaTUk/ON9jssKZDmQz5HsqBMP+IEH8H5X6E1w8ofSsXkHYoJIUQ1aOrHK2oTI4ntJswG+i1Yhgy1iS22x0ffbDlV37pW/z2b/8Bv/dZYFlOiAu3omgJrhJfV4u24gAkFU3UGEZ3MvZUNxilDNoIyV1pI5hdVit2pIslB8XhfObt/UHCuzKootl+9AyNrWoVTec6tNUYp/Gd5TyeWHLCK8m3yUhWSopZCqPiqjCJH6d10jEonTBOM2x7nNWSfliScDfnmbDM1UfUMo8HtsOGb738kKwVc4yEZWGzHwh5xjtHCNKpdF1XsbSWAa2rQ0/ziLoYNVy7lLeLv3Enr7/eimXrVq+NfFvxXJaFvu/XgiuFNVQs/X1i+7Xao9GLWufSfn7bsDeKVM5cKX0qNq+ayFNG2JytUNmypmQHqq8QSsf5+MTh6Ymnh4kPXjxHaUOcJ6wy9MMGowzTccJqxzQdCWmi78UPIIRA13uWZSalKIYe6/1mMEbSBaSDb96Ozb4uM01ntBZd9zTOq8FuWTt+6F23LucKiWXJ9dBosSNq5fjmasDhvJj+Lstct+1KxnXVsGqxfitFrddAzrJAG/pBJtIinEbrG7Ff6kqIEac1h8N5vSZADrLz+bxe1847VIkcTuefWqe+EcVSPrBCSmWlXAhFQG484d/JokCYFALCx1hQMWJU5u5Zz/e/9wFPD5/j8oGTm9jdOVTxGOPRVoFyEiIWDKgI5g0pfUpYfkTOn6PNA1aNqBQoeSGXkVgWsvZk5bDG0bJfcu0ElHegM0YtaBvY68zQR/Y7he9v2e0+5uajM198+ZYvvnhDWCQfBgbpoFZhVMZ5UYtQNDEUjJFArhQXnBNFxVxzWHIS+gzFME+Bw+OJmJ6IMTMtmRwT1lj224G+20p2eP1/KItEuQ6emALjPDHHwNZo5hQoquCKFg33krBebmwK1Y5MoXNBe+FnHg6P9J3j5mZfxx6FQaN7MTXIKWERvK/re3Y3t3z+5ZdoFE8PTwzbOiJWb0G5qeTakNpUqj5b/tk6t5XUrS7FsxWo66+v8co1x6UVsqY1n+dZbhjn2G63f2RrXpIkQLbIiFKkmDfVSClCirb6/bG9da7XXopdd1tHv1y7M8E+pVpKBIbRO7JWvHv3RMxC3xrPZzZdYj5F7t890rtC56VDpSg++uAjnPXM56U66UeR5m6LiDi0GEUsdQHWxlihXl114HWa6DpfHeRVZUfI5944zSnLdQEaZRrHVehErchpbVGr5DMCXrTeWXTwcmDIQtfWSUHUevKZi7GyyESbw1SMVEhA+JrTKPCAVZXKVvF0q6T5SFyMxa9ZEO0AWzHuJFh2qOmyX/f4RhTLduKWAs0QVS62C+XAGjFH7axlu9lRsmWaJ1QWV79OOykeYSH4R8L4JWkYsINClQQxo5nQ6ohWDtI7cvxdlvBrpPIDfHfGOo1WAyV5MQNVE8r0GF27ElpPU+qYrInKoU0EB4WIIuBMwXYZVU48/9Dzp2883/9+z5u3N3z22T1/+IdvWOYsPEOcmCVoWW6lHNGq0V/aJphqSqrQ1rGEKK43SXE6nnl494SzPZ0bKCyoFOlch9EGZzpU0bKZtZbeepxVYDS287x7HDmOI0lDQCzuWheikYuqEYNjDEDGWS+xtUUsxeaw8OWbL0EXDIhprHU4YzHWVUsy6YwfHp/oNltO4xnbew7HA3fLHd4atDWYGv0gqh1bJYARU7fo0s3JAvDCvy3VAGKm+R/+pBdm42G2ouCcw1rBC1vRXJVk5SJ/nOeZQqk3sbryrMzvdapyfVykkq1bvf65IDJBayUyxdpGGZLs87DIQsjaDYPdoHNhPASWAMfDwh++fYXTSlIAbgaolCSrNPvNgDOWOJ15OL3jdB5JZWHTS3TCPC/kXKre2qywg7gtaa7Nj5USSlh7bUpdCPTGSGERVY1kE/XeIaF/SpqaqsISShHyGYZcCybYzrDExHieSFEcloSXmUURhVjBNQ7ttelvU0jR7sUiC0DqtWq9JyWR5KoifpxaaTrv1+kh58zxeGQYhpUmZozhcBy/+U7plCLmrBnQapWIxbgQ4izSrzhgi5eEQidJfKhMBkIoqNnwcB/5xe88w+gnYvhtluWEXfY4A9pplA5Q7snLwjz+mBB/Fz98xma4RzND6cUiLAqwrr3CeYPSRowEpE9CFaGF5BIpag+mpjXmKB+gyTJaR4PXHc/cSGc0L+8+5HuffMDz2y0//OE7Hh5mzueIYqmGvlFc2EuBxFoYnJMxKpbEEmRUd64jA0YntpsbdsMN8xSYqmwwhkzRsgwoWSzs4jSSrKhrMoZYMksIGGe53W4xzmGcBDiVnNBonHFoK510ykJhMdrIu1EvXOct43hinkfiEkghcLe/obMdrmGIOlG05jidmSlMYUGrLIslo1hWfKoRuf+o+e/l62bd5F7jldfF8LpQNX5fSpKFo5Ra1T+NsN66xksejlm7wSbba93J9fdAXTAlSUNsS6BYs4ek+Kh13AthludXeYVaC963zGD0Rqag0vPw9sCXn7/h8WkhRPnezlo0gQ9e7vn+z36br958zjJO/Pz3vk8OEyGCswnvE3NccBZSDigtijdjJYJ2HAWz73yPVo00z/oeeNfXQ7pwPk91rBZOs8hakYRPZ8QrtlrQqdxcjkpdjEbZWldKT65BgfMkuG7fK0IQLuk0TbLwcXWhm8pKWi9FzDtSEphjs+3JWZZOXsmG3RgLdbNPgbAshJiwzmJNO2Qvi6L22bbFj3FO6s4/j9zx/x8PqfZAlb157xjHkVjljTkncoC7/R19rziHI5BQBkLIxGxhcXz2+RN/7mduUe4RZX6XGN8yn5+T47YamU7EcCTlt1gz0bkTnjM6pspLjCR1ouiE7Qcx3tCGRKpqFC3UmRTRJUMxRP0CbSTLvGigeFJcMLanYAgp4Utm0AVlDN0Wfva7d9zsO16/OfH4GLi/f5K4haxxzkAuKKOFW5mEuIzKlCRenN73pJC5f3jk8HDidveMnBTn04y3Pa4znM9nvNXs97s6FkVyXEg6CwjeDZzmhbfv3pINkrHSOQqyNLJaiYlDTKv6SIqEwVmNSlBylvHJ1ax2pdDWkEJgnGaSyWz6AWctuUT80OH6jjlFQk48vLnn9uUtqd6sQjuh+otmqC5Dna3a7RhX70vQa+FrI3Hr3lrRu9YoX4/fcDHK8LXjuP5v7We07st5wUunaVoXOK0wb7dbUkpM5xO6EqJjjO91LK14to4or6YRYkyyzAWtejb9Cw6Hibdf/ojTaWTTd2w2e47HmRAXNoPh7qbjwxd7lvmRw/HM1jmGztM5wzKf0WphvzdkoznNJ3IOKCWGNO1ek2wrTQupC2GqDvYXXDcE2VA3alUpCS2Jsriuk6zu0MZbofa0rs/VrG5oHM647iNiiGhnsNrUSUXs6ZwTQcE0tWgOR8pXooTEOlm036ONJseAqWKAEANpyVjn6apXpVKGoi7epQ0SuRYqGGOIWdRnWv8R/cz6+MYUy67rAAl+SkYxzxPaaG5unjHPE2ba4ownzmcJUtfUYPWINh6U4sc/fsOrLxwffrJl8A/E9I7xfkeOuzreHHF+5O7DCV0SuhRUtJU6pEkqo/qEHTLFyiYt5VjzhbOEjOWIyrEmNQ6UvEHRoZigBNCGzBlj96As4TTi44IzlmU5E8tC38Pz55aXH36I7/Z8+tlrfvSjxKsvI6dTYDzNkhfeD0yTZL+kFCVdT4sh6/39I1988SXzKXCze0FYEiXLmJ5K4sXzZwy9Z+h7cgqiFHGGzluMVszLzLv7B87jyM0Hz2qWdy8BVUZwqRyl00glY6zCeAHom5lJikmWbgqMMxxOB242e7r9njRHjscjYQk8u73Desuw3WIfDzw9PhFyomjFyw8/kDRLK1prpwyleJS63CjiVwq+E9Nbrcw6pjVt9zU1qI3K13rwFJOMY5VjSREhgxS1iilWVYmzlqZGgWqoES5ZOSkmMFVVk1LF5/RqFdYWPW3Ub8VYincWvM5ZtLKU7FAqc3iaeHp4w/kYSNOJ3abj7vkHnCfZag/Dng9e7rFm4XR4w5df3ZNix/bujmfP7vBWMY8BpRIpz+Qy0/WGJTeieHNRatk2XvTf0wQKnHX1QMmcz2PFp209iBKn84SrnpxCdWJNRxR8N6z0IO+7istW44sYsfYCoyhFLbLVxCTL78pZHISMacufy0JthTu0xNzGGDkejriSxWg5iWmwmIAk1JrlBChN1hcsux2KXdexLIG+70QpV356oYRvSLFEFYwVazZURtW4UIVmmRMla/wukXiSD9Z5TqczpoDXGWscxvScw5Hff/dI/9GEXg50/R7DJzw9TFgyL3aPPBtOWLulZDFDzU4xxUTWhm6zR3lHxqKjEayTKgXUYlEmwKURGRsBp35AXjwqZZRJZN5hhneUYshxS+f2RJVQWJLeS5bx4UuKHjF6RuUTL3cL/bc7Pt45Xn115PXbkcN5IUWL754xTrNI1HSS0xw4PJ0Zx0hMhtdvTzy7+4jts+coDJa37PY9gxfj3hAKeAvGcEiRguLt/T2v37zl9u6O3fZG6EtLYtBWIkw1DEPHPAdKEZKxKQanOqGJpESsCYnWF0ou9HZAaYNRHSFNzAsczk8kbfjkwztctrhqV7akiHLCw9QZTMw4LVvgFmeQqgIjZxl7c92MS2dQ1iLUboL1clISOKeTGD2oimcVCs5YQgniI1kSRclBFkvCGDmAVZaM6c5I1s18PtP3nWz1VVU9dY4YA2E6S8plFgPgaRLsuesGnHM1ArevmVKZhKEoy7wYyBaK53hamBbDyxcf8sGHW05fJjoL3/roBT/49AtudqLW+tGnn7NMI713PNs+p9eZT771kk3X83A88RQ04xKYYyYoK3S7VClfTVWjinibppmiE9orPLZyDmcpkBvHPM1oJwuQVBLd0OE7u/JCcxIwfTPISDyGKqW1rnbgic2mIyypEtoLCiMHuhYVljjrC6wSl5rnkxTGWrFMzKVynGsIHoaSIUySK+7MQFkWcUEvIm/VRmJtnZeDNqVMSGfB6wfHsoRq6CHLI2E+wH6743h6XDvir3t8M4pllTW2R0vSa24wm80GYzTLPK2YUm6bypxZmn60JM7nzHSCxcxY1TOHiT/4/C0xFX75F29w/pbteMb1HTEE5hgxXYf3HcZWe4AcUeWAKqMEMFYSbikihyoE+RIGSieOHEVBaBQguShIFpIGfSQlhfWD8EYteDwpzJAdu80erwzOT2zvPuTDbxd+8MMTr76cifEkeEzFzYxWhLhwe7tnv3vGZ59+xeH4hNaeF88du+2GlDfsdreUEJmXBVMvjPPxwGk8E0kczxPDtmd/syXGWZZbuuqajaa530iHZ98z0b3e9JYiWd1UwwJrDU47kssMw4Z8TozTyOFgcAgZe7vd8jSNDP2WvuuwFshiMmHdZsWmrjHIC+XjMkJdcyyNkTF5XfAoRYlpjS9oC6OQI967ypN00NQ+CM+v5EQqEoYnRVi/17UaK+NjyfI7GgSQYmK320unBmtHubq75yzWaymSUmG327DMhc8/fc0yw8uX32a3H9gMG9R4x27TEbLC2I7z45lpOvLmzWsG7/jwxQu+9fKOb724YRh2HA4HHg8HjuOJ4/SEsoV+KwdRjm7dAjf8dFU4VZgB1SaXmr2uDf3QE4N0w74zFIrET1RMr5Sy4tGSaHDZsjeqkLBYUqUKqbXrz0V+t7OuLoI0of7TdBbfVTlsJfQvS2CeJqz1lNKuR4euah6QPHBrBHdsURWioc8YW7nJsSmN5D2QbCTN+TyyNpV/THf5jSiW1xtFELC3dQxrDGoRt5nQcI5cHZvbjlppSrGcj5m8dMzHR0ye0YNi9/wOs3/GaZv4cTjzy/3M8enAkiL9dqDrOyQ8O6wacKVPAkqrDsoAuQfqxlst8s/iUXELSbrgFBUUjzE3EsWqdyjlKOYkoPM8kWMvQe5JY7st291HlMVzck/oPpAw3GbPsL9lnF/x6tUjzt9KfGzJ1XA2gcrsdj23d1usnSll5tWXP2IYtigKt/tnONsRTmfmqhB5e/8GrGJ7s2Wz38rNo+VC22wHQphQQpSklCidd74sWRpIfr20kM9Gipu3jubMY50hn8WL0Hpb5Ztys+ysYT9NYMXN2nvpCsLV2NuWNtf2aRcbv1RH87Iub9qmsz2nzjrmNKGUmGm0TXmzChuGoSpYhDKSs+BpLWtHfo6Qpo0VtYtwOj2pOnnHGGj5OlBYlomu84zjmXE8AWr92UqBopCzbL9jmhFz4MQ4n3j3DpblzC/+4i/jhh1vHw8s8czD4UQuGtDsNls+eH7Lz33ve9ztN3ireHv/wMPjE6dZRAa+M2gnn5/WRZyMKobbNvqNm7o6MDmL1rX4VBu+nEvlJda0yqofv/BVL+qpfFWwVIjs9jvGUd7vvutZlrB+fsYYYuWkGp1IWXSycqDIcigECZeLqYouFNWWsRU6+X9IUaSeWkssRakac2uwWbLOl7qcapv/nDN9v8EaSesURoUVtonKSLTh1z++EcXyent5TRDuuo7dbofWmvF0ZJrn9YOX8UZUHq2glqK5PwZO48Cz4RnL8chyfs1HL3+ez48PEC0qZw6HEUrk5vYG7SxpFtNbERWKVx5KQGuhJ2xQxaHQFGYoicwJlRb0dAtFo4yCuFBSAevIS8Q4MTQWPM5CFLzLOksumnmc0ecDhj24wHarJVMoCun7w488n332irJoYhadsC7Nbr+gTOZbH79gmSPTGPnss1c8HU4M3TMeHk/cbHo2mx1KJZ4O96AS/aZns+tQWl/oGk4xLyeUqmYVGrmRtaHUzOVLBnReFyvtfV/SLDhUigSa1C/VaaEmQWpF3w3sMOiwsBs2PByfSHMgGweqiP1bLYLTNK1829ZhxhgrlexCfRGs+/JoPqgqC9nbGNFds4iln9IQF3G2CnFB6Yp95sumNEXZcstiTFyhSj0spKssckPWZVPj7M3zzDCYdbEkTllCw2md+X47sCyRlBbImpcvbvjgxXNihLdv3nE8vCYVw5fvnjidJzbbHR996wOGzlPijNeF3mmmaeSUE2/u73n77i3bmw3bfY+ymZBGpjCDKqhi3+N6eu9XAn3DVlvstKskc3nuYt7SllUiN23MAJEUCq1HFn/edbK5Norz+Yy1YhYyzxPGyMZ5KRc3p8YvFbMTschtBHShLWkxLK4Qy0XPr2s2fRaHpOofEWIkzBMhReH6Gk2ur7MxGSSOIjONI41+1tWpUrD5uBLqv+7xjSiWcNHaAu+B483hel6CRKoCMRdigRIjvhqnLimAtnx5MPzG7z6x/2XPt54/wyuYx9fc5EA6Bm66gVgKxjriosQkwYqVvVEKbQqqKDFxK9IxquIg92LtVgoFSR4kR3I4oFUWI+F0pqRZ8shLARbQkRw3aN3hhi1miSzLAW01w00HJaD1wrbfUrSh5AntZPv+3e/s+NEfek5jQWdLVlpOyqrtFTqIxmboiuajb92RC5wPltP5xNBZ6aBSIpfAsOvZ3mzAyClKFpt/ayzeO1IK4vyjW1elWeIl7fBa1dI6FQDjRQ44hQltDbt+h7Wa7XYj6qBlIkQDvWczDOQCFsUyToR5xjoIea7L1+qMf9VNNt5jK0rXcsZm7ntNMg8hEFOoFnWWZZmxVq/dqO8c0zzivXSJ5/MZa2TB4b0HB+M4r697XubLEgfRxY/juC5sGl2m7ztCmJFu1ddCEyvpuxmEiIGurbJWrcFZS1gSm61mWR7pug959vIF7jzyyXe+w93NLafDA73bUJYRReL+8ZH7wxPWGT741ksSAW0LqYij/mYzYIzmfBKSdZsM2gJKbA0lidJqQ9RN5ilUtRAiYUn1Pdd1SaTWwtcC4+Z5JsVE328w1jLXwLh5niun1FXFjbixX8M5xuhawKtfpoqEkMQKz7R8IaE/UcREWCnNPE2klNlsthhrictMrtMAuqZqpUQJQdJaFfXAcmw2nSxNywUqyblQ6tJSf9Nzw+Fi4d+2hsMw1NZZ2vmU5Q0PQaq/NgbfiUJEpJKKYjomPJ+9mfhV3hG/3/PByw37vrAxiXl+oL+/R++/jXMdx6cZ12n8xoCJEnVQFFjhjCksaEdJui53NCVplHKk6HG6UMqJzExZJgmKL5qSPYVMjkeMLeT4DMUgWnS1YHxBOwN6ASV4Z8GhjSPHJAW3nLnZe375l77Lr/76G4pSYmaaqFnhotxQqtT0yomu34NSTJueaZx4PL5lHME7RSwL232P6y1LkHyjlAVP0rp2Xaq6PZXKbSyNMNwSGC/QyHX3VLQk6hGLmM6qUrFOJWYP0wmltWzVtcVog9eGzljm08hmJ7na2hrm8QpLq49WGNuNboxeuY5NgdGum1XNEwWH895WvNLhXL92VbLJvYz7jfKTUqk3ebU9ixJP0MZwCSOz9H1PSnE1qzZGVazNYYyqxbRU+7hOaDJKlhZaKYzSKCuZ4TnNaKO4uelJ+cw0P5FZ8F3h089/wBevNNuhYz940nJGpcDT4YjpetxgwSSsLsQ8YawSCWQU4UKbCFpnNtfp7NqExHeukuovOnYZ2RubQMbeeZHFzSUxUw4wo81KkWqHaVuitN2btYawyLRhq4Wcc1bkjUEOZ/H6lMlERnDpOZrHptaWsIjzvC3iTTDW0drW75krTKeNwXhHiJImqldKV6iLwguTwjkl/rGlBeB9/eMbUSwVoq0FWepst9uV/3ZZ/IhaJGUxsvDe45znfD5TSmEYBlmCuJ4lb/mtP3jF4fGJ73//hl/4+W/x8Qd7hjSTjw9MRmFtJ8FPSyDnBdsVvLVkEjoXoK/elgnUAmoE5ShpEa/E7NFWU+xESiNFjWAUVu/QaiCHhaxmtEsoNlJAi6cYJ9Qc34EJiHlXRw5bMelQBW2ecHpmOzh+5mc+4Hd//57jw0hOFrBV8ykjnqojieTASNpg13dsZk1aZjSZTe9Q04yxCq0L1htSzGs0g3RoVQoYIjG10CqFMGou9mbtM3qvMJUkHbjWkArjOOKNhN2roiu2WJdEBXrnuNnuORyPLNOERvwoQw5sNpv157Yx/3o0kk7nou9to3O7Xtpz7PuOHDVLjTJoqpJhGK4WHMLVO5/PxBzXm77U0VKKZyZnfdVds15vTWOdUiJUGlUrTg1KzylxOkkqi/cdVjliieQaYdt3HglEC+x3A+fxTI5H+k6BNvTFAYrdtkPnyLxMGJPZPRtQxlGQ8ZEiIgmDOBnlpIghgcnvkeNbB97+XRoPKSTe+RUKk0YlUOaCaLI1Wila6KQUx9ohmxZJsaA7t+K0OZc1oMwYi9FVkcO8HqjOiQGLMY6corAXlEgZ05VAQJar5b3dxrIE5mWWa7GKLIDqz4X4FwDz+STBarqZFqsV527uSmiNviKvf93jG1EsC6zgc/sgr8H6th1vPLc2krWbqIH8uUR0OrPMkTAP/ParE5+OT/zhofBnf/5b/NyLlwx6x9ND4Dw+sd939NuO8ThKMdEb+o2SAhkT6ERhBFMoagI8hSgkbUTXrSwYRmKGXDQFT2FLRnTdRc9ku1CyRuFRukNZB90erKbMMC8WwgZTIiWO6LKgyPTec1aF3XbDF1/dY92Aoqf5PaYUKVm2mCFIDk3fe1IZ8b1C9zKiYCLd4EgqEXOoSXpKsn0yFTjX62ZSKbksWrff3vufdNOBml2iaoxD1JDhcHjk+d0LNpsBtGKJkkKotUYXQ0gBXQq982ig7zoWHTDKoItef3e7aRsE0IqjmCpcxvN2g8Nl3MxZ/DoLWrbeyOJQ0o5l822sXcnjlOZnKe7fDTeXBYAmxlw7UFk8tOfTfj8qk3OgOf3LNazXbKDWsegsG/hcc+6FwB1wRrPfDRidmccZlJJtOIY5JEIRd3rVU02goShJnNS25t2kKhcGtPJoVVDmctC0A6Bt7Jtru+QeNYjFrPpxa9p7Ww+nOjZfJpDLoeW9kZGYy9eFlWDXDXrr3q2ToLqUgggsYnNJL7TGLiyxuiOJyqm5s+cc1wKqlFrzxkNsEEvNA69FcA4LlIKzlpaT5FxXu2jwXk41ZSyhFvif9vhGFEtgHXueP38OVE1uBfWl69R1LDAygirIRdEPW1r+symRcHrLeRzRdkvyOz5/+5bfuX/LH76e+NMfP+eTXU/fnwlxZH/b873vf0Q/eKZl5PH+ARjoeos20lGWEpGAS0cptgLMPapYcbpOFqU7jI6UqMl5QJsd2vSSaawVyk7VARxQXuIgjCFFzXhW5Lxl0Ht0OhLSA0aNdE4RzuK+Pc9jNUPQ5NBOdzEeKXLk1pCtmmNEwBrDdhiEo5aTGHHETFJyMqfCSkJeCw12zeAxdbGjzfv4YStG12OccjV6IhUMlnEeyTlhbIfSkme9zGe5oGPgdDyiUey3W94dF1JMFFdEnZPNWgTbMq+Ndu3/oNf/1m7M1iWsPoVBOsX2XBt1phThGV4rODabDcsSWGYhUltjqvxSr6/zspUXRoa1F134yhagVJmuLBNat2uMFuLzNItTN6JG0lqWa/JaRb6XcqTvqi57yWjtiEXoTDFFhr5jruFlRkWKlvjZXN3KjbI466F4cgqgwlok2+tp2O/KU605Oq1paR11KRcpqbX1QEjNsOSKQuRkwTPNMzHMV1Z2jnkKzPOMJED6WmgDztsKCZyl66zXW+t4oaBtR4yZGGRRCBptqqFMhROssbLMgRpyJiO4BhFaOIfzHlUK87xUyM5ye3PL+Twyz+J0pFGElET48dNq1P8P6tw/96MUMLrDWoWzHeN0WkF6WZSk2nkKTlJyFOWOkvGgRQ6QQSVP7zsZLUriztyhzZ5wKPzuOPG5C2g7oVSk7ybenDp+5Zc+YrcxLOc3PMYjd7c9+vmIdqBUB6lbQ+8VUgAE0q/mDHQoLK6zFCyoM5qAMjPCnL8h6w0gOeQqQ4kL8wQq7tj4O/R5w/nhiSWe2N9OLMuB8+J5cx/58u1MKnvCDCUcacoEUdrYOs64lUfWGYfVBkK5iraAkpJwIbUl6HYzXEYTVSkZBQHfpeiEq410c8cBo51kZaMJWbqpQsF0jnlaGJcZ5zooiuWcGUNiszXEFHgYT2w2PW7jicfIeTqz6TaU+ppSlbm2sCrZKssNBM1XcV7NXFv323BV2c72IrOMirAEocLmCw8QBPrxrqvLDOnAbN1yh7hgjSbGiXlpKY+zHAL1OcpNLXQWpVkXRIJhXpyLGtQxDAPLJNOAUYDJJETNE0vhPJ3RRhOTJgYZ7dEKoxTLNKK1IS8BlRUlJbRzaO2YxhFnJV9KYJnAPI/y51y10Nowz3XU1gJhSeeeUSpf0apEv+2cq9NTWSEerQ2xHhpC6pY43LYwyiliodKrxLczqsSm38p1EpNciwqJAUmC7Tqr8U6xLJcJsiBTSylgravGxCKbpC5sci5oVwSnLaUmVM6oVCNCtGZwnpIi8zRBzS8qOfPw9Ci2cFF04ZUdhfkJgcP14xtSLAvO9dw9u2FZplX6tIRZRi4aqH8xRDBa+HMNzzBGE2JBqR5IkBKGzMt+h/caSiCGicclkVyPd4rDPDL/1lseHyf+/J/5Nt/5+BPG02c83Z/JaofbiIZaCfen+vEupJKrFtpSYo8yzXOwjgWlqpDsbcWu9sgIX637ywxqwWowXQfLAw9vfogpR/p+IcXAOGp+/OMTv/prD4yzYcmRbugpXMwmco5o3RxzLtiixVBCIVAjOurXVdGUWIgpoipm0zos6bpydb8WHDKnjEJjdN1gUkgrnUOJwiYVvOtkKZDlvTlPE+/u79lvb9HKcjo+cg4zfjtLwqQ1HKaRbvDYXm727e1WiM3VLixUUnFLU8wrYdqtXV7TdjeY5toFKMaM04rNZsc8z0zzmWkSHqTOkkjprHRYLStauiFTKSYJrQu3t3vevbtnmibG8YTzDt9tWPmAqtqHJblpG37anp/k+JS1oCsj9naYyiCYAx2daLMpWGNIeKxRaFtpRyiJzjC2/h5DLAFVFN52RC3u5qsJic4YWx181MA4jnUbDbprS7GCc55pmi4jLQVrPdQu1lYObcmFUMdaYxzNoahtz0slgQOQMhlZDsVloTSXe2txzsjrcZbxPGO05eWLDzgej+t2vsFrpVD9MvNKJfLVkahxP2OU11rE2RpjFLEaaWila4OQSSHSdz3U7lEpVWmImaIU3SApm0a/j4//5OMbUSxl+92R13THuGJGcmNUHW613hbcQdydBfeQP5fcFhMaZw3e9eJsUwIlC/VAF41yAv46vSHEmR/+6EScP6X8xW/z0YcfEPM9b1/tGIYXbHYG2wfckDFecMwcakSDHeqWvGpQC5TiIDhy6tF6S0qa6IRcq61G6YVUTlg942wmzhPL+Svs5h3WHqAUzmfF7/3OzD/6h1/x5p3BdTuW+EQmVNK7ec/wtlFAGo7W6ECr2ilfbMvaNrjbbOAK6G432nub4ZhW/BLep/G05YAoOIQT6Z1FFZGETmFmWkas8qAzw0Y20RrRCDs74HpLLJE3D2/YL+KjKaYM8mijddd16ya8PYeLi094X41CjdWtfL1lmVfS/Eo3WhaibtzRBEpV+pEsZlJMDEPPPC8cj0f6vquv29D3XX3PlnVBgWrLh8zpdFox1AYhhBDqRj1zPf5eP6dW5IVyI0FsrYBY61mNeKuS7GLIO1NyWa0MhX/coZUWz9N8MSVu7+k1b7W9b+fzmRACfS/eosfjUSY5pasWvND1HSm272vjvPweKoNCGXkvO++ZFvl5IcpkOGy2ldEyMwxiEnw4HFaY5fqfkqHU3kfW0TwGgUqcc2w2G87LmRYlXFaLObNem09PT1itsZuB8/lMKgVbF1kFaW6awECpC33x6x7fiGJpraEfPIfDEa1hHMeV3pBSo1tUJQGFrpNx53Q6rW9wSiJrkplSKAu58t+qJEdGziSOPkU5cukwao9Wd7x++8h/++tv+R/+pe9we/chDk9nO9JyEu5bzFgPvtugywD04AdwBUqgmLxug8kOnQeId+iywZsHUl7EfNcnDEqSHMuJkkSPq8xrjF149wZ+59eP/Navzrx70+P8M87LkX5j0baQ48V2rF3s8H5kbFwim2HzHu7bilzrxpSqWTvqki1zfaGUUkQmmMN6Izd+5XwlDhCZaV5xrbTU9zdDSAnTQUJ0v50ZZFzcbIWW5B3b7Y4392+Zpxk/XNyDdrsdOUvxucZI2/NsY+M19acV0Gv8TcZkJBedi0Ew5WL5JpI/sU0ztsbU5iT0r5yxtqPvu+q8ZEgpVDK6wD0li0NSSnllCrTPpxWqtqxs0bttsdTe2+vX2OCB3W5XFzLL+voa5aXY6lg+zXVyEJ/JEBPaSDa3KmD0xUuzdbntPRDyuGWe0woTKKVYlkCLg1BKCtUSAs6ZtYD3/Wa992jbcq1JQSzflmUmRuEBG2M4j2f0LN1xrNG4X3fwXqSZjoKqJin6vS11+9yHzQblFctSr+O6tIkx1a5euuowT/UQqwdubbIykjUOXFGsvuE8S+lOZvreczod1yJQqjOMcx5rhSibUhavu7rwkdNN9LpGKXKWN7cg/Etxbi44I5QKOX0MRvekxUHu0E6TouZHn39F+Ad/yJ/609/iZ7+tUP410/KWOdyD9eTUY7InL5r5bFGuoO+O9BuNGRIQoBiUTYQlMR7PGHVDmV4T4kI3DAy3Du0KOUzE8wGrHVElxrPniy8f+bV//Bmf/bAwnp6h3IZiNJ3tKFr8MltRvC5s7aJdOy4jCpjWbcAlsKsVvVi9C6+ZBT/5M60VZxzB65a1ELWO1ntP3/csUaSCoun23D17wXicOZzOLDFyGI9obbi9e0bKmTgHxvHEEDq0N2w2W2JKeC5KIRn5LnzHcRwB1u1zY0C0G0xiKC6yWa0UMQdJCVTN+q9Da9n+F6Qb2+22NPWIjPxSEMdx5Hw+03Udh+PhauMudm3ei0RuHM9IVMJFP9+KE7C6kUtxNHIt1ue+2WwYx3EtQO11bbdbwSwr4V6iLgbJ8I4XcjzIRr9UbNEYg0fUX3KQWXQtgtc2dO3AbIKPnAXXbgolgT8uEtI29s7zIvna+mL3JvQ+MdYQaENjTCeRDdWGzVrPZiNihEY3UupyWF0f1G1Tv9ls14nifJ5o476rnMt24F37B8gBKBjyxeFIXv88TeKVieL73/9ZpmXh88+/kKVOShW3bwbXX//4k0Th9sB/ySX/4D8tpfy7Sqn/I/C/Ab6qf/X/UEr5f9Xv+XeAvwwk4N8spfwX/7TfsyxTPUXn9UZpUROQSEmcngVjS4znka4TzHKpKgJtxBQ254QqYK3gFjEI7cJ4h3c9p7iIHZjt8WYnvo1pIYWB3/vBa45T4Pio+aWf39MPoLwCEzE+k8uJeU7M04k0W3KeQXk2rqDcSCFBPoLecF4yRu1w7kzf7/B9jXqNE5BRqefwGPjy03d88XnmV3/tnvt7g3U3ZGNBTxQVMdaj1J5xnND6QihuRbJxERvx2Fqz4nDXnMhlWdZNaL461a9pMO3UPp1OuDquXOfVXBfmdsPkKkMrWaGdp99suH/7GW/ffcmm74hx4WZ3KxdvgcPTkXE8Yqzh9mbL3d0zkT7WXPX2eq6pKa2rbTfY9RjebrL2epZFunir2zKniJJDK8bxDHWkbCqSeRa5pkgTw2pCO9RERHlP5H3abAZCWJgXwdMFFjLrwieEwDiObDYbADlMahfZJHfXn9l1kb0e21epLc0M29J1/aWI5swSoozN7aBEMc4ntLU1I6nUsDHWnz9NE33fcz5L1swFhhHsXwqiFJq+Fz+E83ms04gEyl0v1KSQ5lUyWIooYnznCZVeJLJSizWW4/FEq/Xt8G4HYCviy7JwOp1IuXlvlnXx1HcOYy7elsaZC52pursLZ1jTHOEpRUb284jve7788ktKxS37YWAcpzqW59XI+Osef5LOcgb+xVLKUSnlgP9KKfWf1//2fyml/J+u/7JS6k8B/zrwp5Hc8L+tlPql8sfG4crJLpjZJZI0Z7BWRsaShffVOiFRHEgGcxPtG6URO1UZR5Y5YrTCWVMDkWLtCjy7fqBkQ04LKQf5QE2H9S95+27i13595PHB8P2fueOjb+2Z04myzOw2geG24DcjpWyYy5YwTUSfcVocwVN6QpuF5y88pWScFuNhckdKjmmOnE4T928e+P3f+pTXnx05PH7A09NH+KEnlIkpP2LMKDdrHlDplhI92Z7e2wBf01ugntZRQO6fxMPaTbl2oBUTbjfvdefZRvbrjrU9WuffOk6Q7a1siS1P54mnp5nHxwNP7kTfOzabzDQtOCMOstYIYVg02aZGhzisVevrusZP4X0Dj2taUfv69deUvpjueu8xVg7RrvLwgJozM1a3oouUs638RVMcazea6PuOJcykGCoOalDKrsuoVgCv9fPt+besm9j4gLWzu34fr6GUJjiQItLsxtJlrK+8XqGPsXaCWlthtKUaoXzlfNQ+2/Z7VuaA74SDqlSlO6XKb5bwNoEUxO+xaDE8nqsr0zTN5JTQnXgNyHth0Nqii+CqFMH1U47VFejiA9GeQ8PM23vVNurXQoOu6+j7nnGc6ntReDo+4b0EzI3jhFYSt1wK7PeDHBDnE84NQq/TIkJAay7mKK4GxUkm0k97/EmicAtwrP/q6v9/OgoK/yrwn5RSZuAHSqnfA/4S8Hd/2jfIVnCUMTs2ZxtPyaWOBwVTzRMAGrn0/v5hLQQgZq0ljrIkyAVTM4Oz72QLquTCc9piFcQiQfNmyMS8cDgdGYrHuhsenvY8/Vbg0y9Gvve9nk++veX21hHuFLuh4B14b7BxwxIKaU6QM8oYjLOgZPwNMTEFTUhy4r3+6oHPvnjFu3fvuH/zxOObGRU2+K7H9R0hKZZiUa4n6RltgoiIUo9Wwhlri4Cu69ackmv8y15Jtq4XPM1O7Lo7bKdy+7vLsrwnHbwekX4y8Kt1AylqckyEOXN/esebN08cDydygU6BSpmnwwlvOp7d3uJ9V+3/5WabY3PlviyRLuTy/EfwvPacrzXqfX+RZbZuNy6xLjxEBZPrzRdiIKXIbrddsU+gmir4q+chnDzZuE+Ukgnzgu8cVun17zRZpHPS8Vybe1x37sboNT63dVHNff2aW7osiyiiVAvYMrUgqrUjU9rg+57T6VQxxB7XdejKJY1J2AxD169F6HoaaJ93KZI02nJvjLZ0vl8L/zyL4MD7TpZF6bIYWhdEFWYQfbx00vM8gZZIFpLY+C1TWIvjRWnVr2maDa/MtWsOIb7Xcc5zwFpR+EkHP63XibWWvuvq+3SRwrafKe9rIJZSKUMRbey6IOy6DuMuRiJf9/gTYZZKUOl/BPwC8B+VUv5rpdT/BPjfKaX+l8A/BP73pZR74BPg7119+6f1az/1ISFlLRWu1HFbYa2vneXFr1BOAqGLtBOwFY+wnFF5JOeM06YWSNBBNua2Wm7lkJjSUWJcvSTNOQ93docxAzlrtLPEOPHuMPH4G2f+4A8iz24K3//ODd/59i3Pnw3sthZtZ7TqiGMmjppYxLB0nmeWsHA6n7l/G3h8TDw8Zh4PgcfTicPhEassu/4FmY7z/CVBJdAv8f4ZWVsikZzPQMTYSca/ubzXUQHrRbHq6/PFJbxdmCsX7kqlcqHZxLXgXEjIorxoGFYrqO0kbjc1IGT3qDgcTrx7NzKeE6VIsFo/OO5un2GL4Khhs8FpoSM5J7ZZp+lMMRJjWrgot9qNfT1yN4ZE65BaQb/uomUku7hXhRApdbt8Ph/pOr9OJ9Z2sshJuS55AlRaTEyBUI0oBDe3hDgzz1OV9CWcl0C3EAIKvR5erUBdSNaCp7XlWvvv1zBJe62CFcsypOkmnfMgwkgp7toQqplFSrLoGYbquao0XedY5mWVDCulqn/jZaHU7p1xmuWeU+0AbUY2juPxKHCMVcxLgip8sFYOlbaMCYuQ70PlAWujxUYxRWIS70tjLp106xhjjBKB4i8/TymFXlM+DS3C2XvPNE6cjmeaX4TvPPMSLvj5EgR+Q0IQh2EgR0tscSBK4XzP6XzGWFfdtzLTNOI7Q1sIft3jT1Qs6wj9F5RSd8B/ppT6M8D/Ffj3kC7z3wP+z8D/mtYi/MSP+MkvKKX+CvBXAHabrnaUly5RK1P99STqcgmCZW53e0BVNYSuRhriQ0lJaCo52IhBhAZUyVgt9I4cA67zKCO0j6IjKUY2uz1a96RgSMmAy0QKRg2kRfP4tDAeIu++OvPbv3HkxfMtz55r/PAVfb8lBoVzW7TteHw48Pj0iNKK83jm6b6wBMs8a5TpKXqAXEgKxgmcNmif6GwkxpmUzyjl8GZDiAFFwuhMUIG4VJzSGZZZJI7NSj+nXClWGY2wAYzW4pJdt9tKS1ZOrlKywsUCS4qfuK80QF8WPGo1bw2V+G6MIUcpRJ3bkhMcnr5iPE14O7DkQGcdN/s9N/tbjMq4Si0xWtN3Hm89cRnrxtPL88vvF8vrLXgD668DzbruKqe6FlLxDbDIQO1kW69V5eoattuNLBXnGaWbwkfciRpePgwD0zRxHI/03VCvwUUsxa46I9lClirZuzAG2k1/TXnKuRHsRcEj47hMB/OyECtlKeepusSn+uloSlmkM6q5N8Y6UtH0ndCElvr96/u0ERMJixb+pjZyQFbmSC5yjzXebtdXelaSTvl0Ogm7pF5HaQnEJEujwXliymy6bp0Ehd4Dvd+yLAvLeKYgG+mUgwgDshwSYQn4emC1BVHJS70uG+fVVlOWdqhL8JncD1pI7TkR8oIVAwO5Luo/Y0wSE2xgGsXkput7YsrEKoGMYcH5Du8cS7ikX/60x3+nbXgp5UEp9XeAf+Uaq1RK/d+Av1X/9VPgu1ff9h3g86/5WX8N+GsAHzzblXls0ZtqBXVjlFM9p8CcF7KCmCMPj4+EJdUYTYNWEaMmnCt4JdkigtdnjAZrYNsZUpK8lVLVIlNB5IpmIGVPLoqiMq5XxJjRWeF8hzIazEBOgTFHDoeR+2lBvZrR9kzXJfn76oD3PefzRIy5drID49HUC6/Q+46cIpuhQySLkWIzS9igYpHRi0es9lgctmzIFby3Rq2wvykKUhazXgrELNGfXc9isuibS0sRZB2XqF9f4lKT7xwhV5ZBVWbk5u1oPQpT9bdRDH1zpkTpnpW2pJwYp47H+yeOh5HBWpxJbKxms9txe3dDbyznNGIGRzbyfHXJlCXgqs6+32woWpZ518ucJlG8xlVbJ9Y2za1AXG9AtdWM81KxtxETFdvNQNfdYrQcLpIpLZONtQprOoxu1mqJUjRDv6sF70LG976TjKhaeC7b48uIuSzLex4HbWyNlUDvrQPdjK1BGQ85Mc4ZVTzWvL9VT0mud+/9elh4J5aBOE3Oiq7zLCGu3SbGEHMWorbRlBgr9UgMjVMuGCvTl6mb8faeGlefuxEtfMqZVIw8Z+tJcWIe58t+oWScdSwhUXAoJQYhznbEIJhqzplxidiq1ddai0F1CCglkscmv825rAvLhv9a62WzH1JduGk2XU9Owo3NKVWjlo6kDCEk8iK6+1hAq8Km85zGUZI4teY8ihFP13ekUvjnch1SSn0AhFooB+BfBv4DpdTHpZQv6l/714Bfr3/+m8B/rJT6D5EFzy8Cf/+P+x05iyohVPrE0myWrsY/Zz3WVwPeEqtbiSbnsNKJUo50vSdkye0Y+g2dF1cT5Tq0TjVv5X3VhtJqZf2HmEikSoWpetvavqeqJNIqoVSUty/vmM4i/A8xYHTBWY/zPRS5oa0rdQQRb75cEpoWxVnjIkzroCIKdRlHtPAYtdYob0iR9zqr6211G6ti/feu6xjHke12u3Y37cZtyh1ZrnjCEgVLLJfOzhhLDJnT+YQc3g37lMjgcVyIIXE6PfLll19CTOxvtpSUsM6w3W5XKtB4nlHacHPTs+0H0jQyzSemKBJC0eBfTRbr9ji/NzquKhWoywm/jrStQLXvba9jt9tJN0FdAsWGbbLiXe39aZ3q4XBY/5ssYGQj7r27KF6U4qIwaiPxBeO9VhpJR7XgTTPeKJUILZv61oUuy4LK5T01SbMrbNfrNU+yuShtNhsJ8KuYbsrSvaVlWbfaMQiZXHiIl+vnfDxi1mUehHBZqtQqUGlIcu09PR3qAdYc0zOroqaO1/IZyOTRMsfboSeSzgpTGLfCLErli2mOUSvuKDG5HqVSNZBJVwwJWfB2XVehOpHZypJSrSyO3W7D8XQiIku+01n8SEWlVJd76hK7/HWPP0ln+THw1ytuqYG/UUr5W0qp/7tS6i8gI/YfAv9bgFLKbyil/gbwm0AE/o0/fhMuj2leUAg2NE/iUtN4Z85a0OKoHJaWvVLkprSKUARvccOOKRZSDDy7u2N7c4PwzyAWVXE6ccK+2FRVGg41RKlqZhswXLIE36e1u0EchJSmZIsqO8ISWCbI2aKtoygPqcM5jyZznt+S25bPSsj9siwSPp8iSsPgB8Fep6VihYnNpmdZRsZR6B5d1zPni6pFMLjzqkluN2tYltXlp2FWbYnRui9rLUuY6/Po1ptRMmcqzlYEqojJMQwy2grWBBRFjIV5jry9PzLNE16LR+Zus0EruXGzLljvOB5GjscFvWhuv7tH2440HpnCLHK1UqWVxqw3MVwIyO3f22fzk6RuYOWUwiX+1hiDd/VGb/ZfV4W4HQ4tquL6ZrnGEVuWfcppxc5jNdRoHeT1MqoV6vYzW2ffCkMMiZo6LkqVIveA6Lj15eCLcfV0vS6o18svsS0QgxUxBVl4dneLMprDw8N7nboxuf7cgKkLDmUksjmFpo8XPrIok2SpVEqpGUtSbNvrbXiqXLMR09gUnau86Pge5rgZtmQN4zTV11GluNXgpR0GMUZyEahit9vVg63grCzr5H0sK8YNQns6HA7kVOi6HqXEscw7zWGZpJM8n6FCKVprinXVRk5J1PQ/j9yxlPKrwF/8mq//L/6Y7/mrwF/9p/3s9e8DShu62iUoY9Eq09cTNVdApG3BfedqQQi4asTqnEEbS/KK3nd0vadYJ1idKqRSqbtZFjo5SYKfdXa9gEGs8xuf01TplEGthh7QlCtQUJQkcagpKvphoOt6lnlhngMgRbFQ0Fq6SGNqd2JbTGejLCSaG3QpYlKq1VwLiOTuxHiu7toXx5zWybSbCsD1PaoufLz3K/G5xTA0DKjByzkLZCAbyUwpccVuSi5471ZoRDqTTEmKGDKHw5mnp6PwXK1iXhZePHvG7c2eEAOv3nxJUXCeMpthzxIKx9OMVYlijHjJWI3SBa0vGTvXlKhGZWqY5GazWbuOGOOKY7ai0LbUUFU8JcuhhzAv2slttFxP10T397b8V5tUY2T5MY3j6iYv7uH9+v63ot5+TnuOrZgrLvlCTZYqihKR3a30HitF9vpAuGyn57VYSsdY82hiQmm7UqW01pJy6N4PLJNFimKel/X9lHusBvWpal3W1az2LO9du74uHf9FRQWQU0YbQ18niZIV220HtAO7cmSNVOG2+MrpwrJobvRai9kMKtcD8LK4EmFJW1QqCnpdXA1DXz+vRn+rn4MBnYUqtxkGMiK0CzGJ+XLJla7kfqKjfv/xjVHwqDoCL4tIyW5u7qpNf6xUhMA8x6rUAUrGeTndw7LgnQVlMMax2Yshx7xEnDU1SjNjjCPmxPl0hizFeLfbrSeaUhJ1IKhy7ThixDqP855lOhOqYYDk4CTG04iQGhN9bzGmME4TRgmf0HlQWfBKGSVYL4xcRAYYwowi41xfT1YIYeZ4PNH3Q8XIlrpwuZDHrx10rjl47aJtlJIma2s3YzuVmwlJzpklLRWvFBuyNhJrrTH6ohtPqbDMEQqM55nxLLy6XBclKYtTzs3tLeM8wlvFtEzsd8/46KNP2HSW8zyhWCglEHLAenHxSZX21Cg07cZpZPiGU47juNqfpZRqUuPl39t4fF08lVJY7zkdDiQFm2FAG0Mqlw6tdYGNxnJ9fTbyeqPaNNPfS0G78EDbo9GCnp6e2G63bDabtbgbqymLFPt5GbHO4TtR2oznkaHv2W63q067XZ/XU09rJOaqBw8Vd7ZKjLLLT9CuZCPecTqNtcA3fbp4wQ7bHaVeQ9M8s1QFWK4OQilKnO319JXrRt65rv4O+X1LhSC6blg/u2mamMYZv+kJ9XBpneWlU77k/AhvN614sdYWxcUbsyn0tGoQhUUpSe9sru9d15GTTE+vv/qKnKEfBlKGbhgQN6SwvqY/jhT5jSiWSilyHYNd5aiFlEjnEW0Mh9OJ4/EsbzqZvncMvRNakHeiztGGluu9LItsRau5aU4JW7fjShm2g3DBrFFYrdBKNuZyItYPxtUuwXpu9zd1w5yZldiclbJwPI4sYayUkpFpaaqPAiwUhbjUsBERf5Lu9nSaSClU2aWVIqldleZVykiZV+pIikLs9c6vXUvrgFrn0jbD1lqmZZELIqW1G2gFthWaXCR/JwQZk6xxtCLZ9/17m1UpXA2jM2w3HU9PI1o5NsOeOU/0w8C26yAmYkocxzOFwt2zOzYp8tGHn5CCxMGO5zPTfKCw4DslbkpKrP/f40rmy43RilIbl1sRuI6X+ElScwvAkrFXeIDWWpyVbjHESOZCwr8udNdLJHndQs42VkbRlETKtyyinBKsM67P03tft/Ju/fnAVVEWrLzvOwmzUxf8+drq7Zpq02AG+Xwip9OJ29s7piWsuFvjcVrrBM8EWpzCNE3sdvvKSb3EvxZk4y2WaLK82253gl0WiCnhqoLIObf6U3rfVQOOuMIU4/i4QkKt81yWQDML9k5cjRrrgGoTKK+7Rd0KbatNSo0+KFEdwv5YObZxoWQ56IdhoOs6pnFeoaR28OWS2O12LBXGG4Yt53EkJnFMssbS/QRH9icf35BiqTHWYagmwFEibmMuLONZ6Aex2kZZ2W7nHClaiknnO/puIKbMaT5DcfTe4axBUVimM9kapnHEaMXzuwHnZOuuTQGkYOT681OOlHIB8UsRR6NQ6R5LCMzTkWma6kmsGAaJ15ymaaWqNPlcCpllzpQ8S9HRYusVkXE4pUJUoSomBCdyTjDPGNK6jGgXWCtifd+vscFwWYq0jqepna4JxNejrZYYRykMddzPOa/dRNPwvqcWUoYY/7/tnc+LbdlVxz9r7/PrnltV73XHVoIR04KTICJBgqCIiGCMog4zEDLwD1AcSIeA4FAH4lhUCPgjEwUls+APnMVfSaSlbZOo4CDaadPdr+re83Pv5WDtfe55TXes7sZUvfvOF4p776lbxVnn3rP3+vFd3yX03cTh0HN5+ZCBa9pdw8OLSyRRSqJGQgz4suRi13B1tePVr79CPwkTPepn2v2OspLElbOmhDxvOi804zg+RpTPRPy8KGUPO5/3esTrqfPHgxaAjQ7xySaf8o65zz2H+mti8pqjOs8TijJNMVFZbDRCzvuuu4DWXUUPHz40D3AYcGUOwZVcBnCOZbFr25bKl8zjafHP6ZS84eXPPedNwebUDOO8LDwhc3kgFTlsU+m6I5eXD9Jibt6eK4TLywdpTEZIrZDGZy7LkuPxyDCMFOIYR/Ps69ooN5rIGMMwMo0g6frZNfDL9VyT4p0/Ub402tiKvE91XZeu24z4rJU5mKMh1rKaJ3GWpSPqhIqk8+yWaEQVDoeD2R/tc9tf7C2FFCM3N9eoGAVpmi2ancfROFBvg3uyWFrOJr1Ak2JQjNGGWE0jUwoZmrpAZCLMdgFR08Jsmp217809j17/BqhydWlV0O54MAHVcaCpa9raJLxKr4Qp2AcjxsqzXEzAN1ZwORwPhLRzjuOAE6U7HhnHnqpsGPtAmEy9e+yVbhhpd3WStnKEeca7iqbZpzAuGAdQvE2hixCjyew3TaqgJ1tNvXtKofhpOFeukuawcE0YV0x+bpomHjx4sHhjmUC+hHLRBj9lL2CeTk0BtoN7cneKeajWAy1AmKP1c792ZJ5mqro2NWohjWGwvy2riiCRYRx49X/+i3ZfWMvbrqKsWpTAPPc479i3e+Zpokj5tox19Ts/5rzgWhhi3Y3UdR1rJfNs9zgMxDBztd/jvefmcIMT0zjM3mqurmevNntz0zRaVbayGUBTIjmX5WmsRb6WGUsf/ipP6ZKMWBb4CGmmeCbO59D6eDw+5lmvC1y5CSNXwvf7Pc2uZRivFw82xMjN9TU+FYWcc8y1CUac2jvtu+TKiM6BRzcmCuKKkgAM48iQesurqqFI72/qVIwcpvT9sI3K5h6F5Txt4Y6PpVKMejYv18q+U6ltMs1jz7ZmUnrOK4egOPGpcBTI6kIhpZSKwjOOk1XDVw6CTY+cF0+9aXb0bzxiChO+KJdmjKaulg32rXA/Fkt3qkY2dWOE0VXhYsklRfPqCp85b7ZjlmXFOM503ZHj4Ybu2CGiEGfCNOEddNOIEwENjEPHPE/0fbH0Cq9zgQo4NdrN0I8UzkJ5yRzHJcfpTJexLDgeb5DSU3oxrt44Yhp/FYWvU840pt02AM521aig1uNepc4kC3VGCp+Ju2H5kPONnL9smdibc4+qakLFatVoWzgKExtJf7cOkzTdvNmrsk1hYrdrubm5YZ7HRYsxRuOthhDpjh1Ns6Pd7bm5ecOqxENvnUejUFWFjdQQsbywm6hqIcnP22A4JxRFg/OeeRwWG8Q5O9833TjZU+v7nsvLy0WJaN0qmFMPRdEu79c4WyqmKEBPHUBgub1lhlNiYOTHTFuxcNw2m13T4LwwTeZ1e+coUidSVVlUlAs31plSY/ORzBveVTV5bEPb7hjHQFzNFbfv+6m7pu/7hSa01kXI1+Hq6gqcqbjnTcZ7j/MYKX1VGBOxSZPHo83N9r5gv9/zxsG6Waq6SrlX2yCiKmjmup46xXJom6vz6579q6sH6d4aU0qgS5GBJu/RlOt9yqFnMd+6blg0AgpPjJ72wjzoHDrHqKZDm6KHECJ919O2+xVdyT7/uqrZ71sOhyPTOFLv6iX9Yd1qNfuypEvnGWNcvNq3w71YLDVGiIqo5bPyjbEINojjMBySB1HiSqPfxKhoWWJzOq55/bXXLfEeIWhA5xkkovOEaMBHJcaCbqhtRw+Bel9yPFwb5SJG62fFQnYU4hyZEhcxhAkkMs4BxQi6ZQXX16/hnHXFqAbmAMfuyIW/YA4TAW8UIQGJVjzSeUY1oCEQ4mzdFOopi10i857oKNl7sqL8hLjINA+E6EFtRGhuExXxFJrI5/2I10jpAlILYQLijI5QNAVIWrxjwHsj4pdJE/RwfY0T0jzxXOyIRJ2Yw5EpDDz7XMs8P0Jef5Vqd8F+v0d1BA0E72j3F9ROiDrQNCUqEzGAuJKhtx28LG1U7TRb62A3Bwq1G3lWRZJkWlna8LVx7omUzEEBUzXv+8Hyz95BGKkLYR46vHO0VUnX2fNc/e4GK0plTy57puuFJb8uioK+Py48vhAi/TAtQiWUaUKi84xDZJpyBde+DzEY1azvZppmjy8S1zN1ysyTzcSe+oGqakxYWhO3OEUI+WZumsaoMFWFL0v2TcWsytB1ZP5kXRunMoRIUaXcc1TEleBgt98vsmSIMoWR0ke8sxDeu4Kq9AzjSFOaMntdVHR9Z9VyL4QpMEfLr7vCQvN213J96HjUmYPz8MEDBhVGnfBVydwPC21uHjrQgjlNQrBreyp8Oue4ORx57bVHltf3ADPTNDDPI97VmICJSTV2fU/TlIQ4UlY2jiJEB2oOjbqCm+PMHIxfWhQFTdOiCrvapcaFNCQuvD3L8X4slsriQWZlkbyLwUnlpm1b8+3EBDa82JdiHkej7+TRA97k7kUspCVGmqLApxzTMNiiV1bC9c3Bqm4xD2CS1DJlQ78kSVJZIYQUgs1UlXk8w9CnkC/N3k4K3G27wzlhPA64MgteCE4t9A4hLJU9l9IQgqSiy6ml0M4p94OzSEip5h3w1GJnYg7+tAs7G74lkPKxBYJRKzTGZeaO88XinZ6Ue6Buapx3i6wXJNXvfUtd78j9+u979iFXV1fs922iiVgbZlWXTNPAOPT4whZezfbKSXatTGIVOCid9QgjpNG0jjwCVQTqqkK9ncs4jak6LURxxESazlQX59wi0gCkEM0Wskzej3FexpNkryJ7bpkKZDqYpzbGsjiNtjBdAxK1RVI659QLDiEVGyLTeJrFbWyK/LlF2t3Octep4OYSM6Ft24Uilb374/GIKzwhxqT6YwUZVUl5bWtbPBweUdYnmblcGMqCITc3NymaKJPHWjBPIXUnFeQKUNd19EPP5aW1Mjpvs21E7JoZ/cbuO1cYC2SarajqfcE8TlZEimpRlbKkGbK4i81hP6lE1XVF1w9kiptLAsB1bWOcw2zeY1S771hpClSVFSxjFIooqBP6cWSaZtrWuoCGYViuR+7ht/Eb9zxnCafqYw4/8mKZ805FYSHDNNqMGu/dUrkOyfWepilVvY2Zr87yj6Kmdp0py8blNHWSrjvyzDMPloU5RggzoBYeqQreW67n4qKlLAv6MVUY54FCcgiXW93CY73KRvsZ0k1LKhKZ3qaT3Jn9uOufQ5pcSV3Izt7EjS3cOrXanYR5jd7iXUWIKbwsTL1m4a/l/I+EJd9UVC4VTE55wOxV5Zk7tpCcxHVtNooVVy4vL5KHuG5LDMyzLKF9CDPDECmLhna3o6nbFDJ3qeDhLNT1VtEVVcA2Dp8WQVELy2dyQcTmus/zjEZBo3Fec44sf9ZrEv96UdR4muOTkYtJuSCxzpOuu6TWSvXrNsz8+3VqYM29nOfcfmqiDebVTClH3S/nllMoDx8+XNJRh8Ph5F0W/kQdW/LWupy7eacnDdP1NcjIdLL8XZ0my5EPgxHWq6qy5gMJlOHU5XSiIuVKu/1P5x1lUVk33jjavTrNaKqEL/S2JIm2nnfz5v9dFiWxtnbgonAUpU/Cw56ZeDo/cYQwMYcBU1S3ilOMkRisgNr3FinkTqjj0eYxrScN5O94vh5vBVlX/u4KIvJ14AC8etfn8i3At7HZeW54Wmx9Wuz8blV97s0H78ViCSAif6+qP3jX5/H/jc3O88PTYuvTYufb4e011Dds2LBhw4JtsdywYcOGW+A+LZa/c9cn8C3CZuf54Wmx9Wmx8y1xb3KWGzZs2HCfcZ88yw0bNmy4t7jzxVJEPioiL4vIV0Tkhbs+n/cCEfl9EXlFRF5cHXtWRD4nIl9Oj8+sfvfJZPfLIvKTd3PW7xwi8l0i8lci8pKI/LOI/FI6fo62NiLytyLypWTrr6fjZ2crgIh4EfmCiHw2vT5LO98VMoH2Ln4AD3wV+B6gAr4EfOguz+k92vOjwIeBF1fHfhN4IT1/AfiN9PxDyd4aeD5dB3/XNtzSzvcDH07PL4F/Tfaco60CXKTnJfB54IfO0dZ0/r8C/BHw2fT6LO18Nz937Vl+BPiKqv6bqo7AZ7C5408kVPVvgG+86fDPAZ9Ozz8N/Pzq+GdUdVDVfwfyfPV7D1X9mqr+Y3p+DbyEjTs+R1tVVW/SyzL9KGdoq4h8APhp4HdXh8/OzneLu14svxP4z9Xr/3PG+BOI79A02C09fns6fha2i8gHsbEjn+dMbU2h6ReBV4DPqeq52vrbwK/y+PDsc7TzXeGuF8tbzRg/UzzxtovIBfAnwC+r6qNv9ta3OPbE2KqqQVV/ABvr/BER+b5v8vYn0lYR+RngFVX9h9v+yVscu/d2vhfc9WJ5qxnjTzj+W0TeD5AeX0nHn2jbRaTEFso/VNU/TYfP0tYMVX0d+Gvgo5yfrT8M/KyI/AeWDvtxEfkDzs/Od427Xiz/DvheEXleRCrg49jc8XPCnwOfSM8/AfzZ6vjHRaQWkee5xXz1+wIxqZbfA15S1d9a/eocbX1ORB6m5zvgJ4B/4cxsVdVPquoHVPWD2H34l6r6C5yZne8Jd11hAj6GVVO/Cnzqrs/nPdryx8DXgAnbeX8ReB/wF8CX0+Ozq/d/Ktn9MvBTd33+78DOH8FCrn8Cvph+Pnamtn4/8IVk64vAr6XjZ2fr6vx/jFM1/GztfKc/WwfPhg0bNtwCdx2Gb9iwYcMTgW2x3LBhw4ZbYFssN2zYsOEW2BbLDRs2bLgFtsVyw4YNG26BbbHcsGHDhltgWyw3bNiw4RbYFssNGzZsuAX+F6wpCWsU01CbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sample = random.choice(filenames)\n",
    "image = load_img(\"/kaggle/working/train/\"+sample)\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.027572,
     "end_time": "2021-02-25T12:48:32.106385",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.078813",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## splitting of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:32.170668Z",
     "iopub.status.busy": "2021-02-25T12:48:32.169711Z",
     "iopub.status.idle": "2021-02-25T12:48:32.177870Z",
     "shell.execute_reply": "2021-02-25T12:48:32.177360Z"
    },
    "papermill": {
     "duration": 0.041898,
     "end_time": "2021-02-25T12:48:32.178038",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.136140",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "train_data, valid_data = train_test_split(df, test_size=0.2, random_state = 42)\n",
    "train_data = train_data.reset_index(drop=True)\n",
    "valid_data = valid_data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.026806,
     "end_time": "2021-02-25T12:48:32.231707",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.204901",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## generating training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:32.291483Z",
     "iopub.status.busy": "2021-02-25T12:48:32.290759Z",
     "iopub.status.idle": "2021-02-25T12:48:32.295168Z",
     "shell.execute_reply": "2021-02-25T12:48:32.294435Z"
    },
    "papermill": {
     "duration": 0.036387,
     "end_time": "2021-02-25T12:48:32.295346",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.258959",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "train_data_gen = ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    rescale=1./255,\n",
    "    shear_range=0.1,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:32.389584Z",
     "iopub.status.busy": "2021-02-25T12:48:32.384351Z",
     "iopub.status.idle": "2021-02-25T12:48:32.609179Z",
     "shell.execute_reply": "2021-02-25T12:48:32.609916Z"
    },
    "papermill": {
     "duration": 0.287903,
     "end_time": "2021-02-25T12:48:32.610142",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.322239",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_data_gen.flow_from_dataframe(\n",
    "    train_data, \n",
    "    \"/kaggle/working/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='category',\n",
    "    target_size=(128,128),\n",
    "    class_mode='categorical',\n",
    "    batch_size=15\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.0275,
     "end_time": "2021-02-25T12:48:32.666091",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.638591",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## generating validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:32.761548Z",
     "iopub.status.busy": "2021-02-25T12:48:32.760558Z",
     "iopub.status.idle": "2021-02-25T12:48:32.814586Z",
     "shell.execute_reply": "2021-02-25T12:48:32.813768Z"
    },
    "papermill": {
     "duration": 0.117019,
     "end_time": "2021-02-25T12:48:32.814781",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.697762",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5000 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "valid_data_gen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "valid_generator = valid_data_gen.flow_from_dataframe(valid_data, \n",
    "    \"/kaggle/working/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='category',\n",
    "    target_size=(128,128),\n",
    "    class_mode='categorical',\n",
    "    batch_size=15\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.029367,
     "end_time": "2021-02-25T12:48:32.874056",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.844689",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# *CNN Model*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:32.943577Z",
     "iopub.status.busy": "2021-02-25T12:48:32.942843Z",
     "iopub.status.idle": "2021-02-25T12:48:36.049854Z",
     "shell.execute_reply": "2021-02-25T12:48:36.049169Z"
    },
    "papermill": {
     "duration": 3.147595,
     "end_time": "2021-02-25T12:48:36.050031",
     "exception": false,
     "start_time": "2021-02-25T12:48:32.902436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "                         keras.layers.Conv2D(filters=64, kernel_size=3, strides=(1,1), padding='valid',activation= 'relu', input_shape=(128,128,3)),\n",
    "                         keras.layers.MaxPooling2D(pool_size=(2,2)),\n",
    "                         keras.layers.Conv2D(filters=128, kernel_size=3, strides=(2,2), padding='same', activation='relu'),\n",
    "                         keras.layers.MaxPooling2D(pool_size=(2,2)),\n",
    "                         keras.layers.Conv2D(filters=64, kernel_size=3, strides=(2,2), padding='same', activation='relu'),\n",
    "                         keras.layers.MaxPooling2D(pool_size=(2,2)),\n",
    "                         keras.layers.Flatten(),\n",
    "                         keras.layers.Dense(units=128, activation='relu'),\n",
    "                         keras.layers.Dropout(0.25),\n",
    "                         keras.layers.Dense(units=256, activation='relu'),\n",
    "                         keras.layers.Dropout(0.5),\n",
    "                         keras.layers.Dense(units=256, activation='relu'),\n",
    "                         keras.layers.Dropout(0.25),                        \n",
    "                         keras.layers.Dense(units=128, activation='relu'),\n",
    "                         keras.layers.Dropout(0.10),                         \n",
    "                         keras.layers.Dense(units=2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:36.119869Z",
     "iopub.status.busy": "2021-02-25T12:48:36.119223Z",
     "iopub.status.idle": "2021-02-25T12:48:36.127966Z",
     "shell.execute_reply": "2021-02-25T12:48:36.127385Z"
    },
    "papermill": {
     "duration": 0.047347,
     "end_time": "2021-02-25T12:48:36.128098",
     "exception": false,
     "start_time": "2021-02-25T12:48:36.080751",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 126, 126, 64)      1792      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 63, 63, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 8, 64)          73792     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               131200    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               33024     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 412,610\n",
      "Trainable params: 412,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:36.193037Z",
     "iopub.status.busy": "2021-02-25T12:48:36.192252Z",
     "iopub.status.idle": "2021-02-25T12:48:36.204697Z",
     "shell.execute_reply": "2021-02-25T12:48:36.204205Z"
    },
    "papermill": {
     "duration": 0.047933,
     "end_time": "2021-02-25T12:48:36.204825",
     "exception": false,
     "start_time": "2021-02-25T12:48:36.156892",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss= 'categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028692,
     "end_time": "2021-02-25T12:48:36.262806",
     "exception": false,
     "start_time": "2021-02-25T12:48:36.234114",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T12:48:36.326267Z",
     "iopub.status.busy": "2021-02-25T12:48:36.325447Z",
     "iopub.status.idle": "2021-02-25T13:27:42.212640Z",
     "shell.execute_reply": "2021-02-25T13:27:42.213509Z"
    },
    "papermill": {
     "duration": 2345.922196,
     "end_time": "2021-02-25T13:27:42.213677",
     "exception": false,
     "start_time": "2021-02-25T12:48:36.291481",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "1334/1334 [==============================] - 157s 115ms/step - loss: 0.6929 - accuracy: 0.5172 - val_loss: 0.6868 - val_accuracy: 0.5422\n",
      "Epoch 2/15\n",
      "1334/1334 [==============================] - 150s 112ms/step - loss: 0.6790 - accuracy: 0.5784 - val_loss: 0.6697 - val_accuracy: 0.5806\n",
      "Epoch 3/15\n",
      "1334/1334 [==============================] - 151s 113ms/step - loss: 0.6631 - accuracy: 0.6000 - val_loss: 0.5942 - val_accuracy: 0.6924\n",
      "Epoch 4/15\n",
      "1334/1334 [==============================] - 149s 112ms/step - loss: 0.6051 - accuracy: 0.6778 - val_loss: 0.5676 - val_accuracy: 0.6990\n",
      "Epoch 5/15\n",
      "1334/1334 [==============================] - 153s 115ms/step - loss: 0.5648 - accuracy: 0.7107 - val_loss: 0.5929 - val_accuracy: 0.7082\n",
      "Epoch 6/15\n",
      "1334/1334 [==============================] - 153s 115ms/step - loss: 0.5434 - accuracy: 0.7306 - val_loss: 0.5042 - val_accuracy: 0.7512\n",
      "Epoch 7/15\n",
      "1334/1334 [==============================] - 156s 117ms/step - loss: 0.5230 - accuracy: 0.7474 - val_loss: 0.5108 - val_accuracy: 0.7408\n",
      "Epoch 8/15\n",
      "1334/1334 [==============================] - 156s 117ms/step - loss: 0.5051 - accuracy: 0.7616 - val_loss: 0.4532 - val_accuracy: 0.7870\n",
      "Epoch 9/15\n",
      "1334/1334 [==============================] - 157s 118ms/step - loss: 0.4951 - accuracy: 0.7658 - val_loss: 0.4383 - val_accuracy: 0.7932\n",
      "Epoch 10/15\n",
      "1334/1334 [==============================] - 159s 119ms/step - loss: 0.4755 - accuracy: 0.7778 - val_loss: 0.4243 - val_accuracy: 0.8014\n",
      "Epoch 11/15\n",
      "1334/1334 [==============================] - 156s 117ms/step - loss: 0.4610 - accuracy: 0.7854 - val_loss: 0.4269 - val_accuracy: 0.8048\n",
      "Epoch 12/15\n",
      "1334/1334 [==============================] - 161s 121ms/step - loss: 0.4474 - accuracy: 0.7942 - val_loss: 0.4200 - val_accuracy: 0.8130\n",
      "Epoch 13/15\n",
      "1334/1334 [==============================] - 160s 120ms/step - loss: 0.4382 - accuracy: 0.8020 - val_loss: 0.3970 - val_accuracy: 0.8132\n",
      "Epoch 14/15\n",
      "1334/1334 [==============================] - 165s 123ms/step - loss: 0.4466 - accuracy: 0.7950 - val_loss: 0.3765 - val_accuracy: 0.8264\n",
      "Epoch 15/15\n",
      "1334/1334 [==============================] - 161s 121ms/step - loss: 0.4264 - accuracy: 0.8025 - val_loss: 0.3789 - val_accuracy: 0.8306\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f7dc40dee90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_generator, epochs=15, verbose=1, validation_data=valid_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T13:27:57.460115Z",
     "iopub.status.busy": "2021-02-25T13:27:57.459182Z",
     "iopub.status.idle": "2021-02-25T13:27:57.480295Z",
     "shell.execute_reply": "2021-02-25T13:27:57.479613Z"
    },
    "papermill": {
     "duration": 7.364013,
     "end_time": "2021-02-25T13:27:57.480431",
     "exception": false,
     "start_time": "2021-02-25T13:27:50.116418",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_filenames = os.listdir(\"/kaggle/working/test1\")\n",
    "test_df = pd.DataFrame({\n",
    "    'filename': test_filenames\n",
    "})\n",
    "idlist=[]\n",
    "for file in test_df[\"filename\"]:\n",
    "    idlist.append(file.split(\".\")[0])\n",
    "\n",
    "#test_df['ids'] = alist\n",
    "nb_samples = test_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 7.365686,
     "end_time": "2021-02-25T13:28:11.858214",
     "exception": false,
     "start_time": "2021-02-25T13:28:04.492528",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## generating test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T13:28:26.876868Z",
     "iopub.status.busy": "2021-02-25T13:28:26.866594Z",
     "iopub.status.idle": "2021-02-25T13:28:26.980701Z",
     "shell.execute_reply": "2021-02-25T13:28:26.980218Z"
    },
    "papermill": {
     "duration": 7.235679,
     "end_time": "2021-02-25T13:28:26.980834",
     "exception": false,
     "start_time": "2021-02-25T13:28:19.745155",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 12500 validated image filenames.\n"
     ]
    }
   ],
   "source": [
    "test_gen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_gen.flow_from_dataframe(\n",
    "    test_df, \n",
    "    \"/kaggle/working/test1\", \n",
    "    x_col='filename',\n",
    "    y_col=None,\n",
    "    class_mode=None,\n",
    "    target_size=(128,128),\n",
    "    batch_size=15,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 7.518154,
     "end_time": "2021-02-25T13:28:41.818703",
     "exception": false,
     "start_time": "2021-02-25T13:28:34.300549",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## predicting model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T13:28:57.068626Z",
     "iopub.status.busy": "2021-02-25T13:28:57.067666Z",
     "iopub.status.idle": "2021-02-25T13:29:30.194011Z",
     "shell.execute_reply": "2021-02-25T13:29:30.193461Z"
    },
    "papermill": {
     "duration": 41.142903,
     "end_time": "2021-02-25T13:29:30.194164",
     "exception": false,
     "start_time": "2021-02-25T13:28:49.051261",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "predict = model.predict(test_generator, steps=np.ceil(nb_samples/15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T13:29:44.865550Z",
     "iopub.status.busy": "2021-02-25T13:29:44.864579Z",
     "iopub.status.idle": "2021-02-25T13:29:44.867114Z",
     "shell.execute_reply": "2021-02-25T13:29:44.867626Z"
    },
    "papermill": {
     "duration": 7.267187,
     "end_time": "2021-02-25T13:29:44.867818",
     "exception": false,
     "start_time": "2021-02-25T13:29:37.600631",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_df['category'] = np.argmax(predict, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T13:30:00.258335Z",
     "iopub.status.busy": "2021-02-25T13:30:00.257349Z",
     "iopub.status.idle": "2021-02-25T13:30:00.262301Z",
     "shell.execute_reply": "2021-02-25T13:30:00.262900Z"
    },
    "papermill": {
     "duration": 7.901681,
     "end_time": "2021-02-25T13:30:00.263082",
     "exception": false,
     "start_time": "2021-02-25T13:29:52.361401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "label_map = dict((v,k) for k,v in train_generator.class_indices.items())\n",
    "test_df['category'] = test_df['category'].replace(label_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T13:30:14.782094Z",
     "iopub.status.busy": "2021-02-25T13:30:14.781196Z",
     "iopub.status.idle": "2021-02-25T13:30:14.785093Z",
     "shell.execute_reply": "2021-02-25T13:30:14.784593Z"
    },
    "papermill": {
     "duration": 7.384459,
     "end_time": "2021-02-25T13:30:14.785228",
     "exception": false,
     "start_time": "2021-02-25T13:30:07.400769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_df['category'] = test_df['category'].replace({ 'dog': 1, 'cat': 0 })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 7.261102,
     "end_time": "2021-02-25T13:30:29.392387",
     "exception": false,
     "start_time": "2021-02-25T13:30:22.131285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T13:30:44.763026Z",
     "iopub.status.busy": "2021-02-25T13:30:44.761662Z",
     "iopub.status.idle": "2021-02-25T13:30:44.904492Z",
     "shell.execute_reply": "2021-02-25T13:30:44.903875Z"
    },
    "papermill": {
     "duration": 7.653132,
     "end_time": "2021-02-25T13:30:44.904637",
     "exception": false,
     "start_time": "2021-02-25T13:30:37.251505",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission_df = test_df.copy()\n",
    "submission_df['id'] = submission_df['filename'].str.split('.').str[0]\n",
    "submission_df['label'] = submission_df['category']\n",
    "submission_df.drop(['filename', 'category'], axis=1, inplace=True)\n",
    "submission_df.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 7.252899,
     "end_time": "2021-02-25T13:30:59.738243",
     "exception": false,
     "start_time": "2021-02-25T13:30:52.485344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 2590.867857,
   "end_time": "2021-02-25T13:31:10.065250",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-02-25T12:47:59.197393",
   "version": "2.2.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}