Numpy Basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "ac1283b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184e159b",
   "metadata": {},
   "source": [
    "# Creating Numpy Arrays from Python List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "ac86d559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "3b6953c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3.14, 4, 5, 6]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1\n",
    "[3.14, 4, 5, 6] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "1a8ea14c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.14, 4.  , 5.  , 6.  ])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2\n",
    "np.array([3.14, 4, 5, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7bfc97",
   "metadata": {},
   "source": [
    "#Thấy sự khác nhau giữa #1 và #2 chưa ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "d4c534c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4.], dtype=float32)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3, 4], dtype = 'float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "ff226fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1., 2, 3, 4], dtype = 'int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "34f0e183",
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = np.array([1., 2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "039555d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "ed5c8107",
   "metadata": {},
   "outputs": [],
   "source": [
    "a2 = np.array([[1, 2, 3, 4],\n",
    "                 [5, 6, 7, 8]]) # list 2 chiều"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "def96bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a2)  # return kiểu dữ liệu của biến a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "a944315d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.shape      #return mảng này bao nhiêu cột, bao nhiêu dòng: 2 dòng, 4 cột"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "16df2113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.ndim  #return loại mảng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "4eef94d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.dtype # return kiểu dữ liệu phần tử bên trong a2 là 'int32'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "8c9086e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 2.3, 4. ],\n",
       "       [7. , 8. , 9. ]])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3 = np.array([[1, 2.3, 4],\n",
    "              [7, 8, 9]])\n",
    "a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "16e945ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "e4d19210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "25598805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.size  # số lượng các phần tử có trong a2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f2645c",
   "metadata": {},
   "source": [
    "# Creating Numpy Arrays from Scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8acf1887",
   "metadata": {},
   "source": [
    "zeros, ones, full, arange, linspace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b4926ba",
   "metadata": {},
   "source": [
    "### zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "f5c2b85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1 = np.zeros([2,4])  \n",
    "b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "2c6b1ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1.dtype# mặc dù là số 0 nhưng kiểu dữ liệu các phần tử mặc định là float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "36148bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2 = np.zeros([2,4], dtype = int)# tuy nhiên vẫn có thể thay đc định dạng\n",
    "b2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0ded8d",
   "metadata": {},
   "source": [
    "### ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "da68849e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones([2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "eb3d1270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [1, 1]])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones([2,2], dtype = int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d929d4ae",
   "metadata": {},
   "source": [
    "### arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "628efb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "596ad2d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2.])"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "512f71c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(2,20,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1d4cc5",
   "metadata": {},
   "source": [
    "### full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "91eb7d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [1, 2]])"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.full((2, 2), [1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f619559e",
   "metadata": {},
   "source": [
    "### linespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "df86348b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.  , 2.25, 2.5 , 2.75, 3.  ])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(2, 3.0, num=5)# start=2, end=3.0, chia ra làm 5 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed604f81",
   "metadata": {},
   "source": [
    "### Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "30440034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00706631, 0.02306243, 0.52477466, 0.39986097],\n",
       "       [0.04666566, 0.97375552, 0.23277134, 0.09060643],\n",
       "       [0.61838601, 0.38246199, 0.98323089, 0.46676289],\n",
       "       [0.85994041, 0.68030754, 0.45049925, 0.01326496]])"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random([4,4])# ngẫu nhiên mỗi lần ra 1 kq khác nhau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "62b8fadd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5488135 , 0.71518937, 0.60276338, 0.54488318],\n",
       "       [0.4236548 , 0.64589411, 0.43758721, 0.891773  ],\n",
       "       [0.96366276, 0.38344152, 0.79172504, 0.52889492],\n",
       "       [0.56804456, 0.92559664, 0.07103606, 0.0871293 ]])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seed  =>giúp random ra kq giống nhau\n",
    "np.random.seed(0)# trường hợp này khởi tạo với bộ sinh số ngẫu nhiên =0\n",
    "np.random.random([4,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "65b9c3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.44386323,  0.33367433,  1.49407907],\n",
       "       [-0.20515826,  0.3130677 , -0.85409574],\n",
       "       [-2.55298982,  0.6536186 ,  0.8644362 ]])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(0, 1, [3,3])# min=0, độ lệch chuẩn=1, 3 hàng 3 cột"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "f81a40f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 2, 0, 0, 4],\n",
       "       [5, 5, 6, 8, 4],\n",
       "       [1, 4, 9, 8, 1],\n",
       "       [1, 7, 9, 9, 3]])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, [4,5])# min=0, max=10, 4 hàng 5 cột"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146915bd",
   "metadata": {},
   "source": [
    "# Array Indexing & Slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31a64315",
   "metadata": {},
   "source": [
    "### 1 chiều"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "351ed1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.random.randint(20, size =6) # mảng 1 chiều 6 phần tử"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "c68af20a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 11, 14, 18,  0, 14])"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 #ngẫu nhiên từ 0->20 k bao gồm 20, 6 phần tử"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "51fd469f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 14)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[4], x1[-1]# phần tử thứ 3 và phần tử cuối cùng"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228d8d3f",
   "metadata": {},
   "source": [
    "### Đa chiều"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "5416d4af",
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = np.random.randint(10, size=[3,4])# mảng đa chiều 3 dòng 4 cột"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "757331e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 9, 4],\n",
       "       [4, 6, 4, 4],\n",
       "       [3, 4, 4, 8]])"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "fc7a373a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[1,2]# vị trí hàng 1, cột 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "dfd00652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 9, 4],\n",
       "       [4, 6, 6, 4],\n",
       "       [3, 4, 4, 8]])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[1,2] = 6 # thay vị trí đó = 6\n",
    "x2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50add24d",
   "metadata": {},
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a275711e",
   "metadata": {},
   "source": [
    "x[start:stop:step]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "82fa7c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 11, 14, 18,  0, 14])"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "06c9ba6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 11, 14])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[0:3]# lấy 3 phần tử đầu tiên"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "8101aa27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 11, 14])"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[:3]# 1 cách viết khác"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "28339e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 9, 4],\n",
       "       [4, 6, 6, 4],\n",
       "       [3, 4, 4, 8]])"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "eeda1e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 6],\n",
       "       [4, 4]])"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[1:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a7436b",
   "metadata": {},
   "source": [
    "# Reshaping of Arrays & Transpose"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3dfc378",
   "metadata": {},
   "source": [
    "### Reshape: chuyển thành định dạng mong muốn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "855c9386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9,)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = np.arange(1,10)\n",
    "grid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "0bc42ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.reshape([3,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ee7ae5",
   "metadata": {},
   "source": [
    "### Transpose: Chuyển hàng thành cột"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "851f33a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1, 2],\n",
    "            [3, 4]])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "48536bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ccd77dc",
   "metadata": {},
   "source": [
    "# Array Concatenation and Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "d3791976",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3])\n",
    "y = np.array([3,2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "ea3947a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 3, 2, 1])"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((x,y))# nối theo hàng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "9796a8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = np.array([[1,2,3],\n",
    "                [4,5,6]])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "545f34bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((grid, grid))# axis = 0 default mặc định nối theo hàng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "54330273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 1, 2, 3],\n",
       "       [4, 5, 6, 4, 5, 6]])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((grid, grid), axis = 1)# nối theo cột"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18cddff0",
   "metadata": {},
   "source": [
    "### Splitting of arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "4f824645",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1, 2, 3, 99, 69, 3, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "f0750390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 2, 3]), array([99, 69]), array([3, 2, 1])]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# muốn tách lấy array[99, 69]\n",
    "np.split(x,[3,5])# 99 bắt đầu từ vị trí index=3, 69 thì index=4 =>: 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "3c7356f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hoặc có thể viết tách ra\n",
    "x1, x2, x3 = np.split(x,[3,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "07c25b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "cecf86ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99, 69])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "c734eab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1])"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e13fe6c",
   "metadata": {},
   "source": [
    "# Broadcasting và Vectorised Operations"
   ]
  },
  {
   "attachments": {
    "image.png": {
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6w7PFixa7qdMj03U5iSboEoLvD8CwxabNm9xnn30ma0g6ccRreJVAxWt/6NRgWQmjFQcOHnTz5s51ixYvdmPGjI7351aC0NkluVevXtHRY+wRXr16tfYNR7QONYlYOlXtIh+Lh/SicNAx2G88huPYHnjwAffv/t2/c8vvXR557qOojsALIaRqARNhO3bsdG+88br23j362KOyjUmPhZotJ+KBcDYPMUD/k+SfOnXavf/+ZpkAW7hwoXv88ce1wZzefqWAtNiQTmI/3fGpe+P1N/Sbc+oQeJyhR3pgUrmwL0lmOt0BPvMiYhMA5IMwMHC99aOt7pNPPhVxYmz7a1/7mvvSl78UfdDTYNkm7fGWE4rBt3+0+V07d7nXrf45fPeJJ59ws2dFG/zT4OsBViZpmlDY/h5tDjoIZUUB00vFNuuWLVtU/9hxpWf/q1/9qnvmmWfy1EsEwigVMBtoSXtJTQjpvrbWjTLtGzrjbEeFh1NivcADiSrvCNAZLYKe/Y0b12XdhxP0ay3O2bNn6ZQRBZpMtgotuu3P4OSS999/X2b9MCDx4IMP6rQW2hWjSZVA7d+EHYYyEKavvvqqOj+PPfaYW7ZsuRvJWZvVqATR9W2ds8lxaIyGzDLN/umnN1qnapoEojrGCD7Ll+LsRLR8R14OHzrs3nn3HROql3S0GXz513/t192qNau8z76J6ml48JXQAC1EhN0uI5C33npTdimffuZpqc0YcO5MReUD2gQHsG7d+qH7/ODnbuasWe7hhzdEPSKvMVUDMDhMi0HsHC/00IMPmRa5yo2bMM77qA6uXL7qPv74YxN2H8s49aLFi9xPfvIT98TjT7gvfumLVSu3qsLKGBKKG6BPY93NOtPsdrr33tskhvH0xo1uydKlJviGV09TNZo7d+aM+/DDD91h660yhDnctK1du3YZo3jaPfLEY95jAZRCHz4/DTfr3TUTQhgtwJrODRN4dFawqlMz3Gi6CsDwOocZo9lxAv+IESONIV2Q3dlJkydG6U2mOVHe/RJWFpgF3LRpk86bnD//Hrdhw0PWQZgdtf8q4tDBQ+5nP/+Z7N0++uhj7r777pPh+KrRsuWFUQPaP9M/GNV/8sknZag+zkuV6vrokaPu7bfe1rmfCO3T1mG8WXfTPfvMs31e4FWPKhKVgdHnHTt3uM2bN8kyyUbrpaANxcKuFEaTRGjo4Tu73m697U6cOKE4IHZ6dg899KBOOcghkES6ol64/9ER/Hf4R1tgeBEttbmpyT3wwAMido4T6hRCPnDGsDWSYY6T4rdt+8g0op06rggNcp4RO0cHkY5CmkpPgDTXRDlznuFO0+w2b94su6MIu2XLlxkDN2GHv1LqICCUVRr27NTJk26TxYFmxzzXg1Y3dHqwh8rwY4cI4aZdAPc+rfU3blrP+5KEHVodGh097aivmPyoTIRPrWqbG5tNs6vVsDnCjrMShw0dKm3CIvIeM2hO11/V/jdt1tDc3LnzImE3q7rCjvnm3Z/tlrBDw0d7XL16lRszNhJ2pENponmStFKc+eVoMxy/+Z6zOLdv267OGobt4TMIcPIS0Zn/1n9fqoO/hDLjW7TgN9540507f84tX36v8sLoAXwamu7rqLrAq71eK6bNEAMFTC/l3nvv1bE3MVNMMMeSgH9zcbu3e86727Ztm4gdIQeBcOYcc20SIvhNuCDs+Cn4MNs5wx3OnzMwZn9g/wH35htvaE5l3fp1GlOn95WcpyoLqfgQZIzVb9/+sYgdQfqwCbvFSxZbXoYo3dHSaj7ugUjmx8BRTwxjbtnygTS7J594Qh2EuP4TfktG2r/V75lTp90HH3yg+mfoB0Yke6heQLC4RKDcCrl8CM99nI119aofhkpZQIR2N8QEUZykQuGUCkumTN3duG4MtVaMZ+LESW4EQ2XQmIVf3uB/H4YVA50rGDgnsmgY+8hhaXQPPvhAJOw6a64wD1qaWtSZfsPaP1r3hg0b3P1r73cjR430Piw9DC+mOnzFQH3yDUOUof0zqvPZ7s90JNrDGx7WkWMcIM3oUhx+J5w+tTRSZpwS89Zbb+m8zRUrVhjPXC9j+HSqNUiaCbzyUGs9VCqNISZMcDGXBrOjx1oNcEyQekPnL7iPtn4kZsfilA0PbdB1cM1gEXy7OWRVPBWKi34XhL1jvBw7m0dM9X/r7be1iOQhi0PDmGPHtf8epmTpQlDGjl4VfKojXmXhMEH9yaefmha5W2fiPfTQQzrJnXmusPqveEA9AzAFCbsPt0hAP/7Y427FfStUL1FvMyoXlU26U8LvvM77xQH7edYPYx47elTMbv369RJ6zHlVDRZvQ22dCaIbYkpjrW4QdgOHmEA1GgqrQ0l6jJCXnIcdwAJgBIHOVDj2ioUwI0aaJpyhIM6ePWv1v1WHOIf2nzONUYnzaGludQc/P6jhUuj6kUceFZ2NRrMDCb8ihOBAOsyUi4RLNGd76dJlzdft3bs3av+mpS5d6qd+8A9CuAHJ+Io5AwdInzh+wr355pta1HXfivvEZ6ZMjU5+wZ8WzfTYXnX1UDWBh+rPPMqOHTvcLWMQjzz8sM6Eo8fK8GOS0cWMLyEcwu+g6ifdLftezno7nGeHZrf/wH5N5tKzn3/PfFWcwk8SSaLSBd4Vq1N7z1wKPcitxlQ5PBbiuP/+tZGww0sizRCT0ml5hmiCi95Fz5P+Qxook5s3borQ9+7do+XtDz9smp317NBSKaec4YweDobkmBv4lPq3fD/yiNX/ypUS2rdabmlVmMrIO2gkWccqp3zOa2s4yuT6lavWG/5Ewm7uvHmqf84yZJl11crK6qmxoUHnLCKQOO6JlX8DpD3y3i4hbSHOdNTFkmLvoYd6i4eTRCTsjOFx2r5eG81kSMHaMp1q6p9hbDS69evWuxkzZ/qFRSma6oSjbmmbDJd/9NFHpg2dl6Bbv36d5lSRC2q/0GMBUHPFHPHQ0aHt7927T8eVPfjAg5r6kYIQe4yu4h2FoywIVjCfP3fObX7/fa0uX7lylQnVDdLsQKBheCtttK+jaotWGMp8b9N7WqTA4oH7167VqQFoVjANltcH0MMJgih5r4qN/rQxEnsX7qkUeigMl0Lg999/v5tnTA+hyjcIGq1iQptDlzfwbdLpXTLOFPDD8UUsgvlgywcaR4d5M2/D6lI+i1Lj02coFl4hsAKT3h1p2rhxo/IDsUPc9AAvXbzs/p//5//rHnv0MffFL36xqsM11QaMiN7wpk2bTQDNdOvuX6eDcFtaW5Qf6j8qimR5UE/+zh4XKMIY1C2NlqEsFos89dRTqn+EKhoXQoiFUr/85S81/MRwOqs4hRBRQKF6MX+Ewwn3HNY73Gh4qjEH6ogVf1GPeIAYIvQ4ydIBs0qGx51+dZAhyoTVcizugdGwihmBR17UDbUAoENWIHIQcLZK02B5p/3/0z/9kw6YpkON0JO2ZO842qujtlgq2Hqyf/8B9/HH29306TM0nz7BtHtomTlVaAHhSLzJLQmBd5UCOnznz583obpVGiRaF1M/aHbQFfEQdqvdQ7qRgOW4sY75VxqExbzde++9p/M+X3zpRTd92nSlnTAQiD/+0Y81NPz008+4NWtX+y/7Jqq2LYEl6O9/8L777ivf1XAc2pcqRYQYCTtpPhadhA61GDwE2G0s8PhjwF9IIpXOSiaIcfToUW7hgoUaT1fPjPpTuAi86F4P7dsQluKWR/3MC/wwdwcxEs/SpUvcggULXKNpsKSK/V9RmizOKNhOYZAxNpafw9BY0PGbv/lbbsHCe6KXhGtpvHjhovvP//k/a5XmF77whc4fmNsNYKHKO++8477/ve9L0E2dNk0bdMlIVBeRxhvqMqor/y76L6HXERBqLB5hXgWBtvHppyWM9Bn1bG7vvn3u9ddeU68cP9BMHE8JCH7rTNhAA2h2k7Wn67Y6bhJIFtSpk6fEjBC8CDzly+eNv8UWzdAmEHQs8KJjyH4+aFkMNBN4BXH1yjX3F3/+5xpN4kDmUaNGq4MS8xYKpJIysfJFsDGvxpQJHSo6vbxgrjVq//bL4gq03BnQFtDuDx48aOHcFo9hoZK0VKMLdeIsokriAHTS2OJy4MAB93/87/+HCbyXrBM3zBJgL43OGEn44Y9+5I6YxsxK+tVrMoFXHBYClg3YHsAyetRyhuYYGoRRaLjRvNHb5kpFal+dJxw9NEg06T66ivnoV0TDDPGx3eHnv/iF9r+gRcAI6C3DPNhoqmARegTjod6MIY4LT9z4n0mETdEnrQf/y1++qqHMdevWiwjpLYUeHWHxr7Ngvoklztu2b3PDhg5zX/nqV0zgLfBvI5w/d8H94R/+Fwm8l19+uUcLPIoC7e5HP/qhGi/zEBQ3ve4g6BAC5ID6iOo2uvKwFA0PAcNKWTQ45myef+EFN2vmTNEY4dNrxsjBaybwHn74Yc0hB4EX1XlUb0pYAcivgTk1tIkJxoQQeHwfBB4+Tp46pQ3pSYGnsA38Da4QiIcN9DAj6JX9fGxijl6as48RhiwZzwReG+Apf/iHf6jhQEZEKH/oC4EHQv11HpGGz97Ot995W3TMXDQHRkNLWuBh9Ms9/KbjWi4MRovoEL7zzruy2kMHbe68uepsQ0fqWFUKSxrlQmf0n//5n93LX3jZffWrX41G20i2FRW8M9LwjrhnrAO5cvXK6Ns+iqoNaXI4KgsJUJ0xhdVVPQUOWv3G//yGet4vPP+CmzBpgn9TXTAf9dd//dfu2Wefc088+bh/Wl2wCuwHP/yBhs7Iyz1oeJ4QAdYj/uiP/8g9+cSTKtMeK/Aszap/6/C8v/l999xzz7m169b6l9VFa2Oz+8u//EstIHnW4pmCBZIA43mffPyx+8XPf+4ef+IJ91gF9dZY1+BOnjghhjou0JjvFYMLZ89L4DGkOWRYTfSwTMCoWYDB/DcCjeXoioNqNkfnDk2SlbssMBBtJOhD6KEk0RVAG2mxMv+Lv/hzzavSCZw02TojVQZCAO2OodP16x+wjtVz/k318b3vfl97LWkz9yzwIzxVBsPm/+k//SetRWCkKBamRjusQfjJj/uPwPPNt3LQ40F9lrZlPfEYNGDvwuKVgggN2vzKXx6vzHvAKIiLE8YDwsKQOIw835YLmDh5YZyb8OldVhNoJPQ3iAPtISfdVUh/t8Ez6EjDth5y9LQd8tYpv3EJOinoDPJOH4140n01o2aSAlNsaW6OnoFk+AqgA+cRNIWcvUmJ+Ig7uHZIPgrhph0XKw/aDXGEYf+iiJLVLxF1+O6orBhazOEzVQT1ojZp7Z8Riuhh5OAB4gfQciWwz1mIhyGLwGe6AqS1xdIMnTKiRFzwzzY6qjAfvQxVE3jxSeAMVxVrlQkGFsMTlD6FcTG0WCgYew6TSDIjxQ2T4pvggA83h0ATtx2BNAyCGQ0epMaWZkqEqdV0RcKTMM7jL6Q5lFtOuvsC8uRXoCgoO2gg5JmqtOsdGj4v+J12KYTgcmAPqbO8e4p4FOJLfpvvWaUIeU+GnXRcaCu+7uOyKQVleO1roH/R1tEooSBSNAjoeDNSVLbQsujgOfA6/Uh9LnoOfI133hEfLqfTpaT7ACqsz7izn4Y9SvJI8UwW9sBr+inycIXOIWLcXuiV0HgZW87pKfGJT42e5ak/QI9YxM7/hJ+4ESSJCvDIejSqZJ4lvgER47WHyXf2PT0uvosaliH53iDitt/2dVseCgBhqd5pqliYx2wrt5B573o1imSAooDJp6mP51rNO8DdbrHyT7k0LAR/50GdGNSwAw3yjNtkXCQvdEBSQcTw9BTXP0jcRwslUu+TsOfFRjRIf8yQCnvLkAPKnOJNNvIOkKd+We1MJzZocUkHT8pBiMKHg8DQSkl+p8IWPfvq1IiXX8NAfFphnXhPuBHtRIEUpKMk+CYPPYWpjiBYY2dxJ9sIvEYCOynwSoi2LyEUf8VI1X3HMM8UPBoTlcA4Mj0uKikSNp5zFQCVSKXFzMIgBgoVBqIKCbL7sJxfxGzPuUqtN0SM1x7iP3zDT4jDiDvW6njmCQtA3PxWj8mu0ZDnLe05yxGiJUINyGBfRsTfFlWvRdyIQzmQp3S+7J32MlJuODGcO1Zng9q5dgiFlgeiBd2YI34cVR4EXWj03OdBSHtytaWe8ZOL3QcnQLrQMIKZR55+4nTkQaDhQn5ynvto+jui8qYCoksO8j3zoD3S7hFCtFPCYQEKC+qSrqSh5VAt6fi4h8bsSr2yWh0aiOH9B5rhEgVVmEaS4DuENDxGAs14WOQI1DxALwkn+klE3xGt9RckiqN7wRwcm0d/+tOfun/45jfdq6+96k6dPiXNbzDWLArUC5UeOf2IHhpEROEntxI60c8L5y64d95+x+3bv08rlgZZ+BKW9j4Ip+D4FiISYeFu+x4fYQeXAosOjh47JksG//i//tF961vfkl089nIBNTAfdlHgLZGvLgXRdEdU6TjsN5ZsPtv1mfv2t7/j/vZv/sZ9+zvfdu++9567cPGi1b3VTUhb0qXQrvGKBswlHjfcrHOHPz/kPrH6YAUnDkstrUYHxVBSPZifWyboLpw/ry0RbIreuWOHtk5ge7E0Xtaxp26jh14Ctf98BJECbe7qlavuzdffdP/jf/wP93d/9/fue9/9no7eYTVk3DFOujJAOtCi4BfYwsXUHQtd/ulb/+S2bv1IhsAB79X+PeIONnngtsR4d+7Y6X70ox+5b/7DN93f/f3fub/6q79yP/vZz92Z06cVDqbI0D6DUzw58EKwH6P7BB4VSw/D/kEobLre9tE2nUKAYOCKuaAzZ876Dwogpps8BB/q0q4QIhPCBw8c1JJchNDnn38uQRv8KAT+JFxOYwrPA9L33l2/fsPt2b3b7TbH8S5sJA12HoHsO/p8hx4gUFw8k4ueRQQZElcF+HDCcE27IZu7BMpo69atbteunbIjSZ45SYEN/mRfZVI2om+gscBFZETA6p3l/2woVs/b6kMdnshL9FlwnQAdoyuXL8uiBdsmAv1Agx0hWf8xzXUA+e1uEGWhaEk6WUzQdE8CgobjdrCFyTYDtoGgdWGUW/VfIaAzwoF/IYw40gtaxhoUm9aPnzgufzmd3UBznQD2VWkf0Skal2QJ5rh1tFnIFws3LsFlaIduFnjRFQaBpfNz1iuePWuWe/yJx3WFKNn/pgbUAcQeRENtrUxMLgl7hRbBAYenrUfPPb26JBOCJ0IoacdeO82r+d+RZ3M+/UIiOuYTMI7MJvWnnnrSrbh3hY7ekMAjifBWnP2L0h6lO6Rez8Iv0kTY4WWlsLAw+8UZWydPnIzzr0ZIPIl8dCeizf0XYkHUyh63IUNkMDtKV2cSZt+oUqnD6PtGi4cDWxma1D5Qu7LpH1NkQkflbGEke8S656eiiN7hoOebdXXS6LTy1sqWuq6pKe3YoLjuqwGCqiA40s52iGtXr+m4Kgw9IDRY2p4/3ATt9jBQL7RDbG5CY1gDgv6HjxgeWWeqFEYHlBfbirBUQmca28FsyeKA6BCHpm7gI6FuOllc7D3F6suqVSvd6FGjdHTQ0mVL3cQJE70PQzKORDxiOZ7v5AB67kfoPoGXAD0v1H02XnLyANYM2GfU3NxkPf9rbcOIeaDm5Ssv2eNNNzr15K03N2fOXLfSiJD9VBBeTi/ZM68cB8TIuIQHeZAIZsyYsbI+vnr1Gjdq5Cj18iB+mZxKBqH7xIOQB/6HdPnX6fx0Cibb6mrrrNFf1hALhr1pnDA06qAaUXQW5HfcuLGyloMB6NqbtTKgzb60gvDlpGSr3BIZ4DbxLLyhONmzNWniRDdq9CjZx6QH3tTY2OZJ3yZcjGijfIfwNDLUmBt7Q2FEpPDCxQvaUBwjTxzKi1zbs7sNNGKsf7z37nvurTff1DD9m2+9pVND2OythRhhiE5p9p2CogXV/QhtaOasmRJE06ZPc0ePHo3z0mkQrM8vVlkYrcL27tmzZ2QbF6smzS3NsqAjhI6yuVDnnQFmx9iTieCmnuA5OOg6L3waIxK1Dl+gtX6MuyPwrNRlUcAaDZsgsTrChDG9YvaMcM0BlRQqSldIuX3NiZD8YxaSDBs2TBZflpjmlXuGWWko6pfX5jD4i0BFq8B48t49eyVoFy1a3OYPWLak6XmtUXnQ/+AhwN6nH3UCNLqdu3a6d955W/NKn3z8iQ6Y5IR4GqpQhXg6A06Kx7rEUxs36vToObPn6DDfI8aQoiGyAglTHUclli635K9AQTCdRUYD99xzj5s5c5Y6PdIqEXiFEAeUosMCGGI0jDHehYsWuXnz56nHTacCocf8bYwo0W0uBjnJeSB02OHiVWnJKwsa3bB/WBaRa2zSUKD2v5p2RBsiXWqj0f8eC+p66pSpskP7xBNPyPoO6f/80OfSXCW0O4GYL1jm6djW1t7UtAb38BzeM1LFiJU6ljmorMQ4fmvvvr3iIVg0ojOXwy+5DS4G0ykh3Z3Lc1/BXRF4tGOWBdOA2HBNw2LIAc0OO4xUJsQYOxicJ7JQXWp0CYYQN8IEEKbRmPeIqLEibboA9HrRTDATts96eFh/eOzRR3VsvvbflYKEN3KRjwGWC4Zlyfvw4SNklJqeIFoncxiDOUont7i6FdglhCFNmDBexsbH25Uhxwbrud7pQMMvCdCLv2VYi3g4uHX8uHGiCW3yxwfk0AFJFC2eiIsoDsAc5DiLg3ImBQgL+ekuVCkq2h+nN9BmNO9Ou1FWI8MC6rClC6cbs1kqSC8dO4YW6ZAwzDhyxEh1trVSG4HXiXTHfMe+pR3R4aVNTZ48xa1du9YtX75ctoWPHj2WO2dOsVlRhg5vZ4B9V0zeTZs2zU22PEHPpfEKz0M7kd++hK6RAEUAA4IAEW5U3meffaYeEcJp0qSJIojQsHT1DkR719hnxX6YRPJ5zc8ELYVvJDDtn+ztVVLheb7lEfMEnODw1ptvuWvXr7mpU6e5mqE11rAaFe/dwpAhg92yZct0EvT996+T7cE1a9a4GTNnRCth7yKY6Gd+leOkOB2dOV3oYRJ2KymyxBaQchDVdXRFU0SwcrIBKzPPYMbLmN1I6wRgv7QSKB7vEKAMlTKsdfr0GXfl8hXROMagxdyUoOi7LkeF8ZDu0aPHuOkzprsZ02e4OXNmu9mzZ7mJ1i4RHCrXEEfyikOZKeTuAtCu0OQ4XWPXrs802nHt+nVpRTpgt3MkFsHnnTnnadOmu1lWRoOtE8/owU3mOy3socYDdEpIJwVrPkTD8U06EgsD+tK4SxagVUpEL0b3CTzqHMK3K73HefPmy3YcY+k7Pt0hBsjwI/M5QqhDrsEBrtboEF70QAuCujVHjwrmhhaBdpPXAkc5aEczd4zZXdfwxc26mxLEFy9ccHt275HWF5hi+++6AVZWjPsz3Prkk0+4ZUuXyRCxjMcGhHLtZjCkyTD28eMn3PZt28SY5t9zj+ZzO82JKOtwpcwtGI73Ibyz585pERFxqrM1zAu8CpgxcREPgqDGhDXzKtjfZChz/Ljx6vHTOevs0FkSZKc7aKhmWI2bO2eOOkYcRvrAAw/IzZ8/X6MFGiXpprRUCvbAMb8OPv30Ewm+ESOGqxPIfKv2yHWG1MI3vgymTZuqsx85uYHO+6FDnxuNTXVLFi9RHIwAwKvE/yqALKoYIaDdcRrN8GF+RXMRhOYkGmqHzhRA70W3CjxKnAs9EnryHJe/auUqMTqu/JZVeLyGzZQpQDgMfbI5Pd6gbv40kc43Xqgmv2WIkROLFy9arF5XtcFp2GhPL774oluzdo0W4CBYhgyJesTSNPPSVdvzPFnNRWfp0gJGwDGPOTVpaPkuY9LESTqfa/XqVTrI9b77VmpOb8rUqVFWCzGHnNabv1BUlr51j7JOzhLrSNGrx+A0PWMMPqvjI1rBX+S3HESdmIgB1QwfpuN96FRxxMvkSZN1P05M1dLYWQGehM9Pd0Dz35YnNFQ6TAyHR6Ml5CW6lO3uAhjuQ0PduPFpd89860zNnaezJTkBQdtfKoXPGwvVWDyCcWZWTjJfzBmaCxYujEkrIhcjOGiuEG0XAXyTs+zoiHC6RtxxTZJGXjKJ5vDiasjrp3+g+wSeRyh0OoozZ85wjz76qHv55V/RlVVUgirE/nBNVY6GM41Zhc2V0UPLSFDtyRGPueLse1Y2wVjjw2I7g2RaEmkiKnpcTz35pPvVr37VfeHlL7iXXnxJh5POnx8dTpqDRDhRUu2f/fY5icG8IMMjbKeA2YTh2fh7H0ap0GhuGFpJu+5CIq6wgpYFKy+99KKuM+fMcgMZai2khYci8Mw/XWYMV7IyknlAhjKb6hs0N4xmMt3qaM7s2W6MCSFWB+On7uZNd7O21tXV3nT1dh+5OtdQZ+/q6qSxsd2gqb7RNTc06Yy8xpv27NoNd4Pv7J382TcsxBo7bpybboIOYTfcNEj2gRJWPf6IK+FY5s8zrsz3kJ6GuoYoPB8mexUbLIyow4SzTKYznUax98WQjy6MdkSDoW11xnU3LP20Lep+xYp73fPPP6+TAtBcOWg3xwJKEnF67U9HaQ/v8GZlQ+dmtWl5L7zwgnvhued1mOuIkZFQhQdghDpnCqYzsLgYOkWbHDdhXBR/sp7Cfar+8IbEi0e39CAC9HajlgU3LW08pg+jwhooDyrOUKZUiBqRf5B8boBY8yFq9/aPb5MVxG3+TwQIXCbGOvDTOfg8KGEmhC2eOE9JJIkwQYxUAA1hUPjGmAt70jgH7cMtW1xzY6OWuudNdyKcgvDfwayidLY9uysgzRY/5cQ8ImkKLi6jkL6Q1uASQOglh7SbG5u0wvOz3bsl5DgxgeXibMlgov/cuXP6jUWUs2fOqnyZ1+M5Q8/Mv8Xu9GnN93EIKO/we86ubCw/fuKE5h4ZuiRswmQeGn+Eff78ucgRruKxdxZm5LgPzn6ftvj1Hf4tjXbPc745cviItgewH65mSE1Uf0l67wglesuLfOUNkVYSZnchmW6ulm7oDE0IrTW4YnkxX2rO/k7POgR1Y/Fo3pmyKiGOziLkpyBCG0o40Q3/7ap1DMAudLZ27/5MFq8Yqq6K1tvDQfV0H0ptsCAwZxAqz5CsvJIYQAleOg3C7kz4/ht9bnnAur+0Fi/sWMDz3rvvuoMHDmjZ/vJly6IPqomQ9s6kvzPw9VcRFEY0RJwUeLeaWnRiM2cxssmcJejsu6Inr6FM7zjMNcfxLI9j6JPVlpEbq3DG2TNWerIFgYUdrMBlxIChJfb4TeRbrhMnRW7SJA3b4zhANrpOTjj8TdTKUcIfY2ErPtMUWNoOWPlHPJqHBB3VVTXKt5ehS7JsgUJbao/hWgi8SjrIMe303P4E58PtFPgs7UCSLtLt2vwwkUSnOhxiDegU7tq5S4vtGJJ98KEH20bY+jC6V+BVA1aBQdiV1PvqwYBxYvEj5IWjcdAm3nnnHW1eZfhFh1xOmxJ9EAi8NyLZQEtBvqrVswGyyEIDJjiEHabDNm3eLPNeGzZscM89/7ybPW+OGztxvBs7wYSIXcdPMkGWcBMmmcCbPKm943lCgCEAKf8x48dqc7lWGJuQY2/f7LlzNB8ogWrfjZ84QXGlHengENmk49nEqdFcHwscWEhDuhB+mD5jDm3mrFma3x5azgGzvbtJlIwgkHJQDn2VgLKDo+zz0q09TDslPxKowQnFIuV90oFwtWDbOePwsaEAftJu7H7XZ5/pRHfeMZ3E/HnBDex9CL1O4MV1K8LxP3orLA9kIeQJU2sYUEbYsZ8HYTd63JjoZfDUz6HyMuYwxIQPNICRZiyBsDIWs0ucdC6NyMqL0xdUbmU6Hf3D4ijrgGgfVettd/3KNW09oM4mmLbHak/12Bm+wgH7TZzhOKM79l1wOqUh6bwGQByhA4fwvsK8bWOjaXxj3PTp061DZGEHbaEQLDi5foJYOKRRSjnwPpRnHpcUQMEJyXALxQMZ4OCqxZwnmaJIpjdfnKBIWMwhQmesjic/H2//WPZFoeGnNj7lVq1epQ5WfwBF38sQCNH/DCiFMHoSLI0MX7KHiwUWHLH/xuuvu2NHj7r169a5jU8/HQ9tKT+9IU9dDRMUWLOQsQITCnv37HGvvvaaFncg7O63cmMYUDDhM8A0pbJhLWKAP00DhoAGjjk25gFhHCx+0VyHCSgNuyeB4DL/fCPNnflc72LBGJz9Z6M9+cGxOObylStavML2Ge1HND8lA7/l+M9QfYR2GlzgR/lcN7Zn5vygZ/jMRx9tcz/72c9cTc0Q9+wzz2pxjfhMSHMfR68TeOpxqWISvS+PSBD657wq5HoIMGJMcg6blvKWaSns5Vu1erXbuHGjmzB5ohto72NkzEwChoZrFex27tzpXrMOAgLi4Q0boiGZMaOtnKygqH+uFZQZGhfzHGiOrJZl1SX7GWtGGHMIh3kWCz9Jb+E+OPtWQtHyw+pShDbX0aNHaxi13fl/heIK4fd3WDmU0vaTPKKQi2xOtjkQrjnhdQTqCxop5CqgzXYolB57xlYt7QG09H/y6afuFz//uRtt7eS5Z59zi5csiTbgh45bsTz1AVD0vQe+QowMrQKje4GK9cQZO4aXEk7fhm+S394tkCQjxBbrdbEpGsa9atUqaSqY2RKonZ6Q1p4C6tLqtrGpUecPstjnwYcecmvWrnUjWMlKPYNKhJ3vfcMktIXANC9pXJMnRcKO4UlcOb10/JG2pOO/aXjUO8KOLQxop2yIHsycXUi/+dN9sfx0Nr99EZRZR65kGL35u7K+6866SOQpbD1K8jxWpXJkWV19nTqJzBE/88wzOmWBTfgxSHPvkgadQi8TeBHDiyrZ3+s2utdjPeBPAXT0rltxx131lvtZncemVbQUFktoDXpvQ3eUq/VEMePGcTVYt2Cy/b4VK6QVCdBDhZqdvrVgsMHI2XaYjmLFJUYEJOjkJxEB+Y6d/Uk6E5rBiT65eod/log3t7RoaHPkyFFa/DKEIVl7l4NC+Un76wfQ3H2BAoEDRHyg8L+qQfXrHQYwWBjSGp3YH+ZwcRwOnM+pw1QFxDnzNBfRWeTotHHg9YUL5zX/vIp9gi++4JYuWSqDAtgSbW/cum9jgBVQVaiAA0a3bPnQ/fCHP9CEO8upGTtuhQBuWQVbLBBrOKcMpqHJ+uhXUSiR5p/zzbZv36a9SVg0QBsa6A3dYslAYZrT0FdBdBwry3cRRNu2bXeLFi10c+bMiRYp8J2K6442i+dFKk9RA20PPr908aJOX8bSyG9+7WsyeTSIeEotFFCO366CZYZ5NUw3ff/733ejR42WKSos4lAaNMiAjmq8WFYQDKdOn3Y7Pv1Ulm2efe45N8vojLrWeXdGA4ShfX0+tLj889VDHtLHP02i1rQ7tocgTNl6wJwHYRKP4iC8EGaecEC7pwl/hHOrtVUm1Vh1ynmQbIewzFi45iEkl0/CfRohuELv+xosv8xDff3rX9cxP1gdof2zGANaU/1bnXREY8UQ1avTHkxOr+cMukWLFrmhNUMt+shuKvXYLhbFm6iu8MMjpkOD/BiNwhsxRYb2BS/DgMXgQYNFf2o7yby0fV4ciXhZrHLy1Em3edNm97/9u//NfeUrX3Wjx/jVmPTFLC4tjOonqJrAY6z46LGjbvPmzZrz0GZZq7BWK3BZv7dYIsPPVriqSE8E/r4YIiJhKfodWcCgB8NKPTEiC0O9GrJCkNGfToNgOBIFI61YBIksJbSZ8YmDThWdfikNbZErj3lAWhnOpJFi6gh7hfTw2y2EKIYK8lk1qLxuy1bl5s2brP6vtS0eKQNFs2JlhkZEudE5YA4C02ERg4i0KBCHo5voV6nFRH2FCX56xaz45BQEaEBCCnq2qzpuBeq2FCRXguokh7DyM9R/KUEH8ut8MnoVbhuPodw2bdrk9uzdo8U+lBl1FnV4Ix5TjfIgbFbLIoDgMxiGBvAZBe9pTTQQ7j34lUxCxJfanmAhilBIM/PEaPoYTsfplAr7rZMcCMkH7S9lQUVhf+CX0Nnzzz3v7lt5X/s5u2Ri+ziqJvAAAgLLEBAKgRK07MfZD8pULlS8XXVnf0oReUqk/wChpyNLuMtJfvFwisLCCz1FGF+kpbYqnnBCQ4hFcZNH/5v7NOL8pgCDVgfAwsP8EfY4B5nwLhtVyHJVYFmn8XKwL3Ns5SKfebU0gtZO54OVZayW5D631O2XfxBoI/d9x6C+mBsETSb0tFKT3xYWTIN64z6pRZaLqF1AwxaCORjqIOtYCQRJgksJOmSsp9BAV8PyK0HU0ChzWGhboY5BKM/O1ksALZpgxbuSYYV6s9t2MSjePLDnCsyD9PFLT/wH+lL/o2tAoBPdSwCWCR+mBJx9zr7SUaNH+pf9E1UVeBnKAKUeiLuSGkg0kLuCdNq7Mz0h7u6KE/5Hc4GJVSPOQAPJsLo7T70Mt1uto8gq2buJvlhH/YTuqivwQgMO10LobIwhTL5PhBHdRr0y8aJKGVIyfYXCSfrpCIW+h3km222p4aVRST6rgZBu0mH3kJPKv7MoVA4E6eMQ6PGGCqcH21GUhcIshhAm38v5+EBH8RVD+DYoELh8aawkjr6MUFbJ8kmWX1eVW746SqPcuPPlBfC8G+sfZVbR3eW+RFej+zS8dCzdE+vdBRRUCtFWUhbd2Ci6FOWUAX4ZWgRRD6dwOfR0OiPdIY3pPBTKU4YM1USyjfRxmrt7Ag+UG3NPqoxCTCqNYu8rLf2+QqCllgP+qqVp3W2QF9If8p7OS1+p2/6IYvTc0+q2EA32MdxdgVcOekJF5MtDuYSNf57hqlHyfYlAi5UH74Owq8ZwcE9BSH+6LvtS3WboPhSip47QmW96Ie6OwKNQ88V6twubNHWUhkIlFZ4HZhyKNKmFpP10FE9vQzXyEsonDZ6H8IOf9DxDoW97O/oSjWTI0APQszW8rmrwSSbqp4IQRKEoZOyX98Ff8ioX+YsXS/ifcZhYwwds6OQ+CLiw0IJl7snvAsL3vRGdTbuVAcv9+Rzbku3CoX4oM8oyucgjib5UjhkyZOgy3L01OTCptOsuEFfrbddQW+fqb97UqQVY8WD/4I0bN1xTg99H5hkn5oIiwcUPzPFEhyjGjDWkPeTDBBwbZJsbmmQrUeakLAxMVUmoZgw5ghVFQ32Du3H9hso+BmUYTB4ZhVLeLVaWwSSXnAQhHgzJ8szKNkOGDAXQs1ZppplVFzKv2qvX3fkLF2QYeOqUKdpszu9aE3icRI3FepgwG12xsjES48RodCkt7Y4xZjbAYiYKTQVrHFhM4PiaCxcv6jRsbGVyvAy2M6dMmewmTPRHv6TLoAvz2+XoRNqx43f27DnX2NBgZT5Bm+8xdIuVE1nRsHKvqalROdbX1blZs2bJ8kk7DRyEsuzNZZghQ4Yuxd0XeCH65F6qLmZerY3NsmGHkIvscU4wLeO6O3nqlBgtZ55hq6+2tlam0dDSHn3sMR0PEw+toRtbOm8Zgz527Jg7ceKEjnfBP3YRFyxYoON+sP25eNEiCcI9u3frnDPZ5hxWppHgcssC/3zXmW9Bud+VG4eV4fkL52WvctSo0W7e3DnW2ah1B/bvlykkgIWTBx980NWZsKMsOR18qtXNoJrEsUlJdDavGTJk6Beo+pDmLSyEt96Sbc0YCIgkIxIjTrwH6ffhigthhd+EF8A7HM/CNSD4B+He3AXT5OpNq0C7wzFkhrHo69evuZGmQSCcOH0czQzhhXC8YL/R4KIw7A9XS3ODaXIYmgXTpk+X/cVdu3Zp6JKjXk4ZQz93/pxsS3I0x5kzZ6KTsxmyo/S9azWt5szJU+7Y4SPudktkbDsuk0R8ugYk73mHMw1UQ7AgfA/QTHEg+A0OhLDCNTy3a6sJ9fg3SIZjtxpqDOXOq+BSQKOTtSYDnQNOPeA6fPgw0+aGSLDh6IBgNmzHjh3SjKd4DZzTzRkWLohkGjNkyJAhheoIvARzG6TTojntOcF9Qiw8wvGb9/rNlZsEeB/C5BV++R0YbQgP5olmiAP4C9/yDqGCA3jBWRhoFgybYcMSW4nYgEQIIVcYyhxvgu7+tWt1igFpY4iSq+buSAPp9UnBpiNa4ooVK2QAGDuMPOObUQyDGi4ZY+cbhkWZI7zmD/uUgPbhYKORs9dIBwJX+Qj5LVSW3Id84VXh3dGJ3QJZD5+qvM35IGP44onDSoaJMwwe6m18UpZ8Tzj+Hf5l15TvPDRogD8fF4JOnSD7HU4+qjMtjk4GtjA5kQCtjaFljNvOnj1b2p0OyLWPBto7hjaDQLzN8SqJssuQIUOGUlDdIU0LiUMzT5w4ab33SzK8DKOaOmWq5mOOHz8uRs895zFxvMdkY3IxQxXzvSPjsGhCHJ/Dyr0ZpjkhmE6YtsT38Fo0ADQq5siI87T5r7MrQmWOMUws0DMUefjIEc0RMc82zgQS80BoDnDfe01ITZwyyV27fNXt3bNH/POB9evdkOFD3R3TVBlG+/iTTzR/9Pzzz5vfyW2MPpSa/b7TettdMeZ98PPP3bGjR909Cxa49evWSah+9NFHSvPDjzyi9G3fvt1Nt3Qv4bRhBKKEwx0x8mNWPvjhINihJjSpmgHYDbTww7ApAoKyGDd+nGmQ4yQ0KGuMUA+1siaPaKXDrHzQvCT8TDigGZGfS/g1gTVl6lTNmSFg0VAZig1hT540SWm8fOWK8oP/kaNGmqY1VQIbrQttF79orQw1jjBNmY4E9cawMN+g4c6bO090gHbGKRoMX86dO0eLVXbu2qkh3nvvXREdRmlFwdznScsnWjUnIzy9caObNG2KO3Tgcw13crIEdTjU8pcUshkyZMhQDIN+3+DvKwYMmtObP9v1mY7waGhsMKF1SUxylDHMTe9t0lBhizFEnsPk0YJGjYTxR2GgRZ07d06n87KA5PqN6yZMroghf/Lxx+7gwYOa44HpIgwZ6vrcmOnn9vyiMfOLFy6I2aKpwcg3vfee5uLOnj0rZsqClHPnzpqcGiCmPnzkCK3KZJgTLQvtAgbNe4QYwgSr+cy76aDRIPC4kma7Yun/psVB3MQ1xvxxtpUlREIbocT5gAMHDVS+AfOBnJbANwiy4EgzwuTKlcsS2COGDZemgzB948033TUTGliKv3GjVvk8bHnnTD2EOotsEGgILg6SRTNSJ8K0u+bGJvljYQ5lgTDjaCU6Czs+3aH5Re5JE9opHQeE1969eyW4Dx0+5AYPHqL6/XDLFi0kYQiYo4DwT13v3bPXhNVJzctxVBCHmiLQDh85rDjQpDmbq7WlVXV38eIFlemMGdN1LAongFOfaMCEDz2p8zJmtKu/WSfaoaNDHXKGXCj/DBkyZCgF1ekje2HFMRTDjUGPGj1KKxsXL16seTCYNydGw4gZ9gu9dJguwk1DhcZoAUwbTeS6aR4LFy50ixYtliBD80FbRJjwnOFIBCLMFaZ83IQFC09g0uH+jv1jaAwmihAJq/80l6TY2oAfNCLeXzp/UQIArYnhyuOmXSFYGv1iing4zfzfuHLNfW7CCEEAcyfOw4cPS3NCeCCsiDvSoAZLSBEXw5YEIce9OZ6jico1t+g3/hmyDcOlpAeNmfJgOwWMn3nIefPmuYWLFimeQxY/eY1hkVAOIW4ECh0EjvIZZ1oiQoeOB/W1Zu1aCXdO+ibdpBMhvn/ffnfe6orz54LWPXvWbJXZdasHOgxoeCOsA4HmybAumiYHWTKnSRmh4Z0+fVraKsLSgo7yZyDP1N0R0yj5lg7DWerStF7SD+2ITgDf8Fn0aYYMGTKUhOoNCnnuDYNHMDEExpAlDLCuLjqAEE2Aww+51piDYXPaLwy1yXr/cEC0LDRALUu3cBAa2ieHQDAHY55gzB+mi0bCvFdg7gwDIgiJPxJgdyQA+M07mCpaJcKFYdXAbAH3CAXiOmca0L59+6Rh8S3zTTcsjaS17sZNhUNeyfI1Y9wIRDQzmDIPSZMEK3mxtCGUwhlrPKMzQMykgbwgYOabIJtpAmL6jBk6LZxh0RmmFXIALSBsyg3tZszYsZEQpYwsDMJneBEhxTM0Wd7X11rZmnC73XpLApkOBpogcbPYhvLDP+Ey/IjWyenehM8wLJ0O/BMWdUIY1AFxTZwUpYPyrbdyQetG6CHM+IY8odVSftRPS2uLynjEiJEazkb4D/blxWGXlBMCD8FIuTM8S7lz8jiLeEgD5U0eoQkhE3gZMmQoA9UReJ7xwMwbGuqN+V+SEEPTOnf2nDQ8mCFL9E+fOu127dypnvtY0/5GDB+h7QD0/GGew00wsXiBXj5aFnM53MPoGM5DsDDXxRUGTtRoGyw0Qdtg7m7y5En6hqFIvrli/qKThSOtCo0GganNzq23NYTJ/i4EHoKA+S++v3T5sob/GFZDW2HZ/H4ThAxTsu8OBj5s6DD5ZdUnQ24Is7lz5yodCA2ECnNl+EFQImwRyhJ6CDGLa7QJjnHm8Ec+OKhx7ATTkkYOdwMsbQgsNGGELvOYaD4Mk6L5IhAYekVAHzhwQPWAFsjw6RkrU+YwETg4NGLqBSBAETB8R9kTFvXAEK40XasLOgeknzJBi6S89+zZrWHYM2fOKnzK4vy589JoGbpGu2VVKkSB4GcIcrFp6Wi/zS3NWq06cQJ7E8dqJAANEAFNeogDYcZwNeFR5jOtAzDQhD71h3DHD52qDBkyZCgX1ZnDQ4qY5GFIs7W51RjdTWPwzLVMUA+fhQ1Tp04xYXdKQgIthaXnC02LmWiMEK0Khoe/IUNr3HATIjA1mDSaIMN1LLJAWCCs8DfSNEcYJ1oEi1dG2zMWTiAs58yZqyFAtAaEDNoLc1oMo+IfgYYWISZrGmNNzdBIIJgwgKmyCEMC0NKKlokAQ+tCG2JuCQ1n7NjohPKawUOkIaFlIcSYO2QPHnlHQ0HjYXgO7S0IbPaS8X5gWE1pIGw0I/JN+SCwQrmiqV41YYOWSPjkE4GKEGFBDUJPGprlnfwtWrxI5c+8IvnnGzoMCHzKj99TTJujw4EgRijxPYKWd5QJ2hffkn8tTKFTYe/QvPiOxUKkg2/JO6ecU7ezZs5SOd+61Sohxf3o0WNURsOtc4MGyaKVSZMmS2ujU6D4TQCSZjo8hEenIJQ7KziPHzXN0fyychahF/ey/CVDhgwZiqE6qzRDCDAfu0czgIGzyAHGB8OmF//KK69oTu+BBx5w04zxwTARKgyXMZw5xAQIWg9oaWqWUDIuKq0A4STLJ8YkYawgCCQEAcwZh5aGsIJJYo5KWpz54xP2esFMWbGIJgIQRAhHBAoLLRBay++9V2HDYBEUfEMxMWyHRkOcpH8Qq0stv2hgCFcECmmpMabNAgy0MRbZLFu6VAtkWHiCZrZo4UIxfscKTMrO3C1LOwIS7WqspSfWvX3ZNtbVa9Ui+SNtlAHCgsVBp0zTWrNmjbt3+fJ4uJgMt9h7ygShxnf4p14QNHzPM/KlJNgVIcfQ7yCGUa28musbVQaEQTkoKeYvfItGT1jnrUwOHDxg5dqsoVn88pt5yJe/8LIEFO8QitQBnSAEOsIfLXOMCUQWCw217wgPR/jUOatMa6/dcLtN06du7rvvPjds1AgrdEsLZBCRQoYMGTIURfUFXgGcPHbCff973xPze/Kpp6Keu5+f6lawl8wYNhvJGYJDe0GjQpAylIawRpghBE2yRA5ge9M0FpgxjBihGr/jIqnh780hLJiPQiiwyIbvELJoohL2pkXlDCiHMgxhBPC7wMAz2ylef/11bW5ft369tlQMZXl/N4PFLwg4hkYRZgjEupt1boxpwY8/9rgbPjKRpkT+Gq2Mwp7IKabxjR0/LnoRgL9bd/zm/fPSXpnjVHmwcIjyT5ZVhgwZMnSA6gxpwnSKMJ662pti/mh4zMswtMYqyG6HMWOW6Y8cPkKaG9oaTBrtAc2EewQamo42VAcGbcxVC0NqhuqqcAJC/vEL7F5an2lGkyZPdqON8bO3kPjY44ZmSxoKIvkqhJ0HbKdAWDD8h8bI8HBY5NKd0LCulRdAo6b82LPH0O74CeM11N1OkNs9aR08aIj5H+g10xr/0uD9YquUMKkXFtXEezYVnnlKhpkhQ4YMHaD7bGkCi+lWS2u06ABZ130xR4A5EicuyNqQhvAODRBBzHvT6hxzaYGpJtOrYktxW3vGkCTDslwZOpXGZd6aTJthyHAQwjLEnUQy7DTyxQ+8thoDIdpROF0IyEjudrSalmFXhiXR9hg05RrygR+Kb2BS6IchyiT8b4amtcgnaUEG8D79TUAoh2LvO4NCYSZRLHzCKOSnlPAz9DxUQlP5kI8OqhVHP6Wx7hF4xBAKOD0UlY690tQQLo5wCoWFwIFpJrOeb3gsCLyAZJrDp4Qh7u39maDTffDLNTDoEC/P0nElkqJ3+X7LJV4gRELyQhzpcLsLId6QPLtyG0yJJSGBZ45vtDgnIJG1GIQLzZBX8p688g6XBuGEsML7tL+kn3KRL840ioUdwkj7yxd2KfFluLuohJ7ygTpP13sIvxrxJMPuR/TVPQIvMGMJBivdUMDJmNOpKDdZhJnQIhQeTDUf8oXNd+nHgbkiwMI3PNM1uigO3oXngDxWguTnyTQl7wH+KozqbiDS8KLM5Ai8UsGnHeWb98mySpZT+l1PRy+t436HrqCpdL13Fe32Ixrr3iFNECqNaM2FyFXeEli+5MupgEJ+q52zjuJRfqJrZ6NV8OS/HIFZhteeAml3FJLlNZ8GWFWoTnCRkI2GVnH2rLNlF8KsJAxAGMlrGl1dNhl6NgJdJGksD62IhReioQJITjEIldBxL0L3CbzkkBuOWNMxh3eVgnDvRgWG/HSmREM1QIi6mstXPmncjXyWC6t7yAwnja4r0xzKLBkHz7xWqYbe0YKhUpGtEs3Q1TASE81WOmKUIUbXCLw8TKe5gXPM7miVY1UYTiF45talcYAgwLtDQ+kIPbEtJNNs95AYK3Qjs2TRHr8uA/OugD2OXYlM4GXoahiJ0W46NeyfIS+qL/BCaIERGP9pbW3RkTz79uzVdgRtSbCeNpVJ9MGVBfs+bMBmewMrAlkViYFiNktjWQULHtUA2wkAm8tJpayy3Lih1abY9mQ7Q6Ug3Jh32s3AAQNlyos4H3/8cTdr9uyIwabR0xhuqv458BVLOjt27nD79++XKTZMiumd+ZWNUfzSSTGUk52w+R3HVgj2UlIv1A9WYTBYkKYx0Yu5koBff0vYwV4pJt5u3LiuTfTsK423qZRBw/Lp0wSSaSIvmDfHNBvGDh5++GE3b978rh/+7c2wYjx//oLbaXwG03bwGLbrhPIN13JA7aNdUTeRIY3BRq+DZFMXu7KDBw12o8eMjjrxMeF3DqI0+49dWeiCOK9eu+o4Ros4IutCuXSSFx0kg/YC2BNMvshLU3OTLExxyPLKlSvdpMmT5KevosuHNJtMs9v12S73xmuvuWtXr8nqhxiEIYcZ6UnpoNr5nkrkW+xlYmMyWPKH4MWEKgQhIPDYUgADIg5OG2CzOma4YKrEWQ2IIK0sCJ94sGPJ/rPf+73fc089+3TkKV1QlWexS0GD5bio115/3RrWDdU9jANQ7+SZRsy8XrlZwT9hIOCwYIPZN+oJo9vYJRVtBPrC8Y3FJacQSoOEqn1P5wObpnSq2BOIcQJZpvGMpFyoKi1cQFpxOgjY6p1jlDCCgIGDf//v/717auNT8pchP86dPa9jqz7aFp0/meQz1H1nAaWgYREG4Z4+fUa2a9n3yh7TmiE16nDDaiK64sZ/XAb4lk4uwghBB51B13NmzzEaGC4+AKKstOWH75LoKH7e3bYeKHkhHtoMdnCxgPTQgw+53/yt33TLli/zvvsmuk7gWahsGN61c5d7zYTdaNOE1qxeLSFBD5ZIFXWF0cOMzpw9q4NVOb5m+fLlbu3990vg8a4aQJOE4XFI7HvvvSdbnBs2bJCNTxpWZxleGhAh59mhDX+2e7cYKgaav/a1r7kvfOlLlhDzlC6uTjSuboGlE8s0O3fsVP1zQsJDDz2kPEmDkUbXJvDw36msWL1g4WWLMTsMfXOo7zqrfw7CVb0ZDSBE7qihWxz2LGJMpYFUUi/Q6m6rk82bNkmrR+vC1icCjzx0Gp7+g7DDMHeof4QdwvW3/sVvuZd+5SX5y5ALOkoYMkfYYdkIO7XLjWkzkkAlJwVCWjiUCtGo/du5a6d79913pW099thjbu6cuRKq6krpf7iWD06RIX103Ldv2+727tvrFi1aZPE8bryM49ZMeButILDiuARPz4msdZRPzqKElx05fNh9uHWrjumqr6t38+bPc1/96lfd6jWrvc++iS6bw6N3umf3Hh1aikFmTq7mHDyGGeMK6aBiSgHno2GLEWLnVID5JoBgqpifGjLMekTVyJmFQTwIu3feeUdDTOsfeEDqP8xb2l1l2YhgDZee48cff2wNa5fO/YPgf/KTn7gnn3zSffHLX+49As/aJAbEd3+227399tuyqPLEE0+qM0IHAS0sNNx4EUtn6srKjE7OB1b/Bw8ccLPnzFFHBEs+NcON4SFU0zTGzzLL7FZTiztw8KB77dVXZT3nwQcfdCvuu08nXEjYVUTHZBwN97aGsDnkmMOPx5n2MHfuPPfzX/xC9f/lL1uHp5Jo+iKs6M6etfr/4AOd78iw/yOPPCK7rHpJ9Vv9BGHS2fJDSBw8+Ln7+c9/ZvXf6h42GsPergzIGy13JGDKAQddb7OO+86dO2SRijazYME9kXbnowhbeiJlwR6QLdMMQxLaeGt0SYOzQI8dPaZ2iSa5YMFCdRjpnD733LNu1epV3mffRAVd0wKwSuA0bhotho0HDx7kNm58yi1evEgmtQawmIAFJTgqpUDFtAP+SC3Of4Pav337Np0UPs962xilxkgz5sKEcsIPCN8EZ4R1YP9+9ezQWIkDA8Y6UTy2/NFGfGU54K8cy4OWirDjjDwa7rz585UXjtDpNaD+a2/o0NcPtnwgQ9bPPP2MldkKN2r0SBN+UQeBhhkJC/9dKJNiLpSzAe0XYUdvFSb3oNUNRwrVjIjMnCFEggalbxK0UxL4vPW2NAcYBMIOgYqw4xgnji2yTER+xVBLdMzF4vTbvrc0otkj7DiVnlMrHn3kUZlm4/SIOA8ZrCwi19pySwdFf/jhFnf8+DE3d95cdUSof2hMZuvMYbZOqxztf2eAgNi3d5978803fP0/5FasWOEmWIcE7Y5hSLQ/hA70TAeuLGff0BYYXvzkk0/cPtPsOAUFzU7CDnN7pB1avMWwf9RuELQca8YVi0Waawz5TOVV24B04yJhZx13hv+XLVvu1q27XzwTPk3YfR2+tVYHFOzN2ptu7949pnV9qF48xoPRhkaNYk7NewwIv0MlFXNW6YIRIXNcWz/80B0+fERGhRFEHDo6ZNjQtlz5xlEWgn+7YgD64MED7vU33tD83f333289oNU6GideBWhMFQ1QzJX0lRoffsmT4eqliNgZymK45EHTUpcsWRItujFmR6PrFUzPkolB7j179rqtWz9Suh9/7DF1EJiHCOA5Q41twzJlgHKwhn/h3HmLY6s7dOhzHeeE1j1rNszOLyAiaASK7u0Hv0tx1AsrME3QccI9muObb76po5Yw0L1y1SoNy8vMGcFDBklGk3Z6n3LJd9ZGEHaffvqp271nt4biHzLGjcHxYaaliqEFoZpBYDic4WuGsTmNZNrUaW691Q3HTg2u8bZkrQo196pOT/SoZHg6YP75wP4D7q233nI3rt9QZwdaRvtC2AQBE2lc0dxYZ+K6bO3/448/0dmfUf0/JAUhaVs2bi+BdpKuI1g+EKjwZo4qIy+a+lm2zK0yWmYusqbGz3WWnfjeh6q2JCzkM/a8bds2EcOjjz7q1qxdqzPuCm4TSFdeIQf8lVVYxMFBsLNM2NGzYwx6IMQe/CaRDKcUZ0TS3NCo44LefuttMTuYEEx1/MQJuUve1auD+O0Z35YKMTsTdhcvS9Dt3bdP58zRqBYtWugGD6vR0B9MW2H3ZIFnSaNBodnt3bvXGu92FcVD1htmiCRmQh4DrfxihtEJcEQTwo4GzDwKZcaht0OHmVANYVIdydEEyptXxRz+LAwWDBw9elSaPcPzDJXT4eGU93a0nA4j6YqAYSyEHZodnUI6bosXL9aQPIJOTLTEsPoFrBwY2aH9c4g05y0+YG2T01dyjI8L1nakRfufpcL8M5pz6NAh986772hVJiMua9fer3MjB4WRHQM0DD2jrcUaVhngoGNWMHMQM2dCUv8Lrf1zVFoSIY6y82KfICwZBWOUgimge1fcq3imTZsqLTXW7Pq+vKveHB5nn+3bt9+99tqr6rFueGiDtKExY0ZHjdYYIlpQiC4ixKiow30xoEmhacHsOA2dpbTq2c2bp5V/VCwLSIgBv/wmbAkke1YK8cO4YXac0P7epk1ieghUhssgdiZ8JXwIq4KiIy2sxoPRsVyfY4pg3Gh2g/2Q7MXz590f/8mfuIcf3uC++MUvRYw2HWWR/HQLLE2cd7f/wH73hmnDTLxTLyvvW6lGzFCQ6sHqhiv/lA0rvyg7d4wQddNxduxbloPD7OgkcNgvggjNnvqHtogjjsccwYrm4riKwPyxBB2mirCjU8WcjRZCjRqlzofo2LwyNzRwYERb0EWgb97xjPhJs/LEfQL8Yj6YXj2LYTg2ik7VUut5c9I9gXBg8p//xV9ogcwXv/TF6MN+DkaQ3nj9Dbdt+zZpJ+vWrZOw47QNaXMG1YFcoh5AbhXkh30DvZ45e8a99+577uTJkyZQHxA/Y5QiTWNxoKr3KE4FUgTQa11dvdu1c6fyMrRmqOJhTo1DlgnfE445wovi8bG1od0DHkUPSQ+jKdeuXxMtI/TotD34wINu2vRpEqC3Wm+7n/70J+rcP/XUU9milYJIfYXKj7r8gx/8QGo5BcdqNoJH+LDnQwwIwvCEGTMEnJ50DAgaZgrDQyixCELMjmEsCzeER/hamUc89igwpYhAiwOCh9C3fPihtjgwlMUydyDGRlx2Hy1H9mH656UCv+znQiOinF5++WW33hovml0o2/Nnzrr/35/9mXqXL3/hC+qtpcu9pILraliaGMqkUf34xz/WsOxa0+xZKRctgzZZHRqw3dMQ2+XDUDQrFtCly5fd++9vNqF0y923YoVWStJLjTs3vk5C8KoTc6I7/6wY2DfI/BD1P33aNNNUN2gREXRB5ykItnhBhP3jd6AN4iMzMVXnoTvSyJ5BVmOOsvJ66aWXxLzD6RqMMpw4ecL9xV983T366COZwANWrKdPnXbf+J/fMO3+uHUOF0vDYwVjWzOk/O1Gv30NtC/+guDz5uYmCYcPPtjiZs6coQ4vbVTv6bjbv2SnSs99vHrLfRHwPfvfmLdn/o6OLm7IkBrHodkxLds/wuOfkA66SN5oFwyZvvveeybQnnS/9qu/5iZPmSyBy7ctza3upz/5iUZL2PqSCbx8SHwRGA291TfffMt997uvuMmTp2jyGBVfvS7zXwbNFQQEQDwfG5GwNQACkVC1OJjrICvJnnZnICFm1/Om4bGIYJn1uO9ZsEBCm9VSLFFHmIbjb9TSiNO7UkG5ILQ5FJbtDb/5W7/l7lm0oK1sLdjzp3uJwDPU1da5N9960/3whz/UHkWGGMkf8y2R1t1x+ZCNoOUVAt83NDZKw0eoLl26xI0bN17lH2veFYIQtEfp0iWtmGNlMXQGNFeLj8qjEZ1xoj+rP1X/X/uaW7Qsiifg5PGTpuH9uaYGMoEX4dLFS+7PrE2ctc7ggoUL1LlGIwcsIKkU1C9aEe3/o4+2qu6XL1vuWlpbZGiABWTQNM1e4rST7U98xDptLO6rMzpYavFMnzE9fgetB4EaXEGIJNuIMqcZ2A9WTH9onbff+e3fcV/60pfa5tMtSFag/uTHpuEdPeqefnpjJvDyInxhBUaPh9+t1iv5aOtHYnqoxkyIwtRVccF7+THlABWcPSN///d/L6bKcm2GNUIWYHrEBTPpPNiUedudtB7e33/zm+75556TwCEvCLh8DI+fSkMZRYl/5gk4sZxhuueff94tWLLIMmEvlYneJfBoOOxRfH/z++6JJ58wbXV9JPBMw1O5WX6jMvIfpFBKNuhkMAz8rX/6Jw0vP2X1zwozOiDhfaUgjS3GQNHw//Ef/kG0jIs7OpaXsmOxdCnvCdApo3x+8YtfyNAA9b84JfBOnzzt/ut//a/u4UceFqPqKXV9N8E6gf/23/67dXhGuGeffdZNMw0c2qNs1AGtEAhN6orFMN/93ne1gOTZ554VT4iIl0owWpamxy3XAkTdARB2BIWwYQroSdO+WDXJ4hdoLQKCLroWQs6bPN7gW8wT/umf/qmGxtlrp/lBnxXK7seWhqNHjpjAe9qtXtu3BV7nKITC8nWMFkfvQ5qcXVGV6XWNGTvGjRs/TtYI2FOEGzuxMjd63FhtKB5l4aPhwfRGjRvjRo8fq3djLb4xE8bZvT3rtLM4xo2TwzQRKyVHjBmleLiOxMzP2Fw3ypy+JR0lOtLJPj42F1NubUTeO2FUoHkItlGMHj3GDR9p9WP5JI/M4wVaKOQCjXTkKDNWSDK8zHJ96p/yH0N5mqOOKnXUDdsC2HYAnTE3NHz0SL0b62kr/U1RF+gj4VT/lhdoDA047sEneKeehcfl89Q+BzQvRlnY14uGP960e8xujTfeAA1BZ5W60dSV1Tv3w4YNj8x6jRpp/MzozOhOV+Nt0PY4nOqxPT0Xc5OmTIp4jOWDOCZMmKhtO9zTViLn00V8RjP53JikM39pRzgoB2ELRftOQURg/YW8Shd4QciZQ7hpGIHRSiNCfjNHx5APPQbmQLoC7IkiDlq/qskziTstt9xtekxomywppzNWgUP4NFsegv28HFh+SYfioTwCuE+F05EjDDQGz89ygioLfJh23Qz2BzG/hWaE3VTmQAI0AhDQUdqS6S/kDAgBLYLRirhQehFULymX71mHTvUa5ScMlQs8N0Brt5pb27nbeX7fNr/4zwk76dAOoGVjQvm0U/LaJgij+PsztMjC2mbYlxqXiBWtXEfAc/gg3OdzhgGDWAcQrT0QvwmwqhC/oy6JL/Vd0fukM1C3sgZkYebwTHvf3NjsWqEh4srzbbtnuFAOCcf32PykUwUt0zFNIiKviA77A0oTeKmyiMooekgB0mug4nguV6jseB4c4ErFBKQqK+2iHm9ieCikgfjpufj3cUIAfrhP5jS8D35SCHkBOYTAbeJnjPC8jHiSzKyAl84jXxq7EqVmIPgrxTFikCz7NNKv7LfKk60ICZfvmbX+Ntfunfm3b6h3OnIauQAIWAP77waxoTk46xBxZUuMfhtT4Rmb0rU6eLAJzBAnaQkuAdGCDz9DlQB9BGfAQkrydzFQG8HFsG/F76hL+x97CGFy7zt4CMZbxKkf0SUN+GQIApcEWixCKu4IEUYqnDvWySeenEBSDr6oNQ2WLqNqPU4i/buvg2rrGHkqiwYar7qLKyQqTAmL8CyJdDjJCvQCrShgCjCi4AIzIj457hOO3NFbgygAv5OEmnRJ+PS3W/zCbchf8pt0GOl4QPI9IJz4FobHB12LUORJVw2QE1ma8GWTrP+8tGC4k65vvClRPlVBQKQQ0hx5TeQAr/hPPNJ9Mgzi9Jpah/C0hQYeDzXzGd+nP08mkXsEZqh7EDRcfiddEuSjhGRlKAOpsh7CXtBkvZSAHPoKyEfPSX/Uv0F7QO25NPwCyJuUQC5G/+IJGsmInsGP4o94bJ0v+YlHDNreJZ1oGJqGD+bLUz8CJNAxUoUXOc/UUmUXmF0hJpcDvrXCr6+rc3W1tblDB2mk4hYhJiuOXOAKRIv/a5evuobauuKC1cJIpj8v0Qt49LfA7pvqG9yNK9dci87+KwK+9fEQX9yT642wpCv5ulr9K3MeBbLFgrqmxiZtvD5/9ry7fPGSa2mycgsNvMB3oF39c5uvmkI4VueqF8IvsZwDHce0oHAKEw9WeTAicNHycuHMOVd77UaURhhgGALL0L2ATEwQYIbsxvVa12z0VnI9xPSVopfwM0lvvo7hMWdOnnZnTpxyN65eF62V3ZEN4XNNfYoQrbVwT1v4J48ed1cvXYlo0gtZIXEr2G/aS3D9HWXWhgeFCiPQ1TugZ+GHgfJN0k3iVWtzszv0+efaA4KB3JMnT/g3paPD6vNxN9U1uO3btrlv/a//pSXATY2N0fuOAKH6fMREovASMfI6/DRiP2uEzqZr9qGxh6co7PtEcfRuKC/kxpdbkgbyZdLK7aZ1PjCU/a1vfcv997/8S/fKK6/I/ib1VTLSDThKQgTVl3MNdfVu65Yt7pvf/Kb2bzY2WPjJz7y/HPj6T49WiBaSc5IexIEN1G9/+9vur/7qrxTXm0YL586ejTwkyyND14Mqoo9hwgArKWwv+ru//VsZmS6p/RtEFmn6Cj9TNNZoNItpwFe+8x33t3/zN6JpzNFhEShHGJWK5CeJ+4vnzruf/exn7p8sfFaq/+D739c+XnWw6SgW4Oas/Iyuqfz0Q3RO4IHQiLmkb9MNPFXOrU3N7qAJO5bkc5oC9gqxOhAjBBQH2AkYY6q7USvjwj82obr5/ffdiZMntRQ8EGqcrnLpIMkErfcYbNSRnx0mVC9eulQ8DvsNAcrpXy+HFQnFgoBIV38azCfQwaEDwqq7VStXau4LgYe5qILoqJB4l3xvcdRev6HOzo9+9CPVP/YXb/k9W+38J3+rTgogZC74N3fDn1/IAidM6bEZGus5WNERihVIhqqD4WhMwm37aJv73ve/5zZt3uwuXLgYL3gpDVa5+YRE6tG1q1dlkYf5MiwMcWrHPhNEGBUQqlT9V69cdfXWYcM2MfuD2SuISUKOkOoQebLQX1GBhhfdtgMMz98KobAThc4te9BY8s0Sc67tl8t2Aok46N2x0o4e3eRJk7SEXUgTcOpnuVA8fjUntjBzjj8qgjhqbvI1rF6EqNaTrjCYU8CmH9ZZZs+aLZNKM2fOig6ktEZcClRk0W3+8rPfDJNjqGDS5MmyZqIJfPwl/erb6LYoQr2msjhs2DCdbIFFDkw3sTes9uZNMVyhCqSdoXRQx7dNsLFQBQGhrTFjx0TVV0pdiybojKa856Mbc2zF4bQW6h8TdFjnwQQiHSHNrXXcHErG9OnTZeaOM//YrM6p+/C4GInkZciP0ptikcIMr/PWLS8T37PgBQJZvWaNTEOxp67DOSxelUI0wZ/FhdBhjxsb4HHsq2IvihDSk0hTSSCNcnbvv0UzgcCJg/PrYKy8zokjXzx6TqPqE/qdyoV8K++F4LOJ1QqEHZ0F9h1NnDBRHRLqTENOoThCgAUD9h7DuySHsrAQRCvuW6FeN3uROuxUhe8KweJQNCEurv4eaz+cboDQw4gAxwkRN2bvMnQjqENIwBx1zR7HNWtWy0Yte+uKVXE7JAVcB+BcRDQueBkWeti0TieeU1zEL6o0fzva4uHMPzpyHBTMaQ60IVz5meufqEzghcYPihFHYEb2Dcu1J5jWRU+Yzb3hVICqweJhsnjoiGFu+qyZbqLFxdBZxTGQBwIJDgweqM3F06z3xcZUBGCRkoiBP/m1P6V+01MRl63ddFSVDGeyv1EmxwYNNr+R9RoVaTEaKPJa9WPhCgMH6Cy+mTNmysg0AqhikL48aaDOiRtD45vee09DTGutM7fQOkC9vmJ7CdRp5L+vf+pk2PCh1hmd7iZPmqzf1TA9lg9sQ4F2mdrYvHmzOj2cRrBo8eKIwxbjjSWCxX10CLEuhRDnDEi0SGzyxsjorUN0DQXkQ5KZ+QlW9ppAKAxxqZcSUM1Ks6gkhIzoNJzln8WuSpCGauGTlyoG23mQ1VQ5JrMdXNego5BNENUMdZMmoXENUIPlZIorV6+oV44FDX1eoFeskK2so/T7ePTD/gTnn7G0mw4JnZ9gfix+nwSP8jwuCl++DJvu2bvXvffuu+78+fPq7a8xgUdnLmNA3QzqPVGfkfFyOlSdqeA8yBMMQ9ec3sHxO8zfL1m61K287z5plap/+F0VcOr0KcXDFAq2ahmqbWpqcs2sPgZVymJfRvcJPGIKFeKvCLuaIUM0/9WurpKMgvtSGUfan/1GGNEL41QFDWsRWSotJaODtDBkynh+h0Nn/RlW1jCjoUNr3Jw5c9VgORGA1XMnTpzUsOMU671K2CU7QElYAAWrjMCTL7n39U/nCi1fHRNcQNJ/J4EtRFaAsgiHORU6b9jiPH/2rLt9yy+SydAtgDpyKSQyjIGGr7PfknVfJVy8eDGufzQwrKawUptVmmwOrxawYsW5eVusvXy4davmCWkvmCgTf4UvVT97fQrlceY0oy/A+NshXyUYP4MBcZbV4iVLnA7WrAaSafIEQNj3Ll/uZs+epThjhHSVSySEm8o7DYoxe6zrY4sxQwFQ1uamTJnsVq5c5caOHace8sSJE9wK6xUzzB35M0/eb+yA19Sjuc8Ukv48MDrAHNvq1avdTI6SGZIyFVcFkBbiYA6HMxoxcM2iHBauVE2zyFAcof4TRT7ItHzsSd638j4NAbYzFVgNWP3TeWMIm/pvaGhQJ4gFM9WcX2PRCvPEaHVXLl8Wv2GFszqJGUpCaQJPDN7Xmu69qxAjjUlAiOvuv99NmjTJP60A6TT535MnT3ZPPPGEW7JkqRsydGj0sMqoGT7MLVi40G146CEtS68qVPZVKPAeBBYUcJ7dxqeecs8/97wsta+49143cvSoiCoLDAOFUtA10CRIMboAevTQFiceLDaGxHBqtcGcCqdq/O7v/q7OteM4Hw6mnTtnjqxhZOh6qGMRaCBJB3ZPJ+rJJ550ixcvalu4VkUwf/+c1f9v//Zvuy9+8YvuceM10Yni06L53SqBdQ+0l+eef14nRYimFy+OTnqnKSTzXQCFuIg+LeH73o7uG3ujpJPOgO3BkaOxJD/WDeHg02oXOAzR/jOMNWnaFDd85IjcHPt0xNdSkWS0gJ/mYNbjJ0eWyctFnHUfNvv7WPyAtqC5x1SUPRntqtE6uZyI39jQ6LCu0tzUZph70uRJpnnNtB7yBA0738b4eGOzjIGnDTLj2EeJ4/smu/KslfCa7HlDk2u8WS+LOlg+YUMu/hB6aGBo9/xuse+C47tWS1twtywswmdoUn5Z9m1lz3N9Y2kjXDYbN1kPvrmxUelG6M2ZN8/NmTtH+7Cmz5jhhgw34dp6O0oj31pcsbNwm5qbdI3um2XoWumxuJos/ZcuX9IJ3xqGw2XoFLBTOsXa/6hRo7ukHDmhfsqMaW72vDlu5tzZbuacWW7qzOlu5Bg6b8ZwqhWlBcWJLdNnzXCz5891E+A10Bi8oVT+kORddltf3+Bqb9TKWH5/mIq5OzmkzNNEEOqB58FVCojbwo17WYRJPMGBcK0GfJrFoNJCsQjC3IKG6ozZnz5zRiu+OHo/Z0FPb4AvB8w6ARg7py6fOnlKm78vXrygeQ8Wq5w7e07uwoXz+n3BnnOqebjHX3AME3Hi/fUbN1yddQQY1jl/7pwWipyzK/MmBw4e0Kbv43bPnj6+4xpcCCc4Fhkk48CxtJzFNJxIzVliV3162Cd4+vRprcbEWAKr8oiX+M9Yvth8fubMWVlYwWFijHdy9j1hsJwcR9ovWZlwFh6r7MjXpUtRmTD/x3wQiyDOnD2jJe5dtMAwQzVA8yzkQDV5TBqEDe/wba5kmP9GE3b79u11uz7bZXR+NRN4VQUVE1x3g9V6XQmILRmFCLA8CpQWZ2CVJ6uxNm/a5D759FNt0Ne7Kq306g6w5UDXOxx70qo9d6zCRGAgQLCqwzxHQ2PCmfaH2a+OHKvRWBCAtRTKKWhgvLti2jBCBuGCwEQghndlOVa9mbZFj1fHHVk87HfiOYsEEIbnLA6EK8KuvqE+0tCsnuTMH9oa+WNukvoL70i/3ns/fCcbsnRyrFPDRmk0ekzuIex27tjp7pl/j5s6JZujyWBI8k7ug4M1lMoePF9ihOKzXbvcpvc2qeNGeyqzj94r0bdFeqjBJDEkCaUHVTB7EwEaC6eGYyNv2dKlxvDmRxPtPSiteaFGFxV0UtYj4NBeGE7iQErmU+fOnePmGyOHmQfH4iUm5Dtys+fN1cID5kaYl2Nj99x75msyH8MCWKBYsGChjBrMnTfPzV9g4d6T6/KFm+MYlrRw2btHHMQ1Y84spY84WEm6YMECOfxhKYa5On1r4d9jzxWvOcJSOuxd2s2Zb2lkkcvUqVrwMNniIy562ZhXQ3A/tOEh96Uvf8nNnjPbl2aGfotCPKxMQF9MFWgE4Z23rUPXKqMJHECLdZpYK+2jqI7A8wUfhuX6Bzqgtk4UA/ND506fde+ZZrd7zx5ZoWFymglxhjR1NEgnCDxuGF3pUtCwrGl5QdgxJDfOnyBN4eQ7pkSkw58OnSnrJvzVaK082FtXX3tTmiP3nIaOkAqniOcDNNqRQ5Me4DsfaF/axmC4ybCjaankZaLFg5EB5gSVHvMfHeViHnF8Qjg8w3gwR72Ec/GCM2Bwgbzgj0/OnD7t3nvvPXfo0CGZqWIBDBun0WTjg0D7OVRPKq2oXroU3RBF2WpVus2VWRSMMmDo/I3XX9cCLhaLrbt/nSxEKdyINPssqpM9K/DAMOK9bv55OxfAsJcxPpaNl+oA/Ic9VXEcIEkExMEruWSEhuAHIivo7L19RvhoVvH8maU3WjyCBwMX5SGP43lOmPkdVszpVcHQmANi4/L+ffvc+nXr3FNPPqmNq7yLhjtxPRtsJOdfU1OzBB3zYWG7BotGojzcccNHDHcDtSE8qh9d5exH2uElOANDmgwvIuAYNkSoMjRIg6W8EHQajjSnbxA4cfglOGDljSCD2Cj/uuu12mYwcsRI2eUchkBFyHmaF1FSR5BKkg5AoIckrSC7TIBpyNTSCRM6fPiwe/W119znJuxY4cmqPzQ/wqaDoENHffL6N+Ax0eGo4RSAmFaKIV1+/E47D3VE7F/O/Dn0wU/qEuA/xF3MJePAWRAMl0fG7I3eAj+jY5OIUkh/m3SA5BRyRm/8C7zso48+cj//2c+0Qp4Vn4sWLVZ5khamIPo6fClXDlmzsEKjASeJBEGFCs2qO1VAIAAYEUyvDEflMSfEHAiMIN5TkyaCUNkgmUPehbgLOfvPSkGYJgw1XvBi72S5I+2/s87SP3BgZAGGRQrM2cD07jdh99hjj2kzKdoEWgZ+aHxKfw8GWh0NuLb2hlYXIiAYbhxSM0T1AX0kEQlyQ6n5ovHaN2i7WDe5akIVwYYdzvGmdWFKDG1LhgZCvRVDoJXgPAiD9CJQWUCEUXC0xyEsATco7ebHboxgPL1DWzDFZD0nhSmObwzQM3GQXjo771hnBzqgs/PII4+o/ocNjcyhRXHpNoMBHoA91rbOqP03PnOLlb0mMGRejLrEBVBNdJz5JnRA+Dzp/DfiWT7suGPNO5DkJzxLh1HI+bBjZ/WJkINv5ghV+Fyo6/Q3nXG0BeMzxMFxXK++9qpsF0Nj7BukIxq1FdqWXfo4ktVXEYImQs9YvWMPNewa08jYj1RpbMZA0BgQBGwozdHykoBggisX9g3pHVpTo+0FnKweozPhFYIlnSExGikLIFi1t/zee90jDz+s/TYaVrO4Q5laZv2HPRUmIExbvVkXnRKAUWiW6kvYGdBQhg8bLsERyhHNbgBCoSPwOjjzS91T7w0m8Liy0XeiaV2DONHa/IRRBlyMZBhpR7EmHc8sHmlwdsvm8dHGFBDcNcEep73gPR0uMQuYVMiHv8Tgd9KFeAyklQOQD5tWh2BlEzGMCMFK2UjQ4byQzECnqVX1TrnHtjHtAn0NGmzPrC40YhDKO8Du9S7UFS7URXD+G/zQ5qS9h/bv3wn5vi3mwveJcMiHzCvaVXUd4N+3+ya4fOF34BB20PHu3bu1/efJJ5909xqvGeEN3dOZRijCU/s6BlhBJ0q6APBBjwhCCuBZqAC7Z2/R1q1b3WuvvebuX7tWVjNYmYbACOq6CMiDz+MGXQbQ7n74ox+5MaNHa56DRQQMbxF2PCxgUKgWdhyPIRaQ/nk+kEIIhCGsV777XR3H8djjj2vYibwoB9VgQBbWVWNyr7/+mpbrQ4TM2cHABYvj4vnz7g//8A/17qUvvhw9L6+4ugdWHPSs333vXffDH/zQ3bviXtNU1kvoiGcY7TA5fu7ceeuwDHUTrNEhCKkX9cbND41Operrir9p0kQjYjiTgzapy0cefTQ+kYBvoQFWOSI8sK6D5icNGQ/E5a+iQ5z/zb3/pXfUP1sPvve97ykvT298WoI2fEM6zp47p28YdkSzJMWBARNGW/oVa5TPBNDsOEeNAz2htac2btRmYo6yImDes5KTBSxjRo9xU6dNjRh5P0djQ5P7i7/4c2nfDzwQtX+VvW/blLPoype76trAM+p1gD3mmZ6Ga4D/zZenTNv+4Q9/6NaZxv2wdUIRSvAXzad6GorDKQEhHUkQ1i9+8Qvtt8UwBqetaD+m52ekQ995Gg0QXeGKgTAsLFYT//f//t+14Otf/et/7RYuWKCTZBBwrabNcgD38ePH3ManNroVK1f4j/smqiPwDGzM3fLhFveP//i/xBxmzZwp4URPiUIP0eREVkqlJQBRM2S23VTz4cOHucWLFmveBuKHIHkfUkjIxBkcCO/1TE/yg/RChAwBoPazWpK8RISOjxBL50HjY8j0yOHDYtr/4l/+S7dq9eooaBJn1wtnz7k/+qM/ck8+9ZR76Qu/ou86TPjdgqUVgUf9f+fb31EjZcUkwobOCOUNs2DPG7QxcuSIeE4h2sIQDdm2dVfyZ5OiYa4BYcSCjxlGY2iMgUEAtgHU3qx1o0aO0uIV4rcE6J3owPzhV46Huifs6Hv8QE+EcfjQYXVAWG3Jc76BSSF4rl69Ji1jpMXBc2JAEHKN0mJ3UbR5QRzM4V2wTg2rR19++WXR2UArF8GCIC/Hj5/QmZFoy0W14X4AjBb86Z/8idu3f5/xmFmaD4aO6GxIzCXKXPWquojqtRSBJ5hfOk104BcuWugWLVykb3kfRrLgJRqS5JvwXQeQv0Ti+A0NfPbZZzrbDospdHYIXyM65jfQXBo8LxX4ZVvQhx9+6H7nd3/X/dZv/ZYbMWpkxM8t7NbWFut0/dwdP3E8E3gx8NGRwLP75sYmt9UK9afWY2XJNhbjIRKYAleiUVRlVFY7WAUxBPTLX/5Sq+TWP/CAengwVhGxEaBAXLpE8YUsqhdo/jpKhwjR0nv69Cn32quvqYf3gGmS0vBoMPa+GoDYG4yhfWJCFcb8la98xc1ftCB66ZN2/sxZ9yfWuNEwX/7SF6KH+ZN9d2FFgsBjk/ynO3a44cOGaQ4KrS70uNFYTp06qWHcsMJRID/2fURKKQaUAvXLHjx6rAw3o10hOAlCdWuAPlgww6Q855SprlN1pl/+WfKNitb8I1SZI2RjOisyEXo8U8dNPp0MAxOnFspY3ognor9IeCdRiGlBTxwlw+pShpgYmtWnOIsoE3gpWJnQ8fz6X3zdXbt+za1duzZq/+o08T660vpFS1buoezprN5h2b15oWMC9C5RN+GOesHAwOuvv64T7ImHZ1qfQBy+rgmnZH6AP9Lgf9Jho/P+9jvvuFoTSPAZtqlAF0meWYh2SoJ9SjlgqOHb//zP7uUvfMH9xq//emTujiDsPWsrfvLTn6oTuXHjRrdy1cro2z6Kqgo8VgBt2rTJPcNSV6vAaKXcAGNOxpTMj1ZV8V0ngSBiQ/E3v/lNMdSnn3lGcyuxwDNHdkRWii+KLESZ1PAKgTAgajQv4kG7YqiRbQMwPPKTKIWKAFPVkIb1Jl+wOGKBByyJ506fcX/6p3+qobsvfuVL8fOeCBYEyFrI5UuagwonzN+y51ZkKvtwMCoCZKgWZFhmov/S7vAXEVQCibqCydADPnnihAQeKybRGKGrMJyIAMFaCafPs68NxkEYEV10AB8P/qAz5ghZrKK9eNOnaSEDcUszNT8MeeFXqzaZ27N7CTyuUUZi5Is30CCb5WknCLQR2BENn9rrTODlQkPjLa3u6//t61ps8by1mRkzZvg69n644d6KKvAEPbdv8ccvPdE73fBH0DurQzqjB/bvd/9sQgJ7lU8//6y73WIaufGAoH3pe33U9n1R2HeBDsPw/HdfeUXt/7lnn42VBN6LPsylaQnEeSwC8gK9Yizh//N//98amv21X/3VHIEHXf/4Jz8Vv3tq41Nu9ZrV0cd9FBGXqBRWcKj3MDV6J6wColCZ5Kcnzx4kFq0MqhniBg3tvKsZPtSPPVtYVpEsLGGxAgahBw+1e/PDdUjNUD1jbgUHU8Sxwm7wMHs/LHqfz/G9jixi/snyQxyUEt+G8PJ9V7azvNBoh1oZQdKBASYRnkXEr9ueC8sE9V8zpEbCjDk6HOU3lPK2e4YfOe+O6xDqDT/Uq+rN6hj6sOc5jufeQUPUDcIOuqJO8UOdaWEU97y3d8PsnTMBwTc8j2ijA2d0Idqwehk2Yri0bo4xYs6Rjh70TLhcsQGreMyRhvB9SG9IS3A5+fEuxKvvjZbF2CCEDAWh0QL+WXtQ2Vs7Z6FKoCPde9rA8Zwl9zjeU9Zqv2r/Vt9yvv753hz0wjtolPDUiTHwfKjRhfxCK95/vrot6Py32A3mfoTFQWcJmiZdoi38kB/SL9oK6Wxzgb8Vc4RDG4DHEEd+8hL3sU5cpFH2dVRH4BkiwvAMmh4XSJYwr8I+pODwxvPg+B1c2i/OQNhhXigveCwB4d+HHhK9Y7TUAp/FsPf08Bg6Iy6YeEkgGhzeQ77jNHiXAm9jr/6aFz2dEC0TMGyZEmMYM7GfJ11P8hfoIwAvHbkETahucBYGmr2eB9g976g3xeG/URiFkI7D/xaNQS/pilOY9od8QVshf/4SXwP4HcJO0rJ/RvmkP8mQH5RUVL9WYvqfKDm75Z22HlAniVcxQlMOfIA64Jp0wK7wM07jhw/EgBSou3x75QohGXbK0RbEX/K1CRDygSPNwZGO4ID88Y50JZ09MxA2Uwod80zv+jhK5OYlwAozKveoBxYDphDAkCi/wzMuwYHkb/ymXSnAWzIOpSVVk7wi5wUcRBh6dmXRQDKJUWFE98nnCVBOmlvwLi+SeenBKDTQG3cYLHsarrF8xsvJQch2MvvcFyiOgEIlovmZ4Iim1LLDW9IrdeKvMUSHdvXh54TMD7wGZhrA8+Cx1LRkyItAS2ov9i893MfSem11oZ6Sr5L1kaYJ3iXf67f98S75SvB1XxIUVnQrhM/SnydpDMT+iItr9DP2F57hyA+dedGm/VDeE857U+cwg4qrKlBVQCDeCZR0VOZtSD5L1rPvjQjBT9qVgqRfq3ClJQSdbggFEPXurOFAMCEvxYA3Omk47vmWfS2hhPPFq7Lig+g+ukYX/CsN0W3vQrLIfP5VDVbHuBxGFTIYHiW/TcPXSVw3adijdmVG/Hm8Ch3FBVQ/3hOXZDj+XfweFIoHhHepdOtrwtGvDB1BwsyvxuR/TAP85Ld/L6TrwjezvAWdp97wJq/J+i2E4Dmf10B/yTi8X2iHDq86vWhkASEc4vZ+RTeJzmMOwm/Fgz9zXPkN7F5x+J/9Gb4EqwFfoIUKNi58f6VSDJhYwkU/oouu+Vw5IHhz2mhqhIK1lxyh2hGMQEIySwKEifaiIZVbuuZltD5N4TklJSGQLLPO5LWnQ3lq02bjcrFbNt5zVh7XdvnWd9GtoLILHxuSjTog+R5AWn4YSnQWwkyGmwehTmJvKf/5PucMQxY2YKkn7viAkKT46m8oixCP7rnxrqfiLqdNQk5FpT+ChgOt/KKOUPQsnU4ss7DgpR2C/yR8vYS66RDBg7XjW5z5eLNeDlrIGzbPgmu75CI8JDPBL854CsO2jXX1Oi+RhTTR8KpPRMqvrnrl210p+enjqKLAM5RaoFSE4fq1a+7w4UPu2NGjMs6bHHdWQGlXLvjGmN2lCxfcvn37tMFXRFIMIhqfyEIIry3PECFnmmExhdVOrFYUIRaDpS/OFgSZvCYgYi0VSa+J+5bGpogRgyJZqzridEAfUeNDwLHXkTo5eeKk44w8DlPtqBxAVDVRBvJlI64Wf73d2qqz7bDryQrOkjs9wNKQt6jEWO0NL7kajWFvk+0S5IfrdcubypvoELghoLwB9hKEoiujCKuNgsWnukg5A9tlzp877w7sPyADD+3m5CqBLwcEHfsp9+zd67Zt3659wmzRaTDBl9PxScLiDiNJ2t4QtLeAILASeeFgZI6OYuvP9m3bdCYjx2LBfwqCuM2VxUP6MKor8IohQWBXL13WNoZ/+qd/dj/96U91MKiYR0CysoMrFd4v2hbH7fzil790f/u3f+s+/eQTLTcPRJDXlQr5N63FhDR7vzD6/N3vfc/987e/7U6eOhWZm0qGV07YnYXiaItImiYM/tZtHYrKOW7teoPdiKA3s7CATfenT5/R5u5jx4+JQXBoK9Z5YqpMp1G/iyTav1cDN2aDmTO2Q2wzBgFTYmtBKYi0BS/Y0iBsuegnYXLA62HLA3V/+Mhht8/KGyFLeUe9a/MYgiqShbsOKze0bs4BbDIm22wO7UgLRshLTwdJtDy0NrdafZx0r7/xuvsff/U/3Keffuqam5ojP1WsA6z7cHblq8Zn2IuMY6/w8WPHong6KDJe6XW6XPN8w3abN99802354APZXoVv7tq5U/ynx9NUD0H3CjxgFdnS0OQOHDyo3f8wYjZGxquUAnPI58pEc3OT+2DLFveBEQiHHGJxII6nUlg6CYseI0Z/t1hesJrAhmQIUEOoWs3n/UOQSddlsMAJ3+JlpSEb5ilfGgplTeOUxtGJ8qwWSBfaHVrd2HFj3eLFS3ReHvZE2ZMkFBBselrgHYg0r6gAbrW0RAe1mqAjPjYtl8Sw+dzCyBF44Tsudp8MhXDZlMwmdaylcEYe2iSnmeNXYdDR4CMfXE9FY0OjBPaHWz50mzZtdlu2fKAjqzBsjrDoFQLPypga4nDeHTt26sgl2mhzi09/leuAg4HpSHOu3Je+/GW3atUqdYDYzK24iBO2Q9FxxVkbpAORdHof/LDaFP5BWzXHPfsy2Y+58emnZXO33jqNmJ4jfiF8n6Egul/geWAL874VK2RhgoM12VuneT1U+yoRJKsBsbaBQd45c+fKWCqMLCaMfK5cWHDsDVy8aJEsMnCQJ/uqWAZczbyUBOIKNWpChRO8PzGtFosRHChLw3/7rbd03lqzMba4EXYzmKBvbGxygwcNdmPGjLVGPEVWU7A8oaFtUKTc2gRbAnrkhRT5sit7HbEhyAZy7XeLfBZF8BcLvFRc+mV/uLL3i3rHBBk2PIkao8Cy7WqaPkxWjJYXSpd3PRDUAYfP0iFlyIyhwAMH9rtTprkyD6ZFIT007WlwGskEoyuOW5ozZ64lu2sSjhGMBx54QPYwZ86YYfGmTkCAdkLUpSYhj797Fi90v/IrvyLaYliTcyDZYyfeiWAMnSpchrwI7LF74Bs7Gz+Xm7Bbt369m2PMiArD6oAqjBRR2VzTriPkIRA2vj/++ONu3f3367y0YFKoKjDhjFAbMniIbCE+tGGDTifHxmfE3MyF6Mg3rquRzJ7lNVqiPdC1WM8Wu43hOeljAYnSeBdaB73vpqZGxVzDZnMTfEGAVTM10NWsWbNkcUXCDgFUKig/0hSgsuLKbZsA4ymbnIMR6bNnzrgT1uvmHmsvAXHcPpieCvJMuWH6io3blAMbtgMDTxZJTwc2Wx974jH34AMPql3GW2SqjJGjR7mZc2drszpzeHQsOWFjqWn7AtFSbsHpd0RfSZf7Pprfsz+xX8BGddouQg+a1nYDaJGtCTgAjfVwOrtb6BoKKAbqxWKmB8y5c8yBRBVaQS3l+xQaGDpEwwU02DYmX0XQiOw/6cdEFIefQqBiiCCpRVU56g5heR86bLhsAT73/AvuXutgPPjQg7LLOW/+PPUMSefdAFrCYOsoEL3q5fataDGBlRmMttPlZN/FwoismcCHeaOBMYwKHSjokG1+FHAhnLgeuepd2/P4vcXDMPZB04rQnrGSgcbPKe98EzMzEK7AnvMuOL0L7i6BukErnjtvrps3d56bO3eOm28duWlTp6kc72baSoaqzMrUJ5ayDWfCVQWhrkQP/Hbu8oVL7o0333SbTNhhOu+LX/xidJKH0pIHpZQj8QTY/bXLVzVHfI91sP/Fv/wXbvmyZVr7cIG54iT4rJTwDcFrzichX93tugHdK/BSlUAPBQanvMI4GC6xi5b0SkW350lnSDIIMYkSoCFGE0zaMuCfVYxUQAhvmSOze5igevR2ZR7JWpo5MsZL76qFQKnporCywaRSzVDTPiaM13DLvSvucyNHjRRDll1Gap9h5GQYnXFlgDobPHiQGzt2jO6vXb3mzp09p9VmlBnaRE75lFlWEkA4EkbaBltPOXojGtA9r+W83zwuR6DJK8+jb5LvNDxbV6/5mqNHjkjrnzF9unr7Yrh8ky7jPCjwuNuBVsQxNRse2uCeePIJ2ZJ89JFH3ZKlS+IDae86Uu1evCBVgvrlH3XJafERWQhXL11x777zjhwmD+9budJNmDhRHflbYaVuPudB0krhZSyC4fg1zrXjqC3Cp+MadxJxCgzfGfKhewVeCjAgrNpjPR+hJMFg0OGnMUNOOGCPReAQSKjYwIwCwr1/TzwMOWEfUfMqPC/kOgmGDxlu0EnlJlCUJgtQ4+vagG6B80wT0V56dyUSeaLBYwV9zpzZub10kpgst24C9TFu7DidEnD9xnV34OABV1tb66ZMnqx6qgRBEIUshvyNHjU6sicI3YQ8h2GgPA4mAvNS/RlEk9CgPxw0vINuMQLOikwWRTEHyUIZtL3LV66YQEwsGbegezqgYzpJw4YPk4CjAzJ0eGTv00rW+7p7IAUUY7L9S9gly1YevAP2Ee0SPgPtVRsYAN+7b59OWLh44YLmzd984w136PBhCDI3Pcl0ecT8rAhGcRSaCbnNmzdrhSbTQBicZsi+UNgZclH92i8Do40AWeix4aGH3IyZM8RMQqWFDelJFyGq0cibr91CxELrMMfKpqefflrDejTkqsPiGDZsuIYYHjdNisULygulaw1M+7GCkCOtZTa6HCZdLuw7tgFgwNkXWoxIQzEPnQ27HFgexDDtP/mhHmbOnKnhmXvuWeCWLF6s41GGs7AoFE+56cI/DCYJy98Y0ybRWhhqYohbwF8p4QfaSpRbDP+OhSpLli4VLXOdPWeOmz5jhuZxBgz0ve9KEdLR1VAZmiM66sFHi6DoCmHRKShNRcoj8ZrDc5995ll33333ab612uDEjGeeecb963/zb3Su4ZrVq93iJYt1dJEWr4UyTTqlz/+wus2ZXw7vU46TIdC44ZecfLDR7ldanjiiqlQUEqysb+gx9duFuKs5ZGJ/rjEHzs8bPSY6TkawOoEA0s6/iiotuDQglhRgSMuWLxfBDBrsGV41YXHSA6anxdJkepPhOQnOISSl298nEPKHEOJ9Y11DtHfu3Ln4XdkgfnOEidCLwXMD5UiPvuoIQRK3CRyMStfW3nDXr9/QOWY37IpWBBj6GzNmtIZmmM+9iZZ07Ya7cfWaDBMkHc85mDM4nsnfFbuau3Lpkg6ZRbNiG0LdjZvSuFhOrxEEA1YwiINtA7WWDjaM38RZ2HL+nu0RnEiOYwMxfmuVruvaekJPnisr5VgNi+V7GByrAlkZzGgCC4U466zW4iJc4gruJuGRDnNsjOdab+nVO/Jp8V68cFFbNZgb7oJaKg8k4K4nIkLo6MZ9myLp4qiqZcsjTWjg4EhjrybYjrJmzRr3xOOPuw0bNuhotPtW3KcO1gBGdwrBt08VbT5elgKrwe8xXsm8/P3mFlknkVESRhtCmy6KUGhEd+uOFlmxZav25s1M4HU5LPZwdEu7lPA77TxEINFteyRfhvtBA3QkB0d8FP6wQhjBDhrij/mgV4d88QRtlKT3ciAPcUrQRTeuvrZOe/reffddTUrrqwrSTWOSweaQnhB/CLOCsPNBefHxtJh2yxaEixcveaF3XUKCIUAs0vCbocDrJgiv2m+e8R6hxT3PgrtyNRJk0f1VHdBKOAgeOgYMJbIviWX1CCy+D/74lnDZvHuJ8BMOocJVz81vuOqZXRmy4jffnjl9WvN1J05iHSbKAw4BjLUdhma5ki/SqjjsfYgrdnxnVzm7Jx4WH3Al/VgFgRGRFp1AERVt1wNaqDI9VB2WviiJvlSKpJkhfUYUBtH+uwADhwx2Q0cMc8NGjXDDR490I8aMckNHDm/jN/lcgOcLyUcFYZ44mmr4SItn5EgdgSVhF+aISwAHGWt9RCsWaM5p//DOHTvUBksMolcjIUZ6H3pVBQVhVwBhywSWQfbs3u0+3LJFY/ScLahvK+B4ak6+pjmQNRTcHTa0xt3kasIisP+siq2vrxPjv2VMG21u9OgxOhNv5MhRWg3Ib4ZkdB0T7kdrvkXOeuexG4OLnnMoKv7ovaNNBcaBpsUz9nkypxrFNTL+Nidsf694TcvkdxQ/6RqtORP99n602tMYBnN3DFeOM22O76N8tH2v3+bawvPPEy5+bt8TBvnhyl5RwmdFIcPQaL/kg/5KhoCIvvoCOp0NPgxrA8oJxLd3OtJbt26VQQFojW0OWsXex1G1ZtTtZeUZXA5IQ3Ag+Tv5vAcCwdNU3yCbn5hcgwgffOCBtlO7ceWmn+JRMSXLyQLhp12qSeDJkEJ0DQ317vq166bhNUSLVMZPcOPHj3PjJ0xwkydNcpMnT3aTJk2MhgIn2juc/Z5g7yZ6x2q3CXo20Y2bOF7PmDNhIzn3LHLRIZo1NQpn1uzZWiXJe+KQH3s+SXFMVNxceU884yYRJ8+i34rb7hWP90d8CDDiQHhyyj7zjwxZKZzJkZto/vhO+fHxMNyF0++0I3z8+HjIy3DyYj13hDZzmsw/h+HYDKB6NNtToOaS5mWloszPmB7BwtEW61CzhYZpHox/MA2Tc2JDH0X1+o0wT6u0dpPbnazHQkAwBJLP0U6qHA/Bae4sSYjc60UVnCEpcJjD2f3Zbm1cRYvAOgST7GgJ2k7RWeFEXInqCEObBEc9lTJ3UBqCtXq2f9x2dTfr3ZXLVzR/J23KHBuZNbxKr5Q02SU60sWngQuOd0kXnntEYUSHc6IRY2KJHipzZwgkTqrW6sqwcMh/S4OOGzVx8i4g+OM6iH1b0WpMgNkwhkYx04ZGhtBEW40n+kP65KI9mKovHPEUcgF2z1AT842RNnzLa31jdYJ8Xiv//RE0AV+k9pc/ufDvO3SlwNNkcDnIE61QatgBhGNhQyd0aNWpDdC76LYo0vlLOw9t/7K40Oze3/y+O3DggJthnbb169a5+ffMV6exPyDZ5CuHFaiYUZpIAvJVSDnOgJCImQkOBGZVLViwCLtIcPs4QDKOQJSddeFieWDeZx+W1rdtk/HkBx98UJPg2kZhTDdiqv6jChEWqRCcgqxOsFG9+/Kqq68X48Z2IVoLGgx7FENRtmMigHe0+SJORgTMYQKLcmOyHeFED5UhQu4hi5w4uLXn1GnUiYket0PqOT8xcs08GwtTWDTAopRhI4dH+bE4CmrJxJ/jeJZwFJUBRhcEN/N+7K3CKhACjzzQYZD/DCoHykPtP9kuqwnVT1TgohfdeUCDQH4SLiAkqxRnCJ/Gp/enUYU4+Jbyom0yd4wVGPbxsZL4gfXrNYowdKi3DtUP4JtdFUDj9BUXM4F0GSYrsDPOAEODEQTXJbBgCRsiaLe6sYp0wVAVxM5JAR9//LGY+HojwpUrV7qaEcNiDUN51V2VEMqzysVHeWHg9urVK2LcEyZMFONm0cDgIYO0RFt1FsrROzW2Eh1lQpmxSASBR0NG65KdVGjDXN4l1vxE2BN/sbz7KscoNIIb7RuBis3XtgVJlh5DTjD8QJ76dETxRc/iOIPzkGZnwo5FA4ChTkySsTiBsqJNdRmd91IwD93uIGGQLN/OOg+1fSv7qJY98vnvrAOeTnFhv2dBhO/y0VIxZ4CWsb95YP9+CTlWkyL0ho4YrvgzgVcurMBo5BAJvWIOKGxqaHQtjc06HaGZ33X2vAJHWCxnZ4gJxkrvvjmE7eOr1BFWQ129FlsggIiHeJsbm1yLEQ0aGHNt+dJXjmN5PD16hrG4os1hgHbt/fdrpRdMlc4DGo0QGHUlSDSAaoMG02RldONGrRrXSMsP814aQuU92xMsL62t5m5FR83Qmbhtz2lrGqjWTWGnIUn7Dsv0WlVmZRIvWrH3hI8fBCJ1Fwkmc3ZlH+dtixttCks+nGGm/ZHs7/Sr1uJ7AwI7CFXygYEE1QHhG/2RX5xWvRFPcOTJvlVaLD5ZvA9hh/BZLGT3xEFbof5JNxZx1EFAWFq5ANpThjbQSaTTy1aNeg5CtfbPMUa4emtXHbkGc2z36cjR/mnr0DBafThO7FZzq55jVScc8lqJg1+h1cNfGCrnygkrpEH80jvxNXsWeAau4WZdiS7yr5EQcxyPxT5RrC5h95eV6+RNgp021g8wwDJaPKf4oAH64TCBZwkGysGqTIR+//vf1ybfpUuWiCj1KY3frkRVCb+FScLMOHeK4avVq1druAwGgbCN52cqAOmEKbJE/Bc//7kMXLO5E6JglR554l5xSgh1XvuCoXJ8EeWD7T0MXQ8ZPtS/de7MiVPuj//4j92TTz7pvvClL0XdExKYRCUFWi1Ymphn4jw7DvSdOmWqFnYw/IRhaJZPI+RYEYblDuqMsqT84l66hVE0K0Y/lD9WLagD9nCiEQXQS0ZbYhsBWt9U9kFZ+NAddCNA7jyz30EbTIMRCrY+7NmzR4tGlt97r/wjoPiGU805cohn5IVVmQChTfBJOozz5xF+S+CbP7YesF+P/ZtTsZgB+H7wQJ1Dd+ToEWmX06b7d/0cHBz8B3/wB9ZRvCnrQePGjbcyt/Ki3PnTAUJNDOjAW6gfrKZwph08hvl0PoGOqDNVcoWAjqABth4hjDBwz6ke0Ck0qXgqhYUDLbJl59vf/rZ79NFH3b/9t//WTTGaFt3DMy0NP/nJT2QVBuMcq9auVlneFeQ2lS5B1QQePdb339/s/vp//rWIhtVy9FwQDFRsEBBUdGfz1USv3ZL78SefyHwXmzBZ1CH4yq0UgajRJLdv366TEGBGaAzEybwUjU75ID98BPGUCSaRG6xXd/z4MXfv8nvd7/7u77qFSxdHL9ECrKzPnDrt/sQEHj2yHi3wrNg5OubM6TPuqDFwhv5GjR5l5XhHDYteOZoQp8GzApGVmZwyQdqT3YViWeE9wgHBiZkwVrDKZqXVBQWDFnmz7qb2y6H5TTHBC71Qn0kyp94QaqRN39p98j33jB6w304rMmfMULzQF/4ZYTh/4bzqgtWVzO3RaSEezTGn6DAw0SQQzsTDYhgWDMznaKHxbacrADbMZwIvFxzq+l/+y39xn+3+TKt80e6hswjpxpGCvaYmitEZobDxn72wc61eaP/UrzpNnk6ouyKxdQjSQBgYl4DXEA+m9TTHbgEnBV4ynny0VBAWPrwX7ZG8/N7v/Z77yle/quFyRiIygVcI+Cgi8DhfjUMj3377bfUkGJ4TUXhXDVB5MKLvfvcV9ewee+wx9cDV87Z0iIlWAYSHthJ6RRy2iBUQhlKC4Fa+8ByuZYIl7hA62irDcy++9JJbuGRRVK4+wAvWM/ujP/ojmSvDZBGrB9tFlqiSuwZrOzRQbbQ2zWjKlMnSepKNlnsECAIKRkX+Ge5MNuBiWaH+GWqi983yfbQrhTMITS0aTqcs0c5ZsYkw4lmgQeIK8SGYuOU7Fbn9pi4BfxnKwjL9NKMvthoQTqh7BN6Zs2fM1wClgb1/IZ5AG8VAWNCZTqE3//S6h40Y7t9GyARee3AS+5//+Z+boBsp01pTpk5R2QvFiz3JsvIiCBxOyf/2d74j7e6FF15Q551OGx1VdeKpb5z/rlzQYYOef/DDH2pRFJ3aJUuWKFyEK3EAwk/TUyn0lQSWfP7sz/7MPWr88qtf+YobxLFPmcDrAPjoSODZfYs1zo+2btUqIJjzfWtW4avNX5XAmPQ3vvENTey/9OKLbtK0KW3pQyBUCccOHXF/ZfE8Y0Sw8blnooe0q4gOqwLG6H/wgx9oCA6Bt3jZEv/GYNm5eO68+8M//MOeL/AMaFdXrkRWUCabwOMk8+g5wi7aVsHwHT1yBF67o2bS+coH/LfelkUVTnxgyBJLPTyDGSFsmBNF6DLUyb69mDaQbnwf4qQuw+8QdyINDXV1Enhh/55oP/HtWRO6gHc6agmE8EqFxXf+zFkZGGDP4AjTipPIBF57YJbur/7qG+pUvfDCiybwJvs3hkT9dYiO/Hl6OGQC7x//4R8kJJ55/tnoXbVhdPSKdaqh1xeef97NW3iPf1FdYBLv93//992jjzzivvilL/VrDa867NsSivYDU2HIJ16lCewdk7HxhD2vOus8GCKjB8Twop5TUEEYVOo8GHJi7knGXwMorZS/ShBppgOi+Sz/LAne4XoHou0o8UpMYOVE7zs4hF5MGsls8QyhVNRZmVkDJRw+j+lMq0CHaAUn9QYtih4BHkN6knHyOvyWn/aOHrzmoYnHRwUNs2glpCOG+RUT4VE+Z6/aOfOv1758MhQHozjULdpRPHQcyrgYSvEnPxF9QU/p4WkdXQYvq0J1aXGTxYVGx8hFDkrNUzFYOskDvEztJUmz/RDVEXiGjhgz7+JiTjAUucDMSqwHvAWGpxWA3AQk74E8mwtxhd/FYH4i5pzHcwgjuHygMRR6l4CSZWWjsuug/HoDQupzcuGzFfIYueg3KzSZC40ZBxoUnRZpUolrGqFO8r3zyKk7u8BYhHz1wu/gAuy+LZpUp4MX/negQ93zJ/jjh/9e4HG+lhb8p+PIUBgUE87Kt91kQih372Ia8N/If3hWCMlqSPrlNvykM5Wsz9CZT8KvxBW45HEIIIRRTK8hCK6BLxJu0gG+97dxukK44XmA5QfaiuPo56iawAuFGVVcbsFGCzy4SVATXqhAmBwu8apDJCoujHMXBK9xEAIoOQ5LmhEiLpfZ+WsaPE++I84S4iL98mZx9CWGZ7nxd2Qtylt7F827xWUVPgn3sQsvSoMatr8KJlhFJyEeHkN3gUGAnPgiRxr5Tho+wpfvgT3TXG7Ihw97AFZdAh3jF8d9EjwLSLwjnHLzmaE4onL1PwB1X0ox+7oQLQU6AgQHPaTDgAao+yTst1aPovXnizM883UvGuVZSKN/HrskkrQVlAWeJZ8nEI+6JfPST0ERVQUijHwFGh6p4vx9gPnXXpO6hmhPVDEY7RBcCDJH4CVfBPhnLCPn6BX2XpUES5dWZZnLAcQVEOLD+XyxNYP8cM15n88ZkowuLprAhPsITCQon5Gj4xMt/EDWUb7R/in2UzVqr5PyT2H4MtK9L6OSYfUXPgfQJvubVH9iChZeCFI9aX8fwMfQsvnT8u1k9Nwb3ZEHvMWvTIhS99pj5fdSqeePpyBgQSBZPvT5CuWTocqwYmduudXaI3TGFIIKXmXfgTNQbRJ40c/oAUj4iWG/oV1OObl2+aq7evGya7D76Bt7yTV8F1zilrqPeZl/FwuvNk/2xykOjo/iOCyOv4JPtUOcaIN9Bg3n5KUfIzS/ihEKUwULs0hClRVVWAzr+Zw6fdq98/bbOsGXY1GKIoTrr+2YRPInXsyxmAbrAkzMYlKnubExet8RPHEw3JATh+KN4o5hPxnXv3zxkhbtvPnmm+7kyZP+ZRFY2HHJhHjSZdfbYdkKuYzzan9ZXn7x0kV36PND7rPPdmtVHAteNPxIEYSeK9CH0ZfFwCf6zJcjWjpH7Bw8cEAbbyVUQQgOf3LhN87TsA8jB/5R5C3yx9AVRwV9fvCgln+H069v3rwZxRMWvITg8rS60nKXoRxQNwi6/fv3uR/96Edu7969Jvya/dsi8HUbo4MKYoU6tinZt/vdV15xPzZew55k9tkWHL3iGTTteUBb+48uOfDPCG/Tpvfcd7/3Pfcdiwdew3FYrdquFfkRUvHBw6K9ivkC71+ojsCjHENhpguVn1RA8rkxCJaoI+x+8Ytf6Ej8a9ev+5clwhNLXvj46o3hfPLxx+7V114TcbBKkO0FHSKV/JwYFB/x2iX0wIyhsrHzfRPaPzOC/+D993V2WinICbufACbCAo3rN667M2fOuIbGBjd0aI0YE+WI9Ym4YKCZUB9lFZbvsBidnbLOB3udDpowolMV9fI9VI9WkYGO0MKIMk3D+aC0RfFgfUcHxTY0aJsEmit54Uw+xZFMewlBZ6gOmpqbtF/vpz/5qXvNeAD7NzmfsRSEaspLdqk6RBCxqpP9nxwzBX1/+umnoruOINIw2guuHZK0Y1do7PjxE9rSgNaG/V1OV+H8xaJ0ldGdUHUNrx1gDIlKg6mw8o1eNzYKWe2mVXz5VPPOwCcE7QwGdPbcWe2pYhUUwq4kZmaICTBJiNzzMzyyK2Pj5IOlxTA+GGq24q49VIwqr6iuMUFWa27UyFFu9uw52qhOOQabktKKgARLdFsuoAEEaBByaHvxECVhcqUFpFqBYjZ/DDNpji4f7L0YFbfmh/2F7AnFTiFbItDuME0lBHJQwBm6CyyKwmByfUO9OiHxittS4P218QFzyU+59476p85XrlrlHt7wsJsze7YMCmiPJXVfqN592Mlg2yG8tCtm51ZZHMuXL9epIOGQ4WYT7BltlYaqCTwIJK64fIVP5XoPMBLOLFuxYoWbP3++NikXZCwVgN72sqXLdOQ+Z5rlzPkVQcQY/XBDAJ8nH/j8QOzLly1zi5cskUmrvL21fg5KUrLL/ty6zTLsRpUllldGcoKzCQxMunHUT4xQjvowui0HLDhBCM2eNUv1wpYF1Q1hpcPTM+LxL+wq5hh+B/hnCFN11Owec2mT/dl4pLnehCzf5j1yhSylgszQNWCvJmYO165d6yZNnBSbs+sQ1E2izsPpIh2Bw3s5U26Z8QBoAsMHbFCX2TvRi3kimKQDehc56CkHfBO7yA/HUt1nPBNeefrMaVdXX4dPgwUY/GboEFWXMnnLnIfE5F+y03/m3NnunnvukQkomETJPa9i8MQ0YNBAWdBfYkQ4b968yARVMWJPAL8d+oc+b0fHbmASCPNDbKhGUKK1ZsgHGnbk6HGHMma1JuUIBUg7DqRA8Yc66Ax9DBrgOFgWIwVh72YOnSV5DNFoNWZbneM3Enr88M8sfRJ29jwsKyeeGqNpTMUxhHqFzfdGCzCo6KPwcXSJGV6GLgV1snDBQtO650Unj5RY7qp3bkSfCRYZ6i+FqNM2UkOOWz/6SDSA8MNyiubwmI8uAN7E8YGk19RnsvZinULiWrBggYQ4o1iRgWvzrMAivxnyo+oCLy8CoVEp3PvfjEXTEwLtejidRYjLiFVEbjmEiSaiLQ0Qu7/NC4iUNMOQBw9U7zFoEFXLSx+CmjSN0RfqwIGDVC+trS2yU9rUFNlJRSsXQoXJ+Y8A975uEk87QLTSMvhVJwaqDy4gBIjDVJkJR5xGHsJzLjyzMAhT4SJEW2+5SxcvyVYoc3cYNp/nRy7aIWNI3YoBJnCG6KilaIgzp8NTCAk/vto7BIIISyXvvveuFkdhR/iRRx+RJSCByPNAscBH/DUHeZLJHCGLY9AoH3/scTdn7lyZWsT4tOIoJbEAfyFNBdLWV5Fs8hVD5RjfFQeMg563jvzwgq8rQDwMb8gKiH9WETwxinkmAqQxkReZzcqQA9ozxSZhYUICo98IN+a6zpw9665di4wocxSPPIY+A+UrF5U13wenZ0WApi+/hriDUgykFUbkXQ78b/7qzn6zrYIFUdhfJa5RljfmKZkzEpLDYiVEn6GKUF1aHwb+0kVlf/HSJbd58ya39cOtigej5piFQxjFm8UDwSRcTF9c7ZGg34n7BJiz27p1qxb5sViFtoPwQ+OLUWoe1X76HzlWTeDFDMhcu0Kk3nLrTkADw/gu8yxYnO8qwFhnzZwpK/7lCtY8yY4Q8us9wMSxecix+To7LUMKVlBqwJHAmzR5kuodoYBWxOsZM2ZoYj5q9L5gAyAqX+SB1ngkxDcefJr4ns4Ow5oMaxN3DPlr7xCMaJ8y5IuQRPjieOe9JcNHQ2VBlIZNTcCykIAtNzAoMbyQvnQ6M3Q9rMyZI2b6BLuopbT/0EEC7To8eaBz7Roa1WGDZlgRzEkr542umZduR8ulIqTD0zvzxAyTH/z8c/fRtm2aO+awaNpNqZw8mbf+SI/VEXiqj4gBFSzDZJ1zbw6L9usfeEAnEkwPZ4FVGxbPnDlz3HPPP68VTsOM6RVD6HmF3lcOfNrTQMituPdenV23wBpXhlyEYhSNmMYzdNhQ1QuLipYvX6bFBdOt4XLSuxo4cx+lItRHot6iurNnFgwLVhb78AcOSTA8omi334/oIw1SQ5akhee0FG5NYOIYLUB4ho4OZyZCx/etuE/n9LFQRvPTQbvzlwzdj5kzZrrnn3verVu3XkKi2qAz/eUvf9n9v/7P/9P9xm/8hnvKeMD969a5KSag1MFKavjlInxqV4ZIX3zhBTmMTb/44ovuPqM7KQt0yEqIJpB6W7gVpK0XojoCzyNiDt4lQSkHjhdgXqThTZnkJk2d7AZz+m614ZMxxJjrrLmz3diJ4zXEVRFymKP9IV/Wi4cJjp880U2fNSPnENdOwxgxmkM7ay+9Acnqp4j4dweN6ZZly2tNhpqhpnmNHyttj5MNMABNWd4xfwLeZKUkOPt/y763MpEjHOrDnmH1nWeUF89lSolv+N6qfLTFMxRhivUTngG78o3qkEfBdQT59QKVfNnfASZECX+i5YPVwFNnTFdPHIHJ/F4cLvkJDpDOkF7yEufTwrU8qbzsuY8qQxmgZqAFDBxAZ7Ot/Y8eO7rkjpR8qeATKPDpiDGj3Jx75rkly5c6TjxZbNeFixepIwRfEEqLNj/4Fjd4oJtg/JJzM5euWO5mzpnlho70Hfh0Wgsh5e/WrVYthGFUo5Ik9hZUTeBJ2HWEIAiDt1DuyfLvDyVeAHH5QZDGiK9zGvoHH7gTJ09oqKwsjaenwNctiwXYnIs1EjZ/M/l+4fxFd+7sOXf2zFldz58xdy5yFy9cdBfPnncXz5+Pn+E4H5DhT/Y34Vj+fYmwvGMYEcsT2vR99aq7ZO+xrnLp3AW586fPurNnzZ05o/AVljl+n7fnHNVzgbTYM8JWes1dvXxF/vkeCyrHjh6VNR3SqbRZ2i+QfuXlrNKt7ywN7AO7dN7ix1kag7ty4ZLCZ/4Hv+xLvXTJwrNvec6m/FOnTimOSChHZZmhVKCdRxp4WaCZ+baYU+ThB6+4Dw4kOzLJ53Sug3aX/CZ2/KkQIYhytEi+wbul98jhI273nj1qn7Ib28dRNYGXJJS80Pvotu1qN8k6r0L9dzskxHF2nyb4MiCmBqyBwrzf27RJAm/unLnayCqtpJeUj4YUrSyCBsbkOox/yJAae35HB9/yjM3nctdvuGvXr2lZNw0PqztYKWHzLvdJx7wYnQEcFm0umzDCL0KChSMnT5xQ+LU3CDMKB8Ej4XPlirYMhHvFl/Tjr0pDuFq6qA/8I5QQeMyhED/n2DF/Q1ykS2ERDucCci3ilD/uLSzycsGEG+V04cJ5d/TYUXfwwEENwbG9prpjMX0fNEetpLX2VLQzXgjQcVoo8TMZXLE2WShqnnveQfriNOoZ15TLB+JWesxDIT8pKD/ml5GH/fv2yQINtIfBhIlhG00fRukHwBaCL2iMP2/d+pF795133Eu/8pJbuWZ19AJGnayQcCVMudT7juCZ5d/89V9ryIiTiBkSFQgrGQa/k0jGWwQQwzHTFP7nN76hOB576gn/xoBQA4SXjJP7UJSl5MfeY2D4xz/+sZg18XAA5GXr+W82YffxJ59oLohDGTn+X8HlG44tFk93wcoFJo6QYIgSE0uUyfVr101buiDGM3ECh6UOjVbmDhqsIbtAfghCyi80/rC1I8msuGeY97RpPwgCNnpzZaM3gqyxsVEr1jiUdYjFwQIFhrVCL1/DnYrvjr0zrdnu+Z0sQsVtz9jfhJbFoqrJ06a61qZmWYLhdGrzpE3FxBXO3QsnZfNtWBjRUdOiN0160DhZuMMBsITXZPmTQLe4KKuZM2e5IUOzVb8BmKD7y7/8Szdu7Dj34ksvRgfjhmJOFjeVigvPkpVcuFoif8azsIv6j//4j+4xDoB94bn23xQKIxkPSKYh+Y09v2U8E9ubdKaef+45t2DJoshPOoxCoIlAY9BeB99gyPo//sf/6NavX+9+7dd/XcOsu3ftkrlFOp4Y5rj//vsjwwmc+lEob12NUvNdAYr3G0vMvLEpfwdKSDleuiGD3QkxuA6YXEdgHB1h9/7777tdRowsskAIsupTR894xtpjkahPyuG2MQ1Mh125ekUCjhWZ48aPc8NHDNe2DfZHIfTCNg72ZLKalhWVzOUNMUHG3Ctzu8ENMsbPSjgOs8Qh1FgFR6Ntsev48eONAU53w0eNkH+OciGsgUMG62R0wh4qZ+HWDHGDh9Zo07Di8m6wTwNp4lvNK1q+EIAIVtI4zfIyweIaNtLyMtx/Z9qr/FuaBuIszkH2u5CjTvFLvsMeTgQgcRDXqFEj3ZQpU/Xudjh14W4xoh6EqI35H6WgkzwmRFFWkXciLnXw/H1ZCAmjQ1hGAK3WucIG5+uvv6428/Ajj0gQStjRoS4rw70PlXNRX0AQIuVOI84p/3TvI1mgPGduilRwX8wZNExhzAFC0T4nNMh8DCHPt0L6eR5H2MRD7z/eSwUsfDQPFkigbeaA7xBKOMIpBRYH5YZlFnr6GJ7euWOHW7Bwodu4caNWedUY0xcqr6luAGV2W5vIb1y/rtMQYNiTJk2WJgav8t7iK2UtQCeBVoIDoV79tywQoV4QDgwnMgeGZsd+JASejA0EUGbQVwiLezoPuBAHftKO8DUcGw1pcRwLc48IpSkm7NhYTjpihO8s/AGD7Tn3IewQT9qRH09jDP2yreHqtasaGh0xYoQ0GIQ6aYAWQ/5zyqMU18cgPmN0Al1hvCAHyfKtABQb9XLHwuEqpMMNv5OuGFL+JbwN8LNwX3K9EUagu45gvBH6op3RNjFq/fprr+nZ+vXrtHJ9/MQJkbC7myA/3YDq5NJ4g4xA+0rT3E0AjxAOwfE7uHJhDEWE6JmRGAFMLFSW4rBrMo5yHTChBcODSBDgMSwqhJoOgUwzZ1wgwPTzfM4AkWtY68wZzdexmZThy4c3bNCeGzSc4DdOWw/EHet0tLa0Kj/UfW3tDWl2zNmNHzfejTQGjjanTKTLOonku3wO0MExoAUxjMkQJ3v3WBGXU1cVAtqCBtgQTzwwWOLAXJWYE+kIaQoIv7kmXT4EGrA4OK+RDkJ9fYPm6xguRehpKBYaT3ca+zHodGjImGKhXACXfC4geQ/S/pKOCx0XCxsNyLhN9DDUZb5vOnIg33PjU/AXdfjgZdQ18Jc4vkqd8UZ4JiNIzAm/9fbbMqTPEOa9y++NVpLCP9Pp627XTQjFWxlIMMzOmLfKGIIMIAYqNTRc/fauk5AWieAJREJY3KbD74zzgBHpGI5kXoDiMZfy3xlA7PTsWVmItRGE3eOPPSazVDDYOHwKtQcD5qA5KWtUWlRy/YYJBubYJrqRo0ZGybe8FB28CWVayBkQqJxKweIOyo55L/bZMTwpGgOhsXfGAaMrhBpCtbb2puVtoOaMWX6uU9oDkt+AdHo7cgBGZG2GMkNbpWOAsIu3tajdRLcZIkBD1A31kGPnssqgg8aoRI112mIk67pSWB5woVMTa5KgmnVuYTHPzCjIpzt2qOwefPBBt2bNGs1Pt+NvfRzFF62UWMkctLr1ww/da6+/Lg2FkxCoxLDPSD0Zc9LKzEW9G+qjjNq1D1i08t3vflc94EcfecRNnTYtGmbkvcUX95o6CcJhnJtl5xy0SByPmIMxQZykHz8qNnPFiq8QGHqjV//DH/1IB1Q+8vAjmrObPXu25pukQaC9go6i6HxWqwvrsZ46fSqy9Td2rFtwzwLZkowWpkTJJBtUTVRkuZni/YCO8mmgo8NpCsxBIIxmzZqluQfm1XI6WRUA2iEZaHUHDx4wOhsp4+OjTLAGoU6dq8MFrUWfRRkDSXoIzwxpmoRm1dk5dkwCD2sZ2GAcIsFtHqj/oMFUgioE0ZPQ1Njkvv71r6u+aZfUP3sz1TarMSxnxd56q9UdOXJUh0avW3e/jEmgFYFBDKNWWKZ8DuVAS6+++qo6b+RlydKl0sTELyOvFYOwCP8P/uAP3Ljx491v//a/dKtXrXZj7T5G1/UbehyqJvBuNbdofJhVh2gnCCIav3ouPooQVKjMnKCLJEMwpoEA5XRsmB+WM0YMH644YgJJMqFOgFRAJHV1Ny2eQzrGCGYUDXFGhKqUWpzh2hmwwKHu5k131BgeiyBe/sIXZDVBlkCUCPtTCsOrJLPVgiUVpsMeMpbTDx8+wk2ePElMiT148QrGqMSi/OVBsazQ2UATYr4TgcMyaoYAiQeGJ6FCvVRQN0EwsUqSLQjY/GThEL1k5kCUykS48Z1/Fsfr6TAWdOHqAR3D3NiaQPqxOjPBNGJ91BZ85ciNttejxfjM3/3d30kgTZgwXubiqG/m8+IhzgpAWNQLnRAOqZ4yeYqbPWe2u2X0TV1WIw4ALd9mNbi1fzpv0BgLoQI7jummAoS8YFx69+7d7qWXXnK/8vLLmusWbyEqoqlOlnoFqibwYNDMRe0wtZlGrCG5JCwaBZWILhl1qdH0GVjemb+D2XEmIOdpjR43NnoHAZbaw7/bxGrJvK252TvSvq5fv2ZJGqB8lYuOsgID4D0npGOYlw5PtHAhGtoOIweiM+8qAYuVYERojwjVoNkLifD5S7rCO+XBP4j9A77xt4C6j66tbviw4dEcJNpdtaEE9R3QsaJjfezYca91+VLNKezOQ7Vk/+lEU8cUn4QP/xlGjbxVDh8+FoiUdujbXEzH1YCFD53Rgae9bNiwQdMmA1lYlUTVMtXzUT2BB5FYb4J9RPReC/ZQqFh/TaIflbmKFEKk6APj5hqXCUMMvUTgqb3aH+ZUony1dk7YFaMzXzaskEXbRuOK53BTJMyvSopFofkwFRY0G+KyuNNNJmKIwfmHAG9Jv3YfQkVjaNvDZ1eGr3ODrQ6S6entoHwsP7daIu04uZI2lGylgDtpRMLfA4VunbrqxBGFGhw0w1Uhh3xUJyuiyxAuNDacA4nzDftG2ewXqI7Ao8AY7SEoqcp2TTf+DO3hR8jaKL4TuNtlHNJ9N9ORLLtqpoNw0V6h5SSf4LmcjzjQeqVx++CqimqWR08D5dWX89cVyEdj/agMqyfwAsw/hm8LGmkuJby+jHzElSwT3gc/5ZZ9b0apdJGitfiaFD66etdZKEyc/UkLvCR8tO1QStyhk9iVncOuCvdugfImT8ly7415LEQ31UYoLxz0BtLl1RvLr5OorsDrrkrsC6DMKK90mfG8nPLsK8TaU2mnWuV7t/LXV+gjIJRjb8/X3aIH4k2XXV+jkQ5QqN9aHijEu1WBvRXpMoPoqI1+RHy9AqGe0vWV4e6A9pG1kc6jn5dd9RatZKgM+QixlLLvKwSc0VnXIBMOPRM9id77EY1kAq8rEcquI2HGu0IEV0rZ9xViLZTX9PN0fvN9V8xPOe8LpQt0R9kTR0dpKIbuSGOG8lFJnVYb/YhGMoFXLVBOEE4gHiaIWTbNkvN8BJX2nw+llH1H31cZwaJFTpwhH0mEdOdLW/Cf9qO9fP4ehC0ZyWegUFxJFPNTShgg1TTSTUXBhEUyAcnf6XjKRfgeWgqTDyQBV2gyIp2XStOQoWuQrqe7iX5EI5nAKwXGjNlbyLEvOasBk0yHx3rFH09B+O1oBV4xQvNRdYhiYXQS7Dti8zX7AyXkPHgWLJsIpcYf8lLI/607Eija05bcf5j2ny6TfOEV81NuGGn/SeT7thoI4Yb9mNbZEDjpIR9IY0hnMk1dlb4MlaEjmupu9CMaKdRPzJAAjBhTUxfOneNHtEkYIoEHwZDsqgNCr151N2tv6hu9D7Yw0V6Cs/8xsYdrDwQmwTjMlWNx0OzID6eTX7x4SefPYe2Ezb+clFBSPux7LD4EtDS16HDY+rp6lR9MvampUacTNDc2FQ+zlDgL+fHV0mEY+AmOVpL8nXRdBdKGMxpquFnnzhrtybII5Q24tN52t6wca6/dcLXXb6hT1qVpypChl6O4hgeK+2hraKX47QiEU2kYVUajMeWDBw+6sePGye4lR9Jgnw5D1liWwVgyNv048RvNZ/q0aW7osGGRXUzQxudzGVIx5lROuVcLFidaHKewX7hwQXZEOY0bKsGYMka0Z82cpdO4KQMEPOXBwa6cezdy5IgonFS6GhsadYI4BqWx5XeDDoSFjxHw6dOny4TXxfMX3IXz52XFHVusskCS7pJRlpaYSBu0+3xDrOpYeD+8Dx2PAB+GEDTwlJcYePNe26HQN9WAD/tWc6tO38bUFXTV1Nzk6kwAIvwoO8oKOsTG6ESrp7Hjx0Ufgq5MX4bKUIim7gb6EZ2k2UnlSBYe98FRwcn7NCNJ3ie/y+fwC1MD6ecet1tao5vwnCvgu+AfJMMCyXcGjGKfNkaNfVAs5nNkyPETJ9wBY0IYSz5y+LA7sH+/mA7HCV03reiCCYt2x27EDNY7IMYb3Qr03jnMNvTig1/SJyZu96ZttZgGRLrY4J+TdiA/+I9+xsj3O/UpzwizwfJymYNV6xvc2NFjJDRqTUAhBDk2hYNJr1654vZ8ttudtLI4deqU++yzz9zJkydytDjB4oBBb9u2zW3atFl+0QyHDbUOwcBBFs5VdRQYQuU0coA2g2HtmDpDOu169fJl+cfSvJ4n88A95aFh0YHRYaxJYRf8W7g6YcObpopB0kPZ8ZhrqIOQFq44vgufWpx3TCPGtF5cT8EBniVdeB7CDuB58ONx0zoVGMuG7qCxbdu2u/PWUThh5b57zx5p4Bgix+4ntmybrGORE2aGDB2hn9FKaMYdIzTMjlwA7Zn2yrMk70v6CUwj+Uz+aezRzw6B3zCs2MYbcnKjx+FdjrDhjwe3fBPCSrwKv2EkZ06f1lllaHEwUgy8otVNmjxZh0Ry6jb2JEea9kLPG8GgBSsBhEUakqXNs3QZkA4s1Mg8m38G9NycvSI9O3bulABhyDGe78J/iFLfJwLgNsQdHvvPcoAf+672Rq0EESd7I8T5BmZ6yzoRY/2zQQMG6iy6qVOmGDM27czyDPNthfGH+SbiIilWrzeu3xDjbjAhykMMMtOBgIlfuXxFQpDyG27lTL6UNw+sypM3rsRxzQSeBGt6Pos8+XLKKdtQLtCXf8YhvrG911AmfBO+5xXX8C6NoBn6e4TrQA655XmIP4QT6i84nicRhBzP9d6cCd6Whiad2ECHA+PSQ6xDgD1ENGJGEM5ZecpQ++DBss3KPcPNEtoWRMG0Z7j7oH56gutnSDe9zsEaFkxNV2OY9fV1Gupi2Arm2drcqvPyGm7Wu/raOld346arN9fa3OwDMIgZ+RowBoXfproGNfrWphZ328LgmeYrrl13N6/Xukb7zXwP/gizzp4108M1hsvQYpMJhwaLT0Nv9l2zhSVBYOG3NjbLP+GRlhb7jXaFn0YLr7newjF/HJyIJscBoDXWy+Z7jiWaM3u2et0IP5gQGguaD5od59yR9xg+W8RBHoj3pgkA4iVfYkwwOHtfb+XGECoaHGCOBodf0gVju2g9/L1797pTJ0+qPCkjNDOdsWblUWdhoKXh/3bLLWmExEkZ3bT6oLxU3gbKljIiXQyfYZCXo5E4HmW8Hx671WJarjFejFwjBAcOGSwGvHDBAg2pgXBg5mAECQw7CBnDsOHDrHMwyQ0bFpUPdcPZnQyDYiz3Ru0NpXeQhUtZor1xhhfpAVogY/8pRk5kYBg5HokPFGw/qVPyQl2TV+qSZ/UmBKAVtFTK5A5aNN8hMEOHAVgd4I9vKQ/KFSHLHBrlV18XlRNxtFjHRuUKnVtZUWehk0N9EY/qzPxH5W0vzFHe1Bn1TzoJC2HPqef4129f9wwVM7pAmYyzDhZDwfetXOnGjBmtMtJJDtYJoyM2zPxwjiO03qb9+jLKkCGDMOj3Df6+QsA4op787j273SeffOJOnzrtTpw8YQ3RhJUxtE8+/th9fuhze37KnTt3Ts9HjRipIZnQOGHa9OAZIjt85Ig0C4QOvfH9Bw64PXv26JwqtAWYMvNN50wg7d+3T8M8tcbcEDwwRPxv2bJFYXCgK2kYM2aMu3blitu/f7/bZ44DS2HmMFHmQT4/dMgd+vxzHQCKJgPT2bN3j1u6dKkOUAQwIDQ7TipnaG/q1Klu9pw5Wr3IkCZDbgiGcePMv+enCKLDFvbOXbs0BErZMDRFvmtqhpiWc9kdtLQQ/3krG0pjWM1Q/f5s927ljTwwB8YCmiNWBrUmKC6bdoSWNNqeI5DI6y6LgxPUL16Mwkcr+uijj9zhw4c1j8bcGWlFSyIPBy2/hM8wIkKdPJCGpcuWuWGmccFIqQ+E3MyZM91gSy+OwzAvmWZHuji4dIEJwPETxluWLdMIFLvQERpk2s/1q9flb9q06TpjcLAJN04MIN8N1kmBiY8eNdo1myC5ZM+oc8qVE6fRmim/RhN2xyzf0Ii0bSskhhIHWzo4Bf3Q4UNu+/aPlU86A9Qh+YQOoEc0IuqafA63DspQcwK0Z4IP+jh86LDbazQGfSIgESqUDWWqcE9HtAsoV85mJE1XrcwQ9tQB+dy+fbtoC784BBZ5g3Z5juNb6o6DYKk3fiPgWHxCPUPb0DWCbtY8oy8rx+FGE/ihXVFeHC01xsKGdsMcMnN5dB7cHasAT38ZMmSoosCjxw6jY0gFYfKxMR6Y2rWr1yJtwZjl9m3bxKDpjeKPYSuGxXjHUCFMBwbJ/BEMAc0K5oG2BPODKXAWFgyfIUaGwDg5HI0HpsTQIkwIZoFgQ8C89957Gj6rvVlr6bjhJk+erHk35uFg7Mwp1RnTYVgIRsICAbQLhCjMkqGjo0ePuSWLF7vR48aIycKk6O3TmyZvMLlJpukwB3XTnjO0RL4mT5lMyeg/mgL5+cTSD+Mk3QgpFsIwBEiZ7TKhggBBIMF8YeqUA+lFs+Ed2iUaAeXC+VyUI+lheJXwEGhof6SBMoFpomFt3vy+/LHQgXeknbIiXubhwlBhjQkuhDzMkzzXDB+qoUyExlgrU4bTSDsCiHqpM22eMmAxBcO8CEXKLTBa6hONjyHPw4ePaAh01uxZYt5oq5cuXVZZjh07xoTCuEjgWVoYup05a5YYOPRAJ+myCTCEM+/o0JB38kf58xvhTacK+kCTRLihgSL8Prbn0BllR/my3YL0IrhDHaHZUTdHrKN15eoV++6U/LBgZOeOHXo+zOqEoVhojDIiTjoX1C35QdggxD/aulV0RHkfMz9TjH4RSnQGGdY9SX7sPVoxnaZDFi8dIGiZ/E2YMEF5QAiShxkzZmp+jrKGFmhXCEryNNrqhbql3BgloUMRCbwoaxkyZIhAP7xyhEZlV5g0jRWBs2rVajdn7hwxpjHG0Gj8rHLkZG+YmVbqGSOEwYbvddCmMRgYEpoUjP+EMQzODps9e7YaN1rGqtWrdGiqGIO9Y36DZLDKD+aKlodwQ/CsXr3azZs3X1oAWgIMD0ZJWPiZbQwYIYwQgQnRq0cgwIDwr7PX/HArv1mxSRwwMQQiQpMwlW5jUvjnXhoI81l2JX2kH+ZFnCss7ffff7+bbeUAEz5lzBmtirzwGwYK4yYcBDsMFuYYFoVQvotNIM2y72G0LG6gHLknTQgdBCRCfDKrJ63sZ1n869atU+cBhouAPWpMHAZK/iQEL15yjSZ0YJwBzCHxnrQQrhby2Pd8O8Q0MJj8VWPAaC8SRuK0EUK5IZy1SMR+hmdRGeE7Kivi5J2cjxN6IM7wPJwhF3lXYApTKxYtHbi5pm1TvvhhiHaKCVnoYLk9g/bQGClfhIRAcswzZUf5Uv8IauqVThnhUWZ0Tlba9+vXr49XlmoY12gVIQf9oA0j9HGcYr5y5UppbBret/K9cOGi2ghaG4dx0lFBkNOZwx91T8ct1D3pIZ8MKx8/dkwdFM0fWz6hdTo4dBJUXr5sYkTFnCFDBo/qCLwAYxo0UBgHTIwl6kMGD7GeaLMJG2uU1nBhNjAamDOMgvkGetO11nNlboX3NHZ6zzBiBAA9YwQR7+jNI8w4JVpDi8YIGE6C4TPMiB8YCAyGOGB6zHPAOBtM4KGBcaVnjgYIw5w6dZoECGGHOSaGJBlm4xkORgRIe+jRw9xhUqSB92gC5J3v0Sph8DClGyYM8EN6SAfCC8cCDRgmAkrzf55pIeBIj4SLhStGjLPwYMBopvxGKx00aKCG/65Z+fGOYTwYK/FR3mhPlEsoR7RZwkRwkC+Gk7knfhgpaSJctFUJJAPvGd4NdRvSAbMlLYRHXhmiJC1oGuyvQ3NFu7tt9XrTNGy0QcovCJpYwJjQ1Pyo1RYCimfULfSBNjnZBNaSpUvdMnMLFy7UMN6SJUv0jM7KSKsrS6TSYFlWWCo7C59hXzobvING6EApV+YRGqFu2EOJBk66bpgfyolvyX+kxVp6zT9lQlh0jgDvoCcJcgN5p+yhQfJAGki/aNrqB40MwcjwMPWE0EMwEi71A6AFNHU6TpQ5wjGUO0O9aO3Ecet2dGK6hKKnO+qJjg3xCpnAy5AhB1WcwzNYA2OxCkM/9I5h6jBitAaG/BhiQhOCASAs0HTQeGAaCAP807jRnmCmMBZ65jBsGjHzc2fOnDY/dzQnwtxFQ0O95mZgJnwfNAIEDkKJdCA8YDAaYjIGwpWVlwg+hqcmmpaItgfTh8kSDkNFMFbSh/BlWHDi+AkSIjAXHN+j8aFl3nPPPWK0CDjyMt9+w7TQBvFLXk6YBsDQFQyL3wwRoplI8Fu+6STA6EnvDNNiyeORo0fEpDVUaP6UfssLaUI4wqTRoGGSfEfeYKrcI0wRfGgDzBuGTgDlz5zjLIsDAccwHd9P0NDZDDFZ/HFlXxdD1VetU8IzyjwwYX6TbsqBfXnz5s6zPA+WpgcjHjPahPbgQUrr5wc/V/2PGjlKGhfzT9AAGg/ljsY0zBg/aYRG0KZmWlqCpgczp14ZjsU/YQwZZlo9q1qN7lhAQrzHjh6Tf75Da1KHwtzu3bsl6BDU1O88064oB8qPuiBs0nn27BnN50IXCBLCIi8Mi2p+mPqxMrAXmkcOdcn3ODpKCEwNJxvdhjlAOhEISzQ0hB/fQDcIPcqfb3kPTUyzskA7hDYYiic90KElJKY9huehWRYOMbfMsC9lx/7FCVbfEnZkOBN6GTLEqK7AswYmTcd69hNMODC8NHnyJDVWGjcMCAZ7jwkSGvSiRYuihmyNcphpbDT2IcMj7Q1BN3r0KDfNtC+YMMwYJkS4MDvChlnAYHgOc+B7BAXMk3vCmDK1zS9xwajReoifIVH8wERhTAipMRYeDAhhTLwwR4b4EOQwFubxxo0Zq7kumBirDxfcs0CMhiFT5v4QYAhANmnT6yc80sc9vXnSj9YywcKjPIibPJAXGCErQlkFShpg1jBYtK/p060sEFIWHh0IyhRBSHjkGcGGoCNP+Kfs586Zq98IWspw3ry5EkyL/HBo0OpUVhYO8z8IVYQBjHvKpMnaQD/A6hbNDY2QsiQeyob8jLJ6YsiYuTlZaDGGjsYycdJECTyELm7iBDaUT3VTJk9RnCwKuXz5kvI93eoZ4YLQoXMiuoFxh2X61skBLDihTNBkosVOeqx5RgQw5Uw+GCqkbChD4maRx8KFi5S/hUZ3DItDC2j0o3ydR+FG9YBjKBOaoYzIK/OPhM2V8gpbKygLniOgRafml++n+A4dz/CLhgltMMw5d+5cCSiEqoZM7Xu2d0DfhEN9Dho6RNrjubPnlEbokY4YaebKkCjxUgh0Eun0kT9GCCToKDPKLkOGcuDbVIw+REKlWVopFRYSobGAJY0bV6+7V77zHfXcn3jicTd56tToBY3SGF1yeXlXYu/uPW7fvn2yWoGWAxNHC8OayIYNG9xwY0w5A72WHiyAHDiw34SYCTZjMGhaSrPemweY8q070iiZ24I5LV6yJHpfDF1JTD7si2fPu7//+793y5Yvd4899piEOkOAcdyUeVzud7SFhM3f5GW5fTN24ngts/9s1y4JZzoqI2CqAYTjw6JjwEZzhMfoMaMl8JgPE13gDQZs/9mqgvaKRorwQcCzKEMrX00LY8XnpKks+jGE9FkgCGGGcxFsg1hw4uONEefDYPdo4Fs++ECrVH/1135NnS22VcTg+/BNCCvEF8DzdDxl4vL5i+7DDz9U/hcvXiLhzkIYOi8PPPCAmxjymgLlzkIthOpiK3c6hNpnByws5ohZbMRIBqCjNdLKXellpDVPW8yQoSCSdB9QIe33JFS3OVjB5BN2gKXZDCfBRM+bAGEISgWJsAA0Thoyizy60CGwcCwweOftt92e3bvFPBF+9PLzVS6awvRp07U4IMzlCfj1PWgWKzD0SFj0znsErDiZF2VFan1DvVZjogXFwjof7BuG19AecWghYNjI4Zo/Y44p/p5LIqg7VsQMacLEJZAsHMKjzOhXweSDf9KDFonWJy3fwFwZw4DTrDOCtirQAHF8Z7Qy0rTJ0ePGSvtJxp0XFicCjzlXhoEZUkXbawfCKRZWhWD4EWHU3Nzitm7d6t7fvFnlwvAlnQPRfshrgHUU0PgXWbkzVK/9h+whpPcA5P+OypEwCAvNU3lJhpMhQwahuhpeB0DDY66NIRy0K4aJpGUQe3f2Qo2xMPTIsBlzhzASzaXAdElHsjTCvaWZYoKxsJBApyYEBumZCwsfWGQz2DSaEcaUJcBLQRczWtKGOS40WQQOK0UltEJHA5DPRF4Fe88maTZES+vluQl3NlgzV8mwWk7afTkwTBi2Imghhn+m+0Q9s6ilvr5Bw3gMI1K+dBgQSGxQHz58RNSZSKcrCFt+c5+mneAf2H0zw8x0tq5fd1OnTtFwqkyOBSTzEEAYyXDwk89fOTC6Y+6NbQfMG9IOEE4Maw+1zkQM8xctvrEI2RhvnTSmCJg/ZJ51YI3fboDjtWnKLByiLNACB9MRQCZaXFiT6da2laH3I0n3AZXSfg9Ctwm8HBBjKMQQ+90uVEsH9jcjRp5KjGcad1qilXHRsJw9TDIT63mzgAPGHmkyJWaoO/KdjCMI4mTaqYNkPYT74Ce853dSkOdLe3iWDC8f7D2CkLJSeQLCTpJjvjJEoNpFZax6iB7HSHwuIDAszNiQN0j7yYeknzzJKBshDDQ5pd3/DsjXQTJvrB5VGaXNqCXTx7QASI6WFOoQZMjQEZJ0FRBotw+gewVeoZiSBdp9qWkfbzLuQpUc/PC+o3SXmo9C8VQbxBPSxLXa8XYmvGRZcl9qmQWUEmc6jnK+SaIz+UuiUJjheWiGCKlkXGGOOyDc4j0ItY6QCbwM5SDQYxJFSKw3ofsEXvfE0nNQan7vBjGRtmrHW2l4pKlcGiklznSY+b4pN97ehEzgZSgH+drC3eBRXYSsOfRH9CECrhi9RdhldZYhQ8XIBF4vgMyTZejfyAReG+ikJF2G6qGPj4B06ZAmJqUGDh7obrXc0gkE2F5kmbusihSINl3eWpzggSUSNjazsIQl76xmC+GwWIR7Vvlpz9LixdrwG8w+dQoWNTYgAQssiBMzWiztZxM5m4hZgq+9ZiTD/ig1XH260kjmB2hBgjl97tPKPj/2mrGRGLC5uNCWiR6BYumyzLHX8fPPD8kyjrYphIUWMex3/iLrANE3oe7Z6K8VjvY7HFOkPYDeN8iX1A6jTdQjdCfDCreNBo0utPK0ozm0xLeF6CEgvGXVMOmHzlhsA3099OCDOi0h78KWYuiLXdpEUVKualP2/+qVazK2jhUl+AD0QHl2Gvo0+p4611WLoKLFVhxgPNBojH8VwX+OBSnO1SQ8rXYmb4RvtMZzUFJciXQD7qBV0g3gY+SDF6Izi3fe3LmyOTx2wric8i0ZJSSrJ6BrBB4hmqPScGzafuftd2ThhH1FNOhCaFduSYK1pCq59kwCz+5DHAhS9pmxBB1B9+u//uuqQN5VAqKHOGg8CDiWzmPrE/NQwToJ5sBIixwfhXTmQ8iPfx811uhZEOC8waIIJsOIFxuS5EmrEtPI86gngSOgjh077l579VWZycKkGDmkkYlIAuzWiNH/KA2UlcrPwD47jIxTXvPnzZOVFpidQiwz3CTC9wg7OiGcfMBeUh0JNXu2LMbkRTJO0lgkDaHeyQ3bZTDPhzFxrMn83r//9+6pp5+Wv7LRVwReaMbkxxeU59+GO7LQwwktHAOFkMCsHdtAApPvLKCvqHN9RzaBjx8/IfOGEyZMlIUcrAyBgu29JCDUIjpGsGK84djxY9pKhJGHsK9Xwo7/3m9RBIKyPwhm0Zg5ddytbDCUcfTwEcXz4AMPuH/1r/6VW7h0cfRduSgxSXcbXarhNTc26zy3N994w3Eo7JpVq8Uk0GDUk81TcXFiCiQLxqPvfOXB4DgVgPPKsFOIbUPz4H7za19zTz31VMUCD6CRBs0OKyD08jEThQkpNkljNkrpwbNPd7FilW+8UA76HXUQ2MAuI8YmVIkPyyIywZa0OpJEDyY0hB3H27z99tvKz9o1a93MWTPV4Eh4TvVTXum8FQF0QDljRxJGR1wzZs5wDz30kI7TAQipSkE81M2xY8fcu++9p07PemMQy5ct0wb7vHXt61VXn9EOqwo/Fg70y/mLH239SHWP2bt/86//tfvSr34lKp8yy6ivCDyNCNj/AWHrRaIcYNjU/65dn2kP55rVayKTa+a18vY/wHFeJW3+kPGyd955R6M7nDqCFSJsw3pvFQEtjiDYL4oxDPKD+cJnnnlGdl+hMehIVMV/T1MFoeJqKyQ0PIRc2COLreIPPvhASgKWpOAxv/M7v+NWrF6ZU7Ylo8L8dxeqI/BCCCHT9pshBQ7TfPOtN2U8+onHHzdCNJV5YnSIarVw/fJVEQfGgWWz0hxnpj1rhPLok497XxWC/DQ2ifnA7LCTCDNC48NKyNAw3Jis9I5KtQBxYEUDzQ4NEuJEG6Yxc6TShCkJyyNJ9ERCszQirNFS3n7rbXfu/Dn3xBNP6DgkhpvjfXdVQO3V6zrrjg4PHZCHNmyQCa7Bw2q8j+rgzMnTlpe3ZK2FU8fvX7tWdjq1iT1fXYcslpFVTkaXsPtom6z6YOsUBvu00fKXvvrlKJ6O6Cof+tKQpuVdjB9tBcZtTP/qlatu2/ZtOoFi1KjR7sEHH3RLlywxXoBFIP9dFXDm9Bn32muvmYZ3XHSMOTiO3YoFcBWAZgcfo/5v3Wp1Dz34kFu9Zo0bPsIfVFwFoPDSQdy8ebMUhBkzpruLFy6qXF988UV378oV5dMYqGJZdyWq2xx8g+QEawqTQoVhr39gvfWIjNlhXSWAjlc5zocdm2CyZ1cuXIqYnRH78BGRTUKOjcHeY8UgDg9sPDJkQs+OYUxsZXK0DnMsCCbrRpqzD5Lp1e88zv7n+AuwfCFE6eFDfBi/xjAxll3oXcb57qkgbd5xFNQh6+y8v/l9GTV+6KENxiTWqaMgFMpHIoy8Llledn/14mX30bZt7tMdn8Y2KZm7lbAL5dVZ54GxgdPHIzN0nMZAr/6B9etl/FtD8/ilsaddQCq8Qqi7Xut27tzpPtzyoSz6oKUyJE++ksH1a1hBtGnLA2QtaTvtf+cuadoMyy1ZstgNMY1Mml0J5V4MrBk4euSoe/XVV6XhUf8IvEkTEydSVAoL47p13Hbu2CnNvrm5SRrkmrVtwi5ouJXgjrWJc+fOuk2bNsmeMMbeN1jbxJA5fIYRhr6O6gi80CKtvGisMIYP3v9AY8QPP/ywCpXetxhEmtGnQVgdObQDEchVGdWFSWA5H6bKXBdmsyB2hJHApRwX4O9bGptl4BhhxFwdJtE4IYDwwwIJmUgjXZQmTmm1P/lcgBqkRUI8ptkxRIqJKIQbwm7s+PHR0J99Uw0lvFtgyaT+OTX8vXffdWetcT1gPe5HH3nENLvRVkQM21gZkJ1AB7Gzh0UdZWZ+DTeuR5rdx9u3u6FDh0WdnaVLohPM8RO0SO4744BFhxHtN998Ux04jG+jQUycYj37ED6XUhxIhp2Iq762zu3Zs0fGpTmZg0Uqa9Szj06ECJ9kMPiyvHb9mubsdlhnZ+SokaqXxSbshhktMHwHrangoK1yCxAyM3prbmpxJ0+ecu++864Ww3DKxZNPPummTpmqOArTcgnOvkPbumVt/8b1G9Lst3+8XR1o8kL9S0v1kKAnrjId+bhN58/AeYoIu527dmno/9FHH9VwafKsy74O2HNVAE/GIDDC7sMPt+jU5nXr17lHHnlUx8kYh/A+y0BgFp7IBYsHZscwJgIPpgCzYxijJkEgFcMy1FzfKNuHOrHdhBBGpGtM6Bm1SwhBRkpaSB8PYmd/OnD6GiK+dVtaHcOYEF04moaaQeAls97TgbBjGPP999935y+cV7089tijyo+YQ6Wgc2FgGHOXdXRouGjbG0wb4nSKITUcIlsFEI0xorOnT0sI7d27V0dJ0XljGBOoxw3KyRZ+k/4tiPram5qzwaA0xqHRUtDsOBUh1H+v6fB0E65Z/W+3jg6aPR1p6uXe5fdKG64YNE/7B/PnbMQPPnhfc8NodU89tdE0+8luIB3cSmEVC0uEt+yzztQnn3yqzi48c/XqNcqXEKoeQiDaQEMluqhjdsddu3LNhN170iLR6Dg1hSPSOFMyrznFPooq1JzBKgVmx0GXNFzmOdasWe0eefgRI0I0rjJ6DlRwcPSE6J0EZ2ixeJivYSgDC/T0UpaaZqez0aqxX83HzeIRTnfAIDQGfhmOVRyeLlh0w6GkWpoOyqSXIAA4BLXWiB5tEcGAYJXRYENYzdrjGR7VYwzi1OlTmghnQnzNmrXqqdL7rlr6rTia6htU/wgiNPvHrP6XmzCqCrMDVIuREUOxWyyOHTt2aB7l2WefjYSdrxP1uCthEvZpqwk4FnUxLIuwQ3CvNcY6dITlxegdmqATxDVDhPqb9TpWiQ7v2DFjxbiXLlmqYcyq0JnVC50Z6p/hRTTvFfetcE8//bQJu0nSyKpFzy2mQR45ctiE9zbX0mKdHa/ZcQ6o6NCiqUZcCNVNmzdrRIQTTzZufEqHW0twG2kRlTTWfiD0qrZK8/q16+rZ4yjMxx97XMybFY4IBu2L8n47ghKTSFI6eRzr89bbb8sPwwvLli2TIKKy8LvXCPTHP/6xe+zxx91T9l4Mw39bCkRnFg5EAtEzV6dlwRY+cTCZjLA6bUwdf+EwUA2hmtM/azBKdxECwg/DmMwNIrw54Vy9LTBogLvdwvzBEYU/bcaMKHFp9AQatazSOWAYG4E3d17Ug2T4l4bEtg5W5hYpDf3vCDB+NEiGGBGwzHNR/wxjU5YaykIgUfYe4a7UYsI/dY8GCZPgMNYXXnxRxxXRCSIe6I3Vn9zHLvpcIC5oJe3wwzX4oWPIPDfxkZf169dHJ234wM6cOuX+7M/+TMPCv/obvxY9T0ZUCiL53DdgzPnosWPuBz/4vlYyb9jwcDRnZ/Vxy2iMpfdxB1Ql3FZYodyLY4DVR61Gj7Z8sEVa0FMbN7opptmFPXiDBhktlxxefqAEcGI+/PLypctaCLVq1So3gs6OJRvtnrhaW28pfzwMtAbCtRgoF3gIC24Yxvzyl79i17bjy1go97Of/lQrkJ986im3YtV9yWIrHZUVR7ehagLvZm2de/vtt9z3v/99LRphnBvAmMLQTKVlQlgwiVPmEGbPPfecjvbhDDX1UMwPY+1vvfWWmAdMhG0L5fSSAyEzl8LcnebTxo3TcIP2dfni4ow1wPJnJsxhgJrXMyLVHjO776hR6J05hB1aMCe7RydV+2/ofPUigce2g3feedf9xDobDKOQH8oKVxpzMH8WTkd+CevCxQvu84Ofq945X44hTegibD9Q7Zi/AMJLumJAq2aF5JGjR90xcwhUmB4hsvKYEGKmSpjRXd54wzXcQ4cAX9zT2WHf6Nq1a91vfO1rboH1viWg8GbXMycygZcEHUnWBXzjG99wx48dV4eKtgegOTaCq/0Zkw+0VzasrtC24DN0rhcuWOhmz5ktGhMtl9rginhjjvHqtasaLkWALlhwj+VnnLY/kZdBlpdI4LVqNbhox7JjuVIauBYDdMf5lOSD0Zf/8B/+g3v++ee13SHGrTvupz/5iToSbOPKBF6JgMjo3b/yyivaA8XScIY5qbRosULlZUJSOSR064cfas6GrQfsgyMDEIV50PLkX/7yl9onRQWGXnlgNqUA/wiiCxcuaMM3mhcEHxgdYdEg8EfPn3142leI0PXMLSD9O4Z9iyUVTqumgzDdBNrwkX4TMxmyzzgaplcIPAMrwOitfvd739XQ3/Ll96oMEURtve7CCNngWqjMCIch5nffeUedg3uXL5fgwz91gQPJ79vd5w86BsyEjgu9b+Y8Fi9a7FatXm30xWbdaD+mqoewzB/XmC6I36cBhLi5hvSFNOrerruNXtna8tWvftXNW3hPXPcgE3jt0dLc4v74j/7Ynbe2ySI1phpEY4MGigdQrknhUC6oHoQmW6k+2PKBtoawYhJQz9BAR5UQ6hwk79OAJ7beanXbtm1z16/fEC2zR5X0E4/oxP7DP2OBZyDMiJ74VTgdEfAbTZv87Gc/kzGOX/tVoyNogk9JnrVbOql08DYav7xvzariweZD4az2KFRN4EGIzKswRPPSSy8Zk/AFR0GEGCqNySqqsa7B/c1f/7UsaTz37LNu/OSJRhXROyqPoaifmIrO3M7jG5/UZ2XDwmsyIjl16pQE3vhJE/wLD3vPggaKDoYrs1/5EAi+ANO53dwqTREmOnXaVDcsKfCsNwuR9xaBR4dny5Yt7r33Nqle7l9/f/Si3Dovkp/Gm/Xuf1r9TzGh+pz1VsexrzNJX3xfhTI5euiI+4d/+Af35BNP5NARdSamw/67CuO51dSiERG27pCXBYtNwwt5MGQCLwXLO52o//pf/9w6VZPcyy+/rEUkyTKrBqBl9hB/61vfcg8+9KB74cUX/Jvqgi0P3/ve99zly1fcs88+4xYusvr34J2GNY0PDBxkAs6uEqCdyCerQH//939fo16/8eu/4QYNwXKMBWW0gZ3en/zIC7yNG93KPi7wqtYcqAwYNA7hFz2MLjnXSpyB8NG2UPVDryfOxUC0LPro0Ri4gJdyXKLxyB6fuRyYHw1f+rgjIvQfgOQ94Cdh5nMGLXyxtGoZdW+F5YWeL2WiIaVkHypVHBXBOjSytGO3aMfMpwpE54eRO4T8FXH4MUe9qP6NzpLgxPEBHCZLIoL/AP9tO1cI///23jPajuO68y0QOQciR4oIBEgQzJlgkEQxWX4jUVpey2PrLY0t61le/mD7fR7Pmvlo6/lN0oTlJVvBH0hZEkUlBpASCVIkQQIkCBIAiUDknHPG7N+/qvrW6dsn3HPPvbih/8C+3ae7utLetXflMllRd79dqfRUuK31XX+F5TksZj5AzLf4HGjmbMLDpshAa5H9cSt6dZisYvLXSuC315fnZOCA0mAgfNKFsQNZWpsA6SC/CO/8BdNnFoR8syv5SYjaq7Ne+ekDaJ3B419gStVsU84aSTCDKz6B7PdlmK4ug/DMe9ceFo5eFQmBvE46M0hhNYphpATsmhmyfBg84kIcAskvBBPK34OY3pTsv4Af4bZ3IyRIl5i4AH4mFCstKtwkPiXcVEOSV955/GWADxDfp0qPe1NUtKaU75H3kTcq8TgM0Deh6zH/vAhJFHQf3XEf36EoTWGKuA+KkzBE+mVIvy9RFUVDBxmSx7SOJGfwMfKjFiXgk7TipjvcFPCHFqHkrINAfiXDBeFniOHVchNAeVKZ4psknkp/QEV44VZI3PdlxGLfaSAcUQFlslgtE3FgLbG23DaYgVDNmSn56fMceJUZG/+oAuI1ra/I5CJhlCMjeVZAXMz/wv0+7WcWfnyPX2kY/DalxgwoKVnctCPvtN/B0k1NUzV0ZCAi5Jnyi/siKOv8NxVKj0tqyCCeZc+tJcVMN9zDp2h09N7+RFmMFID/sYafPs/As3xccc4z0hFlQvzOETAZ1eeUneSZv/pLiSLUyRyy0ohZm7SUMjmzz9SQgfdFXgRncofDxBH8kT/RTQJN7w9ikoFPa5GB8TlVzgMBXcJ7kPVi5Z5XIDxX71YqywWoKDPptZ8gz6amAcMkFDFDgTEiNtEzJsTXPI4yFZy0Q3yXdxOEox3scVy7lNXOeGa1vMKw4u+UAvQTP/Jh8UiXSkEV4i1XFRAKgiWYXM5TP4fPWv7wwz/LjA+/o1FKyWSJGjtEtzJX79aYC3+LCCBztCqT7mlfKbIb3kHcB1IL1KiQtyC6jbIdoWdc7YZ7+IzfutqNyO4D9HUIo8Lg+UclOoOYh1xhtfEKfooFyBivoozgJiFabCL0CHIi8DJc8pSArkmOQ2sEXl/6axzSwEinMpKBZ8nzzHCDvPsqcROK/O5HaI3qVcYmtdQEFUoDBMahsPbs2uXeeett98qyl92qFe+6/Xv2+a4nEOUsB/xrx894w7uEBIvTwUMH3eaNG92nGze5wwcPuQtxjBGknkGhEGSGE4EPXgkUHD2/LKMqw2r/z5054/bs3OU2fbLBbduyxZ04dgxP+r2AtQPZRb6FdUhMdGKN0Lsr3nEH2MSWbu2YZ14bVJBXEr4yIXmz//4dV33VhvD7xNHjmr3LEoA4Jqtvi6Rf/PQUZU2IN1wjAbsykWXvrt1upaXhpRde0IbPmzdt0gSrDDF+kUDwP3pVorWAf8wUX7d+nVv20jL3wgsvuo8//sQdP35C5Zeczzic8EYtpSBn0RBJXnSTEAjXc2fPuZ07durcR46qqnBXRLq0hROf1QJhsK/n8teWW1qedyveWWE6dE9m+NhCjPsK/VeiAo0ZPC8b1cmgNVRcA0XwPEOsEZsDpuOvXOXPr/p4/Xq/VZDdcyKBEBmW87TNO7uLbrjotu1tfMfpAwjJvv37NZWZ3c45Tki1fX1nf1IKaCcwuZ+CucH4ydiZMmWnmQMHDmpbLWZ4njx5Krjzl/4MFAv/4CN5yxpHdrKA76ydXBm2imND4Awp7yGT1mjoRFl3o71M3QVgPPeZQnjJDCqz4djC6azxSu7z20PF7+SXJ4URHleFOWCnnLUfrXXvWFpYGE96mLHK+XwZ76NHrZYF/K1F/RHksaX9/PkLWj7E+lDxxfjP0qktWz6VIYS74nAun7wBgv9t3ebxmgctwaNHjqlCxdT/ZcuWaUuyxlHsbxH27tmro7bWfLBGeoyy8/7q97WxvYA+CgKWpiseYl2iEYPXYAElb0XmvsLIpYhGxBo+GIeNGzZqSj/7FLInJsf6cFqxEJv2KdUFQmoUFaHVdlioSo2L5QMTJ16tTZrZQeXMaVN8xAdBJqxIhMNFAu+pIvzwjPE97kkRG0sTBop8+vTp2vKINXxMNy+RgMyybENJsBh2w8YNWji8ePFiN2zoUPeJGQoUlED+NgPCoOBbC5Ip7NqGLhhVjKkqMohHkffhGZeqoccXiQMWqhMWW9CxiHzmjBma5s1i3uoeBcQyEdGRdHfAab9Bkp2crg/fWdqzcNEit+Smm0wf7NHGBeymInQiD5ElZvJi4JAzaMf2HTotpCvA5g7IGSeCsAUZu7CwUbtalIZ2OiuAGceImUQtJ279Da0z/VUEJ3tMV2EozHQr0SJiN5NZM2dqr0JOqYaZ7OgtVPGvLiwMGSMDU73xD4ZPmzrVTZ82Xdv0sC0RNbx2yiZFJji5iPCIVgbdHvw2P1jYyTl5rJdjK6qJkyapIKjmRRA5L/ojQm4JF80Y7du7T93F7NbOZrnsZnLa+E/LT6gmmZbf4lpWguM9V/8z+20YPGSI/GbdpqZmG1Ug/S5AjwgnUCHiY7uyjye7pNx+xx26co4hLUm67WsiylY+jPgzyGApPk3A8pBjytiRhZ1FFiyYr2OdRo4YqbWvVFIlSWnmku85VkRUyEF26z9Grtgg3x8GPaLSzxZi3Phx2m+ThfDa7tDihJ6JcsbZfJmxi3EwvesnXtVIXD9C6wxeyuXUSKSFmnu7sO6EkxVYT0PLjkNbMRYYKnU5dQIKLQSJQuUgUgQexYciYteCbKoygpBENYNkwwtH9tr/TOBbd/jD1mb4qTVilgZ25eBegtjuu/6LYKqUJ/AfRTF82HDtHwj/EQ9VRIKbdrBn4psRr4uVkIEKifnNHps33nijTm0YP26c5EsKAeCeSlj6XQL5H6gqQlzY3oz9Y9mGjlYqZ43Ro4Axz5B6GDxVTGJ8CpC9j264pFSiJlinecIMG6eSs8UWZ2ZCnDfHJJaKqoRnpagQyfPohp5CZHjG9BlqcbExM8cTtU10aS0wqAsXLnLHjh/TDi3Hjh93M2fMlCGsBkpKJvMlWmnwAixzC7M3yXRqHJxhxhWjgMFA0bHvJQIk4aomeI0gE8iw3RATTDA+FgUZpvC8ateWIQbfTlgK4oXhpuUo42c1Lr/9kPMH0fJ5Z9LSV5Bmo92TX4yxnDl7Rnl27rxffJstA4jjvR0F4QS6yioe1LzZhkwzOuGltJq9b8T7qtrPI3tr/rIBtE4rN0WEPLOrxTVmBIV6+o/4lmgJ2nhCV55VqIYP0xZe/KYHiYkfQ4cO9ftJ5rWf+F1HMPS6zQ36asSoEf6cTPM3zgDtCjCBZt++vdqkfe3adVahmuNuufVWfxhtiYbQOYMHbwNx8bJQIDA8im4NHH/CKQO0ghhP27pli46UwQhRM5b7ImoAGJ1Y88cQceQOrTwU0uFDh7VzRjxYs0NIw8d/I5KDgqbliAKnW5Nz7Y4bxdarHKG8+T6lHLLWSsG7Xo/A+zh4jiJiD9KBA6/SOOenn27RjM1BgwdZbXW83FS03joBJhVpFxPuzc+KWbchXp0Fm0CvePtt99JLy0zGDmlfRGr7sWtd8lINeteCSJQQ0pzEADGmfvrUaY2pc1YnB8cyxs67ZpFWgnVvLKRChWz5HYdaI7t5oCN/9atfu1XvvafTGzhdgX1rs0piibpoXU4FIajK6lyZRhAxepx8wMyjrdu2aqPWGTNmeCWQc98YvLHLuhQGXaUwmBixbft2TSSgRsZmvUySEJqUzaiQaUXiPycqULOnUDFRhqORCAc00sUh/3AW45MUql6PkJQ4WYyjgmbMmKlxCD+L7jea1Tpr1mzPf0OrumFQBrS06TIdTKs+ooXZyyHBnLzPESvwkXFIZu0hB+JpjVq/2B1kSehLfL8SSLKPcjl//gI3avQo7fH76quvqmI6e84cVVKbQhF77Fnsnh+FnHW0Mt0gPv7kY/fOihXuqBlttm/85JMN7uOP11sl+6gXpPpqpt+j66sGsQAnZRpg8O677z4d8YLwzZ07TwOy083o6Zuc+0bBZ+mn463FOOeaa2TgUHjUiKCBQ5sQyuBxNHYIOop5eJisMt0UOC27IaFmGVsrqunHiEXKQY/wV6RHGfQzZGNfAIaPSUTwe87sOaptz507V2Nt5KGQGoFmQZ5ZpYduTU7P+IzJGmO5rc5L+K3433GHm2rxZ1JUnFEXu9JLdCNCflPRYRz185//vO6RM3TOTUuWaFyvZTBRHTpkqMZxb7n1Fjd1qj8ardWgt+qee+5xSyz+7CBDzwJyxqzQhscN+7ksNm/w2mVcgYKqlblW8545a6Z7/PHH3Ne//nX3xd/7PVN+s/27RpVdrjbMdHdmgDJYHQUAgzTL/F10/fXuuoULdc+sTdWGLlpr8MJFrddK6ZK11Oj6ZExRmwdf8Lt0xHe05NiElWnBdJdxcjU1PI74YYnFYiO67C6am8vn/QQZvksJv/Bf38drIJSkwjF/9Zu0kNReLKx+HNVvwAsx1oGCYCf6P/qjP9I5XVOnTPHpPe8nAXGt5EvIs0Dcs8sKeUeeyQ3fWu2X+7h/Ja1tzk+8ftEiVXpSP/OEP/gXZUgGy6CrxRuKPGfsEXmjsvN//Zt/4775zW+6r33ta+4P//AP3Re/+EUZWMkPacZv4hmIbeeIv8IwUlrM+/ieMDSubddWtXb7G5i1OGrUSJ2+/wd/8AfuT/7kT1TJGjtubOeq+pEdqCl4ZrLNiescWXb/0vvdrFmz/PsW444773Df+LNvuD//8z93/+7f/Ylk7cEHH1QFvlEZyc+wQId5nWnyH571ZdQ/Hii+TfMp/SI8Z4eUt95+27322mvuMVNet3A8DO6gVEBAEW+q+Z/em5CePHbCffe733VXXz3BPfH4E24cR/egiOx7djpg8efy5ctlQO+9916N3yEMUUlq1hKOg4Dobyoslh1sK8VSg507dkiYWGaAAsIfdlY5dfKkpjYz5khLju4yFGJHFBMKn/5+usNQnOpmtZYu7KAmSrwZ32KNDTPAZsy2li9I8wM0HmTXweJEGtg1hV0gHnnkEXfHXXd4w2ZKB2O3f/8BbTZw1tLFB0MGD1ElgSUKpHlg6O+slRydkGB8+OEPf6hJAhxnwgG8GAWyBSPFYn/iEk+PhyeRsjG1GvD+XHTbt21zz/zoRzpmitPb8ZOuWOSD092Z1g6fiEecsMS3Mrx2VUghvCK54Anxfumll8Tn+ywc1ldJTu05i9k59/FFe/8X3/qWe+SxR/2HHUX9JPcqnD51xv3P//k/VKF8nJPoJ7VN2NDMS/I6n2ZjCIp90OCBVpm4ZHJhslbghu95jh8bNmx03/ve99yDDzzgHnn0kUyWKwCj+c54j15gW7CM13n/i2D1saefeUYTUajwLbhugfcz+ZZKouKbgyqQxNdQMfuYS/I97k5Zmfjb//C3Ws7w1JefcoND7xZboK1bu9Y9/fTTkm8qauXxQNWQy9gIPY6ZXw31MrTBzFOt2MDEhO3btrvfvPKKDu2kqc9UcQzRqFGjRHQHoJw4VZjxNhQi5J/5+/ibK9PY6Q6FWFCMQaKlQAsBxYuypr8eGm3+8x3vG6XRo0a7MRYn3ds1xo8DLVGgGAZakoSTjT01I4hXECgICtWhg4fc4cOHNGGARIwj/y1Px4wdowkEMe+Y1p/mUZ7gC/kD/yDu8Yt3Q8izM2fdsaNH3RmrrJCHGU/5PuRxXTJeIjesrRs5YoQ2RsCwjTY/WD6DYoP/TIKCR3SR4h7i2xhP0qTT8o14nifkk/C0LIdv7R45xR/kesWKFdoVhPWp9Bw0PWu1hPQJxg4UGY9Owfym8kqvkSrS3cQmwsTAytjZVZocqqM7Vek3433h/AX3yYZPtDMMO1yxI1V/QOu4X8/IdQHi4PB2a4m99uqrbsPGjRoTYjo4yonDYSdPmeImWSuA65SpU9W/PoX78Ftk97yPblAw02fMsOf2nbXuJkyeqFYeiolWHcaPCTYQYzbxW5F9X/G7GhEnIx+2vyeeKErGGzCkdPHSzaeWhe+h7RWIZf6i1abZ3QRDB68wHMzCJd+mTA3pFz8ayzPGd2lxMxkB40AtH96QZ/iNsZk1e5bnC7yeMd1NmT7NTTEeQeJNHcId44iEQxgY1GmzZphf07KJDow/MjbEpglMvPFy5b/loN4p5lYUwkZGIC9vyNgUczdNMiUDaMaOtEyc4rum1nzwgdbz3Xbbbe6JJ5/UxCsqdSWaRBBI3yrqgnxE9UWD011o0rCq18Mqouw2w/6ix48f1/gz8tcfZKzrWdSoHYSBKTUAuv12b9/plr/2mrYqu/mmm9yDDz0kg9TWxPdX/vodUjguxJIN8a4K+QMR24wq082ZHcW7SShaa6EwWUHr+ejqUC0LMsfZfS0Ku7WYf3G6Pl1ZdJXRemCdkLpLTSHyXP7aZz0SIV7W+Km4IS2Hjxy21wOsQJlyN8U+eNBgpVmwi2qqln++llqP/Gw4Zl7KDwvn/OmzKrQoMlqNkydNlvFTT705EYkn4b4emTsOeaWSQQubR+D44aMy3kxKYhKMWpVWMdGUcOIimTKHUETwL8a/Tf7Cbi8WR54pPfbz8P6D7vXly7W/7PXXX68TvTGqkhEOBC3RcZgYyMjZVd3OUUa7AoTBn64C/seWPpcGgsrKWgAtwvXr1rvnX3hewzb333+/tsNDnlWx7uNIi2fXIeY510j1UI2hElovuOwj9/LLL2tfRial3HnXXW6iGTtNSolQePYnMj6GX40MHCcUF4/TvXT6xClrpRyVsNHVSbdTtvYlLUENCGAGCwv/GMehmwHjffjIEe31SXfaJDMQ1MYQ0C6plXYJ/BgK53zRjcn2cRifSZMnmXHA0F2lLsGKdUrcNkoGWITRgcfMhmT7NroYaY3FQssYbOzuzhC+bwiW3TEc5ObEkWMat2PtJq3SMSYDMnb2PlMoyEG1MIITXRPi+zhJhnWILM9Zba27hQsXuscee0w9DVSoyM/MjxKNA5YESmWop0DG0SLn4xgiVy+ODTrLw+vMy9oc4cUXXpS+YQIP+9jSi4GcNTLG3dvRPQavUcDFSFWAocEAbNywQS07FAUbwzJdl64oWmR8nimioFxQXEpt/F0HMB9DdMRaKLTuUH50x8UxNgEh8ncdB9FB4VlaWLCuaexW46LVSBfd4GGhlYLTTKn6S49DEi8Gv0mLn9QxRK0hWna0zMgv0usP1wzg2w6mCz809d8qBxg2xtowdoOHDQljKQQVPOVCcDGchshPQsBvKiBUROABYWgzAQPvMFZKTyCNszGBqsKvKmRQXI2YAMUasQ8//NDNmTPHPfzZz7oZ1rIbThcqvE++KdGHYDz1GqSNua1mcywHyCknevz61792p0zP3H3X3e7mW25WV6b0mclZVmb6MHqWwWsAKE5aRTp36uBB1YapqTAWhvLIFGo0Eo0ikTv8gfkIxrFjvrsMZTdq7JjQfemzTQLSrJDwmcURoxo3s6Y1RAtyGEpVXid+NxlMd8HnhRm8C+fVVULXJYWJlhcWCINB4fYGIiRGn/h/ysd6ZOB7uhrp9mXZBmO1hHHVYN8do1ax8Sdr5SMG3DZKQWwwZpyCQHcpfmmijdEATuQPIE2aNBAIfoqKwOOUDLREqWmz09DOXbvcNZ+5xj300EMaH2RbNIF046dPfom+AkSaf0G0s7Iery2CvDPxYZLKhx996E6eOOnuuON2d+OSJapYI8+ErXLZD4SsrfS2CNSMuxJ0LVLrRsmxMfCtt93mpk2bKkNE1xAtDKLQoeZ5GmU+u2qAjKp2VDdBYIYe3ZhyRw0+oFMppTvT4hpnY9JFhgDSnRkVYq8xeBZfU/mK71VXDdQGusxCjGNc3sD591RGKgoWt0YNJc8c0b1H4cTAaCblqMqdLXiHIaEiIU9p3cGzDtAFM9r4I8M5yG93N8pkALmQG9Jh76UkAo/0N+VXBI+KyPyhJcxSDYw0awTvu+9+vxY1yC5ygf+qLFhYJbKs6RPw4hIFwkNlo+1np0HFjbHnEydPaM9N1gnetOQmzR6WnjGxoqxAReLb19D8Oryc4F08d8G99eab7ic/+YlmSbKfoJQOoMASTI2gakfCAyPGjMyXly3TkS+sw7nuuuukWPEfBfHhmjWOE7TvX7rUPfDAA2I47yBNGjB/KsLKx4kSZc8wrLQiGRNksTqPpdTNCd2bbImFYmUCC7M3UX58Rxjt/CwA7jF27I+HMmPHmYmTJ4e3BvPm3KkzmoxDlyAz/KRw8yh4dCVwwfjP9k2/+MUv3PQZ092ihYuU57TIKcTc79612xv2cWHtmhlCFXBLRKx51UoOBohKCFOp8efmm2/WmFp8R95roowZEc1+pFAbCLthmFu6K/fs3q31nBz3w0bQGB14Fo2PjjGye1rkLE3gmVp5higDhFutAohL9ndk6QGVt69+9avaaYZTHvjW/riTp05ryzUti5l4tQxjh9FD5KNVYPPn//z//2d36PAhKW4qI1SClNeBB5IF+KRsZF2t35Q8yoFOMc/ni73yssgnXlZf+c0rGuOiB4mWuO+W9+7ayxQe+ufoiXr5TlzRj6+++pqW0rConJY9FXaAHxrvNjfsMQtYk8cz9qDlex8H38tQFJ6X2ctu8+ZN7tlnn3Vf+tKXJGdaqM4/equs4vXsT3/qNm3e7L7whS+4G29eojR2GHXS21PQMoN3yRTeG1Z4/+Vf/kXTt681o5caPJx7r5rJTQ82HcYQfbB6tZQdDGImI0nAV5TO2nXr3IsvvCAhxeh5wTAyxiP0xKUe8AfFiXHFoDF9HMHzhcj8CQaPZwgPLQ3CEbK01k4nEzho4bHomK5MDN7o8WMrsufc6d5j8CiM7616z/36+V9rnz/G7pQHpCcwn5Pg2YKJ1jItJy963kEjBg/Q8mb/Vdbd0WWqyo49j7zheCm6O2lhwhcFXUfE80CpsC4JHmu5gBGVmxgGfKd1hmGjq5sB/2jwCAkFJHdQlIsccIOMUftGqT7xxBNa/iDYJ5fCAbbbt+9w4yeMVxy8/Hon/RUsdfn7v/s7Hew655o56hXhlA1N7DFIkcdyYsxABlH6sccHHtXKQ77nmwP7D7hV762SEVp8w2J11fNtfA9/7X8ngBxZmXnvPavYnLQGwg1u2vRpMnjoTYxyZvBMvgCylBo80qU4hbTlQZpZd8hJ6W+9/Zb7i2/9hfv93/99N2zEMF/sDPj/3LM/04nwj5QGzxDfpgniWS6BbKH1+htvuF/98pdu8Y03Wg1/oZQEgCGqdelX80BJ0p35Cwtj9qxZmcGLhpWkrPnwQ/fz557TLisM/nuD5wVUCqMB4JZxNWrXGDSMHmmRIvMO9I6aI7MpWRcWs1FZgwASpp4Ug9oiwrZv/365pVU0fFToNgV2PWcKr7cYPMBON0x5pkbKwnpAHvllFz7PhpjBo9XMVkyRN4BkVD0pPyDykZYRho5lCHFRPs/VDc3MTTMiTC6i9RW/aRjGO+KCsWGWKUtcaEXQ6uM5YQAqKnB4woSrVWEhLZEV8F/h2j2GsAi4QW4xnPg/f8ECN2qszzMMHpU04rBt23YZPNXKkasYSD8Fdu3/+/a3dawUlVryRa0iy+xsUbndk/viQehFUN5Z5uni7wrzUgbNvmMz+1/84pda6rT0Aas4q3WFDrCWYvC7M6DrH1l6+eVl7sjhI+6uu+928+bN1TPkgjFwwsGY1zJ4MuQxUTnoqf1Bxr73z99zjz/xuHvqqa9oET75Qp6wXd9zz/3Mfbr5U/f5Rx4pDV6W+DRBPMsn0JrGb7z+ultu9KTVVm9Mt6hpYWacOXHK/a///b/d1aYk2I5n0rRko1YLb837q91PrIn+8MMPuwceftDHQWR/igxGEcwpMyZl8MzYjb3abwKdwQrdzu3bJXS0/oZzynEEYcVguK8GyqblGTvqU2BZ4D4k1rz43sLobQbv2JFj7sDBA6oEjI7KO8mPbVu3yVDRDdn0jheWLzss74eaP5OmGO9z3pw+ftJakvtk7NrxrQNAzphIAv/HTGh/wObeXXuULHWpJhNZOop9u/eqpY9hHcr4bYTJK0p9q+UZBi923Qo9iOfdDQzAf/kv/9Xya4LWKU6wa1dg44aN7p/+6Z/cA0sfcI8+3uS2bnVAa/XHP/6xO2hlhu345s2fF960FsePHnf/8T/9J3f7bbe5p77ylCqk8exJKt0/f+7nOuWFTbb7usFrvqTmYbWFOHZBLUTgEqlFUKsAIhy7VsDCIWzyPtaKMkZUqQVVZZT5UxEGl1xaqIHhTn50lOwzanIIHD98DTQB/vYy0JrThtpKkwc1Y/KOK2sbecd4yCUz9prMQr6KyJBAFc8TAsYPasGsvcNQiB/6xq5cEvnIkHNT4WcRmVtadExe4SrwPHpp75nB6d1Yiy98A7EpdIVbrkUESGeAekOgGG/Jif2JYpETj/4KyRN5ZP/blf+WQgxo6x6tBVjWxsqG4MuD36QcVOt9YhuwFKHnVuGp/MTXXPMUQD7RKiQtKhf8j3IWLuif/iBirTN4IQP1N2ZmChiVEooBioCxfJZS6j5ximEVgwqEJDIt7UpiIbm/MZJitXdRWCJJyXriWxQQxkiFSu/DN5BB4Q/w8ahAB6SG7gh9b156X/suVJyCXKh7W/yzZ7AQIh9Sis9TMhaoa9Hes1QgFt7MvV14FimDPc+ujZAB3gwamCzGDf4L9gzZIwzJB+/CbjviZ+oWEJUkOhmQpRjXNL4BeKF8K5HBcqswKwuBw4YdVyKuFa2QoyK/7BkyULGZQh68KngdeYvhqma88/ol+2lXlZ/kdztKoIpCAumuvKN+AIpm6xAKbg3Wt8E4J2GKNWI42dCH6BWvWDJllCAKSNbKNP+1LRMpDanNhDgqpkgJ9IgwMKr+hyegW+Lgr4o/6Yjx51ovLZkfPh+qCXxfgiTD/jP5iBsKoa+x20+yFtmhciJDICc5sj9GOAXK+8jXyIMcGFu+ZCSDhB84wYOaZDyxf/Akk5XUawsvW+cXwXuLt9wrrkaS7fA9/sYrZBU8ZDS89ZU3yVriMLsvEaEyE+5rIvAAdtCb0FH4CSF8X/9buufZHk8I4WYEeBVfIyPwHiNpRpUrlbisVyQNTuLjBU9GkfJSpCfyYUaKsIRUfGdxkfyGOPUn5Ept80AwIknKaoH3CC7Hddg9C4iNI40zAEnk+6h0cgzWz8hgxlfM+YWzofvLBNMbQHsYv9O33k/5G6gwPjzPg0fEBWFGueJf0bd5hHCyfOsnoGYqsrTTvQnJYIQ8kDK4YIYqR+SXJvuYuwvqDsI48p0RomA8qJBDA9P8CYdZxJmM1ctq82ugte6Io6aiRwSRAu34BvstPqSF8/iQhUoDZkhlwuQwLjMgvZk/JVoGn6fMqh7gD0nNtXKqQp95fqR8KeSRPcKgMjOZY3jYqODE8RPu7FmT6cBvDFzsvkdukCsq0llPRyoX6b2JTxzrxh0TZpCvkydOuVNG59FpJIlv0u8ikugS94r4423RN30cPje7G3DY/nO23Zo1a9yqlSvdfmYrNggJSbivBwzQsUNHNO324L79OrcvEwSEMBYCciISRlFhNC4RGG3OtmNd3emTJyuErUeA+OTjVPSsi0B+VsDCZXYa+6G+//777uOP12u/Sgolu4wUUZRW1kEOHTpME1cyCa6SjsuXLur8wo0bN2p9nnhej60mElSY4tTwDHxHq80IY8tGB1wxpkeRsU8/de+/955buWqV221ywBIJjSdiaIPCy/IcMv+ybu2CBLTLsyuJ9tHrubC4im2WfeThvr373MvLXnbr1q2TYaoHvjXzEPzBoxqwMFhCwn6+Lzz/gs7Qe+ZHz7iVK991x02/yUmo4ElWYWmIl6do9HhRHRjM4yeOu5XvrnT/+q//6r7//e9rH2Emm5w5fcY7Cn5XkJAzdv0YUV10PXKM2LVjp/vZs8+6f/zHf3Sv/OY3fiFvHrlvmgFTy99591232pTqnr17/SQDgAJCCBC0Rv2u4o71clu2bLFw3nGrV6+W4u4RQMaDnFPQY0sqdo90dyGI4dFdxFTpDRs3qKJD3FhqgNJgTZIQeZ5SHnWUBGlnE2sOJsYIURnJJrrUojxyzyqcW5rYSo0ZvezrymQpWnqk7YClja2bpMzychY9iGngvgeDWX1CjHcvAPL+0YcfuX/+538WrflgjS8DrYSx78iRoyr3VKpZosSSpuXLX3cfffRhVQObN3Z+6U51UPFiicpry1+zVuQxydybb77pVn+w2p04EcpMibroPoOXA9PtURJMyW83HtIqWMFEkTKjzv8OJTV2IaFsqHmligjYq44YAxYQc3QQMxTVHUeNvieAdBnRxfLKK6+olUNXoGqbJJ+X+bR3IXyeozwvaGEv+cYSBhZeT5k8RYuwMVDesb8UAX8i1YQZ9QNWkULJoVSYiNQ4V9vCyfKIa1BQsUsqks4vnDNH293NtutpqwQdPXpUFQw/oSV+nxBoJB3dCCpCdJfBhyPWamVK+8EDB7VRN11ojbSQegroxmT/SBZex27ErgCzedmA4M677nRPPvl7Om6HXgUqcPBWlUvGEWFzIK8n2vQF90IUhcQtQOZYinHfffdpCcN1CxeaEbwkGaMnIQujRE1cMYPHoYNLly511y1YoK2ZusRImFLhINd58+drnZN297DHEAJUaOwCElnzwH0VsHPIzBkztQ8iOz/0GLGzLGVc4YAprPfef0+7pe/du08KTWMD3cz9qNhZvkANlS5DTh8YPsKfKo4M8FyoxZdgJOSff6B37WBp5PDU+fPmaR2blqpk39SgPNJnyIER0YtRHDpsmNZJEhY9CNqFxZ5zlqHyuEgRKawwcSVQTwCbC3Nqw69++St1nbGm9fnnn3dr1nzgjh47amVoYFFcGMUAADSDSURBVFXe9DTAF4zP5z73OW1F2NTWbA3g6glXu9tuvc0tWXKT8fGSKvKsoWS3Ickc8pLTH/z2FO6rZWp4zOnmU6dN1aHAVBbfeutNd/jQYTd82HCtbaU8a6swyVVCJSpwxQwep3tTI2bLJI3F5ASiVWAnhjGjR0sRaSzFFEs9Y5ehQSU0ZPgwGTq2VMPvbMLMFcbZc2fdG2+84Z577jmNk61cudItX/6a27R5kzt9ygzLFSoUKHfVuJndZlcmhqCMIColtePkX1YYiWrpMP6yGwu7rgh84+/qoqrLApmh5QDvkTG6tXaxJZnJHZUsAXmLcYxUzf8rDHiCoRg1epQWdbMnKRU6iEoJlZTeArafQ8fMnDVT3fhdlefsGsR+qPTyvPbaa6pYsscvh/hGGc+38NsMXhsJ4RIRJmmqMnjx/EXtVMTevuwyc7XpTvYUZaJMRCwXnsLDEhmunPRayByoKcbwMzK8xcD4sLMAAqVWpAQuhBXLQBFVQ1E07Rn+a+2eSWhX1SQ7CmYazpk9x924eLFOAuf4mfnzYmv3ysQxjltQ86V7jC5tDAXdjsgAtVWhBk9k6DIKD6vBlA1jakwqkmIJj+tC3ocwUlTxgFMPNm7apLFo8nfe3LltW4UVlbJ68b5CoGuMXYZQonSXHbXWKmPSHC+jtWa9x96J38gT6ympVPG7K4AMb9i4UcMGmzZtdouuX6RdSziyTGOfVYNFHsPLAjde/Kik23/L+527drr33lulI7HY0HzmzBkav+MkhDbgkacuSm6vxhUVX9937aswXSWMXq/4VgT3mYDxo4gi8ooO1Ikir1UDLvr2CmCQGTWM3JKblqhr58bFN7qFixa6aVOntRmWboKv1jCO4nnBdmnDrGV87Ogx9+mnW9yhQwfVvclYmPLPFG8RpAS4ivjLTbhWAd2GzYzfxHDq4bgZBcZHmbiEkaSFxE4wnCAiRI9S6qEg7rSIGIu8/fbbdXbazTfdrANpOdC3J8c9D+SDcXV4ot4DjF69QtwEmAVKzwm9KWfOnFZrmEqCZgVXg+SAVhjEbcjYmL+68kK/pFzYv5XTNd5e8ba6nU+ePOXGjBltZWZUcGTOzF2kEu1xRQ0eXQ5Tp051M6ZP1+ymrgCCzgbDHN6JMq3b+kLAEEL/q2FQmOjWIJzuNiZVYUJPXMaOHae9RTF2FEb19Xd3gbAMJUgqBGycS5z8ob0DpBiowbKp9GgrwEAVIfEiT/wJ13AreK0RflSCrjhaXbHLudXgWCqIVitywGQcZp9y7IuWMXR3XncCQ4ZQJqfpeKfrr79B3XI3LL5BZ05y0kXGh14ALxKXdGrGnDmzJQMau24xOCiaA5zRYcgahmn9+o81Ya4qkIlAXjyCkISLnpusaoKZ/afCxvAMZ9pt3brVrV271sryCJ2ywPBQBr5PqUQFrpzBMz3AzCaO+aEmOXHSxPCi9WDyAIPWjKtoELkLQPcsBWqm1YRJVyuh3RaaUdRBObFubdbsWRqTwbBoPONKKS1LBq284da6o7JDfs22uNESpeAOGhQOc22VYbJ0UtGJ/NcYYUdRJ69GmqLjGBnOgOQsSAw5Z9gNMeWnmjuN1SrGuB74Kqv9dwcs2wcOusoNHT5UVwwEZxeqdRSUr9CNUWoGRBMRonLFWYKcJE93vg4LbnHcqaixyfRTTz1l4Tys44QWLJivySzkW3XQ3vR52k7cw++0gobBu/e+e9Wdefttt2vG5vU3XK9KbInGMPA/GMJ9bVRhSAZTokzDpfYxf/58N3X6tDbByrsNuGqwtYqsRj9mzFjN1KwL8+f82XPuvfffV61t/vx5/qQCwrF3J48f10L21R98oOODpltNlbPR1HV68aLG885YTeys1cgYpygiZglCnIdGLV37dto/phmfOWU1uTNn3fGjx7S+j4kfCCT99EwNjmv82Cro7OkzNemc+cN6HabNnzC/Gb/CEJ0+eUrvaCUcspYPO/8zcWAMZ+UVoUreCvEdV8sjKayQVxUtneS2M2ABLGmiANJdCWKBJt927d6lcTvSqS6/ixfU4qMVOnjgIMu/85anpzQmQn5C4ok944of5As1Z9zAG8b+CJOjdJRnlp/7rYYNBpmy41vc0K1JMrmPflejM/YNY1iMx1GzHmDfEj7jWKdOnFTLjTV3hItCkxsjavfDjGJLD5kjXlG2uI+yx7fI2UGLKzIK5F7uSPNpre/au3ePG2vlI7Z8K9AivtVFkJma4XVTXBhjXLFihVpT882ojEhPKskBGadyhZFjchyn42cGqFZ8Lb2cYPCB6REqioQTn1eUmwB0EZUcKlVU4DiyjMocE39qhUNFhpbaqdOnNGudCnMh8ENhX+XGjR2nmeeEw7i8znwkTmk4kV8BlJtXf/uqvlmw4DrNaYhg84eV776rc0aZaDN5anL6TEdQI509CVeuhReYmGdOh8C3wAzZCTNCH370kXvzrbe0wBiFwqB7HHw/bkqF6eIoKw3GR7JnKcX3fAehjPjNOqTDR464fXv3ag3hLlN4dMVlfh4Lftk9Exh4V4uOHD0i94z/EA7+0O8PHTx0yO02g8oxOChE1fBjWjsK8tZI3ZjcsotMrKlHajWin4r2ZXX3HD5ieWU8imMb5NnxY5bu45Z/5KFdY36TJ1zJ8yxPyWMjvpP7QLjlGfm0c+cOVbjgEe7hF9/AG7kz/8TX8E01khyYOwwo5y/GOLBOEEMn/puM8TuTk+AeymQtkr0T4U9KhGdX8d9IcTViuvkeM3SEQ16Rh2qVXymgJSJPU5khSlcwWjVRIdcm82YUMmNV8a4FMP8oU5qRaVRRxloEKo5UrJhsxlZpKs8pXyIv6vBD6/2CG4wdOwPRQDhqZaXImPc1XDmD1wJE3qJgPjJjt+Ltt9XyYpkDU9GZtkvXEksGuDJFnTE2kd2zTqaIJkzA7ThNrBhn39JloXPR7DdCQbcY3WQc3EkY1MwmjJ/gz2CD7JtGCL8VHuGaXzGugzWr1I89EQd1w9YR5J4KKh4YBtZ3UbOEL3StcsYbeZzl+7iY9/YuyQ+fN9GN55vnn/92guU9v5lsQWuR2jynneu74Ba/9U3Ic3jE7zw/RHLP+7HiN8/gr8IwuaKFAF/otuY5RFzT+MX4x2tMR0rZt/w2N8gYvwmfsUb4TViE43fh6IHKiCj1ZB0Z4kZZwmC0awn1Mmgme5X4a6jCKsb1ur/VwjU/2FjgnXfece9a644dgcik0uD1cMCgC2fOufXr1mn7MLqMmII/a9YsGaRhI4e70aa4Ro8d48aYgkyVGtcxdkUBVxDvTAmNNwOGsaFbYuTY0epCoYZF9xuHtU6bPl3KdqwpJN4TzlgUM8p8oimyOjTWlLCPiylhwrNwR40boy4KFPfIkaPULUK/vQxeL5RFteyshUKrh8XlKG/STN6S7siPMcYfpvCPtnvykfwcOcbn++jx5sbyZ5zlLQexyqjoO2/w6BLHQGCEMByMpTEJincybHxrxDu+J49HQ+YvMtGe7Lm5o+uIrlnip7iFigfPpk2bJhmbPGWKwiGufEv8dU/aEv8IK0+4QdZkiI2QJZ4PZ2KNyRlywCn4xJtuuX6gi7oMGDuVn76Qh9GeJXbNdwDpT10gw+y8xJ6crMulS5hlFMggE6/6OnqfwbOSH2syNMMZs3vbWnaMvbAYkwkwKKeMeQhBRhILXQVdw31ELBjBjQrLxcsaz6HriZaEauGmYBkTjMsqmilMSgc1M/ODcb/zZrxprTLOhGJFCFHmikmMVw8Hypl0nTt7Tt11tPBQ3rReuJKv6laxbGtLv6VQEzsSKoI95xvtTmJ5xhZlp63lSPcgY3rkGS2qbJYs0q2uH6O8xagRBhSXzPgTGfzen8zGQ2FguFlCAQg3fmMf+HSQHn6D+C6Hyxcuuov2LekBWit4+qy6fJFlDC27wwyOk3hK1EeVvBaKyk4t9z0VMR1J3P0jZLztV4po8ClndJlj6Fa8s0KVKSbZ3HDDYlW0KFN9Hb3O4MFOlAOthjUffqidDU6bgrj77ru11myEKT0pt6BIakJK0AtDRsD4rl3wzQ8MJwPYKFVm3dEdhWKlawDlVzHZJvWnHqmXyoyDXVCaGFMmXDBpAWXHIDtLKLxB7R2lUmMMAwdZei5ofAAjMWb0GA3is/jXJxz7Y4n3tx2DfRPzndY9/tNdyobQ8ITZuHiLESEuKVSYY1ZylZGtQmENIC0tP7HkoMJieQuVKeQA+UIOxR++ifJmvwlL5/rhj97x3IhrJAPfMtEJYsySStVZq+zQ04AM9Bgkcc5ARqfUE5CPYy3ENBWlracizWfpD3/LVb0AIv87gxdlezRAC9R/98bv3KuvvqoekgcffNDNvfZaDQMw9qjuzj6OSq3Q0xGYhzFau26de/N3v5PSuPdeP1WX7iqYFmvNNRFr/InQZPcoLLugiJgMcfTIUXWRMs4yxGpCajUY2FlFu+8DPiB+jZKBWaooSpQdXX+07OhCpQuOVolmbpKWnl4gQ9rZiePUqZNaRI6hoBuYrjpfEFXPFJoeKyDfrGBiaJgEwyxW8kddpUYYJOSB6GRZZt9cNsND5UXQS/tTjYAZOgwmLcg42YTfGKERZlTNoSop2YJ20mP34WvFgXdqieKn/Zbf0fgZkQexQkOrHmNHlzzjhuTZwCFNLJ/oKoQ49zSQpf7GiCzmpsG4whfxpwPf9FjUKE7I1/kLvidh9eoP3LKXl6kb/uGHH3Lz5vpZ7ixbonRSwevr6F0pRMJNUaBsmMFG6+r+pUvVjTlkhBmii5d9F5S5a0ipZho4XBOgVOmOY8kA3YpMThk8fKieX77Q1pWZhcOlIxSAkaO1SrxpnTLBgiNlUNIoUwRVSrOHA8VBWjAOTINmzI51SEOG+nVPaauuMzPYOAgWvjDjE2CEGAu7KhgI8gqeyHvPIhVk1V6RdogKCwcDF1GY/QZ/4QtGCP7T/TN89MhQYsx/DJb5K/7zTLPz+J6Zen6pgt4b8UzdqnFmncjiZATfWfJC5Wq0GW1f2bH3vVkBXwHIdFFW9M/LgW6KkD1v+0bPqrnvKUjjxz1G3ipROikhpheZ5xrcatao/aPMvLdqlZsxY4ZOW1iwYIEbSIXbRJL0I+9ll2YHoIIflX9XwZi6d88e7WRwrTXFH/n857UF0lDWfBmDGRuKNe+G45J3xm9TRKwTYyyFGjfGjoXlCFPsxvLkuz2FKGSNEsb57Hm17lB6tFIYs5PSNaA8yVMJck8HrLd/58+fs7RcUrcfY3YaSyP65GmD7KgJyxomwpy0wosxYQILRkJSTOG3S0UtNQmzXT7ys4iA+UXXIrLEzEy6sdWy433QCRhtjSMa7+Iz/z56kkM+/RZNxm2pUBEOeUZ64Dknpl883/cnEDSFguzNGzeJm/SR/10EGbrUL33kb3sqlE5kzS7IXewOF5m8sI0aMsWVtcEYQp6z/pUekXvvu899+ctfdnOtZUf5iaBnBv9iJb4vY4BlYsr29ohvU2HgWV44TIEvX77c/eD739cYB1v5oPyAhC8HjXNI6ALZM9wVuY1g7IbF7SwIZZeBJ554QjPy4rdIMHvMvfjSS+6+e+919xjxLEK1GLtGt/rHA/4Qtlzx87Jq94TFWBqLSnnGd+qqMuFg+QP3KHa67njvPWsMdIfSZcbi9mHDh7t58+ZJ4cngBW/OW0uJ/Rk1gWH6VP8wH0T17OpWULh27NjpNm3aKOWN8Sa/yBfG9agccDozsynZumqgGXQZC9JjafD1UA91f1qLkN/iWchXCildpZs2b5YswHu2c4N3grmDb+Qpk1euNooFOW79hUv8i362Q/CLMVV6EejG5nw7/KYSBO9JV8p/xg9j3OVrNb8TED4tYhbRs0ifXUDGTZzgX3KckHmI0d22dZtmsSKD7RAD7W6kyevmODAZ6tvf/rZOJJgxc4YqiuQlPBERIeJEFtpvogr/82dusntR/I4PuPhv9VeV6ndXvqvTFtg5BV6hLyRP6DUcdQLsAMOQxUdr15pMn3Kf+cxn3IQJV1uc6NkZqPhKP5kxondCZznqHelgI/w2A4XBIv6pEQP0quBm67atOvz2r//mr83gPWWV98qxuud++jO3YcMG9/nPfc7ddNstlfxtFJ3Mj+5CSw0eY2pPP/20un+YHi6FZlAXVv6DJNjoXRTaaoDxKCLWjyy6/nr32KOPaiYbygxm8yUG75e//KVqMw888ID/MEACYuEq5DT8gmd0AezYuVPr7wiDb2PcED5amjxjvKUZg4d7FObhQ4fUNcs095FxZ4bgDSepo3QxeJOmmcIr8r56dnUbKJRUbljPQ3zZFQRFBMijNoO3xQzeEE3rVyHODN4ANdJiUmilxXvlE3wzwGN1Z1tFhJYXW5ExWxbAG/KTReUYkTjVHwMFqPli8OK4aMbzHJT99o6JUHt279Z4B8tD1HNg8dA39h6Dxz0Gke5be9QGflg4tVhDfEk/O+3QEuYsxRFjGB8MMC9ozfZYgwcRfjfGATmjxfIP//APJgPbVEmkbMAbZAP+yl3ghXhp/+AbRkRRtXzHgee/d+jLdeCXXnv+shk0O6AwZOL5b+6T7zoDJnERr3fefUe6hn1LpxqPqVRh2IYMHqJQqBTTu0T5AcRVsm7lTVsOmiPkniS0M3jkiYXDBgwvvfSi++M//mP3FAYvNz783E+e1ebnnPCw5Nab5WeHoczr+WiZwbt0/qIWfr/8yitu6f33u1tuucUMA0yqbcQicBGZWRX2jjEijCoK7VEzeChPmvSarGIC9P7q1e7ZZ591Dyxd6h75wiNeSANiH7UUKmHxDAEOFBUr97QkZPAmmcGbNFlGTsrS3le08My4R4MHNQoJrbnHSFCYaElqjAgEbzKDZ2mdNHVy9rwC9bO262HxIu3sELJ3315r9bAY31or9hwZQNlQcGnh0fpnI2IKtAqskBg8+xNbd0BGJuQtPGYAfvu27aok6CxF80+VHcbJLLzjR4+6PXv3aq0eFS+yjLyG91KIKIXgH9c8eAJ/6TZlVxW6M1lvx+Qk3qnmbd/t2LlDSof9OZnQpG/xl2uQI+Q1Is8mxvlwjxzReqBSNQL+J1EqDV57sDXdf/tv/90qCYOloCn/MngmM9oyy+IFF8hbSjj3yNBAk0EQ9Uub7AH0Trg1YFgxAM/86Bl35513uscee0x+UEGRrLUg0cQL/nJWJTONH3rwIW3ujkFH15A+GfiLF7Q0JcbXGzUvz17eSF+xwZPcmxt6Pf7+7/5evWJf+vKXtFtLBsuvn/30WbfJ0svY3o233KRnHUY3y0GzqMyhzsAyFoFAIWCMWEQ7zpTeuKvHu7ETxlWn8UbmnokHLDpmpmVVGktX2Vi/X6EpDBQgig7lx4xHfhM+pzCgCO2Hu4qteAKxQwrEvRQkwmuE8hlo3wweNlTETEzW29GKQDHTzTiQ3U8sDMKhxsVUXmYFcvXPzW+7b5RwTxpUgEwwY0HMYD+lU/TObpoRwu6Cxc93Qw5QjVI7kpDfli+0XuhCQRlB5BmGh3ccnOnJfhsx8YR8YdJOnETCs4HmZpC1DMnnIUOHyU+6RCUD5pd4SR7Zhd+RL+QzYcI7jcHip/1n8ghh4F+eMv4SR/Mff3A/aNgQL2cxPpZO3iNr2bchHbgVDSXengbmiPCzyS3wmHyEx3SXce3J/L6CQLlr8/Fhw60yMkGVATaHHm96hp6F0aYjeMbGBmNMZ2hjh3HjdQ/xHtLGBxnZO/sm0lg2BhgzRnJKC5I9OPGb3X14H/3oDOEP3eF0yWvNLRsXWBikY+Kkq7N4sEkD8eZ9DH+Mfc+7caY/2YWISjeVu+gmktJuV44PGsLRTiZnmjyWQrJmf6RnJIV9GrnUdw6qcZB5HUHM8AgqMhypUkTmjFqPam81mOONRHjPJe9UYUJJuDn42pN3JsQfFg1A64xuMrUK9dz+4F8aHrUwI1oMqvWnZE7xAxKK0iP/ihLQc1GR9xUIz+wS1HvjIItCNrUhhJN6ZW4i3/QO6aY7nQfqVm8AIRwu+EVtOwP84IUZpUwGU2+RUcbfgh+C3OeeRRBX8z97hTPC6A1oMDtbDvKskTzCSXBGVDXulrBSIL/RJzyPFECLKvYuVICfqXsj/Ihhcb0UdFUG7tPfwH7jN2F48g7kV4R5E7sy232fwEt8DUhMmVVcUDZ7i7y1CC01eHVB3uYp1Lh1tctlY7CMgNV82xG8CgyDcTnW6V1UQu0EFcf2TArGyCTJ+4UiTMJPkfvpH0D2TayVRwFSeOE+M34WZ96rFUf8aYlECk77EpTjyhRuKkHWxNPOY54J9tElMwgs9VDrBuOQB7yBEsiH1B9AtsPfPO/jb/yOlHOSAS+Jo13gcQWS30qH8bQiKFqQyBKwpBAOy0t8xPwlozzwG7HI5Nw/7pGI8bsCcVT28SfPmyLEvLYsZXp+XoaIv3omeA6lXtp3tIbayRKIaQ9UIc92q7B4FMMP7iqAu6AfPMXHiUO7zfxOHucR3WC4KwxmAoYWZFgrunJ99PoT8iLQ/SD/g06AUIrqi45CmJIh8r2aIOi5+VONkRKuaHzityHstngUfJ0LpwL2LlNU8XviQZxRgGlcI/U1KMv44wtwRFQYFGQeq0CH15Q9usH5qfyLeVdk9CJC3uJeBkkf+1fZ1ZDGQTyQl/aH76FcrVwUZZHKkAE/Kowe/IwgDuG2HeSf/cGN5CL9sBiZX/Gmquf9HZavlrewhWyuhbTlIz3PpynfU6TPwr2WnsBHEN/DlyICFoaMjuTLfvMN5MWpHWJccB/D0dXfFoLKYfo+s18WhxiNLNwA5FiTXIoqg/0M9UtiF+HS+Qvu0P4D7pP16937q1a5dR+tdQf27vMz96KCylMKJB5KgYLJnocPTCBOHDnm9u7ao0kIDBCfP3vWv4vO1N1hN/wmR2LTH//sZ13Yp+fOnHGHDxx0e3bt0rR4zmHLWhIS4ngfqK8hpEnZluZaPq3JK/KI6d9M3GC2KtPwswKZVxjhd2ELLsIe0zvA+wo31npk39UzZ8766Eg+DPBcPIL8I8GMFIaqossZhFv5TzzsXpUzuznHPpgHD7m9O3e7fZYejguSv8hT6keJTqPNkIVrleyFT5wruG3rdrdy5bua3f3p5k+1n2RFS8huYVEqM/6OySH+DmOhhwXEmjf0yvqP1yucDz/8SJO3zp4zPSN9YhTBN3hl/kpW9aAGktd8w5mRO3fsdJs3btYm0MzozOJt4WhGPOKdlDPJaAnhiuUEm+S+//777ne/+51bYYL4uzfe0FEVbLEkIGh5qgcxOqhbnBsxTX3z5s1u65YtjrPSuN9nBilb2Bu+CZraw67qsgxUAX7iNHmP8cbIMQuRWZXbd2y3QrZVSjYT2Lw/fQwkk5xXflVJasxLCuj5s+d1uO2OHTu0Toi827sXJXGuOK94ZIFEYxPHPjJjKLLCH0iGyv5fsHC2bN3i1n70kbaJy7aCUzyNUBAie5aQZmPiDxUwIP8jM7nl/rLkBFnab2nZumWrZvZutzSxhhPZExSWv+0I4icDfFCeSrSDFH5B3mCIkKnXXn3VrXh7hXtv1XvSN5988olmYWdQRgc/uMgvfxONSTbGFp6lxOYR69auc6+//rp788033RtvvC5dxn6yhUAcTCaQHU+ht4JX0WDph8khPVERFhYy/Morr7gXX3jBKte75ZRyVwsql1wtCZKlBLW/7HtIcrN7wR6F1OyZCcVJwSgvNoPeaQpDEONz1FGYkLJHJRs/M9sKIlyE5jyKFSAABX4jyAhYTj68eyPVzgIxLRqhZ3kEM1QHDx4iRc4aK0Hdmv62vyLNSbpXOJQVI8H9qFGcbmEG0ORBRoK8T3O+I3kXZQXemMIjjE2bNsugwnvxNQJ/a/mdvpNb/0AVHW6CVyy5YBkD/GfRPbNHUbTs9Sk0WcqkoLj6n+2RJKVEezBuxQ4ju/fsdrPnzNaaOio/H320VlP1O5J/Yr2ReJ8jlhewPpgZvSyV4Z7NMQ4dPuQ/TpEwlJP0VYky3ZdVpvJxQg8GnLHW6prVH5gB/637cM0apUFnJbY5aUPiT3ydBN1vZeeKGTym4163cKE2fuaUAwSFLq6TZjiEyJ2UmgBT2GfNnOnmzpunXfsB+yPGFoBXroboPz8vts2eopYvSDDtqm+gNoNnUqdDR2fOmqUF5Cg9jKCMagM5jHdZPEBy22eQpImZj3QrnT5zWtOmr7lmjvbdPBMUh/IiKoBGIRnxE4RibRn+UZNnmQRTvzNe1oMFTeiYtaw7KMpgCEdkQI5Qekxjh/cQFSuM37nYdV7iigC+jBg+QhvLs86N/SPpFjxx4rjxx1runoUNoVgOPI0ePcbdsHixwmG9JLw/e9ZvTaeuS01cysG+Q3dEPVOhj7jmYTqJlummTZu0JMYvzQn7wxKPOqKNkxLdYfCKmGdgwS77YLJ+hG7AXWFXCw48rfZNh2G1I9a6oIQQQmrdjBGylVdUiqpBRaGRwPEw3CJ8VZC+Yc0egs4i5bi1FQJJaw9cPh+6xarBwsn8izfVg+61wIAAxh1YbI0SGTpkqNZMDh3mF5B39hBKhRBZawqBXVI4SJUKVpvSMge4Ce6qwtyhNNuB5+EW3rF2kx1XqPQwdneQ3XOsto/hk5N6/C/RJaD3iLJ/+223azeTVatWqXxOnzbdDTOetQrDRwyzyvQ0N2zoMOky9Mxg1n+a/NFFqVmbNcqzXuV1TfKTPXcxdq+/8YZ0DZV3zpakZamlMIDvG6zP9Wd0vcGrATZn5pgfzrSjZUdLz+9baC9z/G8KppVY2My40MYNGzSGxyLNaywMdsBvF4bCTVpu+plzxO9AmTvLRRQgXadbLAy65diVHGMLNFOvGvAj3PZppHajLQu1lRKVEbYmowXGQu646LyzUA1/9Ch1mUbDpSu3aXxSwIzAENwWGjxDfC7+46X9PmatVhQeuwHRm0AFDtTkf4kuA919LLjmHLiXX37ZLX/9dXft3GvdLbfe4re+C3zuLNjfE4PKInC2M7zvvvs1QWrbtu21w5AsehmTPCFSXCOCAaOMrFixQruhUGg4XePAwQPa+g6dk81qRhZL1ETXlsQa+Y+Be/Ott9xvXnlFra2HH35Ye9bpJOmE552ChX/65Cm3bu1aKaIpU6folIUx48MmzRIw79THtX2EM4WXCmQqlIaL5y5oBuj69etk7Njjb8411+g4oZoCXw19UHBpE8XKAwZgpPGZhbAXL15wZ06fsZr3CXV1akeUVsKCRL7YoonWY1aB4VJEwNibVWYir/NuUlgte8/ePe4Tq1TRgmAjYDYdFv/LWvcVA4u22Vf15z//hXt35Up3442L3ZNPPOlmz5qtnYCyst9JMNOY1iNjxROvnmjlf65jazDN1I1hFIWVlvOcTpGc0QVv/3lDa5Gjo/Yf8PvVbvl0i9tgBvDo0SP+W/VUFQVSIkXjBi9f4NP7gKym0gBooj///PPa7DnuGs8909NbJYgsN2CjYfxFmTJri67Tg/sP+K6AfDi59LQD7sM3vkbmf2Dk6Ftnj0f640+dPq0JOSyH6PBYFKAgNPFZT4fasvafSR1so8T+hvv37VfesQ8n3Y6MfwqtMhQm4WQlYbFdmPjGg2oUIGNn1Ig8MzHqk082uJ07dqi1itFjooT4nyq1Et0Kxm+ZNfmTn/zYHTly2IruALf6g9Vu46aNapXled4sOOGf3iNmT/70pz/VqeIYqGnTpwUXBUjCRcKyIZYIPUR4L6sb86tf/ar767/6K/fNP/umzgC9+ZZb3O233eamTbUw0OK4ry+q/R6NG7zOoECo4M1nrBW0aNEi/cYAsmFrNpW/BcxDaWFMp02frj7vI0ePajkEYTCrTkYvCn1imAp1XO5ZVIi6Ghi/oRuLrlLCwHCj+LJ1hSUyMK7B+Oa1c+e6kWbkmLwCfzjuR+Oe4keLLJ75xd6rTChiYhR7bTYF4iSe+58pqOQQ/4mTJmrslt4LDDgypmUNJf+vGIYMGepuveVWN2vmLLXC161br5nglE341gpMnjTJ3XTTTW7K5Mlu3/59Gjtcev9Sd4uFC+JavgqER0hGYUOBn6adkR/2XWU/VvYT5gipW83YsTk+5Yfx4wwFwZSoRPcYvAIwYeVr//fX3F/+5V+6b33rW+4b3/iG+70vflFLFMQ4ZJFrFSYy8aEeBgwaqO6Fhx96yN1///3unnvudrfdequOLtJRPOkalwr49TH1avdx9G3chAnupiVL3N13361xSO5VAKZM8V0nHSlXvVFoLc5aAFsj8mlecs9APuOp1y1YoDxbbPIQj2FiOYE5akFeWIzMLzYlXzB/viZKsXF4XRB0qMzUkwFwtRk6lBBK7u577lHtm/RMmTLZwjMD2yLbXaJj4BSTz372Yfc3/+/fuL+idfTN/8f9mekZTg1gQpEmMcHeTsoZx0PdcMMNOlz1T//0T92//aN/q3FCnXhw0WQo39VIeMiX/+VlrIqcsZm6gOOgE5GtBx98UEeXKf4RxV5UoBF57sto/Higagj5x0w0FnVyCCwHs2bnKkG4gaJf8T5O142D+kSlVj+0vTp94pT7p+9+13HS9aNf+ELbwajm1YF9+9xLy5Zpoekj9u7uu+5SCw8mp4yO0RCi4OWyge927dqlcGgZxGzCEDKlnm4sCgwHjcYDQBmbYsEyYeGe7i0pcH1ZCdzTzcrxQLQCCYPZo9To+IZ3p06d1E4R1OQwBjoVII8a2dVtsAQy9Zo1T0ePHHWTJk9Sl2V8ToFnIgddl+QLymbQIFMG2S4RZgD1Nwc9aHuKWya27DS+xDVPtKjshfL83PlzCp9TxMca32g9wg9ma1bCwixiCgi8Y2IA/Gcn/okTJ+kZfMI/Wgd0wXMSA8spaN1lsO+zxep2b/8DcqkL/rEJAnKC8iK+8Vs2JqeliKzNN2M9Zdq0dl4IRc/6MBjv/c53vqMJQU8++aSbbJUKWlAq32lewGLx2FdcKKuSH3vG2LEQ3cstf+yRveP244/Xux/+4Icyjo8/+bgPo5ZuAvZdBu6j8/xnvDO6ZHz/0Y9+JF3yhUcfddfOn9v2XbVrAPqWR5zuUYjE/anjJ93f/vt/7+4yffjUU0/pZA+AHxww/eMf/1hLtb7yla+4hYuv94Y1RRJuVTTipgegWhOnEmQeiImKvzsD/Iqz8aK2Q6C4RioASkLdhBLeEBFjEFt6cWDjJx9/rBoQi0zpWkAhQhztEomZgCjMeIxMSrilWxLinjVcuOOKf4wLMB0YtxhDzkKjy0xh2PtMuVoBxA1T7qN/KaEkuXI+HBM1dKxNCJ/CywxWCjfGlBllOjKnJ8P4BTsqNqfVs8tS2uygQ3oxdqSRMQ7y0+e7PzqoHdn7QdYii8R3MZ91tJLlE4oMYs3TsWPH1T1KzZ7xQPiC/96flDg2qJhSmYAnHF5LWPLHrsz4Za0gbseNHacxR8ZY0rjF73VvYXniPqHgXkdZxW/tHnlAdhgXppJAOkaMHBEytERHIBuIjITF2f6eG156Ny1F9BeKOq0onOQZ1W1vbKso1fg491rHUUVdA3ifUhV43cnNZc0o/+1vf6sKIkNLWhLWx9FYCw+KzEt/g3DtUAsvIvpRC+k35v7Y4aNW8/qBZix99rOfdVOnT5Oxe/PNt2TsplpNeOnSpW72rFlShLSUioJJk50GEZUo04wRCJYW0DevFleo2SNoKCKMHUqKgkTrTuHYfSbAFC5QkMUofNxxWClGlHBGm58obsYAmUqNsuX5mHFjtet+j23hBRw6eEiGbcqUqTqzi4xlvz/WJdK1i3FgrZJq4zk0kgwMDq1fJgiQfxRQKhRUQNi5BZ6NGDFSh79ihLTQvCCsWohd2cetdcV2dLSsmd3Lsgl202HPRFpk8Coab8JQmoyf+RZ9UVqBWhzmltm9GNHYwpPhtgoCu7SMsEoRlR3Oi5Si7uH87w50pIUXQTnLWmc8L8gzjbPZ/0618DoCE01aeM8880zWwpu7YF5b/LhCyDB8J36kMRUB6pbxN/f56IXfaQvvS1/6ks5j3Lp5i2bI796zxy2+4Qa9mzx1qvcPv1I0kuxG3PQAFJSgDgKmNIP4XWRsg/5QO0bw1FVlArB/z17tk8c6u5kzZ7oHH3hA6+w4gJP+78HxMM4cMWU80pCEeMdBnrEGTu2b3ygi7dphCna8KSAM0UhT6kNGDJMfOuRziJFaJ9YasO+ylgP3OYoHhtIKia0+WhSEg3Fl9waULcaOvCk0dj0F8M4KCUoHRR4NPMaObk74RX4xIYV1UcOGW3ot31Ia2gCRv4Pte4yZWs5mEKh80P2MQho7ZqwmEMAX+CH+wusOEMoAvsB3vyDeDLQZa/bgZCcg5A5De7UZKFpeHEzrec/hrxamfaeWW6DYq5AneK0WJb9p8dk98nwm9CBgUBkDZm2Xxh3J1xIdAoYuUkd0THfDx69K5DAkvMobFIxS+k1MYx1QRndt2+FetZYde9necP31Wg6WGbsG/OjN6FwpajZz4nfpNVId4IRj8BESWkdvvfWW1qNMnzFDk0ZmzZ4thSU0W+uwQKh98zk19tNWQ6LlgjEaY7V67ZdoSqod+AAiV+sRuOjHcQiLbaio2dONheKj9UANP0tDs2npDlh++a5mPybHmreTJ05qL0HyD0NHFy6GT3v/VUOUgWpkRk3bNFkYtKgxdORZrIjQGsKAZO6bAd/BF4s3YEztwplzatmxABjex5a9RaKSLx0J09IA38kfau7kHy1IupcwhowNY9BVu48yVaLzKOJRs7LSSURjTPD+bw7wPcqYyYiuOMsbuzqIFVGINXwsn6Blh7HTdmgYu+g3pPAS6kPoWcUoz8Oi38Zo9p1janHc+XzatGnuHjN2zL5EWVSAbxCQIpJ/VchAOHSTZd1YpuwgKVU5MApuOwP8RtkxNoRwssSBFt8Aa73I/54udCF+LPSlUkBaDh8+IgM4ftx47XSicRTLL3XNkaaUD5Fi3lcjvjXAF1pBRw4fVtcjxgdjR55FN0KRH/UogHgSDmmhsgOPmAhDVxots8IuNHPfMDD+tNrsG/HfDB1Ety09CIzdlssZOgd4FJV9pryhDrCp6xANHX/sb1GceJbGO4t/cJx2saZyD3ASKL6RsfvNb7Sx/fWLFrk77rxTurNi5nLOm76GrjF4+cwHgUdVIaZD3AfKw54xdkOte/369W7Lp59qcSf9zxXGrujbPPJR5Hd8ZvGg5s3EESZcRGWHUiX8LK5F4HEjFMDuH6zXgiichEF3lvIwjWPyTY8DUQ2Fjy2VTljrjvyhlTpm3BhrDScFqpMgFIwqkzrIM7oDqYTQ9Vgodx0F+az0+IkwbN3ElTAYS6MrWoYq5U+Opw0huEfOqFQxIxP+q2XPkpk0Lc34X6IQWbHtMXla2LbzkJ4J9xGKd+5h+kz3/lbXS95/ekM4sgiduSi27KZM8ZMG+xG6JLVNqR0xF+I+UB7m8WkzQtTqMUJsE3bXnXdpGyfNWCryA8ojH8H0N/embBAQhWPGFSNEt5y6SqMiilf870grJZIBwx1PiMA3Zn36mn2okfYq+FOVyTeyZuw4jmMaE1+1UWdgvMX4sCyAadS06Ghx+e3oEs+L8rtREgZozI5lIXQ7wn/It8gyJ5VA7joCc6+ubMsv0oKxw6iOUGXH3kf/O+htiTaouzDrBrf/KH8theGld9PjQZd31CvSMWFilJ6TKP+OtHq3Rtwrkfy85Gf8Hj8uWWMGO8u1ZOwoM95Zv0H9WZoguqAQFrm25xyCyTY+L77woluyZInfQYX8hEH5ReKZf95DCaaYxKMw4y1F/Glutm7d5n727LPaBf+JJ590C6+7rm2mpJH8id9XSxpuw62QhMcd52gdPHDQbdiwQYLBYnimi/MOAaKrSz6Hq75W+Nzwu82/dohuBlylFiQHhnKM0MxZM/1aO1OqCLMmqVSJfgVqBNVtsHjSfblnz251m1A5mDljZvu4NZCeWskht48fP+E2b/ZHpMyaPcsM0VjlF+/UdWU+dCZLkCF4TDc26wZZh3ftZ65VxEij1lkaqWvWINnlH2krkLd2shxhj8+fO6/Dgtmais3GmQwzMF1X1Vv43404c+qM+87/+I4qPnfeeafWYsIXspkylWW35R16x7MkzOL0t8Y/fy9Jsf+Rd/yLOoSZwGx9yAYZLPL2FTnKOF+ZGzzKo/BRwUMDMWAuwrKXl7ljR4+pl4oKPLLno+Xl0P9WTBUv/TOdGuOCzOt54o7ngl0ZG95scvz9H/zA3XvPPe7rX/+6mxyNXXDWErTSry5EYwavAVw4d0GHHv78Fz/X7DxqxMzIw3sY2wrAVKbsc4ArE1QguoBIgPLb/lSkpomkKb7WeqTWTVi0IFjXxSQVhI8Wn7IsCIxErOJqkODFH22oeGZeUGgJh7yiL53Zir0VFy9gJA5qsTZg2UazyApsAvKO/D9prS5aeMyAJN/YI5N85BtNJMmhmsKpBfZcZVnIqZOntIsGBjzKxZDBQ6QwJQP6H/xP7xsA8UUZsbEAsz2nTpmqq9Ju3gxg/A7v2mdFv8a5M+fc08887dasWSMZ0LhtyHuUf5wUpWxrMu843ZzNzDnmh/Fnjn5iZyDkQmEYhUDbwghxyENyUgDGtKlYs2SHig/nQtLCR85jWvAOmed3kT/xgOBqQJbo+eIQZDa4/trXvuY++7nPtVWm04+bzKvehtYYvMAY1pOwHyZH2+MtHrOrgRR9C4DBo4bNWN00a+FNMEFkDIdweEeNTqEhlPasSHHWgvwh3uYX33IgKQuDMXZRQUFqryZe+3DaHuiuIGwexbxQXO0B4VFoR40c5a7qzf3plnlnz5zVeBSnl19lhijNAUkDGVwP5FFR3tk//EDOUDyMpXL0E8+ZFYrfseaeQUGGQKuEnX8sHtkf+IwhZS0kihWXUeERR19q2vzOwmkQ8N2vxXSSMYwdaxXVI2Jp9MqbyIQPSvjstvzYumWrNmqn4kM+xgoIeamWN//tWiRHtZF8E/yVwbF7+IH/F0y2uSqYJAzkkP958G0l2vznDePb+EG3KzpBfLd/fpZw/Fa+t0PRsxS8j3LERCsOwJ0ybWqbt0qIXZUY/6ivo2UtPDJRTW2UzpXIPFLR6nDxM/rblWmKHLgS+dZqIAcSKZSBf9RhdEU+xDzOg/gmL6VaknoHMg3aGdPOgDAtjwr9ND2nd+UMzfaIbKqWNbzvK9nWVWnBX6gX1607g5YaPKErBY5KTxGjCBvFFJVEjEszKcMLiLDifVehO/Ksq5GmgXuouwpTN/Do0gVrUVprj16DgQNDq6sIHY1DUb6lfnRhmnorYutXG7IXIeapXTva4o4Qf0PeZxX4zqBONLz2tXBqtbKaS0qlf6nu5B7wvpPJ621oncHrDvSkmOYFhbh1JH59Wdg6mhcpWpkvdeIhpZh/b2EXKR92vcepNh5uVfzyIICu8rsvIPKqp+QR8anFM3vXleq1qWygUZAaPJDma1Oe9h6UBq9Z9HHB6BTgU7O8aqTQ4XfqpjPh5VEl/IvnL1aMsbQU5l07P4ue9dNuqBIF6Ez5yn+bE7O+jNLgNYt+JCQdBnzqTIGslbfmLyJb0QoL4VW02PS6ikc8rvKqGi5xrhl+tnIsr0SJK4mkrPQXlHXGEr0L0ejUKqR6XeCAR5E6AlMM+gyLV6JEX0E/FOeyhdcsSt1XG53hVRMGSZR+10r+xLSUPC9RolejNHjNolR+PQepXJR8KVGiRBWUBq9ZlIq1ZyHKRle07K4UShkrUaKlKA1esyiVUd8GsnYl5Q35KmWsRImWojR4zaJURn0fV1reShkrUaKlKA1esyiVUYkSJUr0KpTLEkqUKFGiRL9AafBKlChRokS/QGnwmkHZnVmiRIkSvQ69awyvRIkSJUqUaBJlC69EiRIlSvQLlAavRIkSJUr0C5QGr0SJEiVK9AM4938ApaU/Kw1BIr0AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "d42cd876",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "a38183d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(3)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "799f17f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134af6d4",
   "metadata": {},
   "source": [
    "# manipulate & compare Numpy Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "fd9906e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_number = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "4d6084c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ll = np.array(list_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "58edc56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "a5a9f3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ll) #Python sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "e06b163a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(ll) #numpy sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df90ff22",
   "metadata": {},
   "source": [
    "Nên dùng hàm sum() của Numpy vì tốc độ nó nhanh hơn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "54656750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ll)# tính giá trị trung bình của các phần tử"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "f88c61c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(ll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "b5aa1b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(ll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "1a5e0a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147.32277488562318"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_height = [600, 470, 170, 430, 300]\n",
    "dog_height = np.array(dog_height)\n",
    "np.std(dog_height) # độ lệch chuẩn = sử dụng hàm var() -> sqrt()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "cc0fb99a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "394.0"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dog_height)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fa2e74",
   "metadata": {},
   "source": [
    "\n",
    "Sự khác nhau chính giữa hàm mean() và hàm std() là:\n",
    "\n",
    "- Hàm mean()trả về giá trị trung bình của các phần tử trong một mảng, trong khi hàmstd() trả về độ lệch chuẩn của các phần tử trong một mảng.\n",
    "- Độ lệch chuẩn là một thước đo sự phân tán của các phần tử trong một mảng."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "b5811498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21704.0"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(dog_height) # phương sai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "0dd382c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147.32277488562318"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.var(dog_height)) # căn bậc hai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bea2020",
   "metadata": {},
   "source": [
    "# Sorting Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "5955d4ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([2, 1, 4, 3, 5])\n",
    "np.sort(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "5d88fc91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 2, 4], dtype=int64)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trả về vị trí của các phần tử sau khi sắp xếp. VD 2 chính là vị trí index = 0\n",
    "np.argsort(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ed04da",
   "metadata": {},
   "source": [
    "# Sorting along rows or columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "3ab20fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "MatA = np.random.randint(0,10, size=(4,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "de586174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 3, 7, 4, 6, 9],\n",
       "       [2, 6, 7, 4, 3, 7],\n",
       "       [7, 2, 5, 4, 1, 7],\n",
       "       [5, 1, 4, 0, 9, 5]])"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MatA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "09f17cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1, 4, 0, 1, 5],\n",
       "       [5, 2, 5, 4, 3, 7],\n",
       "       [6, 3, 7, 4, 6, 7],\n",
       "       [7, 6, 7, 4, 9, 9]])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(MatA, axis=0)# sắp xếp theo cột"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "0e1b9be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 6, 6, 7, 9],\n",
       "       [2, 3, 4, 6, 7, 7],\n",
       "       [1, 2, 4, 5, 7, 7],\n",
       "       [0, 1, 4, 5, 5, 9]])"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(MatA, axis=1)# sắp xếp theo hàng"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f521c3d",
   "metadata": {},
   "source": [
    "# Linear Algebra : Đại số tuyến tính"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56064d1",
   "metadata": {},
   "source": [
    "### Để nhân ma trận, số lượng cột trong ma trận thứ nhất = số lượng hàng trong ma trận thứ hai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "8282f754",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([[1, 2, 3],\n",
    "            [4, 5, 6],\n",
    "            [7, 8, 9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "30d6eca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "B = np.array([[6, 5],\n",
    "             [4, 3],\n",
    "             [2, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "8f81a7bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 14],\n",
       "       [56, 41],\n",
       "       [92, 68]])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tích vô hướng\n",
    "# A (3x3) dot B(3x2)\n",
    "A.dot(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "08ae4385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1*6+2*4+3*2  # lý do có số 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "aa07d1a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 14],\n",
       "       [56, 41],\n",
       "       [92, 68]])"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cách viết khác\n",
    "A @ B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "64a2ccbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 4, 2],\n",
       "       [5, 3, 1]])"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B(3x2) dot A(3x3)\n",
    "# nên phải đổi B\n",
    "B.T  #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "3ec6791e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36, 48, 60],\n",
       "       [24, 33, 42]])"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.T @ A"
   ]
  },
  {
   "cell_type": "raw",
   "id": "aaa1c112",
   "metadata": {},
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
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