{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from preamble import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "## 1. Introduction\n",
    "### 1.1 Why machine learning?\n",
    "#### 1.1.1 Problems that machine learning can solve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.2 Knowing your task and knowing your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Why Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Scikit-learn\n",
    "#### 1.3.1 Installing Scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Essential Libraries and Tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.1 Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.2 NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(\"x:\\n{}\".format(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.3 SciPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy array:\n",
      "[[ 1.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.]\n",
      " [ 0.  0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "# create a 2d NumPy array with a diagonal of ones, and zeros everywhere else\n",
    "eye = np.eye(4)\n",
    "print(\"NumPy array:\\n{}\".format(eye))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SciPy sparse CSR matrix:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "# convert the NumPy array to a SciPy sparse matrix in CSR (compressed sparse row) format\n",
    "# only the non-zero entries are stored\n",
    "sparse_matrix = sparse.csr_matrix(eye)\n",
    "print(\"SciPy sparse CSR matrix:\\n{}\".format(sparse_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data:\n",
      "[-10. -10. -10. -10. -10.]\n",
      "row_indices:\n",
      "[0 1 2 3 4]\n",
      "col_indices:\n",
      "[0 1 2 3 4]\n",
      "COO representation:\n",
      "  (0, 0)\t-10.0\n",
      "  (1, 1)\t-10.0\n",
      "  (2, 2)\t-10.0\n",
      "  (3, 3)\t-10.0\n",
      "  (4, 4)\t-10.0\n"
     ]
    }
   ],
   "source": [
    "data = np.ones(5) * -10\n",
    "print(\"data:\\n{}\".format(data))\n",
    "\n",
    "row_indices = np.arange(5)\n",
    "print(\"row_indices:\\n{}\".format(row_indices))\n",
    "\n",
    "col_indices = np.arange(5)\n",
    "print(\"col_indices:\\n{}\".format(col_indices))\n",
    "\n",
    "# convert the NumPy array to a SciPy sparse matrix in COO (Coordinate) Format\n",
    "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
    "print(\"COO representation:\\n{}\".format(eye_coo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.4 matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:\n",
      "[-10.     -9.798  -9.596  -9.394  -9.192  -8.99   -8.788  -8.586  -8.384\n",
      "  -8.182  -7.98   -7.778  -7.576  -7.374  -7.172  -6.97   -6.768  -6.566\n",
      "  -6.364  -6.162  -5.96   -5.758  -5.556  -5.354  -5.152  -4.949  -4.747\n",
      "  -4.545  -4.343  -4.141  -3.939  -3.737  -3.535  -3.333  -3.131  -2.929\n",
      "  -2.727  -2.525  -2.323  -2.121  -1.919  -1.717  -1.515  -1.313  -1.111\n",
      "  -0.909  -0.707  -0.505  -0.303  -0.101   0.101   0.303   0.505   0.707\n",
      "   0.909   1.111   1.313   1.515   1.717   1.919   2.121   2.323   2.525\n",
      "   2.727   2.929   3.131   3.333   3.535   3.737   3.939   4.141   4.343\n",
      "   4.545   4.747   4.949   5.152   5.354   5.556   5.758   5.96    6.162\n",
      "   6.364   6.566   6.768   6.97    7.172   7.374   7.576   7.778   7.98\n",
      "   8.182   8.384   8.586   8.788   8.99    9.192   9.394   9.596   9.798\n",
      "  10.   ]\n",
      "y:\n",
      "[ 0.544  0.365  0.17  -0.031 -0.231 -0.421 -0.595 -0.744 -0.863 -0.947\n",
      " -0.992 -0.997 -0.962 -0.887 -0.776 -0.634 -0.466 -0.279 -0.08   0.121\n",
      "  0.318  0.502  0.665  0.801  0.905  0.972  0.999  0.986  0.933  0.841\n",
      "  0.716  0.561  0.384  0.191 -0.01  -0.211 -0.403 -0.578 -0.73  -0.852\n",
      " -0.94  -0.989 -0.998 -0.967 -0.896 -0.789 -0.65  -0.484 -0.298 -0.101\n",
      "  0.101  0.298  0.484  0.65   0.789  0.896  0.967  0.998  0.989  0.94\n",
      "  0.852  0.73   0.578  0.403  0.211  0.01  -0.191 -0.384 -0.561 -0.716\n",
      " -0.841 -0.933 -0.986 -0.999 -0.972 -0.905 -0.801 -0.665 -0.502 -0.318\n",
      " -0.121  0.08   0.279  0.466  0.634  0.776  0.887  0.962  0.997  0.992\n",
      "  0.947  0.863  0.744  0.595  0.421  0.231  0.031 -0.17  -0.365 -0.544]\n"
     ]
    },
    {
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zNDM4IDAwMDAwIG4gCjAwMDAwMDM2MDggMDAwMDAgbiAKMDAwMDAwMzc2MCAw\nMDAwMCBuIAowMDAwMDAzODgxIDAwMDAwIG4gCjAwMDAwMDQwMjEgMDAwMDAgbiAKMDAwMDAwNDM0\nMiAwMDAwMCBuIAowMDAwMDA2Nzc3IDAwMDAwIG4gCnRyYWlsZXIKPDwgL0luZm8gMjQgMCBSIC9S\nb290IDEgMCBSIC9TaXplIDI1ID4+CnN0YXJ0eHJlZgo2OTMxCiUlRU9GCg==\n",
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VBqMlnE4Hf/nL6QC89dYB27YmCYYwFAED/O73Bw60cu3VnBBGklIe8P7cCawETm/phVLK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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f01aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate a sequence numbers from -10 to 10 with 100 steps in between\n",
    "x = np.linspace(-10, 10, 100)\n",
    "print(\"x:\\n{}\".format(x))\n",
    "\n",
    "# create a second array using sinus\n",
    "y = np.sin(x)\n",
    "print(\"y:\\n{}\".format(y))\n",
    "\n",
    "# The plot function makes a line chart of one array against another\n",
    "plt.plot(x, y, marker=\"x\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.5 pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>New York</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Anna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>London</td>\n",
       "      <td>Linda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Location   Name\n",
       "0   24  New York   John\n",
       "1   13     Paris   Anna\n",
       "2   53    Berlin  Peter\n",
       "3   33    London  Linda"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age  Location   Name\n",
      "0   24  New York   John\n",
      "1   13     Paris   Anna\n",
      "2   53    Berlin  Peter\n",
      "3   33    London  Linda\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "\n",
    "# create a simple dataset of people\n",
    "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
    "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
    "        'Age' : [24, 13, 53, 33]\n",
    "       }\n",
    "\n",
    "data_pandas = pd.DataFrame(data)\n",
    "# IPython.display allows \"pretty printing\" of dataframes in the Jupyter notebook\n",
    "display(data_pandas)\n",
    "\n",
    "# also print it\n",
    "print(data_pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>London</td>\n",
       "      <td>Linda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Location   Name\n",
       "2   53   Berlin  Peter\n",
       "3   33   London  Linda"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One of many possible ways to query the table:\n",
    "# selecting all rows that have an age column greate than 30\n",
    "display(data_pandas[data_pandas.Age > 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.6 mglearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Python2 versus Python3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Versions Used in this Book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.6.3 |Anaconda, Inc.| (default, Oct  6 2017, 12:04:38) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "pandas version: 0.20.3\n",
      "matplotlib version: 2.1.0\n",
      "NumPy version: 1.13.3\n",
      "SciPy version: 0.19.1\n",
      "IPython version: 6.1.0\n",
      "scikit-learn version: 0.19.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(\"Python version: {}\".format(sys.version))\n",
    "\n",
    "import pandas as pd\n",
    "print(\"pandas version: {}\".format(pd.__version__))\n",
    "\n",
    "import matplotlib\n",
    "print(\"matplotlib version: {}\".format(matplotlib.__version__))\n",
    "\n",
    "import numpy as np\n",
    "print(\"NumPy version: {}\".format(np.__version__))\n",
    "\n",
    "import scipy as sp\n",
    "print(\"SciPy version: {}\".format(sp.__version__))\n",
    "\n",
    "import IPython\n",
    "print(\"IPython version: {}\".format(IPython.__version__))\n",
    "\n",
    "import sklearn\n",
    "print(\"scikit-learn version: {}\".format(sklearn.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7 A First Application: Classifying iris species\n",
    "<img src=\"images/iris_petal_sepal.png\" alt=\"sepal_petal\" style=\"width: 35%;\"/>\n",
    "\n",
    "#### 1.7.1 Meet the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_dataset = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of iris_dataset: dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n"
     ]
    }
   ],
   "source": [
    "print(\"Keys of iris_dataset: {}\".format(iris_dataset.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris_dataset['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(\"Target names: {}\".format(iris_dataset['target_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature names: {}\".format(iris_dataset['feature_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of data: {}\".format(type(iris_dataset['data'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data: (150, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of data: {}\".format(iris_dataset['data'].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five rows of data:\n",
      "[[ 5.1  3.5  1.4  0.2]\n",
      " [ 4.9  3.   1.4  0.2]\n",
      " [ 4.7  3.2  1.3  0.2]\n",
      " [ 4.6  3.1  1.5  0.2]\n",
      " [ 5.   3.6  1.4  0.2]]\n"
     ]
    }
   ],
   "source": [
    "print(\"First five rows of data:\\n{}\".format(iris_dataset['data'][:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of target: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of target: {}\".format(type(iris_dataset['target'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of target: (150,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of target: {}\".format(iris_dataset['target'].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Target:\\n{}\".format(iris_dataset['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7.2 Measuring Success: Training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], \n",
    "    iris_dataset['target'],\n",
    "    #test_size=38,\n",
    "    random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (112, 4)\n",
      "y_train shape: (112,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape: {}\".format(X_train.shape))\n",
    "print(\"y_train shape: {}\".format(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape: (38, 4)\n",
      "y_test shape: (38,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test shape: {}\".format(X_test.shape))\n",
    "print(\"y_test shape: {}\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7.3 First things first: Look at your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1145e1780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11862dcc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1185aaef0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1185dfef0>,\n",
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       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118850f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118886ef0>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1188f6da0>]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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72z6gFtTZ6GlvYgiv/Px4m9q0dANo/9jOjYX9eUvkNk7per4uoG7M2eilaeMA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\nIG4gCjAwMDAwMTQ2NzUgMDAwMDAgbiAKMDAwMDAxNDkxMSAwMDAwMCB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      "image/png": 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XXhJJtL1DGWtJJHGCE/TTjx8/WWShUMteDyFE+NJJ5xmeoY8+FIqrXOVd3gWMm0e72EUu\nubhxk0YaQwzhw0cWWRFdVAsRDU6cHOUowwwzxtiivofZZPMsz8b0fNlCC+c4Z3YHzSWXh3gIF66o\n7yuUVFJ5iqfoow8HjkU/bZdbOEIIsQjXuEYPPbhxk0wyGs1HfGQ7TmA+DhzkkBOX4G62TDLJJluC\nOyFWCYUim2z66ecud81yjeYyl0km2XzKl0YaOeRIcCfiKpXUJX0PY3m+nGQy6BzeTTc3uRn1fS0k\n0LaX0pVaAjwhhFiELrqCynz4GGAgDrURQqxXdscijY5bFzMhVqN++m2TF9m1r9VAumhOU+rHUd2e\n1l+M6vaEECtLGmmW1ONg3F2cPUZACCFizS5ZE2AZoyeEmF8KKSiUJQsthG5fK508wRNCiEXYwpag\nfvnVVOPBE6caCSHWo0oqg24sFVNsSZ4khJhfEklsZrOlLJFEaqiJU42WJmZP8JRSTwHfnV6sAb6t\ntX5p+r0i4K8AD/B/a63fjFU9hBAiFjLI4EmepIEGxhijiCJLlrCw+Hzw7rvQ2AhuN+zbB7t3x6bC\nQojFOX8eLl0y2mtVFTz4ICQmxrtWJg8eTnCCBhoYZpg88tjIxnhXS6wlfX3w3nvQ1gaZmXD4MFRU\nxLtWUbePfeSSSwcdJJFEJZWkkBLvai1KzAI8rfVrwGsASqlzwOwg7rvA/wVcBv5+zntCCLEqpJLK\nbpYQkP3yl9DUZPw+NgZvv21cOG5d3qkShBAhXLpkXNgGfPKJ0VY/9an41cmGBw/b2R7vaoi1aGoK\nfvYzGBoylru64JVX4Ctfgdzc+NYtyhSKjdOv1S7mXTSVUpuAe1rr4VnFu4APpsuGlFLSUVwIsb4M\nDc0Ed7PV1y97VYQQIdi1x9u3jSBPiPWgqWkmuAvw++Hq1bhUR4RnOcbgfQ74uzllTq11YBTjAJA1\ndyWl1ItKqTqlVF13t2SCEkKsMVrbl/v9y1sPIURoodpjqPYrxFoT6rsubWBFW44A7zng5TllsyeK\nSgf6566ktf6B1rpWa12bu8YeAQshBOnpUFwcXL5ly/LXRQhhz649lpVBUtLy10WIeLD7visFNasz\n+ch6EdNpEpRSBcC41vr+nLcuK6UOY4zBS9daD8ayHkIIEQ/jU2NcanmFrqEGclLK2Vv2PIkJs7Ld\nPfMMPWdeoW3oGokOD+VFR0natSt+FRZCWO3fbyRXuXwZJiagshIefXTm/d5euHHD+H3LFsia6ZDU\nSy93uYsLF+WUh5xCxYuXJpoYZ5xiiskhx/ZzU0zRQgv99JNNNiWUyMTlIvZcLvjsZ40x4h0dxs3J\nw4ehsND6ue5uuHULHA5jHHlGRnzqu0T3uEcHHXjwUEEFbtwRre/Fy1nO0ksvFVSwhz044jBpQazn\nwfs08PPAglLqe1rr7wB/BPwlkAT8ixjXQQghlp1fT/GT8/8nPVOdADSMXuZm7xl+bd8fkeg0plK4\nkdLKhScAbTwluK7u8xiDpJMer2oLIWZTCo4ehSNHjC5pjlkXanfuGMkmAt04P/oInn8eystpoIE6\n6sw5ta5xjUd5lKw5I1IGGeQkJ/HhMz+3l71Bqdn9+HmHdyyTl+eTz8M8HJeLR7HO5OXBr/2akXDF\naXNT4cYNeP31mbZQV2cEhXa9VFawT/iEembG3d7gBo/zeNiZNIcZ5k/5UwYYAOACF6innq/xtZjU\ndz4xPSporb+vtf7Ps5a/M/3fVq31o1rrw1rrX8WyDkIIEQ9XWl8zg7uAvqkePrn7KgATTHCFK8Yb\nSoFS+PBZTi5CiBVCKWtwB3D6tHWMnt8Pp08zxRSXuWyZMNnS3mepp94M7gKucIUJJixlbbRZgjuY\nedIgxLKxC+60Dm4Lk5PwwQfLV68oGGOMa1yzlHnxcp3rYW/jNKfN4C7gBjdopjkqdYxErJ/giXVC\nqR/Huwohaf3FeFdBrEM9I/YH9N7Ru4Bx4ph7EQfGHX0hxCrQ2xtcdv8+vunXXHZt265sgglGGSWD\nmS5ucy8aZ5cXs7qekog1ZmICBm3OW/fnjs5a2YYZxk9wUqVIzslddNmWd9BBGWWLrttizPsETyl1\nWCn1/ymlLiulupVSLUqpV5VSv6WUWp2da4UQYhkUZ9jPSVWYbnTHTCHFtm//BjbEtF5CiCgpKLAt\n8+CxHW9n17btyty4SSV1wc8BIcfrCbFsEhNhg833c+4YvRUugwwSbJ57RXJOLqEkqEyhqGD5J4UP\nGeAppX4J/CbwOvAUUAhsw5ig3AP8XCn1/HJUUgghViofPgYZtHTHAqgpfJhSd5WlrCixnG3FjwPg\nxMl+9uPAgQ8fk0ySSqpMVizEavHww+DxzCx7PPCwMSZuP/stCVCSSWYnO4M2sZ3tlmDOgYO97GWE\nEcsT/gIKgp4AVFBBHnnm8gQTDDJo+xRCiJg6ftxIxhKQnGyMXV1FXLjYy14A85ycSWbQeNj5HOUo\nhRTix88kk2g0BzlIPvmxqnZI83XR/JrWumdO2TBwfvrnT5RScutICLEu+fHzMR/TSCMaTTrpHOaw\nJYnCZ/f+P9y+d4aOgRsUplezucB6wssgAzdueughgQQ2sYlEEpf7TxFCLEZ+PvzGb0BDg7FcWQlu\n46l8McU8y7O0004CCSEzXiaRxNM8TTvtjDOORnOJS3jxkkAC26ZfCsVhDlNFFX30kUWW5endFa5w\njWtMMUUSSRzkIIWsricoYhUrKYFvfAMaG41xeps2GU/2VplMMnHhopdeEkkkh5yIzsmJJPIQD/EO\n7zDMMEUUUUttDGscWsgAb25wp5RKn/15rXWvTQAohBDrwi1u0UCDuTzIIKc5zaf4FApllm/OP8Lm\n/CO22zjDGbx4ySYbgBZayCSTbWyLbeWFENHhdsM2+/aaRBKVVC64CSdOSinFh4+XeZmp6amCJ5nk\nMpfJJpsCjO6gOdOv2dposyRw8eLlNKd5nuflhpFYPklJsH319kDx4+c0p5lgwjwn3+Y2mWSymc1h\nbaONNuqpJ3f6BcStLS6YRVMp9Y+UUvcw5qz7ePqnLtYVE0KIlayNtqCyYYbppz+s9YcYsk2c0Err\nkusmhFh9Ouk0g7vZ7I41C70/yST3uBe1ugmx1vXRxyijQeWRnJNXUlsMJ4vm7wDb5WmdEELMsLsb\np1Bh36Vz4UKhgsbuyR13IdanUG1/oWPCYtcTQsxw4bItj2Si85XUFsOZB68BbEJaIYRYx6qptnTF\nBCODVrgTonrwBCVNUKiIBnQLIdaOfPItUyOAcdG5iU3zrldJZVD2vyyyLAlYhBDzSyedIoosZQpF\nFVUh1gi2ktpiOE/wfg84o5Q6BzMTu2it/0nMaiWEWNcmmeQ617nHPZJJZgtbLMlLVoI88tjXXkjz\nhZfQ3hFSKnZSu+fTzM6j4B3u5lrjq/T62slKLGBLxdOkpM+kVj/AAdJJp5VWEkmkhpqgxAiXucw5\nzjHOOFvZyiM8giOse3NCiJD8frh0yUiQ4vHAnj1GooilGBiAujpj/q+CAqitNbIJYnT/us51Rhkl\nn3xqqAl6YuDAwXGOU089PfSQRhpZZFFHHRpNBhnc5Cb3uGduo48+nDjZxjb66GOYYfLIMxOzCBEV\nPp/x3W5rg8xM47udnR3dfYyMGPu4dw9ycox9pKebb/vxc5vb5vmyiqqoZ6c8xCHe4A0aaSSZZB7k\nwYimIkkjjQMc4G3eZoABSinlCEcsbXGCCfP6JpVUtrCFTDIt22mafikUm9hEKaUR/y3hBHjfB94C\nPgHJvSuEiL1TnKKTTnO5lVZOcCLo7nY89bZfwfeTv6LAP31YbL3Ine4fUPPUPwVgcsLLm/X/iRE9\nBECP9x6tV2/x9N7fI9GdBmBemIVKqnKe8/wdf2d24+ygg/vc54t8McZ/nRBr3MmTUF8/s9zYCJ/9\nLJRGfiEFwOgo/K//ZfwXoL0dmprghRcYcA7zJm+a4+u6p1/HOR60GQ8e9rMfgDvc4RznAOOi8Mf8\nmEQSSSGFO9zhfd7nAR4ggQTaaecwh5d9MmWxDmgNP/uZEXiB8d1uaIAXXrAEYEsyOQk//jH091v3\n8bWvmVORXOACt7hlrtJGGw/xUFSzxdZRZ94kAbjEJXLJNZOuLGSEEeqowzP9Gmecs5zlcR43g7z3\neI9uugHooYc22jjBCdIwrguuc52LXDS32UEHBzgQVsKm2cK5DTyptf5trfV/11r/ReAnor0IIUSY\n+uizBHcAU0xZDuwrQdeFXxlPAWaZuHGF0aEuAO62nDGDuwAvo7S0nAp7H6c5HTRG7wpX8OJdZK2F\nEIyOwrVr1jK/H86fX/w2r12bCe4Cenvhzh1uczsoeco97tFH3/ybZKaObbQxwQSjjOLHzyijjDNO\nO+3mZ65zffH1FyKU1taZ4C7A54MrV+w/vxgNDTPBXcDICFw3vtPjjFuyVgNoNDe4EbUqDDPMXe5a\nyvz4I9pHAw2W+SsB7nPfEtAFfg+YYMLyt81u9wGLadvhBHhvK6VeVEoVKqWyAz8R70kIIcIwxpht\nuW+mh/iK4PcOBxdqzfiYkRnTNzEU/D4wNm5fbvtZm3+LKaYkwBNiKXy+oJszQHCAFolQ646Ohjym\nhSoPmH3MG2ccMC5qA6+5n1loe0Isyjzf7Zjvw2uc6yaYwG/TiTCa3/loXHsstI1Q2wqs58dvtvVw\ntjufcAK8X2d6HB4yTYIQIsZyybXNODV38HO8pVTsCC5MTycjx+hGUZS/13YMTFH+3rD3YZdcIYec\nsLuLCCFsZGYaP3NVVCx+m3brOhxQXm577HLhMufJCmX2eoEuY4kk4sRpjt+bnbxhpR0jxRqxcaMx\neflcm+ZP/hORigpQNmNGp9tVCim2QzSi+Z3PIoskkpa0D7vPOnGa7TSPvKAkLLPXc+Cw7XK6mL9z\nwQBPa11h8xPF/6tCCDEjgQQOcxgPRr97hWIzmymnfPkr4/PBjRtG95Epaxerkj3P4qrZaXxmZASS\nUyh6+psoZRxW07PK2L/hhHkhlkACe7MeIzsn/Ixcz/AMG9loLmeQwef5fBT+MCHWMaXgmWeMBCjd\n3UbXsM2bjaQOAYODcPWq0T0tHCUlcOQIJExfvLnd8PjjkJ5OOeVUUWXe8PHg4QhHbC/0ZtvDHvPC\nMJNMtrHNTDaVTTbb2W5e9OaTz252R/CPIMQcra3Gd35w0FqelARPP2383t1tnO9qa6Mb4GVmwmOP\nQeL0zd2EBHjwQSNZ0bTDHDbHqSkUJZSEHL++GE6cHOGIGeQpFBVUhD3JORiZtLewxUyE5sbNYQ6b\nUy24cFmWHTioocZynq+l1nITN5dc9hL+jeGABZOsKKV+C/hrrXX/9HIW8BWt9X+JeG9CCBGGQgp5\njucYYICk6deya2uDl182AjgwBpN//vOQYVxQOSf81PTlMpq4lXHXOBljWaghF7Nvvm2uepqyjQ8y\nNNhGanqRmVwlXEkk8SIv0kUXXryUUioZNIWIhqEhGBszgryEBOjrg/Fx4/dLl+Ddd2e6cZaWwvPP\ng8t+nizTwYOwa5eRTTMry7xYVSj2s59tbMOLlwwycGLzRGQON24e5VEGGTSzaI4ySgcdFFJIMsn0\n048Tp3nhK0TEJifh5z+Hu9Pjz5SChx6CvbOCir4+I9lKUpLxvb5/37jpafdkb7F27IDqamNfmZnG\nTZJZMsnkGZ5hgAFcuMKekigSueTyHM/RT/+irz32sIetbGWEEdu2Xkwxz/M8AwyQTLJ5MzsgmWRO\ncIIBBlAo0llcIptwrhS+FQjuALTWfcC3FrU3IYQIkxMn2WTHJ7gDeOutmeAOjLuap0/PLNfVQVcX\nySqZTEcmyq+NdSYnLZtxuVPJzq2JOLibLY88yiiT4E6IaJiaMrJo+v2QkmJcSN6/Dx99ZIwFeu89\n6xi9u3fDTyjh8UB+/syTiFmSSCKb7LCCu9nSSTef1CWTTCWVJGNMv5BJpgR3YmmuXJkJ7sAI5N5/\n33hSB8a578wZI/BLTTVudNy5Y/RuibbERKP9uO0nF1coMsmMSXAX4MCx5GsPN+5523rg+mZucDdb\nBhmLDu4gvADPodRMx1illBPiMCW7EEIsl7Ex44Jvrra2md/b24PfD7WeEGLl6O+3T+rQ1mZkC5zT\nHdt8T4i1yO5c5vdDZ+fM+1oHf0baxIoWToD3OvAjpdRjSqlHgb8FXotttYQQIo4SE80Jii1mT+ya\nZTPxutMZvXmBhBCxkZo6M1YTwIeZAAAgAElEQVRutqws48cu2YNdexdiLQj13Q6UL/S+WJHCCfD+\nD+Ak8G3gt6Z//+exrJQQQsSVwwFHj1ov9JxOOHRoZrm21pyA1bR/vzFGQQixcrndxni5uWUHDhhj\nf7Zvt76Xmgq7JYGJWKN27YK0Od18t22buaGZnw9Vc5KDZWQYY+bEirVgkhWttR/4s+mfiCil/gHw\ndcAJvKC1bpsu/x/AVsAL/EBr/TeRblsIIWJq+3bjDuWtW0ZwN/uEB5CVxY9eSKSz/n0c3jFURSWf\nK6+wSXAcmg8f5zlPK60kkkgNNWxhS9T/FCHEHAcPGhn6GhqMmzLbts08fX/sMSgvh5YW48J3x46I\nb9yMM84FLtBCCy5cpJGGFy+jjJJPPvvYZzt2rp9+znOeHnpII42d7KSEkqDPTTDBRS7STDNOnFRS\nyQ52yDhdEbmUFPj1X4f6emO8XWmpkVV2tqefhspKo1tmVpZxfgwxTi5W5p4vq6lmK1sj2sZlLnOS\nk/TRRw45PMMzEWXJ9OPnCldooAE/fsooYw97FsyIGw8ha6SUegX4AfCa1npiznubgH8INGmt/1uI\n9YuBh7XWj4XYxQta69uLqrUQQiyHoiLjx8YrvMKbaWfhgRQgBejnLv8v/5Z/G/bmz3CGe9wDwIuX\ni1wkkUTb+e+EEFG2caPxM5dSxgXu3IvcCJzjHG0YY5QGGKCOOgopZAMb6KCD93iPZ3jGMlfmJJO8\nwzvmpMYDDHCa05zghDk9QkAddTTTbK53laskkBDVtPFiHUlKsk4TMpfDAVu2GD9x8gEf0IkxLtCL\nl0tcIpFEKqkMa/0OOvgpP2UKY4xtF138NX/Nb/PbYScqujr9CrjNbaaY4hCH5lkrPua71fMt4EHg\nulLqI6XUq0qpt5RSd4DvAx+HCu6mPQk4lVInlVLfm07OEqCBv1RKvaKUKlvyXyGEEMvsIz4KKuul\nlxuEl1lshBEzuJvtDneWXDchRPz48NHOTOKKfoxE5H30mWVDDNFNt2W9DjrM4C5Ao81ALmCSSe5y\nl7kaaVxy3YVYibx4zeButki+83XUmcFdwAQTXOBC2Nuw218LLUHbXQlCPsHTWndijLX750qpcozZ\nnbzATa21TfqpIPlAotb6MaXUHwKfBn42/d4/01r3KqWOAX8CfGHuykqpF4EXATba3WETQog1SGOT\nrUwIsW7ZHRPkOCFEdCy1La3UthhWZ22tdZPW+gOt9cUwgzuAAeDd6d/fgpmOslrr3un/ngIKglcF\nrfUPtNa1Wuva3NzcMHcphBDL4wAHgsqyyKKGmrDWTyGFPPKCyqV7phCrmxs3Rcx07c4kE8DSzTKV\nVHKxXtsUUogb67gmhaKccktZAglsJPjGdwUVS626ECtSEkkU2IQLkXzna6kNGqPqwsU+9oW9Dbv9\nlVEW8dyWyyGWowLPMDMh+h6Y6XeklErXWg8qpWqAfruVhRBiKXz4aKSREUYooIBiii3jXfz4uctd\nuukmjTQ2sQkXLutGOjrg5k0jpfq2bZa00M/xHAMM8CEf4sNHCSV8g29EVMcjHOEt3uIWt3Dj5hCH\nJMATIlZ8Prh6Ffr6jLG11dXG2KIYOMQhznOeu9xlAxsop9xMslJAAQkk8CN+RBppHOYwWWSRQALH\nOc55zpvHpQoqaKGFJpooo4xsjERPtdTixEkLLWaSlUgTTghhGhszkqwMDMwkWbGbLiSODnPYTLLi\nwkU11UEJUkYYoZFGxhmnlFLLTdRCCvkCX+BN3qSPPnLJ5SmeChp/1047HXTgwcMmNlkmPN/Odvz4\ng5KszDbOOI00MswwueRSSmlckh8pbTd5YbQ2rtQfA7VAD/DbwNe01n8wncAlC2Ms3re11lfm205t\nba2uq6uLWT2Nuv44qtvT+otR3d56q180RftvXY1qa2uJdRtaSbx4+RW/wovXLKuk0vLU7RSnaKXV\nXE4nncd5nEQSjYL6enjzzZkJXp1O+NznoLg4avW8ylUuc9lcduHiMR4z7/iLlWO9taE1x+eDH/7Q\nCO4CNm+GZ59d9qr8lJ9axv148PAiLwY90e+kk/d4Dz9+wHia9wAPUMbqTV0g7WgFGh012sbg4EzZ\ntm1w4kT86rQIvfTyFm8xyaRZtpe9YfeqAbjIRa5z3Vx24+YJniCV1LDW9+HjDd5gmGGzrIwyDnM4\n7DosRCn1sdZ6now4hpiGlFrr39FaP6K1/oLWukVr/QfT5c9prY9prR9cKLgTQohI3eKWJbgDY3D0\nEEMA9NBjCe4ABhmcSXDi98OZMzPBHcDUFJw9G7U6TjBhycYVKLvGtajtQwgxrb7eGtwB3L4NncGJ\nG2Kpl14uctFSNsYYb/N20Gc/4RMzuANjrM/sG0JCRMWlS9bgDown3b298anPIl3lqiW4A7jClaCy\nULx4g5Kk+fCFnTgNoIEGS3AH0EyzJcHSclkwwFNKHVVKvaGUuqmUalRK3VFKSaomIcSKFQjkZtNo\ns9zufTCCPADGx2FkJPgDUTzhefHannjMOgghoidU213mi9h73LNNytBLcD3sjlMjjKzIjH1iFZt7\n42Oh8hXK7tw5wQQ+fGGtP8KIbduM5Jy84LXFMgrnCd5/Bf49cAw4gNHlMji7gBBCrBAb2BBU5sRp\njl/ZwAbLeLwAM+mBx2Od1DwgxJx4i5FCCh48QeU55ERtH0KIaXZtV6modrkORxlltpMi201mbncs\nyCZ7RSZ0EKtYYWFwmcMBBbY5EFesuUmLAJKnX+HIICN4HH6I7YZi12YVKi7n9XCSrAxorX8Z85qI\nZbWSx8yJNWJgACYnYUNwsBWOqalxhnqb8aRswJNsE2zNYzObaZ9+TTBBEknsZrcZUKWTzna28wmf\nMMYYLlyUUWbNTPfoo/Dyyww4h3H6FanuDXDs2KL+FjtOnNRSywd8YN6RD9QryOCg8VQxx/4kMc44\nwwyTTrrtxaMQ696WLUaXzMbpDkhKwcGDRrsaGoK0BSY6nqcNDvW1AJCWtdHSFp04GWAAFy5SSAGM\nC87HeZzXed18WpBDDo/xWNB297CHPvrooguNJoeciDL+CRGWnTuNdtHYaHzHk5PhwQchJSXiTQ3e\nv4MjIZHUjBA3ToaHwes1rgsWkeAokBwtmeSgwGsHO+iii/vcx4+fFFKopdb2Zq6dQEbND/nQbJvZ\nZFNNddj1K6ecVlrpoAMwgrud7DTbf8AUUwwwQDLJtjd6oyHklYBSKnAUeVsp9e8w5rAzn3Nqrc/H\npEZCiNXN54NXX4Xm6cl5c3KMRAaZ4ScO6W75mHuv/08YGUY7FJ6dB9n8yDdRKrwTghMn6aRzhzuM\nM04qqUGZsjLIAIzxLwpFJpmWTFfDJZmc+lYp/QPN4HBQkFHOEWdSIAVLVJRQwvM8TwcdJJJIAQXW\nbFvj4/DaazMXpdnZxr/lrKeLV7lKPfVMMWWeoCRduhBzOBzw/PPQ3g79/ZCYCO+/D+fOGcHe5s3w\n5JNGxtzZ5mmD3pEe7vz99/B3tAEwWJRK26f2MJXiwY/fbJMAxRTzAA/gwsUxjrGNbVzlKhlksJ3t\ntln2FIq73KWJJgCGGeZBHozZP5FYp5xO4zx97ZoRfKWnWzJGh2NksIOmv//P6K57ADhKy6n81Hdw\ne4zzLH6/kbTs2jVjbHt6Ojz9tP3TwxBaaOGH/JBBBlEoyijjq3zVDJBcuEgllXbamWKKbLLDfnoX\nUEEFBRTQSScePBRQEHaACMa1x8M8TDfdDDNMDjm2WTrPcQ4fPhw4qKY6KBNnNMx3tfQn0z+HMLpl\n/utZZX8c9ZoIIdaGM2dmgjuAnh741a/CXn1ywsu9V/87jBgDlZVf47t0jvZrb4W9jSaauM1tUkgh\niyymmOIMZ8wxb2OMcZazKBRZZOHBQz315l03gHOco981Ypz4srPpdHbFJMGBGzfllFNEUfBF3rlz\nMxeWYIwXeu01c7ELo06BJ4ATTPAhHzKCzfhBIYTRVXPrVjh1yuhlAMYF561bYJfd8ezZkG2w+e2/\nMIO7ccYZa28k8e0zANzhDje5yTjjALTRRj315mayyeYYx9jJzpAp1F/iJfNC04OHXnr5KT9d6r+A\nEFY3b8KFC5CaCrm5RkKxV181bm6EqfmNPzeDOwD/3SaaT/3NzAcuXTIStwQSlw0Owi9+YQR+YfoR\nPzLHsmk0TTTxGjPnw6tcpYMO0kgjk0xGGeUskSdGSyKJCioopDCi4G62XHKpoCIouJtggjOcMccF\n+vFzneu00LKo/cwnZICntT6utT4OfDPw+6yy34x6TYQQa8OdO8Fl7e3Gk70w9LZfMebkmWOo8VLY\nVWinPahsggm66QaM9ON2iQoC640zbn52oe3GVKNNPquuLqObS4j6aLQlUBVCzNHfb/zMZXfssivr\n6oKRESbv3DKLAoFcyp0uJpjAixeNtmTUi/T4EXhyN1sbbea+hIgKu/PM+Di0tgaX25ic8OK/2xxU\nPt44M92A7T6Gh6E7+Dxrp4su+m2mzW5kZrtttAW930//irrh2UWXbXK1WFxbhNPf6Sc2ZTKASwhh\nz2PTn9zlMrqBhCEx2b4rpzM5vHlogJB92t24Lf8N9b4Tp+1g61j1lQ8p2aZ7idNpdC+bpz6h/j4h\nBOB224//sWtvdmUJCcYxLWmm/QWewk0lJ+LAYd75nz0mNtJ2ade+XbhknK2ILrvv+HzlczicLrQ7\n+LutZq+flBT0PkrZl9tIIsn2SffsScjt2pcT58zctitAqGNALK4tQgZ4SqktSqnPAxlKqc/N+vmH\nsNxXOUKIVaPWZv7NPXuCx7aEkJlbhaO03FKmExLI3x3+pKub2RyUaS6ffDOLZgEFQZOJJ5LIJjYB\nxkmhiqqg7UYyYWpU7NtnnARn27XLDPDKKQ86YaSTThHRy/YpxJqTnGx005zN4TDa21x2bXDnTkhM\nJH3/o2aRBw8OHAztqzYz9rpxmxMkKxRb2BJRNQ9xKKisltqQXTqFWJRdu4wbFrOVlISdRdPhSCB1\nb3ACssx9sxIH7d0bfFNl82ZjLF4Y0khjK9Y2q1Ac4Yi5vIUtQV0qN7HJ9mZtvORMv2ZLIIFKKqO+\nr/muuGqAZ4FM4LlZ5UPAt6JeEyHE6jA5aYxVuXPHuFDatw9KS2fer642grlPPjE+W10NO3ZEtIuq\n5/537p5/mbGWGzjTssjb/xTpG8JPHJJBBo/zONe5zggjFFBgCc4UiuMc5zrX6abbPHnMHpAdyHzV\nTLN5AC5meVOqU1kJn/40XL5sdJmpqjJOxtM8eHiABzjJSe5znwIKOMxhSaMuxEIee8zI5NfQYDxF\nyM83xiGdOgXl5caNKpdr3ja4ce/ztCelMXD1A5zAxu17SanJYYABqqnGjZt73MOFi81sphD7hBId\ndJjj9YoppoYanDh5iIdIIomP+Rg/fnaxi2NEL5OvEICRUOVLX4KPPzbGxpWW2t/smEfFoS/T1THI\n5NlTkJCA6/hT5G57fOYDhYXwhS8YbWxkBCoqjKAvAl/ki+SSyw1ukEgiRzhiyTpdRBEP8zC3uMUE\nE5RQYnujNtb66OMa1xhmmFxy2cY2y43Yh3mYa1yjiy5SSGErW4PG6kWD0jp4Uj/LB5Q6rLX+IOp7\njkBtba2usxv8HEXRnjZA6y9GdXsyrcHiRfv/xWpUW1tL1NrQq68ag7IDHA743OeMO35iWXnx8hqv\nWSZyTSONp3hKgrwoi2obEitLRwf8+MfWhA8VFUZgF+td08F7vGeZYLmCCtund2uBtKM16swZ+PBD\na9kTT8B2m2l/1rAhhnid1y3j7LLI4gQnFp2wZS6l1Mdaa5uuUlbh9Jn6daXUV+aUDQB1WuufL6p2\nQojVaWjIyDY3m98PFy9KgBcHTTRZgjswTjDttFNKaYi1hBAWly4FZ/O7cwf6+iJOFx+pG9ywBHdg\ntOvZ83YKsaIFrgHmOn9+3QV4DTQEJVHpo49uuskjb1nrEk5HbjewB7g1/bMLyAa+qZT6jzGsmxBi\npRkbm0lzPJvXu/x1ESGz6UmWPSEiYJO1d97yKLJrqxotbVisHlNTMDERXL4M7WelCdVu596IXQ7h\nPMHbDDyqtZ4EUEr9KfAr4AngkxjWbVWTLpViTcrJgYyMmfmjAiqjP0BYLKyYYq5xzVLmwBFyrI8Q\nwkZlJTQ1WctSU41xeTFWQgm99FrKMsggnfCSTwgRdy6XMW6vZc5cbuvwuqCYYsvUDWAkUckn9seS\nucJ5glcMpMxaTgGKtNZTEIeQVAgRP0rBs89C5nQGSofD6IKxZ0986xUvU1NGkoZPPjEGpy+zHHLY\nwx4zbbobN4c4ZEkWI4RYwI4dsHv3TJa/jAw4dgyuXDECvwVyFSxFDTWUU26Oz0knncMcjtn+hFi0\nnh4j2VBLS3CbePxxa9bNigo4enR567cCFFPMDnaYY+CTSOIIR+IyVUM4T/D+CLiolHoHUMBDwL9W\nSqUAb8awbkKIlSg3F77+dWN8iscT9lw5a47XCz/5Cdy/byw7HMZJbtu2Za3GFrZQSSWjjJJKqiRX\nESJSSsHx4/DAA0a7vnMHXn995iK2sBA++1lzepJocuLkAR5gN7uZYEKe3ImV6exZ4yegtBQ+85mZ\n+W3T0+HLXzZ69zgckBb9rJCrxQ52UE01XrykkRa3aU0W3KvW+r8CR4CXpn+Oaa3/XGs9orX+3VhX\nUAixAikF2dnrN7gD+OijmeAOjIHm775rPxYhxly4yCBDgjshliIpyZgE/fRp6xOKjg7jKX0sd02S\nBHdiZervh3PnrGV378K1a8GfzchY18FdQCKJZJAR1zkrw92zA+gGeoHNSqmHYlclIYRYBTo7g8t8\nPujtDS4XQqwOXV3BGTXBCPKEWI86O+27KUubWNEW7KKplPpD4NeAeiBw1NPAezGslxBCrGzZ2dDe\nbi1zOo07mEKI1Sk72+ihMPeCdsOG+NRHiHgL9d2XNrGihTMG7zNAjdZaEqoIIUx+/HHtfhB3Bw4Y\nCVa8XvxoHCg4eNAYlxgPfv9MkgghxOKkpcGuXcbceAEZGUYSllnmO/6t+2OjWFtyc2HLFrh+faYs\nO9t2jrvAnI7zTuq9xHOVtK/whBPgNQIuFpExUyn1D4CvA07gBa1123T5DuDPMJK2fFtrfTnSbQsh\n4mOUUeqoo4MOEklkC1vYytZ4V2v5ZWRw/euHuNb9PuNToxSm1VC7Yefy569saIBTp4ykN3l58Mgj\nUFS03LUQYnW7cMEYV+v1wsaN8OijRptKTzcSJ7ndAPTQw3nO00sv6aSzm90UUwxAG21c4hKDDJJN\nNvvYRw458fyrhIiOJ5+EqipobTWyaG/dakk6NMEE5zlPM804cFBJJbvYZR0Xfv481NUZbayszGhj\n6eGPOx1jjI/5mFZaceGimmq2s33+YHIdCyfAG8XIonmSWUGe1vqfzLeSUqoYeFhr/ZjN2/8K+ApG\nl8//Anw67BoLIeLqFKfMeZt8+LjEJTx4qKAizjVbXk00cdFzHUpzAWhnjFOc4gQnlq8S9+/DL34x\nM2aoqwteegm+8Y34PUkUYrW5dctIkBTQ3AzDw/DVrxrdNaf58PEu7zKBkUhpkEFOcYqneRqN5hSn\nzCcYvfTyLu/yLM/ixr2sf44QUaeUMa9diLnt6qijmWbAeMJ2gxskkMBOdhofuHED3ps1squpCV55\nBV54IewqfMAH3OMeYEwofoUrJJJINdWL+pPWunCecb6MEZCdAT6e9bOQJwGnUuqkUup7SqnZ6d2y\ntdZ3p5/oyYAVIVaJQQaDJuUFI9hZb+z+5l56GWAg+MOxcvNmcEKI8XFobLT/vBAi2OyuZwH370N3\nt6WojTYzuAvQaJpppoUWM7gLmGCCduaM0xVijZliihZagsot50i7jJvd3UFtLBQvXjO4C7kPYbHg\nEzyt9V8opZKAjVrrGxFsOx9I1Fo/Np2o5dPAz6bfmx1Y2gaZSqkXgRcBNm7cGMFuhRCxEqorxHrs\nIrEi/i3U+vt3FyLqQrWjOeUros0LsQI5cDDF1DwfCPE8KYKxeAoVdBNFxuKFtuC/jFLqOeAi8Nr0\n8h6l1MthbHsACPR5eAssg3T8IX43aa1/oLWu1VrX5ubmhrE7IUSspZFGHnlB5ZXYd9tYyzaxKags\nl9zlnctqy5aZiWYDPJ6Q3WiEEDZskkWQl2ckl5ilhJKg7pZOnJRTThllQfNQunFTQknUqyvESuLE\nSRllQeWW6wK7NlZYGHYmziSSKCJ4bLndeVgYwgl9/yVwEOgH0FpfhLAG25wBdk3/vge4M+u9XqVU\niVKqCJazP5MQYqmOcpQyynDhIo00DnCAUkrjXa1lV0opO4YrGGy/wb32C6R2+zjK0cg31NgIb7wB\n779vTCgbicxM+MxnoKAAXC4oLYXPf95MCCHEutfWBm+9Be+8E7o7WEUFnDgBWVlG4ojqanj++aCP\nuXDxyOQxNl8eZeOvrrPp434e8h0ilVTSSOMhHmIDG0gggXzy2cY2Lk6/BhmM7d8pRBztYx/V3dm4\nb90lqaGDHf0l1uRrlZXwxBMzbaymBp59NqJ9HOIQxU0TJL9xhtT3L7Ctr0gCvHmEk2RlUms9oKxd\nFWxmPLTSWl9USnmVUu8APcDfKqV+X2v9B8C/AH6IkUXztyKvthAiXty4OczheFcj7gZ6m7hx86ek\nM046MMwFrnc62bPzq+Fv5OxZ4yfg8mX40peCnhzMq7QUvvzl8D8vxHpx9apx8yQwp93ly0bgVl4e\n/Nlt24yf+WhN1kvvUts6CGQCY1D/jtH+EhPJJ58neAKA61znAhfMVW9xi+Mcl6yaYk1KuHKdfW9e\nZR8uo8BxHj5damTLDNi+3f5JXpjaP3yJrDPvkgXAKPrS39D3xXyy8muWUvU1K5wneFeUUr+OkTCl\nSin1PYyncwvSWv+O1voRrfUXtNYt08EdWuvLWutjWuuj008EhRBiVbne/DoTjFvKbg6fx+cN8ymc\nz2ekjJ5tYgI+/DBKNRRiHdMazpyxTlju91tvqESqudlIEz9bb6+RIXCWKaaop37BMiHWBK3hgw+s\nZUtta3NMjI8w/NHbljI1Ocm9j16J2j7WmnACvO8A2zGmSPhbYBD432JZKSGEWOmGJoOzifqVn5Hh\n4ExftkZGYHIyuHxAeq0LsWSTk8ZUB3NF2g06nHX7+iyLPnxB2TYBhrGpjxCr3cSEcT6bayltbY6x\n0T5jP3NM9fVEbR9rzYIBntZ6VGv9+1rrA9NJT35faz22HJUTQoiVKi+pPKjMjZuMrOByW5mZkJoa\nXF5cvKR6CSEwxqTm5weXlywh6UmodeeUJ5FEKsFt2y5BlRCrXmKikZRorqW0tTlS0gsgLS141yWS\nUCyUkAGeUuoVpdTLoX6Ws5JCCGFrgXl0eto+oenSy4yPRT/BwZaqZ9ngmBkr59RODhR9BmdCmAlO\nHA54/HHjQjQgLw8OHoxyTVeP/v5xWltHmZpacJj3iqK1pq3NS0+PL95VWT/Gx6G9feYpXU8PdHUZ\n3cXGxozkKkePQlLSzDoZGfDgg4vfZ04OHDpknT5h2zYjScssCsVBDpJAAve5zyCDpJPODnYsft/r\nVGfnGPfuyTMFRkaM77RvCceY69ft56OLhkcfNTI4Byy1rc3hcCSQ/8RXjWBymsrJpfTQ54M+2047\nt7nNJDY9ZNaR+ZKs/PGy1UIIISIxNASvvGJc0IERGD33nHmHz++f5MM/fxFu3kah6Uj8j2Q9+1W2\nHPlG1KqQ6E7j8QO/R3fnFXzjg+Tl78LtiXCKhPJy+M3fhJYW4+RYUrIu57abmtK89FIbV64MoDWk\npibw+c+XUFGREu+qLejevTF++MO79PUZ4zErK1P50pdKcLudC6wpFu3mTTh50rjYnZqC0VHjabhS\nRsDndhs3TpxOOHDASFrkcMDGjcHTikTq8GEjqOvsNFK859gnTfHi5SY36aMPhWKSyaA5vERoAwMT\n/O3fttDZaQR3RUVJfOUrpaSluRZYcw06fRo+/tgY1+ZywSOPRJas5N49+Df/xvjOgvFk+3d/N6pP\n2CgogG9+0ziXOZ1GW4tgjrtw5G7cT+Y3t9Bz9zwJ7lRySnaj1Mw+RhnlL/lLWjHGyaaSypf5MuWU\nR7Ueq0XIf32t9bvz/SxnJYUQwuLdd2eCOzB+f+cdc/GTX/0H1M1bqOkLKsf4BH0v/0+8w9Htr6+U\ng7zCXZSWHYs8uAtwu6GqysiGuQ6DO4C6ul4++WTAzIcxPDzJT37Suiqe5P3sZ21mcAfQ0DDMO++E\nfqoslsjrhddfn3mS0dQE9fVGspOREbhyxQgAwQj+zp41bvxUVCw9uAvIyDDSvIcI7gB+yk8ZZRQ3\nbhJJpJ12fsEvorP/deAXv+gwgzuA9nYvv/xlZxxrFCfNzfDRR0ZwB8Y4tJMnjZuc4frBD2aCOzAC\nvu9/P7r1BCP4rKw0blxGObgzd+FOoXDzg+SW7rUEdwC/5JdmcAfGmNef8JOY1GM1kCnghRCrz507\n85aN3gpOzuuYnOTulV/GslZikW7dCk4+MTIySXu7Nw61Cd/g4IRt9zG7v0dEyd27RuAWEEjkMDAw\nk6BoaMj6GbvjRQz1Tr/maqJpWeuxmt2+HdyG1mW7amoKLvP7jcAvXIEbHrPdvj0TNK4Rdwhu5/30\n0836vOEmAZ4QYvVJsem6N6vMkRo8GBsgOWv9Tci+GqSmBo8WUMq+fCXxeJy4XMGn0bS0lV3vVW1u\n2w+MyXG5ZsazJiRYnyDYHS9iKIkknAQ/LUwmeVnrsZrZtf112a5CfXcj+U6HOl/G6ClbvNi1LydO\nUlj5Xf1jYW393xVCrA8HDsxbtvH4b+BPsF4MTBUXUFLzSIwrJhbjgQc2BAVK27dnkJWVGGKNlSEx\n0cHBg9mWMqXg6FGZzDpmiouhqGhmuajICOjy8iA72xjLWlg40905IwOqq5e1ikkksZvdljKF4ihH\nl7Ueq9mxY8FtyK5szYBhZmUAACAASURBVNu2DZLnBC55edYJxBfy1FPBZU88sbR6rUDHOIbCOsxh\nBzvW7Y2VkLdDlFKvQOgRwVrr52NSIyHEuufDRz31dNJJCilsZas1xfjOncZg7lOnjKx5Dz5olE0r\nLD+E/ta/ouXkf8M/OIC7ooa9n/qudSf37sFf/RU0NhpJGL70JeNkGuD3w/nzRvcWp9PY/uz3wej6\ndf78/8/enYe3cZ2H/v8egOC+7yIpLiIpSrJ2UbtkWd5jO5aXOF6yP06dpE1S/9J0yW37NE9703uv\n0/T2aZo0bW+bOKljO0rieN9tRbYky1psLdS+kRRJifsGgguA8/tjRIDgDEWQIggCfD9+/Eg4mBkc\nijgz55055z1Gkod584wMmI5ZmATgGuXnx/Poo2V88EEbPT1uKiuTWb06c/wdp1hLywC//30Lly/3\nU1CQwA035IwbZN58cy7Z2bEcO9ZNXJyd1aszKCmxvmN84kQ3H3zQjsvlYcGCFDZvziYmZhbfZ/V6\njeQRp04Z7WbpUliwYPz97rkHPvrIWHR8yRKj7dbWGsMy16yB/fuN80NhoZFR88/+zGjDW7bA3WN0\nXerqjP2cTqPzvHatMT92AjrppIYauuiigAJSSOEc53DgYD3rWcjCCR1vNluzJpOUlBg+/rgTpWDF\nigyqqqxHZoylu3uIHTtaqK/vIzs7ji1bcsjPjx9/x5kkMdHItrx9u3HNKiuDW2+d2NO3e+4x1mp8\n803jerl1q9FmRnrtNXjqKWMua2WlkYQlwpbsWcpSHDj4gA8YYICFLGQzgZk8R7bRLLJYzOKofcKn\ntLaO4ZRSW66243QmWqmurtb79+8P6WcotT2kxxfho/UD4a5C2FVXVxPqNjSV3uItWvEnRLFh42Zu\nJpMrnf59+4zMYiNt2BD8EgNuN3zzm8bFbJjDAX//90ayEzASuXz0UeB+t9ziz15WVwfPPQcjz6Hl\n5UY2TxFxnE43P/rRGfr6/HO3UlMdfP3rFcTG2q65DZ082cPTT9cFlC1Zksb9909hJrtI8+67cOhQ\nYNmtt5pvpARrYACefNK44QL+QC8/398hvu8+c+e2oQF+85vAOUnFxca2QXLh4hVeCVjgPIkk7uAO\ny+Gas9V0XYs8Hs2PfnSG9nZ/AqS4OBt/+IcVpKVF0E247m7jRuSg/+cgLQ0+//ngkwYdOADvvRdY\ntm6d8T/Anj3w538e+P3PyTGub1HEhYtXeZVB/P+WiSRyB3cQc9VFBWYWpdQBrXX1eNtJFk0hxIzS\nTntAcAfgxctZzvoLPjYnUbEsG8uuXYHBHRjZyV57zfi72w1Hjpj3GxnwHToUGNwBnD1rXJBFxDly\npCsguAPjCcCxY1Pz+/zwQ3PSjaNHu3A6Z+laTUNDRsbL0SbSjkc7fdof3Hm9RkIWr9dfBkZQOdrh\nw+aEE3V15nPEVZznfEBwB+DESQMNQR9DTJ3Tp3sCgjuAgQEvH33UEaYaTdKxY4HBHRjJhCaSOGi8\n6+Wzz5q//y0txhO/KFJLbUBwB8bSCtHaRscNWZVSlcD/AhYBvmfbWut5IayXEGKWGn0Ctiy3Wux1\nIgvAjuzwjeS6krXR4wnMwuerxKD13ydbDzFjDAxYZ5QbGLD4HkxCf7/5OFobnzvNOUBmhrHa2LW0\nn5H7au0//sjOq1W7HeszJ1CX0cHdeOUitMZuzxGWOXKs68xY5VasvseDg0YbUWrs6+FwhtooEVTf\nIooEM4j3p8C/Am5gK/Bz4BehrJQQYvbKIYc4zHNf5jIiA2ZlpXlHq7KxrF9vPVdu/Xrjz7g4/1DN\nkcrLrf8+LD39qmtjiZlrwYIU0zKENpua8LyfsSxaZF4nMT8/nszMmZ1IJmTi463b2ETa8Wjl5f6h\nmHa7sRA5QEKCf5vFi4P7zNRUY0HoIBVhHmprx04BBRZbi1CrrEy2zHC7cOEk1ysNl4oKc5ndbqw1\ndy3HqKjwJyK64Qbz+3FxxnDpKFJEkSkJiw0bhUTWXMNgBRPgJWit38aYr1ertf4ucGNoqyWEmK3s\n2NnMZlJ6FTQ1YW/rYpFnAcUU+zfassWYbK6U8X9pqfki5fEYa/189BG0jlrgPD0dvvpVoxMHxsVs\n27bA7Jy33GJ0EC9dMvYvKzPm+Q1btsxI8NDRYWyTmAh33TVrFyuPdHl58dx9dwGJica8lpSUGO6/\nv5D09KkJwNavz2LVqgzsduP7UVCQwAMPzOL5d2C0seGMmDabsXh4To6RuKhlnLWrmpuN7c6e9T+h\nS083OqWDg9DUBMuXw8KFxhOMvj7j+F/+svlYixbBypX+OU2ZmUZbnkAiiyyyqKaaWIzvSwIJrGc9\nCSSMs6cIhcTEGB54oMg33y4+3s4nPpFPcXGEZVQsKDCubcMJf5KT4c47zZk129qMa93p0+Yn41u2\nGEnAhq9NJSVGopVhjzxifEZfn/HUzm6Hb3/bmOsXRTLJpJpq3w3k4TYarVk2g5lV2K+M5eJPK6W+\nDjTAyHR2QggxtbL3neeOXY30xXmIHXLiyD4G9y/0X+Ti442AbHhoyeiL3cCAkTShudlfNjoJy8aN\nxhO7xkajQ2d1wezsNN5Tygjiurv9TwX6+ow5Pikpxr4ul7GNPMGLWCtWZLB0aTo9PUOkpDh8wdhU\nsNkUn/xkAbfcksfgoJfU1AhK9BAqqalGwpPeXiNIe/llePVV//sjE0GMtGuXkWhpWEEB3HuvsVzC\n+fPGn5mZRpuMjzeOYbMZZf39RpsdSSm4/nojc+bAgP/GzwRVUEEZZfTTTwIJ2GQlqrCaPz+Fiopk\nuruHSE6OidyMtcuXG0+e+/qMAG/0jYfRSVSys+GBB/zXy7g4I3tsX58xLHP0mPCODqipMbaLiTHm\nx37wAXziE6H9ucKgnHJKKZ0VbTSYn+xxIBH4JrAK+BzwhVBWSggxi/X2wp49KBRJAzE4vDYjUBud\nbQ+MwGp0YAZG0oSRwR0YF6ze3sAymw2KiszH0NpIxuDxGAspOxxGZ3H3bv82+/cbAWBMjHFh1Bp2\n7pzY3Agx49jtivT02CkN7kaKj7dLcDdacrLxtP3y5cDyDz80Jy3q7DTa3kiNjUYyivp6OHnSaNdx\nccbSCS0tRoc1MdEI7t5/f+x6xMVNOrgbNrywcjR3HCOJzWa054gN7obFxBjfzdHBXV+fOaN0a6s5\nAzQYbcBqwu9PfmK0E7vduN7ZbPDOO0abjEKzpY2O+9NprfdprXuBbuCbWuv7tNYfhL5qQohZqbXV\nnNELzAHb1YzuKIJxzPGGfQ0bGDAylY126dLVP2Nw0LgbKoSYmGDb7OXL5uy1w+Ujj+Hx+JMmOZ1X\n/xwhItVY18uJfM+tAjmtA5+Si4gzboCnlKpWSh0BDgNHlFKHlFKrQl81IcSsNDwkcrSJDH202lYp\n//DK8cTFmYdxjT6u1WfExBjzgIQQE2PVNq3a7FhtODs78D273T9EbeQTehlCLaJJZqb1XNGJfM+L\ni81lShnzzEXECub55H8Bf6i1LtValwJ/hJFZ86qUUqVKqctKqR1KqTdGvffdK4HiDqXUtyZVcyHE\nzObxGOvJjcXrNQ+ZBGMYysqV/mNobQRNE7nYLFtmDrRWrgx++JVSsHlz4IUzNjYwyUp1tXm4y/r1\n/k7lDDM46MXrtXjyIaac1prBwQhLxx5uS5candWRli832tjwEwqv10j8sGRJ4HbZ2UYmzDlzArML\nlpQY54GMDOO1wxHYhsWM53Z7cbulLV1ZU8VcnpxsXIvAf71MS4MVK4I/9le+Yuyjtb+trV5tJB8S\nESuYJCs9Wmvf7E2t9ftKqZ4gj/+m1vqzY7z3J1rrt4I8jhAiUng8xly0mhrjYjFvHtx0U2Cq8pde\nguefh54eyM2FL30p8IK0cKGxCPKZM0ZQtnFj4P7jSUgwMoOdPGl8RnGxMdduIubPNzqOp04ZHcOq\nKuNiOiw1FT73OThxwhgKVlYG+fkT+4xp0No6wAsvNFJX10diop2NG7PZuFGeYoTK3r1t7NzZitPp\npqgogbvvLiA3N378HWe7+Hh46CGjvXV3Gx3O48fhRz8ybprExRkd3IEBY3mFm282hlEnJcHbbxtZ\ncbU22ulddxnz7XJyjKd6Z84YN2sWLLB+Mi9mnMFBLy+91EhNjTEHc8mSNO68c47l0gdR78QJY55d\nT4/xfd66NfB6VlVlzDs/fdr4fq9fbz03fSz5+UayoyefNG66lpbCH/zBlP8YYnoF01I+VEr9m1Lq\nBqXUFqXUj4EdSqmVSqmV4+y7VSn1nlLq/7N47/8opd5SSi2feLWFEDPWvn1GQhS32wjwzpyBt0bc\nyzl8GJ56yrhYgTG37p/+yf/a6zWCv+GOXFqakdzk3LmJ1SM21rjTv2HDxIO7YZmZRga+VasCg7th\n8fHGU4b162dkcKe15umn66irM7KN9vV5ePPNy9TUWMwvFNfs1KkeXn31Ek6n8eT64kUXTz1VJ09O\ngxUba2QL3LDBWALh4kWjvL4e3ngj8PXhw8aNn717jXOOx2OcO44fN7Jxbtxo3KTJyjKyY65eLcFd\nBHnttUscPtyFx6PxeDQff9zJG2/MwvmTzc3w+uv+62NbG7zwgv9pntdrvO7vN66X6elGQrGzZ4P/\njN27jey1ubnGDVmbDX7wA0kYFuGCCfCWA/OBvwG+CywENgA/AP7hKvs1XdlvK3CzUmrpiPf+WWu9\nCvga8MOJV1sIMWMdP24uO3fOf0HaudOcJGFgwJ+hsrHRfzEb6eTJqa3nLNDQ4KKtzXyRPnJEArxQ\nsPp37eoaora2Lwy1iWAtLUZHdtjwOpYjy5qbob3dCARHO3HCv4SKiEhWbWlWnrdOnjRfLwcH/QFc\nU5N1QjCr6/BYrDLL9vaas9WKiDLuEE2t9dbxthljvwFgAEAp9RKwGCNRC1rr9it/nlZjLAqslHoM\neAyg2GoCqBBiZoqxOK0o5Z/P5hgjRXzslQWlx1pceHgRYhG0sVKDR3zK8BkqJsb6ejZWuRjD6LY+\n3E8Y3V+IibE+X4w834iIZLcrhoYCy2ZlO7K6no4sH+/9YIx1bR2+JouIFEwWzTyl1H8qpV698nqR\nUurRIPYbORZiI3B2xHupV/7MZowgU2v971rraq11dU5OzngfJ4SYKRYvNpctWOAP7G6+2XxBGZ43\nAEaihNEZwJSyPq64qvz8eAoLA+cuKgWrVmWEqUbRbeXKDFMMkpcXT1HRBOaPCmNodGGh/3VenvFn\nbq6/rLjYmAd7/fXm/VetMoZPi4hldY6aleethQvN18ukJGPONxhtY2S7gIlfL2+5xXxDJCvLn+xM\nRKRgQvyfYWTN/Msrr08BzwL/Oc5+m5VSf4fxFO99rfVepdQPtdbfAL6vlFqMEWD+xWQqrtT2yewm\nhAi14WQpw/Pwqqr8wRtAeTk8/jhs324MvSopgc9/3t8hUwruuccYNlJba3Ti1qyBgoLp/1kiQGtr\nP089VU9dXR8LF6bwyCPFJCb6T+2PPFLMm29e5syZXlJSYti8OYeyMovFbq+iu3uIffva6egYorQ0\nkRUrMia8EPi5c70cPtyF3a5YsSKdoqIJJAGIEHPnJvLQQ8Xs3NlCZ+cQ5eVJ3HJLHsMjVY4f72b7\n9np6e91s2pTD3Xf7v9P79rXxwgtNuN1ebrstnxtuyB3rY2aHu+4yzgHnzxvJUtatM4Zk9vUZ84Q2\nbza2e/BB48+dO415eCtXwhe/GLZqi6lx0025nDjRxfPPN6EU3HtvIVu2TOxmf1+fm6efruPYsR7m\nzk3gkUeKIy/hUXo63Hsv7NljfP8LCoz5pSNHwmzbZrSVCxeMm6Vr1kxs3vnSpUYmzeefh85Oo309\n+qg8BY9wSlstGDpyA6X2aa1XK6U+0lqvuFL2sdZ62pKjVFdX6/2jxgJLgCeCpfUD4a5C2FVXVzO6\nDYnI1909yJe+tJ+2Nn/67NLSJP7rv6qxTdHFuadniH/7t3P09vqXvFiwIIWHHgp+6PyBAx28+GKj\n77VS8NBDxVRVRU7Si2ttQ/v3t/Od7xxhaMif8v3OO+fwp3+6gFdeaeT73z/FyOvxl788j89+tuSa\n6izETBNsO/rVr+r58Y/9C3ArpXj88Uq2bSu8yl5+Xq+Xxx47yJkz/vncGRmx/Od/riYzU4Yeisil\nlDqgta4eb7tgegBOpVQWoK8ceB0wC2e6CiHEzPKb3zQEBHcAFy44efvt5in7jP37OwKCO4ATJ3po\nanIFtb/Wmh07mkeVYSqLdr/4RW1AcAfw+uuX6ewc5Kmn6hh9s3X79ot4vbL+l5idnnmmPuC11pqn\nnqoLev/3328LCO4AOjoG+fWvL05J/YSY6YIZovkt4AWgXCm1C8gBPhXSWgkhhBjXpUv9luWNjdbl\nk9HRYZ0qu7NziDlzxp9b5vFoenrMC953dg5ZbB29WlvNixS73V4uX+63zHTa1TXI4KCX+HgZJiVm\nH6vzzljnIiv19dZZVC9dCu7GlBCRbtwrh9b6ILAFY2mErwDXaa0Ph7piQgghrq662jrpwLp1mVP2\nGfPmmdf/s9sVxcXBzaGLibExd65524nOA4x0ixenmcrS02MpL09m4cJU03tlZcnEx08gE54QUaSy\n0nzemT8/+CHdGzZkYZWlfaxzphDRJpgsmg8ACVrrGuAe4NkgFjgXQggRYrfcYiTjGO7I2GyKT396\nLlVV5oBhspYuTWPx4jRcLs+VO+iaO++cQ1JS8MHHXXfNITXVnxQgOzuO226beQvDh9LXvlZOaWkS\ng4Ne+vs9xMfb+da35hMTY+PxxyvJyfEnf0hLi+Xb354fxtoKEV5/8ifzSU930N/vZWDAS2ZmLN/6\nVvBtoqwsmYcfLg5IBrV5cw633z67zjti9grmCv3XWuvtSqlNwG0Yi5v/K7A2pDUTQggxru9+9zrO\nnu3l1Kkeli1Lo6BgarNTKgUOh0JrjcejUUrhcExs2GBeXjx//MeVXLjgxG5XlJQkWt5dj2YJCXZu\nvDEXh0PR3+9h4cI031PMkpIknn12Lbt3tzE46GXTphxiY2Voppi9cnPj2bo1l48+6gSMJRKys+Mm\ndIzHHpvHXXflc+hQFxUVyVRWRk5SJyGuVTABnufKn3cC/6q1fl4p9d3QVUkIIcRElJcnU15uHtI0\nFY4c6eKjjzpJTIwhMTEGreH55xsoL08KWI5hPHa7ClkdI8H777dy7pyTwkIjAB8c9PLb3zbwzW9W\noJTCZrOxaZOs+SoEwMsvN11ZlsW4CdLWNsirrzZNKHsvQEFB4pTf9BIiEgRzi7BBKfVvwKeBV5RS\ncUHuJ4QQIsKdPes0lbndmtpa6yQGwtqZM72mso6OQcsEK0LMZlprzp41txerc5EQwlowt18/DdwO\n/IPWulMpNQf409BWS4ipM9VrJk71unozvX5idktNtb5MjJxTJ8aXluagqSkwu2lMjJrQXEYhZgOl\nFKmpDrq6AjPtjnUuEkKYBZNFs09r/Vut9ekrr5u01m+EvmpCCCHCrbo6k8REe0BZRUUyhYXjL5Eg\n/DZuzA5I+ACwenUmCQn2MfYQYva6/nrzcGWrMiGENbkdIoQQEayx0cWOHS00N/dTVJTIjTfmkpkZ\nO2XHT0tz8Ad/MI+9e9tpbx+krCyJ1aunP9V4e/sg77zTzMWLfVcSMOQEtQ5fOLlcHt55p5kzZ3pJ\nSYmhpCSBvXs7cDrdrF2bxdat0mEVs5PT6ebtt5s5f95JerqDzZuzA5ZkWbkyndOne3jrrWaUgltv\nzWPpUvNSI0IIaxLgCSFEhOrqGuLJJy8wMOAFoLOzi7q6Pr7xjYoJZ7q8moyM2LCmFx8c9PLTn573\nLZje2TlEba2TP/qjihk9VPSZZ+p8cxVPneqhpqaL5cvTKS1N4vLlfn73u0Y+/em5Ya6lENNLa80v\nflHLpUvGkOWOjkHq6vr48pfLfDdtduxo4cSJHoqKjNc1Nd3k57eyebPcFBEiGJIsRQghItShQ52+\n4G5Yd/cQJ0/2hKlGoXHiRLcvuBs2MODl8OGuMNVofM3N/QGJaBobXVf+9M/DO368m95et2lfIaJZ\nfb3LF9wN83g0Bw50+F5/+GG7aT+rMiGENQnwhBAiQg0NeS3LBwetyyPV0JC2LJ/JP+founk8OuBP\nAK3H/h0KEa2COW9ZbTOT27sQM40EeEIIEaEWLUpl9HrhDoeNqqroWtC3qiqFmJjAH1Qp4+efqQoL\nE8jI8M+FzMkxFmnOzfUv1lxQELiNELNBSUkiycnmGULXXZdm+fdhixfLHDwhgiUBnhBiZrp8GT78\nEI4fB/fsHcbmcnnYv7+dXbtaaW8PXDNtzpwEPvnJAvr63NTWOvF6NQ8+ONeUev/w4U5+/OMz/OpX\n9fT1Tfzf0uPRHD3axXvvtVBfb73+3d69bfzLv5zmuecuTvmd9uTkGB58cK4vGEpOjuGeewrJz4+f\n0s+ZSkopHnpoLh6PZt++9iuJVTI4c6aX11+/xOCgh3XrMnnvvRaOHeump2eIDz9sZ8+eNlN6+BlL\n2qiYhJgYGw8/XOy72REfb+eWW/ICbkx94hP5pKXF8Pbbl3nnnctkZcVy6615Acfp73fz61/X8y//\ncpqDBzuwcuGCk/fea+H48W68XvNIgI6OQXbvbmXfvnZcLs8U/pTTrLlZ2qIIoLS2Hvoyk1RXV+v9\n+/cHlE312mFCBCsS18Grrq5mdBua0T74wPh/WFYWPPAAxM/cDn0otLYO8NOfXsDpNC7YNpvivvsK\nfXeyPR7Nf/93LefO9TI0pHE4FMuXZ3DvvYW+YwwHdsOys+P48Y9Xkpsb3L/lwICHn/3sQsAabhs3\nZnPLLf7O1ve+d5w337zke11UlMhPfrLK8i79tdBa43R6SEy0Y7Op8XeYQpNpQ//6r2d49tl6PB6N\n0znEmTNOUlMd2GyKgQEvixensnlzDt3dQ9TW9rFoUSp2uyImRvHww8WUlyeP/yHhsncv7Nnjf52Z\nabTRhJmd2VSE1+h25HS6iY+3m5YQ2b69ju9856hvqGZsrI0nnljmO7e1tw/y1a8eoLnZf166//4i\nvvGNSt/rF19sDJjXV1KSyOc+V0JMjPFs4/jxbrZvv+gL/BIT7Xzxi6VBnxtnjA8/hN27/a+lLUY1\npdQBrXX1eNvJEzwhxMzS02NcsEZqa4NDh8JTnzB6991mX3AH4PVqXn/9kq9DUlPTxfnzTpRSxMba\nUEpx6FAnFy8aT9mam/v5zW8uBhyztXWAJ5+8EHQdDh7sNC3QvXu3/2mikcr8csD7Fy/28cwzdUF/\nRrCUUiQnx0x7cDcZra39/OY3DQDY7Yr6eheDg16cTjdaawYGPHz8cScul4dz55y0tQ1w+bLx7+x2\na1577dLVDh9eTqcR4I3U3j4r26i4NklJMabgDuD73z+F2+1FKWM49tCQl3/4hxO+93/+8wsBwR3A\n737XQGOjce5ranIFBHcAtbV9HDliJGbS2mhjI5/q9fUZy5pEFKcz8GYoGG3x44/DUx8xY0iAJ4SY\nWVpbwWsxxK+lZfrrEmajAyuAnh63L/Oi1fsjy0+d6glI6jHs3DnnBOrgMpVpjS8L3rFj3ViNBDl7\ntjfoz4hGJ0/24nb7v8f9/cbf3W4dkHCltXXA9/scmVGzpWUgYP8ZZaw22hxhnWMxI3k8HlPwBnDp\n0oDv71bnF49Hc+KEkUF4vHNjX5/Hcij06OyeM15bm7RFYUkCPCHEzJKdDTaLU1N29vTXJczmzDEP\nFUpJifENfbR6H/DNTZs/P8Xy7vi8eUlB18FqnptS/nIj0Yv5M2b08MJpUFWV7BsKBhAfb/w9Jkb5\nfid2uyI7O873+xw5pDUnJy5g/xklK8u6jebIGmXi2tntdnJyzOed/Hx/gqKyMvM5zGZTLFiQcmVb\n63Pj8DkzMdFOWpp5Dc2ZPK/X0lhtMTd3+usiZpSQXT2UUqVKqctKqR1KqTdGvVeglHpHKbVbKXVz\nqOoghBjD0BD0z9A7lSkpsHp1YFlmJixfHp76jMPt9k4qcclILpfHMi341q25JCXF4PFohoa82GyK\n227L9w1RvO66NEpLAzs6S5emMXduIgC5ufHcf38RYNzd1tqYg/eFL5SaPmtgwEN/vznJwKpVGaZA\ncv36LDIzjYQnlZUp3HRTYGeisDCBhx4qDvKnjw6jf4fZ2fHcd58xX2hw0EtRUQKxsTaSk40haXFx\ndpYtS8Pj0ZSWJpCZGUtenvHvHBNj/J5nrORkWLMmsCwjY/JtdGAABgfH307MGt/+9nxiYmy43V7c\nbi8Oh41vf3uB7/3Pf77EFARu21ZIQYFx7isoSGDlyoyA94uLE1myxJi/rFTguRSMoG/rVnNgNDR0\n7ef4a+b1Ql+fMXxipKQkWLs2sOxa2qKIGlM7A97sTa31Zy3K/wL4K+Aw8BLwVojrIYQA4+Kwcycc\nOWJk2ioshFtvhbQZln56/XooK4PaWkhNhcpKiAn16WpitNa8/XYzH37YzuCgl7lzE7nnngKysuLG\n3/mKzs5BnnuugdraPhwOG6tWZXDbbXm+J2IZGbGUlyfxxhuXGRz0Ul2dQXFxom9/u13x+c+XcPJk\nDy0tAxQVJZjubN9/fxFNTf0cONBOaqqDL36xJCCJwOCgl5deauToUWOoZVVVCtu2FZKQYAcgLs7O\no4+Wcfx4Dx0dg5SVJfkCyGF/9VeLuOmmPPbvb6eoKIE77ywgNnaGPn2aYt3dQ/zudw2cO+ckJkax\nYkUGt9+ej92u2LIlh5dfbqKhoYeEBDvf/GY5cXEOOjoGqahI4qWXLvHWW5dJTXXwyCNzWbEiA49H\nc911aZZPF2aUdeuMNnrhgnFTprISHBOss9MJb7wBdXXD617A1q1gt4ekyiJyrF2bicOhqK8fQiko\nL49l9Wr/dSo7O54nn1zNSy81cflyP+vXZ1FdnRlwjLvvLmDJkjTq6/vIzo5jwYKUgIBu0aJUvv71\nCo4f7yY21sZ1Vt1magAAIABJREFU16WSmOi/zmiteest4xw/NOSluDiRe+4p9N3cmjY1NbBrlxHg\npafDjTdC8YgbaGvXQmnptbVFEXVClkVTKVUK7ALOAb/VWv/fEe/tALZqrbVS6kXgEa11z1jHkiya\nYiaJ6CyaH30Ev/994Ju5ufDII1Nah9ngwIEOXnyxMaAsNzeOP/zDiqCP8R//cY6GhsA5brffns+6\ndVkA7NzZYpr0X1aWZPkEzorXq/nhD8/Q0RH4dOT++4t8d7JfeaWJDz9sD3h/8eI0PvWpoqB/jtlg\nrCyaP/vZBS5cCJzTeOONuWzalMWnPvUB7e0DAe994xuV3HRTLg8++AEDA4FPTP/X/1rK+vVZU1/5\nmeq554ybOCOtXWvc4BFRKdhstGvXvsXJk4HdwiVL0njvvRtDVTWTffvaefnlpoCy/Px4vvrV8mmr\nA5cuwbPPBj65czjg0UdnXVZpYZgJWTSbgPnAVuBmpdTSEe/ZtT+y7AIyRu8shAiBU6fMZc3N0Nk5\n/XWJcEePdpnKmpsHLJMDWOnoGDQFd6OPa/UZ5887AzJrXk1Dg8sU3IGRffNqnzHWmlEiUG+v2xTc\nAdTUdHPgQIcpuAPYsaOZd99tNgV3QMBSE1Gvv994cjfayZPTXxcx45w+bU6icvz4mM8BQsLq3Hjp\nUj+treZ2HTKnTpmHZQ4Nwblz01cHEZFCFuBprQe01k6ttRtjGObiEW+PvLKlAqbepVLqMaXUfqXU\n/pZZmD1PiJCwGuao1Iwb/hgJxhqC6HAEd1qNiVFY5CYJOK7VsWw2ZZk4ZbJ1tNpmrLqJQDExynLJ\nBodDBQz1Gik21k58vPUQxOFhsbOCzcYYDWD66yJmHKtzXLDnvaky1rk82HP8lBirPcgQTDGOUCZZ\nSRnxciNwdsTrw0qp9UqpJCBVa909en+t9b9rrau11tU5kplLiKmxbJm5rLzcSJogJmT16kxTWUVF\nMhkZwXVQU1IcLFyYetXjrllj/oylS9PGDBBGy8uLD5izB0afurraP2jC6ueors60zIwpAsXH231D\nXUdavTqT665LY968wHallOLeewu46aY8MjMD52o6HDbuuaeQWSM21phzN9rSpeYyMesMD1MfafPm\n6c2kbHVurKpKmd75sQsXmoO5lBRj/qsQVxHK2/ablVJ/BwwA72ut9yqlfqi1/gbwBPBzIAH4mxDW\nQQgxUkUFfOITcPCgMURq3rzwzHfp6TEWYu3ogIICo1M34k6lBw9nOctlLpNIIvOZTwopVzng9Kuo\nSOamm3L51a/q6e52s3p1Bp/6VGAHvb19kF/8opYzZ3ooK0vi858vITvbP2/innsKSU93cOKEkYRj\n3bqsgKBv2bJ0vF7tS+SyaFEqW7ZM7IbXww8X8847zZw500tKSgybNmVTUuJPxLJhQxZ2u+LgwQ68\nXli2LI2NG2ffkhST9clPziE1NYZjx4xEDWvWZLJsWToATzyxlO985wiHDnWSmurglltyefXVS2zf\nfpEbb8zhwgUnZ886ycuL50tfKqWycmZ9x6dMXZ2RKEJro8M63DnduhUSE41haA6HcR5YvPjqxwqR\nbro5xSlcuCiggDLKsMlKUiEzOOjlF7+4wKFDXeTmxvGZzxRTVua/IfLcc+u4994P2LOnDYBNm7J4\n5pk1Yx0uJKqqUnjggSL27GnD6fRQVZXC1q3T/MAhLQ1uuw1+8xu4fNloO9u2ReWom0EGOc1p2mkn\njTTmM594ZJ7hZIUsycpUkiQrYiaJ6CQrM0FfHzz1lJFBb1hBATzwgG/I1i52UU+9720HDm7l1hkV\n5DU1ufiv/7oQkBq/ujqDu+4qAKC/380Xv7gvYOHc7Ow4fvazNQHrnYnIMJk29NxzDRw6ZMxAuHix\nj3ffbSEvL843LPa22/L5zncWTnldZ5QTJ+C11wLLbroJliwJT30sdNLJW7yFG//c1jLKWMvaq+wl\nJmO4HX3jGwc5csQ/xy0hIYaf/GSl7+bTwYMdvPBCYBKre+8t9N08mTVcLvjlL42bosPy8+HBB62H\nOEcoDx7e5E06R8zYSiaZ27gNBzIcdaRgk6xIL0OICZKbC9eopiYwuANobISLF2HuXLroCgjuAIYY\n4jSnWcnKaazo1e3e3WZau+7gwU5uuCGX5OQYXn31UkBwB9DaOsCLLzbw8MMl01lVEQadnYMcPuzv\nrBw50oXH46W72012tvG0+u23m/nKV8qnP+36dNq717psBgV4pzgVENwBXOACi1lMEuYFtcW1OXq0\nKyC4A3C53Pzyl3W+Gx6//70598LOnS2zL8A7diwwuAMjs2ZtrbE0QpRopDEguAPopZdaaqkg+MzU\nwk/GHwghptfoi9Wo8j76LN8eqzxcuruHTGVer6anxygfHdwNu3x5GjOwibDp6hoKSH7X12fkFvN4\n/DcF3G4vLS3BZV2NWL3mbIg4nebMgGFkdW7R6Bl3zokWVtmDAVpajHOj1try/NrdHebFxsPBqv1c\nrTxCRcp1P5JIgCeEmF4lFk+vbDaYOxeAbLKJsRhckE9+qGs2IeXl5sQ0KSkxvkXEx5rHNqvWOZvF\nCgoSArJi5ucb34uRCXIyM+OorIzyBEdW7X3u3Bk1vMzq3BJHHJmYk2yIa2csYm7ufq5aZSR/UkpZ\nnl/Ly2fh09SRC5oPU8q6PILlkWdZPtOu+5FEAjwhxPSaN8/I5jncwYuJgRtuMDKDYcy3W8vagHH3\nxRQzj3lhqOzY1q/PorIymZ6eIdraBoiNtXHvvYW+VN5Ll6bzqU8VobXx9MbrhbvvLmDt2qkP8Nrb\nBzl1qsf39FCEn8NhfB9iYhRtbQNUVaUwb14yqanG9zopKYZHHy3lzTebOX16etf3mlbXXw9ZI77z\n6elw4/QtVh2MSiopxJ8gafgcZGcWLVsxjdLTY/n61yuw2xV9fR4GB72sWZPJgw/O9W1zxx1zyMhw\nUFvrpK7OSWZmLLffPgs7+2VlsGKF/3pptxvXy1RzBuZIlk46y1jmS2ykUCxiEbnkhrlmkUvm4Akh\nppdSRva8VauMLJp5eRAfmClrLnPJJ5822kgkkVRm3sVMa3C7NTabsRaa16vxBk7JY/HiNGpr+2hu\nHiA7O5YlS6Z+/sgrrzSxb187Whtr5N10U65kwZwhPB7t+73Extp49NEyFi5MobfXw/Hj3fzjP57C\n4zGGKm7YkM3//J/XYbNF2X3X1FT47GeNeUNer5FQaQY9vQOwY2czm+miCxeuMUcRiKlTVpbEhg1Z\n1Ne7SEmJMS3N0tLSz44dLTQ1GcM5OzuH+OQn55CeHsXzVceyZYsR5HV0QG4uJCSEu0YhsZCFlFFG\nBx2kkUYiiePvJMYUZVcSIUTESE01hm/FW6dBduAgn/wZGdwB7N7dyvnzTpKSYsjIiMXt1jz3XIOv\nw97U5GLHjhaSkmIoK0siJcXBrl2t1NVN3ZyC06d7+PDDdt90Jq9X89Zbl2ltlXl+4TY05OX55xvx\neDQZGbEkJcVw+nQv/f1eSkuTePbZet93BYzv00svXQpjjUNIKZgzBwoLZ1xwN1IaaeSTL8FdiLlc\nHl58sQm73UZpaRJZWXEcO9bNxx/7k2z84Aen6OgYJD7eTny8nfb2Qf7hH06FsdZhNny9jNLgblg8\n8cxhjgR3U0ACPCGEmITz552mMqfTTXOzkTTj3Dnz+0b51E2Ot6qD1tblYno1Nrro7/eYys+dc/LB\nB214veYkI/v3t09H1YQIq/r6PlMGYgg8N54+bT5PnjoVxUOZhZhiEuAJIcQkWA0VstmUb45Verr1\n2j1TOcRorGON9dli+qSlOSwfVqWnOygstL4LP5yIRYhoFsy5MSPDfG6zKhNCWJMATwghJmHDhizf\ngtXDVq/OICnJGN61YEGqqcOekxPHdddN3ZDTpUvTTGuozZ2bSEVFlGdmjADp6bEsXx445zI+3s66\ndVls3JjN/Pkppu0//emi6ayiEGGRmxvPokWB58GkpBjWrPFnLX3oobkB7yulePjhwDIhxNhkoLkQ\nQkxCXl48jz02j3372nE6PVRVpbB4sb/TYrcrvvSlUvbt66CpyUV+fjzV1YHpwbu7B/m///c0Bw92\nkpho5+6750xoEfT4eDtf/nIZH37YTkvLAEVFiaxalRGQrEBMr1Onevj971vo6hqirCyRW27Jpalp\ngJSUGFavzvQF5N/97iL+9m+PcexYD3PmxPPtb1eRnS1P8MTscP/9RZSXd3LuXC/p6bGsWZNJWpr/\nyd599xXy/vstvPbaJWw2xR13zGHbtoIw1liIyCIBnhBCTFJ2dhyf+MScMd+Pi7OzadPYGS3/8i+P\ncuRIFwBdXfBv/3aOxMQYtm0rHHOf0RITY7jhBkklPRPU1/fx9NN1vqQ3R45009IyyFe+Mi8g6Ha7\nvfzqVxfJz08gP98Yrvn665coK0siOzsuHFUXYlrZ7YpVqzJ8a9+N9o//eIrDh7soKDDax8GDHfzz\nP5/h8cfnT2c1hYhYMkRTCCHCoLGxj6NHu03lL73UFIbaiKlw8GCHL7gbdulSPxcvugLKzpzppbMz\ncM1Ct1sHZBEUYjZ7551mU9lbb5nLhBDWJMATQogwGBrS6NHRAFhmlxORwe02/z6tyoPdTojZyqot\nuN1ybhQiWBLgCSFEGJSUJFFSkmQq37IlJwy1EVNhyZI0U1lamoOSksA1nSork4mPtweUKUXAHE4h\nZrORCVeGrVuXFYaaCBGZJMATQogQ8Xo1J0508847zRw71m1a++x731tMcXEinZ1DuFxebr45jy98\nIfgkKwAej+bo0S7eeaeZU6d6LJ8Kiukxf34Kt9+eT2KiEbwVFSVQVZXCP/3TKX7+8wtcutTH3r1t\n7NnTxi235JGXZyRVSU11sG1bIUVFsrivEAB/8RdVzJ+fTFNTP01N/SxalMqf/VlVwDaDg14OHOjg\n3XebqavrC1NNhZiZJMmKEEKEgNaaZ56pD1icd968JD772RJsNiPhxqVL/ZSXJ1NUlIjdroiPt9PZ\n6TYtfTAWt9vLz39eG9C5ue66VB54QNKJh8u6dVmsWZPJ0JCXn/zkHE88cQIwhuQ+8cRJbr01j9RU\nY428O++cw5IlacTG2iTzqRAj7N/fwblzfeTlGUmHTp/u5eDBDjZtMkY4OJ1u/vM/z9PePgjA73/f\nwvXX53DjjZJwSgiQJ3hCCBESZ886A4I7gHPnnJw8aZQNDXl5661mtIbYWBt2u6K3183OnS1Bf0ZN\nTbfpzrVVmZheNpuiq2uIF15o8JV1dQ3S2zvE4cNGIhWtjaQRNpuS4E6IUf7jP87jdnux2RQ2m2Jo\nyMu///s53/t797b7grth77/fSnf30OhDCTErSYAnhBAhcPly/1XLu7qG6O/3BL3fZD5DhM/p0714\nPP7hsoODRoKIkdkz+/s9dHVJh1SIkbxeL5cumc9hjY3+MqtznNeraWkZCGndhIgUEuAJIUQIDK/f\nNFZ5errDN1crmP0m8xkifBYuTCUmxn+JjY01ftcjh98mJtpJT3eY9hViNrPZbMydaz6HFRf756gW\nFprft9sV+fnxIa2bEJEi5AGeUupbSqn3R5X9TCm1Vym1Qyn1SKjrIISYfi6Xh95ed7irETZlZUmm\nrIoLFqRQWZkMQEyMjdtvz/fNxwMj4+L11wefRXPRolQqKpIDylatyrDs/IjpMzjoxW5XPPhgka8s\nPd1Benosy5enA8Ywzttvzw8IAoUQhq99rYLYWH/biIuz87WvzfO9XrMmk7y8eDwe7RsJceONuSQl\nSWoJISDESVaUUnHAsjHe/ozW+kwoP18IMf2Ghry88EIjNTVG1sjS0iTuu6+Q1NTZ96Ti/vuLWLUq\ng8ZGF/n58ZSVJQXMt1q6NJ25cxM5ebKHhAQ7ixal4nAE3+G32RSf+UwxZ886aW7uZ+7cRObOlUyM\n4bRjRzO7d7cxOOglOzuOv/mb6zh9upe8vDhuvjmPM2d6cbk8VFWlkJERXDIdIWab9euz+O//Xstr\nr10C4I478snO9j+di4+3U1GRzPHj3TidbioqkiktNS87I8RsFepbHV8GngT+dlS5Bn6ulGoDvq61\nrg1xPYQQ02THjhaOHOnyvb5wwcnzzzfyuc9NLP1/tCgtTbpqxyMjI/aa1ndSSlFRkWx6kiem37Fj\n3ezY4U+S09o6wIEDHTz+eKXvSd2yZenhqp4QESU3N57Pf77U8r2jR7vYtauVzMxYMjNjGRjw8swz\ndTz++HzsdklaJETIxoYopRzAFq31OxZv/4nWegPwf4AfhKoOQojpd+xYt6ns3Lley4QiQkQTq+9+\nb69bspoKMcWs2lpPj5v6emlrQkBo5+B9Dvil1Rta6/Yrf74P5Ftto5R6TCm1Xym1v6Ul+LThQojw\nioszn1ZiYmzExMhdVRHdrL77Rrk5mY4QYvLGbmsyp1UICG2AVwV8TSn1GnCdUuobw28opVKv/FkF\ndFrtrLX+d611tda6Oicn+KQDQojwWrs201S2cmW6JJMQUa+6OsM0PKyoKEGS3ggxxVavzjS1teLi\nRObMkbYmBIRwDp7W+s+H/34li+Z2pdRfaq2/BzyllMrAmIv3tVDVQQgx/VasyMBmU+zf38HQkJfF\ni9PYsGHyc8xmssZGF3v3tuN0uqmqSmHVqoyArJjj0Vpz8GAnJ050k5BgZ82aTIqKJElKpJozJ4H1\n6zP54Q/P0NY2yLJl6fzxH1eEu1pCRKRXXmnktdcuoxTcccccbrvNP+CroCCBz362xLe4eXl5Mlu2\nyMMAIYZNSz5ZrfWmK3/93pXXn5yOzxVChMeyZelRn0yiocHFT396HrfbWMz6zJleGhtdbNtWGPQx\nXn31Eh9+2O57ffRoN1/4QgklJZINLhKdPt3D979/ioEBDzExipqaLv7qr2r40Y9WhrtqQkSUJ5+8\nwE9/et73+tChTlpbB/jMZ/zJusrKkigrk3OlEFZkzJQQQkzCnj1tvuBu2Mcfd9LTMxTU/i6XhwMH\nOgLKvF7N7t1tU1ZHMb2efrqOgYHAZEI1NV0cPdo1xh5CCCu//W1DUGVCCGsS4AkhxCRYBXJaE/Ti\n7n19bjwebSrv6Zm9i8NHuo4O6+D+8uX+aa6JEJHN6vza3R3czTMhhAR4QggxKZWVKaay1FQHeXnx\nFlubDa/fZD6urGcXqdasyTCVxcXZWbs2OuegChEqCxemmsoWL04LQ02EiEwS4AkhxCSsW5fJggUp\ndHYO0tIyQFycjfvvLww6yYpSivvvLyI11eErmz8/hU2bskNVZTGFmpv7qanporNz0Ff2wANz2bAh\nG6WM70BCQgzf+tZ8kpOnZbq7EBGlt9dNTU0XDQ0u03t/9mdVARkxCwsT+JM/mT+d1RMioslVRwgh\nJsHj0TidHuLi7Cjlxe3WuFwTW8y9sDCBxx+vpLHRRUKCnaysuBDVVkwVrTUvvNDIRx8ZK/woBTfc\nkMuWLTnExNj4+79fQl1dHw0NLpYtSyMxUS6zQox28GAHL7/c5BumXlmZzIMPzvUtp1NSksTTT6/j\n4487sNkUS5dGd9IuIaaaPMETQohJ2LWrjfr6PhIS7KSlOfB4jI6/2+2d0HFsNkVRUaIEdxHi1Kle\nX3AHxrzLd99tpqVlwFdWXJzI+vVZEtwJYaGvz80rrzQFzEE+fTqwXQ1bvjxDgjshJkECPCGEmIQL\nF5ymsr4+D83NAxZbi2hRW2v+vYP190EIYXbxosuUgRikDQkxlSTAE0KISbBKkGK3K9LSHBZbi2iR\nkWH+vYP190EIYSZtSIjQkwBPCCEmYePGbOLiAk+ha9dmkpQkw/Ki2dKlaWRnBw6nLS1NYt48WXBZ\niGDk5MSxdGlgRsyUlBhWr84MU42EiD7SExFCiEnIyYnjq18t58CBDpxON/Pnp1im9hbRJS7Ozpe/\nXMaBAx20tAxQVJTA8uXpvsyZQojx3XNPIRUVyZw75yQ93UF1daZkmxViCklrEkKIScrIiOXmm/PC\nXQ0xzeLj7WzcKMtZCDFZw5kxJYGKEKGhtDZPdJ1psrOzdWlpabirIUTEunDhAtKGhJg8aUNCXDtp\nR0JcmwMHDmit9bhT7CLiCV5paSn79+8PdzWEiFjV1dXShoS4BtKGhLh20o6EuDZKqYPBbBcRAZ64\nNv39Ht98kcLCBFasSPctJiqEEGJ6tLUNcPBgJy6XhwULUpg/PyXcVRIiarndXg4d6qK+vo/s7DhW\nrcogIcEe7moJMS0kwItyAwMe/t//O09rq7E218cfd1JT080XvlAiSQGEEGKaNDW5+OlPLzA46AXg\n4MEObrghhxtuyA1zzYSITk8/Xc/Zs72+1wcPdvDYY/OIj5cgT0Q/eYwT5Q4f7vIFd8MuXHBy7pws\nKCqEENPlvfdafcHdsF272hgY8ISpRkJErwsXnAHBHUB7+yAff9wZphoJMb0kwItyHR2DluXt7dbl\nQgghpp7VuXhoyEtPjzsMtREiuo3Vx5G+j5gtJMCLciUl1ovvlpbKorxCCDFdrM65qakOMjNjw1Ab\nIaJbaWkSVrNQysqk7yNmBwnwotz8+cmsWOFfZ0Yp2Lo1l5ycuDDWSgghZpfrr89hzpx43+u4OBt3\n312AzSZzoYWYapmZsdx0U15A+1q2LJ0FCySxkZgdJMlKlFNKsW1bIevXZ/myaKanyx1jIYSYTgkJ\ndh57bB61tX24XB7mzUsiLk6SPQgRKps2ZbNkSRoXLxpZNPPy4sffSYgoIQHeLJGbG09urpzchBAi\nXJRSMjxeiGmUluYgLS0t3NUQYtpJgCeEEJOg1PYpPZ7WD0zp8YQQQggxO8kcPCGEEEIIIYSIEhLg\nCSGEEEIIIUSUkABPCCGEEEIIIaKEBHhCCCGEEEIIESUkwBNCCCGEEEKIKCEBnhBCCCGEEEJECQnw\nhBBCCCGEECJKSIAnhBBCCCGEEFFCAjwhhBBCCCGEiBIS4AkhhBBCCCFElJAATwghhBBCCCGihAR4\nQgghhBBCCBElJMATQgghhBBCiCghAZ4QQgghhBBCRAkJ8IQQQgghhBAiSsSEuwKzVW+vm/PnnSQn\nx1BamohSKtxVEkIIEUJer+b8eScul4fy8mQSEuzhrpIQYdPdPcSFC07S02MpLk4Md3WEiCoS4IXB\nkSNd/O53DXg8GoCiogQ+97kS4uLkYi+EENHI6XTz85/XcvlyPwAOh40HHihi/vyUMNdMiOm3b187\nr756Ca/X6AeVlyfz8MNziYmRgWVCTAVpSdNscNDLSy81+oI7gIsXXXzwQXsYayWEECKUdu5s8QV3\nAENDXl58sdHXwRVitnA63bz22qWA7/7Zs70cPNgZxloJEV0kwJtmly/3MzDgNZXX1jrDUBshhBDT\noa6uz1TW0+OmvX0wDLURInwaGlwBN7mHST9IiKkjAd40S093YLOZ59tlZcWFoTZCCCGmg9U5PjbW\nRkqKzJQQs0tmZqxlufSDhJg6EuBNs5QUB2vXZgaUJSbaWb8+K0w1EkIIEWqbN2cTF2ezKJO512J2\nyc6OY/ny9ICy1FQHq1dnhKlGQkQfuXUYBrfdlk9paRKnT/eQnBzDqlUZpKQ4wl0tIYQQIZKXF8/X\nvlbOwYOduFweFixIobw8OdzVEiIstm0roLIymXPnnKSnO1i5MoOkJOmSCjFVpDWFSVVVClVVkj1N\nCCFmi/T0WG68MTfc1RAi7JRSXHddGtddlxbuqggRlUI+RFMp9S2l1PujyhYrpd5XSu1SSi0NdR2E\nNa01dXV9XLzYh9aSyU0IISbD49FcuOCkqckV7qoIETWG+yj19dJHEWKiQvoETykVByyzeOvvgIcB\nL/BjYFso6yHM2toGeOqpOl8Gt9zcOD7zmRLS0mSoqBBCBKux0cXTT9fR0+MGoLg4kYcfLpZFzIW4\nBu3tgzz1VC1tbUYfJScnjs98ppj0dOsELUKIQKF+gvdl4EmL8kytdb3WugGQ5/Nh8PLLTQHpuZub\nB3j99UthrJEQQkSe555r8AV3YCyHsGNHcxhrJETke+WVJl9wB9DSMsBrr0kfRYhghSzAU0o5gC1a\n63fG+VzJ5DnNvF7N+fPm9WbOnu0NQ22EECIydXcP0dIyYCo/e1bW8xJisrTWlv0RaVdCBC+UwdXn\ngF+O8Z53jL/7KKUeU0rtV0rtb2lpmfLKzWY2myI52Tw6NzVVhmcKIUSwEhLsxMaaL6OpqZK/TIjJ\nUkpZ9kekXQkRvFAGeFXA15RSrwHXKaW+MeK9dqVUkVKqAOiy2llr/e9a62qtdXVOTk4Iqzk7bdkS\n+G+qlLlMCCHE2BwOm2kNU5tNsXmznEuFuBZW/RHpowgRvJDdDtFa//nw369k0dyulPpLrfX3gL8B\nngEU8EehqoMYW3V1JmlpDg4d6sJmgxUrMigrSwp3tYQQIqJs3ZpLbm4cNTXdxMXZqK7OpLAwIdzV\nEiKirVyZQWqqg0OHOgFYsSKdefNk3UghgjUtz7u11puu/PV7V14fBjaNvYeYDpWVKVRWylp8Qghx\nLWQ9LyGmXkVFMhUVEtQJMRkyoDkCaK1pbR0kPt5GSorMkxMiGim1fUqPp/UDU3o8EXqdnYN4PJqs\nrLhwV0WIqOB2e2lrGyQtzUF8vCxdImYPCfBmuMuX+9m+/SKtrQMoZdwpvueeAmJiJPmoEEJEA5fL\nw/bt9Zw7Z2QJLCxM4MEH50riKyGuQU1NFy+/3ERfnweHw8b112fL/Fgxa0iUMMP9+tdGcAegNRw9\n2sXu3W1hrpUQQoip8sYbl3zBHUBDg4sXX2wMY42EiGy9vW5++9sG+vo8AAwNeXn77WbLJaKEiEYS\n4M1gbW0DlmssnTjRE4baCCGECIWTJ83n9DNnevF4dBhqI0TkG6v9WLU1IaKRBHgzWEKCHZtNmcqT\nkmQcuRBCRIvERPNsifh4Oza5QgsxKYmJ1v2kscqFiDZy+ZjBEhNjWL48PaBMKUzrLgkhhIhcGzaY\nz+nr12eTvW4wAAAgAElEQVShlPkGnxBifBUVyeTmBiYrSkiws2JF+hh7CBFdJMnKDHfXXXPIzo7l\nxIkeEhLsrF2bKWvBCCFEFFm5MoO4OBsHD3bi8WiWLUtjxYqMcFdLiIhlsym+8IVS3n+/lfr6PrKz\n49i0KVsykYtZQwK8Gc5mU2zYkM2GDdnhrooQQogQkbX0hJhaSUkx3HZbfrirIURYyBDNadDX56a1\ntT/c1RARaoghBjAn27Hixh30tkKI6Tc05KWvzw0Ya5z29rolmYqYMTQaFy68eMfddoABhhgKaX36\n+z0MDHjGfN/t9uJ0ukNaBxFdvHhx4UIz+fPuEEMMMjiFtZp68gQvhLxeL088cYq3377M0JCXkpIk\n/sf/WEBVVWq4qyYigBcvBznIec7jwUMuuaxlLUkkmbbVaA5xiNOcxoOHLLJYy1pSke+aEDOB16t5\n443L7N/fjtutSUiwAxqXy0tiop0bb8ylujoz3NUUs1gDDRzkIE6cJJDAMpZRSqlpOydOPuADWmjB\njp15zGMFK7BN4TOD/n4Pzz/fyIkT3SilWLgwhbvvLiAuzp8kZefOFnbtamVgwEt+fjzbthUwZ07C\nlNVBRJ/znOcQh+innySSWMlKCikMen83bvaxjzrqAJjDHNayljjixtlz+skTvBD65S/ree21JoaG\njDthtbVO/vqva8JcKxEpTnCCM5zBg3H3splm9rDHcttznOMEJ3zbttHG+7w/bXUVQlzd3r3tfPBB\nG263xu328sYbl3j/fWNN074+Dy+91ERdXV+Yaylmqz762MUunBjrxLlwsZe9dNJp2nYPe2ihBQAP\nHk5zmpOcnNL6vPrqJY4f70Zr4+ZITU03b7xx2ff+sWPdvPNOMwMDRv/q0qV+nn66Hq9XnoYLa510\n8iEf0o8xos6Jk93sxoUr6GMc5jC11KKv/NdII/vYF6oqXxMJ8ELo979vMZU1N/dz9GhXGGojIs3w\nHaKRWmm1PBnVUmsq66bb8uIshJh+NTX+8357+yAej6anZwiXy2O5jRDT6SIXTcMyNZqLXAwo66OP\nVlpN+1tdr66FVVsY2Xey6kd1dw/JTRIxpnrqTcMyPXhooGFCxxitgQbfzfWZRAK8EIqPt/7nlXXs\nRDBiLEZQK5TlMBgH1pnBrI4hhJh+sbH+dutf31RhtyvLbYSYTsFeQ+zYUZiX75jqa43DYW4LI9vH\nWG1F2pAYy1jf0Yl8d+2Y++9jtYlwk5YQQvfcU2hax2jRolTKymSZAzG+SipNZcUUW471rqDCdIKZ\nwxySke+aEDPB6tX++XWZmbEkJNjJyYn1dUgdDpssjSDCpogi4okPKHPgoISSgLI44iim2LR/BRVT\nWp81a8zzUUeWVVdnjLhRYigqSqCgQObgCWullJpuZCSQMKE5eFb9snLKp3T+6VSR2/shdNNNebhc\nHn7964v09LhZsSKdb35zak+CInqVUIJGc4pTDDFEEUUsZrHltnOYwwY2cJKT9NNPAQUsYck011gI\nMZaFC1P51KeK2LOnDZfLw1e+Us7QkJeGBhfZ2XFs2ZJDZmZsuKspZikHDm7kRo5whHbaSSedxSwm\nAXPAtJrVJJLIRS7iwMF85psCwWt1ww05xMbaOHTImGawYkUG69b5A7yiokQeeaSY995robvbTXl5\nEjfemDuldRDRJYEEtrKVGmropJNMMlnCkjGfXlupogobNs5yFi9eSihhIQtDWOvJU1rP/Amp1dXV\nev/+/eGuhhARq7q6GmlDU0up7eGuwlVp/UC4qxBVpA0Jce2kHQlxbZRSB7TW1eNtN/OeKUapiQTS\nVttGQiAuhBBifB7PzJuQL0S0kv6TmI1kiGaI7dnTxq5drfT1eaisTObOO+eQmmr9OPjAgQ527myh\nu3uIefOMbWNjbbzyShMnTvSQkGBn3bpMNm/OmeafQgghxLV6/PGPeP31y/T3e1i0KJV//ucVlJfL\nPFkhRuvrc/O///cJ9uxpQynYuDGbP//zKuLjg++2Nja6ePXVS9TX95GdHcdNN+WycKGsDStmB3mC\nF0JHjnTx+uuX6O114/VqTp7s4dlnzSlWAc6c6eXFFxvp6hpCazh7tpennqpl+/Z6jh3rxuvVOJ1u\n3n67mQMHOqb5JxFCCHEt/u7vjvG73zXgcrnRWlNT08WXvjQz108SItyeeOIkO3e2MDTkZXDQy7vv\nNvODH5wOev+BAQ+/+EUt9fXGsgmtrQNs336Ry5f7Q1VlIWYUCfBC6OOPzWuQNTS4aGkZCHrbw4fN\na70MTzoWQggRGV5//ZKprK7OKeuiCmFh927zWnvvv28uG8upU70Ba0yCsWC6VZ9KiGg07rNupVQ1\nsBkoAFzAUeAtrXV7iOsW8dQYy2LYLMJqq22VGrtcCCFE5BjrvG2XZVGFMBm9xBRY953G3t+6fCLH\nECKSjflVV0p9USl1EPgOkACcBJqBTcCbSqknlVLmxVCEz8qV5jWNiosTycoyr2O2YkW6qWzu3ESW\nLzeXWx1XCCHEzHXXXQWmsvLyZBYuTAtDbYSY2TZvzjaVbdkSfP6B+fNTSEoatUi7XbF0qblPJUQ0\nutoTvCRgo9baZfWmUmo5UAnUhaJi0WDRolTuumsOu3e30dvrpqoqhdtvz7fcdt68ZO67r5CdO1vp\n/v/Ze+/ouK7r3v9zZjCD3gsBECAAAmATuyBRLCIlqtmSLNmWLD2X2HH5+dkpTtZL8uzEiVuS91bW\n+jlZzs8lceQiWXKTrdiSrF4oiRIpsVOsIkH03ssAmBnMnN8fB3MHF/cOMAAGleeDhUXMuXfuPQDn\n3H32OXvvb7+f8vIU3ve+fNxuB88918qFC/0kJjrZuTObLVv0A0qj0WiWEl/+8jr6+/08+WQzIyNB\nNm5M47vf3b7Q3dJoFiX/+3+vw+kUvPFGJ0Io5+4v/3JN1O93ux188pMlPP98Kw0Nw+TmxrN/fx65\nudYFdo1mOaJ18DSaqwCtPRR7tA7e1YUeQxrN7NHjSKOZHdHq4EWTg1cG/DlQOv58KeU9s+mgRqPR\naDQajUaj0WhiSzSCIr8DfgQ8BQTntjtLl/5+Py++2MaVKx4yMlzs25fLmjWpC90tTYzpoovTnKaX\nXrLIYgtbyECHzGo0GisezygvvtjG5cuDpKbGsWdPDtdco3PuNPNLHXWc5zwjjFBIIVvYQjyLO1Qx\nGJS8/noHJ0/2IoRg27YM9uzJweHQVeY0y5sAAc5yljrqACinnPWsRzC9z340Dt6IlPLfZ9DHqwYp\nJY8+Wkd7u5I/8HhG+eUvG/jMZ0opKkpa4N5pYsUwwxzgAH78ALTQQjfd3MVduHEvcO80Gs1i4xe/\nqKexUaWxDw6O8pvfNJKY6GT1ai1urpkfmmjiEIeM11e4wiCD7Gf/AvZqal5+uZ033wzLIrzySjuB\ngOTmm/MWsFcazdxzmtNc5KLpdZAgG9k4retEUzD2O0KIrwshdgohtoe+p9nfZU19/ZDh3IUIBiUn\nTmi9uuVEHXWGcxfCi5cG7MXrNRrN1Utb24jh3IWQEo4f13ZBM39c4YqlrZ12+ulfgN5Ez7FjPVG1\naTTLCYmkmmpLu13bVESzg7cJ+CNgP+EQTTn2WgMEAvaFakZHF38BG030BAhMq12j0Vy9RHr+j47q\nTAfN/BHJPgUXecaN3bxKz6k0VwN2Y3Mm4zWaHbwPAaullPuklDePfWvnbhwlJcmkpbks7Zs26VyL\n5UQxxZYYaCdOiileoB5pNJrFSmFhAtnZ1tBtbRc080kJJZa2jLGvxYzdONFjR7PcEQjbMWvXNhXR\nOHinYJE/CRYYp1Pw8Y+voqgoEYCUlDjuvLOAigqdZ7GcSCON3ewmmWTj9R72kEjiAvdMo9EsNoQQ\nfOxjqygpUXnYSUlObrtthS6yoplXyihjE5twoRah88hjD3sWuFdT87735bNlSwZOp8DpVEVWbr99\nxUJ3S6OZc7aznRJKcODAiZPVrGYzm6d9nWhCNFcAF4QQRwAj0UzLJJhZsSKBz31uNT5fEJdLIISu\n9LQcKaKIlaxklFHDYGo0Go0d2dnxfPrTZfh8QeLihK4AqFkQruEa1rOeIEHiopr2LTxut4MPfWgl\nH/hAAUIoJ0+juRpw4WInO7me6wEVKTYTohnpX5/Rla9S3O5oNkU1SxmB0M6dRqOJGm0XNAuNY+xr\nqREXt/T6rNHEgpk6diGicfDqgRYp5QiAECIRtas3KUKIjcAPgQBwGfiMlFKOHfspsB4YBn4opfz5\njHq/iBgeDvDaax3U1CgdvD17ciguTuLll9t4/PFG+vv9XHttJl/8YjlJSbNfQWtuHub11zvo7vZR\nWprMvn25JCcvjZU5jUajWa7U1w9x8GAnfX1+VqyI5+zZfs6d6ycjw8VHP7qKvXtzASWbcOBAO/X1\nQ+TkxLN3by75+QkL3HuNZnEQDAb52c/qefnlNoQQ3HbbCj7xCXMeUl2dh4MHO+nvH6W8PJm9e3NJ\nSJjdpHgily4NcOhQF0NDAdauTeXGG3O006lZEkTjETwO7Br3OjDWdt0U77sopdwFIIT4CVAFHBl3\n/ONSysvT6Oui5rHH6oyS2G1tI1RXD7JxYzrf+c4lxvxampuHqa8f4jvf2Tare3V1efnJT2rx+1VV\nnfZ2L3V1Q3zhC6t1aKhGo9EsEK2tIzz8cK1RAfBHP7rCwMAoBQUJtLQM841vnOVf/mUT116bxcMP\n19LRobIe2tu9XL48yJ/8STkZGVpTU6P53veq+e1vG43XDz10BY9nlP/5P8sBaGkZ5pFH6oyx1tY2\nQnPzCH/8x6Ux60N19SA//3k9Y1M4WltH6Oz08pGP6MJqmsVPNMsQcVJKX+jF2M9TWiAp5XjBMC+Y\nxMIk8IgQ4ikhxPRLwywyGhuHLHpHo6OSH/+4xnDuQpw+3UddnWdW9zt+vNdw7kK0tY1QWzs0q+tq\nNBqNZuYcPdptTDg7Orx0dnrxegN4vapUfTAoefzxRqqrBw3nLoTPF9QaeRrNGM8912ppe/bZcNuR\nIz0WKYXaWg+trSMx68Pbb3czYQrHuXP99Pf77d+g0SwionHwOoQQRkEVIcS9QGc0FxdC3COEOAPk\nAV3jDv3V2O7evwDfjvDezwshjgohjnZ0dERzuwVjZMRen8LjGbW0SSkZGLC2T4fhYXtdm5ERrcem\n0Wg0C8X4Z3PIqQMIjjMRHk8gos3Qz3CNRmE3FsaPr/mYB9ldS0rwehe3hqBGA9E5eF8A/k4IUS+E\nqAe+DHw+motLKZ+UUm4EmoC7x7V3j/17EMiP8N4fSimrpJRVubm50dxuwSgpSSIpyRr3ffPNeZa2\nnJx4NmxIndX9NmxIs7TFxztYvTp5VtfVaDQazcwZ/2wuLEwkPt6J0ylISAib2j17sqmoSMHlsprf\n9eutz3aN5mpk69ZMS9v27WHFLrt5UEpKHMXFSTHrg914zM2NJzc3Pmb30GjmiikdPClltZTyBmAD\ncI2UcpeUsnqq9wkhxo+AflRBldCxtLF/1wJLPibF5XLw4IPFZGa6jdf79uXyla+s4+ab84zyvvn5\nCXzjG9fgcMwuQbeiIoVbbskzKrNlZLh48MFi4uNjm1ys0Wg0mui55pp09u7NxeVy4HAI7rmnkHXr\n0hBCEBfn4I478nnwwWISE5088EAR6emqGm98vIPbb19BWZlepNNoAP72b9eybp1ysIQQbNiQxt/8\nzTrj+KZN6ezZk2MslGRnu/kf/6M4pnIKO3ZkUVWVaVxzxYoEHnhA599plgZiYo6YcUCITwA/l1La\n7kULIcqBgrFdOLvj9wL/a+zlJeBrwKellP8shHgKyETl4n1RSnlmsk5WVVXJo0ePRvP7LChSSnp7\n/SQnx5nKYvf2+ujr81NSElvj7fMFGRwcJTPTpYuraCalqqqKpTCGlhJCPL7QXZgUKT+y0F1YVkxn\nDHm9AYaGAmRkqGdzTc0gmZluSwGVYFDZjNTUONsdPY1muTFdW9TcPITD4YhYYXbiWJsLhodVHq0u\ngKRZDAghjkkpq6Y6b7IqmtnACSHEMeAY0AEkABXAPlQe3lcivVlK+Xvg9xOa/3ns2Aem6thSRAhh\n7OKNJyPDathjgdvtICtLP3A0Go1mMREf7zRFVJSVpdie53AI/QzXaCahsHDykMuJY20uSEx0kpio\nI6Q0S4uIDp6U8jtCiO8C+4HdwGZUmOV54I+klPXz08XYMjQ0yltvddHcPExeXgK7dmWTlmYvWu31\nBjh0qIv6+iGys+PZtSvb1oGbjObmYQ4f7sLjURoqVVWZSAlHjnRz6dIgKSlx3HBDFgUFibbvDwaD\nPPpoPYcOdZGSEsf99xexY0f2tH/vWXPlCpw9q6oFrF8Pa9ZEPLWNNi5zmVFGWcUqyijDj5/3eI92\n2kkhhbWsJQ2db6LRaJYvPl+Qn/60luPHe8jMdHHNNemcOtXL4OAoVVWZlJUl0dg4QnZ2PCtWxFNd\n7SEQkGzenM7GjekL3f2ri44OOHECBgehtBS2bAHn/EzqO+nkEpfw4qWIIsopR6B2o5pp5gpXCBKk\nZOzrauFrXzvD737XhBCC++9fyT/8wzWm4+3tIxw61EVfn5/y8hR27MgyadSNjKg5XEOD0prctStb\n78ItJO3taox5PFBWBps3z9sYm0/66eciFxlkkFxyWctaXIT9jPmaD0+qgyelDAAvjn0veUZHg/zk\nJ2HtoStXPJw/388Xv1huEceUUvLII3U0NQ0b5547188XvrCa1FR7h3AiTU3D/PjHNUYp3+rqQZqb\nhwkEJO++22ecd/ZsH5/9bJmtk/eP/3ieV19tN14fO9bDt751DXv2zGPhmXPn4IUXwq9rapQR3L7d\ncmoTTRzkIBL1O7fQwgADdIx9gXIAG2jgDu4gGZ1zotFolidf+cppjh/vAaCvz8d//ucVVq9OJjk5\njpdfbqewMIGbbsrj0KEuLl8epKoqk/h4J++9N0B/v59du3IW+De4SujogF/9CkbHKlzX10NrK9x5\n55zfup12XuVVw2a20kovvVRRRS21HOawcW4zzQwzzDrWRbrcsuEzn3mHJ55oMl5/+9vv0dIywve/\nfy2gZEgeeqgGn09lEV254qG+foiPfnQVEJ7DNTeb53Bf/GI5ycnRSEBrYkpbG/z61xAYq0xaX6/a\n3ve+he1XjPHg4SVewodSl2ujjVZauZVbjXPe4A3aaTeO11PPHdxBCvaRHjPlqgr6v3hxwKI91Nfn\nNzlbIWpqPIZzF8LjGeXEiehrwhw61GXRaXn77S6OHes2tY2OSg4fNrcBdHaO8PrrZomIYFDyi180\nWM6dU+zi5Y8csbYB5zlvGKoQJzhBG22mNh8+qpmyVo9Go9EsSaqrBw3nDqCtzUswKGlv9zIyEsDn\nC1BXN8TQ0Cj19UP4/UFaWsIaXm++2WXRUdXMESdOhJ27EJcuQZ91bhBrLnLRYjOvcAUvXs5z3nK+\nnY1djjzzjFUH7/e/bzZ+fuedbsO5CzF+jldd7TGcuxCDg9Obw2liyIkTYecuxMWL0N+/MP2ZI6qp\nNpy7EJ10GhscXXQZzl0IP34ucznmfbmqHLxI+nN27dM5N/L9rGKYXm/QVkNlcNB63Z4ev8VBBOWU\nziseG2H2kRGzuNMYwwxbT2WEINGdq9FoNMuB9nbzYmJobjM6GjSe61LKsQIO6vk4fsI6NDRq94jV\nzAV2Nk5K+/YYY2cHgwTx4mUEq2i3D5+tPV1u2M2TxuvS2c2vxrdPdVwzz0QaY0ND89+XOSTSvDbU\nPtXxWHJVOXiVlSnYFVmqrLRui5aXp9iW27U7N/L9rHp3hYWJrFhhrQZl34dkcnKseivjtWDmhdJS\na1txMdjIPRRSaGkrooh4rL9HAQWx6J1Go9EsOrZtSzeFgqWkqDSA1FTXWMEGQVJSHJmZLrKzVV5Q\n6F+AsrLkmJZ810yCnY1LTIQVK+b81nZ2MIUUUkm1PZZHHk6WX97SRIqKrCkrJSXhgit286vERKeh\ng1denoLDYTeHm50OsWaG2I2x5GRY5DrX08VuDuzEyQrUsyTS+LV732yZ0sETQsQLIT4mhPg7IcTX\nQt8x78k8kJ0dz513FhjlqOPiBPv359kKY6akxHHvvYXEx6tzHQ7B7t05rFkT/cPhhhuyTGKcqalx\nPPBAMQ88UExKijL8Qig9l+uuy7K83+Fw8Hd/t96UFLxpUzpf+EJ51H2ICXv3Qv44PfqcHLj1VttT\nN7GJPMIC7ymksIc97GCHkWQqEFRSSTFaT0aj0SxPEhLi+Ju/WWs4eYWFiZSVJZOfn4DTKSgrS2b/\n/jwcDgeVlals355JdrZaCMvLi+cDH4i9wddEYPNmWLcOYwU4KUnl381DAYj1rGclK43XSSSxk50I\nBFvZSg7hPMx00rmO6+a8T4uBH/7wWtLTw3OfzEw3Dz0Urgy/bVsG27ZljPsvc3LffUXG/C4tzcU9\n94TncE6n4MYbc6ioiG2ekyZKtm5VxfnGj7H3v3/ZFVkpoog1rDGKJLlwsYMdxiaHGzc3cANu1Gdb\nIKigglWsinlfIurgGScI8RzQh5JKMPbHpZTfjnlvIhBrHbyRkQAdHV6ys90kJU2ebOvzBWlrGyEz\n0204ZdOlp8eHxzNKYWGisaIUCEhaWoZJSYmbsqrT6GiQkyd7ycx0U16+gA+nzk61pR7Fiks//Ywy\nSiaZxgd9lFF66SWZZBKxrxqqmRu0Dl7s0Tp4VxczHUM+X5BTp3rIy0ugpCSZ6upBenp8bN2aQTCI\nyb60t48QCMiIVZU1c0x/vwoly8ub94nnAAP48JFJJo4Ja++99BIkaLKnS5XpjqPf/KYBh0Pw4Q8X\n2R7v6/PT3++noCDBVEEzhNcboL3dO6s5nCaG9PWpsMwFGGPzyTDDePCQQQZxNvUsQ/PhpLGv6RAL\nHbwQRVLKZVXmJiHBabtrZ4fb7Yj63EhkZrot8gpOp6CoKLrrxsU5qKqy7vDNOznRV3SzK/kaR5xp\nNVKj0WiWO263g+uuC0vbTFykG29f8vLsxZw180RamvpeAFKJHB2UwTynZSwi7r9/8kif9HQX6emR\nK5vHx0c/39PMA+np6nuZkzj2FYn5mA9H4+C9JYTYJKV8d057ssQJBiWnTvVSU+MhI8NNVVUmaWku\nurt9HD3ajccTYM2aFK65Zvl/sG0JBJTcQmMjZGSokJjk2EgkvM3bXOQiiSSym91zEsus0Wg000VK\nJYlz+fIgqakurrsuM2LERlPTMCdO9BAMwubN6ZSWagmZRUNHh9KB9fmgslJpeC0AQwzxOq/TSisF\nFHAjN0579X8pUV8/xMmTvQgBW7dmzImjdvnyIGfO9OF2O9i+PZP8fL3IMmf09cGpU2oHr6zMHLK5\nSJBI6qmnmWYSSaSCipjLF0RDO+3UUotAUEbZjJzBiA6eEOJdQI6d82khxBXACwhASik3z7Dfy5In\nnmjizJlwSeXjx3v40IcK+fWvG41qUKdO9dLYOMwdd+RHuszy5Q9/UGLpIc6ehY9+dNZO3m/5LSc4\nEb4sZ/k0n76qxGCXK7EOgdQhi5r55umnWzh2LCyVcPx4D5/7XJmRaxfi4sUBfvWrBoJBaZx3zz2F\nbN+eOa/91djQ2AhPPBGuGn3uHOzZA1VTRkjFFB8+fsAP6EF9ni5zmXOc40/5UyOfZzlx+nQv//3f\nTYSyiI4f7+H++4tiukh+6FAXzz8flmM4dqyHT3yihLIyvbgSc7q6lNakb0xC4MIFaGmBm25a0G5N\n5BjHTJIF1VRzG7fNiRB5JGqo4W3eNl5f4Qq72DXtuhWTFVm5G/gA8H6gArh97HWoXTNGR4fX5NyB\nkj342c/qLaV+33mnG48neqmFZUFbm9m5AyWU/u7sNoV76eUUp0xto4xygAOzuq5Go9HMlr4+v0kH\nD2B4OGCreXrgQLvh3IXbOrQO3mLgyBGrJNCRI1bdvDnmKEcN5y5EF12mBc7lxKuvdjD+4y+lGhOx\nIhCQFp1huzZNjDh2LOzchTh9Ws0FFwlDDFn0mf34ucjFee3Hu5jnxhLJWc5O+zoRHTwpZZ2Usg74\np9DP49umfadlTE+Pz7a9rc2qYRMIyPnXsVtoIonFzlLgsptuWz2gPuZenFaj0Wgmo7fXh51/Zmcv\nenqsNqG/314HVTPP2NkvrxeG51fHtYuuabUvZaSU9PbajRP7udZMGBkJMDwcsLTH8h6acdjN94JB\nGBiY/75EYIghJNZn7iDz54QGCTKEVRtwJn2IRgfvmvEvhBBO4Npp32kZU1SUSFycNY54yxZrYnRy\ncpytDt6yprDQVjOPIvuqWNESSV9vLsrNajQazXQoKEg0SrSPxy78y66tuDjJtiqgZp6xs1OZmZA6\nv3pqFVTYtq9hzbz2Yz4QQtjmoMYydDI5OY68POv8QYdnzhF24yghYVHp4GWQYch5jSekYTcfOHCQ\ni/VvMpM+RLQeQoi/FUIMAJuFEP1j3wNAO/D7ad9pGZOUFMfddxeanLx161L55CdLTMKcbreDe+8t\nvPrEa1NSVJz1eCevslLpDs0CN24+wAdMJWjzyON2bp/VdTUajWa2qOf9SkOXC2D16mSuv95aEfmO\nO1aYRM5TU+O4+26ryLVmAdi1yzwJTUiIqAM7l6xnPdvYZsgkCARVVEV0/JY6d95ZYKqOmZnp5n3v\ni239gnvuKTR0KkHpT+7fnzfJOzQzZvt2s5PncsFtt0Hc4pGuiCOO67neJESeRx6VVM5rP67lWlMF\nzhRS2Ma2aV8nGh28/yul/NtpXzmGxFoHb64YGhqlvn6IjAy3qRJTY+MQHk+A0tIk4uOXr+7HlHg8\nKqk2PT2mqzZDDHGBC6SQsixXM2PBUtTBW+xFVrQO3tXFTMfQ8HCAujoPaWkuCgsjl80OBiW1tR6C\nQbWLcNUtBC5mpISmJpVDVFysJqcLRAstNNLIKlbN685CrJjOOAoE1JgANSZCOsKxZHQ0SE2NB5fL\nQVHGv/UAACAASURBVElJEmKRVXVcdrS2qrlgURHEW3dQFwM+fLTTTiKJZJM99RvmgAAB2mlHIMgj\nz6SNOWsdPCHE9rEfHx/3s4GU8vhMOr2cSUqKY906a6WdaPXulj3JyVAR+9XGJJLYjuUjqtFoNAtO\nYqLT1i5MxOEQrF49/+W4NVEgxKxTCmJFwdjX1YDTKSy6kbEmLs5BZeX8htte1eQv/irybtwUsbDj\n3Ylz1uN8sr3Rb4/9mwBUAadQEgmbgbeBPbO6cwwZHQ1y5kw/bW0jFBYmsmFDWsTVz0BAcu5cP83N\nw+TlxbNpUzpxcQ5qagb5/e+b8fuD3HFHPps3T19Y9NKlAa5c8ZCR4WLLlgwSEpbgbl0gABcvKt2f\n3FxYuxacc/d7DDNMDTX48FFEkaH10UILrbSSRBKllNrm2k2GDx+11OLBQx55FFKIQNBFFw004MZN\nKaVKQ8jnUyV7e3pUvmBFRURtlgAB6qmnl16yyKKIItN2vkajufpoahrm3Ll+4uMdlJYm8cILbbS1\nebn++kzKy1MMHbwtW9JJSlJmV0rJhQsD1NcPkZ3tZvPmDNxunXO3KOnvh/Pnla0oL1eFIVpbITtb\n2cgIO3ohe+PCRRllJs261rGvBBIooywqGxeyPz30kEUWxRQb9sePn1pqGWSQXHJx4aKFFuKJp5TS\nSUWXFyuPPVbHd797GYcDvvSlCh58UMsfLRhSqmroIS3j9evBPf/yHN0tZ+m+fIS4+CRWbNhPYsr0\n9OECBKijjj76jDHkiKocSfT00cczPEM77axlLbdx24LME6MJ0fwl8M8hoXMhxEbgr6WUfzz33VNM\nFqIZCEh++tNaGhrCVWfKy1P4xCdWWbbapZQ89lg9ly+Hq9EUFSWybl0a3/zmWfx+VZFRCMGf/VkF\n990XvQf/hz+0cORIuPx1Zqabz32uzBTfvegJBpXeT2NjuK2oCD78YfsiKbOkn35e4iV8hKtWbWUr\nI4xwgQtGWxJJ3MZtURsoL15e5EVT1aEKKsggg6OEP0cuXOz33Ujmr1+Azs7wBSor4a67LNcNEOBV\nXqWT8Ln55LOPfUZexGJFh2jqEE3N7Ig0ho4e7ebpp1sA8HhGeeaZFtLTXbjdDnp6fGRludm3T+X1\npKW5+Oxny0hPd/Gb3zSa5HXy8uL57GfLru4w/sVIezv85jfhEu8XL6oJ7oqx8Mi8PHjgAUsuUTXV\nHOGI8dqFi5u5mSyyOM1pznHOOJZIIrdyK8lELvARIMABDtBBuIx/HnncxE2MMspLvEQ/qlJhBx14\n8Rq7EPHEcyu3ksrC71RFa4v+4i+O88Mf1hhSIUIIvvSlCv7lX7bMdRc1djz3nFoID5GVBQ8+OK9h\nlvUnn6T/wJPGaxkfz6qP/BXpOaujen+AAC/zMt2E5+oFFLCXvTGbw3XTzT/xT6b5ZznlfJkvx+T6\nEH2IZjSz9nUh5w5ASnkG2DqbzsWSc+f6Tc4dQHX1oMmJC7d7LO2NjcP867++Zzh3oBzBn/2sjuBE\n7ZsIdHf7OHrUrG3U0+MzOXxLgpoas3MH6vVEDbsYcZazJucO4AQnTIYPVI7ddHRILnHJUlL2EpdM\nxhbUiufZ5gnOHcClS2p1dgKNNJqcO1CrsC20RN03jUazfBgdDfLKK+3G61OnevF4Runt9Y9J4oxS\nUzNklF7v7/dz+HAXTU3DFu3U9nYvJ0/2zmv/NVFw+HDYuevrU5EeDQ1hbbz2duX0jSNAgNOcNrX5\n8XOWswwzzHnOm44NMzyljWuiyeTcAbTTTgstVFNtOHeh3J1eehlBSTV58Vruudh55JF6kw6klJKH\nHqpZwB5dxXR0mJ07gO5uODt9bbaZMuofpu/Q86Y24fXS8vbvor5GPfUm5w5UtFgbbTHpI8CTPGmZ\nf1ZTzRnOxOwe0RKNg3deCPGQEOImIcQ+IcR/weJ5UnR0eKNuj3Ruc7NVz6a318fgYHRCpp2dXlu9\no0j3W7R0RdDTidQ+Swaw6p948FicPsAwXtFgd64fv+39+r0RRE27rc55JH296fRNo9EsHzyeAEND\nYS2tkMap3x8cWzSUgKS7O/xM6+jw0tkZvd3SLDDj7V9I+250FPx++3NQDpUX6/9lP/0MMGCrtTWV\nHYl0vI8+0zEfPuP6IQcvmusvJvx+PyMjVo268WNNM4/M89zQjmFPF8JrHVOB7nabs+2JNAZiOTYi\nOYs1zP/iRDQO3qeBs8BfAH8JnBtrWxQUFdmH7RUXWwubRDq3stKaxJufn0BaWnTxxQUFCbbVnZZc\ncZWCCAmdkdpniV11ogwybEMxQ7l5M72uGzdZWMuTZycV21/EJhE4UjWl6fRNo9EsH1JT40yl3HNy\nVLhSfLwDt9uBEAIhhKmqclFRIitX2tuiJWczrgYKC8M/p4zNFeLjzflH489BhVzahVtmk00GGbb5\nOFPZkcnsz/hj8cQb1x+f87dQ1QBngsvlIiXFmt4yfqxp5pH8fPu6BHM0N7QjOS0fkq1zdVdB9HmZ\nkcZALMdGCdb+CATXmCXF54UpHTwp5YiU8t+klB8a+/43KeXIVO+bLyorU9i4Md3UVlWVaevgFRcn\nUVWVaWrbuDGdr351Hamp4QeH2+3gz/4s+mqPqakubr01z/T5X7XKeq9FT3ExbNhgblu/HlbNjXD4\nBjaYcgIcOLie601aPwBZZE1Lh6SccpOxFAi2sIWd7DQZ1mSS2Vh4O5SWmi9QVaXiyydQSKFFRH3i\nvTQazdWDwyG4664CQwN1y5YMcnMTyMhw4XAIsrPdbNqUbuRiFxQkcMMN2eTkxLN3r1kqpqIihU2b\n0i330CwwO3dC2lgV1JQU5cyVloYnvBUVsNqcAyQQXMu1FnuziU24cbOd7SYbl0nmlBI/+eRbJo9l\nlJFHnvEvKDtaQAErWIEb5YSmkcZ61s/kt18wvvrVdTid4Smq0+ng61/fMMk7NHNGRgbs2GFuKy5W\n88N5wuGII2f/R8A5zvFPz6Box31RX2MlKynGvKhfSWVMHbx7udciVH4t17Ka6PIEY0nEIitCiF9L\nKR8QQrwL1ngCKeXmue5ciGh08BoahowqmpNpDYEKyWxuHmbFigTDERwaGuX551vx+SS33baCrKzp\nVwfq6vJSU+MhI8NNeXny0tVTaW1VeQW5uXO+QhMgQDPN+PBRSKGxezfIoFFFs4CCaSfASiSttBpV\nNNNQBnqYYZppxo2bQgrDBrixUYVlFhZCzuQOWyedRhVNu13BxYgusqKLrGhmx2RjyOMZ5b33BoiP\nd1JRkcQbb6g8u927c8jOdlNd7SE1NY6KihRTtEd7+8hYFc14ysoiF9jQLDCjoyoX3e+HsjIYHFR2\nMitrUvmEEUZoppk44ljJSpPD58FjVNEsoCDqSn4h+5NJpmliKpG00cYgg+SRRxxxRhXNAgoWTbXn\n6diiixf7+Na3zuN0wte/vp7ycr0AsqB0dSk9yIwM5eAtwBx32NNJZ81R4uKTyVu9A6dz+nP1DjqM\nKppzMYcLEOAgB2mhhc1sZgOxXZiItsjKZA5egZSyRQhhu/8ppaybZR+jZqkInWs0ixXt4GkHTzM7\nluIY0mgWG3ocaTSzY9ZC51LKUGnAW4A3pJSXYtW5hURKSU2Nh+bmEVasiKeiImXSnbb6+iFDp2jt\n2lQcDkF/v4+nnmrB4wlwyy15cy7EqZmcvu5aWtpPEu9Koah4Fy535DyWtr5LnG99Fbczkc3Fd5IS\nv3TyEjQazdKmo2OYhx6qoafHz/33F5Gfn2ixL5p5pKdH7cwlJCh5nJnoenk8qvIyqGskh3diBxmk\nkUZcuCim2AiZnIgfPw00GJEsoYgTUAVb6qlHIimiyJRXt9Q5fryHt9/uIj8/gbvuKrToQDY3D/Hs\ns604HIL3v7/AlMuqWQCam9UOXmam2smeqJHc3w/V1aq9shISl572IkAvvbTQQiKJFFFE3KSS4VZC\nkWkDDJBHnm0aTxddtNFGCimW3f1YEU2vS4FPjO3kHQPeQDl8J2Pem3ngiSeaePfdcDXEysoUPvrR\nVbaG9ZlnWnjnnXA1xVWrkti9O5u//uvTDAyoClq//GU9f/qn09PM08SOy5ee5VjXC0bVsDMdB7h1\n41+QmJJrOfdozW94q+2/CUUcn+58hXvW/W8KM5ZWboJGo1l6nD7dy8c+9jaDg8p2PPxwHZs2pbFn\nj3pWrVqVxCc/WUJcnBY7nxfOnoWXXsIogX34MHzkI+F8u2hoaoLf/S5cUfPNN+GDH4SVK6mnnkMc\nMmzTu7zLfvabnDdQoZov8RLDqAqdpzjF9VxPGWV0082rvIofv3FsD3soYP6KW8wV//qvF3nyyWbj\n9eOPN/If/7HdKG538GAH3/zmOUPC6pe/bOBb37qGHTv0ouyC8NprcOJE+HV+Ptx3H7jG6lfU1MDT\nT0NgrNLpW2+p47nWudhi5iIXOUH490wllVu4hQSiW1zw4+dVXjXJMaxjHVvHqctN1MHMJJP97MdF\nbIsIRVNk5WtSyv3ARuAg8DcoR2/JUVvrMTl3AJcuDXLxorV8flvbiMm5A7Wb93/+zwXDuQMIBiU/\n+UktPl90mnma2OH3DXGq6xVTyWkPg5y78gfLub7ACEfb/sD4dFIfXg7VPDYfXdVoNFc5//iP5wzn\nLhCQeL0BTpzoxedTE6L6+iFOnbKXYtHEGL8f3ngDk77RwAC88870rvP662a5hLHrBglyghMm2zTC\niK0WVkgbL4REcoITBAhwilOGcwdqZ2D85HOpUl8/xNNPm/Vjm5uHefTReuP1D35QbdIn9noD/Md/\nzI0mr2YKurrMzh2oHNRz4zSLX3st7NwBjIzAwYPz078Y4cVr0a8cYIALXIjwDis11Fi09i5y0ZBi\nGGTQoknZQw/VVM+w15GZ0sETQvy9EOJZ4AWgAvhrYEluV7W02Bf/tNPBi3TulStWAfXBQT8tLdZr\naOaWgf4mk/EL0TPSbGnr9TThs9El6vVFr6Gi0Wg0M6W2dsj4ORCQxr9tbeHnkrYj80R/v5qATqR9\nmvagw0ZHtb2d4bGvifTQE1WbDx+DDFomiqA0uwIsbT24s2f7CAat9R8uX1bzq2AwSFOT9e/X0DBk\nadPMA5HGRajd64Xe3ujft0iJNLbsxmgk7M6VSKO9hx5bHczp3CNaookF+TCQDbwEPAE8OS4/b0kR\nKX7brj3SuaWl1kpnyclxFBQszVjjpUxqaoFtbHRG/AprW/JK2/yHdJeWONBoNHNPSUk4d8rpFMa/\nIe08iGx3NDEmLU1p2U1kigrKUZ2fk0PC2NdEMsiIqs2Fi2SSycQqtZRG2qKpiDlTNmxIs619sHq1\nml85HA7bauiR9CM1c0xenn17KPzS7YZ0mwqnSyw8M9LYshuHkbAbz+OvkUGGbVX4SO+bDdGEaG5H\nFVp5B7gNeFcIsbT2XccoK0tmwwZz/Pvq1cmsX2+Nuc/PT+Daa83/qUVFiXz5y+sMTSMAIQSf+lSp\nJTlYM/e44lPYnLnP1JZEMuvL7rKc63YmsC33fTBuYLlxs6vs43PdTY1Go+Hv/349iYnKdjidgvh4\nJ5s3p5OYqCYUK1cmsmVL7I28xgaXC/bsMZd5T062an1Nxd69EDdukTEuDvbuxYnToucaTzwb2Wi5\nxDVcY8gDgdLQ28pW4ohjM5tNeTkOHKZcnqVKSUky739/vqktPz+BT3wiXLT9859fbcpHdbkcfP7z\n868lpgGys2HzBGW0vLywbrIQaiw4xs2D3W7YvXv++hgD7MZoMsmsZW3U11jNaotDWEmlkXubSqrl\nehlkUEH02tvRElEmwThBiI3AjcA+oApoQBVZ+VrMexOBWMokSCm5fHmQpialgzdV5bKaGg91dR5y\ncuJZvz4Np1PQ3e3j979vwuMZ5ZZbVtg6iJr5o6fzMs3tJ0hwpbFq1W5c8ZGrmjb3nOV8y6u44xLZ\nuuoeUhOW1grTTFmKpam1TMLs0DIJsSUWY6i1dZgf/KCa3l4/9923kuLiZIt90cwjnZ2q6l9iIqxd\na7+rNxUDA/Dee+rnNWsgNdU41E8/jTQSRxwllBCP/fV9+KinHi9eVrLStJo/zDB11CGRFFNMCku7\navf4cXToUBfvvNNNQUECd99dQFKSOSKnvn6IZ59VAWN33VVAUdHyqSC6JGlsVN8ZGVBRYV7cAOjr\nU2MhLk6NheSlqe3ZTTfNNJNIIqtYNe3iJwECNNJoVNHMw7oD2j72lUwyq1g1rV35WevgjbvQH4DX\nUAVWjkgprUlPc4zWwdNoZod28LSDp5kdS3EMaTSLDT2ONJrZMWsdvBBSSmu8W3Qd2Aj8EAgAl4HP\nyDFvcuzYf6Di5b4opTwd8UIT8HoDXLo0iNMpqKxMMbbwm5uHaW/3UliYQF5e7PIYurq8NDQMk53t\nprhYrR4FApLq6kFGRgJUVKQYq069vT5qa4fIyHBRUpI0qb7enOH1qnK1LheUloZ1SlpbobsbVqxQ\n2+2T0NN5mZ6+GjLTy8jMUdvGo4zSQgtBghRSGF7R6OqCtjali1IQu9LNDTRwhCNkk80e9qjVDSmh\nvl7pDq1aBSlqJXPY00l33UlcSenkrNqGw6H+P9ppx4OHPPJIZvorSX78NNOMAweFFBorLN1000cf\nmWTGNG66n3666CKVVEM3JUCAVlrx46eAgogrwBqNZn5pahqmo8PLypWJ5OaGx6WUktraIfr6/JSV\nJTMw4Oett7rIy4tn794cHOPCmOzsiyYGdHSoAg95eUrj7q231K7C7t3qdYihIairU20lJeYQs/5+\ntVuRlgYrV5rCOfvoo5tu0kgjmwn2dJyt9Wan0EILLlzkkcclLtFNN2tZSy6Ro0eaaeYKV8gjjzWs\niXjeIIO8y7u4cLGZzfTRxyCD5JJr2umTSFppZYQR8sk3hYROh0466aefHHIscg8zoa7Ow+HD3axc\nmcCuXdmmsQHQ3e3jscfqEELwR3+0ivT0GegUahR+P9TWqnlUaam95mNjo/rcFxXZy4XU1cHp01BY\nCNdeaz3u9ap7OJ3qHhN2+KL6HHZ2qrGbna3mqxPooIM3eZNkktnHvojakpMx1RyuiSbe5m2yyOJG\nbrTsroV07izz4XFc4AKddFJO+YLJmkxPvW96XJRS7gIQQvwEFd55ZOzYPwIfBYLA94F7o7lgff0Q\nP/95PSMjqspNWpqLT35yFQcPdnHyZLiCz44dWbz//bP/g772WgcHDrQblZTXrk3lzjvzeeSROrq6\nfICKC3/ggSK6u30891yrcW5paTIf//gqXK55zM1raICnngKf6htpaUqH5M03wyEkoAbmjTfaXuLI\nyYeoHhkr5dwB5Y3XsGbrAxzggFEVzI2bfewj++B5GL8SV1kJd95pzmuYAb/m17zMy0aloWd5lq96\n/4bUJ15QziQoQ3zrrbTEddD13GMQVOWUO3LzKPvwX3E48RTtqApOoZyG6cRRd9DB67xuVOlMJJGb\nuIkLXKCGmvCvTCXXYvOgmyZnOMNZzhq/80pWsp3tHOAAAygZDydOdrObQgpnfT+NRjMzpJT89rdN\nnDkTljTYvTuH225bgd8f5NFH66irU9X+zp7to6bGQ0qKMrWrViXx3e9uIy3NbWtfHnywWIudz5ZX\nXlGTUFDO1pkzxmIgv/wlfOUrUF6uQjOfeSZc2j07W9nLpCQ4eVKVfQ/95xQVwb33gsvFSU6ayqav\nYhU72YmQwLPPGrZ2gAFOXOuk+cbVjDLKaU7jHPt6nue5iZu4hVss3X+Kp3iHdwxbUEIJn+bTloJi\nF7jAr/gVfvxIJL/gF6xjHamkIhBsYhMb2IAXLwc4YFTpc+BgBzsoocRy70gECfIWb9FIo9G2gQ1s\nZvMk75qcH/3oCo8+Wk8oiqyiIpV///etxoL5iy+28ud/fsKY733ve5f5wQ+2c9NNEQp+aCLT2QlP\nPKEWNEAtaHzoQ2EHanQUfv97NYeEcE7dtm3hazz8MDz3XHhMVFbCP/xD2FFsaoInn1ROHqgxd999\navEfFYZ8gANGZVgHDq7nekopDd9jotbe+vVw++3GnPJFXuS3/JYgar73LM/yV/wVK1kZ1Z9BIjnM\nYeqoM9rWspZthH/PJ3iC53neGH/P8Axf4StkkQWohfhXedWYD7twsY99xqK8Dx8/5sfGWBEIdrKT\nO7kzqj7GkjnzPiaEcnpRuXshsqSUDVLKJsCm9I49f/hDizHYAfr7/Tz2WIPJuQN4++1uGhtnV063\nq8trMr4AFy8O8PDDYecOwO8P8tvfNvL8862mc2trPRw/HvuypxGREl5+OezcgVqJeeIJs3MHcOyY\nbXnn9pZTYedujOqRs7ze86Sp5LMPH8cGXjM7dwCXLimjOQu66OIVzNp23XTzxonvhJ07gGCQwKsv\n0fXirwznDkB2tHP66E8M5w7UoD7FKduy1ZE4xjGTBMMww7zO6ybnDuASl+ikczq/ooV++k3OHagV\npPHOHahVo6MctS2xq9Fo5ocLFwZMzh3Am2920tY2wtGjPYZz5/UGOH68h64unyGLUF8/xMMP19Hd\n7bO1LxOvq5kmjY1h5w6Uo9bbG5ZEGBiARx5RTt3LL5t1u7q6lA7e0JDStxv/n9PYCGfO0EOPRROr\nnnqaaFK2b8zWSiTNNJN17ArxHQPUUUcvvXjwGMdf53VDGytECy0m5w6gjjrewarP9zRPGzZqhBEG\nGOASl4zrv8u7ePBwkYumEuxBghzlKKOMRvlHVRE14507gHOcs/Q/WlpbR/j5z8POHcDlywP84hdh\nHbxvfvOcab43PDzKN75xdkb3u+p5/fWwcwdqPLz2Wvj1u++GnTtQn/033lDRUqCOjXfuQM33nn8+\n/PrVV8POHcDgoNpcGOMCF0yyH0GC5nlWa6tVa+/8ebUjiJp3PsVThnMHagf7F/wiqj8BqJ3x8c4d\nKI26UL8GGOAlXjKNv156+Q2/MV5PnEv68XNsnDT4IQ6ZxopEcohDdGAjqTLHzOn2khDiHiHEGSAP\n6Ipw36j64PMFaWuz6tacO2f/gKmvn52D19AwjF164vnz1vs1NQ0zNGTVzphtH6bF8LC9Dsnly/bn\nN1u14jp77M9t8Fmdtu6hBgLC5g/U1DRpN6fiDGdMAzjESHONpa3f2wmDVpH6kWZrf4MEbTWF7PDj\npxfr37LBtEYRZrYOXhddtk5bPfWWtqGxL41GszBE0uKqrx8yPfPb2rwEAhIpJV5v+Jl27lw/DQ1D\ntvZlXm3GcqRlnIKT368mmWBe+KyrU7ZyyOZv3dysFhKDVhtEU1PESVonnSabOsqoMXFNbu6lD+W4\nTxQtv4JZuLuaaltbUEut6fUggyYbFbruIGGdXomkc+xrIn78Rp+iIZKNm+mk9dSpXmPRYzxnz6r5\nVSAQiKCDp3UiZ4TNfI/m5rDD1mKjfBYMKqcL1C643QPrwthih8+ndgknMm4+OOXn0K6P49rrqWcE\nqw/QQvSqbZE+x6H285y3XfgYPxezu0YPPcb77OZtEjknQuZTEdG5EkI8JYR4MtJ3NBeXUj4ppdwI\nNAF3jzsUjPDz+Pt/XghxVAhxtKOjA5dLkJZmjXNdudI+3268ttBMyMmxj+u102HJyHDbyiTMtg/T\nIiFBVQKbiE0MMwBZWZam1GT7c7PjrCERye4snNImlMjmutOhlFJbjZC4TKveUJIj1bbqWVymfQhH\nKqm27Zb3E2cbG27JtZjmdSMR6f1293PhstVX0mg080N2tv1zPScn3mQ3MjNdY3nYApcr/EwrKEgg\nO9vevsyrzViOZI4rTx4XF7YP43OBcnJU+JjLpjJeZqb6tkszyMqKmHeWSqrp3qFQTABvVjJJJBnt\nIQSCFZhtrl21PcCSr5dEkslGha470TakkWZrXxw4ppWXHslGzTQPL6R3N5GiIvU7OZ1OMjKsYyQr\nS+fgzQi7eVlGRvhznhlB5y3UXlxsf7xwLF3E5QqHQUe4b6TPoZErOkUf8sm31z2eRh2ESJ/jUHsx\nxbbzz/FzMbtrJJFkjMFI88R88m3b55LJds/+X+Dbk3xPihBivKXqB1N8XLcQokgIUQj2y0hSyh9K\nKauklFW5ubkIIbjlljzTczcuTvCxj62yOF2lpclUVs6ulHBRUZJF/iAnJ56Pf3yVxZm7664CqqrM\nAyg93cV1183O2ZkWDgfs2mVui4uDD37QKjZZUqJyCiawsvgGcpxmg5PjyGNPxt04xn1UBIItmftU\nEq3p5BxVanoWlFDCOtaZ2uKJZ/u1/4/FgY3fvoPkGybkMCQksO66T1iMVxllURsjgWAzm00D3YmT\nvew14rBD5JE365y4HHIowvz/kUYa+9hneaBtZOOSF7nVaJYymzenWwTJKypSKC1N4vrrs4yFyNRU\nF2VlyaSmxhm52CkpLj71qdKI9mXrVq2DNytWrw5POoWAdeuUkxeyHQ4H3H+/arvuOvN7Q20ZGXDN\nNeZjqamwZQsrWGGZqGWQofLZ1q0zbK0DB3nkMVCSjacok1Wswo3bZJfWs95SfGENa8w5SUA66exk\np6nNgYN97DNsVCKJxBFHGWXGOSWUkEkm61hnKc61jnXTWigso4z0Cdk0K1k5aaGYyaisTGX3bvOi\nbUaGm499bLwOXpmpUJ0Qgi98QevgzYidO80FhBwOs0bdli3WoiobNoQdtI0brWMiMxM+8AH1sxBq\n/jl+gu50qvuOMeXnsLTU6kjm5Sm5BSCFFHZg1qmMI457oyvhAah82YkadePHdAEFbGKT6bgbt+ke\nm9hkmQ+Pny/eyI0WJ7CCCsu4ng+mlEmY8YWFuBf4X2MvLwFfAz4tpfxnIcRmVHEVAfyplPLkZNca\nL5PQ1DTMmTN9xMUJtmzJICcnHr8/yKlTvbS1qYpmGzemmQQyZ0owKDl3rp+6uiFyctxs3ZpBfLyT\n3l4fJ0704vUGWb8+lZKSZKSUXLgwwJUrHjIzXWzdmmHRdJkXWlpUHoDLpQZoRobaPj9/XuUY6c0I\nyAAAIABJREFU5OcrJ8xp7yQERr3U1r5Gj6eBzORiSkpuJM6VSB991FKLRLKKVcrRCQTg4kW1jZ+V\npe5nV5lpBrzAC5zlLOmkcxd3qZVOjwfOnlX/lpZCmTJm7XVH6LtykrikNFZsuJmk1Dy8eKmhhkEG\nWcEKiiiyXZmZjC66qKceJ05KKSWNNEYZpZZaeuklm+xp65dEIkiQBhrooIM00iijDBcuPHi4whX8\n+CmiKOIK71QsxdLUWiZhdmiZhNgyfgz5fMrmtLd7KSpKZOPGdEPDbmholBMneunt9VNenkxtrYe3\n3+4mO9vN/fcXG85hJPuimSWjoyp0rL1dOVyBABw8qBY8b7tNFVgJ0dCgcucSEpT9Ck1ypYQrV1Q4\nZ1qamtyOOYkBAtRTTxddpJNOKaXhKnoTbG3H2iwanM24cJFNNqc5TQ89VFLJdrabJopG9xnlKEep\noYYcctjJzojad5e5zAlO4MLFNrYxzDCDDJJHnmk3YphhrnCFEUYopHBGVf38+KmhxqiiuYpVtv2f\nitA4CgaDPPNMK0eP9lBQkMB9960kJ8fsdL7yShuPPqqqaH7qUyXs3asLrMyYzk41LoJBVbxk4sL/\nyIiaX/X1qSrl5eVmh210VOWtnjunKqbfeafVKWxtVXPCuDh1jwk7h1N+DkdH1fy1tVVtGKxfb9lp\nf5M3eYd3SCaZ27l92o6THz+11NJHX8Q53Mu8zGlOk0Yad3KnpZ/99FNDDUGClFBiWfgfZJBDHKKT\nTlazmmu51nb3cabEUgevEvi/wAYIL/lIKedtKUXr4Gk0s0M7eNrB08yOpTiGNJrFhh5HGs3siNbB\ni2b55SfAD4BR4GbgEeBns+ueZs4IBJRW3PgEWlCrMjU14apIC4AXL800WytvtbWplVK/3/6N4+no\nUFWVxldr0mg0Gs3yoKtL2aoRa0GFGREMqp26piZlE3t61PWHpyjYEdKU7eqa/Lw5RiLpoIM22mwL\nkGk0MePll+H73zcqVy5GAgRooYUuFnZcLgWi2TNMlFK+LIQQUso64BtCiDeAr89x3zTTpa1N6ZCE\nnLi8PJWD9847cOqUMm6h2Gs7kco55ApXOMYxAqhqo6WUssO7FfHkU+FKSwkJcPfdtvmBjI7C00+H\nHzxuN9xxhzncRqPRaDRLk2BQlV2/eFG9jouD/ftV6ORM6exU+l4DA8r+dXaqsLL4eJWmsG8fbLbR\ncrt4EV58UdkdUJpf73tfxNSGuWKIIQ5wwFgUTSKJfeyz5MNpNLNiZAQ+8hG10A7wwx+q11/96sL2\nawIddHCQg3hRC/w55LCXvTMSO78aiGYHb0QI4QAuCSH+TAjxIZhhIpBmbnnpJfMOXXs7/Pd/Ky2g\n0G5eMKjyEXrmT6NvmGGOctRw7kCVfa47+6xZVmFkRBl4uxLVJ0+aV5V8PmWAo9n102g0Gs3i5vz5\nsHMH4ZyfqXbaJuOVV5RzB9DdrfLt6sfKmAcCcOBA+HgIr9fs3IHS/Dp3bub9mCEnOGGKeBliiKPo\n8EZNjPmnfwo7d6DmYI8/vqh28iSSt3nbcO5ASRacRWsjRiIaB+8vgSTgS8C1wB8Bn5rLTmlmwNCQ\nrXi5rVGSMmzk5oEOOmxDS9oGbXT3BgbsnU+7/o6MKCdWo9FoNEsbu2d8IBBZH2sqRkfN7+3rM/8L\naiLbaBbwprnZ7NyFGD8BnifaaLO0ddBhWizVaGbNqVPWtmBQRYQtEjx4TDqPIezGiEYxZYimlPII\nwNgu3peklFZlac3CEx+vvifmpmXba3JYqh/NIZH0dpJdGcCEnECnE5JtzrfrrxCqfLVGo9FoljaR\nbNJMbZXTCUlJYUHzkCbeRO3UiTYkkk2ZR5sZIplkfPhMbYkkzqh6pUYTkexs+wWMMYmCxUA88Thx\nWhY3QhqTGitTPiWEEFVCiHeB08C7QohTQoj5TeDSTI3TCVUTiuo4HCqnLX1CvH5+vlXDbg7JJttS\nZjaBBMpX32bNadi6VeXiTWTbNqsEw/iy1hqNRqNZumzebNE6pbzcWs49WoSAHeN0s/LylHO3cmW4\nrbjYmvOdkwMVFea2hARlm+aZDWywyPvYtWk0s+JLX7LIEVBUpPJOFwkuXKzFrLPsxMkGZpGju8yJ\npsjKj4E/kVK+ASCE2IOqrGmTmaxZUK67TumOvPeeSlDftEk5c0VFagu+q0vpl2zaZNY3mQf2sIdq\nqumggxRSqKSSxBVJ8NGPwunTKtxy9WolFmtHdnb43MFB5aDOJvleo9FoNIuH1FT1jD91SoVRFhcr\ngeXZsGWLWuC8cEEteH7wgyqsv6dHOXqRrv/+9ytNsPp6tYi4deuCLCYWU8x+9nOFKwQJUkrpjDTs\nNJpJ2b4d/uu/4N/+TeWqbtoEf//3C90rC5vZTAYZNNKICxcVVFiEyzVhonHwBkLOHYCU8qAQQodp\nLlbKy62VJZOSYOfOhenPGE6crBn7MpGToyqlRUNmpqp6ptFoNJrlR1oa3HhjbK9ZWmqOWKmsnPo9\nTqfaUbSrsDnP5I59aTRzyvbt8LPFr4C2auxLMzXRBHK/I4T4TyHETUKIfUKI7wMHhBDbhRDb57qD\nmhjR2wtnzoTzERaAAAG66WYEs76RBw899CCREd65dPHjp5tuSx7FdPDgoZfeZfn30Wg0mpjj9SrZ\noOnqpQ4Oqh0+u0rOM0Ai6aXXtjjEdOinnz76pj5xEoYYopvumGnpSSQ99OCZmEevWbp0dKh5om/m\n85VeehlgYfeAppoz+fDRTTd+7KuwBwnSTTfDzKKC7yIgmh28UOD5RN27XYAEotx+0SwYDz8ML7yg\nKpLFxyt9k7vvntcuNNLIEY7gxYsDB2tYwyY2cZjDNNKIRJJMMrvZTRZZ89q3uaKaak5yEj9+nDjZ\nxCbWESEE1QY/fg5zmCaUlEQqqexmNxlkzFWXNRqNZmlz6pSSAvL7VV7Rrl0qh3sygkElM3T+vKoy\nnZKi8o/sNFmjpI8+3uRNQ+agkEJ2shMXrineGcaLlzd5k3ZUtehMMtnDnoiFy+wIEOAIR6ijDokk\niSR2sYsccqb3C42jl17e5E1jIr+SlexkJ3FRTSk1i45gEL73PTh0SP2clASf/vS0dtMHGeQgB+ml\nF4A88tjNbuKJn+KdsSOaOdN5znOGMwQI4MLFNraxmtXG8Q46eIu3GGYYgaCUUq7juiVZ2GjKHksp\nb57kWzt3i52jR+HZZ5VzB2pF8+c/N+vPzTFevBzikKFfEiTIBS7wGq/RQIOxyuLBwyEOLYudqgEG\nOMpRY4UoQICTnKSLrqivcZ7zxoMqdM3DHI55XzUajWZZ0NWltO1C+qh+P7z2mr2E0HjefVdJCoX0\nYgcH4ZlnwnZzBhzmsEnDrpnmaWt2neSk4dwB9NDDEY5M6xqXuUwttYZdHWKIt3hrVjt5hzhk2qVp\noolzzL9OoCZGvPgivPlmeOd6aEjl5A1Gv/P8Du8Yzh1AO+2c5nSsezopU82ZOunkFKeMSpx+/Bzh\niLHDHiBgOHegdqlrqOEyNpJeS4BoqmiuEEL8SAjx7NjrDUKIz8591zQx4YiNMQgG4fD8OQrttNvq\n9lzikqVtYOxrqdNCi62j2kz0mk7jH1QheunVITEajUZjR01N2Ekbz5Urk7/P7vjQkArznAHDDNOD\nVc91Os//SOe30TYtHTw7OzLEkGkyPh08eGzDRaf7u2kWEceOWdt8Pvt2G0YZNS1EhLD77M0lU82Z\n7D6jEmm099BjG5a5VD/b0eyn/xRVNfOrY6/fA34F/GiO+qSJJZEqf2XMX5hfpC16O/0SgcCN2+bs\npUWk33k64Qp25zpxLou/z0IgxOML3QWNRjOXJEXQxIrUPtv3RSCOOFvNrumGq8UTb0S+hHDjnla4\nWAJW2SGBmHHonAsXDhyWHUC7+2iWCJG0HzOjq1DpwIELlyWnbb4/E1PNmSJ95kP9jNTf+QwzjSXR\nPCVypJS/BjWapZSjMI3lI83CcscdVuHw7OzYVyqbhFxyLfH+LlzsZrdFz6eMsmVhKIooIhXzQzOR\nREooifoa61hn+fusZvW0cjg0Go3mqqGiwqr7mpo6tWDz1q1WTdaKihkvhIZKuI9HIKaVgw2wnvWW\ntrWsnZYO3hrWWBzCYoqnlcc3HjduyjFX6hYIi0aZZglx991WHbySkqiryDpw2P7/T/fzPlummjOV\nUkoiZq3NNNJYidLGTCGFYopNxyP9bkuBaHbwPEKIbFRBFYQQN8Asyzlp5o+cHPjGN+Dxx6GlRZWL\nfvBBq2j4HCIQ7GMfF7hAG22kkso61pFOOimkcIlLePGykpVUEkUJ6yWAEyf72c8FLtBFFxlksJ71\n01oJKqSQfezjEpfw46eIomXz99FoNJqY43arImJHj6pqmLm5UFWliotNxooV6n3Hj4PHo+zkVIVZ\npmArW0kllXrqceGikkryyZ/WNcoow4WLaqqRSEoooYyyaV0jhxz2s5+LXGSEEQoptMoVTZPtbCeV\nVBppxI2bSipZwYpZXVOzgJSVwVe/Cr/7ndLBW7cOHnhgWpfYyEaSSaaWWpw4KafccJzmi6nmTPHE\ncyu3cp7z9NJLNtmsZz1Owos7N3ADmWTSQguJJLKWtUu28J+QdvHq409QUgj/H7AROAPkAvdLKect\ne7KqqkoePXp0vm6n0Sw7qqqqWGpjSIdUzg4pP7LQXVhWLMUxpNEsNvQ40mhmhxDimJSyaqrzptzB\nk1IeF0LsA9YCArgopbQXj9AsDoaGVLjJVKuWQH93HcLhIDWjeMpzZ4tEMsww8cSbVkz8+Bll1LJ1\nrtFoNBqNLVKqKn+JiRA3YSoTDKpjycnKFg4P8/+zd+/BbWX3gee/FyAIEny/KZKSSImiqEfrSUmt\n1qMldbeddrrttN3diWNPnHVqnNqd8m6lNqmd2c3Ubm2lppJN1WSSyVYmrnXNZJxUPA/H7/aju61W\nq/Wm3iLVJEXxJYpviE8RIB5n/7jEJcF7QQIkQIDk74NytXFwcXEo4vKcc885vx+aBhmJWf4f2idn\ntULjGc9w4CCbbNNr00yTRposuxdL8/v1vl1Ojv5dXg6PR79uMiP0tXw+/ZhIe/JSxFLXzRhjPOc5\nm9i0yjVLLUsO8DRNewf4uVKqSdO0PwYOaZr2J0qpW4mvnojJxISe766nB2w2fZr93Dlz44c+sHvw\nd3+IrVePEhbcWsWB3/13uHJKE1K1QQa5wQ0mmMCBgz3soY46bnObxzwmQIAiiniRF01714QQQgjD\nkyd6aPexMf1G5rFjcOiQ/lprq54aYWpqbl+Rz6e3ibW18OqrcduiMMMM17hmRNmroIJjHCOddIYY\n4rt8lwEGjH087/AO6aTzjGdc4xqjjGLHzk52so/o9juJDejePbh8WR985ebq/brq6ujf7/Xq10t7\nu/68ulqPzzD/hsf16/rS5pkZPbjKa69BRUU8f4oVm2SSq1xlmGFs2KihhsMcNvaYevDw1/w1bbSh\nUBRRxO/z+1RTndyKJ0k0QVb+tVJqQtO0k8Bngb8D/iax1RLLEhrcgX4Hs7kZrl2zPLTpH/537L39\naCg0FPauHu599/+wPHalfPi4yEUj/YEPH3e4wxWu0EabEWlshBEucSkhdRBCCLEOeL3w4x/rg7vQ\n848/hu5uveznP9cHdwAPHuhJz6en9TaxtVXP9xUnt7hFL72o2UcvvdzmNoAxuAM99+tDHvILfkGQ\nYFhC6AABmmmmg4641UusI3198Ktf6YM7gPFx+MlP9O90tD7+GB490mfvlNLTiZw/P/d6e7s+gJyZ\n0Z8/ewY/+pE+a5hCLnOZYYYB/Zpqp51P+dR4/Tt8h1ZajRRVI4zwH/gPSalrKohmgBeKmPnrwN8o\npX4IEqc95Xg8c4O7+drMueaCQT9at/lY1dGeiJoxwIApfC5gmRh1lNF1kQdPCCFEAvT06IO6hVpb\n9U5sKFlzMAijo3qH1u2eO86iTVx2VTC3o91084xnxuBuvhZacOO2zGVqdS4hLL+vfr8+SIvWI4tE\n3W1tczkjrT7D49FnylPEFFO4cZvK5183rbSaXnfjXvV8fKkimgFer6Zpfwu8C7ynaZozyveJ1WS3\nm8M8Q8SlKCrNfKyyWMoZD2kRVgJb5XPT0CIeL4QQYoOLtLwyPT38NU3Tl2VCeNsYxwjSVnuAHLMP\nq1x1odeiPZcQi37fo7UwBULo/aG9fJHOZfW+JEkjzTI9yPzrxqrvqKGti9RbyxHNQO1d4BfAryml\nRoFC4I8SWisRO4cD9uwxl+/fbyqy2dKw7z1gKs84cDQRNaOMMvIIz01kx84xjpmOraJKgq0IIYSw\nVlUFhQvCltvtsHevnu8ulJxc06C0VN+DXlQ0d6xFm7hcVmlrdrCDbLItXzvKUfLIo5Twve4amilv\nnhAA7N5tHoDl5cW2B++Aub8Xdh288MLczZCQ0tKU2oPnxMkWtpjK519nJzhher2GGoooMpVvBNFE\n0XwO/NO8531AXyIrJZbp5Zf1qGGtrfqAb98+/Y+DhYYv/zl3XP83nuabgEbmvhc5+OYfJ6RaGhpn\nOMMDHjDIINlks4c9FFNMFlm00MIMM1RRxW6s6yuEEEJgs8GXvgRXruhLyPLz4ejRuUHfO+/orw0O\n6kFVMjP1fUw2m96RfeGFuFVlN7tx4OAxj9HQqKHG6HD+Jr/JL/gFLbTgwMFRjnKc4wCc5CQPeEA/\n/WSQwS52UUJJ3Ool1pHcXHj7bbh6VV9qXFEBL75oGTwvooYGvU/Y1KQvy9y9O3zQV1ICX/yiHmhl\nfBw2b4bjx5cfrTNBjnKULLKM/It11IUlJn+DN1AoLnGJGWaoo46v8bUk1ji5lsyDlwokD54QK7MW\ncw9JHryVkTx48bUWryEhUo1cR0KsTLR58GQv3UYSCJjLgsG5jbYiJSkUQYLJroYQQiRGPNohpeYC\nrIikC2DR3xArY9WHi4VcI6sq2deARLPYCNrb4eJFPaJYaam+lLOkBD76CFpa9Gn4PXvg1KnYpv1F\nQikU97lPG2348VNJJQ00bNgNw0KIdcbj0UPAP3qktz0vvAAnTpj3Ay0mENBTITx4oP//ujo4e1bP\njydW3TjjNNLIIINkksludlvuRxQxaGvTv+NjY3of7uxZ2BRDEm+/X0+V0NysD/Lq6/V+YBwDDok5\nHXTwgAdMMUUxxRzmMAUUrHo9ZAZvvRsehp/+VB/cgb4v4Yc/1HPmNTfrDaLfD3fv6nlQRMpopZVm\nmvHhQ6F4whOucjXZ1RJCiPh4/319z3gwqOfgunlTT7Yci+vX4fZtPZl5MAiffgoffpiY+opFKRQX\nucgggwBMM81Nbm7YMPVxMTQEP/vZXN7HwUH4wQ+sU4VEcumSnizd79f7fE1N+oBPxN0ww1znupEK\nZZhhLnAhKbN5MsBb70KN53xer3UC9IcPV6dOIipddJnK+ulnmhgSnAohRCryeuHxY3P5p5+ayxZj\n1W49eqQP+MSqGmHEMo9tJ52rX5n1oqXFug9nde1EYnWNxHqdiah00mkkWg/x4KGf/lWviwzw1rtI\nS12scubFsixGJJxVzhcNzTK/khBCrDlWUfpibYeszqFpKRcBcCOwarMAabNWItL1EMt1YnWs9PcS\nItI1EKk8keQ3vN7V15sHc5mZ+j6HhfbuXZ06iahsY5uprIIKnMjeEiHEGud06vvlFrLK57oYq7QH\n9fWynzwJCikkn/ywMg3Nsi0TUdq1y9yHc7lgWwz/plZ9u1ivMxGVGmpMg7kssiijbNXrIn8B17v8\nfHjrLX0N9sgIlJfrwVQKCyEjQ5+6t9n0PwBHE5PoXCzPdrYTIEALLfjxU0UVB7BIWCqEEGvRuXP6\nQG9+7larpMyLOXxYX8J2/76+x6i+Hl56KTH1FYvS0DjNaW5zm376ceFiN7uT0rldNwoK4Atf0GMk\nuN16cJVTp/TrJVovvqjPaDc16dfK7t16njsRd4UUcpKTNNHEBBOUUsoBDmDHYtVcgkkePCE2gLWY\ne0jy4K1MvPPgxfv3sdby9K3Fa0iIVCPXkRArI3nwhBBCCCGEEGKDSdgSTU3TjgF/AQSARqXUH8x7\n7f8C3gKeAT9SSv3bRNUjEVpppY02ZphhM5vZz34cRJgub27Wwz4/f66vmT51St8Dt1IdHXD1qh46\nt7JSP29Wlj6N39KiT9/v3w+HDjHIIPe4xzjjFFHEfvab1skn2uRYL08u/iP+J51oufkUH3+Tsppj\nKz6vFy93uMNTnuLEST31st5fCCESbXJSD7Xe3Q05OXDkiPWeukQYHdVzu/b2Qm4uZGfrKYFCefBO\nnIh5D14XXTzkIdNMU045BzhAJku01cGgHpG6qUk/x55sfnhsgFHbGGWU8Tqvs4Uty/0pFxUgwD3u\n0UUXduxsZzu72JWUYA4iyQYG4Nvf1pc65+XBr/86fOYzsZ2jvV3/Lo+PQ1UVnD6tX1vR8vvn+p92\nu743tqEhpmBHz3jGXe7ixk0eeexnP8UUG6/78HGHOzzhCemkU0edKcfiUv3zIYa4xz3GGKOQQg5w\nYNX7w6slkXvwuoBzSimPpmn/oGnaC0qp+/Ne/1+VUh8k8PMT4jGPucUt4/kjHuHBw0lOmg9ub9fz\nzYU0N+sXz9tvr6wSQ0Pw4x/Phc5tb9fLNm3SL66Qjz9mMs3DhX29Rg6OPvpw4+YN3og8KI2zYNBP\n5z/9WyOPi/L0M/jj/4/038qnoHTnis59iUtGzh0vXq5zHQcONrN5xfUWQghhQSk9n+rQkP7c49Fz\ndblceucwkQIB+Kd/0ttS0MPF9/To+8izsuZy4r36atSn7KOPK1wxnnfRxSSTvMZri7/x8mUjb98k\nkzy+doOcQDGDJzfRQw9/x9/xh/zh0gPFZbjJTR4zFyr/HvewYaOe+rh/lkhx/+bf6IM80K/F//gf\n9YHesShvovf1wU9+ol/XoKcZGRmB3/md6AdoH30EDx7MPb90SX9vw5IrCQGYYYbznGeGGUAfiH3E\nR7zO62SRBcBVrho5Fb14uclN0kijhhpg6f75FFN8xEdGf7iffs5zflX7w6spYUs0lVL9SinP7FM/\nmLL8/ZmmaR9omramoka0024q66UXLxZJJ+/fN5c9eQLPnq2sEqGNsvM9e6YnfF2gs/+aKcGiF++q\nJh4d7r49l6RzlhZUDDevLNHmBBPG4G4+q9+REEKIOBkYmBvchSgV3sFLlO7uucEd6ImflQqvz8OH\n+oxClOYPlEJGGGGU0cXfOO/n7aUXhaLigdso8+LlNrejrke0AgQsc8tJ27cB3bs3N7gLUQo+/DD6\nczQ1zQ3uQp4902fIoxEIWOfas+oDR9BDjzG4C/HjN/IBTzPNU56a3jf/O79U/7yLLsv+8BOeRF3P\ntSThe/A0TdsHFCulmucV/5VS6jDwPwL/PsL7vqFpWqOmaY1DCxuSJAoSNJUplGW5aRC2VHnUlbB4\nv1KW5UG1cFw9W25V3wRRAeuGVgWs6xb1ebEOEBSpXAghRBxE+tu9wr/pUVnYzoU6pvM7qEqZO6yL\nnTJCe7hkOzmvLqF2RwuGf66f6Aea0VKzD1N1VrFdFykiHtdipD5ptOeIdL3F0Nddqj8XzXd+qf75\nsq/zNSqhAzxN0wqBvwZ+b365Uso9+9+2SO9VSn1LKdWglGooKSlJZDVjspWtprIyyqyXYNRbLJUo\nLYWiopVVYudO87R5To5leOktxYdMa/IdOKikcmV1iEHx1kPgygov1DSK6i1y8cUgl1wKKTSVV1O9\novMKIYRYxKZN+hKwhazavHjbsiV8H3txcfh/AbZvjymMvFW7nksuBRQs/sadc1sMNrEJgP76ufc4\ncHCQg1HXI1pppFGFeSmstH0b0P79ejqshU6div4cVtdtdnb0y63T0qC2NrrzRlBJpSmVgA2bsd3G\nhYtSSk3vm/+dX6p/voUtpv5wpGtpPUjYAE/TtDTg74E/Ukr1L3gtd/a/xayxXHw72cluduPAgYZG\nJZW8yIvWB+/erefjycjQB2Rbt8Ibb6y8EpWV8Npr+qAO9Nx2b70Fr78OO3boee0cDjh0iPyDZ3iJ\nl8gmG4B88jnFqVVNlm1Pc1L5G/8TtvIKvSA7m/xX36ao0iJBbYxOcIJyytHQSCedfewz1mMLIYRI\nAJtNz80V6gC6XPDyy/rAKtEcDr29Ky/Xn9fXw+c+p+d2tdn0QVcM++9A7/gd4IDRLpZRxilOLR2w\n5PRpPWG03U6evZDNe15n4JQe9KGQQt7lXXLIiflHjMYRjhgd1jTSqKOO3exOyGeJFGazwR/9kd6/\nBH0f6ltvwZkz0Z9jyxZ45RX9vaDfwPnCF8wJ1hfzyiv6tWez6QO+Awf0/HtRyiST05wmD/3GUQ45\nnOAEucwFejnOcSqpREPDgYPd7A4LsrJU/zyXXE5wIqw/fJrTq9ofXk0Jy4OnadqXgb8CmmaL/hXw\n20qpb2qa9rfAXvQB5r9USl1Y7FypmAcvNO0bVfJCpfSp7hijei37vIGAPqC0hY/fAwSSkmwxrA5+\nLza7A02L772FAAFs2CSCWARrMfeQ5MFbGcmDF19r8RpaFX6/3hGMIVpeQj47GNTbxFg6pQvE1K7P\nF1qKNtvmzjBDOunLrkcsAgTQ0LCtkaxXch0lkMcD6emmvl/U4tFXjdD/jOkUS/RVl+rvRXMdp0J/\neLmizYOXsNkzpdQ/Av+4oPjK7Gu/n6jPXS0aWvRfDk2L/+BusfNGaOBS4ctsT0vMnZJU+NmEEGLD\nSUTbtpzPXkGHMiSmdn2+BZ+9WoM7kLZPzJORsbL3x6OvuoIbLMYplvhOL/V6NNfxRrhu1tTyyJTR\n2gp37sDMjL7u+MiRuHypY3L3Lnzve3run+3b9XC2KbRXUQghhAD0SH+hiJO7dunLt5aa8ZuZ0fNy\ndXToS8dKSvSImdPTUFOjh4CPYZ+dEGtWczP81/+qf/+rq+ErX9G36oR4vXpe5K4ufe+syz51AAAg\nAElEQVTckSOwWVJFbXQywItVayu8997c8+FhPWRzrEklV6K9Hf78z+fCQLvd0NkJf/mXcbmLKYQQ\nQsTFzZt6UvKQwUG9Q7rU/pz33tPbNdBz3bW06Pvt8vL0HF2jo/HZ0y5EKuvthT/9U/2GB+j9vceP\n4a/+Sl+OCfCjH82lNHC79XRc7747t09VbEgyGojV3bvmsk8/1dc+r5af/9yc42doyDIPnhBCCJE0\nd+5EVzbf6Ojc4A6gv1/fHzQ/31d7O0xMxKWKQqSsX/xibnAXMjo6d9NkaMicry4Y1GfNxYYmM3ix\nWnihgX4x+XwrX/8crUiDyamp1fl8ISxIUJTUIr8PkRKs2syZGX3AFmmZ5sL3hPJxzc/LpZT1uYVY\nT6anrcufP9f/6/Vavy7XxoYnM3ixsgoDXV4+l7JgNRw/bi5LT4ejR1evDkIIIcRSrPJj1dYuvgev\npCQ8z15hYfh/Q/9/pTllhUh1L71kLktLm+sHbto0l95gPqvrTmwoMsCL1ZEjet6b0F63TZv0/HOr\n6aWX9L0HofXX+fnwL/7F6g4yhRBCiKWcPq3fGNU0/X/V1XD27OLv0TS9jQslL6+shBMn9PYWoLRU\n9t+JjeHgQfjiF+dWiOXmwj//53PXht0On//83M2PtDT9Zn8MScbF+iRLNGNlt+tJxk+f1pdlZmcn\npx5f/ap+0bvdUFEhwVWEEEKkHqcT3nxTX2qmlJ4UPRolJXo7Nz6un8Pp1JejzczIzUyxsbz7rj6I\nGx7WV4wtTGVQVqZHUh8f1weC6auXpkOkLhngLVeowUkmlyv6xlIIIYRIlszM5b0vN3fu/6dCuytE\nMmRkQFXV4sfMv1bEhreuB3jNzePcvv0MgAMH8tmzJ2+Jd6whbrcefnpsTF++cuhQajd8Xi/cuqVH\ne8rNhYaG8P0UQgixDgwMeLh8eYSxMR/btmVx/HgRDoessEiojg64f1+PLl1fD7t3L/2eYFCPiv34\nsd55PnAgPLeYSHn9/R6uXNGvte3bs3jxRbnWlq21FX7wAz0Fyc6d+qxhslaoibhYtwO8Gzfc/PSn\nfcbztrZJpqYCHD26DgYV4+PwX/7LXPSkJ0+guxt+8zeTW6/F/OAH0Df3+6C9XU/WKXechBDrxPCw\nl29/u4OZmSAAnZ1TdHc/56tf3Zrkmq1jLS3ws5/NPe/u1tvIpfLs/epXc8nXQW+TvvjFpWdJREoY\nHPTw7W934PPNXWs9PdP89m9vSXLN1qCODviTP5mLvNnVpQ/4/vRPk1svsSLr9lbHpUvDUZWtSffv\nm0Pj9vXpA71U1NsbPrgDvf737yenPkIIkQDXr7uNwV3Io0eT9PevYp7UjebmTXPZ7dv6DF0k09PQ\n3BxeFgxan0ukpOvX3cbgLqS1dYLBQbnWYvbTn5rTKnR2ht8AEWvOuh3gTU76oypbkyLluwvlRUk1\nkeolefuEEOtIpDZmamqdtD2pyKod8Xr15ZqRTE9bDwBTtQ0VJpGvtYBluVjE+Lh1+cjI6tZDxNW6\nHeDV1ZmjbFmVrUnbtpnL0tJgS4ouTdi82Rz1Cax/DiGEWKOs2piMDDtbtkgwrISpqTGXVVUtHkmw\noEBPLxTNuURKsrrWMjPtVFUtM5jPRnbwoLksPV2P7SDWrHU7wHv99XIqK+cu9MrKTD73ufIk1iiO\namv1Cy+UGiEjA37t1+bypKSahfWz2fQ/KJKIUwixjuzfn0dDQwE2m57EOysrjbffrpLAD4l08qR+\nEzGkqAhefXXx92ianr82tAdc0/T2qKEhcfUUcXXwYD6HD89da9nZcq0t22c/qydOD/UpMzPh935P\n0pGscZpSKtl1WFJDQ4NqbGxc1nuHhvS9aiUlKRxhcrmeP9en1ouLrWfIUo3fr+dxyc2V9A6rrKGh\ngeVeQ9HStP+W0POL9UWpd5JdhZjEcg1NTPgYH/dTXp6B3a4luGYCgGfP9DampCT69ygFQ0P6zUcJ\n+LUq4t0WTUz4mJjwU1Ym19qKDQ3p8RLq6lJ3wkCgadpNpdSSd6PWwKhgZdblwC5kreXBS0vTk3QK\nIcQ6lpPjICfHkexqbCwFBbG/R9OgtDT+dRGrRq61OCopie0GiUhp636AJ+JjiCEe8xiFoppqyllk\noDY4qEfI9Plgxw7Yvn31KiqEEELEanRUz4s3NQXV1bBrlz4AjIEXL220McYYRRSxne04kMGH2BgG\nO2/wrOUqms1O8d6XKdy0J9lV2tBkgCeW1EMPl7mMQl/O20knRzjCdiwGbt3des67UISyTz/V90jI\n3gYhhBCpyO2G7353LlR8a6ue3ue116I+hQ8f7/M+k0wCerv5hCe8witoyNJBsb513/kR4x/9yHje\n+/A2vje+Ttm240ms1cYmu1HFkppoMgZ3IQ+IkB/lxg1z+OkbNxYPWS2EEEIky61b5jxgzc2Rw8db\n6KDDGNyFDDNMH30R3iHE+qBUkPFr74eVaUHF8LX3klQjATLAE1GYwpxnaJppAljkm7FqEL1ec+Mp\nhBBCpAKrdkspmJiI+hTPsc6hZ9V+CrGe+H3Tem7JBdT4aBJqI0JkgCeWVEaZqayEEuzYzQdXVZnL\niorWVjAYIYQQG4dVDtmMjJgCsFi1kxqaZbkQ64kjPQutbJO5fIukwkomGeCJJR3kILnMhZB24aKB\nCHvqTpwIbxRdrpj2MQghhBCrav9+2Lp17rnDobdbjugDpGxiEzvYYey309DYx76wtlOI9arq1d+F\n7GzjuVZUzOaTX05ehYQEWRFLyyKL13mdIYYIEqSUUmyR7g24XPDlL+u5VHw+qKxcGzn6NiDJWyeE\nEOgDubfe0vOATU5CRQU4Y0+xdJjD1FHHOOMUUkgmmQmorBCpJ69kO9n/w/+Du/c+Nns6hRV70DSZ\nQ0om6XmLqGholBLlchVN0xtIIYQQYq2IQx6wnNmHEBuN3Z5OyZbDya6GmCXDayGEEEIIIYRYJ2SA\nJ4QQQgghhBDrhAzwhBBCCCGEEGKdkAGeEEIIIYQQQqwTEmRFCCHEqot3FFel3onr+YQQQoi1Smbw\nhBBCCCGEEGKdkAGeEEIIIYQQQqwTMsATQgghhBBCiHUiYQM8TdOOaZp2WdO0i5qm/cWC1yo0TfvV\n7OuvJqoOq66vD65dg4cPwe9Pdm2EEEKI1DY6Co2NcOcOTE8nuzZCpJ5gENra4OpV6OlJdm3EGpHI\nICtdwDmllEfTtH/QNO0FpdT92df+JfDHwD3gJ8AHCazH6rh0CW7cmHt+8ya88w44ncmrkxBCCJGq\n2trgZz/TO7Cgd2DffhuKi5NbLyFShd8P3/8+9PbOle3eDZ/5TPLqJNaEhA3wlFL98576gcC85/uA\n/0UppTRNm9A0LUcpNZGouiTc+Lh+B3K+4WG4exeOHk1OnYQQYgOJZ1ROici5CoJBuHBhbnAH4PHA\nlSvw5pvJq5cQqaSlJXxwB9DcDPv3Q1lZcuok1oSE78HTNG0fUKyUap5XbFdKqdn/PwYUWLzvG5qm\nNWqa1jg0NJToaq7M0BAYP86CciGEEEKEm56GyUlzubSbQsyJdD0MDq5uPcSak9ABnqZphcBfA7+3\n4KX5s3m5wOjC9yqlvqWUalBKNZSUlCSwlnFQUgKaZi4vLV39ugghhBCpzuWCnBxzubSbQsyJdD3I\ndSKWkMggK2nA3wN/tGC5JsA9TdOOa5qWBeQqpcYTVY9VkZsLR46ElxUXw759yamPEEIIkco0DV5+\nGWzzuiEZGfDii8mrkxCppq4OKivDy/bskeWZYkmJDLLyDnAE+DNNn936V8BvK6W+CVwH3kMfYPo1\nTfsNpdQPEliXxHvpJdi2Dbq79QFfbS2kJfKfVwghhFjDamvha1+DR4/A4dA7sxkZya6VEKkjLQ2+\n9CV4/BjcbqiogKqqZNdKrAGJDLLyj8A/Lii+Mvva36PP7qFp2jXWQxRNgPJy/X9CCCGEWFpeHhw+\nnOxaCJG6bDb9ZogQMdCUVXCQ1fpwTdsG/Dul1OcXO664uFhVV1evTqWEWIc6OzuRa0iI5ZNrSIiV\nk+tIiJW5efOmUkotucUu2WsIvwh8f6mDqquraVyYhkAIEbWGhga5hoRYAbmGhFg5uY6EWBlN025F\nc1yyB3hvog/yTDRN+wbwDYAtW7asZp2ESBl99NFGGz58VFHFDnZgS3x2kzWhk0466ACghhqqqU5u\nhYQQQgixoY0yykMeMskkpZSyi12kk77q9UjaAE/TtHJgRik1YvW6UupbwLcAGhoakreOVIgk6aWX\nT/gEhf71H2KICSZooCHJNUu+Vlq5xdxNrAEG8OJlJzuTWCshhBBCbFSTTPIhH+LDB8AIIwwwwGu8\nhoZFOrUESuZUwBeAHybx84VIaS20GIO7kMc8ZoaZJNUodXzKp1GVCSGEEEKshnbajcFdiBs3Q0RI\nWJ9ASZvBU0r9bbI+W4i1wIvXVBYkiA9fUqb7U4nVv41VmRBCCCHEaojUD0lG/0Q28wiRoiqpNJXl\nk08WWUmoTWqpoMJUZvXvJYQQQgixGqz6IXbslFK66nWRAZ4QKWo3u9nMZmPddi65HOd4kmuVGg5x\niBJKjOfFFHOIQ0mskRBCCCE2skoq2c1u7NgBcOLkJV7CiXPV65LsKJpCiAjSSOMEJ3DjZpppmaGa\nJ5NMXuEVJpgAIIecZZ/Lg4d00iU6aRx48ODAYTRuQgghRCoJEmSGGZw4lx34xIcPhbLcLrOPfexk\nJ895Ti65SWsPZYAnRIry4+d7fI9mmgkQYBObeJd3w2auNrqVDOwGGaSRRsYZx4mTfexjO9vjWLuN\nY5hhbnCDMcZw4mQPe6ijLtnVEkIIIQyddHKXu0wzTRZZHOaw5ZaPSPz4aaSRbrpRKCqo4ChHTTN0\nztlHMskAT4gU9Ut+yX3uG8/76OO7fJdv8s0k1mp9mGGGi1w0ol158XKDG+SRRzHFSa7d2uLHz8d8\nbER39eLlFrfIJZdyypNcu8TStP8W1/Mp9U5czyeEEEI3yijXuGZEJ59iiktc4g3eIJPMqM5xj3t0\n0mk876WXRho5wYlEVHlFZE2SECnKKuz/AAO4cSehNutLH32mUMYA3XQnoTZrWz/9lqk7euhJQm2E\nEEIIsx56TKmnAgTopTemcyz0hCcECKy4fvEmM3hCpAAfPlpoYYghcsihnnrSLC5PDQ0HjiTUcH2x\n+rddrDwSN25aaMGLlwoqqKV2w+3li7S/QPbhCSGESBXRtFUBAjziEX30kUEGddRRSOGi57BjX/Uk\n5tGQAZ4QSaZQnOe8MTM3wAA99LCPfXzAB2HHbmf7ivadCV055WSRxRRTRpkdO9VUR32OEUb4kA8J\nEgT0maxnPOMYx+Jd3ZRWRhm55DLOuFFmw8Y2tiWxVkIIIcScaqp5yMOw1TuZZFJFlfH8Otfpost4\n3kMPr/CKMcirpZY73Ak77za2peSN3dSrkRAbjNWySy9eSijh1/g1iigihxwOcpDf4reSVMv1xY6d\nc5xjK1tx4aKccs5yllxyoz5HCy3G4C6kk06mmY53dVOaDRtnOEMNNbhwUUopZzhDPvnJrpoQQggB\ngAsXZzlLBRW4cLGZzZzlrLEqaoop0zaNAAFaaTWe11PPIQ6RTz655LKXvRzgwKr+HNGSGTwhkizS\ngGCaaU7OPkT8ZZG1oryCVr83hWKa6ag3bK8XLlwbbuZSCCHE2lJIIac5bfnaNNOmPXqgp/+Zr272\nkepkBk+IJCun3HJ6fxObklAbES2r348Ll8xcCSGEEGtMAQVkkGEqX6t9MRngCZFkmWRylKPGMgEN\njXrqJbF5itvJTjaz2XieSSbHOZ6Sa/GFEEIIEZkdOy/xkjHI09DYylZqqU1yzZZHlmgKkQKqqaaS\nSsYYI4usZS/xCxJEoSSC4SqwY+cEJ5hkEg8eCinc8IM7P/6UjSgmhBBCKBQBApZRs0sp5U3e5BnP\nyCSTLLKSUMP4kAGeECnCgWNFSbabaKKFFnz4KKWUoxxd03+c1ors2cdG5sZNI424cZNJJnvZy3a2\nJ7taQgghhKGHHu5whymmyCOPQxyijLKwY+zYV9QXSxUb+3azEOtEBx3c5z4zzKBQDDDARS4mu1pi\nA/Dj5wIXjEiw00xzgxsMMpjkmgkhhBC6Mca4zGUjPdIYY1zkoimIynohAzwh1oFOOk1lo7MPIRJp\ngAG8eE3l83MJCSGEEMnUQ48pSqYfP730JqlGiSUDPCHWgUh7v2Qvnki0SN+9jb4fUQghROrYaG3V\n+vyphNhgrKI8lVJKDjlJqI3YSMooM+1B1NDYxrYk1UgIIYQIt5WtpsAqTpxUUZWkGiWWDPCEWAcq\nqeQYx8gjDydOtrGNE5xIdrXEBmDDxhnOsJnNOHFSRBGnOU0BBcmumhBCCAFAFlmc4QyllOLEySY2\ncZazRoqq9UaiaAqxTtTMPoRYbdlkyw0FIYQQKa2YYs5xLtnVWBUygyeEEEIIIYQQ64QM8IQQQggh\nhBBinZABnhDLsDDUbryOTeQ5hDX5/QghhBDrRyq0ycmug+zBEyIGffRxl7uMMkohhRzkICWUWB47\nxBC3uY0bN3nkcYADbGJT1J+lUDTRRBtt+PBRRRWHOYwTZ7x+nA3NjZtb3GKYYXLIYR/72MzmmM7x\n6ezDi5cKKjjMYVy4ElTjxAgQ4B73eMxjggSpppqDHDRFGxNCCCFSVar0mbro4j73mWKKYoo5zGHy\nyY/6/X783OY2nXRiw0YNNexnf8xpr2QGT4goTTLJJ3xiJA934+YCF/DgMR3rwcMFLuDGDcAYY1zk\nIpNMRv15bbTxgAd48RIkSDfdXOFKfH6YDc6HjwtcYJhhACaY4DKXjd9XNLro4g538OBBoeill0/4\nJFFVTpgHPKCFFnz4CBCgnXZucSvZ1RJCCCGilgp9pmGGucpVJplEoRhiiAtcIEAg6nPc5jbttBMg\ngA8frbRyn/sx10UGeEJEqZtu00Xqx88TnpiOfcIT/PjDykJ/cKLVSaepbIABppmO+hzCWh99ePGG\nlSlUTL+fDjpMZW7cjDO+4vqtJqufo4suggSTUBshhBAidqnQZ+qk07Q0c5pp+umP6v0KZflzWJUt\nJWlrcDRN+x3ga4Ad+IpSqjdZdREiWn78POMZHjxkkkkBBWhopuOsyuIlkefeKFLp37CHHp7whHTS\nqaWWPPJW9fMj/Vuk0r+REEII8YQn9NBDOulsZ3tUSx9Xsy1L1Gct57xJmcHTNK0SeFkp9YpS6owM\n7sRaUEklXXQxwABjjNFPPz30UEGF6dgqqkzJM+3Y2crWqD9vG9tMZRVUkEFG7JUXYTaxyfTvqKFR\nTXXU59jOdlNZMcXkkhv1Oe5xj0tcoosu2mjjl/wypmWi8WD1PauhRgZ4QgghUsYDHvAJnxjt5fu8\nb2yzgNToM1m1nS5clFMe1fs1tIhtcqyStUTzs4Bd07QPNU3795qmxbZzUIgkGGGEcsrJJBMbNly4\nKKU07A9MiBMnZzhDCSXYsVNIIS/zMllkRf15tdRygAO4cOHAQQ01HONYPH+kDSuNNM5whnLKsWMn\nn3xOcjKmjdCb2UwDDWSRRRppbGELJzkZ9ftnmKGFlrCyAAEe8jDqc8TDHvawm904cZJOOnXUcZCD\nq1oHIYQQIhIfPlPbuLC9TIU+UyGFnOAE+eRjx0455ZzhTEwBUg5wgDrqcM4+drObveyNuS7JWqJZ\nBqQrpV7RNO3PgC8A/zT/AE3TvgF8A2DLli2rX0Ox5o0yakQxWk4UpQABBhnEjp0SSphkkiyyTDM3\nU0xZvr+IIl7hlWXVPaR+9iHiL598znBmReeopZYCCvDgoZRS06ztYjx4CMw+nvMcO3ZcuGIKxBMP\nNmzsm30IIYQQqSbUXi60sP8Vjz6TDx+DDJJJJoUUxvz+KqrIIYdJJimiKOYZRDt2Ds0+ViJZA7wx\n4MLs//8V0LDwAKXUt4BvATQ0NCQ/oYVYMwIEuMpVeugB9IulgYaYprif8YyP+djYnJtLLrvZbXls\nGWUrr7RYc3z4uMhFBhkEwIGD4xy3XLJrJZtsAgRopdVouFy4+AyfSVidhRBCiLUmm2yyyDIN6Eop\njevnPOEJV7lqBMkrp5yTnIw6bVCQINe4RhddgH4D9TCHLbd0JFqylmheBuN28QGwCOMmxDJ10mkM\n7kAf8DXSaIqauJgb3AiLvDTOOH30sZOdxvpqDY297KWAgvhVXqwZLbQYgzvQB3zXuBZTOGQIT4aa\n7MSoQgghRKrR0DjGMdJJN8oKKWQPe+L2GX78XOd6WAT0fvpppTXqc3TTbQzuQB/wNdKYlOjnSZnB\nU0rd0TRtWtO0j4Bh4C+SUQ+xPg0wYCoLEGCYYSqpXPL9PnyWgS766ect3qKWWsYYo4CCmPbUifXF\nKuyxFy9jjEW1rGOSSezY2clOppgijTRcuBhhJBHVFUIIIdasUkr5PJ9nkEHSSaeY4rief5RRZpgx\nlQ8wEHEFl9WxC4Xy4W1hdbebJS1NglLqD5P12SK1TTHFbW7TTz8uXOxmd8Tohh483OY2T3lKOuns\nYlfEQVe0g7E00nDiNM34ZZHFIIPc5S6jjFJEEfvZTxFFMf18YmlTTHGLWwwwgAsXe9gTUwTS1ZBN\nNo00GjkP88lnF7tw4Yrq/RlkGBuv50felJsGQggh1pt22nnIQzx4qKCCgxwkk8yo3z/DDLe5baQV\n2sGOuMYocOFCQzOtpJnfJgcJ0kQT7bQTJMhWtrKf/cYSzpX2P+NJEp2LlKJQXOSi0WkeZ5yrXI2Y\nJDIUYt6HjymmaKSRTDJNQVU2sznqCImhpZcLy7axjQtcYIQRIwDLR3wU09JPsTSF4gIX6KU37Dsw\nfzlkKphmmg46mGGGIEHcuOmgI+oN1emks5OdYWV27OxiVyKqK4QQQiTFE55wgxtMMokfP9108wmf\nxHSOq1ylgw6jv3eHO7TTHrc6unCZUhQ4cIQNIptppokmPHiYYYY22rjNbeP17Ww3DVo3sSkpEwFJ\nm8ETwsoII4wyairvoMOUR2SccYYYMh37lKd8ls/SRhtTTFFGWUz5zQB2sINssummGxs2trGNAQZM\n+6t8+HjCk6RsoF2vhhlmnPGwMoXiMY/jvqF6JVpooYACppkmSBAnTnz4eMrTqAOt7GMfBRQkNdG5\nEEIIkUiPeWwqG2GEMcaiavOmmaaPPlN5Bx1x7X810EAJJfTRRwYZ1FJLDjlhn7dQJ50c4hB27GSS\nyWf4DG20MckkpZQuK4ddPMgAT6wJQYIRX+ujjyGGSCedKqpQKFy42M9+07Gh5OTppFNDzaJJqTfN\nPkKs1lYvVTcRu0iBRlItAIlCkUZa2B9/IOYgK2mzDwcOy1w5T3nKFa7gxct+9sd1U7kQQgiRaPFo\n162OjXf/S0Mz2uQ00kxtslUdFpbZsIW9f2Hi82gMM0wXXWho1FCzrGB+MsATKaWIInLJNc3gWN0B\nySWXbrp5xCOjrJ9+XuAFy3N/yqfc4Y7xvJVWznEu6qnzLWyhiaawDnwaaWxmc1TvF9Eppphssk35\n4GKdhU20/eynl96wsmKKY/o+NNHEfe4bz9to4xznjD/mrbTyD/yD8Z1rppmXeZnXeC0OP4EQQgiR\neNVUm2bgCiiIeutMJpmUUWa60R7v2bHb3KaFFuP5Ix7xKq8aN3KrqaaZ5rD3bGGLMRD04uWX/NJI\n5/CYxzzlKSc4EXUduujiKleNgWMbbZzkZFRBAueTPXgipWhonOIU5ZSjoZFJJkc4YrnkbZxxxhgz\nwubasOHCZRnS1o+fJprCygIETBfqYrLJ5iQnjeUEhRRymtMxJ7EUi7Nh4zSnje+ACxdHOBI2m5oK\nXuIlTnGKDDLQ0NjKVr7KV6N+vw+f6fu3sOw8500zgpe5bBnpSwghhEhFW9lqBFXR0KiggpOcjOkc\nxznOZjajoeHEyQu8QC21cavjNNOm/qMXL5/yqfF8D3uop95YcVNDDYc5bLzeTrspV18PPZaR2SO5\nxz1T+qQHPIj1x5EZPLF6Jpmkgw5KKQ1LDh4KTVtEEXbs5JDDGc6gUItObQ8zjIZGHnnMMIMdO2mk\n8YxnpmO9ePHhM5VPMBHTzxBatrlU3cTK5JIb1XdgpWaYwY2bHHIso1wFCTLCCHbslqkPPjv7CBLE\nFuF+mQcPo4ySR17Y5utppi2Xc86fuRxjzPS6Dx/jjIeFiH7Oc8YZp4ACU4AhIYQQItl2zj6W265n\nkMGLvMgwwzhxRj37F60pplAoggR5znMcOHDiDGuT7dg5MPuw+jki9SknmAjrQzznOe20m/rDQYKm\nAeJi512MDPDEqrjMZX7JL/HjR0Ojnnre5m2ucMWIkJlBBic4QQklAEv+AaiiCtA36obWYaeTziEO\nmY514SKLLNOFE/qsWMngbnUk8t+5nXZucYsAATQ0aqnlEIeMzxxllItcNL4zi83YRhrcNdPMAx4Q\nJIiGxh72GBFas8kmk0xTAtT538lKKk3LlXPICWso7nCHFlpQKOzY2c9+6qhbxr+IEEIIkVjLbdeH\nGeYSl4w2s5RSTnEKB4641CuPPDx46KDDuPmaQ07EHHhWP0cJJaZALBpaWLt+lav8nJ8b/eE66vgK\nX8E2+yimmGGGw86xnABzskRTJNwoo/yCX+DHD+jTzQ95yPf5flj6Aw+esHXHS0kjjXLKw46PtCdO\nQ+MIR8L+EOSSa0qHIDaG5zynkUbjj7hC0UZb2J6661wPuyHgxs1d7kb9GaOMco97xs2H0DKL0B9u\nGzaOcMTInwOQT35YEJXXeT0swpgDB2/whjGg7KOPT/nUuAYCBLjN7WXd7RNCCCFSkUJxjWthN0QH\nGTRtvVkJGzaCs4+QAIGIN3CtbGVr2F45DY397Dfy404wwc/4WVh/uIUWLnPZeM9hDoetxHHh4gAH\nYv55ZAZPJFwrrZZL0VppNc00TDHFOONRhc0dY4xiinmJl4womiWUWC5rAyinnLZs3lwAACAASURB\nVM/zefrow4GDMspiunDF+jHAgOWNhH76qaLKWLpp9Xq0Ih3bT7+xvLKCCj7P5+mnn3TSKaMs7K5g\nIYX8AX9AE0148bKXvWGJ1K0iuyoU/fSbonsKIYQQa9EUU5Y3LmNpk5fyjGe4cLGTnUwyiQMHLlyW\n6bgisWPnFKcYYYRJJimmOGz7Rwstlv3hdtqNPYkFFPAmb9JPPxoa5ZRbRtheigzwRMJFWidt1QEN\nDbiucpU++sgii13sspyVCwW3cOI0lmuCfrejl16aaDLy4B3gAC5cOHCwhS1x+snEWjV/kGRVbsdO\nOummYCYL33eZy1zhCtNMs41tvMmbxvd6YbLTSOdIJ33R72QaaZYpP2L5DCGEEGKtCs1oPeUp44xj\nx04RRXENvpZJJgEC9NPPJJOkkUYxxabk59Eomn0sFCndwcL+cBppYf3a5ZDpC5FwddSZomA6cfLr\n/LppBq2WWq5ylU468eLFjZvLXGaQQdN5M8k0XXh27JRRxid8ghs3Xrx0081HfJRyedRE8pRSGhak\nBMK/T3bs1FMf9rqGFrYW/xa3eI/3eMYzPHhoppn/zH82Xq+iyjQTnU12XNNqVFNtGswVUphyEUeF\nEEKI5XLgwI8fN278+PHipY8+ssmO22eE4jSMMoofPx48POVpXIO5bGe7aeDmxMkpTsXtM0JkBk+s\niq/zdS5wgS66yCefM5wxOtmPeIQXL1VUkUuuKUytQhnRhhZqoIFCCnnKUzLIYAc7aKPNNJgbZ5wh\nhpa1UVWsPxoaL/Myj3jEEEPkkksddWEBVHazmxxy6KYbO3a2sz1so3Qjjabz9s0+NrEJO3bOcY42\n2nDjJp986qiL24Zw0BuG13iNVloZY4wiiqijTpYeCyGEWDc8eIxZrdAMXiGFllHTl2uUUXLIoYIK\nJpjAgYNCChliiJ3sjNvnfJ2vc57zdNFFHnmc4cyyA/4tRgZ4Iu6mmKKDDvz42cxmiigigww+y2dN\nxxZSyFGOGs8jrXWev+l1Pg2N7bOPpY4NEGCYYZ7whDTSqKHGMjS+2BgcONg1+4hk8+zDSmiT9ELz\n03GkkUYWWXjxkk12XAd3IQ4cZJONHz/ZZC9rrb4QQgiRqkL9uvzZx8LyeH2Ghkb67CNt9hHrZ/jw\n0UEHE0xQSilVVIXtrU8n3bI/HG8ywBNxNcII5zlvdH5baKGBhrAB2GKKKLJMZ7CVrVHXYQtbTGFq\nM8lkjDHucMcoa6GFs5y1zG8mxFL2sY+nPA0rK6TQ2E8XJMgFLoQtL37MY85yNm6DMD9+PuRD4y5m\nG2100cVpTsfl/EIIIUSyuXBRSqlpu04sfcOlFFDABBN00WWUjTASlsh8KV68fMAHRkCYNtqoppoX\neTFu9YyWrOMRcfWAB2EzGwrFPe5ZRg2yYsPGaU4b+6OcODnAgZg2m25iE4c5bCy3K6KIE5ygmeaw\n43z4TGVCROskJznGMWNWroIKvspXjdf76DM1RqEZ5Hjposu0ROUpTy2jawohhBBr1XGOs4lNaGjG\nCpx45nz14CGDDGNlVyiQy3OeR32Oxzw2RfvspJNRRuNWz2jJDJ6Iq4VJmUG/ozHDTMSIfwMMMMkk\nW9lKGmnkkcervIoPH3bsy9pPtIMd1FKLHz8OHEwxZYqIGKm+Yv14PvsooCAhSxff5E1e53X8+E0J\n0CN9txb+8Q8SZJRRHDhiTm0Q6TPGGaeMspjOJYQQQqSqTDJ5mZfx4zeSgluZYAIfPgooiCmpeig1\nQg01xnJNDS2mfmLoWN/sI4MMbNgYYyyuwVqiIQM8EVfFFJuWV2aTber8gn635Dt8x5gOzyKLd3nX\nWM650v1Kobs8oP9hcOEy3YmxCmMr1j6FopFGHvMYhcKJk2McM0VzjYfQOv2FFkbpDJn/nXPj5hM+\nMb6XlVRynOOW57NSTDEttET92UIIIcRaFql99OHjMpfpow/Q+5QnORkxNcFC+eSTRpoxgAyJpT0t\nppjLXMaNG4XCjp1KKpPSJssSTRFXL/BCWOCSNNI4zGHLuyjv837YWucppvjv/Pe4bpoNsWGjgYaw\nWZwsstjL3rh/lki+Tjppp92IpurFyxWuhAVASbQSSkx7T6upppxyQB+EXuFK2E2HXnp5yMOoP6OS\nSlMQmJ3sjLpBE0IIIdaDJpqMwR3ofcqrXI36/Q4cpv5qAQUxRdBMIw0vXqPvESDADDNR37SNJ5nB\nE3GVTTaf43M85Sl+/FRQYSSoXKiddlPZBBP00UcllXGvWwUVvMmb9NFHGmlUUCERB9ephcFPQL+7\nN8zwquaIO8IRtrOdZzwjn/yw2buJ2cdCffTxAi9EdX4bNk5wgmGGjTQJq70MRAghhEi2+YO7kDHG\nmGIq6ojpNdRQSin99JNJJuWUx7RNqJ9+qqlmiim8eMkiCydOBhgwArCtFhngibizY48qmXMWWQwz\nHFYWClF7gxv0048LF7vYFXFpnQ8f97nPU57ixEk99Yt+dgYZ1FAT2w8k1hyrJcGLlS/XDW5wmct4\n8FBDDW/whmXicatIrU6c2LCZZqyXU8fi2YcQQgixEWWQwRhjYWV27KSTbjx34+YBDxhjjEIKeYEX\nyCU37D1ZZEUd+X2hTDKZYopBBplhhuc8p4yyiDEoEkmWaIqkOclJ09LNXeziDndop50pphhiiItc\nNA0EQy5zmVZamWSSEUa4xCXL2Ruxsexgh2lJRDnlcV26eJe7/JAfMsQQE0xwj3t8h+9E/X4nTrax\nLaxMQ6Oe+rjVUQghhNgI6qk39SlrqTViMUwzzXnO85SnTDFFDz2c53xct26UUEIPPUwxhQ8fo4wy\nxFBS4j2seAZP07QG4BRQAUwDD4APlFLulZ5brG+72MU/459xiUt48LCTnexlL7/iV2HHKRSPeGSa\noZhiynJK/hGPEhJMQ6wdueTyKq/SQgtTTFFOeVzDKQNc57qprIceBhiIOoLlYQ6TRx699JJOOjvY\nQQklca2nEEIIsd5tYhNnOUsbbfjwsZnNYTdRO+gwDeammaaXXqqpjksd+umnhhqGGWaGGbLIoogi\nBhhY1e0hsIIBnqZpvwv8z0AHcBNoATKAk8D/pmnaA+BfK6W641BPkQBPecpNbmLHTgMNlFJKkCBP\neMIznlFAAVVULStNgZU++hhiiCyyjJQIdbOPkFDesCmmmGSSNNLIJz8st15IpLsuVseGPOMZT3iC\nAwdb2ZqUaXOxOtJJ5znPjZQBC2f0FMr4TuaSy2Y2x7QReoYZggTx4CFIECdOHDjw4DGOCRKkl17c\nuMknnyqqwvZ9amgUUcQ00zhxxpwmQQghhEgF44zTTTc2bFRTbdquEA9u3PTSiwMH1VSbtjQ4cDDJ\nJB48VFMdNqMXIECQIOOM48GDCxc55CzaZ4xVKDWXCxd27GSSiQ1bXD8jWiuZwcsCTiilpq1e1DTt\nALADkAFeCrrFLX7AD4z9P9e4xru8yxBD9NNvHFdGGS/z8ooHeTe4ERZUpZVWXuGVsLXRoO8lGmU0\nLBn0CCMc5ajpnPnkk0eeac11pI2s7bTTSKMR3aiZZs5xToJSrEPjjPM3/I0RxKSRRg5ykC/xJeOY\na1yjk07jeSutnONc1Ok5aqnlPveNayi01n4rWwF9AHmRi2GzzKWUcoYzxvXUSiu3uGW83kwzr/CK\naU+AEEIIkap66OEyl8P6V2c4E9e94Y94xE1uGp/xkIec4xx55AHQTTf/if9k5DxupJFXeZWXeRnQ\no07/hJ+EpfLKI4/f4DfiVscKKvg5P8eLF4Bhhiml1IievZqW3WtXSv2/kQZ3s6/fUUp9uNzzi8T6\nkA/DgjsECPBjfhw2uAM9CflK97SNMWaKmDnGGI95bDrWi5dMMo27MjZs5JPPNNZftROcMP6ApJFG\nPfWWm2MDBLjHPeMPA+gzME00LfvnEqnrAhdMESrvcMeYIXbjDhvcgT67u7BsMQUUUEaZMVhz4aKa\naqNx6Zt9zDfIIL30AvqdvvvcD3vdi5dmmqOugxBCCJFMCsUd7oT1r/z4uce9uH2GVR9uYXv5AR8Y\n7W+oXh/zsVE2zjgFFBgrdRw4yCMvpkTmS5lggkIKjZU6TpxkkWXKD70a4rEHrwb4JlA9/3xKqc8v\n8p5q4BrwEJhRSn1mpfUQi/PhY4opcshBoUyzXqDPlFmtQx5jjCqqmGSSIMGYZxdCnxUkaOQDSSPN\nsg5jjOHESS21RrJJGzZGGbU8dy65vKpewTvShz0zi7SsPMvjPHiMOypWdROxCX2HnDgTtszVj59e\neimgIObvXGggN59C8ZSnlFIa8fce6XtmZZxxdrGLXVNbYNoDRYWgaUwySSGFET9jjDE2s5nnPLdc\nZizfSSGEEGuFH78xgPHiNaKhL6st8/lgdBRyc8E5l2LrOc/DBm8h89vsEUYAfTCoUEZOulFGjXY/\nn3yyycaDh0wysWNnjLGo980vJZSuqIACggSNwWTos1dTPNIk/AD4NvBjiClD9ftKqa/G4fPFEh7y\nkAc8IECAdNI5zGGKKDIuhpBIX/BssjnPeQYYAKCIIk5wIur11YUUMsEEvfTix4+GRjHFHOaw6dgC\nCrBjJ0AgbD+UVZh5AAYG4L33cI6NgaZBfT28+irYw/PbZc4+Fs4ERjyviMiNm8tcZpJJNDS2spUj\nHIlrTsEmmvghP+Q5z7FhYy97eZu3o14qvIlNdNARVmbDZizfjfR7jyXSVVGwAOcHF8h/2IemwJuX\nSf/nDpJTlrPouUKfHcqPs/DGg3wnhRBCrBUOHGSQQRNNRh8rm2zLrTWLam6GCxfA6wWHA156CQ4e\nBPQVMhlkhO1xh/D2spRSuuk22lQ7dsooM44ppJBhhhlkkCBB7NgppzyubW4hhXTQYUxOgL7XPhnt\nejyiZ3iUUn+llDqvlLoQ+l8U7zuradpFTdP+IA51EBEMMcRd7hIgAOjLEq9ylTOcCRtApZPOW7xl\n7B8K2cIW+ugzBneg3yVppDHqOqSTjhevsclUoZhiyrT/DvTp7IVJnvPJt46AqBS89x6Mjc09f/gQ\n7twxHWrDxiEOhQ0QMslkL3uj/jmE/ru7xCUmmTSed9JJG21x+wwPHr7H93jOc0Cf+b3HPa5yNepz\nWK39f5EXjT+yeeSZ0hGUUmr6/i9m950ZyppH0GZXjDjHpjn23ggOlWacb+GM+GY2G5G07Ng5xKGw\nTeBZZLGHPVHXQQghhEgF829WevDEFrthbAzef18f3IE+k3fhgn4TH+v20oUrrA9XTXXY1qMgQcop\nN/q6GWQwxZRxTIAA00zHdRVSDTWmvscudiUlgFo8ZvD+UtO0/xP4Jcz9dpVStyK/hT6gbvb4H2qa\n9qFSKn6LdYUhtN9nPoUi5/9n777D2zqvBP9/XwAEQIKdFCkWUSySqN4sW7Ll3msSJ3ZiO7GdsvEk\nmczsTqbs7uzUnZnNtsk+v83slEyS2cSxs4ljJ7E9jnuT5SpZvUuUSIkUi9grSAD398cRyiVACRTB\nfj56+Fi4AC4uLdx733Lec8jiW3yLXezCYNjIRjLJpOb8n3AWzSKKeIZn4vZxlrOECCV1ArfQQiGF\nkThkN26yyKKZ5oQN6uUsp5RSmmkmnXRKKU08O3TuXLRzF6uuDi6Lnx1cxCIKKIhkYCqnfFxZE5WE\nGSSKJW+kMWX1245yNGEoxiEOcRVXJbWPDDL4Jt9kN7vpootaalnEIttr1rOeCioiWTQXsjCuhs6F\neOoaWcISeuklQIAssnB3p8n3coGUOtjCFmqooYOOyPkUazGLKaSQRhrx4KGMMv1OKqWUmjUCBPDj\nZylL6aUXgyGb7HEteaCuTgbpE20vluiyCioooIAmmnDjjrtfDjLIlVzJWc4ywggLWUgGGfTTjw8f\nZzlLBRX00x/JoplBBo00pqyMkgsXN3ETTTTRRx9FFKW0/u74jmXi1gAPAzcSDdG0zj9OyLIsP+c7\ng8aY54HVYF+NaYx5DHgMoKIicVZEdXEePGNuzyY7kl0oVtH5P7GvHd3gduNOujHswYM1MEDu+7tY\neOYcI7k+Bq5YjXfhyjHfk33+zwV5vRKWOfqikD7GaEwwSMaOfSw9fhzcblg/AkuXJvU7KBH+d49d\n6Axjf88uRSaZuPxBqt5vIa+hD39WGvWbFpBRPr6Uy510RoqNttFGEUVxx5l//s8l8Xpx4Ihk8ALk\n+xizboDubha8/zELWlqk07d5M+TbP8+HL+U1+pRSSqmp4MCBK+Sg6mcfU/TBSUIuJ2evW0bvPfHt\nyzGN1W7zRssgBAlykpOc4Qxu3Dhw2AZu3bhx4bJtc+CIZMYOt18yz/+JfMSoUgsTZTCUUZbSfV6K\nVHTw7gWqLcuKH3IfgzEmy7KscIq7rcB3R7/GsqzvAd8D2LRpU4JuvUpGJZUc5rBt6jyb7HEVAl/O\ncj7iI9u2WmqT7uAVWgUUPbMNc07W/Hk6+vE1vE3xQ7cyobDkrCxYtgyOHIluczhg/frEr3/9dTgQ\nkzWzsRHuvFP2oZKSQQYVVFBPfWSbwaS0g1JNNdf+qhPOngMgs32IgoYBlj3wKKMmwMbUSSdv8EYk\nFKObbjro4GZuTtlxsmGDjC6GYpYeL10qi8NBQk1+/nPoPz/j2dEBDQ3wyCNj38yUUkqpWcSBg6v+\naT/Ot/ZEti194gMYriSmMtGF1dRATo49KisjA1asiDzcwQ7b2vpWWrmaqymnHJB2aRtttgHoaqoj\ny4GqqOIwh23r+DLJnBGdscmQig7eHiAXEqStG9s1xpi/Qmbx3rEs64MUHIdKIJ10buImDnGIHnoo\noIAVrBhXQowaanDhoo46LCwqqaSa6qTf335mLwvOGQbIiGTRzAhk0L//Q7h27Fm8pNx6KxQWSkM7\nI0Ma3eXl8a/z+2V93mi7d2sHb5yu4ApyyKGJJjx4qKU2LvRwQlpauOZsDSeQDFkePCwOLSZ/bzvJ\n9s+Oc9wWiw9Sj6ad9nElUrmgsjL49Kdh1y4YGICqKti4MeYgjkc7d2GDgzIgMdYghFJKKTWbDA+z\n8N06hshiiCEMBi9evK/vTb6Dl5YG998PH30k6+4KCuCKKyIzeH78CcsYHeVopINXRhnXci3HOMYw\nwyxiEUuJRml58ETaw910k08+K1mZ0gRxM0kqOnjFwGFjzEfY1+CNWSbBsqwXgBdS8NkqCdlks5nN\nyb3YsuDkSWhthaIiabQaQxFFDDGEhTXudLIBfx8OHLYpcYCgf2Bc+0nI6YTLL5efCxkZsc+0hA0n\nPfGsznPiZOX5P5PC7yeNtPg1ff74MhdjSVR+AEi4tm8iRsqLaShfiR8/pZSSG3tJHet4R29vb5cB\nCq9XBhs8qQt3VUoppSZVIIAJBCLZyiPGcc8GIDMTbrgh8UcQkJm5jg5cJxqwPG4CtdWMeOz3+pLz\nf8aSRdb4s3vOUqno4P15CvahZgLLgueek8ZmWHU15+65krfM25FG8z72cTVXX/AkilW4aCPt7p/G\ndaZyl16kU5ZKmZmwcCE02wu5s2TJ1B2DSk5pqYQwDo4qbj+O9ZLllNNAg22bB09KZxoHGOA1Xosk\nndnLXjayMRquWlMD77xjH1gwRraH7d8Pr70WXUf6/vvw2c9KqIpSSik102VkSFvq2Khs2mvXpuwj\nfPjIPtiI55W3IpmrA+8fpPCzv80Ul5ebNVJRJqEB+CCmPMKHELNAR80ep07ZO3cAdXUcO/mKbUYk\nSJDdxJciGEuax0fxPV+NNlrdbnxbb6Oocgo7eAB33CGdPJC1eitXXnzmT009lws+8QnIzY0+vuKK\ncYXSVlDBKlZFQi+yyOIarklpKMYhDsVlFN3L3ui5kpMDt90mNz+QGbpbbpGQYpBZ5XfesScJ6u+H\nDzRiXSml1CzyzW9CZaX83RhpX335yynbfTDgp/Tt45EyRACZAw4yPtAE/GNJxQzeU2DLXR48v01b\nzrNNa+JllMNtZ6Dansm0m26CBJNuMC9YtIHCR9fS33gUb34pLt80zFDk5MADD0Bvr8R7x2RnYmRE\nGtfZ2dL5U9OrpAQefVT+rbxeyXo6TmtYw3KWM8wwGWSMqwRCMjrpjNsWIEAvvdHMnLW1MrLZ1yez\nyM6Y86WnB4bOL/b2++U5l0vKLIw2OCjf0eyLZJZVSimlJkMoJPetjIz4e3JxMfzX/yrr59LS4rJF\nR/j9ct8bZ5TKQF8brqER8sknSBCDwYGDYOvZS/xl5r5UdPBcsRk0LcsaNsaMvzWmpl9R4vA1d9Gi\nuG055IxvNqShAfPqq2T29MjJv2mTpIyfDlmjCk5+/LGExg0PSyP8xhuhOvkkMmqSGDPhDk3a+T+T\nIY88zmHvjLlwxRc0dToT38xycuSGeeCADC4YI7N7MVnDCATg1Vfh6FF5bXGxZH7VEE6llFJTpb5e\n7kXhAfIrrkgcAVU8Ro4Gy5LC5Xv3yr0sP1+iqs7XjL2YjMwF4E2HoUFb29NVNDczYKZCKqYq2owx\nkYQqxphPAgmGoNWMV1lpXx8EUF3NsspbbI1kJ07WM44sgH4/PP+8jPyAzES8954kc5luZ87A229H\n1wf29cELL8Sv/1JqlJWsxIcv8thgWMva5DuULpd04MKZNi1LUkTHJln58EM4fDi6jq+lBV58MUW/\ngVJKKXUR4TZc7/nqZiMjsH27dPqStW+fZC0P38s6OmSfiYqbJ+B0eci79pMyEBrmy6R0873JH8M8\nk4oZvK8BTxhj/u784zNI4XM12xgDd98tJ21rq4ysVFZSYAx3cRcNNGBhsYhFZDCOotOnTyfOVnn8\nuGTpnE7Hj8dvCwRkPWLsTIpSo6STzu3czmlOM8TQ+Sya41jt3dUlYS7r1kFnp3T48vPhbEzISaLv\n59mz0in0+eKfU0oppVKpvl46daMdPw6LFye3j9EJWEAGNNvaxoweG61s5c1klyyj/cSHOD0+Fi67\nljSP3gfHMuEOnmVZJ4AtxphMwMQUMFezkTEykxdeLHueF298Mev+fqlZcvas1Cy5/HLIy4vf51hp\n38ebDr6xUWqO9fXJ8V12mYQKTESqjk3NPZYl4SRHjkjna+3auKyraWdaqP54v9TBq+6TOniuJC+r\nbrecb+np9sLnsd+9RN9DhyP5z1BKKaUuprVV2nM9PbBokbTnwvcfj0c6eE1NMovn8Ugd2PG0k8Z6\n7TjX12flVZC1qeLiL1SX3sEzxnwBeNKyrBCAZVl9o56vAUosy3pnYoeoZqRgEH7xC5l5AAkdq6uD\nhx+On1koL5fZwLa26DaXC1avTv7zmpvh6aej0/vNzXJB+sSY5RaTs2qVdBpjZxjz8uI6uGoe2r4d\nduyIPm5okKyY4ZndM2fgmWfs38m2NrjrruT2n5EhSVgOH45uM8ZeBH3DBvuMHsh3VgcglFJKpUJ7\nO/z85xK9BNKea2qSkj0gbbjTp6OJ+Pr6pKP38DiC9davlzZibNmgqqpotmyVchMZBi4AdhljdgI7\ngTbACywBrkPW4f2HCR+hmplOnox27sKGhuDgwfiFt8bAvffKeqLTp+WEvvzysbMsJRIbux1WVyfH\nkGjWMFnZ2XD//ZKavrNTarBt3qyZNOe7YBD27InfvmtXtIOX6Dt5/LiMgCabHObmm+V8OHFCsoWu\nX29P8LNsmZw/e/bIIMTSpTJzrZRSSqXCvn3Rzl1YU5MMLpaUSPTUokVyL+rpkYiTsjIZ9FwUn4Qv\nofJy+NSnYOdOif6qqpJELWrSXHIHz7Ks/+/8ursbga3AWmAQOAQ8bFlWw4XeP5uNMMIZzjDCCGWU\n2RItzEiWJTHUHR1SB660dPz7GBqSxmsoJGFqYyUhGRyUBbknTkiDtKZGslZmZMD111/673Chz5tI\nBw9kdvHuuye2j/mmq0s6+enp8n2YhJDBLrpooYUMMiilNKU17CKam+VGFp61DS/gDgQSrzmI/R6G\nSxzEsiz5/ifL5YItW+RnLEuXXrjIeyrOb6WUUvPT4KDcRzo75f6VnS2RWOH73eCgLIcZnTNhdLus\nv1/aiS6XtAtGR5qUlsp7BgbkfptgiU0bbbTTTi65FFOc8vJG88mEWmWWZQWBV87/zAt99PEarzGI\nfLF3s5stbKGCGRoTHArBs89K0pCw1atl5iBZra0SihZu0G7bJu93OOJnMAoL4Uc/khMYJEPlXXfF\nZ+ccr5qa+IxNPl+0cLmaOgcOSLrkcParnBwJ5Uhh0o+DHGQv0QKmeeRxIzemtuTBW2/JjFxYeITR\n5ZIbU3m5hGHGiv0eV1fHP5+TEy1kPhVScX4rpZSavyoqZMlNOKMzSGeuvFz+vnhxNOtzrNj7YX09\nPPdc9DXbtsFnPhMtgzAwAE89FY38evttuOEGWdt+3gd8wEmi2dXLKGMrW3GkJOH//KP/18ZpP/sj\nnTuAECF2sYsQoQu8axodO2Zv/AHs3y8zF8nats0+WzEyImuTbrstWiw8LQ2uvlpmdcKdO5AG6Ftv\nJZ0Kd0xr1shPOHQyJ0dm3TSUcmoND8f/e3Z329eqTdAgg+xnv21bJ50cI0EWrkt17py9cwfSWTty\nJPr41lvt2b2qquDKK6OP16+HlSvt38m77rKncZ5sqTi/lVJKzV9DQzJrF76Xud0SnROORvF6pWZd\nxvns6eHIk9gZvTfftHcAh4ZkHXvYzp32ZT2WJW3L85/RRputcwfQSCNNNKXol5x/NBXbOHXQEbdt\n8PyfGRmq2dIy9vZkZ78S7aOtTabga2okXC8rS2Y9fvjD+Nf29Einz+eTESKvV4o/j4cxcNNNUiC9\nt1fiv2Mb0gMD0smcaFZNdWFdXYlLXiTqUPj9chEPDwIk+xF0JRwwSXTuXbLwYvHRmpsliQnIDe+h\nh6IlDLJGFTB3OKQTuHlz4u9kWCgk38+MjNQPSKTi/FZKKTV/tbTILF1JiYRQZmfLvay1NXrfq6mR\nmb4TJ+ReF3s/HB6Oz8kQ3m+iv4eNjMjSgpIS2mlPeGgddFBO+QR+uflLnDkAzQAAIABJREFUO3jj\nlEsuPfTYtnnw4GV8jdgpM1a42HjCyAoLZZ1SrLy8aCctdl+FhdGC5mE+n5z84aybXq80ijdsSP4Y\nAN59V2ZdRkZkZuXWW+UYXnpJGubhVPbXXDO1syjzSU5O4lCN2O9AIACvvx4t0F1RIbO9SYZwZpON\nwWBhn/UdV425ixnPeTHWGk/LgnfekQQogYB0qG691Z486PhxmfHs7ZXf/9prJXNmqqTi/FZKKTV/\nFRRIG6+xURKMpadLhy72PrJjB/zgB9E23J13RrNspqVJ26C7277f0W3D0UsanM7I/XWs+3sOORP9\n7eatCQ8nG2M8xpiHjDF/bIz5s/BPKg5uJlrFKjxEF44aDGtZOzkJIFJh2TIZlYm1dKmMwCRr61Z7\nEg2HQzpRiVx5pX1hrTHSmXv++egIz9CQNHobxpGH5+BBycIZTnzR2irx3s89F509CgTg448lI5Sa\nHB4PXHWVfZvPZ8+c+v778u8VXp/Z0ACvJL9M14eP5Sy3bcsii6VcINHIeBUVSXjl6G3jKW6/b5+E\nnYQ7u83N8j0P6+mBF16Qzh3I7PVLL8mIZaqk4vxWSik1f2VnS5sqGJTHg4PRQUmQv3/3u/Y23DPP\nSN08kHbeNdfYI1TS0uxthcsug8xM++du2RKJ8CmmmDLs960iinT2bgJSMYP3a6AbKZUwjvRxs1MO\nOdzBHZziFAEClFOe2pmFVHO54L77ZK1Oe7vMMsSmYU9GWRk88oisTwqFZAZirNolCxbIaw8fls7Y\nkiUyqpMo4+DRozK7k4xjCdZfnT0rxzP6onH0qG3hrkqxjRslG1Y4i+by5fYwzET/VvX1ErKZZP22\ndayjhBKaacaHjwoqUptgBeCWW6SD1Ngoo4jLlo0vG2ii37OjQ8KXFyyQUJbRSYhCIZnVS1V66FSc\n30oppeavhgbJcdDeHs2imZMjM26VlVJGKlF26HfeiQ7uLlkidfGOHJH7Um2tPYwzK0ueP3w4WiYh\nZnDSYNjKVppoooMOcsihnPKZO3kyC6Sig1duWdbtKdjPrOHFGzfDMKM5ndIIn4js7Pj6dmPx+ey1\numKTrsRKS5PG9e7d0gGsqZGOWaJ1SonW1o21jk/X4U2+hQvHXuOV6P+/wzHu9WdF5/9MGmPk5nWh\novYHD0ZvWGvW2F871vfM7b7w86n+fqbi/FZKKTU/paVJB663V/5rjAych+9V6emJ3zd6fX1e3oVL\n/ng8sG7dmE87cFB+/o+auFSs+H/XGLMmBftRc9WiRfFFzZ1O2fb00zL7cPq0ZGF6443E+1i7Nn5d\n3ZIl0YQYYcZc8AKipkCi//+rVs2+jvf778PLL8vs44kT8Ktf2WftEn0nKytl5BMkVHL0jdHrTe0a\nPKWUUmoiKipkMLOtTZYWnDkja/LCNVU3b07chrv11qk/VpW0S57BM8bsA6zz+/iSMaYOCdE0gGVZ\nlsbIKWEMfPrTkiSlsVEawFdcIevlRoewHTggcdujG8bl5XDPPbLmqa8vmrLeGHjvPairkyyFl112\n4RkZNfnWrJF/l717JUy3tjb52d+ZIhiU7+doO3ZEi45XVkqpjh07ZJa6uto+eunxwP33y/c+HLa5\neXM01bRSSik13Rob5b7W2CgzeDk5UFws6/KKiyWC5U//FB5/XNpa+flS426i9Y3VpJpIiObdKTsK\nlTqDg7I2yuWSBmd4TdHOnRJnvWLFxcO5mpvlxC4qSl2q9czM+NGebdviXxcKye+QKCSgujrx+qLr\nrpMfNXOsXi0/08my5Dvf0yOzyGOtG00kEJDUzyMjUhrC4ZD3xxaCBUktffq0vGbZsvg1hvn50glU\nSimlpsvwsHTOLEvaUbH3qoEBWYbT0yODs3l5stQg9n5XUgJ/9EcX/oyhIfkMp1M+Y7ZF7cwxl9zB\nsyyrHsAY87hlWQ/HPmeMeRx4OOEb1eSpr5eskuGsfllZ8KlPwd/9HRw6FH3d1VfDN7+ZeB8vvyxT\n9WErV07eNHxVVXxtlNzcsdPSK5Ws4WH45S8lEQ/ITevaa5MvzeHxSDjlhx9GZ5ndbhm1DOvogD//\nc5mdA/j5z+ELX5D00UoppdRM0NYmy2HCye48Hrj33ugA/sKF0k4Mlzk4cEDafl//evKfceYMPPts\ntE6uzyf3y9GhnWrKpGINnm0RlDHGCVw2xmvVZLEsqT0WW5+stxf++Z/tnTuQzEejt4HMRMR27kAe\nnz6d+uMFCadcsiT6ODsb7rhDa9ipidu3L9q5Azk/tm2Ln4G7EMuyj3J6vdE00gA/+1m0cwfSEfzZ\nz8ZOKqSUUkpNtbfftmcy9/sl50HY4cP2e5vDIfe2rq7kP+ONN6KdO5B77fbtl3zIauImsgbvPwJ/\nDKQbY8KVrQ0wDHwvBcemxmNwMDr6EgxKJ8nhkJIBII1Vy4pmMjxwQMI1wx1Cl8veII7V1CQhbqmW\nlibha+EyCgsWjDvTolIJhb/LliXng8slHbDWVpk5juX3y+xc7MDC0JBsX7tWOmwOh4QNt7dHX1NX\nF/2M8Lnl98v26Q5PVUopNXdYlnSgkiw1ZJOobdfcLPs0RpKI5ebKrNvIiNzrjJGJgOLii+9/eNh+\nbwxrahr/saqUmUiI5reBbxtjvm1Z1n9M4TGpS+H1SiP2wAEpRulwRNfQ7d8vnahwQzc3V7Ij/eY3\n0ayAy5fLeqJECgom99hzcqKZB5VKhfx8uak1NckNKzNT1gTEhos0Ncms97lz8vxVV0WLn3s8sq2v\nL1rsNbzfsIUL5Xzr65POo8cDhYWSEEgpNesZ81RK92dZ96d0f2qeOHRIZsP6+qQ9dsMN47vP5OfL\n4ObobeFBzdJSuRd2dMi9zO2WbWO1CUdLS5MlQb299u2T3XZUF5SK6ZKnjDEbR/3UGGNSUWNPJcvh\nkJO1s1Meh2crVqyQ0ZXw9HsgIK9rb48WLg+FJBSzsTH+olFWpoWT1eyTkyMjlCMj8rivTxaQZ2bK\nY78ffv1r6dyFn3/lFfuavauuss/quVySuTVs3TqZOQ+v0fP7pYM3nmQuSiml1FhaWyU3Ql+fPG5v\nl7VusSGXF7N1qz06yuGQbWGlpRKOGb6XDQ9LZ62sLLn9GyP7G32/vFBNPDXpUtEJ+3tgI7AXCdFc\nA+wBCowxX7Ms6+UUfIa6GL9fOm+rV8sFwOmUkMcTJ+Cmm+S/3d0yolJVBR98II3RWEePwle/Kh2/\ncFr3Zcs0bFLNPqdPS7mGtrZo2ue8vGi48alTsj2WZck5UFIij1eulHPk6FG5Wa1YYZ9p7u6Gm2+W\nc2toSG6GZWXSaRx9bimllFLjdeSI3JtihTNihiNOLmbxYkkAFh7Ur62136P27pWB/I4O2Xdmptzr\nduyQgc5kLF8u7csjRxLfL9WUS0UH7xTwFcuyDgAYY1YCfwj8FfAMMGYHzxjzLeDTlmVdnYLjmF/O\nnJEsga2tcrLefbeMnmRmRmcpQE7WtDR7aQTLkm0dHdIYNUY6c+Xl0jFM9qKh1FjOnJHQ4EBABgmW\nLRvf+4NBuek0NEjox4YN8dlVT56MJgVaudK+ts7hkJtMuLMW5nTKf11jXPrCz4cVFclPIi6XhG+u\nXRu/XSmllJqose4n473P9PZKhJdlSTRLbAfP6bSXBvL75XVu9/g+Y8EC+VEzQipaIsvDnTsAy7IO\nGmM2WJZVZy6QDdEY4wHWpeDz55+zZ6Xo5OCgPD52TBrTN94YnwXzppukYHNsdk2fTzJWvvJKdFt7\nu3bsVGrU1Um5jvCo4/HjEv5xxRXJ7+OFF2RmLOzIEXjwwWj444ED9u/vsWMymxZObrJ6taxbiB35\nLCyMdvgWL45fMzDewY01a6STGWu89faUUkqpsaxYIXWMY9twmZnxycIu5OBBCfMMO3ZM2ovhwcl1\n66SIeThEM7z0YOPGiR+/mjap6OAdMcb8A/D/zj/+HHD0fAdu5ALv+zfAj4D/nIJjmF/+9V+jnbuw\nU6dk0exll0VDytaulZmPykp47z3pxJWUyJT7009Hw8nCM3ij96nUpfjoo/iQkp075bs5eoYskXPn\n7J07kBHFPXuiBe0//DDx54Y7eKWlcM898rqeHqiokPqP4UEnl0tq9LzzjoRt5uXJ+rrx1Oyprobb\nb5ffbWBAbrhXazCCUtMl1UlRlJp2ublSz/j99yXqqrRU1ruNp4j4Rx8l3hbu4B05Im3Dc+ekI+n1\nyr3wyBHpYKpZKRUdvC8C3wD+HbIG7x3gD5DO3Q2J3mCMSQOusyzr/xhjEnbwjDGPAY8BVFRUpOAw\n55COjrG3X3edpLV1uaIZkEpL7QWaQyHpzJWWRjME5uaOr0aYUmMJLwaP5fdL6EcyHbzw+/1+6Zx5\nPDLjHDvbNjpbV6Jt1dXy3p4eCT+ODV0G+c7ffffFj+dCli+3hz8rpZRSqVReDvfdd+HX9PXJ2vOs\nrPhkeeFMz+G6djk5si1cJqGzM3E285YW7eDNYhPu4FmWNQj87fmf0RK09AB4GHjyIvv9Hufr6W3a\ntMm60GvnnfXrJewylsMhadt/+MNo8oicHOnYZWfHvzYnB157LZpl0O2Gu+6a/GNXc9/ixRIyHKu4\nWEYFk1FSIoMVx45FZwKzs6OzdyCz0uE6dLGfGzYyIpnGTp+Wxw4HXH99/Ho5pZRSajY7cEDac+EQ\ny/Jy+OQno7N8+flSFitciDwtTZY0hCNa1q2Lv2e7XBqiOctNOD2iMWarMeYVY8xRY0xd+Ocib6sF\nvm6MeRFYZYz5nYkex7xy882ynil8cqalSYakXbvsmQG7uyU0MxHLss+muFzxYXVKXYqtW2WwISw7\nG265Jfn3h0Ly3YzN3up2R29eIJ212Bo7+flSGyhs//5o5y68z7fe0jBkpZRSc4ffD2+8Yb8/njkj\nSxpixSZlGR1Jc9dd0pkLtyndbvjiF+MnB9SskooQzR8AvwfsBILJvMGyrH8f/rsx5h3Lsr6bguOY\nPxwO+Na35CQ+cwZWrZIT9nvfi39tY2P8tlBIOn/r1sk0fTj7Znv75B+7mvvS0+GBB6REwciIdPbG\nKrURCsU/19IiM8wbN0rYsNsts3/hGnUgN54vfEFeCzJDGJvUKdH3PhiU2njjWZyulFJKzVQtLdEE\nLOGQS5B74KZN8vf2doleCYdlZmZKuGb49Q4H/NEfyaBoc7OEZY5e0qBmnVR08Loty/rNpb5ZSyRc\nokOHZHaup0dSyV97rTSCRxe/TJTRLxyi2d0t8dpho9PQKzURF0qXfPQobN8u38GFC2X2rbhYnsvN\nlZuO02kfQRz9XTbGPlMYK9H33hjNcKmUUmruyM2VWby6Ormfut2SQG/Dhuhr8vJkwDW205aTYx8U\nBckCvWjR1By3mnSpqGD9hjHmfxhjrjTGbAz/pGC/aixNTZLytqcn+vjZZ+PT0DudsHlz4n1cdZX9\n5HY4JIugUpOttVXWA3R3y+PmZqnpGF4fkJsbX67A55MZ52StXx8/ArlypQ5iKKWUmjuysqQtGL6f\nDg9LZFfsvW7LFnukjDHJFzBXs1YqZvDCPYhNMdss4MYU7Fslcvhw/Hq5gQEZkbn/fpkdSUuTBu1Y\nad9ra+X1R47I45UrtUClmhqJvr9DQ1LqI1wQ/eabJZFKfb3M4q1eDRkZyX9GZiY89JCsxevpkQQs\nS5ak6jdQSimlpt/Zs1Lj1emUsEu3G4qKJNwynFSspkbqyIbrJC9fHo2YUXNWKrJoJiyFoCaRwyGj\nNM3NMjWflSUntMMhU/NlZcntZ+HCsUPclJosxsj3t6VFOnaZmfL9jZ1RNgaWLpWfS5WRMb7i6kop\npdRMMzAgSVPa26V9t3p1NEOmwyH3y/x8+4D+6LXtCxbYM1GrOW/CHTxjTDHwX4BSy7LuMMasBK60\nLOsHEz46lVhlJfzLv0QzZra3S0NZ6wWq2aCqCn7wg+h60fZ2SaZSWTmth6WUUkrNKEND8P/+X3RJ\nzvHj8nPffdKxKy6WGbxz5+zvW7Vq6o9VzSipWIP3f4GXgNLzj48iRc/VZGlulpAzn09GaXJz5SRv\na5vuI1Pq4pqbpTMX/v7m5EBpaTQjplJKKaUkrDLcuQtrbIyWATJGat4tWSLhmfn5cPvtOuCvUrIG\nr9CyrJ8bY/4jgGVZAWNMUuUS1CXq6Ymfjg9vLyiQEz+cSWl0liSlpltPjwxKjM5oOfomNhMEAnI+\nuVxSPFbPJ6WUUlOltzfx9tj7ZVYW3H33xD+rrU2StZSWjm/Nu5qRUtHB6zfGFCCJVTDGbAG6U7Bf\nNZZFi6KLZcMcDllk+8MfRos5FxTAvfdqPRM1s1RUwN699m3GSAdqJmlpgV/9Kno+5efL+RRbWkQp\npZSaLOXlsGuXfZsxqS1nEAxKZuvjx+Wx0wk33qhhnrNcKkI0vwU8C9QYY7YDPwZ+JwX7VWOprZVC\nlGFOp9QRe/fdaGMUZG3Tu+9O/fEpdSE1NfYbh8MhdRxja97NBK++aj+fOjqkdp9SSik1FaqrYc2a\naPSIwwHXXCNLG1LlwIFo5w6kw/f66/b7n5p1UpFF82NjzHVALWCAI5ZljUz4yNTYHA647Ta4/HJJ\ni1tSImnnX3st/rVnzkz98Sl1IcbALbfApk3Q2SmZXGdaOMjQUOI1reF1D0oppdRkMwZuugkuu0wG\nGYuLZf16KiVqJwaDUmO5pia1n6WmzCV38Iwxnx7jqWXGGCzLeuZS9z0rdXXBW29BQ4PMRGzZIjNt\nkyl2HV4wCF5vNDNhWE6O1BLbvl1m9EpKZLakqGhyj03NXLt2wccfy3elpkZSJ6enT/1x5OVdeuHx\nYFC+0+FQ5RUr4OqrZTY7Fdzusc8npZRSaqps2wa/+IUMOlZUwKOP2qO4Jmqs+5re72a1iYRo3nOB\nnxSs9pxFQiH45S/h5ElpeHZ2wosvTu3smdMZX/PL4ZBO5q9/Da2tcmxnzsAzz0RLLKj55eBBGYjo\n7YWRESk6/sIL031U47d9e7STOjQkndZ33knd/h0O2Lz54tuUUkqpyXL8OPzDP8ia8FAITp2C//7f\noa8vdZ+xdm18FM3SpVJ+Qc1alzyDZ1nWl1J5ILNaU5NkHoplWdKYnsrEERs3yoze0aNSBHP1ajhx\nQi4KsYaGZPvKlVN3bGpmGJ2cByTssKdn5q2Bu5BEv8fBg6kt5Lphg5xPR47I+bRqlc58K6WUmjqv\nvRbfhhsclFm9O+5IzWdkZcFDD0nys54eSeCSyhlCNS1SkUVTWdZ0H0FUZaW9YHTswlmlZtJ3dSIS\n/R6T8bstXiw/Sik1QcY8ldL9Wdb9Kd2fmscyM+Gqq6b7KFQKpSKLpiorSxyrPBNmyGprJbQslsej\nC2fnq0SjcuXls2v2DhKfWzPhfFNKKaVS5YYb4ttwXq+sOVfqArSDlwoOB3zqU7L41Rjp7N1668yo\n65WfD/fcIzXxjJEkK/feK508Nf+sXi03howM+d4uXQp33jndRzV+W7fC+vWSDCUtTf6uNzyllFJz\nybJl8Nhj0TbcokXwB3+g9VjVRU1GFk2AuZ9Fs7VVEpWUlkqCk7w8+PQF/5dMn6oq+VEKpDzBpk3T\nfRSybrWrS9a1jTeLp8sF118vP0oppdRcdf31kmMhXCZhOrJeq1lnImvw7rnAcxYwNzt4fr9kpWxq\nkscZGTJDVlIyvcel1GxhWfDGG7Bvn/zd6ZTkKGvXTveRKaWUUjPLm2/Cnj3R++U110jUilIXoFk0\nx+v996OdO4CBAXjpJalLYsz0HZdSs0VdnWTrCgsG5QZWWTn71gIqpZRSk+XkSdi9O/o4GJRSR9XV\ner9UF5SSLJrGmLuAVYA3vM2yrP+cin3POPX18du6uqSumJ5sSl1conMoFJIajZooRSmllBINDfHb\nLEu2r1499cejZo0JJ1kxxvwj8DngdwAD3A/M3bziiTpxLpdkNVJKXdxYAyG6aFwppZSKGuu+qPdL\ndRGpyKJ5lWVZjwCdlmX9JXAlsCgF+52ZNm2KT1m7caNk81NKXdyqVVJzJ1Zp6czIOquUUkrNFCtX\nxnfmSkoka7tSF5CKEM3B8/8dMMaUAu3A3E3ZWF4On/ucrCHy+yXNfG3tdB+VUrNHejo88ICsK+js\nlM7dmjW6hlUppZSK5fVKmzN8vywpkYRker9UF5GKDt7zxphc4H8AHyMZNL+fgv3OXMXFcMst030U\nSs1emZlat04ppZS6GL1fqkuQig7ef7csyw88bYx5Hkm0MpSC/SqllFJKKaWUGodUrMF7L/wXy7L8\nlmV1x25TSimllFJKKTU1LnkGzxizECgD0o0xG5AMmgDZQEYS718NfA8IAseBL1uWZV3q8cxoXV1S\nL6+4WIpUKqUuLhCAlhZZYK4lSJRSSqnEAgFobZVwTr1fKiYWonkb8EWgHPhOzPYe4I+TeP8Ry7Ku\nAjDG/AuwCfhoAscz8wQC8OKLcPy4PM7IgDvv1GyBSl1MXR28/DIMDcli8hUr4Oab4zPYKqWUUvPZ\nqVPS1gzfL5cvlzwRer+c1y75X9+yrB9ZlnUD8EXLsm6I+fmkZVnPJPH+kZiHfuD0pR7LjLVrV7Rz\nBzKL9+KLUtRZKZXY8HD0ZgVS1PXgQTh0aHqPSymllJpJRkbi75eHDsH+/dN7XGrapaJ7v90Y8wNj\nzG8AjDErjTFfSeaNxphPGGP2A0VIeYW55dSp+G19fXDu3JQfilKzxtmz0skb7eTJqT8WpZRSaqZq\nbo527mIlan+qeSUVHbx/AV4CSs8/Pgr8u2TeaFnWs5ZlrQYagbtjnzPGPGaM2WGM2dHW1paCw5wG\nPl/8NmMkVFMplVii8wbii6MrpZRS89lY98uxtqt5IxUdvELLsn4OhAAsywogiVMuyBjjiXnYQ7Rg\nOuf38z3LsjZZlrVpwYIFKTjMabBxY3xSlRUrtKGq1IUUFkJVlX2b2w3r1k3P8SillFIzUX4+LFli\n35aWBuvXT8/xqBkjFXXw+o0xBUiBc4wxW4DuJN53uzHmW+f/fgx4OQXHMrMsXAif/Szs3i3r76qq\nYO3a6T4qpWa+u+6CPXugvl6yaG7cCHl5031USiml1Mxy++2wd6+EZWZlwYYNUFAw3UelplkqOnjf\nAp4Faowx24EFwH0Xe5NlWb8Gfp2Cz5/Ziovhttum+yiUml1cLrjsMvlRSimlVGIulwyCbtw43Uei\nZpAJd/Asy/rYGHMdUIvUwjsyKkOmUkoppZRSSqkpMOEOnjHGC3wDuBoJ09xmjPlHy7ISpPVRSiml\nlFJKKTVZUhGi+WOgF/ju+ccPAo8D96dg30oppZRSSimlkpSKDl6tZVmx6e3eMMbsScF+lVJKKaWU\nUkqNQyrKJOw6nzkTAGPMZmB7CvarlFJKKaWUUmocUjGDtxl4xBjTcP5xBXDIGLMPsCzL0roASiml\nlFJKKTUFUtHBuz0F+1BKKaWUUkopNUGpKJNQn4oDUUoppZRSSik1MalYg6eUUkoppZRSagbQDp5S\nSimllFJKzRHawVNKKaWUUkqpOUI7eEoppZRSSik1R2gHTymllFJKKaXmiFSUSVBKKaWUUlPImKdS\nuj/Luj+l+1NKTR+dwVNKKaWUUkqpOUI7eEoppZRSSik1R2gHTymllFJKKaXmCO3gKaWUUkoppdQc\noR08pZRSSimllJojtIOnlFJKKaWUUnOEdvCUUkoppZRSao7QDp5SSimllFJKzRHawVNKKaWUUkqp\nOUI7eEoppZRSSik1R7im+wCUUkopNf8Y89R0H4JSSs1JOoOnlFJKKaWUUnPEtHTwjDGbjTHvGmO2\nGWP+13Qcg1JKKaWUUkrNNdM1g1cP3GhZ1jVAkTFmzTQdh1JKKaWUUkrNGdOyBs+yrOaYhwEgOB3H\noZRSSimllFJzybSuwTPGrAUKLcs6mOC5x4wxO4wxO9ra2qbh6JRSSimllFJqdpm2Dp4xJh/4O+Ar\niZ63LOt7lmVtsixr04IFC6b24JRSSimllFJqFpquJCsu4CfAH44K11RKKaWUUkopdYmmawbvfuBy\n4L8ZY940xlw5TcehlFJKKaWUUnPGdCVZ+Snw0+n4bKWUUkoppZSaq7TQuVJKKaWUUkrNEdrBU0op\npZRSSqk5YlpCNOeTs2cH+eCDDvr7A9TWZrFxYx4Oh0n6/aFQiJ/+9DTvvttOZqaL++8vZ9Om/Ek8\nYqXUTNLQMMBHH3Xg94dYtSqbdetybc/39Izw3nvttLX5KS9PZ8uWArxe5zQd7dgOHuxh794unE7D\nxo151NRkTvchKaViGPNUSvdnWfdP6P1HjvSye3cXxsCGDbksXZo1rvf7/UHef7+D06cHKCz0cOWV\nBeTkpNles29fN/v3d5OW5mDTpjwqK30TOmalZgrt4E2ipqZBfvjDkwQCFgDHjvXR1DTEJz5RmvQ+\n/vqvD/H6662Rxzt2dPDXf72GK68sSPnxKqVmlhMn+njiiQZCIbmGHD3ay7lzfm66qRiAwcEg3//+\nSXp6RgA4fryPw4d7eeyx6nENJE22995r56WXogmTDxzo4b77ylm9Omcaj0opNVPt2NHB88+fjTw+\neLCHT36ylA0b8pJ6v2VZ/PjH9TQ2DgJybTxwoJuvfa0Gn0+avm++2cqbb0brLB840M2DD1awbNn4\nOpJKzUQaojmJ3n23PdK5C9u9u4ve3pGk3t/RMcxbb9mLvAeDFk8+WZ+yY1RKzVzvvHMu0rkL++CD\nDkZGQgDs2dMV6dyFNTcPcexY35Qd48VYlsU775yL275tW/w2pZQCePvt+OtDouvIWE6c6I907sJ6\newPs3t0FQCAQ4r332m3PW9b4PkOpmUxn8M4LBEIcP95HIGCxdGkmHo+EOJ040ceOHZ1UV2dw+eUX\nnjVraBjg/ffbKStLZ+vWQnp7R7Asi87OEUZGQuTluXG7HfT1BcjKSrvgvgDa2/0Eg1bc9s7O5DqI\nSqnJZVkWdXX99PcHqKnJjIwMx2ptHaKpaYiFC70sXOiNe35gIMCE7AW7AAAgAElEQVTx432kpzup\nqcm0zbz19gbiXj88HGJoKEhamiPyfG/vCP39QbKzXWRkuOIGkXp7R6ir6ycry0VVlQ9jUj+719k5\nTH39APn5bioqMiLbg0GL/v743yPZgS6l1PzT1xegu3uEt95qxRjDtdcuIC0t+TmJsa4v4QGx4eEQ\nfn8owfvs16pQSK7xAwNjX+OVmon0m4rMlP3oR6fo7pYT3+t18vnPV/CrXzXyzDONWJZ0stauzeU7\n31mHyxV/kfmXfznJ44/XR0bbly7N4t57S3n66TP09ckFw+EwbNqUR3FxfCMvkZoaHwUFHtrb/bbt\nGzbkjvEOpdRUGRwM8uMfn+Ls2SEAXC7DvfeWsWpVNOzwN785ywcfdEQeb9yYZwvRPnKkl1/84kxk\nRq6oyMOjj1ZGGhFLlmRy7pz9/C8u9kYGiGpqfPzzP9fR2joUeb6iIsO2vm337i6efbYpcm1atCiD\nL3yhIjKIlQrvvnuOV15p4fylkqVLM3nggQqcToPL5aCqysfJk/229yxZomvwlFKJ1df38fjj0fD0\nffu6+Tf/pjLp91dXy2DZ6AiI8Dq+jAwXpaXpNDXZZ/lir0sDAwF+/ON6mpuj1/jPfKacFSuyL+VX\nUmpKaYgm8OqrLZHOHcDQUJAf/vCkrXMHsHdvF7/6VWPc+5ubh/jJT+ptF5Jjx3p55ZUWW7IDl8vg\ncJiEs3KJOBwO/sN/qCUnxx3ZtmJFNr/1WzXj+v2UUqm3ffu5SOcOIBCweP75s5HO2unTA7bOHcDH\nH3dSVyfhk8GgxXPPNUVeD9Da6uftt6Nh2ddfv4Cqquii/9zcNO69tyzy2O8P4fU6IjNyDofB63Uy\nPCz7HBoK8sILZ23XpkTHNRHd3SO8+morMZdKjh3rY8+ersjje+4ppbDQE3lcVpbOrbcuTNkxKKXm\nlmeftV+3QiGLZ55pSvr9OTlp3HNPCW63NHMdDsPWrYW2DtynPlVKXl60fbV4cQY33lgUefzOO+ci\nnTuIXuMDgfiZP6Vmmnk7gxcMWjid0ihqaBiIe37fvm6CwVBcooK9e7u5775FhEIhAgFwux3s2dOV\nsNN24EAPV11VSG+vn6Ehi4ICDw6Hoa3NT2lpOsGghcOBLVzKsixCISLHdvnlBTz99JW89147hYVp\nrFhx6bN3sb+zUmpiEl03BgeDkfM79vnh4SButzPyvurqTDo7hyOz+2Pt1+t18uijlbS2DjE8HKK0\nNN12TTp9WvZVXp5BT88wubluXC4HDQ0DFBd7aW4einT2LnbsyRgcDJKebp/5O3NmINIQC4WsyPE1\nNAywcaMkRMjPd/Pbv11DU9MQTqdJGKqarKm4jum1UqmpkeiaAhKi6XSayCC7w2FsA/Fho9tMsTZs\nyGPFimxOneqnrCw9bmlMUZGX3/3dJTQ2DuJ2Oygqsl+XEl0n+/sDtLcPJx2JpdR0mXcdvFOn+vnN\nb5ppaRmisNDDrbcWU1DgjmtolZen09Y2FPf+8nIvf/u3R3jllRaGh0OsWZPDgw8uSvhZ5eXpvPRS\nM8eO9RIIQH5+GnfdtRDLsnj88Xrq6vrIyHBx9dWFXHllAR9+2MHbb7fR1xegstLH3XeXMDwc5Nvf\nPsz+/T14vU5uu62Y3/3dJTgcyU++HjrUwyuvtNDRMUxJiZc77iixrZFRSo1fYaEnrgHgchlyc6UR\nUVDgZt++Lt57r4OBgQDp6U6uuCKPz3ymHICsLBdutyOuA1ZQ4GG00Q2P2NeGU4kPDsra3iuuyKeg\nQEal8/PdCcOUYmfTkvHtbx/kiSdO09c3QllZOn/zN2u4/vqiyDH09QU4erSXnp4RPB4nVVW+yDGE\nGWMoK0sf1+fGqq+Xa3dz8xAFBW5uuaWY5ctTGyp19GgvL7/cwrlzfoqLvdx++0LbDKpSKjXefruV\n//Sf9nP69ACZmWl87nOL+NM/XRl5Pi3N0Nsbe220yM21dwQTtZlir23PP9/IX/3VYVpaBsnOdvPV\nr1bxO7+z1LYPYwzl5YnbQwUFHs6csYdwpqU54kotKDUTzasQzf7+AE8+2UBLi3Tczp3z87OfnWb9\n+lxcrujojzHwxS9WsW6dPR3vggVehoZCPPdcE0NDQUIhiz17uvi//7eeq64qtL02N9dNfr6bw4d7\nzmfStOjoGOaVV1r59a+bOHGiD8uSY3rppWb+9V+beOGFs5GO5qlT/Tz5ZAN/8if72bevG8uyGBwM\n8KtfNfKTnzQk/Tu3tg7x1FNn6OgYBuDs2SGeeKKewcHgpfwvVEqdt3VrQdzI81VXFZKRER43s9i2\n7RwDA3JODw4G2b69A79fHns8Tq69doHt/R6Pg2uusV9LLsTplLDPwcFospUPP+yINECys6XDFysz\n08WWLcnX0nz66TP80z/V0dcno+eNjYN84xsf09cn15TCQjetrf5I8gK/P0h9/QBFRePrRF7IwECA\nJ55oiIRLtbcP8/Ofn4lbnzgRHR3D/OxnpyP7bGkZ4sknGxLOsiqlLt3gYJDf/u1dnD4tA2R9fSP8\n4Ad1tgzhGRnxs3o+X3Tb0aO9CdtM4Rm/trZBvvWtvbS0SAetp2eY73znKK+/3pL0cV59dWFcTdFE\n25SaiebVDN6hQz1xo+XBoEVvb4Cvfa2G3bu7CAQs1qzJoawsne98Zx2//nUTe/Z0UVGRwX33lfPN\nb+6K2+/hwz08+eRmtmwp4OOPOyku9nD//eV89rPvk5WVxvBwiFDIIi1NMmju2NFBRYV9VPjll1ts\nseAAdXV9HDzYi9dr74e//norjzxSmdTvvH9/T9zovd8f4vDhnqTrySil4hUUePj612vYtauL/v4A\ny5Zl2dZ3PPFEA+npTpxOEwn5c7sdPPnkaTZtkoy8V19dSFlZOocO9ZCe7mTjxrxxjQ6/9lorxcVe\n+voCjIyE8Hgc+HwuXn65mQcfXAzA7bcvpLrax7FjfWRnu9iwIY/MzOQv/T//+em4bQMDAX7xi0a+\n+MUq6ur6qarKIDPTSVfXCOnpThYu9HLsWB+1tamZYTt8uDfu2h0KWezb180NNxSN8a7xOXCgOy7U\nfmQkxKFDPVx+efIdYqXUhf3yl2cSZrl86qkzPPSQXLdGRiy8XsPwsJyTbrfB74/NidAd9/6OjmFO\nnx6koiKDxx9vYHjYPpBtWRZPPNHAjTcWJ3WcCxZ4+MY3avj4404GBoLU1mbZElgpNZPNqw6ew2EY\nGgqwb18P5875yctzs3ZtDg6HobDQw8032096y4LS0nQCAYvCQjdOp8HpNPj9IXp7A1iWRUaGk8xM\naZB1dg7T3T2Cx+Ogvz+IwyGLcsOzfZYlI/QOB5w9O0hHxzBut4OysnTbDGL0eGU2cbREWTzH/p0T\nb9f1JUpNXGPjIG++2Up39wh9fQGqqzMi4dMulwNj5JyPNTrVd19fgP7+AKGQXCtGd/CamgbZubMT\nvz/EypXZrFwZ7TQ5nYZAIER39zBDQyEyMpykpzttnxEIhPjoow527OgkLy+N0tJ0MjPtjZT6+n52\n7eoiFLJYuzbX1lEd61oRvmY5HAZjDEVFXlsoaSqvMWMVbZ+Kz5hJBeOVmgvS0gyWJaUKAgHJReDx\nOEe1g2RgLHR+XCcYtGz5ChwOaXPt29fNwECQ0lIvq1fnRNo8Y5VUGO81Izs7LRKOrtRsMq86eDU1\nmbz0UgudnRJa1NIyxOnTg/zbf7ss7rXhkZ5Tp6Kpvffs6Wb16uzzxcdlJKm/P0BFhY//9t+O2LLG\nbdt2jtraLFvRzOHhEBkZXrxeF/v3R0efmpuH+OY3l7B3b7ctE93SpdkMDsoIcqzbb09u9AmktMM7\n75yzFVz3+VzU1mYlvQ+lVLy9e7v4/d/fE8mC+fHHnRw61MOf//kqAL70pcU88YR9FDktzcFXvlIZ\nefzKKy1s3x69Ruze3cUXv1gZWRNy8mQ/P/lJfWRmaf9+mbG67joJ7bzuukL+y385FJnd6u8PMDgY\n5Oabo6Gff/qn+20Ffbdvb+d//+/1kdm1gwd7eOqp05Frz9693dxzTymXXSYz/I8+Wsl777XbMgrn\n5Lj57Gdl7XFVlY+8PHfkugrSKVq/PnXlXFasyOLll50MDNj/X65Zk3OBd43PmjU5vPVWm22mMD3d\naetQTzZjnkrp/izr/pTuT6lU+PSny/jDP9xnWyoyMmLZMgRbVoiRmEm+kREwJnoNKihw86//Gs1o\n2dw8SG9vgL/8S7n+PvLIYv7+709EwtdBrktf/nLlJP1WSs0s82oN3rZtbXi9TrxeJw6HweNxkpXl\n4o03WuNeW18/YOvcgRTI7O0NsHZtDunpLtLSHCxe7KOw0M2uXV221w4MBNi/v4eSEi8ulwOHw5CZ\n6aKoyIPTaSgulu0ZGS6WLs3C6XRw333lFBd78XqdrFqVzec/X8G3v72GrVtlXU9hoYcvfamK++5L\nnNQlkfx8N1/4wmIWLcrA63WyZEkmjzyyOKU1sJSaj37yk3pbiQOAt95qi6wTq6jI5Lvf3cDixT7c\nbicVFT7+9m/XsWyZdBiGhoJ88EG77f2BgMX27dFt27a1xYUNvvvuuZhOZRdlZd7INS0jQ8IjP/pI\nrkcnT/bx/vv2kgh+f5Annoiu433rrTbbwFJ4W9htty3kj/94OUVFXtxuJytWZPOjH10eyQrqdBoe\nfngxy5dn4fU6KS1N54EHFlFScukJVUbzeCSbaE1NJl6vk4oKqeU3Oqx9IrKz03j44cUsXizXyupq\nH488sjhhhj+l1KU7d26EO+8sZsECaQ/5fC62bClgwYJoBEB/f3yegPA6X5BrVF5eGm63tK98PheD\ngwGOH5cyNDk5br7//U0sWZKJ2y3Xpb/+69Vs2ZL8GmelZrM5M4N35EgPR470sWZNNlVVEl4UDFqR\nmlPV1Zk0Nw/h8TjiUnS3tAwRClmcPNlPMGhRVeVLmI4XoK3Nz8aNeZH03wDHj/fa0oOHtbcPs3ix\nj8WLo+vthoaC+P3BuEKZXV3DrFpVaiuSHPY3f7Mm4bE0Nw/R0THMokXx6X9jVVb6+MpXqsZ8XqlU\nC4UsTp3qZ2TEorraN2a4zGx27pzMWA0NBQkGrUhHoLl5MHKNue22hdx2W+J6b319AQIBC78/SEPD\nAB6Pk0WL0unqis6EJboO+f0hBgeDpKU5aG31k5PjttXKBGhqGor817IsgkEJ/3S5ZGArNjlJos/o\n6RmxlQr46ldr+OpXx66/mZ/v5oEHKsZ8PhWKi708/PDiSf2MRYsy+NKX9Fqp1GTq7h6hqCidz3/e\nfj7HXvtCIQnDDIVkqYoxEIjJd3Tu3DA+nwuXy0EgYOH1OnA6DY2Ng5Fi5lu3FvLqq9dPxa+k1Iwz\nJzp4f/mXByKzcMYY7r67hEcfreTxx+sjjZecnDQ2bUocMrRyZTb/5/8cp71dLi4+n4t77ilJmF58\nw4bcSOMpbPXqHJqb/XHFLzdsyI1kiQrLz/dQUeGLSxgwnoW7oZDF00+f4cABCd10OAy3374wLlue\nUtOhp2eEH/+4PtKJyMhw8tBDFWOmop6tVq3KZvv2c/j9MtJsjGHxYh8rVyYXNlhQ4KatbYgXX2wh\nGJTrQXZ2Gn/xF6sir6muzqS93T4Dt2CBh+xsGdC54op8Xnqp2fa8MYarrpIkLhs25DIyYtHUNBgJ\nsfR6nXzuc9HOWHW1j4MH7WHglZU+XaerlJoUixZlkJbmiIuAiG0Hud2GgQG5ZlmW/Ph80YHCtWtz\neOWV5kiYpzGGkhIvGzemLjRcqdls1g+rv/lmqy3E0rIsnnuuiX/8xxO2kenu7hFOnhzgs59dFGm4\nGGO4/fYS+vuDkc4dyDqWt95q4447FkYW/RoDl12WFwl3DPP5XHz+84v5+tdrbLMUl12Wz3e/u8FW\np8nrdfIHf7CM++4rtyVeWLo0k82bk++c7d3bHencgXT4XnyxOWFWKqWm2quvtthmiAYGgjz33Nlp\nPKLJsXx5NllZ0TEyp9NQW5s5ZmKj0YJBi6NH+2xr24aHQ+zbF12fe8MNC2y14zIzXXzqU9F1Kjfd\nVMzNNxdHkg84nYYHH6yIjGC7XA6WLs20RRdkZ6dRWxttSN1220IWLIiWNMjLc3P33SXJ/RJKKTVO\n6elOPvGJUlubafXqHNu63czM+NDo7Ozo9Xbp0qzIQBfIbF9NTWYkdFyp+W7Wz+Dt3NmZcPuHH3bY\nwigBTp0a4M/+bCWf+lQpBw70UFubTUVFBv/zfx6Je//Zs0OsXZvDqlXZNDYOUlDgIT9fwqC+8pUq\nGhsH8fuDVFRk4HI5WLQog5tuKmLnzk7Ky9MjCQz+8R8vY+/eLs6d87NlS0GkRtbv/34tDQ0DZGW5\nKC5OXMR4LKPXBoJ08urrB1i9OnVJB5S6FKdODcRta2kZYmAgEFMjbvZraRni7rtLI79beXlGJGyy\ntPTi689OnOhjZCTEokXpDA2FIpnkjh7tjbwmI8PFV79aHbneLF4cP7P2J3+ykocfXszRo72sWZNr\nC0FvahqkstJHaWk6Z84MnF8H7LVFIeTkpPGNb9Rw+vQgoZBFRUWGZo5USk2qNWtyWLo0kzNnBsnN\nTbMVKA8EAgwPQ2amlEYwJjyjF53xa2oa5I47SmhtHaKvL0BpqSSwa2wc1FIGSjEHOnhjhX0lamCF\nO2ilpRmUlkbfl5fnjitmm5kpSVQ8HhMZDY8VO6oOUn/lhRfOcvJkP7m5afj9IdauldGo8H9jud0O\nWyry8Qj/HsluT2RkJMQrr7SwZ08XDodh48Y8bryxSMOy1ITl57tti+FBwjTnWnHY/Hw3DQ0DtgEa\np9MkXceupMSLw2FoafEzMCBlVbKz0+IGpiD+ejPa6LW+YXl57vONIwfV1dHrzehrhTGGiorpDaHd\nubOTbdva6O0NsHRpJnfcUTKumoBq8qU6y6ea38KJ30ZzuVykpzvo6CBSJmFkBHJzo/eQ8DVMyrPI\nNmNIaeIlpWazWR+iec89JXHZ2hYv9vH1r1fbasgZI+FOiVx//YK4js311y9IehQ7FLJ4/PF6jh/v\nIxi0aG8f5pe/bEw405YKl10WXwx5+fKspGYNwl56qZkPP+yIJGzYvv1cwmyiSo3XddclOp+K5tys\n0NathXE17jZvzsfnS27cLDvbzcKFXvr7A5FEKD09I1xxRXwH71Ll5KRFyh2EZWQ42bKlIGWfkQpH\njvTy3HNNdHVJcpfDh3v56U8bLv5GpdScVFXls5V3CgQs28xcomvt+vW54xroVmoum/UzeBkZLv7p\nnzby05+epq6un2XLMnnwwQoyMlwUFHjYs6cby7JYvz53zNm+mppMvvrVanbt6iQQsFizJofKyvjR\n8LHU1w/YakCBLAjevbtrXPtJls/n4rHHqtmxo5OOjmEqKzMSzhKOxbIs9uzpjtu+e3dXXLF3pcar\nqsrHY49V8/HHnYyMWKxenW2bPZorFizw8LWv1bBjRyf9/QFqa7PisuNeSHf3CMuWZWEMNDQM4HY7\nWLky2xaGlAp33VVCZaWP48f7yMpysWlT/oybGdu1Kz7Uvrl5iLNnB1NabkEpNTv4fC5qanw0N8t6\n7pISry3EPzfXzW/9lrSDurtHqKnxpbQuplKz3azr4J082c/+/d04nYYNG3IpKUknO9vNb/1WfArv\n8vKMpDP3LVzo5Y47UptYwEzihIXP54oUO1ZqpikuTv35NNONriWXrKVLs2xh4Km+bhhjWL06Z1au\nzzWTeRFVSk2bUMhi797uyLKWTZvy4so9lZSkX3CAJzs7jRtvLJrsQ1VqVppVHbydOzt57rmmyOMd\nOzp56KGKS17LliqLF2eQl+e2zeIZgy0j1ExijGHduhx27LCPmm/YMDOPV6mZqK3Nz/e/X4ffLzNu\nu3d3cdVVBdx6a+K6d6Pl5KRRXe2jrs4eyj1TrxuTacOGPA4f7rVtW7jQG1ezVCk1N/zyl422jME7\nd3by2GPVkcyYW7cWxi0bueYaLVKuVLJmzRo8y7LiTvZQyOKtt9qm6YiiHA7Dww8vZsmSTJxOQ0GB\nm3vvLUuY9GCmCNfN83gcpKc72bq1kOuv15EwpZIlNfDs4ZQffNBBf39gjHfEu+++ctasycHlMmRl\nubj11mI2bEjdGrzZorY2i3vuKSU3Nw2n07B8eRYPPTS5hdOVUtOjrc1v69wB9PUF+OijaM3Pf//v\na7nxxiK8XknQdcstC/n936+d6kNVataathk8Y0wp8DywEsi0LOuCraKRESsu0yVI9sqZID/fzRe+\nsHi6DyNpLpeDO+8s4c4751cYnVKpkujaEwxadHePJJ1oJSPDxWc+U57qQ5uVLrssLy4hjFJq7hmd\nsyAs9prq9br4sz9bNVWHpNScM50zeB3ATcD7ybzY7XYkzBJZWTm9qb2VUvNTogRKPp+LoiJPglcr\npZQCKC9Px+WKX187GUnplJqvpq2DZ1nWkGVZiauUj+Huu0tsI+MFBW5uuUWzPiqlpt7WrQW22nFu\nt4NPfKIUl2vWRL4rpdSUy8hwcdddJbZyOsuWZSWsAaqUujSzKslKaWk6v/d7S6mr68flMlRW+uZc\nbS2l1Ozg8Tj58peraGgYoL8/QFWVb84Vc1dKqcmwYUMey5ZlUV8/QG5u2rjq+CqlLm7GdvCMMY8B\njwFUVEQX27tcDpYtyxrrbUopNaViZ/GUUkolx+dzsXJl8rVDlVLJM9alFm9K1QEY8yZw84WSrBQW\nFlqVlZVTdkxKzTWnTp1CzyGlLp2eQ0pNnJ5HSk3Mzp07LcuyLroWZDqzaKYBvwHWAS8ZY/7YsqwP\nEr22srKSHTt2TOnxKTWXbNq0Sc8hpSZAzyGlJk7PI6UmxhjzcTKvm7QOnjFmM/C/gCCww7Ks34t5\n7i+Ae4FO4K8sy/rOZB2Hmtv8+NnNbppowoOHFaygiqpJ+7xznGMve+mii3zyWcc68tCF4WryHOUo\nxzjGCCMs4v9n786j47juA99/L/bGDpAgQRAgCYIUV4GkCIqbSIqidtGWY1leFMdO7Bnn5ORlm7xk\nknfeyUwmz5PEmSTnePzmJD4vmRnHjpPItmxrsWSJEmWJosR9EfcFxL7v+9J93x8XXd2NqgYaQFd3\nA/h9+uAAXVVddQFU1a1bde/vV0YllaSSGrX1azRXucpd7uLDxxrWsJWtJCPjCYVIJB10cJGLdNNN\nAQVsYxuFFE77uWqquc51hhmmhBK2s510JNqvEAuZm0/waoBHtNbDSqnvKaXu11pfDpr/+1rrt1zc\nvlgETnCCVloB09j7iI9IIYUyyqK+rUEGOc5xxjG9iZtpppNOnuEZqSyFK+5wh3MEbtbd4hYjjLCP\nfVHbxhWu8DEfW++vcY1xxtnJzqhtQwgxN0MMcZzjjDEGQAstvMM7HOXolPVPAw18RKBzVDXV9NPP\nEY64XmYhRPy4Fs9ba92stR6eeDuOeZIX7C+VUm8ppba7VQaxsPXRZzXugt3hjivbq6HGatz5jTJK\nPfWubE8Ip325jjpGGHF1G9VUo4nv+GwhREAttVbjzm+MMWqpnfJzd7lrm9ZGG730RrV8QojE4nrC\nJqVUJbBUa301aPI3tdY7gd8A/nuYz31NKXVGKXWmra3N7WKKeSjcBahbF6bh1uvD58r2hIjFvuW0\nX/vwSQNPiAQS7lww3TnCa7u3HtnnhBDzm6sNPKVUIfAt4KvB07XWnRPfb4X7rNb621rrKq11VVFR\nkZvFFPNULrmO4w/WsMaV7ZVRRtKkQyaZZEopdWV7QqxmtW3acpZHtUuw0zZWs9q2rwsh4idc/TPd\ncASn+jB/4iWEWLhcq8GVUinAd4E/0Fo3T5qXO/F9KQmci08kvv3sp5hiFIo00rif+10LspJDDvvZ\nTw4mD2MeeRzgAB4kQatwxwY2sJGNpJKKQlFKKXvYE9VtVFJJBRUkk0wSSaxhDQ/wQFS3IYSYm2yy\n2c9+cjF543LJ5SEeIpOp83CuYQ2VVJJOOgrFcpZzgAOxKLIQIo7cbFw9D+zCjLUD+GPgBa31bwF/\npZTaimlg/pGLZRALXBZZPMzDePGSRBIK5er2Vk68xhknRe5NCJclkcR2tlNJJRrtSmTLZJLZxS52\nstO1bQgh5m629c9mNrOJTfjwyfEtxCLh2hWq1vr7wPcnTT45Me/X3dquWJxiXWlJ407EUiy6S0qX\nTCHmh9nUPwoljTshFhG5ShUxMcwwl7lMG21kk81mNrOUpXNe7zjjXOGKlQdvAxtYycoolFiIueuj\nj4/5mC66yCefrWy1ulhFSzfdXOEKPfSwhCVsZStZZEV1G0KI+HiXd7nABXz42MxmHuOxBXczRqkX\no7o+rZ+P6vqEmI+kgSdcp9Ec5zjddAPQSy8ttPAET8z5YvckJ2mgwXrfRhsHOcgKVsxpvULM1Rhj\nvM3bDDEEBPb7p3k6akFSBhnkGMes8Om99NJKK0/ztNytF2KeO8Yx3uEd6/17vMcQQ3yKT8WxVEKI\n+WBh3QYSCamVVqtx5+fF65ifZyYGGAhp3IFpTN7m9pzWK0Q01FFnNe78RhihhpqobeMe92y5sZyO\nCyHE/HOWs7ZpF7koKQ6EENOSBp5w3SijM5oeqckXttNNFyKWwu3f0dw/5RgQYuEaYcQ2bXziJYQQ\nU5EGnnBdMcWkkmqbPl3+nunkkefYxVPy0olEUEqpLaqrP9VBNLcxWTLJlFAStW0IIeJjHets01ax\nijTS4lAaIcR8Ig084bpUUnmIh6zAD6mkUknlnMfJKRT72W8lbE0mmfUTLyHiLZts9rCHDDIASCed\nB3mQPPKito0lLKGKKuuCz4OHveyV3IxCLADP8izllFs3ikoo4XkkgIgQYnoSZEXExHKWc5SjDDJI\nOulRSzOQRx5P8iQDDJBGmuOTQiHiZTWrKaWUYYbJIMOVwCfrWEc55QwzjAfPgouwJ8RilUkmX+Wr\n9NKLFy8FFMS7SEKIeUIaeCJmFMq18O0SFl4kqmSSXd8/Y62XKs4AACAASURBVLENIUR8RDu1ihBi\n4ZMGnoi6Jpq4wx3GGaeMMtay1jYWya+NNm5yk1FGWclK1rFuRk8gfPi4xS0aaCCDDO7jPpaylG66\nucENBhhgGcvYwAaSSeZd3uU610kjjT3sYQtbovVru+IqVznJSUYYYRObOMQheUIzoY46qqkGoJzy\nOY/pnI2b3OQEJxhkkPu4j0McChkfM8QQN7hBJ53kk89GNpJJpjV/lFF+wA+4ylUyyOBRHmUPe2ZU\nhgEGuMY1eullCUvYyMaopWEQQsyORnOXu9RRRwopLGc5nXQywABFFKFQtNKKBw9LWUorrdPWg4MM\ncp3rdNNNAQVsZKPVHbuXXq5znX76rTrP36Olgw5ucpMhhiihhPWsdy2NihcvN7hBM81kkskGNsiT\nRyHiQBp4IqrqqecEJ9BoAJppZoABKqm0LdtKK+/wjrVsCy300MMudkW8vdOcti7y/dt/kAc5y1kr\nkmArrbTRRjvtXOSitew97vEcz7Gd7bP6Xd12kYv8gB9Yf59GGmmjjc/y2TiXLP5ucSskhHgjjTzA\nA9zHfTErw01u8l2+a4Usb5p4fYkvASba3TGO0U8/YPbDeup5iqesC69v8k1uctNa5//kf+LFy372\nR1SGEUZ4kzcZZtjaRiONPMETciNAiDi6yEWucx0wx+nP+TmllJJLLmc4gw8fa1jDAAPUUEM55Xjw\nhK0HxxjjGMcYYAAwx3oDDTzJk9Z5ILjOa6aZR3mUDjo4xjHrPNVKK110sZe9rvzeH/BBSJqWOup4\njMessfJCiNiQKwARVde5bjVI/G5y0zGs8w1u2Ja9y13H0NBOhhjiHvdCpvnwcYITtjDx9dRznvMh\n0zSaE5yIaFvxENxQ9rvCFQYZjFOJEof/wmm6aW56n/dt+ahucYt22gGzz/kbd36DDFJLLWCeXt/i\nVsh8jeZN3oy4DNVUW407vx56aKQx4nUIIaJrjLGQY7uTTnz46KCDEUbopZd++hliiHba8eGjk05r\n+WqqbfVgLbVW486vn37qqOMOd2x1XjvtVg+Zyecpp3VFQw89thycXry285wQwn3SwBNRNTmxM4TP\n2+O0rEZH3MAbZtjWAAJsF9VgusI55QZL5MaSU9m8eF2pmOebyY2acNPc5PR/0Gh66Z2yPP79vosu\nx/13JvtkuG3E+m8hhAgYZxwv3pD3/u/BdWHw++D6yYfPVg9Odaw71aVgzjVzrWdnQs5HQiQOaeCJ\nqHLKv1VIoRUqfrplcyZekcgjzzGwhFM3vXzyWcYy2/S1rI1oW/HgVLYCCiiiKA6lSSxO+06sc785\npePIIotVrAJgBSscx576y1lBheP+65T7Khyn31mh5pyCRAgxex48IePOsskGTP3mwUPyxCuTTKu+\nC673nOpBp2Paf6w7nQeSSWY5yx3nefC40mVyCUscx/9KXk4hYk8aeCKqKqmkmGLrfS65YYNGbGRj\nSKLmLLLYy96wAVkmSyIpJOeXQlFGGQc4wHrWW+tJJ5097OGzfDak0iyjjKd5esa/Y6w8xVNWYwFM\npf8ZPhPHEiWOB3iAQgqt94UUspOdMS3DIzxCBRXWfpZJJs/xnJUCJI88drDDep9MMtvZbpU7mWS+\nzJdDctatZCUv8ELEZVjGMray1QqYkEoqu9glETWFiLPd7Lbqm3zyqaCC5SwniSTWsY4KKkgmmSKK\nqKDCOi+EqwcLKWQ7261jPYUUdrCDfPIpo4wNbLA+4w8ilk4661nPalZb8zx42Mc+V8boppDCXvZa\njTyFYi1rKac86tsSQkxNaW3vIpRoqqqq9JkzZ+JdDDEDffQxzjj55E/bYBtggBFGKKAg4sZdMB8+\nuukmnfSQC9shhhhkkHzyrUrRh4973MODZ9485WihhQEGWMOaWVfKVVVVLMRjqIceNDquA/jbaKOX\nXlaz2jG/4xhj9NJLDjkhETb9vHi5zGVyyZ31E+URRuinn1xyJRekSxbqMSTco9F00UUqqeSQE1In\ngTl/ZZBBJpn0088oo9PWg6OM0kef4/nEqc7z89ez+eS7HoDJi5duuvHgCYkaDM7HkVIvRnX7Wksy\neLFwKaXOaq2rpltOomgKV0TazRLMHcu5PHFIIinkaY6fZ+I1edlE7pbpZDnL412EhJVHXryLQNHE\nK5xUUlnCkrDz/U/25iJ94iWESBwKFVI3Ta6Tguf5u3FOJ420sOcTpzrPb6717EwkkzzlOU8I4T5p\n4ImE43/K5s+js571i7bLWTfd3OY2Y4xRSmlccr2J8Hrp5Ta3GWGEEkpYxapZPYWe77x4uctdWmkl\nhxzWsz7shaYQIjqaaKKGGpJJZi1rY9qoqqOOeupJJZV1rJM0CEIkGGngiYRzilMh6Q/ucpfHeGxG\nTwUXgjbaOM5xKxpbDTVsnXiJ+Oumm7d4y4qCV0MN7bTHfCxgIjjBiZDUDNVU8ziPSyNPCJfc5Cbn\nOGe9v8tdDnAgJgFNLnOZK1yx3ldTzWEOs5Slrm9bCBEZCbIiEkoffdRQEzJtlNGQZNCLxVWuhoTa\nBpPrzSndg4i961y3pf+4ze1FFxK8gw5b3r0hhrjDnTiVSIiFzYcvpIEFZrzfVa66vu0xxmw5R714\nucY117cthIicNPBEQhlkcM65wRYKpzxr44wzymgcSiMmC5cHb7Htq+HyMkq+RiHc4cXrmMcuFsfc\nCCO2G4/gnH9WCBE/0sATCaWQQscogE457BY6p+Aq2WTbopKJ+HD6/6STnhCBX2KpiCLHqHwSHEgI\nd6SS6hhYLBb1ZLhgLXK8C5FYpIEnEoo/j1dwiOeVrJxR8ueFYitbQyrxdNLZze5FGcQjEW1gQ8gF\nVSqp7GGPLTz5QufBw052hvzeq1kdksNRCBFdu9gVMsY1j7w5R+ONhEKxm90hUXuXsIQtbHF920KI\nyEmQFZFwVrGKYoppo41MMimgIN5Fiot00nmcx2mnnTHGKKLIMc+aiI9UUnmER+ikk2GGKaJo0eag\nq6CCUkppp51sshfdU0whYq2AAo5ylFZarYTpsbr5t4xlfIJP0EYbqaRKcBUhEpBcLYqElEYaK1kZ\n72IkBKk8E5tTV6nFKJ10OWaFiKFkklnBirhsO4WUuG1bCDE9aeCJuBpkkGqqGWWUlayc1RiCFlpo\noIEMMiinHA8eRhihmmoGGGA5y1nJShSKdtqpo45UUimnfMr8ep10UkstSSRRTrmraRp8+Kinnjba\nyCGHcsoX7dOgWInG37yJJl7jNfroYyc7OcCBkPlevNRSSyed5JPPGtbMuAvnAANUU80YY5RRJg1+\nIRaY4HpwBSsYZdQ6Ly1lKfXU48NHKaX00ksXXRRSyCpWWeeTUUa5xz366GMZyyilNKpP9MYY4x73\n6KWXpSyljDLHsbdCiMQgDTwRNz30cIxjVlTIG9xgG9vYxKaI1/HxxMvvBjd4iIf4kA+tiGK3uEUF\nFeSRF5I36AY3OMxhxycwNdTwIR9aET1vcINDHHJtEPtJTlJHnfX+Nrd5jMekkeeiuf7Nb3Obv+Vv\nrbQVV7nKNa7xNb4GmAbku7xLK63WZ/z5oiJt5HXSydu8baVjuMENqqhalGNShViIeujhLd6yziNv\n8iaZZLKUpfTTTzPNlFOOQvEar7GEJVYX6GqqeZiHGWOMt3iLPvoAU+etYQ172BOVMo4xxjGO0U23\ntf5aam03tIQQiUNuv4i4ucpVW8j/K1yJOM/bCCO23DsjjHCc47Zw0be5zVnOhkwbY8wxb5BGc5GL\nIekavHi5zOWIyjVTHXSENDQAeumlmmpXtiei8zf/MT+27avnOEcHHYB5uhfcuANop5166iPexhWu\n2HLtXeayY5hyIcT8c5Wr1nlkkEF66aWVVrx4aaWVYYbpoos++hhkkGaarc+20koTTdzlrtW487vH\nPatBNldO62qggTbaorJ+IUT0SQNPxE0vvbZp44xHnEdsgAHHC13/BXawMcZsFeBMy+C0bDSEW28P\nPa5sT0Tnb+60n/nwUUPNlNtw2g/DcSrPyMRLCDH/BZ8n/Me1Dx9jjFnvg4/5Mcbw4bM+00OP63VI\nuPW7VScKIeZOGngibpzGEqWTTjbZEX0+l1zSSLNNL6XUcb1LWGKb7jQtlVTyyY+ovNGwhCWOYyVk\nrJV7ovE3dwookkIK93HflOty2ufCKaLINi2LrJDw6EKI+Sv4POHPcZpCCmmkWe8zJ14AGWSEjH1b\nOvGaTKGiVoeEW4/UUUIkLmngibjZzGZyybXeJ5NMFVURj09KIYWd7Ayp7PLJ5zCHQ8bKKRTb2MZe\n9oasO5ts7ud+x3U/wAMhY7E8eNjGtoh/t5nIJZfNbA6ZVkKJ5BFzUTT+5p/jcyH7r0LxJE9aNyiK\nKKKCipDPrGENxRRHvI2tbA0JBOQ/RiQXohALQ3A9mE46xRRTQglJJLGc5daYuyyyWMpSSiixPltB\nBctYZjuvKBT3c/+UQcRmoowy2w2tzWyWdChCJDCltZ5+qTirqqrSZ86ciXcxhAt8+GiiiVFGKaZ4\nVk8mhhiiiSbSSWcFK0giCY2mhRYriqb/onuEERppJJVUVrBiysbkKKM00miFonY7B10vvVbktGgH\nc6mqqkKOIbu5/s1HGeVd3qWHHvay1/GpXieddNFFAQWzSqngxUsjjYwzTgklIQmGRezIMSTcMrke\nHGPMOi8VUkgjjfjwUUIJ/fSHPZ+00koffRRRFHLzKVraaLOiaM62ced0HCn1YjSKZ9H6+aiuT4hE\nopQ6q7Wumm45iaIp4iqJpDnnzvLgYS1rQ6YplOOTknTSKac8ovWmkcYa1sypbDORO/ESsTPXv3ka\naTzGY1MuUzjxmq1kkimjbNafF0Iktsn1oAdPyHkpuGfBVOeTZRMvtxRNvIQQic+1Bp5Sajfwt4AX\nOKO1/r2geSXAd4EM4E+01m+5VQ5h7g420kgPPSxhyYy6iMXLHe5wgxvkkksVVWSQEe8ixcUoo9RS\nyxhjrGTllI2RMcaoo45hhimhxHEc4XzSQQcttJBJJmWUzTh/XCRaaaWNNnLJpYSSGW8jGn/zHnpo\noIF00imjzHFc6XzQRhuttJJNNqWUuvL/EiKRTa5rPXisHiMllNBKK4MMWl0vE41G00yzlbfT3yPG\nSS+91FNPGmmsYtW8PW8JsVC5+QSvBnhEaz2slPqeUup+rbU/zvwfAf83cAl4BZAGnku8eG25uKKZ\nH8cNr/AKH/Kh9f4kJ/l1fn3RPd3qpZe3eZthhgG4xCV2s9vxqeIggxzjmJUe4jKX2cEOK+DHfDM5\nv+E1rvEIj0S1e+JpTnOHO9b7pSzlYR6OuCtuNP7md7nLaU5bKTk+5mOOcCTiQEOJ4jznucEN630h\nhRzmsORxFIvG5Lq2m24GGGAlKxlnnHrqWcEK6xy2hS1hx4DHywd8EJI+pphiDnLQ1sirpppTnLKd\nt3LIiWl5hRDhuRZkRWvdrLUenng7DiHx7CuBk1rrfqBPKSVnBZfUUWfLxXWPe7TTHqcSTa2LLk5x\nKmRaDz28wztxKlH8XOGK1bgDc3f1POcdU0Nc41pI7j+N5hKXIs4pmEgGGeQKV0Km9dDDLW5FbRvd\ndIc07sDkqPOnOIjEXP/m44xzgQsh+RaHGLL97omul15ucjNkWied3OVunEokROwF17X+J2FddDHI\nIJ100k9/SF18lasRpwSKhVZabblBm2mmgYaQaV68tvPWMMPz7rwlxELnehRNpVQlsFRrHZxROlkH\norv0AAVul2Ox6qJrRtPjrZ76kBw/fi20xKE08eX0PxphJKTRN9Wy44zPKOdaouimO+TiIXh6tITb\n/zvpnNM6ZvI3H2CAUUZt06P5e8ZCLP5fQiS64PPBGGOMMw6Ymzb+c/YQQ9YyGp1Qx0i4c9/k89wg\ng455OBP1mkKIxcrVBp5SqhD4FvDVSbOCH0Hkgv0sp5T6mlLqjFLqTFtbm4ulXNgKwrSdw02Pt1JK\nHfv8L2d5HEoTX07/o3TSHccjOi2bQsq87DKTT75jGoBojikMt//PJBjKXP/mWWQ5jluZb2MnY/H/\nEiLRBZ8PUkm1unp78Fjn7OAo0QqVUMdIuHPf5PNcJpmOXeUT9ZpCiMXKtQaeUioFE0jlD7TWzZNm\nX1JK7VVKZQG5WuveyZ/XWn9ba12lta4qKpKoTbNVRpktqtYa1iRsgtICCniQB0Om5ZHHYQ7HqUTx\ns4UtIY05hWIHOxyDV2xiU0jOI4Wiksp5OQYqk0y2sCVkWh55rGd91LaRT74tR91SlrKa1RGvY65/\n8xRS2M72kMaRB4/td090ueTaxh0WUmiLbCvEQhZc1/qjOBdQQCaZFFJINtkhdfFmNlvJyxPBMpbZ\novUWU2yLcp1Msu28lUHGvDtvCbHQuZYHTyn1BeCbYHXM/mPgBa31bymlSoHvAB7gP2mtfz7VuiQP\n3txIFM35K1pRNOdjDq/FEkXTH40unXRWsWpeNsrBjGFsoWXBRtGcj8eQiK3JdW0WWdRTP2+jaJZQ\n4vh0Hsx5q4EG0kibUfRfyYMnxNxEmgdPEp0LsQjIxakQcyPHkBBzJw08IeZGEp2LaQ0yyEUuolBs\nZ7vrT8l66aWNNnLIsbqqePHSTDOjjFJMccgYhYWgiy466SSPvITtFpuoBhigmWYyyaSY4rB3ksPx\n4uV93qeVVnay07HLoNM+KYQQs9VOu/UEL4kkLnGJdNLZxraQp1wDDNBCCx48szq/zYQPH800M8ww\nxRQnVNdQIYQ7pIG3SN3mNv/MP1tR/N7iLb7El1jFKle2d5WrXOayFW1vOcvZxS5+wS/oxQzBTCaZ\nPeyxjQOYr85yNiS0/2pWs4c9rlbkC8VNbnKe89b+spSlHOJQxN0X++jjv/Jf6aADMPv3AQ7wRb5o\nLXOZyyGhvUsoYT/7F1zXQiGE+3z4OMlJK9VAM83UUms1po5xjK/yVYoo4g53OMMZ6/xWSCEP87Ar\nycJHGOEd3rEidipU2HyqQoiFw/U0CSIxvcIrISHahxnmVV51ZVsDDIQ07sCkPTjOcatxB+aJyxnO\nOOZ5m2/aabflbauhhkYa41Si+WOYYVuepXbabXnrpvIjfmQ17sCMLXmP96x0G730cpWrIZ9ppJFa\naudYeiHEYtRAg9W48+HjFrfop9/Ki9lPP6/zOqOMhty8ApOiYHIuyWi5wY2QdAwazVnOzsscqUKI\nyEkDbxEaZ9wx0blbueY66HDMk1VPvW3aCCP00+9KOWIpXCL54EaHcNZJp2MuxHB/UydOCcs1mo/5\nGAi/T85kG0II4Rd87hhiyGpABTekmmiim24rR164z0dTG/Y0U2OMhdxcFUIsPNLAW4RSSHGMxuhW\nTp5wkR+dooilkLIgxgfkkec4faoomMLIJdexG+tM/nZF2FOrKBTllE+5rnD/NyGEmErwOSWddKur\nd0rQSJgCCsghZ87nt9mWyy+ZZLLJdmV7QojEIA28Reowh0MqmSSSeIRHXNlWPvm2/v455DiOOdjC\nlnkbJj5YMcW25OyFFC6Y8YVuyiabdawLmZZJ5ozy4H2KT9kC9tzHfVaglSUssf0vcsm1GoBCCDET\nq1ltJftOIYVVrCJt4gUm+fkRjuDBwwY2hHzWaVq0bGKTLTH5RjY6JisXQiwcEmRlkdrFLpaxjLOc\nJYkkdrHLltA0mnazm5WspJVWcshhDWtII40neZJqqhlllJWsXDCRDBWKgxykllorp9BqVksAjwjt\nZCfFFNNMM1lkUU75jC5IVrCCP+FPeIVX6KSTrWzlMR4LWWYveymjzIqiWU75gri5IISIvRRSOMIR\n7nGPHnrYy15GGOEyl0kjjd3stuq37WxnOctpogkPHsopdy2KdTbZVj3rz9c5H3LhCiHmRhp4i9jq\niVcsKBRlE69gmWSyhS0xKUOsJZNM+cRLzNzKiddsLWEJX+bLYecnkcSqiZcQQsxVCim23gf3cZ/j\nsismXrHgwcNmNsdkW0KIxCBdNIXNQG8TLXdOMtDbZE3TaFpppZnmWUe57KWXc5yzIo1NZZRRGmig\ni65plx1jjEYaYxbApJ9+GmhgkMGYbG+xGmGEBhpCIsDNVDfdNNDACCOz+rwXL0000UqrY1AWgHOc\n4xjH6KNvVtsYHxuipfojOpuuTL+wECLuhvrbablzkr7uenz4aJl4TQ4O5T9/tNMe9vzhZJhhGmig\nl94p696pzk+xrheFEIlFnuCJEHdP/BODZ34BWtOmFJlVB1m+/9Mc57h1AZtJJoc4NKOAFO/yLm/z\ntlVBrWMdX+SLIQPQ/Wqp5SM+spadKj9ZE018wAdWpLJlLOMAB1zraneBC9zgBhqNQrGFLWxlqyvb\nWsxqqOEUp6x9YCUr2ce+iLu4evFykpNWpNZkktnFrhnlfuqkk/d4jyGGADOW9BCHrLF9ffTxF/yF\nFaXuh/yQ53iOIxyJeBttdedpefUfYHgYgOYVK1n3qf+TtPSciNchhIid6tMv0n/y5yifZlx5qa9c\nQv/hKgCyyOIQh8gll3baeY/3rJtLhRRykIPTdsW8yU0ucAEfPkYZpZ9+CihAofDg4RCHyCffdn7K\nI4+HeRgPnpjXi0KIxCNP8ISlq/kag6ffBT1xJ1BrBk+/y6mmn4Q8nRhkkDOciXy9dHGMYyF3H29z\nm1Ocsi07xhinOR2ybCON3Oa2bVkvXj7io5Aw1K20cp3rEZdtJvzr9t8p9Yfdj+Qpo4jcKKMhjTsw\nOabucjfiddzlbkgaDi9eTnM6JPfjdE5z2rp4AvM08CIXrfff5/shIcjHGeclXmKY4YjW7/ON0/Lz\n71iNOwBfUwO1p34YcRmFELHT11nDwIk3UD5TB/TrPjIv3kTdM2lZBhjgHOcAOMWpkJ4DnXRaaVrC\nGWCA85y3ngS20EIjjVZKgyGGrLr3DGdCzk899HCBCzGvF4UQiUkaeMLSU3vZcXq3w/Q22iLuqnmT\nm455zZwSV3fS6ZiAtZVW27Reeh0vpt3K5xduvW5tb7HqoMNx35rJ39lpf/HijTjX1Cijjg334DJU\nU+34uUgvpAa6G6DP3q1zpO5WRJ8XQsRWV11oXei/YZRa12xNa6WVQQYd88xNdw6b3NXSnxM2ODds\nO+0MM0wnnY6fj3W9KIRITBE18JRSBUqpLUqptUopaRQuUGm59txhZvpS2zQPnoi7yznluwPnvHuZ\nZDrmCHLKjefBQ5LDLpxFVkTlmqlw63Vre4tVNP7Oc11HCimOUTuDP+/URVmhIg6ckJ5VCMn2LsrJ\nuQURfV4IEVvpk+pIfx3ozQ2cF7LIIo00x+6Q051/Js/3ryM4nZAHD6mkhj0/ZZIZ03pRCJGYwjbW\nlFJ5Sqn/Syl1GfgQ+Hvg34AapdSLSqnDsSqkiI3l6/ahloZWYGppEVvXf8rW6JrJuLN1rLNFKvTg\n4SEesi2bQ44tsmcaaY45gjLIsEUsSyGFTWyKuGwzsYpVtqSxhRS6ml5iMcol17YPpJMeNhqdk/Ws\nt10ArWJVxONGk0iyRZ3zj7n0O8pR202OjWy05T8MJy09h8wH9odM0ykpLK96JqLPCyFia9maXSSt\nCJzvs8hiNC+T8Y0mR6dCsZWtjvWQ0znFtn6WhZw/lrGMVFKt/Hpg6t5kkm3RpxWKzWwmnXRbztAU\nUtjIxpn9skKIeU1p7RzZSSn1JvAd4GWtdfekeTuBXwEua63/we1CVlVV6TNnIh/zJWZvdKSPhkuv\nM9JaR/qyMlZWPklaeg7ttHOXu2g0q1k94zw6o4xyghNUU20NNi+k0HFZHz7ucY9mmq0E1+HuPmo0\ntdTSSCPppLOOdbZGWDSNMMJtbtNNN0tYQgUV82LgelVVFfPpGJrJPhDOAAPc4haDDLKc5ZRT7nhn\neyqNNFJLLckkU0GFbZ+9yU1e4zUGGWQrW3mGZ2ac67Dp1nv03j5HUkYmy+4/Qt7StTP6vIiN+XYM\nCXeMjQ7Q8PHPGW6qJm3JCtK37aLeYyJVllMeksu1gQZqqSWVVCqoCGmohePFyx3u0EYbueRSQAGN\nNDrWvU00UUMNySSzlrVWb5lY14sz4XQcKfViVLeh9fNRXZ8QiUQpdVZrXTXtcuEaeIlEGnhCzI1c\nnAoxN3IMCTF30sATYm4ibeBFlCZBKVUJrAleXmv9o1mXbgHoo48xxqzwxSKOfD5oawOPB3IT4y6l\nmDsvXrrpJpNMKzVBQurthaEhKCqCJBmiLMR8NcggwwyTR96Mn8RbhoagpwcKCyEtbfrlhRDCBdM2\n8JRS/whUAlfACoWogUXZwBtjjJOcpJFGALLJ5iEecgwYImKgqQlefRX6+0EpWLcOnnwSkmdZOYuE\n0EQTH/IhI4ygUKxlLVVUJdbNFK8X3ngDbt0yqUWys+Hpp6GkJN4lE0LMgA8fpzhFDTVoNB487GVv\nSHfLiHz4IZw+bc4NaWlw6BBs2TL954QQIsoiud28R2tdpbX+stb61ya+vuJ6yRLUVa5ajTsw4Ys/\n5MM4lmgR8/ngZz8zjTswF9m3bsG5c/Etl5iTMcb4gA+sHFIazR3ucI978S3YZOfPw82bgbyR/f1m\nf/TZU4IIIRLXbW5zj3tWioIhhviADyJOBQRAfb1p4HknPjM6CseOmSf8QggRY5E08E4qpaYO/bSI\nBDfu/LrpZoCBOJRmkevocK48q+35ycT80U67Yy5Ep2Mvrpz2s74+aI8s154QIjE4nVuGGXbMhRmW\n0/nA54N792ZfMCGEmKVIxuD9b0wjrxkYARSgtdaVrpYsQWWQQQ89IdOSSQ7JUyNixOMx3TInBwrK\nknw/81m48XYZZMS4JNPItOdmRCmzXwoh5o2onHPC1TtSHwkh4iCSJ3j/iEmJ8CTwCeDoxPdFaSMb\nbeOA5kuo/AUnOxs2Tcp5l5QEO3bEpzwiKvLJt6XhSCHFltsp7h54wB5UZeNGyMmJT3mEELOynvW2\noCpllJFNduQr2bTJftNnyRIoL49CCYUQYmYieYJXpwwLUgAAIABJREFUq7X+qeslmSdWsILDHOYW\ntxhjjFJKqaAi3sVavB591EQvvHvXVK7bt8OKFfEulZijh3iIm9ykmWayyGIDGxImj5NlxQp4/nm4\ncAEGB2HtWti2Ld6lEkLMUCGFHOEIN7jBEEOUUDLzG0oeD3z+83D2rBk+UFwMO3dKZF0hRFxE0sC7\nrpT6Z+BlmIh6wOJOk7Bs4iUSgP+JnTy1W1BSSGHzxCuhrVghNxSEWAAKKWQve+e2ktxcOHw4OgUS\nIg4kJ+HCEUkDz4Np2D0eNG3RpklY1IaGzKDx2Y4p0NoEofB4IHX+dmn14mWYYTx4SIqol7NYyMYY\nY5RRsnBxrM3wMIyPm27JTsbHzVPE7Gx5YiDEbI2MmC+nfKrTHYNxMsQQSSSRTnq8iyKESCDTNvC0\n1r8Wi4KIBDYyAm++CXfumEbaqlUm15xTkIlw6urgrbdMAti0NNi1y3zNM3e5y0UuMsIIHjzsYAer\nWBXvYok40GjOcY673MWLl3zy2cOe6ObEHB+Ht9+G69fNzZUVK+Cpp0IvQC9dgg8+MBeg2dnmCUKF\ndBsXImJaw3vvwcWLJs3BkiXwxBOwbJk5Bo8dgxs3wh+DcTDEECc5SSutKBRllPEgD5IS0X17IcRC\nN+2tXqXU/1ZK5Qe9L5hIfi4Wi/ffh9u3A9Eqa2vNRWekRkbglVdM4w5MfqATJ+Zd+OhuujnNaSs/\nm7+ClRQZi9NtbnOLW1aurG66OcEJK5dWVJw+DVevBnLrNTXB668H5jc3m2NxeNi87++H116DAdkn\nhYjY1asmf6o/h11Hh6mzfD5zDF67Fv4YjJNTnKKVVsDcbKqllstcjnOphBCJIpK+PJVa627/G611\nFyADnhaTW7fs0+7ejTyhc12daeRFst4EVk+97eJdo2mgIU4lEvFUR51tWh99tjQqc+J0jDQ2Bhpw\nt2/b53u95vgUQkTG6Tjr7YWWlvDH4OCg++UKY4wxmmm2TXc6JwkhFqdIGnhJSqkC/xulVCGRjd0T\nC0W6Q9/+1FST82u2n59qeoIKlwpDUmQsTjHZH5yOkaQkSEkJP3+q6UIIu6mOozSHHLdJSZCcbJ8e\nI0kTr8mkLhJC+EXSwPtr4AOl1J8ppf4L8AHwDXeLJRLK9u32adu2Rd7AKy2FpUtDp6WkwNatcy9b\nDK1mtW0guwcPpZTGqUQintaz3pYTcyUroxtsxenY27w5cEG6aZP94jQ316RsEEJEprLSHpxo1Soo\nLJz+GIyDZJJZxzrb9Pu4Lw6lEUIkokiCrHxHKXUGeARQwKe11lddL5lIHDt2mAbZxx+bcXibNjlX\neuEoBZ/+NHz4IdTXQ36+CbBSWOhemV2QQQaP8AhXuEI33RRSyFa2yl3TRaqYYg5wgOtcZ4QRSihh\nC1uiu5ENG8z3ixdhbAzWrYOqqsD87Gz4zGfgo4+gs9Pk3tqzJ/CETwgxvZUr4dlnTQ67gQFYvRp2\n7zbzNm40dVi4YzBOtrGNDDKopZZkkqmggnIkqboQwgh7FaCUytZa9wNMNOhsjbrgZcQCd//95mu2\nMjPhkUeiV544ySOPfeyLdzFEgiiZeLlqw4ZAQ89JUREcPepuGYRY6FavNl9OpjsG4yCJJDZNvIQQ\nYrKpumj+RCn110qpg0opq8+RUmqtUuqrSqk3gCfDfVgpVaKUOqeUGlZKpUya97+UUh8ppY4rpV6Y\n+68hom5szDbJO/GKiM8XiEgWZHxsCK0jDM7ilvHxyAPEiLC84yPu/y/HxwPRW93aBONzi3yptSnn\nVByOJyFEjHi9kZ3zIzmWnYyOmq8J4wTW4cWLD6lvhBCxFfYJntb6iFLqaeDXgf0TwVXGgBvAq8CX\ntdb2ME4BncAR4KUw839Za+0QAk7E1b178O670NUFBQVw8CDe8lWc4xz3uIdGU0YZVVQ5d030+Uxa\nhcuXTaW6di0cOUJXXy2Nx76LbmmC7Gzy9z5F6ZYnYvu7DQyYkPJ375ogMZWVsH9/5GMJBQADvc3U\nvvWPeOuqIT2drB0HKN/9uehupKPD5J5qbDRPfx98cGbdgiPQTjtnOUsXXWSSSSWVrGHNzFZy6pQJ\nrz4yAmVl8OijofmxamrM8dTZabomHzwo4+OEiJWREXPOv3XLjLHbvBkOHXIOkHL2rEmJMDxsjuUj\nR8wxO5XBQfjWt0z3TaBnWznv/x876MscI4sskkiin35SSKGCCiqpdAyOIoQQ0TblmUZr/ZrW+pe1\n1mu01rla6yVa631a669P07hDaz08kVLBcTbwHaXUy0qpMH0iRMz19sLLL5vGHZjvr7zCpYEPucMd\n605kDTWc45zzOk6fNhe8Y2OmsXf7Nr6fvUrDS98yjTuA/n6633yR9vqLsfm9/F5/PZCsfXQUzpwx\nZRUzcu+Vb+GtvWv+jsPDDJx8k4arb0ZvAz4f/OQnpnEH5iLq+HGoro7aJsYY4xf8gi7Mvj7IIB/x\nER10RL6Sq1cDCca1NvkhX3klML+/H376U9O4A+juDs0HKYRw1zvvBBKUj4/DpUvmmJ3s5k2T6Nyf\nT7Kuzhy70/Ue+Lu/s/LnjXtHGDn3Aav+/mcAXOEKH/ERYxOv61znBjei/AsKIYSzeN1K+n2t9T7g\nLzFROkUiuH3b3q3S66Wm67xt0RpqnLud3LBXYIMXP0T12Ydqdt44OeuiztjAgKm0J3Morwivr7MG\n3Wq/t9Nz7aPobaSx0dxsmOz69ahtookmRhkNmeZPFhwxp/K0tpqnj2CeGkw+nnw+czEphHCX1+t8\nrDmd852O5c5OczxP5cIF68dhTONw6flaxhmnn358+Oijz1qmhpqIii6EEHMVlwae1rpz4vv7QLHT\nMkqprymlziilzrS1tcW0fIvW5DDRE5TDbpJEki1EfNh1JCeBw7IqlnmEkpKcu2JK98yZSXL+n6kw\n+87sthFmXVHcXxz33SmmOwpXTv/0cOWNY/4sIRYNpZyP0UinTTXdYb7/3KGTVMj74HOKdM8UQsRK\nXM42Sqncie8bgG6nZbTW39ZaV2mtq4qKimJavkXrvvvsuX3S0qhY8qBt0XLKnS+GHXLbZVYdQi8p\nCJmmkxRLNx+cU3FnxOOBigr79LlEBl2EcvJLSSqz96rOv/9A9DayYgUsWRI6TSnYEr0UBCWU4MET\nMi2JpJmFGXfad0pLzdhVgPXrHY8n7pNcVUK4zj/mbjKn/KtO04qLTYTaqezZY/2YQQYArXvWkkIK\nueSSTDI55FjLVOBQBwkhhAsiSpaklEoGlgcvr7Wesi+TUioV+BmwDXhjIkn6Q1rrrwPfU0oVYMbi\n/cYsyy6iLTPT5Kt7/31oazOV2/79bPEsR+HhLnfx4aOc8vD5vrZvN+MWLlww4/A2bCBp3z7WDG6n\n/r1/Yby+GpVXQNGeoxQsi3HY6ccfh6ws03UuLc2UdZ4lW08EFc/8NjW/+C6j1ddRmVnk7zzCinVR\nbOApBZ/6lNkPa2pM0JIHHzS5qqIkmWQOc5iLXKSNNnLJZStbySMv8pVUVJh96uxZM06wvNwEUfHz\neOC558zv0dpqjqd9+0zuOiGE+w4dMuf669dNg2/rVpODdbI1a+Cpp8wY8v5+cywfiOCc9u/+nck5\neeoUyYBnz0N0fGkjaQywk52kkEInnaSSynrWSwNPCBEzSk8ziFgp9VvAfwJawBp0pbXWlS6XzVJV\nVaXPnDkTq80JseBUVVUhx5AQsyfHkBBz53QcKfViVLeh9fNRXd9iIv+LxKeUOqu1rppuuUie4P0O\nsEFrPYPwckIIIYQQQgghYi2SBl4dIHG9hV1bmwkt3dwMhYUmp1xZmXvbu3rVpDYYGIDVq013uORk\n+MUvTG47jwd27pz5uLp790zo7O5uM/7r4EH7GDAhnPz4x/DGG6aL5qZNpsvW0qWB+c3NpotmS4vV\n5Tmkq+nwcGD/TU+HHTuinu9PCOGguxu+/W1Tr2RkmByWn/mMmdfba47L2lrTRTw720TVHB01Y2sf\nesg+vlYI4Tp5whi5sA08pdR/mPjxLnBcKfUqMOKfr7X+G5fLJhLZ6Cj86EcwNGTeNzebi91f+ZXp\nk8PORnU1/Pzngfc3b5p8YqmpUF9vpo2MmOTYGRmmEo5ER4fJd+Sb6H1cU2N+r1/9VbNuIcI5dgz+\n5V8C7y9cgG98w3yBOTZeesnsl2DSP/z4x/ClL0HOROCFn/3M7HNgGnvHj5sLx02bYvZrCLEo/dVf\nmbyoYI69H/zAjEN/6ilznPrzV378sbkJuHmzaexdvmyWf+aZuBVdCCGmM1UUzZyJr1rgTSAtaJpE\nCVjs7twJNO78vN6o5ioLceWKfVpNjXNOo48/jny9164FGnd+AwOmQhdiKseP26fV1gYSst+6FWjc\n+Y2NBXJz9fUFGnfBnPZ1IUT0NDQEGnfB3n3XzPM37sD0VAn+DiZnrD8puhBCJKCwT/C01n8KoJR6\nXmsd8kxUKbVwn2mKyIQLzjO5sRQtTuuNRhnCLTtN8CEhbEnMJ0+fbt+K9TEkhDCmOvYmzwt3vEod\nIYRIYJHkwfvjCKeJxaSiwj4GISnJvRxfTl3WSkth7drIlg1n40Z7svOMDBM2W4ipPPSQfVpxMaxb\nZ35et86EUA8WfIzk5jqnfti4MbrlFEKEKi11Hi++b585JnNzA9P8Y2qDx9auWWPGfAshRIIK28BT\nSj2llPrvwEql1DeDvv4XMB6zEorElJ4Ozz4Ly5aZ9/n58PTToZVgNK1fb3IaZWaaBtnq1fDJT8LR\noyZnkVKmwn3oIefktuEsW2bGXORN5D8rLjY52NLS3Pk9xMLx9NNm//Pvk/fdB3/wB4H52dnmGPEf\nE0uWmH02+OLx6afNzZKkJHNjYc8eqIxZBhohFq8//ENzMyUpydQdTz4Jv/RL5v2nPmUagWDqnqNH\nYflyM2/9epP/UgghEljYPHhKqW3ADuBPgT8JmtUHvKO17nK/eIbkwUtwPp+p+OK5vWiUIda/RwxJ\nDi+XjY/bn9YFm27f8vlMI3Hy02SRMOQYWqCmOnaDj1utzdcCrSNiRfLgJbZE/18kevliYc558LTW\nF4GLSqnvaa3Holo6sbDEusJz2l40yiAVt5itqRp3MP2+JfueEPEx1bEbfFzKDRghxDwyVZqEy4Ce\n+Nk2X2st/Yji6fx5E9a5o8N08frSl0zXr7/4C5MrLi3NdCP5jd+AEydMvp/WVjPu4Pd+L/Zh2AcG\nTK65+nrTnfPBB81Yh1u34Nw5E5GsogJ27w6fnqC6Gs6ehf5+0y1zzx7JRZSofvpTePttE3Bk5074\n4henbwQFGxqCkydNVMqcHLO/zDTHYl0dnDplolWuWgV794aOm2lqgn/6J5ODbtkykwMruHvk8LA5\nVi5eNPkWH3kE/vRPQ7dx547ZfwcHA/ukdO8VYnbq680x29Njjvd9+0wX6On8+Mcmqq3Xa7rvDw6a\ntCQrVpjzx717prF2//2mq3RXl+mOn5JilsvIMF0w29tN/VJSYp7WNTWZz+/eHeiyKYQQ88BUV1xH\nJ77/5sT3f5r4/svAoGslEtO7fRv+238LROs7e9ZUYEqZXFx+3/ueCe38zjsmPDuYpK6/+7vwb/8W\nGHfmNq1NbrmODvO+p8dU5Pv2mUTpfmfOmOSzR4/a11FfbxoN/i7F58+byvi559wvv5iZH/84ND/c\n66+b//nv/M7M1tHSYn7u7jahyz/3OXMRFomWFpODzh+RsrvbTPvCF8z70VHTWOvuDsz/xjfg6183\nF4gAX/lKaMqNl14y3bm+/nXzvqYGXnklsE92dZmvZ5+N/PcUQhhtbaae8B+zPT3mmH3hhamfnP3g\nB+YLzPH53ntm/GtpqUk50tdngnElJZm6csMG2LoVTp82DcFt28wyL71k5hUUwPvvm+U3bw6cfz7/\n+cCYcyGESHBh+wVprWu01jXAfq31H2qtL098/RHwROyKKGxef90eor252VRYk738cqBx5zcwAD/8\noXvlm6yuLtC48/N64Y037MveuWPuoE526ZI9LLXTekX8vf22fdqpU+ZiKhLNzYHGnZ/PZ/aBSF2+\nbE830NJi7siDeZrsb9z5jY/Dz39ufu7uNjdDJgvOfXfxon2frK42F6ZCiJn5+GP7MdvWZhpXU3nn\nncDP3d1mHX19po7p7TXvu7tNvae1uRk6Ompy3Q0PB27+gDn39PWZOqi3N3DOmun5Rwgh4iySgR9Z\nSikrHrhSah+Q5V6RxLQmJ08GU3GNOwQ3ndy48xsYiG6ZphKuDDP5PUZHZ7ZuET9O/yufL3zeuEg+\nDzP7X0+3jqEh5/n+6UNDznmugssQrjzhti2ECG+2x33w5/znGH9AlOAcdv6fvd7QhqTXG/hc8M8Q\nupzUNUKIeSSSBt5Xgf9XKXVPKXUP+B/AV1wtlZja3r32aTk5zjnhtm+3d29JSjLh2mOlrMx5rNzu\n3fZpRUVmjN5kTvn18vIi77InYmfbNvu0tWvNPhqJlSudx92sXx95GZz2l8xMM7YGzDHkNNZz3z7z\nfcUK5+5YwWNX/fnughUUuJcqRIiFzOn4zsiYfuxb8PnGP+zA4zHj6/znkdzcwPjb5cvNejMzzdja\n/HwzLg/M99xcc25IT4esoHvZMzn/CCFEnE3bwNNan9VabwMqgW1a6+1a63PuF02EtW+fGefjbzQV\nFMBv/Rb85V8GEif7B5R/61vw2c+aCg3M2ITf/M3AOKNYSEuDT3wi0HBLT4cDB0zeoe3bTSULZtD7\nM884r2PTJti1K3BRXlRkxupJVLPE82u/Zv6v/gh05eXw278d+eeTk80NCP9FV1qaCV4ykwusdetM\nI84f8KSw0KzTH+glP98cB/59MiMDPv1pqAqKPPzNb5r9DMx+tnYt/PVfB+ZXVsIDDwTWuWyZ2c9l\nnxRi5tauhf37Q+u1T34yfNAtv698xTTy/Lkkt283Y+nA3OjZvt005jwec3z7b5BWVZm8qSkppu45\neNDcjExKMnXs7t3mWE5LM59xuqEjhBAJaqo8eF/UWn9XKfUfnOZrrf/G1ZIFkTx4YQwPm6AO/gSs\nfnV1pkLzXyCD6XLW0GAutv0NqljT2oxt8N9d9RsdNd1fsiLo+Ts2Zrp2Zme7V84FKC45vHp7zf92\nLk+0+vvNBd90F3nhTLe/+Hxm/E1BQeAmyGR37pgyhHuSMJP9V8xbkgcvRsbHTd0203N8b6/5bGGh\n+d7WZm7QpKSY8XYpKebp3Pi4qQ+zs00DbmDAnF/S0uzni7mef4SN5MFLbIn+v0j08sXCnPPgERhn\nF2G/KhFzGRmmK9lkTuHkPZ7434FUyrmbXlpa5KHlU1Olsp0vcnPnvo65NuSn21+SkpyPoWAVFVPP\nn8n+K4SYWkrK7I774PNNSkrocV1YGDovuB4KvjEz+XwhNxKFEPPUVInO/37ix7/UWg/HqDzCDT09\nJsJmZ6fpilJVZZ7wvfKKiSaoFBw+DI8+Gu+SChG5GzdMGHQw4cw3bgyd39ZmwqL39po8eA88EP2G\nWGOjyYM3NGS6mAV3ORZCuGNkxKTVaWgIjKGrqzMBUvxpEPyuXoVr18xxef/909+wEUKIBSCSzMMf\nK6VagPeAXwAntNYSB3y+GBw0Ocn80QEbG02YaIDXXgssd+eOCQ/9S78U8yIKMWMXLoSmLKitNfv4\njh3mfUcH/Ou/BiKyNjaai8Fo5k1sbDT5t/yR9hoaoLUVnnoqetsQQoTS2uSsa2427y9dMulJKitN\nd8q6OtO1cs8e+OgjOHky8Nl79+Dxx80NISGEWMAiCbKyDvgCcBmT/PyiUurC1J8SCePaNXtI+M5O\nePVV+7L+HGBCJLqzZ6eedumSPd1GXZ1pgEXL+fP2vF03b5obJUIIdzQ0BBp3YG60eL2huTPPnzfT\nzp+3f97p3CGEEAvMtA08pVQpsB84AOwArgD/6nK5RLQ4JZf2+ZynR5qIWoh4m27/DbcvR3Mfd8ql\np7UJECGEcMfk486fny74hs7ISOBrMqnnhBCLQCR58GqB3wV+prXeq7V+Rmv95y6XS0RLebl9WkoK\nbNlinz55DJMQicop52PwNKf9Pj09kEYkGpy2kZsrefCEcFNZWWgU5oKC0O/+ZTIznQOOOR23Qgix\nwETSwNsBfAd4QSl1Uin1HaXUV10ul4iW0lKT08dfIaanm2Aqv/M7gaTPYIJQ/Pt/H58yCjFTDz8c\nGiVvxQoTKMhv06bQXHyZmfD009GNwLp9u7kp4s97l5trtiF58IRwT0aGGefqT2tSXm6ORX8Dr6go\nEDDs0UdNfkq/sjKT704IIRa4aYOsaK0vKqXuAHcw3TS/CBwE/sHlsoloefBBkwi2p8dUgv6L3L/5\nGxNcJSlJ7mqK+SUrCz73OZMHUuvQMOhgGlkPP2z2/f5+E2Uv2tEtk5PhySdNcubhYfPkThp3Qriv\nogJWrzbjyXNyTBqgnh7TTTM4/2tuLrzwggm6lJxsIm4KIcQiMG0DTyl1BkgHPgDeBw5qrWvcLpiI\nsvT00DuZfhIyWsxnwd2ynGRmmi835eQ453cUQrgnJSW0TsvLC79scKNPCCEWgUjSJDyltW5zvSQL\nVUeHieg3OGiekm3aFJ27/H19JlR8T48ZV3T//abCu3PH5AdLTTXTiovnvi0hoq2/Hy5eNE/gSkrM\nvhrcfXJ8HH7yE5PnrrAQnn3WeTyNECI+fD5zfNbUmBsc27a5+4Ssvd3UpUND5und6KiJoJmXZ7po\nyk0WIYSwRNJFUxp3s9XWFpqL69YtE8o5eKzQbPT3w/e/H4gGdvu2yQNUVgYnTgSWu3bNXBivXj23\n7QkRTUNDJjdjf795f/u2uTHxmc8Ebn78+Z8HkpiDSWr8Z38mjTwhEsUbb5ibiX5Xr8IXvuBOI6+1\nFf7t3wJ16U9/arplrllj3l+7ZrpiZmdHf9tCCDEPRRJkRczW2bP2XFyXL8PAwNzW+/HH9lDP9+7B\nO++ETvP54PTpuW1LiGi7ciXQuPNraDBfYBp8wY07MGPcfvKT2JRPCDG1rq7Qxh2YlAQXXEqRe+ZM\noC7t6zM9V1paAikSBgdNvSiEEAKQBp67nBIe+3xzb+D19tqneb3O23NaVoh4CrdP+qcHJywO1tHh\nTnmEEDPjVNeAe/VN8PZGR813rQM/u7ltIYSYh8J20VRKfXqqD2qtfxT94iwwZWWBpxJ+mZlzH/C9\napXpDhMsPd2M8Ztcya1aNbdtCRFtq1ebsTTBkpJMSg+AykozHs9/d95v69bYlE8IMbXiYkhLC21g\ngXv1zapV0NRkfs7JMV25U1NDAyjJUAQhhLBM9QTvE1N8HXW/aAvAzp2hFV56OjzxxNzDtW/YAJs3\nB96npMCRI3D0qAkf77dsmcmBJ0QiWbvWBFXxj7dLToZDh0xIczAXcL/yK6FBVzZvhk98IvZlFULY\npaXB44+HHqPr1pnj2g07dwbG36almfyT69cHziGbN8N997mzbSGEmIfCPsHTWv9aLAuyIKWmwqc/\nbYKtDA6aaIHRSLSslKlcd+2C7m6T5Nmf9PUrXzGRxVJSQhNBC5EolDI3JHbuNGN5iotNwIRgjz8O\ne/eaJ33Ll5uLRyFE4li3zjS6mppMcJOlS93bVloaPPecCbYyNGTqUq/XbDs/f/p0KUIIschEkiYB\npdQzwBYgwz9Na/1f3CrUglNU5M56CwrsFVtyskQaFPNDfv7UEfdyckwScSFEYkpPD0SyjIXgvHep\nqWZYghBCCJtIEp3/HZAJHAb+P+AzwKkIPlcCvAJsBrK11uNB87YCfwco4De01pec15KgenrMGLiR\nEXMXs7TUDPi+fRvq6kxXsy1b7E8loml42JShu9vkwVu/3oxjcjI6apbt7DRPSzZsMA3B1la4ft0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ow9bcRHHx2kbV71hoYga9ZE5ugTkdTSE7/vaOVYVVUTGzdGlk3RNDc7PvooMs9btO0m0kcf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\nPnx4H775zXGh8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/1tZwD/iTdDmAN+45y7x8w2+/s4C29q6K/h/WjHAj9xzt3nb+tp4Eq8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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118500ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create dataframe from data in X_train\n",
    "# label the columns using the strings in iris_dataset.feature_names\n",
    "iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)\n",
    "\n",
    "# create a scatter matrix from the dataframe, color by y_train\n",
    "pd.plotting.scatter_matrix(\n",
    "    iris_dataframe, \n",
    "    c=y_train, \n",
    "    figsize=(15, 15), \n",
    "    marker='o',\n",
    "    cmap=mglearn.cm3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7.4 Building your first model: k nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 1.7.5 Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_new.shape: (1, 4)\n"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
    "print(\"X_new.shape: {}\".format(X_new.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [0]\n",
      "Predicted target name: ['setosa']\n"
     ]
    }
   ],
   "source": [
    "prediction = knn.predict(X_new)\n",
    "print(\"Prediction: {}\".format(prediction))\n",
    "print(\"Predicted target name: {}\".format(\n",
    "       iris_dataset['target_names'][prediction]\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7.6 Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set predictions:\n",
      " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(\"Test set predictions:\\n {}\".format(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.8 Summary and Outlook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state=0\n",
    ")\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  }
 ],
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