Notebooks/Lab_Iris.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## Лабораторная №1\n",
    "\n",
    "## Учимся распознавать ирисы\n",
    "\n",
    "Мы используем знаменитый в области Data Science набор данных, содержащий измерения 4 параметров длины цветка для трех разных типов ирисов."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import cntk\n",
    "from cntk import Trainer\n",
    "from cntk.learners import sgd\n",
    "from cntk.ops import *\n",
    "from cntk.io import *\n",
    "from cntk.layers import *\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"350\"\n",
       "            src=\"https://en.wikipedia.org/wiki/Iris_flower_data_set\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x948494d358>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('https://en.wikipedia.org/wiki/Iris_flower_data_set', width=800, height=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для чтения данных в формате `CSV` используем библиотеку `pandas`. Объекты, с которыми оперирует `pandas`, называются *data frames*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "5             5.4          3.9           1.7          0.4     setosa\n",
       "6             4.6          3.4           1.4          0.3     setosa\n",
       "7             5.0          3.4           1.5          0.2     setosa\n",
       "8             4.4          2.9           1.4          0.2     setosa\n",
       "9             4.9          3.1           1.5          0.1     setosa\n",
       "10            5.4          3.7           1.5          0.2     setosa\n",
       "11            4.8          3.4           1.6          0.2     setosa\n",
       "12            4.8          3.0           1.4          0.1     setosa\n",
       "13            4.3          3.0           1.1          0.1     setosa\n",
       "14            5.8          4.0           1.2          0.2     setosa\n",
       "15            5.7          4.4           1.5          0.4     setosa\n",
       "16            5.4          3.9           1.3          0.4     setosa\n",
       "17            5.1          3.5           1.4          0.3     setosa\n",
       "18            5.7          3.8           1.7          0.3     setosa\n",
       "19            5.1          3.8           1.5          0.3     setosa\n",
       "20            5.4          3.4           1.7          0.2     setosa\n",
       "21            5.1          3.7           1.5          0.4     setosa\n",
       "22            4.6          3.6           1.0          0.2     setosa\n",
       "23            5.1          3.3           1.7          0.5     setosa\n",
       "24            4.8          3.4           1.9          0.2     setosa\n",
       "25            5.0          3.0           1.6          0.2     setosa\n",
       "26            5.0          3.4           1.6          0.4     setosa\n",
       "27            5.2          3.5           1.5          0.2     setosa\n",
       "28            5.2          3.4           1.4          0.2     setosa\n",
       "29            4.7          3.2           1.6          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "120           6.9          3.2           5.7          2.3  virginica\n",
       "121           5.6          2.8           4.9          2.0  virginica\n",
       "122           7.7          2.8           6.7          2.0  virginica\n",
       "123           6.3          2.7           4.9          1.8  virginica\n",
       "124           6.7          3.3           5.7          2.1  virginica\n",
       "125           7.2          3.2           6.0          1.8  virginica\n",
       "126           6.2          2.8           4.8          1.8  virginica\n",
       "127           6.1          3.0           4.9          1.8  virginica\n",
       "128           6.4          2.8           5.6          2.1  virginica\n",
       "129           7.2          3.0           5.8          1.6  virginica\n",
       "130           7.4          2.8           6.1          1.9  virginica\n",
       "131           7.9          3.8           6.4          2.0  virginica\n",
       "132           6.4          2.8           5.6          2.2  virginica\n",
       "133           6.3          2.8           5.1          1.5  virginica\n",
       "134           6.1          2.6           5.6          1.4  virginica\n",
       "135           7.7          3.0           6.1          2.3  virginica\n",
       "136           6.3          3.4           5.6          2.4  virginica\n",
       "137           6.4          3.1           5.5          1.8  virginica\n",
       "138           6.0          3.0           4.8          1.8  virginica\n",
       "139           6.9          3.1           5.4          2.1  virginica\n",
       "140           6.7          3.1           5.6          2.4  virginica\n",
       "141           6.9          3.1           5.1          2.3  virginica\n",
       "142           5.8          2.7           5.1          1.9  virginica\n",
       "143           6.8          3.2           5.9          2.3  virginica\n",
       "144           6.7          3.3           5.7          2.5  virginica\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы можем посмотреть на то, как коррелируют друг с другом те или иные признаки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6f4qIyDgRi8HSpdDaCpde6ny/5prCJ0He10p5wlTObYtIdOXGgjvucMapCy+Es892wo69\ndTRuiIwszPF2rMfm/JDiXGD5tdeWrq/q3EHqXCVuszobOMda+7sSbe/NuK/mORP4D+Bhv8rGmNcA\nG4E1wBXAxcB9xpjnrLU/KlGbRETqT1eXc3KVy56opUeOiojkW7XKGa9yHn54+JOrRKT2KKRYZNQq\ncWXOb4ETSrUxa+0frbUHcl/Au4BnrLU/C1jl48Bua+2N1tqd1tp24DvAilK1SUSkLoUMDswMZAou\ni0j9q9o4oKBTEV81f2xW3xUZtbJM5hhjjs19AZ8C7sje6vSq/Neyr49lP41AG7C2QLVzgR97yn4I\nnDeWfYuI1LVccGBbG8yY4XzPBQf6VR/I0Hu0l7b1bcxcOZO29W30Hu2tvZNGESmbqo0DIccrkfGi\n5o/N6rsiY1Ku26xexAk3zjFAp6dOKQKQ3wtMBb5eoM4pwAuesheAY40xE621r4xh/yIi9SlkcGCs\nIcbyR5az7tfrAFj363UYDB2Xd/jWF5H6U7VxQEGnIr5q/tisvisyJuWazLmoTNv1+ivgUWvtH8qx\n8RUrVjB16lRX2ZIlS1iyZEk5diciJaB+W0IhgwO79rjve9+0R/e9S3HUb+tH1cYBBZ1WnPptNNT8\nsVl9V2TUyjKZY60dHCWMMacBe621+VfqYIwxwMzR7iO73YuB94xQ9Q/AyZ6yk4GXR7oqZ+XKlcyd\nG/qBWyJSReq31ZOYlWDL/i3MmTaH7sPdzD91frWbJBGhfls/ErMSg1cBACyYpfyLeqV+Gw3qkyL1\nqxJPs3oWmA4c8JRPy7422unXv8K5XeqREeo9DlzqKbskWy4iIiHZVArT2OhaHog18MA719I4oWmw\nPHU0SWYgQ6xBf2UTGQ8yAxnaF7djMGzas4kFsxbQvriddCZNPBZ31YtZ3H+B995aUWh5pLoiAvj3\nydWLVw9m5uQfn6t2vA7bn9NpiMeDl0XGkUo8zSqXjeM1BRjVc26zV/V8BHjAWjvgee0Lxpj8DJ1/\nAWYbY75kjDndGPMJ4H3Al0ezbxGR8cymUtDf74QUzpzpfO/vpyEzQPxI0lUeP5J0PrCJyLgQa4jR\nPKGZjss72LtiLx2XdzC5cTJ9qT5XAOtAOoXt7XWPI729Q6GnmYyz7Pd6oddExMWvTzZPaAaojWDk\nsP05nYa+Pnf9vj6nXGQcKts0pjEmN1ligc8ZY47kvRwD5gNPjnLzF+PconW/z2vTybt9y1r7e2PM\nO4CVwN8A+4Cl1lrvE65ERGQEprERli+HddlLttetwxjjhBeuWeNfLiLjhvcv+/FYnOUbhgJYt+zf\n4lzBd/VS13hB/ngRiw0bZ1yvF3pNRFy8fTK3XBPByCP1da94XP1fJE85r0l7U/a7Ac4Ejua9dhTY\nDtw1mg1ba39EwO1Z1tqrfcq6gHNGsy8REfHococpsikbk5Z365WrXETGtfwA1jnT5mQLA8aRwZUK\nvD7SuiIyopoJRg7bn9X/RQaV7TYra+1F1tqLcB4bfmluOfv1dmvtx6y1T5dr/yIiUiaJhHt5QTZM\nMZWC1lZYtAhaWobKRWRcu27edbRMawHgmcPPOIVB40hOoddHWldERpSY5e5HVQtGDtufEwmda4hk\nlT0tyu9KGRERqQ5vwGHBwEOfUEI7MADt7c4tVJs2wYIF2NWrIZXCXH89fOYzQ/WTSQWTioxjufHl\nxvNv5Mbzb6Rzdyff2vEtUkeTxD3jCKtXD40XmQy0tzu3T3hft3b4a+3tw0NQNfaIAP7HfSAwGLmi\nIciF+rpf/02nYe1aaBp62ALJpEKQZdwq+2+9MWZ9wEsWJwC5G1hnrd1Z7raIiIxnmYEMvUd7Wf7I\ncrr2dJGYlaB9cTvNE5qHn7zlQgmXL3cuaU4knEmc5mbs5Mnu+9NTKUxDgxOMvHSpq/6wW69EZFwI\nGm8ufM2FAJjmRvc4kv/hLRaD5mb3652d8NBDcNddMGWK+7VcKKpnvKK5WRM6Mq4VOu7ngpHz61bl\naVZNTe7+nCzwfBxjnNe95xrNzeVvp0gNqsQU5svAe4AXga3ZsrnAccB/AB8EPmWMWWit/UUF2iMi\nMi7FGmLFBx4WCCU0ng9Hg48pVyihiGSFGm9g+KRLbvmOO+Bf/xW6u53lP/1p+LiiUFQRX2H6YVUm\ncmIxuPJK2LzZuWWquxvmzw/uu2EDk0XqXCUmc/YD64Brc48RN8Y0APcAfcCHcB4f/iXgggq0R0Rk\n3AoVeNjV5dyXPmeOc4KlUEIRCaHnpR7X8qgCVletgn378jba419P44+Ir5oJOg7S1QXHHDO0XMy5\nRphzE5E6VrYA5DwfBf45N5EDkP15FfBRa60FVgNnVKAtIiLRlckUXi5CqMDDDRtg1y549FF4+mln\nGRhIp1zVBpeDQgzTaXe5d1lEIiWXueG3nP/zz67+GRuXbOS4puMAWNyy2H/dQmNbblw5/njYuNH5\nIOdXf9Eid5lCUUWA4ON+oX7sFVi3BOclQecaoev7taUU7ROpYZWYzGkE/syn/M8Yerx4EidDR0RE\n/OQybNraYOZM53tvb6gTk8xAhvbF7bSd2caMY2fQdmbbYOChl02lsLNnu/ZnZ8/GplKYvn5Xuenr\nx6ZSzn3rbW0wY4bzPRuMTF+fu919fZrQEYmoXAZH2/o2Zq6cSdv6NnqP9pIZyPi+dt7M8/jO+79D\n25lt3HPpPcNeH0insEFjWy4cta0Nvv1tOO882LYNXnxxeP1Vq4aPP/rgJuNcOpP2Pe6nMqnAfuwV\n1OdtOj3m8xLSafCcazB7dvA5QlD9VGp4W/zKwrZPpMZV4jarB4G1xpgvAFuyZW8BPgN8I7u8ANhR\ngbZIEXp6ejh06FCodU444QROO+20MrVIREpxn3isIVZ04KFpbBy2P5Pb3223+Zd7A0tzgaa6v12k\nboyUwRH02sLZCwFY+r2lg69v2b+FxglNcPXS4DEif1y54Qa4+27nQ5lf/aBAZZFxKh6Ls/vAbtdx\nf8eBHbzhpDewfEPxWTrefj19ynTuuuSusR/fw+ZdFaqff26yZYsTrLy0wNgiUgcqMZmzAngBuBE4\nOVv2ArASJycHnCDkxyrQFhlBT08Prz/9dI4USpL3Mampiad27tSEjkg5lSATwjtxUzDwMGh/Bw/6\nlwcFmCrLQqSuFMrgGCmfI//1OdPmZAsLjBH548qBA4Xrr1zpXLnz4IOayBHJuuyhy2iKN9EyrYXu\nw92cO+NcHnzvg6GydLx1X+h/IftCCY7vYbdRzLnJnCLGFpE6UPbbrKy1GWvtP1lrp+M8weo4a+10\na+0XrLWZbJ0ea+2+wluSSjh06BBHkkk6cB49VsxXB3AkmQx9NY+IhBSUSVPp/Z14Yrh2VLrdIlJW\niVkJWqe1sqhlES3TWlzZWyPlcuW//szhZ7KFRY4RJ51UuP4PfgBWd+2L5EvMSmAwg8snTz55sDxf\noQw9b93cNkpyfA+7jWLOTZ4JObaIRFQlrswZZK19uZL7k9F7Pc7z40WkRuSyI4xx/rK0YMFQJkQJ\n/gLtvd1qIJ3CtLc7t1Bl92dzGTi33oo5cMBVbtMpGuKNwzecTvu3O512LpcWkUjJDGRY++61NMWb\nBsuS6STpTBpjDO2L2zEYNu3ZxIJZCwZzuWINscHcrtzr806dR+pokrhnrPEd2zIZuOUW58qbe+8d\nPqYcOACnnFLScVEkarzH8nQmHdhfC/VV7za9dW9O3IxNpzFjPS8Je24TVD+dhltvdcaBTZtg3jxI\nJst63iRSC4wt818wjDEnA3cBC4GTIG9qGLDW1lxvMsbMBbZu3bqVuXPH15TGtm3bOOecc9hK8ZM5\n24BzgPH4/zXOmZGrVM646Ld+H25KNJHTe7SX5Y8sp2tPF4lZCda+ey2NtoFY44SheqmjEIthBgZc\nEzcDQRM5Od6JG03kVJP6rYyJ33jRvridpngT1z16HR96w4cG83Fy9fM/HPouW4ob23Ll3tfzx5T6\n/KCmfisjCjqWJ9PJYf21eUIzQMG+6d22b91SnJeE3UZQfb9yKMt5U5Fqqt9KfarE2fQDwGnA54Dn\nKcFTq4wxr8bJ27kUmAQ8DVxtrd0WUH8B8BNPsQWmW2sPjLU9UnphQ5gVwCzjQlAmzVg36wk33LJ/\nC03xJtrWt/FC3wucN/M8Ht/7OKdMOcUJR/Sc7BWcyIHhEzeayBGJrEIByD/9/U+5b9t9tExr4aNz\nP8qN5984Yk6X74fHoLEtV+59PX9Mqb+JHJGiBB3L80PHRwo6LrRt3+VSnJeE3UZQ/WK2o/FB6kwl\nzqgvAN5qrX2yFBszxhwH/ALoBN4OHAJagf8eYVULvA7oHSzQRE5NGk0IswKYRcbGL5S0a08X+17e\nR+eznQDMOHZGVdomIrUlKDg1F7DafbibVZtXceP5N1ajeSLjVtCxPF+hoGMRiZZKTObspbSXmd0E\n9Fhrr8kr21PkugeV21P78kOYX19E/aeAD2cDmDWZIzI6iVmJwb/c5UJJE7MSTGiYwEWvvYjO3Z1k\nbKaaTRSRGpE/XsBQcGr34e5hZSJSOUHHcr/+KiLRV4nJnL8FbjfGfMxa+/sSbO9dwGPGmIeBBcB+\nYI219r4R1jPAk8aYJuA3wD9aa39ZgvZImSiEWaT0/O57B4aFkibTSTre9QCm0bmF6sqzrsSmUgXv\nqS9Xpo+I1A6/MNTVi1eTTCeZf+p8kulkwTDVoQ3ljQ+Fxo6RxhWNOyLA8L6ZO5YHBR1D8Zk5wTsN\n2f/CZOipb4uMqOyPJge+BVwIPGOM6TXGHM7/GsX2ZgMfB3YClwBfAe41xvxlgXWeBz4G/E/gcpyr\nhX5qjDl7FPsXEYmkXDhi2/o2Zq6cSdv6NnqPOneeNk9opuPyDvau2EvH5R1MtDHo74e2Npg50/ne\n308sMxCw8Qz09rrr9/YOBRCKSF2INcSGjRdTGqcAuMryn54zTP540dkJL73kP3aMNK5o3BEZ5Nc3\nGxsah5Xlwo/9zgdykzxFCdv/0mno63PX7+tzyse6bZFxqlJX5pRSA7DZWntLdnm7MeYMYBnwoN8K\n1tpdwK68oieMMXOAFcBVJW5fXXiqTHVFpHoKBZd6mcZGWL4c1mUvzV63znl0cMfwus7GY8PqU6i+\niESW96/38Vicq9Zfxeb9mwdzc+afOt93bHE2kB0vtmyBb37T+aAWNHYUGlc07oi4FBMwnisr9nwg\neGch+188Xnx99W2RopR9Msda+/USb/J5hs8fPIVzxU0Ym4HzC1VYsWIFU6dOdZUtWbKEJUuWhNxV\ndLzyyis0AB8OuV5Ddl2RahuP/TaMrj1dvG3225g/Yz6P7328cBBilzs0kU0jhCZ2dUFrK8yZA93d\nI9cXyVK/jb5cYHouNyeZHuEhBl1dcMYZQz/nyx87vOPKgOfqwLDjlJSM+m20lSQYOexxP0x/1TmF\nyIgq8nzY7FUwVwNzgE9aaw8YYy7FCTLeEXJzvwBO95SdTvEhyDln40wMBVq5ciVz546v1JaJEycy\nAHARcHyRK/03DPzEWVek2sZjv/UVcK/5jr/ezrGTpw0Wv9zv3O3qe698IjH0VzGABQsKbpsNGyD/\n/37btlL9a6TOqd/WvqC8rVxZ6JDVRMK5Mif3s99YA8PHlfzbLNJpuPxyuPde/3WlrNRva49fPw3K\nwSlJMHLQcT/oPKFQX/fWDbttkXGo7Jk5xpgFwK+B+ThXz0zJvnQW8L9GscmVwLnGmE8bY+YYY64A\nrgFW5+3zC8aYr+ctf9IY8+5s/TcYY/4ZZ7pi9bCti6MVeGORX61VaqOI+Au419ymUjSnGlzlzakG\nbCo17N55m0ph29udujNmON9Xr4ZUKnDbdvZsV7mdPRubSlX7f0NExigobys1kKJtfRuduztZdekq\n2s5sY8axM2g7s80Vsjp8gxlob4d585zMnFWr3GPNPfc4H9yOHgXPuMJLLznr5PI27rxz+DilXA0Z\nh4L6qV8/TGfStC9uH9Zn0xmf/Jog6fTw/jl7duB5AqmU0++9/TWZ9K8bZtvq8zJeWWvL+gU8Dlyf\n/bkXmJ39eR6wb5TbXAz8F3AE2AH8lef1+4H/m7f898DTQD9wEOgEEgW2PxewW7dutePN1q1bLWD5\nayz/WOSzJvknAAAgAElEQVTXX2NL+f+Va8NWsLaIr62Udv9StLKPH2G+xnO/9XXFFe6+8sUv+pe3\ntVlrrf2nrn9y9eubfnSTtUePureZWw7Yhl2xwr9caknV+2r+l/ptdFzxf65wjRFt/8fp3y33ttjj\nbj/Obty50VU/nUkX3mA67f+ztdZu3GjtOedkdxww3rS0+I8x3m3Vh6r31fwv9dvaFdRP/Wx9bmvB\n5eJ2GPJ8IJVyr59OW3vNNe66N9wwum3Xnqr3VX3V/1clbrM6E7jCp/wAcMJoNmitfQR4pMDrV3uW\n7wTuHM2+REQiyXtfeu4S5ID71Rtjja7ijl938MWLv+ium31MeeA97wcP+peLSOQF5WvkAo/f+W/v\n5ILTLuBnV/8M8A9edcm/LSL381/+JTzxhJOPsWhRdscB401Li/8Yo9stZBwLk4Nz2UOX0RRvGuzD\nyXSSvSv2htxhyPMB72PIYzF47DF32QsvjG7bIuNQJR5N/iIw3af8TcD+CuxfRGT8SSSc4MBFi5wP\nPblLkL3l2fvVU5kUrdNaWdSyiJZpLYXvnU8k3Mu5e95PPNG/XEQiLzHL3e8/+IYPAs7YkTNr6qyx\n7WRgwJnIAXjmmeyOA8abVEpjjIhHYlai6GN5YlaC1x73Wt786jcza+qs0WXmFDof8DnXKGobJ588\n8rb9ykXGoUpcmfMQ8CVjzPtxbodpMMacD9wFfKMC+xcRqUlhggoBbDqNyfurlnd5aEMZWLsWmpqG\nypJJ58OPT7lNpfj7+dfzmbd+ZrA4dTTp355c1oUxzl/DFiyA1audbJxbb8UcODBYblevdu6ph+Ht\nNkYBhiIRkRnI0L64HYNh+x+28/X3fp25051g0h9f+WM6d3fyrR3f4s633Uk6kyYei7vWbRiwQ2NA\nUHhpKgVr1gyNLfPmOeOWz3hDJgM//rHzejo9/K/9CkiVcSgzkGHtu9fSFB86xifT/sfyzECGb7zr\nfmKNE4bKUkeHBZvn6vqemwScD5BOwxe+AF/+8lDdZNK/H/pt4+abnW0EbfvWWyHvXMOVk+Xt935l\nGgukjlTiypzPAL8D9uKEH/8W6AJ+CXy+AvsXEak5YYIKwZkAoa/PHfrX1+eU+/EGCiaTzkmRT7kx\nhvgRd3n8SJKY9dluLAbNzdDRAXv3Ot+bmzGNjTBlirt8Sjbv3tvudBqrAEORyIg1xGie0EzH5R08\nuexJZh8/2zV2nfPqc/jKO77C5MbJ9KX6XK+RyQyNAZ2dToBxft9/8UUn7Li/HyZNco8h8fjw8Wb3\nbjjrrKFxzSsgAF7ji4wHyXTS1f+SaZ8+AjQMWBr6j7j6SUP/EczAQPHnJtY6fxzK759NTU653zmI\nn4BzCt++X6gcgkOXNRZIHSv7lTnW2qPAR40xnwPOwJnQ+ZW19uly7zuqenp6OHToUKh1TjjhBE47\n7bQytUhESi3WEGP5I8sHHwu67tfrMBg6Lu/wrW/icVi+fOiRnuvWOVe3dPjUj8WG1WX6dLjrruHl\n2W2Y224rbtu57fsse68SGlzO3+eWLZimJli61LcdIlKbcn+ZH2nsWr5h6LWD/Qedv/wvv9p5DPk3\nv+l8oPLr+zfcAHff7d6p94qb66+HlSudn3fs8B83/MY/jS8yDoQ5rwg6pzAdHax5fE1x5ybxOFx1\nFWze7NxK1d0Nl11W8FzDv+H+5xShy4P2uXkz7NunsUDqUiVuswLAWtsD9FRqf1HV09PD608/nSNB\nM9gBJjU18dTOnZrQEYmQMEGFzgoBYYDF1K1moGD+PufMKdwOEal5hcau/Nfmz5ifLeyCM84Y+tm1\ncnbdAwdG3vG3v+2/7rAGanyR8SnUeUWRD0QYcRv79g1lXY10rlFOhcLSc+3TWCB1piy3WRljvlzs\nVzn2H2WHDh3iSDJJB7C1yK8O4EgyGfpqHhGpLm+g6Ijhg0FhgMXUrWagYP4+80NNiw1HFJGa8v4/\nf79rOX/syh/X/t++/+f8cN11g/lZgWPQSSeNvONix8AwY6VIHQkTgBzUT/JDzWGEcxPvsXykc41y\nCtpnbiKnUu0QqaByXZnzpiLr+SUyCPB6YG61GyEiJeEXdAwMBopu2rOJBbMWsHrx6sCgQZsNAzR5\nYYC5gOFhIcijCBS0PuHFpgRBgcPaPW8eNpmEtWud261ygsIRRaQm5I9NX377l/nwGz/Mld+9krNP\nOXtw7LLWsmbxmsGg5Dv/x+3Oyjfe6HwdOACrVrnHoHvucTJzbrnFPQZ4xwNvQHJ+IGr+GBgUyqrx\nRepcmADkoHMKm07xibd8gt8e/O3I5ybpdPDDFsrZB/0CzsF/n8kkzJ/vfNdYIHWoLJM51tqLwq5j\njJkBPGetHShDk0REqiIXdLz8keV07ekiMStB++J2mic0DwaK5tcNepqVicexuYDhnKCnWeUHCg5u\nPHuykwsrzEkmsUB6UhONeeXpo0kaDIz1dMe33akUpr/fyc3p6nL+mtbeDo2NwRsSkaoJGse2L9tO\nrCFG5+5OHtrxEHe+7U6mNE5xxrX8IOJcP1+zBiZPHj42vepVwydyenudDIz8McK7rt+TrILGP314\nk3EgmU6y9HtLXf20ccLwY+tAg4HJk4jl9ZOB1FFoaCj+3CT3UAXvsby5uXx9MGhsCNonaCyQulax\nzJwi/BY4G9hd7YbUgqfKVFdEKitMIGGhx5JDgYBh3x0HBAReeaU7rHD+fExHB5/64Y18f9f3aZnW\nQvfhbuafOj8wjDmsYe1ubFRAqUiEFBrHLv7GxXQ+2wnAn1J/Gho3ig0i9gs1LXbdoDEwaPwTqWNh\nzzfa/v1KXuh7gfNmnsfjex/nlCmnhDs3CRM2Xqo+ONZ9aiyQOlNLkzmm2g2oBa+88goNwIdDrteQ\nXVdEak/ooONy8oYVZsPWDx45SPfhbroPO+VBjzMtaTvyKZRQpKYFjWP5Yak9L3mec5Hfz1tbnQ9d\nRe9QY4RIWGHON7r2dLHv5X2Dk7Ezjp0xih12OX17zhznvKKaQcci41BZApBl9CZOnMgAwEXA5UV+\nXQQD2XVFpPaEDjoOI3cZcdDysMb4BwSeOMkdgLy4ZfGwbdlcgGkpKKBUpObk8rz8loPGse7D3Rzf\ndDwbl2yk62rPh6xEAo4/HjZuhF274MEHsxseYZzKrevaoc8YUcx2ROpQUF/19lO/Y/lAOuVbd1Tn\nJhs2OH370Ufh6aed5XLT+YPIoFq6MkfytQKvLrLuc8BPytgWERm1zEAmVNBxuI0XuHfc51LigXQK\nExB4eOuCWznQf2CojW+/B9vbi/Fs206ZUvj2rmLbrYBSkZpSKN8L/APbk+kk80+dz9VnX82bpr+J\ntvVtg+ve/877aGxvx+zeDbNmubNzCoxTTmMCxoigfA6NGzKOBPXVyY2Th/dTn2O5aW9nYIpP3bDn\nJuk0zJ49vG/7ZVmV7B+v8weRfJGczDHGvBr4EnApMAl4GrjaWrutwDoXAncDbwB6gH+y1n69/K0V\nkfEs1hALFXQcbuMh7h0HGuKNbO/fzVl5r//X/m2cddxcDrx0YPi98lcvdW3blCrXRgGlIjVnpLwN\nv3EMGCxrW9/mWndy42S+uvgrMHeu82EvTEZW0BixbJmytmTcK9RX9/9xf1HHctPRQXPDGM9N4vHK\n59/p/EHEpZYmc4p6TLkx5jjgF0An8HbgEM51LP9dYJ3XABuBNcAVwMXAfcaY56y1PxpTq0VERuA9\nOSrJRE5OyHvH3/nwZTTFmwaDjpPpJHtX7GXDzg18detXaZnWwiVzLmHFuSvKe1+6AkpFak6hvI2R\nxjHvuo92PzrUr0czlviNEY89Fn47InUoqK/e/ovbeWLfE0Udy0tyblKN/BqdP4gMqqXMnGJT8W4C\neqy111hrt1pr91hrf2ytfbbAOh8Hdltrb7TW7rTWtgPfAVaMtdEiIlUV8t7xxKwE3Ye7eaz7MboP\ndw/eI5/KpDDZYXj7H7YPbbu1FRYtcp5+pfvSReraWDI0Cq573XXOGDL44ijHEmVliADB/e3ESScO\nHuMfffrRbOUy9hudJ4hUVS1dmfPnOOkvI3kX8Jgx5mFgAbAfWGOtva/AOucCP/aU/RBYOZqGioiE\nYdNpV86Md3nUCtw7njHuv7JlBjJYa33vkU9lUlx/3vV85q2fGaw/kE5h1q7FNDUNtTuZhBK13Xs5\nd8luPRORURlNvlc6kyYei/uue++ie3mp/zBTJ0+DG290vjo74VvfgjvuGLo1Ivt9xDFAWRlSx8Ic\nE4P6ajqTduXfnTT5JDKpozQEZOUZjO+5Sa5f53iXhxqSgbVrIe88gWRSfVKkgsoymWOMWV9sXWvt\n5dnve4tcZTbOlTZ3A/8EzAPuNca8Yq19MGCdU4AXPGUvAMcaYyZaa/VMbxEpC5tOQ1/fsJDikgQJ\nB9w7njEEBplOaZziukc+nUljjKE/1c/S7y0drP/AO9cS9wkbNc3NY2szhYNWNaEjUj1N8SbX+JBM\nJwPrpjNp+lJ9LN+wnO1/2M433vsN17o2ncb09Q0PR73wQti+Ha68Es46i/Tqe9lxZA+zXjW78Big\nrAypU2GPiYWy+JrN8HI7ZTKmI79vZq/E9Tk3GZgymb50P8s3uNsyhSn+Ezp+oeSNjaX9DxKRQOW6\nMuelMm0XnFvDNltrb8kubzfGnAEsA4Imc0REqsL4BASWLEgYfO8dj0HBINN8uZOz/Ppb9m+hcUJT\n2QKQRwpaFZHKizXEuPLfr2Tz/s2DmVrzT50f2C/jsTjLNwz143O+eg7Xn3s9d7/9bsB/7BsMR/3g\nB6G7G3bsIG4MZ3V0cMMPbxhcN7iRysqQ+jOaY2JQ3o1vuaesIZ6dbPE5NzEdHdzWeVtxbQn5EAYR\nKb2yTOZYa68ux3azngee8pQ9BVxeYJ0/ACd7yk4GXi50Vc6KFSuYOnWqq2zJkiUsWbKk+NaKSEXV\nZL+tQkBgoSDTkerPmTYnW9jl3As/Z47zwauE7Q7bPqlvNdlvx6GuPV3se3kf3Ye7gcJX5uTq53v4\ntw+7J2SCxr6WFmdMaW11PvwBB44cGFvjpeLUb0unKsfEgP558MjB4ttSxvMEERlZLWXmFOsXwOme\nstOBPQXWeRznMeb5LsmWB1q5ciVz584N3UARqZ6a7LeJxNBfrqAiAYGJWYnBv6zByEGm+fWfOfyM\nU7hhg/NY4Zxt2wpuI8w9/2HbJ/WtJvvtOFRMv8zv1yPWDxr7Dh6EjRvhHe8YfOmz591Ugn+BVJL6\nbelU5ZgY0D9PnHSiq1rBtoQ8TxCR0qrIZI4x5n3AB4DTgAn5r1lrwx4FVgK/MMZ8GngYmA9cA3w0\nb39fAE611l6VLfoXYLkx5kvA14CFwPuAxeH/NSIixbPptJM14wkfLFWQsJ+wQabe+vNOncdA6ihm\n9mxMXt6FbW+HVArjcz98mHv+RxO0KiLlFdQvk+mkK08r16+DAtVzYamBY18yifnqV2HWLFeezunt\n7aULhxeJkGocEwfSKUxAMHJ+iLK3X+ezqRSEOE8QkdIr+xHTGPM3OEHFDwCXAfcDc4C3AO1ht2et\n/U9jzHuB24FbgGeBT1prH8qrNh2YmbfO740x78CZCPobYB+w1FrrfcJV7ThUproiUlEmHsdOmeK+\nh7zMH1gKhSMWWx+A5VcXnZkT5p7/sO0TkfIL6pfLfrAssF9PYXigeu4Dn4nH2d6/m7Pyxowdz2/n\njOYznL/kt7WVL0tMJEKqcUxsiDfyUuMAU/P63Mv9h5kab2SKCe7X+UxjY3kzAUVkRJX488cngL+2\n1v6bMeYjwB3W2t3GmNuAaaPZoLX2EeCRAq8Py+yx1nYB54xmf5X0yiuvgAGKfh5YlsmuKyI1xztx\nU4m/PAeFIxZbHwid9RPmnv+w7ROR8vPrl491P+Yqy+/X3g943uV3PnwZTfGmwUDlZDrJ3hXZh5dW\nIUtMpFZV45h4xlfP4vRXnc55M8/j8b2Ps/OPO9m7Yu+I/dpF/VikqhoqsI/TgF9mf/4TkHuu7YOA\nEtI8Jk6cCBbgc0BHkV+fA5tdV0SkVBIJ9/IIWT+JWe76ysERib73//n7Xcth+nViVoLuw9081v0Y\n3Ye73euGHF9EpLQSsxJ0PtvJ57s+T+eznaM7Zqsfi1RVJa7M+QPOFTh7gB7gXGA78Fqca1DE12Kg\n2DihbTh3nInIeBMUOmw996wPLmcy7sf5epdz9UNm/UQ5B6fI/xKpE3q/3fL7qN948uW3f5kPv/HD\nXPndKzn7lLOL7tcFxwQLtLc7T7LKji+sXq03Q8Ysqv07zAMEYPitT0G3QhXa31iP2QXPEwDyzxXS\nafdyOUT1zRcZg0pcmfN/gXdnf74fWGmM+RHwLeC7Fdi/iEhdyoUOt61vY+bKmbStb6P3aK8TStjf\n72RSzJzpfO/vd8p7e93lvb3OCY+Hicchl/Wzd6/zfcqUwFvE8u/537tiLx2Xd/iGH9eaTKbo/xKp\nA3q/3fLHkM7dnbz0ykuu8eTF5Itse34bs4+fzfZl20P164JjwsAANDW5x5emJqdcZJSi2r+DjuWZ\nAf+GpzNp+lJ9rvp9qT7SmXTR+yzFMTvwPAGgr8/9RvT1ORM65RLVN19kjCpxZc5fk500sta2G2P+\nCPwF8D3gf1dg/zIO9fT0cOhQ8cnQJ5xwAqeddloZWyRSen6hw2eedCY3XXBTcCjhbbe5yikQVhg2\n6yeKOTix2LD/qkL/JRJxer/dcmPIlv1b+Obl36RtfZtv2PENP7yBu99+9+A6Ybbvu9zYCB/5CGze\nDC0t0N0N8+eP3zdCSiKq/TvMAwTAybBZvqH4+oX2W2i5GIHnCZV+I6L65ouMUdknc6y1A8BA3vJD\nwEPBa4iMTU9PD68//XSOJJNFrzOpqYmndu7UhI5Ejjd0ePBkLCiU8OBB//JxTPmN44veb7euPV2c\ncdIZgz/ny4UdHzhyoAw77oJ9+5yJHIAQx2yRIFHt32EeIDCa+hVXjTciqm++yBhU4jYrjDHHG2P+\nzhizNvt1gzFmVE+yEhnJoUOHOJJM0gFsLeKrAziSTIa6kkekVnhDhwcvyw4KJTzxRP/ycUz5jeOL\n3m+3xKwEzxx+ZvDnnNZprVw37zoALph5QRl2rDdCSi+qv1aJWQlap7WyqGURLdNaRgwjDlu/4qrx\nRkT1zRcZg7JfmWOMSeDcUvUy8J/Z4r8BbjXGvCv7yHCRkns9xUdIi0SCJ8zPptOsWbzGFWC47M3L\nnGwcv1DCVApuvRVz4ICr3GQyZAzDwhdheFkUbp0Kk4GYyZQ3h1V5jLWlmPfb7z2D6L2PxQSq5kJQ\nr33kWjp3d7Lq0lVMaZzCB97wARbOXjhY75q51wz+nB+0Gja01amUAWv934hUyrkFS2QUCvVv8O/D\ntTBGZwYyrH33WpriTYNlyXQysD+lM2nf+ulMGoulMTbUh1KZlGu5IrLByMPeiHTaKSvHf3i5D+Yi\nNaoSV+a0Aw8Dr7XWXm6tvRyYjXOrVXsF9i8iEn1+4X59fUyJHeMKMGyKNzEQa4DJk92hhJMnk2kw\npCe5Q0fTk5pIMeAbvpgaSBUdyFgrwmYgxmLQ3Oz+r2puLt25pfIYa8tI77ffexYiN7xmFBuomh+C\nunD2QqZOnMqad6zhnFef41r3pVdeonN3pytoNWxoq9Ow7H/wDTfA0aPDxihN5MhYBPVv8O/D6XTt\n9O1kOunqS8l08G2Hxhjf+hZLf6rfVd6f6ieVSVXwX4Lz1CqfYGSMKd9/eDkP5iI1rBIByC3A+6y1\ngz3VWpsxxnwZuLIC+xcRiT6fcD9jDLGODi7+xsU0xhrpPtzN/FPnOyGInr/mmcZG4kDbhqvYvH8z\nLdNaXPVv+/FtvmGKm/dvZt/L+0YdsFhpo8lA9J7rlercT3mMtanQ++19z7ZscR6ytHRptN7HMIGq\n+X/5z/0ctO6yHyzj2keuHdxOmNBWZwd5/8H33gsLF8LHPgbvf78mcqQkgvp30Fgc4pkAZRM2ALnQ\nww9KEYxcEt4HJlQiGLlcB3ORGlaJyZxtOHe87PSUvx7YXoH9i4jUh4Bwv8ZYI491PwZQ8K954IQm\nHhM/ZmgT2dDEg0fcwci58tykT35ZraulDMRaaosUJ/89mzNneBlE430cS0Bq0Lot01pc2+na00Xr\ntFbmTJtD9+Hu4vbR0zP0c2cn7NzpTOaIlFGtPxNgrAHIuYnYSAQjt7Y6g2t3dzQGU5EaVonbrO4F\n7skGIF+Q/fo7YCWw0hjzxtxXBdoiIhJdAeF+uckWYDAE0XurQ2554wc2sOu6XTza9ihPX/c0Gz+w\nAYATJ7mDkXPb8dt2raulDMRaaosUJ/89e+aZ4WUQjffRG46e33/zxwe/26KC1u0+3O3azoYPuceT\nDR/a4N+Y/NsofvYz2LgRjjsuu/EI/GdK5NX6MwGC+lzQsTzo4QeF+r1X0LbLasMG2LULHn0Unn7a\nWRaRUavElTn/lv1+R8BrFjDZ77oeTkTEh80GCnpDjdNHk8w/dT7JdJIFsxawevFq0pk0fak+lj+y\nnK49XSRmJWhf3M6x8cm8cfJs5z71ri5IJHhjezsD6RS3LriVA/0HBoOUVy9eTTI9fNtVCVMMoZYy\nEGupLVIc73s2b57zxOyovY+5YOP8cPR7L72Xzt2dfO3Jr9G+uJ3mCU6YSO/RXtdYcf9l9weuO//U\n+YPjgMEw+/jZtK1vc40z+QHJTmOyOTnLlw+OO6xaBd/5Dtx/f+3/Z0rkBY3F6TTceivkPROgKr+O\nfv210LF8SuOUYfWXvXkZqUzKdzt+x+1c5pV3280Tmsv3oIN0Gma7z0Fob3fKvbdliUhRKtFzXlvK\njRlj/gH4B0/x76y1fx5QfwHwE0+xBaZbaw+Usm0ypKenJ9Sjvk844QROO+20MrZIJNpMPM72/t2c\nlXdv+Y7nt/P6Y89w3Q+fe/qF97756VOmc9cld/nm7piODvYf3j9sO7858BtX2bbntzF3em0/Iy4/\nAzGnWp8Ta6ktUpyg96yxMVrvY36wcc4Pdv2AD3/3w7yYfNGVo5GfvbFl/xYmxCYM6/uZgQwLZy9k\n4eyFHP7T4cEPhkXlcxQKj1q4sPb/MyXyCo3FtTBG+/XXoGN5ro81G//6A0cHXOX5/dW7z9CZV2MV\njytITqTEyj6ZY63dU4bN/gZYiHNFD0B6pGYArwN689qliZwy6enp4fWnn86RZOHsjnyTmpp4audO\nTeiIFPDOhy+jKd40mGOTTCfZu2Kvq07QffMv9L/g/BAQHHD7L27niX1P0DKthUvmXMKKc1fwzn97\n54j7q0W1lIFYS22R4hTznkXhfcz/6/pb738rP+/5+eCyN/cmZ840JyTosocu45I5l7D23Wt56/1v\nZWJsIufNPI/H9z7Ozj/uHBwHis7nKBQ6FIX/TIm8oH5dK2O092qYkTJwguqf9S9ncfqrTvftr15V\nydeJYgCZSA2rRGYOxpi/NMb8whjznDFmVrbsb40xl41yk2lr7UFr7YHs1+Ei1smvr4mcMjp06BBH\nkkk6gK1FfHUAR5LJUFfyiIxHiVkJzOAcduF74b33zZ88+eTsC/7BASdOOpHuw9081v0Yjz796OA2\ncmXerAwRiY7Tpg79oaR1WivXzbtucPm6edfRMq0FgGcOPzNY1vNSz+C6nc928vmuz9P5bKdrHMhf\nFwqMSVEMHRKpAYlZCVqntbKoZREt01pGPA4nZiUC+6tf3XwVOcZrLBApqbJfmWOM+ThwG/DPwGcZ\nysV5EfhbYDTJV63GmP1AEngc+LS1ttCfiw3wpDGmCeeqnn+01v5yFPuVEF4P1PYNGSLVlbss2rvs\nzZxIZ9IYY1j77rU0xZsGy5Pp5LC6uQBD733zNyduxqbTGJ/gAJtOuzJzTpp8EkczRwPv4XflYZT6\n/8RziXut3YFR6+2TcILez3p6X3N5HFMap/CBN3yAhbMXul678fwbufH8G9n0+00c1+SEEufKDvQd\nYNWlqwLHgVy9zt2dfGvHt7jjbXcMG9cUHiWV5I1fiUoci9/5AOB73M+95q0/YAdCZeb41R3Wf0v6\nj9RYIFJqlRjergM+aq39d2PMTXnl/wncNYrtPQF8BOdR59OBfwS6jDFnWGv7feo/D3wsu7+JwEeB\nnxpj5llrnxzF/kVExiwofHBy42T6U/0s3zC8PJlOsvR7SwfL1757rROOuGF4gKHf/femwT8gwMRi\nNDcMrz+lcYqrrBITOd6c1PZ2p8m1cJ5X6+2TcPzez3vvhT17nIzOenlfc3kca96xht6jva7A4lWX\nruJXz/+Krz35Nda+ey3JdNL1+prFa5jcOHnYONCf6ucTGz7hGncufM2Fg/tzN6BGgkmk7qXT0Nc3\nfIyeMqW2J3SCzgemNE4ZdtxvX9xOvDHuG4w8uXEyTfEmV39NppOuK3pzCuX0lI3GApGSq1QA8q98\nyl8BJofdmLX2h3mLvzHGbAb2AB8A7vepvwvYlVf0hDFmDrACuCrs/kVESsEvfPDMk87kpgtuCgwv\nzi/fsn8LTfEmln5vaVEBhoMnaAEBAUH33+cr50ROrim1nI1Y6+2TcAq9nzfcAHffXd32lVKuPwcF\nnt7+i9uLHk/isTif2PCJcMGptRJMInUtqvm6hcKIb/vxbSOeD+TX/9QPP8X3d31/MOtu/qnzA/tm\nMcf9ktNYIFJSlZjMeRY4G2fCJd8i4Kmxbtxa+5IxZhfQMmLlIZuB80eqtGLFCqZOneoqW7JkCUuW\nLAnXSBGpmCj1W2/44EjhxX5BpVUJMCyjWs9GrPX2RVW1+m3Q+3mgTpP1gsaLc2ecW/D1Yrcj40st\nHm+jOkYH9amDRw66yv3OB7z1uw930324G3CuzBGR+lWJyZwvA+3ZvBoDzDPGLAE+DVwz1o0bY6bg\nTOR8I8RqZ+PcflXQypUrmTtXqS8iURKlfpuYlRj8yxoM3SPvLc+FF+eX54JKvXWjHlKcSAz9VRVq\nL4Zh+04AACAASURBVBux1tsXVdXqt0Hv50knVbwpFRE0Xjyx74mCrxe7HRlfavF4G9UxOqhPnTjp\nRFc9v/OBQvXVN0XqWyUeTX6fMeZPwOeBScA6YD/wSWvtQ2G3Z4y5E/g+zpU+pwL/C0gB/5Z9/QvA\nqdbaq7LLn8S5OmgH0ISTmXMR8Lax/ctEREbPL3xw2ZuXkcqkfMOLveXzTp1HMp0sa4BhUEBzKfiF\nVBoTLhsxbBjxWIMxld1YX/zez3vugW3b4JZbht7XdNqpH8VQ1Xx+Y869l95L5+5OTp58cuB44hey\nHjTuQIVu1RDxkU77j9GF+msqBY2NwcuVENSn0hn3wwly5wPpTDow6Nhbv9CDC8p5jBeRyqjE06yO\nAb5rrf2mMWYScAbOLU77RrnJGTgTQq8CDgI/B8611v4x+/p0YGZe/QnA3cCrgSPAfwELrbWeCzFF\nRCqnUPjgFPyDh5vN8PqNExrLEmAYFMjYPKF5zNsvFFJZbDZi2DDiUgRjKrux/jQ1DX8/X/UqZ0Ln\nyivhrLOc35OmJrjqqmiFqnoFjTkLZy9k4eyFZAYyxBvjI4ae+22nc3cnD+14iDvfdmdJxgiR0Zo0\nyd2njx4NrptKQX//8OPC5MmVndApdD7gd9wvVB50/uBVzmO8iFROJU5DNgDrgX/BmVj5Hs6VNCcY\nY6631n4lzMastQVvxLXWXu1ZvhO4M1SLI27Lli3s2rVr5Ip5Xve61/GWt7ylTC0SET9B4YPeE6/c\ncjFhhaU6CSsUyDhWYUIqgyZKwoYRlyoYU9mN9SMWcyZsNm+GlhY4+WR44AG4/npYudKps2PH0O/J\n5s2wb190QlX9FBpDwoSe5+re8Ys7+Ndt/zqYz/Gn1J9KMkaIjEY8Dm1t8MILcN558PjjcMopwX21\nsbF2ApOD+mbY8qDzB7/9lesYLyKVU4nJnLk4T44CeB/wAvAm4H8CtwGhJnOksJ6eHs77i/PIpDOh\n1ovFY+x+ZneZWiUiUVTOkNNShFR2dUFrK8yZA93dI28jqsGYUj5dXc4ETXc3LFrklH372/6/Vy0t\nzjLodydn1eZV7Ht56EJrBSFLtXV1wTHHOBM1e/bAzp0j1883nvq2gsxFoq8SkzmTgN7sz5cA6621\nA8aYJ4BZFdj/uHLo0CFnIuci4PgiV/pvyPwkw6FDh8rZNBGJmHKGnJYipHLDBsjP3ty2rfz7lPqS\n/zvxjJMpHvh7lZvIAf3u5CgIWWqNjgvFU/8Vib5KTOZ0A+8xxnwXeDuQvXiZk4CXK7D/8akVJyWo\nGM8BPyljW0QkcgqFnJYiMydsSKXfNmbPdi6pz886CNpGKfYp9cUbgDxvnpOv4fd7dfQozJ8PyaR+\nd3LKOUaIjEYq5d9/g0KNUyn/40I1QpArTf1XpD5U4jTkNpzA4pVAp7X28Wz5JcCvKrB/EREJqVAg\n41jF4054bH4uQdgPxmEzcEqxT6kvfoHWAFdf7f97pd8dt3KOESKjETYDp7HRCTvOf308TOSA+q9I\nvajEo8m/Y4z5Oc5TprbnvdQJfLfc+xcRkdEpJnB5tLwfhEfzwThs1kEp9in1xS/AupjfK/3uOMo5\nRoiMRtjjgnfiZjxM5OSo/4pEX0MldmKt/YO19lfW2oG8ss3W2t9VYv8iIlJ/Egn38njKOpDy0e+V\nSHSp/4rIeKK/LYmI1CHv5dK1dvl0JuO+KiK3HFTut75f1kFQ/VK1T+pT/u9fOX+valGtjxUiQbz9\nMigbrZ77bzHUx0XqV0WuzBERkcrJDGToPdpL2/o2Zq6cSdv6NnqP9pIZyFS7aYBzYt3b64RUzpzp\nfO/tdU7E/cozPs3OzzvZu9f53txcuomcYtsh0Zf/fv/85zBxovv3qqkJrK12K8uj1scKkSB+43R/\nP0yaNH76bzHUx0Xqm67MqVdhnjKuJ5KL1JVYQ4zljywffOToul+vw2BcQYfVFIsFh1Tedlvx4ZXe\niZtS/eW1UPuk/uTe7y1b4JvfdD4Ubt4MLS3O48jnz6/f977WxwqRIIXG6YsvdrJv6r3/FkN9XKS+\naTKnzrzyyitggPUhVzTOuhMnTixHs0Skwrr2uFMgN+0ZIQWywoJCKg8e9C+vtLAhmhJtXV1wxhlD\nP+/b53wQBOdx5PWs1scKkSBB43RjIzz2mPNzvfffYqiPi9QvTebUmYkTJ4IF+Bzw2iLXehbsLZrI\nEakjiVmJwb/EASyYVVspkImEcyXEnDlDfz0FOPFEd71qhVcmEkN/8a1mO6Qycr+P+T/PmePc+ve2\nt1W3beVW62OFSJCgcfrkk+Hmm+Hxx+GUU6rTtlqiPi5SvzSZU7cWA3OLrLsNuKWMbRGRUig2xDAz\nkKF9cTsGw6Y9m1gwawGrF6+uWuihX0jl2rVOlkFOMumU33orHDhQXHhlUEhxKuV+vKx3uZj2KkRz\n/Mi939de67zf990HxxxTOAQ7l59UyZDscoSY1tpYIVIsv3F6zRpnvH/ggaF6R486xxaAeN6nnnTa\nWc5995bXSgj+WPu9+rhIfYtcALIx5h+MMQOer9+OsM6FxpitxpikMWaXMeaqSrVXRKQUwoQYxhpi\nNE9opuPyDvau2EvH5R00T2iu2kSON6QynXYmb/LLkkknpLKpqbjwyqCQ4lTKCcH0hmKmUsW3uZzh\nylJ78n/vLrgAXnkFtm2DF190/x699BJ0djo/p1KVDckuV4hpLY0VImH4jdPHHDN8/D9yxOnjfX3u\n8r4+px8HlddCCH4p+r36uEh9i+qVOb8BFuKkwwCkgyoaY14DbATWAFcAFwP3GWOes9b+qLzNlLF4\nqsT1RKIsbIih90StWidu3pDKLVucD85Ll/oHV37qU/D9748cPusXfnnmmXDTTaUJLy5XuLLUnngc\nrroKZsyAL30JPvc5uPtu5wOc3+/R7bcX/h0uh3KGmNbKWCESlndcbmwMHv/XrAl3vAgTxl8uper3\n6uMi9Suqkzlpa+3BkasB8HFgt7X2xuzyTmPMBcAKQJM5NeiVV16hAfhwiHUasuuJ1LOohhh2dUFr\nq5NB8upXD5Xlyw9Afu1r4c1vdv46Wih42LuN3Im9woslrIYG5wk44Hxog+Dfo3PPLfx6uUS1/4tU\nUv7xprvbHYqcXz7S8aJmwvjV70WkgKhO5rQaY/YDSeBx4NPW2r0Bdc8Ffuwp+yGwsoztkzGYOHEi\nAwAXAccXscJ/w8BPUICzVFQp8ivCbqPSIYZhMwOC6m/YAHPnussXLXKySXJywZWrVsFxxw2VHz4c\nvG9v+GXuEniFF0sh3uybWMzJ2MiV3XVX4d/RJ55wvlf696xQ//cbS4CSZ+yI1Drv8WbbNuf7e94D\nf//3Q+VPP+18D+rH3jD+xYvLm6OTzqSJx+LDlhVeLCKFRC4zB3gC+AjwdmAZziObuowxkwPqnwK8\n4Cl7ATjWGKNP/7WsFXhjEV+t1WqgjFeluI897DZyIYZtZ7Yx49gZtJ3ZNhhiWA5BmTRBmQGFMmxm\nzx5evmqV8/OMGc731auHcm3y6zY0+OcXJJNO+GX+NpYtc+p6y/O3LeNb/u9pZ6eTg+OXjxP0O3r0\nqPOkHL/fv1xIdlnaXaD/B40lqYFUyTN2RGqZ3/Fm9myn/MQT3eUnnlj4eHHrre7ye+4pX45OOpOm\nL9Xn6q99qT5SmVRFj/siEj2RuzLHWvvDvMXfGGM2A3uADwD3V6dV4keZN1KvSnEf+2gycHIhhjnl\n/Eu7XyZNocwAv/rTpztXOQRtJ39bTz/tXJFTbN7B5Mnwla+4t5H7K+nkye7ysE+zkvqV+z3dsgW+\n+U244YbC+Tj5v0fbtjl/8e/ocH7XGhv9f//K0u4R+n/QWLJ5/2b2vbyvpBk7Iv+fvXuPk6Ou8/3/\n+s6FxFzILpKbEEBJlsMq4k6UBPVMVC6r6O+w6889u2G4yC8uGOPqSXzo6pqwirsRBYwGEnWP63VO\nPD/OiiK7KK4BibuAsDOCClkgoIQgmwvZJDMDnXT3fM8f1TVTXV3VXd3T1V1V/X4+Hv2Y6epvXbqq\nvvX9zneqPp+kqhYzJywGzrPPBtdzN7iyV1xxsnq6e1hzW3Adnm1a1+6LSPqkbjDHz1p72BjzOLA4\npMh/APN90+YDR6y1VYOsrF27ljlz5pRNW7lyJStXrmx0czuCYt5IO7Wq3jbjOfZ6l9HqIIb1xgTx\nl9+7t/pyVq2C3/7WiV9w1VXOLfBhZf2DMT/4QXiQYn9ZDeQkXyvb2x074FWvcn7ft29ymlfQOZrL\nOVlzIHjQJu4g2dXqf9i1ZPEJi9l1cFfZNJFmSWI/ud4YONdd5zw66Qbdd+t5UH2OM05WWB1W8GIR\nqSb1gznGmFk4AznfDClyH/A237QLS9Or2rRpE33eB28lEsW8kXZqVb1txnPsSX8Wvt6YIP7y8+dX\nX86OHU7nGWrHu8nny4NXLltW//eR5Gple9vf79yZAzBv3uS0WufowEBLNq8hYdcSdyDHO02kWZLY\nT44aA8c7fdeuaPU8zjhZSe8PiEgypW4wxxhzPXA7zqNVJwGfBPLAt0ufbwROstZeUZrlS8AaY8xn\ngK/ipDR/F3BRize98ywBXhah3G+Bu2PeFpEmcuNXGAz3PH0PK05dMfEce9T/mlVbBoQELo0z+qJ/\n+4pOLAFjnP8+rlgxGRPEWieds6tQcMr5y69f73wWtJxczhmQyeWcad54N/6y+TysWwd/9VeT68zl\n4n2sRbLJPa/f/34nZs7HP+48VrF5c+1z1D0Xk3anV9i1JFfIseykZeQKuYauUdFW3rprkohfoVDe\nFlVrQ665xrkTzzu9UAieHnQaV2sTp3rKF4qFwDrsD4rcVqrrIomUkCtEXU4GtgEvBfYD/wIst9Y+\nX/p8IbDILWyt/Y0x5u042as+AOwBVllr/RmuREQiaUb8mrBlAIwcG2HNHWvY8fQO+k/tZ8tFW5jT\nMwszOuoEBNixw/kX4ZYtzoP9MXWopk8vjweQy8H4OIyNVW7GrFmVMQbcvl7QdGvLp7md8lmzKqcb\n46xz1arydSbtj2pJPv/5WCw68TGKxcpzfWQkPbGXpvdML7uW5Ao5ekxPvLE23GjSLbwmibgKBfA3\niVu3VsZMO3bMaUPqaZ/CHqOMWrZexhhm9M4oq6/Hiscwxkx94c2gui6SWKkbzLHWVn0Q11p7ZcC0\nHcDS2DbKY/fu3fze753B0aO5uuZ729vewR133B7TVjXgQExlRTKiGc+xhy3DH8x04ayF3HBhlUjC\nMejuhssvhwcemIwlsGoVfPSj0TfD7eOFxbbxcv+72tMTPL2FX10yznv+ub/feCP86EfOYI37GN/g\nIJx/Plx4IXzkI8kdyOnu6uby713OA88+MBEjZ9lJyyqCHTc91ka9UdJFmqinJ/z0u+IKOOUUuO8+\nWLBg6u1T2GfNGsfo7urm8lsvZ+/oXs5ddC73PXMfC2YtSE7ActV1kcRK3WBO0j3wwAOlgZyTiZ75\n/RD33Xd/jFsV3dGjR8EAt9Y5o1EAY5Fm8QdC3DtWI5JwXNuxA848E177WueuBLfj2uLNaNs6pXPc\ndBO85CVOTCYoD7x9003OYE6S7Xh6B3uO7JmIkZMr1PcPpcZXrIop7RN2+u3bB98sRdI8+eTWblOj\n3Dq8/dfbATj5+IRtuOq6SCJpMCc2vwLm1Czl+CS9vX8X58ZENm3aNLAAnwJeHnGuX4PdoADGIk3i\nD4Q4f2aNSMIxefhhOOGEyfdHjjRvM/yxDvzv/Vr81aXD3Hab87iVa3jY+blr1+S55j9HkxRCom3B\nU1UxpY3CTr9duyqnJam+Bkl8AGTVdZFE0mCOhLgIiJqhYBjYEOO2iHSOoGCm6/vXYwsFTFzRFwPk\n89DV5WT28D4iXy3AZNTHUIJiHbhxd4IGdOIMPCmSz8MrXlF5ru/b5zxu9YUvOIM7r3jF5DmapBAS\nzQjI3tiKVTGlfcLaorDA5UGx3pIS8qVtdTjyBqquiySVBnNERBIkLDCy6Yox+mKA3t7wR+SffbZ8\nM4aHy+9qqKVarIMgcQaeFKl2rg8Owj/9E1x6Kbz97ZPnYJJCSDQjIHtjK1bFlPbp7YWnnqo8/X71\nq/JpjzwCr3xlcuprkLbV4ahU10USS4M5IiIJExpcOa7oiyHCHpG/7jq4//7JwMi5HDzzTHOWHabF\nX106TNj5uGoVfPWr5dNqzdMOzQjI3tiKVTGlfS6+2Mm6uHixE6h87Vp4xzsmp+3aBcuXw7e+laz6\nGqRtdTgq1XWRRIoaoVdERDpMf3/5e/cR+blznU7yD39YHlOkGcsWaYew89H7B6D/HNU5LNJe/f2T\nbdEPflA5bdcumO8JOeel+ioiWaA7c0REpEKhEB4b55prnHgi/kfnrY0W1Dhs2bWCIIs0m3veRo29\n4Z6jCiEh0l7+OjhvHhw7Vlkv168Pj6+j+ioiaadus4iIVOjpcYK9eh+Rd/+QDXp03troQY2rLVuk\nVdwgxn/91/Dxj5efj/m887l3Wi7n/DEICiEh0m5BdTCfdx6xCqq3qq8ikkXqOktm7WxyOZFOEzQI\nA+GPztcTYDJs2SKt4g1ivHkznHceXH01/MmfOMFV3/1ueOCBydgby5aVn88KISHSXv46F6Xehs0r\nIpJG6j5L5hw9epQu4NI65ukqzScijUt6gEkRP+85u307PPaYM5jjfrZnj/MHITj/4ReRZFO9FZFO\nosEcyZxp06YxDvBm4HcjzPCfMH63M5+INK6/Hx58EE4/ffI/oiJJ1t8/eTfZBRfAn/958GeggKki\naaB2SEQ6iQZzJLuWAC+LUO63wN0xb4tIhvhjDbgxc/7+7514Ba5cLjwWTtAyqt32Xm95Eah+3rgB\nVOfNg499zPnpyudh61YFTBVJk0IhvB0yRm2IiGSPUpOLiEhkbtDYgQFYtMj5OTLifJbLlU/3BoyN\nsoxisb51hpUXgdrnjRtA9frr4bjjysuNjTnxNwYH4ZlnnJ+zZ+uPP5Eksza4HbJWbYiIZFOq78wx\nxnwU2Ah83lq7LqTMCirvu7DAQmvtvpg3UVJq9+7dHDhwoK55TjzxRE455ZRMrF8kjDdoLDg/Fy6E\nG26IHgA5aBnVgiXXW14Eop03tYJ3X3EFvPKV8JGPaCBHJOl6e8Pr8tatakNEJHtSO5hjjHkdcBXw\ncITiFvg9YGRiggZyJMTu3bs584wzeKHOqHkzpk9n52OPTXlApd3rF6nFH+h4797g6dUCINcbLFnB\nlaURUc+bsHKnnAI33eQM5ohI8oXV5d5eWLJkMpaO2hARyYJUPmZljJkFDALvAQ5FnG2/tXaf+4pv\n6yTtDhw4wAu5HIPAUMTXIPBCLlf33TRJXL+0jv8W77Tc8t3fX/5+/vzg6dUCxtZTtpHyIlD7vHHr\nXFi5++7TuSaSJmF1+Y/+CB5/HH7wA3jiCbjtttZvm4hIs6X1zpwtwO3W2ruMMRsilDfAQ8aY6cCv\ngE9Ya++NdQsl9c4E+jp4/RIvN57HmjXOfxL7+51grEmPy+EGjfUGhl2/3gkw6Z8eFjA2aBnVgsvW\nW14Eap83bh186inYvLmy3L59sGCBzjWRtMjng+t8Pg9z5zqxcrztbViAfhGRtEjdJcwY82fAa4DX\nRpzlOeBq4N+AacCfAz8xxpxjrX0onq2UTqeYN1JLWuPAuEFjvdvp/qEbNr2eZdS7TpEwtc4btw7e\ncYdTxlvO/SNvcFDnmkha9PbCzJnldTmfrx5LR0QkzVI1mGOMORn4PHC+tTYfZR5r7ePA455J9xtj\nTgfWAldUm3ft2rXMmTOnbNrKlStZuXJlXdstnWWqMW9katJUb9MaB8b/h633j+Nq5aIso1nlJV3i\nqre1zpsdO+DQIXjHO2DxYli+HL71rfL/1utcEwmWxPa2tzf4fVrbWxGRalI1mAMsBeYCw8ZMJLzt\nBvqNMe8HpllrbYTlPAC8oVahTZs20denB12kPt6YN2dGnGcncKli3jRFmuptf//kfwpBsTmkc7Wr\n3vb3w4MPTgZFjdSDEBFA7a2ISLulbTDnx8BZvmlfx/lb+LqIAzngPKb1XBO3S6SCYt5INUmLA+Nf\nrxsY1j+t2rYFLUN3NUhSFYvw938P06dPTsvlgs99EUkH97Eq7/uurmS1tyIizZKqbFbW2jFr7aPe\nFzAGPG+t3QlgjNlojPmGO48x5oPGmP9mjDndGPNKY8zngTcDN7fnW4iIlMfzeOYZ52e7gh+7gWAH\nBmDRIudnPl85bWQkPONW0DKqlRdpN2udwRvvOZvLwZEjOndF0iifh7Gx8jo9Ngbj48lpb0VEmilt\nd+YE8d+NsxBY5Hl/HHAj8DLgBeAXwHnWWt/TsyIirZWUODD+YMwPPujcrbBqVfSAkWkN6Cydq6cn\n/Jz99KfhYx9r7/aJSH3qCXSsgRwRyYLUD+ZYa9/ie3+l7/31wPUt3SgRkZTxBoc8/fTKaVA7YKQC\nTErahJ2zSlcskk5qh0Skk6TqMSsREYlHf//k708+WTkNageMrLe8SLuFnbOFQuu3RUSmTu2QiHQS\n/e9JRKTD+YMxn3OOEzuknoCRSQvoLOLnPxfz+eBz9uBBWL1a565I0kWt0/6gyCIiWaHBHBGRDucN\nxuwqFJy4Od5puVx9y9Afw5IUboDuNWucxzD6+2HrVpg5s/K87+nRuSuSdFHrtAZyRCTLNJgjIiIV\nf7j29MAVV8ADD8DixbBrFyxbVj2gcVICOov4RQ3Q7cbK0bkrkmxR67QGckQkyzSYIyIigXbsgD17\nnIEcqH5njkjSKTCqSLaoTotIp1MAZBERCaRAkpIlOp9FskV1WkQ6ne7MERGRCgpoLFmi81kkW1Sn\nRUQ0mCMSamdMZUXSQAGNJUt0Potki+q0iIgGc0QqHD16lC7g0jrn6yrNO23atBi2SqT1FNBYskTn\ns0i2qE6LSKfTYI6Iz7Rp0xgHeDPwuxFn+k8YvxsN5IiIiIiIiEjsNJgjEmYJ8LKIZX8L3B3jtoiI\niIiIiIiUKJuViIiIiIiIiEiKpHowxxjzUWPMuDHmczXKvckYM2SMyRljHjfGXNGqbWylH/7wh+3e\nhHIHcO5YqfU60K4N7Azf/va3Y1muMWZlLAtOmbj2b5LW2QnfsR3rbMd37NR626p93Yr1aB2dt45O\nrbd+ahe0zrSsD1RvpTVSO5hjjHkdcBXwcI1ypwH/CGwHzga+AHzFGHNBzJvYckkZzDl69CgY4Fbg\n7yK8bgVMaT5puhgbMDVSqBOkdaZnfSUdWW81mKN1pHkddGi99VO7oHWmZX0lqrcSu1TGzDHGzAIG\ngfcAG2oUXw08Za39SOn9Y8aYNwJrgX+Obys717Rp08ACfAp4eYQ5fg12g4IHi4iIiIiIiESQysEc\nYAtwu7X2LmNMrcGc5cCPfdPuBDbFsmXicRHQF6HcMLXH5EREREREREQEUjiYY4z5M+A1wGsjzrIA\n2Oubthc43hgzzVob07M9TwGzI5Z9Pp5NEBEREREREZHMSdVgjjHmZODzwPnW2nyMq5oOsHPnzrpn\nPHz4cOm3KHekTJoz5/cZHh72rLOedTtlR0dHpzR/+fdtfP76tyFk/qiBkUvl2jZ/wDKgkT3Y3Pld\nhw8fZnh4uI6lRTbHGDPDWvtCHAtvQMP1dipi3L+JWWcnfMd2rLMd35EOrbet2tetWI/W0XnroEPr\nrZ/aBa0zLesrSVq9lQwy1tp2b0NkxpiLccLlFnFC7AJ040RoKQLTrO8LGWPuAYastes8094NbLLW\n/m7Iei4B/lfTv4BI9iy11ra8dQyieisSmeqtSPqo3oqkT2LqrWRT2gZzZgKn+iZ/HefGhOustRX/\nIjDGXAe8zVp7tmfaNuB3rLUXhaznpcAfAr8Bck3ZeJFs+vek/MdB9VYkMtVbkfRRvRVJn8TUW8mm\nVA3mBDHG3A383L3zxhizETjJWntF6f1pwC+BrcBXgfNwHtW6yFrrD4wsIiIiIiIiIpJoXe3egCbw\nj0YtBBZNfGjtb4C3A+cDD+GkJF+lgRwRERERERERSaPU35kjIiIiIiIiItJJsnBnjoiIiIiIiIhI\nx9BgjoiIiIiIiIhIimgwR0REREREREQkRTSYIyIiIiIiIiKSIhrMERERERERERFJEQ3miIiIiIiI\niIikiAZzRERERERERERSRIM5IiIiIiIiIiIposEcEREREREREZEU0WCOiIiIiIiIiEiKaDBHRERE\nRERERCRFNJgjIiIiIiIiIpIiGswREREREREREUkRDeaIiIiIiIiIiKSIBnNERERERERERFJEgzki\nIiIiIiIiIimiwRwRERERERERkRTRYI6IiIiIiIiISIqkbjDHGPPXxphx3+vRGvO8yRgzZIzJGWMe\nN8Zc0artFRERERERERFpptQN5pT8CpgPLCi93hhW0BhzGvCPwHbgbOALwFeMMRfEvpUiIiIiIiIi\nIk3W0+4NaFDBWrs/YtnVwFPW2o+U3j9mjHkjsBb451i2TkREREREREQkJmm9M2eJMeZZY8yTxphB\nY8yiKmWXAz/2TbsTODe+zRMRERERERERiUcaB3PuB94N/CHwXuDlwA5jzMyQ8guAvb5pe4HjjTHT\ngmYwxswwxvQZY2Y0Z5NFJG6qtyLpo3orkj6qtyIiyWCste3ehikxxswBngbWWmu/FvD5Y8BXrbWf\n8Ux7G04cnRnW2qMB8/QBQ/39/cyZM6fss5UrV7Jy5comfwuRVDLt3gAv1VuRSFRvRdJH9VYkfRJV\nbyWb0hozZ4K19rAx5nFgcUiR/8AJluw1HzgSNJDjtWnTJvr6+pqwlSLSKqq3IumjeiuSPqq3IiLt\nlcbHrMoYY2bhDOQ8F1LkPuA837QLS9NFRERERERERFIldYM5xpjrjTH9xphTjTGvB74L5IFvlz7f\naIz5hmeWLwGvMMZ8xhhzhjHmfcC7gM+1fONFRERERERERKYojY9ZnQxsA14K7Af+BVhurX2+85SY\naQAAIABJREFU9PlCYCK7lbX2N8aYtwObgA8Ae4BV1lp/hisRERERERERkcRL3WCOtbZqVDVr7ZUB\n03YAS2PbKBERERERERGRFkndY1YiIiIiIiIiIp1MgzkiIiIiIiIiIimiwRwRERERERERkRTRYI6I\niIiIiIiISIpoMEdEREREREREJEU0mCMiIiIiIiIikiIazBERERERERERSREN5oiIiIiIiIiIpIgG\nc0REREREREREUkSDOSIiIiIiIiIiKaLBHBERERERERGRFNFgjoiIiIiIiIhIimgwR0REREREREQk\nRTSYIyIiIiIiIiKSIhrMERERERERERFJEQ3miIiIiIiIiIikiAZzRERERERERERSRIM5IiIiIiIi\nIiIpkurBHGPMR40x48aYz1Ups6JUxvsqGmPmtXJbRURERERERESaoafdG9AoY8zrgKuAhyMUt8Dv\nASMTE6zdF9OmiYiIiIiIiIjEJpV35hhjZgGDwHuAQxFn22+t3ee+4ts6EREREREREZH4pHIwB9gC\n3G6tvStieQM8ZIz5rTHmR8aY18e4bSIiIiIiIiIisUndY1bGmD8DXgO8NuIszwFXA/8GTAP+HPiJ\nMeYca+1D8WyliIiIiIiIiEg8UjWYY4w5Gfg8cL61Nh9lHmvt48Djnkn3G2NOB9YCVzR/K0VERERE\nRERE4mOste3ehsiMMRcDtwJFnEenALpxAhwXgWk2whcyxnwWeIO19g0hn/cBQ/39/cyZM6fss5Ur\nV7Jy5crGv4RIdpjaRVpH9VYkEtVbkfRRvRVJn0TVW8mmtA3mzARO9U3+OrATuM5auzPicn4EHLHW\nvivk8z5gaGhoiL6+vilssUimJaqRUr0ViUT1ViR9VG9F0idR9VayKVWPWVlrx4BHvdOMMWPA8+5A\njjFmI3CStfaK0vsPAr8GHgGm48TMeTNwQQs3XURERERERESkKVI1mBPCf2vRQmCR5/1xwI3Ay4AX\ngF8A51lrd7Rm87KtWITu7vD3IiIiaaJ2TaRzqL6LSJqlNTX5BGvtW6y16zzvr7TWvsXz/npr7RJr\n7Uxr7VxrrQZymqRYhJERGBiARYucnyMjznQREZG0Ubsm0jlU30Uk7bJwZ460SXc3rFkD27Y577dt\nA2NgcLC92yUiItIItWsinUP1XUTSLvV35kh77fDd43TPPe3ZDhERkWZQuybSOVTfRSTNdGeOTEl/\n/+R/NABWrGjftohI8+3evZsDBw5EKnviiSdyyimnxLxFIvFSuybSOVTfRSTNNJgjDSsWYcsW55bU\ne+5xGsCbb1bwOJGs2L17N2eecQYv5HKRys+YPp2djz2mAR1JLbVrIp1D9V1E0k6DOdKw7m6YPbv8\n2WI1gCLZceDAAV7I5RgEzqxRdidwaS7HgQMHNJgjqaV2TaRzqL6LSNppMEemxN/gqQEUyZ4zgb52\nb4RIi6hdE+kcqu8ikmYKgCwiIiIiIiIikiIazBERERERERERSREN5oiIiIiIiIiIpIgGc0RERERE\nREREUkSDORKqWKz+XkREJAvU3olkg+qyiHQSDeZIoGIRRkZgYAAWLXJ+joyoURQRkWxReyeSDarL\nItJplJpcAnV3w5o1sG2b837bNjAGBgfbu10iIiLNpPZOJBtUl0Wk0+jOHAm1Y0f5+3vuac92iIiI\nxEntnUg2qC6LSCfRYI6E6u8vf79iRXu2Q0REJE5q70SyQXVZRDqJHrOSQMUibNni3J56zz1OY3jz\nzc707u52b52IiEhzqL0TyQbVZRHpNBrM6RD+hqxWw9bdDbNnlz9nrMZQRESyplZ7p7ZPJJmC6qb6\nriLSSfSYVQdoNLq/v/FTYygiIlnkHbg5cgQuv1zZcESSLKxv66e+q4hkme7M6QCK7i8iIlJbdzes\nXq32UiTp1LcVEUn5nTnGmI8aY8aNMZ+rUe5NxpghY0zOGPO4MeaKVm1jUii6v4iISG1qL0XSQXVV\nRDpdagdzjDGvA64CHq5R7jTgH4HtwNnAF4CvGGMuiHkTE0XR/UVERGpTeymSDqqrItLpUvmYlTFm\nFjAIvAfYUKP4auApa+1HSu8fM8a8EVgL/HN8W5kciu4vIiJSm9pLkXRQXRURSelgDrAFuN1ae5cx\nptZgznLgx75pdwKbYtmyBGp1Zqp6M2eJiIi0SrU2SpkcRdIhrK76qf6KSJal7jErY8yfAa8BPhZx\nlgXAXt+0vcDxxphpzdy2JGtVZqpGM2eJiIjELUobpUyOIukQVDfVBxWRTpKqO3OMMScDnwfOt9bm\n2709UknZBUREJKnURolkl+q3iHSaVA3mAEuBucCwMcaUpnUD/caY9wPTrLXWN89/APN90+YDR6y1\nR6utbO3atcyZM6ds2sqVK1m5cmWj298RlF1A2kn1ViR9Wllv1UaJNEcS21vVbxHpJGkbzPkxcJZv\n2teBncB1AQM5APcBb/NNu7A0vapNmzbR19fXwGZ2tv7+yf+KgLILSGup3oqkTyvrrdookeZIYnur\n+i0inSRVgznW2jHgUe80Y8wY8Ly1dmfp/UbgJGvtFaUiXwLWGGM+A3wVOA94F3BRyza8gyi7gIiI\nJFVQG7V1q9ookSxQH1REOk2qBnNC+O/GWQgsmvjQ2t8YY96Ok73qA8AeYJW11p/hquMVCtDTE/4+\nCmUCERGRpPK3UVGzLypLo0jyNdIHbUbfV0SkXVJ/ubLWvsX3/sqAMjtw4u1IiEIBRkedwHE7dji3\nqW7ZArNmNTagU+29iGTf7t27OXDgQKSyJ554IqecckrMWyTicNskN7OVv92bPbty4CZKORFpv3r6\noM3s+4qItIMuVQI4jZYyAIhIM+zevZszzziDF3K5SOVnTJ/Ozsce04COtFTUzDfKkCOSTer7ikja\ntWQwxxjTBSwG5gFd3s9Kd81IAigDgIg0w4EDB3ghl2MQOLNG2Z3ApbkcBw4c0GCOtFzUdk/to0g2\nqW6LSJp11S4yNcaY5cAunD77DuAnntfdca9fouvvL3+vDAAiMhVnAn01XrUGe0TiFLXdU/sokk2q\n2yKSZq24M+dLwL8BbweeozJgsSRAoRCcAUCB4EREJIuiZr4pFuGLX4SFC2HvXpg3DzZsUBBkkbRz\n+77+uq2+r4ikRSsuVUuAd1lrd7VgXQLk89DbG/4+SE+PE/DN+5xwlMZMGT5aIMpO1oEQEalLWOYb\n7+/d3c7r+OPhM5+ZvKwWCpXL02U3haq1nWpXUynssIVlrZo5E264YXJ6Pq+BnFjVU69UB0Vqiv0x\nK+BnOPFypAXyeRgbg4EBWLTI+Tk25kyvxd94RRnIGRkpX9fISHlnWKYoyk7WgRARaUjQ3wUjIzA8\nDIcOlV9WDx+G7dud3wsFXXZTr1rbqXY1lcIOWz7vZK3yTh8dDe8z6zDHpJ56pTooEkksY8/GmFd7\n3t4E3GiMWQD8EigbVrDW/iKObehUvb2ti8yvDB8tEGUn60CIiDRFdzd86lNw443O3w5Bl9XrroPp\n02HVKl12U61W26l2NXWqHdJrry2fvnChc0eODnML1dNfVd9WJJK4biR8CCc2jvFM+6rnd/czC+h+\nuSZrZWR+ZQFogSg7WQdCRKQp9u1zfoZdVpcvr/65pEi1g6gDnEphh23//vLpe/dWLy8xqWeH6+CI\n1BTXY1YvB15R+hn0eoXnpzRZKyPzKwtAC0TZyToQIiJNMW+e8zPssnr//dU/lxSpdhB1gFMp7LDN\nnVs+ff786uUlJvXscB0ckZpiuTPHWvu0+7sxph+411pbFi7QGNMDvB54GmmafD44O0eUIMj1ipoJ\nRKYgyk7WgRARaYpi0clm88QT8KUvwYIFcMstzmV182YnZs78+ZDL6bKbetXaTtABTiH3kPqzU+Xz\ncM01zl137uFcvz68z6zDHJN6+qvq24pEYqyNN1O4MaYILLTW7vNNfymwz1qbuBppjOkDhoaGhujr\n62v35tQtSjaroADxUH/QeAWab4HkZrMytYu0TtrrbRINDw+zdOlShoBae3QYWAoMDQ0BNDSfjltL\nqN7W4G8z3cupfwwd1P6lXnqyWaneRhTWBw7LZhU2XWLSWdmsElVvJZtakc3KjY3j91JgrAXr7yjF\nohOJf+NGuP5656c/Mn+1aP/1Bo33X1PTdY1NiSg7WQdCRGTKgrLbuO2j97Lqpiz30mU3haodRB3g\n1CkUgrNTBQ3Q9PQ4fdzRUVi3Di67zPk5OqqESbGqp16pDorUFNvYszHm1tKvFvi6Meao5+Nu4NXA\nvXGtv1P5g78DPPpo9ORHDzwAe/YoaLyIiHSeVmaEFJHm6umpr/4G9Zn37VN9F5H0iPNGwsOlnwYY\nAV70fHYMuB/4nzGuv2NNJfnR4sWwa1f4fCIiIlmmBCoi6VVv/VV9F5E0i+0xK2vtldbaK4FPAqvc\n96XX1dbaT1trD8S1/k42leRH7kBO2HwiIiJZpgQqIulVb/1VfReRNIs9xJe19pNxr0MmTSX5US4H\ny5Y5PxU0XkREOk2h0LqMkCLSXGH1NyyosRImiUjaxTKYY4z5OcFBjytYa5MVBr+FGg3S7i/nbaS6\nu2HWrPLnff3lu7th9uzKMlB9Pqkh/VH3RUQyJepl2W1He3oq20cN5KSY2uVM8x9OY2DmzMr6G5ad\nKqw/rFMkQ3QNkIyL6zGr7wG3lV53AqcDR4GflF650rQ7Y1p/4oVllKoVQT9ovtFRGB4uf+9dTtTk\nRwoaPwWNHlAREYlF1MtyoeC0m265yy+HI0cmy2kgJ6XULmdatcPrzU7lZrMKo75vhukaIB0gljtz\nvI9WGWO+Amy21m7wljHGfBJYFMf606BaRqlG55s+XZk32qbRAyoiIrGIelmuNwOOpITa5Uyrdnhv\nv30yBqSyU3UwXQOkA8QWANnjT4BvBkwfBP7fehdmjHmvMeZhY8zh0uteY8xbq5RfYYwZ972Kxph5\n9a672RqNoF8tE1U9y5EmU0oEEZFEiXpZ1uU7o3RgM61Wf9g7TTqUrgGSca0YzHkReEPA9DfgPG5V\nr2eAvwT6gKXAXcBtxpgzq8xjgSXAgtJrobV2XwPrbqpGI+jXykSlSPxtopQIIiKJEvWyrMt3RunA\nZpoys0pNugZIxsWezQr4PPBFY0wf8EBp2jLg/wM+Ve/CrLX/5Ju03hizGlgO7Kwy635r7ZF61xeX\nRiPoh833xBNw1VXOc8GrVyu+V8spJYKISKJEvSwXCvDlL8OCBXDLLbUz4EhKqF3OtGqZWVetcg6x\n2ydWXe5QugZIB2hFavLrjDFPAR8ELi1N3glcaa29ZSrLNsZ0Af8dmAHcV60o8JAxZjrwK+AT1tp7\np7LuqWo0gn7QfPk8/M7vwIc/PPne31E1pv5g7goAXwelRBARSZQol2X3j7xZs+DGG+Gzn3U+D4qP\nqUt6yqhdzrSwwzs+Dh/96OQ0N5uVf0CnkQEe9YtTRtcA6QCteMwKa+0t1to3WGtPKL3eMJWBHGPM\nq4wxIzgZsrYCf2yt/feQ4s8BV+PE53knzmNaPzHGvKbR9TdLoxH0/R3RsbHyQO1jY3Dw4OT7QqH+\nYO4KAN8ApUQQEUmUapdlfxYrt507dgwefhgOHVIbmHpqlzPNfzjHx4P7xPl8ZV0fHa2e5cpP/eKU\n0jVAMi6tNx3+O3A2MAd4F/BNY0x/0ICOtfZx4HHPpPuNMacDa4ErWrGxcaqWheOMM+DBB50sV6tW\n1RfMXQHgRUQky6q1n319zh9ragNF0qO3t7JOL1wIN9ww9T6t+sUikkSxDOYYYw4Cv2etPWCM+U+c\nAMSBrLUn1Lt8a20BeKr09ufGmHNwHuNaHXERDxAclLnM2rVrmTNnTtm0lStXsnLlyjq2Nn5hgdrP\nPddp2KqVaWS5IkmWlnorIpPaVW+rtXNqA0WqS2J766+3e/cGT2+kPuuaICJJE9edOWuBEc/voYM5\nTdIFTKuj/GtwHr+qatOmTfT19TW8Ua3S3z/5nwKYDNR+332we3f1Mo0sVyTJ0lJvRWRSu+pttXZO\nbaBIdUlsb/31dv784OmN1GddE0QkaWIZzLHWfsPz+9ebuWxjzEbgB8BuYDYwAKwALix9/mngZdba\nK0rvPwj8GngEmA78OfBm4IJmble7FArBgdoPHXJuLT37bCey/5Ytzvu9e2HePNiwoXoMMAWAFxGR\nLAtrP48dg6efdn5XGyiSHvl8ZX93/frJ6f76XE8QZPWLRSSJYo+ZY4z5JnA3sMNa+2QTFjkP+Aaw\nEDgM/AK40Fp7V+nzBcAiT/njgBuBlwEvlMqfZ6313SzZelGi4teKvt/TAzNnVma36u2Fb31r8v2s\nWc4zw97lVFtXd3flcpvZYBXHi3R3dYe+D9w57obFsUEiItJR3CxW3nbODYi6ZInTxPg/c5scNUmO\nmm35lBaufoDUp7fX6bt6+7tunzior1xPlqtOSowUa71uhrA/oJRuTDpQK7JZHQM+BjxhjHnGGDNo\njHmPMWZJIwuz1r7HWvsKa+1LrLULrLXegRystVdaa9/ieX+9tXaJtXamtXautTYxAzm1ouIHZdrw\nR993s1lt3AjXXw9PPBEcyb9QqFxOrXW587maOZAzcmyEgVsHWLRpEQO3DjBybITieLH6zsnnlUZA\nRESaxv1j7sUXnXb04MHJtvC002DdOuf9oUNw5Ahs3+58ls8rs03NtnxKC1c/QOqXz4dns/L2lTdu\nbCzLVSckRoq1XjdD2LWhkdS9IllgrW3JCzgJWAl8CdgJFIE9rVp/ndvaB9ihoSEbp0susRYmXwMD\nUyuzZEn1eRYvnvq6muWS71xi+QQTr4Hv+FbW6JeQVmp7XfW+WlVvO8nQ0JAF7JC3Loa8hpzYaHZo\naKjh+aQl2l5Xva+k1NtLLrH2ggsmfw9qfjZudH6ef371cp2kZls+pYWrH+DR9rrqfSWl3gYJO222\nby+f/ulPVy/fyWKt180QdtDWrk3awWx7XdUr+69W3Jnj+k/g+dLPQ0AB2N/C9SdOlKj49ZQ5/fTq\n8yxePPV1NcuOp8tXds/TvpU1+iVERETqtGMHLFs2+buX29S4j14sX169XCep2ZZPaeHqB0j9wk4b\nt4/scu+qUT2uFGu9boawg7Z/f/B0kQyLfTDHGLPRGHMvzkDOdThBiK8DFlhr/yDu9SdZf3/5+6Co\n+PWUefLJ6vPs2jX1dTVL/6nlK1txqm9ljX4JEQm1e/duhoeHI712u6nwRDpAfz/87GeTv3u5TY37\n6MX991cv10lqtuVTWrj6AVK/sNPG7SO73KdvVI8rxVqvmyHsoM2dGzxdJMNiD4AMfBTnDpxPArda\nax9vwToTL0pU/LBMG97gbG4ZN3L/I48Ez5PLOf91zOUaX1fTvvt4kS0XbcFguOfpe1hx6gpuvujm\nyQBrYTun1pcQkVC7d+/mzDPO4IVcLlL5GdOnc8s//EPMWyXSfm779zd/48TG+eIXK5ufgwfh6qvh\n5z+HV796MktkJ2e2qdmWT2nhEfoBf/qn8IlPdNZOl6qCsllt2OBM7+tzwqi4p9N739ucLFdZE2u9\nbsoGhlwbCgW45hrYt69zL8rSkYy1Nt4VGHM2TurwNwH/FScg8j3AT4CfJHFwxxjTBwwNDQ3R19cX\n23qakc0KJiP1h70vFJxrXjPW1SyNZLOy4+MYzxez+XzZ+8ha+UWzzbR7A7xaVW/Tanh4mKVLlzII\nnFmj7E7gUmBwcJBLL72UIZwACVWXDywFhoaGAFi6dGnd8+m4tYTqbQB/u+n/Z0dPT+W0KO1q1gW1\n5UBzMuFUy2bl/8zfjmcvq43qbURhfeKwUyKsS5i9Uyi6QrFAT3dP6Pumq3dn15PNytp29vkTVW8l\nm2J/zMpa+7C1drO19p3W2rnARTgDOltw/mboWFGi4vuvN/73xWJw5P5HHoHLLpvMxFFr3VHW1Uz+\njl1FR8+3gdZa54utWzf5xcbGsGEpB8JESREmkmFn4gywVHvVGuwRyZKgdtSbBMUYJ5PV5ZeXNxt+\nnfKHnlfQIE3TMuEEdZLcv779WWu87XiUdKGSSWF94qCxAfd9UN+3k0+h4niR0fwo6+5cx2XfvYx1\nd65jND8aXzarRnZ22MH0T7dWfX7JvNiHJo0xBvgDnDtz3gS8ETge+AXOHToyBd3dsGYNbNvmvN+2\nzel4Dg46z/Xv2uXccTg42N7tnCrT01P+RQHTyBfzL8e7w0REpONUa0fdz1evVrMRRXdXN2vuWMO2\nXzo7a9svt2EwDL6ziTurVjte64BKZjXr0HfyKeSvwwD7xvY1tw6XrTDGna0+v3SAVtxndhCYBTyM\nM3jzP4GfWmsPtWDdHaFawodduzIUzL1ZKQeUukBERDxqNQtqNqJrSSYcHTAJoa7i1LU8m1WcO7uT\nD6R0hFakJr8UeKm19rXW2g9Za28PGsgxxpxsjGllqvTMqJXwITPB3JuVckCpC0RExKNWs6BmI7qW\nZMLRAZMQ6ipOXcuzWcW5szv5QEpHiP3OHGvtP0Us+ijwGuCpGDcnc8KCug8PO8keBgayEczdltKN\nGM8XtaXo9aae4D6tTNslIiKJVyu7ZLEYnOEq7e1qHIIy4Wx9+9bmZsKp1Y67B8yf0kgHLPOiZIpt\n5XLSqOXZrOLc2erzSwdI0pmcqYjfUQKzNyPoenc3zJpV/vinm4LxmWfC191KNTNXRWB6erABX9Q7\nkGMLBWewp9qO7+kJ3mH+LBhQNp/1DRoFfodOTn0gIpIS3kuz+7u/WfDGx+zuhuOPh298o3K+ThfU\nFs7qnTURX6Nq+18tW5V3mn9H9/TAzJmV7bgxk/Mffzx85jPl6cd0wDKvuzv41Kj30Hd3w+zZ5ctJ\nc50Pq4dh02cfN7ssRk61fru/f+x/X7kxATuy3p1dK42vK6jPr4EcyRg91hSDKIHZw8oUCvUFXS8W\nnTK+JE9l62r3QE6zMlv4G4uKlAWjo9h8vnZEfP/ATVAaE/9yRkd5+Nnh8O/QyakPpOV2797N8PBw\npNfu3bvbvbkiieG9VG/fDocPO3eyHj5c3o6OjlY2A95Lelr/qGumsPa9YAusu3MdP3/u5xw+eji4\n/Q9qM/P5aO2o225v3AjXX+/8DFre4cPOQXY7U2qPMy+fD0x8Sj5f/7KiZJxNg9B6WiyE9s9rZpwt\nsSEZYkMzzYb1lf1qDeQEpSwLO8itTNUr0g7W2kS8gBHgFe3ejtK29AF2aGjINuqSS6x17rNxXgMD\n0cssXlx9vkbW1U6XfOcSyyeYeA18p0kbGPcODFjO4s2Lw79D0g9E87W9rnpfzai3afD000/bGdOn\nWyDSa8b06fbpp5+2Q0NDFrBD3nM05DVUmndwcLDueYaGhhpaV9aPW4K0va56X+2ot5dcYu2SJc7v\n69ZNTmtGc9Jpwtr38795ftXPnQ89O909IFHb0XrmXbw4Cwev7XXV+0pye9t5XbHa/PXwQ3d+KHB6\nQ/3zend4Mw5Qeg5y2+uqXtl/6c6cmEQJnl4tC1W1+RpZVzvFFhU/7h0YsJzFJzjTAr9D0g+EZMKB\nAwd4IZdjEBiq8RoEXsjlOHDgQLs2VyRxduyA0093ft+3b3KaV6PNSacJa9+Xn7y86ufOh57P3AMS\ntR2tZ97Fi3XwOoi6YpX89XDv2N7A6Q31z+vd4c04QDrIIhOSNJhj270BzRQleHqtLFRh8zWyrnaK\nLSp+3DswYDm7DjrTAr9D0g+EZMqZOP8arfY6s21bJ5Jc/f3w5JPO7/PmTU7zarQ56TRh7fv9e+6v\n+rnzoecz94BEbUfrmXfXLh28DqKuWCV/PZw/c37g9Ib65/Xu8GYcIB1kkQlJenAwMwGQowRmDyuT\ny8GyZc7PKEHXkx5xP66o+BPZrTzZKuyGDZDLYfw7sNrOqHYgVq1y5isUsKtX84tnh8kVcgycNVD5\nHZJ+IEREZOJS/f73O+FUPv5xJ2bO5s2Vl++jR51m4JFHlBApSHG8yJff8WUWzFzALY/eMtG+5wo5\nzp5/Ng8991B4+29xDoQ341QuV9mObt0aHCj5y1+GBQvgllvgnHOC59282TnIy5apPe4Q+XxwVyws\nPm4ncPvhC2ctZO/YXubNmMf6/vUUigW2XLSFs+adRXdXN4VigdWvW11X/zy0Lx4WBLkZfWUdZJEy\nxtpk3BBjjFkE/NZa2/YIdcaYPmBoaGiIvr6+hpYRJbGRf5DGjRfmnea/NgVlvAoqk6T+SjOyWQWx\n+TzG88VtPo/p6qq548cLebp6fPP5dqAdH69ctue9slkBCRuAbUa9TYPh4WGWLl3KEM7dN1XLAkuB\noaEhgLrnGxwc5NJLL2WQ2nf57AQu9a2r3vmyfNwSpOPrbVA2q2qXb/d3JUGZVC07jrWWnu7g7I9l\nWXTGbWUnyJuRsp5sV7UyYaW/Pe74ehsk6BQZH4+W6KiTFIqFsjrpvs8X8/R2T+4c//soAvvi1XZ4\no1nsvMKyWSWvH56oeivZFMtjVsaYW6O+3Hmstc8kYSCnWWpFwQ/KQjU6OplZ0y3jD9iez1cGjh8b\nq0yjmiRRo+LXpVjE+HaOGRsLWHnlQI4ZrYyCP16YjIJvx8cDI+VbT6T8wO+QldQHIiXHjh2jC2ew\nZWmN16U4DcrRo0c5evRoQ/OJtIL30uz+7s1gfeQIXH755OX/0CHn7h0lRHLUylI5mh+t+Oxo4Sjr\n7lzHaV84jYFbBxgv5LEBWXAmVxKQ9ebYsfBsV93d1dtgtceZE3SKuP3mL34R/uEfnJ+NZrPKiuJ4\nsaJOjuZHyRfzjOXHyqaP5ccoFKuk0K1YeEhfvNqFMqgu1pMN1j3I/pRlhYKyykpHiut/TIdjWm5m\ndHfDmjWwbdvktH37YHAwvMyDD8L06c5t3+60bducASDvfB3Bv3Mi7oiunt6K+YwxGM98pje4TOft\nZOl0xx13HOMAbwZ+t0bh/4Txu2HatGkADc8n0k49PbB6dXDT8qEPwY03tnf7kqC7q5vZL9evAAAg\nAElEQVQ1d6xh2y+dnbTtl9swGAbf6bSRYZ/d/vjt7DmyhweffZDe46bDlVU6M0EdoJe8BN7zHnWA\nBKg8Rfbvh+OOgyuvLO9b/+u/dvYpElRfF85ayA0X3sCa28LrcbSFN9YXn9Iyqv0Bde21uj5Ix4ll\nMMdae2UcywUwxrwXWA2cVpr0CHCttfaHVeZ5E3Aj8EpgN/C31tpvxLWNUdWb8areZA+Z1+iOmEqq\nMZFOtAR4WY0yvwXubtJ8Im0Udvl3M19J9Sw4YZ8tPmExuw7u4vQTInZm1AGSGrynw7JlldNApwgk\nLJtVM5YRVn7//qlvi0jKJCmbVVTPAH+JE/ZhKXAXcJsxJjA0gzHmNOAfge3A2cAXgK8YYy5oxcZW\nU2/Gq3qTPWReoztiKqnGREQk08Iu/27mK6meBSfsMzcb5JMHI3Zm1AGSGrynw89+VjkNdIpAwrJZ\nNWMZYeXnzp36toikTEtC+Rlj3gX8d+AU4DjvZ9bauiKnWWv/yTdpvTFmNbAcJ5am32rgKWvtR0rv\nHzPGvBFYC/xzPetupnoyXtVK9lAr41UmNRgRf7yQxwRE3reeoMi2FCnfeJZtS5HyqwZ1ExGRVCsW\nnTgb3qblC19wYuYom5XDnx3njYveyOVnXz4RM8ef4Wrr27dyrHiMVX+wikf2P8K8GfPI53P0+Npi\nPvnJ8oDG3jb+nHPgxReVNVImuKfIWWdNBig/dqyy37xhQ2cHQXbrqz9rVb6YD8w25w+WXH3hAQdh\n9er66mS9/fmgP45KGbS45hrnFkpdH6SDxJ7NyhjzAeBvga8DVwFfA04HXgdssdZ+fArL7sIZJPoa\n8AfW2n8PKHMPMGStXeeZ9m5gk7U2MJpDI1H6/dcK/+BK0LUkStD1oIDtUD1Sf6PXrShZp/wXeX/k\n+6B5rC9FoS0UnIESz0ZWRMMPS2tYtsG10xgEZaHqKlbPVBUlm1Xg+qMc9CjiWm7zJSpKf1Kya8St\nHdmsuIpoj0v9Xfm66p0vy8ctQTq23lZLoqJsVuWCMlC53ACpPd09VctNpCAPyz7lT83p3en+zFbu\n5wQsz78c90A1kiUnqEwy2tyOrbfV1NNHTuZhbVy1jHL+6eN2PDBrVWg2q4CdVTQE/40QllkqdMPr\nSEEWdtCUzUpkQises3ofcJW19i+AY8BnrbUXAJuBOY0s0BjzKmPMCHAU2Ar8cdBATskCYK9v2l7g\neGNMUyJu+iPqu1kvagVUr5X8qFAITKqEtbBxI1x/PTzxRGWZRoK318pOAU4HzhsRf/i54YpI+P55\nbKFQuTNGR527X/xfbHi4vEyhRkR9/w4LyUKVP/rixPYFpgjzr3tkxElx7hE4kNPIQa95IGJarohI\nBwvKfJPPO9OGh52MVf7L7NGjTlv77ndPZpzshEuvtz+w/antHD56uCITTsEWGH5umEO5Q6Hlxgt5\nrH+nl1KD2fwx7Kjvs8OHYfv2ycxW1jododHRyQPx4Q876cbcA2FtYB+DgwcnD3A97WfQiaI2N5GC\n+sjWBnfxGjkVkiysz14oFiqm58eDs1a52azW3bmOy757GevuXMdYvpTd1bez7MgI44V8xfpsPh++\nwwM3vEr98menCjtoYessFJRVVjqTtTbWF/ACcGrp933A2aXflwDPN7jMHuAVwB/g3PWzD/gvIWUf\nA/7SN+1tQBGYFjJPH2CHhoZsVJdcYi1Yu2RJ+Xv3NTAQeVGBy/Uv57zzYljXdy6xfIKJ18B3Khfk\nllmyeUnkeUI3cPHi8GlN3mGX33q5veCbF0TbnqjrjvugN3u5zRf79aOeVyP1No2GhoYsYIe850TI\nawisu08amW9wcNAClqsoq+eBr6sq11XvfNISba+r3lcr6633UupeXtetq/ysVjPVCS75ziWR2vmw\ncu60qu1XtZ0epVyUzxtpP5PZ5ra9rnpfSWlvvYfqgpAu3oc+FDw9GYe1cWH1cu0P11bUwyhl+QT2\nQ3dW31mLNy+eKPvtX3y7atnwDa/jYvu3f1te9tOfbmyd7dP2uqpX9l+tuGH4P4ATgKdxMkktBx4G\nXk6Dt59ZawvAU6W3PzfGnAN8ECc+TtD65/umzQeOWGuPVlvP2rVrmTOn/OahlStXsnLlyoqybmD1\nZidcCFvOuedO3mHYtHVFiGrvlnGzUUSKhB+2gYsXw65dwdOavMPOe8V5PHHwiWjbU29WrLgOurJ3\nNKSeeisiydCKehuUHMnNUFVPM9UJdjy9g1fNe9XE717+zFVB5SJlrKq206OUi/J5I+2n2tzI2t3e\nRslmtXdv8PS0H9awern/hcmMTrX66t6yMJnlKmxnuRnpAE6ec3LVsuEbXsfF1n9XvHunTdYOpsgU\ntOIxq7uA/1b6/WvAJmPMPwP/P/DdJq2jCwh7ZOo+4DzftAtL06vatGkT3//+98teYQ2UG1i92QkX\nwpZz330xrCtCVHu3jJuNIlIk/LANdC/aQdOavMO2P7Wdn+2pkeqg3nXHfdCVvaMh9dRbEUmGVtTb\noORIboaqepqpTtB/an+kdj6sXKSMVdV2epRyUT5vpP1UmxtZu9vbKNms5s8Pnp72wxpWL+fOmMzo\nVKsOe8vCZJarsJ3lDuQA7Dm8p2rZ8A2v42Lrf1zLfS4uawdTZApacWfOVZQGjay1W4wxzwOvB74P\nfLnehRljNgI/wLnLZzYwAKzAGaDBGPNp4GXW2itKs3wJWGOM+QzwVZyBnXcBF03hO5XxB2IfHm5O\nwoVCIXg5x47BggXw2GPNW5cb7d4f1d4bSK1QLJSVGX5uuCyjxbwZ89iwYkPZPLb0JSoyQ+VymGXL\nnPRcpWlmeNh5PzDglIkSBNmrlIUK37oKR1/krt/cxYpTV1DMH6MrYHu86460A+M66HEtV0SkgwUl\nR8rlnCQow8OweXPlZTaXc/7bX2qmOubS6/YH3n/H+9n+1HZuettNE+3875/4+/zFsr/gaOEoj+x/\nhM1v3cwHf/jBiXJu/+Cck84hf6yUsSogNZj9/TNhy83ln23e7MTMWbZsMk0nVE/hGdZROngQZsyo\nv/1sMFOmtJ7/0M+bN5nNynv41q8P7B6m+rCG9dkLxQLXrLiGeTPnTWSuyhVygWXzxTzXrLiGfWP7\nJqav71/PeCFPV1Bf+liOZSctI1fIseLUFbx1yVud5CBBOzYsCHJY/Qq62Obz8L73waOPTpZ973vD\nD2YnRagX8Yg9m1WzGWO+ArwFWAgcBn4BXGetvav0+ddwYvS8xTNPP7AJ+H1gD3CttfZbVdYx5WxW\n/utYo9cY/3xBCR6alkQpQjar0Mj37rYEpDQMymYFlE/zZYsa96QKn9gWS3kGLN9yi+NFuovVs1kV\nigW6bfV1R96BtbJONXpglM2qIUnJrhE3ZbOSKerYeltPNqt8Hrq6kpYYpXFR2vew8m67HrQMa+1E\nm193NitCPoPydq9a29pINqtq643yvj06tt5WE3RqWJuWREdTE1ang/rpQLayWdXqbydHouqtZFNL\nznxjzO8Cq4AzS5MeBb5mrT1Y77Kste+p8fmVAdN24PxtEhv/tWZsDK69Fvbvh7lz4ZprYPbs+hsO\n/7Up6Frln9Zo4+Tv2PnfF8eLjOXH2HrvVnq7e7n4jIuZN3Mea25bw46nd9B/aj9bLtrCbDO7bF7/\n3TXjXYaRYyNcu/1a9r+wn7kz5nLNimvYf2Q/33vse/zRGX/E3JlzWfP9yeV+/R1/T9cLOcyaNc6z\nsv39sGULD489xTtuuXhy3cfNptuzvSPFMa69q3w93jIQkKkq6g70l/MPuIyOgm97I50E1ZZbz/aJ\niMiEsEQnxSK8+CJ861vOZdv9h/Ds2dXnTws3882aO3xt9XGzQwd0vOnGR/OjPLXvKU6dcyof+OEH\nypYxvWc6H7nzIxNt7Cfe9AmmdU/jY9s/xv959P/Qf2o/X3z7F5nZO9NZprsTg3amd5q33fMO3Lz4\nIlx9dWW76uWWDztgIyPV22ZlxEkNf/fIGKcOb93qjAV463LWDmtQn93NUOXvl8/sncnGn26cGMB5\n3+vexyxmMZYf49of+/rIZjbdATvLv7u6u7onU4rdcQeccgrs3g0XXQSzZoUPrgQdCP80t18edtCi\n/HEk0iFiP/tLd8V8HzgC/Ftp8geAa4wx/09poCVTurudfsK2bZPT9u2DwcH2bVMzdHd1s+aONWz7\n5TaWnLCED7/+wwzcOsC2XzpfdNsvt2EwDL6z+hf1Lse1b2wfg+8c5EdP/qhiuQ8++yC9x02HK1dN\n7tRt2zDGcPbgINN7pgeuu9p6Yuc/CbZtc3oZaT8JREQyprvbGR/wttmPPpqdy7W/LYzaVrvzfmrH\np7jxwhtD2/vbH799IpaG28be/vjt7Dmyp6511d4Y34FqpF1V25xpQf3vLNXlWnq7e1lzW3BdP/Ti\nIa6/73oADr54kBsuvKGsLDTQR+7pqdzhAwOds8NFEqAVAZC3ALcAL7fWvtNa+06ctOL/u/RZJmU1\n0HpD2ayqLMc/3/KTl1d8XisrxuITFoeuu9Hta4qsngQiIhmT9cv1VNrCfWP7qi7DbYNrTWuKZhyo\nrB/sDtfphzesnl64+MKJaW7Wqqb0kTt9h4u0WSsGcxYDN1pri+6E0u+fK32WSVkNtN5QNqsqy/HP\nd/+e+ys+r5UVw/2PYLXsW/VuX1Nk9SQQEcmYrF+up9IWzps5r+oyvBluqk1rimYcqKwf7A7X6Yc3\nrJ7+aNePJqa5Waua0kfu9B0u0mateMhwGCdWzmO+6WcCD7dg/S2X1WQIbvR8N6vFI/seqZnNqtpy\n/JH1n3j+CS48/UIef/7xiuVOZMVYuBD27oV587AbNvDUgSe4aulVFIoFVr9uddm6o2Toim9nZfQk\nEBHJmKxfrqfSFhbHi/z1ir9m+LlhNr91c8UycoXyDDc3X3QzL+Zf5OIzLp5ovz/55k82p911D9RZ\nZ00GQV29ur4DlfWD3eE6/fDmi/nAfnm+mGeccS599aXMmzGP9f3rJzLU1tuHL+OmFPP0zdmwIckB\niUUyJ/ZsVsaYPwU+C9wE3F+avBxYA3wU2OmWtdb+ItaNiagZUfqzFjXf5c8g5c86lS/m6aV2ChB/\nY+GPqO9fri0UnJDwVbJQ2Xye8e6usuX6s2sFNVL1ZvmIrFrqlLAyUZeTjJMpUVH6k5JdI27KZiVT\npHobILmX2eaI2s6504PKAxXtK1DRxnozXPn7DGXZrMKyUFXTjMw5EO1gJ+ukUL2NKFmHLT71ZLMy\nmLJ66vaN/X1t//tIwupkPZmo/Gl6vdeJeg5m8g5+ouqtZFMrHrP6NrAIZ0BnR+n1WeDU0mc/Bx4q\n/cyMrEXNB6BYxIyOOsHNFi2CgQHM6BgPPzvMok2LGLh1AIpF7MhIWRlGRiY7UCX+DuFYfoyBWwdY\ntGkRDz87jBkdK19PoeCkKPBOGxuD4eHJ9YyNYYrFieUM3DrAaH50ohPqXy9MZvnwzjNybKRsnoYF\nHfQI+6Z8A4v1zyMiInXJZJvtUStbJUy2h8PPDXMod6iiXfQzxjCaH2Xdneu47LuXse7OdeTH84zm\nRxm4dYDnR/ZVtNscOuS02/l85Wejo84fdmHczDneecbGwucJaz8rdk7IQI7a3lTKel2G8L6rm83K\nO30sP0bBFir6xuOFfGVfe3QMW60OVmxIMbxOBtWfoHofVjZselgdVJ2VDtWKe+Be3oJ1SCsEZIHw\nZpQKyzpVK1NET3fPRET9JScs4eyT+pyLsLuMBx+E6dNhVchyp0+f2BYzOMje0b2Rs2hMJctH3RrJ\noqHMGyIi0gJRMlf5y3vbzyUnLOFzf/g5Vn1/FfvH9vPS2fPK23J/+xXWpofxZ86pNc9U2k+1vZJg\nQX3XhbMWVmSo8tbdB559YKJv7JYN6tNPOTvcwoVwQ+WyJ+rPtdfW7t/XWkbUbVGdlQ4Q+5051tqn\no77i3hZpgioZpWplnaq6WF+WrLJlnF5juYsXl70/d9G5k0UiROZvacarRqL+K1OAiIi0QK3MVX5B\nmSd3PL2DZScvKxWo0n61oj2cSvuptlcSzF9Ha2Wo8maYc8vGkh1ub41l798/OS2sf19rGVG3RXVW\nOkArHrPCGHOZMeZfjTG/NcacWpr2P4wxF7di/dJEVTJK1co6VXWxvixZZct4ssZyd+0qe3/fM/dN\nFokQmb+lGa8aifqvTAEiItICtTJX+QVlnuw/tZ+f7flZqUCV9qsV7eFU2k+1vZJg/jpaK0OVN8Oc\nWzaW7HDzayx77tzJaWH9+1rLiLotqrPSAWJ/zMoYsxq4Fvg88HHAfXr1EPA/gNvi3gZpkoA0Afbm\nm/nFs8MTmSyCsk6xYUPVIGRuRH03S8bDzw7z6i1bnNs977kHzjkHcrngFAXDw85nAwPYm2/G5o+x\nYNYCTj7+5EgZO1qa8aqRNAudnppB0u1Ak8qISOxqZa4KCrjqbT/POekcXsy/yM1vu5m/+MFf8PzI\nPk7wtuUrVsDmzfDII85/5OvNguNmzvG3h2HzTKX9VNsrCebPLutmqHKzWdXKOre+f70TM8dXP22p\nPpmomaiC6sn69eFZrvJ5uOYa2Levev/eu4yodVB1VjpUK7JZPQr8lbX2e8aYEeBsa+1TxphXAT+x\n1p4Y6wY0IMlR+hvVrIxNtbJZFYoFusdtRZYp48s2UW82q8DMFwER9Iu+bFZRU6/Gks0qcGUNRNpP\nXnR+V6Ki9Gex3gZJQzaro0eP8vo3vB6iNi8G7v3Xezn33HNrl5WpUr2VMkFZH4HQdjHs96B5Q7NZ\nFQpYG9BX6KqRDTMoE061Pzyn0n4mq+1Vvc24evui/nrrvq8nm5W/frrv69qWsDoZluVK2axEmqoV\nj1m9nOBMVUeBmS1Yf8drVsam4niRw4XyzBVHCmPl2aLGbWBke5vPV92esXz5cgJTI46Owrp1cNll\nzs+xMXjiCbj+eti4EcbG6Pb98RhlUCZKlo+maSTNQiekZpBMmTZtWmkg51PAYI3Xp8CW5hGRlioU\nCxPZp7z9g8J4gY0/3chDzz3EodwhLv/e5YH9h6D20zvN9PQ4f1C57fdppzl30oZkprL5fPVsNP6B\nm1p3EEyl/VTbKy1Sbz+9OF6sqLej+dGJ7LDefrqbzco7zc306r8Dxx3Iibwt3rrt9s1HR52Bm6As\nV/l8cB0Oq2v11kHVWelArchm9WvgNYA/wPFbgZ0tWH/Ha1bGJv9ywAmW6F2O6e2tGR2/oe3xR6kH\n5zbNwUH4u79z4uY8+qii1oskykVEuw9oQwu2RUT8vNkkobw9vvs3d/NX//WvImW2qqq7G66+uiyD\njQnIYDPRV3jgAdizR9lopGPU2y+Oms0KJvvptz9++0TcHH/fveFtCeqbd3XVn4lKRBrWijtzPgds\nMcb8Kc7tZucYYz4OfBr4bAvWLzQvY1Ok5USIJt/Q9kTMZiUiIiLRhLXHy09eXvXz+lbSQIZK7zSR\njKu3nk0lm1W9y65avlmZqESkIa1ITf4V4C+BvwFmANuA9wIftNb+77jXL45mZWyKtJwI0eQb2p6I\n2axEREQkmrD2+P4991f9vL6VNJCh0jtNJOPqrWdTyWZV77Krlm9WJioRaUgrslm9BPiutfZ/GWNm\nAK8C3gDsiXvd4mhWxqYoy7H5PARFx/cEQW5oe8Ki1HuyWSlqvYiISHT+bJIrTl3B1rdv5VjxGK+e\n92q2P7Wdm95209T6D/72+5xzsKUMNhV9hVwOs2yZ064rG410iKDsVBtWbAitZ275s+adRXdXN4Vi\ngdWvWx1Yn91sVhefcXFdy45U58OyWZX+Fqjos/uDIovIlLUim9WPgFuttV8yxvwO8O9AHjgRWGet\n/WKsG9CALEbpbyRjU60sFRCcBaOrOF53NqvQjBdlM9XIapHVDl/yovO7EhWlP4v1Nkgaslm566KO\ntWX9uCWI6m2HiNrue9vxKO1+rWxXwRtT2Y7Z8YC+Qq1sVu1uD9u3ftXbjAvLThUmKGtVb3dvYB3u\nGreBWavC1PU3Q1idCMtm1Qztvg5El6h6K9nUipg5fcBPS7+/C9gLnApcDnygBesX6s/YFBbN3l/G\nH00/P57ncHGMjT/dyPX3Xs/Gn27kcHGsIgp+2fqLRczoaPUMFlB5ofY3RMm8kE9Nsejsi1r7RkRE\npKSejDTegZxa7b6r7gyZAVlm/P/kMb291bPRtLs9bPf6JbPCslOF1Sk3a5U/K2yhWKjs71sq+thm\ndLTqeVvX3wxhddY/cNPMgRzVQ5EJrchmNQNwewMX4tylM26MuR9nUEcSKEo0e3+ZB599kOk901n1\n/VVlkfQf3f9ofZmqFPV+kvaNNFmUFIJKMyiSbo1kjYw6T7MyZNat3e1hu9cvmVVvnaqWha5y4Rk7\nb7P2fUSmqBWDObuAPzLGfBf4Q2BTafo84Ei9CzPGfAz4Y+C/AC8C9wJ/aa19vMo8K4C7fZMtsNBa\nu6/ebegUUaLZe8ucfsLpkeerXJCi3ofSvpEmOHr0KF3ApRHLdwHHjh2LcYtEJE6NtMVR52lWhsy6\ntbs9bPf6JbOmms2qroxTaT9vs/Z9RKagFY9ZXQvcAPwG+Jm19r7S9AuBnzewvP8K3AQsA84HeoEf\nlQItV2OBJcCC0ksDOTVEiWbvLfPkwScjz1e5IEW9D6V9I00wbdo0xgHeDLyzxuvNMA4cd9xx7dlY\nEZmyRtriqPM0K0Nm3drdHrZ7/ZJZU81mVVfGqbSft1n7PiJTEPudOdbafzDG/AuwEHjY89F24LsN\nLO8i73tjzLuBfTjxOv+lxuz7rbV13w3UiaJEsw+Kvp8r5P4ve3ceHXV1/3/8eWcChB2UzQXD+kUU\nZbOiVQJ1AYVaKVUpIqDgDmgBS6uIIPzcQSqLStUqiIhYsFaLBUUFVBQN7qKCIAhFNlkSIJDM3N8f\nn0ySmcwkM8msyetxzpwwN/dzPzdh3nNv7twleidVJe+GZvGj341EW1vC28w4cC6jiKSM8pwaGe41\ngfkGnD6AST0nRXxCZuQ/VILbw0TfXyqtSOM11KlVQTdNrmyv28r284hUUDyWWWGt/Rn4OSBtbZSK\nb4Az6+aXMvIZ4DNjTDrwFTDJWvtBlOoQX2Xs4l6ek6sCuV1u6lSr47f+Nt+TXyJPvbTaTO01tTDN\n5udTrXpdv+vKvL/bDXXr+q93DfKmHLj7flm78YeUOrvgh/27ERGRyi2Stt3tclM3zLbYlx6q3Q+2\nGWph2WG2p373DjyJMvB5KKHawxI3i1EbqfZYYqS0eA11ylUdSsZqmjut5PuEAXeI121UTq2KVnq4\nFIcifuKxzCpmjDEG+BvwnrX2m1Ky7gBuAv6As4jgJ+BdY0yn2NcyysrYxT2SEyxKvU3Bzvpjlo1h\n8CuDGbNsDDl5OeR58wrL/nz7OkzOoRI75LsDTrsPayCptBMscAZuCDzxKifHSY/oB0vBXfDL+N2I\niEjlVp62PZwTaYqXu2LTCg4eO1ii3Q92D7fLHXZ7Wvwee7N3YYO05YTblgdr/+LZpqs9lhgJFq/5\nnvygp1wFm4HjG8gJ+j4ReEB2wUBO2O8poWI9Pz866ZHGq+JQpFBcZubE0OPAacB5pWUq2By5+AbJ\nHxpjWgOjgaGxq14MlLGLe7ROmQgsB2DXoV3M7z+ftdvXUjOtJh1P6uK8EcdhR3mTllbi5zbluZd2\nwRcRkRQTqxOkfOV+vP1jXuj/AoOWDAra7ge/OLz21HeP3Yd2c3zdJtHtN6hNl0osolOriOx9IqL3\nlGBxdsIJMHVq+Om+uJw8WfEqEkUpO5hjjJkF9AG6W2t3lKOItZQxCDR69Gjq16/vlzZw4EAGDhxY\njttFURm7uEfrlIlQ5bQ5rk3YdYmqaN1Lu+BXakkbtyISkuK2bLE6QWrVllV0aNKhfPcIsz1dtWUV\n13a6NqJrwqY2PWEUt7EXy1OuKnQi1s6dkaX74nL37uDpIlIuKTmYUzCQcznQw1q7tZzFdMJZfhXS\n9OnT6dKlSzmLj6HMzKJRbSixi3tmRqbfJ2vlPWUiVDkbf9mIwYRVl6iK1r3iWWeJu6SNWxEJSXFb\ntmi17YFGnT2KNze9Wb57hNmeZmZk8tG2jyK6Jmxq0xNGcRt7kcZkJPkjKjswzpo2jSzdF5eNG/uX\nq3gVqZCUG8wxxjwODAR+BxwyxhS8a3DAWptbkOd+4CRr7dCC57cDm4GvgXTgBpwDei+Oc/UrzuPB\nzp7tLDEq2MXdzpqFKbaZWcSnSQW7TYhycvNz6XZSN1ZuWcnn29dxZkBdYrWjvM3PhyA/N5Fugqxd\n8KWSWB+lPCKS/Mpq28PdyDRYvnHnjWPceePYlbOLmZfODL//EGZ76qv7yKUj2Zu9i+OC9RvC3QS5\nnHUQSXbBNjoGwj+1ishOxYro74VgcXb33U7chpvui/N77oFduxSvIlFirLVl50oixhgvzulVga6z\n1s4ryPMskGGtvaDg+Z+BG3EO5D0MfAHca61dFaQcjDFdgKysrKyk+8TB4/Xgzc+jWvX0wrS8Y7m4\n0qr5HRle0dOs8j355Nt80tOK7pObn4vbuKnmrlaYVuJEqRi+IVfJ06ySW+CWegmVzHEbTWvWrOH8\nX/8ab5j5XcB7H3xAjRo16Nq1a9E7YWn+B/wd5s+fzzXXXBPRNVlZWQDOvcgCyvq/WAd0pbL/vyUR\nxW2KCtW2+zYyHbF0BKu2rCIzI5PZfWZTt3rdEvkD8828dCaf7viUf3z2Dx7v8zi1q9X2+yOxzP5D\nPE+zqmAdUpzithLzbXQcGMO1q9XGYz0l+uLVXNVCxmUkfwOk5GlWqSWp4lYqp5SbmWOtLfMELmvt\ndQHPHwEeiVml4sjtcjPk9SGs3b6WNse1YeMvG+l2Uje/DcvCOcGiLGnuNIYuGVrqfYCSgykxfEMO\nvFe5BnJAu+BLSqtRo4YzkPMboGEZmfeB9x3nGhFJbaHa9nA3Mi0t383/uZkRS+L6WWUAACAASURB\nVEcEvab0SoXXnvqVE9h2V2QgJ4I6iCSr0jY6/suyv/Da96+V2hcvLpK/ASL6eyFUnEUrXUTKJeUG\nc8TZsGzbwW1s/GUj4IzSp/J9RKQc2hLebJl34lAXEUmocDcyLe1gg2htqCwikQsVm7sP72bjLxvV\nFxeRoMqc5SLJJzMj0+95tDZBTNR9REREpPzCba9D5dv4y0a18SIJFCo2G9dqHDRdRAQ0MyflRGuD\n47Lke/Ij2nRNRCSRtm7dyp49e8LK26hRI0455ZQY10gkPnz9ghPqnMDOQztpUqsJE3pMCLofRmC7\nPuPSGazYtIJuJ3WLSV9CRMrm63MHxnCeJ497etzDrkO7YtrnF5HUpb/K4ygae365XW7qVq/rt142\n35Nf4Q2PA6W506hNbb/75HnyEjqQE42NnUWk8tm6dSvt27XjcG54089rpaez/rvvNKAjlWIvTrfL\nTZ1qdZjaa2phWmC/wJcvsP/g8Xq4sNWFXNjqQjxeT4myIzkVS+2xJLNkjnVfn7t4DOd58qjmrkZd\nUzJmS4u1YKdi6UNYkcpLy6zixOOB7GwYNAiaN3e+Zmc76ZEK7EDl5OUwaMkgmk9vzqAlg8g+lh20\nUxZRfb0eDuUd4v7V9/PIB49w/+r7OZR3qMLlVqQ+2ceyo/5zikjq27NnD4dzc5mPc35WaY/5wOHc\n3LBn8UjlFc12OZGC9QNy8nKCto9lbXgaTjur9lhSTbLHuq/PXTymfH3uSDYp9p2KFfhe4DvmXEQq\nHw3VxonbDSNGwAJno3oWLABjYH7oDenDKzfMUywqWi7AN7u/qXC50apPtH5OEak82lP2QegiPrFq\nl+MtWu1jNE7FEklGyR7r0Yqp0k7FEpHKSTNz4miV/0b1rIzSwRHhnmKRLOWWV7LVR0REUlus2uV4\ni1b7WNFTsUSSVbLHerxjWEQqBw3mxFGm/0b19IjShvSxOnUq2U6zSrb6iFQZe3COOS/tEaVVS1u3\nbmXdunVhPbZu3Rqdm0qVFat2Od6i1T5W9FQskWSV7LEe7xgWkcpBy6zixOOB2bOdaZ0rVzqNyKxZ\nFd+ALVanW8Xr1KxUrY9IVXDs2DEwwJIwLzBw9OhRatSoUa77lXcjY5HyiFW7HG/Rah8rciqW2mNJ\nZske69GKKZ1EK1L1KLLjxO2GunX91+dGoxEJdTpFRTtUsSrXx+bnY9LSQj4Pdq9Y1kdESqpevTpY\ngClAyzJybwY7odwDOeC/kXH7MvKuB67RRsZSAbFql+MtWu11RU/Fcrvc4d03mY8Vkkop2WPdF3ul\nnVQbjjR3GnUoWU6ogZyy+uIikvy0zCqOAhuNaDUikex0nwzl2vx8yMnxP1YgJ8dJJ/RJGWXVT0Ri\npQ8wqIxHn6jdzbeRcWmPsgZ7RMIRq3Y53qLRXkd6KpbH6yHnWA5jlo2hxWMtwjvVKtmPFZJKK5lj\n3Rd7Y5aNYfArgxmzbEzI2CtL4MBNaQM5pfXFRSQ1aPhV4s6kpZU4VsAUO1ZAJ2WIVG3ry5mnvNeJ\nSORtr9vl5qbXb4qsrU72Y4VEEiDYCbK7Du2Kab+3rL64iKQGDeZIYpRxrIB24xepeo4ePYoLuCbM\n/K6Ca3z/Ls91IlIk0ra3XG11sh8rJJIACen3KhZFUp4GcyQxMjOLPg2AEscKZGZk+n1Cod34RSq/\nGjVq4AXgFuDEMnL/Dy9PFO7RU97rRKRIpG1vudrqMtp/kaooIf1exaJIytNgjsSdzc+H2bOd6ZwF\nxwrYWbOgYOM1nZQhUtVdj7MzTmnWAU9E6ToRibTtLVdbnezHCokkQCL6vWX1xUUkNShaJe5MWhq2\nTh3/dbnFGo9Yn6QlIiIi/iJte8vVVif7sUIiCZCIfm9ZfXERSQ06zUoSIrCxCHweq5O0REREJLhI\n295ytdXJfKyQSIIkot9bVl9cRJKfBnNERERERERERFJIyg3mGGPuNMasNcYcNMbsNMa8Yoz5vzCu\n62mMyTLG5BpjvjfGDI1HfUVEREREREREoinlBnOA7sBMoBtwEVANWG6MqRnqAmNMC+B1YAXQEXgM\neNoYc3GsKysiIiIiIiIiEk0ptzjSWtun+HNjzLXALqAr8F6Iy24BNllrxxU8/84Ycz4wGngzRlUV\nEREREREREYm6lBvMCaIBYIFfSslzDvBWQNoyYHqsKhVPgTve6+QnkdTw8ccf8/3334eV9//+7//4\n1a9+FeMaiUhlob6BSOpQvIpIeaT0YI4xxgB/A96z1n5TStZmwM6AtJ1APWNMDWvt0VjVMdY8Xg/Z\nx7IZsXQEq7asIjMjk9l9ZlO3el01AiJJbOvWrZz763Px5HvCyu9Oc7Pph00xrpWIVAbqG4ikDsWr\niJRXSg/mAI8DpwHnJboiieJ2uRmxdAQLvlwAwIIvF2AwzO8/P8E1E5HS7NmzxxnI+Q3QsIzM+8Dz\njoc9e/bEo2oikuLUNxBJHYpXESmvlB3MMcbMAvoA3a21O8rI/jPQNCCtKXCwtFk5o0ePpn79+n5p\nAwcOZODAgeWoceys2rLK7/nKLSsTVBORxEuVuC3UFjixjDz/A96JQ11EEiTl4jYFqG8gsaa4jR7F\nq4iUR0oO5hQM5FwO9LDWbg3jkjXApQFpvQrSQ5o+fTpdunQpXyXjKDMjs3A0H6BHRo8E1kYksVIl\nbkWkiOI2+tQ3kFhT3EaP4lVEyiPlBnOMMY8DA4HfAYeMMb4ZNwestbkFee4HTrLWDi343pPACGPM\nQ8A/gAuBK3Bm9qQ0j9fD7D6zMRhWbllJj4wezOozSxuniYiIVFHqG4ikDsWriJRXyg3mADfjnF71\nbkD6dcC8gn+fADT3fcNa+6Mxpi/O6VW3AduA4dbawBOuUo7b5aZu9bp+62r15i8iIlJ1qW8gkjoU\nryJSXik3mGOtdYWR57ogaauArjGpVIIFvtnrzV9ERKRqU99AJHUoXkWkPMocGBERERERERERkeSh\nwRwRERERERERkRSScsusRERERERERFKJMeYUoFGi6yEpZU9pp3drMEdEREREREQkRowxp7hcru+8\nXm96ousiqcPlcuUaY9qFGtDRYI6IiIiIiIhI7DTyer3p8+fPp3379omui6SA9evXc80116TjzObS\nYI6ISCwcOHCA3NzcsPLWqVOH2rVrx7hGIiIiIpJs2rdvT5cuXRJdDakkNJgjIlIB3333HWd17kzO\nkSNh5T/lhBP46rvvYlwrERERERGpzDSYIyJSAZs3bybnyBGepewd7bKASTt2cODAgTjUTERERERE\nKisN5oiIRMFFwMll5NEbroiIiIiIRIMr0RUQEREREREREZHw6YNiEZEKsNYC8CTQoIy838a8NiIi\nIiIilcN1113HgQMHWLJkSaKrkpQ0mCMiUgHZ2dlUAx4MM381ID8/P4Y1EhERERFJfTNmzCj84FRK\n0mCOiEgF1KtXjzyA0UD9MjJvAM8LkJamt14RERERkdLUrVs30VVIatozR0RERERERERK+Oc//8mZ\nZ55JrVq1aNSoEb169eLIkSNcd911/P73v2fy5Mk0adKE+vXrc8stt/jNQLfW8sADD9CqVStq1apF\n586dWbx4sV/533zzDZdddhn169enXr169OjRg82bNwPOMqv+/fuHXd7+/fsZNGgQTZo0oVatWrRr\n1465c+fG+DeUOPp4WERERERERET8/Pzzz1x99dVMnTqVfv36kZ2dzerVq/F6vQCsWLGCmjVrsnLl\nSn788UeuvfZaGjVqxJQpUwC4//77WbBgAX//+99p06YNq1atYvDgwTRp0oTu3bvzv//9j8zMTC64\n4ALeffdd6tWrx5o1a0JuSVBWeXfffTfffvsty5Yt4/jjj2fjxo0cOXIkbr+veNNgjoiIVElvvPEG\nWVlZYeXt2rUrl156aYWuExEREUklO3bswOPx8Pvf/57mzZsDcPrppxd+v0aNGjz77LPUqFGD9u3b\nM3nyZMaNG8eUKVM4duwYDzzwACtWrKBbt24AtGjRgtWrVzNnzhy6d+/OrFmzaNCgAS+++CJutxuA\n1q1bB61LOOX99NNPdO7cmc6dOwNwyimnxOx3kww0mCMiIlXOjh07uOyyy8LeVM8Yw48//ghA3759\nI76usncmREREpPLp2LEjF154IR06dKB379706tWLK664ggYNGhR+v0aNGoX5zz33XHJycvjpp5/I\nzs7m8OHDXHzxxX79pry8PLp06QLA559/Tvfu3QsHckqzcePGMsu75ZZb+MMf/kBWVha9evWiX79+\nnHvuuVH5XSQjDeaIiEiVs3///oKOwC3AiWXk/h/WPsGePXsAynWdBnNEREQk1bhcLpYvX86aNWtY\nvnw5M2fO5O677+bDDz8s89qcnBwAli5dyokn+veZfANANWvWDLsu4ZR3ySWXsHXrVpYuXcqbb77J\nRRddxIgRI3j44YfDvk8q0WCOiIhUYdcDXcrIsw54IkrXiYiIiKSWc889l3PPPZcJEyaQkZHBv/71\nL8CZWXP06NHCwZQ1a9ZQp04dmjdvTsOGDalRowZbtmzh/PPPD1rumWeeybx58/B4PGXOzjnttNPK\nLA/g+OOPZ/DgwQwePJjzzz+fcePGVdrBnJQ7zcoY090Y829jzHZjjNcY87sy8vcoyFf84THGNIlX\nnQO9+OKLKjfGZavc2JdtjBkYk4JTzLvvvpvoKkiKimXch1JV4zZev+t43Ef3qHr3qKpxGygR75nx\nvmdV+BkTcU+1t+W3du1aHnjgAbKysvjpp59YvHgxe/bsoX379oCzj83w4cNZv349S5cuZdKkSYwa\nNQqAOnXqcMcddzB69GjmzZvHpk2b+PTTT5k1axbPP/88ACNHjuTgwYMMGDCArKwsNm7cyPz589mw\nYUOJuoRT3sSJE/n3v//NDz/8wNdff83rr7/OaaedFqffVvyl3GAOUBv4DLgVCG/TAidfW6BZweME\na+2u2FSvbKk2IJCKAw0qNy5lV4pGqqIqPJizB/hfGY89FbtFQu8nISWic0kVjVsN5ugeqXwPqmjc\nBtKgg+6ZKvcrUCnitl69eqxatYq+ffvSrl077rnnHh599FF69+4NwIUXXkjbtm3JzMxk4MCB9OvX\nj4kTJxZeP2XKFCZMmMCDDz7IaaedxqWXXsrSpUtp2bIlAMcddxxvv/02hw4domfPnpx11lk8/fTT\nVKtWLWh9yiqvevXq3HXXXXTs2JGePXuSlpaWqP//uEi5ZVbW2v8C/wUwxpgILt1trT0Ym1qJiETm\n6NGjYIAlYV5g8JvGGo/7HTt2rFz3EhEREZHUd+qpp/LGG2+UmmfixIl+AziBRo0aVThbJ5gOHTqE\nvMezzz4bUXnjx49n/Pjxpda3Mkm5wZxyMsBnxph04CtgkrX2gwTXSUSqsBo1ahTMLZwCtCwj92aw\nE8o9kFPe+1WvXr3c9xMRERERkdipCoM5O4CbgE+AGsANwLvGmLOttZ8ltGYiIvQhvI10J6To/URE\nREREJNoq/WCOtfZ74PtiSR8aY1oDo4GhIS5LB1i/fn1M6nTgwAHWrVtX5cuNZdkqNy5l1zfG1LLW\nHo5F4eUQ07gN5dChQ84/VgNlTWTZ53z58ssv+eWXXwoSw6mvk8fvZwtnX5uCPP6/k/Dvt3nz5oiv\nKe+94n1d4n62IrGM+1JUybiN1+86HvfRParePaiicRsoEe+Z8b5nVfgZE3FPtbexEWwJlMSXsTbc\nPYSTjzHGC/Sz1v47wuseBs6z1p4X4vtXAy9EoYoilV1Xa23cW8dgFLciYVPciqQexa1I6imMW2NM\nFyArKyuLLl3KmiEtAuvWraNr165Qyvt/pZ+ZE0InnOVXoSwDBgE/ArnxqJBIivo20RUoRnErEh7F\nrUjqUdyKpJ5kiluphFJuMMcYUxtog7OpMUArY0xH4Bdr7U/GmAeAE621Qwvy3w5sBr7GmRZ6A/Ab\n4OJQ97DW7gUWxO6nEJFoU9yKpB7FrUjqUdyKiCSHlBvMAc4C3sE5l8UC0wrS5wLDgGZA82L5qxfk\nORE4DHwBXGitXRWvCouIiIiIiIiIREvKDeZYa1cCrlK+f13A80eAR2JdLxERERERERGReAg5KCIi\nIiIiIiIiIslHgzkiIiIiIiIiIilEgzkiIiIiIiIiIilEgzkiIiIiIiIiklBr1qzh3nvv5eDBg4mu\nSkrQYI6IiIiIiIiIJNQHH3zA5MmT2b9/f6KrkhI0mCMiIiIiIiIiCWWtTXQVUooGc0RERERERESk\nXHJycvjTn/5Ey5YtSU9Pp2nTpvTq1YvPPvusMM9HH33EJZdcQoMGDahduzY9e/bkgw8+KPz+vffe\ny7hx4wBo0aIFLpcLt9vN1q1bAfB4PEyZMoU2bdqQnp5Oy5YtGT9+PMeOHfOryyeffELv3r1p3Lgx\ntWrVolWrVgwfPtwvz9SpUznvvPNo1KgRtWrV4qyzzmLx4sWx+vXETFqiKyAiIiIiIiIiqemmm25i\nyZIljBo1ivbt27N3717ee+891q9fT6dOnXj77bfp06cPZ511FpMmTcLlcvHss89ywQUX8N5773HW\nWWfxhz/8ge+//56FCxfy2GOPcfzxxwPQuHFjAIYPH868efO46qqruOOOO/joo4944IEH+PbbbwsH\nYnbv3k3v3r1p0qQJd955Jw0aNODHH39kyZIlfvWdMWMGl19+Oddccw3Hjh1j4cKFXHXVVbz++utc\neuml8f3lVYDRVCYRERERERGR2DDGdAGysrKy6NKlS1TLzstzvrrd4ErQupuGDRsyePBgZsyYEfT7\n7dq1o02bNvznP/8pTDt69CinnXYabdu25b///S8A06ZNY9y4cWzevJlTTjmlMO8XX3xBp06duPHG\nG3nyyScL08eNG8e0adN4++236dGjB6+++ir9+/fnk08+oXPnziHre/ToUWrUqFH43OPx0LlzZ5o2\nbcqbb75Z7t9DNK1bt46uXbsCdLXWrguWR8usRERERERERFJIfj4cPQrz5sHDD8NPP4HHk5i6NGjQ\ngI8++ogdO3aU+N5nn33Ghg0bGDhwIHv37i18ZGdnc+GFF7Jq1aoyy1+6dCnGGEaPHu2XPnbsWKy1\nhYNEDRo0wFrLv//9b/Lz80OWV3wgZ//+/ezbt4/u3buzbl3QMZOkpWVWIiIiIiIiIikkLQ1694a3\n33aeP/AAfPYZtGoV/xk6Dz/8MNdeey3Nmzena9eu9OnThyFDhtCyZUs2bNgAwJAhQ4Je63K5OHDg\nAPXr1w9Z/pYtW3C5XLRp08YvvWnTpjRo0IAtW7YA0KNHD6644gomT57M9OnT6dmzJ/369ePqq6+m\nevXqhde9/vrr3HfffXz22WccPXrUry6pJLVqKyIiIiIiIlLFbdxYNJADcOgQPPUUJGIXlSuvvJJN\nmzYxa9YsTjrpJKZOncrpp5/OsmXL8Hq9gLOE6q233irxWL58OXXq1AnrPsaYMvMsWrSINWvWMGrU\nKP73v/8xbNgwzjrrLA4fPgzA6tWrufzyy6lVqxZPPPEEb7zxBm+99RZXX311yp2mpZk5IiIiIiIi\nIimkTh1nBk7BWAkADRokZjAHnFkyN998MzfffDN79uyhc+fO3HfffTz66KMA1K1blwsuuKDUMkIN\n1mRkZOD1etmwYQPt2rUrTN+1axf79+8nIyPDL//ZZ5/N2WefzZQpU3jxxRcZNGgQCxcuZNiwYSxe\nvJiaNWuybNky0tKKhkOeeeaZ8v7oCaOZOSIiIiIiIiIppFkzGDu26Hn79nDLLc7yq3jyer0cPHjQ\nL61Ro0aceOKJHD16lLPOOovWrVszdepUDh06VOL6PXv2FP67du3agLOPTXF9+vTBWsvf/vY3v/Rp\n06ZhjOG3v/1t0OsAOnbsCFC4nCotLQ1jjN+eOj/++COvvvpq2D9zstDMHBEREREREZEU8/DDzgDO\njh1wzjmJ2QA5Ozubk08+mSuuuIKOHTtSp04d3nzzTT755JPCWTlPP/00ffr04fTTT+e6667jpJNO\nYvv27bzzzjvUr1+/cCCla9euWGu56667+OMf/0i1atX43e9+x5lnnsnQoUP5+9//zr59++jRowcf\nffQR8+bNo3///mRmZgIwd+5cHn/8cX7/+9/TunVrsrOzeeqpp6hfvz59+vQBoG/fvjz66KP07t2b\nq6++mp07d/L444/Ttm1bvvjii/j/AitAR5OLiIiIiIiIxEgsjyZPtLy8PCZMmMDy5cvZtGkTXq+X\nNm3acPPNN3PjjTcW5vviiy+YMmUKK1euJCcnh2bNmtGtWzduuukmevbsWZjv/vvv58knn2THjh14\nvd7CY8q9Xi/3338/zz33HNu2baNZs2YMHjyYe+65h2rVqgHOyVlTp07l/fffZ+fOndSvX59u3box\nceJEv6PKn3vuOR588EG2bt1Ky5Yt+ctf/sLmzZuZPHkynkQdCRYgnKPJNZgjIiIiIiIiEiOVeTBH\nYiOcwRztmSMiIiIiIiIikkKqxGCOMcZljJlijNlkjDlsjNlojLk70fUSEREREREREYlUVdkA+a/A\nTcAQ4BvgLOA5Y8x+a+2shNZMRERERERERCQCVWUw51zgVWvtfwuebzXGXA2cncA6iYiIiIiIiIhE\nrEosswI+AC40xrQFMMZ0BM4Dlia0ViIiIiIiIiIiEaoqM3MeBOoB3xpjPDiDWOOttQsTWy0RERER\nERERkchUlZk5A4CrgT8CnYGhwJ+NMYODZTbG1DLGdDHG1IpjHUWkAhS3IqlHcSuSehS3IiLJoarM\nzHkYeMBa+3LB86+NMS2AO4Hng+Q/FcjKzMzkd7/7nd83Bg4cyMCBA2NYVZGUYRJdgQCKW5GyKW5F\nUo/iViT1JFvcSiVUVQZzagGegDQvZcxMmj59Ol26dIlZpUQk+hS3IqlHcSuSehS3IiKJVVUGc14D\n7jbGbAO+BroAo4GnE1orEREREREREZEIVZXBnJHAFGA20AT4H/BEQZqIiIiIiIiISMqoEoM51tpD\nwJiCh4iIiIiIiIhIyqoqp1mJiIiIiIiISCV27bXX0rJly5iV37NnTy644IKYlR8JDeaIiIiIiIiI\nSMozxuByxW6Yw5jkOaisSiyzEhEREREREZHK7emnn8br9Sa6GnGhmTkiIiIiIiIikpQOHz4cdl63\n2021atViWJvo8Xg85OXllft6DeaIiIiIiIiISMQWL16My+Vi9erVJb43Z84cXC4X33zzDQDffvst\nV1xxBccffzw1a9bkV7/6Fa+99prfNXPnzsXlcrFq1SpuvfVWmjZtSvPmzQHIycnhT3/6Ey1btiQ9\nPZ2mTZvSq1cvPvvss8Lrg+2ZY63lscce48wzz6RmzZo0adKESy+9lHXr1hXm8Xg8TJkyhTZt2pCe\nnk7Lli0ZP348x44dK/N3sHv3boYPH06zZs2oWbMmnTp1Yt68eX55tmzZgsvl4tFHH+Wxxx4rvM/6\n9evLLD8ULbMSERERERERkYj17duXOnXqsGjRIrp37+73vUWLFtGhQwdOO+00vv76a84//3xOPvlk\n7rzzTmrXrs2iRYvo168fS5Ys4fLLL/e79tZbb6VJkyZMnDixcGbOTTfdxJIlSxg1ahTt27dn7969\nvPfee6xfv55OnToBzp42gfvaDBs2jLlz59K3b19uuOEG8vPzWb16NR9++CFdunQBYPjw4cybN4+r\nrrqKO+64g48++ogHHniAb7/9lsWLF4f8+XNzc+nRowebNm1i1KhRtGjRgpdffplrr72WAwcOMGrU\nKL/8//jHPzh69Cg33XQTNWrU4LjjjivfLx4N5oiIiIiIiIikpP25+9l3ZB8tG7Yk35tPmiu+f+Kn\np6dz2WWX8c9//pMZM2YUDqTs3LmTlStXMnnyZABuv/12WrRowccff0xamlPHW265hfPPP5+//OUv\nJQZzGjVqxIoVK/wGZpYuXcoNN9zAww8/XJh2xx13lFq/d955h7lz5/KnP/2JRx99tDB99OjRhf/+\n4osvmDdvHjfeeCNPPvkkADfffDONGzdm2rRprFy5kh49egQtf86cOXz33Xe88MIL/PGPfyy8NjMz\nk7vvvpthw4ZRu3btwvzbt2/nhx9+qNAgjo+WWYmIiIiIiIikmEc+eIQmjzSh1YxWZD6byTFP2UuC\nYmHAgAHs2rWLd999tzDt5ZdfxlrLgAED2LdvH++88w5XXnklBw4cYO/evYWPXr16sWHDBnbs2FF4\nrTGGG264ocQMmwYNGvDRRx/55S2LbxnYPffcEzLP0qVLMcb4DfAAjB07Fmst//nPf0Je+8Ybb9Cs\nWbPCgRxw9u257bbbyMnJYeXKlX75r7jiiqgM5IAGc0RERERERERSyt7De7lrxV3keZ0NdFdvXc2T\nnzxJnqf8G+qW1yWXXEK9evV46aWXCtMWLVpEp06daN26NRs3bsRay4QJE2jcuLHfY9KkSQDs2rXL\nr8wWLVqUuM/DDz/MV199RfPmzenWrRv33nsvmzdvLrVumzZt4sQTT6RBgwYh8/j2s2nTpo1fetOm\nTWnQoAFbtmwp9dq2bduWSG/fvj3W2hLXBvu5ykvLrERERERERERSyO7Du8n35vulbc/ejsvEf75G\n9erV6devH6+88gqPP/44O3bs4P333+fBBx8EKDwq/I477qB3795BywgcSKlZs2aJPFdeeSWZmZm8\n8sorLF++nKlTp/LQQw/xyiuvhCw3EoEzgWIh2M9VXhrMEREREREREUkhpzY6la4ndCVrRxYA1VzV\nGNpxaMLqM2DAAObNm8eKFSv4+uuvAbjqqqsAaNWqlVPHatW44IILKnSfpk2bcvPNN3PzzTezZ88e\nOnfuzH333RdyMKd169YsX76c/fv3h5ydk5GRgdfrZcOGDbRr164wfdeuXezfv5+MjIyQ9cnIyODL\nL78ske47paq0aytKy6xEREREREREUki+N5+3h77NfRfcx6izR/HR9R9xdfSoIQAAIABJREFUeuPT\ncbvcCanPRRddRMOGDVm4cCGLFi3i7LPPLhzIaNy4MT179mTOnDn8/PPPJa7ds2dPmeV7vV4OHjzo\nl9aoUSNOPPFEjh49GvK6P/zhD3i9Xu69996Qefr06YO1lr/97W9+6dOmTcMYQ9++fUu99ueff/Zb\nYubxeJg5cyZ169YNuXFyNGhmjoiIiIiIiEgKSXOlUa9GPcadNw63ceO13oQN5ACkpaXRv39/Fi5c\nyOHDh5k2bZrf92fPnk337t0544wzuOGGG2jVqhU7d+5kzZo1bN++nU8//bQwr7W2RPnZ2dmcfPLJ\nXHHFFXTs2JE6derw5ptv8sknn/idUhWoZ8+eDB48mBkzZvD9999zySWX4PV6Wb16NRdccAG33nor\nZ555JkOHDuXvf/87+/bto0ePHnz00UfMmzeP/v37lzogc+ONNzJnzhyuvfZaPvnkk8KjydesWcNj\njz3md5JVtGkwR0RERERERCQF+Y4id5vEDeT4DBgwgGeeeQaXy8WVV17p97327dvzySefcO+99zJ3\n7lz27t1LkyZN6Ny5c4mTpoLtXVOrVi1GjBjB8uXLeeWVV/B6vbRp04YnnniCG2+8sdTrn3vuOTp2\n7MgzzzzDuHHjqF+/PmeddRa//vWvC/M888wztG7dmueee45//etfNGvWjPHjxwc9Bat4+enp6axc\nuZK//vWvzJs3j4MHD9KuXTuee+45Bg8eXOK6aO7LY4KNelV1xpguQFZWVhZdunRJdHVEklXsdwiL\ngOJWJCyKW5HUo7gVST1+cau4kUitW7eOrl27AnS11q4LlqdK7JljjNlsjPEGecxMdN1ERERERERE\nRCJRVZZZnQUUn3d2BrAcWJSY6oiIiIiIiIiIlE+VGMyx1u4t/twYcxnwg7V2dYKqJCIiIiIiIiJS\nLlVimVVxxphqwCDgmUTXRUREREREREQkUlVuMAf4PVAfmJvoioiIiIiIiIiIRKoqDuYMA96w1v6c\n6IqIiIiIiIiIiESqSuyZ42OMOQW4COgXTv7Ro0dTv359v7SBAwcycODAGNROJASPB9zu0M/FT5WL\nW70+pBKocnErUgkobiUh1O8RKVSlBnNwZuXsBJaGk3n69Ol06dIltjUSKY3HA9nZMGIErFoFmZkw\nezbUrauGK4QqFbd6fUglUaXiVqSSUNxK3KnfI+KnygzmGGMMcC3wnLXWm+DqiITH7XYarAULnOcL\nFoAxMH9+YuslyUGvDxEREakq1O8R8VOV9sy5CGgOPJvoiohEZNUq/+crVyamHpKc9PoQERGRqkL9\nHpFCVWYwx1r7prXWba3dmOi6iEQkMxPatoVLLoE2baBHj0TXSJJJZqb/c70+REREpLJSv0ekUJUZ\nzBFJSR4PPPMMfP89vPEGbNgATz/tpIt4PM5a8UGD4OSTna+zZpX++gj8XmV8LVWFn1FERCTVRNI+\nB8tbnn6PJI1JkybhckV/+KEi5T733HO4XC62bt0a5VrFhwZzRJKZtZCb6zRWzZs7X3NznXQRt9vZ\n9G/+fPjpJ+draZsA+jYOLP56ys6uXJ2gqvAzioiIpJpI2udQeSGyfo8kFWNMTAZzKlKuMQZna93U\npMEckWSWlla00du2bc7XkSOddBEo2YEprUNTfOPA4q+nytQJqgo/o4iISKqJpH0uLW8k/R5JKhMm\nTODw4cNJVe6QIUM4cuQIp5xySpRrFR8azBFJNoGfUGijN4mmSF5PqbpcSTEjIiKSfMJpn319DbXl\nlY7L5aJ69eql5rHWcvTo0aiXG4oxptzXJgMN5ogkk+LTSq++2knTRm8STeG+nlJ5uZJiRkREJPmE\nap/HjCnqaxw6VHpeSTqLFy/G5XKxevXqEt+bM2cOLpeLb775JujeNi6Xi9tuu40FCxbQoUMH0tPT\nWbZsGQC//PILgwcPpn79+jRs2JDrrruOL774ApfLxbx58wrLKK3cV199lTPOOIP09HQ6dOhQWLZP\nqD1z3njjDXr06EG9evWoX78+Z599Ni+++GLh99977z2uuuoqMjIySE9P55RTTmHMmDHk5uaW75dY\nTlqrIZJMik8rBZg2zdnozRjnE4kePZyN3vLztdRKIufbODDw9eTxBJ+2XPy1uGCBc938+fGvdyQi\n+RmlSnnnnXcYOHBg2PkbNmzI2rVrqVu3bgxrJSJSRYRqn9etg+nTnTy+vsbcuWrLU0jfvn2pU6cO\nixYtonv37n7fW7RoEWeccQannXZayP1pVqxYwaJFixg5ciSNGjWiRYsWWGv57W9/yyeffMKtt95K\nu3btePXVVxk6dGiJMkKVu3r1apYsWcKtt95K3bp1mTFjBldccQVbt26lYcOGIa997rnnGD58OB06\ndOCuu+6iQYMGfPrppyxbtqywH/Hyyy9z5MgRbr31Vo4//njWrl3LzJkz2b59Oy+99FKFfp+R0F+D\nIsmm+LTSc8+FtWv9/4DWQI6UV/ENk31K6xil4hTnSH9GqTL+/Oe/snPnzrDz79y5kzlz5nDHHXfE\nsFYiIlVEqPZ5yBD/fCtXqi2PRF4eVKvm/DtBfyOkp6dz2WWX8c9//pMZM2YUDo7s3LmTlStXMnny\n5FKv//777/nqq69o165dYdqSJUv48MMPmTFjBiNHjgTglltu4aKLLgq7Xt9++y3r16+nRYsWAPTs\n2ZOOHTvy4osvcuuttwa95uDBg9x+++2cc845vPPOOyGXYD388MPUqFGj8Pn1119P69atGT9+PNu2\nbePkk08Ou54VoWVWIsmm+LTSLVucqafFaSBHKiKSjQNTdYqzNkeUIKpVqw4MAWxYj7S0hni93gTV\nVkSkEgrWPnfs6J/m62uoLS9bfj58/TX07u38Hp94ImFVGTBgALt27eLdd98tTHv55Zex1jJgwIBS\nr+3Zs6ffQA7AsmXLqF69Otdff71f+ogRI7Bhnup78cUXFw7kAJxxxhnUq1ePTZs2hbzmzTffJCcn\nh7/+9a+l7qVTfCDn8OHD7N27l3PPPRev18unn34aVv2iQX8ViiST8i4RCfy+Pr2Qikrl5UqKBxER\nkeQT2B7n58Pjj6dmXyMZWAuXXAK+Wae33QYtWzqDO77ZOnFyySWXUK9ePV566SV+85vfAM4Sq06d\nOtG6detSry0+4OKzZcsWTjjhBNLT0/3S27RpE3admjdvXiKtYcOG7Nu3L+Q1P/zwAwCnn356qWX/\n9NNPTJgwgddee82vPGMMBw4cCLuOFaWZOSLJpPi00p9+cr7WrVv2QE6qblQryas8r8VkoHgQERFJ\nPsHa55wcqF079foayWL9+qKBHJ9lyxLy+6tevTr9+vXjlVdewev1sn37dt5//33++Mc/lnltzZo1\nY1Ind4jfQ7gze0Lxer1cdNFFvPHGG9x55528+uqrvPXWW8ydOxdrbVxn9WpmjkislHd2QKTTSlN1\no1pJLuG8XlOhc6V4EBERiY9I+rrhts+p0NdIFm3bQoMGsH9/Udp55zn/D674z9kYMGAA8+bNY8WK\nFXz99dcAXHXVVeUqKyMjg3fffZfc3Fy/2TkbNmyISl1Dad26NdZavvrqK1q1ahU0z5dffsmGDRt4\n/vnnGTRoUGH6W2+9FdO6BaOZOSKxEO/ZAam4Ua0kj8o2m0XxICIiElvl6TuofY6u6tVhyRLo1Ama\nNYPx4+GPf4z7Eiufiy66iIYNG7Jw4UIWLVrE2WefTUZGRrnK6t27N8eOHeOpp54qTLPWMnv27KAn\nV0VLr169qFu3Lg888ABHjx4Nmsc34ydwBs7f/va3mNYtGM3MEYmFeM8OyMwsuhekzka1khwq22wW\nxYOIiEhslafvoPY5utxu6N4dfBvuJnjT/rS0NPr378/ChQs5fPgw06ZNK3dZ/fr14+yzz2bs2LFs\n2LCBU089lX//+9/sL5iFFKtBk7p16zJ9+nRuuOEGfvWrX3H11VfTsGFDPv/8c44cOcKzzz7Lqaee\nSuvWrRk7dizbtm2jXr16LF68uLBu8aTBHJFYidenD6m8Ua0kj8ryaZniQUREJD4i6TuofY6N4qfc\nJmBpVaABAwbwzDPP4HK5uPLKK0t8P3AQxhgTdGDG5XKxdOlSbr/9dubNm4fL5eLyyy9nwoQJdO/e\nvcTGyOGWGyq9uGHDhtG0aVMefPBB/t//+39Uq1aNU089ldGjRwPOoNXrr7/ObbfdxoMPPkh6ejr9\n+/dnxIgRdAw8nS3GNJgjEivx+vSh+Ea1PmoYJVKV5dMyxYOIiEh8RNJ3UPtcJVx44YV4Qiy1mzhx\nIhMnTvRLC5UX4LjjjuP555/3S/vXv/6FMYaTTz65XOUGHks+dOhQhg4dWiJf37596du3b8i6tWvX\njmXLlpVIL+3niYUqM5hjjDkReAi4FKgFbACus9auS2jFpHKK96cPqbhRrSSPyvZpmeJBREQktsrT\nd1D7LBEI3PzY6/Uyc+ZM6tWrR5cuXRJYs+RRJQZzjDENgPeBFUBvYA/QFgh9yLxIpIo3Xm431KlT\n8tOH0q4Jp9xwrxEJJdTrKdinZWVdG0+KAxERkeRR2kybWLbZkZStvkNKGzVqFEeOHOHcc8/l6NGj\nLF68mA8//JAHHniAGjVqJLp6SSHxC+vi46/AVmvt9dbaLGvtFmvtW9bazYmumFQSvh39x46FwYNh\nzBjIySn5B3Gku/5XtlOGJLFKez0F69wky2tPcSAiIpJ8gs20iWWbHUnZ6jukvAsuuIDvvvuOu+++\nm/Hjx3Pw4EFmzZrFuHHjEl21pFElZuYAlwH/NcYsAnoA24HHrbVPJ7ZaEpFkHl13u2HTJii+a/u6\ndVB8CmB5dv2vbKcMSWJF8noqz2svVIxWNHYVByIiIsknVPseqzY71v0YSSoDBw5k4MCBia5GUqsq\nM3NaAbcA3wG9gCeAGcaYwQmtlYQv2UfXPR7IyPCvX0ZGyfqV58SgynLKkCSHSF5PkZ5SESxG8/Oj\nE7uKAxERkeQRrN0/csT5Xizb7Fj1Y0RSUFUZzHEBWdbaCdbaz621TwFPATcnuF4SruKj69u2OV9H\njkyumTm33eZfv9tvL1m/zEz/5+GcGFSea0RCieT1FEleX4x+/DF06ABr1zoxmpYGkydXPHYVByIi\nIskjWN980iTne7FsszMzoW1buOQSaNMmev0YkRRUVZZZ7QDWB6StB/qXdtHo0aOpX7++X5qmeyVQ\nrEbXy7sEJDBfWfUrz67/le2UoThQ3JYiktdTeV57w4bBCy8UPV+xwvm6e7d/vkhjV3FQ6SluRVKP\n4lZK9H1fegkeeiiyNjvSDY2feQaKnXBEbm7ofsyMGf71eOwx9R2kUqkqgznvA+0C0toBW0q7aPr0\n6Tr2LJlkZhate4XojK77poiOGOE0SJmZTgNUt27pb/TFr/vpp6JrP/4YWreGjRuhWzf/a0rb9T+U\n8lxTxSluSxHp6yk93T9vbm7osj0e6NzZmWbti4eZM530xo3980Yau4qDSk9xK5J6FLcStG8eSZtd\nnn54bi4MH+6fv1q1kvncbtiyxb8e69bB8ceX72cVSUJVZTBnOvC+MeZOYBHQDbgeuCGhtZLwhfvJ\nfDij+4FHiJdnc7TA65Yvh6efhpo1i/IE+6Qg2K7/ZSnPNVI5RWMT8HBfT243HDjg/+nX4cNw3HGh\n848aFTyWJk2CXbsqNqtGcSAiIhJ74fY1Ipk1W1pfI1Q/vKKbK3s80KqVc9Lsrl3QpAlMmKAPg6RS\nqRKDOdbaT4wxvwceBCYAm4HbrbULE1szCVs4o/zhjO4Xz+NywfPPl3/5VvHrrHUGb66/vuxPCkTK\no7yzyMorP9+JkeIzbWbPdtLTQjQdoWKpdm3NqhEREUl2kfY1IpnBG0qwvkOwesyZA3XqhN9v9/3t\nUPyk2STof6xfH7jzh0hw4bxWqsRgDoC1dimwNNH1kAooa5Q/nNF6X57du6FPHyetvMu3fNe1bQu9\nezt/9Or4Q4mVeB+xmZYW+f1CxZJm1YiIiCS/SI/+HjLEOfCgTZuiLQYi7ZcEblNw+eXB63HCCTB1\namT99uTqf+xxuVy511xzTXrZWUUcLpcr1+v17gn1/SozmCNVRDij9dOnO1MtfZ55xpk58MYbRVNE\nA2cfBJsF5Jtaakz49xapiHi/xiI9mjzUdGtr/eOptNk9IiIikjiRHv29bZszCAORz8wJtqHx0aPB\n6xHrzZVjzFq71RjTDmiUkApISvJ6vXustVtDfV+9aalcyhqtz8+H6tVLLh154omiN/f8fDh0CG69\nNfQU02DLvmKxQbNIcfF+jUV6v2DTrb1eJ54Cp2zXqaMBHRERkWQTSdsfjX5JsA2N09JClx3u0q54\nL08PQ8Ef5SH/MBeJlHrSUnmEsxFbOEtH0tKcgZyyppiGmqmjo5MlFuL9GsvPD36/ULNqgk23Hj4c\n/vrX+C4PExERkfKJpK8RjX6JbznVxx9Dhw5OH2LkSKeP8MQTztKqnTudGfUTJ0a2tCvey9NFEkCD\nOZIcwpkGGfhHZF6e/wbD1oZ3FGI400cjXc6io5Ml1kK9xgKV9boLjCPf88DrjHFmzxS/X6i8vueB\n0619ecKJJ8WLiIhI9EWy1CiS/qzbHbyfEGlbPmwYvPBC0fMVK5yvtWo5e+QULxtCL+0K9mGTtkCQ\nSs6V6AqIFE6DHDQImjd3vmZn+/+hmp8POTn+eQ4dgq+/hsGDYcwY5/vW+pcdrEHJzPR/HmxKaDh5\nAiXXJmtSGQV7TZUVO8UFi6OcHGdgNFg5vv2gfHwDOaHuGRg3vnqEiqcxY8Krt4iIiEQunD52oHD7\nsx6P04eYMwf++U9nJk1OTmRtuccDnTv7169z59D9lWB9jR49SuYfO9b5Xnn68yKpxFqrR8AD6ALY\nrKwsK3Fy9dXWOkMxzmPQoNLzNGxobeD/T7D/r/z8ks/37nXKP/lk5+vevf758vKs3bfPP8++fSXL\nkoTHavFHlY3bcGInnPz33eef/qc/ObFQnO95qDIOHCgZN8eOBY+nrKzI6i3RkvBYLf6IZ9yec875\nFob4vexKe6SlNbQPPfRQzOslEoaEx2rxR5Vtb1NVpP2ESOzdW/rzcATWb+zY4Omh+hrZ2cHzZ2Ul\nuj+f8FjVo/I/tMxKkkOkS5+efx4yMvw3Mp4xwxmxX7LE+cQ/1MbFn37qPyV0xQq48EJnho/XC48/\nXnLaqJaASLKKdApxqPzFlywC3Hmn8ylX8Y0D58xxYiNUGbVrB4+bYPE0ZEhk9RYREZHIxWqpUX4+\nuFwlDxWJ9MTKwPrt3Bk8vbS+RrD8Q4bA55+rPy+VmpZZSXKIZOlT27bQty/cdpuzmdm2bc7X2293\n3qBff70obeTIkm/a//iHU8allzpfn33WSf/wQ+eaESNKNkJ645dkFekU4lD58/KK0i6+2Nls0Ldx\noC+eZs0qvYxQU7ODxVPHjk78XXKJs4mhpj6LiIhEX6yWGhU/VKR4vzvSkyozM/37A02bll7vUH2N\nwPydOmkLBKn0knpmjjGmLfAboAkBA0/W2skJqZREX+Bu+H36wGOP+efJy3NmzBhTtI9HqBH7XbtK\npgXea+TIop33H3sM/vOfoo3UPv88ss3iRBIl0pMkQp1QlZfnnOD2zTdO+g03OPkDY+zrr52yZ83y\nL2PmzMhixOOBZ55xjhf1yc1VnImIiERTrE/CrOisH48Hnn4aatYsSjtyJHR/xTcLJ7CPbm1kJ3CK\nVBJJ++o2xtwAPAHsAX4Giu9sawEN5lQWgTvn+zZrGz7cf9pm4LTKzMyi4wahaMTeNyhTPC3UvQDW\nrYNrril6Pneuc//iy0sCl2uJJINIT1FLSwt9QlVgOVAyxnyflqWn++c9ciTyuufmlozxwKVeIiIi\nUn6xPm01VF88EkePwvXX+/cHqlcPfYJnqD56qP6NSCWWzK/wu4Hx1tqHEl0RiYPijYrbXTRtE5yv\nxpR8Qw82Ap+bC926OV9DffoQOJrfqpWzbGvlShgwALp0cdb/lnZ/kWQRagpxqNllgR0b3/PAcoJ9\nKnbPPU6+66+HtWud6dAbNzoxN39++DPawolxERERqbhYLTUKNXsmP99Ji3Z/wHd9uPk1kCNVQDK/\nyhsCLye6EpIgZU3btLbk7IDcXOeNPpJPH4J9YhHO/UWSmW92W0VmlwWbxVN8k8Ft24pmweXmOl+P\nHIGbbgrvnooxERGR1BVqtq8xkfVBonWQg0gVlMwbIL8M9Ep0JSRBytqsLS3NWaJRfCPj668vuUwj\nnD9cg+WJ1WZxIvFQ/JOu0jYDL0uojcBDxcc994R/T8WYiIhIags22zfSPki0DnIQqYKSamaOMea2\nYk83AlOMMecAXwJ5xfNaa2fEs24SR+Fu1hY4OyA9PbxlHmXlifVmcSLxEI1PrgLXm/s+cQsVHy8H\nTKYMdU/FmIiISOUVqg8S2M7n5xcdcBJOfyAahzCIVCJJNZgDjA54ngP0KHgUZwEN5lRW4W7WFrjp\nWjgbF4ez/CTWm8WJxENFNyXMz4ecnJKxUqdO6PgI956KMRERkcorWH8gVB882JLu0voD0TiEQaSS\nSKrBHGtty1iUa4yZCEwMSP7WWntaLO4nUVDWZm2Bm66Fu3FxuButxWqzOJF48M18OeEE2LkTmjSB\nCRMiGzBJS3Ni5eOPoUMHZ8PjkSNDb0oY6WwbxZiIiEjlE6wPMnFi+fvggd8LdQiDSBWUVIM5xRlj\n7gGmWmsPB6TXBP5srY30aPKvgAsBU/A8v+K1lKDy8vz3rgl87vHgMeB2Fb1Ze7wev+dBFf+jMC2t\n5FHlEN7SknDyhHsqj0gycruxtWtjpk4tTLJ5eRi3O/jSqVAnPgwbBi+8UPR8xQrna6j4CLYRouJG\npErZunUre/bsCTt/o0aNOOWUU2JYIxEJKkRbHtgnL7WPHqo/ULs2FOuDkF/wZ1c0loCHOoRBfXep\ngpJ5A+SJQJ0g6bUoOcsmHPnW2t3W2l0Fj18qVj0JKi8PDh1yZsg0bw7r1vk/HzQIm52NNz+PscvH\nMviVwYxZNobsY9l4vJ7Q5fqmZo4dC4MHw5gxTrmegGvC2RStrDy+exWrM9nZJe8lkqRsYBwOGgSH\nDjnpOTn+6Tk5RZ2s4jwe6NzZP2/nzk76kSMl48O3LGvMmKIYzclR3IhUIVu3bqV9u3Z07do17Ef7\ndu3YunVroqsuUrWE6Ova/Hyyj2UzaMkgmk9vzqAlg0L30UP1l319kOJ99sMFn81HY/PiUGUE9nvU\nd5eqwFqblA/ACzQOkn4BsDvCsiYC2cB24AdgPtC8lPxdAJuVlWWlDPn5JdOuvtpasLZtW//nvseg\nQSUu+WxbGL/rwP+PwOf5+dbu3euUf/LJzte9e/3rmJ9vvfv2+eXx7ttX8ucIo86S+PeJ4o+kjtvA\n11ewuAlHXl7pz4sL9RoePTr813akZURStiRKwmO1+COecXvOOedbGOL3Ei3tkZbW0D700EMxr1dl\nkpWVZQE7H2xWGI/5zh6Iyfm+nVwSHqvFH0nd3kr4QrTxo/872jKJwsegxSXb8nxPfqll2K++8r8g\nK8vp+wT0wW2wPnhpSuvrJ18fJOGxqkflfyTdzBxjzD5jzC84Dfz3xphfij0OAG8CiyIs9kPgWqA3\ncDPQElhljKkdxapXPYEj8r6TbHxTKFu39n/u45tS+fDDhaPnp9fKKH303OOBjAz/EfeMgGvcbvj0\nU2eZx08/OV8//dRviqXHQH6tdL88+bXS8ZiA+0VjGqgIRG+ml2/mSzizaiD0a3j37uDp0SgjkrJF\npNJqj/PXflmP9omqoIiEbON3H/Zvy1ducdIffv/hwtk6R/KPlFpGYT+leJ8dijYv9vXTa9SIrM6l\n9fXVB5EqKOkGc4A/AWNw9raZiHPCle9xM3C+tXZEJAVaa5dZaxdba7+y1r4J9AEaAldFteZVTfGN\nzLZtg6eectJ90x9/+MH/uY9vOuRTTznXLVhA2qjby97w7Lbbiu61YAHcHuSaf/wD2raFSy91vj77\nrH8xLjfXvj6ctjPbcukLl9J2Zluue/36kmuBozENVARKxsmCBc5GwpGu4/ZtSBxYTqj9bkK9hhs3\nDp4ejTIiKVtEREQSJ0Qb376R/zBrjwwn/al1T7Ht4DYWfLmASe9OKrUMJk0K3md/9FG4+GLnUIaL\nLoIbboi8PxSqr68+iFRBSbcBsrV2LoAxZjPwgbU2Lwb3OGCM+R5oU1q+0aNHU79+fb+0gQMHMnDg\nwGhXKXUEbiY2bBgsXQr798Obb8KuXf4n2qxbF/yEmxUrijYua9gQrruu9PtA2bNlfLvnjxxZ6mk6\nq7asYtvBbWz8xbl/bn5uiXLs7NmYYnW2s2ZhtJFaSkjKuI3WTK8wy7F5eRDkNUxeHuaee5w4LR4j\nwTZB9niws2cFlDETk5+PveceTLEy7KxZmPx8CFa24kbCkJRxKyKlUtymMI8HZszw758/9hh4PNzW\n7Ta+2f0NK7espEdGD2b1mcWKTSsK+80AL339ElMvfCj0KZa+2fo+vv5Knz5w111F6b6DFSKpd7C+\nvvogUkUl3WBOMZ8CNQtOryrOAkettcfKW7Axpg7OQM680vJNnz6dLl26lPc2lY9vuciIEc4flZmZ\nMHOmM8Xxt7918tx/v7N7ffETbfLy/J97PLBwYdHz558v2mjVV+7s2VC3rv8bcGZm0XGGUHLE3e12\nrgm8V8CbeGZGJgu+LCrH94lD4SUGvLXSqVasnPxjubgMqDlIfkkZt2W9dqNcjnG5sL6pzD65uc7A\nTJB0TOA6wwIl8h4BY8ivHSQ+XAZ3GPEnEkxSxq2IlEpxm8Lcbtiyxb/NXrcOjj+emqYm8/sXpXu8\nHhZ+vdDv8h4ZPZydaYL1Kdzu0P2VFi38+/szZ0bWVyitr68+iFRByTyYsx9n4CYoY8w24DngXmut\nt7SCjDGPAK8BW4CTgHuBPODFaFW2Sii+XAScr8Y4b5znn++sh51xYRE7AAAgAElEQVQ0qeQfhsWP\nJfd55BGoX9/J27ev88YerFwfj8d5wy8++j9jRsk36sA37YDnHq+H2X1mYzB+nzgUP3bR7XIz5PUh\nrN2+ljbHtWHjLxvpdlI3v4ZNJGy+T5GCfXIVSScjPz/4jLFgs2rcbsyQIbB2LbRpAxs3Yrp1c2Jq\n3Dj48ks45xxYswaaNfOPNb8yrnc+5SrIawry/uWtP/Pa96+VHR/qRImIiCQfjwdPyxa4x4512vkm\nTfDcPR63x4M7SNv9yMWPcCTvSGHf+cnfPun0PYYO9esn0KwZzJ0but8T6u+ISITq65fxN4BIZZTM\ngzlDgftxBmzWFqSdXZB+H9AIuAM4WpCvNCcDC4Djgd3Ae8A51tq9Ua91ZRdqmcfq1c7XIH+gFh8o\nAWfmi7tOHWcGj0/x5VrFyy3uyy/93/BXrIALL4yo+m6Xm7rV6/r94Znvyfevn9fD5z9/zsZfNoZe\niiUSrmh9WpSWxi/uYxxfrJxfsndxfKg9c1atctaq+5Yz5ha8hgcPdmbC+ZQ2xXnYMP8YK8i7+/Du\noPFRItYDnouIiEh8BW2b3W5W//IpPadNK0xf/cMKejYs2a8O1ncuPKo8WD+htH6PDhgRiapk3ADZ\nZzAw1lo7wVr7WsFjAs4AzlXW2vuA24AhZRVkrR1orT3ZWlvTWnuKtfZqa+3mGNe/ciprY+AgAznZ\nx7IZtGRQ4Q743vw8bOCpPJ07+7/p+8rt3r3o9J+6dUvd3DhcgQ1aTl6OX/2yj2Uz7/f+K/ACl2KJ\nRCRKnxbd9uZoLpp3ERPemcBF8y7i9jfHhM4cLFaDnQrXuXPwk7U8nqLljwF5G9dqTNvj2nJJm0to\nc1wbemT0CBrr2ceyizp8IiIiEleltc1Pff4PLp53cWGf4unPQ/erAz+YcbvcpfYTQvZ7dMCISFQl\n88yc84Fgp1Z9Cpxb8O/3gFPiVqOqrhzLRdwuNyOWjijco+bj7R9TrXo6XDe89OVaM/4/e3cfJ2V1\n3///fXZm5V7UCEqNYAS03iW6VtEmLjUS402r1TY1m/Ue26iYpOC3NneS/EiruZEQETVp1XhDaGP6\n0KrxLoYYiY0pllWTgFGByIKxLFsU2YWBmdnz++Paa3fm2uuauWZ2Zue6Zl7Px2Mey1xzrjNnl+uc\nc82Zc85nqfT449Lzzztp3NfPO0968MHBDV0zGZmgmQlBv4ZnSdXGdzbqiTee0Lupd7XiNytkZLT8\nwuVacMoCPbjuQd+lWMw4wEhzlwhe98R1uvfle32vy8HEPnX1jjucevqZz/jWvSH5FEh78xk369sf\n//ZA0lQmNaSu59YlZuwAAFA5YfvVQn3z3efdrdHJ0QNpU5mUMtmMjDHh+uwC9wn+ha7QsnMAA6I8\nM2eLpLk+x+dK2tz/7/dJemfEStTocqdNbt7s/PRuUuxj1abBKZXTD5jef7DAcq3ly6WXXpIuvnjo\n64sXD7x337ix6msK2Lg1gN83FNMmTtPyCwY7nuc2Oe+1+OOLtXn+Zi2/cLlGJ0fr6sevZsYBaiZ3\nmrN7XU7YZ0LwDZa3ro4b57wWUPf2ZvcO1Itv/tc3C6ZN96Xz6lAqk1K2L5tX16XBurQ7s5sZOwAA\nVECpM2H9+uZsX1apTGpIXy6ptFm2pSybKvNzBIBgUR7M+X+S5htjXjHG3NX/eFnS30u6vj/NSZJ+\nWLMSNqIylou0ThucUrlh+4b+g/7TLO99+V7n+T33DO6fk/P6ZQ9fNjAd9LLHriz52/3cbyi2vLdF\nK36zQp976nM694hzNeMAJ1K935KquY/O1V0ddw2cc90T1zGzACPOd5pzYOLSpjjf+/K9A/Xiro67\nCqZd+OzCvDrk1odPHP2J/OTTCqcHAACl8buXLdSvtk5rzVsafdExFwXmkUwktei5ReH77FKXTbFJ\nMVBRkV1mZa191Bjzx5I+LemI/sNPSvpLa+2b/WnurFHxEJI3etTJh5ys9N6Ukp6oPH3LblNTNqvL\nj79cymbVd/syNXmmYf58w0rd/+vBvWzev+/7fd8vt8PJZDNKJvIv86DZA6e8/xTNOmSW79KVoHOA\nOLGZjOQTEUuZjH655ZcD6d7Y/oae//1z+rBf9KxsVj9a96O8fN368NXZX1VXb9dAtIulZy9Vti84\nPQAAKF3Qfanf8qu7/uIujWkeM3DMnYETlMe2Xdt8j3vZdNr/niKdlvGLZAug4iI7mCNJ/ZsUf77W\n5UD5gnbANxOa89bUZvbu1rwnrtZT65/SWTPO0u0fX6p9PLvg/+AX/56Xt3cGjTvtdN4T87Rq0yo9\n8slHdPj+h2veI87zvznmb7T4zMVqndY6sHY4N58HLnhgIB/vNxBB5wBxYpJJ7Wju08ScuvVe73ZN\nTCYHNjWefsB0rd++XhPG7KfM2NFqzqunKTWZ4Pqw+b3NeXV9bddaHTDmAOoPEDOdnZ3q7u4Onf7A\nAw/U1KlsYQiMlKB+tTfdq2sev2bgvvebc76pPdk9uuqxq7Rq0yq1TmvV7efcruam5sA8vPcDsw6Z\n5VsG09wsO25c/h45DOQAIyrSgznGmP3khCOfLM+SMGvt/b4nNQLvRmEV2jgszGZqxWa++J3j9zyT\nzeRdfFf8+KqBDuWujru0O707P3y4rG752C3and498K2/dwZN7pTRmQfMVMuUFrU/1D6Q77df+Lba\nj2vPmykUlI/3dy52DlBIxTYAHmbdz/ZlZRNNuvkXNyuZSCqTzeiak65RJpvRP3/0n/M2NZak9ofa\ntfqt1ZpxwIyBG7rlFy7XnefeOaQ+ZLKZwT2x+k0/YLoy2cyQ+nP7ObeHajsAjLzOzk4ddeSR2pVK\nhT5n7OjRevW116pYKgCuQvel1zx+zcB972OvPabFZy4O3AA5qC+/6YybhgQ5sN6AI/33H96BGwZy\ngJEV2cEcY8xfSPqBpPGS3pNkc162khpzMCebdcJ0z5vnbDrW2ursDD/MDcS8s1rckfvcDVaD0mzs\n2qjz//1833P8ZLIZ9aR7NO+ReWoyTXrgggcCp3pe8vAl6rN9uv2c2zW+eXzeAE8qk9LVjzuzeT5z\n8md0w4dvGMjH/VDpzffShy/VK1e/MmSmUKHyBs0u4oMnwghTt8JlVJm6PyoxSl847QsDz1OZlIwx\nSmVSA9/c5danLe9t0frt6wfSStK45nFD6oMkpdIpzX107pDf01t/ctuAYf1NAFRcd3e3dqVSWi7p\nqBDpX5V0cSpV0kweAOUrdF/qF3Qk6B57dHL0kPtqK6tUJr8vv/fP75Z29VT8sweA4YvsYI6kxZLu\nkfRFa+2uWhcmMhIJpzENGwYwbLYFQheGSTM6Odr3HD/JRFLzHhmcQSMFTxf91ZZfaf329b75zn10\nbt5snhs+fMNAPu5Gy63TWvXiWy8OTBU9/uDjS9tENiANHzgRVpi6FS6j4df9RFNCV/3nVXmzbeae\nMFef/8jndd2T1w2U8acbf5pXn1xuvQyqD2F/z9w2oFhaALVxlKSWWhcCgK+gfjj3vjfTlxk45teX\nf/6nn9djrz82cD9w/pHn65Yzb8nrn19860U17zNaumJuxT97ABi+KA/mHCJpKQM5PkoJA1hKtjkj\n9/uP3l9XHH9F3uvZvqxe+d9X8t+6f3Tf7QjCbmzqvtcb29/Q468/rqVnLc2b6nnrWbfq8dcfH5gR\n8Mr/vjJkNsyVx1+pJ954Qu+m3tUb29/Qyo0r86advvK/r+ju8+7W6OTogXNSmRTLOzDiStmosOC1\nGKLuF8vDO9tm7ba1Q8qYaEoo25f1rZfuLBy/cpeyUTibigMAUB6/+wdJQzY73pvdG7gk60frfpR3\nP7C1d6sk+c7uqdZnDwDDE+XQ5E9L+pNaFyKSSg0DGDbbnBDiD1zwgE6YcoLaH2rXoUsOVftD7dq5\nd6fuvyB/dZs7uu92BGE3Ns19r4sfvlibdmzS8guXa/N8ZwPVTTs26eKHLx5Ic98F92nn3p155Tlh\nyglafsHgtwI/XPvDgWmnm+dv1rGTj1Uqk8o7J5VJKWMzQ34vtxMEqiH3epeceuIuvyrpWgyo+wue\nXhA6D29ZDhp30JDjG7ZvUKIp4VsvE00J7c7s9i233+9Zyt8EQHS8KqkjxOPVWhUQaFBB9w/WWu3J\n7sk7vju9W2Obx+b15WOSY5RoSoS+H3AOVuezB4DhifLMnMclfcsYc7Sk30hK575orX20JqWqtWzW\nWafqCds93E2QczdT69zRqXOPODdv8+DcZRALTlmgB9c9ODC63/F2hzNoclx7qI2B/TZEPXz/wwdm\nzGT7sjp8/8M194S52tq7VcdMOmbIZsa55bn+1OtlrdWNs2/Me59Cy1tWv7VaW97bwvIOVJ3fRoV3\nnHtH6cuvAur+K291aMmvloTKw68sX2798kCdnDJ+irb2btXksZOVyqR0+P6H6/qnr1fXri5NHjtZ\nX2r9krJ9WS18dmHozRSDNlJnU3Egmvbs2aMmSRcXTTmoqf+8UaNGValUAFyF7h8W/XSRXnzrRR07\n+Vitfmu15j0xT8svXK45989Rc6J5IJjBveff63s/kM6m846ffMjJSu9NKekJQV6Jzx4Ahi/Kgzn/\n2v9zoc9rVlJjth6JhLPhmCds93AbU7/N1IKWQSz++GIt/vhiSc7ATMuUFm2ev9kpSogPY8lEUuM1\nfsiGqO7Sp0RTQuObx+uWM28ZeL3QEi83XSabGfLeQb+Duyws9xhQDUEbFUolLjUKqPvtKy4NnUeh\nTRPHaVxenctkM1rbtXagrkvSyo0rdcbhZ2jbrm2+7+m3MbJfe8Cm4kB0jRo1Sn2SdLqk/UOc8I7U\n96wYyAFGUGDgkA9ekheJauXGlZKk5kSznlr/lCRny4FkIqlxyu+z09m0mhPNmmCG9s9mQnPFP3sA\nGL7ILrOy1jYVeDR26+FtPCvUmHo/SIVZBpG794xfHkG853n3sOlJ94Re4nXa909T+0Pt6kn3DFle\nEvQ7uAM5Qb8XUEmFNirMVfRa9Kn7Hzr4QyXl4VeWTDaj3nRvXp3rSfdoTPMYfeyBj+nGZ2/UnPvn\n6Psvf1+SNGnsJN/3LGWjcDYVByJupqQPhnjMrFUBgcbVOq1VMw+YqbNmnKUZB8wYWL49bb9pQ7Yk\nyPZlfe97mxP5YcTd5779c5U+ewAYnijPzBlgjBltrU3VuhyNxG8ZxO3n3D5k82B3FN/lfd3Nq5RN\nXgtNH81d4rX07KV6/PXH9Xzn85I0ZHlJ0FKOVCalWYfMUiqTYnkHaqYSS40K5SH5b1Lsp1B0qWcu\neWYgnbuB+MLZC9XV28USKQAARli2LzskwMeezB4lmhL6zJOf8e3LzzvivLwtEjLZjIwxpQVhABA5\nkR3MMcYkJH1R0tWSDjLGHGGt3WiM+ZqkN621d9e2hPXNbxlEJptRT7pH8x6Zp1WbVumVq19Rk2ka\neN46rVW3n3O7xmv8wICOu0nbvCfy00zYZ0LRiDu5/JZ4rdy4Mm+TZO/ykkLLW1jegVqrxFKjQtd4\nqfUuqM5987++qdtW3zZYv5vHs0QKAIAaSmVSmvvo3IE+/nt//j2N0qjQWyQYY8q6PwcQLZFdZiXp\nS5Iul3SDpL05x38r6apaFKjReBvzZCI5MGPmqAOP0gFjDtC8J+blbbR23RPX5c3MyZ1l4244fN0T\n1xXtKMIsP7nn5Xv0burdgmn8poqyvANRUYlrMegaL7XeBdW5n2z4SV4eyUSSOgQAQI349fHLVi+T\n5L/8ysvtx8u5PwcQLZGdmSPpUkl/Z61daYz5bs7xVyT98XAyNsZ8XtJNkr5jrV0wnLwajTviP+v9\nsyRJVx5/pX5w4Q8GXnc3Wst15fFX6ok3nhgYeCm24XCY5SdEwwEKC/p2zm/ZoyQtPWtpXn269axb\nle3L5i2jZLNwAAAqr9QtCbx9/Npta5Xty+quv7hLY5rHDBzfnd4dmFdJQRgARFKUZ+YcImm9z/Em\nSc0+x0Mxxpwk6e/kDAqhRO639/+95b+V7cvqhCkn+G609t3/+a4uefgSLXh6gU6YcoKWXzC4JCPM\nBq3uMo7N8zdr+YXLh0z7DJMGaGR+M23cZY/ezcUladOOTXn1adOOTUo0JdgsHACAKgrqm71BPXJ5\n+/iDxh0kSdqT3ZOXz57sntB50McD8RPlmTnrJJ0maZPn+F9LeqmcDI0x4yUtl7NM68Zhla4BeDcz\nzmQzuu3s22Rk1Lmjs+BGa1f/ydW6+k+uliR1vN2hc484Vx+Z+hFNmzjNdyNlv42TcwWFNy6WBoiC\noG/cSv0mLmhWjd8xt67mzlwrtLn4jANm6Pqnr1fXri5NHjtZN86+UbvTu/M2C1969lJmvwEAUEGF\n+mY/frPTF85eWFI+zHAH6kOUB3MWSbrPGHOInNk4FxpjjpSz/OrPy8zzdkmPWWt/ZoxhMKcA72bH\nrdNadcc5dyiTzeR1CmE2TV16lvMB8BdX/MI370c++YgO3//wIRspM9MG9SBoE/DxzeOdehBy80G/\nfO4+726lMinfPF56+6W8uvry2y/r+CnHB9bZcc3jBjZHdN/v+c7n8/JYuXGlzjj8jEr/iQAAaGil\nLnkanRyd1z/vze4tKZ9KBGEAUHuRXWZlrX1E0l9ImiOpV87gzlGS/sJa+0yhc/0YYz4p6XhJX6hk\nOetV7mbH7sZo856Yp8njJ2vO/XN01aPOHtRBUzT/teNfB8773FOfy+sccvMekxyjliktbMKGuhW0\nyWAykdSi5xaFvu69+bz41osanRwdWHcef+Nxfez+j+nGZ2/UnPvn6KkNT0kKrrN+M93+peNfNPO2\nmTr7B2dr5m0z9f2Xv1+FvxAAAI2tlCVPiaaE5j46N69//uLKL5aVT6HnAKIvyjNzZK39haSPDTcf\nY8z7JX1H0hxrbTrsefPnz9fEiRPzjrW1tamtrW24RRpxpS7nkIJH98+cfqZuW32bOt7u8J2iuXLj\nyrx9Nvy+FXDznn7A9ILvBZQqivU26Pretmub7/Ew+RSrO1+f83Xtk9hn4PiezB5l+7KBGx172wN3\nCvZ1T1zHFGxUXRTrLYDCqLeVUahvloYupU40JbRq0ypteW/LwP32D9f+UN+Y840hS6z9+nj6caB+\nRHowp4JOlDRJUocxxvQfS0hqNcZcJ2mUtdZ6T1qyZIlaWlpGsJjVEbTMo9gyptZprQPrbqXB0f0b\nPnyDbvjwDZKcaZ3eKZr/vvbf8/Lx+1bAzXvD9g0F3wsoVRTrbdD1PWnspLx0xa773HyK1Z1UJqUr\nHrliSJ13Nzp2dbzdofeNfd+Q92IKNkZSFOstgMKot5WRaEr49s37jd5vyP37nefeqX1H7evb9yea\nEnp759u+ffxNv7hJd/7PnWxlANSZSA3mGGPekTRkUMWPtfaAErL+qaTjPMfulfSqpK/7DeTUk1I3\nVpOcfW38Zt10vN2hi/7jIs04YIbWb1+vWYfMGpLPN+Z8Q7vTuwfO826a6t10LWiGDx8cUQ+CNhnM\nZDNaOHuhunq7Ql332b5s3jduJx9ysnandwfWnWsevyb0RsdB78kUbAAAqivbl9Xh+x+e1zd/5c++\nMnD//uJbL+rYycdq9Vurtei5Rb4zcJaevVSZbEaH7HvIkD5+++7t+tLPviQp3GcAAPERqcEcSX9f\njUyttb1yomMNMMb0Svo/a+2r1XjPqCl1GVMykdQ4jctr7DPZjK574jqt375+YFpnKpPKOy/RlBiy\n8ap301S/b/y9GyszkIN6UWiGywTjfzxoSrS3bj335nP6yNSP+OZRykbH1DUAAGrDvU/w9s2SdOXx\nV+oHF/5g4PjLb79c8F57vMbn5ZPJZtR6b/4+OmxlANSPSA3mWGvvK/UcY8znJX3XWvtuqW9X6nvF\nWanLmLJ9WfWme3XnC3cq2ZRUOpvWtSddq2XnLNOJ/3JiwXzuefkeXf341Xmzd7wRcLwfHr1hyflw\niXoSNMPF77i7LPJrq76mrt4uTRo7SQtnL9SEfSb41q3Zh832zTuozjPbBgCAaAm6Hzhhyglqf6g9\nb8m0FHyv7b2fTiaS+sD+H9ALW14YOMZWBkD9iNRgTpm+KOlBSSUN5lhrP1qd4kRP0DKPQt/IJ5oS\n2vjORn3hI4PBvzre7lDLlBYtOGWBHlz3oG8+bJoKDI9b9xafOfjNmlv37jz3Tl37+LVF61bQMslM\nNjPkRg8AANRW0Izczzz5mSFLpu/7y/tC32uX8xkAQHzUw129KZ6ksZWzkWm2L6tpE6flfRuw9Cxn\n75vFH188MIXTmw+bpgLDU6jujWseF6puJRNJjdf4IUsZGcgBACBaCgUq8VsyXcq9NvflQH3jzr5B\nlLq0ItGU0Gef+mzRTZPZNBWorLB1z00bxG+qNQAAiJZCgUo+cfQntORXSwbSlrNkmvtyoH5xd49A\nYTZNZnQfqLxSNyyvhKAp3gAAoLqC+v1Fpy8KHfVSoi8HGk1TrQuA6Gqdlr/7vfttwIKnF+jQJYeq\n/aF27dy7c2DHfQCVEVT3qsWd4t3+UDt1GwCAERbU749JjtHyC5dr8/zNWn7hck3YZ0LBgRz6cqCx\nMDOnDlRjFD5ow7SOtzsGpns++caT2vjORrVMaanoewNxUK1vv0rdrLAS5Sg0xRsAAFRPKf1+seVU\n9OVAY6mHwZxfSNpd60LUSqFN04bzwTJow7RLH7504PkDFzwwZKPWSrw3EHXVqndSaZsVVrIctVja\nBQBAo7PWanRydF6/n8qkZK0tOS/6cqCxRGowxxizb9i01tr3+n+eU70SRV81R+H9vg340MEf0tpt\nazXzgJk694hz1f5QO98AoOFU+9uvsN/EVbIcrdNaB/KRqr+0CwAAOAEKLnvoMq1+a7VmHDBD67ev\n16xDZtGXAygqUoM5kt6VVGwY2vSnYepHv5Eahc+dBmqMGdH3BqImKtd+JcpR6tIuAABQOas2rdKW\n97Zo/fb1kpyZOaWiLwcaT9QGc06vdQHiaKRG4f2Wf/ANABpVVK79SpSjlKVdAACgsujLAZQjUoM5\n1lqmdZTIHYWfMn6KtvZu1eSxk3Xj7Bur1nh7N1rlGwA0oqhc+5UsRymbLAIAgMrIZDO+9/KZbEbJ\nRGkf1ejLgcYSqcEcP8aYsZKmSton97i19te1KVHt5X5QSzQlNK55nG4585aB1zPZzIg03nwDgEZV\nqWs/KBJV2AhV1EEAAOItmUhqnPLv5dPZdMkDOQAaT1OtCxDEGDPJGPNjSTslrZX0kufRkNzoNe0P\ntevQJYeq4+0O9aZ7B563P9SunnSPsn3ZESkP3wCgUQ332vfW5faH2rVz705lshnf40F1mjoIAEB8\nZbKZIffyveleZbKZWhcNQMRFecj3O5L2kzRL0s8lXSDpIElflnR97YpVW7nRa2YeMFMtU1qIKAXE\nUKFIVIt+uog6DQBAA0gmkpr3SPUiZAKoX1EezPmopPOttf9jjOmTtMla+4wx5j1JX5D0eG2LVztu\n9JrpB0zPe+4iohQQD0F1d9uubb7HAQBA/eFeHkA5ojyYM05SV/+/35E0SdLrkn4jqaVWhYoCd8f7\nDds35D13EVEKiIegujtp7KS8dNRpAPWus7NT3d3dodMfeOCBmjp1ahVLBIwc7uUBlCPKgzmvSTpS\n0puSXpH0aWPMm5KulvR2KRkZY66WdI2kw/oPrZW0yFr7VIXKOmK80Ws63u6IRFQdAKUJikSVyWa0\ncPZCdfV2UacBNIS3335bp334w9qVSoU+Z+zo0Xr1tdcY0EHsudGs/O4H2AQZQCFRbiFulTSl/9//\nn6SnJLVL2ivp8hLz2izpHyW9Icn0n/+IMeZ4a+2rlSjsSPGLXpPJZohmA8RMoUhUEwwRqgA0jnff\nfVe7Uiktl3RUiPSvSro4lVJ3dzeDOYi9ZCKp8Ro/5N6egRwAxUS2lbDWLs/59xpjzDRJfyyp01ob\nfh6uc753f50vG2OukXSKnHuCWPF+qCvW2PNBEIimoEhUQcfDhiwHgDg6Sg2+jh4Ny3sv7z6n3wdQ\nSJRDky80xox1n1trd1lrOyT1GmMWDiPfJmPMJyWNlfRCBYoaKUHhjkcqVDmA6qBuAwDQOOj3ARQT\n2Zk5kr4i6buSdnmOj+1/bVEpmRljjpUzeDNa0k5JF1hrf1eBckZKoXDHAOKLug0AQOOg3wdQTJQH\nc4wk63P8Q5K2l5Hf7/rPnSjpryXdb4xpLTSgM3/+fE2cODHvWFtbm9ra2sp4+5FDeEM0srjW2zCo\n26hX9VxvgXpFva0++n0AhURuMMcY846cQRwr6XVjTO6ATkLSeDkzdkpirc1I2tj/9CVjzMmSPicn\nypWvJUuWqKUlfqu3CW+IRhbXehsGdRv1qp7rLVCvqLfVR78PoJDIDeZI+ns5s3LukbOcakfOa3sl\nvWmtrcReN02SRlUgn0gJCnfMhmlAvFG3AQBoHPT7AIqJ3GCOtfY+STLG/F7Sf/XPqBkWY8xNkp6U\n1ClpgpwQ57MlnTncvKOmULhjAPFF3QYAoHHQ7wMoJnKDOS5r7XPGmOnGmCskTZf0OWttlzHmbDnh\nydeWkN1kSfdJmiJnps+vJZ1prf1ZxQseAUFhjQHEG3UbAIDGQb8PoJDIDuYYY2bLmU3zX5JaJX1J\nUpecTYznytnEOBRr7VXVKCMAAAAAAMBIa6p1AQr4uqQvW2s/JmevHNfPJJ1SmyIBAAAAAADUVpQH\nc46T9LDP8S5JB45wWQAAAAAAACIhyoM578rZ48brBElvjXBZAAAAAAAAIiHKgzn/LukbxpiDJVlJ\nTcaYD0u6RdL9NS0ZAAAAAABAjUR5MOeLkn4nabOk8ZLWSfqFpF9K+qcalgsAAAAAAKBmIhvNylq7\nV9LfGmMWydk/Z5ykl6y162tbMgAAAAAAgNqJ7GCOJBlj5kqaL2lm/6E3jDHfsdbeVcNiAQAAAAAA\n1ExkB3P6Z+QskHSbpBf6D58qaYkxZqq1dmHNCgcAAABfry8D16EAACAASURBVFY4HQAAGCqygzmS\nrpH0t9baf8s59qgx5tdyBngYzAEAAIiIPXv2qEnSxSWc0yQpnU5XqUQAANSvKA/mN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pguq+95j7POi453rNNhnt3NujJ159QlP3m6rOdzt1zhHnaEJighIBeYSq6wDqQ4j7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\nIiIiIiLSQzSYIyIiIiIiIiLSQ/4/awH+qP//shAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9488c78c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn.apionly as sns\n",
    "pp = sns.pairplot(iris, hue=\"species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNTK\n",
    "\n",
    "Для обучение сети будем использовать `CNTK`. CNTK умеет получать входные данные из разных источников:\n",
    "\n",
    " * Из объектов `numpy array`\n",
    " * Из внешних текстовых файлов\n",
    "    - текстовые данные представляются в специальном формате\n",
    "    - картинки описываются map-файлом и множеством файлов с изображениеми на диске\n",
    "    \n",
    "Преобразуем наш набор данных к текстовому виду, пригодному для CNTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'setosa', 1: 'virginica', 2: 'versicolor'} {'setosa': 0, 'virginica': 1, 'versicolor': 2}\n"
     ]
    }
   ],
   "source": [
    "# Строим отображение типов ирисов на номер класса\n",
    "fwmap = dict(enumerate(set(iris.values[:,4])))\n",
    "bkmap = {fwmap[k] : k for k in fwmap}\n",
    "print(fwmap,bkmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|label 1 0 0|features 5.2 3.4 1.4 0.2\n",
      "|label 0 0 1|features 5.6 2.7 4.2 1.3\n",
      "|label 0 0 1|features 4.9 2.4 3.3 1.0\n",
      "|label 1 0 0|features 5.4 3.9 1.7 0.4\n",
      "|label 1 0 0|features 5.0 3.5 1.3 0.3\n",
      "|label 0 0 1|features 6.0 2.2 4.0 1.0\n",
      "|label 0 0 1|features 6.3 3.3 4.7 1.6\n",
      "|label 1 0 0|features 5.1 3.5 1.4 0.3\n",
      "|label 0 0 1|features 5.7 2.8 4.1 1.3\n",
      "|label 1 0 0|features 5.2 3.5 1.5 0.2\n",
      "|label 0 0 1|features 6.2 2.9 4.3 1.3\n",
      "|label 0 1 0|features 6.8 3.2 5.9 2.3\n",
      "|label 0 1 0|features 7.7 2.8 6.7 2.0\n",
      "|label 1 0 0|features 4.8 3.4 1.6 0.2\n",
      "|label 0 1 0|features 7.2 3.6 6.1 2.5\n",
      "|label 0 1 0|features 7.7 3.8 6.7 2.2\n",
      "|label 0 0 1|features 5.6 3.0 4.1 1.3\n",
      "|label 0 0 1|features 7.0 3.2 4.7 1.4\n",
      "|label 0 1 0|features 6.4 2.8 5.6 2.2\n",
      "|label 0 0 1|features 5.7 2.6 3.5 1.0\n"
     ]
    }
   ],
   "source": [
    "def conv(n):\n",
    "    \"\"\"\n",
    "    Преобразует название класса в трехмерный вектор из нулей и единиц\n",
    "    \"\"\"\n",
    "    return [ 1 if x==n else 0 for i,x in fwmap.items() ]\n",
    "\n",
    "def dump(seq,fname):\n",
    "    with open(fname,'w') as f:\n",
    "        for x in seq:\n",
    "            f.write(\"|label {}|features {} {} {} {}\\n\".format(\" \".join(map(str,conv(x[4]))),x[0],x[1],x[2],x[3]))\n",
    "\n",
    "data = np.random.permutation(iris.values)\n",
    "dump(data[0:130],'iris_train.txt')\n",
    "dump(data[130:],'iris_test.txt')\n",
    "!cat iris_test.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reader = Minibatch Source + Deserializer\n",
    "\n",
    "Для чтения данных используется MinibatchSource, который умеет:\n",
    "\n",
    " * Преобразовывать данные из текстового формата на лету с помощью `Deserializer`\n",
    "    - по умолчанию доступны десериализаторы `CTFDeserializer` для текстового формата CNTK, `ImageDeserializer` для картинок и ещё один для последовательностей\n",
    " * Перебирать данные по нескольку раз\n",
    "    - зацикливаться или заканчиваться\n",
    " * Автоматически обеспечивают рандомизацию данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reader_train = MinibatchSource(CTFDeserializer('iris_train.txt',\n",
    "         StreamDefs(\n",
    "           labels   = StreamDef(field='label',shape=3),\n",
    "           features = StreamDef(field='features',shape=4))))\n",
    "\n",
    "reader_test = MinibatchSource(CTFDeserializer('iris_test.txt',\n",
    "         StreamDefs(\n",
    "           labels   = StreamDef(field='label',shape=3),\n",
    "           features = StreamDef(field='features',shape=4))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Описываем переменные, модель и ошибку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_var = input_variable(4)\n",
    "label_var = input_variable(3)\n",
    "\n",
    "model = Dense(3, init=glorot_uniform(),activation=None);\n",
    "\n",
    "# Подсказка: попробуйте использовать model = Sequential([....]) для комбинации слоёв!\n",
    "\n",
    "z = model(input_var)\n",
    "\n",
    "ce = cntk.cross_entropy_with_softmax(z, label_var)\n",
    "pe = cntk.classification_error(z, label_var)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Описываем `learner` и `trainer`. Переменная `progress` будет хранить достигнутую точность на разных шагах обучения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 15 parameters in 2 parameter tensors.\n"
     ]
    }
   ],
   "source": [
    "minibatch_size = 16\n",
    "\n",
    "lr_per_minibatch = cntk.learning_rate_schedule(0.01, cntk.UnitType.minibatch)\n",
    "pp = cntk.logging.ProgressPrinter()\n",
    "    \n",
    "learner = cntk.adagrad(z.parameters, lr = lr_per_minibatch)\n",
    "trainer = cntk.Trainer(z, (ce, pe), [learner],[pp])\n",
    "\n",
    "input_map = {\n",
    "    input_var: reader_train.streams.features,\n",
    "    label_var: reader_train.streams.labels\n",
    "}\n",
    "\n",
    "cntk.logging.log_number_of_parameters(z)\n",
    "progress = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Начинаем обучение. Обратите внимание, что вы можете менять количество эпох и минибатчей в каждой эпохе. Можете запускать этот блок несколько раз, он будет дообучать модель и достраивать график."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate per minibatch: 0.01\n",
      "Finished Epoch[1 of 300]: loss = 1.273110 * 8000, metric = 41.66% * 8000 2.234s (3581.3 samples/s);\n",
      "Finished Epoch[2 of 300]: loss = 0.440873 * 8000, metric = 13.44% * 8000 0.578s (13837.0 samples/s);\n",
      "Finished Epoch[3 of 300]: loss = 0.367969 * 8000, metric = 5.94% * 8000 0.563s (14221.4 samples/s);\n",
      "Finished Epoch[4 of 300]: loss = 0.323731 * 8000, metric = 4.54% * 8000 0.547s (14627.7 samples/s);\n",
      "Finished Epoch[5 of 300]: loss = 0.292336 * 8000, metric = 4.51% * 8000 0.625s (12799.2 samples/s);\n",
      "Finished Epoch[6 of 300]: loss = 0.268508 * 8000, metric = 4.91% * 8000 0.531s (15058.0 samples/s);\n",
      "Finished Epoch[7 of 300]: loss = 0.249438 * 8000, metric = 4.95% * 8000 0.578s (13837.1 samples/s);\n",
      "Finished Epoch[8 of 300]: loss = 0.234228 * 8000, metric = 5.12% * 8000 0.563s (14221.4 samples/s);\n",
      "Finished Epoch[9 of 300]: loss = 0.221420 * 8000, metric = 5.15% * 8000 0.594s (13472.9 samples/s);\n",
      "Finished Epoch[10 of 300]: loss = 0.210433 * 8000, metric = 5.08% * 8000 0.578s (13837.1 samples/s);\n",
      "Finished Epoch[11 of 300]: loss = 0.201486 * 8000, metric = 5.06% * 8000 0.547s (14627.7 samples/s);\n",
      "Finished Epoch[12 of 300]: loss = 0.193607 * 8000, metric = 5.03% * 8000 0.641s (12487.1 samples/s);\n",
      "Finished Epoch[13 of 300]: loss = 0.186339 * 8000, metric = 5.03% * 8000 0.625s (12799.3 samples/s);\n",
      "Finished Epoch[14 of 300]: loss = 0.180002 * 8000, metric = 5.03% * 8000 0.656s (12189.8 samples/s);\n",
      "Finished Epoch[15 of 300]: loss = 0.174973 * 8000, metric = 4.95% * 8000 0.578s (13837.0 samples/s);\n",
      "Finished Epoch[16 of 300]: loss = 0.169677 * 8000, metric = 4.89% * 8000 0.594s (13472.9 samples/s);\n",
      "Finished Epoch[17 of 300]: loss = 0.165416 * 8000, metric = 4.83% * 8000 0.609s (13127.5 samples/s);\n",
      "Finished Epoch[18 of 300]: loss = 0.161413 * 8000, metric = 4.79% * 8000 0.547s (14627.7 samples/s);\n",
      "Finished Epoch[19 of 300]: loss = 0.157512 * 8000, metric = 4.76% * 8000 0.547s (14627.7 samples/s);\n",
      "Finished Epoch[20 of 300]: loss = 0.154275 * 8000, metric = 4.78% * 8000 0.563s (14221.4 samples/s);\n",
      "Finished Epoch[21 of 300]: loss = 0.151186 * 8000, metric = 4.67% * 8000 0.625s (12799.3 samples/s);\n",
      "Finished Epoch[22 of 300]: loss = 0.148222 * 8000, metric = 4.70% * 8000 0.578s (13837.0 samples/s);\n",
      "Finished Epoch[23 of 300]: loss = 0.145429 * 8000, metric = 4.61% * 8000 0.547s (14627.7 samples/s);\n",
      "Finished Epoch[24 of 300]: loss = 0.143068 * 8000, metric = 4.64% * 8000 0.688s (11635.7 samples/s);\n",
      "Finished Epoch[25 of 300]: loss = 0.140626 * 8000, metric = 4.62% * 8000 0.781s (10239.4 samples/s);\n",
      "Finished Epoch[26 of 300]: loss = 0.138767 * 8000, metric = 4.62% * 8000 0.547s (14627.7 samples/s);\n",
      "Finished Epoch[27 of 300]: loss = 0.136417 * 8000, metric = 4.60% * 8000 0.563s (14221.4 samples/s);\n",
      "Finished Epoch[28 of 300]: loss = 0.134790 * 8000, metric = 4.67% * 8000 0.578s (13837.1 samples/s);\n",
      "Finished Epoch[29 of 300]: loss = 0.132689 * 8000, metric = 4.61% * 8000 0.594s (13472.9 samples/s);\n",
      "Finished Epoch[30 of 300]: loss = 0.131246 * 8000, metric = 4.62% * 8000 0.563s (14221.4 samples/s);\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9489f28be0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vk0Fi+XJYvdogIUnSYuvJIAHOJSFJUif0bJBwdktJkhZfzwYJRyQkSVp8PRskHJGQJGnx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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9488a3da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for x in range(30):\n",
    "    tloss = 0; taccuracy = 0; cnt = 0;\n",
    "    for y in range(500):\n",
    "        data = reader_train.next_minibatch(minibatch_size,input_map)\n",
    "        t = trainer.train_minibatch(data)\n",
    "        tloss += trainer.previous_minibatch_loss_average*trainer.previous_minibatch_sample_count\n",
    "        taccuracy += trainer.previous_minibatch_evaluation_average*trainer.previous_minibatch_sample_count\n",
    "        cnt+=trainer.previous_minibatch_sample_count\n",
    "        pp.update_with_trainer(trainer,with_metric=True)\n",
    "    progress.append([float(x),tloss/cnt,taccuracy/cnt])\n",
    "    pp.epoch_summary(with_metric=True)\n",
    "\n",
    "progress = np.array(progress)\n",
    "pylab.plot(progress[:,0],progress[:,2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eval error = 0.0\n"
     ]
    }
   ],
   "source": [
    "test_size = 20\n",
    "\n",
    "data = reader_test.next_minibatch(test_size, input_map=input_map)\n",
    "metric = trainer.test_minibatch(data)\n",
    "\n",
    "print(\"Eval error = {}\".format(metric))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание\n",
    "\n",
    "1. Попробовать достичь минимальной величины ошибки на тестовых данных за счет изменения архитектуры сети\n",
    "   - попробуйте многослойный персептрон\n",
    "   - попробуйте разные функции активации\n",
    "2. В процессе обучения строить training error и testing error и посмотреть, если ли переобучение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py35]",
   "language": "python",
   "name": "conda-env-py35-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
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