{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Chapter 7 - Moving Beyond Linearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- [Lab 7.8.1 Polynomial Regression and Step Functions](#lab-7.8.1)\n",
    "- [Lab 7.8.2 Splines](#lab-7.8.2)\n",
    "- [Lab 7.8.3 GAMs](#lab-7.8.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Imports and Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# Standard imports\n",
    "import warnings\n",
    "\n",
    "# Use rpy2 for loading R datasets\n",
    "from rpy2.robjects.packages import importr\n",
    "from rpy2.robjects.packages import data as rdata\n",
    "from rpy2.robjects import pandas2ri\n",
    "\n",
    "# Math and data processing\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "\n",
    "# StatsModels\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from patsy import dmatrix\n",
    "\n",
    "# scikit-learn\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "# Visulization\n",
    "from IPython.display import display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Load wage data set from R ISLR library.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>maritl</th>\n",
       "      <th>race</th>\n",
       "      <th>education</th>\n",
       "      <th>region</th>\n",
       "      <th>jobclass</th>\n",
       "      <th>health</th>\n",
       "      <th>health_ins</th>\n",
       "      <th>logwage</th>\n",
       "      <th>wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231655</th>\n",
       "      <td>2006</td>\n",
       "      <td>18</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>1. &lt; HS Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>1. Industrial</td>\n",
       "      <td>1. &lt;=Good</td>\n",
       "      <td>2. No</td>\n",
       "      <td>4.318063</td>\n",
       "      <td>75.043154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86582</th>\n",
       "      <td>2004</td>\n",
       "      <td>24</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>2. Information</td>\n",
       "      <td>2. &gt;=Very Good</td>\n",
       "      <td>2. No</td>\n",
       "      <td>4.255273</td>\n",
       "      <td>70.476020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161300</th>\n",
       "      <td>2003</td>\n",
       "      <td>45</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>3. Some College</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>1. Industrial</td>\n",
       "      <td>1. &lt;=Good</td>\n",
       "      <td>1. Yes</td>\n",
       "      <td>4.875061</td>\n",
       "      <td>130.982177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155159</th>\n",
       "      <td>2003</td>\n",
       "      <td>43</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>3. Asian</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>2. Information</td>\n",
       "      <td>2. &gt;=Very Good</td>\n",
       "      <td>1. Yes</td>\n",
       "      <td>5.041393</td>\n",
       "      <td>154.685293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11443</th>\n",
       "      <td>2005</td>\n",
       "      <td>50</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>4. Divorced</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>2. Information</td>\n",
       "      <td>1. &lt;=Good</td>\n",
       "      <td>1. Yes</td>\n",
       "      <td>4.318063</td>\n",
       "      <td>75.043154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  age      sex            maritl      race        education  \\\n",
       "231655  2006   18  1. Male  1. Never Married  1. White     1. < HS Grad   \n",
       "86582   2004   24  1. Male  1. Never Married  1. White  4. College Grad   \n",
       "161300  2003   45  1. Male        2. Married  1. White  3. Some College   \n",
       "155159  2003   43  1. Male        2. Married  3. Asian  4. College Grad   \n",
       "11443   2005   50  1. Male       4. Divorced  1. White       2. HS Grad   \n",
       "\n",
       "                    region        jobclass          health health_ins  \\\n",
       "231655  2. Middle Atlantic   1. Industrial       1. <=Good      2. No   \n",
       "86582   2. Middle Atlantic  2. Information  2. >=Very Good      2. No   \n",
       "161300  2. Middle Atlantic   1. Industrial       1. <=Good     1. Yes   \n",
       "155159  2. Middle Atlantic  2. Information  2. >=Very Good     1. Yes   \n",
       "11443   2. Middle Atlantic  2. Information       1. <=Good     1. Yes   \n",
       "\n",
       "         logwage        wage  \n",
       "231655  4.318063   75.043154  \n",
       "86582   4.255273   70.476020  \n",
       "161300  4.875061  130.982177  \n",
       "155159  5.041393  154.685293  \n",
       "11443   4.318063   75.043154  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wage dataset is in R ISLR package\n",
    "islr = importr('ISLR')\n",
    "wage_rdf = rdata(islr).fetch('Wage')['Wage']\n",
    "wage = pandas2ri.ri2py(wage_rdf)\n",
    "display(wage.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3000 entries, 231655 to 453557\n",
      "Data columns (total 12 columns):\n",
      "year          3000 non-null int32\n",
      "age           3000 non-null int32\n",
      "sex           3000 non-null object\n",
      "maritl        3000 non-null object\n",
      "race          3000 non-null object\n",
      "education     3000 non-null object\n",
      "region        3000 non-null object\n",
      "jobclass      3000 non-null object\n",
      "health        3000 non-null object\n",
      "health_ins    3000 non-null object\n",
      "logwage       3000 non-null float64\n",
      "wage          3000 non-null float64\n",
      "dtypes: float64(2), int32(2), object(8)\n",
      "memory usage: 281.2+ KB\n"
     ]
    }
   ],
   "source": [
    "wage.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Utility function definition.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def ortho_poly_fit(x, degree = 1):\n",
    "    '''\n",
    "    Convert data into orthogonal basis for polynomial regression by QR decomposition.\n",
    "    Ref: http://davmre.github.io/python/2013/12/15/orthogonal_poly\n",
    "    '''\n",
    "    n = degree + 1\n",
    "    x = np.asarray(x).flatten()\n",
    "    if(degree >= len(np.unique(x))):\n",
    "            stop(\"'degree' must be less than number of unique points\")\n",
    "    xbar = np.mean(x)\n",
    "    x = x - xbar\n",
    "    X = np.fliplr(np.vander(x, n))\n",
    "    q,r = np.linalg.qr(X)\n",
    "\n",
    "    z = np.diag(np.diag(r))\n",
    "    raw = np.dot(q, z)\n",
    "\n",
    "    norm2 = np.sum(raw**2, axis=0)\n",
    "    alpha = (np.sum((raw**2)*np.reshape(x,(-1,1)), axis=0)/norm2 + xbar)[:degree]\n",
    "    Z = raw / np.sqrt(norm2)\n",
    "    return Z, norm2, alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<a id='lab-7.8.1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Lab 7.8.1 Polynomial Regression and Step Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Polynomial regression of degree 4 on orthogonalized X.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            GLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   wage   R-squared:                       0.086\n",
      "Model:                            GLS   Adj. R-squared:                  0.085\n",
      "Method:                 Least Squares   F-statistic:                     70.69\n",
      "Date:                Tue, 21 Mar 2017   Prob (F-statistic):           2.77e-57\n",
      "Time:                        18:05:48   Log-Likelihood:                -15315.\n",
      "No. Observations:                3000   AIC:                         3.064e+04\n",
      "Df Residuals:                    2995   BIC:                         3.067e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const        111.7036      0.729    153.283      0.000       110.275   113.132\n",
      "x1           447.0679     39.915     11.201      0.000       368.805   525.331\n",
      "x2          -478.3158     39.915    -11.983      0.000      -556.579  -400.053\n",
      "x3           125.5217     39.915      3.145      0.002        47.259   203.785\n",
      "x4           -77.9112     39.915     -1.952      0.051      -156.174     0.352\n",
      "==============================================================================\n",
      "Omnibus:                     1097.594   Durbin-Watson:                   1.960\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4965.521\n",
      "Skew:                           1.722   Prob(JB):                         0.00\n",
      "Kurtosis:                       8.279   Cond. No.                         54.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Polynomial regression of degree 4 on orthogonalized X. Refer to chapter 3 notebook.\n",
    "X4_ortho = ortho_poly_fit(wage[['age']], degree=4)[0]\n",
    "X4_ortho[:,0]=1  # Replace constant column with 1s for Intercept estimation.\n",
    "poly4_ortho = sm.GLS(wage['wage'], X4_ortho).fit()\n",
    "print(poly4_ortho.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Polynomial regression of degree 4 on raw X without orthogonalization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            GLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   wage   R-squared:                       0.086\n",
      "Model:                            GLS   Adj. R-squared:                  0.085\n",
      "Method:                 Least Squares   F-statistic:                     70.69\n",
      "Date:                Tue, 21 Mar 2017   Prob (F-statistic):           2.77e-57\n",
      "Time:                        18:05:49   Log-Likelihood:                -15315.\n",
      "No. Observations:                3000   AIC:                         3.064e+04\n",
      "Df Residuals:                    2995   BIC:                         3.067e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const       -184.1542     60.040     -3.067      0.002      -301.879   -66.430\n",
      "x1            21.2455      5.887      3.609      0.000         9.703    32.788\n",
      "x2            -0.5639      0.206     -2.736      0.006        -0.968    -0.160\n",
      "x3             0.0068      0.003      2.221      0.026         0.001     0.013\n",
      "x4         -3.204e-05   1.64e-05     -1.952      0.051     -6.42e-05  1.45e-07\n",
      "==============================================================================\n",
      "Omnibus:                     1097.594   Durbin-Watson:                   1.960\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4965.521\n",
      "Skew:                           1.722   Prob(JB):                         0.00\n",
      "Kurtosis:                       8.279   Cond. No.                     5.67e+08\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.67e+08. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Polynomial regression of degree 4 on raw X without orthogonalization.\n",
    "X4 = PolynomialFeatures(degree=4).fit_transform(wage[['age']])\n",
    "poly4 = sm.GLS(wage['wage'], X4).fit()\n",
    "print(poly4.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Predict over a grid of age.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAGhCAYAAABmnh1DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlUXPXdP/D3nZ0ZBoYBhn3fd8ISAgRIIpqqdWn7qK0+\ntsmxtVqrrUuP/roYrUt9qsajPmlt+7i12tbGGqvGlcSEAGELIQlL2JewL8M2zMBs398fOdyTyQAh\nCTAw+bzO8Ry5c5fP/c7NfO79bpdjjDEQQgghxGkJHB0AIYQQQlYWJXtCCCHEyVGyJ4QQQpwcJXtC\nCCHEyVGyJ4QQQpwcJXtCCCHEyVGyJ2Qd4zgO77//vqPDWJItW7bgpz/96ZLX7+zsBMdxqK6uXsGo\n1rZDhw6B4ziMjIw4OhSyzlGyJytux44d4DgOHMdBLBZDo9Fg69at2LNnD0wmk6PDW1YlJSUQiURI\nTEy84LpzZcJxHJRKJTIyMvDBBx+sQpSO8cEHH+B3v/vdsu7z3GtLJBIhODgY9957L8bGxpb1OI6S\nk5OD/v5+eHp6OjoUss5RsierorCwEP39/ejs7MSXX36JG264Abt27UJeXh6mp6dX9NhWqxUWi2VF\njwEAY2Nj+P73v4+rrrpqydv85S9/QX9/P6qqqpCSkoJbbrkF5eXlKxil46jVaiiVymXf77nX1v/9\n3//hk08+wU9+8pNlP875zGYzVnpOMolEAl9fX3Act6LHIc6Pkj1ZFVKpFL6+vggICEBqaioeeugh\nHDp0CDU1Nfj973/Pr2c0GvHoo48iMDAQcrkcmZmZ+OKLL2z2tX//fsTExEAmk2HLli147733wHEc\nOjs7AQBvvfUWXF1d8emnnyIxMRESiQSNjY0AgDfffBPx8fGQyWSIjo7GSy+9BKvVyu97YmICd999\nNzQaDZRKJQoKCpZcjXzXXXfhBz/4AbKzs5dcLiqVCr6+voiNjcWf/vQnyGQyfPTRRwDO3qQ89dRT\nCAoKglQqRVJSEv7zn/8suK9t27bZVZNPTk5CLpfzNQahoaF4+umn8eMf/xhubm4IDAzE888/b7NN\nd3c3vvWtb0GpVEKpVOLb3/42enp6+M+feOIJJCYm4u2330ZoaCgUCgV27twJo9GIP/zhDwgKCoKn\npyceeughm7I9vxr/nXfeQWZmJpRKJTQaDW655Rb09vYuuezmzF1bgYGBuOaaa3Drrbfiyy+/tFln\nKd/rG2+8geDgYMjlctx888344x//aJNk5877rbfeQkREBKRSKaanp8EYw+9//3tERETAxcUFSUlJ\neOedd2z2/dvf/hYhISF8rN///vf5z4qLi7Fp0ya4urrC3d0dGzduRF1dHYD5q/E/+OADJCUlQSqV\nIigoCM8884zNTcdSvmNyBWKErLAf/OAH7Prrr5/3sxtuuIElJCTwf99+++0sKyuLHT58mLW1tbFX\nX32VicViVltbyxhjrKuri0kkEvbggw+y06dPs71797Lg4GAGgHV0dDDGGHvzzTeZUChkmzZtYiUl\nJaypqYlNTk6yP//5z8zX15ft3buXtbe3s48++oj5+PiwV199lTHGmNVqZbm5uey6665jFRUVrKWl\nhf36179mSqWS9fX1LXqOe/bsYTk5OcxsNrNdu3bZnNNCALC9e/faLHNzc2MPPfQQY4yx3bt3M6VS\nyd59913W1NTEfvOb3zCBQMCOHz8+7z7+/ve/Mw8PDzYzM8N//tprrzFvb29mNBoZY4yFhIQwtVrN\nXn31VdbS0sJeeeUVBoCVlZUxxhizWCwsNTWVZWdns6qqKlZVVcWysrJYeno6s1qtjDHGdu3axRQK\nBfvWt77FTp06xT7//HOmUCjY9u3b2Y4dO1hDQwP74IMPmEgkYu+//z4fS0FBAbvvvvv4v19//XW2\nf/9+1tbWxioqKtiWLVtYXl4e/3lHRwcDwKqqqhYsw/Ovrba2NhYfH898fHz4ZUv5XsvKyhjHcey5\n555jTU1N7M9//jPz9vZm5/5E7tq1i8nlcnb11VezY8eOsVOnTjGTycR++ctfsujoaPbZZ5+x9vZ2\n9u677zK5XM4++eQTxhhj77//PlMqleyTTz5hXV1drKqqir/mTCYTU6lU7OGHH2atra2ssbGRvfvu\nu6yhoYExxtjXX3/NALDh4WHGGGPV1dVMIBCwxx9/nDU1NbF33nmHKRQK9sorr/BxXug7JlcmSvZk\nxS2W7B999FHm4uLCGGOstbWVcRzHurq6bNa56aab2L333ssYY+yxxx5jsbGxNp8/88wzdskeAKuu\nrrZZLygoiP31r3+1WfbSSy+xuLg4xhhjBw4cYAqFgun1ept1UlJS2P/8z/8seH4nT55kGo2Gtbe3\nM8bYJSX7mZkZ9tvf/pYBYJ9++iljjDF/f3/25JNP2mxTUFDA7rjjjgX34enpyf7xj3/wn2/cuJE9\n/PDD/N8hISHsu9/9rs0+IyMj2VNPPcUYY+zLL79kAoGAL0vGziZQjuPYV199xZ+fTCZj4+Pj/Drf\n+c53mJeXF5udnbWJ9dzkfv7f52tsbGQA2JkzZxhjS0/2QqGQKRQKJpPJGAAGgO3evZtfZynf63e/\n+122fft2m89/9KMf2SV7kUjEBgYG+GU6nY7JZDJWXFxss+3PfvYzdu211zLGGHvxxRdZdHQ0f8N1\nrtHRUQaAHTp0aN7zOz/Z33777Wzr1q026+zatYsFBATwf1/oOyZXJqrGJw7FGOOrSmtqasAYQ3x8\nPFxdXfn/9u/fj7a2NgDA6dOnkZmZabOPrKwsu/2KRCKkpqbyfw8PD+PMmTP48Y9/bLPvxx57jN/3\nsWPHoNfr4e3tbbNOXV0dv865y++55x7Mzs7itttuwwsvvICwsLCLPv8777wTrq6ukMvleOmll/DC\nCy/g2muvxeTkJPr6+pCbm2uz/ubNm9HQ0DDvvqRSKe6880688cYbAID6+npUVlbirrvuslkvOTnZ\n5m9/f38MDQ0BABobG+Hv74/Q0FD+8/DwcPj7+9scNzg4GO7u7vzfPj4+iI6OhkQisVk2t9/51NTU\n4KabbkJISAjfQRE424xwMfLz81FbW4vKykrcf//9uO666/DAAw/wny/lez19+jQ2btxos9/5rqvA\nwED4+Pjwfzc0NGBmZgbf+MY3bPb9xz/+kd/3LbfcgpmZGYSFheGuu+7C3r17MTs7C+BsP4YdO3Zg\n+/btuP7667F79+5Fz7+xsXHea6K3txeTk5P8ssW+Y3JlEjk6AHJla2hoQHh4OICzbdQcx6Gqqgpi\nsdhmPRcXl4var1QqhVAo5P+eazt+7bXXkJOTM+82VqsVPj4+OHLkiN1nbm5uAIDa2lqbZf39/Whs\nbMTOnTuxc+dOfj+MMYhEInz66ae45pprFozz+eefxze+8Q24ublBo9Es6dwW66z1wx/+EMnJyeju\n7sYbb7yB7OxsxMXF2axzftlyHGfTtr6U4863j/mWLdQxcnp6Gtu3b0dhYSH+9re/QaPRYGRkBHl5\neTAajReM5VxyuRyRkZEAgFdeeQVbt27FU089hSeeeALA0r7XpVIoFDZ/z5Xbxx9/jODgYJvP5soj\nKCgITU1NOHDgAIqKivDwww/jySefREVFBRQKBd588038/Oc/x+eff46PPvoIv/rVr/Dhhx9i+/bt\nFxXbhb6fpXzHxHlRsicOU1dXh88//xy//vWvAQAbNmwAYwwDAwPYunXrvNvExsbadVKrrKy84LF8\nfHzg7++PtrY2m85R50pLS8Pg4CAEAgF/A3K+uaQyx2Qy4dSpUzbL/vCHP+Crr77Cvn37bJ6Q5+Pr\n62u3T+BsEvL390dpaalN7/6SkhLEx8cvuL+EhARkZWXhL3/5C9555x0888wzix7/fHFxcejr60Nn\nZycfe3t7O/r6+hY97sU6ffo0RkZG8Oyzz/I1Iss17HDXrl249tprcffdd8Pf339J32tsbCyqqqps\nli3luoqPj4dUKkVXVxe2bdu24HoymQzXX389rr/+ejz22GPw9fVFaWkpfyOYkpKClJQUPProo7j2\n2mvx9ttvz5vs4+LiUFpaarOspKQEgYGBKzLSgTgPSvZkVczOzmJgYABWqxXDw8M4cOAAnn32WaSn\np+ORRx4BAERHR+OOO+7Ajh078OKLLyItLQ1arRaHDh1CeHg4vv3tb+Oee+7B7t278cgjj+BHP/oR\n6uvr8ac//QnA4k+8APDkk0/i/vvvh0qlwnXXXQeTyYSamhr09vbi//2//4fCwkLk5ubipptuwu9/\n/3vExsZiYGAAn3/+OQoLC5GXl2e3T7FYbDemXqPRQCqVLmms/WJ+8Ytf4PHHH0dUVBTS09Pxzjvv\n4MiRI6ipqVl0ux/96Ee45557IBaLcdttt13UMQsLC5GcnIw77rgDL7/8MgDg/vvvR1pa2qLJ7GIF\nBwdDKpXif//3f3HfffehsbERv/nNb5Zl31u2bEF8fDyefvpp/OEPf1jS9/rAAw9g8+bNeP7553Hz\nzTejuLgY+/btu+CxlEolHnnkETzyyCNgjCE/Px86nQ7l5eUQCAS4++678dZbb8FsNiMrKwuurq54\n7733IBaLERUVhY6ODvzpT3/CjTfeiICAALS3t+PkyZO499575z3eww8/jMzMTDzxxBO4/fbbUVVV\nhRdffBHPPvvsspQdcWIO7TFArgg/+MEP+I5TQqGQeXp6soKCAvbqq6/adOhijDGj0ch27drFwsLC\nmFgsZj4+PuyGG26w6Wz38ccfs6ioKCaVStnmzZvZ66+/zgDwHafefPNNplAo5o3l73//O9uwYQOT\nSqVMpVKx3Nxcmw5tk5OT7IEHHmABAQFMLBazwMBAdtttt7HW1tYln+/l9MY/l8ViYb/97W9ZYGAg\nE4vFLDExke3bt++C+5ienmaurq5s586ddvsMCQlhzz//vM2y8zvOdXV1sZtuuom5uroyV1dXdvPN\nN/Od5hY6v/vuu48VFBTYLLvtttvYd77znQWP889//pOFh4czqVTKMjMz2eeff84AsK+//poxdmm9\n8ee8++67TCKRsM7OTsbY0r7X119/nQUGBjKZTMauv/569vzzzzOZTLboeTN2trf/K6+8wuLi4phE\nImFeXl6ssLCQffnll4wxxvbt28c2bdrE3N3dmVwuZxkZGezjjz9mjDE2MDDAvvWtbzF/f38mkUhY\nUFAQ+8UvfsF35ju/gx5jjP373/9miYmJ/Hk8/fTT/EgJxpb2HZMrD8fYCs8KQcgKe/nll/H4449j\nfHycJh8B0NfXh+DgYBw+fNiuMxdZugcffBBFRUV2zTSErEdUjU/WnT179iAzMxPe3t4oLy/HU089\nxU+beiUzmUwYHR3FL3/5S2zYsIES/UV6/vnncfXVV8PV1RVFRUV47bXXqHqcOA1K9mTdaW1txbPP\nPovR0VEEBgbinnvuweOPP+7osByutLQUW7duRVRUFP71r385Opx1p7q6Gi+88AImJiYQFhaG3/3u\nd/jZz37m6LAIWRZUjU8IIYQ4OZpUhxBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBCCHFy\nlOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBC\nCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJ\nnhBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQJ0fJnhBCCHFylOwJIYQQ\nJ0fJnhBCCHFyIkcHsJz6+vocHQK8vLwwMjLi6DAcisrgLCoHKoM5VA5UBsDKlIG/v/+S1qMne0II\nIcTJUbInhBBCnBwle0IIIcTJUbInhBBCnBwle0IIIcTJUbInhBBCnBwle0IIIcTJUbInhBBCnBwl\ne0KuMIwxR4dALgN9f+RSONUMeoSQhel0OtTU1EAgEIAxBrFYjIyMDIhE9DOw1lksFlRXV8NoNILj\nOFitVqSmpsLNzc3RoZF1gv6VE3IFYIyhvLwcBQUFEIvFAIDJyUlUVFQgNzfXwdGRC6msrERsbCw8\nPDwAAGazGYcOHcK2bdsgEFAFLbkwukoIuQL09PQgIiKCT/QA4ObmBpFIhJmZGQdGRi7EZDIBAJ/o\nAUAkEiEmJgZdXV2OCousM5TsCVkEYwwGgwFWq9XRoVwWvV4PV1dXu+VyuRyzs7MOiIgsldFohEwm\ns1vu6uqK6elpB0RE1iOqxidkAc3NzRgYGIBcLoder4darUZiYqKjw7okgYGBqK+vh7e3t83y0dHR\ndXtOVwq5XI6JiQkwxsBxHL+8ra0N0dHRDoyMrCeU7AmZR29vL2ZnZ5Gfn88va2lpQVtbGyIiIhwY\n2aVRKBSQyWQ4fvw44uLiYDKZcOLECURERNgkELL2cByHqKgoHDlyBKmpqZBKpTh9+jSEQiF10CNL\nRsmekHl0dHQgOTkZ5eXlsFgs4DgO8fHxOHXq1LpM9gCQnJwMrVaLU6dOQSQSIS0tDXK53NFhkSUI\nCAiAWq1GU1MTjEYjIiIi4Onp6eiwyDpCyZ6QeczMzKC6uho5OTmQyWQwGo0oLy+HXq93dGiXRa1W\nQ61WOzoMcglcXFyQmprq6DDIOkUd9Mi6Y7Va0dHRgRMnTmB4eHhFjjE8PIyoqCi+Y5REIkFiYiJ6\ne3tX5HhkbWOMoaurC7W1tRgYGHB0OIRcNEr2ZF0xGAw4ePAghEIhIiIiMDQ0hNLS0mWfVSwwMBDt\n7e1obm6GTqdDW1sbamtrERkZuazHIWvf7OwsDh48CKvVisjISIyPj6O4uHjdj9AgVxZK9mRdOX78\nODZv3ozg4GC4uroiISEBAQEBaG9vX9bjiMVibNy4Ea6urmhra4NEIkFeXh6EQuGyHoesfbW1tdi0\naRPCwsLg6uqK2NhYREZGoqmpydGhEbJklOzJumK1Wu3GHIeEhCx71WpCQgIqKirg5eWFlJQU+Pn5\noby8HPHx8ct6HLL2mUwmKBQKm2X+/v4YHR11UESEXDzqoEfWlfmqTk0m07I/cSuVSqSlpaG6uppv\nIkhISKDObVeg+ZqI5kZoELJeULIn64parUZvby8CAgL4ZbW1tSsyuYibmxtycnKWfb9kfdFoNOjo\n6EBYWBi/7OTJk9R/g6wrlOzJupKQkIDa2lq0t7fDxcUF09PTCA4OpidusmJiYmJw8uRJFBcX87Mp\n+vv7w8fHx9GhEbJklOzJusJxHDZs2ACr1Qqj0QipVErVqWTFJScn0zVH1jVK9mRdEggE874chJCV\nQtccWc+oNz4hhBDi5Fblyd5oNGLXrl0wm82wWCzYtGkTbr31Vuh0Orz00ksYHh6Gt7c3HnzwQf41\nnPv27cPBgwchEAiwc+dOmiaSrBtTU1MYGBiA2WyGSESVZ4QQx1uVXyKxWIxdu3ZBJpPBbDbj8ccf\nR2pqKiorK5GUlISbb74ZH374IT788EP893//N3p6elBWVobdu3djbGwMTz31FF5++WUIBFQRQdYu\nxhiqq6shEAgQFBSEkZERNDY2YvPmzRCLxY4OjxByBVuV7MlxHN/WZbFY+DGqVVVVKCgoAAAUFBSg\nqqoKAFBVVYWcnByIxWJoNBr4+vqitbV1NUIl5JKdOXMGHh4eSE9Ph0ajQWJiIjIzM1FTU+Po0Agh\nV7hVq2O0Wq149NFHMTAwgO3btyMqKgoTExPw8PAAAKhUKkxMTAAAtFotoqKi+G3VajW0Wq3dPouK\nilBUVAQAeO655+Dl5bUKZ7I4kUi0JuJwpCulDAwGAywWC9/0VFdXh4KCAr6ntkgkQmhoKDo6Oq6I\n8pjPlXItXAiVA5UB4NgyWLVkLxAI8Pzzz2N6ehovvPACuru7bT7nOO6ih7MUFhaisLCQ/3tkZGRZ\nYr0cXl5eayIOR3L2MjAYDKisrIRcLodQKMTExARSUlIwOTmJwcFBvp1+rhzGx8edujwW4+zXwlJR\nOVAZACtTBv7+/ktab9V7DykUCn5iFHd3d4yNjcHDwwNjY2Nwc3MDcPZJ/tx5p7VaLU2aQtaMiooK\nZGdnQyqVAjhba/X1118jISEBdXV1Np1Jh4eH+Sd/QghxlFVps5+cnMT09DSAsz3zT548iYCAAGRk\nZODw4cMAgMOHDyMzMxMAkJGRgbKyMphMJgwNDaG/v5+mpiRrwvT0NJRKJZ/ogbO1VhERETAajZDJ\nZDhy5AgaGhpQUlKC9vZ2GklCCHG4VXmyHxsbw549e2C1WsEYQ3Z2NtLT0xEdHY2XXnoJBw8e5Ife\nAUBQUBCys7Px0EMPQSAQ4K677qKe+GRNMJlMNol+jkQigV6vR2xsLN8fJTg4GDqdzgFREkKILY7N\n90qndaqvr8/RIVC7FJy7DBhjOHToELZs2WLTx+TIkSPIysqCRCLhlzlzOSwVlcFZVA5UBsAV1mZP\nyHrGcRwSEhLw9ddfIyYmBmKxGM3NzfD397dJ9IQQspZQsifkImk0GuTn56Orqws6nQ4ZGRk0Zzoh\nZE2jZE/IJRCJRIiIiHB0GIQQsiTU640QQghxcpTsCSGEECdHyZ4QQghxctRmT8gqYIzh5MmTmJiY\n4OeMSE9Ph4uLi4MjI4RcCSjZE7IKampq4Ofnh5SUFADA7Owsjhw5gm3bttGEUYSQFUe/MoSsMIvF\nAoPBYDP5hVQqRUxMDDo7Oy+4/fT0NEZGRmC1Wi87FqvVira2NvT29l72vi7VzMwMmpqaMDY2tuRt\nLBYLRkZGoNfrVzCy1TUxMQGtVgsnmteMrGH0ZE/ICjOZTPOOw/fw8EBbW9ui2x09ehRyuRyurq5o\naGhAQEDAJQ/5q6urw1dffYWIiAjMzs6it7cXt99+OzQazSXt71L85z//QX9/P0JDQ3H06FGYzWbs\n2LGDf1PgfFpaWtDf3w+NRoOpqSnMzMwgOzt70W3WssnJSVRXV8PLywsCgQC1tbVISUmBp6eno0Mj\nTmx9/mshZB2RSqX8i6DO1dnZiYCAgAW3q66uxoYNG6BUKgEAsbGxKC8vh0aj4Zct1czMDIqKivCz\nn/2MbzbQ6XR444038MADD1zUvi7ViRMnwBjDPffcwy87ffo0PvjgA9x6663zbjM2Nobx8XHk5+fz\nyyYmJnDs2DFkZWWteMwrobq6Gvn5+fzNytxbE7du3UpNOmTFULInZIVxHIfQ0FCUl5djw4YNkEgk\naG9vx+TkJBITExfczmw22yX1lJQU1NfXIyMj46JiKC8vR15enk0ycXV1hVqtxsjICLy8vBbcVqfT\noaWlBRzHISoqCgqFAgCg1+tRV1cHk8kEkUiEhISERV/nW15ejp07d9osi42NxcGDBxfcprm52e6t\nge7u7jAajYue71o1Pj4Ob29vm1oJgUCAsLAw9PX1geM4tLa2QiQSwWKxQKPRIDY21oERE2dByZ5c\nMbRaLbRaLby8vKBSqS5rX2azGdXV1ZidnUVmZibkcjn/2dTUFAYHB2GxWCAUCgEAISEhcHNzw5df\nfomZmRmkpKQgOzt70WPMvWhndnYWs7OzUCqVEAgEfNv9zMwMenp64OLiAn9/f5sX88wX79zc/Tqd\nDkKhEC4uLhCJRDCbzQtu19LSgtHRUYSHh4MxhhMnTsDHxwe+vr6oqKjAxo0b+fOsqqpCeno63Nzc\nYLVacebMGYyNjcHV1RVisRiMMYhEIr583N3d4e3tvejTrNVq5ctwvrJhjKG/vx96vR4BAQE2oxsY\nY9DpdBCLxcsynbHJZIJer4erq+u8Mc3HYrFAp9Px15vVap33fEUiEUZHR2EwGJCfn8+fX3NzM1pb\nW+kV3+SyCZ944oknHB3EcpmamnJ0CJDL5U7ViehSXEwZMMYwMDCAwcFByGQyiMVi/rOZmRl0dnZC\nr9dDqVQumswWY7FYUFJSArPZDA8PD/T09KC9vR2BgYEX3Of4+DjOnDkDjuP4RNLS0oK3334bYrEY\nRqMRBw4cgNlsRnBwMKqqqjA6Ogpvb2/odDpUV1cjMDAQ09PTqK6uhru7O9zc3DA+Pg6TybRoO21H\nRwc6Ojpw+vRpdHd3o7OzE21tbUhOTkZXVxfa29uh0WhgMBhQU1MDb2/veV+/C5ydz/8f//gH9Ho9\nWltb0dHRgTNnzqCpqQnbt28HAIyOjqK3txcikQgymQwmkwk1NTUAzn4Xk5OTMBgMGB4extjYGBQK\nBc6cOQOj0YjOzk4oFAr09/fDxcUFFRUVUKvVcHNzQ2VlJQDAxcUFn376KWQyGby9vTEyMoLDhw/D\nYrFgw4YN88YtlUrR0dEBHx8ffpnBYMDAwAC8vLxw5MgRKBQKKBQKNDU1YXx8HBqNBr29vaipqcHM\nzAx6e3vR2dkJPz8/CIVCMMYwNDSEgYEBSKXSC77AiDGGmpoadHV1YWZmBi0tLdDr9fD29l50u1On\nTqG1tRVGoxFdXV0YHBxESEgI6urqEBoaanPDUltbC7PZjMzMTJsbCU9PT9TV1SEkJGTRY10Kg8GA\nrq4uGAyGy/r3tVT027gyZbDUJj16sicOYzAYUFZWhuDgYCiVStTV1UEmkyE5ORlNTU0YGRlBeHg4\nDAYDDh48iMzMTLi5uV30cU6ePInExESo1WoAgI+PD3p6etDc3IyYmJh5t2GM8Z3j/Pz80NfXh7q6\nOmRnZ+Nf//oXMjMzERMTA5FIBJVKhUOHDkGj0UClUiEqKgrA2ddZKpVK1NTUYHR0FEKhEL6+vnBx\ncUF7eztqa2sREBDAV4ufz2g0oqqqChkZGdBoNDh27BiGh4eRlpaGiYkJ5OTk8OuGhISgtLQUBQUF\n8+7LxcUFer0en332GQoLC6HT6bB//36kp6fDYrGgtLQUarUa3t7eaG9vx8zMDPz9/aHVanHLLbfw\n1c4mkwnvvfceent7kZOTg/T0dP4YbW1tOHbsGGZmZrB161ZwHAcvLy/I5XIcPnwYAQEBMBgMaGho\ngEgkQmdnJzo7O7Fp06YFvzuNRoP+/n5UVFQgKCgIExMT6O/vR05ODiorK5GXl8cnaz8/Pxw7dgwD\nAwNoa2s5asRRAAAgAElEQVRDQUEBn8Cmpqb4mofS0lL4+/vDw8MDDQ0NEAqFSEtLWzCGxsZG+Pj4\nIDAwkF926tQp9PX1Lfh60Y6ODkilUmzevBnA2WuhvLwc7e3tSEpKwsGDBxEeHg6hUIj29nZERkai\nq6vL5mZ3zlJrES5GfX09JicnERYWBp1Oh4MHD2LTpk0LXotk/aPeIMRhqqurkZubi6ioKPj6+mLj\nxo2wWq3o7u7G2NgYcnNz4efnh/DwcBQUFOD48eOXdJzp6Wk+0c8JDAzE8PDwgtucPn0aISEhSE1N\nhY+PDxITE5GQkICioiIwxlBYWIiQkBAEBAQgPz8ffn5++OKLL+yqW11dXWEwGNDd3Y2rrroK4eHh\n8PPzQ25uLlQqFRoaGhaMoaWlBQ899BBSUlLg5uaG22+/Hfn5+di/fz/i4+Nt1hWJRJBIJAtWybe2\ntiIpKQlPP/003NzcEBERgVdffRUWiwUVFRVISkpCYmIifHx8sGHDBgQEBKChoQHu7u427ctisZh/\ngg8LC7M5RnBwMHp6eqDRaOyeEiMiIlBbW4sHH3wQV199NcbGxhAfH49HHnlkwfOfk5KSguTkZBgM\nBnh6emLbtm2QSqUQCAR2T+UJCQk4dOgQkpKSbGJQKpWwWq2orq7Gpk2bEBsbCx8fH2RmZkImk6Gv\nr2/B44+Ojtok+rnjdHR0LLhNT08Pf9N3bhn09fXB29sbW7duhUgkAmMMmzdvRmBgIFQqFQYHB222\nmZ2dXfYn7vHxcej1emRnZ8PX1xeRkZHIy8vDsWPHlvU4ZG2hZE8chuM4u7bUhIQElJSUIC4uzmb5\nuVXLF2u+ccyMsUXHN4+Ojtr1lPf09MTExARkMpldu6uPjw90Ot28Y+FNJhMkEondULHw8HD09/cv\nGINAIIBMJoOPjw/Cw8Ph4uKC5ORkDAwMzFsOVqt1wcTQ3d2N6OhoyOVybN68Genp6RCJRPDw8MDI\nyAg8PDxs1g8KCsLMzAwmJiYwOzvLLzcYDNDr9YiMjERFRQV/c2GxWFBZWYmQkJB5bzjMZjNEIhGs\nVit8fX2xZcsWPhkuZZy5i4sLIiIibIYJzred2WwGY2zeqnmBQACLxWLTvwLABec7mK9MOY5bNG6O\n4+y2O3eZQCBAUFAQQkJC+OsiNjYWDQ0N6OrqgtVqxdDQEI4cOWLXQfFytbW12XUMlUgkEAgENObf\niVGyJw6z0I/1Qp3GLBbLJT3leHp6oqenx2ZZc3MzgoODF41tvvjUajWGh4eh0+n4ZVarFVVVVbjq\nqqtQV1dns/7w8DBUKhVMJhMMBoPN/hsaGhZti7VarXa9zuvr65GWloa6ujqb+PR6PRhjC1b5BgYG\n2o3pZ4xhfHx8wc5rnp6e8PT0REVFBUpKSlBSUoKqqip4eXkhLi4OPj4+KC8vR2lpKcrKyvibkrGx\nMZubkbmJfDZu3GhXkzE0NLTkppm5RA6cTZxCodCun86JEydQUFCAxsZGu21NJtO8189CnebmSKVS\nTE5O2iybq8FYiLu7u13N0cjIyKLtq0KhEFu2bIHVakVFRQVGR0exZcsWu5uTyyUUCue9WaRE79w4\n5kTf8GJVcavFy8sLIyMjjg7DoZZaBhUVFYiNjYW7uzu/rLy8HJGRkWhoaEBeXh7/42wwGFBdXY28\nvLyLjocxhmPHjsFkMkGlUkGr1UKpVCI5OXnBbTo7O9HT08P3BrdYLBCLxVCpVGhqakJbWxsCAgIg\nk8nQ3t4Ob29v3HHHHXxfAy8vL3Ach/HxcWzcuBFff/01LBYLPD09IZPJMDg4iPHxcVx//fXQ6/U4\ndeoUfxxPT0/Ex8fj4MGD+Pe//w2LxQIXFxeMjY0hLi4OP/3pTzE+Po7W1lZ4e3vzT9tZWVmQSqX4\n/PPP0dbWBpFIBJPJhKuvvhqRkZH4xz/+gdDQUERHR8NkMqGurg6zs7OIiIjA1NQUjEYjHwMAhIWF\ngTGGzs5OvnmiubkZUVFR8PPzQ0lJCdRqNfz9/TE4OIihoSFs3rwZBoMBVVVVUKlU8PLyQktLCz9p\nTENDA0ZHR+Hl5YWJiQlwHIeNGzcuehPX19eH5uZmyGQyGI1GKBQKpKWlwWQy4Z///CeMRiPkcjm0\nWi0KCgqQlJSEuro6TE9PIyIiAtPT02hpacHGjRvR3NyM8PBwm2adY8eOISwsDC4uLnxVNmMMLi4u\nSEtLg8Viwd69e8EYg1qtxtDQENRqNb75zW8uGLfVakVJSQk8PT3h5+eH2dlZtLS0IDc3d0Xa4C+G\nXq9HTU0N358AONvUdeLECZt+IMuNfhtXpgwW6jdyPkr2y4wu6KWXgdlsRnl5OaRSKRQKBUZGRvgZ\n4np7e9Hc3AyNRoOZmRlMT0/zyexSGY1G6HQ6KJXKeTtCnWtkZARffvklgoOD4enpifHxcXR2diI/\nPx8qlQpVVVX80xHHcUhNTeXHqlssFkxMTCA4OJivAZiZmUFFRQWEQiGEQiEMBgNiYmLg6emJsrIy\nbNmyhX+67OzshE6nw1dffQWTyYT4+HhwHAez2Yx9+/Zh9+7dUKvVYIxhbGwMMpmMf/orKioCABQW\nFgI4m3T27NmD2267DcDZOfrnak3kcjk2bdqEkZERlJWVISQkBCqVCqOjo+jq6sK1114LtVrN9ybn\nOA4hISE2ZTcyMoLh4WF4enraPelOTU1BpVLBbDbbJEWz2YzJyUnI5fILDombnp7mE9PcPvr7+zE4\nOAiO4/jah7ke5cXFxcjPz4dYLIZer0d3dzdcXFwQFBTED1ssLy+HSCSCUqnE8PAwfH19ERUVhQMH\nDiAvL4+/xkZHR9HS0gJ/f39MTEzA29sbY2Nj0Gg0aGpqQlxcnF3zx/mGh4cxMjKC6Ohohyf5c3V3\nd/M3qXq9HgaDAdnZ2Rf8d3E56LeRkv2yoWS/NlxsGRgMBhgMBqhUKpvq1LlkNnczsJpKS0uRlZUF\nxhimpqb4seJHjhzhZ3ObmpqCxWKBu7v7vE9485XD9PQ0Zmdn4eHhAY7jcOLECT7Jnqu4uBgfffQR\nXnjhBZvyOX78OMrKynD//ffPG/eePXtw33332SwbGRnBZ599hjvvvJOvuheLxfwEOMXFxcjLy4PR\naMT09DRf1VxVVXXZT3qX+++huroa8fHxdlXZxcXFAGAzsx5w9lwHBweRkJCw6H7nytTd3R1CoRBn\nzpyByWRCeHi4zXpHjx6FwWDgRxfMMRqNOHbs2AXnSpizFn8X5rtZXElrsQxWmyOTPQ29Iw7n4uIy\n76teOY6z60W/muY6Tp0bw7lPZxc7ZS0Afkz4nOnpaZtmjHOPM3f8c8snKSkJ+/btu2DM51Kr1fzY\nXo7j7J5GhUIhOI6DVCq1qTlZC88BRqNx3qd/gUAw7w2WSqVCa2vrBfd7/jU3NTUFX19fu/Xkcjl0\nOp3dsSQSybK8mMiRHP3vi6wuSvZObnZ2FtXV1fwPE8dxyMjIWJYZxVaS1WrFwYMH0drayndcOn8o\n0/mmp6exf/9+aLVaAGeHgl1zzTUQiUQwGAyor6/H7Ows5HI54uPj+cTW09PD98YODQ1FYGAgZDIZ\n6urqoNVq+XZsDw+PC1bF9vb28tOdKhQKiMXiRZ8y/f390dnZaTOMjTEGs9kMo9FoMwsfAOzduxdZ\nWVmwWq1obW3F0NAQhEIhoqOj4enpyW93bm/0udqDhQgEAszOztok+rlJcxYzMzODhoYGGAwGSKVS\nJCQkzHvTdi6TyYSGhgbodDpIJBLExcUtOsVucHAwKisrMTs7y08h6+rqyvcrYIzZJOKOjg4EBQVh\nbGwMp06d4qvu5XI50tLSwHEc6uvr+e/VbDYjJiYGAQEBqKurg8Vi4XulSyQS6PV6yOVyu/KZ6/cB\nnK0S7+7u5qdFnhvF0d7ejp6eHgiFQigUCiiVygu+xKinp4fvb3H+dLnDw8NoaWnhRzRERESs+EQ4\nxHnQDHrLbK3NElVaWoqMjAxEREQgJCQE3t7eqKioQGho6IodcznK4I9//CPCw8Nxww03ID4+Hl99\n9RXGx8cXjJsxhr/85S/Izs7G9u3bkZaWBq1Wi9LSUoSGhqKiogKpqamIiIiAXC7H0aNH4e/vj7q6\nOlitVqSmpiIoKAh9fX3o7e2FRqNBUVER8vLy+IR04MABxMbGLjjr3cTEBFpaWrB582YEBwcjMTER\ng4ODGB4eXnDueXd3d5w8eZL//+npaZSXlyMhIQHe3t549dVXERMTA6VSiX//+9+oqqrCvffei9LS\nUvj4+CAhIQG+vr5obGyEyWRCTEwM/vrXvyIwMJCf0Ofw4cP43ve+t2CPc09PT5SWlkKlUsHFxQWD\ng4Oora21m83tXAaDAaWlpUhKSkJUVBTc3Nz4l/Sce6Nx7rVgMplw+PBhxMbGIiYmBmq1GlVVVXB3\nd1/wJkEoFOKLL75AbGwsEhISoFAoUFNTA09PT0RFRaGmpgZeXl4Qi8Xo6OhAX18fYmJiUFFRgfz8\nfISGhvL9DE6fPo3x8XEolUqkpKQgODgYISEhOH78OPz8/FBUVISYmBikpaXB19cXbW1tGBsbw+bN\nm1FWVsaXz8DAAE6cOIGMjAzU1tZCKBQiNTUVgYGB6Onp4adK1mq1yMrK4q+FpqYmWCyWBUcfaLVa\ndHR0IDc3l49Nq9VibGwMWq2Wn1ApJCQEOp0O9fX1i44oWWvW2m+jIzhyBj1K9stsLV3Q4+PjMBqN\nNhOCiMVi6HQ6iESiCz6FXarLLYNTp06B4zhs2bIFHMdBLBYjJSUFH3/8MTIzM+d9mjl9+jQsFgty\nc3PBcRw/jrm8vBwGgwGbN2/mz1cmk0Gj0aCmpoZP9HNjoL29vdHZ2YmBgQHceOONfEdBk8mErVu3\noqmpacGn5BMnTiAtLY2vSp/rgLbYELu5Tm9jY2NobGzExMQEUlNT4e7ujuDgYMTFxeHdd9/FZ599\nhoCAADzwwAMYGhqCWCxGWFgYPwQtICAAJ06cQHJyMuLj43Ho0CGUlpZCLpfj1ltvXfR1sGKxGIGB\ngejo6OCrwDMyMhbd5vjx40hPT+efyiUSCR9DUFAQv96510J9fT2f5OeOGxQUhGPHji1YPqdOncK2\nbdsgkUj4oYO5ubno6upCQkICvLy80NjYiM7OTqhUKiQnJ6OlpQVhYWE2NQYKhQJtbW2Ynp5GSkqK\nTflrNBocOnQIubm5UCqVqK+vx/DwMJKTkzE7O4vAwECEhISgvb0dra2tEAgESE9Ph9FoxNDQEJKT\nk/nrR6PRoK2tDYODg8jKyuKvVblcDqVSuWgtS21tLTIyMuymyz158iR0Oh02bdrEN1+oVCpMTk5C\nKBSuSnv7clhLv42OQtPlkhWh1+vnrYp1dXWFXq9fs+11jY2N2LJli91ylUoFnU4375NRT0/PvFWk\nPj4+GBsbs5tkxdXVFSMjI/POye7r64sTJ05AKpXazVS3WDW+2Wyed7TAhar+OY5DeHi4Xecw4Gw1\n9i9/+UubZQMDA3aTDgFn26HNZjPc3d0XfGXsQiQSCZKSkpa8vslksksyUql00Xbsqakpu2tOKBQu\nOsZ9ZmYGCoUCrq6uNr3958pUoVDYvQFwenp63hqgheZvkMlkmJqaglKphKenp02HJxcXF/4lROcP\n1RwaGpq3c5RGo0FjY6Pdec3dhC7EarXO2xveYrHM++KmuZqExd6vQMgcmlTHiWk0mnlHKPT09Ni8\nWGSt8fPzQ3t7u93yqampBZ9ioqKi7Ca0Ac62oatUKrskZDQa4erqyrfvn0ur1fLDrM6l1+sXHZrk\n5eVlV95Go3HZO7q5u7tjdHTUbvnMzMyiT+PL7fyJWSwWy6LJXiKR2EwsBJxtfplrf59PYGCg3bVg\nsVgW3SYkJGTeCYTmvovzE353dzcSEhLsjjPXW32hPgULfQ9jY2NQq9V2E/FMTU0t+tKdhabLlUgk\nmJ6etlt/dHT0st/eSK4clOydmEgkgq+vLyorK/mhRlVVVfD29l7R8bSXKzs7GyUlJTaJuLy83O49\n4OcKDQ2FTqdDaWkpZmdnMT09jY8//hgBAQFITExEdXU1n3TnZijLyMiATqezOY5Wq4Ver0dmZiZq\namr4iXX6+/tRVla26EQ8UVFRaG5uRkdHBywWCwYHB1FcXLzs052GhYXh9OnTNolzrhp7tTpsxcXF\nobKykk/ujDFUVVUt+u71+Ph4VFRU2HSuq62ttZtj/1zBwcHo6+tDS0sL3w5+6NChRb+HubHj9fX1\nMJvNmJiYwOHDhxEXF4fU1FQcPnwYo6OjsFgsaG1tRVdXF5KSkiAWi3HixAl+PobS0tJFO8F5eHhg\nfHwc4+Pj/LLR0VGYTCZkZmaisrISvb29/Ot+KyoqFo17brrczs5OWK1WDA4O4siRI0hPT4dMJkNv\nby+/rl6vR2dnp92UzoQshMbZL7O1OJZ0cnISTU1NAIDo6Oh5h3otp+Uog4mJCezduxdmsxkWiwUh\nISG47rrrFq0GnesA1tnZCYFAgPj4eL7dtK+vj+/Zb7FYEB8fDy8vL1gsFhw/fpxPnHK5HKmpqRAK\nhbBarejo6MDQ0BA8PT0RERFxwSp5xhi6u7vR19eH0NDQRW9QLofBYEBtbS3/NO3t7T1v1f5KGh4e\nRmNjI1+m0dHRdsPXzr8WxsbGUFdXx89THxYWZtPGPx/GGPr6+tDd3Q2lUono6OgLvpYWONvc0dnZ\nCRcXF8TExPAjUEwmE5qbmzE5OYnAwECbVx2Pjo6itbUVYrEYsbGxF2wPN5vNOH78OGZnZ8EYg0Kh\nQGpqKj8KoL29HcPDw4iMjISnp+ei1+/cuc71GZl7g+LcC3Pq6+sxNjYGjuMgEomwYcOGy5pkarWt\nxd/G1UaT6iwTSvZrA5XBWVQOVAZzqByoDADHJnuqxieEEEKcHCV7QgghxMlRsieEEEKcHI2zJ2QN\ns1qt6O7uxvT0NIKCgpb87neyemZnZ9HZ2clPl7uUzoOErDZ6sidkjdLpdDh48CA4joOfnx+am5tR\nU1Pj6LDIOTo6OlBRUQG1Wg13d3eUlZXZDJEjZK2gJ3tC1qiamhoUFBTwcyKo1WqcPHkSw8PD8Pb2\ndnB0xGw248yZMzav2Z2betfPz++Cw+wIWU10NRKyRgkEArvJj+Li4uxmhyOO0dfXZzfPPcdx8Pf3\nv+KHmJG1h5I9IWvUfFNgzE2fShxPLBbDaDTaLTcajWt6hkpyZaJkT8gaJZVKbebnZ4zh+PHji05J\nS1aPr68vuru7bebaNxqNGBkZgYeHhwMjI8QetdkTskalp6ejsrISwNm3r42PjyMyMnLdvNLU2XEc\nh8zMTJSUlMDV1RWMMej1emRlZTk6NELsrEqyHxkZwZ49ezA+Pg6O41BYWIjrrrsO//rXv3DgwAF+\nONH3vvc9pKWlAQD27duHgwcPQiAQYOfOncv+MhFC1jqhUIjs7GwYjUYYjUYoFIpVe9ENWRo3Nzds\n2bKFf7eCi4uLgyMiZH6rkuyFQiHuvPNOhIeHw2Aw4LHHHuPf/nT99dfjxhtvtFm/p6cHZWVl2L17\nN8bGxvDUU0/h5Zdfpt6t5IokkUionX6NoyRP1rpVyZ4eHh4IDw8HcPYfRUBAwLzvEZ9TVVWFnJwc\niMViaDQa+Pr6orW1dTVCJYQQQpzOqrfZDw0NoaOjA5GRkTh9+jQ+//xzFBcXIzw8HN///vfh6uoK\nrVaLqKgofhu1Wj3vzUFRURGKiooAAM899xy8vLxW7TwWIhKJ1kQcjkRlcBaVA5XBHCoHKgPAsWWw\nqsl+ZmYGL774Inbs2AG5XI5rrrkG//Vf/wUAeO+99/DXv/4VP/nJT5a8v8LCQhQWFvJ/r4WxrfQa\nRyqDOVQOVAZzqByoDIAr5BW3ZrMZL774IvLy8vjeqiqVCgKBAAKBAFdddRU/WYharcbo6Ci/rVar\nhVqtXq1QCSGEEKeyKsmeMYbXXnsNAQEB+OY3v8kvP3cMcWVlJYKCggAAGRkZKCsrg8lkwtDQEPr7\n+xEZGbkaoRJCCCFOZ1Wq8ZuamlBcXIzg4GD84he/AHB2mF1paSn/tihvb2/cfffdAICgoCBkZ2fj\noYcegkAgwF133UU98QkhhJBLxLH55uRcp/r6+hwdArVLgcpgDpUDlcEcKgcqA+AKabMnhBBCiGNQ\nsieEEEKcHCV7QgghxMlRsieEEEKcHCV7QgghxMlRsieEEEKcHCV7QgghxMlRsieEEEKcHCV7Qggh\nxMlRsieEEEKcHCV7QgghxMlRsieEEEKcHCV7QghZAsYYjEYjnOjdYeQKsiqvuCWEkPWsrq4OWq0W\nLi4u0Ov1CAwMREREhKPDImTJKNkTQsgiWlpa4OLigvz8fH5ZTU0NhoaGoNFoHBgZIUtH1fiEELKI\n/v5+u6f4lJQUtLS0OCgiQi4eJXtCCFmEQGD/MykUCqntnqwrlOwJIWQRYrEYer3eZll/fz/UarWD\nIiLk4lGyJ4SQRaSmpqKsrAxdXV0wGAxobm5Gc3MzYmNjHR0aIUtGyZ4QQhYhlUpx1VVXwWq1orGx\nEQqFAvn5+fNW7xOyVlFvfEIIuQCO4xAWFoawsDBHh0LIJaFbU0IIIcTJUbInhBBCnBwle0IIIcTJ\nUbInhBBCnBwle0IIIcTJUbInhBBCnBwNvSOELCvGGFpbWzEwMACVSgWO4xAfH0/j0glxIPrXRwhZ\nVsePH4dIJEJeXh62bt0Kb29vlJWVOTosQq5olOwJIcvGaDRiZmbGZvIZHx8fKJVKjI2NOTAyQq5s\nlOwJIctmcnISnp6edst9fHwwOjrqgIgIIQAle0LIMnJzc5s3qQ8ODsLLy8sBERFCAEr2hJBlJJFI\nIJPJ0N7ezr/vfWBgADqdDiqVysHREXLlot74hJBllZaWhtbWVpSUlPC98XNychwdFiFXNEr2hJBl\nFxkZicjISHh5eWFkZMTR4RByxaNqfEIIIcTJUbInhBBCnBwle0IIIcTJUbInhBBCnBx10LuC6fV6\nnDp1CmazGQKBAPHx8XB3d7/k/fX29qK9vR0qlQqMMSQkJEAoFC5jxItjjKGxsRFarRaMMQQFBSE0\nNBQAMDExgYaGBlitVohEIiQmJkKhUNjM4w4Avr6+iIyMBMdxGBoawv79+2E0GiEUCnHttdciICBg\n1c4HAOrq6lBSUgKO4+Dm5oabbroJcrl80W2OHTuGvXv3AgBcXV1x9913Q6PRXNLxGWNoaWnB4OAg\nAMDPzw8RERHgOO6S9rfcBgYG0NraCo7jIJfLkZiYCLFYfEn7slqtaGhowPj4OBhjCAkJQXBw8AW3\n+fLLL9He3g4AiI2NxbZt2y7p+Bdy5swZdHZ2guM4qFQqet8AuSgcmxsMu4JGRkawZ88ejI+Pg+M4\nFBYW4rrrroNOp8NLL72E4eFheHt748EHH4SrqysAYN++fTh48CAEAgF27tyJ1NTUCx6nr69vpU/l\ngtZL72O9Xo+jR48iJycHLi4uMBqNKC8vR1JSEjw8PC56f83NzZiZmUFiYiI0Gg2amppw8uRJbNmy\nZdUSQ0lJCcLDw+Hv788nqZmZGQQFBeHUqVPIysqCVCqFwWDA0aNHsWnTJtTX10Oj0fA/6l1dXRge\nHkZISAjeffdd7NixAx4eHpicnMTbb7+NG2+8ESEhIUuK53KvhZKSEnR1deGWW26BRCJBV1cX9u7d\ni5///OcQiea/Tz906BCKiorw2GOPwdXVFW1tbXjxxRfx9NNPQ61WX3QMlZWV8PX1RXBwMBhj6Ozs\nxOjoKDIyMpa0/Ur+e+jo6MDY2BhSU1MhEAgwNjaGmpoabN269ZKSYHFxMWJiYuDj4wPGGE6fPg2L\nxYLExMQFt3nzzTcRHx+PrKwsWK1WHDlyBH19ffje975ns97llkNDQwMYY4iPj+dvRE+fPo38/PxL\n3udqWy+/jStpJcrA399/Seutym2hUCjEnXfeiZdeegnPPPMMvvjiC/T09ODDDz9EUlISXnnlFSQl\nJeHDDz8EAPT09KCsrAy7d+/Gr371K7z++uuwWq2rEeoVo66ujk/0wNnJUHJyclBfX3/R+2KMYWBg\nAMnJyfyPrKenJ4KCgtDb27uscS9Eq9XCzc2Nv/A5jkN0dDQmJydx8uRJ5OTkQCqVAgBcXFyQnZ2N\n6upqAEBISAg4jgPHcQgNDQVjDB9++CGf6IGzM8Pddddd+Oyzz/jjVVRUoKyszOYm02KxoKmpCaWl\npaiqqoLBYLhg7IwxtLW1obS0FNXV1dDr9QDOvlDmjjvugEQi4ePcunUrDh8+vOC+Pv74Yzz55JP8\nTXNERATuuusu/O1vf7uo8gSA6elpcBzH3whxHIewsDBYLJYlnddK6+7uRlpaGn/NeXh4IDo6Gh0d\nHRe9r5GREXh6esLHxwfA2XONi4vD2NgYLBbLvNsMDAxALBYjKysLACAQCFBQUAC9Xo+JiYlLPCt7\nVqsVIyMjSEhI4G+cNRoNvLy8+BoXQi5kVZK9h4cHwsPDAZz9oQ0ICIBWq0VVVRUKCgoAAAUFBaiq\nqgIAVFVVIScnB2KxGBqNBr6+vmhtbV2NUK8YJpOJT/RzRCLRJT2FG41Gu30BZ+84h4eHLznGizEy\nMgI/Pz+75R4eHpiZmbF7EnZxcYFWq523eluj0WB8fNyuhkMul4PjOLS2tqKtrQ0bNmxAVlYW/0Rp\nsVhw+PBhKJVK5ObmIioqCkePHl30h58xhuLiYojFYuTk5CAuLg5VVVUYHh7mk/y5UlJS+Crj+YjF\nYrumk/T0dPT39y+4zUJGR0fh6+trt9zb29vhL7VhjM1bu3Gp19zQ0NC814+7uzt/83W+1tZWxMXF\n2S0PCwtDd3f3RcewEL1eP2/z2mr++yLr36q32Q8NDaGjowORkZGYmJjgf1BVKhX/o6jVahEVFcVv\no1arodVq7fZVVFSEoqIiAMBzzz23JubeFolEayKOC/H09IRCobBJ0haLBUql8qLjn6vynNturgxa\nW2RenikAACAASURBVFv5iVUuxdTUFDo6OiCVShEZGblo+z9jDH19ffMey9vbGx4eHjbbz1Xvm0wm\nKJVK/mYyMjISJpMJvr6+kEgkcHNz47cxGo2QyWTQ6XS49tpr+eU+Pj44evQoOjo6kJ+fzydIkUiE\nb3/72yguLl6wHbetrQ2pqamIiIjglwUHB6OoqAgSiQRqtRrt7e2Ynp5GSEgIurq6kJiYuGCZCoVC\nqFQqnDlzBhMTEwgMDERPTw+Cg4Mv+nuQSCRobGy02665uRmRkZEX7DsArOy/B4VCYbfvM2fOICIi\n4qKPGRUVBa1Wa7cdx3EICgqa98YiPT0dBw8etNtGq9WisLDQptnk/HIwGo1obW2F1WpFRETEvDfL\nczw8PNDe3m53nOHhYURFRa2L3xtg/fw2riRHlsGqJvuZmRm8+OKL2LFjh90PxVw16sUoLCxEYWEh\n//daaA9aL+1SgYGB2L9/P3JzcyEWi2GxWFBeXo6YmJhLil8sFqO0tBTR0dHw9vZGe3s7jh07hm3b\ntl3S/urq6qDX6xEZGQmDwYD3338fGzZsWLDdmeM4tLW1QSaT8W9d6+zsxOzsLEJCQvDJJ59g06ZN\nEAqFMJlMKC0tRXp6Og4cOIC6ujrk5uYCAP72t7+B4zhcffXVeOGFF/DDH/4QcrkcMzMzeOutt7Bx\n40ZIJBK7c3Jzc8Phw4cRGRnJf+bl5YXx8XFMTU0tWAb19fXIzv7/7L15cJv3fef/wkGAIAkSvMH7\nvm+JlMRDpyM71qiOnCiHnTSxm6y9jetp0263zXQ3P3fSTGe7m2zqqVNvEjuXncaxXF+yHNeSTfES\nxfsmwfsQbxIgDgIgCOD5/aHBt6J56LBsyQlfM5rRPCSAhw+A5/P9fo73u3zTz+12O1FRUfzFX/wF\nX/va1wgJCeGdd97h3Llz/OM//uO2z1daWsrnP/95/vqv/5qoqCiqq6v50Y9+xDPPPHNL78P8/Dw9\nPT1iATMzM4PJZMJut2+7472Wj/L7EBgYSHV1tUhvr66ucunSJY4dO8bo6CgdHR3I5XK8Xi/BwcEU\nFRVte4/x8/Ojv78fPz8/8RkbHR3F6/WysrKy5WM0Gg1XrlyhpqaG3Nxc4GpzpM1mE6l3H9deh+np\naYaHh8nOzsbPz4933nlHND5uh0wmo7GxUTRHmkwmenp6OHLkyCfifgOfnHvjR8mdrNl/bMHe7Xbz\n/e9/n4MHD4oaV0hICCaTidDQUEwmk9hFhYWFbXDOMhqNt9RctMv2BAUFUVJSQlNTEzKZDK/XS05O\nzpb2pDdCbm4uY2Nj1NbWEhoaytraGocOHbqlsoDFYsHhcLBv3z5xLCYmhpqaGo4cObLt4yorK+nu\n7qa/vx9JkoiKiqKkpASZTEZ2djaXLl0S51NSUkJAQAA6nY6IiAgGBgaAqzv7xcVFoqKiePDBB0Wt\nW5Ikjh8/TlxcHF1dXZtee2VlheDgYBwOB35+fqysrBAYGAiwbc0XrgYss9m8ySTGl2X51Kc+xVtv\nvYVCoUCtVvO5z30Os9lMZGTkls8XFRXF5z//eX7yk5+gUChQKpU8+eSTNxSYt6K8vJyenh6GhoaA\nq9/Na9+XO0lGRgaTk5PU1taiUChQqVQcPHgQr9dLU1MTR44cETvy2dlZ2tvb2bNnz7bPd/DgQTo7\nO0UznF6vp6ioaMdz+PrXv84bb7xBTU0NXq+XxMREvvrVr277+5IkMTg4uKFxtaqqipqaGhITE7ed\nJCgoKBB+A77Jg4MHD941UxG73P18LMFekiSeffZZ4uLiOHnypDheWlrKxYsXOXXqFBcvXqSsrEwc\nf/rppzl58iQmk4nZ2VnS09M/jlP9gyIkJETsaG8HKSkppKSkfOjV6/j4OFlZWRuOyeVyMTWwVS0b\nru5+rk23+vn5iZtheHg4VVVVG35/enqaxMRE0U/iY3h4mPn5eRISEnj88cc3vY7b7d6wAHU4HExP\nT1NRUcGZM2dITk4mKiqK5eVl2tvbd+xcz8rKoqGhgcOHD4syw9jYGOHh4ZhMJioqKjaYyKyvr9Pa\n2rptsJckiXvvvZd77713w7GGhoYbniK4FplMRkFBwU0/7nbicDjo6urC7Xbj8XhIS0sTI5CJiYmb\nxuMGBgYoKCjY8FmIiYlhaGgISZK2DZAKhWLHxcBW+Eo1N4rRaESv1286h9TUVKanp8Wo6Fb4/AZ2\n2eVW+FiCvcFgECvXv/7rvwbgoYce4tSpU/zf//t/ee+998ToHUBCQgLl5eX85V/+JXK5nK9//eu7\n86R/QPj5+bG2trbpuMfj2bFu7ysj5Ofniw73zs7ObXdnarV6y+a5tbW1HccP9+/fT1tbG729vchk\nMuRyORUVFZjNZjGitrS0RGBgIGlpaVit1m2fy9/fn5KSEurr61EoFHg8HiIiIsjLy6O2tnbLc9tu\nsQNsObWyvr7+seod3E58JZfKyko0Gg2SJNHZ2cn6+vq2gdFms235M5VKhdfrvaPXYrvPttPpvKEe\niF12uVU+lmCfnZ3Nb3/72y1/9p3vfGfL45/97GdvasW8y+8PaWlp1NfXb0h12mw2JEna9kZtNBrR\n6XQbRqfS09Opr69nfX19y/RoeHg4XV1dZGRkiJ+vr68zPz9PXl7etuenUChEFupa2traqKqqEufo\ny3BsFbSvJTQ0dMt5aZVKRUNDA263G4VCgdvtxuFw7DhbHRISwtzc3IYu+s7OTjIzM3c8h1thenqa\nkZERsUhJSkq6bvZgfn4eg8EgHhMbG0t6ejqSJNHe3i7G/SRJYs+ePYyNjVFcXCwa2GQyGcXFxdTU\n1Gwb7BMSEhgdHRV1dLia3VhbW7vji57g4GBWVlZYW1sTo6But5uJiYmPTIxnl11gV0Fvl7sQlUpF\nXl4e1dXVBAcHs76+jtvtFr0eW7G0tLTlmJhOp8Nms225U5fJZOzbt4/6+npRX7fb7Tu+zk5IkrRl\nBupW66pqtZrJyUkiIiLw9/dnZWUFs9m8raAOQGFhIS0tLYyMjBAQEIDFYiEhIYGwsDAcDgetra3i\nXDUazYY59ZthcXGR6enpDXXjtrY2/Pz8tm0YslgsDA4ObnhMf38/o6OjLC8vk5SUJEYhXS4XNTU1\nqNXqLUVtdgra0dHRjIyMMDQ0RFpamvi7P4oFz62wf/9+Ll++jL+/v2gsLC0t3a2/7/KRshvsd7kr\niYqKIioqCofDIZqvdiIiIoKZmZlNteyVlZUdb/JBQUGkpKQwMjICXK2L+gL/zZKYmMjQ0NCG17Na\nrdc99+2wWCw88MADrK+vC10Ek8nE4ODgtqpuMpmMsrIy3G43a2trQhvAV7evrKzE398f+E9hoPLy\n8ps+N4PBQHl5+YYAVVxcTH19/bbBvr+/n6KiIjo6OrDb7SiVSnJzc2lpaUGlUm3QPFCpVGRmZjI4\nOMj8/LzI2PjYqekRrjYWTk9P09jYiFqtZu/evbf8vl6PsbExIR6VnJxMfHz8jr+v0Wg4dOgQa2tr\neL3eHcfu/lC5cuUK4+PjAMTFxZGSknJnT+j3gN1C+C53NRqN5oaCpU+LwScyIkkSo6OjBAYG7qiV\n3tbWxtraGkePHuXo0aM4nU7a29tv6VwTEhKwWCw0NzczOztLV1cXra2tlJSU3NLz+Xavfn5+Imjr\ndLodewB8KJVKAgMDRTCemZkhKSlJBHq4es1kMtmWNeTrIZPJNu2u5XL5jlkCp9NJU1MTKSkpVFZW\nUlhYSHt7OxaLZcuAFxISQnh4ON3d3VgsFuBqkG9ubt7UVLnV+cXHx1NRUfGRBvqWlhY8Hg+VlZVU\nVFRgtVq3nNbYCrVavRvot6Crqwur1UpFRQWVlZXiPd/lw7Eb7He5bUiSxNTUFP39/XdETrWqqorZ\n2Vnq6uqoq6vD6/Xu6KngcDhYX18nMzNT6DxkZmaytraG0+kEru5+BwcHb3i6oLS0lJycHFZXV9Hr\n9Rw5ckQsNrxeL5OTk0Kz/1pWV1cZHBxkenoan12F2+0W//cxOjp63Z3jVlit1k3jfXA1s/HBc7kR\n1Gr1pkWHw+EQJQaPx8PY2BgGgwGXywVcVeRLTk4W56HRaNi/fz/T09NbLmDGx8dJSkri0KFDDA0N\nUV9fz6VLl25o9/xxsLq6iiRJwjhJLpeTk5ODzWYTf/MuN4fL5cJms5GTk4NcLhe9N3D1eu9y6yie\neuqpp+70SdwubmTH81ETEBBwyzPNn2QsFgt1dXXodDqioqJoa2vDZrNtOyJ2I1itVoaGhrBYLAQH\nB1+3tiyTyYiOjiYxMZGkpKTrajPMz88TGBi4KQi63W6cTifd3d2sra0RGRnJ/Pw8AwMDxMfHX/c8\nfMp3kZGR4rNgNpupr69Hp9MRGBhIX18fq6urRERE0NHRwfz8PHq9ntXVVTo6OoiJiSE4OJi2tjYi\nIiLw8/NjfHyc6elpCgoKbrq+q1arGR0d3ZRiHxgYICMj46afLzw8nIaGBoKCgggMDGR5eZmmpibK\nysowmUw0NzcTGRmJTqfj8uXLSJIkRhb9/f0JCgrCbDZz+fJlwsLCSE9Pp7e3l4iICBQKBYODg9hs\nNjIyMlAoFMTGxooxu49ql36zzMzMoNPpNqgswtWJCY/HI/wJ4A/3vnAtN3INFhcXNwgbXct2C9ZP\nEh/F50Cr1d7Q7+3W7He5LbS3twsRk4iICMrLy7l8+TI2m23DTe9G6e7uxul0CgW9ixcv7qigdyuE\nhIQwNDS0qYPcJ+iUnJws5rkjIyMxGo10dXXdUlq+o6ODo0ePitS3Xq+nsbGRiYkJ5HK56O6Piooi\nISGBpqYmDh48SFBQEP39/TidTuLi4m5ZSEWr1eJ2u/n3f/93IiIicLvdGxzjbha1Ws2RI0cwGAwM\nDQ0REhLC4cOHUSqVXLp0iaNHjyKTyURzYU1NDSqViuLiYmZnZ8VCobKykqamJhITE9HpdPT09LC+\nvk5SUtJd01C3HTqdjomJiU22xyaT6YZVzXbZSEhICFNTU5uOLy0t3ZJOxC7/yW6w3+VDs76+jlqt\n3tQlnpOTw8jIyHVVyD6IxWLB6XSKABgaGoper7+ugt7NotVqcTgcLC8vC+XA5eVlnE4nkiRtuomH\nhYXR19d306/jMwr6YI07Ozubc+fO8YUvfGHDcbVaLQK63W4XaXaz2YzH49mxG38nnE4n2dnZrKys\noFarCQ8PF7XwoaEhZmdnxYhfamoqCQkJOz6fUqncNKLoMxf64IIkLS0Ns9lMa2srVVVVZGZminE7\nn0xscHDwliONdys6nY6urq4NAkuLi4u43e7dmflbRKPR4Ha7WVhYEA2bRqNxS5XJXW6O3WC/y4fG\npz/+QdbX128pMI2NjW2poBcQELCjgt6tUF5eTkdHhwjiQUFBlJeXU19fv6Xa2gdr6DeCTCbbsnvc\nN/+/1XWSJInJyUnm5+fZv38/SqUSo9HIxYsXb8mvfXFxkcjIyA2z53BViGh4eJj19fUN8/vNzc1o\nNJqbNu1QKpW43e5Nx10uF1qtltjYWC5duoRcLt+khvdJpLKyUnx+JElCq9Vy4MCBO31an2j2799P\nV1cXBoMBuJr6vp1Kn3+o7Ab7XW4Kj8fD0NAQy8vL+Pv7k5ubi0ajwev1Mjc3x+TkJAEBAQQGBjIx\nMXFLY11qtXrLDvGPQglOLpcTFxcnRu9iY2ORy+UkJiYyODi4YdExMTFx3R4Ej8fD4OAgRqORmJgY\nYmJixG7F4XCI7mtJkujt7eXo0aO0t7cTGRnJwsICKpWK2NhY1Go1Y2NjwgIarmYWfH7tqampjI+P\nMzMzg5+fH1lZWVvaoPpYXl7eNL4G/1nKOHLkCB0dHayurhISEkJ+fj7t7e03HeyDg4OFSUtjYyP+\n/v7k5OSwsrIiFikHDx68qee8m/H1E4yNjSGTyYiLi9udl/+QyOXyHRtrd7k1drvxd7lhPB4P1dXV\n6HQ6KisrycrKorGxEZPJRExMDK+99hrr6+uEhITQ2NiI2WwWKmE3Q1paGj09PRt20Vardctxrw9L\nT08P09PThIaGEhoayvT0ND09PSQlJeFyuXjrrbd44403OHv2LEtLS5syDtfidruprq4mLCyMyspK\nsrOzaWxsZGVlhX379tHc3ExzczM9PT1UV1eTmZlJeHg4ExMTdHZ2CiOdN954Y1tb1bi4OObn52lo\naACgoqKCwsJC+vr6tqx1+oiMjNzS095kMiGXy2loaBAjcXq9XigP3goWi4UzZ86Qm5tLeXk558+f\nZ2xs7PdS8rqzs5Pl5WX2799PWVkZ09PTt1Tq2WWXj5rdbvzbzO9z163BYCApKUko1alUKhITE2lu\nbsZisXD69GkiIyOJiooiNzdXpK5vtntaoVAQFBREU1MTy8vLTExMMDc3x759+25rsF9bW6OpqQmF\nQkFCQgL+/v7Mzs6KmfSRkRHCw8MJDQ0lODhYqNFtF7T6+/tJTU0VtcbQ0FAiIiJobW0lNTWVwMBA\nrly5gslkEtKy4+PjJCYmcuDAAdRqNcnJyRw4cIC2tjYkSdrUlDQ/P4/RaCQ0NFSMDCqVSuLj4+no\n6NhWQjYgIACDwYBKpUKr1SJJEgaDAbVaTV9fH8ePHxeZgYCAAORyOUNDQzvKBm+Fy+Wiurqa//7f\n/zthYWHEx8cL57yYmJhb7qT3er3Cx+BWexZuNz4DJJ8KoVwuR6/XYzAYiImJ2fBZ/X2+L9wou9dg\ntxt/l7sUq9WK2+1Gp9Mhk8kwGo3k5ORs+B3fDc3XkKTRaAgNDWVpaYmUlBQMBsMGZbQbJTIykqNH\nj4rZ7Z2EcW6Eubk5BgcHhSZ7fn4+VqtVWPH60Ov1/OY3vxF1Zbvdjk6nw2w2I5PJ6Ojo2NbFbmVl\nZVNwVCgUyOVyRkZGMJlMHDx4EIVCwfT0NHV1dSgUCqFEd62kr1KpJCoqit7eXnJzc5HJZDgcDnp6\netDpdJsUxWQyGRqNZlsfALhaX+7v72dkZARJksSI4sLCAs3NzRQUFBAVFSU077dK+1+PiYkJIXgT\nGBhISEgIS0tLlJSU0NPTw7FjxzCbzczPz4sxzesxOjrKlStXCAsLY3V1FY/Hw/79+++4zv38/PyW\n/QZ6vZ7l5eUt5Zt32eVOsRvsd9mE3W6nqamJkJAQ/Pz8aG9vJz8/H5VKhd1u39RpLEnSlsIsJpPp\nhled23E7FMYWFhaYmJgQY2ter5e6ujrRW/BBAgMD6e/v55577iE7O1scn5iYoKGhYdtgr1KpNtTl\nfXg8HqanpzcsKuLj47FYLCwsLLCysrJJu9/j8ZCRkcH4+Ljwa1coFFRWVjI9PY3RaNwUaNbW1nbc\n9crl8i136kqlkvLyckZGRkSQr6ysvCUlwYiICOrr6zcdn5ycJDU1lcuXL+Pn50dCQgJLS0v09vZS\nVVW17QLFbDazuLi44doZjUba2trueOd+cHAwMzMzmwR+VlZWiImJuUNntcsuW3NTwb6rq4v6+nrM\nZjN/+7d/y8jICA6HY1ud7l0+mTQ1NVFRUSG63iVJ4v3336e0tJTm5mYOHjwoUtn9/f0kJCRgNpuZ\nnp4WAcjlctHf38+xY8dwOp2cO3eOxcVF5HI5x44dE+NWHwcGg4Hi4mJaWlpwuVyo1WqKiopoaGhg\nZWWF//iP/2B0dBS4qo1vNptxOBxotVq+973vYbVa8ff355FHHsFkMm37Ojk5OdTU1LC8vCwEQKKi\nooRYzvz8PMPDw0iSRHh4OPHx8ayurtLY2IjNZsNoNKJQKEhPTxc7Xo/Hg8FgwOv1Eh0dzZ49e0hJ\nSeHChQssLS2JXgatVktQUBAymYypqSn+4z/+A7fbTWhoKCdOnNhR6yAtLY2amhqmpqZYW1tjbGyM\niIgI9u3bB0BtbS29vb3AVf17X7e5yWTi7Nmz2O12NBoNJ06cICIigvX1dd544w1mZmYIDAwUzZq+\nSQCn08ng4CBKpZKsrCza29vZt28fPT09YgoiKSmJ++67D4PBQFZWFq+88gpLS0soFAo+9alPicXl\n8vIyBoMBSZIICQkhJyfnlnf8Xq+X8+fPi89CeXn5jmOjYWFhdHd3b9CSWFlZwel0Xrdcca32e0xM\nDKmpqbuNfXeApaUlBgcH8Xq9hIaGCuW+30duuGb/9ttv89JLL1FYWEhtbS0PPvggq6ur/OIXv7hr\nrBl3a/YfntXVVaxW64YZa5lMhp+fHzabjcTERFpaWsTNSqfTkZ6ejl6vZ2JiAoPBwNzcHKOjo+zb\ntw+5XM4zzzxDZWUl9913Hzk5OZw9exaPx/OxjVwNDg4yNzfHnj17SE1NJTg4mObmZhQKBa2trTid\nTgoKCoiMjKSnp4fJyUn0ej0/+clPePLJJ/nCF75ARkYGTz31FBERESLYra6u4nQ6N1iVvvzyy+j1\netLS0ggODqa6upqCggL6+/vx8/OjtLSU5ORk1tfXee+990hNTeXChQuEhISQkZFBQEAAjY2NpKSk\ncOXKFZqbm3nooYfEVMOvf/1r9u/fL5zifKn7yclJUlJSWFhY4J133uGhhx6isrKSwMBAfvnLX7Jn\nz55td/1ms5k33niD9PR0EhMTcblctLa2Ul5ezpkzZwgICOD06dPs3buX3t5e2traiI6O5rnnniM7\nO5vY2FgCAwN56623SE1NZXFxkXfffRer1YrVaqWnp4e4uDj8/PxYWVkhPz+fjIwMIiIi6O7uxmw2\nMzExweTkJA899BD79+/HarXy6quvEhERwZtvvsnRo0c5fvw4mZmZvP7665jNZsLCwpiammLv3r2i\nV6G5uZnk5ORbCpw//vGPSU5O5oEHHqCkpIRLly4xMTFBRkbGto+Ji4ujq6uL8fFxJiYmsFgs4nN/\nLdfeF7q7u3G73ZSUlJCYmMjKygrDw8Of6BHEG+FuuzeOjY0xMzMjvpOSJNHa2kpSUtJHtvC6kzV7\nmXSDg8NPPvkk//N//k+ioqJ49NFH+dnPfobX6+Ub3/gGzz///Ic62dvFzMzMnT4F4WH+ScVqtTI6\nOrppRzM7O8vq6qrQqd6Ja69BdXW10ED34fV6eeaZZ3jyySdv78lvw//7f/+Pr3zlKxt2WwsLC7z6\n6qt4vV5ycnLEzsxms9Hf309nZyf33HMPer2ekJAQLBYLZrOZV199laeffpqmpia0Wq2Yfy8sLKS6\nupqSkhJRs46IiGBubo5nn32WwMBA7rnnHhITE4GrzV0vvfQSgYGBpKamsnfv3k3XB9h0jRobG5mZ\nmaGgoGBTELp48SJdXV088cQTG4KNwWBgYGCAz3zmM1ten2effZZHHnkEpVLJ6uoqQUFBLC4u8vrr\nr6NWq3nkkUc2/P5Pf/pTHA4HCQkJHDt2jODgYKxWK9XV1QwPD2M2m/n85z9PTk4OkZGRNDc382//\n9m9otVoefvjhDeN8Xq+XH/3oRwD82Z/92YbXOXv2LENDQyQkJHD69Glx3G63853vfIc/+qM/2jCa\nCIjd8naNitsxMjJCZ2cnn/3sZzccf+aZZ3j88cc/dFOg7zuxvr5OU1PTprnxlpYWsrOzb0lt8pPC\n3XZvvHjx4qbPz/DwMP7+/h+Z98JHcQ1uVK3xhvMVDodj08yt2+2+azpjd7k9aLVajEbjJpGcoaEh\nEahuhqGhoU21VblcfluFca5HamoqTU1NYkV9rTNZUVERR44cIS8vj7y8PI4cOUJBQQEul4uYmBiC\ngoIIDg4mODiYoKAgwsLCRJlj7969FBUVcfjwYTo6OlhcXNzkxub7fhQUFGCz2aipqaGuro6Ojg5O\nnjxJT0/PJvld3/XZ6hqVlpbS0dGx5c3Ip9L3wV1lVlYWc3NzO16jvr4+Ll++zOTkJA0NDRiNRiYm\nJjY1ZAJkZGTQ3d3NvffeK3ThtVotx48fx2AwEBwcTF5enjAySU1NJT09HZPJJKxgfSwuLiKTyYSC\n4bWUlJQwMjJCcnIyzc3NLC4uMjw8LMxwtrr3JCQkMDc3hyRJDA0NUVdXJ8ZDd6K/v3/L2W69Xi+c\nFG8HJpNpS62G2NhYFhYWbtvr7LIzHo9ny7HghISELUdUfx+44WCfk5PDa6+9tuHY22+/fdOjObvc\n/RQVFfH+++8L45WamhoSEhJuKUDHxMQwODi46fitznDfCv7+/pSVldHb20t9fT1DQ0Ps27ePuLg4\nJiYmgKuB0tdYNzExgU6nIzc3l6SkJNbW1oiJiaG8vByXy0VwcPCGayGXy0lPT2dtbW3TDdvr9SJJ\nEg6Hg9zcXA4dOkRVVRXl5eVYrVYSExOFoM+1+DzsP8jg4CDJyclb7g6cTueWSn3T09ObzFquZXZ2\nVmgnFBQUcPDgQSwWC35+foyNjW36/ampqS1r0r7gvpUgksPhICoqCq/XS21tLX19fTQ2NjI+Pk52\ndvaWwdi3wPTVUo1GIxqNRpQNt/pbFxcX0el01NbWotFoqKysZM+ePRgMBiYnJ7e9BgkJCQwPD286\nvrS0tKl58sOg1WpZWVnZdHx5eXlXDvZjRC6Xb+lMeLvf77uJG67Z5+Tk8Nvf/pZXX30Vq9VKbW0t\nc3Nz/Nmf/dkGj+w7ye97zd5ut3Pp0iWmpqaYmJhgZmYGvV5/20eQNBoNycnJwqozLy9vy53Xdlx7\nDRITE3nhhRfQarUMDw8zPz9PU1MTMTExH1uTnkajwWAwUFZWRlJSElFRUTQ3N7Nv3z5aW1uFbzpc\nTXkPDg7ywAMP8PTTT5OcnCwa9v71X/9VzKN/cCzNbrcTHh7Om2++iVarZWhoCKvVyltvvUVhYSFB\nQUHY7XZxI1ldXaWrq4v777+fF154gfz8fNRqNV6vl7feegu9Xi+MYaxWK+Pj45jNZn73u9/x6KOP\n0tHRgV6vF13sIyMj+Pn5ER4eTmNjIzk5OchkMux2O7/85S85ffr0tpMNV65cYWRkBKVSyfDwMCsr\nK9TV1ZGdnS1ufj7t94GBAUZGRsjKyqKmpoa9e/cil8txu908//zzpKSkiMfo9XoCAgLo7+/nUgpV\nWwAAIABJREFU/PnzPPzww8zMzAhNgYSEBGF7u7y8jNlsFr0ivt6Dxx57jBdffJE9e/YQFxdHUFAQ\nZ8+eJS4uDr1ej9FoFO+d0+mkpaUFvV6PWq3Gz8+P3t5elpaWyMvLo6+vb9v0flRUFG+++SbJycmi\nBtrc3IzVamXPnj07fr4WFxfp6upiZmaGgICALa+z7zvh5+fH1NQUfn5+ImVvMpmYmJjYMPmxFRaL\nha6uLiYmJlAoFB960uXj5m6q2ctkMmw2GxaLRXy2HQ4HbW1t7Nmz5w+7Zg9Xu7KHh4dZWloiPDyc\n9PT0u6pz8fe5Zi9JEu+99x5VVVUi/WS1Wuno6Ljr5EevvQaSJHH27Fl6e3vR6XRibv5LX/rSh7K/\nvVnm5+cxGAwoFAq8Xi95eXmEhYXhdrs5c+YMRqMRuNphffr0abxeL88//zw9PT0EBQVhs9lIT0/n\nscceo7GxUbi6+aipqaGsrIw33niD8fFxQkJC8Hq9KBQKHnroIYKDgzEYDCwsLIiGx+LiYtRqNYuL\ni/ziF78Q3fhHjx7l2LFjLC4u8pvf/Aa3241GoxFz/CdPnmRtbY33339fBI7S0lIKCgqAq0GqqakJ\npVKJJEl85jOf2XEU7MKFC4yNjWEymQgODsZsNhMbG0tCQgKVlZW8/PLLYucdERHBZz/7WSwWC+fP\nn2dxcRGlUsn6+rrQRhgZGRFOfVqtFqfTSWpqKvfccw9ms5nu7m7hF+DTxvd6vbz99tvCBdDf35/T\np08TFBTEwsICr7/+Ol6vF7fbTVFREVVVVcDVRc709LQoXxQXF9Pb24vNZiM+Pp7U1FScTietra1Y\nLBY+/elPb7s4ttvtnDlzBrvdjtfrJT4+npMnT+54j+vp6cHj8ZCbm4skSXR3dxMUFLRJafGD3wlf\nY6JPH6G4uFicl91uZ3FxkZCQELHb9y3ui4uLUalUwgL4To8f3gx3W80e/tMASi6Xo1QqKSoqui3j\nvttxJ2v2NxXs73Z+n4P9zMwMq6urm5qympubyc/P/0g/oDfLtddgcnKS9fX1Dbt4SZKora3dMDt9\nt9HW1kZaWtoGvXmHw0FnZydpaWl0d3eTkZGBUqkUTWQOh4Pw8HCx6/c16F26dGnbBZkkSdTX1xMd\nHU1qaipms5mOjg6Ki4vp7u7m0KFDGxYVvgYi3w3K398fSZJYXFwkNzf3loRwfv7zn3PixIkNAjd9\nfX0MDg5y6tSpbR+3sLDAwMCAECrKyMggJiZmQzDT6XQ4nU5KSkpue3+PJEl0dnZisVg2iCUNDg6y\ntra2oflKkiSeeeYZnnjiidu2a3M6nbS3t2/yf6itraW8vHzD33uj94XLly8zOjqKRqPBbrcTERHB\nsWPHqK+v39RM1t7eTkpKyicm/X83BvuPmzsZ7G/42/enf/qnWx73pQ737dvHvffee8dVrX5fsdvt\nW3bqBgYGbinkcrdw5cqVTS5gH1bjfnh4mJmZGXGDz83NvWnDFh9ut5vW1lZRv1OpVOzduxeHw7HJ\nWEaj0eDxeIiMjOTw4cNMTk6ytrbGgQMHUKlU1NfXb3KVUyqVO+4Mr1y5gl6vF1MOYWFhHDlyhLq6\nOpRK5abAlJKSwrvvvovT6USv15OUlMTq6iqzs7O0trZy4sSJbV/Lt8OVJAlJkggICBDTA319fcKe\ndnp6GrPZfN1rGhUVtaUCnkwmo7CwEPhob/AdHR1ER0eLxjqv10t1dTVqtZrV1VXW1tZEFmxycpKw\nsDCRbbkdzM3NbWkDHBMTw9LS0k0r6A0PD2MwGDh58iRhYWHY7XbOnz/P+fPnt3yu5ORkrly58okJ\n9rvcWW442N9///3U1tZy//33Ex4eztLSEu+88w4HDhwQdbTl5WW+8pWvfJTn+wdLXFwcnZ2dm9Kx\ni4uL16313UmCgoIwm81bKsTthNPppKOjA7fbjdfrJTIykpycHEZGRjbYsfrU8Pbs2XNLY0sNDQ2U\nlJSIupfFYqGhoUH4ul+7O5MkSZy3QqHYJFnr5+e3rYLedkxNTW1aDMnlcrGQ8Y0CulwuUcefn58n\nNTVVPC4iIoL4+Hh++tOfbiuX68sgVFRUiPNbWlqiubkZgMOHDzM/Py8kYLOzs8XP7lasVuuGSQZf\nGr+hoYH8/HxaWlqQJAmv1yuu0e0sO2q1WmZnZzdNRpjN5luSyq2rq+OBBx4QNeSAgABOnDjBs88+\nu2WDpdls/sTV7Xe5c9xwsK+uruZ//I//IT6IcHU05h/+4R/4wQ9+QH5+Pt/97nd3g/1HhEajISgo\niLa2NvLy8nC73XR2dpKYmHhX9U18kKysLOrr66msrMRoNBIQEIDFYtmx41WSJOrq6qisrNzQId/V\n1cXKygqHDh3CZrNhtVoJDw+nrKyMnp6em65fms1mQkJCNtwwfWN2ISEhtLW1kZ+fz8rKCjqdjsHB\nQVGOkCQJo9GI2+0mKioKmUxGTk6O2JFPTU2RmJiIWq3eMc2m0Wiw2WxoNBqWlpYIDAwkODgYj8eD\n1+vlnXfe4f7770ej0TA+Ps6vfvUr4uPjiY2NxeVyiVE3nxCIryp37fVRqVTC3MfPz4+enh40Gg1p\naWkMDw8THx9PT08PBQUF6PV6JEni8uXLN7SIdDgcmEwmQkNDbzi7tLa2htFoJDg4+JaNcSRJEjt0\ns9mM3W4nMjISrVZLWFgYw8PDHDx4EJPJhL+/Pw6HA6fTeVsbr8LDw+nu7t4gIW2xWIRWwc3idDo3\n7dKVSiVKpRKFQsHS0pLItrhcLoaHh7nnnns+/B+yyx8ENxzsfV+aa1Gr1aJxJyYmhtXV1dt7drts\nID8/H6PRSGdnJwqFQnR5382o1WqCg4N57rnniIyMxGazIZfL+dKXvrTtY3yqZdcGj6SkJGpqasRC\nQKPRoNPpGBsbIzg4eMsxmuthtVq39ID3eQIYjUZ+9atfERYWxsrKChERERQVFWGxWGhpaSE6Oho/\nPz/6+vrIzs4mNDSU9957D7fbTXJyMo2NjUxOTvI3f/M3255DdnY2r7zyCklJScTExDA/P8/o6ChJ\nSUlYrVYyMzNpaWkRO/3Dhw+zsLDAyy+/TEBAAHl5eQwODvLiiy+KRUttba0woRkdHSUkJASlUsnM\nzAxnz54lNzdXWOkWFxeLbvaamhrxOmlpaRsW9h/EpzbmcrmEHK5cLmffvn07BtSuri5sNhvR0dFM\nT0+ztrZGeXn5TS9YfcZA1dXVhIaGEhgYyNDQkFBDXFxc5Kc//SmRkZE4HA48Hg8PPfTQTb3GjVBe\nXk5ra6sYsVSpVFRUVNzSc8XHx9PV1bVh3n9xcRG1Wk1ZWRnt7e309/eLRV1FRcWuxO4uN8wNB/u9\ne/fyv//3/+bBBx8kPDyc5eVlXnvtNaH8NTg4+LF2V/+hEhYWJjTLPwmsrKwgSRLf/OY3xbHV1VVa\nW1u3vSmaTCYyMzM3HddoNLS2tnL69Gmxw0lLS+PixYu3dNOLjIyktbV10zjW7OwsYWFhpKamEh8f\nj9lsJjMzk5CQEHp7e4Uxiy/Fn5GRQXV1NbOzsxQVFYmFTEREBJcvX+b555/n7//+77c8B6fTSXR0\nNF6vl/n5eTweDzExMaytreHn5yc67H14vV7OnTvH7Owsn/3sZ1EoFERGRhIQEMB7771HZ2cnBQUF\nYoeYlpZGV1cXHo+Hd955h3/8x38UgdVut/N3f/d3HD58mJCQkE2CQDsxMjIiMjW+CQebzUZfX9+2\n2hvT09MolcoN77vRaKSjo+O6421b4XQ68fPzE+NyXq+XixcvUlhYiM1m44knnhC/63A4aGlpue2T\nK2q1etvPsS/z4rNG1uv1O2pVHD58mDNnzmCz2YiJiWF5eVkYMsnl8g0qi7vscrPccLB/7LHHePnl\nl/nJT34i/LTLy8uFjGV0dDTf/va3P7IT3eWTydDQ0KaAFRgYuGMdOyYmhqmpqU1pZLvdTmJiIu3t\n7YSFheHxeFAqlaytrV03hex2u7l48SJTU1Okp6dTUVEhsg5dXV2isa6vr0+4mXk8Hvbs2UN+fj4m\nk4nW1lYhE+tyuejp6cHtdpOenk56ejovv/wyP/zhD2lubqa3t5esrCz27t27pSPgtdfn0KFDm9S8\namtrkSQJl8vF0NCQ+NuVSiWjo6N85StfYXp6GrvdjkwmIzo6mrS0NGHKMjAwgMViIS4ujtzcXJ59\n9lkKCwsZHBwkMzMTt9tNb28vSUlJIj08OjqK0WgkOjqaxMREsYCanZ3lvffeQy6Xc/ToUfR6PZ2d\nneTl5W14j0ZHR2lubt422I+Pj2/qXA8LCxMmOzeDz/imsLCQ8+fPYzab2bt3Lw8//DBvvvnmpkZF\njUaDXC7H6/Xi9XqFDkJcXByxsbG3fYfscDior69nz549FBQUIJPJePfddzeUpj6IVqvlM5/5DO3t\n7fT19aFWqzl27NiWTYC77HKz3HCwV6lUfPnLX+bLX/7ylj/f7QjdZSskSbrpG6ler2dwcBCtVkts\nbCwej0dIxI6OjrK+vo7RaESlUrG2tnbd7mqLxcKPf/xjjh49Snl5OW1tbfzzP/8zf/qnf0p+fr7w\nc4erO+GoqCiee+45qqqq6O3tFantpKQkzp07h0qlwmazUVhYKIRbfONfP/jBD9i3bx9f+MIXWF5e\n5umnn8Zms93S9VEqlbzyyivce++9aLVaDAYDNTU1REVFsbCwQGpqKrGxsayvrzMwMCDq0j7zndTU\nVCYmJqirq0OSJPLz89FqtVy6dAmFQkFmZibDw8Osra3x3nvvkZOTQ0lJCdPT07z//vscPnyYCxcu\nMD09zR/90R/hdrs5e/YsqampzM/Pb9KRT0lJ4Xe/+92Of+vtxGKx0NraytGjRwkMDGR4eJiWlha8\nXu+219RqtdLc3ExhYSFpaWnCQthnf3y76OnpoaKiQtTyw8PDqayspLu7e8fMXFhY2G4dfpePhJsq\nlLndbiYnJ+np6dnwb5ddtiM9PZ3u7u4NxxwOx3VrtFVVVdhsNurr62lqaiI5OZm0tDQmJycpKyvj\n+PHjHD58mHvvvVfItG7Hv//7v/PVr36VvXv3EhAQQFVVFQ888ABvvfUWbrebsbEx3G43breb8fFx\n3G43cFUv/eDBg1RWVnLo0CFRl+7v76e8vJzg4GA0Gg179+7FYDCQmJgoFOICAgIoKCggLi5uR2XH\njIyMTd8hu90uVOny8/Pp7e2lubkZk8nE/v37SUhIoLW1lcLCQiIiIoiJiaG0tJQrV64wNTVFSUkJ\n0dHRqFQqMjIyxPPU1NQQFxdHZWWlmKKZn59nZmaGiooKdDodCwsL6PV6CgsLaWtrY3R0lEcffZSI\niAj0ej3f+MY36O/vJzg4mCtXrmw47/n5+R27w5OSkhgaGtpwbKteoBtBJpMxNjbG/v37CQsLQ61W\nk5eXx8LCAnl5eZuyBWtra3g8Hrq6ujh8+DBRUVGoVCoyMzNJTEzcUhb4w+ByuUSg9+FzKNxllzvB\nDe/sBwYG+MEPfsD6+roYL3I6nYSHh/Mv//IvH+U57vIhmZiYYGpqCrjaBHSzjmDX4nK56OvrY3V1\nFYVCQU5OzpZNbj7CwsKYm5ujvr6ehIQELBYLi4uLm9K5H0Qul5OVlbVJiSw9PZ2WlhaWl5cJDAzE\nZrORlZXF+vo6kiQxMjLC3NwcMpmMlJQUYmNjWVtb2zQPnpaWxvnz52loaKC4uFiMNvlG79bX15HL\n5Tz55JNC7e3o0aM4nU6qqqp45ZVXxMz2+vo6Bw4c4Ny5c+Tl5fH1r38df39/0ah3rePfVtdndHSU\nv/qrv2JtbQ23201paSl//Md/TEtLi7C89fnZl5WV8dvf/paCggIefPBBsUhJSUnhM5/5DEqlkurq\navr6+lCpVHi9Xg4fPowkSZSVlfGd73xH9ASYzWb+5E/+hImJCV599VVef/11JElCqVTy1a9+lenp\naWFpfa2ffU5ODiaTiba2Nqqrq9FqtaLZcSevjISEBC5fvsyvfvUrgoKCWF1dJSIiguPHjwNXFwvD\nw8NIkkRERARZWVnIZDLcbjd9fX1YrVbkcjnZ2dnCu6CpqYmoqCiCgoKYmpoiJyeH1dVVQkNDeeGF\nF9BoNLhcLvz9/fn0pz9Na2vrptHEpKQkGhoaSE1NxWKxMDY2hlKpJD09fUuzlA+yurrKyMgIcrmc\ntLQ0NBqNGNO8NuvkKyHA3e1nbzKZGBgYwOv1EhQURG5u7pbjnLt8srjhYP+LX/yCBx54gJMnTwqL\n2zNnznys7mW73Dzt7e0EBgYKS82RkRFaW1tvqdlnfX2dmpoaSktL0el0rK2t0dTURHZ29o7Nmbm5\nubhcLubn54mNjSU/P/+W/x6r1UpwcLDoig8JCaGjo4Pg4GAuX76MXq+nsrISSZLo6+tjZWVFzOpf\nm01wuVw4nU4xaucjODgYrVaLQqHg17/+NX/3d39HSEgIdrudf/iHf2D//v1MTk6Sm5tLeHg4Xq8X\nl8tFe3u7GI/69re/jd1uJyEhgddee21HURmXy8XPf/5zHn74YRISEvB4PPz0pz+lurqaqakpBgcH\n+fKXv0xAQAADAwP88Ic/JCMjgxdeeIHPfe5zZGRkoFAo+OUvf8m7775LUVERVquVRx55RBjwvPba\na1RWVuL1evna176GyWQS31ur1cr58+fp6OjgiSeeIDAwEKPRyNNPP01lZSUGg4H777+fxx57DIDz\n58/z7rvv8uijj3L+/HmKi4vR6XSYzWba2tp2VEV0uVxYrVaOHz8uAvDAwABms5nl5WVWV1c5cOAA\nCoWC6elp6urqqKio4OLFi5SUlBAWFobL5aKlpUW43h06dIjl5WXsdjv79u1jdXWVqakpZmdnOXLk\nCJIk4efnx5UrV5ibm9vk5ghXG/1UKhU9PT04HA6ys7NxuVxcunSJrKysHaWGh4eHhXqh1+ulpaWF\nxMREsrKyaG1tpaysTHTPt7a2kpmZSVdXF35+fqKbfmxsTHg13Gnm5uYYGRmhrKwMlUqF0Wjk4sWL\nHD16dFcw7RPODafxZ2ZmNjW9nDp1irfeeuu2n9Qutwen08na2hqZmZnIZDJkMhnp6el4PB4cDsdN\nP5/BYKCkpET0Z6jVaqqqqujv77/uY1UqFQkJCTuOc90IJpMJk8nE2NgYTqeTwcFB5ufnWV1dxd/f\nn+TkZGQyGXK5nPz8fJaXlykoKODtt9/e8DyvvPIK+fn5W/aa6HQ6Ghoa+OY3v4nJZGJqaoqFhQW+\n9a1v0dbWxvLyssgaxMfHEx8fz/LyMv7+/iiVSvLz8zlw4ABFRUXk5uayvLwMXN3ZjY2Nifo6wEsv\nvcTJkyfJz8/HZrOhUCh46qmneOeddxgZGeHEiRMiHZyVlYVOp6OtrY3o6Ggefvhh9u/fT2lpKU89\n9RT9/f0MDAxw4sQJEhISSExMJDMzk6SkJAwGA0FBQaSlpVFaWkphYSGFhYUsLy9TW1vLfffdR3Fx\nMeXl5ZSUlFBWVsalS5dYWFgQZjdyuZzS0lIWFhaYn5/nv/yX/yJ2fVlZWfzX//pf6evr2/a96+/v\nZ+/evej1ehITE4mKiqKqqoru7m5mZ2c36MPHxcURHh5OU1MTOTk54nOjUqkoLy9neHiYwMBA4TUg\nl8uRJImOjg6CgoLE++Lrgi8tLWVqaoqwsDCR5YL/HCFMTExkdXWVsrIytFot4eHhHD58mIGBgW17\nDdbX15mdnaW8vJyQkBBCQ0M5ePAg4+PjhIeHExcXR21tLfX19Vy4cIHY2FjCwsKwWq3k5ORssACW\ny+U79nZ8XAwODlJRUSEWg2FhYeTn528qv+zyyeOGd/YBAQE4HA4CAwPR6XRcuXKFoKCgHTuNd7mz\nLC8vb6mVrtfrWVpauukuX7PZvGlX/mGlb28Fn+iMzz0qKiqKqakpCgsLaW9vZ3V1FUmSiIyMJCws\njNjYWC5fvsy//Mu/oFarcTqdFBYWcuDAAdra2rYdvXO5XBQXFxMREYHRaKStrY2goCCqqqrEOJtM\nJsPr9XLvvffywgsvcN999/HMM8+IwB8aGkpeXh4mk4m33noLjUaDv78/ly9fpqSkhN7eXj73uc8x\nODgotPGrq6tRKBQkJSVx5coVYeDjdrs5deoUf/Inf8L3vvc9amtrUSqVeDweNBoN8fHxhIWFMTY2\nxrlz51AqlXi9Xu655x5+85vfEBkZyb/9279hNpvxer2iwc/tdlNRUUF/f78wtTl16hTvvvsu3/rW\nt3jxxRdFJ7tcLueLX/wik5OTHD16FI1Gs0FW1yfFOzAwwNLSEjqdDo/HI8bhPiim5OtN2EpkKT4+\nnra2tm3llnNzc/nVr36FWq0mKCiIubk5SktLxQLvg6hUKrKzs+nt7RWp+vX1dTIyMjAajaSnpyNJ\nEsvLyyiVSnQ6HaGhoduK5PiEij5ITEwMi4uLxMXFERcXB/ynbPDCwsKOfvZ3WjdDoVBsKifo9fot\nbZh3+WRxw8F+//79tLe3U1VVxdGjR/n7v/97FArFpi/iLncPOp2O/v7+TbKuS0tLQov9ZtBoNFit\n1g1NWNdKyH4cWCwWjhw5IlKjMpkMk8nEiy++yPnz5zlx4oTYBU5MTFBdXc1DDz3Epz/9aYBN6Xyt\nVrvl6J1Go6GwsJC5uTkMBgNarZYDBw7w9ttvs7q6Snp6OkNDQ0iSRHJyMlarlYiICJKSkigrK8Pr\n9Yqu+R/96Ee8/vrrHDhwQNShLRYLL7zwAgEBAYyOjvLFL34RuLqTiouL42c/+xlJSUmkp6djMBiE\nXO7y8jJpaWl0dnby6KOPil2nTCbjn//5n7Hb7Wi1Wh5//HHg6u7z9ddfJzQ0lH/913/lkUceEfP0\ndXV1vPHGG8DVbvGioiJxTevr6wkICGBpaYlvfvObIv0tl8t54YUXiIyM3CTN6/F4kCRJ2Mzm5OQQ\nERGBwWAQQkjXqs3BfyrhbWX7ubi4SHR0NCaTSeja+zJUHo+H1tZW/viP/1g0+MlkMpqamvDz82N5\neVkEWh8ulwuFQkFRUZF4bV9gW1tbY3x8nK6uLmJiYkRpRi6Xb1uv9vf339Kb3uFwbGtIpNVqRa3+\nWrY63zvBVt9lk8l0xxchu3x4rpvGb2hoYGVlhUceeUTYSj7wwAP85V/+JY8//ri4qexy9xEYGIjL\n5WJxcVEc89U3t9Lavh45OTk0NzeLjmKf69iHafi7WWJjY+no6BA36vX1ddra2oiPjxda9jabDYfD\ngZ+fn9h9+/jgFEB+fj7R0dE0NTXR1NREdHQ0+fn5PPzww/zTP/0TaWlpVFZWkp2dzf/6X/+Lz33u\nc0xPT9PV1cX+/fuprKxkaWmJ+vp6vvCFL/DrX/9adNN7vV5eeukliouLcblcYiZdkiSCg4OFTn11\ndbWo63s8Hp5//nmhYPfcc8+Rn59PVVUVkiTx7LPP8t3vfpc333wTg8Eggt/TTz9NUlIS4eHhVFdX\nU1tbS1NTExcvXqSzs5PQ0FACAgI27MJTUlJwOp3cc889/Pmf/znNzc1MTU1x8eJFvve97/GVr3yF\nlZUV0Xwml8sZGBjA6XRSXl5OY2OjCA5er5fLly+TkpKC2+3ekDUKDw9Hp9OJDItv2kGSJNra2sjI\nyECr1TIxMSEeY7PZGB8f59ChQ1y4cIHz58/T2NhITU0Nr7zyinDX02g04hoAFBQUsL6+Tn9//4ZS\n1fDwMOHh4Rs+C9f+PzY2lpqaGg4ePEh2djaFhYUUFRXR3d29bZNeVFQUV65c2aDe6HQ6MZlM2zat\n+pr35ubmxDFfaerDlrhuBz7pZN8i0uVy0dbWdlf7b+xyY1zX4vbP//zPmZubEyv13NxccnJy7kq1\nvN9ni9tbxev10tXVJca/goKCKCoqumU9fYvFIkbpPB6PUJnz4VNV8/Pz2+DP7QvQHo+HuLi4DZa3\nN0NDQ4OQWlUoFEiShFarxWg0EhkZyW9+8xux89NqtXzqU58iPT19Ry3+7Xjvvff42c9+JlLoX/zi\nFzlx4gR1dXWEh4djMpmQyWT4+/vj9XrJycmhtbWV73//+yIAHT9+nK997Wuig35tbU2krnU6HRcu\nXODkyZP8/Oc/F37tBw4cICoqCkmSeP/994U3vVwu5/HHHycnJwe3283f/u3fipGy/Px8/umf/okX\nXniBhYUF2trahJ/9sWPHmJmZ4dSpU9hsNtbW1pAkibCwMAYHB4mJiaG5uZn6+nqx0z58+DCnT58m\nJCSEM2fOYDQagauqgw8++CBKpZLm5mZeeukllEolbrebBx98kKysLJaWlkRw8H0f5ubmsNlsREZG\nbutn/7vf/Y7x8fENfva+LINPQ9/j8eDv749KpcJqtRITEyMcEGUyGdnZ2YyOjpKfn7/BSCk2NnZD\nNstisWC1WomMjESlUjE8PIzH42Fubg61Wr3B8KioqIiVlRWGhoaEWmBeXh5hYWFCDdLPzw9JknC7\n3ZSVlQmvg76+PhQKBUFBQcTGxhIZGXldP/s7zeTkJOPj4+J8ioqKbsvO/m67N94J7no/+5WVFfr7\n+8U/X6OLL/jfLSIQu8H+zuFwOGhsbCQyMpKYmBh6e3tJSEggISGB6upqDh48iFqtRpIkDAYDcrl8\nS0nc6+F0OqmvrycpKYnQ0FCmp6dFWv373/8+f/M3fyMC+9jYGD/+8Y/57ne/e9Ne6isrK3R1dVFZ\nWSlu8E1NTYSFheF2uzdZ2S4sLDA4OMjLL7/M9773PYKCgggNDeXb3/42OTk5NDQ08MQTT4iFkdvt\n5v/7//4/vvSlL2G1WjdJyI6MjPD666+TnJzM17/+dSRJYmlpiaeeeooHH3wQmUy2QY1tdHQUu93O\nL3/5S0pLSzl9+jRyuRy73c7/+T//B7VaTVZW1iZ/+h/96Ed84xvfYHJyUiygfEH4g25u1zI+Ps65\nc+d47LHHRLD/+c9/Tnl5OWazmZSUFEZHR8V0g9FoJC0tjYCAAPr7+7FarahUKnJzcwlhSUKuAAAg\nAElEQVQMDKSjo4PQ0FBRA19dXaWxsZGQkBBycnI2GebU1NRw5coV8vPzhZ2u0+nkueee4+GHH952\nced2u2loaECr1RISEsLs7CwhISGoVCpCQkKIjo4WY5cKhYLW1lZCQ0NZXl6mtLRULFJ8Tou+kpbb\n7d7Qv+KTAQ4ODhajn1NTUxQVFQkvgj80/lDvjddy1/vZ63Q6ysvLxWy0zWbjwoULnD17lrq6ursm\n2O9y52hubhYStBEREYSHh1NXV4fJZKKoqEikQn27r5qamlsK9mq1msjISDo6OnC5XAQHB1NcXMzU\n1BTx8fHY7XZCQ0OF1KxvBvxmd/b9/f1iDAwQJi/vv//+lk5ty8vLvP3223zrW98SuyCFQsFf/dVf\n8d/+239j7969XLhwgaysLPz9/RkfHyctLU1I9l68eJHQ0FDsdjuSJLFv3z5++MMfcurUKdGI53K5\nqKqq4ne/+x1/8Rd/sUF2NTU1lZqaGqxWK/Hx8Xg8HuRyOR6Ph8zMTAYGBpibm6Ozs5OioiLhqBcb\nG4tKpWJ9fV0EK4/HsyE1bTQaqa2tRSaTCR39d999l9OnT9PS0iJe69SpU5w5c0bU2e+77z6io6N5\n4403GBwcpKCggIsXL7Jnzx7CwsJwOBxcvnyZgoICrFbrBgOYwMBAkpOT6e7uFrr519bY5XI5UVFR\nGI1Guru7CQwMZHp6mpKSEhYWFrZ9v9va2jboKqSmptLZ2UlAQAAGg0GYG8HVLILPxa6qqorJyUlM\nJhN6vZ59+/aJUg6waTHZ0dHB+vo6mZmZBAcHo1KpGBsbo729nU996lM38UncZZfbww0Fe0mSGB8f\np7+/n76+PgYHB4U2fk5Ozkd9jrvc5bjdbvz8/DbVNvPy8jh37twGz3Ef10tZulwuEdCvTcP29/fj\ndDqJiooSaXyDwYDNZuPw4cOYzWbGxsaQJInExERyc3N3tNT1er10d3djsViAq453BQUFeL3eTRoS\nvvQyXG0e85WybDYbc3NzeDyeTf0LCoUCpVJJVlYWsbGxGAwGrFYrqampHDlyhImJCaHiZrPZUKvV\n4nXX19fxer1idt3lcvH000+zvLy8ofbuw9/fH61WS2pqKs3NzUiShFqtZs+ePYyOjvLYY49x7tw5\nLl26BFwVyDlw4AAjIyOsrKyIyQKFQsHU1BRBQUFinO/48eNIksSLL75IcXGxyOQEBQWhVqtxOBw0\nNDRgt9uJiIggISGBS5cuodPpCA8Pp6CggJaWFrEbXlxcJCgoiIMHD/L+++9vWa+OjIwkMDCQpqYm\nYQzkdrsJCAjA7Xaj0WiorKzEZDJht9s5cOAAcrlcSB9vxf/P3nnHV1Xf//95b3Izbm5u7s3eeyeQ\nAQnLsAUEZIlbcVWt1irVamtbtRVFcVWt1oKK0lJFEYTIDCuBkEASEhKy9947uUnu/v3B436+hKBt\n/epX64/XXzwuueeee+45n/F+v4ZWq53AV4mJiSE3Nxc/Pz9OnjxJYGAgOp2OhoYGpkyZwvnz5zlx\n4gTBwcGEhobS3NxMZWWl4GVYkvzMZjMqlYrY2Fhqa2u5/fbbxYJMqVSSkpLC9u3bv/bcfixobm6m\ntrZWtEbi4uK+dRTxVfx48C8n+5deeon6+nq8vb2JiIhg4cKF/OIXv/i3s6uv4v9fSCQS1Go1zc3N\n4whblt7m18FsNnPq1CmmTZsmdsnV1dUUFxdTWVlJQECA8DLX6XScOHECiUTCuXPnuPvuu8cdJzU1\nlWXLllFfX09jY6Pov4eFheHt7c2ZM2cIDQ0VDO3Ozk7OnDmDjY3NBOa4ZbebnJxMYWEh5eXlSCQS\nZDIZs2bN4vz58xMMi3Q6HWazGY1Gg7e39zjGdUFBgVBKSCSSCVaz1tbWODo6iuhZi7Ocv78/zc3N\nhIWFjfv70dFRPD09uXDhAgEBAYLlnpeXR3R0NFKpVJjTWFlZifeXlJTg5uZGVFQU/f39ODs7U1hY\nSG5uLnV1dTzyyCPiMx5++GH+8pe/0NTUhL+/P3PmzBHVgLS0NKqrq1m6dCmBgYEEBgaO69mfPHkS\nR0dH+vv7cXd3p66uDqPRiK2t7RU15pbvePDgQebMmUNISAgDAwOcOHFCEDIB1Gq1WMzV19fj6en5\ntffW18FsNhMUFISfnx8tLS3I5XLmz5+PRCKhurqaG2+8UZDuIiMjkUqlXLhwgaysLCIjI8Xiq62t\njZycnCtq838sDnnfhLq6OgYHB8XzpdVqOXXqFLNnz75qoPZfjn852be1tWFtbY2bmxuenp54enr+\nxxP9X//6V/Lz83FycuL1118H4PPPP+fYsWNilX3rrbeKct2XX34pUrbuueeeceW9q/jxwaJX1ul0\n4waEkpISZs+eLQY/y/+3tLR8o/SvsbGR4ODgcaSg0NBQTp48SVtbm+hZw/+YrOzevRtHR0d27txJ\nSkoKer2eI0eOiLJuf3+/2CGbzWbBCre1tR1npevu7k5jYyOBgYFkZWUJD/mRkRHOnj0rDGauVK24\n9957efrpp7GxsWHSpEm0t7fz9NNPc8stt+Dn50dGRgbu7u5IJBI0Gg0SieQbA6QSExN5++23WbFi\nBUqlkgsXLpCfn8+6detobW1FoVDg5eWFwWDg/Pnz+Pn54eLiwscff0xSUhJyuZyuri7KysrYsGED\nubm5QkJoMBgoLCykt7eX5uZmlEol7e3tuLq60tDQgJWVFWfOnGHx4sUTzis6OpqWlhaqqqro6urC\n09OTvr4+SktL8fPzY2BgYMJ7LMZCFrthCzo7O9m/fz8zZ87kk08+ERa/cHESd3Z2Zt26dQwNDVFU\nVIRCoeCGG24Q9rY5OTkkJCQgk8no7OyktraWefPmfe01tRD7Ll1YlZaWCsKotbX1BO28r68v+/fv\nF1HCFnKfXC5HoVCMq7JYEhsthjqzZs0Sts6ZmZn/p8qVb4OmpqZxLoiWylBFRcUVvQuu4r8H/3Ky\nf/vtt8cR9Pbv38/Q0BARERFERUURGRn5L2/guXPnsmTJEt59991xry9btowVK1aMe625uZmsrCze\neOMN+vr62LBhA2+99da3Zo//lGA2m6msrKSrqwuz2UxAQAD+/v7fy2c1NTUJKdSlPuVfh6lTp5Ka\nmopWq8XFxYWOjg5mzJiBQqFAqVSyY8cOpFIpOp0OX19fsYCrqKggPT0duDi4L1++nN7e3q/Ns4eL\nfVeLN72joyPLly9HLpezevVqDh48yPvvv49UKuXaa68lOTmZkydP4unpyTPPPINer0cul3PXXXdR\nUFDA5MmTOX78OOXl5cDFiczf31+Qqu69916Rm/7rX/9aLE5fe+01MjIygIthNps2bcLJyYn169fz\n5JNPMjg4iI2NDb/4xS9ISkpicHCQkpIS9uzZI77rjTfeCFz0Pdi4cSPd3d1IJBJuu+02Fi9ejKur\nK97e3nzxxRcYjUa8vb2ZMmUK/v7+uLq68vLLL9PY2AjAypUrmTp1qiAR/ulPf8JsNiOXy3nqqado\namoS3gRZWVnARU/4uro62tvbiYmJ4bPPPhPXZ/r06ej1evr7+zl27BhVVVWChT4wMIBKpWL58uU8\n//zzjIyMYGdnxy9+8QuysrIwm80cOHCA+vp6HB0dUSgUopzf2NjIzp07hePgnDlzRKRwR0cHo6Oj\nov9tqayYzWbS09PFIsKiTPDz8+PAgQP85je/EffpO++8I0h0paWl4j2hoaF4enqSmJhIWloavb29\nODg4MDQ0xKRJk/Dw8MBkMnHgwAER8DNlyhSSkpIYHh7GxsaGzz//HL1ej0KhYP78+fT29hIfH09V\nVRUdHR2YzWZhbKRUKqmtrWXXrl2Mjo7i4uKCg4MDSUlJAHzxxRecPXtWeP2vW7cOKysrRkdHKS4u\nRqfTIZVKiY6O/sbsiW+LS/MGLNfHw8Pjiu01Z2fnf+mSOTAwQGlpqWh/xcbG/p9Xfy/NG3B2dhYV\nmKu4iH+LjX85LiXoDQ4O8tlnn/3L93R2drJp06ZxO3s7O7sJk/2XX34JwOrVqwF48cUXufHGG/8t\nMtdPnY2fnZ2Nn58fvr6+YuLXarWCjfxdobi4GCsrKyIjI5FIJLS0tFBfXy/89a+E6upqNBoNXl5e\nODg4MDg4SEVFBdHR0ezdu5cbbrhBMNlzcnJoa2vD19eXiooKbrrpJkFa2717NzfeeCOjo6MTfvOT\nJ09y7tw58vLyuO+++4Q2e/v27Tz++ONYWVkRGBiIj4+PcHGzxOPm5+fz29/+VuzcX3nlFVJSUqis\nrGT27NmkpKQAF3PkT548yYoVK9iwYQObNm0iJCSEpqYmnnzySR5//HE++OADFAoFmzZtQiaT8c47\n73D06FG2bt3KH/7wB371q18RFhaGVCrlySefZOXKleK3W7NmDdbW1pSWlvLRRx/x+OOP88ADD7Bk\nyRKSk5Pp6elh69atLFu2DLhYml+1apWwyc3KymL+/Pn8/e9/56abbmLmzJmil+/h4cGZM2fIy8tj\nx44dBAUFsXv3bp577jkeeOABgoODiY+PF9enrKyMqqoqTp06RXNzM08//TSRkZHk5eXx+uuvM23a\nNEpLS7npppvw8/NDIpFQU1NDamoqy5cv5/jx4zzzzDN4eHjQ09PDhg0bSE5OxsbGhq6uLmJiYlCr\n1eTl5aHX6/H29uaDDz4gKSkJDw8PBgcHyczMZN68efT09LBq1SomT56MVCqlo6ODrVu3MmXKFDIy\nMnjggQcICAhgbGyMf/7zn8DF4Jzi4mLee+89nJycSE1N5a233uLgwYOcPn1a8CBkMpkgbsrlcurr\n6wkNDWVsbAw7OzsKCwuZM2cO77//vrALNplM7Nu3D6lUSnFxMbW1tWzcuBFXV1fq6+t5+umnufba\na7GysmLu3Lli0V1TU0NWVhbLli0jPT0dvV6PVqsVjoyLFi1i8+bNuLu7i7jwffv2kZeXx+9//3sy\nMzOZMWOG8Mg4c+YMMTEx3ymD3yLptCgDjEYjhYWFqFQqsbO/dGHf0dFBT0/PBAWKBb29vRQXFzN9\n+nRsbGzQaDRkZ2dzzTXXjEs0/D7HxtLSUsxmM9HR0UgkEtra2qiurhbP9Y8FP3rp3eUEvYqKCjQa\nDSEhIURFRXHbbbf9yw+60mSfnp6OXC4nODiYdevWoVAo+PDDDwkLCxOlpPfee4+EhIQrOvUdPXqU\no0ePAvDyyy+PYxD/ULDIkL5r9Pf3U11dzdSpU8e9np6ezqxZs751KpWFcaxQKAQB6uTJk8yfP3/c\n3+Xn5wvTlsthNps5duyYYBlbrkFZWRlHjx7luuuum1C2f/311zEYDGJXZsHZs2fp6urC1taW2NhY\nvLy8MJlMwqr2+eefFx77lr56Q0MDLi4urF27dkLAz4kTJ3j77bd5//33x5VbCwsL2bhxo5C2WfrX\nVVVVfPDBB9TV1fHiiy+OW3B0d3eLkvKpU6fGfc4NN9yAXC5n/fr1xMXF0dTUhJeXFzKZjAceeAA7\nO7sJla1PPvmEzz77jKioKF566SUxwHZ0dHDDDTewevVqHn30UcrLyxkeHiYoKIjR0VHeeustYmNj\n+dnPfjbueA8++CCpqanU19ePI0u++uqrvP/++2zatEksoi146623OHToEDfffDMGgwGtVisWa8eP\nHyciIoL+/n5hJVtbW4uTkxNOTk54e3vj6emJnZ0dWq2Wzs5OGhoaUCgUrF+/fty9sG3bNvbu3UtI\nSAh33XUXgYGBdHV1kZGRwXvvvUdKSgqvvfbauHM7ePAgqampJCYmMnPmTMLCwujr6+PcuXPk5ORw\n6NAhzpw5M+49f/rTn2hra2PRokX4+voSGRnJ8PAwhYWFjI6OolQqWbBgwbjJrLOzk9OnT2MwGES1\nxYLXXnuNkydP8pvf/IaxsTFRkjcYDHz00UcsWLCABQsW4ObmhkQiobm5mVOnTgmSX1hYGL6+vnR0\ndFBSUoLJZOL48eO89dZb4z7nD3/4A3FxcSxatGjcTt5kMnHixInvVPFksb693Ovi6NGjREVFUVNT\nw4wZM5DJZPT19ZGdnc2iRYu+Vr56/Phx5s6dO24XPTAwQEVFxbhwn+9rbDQajaSnp0+4RoWFhXh5\neU1IvPwh8X1cg3+XS/FvEfQqKysxGAyEhoYSHR3NkiVLCA8P/18RNhYtWsTatWuBi2Egf//733n4\n4Yf/o2MsXLhwnIzlx6Dh/L5Wr9XV1Tg6Ok44tpWVFXV1dd/Kfau4uJi+vj7UajWDg4PY2toSFBSE\nVCqd8DmW1LXLI2fhIsNZr9eL91iugYODA9XV1djY2Ew4niVT4fLXg4KCOH78OPfddx8ZGRnk5OSI\nCpCrqyudnZ1ce+21VFRU0NfXR3BwMBEREaSlpbFu3boJx7Mw6NPS0lCr1RgMBqysrBgYGGB0dJSZ\nM2fS398v4l19fHyYOXMmWVlZqFQqzp49S2dnJy4uLoSEhNDZ2Ul4eDjFxcUcO3YMo9HINddcw+zZ\ns3n//fepqKhg3759Qgao1WrFJHH5ufn6+nLhwgVef/11ioqKBAM+PDxckPo6Ojro7u5Go9GIsnNN\nTQ333HPPhOPJZDKkUim1tbV0dnYyNDSEt7c3iYmJaDQauru7KSwspKWlRfBwLAz8gYEB4d3u5+cn\nrpGTkxMPPPAAhYWFWFlZsXbtWrZt20ZTUxO33XYbR44cobu7G2dnZxYuXCgWhZffC/7+/tTU1LBi\nxQqampowmUz09vYK7wWNRkNlZSVHjx5Fo9Ewc+ZMOjs76e7uZtWqVbS3t3P06FEUCgUJCQmcO3fu\nivfpHXfcwerVq1mxYgXBwcGCJzJlyhS2bNlCTEyM4A9ceo/k5OSwevXqCcdzdHRkdHQUPz8/6uvr\naWlpwc/PDycnJwYGBoiMjGTfvn0iYlkulxMVFUVGRga33367kBgGBwdjNBp54403CA4Opq6ujsrK\nSkwmE6GhocTExHD69GnmzZs34Rwsv913hYqKCuLj4ycc0+I66enpycGDB8X3iY+Pv6ItsAVDQ0PC\ndOlStLe3j/uM72tsHBwcRCKRTDi2vb095eXlP6pS/o9aZx8VFcWaNWsICQn5j41JvgmXEpMWLFjA\npk2bgIu9lksfxt7e3h+FjeQPDWdnZ1pbWyd4bvf39wuv8/8Ezc3NSKXScWUui7GKxSXPIseysbHB\n1tb2a28qGxubK6bodXZ2kpCQQHp6+rjERIvMzWK7eynKy8vx8vIiNTWVoaEhbr75Zvr6+ti7dy/L\nly+nu7ubsrIyMZhnZ2dz4MABRkdHaW1tpb6+XjjEqVQqBgcHsbKyYmxsjOHhYaytrRkdHRWWppb2\nxKXXMCsrC7lczs6dO5k5cybJycl0dHRw8OBBFAoFFy5c4G9/+xtTpkxBJpOxY8cOsrKycHZ2pry8\nnEcffRS4+GAfP36cPXv2IJPJKCgoYHh4WOjZz5w5g4+PDx988AErVqxg6tSporQ9OjqKSqVi8+bN\nrF69msjISNLT0zl//jwJCQmcPn1aROJasu6HhoaQSCR89NFHorefk5PDyZMncXZ2RqvV8sEHH+Dk\n5CSSDy1xrunp6Xh7e4ugmK6uLkwmk5CZWXrNRUVFtLW1YWNjw/Hjx1Gr1bi6umIwGMjIyMDOzo7W\n1la6uro4evQotra2TJ06laqqKuzt7YUE0RJ3HBUVxRtvvMHQ0BAbNmzgkUcewdXVlb///e+cOXOG\nuXPnUl1dTVhY2LioWaPReMX7xxL4c6XAHeCKu6q+vj4CAwNFGNGl6OrqQqVS8Ze//IUVK1Ywa9Ys\nCgoK2LZtG97e3pw+fZqlS5fS0NCARCIhICBA5A1crq5wdHREpVJRWFhIZGQkAQEBojVy7Ngx4uPj\nr5gd8F3vBJ2dnUVQz6WwmAk5OzuPM3n6V7BkIlxaLRkZGfnW1cb/FBbS5OXo6uq6Ondcgn+55Fm1\nahURERHf6UQPFx8wC3JycoQ0a+rUqWRlZaHX6+ns7KStre1bhbb81ODs7Exvb++4FXRzczPW1tZf\n6939Taivr5/gkeDv709/fz+1tbVotVpSUlK45ppr8PLyIiMj42stkiUSCS4uLiIYBi7yOmpqali8\neDG9vb2kpaVRWlpKTk4OqampzJkzh+joaD7//HPRiy4qKuLAgQNERUUxNDTEnXfeSWBgIAkJCTz8\n8MMcOHBA9F/DwsIE690it7Kw3a+55hpSUlLQ6XTU1tYSFxdHZmYm06dPJyUlheTkZDIyMpg/fz4j\nIyMUFxeL71JcXMzY2BgrVqzgiy++ENn2SqWSzz77jLlz5zI4OIjBYMDT0xOVSoWHhwcNDQ0kJCRQ\nXl4ufiOdTkdqaiqBgYF4eXmxd+9e5HI5arVahK488sgj7N+/H5lMhrW1NUqlkkOHDomWw8yZM+nq\n6iI3N5eAgADc3NxYvnw5n332GWNjY0ilUuzt7dm9ezdyuZzo6Gj279+PtbU15eXl2NnZcfz4cR56\n6CGysrKIjo5m1qxZzJo1C09PTwoKCpDJZGg0GlJSUnB1dWXu3Ll0dHTg5uZGR0eH0JJfuHABo9FI\nT08PoaGhYvdn8c1PS0vDz8+Puro61q9fT3t7OwMDA2zYsIFDhw5x++238+yzz6LVaoXa4je/+Q0J\nCQnY2toyefJksrOz2bdvH2q1Gk9PT2bPns2ePXsoKioiKyuL/Px8duzYwaRJk5g0aRKPPvqomPTP\nnz/Pjh07ePTRRykpKUGj0YjFkCXMKDg4mIKCAnGfarVazp07x/Lly6msrBznz5+ZmYmzszNxcXH0\n9vYKEyArKyva29tJTEwUvg7Tpk0jKSmJ0tJSwV3JysoStrtGo5GsrCzRFsnMzKS7u5ve3l5yc3Op\nr69n0aJFnD17Vnwfk8lEbm7utzKf+iYEBwdTVlaGRqMBEPyWb1vuDg8PJycnR6go9Ho9Z8+eJSYm\n5js752+CtbU1CoViXMBQf38/ra2t30qG+VPFtyLo/ad48803BfPTycmJm266iZKSEurr65FIJLi5\nufHAAw+I1fju3bs5ceIEUqmUu++++4oypyvhx0rQ02g0aDQaXFxc/lf+10ajkaKiIhHh6uLiQlRU\n1LfS72ZmZo6TQFmQnp6OjY0Nzs7O5OTkMDY2RkJCAg4ODri4uHxtmhdc7AUWFRVhZWWFm5sbU6ZM\nEbv+8+fP09/fLwxm/P396enpYefOnWI3Mzg4SGxsrCjjXr7z+Pjjj8nIyGDJkiXk5eVhZWUl2Mx5\neXk8+OCDjI2N0d7eLiJi+/r60Ov12NrasmvXLqFXv+OOOxgcHGTGjBl8+eWXdHR0ABfjPFetWkV2\ndrYImNFoNNjb23PffffR29tLYWEhTU1N9Pb2ilS0mTNn0tDQwPr169m8ebPYVVgmkdjYWPR6PUVF\nRZhMJlxdXUlOTqasrAyTycSnn36KVCpFr9cTERHBggULkMvl+Pn5idhef39/EhISOHz4MFqtlqys\nLAYGBpBKpQQFBREXF4e9vT0ffvghJSUlIv72lltuwdnZGaPRSHJyMrW1tchkMkJDQ9m9ezelpaXM\nmzePyspK4GJrKDQ0lIyMDKZOnSoG8UsndqlUilKppLm5mdHRUezs7PDz80On05GXl0dUVJR43UIG\nfOaZZzh27BinT5/GysoKvV5PfHw8M2fOpLa2lmnTplFXV4fBYMDFxQU3Nzfy8/OxtbWluLgYmUyG\nXq8XSgZnZ2c2bdokZLoKhYKXXnoJPz8/vvrqK7q7uxkZGUEikeDu7k5wcLAI8Dl16pRgya9evRpv\nb2/GxsbYtWsX7e3tSCQS0bK0ZAbs3btXPL/r1q0TITmBgYFi7PH39+fChQuEhYWh1+tFEJNSqUSj\n0WBra8v58+fRaDQiI97b2xtfX1+mT58usgPg4mQfHh7+jc/ct4VWq6WwsFCYVvn5+U1Ix/xP0NHR\nIYyG4GIg0eXmRd+3XW5FRQWdnZ0ibyAuLu4736T+b/GjJ+j9t+DHNtkbDAays7ORy+U4OjrS3t6O\nl5fXBDOUHwLl5eU4OjqOm1D7+vooLCxkbGyMgwcPEhgYiFwup7S0lPnz5xMZGXnFnj1cJORs27YN\nT09P/Pz8yMvLIykp6RsjkDMyMkhJSRnXU6uuriYrK4ukpKQJlYctW7bQ1dWFr68v/v7+KBQKenp6\naG5uprOzk1WrVtHR0YGnpycGg4Hu7m4cHR3p7OzkuuuuG7coMhqNnDlz5msVBpmZmWRmZjI8PEx4\neDg1NTVYWVkRHBxMVlYWf/3rX8f9/auvvopWq6Wvrw+ZTEZ0dDQdHR20trYSGhrKtGnTJpArh4eH\nOXDgAOHh4TQ3N2MymTCbzTg5OYnybWVlJc7OzqJy4u/vT0dHB0NDQzQ1NRETE0NfXx8tLS34+voy\nNDTEqlWrhLTRaDTy2WefkZubS0REBGNjY4SFhTE2NkZTUxNdXV2UlJSwePFiIUFrbm6mqKhIMKqN\nRqMI5rHc242NjSgUCgwGAxEREVRXV2M2m2ltbUUmkxEQECBMgSycg5iYGGbMmEFjYyN2dnbo9XqU\nSiXh4eFs376d+++/f9z1OXfuHFVVVXh4eGBtbS0Y/BqNBpPJxKxZs4SLnyXP3sfHBz8/P7Zt28bI\nyAg+Pj5oNBrhbe/u7s6OHTuEFK+yspKhoSEeffRR+vr6qKioEBG3PT09TJs2jby8vHHac7j4bH/5\n5ZdiEebp6YnJZKKjo4O6ujqWLVvGwMAAnZ2d2NvbY2Njg42NDUqlkqNHj+Ln54e7uztSqZT29nbR\njvspe4pc9cb/kffsr+LbIy8vj7i4OLHCDQ8PJzc3V5DifkhERESQlZVFT08P3t7edHZ20tnZyYwZ\nM7jrrrvYvHmzSGdzdHTknnvu4eWXX/7a4/3zn//kzjvvFD3cGTNm8O677xIREYFarcZkMtHf34+9\nvb3Q31r00pciODiYxsZG0tLSCAsLE8RBS8JcYmIiRqNRSHzUajU1NTVcf/31nJ4uKfMAACAASURB\nVD59mp/97GdiUtfr9WzevJmlS5eya9cuLly4gE6nw87OjtjY2G+U5ZSWlmI0GnnhhRfEa1u2bKGs\nrAydTkdbW5voIVviWCdNmkRJSQnR0dHY2NgIcpdlh3k5mpqaiIqKIjs7m/vvv1+cd2VlJTk5OVRV\nVZGcnCxaCXFxcezZs4fAwECKi4v58MMPxbHa2tq4//77SUhIQKPRCNc9k8mEvb09MpmM48ePs23b\nNqF2MJlMPPjgg6jVasEj6OzsxMHBgYGBAVxdXenq6uKpp57CxsYGiUTC6Ogor732Go2NjSQkJPDH\nP/5RnMNf/vIXYW172223CYldeno6zz77LIGBgQQHBxMUFIROpxOEQkAQC93d3dHpdNjb23PixAki\nIyNxcHAYx+oeHBzk888/F8Q7i5VreHg42dnZtLW14ePjw9KlS0WGvU6n4+OPPyYtLY34+Hg6OjrQ\naDSiMnPgwAFcXFyYO3eu+B10Op2QTFZUVIxb6Fo89i1hRSqVCrPZTEdHB319fTg6OuLl5UVERAQ6\nnQ5vb296enro7e2lsrKSVatWCdZ9UFAQL7/88k96or+KHx5XJ/vvEZady6WYPHkyhYWF4wavHwKW\n1LTe3l66urpwdXUlOjpa9L93797NpEmTRAk1MjKSU6dOccstt0w4lsV05vIFzHXXXcfJkyeZNGkS\nzc3NuLq6otFoRB66pZ95KQYHB3FxcSEuLo4nn3wSX19f0Qb57W9/i1qtZu/evaLXrNPpmDVrFgqF\nAk9PT1GalEqlmM1mvLy8aGlp4dixYyxatAg3NzdaWlo4fPgw8fHxX9unrKqq4sEHH2THjh0MDw/j\n4ODAnDlz2Lx5s9Bku7i4CEZ4UlISubm53H333YSGhlJUVMTChQuZNm0aW7ZswcfHh3PnzhEXF4eV\nlZUgsdnZ2bF06VJOnjwpyu5KpRJ/f38Ru3rpdZ48eTKpqamkpKTQ2tqKt7c3BoOB5uZm5HI5UqmU\nzs5OQUzS6/WMjo6i1+txdnbml7/8JcHBwWi1WpqamnB3dycgIIChoSGys7OF7E6hUGAymUhISCA/\nP1+cg1QqJSYmRlj9fvjhh0Lh4OnpiVQqFR4JdnZ2yGQywsPDGR4eFgFFTU1NIm0uNDQUa2trbrrp\nJrGYlMlkDAwMcOedd1JSUjKhOqRUKkX52dramuLiYkZGRvD392fy5Mm888473HXXXcD/yJLs7e2F\nciU4OJiYmBhx/2RmZnL48GH+9Kc/iQWNVCrF1tYWuVwuUhw3b94snp25c+cil8vx8fFBIpHwj3/8\nQzxTlsqA5W9tbW3HVZUCAgIoLi7GZDIJo6OQkJAfFWv8Kn56uDrZf4+4Ui/dEp7xY4Gzs/M4xqpe\nr8dgMLBgwQKampoYGxsjOTmZlpYW9Hq90CxbyGERERE4OzsjlUpJT0+npKQEpVKJ2Wxm+vTpDA0N\n0dnZOa4MOjg4SF5eHiqVSvACLKEbQ0NDTJ8+ne7ubm6++WZ6enqwtrbGx8eH4uJiUlJSWLlypdCE\ny+VywWi2s7MjIyMDk8mEyWRCLpcTFBTEnj17eOqpp2hubkYikeDj48Njjz3G9u3bee655654XSQS\nCQ0NDcycOVOwx8vKykR/fNq0acLJ0MXFhZaWFk6dOiX+HRoaKiSJw8PDhISE0NXVJYhM7u7upKSk\ncPbsWWQyGdXV1RgMBgwGA1OnThXxtPHx8YJ45OjoSFJSErt27UKpVNLX10dtba1gUKtUKvR6Pe7u\n7mKRYMlmt7OzQ6PRsHjxYhwdHUUa3qFDhwgODsbT01OU5a2trenv76evr0+QzSwe/5YJ0s3NjbKy\nMlQqFXK5nKGhIZqbm3F0dMTPz4+NGzfi7++PWq2mqKiIyZMnYzAYOHjwIKOjo7i5uVFRUcGFCxdY\ntmwZBQUF3HLLLXR3d2MymVAqlfT29uLp6UlOTo7YOVuqOkqlkqGhIU6fPk18fDwKhYLq6moqKipQ\nq9XU1dXh4eEh5GSWTIT29nb8/f3FxGohX1rIhJb2liX5TyaT0dPTQ2trK2FhYdja2jI2NkZ5eTnR\n0dF0d3fT2toquA2nT5/G0dERk8lEQUEBO3bsEPkJq1atIigoiJiYGMbGxggODsbKyora2tr/c7c3\nS8/ewiv43/bsr+LHj6uT/fcIyy7hUtvIkpKSH7W6wNvbm/7+flxcXMSO0MvLi+rqaubNm8eePXvw\n9PTE1dWV4eFhjh8/zpw5c8jPz8fDw4OHHnoId3d3iouLeeKJJ1i3bp0ImbFAqVQKW9aamhqx2NBo\nNIyOjtLQ0MDg4CDR0dEiVc1oNNLY2IherxeLDcsE5uPjg5ubG2+88Qb+/v64ublhMBjo6enhyJEj\nGI1G2tramDlzphj0T58+fUW5jgUKhYLOzk7c3d3Jzc0VltAmk4np06eTn58vdmY9PT3MmjWLrKws\n9u/fzy233EJJSQkRERFotVq0Wi1wMcXtckWDj48Pr776Kr///e9Fu2Pnzp10d3czODhIa2urCCUZ\nHR3lhRdeYMmSJZSXl4vdoMFgoLOzE4VCQVRUFOnp6SQlJeHg4EB7ezu1tbWi6rJ06VLq6uqwtrbG\n19eXI0eO4O3tjZ+fnyCmGY1GfHx8CAoK4uDBgzg5OYkKVWdnp2hD6XQ6EV+s0+k4deqUcKlbs2YN\n9fX12Nvbs3r1ag4fPoxKpUKj0bBixQrhgX/mzBkOHTrE8PAwLS0tyGQy0fKxpAOWlJTg7+/PyMgI\n1tbWmM1m7OzsqK2t5b777hPPV3R0NDt37iQpKYnMzExKS0uJjo4WBi+W37CkpIT7778fuVxOc3Mz\nW7duxc3NjbS0NB555BEx6Q4PD/Pxxx8Ln4UZM2YIEuXRo0cpKyujtbWVjRs3ioX9woULefLJJ6mu\nruaLL75gw4YN2Nvb4+DgwMMPP8ytt96KVqtlzpw5tLW1Ce7B6dOnJxBSL4der6eurg6dTkdQUNC3\nTqIzmUycOnVKOPVZkiPLy8uJjIz8Vse8ih8/rP54adPtvxwWn+cfEnK5XPRnPTw8yM7OZnR0FJ1O\nR2lpqTCu+bFCq9Wi0Wh4/fXXkUgk9PX18ec//5mwsDCMRiO+vr7Mnz8fT09PMbEePnxY7AaHh4cZ\nGhri4MGDeHl5MTY2Rnx8/AQVQlNTE4ODgyxZsoTAwED8/f0JCQnB2dmZ9PR0ZDIZ9fX16HQ6QXRr\nb2/H1taW/v5+pFKpmLhbW1tpa2ujsLCQhQsXkpycjL+/PwaDgYKCAgYGBrjnnnuEftkymB8/fpzr\nr7/+itfBaDTywQcfiIlm79697N+/n9tvv53w8HB8fHxENoG/vz8ymYyGhgaOHDnCnj176OvrIzU1\nlQMHDjB79uwrKh/gIkkxMDCQkZERpFIpLS0twEWyqVKppKWlhb6+PoaGhjh8+LAg/1lbW7N//360\nWi0lJSVkZmby1FNPceHCBe6//35GRkbEpB0fH8/58+eJjIwkKysLNzc3ent72b9/P35+fqSkpFBe\nXk5raytGo5H29nbs7OwICQlBp9Nha2tLQECAYJRbrF+Hh4fp6enBxcVFqC3Cw8MJCwvj/PnzxMTE\nEBAQwMmTJ3FzcxPWv5bJWSKR4OvrS1pamtgpT5s2jfDwcPR6Pfn5+WJ3DRe96s1mM9XV1Xh4eODt\n7U1zc7NYUBUXF+Pt7c3Q0BBarRY3NzfhuaBQKHBycqK8vBxXV1fy8/OpqKjg9OnTqNVqNBoNS5cu\nFQohC3HRzc2N6upq1qxZg8FgYGBgADs7O/z9/fn0009JSUkRFaPe3l7Onz+Pq6sru3bt4oUXXhBh\nTk5OTsTHx/Pee++xZs0aUlNTxT19/vx5EhMTvzEUqbOzk127djEyMoJGoyEnJwfgW0nLampq8PLy\nEm6YEokEV1dXSktLJ4QAfZe4dGz8/xXfxzW43M/h63C1SfQ9wtbWlnnz5uHm5oZOpyM+Pv5Hnxyl\nUCgYHh7mn//8J0FBQcjlcv72t7+hUqno6elhxowZ4/7ey8tLOLWtX78eLy8vtFotd9xxB7fddhtm\ns5nS0lK0Wi0NDQ10d3eLAdiyAOju7qahoUEM0MPDw+Tm5jJjxgxUKhVBQUEEBgZSVlbGhQsXiIiI\nwNHRUZTL1Wo1R44cYfr06cyfPx+z2Yy9vT1r1qxBpVIxadIkNm3aREFBAZmZmRQWFvL2228Le02T\nyURLSwstLS2ixZKbm8uLL77IvffeS11dHatXr2bLli0iC95oNHLkyBG++uorYShkYV4/++yzLFy4\nkGeeeYa4uDgqKiqAizuziooKioqKRFWhra2NZcuWiWS1lpYW5s2bh4ODAxEREcyYMYOmpiZ27txJ\nTEwMkyZNws7OjqCgIJ544gkUCgWJiYk8/PDDGAwGgoODKSwsxMfHB19fX9RqNY2NjUybNo3BwUHR\nB+/q6hLvsZAmAwICaG1tJTo6WiyyIiMjmT17NlZWVshkMubOnUtoaCg6nY7nn3+elJQU8vLymDp1\nKhs3bsTa2pq1a9cye/Zs9u/fzz/+8Q8mTZrEXXfdhdFonGC0YtkR9/f3M3/+fMrLyzl58iSRkZHY\n2NhQUVFBSkoK1157LWVlZSgUCh555BHhMTFv3jx6e3s5e/Ys4eHh+Pr60t3dzdKlS5kzZw5OTk4E\nBASwcuVKrK2tCQ8PF2mB7e3tTJkyBZPJRHBwMGFhYSQnJ9Pa2srw8DDz5s0TeQ55eXlkZGRw7tw5\nTpw4QVlZGY6Ojmi1WnF9bGxsmDt3rpAcXh5go1AokEqlNDU1MWXKFGbOnElSUhLXXHONCN/5OqSm\npjJ//nyCgoJQqVRcf/31ZGVlXTEa+F+ht7f3ip4ZlqrKVfw0cXVn/x3jSis3BwcH1Gr1j07zeSVY\nHOfq6uoICwvD399frPhra2uJjo6e4PB17tw5tFot06ZNw8PDg5iYGAwGA1lZWQQFBdHY2MiZM2dw\ncnKisrKSQ4cOsWjRIhoaGkRqm52dHeXl5dTU1IhScW5uLu7u7tTU1HDgwAExiR89epT+/n7c3NxI\nTU2lubmZgYEBURJ1cXFBqVQyPDxMWloacXFxdHR0sG/fPkpKSjh+/Dj+/v7CAc8S/WqJfXVwcOD8\n+fNC72zhLBw5cgSZTIajoyMbN25EpVIJxzqj0Shsfl1dXbG1tUWlUnHttdeyY8cOFi1aRG5urqiG\nVFZW0t3djUwmY+vWrRw7dkzYe+7evRuZTIZKpeLNN9+ksbERk8nE3r176enpITY2Fp1Oh1arZerU\nqahUKrq6uujt7UUmkzE6OkpFRQXd3d3U1NQI8mJjYyN/+9vfCA0NFRWblStX0t7ezr59+4QH/qlT\np6ipqRGLVF9fX1HKl0gkVFRU4ODgwLZt27C2tmbmzJnU19fz6aefCp+E7du3M2XKFKKjozl06BAy\nmQwvLy/q6uoICgoSk3xVVRWtra10dnby8ccf09HRQVdXF1u3bhXyQ6PRSHl5OaGhobS3t5OWloZe\nr8fT05Pt27ejUqnw9PTk+PHjZGVlMWnSJEZGRmhsbMTPzw+DwSAIcba2tuzbt09c04yMDEZGRrjx\nxhspLi6mt7dX2AWfP3+evr4+BgcHqa6uJjY2lsDAQLRaLXv37iU2Npbm5mYRTqVUKqmrq6O8vByt\nVktsbKwotcvlchobG8nMzCQkJISEhASUSiVOTk6oVCo6OjpEy+pKz2R2djZlZWXI5XJkMhlHjhzB\nzs6O0dFRAgICqKmpoaysjO7ublQq1Te61+n1eoaGhiZUEurq6r7XCN6rO/sfdmd/VWf/HeO/SUtq\nSauysrIShC0nJyeKi4uZMmWKIOX5+fnR2tpKQ0MDDQ0NJCcnMzg4iFwup62tTZR6e3p6WLNmDZ6e\nnmRmZpKWlsbtt99OTk4OlZWVQuoUHh6Om5sb3d3dGAwG3NzckMlkDA8PC6OR0tJSfve736HVarGx\nsaGlpYXXXnsNg8HA888/Py7pb+PGjWKynzFjBu7u7mK3XlhYSEREBDKZjLVr12JnZ8fIyAhbt24l\nICAABwcH5s2bJyYfSyJYamoq11xzjTg3g8HA0NAQX331FTKZjNdff31cNsTjjz9OV1cXK1asoKio\nSJS6nZycyMzM5L777sPd3Z329nbg4gLQZDKh0Wh46aWXeOGFF5gxY4ZIj+vp6aGvr4+lS5eSkpKC\nVqvF3t6exx57jFWrVjFp0iTmzJkzzpb0448/FmVsDw8PbG1t0Wg0VFdXM3/+fKFAKCoqQiaTkZiY\nyOnTp6mvr+eOO+4QlrgAO3fupLS0lMWLF9PV1SV2qQMDA3h7e9PY2CjcCe3t7RkdHaW0tBS1Wk1O\nTg533XUXAwMDKJVKjEYjn3zyCc899xx79+6lv78fBwcHQfJcu3Yta9euZfHixQQGBiKRSBgZGWHb\ntm0sXrwYjUbDggULUCgU6HQ6mpqayM3NJTIyEhcXF2GkYjHqmTFjBhUVFdx5553i+4yOjvLhhx9S\nW1tLfHy8aPPY29vz2Wefcd1119Hb2yuyFiztEY1GQ1NTE87Oznh6egqCniWrYuXKlezcuZPR0VHM\nZjNKpZK1a9fS0tLCm2++ye9+9ztR7Xrqqae49dZbcXZ2nuCK19raKkh7V3pOn3jiCd57771xKXJ/\n/OMfiY2NxdXVlZCQEHx9fRkeHubcuXNMnjz5a61ivyn17ttYb/+7+G8aG78vXNXZX8X/OQYGBigu\nLmbWrFnCVS4nJ4fAwEBGR0fRarXCwthoNFJdXc2cOXMoKiri8OHDhISEiF76Qw89hIuLCwUFBSL2\n1d/fn5///OdkZWVRXFzMI488Inq1J0+eJDs7G39/fwICAkTp0NnZmcDAQDIzM4mKiqKvr0/kkXt4\neIiecUtLCwqFAmdnZ5qamggPD+fYsWMsW7aMwsJC6urqAMSOLTc3l8TERN555x00Gg1KpZLrrruO\n1NRU1qxZg16vF4z3wMBA8b0teeIWglZJSYkwkbk8BGrRokVs376dXbt28cc//pHq6moiIyPFRNDc\n3ExPTw9+fn7IZDIR8nL48GE2btxIY2MjW7ZsAeDhhx9mw4YNGI1GvLy8hKyuubmZO+64g08++YS4\nuDgOHz4sdglubm6C+e/r6yt4CU5OToJop1QqSUxMZM6cOQC0tLSg1WpFOftSLF26lAMHDgiNuKVV\n4e3tjVarpa2tjYceeoji4mL0ej2Ojo7ce++9vPrqq8hkMpqbm/Hx8UGtVlNRUYGLiwuHDx/G19eX\nmJgY4cNuIRdaEtYcHBywtbWltbUVFxcXiouLSUpKQq1Wo9frkclkuLq6YjQa6erqwt3dnaSkJGxt\nbenu7qalpYW0tDQWLlxIbm4uISEhjIyMUF5eTlhYGPX19fj4+FBVVYWNjQ1Go5H77ruPzZs389xz\nz+Ho6EhdXZ0oyRcWFtLQ0MDy5cuF94JSqeSOO+7gH//4Bw4ODtx9993CfteyaAwJCeGJJ55g8+bN\njI2N4eTkxK9+9SshD70cXV1dIib3clicCy+VYVpy41taWpg2bZq4Zx0dHZk9ezaZmZkTjIAskEgk\nzJ49m9LSUsrKypBIJCLP/ip+urg62f9EMDo6SklJidgBRkdHi12AJY/ebDYTEBCAn58fpaWlTJs2\nTfTNJRIJSUlJnD59mmnTpnH48GE6OjqQSCQ4ODiwePFi6uvrWbp0qQiccXd3Z8WKFZSVlREbGyuM\nZZydneno6KCiooK8vDyWLVs2TpEwe/ZsvvrqKxwcHCbo9pubm8nMzMTa2pqmpiZRSi4oKCAgIICe\nnh70ej0nT54UkjJXV1fUajVubm6CxS2RSFCr1Tg7O9Pd3U1BQQHXX3897u7uQian0WhobW2ltbVV\nGKacOXNG6PMrKipIS0vDxcWF3t5ePDw8RPhMeno6hw4dwmw2M3XqVGxsbHBycqKpqYn169cLBYHZ\nbCYyMpLi4mKWLFnC3r17MRqNREdHo9FoGBwcJDAwEEdHR/r7+7GyssLDw0NI7+bMmcO5c+eEw1pr\naysmk4k9e/aQmJiIi4sLWq2WvLw8enp6MJvNODg4sGnTJvR6Pa6urrzyyiukpaWRkpJCZ2cnhYWF\nYoDX6XQoFAo+/fRTysvLhbWwxcxGqVTi4uIigl48PT1pa2tDr9fT09MjJnuLzt4ib0tISBBWupGR\nkRw+fBipVMqyZcuor6/HYDAgk8mYNGkShw4dwtramiVLljA0NITJZBLkwPz8fFJSUkReudFoJDg4\nWLDx169fL8rRZrOZ7du309vbS2RkJLW1tRw4cAB7e3tmz55NRUUFPT09BAYGCpmhk5MTra2tol3Q\n1tZGX18fUqkUJycnoeXv7u6mqqqKkZER1Gq1WGxacCWZbWBgoDAdunRHJ5VK2bFjB+fPn8dsNhMT\nE0NQUBBqtZre3l4++ugjurq6kMvlrFu3DrVaTVhYGDt37iQ0NBQbGxsR6zw6Ooqvr++4z7WQV78J\n1tbW46pjV/HTx9XJ/ieA4eFhcnJySE5ORqFQiOS0WbNmUVlZiY2NjSDWVVVVUVBQIPqXl8LieV5X\nV0dAQACLFi3CbDbT1tZGZWWl6BNPmTKFa665htHRUXJzcxkeHubw4cMEBAQwZcoUPD09KSoqEkEp\nlt25BRqNBplMho2NDcPDw4KxDBfLmZYeuZubm+BhhIeH09XVhV6vp6Ojg1mzZjE2NoZcLufdd99l\nyZIlfPHFF/z85z8nODgYk8lEYWEhH3zwARqNhtmzZ4sJXa1WU1paSmNjI/n5+Tz66KNiJ+zm5sbb\nb7+NSqWis7OT559/Ho1Gg0Kh4JNPPmFsbIyjR49iNBp58sknsbKyYv/+/bzzzjukpKTQ3d3N5MmT\nx5VHLSEqJ06c4He/+x329vYcOHCAvXv3EhERwW9/+1teeeUVYmJi0Gq1pKamCr+Dt956i2XLluHt\n7U1tbS1//vOfiYmJwWg0Cuc7Ozs7vL29KS8vp7GxkQ8//JDNmzeTnJzMu+++y9q1a7njjjs4fvw4\ncXFxzJo1S4SstLe3o1Qq2bNnD7fddpvQzL///vvMnTuXtrY27O3tmTVrlghMsZgB/fnPf+bZZ59F\npVIxNDTEiy++iKurKzk5ObS3tzN9+nTc3d3Jzs6murpapBTa29uL9MFTp04BF+1np0yZIhL5ZDIZ\n27dvJyQkhP379/PLX/5StASOHTsmdruXJsJJJBL6+/vx8fFhy5YtmEwm7OzsMBqNVFVVERISgslk\nIjMzk1WrVmE2m9HpdLz99tskJiayd+9elixZIq7PuXPnqKysJCgoiE2bNrFu3TrCw8PJy8vjlVde\n4de//vW/fDYNBgPDw8Pj+uNnzpyhs7OTxYsXI5VKOX36NA0NDcTExPCHP/yBn//850yePJnGxkbe\neOMN7rvvPhwcHJg7dy6NjY0MDw8zefJkDh8+zNSpUxkeHp7Qt72SYdV/Aq1Wy+joKEql8qrZz08E\nV3v23zF+iL5UdnY2iYmJ4ybvkZERzp8/j0QimcCgtyRrWcxILNDr9WJne3kJsKCggPz8fJYsWYK9\nvT2tra04OTnh7e3NO++8g52dHQ8++CDwP9fg3LlzZGVliZ232WwWA3BxcTGxsbHCP12pVNLW1obZ\nbBb2vWlpaSLoxdvbm3vvvZdz585x5MgR4GLZ35I3YIkPvfXWW8ed94svvohUKsXNzY2qqip0Oh1y\nuZzY2FiamppYsGABAwMDQsLU3t6Ok5MTGRkZjI2N0d3dLb6PUqnE2dmZwsJCoSqQSCQiG761tZUH\nH3yQ1atXi+vQ2NjIokWLUCqVPP300yJFTqlUcuTIEeRyuZASWjwOenp6SExM5MyZM1RVVREQEICP\njw8FBQVIJBLCwsJITExkZGSEoKAgkSdeX1/P/v37efbZZxkZGcHR0ZHBwUFBNrz++uuxtrYmMjKS\nsbExodlua2tjaGhI8Cfa2tpwdHTExcWFsLAwli9fzp49e5BIJKxevZrdu3eP8zlQKpUMDAxgb2/P\nwMCAUDVERETg4uJCVlYWtra2qNVqAgMDiYmJoaGhAXd3dyEj6+rqYnh4GE9PT+RyOU1NTQCEhoZi\nZ2fH2NgYKpVKtC10Op0wq3F1dcXV1ZXy8nKUSiVjY2NkZ2cTExNDVFQU/f39FBUVYWNjI84HLi7s\nGhoaCA8PJyoqiuDgYPR6vWgrWbwALOQ8C8fAMmR2d3dPuN8uRVFREQ0NDZhMJpycnMT3f+mll9iw\nYQN1dXWYTCYCAwN57bXXGBsb4+abb8bb25u2tjbUajVqtZrnnnuO3/zmN2zfvh1/f3/kcjmVlZXM\nmjWL6Ohozp49y+zZs8WkXFNTg06nm5At8e/AZDIJWZ9CoaC3txcfHx9CQkL+42Ndjqs9+x+2Z3+V\njf8d44dgnDY2Nk7Q7stkMvLz8wkJCZlgY2uZcC06YltbW0ZGRsjOziY4OBipVDrBRlYmk1FZWUlV\nVRUKhQJ/f380Gg1paWnARSeyxMRE4H+ugcFgEJONxY/ekh62dOlSgoKC6OjoYGRkhI6ODuzs7ETl\nIDU1leHhYZKTk0Xv1hLgEhwcTGRkJE5OTkydOpWAgACKioqIjIycQDDKy8sTxD8HBwdcXV0ZGhoS\nWu3IyEiSkpKwsrJCoVAQHR1Nf38/ubm5BAcHM3fuXPz8/EhISEAqlZKfny9c/VJSUoiIiKCvr4+R\nkREGBwdxdnbGx8cHlUqFTqfjvffeo7W1VVjZqlQqbG1taW9vp7Ozk+HhYdasWYNEIsFkMgmSlCWG\n9JlnnkGtVmM0GvnZz36GnZ0dTU1NKJVKHnvsMdRqNdHR0cTHx7Nr1y66urpISEhg5cqV+Pv7ExgY\nSEtLC7m5udx+++3CWtiStGZjY8P58+eZOnUqvr6+Ql/v4eFBZWWlaBksWrQIJycnXnvtNcHxiIuL\nIzg4GLVaTUBAgCiJOzg4cOutt+Lj44O3tzezZs2ira0Ng8FARUUFMpmMZxiYiwAAIABJREFUadOm\n0dzczFdffYWjoyMBAQFoNBp0Oh1DQ0M4ODjg6OhIcnIyzc3NojRtZWVFS0sLERERqFQqMVHb2NgQ\nFxdHVVUVPT09hIWF8fDDD4vrM3fuXD799FM0Gg1r164Vba4bb7yRlpYWdDodkZGRyGQy8ftZKlBt\nbW3cdNNNeHt74+TkRGRkJOHh4WRlZU0IN7KgqqpKeA3ExsaiVqs5e/YstbW1wMWFtY+PDwqFgqKi\nIsxmM6dPnyYxMRG9Xo+vry+Dg4OUlJRQU1PDypUrCQ0NpampiYGBAebOnSssf5VKJXl5eTQ3N1Nf\nX4+dnd23mujhYvhQSEgI4eHhwkq5uroauVw+rhX3n6CpqYni4mLa29sxmUzjNhj/v+GHZONfLeP/\nCNHU1ERdXZ0IMpk0aRIqlUqYxFh06kqlksmTJ2MymWhtbaW6ulow68PCwsTK/PKFQE9PDyEhITg4\nOPD555+LGM558+aJ1DO4OCCZzWZsbGzo7u7GxsYGLy8v+vv70Wg0GAwGQkNDyc3NRSqV8vbbb9PY\n2IiDg4PQfFukejt27GD37t1IJBIWLlwo/MctTnpyuVyQtqqrq0VSnSWPPC4ujq1btxIZGcldd901\nbuApLi4mICCAY8eOiV6sxWWtpaUFjUZDZ2ensNG15AG4ublRX1/Prl27RKtBqVQybdo04dY2OjqK\ntbU1IyMj2NnZMTQ0RGVlJfn5+eOklDNnzsTd3V3IwSx++lOmTEGtVtPT08O+ffvE97YY3kRHR2M2\nm0X/1JIwd/ToUVQqFYcOHaK0tBSpVEpxcTE33HADJSUljIyM8PHHH+Pl5SXCdtRqtWCY//rXvxYM\n9cjISGxtbfHz8+PChQs0NjYikUhISEgQ9r6xsbG0traKc3N0dKSrq0sQ9DZv3iyqLuXl5QQEBIiB\ny8rKSuy8+/r6mDFjBm+++aYIMQK44YYbSE9PZ968eQwMDHDo0CEkEgm33XYbW7Zswc3NDalUiouL\nCzY2NnR1dWFlZSUW8BqNRsQQx8fHc+HCBZ588kmee+45GhsbsbW1Ra/Xc/fdd1NZWUlUVBQPPPCA\nsPmNjY1FKpUyNjbGnj17gIuDZHZ2tkifq6mpITg4WNggu7m5kZGRIRZil4Y4DQwMTCBpXoqsrCyS\nk5OFJNLih3D48GEuXLjAY489Jvr811xzDV9++aVQtBQWFgplwaRJk+jo6KC6upr+/n6WL1+OlZUV\n9fX1ZGdnM3PmTJydnb+WjPd1GB0dpaCgQPAgPD09CQ8PF7G/lyIuLu7/sffegVGWadv3b/pMJmWS\nkN47SYA0EjCEIkUposDaWUXsyrq7uo+rq++6KuoCq4iriLiuYsEOItIhkE5JgEAgvZKE9DqTTJ95\n/2DnWiO47X3Kft/D8R8BMjP33Pd1Xtd5HoXy8nKmTJnyT70GXNo8eHh4iPdZUFBAX1/ff1vW/VX8\nFVeL/b8ZWltb6evrExapdrudwsJCEbSSnp4urEu7uro4ceKEYDqvWLFCzDHfe+895s+fT3t7O/39\n/YJMNDAwgMFgEE5kK1euFK3JsrIyEYDy7bff4u/vj0wmo6+vD5vNJtpFaWlpoq3d0dFBQEAAx44d\nQyKRMHv2bDw8PGhpaeHzzz8nICCA3bt3s2HDBvEePvjgA0Fumz9/Pj09PYyMjBAYGEhPTw9/+tOf\niImJISkpiYGBAVQqFWazWbTg6+rqmDhxorg+3d3dhIeHU11dzYEDB8jKysJsNnP06FEiIyPZv38/\nISEhYr4ZExNDcXEx586dY3h4mOTkZObPn4/T6aS8vJx9+/YJf/Xg4GA8PDwYGhqivr4euLQhKC4u\nFqz2uro6dDodISEhfP3116xatYrAwECcTidvvPGGSADMzMzkwQcfxGAwMDIywhNPPEF2djanTp1i\n5cqV6PV63N3dOXnyJDabjcbGRoKDg3nkkUdQqVRcuHCB119/naVLl9LT04Onp+eYzYO/vz8+Pj6s\nX7+ewsJC3NzcqK6uZsGCBTz88MN89913REVFkZWVhd1u59y5c6hUKuRyOdu3b+eZZ54hICCAlpYW\n1qxZg06nw2g08tBDDwlJXFVVFWvWrCE2NpbPP/9c2Py6Zvbx8fF8++23xMbGEhsbi1arFRr80NBQ\nKioqmD59OkqlEpvNRnl5uQjycZn8WCwWEUV79OhRhoaGeOGFFwgKCsJms/HWW2/R3d3N1q1bSU1N\n5bHHHmN0dBStVsu6detISUnh+eef54knnmD8+PFYLBY++ugjqqqq8Pb2JiMjg/vuu09wMX7xi18w\nbtw4GhoamDFjhsgH6OrqYnh4mFmzZvHpp59y5513iufr448/FuOaK8GVcJeTkyM2Mbt27RIbIFeh\nhksyU9ccfuPGjXz55Zd4eXlhtVq59957CQgIELbJLqmrS2b3/Wf7SnA6nXR3dzM6OkpwcLAgjhYX\nF5OTkyNIvA0NDZw/f/6KRENXQNM/C1cUsUtmKJPJmDhxIkVFRUJZcRX/fbha7P/N0NTUJAo9XHpA\nsrKyKC4uJjg4eEyKXkBAAE1NTXR0dHDddddRVFQkTvY33ngjzc3NXHPNNZw+fZrz588Dl9K/srOz\nOXHiBFOnThVzPolEQkZGBsXFxTgcDoKDgzGZTNjtdry8vBgeHhaz0y+++AJ/f3+GhoZQqVSMGzcO\ns9lMeno6vb292O12Ebry4YcfsmrVqjEL0sqVK1m+fDkOh4MjR44QFBSEp6cn5eXlY+aEZ8+eJSoq\nCoPBwIULF9Dr9QQFBaHT6SgsLBzT+SgtLWXZsmUUFxezY8cO5HI5qampJCQksGXLFp544gl0Op0g\nMIaEhPDss88yZcoU7r//fiEty8zMZM2aNXR0dHDffffR399PV1cXCoWCRYsWUV9fT2BgIKWlpeza\ntQupVIpGoxHs9bvuuott27aJUclNN90k3NU8PDxYv369OHFPnz6d/fv384tf/ILt27cTEBAglAQu\nL/zExEQxgujr6yMpKYmWlhamTJlCf38/JpMJgPDwcOx2O+Hh4Xh5eTF16lQkEglSqZSHH36Y5uZm\n0tPTCQ0NFafdlJQUysrK6O/v5+GHHxbfvVwuZ/ny5Xz00UfCubCrqwun08nAwABqtZrBwUF++ctf\nsn37dmw2G1KplPvvv59vvvkGh8PB7NmzsVgs6HQ6Ib/r6enB3d2diooKwY1QKpViBv5DQ5fk5GT2\n7dvHb37zGxoaGmhsbMRut3PDDTeIrPuXX36ZmpoawZK/9dZbefPNN9HpdJhMJkwmEyMjI/j5+eFw\nOBgaGiI7O5v3339fxDfPnTuXXbt2MW/ePHbt2jVGxujh4SHSHzdt2oRcLsdms7FgwYK/6WU/NDRE\nZGTkmOcrKiqKkZERVq5cyZEjR/jmm2+QSqVotVp+9rOf8cEHH/D000+zevVq8Tq33norubm5SKVS\nDh8+TEREhNgsuMYmP1bsXaO50NBQ3N3dKSsrw8fHB5lMRnJy8hjNfkxMjIhFdvExXPh+rsA/g66u\nriteo4CAAAYGBn40cfIq/mtwtdj/m8GV/vZ9uLm5MTAwcEWpjJeXFwMDA4SEhFz2YDU1NSGVSi/T\nUMNfdbrfh6ut7kpX+z6am5upq6tjcHCQBx54QPy8vr6e3NxcjEYjy5cvF4Sp3t5e4RHv6hy4Pld/\nfz8qlYqqqipWrlwpWqMhISHk5eURFxfH4cOHeeCBB4SEyOFwMDw8jLe3NwqFYkzbsqysTBCvpk2b\nxqpVq0TYzenTp5HL5TQ0NIx53y+88AJyuZy4uDiAMWOB2NhYjEYjZ8+eJSUlRaTelZeXM3PmTLZt\n20ZYWBi33347crmciooKNmzYwH/8x38wdepUEcnqug6ffvopdrud66+/nunTp6PRaKiqquLll1/G\nZDKJAJhz586hVCqZPHkyVVVV+Pj4CBKbq6tx8803s2nTJhISElCr1YSEhGA0Gunq6kKj0eDu7k5y\ncjLPP/+88Ds/dOgQw8PDZGVlXRZK1NTUJKJo77rrLiFNPHr0KAqFgv7+fsLDw4UPQVRUlIghHhgY\nYPz48WLGbjAYMBqNhIWFiZav6xo0NTXx1VdfUV9fT0hICNdffz2tra0UFhaKzsTIyIhwnHM6nTQ1\nNTFu3Dg6Ozv5yU9+It5zT0+POG0eO3aMtLQ0kXpnt9tpbW3l6aefFp0vp9NJUlISZ8+eZXR0lC++\n+IKHHnqI2NhYDh48yM6dO1Gr1bS0tPDkk0+OCcJ55ZVXgEu+/Fd6jn4MEydOJC8vj5SUFBGznJ+f\nT0ZGBgaDgV/84hdixOGScgYHB+Pt7c2rr74qfk9bWxsnT57kzJkzPPTQQ2J0FBYWxpdffvk331NZ\nWRk5OTnidUJCQoR6Ztq0aZf9e7lcTkpKCsXFxURERKDT6cT3npSU9A9/dhd0Op2IYf4+BgcHL5ML\nXsV/Pa4W+38zuGbU35e7uCw829raLjO+6Ovrw93dHbPZfBkb39UqPXXqlEheUyqVgrlvMBhwc3MT\nXt4OhwObzTZmV++CRqOhv7+f9PR0CgoKhMGNUqkkLi6OwsJCqqqqGBwcxNvbm6GhIRoaGkhISKCi\nooKzZ8/i4eGByWTCw8ND2IRu3boVs9mMXC7HbDbj4+ODQqFg5syZLF++HC8vLywWCzKZjGuvvZaJ\nEyeyd+9eOjo6hExs6tSpKJVKjEYjZ86cEYl0np6etLW1ERERQUlJCQUFBfj6+tLT04NCoSAuLo4T\nJ04wODgoTvYajYba2lrmzZuHm5sbLS0tIkY2OjoaiUTCK6+8wl133cUf/vAHMUp46aWXhOzsxIkT\nQloYFhbGuHHjsNls1NbW0tbWJvLaOzo6CAwMFOqC8PBwhoaGyM/PJz4+npGREex2u+BAaLVacnNz\nCQgIYHR0FH9/f/R6PXK5XJj+GAwGJk6cyLZt24SU0svLCy8vr8skkAAmkwl/f3/6+/s5e/ascBgc\nGhoiIiKC7u5ufv3rX+Pm5obT6RQz3ejoaD777DMx7+3u7gYuFcXc3Fw+/vhj9Hq98I+vrKwUKoja\n2lr6+voYGhoiKiqKyspKwsLCOHDggJjd6/V6DAYD1113HTU1Nbz44ouEh4cL+9rExEROnz6NTqcT\nM2673c6pU6eIiori3LlzZGdni+86Pj4ei8WC3W7ngQceYGBggNzcXCIjI4mJiaGpqYmYmBg2bdqE\nRqPBbrcjk8kIDw9HIpFQVFREeXk5CoUCq9XKrFmzmDBhwo8+x2FhYTQ0NPDOO+8gkUhQKpWkpaUR\nFhZGSkoKx44dExt7q9XK5MmTxTUpKCjAy8sLg8GATCbD29sbT09Pjhw5glqtRiKRiO6M67n+IVzS\nxB/Ka5OTk8nPz6e1tfUyhr3VakWr1RISEkJFRQUmkwlvb2+ysrJ+9HP+Lfj4+FBRUcHw8LDoSLpG\ngv8q2e8q/nVcLfb/ZpgwYQKFhYVkZWWJAnvq1ClmzpzJ6dOnBYnI4XAIe9KIiAiKi4vH6OxLS0vJ\nzs6mpKSElJQUYXeq1+s5evQoWVlZYqYeFBRET08PFy5c4Oabb+bUqVNjTuJwaabn7+9PVFQUsbGx\njIyMoFarkcvl5OXl4ePjwyeffMIDDzwgZG7Hjh3j17/+NevWreORRx5hypQp6PV6XnrpJVJSUmhr\nayM8PJz4+HhkMhlms5ni4mJCQ0MFA9nT0xOZTEZVVRUWi4Xm5mYSEhK44YYbMJlMaDQaioqKGBoa\noq6ujvnz55OamorD4WDfvn10d3eTmprKqVOnWLp0qdD2f/vtt9x8883s3LkTHx8fcXLctm0bjY2N\nzJgxg/z8fMH0ttlsNDQ0MH36dFavXs3bb7+Nv78/FosFtVrNmjVrePLJJ/nzn//Ms88+i5+fH1qt\nlt/97ndER0cTEhLCnj17uOGGGzAajdTX12M0GlEqlQwODrJp0yZ8fHxwOp188MEHbNu2jdDQUAoL\nC3nxxRfx8fHh8OHDlJaWMm/ePGFNHBERgc1m4+DBgwwODpKYmMjHH3/Miy++SFBQEKWlpaxdu5bl\ny5dTVVVFfHy84BPU1NTgcDi45ZZbOHToEPPmzcNgMCCXy6msrGTx4sW8/PLL3HHHHdx33304HA42\nb97M9u3baW9vR6PR8MQTTwg74TfffJO+vj46Ozupr6/n2WefJSAggN///vecPn2ajIwMJBIJTz31\nlPB52LFjB/X19cyYMYPt27fj5eWFXC6nv7+fpKQkrr32Wp555hnuvPNOTCYT8fHxFBYWEhERQW9v\nL1u2bOG5557Dx8eH3Nxcjh8/zvLly1m/fj0xMTEsX76c3t5ennjiCYKDgwkMDOTjjz9m0aJFhIeH\nU1VVRU9PD+PHj6e2tpZly5YRFhaGRCLh+PHjlJWVcerUKbq6uvjZz34GXOoyvfvuu0JFcCUolUoq\nKip48cUXUSqVeHp68vTTTwuvBxe+r3xeuHAh77zzDsuWLcPf35/R0VE+//xz5s+fz7Fjx8bMuF2K\niL9lnnMlVbXdbicgIIDW1lbc3NwICgoSCYNRUVHU1NQgk8kEH8Fut1NQUMDUqVP/pQI9bdo0kV7o\n6emJzWb7l4h+V/H/jqs6+/9k/GfoKA0GgyhuLjmYQqHA6XTS3NxMe3s7EomE6OhogoKCgCs76Fks\nFhoaGkhLSxvz+8+cOYNSqWR0dJTu7m46Ojrw8fEhLi5OBL3U1NQIaU91dTU6nQ4fHx86OjrGtIL7\n+/tpaGgQrnfV1dWoVCrc3NwIDAwkNDSUzMxMvvvuO5qbm1EoFNxyyy0MDg6ya9cufvvb346RjuzY\nsUM4uV1//fWCRDZp0iQ+/vhjFi5cyKxZs8Z8ntHRUd59910GBwe5++67uXjxIhKJhJiYGJ555hnS\n0tLIzs5m37596PV6fHx8yMnJobKyErjUFm5tbRUOgz4+PixatIjBwUFhkSuTyYiLiyM0NJSNGzfy\nwgsvjHkPLt/1e++9lz179tDV1YWvry+LFi1i79691NXVodfrRYSqh4cH8fHxnDx5klWrVqFSqTh1\n6hReXl4sXryY+++/n5tuugk3NzeOHTuGzWYjICCAa6+9lr1797J48WJ0Oh3nz59HrVYza9Ys3njj\nDYKCgpg/fz6bNm3CYDAQGRnJ8uXLhazr2LFjY9Qc8+bNQ6fTsXbtWsHrkEgkZGVlodVq+e6773jo\noYcoLi5GIpEwffp03njjDTw8PNiyZQt1dXUYjUZUKhWJiYk888wzREZG0tbWRnV1NXK5nMDAQMGg\nnzVrFsHBwUgkEpxOJ3q9nj179nDbbbcRFhYmNPoBAQFcuHABLy8vgoKC+O6772hra0OlUrFkyRIG\nBgaoqKggOzub7du3Y7VaiYqKIicnh6KiIhGd29PTg0QiYfLkySQkJNDY2MiECRP45JNPhK/Arbfe\nysmTJ/H09CQrK4vOzk6kUikRERHC4OjRRx+lubmZgYEBAgMD8fT05Ouvv+a+++674jP8zjvvsHDh\nQg4dOoTVakWn03HNNdewa9cuEhMTyc7OFqdum81Gfn4+s2fPZmRkhL1799Lf349Go2H+/Pn4+Pjw\n7rvvct9994n/43Q6+eKLL8jMzPxRDXxRURHp6eljgquKi4tJTU0VHaze3l5kMhkJCQmCLf9DZv/w\n8PAV15F/Fld19le98a/iB3B3d79sZg5/Jfn8UEoHl9rPP9T89vX1XRazCZdmaWVlZeh0OhISEpg9\nezY9PT1UV1cjlUqZPXs2vr6+NDY2YrPZmDhxopil9vb2UlRUJAh6FouFjIwMysrKiIuLIycnh+Dg\nYKqqquju7qa7uxt/f39mzJjBxIkTRWtUr9cTGRnJ6dOnhaa6s7OTpKQkDh8+TEZGhgjkGRwc5MiR\nIwQEBFwxgtO1mMnlcvr6+sjKyhIz+6CgIIKCgrDb7cydOxcfHx9hReoKXHn66afHBOG89dZblJeX\nExUVxa233ipep6mpiZKSErHB+j5iY2MpKipi9+7dzJ07V7i1bdmyhaGhIfR6PTfccANxcXFCEvjZ\nZ5+hUqkoLCwkLCyMxYsX09zczB//+EfkcrmQf6WlpWEymQQb3dPTk1OnTpGWlsbtt9/O0NAQ33zz\nDWq1WrD5f/Ob36DT6WhpaWHfvn3ExcXhdDqJj48XYxq9Xi9c/iZOnMjChQuFzKyxsZGzZ88SFBSE\nu7u7CJXp7u4mICBAkDNd3gpwqWiZzWb6+/vx9/cnOTkZd3d32tra6OzsJDg4mL6+PsGJcHVMYmJi\nkMvlY4qSRCIhKSlJmBvNnDlT8AXOnj2Lw+FAq9USGBjI6tWrgUsn7tzcXORyOXPmzCEnJ4eWlhY0\nGg1RUVGUlZVhNps5ceIEGzZswN3dnaamJjZs2MAtt9wishYCAwOxWCzU1taSkZFBYWEheXl5JCYm\ninHa6dOnsVqtl90HLphMJpqbm7n77ruRy+V4e3uzc+dOwTv5fntdLpcTERFBR0cHwcHB3HLLLWN+\n18jICBkZGZSUlODj44NSqaSzs5OUlBQxfroSsrKyOHbsGO7u7mi1Wjo7OwkPDxfP8vjx4y/7P1fq\nFHh4ePyvT6v7/wOuFvt/Q3R2dlJbWysevEmTJuHh4YHFYhGacIfDQXh4OAsXLkQqlVJXV8fBgwdF\ne2/+/PkEBwdz/PhxysvLRdpaQECAKNQzZ85k3LhxACL7/NNPP2XmzJl88MEH1NfXi5nv448/jkaj\nITw8nL6+Purq6lAoFEyYMEHMHouKigSxy83NjZ/+9Kd4eXnx1VdfMXv2bJKSkoSPe2dnJ3a7nc2b\nNwudvlwu58EHHyQ8PByZTMZdd90lfAJmzJjBr3/9a5xOJ19++SU7d+5ELpcjlUq5++67SU9Pp6Ki\ngrVr19Lb2ysKW3JyMgEBAXR1dfHZZ5/hdDqRy+WsWLECX19fOjo62LBhg+gKucxMRkZGiI6O5vTp\n04yOjqJSqUhOTqaxsZHCwkI2b94sro+3t7dwrFMoFLz66quCdzFr1izy8/OJiopiz5491NbWIpFI\n0Gg03HbbbVitVioqKoiJiSE/Px+4JO0zm81cuHCBpqYmJk+eTGRkJNXV1ezZs4elS5dSXl7O9u3b\n2bRpE1KplPT0dOx2u8gRWL9+PXK5HIfDwbJlyzCbzfT29lJSUiLun/j4eJxOJ42NjfT39/PKK6+I\n++fBBx8kICCAo0eP8vnnnzM8PAxcshpubW1l2bJlrFmzhrq6OqEJT0hIICEhgYMHD3LfffcJFUNA\nQABFRUUsX76cjo4OHnroISFhmzFjBjfccAOdnZ1ChieXyzl58iSRkZFClrdlyxZsNhs2m405c+Yw\nOjpKcnIye/bsoaSkBIlEgqenJzfeeCNpaWnk5+ezfv16zGYzdrudyZMns2TJEnx9famurmbhwoXI\nZDLkcjm//OUvgUsdInd39zEcja6uLnp7e1Gr1Xz77bfimQwNDcVoNGKxWHjzzTfF8xUeHs6jjz7K\nwMAASqWS9957T1gaJyYm0tfXd8UYW7VajcViYXh4WEQr2+12EhIS8Pf3x2QyYbVahVWwv78/7u7u\nIujnzJkzOBwO7HY7MTExhISECM5IbW0tFy9eZNy4ceIUaDabOXPmDBaLBYfDQVhYGFFRUVfkDLW3\nt/+XhOT09/dz/vx5pFIpDoeD5OTkvykjBMZcH5dL41VW/z+Gq8X+3wzt7e1cvHhR6HMtFgtFRUVc\nc801/PnPf2bBggWibVdaWsqnn37KlClT+Prrr8nKyhIZ11u3buXuu+8Wc+cVK1bgdDrJzc3l8OHD\nxMbGXnYycUmo1q5dS3JyMitXrhS+4s8++yyvvPIKx48fF97hSqVSLBh79+7F29ubzz77jJCQED79\n9FNWr17N7373O4xGozDosdvtgvy0fft2uru72b9/P/7+/vzpT3/itddeY926dfT399PU1CRO5SUl\nJfj6+lJbW8uRI0fYvHkz3t7eVFRU8Mtf/pLNmzfz3HPPsXDhQu69917sdjvPP/88xcXFREVF8d57\n7/H2228TFhZGfX09jz76KL/61a84fvw4YWFhY2b2bW1tJCcns2PHDpHj3tfXxzfffIOfn59IsHvj\njTfQaDS89NJLlJSUMGPGDP785z9z9913Exsbi81mY926dYSFhVFcXExgYCDPPPMMUqmU8vJyNm7c\nyJQpU1AqlZSVlREbG0tfXx81NTXExcXR39/PpEmTCA4ORqPREBMTQ11dHVVVVZSVlTFt2jSWLFnC\n6OgoX331FV5eXjQ0NNDV1cWuXbuECuGhhx5CIpHQ3NzMo48+KpL3Dh48yO7du2ltbaWtrY2DBw+i\nUqno6OjgJz+5mZkzZ6HXm6iubmLhwhtwOBzs3bsXs9lJUlIGzz77DMuWLSMqKorW1ma+/vpLfvrT\nO0Xm/SOPPEJAQABvv/02ZrOZlpYWPvvsMx5++GEiIyMZHBzkjTfeICIigurqam699VZmz54tnoPf\n/va3TJ8+nffff1/wPEwmE48//jienp5oNBrRxXBxGjZv3sxbb73FihUreOyxx0SOwGuvvcbbb78t\n+CYHDhzA4XAwMDDAY489xooVK0T634IFC7DZbOzbtw+ZTEZoaCjvvPMOq1evJioqivLycl566SXu\nuOMOfvvb33LTTTeRnp6ORCKhoKCAF198kfj4eF5//XWef/55EhMT6erq4he/+AULFiygs7OT8ePH\nj+HEuOKCT548SU5ODgqFAofDQVlZGU6nk/Pnz3Px4kVmz56NQqGgoqKCrVu3smbNGoqKioRm3ul0\ncubMGaxWK2azGYfDwXXXXYdEImFkZITCwkJmzZpFYWEh11xzDVqtVmQeVFdXk5ycTEFBAZmZmbi5\nuXHx4kXq6upEnPLJkyfFCEaj0ZCenv4veee7Cn12drYgVxYXFzNx4sTLHD9dGBkZuez6uNQWVxP7\n/j6uzuz/k/H/OpMpKCgYo7OHSzP8w4cP43A4WLJkyZh//95779EsH+G3AAAgAElEQVTR0cH8+fPH\ntP7z8vIoKioiMDAQnU5HbW0tcKndbDAY8PHxEXN4lznLxYsXGR0dpaioiEWLFqFUKpHJZOIhGzdu\nHGlpaQwMDAg2vktz/fzzz/P000/T3d2Nu7s7TqeTffv2odFoePPNN6mrq6O2tlbI+s6cOcPKlSv5\n4x//yLFjx3A4HPj4+FBdXU15eTlPPfUU586dE37kHh4eJCUlsXXrVt59910xK/b19UWv17Nu3Trh\nDeDiNCQmJtLR0YHZbGbTpk20tbVhMBjw8vLCzc2NZ555BovFwlNPPUVxcTFwyc3s97//PdnZ2cTG\nxo4xTjl8+DDHjx9nZGSEm266iQ8//BCr1cpNN91EY2MjX3zxBampqWJT45Ih7ty5E5PJJH6XSzZ2\n8OBBtFotTzzxBJWVleL63Hzzzbz55kacTi0vvvgGZ882MzzsRKPxZ3DQynvvfU54eBJ+flEMDFiw\n21VoteM4daqK0VEHUVFJDA9bcTgUOBwKJBIlfX16tFofJBIVTqcSu12K3S7BanVgszlRKDQ4HFIc\nDilO5+XGKv8sJBIH4EAqdSKTOXE4LDgcFtRqOXK5BKnUjkzmQC53MDDQjbu7Cl9fDzw91ahUEqxW\nAwZDHx0dzcTEhKJUOpFKLcjlNvz9PSgqOkhgoI60tPEMD3ejVNpwd5ditw9z9Ggu48Zp+MlPFqDV\nglJpwWod5fnnn8fT05OXX34Zi8WCRqNBr9fT1NREbm4uzz77rJDrKRQKrrvuOvr7+/nmm2945pln\nyM3NZWBggKCgIFJTU1m7dq2wA3YVTaPRyMGDBxkeHuapp57i+PHj9PT0EBUVxYQJE3jzzTf52c9+\nRl1dHYmJichkMqqrqwkKCqK/v5/k5OQx4wxX9vyhQ4e477776O3txWazERQUxLZt24iMjGTOnDmX\nnYgLCgoALpu/t7e3U1NTQ0JCwmVSXde8fnR0lMrKSsxmM35+fsTGxiKRSMjNzWXGjBlCstvX10d9\nff0/TLj7/troStf8vgul1WoVxOIr4cSJE0ycOHEMUdAVbPTPOgj+T+HqzP4qBK6ks3d3d+fChQuX\nEdMAgoKCOHnyJJMnT6a7u5u+vj78/f2ZPn06n3zyCQ6Hg+joaBFSc+7cOWprawkKCqK3t1dEikok\nEiG7s9lsZGZmiofKbrdTWVnJ2bNnSUpKIiUlhY6ODvz9/ZFIJBQWFqLRaJBIJAQFBeHl5YVerycp\nKYni4mJKS0uxWq0kJydjMBg4efKkcOTKzMwkISFBRLiWlZWRl5dHfn4+wcHBhIWFifz0xsZGVCoV\nHh4eY2bFcGmjZzKZuP3228nMzMRisZCfny9cAX19fbHZbGKe7IqtnTJlCiUlJSQkJAh/8uTkZNE+\ndxEPXddIr9fj6elJUVER8+fPF6E+3t7eGAwGDAYDq1evRq/XExISwubN72IyKZFKw+nvH49CEYLZ\n7ElfnxObbRYtLTKefTYMX9/FmM3u6PVStm8Hi2UpIOXSVz7W7x/S6ej4658UCjtyuRWLxQ+HY4Se\nHjXe3mrUailOp4menlagE4Wil6lT07DZRlAqQSaDgoIj6PX9ZGSkEBzsj1arxmgcobX1AidPlpKc\nPJ6cnGnU1dUikUBCQjyHDx/h4sWLLFmyBH//AJxOcDigt7ePPXv243RKmDo1G6lUjlSqxGp1cOxY\nGXq9kZycWfj6BmC1yrDZYHjYRElJK1ptCP7+wfT16envB6VSx8iIFoPBg9paHXK5FpMJLBYpdrsU\nmERHB5w+faWnaCUAf6l3ACiVTmy2pXR2GnnkEQ3+/m64u4NabaG7u46urlheeskNrTac2NjJgJEt\nWyrx9pZisSRQUzPKggW3odU6kUoR83+FQkFOTo4oWmazmUOHDiGVSqmpqWHBggVoNBq0Wi27du0i\nODiYkJAQYdXsGjGo1Wo6OzvHFHpA3HOulv33yXjTpk1jz549l8344crrCIC/vz8HDx4kJyfnsr9z\nnZbd3Nwu4/+0tLQQFxc3xpvD19eXmpqaf9kN7/uF3vX6fwtWq/UyRYBr/HcVfx9Xr9L/IPr6+kQB\ni4uLE/reH87MBgcHiYuLo7Ky8jJtb1tbGxqNhgMHDhAeHi6sNU+cOCGIYJmZmdTU1OB0OklPT+fL\nL78Up/bBwUEMBgMajYaAgADUajUDAwNjHiqZTCbmr64QmIiICJG0ZrPZGB0dRa/Xk5eXh8lkIjU1\nlcrKSuLj4zl16hT333+/WCjOnj1LZWUlVquVjz76iK6uLmw2G+7u7pw4cQJ3d3fGjRuHp6eniESd\nPn06R48exWKxMDg4yNdff01bW5toN7oS3nx9ffnd736HWq3mZz/7Ge+++64gR7k0yhcuXGBwcBCd\nTkddXR2bN2/m1KlTwCV3v4cffpjY2FicTidnz56ls7MTPz8/xo8fj5+fHwcOHGD79m8wGjV0dMiA\nhbzwwp8wGp/kwoVMli6VYTJFYTJ5YTavxW5fD8BfLNkFJBIzUukAarWdnp4+7PYaVKpRUlK86O6u\nIyhIQ3CwByqVCbl8FG9vBU1NFYyMdCGXm8nJSaW5uRI3NxVpaWm8/PLLdHV1sXbtG5w7dw6DwUBI\nSAiVlZUcPHiQtLRMPDwu2cK6rEtDQo7R0tLCo4++RWtrEQMDA38hBdqoq3sDiyWAlpYYQkMvzUUb\nG7uxWusIDJRx991TkEq76OnpwdfXFzc3N8rKNv1F2vUuUqlU+B+kpFwy+Lnjjpfw8vKiqqqKwMBA\npk6dypw59zBt2qXAodLSUoxGI+np6QwPD9Pe3i6y3pVKJVarFblczbffHkCt9sbPLxyVSodUqmV4\n2IbRKKGpqZuIiESmT59PTU0bEomWoKB4tm7dgUrlS3LyLHp7B+jtlWM2K9DrYxkaiiE/3x2H4/sE\ntb+aWG3f7vrOHGg0Zry8ZmOzTeL06SFaWsbh5eXE29uBj4+W06eTCA93o7c3gj/+cSdubqPodAqS\nkhLFc2W1WoWPgtlsRq1Wo9FoxmjS4dLJVaFQMDIygtFoFFHTMTExtLa2EhISQmdnJxcvXuTs2bOM\nGzeOa6+9dkz07/fR3t5ObGwsFy9evMwVz5VNcSW4Nq8/hEajwWKxiGwBV+fDJV/8MUil0su8QVxd\nvB+Dyxvk+0E6rrHgVfx9XE29+0/GP5pqdOrUKQwGA0lJSWi1Wk6dOoVarcbf35/Tp08TFBSETCYT\nWfUzZ86ktLRUBKg4HA7279+PQqFAJpPR1NTE3Llz8fb2Ri6XC3/t0dFRcnNzGR0dpb29na+++kpY\nufr6+jJx4kSys7Px8/Ojp6eH4eFhEQnqciMrLCykoaGBqKgorFYr06ZNQ6fT4eHhQWtrq8i7P3jw\nII8//jirVq1iw4YNnDhxghtvvJGbb76ZPXv2CEe7kJAQVCoVubm5FBQUsHDhQubMmcP27ds5efIk\nU6dOFVnjc+bMwc/Pj7y8PPR6PZmZmTz++OMsWLCAO+64g927d/Pqq68yd+5cQUZ89dVXiY+PF17r\nOTk5bNu2jcWLFzN16lTa29v5wx/+wG233cb58+dpb29n6dKlBAUFsXHjRlpbW4mLi2f37uPEx99M\nUNDt1NYmsGWLlYqKmVy8eA9vvhnMW2/p+PhjLTt2uDEwMB2LJZv+fnc8PXWMH+/N+PEW+vq+Qqs9\ngFT6KVrtJ6xe7c0TTxi5cOFuenpWMW3acWSyd7nxxn42bJhKZmY7eXlPER/fzuOPT6W09B0iI0eI\niLDS2lqMh8cIU6bEsXfvl2i1CmbNmoFWq+Xdd99FoVDg5eXFtm3bmDx5Munp6RQWFrJv3z4SExOF\nhnrdunWkp6ezefNmRkdHmT59Om+99RZ33nkny5Yto6amhpdeeonbbruNY8eOERQUxNNPP01GRgbf\nffcdjY2N3H777axfv5709HSysrJoamri5z//Oddffz3l5eWEhISQnZ0tJKB1dXVMnz6dP/7xj4SH\nh3PnnXfS3t7O8uXLSUlJESY38+fP55prrmHHjh1UVVWxfPlytm3bxrx587jzzjsJCgpi06aNBAZ6\n09ZWg1pt4plnHuTmm3MwGCo5cGATy5YlkZf3JirVeW67LQIfn/N89NHdREVVM3++maGhP/H229k8\n8oiK1NRjnD//IDfcUMnUqYe4//5e/s//0bFsWS863Xf4+R1h8WInVusubrzRmylTZOh0vfT01P5F\nXeCgrU1FZaWW/HwVR46o6ezMpqYmk/37Izh+/BqKimZQVJTN9u0+NDdns3u3jG+/HeH8eQ/q6zWU\nlfXR2WklJCSG06dPEBLih1KpFPkOqamplJaWUlxcLBIQCwoKKCkp4bHHHmPt2rV4eXlx00034XQ6\nee2118jMzCQgIIDGxkYCAgKQSqX09fVRXV3N9OnTOX36NJ6enri5uWG32ykvL8ff3/9H5+VKpZLm\n5ubLlCi1tbWEhIRQWFhIaGgoMTExDA4OcubMGcLDw8cU7++vjTqdjuPHjxMYGIhCocBoNHL06FHS\n0tJ+NGDI29ubY8eOERAQMOb6TJgw4f8zJj3/k6l3V2f2/8n4R2Yyg4ODNDY2jmlFO51O8vPzmTVr\nFoODg1RVVQnjkQkTJgiHuwMHDtDc3IzD4SA1NZXs7GwOHz5Mb28vXV1dKJVKLBaL2IVv3bqV66+/\nHqvVKrLX9+3bx8yZM/npT3+Km5sb/f396HQ6JBIJn3zyCW5ubvT29orr6eXlhb+/Px4eHsTExIiZ\npFQqZcmSJZSVlfHpp5+yfPly9uzZg1wuJyAggL6+PqKjo5k7dy6JiYmEhIRgMpmEH/trr702hoWu\nVqvJysqivb0dDw8PXn31VaKiorBYLJw4cYInn3ySnJwc5syZw7Zt24S3+eTJkykpKeHYsWPceuut\ngp+Qk5PDa6+9xv3338+0adN4//33RcrePffcw9GjR9HrpXR3+9De7snQUABWazjDw34MDPhitY5t\nqbq5jaBUdhIe7mTcuBH6+8tRq3uZMMGbjIwgvvnmbby9taLbIZPJmDBhAo2NjTQ0NHDttdcKJrGL\n6Ofp6UlaWhpqtVrcNwsWLGDNmjU8/fTTjB8/nnPnzmE2mwkKCkKhUHD06FHy8/MZHBxEr9cL5UF/\nfz+jo6NkZGQIHoS7u7v4Lvr7+7nzzjtFzGpWVhavvPIK99xzD3K5nD/96U/Y7XaUSiWrVq2iurqa\nmpoacnJyOHv2LACTJ0/m0KFDpKamkpmZyYcffojZbEapVLJixQrq6+vZv38/d9xxhzilWq1W9uzZ\nQ3h4OHK5nDNnzohkwqlTpzI6OsqpU6dYv3696LAEBQXxySef4O3tzbXXXktpaSn9/f0Awi/+xIkT\nPPTQQ1RUVCCTycRC2tDQIAyphoaGcDqdJCcnI5PJuOOOOxgZGeHzzz8XkcK33HILra2tGAwGsrOz\nxetERETw9ttvM2fOHCIjI8nPzxfub1OnTsVoNDIwMMCFCxc4f/48DoeEpKRsVKoQRkfVuLtHUVRU\nzfCwCrvdBze3SNrbLfT0SDCbvRgeVmO1Xn76VSpt6HQmfHxMREUpCAuT0tBQgNPZTkCADV/fUczm\nJpxOiyDyent7YzQakUgkREREsGPHDlatWiUS81y+CklJScJ3v7KykuHhYSQSCbGxsX+X5FZaWopW\nqyUhIUGoPkJCQrh48aK4h13o6uoSWQ4u/HBtNBgMwsdCoVCQnJwsZIE/BqPRyLlz50Q3ISkp6Yry\n4n9XXJ3Z/y/DhQsXiI2NHfMziUSCu7s7RqNRGHD8EFKplPnz51/2c4VCwdKlS6mtrWVwcBBfX19i\nY2PZunUrXl5exMbGipS4iooKTpw4wejoKHl5eQwMDODt7c3g4CBeXl7IZDJBNoqOjkahUIgAE6VS\nyccff8ySJUvIyMjg4sWL/PnPfyY0NBRfX188PT1FSpdrgdm5cyc5OTnCC1uj0ZCRkcGWLVuAS4v6\n/v378fPzo6SkhO+++47u7m6Cg4MpKiqitrYWo9Eo/PTNZjOhoaGkp6eLfPPrrruO999/n5CQEDw8\nPHjuuecwGo1s27ZNFNUJE7J54IGZVFbKqa+X89prck6fnsXIiO5719eOv/8o3t49uLvn8ZOfpNLX\ndxyVqo2gIAuLFl3L66+/jre3tzgtyeVyDIYWKipOkpqaRFVVFe7u7nh6emKxWGhqahItTafTSWZm\nJkqlUvjK79q1i9bWVpRKJVqtFqPRyFdffSXcCQMCAsYswna7nQ8++EBIJydOnIjBYKCgoIDW1lZ0\nOh0ajYYXXniBiIgISktLOX78OLW1tVxzzTXIZDIefPBB0fGJjo4Wsq5f/epX+Pn5cfHiRTFaCQkJ\nEe/P4XDQ1NSEn58fdrudsrIyEhISBEejtLQUT09PYfYzOjqKm5sbJpNJtJuzs7PJzs7G3d1dnPhd\n79vHx4fk5GQhVbvlllt46623+OlPf0piYiKRkZEMDw9TW1tLaGgolZWVDA4OCmb+yMgICQkJFBQU\nsHr1amQymZB1ueKcJ0yYQGlpKU8//TRqtVqECY0fP559+/YRFhYmClRTUxMqlQqn04mXlxfx8fHi\npBoYGEhFRYXYwCxcuBCHw0F1dTXR0ZeSA5OSzISGjiKRGPH0dBASoqK4uBiDwcCiRYuorq7BYJDj\n4RFPQUEtSmUEDoc/VVWDDAyo6O9Xc+6cB0eOKDCZFv1gLXDi7W3k22/byM4OJSzMTnCwnfBwO0aj\nDafzUnv8h/ePC3K5/IpZG38LmZmZdHR0cPz4cRQKBRMnTsTDw4O2trbLLLZda8Dfgru7+z/tpqfR\naK7oQXIVfx9Xi/3/ANzc3DAYDGNmcwAWi+VfIroEBgbS2to6JiO6uroarVbLlClTRLocXGqFZWdn\nU19fz7lz51i8eDHh4eF0dnaye/duoavPzs7GbrdjtVpFOlplZSVz5swhJSWF1tZWPD09+dWvfsXr\nr7+OwWAgMTFRLLxRUVE899xz+Pn5ER0dLWxw9Xo9vb29JCUlMTo6ys9//nOMRiNtbW1kZ2eTn5+P\nWq1Gq9XS1tYm0t+am5vx9vamv7+f8vJyHnzwQeRyOcPDw8IgJSEhAZvNzoYNOxgYiMRuv52BgTvY\nv38iX33114xurdZBSIiehIQ2dLpSoqPNzJoVQFCQifPny+nt7aW5uRmZrJBrr01GpQrEarWyc+dO\nkSTX0NAg5E4lJSXo9XpUKhUymYwnnniC5uZmYmJiyM3NpaKiAqPRKDYeNpuNwMBAGhsbxWJ5ww03\nEBAQgNFopLi4mIGBgSvOIvV6PcPDw/j7+wvJlN1uJygoiLNnzxIQEEBOTg5NTU1UV1fj7e3N4sWL\nqaurw2q1smjRIvbt24ebmxt33XUXhYWFDA4Ocv3114timpycTHl5OSdOnODAgQPccccdInDl2LFj\nHDp0iMWLF2MymcYQuU6dOsXFixepr69n6dKlpKam4unpSW1tLU8++SQJCQnMnTuXhoYGuru70Wg0\nLFmyhPXr14uiO336dOBS9+sPf/gD0dHR2O12IiIiaGtrQ6vVkpqaSnNzM6Ojo0gkEm677TZMJhMK\nhYKHH36Y8PBwsZlwNS5dJkpqtZrh4WG2b9+OVCpFIpGI8deUKVP4/e9/j1ar/Qs3QE5OTo6IN542\nbZqQwxYUFAgXy+DgYKHNd+U01NbWUlpayuzZs5HL5eh0Or788kv0ej16vZ7KykpSUlKQSqWcP38e\nf//zREYasFrLeeSRHJRKBQ6HldLSPXh56cjPLyc4OIvmZhv9/Vr6+7UMDHhw7pydkydl7Nmj+UGX\n4P/wwQdOIiJshIXZiYy0ERVlJyrKRlSUDU/Pf62h6zKp+j6udJ/+rfn/VfzP4Gob/z8Z/0ibxm63\nc+TIEa699lph0jEwMEBNTY1ITLsShoaG+Pzzz3E6nYI1e+edd6JUKikvLxeyMlcYTUhICGvWrBGn\nDpeX9t69e3F3dxeOdq5AHJVKJRziRkdH6ejoEAYwSUlJVFdXk5SUxI4dO8SJLSoqipCQEPbu3Yuf\nnx++vr54eXnR0dHBuXPneOyxx1Cr1Xz00UfY7XbRMpw3bx6vv/46KpUKpVKJUqlkZGQEs9nMuHHj\n6OvrY9q0aQwODqJQKNBoNBQXFwvpnsu6c2BAzgcfnEMiyaKiQo3FkoLd7irsDpTKFqZN8wDO4O9/\nkagoA9CKWq1izpw57NixQ6ShAWi1WiZOnIharebgwYOCST06OiqIh4GBgVy8eJHW1lYcDge+vr5k\nZWVx6NAh0T1Rq9XCh9zDw4OmpibhfueSM/b29jJp0iShRHCNWtRqNcXFxWzYsIG3336b7u5u5HI5\nFouFefPm0dXVRUVFBbGxsUIlAJeK7YIFC0Tr3NWSd6kO3nzzTWbOnElKSoqYd7a2tjJp0iRhJuRq\n8Xp4eFBXV8eBAwcYHh7G6XSK+8fd3Z24uDiSk5Px8vIS4TAuHfaFCxeEOY5cLhemRHPmzOHChQt4\ne3sLExmj0UhQUJCIbw0JCUEulzM4OMjRo0fZvHkzhw4dEvHHJpMJLy8v7rvvPtavX8/o6KggAur1\nemQyGTNmzODo0aMEBgaK78FkMolo2oaGBlQqlQhfGhoa4p577uHDDz/Ezc1NFCrXxtdlaDU0NIRC\nocBisQgb33nz5gF/9aF3fRd5eXmYzWYmT56Mr++l+3H//v0i4W7RokVis2G329m/fz85OTn4+flx\n7NgxQUZMSkpCKpVy4MABzGazeF8uy+O77rqLI0eOkJGRxciIO52dGurrHfT1eeHuPpELF2S0tMjp\n7BzrjOfra2PcuAGCggwEBQ0RH29n7twQIiIc/Jjd/o/p7F1joe93K0+cOEFsbOwYSeBVu9yrbfz/\ndZDJZGRmZlJcXCwMJdRq9d9sTzkcDt5//33uueceQaJpbm7mgw8+4KGHHiI1NVWw4pOSksQmQq1W\nk5+fL2byAwMDwprUNbP08fHBZrMxNDSERCLhyJEjTJ48mRtvvBGJREJTUxNbt25l4sSJ5Obm8txz\nz3HhwgV8fX0pKysTftu1tbUYDAZUKhVGo5Hx48cjlUpZs2YNv/71r9HpdNjtdnbs2MEXX3yBTqdj\nYGCAcePGiTCVgYEBYmNjhY+8RqPBaDT+hXkchkIxic8/V7FxoxqzOQObLQy4GXCgUNTh5paHTleL\nWl2JzVaOh4eUtWvf4+TJur8UbSlubono9XpGRkbIy8sjNTVVnFY6OjrIy8sjKioKnU7HpEmTxGm+\nsrKS5uZmKisr+c1vfkNycjJOp5Oenh6efPJJhoaGMBgMPP/880gkEry9vdm4cSO1tbVYrVahMHBx\nJ/R6Pa2trbi7u2Oz2QRZqqurC7lcznvvvYefnx/33nsvMpmMoaEh1q5dy5QpU+jt7SUyMhIvLy9s\nNhsXL14UhbOiooK4uDjkcjlGo5GWlhZWrFjBHXfcwYEDBzh9+rQYGy1fvpySkhLq6+vx8/MTLnUu\n4qIr/c5FCm1vbxcmPCkpKUyYMEHY3paWltLb28vIyAjTp08nNDRUSMp2794tpGbh4eGoVCph2NTc\n3Mz06dMpKyuju7tbRC2npaUJk6FrrrlGpCP29fVRVFSEj48PBoMBm80mTvHf5yekp6fj4eGBw+Gg\npqaGoaEhcnNzWbJkiTDvsdlsvPbaa3zyyScMDQ2RkZEhugCuWOOwsDDsdjuTJk0SG4QTJ06IsYvL\n+MblzpiZmYlCoWDatGnU1NRw7tw5oqOjmT17NgcOHMDNzY2+vj78/PyAS65wCoWC1tZWWltbWbVq\nlTgV79y5k7a2Nmpra8nJySEjIwOpVEpXVxebN28WypVz584Ii+WICAl33z2T5ORBsX4YjdDSIqep\nSU5Dg4SSkl5MpjAqK33Iy7u0VrzwAqhUTqKjbcTF2YiPt5KYaCMx0UpoqI2SkhKmT58uGPS9vb2U\nlZWRlZVFZWUlBQUFyOVykVPw99zwruK/F1eL/f8QvLy8mDFjxmUngh/D+fPnSUxMHMOWjYyMRKVS\nibm7XC4fMxoYGhpi0qRJDAwMiBQ7jUaDt7c358+fJzo6mkmTJomIW1ehM5lMYzoMERER+Pr60tDQ\nIE4+06ZNw2Aw8M0332CxWNDr9fz85z/HbDbj6+tLb28v7e3tfPTRR9x4441j3tfSpUv5wx/+gLu7\nOytWrCAtLY3BwUHGjRvHunXraG5uJicnh4aGFnp7Y+noiGN4eBIdHdFYLJdkN1qtnvj4Nvz8dtPb\nuweptJy0tFh+//vfo9dPQCq9Ga1Wy80330xhYSFarVbM01taWoiPj6eoqAiHw8Hjjz8ucrsjIiK4\n6667OHv2LGvXrsVkMgnS44wZM/iP//gPNBoNQ0NDFBUVic+kVCppbW0V7nk9PT2EhITw4IMPcv/9\n9yOXy/nwww/HkIkeeOABjh8/TmJiIosWXZrJuhLfXnvtNdra2nj33XcBxPdns9l45ZVX0Ol0LFy4\nEC8vL5xOJxUVFdTW1jI8PMy8efMEiVOhUPDAAw/w7bffMnfuXCIiIkRan8vSta2tjcDAQLEBGBoa\nwmQy0dHRgc1mIzExUei/vb29aWxsZGRkRAS6uAhWRqOR7du3o1AouPfeexkZGUGn0zE6OkpZWRmN\njY0sXrwYqVSKm5sbIyMjhIeHU1JSgp+fH3v37hWujgqFgi+++IJPP/1UkDR9fHwwmUy4u7tz9uxZ\namtrefjhh0U3YGRkhPfee4+WlhbuueceBgcHxaZ32rRp7Nq1i97eXlHoXdd7xYoVrFy5kpUrV4oo\nX6fTSXBwMOfOnaOiooINGzaIMCqXb//bb7+NRqNBqVSK6zM6OkpBQQFarZaamhoRNNPT08Pg4KAI\naPL09BSkx8jISNLT08nNzeU3v/nNmPb3woULeeyxx/D29mbWrFkMDw/jcDgIDAzktttuY8uWLaxc\nuZKOjg5UKpWIqe3r6xuzfmg0MH68jfHjbVRVVXHrrb74+zQMsq8AACAASURBVPf/ZZ2QUF8vZ/fu\nRiCJ+noFZ84o+O47tTBY0mjsREcv5cABKUlJVpKTbSQl+WGz1YoOxPfv06v498PVYv/fAJd1pVar\nvexB+EcfjO7u7ivqXMeNG0d/f/8VJTODg4M4HA7uv/9+UfB9fHzYvn07MTEx7Ny5k6KiIqKiomhr\na6OtrY3IyEiGhoYE+9/hcIiEtgMHDvDkk0/y8ccfi9N7SkoKjY2N2O12RkdHRVa7r68vRUVFdHZ2\nctddd13mX71u3TqR375x48a/LKQwb97PWbfuJMeOTUWvn4zJdOkU4ePTjbd3HnZ7HjNmSFm9eiVy\nuYzh4QB+/vPTNDY2Eh19HXl5eWi1WuET7+vrS0lJCTfccIOIKAX48ssvKSsrw9PTk40bN4rM7l27\nduHp6cnQ0BBlZWWsXLkSjUaD2Wzms88+Ex7su3fvJjMzU6TVuU51tbW1HDp0iLCwMHp6eti1axc6\nnU44++l0OqRSKT09PSiVSuRyOTU1NRiNRiIiIhgcHKS6uhpfX98xZiGu+2Tu3Lk8++yzzJ8/n9bW\nVtrb20VXwNfXl+7ubkFmc6k5urq6GBj4v+y9Z3jUddr2/5lJJtMyk0nvlVRCSIWEZigC0gQRRSm6\nPororm1dZHdduy6i3oqouI9lLStiUEQUpYYeWiCFkN57m/QyM5n6f8HO95F1vW931/Xe/x57HYcv\njBAnk998y3Wd5+fsp7y8nPDwcJqampBIJPj4+FBUVCREodHR0ZjNZoKCgmhra6O3txe73U56ejqr\nV68G4O233xaCtwMHDlBVVYVGo2FkZISGhgb8/PwYHR2lrKyM1atX4+/vz/bt2wUHQaFQkJaWxuDg\noAhEOnToEAEBAZSWloq2dXd3Ny4uLrS1tZGQkMBDDz0k0vUqKyt56KGHkEgkDA4Osnz5ctzc3Kir\nq0Mmk9Hf38/g4CD33nsvRqMRmUzGyMgIOTk5YiTzbXhNdXU1SqWSsrIyfHx80Gg02Gw2hoaG6Ojo\nIDg4mLNnz5KRkSFGUaWlpahUKrq7u8UoDRCo6rS0NPbu3cukSZPQarWiVX/NNddw6dIlzpw5g0aj\nwcXFhcuXLxMUFISPzxUWgZPwaDabOX/+PL6+vvT395OSknLVZ6ijo4OBgQG6u7vJzs4Wz0hHRweH\nDx/+XqrcyMjIVXAeDw8H6ekWxsZayMoKFocNg0FCVZUrlZUy8vIGaWvz4ZtvlHz8sfrPz6SDkJD5\npKY6SE11MHGihcRECxrNv81k+N+q/rPZ/xPr2+xm520wIiLiezOw/7tKTk5m3759JCUlXfX1pqYm\nodC3Wq2Mjo7i7u4ubjRDQ0O89tprqFQqpFIpIyMjaDQaXF1dmTp1qmCmh4WFkZaWJvz/HR0duLm5\nIZFI0Ov1NDY2CurXs88+K1TZPT09fPbZZ6jVaj755BNGRkZwc3PDZrPh4eFBVFQUO3bsEGEjztdp\ns9loa2sjPf1atNppXLoURHV1KNu3q4CZqNWdZGd34ulZwMSJ/WRmRrJhwxMMDg4ya9Yv2bz5eeDK\nDXDNmjW89NJL7Nq1i3379omf3Wg08uijj5KZmcm0adOuen/mzJnDkSNHqK+v5/HHH6eurg6AFStW\ncMcddxAeHo5EIhGOBadTwcfHh8HBQR577DEKCgoYHBxk6dKlPP/884Ie6Dw4ubi4MHnyZEpKSlAq\nlRw5ckS0skdGRmhubiYqKgpPT09iYmJobm5GLpezZs0a3nnnHRQKBRaLhcHBQZEW9/zzzwtkcVZW\nFgMDA0K57+7uTnt7O0NDQ4Ix3traSn5+vrAvtrS0AAihWX9/PwaDAU9PT44dOyayFcLDw4WWoqqq\nivXr12O32wkJCcHNzU204mtra0VSnkajYeLEiVy4cAGVSsX9998vAoScUbc1NTWYTCa8vLwYHh6m\nu7tbHJQ8PT05c+YMcMWHbbfbUSqVuLq6curUKTHqkUqlmEwmITZ9++23hUjxxhtv5A9/+ANFRUV8\n/vnnaLVaHA4Hzc3N4kbuZNdrtVrROneOEW677TZUKhWurq7U1NTQ09NDcnIyBoOBN998k/7+fvz9\n/YmMjMTFxYWIiAjc3NwoLy8XUbHOAKG5c+cyOjpKZWUlCQkJzJ8/n6qqKvLy8rj55puJiopCIpHQ\n3t7OBx98wMqVK/Hz86OsrExYy5wx1u3t7RQUFJCcnIyLiwtNTU0cP36c8ePHi66Ms/z9/UVwEVyh\n+o2NjaHRaJBIJISEhFBfX39V6p3D4fiOqE6lcpCaaiE11cKCBaM0NJSSkpJKZ6eU0lIZly/LOHZs\niPz8QL766spnTiJxEBtrJS3NTFqahbQ0MzEx1r+qA3CKOZ2jzJiYmL+aKPmf+nHqPwK9H7m+LcC4\ncOEC48aNu2p2debMGWFZ+VsrJycHhULBwoULMZlM7Nmzh+DgYObMmUNxcbGY/TlvtKGhoTz00ENM\nmjRJqKmLioo4f/48y5Yt4/Lly7i6uhIaGkpnZycDAwNMnTqVjz/+GJ1Ox/XXX49cLic3N1eEVuTm\n5gpveFNTE+Xl5cyYMYP8/HxGR0fJysoiODiYI0eO0N3dzYMPPsjnn3/OvHnzuPvuu6moqOCxxz5C\nqbyJioooenoSAFcUihE0mnyk0mMkJ/dgtVbT0dFBSkoKfX191NXVERERQX19PXPnzmXy5MlotVra\n29v55ptvBFDJz8+P559/nv7+fh555BG8vLyIjY1Fo9EQGhpKSEgIDQ0N9PT0UFdXx8WLFwFYvnw5\nALv/jEpbtmwZu3btEmRBJ3Dotttu48KFC3R0dIj55enTp1EqlURFRbFv3z5Wr17NypUrqays5NFH\nHyU7O5uxsTFOnTpFREQEGo2G2tpa3NzcmDhxImVlZUIE2dvbS3l5OQEBAcydO5dXXnmFrKwsIiIi\nOHLkiPi9r1+/XjggnNyGhQsX0t7eLqA1wcHBFBQUsGvXLkZGRmhtbWV0dJTw8HDGxsbo7OwUM3+1\nWs3y5cuJi4ujqKiIL774ApPJREtLC4GBgaxbtw6JRMJbb71FZ2cn119/PQUFBeIA1Nvbi9lsZtWq\nVRQWFpKXl8ecOXPw9/fns88+Izg4mMTERI4dO0Z0dDRxcXG0trZSWlpKREQEP//5z7l8+TIrVqxA\no9GQm5tLZ2cnNTU1FBUVMX36dCGu27lzJ2NjY7i7u5OQkMCMGTNEKNKhQ4eIioqioKAApVLJhAkT\nGBgY4PLly0yYMIH58+eTk5NDZGSkOIB3dHRw/fXXc/r0aTo7O0UEb1NTE+Hh4UyZMoXf//736HQ6\nIiIiqKmpwWw28+yzz1JQUMDFixdFYJNerxcdAGeoUEhICD09PXh4eNDf3097ezv33nsvEydORCqV\nUlFRwXvvvSeAT7NmzSI5OZnm5mZ2797NqlWrOHv2LHl5ecyaNQu1Ws358+cxm83MmjULi8VCdHQ0\noaGhjIyMcPHiRUwmE3PnzuX8+fO4uroK7kBMTAxBQUEimGncuHFCdxAbG0tAQMD3rj8XLlwQrpex\nsTGKi4sJDAwkIiICvV5KSYmMkhIZhYVuFBa6MTBw5eDg7m4nJcXCNde4kpTUT0aGhdHRburr65k8\nebIQ/J0/f57o6GiRxPnvWP+bAr3/bPY/cn37l3nq1ClmzJiB2WwWi5PJZKK0tPTv9oqWlpZy7tw5\nXFxcmDVrllh8ampq8PDwEAvK4OAgGo2GnJwcNm7cSFdXF3Dl1P/SSy8RFBSEp6cn7u7ugr1ts9nE\njb6zs1P8HFqtlqCgIMbGxqiuriYqKoqWlhY0Gg06nY7GxkZKSkq4//77aW1tRSaT4e3tzc6dO8nM\nzGTRoiV88kk9BQWRDAxMx2Bweu6riYmpRqM5gU5XjUZzBebjjOx98cUXKSwsRKVSERkZycaNG4mM\njMRgMJCWlkZYWBilpaWUlJSQkZHBww8/zP79+0X87bp16wgMDGTPnj1ce+21aDQaBgcH8fLyoq6u\njoKCAmpra0U8LiBshs7ccolEInDCTupgS0sLQUFBtLS0CFFYX18fcXFxXHfddWzbtk0IGFevXk17\nezujo6PI5XLKysoYGxsjISEBvV6PRCKhtrZWQFHsdrsQ2TkDVvLy8jAajcTExACwfv16Xn75ZUGu\nk8vlBAQE0NDQgEKh4JFHHuHo0aNC7BgSEsLjjz9OU1MTx44dY3h4GJlMxtDQEEuXLiUkJIQFCxag\nUqnEuKmhoYGamhoGBgZE7CtATEwMg4ODwvrnTOfz8vKiublZdAlmzJhBTk4OUqmUefPmUVxczOjo\nKJGRkYSGhlJVVYWvry9xcXG8/vrrHDx4kLa2No4cOcLY2Bjp6emkpKTw1ltvUVpaSnp6OtXV1Wi1\nWkJCQti3bx+JiYn09PQQHByMUqmku7ub9vZ2Zs6cSVFREenp6Rw4cACdTsfKlSt55513uOaaa8jO\nzubixYsMDg4SExODRCKhoqKC0tJS7rzzTgoLC5HJZGRkZPDSSy8xPDxMVlYWkZGRdHR0MG7cOD7/\n/HM8PT3Jz8/nueeeExjrCxcusGnTJpKTk4mPj+cXv/iFWBc2bdrEoUOHmDBhAg888AA1NTXY7XbG\njRsn4nPXrFnD0aNHaWpqwsfHh7lz5wq9S1VVFTt37sRkMpGdnc38+fNpbm7GZDLh4uIi2PrR0dEU\nFxfj6upKYmKi+PsOh4NTp04xadIkFAqF6GrI5XISEhL+R6ANXMHtNjU1IZPJiIuL+4592FkOBzQ0\nuIiNv6BARnm5DLtdgqurg/DwPq69VkZWloVJk8x4el4Jjzp79uxf5fb/u9R/Nvsfqf4VN3unqEel\nUjEwMEBISAjd3d1/FZrzQ8toNCKVSoUqNicnh8zMTCIjI8WfqaioYPfu3fj7+zN+/HjhhXVupPv3\n7ycxMRGDwSCgKO7u7tTW1uLj40NAQAD9/f0AYn5YUlJCZGSkUPEPDw/j5uZGQUEB7e3t3H777SJN\nKz//It9800tz82Ts9uX09qqQSi0EBVWSlNTIo4+mcMcds9m1axc6nU5sMq+//jofffQRCQkJREZG\nMnPmTPR6PadPn6a0tFRQtlxdXTGZTGi1WoaHh/H29mb69OlMmzZNvAcOh4Pjx49jsVgoLS0lKyuL\n+Ph4iouLKSkpQafTceHCBZ5++mlB3YuNjeXxxx+no6MDHx8fwsLCCAsLE635zs5OjEYjDz30EKmp\nqcJz//zzzwsld0ZGBi4uLmg0Gi5fvkxzczMBAQGsXr1abHrJycn09vayYcMGFAoFjz/+OAqFAhcX\nF06dOsW2bdtITExk69atwvetVCrZvXs3H330EWvXrqWqqorh4WFcXV0JDg6mrq6OwMBA4uPjSU5O\nxsvLi8bGRoqLi9mxYwe+vr7cfvvtBAQEYLfbaW9v58knnyQsLIzU1FRCQkJQq9UMDw+j1+upqakh\nODiY0tJS4Uq4dOkSEydO5KuvvmLx4sXodDoBZRoaGmLXrl3cdNNNbNy4kaGhIdGOz8nJ4YMPPmD+\n/PnEx8eTlJREa2sreXl5FBQU8MknnwBXok+NRqPQp7z77ruYTCbR5u3p6aGpqQm5XE5SUhJWq5Wu\nri6MRqOYe3/99dc4HA4RETw4OEhVVRVqtZrk5GR+8YtfUFNTQ1dXF+PHj8fd3Z3f/e53eHt7c+21\n1xIfHy+e96+++or8/Hxef/11jEYjGo1GHIDuuusuZs6cSUhICBqNBofDwcjIiMiP/9nPfkZycrIY\nAR0/flx0jrZu3So6fiMjIzz44IM8/fTThISE4HA4MBgM4nn478rhcHD27Fk8PDyIjIxkYGCA8vJy\nMjMzKS4uFtwCZw0ODtLY2EhycvLft/j8A+Xm5sOhQ8OcO+dGbq6JujofzGYJEomDCRMszJgxhpdX\nIT/7WQz/P6Hf/s31H+vdv2lVVVWxZMmSqwhWn3zyyd99qx8YGKC4uBi1Wo3dbmdsbIxJkybR399/\n1UYPCNtbS0sLd91111X/LTc3F7vdTnNzM7NmzcLT05OhoSHOnDlDd3c3fX19pKamCohId3c377//\nPgMDA5jNZtLT0xk3bhxdXV2UlJSIvPqAgEDKy93ZuTOUwsJsBgbckUjMhISUMWVKFZmZemy2Pvr7\n+zl+/BJKpZJ3330XqVSKWq2mr68PNzc3HA4HERER+Pr6cuTIEVxcXJg4cSINDQ1IJBIeffRRzp8/\nT0dHB8nJycjlcr744gt8fHw4e/YscXFxWCwWysvLiY+Pp66ujoceeogzZ85w8OBBoqOjeeCBB/jw\nww9JTk5m06ZNREVdSZbbtWsXaWlpvPvuu8yfPx9/f3/MZjPx8fGEhITwxhtvkJGRQVVVFS0tLUil\nUoaHh8nIyKCxsRE/Pz/27t2Ll5cXZrNZYGHtdjsffvghMpkMhULBJ598woQJExgeHiY0NJTf/e53\n+Pn5CUugp6enILLZbDZhowsLC6Ovr48jR44QExNDcHAwY2Nj9Pf3U1NTw/z582lvb2fv3r3AFbW5\nc1ZdV1dHU1OTwME6uygeHh4kJydTV1cnOhKhoaH09fURERHBzTffzGeffYZEImHTpk3k5eVhsVjo\n6OggPDycsLAwJBKJCGmRSCS88cYbqNVqdDqdIOSpVCqUSiUffvgh/f39yOVyli5dKlrML7zwAq6u\nrsjlcvr6+rjlllsICAggPDwcmUxGUVERQUFB3HDDDWzfvh21Wo1UKsVoNKJWq4WWwGKx0NDQQFZW\nFiaTCVdXVyZMmMCBAweIj4/nt7/9LR4eHgQEBHDgwAE0Gg1yuZzk5GSGhoYo+nOUntlsJiEhgTNn\nzqBUKklJScFiseDm5iY88y4uLtx11100NjYikUgICwvj17/+tQi12b9/vxDmOhwOAgICiI6O5sEH\nHyQxMRGZTEZpaSmxsbGEhIRQU1NDR0cHGo2G0dFRARH6PiGvRCJh6tSpdHd3U1lZiUajYdasWcKP\n/5fl9O//1GWxWNDprMycOcbMmWPMmnUeu92Ny5eV1NaGUFio4+23fbBas3npJQcZGWZmzBgjO3uM\nCRMs/IfP84/Xfzb7f2KFh4cLeppWqxVCOOeNGb4L4/i+cjgcFBQUMHPmTPEhHhsb4/z582KxdG4w\nbm5udHV1CRb9a6+9JhjaeXl5+Pn5MTQ0JDjZTu+9M3XM39+fkJAQTCYTEokEjUaDl5cXHR0deHp6\nsnjxYlpaWggNDSUyMpJvvinBbv8ZTz99M6OjYbi6WgkIuAS8x4QJjaSnx7BhwwasVitubm7U19ez\nadMmrFYrzc3NQpUPVw4iaWlpFBUVsWnTJmpqalAqlahUKoaHh4mOjuZPf/oTPj4+It7XZDIRExND\nXFycYKO7uroybdo03NzcaG9vZ3BwkOnTpwtrUHt7OykpKXzwwQc899xzQmi2du1annjiCSQSCTKZ\njOXLlwtAzN69e7FarXR2drJ582ZaW1ux2WxERUVx3333IZFIkMvlrFq1iqCgICQSCZ9//jmFhYU0\nNzezefNmIUCSSqXcfvvthIeHU11dzd13301zc7M4yL377rvI5XLa29uFoNHd3Z2PPvpItMFXrFiB\nQqHA4XDQ0dHBZ599RmxsLHV1dcL9YLPZGB0dZd26daxdu5azZ8+KA0JHRwcOh4Pp06cLyFNUVBRl\nZWWcOHGC6dOn09vbS3NzM4sWLcLhcFBfX8/w8DASiQS1Wk1oaKiwqel0OmQyGYcOHWLHjh0MDw/j\n4eGBQqFg+fLlLFu2jHfeeYc77riDRYsWUVRUxKuvvsr48ePZvHkzWVlZ+Pn5CRvde++9x8aNGyks\nLBQgpuHhYfLy8oiNjaWyspIbbriB6dOnC+LdBx98IHz3s2fPFjPpY8eOYTAYxAht1apVqFQqOjo6\n+N3vfsfChQtpa2tj1apVNDQ04ObmRmhoKG+99RYeHh5s376dCRMm4OfnR2trK8ePHycqKkqMFpzo\nWavVKmiWcrmcefPm0dvbS0xMjEheTEpKIjw8nPz8fIxGI8uXL0ej0dDd3c3IyIiw5EokElpaWigv\nLycxMVFwDsbGxggNDb0qMc7Pzw9fX9+r1hGbzSaeXWdVVFRcpcT/y4TNH7uckdZubm5otVr6+vqE\nQyE/P5+bb16Kh4eawcEedu78EKVyHg0N4zh1Ss7mzVo2bwY/PxuzZo0xZ46Ja64Z+2/V/j90Pf2h\n9e9kJfxJ2vhvvvkmhYWFeHh48PLLLwNXHoItW7ag1+vx9fXll7/8pYgu/OKLLzh69ChSqZQ77rjj\nO5aT76t/tTZ+Xl4e06ZNo6WlRdzgVCoVFy5cYPz48RQXF+Pi4iJS6NLT07+3bdfa2ipU2VKpVNDM\nXFxcGBsb4+WXX6a9vV1AaCIiInjiiSfo6elhw4YNwjIkkUjYvHkzr732GhaLBavVire3t7DpmUwm\nsRg42+V9fX0iu1qtVjM4OEhwcBy1tRPp7r4OgyETkOLqehaFYifu7gcwmbqIjY0V/vSvvvpKwFc8\nPT3RaDQYDAaamprw9PRErVbT2dkpEKUVFRU0NDQQFBSEwWCgv7+fa6+9VgjaXFxcBODE4XDws5/9\njMDAQD799FORPy+TyVi9ejVubm7k5OQAVwJoOjs7UavVZGdns2/fPiHqgis89Hnz5rFnzx56enqw\n2Wz4+fnR29uLzWbDy8tLIGDVarWA3URERIiAHYPBwNDQED4+Pnh6erJ161ZiY2M5duwYKpUKhUKB\nXq9nypQp1NTU0NvbK7jw3d3d1NXVYTQaSUpKorq6msmTJxMSEiLQtt7e3gwODqLX6wXt0BnZ+8AD\nD7B9+3ba2tqQSqXYbDbmzp3LlClT+MMf/kBFRYXwkhuNRubOncvSpUvZtWsX9fX14gaalpZGVlYW\nTU1NYuOz2+20tLRgs9k4c+YMvb29aLVaAgMDhb3P29ubkJAQ8vPzBT754sWLJCUlCbKj09/vtAw6\nv8/atWsFz95qtQqao81m48iRI8LlodVqefHFFzl16hRbt24Vt+2RkRGuu+46ioqKiIiIENHERqOR\n0dFRurq6hOuht7cXFxcXLBYL06dPFzHKDQ0NzJgxg9HRUU6dOsWSJUs4evQo77//PiEhIeIwNDQ0\nxO23305vby8lJSWsWLECs9nMF198wZQpUwgNDeX06dOMGzeOzMxMoS2ZM2cOarVadGycv6OJEycy\nMjJCfHw8zc3NwnngXE+SkpIoKCggOjoauVxOXV2d6BJ0dnZSVVWFq6urcME4v59T9ObUXSgUCiZO\nnEhdXR379u0TvwcPDw9uueWWHzUb3uFwcODAAUHJ1Gq14nMkl8vx8vIiPz8fi8WCTCYjMzOT3t5e\nMXrQ66UcPy7n6FEFJ07IGRyUIpM5mDzZzJw5Jq67zkR4+JXxpNVq5eLFi4JCabfbSU1N/buE0HAl\nfry0tFS8pyqVivT09H944/+3n9mXl5ejUCjYtm2b2Oy3b9+Ou7s7y5YtY8+ePYyMjLBmzRpaW1vZ\nunUrmzZtor+/n2effZatW7f+oNPnv9pmf+rUKSZPnnzVCby0tBRPT08qKiquwuX29fVRXV1NVlaW\naOW5uLhgtVqJj49nZGSEwsJCli5dKjy9IyMj7N69m0uXLlFRUcGjjz4qcKePP/44S5cu5dNPP2XL\nli0C2Tk6Oso999wjNvL7779f4HJ37NhBTU0NcrmcdevWERsbi0Qiobe3lxdeeIEJEyZQVaXE0/NR\nCgrisFjkuLt34e6+G6XycxYtSsDLywuJRIJOp+O1114jICCAqqoqcbqHKx77Dz74AA8PD5YsWSK8\n2cHBwXzwwQcCQrJixQpqampE+/f48eNcd911mM1mAgMDxdy+rq6Oa665hurqauF9d85P6+vrWbRo\nEQaDAW9vb/r6+vDz86OxsRFXV1dycnIwGAzo9XrgymFAqVTS1NRER0cH8+fPZ2xsDJlMRl5eHjKZ\njJ6eHjIyMpgwYQJSqZTW1la+/vprMjMz8fb2JiIigsDAQJEsVllZSXt7O7/61a+EeHLGjBls2LBB\n4IH9/PywWq1IJBLxM4WGhrJgwQKMRiPDw8OEh4dTUFBAQUEBvb29fPjhhyL2VC6X8+tf/1p0W269\n9VZcXFwwmUw89dRTQni4bds2cnNzUalUxMbG8uSTT5Kenk5QUBB33nmnsJY9+eSTeHl54XA4eOSR\nR+js7EQikeDr68tLL71ET0+PyCMfHh4W3nSDwcDY2BiPPPIIBw8eFELSN998k46ODhYsWEBYWBgt\nLS14enricDjYuXMnHh4evP3224IXYbPZ2LJlC+3t7UgkEh577DE6OzvRaDSUlpZSXl5Obm4uN998\nM7Nnzxa/31tvvZWwsDCam5u57777qK+vFyjo//t//y8SiYSbbrqJW265haGhIXx9fVmzZg2BgYG8\n//779Pf3i27ZlClTcHNzY/PmzcyePZuysjKKi4uZM2cOEomEjo4O4uPjiYuL46OPPhICO+cmMWPG\nDM6cOcPly5eZPn06iYmJfP3111y8eJGf//znovsyNDTEli1bRAjPt4FWtbW1FBYW4ufnx4wZM666\nDJw+fZq4uDjhiHFuRK2trfT395OUlCQOaEajkdDQUNRqNSMjI7zzzjvcf//9YnOvrKzk4sWLrFmz\n5kdbC9vb28nNzWXlypXI5XJ8fHxobm4mJycHpVKJj4+PyJOw2WwYjUaMRiPLli37zveyWuHiRTeO\nHLmy+VdWXskQGT/ewsKFRgIDz3LddWHodB5//vNWjh8/zuzZs//mzsXY2BhnzpwhOztb/N3u7m5a\nWlqEq+nvrX/7mf348ePp7u6+6msXLlzgqaeeAiA7O5unnnqKNWvWcOHCBaZOnYpMJsPPz4+AgABq\na2uJjY39KV7qj1rp6emcOnWKcePG4eHhQUNDA3BFYPdtpC2Al5eXwHo6HA4BxHA4HOTl5YmF8dtZ\nz05c57lz5zh27BgDAwP09vaKufE111xDZGQktbW1/RH1YwAAIABJREFUFBQUAFeU9REREVRUVDBt\n2jQ6OjpQq9X09PTg4+NDbW0t/v7+nDp1itzcXAAkEhUKxd0cObKQ0dEkXF0tZGU1kpZWjN1+iqKi\nQoaGhkhLWy1Sz/R6PVOnTmX//v3Ex8ezadMmcXN1PuwWi4UNGzbQ1dXF6OgoISEhjIyM8MILL5Cc\nnMzkyZO57bbbMBqNnD9/ngMHDlBXV8dtt91Gc3MzAwMDhIWFERgYyFdffUVSUtJV74+7uztdXV2i\nDXro0CEMBgMajYaZM2eSl5fHuXPnWLZsGXFxccCVD7qzC5GSkkJVVRVyuRyz2UxsbCyXLl3C3d2d\ntWvXcv78eaxWKzNnzhSzcCeH3ekfdvqlnb56X19fPDw8OHfuHDqdjvr6eqKjo/n1r39NSUkJPj4+\nKBQKobNISUkRt3F3d3emTZvG4cOH8fX1ZevWrURERNDY2CjijVtaWvjNb34j3gM3NzeWLVvGW2+9\nxbRp00hPTxcLlsPh4JlnnqG8vJxnn30WLy8vYb164IEHuP/++1m5ciW/+tWvxPtqs9lIT0+ns7MT\nmUxGQEAA48ePF5YyJ862qamJ7OxsPD09qampwWg0YjAYsFgs+Pv7ExwcLMA1AwMDIvXPWU4vuV6v\n57HHHqOsrEwwH2JiYsjNzWV4eJg1a9ZcdRu94YYb+PTTT4U9LSMjg97eXs6cOSMyG9LT06mrq8PF\nxUX46J18id27d2OxWLDb7fT29rJs2TLc3d0FA2HBggV0dXXh7e2Nw+EgLCyMqqoq1q5di91up6ys\njLi4OPR6Pb29vSLlz8fHh+rqatzc3JBKpVdBprRaLd7e3uj1+u/ApwIDA9Hr9URFRX2n6xcfH8/x\n48dZsGDBVTdOp48eroyL/pLrceTIEa6//vqr3rf4+HiOHDnyo7b129raCA8Pv+qyo1KphE3S39//\nKvBPbm6u4ED8Zbm6QlaWmawsM7/73TAtLS7s369g3z4FL7+sweGYz7ZtVhYuNLJokYmkJEQXJyIi\n4m963dXV1aSkpFz1Pvj5+VFZWfm3vQH/YvW/NrMfHBwUp3idTidmtk4vqLO8vLyEoOgvKzc3V2xI\nmzdv/pfwZ7q6ul71Onx8fMjNzaW0tJTU1FSSk5MpKioiPDz8OznMfn5+gpRVUlIigjKmT59Obm4u\nEyZMEIp0q9XK5cuXycrKIicnh6CgoO+c8CwWC2azmXHjxglimPP07OLiQmxsLMPDw3R1dSGXy8Ut\nQSKRYLFYCA2dSVnZNZSUpGGxaFEqm1i/vpKsrCqsVv2fb9yz6O7uwsvLC51Oh1arxdXVFQ8PD667\n7jq+/vpr4uLihIjLuahpNBq0Wi1//OMfiYqKQqvVcvnyZRoaGnBxcSEtLQ2FQsHZs2eRyWRERUXh\n7u5Ob28vlZWVBAQEiFZtXV0d/f39wvJns9kEqMVisdDZ2YlerxdEt9bWVj7++GMxNlAoFAIwUlVV\nJTZprVbL8uXLGR0dRaVScfLkSQDRir777ruRyWRcuHABnU4nZr1z5swRymhnXK1SqaSmpgatViuo\nds6gGJlMxmuvvUZaWhrV1dWUlpYSGRmJSqWirKxMhNuUl5fz6aef4uHhIUh+zgAfqVSKUqnEarVi\nMplYtWoVUqlUoGrtdjtBQUGUl5czfvx4oTZ3Pqv19fUCw9rS0oLBYMDLy4sDBw7w+9//HoVCgUQi\nYWhoiKeffhpvb2/S09OJiYkRTo76+nqOHj2KWq2mra2NadOmoVKpyM/PR6VS4ePjw4kTJ7j++utJ\nTk6mtbWVhx9+mNDQUNLT07nvvvuYNGkSnp6e5OXl4e7uLoJqFi1aJBIhy8rKBJinsLCQ8ePHo9Vq\nqampYfLkyWzfvp077rhDPMcajUao4q1WKzk5OUyfPp2IiAjOnj2L0WhEqVTy8ccfc+edd4oDz4UL\nF4SNMy4ujpiYGNH5OHr0KDqdjvT0dOx2u+A1TJw4Udyovw1RckYEO+2Czc3NJCcnI5FIqKysJDQ0\nlIGBAUZHR2ltbRW6moaGBjIyMtBoNN9Z38xmM0qlUkQof7s8PT3Fn7dYLMLN4UQuO0csf/l3vLy8\nfrRWvlOz5HwdzrXRmRuh1WpFnLWTLunh4fGD1nEfH0hNhUcfherqPj78cIDCwkj+8Ad33nhDQ0yM\ng+uvd2fatGYyMv62fUEmkxEREfGdBFIvLy+8vb3/oVb+X+4PP2X9Swj0nLPkv7WuvfZarr32WvHv\n/wqJSt9u0/T19XHp0iXS09PRaDQ0Njaye/duJk6cyNmzZ+nr66O+vh4XFxemT59OV1eXIF85Ay/M\nZjPHjh3DbrczODgobnqurq4kJSVx5swZ5HI5u3btYubMmeJ15OTkiK6Ii4uL+L5yuZzq6moSExNp\nbm7Gz88PDw8PRkdHRTZ4dbU3SUkf8NFH3kgkdqZN01NX93Oysx3ExWWQn19Dc3Mz7u7u3HLLLbi5\nudHR0UF3dzevvPIKgLjtpaam0tzczPr16yksLMTf35/Zs2ezd+9eEWPrnL8bjUYKCwuZMmUKR48e\nJSAgAIPBgMPhEPYqZ4DMggUL8Pb2pra2lkOHDglb2KpVq1AqlUilUgFlMRqNPPzwwxQUFDA6OoqH\nhwdZWVk8++yzmM1m5s+fj8lkAiAsLIzt27djMpmYPn26uKU5DzO5ubm0tbUB8OCDD2Kz2Zg2bRpl\nZWV4enry4IMP0t7eLrLin3nmGR5++GGGh4dZuXIlAwMDjIyMsHz5cn75y18SHh6O0WjE4XDw9ddf\nI5PJBPNApVKh1+t54YUXcDgcaLVaRkdHiY2Npby8nKlTp4poV2coCVwJP9q8ebOwfDY2NhIcHExR\nURETJkzgzJkzgoM/PDwsQn+coBQ/Pz86Oztxd3fHYrHw8ccfo1arxZxfJpMxMDDA5MmT6ejooK+v\nTxxKDx8+jNVqJT09nf/6r//C1dVV3N5UKpXwuzvto7feeiufffYZWq2W8ePHU1NTw9jYGFFRUfT3\n9zNu3DjKysquslRqtVr6+/sZGxsjMDCQQ4cOYTQaiY2N5cMPP2Ty5MkoFAqys7PJz8/Hw8ODG264\ngccffxyz2Syslm1tbdx444189NFH6PV6xo0bR2NjI+fOnRPpjM7nzsmV8PPzE/wBm83G3r17iYqK\nEtkGra2tHDhwgNTUVEpKSgRfw+FwCOhQb28vbm5ufPPNN6I70N3dTXJyMqOjozQ2NjI2NoaLi4vQ\nLHR1dV2VG+9wODh9+rTIeKioqECv1+Pq6srs2bPp7++nq6uL/Px8AJEKGBcXR0JCAp999hlLly4V\n76nVaqW3t5eBgf8XnvOPlnNGv3fvXhISEtDpdJw8eRKbzYZarSYmJgaDwUBxcTHe3t7ExMRQWlr6\nN6/jnp4OUlPP89BDCgYGpOzfr+SLL5S88oqGl1+eQEqKmWXLjFx/vRF/f/v/+P10Oh1nz54VvH9A\ndHr+MnPgb61/+5k9XJl5vPDCC2Jm/+CDD/LUU0+JW9JTTz3F1q1b+eKLL4Ar7TiA3//+99x0000/\nqI3/rzazP3nypFDBO8tpH3vnnXcICwv7cwa7lWPHjpGamipY2ydPnqSrq4v4+HgCAwMpLi4mPj5e\nxJ06F4/Y2FjGxsZYtWoV/v7+aLVaBgYG6Onp4cknn+TFF19kbGyMuXPnIpVKOXz4MC4uLixatIjD\nhw8zc+ZMpk+fTmHhJXJyjNjtD9PVFYWr6xAzZ1Yxb141+fl7aG9vJykpiX379pGamkpCQgLd3d3k\n5uayceNGDh8+TF5eHrNnz8bPz0+8/ldeeYWnnnoKi8Ui2pAjIyOsWLGCoKAg9uzZw9jYmEj+8/Hx\nYcmSJWzZsoWwsDCmTp3KwMAAx44dE1x5J7LVeXM3m820traKeN1x48YxNjZGfX09IyMjgiwWEhKC\nu7s7AwMD6PV6hoeHKS0tZcaMGeLZCQ4O5uTJkyQmJtLZ2Snm3jKZDJvNRmpqKvn5+XR2drJ+/Xq0\nWi1vvvkmVquVyZMnC0dEW1ubiLdtbm5maGgIq9VKcnKyEGgNDAyILPQlS5aQmJjIwMAAX331FRaL\nhZSUFC5evEhWVpZgIDg34Y6ODoxGo8DHOoVrGo2G06dPM3XqVKKjozl79iy1tbVs3LiRPXv20N/f\nT1BQEDabjaamJmbOnMm9997LfffdR2BgICqVipGREXp7e7nppps4ePAgqampjI6OiiClgoICPD09\nGRkZwd/fH6vVKlriwcHB1NTUMDg4iK+vrwhYkslkTJgwQbgcBgcHRbdJoVCIg4zRaMRsNgsdR0xM\nDGfPnhXPhsFgoKGhgZkzZ2I0GgUox8/Pj7Nnz9Lf38+OHTv48MMPiYuLY/bs2fT09PDll1/i6+vL\n6Ogon3zyCQsXLiQhIYGDBw9SV1fHzJkziY6OxtPTU4CKnJ2QgIAAVCoV1dXV9Pb2EhwczLhx47Db\n7fj6+grWv7Pbcv78eTo7O/H39xfzdx8fHw4cOCB0B9XV1SxZskRs+p6enmRkZPDll1+KbAanMyMp\nKYmEhATy8/Nxd3dHLpej1+tFMNbGjRtZvHgx2dnZtLa2sm3bNm6++WYcDgfx8fHodDrgygHh5MmT\nZGZmCsFqaGgoJpOJiooKlixZIsZZP1ZVVFTQ09ODQqHAw8NDEB6dh0RnCiRcsRb7+fn9tzHf31dt\nbW3U1dUJK25FRQV9fSpqatLYs0fJ5ctuSKUOsrPHuOUWA/PmmfjWxO87de7cOTQaDTExMYyMjFBc\nXExKSsrfneRnsVgEy0OpVBIZGfmjqfx/6Gbv8pRzcP5PrtHRUU6fPs38+fOBK7dwp8Dl4MGD+Pr6\nMnHiRNzd3dm1axezZs2it7eXb775htWrV/+gN8aJTP3fLJVKJVqrra2t35mX6XQ69u/fj0QiISUl\nRfDHg4ODKSkpISgoiPfff58ZM2awcOFCLl26RE5ODmlpaSJxzrnR2e120RbOy8vDarXS09ODUqnE\nw8OD2bNnc+nSJUJDQykqKqKxsZHo6Gjc3Nzw8vLil7/8JefPl7B9u4qTJ9fT17cSFxcb3t6v8cQT\ndbi6Hkavryc9PZ36+nr6+/uJjY1FoVBQVlYmNr/i4mKamppISEjAZDIxODiIj4+P8M9fvnxZKKmd\nCujIyEjkcjmNjY3ExcURFBSEWq3GZDKhVqsJCAhAKpVSWlrKwMAA0dHRBAUFiZCdwMBApFIpAQEB\ngtrncDhITEzEzc0NhUKBTCaju7ub3t5eUlNTycjIwM/Pj5CQEPr6+mhraxNEMD8/P6RSqcCQJicn\nU1pailarFZtSV1eXCAq6/fbbOXHiBOXl5SxevBiTyYRKpeLEiRNXCeQ+//xzlEolWq2W8PBwKisr\nqaurIzIyEpvNhs1mY/Hixfj7+9PU1ITD4WDixIk0NTUJ4ZZKpUKlUjFv3jzkcjktLS3CBqnVanF3\ndxcRr6GhoWi1Wtra2igqKsLLy4vQ0FCioqLERuoU1/n6+hIWFsbEiRMpLCzEYDDQ3t4uFuapU6ey\nd+9eUlJSSE1NJTQ0VPAYoqKiRPCNVqvFarVSX18vqIaRkZEEBgbi7e2NXC6noaGBRYsWcfDgQTQa\njSAhFhQUkJKSQkdHBwqFgsDAQHQ6nci012g04rDlfH/8/f1FO9jHx4fW1lbq6uqIjo4mMTGRhIQE\nIS7Mz8+nu7ub0NBQpkyZgtFoJDU1lYKCAs6dO0dUVBTz5s3Dx8eHffv2ERoayg033EB4eDinTp3i\n7NmzJCUlCd2HE3FbVFSETqfD39+f0tJSEc/sTFbMz8/nxhtvJD8/n1OnTmE2mwUueMWKFYSGhnLs\n2DGqqqqYOXMmkyZNoqCggMbGRvz9/YmOjsbFxUVQERMTE4n4c6SxWq0mPj4ejUbD4cOHmTFjhiBp\nSqVSbrrpJr788kv8/Pyu2rydwtn6+noBo8rLy6O/v5/x48cTExPzgyh6f0v5+vqi0+no6+vD3d2d\n2NhY/P390el0HDx4EC8vL5G0WFFRwcKFC0V6X0NDA729vSIwyFl9fX00NjZitVpxd3cXhyw/Pz/q\n6uro6OggMjKSxMRQMjIsrFljYOlSIxqNgxMnFOTkqPnoIxXd3S4EBtrw8fnubd8JN6qqqsJkMpGa\nmircYn9rjY6OkpeXR3R0NOnp6XR1dVFYWEhYWNiPoo/4oY6Dn6SN/+qrr1JeXs7w8DD33HMPN998\nM8uWLWPLli0cPXpUWO8A8aF8+OGHkUql3Hnnnf9UH+g/s5zUum/X8PAw9fX1ZGVlfYf2dvLkSc6c\nOcPTTz9Ne3s7586dIzExEU9PT4qLi8nMzPwOEevkyZNs376dm266ibi4OGHnKSoq4o9//CN9fX2s\nWrVKtPjz8vLYunUrdruc997TcPLkO3R3u5KSYmLWrDOYTJ9w5swpzOZb+NWvfoWrqysnTpxAr9cj\nlUoJDQ0lMzNT0MxOnTpFbW0to6OjfPnll7i5uYnuxsaNG9mxYwdKpZLs7GwCAgJEm/LgwYN4e3uz\nZ88eoeL28vLiueeeo6SkhOTkZCZNmiSsiXa7nWPHjon5pzP8wwmtkclk6HQ60tLShM3QZrNdFdQS\nHR2N3X7lg3369GlkMhkWiwVPT09GR0eBKx8cvV5PaWkpwcHB3HjjjQQEBNDT08NXX33F6dOniYqK\nIi0tjSVLlojxivP3NW7cOHx9fcnNzcXb25tp06aRn58vwm+mT5+OWq2msLAQvV5PUFAQM2bMwGAw\n0N3dLVCzx48fF6l08fHxIsXPmR5nMpm49957iYyMxOFw8M033/Dee+8JN8H8+fPx9vamqamJpqYm\nzp8/z8jICNu3b8dgMAhL0R133EFvby/h4eGkpaUJEaKTqe/sIvn7+yORSCgvL0cmk1FTU8OECRNI\nSUnBw8ODwcFB8dxJJBICAwMJCAgQLWyVSkVubi4zZsxg6tSpYoNJTEzk6NGj6PV6Met2QnWcITkj\nIyPMmzdPdL72798v8ulffPFFDAYDRqMRb29v9u3bR15eHqtXr6a1tRV3d3fc3NwEDvbixYt4e3uz\nZcsWlEolPT095ObmYjabsdvt+Pn5oVAokEql6HQ6HA6HGHtdc801qNVq+vv76e/vF5vlunXrxBpl\ntVp5++23kclkvPHGG6SkpDBv3jxaWlp44403xKXFz8+PW2655arP8vnz51m0aBGxsbGC0jd16lSe\nf/55Fi9eLNYU5zMM0NzczIIFCwCuSsV0CgH/suRyubDqaTQa7rrrLvR6PefOncNqtYqMiB+zPDw8\nyMjIuKrrWVVVxbp162hra2NgYICIiAiysrKoqqoiKCiI6upqYmNjkUqlnDt3TsCbnGmBISEh6PV6\nKisrmT59OjKZDKVS+b1kwOhoK7/+9TAbNgxz4oScTz5R8cEHat55x53UVDOrVhlYtsyISvX/Gt3+\n/v5XAdH+3iouLuaaa64RwWLh4eEoFAqqqqquGhX8s+sn2ey/nXj27XriiSf+6teXL1/+T3nofury\n8vISYRpwZe5TUFBASEiIOAg4oQ3OjSsgIIDKykoyMzNxcXHBYDDQ2NjIwMCA+D7fBj34+PjQ1tbG\nyMgIJSUlBAcHC0VrU1MT0dHR1NXVUV5eDoBUqsbVdQM7dtyEyeRJREQjjz5ag7t7AWbzGDablCVL\nlogYUalUiqurK6tXr+bNN9/E09NTtAXlcjmpqal8+umnokPxlz9/T08PN954I1OnTkWtVjM2NkZQ\nUBAnT55Eo9GwY8cOkpKS0Gq1FBQUEBwczJkzZwgPD2fatGnihnb58mVxIBgeHqayshKtVktPT4+Y\nPY8fPx69Xk9ra6tQIcfHx1NVVYWnpydbtmxBoVBgMpnIyMjg9OnTuLu7c//994s5fFBQEC+88AIN\nDQ088sgjVFdXU11djd1uZ968efzhD38QoirnxiaRSHBxcRFpcZWVlahUKvr6+gQTwWw2M3PmTEF9\nmzhxoiC/PfPMMygUCkFry8nJEdCUwcFBvv76a9HJcdoHFyxYQG5uLlarVSTOOUN+tm3bRk9PDxaL\nhbS0NEpLS3n99ddZvHgx586dw8fHB7vdTl9fH0qlktLSUv74xz8KQM64ceMIDw/nt7/9LfHx8RQW\nFnL06FHxO42KiqKmpoZ169bR1dVFd3c3ISEhTJ06lbVr1wohWG9vrxizOKFS99xzDyUlJchkMvR6\nPWFhYYLrPnv2bBITE4Ero57XXnuNrq4uHnvsMVxdXamsrMTV1ZW1a9eKcWBeXh5qtRq1Wk1FRQVa\nrZbS0lIkEokIPvp2V9DLy4uJEydy+fJlLBYLHh4e3HjjjWzatIlVq1Zhs9l46623BJTJGd/stN45\nZ/C33norW7duJSoqSsTgOhwOWltb8fHx4cKFC2RmZopDdlxcHA0NDVy4cOF71wsnlXBgYABfX18a\nGhowGAxIJBLhSNHpdMjlckpKSoiLi7tqjfn2umC1WsU/3xbcVVVV0dPTg06nY+3ateK1ZWRk8Pjj\nj7Ns2bKf5HJlNpvR6XRiLXFWeXm56HY4f5aAgABOnDjB6OgoYWFh4jn18vISI84fSiV1cYHZs8eY\nPXuM3l4pn3+uJCdHxSOP6HjuOS0rVxq4/fZRIiK+e1H7R+ov10Z/f39qa2t/1P/H/1T/EgK9f9dK\nTEykrKyMkydPCi9pcnIyISEhvPfee5jNZoFFraurQ6VSidjNL7/8UmwkzjCM7u5uqqurxQbi/BD3\n9fWJD3ZjYyNmsxmz2czw8LAIzRgddZCbO469exMYGFAQGFjJ7Nm7mD3b5c9+aT9aWlpE+9vX11fk\ncCsUCkZGRvDy8iI3NxeHwyGU0kVFRSgUCuFjXr9+PTabjbq6Og4dOiTsWc7xDVw59DzzzDMYjUay\ns7Npa2vDYDDg6+tLS0sLKpWKsLAwampqsFgswnLo5L9PmDCB2tpaAWcJDg6mubmZpqYmuru7BUu+\ntLSUkZER0Zpcv349fX19+Pj4sHfvXqRSKb6+voyMjIjbksFgEDaphIQEVq5cKRbM5uZmXn31Vfr6\n+tDpdKxYsQK44lHeuXOnmKU6fcUqlYqzZ89y7Ngx8X41NzeLA1RgYCB9fX10dXVx00034ePjg7+/\nPxaLBb1ej4eHB8ePHycsLAxXV1eGhoY4evQosbGxApHsvJEGBQVx9OhRlEolIyMjTJo0SRwgnZav\nc+fOUVxcjEKhwG63i5Q2iUTC8PCweE6tVqsIuqmpqeHnP/85U6ZMETbQt99+G3d3dw4dOkRKSgrJ\nycno9XoOHjyISqWiq6uLRYsWkZKSIroTt9xyC35+fhQUFGAwGAS21SnsBET71inWdIY6OUE93y6b\nzcbAwAAdHR0CLDQ2NsaRI0dISkqiv7+fkpISsWGoVCrR9dFoNEyePFl8L4PBQFhYGJcuXSIpKYmk\npCRBC3RG6TY2NgoXy+DgIA0NDQI+5FTNOxwOAgMDSUpKoqmpCZPJxMcff0xwcDDDw8MMDQ0RGhr6\nveuFXC6nqamJzMxMMQZraWnBbrdz4cIFpkyZImxs8fHxnDhxgtmzZ7Nt2zYyMzOFoPLy5cuMHz+e\nlJQUjh8/LroaTrFua2ur0EQ5y9nN+KmiUpzdum8fLOx2O/39/SKc6NsVERFBQUEB6enpnDhxQnSm\n/Pz8GBsb+7teg7e3nbvvHmXdulEuXHDj/ffVvPeemnfeUTNr1hj/5/+Mkp099g+jev9ah9cpwPwp\n6z+b/T+xJBKJSMP6drm7u+Pu7i5mxs7WdFBQED09PbS0tAjxjsFg4O233+a+++4jNzeXdevWiRlN\ne3s7e/bsQaVScfDgQR555BHc3NyE4tjDwwOZTMn+/cHs3p1Mf7+SxMQO4uJeIS1tlPHjx6NS+YiZ\na0REBIWFhbS1tTFu3Djkcrmw/xUWFopktaNHj4oWuEKhQKVSMXXqVI4ePconn3yCTCZDIpEQFBRE\ndHQ0O3fu5O677xYzrxdffBGlUolOp+PVV18lPj4euVzO0NAQhw8fZsmSJbi6urJv3z4RghIVFUVI\nSAhVVVV0dXWxceNGvLy8aGho4LnnnhN+85iYGHHbdQa6TJkyhdOnT9Pd3U14eDhHjhwRi+rOnTtJ\nTk4W7bRLly7R0tJCbGwsv/nNb1i8eLGgEh49epTQ0FAWLlzIxYsXOXjwoAgkWr58OXv37qWhoUHM\nv512ObVajV6vF6Akp6DOyU/YsGEDu3fvxmg0ApCZmUl5ebkQqDkXM7VaTXh4ON7e3nz22Wds2LBB\n2DdzcnKw2+0EBwdTVVXFwMCAOGDW1dWRmprK119/zT333INKpRKwlcLCQtLT0/nTn/4kFOWurq68\n9NJLBAcHo1arkcvlHDx4UMzII/6c4V5ZWcn48ePFM1xTU0NISAgWi4U333wTs9ksNnAni/6rr77i\nrbfeErfLbdu24XA4kMvlBAYG4uLiIqxZbm5uhIeHs2PHDoE6dnrzvb29MRgMgkDopCm2t7czZ84c\nTpw4gYeHB3K5HJvNRl9fHxcvXsTHx4fGxkbhvXY4HFy8eJEbbriB3/zmN4LW6ezGWK1WEQm7bt06\n4Mqm9PTTT7No0SIaGxu/I5Dq6OhApVJx66230tXVRUdHB5mZmchkMt5+++3vXS8iIyMpKyvjm2++\nYWxsTOQE+Pv7o1Qqr/KrSyQSEhISaG1tJSkpifr6ekEeDA4Oxt/fHw8PD2bNmkVjY6PQ22i1WgG5\n+rZ332lD/anQsFFRUeTl5Yn1xfm7jYmJ+avsfrPZjMlkoqOjg+zsbPH1qqqqf1iYLZHA5MlmJk82\n09Eh5eOP1WzfrmLNGm8iIqzcddcIt9xiRKn8+w5CQUFB1NTUCEu5E33+U7bw4T+pdz96/RBrRW1t\nraCrOWfhzvnUoUOHBNTGaDTi5eVFVlYWvb29LF26lPr6ejo6OpBIJERGRqLRaHjqqaeIjIykpqZG\nhKZERY2jtDQCk+kpOju98PGpJiUlBz+/KqbHkb+gAAAgAElEQVRMmUJVVRUpKSl0d3djMBiQy+X4\n+vpSUVEh5p+XLl3CZrMRHx+Pi4sL586dY3R0VCSKyeVywsPD6ejo4N577+XTTz/FZDKhUCiEU2D6\n9P+PvfcMj/I8079/UySNNBq1GfXeUK8IkMB0RFEwBmODMW4xISaL/V/bh9OzXnv97iZxjjgbO47t\nuAWMCwYCsg2YDkJU9d67NJKGGdWRZjQzGr0f+D/3i2yc6s2xx3v4+iikYfTomee+7+s6z995B4WF\nhZSXlwv/u5Rt7unpSVtbG319feKBHxsbS0FBAfv376e7u5u+vj4UCgVpaWnMnTuXpqYmkZk+Pj5O\nSEgI/v7+HDlyRLALLBaLUI4PDg4SFhZGRkYGf/jDH8ScfOfOndTW1nL27FnR0oabbV65XE5SUhK1\ntbWCfy+5AOLj47n//vtpbGzk7NmzAjKzdu1afvrTnyKXy6msrMRut4tRgoeHBz09PSQkJDA4OChm\nw21tbWi1WlasWMETTzwhMLbnz5/n9ddfJyIigueff56GhgasViuhoaHY7XZ+8YtfsGbNGn7/+98z\nPDyMTCYjLS2N3NxcESh07do1LBYLOp1OxOk2NzeLUB5JFW8ymcjMzGRkZISBgQGmpqZwd3dHq9WK\nUcqFCxdEOExOTg7z5s2ju7ubqakpjh07xuTkpNAJaDQacV26u7uRyWRisdJoNCQnJ1NaWipO9mFh\nYTgcDsbGxkhNTaWhoYHp6WlCQkKEMEqyuo2NjYmOSHZ2NiUlJQQEBFBXV4fdbken0xETEyOia6Vu\nhlqtZv369ZSUlLBjxw6Ki4v5+OOPBZVw586dTE9PYzKZOHTokEANp6WlkZOTg81mY3R0lAMHDoiQ\nokcffVRcvxs3bqDT6ZiZmcFgMBAdHY2Xlxd6vV7Q4HQ6He+++y5arZaFCxfe9rlw+PBhrl+/Lror\n0sZQUvXn5OTM+n6j0ciFCxdEPPGtVVRUxJIlSzAYDJw8eVIkLc6fP5/R0VHefvttcnJyxHjIYrHQ\n3d3Nzp07sVgsNDY2Mjk5SVBQEDExMWITUFFRQWlpKW5ubqxcuXKWTuCrSkJA+/r6EhAQgJ+fH8PD\nw3z++edMTU0xNTUlRLXr1q2jvLyclJQUOjs7cTqdYhM0MTFBeHg4WVlZ4rV7e3s5d+6cGEl8XWWz\nwdGj7rz9tpqKCld8fad59NEJHnlkEj+/v2zf+2JVV1dTXl4uNlRLliz5s12ev6X+16nx/xn1v02N\n/1XV29uLwWAgNjaW/Px8UlJSGBwcZHR0lJKSEkZGRnj66ad54oknUKvVXLx4keHhYSIjIwkPD2f1\n6tUiunN4eJjS0lI8PDxYv349a9euRS7P5cSJBxkYeASVapIdO66xefM1EhPdSU1NFS3iqqoqkpOT\nBcBF4lS3tbXh5ubGT37yEzZu3Eh9fT1VVVXo9XoMBgPBwcFCGV5eXi7a0unp6Tz44IN861vfIjIy\nUgSCFBcXk52dzb/+67+SlJREe3s7+fn5om363HPP8S//8i9ER0fT1NTEwMAAn3/+OWq1mh/84AfE\nxMTw+eef09vbS1xcHHa7nbS0NBYsWIDNZqO9vZ2hoSGUSiVbt24VGgF3d3fOnj2Lq6srhYWFrF27\nlqVLl6LRaNizZw8hISGCc5+eni4U8QsWLBA5BFu2bOGZZ54hODiY+vp6vL29aW9vx2QyietTVlbG\np59+ilKp5NKlS2zbto2PPvqIxMREDh06xNTUlBitLFmyhJycHNra2gQ5LigoiNWrVwOIgJkDBw7g\n7e2Nu7s7oaGhYgxw/vx5zGYzFy5cID8/nzfffJN169Zx/vx5Mct/8803WbhwIQUFBfT09NDQ0IBW\nq2V4eJi7776bgoIC0fatq6tDq9Wi1+tJTU1lyZIlOJ1O+vv7SU9P5+DBg7i5ufHCCy+wZs0azp8/\nLzLHDx48yJo1a3j66adxdXUVIUZSUuEPf/hDHnzwQYqLi+no6CA0NJSwsDC+973vsXLlSvLz81Eq\nlVRWVhIYGEhNTQ1LliwhOTmZpqYmDAYDd9xxB21tbcTGxnLvvfcSEhIiYpjr6urw9/enoKCAjIwM\nQkNDKS0tZXh4mO7ubnQ6HY8//jixsbG89dZbIofjww8/ZPv27axbt46ZmRk+/PBDkpOThSD2ySef\nZOXKleJvZLFYuHjxIk888QRPP/00Xl5eHDp0iKCgIKxWK8nJyYSGhhIQEICPjw/9/f0sWbKEvr4+\nTp48SWNjoxCWrVix4iufC59++ikhISFs376djRs3kp2djd1up7q6mqioKMLDw2e1vcvLy3Fzc7tt\n27unp4fR0VGOHz/Oxo0bxXU8ceIEixcvxs3NTXAZBgcHMRqNPPzww4yNjVFWVkZKSgpxcXHCdhYR\nEcF7773H9PQ0d999N9HR0XzyySdYrdY/u2hVV1djNpvJzMwkLi5OkApbWlpwdXVl2bJlLFmyhLCw\nMGEZ9vX15ezZswQGBqJUKikuLiY4OFhsGBsaGhgbG6O1tVVoKCIiIv7is/lvKYUCkpIcbNs2yeLF\nU/T2Knn/fTXvvuvBjRty4uIceHv/defk8fFxWlpaWL58OQUFBbi7u9PU1ERkZOQ/VY3/zWL/Nddf\ns9hPTk7S2trKkiVLxAkrLCyMkydPUlxczCuvvEJsbKwAewwMDFBdXS0WBUkQFhoayueff05XVxca\njYapqWA+/XQ9R4+uwul0JSrqVXbvrsRur2TOnHg8PT2pqKhArVaj1+uJjY0VuFm5XI6rqytNTU00\nNjayZ88ePDw8cHFxYd68eezZswe9Xs+cOXNYt24daWlpBAQEoNVqZz3IpDS64eFhTCYTZWVlbNiw\ngdjYWLq7u3F3d+fuu++mqamJ9vZ2sfB1dnbS09ODn58fH374IR4eHrz44ovExcURFxdHbm4u+/fv\nF977wMBA8f5MJpPQJyQkJHDq1Clqa2sJCAgQboGIiAjCwsJISkqir6+P0dFRETjz/vvvCzX67t27\nee+99xgaGuKee+4RljVvb28SEhI4f/48bm5urF27Fnd3d6xWK25ubrS3t4vFIjAwkO7ubvr7+wkJ\nCaGsrAyHw8F9991HWloa/v7++Pn50djYKEY9p0+fZnp6mosXL/LRRx+Rnp7O8PCwSB5UKpVcu3aN\nxsZGPD09mZiYYNOmTbNOyMeOHcNoNPLGG2+QlpaGSqXi3nvvJSAggKqqKjo7O9m0aRPR0dF4enri\n4uIiWsY7duxg0aJFBAcHk5KSgo+PD+fPn8fpdJKdnY2Xlxdms5mpqSk6Oztpbm5m165dPPDAA7i7\nu3PHHXcwZ84cfv/73zMxMcHdd99NcHAwKpUKpVJJW1ubEBJKKZBNTU20trZiMBhEGpykNfH398du\ntzMyMsK6devYtGkTHh4eJCYmkp+fz759++jv7xe5BR4eHhQXF3Pjxg3MZjNBQUE89dRTQlW/YsUK\nXn75ZXp7eykoKECn04noWbvdTkNDA5OTk+zYsQOFQoFKpSIrK4u9e/dSX1/Ps88+S0JCAjMzM8TE\nxODm5kZhYSHJycksW7YMrVaLn58foaGh1NbWotPpyM7OJisri5iYGNasWfMXT3InT54kPT1dhLkM\nDg5it9tpb29ny5YtXLp0CafTKUA0ISEh+Pn5iYAhqaSuyrVr1/iXf/kX8XtGRUVhNBqZmJggKytL\n0AozMzNZtGgRSqWSkpISlixZIoiJ3t7eKBQKIZC9++67USgUuLu7k52dzZEjR4Q+5ItlsVjo7e0V\ngDCNRoOPjw/19fW0tLSwfv16MYaS7LaScPfuu+9Gq9Wi0+lYsGABfX19wpqZkJCAh4cHUVFRaLVa\nYa38S/X3JNjJZBAaOs2mTRbuvNOC2Sxn/34P3npLTVubkvh4B1rtnz/pl5SUsGjRIiEkVSqVArL2\nRTzy31P/q6x339TsGh8fJygoiOrqahITE7FYLFRVVQkP6q9+9SsyMzOJjIykqqqK3t5eMdN8++23\niY6OFmK8m9CRCurr76az835kshkSE/cTEPAeKpUdF5d8nnjiCRFnumDBAg4dOiSIUIWFhUKhrlar\n8fDwIDIyktdff52NGzfi4eHB0aNHiYqKEvPn4eFh+vr6GB8fx2g0CkiKWq3GYDAItrjZbKarq4uC\ngoIvITivX7/O1NQUcXFxhIeHC7LdH/7wB0ZHR1m6dCkzMzMiJUt6mEn0OB8fH1QqFcPDwzgcDmJi\nYrh69SqffPIJq1atwmq1smfPHmQyGRMTEzz44IOkpqYyPDzMQw89RGhoKL/4xS/YtGkT77//vpiH\nXr16leTkZM6ePUteXh7+/v7itDE1NcXrr79OUlKSUEQ7nU78/PzIz8/nk08+YcWKFXR3d1NXV4en\np6cI7FEoFIKp7u7uLihqki7j3LlzHD9+HIVCQUxMDHFxcQQHBzMzMyN4+VKAT0NDA3fddRdNTU30\n9fXhcDgwm80ifMfd3V204gFWrFjBH/7wBwICAnj99ddZvnw5FouF06dPExcXx/DwMJWVlVy/fh2l\nUikwrHq9nnvvvRe5XM6f/vQnZDIZMTEx5Obmcvr0aaxWK++88w46nQ6TyYSPjw92ux2tVsu8efNo\namrixo0bpKSkUFlZSXd3NzMzMwQEBKBWq0ULXoIB6XQ6xsfHUSgUQvx26tQp7rrrLoqKioQoS8K8\nms1mHnnkERHYFBwczBtvvEFfX5/YoEZFRTExMUF3dze+vr4YjUZMJhOtra3id5UofDt27KCyspKk\npCRsNpsAWVVXV3Pq1Cl8fX3RarX09/cLgeMXVdaASKxrbW0VeQEdHR14enoSFxf3lc8FlUpFdHQ0\nQUFBGAwGQkNDiYmJ4dSpU3h7e7Ny5Ur0ej12u528vDzBlrh48SIOh0MgmquqqsjOzqapqelLJ0ep\nI5ORkYFcLv9SG14aHdxaYWFhfPjhh2zZsuVL71n6fNxu0erv77/tIhwYGEhJScmXngkuLi6MjY2J\nGGh3d/dZ72FqaoqamhpiYmIIDw/nxo0bImToz1V/fz/Nzc3i/vHx8SEtLe1vXvjnzHHw0ksjfP/7\nY7z1lifvvedBYaE7d91l4cknzcTHO277c9KB7tby9/cXkJ1/Vn2z2P8PV1NTE+fPnwdu+k3Xr1+P\np6cnvr6+qFQqKisrcXNzE1ANtVpNbGws7e3t1NXV4e3tjUajQavVMjAwwPr16+nu7kahUJCRkcGv\nflXP1atvYbWGEhtbRmbmPoKC7AwPe9PT04Naraarq0voGWw2G56engIBm52dzY0bN/Dx8RGiMY1G\nw8zMDM8++yxOp5PExEQ8PT3FDjIwMJCxsTHc3Nzw8fHB1dWVmZkZysvLiY2NFQtFa2srAQEBdHR0\nYDabRcdjzpw5OBwO/P392bdvn7CgSbGwGo2GiooKNm3aJAJHJL57bGwsq1ev5ty5czQ0NJCSksKO\nHTs4fPgwSqWSVatWoVKpUKlU5Ofn88EHH6BQKERamoeHB729veIh1NraSmxsrBAPms1m2traCAkJ\n4aOPPiIgIICoqCh6enrQ6/VERkZSWVnJ8uXLxUlByjvXaDR89tln3HfffSKFTULTSr5np9Mp5r6S\nKLKwsJCXX36ZnJwchoeH+dGPfsTFixdZsWIFa9eu5cCBA5jNZtLS0ti5cyc/+9nPaGlpYdeuXaJb\notVq+eyzz4Q1r7GxUeggfHx88Pf3Fx7tgwcPolAoiI2NxWazcePGDfr6+pg/f75QZJ89exalUsnx\n48fZvXs3y5YtY2ZmRlj7FAoFAQEBrF69WrgTpAfqyMgI7e3t3HPPPQQFBbF//37a29uFuFEmk1Fc\nXExISAj3338/b7zxBnq9nqeeempW6t2vf/1rvLy8OHLkCP/n//wffHx8cDgcnDx5ErvdLu69wcFB\n4KZwbsGCBaKrkpmZiUwmE58fuClEk2JnpZ/5r//6L6HSDw8Pp6qqChcXF6ERsVgszJs3DxcXF4aH\nh5k3b54YdRmNRgwGg1js+vr6mJiYwGazYbFYBCpYp9Nx7tw5ent7v+QskCovL48jR46wevVqEhIS\n0Ov1FBYWiveq1+tFmNbExARJSUnI5XJBgCwtLUWj0bB48WKxkfliSeONr6rbKcclUmJvb68QNkol\n5SLcriRmxRc3FGNjY8TFxXHx4kUWLVqESqXCYrFw+fJl4uLiRE7KrTUyMkJwcDAxMTF0dnZSUlKC\nr68vK1as+LOq9vHx8VldVLg54qirq7utePqvqeBgJ//2b2Ps3m3m9dfVvPuumsJCdzZutPDkk+PE\nxc2+hre7plar9WuNE/5r6ps2/tdUAwMDXLhwgfHxcRGWUFJSQnl5OQ888AC5ubloNBr27t3LypUr\nqaioEAExQUFBDA8Pi5Pw+Pg427ZtIy4ujoyMDD7++GOys7Nxd3cnKyuLyMhIbLYwfvCDQD77LAcX\nl2Hy89/hmWec+PkpSE9Pp7m5GbPZzMzMDJGRkfj5+aFWq5mcnKSoqAi73c7Q0BAbN24UIpyzZ89i\ns9kYHBxEo9EQEREh2p2nTp1i6dKl1NXVsWLFCrFAt7W14XA48PLyEqFFvb29wrqUn5/PoUOHWLZs\nGa6urvj6+gqWfU9PD3a7nXXr1qHT6UhISOCTTz5h6dKl4sFVU1NDS0sLly9fZmxsjMcff5zR0VHG\nx8cZGhoSJDen0ylOwFKbXq1W4+LigtVqpbKyktTUVKHu//3vf09UVBRNTU387Gc/E6fp9PR03nnn\nHbZv305ZWRnLli1Dr9cTGhpKZWUleXl5VFdXEx4eTn5+PpGRkej1eg4cOEBKSorg/0s43osXL+Lp\n6YmXlxdBQUFcu3aNuro65HI5Q0NDjI2N8fOf/5zR0VEKCwtpb2/n8ccf59ChQ0RGRjI9PU1CQgI6\nnY45c+bwpz/9ibS0NM6ePcu6desEpbCkpISWlhbS0tI4d+4cCQkJwmb4/PPP89BDD3H8+HERXZyd\nnU1cXByXL18WNL2YmBhGRkbQ6XTU19fjcDgYGRnhu9/9LklJSQKN+/HHH7NkyRKuXLnC/PnzMRqN\nyGQyXn/9dWJiYtDr9SQmJnLs2DHOnj2Lp6cnJSUlbNu2jbGxMcxmM2NjY9jtdoxGI1qtlp6eHtLS\n0sRCNDk5ybFjxwgLC0MulwuBaHt7u/Cd22w2FixYwNTUFCaTifj4eIqLiwkKCmJwcJBNmzZht9vx\n8vISfHuLxUJISAjp6enATW/7/v37CQgIYHx8nPT0dGZmZnB3d2d4eFjYUfV6PQUFBYSFheHl5cVb\nb71FZmYmoaGhTE5O0t7eTnd3N5OTk2LMkpOTM8v65+npSXV1NZGRkYI4KHEilEqlgPiYTCauXr2K\nyWQiKSmJ+Ph4BgYGmJycJDs7m8jISJxOpwjUkghyYWFhBAQECEeDxNiXTsqSFXDLli1fOmlKNTk5\nyfj4uNh0OZ1Orl69Sn5+PkeOHCE1NVV0waTO0q02xltLrVZTW1tLUFCQGLX09/fT19dHeno6Y2Nj\nGAwGkW44MzNDRkaGoENK/8/k5CTNzc0kJycLp0ZYWJiwJ/65qq6uJjMzc9bv6+3tTWNj45fopn9r\nubvPsHixje3bJ5HJ4OBBD95+25OODiUJCXb8/G7O9Kenp+nv7xeWWpvNRlVVFSkpKSLr4B+pv7aN\n/40a/2uoAwcOYLVayc3NxWq1cuLECR544AEOHDjAE088Met7q6qq6O/vZ/HixZSXlwu6m6enJ1lZ\nWbz22mt4eHiITGy73c6qVauoq6vjnnvuoatLz+HDiRw5koRCMcP27W10dj7FnDlR2Gw2/Pz8MJlM\nuLm50d3dTUREBFqtVig29Xo9JpOJsbExYmNj6enpQafTCT9zV1cXarWaqqoqYX/q7+8XOE2TycT5\n8+eF9S4uLo7U1FQBkvHw8BCgD6VSSUREBO7u7tTV1QneuUwmIycnhz179nDffffR29uLUqkUNLzq\n6mqMRiNVVVXipOni4sIdd9zBc889x49//GOioqLEKVulUrFt2zb6+voEhGdmZgaVSiVeo7i4WISv\nSJS2tLQ0jEajiBCVTq46nU6Ew1y/fl1Y79LT0/H392dwcJCenh7Gx8eFdWj+/PmcPHlS8PalBDpP\nT89ZiFUvLy9cXV0xGo2iXfuf//mfuLq6Eh0dzdjYGG1tbbz00kssW7aM/v5+4uLi8Pf3p7a2lomJ\nCebNm4fT6eTDDz8UqFxXV1e+/e1vMzU1xUcffcTMzAze3t4MDAwQHx9PYGAgLi4u6PV6dDodDoeD\n8fFxsWhotVocDgd+fn7cuHFDxLree++9XLlyRXRl1Go12dnZKJVK9Hq9GFeYzWbhvpiYmOCll14S\ni7TVauU73/kOmZmZfPrpp2LeajAY6O/v51vf+hatra2oVCpxTaenp/Hz82NqagofHx+Ki4tn2fjy\n8vKoqalhYGBAzEKlEVJ+fj5BQUEiY356eprg4GAWLFjA0aNH8fHxoba2VpwIlyxZwsjICIsXL+bQ\noUPodDrsdjsTExM88cQTtLS00NPTQ2lpqbjvJazr/PnzRddJ4kHMnTuXq1evzmovSy6d4uJiYmNj\naWxsJCEhAYVCQVNTE+Hh4URHRzMwMEBzc7N43yEhIcKmdmscLNwE0Oh0uq+c+zqdTg4fPixsdQ6H\ngw0bNvzF+bYUqiO9h5SUFLRaLUajkYMHD4rX9vb2ZuvWrX/2hDo1NUVZWZlAe1utVnH/dHd309nZ\nKf6f6OhowsPDcTgclJWVYbPZkMlkyOVycnJybjsy+Usl5UR8sWVfXFz8F9v/f2sZjXJee82TP/7R\ngw0brPzmNzdDhWw2Gx9//DEKhYKIiAiam5sJDAz8UjTx31v/q/Ls//9cLS0tjI+PExsbi8FgEIjL\ngwcP3vbmTEtL4+rVqwC88MILYjHTaDQcOHAAhUJBZ2en+IDabDa6uroIDAzk9OlR3nprFT09PuTl\ndfPIIzW4uw9hMNycCQ4MDIiFKTg4GJlMhr+/PzabjbGxMQFZCQi4CdAZHh5mbGxMkN7UajX9/f1o\nNBoOHz48a9f8H//xHyIuV2qLjo+P4+LiQnh4OI2NjRgMBhwOBxqNBpPJJJCWTqeTCxcuiIVWp9Ph\n4+ODr68vjY2NAnozMTGBv78/IyMjyOXyWT5pSRfwu9/9Dq1WS0tLC21tbUxPTxMUFER7ezuNjY24\nurri5eWF0+lkZGQEPz8/EhMThcXP19dXnAJ9fX1pa2sjJSVFgF1SU1NpampCpVKxYMECgoKCRLte\nUoKbzWYqKipEVv309DSRkZGMj4+TmJgo2uPu7u6EhITw6aefolKpyM3NxcXFBVdXV0ZGRsT7eOON\nN3j00UdpbGwU+QFjY2MMDAxgsVgoKSnB3d2d0dFRIiIixGbu+9//vrimrq6uIqCnoKCAkZERcf+4\nublRVVUldApSOZ1Odu3axeTkJLGxsYyPjyOTyQRYydXVlY6ODnbu3ElUVBQzMzN0dnZSWFgoxilr\n1qwRD2uFQkFraysnTpxgw4YN+Pn5odFo6Onpobi4WGTYNzY2otfrmZycFO6GkJCQWSOPkZERfHx8\niI6O5tq1a0RHR4sNlKRQT0pKEul9arUahUKBh4cHMTExXLlyBb1eLwKMpqamSE1NRaPRYDabWbx4\nMd7e3vT29lJXV8fq1asZGxujoKBgFrRKGv1IinvJFuft7Y1SqaSvr4/XXntN/B0UCgW//OUvxd/r\n1hhrafRVU1MjumISOKu1tVXYEKXTtbu7O/7+/oLZ8MUKCQkRcbi3K7lczubNm2/7b0NDQ2LDI6XQ\nZWdnC/9+UlLSl35Gp9Oxa9eu276epE+QXm/OnDkEBQXh5uYmrIZftCVHRETcVkWvVCrF6OIfreDg\nYLq6umaNH6RDxdddOt3N9v6uXWZu7dxLI0m1Wi2uQUdHB+3t7cTGxn7t7+Or6pvF/h+sCxcuEBoa\nyuLFi5HL5eh0Omprazl+/PhtucotLS1oNBp++tOf8pOf/ISEhASsVitHjx5l48aNgt/+8MMP4+rq\nysTEBL/97R/w8fktly4twNfXyh/+0M+SJRauXjUyMjLG5OQkAwMDLF26VDx0Tp8+TXh4OCaTiSef\nfFJEuKpUKl5++WVmZmY4fvy4mA+2t7dz6tQp/Pz8bivS8fPzQyaT0dvby9jYGA0NDSJL28PDg9bW\nVlauXCly58fGxnjrrbcIDAykrKyMl156iYSEBMbHx3n55ZfZu3cvYWFhbN68GTc3N7HISPae+vp6\ndu/ezeTkpCCJnT59GqPRSEZGBuvWrROL2eHDh4W3+tb5oaRDqK2tZcWKFSxcuJDx8XG8vb357LPP\n6OzspKWlhQ0bNvDYY48JsdPRo0fJy8sT11SqqqoqxsfH+eCDD7jnnnvETLivr4+XX36ZlJQUioqK\n2L59u6Dbvfnmm0JRn5WVxaJFi5DJZOj1eoFlldwPGzZsoKmpiU8//ZSFCxdSVVXFjh07xIgnMTGR\nAwcOMDw8TEJCAq2trQLEI7UDW1tbKSgoYPv27cDNBf03v/kNo6Oj+Pj4UF5eTlJSEna7naqqKtzc\n3FCpVHR1dbF161bUajWjo6Ps3buXwMBALBYLbW1tJCUl4XQ6BdUwMDCQiooKhoeHsVgsqFQqtFot\ncrmc8fFxYmJiCAoKEsEt5eXlHDp0iJCQEJ555hn0ej2+vr40NDRw5coVUlJSiImJEX51mUwmRHnt\n7e2sWLFCROpKsKWMjAzi4+Pp6OgQeo/MzEw6OzvZt28fu3btws/PD7vdzuXLlzlw4AAZGRmMjY0J\nJ4eXlxft7e1ic7RhwwbR8h0ZGaGwsJC0tDQuXbok4C9KpRKr1cp9993H7t27eemll8Rpuampiaef\nfpo//vGPFBUViUWvq6uL69evk5KSQmNjo5izz8zMEBISQklJCbW1tVRVVZGamkp6ejpDQ0NUVlYK\nsesXS9Ld/K0liQ+XLVsmPucGg4GyslgGecYAACAASURBVLIvefn/mpLGBVLnYWZmhsuXL6NSqb6E\nw/1nV3R0NBcvXsRutxMVFSXIil/XZuJ25e8/W50vMTfKysrEaCIhIYFr1679Uxf7b2b2/2CdOXOG\n3Nxc/P39gZuzOblczvHjx1m8eDFlZWUkJiYik8kYHh7mo48+oqKigrCwMJ5++mmBiV2xYgVvvPEG\nDoeDpUuXEhUVhd1up6cnmuPHn6ClJZH16/t5/fVBZLIaRkZGyMrKwmazcfDgQZKSkkhPT8fDwwOd\nTieEZklJSYJQJ806a2trKSsrY8GCBeTk5DA6OipoaTU1NYSGhgpkrUwmo7m5WUTM1tfXk5eXx+LF\niwkPD+fy5ctMT0/T1HQT1hMVFSUSybq6ukT7cceOHfj5+YmRwocffkhCQgIymYzNmzcLtvexY8fo\n6OjAy8uLlStXkp6eTlhYGEajkfLyciYmJvjOd77DqlWriI+PJyoqCr1ez7lz59ixYwdbtmwR4Jd5\n8+ZRX19PfX09Dz/8MB4eHsL64u/vz4EDB0hISGBsbIyrV69y7do1TCYTfn5+gvAneZslEl1HRwdN\nTU0UFBSI3HlPT08aGxvp7Oxk/vz5AvsrWc3q6+tRqVT827/9mwj+iImJwWQy0dDQwCOPPILRaOSz\nzz4T0bIymYzq6mrkcjlZWVlER0cLz/zAwABBQUHExcURGBhIcHAwBoOB3t5ejEYjK1asEJ0dKW63\nsbGRjIwMAVHq6ekR6XQVFRXk5OQwMjIihH1arZbW1laeeuopLBYLJ06coLy8nPDwcBYuXEhxcTFd\nXV0sX76cefPm4efnx6VLlzAajVitViHEc3V1RaPR0NbWRlVVFffccw8ZGRlkZmbi6+uLxWLhs88+\nIyIigqysLLRaLZ6enmKROHnyJPfffz9z584VXad169Zx4MABHA4HERERbNiwQeQoHDt2jHPnzpGb\nm0tWVhYKhQK1Wk1oaCgXLlwQgBqZTMbY2BhhYWHExsZy/fp15s2bN2uOq1KpqKioYHR0lKGhIXJy\ncggPD8fb25vOzk4qKyvx8/Njw4YN4md0Oh2XLl0iMDCQvLw8TCaTSJlLTk7GbDZz/fp1RkdHhdq8\nubkZo9EoArJsNhtDQ0NMTU2RlJREUVERMTExQtgJMDo6SlNT09+lKm9sbCQuLm5Wwp2E05U+839L\nVVdXz9InSCCl2tpakR43OjoqQq3+mSWTyYiIiMBqtdLc3Mz09LTQP/2zqr6+noGBAdLT08nMzBRi\nRKVS+Q/rBuAb690/rdLS0jhz5sysKMmuri5UKhVLly6lpKSE1157DaVSiVKp5NFHH+XBBx9k2bJl\nvPLKK0RHR2M2mwWRbmJigqioKK5dq+PChQKuXs3G399MTMxOnntuF8HBwURE/H+7b7VajcVi4d57\n78VisTAxMYFGo2HTpk08//zz+Pj4cPToUSHakWae09PTQrnu5+cngl7OnTtHTEwMw8PDoj3p7e0t\ngjnuv//+Wcl7ixcv5qGHHsLV1VWcRtzc3HB1dSUmJoYzZ86wcuVKqqurhSVPEt/MmzePkJAQfvjD\nHwpv8wsvvMCdd975f3/PCMF+z8/P549//CPT09NC6S8F4fT39wPctp0p2aWuXLlCeHi46Abo9Xq0\nWq0giN2KsZXU/FlZWVy9elXoKpKTk7ly5YpIIZQ47jabjbi4OKGMt9lsImxGai97enpSVFTE8uXL\nUSqVdHV1UVNTg06no6mpif/4j//A09NTjDwqKiqwWq2kpaWJYBmpFWm1WpmcnBT2NYvFQlRUFBcv\nXkSr1WI2m7l48aL4m0shQXFxcURERHDlyhWB+G1tbcXNzU0otyMjI7lx4wZTU1Oo1WrMZrPQdUj3\ntqS8f/TRRwUMCMDLy4tf/OIXWK1WRkZGCAkJQavV0tjYKDIPent7qa+vFzYok8kkCG5fTHSUxkzS\nCEWaTQ4NDaFQKDCZTKhUqlnUOXd3d/r6+ti2bRvZ2dnitWZmZvj1r39NX18fzzzzzKzOVUNDA+fP\nn2dgYGCWF1vqmpSWlvLCCy/MotRFRUXxzDPPUFBQ8KV7LiQkhJ6eHubOnUtsbCyxsbGifSuTyWho\naGD79u2i8zcyMsKPf/xjMRdftGiRGFlIG4P09HRaWlooKipCLpejUqlYvHjx3zXzlUSEXywp4fBv\nfU3p2ktJhIGBgcTExAhMcWVlpRjPdXR0kJOT81cvUF9HyWQyQkND/yra3/9EGQwGli9fLq65v78/\nKpXqn5ZDINU3i/0XampqitraWtF6TUxMFK2y/v5+2tragJuKzuTkZNLT0zGZTOzcuVNktgcHB4v2\nr3TCvLWWLl3K/v37hddcoVAwMjLCK6+8QmhoKAZDFHv2bGFw0JM772wjJOT3tLZ20NnZyejoKEaj\nkZmZGSIiIoSY6tSpU9y4cQONRiPUtFKQiKurKzdu3AButuOnp6fR6XQMDAwwPDyMzWZDoVAQGRmJ\nRqPhwQcf5NVXX8Vms+Hi4kJPTw/f/va3eemll4iOjp71u9xqKzMajSLMx2QyMTo6ypw5czh48CA7\nd+5ErVZjs9kwGAzI5XKRay8JKyWqX1paGnq9nsOHD+Pp6SkQvC4uLqhUKmpqaigpKcHHxweTycTU\n1BRhYWHioW8ymYQLobu7m7CwMAwGA5WVlcDNWaaUfnbo0CEee+wxcWIaHh7mjTfeID8/H6fTOUvE\nMzExQWJiInv37uXNN9/E6XTicDjw9fXljjvuIDw8nOnpaRwOB25ublitVhFLm52dTVVVFZcuXcLV\n1RWr1UpcXJyYJZeVleHr6ytCYqQgHamN7uPjg16vF/kJGo2G9vZ2amtrAQRtLDk5mYqKCnEvjI6O\nEhISQl5eHhkZGRw9elTExl65ckXw8F1dXYmIiBARuTU1NXh5eXH48GF+9atfiZOQ2WzmJz/5CWq1\nGj8/P4qKioROxMPDA29vb2Hd8/DwQKPRCERwZGQkLS0tYg4upRjeBEJN0dPTQ1dXF3BzBNPd3U1a\nWhoNDQ0oFAoRRTw2Noa/vz8uLi6CoiaxIhQKBf7+/pw8eXIWrW56ehq73Y6/vz8VFRViPi2FOSUn\nJ6NWqzl//ryw+E1OThIaGkpzc/OXFkCJR3H+/HnuuOMOBgcHBQq6srKSXbt20dnZycmTJ0VwVF5e\nHgqFgrCwMLEBksvljI6OEhcXR0dHB1FRUeJaK5VKMjMzKSwsBCA+Pl7w1f+RioyMpLW1VSQMws3N\nkNRuNplMNDY2Aje97qmpqX9WHGe1Wjlz5gzz5s0jJSWFvr4+CgsLiYqKoqKiguXLl4sRZ1hYGBcv\nXmT58uX/8O/xddTAwIBIn/Py8iIlJeVrD6iJiYkRot7w8HA6OjoIDg4Wz+R/Vn3Txr+l7HY7RUVF\npKenEx8fL8A3Li4uYmHMyckhMjIShUJBSUmJePjfeeedLF68mLVr11JSUkJISMhXfjANBgOXLl3i\nwIED+Pv7c+rUKX7wgx8QHByGUvlvfPjhOlxc7Dz7bDkq1V5OnTpKfn4+FotFtPZ8fX25fPkyCoUC\ng8FAUVERd911Fw8//DBGo5FDhw6RkpIiwku+/e1vk5ubS1dXF319faSmprJnzx6io6NZu3YtfX19\nvPnmm6xYsQJ/f38BUAkICBCc8pGRESYmJnB3d8fb2xuTycS1a9fo7OwkMzOTa9euCWvQiRMnGBoa\nYvfu3fzxj3+krq6OoKAgrly5wiuvvMLmzZuJi4vjnXfeYfPmzWRkZNDc3ExhYSGPPPIIx44dw263\nC6LZO++8Q1RUFKGhoXR0dLBlyxaWLVvG2NgYJSUlrFy5ksnJSeGvVqlUfPDBB4SEhBAcHMznn3/O\n8uXLyc/Px2q1cvr0aRISEnB1dWVgYACr1Sr48YGBgaSlpdHa2oqLiwuenp4MDAxQUVHBnDlzKCoq\n4v3338fHx4ehoSG+//3v4+LiQnx8PFarlcTERGw2G1qtVliPNmzYgF6vZ9WqVcTFxeHm5oa7uzte\nXl54e3sTHh5OcHAwGo1G2KwkxfzSpUuJiYmhp6eHnp4eMTOWWAUOh4OqqirMZjNbt27lwoUL3Hnn\nnSQkJODl5UV5eTnf/va3qampIScnh6ysLNLT03F1dRWAGYvFwoIFC0hKSsJsNgs745133im85L29\nvSLCtbOzU0BfNBoNnp6eYrH28fFBLpeTmZlJeHi4gEIFBgZSWVnJ2rVruf/++0WmwZw5c/D39+fy\n5cusXLmS6OhoQUHcvn27sCp6eHgwMjJCXV0dCxcupKysjBUrVpCfn8+cOXNISEjg9OnTIieioqJC\nLL4/+9nPyMvLIzMzk4GBARobG2lra6O6uhp3d3fmzJmDp6cnKpUKT09PPDw8hMOgu7tbbCQsFguD\ng4NC72A0Gjl37hzr169HpVLx7LPPEhYWRnp6Op999hkPPfQQeXl55ObmsnfvXtEtCQ0NJS4uTmzw\nzGYz7u7ugnkhedTPnDmDh4cHc+fO/YeebbeWWq0WOgcfHx/Gx8e5du0aqamp4vQ9f/58oqKiRHJj\nRETEV9rc2tvbcTqdhIWF4e7uzsTEhMiUiImJEYclDw8PrFYrZrNZaEX+nrLZbJhMJuRy+VdaCP+a\namtrw2QyifGNUqnk+vXrREVFfa2BQJ2dneTm5hIcHExAQIAYmRqNxq+l2/CN9e7vKMkyJYWvSKd0\nvV4vwgturba2NsF6Li0tFeKdkJAQ3N3d+dd//Veam5sZGBgQ86r09HROnz5NQ0MDZ86cEf8WE7Oc\n7u7/hxs3koiKukpAwL/jcNxMBouMjESn05Geno7RaBR57RLTfv/+/WRnZwvmdEpKCk6nk6qqKpKS\nksjNzaWtrU2gPsvKygToweFwMDAwIHCfkjJ3y5Yt4rRrtVrZt28f8+bN49133+Xxxx/HaDTi5eXF\nuXPnhOIdEF793NxcpqenWbBgAW+//TaXLl0SnuW8vDwKCgoEg7u2tpapqSkRkjExMUFdXR0hISF8\n8sknaDQa7r33XpqamgDEImo0GkWmt4uLC7GxsZSUlGA0GgUdLD09nY6ODkFH6+7uJi4ujvj4eH79\n61/j6emJ0WgU7WJvb298fHxYvXo1aWlpdHR0CCGUZFX8r//6LxISEgS5bP78+QwNDRESEkJXV9es\ndDtXV1fWrFnD5s2bOXPmDI2NjSKZbPny5QwNDVFVVSVa+xqNRszz+/r6SE5OFn7s3Nxcrl27hlar\n5cSJEzz33HPCllhaWsqRI0d4+umnAairqxOBRtL94O3tLXDB0n1aXFzMpUuXBEOhq6uL5ORkQkJC\n+Oyzz1i/fj1Lly4VyXtubm6cPHmSkZERTp06xapVq8Rn5fz586xdu1YwDWpra4WlsKuri66uLpYt\nW4ZCoaCyspKwsDDS0tLYu3cvOTk5LFq0iJqaGiYmJpg7dy7l5eWsWbMGvV7PiRMnhHUyPj6erVu3\n8txzzwkxm9TpKC0tFR0WCUylVqvJyclh7ty5eHl5cezYMcxms1i8/Pz8eOSRRxgZGRHdNrj5EF26\ndCm1tbUcOnRI+OGnp6cZHh5m586dmM1mbDYbH3zwATKZjB07dmCxWKisrOTBBx8UrVudTkd3d7eg\nOn7rW9/CYDDgdDoJDg7m2LFjpKSkoFQq6e3tFbPtoKAgnE4nS5Ysobq6Wtyncrmc7Ozsf8inPTAw\nQFdXl9jsqFQqioqKvjQeMBgMGI3Gr0xpu3TpkiAmSl2X2NhYCgsLycnJEYp7aZRRX19PYGCgAB39\nLVVTUyNwysPDw8zMzDB//vy/eXGemZmhqKholgAXbm5cJMvw11VdXV2MjY0JjoTBYKC4uJisrKyv\nZZzxjfXu7yiDwSBa3FIS3MjICHq9XjyAm5qahH0sPDxcPBx+9atfCXHcBx98wDvvvMOaNWtQKpVi\nk+BwOLhw4YIIerjzzjtpaWlhYGAZVVW7sNunycx8iXXrTJw4YRQBKi4uLgINKQmspAhX6VSVlJTE\nvn37mJ6epqamhh07dlBbWyuiVSU0o+RHlvQD7733npg7L1u2jFOnTuHl5TWLta1SqYSKeePGjTz6\n6KPigbhp0yZ2797NzMwMdXV1grc9NjZGVlYWpaWlpKens3z5cnp7e8WD5ejRoxgMBpKTk6mvrxe+\n5ujoaK5evcpjjz3Gyy+/LIAgRqORtWvXCq+zRqMRLWN/f38RIuTl5UVHRwcymYy5c+dy+fJl/Pz8\ncHNzIzMzk9jYWGGb8vb25sSJE/ziF78QXSGNRsOPfvQjHnvsMeRyuZi5SiWl6g0NDTEyctNH29PT\nI2adUmbB+Pg4arWaiIgIysvLiYiIQKPRMDQ0xPT0NP7+/rS0tJCZmcm7775LVFQUycnJjI6OcubM\nGVatWsXChQt59dVXKS8vB6CyspJ7772XoaEhVq9ezYkTJwQ4KTk5GZ1OJ+JsJYGXm5sbTqeT+vp6\n5s2bJ4JtHA6HmIX7+/tjtVqZmZkhLCxMWL+WL19OVVUVCoVCjAuysrKor6+noKCA6elprl+/Lrzn\nCxcuFFqFoaEhLBYLTqcTg8GA1WrF1dWVxMREysrKCAsLE8mEGo2GrKwsmpqaKC8vZ2ZmhrGxMbZu\n3Upvby9DQ0OsWbNGeOq9vLxoa2sjNDQUnU7HW2+9hd1uJzw8nKVLl9LX14dWqyUiIoJly5YJ7cTk\n5CS9vb1i86xQKISNb2BggM7OTpKTkxkcHEQmkxEZGUlJSQkOh0Oo7SX2hbu7O729vSQlJdHR0SFo\nbNLfdmJiAoVCQWlpKVNTUwQGBhIaGopcLmfRokVcunSJoKAgFAoFly9fJjMzk7S0NIqKikS2hDT2\nSktLo7S0dFbmvJeXF8XFxaxcufLPLnRDQ0PimeXr60tiYqLY5AQFBREUFDTr+yWdx60VEBBAS0vL\nV/4fEjY4LS1NfM1msxEYGEhHR8eshXNmZoaBgYHbWvv+Ukk8jltTAwcHB6mpqRGQpL+2JNaGyWQS\nwj2tVktUVBS1tbVf62IfGRlJZ2cnRUVFAjyWkpLyT9UtwDeL/azq7+/HxcWF7OxsNBqNgDt0dXWJ\nk9r8+fMFUOTYsWMYDAbWr1+Pw+GgsrKSqKgoVq1axX//939jMBhEipjD4cDV1ZXs7Gx++ctf4nQ6\n6egwMDb2CtevR6BWVxMc/BRubhN88MEAjz32GDk5ORw+fJhPP/2UhIQEqqureeyxx8jPz2dmZob3\n33+fw4cPExUVxdmzZ/n5z39OQEAA/f39/OY3v8Hb21uIob73ve8Jylltba148H/3u9/F39+fsbEx\n9u3bJ0ArgMCruri4MDU1hdVq5bXXXuOFF15g6dKl9PT08OMf/5gTJ06wZs0a8cC71U/b1tZGQEAA\nwcHBZGRkYLPZKCsrY3h4mIaGBgGVCQwM5NixY/zyl79k3rx57Nu3jwceeIC5c+ditVo5dOgQ169f\nx2q14nQ6ue+++/Dz86OtrY1z586JduNdd93F22+/jcPh4Pnnn6eiooJvfetbHD16VFAEGxsbhWo8\nJiaGAwcOsH79euAmICkmJoa6ujry8vK+dI9YLBb6+vrQ6/X85je/wdPTk5/+9Kci8tVisbB582YW\nL17MjRs3ePHFF3FxcRHCqp/85Ce4u7tz6tQpDh06RG9vL+vXr8fLywubzSbm/8ePH8dkMjE4OMi2\nbdtwc3NjYGCAwsJClixZIpLEcnJyGBsbEzPy5uZmmpub+eEPf0hAQAAVFRW8+uqrJCQk8Omnn/LQ\nQw8JAlt1dTVHjhxh165dvPjii+Tl5ZGcnExJSQlFRUU899xzVFdX8/HHH7N9+3amp6fZs2cP8fHx\nDA4OsnXrVvLy8tDr9QQGBhIdHc3JkycxGAy4urqyc+dOAgMDeeedd+jr6yMuLo7CwkI2bNhAamoq\nfX19HDx4EG9vb4qLiwkICODf//3fkcvlVFdX8+abbzJ//nympqaIjIxk7ty5TE1NUVpaSnd3NxaL\nRcSburm50dzczLvvvktBQQHz5s2b5XGHmyCVvr4+Hn/88VlfT09P591332XOnDm4urqyaNEinE6n\n6B719vayevVqAZpxOBxiNFRSUsL8+fP57ne/i9Pp5MSJE5w5cwaVSsXp06eFMEulUnHw4EEcDgep\nqakEBATQ2trKzMwMixYtIjQ0FJvNht1uZ3JyEh8fHyHElMlkdHR0EB8fT25uLnK5nN7eXhobG2eh\nn79Y3d3dDAwMkJOTI0aR0mn2qzYIt0O7Dg0N3dbnL5WkQZC6e7fmtUsJjXFxcYyPj1NSUkJqaurf\n1Sbv7Oxk0aJFs74WGBj4dzHmJTuvQqEQ16e/v5+jR4/OitD9uioqKko4lf5SBPr/VH2z2N9Skiin\nvLxcfNh6enoElMPLy0uINyQEp4R/7e3tJS0tDYPBwCeffIKXlxdyuZzS0lIsFgtubm5YLBZiY2Pp\n6OggKGgJV6++iF7vw7JlRezcOcAPf9hMS8sku3fvxtPTk/r6elJTU8WMPzY2lpmZGSoqKsRO3eFw\noFQqmZqaorm5mdHRURGWISVXbdu2Dbi5m73//vupqqoS2eDNzc309/czMTEB3GzZj4+Pc+jQIZGG\nJ9mC9Ho9jz32mGh9hYeH89Zbb/Gd73yHNWvWMDw8TFNTE56enri7u4scbLlcLpTyEmHs0KFDGAwG\nMbsFyM3NZd++fXR1dZGSksKcOXMABKlsz549IkXtzJkzyOVyAfFpbm7GxcWF++67Tzgfvve977Ft\n2za0Wi1FRUVER0eLzVBnZycpKSlMTEzw05/+lJKSEmQyGc8++yzPPfccdrv9tveINHM8efIkly5d\nwm638/LLL/PAAw/Q3NzMj370I1QqlWB3P/300zz55JMA7N27V7zO2rVraWxs5OLFi/zud7/7kgBK\nLpdz5MgRlixZglarxdvbm6mpKaKjo+no6EAulxMcHMzZs2dRq9VkZGRQUlKCwWDgxz/+sbimycnJ\nZGRkcP36debOnYu3t7foQEhMhZqaGp5//nnOnTvHuXPnSExMZOPGjVy9epWgoCCefPJJcX3++7//\nm71792I2m3n//fdRqVSEhoZSUVEhEstkMhkPPPAAcFMHs2XLFnp6eoRw08XFhevXr6PRaEQSWmtr\nKyEhISLz22QyYbfbqaurY+vWrcINIEFaXnvtNYxGo8AVS64KCZp0O5GVlB44MTFBfX292FzFxcUx\nMjKCRqMRVigpe6K0tFRAp24d412/fl0gfyVComSTvHLlCrGxsTQ3NwuBp8PhoLm5WQThBAQEfMk9\nUl9fzx133DHLmz49Pc2ZM2dEUp1Ukhaiq6vrKxf7jo4Oli5dKrosgYGBhIeH09PT85Un14iICCor\nK8nIyEAmk2G1WikvL//SCPPWiomJobm5maKiIrEZkvQIvr6+BAUFie7X0qVLv3YB3N+zcZB+N41G\nIwiAGo2GycnJf0gH8L+5vlnsbykJ0Tk9PU1DQwMqlUqkrM2ZMwe5XM5HH30kWoZ33XUXfX19NDQ0\nMDg4KPzU6enpfPLJJzQ2NlJQUMDk5CQTExPExsZy/vx5RkdXUV//DCqVnKeeOkFiYg/9/TdP1DKZ\nTMziJRRrZGQkp0+fRq1Wo1KpBI1OIrK5ubmxZcsWPvroI4FO3bFjB2+++SaLFi2is7NT6BkCAwNZ\nvnw59fX1ZGVl0dLSgo+PD6Ojo2i1WuHlnZycxGKxoFQqMZvNApu7Zs2aWddMWny7urq4du0aHR0d\nKBQKdDodvb29BAUFYTKZKC8vJzo6WuBkJWykQqFg165dIt3t4Ycf5sUXX+TnP/85jY2NTE1N4XQ6\nCQ0NJSEhgeLiYu666y4mJibETLqrq4vBwUFiYmI4ffo0Q0NDOJ1OoqOj8fPzo729nc2bN4sYXMlF\nUVdXh8lk4sKFC4KgNzk5ycjIiPAH9/f3YzAY0Gq1hIWFUVtbS2hoKFeuXAFubl7Ky8vJyMjgzJkz\nVFZWsnv3bvr7+4mMjOTatWs4nU7c3d1pbGykoqICu91OTEwMwcHBVFVV0dXVNUvMOTMzg8PhwGg0\nkpOTg91uR6FQkJKSgs1m49ixY8TFxfHb3/5WEOLsdjtz586luroab29vgXDW6XQsXLiQCxcusGLF\nCjo6OqirqxPpc4mJiYyMjFBSUsKKFSvEqKGsrIyWlhays7OZnJxkaGhIcBri4+M5f/48GRkZzJs3\njxs3bpCbm0tDQwNFRUXExsYyPDxMbW2tiC7NyspiYGCA++67j9/97neYzWaUSiX3338/4+Pj6PV6\noqOjaWpqYnp6moiICJKSkigvLxcJjb29vajVaqKjo3E6ncKSKWls/Pz8CAwM5MqVK9TV1c0Cp9hs\nNqanp0U2xNKlS/Hw8GBwcJBXX32VmJgYzGazACfNzMwIVoFEsLt8+bJo3SuVSm7cuMEDDzyAXq8X\n6nVfX1+WL1+OXq9n27ZtHD58GLvdjq+vL1u3bqWzs/Mrnz8TExNfgtDcrq0ulfS8uF1JyOhz586J\nfIsrV66g1WoxmUxEREQwMjJCV1cXHh4eREdHo1QqiYqKoq+vT1g35XI5Cxcu/Iuo2jlz5ojN+RfL\nxcVFAJP+kVNtcHAwHR0dxMTEiK8NDw8L94LNZqO9vV24fL4qoAduPuvj4+Nxc3Pj4sWLyOVy3Nzc\n2LhxI01NTV9rG/9/S32z2N9S0kk3IiKCkJAQzGYz586dw93dHaPRiNlsZvny5Xh4eNDS0sK1a9dw\nc3NjcHBQJK4NDw+zf/9+oqOjxUN3/fr1REdHc+VKKe+9l0FNzRJUqiruv/9TVq/OYXJSJ5jhSqWS\nhoYGov4v+72mpkZ0FXp6ejh16hSbNm1iYmKC/fv3i4XYZDKxdetWQYjr6urCz88Po9FIXFwcd955\nJ3DTUyzRre666y48PDwYHR1Fo9GgUCj4wQ9+wJo1a0hISKCpqQmHwyEiUUtKSqipqZk1m3M4HALs\nI0FlIiIiOHfuHKWlpdx7771E9wxuQQAAIABJREFURESIGbc0K0tOTubkyZNcuXKFbdu2ERoaysWL\nF0W2fFdX16zZHMDnn38uMtUXLFggGPydnZ2o1Wp6enro7e1l3bp1TE7+v+y9Z3ib53n+/QNIkAAJ\nEiQILnHvvcQhUntY3k4kr7ipE9txPVq7adPUR5p0JnHeOGmapM1o0jROHMt26thWvWQrlixKpEhK\n3Atc4iZBgAMkBgGSIID3A//PXUKkZDu2rKTV+ckHRGPcePBc931d53Dw2muvifHJxZnbHo+HlpYW\n/Pz8eOGFF0Sk7vPPP49Go0Gr1XL69Gm2bNlCeno6JpOJd999l5ycHBwOB729vRQUFKBQKNDr9WJj\nMzQ0xHe/+13Ky8upqamhvr6emJgYbDYbzzzzDLfddhvh4eG88847tLS0cODAAcG4llQPp0+fJi0t\njcXFRSIiIsRNdHl5mbfeektE2qanpxMfH4/T6RRugGq1mjfffJOCggISExNpbW2lu7ubzMxM2tvb\nuffee8U6rKyscPLkSTweD5/4xCcE2Uur1VJcXEx3dzfHjx+ntbWV66+/XoyOZmZmROCQTCYjPz+f\ntrY2hoaGhFVyS0sLN998s0i96+7upqCggKNHj/LZz36WjIwMpqenxXhFIklK3g/9/f1MT0+zZcsW\nXnzxRYqLi6moqGB6epof/OAHxMbGMjIysoFTIbmUSX76KSkpLC4uMj4+TmVlJWazmf7+fqKiooTa\nxuPxkJqaSnt7OwBFRUUsLS1RV1fH6uoqlZWV/MVf/AXXXXcd27Zt49ixY3R2dnLjjTcyMjLCXXfd\n5XOdtrS0CM/6z33ucwBC6iql1m0GhUKB0+n0MXyRCJhS2FFRUREKhYKhoSGmpqY2HTXB2slVr9fz\nwAMPiOdLTU3ljTfeIC4uTvBAMjIysNvtVFdXU1paSnh4+FXVpF8OqampotMiSVAXFhbYsWOHOGxJ\nMkEpjnu9vHA95HI5KysrpKSk+MiJJUvt/424Jr1bh66uLgwGA4WFhSiVSkHQW1hYEHnic3NzTE9P\nix/T5OQkaWlp7Nu3j4GBATQajWBex8bG8sd//McYDAb0+nn+7d8O0tqaRUHBaRITv0RubjRvvvkm\nPT09YqYVHx+P0WgkPj4evV5PREQER48eFTNyqQBKHuEjIyOUlpZy4sQJEhMT0Wq1TExM8PLLL7Nj\nxw6mp6fFzXRychKz2czIyAh79uyhrq6O8vJyQkJCkMvlHDlyRJBU3G43JpNJELlCQ0OJjo7mu9/9\nLtdffz1KpZLV1VW+/OUvU1RUxKlTp/jrv/5rDh48SFlZGRkZGWKOHBwcTEVFBYGBgSQkJBAQEIBG\no6GmpobExESuu+46HA4HGRkZPPfcc3zuc5+jtbWV7OxscaOqra0VNrMul4uGhgZaWlpoa2tjeXlZ\nyOMOHjxIS0sLMzMz4tRfWFjoo1+GtR/1xMQEdrudG2+8keHhYRYXF9m/f78Ii5FSCQMCAtBqtaJb\ncfLkSTQaDWazWSSpNTY2cuedd2I0Gtm+fTsDAwOC/BQQEIDNZhPabqPRSHR0NGfOnOEzn/kM6enp\n1NfXo9fr6erqQq1Wc9111/Gb3/wGs9lMQEAA4+Pj9Pf388orrxAcHExkZCSHDh0Sp1lp8yR1oG64\n4QZWV1fJysrihRde4N577xWn2oGBAVpaWjh//jwFBQUolUocDgdbtmxBJpPhdrtpaWlBrVZTXV3N\n1q1b8Xq9gs9RV1eHSqXiwQcfJD09HafTSVZWFoWFhbz77rt4vV62bduGxWIRG8m2tjYRxhMeHo7R\naBQE056eHiFrlUY/arWao0ePUlJSIhj4kqY+ISFBMLEdDoc46VksFp5//nn27NlDUVER0dHRLCws\noFarKSgoEJvim2++mf7+fuEY98lPfpLu7m7cbreQuErZ8kNDQ/T09PCFL3yBgIAAJiYmKC4uJicn\nB6PRiN1uJyoqSpzIe3t7GR4e5uDBg0JxIFlBSyOS9eY866HRaISaQCLitbS0kJSU5GNrPDY2RkBA\nAC6XS7TbL4bX66W/v19sXGFtBDU0NCQCmkpKSggMDBTji8bGRmHcdCUgeSv8rpDMcUJDQ7FYLKIz\nJSWM7t27V3Q/4+LiGBoaEkqdzZ7LZrNhs9nE+jidTlpaWsTv9Ergw67BZrgmvfsd8MMf/pCcnBxh\nLStJ737605+ybds2lEqlmNs7HA4cDgevvvoqW7du5fHHHxea9r6+Pu6//35uuukmHn74YVpbg3j4\n4XBsNhl/8Rd6lpZ+jlqtpq2tTTiuqVQq4uPjSUxM5IUXXmB6elpoVrOysqiqqsLr9bKwsMD09DRe\nr1ek0FksFiorK8XsUgq7mZiY4LHHHuP73/++MHAIDw/nr//6r+nv78fhcAh2+8rKClu3bsXPz4/W\n1lZCQkJ8HMtGR0d59NFH6ezs5Ec/+pEI1Tlw4AC33norDz/8MEePHgX+h6D30ksvcebMGZ566ina\n2trwer243W7i4uJIS0vj3//93xkaGkKv1wtDoscff5zR0VHuvvtuXnrpJVwul9Dr3nTTTTz00EMk\nJSVRUlIiWrlvvfUWWq2WqqoqkV8vdRDuueceMUeMi4sjMjKSqakpjEYjVqsVi8VCQkKCaPnZbDbG\nx8dFitzFePvtt+nq6hJSMGlWvHfvXpEcNzw8LKx0Q0NDCQwMxOv1inm/lF+Qm5uLWq3mrrvuEnIu\nKTkQ4LHHHhNRuFK7Xq1WMzMzw65du8jJySEoKAiPx4PVauVXv/oVDz30EA6Hg5qaGuGI9sADDwjz\noeXlZbxer3ALTEhIYGFhgfT0dC5cuCDWLiMjg7feekt46ks3KEmqKKkjLp5jHz9+nMOHD3PixAkm\nJyeFB/0999zDc889R1FRESqVSgTnLC0todfr0Wg0LC4uMjc3J9Y0Pz8fp9PJjh07OHnyJCqViqWl\nJeLj48VN3Gg0Cpa2pC44cODAJe1Qz5w5s2H+bLPZ+O1vf8vOnTsZHR1lZWUFr9dLeHg4brebEydO\nUFRURF5eHpGRkUxMTIjgpT/5kz/h+eefZ2xsDK/XS2ZmJnfddZdo80uGQGq1WuisLwdp/CF9P2lp\nacTFxeH1etHr9WKcolAoKCkpuWR73ev1Ctve9VLQjIwMjh8/zh133LFBtnephLiPCleKnCZJji8m\n1kkHs8sx/yVptJTQWFxcfEWtdK/EGlyT3v0OCAwMFE5dKSkpgjEupbf92Z/9mc/ft7S0EBQURGBg\nIL/4xS9EwEhnZycpKSmkpaXx5JNj/PKX29myZZWvf70JtXqE6ekkTCaTKFwul4uenh6ampqYnZ3l\nzjvvJDs7W8xq6+vr8Xg8BAUFiXhMhUJBSkqK0MlLp2ApZ16SfNntdiERg7XkLavVSk5ODjU1NZSV\nleFwOAgKCsJms1FVVcXx48ex2WwiYtJqtTI7O8vi4iIVFRUb8qsl7e/ExATx8fEAgjAYHR1NUFAQ\nZWVlmEwm4bwGCE13cnKykP/19vYSGRmJRqPhvvvuw2g0EhgY6EPQCgwMJCoqSiSixcXFsbi4iM1m\n44UXXvB5bz/60Y+47777xOxc2vSoVCqSk5N59dVXefjhh33+n6eeeorU1FQ8Ho+PkYjkMtbX18eR\nI0eE3anNZuOrX/0q/v7+JCQkCCOi1dVVJiYmsFgsGI1Gvv3tb/u8zksvvSSeX+I5rEdSUpKIg5Vs\nZxcWFlCpVIyNjfHoo4+KYA1JDeDv7y/IUdImJygoiLGxMbRarQgWkuJYFxYWSE5Oxmg0Cn21VCgS\nEhIE18RsNgMInbBkNvPCCy8QGxuLyWTC6XRSWlpKW1sbn/rUp4iNjUWn03HhwgXefvttYfEbFBQk\n5JLLy8vMzMwQEBBAfn4+ZWVlKJVKent7OXv2LDqdDr1eT15entjweL1esaE+f/48UVFRKJVKEYa0\n/ma9tLQkCJuwdmPs6ekRBcDtdtPU1ERFRQVzc3Mbru2amhqUSiX79u1jcHCQsbExoqOj2bFjBwMD\nAxgMBtLS0rjlllvweDz09vZiMpnExlIiSr7fm3x4ePgG22BYO4leqiW9GaTvMT4+3ocP0tDQILox\nFxf7j9u3/qOCdO+4GBIx+nK4HNfgfxuuFft1yMjIwOv1otVqaWxsRKfTkZ2dzejoKHK5nIWFBVZX\nV7FarcJDXDLduO+++xgYGCAtLY3V1VUsFhtvvrmTl1/OYOvWeZ54opHc3Bh6e/2F5/vExAT9/f2C\nHBITE8PCwgLZ2dliFy/lcDc3N3P8+HEcDgcFBQUsLy/z2muvkZ6ejlar5fnnn+eGG25gy5YtLCws\n8Mwzz6BWq3nyySf5y7/8S0E4mZyc5Mknn+S73/0uY2NjovhJlp6ZmZkitGJ1dVW4hsnlctrb2zlw\n4MCGdQsJCSEqKoq3336bzMxMwsPDRWjL3//939PT0yNMeCT3se3bt2M0GklNTeX+++8XRLxHH32U\nBx98kMHBQSYmJtBoNHg8Hjo7O9m2bRs6nY67775buO2Vl5cTGRnJa6+9hlqtxmAwMDs7KzTJsbGx\nuFwu9u/fz4ULF5icnCQlJYXi4mImJyfxeDwMDQ0Jg4+5uTlxopLmzBIkH3nJEthmswkTIq1WKzgJ\ne/bsob29XZxiGxoaxDWVk5Mj/P4HBwfFekqRvCqVShSrrKwsdDqdIMxJcr6goCB6eno4cuSIsHad\nnJwkMzNTEJaS/19efH5+Pv/1X/9FRkYG4+Pj3HLLLeI0uLCwwBtvvCFawW+//TY2m42wsDAR2PGL\nX/yC+Ph40d4dGhqitbWVL37xi8JdcHh4mNLSUlZXV4G1jcd6oplarWZxcZHExERqa2t54oknWFlZ\nYcuWLRw9elQ4CVZUVIgNjeT2aLFYmJub49ChQ2Jj1NbWJk6rkvTPbDazY8cO0bI1mUz09PQQHByM\ny+VCJpNRUVEhOhgSc9ztdotQnjNnzjAzMyM2B1NTU4SFhZGWlkZdXR0VFRVCGfHSSy9RWVnJ2NiY\nT6cgKiqK6upqMRK5HKxWqzA7uhKn6fLycurr64mMjBScltjYWBISEmhoaPCR4EmZFlfqVH8loVQq\nWVlZYXFxUYT7SOFce/fuvbpv7vcI19r46+BwOPjZz36GXC4XhddsNvPJT36S4eFhTp06JUw9RkdH\nKS8vZ/v27Rw7doz29nZRYJaX/VlY+AFnzkRw/fUj3HdfI8HBa3G1GRkZWCwWOjo6xClY0rJ3dnYy\nMDAggi6k/HQpmKWpqYndu3eL6NbFxUWqq6tFNrsk95NOsGazmaSkJL74xS/6fM4f/OAHwlAiPDyc\nhIQEpqamhJ72+PHj/PCHP2R0dFScDr7xjW9w8ODBDWQkCR0dHTz33HMAgtFdVFTEbbfdRm1trQjT\nkbzNFQoFr776quiaSLG4krlKUVGRmDeurKxgsViANWc4jUYjZFNGoxGdTsf8/DwRERG8/vrrREVF\nsby8jMPh4PDhwxw4cEAY90gWtyEhIaSlpVFTU8PZs2eFHMjj8bB9+3Z2797NxMQERqPRx8c9LCyM\nH//4x8TGxqLRaFAoFILbMDs7S2JiIkNDQyQmJmK1WnG73cK5rrm5GYfDgVKpFJnqRUVFLC8vMzk5\niU6nY3FxkeXlZSorK/ntb3/Lr371K7xeLxqNhqmpKbZv305VVRUmk4kzZ86gVCpZXl4mKChIhOxU\nV1ejUqmIiYkRc+mwsDB0Op1PcA3Af/7nf/KpT32K//7v/2Z5eZnY2FgmJiaIjIyksrKSb3zjG8zP\nzxMSEoLX6xWM8a9+9aucPXsWg8FAVFQUMzMz6HQ69uzZw9TUFBaLBX9/f8LDwzGZTKKwS6TBsLAw\nrFar2Pjm5eXx9ttvExoaSnBwMGNjY1x//fV0dHRw6NAh0RWSjIIaGxu5//77+eUvf0lISAjh4eEM\nDw9TUlIiitz6YiYl+lVWVl7y9y/F50rBQEFBQXzyk59EqVTyne98h9nZWSIjI4UpjOR3cXFyWV9f\nH+Hh4T4t+/Une5vNRlNTk+i+zMzMUFxc/J5xtR6Ph7GxMVZWVkhMTHzf7nnrpXdSh2P9ZkjKxigv\nL//IZXHrcSU15isrK5w/fx65XI6/vz8Oh4OioqLfO7LdtTb+7wmcTqc45Xg8HrEjXlxcpLGxkUOH\nDlFRUSF09//4j//Irl27UKlU3Hbbbfj7++N0RvDUU9sxGML56lctPPhgANPTazre/Px8VCoVFouF\ngIAASkpKaG9vFxapUrG//vrreeSRR8RM89FHH2VhYYHDhw9z7733MjAwIGxIpSItnXqSkpIYHh4W\nxW4zVm1sbCwvv/wy9957L3v27GFmZoYdO3YwODjIv/3bv+HxeGhtbRUOgJJXgKTF3wyFhYWEhYXR\n3NyMn58fMTExbN26ldraWsLCwnxOP1arlV//+tfMzc3xla98RZxOAwICeO6555idncVmswlbV5fL\nJTSw0glaMjFZWlriySef5L777uNrX/saTz/9NPPz86Iz8eUvf5n09HRiYmJQq9XMz89TVFTE5OQk\nMzMzDA4O8sgjj4iWvNFo5I033uCGG24gMzOTjIwMlpeXRdiP2+1menqa6667Tmwq8vLy+M53vkN5\neTlms5lvf/vb4uR65MgRTp48iU6n45ZbbiE3NxePx4PD4eBXv/oV5eXlTE5O+qyPxWKhubmZZ599\nljvuuIM777wTmUyG3W7n05/+NAcOHMBoNPKNb3xDdIW6u7vp6+ujt7eXRx99VJi0BAYG8sorrzA8\nPCyY8hEREXi9XoaGhkQqYl5enk9K3Jtvvsk777yDw+Hg4YcfprCwEK/XS1tbG88++yxNTU3k5eXx\niU98QqzP+Pg409PT2Gw2du3axerqKlqtFqvVSktLC7Gxsdjtdv7pn/5JaP1PnDjBhQsXeOutt/jK\nV74iEhkVCgVf+tKXRISrZNmrUCjweDw0NTXx/PPPc/vtt/u4wP30pz8VttQulwuDwYBKpSIqKgqX\ny3XJVLfV1VV6e3v53Oc+J/59eXmZhoYGIiIi+OM//mOioqJEqmRPTw9zc3Obyrsk18tLoampiV27\ndonC6/F4OHXqlAiM2Qxms5nW1lYyMjLQaDQ0NzcTGRn5vlrQm20ioqOjiY6OFqOfK1nkPw4EBASw\nc+dOVldX8Xg87ykV/L+Ia2z8dWhvb6eyspLU1FRSUlJE8Ep/fz9LS0ts3bqV7u5uDAYDFouF3bt3\n89xzz7F9+3b27duH3Z7LE09sxWYL4vDhZ/mrv4qipqZGsNmlCEiPx8Pk5CTPP/+8cP566aWXKC4u\npqenh23bttHd3c3ExATNzc2UlpZy6tQp4uPjRXKX2+2mra2Nzs5O5ubmeOKJJ9i6dStqtZodO3aQ\nnJxMc3OzsFZdj1/96lfMz8+zd+9e5ufnRaLa4uIiNTU1qNVq4uLiGBsbY2hoiPHxcTFekHTZIyMj\nLC8vCxMVu91OR0cH6enpFBYWMjExwerqKnq9XmyIJAQGBnLu3DlsNhtqtZrAwEDkcjk9PT0MDAwI\nEmJZWRnx8fFERkbicDg4e/YslZWVFBYW8sorr9DR0SFmty+++CK5ubk0NTUJ8lRfX5+Y2bndbmEe\nJL23kZERysrKcDqdDAwMiMwBKQhDpVIxODjI4OCgj6uZ2Wzm6NGj6PV69Ho9b7/9NmVlZUxOTnLo\n0CEGBwcxGo3itN7a2sqOHTuIiIhAr9djMBiw2+1UVVUJL/L1xUGpVDI8PExraysPPPAA7e3tTExM\nMD8/zw033MD3vvc9vvjFLzIyMsLk5CSTk5NCE97R0cHhw4fx8/MTpDWpK1RUVMTi4iIXLlxgbGxM\nkAjHxsZ8MtkB0tPTOXLkCFqtVkThzs/PU1BQQGNjIwEBAVx33XXCqEYmkxEWFkZfXx9hYWHiFBwS\nEoJer/fxOJiYmMBgMDAxMUFycrLYdLjdblQqFV6vl87OTiIjI3E6nVitVlZXV0X40vnz5wkPD2d2\ndnZDelp8fLzo7gwPD6PVarHZbLS3t4vkvc0K6tDQELGxsT5OcRLJTupSSR03iYg7ODiI2Wz2CYlZ\nWVmhr69vAylM6g4tLCzgcrmEDTT8jymM0+kkNDQUt9vNwsICfn5+ogifO3eOvXv3EhYWhlqtJjEx\nkb6+PnQ63YcygfH397/kBuOjxpVgol8MiWj3+4qryca/drJfBynXfDPI5XKSkpJ8WnaS4UpOTg5v\nvinjz/9cS2TkKq++aqa6epCmpiAqKytFoZMSuGBN7/4v//IvOBwOMTv827/9W1QqFUVFRdTW1jIx\nMUF6ejpRUVGCRfv5z39e/Li1Wi3f+ta3qKiooKWlhYKCAqGb1+v1xMfHk5CQwNe+9jUeffRRAP7j\nP/6D9PR0zGYzY2Nj3HjjjczMzJCQkEB3d7e4kYWFheF0OoVxTWBgICkpKZw/fx6Xy0VAQAAWi4Xe\n3l527twpXLYUCgU6nQ61Wk19fT1arZaenh6Ki4sxGAxi3u3n58fOnTvp7u5meXkZuVyOw+EQxDGX\nyyVCVmDtxC2R73Jzc31COaQTvpQql5aWhsvl4sKFC3R2dgqGe1JSkmDjj46OCmKjZPMp4cKFCywt\nLQldfVVVFdPT01RXV4twnG9+85sisCQuLo5nn32W4OBgLBYLqampJCUliajXLVu2iOCi9Zre+fl5\nXC6XOOGth+Q8eLHm2WQyCfXGxUloBoOB2NhYTp8+LebQo6OjDA4OEhMTI2ay27Ztw+Vy0draSnJy\nMj09PcBa/sD8/DyRkZGEhoYSEBCATCZjYGBAnOylcZX0b5u9b8kK97e//S1qtZqYmBgqKio4e/Ys\nbrcbf39/4uLiMJvNwqt+bm6O3bt309DQwOLiouBndHV1ER4eLgifXq9XsKY306yrVCqUSiUdHR18\n5jOfEY8nJiby85//3Cf2dj2k93Ux/Pz88Hq9gqwrhV1Jlq/l5eXU1taiVquFFHC9mc/F8Hg8m76O\npKLR6/XMzc2Jjoi/vz95eXmo1eoNRTkrK4vh4eFLBtRcwzWsx7Vivw7JyckbduVSII7kU71eI3v8\n+HH27dvHV77Sz9GjN5Od7eSHPxxhePicCBa5WMaRl5fHv/7rv7Jr1y4aGxtFLKperyc9PZ2enh6+\n9a1vcfDgQZFN/uyzz7J37162b9/Opz/9aQoLC1laWmJgYICHHnqI6elpxsfHMRgMaDQaQRRLTU3l\n3nvvRa/X85Of/ASPx8Mdd9xBQUEBVquVM2fO0N/fT1FRET09PYyPj5OVlUVeXh7Hjx8nKSkJlUpF\nXV0d+fn5rK6uYjAYhETQbDYzPz8vTHouPmHk5ubS0dFBc3Mz3d3d4nX6+vrIz88XRKjx8XFWVlbQ\n6XRER0eLbkNtbS1xcXEiVlSav9ntdpxOJxaLhZiYGAwGA+Xl5fzyl7/ka1/7mnj9mJgY/uVf/gWd\nTkd4eDjz8/Niji7JGsfHx0lLSxOFy+v1CmtXyWQE1tqeISEhwvdcWgMJEhFQ8havr68nOTmZrKws\nocWXrI0lSBulnp4enyCPpaUlYK0ASRn2Er71rW/x2c9+Fr1e71NUJHOjiIgIUlNTmZqaoq+vT4Ty\nbN++nYKCAmZnZ4WZUFFREWq1GrVaLTojUVFRjIyM0NTURFVVFb29vRgMBuH8plQq0Wg0FBUVbXhv\ns7OzhISEYLPZxGvrdDp6enrQaDTEx8cLX3lpjp+UlERPTw/d3d2cPHmSrKwslEoler2e06dP8+ij\njxIeHs7k5CRjY2OEhISwc+dOAgMDcbvd2O12n1b6G2+8QWZmJmFhYTQ1NZGRkcHi4iI9PT1kZGQI\nhcX09DRWq5XY2FiCg4NJSkqiqanJZ87u8XiwWCzIZDLOnTvHgQMHhPHN0aNHKSwsJDQ0lL179+J0\nOgHeU7YVHh5OW1sbOTk5Ppul4eFh0tLScLvdPmz8qakpenp6NmXKv9e44BquYT2utfHXITQ0VLSu\npQI6MjLCtm3bSElJ4emnn2ZpaQmr1crrr79OdHQMev2tPPNMGbm5UzzyyGsoFDZmZ2dZWVkhKipq\nA3lndXWVlpYWysvLiY6O5re//S1zc3PCwjYwMFCwvBcWFpicnBRdhQMHDnDfffcxNTVFcnIyX/7y\nl8Xfud1uYQQECA11RkYGkZGR7Nmzh71794rZtBTlGBsbi0KhELa3MTExOBwOHnroIdLS0khISODQ\noUM0NzezurpKamoqy8vLzM3NYbfbhRmHNP+rra0V9sGhoaHMz88DkJaWRlBQEGq1muDgYNH6lYhB\npaWl+Pv7Y7PZBNM9Pj5ezMmdTidjY2Pcc889/OIXv2BpaUmYvkxMTBAVFYVMJuPXv/41zc3NnDp1\nipdfflmE70h+3QkJCaysrIiOwr59+2hubsbf31/MlrOzs5mZmdkwD1UoFPT29hIQECB4AYuLi7S1\ntYlYzMnJScbHx0lKSmJycpLq6mo++9nPEh0dzdmzZ4G1Yt7R0YFOpyM1NRWj0cjY2Bh+fn4YDAa6\nurrYtm0bZWVlfPGLX2RsbAyTycT3v/99QkJC+NM//VPsdjt6vZ6ZmRkmJibo6+ujvLycoqIifvOb\n3+BwOAgJCeHcuXMolUrBSg4KCiI+Pp4tW7aIuebi4iKdnZ1i/q/X6zGZTGzfvp2QkBAMBgO5ubmE\nhYUJPXNFRQWdnZ3Mz8+LoJaJiQnKysqoq6tj165deDweQkNDRfSwFDdsNBqBNeWD5LpoMpno6+sj\nJCQEl8slPPK3bduGQqEgNDSUhIQEH5JZamoqTz/9tLCjfuONNwgLCyMxMZHMzEwiIiIYGxsD1tL6\nTCYTUVFRwgpWo9EwODiIyWQiISFBePFL3vytra0UFxeLAJzp6WlMJhMTExPCzlrquigUik0Lr81m\nY2pqiuDgYGFvGxwczPnz5/Hz88Nut9Pa2kpqaipjY2MbDF1CQkIYHBwU3hnSa3i9XhobGykuLr4i\nbXhJox4YGPiRbSg+jjZRctLcAAAgAElEQVT+7zuumep8RPiwbHxYm9vPz8+L3GoplUoiBrW2tmKx\nWCgpKeVf/zWBn/1MTUFBN1lZ/x/x8VE+BC6tVrshyrCxsZGYmBi+973vkZaWRmlpKVarlaamJuRy\nOaGhoeTn5zM6OsrCwoI4/TQ2NhIREUFkZKTQ1w8NDYlToNlsJiEhQbzvsbExoqKiuPnmmzf9nC+/\n/DKFhYUEBAQIxnZwcDDHjh2jrKwMWLtRSTPZLVu28Mwzz1BWVsaBAwcICAgQDl/vvPMOOp1OxHdu\n2bKF+vp66urq2LdvH1VVVbjdbiYnJ4VXulT4duzYgdFoZHFxkbi4OMEmb2hooKioiJSUFFwuF+fO\nnUOhUFBQUEBeXh4OhwOr1SpMQ6anpzl27BgWi0WoIiwWC3FxcSwtLfH444+jUqkwm83odDqmpqY4\nevQoX/nKV0TMqUwmo7CwEH9/f2pra6mqqvIZ63i9XmpqamhpaeHRRx9lenqa1dVV4uPj+fd//3ce\neeQRlEolU1NT6PV6cnJyfE6+Xq+XyclJlpaWSEhI8NEAOxwODAaDWB/phr+6uirS8Q4dOiSsYQcG\nBhgbGxMRtisrK1RVVQnp0djYGNPT04LQdTnU1tayc+dO8ZyZmZnEx8dz9uxZdu7cydTUFPX19cIn\nff3pd3p6WoSxxMTEYLFYaG9vx+VyibFJa2srKpWK2NhYwceQRlcqlYqSkhK6u7spLS2lubkZm81G\nWVkZq6urTE1NXVZf7vF4aG9vx2w2s3XrVpHrIPEkJDidTkEeLSoq8unQ6fV6wsLC2LJlCysrK8JU\nSYqkrampEa8lEUYlZYAUdHMxvF4vDQ0NBAYGio2w0WikqqpKGHZNTEyITblCoaC2tpYdO3ZsGI/U\n1tZSVlbG+fPnUalUBAQEiJjU9zLp+aBYWVmhrq5OjPKkjc0HjZDdDFcz8e33BdfY+L8nkLS767WZ\ndrudlpYWtm3bhlwup7S0lJUV+Ku/CuPo0SD27etiz56jPPTQP7O8vExMTAwjIyM8+eSTfPOb36Su\nrk7I9aampoiJiSE4OFi4iC0sLGCz2cRmYmxsjE9/+tM+7OympiYcDgexsbF4vV6cTqdwIPPz8xPu\ndhIBSKvVsmvXLn7+85+L55CY9FIxiI6OFu3wpKQkzGazkOq53W5KS0tF6zkkJITx8XFBUJJOhDKZ\njPj4eKxWK0qlkry8PDHnDwgIYMuWLUxPT4ubupT4BWs3Q+k5tFotQUFBKJVKlpaWCAsL48Ybb+Tc\nuXM0NDTg5+dHVlYWBw8eFP7vAQEBBAYGEhwcTHp6OoODg/T29vLYY48JQ5uxsTGefvppbr/9dlpb\nWwkPDycoKAij0cjs7CzZ2dnMzs5y/PhxcYPt7e3lxhtvJDMzU3RgJHR1dQni5o9//GOys7NRKpW8\n/vrr7Nq1S0ihYmNjhaHM+h+2tF6bQaVSERkZKQhgEvz9/fnUpz7l87eLi4vMzs76eB6srKzQ0NAg\nrpvExMT3HeYhfRcZGRnCgGX9GSA2Npbbb799w//X2toqOB3SyT4lJYXBwUHuu+8+5HI5Op2O0NBQ\nXn75ZVZXVwkLC+O6664TzzE3N0dnZ6dw9Vu/3tPT05vOt9dDSppbj6CgIOLi4jh9+jSxsbFC7llZ\nWUljY+MGu9qsrCzOnTsnuh3rvfZhbWP1iU98wsf0qKuri8nJyUu+r56eHhITE8XJX6fT0dnZSXd3\nN/n5+fj5+REZGSlkt9I6j4yM+PA6rFarsMrdvXs3TqdTJO1dCU18c3Mz5eXl4j4RHx9PR0cHJpNJ\ndAWv4Q8T14r9OgwPD7Nt2zbh6hUSEoJGo/GJO11clPHww+FUVyv50pesKJUvC8MRyYFPirv09/dn\n9+7d2Gw27HY7O3bswM/Pj9dee40HH3yQxMRELly4QHBwMHfddRe//OUvkcvlNDQ0cPDgQSG9a2lp\nITg4mLKyMuLi4kSEZ35+vvgh9vb2YrPZxFw9MDCQyMhIoemVTncWi4WtW7eSmJjI4uIiubm5LCws\niOeVCvuJEycICwvD398fs9mM1WqlsLAQm83GmTNnCA8Px263Y7PZSEpKIiAggIMHD2K1WgkKCmL3\n7t1kZmaKJLj1gR2Szt7j8fDOO+8QGhpKUFCQ4ADs37+frq4uKisrxWlKcvTzeDycP38et9st9Pw6\nnY6mpibuueceYG127PV6CQ4OprS0FLlcztzcHKOjo4LToNVqyczM5OWXX2b//v1iIzIwMMBLL73E\nQw89hNPp5PTp06JbsmXLFhEn+oUvfIGenh6Wl5f5sz/7sw/V6hwZGRHBRQ6HA7fbzbZt2y5JFh0c\nHNxw2g0ICEChUGzgBbwfaLVaJicnfYiAvb29G0ZQ6yGF3qwvtGNjYyKjfT0kNzeDwbAhuEVSKEib\nWGnmLdnDXhyG9H6RnJxMYmIis7OzxMTECJa91+vFbDbT29uL1+tFqVSSlZV12VZ4VFQU/f39Qhe/\nvLyMyWS6bKra3NycIM5J8/bY2FguXLggpLxSJsXCwgLFxcWkpqZy7tw5kbo4OzuLyWTy6VCsN126\nElhdXRWFXkJubq4Y1V3DHy6uFft1kMlkdHV1sbi4yJYtWxgeHhYuVwA2m4x7742gpUXBP//zAp/+\ntIMjR1TodDpOnz4tZDAmk0lEpLa2too4z/7+fnFjkVjY65ngbreboqIivF4vTz/9NMHBwdjtdkpK\nShgfHxczv/UnDI/Hg06no7q6mtzcXNHWlhKyGhsb2b17tygAbreb6upqUVCl8cDQ0BAKhYLS0lKR\nPS3JWLxeLx6Ph+TkZFZWVsTpXSq4aWlpDAwMAGu8B+lEazAYiIuLIyYmRqyPw+EQpjGNjY3iM0lx\nvm63m7m5OTHrnZiYIDAwkOLiYmpqapiamqKoqMiHRPnrX/9amLHs37+fhYUF/P39CQkJ4fz586jV\naux2O5/61KeEp/+xY8dwOBzodDofO1EpHU6au1+q4Mlkso+EBW21WpmammLPnj3isfn5eZqbmzdY\nt65/bYkIuB6X0pC/F3Jzc2lubhbOd3Nzc4SFhV0yJx3WnBgvbmEnJiai1+vJyMjg1KlTJCcnCx6E\nxGe4FIO/vLycc+fOCSnmzMwM2dnZH0ovLZfLN7S5pUK7f/9+FAoFVquVI0eOcOjQoUs+j0ajISsr\ni/r6enGtVlRUiJS8S2FgYICpqSkUCgUqlUoU0fPnz7Njxw7x2db/JisrK7FYLJhMJiIiInyu8/7+\nfkwmk5BTSgl4Vxq/63V1Db9fuFbs10Gj0aDX67nzzjvFY+Pj47z77rvMz3u59VY/xsb8OHz4v7DZ\nepidvYN9+/bx9ttv88ADD4gd+cjICL29vbS3t4uZulQ829raKC8v5/nnn8doNIp43NjYWBwOBxER\nEZhMJhYXF1lcXMTf35/Z2VkqKiro7+8nISHBZ547Pz9PcHAwVquVF198USgHUlNTsVgsIqdagp+f\nH3FxcczMzBAdHc3s7CwjIyMikc7pdKLVahkeHqarqwtAMKD9/PyYnZ2ltLRURG+2t7dTWFjIzMwM\n586dE/P+6elpGhoa+NznPidkbxfbwa6urpKSksLY2BhmsxmVSsWuXbs4ceIE2dnZtLa2kpaWhtPp\n5N1338Xf35/IyEgWFhY4d+4cGo2GmZkZ8vPziYqK4tVXX+XGG28UDHqLxcLk5CRVVVXk5eXx2muv\n4XK5UCqVHDx4kHfeeYfk5GQaGxvF96RUKtFqtUI/faXR19dHcXGxz2OS1Ez6d5PJJAJqSkpKSE9P\np7m52afYrh/tXArLy8u0tLQILkpISIgwkYqJiRE+ENKc+XKQyWSbFgC1Wo3T6RQbTqnIxcXFsXXr\nVrHhlbCwsCDa1Hv27GFxcVE46knPb7VaaW9vF2RNKXb4cpDm9tJGNSIigtzcXKEakcKeJPc6qQOw\nGTQaDWNjY0Ka63K56OrqEteH0+lkcHAQuVxOamqqGOcMDg5y4403Amtt/F//+tesrKwQFxfns4nx\n8/MjKSlJbI41Gs0GnoVkviUx9S0WC7W1tezdu/cjLcSSpHb963d2dvpsiK/hDxPXiv06SMYxtbW1\nwi53aWkJnS6Tm27yZ2oqkp/9bIEbb9yN1VrMf/zHf/D5z3+elJQUMcOtr69neHiY+++/n1dffRWt\nVit29Gue+RaGhoawWq288sorbNu2jdnZWY4dO8bhw4dxOp10dnayf/9+wsPDMRgMHD9+nJKSEvLy\n8jh16hSxsbGCCFhWVsaRI0dwOp38yZ/8CU6nE7VazQsvvMDw8LDPjVVCQEAAs7OzmM1mcbPwer3C\nzvX48ePcfvvt3H333cBa4f7pT3/K3Xffza5du+ju7hZ++Xl5eYSHh3Po0CGOHTtGfX09wcHByGQy\nbr75ZjEflcvlG5y8JK9uaYbrcrk4efKkGEXceuut4m8TEhL4yU9+QmFhIVVVVTidThwOB+np6fj5\n+WGxWDh8+DB/+Zd/SVFREU6nk/7+fj7/+c/T09ODUqnkgQceEJ+1rq6OsLAwjh8/zpe+9CWxQZif\nn+epp57i7/7u7z76C2wTrK6uXjKCU1JnSHN4l8vF6dOn2bNnDwkJCWImLXE1LmcFK4XbVFZWiu/E\nZDLR2NhIfHw8JpOJ/fv3C/fIs2fPolAofDzu12Oz1v/MzAyhoaG0tbURGhrKrbfeik6n4+zZs7S2\ntnLTTTfR0tIi5uNmsxmLxeLTpg4ODvZpIy8vL9PY2MiePXvEprW/v5++vr5Nr21YOylLigCJBDk6\nOkpnZycKhYIdO3aI6yczMxM/Pz/q6uouuXZarZZ3332X7OxstFotRqORvr4+cnNzhYFSTk4OXq+X\n8+fPk5KSgtfrRafT0dDQgE6no7e3F41Gg8lk2vT7DgwM9BkXXvx5rFYrRUVF4jFJyjg1NfW+CVrv\nB6WlpdTX1xMUFCQsmnU6nciO+EPE8vIyXV1dYkOfk5PzntbE/xtxrdhfhJycHDwejwihWV5Wc9tt\ngRgMofz85/OUlk4xObnmM71//35qamo4cOAAO3fupL+/n4yMDHGD6e/v54EHHhAzfY/Hw+uvv86p\nU6coKiri1ltvpb+/n5CQEB555BF+8pOf4Ha7eeKJJ1haWsJms1FZWUl+fj5vvPEG9957L1FRUQwN\nDSGTycjMzCQ0NJSGhgZ++tOf+szyYmNj+Zu/+Rvy8/N9dOSwNlv19/enoqJCPC6TySgtLRVhKOvb\nh1FRUaIVHhgY6GOrKmFlZQWNRkNmZiZarVY47F0OCwsLPq1ihUJBcHCwsLxcWVkRJ6Dp6Wnhzy75\nHUif98KFC8THx5OUlMSuXbtEO/jP//zPgbXksvUsZ5lMRkJCAn19fcTHx3Pq1CnBNu7o6CAhIWHD\n3PlKQUoxzM/PF49JMbSzs7M+RE2FQkFxcTH9/f3k5eWRkJDA7Ows0dHR78m4n5qaIiEhwYecFh0d\nLdwG9+3bJ9ZHLpeL8KWLZ+wScnJyqK+vx2g0Cgtju90uYns/+9nPYjabCQsL4/bbb+f111+nt7eX\nrVu34nQ6mZmZISkpyWcz4XQ66evrY2VlhdTUVFEkJVmmhMzMTM6cOXPJYj80NERubq6P2iEpKYkz\nZ86I73V9h0nqjl0KQ0NDfOYznxGy24SEBMrLy8UYav13tHv3bqqrq5HL5ZSVlbG0tCSMliSHSmlz\ncLHO/lL8BKfTuam8Sorb/SiLvb+/P7t27cJut7O4uEjy/0uk/EOFy+WipqaGiooKQkNDWV1dpbGx\nkaSkpI903f4Q8If7LV4BpKWlodfrKSgoIDo6mrk5OXfdFcbkpIJ77nkBf38ZBkMkQUFB1NXVERIS\nIjTDUkTnega2w+HwYV/L5XISEhI4duwYn/nMZ/D39/eZ+4aGhjI3N4dcLvfJNddqtTidTmpqanA4\nHCIJTiLlabVaWlpaqKysFJ7wvb29hIWFkZGRwenTp8nIyBBuaOnp6YyOjm6Yh8pkMpxOJwUFBZw6\ndYrMzEwUCgX9/f0UFBSIhLzN0NbWxt69e/H390en0xEXFycscS+lA83KyqKmpoaUlBQ0Gg3Dw8NC\nqVBSUsL58+cBRDJYWloaxcXFNDQ0iEzz8fFxXC6XYHH7+fltuGlmZGTQ1dVFZGQk0dHRwu44PDyc\nffv2sbCwIEYWqampFBcXC73/lUZUVBSTk5M0NjaSkpKCxWIRN/7W1tYNf6/Vaunv7wc2n0lfClar\nVcStrodkgrO8vExPT4+4vrKysi4beSqTyQgPD2dkZASDwSDifc1msxgBaLVa8XtISUnBYDCIKOaL\nRyQGg4ELFy5QXFxMYGAgfX19jI2N4XQ6N93IXG5cYbFYNuUbBAQEEB0dTUtLCyUlJchkMpaXl0Wi\n4KXg5+cn7JPXb6hnZmY2uBjCmhRKspgOCwtDqVSiUqmET39GRgbV1dVkZmYil8sZGBggMTHxkkU1\nKCgIq9W64XEphOhKQDJb+kOHNCaTfsv+/v5UVlZSU1Nzrdj/X0Z0dDQzMzPU1dURGJjI44/nMDXl\nz89/PsOxY29SVfUNMY9LTU3l+9///iVPPrB2uq6urqawsBCNRsPQ0BCjo6MkJiYyMjKy4YbkcDiQ\nyWQ+J1pYI/oEBQUxODjI3XffLX6EMzMzvPrqq6hUKiIiIqirqxNZ5VJiXFxcHFFRUYyOjgKwfft2\nQSK8uBCvrKwQGRlJR0cHf/RHf8To6ChOp5OKigpeeeWVS2qKXS4XgYGBG25WOTk5DA0N+bQf10M6\nRRgMBubm5sjJyWFubo6SkhKGhobYvXu3OP04HA6hSti3bx9TU1PMzs6Smpr6nqdaPz8/KioqsFgs\nzMzMEBcXR3JyMp2dnUL7vJ5VXl9fv2n34v1gcXGRjo4OPB4PISEhInDkcigpKcFutwsfggMHDggi\n4cXkqNHR0d/pJhUbG+sT5StByqk/ffo0VVVVYsP51ltvXfZ9SyTOm266STw2MDAg/B+kWGPJ8769\nvZ0bbrhh0+fyer309/f7pNQVFhbS3NxMUFAQk5OTPptmr9cr4nQ3w5YtW4RfwHosLy+TkpJCQECA\nMNaRy+VUVVVdNvfc6/Xicrl82u8LCwuEhoaK1vB6LC0tUVRURHNzM+np6cTGxjI8PCxSKyVLaSlV\nsrKy8rJERLlcTmRkJF1dXeTm5iKXy8X1//tulev1eunt7RWk26CgoPfkW3yUsFgsPl0zQJh5/V/D\nVS/2jz32mDBB8fPz46mnnsJut/O9732PmZkZIiMj+cIXvvCx7TLXiEoWvvGNDObnlXzzm53s3atj\neDiH5557jrvuugu1Wi3kX5e7SaSmpjI+Pu7Dbl9dXeXOO+/k5MmTIifc5XKJcI+Kigq+/vWvU15e\nLvTLg4ODos26fh0kD/NDhw7x1FNPcfDgQZKTkxkYGOD06dN8+9vfBtbavxf/wPLz86mpqWHr1q0i\nLOT8+fNs27YNp9PJiy++yOHDhwkICODUqVMsLy9fkp0txepejEv5vkvIzMzk+eefJyYmBpVKhV6v\nx8/Pj9tuu43Q0FCqq6vR6XQsLS3hdDqFPaxMJmPLli3vu+gVFhZSW1tLRUUF2dnZzM/Pc+7cOTF6\n6e7uFjNXKfbzct/rpbC0tERDQ4Owc9VqtRw/flyQyi4Fs9lMV1cXfn5+mEwmFhYWyMvLIz09neee\ne46YmBiUSiUzMzMoFApuueWWD/zeNBoNNpuNF198UfgQTE5OUlpaKjz+1485pHb3ysoKzc3NgtQn\nKTZGRkY2bP7S09Opra3F7XZz+vRp7r//fmJiYvjP//xPhoaGLkmAW1paEoFKFz/fyMgI/f39wt3R\n6XTS2Ni4IWhmPWJjY6mpqUGlUhEfH8/KyopwqQM25A28FwoLC0UrWK1WYzabaWlpYdeuXdTW1pKS\nkiI2AsvLy5jNZgoLC9m/fz8jIyO0t7eTnp4uOBGwttG9WM9/OeTk5DA5OSm4BVqtVqz/3Nwc3d3d\nwqhIp9P93mwCGhoaSExMJCcnh4iICBobG2lpafmdN9MfFFLS5cVRt5upWf6346o76D322GN885vf\n9GmZHjlyBLVazaFDh/jv//5v7HY7995773s+14d10FtcXKS5uZn8/N08+GAEX/qSDZ1Oz8rKinCz\nO3nyJC6Xi4yMDLZu3crY2JjPyXV9G//s2bNcuHCBpKQk/P39WVpaoquri0ceeQSz2cyZM2dwu924\n3W60Wi0HDx5kYmKC2dlZoceNiooiNDQUhUKB2+3ekPL1wgsvkJubi9lsprGxEbPZTGRkJMXFxRQW\nFm7aupWwsrIiRgEqlYqcnBzRpuzt7aWmpgaPx0NBQQGVlZWX1SLX1taydetWgoKC0Ol0zMzMcObM\nGSorKy9ZOLu6uoTVqs1mIyMjA4vFIoql1+sV7PAPqy12Op309PQIAmNOTo44TRkMBhGqkpKSIgrz\n+vUJCgoiJyfnshniLS0tZGRkiG6JtA41NTU+c931WFpaEtnr0vqOjo5is9mE4YrNZsPlcglS3O7d\nuz+wJM3r9XLy5EkSExOZmZkRp8WZmRk8Hg9bt25Fr9eLwpudnU1jYyMul4uEhASMRiMymYyoqCgM\nBgNyuZydO3diNptFbK5Wq+XUqVPCP//s2bOibS0V3s2IUaurq5w/f57t27djt9tZWVkR5NSlpSWx\ngZ2bm0OhUJCTk/OeFqFer1eQ5/z9/cnKyvpQ2eZOp1OsT2hoKNnZ2SgUCux2O83NzSiVSrxeLysr\nK5SXl2+4Xq+Ue5zT6eTcuXPs3r1bXD+Ss+bVLvhWq5ULFy6Iwi6tgdQ5+1021B8Uy8vL1NTUsHPn\nTvEdtba2otPpPha1zcW45qB3ERobG5Es+/fs2cM//dM/va9i/2Gh1+spKysjKAheeWWOtU14OmfO\nnAHWTv0PPvig+Pu6urpLtqhhbfd49913iyKTlpZGWVmZmCPdc889zM/PCyc4WCs8u3fv9mFWSyfG\n4eFhH4vM4eFhnE6niKtdvxFwu900NDRcttgHBARc0gZzszS4y6GiooKGhgZUKhUxMTEMDg6SmZl5\n2R/01NSU0KuHhoYyPDxMQEAAfX19wiHsUmzwDwqVSnXJ08RmXYKlpSWxgZESyNaz2fv7+5menhbd\nlsLCwk2JVO/VMpRIa+s3UhKZDNiwSYiOjqavr4+CgoIP9PkNBgPJycmkp6f7tLdnZ2dZWlpCqVT6\nzJ9XVlZYXFzEbrcTGRlJVVWVMLqZnp4mLi6ON954g8TERBFGJNnDhoeHk52dTUxMDBqNBrlcjslk\nwmw2b1rsJQvaN954g9DQUGQyGUtLS8zPz3PHHXfg5+f3ga5FWFv3lJQUQkJCxObjw2CzlEFYOznu\n2bOHlZUVZDLZxx5MI92z1l8/qamp4vq5mriUnFGn02GxWK4Y32A9AgMDBf/F4/Hg8XhIS0v7Pzev\nh9+TYv/1r38duVzOwYMHue666wR5CiAsLAyLxbLp/3fixAlOnDgBwFNPPeVjNvO7QKVS+ejYJWi1\nWioqKqipqSEmJoagoCDGxsbIysoiKSkJh8NBU1OTaGXHxcWRkZGBVqslISHBp/3t9XqZmJgQ7/Xi\nYiyRmi6GTqcjJiaG3t5epqamxKn35ptvFtnhF0Oj0XzoNfkguP3224UDXHFx8Xvqf6empnj88cfF\nKTU7O5vm5mbGx8c/1ve9GRoaGrjlllt8TukS6TAwMJDY2FghGZuZmaGjo4O4uDj8/PzEtevv709E\nRATBwcGX/DwKhYLU1NRNrznpddcjPDwco9H4gdfHZDIJhvt6SJrvkZERysrKRAv/9OnTIpxlveGP\nRHCENU+C6Oho4uLikMlkwkzKYrHQ1NREbGwsHo+HkZERvF4vVVVVl9y8BQYGMjg4KDYeEss/PDz8\ndzLWuXDhAsPDw8THxzM/P8/g4CC7d+++bGfmSkIirn7UCAwMJCkpadPr52r/hvz9/blw4YJ4H9Ia\n9PT0+PgRfBy4Gqf4zXClroP39dpX5VXX4etf/zparRaLxcKTTz65Ycd1KfMOgOuuu87HZ/vDtkcC\nAwPFTVuC2+3GbDbjcDgoLS0V/52Xl4e/vz9Go5Hq6mp27NghiHJnz55lenoas9nM9PS0z657eHgY\ntVotdO5DQ0MolUoh2ZufnxeuWxJMJhNyuVwwmSWf+uzsbKKjoxkdHWV8fNyndWi1WlleXr4qwRPv\nt1Vls9lEhKgEKVHvagdmmEwm0tPTN8gHpbl5WlqaeI/S6T0wMJDjx4+LQBqNRsOxY8fIyMi45OdR\nq9U0NjaKeTIgpJ9er5e2tjaGh4eRy+UEBASInIUPuj5BQUG0tLQI0yMJw8PD7Nq1i/HxcY4ePSrm\nvtnZ2VitViwWy4bXslgs2O127rjjDoxGIy0tLWi1Wvbv38/Zs2fp7e0VgUiRkZEsLS3x6quvsnXr\n1k3f98rKCh0dHdx3331ifCOTyXjttdeora39wCEsDoeD7u5usRmTVBhvvfXWZVn3VxJXqo2vUqlo\nbW31KWbS9XO1f0Ow5rSoUqmIjo5Gp9PR2toqOkZ2u/1qv72PHVezjX/VI26lAqVUKllYWMBqtWIw\nGKisrESlUjE/P099fb0P6/dS+LARt+Hh4SJaMzQ0FKvVSn19PcXFxWIXKoXDqNVq5HI5/f39JCYm\nolarMRqNgmmr1+vJy8sTdrQBAQGMjo6KGb+UrhcWFoZCoaC7u1t0Furq6ggPD0epVGIwGOjp6REe\n79L8XqfTiVhXaYMhsV1NJhOtra1UVFRcFdbpZjGOLpcLo9EoQnFgTTtvNBrF+oyMjDA8PCxO0VcT\nExMTxMTE+Kyfx+Ohp6eH2NjYTTspNpuN/Px8WltbaWlpYWZmhqysrMvu5DUaDV1dXXg8HsLCwrDb\n7TQ0NJCfny/+e//+/aSlpTE/P8+JEyfYv3+/IHtOTU2xurr6npyGwMBAjEYjMzMzuFwurFYrer2e\n2NhYtFotGo2GpAxNk+UAACAASURBVKQkEhMTSUpKIjg4GLlczvDwMMvLy0REROB2u2lraxMqjtTU\nVKE4kNrver2e7OxsgoOD6erqEt95bm4udrt903b64uIi/f39YiOynig4NDS0gVX/XtDr9WRlZfmM\nkBQKBePj48THx18V69crFe8aFhZGW1sbMplMkDDr6+spLCy8oh767xeSo+jAwAAGg0FwgP6v2u9e\nzYjbq1rsl5aWhKRlaWmJ3/zmN1RVVeHv78/U1BTZ2dkcP36cyMjI97W7/7DFXiaTkZSUxNzcHD09\nPSwuLlJSUiIsQCXCmlwup7u7m9XVVebm5vB6vQwMDBAcHIzNZhPzIcmrvr+/n6GhIUJDQykqKsJq\ntQpymkajEQY6k5OT5OXlERcXx9DQEIODgwQGBlJSUiKyziU5kiTx0mq1hISEkJCQwNjYmPCoLysr\n+9jnhxIuvqB7e3vF+szMzIiCabfbhV3u8PCwkDLl5+d/LOSdyyE4OJiOjg5RHCSHwfT0dIxG44a2\n4IULF4S8bXV1lYyMDCGXjI+PvyS5USaTkZiYiMViQa/XY7VahS54cHCQG264gb6+PoaHh4mMjCQ/\nP5/p6WkWFhbQ6/UEBwcLNn9MTMxlv3O3201nZycTExOMj4+zvLy8wXxmPSQFQHR0NAMDA2J8IOUk\nzM7O+kj5RkZGxNxaGnHl5eURFBQkCIeXauM3NzdTUlLis07nz58XCYIfBOs3kOsxPj6+6Zju48CV\nKvbSPctsNtPT04PdbvfRlV9tyGQyoqOjSUxMFNfC/9VCD1e32F/VNr7FYuE73/kOsHYj2rlzJ8XF\nxaSlpfG9732Pd999V0jvPi7I5XIxx5TIPbBGGtyxY4doM6amplJXV4efnx+Dg4PcdtttwFqbRqfT\n8Ytf/IJ9+/YJg5j1kKRE68lXGRkZPPPMM2JmefHmZnV1lb6+Ph8vbMkwRwr1uFzu99XC/Pw8NpvN\nR6YlcRwqKys5d+6c6FiMjo6SlJT0vi/eKwmtVktaWprQY7vdbtLS0kQ6oGROJJPJMBgM2O12HA4H\n/v7+wntBp9OJbtH66NaLIZPJSE5OFk6LEvz8/DYQCz0eD9XV1SLyVMLy8rJgZW8Gl8vF4OAghw8f\n9slWqKmp2aDwWI/t27fT0tIirFxnZ2epqqrCz8+P9vZ2zp49S3h4OAsLCyiVSkpKSqiurhabYvgf\nZvylfBqkXIYTJ06Qnp6OUqkUIVTr46bfL9LT0+nu7hZSTWl93G73ZRUlf6iQyWSkpqb6jIKu4Rou\nxlUt9tHR0fzzP//zhsdDQkL4h3/4h6vwjtZCH+x2O3FxcYyNjdHR0SGy7Ds6OnA6neLmn5SUxNmz\nZ9FqtcITwOVy0dnZeVlDktnZ2Q1pagEBAZfUq8NaW1kqLhL8/PwEs/XDso2vFAYGBjZsXIKCgv7/\n9u48pu37/AP4+2sb25jD5jCHDQQMJA1QbCCc4cipTl0WVVOXaVGnZZvStMkUdX9Mi6a1q5Rqi7Sm\n0fpLpiha1v3RVdomrZmWVqsWNeF0Go4QThMwtyE2NmBu378/kL+LYyCEAHbs5yVVKgbHHz8YP/5+\njudhm4qUlZXBYrHAYrEgJyfHrz71r1YQR6lUYmBgAHV1dWAYBtHR0SgrK0NDQ4NXkSWJRLJi4ZX1\nWOks8Pj4OBYXF712hruLGjkcDszOzkKj0cDhcEAkEiE7OxvDw8NsFUU3Ho8HsViM+fn5VcvFussq\nr0SpVMJms2F2dhYZGRnslbRKpcLt27fZ5ketra3IzMxcs+aCu8yxuzOeuyriRqaiw8PDERUVhbq6\nOraV8/j4+Jq9AwgJdD7foOdP3GeJ3W/YKSkpmJ2dRXNzMx4+fIijR4+y668OhwNffPEFnE4nysvL\n2eIfEokE6enpa9aFl8vlbPcv95rw9PQ0GIYBj8dDV1cXjEYjW/p2rYpTPi6TsC5GoxFarZZNRpGR\nkR7jFggEPp+2f1ZpaWlIS0vb0sdITk5GS0sLVCoVOBwOzGYzuru7IZfLV/29GwwGaLVaFBYWQiAQ\nYHp6GjU1NR4V6DZTSEiI13E692Y990bTioqKp+4diYiIwMGDB2EwGGCxWCCTyZ6rJvvOnTuRmprK\nTum/9NJL7HJMZ2cnJicn2b+vXbt2rVrwh6yPRqOBwWBgY5qenr4lrzn3Eqi7m+FLL7205vFi8j+U\n7B/T39/vMfUHLL8J2Ww2LC0teVSv43K54HK5SE1NRVdXF3v1Exsby+6WX83OnTuh0+lQV1fHFstx\nT8UNDg6Cz+ez07EulwtqtRq7du3yWEMG/ndSYK2z/r62Y8cO3LhxAydPnmRjotVq0dbWtuqU84sq\nLS0N3d3dHsspU1NTHlenk5OTbFnipyXAtLQ0hIaGoqGhge1hXllZCbvdjvv373t0i7NYLLDb7ejt\n7UVFRQX7GpFIJFCpVBgdHYVer0diYqLHNL7ZbF6zCcxGuascPsvuY/f67mbh8/leeyu6uroQERHB\nllB1uVyora0NmFrwvtDX1wcOh+PxnvXNN99AJBJtanc5d9GzvXv3gs/nw+Fw4N69e3C5XNtyZv9F\n5/Pd+JvpeTfojYyMQC6XeyXqwcFBxMXFYWRkhD020t7eDplMxm6y6+rqgsPhgF6vR0dHB4qLi1e9\nMuHxeAgJCYHZbEZCQgJ4PB5mZ2dRXFyMhw8feqzxu6uWdXV1QaFQoKmpiX2c9vZ25Ofnr3le1el0\noqWlBVqtFsPDw+yGq61cu3x8E4pWq2W7qzkcDoyMjECv1yM8PDzg1hgjIiIwPj6OgYEBthytVqtF\nUVERLBYL6urq2MIenZ2d6yoaFB4ejpSUFKSkpEAmk4HL5SIkJITtqe5uB6zRaFBcXAy9Xu+V4Nwb\nBRUKBVv61v063bNnz5bOqmzVxrSN0mg0Hh+OH//72soTIP4Wh83U0dHhsa/E/aGtvb3do8bI88bg\nwYMHHu937v1VLS0tXsui/ipoN+j5m4yMDHR1dXmsMZvNZvYXtG/fPkxNTWFxcRFlZWXo6upCamoq\nYmJikJaWBoPBgOTk5HUVcEhNTUVycjL0ej1iYmLYimjuqz29Xg+z2cy2L7Xb7ZDJZEhISIBerweP\nx2OnJteiVqs9+jdPTU2xvb63w8LCAoqKiuB0OmEwGBAdHY3c3FzU19dvy+NvN6VSydY3SEtLYz/Q\nNDY2oqysjE2sGRkZqK2tRVxc3Jrr0i6XC2NjY1hYWIBcLmc7Iaanp7PT1FFRUeyVqruGvV6vx8zM\nDBITE+FyuSASiSCXy5GYmMj2VF/P6yfQrDSbIhQKYbVafTCawLBSTENCQtbsmrgRVqvV62/F3VOF\nPF3gbU19DnFxceBwOKivr0d/fz9aWlrQ1taGgoICZGVlobq6GgzDQCqVQqPRsOePgeUXt1wuf6Zp\nKy6XC5lM5nEfm82G//73v2zzBq1Wiy+//JL9GQ6Hw57zftob9dzcHIRCoce/HxUVhfDw8BVbZm6F\nlJQUaLVaNj4SiQROpzOgG1EIBALI5XL2+JPNZgOfz/e6gs7OzkZ/f/+q/87CwgK+/vprts1rR0cH\n2tra2O9zuVyv15xMJsP169fZ9qpdXV347LPP2HKz7tdPbGxs0CV6YPnD0JMd84aHh2nN/jm4WwU/\nzmAwbFqpazeBQOBViMfhcGz6h4pARVf2T8jJyYHVaoXRaPRoauJuSPPw4UO2OcfzrhPNzMygpaUF\nERERHmeMjUYjrFYrJiYmMDk5iampqWeuDQ4sz0qs9OEjJiYGZrN5zbO4TqcTOp2ObYSy0TP7MpmM\n7dSVnp4Os9mM1tZWqFSqDf17W8G9vAAsb4rb7CuFJ9vUPvm91TQ1NaGiooLd5R4XF4fW1la28cxK\nJiYmUFVVhfHxcZhMJgDLfQtMJtOWbdJ7kahUKlRXV0OlUkEikWBwcBCjo6Nbsn9kdnbWo2BScnKy\n35x/30wqlQq1tbXIzc1l9yxptVqPMsubITs7mz3xIhKJYLVa2QJU5OlozX4FXC4XERERXldiPB6P\nrQW+2qam9a7J3Lp1i60/Pjs7i88//xwpKSkYGhqCVCpFQkIC7HY7kpKS4HQ6MTMz88y7vwUCAXp7\ne71a02o0Go+2nE+anJyEWq2GWCxmSwg7nc51dw17PAYMwyA5ORkWiwXd3d1YWlpCXl6eX5ylB5aP\nsjU3N7NXuq2trRAKhZsyPnccuFwu+vv7kZiY6PFBorm5GdnZ2av+HkZGRrx+59HR0ejq6lo1cQ8N\nDSEvL4+thpeSkoL4+Hh0dHT4pD64v61VCwQCJCUlYWhoCAMDA4iKitqSim7uD/JFRUVIS0tDRkYG\n7ty5w1bGDCQhISFISUnB6OgotFotwsLCvAokAc//WggJCUFiYiI6OzsxODiIR48eIScn57m6GW43\nWrMPMkajEQMDAzh16hTm5+fB4/FQUlKCa9eugcfj4ciRIx5vPlKpFDdv3nzmxxEIBBCJROju7sau\nXbsALJ975/P5a64Tt7a2Yv/+/ewfq1wuR01NDVts6FkxDPPMPcS3g9PphEaj8ShUlJycjDt37mz6\nJsaCggLU1tZCJpMhNDQUw8PDSEpKYn8PLpcLc3NzEAgEbIxXuuq32+1rzjysdJ9ALSazUXw+f0NX\ng0tLSzCZTIiPj3/qscDOzk6UlpayFwx8Ph9lZWVoampCWVnZhsbtz3g8Hnbv3r3ljxMaGrpmgSqy\nOkr2z2hubg5LS0uIjo7e8BtofX09lEol7ty5A4lEwh7tCwsLg91uh8Fg8DiCNDIysuq07dMolUro\ndDqo1Wq4XC7s2LGDTfwrmZ2dXfG5ZWZmYnh4eM0z/y+aR48eeZVPdc9EuI+pbZawsDAcOHAARqMR\nFosFZWVlbMIYGhrC4OAgJBIJ+6m/qKgIISEhmJmZ8Zj6bW1tXbN0dFhYGIxGo0c9/ra2NmRmZm7a\ncwk2TqcTf/vb3zAzM4PY2Fg8evQIqamp+Pa3v73mfZ6cGeTz+S9EXQwSmCjZr5PFYsEXX3wBs9kM\ngUAAi8WCffv2baiwCsMw0Gg0+OEPf8gmmoWFBXz11VeorKzE4OAgW0vfZDJBJBI9V//lZ7mq5nA4\nK26eczgcAbfr1V3k50l2u/25Crqsxr2583HungiPr29OTk6yHeru3r0LPp8PkUgEk8mEHTt2rHke\nXKVS4d69e+jr60NERARMJhMSEhJ83u70Rfbll19CoVB41OD45z//iQcPHqxa44JhGFitVo+ZMHfJ\nYUJ8geb21ulf//oX5HI5fvSjH+H48eM4evQobt68ua71l/n5eRgMBjaxyOVy6HQ6j0/5FosFNpsN\nWVlZcLlcUCqVkMlkKCgogNls3pYpMgBsM5/HjyK5XC62u18gkUqlGB0d9Uj4DocDY2Nj25YcNRqN\nV++E6OhoLC4ugsfjoby8HC+//DLkcjkqKyu96uc/icPhoKSkBHl5eZDJZNi7d+8zd40jnoaHh72K\nbR09ehRqtXrV+2RnZ0OtVrMJ3mazQa1W+2X/ChIc6Mp+HRwOB0wmE44dO8beFhMTg4qKCtTX1+Pw\n4cMr3s9ut0OtViM0NBSRkZHQaDRITEyEQCBAWVkZPv74YygUCszPz8NoNOJb3/oWpFIphEIhWlpa\n2D7ppaWl29qusqioCPX19Wz7XYPBgJycnIC7smcYBgUFBaiurmaTu9FoRH5+/rYdS7Pb7Stu0Ht8\nGSU0NPSZf/8vYglif7XSct3TlvDEYjFUKhUaGxvhcrkgFouRm5u76cfRCFkvSvbr4D4n7eY+SiWT\nyaDRaFa9X1NTE5RKJbvmunPnTjQ2NiIxMRHz8/M4e/YsBgYGEBYWhri4ONTU1EAoFEIoFEIqla55\nZGsriUQi7N+/H7Ozs7DZbMjOzg7YM9kSiQQHDhzA9PQ0AKyrlfJ6rHdtVqFQQKPReGwYs1qtPlvb\n9dVrzp+JRCLodDqPpbDm5uanVoAUi8XsZrxnKRtMyFagZL8OAoEACwsL7LScu+vdyMjImnXpbTab\n17na3NxcPHjwABEREbh37x4yMjKwtLSE27dvs9O5nZ2dMJlM4PF4sNlsyMjI8MlOdn85HrcdNuuK\nS6fToa+vDyEhIRCJRODxeGvu/I6Pj8fo6CiampqgUChgNpuh1Wq3fcd2T08PW5nRbrcjJSXlqUsG\nweL111/HtWvXkJWVhYyMDLS2tmJiYgInT5709dAIWTdK9uvAMAzbl/zw4cMICwtDZ2cnOjs78b3v\nfW/N+z3J3cY2KysLCwsLGBoagkAgwL59+8DlctHT0+PRq9zlcuHu3bsQiUQv1HnSYDQ9PY3h4WFU\nVlaCYRjExsbi3r170Gg0axZFKigowMzMDEZHRxEeHo6DBw9u69X1wMAAXC6XR2GZpqYmhIWFUUcx\nLF/Znz17Fu3t7ejq6oJSqUR6evqWPZ7VasX09DQiIiK2dfmOBDbaoLcOTqcTO3bswKuvvoru7m40\nNDQgPDwcZ86cWbPcKcMwWFxc9Lits7OTPb4mEomwe/duKBQKj5r4j7+RMAyD/Px89PT0bMEzI5up\np6fHa71foVDAYDA89b6RkZHIyspCSkrKtk+jj4yMeB3HVKlUePjw4baOw59xOBwolUocOXJkSxN9\ne3s7GhsbYTab0d7ejrt379JxPbIp6Mp+HZxOJ0JCQrx6mC8uLuLRo0er3q+goABqtZptZjMyMoKw\nsLA1d3qvtAmOz+d71fPeLEtLS5icnIREImGbrJBler0ePT097LJNenr6msspdrt9xaJD/l7Qhsvl\nen3A4PF4cLlcsNvtaGlpYfcRCIVC5OfnB9xmTX+g0+nA4XA8WhcbDAa0tbX5dRtr8mKgZL8OPB4P\nS0tLXpuXent711zXFAqF2L9/PyYmJjA3NwelUrmuaTmLxeKxk3psbGxLplPdb+JSqRQ6nQ4OhwPF\nxcW0QQvLZ921Wi3bG97lcqGpqYltJLMSqVSKsbExjw8EVqvV7+PJ5XKxuLjo8do0Go0Qi8VoaGiA\nSqVi955MTU1BrVajvLx8U8fgcrnQ2tqKubk5Nl75+flB9QF0cHAQpaWlHrfFxcXRrB7ZFP59yeFH\nsrOzUV1djampKVitVnR0dHh0vVuLVCpFWlrauhJ9Xl4eamtrodfrYbfbodVq0dvbu+kV0NxFe0pK\nSpCeno7CwkKkpqaio6NjUx/nRdXd3Y3CwkI28biP6fX19a16n4yMDPT19UGr1cJms2F8fBw1NTV+\n1fRnJSqVCvX19RgbG4Pdbsfg4CDa2togk8kgFos9NplGRUUhNDTUq/vY82pqaoJMJkNFRQXKy8tR\nWFiIhoaGoOpoRichyFaiZL9OUqkUJSUlGB0dRWtrK+Lj47ekRrNIJMK+ffswNTWF5uZm8Pl8VFVV\nbfpUsE6n81p7TEhIYI+g+dr8/Dxqa2tRV1eHmpoatLa2bvva5ZPn3zkczpq/Bw6Hg8rKSvD5fLS0\ntGBychL79u1btWnSetlsNiwsLGzZ83fPQM3Pz6O5uRkA2K9X2hQqkUg2tUWyw+GA1Wr1KBEtEAjY\nEs3BIikpCVqt1uO26elp2qRHNgVN4z8DoVCIl19+ecsfh8fjbailbaBwOp1Qq9WoqqpiE65er0dz\nczP27NmzLWNw985+vDStxWJ56ocud2395OTk5z5b7XA42KIsQqEQZrMZmZmZW3IMk8vles0eSaVS\nPHjwwKtyol6v39Tfg9VqXTGhRUVFYWhoaNMex9/t2LEDTU1NaGxshEwmg8lkwvT0tMcaPiEbRVf2\nQSoxMRGDg4Met01MTPhFv+3+/n7s3r3b48o6Pj4eS0tL2zatm5OTg7t372JychIAYDabUVdXt2lF\nd9ajubkZO3fuRGlpKfLy8lBVVYX+/v5taxnrrtzX2dkJp9MJh8OB9vZ2REZGbqj74WqEQuGK7akH\nBwf9rlPiVmIYBoWFhcjKyoLVakVSUhIqKytpMyTZFHRlH6TS09PR1NQEo9GIhIQETExMYHFx0WuD\nkC/Mzc2tuAlOIBDAZrNtSxlYd60DjUaD7u5uiEQi7N27d1t7kVssFkRHR7NfMwyDvLw8aDQa5Ofn\nb8sYlEolxsbGoFarwTAMFAoFEhISNvUxGIZBamoq7t69C5VKBT6fD61Wi4WFBY/nHyzCwsI21GCL\nkLVQsg9S7quIhYUFGI1GZGRk+MVVPbC8djkwMOBRec7lcmFhYWFTryifhs/nb+uV/JNW2qwlEAg8\nmhRtB5lM9lxdF9djx44dkEgkaGtrg81mQ0pKilfzGULIxlGyD3IikcjvutnFxsair68Pvb29SE9P\nh8ViQUtLC9LT04Nqt7LT6YTNZvNYztBoNFta1MWXxGIxioqKfD0MQgISJXvil4qLi6HT6XD37l2E\nhIQgNzc3qGr1A8vHMKurq7Fz505ERESgv78fDMNQCVtCyDOjZE/8EsMwSEpKQlJSkq+H4jMRERE4\ncOAAhoaGoNPpkJGRAbFY7OthEUJeQJTsCfFjHA6HNmsRQp4bHb0jhBBCAhwle0IIISTAUbInhBBC\nAhwle0IIISTAUbInhBBCAhwle0IIISTAUbInhBBCAhwle0IIISTAUbInhBBCAhwle0IIISTAUbIn\nhBBCAhwle0IIISTA+XUjnNbWVnzyySdwOp04ePAgXnvtNV8PiRBCCHnh+O2VvdPpxPXr1/GrX/0K\nly5dQn19PUZHR309LEIIIeSF47fJvq+vDwkJCYiPjwePx0NZWRkaGxt9PSxCCCHkheO30/iTk5OI\niYlhv46JiUFvb6/Hz9y6dQu3bt0CAFy4cAEymWxbx7gafxmHL1EMllEcKAZuFAeKAeC7GPjtlf16\nHDp0CBcuXMCFCxd8PRTWuXPnfD0En6MYLKM4UAzcKA4UA8C3MfDbZB8dHQ2TycR+bTKZEB0d7cMR\nEUIIIS8mv0326enpGB8fh8FggN1uR0NDA/bs2ePrYRFCCCEvHO7777//vq8HsRIOh4OEhAT83//9\nH/7zn/+goqICJSUlvh7WuigUCl8PwecoBssoDhQDN4oDxQDwXQwYl8vl8skjE0IIIWRb+O00PiGE\nEEI2ByV7QgghJMD57Tl7f2c0GnHlyhVMT0+DYRgcOnQIr776Kubm5nDp0iVMTExAKpXi5z//OcLD\nw3093C1jtVrxm9/8Bna7HQ6HAyUlJTh27FjQxQFYrvp47tw5REdH49y5c0EZgzNnzkAoFILD4YDL\n5eLChQtBF4f5+XlcvXoVIyMjYBgGb7/9NmQyWdDEYGxsDJcuXWK/NhgMOHbsGKqqqoImBm43b97E\n119/DYZhkJycjNOnT8NqtfokDrRmv0FTU1OYmpqCQqHA4uIizp07h1/84he4c+cOwsPD8dprr+HG\njRuYm5vDG2+84evhbhmXywWLxQKhUAi73Y733nsPJ06cwL1794IqDsDyH7ZWq2VfD59++mnQxeDM\nmTP43e9+h8jISPa2YIvD5cuXsXv3bhw8eBB2ux0WiwWff/55UMXAzel04tSpU/jtb3+Lr776Kqhi\nMDk5iXfffReXLl0Cn8/HRx99hPz8fIyOjvokDjSNv0FRUVHsrsrQ0FDI5XJMTk6isbERVVVVAICq\nqqqAL/HLMAyEQiEAwOFwwOFwgGGYoIuDyWRCS0sLDh48yN4WbDFYTTDFYWFhAd3d3Thw4AAAgMfj\nISwsLKhi8Lj29nYkJCRAKpUGZQycTiesViscDgesViuioqJ8Fgeaxt8EBoMBAwMDyMjIgNlsRlRU\nFABAIpHAbDb7eHRbz+l04pe//CUePXqEV155BZmZmUEXh7/85S944403sLi4yN4WbDFwO3/+PDgc\nDg4fPoxDhw4FVRwMBgMiIyPxxz/+EUNDQ1AoFDhx4kRQxeBx9fX12Lt3L4Dg+3uIjo7Gd77zHbz9\n9tvg8/lQKpVQKpU+iwMl++e0tLSEixcv4sSJExCJRB7fYxgGDMP4aGTbh8Ph4Pe//z3m5+fx4Ycf\nYnh42OP7gR6H5uZmiMViKBQKdHZ2rvgzgR4Dt/PnzyM6OhpmsxkffPCBVx3wQI+Dw+HAwMAAfvKT\nnyAzMxOffPIJbty44fEzgR4DN7vdjubmZhw/ftzre8EQg7m5OTQ2NuLKlSsQiUT46KOPUFNT4/Ez\n2xkHSvbPwW634+LFi6ioqEBxcTEAQCwWY2pqClFRUZiamvJYuwx0YWFhyM7ORmtra1DFoaenB01N\nTbh//z6sVisWFxfx8ccfB1UM3NwlrcViMQoLC9HX1xdUcYiJiUFMTAwyMzMBACUlJbhx40ZQxcDt\n/v37SEtLg0QiARB8743t7e2Ii4tjn2dxcTEePnzoszjQmv0GuVwuXL16FXK5HEeOHGFv37NnD6qr\nqwEA1dXVKCws9NUQt8XMzAzm5+cBLO/Mb2trg1wuD6o4HD9+HFevXsWVK1fwzjvvICcnB2fPng2q\nGADLs1zuZYylpSW0tbUhJSUlqOIgkUgQExODsbExAMtv+ElJSUEVA7fHp/CB4HtvjI2NRW9vLywW\nC1wuF9rb23363ki78TdIo9HgvffeQ0pKCjsN84Mf/ACZmZm4dOkSjEZjUBwvGRoawpUrV+B0OuFy\nuVBaWorXX38ds7OzQRUHt87OTvz73//GuXPngi4Ger0eH374IYDl6ezy8nJ897vfDbo4DA4O4urV\nq7Db7YiLi8Pp06fhcrmCKgZLS0s4ffo0Ll++zC5vBtvrAAD+/ve/o6GhAVwuF6mpqXjrrbewtLTk\nkzhQsieEEEICHE3jE0IIIQGOkj0hhBAS4CjZE0IIIQGOkj0hhBAS4CjZE0IIIQGOkj0hhBAS4CjZ\nE0LW9P777+PHP/4xbDabr4dCCNkgSvaEkFUZDAZ0d3cDAJqamnw8GkLIRlFtfELIqmpqarBz505k\nZGSguroapaWlAJaroV25cgXd3d2QyWRQKpXo7OzE+fPnAQA6nQ5//vOf0d/fj8jISHz/+99HWVmZ\nL58KIUGN5jBzIwAAAjpJREFUruwJIauqrq5GeXk5Kioq8ODBA0xPTwMArl+/DqFQiGvXruHMmTNs\nrW9guVTqBx98gPLycvzpT3/CO++8g+vXr2N0dNRXT4OQoEfJnhCyIo1GA6PRiNLSUigUCsTHx6Ou\nrg5OpxPffPMNjh07BoFAgKSkJFRVVbH3a2lpgVQqxf79+8HlcpGWlobi4mKo1WofPhtCghtN4xNC\nVnTnzh3k5uayLTjLy8vZK32Hw4GYmBj2Zx///4mJCfT29uLEiRPsbQ6HA5WVlds2dkKIJ0r2hBAv\nVqsVarUaTqcTJ0+eBADY7XbMz89jenoaXC4XJpMJMpkMAGAymdj7xsTEICsrC++++65Pxk4I8UbJ\nnhDi5d69e+BwOLh48SJ4vP+9TVy6dAk1NTUoKirCP/7xD7z11lswGo2orq5GbGwsAKCgoACfffYZ\nampq2E15g4ODEAqFSEpK8snzISTY0Zo9IcRLdXU19u/fj9jYWEgkEva/V155BbW1tfjpT3+KhYUF\nvPnmm7h8+TL27t2LkJAQAEBoaCh+/etfo76+HqdOncKbb76Jv/71r7Db7T5+VoQEL+pnTwh5bp9+\n+immp6fxs5/9zNdDIYSsgK7sCSHPTKfTYWhoCC6XC319fbh9+zaKiop8PSxCyCpozZ4Q8swWFxfx\nhz/8AVNTUxCLxThy5AgKCwt9PSxCyCpoGp8QQggJcDSNTwghhAQ4SvaEEEJIgKNkTwghhAQ4SvaE\nEEJIgKNkTwghhAS4/wcj+w2C0JT4fgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1021db550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a sequence of age values spanning the range\n",
    "age_grid = np.arange(wage.age.min(), wage.age.max()).reshape(-1,1)\n",
    "\n",
    "# Generate test data\n",
    "X_test = PolynomialFeatures(4).fit_transform(age_grid)\n",
    "\n",
    "# Predict the value of the generated ages\n",
    "y_pred = poly4.predict(X_test)\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "fig.suptitle('Degree-4 Polynomial Regression', fontsize=14)\n",
    "\n",
    "# Scatter plot with polynomial regression line\n",
    "plt.scatter(wage.age, wage.wage, facecolor='None', edgecolor='k', alpha=0.5)\n",
    "plt.plot(age_grid, y_pred, color = 'b')\n",
    "ax.set_ylim(ymin=0)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Wage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**ANOVA on polynomial regression of degree from 1 to 5.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2998.0</td>\n",
       "      <td>5.022216e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2997.0</td>\n",
       "      <td>4.793430e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>228786.010128</td>\n",
       "      <td>143.593107</td>\n",
       "      <td>2.363850e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2996.0</td>\n",
       "      <td>4.777674e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15755.693664</td>\n",
       "      <td>9.888756</td>\n",
       "      <td>1.679202e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2995.0</td>\n",
       "      <td>4.771604e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6070.152124</td>\n",
       "      <td>3.809813</td>\n",
       "      <td>5.104620e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2994.0</td>\n",
       "      <td>4.770322e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1282.563017</td>\n",
       "      <td>0.804976</td>\n",
       "      <td>3.696820e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid           ssr  df_diff        ss_diff           F        Pr(>F)\n",
       "0    2998.0  5.022216e+06      0.0            NaN         NaN           NaN\n",
       "1    2997.0  4.793430e+06      1.0  228786.010128  143.593107  2.363850e-32\n",
       "2    2996.0  4.777674e+06      1.0   15755.693664    9.888756  1.679202e-03\n",
       "3    2995.0  4.771604e+06      1.0    6070.152124    3.809813  5.104620e-02\n",
       "4    2994.0  4.770322e+06      1.0    1282.563017    0.804976  3.696820e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Polynomial regression of degree 1,2,3,5\n",
    "X1 = PolynomialFeatures(degree=1).fit_transform(wage[['age']])\n",
    "X2 = PolynomialFeatures(degree=2).fit_transform(wage[['age']])\n",
    "X3 = PolynomialFeatures(degree=3).fit_transform(wage[['age']])\n",
    "X5 = PolynomialFeatures(degree=5).fit_transform(wage[['age']])\n",
    "poly1 = sm.GLS(wage['wage'], X1).fit()\n",
    "poly2 = sm.GLS(wage['wage'], X2).fit()\n",
    "poly3 = sm.GLS(wage['wage'], X3).fit()\n",
    "poly5 = sm.GLS(wage['wage'], X5).fit()\n",
    "# ANOVA, as in chpater 3 notebook\n",
    "with warnings.catch_warnings():\n",
    "    warnings.filterwarnings(\"ignore\")  ## Supress warnings\n",
    "    display(anova_lm(poly1, poly2, poly3, poly4, poly5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    ">The p-value comparing the cubic and degree-4 polynomials, Model 3 and Model 4, is approximately 5% while the degree-5 polynomial Model 5 seems unnecessary because its p-value is 0.37. Hence, either a cubic or a quartic polynomial\n",
    "appear to provide a reasonable fit to the data, but lower- or higher-order models are not justified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            GLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   wage   R-squared:                       0.087\n",
      "Model:                            GLS   Adj. R-squared:                  0.085\n",
      "Method:                 Least Squares   F-statistic:                     56.71\n",
      "Date:                Tue, 21 Mar 2017   Prob (F-statistic):           1.67e-56\n",
      "Time:                        18:05:49   Log-Likelihood:                -15314.\n",
      "No. Observations:                3000   AIC:                         3.064e+04\n",
      "Df Residuals:                    2994   BIC:                         3.068e+04\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const        111.7036      0.729    153.278      0.000       110.275   113.133\n",
      "x1           447.0679     39.916     11.200      0.000       368.802   525.334\n",
      "x2          -478.3158     39.916    -11.983      0.000      -556.582  -400.050\n",
      "x3           125.5217     39.916      3.145      0.002        47.256   203.787\n",
      "x4           -77.9112     39.916     -1.952      0.051      -156.177     0.355\n",
      "x5           -35.8129     39.916     -0.897      0.370      -114.079    42.453\n",
      "==============================================================================\n",
      "Omnibus:                     1094.840   Durbin-Watson:                   1.961\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4940.229\n",
      "Skew:                           1.718   Prob(JB):                         0.00\n",
      "Kurtosis:                       8.265   Cond. No.                         54.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Polynomial regression of degree 5 on orthogonalized X. Refer to chapter 3 notebook.\n",
    "X5_ortho = ortho_poly_fit(wage[['age']], degree=5)[0]\n",
    "X5_ortho[:,0]=1  # Replace constant column with 1s for Intercept estimation.\n",
    "poly5_ortho = sm.GLS(wage['wage'], X5_ortho).fit()\n",
    "print(poly5_ortho.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    ">Notice that the p-values are the same, and in fact the square of the t-statistics are equal to the F-statistics from the anova_lm() function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "** Classification: wage > $250,000 per year?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                   wage   No. Observations:                 3000\n",
      "Model:                            GLM   Df Residuals:                     2995\n",
      "Model Family:                Binomial   Df Model:                            4\n",
      "Link Function:                  logit   Scale:                             1.0\n",
      "Method:                          IRLS   Log-Likelihood:                -350.61\n",
      "Date:                Tue, 21 Mar 2017   Deviance:                       701.22\n",
      "Time:                        18:05:49   Pearson chi2:                 3.70e+03\n",
      "No. Iterations:                    12                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const       -109.5530     47.655     -2.299      0.022      -202.956   -16.150\n",
      "x1             8.9950      4.187      2.148      0.032         0.789    17.201\n",
      "x2            -0.2816      0.135     -2.081      0.037        -0.547    -0.016\n",
      "x3             0.0039      0.002      2.022      0.043         0.000     0.008\n",
      "x4         -1.949e-05   9.91e-06     -1.966      0.049     -3.89e-05 -6.41e-08\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "# Create binary qualitative response\n",
    "y_clf = (wage.wage > 250).map({False:0, True:1})\n",
    "# Logistic regression\n",
    "logreg = sm.GLM(y_clf, X4, family=sm.families.Binomial()).fit()\n",
    "print(logreg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAF2CAYAAACcW7pkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133c6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict on age grid\n",
    "y_pred_clf = logreg.predict(X_test)\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "plt.plot(age_grid, y_pred_clf, color='b')\n",
    "plt.ylim(-0.02, 0.12)\n",
    "# Rug plot showing the distribution of wage>250 in the training data.\n",
    "# 'True' on the top, 'False' on the bottom.\n",
    "ax.scatter(wage.age, y_clf/10, s=30, c='grey', marker='|', alpha=0.7)\n",
    "plt.title(\"Logistic regression with polynomial of degree 4.\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Step functions for piecewise-constant regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.938, 33.5]     750\n",
       "(33.5, 49]        1399\n",
       "(49, 64.5]         779\n",
       "(64.5, 80]          72\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cut predictor data into intervals of a step function\n",
    "wage_cut, bins = pd.cut(wage.age, 4, retbins=True, right=True)\n",
    "wage_cut.value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(Intercept)</th>\n",
       "      <th>(33.5, 49]</th>\n",
       "      <th>(49, 64.5]</th>\n",
       "      <th>(64.5, 80]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231655</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86582</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161300</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155159</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11443</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376662</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450601</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377954</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228963</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81404</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302778</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305706</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8690</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153561</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449654</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447660</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160191</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230312</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301585</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153682</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158226</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11141</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448410</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305116</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233002</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        (Intercept)  (33.5, 49]  (49, 64.5]  (64.5, 80]\n",
       "231655          1.0         0.0         0.0         0.0\n",
       "86582           1.0         0.0         0.0         0.0\n",
       "161300          1.0         1.0         0.0         0.0\n",
       "155159          1.0         1.0         0.0         0.0\n",
       "11443           1.0         0.0         1.0         0.0\n",
       "376662          1.0         0.0         1.0         0.0\n",
       "450601          1.0         1.0         0.0         0.0\n",
       "377954          1.0         0.0         0.0         0.0\n",
       "228963          1.0         1.0         0.0         0.0\n",
       "81404           1.0         0.0         1.0         0.0\n",
       "302778          1.0         1.0         0.0         0.0\n",
       "305706          1.0         1.0         0.0         0.0\n",
       "8690            1.0         1.0         0.0         0.0\n",
       "153561          1.0         1.0         0.0         0.0\n",
       "449654          1.0         0.0         1.0         0.0\n",
       "447660          1.0         0.0         1.0         0.0\n",
       "160191          1.0         1.0         0.0         0.0\n",
       "230312          1.0         0.0         1.0         0.0\n",
       "301585          1.0         0.0         1.0         0.0\n",
       "153682          1.0         1.0         0.0         0.0\n",
       "158226          1.0         1.0         0.0         0.0\n",
       "11141           1.0         1.0         0.0         0.0\n",
       "448410          1.0         0.0         0.0         1.0\n",
       "305116          1.0         1.0         0.0         0.0\n",
       "233002          1.0         1.0         0.0         0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dummies for predictor intervals\n",
    "wage_step_dummies = pd.get_dummies(wage_cut, drop_first=True)  # The first interval is the base and dropped\n",
    "wage_step_dummies = pd.DataFrame(sm.add_constant(wage_step_dummies.values), columns=['(Intercept)'] + list(wage_step_dummies.columns.values), index=wage_step_dummies.index)\n",
    "wage_step_dummies.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                   wage   No. Observations:                 3000\n",
      "Model:                            GLM   Df Residuals:                     2996\n",
      "Model Family:                Gaussian   Df Model:                            3\n",
      "Link Function:               identity   Scale:                   1634.08459238\n",
      "Method:                          IRLS   Log-Likelihood:                -15353.\n",
      "Date:                Tue, 21 Mar 2017   Deviance:                   4.8957e+06\n",
      "Time:                        18:05:49   Pearson chi2:                 4.90e+06\n",
      "No. Iterations:                     4                                         \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------\n",
      "(Intercept)    94.1584      1.476     63.790      0.000        91.265    97.051\n",
      "(33.5, 49]     24.0535      1.829     13.148      0.000        20.468    27.639\n",
      "(49, 64.5]     23.6646      2.068     11.443      0.000        19.611    27.718\n",
      "(64.5, 80]      7.6406      4.987      1.532      0.126        -2.135    17.416\n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "# Piecewise-constant regression as a step function\n",
    "logreg_step = sm.GLM(wage.wage, wage_step_dummies).fit()\n",
    "print(logreg_step.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<a id='lab-7.8.2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Lab 7.8.2 Splines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Cubic splines**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                                                                 60.493714\n",
       "bs(wage.age, knots=(25, 40, 60), degree=3, include_intercept=False)[0]     3.980500\n",
       "bs(wage.age, knots=(25, 40, 60), degree=3, include_intercept=False)[1]    44.630980\n",
       "bs(wage.age, knots=(25, 40, 60), degree=3, include_intercept=False)[2]    62.838788\n",
       "bs(wage.age, knots=(25, 40, 60), degree=3, include_intercept=False)[3]    55.990830\n",
       "bs(wage.age, knots=(25, 40, 60), degree=3, include_intercept=False)[4]    50.688098\n",
       "bs(wage.age, knots=(25, 40, 60), degree=3, include_intercept=False)[5]    16.606142\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specifying 3 knots\n",
    "transformed_3knots = dmatrix(\"bs(wage.age, knots=(25,40,60), degree=3, include_intercept=False)\",\n",
    "                         {\"wage.age\": wage.age}, return_type='dataframe')\n",
    "\n",
    "# Build a regular linear model from the splines\n",
    "spln_3knots = sm.GLM(wage.wage, transformed_3knots).fit()\n",
    "pred_3knots = spln_3knots.predict(dmatrix(\"bs(age_grid, knots=(25,40,60), degree=3, include_intercept=False)\",\n",
    "                                          {\"age_grid\": age_grid}, return_type='dataframe'))\n",
    "spln_3knots.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                                         56.313841\n",
       "bs(wage.age, df=6, include_intercept=False)[0]    27.824002\n",
       "bs(wage.age, df=6, include_intercept=False)[1]    54.062546\n",
       "bs(wage.age, df=6, include_intercept=False)[2]    65.828391\n",
       "bs(wage.age, df=6, include_intercept=False)[3]    55.812734\n",
       "bs(wage.age, df=6, include_intercept=False)[4]    72.131473\n",
       "bs(wage.age, df=6, include_intercept=False)[5]    14.750876\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specifying 6 degrees of freedom \n",
    "transformed_deg6 = dmatrix(\"bs(wage.age, df=6, include_intercept=False)\",\n",
    "                        {\"wage.age\": wage.age}, return_type='dataframe')\n",
    "spln_deg6 = sm.GLM(wage.wage, transformed_deg6).fit()\n",
    "pred_deg6 = spln_deg6.predict(dmatrix(\"bs(age_grid, df=6, degree=3, include_intercept=False)\",\n",
    "                                  {\"age_grid\": age_grid}, return_type='dataframe'))\n",
    "spln_deg6.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Natural splines**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                79.642095\n",
       "cr(wage.age, df=4)[0]   -14.667784\n",
       "cr(wage.age, df=4)[1]    36.811142\n",
       "cr(wage.age, df=4)[2]    35.934874\n",
       "cr(wage.age, df=4)[3]    21.563863\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specifying 4 degrees of freedom\n",
    "transformed_deg4 = dmatrix(\"cr(wage.age, df=4)\", {\"wage.age\": wage.age}, return_type='dataframe')\n",
    "spln_deg4 = sm.GLM(wage.wage, transformed_deg4).fit()\n",
    "pred_deg4 = spln_deg4.predict(dmatrix(\"cr(age_grid, df=4)\", {\"age_grid\": age_grid}, return_type='dataframe'))\n",
    "spln_deg4.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAF6CAYAAAA0zb+0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4lOW9+P/37JNMMplkspI9JEERlwpqWCQEKeBx+aKt\nVWu1UnfP0Z56Kbb9ilU5Xdy1PdL22KO03+ZU/BXFy+tUxRQEBRGPikvFkH3fZ7LMTGaf3x8585gh\nCwFCJiSf13V5Cc/M8zz3PfeQfOaez/25VaFQKIQQQgghhBDimKij3QAhhBBCCCFORRJICyGEEEII\ncRwkkBZCCCGEEOI4SCAthBBCCCHEcZBAWgghhBBCiOMggbQQQgghhBDHQTsVN/F6vfzsZz/D7/cT\nCAQoKSnhO9/5Di+//DJ///vfMZvNAFx77bWce+65ALz66qvs3LkTtVrN+vXrOeecc6aiqUIIIYQQ\nQkyIairqSIdCITweD0ajEb/fz4MPPsiNN97IwYMHMRqNXH755RHPb25u5tlnn+UXv/gFdrudTZs2\n8eyzz6JWywS6EEIIIYSYHqYkMlWpVBiNRgACgQCBQACVSjXm8z/88EOWLFmCTqcjNTWV9PR0qqur\np6KpQgghhBBCTMiUpHYABINB7r//ftrb21mzZg1FRUV88sknvPnmm+zZs4eCggJuuOEG4uLisNls\nFBUVKecmJSVhs9mmqqlCCCGEEEIc1ZQF0mq1mscffxyn08kTTzxBY2Mjq1ev5tvf/jYAW7du5U9/\n+hN33nnnhK9ZUVFBRUUFAL/61a9OSruFEEIIIYQYzZQF0mEmk4kzzjiDgwcPRuRGX3TRRTz66KPA\n0Ax0T0+P8pjNZiMpKWnEtVatWsWqVauUv7e2tp7Elo8vOTmZ7u7uqNw7M3MOAC0ts7P/0TYdXv9o\nm63jL2M/e8c+LNr9nzNnTtTuLYSYohzp/v5+nE4nMFTB47PPPiMzMxO73a4858CBA2RnZwOwaNEi\n9u3bh8/no7Ozk7a2NgoLC6eiqUIIIYQQQkzIlMxI2+12nnvuOYLBIKFQiMWLF7Nw4UJ+85vfUF9f\nj0qlIiUlhVtvvRWA7OxsFi9ezD333INareamm26Sih1CCCGEEGJamZLyd1NFUjtmZ/+jbTq8/tE2\nW8dfxn72jn1YtPsvqR1CRNeU50gLIYQQQpxMoVCIrq4ufD5ftJsiTnE6nY6UlJQxyzZLID0DzObZ\nsOmgpaX1f2elot0SMdVk7IWYnrq6uvD7/ej1+mg3RZzifD4fXV1dpKamjvq4JB4LIYQQYkbx+Xzo\ndLpoN0PMADqdbtxvNiSQngE2bEhgw4aEaDdj1tqwIYE77tBEuxkiCmTshRBidpNAegYoLzdRXm6K\ndjNmrfJyEy+8IMHUbCRjL4QYy9NPP82FF15IaWkpZWVlfPTRR5N6/WuvvZa+vj4Ann/+eZYuXcrt\nt98+5vO3bNnC1q1bJ+Xev//973G5XMrf8/LyJuW642lsbGT58uWTcq1nnnlmUq4DkiMthBBCCDGp\nPvzwQ3bs2EFFRQUGg4Genp5JX/j4l7/8Rfnziy++yF//+tdxq7jceOONk3bv//iP/+Db3/42sbGx\nEz7H7/ej1U6PsPOZZ57hX//1XyflWjIjLYQQQggxiTo6OrBarRgMBgCsVivp6ekALFy4kIcffpjS\n0lLWrFlDbW0tAN3d3axfv57Vq1ezevVqPvjgAwAcDgd33303paWllJaW8vrrryvX6enp4d5776Wh\noYFrr72WzZs3c8EFFyglGYPBIOeffz7d3d089thjPPfccwCsW7eORx55hDVr1lBSUsL+/fsBcLlc\n3HzzzSxbtozvf//7rF27loMHD0b07fnnn6e9vZ0rr7ySK664Qjn+i1/8ghUrVnDxxRfT2dkJwF13\n3cW9997L2rVrefjhh3E6nfzwhz9kzZo1rFy5kjfeeAOAQCDAQw89xOrVqyktLeWPf/zjuK9vfX09\nK1eu5JNPPuGll17ixhtv5Oqrr+aCCy7g4YcfVp73yiuvUFpayvLly3nkkUcA2LRpE263m7KyMm6/\n/XacTiff/e53WbFiBcuXL2f79u3HNNbT46OBEEIIIcRJ8MADJr74YnLDnQUL/PzbvznHfHzFihU8\n+eSTlJSUsHz5ctatW8eSJUuUx81mM7t372br1q1s3LiR8vJyHnjgAW677TZKSkpobm7m6quvZu/e\nvTz11FPK8wF6e3sj7vXEE0+wa9cuXnnlFaxWK06nk23btnHbbbexe/duzjjjDJKTk0e00e/389Zb\nb1FRUcHjjz/Otm3bePHFF0lISOC9997j0KFDrFy5csR5t9xyC7/73e+U+8FQAL5w4UJ++tOf8vDD\nD/PnP/+Ze+65B4C2tjb++7//G41Gw89//nOWLVvGs88+S19fH2vWrGH58uVs27YNs9nMjh078Hg8\nXHrppaxYsYLc3NwR96+urubWW2/l17/+NQsWLKCyspIvvviCnTt3otfrWbJkCTfffDMajYZNmzbx\n9ttvY7FY+M53vsPf/vY3Nm7cyH/+53+ya9cuAF5//XXS09P5r//6L2BoN+5jITPSQgghhBCTKC4u\njoqKCp588kmsViu33HILL730kvJ4eCb3yiuv5H/+538A2LNnDz/5yU8oKyvj+uuvZ2BgAIfDwZ49\ne/jBD36gnGuxWMa993e/+11efvllYCj949prrx31eZdccgkAZ511Fk1NTQB88MEHSttOP/105s+f\nP6H+6vV6Vq9eDcDZZ5+tXA/gsssuQ6MZWkvyzjvv8Jvf/IaysjLWrVuHx+OhpaWFd955h5dffpmy\nsjLWrl2L3W5XZuqH6+7u5oYbbuC3v/0tCxYsUI4vX74cs9mM0WikuLiYpqYmPvnkE5YsWUJycjJa\nrZZvfetbvP/++yOuOX/+fHbv3s0jjzzC/v37MZvNE+pzmMxIzwBSRzq6pJbw7CVjL8T0N97M8cmk\n0WhYunQpS5cuZf78+WzdupVrrrkGIGJzj/Cfg8Egb7zxBkaj8YTum5mZSUpKCu+++y4ff/wxv/3t\nb0d9XjjtRKPREAgETuieWq1W6YdGo8Hv9yuPmUxfF0MIhUK88MILFBYWRpwfCoX4xS9+MeoM+HBm\ns5nMzEw++OAD5s2bpxwfXi/8WPszd+5cKioqqKio4Je//CUXXngh995774TPlxlpIYQQQohJVF1d\nHTGj+sUXX5CVlaX8/bXXXgNg+/btLFq0CBhKB/nDH/6gPOfzzz8HoLS0lBdeeEE5fmRqx2iuu+46\n7rzzTi6//HJlNngizj//fKVtlZWVHDp0aNTnxcXF4XA4JnzdsLKyMv7whz8QCoWAr/tYVlbGli1b\nlAWZNTU1OJ0jPwDp9Xq2bNnCyy+/zLZt28a917nnnsv7779PT08PgUCAV155RUmvGV4bur29nZiY\nGK666ir++Z//mc8+++yY+iQz0jNAuIb0Y4/1Rbkls9OGDQkYDBo2bYp2S8RUk7EXQozG6XTyk5/8\nhP7+fjQaDfn5+Tz55JPK4729vZSWlmIwGPjd734HwM9//nN+/OMfU1paSiAQoKSkhCeeeIIf/ehH\n/PjHP2b58uWo1WruvfdeLr300nHvv3btWn74wx+OmdYxlvXr13PXXXexbNkyCgsLmTdv3qipDtdf\nfz3XXHMN6enpvPrqqxO+/j333MMDDzzAihUrCAaD5OTkUF5ezve+9z2amppYtWoVoVAIq9U65oJD\nk8lEeXk5V111VcRs95HS0tJ44IEHuPLKKwmFQqxatYqLL75Yaf+KFSs488wz+c53vsPDDz+MWq1G\np9Px2GOPTbg/AKpQ+GPBDNDaGr0Uh6Gvd6Pz/W5m5lC5m2imeESz/9E2HV7/aJut4y9jP3vHPiza\n/R+v3Nls1tLSMm23B1+4cCE7duxQFuqdDAcPHmTjxo1KhY+JCgQC+Hw+jEYjdXV1XHXVVezbt2/a\nvpZTxev1kpmZOepjMiMthBBCCDFD/PrXv2bLli1s3rz5mM8dHBzkiiuuwOfzEQqFePTRR2d9EH00\nEkgLIYQQQkyRyd7h8Eh33303d99993GdGxcXx9tvvz3JLZrZZLGhEEIIIYQQx0ECaSGEEEIIIY6D\nBNIzQEtL66xe7BRtLS2teDzeaDdDRIGMvRBCzG4SSAshhBBCCHEcJJCeATZsSFBqSYupt2FDAnfc\nMfGC92LmkLEXQozl6aef5sILL6S0tJSysrJJX2R47bXX0tc3tH/E888/z9KlS7n99tvHfP6WLVvY\nunXrpLYBYO/evVx33XUAeDwevvWtb1FWVsb27dsnfH5ZWRkXXngh/+f//J9Jb9/JJlU7ZoDy8qGC\n5LIhS3SEX//ptilHeNcmnU4X5ZbMXJM59sFgEK/Xi8FgiNg+WAhx6vnwww/ZsWMHFRUVGAwGenp6\nlJ/Jk+Uvf/mL8ucXX3yRv/71r+PWFb/xxhsn9f6jCe9UuGvXrgk9v6+vj/vvv5+XXnqJrKwsurq6\nTmbzTgqZkRZihvF4PBw+fJiWlhZaWlqorKzE7XZHu1liHE1NTdTW1tLV1UV1dTXt7e3RbpIQ4gR0\ndHRgtVoxGAwAWK1W0tPTgaENWR5++GFKS0tZs2aNspV4d3c369evZ/Xq1axevZoPPvgAAIfDwd13\n301paSmlpaXKJisLFy6kp6eHe++9l4aGBq699lo2b97MBRdcoGwSFAwGOf/88+nu7uaxxx7jueee\nA2DdunU88sgjrFmzhpKSEvbv3w+Ay+Xi5ptvZtmyZXz/+99n7dq1HDx4cET/du7cyZIlS7jooov4\n7//+bwC6urq48847OXjwIGVlZdTV1R31ddq2bRuXXHKJsn16SkrK8b3gUSQz0kLMMHV1dRQVFaHR\nDKUcBINBDh8+zGmnnRbllonRtLW1YTKZyM7OVo61tLTQ19dHQoKkbAlxokwPPID2iy8m9Zr+BQtw\n/tu/jfn4ihUrePLJJykpKWH58uWsW7eOJUuWKI+bzWZ2797N1q1b2bhxI+Xl5TzwwAPcdtttlJSU\n0NzczNVXX83evXt56qmnlOfD0Pbiwz3xxBPs2rWLV155BavVitPpZNu2bdx2223s3r2bM844g+Tk\n5JF98Pt56623qKio4PHHH2fbtm28+OKLJCQk8N5773Ho0CFWrlw54jy3280999zDK6+8Qn5+Prfc\ncgswFAQ//fTTbN68mfLycgA2btzIe++9N+IaV1xxBXfffTe1tbX4fD7WrVuHw+Hglltu4eqrr57A\nCEwfEkgLMYM4nU7i4+OVIBpArVaTkJDAwMAA8fHxUWydGI3T6SQjIyPi2Jw5c6ipqZFAWohTVFxc\nHBUVFezfv5/33nuPW265hY0bN3LNNdcAQ4EkwJVXXsmDDz4IwJ49e6isrFSuMTAwgMPhYM+ePfzH\nf/yHctxisYx77+9+97vccMMN3HbbbfzlL3/h2muvHfV5l1xyCQBnnXUWTU1NAHzwwQfceuutAJx+\n+unMnz9/xHlVVVXk5ORQUFAAwLe//W3+3//7f6PeY9NR8t78fj+fffYZf/3rX3G73fzTP/0TixYt\nYu7cueOeN51IIC3E/woEAvT09ABDX8MND0ZPFX6/f9ScaJ1Oh9/vj0KLxPGQHGkhJs94M8cnk0aj\nYenSpSxdupT58+ezdetWJZAe/m88/OdgMMgbb7yB0Wg8oftmZmaSkpLCu+++y8cff8xvf/vbUZ8X\nTjvRaDQEAoETuudYjjYjPWfOHBITEzGZTJhMJhYvXsw//vGPUyqQlhzpGUDqSJ84m81GTU0NBoMB\ng8FATU0NNpttQudOp1rCZrMZu90+4rjNZpPZzZNgMsZeq9WOyGG32+2YTKYTuq4QInqqq6uV3GeA\nL774QskDBnjttdcA2L59O4sWLQKG0kH+8Ic/KM8JL9wrLS3lhRdeUI4fmdoxmuuuu44777yTyy+/\n/Jgmhc4//3ylbZWVlRw6dGjEc4qKimhqalJyoF999dUxr7dp0yZ27do14r/wFuZr167lwIED+P1+\nXC4XH3/8MUVFRRNu73QggbSY9UKhEF1dXRQXF2M0GjEajRQXF9PV1UUoFIp2846JSqUiJSWFw4cP\nMzAwwMDAAFVVVVitVtRq+ec+HWVnZ1NfX09nZyeDg4O0tLTQ09OjLEwSQpx6nE4n//Iv/8KyZcso\nLS2lsrKS++67T3m8t7eX0tJSnn/+eR555BEAfv7zn/Ppp59SWlrKsmXL+OMf/wjAj370I3p7e1m+\nfDkrVqwYdYb3SGvXrsXpdI6Z1jGW9evX09PTw7Jly/jlL3/JvHnzMJvNEc8xGo08+eSTXHfddVx0\n0UWj5l9PVHFxMWVlZaxYsYK1a9dy3XXXcfrppx/39aJBFTrVIoVxtLZGb1Y2OTlZWSU71cI1pKNZ\n/i6a/T9Rvb29OBwOnE6n8pWa2+3GZDIRFxd31Hy0DRsSMBiMbNrUMRXNnRC/36+MR3JyMlrtyc3i\nOpXH/0RM5tjb7XZcLhdms/mUymWfrWMfFu3+j1fubDZraWlBr9dHuxmjWrhwITt27MBqtZ60exw8\neJCNGzcqFT4mKhAI4PP5MBqN1NXVcdVVV7Fv375p+1pOFa/XS2Zm5qiPSY70DCB1pE+MRqOhrq6O\npUuXKrO2wWCQvXv38o1vfOOo50/HOtJarVZmNKfAZI59YmIiiYmJJ34hIcSs9utf/5otW7awefPm\nYz53cHCQK664Ap/PRygU4tFHH531QfTRSCAtpj2bzUZ3dzdqtZpgMEhycjJJSUnRbpaYQXp7e+ns\n7FTeY1ar9aTOFgkhZq/J3uHwSHfffbeSg3ys4uLiePvttye5RTObBNJiWuvr62NgYIDi4mLlWH19\nPTqdbtK+/g4EAuTn51NVVRWR2pGfny+VLmYBh8OBzWaLeI81NTWhVqtlhlgIIcS4ZPWRmNa6urrI\nycmJOJaTk0NHx+TlIyckJOB0Opk3bx4ZGRlkZGQwb948nE6nVLqYBdrb28nLy4s4lpWVpZRCFEII\nIcYiM9Ji2juypu5kV59QqVSkpqZy+PBh0tLSgKFZ79TUVKnnO0sc+Z6ScRdCCDERMiM9A8zkOtIa\njQaPxxNxzO12T3oVisTERObOnYvH48Hj8TB37twJf60/nepIi2On0+kYHByMOObz+Sb0gU3GXggh\nZjcJpMW0lpWVRW1tLQ6HAxjKZ62rqxuzDM2J0Gg0pKamkpqaekruaiiOT2ZmJvX19QwMDABD9V+r\nq6sjNk8QQohjlZqaqmz/DfDcc8/x2GOPjXvO3r17OXDgwKS2Y+HChSecqvbSSy/x4x//GIAtW7aw\ndevWyWhahMcee4znnnsOGNqGvKysjJUrVyobv0zE5s2bSU1NndLUPAmkZ4ANGxKUWtIzjU6no7i4\nmL6+Pqqrq+nr66O4uHjUbbCjZcOGBO64QwLvU5VWq2XevHkMDAxQXV1Nb28vxcXFEyr5JGMvhBiL\nwWDgb3/72zEFdXv37uXDDz88pvtM9aL4G2+8kauvvvqk3uONN97gsssuY+fOneTn50/onJaWFt55\n550pnwSRQHoGKC83KfVsZyKNRkNmZiaFhYVkZmZOu9ni8nITL7wwvdokjo1arWbOnDnH/B6TsRdC\njEWj0XD99dfz+9//fsRjb731FmvXrmXlypV861vforOzk8bGRv74xz/y+9//nrKyMvbv389dd90V\nsalKeGH03r17ueyyy7j++utZtmwZADfccAOrVq3iwgsv5E9/+tO4bQsEAtx1110sX76c0tJSfve7\n3wGwbt06/u///b+UlZWxfPlyPv744xHnDp85XrduHY888ghr1qyhpKSE/fv3K9d/6KGHWL16NaWl\npcoujUd6+umnKSkp4dJLL6WmpgaAiooKfv/737NlyxauuOKKcfsx3MaNG3nwwQenfI2LLDYUQggh\nxIz1wHsP8EX3F5N6zQXJC/i3Zf921Of94Ac/YMWKFfzLv/xLxPELLriAN954A5VKxZ///Gf+/d//\nnUceeYTvf//7mEwm/vmf/xmA8vLyMa/9+eefs3v3bnJzcwF49tlnSUxMZHBwkDVr1nDppZeOuefC\nF198QVtbG3v27AGGSs2GDQ4OsmvXLt5//33+9V//VXnOWPx+P2+99RYVFRU8/vjjbNu2jfLycsxm\nMzt27MDj8XDppZeyYsUKpa0An376Kdu3b2fnzp0EAgEuuugizjrrLFatWjXidbjsssuUFM/hHnro\nIUpLS3njjTfIyMhgwYIF47b1ZJiSQNrr9fKzn/0Mv99PIBCgpKSE73znOzgcDp5++mm6urpISUnh\nRz/6EXFxcQC8+uqr7Ny5E7Vazfr16znnnHOmoqlCHBOvVxaaCSGEGF18fDxXXXUVzz//vLJPAUBr\nayu33HILnZ2deL3eEWVeJ+Ib3/hGRGD6/PPP87e//Q0YSnOora0dM5DOzc2loaGBn/zkJ3zzm99k\nxYoVymPhWeDFixczMDAQEWSP5pJLLgHgrLPOoqmpCYB33nmHL7/8UplNHxgYoLa2NqK9+/fv5+KL\nLyY2NhaANWvWjHmP8bY6d7lcPPvss7z88svjtvNkmZJAWqfT8bOf/Qyj0Yjf7+fBBx/knHPO4cCB\nA5x55pmsW7eO7du3s337dr73ve/R3NzMvn37eOqpp7Db7WzatIlnn3120sueCXEiWlpa/reiSB4A\nhw4doqCgAIPBENV2CSGE+NpEZo5Ppttuu41Vq1ZxzTXXKMd++tOfcvvtt7N27Vr27t3L448/Puq5\nWq2WYDAIQDAYxOfzKY+FA1AYSvXYs2cPf/vb34iNjWXdunUjKl4NZ7FY2LVrF7t27WLLli289tpr\nPPvss8DI8p9HS5UI/87TaDQEAgEAQqEQv/jFL1i5cuW4507UeDPSKSkpNDY2UlZWBgx9SFm1ahVv\nvvmmUtL2ZJqSyFSlUimfxAKBAIFAAJVKxYcffkhpaSkApaWlSoL9hx9+yJIlS9DpdKSmppKenk51\ndfVUNFWICXG5XAQCAQoKCpRjRUVFNDY2RrFVQgghppvExEQuv/xy/uu//ks51t/fT0ZGBkBEBYy4\nuLiIgDE7O5tPP/0UgDfffDMikB6uv78fi8VCbGwsVVVVR92GvKenh1AoxGWXXcZPfvITPvvsM+Wx\n7du3A0MzxmazGbPZfIw9hrKyMrZs2aK0t6amBqfTGfGcxYsX88YbbzA4OIjD4WDHjh1jXu/1119X\nAv/h/5WWljJ//ny+/PJLPvroIz766CPmzJlDRUXFlATRMIU50sFgkPvvv5/29nbWrFlDUVERfX19\nSq1ei8WifH1gs9koKipSzk1KSsJms424ZkVFBRUVFQD86le/Ijk5eQp6MjqtVhu1+39dx3Z29n8q\nOJ1OXC4XCQkJ6PV6ampqOPPMM9HpdHg8XrRaLX5/Or29vTP6dRjLTB//sXw99rOv72GzdezDZnv/\nxcTccccdvPDCC8rf77vvPm666SYsFgvLli1TJmHWrFnDD37wA958801++ctf8r3vfY/vf//7rFix\ngpUrV0bMQg+3cuVK/vjHP7J06VLmzp3LwoULx21PW1sbP/zhD5XZ7gceeEB5zGg0snLlSvx+P888\n88xx9fd73/seTU1NrFq1ilAohNVqHbHg8KyzzmLdunWUlZWRnJzMN77xjeO6V7SpQqFQaCpv6HQ6\neeKJJ1i/fj0PPvggW7ZsUR5bv349L774Iv/5n/9JUVERy5cvB+C3v/0t3/jGNygpKRn32q2t0duU\nJDk5me7u7qjdP9pmav+DwSA1NTXExMQQGxtLX1+fUtEhLS1N+Uor3P/q6moKCwuj2eSomKnjPxGz\nue8g/Y92/+fMmRO1e09nLS0tEyphKSKtW7eOhx56SNalHcHr9Y65f8WUJx2bTCbOOOMMDh48SEJC\nAna7HQC73a58fZCUlBRRd9Fms42ZMC9mdh3paGtsbCQrK4usrCySkpLIz89Hp9Oh1+uVRRXhWsJu\nt1u2lp5lpI60EELMblMSSPf39yu5MV6vl88++4zMzEwWLVrE7t27Adi9ezfnnXceAIsWLWLfvn34\nfD46Oztpa2ublbN8EzXT60hHk9/vJyYmJuJYeno6DoeDxMREDh8+rNQSbm5uVmp8itlB6kgLIWaS\n7du3y2z0MZqSHGm73c5zzz1HMBgkFAqxePFiFi5cSHFxMU8//TQ7d+5Uyt/BUHL94sWLueeee1Cr\n1dx0001SsUNMG+FsKKvVGvFNiXzYE0IIIWaXKQmkc3NzR91fPj4+PmIf+uGuvPJKrrzyypPdNCHG\npdPpcDqdmExfz/i3tbUpi4sklUMIIYSYvWRnQyHGkZOTQ01NDTqdTllsGBMTg8ViiXbThBBCCBFl\nEkgLMQ6VSkVhYSEejwe3201+fr5StUMIIYQQs5skHs8ALS2ttLREr/TfbGAwGEhISBg1iG5paR1W\ny1vMJjL2Qggxu0kgLYQQQohZLRAI0Nvby+DgYLSbIk4xktoxA4RrSD/2WF+UWzI7bdiQgMGgYdOm\naLdETDUZeyFOfa2trcrOtX19fXg8HgoLC0+oWti///u/YzAYuOWWW9i4cSP/+Mc/eOWVV3j33Xcp\nLy8nPj6egwcP4na7ufTSS7n//vuBoR2bH3zwQWJjYzn//PNpaGigvLwcp9PJT3/6U7766it8Ph/3\n3XcfF1988WS9BOIEyIz0DCB1pKNrtFrCTqeT2tpaqqurR93eXswMUkdaiFPbwMAAfr+fwsJCUlJS\nyM3NJTs7m/r6+hO6bklJCfv37wfg4MGDOJ1OfD4f+/fvZ/Hixfz0pz/l7bff5p133uH999/nH//4\nB263m3vvvZe//OUvVFRURGxM98wzz7Bs2TLeeustXn31VR5++GFlfw4RXRJICzHJurq66OrqIjc3\nl7lz5+L3+6mtrY12s4QQQhyhs7OTrKysiGMxMTEEAoETuu7ZZ5/Np59+ysDAAHq9nkWLFnHw4EH2\n799PSUkJr732GhdddBErV66ksrKSw4cPU1VVRW5uLrm5uQBcccUVyvXeeecdfvOb31BWVsa6devw\neDy0tLQfh1sSAAAgAElEQVScUBvF5JDUDiEmUSgUwm63U1xcrBxLTU3F4/HgcrmIjY2NYuuEEEJM\nBZ1OR25uLi+99BLnnXceZ5xxBnv37qW+vh6j0cjmzZvZsWMHFouFu+66C4/HM+71QqEQL7zwgmz8\nNQ1JIC3EJPJ4PKMGy1arld7e3jEDabfbTVNTk7LBi1qtJi8v77hy9NxuN3v37o04tnjx4ikL4vv7\n+zlw4IDSF41Gw7Jly9Bqx/5x09vbS2dnJ2q1mlAoRGxsLJmZmVPS3snW19dHR0fHjOiLEDNdamoq\nra2tEbPSbrd7UsqcXnDBBWzevJlnnnmG008/nQcffJCzzjqLgYEBYmNjMZvNdHZ28ve//52lS5dS\nWFhIQ0MDjY2N5OTksH37duVaZWVl/OEPf+CXv/wlKpWKzz//nDPPPPOE2yhOnATSQkwinU6H2+0e\ncdzpdGI0Gkc9JxQKUVdXR3FxsfLDe3BwkNra2uOafdizZw9LliwhLi4OAJfLxZ49e1i7du0xX+tY\nBYNB3n33Xb75zW+i1+sBsNvt7N69m4suumjUc9xuN93d3RGz+D09PbS2tjJnzpyT3ubJ5Ha76erq\niuiLzWajpaVFgmkhpqH4+Hj6+vqoqanBYrHgcrlwuVyTMvNbUlLCM888w6JFizCZTBgMBkpKSliw\nYAFnnnkmS5YsITMzk/PPPx8YSil59NFHueaaa4iNjeWcc85RrnXPPffwwAMPsGLFCoLBIDk5OZSX\nl59wG8WJk0B6BpAa0tHV0tJKcnIy3d1Ds68ajYaBgQHi4+MB8Pl8dHd3c/rpp496vt1uJzU1NWIG\nJCYmBrVajd/vH3cm90g2m43ExEQliAaIjY0lNTWVzs5OUlNTxzx3cHAQm82GTqcjOTlZmQ13OBx0\ndHQQCoVISEggJSVlzGs0NjZSWFioBNEAiYmJ6PX6MVNb2tralJzAMKvVSlVV1YT7HS3Dxx6gvb2d\nnJyciOckJSVRVVVFMBiksbERv98PDG32k5eXJxv8CBFlWVlZ+P1++vv7SUpKIjs7e1Kuu3z5clpb\nv/79HF58CPCb3/xm1HOWLl3Kvn37CIVC3H///UowHRMTw5NPPjkp7RKTSwJpMWP19vbi9XqxWCwR\ngd3xaGtrw263k5mZSUJCgnLc6/XS29sbEQzl5eXR1NREZWUlwWAQs9k87uyGx+NRgm63241arUav\n16PX6/H5fGi12gn3ZXgAH865MxgMxMfH43A4xgykGxsbgaHZGb/fT2VlJXl5eTgcDlwuF1lZWQSD\nQVwuF9XV1Up/XC4XDodDmW13OBxYrVYCgQA2mw2NRkNiYiImkwm32z1qIB0IBNDpdCOOh1NDgsEg\ndrudUChEUlJSRLpLIBDA6/ViMBhOqFRV+D4ejwe9Xj/h4Nbv9+NyuQiFQqhUKvx+/6jjo1KpqK2t\nZc6cOcpr4PF4qK6uZt68eSfUbiHEidNqtSQlJUW7Gfz5z39m69at+Hw+FixYwA033BDtJomjkEB6\nBjjV6ki73W7sdjsGg4HExMSIgKmnp4dAIIDVah01uJoIj8dDbW0tVqsVg8FAc3MzBoPhqF+t+/1+\npdxQcnIyGo0Gr9fLzp07MZlMmEwmPvroI/R6PcuWLaOlpQWPx8Ovf30GWq2Om276gIKCAgKBQERA\nGwwG6e3tHXMmNzExkZqaGqXtMDSLHAgESE1N5dChQ1itVoxG41H7kpmZyY4dOwgGg0qKidFopKGh\nQUmt6O3txeVyYbFYiI2Nxel0MjAwgFarVfqs0Wioq6sDhtJV2tra0Gq1OJ1OQqEQAwMDdHR0YDQa\nSUhIwGaz0draSnZ2Nu+99x7FxcWkpKQQCASorKykubk54mvK4cLnD/8lFl4x39/fT2trK6mpqajV\naqqrq0lJSSExMZGGhgZ8Ph8xMTEMDg5iMpnIyMhQXkebzYZerycpKUl5j42lpaWFwcFB5Vp6vX7E\nzPJwwWCQuro6Hn+8iNhYFbffXkVSUhIWi4Wenh6sVmvEcz0eDyaTKeKDxPAPOMO/QZgMDoeD/v5+\nTCZTxAc/IcT0dvvtt3P77bdHuxniGEggPQOEa0ifCoF0Y2MjoVCI5ORk3G43X331Ffn5+fh8Plpa\nWkhPT1cCv/j4eNLS0o7rHkVFRUpKREJCAg0NDeNWzbDZbHR3d5Oeng5ATU0NycnJHDx4EKvVSm5u\nLnFxcdhsNr788ku++OIL4uPjKSgo4P/7/xIB+NWviqirq1NmaFNTU9HpdHR0dFBdXY3FYhn1w4Fe\nr6eqqgqr1cqCBQvwer189NFHxMXFYTAYIvpiNptpbGzE6XRiMo2sHa7VanE4HHz00UesWrUKgF27\ndgFDCxi/+uorJeCz2+20tbURDAZRq9URM6Nut5t3330XvV7P4sWLI2ZZP/vsMyorKyksLMRisQBD\nHzyCwSDd3d34fD56enrIzMzE5/PR3NxMbGzsmDPGycnJVFVV4ff7SU5Oxul00tzcTF5eHg0NDZx2\n2mnKcxMTE6msrMTpdGI2m0lMTFQea2lpoa+vD4fDgdfrVaqlfPXVV+Tl5RETEzPm2KvV6ohvDXp6\nemhvb1feD0dqbGwkIyOD118fevypp4qpra0lPT2dlpYWAoEAycnJuFwumpqaSE1NJRgMjrhObGws\nbrd70gLpUChETU0NMTExJCYm0t/fT3t7O0VFRSc8Yy+EEGIk+ckqpszAwAAajYbc3FxMJhNWq5Xi\n4mKamppobW1l3rx5WCwW4uPjKSwsZGBgAJ/Pd8z3UalUI/KK58yZQ2dn56jPDwaDygIxs9mM2Wym\nuLiYrq4u2traOPvss0lNTSU2NpasrCzOOecc9u3bN2IhnFarxe/309vby2mnnabkKs+dOxeTyUR7\ne/uo9+/o6CAnJ4dzzz1XSeG46KKL8Pv9o+ZIz5kzh46OjlGv5XA4mDNnDpdffjl1dXXU1dVx8cUX\nk5ubyxdffEF+fr7Sl8zMTCwWC/X19SM+sBiNRoLBIMFgcESqQnx8PHa7XQmiw+Li4ujo6KCsrIzC\nwkI+/vhjDh06xPnnn89pp5025ta7KpWK4uJitFot9fX1OBwOiouL8fv9I+4BQ4F3e3t7RBAdfl2a\nmprw+/3k5+djMplISkpi3rx5NDU1jXpvGAqkjwyYrVYrDodjzHP8fv+ID2XZ2dl0dHRQVFSETqej\nvr6e/v5+iouLsVqtDAwMjLiO3W7HbDaPeZ9j1d7eTnJyMpmZmcTGxpKenk5OTs64/RdCCHH8ZEZa\nTJnwJiXDhVMJzGbziK/f09LS6OnpGXNW8FgEAoExZ+TCC0yOlJSUhMPhGBFIJiUl4Xa7R51h9Pl8\n6PX6EX2xWq3YbLZRF7F0d3eTkZGByWQiPz9fOW4ymUYNPoPB4Jg5vL29vSQlJZGQkMCSJUuU4+Hg\n02AwjGiXWq2mvb094jVwuVxoNBp0Oh12u10JWr1eL3a7fdTZcBia9Q7Pxi5btkw5brfbjzojmpSU\nFNEGjUYz6qYIY42lSqXCbrdTVFQ0ok3hUnRjpXgcLfVjIjQajfKeSExMHBHom0wmGhsbyczMRKVS\n0d7ejlarPeH8/eGcTqeS3hIWExNzXB9IhTiV6XQ6fD7fcacIChF2tPeRBNJiymg0Gvx+/4ggMBx8\nHSm80O5YqVQq3G53RLm55ubmMVdia7VaJWB1uVzA0Ffufr+fuLg42traIoKTjz76iLPPPpumpqaI\ndAC3243BYFDSG4b/w2ttbVVybv1+v5KPq9VqSU5Opq2tjdTUVKVY/5w5c3A6nSQmJirPDWtqalJy\npL1eLz09PcTHxxMXF4fFYlFym4fr7u5WZj69Xq9S71qlUilpB5WVlRiNRnw+H6FQiKysLFQqFU6n\nk+7ublQqFSqVCpPJRHp6+ogqIOF0js7Ozojcd7/fj8fjGRHEH8nr9dLf309MTIyST9zY2Kjke4dr\nMttsNiwWy4gxttlsmM3mo+5INjg4SCAQwGQyoVKpiI2Npbe3l7i4OAYHB5XX4GgBbrj6RlhbW9u4\nFU0yMjJwOBzU19cTCoVISUk5KfnL431gEGK2SElJoaurC6/XG+2miFOcTqcb92e7BNJiyqSnp48I\nPl0ulxK4DA8+Q6EQnZ2dEfV4JyovL4+6ujplti9cSWKsQC4uLo6qqirsdrsS2NTX1+PxeFi+fDmf\nfPIJhw8fxmKx0N3djd/vZ8mSJXi9Xg4fPgwMpXg0NzdTWFioLLAzm81oNBr6+/tRqVSkpKTQ0NCg\nBOhdXV1oNBqys7PZtWsXb731FgsWLMDlclFVVcWFF15IYWEhtbW1EX1JSkrCaDTyySefYLPZSEtL\no6amBq/Xy4oVKzAajXz55ZdK6kl7ezsajYaUlBQOHjxIQkICsbGxdHR0YLfbOe200+jv71eqhwQC\nAbq7u8nPzycYDNLQ0EBaWpqS752QkEB6ejrNzc1UV1djMpmw2Ww4HA5yc3OJj4+nsrJSudbg4CB5\neXnjjlk4JcNisdDb26u8lmlpaezYsQOr1YpGo6Gjo4NFixaRlJREdXU1CQkJxMXF0dvbi8fjYf78\n+UpN7rDBwUGlokZtbS2xsbFotVpaWlpITU0lIyOD9957D41GQ3p6Ol1dXTidTlasWDFme3Nycv63\nPN/Qh6OGhgZUKtVR0zTi4uImfWHhcFarldbW1ojFqD09PSf1nkJMR+FJAiFONlUoFApFuxGTZXi9\nxqk2VEu2O2r3j7aJ9j+8qC82Nhav10soFCI/P59AIEB9fb1SOWJwcJCsrKwxUwgmwuv14vP5lJnX\n8Xz66acYDAZl5ndwcBCPx8PZZ5+t5O0GAgH0ej1Wq1X5AR0KhXC5XGRkZNDf3w8MzVTW1dURCASU\n+2ZmZirpEsMrOvT09OByuXj99dcpLS1VqnZoNBo++OADbr311lH70tLSQmtrK+edd55yrc7OTqqr\nqzn33HOpra3F4/EQCoUwGo3k5+fT3d3NwMAAer2emJgYZeOB3NxcLBYLHo8Hu92OXq+PmFEOhULY\nbDb8fj9JSUkRM+2BQAC3201mZia9vb0Rr6nL5UKtVo+5EU1YuIrI8Jxzt9tNa2srXq+X4uJi5XUx\nGo1UVlYyb948VCoV/f39uFwuzGazkrPc29tLR0cHsbGx+Hw+gsEgBQUF1NTUkJeXF9H+w4cPYzab\nUavVmEwmBgYGMJlMyrcGR9sQJpzmEgwGj9rPqdLW1obD4SA2NpbBwUEMBsOk1cUdjfzsi27/T7VN\ni4SYaWRGWkypcB6s2+1Gq9UqqRtqtZqioiIl8DlaGsBEhGsxH83AwABpaWmkp6crXwPq9Xra29sZ\nGBggLy+PQCCAz+fDYDBEBOXhVIfh99FqtaP2pb29fUQ9aavVys6dO7nwwgs544wz8Hg8qNVqdDod\ntbW1Slm4I/tSVVUVkYMMQ1vdfv755xiNRubPnx/RF0CZsQ0Gg3i9XuUXcHhHL4PBMGo+ukqligj+\nh9NoNJhMplFTcCa6JXl3dzdz586NOGY0GnE4HMrGMMNTW5KSkujr68NisSiLQ4ezWCxK6kf4PRYI\nBJSc7+HmzJnD559/zuLFiwEiPriFSyGOJzExMeqB1JEyMjKUMdbr9VKtQwghTiIJpGeA0epIh2cR\nnU4ncXFx06LQ/HBjzd4FAoFRF/GNZXBwkLa2NnQ6HRkZGREBXSAQUGaRhwuFQni9XnQ6HWq1Win/\n5vV66erqAoby68KPwdc7Fh7J6XRy331mYmI8PP54MCJomegil+H5uMM/QAy//5F9Cbd5PGN9iBg+\nSzzRL6TGei3H4/P5Rq2gciSVSqXUpu7t7cVoNJKcnDxmrm944WAoFFJm9M1mc0SFD5fLRU9PD1qt\nltTU1IgNU7q6upRSe+FrHU9fgsEgd99tRKt1sWnT13XDj3adzs5OpQTk8H8HwWBwzMWqx2oi3wQI\nIYQ4cRJIzwBH1pEOBAIcPnyY1NRU0tPT6evro7KyclrXknW73bz33nvodDp0Oh39/f0sWrSI5OTk\nMc+prq6msbGRrKwsXC4XX331Feeeey6JiYkRm4kMDg6SlpaGxWKhs7OT3t5eYmJi8Hg86HQ6cnJy\neP/990lNTVVySxsbG+ns7FRmKkcTTkV57bWhKhE333xg3HrF4UVtwwO+cD/3798fUcfZ6/XS3d1N\nUlIStbW1hEIhpS+pqakUFBTw+eefc/bZZyvn9PX1jRu0hhdVDm9fV1fXiOoSw/n9furr61Gr1Wg0\nGiWNY7yc23DtZIPBQDAYJBAIjEipGC41NZW9e/dSWFhIeno6TqeTTz/9FJPJRF9fH+np6RGBZU9P\nD3l5eXz11VfKNwl2u53Dhw9TVFREY2MjarWa1NRUvF4vVVVVZGdn09vbi9vtJjs7G61WS0dHB83N\nzeTk5IzYECZcC3xwcJDGxsYRfQkEAtTV1fHqq0Ob3Nx3XxVdXV0UFBSM+brYbDZsNhtz5sxBpVLR\n2tqKyWQiLS2NpqYmZUGm2+0mPj5+UqrVCCGEOLkkkJ6BwptZhAOmlJQUYmNjaW1tJSsrK8qtG917\n771HSUmJEqAFg0HeeOMN1qxZM2pwODg4SENDAytXrlSCrOLiYnbv3s3cuXPJyMiISC2oqqoiGAwy\nODgYsRDNbrfT3NxMTEyMst0zDAWQMTExY86KDgwMoNPpIhZ1zZs3j+rq6jEXSGZkZFBTU0N/fz9m\ns5mBgQE8Hg9FRUX09fXx4osvcs455zA4OMjnn3/OxRdfTENDA+np6RF9qa6uJjs7m9bWVt59911y\ncnLo6emhs7OTlStXjvka5+TkKIsDTSaTktM83kLA+vp6srOzlZnyUChEZWUlxcXFo34oC4VCyiYq\n4dfN5/ONWAA4nMfjISkpCafTiVqtjgj2MzMzqaysJDk5GZVKRXd3N2lpabS0tDB37lxlhjwtLQ2D\nwUBtbS16vV7JCTYYDMybN4+qqiq0Wq2S067VavH5fMrsd1tbGwMDAyQkJOBwOHC73RQUFCj52OG+\nhvuiUqkiPvhkZmYqizdH+2ASCoXo6uqKOKegoIDq6mqlJvXwPObm5mb6+vpkV0IhhJjmpuf0pDgh\n4W2ThzOZTMqW0dNNuHLH8FlOtVrN6aefPmopNxjK6x0erAHKIrrwQqvhsrKyOHTo0IgPEomJiXR0\ndJCdnU1xcbFS4m3evHlkZ2fT1zf6bpFdXV0jZgzD9YrHolKpKCwsJCUlRUktKCwsRKVScd5553HN\nNdfg9/uJj4/n5ptvJicnh0AgMGpfOjo6WLJkCWeffTZut5usrCzWrl07buqFWq2muLgYi8WC3+8n\nIyNj3CA6XK95eLqJSqUiPT19zJzgcBWR4eOi0+nQ6/VjlqHq7e3ljDPOoKCggNjYWObMmcNpp52G\nWq0mPj5eCWRVKhVFRUVYLJZRN4qxWCw0NzeP2FwmnDpiMpmYP38+BoNBWeQ6f/58bDYbBQUFpKWl\n4ff7sVqtFBYWKtu6H5muYzAY8Pl8I8Y6NTV1zLxqh8MxalBstVppa2sbkYOemZmppBkJIYSYvmRG\negYaLedzOhdnCS/iO1JMTMyYAVs4OBrt+GgzyOHi/KPlOev1emWR3/Cv9z0ez6g764XP8Xq9Iz6w\nTOR1jomJGTX9IyYmJqIKx1h0Op1Sw3i0xXZHE56RPprwAr0j6fV6Zeb+SOGZ/NHaPFZ9Z7VarZQ+\nHJ5nHH4tVSrViBz/0V7nYDCITqdTFtkNp1KplPsPD2i9Xq/yjYfRaIzIKw4EAqOmo4R3sDxSOO9+\nNOH332jnjPYaSx1oIYQ4NciM9AyUlJREW1tbxLHW1tYxKy9EW0JCAj09PSMWGVZWVo6o5hA2d+5c\nDh06FHHO4OAgLpdLyWcdrr29nby8vBEzhl6vVyl7Njw48vv9OByOMQPOcF7r8IDO4XBM+i5aoVBo\nRADa3t4+bnH4yaLX65WNUI68/1jvpaSkpFG3Ync4HGPmjmdkZNDY2BhxzG63j1v1Iz4+fsSHrKam\nJk4//XRaWloi2hxO5QinjYSFQiFaW1vHfC0TExNH/SDncDiwWCxKucPh9x8rrzmcbz08mA4EAhGb\nywzX29t7QqUfhRBCTA2pIz1JplsJrHAt2fDsmdlsHvGV92Q60f63tbVx8OBBFixYgMFg4Msvv8Rq\ntXLmmWeOeU5dXR21tbWkp6fj8/no6urivPPOw2AwUF9fT1paGkajka6uLlQqlZIjHBMTo+TkdnV1\nUVRUpGw8Ev66PhgMkpubO26qRH9/v7LNczjnOS8vb1JnE91uN3V1dUpfuru7CYVCI7ZaP1kcDgfN\nzc2kp6crJQHDOy8ON3z8Ozo6cDgcpKen4/f7aW9vJyMjY9yZ83B98fDMtV6vV3aCHEtLS0tEvnNS\nUhLJyckMDAzQ1taGVqtVqpuEU1jq6uoIhUKo1Wr8fj9ZWVnjBuydnZ309/eTkZFBIBCgra2N9PR0\nzGYzjY2N+Hw+rFYrXV1dpKamjvkNBgx989LQ0AB8PUOek5ODTqejqqqKpKQk4uPjsdvtuFwu5s6d\ne0rMTE+3n31TLdr9lzrSQkSXBNKTJNo/TMcy1tfzk20y+h/eKdDn81FUVDSh3dg8Hg9dXV3KFp7h\nQDhcGi28kG34bKjD4aCvr08JqIcLz2QfS3WTQCAwbn7siRqvL1MhGAwqOzqOtUPkkeMfrjqi0WhI\nTk6e8HswnJc90QAyFAoRDAZHvf5Y1wqXzpvoGIe3YVer1SP6EgqFSEpKwm63T+haMPZ7LBxAm83m\nCZXSmy6m68++qRLt/ksgLUR0SSA9SaL5w3S0OtJTLdq/TKJpw4YEDAYjmzZ1RLspUTNbx1/GfvaO\nfVi0+y+BtBDRJTnSM0B5uUmpJS2mXnm5iRdeOPmz/mL6kbEXQojZTQJpIYQQQgghjoOUvxNimgjn\n4obziqfrLpSzjc/no7u7e9QcaSGEELOb/KYWYhpob2+nqamJ+Ph4DAYDhw8fZmBgINrNmvU6Oztp\naGggPj4eo9FIdXX1iLJ3QgghZi+ZkRYiynw+Hw6Hg8LCQuWY2Wzm8OHDEVtKi6nl9/vp7++PGJeE\nhAQqKyuPeRMcIYQQM5PMSM8ALS2ttLREr2LJbNfS0orHM/r21xPR09Mz6rbWsbGx03Zb99nAbreT\nnJw84nhcXJyys+OJjr0QQohTmwTSQkRZeEORI/n9fsnHjSKNRjPqtuYyLkIIIcIkkJ4BNmxIUGpJ\ni6m3YUMCd9xx/IFVeFvt4SXdvV4vfr9/0rccFxOXmJhIZ2dnxHbzPp8Pj8ejbEpzomMvhBDi1DYl\nG7J0d3fz3HPP0dvbi0qlYtWqVfzTP/0TL7/8Mn//+9+VfMNrr72Wc889F4BXX32VnTt3olarWb9+\nPeecc85R7zNbN2TJzBwqyB/N9I5ob0oQTZPx+judTpqbmzEajQSDQfx+P3l5eadMID1Tx9/lctHU\n1KSMi8/nIz8/XxmX6fBvL9pm6thPVLT7LxuyCBFdU7LYUKPRcP3111NQUMDg4CA//vGPOeusswC4\n5JJLuPzyyyOe39zczL59+3jqqaew2+1s2rSJZ599VsqBiRnLZDIxb948vF4varUarVbWAU8HsbGx\nyrioVKpT5oONEEKIqTElkWliYiIFBQUAxMTEkJmZic1mG/P5H374IUuWLEGn05Gamkp6ejrV1dVT\n0VQhokqv10sQPQ3p9XoJooUQQoww5VO8nZ2d1NXVKSWl3nzzTe699142b96Mw+EAwGazYbValXOS\nkpLGDbyFEEIIIYSYalM69eV2u3nyySe58cYbiY2NZfXq1Xz7298GYOvWrfzpT3/izjvvnPD1Kioq\nqKioAOBXv/rVqKWqpopWq43q/YFZ3/9om839n+3jP5v7PtvHfrb3X4jZbsoCab/fz5NPPsmFF17I\nBRdcAIDFYlEev+iii3j00UeBoRnonp4e5TGbzUZSUtKIa65atYpVq1Ypf4/mgo9oLjhpaRn6fzTX\n+0R7wU00tbTM7v7D7O2/jL30P9r9l8WGQkTXlKR2hEIhfve735GZmcmll16qHLfb7cqfDxw4QHZ2\nNgCLFi1i3759+Hw+Ojs7aWtri9hdTAghhBBCiGibkhnpyspK9uzZQ05ODvfddx8wVOpu79691NfX\no1KpSElJ4dZbbwUgOzubxYsXc88996BWq7npppukYsc4wjWkH3usL8otmZ02bEjAYNCwaVO0WyKm\nmoy9EELMblNSR3qqSB3p2dn/aJsOr3+0zdbxl7GfvWMfFu3+S2qHENEl07xCCCGEEEIcBwmkhRBC\nCCGEOA4SSAshhBBCCHEcJJAWQgghhBDiOEggPQO0tLTO6sVO0dbS0orH4412M0QUyNgLIcTsJoG0\nEEIIIYQQx0EC6Rlgw4YEpZa0mHobNiRwxx2aaDdDRIGMvRBCzG4SSM8A5eUmystN0W7GrFVebuKF\nFySYmo1k7IUQYnaTQFoIIYQQQojjIIG0EEIIIYQQx0ECaSGEEEIIIY6DBNJCCCGEEEIcBwmkZwCp\nIx1dUkt4Zujr66O5uRmbzTbhc2TshRBidpNAWggxq4VCIQ4fPozb7SY5OZlAIMBXX31FIBCIdtOE\nEEJMcxJIzwBSRzq6pJbwqa21tZWMjAzS0tIwGo2kpKSQm5tLc3PzUc+VsRdCiNlNAukZQOpIR5fU\nEj61DQ4OEh8fH3EsJiYGn8931HNl7IUQYnaTQFoIMeuFQqEJHRNCCCGGk0BaCDGrWa1WWlsjF+t2\ndnZisVii1CIhhBCnCm20GyCEENGUmJiIx+OhqqoKvV6P1+slNjaWzMzMaDdNCCHENCeBtBBi1ktP\nTyctLQ2/349Wq0WlUkW7SUIIIU4BktoxA0gd6eiSWsIzg0qlQqfTHVMQLWMvhBCzmwTSQgghhBBC\nHAcJpGcAqSMdXVJLePaSsRdCiNlNAukZQOpIR5fUEp69ZOyFEGJ2k0BaCCGEEEKI4yCBtBBCCCGE\nEBpdpLMAACAASURBVMdByt8JIY6Ly+VSNjLp7u4mJiYGk0lSjIQQQsweMiMthDhmg4ODNDc3U1BQ\nQGFhIcXFxbS1teF0OqPdNCGEEGLKSCA9A0gd6eiajbWEW1tbKSgoQK0e+hGiVqspKCigra0tyi2b\nWrNx7IUQQnxNAmkhxDELhUJotZGZYeGgWgghhJgt5DffDCB1pKNrNtYSVqvV+Hy+iGOBQCBKrYme\n2Tj2QgghviaB9AwgdaSjazbWEs7MzKSmpkYJpn0+H9XV1WRmZka5ZVNrNo69EEKIr0nVDiHEMTMY\nDOTn59PU1EQwGMRqtZKbm4vRaIx204QQQogpI4G0EOK4GAwGCgoKAEhOTqa7uzvKLRJCCCGmlqR2\nCCGEEEIIcRwkkBZCCCGEEOI4SCA9A0gd6eiSWsKzl4y9EELMbpIjPQv5/X5sNhtarZbExERUKtVx\nXysYDGKz2QiFQoRCoSmvJez3+7Hb7Wg0mhF9sdvt+Hw+LBYLer1eOT44OMjAwAAxMTHEx8dHXKum\npgaAuXPnjqiTfLK53W7q6+vR6XTk5+dP6LXs7Ozk8OHDpKSkMG/evBO6v9frpbe3F51OR2Ji4gld\nazIFg0HsdjuhUIikpKQTeo+FQiHsdjt+v5/ExER0Ot1Rz/F6vdTW1qLRaJg7d+5Je487HA6cTidm\ns5mYmJiTcg8hhBCTS/PQQw89dLJv0t3dzeOPP85rr73Gjh07CAQCFBUV4XA4ePTRR9m2bRv/8z//\nw8KFC5WA59VXX2Xz5s289dZbZGRkkJ6eftT7DAwMnOyujCk2NhaXyxWVe2/YkMDbbxv55jc9R31u\nR0cHnZ2dmM1mgsEgDQ0NxMXFTSigOJLdbqepqYn4+HhiY2M5fPgwOp1uyio3dHZ20tHRQXx8vNIX\nk8lEMBikqqoKg8GA0Wikvb0dl8uF2Wymrq5O+bPD4aC1tZXExESampo4cOAA6enpaLVaDhw4gE6n\nw2KxHLUdGzYk8OabelauPP7tsb/66iv+8Y9/kJaWht/v54MPPsBisWAyjV3W8K9//SvNzc3k5eXR\n3NzMzp07Of30049rLFtaWrDb7ZjNZnw+H42NjZjN5gl/mDhZ7//+/n7q6+uJi4tDrVbT1NSEWq0+\nrkBzcHCQ6upqYmNj0ev1tLa24vP5iIuLG/OcqqoqPvvsM9LS0giFQhw4cID4+HjlnMkY+/D7NRQK\nERcXh81mo7u7m6SkpOO+5lSK5s++6SDa/R8+GSCEmHqqUCgUOtk3sdvt/z97bxrcyHmd+z/d2Ddi\nX0gQJAiAy3AWjTSyZjSasTTWYtlxOYo/3Bs7dmQ5f7uSSlVSsWOVUzdJxVFKXq6Xsiu2b+xUVHas\n3ETX8ZbE1q7ROhrNLg45BImFIHYQ+47uBvr/genXBAFyyNlH6t83NoDufrsbxHnP+5znoFAowOPx\noNFo4Atf+AI+//nP4+jRo9BqtXjooYfw85//HNVqFR//+McRi8XwrW99C48//jgKhQIee+wxfOtb\n37poJiiRuH7yhuvpWuB0DgHAReUdDMNgeXkZPp+PbBN+xLebzVz/OWH8fr8fExMTl5Xl3gosyyIS\niXSNhed5LCwskHbVEslv/H0jkQhomoZCoYDNZiPbm80mYrEYAoEAHnzwwa5jPP3003jggQdA0zTq\n9ToajQZ0Ol1XdpvjOIyOjgC4+PVfe8xarQa1Wg2VSoV6vY5jx47h3nvvJe/pdDp49tlne85J4MyZ\nMyiXy7j77rvJtmw2i+effx6/+7u/u6XzEGg0GshkMhgdHSXb2u02QqEQxsfHt7SPq/H88zwPv9+P\nqampru1+vx/j4+Pbzgz3+1wwGMTw8DAUCkXP+xmGwSuvvIL77ruPbOt0OnjmmWfwgQ98AMDWv3ub\nEYlEYLPZuiYH+XweHMd1Pas3Ku92x5brPf6hoaHrdmwREZFrpJE2Go3EJkulUsHpdCKfz+PEiRMk\nELj77rtx4sQJAMCJEydw8OBByGQy2Gw2OBwOBAKBa3Gq72iy2SwGBwe7tgnB5foudRejVCrBbDb3\nbDcYDNdkZSCbzfasUlAUBaVSCY7juoJoYPXHJhQKwWq1dm1XKpVIJpMYGRnpOcbIyAii0SgWFhZQ\nKBRA0zTi8TgikQgAIJlMYmlpibx/fn5+0+vI8zyCwSAymQxomsbKygoCgQCCwWBPsEjTNAwGA0ql\nUt99zc3N4dChQ13bLBbLtu8jsJrZX/9cSCQSUBSFazDP3pBqtdp3RcBisaBYLG5rXxzHQS6X9wTf\ng4ODGwZBoVCoa6IGrN4Xm82GfD6/reNvBsuyPRl2k8mEcrl8xY4hIiIiInJ1uOYa6Uwmg3A4DJ/P\nh1KpRLSYa4OGfD7flQkzmUx9f7ief/55PP/88wCAL3/5y7BYLNdgBP2RSqXX9fgALnp8hmGg0Wig\n13e3Ey8UCrBardvSBMtkMtTrdXJMYfzNZhMGg2HT5fLNqNVqaLfb0Ol0m2a1OY6DUqnsCbSKxSIU\nCkXPtRDOy2w2g+d5VKtVKJVKKBQKmM1maDSans9oNBo0Gg3s27evK9BJJBJoNBpQq9XYvXs32b5/\n//6+QbFALBbDxMREl/64WCzi5MmT0Gq1MJvNqFQqkEgk0Gg00Gg0sNvtfa/lwMAAjEYjJBJJ11i0\nWu22n8NqtQqj0dgTzGWz2Z6Jx0ZcjedfpVIhn8/37JfjOCgUim3puNvtNiqVSs++yuUyJBJJ33MX\nssHrX1Or1bDZbF3P3vr3tNttVKtVIi/ajGw22/N5nuf7br8RuRH+911P3u3jFxF5t3NNA+lms4mv\nf/3r+OQnPwm1Wt31GkVR25YD3HfffV3Lrtdzee36Lu+tLu1d7PgSiQTnz5/vknGwLItsNntJxWWB\nQAASiQQ0TcNisSCTyWBpaQlTU1NoNpvb2pdQaKfT6SCRSFAqlWC32zfUKFMUhdnZ2Z6xZDIZSCQS\nxGKxrgBGaF/97LPPwmAwYGBgAPV6nUzmZmdn4XQ6yWSC4zjMzc1hfHwctVoNtdpvNLAymQzHjh3D\nwYMH//uar17/UqmEYrG44X2IxWLw+Xw9ryuVSjz33HPYuXMnjEYj2u02crkcotEodu/e3fdaTk1N\n4Vvf+hYOHz6MgYEBNBoNUii53edQLpfj3LlzXdnXZrO56VjWc7We/0gkAoVCQTLJPM9jcXERU1NT\nCIfDSKfT5L1Op3NT7XQ2m4VGo+mS5vj9fni93r7nbjAY8Mwzz8BgMJDjMwyDSCSCHTt2dN37tZ9P\nJpOkaLDRaIBlWXg8ng2lKK1WC4FAoOtZTyQSUKlUN4Vk4npLG64313v8orRDROT6cs0CaY7j8PWv\nfx2HDx/G/v37AQB6vR6FQgFGo5EUOgGrGehcLkc+m8/nb5rCmxsZiUQCh8MBv98PrVYLjuPQarUw\nNjZ2Sftzu91YWFiAVqtFtVpFPB7v0tluh0gkgvHxcSLJEM5zYGCgbwAikUgwODjYdyw0TSMcDkMq\nlUIul6NarcJsNkMikZD9t9ttdDodSCQSdDod7N+/H88//zyRq+RyOezfv7/vSgjP86AoCp1O55LG\nuh6KouBwOBCJRFCv18GyLPL5/KY/kHa7HTqdDm+88QacTidWVlbQarVw+PDhbR9fLpfDZDLB7/dD\np9OBYRhwHHfJz8WVxO12Y3FxEWq1GhRFoVqtYnR0FJVKBdlsFl6vFxRFod1uIxAIwO1299U7C/sK\nh8NQKBSQSqWoVquw2+0brsTQNI1bb70VzzzzDGw2G1iWRbFYxMGDBzc832q1CpZluyYljUYDkUhk\nw+vpdDqxtLSEXC4HtVqNWq0GlUp1QzmniIiIiIj055oUG/I8j+985zvQarX45Cc/Sbb/8z//M3Q6\nXU+xYTQaxbe//W1SbPi3f/u3+Pa3vy0WG15B6vU6JBLJhkHHdmg0GrBYLF1Z2+3AMAySyWRPEF4s\nFsEwzIYFVzzPI5VKIZvNQiKRYGhoqCurJwSEKpUKFEUhEAgQrX6j0SABVSAQIIFPoVAAABLERCIR\nWCyWLveMeDwOmUxGsoYqlQparRbpdBpSqXTDAj3h9bXa8nw+j0QigeHhYRgMBuTzeUgkEuj1ekQi\nEQwODnZlUAWEsXAcR5xH9Hp911i2C8/zaDQaZAKyHS73+WdZFvF4HBzHAVidSK2VtDSbTfA8TzLO\ni4uL8Pl8XatYDMMgkUjA7XZveqxWq4V2u92zKrYZa+/LetaOPRgMwu1292j0t3JfhMmgSqW65jaS\nl8PN9r/vSnO9xy9mpEVEri/XJCPt9/vxyiuvYGRkBJ///OcBAB/96Efx0EMP4Zvf/CZefPFFWK1W\n/Nmf/RkAwOVy4c4778RnP/tZ0DSNP/iDP7ipflhuBrYTRFwMIZi81EBayPCu52JSn2AwCIvFgt27\nd4PneaJdFgrn5HJ5T0AoSIg2spVbnwUcGRlBMBiEVCqFWq1GuVyGSqWCwWBAOByGRqOBQqGATCYD\ny7I9AdRa7HY7IpEISqUSyeILxWvC87125eVixX4URUEul180cNwqFEVd0ediqwjZ5LGxMSiVSnQ6\nHSwtLaHdbpPAdb3OuJ8UTC6Xk0B8My5l8ridFbFLdayRSqXX3LtcREREROTyuCb/taempvDUU0/1\nfe2v//qv+27/yEc+go985CNX87TeMTz66Gqw8dWv9nd4uNFRKBRoNBrodDpdE6ZMJkMyyOupVqsk\noAVWgxen00n8ePsFMyaTCel0usvtQ8jAbgRFUfD5fGi1Wmi1WkQiEo1Gccstt0Aul+Ozn9VCrVbj\n8cd1F3WXGR0dBcuyaDQapCEIx3Hw+/2Qy+XkvCmKAsMwGwZ9ZrMZqVSqy22j0WhsGshfCjzPIxKJ\ngGVZEti7XK5NC+gEm0UBmqbhdruJ60m9XifSGKfTiWKxiNHRUbJPwb5wYWGhbwZYYP3z0mw2L8lD\n+3J49FE9FAoJHnts9W+r1YpkMgmn00ne02q1xESAiIiIyDsUMf3xDuDJJ1ezqzdrIA2sZn79fj9M\nJhOkUilyuRwsFsuGgeFax5e1qFQqtFqtvoGeyWTC8vIywuEwDAYDarUaGo0GvF7vRc9PoVB0BbUc\nx0EqlYKmafy//7cazD/++NbGKpPJugI+iUSCSqWCgYEB0pAlkUhsKq8QmsiEQiEYDAY0Gg3UajX4\nfD7isb2W0dHRSwoyw+Ew7HY7yeC3220sLCxgampqw8xrMBjE+Pg4maA0Gg2EQiGoVCqo1WoSZAq+\n3zzPdwWeApsFn4ODgwgGgxgbG4NUKkWr1SJuQNcS4bsnBNIDAwMol8td96VarV7z8xIRERERuTaI\ngbTIDYFKpcKOHTtQKpXQbrfh8/k2DaQ0Gg0qlUqPFKHZbG4agNpsNoTDYQSDQcjlcoyOjl5SttBq\ntSKVSmF4eJhs63Q6l+S7XCgU4PP5YDKZUCwWoVQqceuttyIUCpGAvR8ulwsMw6BSqcBoNJJgdHFx\nEWNjY+Q6sCy7qS3fRggFmWtlMBKJBHa7nUx0+o3FZDIhHo8TT2uNRgOJRIJyudwVMFMUhZGREczM\nzKDRaPQ4bmx2LTUaDYaHhxGJRMDzPKRSKXw+3xXPSHc6HcRiMTAMA2BVEnUxTerw8DBYlkW5XIbB\nYOg7SXg30m63EY1GwXEceJ6HXq+/KRrOiIiIiGyGGEiL3FBstpS/FoPBQFw9hAAsl8t1WaWth2EY\nLC0tYWJiAhKJhMgWOp3Oth0StFotCoXCf2d+VwMrv99/SU4XrVYLOp0ONE13aXGFRjmbSU/kcnlX\n8WKj0eixeJPJZNDpdKjVapu2HF9Pu93uOykR/J37IRT8TU5OkklOPp9HNBrte2ylUgm9Xo+lpSUS\nCAv35WLevCqVakurCZdDIBDA8PAwGUuhUMDS0tJFdekymaxvw6J3K8Lqg6CDB1alW/F4XJxoiIiI\n3NSIgbTIZVEqlbCysoJ8Pg+pVEosDK8FPp+PZLiA1eDW5XJt+P5kMtnlqEBRFLFXMxqNKJfLyGQy\nAFYz1xcbi8vlQqPRIH+vlTvUajWk02l0Oh2YTCYSIHMch1QqhWazCaVSCYfDAaPRiFQq1dN4pVar\nbbsiv9Vq9dVVK5VKtFqtbQXScrm8a3wCKysrsFqtaLfbSKVSRL6gVCohkUjAMEzXSoHQpU+r1fbo\n14V9abVaRKNRYim4let/talWq9BoNF1jMRqNpH23WBi4dYSmT2slVzabbdOaBhEREZGbgW39Erz9\n9tt4/fXXUSqV8IUvfAHBYBCNRgO7du26WucncgMTi8VAURQ8Hg8sFgtmZmZQqVQuOcOUz+dJplOt\nVmNwcHDTH1iJRLItxwqWZftqpymKQjweBwBS3JhMJrc0lrVyBOFcV1ZWUKvViGxkZWUFoVAILpcL\ngUAAo6OjUKvVqNfrWFxchNfrJYV4g4OD4DgOy8vLMJvN2w4wBgYGEAwGYbVaibRCJpOhUChcUrbc\nZrMhEAjA5XJBJpMhmUwSKcXCwgJGR0fhdDqhVqtx6tQpaLVaGAwGxGIxDA4OkuX8wcFBWK1WLCws\nYGRkBEqlEisrKyiVSsQucKPC0uuF0FRlPWq1Gq1WSwykt0GtVusr45DJZGi32+K1FBERuWnZsjj0\n17/+NX7wgx9gcHAQFy5cALCasfrXf/3Xq3ZyIlsjHk8gHr+2Htosy4JhGDidTtA0DZqm4XQ60Wg0\n0G63t72/eDyOVqsFr9cLn88HtVpNOvVdKQR3kLXwPA+WZdFsNnvG0mw2t2SnFo8n0GoxZH+FQoFk\nvimKgs1mg0wmw+LiIjweD8lwqtVqeL1exGIxjIyMkCA4Ho9jeHj4kqQBNE1DpVLhhRdeQCgUQjgc\nxgsvvAC5XE4y8aVSiWSSL4bRaITL5UIqlUI4HIZOp8Po6Cji8XjPWHw+H8rlMjQaDQwGAyKRCBKJ\nBJxOJ2nF7vF4kMvlEAqFNvXcvhEYGBhAsVjs2S40TAG6773Ixuh0ur7X8mLSJREREZEbnS0H0r/6\n1a/wV3/1V3jooYeIBtXpdF7XJigi149KpdK3fbder0elUtnWvjqdDvF/FjKwBoPhsryp+zE4OIil\npSXScpvjOASDQeh0ur5jMRgM2x5Lq9Xq68VsNptRLBZ7ZBdyuZyMP5FIkE6LsVhsS0F8P6rVKg4c\nOACVSgWlUok777wT9Xod7XYbfr8fjUYDWq0W2WwWoVDoovtTKBQYHR2F1+slGVqO43rGIpPJoFAo\n0Gw2SVvs0dFRFAoF0mREJpNheHgYXq/3hu9WqlKpwDAMWSXheR7JZPKma5hyI2AwGFAqlcj3ied5\nxGKx6y7fEREREblctpwKELrXrUXUCd4YXCsfaSF7RFEUVCoVcrkcTCYT2u02yUI3Gg3odLpt7Vfo\n5rYeg8FAMpxXAsHZQWjcIpVKiXQjm832ZIDr9fpFC94A4M//fAAqFY3HHlsNJoVAfS21Wo00G6Eo\ninx3eJ4Hz/NYWlrC5OQkCdBarRYpwANWJxudTuei37darQadTgeNRtN13fR6Pebm5jA+Pg6FQgGO\n4+ByuZDNZpHNZrc0zvUIeuZms9nlWOL1epFOp4mn9lqN+M2G1+tFKpWC3+8HRVGwWCxdY1nvIy2y\nMePj40gkEkin0wBWnW+2WlwsIiIicqOy5Sh4x44d+PnPf97VJOXXv/41du7ceVVOTGTrXG0f6Ww2\ni3w+TzrHKZVKDA8Po1gsIp/PQ6vVolQqIR6PQyqVbtqsox/9JBcASIHalaRUKhGfaY7jkM1m4XK5\n0Gw2uyzYGo0Gms1m3wB/7b5SqRT+7/89AgB45JEQxsbGiNWbkG1jWRbZbBYTExM4ceIETCYT5HI5\nGIZBoVCA3W6H2WzuynIqFApSuBePx4mOtNVqweFwbBiAcBzX1wJO6LyYzWbRbDYhlUrBMAyx3Ntu\nIO1wOPDss8+Sjo8zMzPI5XI4dOgQKIrqanpzM9NoNFAul4mbSD6fx8DAAJnQrPeRFtkYoWmSiIiI\nyDuJLQfSn/rUp/CVr3wFL7zwAprNJv70T/8UKpUKX/jCF67m+YlcZ6rVKmq1GiYmJsi2fD6PZDIJ\nmqZJlrXT6UClUpECt+1A0zQUCgVyuRzJCtfrdVQqla7OfZdLrVZDJpOBVColXrbNZhOJRAJjY2N4\n4403ugr0Dh48uOG+WJZFOp3G5OQk2Sa0AHe73YjFYlhaWgLP89BoNPD5fKhUKiQAFo6j1+vRaDT6\nZmxlMhnC4TCcTmfXhGJxcREqlaqvNd3AwAAWFhZ6Crvy+TwajQbUanWX93U4HEa9Xt/K5esim812\nFYTq9XpwHIdkMvmOWa7vt1LAMAxCoVDX90FERERE5N3LlgNpo9GIL33pSwgEAmQZ/GJNM0RuftLp\ndI/bg8lkwtmzZ2G320mga7FYkM1mSQvofjrhzRAK2gQ5gEwmu6xucO12G5lMBgzDwGw2Q6vVYnl5\nGTRNY3x8nGixE4kEYrEYarUaXC4XkahIJBKEw+ENi+HS6XSP1Z5Go0EikQDDMKjX66SdeKlUQrVa\nRT6fx+TkZI8Tx4ULF7CystIjianVaqBpuicrL1yrkZGRnvOiKIq4YzgcDlAUhVQqBYvFgkwm09Mp\nUvDT3i7BYBBHjhwh33+LxQKr1Yrnn3++a3KxVViWRSwWI9ffbrdvWyJ0pREs29b+j5PL5VAoFBva\nDIqIiIiIvLvYlsCZoiiMj4/f0JX2IpfOysoKisUiKIoCTdNwuVzgeb7vZGmjZh2ChOBSuFJygHq9\njuXlZeIWsbKygpWVFaTTadx1111dgezQ0BDOnj0LlmUxPT1NGrMUCgVcuHABw8PDfeUdLMv2HT9F\nUYhEIvD5fCRodTgc8Pv95PX1yGQyKJVKhEIhYn+XSCTgcDiwsrLS835BYrMRJpMJAwMDyGazAEDa\naDscDhQKBWSzWSiVStTrdWi12kvWoPd7LiiKAsMwWF5e7nqf2+3ecNLNcRwCgQC8Xi8pvoxEImi3\n232LQK8V/QoqgVWt/aU404iIiIiIvPPYciD9R3/0R323Cx287rjjDjzwwAM9GS+RmwNBqiFMkhiG\nQSAQgMFgQKFQ6Or8xzAM9Ho98vl8T4FesVgkmeRQKIRSqQSXy3VJxWyXSiwWw8TEBAqFAiqVCsxm\nM3K5HHieR6VSAcdxiMfjpK12s9nE0NAQZDIZjh49CgC4/fbboVarUS6X+wbSFotlTROV1aYpHMeR\nZiQURSGTyaDdbsNsNsPhcCAUCqFer6NQKCCdTsNut8NoNEIul2NoaAjZbBavvfYa5HI59u/fD6VS\nSfZRLBbRarVIQxDhemazWUSjUej1+i4fZiFwXgtFURgaGkIsFkMkEoHH44FcLicBbqlUQigUglqt\nxvj4ONkuBLqCPaFWq8XAwABSqRRxspicnCRFo8FgEG63G8ViERKJBBqNBqFQCD6fD51OB8FgENVq\nFW63mzSjGR0dRblc7hrL4uIiDAYD2u02stkseJ6HxWK5rALntWMZGxvbVIZiNBoRiUR69OjVavWi\nWt96vU7avRuNRrHhyDVEsKBsNpswGAzbXh0TERER2Q6Sv/mbv/mbrbyx0+kglUrht3/7t/He974X\nbrcb0WgUhw4dgtfrxa9+9SsUCgXs2bPnKp/yxmzXquxKIjTYuB587nMVfO5zlzf2ZDKJ0dFR8rdE\nIoFUKkWn00GhUADDMJDL5STYWdu4ROhoNz8/D5PJBIqi8Oyzz0Kv12NwcBDhcBiBQGBbzVMuh1Qq\nRYogtVot6TDYaDTw2muvoVarYWxsDIlEAk8//TRGRkYQCAQwMzODXbt2YWBgAE8//TQKhQLGx8f7\n/hArFAq89NJLCAaD+OIXKTz88BJ+8pOfYOfOnajVasjn89Dr9VCpVEgkEsRN45e//CV4nofb7cbC\nwgJeeeUV3HXXXZibm0MkEsHOnTuh0+lw4sQJaLVa6PV6vPTSSxgYGIBWq8WFCxdQKBTg8/nw8ssv\nk8YvlUoFx48fx/DwcN9iQ2B10vvkk09Co9FgdHQUMzMzOHv2LO68806cPHkSiUQCXq8XHMfh2LFj\nsFqtqFQqeOWVV0jxo9/vR6VSwa5du/C9730PuVwOer0es7Oz+K//+i/cc889aLVaqNVqpIAyHo+D\nYRhIpVK89NJLMJvNsNvt8Pv9iEajUCgUmJ+fJ2OpVqs4fvw4tFotZDIZotEomXDEYjEAuKTgKJvN\n4uWXX8bQ0BBMJhNmZmZQKBQ21OELxZ6ZTIZ0hlxaWoLNZiOTq899roIvfUnR9d0Ph8NotVowmUwk\nO6/X69+xSYbr+b9vPSzLYmFhAWq1GjqdDrlcDvl8visRcKW53uO/3hIoEZF3OxS/RYHkZz/7Wfzl\nX/5lV1FUPp/H3/3d3+Eb3/gGEokEHnvsMXzve9+7aid7Ma6np7WgEb4Z4XkeoVAIXq+3a3u73cby\n8jLGxsZQLpdRKpWgVqtJsAysZqdXVlZgMpkglUpJVvf222/v0vbOz89DIpFcE1nQc889h3vvvbdL\nShAIBBAMBsHzPHbv3k1s6NLpNKLRKM6cOYPPfOYzJKhKJBL4/ve/j89//vOo1+sol8ugKAqdTgeD\ng4Oo1WqIRqPYuXMn8vk8HA4HOp0OXnnlFXAch/vvv78rcHrxxRchk8mI/Vy1WoVWqwXLsrhw4QLa\n7Tbe9773kfd3Oh08++yz8Hq9cLvdZDJjMplQKBSwsrICpVKJqakp8plqtYqTJ0/innvu6XtdXn75\nZezduxccx6Fer0Ov1yOdTmNlZQUSiQT79+8n7+U4Di+++CJqtRomJyfhdDohk8mQSqVw9uxZcr9V\nKhUajQaRoWQyGbjdbtxyyy1kXzzP47XXXkO5XMb999/fJYk5ffo0otEoXC4XbrvtNrK9Xq/jJz/5\nCfbv39+juV5YWLik+oynn34aDzzwQNfnhGuymQ1bq9VCNpsFTdOwWq09GfG1333hPtntdvI6QLuI\nxgAAIABJREFUy7JE7vNO5Eb63yd0Dl07mUyn05DJZFfNgvF6j39oaOi6HVtERGQbDVkKhUKPrZlC\noUChUAAAElyIXHsefVRPvKQvBYqi+mo+C4UCCTAGBgbgcrl62lbL5XI4nU7SQhpYDcDXF8hNTEwg\nGo1e8jluB7lc3pUh4nkeDMMgnU6TQKrVaqHT6WDPnj3I5XLYs2cPOp0OwuEwwuEweJ7HLbfcgjNn\nzoDneYyPj8Pn82FiYgKpVAqzs7PYvXs31Go1vv3tnfjiF51Eu+zxeLCwsIB0Oo1cLkcCv1QqBYfD\nQboFGo1G0oJ7x44dXWOgaRpGoxHVahUymQw2mw3Dw8NQq9VwOByYm5vrcY7QarWbanc5jkOj0UA+\nn0er1UIqlYLVasWpU6ewe/furvcKOuBms4kdO3ZAr9dDrVaTJivHjh3D3r17sW/fPhw6dAgHDhzA\n3r17EY/HewJc4XmhKKpHV75nzx4Eg0EoFAowzGqHwE6ng3Q6DYVC0TfAFZrbCO8LhUKkacpG1Ot1\naDSannPbvXs3KXDdCIVCAafTicHBwZ4g+tFH9fijP/rNhCmXy/U4pmy0QiBydVh/vW0220WfDxER\nEZFLZctiw3379uF//+//jd/5nd8hmtOf//zn2LdvH4DVLJHVar1qJyqyMVfCR9psNmNpaQkjIyOg\naRqVSgW5XO6SHBj6wXHcNXN4EfTGqVQKNE2D4zhYLBaoVCqSUV6LQqGAwWAgPs3AaiBpMBjAsmyP\n1tjtdmN2dpb4Ua/1EqYoCjKZDBMTEyiVSmi32/D5fCiVShsu7ctksr4+2gzD9C0EFJq5cBzXt+Bx\nI0ql1edj7arA/Pw8KIpCs9nskUtwHEfanK9Fp9P1dWYROv5RFIVYLIbBwUG0221Eo1HodDpUq9We\ncxK8ut1uN/HLBn7j2tFvYiCc1/z8PJxOJywWCwqFAvx+f5e2ey0bFQg2Go1tXcP1rPeRpmmaeH6L\n3Bi0223RXUpEROSqseX/Lp/5zGfg8/nwgx/8AI8++ii+//3vw+v14tOf/jSA1R++v/iLv7hqJ/pu\npl6vY3FxEYFAAIuLi1dFwmI2m2E2mxEKhRAIBFAuly9ZhjEwMIClpSXE43EEg0HkcjmcOHEC09PT\nV/is+yORSGC324ksQghqDxw4gLfeeqvrvZFIBDt27EA4HCY6YIlEAoVCgXA43KUbF5DJZPB4PDhx\n4gSKxSLZnk6nSaMXoSBTkMGk02ns3LkTMzMzXfs6f/489u3bB7/fj3K5jFAohEgkgmw2i1arBY1G\n06O/jEajOHjwIE6ePNmzfbPiOYlEgnq9jkQiQWwss9ksdu/ejdOnT3e9V9CYsyyLcrlMtnc6Hbz9\n9tvYs2cP3n777a7PzM3Nwe12QyKRQK/XIxKJIJlMwmazEc1qJpPp+sxbb72FBx98EGfOnMHY2Bh8\nPh98Ph/K5TLMZjMqlUpXANzpdJDP51EqleD1elGr1cgKgsPhQCqV6jt2uVyOdrvddywXmyzWajVy\nX/p1rVyLw+HociwBVms3LhasdzodJJNJBAIBrKysXJIlocjqfV5fKxONRt8xDYJERERuPLaskb4Z\neCdqpFmWRTAYxMTEBMmq5HI5NJtN4hzgdK5q5OLxG2P8zWYTP/3pT2EwGGCxWBCNRqFWq/Hggw9e\nE/cCQaIB/EbmYbPZYDQaMTc3h1gsBqPRiEqlAqVSiYMHD+L8+fP49a9/TbyZl5eX8YEPfABKpRIe\nj6cro1Uul1GtVrG4uIhkMonPfe6zAICvfOWr+NCHPgStVotwOEyCcuFeabVanDp1irzWbrcxOjqK\n97znPTh79ixmZ2cxOjqKVquFeDyO973vfRgaGsLi4iK5tnK5HF6vFyaTCfPz81heXiZjkcvlOHTo\n0IbX5cKFCzh79ix0Oh1sNhvi8ThkMhkmJychl8tx4cIFGI1GNBoNdDodvPe97yUFgSqViki5tFot\npqencerUKdA0TeQX+Xwe+/btg1qtRjQaJc16OI4jAfarr74KnuehUqlIMafX6+07loMHDxJ9sVwu\nJ5nzkZERLC0tAQCGh4eh1WpRKBSQyWRA0/SGzVIYhsGrr74KqVQKhUKBYrGIW265ZdOmP/F4HJ1O\nB0NDQyS7rtfriWtKv++e0AlUpVKh1WpBIpHA7XaDoijwPI9isQie52EwGEDTNBiGQTAY7BnL2u/8\njcz11givRWii0263ScdUk8l0VV2Drvf4RY20iMj1ZVuBtOBvuzarAwC7du264id2KbwTA+lYLAaz\n2dxjwba4uEgyxjdaIB0IBDA2Ngae54lbRalU6inCutq02+2+XsCdTgeVSgUajYYs+YfDYSLBAFY7\nDgYCATgcDsRiMYyOjpLgL51Ow2azgWEYOBwOcv1jsTgWFxdJIMdxHPlBF/5eXFyE1WqFTCYDx3HI\nZDJwuVzIZrNwu92oVquQSCRQqVRYWFjA0NAQCUoFSYcgF+k3ls148cUXcfDgQUilUnJfUqkU0uk0\nbr31VgCrkwSlUkkyqMK1YVkWnU4HUqmUTApSqRQKhQKx+ON5vutHnWEYInVZC8MwaDQaPfrnfmPh\nOA5LS0ukE6VUKsXo6CheffVV3H333V1jLhaLmJub27QjJbA60WMY5qIdGBmGQSwW67IVBFZlbEJT\nn42+ezzPo9VqQSaTEUlPtVrF7Owskb+wLIupqSnk83mMjIx0XadqtYpSqXRTtNS+3oFkP9rtNliW\nhUKhuOqT9+s9fjGQFhG5vmxZyDc/P49vfOMbYFkWjUYDKpUKzWYTZrMZf//3f381z/FdTbPZ7Cny\nBPo39riREIIHIVgyGo0IBAKXFEhnMhkUi0XQNI1OpwOj0bglPb6QEV6L4HhB0zQymQw0Gg1UKhWx\nx1ob3AkaaaFQsNlsQqfTYXJyEuFwuMfOT2hkIyCVSrsCvXg8jrGxsa77qdFoMDMzQ4oN1xZpqtVq\nzM/PQ61Ww2g0QqlUIpvNIpfLEW/vtRlhASH4XMvo6CjsdjvC4TBcLhfxAS+Xy1067PXBpUQigc/n\nQ7vdRqfT6Qr2HA4H7HY7OI6Dw+FALpfr+uxGcga5XN73tX5jCYVCcLvd5P3CCo1Op0M+n+8q7CsU\nCluyAlMqlX2/U+spFot9M5larZa0W98IiqJ6jnHq1ClMTEyQjpO5XA5nzpzpKtRde4yNZCoiF6ff\nd19ERETkarDlQPqHP/whPvzhD+NDH/oQHnnkETzxxBP4yU9+clmFOiIXx2g0IpfLdf2g8zyPTqdD\n/r6emeh+9FvkYFl20x+2TqeDpaUlMi6e5zEyMoJ6vQ6WZbuW62OxGIrF4ra73tXrdeTz+a59Cd0c\n+z3HQgMUqVSK4eHhrtcUCgUp0IvHE/+dleo/dgGWZXuCK6VSCZqm0Wg0UCqVSNGh0WgEwzDI5/O4\n7bbbSIA+PDwMnueJDKIfwWAQY2NjXcFnKBSCXC6Hx+NBKpVCKpWCXq+H1+vdkpvKRoGJkHG+GhM7\nYcK+9t7IZDLo9Xo0m020220sLi4SW0Kj0dj1vbhc5HI5ms1mz+RC8MQG0HXvN6NcLkMmk3XJSIS6\nhJWVlZ56BLFzooiIiMjNwZYD6UQigQ9+8INd2x566CH88R//MT784Q9f8RMTWcVsNmNhYQEURcFs\nNqPZbGJ5efmGXs5bW1QmdBfLZDKbLlOHQiE4nU4iYWm321hYWIBUKoXP50M+n0elUoFOp4PT6UQw\nGNx2IC100FuL1WpFsVgEwzBkOR1YzUwLHex4nkc2myVyBL1eD7vdjpmZGTSbTSQSCVitVhiNxoue\nk9B1TRiL0WjEwMAATp06hQMHDpBrtLS0RLpNdjodzM7OolarkW6AQmOSUqmEUqkEpVIJq9WKZrMJ\njUYDhmFIYeP4+DgpHMxms133IRgMbqoRBkAkKO12m7ifXIxms0ky/zab7ZIs4ISlecGrnOd5WK1W\nKBQKkg02Go2o1Wokw36xsWwHg8GACxcuEM9sYDW4365bivC5fg4sOp0OLMsimUx2nfvy8vIVHYuI\niIiIyNVhy4G0Wq0mPwYGgwGxWAxarfaiVewil8/4+DhyuRwCgQBkMlnXUjcA4iF9OfZ3VxKTyYQ3\n33wTGo2GFJ6p1eoNl9MFa7y1AZpEIoHVakU4HIbf74fFYsHQ0BBKpRL8fv8lFWEJGt/1UBSFgYEB\nvPXWW0Qq4Pf7sWPHDlII5nA44HA4kM/nSdORc+fOQavV4le/eggUReE97/kB7rzzTrhcrr7HN5vN\nOHr0KHbv3k3G8vLLL8Pj8WBychIrKytEaymM3+/342c/+xkOHjyIsbExzM/PY3Z2FocPH0YgEIBG\no4HD4UCtVsP8/DwsFgtisRj8fj/RPZ8+fRpyuRxTU1NgGKYriys4amxEuVxGPB4nOu9oNAqNRrPp\npCiZTKLZbGJwcJAUfgoTje2g1WpJt0zheLFYDNlsFnv27MHJkyehUqmg0WiQSCQgk8kwNja2rWNc\nDK/X2yWToWm6SzP96KN6KBQSYn+3ESaTCefPn0en0yHPLs/ziEajuP3220nxqnBfLBZL38BbRERE\nROTGYsuB9P79+3HmzBkcOnQIR44cwRe/+EVIJBIcOHDgap6fCFYDPYvFsmHl+ZXwkb6SLC8vk6I2\ngXw+j2w221fbLGQe16NUKpFKpTA+Pk4yvRaLBTRNY2FhYdvnpdVqeyQhQhFbuVyGx+MBx3EAAI/H\nQ9w5fD4fyag6HA7I5XK8+uqrGB8fx1133YU//dPV1YF/+If/Dz/+8Y+7uvqtRdhXPp8nDUW8Xi/S\n6TR27NjREzjF43HihFGv19FoNDAwMAC1Wo1CoYCxsTHSrU0ul0On02FxcRHz8/P45Cc/SfZzzz33\n4Ec/+hH27dsHk8m0LSuwYDAIlUoFs9kMmUyGdDqNeDwOs9ncd2LEMAzq9XpXl8yJiQn4/f5tB9JC\nUElRFNGf0zQNnucRj8exf//+rgllqVRCKpW6oplcuVy+YUfCTCaDJ59cvfcPPxzoWlFZj0wmw9DQ\nEE6fPo3BwUFiiWgwGKDRaKDRaK5pIa6IiIiIyJVhy4H0kSNHyLL4hz/8YUxMTKDRaGwYNIi8u1mf\n+TWZTAgEAn0DaaVS2bcrZjabxejoKGntrdfrUSqVQFHURYOy+fl5xGIxEnzdcccdsNvtWFhYAMuy\nMJvNqFariMfjMJlMWF5exs6dO8kKi1KpJA4L1WoV2WyW6ICdTidmZmbwiU98AkePHgXwMQCrbiWb\nOWe0Wq2+QVk+nydFkEIgL6wADQ0NgWEYpFIpyGQysCyLQ4cO4eWXX8b09DTpysfzPHEPmJiYIFIZ\niqIQj8cxPj5OxrqystI1FpVKhXq9jhMnTpCiwuHhYXg8HhSLRezdu5e8f2xsDPV6Hclksm/2t1Ao\n9HT2A35ToLcVWYhApVKB2+1GrVbDW2+9BZ7nMTg4CJ/Ph6WlpZ5rqdfrsbKyApZlEY1G0el0yHPT\n75wuh1Qq1aULF7pZ+ny+DZ+BHTt2kMkRz/Mwm8092nsRERERkZuLLQfSX/7yl9FqtTA1NYXp6WlM\nT09jcnLyhnePELn29Cu426yzoZBxDwQCcLlckEqlSCaTXc4fOp0OtVoNIyMjKBaLpANhP86dOwea\npnHfffcBWC0yfOmll/D+978fk5OTiMfjOHXqFIxGIyYnJ5HP51Gv17G8vEwC/eXlZdRqNdLEQygG\na7fbmJ+fh06nw7//+7/j937v98hxGYbpacbR79qs/85oNBrMzs7i9ttvh9lsBrBqbyhkrnft2oVb\nb70V7XYbPM/j9OnTkEgkCIVCmJqaItdpeXkZzWYTJpMJQ0NDxPXB4XCgWCyCZVmUSiX4fD6S7V1c\nXMTw8DCOHj2Ku+++m7iGnD9/HmfOnIFUKu05X6PRuKHdl0wmI62+18Ky7La7/clkMoRCIezevZtk\nuGu1Gt5+++2+AblQaBgIBOD1ekm2OpPJIJFIXNG6gkql0lUgSNM0RkZGkEqlNg2OnU7nTWFpJyIi\nIiKyNbYsNP3e976HL33pS7jjjjuwvLyMb37zm3jkkUfw5S9/+Wqen8hl0G63Ua/Xr4iTQafTQb1e\nJxnTzVCr1cjn813blpeXN5UUmEwmuFwupFIpRCIRGAwGuFwuMAwDpVIJi8UCh8MBi8UCpVLZFaw1\nGo2uv1dWVrB79+6u89mzZw8uXLiAcDgMjuMwNTUFjUaDxcVF8DyPdDpNbN4En+ZMJgOWZaHT6cBx\nHBqNBiiKgkQiwcDAAGq1Wldnw2q1umlRnclkQiKRIAF3o9FAIpGAVCrF7t27Ua/XcerUKZw7dw56\nvR5ms5m4N+RyOUQiEVAUhWQySazveJ5HPp9Hs9mEVquFWq3GysoKOp0O7HY77HY7KIoi7dJtNhve\nfvttPPfccwgEVuUIx44dw65du8h9q9fr2LVrF2lxLlxfoVFLLBbbMMNrNBqRyWTQ6XTQaDTQarXQ\narXAsiy5NoKF5voJF8/zpJgPAGlms1ZColQq0Wq1oNfrkc1mu57xaDQKqVRK5DcCNput74rH5dAv\ngaBWq8mKxvqx3Eg0m81NJ6IiIiIiIltnWykiu91OmlxwHIezZ88SlwORG4vl5WUwDAO1Wo16vQ6V\nSnXJmbBkMolarUa8w4VObRvhdDoRjUaRy+UglUrBsiwsFsumRW3AqqXcelcNwfFDyAJXKhVYLBbo\n9XpUKhUkEgloNBrSlntsbKxv5ntwcBBnz57F/v37SRAouGbMzs5CpVLha1/7GtEc5/N57Ny5ExaL\nBX6/H/V6HRqNBpVKBcPDw+A4Drfddhv+4R/+AcDtAIDjx49jz549G47PZDLhX/7lX5BKpWCz2ZDJ\nZGC1WnHfffeRNvBmsxm1Wg1nz56Fx+OBz+fDV7/6VVIQmUwmceTIEej1ehQKBTz55JMwm80olUpQ\nKBTYs2cPpqen8dRTT0GpVEIikaBWq+G3fuu3kEwm8bWvfQ1arRZWqxWnT58mk5Th4WE8++yzsFqt\nqNfrYBgGNE3DYrHg5ZdfJjZ0pVIJWq12w0CaoihYrVY899xzZALCMAzuuusudDodhEIh0DQNhUKB\nWq1GLOAymQxKpRLUajUJ8kwmEyYnJxGJRMi+O50OduzYAalUikAggFqtBrVajWq1isHBQWg0mi4v\nbgGhk2Q8Hker1SKBsMvl6qvPFxA6KwpIpVKMjIz0nZzm83kMDAz0jIXneXg8nuu+elev10nhb6fT\nQbPZhNvt3nT8IiIiIiKbs+VA+pvf/CYWFhZgMpkwPT2NQ4cO4dOf/vS2NI8iV4f1PtLpdBoajYa0\nuxa25XI5Ih3YKuVyuauTHrBa1BWPxzcNzF0uF/G7vpzGCIVCAXa7HSaTiXRGpCgKmUwGHMd1yYtY\nliVd8NYTDoehVqt7NNpKpRJSqRQnT57EI488Qhw3otEonnjiCRw8eBAHDhzAwMAAGcvCwgIUCgUi\nkQi++MUv4n/9ryU4HA4Eg5/EE088gfe///19x/Lmm29iZGQEH/vYx9ButyGRSPDGG2/g3LlzyGaz\n+NjHPkYmAfV6HU899RRCoRD+5E/+BKOjo8R27fHHHwewOrF9+OGH0W63QdM0kskkjh07hp07d+L3\nf//3SbBH0zT8fj/+7d/+DXfccQemp6eJBOOXv/wlIpEIOI7Dww8/TM61VCrhH//xHzE8PAy32w2p\nVAqapmEymfpmkwU6nQ6y2SweeOAB8h4hgJbJZD0FecFgEMDqqsJaqUS1WkUmkwHP8/D5fF1jWVpa\nAs/zmJychF6vJ/clHo+DYRgUi8We+8xxHKLRKEwmE/GFFjpNTk1NbRjkBoNBeL1ekk2v1+sIhUIY\nHBxEIBDA0hKLwcFBBIMl0qVyvS90rVbD8vJyzyTxWsLzPCKRSNdY2+02AoEAJicnr9t5iYiIiNzs\nbFnaEQ6HQdM0RkdHMTo6CrfbLQbRNyiVSqUnYLbb7SgUCtveVyaT6dGW6vV60jhkMwQZxOUglUox\nOzsLs9mMiYkJmEwmzM3NoVqtwuFwgGEYxONxpNNp0DRNMoavvvoqCaiTySQCgQBGRkb6BoDZbBYe\njwd6vR7hcBjhcBh6vR4ejwcsyxIPZYlEgkajAZZlwbIsVCoVkskk5HI5Op0Ozp8/D5vNBp7nEQwG\ncezYMbz++uuYm5tDq9XC4uIipqensbCwgHA4jIWFBUxOTmJmZgYul4vIRASvaZPJROQbPM9DLpej\nWq3i0KFDOHXqFCwWC1ZWVpBKpZDJZFCr1cDzPAYGBtBsNjE7O0u8roXGPkNDQ3C73XC73XC5XNix\nYwcWFhag0WiQTCYBrAaYMzMzMJvNxDZvbGwMLpcLk5OT8Hg8RCMtTKoEKY9QbLjWZUMqlUKhUPQt\nNnS5XJibm+vRFWu1WtJJ8fTp0wgGgwiFQjhz5gw6nQ7a7TYMBkPXMzY0NIR2u006NgKrQbwgFeI4\nrqu5ilQqhd1u7+nIKFAqlWAwGLrkOmq1GhKJBEqlEsPDw8RqsF6vY2JiAul0umcsGo0GLMtu9phf\ndQqFAqxWa9eEQSKRkKJWEREREZFLY8sZ6W9/+9soFAq4cOEC5ubm8Itf/AIMw2DHjh34wz/8w6t5\njiIX4Wr7SF+KZ/OVolgs4u6770Ymk0E6nYZUKsV73/tePPPMM8hms6BpGg6HAxzHIRAIkOy5wWDA\na6+9hk6nA4PBgPvvvx/1eh2JRKIr0KlUKqSLYjqdJi2m0+k0RkZG0Gw2MTw8jIWFBdTrdRgMBkxM\nTGBmZgYf/OAHsbCwgD//8wEoFCwef3wcDMPg/Pnz4DgOO3fuhFQqRSwWw1tvvYVGo4FKpdLVWVHQ\nSk9MTKDVauHkyZNEOmO32yGXy2G320kWVqVSYe/evfjpT38KmUyGSCTSJbkRjjc3N4ddu3aBpmkc\nO3aMyCx2795NHC3kcjkOHz6MH//4x7jvvvsQCoVw4cIFAMD09HSXrnhtZ0OJRAKO4+D3+4mWW/Db\n1uv1fSfYEomkrxxCkFxs9IyxLAur1UrkHmazuUuasRZh28TEBJLJJGkKZLfboVQqiSa9Xq8DANGM\nr9W5r6XVavWVIwkafY1Gg+99by8UCiUeeywNAJe9AnO14Hm+73ltdF9ERERERLbGtjTSRqMRQ0ND\nyOfzyOfzmJ2dxZkzZ67WuYlskfU+0oL+dK0vsaDZ3C5CUddaD2shaLsWCNnetZrscrmMgYEBxGIx\n3HPPPWS7VqvFs88+i+npadJAZS1CgaDQyETIxN1zzz344Q9/iCNHjpDg0el04vXXX8fhw4cRDAZh\nNBoxODiIYrGI8+fP47bbbsP58+dx4MABHD26mrH/P/+HB8dxSKfTuPfee7sCu0qlQorv1tJsNmGx\nWHD8+HFMT08TL+tEIoF0Og25XA6WZbt8mZ944gncddddJNhvt9tQKBRQKBQ4f/48Zmdn8cgjj5D3\n33vvvXjiiSdgtVpx5swZHDhwABRFod1u44c//CEOHDiAubk5HDx4kHyGYRhIJBKwLNvVRARYtX6T\nSCRwOp1Ej2yxWIhNX7VahV6vJ+/neR61Wg1KpRIcx3W5d6RSKXg8HsTjcVJ7AazqozmOg1Kp7Glw\nE4vFUK1WezLcuVwOAwMDoCiqr0NHJBLBrl27yGsMw+DYsWPYt29fz3uB1Wc/mUz2tAgXVkOA33z3\nhIYsZrO5x8v6emejgdVaA+E5FhD802/kLqkiIiIiNzpbDqS/8pWvYH5+HiqVCtPT09i3bx8+8YlP\niG1sb0CcTicJFgXv5Uaj0RWMbRWr1YpQKIRGowGj0UjaaK/VTAvSg7UwDINkMgmWZUmziUvJbNts\nNsTjcWi1WjKWarUKtVoNm82GX/ziF5BIJET64Ha70Wq1NiygErrtNZtNWK1WEtS95z3vwXe+8x14\nPB50Oh0sLS3h8OHDoGmayDpkMhkJLi0WC4rFIr773e8C+DsAwFNPPYUjR46Q7nvFYhEURUGhUMDp\ndMLtduP48eMIhUIYHx9HIBBAOp3G3XffjeXlZbz00ktgGAY8z0On02Fqagq33347vvGNb8Dj8cBi\nsWBubg52ux179uzBm2++iXA4TDyaa7UanE4nbDYb/H4/kVhkMhns2bMHQ0ND+NWvfoWzZ89idHQU\n58+fR7vdxqc+9SnEYjG8/PLLmJiYQLlcRiAQwKFDhyCTyXD8+HHio61SqbBr1y5UKpWeoj6hiE2v\n1xPP8Ha7jUwmg+HhYcjlcszOzpLrybIsbDYbnE4njh49ipGRETgcDlSrVZw+fRoejwdqtRrlcpm0\nCBc6/snlciwvL2NgYAA6nQ6FQgEMw3Q942utBnmeJ41sdDodpFIpCoUCtFotGIZBu91GKpVCo9GA\nQqGAw+GAQqEATdOIx+PkuYnFYtDr9aAoqqurazqdhs1mg9FoRCQSQSQSIXryfD5/Sd+9K4lEIoHZ\nbMbi4iJsNhu5L6IVn4iIiMjlQfEbVQ2t4+jRo5ienr7ijQ2uJIlE4uJvukoIzTCuB07nakZpfdFh\ntVpFtVqFTqe77HbDjUaDZLWFDB3DMMSSzWAwoFgsYnR0lDTEEBwBhDbTk5OT2w6ma7Uakskk7HY7\narUatFotUqkU5HI5Tp8+jVtvvZUEPIJ13gc+8IFtexbH43E0Gg2k06tL9Ha7HSqVCo1GAz6fj0xG\n9Ho9lEolgsEg6W74P//n/wAAPP74l7B37174/X7cc8895LtSr9fxwgsvwGKx4NZbb0UqlcLS0hJc\nLheGh4cxMzODubk5DA0Nke528/PzkEgkOHz4MNEhFwoFTE5OQiKRIJ1OI51OY3p6Gu12GzKZDEtL\nS0ilUtizZw/27t1LtL9Ce2qTyYRqtYpUKoWVlRWMjIzAbDYTX+lqtYqlpSWo1Wq43W5SpJhOp+H1\nekHTNHK5HHK5HJxOJ7xeLwqFAiqVCilsDQQCGB8fR7VaJQ1qxsfHyXOQTqeh1+vJxKu1rjVaAAAg\nAElEQVRYLEKj0cBkMoGiKJRKJahUKuj1ely4cAGFQgEjIyMkaypcu3379pF91uv1LklJrVZDPB4n\n+nKpVEp8tW02G1KpFNrtNsxmM1QqFQmgR0ZGyErF0tISPB4PFAoFSqUSmUg4HA6o1WriGPO+9x0B\nABw//hZqtRomJiZIkF0sFqFUKrs6aV5v2u02crkcKRy9ErKt6/m/70bgeo9fXFEQEbm+bDnaWLuE\nLnJzoNVq+1qBXQoqlapH+xoKhYijgcViQTKZJPZm4+Pj5EdaWG5PpVLb/qev0WigUqlw/Phx0DSN\nTqeDnTt3guM4UBSFkZERknXU6/U4d+7ctm3GOI5Ds9mEz+fryrQHg0EiNdDr9USu0Ol0MDc3h8nJ\nyS45xMMPP4x/+qd/gslkQi6Xg8FgIJnPVquF8fFxhMNhkp0ul8sIhUKgKApSqZRkwwHgzjvvxFNP\nPQWfz4c9e/Z0nVc2m8Xrr7+OnTt3Ynp6mmy3Wq148sknkclkwDAMca5gGAaJRAK33HILcrkcKpUK\npFIp5HI5hoaGyPUSigKFiYlQqHfo0CGkUil0Oh0MDQ2h1WqB4zi8+uqrmJ6eJvf09ddfx/j4OPHK\nnpqaQqfTwfLyMimMXKsPF44ZDoeJbn3tMyacg0Qi6TpHoW04sPpsrZVetNttRKPRLjcXoYMlx3FQ\nqVRdHRkFzbTH4yHXVqPRYHx8HJFIBF6vt+veC4TD4a7JGk3TqNVqyGazsFqtUCqV22rFfq2QSCQ3\ndDJERERE5GZje2k7EZH/plqtYmBgoMvRQCaTQaPRoFgs9mS6dDodyfZuhKC9B1YDqqGhIVSrVZTL\nZRJkUhSFYrGIfD6PW2+9lVjRCV3/hGzo+sBHgGVZElQBIIFhv6yhwWBAIpHo0Zsnk0nkcjk8+OCD\nPZ+haRp79+5FrVbDm2++CWB1MnD33XeDYRhMTk4inU4jk8lAo9FgcnISL7zwAliWhdPpJJKUQqGA\ndrvdt5GI0N58aGiIjF9oeGI0GnHw4EG88sorJCBuNBo4ePAgub5msxk8z4OmaUQiEXg8Hvj9fuRy\nOXg8HuTzebz99tvYu3cveJ7HwsIC8aSORqOgKIp0VSwUCsSnfHBwEJVKBTRNw+PxoNVqQSaTYXx8\nHH6/v6+u3mQy4cKFC1hZWSFSGKGtt+CEIZPJiE2e0WjE9PR0X2caAMQ1Y+1kSqvVkiY2kUgELpcL\nNE2T5jNyuRyJRAISiQR6vR65XK6rsUoqlSL3wWg0wmw2Y2VlBUeOHCHHGBoaItdnvfXejcL6sRgM\nhq7aBxERERGR7SMG0u8A1ks6rgUMw/TVIcvl8r4tolut1qZyC2Ep3uv1kiX+YDBIChtdLhdkMhlY\nlkUgEADLsigWi9izZw9YlgVN05BIJIhEItBoNEilUqhUKl2NNyQSCQKBAFmyb7fbxOqu2Wz2BGb1\neh1er5e0Kxf0uiqVCsPDw4hEIhgfH0c8nvjv5d3VzwmOHe12m1i4JRIJ6HQ60DTdU1egVquhUqmw\ntLRE/Kqr1So55noELXQqlcLOnTtJULmyskIytIcOHSKNRATJwrlz56DRaGCz2dBqtSCXy7G0tITZ\n2Vk0Gg28733vI8cYHx/H0aNHEQgEsHv3bgwODoKiKFQqFfznf/4nsQfkeR4su+qlnMvlcPz4cbjd\nbvj9fqhUKnQ6HfKsVCqVnrE0Gg3Y7XacPHkS9957L+RyOXiex8mTJ4k1m6A9XvusrP17LSzL9n0u\naZqG2WyGUqlEKBQCsDq583q9OH78OLxeLwmADQYDKpUKzp07B2B16V64Z+l0GrFYjExE1t57nU5H\nHEFuREKhEEwmExlLJpNBNBrtKeYUEREREdk61ySQ/u53v4vTp09Dr9fj61//OoDVwqwXXniBLMt+\n9KMfxW233QYA+NnPfoYXX3wRNE3jkUcewd69e6/FaYpsA8FzeX3wWSqVMDw8TDoONhoN4rCxUUMK\noZ30WvmCXq9HtVrF4uIi7r//fhKECxnOVCqFdDqNRCJB/INnZ2dhMBiQTqehUChIU4x2u42FhQWi\n/RUCLYlEAq/Xi0AgAABdLhCNRgPNZhNqtRperxcMw5BgWyKRwGQy4Uc/+hGGh4fJZ06cOIHBwUGo\nVCokEgkoFApwHAeZTIZarbahrMXj8eDUqVPweDxk4pDL5aBWq6HT6bpcU4RW2EeOHMF//Md/oFKp\nkABO0Ecnk0kwDIPx8XFQFIV4PI5SabVhyMjICMrlMjQaDfL5PORyOd544w18/OMf7zonpVJJivXa\n7TaZkAhe2kajkbwul8vJ6zRNIxwOY//+/V2Ncl577TX4fD7MzMyAYRhShMqyLAwGAw4dOoRYLEak\nLULmnmVZ1Ot1MqFoNptkItGP/5+99wyP8yzT/n/Te5M0GvVuS7Ik99iObcXd8eIAgYQA2U1CXeoC\nS1l2l+PYl/fg2A3LwQu7CyxLIBAS0glZAk4cx3KLLVdZzbJ6G2k00mik0fSiKf8Pep/7tWLnT4tJ\nWOb8lDzWlGfmmZnrvu7z+p1SSuK1/57JZARiz2QyCcShJL1ez+TkJD6fT9hJIpGI4EVfu1vhcDgY\nGhpCq9Xi8XiETSKdTjM0NPSWHb6ORqNix0JSfn6+QEa+FZF9WWWVVVZ/CvqjFNI7d+7kwIEDfO97\n31t2/ODBg7zjHe9YdmxycpLW1la+9a1v4fP5+NrXvsa///u/v6ks4zdbyWSSyclJYUew2+3LrAtv\nFEfa5/OJATWVSkVJScnr/sAqFApMJhPnzp0TUdtOp5Pq6mpsNhsnT57E5/Oh0+kIhULU1dWh0WiI\nxWJcunRJ0ClqamrIz8+/IXvYarUSjUZxuVycPn0aWCrUtm3bhkwm47bbbqOlpYWWlhZkMhnV1dVs\n2bKFoaEh5ubmeOmll5DL5ej1erZt28bw8DC5ubmcPHlSWEEaGhqQyWRUVVXxgx/8gMnJSQBKSkr4\n2Mc+BixFf7/wwgsiXOTDH/4wJSUl7N+/n6985SucP/9h5HIZDzzQyUc+8hHC4TAvvvgic3Nzgr8s\npR2Ojo7yy1/+UnTo77zzToqKiqisrOTs2bPidcnJyaG6uprKykpOnDjB5cuXkcvlqNVq3ve+9yGX\ny7FarXz7298mnU6TTqc5cOAAGzZsEFaUsbExABETPzMzQ2FhoVikKhQKamtricfjxGIxurq6iMVi\nZDIZqquryWQyrFmzhosXL/L0008jk8kwGAzs3LlTMOU9Hg/pdBqz2SwoMV6vl1/84hfCBtLQ0IBK\npSISidDX14dcLker1TI+Po7ZbEav14uCf3FxkUwmg8FgEO/Lt771LUZHR4ElIs3nP/954P8NokrX\nhYTjm5mZ4cKFC5hMJhYXF4nFYsJLPjg4iNPpBJYsHxL6zu/3c+LECWCp8Jai3s1msxhElf7fYDBg\nt9txuVx87nN6NJoE999/BqvVSklJCX6/nyNHjpBIJEin02zatEmkB87MzIjOvGRfeqOjw2OxGC6X\nSwx0FhUVEQ6Hb2h3kogvrzdLsbi4yOTkJKlUClgqvl+LA7yZeu25SDHwWWWVVVZvFf1RCulVq1aJ\ncITfpIsXL7J161ZUKhX5+fkUFBQwNDR03ZDSn4vS6TSDg4NUVlaKLuHExATJZFJ0g1/Lkf595PF4\nBK9YJpMRjUYZGBj4/41PDofDVFZWEo/HMZvNVFRUEAqFROjJrl27kMvlJBIJLl68yNTUFO3t7Wzf\nvl1EO1+8eJFoNHpD24dko/jZz37Gu9/9bmHteOyxx2hubmZ8fJwdO3aIH1apQ93Z2YlOp+Pee+9F\noVDgdrv5xS9+QVVVFUePHmX//v3o9XqSySRnzpxBoVDw0ksvsX79ej7xiU8AS4Nz3/ve92hsbOTU\nqVN85StfQa/X43a7+frXv86XvvQljhw5wpe+9CU2blwqxgoLR2ltbWVmZoa6ujqamprEa/vSSy+x\ndetWHnnkEd773veKSf8f//jH3HXXXSSTSd7+9rdTXl5OJpOhs7MTt9tNa2sroVCIz33ucygUCjwe\nDz//+c+pra3lpz/9Kd/85jcFsu4LX/gCkUiEffv2LdvCX1hYYGZmBq/XyyuvvMJHPvIRTCYTU1NT\nPPTQQ9TV1fHwww9zzz33iO7kyMgIbrebqakpGhsbefe73w0s2SqeeeYZ3v3ud3P+/Hnq6+sxGAxo\nNBrOnDnD6tWrOX78OIWFhaxevZpUKsWVK1dQqVS43W42bdpEZWWlIFucOnUKl8tFd3c3f/EXf4HR\naCSZTNLa2koqleKZZ56hsbGRL37xiwAcOXKEr3/963zhC1/A7Xaj0+mETWVkZISqqioSiQSVlZWE\nw2FMJhPpdJpwOMzg4CByuZw9e/YAS4OFR48eRS6Xc/XqVT760Y+KwvKRRx7BarXS3d3N6tWrRYfb\n6/XS398vOuYnTy59L334wxdIJBIkk0meeuop3vOe95CTk0MqleL5558nHo9jsVgwGo1i50WyL127\nE/OHKh6PiyFJaTBzZGQEi8VCMBi8rpgOh8Ov65OWIsQlK1Qmk8HpdJJMJsnJyXnDnvPrKZFIMDY2\nJgaapXMpKCh4w4aos8oqq6z+UL2pHunDhw9z6tQpqqqquP/++zEajczPz4steVgaRpIG0F6ro0eP\ncvToUQC+/vWvv6mDM0ql8qY8vsvlYvXq1ct+AO12O729vdc93m/7+Mlkkrm5OZRKpcCOzc7OsmbN\nmmV/J3lcbzQ8JQWJSLQDpVIp0gWnpqZEsSJp7969PPHEE+zdu3cZNeHgwYP8+te/prGxkXQ6LbbK\nw+EwSqWShYUFVq1ahdfrFQl+9fX1wj5w7RZ+Xl4efX19uFwuviYlZPzf49PT05w/f57du3dTVFSE\nUqkkk8nQ1NRES0sLVquVd77zneI273znOxkZGeGll17iBz/4gRiqzMvL4x//8R/55je/yac//ell\nfOAHHniAb3/721it1mWDaFLR/KMf/Ygvf/nLyxaFFRUVojCUaB9yuZzNmzfT1tZGR0cHf/u3f7vs\nvhKJBJ/97Gf56le/KqgheXl5PP/882zfvp23v/3tyx5DOv9QKMQ73/nOZdv5+/bt49VXX6Wuro6+\nvj4aGhrw+XzMz89TWFhIJpPB4XAsw3utXr2a3t5ePvShD6HVakVKYn19PQ8//DAymYz3v//9gr28\nceNGvvGNb5Cbm8umTZuWXRe333473/3ud9m1axcWiwWbzUYsFqOuro7W1lZUKhUf+tCHxN/fe++9\n9Pf3093dTWVlJZWVlRiNRnw+H2NjY0xNTVFXV3fdNdvT00MikeDgwYPXvZaPP/449957r7CcxGIx\n7rrrLp566ikaGhqw2WwkEgnhszabzWKXRdK2bdsYGxvj6NGjvO9971t2XXz84x/nP//zP9m3b9+y\n77a8vDwUCoUg07wRGhoa4pZbbkGtVi97HGlgVKPRCGvL3Nwcdrv9dekiExMTrF27dpkV5kbfPTfr\nu294eJhNmzYtOxe73U5fX9+ygKY3Wzfr/LPKKqs/Db1phfT+/fu5++67AXj66ad59NFH+eQnP/k7\n3cfevXvZu3ev+P83k+V5s1iiExMT1NTUXHffEtt2SUve29/m8T0eD4FAgLy8PJLJJFevXqW8vJxA\nIIDX6yWdTgsucSqVwul03tBWIy1upMeUzl8q0l/7XKT7MhgM1/1bJBLBaDTS3d3NU089RSaTobS0\nlG3btjE+Ps5tt92G0+kUxV95eTkdHR2k02m8Xu+yYcNYLIZSqaSlpQWNRoNSqSQej6NQKJienqas\nrIy2tjaxVZyfny86yFevXiUQCABLW/iFhYWcOHGC4eFhurq6SKVS6HQ61q9fj9PpFExjeD8Ajz32\nGD6fT9gbrh021Gq1zMzMYDAYuHDhgkgLlMgkMzMzxOPxZcNqCwsLJJPJ696XwsJCfD4fZrOZtrY2\nkZxns9mQy+WEQiE6OjoIhULA0vZ9IBAgmUwyPz8vbEI6nQ6TyYTb7eb973+/sFdotVr2798vusHJ\nZFIM3plMJtasWcMvfvELwSmX3v9AIEBPTw/19fXLkgpnZmbIycnB6XTS1dVFX18fsGQ5KSwsJBAI\nUFdXh8vlor+/X5zj6OgopaWl110vK1eupKWlhX/4h38gFosJwobNZuPIkSM3vM3s7CyLi4vXHTcY\nDMzOzorrcmJiYhmBxGw2c+zYMYxGowjyKSgooKenhzvuuEPcj0wmw2w2c+XKFQ4ePLjsfZHu60aP\nn0wmGR8ff8MKMa/Xe0MCzcLCAtXV1WKAVzr3wsLC1/3ecLlcouN/rZZ/99y8777Z2dkb2lFe+/hv\ntrIc6ayy+vPWm1ZIX/tlv2fPHv71X/8VQHTlJM3Pz/9RthHfqpKKoNf6EqWhrN9FiUSCQCCwbCs5\nJyeHwcFBMpkMw8PDZDIZVCoVsVhsWffqtdLr9aJAulZS4p00BCdJ2sKemJi4buhQKuhDoRB33XUX\nSqWSwcFBWltbmZ+fZ3Z2lne84x3C63v48GERiJFMJpfh76TiVavViq6zwWCgo6ODoqKi6xIepY77\nhQsXWLdunXhtvF4vFy5cQCaTcfjwYdavXy+Krf/6r//CaDQyMDDAfffdJ+5r165dHDt2DLPZTF9f\nn0hClPzHFouFK1eusHr1amFT6erqQq1W09/fz6pVq1ixYgXJZJL29naCwSCpVIqRkRFRkMdiMebm\n5nA4HPz6179m165dOBwOgsEgHR0dyGQygd6T/O1S2qB0vw0NDeTk5OByuUSB3NvbS3NzM2VlZSLB\nLxqNMj4+jk6nE7zueDxOa2srNpuNmZkZLl++zMLCAsXFxdTW1mKxWIhGo5SXlwuP+MqVK4nFYlit\nVl555RXuuusuDAYDQ0NDPPLII6xZs4b5+fnrOqPFxcX09vZed+11dHRQUVFxnX9fYmP7/f7rhmCV\nSuWyJEJJExMTFBUVcfToUXbu3ElNTQ0LCwscP34cs9nM2NgYFRUVYj5Bq9Vy9epVNBrNdZYnlUqF\nXq/n5MmTNDY2imNS4EsoFLqO4RwIBN5QXJ5CoSCRSCzr4krfFTKZTPC6fxtJwTOv/Q74fb57fh9J\ni+BrKSw3SlHNKqussnoz9aZN8Pl8PvHfFy5cEAimjRs30trayuLiIh6PB7fb/YZ6CP/UJEVkX8u1\ndTqdN2To/iZ5vd7rqAJyuRyNRoPP5xNDZuXl5ZSVldHX1/e6g0VarZZUKrXsfZyfn0cmk7F+/Xou\nX77M0NAQLpeLq1ev4vF42LNnDx0dHXR0dDA0NER/f7/oIE5NTXHrrbei0WhQKBTU1dWJQnR6eloU\nLel0mpmZGWw2G0NDQxQVFVFRUUF1dTUajQav1ytoBBUVFdTU1KDRaBgdHWX//v3CcwxLBfypU6fY\ntGkT6XRadEphyQqQTqcpLi7m0qVLJJNJFhcXCYfDXL58merqavx+vxh2A2hpaaG8vByz2Sy4yUql\nEq/Xi9frpbm5mRdeeEFwfMPhMIcOHWLdunXY7XaMRiOjo6O4XC4KCgqwWCzk5uZy4sQJ8fiBQICj\nR4/yN3/zNxw5coTR0VF6e3sZGxvj0Ucfpbm5meHhYYqLi9m1axe7d++mpKREDOtJsdxKpVJ0tvPy\n8kQ3eGhoiMHBQUE5iUajjI6Oitff7/czMDDAqlWrePDBB1Eqlaxbtw6dTseDDz5Ic3MzY2NjDA8P\nU1BQgN1u58SJE2g0GvLy8igrK6Ojo0N4yVevXk1JSQmnT5+mq6uLoaEhent7efHFF9m8eTMqlYoX\nXnhBvMZHjhwhGo3S1NTE8PAwPp8Pt9u9zG88OztLJBIhGAyKYVWbzYbdbhcLB1janZmcnGT79u3M\nzc3h8/kYGhrC6/UyOztLRUWFGJiVwnqkwUy9Xi/wgrBkdRoZGWHDhg28+uqreL1eioqKUKlUHDp0\niMLCQkFjkeT3+0kkEm+YrQOWupMjIyOiGy4txH4fmkhBQQGTk5OiI53JZN7Q7vlvUlFREaOjo+Jc\n0uk0w8PDb1kySlZZZfXnqd86IvwP0b/9279x9epVMexyzz330NPTw9jYGDKZDLvdzl//9V8LNNMv\nfvELjh8/jlwu5wMf+ADr1q37rR7nrRQRLhV84XBYpJz9rrHVkpLJpCAawPXUjt9W09PTyyK+JY2M\njAgP6NGjR4nH49TU1LBz504CgcDrosYAOjs76enpwWg0UllZKQbsQqGQsBDo9XpKSkpQKpV0d3cT\nCAQEncFms6HX6wmHw4KgIGlmZoYf/OAH7Nu3j7GxMWGHKCsr4/Lly9x7771MT08zPT2NQqGgpqaG\nRCIhvK4dHR2C2rF//35mZmbIz8+nra1NUAhWrVqFRqMhnU7zxBNP0NbWBsCGDRu49957efTRR6mq\nquLSpUvA0sJjx44dnDp1io997GP87Gc/Y2JiAqPRyMGDBwmHw9TU1CzrJOfl5VFRUcHY2BiBQICX\nX35ZxFfv27cPvV7P6tWr6enpEQNxGzZswGg0MjQ0RDAY5Pjx4ywuLlJUVERzczOxWIyZmRl+/etf\nk8lkSCaTbNmyhfr6etLpNOXl5WIxsGrVKtxuNy0tLdx1113CdmAwGKivr+fw4cNs3LhR2EKkz6Tf\n70cmk6HX6zl//jzJZJKVK1dSVFREZ2cnTU1NOJ1OgsGg8NpOTk4K0odEpygsLGTTpk04nU42bNiA\n2+0mmUxiNBopKSnh2LFj2O12xsfHCYfDKBQKKisrqampITc3l4ceeoiuri4ymQy1tbV85jOfwePx\niPdRQtYplUpuu+02QqEQx44dEwu7+vp6tm/fjlKppKenh87OTlKpFAUFBezatUvwu1taWkS3defO\nnYLyIg2sShSRiYkJ8vPzUalUJJNJbDYbgUCAaDRKIBAgPz+f48ePEwgExHCjx+Nh9+7dwloF/8/a\ncjOoHVNTU+JcioqKbsgj/2302gCjG1E7bqa1IR6P43K53pBzuVnKWjuyyurPW3+UQvqPpbdKIS1x\ni4uLizGbzUSjUcbGxgR5482SNIUvobhg6YdydHSU0dFRPB4PBw8exGKxcPHiRfr6+ti5c+frDva0\ntbWRTqdZt24dOTk5vPLKK6jV6tdd+LjdboxG43VbxT09PczOzl4XQy8VPWVlZSLuPBgMCjJIY2Mj\nubm5OBwOkskkExMTBINBFAoFjY2NuN1uQqEQpaWlxGIx4vE4DofjuucVj8d59tlnsdls7Nu3j1Qq\nxdGjR/H7/UxOTvL3f//3y/7e4/HwxBNP4PF4eMc73sGKFStQKpU8+eSTaDQa9uzZQ1lZ2bLbSL5l\nq9WK3W4X29Ner1cUC6WlpZSWlorrRyqIZDIZb3vb28jLy+PixYvidZesCMFgEJ1ORyaT4Uc/+hE6\nnY41a9awbt065HI57e3tdHR00Nvby549e7j11luFZ/ncuXN0dnayY8cOrFYrOTk5JBIJ5ufncblc\nDA8PU1VVJYgap0+f5uLFi0xMTHDrrbdSVlZGfn6+WLiMj4/zhS98YVm3VvK1S8SU114TnZ2dFBYW\nUlZWhs1mIxqN4nQ6icfj2O12kfook8nEDkAwGMTtdpOXl4dOpyMQCODz+Vi5ciUnTpxgzZo16HQ6\nZDIZw8PD6HQ6Ghsb8fl8lJWVoVQq8Xg8YndAOiYpnU5z4cIF5ubmWLt2rRicHB8fp7e3l127drGw\nsEAkEhED0UVFRVy+fBmTyUR5ebkYnBwfH2doaGjZoOP/JL3ZheSbrTf7/LOFdFZZvbnKJhveBLnd\nbpEmB0v0i5UrVzI6OnpTbCq/LUdaoVBQUFBAf38/JpOJZDJJLBajsrKS559/nk996lN4PB7m5+ep\nqKjA6XTi8XhuWEhLW+dS8atWq9m8eTMtLS2Cn+t2u8WAXkFBAZFI5IaEAMnO0dfXJ4oZKW553759\ndHR0UFZWRiKRIC8vj9nZWTZt2sTU1BQNDQ3Akhe1srKSI0eOsHLlSr773e9SXl6OyWTi9OnTGAwG\nMdz6Wi0uLgoftuQt3bFjBw8//DCFhYWcPHmS5uZm5HI5sViMEydOYLPZ0Gq19Pf3873vrSGTSbNv\n3xJVJBqNirASSR6PB4vFIrq9UhfV5/ORSCSYm5ujvLwcmUyGTCZDqVQKW8w///M/C2rILbfcgtvt\npq2tDbfbjUqlIi8vj/n5eQYHB8nJyWFkZIStW7cK9rbD4cDtdpObm4tKpSIUCjE/Py98w7m5uWg0\nGtasWSP8qG63m4mJCaanp/noRz+KXq9ncXGRTZs2cfbsWdxuN5WVlWzevBlADK+eOnUKlUp1w+u8\nrKyM1tZWCgoKRIext7eX3NxcysvLxdyETqdjxYoVtLS0oNfrl91XcXExIyMjjI+Ps3fv3mU+6XA4\nzJNPPikiwfPy8ohGo8K243A4li0iHQ4HLpcLjUbDyMiIQDmq1WqcTidlZWWEw2GCwSCxWEzwuqWg\nluLiYv7u7yxoNFq+9rUZcR1Kw7PSta3Vaq8b2Msqq6yyyup/hrKF9E1QLBa7LjTgZiaH/S4caYvF\ngsViIRKJiGCMVCqF2Wzm7NmzrF+/Hr1ez9DQECaTifHxcTZt2kQ0GhWdT5PJxPT0NKWlpSSTSYaH\nhwUtoLi4mOHhYbE9L6X6DQwMkJOTg8/nw2g0srCwgFqtxmq1kk6nqa6u5vLlyyJeXC6XU1tbi91u\nZ926dXR2diKTychkMqxfv55EIoHNZuPo0aOi+E0kEpSVlXH69Gl2795NNBollUpx6623cunSJYH7\neq1GRkbYvHkz3d3dwouu1WrZvHkzHo+HTCbDCy+8gFarJRQKUVJSwqVLl9i/fz8mk4l//Mel4cXv\nfe9OfvzjH1NUVMTAwACFhYUolUqmp6cFJq6oqIi2tjYxRNvQ0MDg4CB1dXWYTCaGh4eRy+WUl5fj\ndrvFUF5BQQFarRav10tJSQknTpygsLCQxcVFgTarr6+nu7sbjUbD+fPnWb9+Par41rcAACAASURB\nVHK5nEuXLomAHaVSSVtbmwjKqaysFN54p9Mp0v0qKytRq9UUFRVx7tw5sTDIZDKUl5fT3d3N4OAg\na9asQavVkkgk6OnpwWazEY/HGR0dFdYGm81GZWUlhYWFTE9P09nZKYZaS0tLiUQioqgPBAJotVpM\nJpP4HEnJk5KkJMfXfqZ0Oh1Op5OVK1dSXl6OQqHAbDZjMpk4ceIEu3fvJhwOMz09TSaTER50r9eL\nXC6npaVF7BSUlZVhNptRq9VcuXJFJDnOz8+LHQ6DwSA+e5/97FL6pNlsRiaTMTExQTqdFuf5eoi5\nmyXp82owGLIBJllllVVWN1HZQvomSCaTkUwmr/NEv5VcNNf6DBUKBaFQiD179uD1epmfn6empoZw\nOEwikWBwcJC5uTl0Oh3RaBSDwYDD4eD8+fOiCFQoFBw9epREIkFJSQnr1q0THVmDwUBpaSl+v5++\nvj5ycnIoLy8nHA5z8eJFVq9ezczMDNXV1eTn56NQKJidnSUajRKJRDCbzVRXV4vX1Gw2EwwGGR0d\nJZPJMDk5iUKhwOFwMD4+TiKRQCaTUVpaikajYW5ujjVr1nDmzBnuvPPO616L3Nxczp8/zwMPPLCs\nKP/JT37Cnj17SKfT1NXVkUwmUavVzM3NYbVaGR8fZ8uWLeJ+5ubmCIVCmEwmMfSWSqUoLi5Go9Ew\nMDBAa2srFouF1atXMzU1JYJHwuEwqVSKlStXkkql8Hg8hEIhUcxJaDy1Wo3P56OyspKenh6ampqo\nr6/H7/dz6dIlbDYbMpmMLVu2EIlEkMlkbNy4kVOnTlFcXExBQQFVVVWiKA6Hw6hUKnp6eigoKKC2\ntpZoNEpHRwe5ubl0dnaSTCbF4Nzs7CxXr16lpqYGs9nMU089hVqtRq1Wo9Vqqamp4fLly1itVnEu\nTqeTnp4eUdjq9Xo0Go1YzGk0Gk6fPk15eTnFxcUEAgHOnTtHbm4u/f39YggVlnYPzp49i9lsZnp6\nmkgkIjjWiUQClUrFwsKC6NQnEgmi0Sh+v1+w0yXix9zcnPD3Lyws0NDQIIJL3G43c3NzTExMsHPn\nToElXLVqFceOHWPFihU8+eSTwD8BcPz4cbRaLatXr0aj0RAIBFAoFGQyGRQKxRtK5vhNGh4eRq1W\nY7FYWFhYwOVyUVNT82edDptVVllldbOULaRvgoqKihgbG1u2Je1yuX4v0sYfQ5lMRqDJpCJrZGSE\nCxcusHHjRmZnZwWZYXFxkd7eXmZmZujp6eETn/gEFotFTPI/9NBD1NfXXzdAJREpKioqhD9VJpOx\nefNmnE6n6PSNjo6STqcxmUwkEgncbjeRSASNRiNSEvv7+7FYLLS3t/Oud72LgwcPkkgkOHXqFFeu\nXAEQvmVY6sJ3d3cvYzRfK5vNhs/n41e/+pWwFywsLOD3+0WEs9vtFh3GFStWsLCwwMsvv/x/CSD1\nAJw+fXqZNeW1XciZmRlMJhO33HILsDSEd/HiRQKBAH19fWzdulUscBKJBAsLC9TU1AhvudSFHx4e\nZu3atWg0Gubn54lEIsTjcQoKCgSu7NqhLKl7arVaMZvNxGIxfD4fRUVFaDQaHA4HHR0dwjYjk8lY\nsWKF6I6XlZWJ4Var1crp06dFUMauXbuE97ytrQ21Wo1cLqe+vl6cd05ODsePH8fn82G32wU1JJPJ\nCGtFJpMRlB4pilyyUVyLW5MGTouLizlz5gwNDQ2YTCZ8Ph89PT1UVFQwMTEh7EXSokStVhMMBqmv\nrxedbIvFQigUwufzsWHDBjHsbDabsVqtHDp0iNzcXPLy8sQCKxKJoNVqef7557nnnnv4X/9r6Xm9\n5z3v4fDhw4TDYWKxGGVlZfj9fvR6PbFYTFhzXk9ut5vx8XHi8Th6vZ7q6urfC/spEW2k25rNZiKR\nCJOTk9f59rPKKqussvrDlS2kb4J0Oh35+fkMDAwIL2xOTs5bloctk8lYuXIl8/PznD17FplMRjqd\npra2Fo/Hw+233y4QXSqVisbGRp566inuuOMO2tvbhTUkEolw4MABZmZmliW4wVIx5/f7qa+vv65T\nn06nmZiYwGq1UllZKZB3o6OjeL1eSktLKS8vR6PREI/HGRwc5MSJE6xfvx6NRiP41ytXrqSvr49A\nIEA4HF5GNhkYGHjdocl4PE5RURHhcFgU25lMhqKiIuLxODabTSQ4SpJII5cvXxbHuru7lw3+JJNJ\nUqmU6KYmEgkaGxuXXRfV1dWMjY2xfv16zp49K26r0WhobGzklltu4ZlnniEajaLVavH7/ezcuROl\nUklubi75+fkiSl0mkzEzM4PZbKarq0uQFpRKJVVVVZSVlQlmuGSZyMnJQaVSsWnTJmZmZsR7r1Ao\nyMvLY/fu3UxOTtLT0yMWR/fff78Y7uvs7MRoNAqyQ3Fx8XV4NJlMhsVi4dy5c+zZs0csVjKZDAsL\nC/T29rJ161Z0Op2gmaRSKSYnJ2lsbGR6elpQVmQyGXV1dYyNjbF9+3Z8Pp/oQO/atYvjx49TXV3N\nxMQE/f39yOVyrFYrpaWlVFVVibh06VzWrVvHSy+9hEajWWYhcTgcRCIRNm7ciNPpFMeljrbX672O\nnLN7926ee+451q5dy7lz58TgZCqVWrZz8Vq53W6GhoZobGwUi4L29nZuueWW18VPvp6CweB1/nS9\nXi8sU1lllVVWWb2xyhbSN0lms/l3/hF8M6VQKGhubhZFUDKZpKOjg9HR0WWBCLDkX5Y6bNKwoTS5\n3t7eTl5eHu3t7RiNRmFzCYVComM6OzsrCiapQJ+bmxPb6YAgR0SjUUwmE0888YTYvq+rq2NmZkYg\n0q6VVBR++9vfxmaziVCN2tpaSkpKWFhYwOPxiMc3Go3I5XLUajV5eXnCfiOTyfB6vSQSCVpaWnC5\nXCLsYvPmzQwMDNDY2MjWrVt55JGlx77//vt57LHHSCaTjI6OCo50NBoVCMGhoaFlxfeaNWtQKBQo\nFAp+/etfi+MGg4H3vve9TE5OsmfPHhYXF0WRm8lkBFNaYn1Ho1HUajUrVqxgZGSESCQiCj2p0AwG\ngwSDQYELlCwwUjT7axc/wWCQeDxObm6uGJ7LZDLMzs5SVlbG4uIiV65cIZPJoNfraWhowG634/F4\nrkMmBoNB9Ho9hw4dEhxvmUyGw+FgdnYWo9HI8PAwSqWSdDpNbm6u8CR7PB5RSMNSkSs9H+k1lgJr\nCgoKGB8f59ixY4KakU6nWb9+Pclkcpm9IZPJEAqF0Ov1nDt3TkTHp1IpLl++TF5eHmNjY+zevXvZ\nbfr6+m6IYItGo8TjcaxW67KOfCKRuG6H6lr19fVhs9loa2tDLpeTTCZxOBxcuXKFrVu3kkwm8fv9\nKJXK3wp7+dpB16yyyiqrrG6esoX0/wC5XL8b9k8Kn5DL5TgcDvR6PQUFBSSTSQYHB8WP8KpVq5ib\nm8PpdFJcXCyGDRcXF0XBUldXJzrMyWSS6elp1q1bRyAQ4OLFi2QyGbRaLRUVFVgsFi5dukRTUxOh\nUAilUiluEwqFKCoqEoNRiUSC7u5uZmZmsFgsfPCDHxTP//nnnyeTyTA2NkZVVZV47lLoiM/n433v\nex+Li4sEg0He9ra3cejQIdRqNV6vl5UrV4r78ng8OJ1OgdOTisnBwUG6u7s5efIkRUVF3H///eI2\njz/+OOPj4+zatYtIJMIPfvAQDocDjcYiLCwSSSORSJCbm8vk5CShUIjW1lYeeOAB4fP+6U9/Sl1d\nHT/84Q/54he/iF6vR6lU0t/fz6OPPso73vEOmpqalnVxe3t7CYfDxONx6urqmJubw2azkU6nCQaD\nZDIZ5HK5wEGaTCaBcysoKFh2Lk8++STJZJLJyUksFosYEMzJyUGpVDI5OUlVVRXr1q1DrVYzOTnJ\noUOHMJvNuFwu7r77bhGbLUVp+/1+urq6BJ87Fouh0+mYnJxEp9ORl5eHSqUiHo9z8uRJSktLOXfu\nnBgGlKw/k5OTLCwsUFpaKob9gsEgXV1d1NTUYLFYlhX//f39aLVaBgYGeN/73kcmk0GpVDI+Po7T\n6RQ8b2lnIh6Pc/ToUUwmE/Pz86xYsQKz2Syiz1UqFQ6Hg1OnTlFfX08qlaK7u5v6+nqCwSAulwuX\nS1pEIogxr0UsXpsyeCNNTk6Sk5PD3r17xbGTJ0/i9XqZmZkhGAySm5tLLBYTtJTXLm4l5eTkMD09\nvSy0xOfzveXYy1lllVVW/1OULaT/zDQ8PIzFYqG6ulpYKoxGI+l0GofDsczX63K52LBhAydPnhTp\naHNzc7hcLvbs2QPA0aNH0ev1WCwW3G43W7Zsobe3l2g0yrve9S50Oh1zc3OcOXOGcDiMRqNhcnKS\noqIiIpEI4+PjaLVajEYjWq0WtVqNQqEglUpRXl7Oq6++Sm1t7TJqRF1dHf39/eh0OsbGxujr6xO3\nqaurIx6PC8SbXC7nypUrbNmyhUuXLnHgwAEx3KfRaMjPz2dwcBCdTkdlZSUDAwMAVFVVodVqmZ6e\n5p577ln2Gt5xxx08+OCDnD17lnXr1lFSUoLBYKCzs5NoNMrCwgJerxebzYZCoWBkZAS5XE5fXx+b\nN29maGiI3Nxc5ubm2LRpE4cOHaKpqYmxsTFKSkqIx+PE43GUSiVzc3PC45pMJtHpdOTk5NDf34/N\nZmNubo78/Hz8fj8ulwu1Wk0mk+GWW24RxZREyujt7eW9733vsnM5ePAgjzzyCCaTSUR9RyIRLl26\nRENDAyUlJWKIU8Ly7d27lxdffJHm5mbm5uYwGAzMz8+LnYhNmzbhdrvR6/Wk02kikQj19fWEw2FB\nyzAYDExNTaHRaJiYmKCiooLp6WkKCgoIBAL4/f5lBaS0KJDL5VgsFvx+P/n5+eK6kPzQvb29HDx4\nEI/HI/zVVquVmZkZUeBKFg6ZTMamTZs4ffo0t9xyC36/H79/iXzT2NjI4OAgDQ0NBAIBhoaGUCgU\nbN++HZ1OR2FhIc8++yxqtZrKykoxgFlQUPA7DxV7PB5WrVq17FhDQwM/+9nPRMCPpLy8PIaHh5ct\nBq+VFP0+NDSEwWAQdJPXszVllVVWWWX1hylbSP+JKxaL8bd/ayCTyfDNbwbElnc6nRaBEyaTCbvd\nLgalJPuG9AMrodquXr0qCjGJhKDT6Vi/fj0ej4fR0VGMRiPbtm0jHA6Tl5dHQUGBsEAUFBQIr+l9\n990nOtu5ubls2bKFxx9/nL/6q78iLy+PSCRCXl4eNTU1/Pd//7fYypdYy6lUCrVaTX5+PrFYjHPn\nzonOpuRB37hxI0888QTRaBRYSl378Ic/zKFDhzAYDIJUEAqFmJ2dxe/3MzMzQzQaxWw2Mz8/TzKZ\nFFvpTz/9tPBCX7x4URSQ0WiU06dP4/V6qampYf369dhsNlwuF1qtlkOH3olSqaS5eQyFQsHw8DBb\ntmyhvb2deDzOmjVrGBsbIxwOs2/fPqamppiamhIpgY888gjNzc1s376dgYEBNBoNO3bs4OzZs4yP\nj2M2mzEYDMjlchYXF+ns7GRxcZHm5maefvppLly4QElJCZ/5zGd45ZVXcDgcInAlk8lQUFCATqcj\nnU4zOTlJe3s7wWAQh8NBZWUlAOXl5ahUKiYnJzEajezYsYOenh5MJpM418XFxWUYO6vVSm5uLsFg\nkIKCAuLxOMeOHROBJOPj46hUKsrKypiYmBBIRJvNhlKppKioiMbGRg4fPkx5eTk6nQ6PxyPiy7u6\nupifn2ffvn3CAhSLxXjuuefIz8/HYrFw9uxZ4vE4RqORhoYGTpw4gVKpZMOGDQI7aDabRWKl3W5n\nenqaZDKJ3W4XthC73S4IFwUFBRQUFDA8PAws2bTWr1+/7HOnVqv5y7/8Sz73OR1nzsD/+T/rUCgU\nhMNhJicn8Xq9Avm4du1aYYVyu92cOXOGTCbD5s2bKSsro6ysjPPnz9PU1CQ80l1dXVit1uvisCUb\nkDR0eSMVFxeTSqWIxWLiHLPKKqussro5yn7D/glLsmi88MJtAHzlK+MsLCzgcDgYGhqiqKgIh8PB\nwsICfX19GAyGG2K4rg2MkH6cpe6v1+slnU5TU1PDpk2bRPR3Op3G6XRSUlJCQ0OD6Ea2t7eLQbpr\nOb/S9nY6nRZUBkD4a9VqNYuLi1y9ehWZTIZGo8FoNFJWVsbs7Cy5ubnY7XYCgQAzMzMYDAZOnTrF\nfffdR1FRkeApHzlyhNHRUe644w5RfAE899xzaDQagsEgdXV14nggEGB8fJyJiQluu+025ufngaUh\ntFOnTiGTyXj44Ye5/fbbaW5upru7mwcffBCNRoNOp2N6epqOjiUKR2Xlg+Tn5wsE34EDBzCZTJw8\neZKuri7S6TStra00NDSwceNG/H4/Z8+epbCwkNbWVux2O6WlpSQSCc6cOSO2/KPRKA6HA4PBgNvt\nFmi1j3/84+zYsYOHHnqIY8eO8clPfpJt27YJu0xJSQmwtLMwNjaGTqfjpz/9Kbt27aKpqYne3l4e\neughVq5cid/vF8VoPB5nZGQEmUzGlStXSKVSbN26FbVazeDgIM8++yxarZaZmRmB05PQeFLaXyAQ\nQK1Wk06nhV0oHo+zc+dOkeZYXl6O3+/HYDDg9/sxm82i4JQSKqXF07XXqhQv39nZSTAYRK1W43K5\nCIfDlJSU0NbWRklJCTU1NSSTSVwuF8FgkIWFBTKZDKWlpahUKqanp5mYmKCqqoof/ehH7N+/n/37\n9zM2NsZPfvITNm3a9Bs/g88+u0T6+Ld/i4rn98tf/pKCggKam5sZHR3l4Ycf5gMf+ACtra04nU72\n79+PWq3mlVdeob+/H7vdTmFhIT09PSQSCfR6PUVFRSQSiRt2t3+bjrdCoXhdfnQymUQmk91Utn1W\nWWWV1Z+LFF/96le/+mY/iTdKwWDwTXtsvV7/uni1myGJn7xixQq+9a2lyO3//b/lzM3N4fV6qays\nxGAwIJPJ0Ol0GI1Gpqam0Ol01/klZ2dnCYVC1NfXC7qItKUuRYpLPGalUklBQQHnzp3DarXS2NiI\nQqHAZDKh0WhwuVxiOzmRSBAKhfD7/aI7KRVKBoOBWCzGyMgIWq2WoqIinE6n8DVLg4BVVVW0tbVR\nXFxMbm4uPp+P7u5uQWJobGxELpejVCoFgs1isTA9PY3L5WJwcFDYAiKRCA6HA7lcjk6nE93DVCpF\nKBRibm6O3NxcFAoFg4ODYkBPiiRvb29ncnISn8+H3+9n7dq1fOpTn+Lb314aKj18eAuPPfYYAG97\n29tIpVIEg0EsFgtDQ0PY7XYGBwcxGo0oFArGx8d59dVXaWxsFF3miooKgsEgv/rVr4AlRF5+fj5a\nrZZMJrMMv9bU1MTevXuJRqMiAvupp56ioaFBcLVhiVgidd8TiQQGg4FoNMrs7KwIyKmoqMBoNHLu\n3DmBibty5QrxeFxYbiKRiOCMS93jnJwckRB4/PhxTCYTmUyGkpISkfCo0+no6ekRvGppUXDlyhUu\nX75MZWUlqVQKi8WC2WwmkUgIv7vVahVBOqlUigsXLpBIJIjH4/T397N69WpKS0vJZDK0traK59LT\n00M8Hqenp4eWlhbKysrIzc0VAUThcJhkMineXyk8JxQKEQ6HycnJIRwOi479jT5/Y2Nj/PjHS0i5\n/ftbUalUdHZ2UllZydatWwkEAhQVFbF7925+/vOf4/f7ef/7308oFCKRSLB27VouXbrEhg0buHLl\nCqtWraKxsZFEIsHQ0BA7d+5kampqGe1ncXERv9//e6E0o9Eoo6OjhEIhYT8ym81/cEH9x/7ue6vp\nzT5/k8n0pj12Vlllle1I/8nqRumJsOSh7OnpWRaFDIgC2uPxLOv8STQA4LpJ//z8fNrb2+nt7aWk\npASbzUYoFGJkZIRYLCa4u9fKbDZTUFBAZ2enCGqJx+NcuXKFu+66i4mJCRHqITGM7XY70WiU/v5+\nrFariAdvampCqVSyY8cO+vv7xXb3rl27aGtru267HZZ41XNzc/T395NOp1EqlcTjcUpKSjAajdTW\n1jI/P8/w8LCIbj9z5gx6vV5QPQAqKyuZnJzE7/cLjJxGoyEUCqFWq5mYmEClUol/g6WERKmTPjs7\nK0gbSqUSk8mEXC5n3bp1tLe3c/78eVQqFWvXriUWi7F7925ycnJ49tln0Wg03HHHHXR2duL3+9m4\ncSOhUIh4PM6qVau4evUqXq+X9evXk5+fTzQaJScnh0AggFKppK6uTiQoSueiUqk4fvy4sJlIr/WW\nLVvo7u7m+PHjWK1WNm7cyPz8PN/5zncoLS0Vr9vAwICw3tx6660cP35ceKQff/xx6uvr2blzJ5cu\nXcLlcqFSqVizZg3xeJzLly8zPz9PbW0tk5OTPPfcc6TTaSwWCw6Hg5qaGnw+nyjcFQoFRqORyspK\nEokElZWVXL58mUwmQ1VVFdFolCtXrnDvvfeSSqWIRCLU1NRgtVp57rnnRLBLd3c3er2eAwcOcPHi\nRUwmE9FoVKRXSraXS5cucffddzM7O0sgEMBut9PY2MiJEyde9/M3PDwsrhXpur9y5QojIyPs3LmT\nubk5gdCT/lZKsZQWc6OjoxQXF+N0Otm5cyevvvoqx44do6ioiD179qBWq4Uf3mKxsLi4SCQSuQ7F\n+NtIKvxra2vFrtPi4iKjo6Ov67f+TfL7/czOzuL1epHJZG9ZTn5WWWWV1c1UtpB+C8nj8eD3+4Wt\nori4GL1eTzQaZXJyUhw3Go3k5eURj8cZHx8HltjFw8PDgvrwWgSWFGohDfBlMhkymQxms5lbbrlF\nDGAlEgkx1BaLxVAoFILfLBUhGo0Gk8mE0+nk8uXLLC4uYjQaWVxcxGazUVxcjNvt5plnnhF+zv37\n97O4uCiGwkpLS1lcXMTr9VJbW8uLL77IHXfcsQxzdubMGUKhENu2baOxsVEcDwQC9Pf309HRsayQ\nValU+Hw+4U2VOpA+n48zZ85w3333cfHiRfr7+5cNNEqWibVr15Kfny9er4sXL9LV1cX3v//9ZXzo\np59+msuXL6PVann66aeBLwMwOjpKaWkp8/PzPPbYYyiVShQKBcFgkOLiYiorK6mrq6O4uJjFxUVU\nKhUmk4mXX36ZkpISEbENcPXqVXJycshkMnR1dYlQkOHhYWZmZlCpVKRSKZ588klxLqtWrSKVSuFw\nOJiYmBA7NOPj45SWljIxMcGKFStEbLSEl5uYmMBkMjE5OcnU1BSpVEpw0CORCDt37hTUEFhaMFwb\nE67RaJiamqK4uFj8nVar5erVqwDi+UgLtqamJtRqtbCBSNdcOp1Go9GQSqXQ6XTMzMywYcMGfvaz\nn4nbnjp1irvvvpuOjg66u7u5dOkSMpkMmUzG7t278Xg8WCwWsbsg2WSkHZy6urplTGWv1yu41fn5\n+eL9v/YzcyONjY1dh7jLz8/nzJkzxGIxYR+SgnBCoRBDQ0PidYYlJvXIyAjr169ncnKSXbt2odFo\niEajDA8Ps2LFCnJycsQCVqlULrO5vJ4CgQDT09PCYpWXl4dMJiMvL2+Zr1qlUqHVaonFYmi12t94\nv9dqampKLGzsdju9vb2MjY1lhxqzyiqrPztlC+m3iDweD+l0WuC8MpkMAwMDlJaW4nQ6WblypfgR\nlLpATqeThoYGcR+FhYWcOHGCLVu2iMJG0sTEBHa7nfHxcZqbm0VHWvIIKxQK2tvbsdlsqNVqnE4n\nfr+fgoICMXDmdrtFalowGOTll19m+/bt2O12rFYrV69e5cKFC6xcuZLc3Fz+5V/+BYBUKsVPf/pT\nFhcXWb9+PVqtlvn5ecxmM7W1tZw/fx6ZTIbVahXcX6mzLXWFr92+XFhYoKKigvPnz6PT6Vi1ahWx\nWIwzZ85gs9mYn5+nvr4eu90u7tftdnP69GlkMhl33nknOTk5RCIRjhw5Qk5OjhgUk3zFUuy4RqNh\nZGRkWSEdi8UoLCzk9OnT3HfffeL4/Pw8FouFEydO8MADD1BVVUUqlUKhUPDlL3+ZtWvX4nQ6WbVq\nFaFQCKPRKCLTX3jhBe6//34xbDkwMEBLSwubN28WhBG5XC46sNu3b+db3/oWR48eFVvzn/70pzGZ\nTKIgX716NbBU9EgBLa2trXz84x9Hr9czPz/PN77xDTKZDFNTU3z2s58lHo+jUqno7e0VMd3BYFBc\nZz6fj5dffpmVK1cyPDzMnj17kMvlmEwmfvWrX1FYWEggECA3N1cUfcFgkHA4zMTEBAcPHhQDnhIq\n8OzZsxgMBj7wgQ+IlMOWlhaGh4c5ffo073rXu0QhnclkePXVV3G73QSDQe6//350Oh1+v5/vfOc7\npFIpxsfH+cxnPiO626Ojozz33HOUlpYil8sxGo0kk0lMJhNdXV1UVVVx4cIFtm7dKt5LyXLyevL5\nfMvSCqurq+nt7cVsNnP+/PllPvyOjg6Kioq4evUqe/fuFQX4xMQEzzzzDE1NTTQ2NuL3+8WOUUVF\nBS6Xi/LycmQy2Q2371OpFLOzsySTSfF6RyIRPB7Psi6zZJm6USCUtCD7XSSRWCSaiEwmE7MBv09R\nnlVWWWX1p6xsIf0Wkd/vX8bElclklJeX09nZSVNT07JOktQBKi0tJRgMcvLkKQBcrgxNTU0oFApU\nKtWyBL3c3FzRdbu2ADCbzaKIl7rKUnCFXq9HoVDg8/mYnp4W9IZYLEYymaSqqkqwiyULg9RZvdZ6\nr1AoOHjwID/84Q+prq4WxI5YLEZ/fz+pVEoUEcXFxSLZMBwOo1Kp0Gg016VEptNpdu/ezYULF+jq\n6hLpjDU1NQSDQTZt2oTZbBZDbolEgscff5yHHnoIo9FIMBjEZrPxwQ9+kP/4j/8QFoalDv9SfHRh\nYSHFxcWEw2EeffRR4fsuLy9nYGCAe++9l7Nnz/JP//QqRqMRnc5ISUkJra2tTExMMDIyglKpJBaL\n0dzczNmzZ7nnnnvo6+vDarXicrnIZDK43W4KCwsJhUKiIyv5kk0mE2aztKOYQwAAIABJREFUmdnZ\nWZGSKKUh3nPPPdx9992iGJJCZ6RwGcnCYLVa8fv9hMNh7rzzTs6cOSO62HfccQff//73uf322+nt\n7RW3S6fTJBIJVqxYQTQa5ejRo+I93rVrF8899xx33nmnKPAtFgt33nknjz32GHl5eQwNDYngFYvF\nQiqVIplMioWKJK1Wy2OPPcaXvvQlxsfHRVBOfX09Z8+epaSkBLvdLoZAJZJFS0uL4HhLhV1paSmt\nra28/e1vF13oVCqF3W4nmUxiMBjEboVKpWJ4eJi8vDwMBgMWi4UjR44ItnlJSQnV1dWv+3mNRqPk\n5ubick39X470UqiPxWJh3bp1/OQnP0GtVovP3Nq1a7FarXR0dHDp0iVxPwcOHGBubo6BgQHREff5\nfMvSF28kaei3qKgIlUolcH/xePy6BUBpaSl9fX2CN36tAoHAdVH2v0mRSETQga6V1WolGAxmC+ms\nssrqz0rZQvotohv9aGq1WsLh8A3DFKRi60YJcuFwmIKCAnJzc8WwmFwux+Vy3bArpVarCYVC17Fs\no9EobW1t2Gw2Nm7cKI57PB5eeuklLBYLe/fuJRaLkZeXRygU4umnnyaRSBAIBEQ0sbR9LBUp5eXl\nzM/Po9frqa2t5fDhw8zNzVFQUIBKpSKdTlNWVsZjjz3GgQMHyGQyVFdXE4/H0Wg0eL1eUVwfPHgQ\nWCrW5+fn6erqIpFIYDabyc/PZ3FxEZ1Ox9NPP41GoyEnJ4dkMilCS6TXWWIeS51El8uFXq+npqaG\nQCDAjh07hB3jySefZOvWrdTX11NfX8/8/DxlZWVEIhGOHj3K4uIi733ve3E4HGQyGaLRKN/85jcJ\nhUIkk0lhuzCZTJSVlTE9PU1zczMymYzZ2VmxEKquriYYDKJSqbDb7SgUCiKRCH6/X2DlpIJNJpNx\n/vx5zpw5Q0FBwXUphVLyn8ViYePGjYL93NXVhclkYnR0lPz8fC5duiTsCFLyofTfkn0hHA6TSqVo\namoimUyysLAgtvil62Pt2rXLwm0ikQhKpZJIJLLsenY6neTn5xMMBmlqahIJjeFwWFArEokExcXF\nyGQyQqEQ6XQarVYr4tYVCgWZTIa3ve1tXLhwAafTydvf/naampoIh8O8+OKLKBQK5ubm2Lt3r+js\nNzQ0cPjwYRQKBStWrKCyspKFhQXMZvNvDFEpLCyks7NT+KC9Xi9ut5v6+nr0ev2yAKFQKERvby8G\ng4EDBw6IHYR0Os2JEydwuVzs2LFDhNRIi91rI8tfK5fLRW1trfjekHCD0Wj0uuFBmUyGSqXCbDYz\nPDwswnvm5+fJz8//nVMQNRoNHo/nuuOhUOi3Sl7MKqussvqfpGwh/RaR5Fm+9kfN7/fjcDiE3/Pa\nv1WpVGJS/O/+bunH6xvf8IsfR6fTKVBakUgEnU6H1WoVwRkSgSMvL08UL6+V1LXMzc1lcHBQ+LWV\nSiUVFRX09PTQ39+PXq8XmDGPx0NhYSEXL15EoVAIXnM8Hqe4uJihoSGGhoZwOByEw2E8Ho8Yevv+\n97+PUqlErVYTCARYs2YN+fn5jI2N0d3djc1mw+fzYbFY0Ov1LC4ucuHCBex2uwhhmZubo6qqip/8\n5CdYrVYsFgtOp1P4Vl966SVUKhW5ubkEAgHBqy4pKRH4PUAMH+7YsYPPf/7zvPDCC9hsNmZnZ2ls\nbOTWW2+lra0Nn8/HU0/tQiYLcO+9J7FarcTjcdrb27Hb7ajVaqanp0XYyK9+9Stqa2uprKxkYmKC\nX/7yl+Tn59PW1saBAwdEBLhMJmN8fJyVK1cK6wkgglNkMhlqtVp4tROJBEajkVQqJbja1yoSiVBc\nXIzf76evr0/43oPBINXV1fT09Aibw8LCArOzs+Tl5eH3++nt7aWqqgq9Xs/Zs2fF0OGRI0dQKBTk\n5OQwNja2DC83OjpKJBIhHo8TCASw2WyUlJTQ2dkpCn+5XM709DTbtm2ju7uboaEhKioq8Hg8eL1e\nmpqaGB4epqGhgUQigVwuJxwO09/fj8lkErSRTCaDQqEQlhKLxYLVauXy5csolUq2bdvGuXPncDgc\nnD59Gq1WKyg0kmWira2NhYUFcnJyRBLgli1bXvfzWlxcTHt7O1/9ahE6XYLdu1+isrJS2DY6OzsF\nZs5isbB27VpaWloYGhpCq9Uik8mIRqOk02kxTOlwOARNRvJ630jSNfvaAtjhcDA9PS0oLpKk5/H/\nsffeUXLe9b3/a3qvOztte69qliWrWLJc5CILyT6m2BhsB4ydA/dSEpNASC6XA0ng5AZysCHO9flh\ng61gYxyMjWwkIa2tsuqrstJqe9+d3Zmd3vvvj/V8rcWm5RIgsO9z5uzsnJ2yzzzzzOf5fN+f11un\n0+H1egkGg2If/88kHl6NKCxtv2QySSwWe8eJ/bKWtaxl/bFrGX/3W9L/KwJJo9EwNjaGwWBALpcT\nDofxeDyCdCCTydBoNAIN5nK5kEoXcXef/nQ9vb1K7r9/SAxtqdVqKisrMRgMgkVcKBQYHx/H6/VS\nWVlJPp8XBUYul8NsNi+xkExNTaFUKqmpqaGsrAyFQoHNZsNqtRIKhRgYGCCbzWIymZDJZJw8eZJQ\nKMTq1avp7+/n+uuvF2iyo0ePsmrVKpLJJM3NzYJu4XA4uHLlCrlcjsbGRrZs2UJjYyMul4vp6Wkc\nDgcVFRW0t7djsVhobGwUy/JjY2PccsstNDY24na7yeVynD9/HqfTKUI5ysrKBEXjpptu4uDBg7S0\ntIhQmmPHjlFTU0NLSwuJRAKlUimKFLvdzuHDh7nzzju57777WLlyJQ899JDYz/r6+ti2bRt/+ZfN\nDA+b+OhHp5mcnCQSiTAyMkIul2NhYYHLly8Tj8dJJpNs3bqVu+++m7q6Ojo6Oshms1y8eJFsNkt1\ndTXXXXcdZWVldHd34/f70ev1NDQ00NbWRlVVlUgCLEWir1+/njVr1qDVajlw4IAgdKjVakF18fl8\nTE1N0dHRwfnz58UgaYnAUllZSX9/P5///OfZvn07W7duJZFI0NfXh81mY/v27axcuZKKigpqa2vp\n6+ujrKyMU6dOsW3bNkF0+cEPfoDVakWlUvGe97yHhoYGmpqasNlsDAwMcN111zEwMIBKpUIulzM/\nP49UKmXdunX09fWxZs0apFKp8GWX0h8LhQKrV69Gp9PR09OD3++nsbGRQ4cOsXnzZtrb25mbm2PP\nnj1s3LgRqVRKMpmkoqKCXC7H4cOHqaqqEp3ntrY27HY7NpuNwcFBgYjcsGEDLpeLuro60uk0MzMz\n74j7Lqmnpwe3282//MtNTE6W8/jjNi5evEhTUxPRaBStViswf7FYDIvFIrZ5XV2dYJCXvN9ut1vY\nXwCBKPz5QJaSAoHAO1aXkskkKpWK+fl5NBoNSqWSZDLJ6OgoVVVVTExM0N7eTkVFBW63G6vVyujo\nqPgs/CYym814vV6BzgwEAtTX1//G3e0/Bi3j75a1rD9tLXek/0Ck1Wqpq6vD4/GQzWbR6XRiwLC5\nuRmfz8fw8DByuZyqqirUajUGg4F4PC4eQ6VS4XQ6GR4eXhIrDIvdqhIJIh6Pc/LkSaRSKbW1tSST\nSZFwWFZWJuwTGo0Gm83G1NSUSJ6DxS/xUnJgKTXOYDDgcDgEsuvee+/lzTffJBaL4Xa7+eQnP8nL\nL79MW1vbktdW8tHmcjncbjfj4+MUCgUsFgs6nY5gMCi6fKXnt9vtgpUtl8sZGRlBKpVSXV2NUqmk\ntraW1tZWzpw5I/Bpu3bt4vLly9xzzz14PB6mpqZQq9XccMMNLCwsAIthNKVuWzabBRYHG0sowZIv\n9LbbbuNrX/sa9957L11dXcAiUSSRSKBSqUSRMjY2RqFQwO12Y7FY6O/vp7Gxkddee41sNotMJqOl\npYVYLMZHPvIRzp49S3d3NwBNTU3ccsstDAwMYDQa6e7uFj725uZmhoaGePDBBzlw4ACvvfYaKpWK\nHTt2MDw8zKpVqzh69KgYIpNKpWzevBmlUsnw8DC9vb2i4GltbeXEiRN87GMfE8mESqWSnTt3cvLk\nSRwOB5WVlXg8HuFPv+222/jhD3/Ixo0beeqpp8T+umbNGsLhMCaTiWAwKE44stksZrOZZDLJzp07\nBRqwurqaTCbD1NQUu3fv5s033yQejyORSNiwYQOhUIj169dTVlYmkHlr1qzB5XKJE7KXX36ZdDqN\nwWDg0UcfZWFhAZlMxuzsLF1dXUilUlatWkU4HBb7c+l/MZvNVFRUMDIyIrYdvI3FO3Xq1C/8vEql\nUoxGo/h9cHCQLVu2cPLkSdauXbukALdarUxMTLBq1So8Hg+nTp2iUCjQ0NBATU0NJ06cECSekiYn\nJ5d8tn/+uUsd7dJnolgsMjMzQ1NTE06nU6yCqFQqMTdQGr4tSSaTiVWmX4cGcrUkEglVVVUAb3nE\nF36j+y9rWcta1h+LlgvpPyAplUocDgepVAq9Xr8kZbBUiBgMhiXDPFezpH9+KbhkdSgvL0er1RIK\nhdBqtbS2torH8Pv9DA0NUV9fL0I4AoEAa9euFY+dSqXo6+sjn8+LwTKTySSIHoFAQPi1FQoF2WyW\nZDIpwlpyuRzxeJxisYjb7ebw4cN0dXVRXV3Ntm3bMBqNFAoFksmkICtEIhHMZrNg9c7PzzMyMkJD\nQ4Po9BUKBWZmZlhYWECr1eLz+XA6nYKwYbPZCAaDGAwGkR7Y1NRES0sLY2NjOBwObDYbzz77LKFQ\niNbWVlF8lkJZSilyXq+X+fl50U0sbZNbb71VbO+KigrOnDmDXC6ns7MTg8FAIpGgra2N3t5egbLr\n7OwknU6LoBKVSiWYwmq1GplMRnl5OR6PB6VSyfj4OC6XS8S9nz9/nqqqKnp6eli/fj1nzpyhqakJ\nj8cjvMBbtmwR7OfW1la8Xi+RSIRNmzZRW1vLxYsXufnmm0kkEhw/fhypVIpOp0Oj0YgOl16vJ5VK\nodVqRTe1traWsbExUqkU6XSaBx54gHg8Tk1NDcePHyeRSGA2m1EqlZw9exaVSkVra6vweC8sLODz\n+UilUqRSKaqqqrhy5QoKhYItW7YwMDAgCrRYLIZarcbhcLBt2zZBcxkYGEChUHDttdcikUjo6+tj\n8+bNNDY2cuXKFU6fPs21116Ly+VCq9WKlMXq6mqCwaAYpszlclRXV9Pf3y8GPv1+v+i2liwy+Xxe\nnMSWBhB9Ph+bN28W7/3atWuZmJjA5/MtQegBS4Z7XS4Xer2eYrEotnMJZXnu3DmSySR6vR6n0ylO\n3EoDvQqFQlgx6urqGBsbEytNCoVCdN0B4WXXaDQoFIolMwFXq+Qv/22rhMvU6XRL/v9fpkQiQTab\nFcz1ZS1rWcv67yBJ8b/iKPp7UinB7veh/9euTKFQYGRkBJVKJYpejUYjgiVKRItwOEw4HGbbtm3E\n43GOHTvGo48+AsCTT/4bK1euRCqV0tfXJyLBvV6vmLR3Op3voBF0dXXR2dnJK6+8QkVFBUajkeHh\nYerr67n++uuZnp4mnU6j1WpJp9PCBvLqq6/S29tLZ2cnWq2WU6dOCQvAihUrltAATp48iVqt5p/+\n6Z8IBAKsWLFCpA/ed999qNVqPvjBD1IsFkWQyv79+9FoNJw5c0bQKkZGRkilUuzevZsf//jHogBK\nJpNEIhFWrlxJW1sbr7/+ukhDHBgYwGazYbfbRZCKzWYTHGOAXbt2EQwGiUQiwCLNxGKx8JOf/ER0\ny51OJ2NjY3g8HtRqtfCZP/HE4wA8+uifk8lkRCCNzWZDo9EwNTVFc3Oz6ITedNNN4n85dOiQWPa/\nfuv11LXX4U/4OXbmGKMzo9gqbQxNDiHXy5GoJASiASRKCQargSnPFOliGq1BSzwdJ5PLYCmzIJVL\niafiyJVyCsUCmWxGECSi0ShS6SJhIpPKIJfK0Wv1hAIhtGotJoOJQrZAJBhBLVNjMVqIBqM4bU50\nKh0LswvIi3K0Ci0OqwO3zY3D6kBZUOIZ9+CZ8FBtr6b7jW6c5U7hXb/vvvtQKpVEo1FcLhcmk0lE\nd6tUKoaHh8nlcrS3tzM9PY3P52PlypWYTCbOnDkjCDKhUIi1a9fidDr55je/iclkEh5vuVzOhz70\nIYaGhjh58qQoJjOZDHfffTcqlUqkcMpkMmKxGLFYjKmpKbLZLOXl5TidTnw+H36/n0wmQ0dHB8eP\nHxdx42NjY9x555389Kc/xWaz8ed//igA3/ves8zPz6NWq9m1a5ewbJQ0PDws0jtNJhNSqZRgMIjb\n7V4SIKRSqUilUiLQpjREa7FYRFJofX09xWKRsbExsV1isRhlZWWUlZUxMTFBPp/HaDQSi8UEL310\ndHQJFq9YLDIwMEBrayuFQkFEt1sslnelcvwiXX3sKxaLjI6OilCdUpx9TU3NL7x/NptldHQUvV6P\nUqkkGAxitVr/U5aT34d+3x35q9Gcy1rWsn73Wu5I/4FoYmKCyspKscRqs9mYnp5mfHycaDTKzTff\nLP42EonQ3d1NIBBYwqvt6Oigu7tb+IhLX8pqtVoMfsVisSVDjel0Grlczk9+8hPuvPNOgRNraGjg\n0KFDDA4OotVqBUGkNKg3OzvLkSNHePjhh1Gr1ZjNZmpra/m3f/s3Pv3pT4tOcokOYbPZOHLkCHNz\nc3zyk5+kvLyceDzOuXPn+MEPfsBnP/tZTp8+jVwuRyqVks1mkcvl9PX14Xa7uf766zEajSJx7vjx\n48zOzrJixQrx/2SzWQYHB5mZmWHr1q2i897R0cH3v/99pFIpU1NTbNy4kc7OTiYnJzlx4gTFYpGz\nZ8/S1NTEqlWrgMWl9VJHtdQdLysrY35+nnA4DCyypq8O5ZBIJMzOztLf388dd9yxGJctKVJWV8be\nw3uxN9qRGCU8NfoUifEESWmSXFuOaD5KRpHhe9Pfg+mrdooyIAmUv32T3CRHkpcii8iRaRToVEbS\nRRlKjQV5TorfE0en0VJmriYTz0FRSplKQ2A+QCwcxuVoQ6NeJJSgLTLvmyMWzkJRQzonYSEtJU+e\nnFRJQpZlIeVBYsgzm/GRz2bJmdNvv5gMMPvWpaRyoAjcAAqJEq1Ug6qgoqe/B4vGQnN1M86cE3PM\njNlu5sr0FTKhDDqJjk3XbkKv1bPSuhKfzycGSa+55hoRdx+Px+np6eHAgQO0tbVxzTXXoNfraWpq\nYu/evbz66qsolUoee+wxIpGIsOs8+eSTvP/97xfhLbBokSjt/+FwGK1Wi9FoJB6PMzk5iUwm4/jx\n49x9990EAgEkEgmrV6/m5ZdfRi6XLwl2UalUxONxwX2/On0wGo0KNvvVPHiHwyGwdMFgkJUrV+J0\nOpmYmODChQtUVFSgUqmWFL/ZbJbx8XFgkV19dcE+PDxMJpNBo9GIrritrIzY3BzBixdxp9PMv/QS\nZTIZJBLE/H7adTrkR48S8HhwaTQopFLimQxJiYQyt5uiUgkqFUWVioLVSt5up+BwUPwFQ4qzs7OU\nl5cL24vNZhPc+19UGI+Pj1NfXy861+Xl5QwPD/9a9JRlLWtZy/p9a7mQ/gNRKc3tarndbl588UV2\n79695Haj0UgkEhEdvJmZUiVTz/T0NOfOnaOzs5NCoUAqlRLkgLm5Oaqqqjhy5AgSiYR8Po/BYBCx\n4qUlerlcTjAYpK6ujoMHD3LDDTeI2OV0Os34+DjHjx+nqqoKh8MhBhrz+TxOp5PBwUFuvfVWRkdH\nyWQyGAwGGhoaePHFF/n0pz/N6tWrCYVC2Gw2amtrOXDggLA5lJBnEomE+vp6gsEgjz76KPF4nFgs\nhtls5v777+fhhx9mxYoVYrsVi0WxZJ9Op9myZQvBYJBEIkFlZSUPP/wwjz32GB/72MfQ6XT09fVh\nNBrZsWMHL7zwArFYjMbGRlGYNDY2cu7cOTKZDB/4wAc4dOgQFy9exO1286lPfYpPfOITNDc3c/P2\nm2nb/DQpTYqTA0nOR86Trc4yJ50jmoqykH6rU7X+7fdPIpOizllR5qyo4hXkF+RYFbVoi9XkomaK\nCSvFpIXgnIJ0RI887yYTsZJLmMgVFl9f9q3HejcXbQTwlJ6LAmpSaEhiJomUBFKSaEmhJIOTDAqy\nKMmgfOu6jDxSCuJn6SKhSEFSJCvPkVVmyCqyZBUZssoMKVWWtCpNSpUmrUqTUaXJqJKk1UlymjhF\ndQyvJo4n1ktW1U1OFQXJW4thb9Vkz/U/B4C0qECVt1DQqTCkbNgvX8SksGKSW7AqrcSLOfrGp/mb\nXR+g3O5CIy8Ib/AnPvEJHn74YeRyuegiT0xMCPxeOBwWvOqSR/3cuXN89KMfJRqNMjc3h9vtZtWq\nVfzDP/wDa9euJZvN0tDQIHzIKpWKYDDI/fffz7XXHn8rAn4llZWVIpRlcHAQjUYjQmHsdjsymewd\nloXy8nIOHz7Mgw8+SCaTEVaguro6XnzxxSWBPxSLKNNpVDMzqKJRdBMTSAMBcVnl9RIbG6McxG2S\nSATJL0loLOnqEvedgMx3qqDXU7DbyTscyJqa0DU0kO3oIKNWY/w5ckepMH63QrpQKAg839WqqKjA\n5/MtU0CWtaxl/cFruZD+PSmdTguE2S/TL3LelPjMY2NjS5LJSmlxt9xyC7W1tYK9PDw8TF9fHxMT\nE7S2torl7f7+ftGpvtryYbFYhFe0FB5SCsswGo0sLCyg1+uZmppiYWEBo9GIWq0WRb7f72f16tVI\nJBJyuRxnz54VgSIjIyNiybe+vp5sNkt/fz8tLS20traSzWaJx+O88sor4vWU+NKlL9xkMsn8/DyP\nPPIIMzMzomP5mc98hubmZqanp4lGoxSLRcLhsLA2lJBf1dXVoiOZSqVob2/nzTffFCEmarWa9vZ2\nXn75ZSYmJnjP+9/DcHiYS55+Pv6jz3GueZQe2yD/37GnKUquSoZrkCGJuoiMVZH310OoBiIVEHWL\nSzFhI1mUkQQkkjxKZYqEJofWpsJiAK22iEyWwsAkqeIUG1vmabJ4MOd8GPNBwpN9BIbP01lppdGm\nQ5sKoc7GyId8JOZnsCokmCQFlJkEiuxVHeTfhoosVvHZX/WHv1wFCYTU4NfAghb82sXr81op81qY\n14XxaiUs6Hz4s5eZ1OWJqfOLXXAtcAP82YUDAKjSCgwxHfqEAU2nhufffIHXDhzAmLdiyFnQ5Yyo\nZ6s48Fe9VNRUYLJXotDLUemlvPlGD8qikZ6Xz1HfWkmNyYpKKmXs4kVMCgWKQgHHWwWgRCqlqqqK\nVCqF1WrlypUr5PN5Qb/QaTTolEpsej1lajXRUAitXo+yWCQ1NYV6YQF5KIQkk0GSSiFJJDDNz7Oq\npweLQoEkHicfiaCMx5FGItx74QLuV19FHotBMLh432yWd+N4FBUK8hYLRY2GYmUl2c5OChYLBZOJ\ngsnEfCqFrbGRotFIwWCgqNNRVCgoKpWMzc5S29y82H2WyZBks5BOMzE4SJ3bDek0mUgEVSSCwudD\n5vUi9XqRzc8jnZ9H+tOfYnqLK20DclVVZDs6yHZ2kt6yheyaNf+pfeSPyHW4rGUt649Yy4X071jJ\nZJLJyUkx7JdKpaitrRXIrqu70h6Ph+uuu46enp4l8cWxWAyj0Uhvby833ngjX/7yYlrcP/yDn299\n61vCejE8PCwiqhUKBalUiurqaiYmJigUChQKBQwGg0inC4fDwlZhNBo5d+4c1dXVTE5OLmHqlgbj\nSsQEh8NBJpNhcHCQ3t5ePvvZz5JOp/F4PEilUpEqt3XrVr74xS/ykY98RDCKjx07RiKRQCaTcenS\nJUETAYSv81/+5V8oKyujvLwcn88nhgonJib4u7/7O9xuN4lEglAoRDKZJB6PMzQ0RE1NjfDlPvnk\nk6xevZqBgQHWr18vBujC4TByuZyzZ89iMrlIZpwMBhboDwwwmXkdv2yUZ3r2krky8/abmFOCsglG\nW8DfDP5mZJFaTMVyUr4sMkmUmhod5eVgNOXJar1cCXZT0ailudlOMjhCrTKGLjJDg16BIRbDmEhg\nTiaxZDKoZsMoQiG0ySTKVGqp3eMq5YclpGe1pNVqUioVKbkcvyaHX68nr9NR1OlIy+XECgVC6TSB\nZJKOdeswuVxINRqyEgndZ84QSiTwhULceffdWB0OEpkMVwYHOXX6NFW1tezctQuDyYRcoSCZSPDK\nyy+jUauJRiJo1GqkEgkapZJYKIS0WGRhbo7bbr4Zk05HIZMh6vdztKuLzuZmJoaGKDebMWs0BOfm\nkOZy1LlcZEMhrqmqQp7PQzpNNhDFd2GWfCKD22pDJ1WQzabxFqPM5cKEdTmieghqc/g1Wby6CD5d\niLlyGNRD6F0gFLIC2OPgjoJrbvGnOwquGKhOQDEKxuji39QWYevP3b/AoiXkb4EiEiQSKBQlSCgi\n41d3fd9NZUA1wKuvAlCUSMjp9aQ0GmxqNZFMhoTBgLS6moRGQ0yjQVNdTdFqpby9HUl5OQWrlaLB\nwPDICBqNBpVKtaT7G41GCYfD6H8uVbKkTCJB8apB5aJSSUahoOB0Mq/VEkil0DmdpMxmJDU11NXV\nLaF/2Gw2An19KC5fJtHdjWl8HM3gIOp9+zD+n/9DzmJBvWkT3Hkn6RtuoGg2i/uWTs5zudwSm8rs\n7Ow7UjCXtaxlLesPUcuF9O9Y4+PjtLS0iGKxRARoampieHhYdEyDwSBqtZq6ujoikQiHDh2iqqqK\nSCQiiAHJZJI33niDPXsWB55uvPF5KioqCAQCdHV10dbWRkNDA0NDQ/T39+N0Okkmk2zatEl8EWYy\nGU6ePMn69ev59re/TW1tLXa7nfPnzwuMm9Vq5fjx4yLC2ul0otFokMlk+Hw+6urq0Gq1YvhOoVAg\nkUjwer3ieUrDQ0qlkueff57m5mYWFhaYnp6mpaWFnp4eHnjgAaqrq5HJZHi9Xp577jlcLhdjY2Po\ndDrhQ52ZmcHlcnHp0iU6Ojro6OggEolw5swZ4vE4q1atEn7s8vKCXkBVAAAgAElEQVRyLly4gMvl\norq6hgsXPExMzCGRtDA0DIPBPGHdKsLBPvL2V8F5AZRpcAJ5BVJ/K+rp67Clm3ErmtEldVQayggH\nh8lkJnFXyXjm4J+RB3b/2UeIRSKkxsZo12pZayzDkU6THh5GFfBRHZZh60mhfxfmbFEqJWkwkDAa\nSRoMxFwuwnY752dmmMlkUFdWQlkZY6EQnlQKzGZGvV5UajWrVq1idHQUj8eDe8MGpqen2bZtm/B7\nX7hwga6uLnZ/+MP88+uvY37rhKq/v3/RArFmDbHZWU7E46wyGPD5fAxms8RsNqo3b+aFo0epqKhA\nq9UyNjaGuboaXziMwu3GXl9PTU0N4XCY8+fPE41Gmc9k2NPXR3t7+2IM/NgY+s5OxpJJWnfsQNPY\niEStRhmL8fLLL7NixQrS6TQnVSpWrFjB1NQUExMTGDdt4vz582zdulUQOs6ePcvhw4dZvXo1b7zx\nBtX2ajo6Opg8c4bMzAJfePhhwvPzzE6OUtnoZDowwah3FEuVhZmEh7xBStCYYCAf5mgxREgWe8d7\nIS1IMCZ1mJMGDFEd+ogOc8yAPmxAGzKgDxvRR43oEmokbxXXeWRLLkUkyFRSJCoFRYUEuU6Jyqgh\nLy+gMalQGFXk1UWsVVYihSh9E5eo7aigrt3F3Pwgk5Pj3HXXXZw7d47Vq1djtVoXT05nZwkEAtxw\nww30jY1hVCpRZbMEhocxm82Ul5eLuQqTyUQsFiOTyfzSuHO1Wk0kElmC85ucnKSsrIxgMLjEo13y\nj//88GDBbidttyPdto3zIyMoFArMhQLSAwewHj+Otbsb2d69FGUyMtdeS3L3bpL33ENRr6empkZ4\nolUqFYFAAJPJhEql+hVH02Uta1nL+v1rmdrxW9KvM7kdDodFBPDVmpmZwWKxCKZrCXN3tW8wlUox\nOzuL0WjEZrOJWOJMJsMNNyz2zg4c+Bk2m41//dd/ZceOHTQ3N4tUxN7eXg4cOMAHPvABETMtlUpx\nOp14PB4kEgmNjY1MTk4KqsbQ0BBms5krV67Q3t6O3W4nFotx5swZotEoyWSS++67j4MHD2I2m1m7\ndi3PPPMMZWVlPPDAA0tS00ZHR/nWt75FXV0d1dXVnD9/XhRnpYS8r3zlKwLnZTAY+OY3v0k6nebv\n//7v+dnPfsbg4CAdHR3ceOONfPzjH0ehUPClL32JgYEBdDodFouFT33qU9x334dYt+4DHDgwx5Ur\nEtLpWsbGlAxMLZCxn4PqY1DVDa6zoFwsamU5HbZMB7ZMFVWKCjbWd+JSKvDNzXLTTTfR09OD1+ul\nvr6epooKel58EaPXizMaZexnddQxxjXWHoyhEIqf86SmdTr8Gg1TgLmzE69SyQwgraxEXVvLwcuX\neeRv/gaT1So65AaDgfHxcb797W/T1NQkuMElG8Hs7Cxr167ltttuo6enh4aGBiKRCN/5znewWCw8\n9thj+P1+YNGj+vWvf52amhqamprwer2MjY2xevVqpFIpBw4cYOfOnRSLRQ4dOoTdbuf222/niSee\n4I477kCtVtPf3y8sMPl8noMHD/L+97+fubk5RkdHWbFiBSaTiWeeeYZNmzah0+nw+XxiEDUQCNDd\n3c3jjz/O5cuXCYVCNDU1USgU+NznPsff/u3fYrPZuHLlihi6ffbZZykWi+zcuZPDhw9TKBTYvHkz\nx44dY3JyknvvvZdEIiHsSiW7T1tbG263m8uXL2OxWHA6nUSjUQ4fPswHP/hBEokEuVwOk8nEgUMH\n8Kf8tFzbQu94L0NzQ2TVWXLqHGP+MRbSCwRyAULp0Ds+zwqJkmzQBVE3q+oNWOQ1hKcllClb0BUa\nyAbKkUQrSUWszEzHkUishMMQDEpIJn9xD0MmK2AyZVEqw1RXa3A4ZBgMSez2ItXVMjyeHm6+uR23\nW4JCESGfzwoSSEklwkdpUPiXqVgsMjk5KQZ8M5kM5eXlhEIhKisr3+Ff/nlO/bsd+1KplCAFKZVK\nyOdR9PSgPngQ9YEDKPr7Kej1JN/7XuIPPkiuuZlYLCYCnv474e+WqR3LWtaftpY70r9D5fP5d2Cx\n4O0oblgMHXm3cAS1Wr2EBFBiGV/dLWpvb2d8fJzKykqBx9Lr9YyMjKBWq7FarRw9epSbb76ZtWvX\nksvlOH78ODMzM7S1tTE1NYXFYsFut+P1eikvL2dgYIC2tjZMJhOZTAaFQsHWrVv57ne/i9FoZGpq\nig0bNmA2mxl5qxNlNBqZnZ2lrq4OmUwmmMH5fB6bzUZ7ezvt7e0Ui0U0Gg0vvfQSjY2N7N+/n2uv\nvRa5XM6JEyfQarVEo1EOHDjA2rVr2bFjBx6Ph3379pHP51m/fj3PP/8aiUQrk5N6xsfVTE09z1//\ndSXZrAIsMah9E23LNyjcdoyMfmhxe6OgxdjJGvs9aINqnAUnxqyRjvaOtwNZ0mlmjh9ndSTCwhe+\nwH1lZWinp5EMDaFcWGDjVe+NDxuj1ONxOjlbU0PEaiXjcjFWLBI2mShqtdjtdg4ePIjFYuG2226j\nubmZwcFBfrRvH2qNBvlb9hqLxbJkvyixlEvd/EgkglqtJpfL0draSm9vLwaDgdnZWWQyGQ6Hg6qq\nKkZGRgShJRAI0NjYSCgUQqVS0dnZybp164hEIkQiEUwmEz/60Y9oamri5ptvJhgMsmfPHpLJJIFA\ngFgsJsJupqenxWDnm2++yTXXXMN73vMewuEwhw4dEvYhlUolfOtO5yIGL51OMzY2Rltbm8AH9vb2\nolQqMZlMRCIRKioqBP6wxOUeGhpi27ZtSKVSBgcHmZiYwGq10tLSQl9fnyj2VqxYwQsvvIDL5WL/\n/v2k02mKxSJ1dXVs2LCB+vp69u3bh8ViQaFQLHrnZUoqDBVog1ru6bgHwwaD6LrigObmZiYmJkjl\nUvjTfsKFMEVDkXND5wgXwzx/XgGGGaLqYYYTx4iXXTX++ZZbQi1VY5QaqTJVUamwUmerw6Z2UAxr\nUWeceEcLtFfdwOxEjmBQQiymJZs1cfbsAtlsLZcuKfH71UQipeLyZv7pnxavKRQOHI48FRVvXyor\n81RWqqis1KLT5X7lcUkikVBTU0OxWFxyjAoEAoJL/ZtKrVYvLeBlMrLr1pFdt47oX/81ip4edM88\ng/bf/x3dM8+Q3rgR9UMPkbrtNvhvVEQva1nLWtZyIf07lNlsZnh4+B3RvuFw+BdGEcOiJ7o0rFco\nFKirq6OhoUF0J2GxIzEzMyPSyiorK5mYmCAQCAgetVwux+12Mzc3x/j4uEhwGxsbw+/343A46Onp\nEWERra2txGIxUqkUXV1d6HQ6QcFYuXIlBw8epKmpiUgkgsFgwO/3MzExwc6dO5mamuI73/mOIBV8\n+MMfFilyAHK5nHw+L7pfsDjg+PrrrwOLXdRisYhSqcRmsxGLxbh0aZThYT2nT69jeHgF3d01RCJv\nj16p7P2o2r+Pc9U5guZjxGRzAEhleppVjXSaPkSDooGJ7gmkBSn5fJ7rt21Ct7DA7P79yH+yF1cg\ngM3rxbKwgDL3dhESVamYNJnwWa2U3XEH56JRfGYzl5JJ/u8LzwPwuV2fJxwOMzMzg0kiwWyxIJfL\nCb1VMMpkMjZv3kxfX5/4fcuWLezfv5/x8XFee+01wbFubGxk06ZNZDIZ5HI5ly5dQi6XI5FIRFw8\nLKIQg8EgxWKR9vZ2UVw2NzfT0NCARCJhcnJySZhNiVoBiCTL6elpAoEAFy5cEAi1WCxGf38/d955\nJ7FYTJBUTp8+Lfzle/fuRa/XC4+rVCrl8uXLInSkWCzi9/tF4uClS5cYHh4Wj//GG2+wY8cOLly4\nwMzMjIhbLi8vp6Ojg2PHjuH1ekVsu9frFYSV/fv3EwqFUCqVjI2NcfnyZcxmM0eOHMFoNKJQKMjn\n84TDYS5fviwGXy9cuAAg0gRLloZTp04hkUgoFotUV1fj9XrFQOzPy+pZLOSf/+hKAA5Pz3Dq1CnG\nZ8dRWBUUdUW8ae9i8Z0Pc2nqEtlclkuxS7wx/wbZ4lXTmjJgFtQKNSaHCb1Dj11tp/FaC3aNnSZn\nE1a5FQPlqPN19Jyeo7p6A16vDK9XyuysjJkZGadOKfF4ZOTzb/uXJZIiLleOhoYCdXU5cWlszFFd\nnadUJweDQRYWFgQesKqqSuAer44pz+V+dWH+KyWRkF27ltDatUT+9/9G+/zzaL/3PayPPkquooLo\n5z5H8q67lgvqZS1rWf8ttGzt+C3p113e8/v9BAIBHA4HxWKRubk5HA4H5qsGcK5WLpdj37593Hjj\njcIqcfbsWXQ6nVjObmxc9D/29w9gMBi4cOEC3d3dIhY6nU5z9OhR3G43SqVS2CsymQwDAwNMTU0x\nNTWF1Wplw4YNKBQKPB4PXV1dIlZ48+bNIiVwYGAAn88nhpg2b96MXq9n3759lJWV4XK5mJ2dZdeu\nXSgUCiKRCHv27GHDhg0cOHCAyspKEVt87tw5KisrxRf4zp070Wg0dHV1cfHiPCrVTZw7p8XnayMY\nrKFYXPzmN5vjFBRd6Ff+DPumUaYVp1gozANgVBhpUbZwre1aVplW4ZQ5icfiaPN5zj79NDsrKqjw\n+dCMjKAbH0d9VXEQLy8nUlnJqEpFf7FIur4eeWcnd3z4w8hkMpLJJHv27BHb4P777+ehhx4E4KMf\nfZjZ2VlBVNm4cSNyuZzLly8zODiI0+mkUCiwc+dOVq5cycWLF/nJT36C3W7H7/fznve8h82bN5PL\n5Xj++ecJBoNcvnyZiooKbr31VgwGA5cvX6arq4sVK1YQi8XYvn07LS0teL1eXnvtNdatW8fc3ByD\ng4O0t7cjkUi4cuUKNTU1yGQy4vG4SF30er0MDAxw5coVrly5Qm1tLS6Xi0wmQ29vL1KplJaWFrZv\n38769etRKpUMDQ2xb98+Ll68SCaT4Y477qCsrIxcLscPfvAD7HY7CoWC6667jhtuuIF0Os3evXuZ\nnp6mqamJgwcPsmXLFlwuF6dPn8br9fLII4/w05/+lM7OTpRKJblcjqGhIdra2hgdHeXy5ctoNBok\nEonAw6XTaebn53nggQdwu90MDAywZ88eNm7cSDab5b3vfa8okHt7e+nu7sZsNmMwGNiwYQNKpZKR\nkRG6u7vp6OhgbGyMbdu2UV5ejt/vp6uri6amJtatW8fExIQgSCgUCqqrqxkZGUGr1bJu3bUAdHW9\nQUVFBYcOHSIajbJhwwYcDofgXgeDQXQ6HatWraK6uppIJsKlyUscOX+EicAE3qSXqvYqQoUQM+EZ\nJgITpOQp8lxFhAEkSDArzFSaKnFoHTi0Dlw6l7hukliZGwKrZi2eWRVjYzJ6e1PMzxuYnFQRDr9d\nnKrVBZqbc9TWxqmpiXLddTra2rJYrWlGRhbnNmZmZpBIJJSVlZFMJvH5fDQ0NCyhDf1WrA35PKqD\nBzF8/esoe3vJrFpF5H/9LzJXDTn/oWrZ2rGsZf1pa7mQ/i3pNzmY5nI5/H6/+IL6Zcun586dw+l0\nLukKAezfv39JPPXVGhgYYGBgQMQ7x+NxETmu0WjYtGkTXq8XhUJBeXk5zz33HH19fdxzzz04nU4x\n8NPV1cXg4CCdnZ3s3LlTREafOnWKN954g40bN1JdXc2pU6dEWMrAwACnT5/m7rvvxu12o9FoiEQi\nnD9/nhMnTlBXV8eNN97I0NAQOp2OyspKnnnmGZqbm4nFDJw+bWR6uomFhVYCgUXygFyepapqlnJ7\nP9bOHnzG/YzJLhLShyhQQIWKRnkju1ft5tIrl3h418MU/QGKp09jm5jA6fHgmp9HPT1NqYxIGwwk\nm5pINzXx3PnzqNaupeP972f0rfewpqaGffv2ceXKFb74xS8Si8UoFArIZDLC4TBf+tKX2L59O/ff\nfz9jY2NUVlai1Wq57777MJvNPP744wwMDJDJZOjs7OTxxx/n6NGjfOlLXyIejzM9PU1lZSUWi4V/\n/Md/ZMeOHTz00EP4/X5kMhllZWV88YtfpK+vj4cffhitVit8vS+99JLwp1+dDlhdXY3P56Ozs5Nw\nOMzFixfJ5/OsWbMGi8XCs88+S0dHh4htVyqVLCwssGfPHqqqqli3bh3RaBS5XI7VauWb3/wmW7du\n5atf/SrRaFSk/33nO9/h+9//Pt/4xjfI5/MkEgkcDge5XI6/+Iu/YMeOHTQ0NODz+cQqyJEjR7Db\n7Tz66KO8/vrrzM/Pc+ONNyKVStmzZw9tbW1IpVIKhQJSqRS5XE5/fz8zMzPce++9hMNhisUiVquV\nF154AY1Gw5133snMzAzZbBa1Wo3T6eTpp5/m05/+NB0dHUs+E1/96ldxu91s3rwZr9dLLpejrKyM\nWCzGT3/6Ux577DERxV2KSv/+97/P6tWrkcvlonBMpVKiO67RaIjH48KWYjabRWHu8XgEdrG6ulqg\n8t73vvcteV2vv/46R48e5aGHHhLM92KxiFwu5+Ufv8wd772DobkhhuaGiEvjSIwSvEkvSUWS+cQ8\nc/E5/Cn/O44BCqlCFNdOnRN1Vk2LqxU9TgqhCmKzFcwNVTPab+byZQl+/9uDfXZ7ns7OJI2NYa6/\nXk1LSxSZzI9KpXrHahr8lgvJQgHNj36E8R//EZnHQ/L224n8zd+Q/yXDkr9vLRfSy1rWn7aWrR3/\nhSoFoiiVyiXeaLlc/kutHFcrGAyy5l04rO/mtS5pbm6OW2+9lXw+z/z8vHiuAwcOkE6n6e7uFgSQ\nU6dOUVZWhlKppKqqilAoRCAQwOl0smLFCk6cOEFNTQ2vvfYaZrNZoPdUKhXhcJiKigo2bNiAxWJB\nrVZz9uxZ0uk0GzduFIzmUgf8xRdf5P777+f48eNotVq83jjHjhkZHHyUvXtrCQQWwxcMhgzt7QHk\n8peJ516j/rYwxaYsb0y/QTAdhCJoQhruLr+bLe4trNW1YhgYQv/6FSKHB6h+8QHKQm8Ph+Wqqpgu\nL+eYTodhyxZYswZjWxvhSISJiQn2er20GI1sb22lxWAAFpFhJUpJybttNBrxer2cPHlSbP+LFy9i\ns9mIx+McPHgQrVYrUiVLyMJSAazT6XjllVfo7OwUy+YHDx5ELpdzzTXXoFAolsSql2wYpf2oREm5\n5pprxIBmfX094XAYjUYjuv+l2PRVq1ZRKBSEZ70Uh97U1EQmk0GlUjE7O0s8Hsfv9/ORj3xEWEZ+\n/OMfi5jp1157DaPRiEwm49y5c8TjcRwOB6Ojo9x1110kEgmqq6v53ve+h0ajIZvNUl9fT1VVlcAf\nZrNZZDIZkUiEXbt2ic/H4OAgHo+HtWvXcvvtt4t47PHxcV599VVUKhX19fViH/b7/RSLRYLBIPX1\n9axevZpIJILVahVM9asTB69WKRK+FI6STCaZm5tDoVAwNzcnMJSl7VLax0srC7BYSJf233Q6jdvt\nFpSdI0eOCF93KBRCrVaTTqeZmZlBo9FgNpuXIC5LFppisUhjYyPZbJZkMolOp6NYLJJMJJEmpNyz\n6R5hxYnFYpw7d45NmzaJE/BMPoMv6cMT93Bh5AISo4S5xByeuIf5xDwDwQFmo7O8NP3S0g3iBn2N\nHutOK426alRZF4WQm+hsJX3DlRx6vZr/+0IFxNxUOCu59toM112XYcOGDE1Nuf8a54VUSvKee0ju\n2IH+qafQP/EE9ptuIv7gg0Qfe4ziVWSRZS1rWcv6Q9ByIf1fpFLhUiJxyGQyamtrf+PHsdvtTExM\nvAM3dbVX8TOfWfzi/cY34iKx8OzZs8TjcXQ6HSMjIxgMBuRyuYhB7unpQaPRUF9fz+zsLAqFgpG3\nOLSwyIq+ugDYsGEDAwMDVFVVIZFIOHLkCOFwmKeeeop8Po9arSafz6PT6cTrU6lUAmE1MDCA1Wrl\n1KkF5uffx5EjJgYH7eRyMmSyLA7HII8+msbtvkzRfJ55s4fXhl9jUjLJeWkR7YiWZmkzN0tuZpd+\nJePff44tB66wSTqA4soVJG8Na/o0Gry1tSj+/M+JtbURbW5GV1nJxMgIT3/5y+zs6KC9sZF5rxe9\nXk9zczO5XA6JRML4+Dg+nw9AWFqUSiXr169nenqavr4+nE4nTU1N9PT0kMlkmJqa4lvfWolUKuOD\nH9Sh1+sFl7pUVJaG9KxWKx0dHUSjUTweD3q9nhUrVhAOhzl+/DirV68W/uHq6mo8Hg9VVVW4XC5i\nsRiRSISqqir6+vowmUzMzMywZs0aGhoaSCQSjI+PMz09TTabJRAIEI/Hhd/d5/MJv/WZM2dQKBRk\nMhkqKiqQSCQ0NDTw5JNPotFoyOVyqNVqtFotNpuN2dlZZmZmxCBasViktrYWm83GU089RTwep7y8\nnJqaGsrLywkGgwwPD6NUKhcLwmSSWCyGw+EgnU4zNDQkOq8lnnkpDVAikVAoFMRg5ebNm+nv7+f0\n6dPAoq+5ra2NM2fOcOXKFaxWK9lsFp/Px8jICDU1NZw8eZJwOIzJZKJQKAiUW2mYtlTglpeX8/rr\nr+NwOAgEApw9exav10tZWRn19fXkcjnR7Tt//jxKpZL29nb0ej3z8/M0Nzfz5S9XUSwWeeyxLHV1\ndQwODjI0NMR73/teysrKSCQSnDhxgsnJSZxOJ3Nzc0sClORyuUgDbW5uRqlUUigUOHHiBBaLBZ1O\nt4RgodfrSafT4rYSgcSld1Ghr8CWtOFyuZag4wqFAqOjozirncwl5piLzzGfmF/saCfmGJ4bJi6N\nM5Y7wbx0now7szh2cRVI25ctZ2+4gh9fcsPxClRZFw3ldlbWlbPj+jpW1Kqwaa1IJVIWFhYIBoPi\nvbTb7b/QuvYLpdEQ++QnSdx3H4Z//md0Tz+Nev9+go8/Tnbdut/ssZa1rGUt679Qy9aO35KuXt4L\nhULE4/El8bbvdtuvo5JH+rrrrsNmswnSRkVFBW63m/HxcW6++SYAfvazg7jdbrq6uoRPs4QeO3bs\nGFqtFp1ORyaTweVykc1mmZ6epqKigtOnT2OxWNi0aRNyuZy5uTn27t0rIpRbW1tpaGhgfn6eU6dO\n0d7eTnd3N9XV1WzYsAGDwSCW7G+//Xbm5uZ45JFH0Gr17NsX4JvfHGdhYROzs4tLw253gObmUWA/\ntXXTRDVe+ugj6AgyV1wcEjRnzRhn9XzIuI77E1bsff0YLl9G9Va3OSmXE2ppQX/LLSRXruQ7vb3M\n5vM0NDRQXl5OXV0dGo0Gj8dDKBRi3759LCwscMstt7By5UpmZmb4j//4DywWi+holk52xsfHxf2n\np6e57rrr0Gg0+Hw+QYo4ffo0jz76KHfffRcAH//4JygUCmg0GoaGhlixYgVarVZEn9fX1zM6Okpr\nayt2u51AIMDly5dpaWkR9I1t27aRSCR4/fXXaW9vp7Kykq6uLlavXk1ZWRmXLl0iHA6zevVqlEol\nO3fuFESVUox5IBAgnU6zZs0aJBIJPT09yOVykskkzc3N3H777VgsFmZnZ3nppZcYGRnB7/fzoQ99\niM7OThKJBP/+7//OxMQEN910EyMjIzgcDpRKJXNzc6jVau666y6+9rWvsX37dlatWsXk5CQ/+MEP\nePDBB/nxj39MY2Mja9asEVagUCjEI488QiwWEx3ehYUFTp48yezsLCMjI+zevZvGxkYCgQA/+tGP\n8Hq9GI1GbrzxRpqbm5FIJIyMjPCzn/0Ml8tFX18f11xzjRiY7e3t5X3vex89PT1YrVYMBgPZbBav\n10ttbS1zc3P4/X5aWlrEe1QsFmlpaeG5556jpqaGzs5O+vv7GRoa4uGHH2Z+fp6RkRHa29vJZDIM\nDw/T2tqKRCIhFovx+c9/DoCvfvVrOJ1O9u/fz+7du1mzZo2wAnk8Hp5//nluueUWdDodnZ2dAIyM\njDA9PU1dXR179+4V2MBgMMjJkye58cYbKRQK1NbWYjAYKBaLTE9PMzc3R2dnJ5OTk+j1ehQKBaFQ\niLKyMgwGAyMjI9TX16NSqchms0usR++m0ipUbW0tEomE2dAs54bPobFrmE8uFtyeuIf5+DyToXk8\nsXmiBd/bEe9vSVJQYJLZcenKqCuvxKVd9G5L41KaXc3UWGtw6VzoFLrf6BgIoDh7Fsv/+B/IZmaI\nfuYzxP7n/4Rfsir3u9SytWNZy/rT1nIh/VvS1QfT4eFhGhoaSKfTRKNRUcD+PH/111UmkxFL6rCI\nuXM6nVy4cAG5XM6tt24H4NChLsLhML29vaxbtw69Xi++zP1+v/CpVlRUCC+u1WpleHhYDJ+F3ipS\nSzaDUChEOp0WCYEKhQKbzUZvby9er5cPfOADhEIhDAYDSqWSZ599lr/6q8/R3Q2vvKJlbGwNyaQZ\nqbRAY+MsDscpVqwYx+VOMJwaZlg+zIXMBWLKGFKkNMrr2Rxxc6/Xgu3Vo7SHw2IYMOZyMV1VxbFC\ngfmGBu76whd48T/+g/HxcQC2bt1KW1sbBw4cYNeuXYTDYWARxXX+/HlGR0cxGo1Eo1FyuRzFYpGq\nqiomJibw+/1YrVZBzTAajWIoVK1Wi4CcZDIp/Mq33HILP/zhD3n66e8A8M///HUsFgvhcJhCocDU\n1BQSiQSLxYJUKmVkZASTyUQ6nUav1xONRlEqlcJKk0wmWVhYoFgs0tTUhFQqRaFQiKI3l8ths9nw\n+XyYTCZuv/12xsfHSaVSSKVS2tra+O53v8v58+d5+umnCYVCFItFzGYzH/3oR9FqtcKjK5fLKRQK\neDwerly5QqFQwO/3C453Q0MDxWKRUCjEli1bCAQCFItF7HY73d3dtLS04HQ66e3tJZ1OYzQaaWtr\nY3JykpmZGW666SbxeWxtbeW73/0uX/nKV0ilUvT19QGg0+lYs2YNe/fuFSEcpW5rWVkZZ86cEZaY\n0ozA/Pw8oVAIqVTKtddeyxtvvEE0GsXpdNLc3Mz4+Djr1q0Trx2goaGBgYEBent72b179yLajkVK\nzIULFxgcHKSlpYXKykrBXk4kEuIE4Atf+ILo8C4sLPDEEywSOOIAACAASURBVE9gsVj4xCc+wdq1\n1wAwMzPL008/zZkzZ/jqV79KMBgUFhOXy8WTTz7JX/7lXzI0NCSe3+FwiKLa4/Hw5ptvks/nUalU\nbN++HblcLj6nyWRSHGdKQUe1tbVLGM/Dw8MCY1faX6RSKW63+1eypOPxOB6PBwClUklFRcUvnd3I\nFrJ4E176pryc7k9w5MIkg7NeUkoPGGZRlU9T1M+SkUTfcV+DwoBD58CpdS4ZlnTqnOKnXWtHIV3K\nr5ZEo5i+8AW0L71Eet06Qk88Qf4PIP1wuZBe1rL+tPWHcUr/R6jx8XGkUilms5lQKMTMzMyvvtMv\nkFKpZPXq1YIDbDabSaVSBINBtm59e/21paWFM2fOkEqlsNvtRKNRZDIZ2WyW6upqUVgWCgWqqqpE\nh21ubo5UKoXT6cRms6FQKMjlcmJJuK2tDZ1OR0NDA9FolEQiwezs7P/P3puHx1me9/6f2XfNjDTa\nNVpGm2VZlmx5wyvYBmNDwBAgZkmBBHLaBJKctE1+JE3KSUranLRlaUsv1rCvjU0DBox3g7zKsmxL\n1r7vo9GMNPs+5w/xPrEwyUnahOR3ou91cfnyWMzM+76jd+7nfr7350t6ejoGg4Hy8nLMZgu7d48y\nPPwN7rxzPR6PCaUyxoIFvSxefJRNm6KcO3+ItKo0hsxDPNr3Di6lC5VcRd60nlsH8/hKr4ql3YPI\no92kZDKGrVYOl5QQXr6cfrud0Mfbw3K5HEUqhXtmhnvvvVccfyqVEsORp0+fFr7mtrY2EYNeU1ND\neno6TqcTk8mE2Wzm3Llzokhdu3YtAO3t7TidTrq6urj11ltxOBzCNnPgwAF6enro6Ojglltu4Wc/\nm319h8PB+fPnqayspK6ujsbGRiKRCDU1NaRSKb7zne9www03UFZWhsfjwWq10tfXx0svvcT69evZ\nsmWLYPdaLBaeeOIJ4emVuolKpZIVK1Zw9OhRBgYGKC8vJyMjg0AgQH9/P/F4nIKCAnp7e8WAnOQn\ndrvdhMNhgYYLh8NkZGSQlZWFUqnEbrdTVVWF1+vl3LlzLFy4kLfeegu9Xk9RUREqlQqXy4XBYOD0\n6dM8+eSTrFmzhvHxcRYsWIBSqeTb3/421dXVaLVa1q9fTzKZFJaLmZkZioqKmJycxO12C/qD2WxG\nqVSi0WgoKirC7XYLyseCBQuEFSaVSuFwOLBYLOzdu5cDBw5QVlZGeno64+PjnDt3jkAgIM7v5OQk\nBoMBm81GZ2cnJpOJZDLJ6tWrUalUTExMYLPZ+OCDD7jnnntQqVQiDTAYDPLCCy9www03cObMGTIz\nM0kmkyKUx+l0isAbgKGhIYqKiujs7OTAgQN87nOfE9z18fFxcS0kbGUymZwzuJebm8uOHTsu+d2f\nnJxEoVBQUlIirEQS5eWTQSl5eXk4nU7y8/N/axuZwWD4rRb5KrmKfGM++VX53LputpBMJODdd8fp\n7i5j/34VZ89qQeknr3KQBctbWLJhCrVthIngOOPBcSYCExwfP44z6CSWjM15fhkybDrbL4trfQ45\nhhxyvnoZhauzqPiX58j73CaUf/sTItu3/1bHOq95zWtev0vNF9K/B8ViMZLJpAhLMZvNeDwewff9\nbSX5Xm02G6FQiPb2djIyMtDpdJckgEn8ZUAkx8nlcgYGBoDZjpqERVOr1aLz5/F4qKmpYcGCBWg0\nGiYnJ9m1axeTk5PU1tZSWVlJYWEhLpeLCxcukEgkcLvdxON5PP98EUeOFDA4uAyZLI7D0c211w5y\nww1y7HYL+9rO8dzAu5wxnsE/5kc5qmC1L5sb2gq4tcFJtncGmGGqoIDTK1cyUFLC9KJFjASDdHd3\n893vfhdZVxfDw8MUFRVRUlLC3r170el0s97PnBzi8Tijo6Oik1ZbW0trayt+vx+Hw0FVVRVTU1MM\nDg7S399PaWkpQ0NDNDY2Cj720qVLxflcunQpLS0txGIxZmZmRHy7lPbW0tKCy+Xi/PnzwNXArI82\nHA7j8/k4fPgwFRUVaLVa2traUCqVaLVaTp06RWdnJ3a7naNHj+L1ekXnuaurSxRH4+PjpFIp2tvb\nycvLY+HChWi1WsF6NplMeL1eTp06JZjcMpmMxYsXs2fPHsbGxn45tHaRRzkrK4tQKCR2SlKpFGaz\nmTVr1hAIBDh27BgWi4Wvfe1r7N+/n0AgQCKRIDs7G51OJ5CHkUiE/fv3k5aWJpjlfX19WCwWZmZm\nxP8rl8tJT08XvuAnnniCwsJC0tLS+OCDDzCbzeTm5rJgwQLC4TBDQ0OkpaWxdetWjhw5gkajER16\nSVqtlq6uLlatWoXNZkOv15Odnc3U1BTj4+O88cYbZGdns3TpUmZmZnjjjTdQKBQUFRUJq4Pk3y0q\nKiKRSKBQKLDZbCIqW6lUEolEUKlUbNy4EafTiVwuZ8mSJTz22GNiESRJiluvqamhp6eHp556ivLy\nciYnJ2lvb+fOO+/E6/UyOjoqEIE9PT3YbLZPJWFIKi4uxufzMTAwINCVcrkct9v9W99HftdKJpMf\n3wNmd4yUSiVVVX5KS09yyy1m4nEr77yjYM+ebA4/X8WBZ+Xk5cXZti3MV7eHqNsUQyaDZCqJO+wW\n/m3Jwz0emC24R/2jNDmbcIcvOua7Zv/Qjn6N3H/7Dpn2KnIu6mxLhbf0d53y0pCrec1rXvP6Xegz\nKaQff/xxmpqaMJvN/NM//RMwO33+8MMPMzk5SWZmJv/zf/5PjEYjALt27eLAgQPI5XLuvvtu6urq\nPou3+TuTSqVCLpczODiI1WrF5/Ph8/lE8IhUhMpkMtLT039tHK7P50Mmk5GXl4fH40Gj0VBaWkpn\nZyehUOhjK8Ls1t74+DiTk5OsXr2akydPMjExIQIVbDYbmZmZDA0NodPpBJvX4XAQCATQ6XTk5ORw\n7Ngx/H4/FRUVlJWVceLECTIyMkhLS+PIkSMUFRWxcOESvF43o6Pb+PKXlwNyCgt72bLlDNPTz7Jk\nSSGfu/tz/OuH/8qx5mNMy6dRpuQsm9DxxSYVt7fEMEdGcVqt9JUt4lngQlYWd3zzm5w+fZpwOMxl\nixbx/jPPsHbtWg4dOoROp8NsNuNyuRgcHCQvL4/c3Fw8Hg8nT55Eq9VSX18vOo1yuZySkhJisRgW\ni4XR0VGKiooERaSjo4O8vDxqamp44YUXmJqaori4mIaGBgA2bNiAy+VCo9GwZMkSTp06RWNjI+vX\nr2d6eppYLDZnax5gxYoV7Nmzh+bmZr7xjW9w9OhRwuEwq1at4vnnnyeRSFBSUiL8ukuXLmV4eJih\noSEOHjzI1VdfTSKRIJVKoVAo6Ovrw+PxiCLR6/XicDgYGhrC4XBgNpsZHBzk9OnT5OXlcdlll5GX\nl8fOnTs5c+YMLpdLFLIej4dt27YxMjJCPB4X2LZgMEh+fj4jIyMUFxdz3XXXkUwmGR0dRSabDfYo\nKCigs7OTWCwmUgoNBgPhcBir1cqZM2coLy9HoVCgVqs5evQot9xyi1jUdXZ20tDQwMGDB1m+fDkm\nkwm5XE5mZibHjh0jLy+P4eFhZDIZgcDswGxvby9lZWX09PSwfv163n33XRKJBFu3buXYsWPC52+x\nWIhGo2RnZzMyMiIIJHV1dXzwwQekpaVRU1PD+++/T25uLmVlZczMzAj8XUNDA3V1dbzyyiusX7+e\ntLQ0vF4vTU1NlJeX09bWxo4dO0SHV9oJSk9Pp76+Xlz7NWvW8MYbb1BVVUV9fT39/f00NzdjtVq5\n++67gVnrWUVFhSg+S0tL6enpwWKxEIvFGB0dJZFIoFarycvLE6QQKSVSpVKJUJ54PC4WAJJGR0cp\nLCwEZhfMY2Nj2Gy2S9CZvwv5fD6GhobQaDQiKCcrKwuZTEYkEkGhUKBQeLnpJhnLljWTllZES0sJ\n776r5cUXDTz9tJHy8hi33BLixhuD5OTYsOlsLMpYdMlrBYNBvF4vKq2KoCL4y4FJ3xjuQ7/A1X2G\noWAXLfZJ9oYmCMVDlzyHRWMhW59NljYLq9JKjiGH4oziObYSm9aGQv5fS3Kc17zm9aerz8QjfeHC\nBbRaLf/2b/8mCumXXnoJo9HI9u3beeutt/D7/dxxxx0MDw/z6KOP8uMf/xiPx8OPfvQjHn300V9b\nbEr6Y/NIh8NhvF4ver0ek8kkUg1dLhdZWVmkUimcTic5OTmYzbN5wjMzM4TDYcxmM1qtlt7eXhQK\nBalUioyMDMLhsHido0eP8v7777N582Yx7FdcXMxf/dVf8dWvfpXMzEzKy8tpb2/H5/OxadMm9u7d\nS1paGps3b8bj8XDo0CEikQhFRUXodDqWL18ufLCBQEAEMqSlpVFUtI3//E8bFy7UE4uZ0eunSEvb\nybJl57FapznTdQbtci09hh48Wg/yFKwb03HHqRCfbwN1XMkpi4XW/HzYsoX2YJDTp09TUFBAdnY2\nBw4coK6uDp1OR1NTEw6Hg40bNzI4OEhmZqbowk5OTrJ+/XqRyJeTk0MsFmN8fJyVK1ei1Wr58MMP\nsdvtpKWl0dvbi9lsxmQysW/fPnw+H1arlUAgQCgUYuHChYLNLLG5P/jgA3Q6nbDLLF68mNraWvbv\n34/T6WThwoWsXr2avLw8QqGQYBK//fbbZGdn09DQwPr167FarXzwwQc4HA46OztRKpUUFhaKIrGv\nrw+FQoFWq8VqtbJmzRqCwSCHDx9Gr9czOjqK2+2mvr6ekpIS9u3bJ/CJkse6pqaGkZER2tvbufXW\nW9m/fz8nTpygrq5OIOukiPXdu3eTk5NDdna2SLdcsWIFsVgMo9GIw+HA+zEW0O/3c/LkSdxu95zB\nSblcTk5ODiMjI9jtdurr6+no6KC1tRW73Y5KpSIUCmG1WkkkEsLTHIvFuOWWW4TvfGpqiqGhIXp6\neohGo8RiMSoqKpiYmGBwcJA777yTw4cPc/DgQTZv3oxCoWDv3r0sW7aMlpYW8vPzMRgMZGRkMD4+\njkwm48SJE1RUVOByuVi2bBkul4uWlhZqamq46667OHnyJAUFBWi1WgYGBrDb7UxNTfHqq6+i0WhY\nsGABPT09eDwerrrqKvR6PWfOnGHp0qWEQiHOnTvHhg0bMBgMnD17VoQLnTx5EovFgk6n+7iInLVj\neDweEWkudeazs7NF8Slde7/fT0lJCWq1mlAoRH9/P2VlZUxOThIKhcjKyiIajYrBSblcTl9fH2az\nWfjsLRYLGRkZHDp0CJPJhN1uZ2xsjMnJSTZu3PhrkZm/raRQqOzsbDIzM2lra2N6elo0Q9xuN7FY\nDI1GQ0ZGBjKZTOwq+Hwy3nlHxxtv6Dh5UoNcnmLDhgg33xxky5YwF9u5e3p6hJ3N5/Ph9XrF/IAk\n/YsvYv7ud4nV1jL13HPMpKlFZ/tiSkmvq5fJ8CRTsSlcIRdJknOOSSFTkKnPFEOSOYYc0d3ONmSz\nJHMJJrXpknMx75Ge17z+tPWZDRs6nU5+8pOfiEL6G9/4Bg8++CBWqxWPx8ODDz7Io48+yq5duwC4\n4YYbAHjooYe4+eabfyNLxB9LIe3xeAiFQnNucF6vl+npacLh8CXH0tHRgcPhEN0pg8Egtm79fj9G\no3EO/i4ej3P48GFefvllvvnNb6JWq5HLZyOvH374YcxmM9dff72wLxQWFtLY2MiuXbvwer08+OCD\nBAIBlEol8Xic73//+xQUFHDvvfcSi8UIBoPk5OTwyiuvEI+niESupKfnGs6ds6FUJlm8uB+N5iWs\n1kb+x3338G7/u5wMHues/xxJUiwelXH3uRQ7WiCtcCGRjRv5l44O3hwc5LrPf561a9ficrnQ6/Xo\n9Xr+/u//HoVCwf33308wGCQWi5GVlcWjjz7K4sWLKSkpEVi5eDyO1+slHo+jUqmorq5GpVKJbqZE\nWJB8rH6/H7vdjtPppKWlhY6ODiwWi1icSNHXhw4dYseOHeIzlJ+fz6uvvkpGRgbr1q3DYDAIlF1r\naysTExMsWLBAeIclJNq5c+cYHR3lu9/9Lq2trYTDYRYuXMhLL71ET08PGzZsQKVSoVarRfF45MgR\ntm3bRnV1Nf39/ahUKoqLi3n//fd5//33efbZZwWCbs2aNfz0pz+lu7ub5cuXc/PNN4vBwe7ubp55\n5hmMRiNPPvkkJ0+eBGY75X/+53+O0WjkL//yL4VFyGg0sn//fpqamrjsssuor6/nxIkTZGZmUllZ\nyZtvvsmZM2e47LLLcLlcRCIRcnNzaWxsRC6X84UvfEGEy1RVVaFQKPjpT39KXV0d27ZtY2pqCqVS\niclk4mc/+xmRSIRHHnlkTkHX1tbGz372M7Zt28by5csZHR0Vnuldu3bR2NjI448/LgJZLBYL9913\nH+FwmA0bNrBs2TLxWdi5cyfnzp2juLiYH/7whwwPDwt842233cbOnTuRy+WcOnVKUG30ej179uzB\n4XCQTCZpbGykuroam83GqVOnqKqqoqioiKamJlQqFfX19XR3d6NQKCgrK6O/v18UxxqNhl27dlFR\nUTFnFy0YDPLaa69RVlY2Z6YB4Pjx44IjfnFxGA6HGRwcRKVSUVJSIh6XrrN0D5GCcsxmMwqFQtBf\nLvZIu1wuOjs7Bdf8v6tgMEhjYyPr1q1DJpOJe19jYyODg4OUlpaK6PXp6WmBXlz1KSmFvb0K/uM/\n9Lz5po7RUSUWS5Jbbw1y550B1OpZLOfF1pdQKMTExMQlHnDtnj1Yv/pVErm5TL38MolP4EK9Xi8+\nn09QkxLJBGO+MZp7mlGlqxgLzlJJLi68xwPjzERnxHO8ff3bLM1aeskxzBfS85rXn7b+YB7pmZkZ\nrFYrgPBVwmwn42I/ZHp6+h+FH/C3kdVqJfixv1ev1xMOhwFEkfxJmc1m2traWLBgAfF4XBThLpcL\nv99/ycR9LBajv7+f4uJiFi9ePIcjfd111/HII49w9dVX09vbi9FopKurC6PRyMDAAFdddRVHjhwh\nIyODaDSKz+ejqKiIjIwM3njjDbRaLQaDgaEhH9PTn+f06VUEgzmkp/u5/fbzrF7djko9zasNhxgr\ncHHriZsJyqKUTMv43tkUt7QqcIX0eNas4b3/by2mykr6+/vxajR4LlygqqqKhQsXMjo6islkQqvV\nikLAYrFgNpsFY3jRokWcOXOGNWvWMDMzQygUQqVSUVBQwM6dO9m4cRb7J/28XC7H6/UyMzPDkSNH\n0Gq1aDQaGhoayM/P5/jx4+j1ekpKSkhPTycQCNDV1cX7779PdXU1Fy5cEF/Qra2tVFdX09bWxsKF\nC9FoNIKjnJ6eztNPPy18yHv23IhMJmf58qeIRCLEYjF6enoEsk2ihAQCARwOB/X19SLZsLm5mUOH\nDqFSqdBqtdTV1SGTyWa3sj8efnvqqacoLS3FYrHw0ksvoVAoGBkZ4b777kOpVIruek5OjuiWy+Vy\nUTglk0lKSkoEf1qn0wle+OLFi9m9ezdjY2M888wz1NbW0tHRwXvvvUdJSQlZWVl4PB7sdjsajYaR\nkRHy8vLEsOW6deuora1FqVTy7rvvotFoRJe9urqaRCLB+Pg4ExMT5OXlMTExgdVqFaSP06dPk0ql\nKCoqwul0kpmZSTAYFJ1ciUIhcYiTyaTgSEciEZqbm7FYLExNTQlutF6vp6+vj+zsbKLRKMeOHUOl\nUuF2uzlx4gQlJSXCVpKbm4tSqcTpdJJMJlm+fDkul0uw1/Pz8+nv7ycjIwP4JRljbGyMYDDI44/X\notFo+dGPJujt7UUmk5GVlTXn91WlUonP58V2jFQqNeeze7G0Wi0TExMs/wQzWS6Xo1AoxOyDyTS3\nQ+p0OqmpqZnzmM1m4+zZs7/qdvVbKxwOi8j2i2WxWGhsbGTFihWiYDWZTPj9fsFn/6QcjgTf/raP\nv/orHw0Nal56ycCTTxp44gkDq1cruO8+WLs2ivRSEuf8kve0ZQuu114j4667sF13He4XXyS2eLH4\n98nJyTkLEoVcQYG5gHBamLLiXz1kGYwFBW+70lr5G5+jec1rXn86+qMYNpTJZJfclH8T7du3j337\n9gGzEcA2m+13/dZ+YymVyjmvL034x+Nx0RWTwhQkhBXMBq4Eg0EikQgzMzOCMuB2uzGZTGg0GgoK\nCpicnMRkMhEOh0kkElRXVzM6OorNZuONN2aJAC+9pCeZTIogiaysLNH5lCwaCoWCTZs2iQLd7/cL\njvBNN92EUrmA117LZf/+fMJhJZmZ7fz93yepq+unub+RxsgJ9o++z2CxG1MEbj0Ht3bqKS24jOR1\n17LvOnjx5z/nL//H/8D8MXqvqKiIgoIC9u3bR0dHB7FYjOrqaoaGhmhtbSU3NxetVovP5yM3NxeZ\nTIbb7cZoNIp0xnXr1ondiyNHjswZZMvIyBAhJKFQiO7ubnbs2IHRaCQajWI0GnnnnXcYGRnh+uuv\nx2g0kpGRgVwup6ioSGDKvvKVrwjM2FVXXcW///u/o9VqRZCIFHAjFT+1tbVkZ2dz8ODswu/P/7ye\nI0eOiO60FCPd19dHRkYG+fn5dHV10dTUhMViwePxkJaWRl1dHcFgUHCPZTIZFosFmUwmaClGoxGF\nQkF+fj7BYBCVSoXBYBB2gGQyKdBpWq1W2AaAOb5baUBMr9fj9/sFucTn8/HVr36Vvr4+ysvL2bZt\nGz/96U8pKChg+/bt+Hw+otEoixcvpqOjg66uLrHjoVKpBO9YOl/Nzc1s2bKFUCgkiteVK1fy5ptv\nYrFYsFqtDA0NkZ6ejt1ux2KxzFlARyIRXnnllTndPilCPDMzk9LSUlasWEEgEECtVmO1WsnKyuLc\nuXNUVlbS0tLC6OgooVCIVCpFXl4e7e3t3H777cRiMeLxOPX19ezZsweZTIbD4WDhwoUEg0F0Op0Y\nRL04jVAa3ExPTycvL4/9+/fz8suzRdiqVbtZu3Yt5eXlKJVKfD4fWVlZ+P1+pqamKC0tFTsOfr8f\nuVwuAl4mJiaE/UFSLBYjIyMDq9WKTqdjfHwcvV4vFg3SrsLIyAjJZBKLxUJ2djZpaWmfOneRlpb2\nO7s/GgwGBgYGsFqtKBQKlEolGRkZtLa2UlZWJtCR0messLAQv9//f339G26Y/W94OMaTTyp48kkL\nO3YoqapK8hd/keSLX0yi18922D/1ubZuJX74MKrrrsN2883EX3uN1JWzaNDp6WmsVuslpJNf+VwX\nqZDCX/vvn7z3z2te8/rT0h+skJZIFlJxlPZx9Gt6evocrJTb7f6VU+2bN29m8+bN4u9/yO21i7/w\nfT4fbrd7jh1jcnKSmZkZent78Xg8YhBrcnKSrKwsAoEAl112GXK5nOnpacxms6B1DA8P43A48Hg8\ngrSQnZ3N4OAgLS0twOx24+joKG+99RYlJSW89dZb3HTTTeLL+fXXX6ekpISBgQFaWloE4s7v95NM\nJpmczObZZzfS0GBHoUixceMEOt3TZOeP8F5PJ08Pt3Le4ESWgk298INOLRnTefSUL6Th5iU0mUx0\nt7UJOoTX66Wuro6Ojg6WLFnCj3/8Y7Zs2UJPTw+1tbUcPXoUk8mESqUiKyuLzs5OnE4nkUiEVCpF\nKpXio48+EiE0586dm9PVSyaTnD9/noULF9LR0YFMJsNoNAory6lTpwgGg8jlcuRyOaFQSPCdFy9e\nLDr6Uqd5enqatLQ0YrFZDJe0LS1h6CorK0lLSxMhIjabDbPZLDqVwByf+xVXXIFMJiMUCrF06VK+\n/vWvk5OTg8fjYeHCheh0OrKzs2ltbRWF7FtvvUVhYSHRaFQUKjBrM5GG4EpLSzl58iQVFRW89tpr\npFIpgsEgarWa3t5ecnNzGRoa4tixY8KvKi2W1q1bJ/CA4+Pjwr5isViQy+W8++67WK1WotEoY2Nj\nyOVyfD4f4+PjyOVyotEoCoVCBLRIfuR4PI7VahVBKMXFxdTW1vLKK6+g0WjYtGkTb7/9NslkEofD\nIfzSUvhKaWkpBw8eFOdBKtTy8/M5d+4ctbW16PV6Mch25MgRrrrqKiwWC5WVlfj9fiwWC83Nzaxc\nuZKmpib+4R/+QRSfLpeLd999l1gsxokTJ0SRKRWgHR0dYig4kUggl8uJRCJoNBouXLhAZeUvO5FG\no5EzZ84IFKKkNWvWcOzYMRYvXkxnZyf19fX09PSg1WpF0dzS0iIWldKC99SpUyxdupTTp09TVFQk\naCLd3d0UFBTwzjvvEIvFyM3NJRQKMTU1Je4rXq+XwsJCFAoFbrdbdM4PHjzI4sWLBf+9v78ftVr9\nO70/KpVKGhoahEd9eHiYSCQiGOtSJ9pqtYpF5W/6+lotfP3rcMst0/znf2p5660Cvv51Nf/rfyX4\nsz9zcd118V/9XDYb8p07yfjiF1Fu3477+eeJXH45Go2GM2fO4HA4xI8Gg0F8Pt9/67xEIhGys7MF\ns/wPoXlrx7zm9YfVH8wj/eKLL2IymS4ZNhwaGuKxxx4Tw4Y//OEPeeyxx/5/N2woFQwXq6OjQ6DL\nrFYryWSS6elpMawWi8VQKBQYDAZmZmZEp0Ov19Pb20t6ejp+vx+AdevW0dzczLe+9S26u7sAcDhK\nuffee/F6vTQ2NorO1eTkJLm5uZSUlFBaWkpLSwtpaWmEw2GczkJ6enZw9mwJKlWY+vqTbNx4jj7/\nh7jkZzmu68KnSVLsgdta1SycLqLbUkTm9dez78ABpqamyMzMRKlUMjIyQm5uLnfffTf33XefKLCc\nTidVVVXcc889nD9/ng8++EBwrpVKJffffz8NDQ309/cLNNjIyAgWiwWNRiM6+tI5npmZweVykZ2d\nLfjH0WiUwcFBUThbLBbsdjtGo5Hh4WE8Hg+NjY3A7JZ6bm4uLpdLFM4rV67k5ZdfFsWrx+Ph9ttv\nZ2xsjIGBAdFpHRwcFGEY0WiU6elpXn31FQC++MU/w2g0YrPZOHPmjBgYHRsbY+nSpbhcLioqKgR/\nOxgM4nK5GB8fx2azYbfb56DE+vv7CQQCHD58GJgtPHz+ZwAAIABJREFU1MfGxoRlo7e3F5fLRUFB\nAU6nk0QiwU033YRCoeC5555Dr9eLYvi2227jjjvu4Gtf+xrBYJDs7GyGh4eFz1uKsZesFeFwmNbW\nVhwOB319faSlpWEwGBgZGcFms2EwGBgeHsbv95OZmYnP50Mul1NQUIDFYuHcuXPY7XYikQgul4uV\nK1diMBiYnJxELpej0+mYmZkhPT1dJEa2tLSQl5cnvNU33XQTHR0dvPbaa2RmZoqUwBtuuIG1a9fS\n2tqKRqMhLS0Nj8cjOs/Nzc0cPHhQsKuDwSC33XabWABIlh+JviLZQcbGxjCbzSIlMDs7m5KSEkKh\nEJFIBJjtxiYSCc6ePcs111xDfv5sETM8PCIsLGq1WnDWfT4fqVSKNWvWcPbsWc6dOyf8zFNTUyxc\nuFAMMk5OTordjry8PGQyGfv27aOyshKj0ShQjE6nk6KioktmLSSr0fHjx8VnStrZ2f47Zi1HIhHB\nypcGqAsKCpiZmeHChQvI5XLRLEmlUtTX14uGiGRp+TR98t9GR0cJBIK0t2fy7LPZnDyZjsWS5Mtf\n9nP33QGs1k//+pJ5vdhuuglFby9Tr79OrL4el8uF2+3GYDCIBbuEE/xt5fP5GB0dRafTYTKZmJqa\noqSk5NeG2Py+NF9Iz2tef1h9JoX0I488woULF/D5fJjNZm655RaWL1/Oww8/jMvlugR/t3PnTg4e\nPIhcLueuu+5iyZIlv9Hr/DEV0p8WbnD48GGCwSBbtmwhGo0ik8lQKpX84he/wOPxsGDBAnJyctBq\ntQQCAVpbW8nKyiIrK0tQQJRKJdFolMnJSV577TWuvPJKrrvuc8BsRPhrr70GQFFREVdeeSVOp1Mg\n0QYGBti6dSu1tbUcOZLkxReLOH7cjF4fxm5/i4f/rZimvud4s/tNzurcqONwXYeMq/wV5Cy8hpES\nBwq1mlgsxt69e1Gr1fzoRz8SOwvJZJLvfe97BAIB6urqxDkpKyvj5z//OUuWLCEUCvGtb32L8fFx\njEYj4+PjvPjii1RVVWE0GnG73SQSCdLT01EoFDQ0NFBfX4/ZbBZb+9PT04KYsGnTJpLJJDKZjFgs\nxp49e5iamuI73/kORqOReDyOVqvlmWeeoauri87OTrKzszEYDIKvLCUH/uAHPxCdJYvFwg9/+ENq\namr48pe/jEajYWJigpKSEpqamti5cyfT09Pcd999fO5z1wLw+OP/zq5duygqKqKsrEz4vzUaDY2N\njeTm5nLDDTcQDAZJJmeJAQaDgVdffZWVK1dy+eWXi4JWoVDwH//xH7z//vvcd999cwJNvvGNb1Be\nXk4ikRCFn16vF8SHqakpbrvtNlGUKJVKnn/+eerq6sTApN/vF4V5IpGgr6+Pn/zkJ+Iz1t/fz0MP\nPUROTg719fVixyiRSHDo0CGUSiVpaWmsX78epVL5sVd8D16vl0QiwY4dO4RNJRKJsHfvXkwmE7W1\ntbhcLuLxuAg/8fv9qFQqli5disfjwWAw4Pf7GR4eFosDKamvuLiYN954g23btuHxeERBJJFlzGYz\nx44dY/PmzbS2topFy8mTJzl48CD3338/OTk5wGxH+kc/+hE1NTWYzWauuOIK0cU9c+YMY2Nj5Obm\nikAimN0h6+rqYmpqCpPJxI4dXwDgueeep7S0lP7+fjZu3Cg+p3q9XtiW3n33Xa6//npRLEsDlVdc\nccWnWgOkLrVk41AqlahUKvbu3YvD4aC0tHTOz0ciEbq6upienhbXQalUYjQaKS8vF8f9u1QkEhHE\nGkAsJvR6PUqlklgshs/nE5Hlw8PDyOVycfxSF14KE5I68hdHmieTSXFdzp/X8thjRvbs0WEwJLnr\nrgBf+UoAmy15yXuTT05i274d+fQ0rp07iVdWkkqlxGf8kzaP31SJREIMfErDliMjIwwNDf2Xkmv/\nu5ovpOc1rz+sPhNrxze/+c1PffwHP/jBpz5+4403cuONN/4+39LvVVJHWdrqh9mY70AgIPyLFw8Q\nGo1GBgcHWbJkifjyycnJQSaTcfToUZYtWyb8tjA7vHTu3DlisZiIDYfZjpTdbqehoYHq6mqOHDlC\neno6bW1t2Gw2+vr66OrS89hj2Zw8mYnVGuXuu9tJtz9B6+TPufnAMAF1ihov/E1nHgXKJfx83Iv8\nzjtJKyvD/HEB4PP56OvrY/369RiNRtGFdblcpKen09HRwerVqyksLKS2tpaRkRGqq6t56623+Nu/\n/VsxlCX5UaXib/v27YKmoVareeWVVzAajUxNTZGWliYKRbfbjVarpbS0lGQySSAQQKFQoNFoRBzy\nnj17SE9Px2g0Ckzbvn37KC8v54orrhCdo/Pnz3PkyBFqampoaGgQzN3W1lbKy8vxeDyMjo4SDAbR\n6/UcPXqUvLw8BgcHufrqqz+mzMwW0lK3MRaLoVKphDc5Go2iVqtJJBIMDw/T3t4uir+Kigo0Gg02\nm429e/cKX3g8Hic3Nxer1UpnZyc9PT0YjUYaGhpYtWoV+/fvZ/PmzSxfvhy1Wk08Hqe3t5f33nsP\nh8MhXl9Sbm4uBw8e5Ktf/SoLFiwQj+/bt4/XX3+dDRs28MADD5CTkyOKIolJXFFRgdFoFEXx5OQk\nb7/9NrfffjvNzc0C4eZwONi5cyd33303o6Ojwjstl8spKyvjww8/pKioiNraWmCWwPDee+8xNTXF\nNddcg0qlorS0lFAoRCwWY2pqCofDIYpogMHBQQoKChgeHmbRokXE43EikYgIJxoZGaGyspLOzk4x\nUCt1ZysqKgTbWdopuPzyyzl9+jSf//zn6evrw2g0EggEBFIvGAzOGRCWMGxdXV3ccsst4vErr7yS\nN954g4KPI6ulTq2kUCiEVqtlfHxcHMvF3Vy5XC7+TCaT5Obmkvx4xgAQv/uAuEd8UhJBxWQysW3b\nNvH42bNnOX/+/O+lkJY8+dJnxu/3U1tbO+e6yGQyurq6SCQSVFZWig5wMBikv79fDGhK5zmVStHR\n0SHY5NIOBkBdXYxnn/XQ1ubjX/7FyOOPG3nuOQN/8Rd+vvKVAAbDL/tCycxMpl59Fdv27WTcdhuu\n//xPEgUFc87lf0XSDt/FnXOJp/1Jtve85jWv//f1RzFs+P+acnJy6OzsJBKJCO/lxMSE8Abb7XYx\n1KbT6XC5XKLTOTg4iEajIRKJUF9fLwrLvr4+URjZbDYRPCGTyXjhhRcBUCpnI5yTySRms5mioiIx\nBHby5BQnT17Gq6+uwmCIcNMXjpBX/BMOR49wVu5Hlw7b+40snC7FlbEYxWIH9hUrkD/2GCMjI+h0\nOpLJJKlUSvhIPy323Ol0Eo1Gue2220Q6XFVVFXl5ebz++uti+MtqtRIOhxkeHhbDYgMDA1gsFpRK\npQi96OvrIysrS/igU6kUdrud6elplEolzc3NmM1mQTuxWCyMjY2xdu1avF4vXq+XDRs2cOzYMYLB\nIHfccQcTExPiGDZv3syePXtQq9XceeedYmFiMBj43//7f+N0OlEqlaxatYpwOIzBYOD1119Hq9Xi\ncrm444472Lbt5+Tk5DAxcaXAv6lUKoLBoPhitdvtuN1u3n77bb70pS9htVqZnp7mmWeeYd26dZw4\ncYLKykrcbrewpki0FZvNJs59Xl6e6PJddtlllJSUEAgE0Gg02O123nzzTXw+Hx999JEYaLXZbCLQ\npaCggO7ubmB2yHflypW88MIL9PX18b3vfY/x8XHMZjN9fX28/PLLYjEiDblqtVoRUR+NRsWAZkFB\nAS+88ALBYFBcB4/HI4qkgYGBj8ODZgu+tLQ0pqamMBgMXLhwgZUrV6JSqQgEAlitVoqLizl+/DiD\ng4NzhhBhli2cl5eH3+9nfHxcHEt+fr5A0anVaoF41Gq1tLS0CMzc0NAQwWCQBQsW4HQ60Wg0ZGVl\nMTg4KOK8JeSdw+Ggvb1dMKBnZmYoLS1lcHCQvr4+enqsFBQU0NY2ifrj3ZpPUzKZFPHqUjDT9PQ0\nH330ERaLhUAgIKwaqVSKnp4e7HY7zc3NOBwO8bwShlFCZErFeiwWY2JiApfLNWduBKC2tpYXXnjh\nN7t5/Y6kVCrnYA6lHbc5/Ge9XgQQRSIRxsbGgNlGgUSg+VVhMlVVcR5/fJpvfcvPT35i4h//MY3n\nnzfwrW/5uPXWIFKzOVFYyNTLL2P7/OfJuPVWXLt2kfxvDgZKnf5PSqKpzBfS85rXn5bmC+nfg2Qy\nGZWVlaKbqdfrWbBggShCT548yeLFi0kmkzQ0NKBSqcTQX3V1tbAq7N27l/r6eo4fP86qVatEXPLA\nwAByuZze3l7i8bgYPvJ4PDQ1NZGfn4/FYkGlUjE9reXZZ/N5++0cZLI4m7c+Q2blw+zWX8CrTrHQ\nL+Ov20qoLPocnjq76AJJdAyJQT02Nia8uIODgxQWFmK1Wvn2t79NYWGhKIhycnIYGhoSNgi1epYo\ncvToUfLz8+nu7kYul4vY7JycHBKJBDk5OSJyO5lMijRFhULBzMwM99xzj+j0vvLKK+j1epqamli/\nfr2wQ5jNZlpaWsjOzubDDz8UfuSLt+lPnz7Njh07CIfDaDQaWlpaMJlM9Pf3c/bs2TmF9MDAAOvW\nreP06dPCItPR0UEkEqGwsJCxsTExiOf1epmYmCAejzMyMsLChQsFHSIzM5OmpibkcjlXXXUV7777\nruhIb9q0ie7ubjHgV19fTzQa5ciRI8JHXFhYKHzgarWaRx55hPLycvE5sFqtTE5OCu/s0aNHycnJ\nYcOGDcBsINLRo0cpKysT50cqBt577z2sVit2u51Tp04JKoQUAuL3+8XgrEqlwul0cubMGbKzs/F4\nPDz77LOkpaURj8cJBoOkp6ezb98+/vqv/5pFixahUCgYHBykqakJrVZLUVERaWlpyOVy8vPzGRwc\nFIjG9PR0gsEgSqUSs9ksrD9tbW2imzoxMSGGCLu7u0UASiwWo729XaRGFhUVCatYMpkUXfv29nYW\nLFggfPinT5+moqKCU6dOsWbNGlEMNTU1ic5lXl4efX19KJVKHA4HIyMjFBYWUlRUxMmTJ2lpaSGV\nSrFp0ybhw/+kpAXi+fPnhQXNZrMJBrjdbp9z/yguLhaEjwsfYyP9fj/Hjx9nxYoV2O12RkZG6Orq\nEp3RsrIyWltbxfClJAkb+VlIpVIJZn40GkWr1aLX61GpVGg0mkt+Xi6XMz4+jslkErYIibwj2Yl+\nncrK4jz1lIfGRj8PPZTGAw9YeOopIw884GXr1jAyGcSrqnA//zzpO3aQfscdTL35JinTpcEqv6ky\nMjIYGxubg9OTFgOf1Xme17zm9cejz2zY8LPQH4tH+lcpFArR2dkpUs1kMhnZ2dnE43HOnz8v/LwS\nMk8a4CkpKRHoOqkYdzqdPP/88+Tn59Pa+g0UCjl1df/O4OAgS5cuRa0209y8md27FxFLJli/8dsE\ny57nZOYM6jhcO2ziqvQtLLvxmzR8HMudnp4ueM05OTn4fD5Onz5NIpHAarUyPDwsvvw9Hg+LFy9m\naGhIdP18Pp8gMUgFSm5uLu3t7Xi9Xurr62lvbycnJweHwyGef8WKFRgMBrq7u2lubiYajVJbWyve\nw/LlywkGg6LI0el07N+/n4KCAhQKhUjQc7lcggKiVqtpbGwkFApRXl4uuntOp5P8/HxBFhkaGhJD\nXDKZjJqaGhKJBC0tLcjlcm6//XZMJpNYTNTU1FBVVcXPf/5zxsbGmJ6epqXl6ygUCior/wmr1cr4\n+DiVlZXYbDbxBSv5XTdt2kR9fb0opJuamvjoo4/YunUr1dXVNDc3o9FoqKur46233iIYDNLX14dG\no0GhUIhdDmn4LpFI4PF4RIT61NQUBw4coLq6mvz8fLFzIDGyZTIZubm5qNVqgsEgY2NjZGRksGrV\nKkZGRhgdHUWj0YhOoN/vx+PxCKKF2WwW1qWRkRHUajU+n4/MzExmZmYwGo0i8nrhwoVEIhHa2tqE\nRWf58uXEYjFSqZToQPt8PtG51ul0xGIxRkZGKC0tZWBgAJPJhFKpFJ99n8+H3W4XyX9SQIdCoRDh\nOAMDA4yMjKDVarHb7VRUVNDS0sLY2Jjo7qrVavF+nU4nXq9XFENGo5GysjJcLhdGo5H09HRSqRQj\nIyNiaHLdunV8//tZgiN99uxZrFarsMRcLJ/Px65du4hEItTW1qJQKGhpaSEajVJXV3cJLxqgsbGR\nkpISsajT6/XY7Xa6urooLy8nGo3S1tZGOBymvLyc9PR0Ghsbicfj2O12sRs2OjrKzMwMl19+ubDm\nSOdbKvzi8TidnZ14vV6Kiop+o1jxWCyG0+kUAUdarVakckoWq2AwiNPpZNWqVXi9XnGfVCqVZGVl\n0d3dzeDgIJs3b55jlTh//rwYGP5NlUrB3r0afvzjNLq6VCxbFuXv/m6GmprZ663Zv5/0L32J6IoV\nTL34InMiFH9LjY6OMj09jUKhwGKx4HK5KC4u/tScgN+35j3S85rXH1aKBx988ME/9Jv4Xcnn8/3B\nXluv1xMMBn/tz0iJfBKNQ/pyVqlUnD17FrlcTnZ2NhkZGYRCIZLJJOPj41RXV4siQaFQiO5fX18f\nFRUVvPLKDoaHs9m+/SyhUBiP53JeeWUHZztSrNz8BcLX3kur4zjKWIS7hoq4w/RnXH71d1DnLqDp\n3DlUKhVjY2OEQiEWLFhASUkJ4+PjzMzM0N7ejtPpFPHhgUCA5uZmdDodJSUlIm45NzdXRHWXl5cL\nTrXb7SY7Oxu5XE5lZSXp6eliul+r1VJVVYXT6aShoYHjx49z7bXXsnjxYvbt2yfCUCQGcm5uLgqF\ngomJCUEIWb16Nfn5+eTm5gpcWjQapb29nbKyMvLz8/F4PIKPLHV/Q6EQHo8Hp9PJFVdcgdvtJjMz\nk0AgQDgcFoxcu93OmTNnqKiooL6+nunpac6dO4darebYsWMUFhZy7NgDeL1l5Oc/jcvlIpVKEYvF\ncDgcwjMupV3ecccdZGVliVRHiXFdWloqCliLxcL4+DgjIyMEAgHR6dXpdCiVSsLhMHV1dRw8eBCN\nRkNFRQXRaJSGhgbKysoIhUIUFhaSSCRE2E0ikRBBLxaLRQy0ut1uUcBHIhFBMWhra0OtVmMwGDh/\n/jwOh4PMzExGRkZEIuTAwACFhYUsXLiQRCJBT08PAMXFxWzYsIHJyUk0Gg2rVq2ip6dHWEby8/PF\nwqu1tZVVq1YJ7rDZbCaVSomifHp6WuxayGQyERPv9XqF/aG2tpbBwUHOnz+PQqGgv7+fmZkZ8vPz\nRRc4IyODSCRCMBgUux0ymUwUe4ODg6SlpZGdnQ3AyMgIWVlZyGQy4WmWfidTqRQ1NTUcPnyYhx5a\nxZkzctavP0AgEGDxRSEgFysej4twIcnOUFRUxPj4OJmZmeTk5MyxPYRCIdH1lDz00syF1+slGAxy\n/PhxSktLyc3Npa2tjdHRUZYuXSpi3BOJBG63m5GREdauXYvb7WZqaoqsrCy0Wi1DQ0PIZDKi0SgH\nDx6koKCAwsJCBgcH6ejouCQ98GK53W7GxsbEMHRPTw+RSISJiQlMJhNGoxG1Wo1arUaj0Yh72fj4\nOIWFhcTjcRoaGigoKPh452xaDBdKdh3J4vSbSiaD0tIEd9wRJD8/we7dWp5+2oDbLae+PoqyqoRE\nURHGJ59EMTZGeMsWkP32+QXSNZB2OiRfvcT9/6z1yVCeec1rXp+t5q0dn6G0Wi2jo6Niy13S/v37\n6ejo4B//8R8xGo2iW/n+++/T0dFBPB6noqJCPJ5MJuns7CQUCokvH4BAoIj33r+GoGYCx6ZVeMpb\n+EgBVw5puNK9Gsfau2kdHGL19deTkZFBMpkkKyuL3bt3097ezuOPPy78fYsWLeKpp55iaGiI9evX\ns2bNGpLJJHl5eZSUlPD0009TV1fHsmXLsFqtWK1Went7OXToEKOjo2zZskUUlQqFgpqaGlpaWsjN\nzaWwsJCqqipisRh+v5+TJ0/S2trKE088IWgmNTU1PPDAA1y4cIH6+noyMjJIJBJkZmaKn5G6h/39\n/SiVSsrKyggEAnR2drJs2TJqamqwWCwCuZdIJAR/eHBwEKvVSk5ODm+//TYAW7duFUOg4XCYN998\nk1OnTrFx40YWLVokwnE++OAD9u7dS1paGlu3buXVV2fP/6ZNm3j++efR6/Xcc889IiTlyiuv5MUX\nX8RqtfL2229z7bXXkpmZyeTkJLt372bFihW0t7eL8BUpQMfpdNLX18e9995LZWWl2AmQ4r+3b98u\niu7y8nKuvPJKnnvuOUZHR7nvvvtEV3FsbIwjR46g0Wi455578Hg8hMNhiouLKS8v59SpU0SjUW65\n5RZKSkoIh8McPHiQo0ePEolEeOihh3C73cRiMbZv384jjzxCV1cXK1eu5OqrrxYx4Hq9nn379uF2\nu/H5fKxdu5ZYLCY6nbm5uVRUVOB0OsUgot1up7+/n/Xr14sURZ1Ox7p163juuecE0q2mpga5XM75\n8+cZGhrC6/XyN3/zN2RkZJBKpdiwYYNYlJSVlbFixQphOcnKyuLQoUMoFAruuuuuOQXr22+/TWdn\nJ9dcc42wX8lkMsbHx0V0uTQcKenDDz9EqVSyZcsW8VhxcbHwPn+apLCltLQ0qqqqkMlk4tjUajVd\nXV0UFRWh0+nw+/2CADE9PX3J88bjcTo6OtiyZYs4ltWrV3P06FG8Xq/gtQ8NDZGRkcHWrVtJJpNi\nx0hSeXk5HR0dDA0NceWVVwob1tKlS7lw4QK9vb1zuMuSJP69xNfW6XQ4HA66u7sZHR1l9erVc4ap\nfT4fjY2NOBwOMjIyxM7Rxo0bxW5LSUkJExMTxGIxbDYbGo3mv4SlA1Aq4bbbglxzTYif/tTE888b\neOcdHd//vpcbb7wRZW8vpocfJrp8OcHbb/+tnz8cDouGASAWOZ2dnXOY4/Oa17z+NDRfSH+GikQi\nmEwmgYuD2RAZKRpbiqqWfIZjY2Oo1Wqmp6c5f/48Op1OdJmMRiNWq/WX4TXKME+8N4Z++7eYzJtk\nIAz3DmRzzcI7ybr3RlQaDXv27KGoqIi9e/eK0BOVSoXdbmdgYICmpibq6+sFQUCr1RKJRESXULKW\nSFvjkUgEhUKBz+cTyDa/349arWblypVi8MxkMqFWq9m/fz+lpaWkp6cTiUTQarWoVCqGhobQ6XRo\ntVrhS5WioPv6+kT6WCqVIplMMjU1RUVFBX19fZw8eZKVK1cSCoXYtWsXMpmMQCDA5z//eXw+H+Fw\nmGXLluF2u9m9ezcbNmxAoVCIrnAgECASiZCfn09xcbGIMi4uLiY/Px+n04nNZuPEiRMiOXDDhg38\n67/+K/fffz/9/f3i+mZmZqLT6cSgmzQkKAVVwOyg0sUUmyuuuAKr1So6xxKPWepWS9f4o48+Etdg\n7dq1PPvss9x///0Ac768ZTIZGRkZ7N69m5KSElKpFENDQ2Kgrbe3VwTa+P1+UqkUbW1tPPjggxw8\neJA33ngDlUrFokWL0Gq1ZGRkcPz4cWEDaGpqYvny5Rw6dIhrr72Ws2fPEgwGyczMZPPmzbz33nuo\n1WpBt5CCcgyG2Rj7rKwsMjMzRSiLVqtl7969XH311YIxLc0LRKNRotGoWMSlUikuu+wy0XGVaDTS\nUGsgEGBgYIC1a9cKT3MymRTx4OXl5YyPjwvighQe1NTUhMfj4cKFC2JgcHp6WiRLflJSmNGslWzW\neuDxeATT+tMkIQItFgt9fX2kUilsNhtWqxWTySQ899FoFJ1OR0VFBQqFgo6ODvR6vTh/ErvYYDBc\nUmhWV1fT2dnJ8uXLKS8vnzOk6Xa754QHSUpLSxNouYu1YMECDh069KmFtJTC+UlJUeSf7MpKoTir\nVq1Cp9MJ7zogutZShLxE83G5XHPoMp+UFFp0sR2kuLh4zhCg2Zzi7/7Oyxe+EOKBB8x8/etWXn1V\nz49/9NesamrC/P3vE1u8mNgnItX/b5qamhI7F5JkMhkajUbMccxrXvP609F8If170szMDGfOnBEF\nQHV1NXq9nry8PMLhsCAnSNvJUkjDqVOn5iDg0tLS0Gg0mEwm4vG4sBs0NzeTSkH3xAIyN36FcP0L\nuA0Rcpzw3dO53HHXPzNpm/XOhSIRrB9vbQ8ODlJVVSWoISaTiePHj5Ofn8/4+Dj//M//LFB7ks9S\nOgaYLXClokCyf0hbztLWcU5ODl6vV1AjZmZmxHb6yMgIw8PDgj6QTCbJzs6mvb1dkDpgtusmpbut\nXr2as2fPion4NWvWcOLECUZHR6murhaWnlWrVvGLX/wCg8GAy+US3lppm18KbVGpVNhsNsbHxwmF\nQqKoa2xsFPgyyWsq8Z7z8/OB2S9MqTA9cODAnKALqUCVfL4Xc3Glgq6trY0XXngBrVZLOBzmgQce\nIBKJcO2119LZ2SkWRuXl5VRVVdHZ2SkY4jBbxHV3dwvbzPT0tHiPWVlZaDQasT1//vx58RlzOByc\nPXuWyclJiouL0el0BINBTp8+TSqVYt++fVx99dUi2VDyGOfk5LB06VK8Xi+xWIza2lp2796N0Wjk\no48+4uabbxahFM888wwGg0F4o6Ut+vz8fAoLC+nv72fZsmXo9XpRRJ8+fRqZTMbp06e5+uqrRXF4\n4cIFAoEAdrtd2IRglvghhQpJXmtAJIKqVCr6+vr40pe+JJ5rfHycN998k2XLlqHVaoUFRQrZkclk\nWK1WEbWuVqvJzs5GpVIxOTkpBi1h1nIRjUaZmpqag7erqKigq6sLjUbzqdv7JpOJVCqF0+nE4XCI\nz5HP58NmsxGJRERUvdTxVCgUlJeXMzIyIogWGRkZmEwmOjs7L3mNi8/TJyXx5z8pifX9SUlpmZ8m\nabjzk4pGo9hsNgYHB8UAdCqVore3l8zMTGKx2CXouUQiQW5uLjMzM+K6/B/23juwzfM89/5hEXsQ\nABe495Y4NKllDduRvOPEzqib2K0zvjhN2iZZ3ZOcAAAgAElEQVRukp4enzbtSducNKfNaXyS1k6c\n2InjvS1ZtoYlasvUpERxgiQIEABB7EGAwPcH/T6fKMux4+TU32l4/SebBN4F4n7u57p/l2RX+nUa\nGRmhrq5OFM6pVIqRkZGr/l57e5oXXvDzi1/o+M53TFz7kWLuv+en/PWlHvLvvRffzp3kfgMLiUql\nEgueK8/lg3bRl7SkJf3fq6VC+rdUPB7n/PnzqFQqCgoKKC0tJRqNcujQITZv3iyGBw8ePEhjYyOh\nUEiQMWDhi2xkZASj0SiKRCms4IknnqCyshKFQrHIrxiJRDg94+GN3DC+8sfIVubYMaDgU6r19M05\nyOtu4dy8glqDQfCPT5w4gU6nY3x8nBtuuEEEPESjUYLBIFqtlpmZGeGxtVqtvPrqq1xzzTWi2yb5\nu1OpFOXl5ajVaqamphgeHsZoNIriyGQycerUKerq6gQy7oUXXqCjo4NXX32VW265RRR/Q0ND6PV6\nFAoFu3btEoW7tPX99a9/XWDMJicnKSsrIxwOk5eXR2dnp+gcShSKlpYWQRaRukZSiEVtbS2hUIg7\n77xTnLPT6RRdVa1Wy8TEBIDoVhcXF3Pu3DluuOEGEfrygx/8gJ6eHvbs2cP09LS4L/39/RiNRkpK\nSpienubo0aPkcjmKi4uFj/KBBx5g586d4r58/etf57vf/S5nzpyho6MDl8uFSqUiPz9fEEWkGHlp\nd+Dw4cPs2LGDRx55hB07dghk3YULF7DZbJw6dYrbb79dePa1Wi1PP/20KPKlYVWLxUIulxPJmXV1\ndeK5lAbvpJ9VqVTkcjnm5uY4e/Ys69evx+Vy8dRTTxEIBCgrK2NiYoKOjg4RyS4tUmZnZ7HZbILH\nbTabRWHq8XhEJ/Ktt94S4SxqtZrS0lKSyaTgrMMC2zqbzYrPhUajYX5+XiD6KisrRZqepLm5OVHg\nZDIZ0Tk0Go0cOnSIzs5OBgcHuemmm8Tv7N+/n4qKCgwGA6Ojo4tixQsLC5mfn3+Hf7eiokIQQ6RF\njlSsa7VaWlpaCIfD7N27l1wuh8lkElxlj8cj0lAzmQzDw8Miwv5qA3dyuXwR/i6bzXLmzBm2bt16\n1b9TFotFPB/SPZ6bmyOZTGKz2ZiYmFj0PseOHaO7u/uqryXtVEkUGen9paTGYDAoaCK5XI5MJsOy\nZcuYmprCaDSKwj2RSCCTyQR5Rtqley9JPv/Lu88SzzqZTF51MSGXwx/8QZzt25P89V+b+O8/rmKk\n6gkem9xC/le+QuAnP1n4ofchu93OpUuXxM5dNBoVvvcl9N2SlvT7pyVqx28ht9vN6dOnWb58OQ6H\ng5MnTxKLxZDJZCxbtmzRNm82m2XPnj1s2LABp9PJqVOnUCqVtLe3U1VVxf79+4nFYrhcLpLJJEaj\nkba2NiYnJ9m2bRvT09Mk55K8OXOQx88+hVM7iCkJd51SsU62Enf3FubfjkpWKpVUVFRgt9vRarXE\nYjH8fj+Tk5MicOPywkDq7I2MjAh03uDgIBaLhdLSUpHmptfricViFBYWUldXJ9IJc7kcRqORSCRC\nPB6nubkZj8dDOBwmEolgs9lE+ldxcTEKhYK5uTlB3HjjjTdQKpXs3r1bDJVJscKf+cxneOONNygv\nLxdf4OPj42zfvp1MJkNeXh4+3wLD126343Q6mZqaYnJyUnip5XI5MpkMq9UqEs0sFouIZVYoFKxd\nu1YMEcJCkbFs2TKmp6fJZrMMDg4KPnJbWxuHDh0ikUhw9uxZzGYz2WyWZDLJqlWryGazDA0NiQjz\n6elpDG8var74xS9SUFCASqUinU7j8/l46KGHqKurw2q1YrFYxCICFrzqL774oiCjBAIBurq6KCgo\noK+vj1gsJrjOkUhEdI+z2axAxkmEmH379lFSUoJOp0Or1QoSx9DQEHfddRcHDhwQ8ck6nQ6TyURb\nWxuvvfaaGHScnZ2lp6cHvV7Pc889x/T0tLiWBoOBz33uc/T39wOIzmw4HKaoqAiFQoFSqRTvm5eX\nh9lsJhQKkZeXh9PpFLsxErlEwslJdgyPx0MqlWLDhg2MjIwwPDwsFmsdHR2MjIxgMpnEMUhMdZ1O\nx0c+8hFef/11jEYjKpVKsI1NJhMej4eBgQHUajVzc3OUl5eLYdrx8XHGxsaEDUpC00l+Y4lEkcvl\nxE6T0WgUixaJKqJUKnE6nczNzYnOd0VFBZOTk9TW1i7qDEs++csRa5dLWpxLuzSJRIKuri4KCwuB\nhe5oPB4X9ilY6HSPj48Lywsguu3S8yyFHjU0NIjFu5QGqFKpRPGayWSErclms+Hz+aioqECj0eBy\nuYjH46Jza7FYKCwsFIExkj9eJpN9oIjumZkZUXxfLilR8f0M3+3apeH++818OvCv/M/sVwje/w3i\nX/ny+z4GaQi7uLgYvV7PxYsX6ezsXLRL8R+lJWrHkpb04WqpkP4t9Morr1BTU4NOp6O4uJixsTEm\nJycJBoNXTWbcs2cPeXl5PPzww5SVlYkQhS996Uv4fD7h0czPz8ftdlNQULAQlWvTscu/i2dHn8LP\nLHUzcO8xLfZUB721Duy1tWi1WgYGBtBqtTQ2NlJSUoJarcZqtTIzMyO++AKBgPBYStYKqeN4//33\ni66jVqvl6NGjXLhwQcT4SimEc3NzGI1GTp48SSgUorq6Gp1Ox/nz58WX5r59+wCwWq243W4qKiqQ\ny+UC/VVUVEQikWBiYgKPx4Pb7cbpdArv8sjICA6Hg5qaGpH0Z7VamZ2dBaC2tpZIJCIQWVJnXyra\n33jjDWw2G3q9XgxuDQ8PU1NTI7qSUsHY19cnwlikYiaXy6HX6zl+/Dh9fX0olUpKSkpwOp3YbDbS\n6TQ6nU4QMKQC7PDhwxiNRsrKyigoKECtVoutebfbzY4dO2hqasJgMBCNRhkcHOT5558X3crGxkbi\n8TgulwuTyURZWRlNTU0C/yb5ZCcmJkRCn9FoJJlMkkgkxD245557BG1CqVTy85//nF27dtHS0oLZ\nbKakpITx8XFCoRDT09Ns2bKFTZs2iWvj9/vZs2ePwMxJi5H5+XnGx8eZnJwUA1eSz/X06dPAQvEq\nDaNJxVtlZSWtra309fWRn5+P2WxmcnJSWJvGx8exWq2C9yzdC7vdLugfkn+2oKAAs9nM66+/jlwu\np6CgALfbjcFgYOXKlbz55psiEjuVSuF0OoVXXqlUkpeXJ4pP6VmXdjQkK45U6IdCIV5++WXx3IyP\nj/OHf/iHJJNJamtrkcvlopD2er2EQiEymQwqlQqj0SjeI5fLUVFRwcDAgNgp0Gg0NDQ04PF4rhot\nPTQ09J6R05L96HIbxuTkpFiMS9fx8oJcWqxeWcBKvvTLO7p+v59AICAWWLlcTlhTpN+x2WzicylJ\nQhVeLbjk3d7//Ur6W3bltbl06RL19fVXtapcTYGAnG9908QnX/oj7uAJzn3/SQruWPOevyctwCUy\nTkFBAYFAgIGBgQ9l2HCpkF7Skj5cLVk7PqAymQyzs7PU1NSIbmheXh6pVIqLFy++IyJc6lg+/PDD\nfOtb3xKpg0NDQ3z/+9+npqaG5uZmGhoaRGrZL1/7JbuUuzgycogUabaOwD1HTJiKP4vsqz3807/9\nmBW1tbz55qcBGVu3/opDhw5RWFjIRz7yEYxGI/F4nLq6OmZmZjh9+jT9/f2sX7+ezs5O4vE4Bw4c\nwOVyUVVVJb7YJO+fXq8XW7YSX3hubg6r1YrRaMTpdLJjxw70er0I0Ni1axd79uzh9ttv59Zbb0Wn\n0xEIBHjggQeYn5/H4XCwdetWMVxXWVnJP/3TP3H+/Hk+85nPoNFokMlkdHV18bOf/QyPx8OWLVtE\nhz8cDgv28saNGzG93YWHha5lMpnk3LlzfOlLXxKDfhaLhQMHDgAL3sq77roLm81GNBpl//79ouO8\nefPmRYX0nj17OHToEM3NzXR2dmIwGAgEAjz77LMiofHmm2/mySe3vU0m+V/4fD60Wi1r1qwR+LSm\npiaef/55VCoVzzzzDKtWraKrq4u+vj6OHj3KsmXL8Hq9bN26lWAwSH5+PnV1dTzxxBMUFBRw4cIF\nYWOQLCkjIyNs3ryZDRs2iGfM6/Wyd+9eLBYLw8PDlJaWIpPJGB8fF7acXC5HY2MjBoMBtVrNa6+9\nhsViIRgMMjExIQr6S5cuiedXunZKpVL4lgcHB8U1kQJWbDYbe/fupbKykk984hOEQiEUCgVr1qzh\n0UcfRaPR4HA4sFgsyOVyqqurGR0dBRZ89MuWLaO+vh6fz0dvb68oMCWLSTabxWKxiF2M/Px8Wltb\nMRqNBINB9u/fz8jIiNjil9IX0+m0WBz09PQsGgY7e/Ys4XAYl8tFOp3GZDKJ4dmWlhZefvll/uIv\n/kIUhNFolB/84Ad89atfZWBggB/9qBuDQc5993mIRqMiCjybzTI9PS2IJiMjI2JIraWlRew6nDx5\nEofDIaLUJaVSqasWoVfqyp+ZmZlBpVIJrz8sJCi6XC7h8383+4FcLl9URCcSCcLh8CLfsTTQKXWr\n5XL5VV9PJpO96/H/tvYHaUZDQilms1kmJyexWq3vu4gGsFqz/O8fBXnliX9g+M9PUfynX+TR8X18\n4s/Nv5aKFwgEyM/PZ3h4mFwuRyAQEAuQpWHDJS3p909LkxEfUFLn6sqBHL1eT3l5OQcPHiQajQII\nTqvH46GlpYXi4mL6+voEn7eoqAin0ykKnz2je/je1Pd4uuRpDkXe5JN9aQ48aOBTh/6Skv/xKssf\n/RoRFogbK1eu5MiRZRw50k5PT4/Aqs3MzDAzM8Ps7Cx+v59QKMTMzAw1NTWsX7+eSCSCSqXiuuuu\nI5fLCe+tFEM+PT3N2bNnBUHkjjvu4LrrruOOO+4QA4oWi4WKigqKiopwOBxUVlYKzu/KlSvFl7Je\nr+fWW29leHiYWCxGXl4edXV1IhbdbDYjk8mor69n9erVdHd3093dLQIb1qxZQ01NDRqNhpqaGnp6\nejh//jzd3d1s3ryZrVu3snXrVq655hoMBoP4UjMYDMIT6nA4mJ2dZdWqVTz//PP8y7/8C4899hha\nrZaioiJBU5E0OzuLyWQSw22JRELsGKxevZqhoSEaGhpQKpW8/HIpL7xQLPyuC+EsjczNzQkrTFlZ\nGfF4nLvvvptUKsUTTzxBIpHg7rvvFmEgktWnubkZv99PMBhkfHyc6upqSktLUavVdHV1kUgkCAQC\nwuoCCLyZVqulvr6ewcFBBgYGGBgYYGhoiNq3dy0aGxsZGBhg165dTE9Ps2LFCkKhENu3b2d8fJwf\n/OAHPPnkkxQWFtLS0sKZM2fo6upi+/btXHPNNdxyyy309/fj9/tZtmyZSB/U6XS0trYyNTWFXq+n\nqKiI8vJy0ZlPJpP4/X7a29spLy/HbrfT0tJCW1sbvb293HzzzaIzncvluOWWW4hEIpw6dYru7m66\nurro7u5m5cqVnD17Fq/Xy8aNGwWHuLa2lhtuuIE9e/bQ0dFBd3c38/PzGI1GNm/eDCx0Qq8scvLz\n87l06ZJA7tXW1rJu3TosFguvvPIKPT09iwpCg8EgUjgbGhp48kkLP/mJEq1WK8KIJicnBaFEp9Mx\nMTHB+Pi46EyPj48zPj5OMpkkLy8PlUrF8PCw+Oyl02lGR0c/UKdxdnZW2DskSdab31QS8/ly6fV6\nsdORTCZxOp0MDQ29J0P/dy2JW+92u/H5fOKZ+iDacYeS+Sf+NyZFjBXf/wJ3f8ZCIPDulbRKpaK/\nv1/s1jQ3N4v5kyWP9JKW9PunpY70FZI8w3l5ee/obkhDS0qlErlcjlqtFslw0WiUTCbDuXPnaG1t\npaioSKQCAixfvpzR0VGSyST9/f10dXWRzWY5efIkc3NzxFNxjiaO8tzYc4wkRshP5/GtQzL+6LiK\nl+fvY/wfvsDmGxc8ppLHWLIWSMrlcjgcDhEwIRWRoVBIeIFramrQarXYbDaBJjOZTGJ4cO/evWQy\nGfLz8zEYDAwPD4sOrqSbb76Zr371qzQ2NqJWq0kmk6RSKZGuJ4UwjIyMAIgUM6VSyR/8wR/w5ptv\n4vf7hTd5ZmaGEydOYLVaBZJNq9WK0IzDhw9z5513UlpaSjwe5/Dhw+Tl5YkC58r7YrPZBDFFCrDR\n6/Xk5+czNDTEypUrMZlMzM3N0dfXh0wmo7KyknQ6vcjjWllZSTKZJBgMin9PTU2J18xms4tQXlLn\n02g0cubMGdrb24Uv1uVyodPpyMvL40/+5E/E75w4cUIsuEpKSsSQXF1dHXNzc4KWIXGkg8Gg6JwG\nAgGmpqZIJpOiWyylNtrtdgYHBwFYuXIlXq8XjUaD2+1mxYoVwnLT29uLXq/nzJkzVFZW0tPTI55R\nKfylvr5e+E4zmQwOh4MLFy5w7NgxbrjhBqqrq9FqtTz++OMCZSZxuiXfcCqVIpVK4XA4hFVHsmtk\nMhkxqCb5XmdmZsRidWRkBLvdjkwmE/atYDD4jsAQk8lENBpFp9NRUVGxqAiUYuRjsZhA2kkWlJmZ\nGT760Y+Sl5cnmM12u51XXnlFDNxJdAvJGiJ5/CVJz4E0YHn5Fr9SqeTAgQPk5+cvIoBINpl0Ok1Z\nWZnozkvd+nejZryX3qsre/m5/DpJOzqjo6NibkHapZAW52VlZRQVFXHmzBkUCoXoev9HSKvVCjrI\n1Y5d+jy+H5nX1pP8x2+z6c//jGf2PsT113+RBx+cZcWKd9JJ5HK5oBdJUqvV4m/9kpa0pN8vLRXS\nl8nlcpFIJERxaDAYKCkpETG7EiEgm81SXV1NW1sbTz31FOl0mvz8fKanp1m+fLlIbrsyeGXLli3c\nd9993H///Xg8HmQyGTUtNRx48gChdSEOjh2kNm3mwdfz+IOTaX6Z+SM+13Q7n/92Hnb7JNPTC1uH\nUlRzPB5n9+7dwDYADh48yMDAAD09PUQiETEMGI1GMRgM1NTUMD4+LpjE2WwWnU6HQqFg06ZN7Ny5\nU8QHO51OtmzZwtTU1Du+IBKJBIWFhWLqX1IymRRx4hIKTCr4d+3axebNm9m3bx9arZbW1lbR0ZIo\nDvv37xfWjrm5ObxeL9XV1UQiEf7t3/5NpEfmcjnRza6rqxP3ZW5uTnSVvV6vSI6cn5+nt7eXiooK\nVCoVGo1G0CGam5t58803CQQCNDU1LeLDXrx4kWQySXFxMYWFhWSzWdrb23n88ceFB/3yLrZWqyU/\nP5+mpib8fj9PPfWUoDNUVVURjUZFB1T6MrZareL8pYJFkhQD7vf7uXDhArCwLW40GnE4HIRCIfbt\n24deryeZTIpudGlpKYcPHxae35MnT7J69eqF562mhlgsRiKRIJPJUFBQwPz8PMlkkmXLloniUKfT\n8cwzz1BSUoLf7xfPKywMqOXn5+Pz+Xj66afFUO3U1BTNzc2Ew2FefPFFdDod2WxWYBv1ej39/f2o\n1WpUKpW4/8uXLxehOnK5nFwuRzgcpqCgQAxrBgIB8VwolUoxE3B5wbp7925aWlqYmpqirKwMi8XC\n/Pw8o6Oj5OfnU1hYiMfjEYNuEmLSbrcTjUYXDYrF43FaW1vZu3cvdrtd2DWSySTDw8Pceeed7N27\nF1gI9Ni7d6+wGVRWVop7LBF4bDYb8Xh8UfEl+bQlfvp7+aHfj6QF1uXnEovFUKvVJBIJxsfHUavV\ngqZRWVn5rgW72WzmrbfeYt26dcJ2Mjk5id/vJ5vNCsuHNNw8PDz8oVsbcrkcTqdTLNQSiQQ2m+2q\nDO0rlbjzDrQvvsD3jnyDE2zn9ttr+cY3wnz+87FFQI9UKkVzczPDw8PIZDL8fj+RSISqqioxAL2k\nJS3p90dLEeFvy+v1olQqRRqazWYTHsvp6WkaGxuFP1PaxhsfHxfsXrPZTHl5OYFAgObmZoGW8/v9\nzM/Po9Fo8Pv9uFwufvazn9Hv6ufh4Yf5x6F/JOKIUB8z8z9fyvHjp2NMTt7IZ/N+wvjWeT75+TmU\nyrQ4lkgkQiwWw2Qycfz4ceRyOX19NwOwfPlzzMzMUF9fj9lsprm5mcLCQhwOB+FwGIPBwOnTpzEa\njXR0dKDT6Th27Bg6nU5EU69fv57u7m4cDoeYsJcKZED4pcPhMDU1NZw5c0YEtuzfv5+Ghga2bt3K\nP/zDPwiO9AsvvIDP5+P+++/n+PHjAm8Wi8Xwer1ieO/06dOUlJSg0Wg4cOAAJpOJrq4u3G43lZWV\ntLe3E4vFGB8fp6OjA4PBwMDAAC6XS3R9q6urBcNZiiqW2MxyuRy5XM6JEycIBAJcvHgRv98vtmel\nQlYqOCQ7yCuvvILT6RQFYjAYpKamRvg0jx7dDkB5+cM0NDSwefNmLly4IJ6XcDhMS0uLGGCUhgYl\nvnQsFkOn0xGNRsVQ2MWLF8XvSh3Ompoa4vE4Z8+eFWErGo2G4uJiUWQ2NzczOztLd3c3NTU1VFVV\nUVlZydjYmHh+JHvC1NSU4C/feeedTE1NCRa4FDEdCoUwmUzE43HS6bRYVJrNZqxWq8COSVQPlUol\nOuOrV6+msLAQn89HNpulqqqKmZkZ9Ho9arVaDJr29PQwOztLIBAgHA7j9/sJh8MsX76cU6dOcdtt\nt1FfX09VVRXl5eXs27eP9evXs2vXLk6ePMnk5CQvvviiSPXT6/W89dZbHDt2jNOnT6NSqSgtLaW2\ntpbx8XHi8TiZTIZIJEJpaSnpdJp4PE5/fz9DQ0OMjIyQTCZRKBTI5XKOHz+OXq/H7Xaza9cuVq9e\njdfrpa2tjQcfXFh4/eu/FjAwMCDsJGq1Wuz6xONx4vE4+fn5RKNRFAoFyWSSkZERQQjR6XQEg0Gx\nYPigxZh0nOFwGEBYhCorKxkeHqahoUGg5kwmE6Ojo+9qiZiZmSEvL08sAILBoOCW2+12sUshLXDl\ncjnxePyqITbvpUgkwsTEBIFAQDQxfhO/s6SJiQmsVislJSWYzWZsNhsej2cRveRdJZMxt2YNhp89\nwieajtPXcicPPWzkzBkVmzYlkbDRUuhTVVUVVquViooK1Go1Xq+XgoKCD3Tcv42WIsKXtKQPV0uF\n9NuSutFScIY0OCShqaTCb35+Hq1WSyAQ4Pz584TDYZqbm2lsbMTlcok0t3g8LrqzEp0iLy+PYxPH\nGKgaoNfai1/tpz1Sxo9eyeMHz/lRzjTwiezjvLrsWr7ybRdq9QAARUVFZDIZRkZGyGaz6PV6ZmZm\nUCgUbNy4kSeeaAHgv/23haSw4eFhNm7cKLbMNRoNdrud3t5eenp6cDgcXLx4kUQiQWdnp+jc9PT0\noNPpBB5OSmI8efIkdrudsbExcrkcx48fZ9u2bdjtdlHc6fV6GhoahB921apVgo97zTXXsGbNGkZH\nR2loaKCpqYnR0VEKCwtpbm5mbGyM2tpa1qxZw+zsLMFgkB07duBwONBoNDQ3NxMMBrlw4YLAnElU\nhZqaGtLpNAUFBVitVmKxGAA7duxAo9Egl8tpbW2ltrZWYPZWrVqFxWIRRbfH42Ht2rUicVEmk4ld\nhePHj7NhwwasVisjIyNs3boVm81GJpPB7/dTUVHB66+vA2DDhj3odDoqKytxOByk02nS6bTw65rN\nZvr6+sR/9/v9OJ1OOjo6CAaDIplubm6OmpoaVCqVCJ2RwiesViuDg4NkMhk6OzupqanB4/GIIlP6\nMo9GozidTrxerwjCmJ6eFrYWr9dLa2urwM/V1tZSVlYmcGUFBQVicG9kZIRVq1ZhtVpxOp0MDw9T\nV1eHWq0WPt6qqiqSyaQoMDds2IDH40GpVNLV1UUgEKC4uFhQFaanp6mpqcFkMqHVasUzCIjutcPh\nIJVKEQwGcbvdTExMCA92YWEhRqOR1tZWMpkMXV1dKBQKVqxYwf79+ykuLqazs5PS0lL6+/tFwIpG\noxGkleLiYkKhEFVVVTz22GM0NTVRU1NDNptl586drFq1is7OTjZu3Mjp06fJy8vj7rvvRqPRMDU1\nxbJly/inf1ooYv70T0MUFRUxOjpKJpMRvOu8vDzC4TBms1lQVsbGxvD5fGJAt6ioiKGhIWG3iMVi\n+Hw+8vPz37MoSyQSwmst/az0PslkUlBRJKb15VYkhUJBPB5HrVZf1ebh8/loaGhAo9EIdnt5eTnB\nYBCFQiFsZVIhHQqFxMLqN5G0cKqqqhKR75I17TeVz+ejpKSEVCpFIpFApVKJIKj3w6nOmUxkTSZM\nP32YHV+wYdrUzM9+pufpp3X09KQoLMyiVqsFP156dgcGBjCZTB9oEfHbaqmQXtKSPlwt4e/e1v79\n+0V3Wa/XE41GSaVSDA4OsmLFCqLRKGazWXSvstksjzzyCH/7t3+LUqkUCKynn36acDjMLbfcIrZX\nc7kcr428xvePfZ+z0bPk5fLYpu7ha3vibHr9GEG5kb/Mfodf6D5K16qf0NLiwuv1irQ1i8WCyWSi\nvr6emZkZRkZGBBdZ6mhK3daysjIOHjxIYWEhdrtdBGUEAgFmZ2e58cYbGRgYEENDkvUgmUxyww03\nLLom8XicU6dOcenSJY4ePSo40hUVFXz9619HqVQSCATEFLvEdn399deFF1TiFQM8++yzlJSUCBqD\nz+cjFAqh0+nYsmULfX19gvQgl8tZt26d4DdL/OlMJoPb7SaXy4kOmRQi43K5sFgsVFZWCu81II7j\noYcewmKxcO211wo02fnz5zl79izf+MY3rvpc/PCHP6S/vx+9Xk9BQYEYJKuqqiKRSIj/DgsDmnNz\nc2zevJmzZ89SXFwsCqfq6mpRjFdXV4tCZ2xsjKamJkZGRnjxxRcpKCgQcddf/vKX2blzJ8uXL8fn\n86FUKoWt4ciRIyxfvpxQKERFRQU+n0/wlO12u0AySsclWVkCgYCwkqTTaaampli/fj2vvPIKZWVl\nlJaWEovFGB0dpaSkhNLSUuRyOUeOHCGTyYjO8PHjx4lGoyiVSiwWC9lslkAgILa4JS+t5CV1Op1U\nVFQwMzNDMplEp9MRiURoamoS2+ESyvdPOpIAACAASURBVE8qDhOJBB6Ph0gkIoozqTi02Wy0tbUx\nPT1NJpMRBbLk7S4tLRVx64WFhfT29lJeXo5CoRCLxVAoRC6Xw+VyUVdXx9mzZwmFQhgMBrq7u3nr\nrbdobGwUKMZMJiPsRsPDwzQ2NqJSqXA4HIyNjaHVahkaGqK6uhq3241GoxF4vebmZrxeL+fOnRPB\nKyMjIwIlJwWJSNcllUphs9nEPbxSEu7x8nORFpNXkzSTcGUxKe0uXK0AlLCYl/9OLpcTYSsS211K\nCR0aGvq1sd7vpkuXLr0jkfBq7/1+X0ual5D+9kiF/vu2zmSz2D7+cVT9/Xj37uWUt5wHHjDx4x/P\nUlCwwN/O5XL4fD7C4bDANl6+SPmP1BL+bklL+nC15JF+W4FAQCDpIpGI8Gtms1kuXbokJv9hYbht\n9+7dKJVKzp8/L7pLyWRSJI4ZjUaGRofYObmTX038ipHYCGaZmWXuNv4mWMf1z+9EmUrzY80X+Wby\n78gr3M3X7nucqqpyFIoqAF5++WXGxsZYvXo111xzjUgha2lp4Xvf+x56vZ4/+7M/E1P6iUSCBx54\nQHy5t7W1iULixIkTuN1uDh48yO233y46twaDgZ///Od0dHSIoURAfAm5XC7UajUPPvigOP9Tp07x\n85//nLvvvpv8/HwUCgV2u11EEPt8PtauXSu+nIuKihgbGyMWi3Hu3Dn+/u//XrzW8PAw3/72t9Fo\nNHz6059eNBz005/+VHQmJyYmBCJM8gBPTU3xz//8z+LnZ2dn+dM//VPsdjvnzp2jpKQEWCi+9u7d\ni06nE6EdUiiFxWJBp9O963Nx7tw5VqxYQVVVFRqNhng8zt69ezl58iQbNmygpaVFpORVVVXx6KOP\n0tvby7333rvoi/XBBx/EZDLxyU9+kmAwSDQapaioiOXLl/Pqq69y8uRJPvnJT+J2u1EoFBQWFvLM\nM8+gVqspKSkRXXCtVis60rFYjLvuuku8x9DQEI899hhVVVXcddddwtfqcDh49NFHWblypbhOkpdV\neubtdjtyuVzYMKSdBSm4pKSkRHwe8vLycLlcNDY2Ul5ejlwux2w2c/HiRfr7+5mYmKCqqgqj0cj8\n/DzRaJTZ2Vnm5+dZt26deE40Gg1Hjx4VxI/LhwPj8TjHjh0TxaZEdgmFQgwNDVH1dsz5lQOHTqeT\n7du3i8HOvLw89Ho9crkct9vNxo0bxRa/lFrpdrtxOByC+pJIJBgbG0Mmk9Hb28s999yz6LP/85//\nHK1Wi8ViwW63Y7fbUalUvPjii7S0tFBZWSlir6XFASwE02zatEmEt6xbtw6n0yloJtLio6ioCI/H\nIwKUrqaxsTGxayGdy+DgoAibuVL5+fnCK365IpGI+JxcqYKCBbuKdH+l6Hgp1EfaoZLsIzU1NVd9\nnV+nq5FUpOOdmpr6jQtpt9tNR0fHomv51ltvicXu+5JcTvB//A8Ktm3D/M1vsuzhh3n22ZlFPyKT\nySgsLBQNC7/f/xsd55KWtKT/PFoqpN+WWq3m4sWLtLa2otfrCYVCOJ1OEokEbW1tjIyMiM6b3++n\nvLycQ4cOcejQIZYvX47ZbGZqaopTp05RXFnM3+39O17wvcB0fJpGSyPfaPwGtt1ubn7+JRpmznHe\nvo6PJf+NqL2K1sq/IRR6ibKyP6O9vV34HJcvX86PfvQjbrzxRmKxGGazmUwmw4ULF8hms6xbt46d\nO3fy0ks3IZPJueGG59myZQt79ixYDObm5kQRarPZSKVSqFQqxsfHaW9vJ5VKceDAAeHpPHToEKtW\nrUKr1TI2NkZfXx8ej4evfOUri65VR0cHb7zxBpFIhNHRUWKxGEajUYQ15OXlLaIjwMIXttfr5dpr\nr+WNN94QRZbf78dkMlFZWSm6oEqlEo/HQ3t7O0ePHiUUCnHNNddgs9mYmZlh//794vpcuHBhUfyz\nRA2RQjy0Wi1ut1uwkktKSmhubsbn82E0GjGbzRw9elQcp1TkSR7VdDrN1q1bRYCMXq/HZDJx//33\nYzab6ezs5L/+12K0Wi333nucSCQitrclIoO0qDl8+DByuRyr1bqoc3ju3DnB1C4rKxMR29FoVDCw\ni4qKxHBjIBBgfn6elpYWJicnMRgMpFIp4Q2vr69naGhIFEjT09M0NDTgdrspLy+ntLRUPONKpZJj\nx46xdetWYfOQjv/111/n5MmTdHZ20traSi6Xu2zAFcrLy9Hr9eh0OnQ6HYWFhZw+fZquri6cTqfw\ng4fDYTo7OxkeHmZgYID29nY0Gg3T09NotVqGh4fZtm0buVxOPKM6nY6ZmRny8/MpLy+noKBg0S6E\n1+ulsbHxHWmAUqS3RqMhPz+fYDAowmMkq4/b7SaTyQifr8/no6ysDI1Gw+zsLEajkdraWl599VXW\nr18vruX8/Dxut1tQSk6cOMFTT12HyZTgttsOCLKI9PxcXrzF43HxObucbGG32xkYGFjEnAcoLCzk\n/PnzV/1bJS0op6enRcy29Fo+n09wqVOpFGq1WiAZzWYzQ0NDFBcXk81mcbvdFBUVvat9RCaT0dDQ\ngNfrFTsiUhcaFkKRpPf9oIWkFIl+pSKRiPj7ISUrSufy61RYWMjU1BSZTEY8Q0ajUXyu36/mq6uJ\nfP3rmL/9bRIvvEDyllt+o99f0pKW9PujpUL6bSUSCaqrqxkfHyeRSGA0GsnPz8fj8QhP6ezsrLBC\nXI476u/vR6vVEpwP0qvtZXR+lKQzSZeli2+1fotOeQOOf/93qp99lpBMwxc0P+bfZu7h5ltH2Lbt\ncV5++TTptA6z2SyGeux2uxjeCgQCXLhwAbVaTSaTIZlMks1m0Wq1VFdX881vtgLwl3/pFFvJ3d3d\nxONxxsbGKCoqEmmDq1evxmAwcOrUKRQKBatXr2bv3r0YjUZKS0sZHR0lm82i0WhYs2YNv/zlL696\nvXK5HIcOHcLpdIoBTJfLJeKQpdQ8mUxGOp0mlUpRUFAg8GBSRzwej4ukxYaGBvx+vwj90Gg0TE5O\n8tnPfpZIJCJsHz09PRw9epTp6WnRDYaFbpRSqSSdTnP77bczMzNDKpWirq6OWCxGJBLh0UcfpaWl\nhfr6eoaHh+nr66OtrU0QJC63EFRWVmIymbh48SKDg4PodDpx3FJRcubMGX71q4Vt6ba2w4JU8vzz\nz9PU1EReXh5vvvkmU1NTWCwWJicnF4VlSN58k8mESqWioqKCTCbDxMQEsVhMJBmGw2ERbqPRaNDr\n9SIK3OPxIJfLSSaTWCwWlEolNTU1ovNZVVVFKBQiEongcDhEB95ms4nBNJfLxeHDhwVdYn5+nnR6\nYchVQtFJz6ZkMYnFYu+YBZAKa2lwDhaoDtKMwapVq3C5XIKYUVtby+joKGfOnMFoNKLT6UilUoLO\nYrPZBAUll8tRW1uL3+9HpVLhdDpF6mY2m2V0dJSVK1fy0ksv8alPfUoshkZGRsQC58SJE7S2tqJU\nKhkfH2d0dJTa2lqeeeYZSktLyc/PZ3Z2FpfLRUFBAYWFhZSWluL3+5HL5dTV1QlE4/XXX88f/dFC\nUfw3f9OJVqvF6XS+6+flagWrFIJyJWnD7Xa/q00jl8vhdrtpa2sTz5KE3rTb7bjdbmKxmEgiVavV\nYjFisVgWnct7cY/lcvm7dsV/F5LJZOh0Onw+n1h4SAPeTU1NuN1ugTSUIsyvZFtfLokpLi04rVYr\nGo1GoAV/E8XuvRftSy9h/i//hbn168l+AM/2kpa0pP/8Wiqk35aEHysuLqasrAyv18vU1BR2u50z\nZ86wevVq/H4/SqUSs9nMoUOHMBgMzM/PE9fHeV32Or3JXnLWHC25Fu5tuZeOog6KT56k8rt3o/F4\neMp0J58P/yu6kgRfveMXOBxeAoG4KMwly4jEPZ6dnWX58uUifESyjwwMDKBQKLh06dKiISFpALKj\no4OBgQGBbUskEjidTlpaWnC73Wzfvl34BTOZjOjWVVRUCCRXSUkJRqOR6upqdu7cyY033ijeR8I+\nHT9+nDvvvJPKykpsNhsnTpzgySef5KabbqKoqEh4o1UqFXa7nfXr17Nnzx7++I//WJArotEovb29\nggZweZjEhQsXKCsrY2RkRAyGST5jqeiWPOOSP1smk2GxWES8tCSJKtDQ0EA6nebYsWOoVCpWrFhB\nOBxmbGyMhoYG0fGSLD0qlUrQCLxeL0VFRRw7dkzETU9MTIj3MJlMFBcX43K5uO6664RNwuFwsH//\nfr70pS8xPDxMOBwWCXper5eenh5effVVzGYzY2NjKBQKbDYbwWAQp9PJl7/8ZUKhENFolKqqKl54\n4QUSiQSjo6P09PQACP60VqslFostwhZOTU0J+saVxZxk/Xnttde47bbbBMoxnU7z/PPPU1dXR1FR\nkUgX1Ov1FBcXEwgEcLvdYhhNGkosKCjg9OnTfPSjHxXscqvVypNPPil2VaROJsAzzzxDWVmZ6BxK\n53I5K/vKYs7j8bBx40bUarUY0gOoqKjA7/ezbds2Dhw4QF5eHul0Gr1eT2dnJyMjI5SVleF2u5HJ\nZKhUKuEVl54daeBROudLly5RVVUluvvZbBav10t9fT3p9P/HGDYYDExOTr6rhUCKqi8pKVl0D6Qo\ndZ/PJxZJiURCsKWvJmnRdPmQmZRaGQqFsFgsi/zAMzMzeDweiouLUalU72rl+LBUWlqK1+sV3mu5\nXE59fT2hUIhsNkt9fb34Wem5e7dzkOg1ly9CXC7XBwtrUSgIfu97FFx/Paa/+iuCP/zhb/4aS1rS\nkv7Ta4na8bakwaKioiIMBoNAdI2MjFBUVITf7xckhr6+PiwWC28MvoFruYvHAo/hy/n4dPOnuVN5\nJ/oBPfdefzsN3/0uJf/8z/jzSrk5/Qz/kruPa28+wooVP6G6WoNCocDpdNLW1kZrayuPPPKICC/p\n7e3F6/XS3t5OU1MTQ0NDgsQQi8XEZH46nWbXrrUAXH/9YeLxOJs2beLUqVOEw2EUCgWDg4OEQiF2\n7NhBJBJhYGCBBuJ2uzlx4oSgV4yMjKBQLIS+TE5O4na76e7u5ujRo5w4cYK5uTnefPNNjhw5wsqV\nK0mlUlx77bUoFApMJhM6nY4zZ86IrWu9Xi/oHxIhIJPJcODAAbLZLKdOneLIkSOsX7+edevWsW/f\nPjKZDOFwmBMnTlBVVYXL5cLv95PJZCguLmZ0dFR0NKuqqjhy5AiVlZVEIhF2797NjTfeKLbwjUaj\nKI4GBgYEK9jhcNDd3Y3NZmN2dpZMJkNdXZ3gIQOik24wGPjxj38sfve1115jZmaGTZs2MT8/TzAY\npLf3WgDKyh6irq4Oh8PBkSNHKCsrw2QysX//fhwOB1arleXLlxOPx4lGo2g0GlpbW5mbm2NkZIT9\n+/djs9kYHR3l0UcfFWETkk9a2jGQurFlZWWi0z0+Ps6lS5dYt24ddrudU6dOAQsM4cHBQWpra0Xq\npLSj4fF4SKfTTE5OolQqicfjFBYWEo1GOX36NHq9nvn5eTZs2IDNZsNqtYpoZI1GQ15eHhMTE0Sj\nUaanp5mfn0cul2MymfD7/SJg5tSpU8IuMjg4yNjYGB6Ph4MHDwrcZE1NDW63m0QiQTAYJJfLCTvL\n+fPnyWazzM7Ocvz4cfLz82loaECn04njslqtIoXTbrfT0NBAcXEx1dXVYhhTehbz8/PR6/XE43Hs\ndjsXL16ks7OT7u5uioqKqKurIy8vD4/HQ3NzM0eOHCEejzM1NUVfXx8rVqygpKSEkZERHnlkAVd4\n222nxdDxu+nyzmgymRT4TImqoVarycvLEwOa7xZuMj8/L2xR2WyWubk5XC4XZrMZt9tNa2vromJd\np9Ph8XjetcP920oa5vttpNfrFz1jcrmcyclJseMgSavV4vF43pXoYTabBVZQGkyWFosfRNm376fh\n4YdJrVzJ/FUCYH4X5//baInasaQlfbhaKqTflhTRXVRUxPz8PBaLRQzXtLa2Ck+xRqvBbXTzwMkH\nOK4/ji/q4/Ntn+fxjz9O3Vwd085pWoaHWf3Xf436zFn+2fSX7Aj+ktptBr761T2UlQ3x8Y9/TPxx\n37x5swijuOaaazCbzUxPT3P99ddjNBqRy+W0tLSwYsUKgY/r7OxkcnISk8nEmjVr+Pd/X+hcfe1r\nUZHYV1VVxYkTJzh//jypVIrNmzfjcDiIxWI0NzczPj5OXl4eTU1NqNVqLly4IOgW0nbwqVOnBMpM\n8lq3tLRwyy234HK58Pl8dHV1ie3ZWCzGiRMnqKiooK2tjWw2Szgcpri4GJvNxtjYGIWFhcLHqtfr\n2bBhA8FgkBUrVlBXV0coFCKRSLBq1SoKCgp46623KCkpYevWrRgMBhobG8Vw04oVK7jtttuYnp6m\nsLCQP/7jP2ZsbExYOSR7gURv8Pl89PT0UFZWJljhjY2N9Pb2Cm/85Uomk+zbt4977rlHxITfdttt\ngslbVVVFY2Mjjz++QCr4m7+Riy7s5z73OWKxGOl0mu3btzM/P49MJqOsrEyEg0iDmvPz83i9XrZt\n2yZQacuWLSMajeL1ernnnnuEraeqqoqJiQm8Xi+f+tSnxALIYDBwzTXXiMjwsrIysYCprq4WxVlJ\nSQlarVZQIaQ46p6eHkGdSKfTrF69msHBQWFrMJvNZLNZhoeHCQQCaLVaSkpKqK+vR61Ws2zZMnK5\nnPA9FxcXMzMzQzweF9HfMpmMtrY26urqUKlUdHZ24nQ6USqV6PV64ZvX6/Xo9XoikQg1NTWCKTw/\nP09BQQFFRUXv8BRL0mg0uFwurFarGBhOp9PCFtTY2ChSKaUBysnJSdrb2xkfHycSiQhvdiAQoK6u\nTuxISMW9VODb7XaBv3vggdyilNGrKS8vD5vNJvy6paWl6HQ6ZDIZNpsNlUrF/Pw8Vqv11w7HSQPN\nTU1NIiCqpKRE2GyuZsW40jryu9T/qUIyEAhctWCenZ1913ORy+XCq345FvO30Vx3N5nyclIf+Qhc\nxaO9VEgvaUm/31qydrwtKbxDGkxSqVRiGC2TyZAhwwsTL/CTgZ8wHB7GrrRzo/xG7lt3HzPuGY7t\nO4Yxk2H7L39J47FjXNK0ccf8GwQt9Xznry6wbRu4XGpCoYUhImlbWMJWSQlnCoWCsrIyAoEAXV1d\nnDlzhjfeeIPp6WnKy8sZGBggk8nQ0tKCx+PB5XKJc5DCOsrKygiFQtx3332iUPvFL34hOLrPPvus\n+NJWqVTceOONRCIR4vE4Pp9PeFwlf/DatWtxOByLMEutra28+eab9Pf3C2LHpUuXCIfD1NfXMzIy\nIl5/YmICo9GIyWTi5Zdf5s///M+Bhe3o48ePi7AQqdsqRZM7HA6MRiNtbW0cPXqUTCaDUqmkvb2d\n6elpkczY0dGBSqUSqXeFhYWEw2FhrdBqtTQ0NBAIBBgbG6OzsxODwYBKpRKYwUAg8I7iJRAIiG6p\nSqVi+fLlYoCxt7eXurq6RcWTlCQodQmbmpoEl9zn89He3g7wjkGwXC4nUgWlSHApJCYSiXDgwAFq\na2vJy8vj0qVLjI6O0tbWxqlTp+jq6qKsrEwMzUmc5ebmZtHRlsJGJCyb1OELhULCp/z666/T09ND\nc3OziOUOBoOsWbNGeOrT6TSlpaXidQoLCwWizW63i2jxCxcusHHjRtGhjsfjuN1u6uvrKSwsxOv1\nkslkCAQC1NTUMDAwwOzsLBs3bhTWmrGxMS5evMiqVaswGo2Mjo6Sy+WoqKgQHm8JjSddc7vdjtVq\nxWQyMTg4SH5+PqlUimg0Sk1NjRge1ul04tjcbjdVVVWEw2Hq6urEfQkEAqjVajweDy0tLeIeZ7NZ\nBgcHFyUqAu8gT1w+OHm5D1kmk70riUIa2nw/stvtDA8PU1FRgVKpxO/3ix2FYDC46LmUeMr/t8lg\nMDA7O7voeiUSifeMNpd+93eGo1OpSHziE7+b11rSkpb0n05LHOm35XK56Ovrw2azUVBQwPT09ALl\noaqExwcf50Xfi4QJU6YqY+38Wj61/FMY9Ub27NmD0Wik0+VizU9+hj4W4+/4Sx7M/zp3f97PZz6T\nIZGIMDs7i91u5+zZs6JLksvlmJmZwe/3i/ALySbh9XoxGAzEYjEuXLjALbfcIjq2Bw8eFIVkR0cH\ncrlcFBOnT59Gp9PxhS98YVE6WiKR4IknniAvL08km83NzYmBwEOHDvEnf/InoqtlNpvZs2cPCoWC\nj33sY1e9Zjt37mRycpKKigqMRiPnz5+nurpaDGRJ2/lSspsUGx4Oh3E4HEQiEaLRKPF4nHXr1iGT\nyVCr1chkMjFw5vf7RSpZQUGBKMK8Xi8VFRW88cYb2O12UqkUqVSK6667joaGBiYmJpDL5YIgUlhY\nyOzsLJcuXRI4t5mZGebn52lvbxf2Hamb5/F4sNvtPP/881RWVmIwGLBarYJh3dvbS0dHBz6fj6qq\nKnK5HGNjY+Tn5+NwODh//jw2m010SOvq6mhqahJ8ZKnzr9Vq0Wq19PX1sWvXLkFXSSQSfOxjHyOT\nyXDixAkxkOn1eqmsrBSItEQiQV1dnYjRlgJPVq9eveheHT9+XBSt4XBYcKzLysrIZrO89tprqFQq\n6urqmJ2dZWpqCoPBwKc//WmOHDlCcXExarVabLdLkuw7kk1pYmJCbKlLqYFSemVRUREXL17EYrEI\nO0pFRYUgsEjb+7FYDL/fz/j4OGvXrhXzCHK5nHA4zOrVq0VX+fJId8mGpdfrxW5IXl6eKE7n5uY4\ncOAACoVChC51dHRQUFDA008/jVqtprq6momJCWZnZ9m2bRsqleodHc2JiQnsdjvat6PurqRW+P1+\nAoGAGJyUyWRUV1e/Z7jK5aml76egTiQSuN1ustks+fn5IsxkeHhYxNWHw2FCoRD19fXvSbz4oPo/\nhX+78lwikYjgxr/XkOR/pD5s/N0SR3pJS/pwtdSRfltKpZKysjJkMhnRaJS0Os2u+V3sOraLaCbK\n2uK1fKriU2xwbGBubo59+/axfPlyWior6XrkVzQf2sMZ2rlX+e+s+X8aePNLEZLJMGNjU1itVpqa\nmgiFQqhUKhoaGujv70cul9Pc3MzBgwe5dOkSH/3oR1mzZo04pn/8x39kdHSUv/3bvyWdTnP+/Hks\nFguf+cxn+M53voPZbMbv99PY2Ci6lUajkXg8/o6IYa1Wi8vloqKigi1btgj6gkql4qGHHhJfyq2t\nrahUKvx+vwgzeDdt3bqVgYEB+vv7SafTrF27lsbGRo4cOcKyZcsWeY5HR0eZmppi9erVXHfddfj9\nfrRaLRqNhgcffFCQJ9LptCg4lEqlGH7s7OwkEonQ3t7OgQMHqKioYPfu3Xzta18jEAigUqlESI5e\nr6e0tJRkMkk6naawsJDx8XFBu9i4cSMzMzMsW7aMQCBALBbDarWK6wkLaC+p4JKKaI/HQ0NDA7t3\n78bhcOD1ernzzjuZnZ2luLhY0B/0ej033XSTSDBct24de/fuZX5+HqVSuWgQzOv1EovFeOmll/j7\nv/97wdGemJjghz/8ITfddBO33noryWSSaDTK9ddfz/Hjx4nFYpSUlNDW1kYwGKS2tpZ0Os2rr75K\nU1OT6KzCQgGZy+U4c+YMNTU1YkAR4OjRo0xMTKDVarntttvw+Xxi0GvPnj14vV42bdq08JlIp6mu\nrmZ0dBSFQiFSLaUI6rq6OgKBAC6XizvuuEMk4KnVanbt2sWFCxfYvn07SqWSVCrFypUr2bVrF0ql\nEofDIZjVWq2WpqYmpqamOHToEFu2bBHPcyaTYffu3dTV1VFfXy92UiSEopSSKZfL32G1GB8fZ/36\n9cKHLJfLRYDHunXrRIda2nnw+XxXtUn8uoI4Ho8TiUQWBYxEo1EmJibelTYhdblNJhNGo1EM1F0+\nlHk1abXad7CbZTKZWMgGg0ExHPp/o6RzkWxaer3+HTsBS1rSkpb0YWupkH5bkUhkIaxjdoT/dep/\n8dzoc6SzaTo1nXyh8wvU6xc6fcFgUEy+Tzz/FjsefYii8DDf4Rv033E/X/tYP0VFZ/B6F9K1qqur\nxYCflET46KOP0t3dTSaT4ZFHHhH+XAlhpdFoCAaDrF27lr6+PrxeLxqNhu7ubpLJJJcuXRJEhhtv\nvJH777cAOf77fy9l7969jI2NEY1GF21tBgIBsVU/Pj5OQUEBmUxGJPVJMcCDg4OioLl8qzkYDIoU\nQsmDKKXlXXvttVitVi5evEgsFiOVSi0qomGBOSxxgOPxuPDwjo+Po1QquXDhAitXrsRms4nO47Fj\nx6ioqKCqqorDhw+jVCoFJ/mtt96is7OT119/HYvFQjqdJhaLUVBQgM/nE3xduVyO3+9HrVYLv/vk\n5KQgH1x+jSREHiwUKSaTibq6Ovbs2SMino8ePSrSLzs6OnA6nfzLv7SiVKr48pcXXk/CzWUyGWQy\nGZOTk3R1dTE2NsaqVasWXZfCwkJ++tOfcvPNNwsfMSzEN1dUVKBSqcjlciIa3uv1smLFCn71q1/x\niU98YtE9kkgo+fn5i2gigMC6VV4xLNXd3c3OnTu5+eabBS0GoL6+nj179gg82+WxzyUlJczOzgp0\nnGTbOXbsGIWFhcjl8kW7Q7FYDLvdTigUEkW31WolEAhQVVXF6Ogog4OD1NfXi1hwiUhjsVgWLQol\nlvHU1BTZbBaj0ShsLVJS59UkedSlxYXU0SwpKaG/v5/Vq1cjk8kWFZ2BQEBYfyRls1mi0aggatx/\nvxm1WsG3v73w/z0ezzsKZoPBgMfjEf8Oh8MCoymTyUSRLXW4DQYDgUCA6enpRR3330S/U2vDhyzJ\nM7+kJS1pSf9/1FIhfYWm4lM8N/ocH6//OF9Y9gXO7DvDmoo1i7Z3TxxTMPPNl/ni5HfxUshf9TxD\nz7eWcXu9j1zOzoEDB1i9erUYlJFij8fHxxkZGeGzn/0ss7OzyGQyOjs7+elPf4pOp6O8vJy+vj4S\niQQFBQVUVVVRU1PDrl27+OpXpQUQSgAAIABJREFUvwogKAfhcFikp736ahsADzywULysWbOGxx9/\nnFtvvRW73Y7X6+XZZ59l06ZNDA4OsmbNGhGwIMU2S4EUPp+P+fl5DAYDCoWCyspKBgcHyWazKBQK\nEYZRV1cnQjEAMbg3MDAgSBnhcJhEIoHZbCaVSlFTU0Mmk2FgYED4maPRKPX19Rw7doxly5aJgi2b\nzdLb20s4HKaxsfEdnahDhw6hUCiE31kmkxEIBBgcHGR0dJT29nZhR5iZmcHpdBKPx98RhgILiYCS\n11zq8LndbiKRCE6nk09+8pPMzc0hk8lYsWIFwWBQvH9xcTHPPbdABPjKV+QiWe7KYioajYrFz5VK\nJBLk5+cvwnwBi+wXl0u6Py6XS1gX0uk0Y2NjIh5cOjZY6HqnUimR5ij5aU0mE1arFYPBgM/nEwmC\n8/Pz/L/tvXlwW+d57//FSgAESWwkwBULV1mWJSuivMSOZUtJf9k9nfx8k9zc1Df3TtvEnaZJm9S3\nnTpunDaabJomca6bTpK2iafTTtMorpvEiSxbsSXbki3JUixxAQECBEBiB4jtYDu4f0DvG4AEKYni\nJvL5/GVTIM95cQ6J533O9/l+XS4XH3ZdiEKhgFKp5FKlbDaLzs5OnoZYKpUgk8l4iInf70dnZyfy\n+Tx6enpw8eJF7hW+c+dObvsWDocRCoUAVDvPXV1dEARh0fGZdnnv3r180KqlpYU72jSCdaAbraVc\nLiOTyXD9eKVS4RsCo9GI8fFxGI1GlMtlxOPxumv79NPVAo8V0uz3pBHZbBYejwcGgwESiQTj4+Po\n7OxEsVjkRTTDYDDA6XSuuJAmCIIg1gcqpK+gUCiqWt2ut+PMR87AqDYimUxiZGQEFy9exOjoARw7\n1oSj/zeFz154GJ/Gr/Cy+QFE/vYzePjeYXR1mXHp0iWMjY1BpVItmjZn0gqm3+3q6kKlUoHb7YbD\n4YDf78dzzz2HBx54gH+I/vCHP8To6ChSqRS+9a1vYXR0FD6fj8tAmK4beDuAqoUf80retWsXTpw4\ngWw2C41Ggw9/+MMIBoO4dOkSnnnmGYyOjiIej+O//uu/YDQaceutt+KNN96Aw+GAWq3G+Pg41Go1\nZDIZkskk+vr6oNfrkclkMDMzgzfffLNhJLBOp0MymcSxY8cwPDwMk8kEl8uFmZkZ7NmzBz6fjwda\nMD0oS5ljGleZTMYDGlgyHkv6a2lpgUqlQnd3N86dO1en32byC1bMMg9iFggjk8kQiUTqLMry+TxP\nTqt9lN7d3Y2pqSnk83lekDNCoRDeeustHkhSe3wWTLIwgCMYDKKzsxOpVKpuyj6dTuPee+/Fr371\nK+zZs4d/vVQqYW5uDvfccw/K5XJdcTY7O4vbb78d+XwesVgMgUCAa+tbW1v5sOrU1BQ/L7PZjMnJ\nSZw9exYDAwO8I/yrX/0Ko6OjEAQBY2NjaGpq4vIaq9XKZSG1a5mbm0NXVxd8Ph+MRiMcDgdkMhnX\ny7NNXjgchlQq5V3nSqXCB/VY4fzzn/8co6Oj6Ojo4JpvqVQKh8OBpqYm/PKXv+RDmgy3281THbu7\nu7kcgqXfAdVBymg0CqVSCbPZzIdRF65ldnYWRqMRk5OT2LNnDy+kL1++DKDayWeJj+xnLYfRaEQw\nGKzrbLPBYq/Xi5GREX78jo4OvvFcyBYaXSEIgtjSUCF9BeaIUSwWq5ZYZR8qlRa4XLvxL/8ygtdf\nb8ehwnN4WvowtLIUXJ/7Asb7e9DaksPJkyd5HHIymYTdbm+YZMYeVUulUvzkJz/h2kwWfDI/P483\n3niDR9t2dXWhqakJ733ve+HxeHDy5EmYzWZ87nOfw/T0NPx+f10nq6mpCdlsFjqdjmtea4nH4xgY\nGOAR5JVKhT9KF0URDz30ENec3n777XjjjTcQDAYxMDCARCLBH0m3tbXB6/Wiu7sbFy9eRDQaRWtr\nK5RKJU/os9vtkMlkSKVSaGtrg1QqRblchkqlQm9vL3d5YMVTc3MzJBIJ5ufnAfy2kNi5cydOnToF\nu92Onp4eeL1eeL1emM1mOBwOHDlyBHK5HOVyGZVKBfv27eOWdzabDSqVCqFQCOVyGWq1GslkEoIg\nwGAwIJ1Oc4utRt1KnU7HrfpY5zYWiyGXy6G3txdSqfRKsfo2AMDJkydxzz33QCaTYXx8nBdwwWAQ\nzc3NvJhNpVJ8w5FKpXDLLbfA6XTiyJEjuP/++5HJZPDiiy/iAx/4AHp7e3Hp0iV+PzHpxs6dO/HK\nK6/A7XbDarUiGo1icnISBw4cAICGnXfmw83ivGdmZlAoFLBr1y5MTk5yXTGzxNu7dy9aW1sXrYUl\nLDIrt7m5OfT29sJsNiMUCqG1tRVOpxMymQyVSoVLSrLZLLxeLzQaDQ+fYU4yMpmsoW/yjh078Itf\n/AI7d+6EVCrF5cuX+eCew+FAMBjkHtHMLcblckGtVsNmsyGfz8PlcqGrqwu9vb0YGxuD2WyGUqlE\nKBSCWq3m99qFCxcgk8m4VSBzh2BhPwupjbZm9nJ6vR5utxterxcmk4kPTnZ1dSGdTi/6m2AymRAM\nBhe5U7ACf7XJZDIIBoOQSqUwm82LOuEEQRDE9UGuHVcoFotwOp0A9DhxQo/jx/U4fboVxaIUXR15\nfMf0f/DBS0eQcvQj9d2/hzg8jFOnTnE/4Y6ODoyNjUGhUHANaG1hkEwmMT8/j2eeeQa7d+/Gvn37\nIIoiXnvtNUxOTqKvr48HiyQSCT58xYqCW265hT86ZxrpeDyO9vZ2vPvd/x8A4LnnfolwOIwdO3Y0\nHDCamZlBNBrFwMAAD9Xo6+vDa6+9xr15M5kM/7A3m834j//4D9x99911soNwOIwTJ06gUChgdHQU\ndrsdRqMRp0+fxtmzZzE6OsrdH3K5HHQ6HTQaDV544QXcd999KBQKiMfjaGpq4gEfbIDNZrNBIpEg\nFArB7/dj165d6O3tRbFY5N1cuVyOS5cu4cSJE7DZbLwYmpub4wN9H/7wh5HNZlEoFKDX67mf9l13\n3QWfzwefzwedToehoSEUCgVEIhH09vYuer+mpqawY8cOKJVKvpaxsTGYTCb09fUhmUzillt2XHm9\nry4ZMRqN8m57rf1YKpXia6ntTkciEbz00ktoamrCAw88AJVKhXQ6DZ/Px8NlyuUyjz0HqnpbFspy\ntaEyp9OJgYEBeDwexGIxvqFxOp3o7++H3+/HzMwMVCoVhoeHufaaaZYFQcDg4CB0Oh3Gx8e5f7FO\np4NCocCFCxcgkUjQ2tpaN/Cay+XQ0dGBaDQKh8MBt9uNdDqNvr4+6HQ6uFyuZQfrCoUCnE4nRFHE\nwMAAd0LRarV1ftIsqEcikdT97rFO+NDQEERRRCwWQ7FYhNFo5EO65XKZa5bZUxCJRIKRkZGG58Q2\nBQcPPgAAOH/+TSQSCf57ksvlEI/HoVKpYDAYkM1mEY/HF20WWPx6KpVCqVSCWq3mT5FW243B7/fz\npE1RFLkt5XKe1dfCRrtWbDQbvX5y7SCIjYU60leYmZlBf38/jh9vxRNPGNDTU8L/+B9JHNhxAf//\nT/4CqlOnkPnv/x2pL34RUKkguWLN9Hu/93vcfoo5cLDBJqfTyRPUpFIpOjs70dPTg1QqhYsXL3I/\nYbPZjEAgwFMCGS6XC5lMBg6HA4lEAqVSCfl8noefqFQq3Hbbbfz1O3fu5AmIlUoFkUiEyyGYFZte\nr+eRuaVSiccWM11oMBjkRYZUKuVdxVqkUinS6TSsVivK5TLm5ua4ltVsNnOdKrPZA6rdO6VSiXw+\nj2KxCEEQ+BBdpVLB/v378dJLL+HkyZOQSCRQq9XYv38/d59IpVLI5/NQKpXQarX8+3fs2MHlIX19\nfXj22Wexf/9+jI+P83CSubk5FAoFmEwmvP7666hUKjCZTMhmszh58iT2799fZ0sHVAshQRBw7733\n4qWXXoJCoYBer8f4+DhaW1u5Tra2kKvtakul0oYFCvMXZh7DTN8NVD+QFz5FCAQCGB4eXiQTYR1M\nJue4Fth1tFqtfOiQ3assLGahHjuZTCIYDKK/vx8KhYJ7NweDQYyOjvL3q62tDQ6HAy+99BIcDked\nMwkAjI+PQyqVQiqV1hXNgiBc1eNYqVTWeTkD4CmPkUiE/z5oNBqurWZrY9aQrNPMAjtqiUajGBkZ\nqesIGwwGvPbaa0sW0n6/v063397eznX6BoOBX2NmS6nRaOD1ehc9qWIR4QaDgW+SLBbLqlvVMXkT\nk2Mx15XJyUk+PEwQBEFcP1RIX0EURQCAwzGJ735XgV27irD5fdD/4R9CmU4j/o1vIPff/ht/fblc\nRlNTEzKZDJqbm7m7glar5Q4giUQC4XAYbW1taG9vh9PpxK5du+BwOBCPxyGVStHW1oZLly5BEASc\nO3cOu3fvhkKh4NHOGo2GW2mxYAWFQoG33noLpVIJb731Fk6ccEIqlWJsLI9oNAq73Y6xsTFYLBbe\nNR0fH0d/fz+3y8vn85BKpRBFEYFAAPl8HhcuXMDw8DAf5hofH8fOnTuRyWQwMTEBhUKBUqnEBwh7\ne3sxMDDAhyO1Wi3XPi9MUvN4PNi1axdOnjyJHTt2oLe3l6+ZaVzvuece5HI5iKKI1tZW+P1+SCQS\nXL58uW4tly9fxuTkJN73vvcBAJeDqFQqHigSDodhNBp5uEY+n+fa6Vot8tzcHM6fP4/9+/fD4/Hw\ngUCZTMZt1A4cOIBUKoX5+Xn09/fX6aUBwO8PXOlKLX+PlctlTExMwGw287WMjY1haGhoyQE1VgjW\n0t7eDrfbvWSwx1K0tLQs0oh7PJ5lO9lzc3N1BWNvby88Hg8Pj6mVBjCLvEYbCOaJnkwm+eajUqnA\n6/XCZrNd1zoYdrsdpVIJhUIBZrOZ29cx60Y2XCmVSlEqlZYsTvV6Pebm5qBSqXjqo8fjWbZTy67J\n+fNvXinkq/polvzY0tKCvr4+ZLNZjI+P86jy8fFxLu+KxWLo6uriP0sURe6PvdqFdCKRaJjwx67L\nVnH4IAiCWG+okL6CIAhwuVyw2WwYGmyC8qmnYPjylyGYzQg/8wzS/f2YHhuDUqmEzWbjE/0sICQa\njSIajfJH9ZOTk5DL5dx5YWxsjAetiKLIo31ZcMTg4CBaW1tx9OhRHqu8e/duTE9PQxAEqNXqOk9n\nJjUQRZEnJTItczabhd1u5w4YBoMBKpUKc3NzsNls3KaOpazZ7XaeBHfmzBneFWM6YLlcjkKhwO3F\nWPeR6aeZHzSLtH7b296GRCLBdbcs2lkURdhsNiQSCQQCAf4esnUUCgXezRZFETqdDlNTUzCbzbwI\nMBgMiEajUKvVmJ+fx+joKNe1y2QynDlzBhaLhYdaVCoVaDQahMNheL1evOtd74IgCLwItFgseOut\ntyCVSmG325e8PxbKMFaC3+/nUhS2FrVazUNOSqUS4vE4ZDIZ9Ho9lxk0uldr7egaIYoi4vE4gGqh\nKJVKYbFY4Pf7cf78eRQKBajVanR3dy9pLcZes5D29nb4/X4UCgWcOXMG2WwWFosFKpUKHR0dfNOy\n8GfZbDbMzs4iFArx+8JisXAJCLsuKpXqmrvsgiAgm83ypxgGgwHHjx/Hu971Lr45CQaDDdMIGcxS\nz+PxIJFIoKWlBf39/fB4PMsel21WWdDMzMwMBEFAb29vnWPP8PAwXC4XD+VJpVIQRZE/aWBDxxKJ\nhFscqlSqRU8HbgQ2P7GQfD6/ZtHhBEEQ2wEqpK9QKBTQ0tICdakE3Z/8CdT/+Z8Iv/3t+M2f/imK\nlQpCp05hcHAQ2WwWP//5z3H33XejubkZSqWSa4SDwSCee+457N69G9FoFDKZjEdAl8tl5HI5OJ1O\nhMNhmEwmVCoVnD59GsViEbfddhtee+013HLLLdDr9ZiensZLL72Ed7/73fB6vRgaGuKdq3g8DoVC\nAalUip/+9Ke4fPkzkMvluO22b6O3t5cnmdWi0WgQCASQSqWgVCq5NVk4HIYgCBAEAU6nE3a7HVqt\nFi6Xi0s82Ictc+0IBALYsWMHXn/9dYyPj8Nms/HCQqlU1vm+1j7Knpqa4rHYzEc6Go2iUChwOzYm\nCchkMnA6nbxrz4IzRFGEwWDAnj178OKLL+LWW2/lxZ7X6wVQtQgURZHHRwPVQTuXywW3283THdPp\nNPcjvhEWegkvd48tLEzVajWKxSLC4TASiQTa29tRLpcxNjYGq9UKlUpVN4hWqVS4DGkpkskkj/AG\nqpu6jo4OtLa2Ip1Oo6OjA83NzdwbfKlCSiaTcceJWvL5PDo6OnDy5Ek+BFoqlfDKK6/g0KFDCIfD\nMBgMvKsqCALK5TKUSiWXlCyUOHg8HlQqFRgMBn6PDQwMLBkHzUJMWlpauH1fqVSCSqXCvn37MDU1\nxX/3mNtGowFgoOqe8eqrr8LhcMBisSCVSuH06dMYGRlBuVyGx+Ph94hSqeS/Y8PDw/jyl21oalLh\ni1/sxqlTp6BQKBYNCS7sLi/cZAQCARiNRt6pZw4mC5/q3Aitra38OExKk8vlUCqVFj1huZlIp9OQ\nSqXXHK1OEASx2tCw4RWcTifa2togf+YZ3PL443D97/+N3B/9EWZ8PsTjce6GAFT1vseOHcO73vUu\nnD59Gul0mltk7d27F5OTk5BIJHWWcV6vlxetLOQCqBYZLP2tu7ub61WZ329XVxdMJhO8Xi+y2SwU\nCgVMJhMMBgOefPJJfPrTn4bNVi1Opqc9+P73v4+hoSHcf//9deurVCp8GLJWv8oKktdeew0f/vCH\n68Ivjh07hs7OTu71ywJZ2tvbIZVKUSgUcOLECUQikeomRK3Gfffdt2TxMzExAYlEUlfkl0olHvxx\n6NChutefOXMGoihi//79dQUQiw7W6XT42c9+xv9NpVLhgx/8IH7961/jwIEDdecxNjaG8fFxtLS0\n4IEHHuBfd7vdOH/+/CJt8vXQ3V3V5Pr9y99/bKhv4VrGx8cXJR5WKhVMTExgeHgYPp+PB8wwp5Wl\nusi131fL+Pg4DxKqLebD4TAkEski3TBjamoKXV1d/HtEUcT4+Di/F1UqFd/shEIhZLNZ7Nq1C16v\nt66AZC4u7Bxr/Zbj8TgEQUBnZyd/fT6fh8/nW3LDMD09vch1IhqNwuv1YufOnYuSPaempmC32xtK\nJmZnZ3kKZm3xLZVKkc1mYbPZeLGZSqUQDochiuIVHf8oAOCFF15EZ2cnzp07hzvvvHPR8VngTCPY\nEGgt7B5f+PUboVgs1nXZmTXhjcpINmLYjg3htrW18U2z1WrdEBcSGjYkiO0NdaRrMJlMkPyv/4XQ\ngQNQ9/dDWS7j5SvDaG63m1ustba2orm5GYVCgUd61/4xrU7zH+TdpKamJmi1Wnz3u9/FO97xjroB\nQQA4ffo0AoHAouJ3YGAAL774Itcms2jj+fl5iKKItra2ukEtZjuXTqfh9/vrHAJYml9tsQJUP0wV\nCgUkEgkf5gPAu4OxWAxAYzs1j8eDt7/97Tzt0OPxYHp6etkP/2KxWNcZZIOY7e3t8Hg86OnpgVQq\nRTgc5pZ4gUCgbi1sWFKn0+HjH//4omMw2Qvr/LEnAlqtFi0tLXjttdd45zOVSq1b7LDRaITf7697\nZM8kLqx7zGBSBRZicq0wa72FGAwGzMzMwOFwoFAo8KFVpt1fqpC22+3c4pAlYN5+++145ZVXcPDg\nQf46k8mE9vZ2PP/881CpVNwhg20Mgep18Hg8XK9cLBZhsVgQjUYXFcxNTU3Leikzh4ta2PsbDAbr\nHFjY9V+qYMxkMg3v2TfffBMdHR11HdvaFMXae5K5v5jNZni93rqfF4vFrrtjuhbDfws30TcrlUoF\nPp+vbghXFEVMTEwsORxKEASxVlAhfYWOjg5MT0/Dbrej3N/PdYvM+3d0dJR3kSORCPdibYRarUYq\nlaorPFOpFGQyWcOOiUqlqvOkZbBBKmYPxuzvmE+tWq3GxMQEgGpHwu12cy2sXC7HxMQEZDIZyuUy\n9Ho9T3Fbav3RaBRzc3O8I97R0YHZ2dmGr8/lclCpVHUFAhu6XErD29TUxMMvajt/vb29kMlkMJlM\nmJ6e5kV8W1sb2trakM1m69ai0+kaFouMzs5OxGKxurWYzWbEYjGu304kEjCbzbjtttvgcrmW/FlX\no1Z3erUhMb1ej2KxuGgter2+4XVhhej1wOQvS/0sl8sFiUQCjUaDYDB4Va01G9pkm79kMln35Cce\njzf0R2bnUsvMzAyMRmOdtGFycpIP2C01cHmtMD08k790dHSgUCjA7/cvsja8FgRBaNj5V6vVKBQK\nPE6e4fV6YbVauUUlu8fVavWymyGlUsmHlhmRSKTOEWYzwp6asdTPq7mvrBaJRGKR04hUWk0VZdaB\nBEEQ68WGF9KPPPIIfzwsk8lw+PBhpNNpHDlyhKfbfeYzn1nzqfLW1lbk83mcOHGCexHv2rULGo0G\np06dqis4NBoNf7TdiK6uLkQiEf4YtVKpcM/imZkZDAwM1HVSgsEgzGYzfD4fisUiDw9xu91QKBRw\nOBz88Z1CocCtt96K48ePI5/P1xXmcrkc09PT+PjHP95QF1oul+F0OusGudjxmL1bW1sb/57nn38e\nd9xxR8M1shCVhTQ3Ny9ZSLM1trS0IJPJQCaToaWlhTugpFIp3plkaYbd3d3QarXo6OhYUuO6EBY2\noVKp+Pc4nU6MjIwgEAigp6eHb3JSqdSKNaIej+dKsVjt8jG9+HKPl/V6PQRBQD6f53aEMpkMExMT\nKJVKSCaTAMDDThZKBK6GVquFz+eDUqmsGzZk3skqlYrfS2azGWfPnuV6/WAwyDWnFouFD3TWShJ0\nOh2y2Sza2trws5/9DPv27UNHRweXOy2X/NdoCLG3txculwt+v78uejuTySxbmLFwndpiMxAI8CcV\n2WwWoVAICoUCg4ODyxbpzLWj1r0kFovBbDYjHo8vGnzMZDIYHBzEzMwM/9rExATa29uhUqmgUqmg\n0+mu+X7t6enB1NQU1Go1tFotkskkRFHkwU6hUIjr/S0Wy5KynvUkmUzy90yr1eLSpUvcnWitWep9\nXWo4lyAIYi3Z8EIaAL7whS/UfVgdPXoUu3btwoMPPoijR4/i6NGj+NjHPram5yAIAqLRKO644w6o\nVCrkcjlMT09zr+Zf/OIX6OjogCAISKfT2L9/P0qlUsMPe7vdziOy2VDb2NgY9uzZg1wuh9deew29\nvb0QRRE+nw82mw1msxnPPPMMurq6YDQaeUQxKyJrYZZod911F/71X/8VQLXY/fGPf4zdu3fXFV+1\nHzgymQwWiwXj4+PQarXcl9put8NqteLXv/41VCoVmpubEQwGMTg4uOQGpqWlBTMzM4vkHslkkg+U\nLaSpqQmRSATz8/PQ6/UoFAo4f/489uzZw904qqE41UJp4eP+a+3OskhquVwOpVKJdDoNo9EIo9GI\n2dlZ7u+dy+UAgLt1lMvlOinLcsUXs1erfbw/PDwMp9PJ7QoXUigUeICPWq3mw6cOhwPFYhFTU1M8\nfObNN99c8WN4hUKBy5cv8+tw+fJlmM1m6PV6nvSnVquRyWRgNpuRy+UwOTnJkxxlMhlmZ2fR3NzM\nN0TMZYZtuAKBALRaLX7zm9/AaDTyYcLlLPkaXT+lUommpiYolUpMTk5Cq9XyiPVGEfQM5iPNHFyY\njzR7UqHRaK65M8kkIbW+73K5HDabDdPT0wgGg3wj5/f70dzczMODGI2u+bXer1KplA8yM8ce9r47\nnU4YjUYMDAygXC7D6/WitbV1TVIPr4daW0Q2JDw5OXnV35vVQKfTYXJysu49YCE6pBcmCGK92fBh\nw0ceeQRf/vKX6wrpT3/603j88ceh1+sRj8fx+OOP4+/+7u+u+rNuZNhwamoKVqu1bkCNOVmYTCZY\nLBZEIhEegz0xMYHBwcG6MA2mkZ6ZmUGlUkE2m+UdEq1WyxPpQqEQQqEQJBIJzGYzTCYTJicnuSdz\nOp3maWgXL16EyWSq0/EKgoCXX34ZVqsVg4ODCIVCKBaL6O7uhtfrRXt7+1WHbrLZLB9yrCWdTvMI\n7asNIXm9XigUClgsFphMJh4ys9Rj7Gg0ing8jlKpxLtHCoUCxWJxTbSNzLJPrVbXFTWiKCKXy0Gp\nVPKNUDweRzgc5psWVjwxOcbc3BzvqDI9d19fHy8a2PVvNDjGcLvd6O7urtvoFItF7j7R29vLNeMq\nlYrfE9cj7yiVSvB4POjv7+cbBbVaDZfLhUKhgJGREb6BYu/L2bNneThMe3s78vk8wuEwisUimpqa\nIJPJeKgJC8iJxWLYu3cvT6kcHBzE/Pz8suufnJzkGwh2/WUyGQwGA3Q6XcPrci3rFQQBGo3mhofm\nyuUyBEFAU1NT3d+BWCyGWCwGiUSCjo6ORV31tRo2SyaTyOVyi3y+JyYmltysrQfZbJanrwK/XX88\nHke5XF5Sb7+aJBIJBINBmEwmniLa09OzIX7YNGxIENubTdGRfuKJJyCVSvHOd74Thw4dQjKZ5J0t\nnU7HH3cv5NixYzh27BgA4PDhwzf0BzwajTYMpshkMhBFEXK5HCMjIxBFEV6vF3a7He3t7UilUggE\nAkgkElCr1ejq6kI0GuWFb6lU4oXI+Pg4TCYTTCbToqS2aDTa8LFoNptFLBZDOByGXq9HLpdDJBLB\nLbfcAoPBwH8eg3VgV/peXM/3mUwmRKNR3mWulUw0Ym5uDm1tbdixYwf/WiaTwalTp9blw3cp2OPz\nu+++m39tYGAAly5dgkajgdvt5pHZ8XicSwhaW1v5hkUul/MP1KXWEo1GG37oOZ3Oht3/bDYLrVZ7\nXU4E8Xgcdru94Tl4PJ5FnVoWRNLd3V1XAA8ODuK5556DKIrYsWMH77zncjn88pe/xNDQEJeiKBQK\nTE9Po6WlBW1tbUuuPxaL4c0334TVakVzczNmZ2cRj8dx++23r0hf6/P5+OxBLpfjA4+rzdXuTXbt\nV5tUKoWhoaFF8p5EIsGh4mS+AAAgAElEQVQlQRuBIAgQRZGvma2f/Z1cD19qk8nEg61YlPtGpTOu\n1fUnCOLmYMML6SeeeAIGgwHJZBJf+tKXFhUatVP/Czl06FCdZdqNdAVYR7L2WOVyGYlEAg6HAzMz\nMxgbGwNQDaRQKBQYHx9HKpVCX18fOjo6MDU1hVdeeQVyuRyzs7OLioNYLIZIJIJSqcQlBEajETKZ\njP9bLYIgoFAooKenB4FAAIFAgOuK29vb4fV6oVKp8PnPV3WiX/lKVbeo0WjWtUNiNBp5EbnccZlr\nxMLXsGJ8o0gkEpBIJIvOQSKR4OTJk9i9ezd/7A6AF9Szs7MYGhriPtKPPupBOp1eci3xeJyHkTBY\nF9bj8UAikfCUxo6ODoRCIZ6eea0IgtBwEHZmZgYmkwnnz5/nPt+JRAJAtTguFouLzjuRSKC/vx/x\neJzrgWUyGUZGRuDxeODxeLBv3z4A1cLm+eefX/be+81vfoM9e/Zw15D+/n643W6cO3duWRlHI0Kh\nEADUyZ6mp6fXPaXvtx7iq3//CoIAj8ezSC7D0lI3Mtbb7/dDpVLxIjIUCmF8fBwjIyPr/rtcqVQQ\njUbX9Zi1UEeaILY3G15Is+5FW1sbRkdHuZ8zC6FoNOyzFtS6dgDgrh0swtdisSCdTkMul/MOYTQa\nxdDQEHK5HObn5/nAYrlchtvtrpN+eL1emEwmxGIxnoBYqVTgdDq5frXWsk4URUxPT8PhcECpVEKr\n1fLUN6aflEqlmJ+fx9NPV/+QfvGLQSSTyWUjn9eTfD6PfD7PNaU6nQ5zc3PQ6XS8mxYKhTZ8eIp1\nNBfCbNoaBWqwAdHLly/j6aerNnD/838Gl01H7OzshNvthsPh4NKG6elpDA0N4ZVXXsGtt96KgYEB\n7tWcTqd5l5j5HDc3Ny8rYVCpVCgWi3WyIqlUyh0ohoaG+Kags7OTB+T4fD7o9XouaYhEIlzisPB+\nymazyOVycDgcmJychEKh4IPBjdLzGMVicVFRODg4iNdff/26C+lGoUN9fX1wu93rWkg//XT13r1a\nGM9KMJlMGBsbg1ar5ZvyWCy2SKq0EdjtdrhcLiiVSszPz2N2dhZWq3XDz4sgCGK92dBCWhAEbg8l\nCAIuXLiAD33oQ9i3bx9OnDiBBx98ECdOnMDo6OianwtLw2NhKpVKBRaLBRqNhksr2traUCqVkMlk\nYLPZIJFIMDExAbVaDblcDrfbjdbWVhQKBXR3d8PpdPKfxZLLLly4AJVKBVEUUalUuLfubbfdhlgs\nxo8PAFarFUqlEqFQCMlkEq2trUgmkygUCnA4HLDZbPD7/XwNLA1uo2FhEnK5HBqNBqFQCBqNBhaL\nBeFwGG63m7+uubl5wx+LtrS0wO/3w2w28yJVFEUeQLOQYrEImUyGQqFQp6W9mo1bc3Mz921m9wU7\n5uDgIB8+rFQqaGlpgVwuR6lUgtvtRlNTE5qamrg8ZuEAai0WiwWvvvpqXehP7e/QQnkH2yROT08D\nALcl7Onp4b+jtQXS3Nwc9Ho9Ojs7uUez2WyuGxZtBLvfa39WOp1ekV3ZUnZ7W8m1QSKR1EWVVyoV\naLXaVY0OXylKpRJDQ0MoFoswGAzr0uwgCILYjGxoIZ1MJvG1r30NQFVGcc8992DPnj3o7+/HkSNH\ncPz4cW5/tx7odDru6cq0qcVikWuey+UyJBIJyuUypqen4fF48I53vANKpRImkwkKhQJnz56F2WxG\nc3Pzoo5ZNBqFIAjYvXt3nf3dqVOn6pwlasnlctxui8Ee+TocjroP1evt6q0VXq8XXV1dvEDq6OiA\nz+dDpVKBXC5HpVKByWSCIAgIhULLdnHXC6vVyofhgOr7brVakU6nMTs7y4NsWBe5o6MDkUik7rpY\nrVa43e5lNzOtra2Lio7Z2Vm0t7cv6sz7/X5MTEzA4XDwothsNsPlcvEkyUb4fD4cOHCAbwpYquVS\nOlKlUsk3OVqtFoVCAcViEXa7HblcDuPj4+js7IRCoeCaaoVCwT175XI5JBIJBEFYMtUSqEqixsbG\nYLfb+cAisyW8XiQSCYrFYp18an5+fsOfbqw2SqVyU2yOl0KhUHDrSoIgiO3IhhbSZrMZX/3qVxd9\nvaWlBY899ti6ngvrojIvV+ZaoNFo0NrayiOWa8MuVCoVkskkH3BiEcONAjEA8CjxhUECGo0GgiCg\npaUFoihytwSg2v1b2IFiHe3NCnvf2FqUSiW6urrgdru5m0Q8HkdTU9OGDgnVolarMTw8DEEQAIAX\nrmq1GqFQiD8pEEWRB74svC4r9aPWaDRIp9OLisBcLgeZTLbIk5tp5ru6uuDz+fi9YLFYUKlU0NbW\nVif/kEgkMBqNmJ+fXzLkgyVX5nI5yOVyXqBqtVoMDQ0hHA4jnU6jq6uL33/MO7mtrQ3RaJTLmZZi\naGgIk5OTPDioXC6jvb19Wcu8pejr64PT6URnZye0Wi3i8ThisVidm0WhUIBMJtuwobytRD6fh0Kh\nuGFnFIIgiK3GhmukNws+n4+HCwBVzXYsFsP09DSkUmldFzmXy+HVV19FV1cXl4NEIhEkk0n09/fz\nIa6FtLS0IBAI1CXglctlZLNZqFQqTE9P8yhwZkFXqVRuyg8vj8fDi2hBEHhABVAtTq/HiWI9aRQk\n09HRsUhKEY1GV+26tLW1YXZ2Fm1tbfz4iURiSScLqVSKUqmEqakp9Pf38+j46enpJa3jrjWsotF1\nYQE3C782PDyMWCwGn8+H3t7eq26K1Go1du7ciVAohHw+f0OSAIVCgeHhYUQiEfh8PrS2tnKnHBYW\nwhJDmSf1zfh7tNGwIWiVSsU3JkzWtpr4/X5ks1n+c3t7e1e8MSUIglhPqJC+Qj6fXzSkZDAYcOnS\nJTQ3N9d9cCiVSh7pbDQauRd0JBKBy+Vacopap9NBo9FwCUGlUkEul0NraysSicSiwsLtdvPiuzbi\nuFgs1v1cv7+xf3alUkGpVOKP3teLcDiM/v7+umCWM2fObKnpcrPZjEAgAKvVCr+/aoc3O/vb61Iu\nlxEKhSCKIoxG41WjuAcHB+H1evl9pVarYbVa4XQ6F0kY/H4/RFGEzWbj1mhSqRQOhwMTExM83r32\nmtdaMq4WrNNd69pyNWQyGZfJ3ChSqXTRBod5fteuNZfL8achqw279hto2rBmsCdHtV3+VCrFo9BX\ni5mZGWi1Wj5oXSqVMDk5ieHhYdr8EASx6aFC+ipIJBIYDAaepFUsFhGLxdDb28sHCnU6HY+h1mq1\nSxZNUqkUVqsVwWCQF0bM/WBubm5Rd663txderxdNTU2Ymprij95TqdRV9dCBQACZTAZKpRKFQgEt\nLS3r5uZhMBiQSCS4xCORSMBkMiGfz6/L8dcDtVoNpVLJk+fy+Twv1piLQVdXF7dCbGpqWnYjIZPJ\nGmrFrVYrpqameLeauTYUi8Ulu8ednZ3cs1wikSAcDqOzs3NTSGjWmlAoVLfpBMA3rdca2U1UmZub\nq4ttB6pP1ebm5lbtGJVKBYIg1F0zuVyOzs5ORCKRZYdqCYIgNgNUSF+BeQPX6jWDwSBsNhuKxSIc\nDgcSiQRUKhVGRkYwMTEBrVaLkZERzM/P8w7hcsNWQLUrzTrQzFaPfbgLgoDZ2VmUy2Wo1WpYLBaI\nooju7m4Ui0XMz8+jpaVlUUFW6yMNVK3LmBMEw+/3L1rfWiGTyTAwMIBMJgNBENDd3Q2FQoGpqak1\nP/Z60tnZiVKphM9+VguNRoPDh6uPol0uV11HlFmFsVTApYhEIvy+YAl6CoUCIyMjSKVSKBQKPH1z\nZmYGmUwGiUSC65o7OzshiiJaWlowPDyMRCKBSqWC4eHhbVNAsicwC2HSltV+H37rI72qP3ZTUC6X\nl3wvV4ulXG40Gs2SQVwEQRCbCXpudoWuri4kk0m4XC7Mzc3xR+qsCPT5fDyGeHx8vK6YbW1tRXt7\n+1WLaIZUKoXBYIBer+cfStlsFi6XC52dnejr60NrayteffVV6HQ6AFVNqNFoXBRPDFS9bJmfLVAN\n/ljYfWaJi+sF8zw2Go1QKpWIRCLrUsSvN3K5HD/+sQE//GH1KQSL3l6I2WzmITyNcLlcqFQqGBgY\ngN1uRzweRzAY5P/e0tICo9HI7zGz2YwXX3wRarUaAwMDsFgsePnll7k8SSKRQK/Xw2AwbJsiGqg6\ngyzsmIqiiHK5vCYygaefbsb3v781hxmNRmPdPQhUNyqriUwma/gzo9Holvx7QRDE1oM60jXYbDaU\nSiUIgoD29nbeKenu7kYul0M0GoVcLsfg4OANOQFcunQJs7OzAKoODMPDw1wCkM1m0drairm5OW7x\ndb00KpyupZian5/niXEqlQpdXV0rKj6YtpdZusViMQiCsCYa1eulWCwiEAigWCxCIpGgs7NzRT7G\nS7FUYVAoFJYcHsxms1AoFNz9RSqVoq+vD5OTk4u0zoxwOIw777wTyWSSF+j79+9f1cfuNyNss8ss\nCvP5PObm5mCz2Vb1OMwGEahuqCcmJmAymdYlHnu90Ov1cLvdPBUzm80iFAqt+u+xyWTC1NQU+vr6\noFAoEI1GkclkVk1LTxAEsZZQIb0AuVzeMBlNrVavShDCSy+9BIvFgoMHq2l4ly5dwquvvopSqYS3\nve1tPDL6bW97G4LBIKLR6HV/oCgUCuRyubrOaDqdXnbgLRKJIJvNcneDTCaDiYmJFckCmByBWZLp\ndLpN4e9bKpXgdDpht9uhUql4AqXZbG7Y6V8JcrkcoijWyThEUVw0AFdLMplsWIAxW8RGHe5cLofu\n7u5FvuMLO4jbERYkE41GoVQq12Rozev18o0PULX2czqd0Gg0Vx0svZmw2+3IZrOIxWJc1rbaTzgM\nBgPUajV8Ph/K5TJ0Ot2m9s4mCIKohQrpdSSZTEImk8FkMvEEuJ6eHpw9exbZbJbLOBgdHR04f/78\ndR+np6cHk5OTMJlMaGlpQTKZRDweX9bjd6EHL0vhi8Vii4q1a4ENaW4mZmdnYbPZeKEjk8nQ398P\np9O5aoU0UC0+WHojS0C0Wq3ctm52dpYX2p2dnVCr1chkMos2G4IgcFeOhSgUioaa662U7HcjqFQq\n7gJxLQiCgIsXLyKVSkGj0eC2225b9klFsVhctOHu6+tDIBBY9e73RrMwCXMtUKvVmyKYiSAI4nqh\nQvoaEEURfr8foVAIcrkcfX19K9LvMdkEkzlUKhUu8ahUKjxkgx3T5/OtqIiVyWQYGRlBLBZDKBSC\nVqtd1vqsUqk0lKrodDp4vd4VncNmpJF+WSKRrHqHjQ1biqIIURS5rrlQKGBqago2mw1qtRq5XA6T\nk5Po7+/n0d+sMGabrqUkRBaLBdPT0xgcHOTdVr/fv+k2LzcD2WwWL7zwAu644w6YTCbE43G88MIL\nuPfee6/L51oul3P7QoIgCGJ7QIX0NXD69Gmo1WrYbDbk83lcvHgRVqv1ur1UW1pa8NZbb+Huu+8G\nUC3iuru78fLLL2NoaAgSiYQn6AHVLs21dEqX8pFmaXVXg8WeL2SrRS4rFAoIgrDo0fuNdnGX8hKW\nSqV1kgK/388DVADwQcGZmRkMDAzA6/XylEKVSrVsZ7OpqQm9vb1wuVx8DQaDYctsetaT8+fP4557\n7uGpj3q9HgcPHsSrr76KAwcOLPl95XK57toHg0F6/wmCILYZVEhfhUgkAolEgt27d/Ovmc1mHD9+\nHH19fUt2M3O5HPx+P4BqkdPR0QGFQgGJRILZ2Vmue/Z4PFCpVOjt7UU4HEZHRwc0Gg1isRhPf1sP\nWltb686LDWmNjIysy/HXg87OTrhcLgwMDEAul6NSqcDj8dRpXdeScrm8SKrBYuflcvlVvcEXotFo\nSEu6CgiCsCg6ncWgL0Vvby8mJiZgNpuh0WgwMzODcrlMA3IEQRDbDCqkr4LH41mkLZbJZFCr1UsO\nggmCAK/Xyx+7swl/lUqFvXv3wufz4dKlSwCq3a89e/Zwe7NYLHbdA3oLfaRXgsViQTQa5R1xmUyG\nwcHBLWWdplQqYbPZrrgt/HaDs7CIul6ux0u4Nh6e/T+x8RQKhbpNjiiKyz6paGpqwsjICD79aTXk\ncgm+9CXjmuuICYIgiM0HFdJXgQUDZDIZnsxnMBiWtTObnZ3l7hdAVTphs9ngdDpRqVRw66238gI8\nm80iEAigqakJqVQK8XgcQPWDvaenZ8lj1MI8pG+kkAbA4563MiqVatW6uOVyGT6fD08/fRcA4E//\nNLFoYLQWpmuu7TxPT0/DbDavyvlcC5lMhuvypVIpenp6lhxo3C7cdtttePnll3HgwAH+O3vq1Km6\n4dtGSCQS/PjH1SdG3/hGZs3PkyAIgth8UCF9FQYGBvDDH/4QH/jAB3iM7blz55BKpZYMYCmXy4sK\nYDbU1t/fD4/HwzXJCoWCpybGYjFegOfzeTidTgwNDd2QZzWxNlQqFUxMTNTpmNPpNIrF4pJSEa1W\ni3K5zLv+lUoF7e3tq+oYshypVArBYJDfY8ViEU6nEwMDA9e0YduqmEwmDA8P4/jx45BKpRBFEQMD\nA4uixlcTiisnCILYGlAhfRUymQzuvPNO/OY3v4FarUapVOKyh6U+DJVK5SIfZ/YIXyqVNrR5CoVC\ndR2wpqYmWK1WzM3NXZeNF7E+hMNhWCyWumvc09ODiYmJZTXXbW1tNywlWSlzc3MYGBjg96xCoYDd\nbkcgELjuwdmtRnd395r/npVKJS4rAqrFtNVq3fZPBAiCIG5mqJC+Cul0Gg6HAyMjI0ilUlAoFNBo\nNPB4PCiVSg07eZ2dnXXBH8ViEW63e9lAl0aBERqNBoFAY0eOG0UQBMhksm3diayFWRFms1kA1fe+\nNgZ+Iel0uqGrxlrEUK8Wjaz+2P1ZKpXg8/l4KuO1Or4Q147L5YLVauUWhywgaCsN9BIEQWw3qJC+\nCs3NzZifn180lFYoFJaUdigUCgwMDPAoahb5vFziWaOhs2w2u+opaalUCoFAAM3NzSiVSigWi7Db\n7UuuZbvgdrthMBh48ZxIJOByuZZ00tBqtUgmk4v8xDfz8GClUln0FEUQBMjlckxOTvKNH1C16gsG\ng6uu3w6Hw0gkEvwcent7F4XKbEWY7WLtWuVyOXQ6Hebn56/Lr5ogCILYPGze9tkmQa/XIx6P804l\nUB0mbG5uXlbjqFAoYLVaMTAwAIfDcdWCuL29HW63mzsFFAoFeDweWCyWq56j3x9Y0ku6lqrvrR/D\nw8Po6emBzWaD1WrlKXzblXw+D6lUWjcoqNPpuFa9Ee3t7Zibm4MgCPD7AxCEPPx+/4qCetYLs9kM\nl8vFi/1isYjp6WkolUqYzea6e7S7uxvJ5I0Nry4kGAyiXC5jcHAQAwMDsNlscLlcN3WIid8fQD5f\nuOrrisViQwlHU1MTCoWrfz9BEASxOdnebchrZHBwED6fj3/g6fX6VXe30Ov1kMvlmJqaAlCVCPT3\n969qpzgSiSySKyiVSshkMpRKpQ3tSouiWDeECVQjl9dDP5rNZhsO/LW0tCCbzTbsmEokEgwODsLv\n96NYLCISiUCj0dxwIZ3NZiGVSlf9SQRQ9QqXSqU8xIXp9QOBQMPOs0wmW9WhuPn5+TorSblcjt7e\n3m0xB6DVajE7O7toYxyNRrdcpDhBEMR2ggrpa4BJM9aalpaWFTk4XKuPdLlcbugAwgqmjcTtdqOz\ns5N78ZZKJUxOTmLHjh1rfuzm5mb4/f5Fm6P5+fllCzy5XA6r1XrFR1qFJ54IrvgcmOSGOXvkcjnY\n7fZV30hotdpF9n8tLS1IJpOLNNHlcnlVnSUa/SytVou5ublVO8Z6c60e4hKJBAaDAU6nEz09PZBK\npfD7/Whubt72siqCIIibGZJ2bAGefrqZe0kvh8lkWlS0VCoVCIKwoUOHpVIJEomkLtBCLpfDZDIh\nFout+fFZsVp7LPbf11LIPv10M77//ZVbFIqiCL/fj6GhIXR3d6Ovrw/9/f11Dg9ridFoRCgU4vKl\nSqWCmZmZVZepNNKPx2Kxm1offD3X3mQy8QRT1p2mJESCIIibG2qFbCOUSiWam5vhdDphNptRLBYR\nDAY33PqsWCw2lE+o1WqkUql1OQe73Y5gMAin0wmg2qVtZFO4FkSjUZjN5rqOrVwuh1KpXJS4txbU\nylSYfMlkMi0bLrMSOjo6MDU1BavVCrlcjlQqhVAohOHh4VU9zmamqalpTf2pCYIgiPWFCulthsVi\nQbFYRCwWg1wux8jIyIYHQ6hUKmQyi5PhWIG5XpjN5nU9HkMUxYaSGxYOsh7IZLI1ly/pdDoolUp4\nvV6Iogi1Wo2hoaENv/8IgiAIYqVQIb0NUSgUG1IwLoVEIoHRaMTU1BT6+vogk8kwOzsLiUSyJkN3\nmw2j0Yjp6ek6iUOlUkEul9ty69doNEtaChIEQRDEzQYV0sSmwGg0QqPRwO/3o1wuo729/abWzl4P\ncrkcbW1tcDqdMJlMKJfLCIVCGy65IQiCIAhieaiQ3gJci4f0zYBarb4prcD8/gBMJhMikZX/jPb2\ndu5ZLpPJNoXkhrg6q3HtCYIgiJsXKqQJYpMgl8vR3t6+0adBEARBEMQ1QvZ3W4DPf76Ne0kT68/n\nP9+GT35y5fZ3xM0LXXuCIIjtDRXSW4Br9ZEm1oYb9ZEmbl7o2hMEQWxvqJAmCIIgCIIgiBVAhTRB\nEARBEARBrAAqpAmCIAiCIAhiBVAhTRAEQRAEQRArgArpLYDfH9gyXtI3I35/APl8YaNPg9gA6NoT\nBEFsb6iQJgiCIAiCIIgVQIX0FoB8pDcW8hLevtC1JwiC2N5QIb0FIB/pjYW8hLcvdO0JgiC2N5s6\nIvz8+fP4wQ9+AFEUcfDgQTz44IMbfUoEQRAEQRAEAWATd6RFUcT3vvc9/MVf/AWOHDmCkydPwufz\nbfRpEQRBEARBEASATVxIO51OWCwWmM1myOVy3H333Thz5sxGnxZBEARBEARBANjEhXQsFoPRaOT/\nbzQaEYvFNvCMCIIgCIIgCOK3bGqN9NU4duwYjh07BgA4fPgwurq6NvR8Nur4lQo/gw05Pj/6Br//\nG8Vmef83mu14/enaV9mO176W7b5+gtjObNqOtMFgQDQa5f8fjUZhMBjqXnPo0CEcPnwYhw8fXu/T\nW8Sjjz660aewodD6af3ble28doDWv93XTxDbnU1bSPf392N2dhahUAilUgmnTp3Cvn37Nvq0CIIg\nCIIgCALAJpZ2yGQyfOITn8Df/M3fQBRF3H///ejt7d3o0yIIgiAIgiAIAJu4kAaAvXv3Yu/evRt9\nGtfEoUOHNvoUNhRaP61/u7Kd1w7Q+rf7+gliuyOpVH47LkMQBEEQBEEQxLWxaTXSBEEQBEEQBLGZ\n2dTSjs1KJBLBk08+iUQiAYlEgkOHDuE973kP0uk0jhw5gnA4jPb2dnzmM5+BVqvd6NNdVQqFAr7w\nhS+gVCqhXC7jzjvvxEMPPbQt1l6LKIp49NFHYTAY8Oijj26r9T/yyCNQqVSQSqWQyWQ4fPjwtlp/\nJpPBU089hZmZGUgkEnzyk59EV1fXtlh/IBDAkSNH+P+HQiE89NBDuO+++7bF+p999lkcP34cEokE\nvb29+NSnPoVCobAt1k4QRGNI2rEC4vE44vE4HA4HcrkcHn30UXzuc5/Diy++CK1WiwcffBBHjx5F\nOp3Gxz72sY0+3VWlUqkgn89DpVKhVCrhsccew8MPP4zTp09v+bXX8uyzz2Jqaopf/x/96EfbZv2P\nPPIIvvzlL6O1tZV/bTut/9vf/jZ27NiBgwcPolQqIZ/P4yc/+cm2WT9DFEX8wR/8Af72b/8Wzz33\n3JZffywWw1/91V/hyJEjUCqV+MY3voG9e/fC5/Nt+bUTBLE0JO1YAXq9Hg6HAwCgVqvR3d2NWCyG\nM2fO4L777gMA3HfffVsy0lwikUClUgEAyuUyyuUyJBLJtlg7IxqN4uzZszh48CD/2nZafyO2y/qz\n2SwuX76MBx54AAAgl8vR3Ny8bdZfy8WLF2GxWNDe3r5t1i+KIgqFAsrlMgqFAvR6/bZZO0EQjSFp\nxw0SCoXgdrsxMDCAZDIJvV4PANDpdEgmkxt8dmuDKIr48z//c8zNzeF3fud3MDg4uG3WDgD/+I//\niI997GPI5XL8a9tp/QDwxBNPQCqV4p3vfCcOHTq0bdYfCoXQ2tqK73znO/B4PHA4HHj44Ye3zfpr\nOXnyJN7+9rcD2B73v8FgwPvf/3588pOfhFKpxO7du7F79+5tsXaCIJaGCukbQBAEfP3rX8fDDz8M\njUZT928SiQQSiWSDzmxtkUql+OpXv4pMJoOvfe1r8Hq9df++ldf+xhtvoK2tDQ6HA2+99VbD12zl\n9QPVItpgMCCZTOJLX/rSonjkrbz+crkMt9uNT3ziExgcHMQPfvADHD16tO41W3n9jFKphDfeeAMf\n/ehHF/3bVl1/Op3GmTNn8OSTT0Kj0eAb3/gGfv3rX9e9ZquunSCIpaFCeoWUSiV8/etfx7333os7\n7rgDANDW1oZ4PA69Xo94PF6nId2KNDc3Y+fOnTh//vy2Wfv4+Dhef/11nDt3DoVCAblcDt/85je3\nzfqBamcOqN7vo6OjcDqd22b9RqMRRqMRg4ODAIA777wTR48e3TbrZ5w7dw52ux06nQ7A9vjbd/Hi\nRXR0dPC13XHHHZiYmNgWaycIYmlII70CKpUKnnrqKXR3d+N973sf//q+fftw4sQJAMCJEycwOjq6\nUae4ZszPzyOTyQCoOnhcuHAB3d3d22LtAPDRj34UTz31FJ588kn8yZ/8CW699Vb88R//8bZZvyAI\nXNIiCAIuXLiAvr6+bbN+nU4Ho9GIQCAAoFpc9fT0bJv1M2plHcD2+NtnMpkwOTmJfD6PSqWCixcv\nbqu/fQRBNIZcO1bA2NgYHnvsMfT19fHHeB/5yEcwODiII0eOIBKJbFkbJI/HgyeffBKiKKJSqeCu\nu+7Chz70IaRSqaTsBp0AAAPCSURBVC2/9oW89dZb+M///E88+uij22b9wWAQX/va1wBUZQ733HMP\nfvd3f3fbrB8Apqen8dRTT6FUKqGjowOf+tSnUKlUts36BUHApz71KXz729/mkrbtcv3/7d/+DadO\nnYJMJoPNZsMf/uEfQhCEbbF2giAaQ4U0QRAEQRAEQawAknYQBEEQBEEQxAqgQpogCIIgCIIgVgAV\n0gRBEARBEASxAqiQJgiCIAiCIIgVQIU0QRAEQRAEQawAKqQJgiAIgiAIYgVQIU0QBEEQBEEQK4AK\naYIgCIIgCIJYAfKNPgGCIDYvR48exfPPP49kMgmj0YiPfOQj2L9/P0RRxI9+9COcOHECKpUK73//\n+/H9738f//Iv/wKZTIZsNot/+qd/wrlz5yCRSHD//ffjoYceglRKe3eCIAhi60CFNEEQS2I2m/HX\nf/3X0Ol0ePXVV/Gtb30L3/zmN3HmzBmcO3cOX/nKV9DU1IQjR47Ufd+TTz6JtrY2fPOb30Q+n8fh\nw4dhNBrxzne+c4NWQhAEQRCrD7WHCIJYkrvuugsGgwFSqRR33303LBYLnE4nXnnlFbznPe+B0WiE\nVqvFBz/4Qf49iUQC586dw8MPPwyVSoW2tja8973vxalTpzZwJQRBEASx+lBHmiCIJTlx4gSeffZZ\nhMNhAIAgCEilUojH4zAajfx1JpOJ/3ckEkG5XMbv//7v869VKpW61xMEQRDEVoAKaYIgGhIOh/H3\nf//3eOyxxzA0NASpVIrPfe5zqFQq0Ov1iMVi/LWRSIT/t9FohFwux/e+9z3IZLKNOHWCIAiCWBdI\n2kEQREPy+TwkEglaW1sBAC+88AJmZmYAVCUfP/vZzxCLxZDJZPDTn/6Uf59er8fu3bvxz//8z8hm\nsxBFEXNzc7h06dKGrIMgCIIg1grqSBME0ZCenh68733vw1/+5V9CKpXiHe94B4aHhwEABw8eRCAQ\nwJ/92Z9BrVbj3e9+Ny5dusRdOf7oj/4ITz/9ND772c8il8vBbDbX6agJgiAIYisgqVQqlY0+CYIg\nbm7OnTuHf/iHf8B3vvOdjT4VgiAIglg3SNpBEMR1UygUcPbsWZTLZcRiMfz7v/879u/fv9GnRRAE\nQRDrCnWkCYK4bvL5PB5//HH4/X4olUrs3bsXDz/8MDQazUafGkEQBEGsG1RIEwRBEARBEMQKIGkH\nQRAEQRAEQawAKqQJgiAIgiAIYgVQIU0QBEEQBEEQK4AKaYIgCIIgCIJYAVRIEwRBEARBEMQKoEKa\nIAiCIAiCIFbA/wP/SfPmvSP/egAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot splines\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "plt.scatter(wage.age, wage.wage, facecolor='None', edgecolor='k', alpha=0.3)\n",
    "plt.plot(age_grid, pred_3knots, color='b', label='Specifying three knots')\n",
    "plt.plot(age_grid, pred_deg6, color='r', label='Specifying df=6')\n",
    "plt.plot(age_grid, pred_deg4, color='g', label='Natural spline df=4')\n",
    "[plt.vlines(i , 0, 350, linestyles='dashed', lw=2, colors='b') for i in [25,40,60]]\n",
    "plt.legend(bbox_to_anchor=(1.5, 1.0))\n",
    "plt.xlim(15,85)\n",
    "plt.ylim(0,350)\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('wage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<a id='lab-7.8.3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Lab 7.8.3 GAMs"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "name": "Chapter 7 Moving Beyond Linearity.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}