{
 "cells": [
  {
   "metadata": {
    "id": "nNzmA4NX9JSe"
   },
   "cell_type": "markdown",
   "source": "## X Learner\n\n### <b><span style='color:#686dec'>Background</span></b>\n\nThere are various approaches to estimating the `uplift`\n- Our aim will be to replicate the `x-learner` (Two independent Model) approach to uplift modeling using only `sklearn`, the appoach itself is not implemented in `scikit-uplift` so we need to realise it!\n- This is desired to show an understanding of what the approach actually involves, rather than using ready modules"
  },
  {
   "metadata": {
    "id": "tdeEWY9-M5Ts",
    "trusted": true
   },
   "cell_type": "code",
   "source": "! pip install scikit-uplift -U -qqq",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "text": " System commands are not supported in Juno (yet)\n",
     "name": "stderr"
    }
   ]
  },
  {
   "metadata": {
    "id": "DfrkF0FP9JSi"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Read Dataset</span></b>\n\n- We'll be using the `fetch_x5_clients` dataset. `df_train` contains the treatment & target vectors.\n- `df_clients` contains more `client_id` than treatment information, so we need to merge and keep only those clients for whom we have treatment data\n\n`control` and `target` groups are:\n - The `control` group contains clients not targeted (`treatment_flg` = 0)\n - The `target` group contains clients targeted (`treatment_flag` = 1)\n\n<br>"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "Dq7g7mdA9JSj",
    "outputId": "3ee80e1a-418e-4156-f257-a372cbec4bd5",
    "trusted": true
   },
   "cell_type": "code",
   "source": "import pandas as pd\n\ndf_clients = pd.read_csv('fetch_x5_clients.csv')\ndf_train = pd.read_csv('fetch_x5_train.csv')\n\ndf_clients.head()",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 2,
     "data": {
      "text/plain": "    client_id     first_issue_date    first_redeem_date  age gender\n0  000012768d  2017-08-05 15:40:48  2018-01-04 19:30:07   45      U\n1  000036f903  2017-04-10 13:54:23  2017-04-23 12:37:56   72      F\n2  000048b7a6  2018-12-15 13:33:11                  NaN   68      F\n3  000073194a  2017-05-23 12:56:14  2017-11-24 11:18:01   60      F\n4  00007c7133  2017-05-22 16:17:08  2018-12-31 17:17:33   67      U",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>client_id</th>\n      <th>first_issue_date</th>\n      <th>first_redeem_date</th>\n      <th>age</th>\n      <th>gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>000012768d</td>\n      <td>2017-08-05 15:40:48</td>\n      <td>2018-01-04 19:30:07</td>\n      <td>45</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000036f903</td>\n      <td>2017-04-10 13:54:23</td>\n      <td>2017-04-23 12:37:56</td>\n      <td>72</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>000048b7a6</td>\n      <td>2018-12-15 13:33:11</td>\n      <td>NaN</td>\n      <td>68</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>000073194a</td>\n      <td>2017-05-23 12:56:14</td>\n      <td>2017-11-24 11:18:01</td>\n      <td>60</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>00007c7133</td>\n      <td>2017-05-22 16:17:08</td>\n      <td>2018-12-31 17:17:33</td>\n      <td>67</td>\n      <td>U</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "ODjWasCR9JSk",
    "outputId": "23843a31-54a4-43eb-c24e-cd7ae296c2d7",
    "trusted": true
   },
   "cell_type": "code",
   "source": "df_train.head()",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 3,
     "data": {
      "text/plain": "    client_id  treatment_flg  target\n0  000012768d              0       1\n1  000036f903              1       1\n2  00010925a5              1       1\n3  0001f552b0              1       1\n4  00020e7b18              1       1",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>client_id</th>\n      <th>treatment_flg</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>000012768d</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000036f903</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00010925a5</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0001f552b0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>00020e7b18</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-FCG66tm9JSl",
    "outputId": "9dd15945-3e73-4d36-b457-7d7cc16dc20b",
    "trusted": true
   },
   "cell_type": "code",
   "source": "print(df_train.isna().sum())\nprint('')\nprint(df_clients.isna().sum())",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "text": "client_id        0\ntreatment_flg    0\ntarget           0\ndtype: int64\n\nclient_id                0\nfirst_issue_date         0\nfirst_redeem_date    35469\nage                      0\ngender                   0\ndtype: int64\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2oWQCATo9JSm",
    "outputId": "7bb95649-f58c-45b5-8022-41bf2ce133a0",
    "trusted": true
   },
   "cell_type": "code",
   "source": "df_clients = df_clients.dropna()\ndf_clients.shape",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 5,
     "data": {
      "text/plain": "(364693, 5)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "id": "eZ_6mvWc9JSm",
    "trusted": true
   },
   "cell_type": "code",
   "source": "df_features = df_train.merge(df_clients,on='client_id')\ndf_features.index = df_features['client_id']\ndel df_features['client_id']",
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "PWMeX6Qu9JSn",
    "outputId": "775e06ee-27e0-4ad1-944e-3518cd6e6dfb",
    "trusted": true
   },
   "cell_type": "code",
   "source": "df_features",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 7,
     "data": {
      "text/plain": "            treatment_flg  target     first_issue_date    first_redeem_date  \\\nclient_id                                                                     \n000012768d              0       1  2017-08-05 15:40:48  2018-01-04 19:30:07   \n000036f903              1       1  2017-04-10 13:54:23  2017-04-23 12:37:56   \n00010925a5              1       1  2018-07-24 16:21:29  2018-09-14 16:12:49   \n0001f552b0              1       1  2017-06-30 19:20:38  2018-08-28 12:59:45   \n00020e7b18              1       1  2017-11-27 11:41:45  2018-01-10 17:50:05   \n...                   ...     ...                  ...                  ...   \nfffd8c9d7d              1       0  2018-02-20 13:40:04  2018-06-30 20:02:51   \nfffe0abb97              0       0  2017-11-27 08:56:54  2018-02-11 09:26:08   \nfffe0ed719              0       1  2017-09-15 08:53:24  2017-12-12 14:50:12   \nfffea1204c              0       1  2018-01-31 16:59:37  2018-03-12 17:02:27   \nfffff6ce77              0       1  2017-08-03 20:25:12  2017-08-26 16:41:41   \n\n            age gender  \nclient_id               \n000012768d   45      U  \n000036f903   72      F  \n00010925a5   83      U  \n0001f552b0   33      F  \n00020e7b18   73      U  \n...         ...    ...  \nfffd8c9d7d   48      F  \nfffe0abb97   35      F  \nfffe0ed719   69      U  \nfffea1204c   73      F  \nfffff6ce77   42      U  \n\n[182493 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>treatment_flg</th>\n      <th>target</th>\n      <th>first_issue_date</th>\n      <th>first_redeem_date</th>\n      <th>age</th>\n      <th>gender</th>\n    </tr>\n    <tr>\n      <th>client_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>000012768d</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2017-08-05 15:40:48</td>\n      <td>2018-01-04 19:30:07</td>\n      <td>45</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>000036f903</th>\n      <td>1</td>\n      <td>1</td>\n      <td>2017-04-10 13:54:23</td>\n      <td>2017-04-23 12:37:56</td>\n      <td>72</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>00010925a5</th>\n      <td>1</td>\n      <td>1</td>\n      <td>2018-07-24 16:21:29</td>\n      <td>2018-09-14 16:12:49</td>\n      <td>83</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>0001f552b0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>2017-06-30 19:20:38</td>\n      <td>2018-08-28 12:59:45</td>\n      <td>33</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>00020e7b18</th>\n      <td>1</td>\n      <td>1</td>\n      <td>2017-11-27 11:41:45</td>\n      <td>2018-01-10 17:50:05</td>\n      <td>73</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>fffd8c9d7d</th>\n      <td>1</td>\n      <td>0</td>\n      <td>2018-02-20 13:40:04</td>\n      <td>2018-06-30 20:02:51</td>\n      <td>48</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>fffe0abb97</th>\n      <td>0</td>\n      <td>0</td>\n      <td>2017-11-27 08:56:54</td>\n      <td>2018-02-11 09:26:08</td>\n      <td>35</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>fffe0ed719</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2017-09-15 08:53:24</td>\n      <td>2017-12-12 14:50:12</td>\n      <td>69</td>\n      <td>U</td>\n    </tr>\n    <tr>\n      <th>fffea1204c</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2018-01-31 16:59:37</td>\n      <td>2018-03-12 17:02:27</td>\n      <td>73</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>fffff6ce77</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2017-08-03 20:25:12</td>\n      <td>2017-08-26 16:41:41</td>\n      <td>42</td>\n      <td>U</td>\n    </tr>\n  </tbody>\n</table>\n<p>182493 rows × 6 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "id": "lprp0mLf9JSn"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Feature Preparation</span></b>\n\n- We'll be using basic features, similar to the [uplift notebook](https://shtrausslearning.github.io/notebooks/course_recsys/Uplift_Modeling).\n- We'll be using `GradientBoosting` so we'll need to convert categorical data into numeric."
  },
  {
   "metadata": {
    "id": "IhZ5vebo9JSo",
    "trusted": true
   },
   "cell_type": "code",
   "source": "from sklearn.preprocessing import LabelEncoder\n\nencoder = LabelEncoder()\ndf_features['gender'] = encoder.fit_transform(df_features['gender'])",
   "execution_count": 8,
   "outputs": []
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "3NpGvllM9JSo",
    "outputId": "3b8e25d5-f97c-45e6-98a4-de7df242e228",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# feature extraction\ndf_features['first_issue_time'] = (pd.to_datetime(df_features['first_issue_date'])- pd.Timestamp('1970-01-01')) // pd.Timedelta('1s')\ndf_features['first_redeem_time'] = (pd.to_datetime(df_features['first_redeem_date']) - pd.Timestamp('1970-01-01')) // pd.Timedelta('1s')\ndf_features['issue_redeem_delay'] = df_features['first_redeem_time'] - df_features['first_issue_time']\ndf_features = df_features.drop(['first_issue_date', 'first_redeem_date'], axis=1)\n\ndf_features.head(2)",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 9,
     "data": {
      "text/plain": "            treatment_flg  target  age  gender  first_issue_time  \\\nclient_id                                                          \n000012768d              0       1   45       2        1501947648   \n000036f903              1       1   72       0        1491832463   \n\n            first_redeem_time  issue_redeem_delay  \nclient_id                                          \n000012768d         1515094207            13146559  \n000036f903         1492951076             1118613  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>treatment_flg</th>\n      <th>target</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>first_issue_time</th>\n      <th>first_redeem_time</th>\n      <th>issue_redeem_delay</th>\n    </tr>\n    <tr>\n      <th>client_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>000012768d</th>\n      <td>0</td>\n      <td>1</td>\n      <td>45</td>\n      <td>2</td>\n      <td>1501947648</td>\n      <td>1515094207</td>\n      <td>13146559</td>\n    </tr>\n    <tr>\n      <th>000036f903</th>\n      <td>1</td>\n      <td>1</td>\n      <td>72</td>\n      <td>0</td>\n      <td>1491832463</td>\n      <td>1492951076</td>\n      <td>1118613</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "id": "xiNslQ0l9JSp"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Train/Test Splitting</span></b>\n\nWe split the dataset into two parts, we'll be evaluating the `uplift` on training and the generalised set"
  },
  {
   "metadata": {
    "id": "KFiXnW4a9JSp",
    "trusted": true
   },
   "cell_type": "code",
   "source": "\"\"\"\n\nTrain / Test Splitting\n\n\"\"\"\n\ny = \"target\"\nT = \"treatment_flg\"\nX = [\"age\",\"gender\",\"first_issue_time\",\"first_redeem_time\",\"issue_redeem_delay\"]\n\nfrom sklearn.model_selection import train_test_split\n\nrs = 47\ntrain, test = train_test_split(df_features,\n                               test_size=0.3,\n                               random_state=rs)",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {
    "id": "yQPhYMrw9JSs"
   },
   "cell_type": "markdown",
   "source": "\n### <b><span style='color:#686dec'>Target / Treatment Distribution</span></b>\n\n\n- In both dataset, lets check the distributions for target variable (outcome) and treatment vector (communication)\n- We'll also check the balance between the two binary groups in each subset"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ERag4erj9JSs",
    "outputId": "e4679e4b-69cb-4d05-f0ce-57a5de02cee9",
    "trusted": true
   },
   "cell_type": "code",
   "source": "print('train target ratio',round(train[y].mean(),3))\nprint('test target ratio',round(test[y].mean(),3))",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "text": "train target ratio 0.645\ntest target ratio 0.644\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "id": "_wmmg6ra9JSt"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Modeling Approach</span></b>\n\n#### **About X-learner approach**\n\nThe X-Learner has `two stages` and a `propensity score model`. The first one is identical to the T-learner.\n\n\nFirst, we split the samples into treated and untreated and fit a ML model for the treated and for control.\n\n\n- $\n\\hat{M}_0(X) \\approx E[Y| T=0, X]\n$\n\n- $\n\\hat{M}_1(X) \\approx E[Y| T=1, X]\n$\n\n<br>\n\nFor the second stage, we impute the treatment effect for the control and for the treated using the models above\n\n- $\n\\hat{\\tau}(X, T=0) = \\hat{M}_1(X, T=0) - Y_{T=0}\n$\n\n- $\n\\hat{\\tau}(X, T=1) = Y_{T=1} - \\hat{M}_0(X, T=1)\n$\n\n<br>\n\nThen, we fit two more models to predict those effects\n\n- $\n\\hat{M}_{\\tau 0}(X) \\approx E[\\hat{\\tau}(X)|T=0]\n$\n\n- $\n\\hat{M}_{\\tau 1}(X) \\approx E[\\hat{\\tau}(X)|T=1]\n$\n\n<br>\n\nLet $\\hat{e}(x)$ be the **propensity score model** , we can combine the two second stage models as follows to get the `uplift` value:\n\n- $\n\\hat{\\tau(x)} = \\hat{M}_{\\tau 0}(X)\\hat{e}(x) +  \\hat{M}_{\\tau 1}(X)(1-\\hat{e}(x))\n$\n\n- For the propensity score model, we'll be using `LogisticRegression` model"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "markdown",
   "source": "![](x-learner.PNG)"
  },
  {
   "metadata": {
    "id": "AT5qbT399JSt",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# lets use gradient boosting\nfrom sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor\nfrom sklearn.linear_model import LogisticRegression\n\n# metrics used for uplift evaluation\nfrom sklift.viz import plot_qini_curve\nfrom sklift.metrics import uplift_at_k",
   "execution_count": 12,
   "outputs": []
  },
  {
   "metadata": {
    "id": "R5jpSeaS9JSu"
   },
   "cell_type": "markdown",
   "source": "#### (1) Training Initial models\n\nFirst we train separate models for:\n- control group `mu0` (treatment_flg = 0)\n- target group `mu1` (treatment_flg = 1)\n\nThen we train the **propensity score** model `g` on the entire training dataset"
  },
  {
   "metadata": {
    "id": "WFwWvI889JSv",
    "trusted": true
   },
   "cell_type": "code",
   "source": "mu0 = GradientBoostingRegressor() # control group model\nmu1 = GradientBoostingRegressor() # target group model\ng = LogisticRegression() # propensity score model",
   "execution_count": 13,
   "outputs": []
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "FgvtU6M39JSv",
    "outputId": "19dda66e-3de4-4920-80a1-ab6dc3626a1c",
    "trusted": true
   },
   "cell_type": "code",
   "source": "mu0.fit(train.query(f\"{T}==0\")[X],train.query(f\"{T}==0\")[y]) # control group model\nmu1.fit(train.query(f\"{T}==1\")[X],train.query(f\"{T}==1\")[y]) # target group model\ng.fit(train[X],train[T])",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "text/plain": "LogisticRegression()",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "id": "AO_bk-fy9JSw"
   },
   "cell_type": "markdown",
   "source": "#### (2) Corrected labels for control and target\n\n`d_train` : `CATE0` and `CATE1` targets for the control & target groups"
  },
  {
   "metadata": {
    "id": "vecYClXb9JSw",
    "trusted": true
   },
   "cell_type": "code",
   "source": "import numpy as np\n\n# corrected labels for 2nd group of models\nd_train = np.where(train[T] == 0,\n                  mu1.predict(train[X]) - train[y],\n                  train[y] - mu0.predict(train[X]))",
   "execution_count": 15,
   "outputs": []
  },
  {
   "metadata": {
    "id": "Zdwm3ZTC9JSx"
   },
   "cell_type": "markdown",
   "source": "#### (3)  Newly Adjusted Model Training\n\nUsing the adjusted target variables ( for both the target and control groups, we train the main models"
  },
  {
   "metadata": {
    "id": "YSwLETb-9JSw",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "outputId": "ca84709b-a523-44d8-c0ee-0f8b2e33a391",
    "trusted": true
   },
   "cell_type": "code",
   "source": "mx0 = GradientBoostingRegressor() # control group model (updated control)\nmx1 = GradientBoostingRegressor() # target group model (updated target)\n\nmx0.fit(train.query(f\"{T}==0\")[X],d_train[train[T]==0])\nmx1.fit(train.query(f\"{T}==1\")[X],d_train[train[T]==1])",
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "GradientBoostingRegressor()",
      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "id": "4jpZu6u29JSx",
    "trusted": true
   },
   "cell_type": "code",
   "source": "def ps(df,t):\n    return g.predict_proba(df[X])[:,t]\n\n# CATE on train & test subsets\nuplift_train = ps(train,0) * mx0.predict(train[X]) + (1 - ps(train,1)) * mx1.predict(train[X])\nuplift_test = ps(test,0) * mx0.predict(test[X]) + (1 - ps(test,1)) * mx1.predict(test[X])",
   "execution_count": 17,
   "outputs": []
  },
  {
   "metadata": {
    "id": "q1mV-ErxMK9k"
   },
   "cell_type": "markdown",
   "source": "### <b><span style='color:#686dec'>Metric Evaluation</span></b>\n\n#### Uplift @k metric\n\nWe can now evaluate the metric `uplift_at_k` (k=0.1)\n- In the context of uplift modeling, `uplift_at_k` refers to the estimated incremental effect (or uplift) of a treatment when targeting the top \\\\( k \\\\) percent of a population based on their predicted uplift scores.\n- `by_group` : sort by uplift within each percentile "
  },
  {
   "metadata": {
    "id": "8fWMQSf09JSx",
    "trusted": true
   },
   "cell_type": "code",
   "source": "# uplift @k metric on training dataset\nct_score_train = uplift_at_k(y_true=train[y],\n                             uplift=uplift_train,\n                             treatment=train[T],\n                             strategy='by_group', k=0.1)\n\n# uplift @k metric on test dataset\nct_score_test = uplift_at_k(y_true=test[y],\n                       uplift=uplift_test,\n                       treatment=test[T],\n                       strategy='by_group', k=0.1)",
   "execution_count": 18,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "print('train uplift @k=10%',round(ct_score_train,3))\nprint('test set uplift @k=10%',round(ct_score_test,3))",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "text": "train uplift @k=10% 0.142\ntest set uplift @k=10% 0.054\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### qini curve \n- **Cumulative Uplift**: The Qini curve plots cumulative uplift against the proportion of the population targeted. It shows how much additional benefit is gained by applying the treatment to different segments of the population, ranked by their predicted uplift scores.\n\nHow to Interpret the Qini Curve:\n\n**Axes**:\n \n> The x-axis represents the proportion of the population targeted (from 0% to 100%).\n> The y-axis represents the cumulative uplift achieved.\n\n**Curve Shape**\n\n>A steeper curve indicates that the model is effectively identifying individuals who will respond positively to the treatment, resulting in higher cumulative uplift.\n> If the Qini curve lies above the diagonal line (which represents random targeting), it indicates that the model is performing better than random selection.\n\n**Area Under the Qini Curve (AUC)**\n\n> The area between the Qini curve and the diagonal line quantifies the model's effectiveness. A larger area indicates better performance in identifying individuals who will benefit from the treatment.\n"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "id": "fxhJxaHvFdaK",
    "outputId": "7e128913-45d8-4b03-877d-461ea37cc58b",
    "trusted": true
   },
   "cell_type": "code",
   "source": "plot_qini_curve(train[y],uplift_train,train[T],perfect=False)",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 19,
     "data": {
      "text/plain": "<sklift.viz.base.UpliftCurveDisplay at 0x14f974580>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 480x360 with 1 Axes>",
      "image/png": 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e2d1HmXuNGDECXbt2RUREBObMmZPjLOrAFVJERERERYhIJMKaewE/PM/aYeNyvQrn+6JUTlzZ/w+u7P8n1+NVISEmBo+vXgfw3/eW3Va+4qalseT8caXvsfzKfwe9L2zRCSuvZv7Jd72frLOcVTX/xEFc3ncY/rsPIuztO6XvRUTqoaGhgeHDhwMAvLy8sqyYiYyMhLe3N/r374/hw4fD0dFR5SuWMhZnMhZEVG3Tpk34558f+7M+t9vVihUrpvQcCQkJ0te6urq5vpcyWRXdy9jYGH/++ScAYOHChQWmeMiCFBEREVE+JxKJYFiqJGLCI5Dy/4+Yzq0fLUZ9fPoc+3/57YfmIOSoGPW974tRirQZPghthg8CANjXbZnrexKR6nXp0gXlypUDkHW7Xro9e/agf//+KFeuHDp37owzZ87I7JdXoqOjpa9z8xS53Pr8+TM+f/4s2P0ySl+FBgDa2toK++ro6Ehfx8XF5fpe2d0nu3v9+eefMDU1xbVr1/D333/nOIe6sCBFRERElI/Z/7ML5WpUk37998QZePL/q3FyKjdPewOAjTZTkJiLN9pF2Y7pczHuL+XPlhJCxn//fw4eg3cPHqsxDRUGsRGRWNyuu7pjCCpWhQ8ZGD16NAAgPDwcXl5eMvt4eXkhPDwcxsbGGD16tMyCVG5Wr4pEIpljv3z5b0t1+llShV3GIlx22/AyFumU2XYn717KbPeTdy9ra2vY2NggOTkZkydPLlBnCLIgRURERJRPySog2W5dn2mli1kVCwxYNA9VmzQEACzv8nOmJ8kBOd8ellFk6BcWo3IhMjT3P9nft3AZhq9ckodpsppzaCfmNWz7wyvuqGiTSCQqPeC7KDE0NETfvn0BpG2/UmYLV79+/WBgYJClEJJx9Yyenp5S908vdny/Le/Dhw8IDQ2Fqakp6tevDw0NDUEONi9dujRMTU1/aI7ExEQ8efIkx+MyHmRubm6usG/Gg8wzHnCe03sZGBjAyMhI4cHm6fcKDQ3N9N+Ho6MjAODGjRuoUaMGatSokWVsxsPde/fuLV19dvDgwRxnzkssSBERERHlAyINDay5e0X69YruA+T2VbTS6dcznljQzDrToeLKFKOiw77CsKRJlus7pjtkO5Zkc7WdhUl/r1W6/7rh45GakgIA2DNvMUauVu3WyNW3L+HUxq04+/cOld6HiLI3ePBgpYtH6fT19TFw4EC4u7tnuv7161fp6zJlyuDBgwcK59HW1kaJEiWyjE3n7++PgQMHwsDAAO3bt8fFixdzlDM3pk6dqran7D1+/BjJycnQ1NREzZo1FfbN2J7d77MsISEhmeYKCgqS2U8sFqNq1aoy75O+la9FixY4cCD7p+T+9ddf0tcsSBEREREVYdq6xbDqWtY397/4HMn1nKuuXcSR5U4oaV4e1Vo0Udg34wHjBibGqG3dFk+uXkf4+48Fatl/fhQTHoEtk2agQh0rfH75WvokPFm+P+j986vXeHApALXatlJpxm52ExEbEYkrB3L/3xsR/bj07Xrv379X6glpf/zxBypVqoTRo0dnKUjdu3dP+rpRo0bZFpDq168PTU3NLGPTubm5YeDAgQCAWbNmCVKQUqekpCRcu3YNrVq1QsuWLaGlpYWkpCSZfdu3bw8g7SyoGzdu5Phely//94Ta9u3byy1INWnSRLqt78qVKzL7FEQa6g5AREREVJTJKkblhQG/zkUHm+EoX7O63D7fF0G+fQ1H0JHj+PruA4tReSQ2MgqPrlzF13fvsXnCNJl95D118NTGrd997Sqz3975S+AyxAZHlq9GcmIiLmzfnaOM/X9xgHNwIGq2aZGjcUSUNywsLNCmTRsAwJEjR3Dw4MFsfx0+fBhAWhEj47YxIG1FU3oBZdiwYdnef+TIkdLX589nfWiCt7c3bt++DQDo06cPRowYofT39vPPP+d45RcALFu2DCKR6Id+5WZ1VLqjR48CAIyMjNC/f3+ZfcqXL4/OnTsDSPt9y80ZUr6+voiIiAAAjBkzRm4/Gxsb6WtPT89MbdbW1tn+XmQsWlpYWEivqxsLUkRERERqkttDxvPC51ev1Xbvoio++htchthgz7zFOLzsd7gMsZFbjAIASCTSPi5DbPDAPwA+f23J0i30xSsAwOvgEPw1ahLunjmPvydOx+McHn4/cbMLSlZQfF4KEeW9UaNGQUMj7aP5P//8o9SY9H4aGhoYNWpUprZPnz5J2xs3boz58+fLncfa2hq2trYA0ra4nThxQma/kSNHSs+XcnNzw5QpUxQWNEqWLIn169fDw8MDWlpaSn1P+cm2bdukhaLff/8dJiaZt7RraGhg06ZN0pVla9askTmPm5sbJBIJJBKJdDVVRklJSVi/fj0AwMrKCg4OWbfJt2jRAuPHjweQVsDKzUqs/Ipb9oiIiIh+gFYxHSQnJkHy3SGvJSuYAxIJNLW18PnVG+nZQPnBzZOn4L87+3MmSDV+pBj48HIgnt24jRJlTBH+/iOS5Rx8HBcVDS+XjfBC2rbQHjOnonLDetnOv9D7cKZD84lI9dILSp8+fcKlS5eUGhMUFIQ3b96gQoUKGDVqFFauzLwleM6cOejYsSPMzMywatUqdOjQAXv27JGej2Rubo7evXtjzJgx0NLSQkpKCsaOHYsUOX9XhYSEYODAgTh06BAMDQ2xadMmTJkyBQcPHsT169fx+fNn6Ovro2LFiujSpQv69esHIyOjH/uNUaPw8HA4Ojpiy5YtsLCwQFBQEFasWIHg4GCUK1cOs2bNQseOHQEA+/btg6+vb67v5eTkhCFDhqBGjRpwcnKCpaUlDhw4gLi4OFhbW2PhwoXQ0tJCbGwsZs2alTffYD7BghQRERFRDolEIsw9ug9mVSyUHjOvYVsYmZVGQkwsYiIi0W7U0DzLk5qaKv3penYUrsihAiEpPh6fXypf1EqMi8fR3/8EAFRr0RS9ZtupKhoR5VCrVq1QrVo1AGlbsXKyXdrDwwMzZ85EzZo10axZM1y7dk3a9vHjR7Rr1w6enp6wsrJC165d0bVrV5nzhIeHY+TIkdkWVU6dOoXWrVtj06ZNaNOmDerWrYu6devK7f/lyxcsW7YMUVFRSn9P+YmrqyvKlSuHRYsWwdLSEm5ubln6eHl5Ydy4cT90n2/fvqFnz57w9vZG9erVMXnyZEyePDlTn8jISIwYMQJ37979oXvlNyxIEREREeXQmnsBOR6z+vZ/P/W+c/o8GnTtpPRYlyE2KFnBHCN+X4rnN27j5slT+PD0OfDdB5fZB90VzuOx0jlHmanweXL1OlyG2KDN8EFo2renzD6thw3Elf3KbRsioh+Tfpg5kHZ+VE4cOXIEM2fOlM6TsSAFpD0trl69ehgyZAj69euHpk2bonTp0tDU1MTXr19x//59nDp1Clu3blW6aBQcHIy2bdvC2toaffv2Rbt27VCuXDmYmJggNjYWHz58wI0bN+Dl5QVPT08kJCTk6HvKb5YuXYrTp0/Dzs4Obdu2hZmZGSIiInD37l24ubkp9VQ7ZTx79gwNGzaEnZ0dBg0aBEtLS2hra+PNmzfw9vbGunXr8Pp14dtqLwLAEyspT5QvXx5v374FAJibm+Pdu3dqTkRERJT3qrdshsmu61Q2/8dnz3F0lQv6Os7CuwePcHnfYaV/Yt562EA069dLbjtXR9H35BUxuW2vcPiR9+fr1q2DsbExwsPDpUUPIiq6VPFnAldIEREREckgEonwv8unoVvcENePeeHAr8sBQKXFqIwFowO//i/H4wMPeSIxLh5thg3MdP3hlavwWf/3j8ajQuiv0ZMwfVfWp/eVq1EN7x89UUMiIiIqKliQIiIiIspA1pPvmvbtKXd7U17ZNHbqD8+RmpKC60dP4vrRk3mQiIqC5ATZh6Lb/7OLq6SIiEillDv9koiIiKgI+N/l02q5b4jfZSTExqrl3kTrR06Ueb3XnGnQKqaTo8P7iYiIlMUVUkREREQAylSrCj2j4j88z0abKUiMiwMA1O3cAZ0n2ijsf2z1Wjy/eeeH70uUWylJSTKvW48dAeuxI6RfL+vYG1GfvwgVi4iICjkWpIiIiKjI09AUY67HnhyPu3vmAgIPecJ2218AgG129tJiFAAEn/NF8DlfAICuoSH0Shhh9Jq0s6g+v3qDM5u3IfTFqx//Boh+0IYxtpi2U/E5Y0sunOA2PiIiyjNqKUjp6OigcePGKFOmDPT09HDs2DFER0erIwoRERERnG5fztW4C9t3AVDu6XVx0dGIi47mk+4oX0qKj1eqX/oZa2uHjcebf0NUGYmIiAo5Qc+QMjc3h7u7OyIiIuDv749Dhw7B3d0d5ubmmfqNGzcOQUFBOHPmjJDxiIiIqJAbvHQBHI8fQAkzU+m1btMn5WouFpaosNm3cJnSfWft367CJEREVBQIVpBq2rQpbt++jZEjR0JbWxsikQgikUhm3+PHj6NevXro2LEjunTpIlREIiIiKsScgwPRfEAfmFauhEXnjmH67rRH3f80aazcMac3bpV53dV2lioiEqnVp2cvctRf1hMpiYiIlCVIQap48eI4duwYTExM8PHjR0ydOhV169aV2//Lly/w8fEBAPTsqdpHLBMREVHhZdGgHpyDA2V+cLZoUDfbD9Qh/lfgNssRAJCamoq/Rk2CyxAbxIRHqCIukdq5DLHB9aNeSvfXLW6owjRERFSYCXKG1PTp02FmZoYvX76gZcuWePPmTbZjzp49i759+6JZs2YCJCQiIqLC5kdWb0hSU7F22DgAQMSHT9yeR0XK5f2HcXn/YTTq1Q1thg2EWFP+R4blV85gSfse+PY1XMCERERUGAiyQqp3796QSCT4888/lSpGAcD9+/cBAFWrVlVlNCIiIiqEfqQYtXboWGkxiqgou3XyFNaPmJDtisBlft7Q0dMTJhQRERUagqyQqlatGgDA399f6TEREREA0rb7ERERESnDyKw06nRs/0NzSCSSPEpDVDi42s6Cjr4+tIrpYOKmP2X2WebnjflNOwgbjIiICjRBClK6uroAgJiYGKXHGBgYAADilXwELRERERVteXHAMrfmEcmWEBODBAXv5bWK6UCsqYmU5GQBUxERUUEmyJa9z58/AwAqVKig9JjGjRsDAD58+KCSTERERFR4dLWb+MNzuAyV/7Q9Ikqzy+EXuW2rb18SMAkRERV0ghSkrl27BgDo3r27Uv01NDQwadIkSCQSXL58WZXRiIiIqIAzLGmCLrbKn/l0fuvOLNeOLF8NcKseUbbC3ryD5ypnue15sVKRiIiKBkEKUvv374dIJMK4cePQoEEDhX1FIhH+/vtvWFlZAQD27NkjQEIiIiIqiFoO+hlLfRU/ot5v9wG4DLGR/rp37iJiI6My9XkdHKLKmESFyss7wQq3tzoHB0JDLBYuEBERFUiCnCHl4eGBgIAAtGrVCufPn8eiRYtw+PBhabtEIoGpqSm6dOmC2bNno379+pBIJDh16hT8/PyEiEhEREQFjFhTEwMXz8u2362Tp7Jc2zJpBnT09aClo8PH1ROpgNOdy9g4diqe37gNABBpaKDeT9YI//ARr+/dV3M6IiLKDwQpSAFAv3794O/vj5o1a2L9+vVYv3699Ck2t27dgra2trSvSCRCcHAwRowYobI8DRs2RLdu3dC2bVvUqVMHpqamSEpKwvv37xEQEIDt27dnu11wzJgxcHd3V+p+NjY22Lkz6xaBjHR1dWFnZ4dBgwbB0tIS2traePPmDby8vLB+/Xq8efNGqXtZWVlh+vTp6Ny5M8qXL49v377hwYMH2Lt3L7Zv346UlBSl5iEiIsqvipcuhSUXTsht917/N57duIXkhES5fRJiYpEQE6uKeERFwp55izFy9W9y2+3cNgEAQl+8gmnlSpna7Ou2VGk2IiLK/wQrSIWFhaFJkyb4448/MH78eBQrVkzapqOjI32dlJQENzc32NvbIzZWNW8SfX190b591kdC6+jooHr16qhevTpsbGywa9cuTJgwAUlJSSrJkVGVKlXg5eWFmjVrZrpeq1Yt1KpVCxMmTMDw4cPh4+OjcJ5x48Zh48aNmX5/dXV1Ubp0abRr1w42Njbo1asXvn79qpLvg4iISNWyO6PGd+c+PLpyVaA0REXX51evsWPGPIxbv1phv++LUUDa/8csShERFW2CFaQAIC4uDjNmzMDSpUvRtWtXNGnSBKamphCLxQgLC8Pt27fh4+Oj8ifrlS9fHgDw7t07HD58GJcuXcLr168hFovRsmVL2Nvbw9zcHKNHj4ampqZSK7W6dOmC9+/fy21/+/at3DZ9fX2cPHlSWoxydXXFgQMHEBcXB2trayxYsAAlSpTA4cOH0bJlSwQHB8vN4OrqCrFYjI8fP2LFihUICgqCiYkJJk6ciAEDBqBly5bw8PCAtbW1dIUaERFRfiUSiaAhFksfJf/Hzey38t/2PqPqWET0/yI/hcJl6FjMPuCW47G12rXGA/8rANK29FVp3ABmVSyQGBePYSsWAQB+bd0FqSkpSElKRnKi/BWPRFSwpX82Xbp0KZYtW6bmNCQUQQtS6b5+/Yr9+/dj//796rg9Hj58iIULF+LIkSNITU3N1BYUFITdu3fjypUrqFGjBoYPH47Nmzdnu33v8ePHePXqVa7yODg4oFatWgCAuXPnYs2aNdK2q1ev4uLFi/D394e+vj7Wrl2LTp06ZZlDLBZjw4YNEIvFiIyMROvWrfH8+XNp++nTp7FhwwbY2dmhffv2GDlyJHbv3p2rvERERKomEomw5l5AjsbsmbcYn1+9VlEiIpJLIsHaoWMxK4dFqQkb18B35z6c/XsHVgSek9ln+ZX/Cswhfldg1b619GuusKLCpn379vD19ZXZFhcXh7CwMAQHB+PEiRPYtWsXYmJihA1IlMcEecpeftO7d28cPnw4SzEqXVhYGOzt7aVfDxw4UGVZNDU1MXPmTABASEgInJ2zPkb36tWr2L59OwCgY8eOaNSoUZY+P//8M6pVqwYAWLVqVaZiVLq5c+dKt+rNnTs3z74HIiKivKIhFqNkBfMcF6MAsBhFpEYSiQS7HH7N8bgOY4bLLUZ9L2MxCsh++y5RYaKrqwtzc3N0794dmzZtwr///ot69eqpOxbRDymSBSllZKxMV61aVWX36dChA4yNjQEAO3fulLuNLuPh6f3798/S3q9fP5l9M4qLi8OhQ4cAAHXr1oWlpWXuQhMREalAvS4d4XTnMhZ6H86+83c2j7dTQSIiyomwN2+xfuREPL12U7B7OgcHZvk1ZfsG6OjrCZaBSBU2bdqEOnXqSH916NABkydPxqNHjwAAFhYW8PHxgYGBgZqTEuWe4Fv2RCIRrKysUKVKFRgaGkIsFmc7Rh1byzI+9U/eSqq80LZtW+lrPz/552LcuHED3759g4GBAdq0aSN3nocPH+LTp09y5/Hz84OtrS0AoE2bNnj69GluoxMREf2wH13h8On5S5z9ewfiv3HbAlF+kJKUhBPOf8GiQT38vGCOWjJYNmuMlVfPY3mXnxH+4aNaMhD9qNDQUNy/fz/TNT8/P7i5ucHHxwedOnVCuXLlMGnSJPz5559qSkn0YwQrSOnq6uLXX3/FhAkTULJkSaXHSSQStRSkMj6F7+HDh9n2d3d3R61atWBsbIyoqCg8ffoU586dw+bNmxUedp5+dlR290lJScGzZ89Qv379TGOAtEPRzc3Nlcqasf37ebKTfhi8PGXLls3RfEREVHTl5oyo7yUlJGDfgqV5E4iI8tTLO/fgMsQGTfv1Qpthacdf7JgxF81+7o061u0EyfDrGU+eM0WFTlJSEpYuXSo9V/inn35iQYoKLEG27Onr68PPzw+Ojo4oVaoURCJRjn4JTSQSYf78+dKv07e5KdKhQweYmZlBW1sbpUqVQosWLfDrr7/i6dOnmDRpktxxFSpUAAB8+/YNkZGRCu/x5s0bAICpqWmmFVzm5ubQ0Ej7V6noaX4Z58h4b2W9fftW4a/r16/naD4iIiq6pu3e8sNzbBg9OQ+SEJEqXT96Ei5DbOAyxAaRnz7j7N874D57gWD35zlTVBjdunVL+lrWZzqRSARra2s4OTnh8uXL+Pz5MxITExEeHo7bt2/Dyckp28+CFy9ehEQiwcWLFwEA5cqVg7OzM548eYLY2Fh8+fIFp06dQrdu3ZTKPHz4cFy8eBFfv35FdHQ0goODsXTpUhgZGSn9fWtpaWHKlCm4cOECQkNDkZCQgA8fPsDLywsjRoxQWDtwc3ODRCLBixcvAABmZmZwcnLCo0ePEBMTg7dv3+LgwYOwsrLKNK5SpUpYt24dHj16hNjYWHz8+BF79uxBlSpVlM5N8gmyQurXX39F48aNAaQd0O3q6oq7d+8iIiJCpdvhcmv27Nlo3rw5AMDDwwM3b8rfB//s2TN4eHggMDBQWuypUqUKBgwYgIEDB0JXVxdbtmyBRCLB1q1bs4w3NDQEkFaQyk7GpygYGBhIDyhPn0OZeb6fg4iISGhiTU1Y1K+rdH//PQfx7wV/NO/fG7Wt2yHgwBHcPXNehQmJSJXC33/A3xOmY7LrOog0Mv98/OyWHfj3gn+WMbMPuuf6fs7BgdjjuAS3vc9k35moAEhJSZG+Tk5OztK+ePFiLF26NMv1EiVKoEGDBmjQoAGmTJmCkSNH4ujRo9ner3Xr1jh69ChKlSolvaarq4uuXbuia9eucHBwkPlwLiDtafAHDhzI8qCw9LOxRowYgZ9++inbDBUrVoSPj0+WglGZMmXQo0cP9OjRA5MnT0bfvn0RHh6ucK569erh1KlTmXb46OnpYfDgwejZsye6du2KK1euwNraGh4eHihRokSm73vEiBHo3r072rZti5CQkGyzk3yCFKQGDhwIiUQCb29v9O3bV+7B3flBu3bt8PvvvwMAPn36hClTpsjt6+npiZ07d2a5fuPGDRw6dAg9e/aEh4cHtLW14eLiguPHj2c536lYsWIAgMTExGyzJSQkSF/r6upmmUOZeeTNoYz0bYHylC1blqukiIgoC5GGBnrOnAKDkiY44fwXfvP3UXqs3679uOV1GgDgv/sA/HcfUFVMIhJQXHQ01g4bJ/1a37gEYiMi5X5OcBliI33deaIN6nbukKP7jfxjGTS1NHH9mHdu4pIcIpEIJUsaZt+xEAkLi1b759mMRZmXL19madfU1MT79+/h6emJwMBAPH/+HPHx8ahQoQJatWqFqVOnwtDQEPv27UOjRo0UHvtStmxZeHp6IiUlBY6Ojrh8+TISExPRpk0bLF68GMbGxli1ahV8fHxkFmdcXFykxaiHDx9i9erVuHfvHoyMjDBo0CBMnDgRBw8eVPj96uvr48KFC9KHjXl6emLHjh14//49KleujGnTpqFDhw5o27YtTp48ibZt28pd+KKnpwdPT09oa2tjwYIF8PPzQ0pKCrp164aFCxdCX18fu3fvxk8//QRPT09ERUVh8eLFCAoKgqamJgYMGIBZs2bBxMQE27dvR8uW3Bb8IwQpSKWfPbR+/Xq1/8+riJWVFTw9PaGlpYX4+HgMHjwYoaGhcvtHRUUpnM/LywvLli3DihUroK+vj/Hjx2PlypWZ+sTHxwPIfIi6PDo6OtLXcXFxWeZQZh55cyjj3bt3OepPRERkUNIYy3z/+wDYtG+PbMdss7NH9JcwVcYionwmJjxC6b7ntrrLLEh9ffcBJuXln2k6dPkiFqTyWMmShgj9vFfdMQRlWnoEvnxR/DlQ1RwcHKSv//nnnyzt27Ztw7Jly7Ksnrp9+zaOHz+Ov/76C1evXoW5uTkWLlyI0aNHy71XjRo18PLlS7Ru3TrT2cg3btzA9evX4e/vDy0tLUyaNAmzZs3KNLZu3bqYOnUqAODmzZto3759ph07Fy5cQEBAAHbt2qXw+12yZIm0GPW///0PixcvlrbdunULR44cwe7duzFy5Ei0atUKkyZNwt9//y1zLlNTU4hEIjRr1gzPnz+XXr927Ro+f/6MTZs2oXLlyggICMCnT5/QunVrfPnyRdovICAAycnJmDdvHlq0aIEGDRrgzp07CvOTfIKcIZVe1Mn4LzK/sbCwwJkzZ2BiYoLk5GQMGzYM/v5Zlwvn1NatW6XV2YwHpaeLjo4GoNz2OX19fenrjFvz0udQZh55cxAREeW1xr26ZSpGKbJ1ymysHzEBLkNsWIwiomxlXDEFAC5Dx2LnnAVYP3KiwnHTdv34+XVE6mBiYoLWrVvDy8sLw4cPB5BWHDlwIOvK4VevXsncypfu3bt3cHJyAgD06dMn23tPnz5d5oO6rly5gqCgIACZnx6fztbWFmKxGAAwadKkTMWodLt374a3t/z3Ctra2pgwYQIA4P79+zK3IgLA1KlTpfWGadOmKfx+Fi1alKkYlc7NzU26aMPU1BTTp0+XWcPYvHmz9LWs75uUJ0hB6tq1awDSqqv5UdmyZXHu3DmUL18eqampGDdunFJ7aZXx+fNn6X/Esp5Sl34IuYGBQbYHuqUfPBcaGpppa17Gg8yz21aX8fC6jAecExER5aVlft4YvmqJ0v2/fQ1HioI3z0RE30s/LN1liA3w/7swUpKSFD59s3LDesKEI/pBS5cuhUQikf4KCwvD5cuX0aNHDyQlJWH37t3o1q2bwsJTOkNDQ1hYWMDKygq1a9dG7dq1ERsbCwAwMjJC5cqV5Y4NDw+Hl5eX3Pb085ZlHfLduXNnAMC9e/cyHcT+vR07dshta9y4MYyNjQGkPdle3la86Oho6cPIateujTJlysjsl5qaKvehZfHx8Xjy5AkA4OvXrzhzRva5cy9fvpTuluLh5j9GkIKUi4sLgOwrlepQsmRJnD17VroEcPr06di9e3ee3kPRaf8Z99nWrFlTbj+xWCzN+ODBg0xtMTExeP36dbZzfN/+/TxERER5xcDEWOm+3690ICL6EZ+ev4TbLEe57c7BgXAODkS7UUMFTEWUdx4/fozVq1dn2inzvYoVK2L9+vV48eIFoqKi8OLFC9y/fx///vsv/v3330wP3Mp4WPn3njx5ovDYHVkP2gLSjoqxtLQEgGzPGU5fwCJLnTp1pK/TV2PJk7E947iMvnz5ovDQ84iICADA06dPFd4rvd/33zfljCBnSAUGBsLR0RGrV6/GgQMHMHnyZERGRgpxa4WKFy+O06dPo3bt2gAAR0dHbNq0KU/vUbp0aZQsWRIAZC5zvHz5svR1+/bt5f5P1qRJE+l2vCtXrsicZ/jw4ahZsybMzMyyHJ6e8R7pZM1DRET0o4zLyv6ppCxrh45VYRIiKqoiPnyCy9CxmH3ATW6fvvNmQlu3GM65ugsXrJAJC4uGaekR6o4hqLAw+UWgvLRp0ybpZ1NNTU2UK1cOffr0wbhx41C7dm34+vqiVatWePz4cZax3bp1wz///JPpuBZFFD3sKn0llTzpK5bSt+alK1GiBDT+/ymais5lBiD3syuQtlVRmX4A8PHjR5njMlL2+8nt9005I0hBCgCcnZ3x9OlTbNu2DW/evMHZs2fx+PHjbP9FA2kHl+U1XV1deHl5oXHjxgCA5cuXY/Xq1Xl+n0mTJkn/R/Tz88vS7uvri4iICJQoUQJjxoyRm8HGxkb62tPTM0v70aNHpXuJbWxs8Mcff2Tpo6uri8GDBwNI23+bvhyRiIgor4g1NfHrmax/T33Pa91mPAm8lq8fdkJEBZxEApchNph90F1ul+7TJyPw8NEcHapO/5FIJGo/4LuwCg0Nxf3796Vf3717Fz4+Pjhx4gSOHz+OkiVLYt++fWjWrFmmbWwmJibYt28f9PX1ER0djTVr1uD06dN49uwZIiMjkZSUBACwtrbGhQsXACje0ZNbGefMq7/rs5tHFd8HqZZgBanSpUujf//+MDIygoaGBvr27av02LwuSGlpacHT0xNt2rQBAKxduxaLFi3K0RyVKlWCsbGxwhP1e/bsKZ03Li4Obm5Zf0KTlJSE9evXY/HixbCysoKDgwPWrFmTqU+LFi0wfvx4AGkFrBs3bmSZx9PTE0+fPoWlpSUWLFiAw4cPZzmozcnJSVopTj/EjoiIKK+YVq4Ex+NZD1cFgITYOGwaO0XgRERE2fvN30f6+n+d+yLik+LVHETq5O3tjb///ht2dnZo3LgxbGxsMp3BNGjQIOmZS/3798e5c+dkzpPeR1UyboszMzNT2FdRe/qWQAAoU6aMwkUVGefJOI7yL0EKUiYmJvD390e1atXyRdVy//796Nq1KwDg/Pnz2L59u3TbniyJiYlZ/sO3sLCAr68vAgICcOLECdy5cwehoaEQiUSoUqUKBg4ciIEDB0pXRzk4OMjcsgekFYeGDBmCGjVqwMnJCZaWljhw4ADi4uJgbW2NhQsXQktLC7GxsVkepZkuOTkZM2bMwIkTJ2BkZIQrV65g+fLluHbtGoyNjTFx4kQMHDgQAHDp0qU8PyeLiIiKNufgQIXtLEYRkbqsHzkRM/Zszb4jgEXnjuGky0Zc3LFHxamIcm/ZsmUYM2YMDAwMsGTJEuzevVu68in9c21YWJjcYhSQdiSMKiUkJODx48eoXr06mjZtqrCvovZ///1X+rp58+a4dOmS3L7NmjWTOY7yL0EKUgsXLkT16tUBAIcPH8amTZtw79496UFgQhswYID0dadOnRAcHKyw/8uXL+U+eaBVq1Zo1aqV3LExMTGYPXt2pkPjvvft2zf07NkT3t7eqF69OiZPnozJkydn6hMZGYkRI0bg7t27cufx8fGBra0tNmzYgDJlymDDhg1Z+gQFBeHnn3+W+3QCIiKinFrmJ/9xzQBwaqOrQEmIiLJKSUrCoSUrMXjZQqX695ptx4IU5WufP3/Gli1bYG9vj4oVK2LMmDHYtm0bgLTzpoC0Q8VFIpHMbW66uroYPXq0ynOeO3cO1atXR7169dCgQQO5u4vGjRsnd46bN28iPDwcxsbGGDNmDP7880+Zn2UNDAwyHU+T8Twpyr8Eecpenz59IJFIsHv3bgwdOhT+/v5qK0bllZs3b2LEiBHYsGEDrl69ilevXiEmJgYJCQn4+PEjzp8/j4ULF6Jy5coKi1Hpnj17hoYNG2LevHm4fv06wsPDERMTg4cPH+LPP/9EvXr1FD5uM922bdvQuHFjuLq64tmzZ4iLi8OXL19w6dIl2NraonXr1ggLC8uL3wIiIiIA2T9R74F/gEBJiIhke/fwMVyG2GDTuKlK9dfR01NxIqIf4+TkhLi4OADA/PnzpTtz0nf2GBgYSHfIZKShoYFt27ahfPnyKs+4ZcsWafHI1dUVejL+vxo+fDh69uwpd47ExERpsa1OnTpYsmSJzH4bNmxA6dKlpa+pYBBkhVT6f+wZ97aqU15sG/z27Rv27duHffv25UGiNLGxsXBycvrh853u37+fZYUVERGRKvzvymm5bTdO+ODSnoMCpiEiUiwhJjbbg84BYGXQedjXbSlMKKJc+PTpE7Zv345p06ahatWqGD58OPbs2YNDhw5h5cqVKFasGNzd3dGgQQOcO3cOUVFRqF27NqZPn44mTZrg8uXL0jOVVeXevXvYuHEjpk+fjqZNm+LGjRv4448/EBwcDCMjIwwaNAiTJk3C9evXFW7b++2339C/f39UrVoVixcvRp06dbBjxw68f/8elStXxrRp02BtbQ0ACAgIgKsrV2YXFIKskPry5QsAIDpamEdkEhERkTD0iheX28ZiFBHlV5snTMu2T3Zn4xGp2x9//IGEhAQAacfkiEQivHv3DlOmTEFKSgr09PSwcOFCXLhwATdu3MDOnTvRpEkTHDhwQO5Ko7w2Z84cHDlyBABQq1YtuLu74+bNm7hw4QKmTJmCV69eSbfayfPt2zd06tQJDx48AJB2WPvJkydx69YtHDlyRFqMunz5Mnr16sXjaQoQQQpS6QeP1alTR4jbERERkQB+PeMpt02ZD3tEROoSH/0NLkNs4DLEBq62s+T2y25LMpE6vX37Fjt37gSQVuxJPyvZ3d0dbdu2haenJ0JDQ5GYmIj379/Dx8cHgwcPxrBhw5CSkiJIxuTkZAwcOBAjR46UHt0TExODkJAQrFixAo0bN8bLly+znefVq1eoX78+7Ozs4Ovriy9fviAxMREfP36Ej48PRo4ciXbt2mV6uh/lfyIAWU85y2MNGzZEQEAAHj9+jGbNmkmruFS4lC9fHm/fvgUAmJub4927d2pOREREqiDW0sL0Pa6oYFVTZrvLEBthAxER/aCm/XqizbBBWa4HHj6Kf377Qw2J8saPvD9ft24djI2NER4ejpkzZ6oqIhEVEKr4M0GQFVK3b9/GhAkTUL16dZw5cwbVqlUT4rZERESUx1oO/hmrb/mzGEVEhcqN4z4yr7cc1E/YIERERYggh5pv374dABASEoI2bdrgwYMHuHv3Lh4/fozY2FiFYyUSCSZMmCBETCIiIlKA56kQUWElSU3FRbc9sB47MkubaeVKCH3xSg2piIgKN0EKUjY2NpBI0nYGSiQSiEQi1K9fH/Xr11c4TiQSsSBFRESUD1jUr5ttH66OIqKC7M6pczILUo7HD/CJe0REKiBIQer169fSghQRERHlb7rFi6PvvBkIf/8Rd89exFyPPdmO+XvCdAGSERGp1tHfXdBv/mx1xyAiKhIEKUhVrlxZiNsQERHRD9LQFGP5ldPSr7tMGZ/tGF/3vYiLjlZlLCIiQby4fVfm9aHLf8WBX5cLnIaIqHATpCBFRERE+U/Tvj0wdPmiXI9/EnQDJ//ckIeJiIjU71FAEGq0ap7pWtO+PVmQIiLKYyxIERERFUE/ekB52Lv3LEYRUaF0aoNrloIUERHlPQ113lwsFqNUqVIoVaoUxGKxOqMQEREVCSKRCA26dvrhefbMzf3KKiKi/Cw1JUXmdetxWQ88JyKi3BO8IFWzZk2sX78eISEhiI+Px8ePH/Hx40fEx8cjJCQE69atQ61atYSORUREVCSsuReAUWt+bNtJkMdxuR/YiIgKA1/3vVmu9Zpth2KGBmpIQ0RUOAlakFq5ciXu3buHqVOnokaNGtDQ0IBIJIJIJIKGhgZq1KgBOzs73L17FytWrBAyGhERUaHXtG+PXI+NiYgEAHisXIOAgx55FYmIKF+6feqczOsrAs4KnET9NDTUuqmGiPIJVfxZINgZUuvXr8fUqVMhEokAAA8ePEBQUBA+fvwIkUgEMzMzNGvWDFZWVhCLxXB0dIS+vj5mzZolVEQiIqJCLacHmB9Zvhqvg0NUlIaIKB+TSOQ2aRXTQVJ8goBh1CM2NhbGxsbQ09ODhoYGUlNT1R2JiNREJBJBV1cXABAXF5dn8wpSkGrVqhXs7OwgkUgQEhKCSZMmITBQ9mGqLVq0wN9//426deti2rRpOHjwoNy+REREpJieUXH87/LpbPutHToWEokE5WvVQGJsHD6/ei1AOiKi/OuvUZMwfbdrluu/X/eFfd2WakgkrA8fPqB8+fLQ0tJCzZo1ERLCH1AQFVUVKlSAtrY2ACA0NDTP5hVk/eXkyZMBAC9evEDr1q0VFpiuXr2Kdu3a4fnz5wAAW1tbISISEREVOtq6ukoVo1yG2EDy/6sB3j14xGIUERGA5MREdUdQq6CgIOnrbt26SXe6EFHR07Llf0X4e/fu5dm8ghSk2rZtC4lEgt9//x1RUVHZ9o+KisIff/wBkUiEtm3bCpCQiIio8Og12w7OwYFYde1Ctn33LVwmQCIiooLJZYiNzOvOwYHQLV5c2DACu3PnDpKSkgAADRo0wOzZs2FlZcUzpYiKED09PfTs2RM9evx3DmlwcHCezS/Ilr0yZcoAAG7fvq30mFu3bgEAzMzMVJKJiIioMJp/8hBKV6qgVF95H7SIiOg/26c5YPyGNVmuL79yGn+NmoyXd/JutUB+kpCQgA0bNmDGjBkQi8Vo0KABGjRogKSkJMTGxvJMKaJCTiwWw9DQMNPqyCNHjuDTp095dg9BClLx8fHQ1taGvr6+0mMMDNIeqZqQUPgPDCQiIsqtReeOoYSZKQAg7O07lDQvr+ZERESFS9TnL3Lbpu/eUqjPk7p9+zbWr1+PadOmQUtLCwCgpaUFIyMjNScjIqFdvnwZx44dy9M5BSlIvXjxAvXr10efPn1w6dIlpcb07t0bAKRnSREREVFmzsGZz2RUthgVcMgDQUeOqyISEVGhdPPkKTTu1U3dMdTi9u3bmDp1Kho0aIDmzZujbNmy0NPTU3csIlIxiUSCb9++4dGjR7h48SLevn2b5/cQpCDl7e2NBg0aYNq0afDx8cGFC4rPtOjYsSOmT58OiUQCb29vISISEREVKJo6Orka57VuMx4HBGXfkYiIpPx3H5BbkDIsVRLRX8IETiSshIQEBAUFZTronIjoRwlyIt3atWsRFRUFLS0t+Pj4YMOGDWjUqFGmvYgikQiNGjXCxo0b4ePjAy0tLURFRWHt2rVCRCQiIipQZuzJ+ihyeaK+hMF35z642s5iMYqIKJfWj5wo83qDbp0FTkJEVDiIAEiEuNFPP/2E48ePQ1tbW/po6cTERHz9+hUSiQQlS5aEtrZ2WiiRCImJiejVqxfOnz8vRDzKA+XLl5cu4zM3N8e7d+/UnIiIqHDSKqaD36/7Zttv+zQHhWefEBFRzs0+6J7l2i+tfkJ89Dfhw2SD78+JKD8T7JmdZ8+eRYsWLXDjxg2IRCKIRCLo6OigbNmyKFeuHHR0dKTXr1+/jubNm7MYRURE9J1SlSooVYxym+nIYhQRkUBWBJxVdwQiogJHkDOk0t29exfNmzdHkyZN0LlzZ9SpUwcmJiYAgK9fv+Lff//FuXPncOPGDSFjERER5Svfr4D6vfcQfH75Gn3mzkD70cOyHf/P//5AxMe8eyQvERFlT0dfDwkxseqOQURUYAhakEp348YNFp2IiIjk+H4F1PwTB7Mds274eKSmpKgoERERpfvy+g1KVayQ5frKq+dhX7elGhIRERVMgm3ZIyIiouyNcvpfjsccXvY7i1FERALZPW+xuiMQERUKLEgRERGpmYZYjGKGBug5a0quntb0NuShClIREZFMEglchtjIbDK3qilsFiKiAkyQglSDBg2QnJyM2NhYlCtXLtv+5cqVQ1xcHJKSklCrVi0BEhIREQmvXI1qcA4OhNOdy1gRcBYdx4/O8RzyPhQREZFqXf3naJZrsw+6CR+EiKiAEqQgNWTIEIhEIpw8eRLv37/Ptv/79+9x/PhxaGhoYOjQoQIkJCIiEp79P7t+aDyLUURE6hN4+Ki6IxARFWiCFKQ6dOgAiUQCHx8fpcd4eXkBADp3zvnWBSIiovyu/y8OPzR+29Q5eZSEiIiIiEh4ghSkKlRIewpFSEiI0mMePXoEADA3N1dJJiIiInVqPXRArsfucVyM6LCveZiGiIhyI/iCX5ZrlerXUUMSIqKCR5CCVMmSJQEA8fHxSo9JSEgAAJiamqokExERkbqMWrNcqX677Bdi7dCxma7tnvsrPr98rYpYRESUQxe2Zd16PWPPVjUkISIqeDSFuEl4eDhMTU1RsWJF3L17V6kx6SujoqKiVBmNiIhIcA26dsq2zzlXN4S9TTt3kWdFERHlT6kpKeqOQERUYAlSkAoJCYGpqSn69OmDEydOKDXm559/BvDf1j0iIqKCzMDEGHHR37D6lr/MdpchNtDW1UXJCuXx+eVrJCcmCpyQiIjySvWWzfA48Jq6YxAR5WuCFKS8vb1hbW2N0aNHY+fOnbh8+bLC/m3btsWoUaMgkUhw8uRJISISERHlWNO+PTB0+SK57R+ePIP/rgMY8r9flJovMS4OHx4/zat4REQkgOe37qJKo/qZrk12XQf7ui3VlIiIqGAQ5AypLVu24MuXLxCLxfD29sa0adOgo6OTpZ+Ojg6mT58OLy8viMVihIeHY/PmzUJEJCIiyhHn4ECFxSgAKFutqlLFKG7JIyIquI794aLuCEREBZIgK6RiYmIwfPhweHt7Q09PD2vXrsXKlStx48YNfPjwARKJBOXKlUOTJk2gp6cHkUiEpKQkDBs2DNHR0UJEJCIiUprTHcUrfXPiy5u3eTYXERHlH2WrV8WHx8/UHYOIKN8SAZAIdbMOHTpgz549KFu2LABAIsl8a5FIBAB49+4dRo0aBT+/rI9RpfyrfPnyePs27YOVubk53r17p+ZERESq4RwcmGdzcXUUEVHBZ1iqJCZsdM5yXd3b9vj+nIjyM0FWSKXz9fVF1apVMXr0aPTs2RMNGzZEqVKlAABfvnzBrVu3cOLECezZsweJPMyViIjymR4zp6DThNF5Nt8u+4V5NhcREalP9JcwdUcgIipwBC1IAUBCQgK2bt2KrVu3Cn1rIiKiXOtiOy7PilHnXN0QfJ6rgImICjsNTTFSk1PUHYOIKF8S5FBzIiKigqq2dVs4Bweiq91EuX281m5CkMeJTNeOO63P9PXNk6fgMsQGLkNsWIwiIiqE/Hbtz3KtSe8eakhCRFQwCLJC6vnz50hNTUXXrl3x7JlyB/tVqFABvr6+kEgksLS0VHFCIiKi/xiYGGOZn7fS/R8HXsPjwGsIOHgEGmIxUlPSfhq+Y/pcVG/ZDG9DHuLDEx5sS0RUmN3yPoP2o4dluta8f29c8zwhZwQRUdEmSEGqUqVKkEgk0NbWVnqMlpYWLCwsshx8TkREpGo5KUYFX8i82im9GAUAkaGfcf2YV57lIiKifEzG55bipqXUEISIqGAQ/AwpIiKi/Kz10AFK9z2ywgmv791XYRoiIirITMqVVXcEIqJ8K98WpIyMjAAAsbGxak5CRERFQaX6dTBjj3IP3Ag87ImgI8e5ipeIiDJJTU2FhgaP6SUiUka+LUiNHDkSAPDq1Ss1JyEiosKuesummOy6Ptt+57fuxL1zFwVIREREBVHgYU+0HpJ5pa1IQwOS1FQ1JSIiyr9UUpA6f/68zOtubm6IiYlROFZHRwdVqlSBqakpJBIJzpw5o4qIREREAIBqzZsoVYzaPs0BUZ+/CJCIiIgKqn/P+2UpSJUwM0X4h49qSkRElH+ppCDVoUMHSCQSiEQi6TWRSISmTZvmaJ7nz59j1apVeR2PiIhIynbbXwrbdzn8grA37wRKQ0REBVlsZFSWa5bNG+P6UT7ggojoeyopSPn7+2c6V6N9+/aQSCS4efOmwhVSEokE8fHx+PDhAwICAnDgwAGeIUVERCrTpE8Phe3rR05ESlKSQGmIiKgw0iteXN0RiIjyJZUUpKytrTN9nfL/j8C2sbHBgwcPVHFLIiKiHBFraWHYikUK+7AYRUREP6r9mGHw27Vf3TGIiPIdQQ4137VrFyQSCcLDw4W4HRERUbZW3/JX2B5wyEOgJEREVJgZmZZWdwQionxJkILU2LFjhbgNERGRUloN6S+3bfN4OyTExvGJSEREREREKqSh7gBERESqJNLQQNUmDVE8w0+oB/w6V2bfo3+4IP5bDItRREREREQqJsgKqQoVKvzQ+Ddv3uRREiIiKiqm73GFRf26Wa7PbdBGZv9Lew/hxa27qo5FREREREQQqCD14sWLXI+VSCTQ0tLKwzRERFTYOQcHym1zunNZ5vUbx71VFYeIiIqQII/jaN6/j7pjEBHle4Js2ROJRD/0i4iISFkijZz/1bZhjK0KkhARUVH0KCAoy7Xc/N1ERFTY5ZtDzfX19VGjRg0MGDAA5cqVQ0BAALZu3SpAOiIiKkzW3L2S4zFJ8fEqSEJEREVR/LeYLNd0DQ0QGxmlhjRERPmXIAWpXbt2Kd3XwcEB69evx6RJkxAQEABHR0cVJiMiooJOq5gOfr/uq+4YREREAP4rSIlFEjQwicOrGG3oGRVnQYqI6DuCFKRyIjk5GVOnTkXNmjVhb2+P8+fP48yZM+qORURE+dSPFqP2LVyWN0GIiIgApCQloYpeDDqYJ8BIOxWvvyVBt3hxdcciIsp38u1m5s2bN0MkEmH69OnqjkJERPlUuRrVfmh86MvX+PQs9w/eICIiyqh+3Qrw2GOHvlXiYKSdCgCoaJCE7t0aqTkZEVH+k+9WSKV78uQJAKBJkyZqTkJERPmV/T/ZbwlfN3w8Wgzsm+WJRy5DbFSUioiIippyZUvAcVYP/NxLduFp3vSfsNV5LyQSicDJiIjyr3xbkDIyMsr0TyIioow0tbWz7eM+az5SU1IQcNADjXt1k45xnTxT1fGIiKgI0NfTwdQJ1pg4pj2KFdOS2eddjCZ+3xrIYhQR0XfybUFqzJgxAIAPHz6oOQkREeVHdu6bFbbfPXMB4R8+Sr/+a9QkVUciIqIiQkNDhCH9m8FhejeULmUos8/rt1/x2yoPHDrgj9goHmhORPS9fFeQsrS0hL29PcaMGQOJRAJvb291RyIionymSuMGqFjXKst1t5mOiPj4SQ2JiIioqGjbqjp+deiFWjXKyWyPiPiG5f87iA0bTiIxMVngdEREBYcgBalnz55l20dDQwMlSpSAoeF/P2EIDQ3FihUrVBmNiIgKmHpdOmKMs+y/G1iMIiIiValW1Qy/OPRCx3a1ZLYnJ6fg783eWLbsAMLCuCKKiCg7ghSkLCwscjzm6tWrGDt2LD5+/Jh9ZyIiKhLqd+2E0WuWy2yL+vxF4DRERFQUlDQxwGy7Lhg+sDk0NcUy+5w4cQ3z5rrh0aO3AqcjIiq4BClI7dy5M9s+qampiI6OxosXL+Dn54e7d+8KkIyIiAoKTR0ducUoIO0AcyIioryio62JsSPbYNqkTihuqCuzz927L+Bgvx3nz/OzCxFRTglSkBo3bpwQtyEiokJKQ1OMP274KuyTksxzOoiIKG/07lYfjrN7oqK5icz2Dx++YtGve+Dufh6pqakCpyMiKhw01B1AXRo2bIgFCxbA29sbr1+/Rnx8PKKjo/Ho0SO4ubmhTZs2OZqva9euOHLkCN68eYP4+Hi8efMGR44cQdeuXZWeQ1dXFw4ODggKCkJYWBiio6MREhICJycnVKhQQel5rKyssHnzZjx58gSxsbEIDQ2Fn58fJk2aBLFY9jJjIqL8qtOEMXC6fVlhHz5Bj4iI8kLDehXhsWcaNjqPklmMio1NwP9+O4Dq1SZjx46zLEYREf0AEQCJukMIzdfXF+3bt8+2365duzBhwgQkJSUp7Pf3339j8uTJctu3bNkCW1tbhXNUqVIFXl5eqFmzpsz2iIgIDB8+HD4+PgrnGTduHDZu3IhixYrJbA8MDESvXr3w9etXhfPkRvny5fH2bdq+eXNzc7x79y7P70FERYNx2TKYtnsLHvgHoOWgfnL7PQoIgve6zcIFIyKiQsm8nDEcZ/dA3x4N5fbZtesCfv1lN96+LThnFvL9ORHlZ2opSGlqaqJRo0aoU6cOTEzSfvLw9etX/Pvvv7h16xaSVbzt4smTJ7C0tMS7d+9w+PBhXLp0Ca9fv4ZYLEbLli1hb28Pc3NzAMC+ffswYsQIuXMtX74cv/zyCwDg1q1bWL16NZ49e4aqVati3rx5aNSokbTfokWLZM6hr6+P69evo1attCd2uLq64sCBA4iLi4O1tTUWLFgAQ0NDxMTEoGXLlggODpY5T5cuXeDt7Q2xWIyPHz9ixYoVCAoKgomJCSZOnIgBAwYAAPz8/GBtbQ2JJG//1fMvPCLKC3pGxfG/y6ez7ff0+i2cWLNegERERFRYGejrwG5iR4wf3Q7FdLRk9vH3/xf2c7bj5s2nAqf7cXx/TkT5maAFKV1dXSxatAgTJ06EsbGxzD7h4eFwdXXF8uXLERcXp5IcJ06cwK5du3DkyBGZy2xLliyJK1euoEaNGgCAtm3b4vLlrNtFqlatigcPHkBLSwvXr19Hu3btEB8fL23X1dWFn58fmjZtiqSkJNSsWRPPnz/PMs+SJUuwdOlSAMDcuXOxZs2aTO0tWrSAv78/tLS0cOHCBXTq1CnLHGKxGA8ePEC1atUQGRmJRo0aZbnXhg0bYGdnBwAYPXo0du/enc3vVM7wLzwiygvOwYFK9Vs3fDxSU1JUnIaIiAojsVgDQ/s3g/30bihV0kBmn6dP38Nxnjs8PZX7eyk/4vtzIsrPBDtDqkKFCrhz5w7mzZsHExMTiEQimb9MTEzg6OiI27dvo3z58irJ0rt3bxw+fFjunu+wsDDY29tLvx44cKDMfrNnz4aWVtpPUqZPn56pGAUAcXFxmD59OgBAS0sLs2bNyjKHpqYmZs6cCQAICQmBs7Nzlj5Xr17F9u3bAQAdO3aUrrrK6Oeff0a1atUAAKtWrZJZ+Jo7d650q97cuXNlfk9EROqiIRYrXYwCwGIUERHlSrvW1XHqyBysWjpQZjEqIuIb7OdsQ53adgW6GEVElN8JUpDS1NSEj48PLC0tIRKJ8PDhQzg6OqJDhw6oWbMmatWqhQ4dOmDevHkICQmBSCRCtWrV4OPjo7ZDuH19faWvq1atKrNP3759AQAPHjxAUFCQzD5BQUF4+PAhAKBfv35Z2jt06CBdLbZz50652+jc3d2lr/v375+lPePcGftmFBcXh0OHDgEA6tatC0tLS5n9iIiEVq15EzjdUXxwORER0Y+oblkGu7ZMwB7XSahRrUyW9qSkZPy1/gSqWU6Gi8sxJCby6a1ERKokSEFqwoQJqFWrFiQSCVasWIE6depgzZo1uHTpEp48eYLHjx/j0qVLcHZ2Rr169bB8+XIAaU+LmzBhghARs9DW1pa+lrWSqnLlytJzpvz8/BTOld5eoUIFWFhYZGpr27Ztln6y3LhxA9++fQMAmU8ATJ/n4cOH+PTpU7ZZ5M1DRCS0pb5esN32V47GbBpnp6I0RERU2JQqaYCViwfgtMccdGgj+wFCx45dRd060zBzpivCwqIETkhEVDRpCnGTQYMGQSKR4OjRo1i8eLHCvhKJBEuWLEHt2rXx888/Y9CgQdiyZYsQMTPJ+BS+9BVOGaUfQC6vPaOM7bVq1cLLly9zPE9KSgqePXuG+vXrZxoDpB2Knl4cy2mWnMhuC2XZsmVzNB8REQAYlsz6WO3vhb17jwvbd0FTSwuv7v6b5w9lICKiwkdHWxPjR7eF3cROMDSQ/QTq27efwcF+By5evCdwOiIiEqQgVadOHQDAjh07lB6zfft2/Pzzz6hbt66qYsklEokwf/586dfp29wyqlChgvR1+kGB8rx580bmuIxff/v2DZGRkdnOU79+fZiamkJbWxuJiYkA0g4o1NDQ+OEs2clubiKinFLmzKjN4+0Q/y1GgDRERFRY9O7eAPNn90CF8rJ/6PH+fRh+/WU3du26KPdcWSIiUi1BClJGRkYAgPfv3ys95sOHDwCA4sWLqySTIrNnz0bz5s0BAB4eHrh582aWPoaGhtLX6Vvp5ImJ+e+DlIFB5oMT0+fJbg5Z86QfUJ5XWYiIhGJYqiSWXjyZbb/1IyYgJZlneBARkXIa1a+ERfN6o3EDC5ntsbEJWOPkAScnD8TExMvsQ0REwhCkIPX161eYmpqicuXKuHPnjlJjqlSpIh0rpHbt2uH3338HAHz69AlTpkyR2a9Ysf+W/aavVJInISFB+lpXV1fmPNnNoWievMqSnfRtgfKULVsW169fz9GcRFQ0ZVeMivj4CW4zHQVKQ0REBV2F8iZwnNUdfXo0lNvH3f08Fv26G+/ehQmYjIiI5BGkIHXr1i10794ddnZ28PT0VGqMnZ0dJBIJbt++reJ0/7GysoKnpye0tLQQHx+PwYMHIzQ0VGbf+Pj/fqKS8QB0WXR0dKSv4+LiZM6T3RyK5smrLNl59+5djvoTEeUWi1FERKQMQ4NimDapE8aNagsdbdkfbXx9g+Fgvx23bj0TOB0RESkiSEFq//796N69Ozp06IDt27dj+vTpiI2NldlXT08Pf/31Fzp06ACJRIK9e/cKEREWFhY4c+YMTExMkJycjGHDhsHf319u/+joaOnr7La+6evrS19/v6UufR5lts/JmyevshARCUHRuVGutrMQEx4hXBgiIiqQxGINDBvYHPbTuqKkiez3v0+evMe8uW44duyqwOmIiEgZghSk9u7dC1tbW7Rq1QpjxoxBz549cejQIQQFBeHTp0+QSCQoU6YMmjdvjkGDBqF06dIAgCtXrmD//v0qz1e2bFmcO3cO5cuXR2pqKsaNG4ejR48qHJPxgO/strJlPDw846HiGecxMDCAkZGRwoPN0+cJDQ3NtDUvr7IQEamShqYYTrcvy213GWIjXBgiIiqwOrSpiUXzeqNaVTOZ7V+/RuN/vx3Apk3eSEriOYRERPmVIAUpAOjduze8vLzQokULlC5dGlOnTsXUqVOz9BOJRACAwMBA9O3bV+W5SpYsibNnz6Jq1aoAgOnTp2P37t3ZjgsJCZG+rlmzpsK+GdsfPHigcJ6goCCZc4jFYmnG7+eIiYnB69evUbFixR/KQkSkSixGERHRj6hRrQx+ndsb7VvXkNmelJSMTRu98NtvBxAezp0ARET5nYZQN4qIiECbNm0wffp0PHjwACKRSOavBw8eYNq0aWjbti0iIiJUmql48eI4ffo0ateuDQBwdHTEpk2blBr74sUL6ZlK7du3V9i3Xbt2ANJWMr18+TJT2+XL/31AUzRPkyZNpNvxrly5kqU9fZ6aNWvCzEz2T4u+v4eseYiIVEFDLJbbtv+X3wRMQkREBU3pUob4felAnDoyR24xytMzEHVq22H27G0sRhERFRCCFaQAQCKRYNOmTahTpw7Kly+Prl27YtiwYRg2bBi6du2K8uXLo06dOti8eTMkEolKs+jq6sLLywuNGzcGACxfvhyrV6/O0RzHjh0DANSqVQvNmzeX2ad58+aoVatWpv4Z+fr6SgtvY8aMkXsvGxsb6WtZB8Nn3GKYsW9Gurq6GDx4MADg/v37ePLkidz7ERHlFYOSxnC6I3911MenzwVMQ0REBYWOjibsJnaEn7cjhg9qAbE460eXW7eewbrDAgzovxJPnrxXQ0oiIsotEQDVVn7yIS0tLZw4cQJdu3YFAKxduxazZ8/O8TzVqlXD/fv3oaWlhevXr6Ndu3aZnnhXrFgx+Pv7o2nTpkhKSoKVlRWePn2aZZ5ly5Zh8eLFAIC5c+dizZo1mdpbtGgBf39/aGlpwdfXF9bW1lnm0NTUxIMHD2BpaYnIyEg0atQIz59n/pC3YcMG2NnZAUgrWu3cuTPH37Mi5cuXl55nZW5uzqfyEREAxYeYc6seERF9TyQSoU+PBpg/uwfKlzWW2efduzD8snAXdu++qPIfZBdkfH9ORPmZYGdI5Sf79++XFqPOnz+P7du3S7ftyZKYmChzNdGTJ0+wZs0aLFiwAE2bNsWVK1fwxx9/4NmzZ6hatSocHR3RqFEjAICTk5PMYlR625AhQ1CjRg04OTnB0tISBw4cQFxcHKytrbFw4UJoaWkhNjYWs2bNkjlHcnIyZsyYgRMnTsDIyAhXrlzB8uXLce3aNRgbG2PixIkYOHAgAODSpUtKnZNFRPSjylavKrft74nTBUxCREQFQZOGFlg0rw8a1qsosz0mJh5Oq49gzRpPxMYmCJyOiIjyUpFcIZXTn6K8fPkSlStXltkmEomwdetWjB8/Xu74bdu2YdKkSQrvW7VqVXh7e6N69eoy2yMjIzFixAh4eXkpzDphwgRs2LABOjo6MtuDgoLQs2dPhIWFKZwnN/gTGKKiydyqJmYfdJN+PbdhG6QmpwDg6igiIlJORXMTzJ/dE7261ZfZnpqaip07L2DRr7vx/v1XgdMVXHx/TkT5GQtSSlBUkErXvXt3TJo0CU2bNkWpUqXw5csXXL9+HVu2bMGpU6eUuo+enh7s7OwwaNAgWFpaQltbG2/evIG3tzfWrVuH169fKzVP7dq1MWPGDHTq1AnlypVDTEwMHjx4gL1792Lbtm1ISUlRap6c4l94REVPV7uJ6GI7LsfjWIwiIiIAKG5YDNMmdcbYkW2goy1788bFi/fgYL8Dt28/Ezhdwcf350SUnxXJghSpBv/CIyp6FK2AkofFKCIi0tTUwPBBLTDHritMjPVl9nn8+B3mOuzAiRPXBE5XePD9ORHlZ0XyDCkiIvpxuSlGbRo3VQVJiIioIOnYrhZ+ceiFalXNZLZ//RqNZUv34++/fZCUlCxwOiIiEgoLUkRElCMikQhr7gXkamxCTGwepyEiooKiZvWyWDS3N9q2kn1mamJiEjZu8MLy5QcRHv5N4HRERCQ0FqSIiEgpZatXhcORPbke//HZ8zxMQ0REBUXpUoZwmN4VQ/o3g4aGhsw+Hh4BmO/ojqdPPwicjoiI1IUFKSIiypZJ+bI/VIwCgP0Lf8ujNEREVBAUK6aFiWPaYeqEjtDXk/0E6Bs3nsB+znZcunRf4HRERKRuLEgREZFCIpEIv5zyyLbf46vXcXHHbkx2XS+9tnbYOEhSU1UZj4iI8hmRSIR+PRvCcVYPlCtbQmaft2+/YOGCXdi71zfHT8AmIqLCgQUpIiKSy7BUSSy9eDLbfre8TsNv9wFAIuFT9IiIirBmjSpjkWMf1K9TQWZ7TEw8/vj9Hzg7H0VcXILA6YiIKD9hQYqIiORSphi1Y8Y8RH4KFSANERHlV5UqlMSCOT3Ro0s9me2pqalwdzuHRYv24sOHrwKnIyKi/ChPC1Lbt2/Py+kAABKJBBMmTMjzeYmISLFhKxdn22fH9LmIDP0sQBoiIsqPjIrrYtqkThg7sg20tWR/tDh//i4c7Lfj7t0XAqcjIqL8LE8LUjY2Nnm6B1wkErEgRUSkJk16d1fY7j5rPotRRERFlKamBkYObonZdl1gXEJfZp+HD99i3twdOHnyusDpiIioIMjTgtTr1695KCERUSGgb1xCbpur7SzEhEcIloWIiPKXzh2ssNC+FyyrmMpsDwuLwtIl+7BlyykkJ6cInI6IiAqKPC1IVa5cOS+nIyIiNfnN30fmdR5YTkRUdFnVLIdFc3ujdYtqMtsTE5Pw1/qTWLHiICIiYgROR0REBQ0PNSciIqW4DB2r7ghERKQGZqWLw356Vwz+uSk0NDRk9vnnnyuY7+iO588/CpyOiIgKKhakiIgok+4zbGU3cEs2EVGRUqyYFibZtMeU8dbQ19OR2ef69Sewn7MNly+HCJyOiIgKOhakiIgok84Tx6g7AhERqZFIJEL/3o0wb2Z3lC1TQmafN28+Y+GCXdi3z49nyBIRUa6wIEVERKjRqjkmbVkrt339yInChSEiIrVp3qQKFs/rg7q1zWW2f/sWh99X/QMXl2OIi0sQOB0RERUmghaktLS0MGLECPTr1w/169dHqVKloKurq3CMRCKBlpaWQAmJiIqW0hYVMf/EwWz7pSQlCZCGiIjUpVLFklg4pxe6/1RXZntqaip2bD+LxYv34uPHcIHTERFRYSRYQapatWo4evQoatSoAZFIJNRtiYhIDufgQKX6bRg9WcVJiIhIXYyK62KGbWeMGd4a2lqyPxqcO3cHDvbbce/eS2HDERFRoSZIQUpPTw8+Pj6oXLkyUlNTcezYMXz+/BkTJ06ERCLB8uXLYWxsjCZNmqBFixaQSCQIDAzE2bNnhYhHRFRkaGpr44+bfjkak5TALRlERIWNpqYGRg1phVlTf4JxCX2ZfR48eIO5Djvg7X1D4HRERFQUCFKQsrW1ReXKlZGSkoKuXbvi4sWLsLKywsSJaWeSLF26VNq3fv362LNnD1q0aIEDBw5g48aNQkQkIir0NDTFOS5GHftjrWrCEBGR2vxkXRsL7XuiamVTme1fvkRhyeK92Lr1NJKTUwROR0RERYUgBanevXtDIpHg0KFDuHjxosK+d+/ehbW1Ne7evYs///wTgYGBuHXrlhAxiYgKNafbl3PU33XyTMRERKooDRERCa12zXJY5NgHrZpZymxPSEjC+nXHsXLlYURGxgicjoiIihpBClJWVlYAAE9PT6X6f/nyBX/++SdWr16NadOmYdy4caqMR0RU6JUwk/1T8O/9PWE64qKjVZyGiIiEZGZaHHNndMfAvo2hoaEhs8+hQ5exYL47Xrz4JHA6IiIqqgQpSJUoUQIA8OrVK+m1hAxnkhgYGODbt2+Zxly5cgUA0L59e9UHJCIq5BadO5ZtH5chNqoPQkREgtHV1cZkm/awHWcNPT1tmX2Cgh7Bfs52BAQ8EDgdEREVdYIUpGJjY2FoaAiJRCK9FhERIX1dsWJFhISEZBqT3rdMmTJCRCQiKnT0jUvgN38fpfqyGEVEVHiIRCIM6NsY82Z0RxkzI5l9Xr/+jAXzd+LAAf9M79GJiIiEInvNbh578eIFAKBcuXLSa2FhYfj69SsAoHXr1lnGNG7cGACQmJgoQEIiosKlZpsW2Rajwt9/xNUjx+EydKxAqYiISNVaNK2Kk4dm4s8VQ2UWo6KjY7FwwU7UrGGL/fv9WIwiIiK1EWSF1I0bN1CvXj00adIEJ06ckF4/f/48Bg0ahLlz5+LIkSPSAlWlSpXg6OgIiUSCO3fuCBGRiKjQcA4OzLbP4d9+x9v7DwVIQ0REQqhcqRQW2vdC1051ZLanpKRg+7azWLx4L0JDI4QNR0REJIMgK6TOnj0LkUiEPn36ZLq+fv16AECVKlXw+PFjHDp0CCdPnsTdu3dhbm4OAHB1dRUiIhFRoTDmz5VK9WMxioiocDAy0sWS+X1w7thcucWoM2duo2GDmbC13chiFBER5RuCrJA6efIk/P39IRaLUaVKFTx//hwAEBAQgN9++w2LFy+GsbEx+vfvDyBt3zsAuLm5Yf/+/UJEJCIq8DS1tVHvJ+ts+6UkJwuQhoiIVElLS4zRQ1th5pSfUMJIT2afkJDXcLDfgVOnbgqcjoiIKHuCFKTi4uJgbS37Q9KyZctw6dIlTJgwAbVr14ampiaePHmCXbt2wcPDQ4h4RESFwh83/ZTqt37EBBUnISIiVerSsTZ+ceiFypVKy2z//DkSSxbvxdatp5GSkipwOiIiIuUIUpDKzoULF3DhwgV1xyAiKrBMK1fKto//7gO4efKUAGmIiEgV6lqVx6J5fdCiaVWZ7QkJSVi39hhWrjyMqKhYgdMRERHlTL4oSBERUe5oaIoxcbMLqrdoqrCfyxAbYQIREVGeK2NmhHkzu2Ng3yZy+xw44I+FC3bh5ctPAiYjIiLKPUEKUs+fP0dqaiq6du2KZ8+eKTWmQoUK8PX1hUQigaWlpYoTEhEVTE63Lytsj4mIhOvkmQKlISKivKSnpw3bsR0weWwH6Opqy+xz9epD2M/ZjsBAPqyCiIgKFkEKUpUqVYJEIoG2tuy/SGXR0tKChYUFJBKJCpMRERVcvR2mK2znqigiooJJQ0OEAX2aYN7MbjAzNZLZ59WrUMx3dMfBg5cETkdERJQ3uGWPiKiA6jBmuNy2HdPnCpiEiIjySqvmlvh1bm/UqVVeZntUVCxWrTyMdeuOIz4+UeB0REREeSffFqSMjNJ+GhQbywMZiYhyKjL0s7ojEBFRDlSxKI2F9j3RpWMdme0pKSnY6noaS5bsw+fPkQKnIyIiynv5tiA1cuRIAMCrV6/UnISIKP/RMyout41b9YiICo4SRnqYPfUnjBzSClpaYpl9fHxuYt7cHbh//7XA6YiIiFRHJQWp8+fPy7zu5uaGmJgYhWN1dHRQpUoVmJqaQiKR4MyZM6qISERUoJmUL5fl2pZJMxAbGaWGNERElFPaWmKMHt4aMyd3hpGRnsw+//77CnMdduD06VsCpyMiIlI9lRSkOnToAIlEApFIJL0mEonQtKnix5J/7/nz51i1alVexyMiKtCa9++NwcsWZrnOYhQRUcHQrXMdLLTvBYuKpWS2h4ZGYPGivdi+/QxSUlIFTkdERCQMlRSk/P39Mz0dr3379pBIJLh586bCFVISiQTx8fH48OEDAgICcODAAZ4hRUSUgXNwoLojEBFRLtWrbY5F8/qgeZMqMtvj4xOx1uUYVq06jOjoOIHTERERCUslBSlra+tMX6ekpAAAbGxs8ODBA1Xckoio0GMxioioYCpbxgiOM3ugf5/Gcvvs2+eHXxbuwqtXoQImIyIiUh9BDjXftWsXJBIJwsPDhbgdEVGhs8zPW2G77859AiUhIiJl6elpY8p4a0y26YBixbRk9gkIeAD7OdsRFPRI4HRERETqJUhBauzYsULchoioUFJmZdRtbz4Agogov9DQEGFQv6aYO6MbTEvLfirqixefMN/RHYcPXxY4HRERUf4gSEGKiIhyJ7tiVJDHCQQcPCJQGiIiyk6bFtXw67zesKqR9WmoABAZGYOVKw5h/foTSEhIEjgdERFR/qGWgpSBgQEqV64MQ0NDiMXibPtfunRJgFRERPlL8/69Fba7DLERJggREWWrauXS+MWhNzp3sJLZnpKSAtctp7F06T58/hwpcDoiIqL8R9CC1IQJEzB16lTUq1dP6TESiQRaWrL33BMRFWaDly2U2+a56k8BkxARkTzGJfQw264rRg5uAU1N2T9o9fa+gXlz3RAS8lrgdERERPmXIAUpDQ0NHDlyBL17p/20XyQSCXFbIqJC6+Wde+qOQERUpGlriWEzog2mT+4Mo+K6MvsEB7+Eg/0OnD17W+B0RERE+Z8gBSlbW1v06dMHAPDp0ye4ubnh5s2b+Pr1K1JTU4WIQERUIJSqaI6Rq39DZOhnuX24VY+ISL16dKmHhXN6omKFkjLbP30Kx6Jf98DN7RxSUvhel4iISBZBClKjR48GAISEhKBt27aIiIgQ4rZERAVKxgPMK9SulaX9yAonvL53X8hIRESUQf26FbB4Xh80bVRZZnt8fCL+dD6K33//B9++xQmcjoiIqGARpCBVq1YtSCQS/O9//2MxiohIhhqtW2TbJ+zNOwGSEBHR98qVLYF5M7ujf+/Gcvvs3euLXxbuwuvX8le4EhER0X8EPdT80aNHQt6OiKjAmPS3S7Z9YsIjVB+EiIik9PV0MHWCNSaOaY9ixWQ/ZOfy5RDYz9mG69efCJyOiIioYBOkIPXkyRM0aNAAJiYmQtyOiKjAEIlEsD+yW90xiIgoAw0NEYb0bwb76V1hWqq4zD7Pn3+E4zw3HDkSIHA6IiKiwkGQgtSBAwfQsGFD9OrVCxcvXhTilkREBcKae8p9kFk3fLyKkxAREQC0bVkNv87tjVo1yslsj4yMwfL/HcRff51AYmKywOmIiIgKDw0hbrJ+/Xrcu3cPU6ZMQZs2bYS4JRFRvlanY7tMh5gr8teoSUhNSVFxIiKioq1aVTO4bRqPvdsmyyxGJSenYOOGk6hmOQnOzp4sRhEREf0gQVZIJSYmokuXLvDw8MDZs2exfv167Nu3Dw8fPkRCQoIQEYiI8o1JW9aiRqvmCvusHzkRKUlJAiUiIiq6TIz1MduuC0YMagFNTbHMPidPXse8uTvw8OFbgdMREREVXoIUpJKT//sJkkgkgr29Pezt7ZUaK5FIoKUl+xBJIqKCpHGvbhi+aolSfVmMIiJSLR1tTYwd2QbTJnVCcUNdmX3u3n0BB/vtOH/+rsDpiIiICj9BClIikUjh10REhV3rYQPRf6FyhXiXoWNVnIaIqGjr1bU+5s/piYrmsh+48/FjOH79ZTfc3c8jNTVV4HRERERFgyAFqWXLlglxGyKifEuZYpSr7SzEhEeoPgwRURHVoG5FLHbsgyYNLWS2x8UlwHmNJ1av9sC3b3HChiMiIipiBClI/fbbb0LchogoXzKtXCnbPi5DxwISiQBpiIiKnvJljeE4uzv69Wwkt8/u3Rfxy8JdePv2i4DJiIiIii5BClJEREWZ4/EDCts32tiyGEVEpAIG+jqYOqEjJoxph2I6ss8kvXTpPuznbMeNG08ETkdERFS0sSBFRKQmUZ+/wG2mI1JTUtQdhYioUBGLNTC0fzPMmdYVpUsZyuzz7NkHOM5zh4dHgMDpiIiICFBTQapy5cpo2bIlypQpAz09PWzevBlhYWHqiEJEpFIDFzvKvO4yxEbYIERERUS71tWxaG4f1KhWRmZ7RMQ3/O+3A9i40QuJicky+xAREZHqCVqQatCgAdauXYs2bdpkun7kyJFMBampU6diyZIliIyMhJWVFZKT+WaBiAqmloP6ZbkW+SlU+CBERIVc9apm+GVub1i3rSmzPTk5BZs3eeO33w4gLCxK4HRERET0PQ2hbtSjRw8EBASgTZs2EIlE0l+y7Ny5E7q6uqhSpQp69eolVEQiojzlHBwo8/qlvYcETkJEVHiVNDHAikX9cdrTXm4x6vjxINStMw0zZ7qyGEVERJRPCFKQMjMzw/79+6Gjo4OQkBB0794dhoay9/MDQExMDI4ePQoA6N69uxARiYjyVMV6teW2PQm6IWASIqLCSUdbE1PGW8PfZz5GDW0FsTjr29o7d56jc6df0K/vcjx69FYNKYmIiEgeQbbszZ49GwYGBnj16hXatm2LyMjIbMf4+vpixIgRaNy4sQAJiYjy1sy922Re3z7NQeAkRESFT+/uDTB/dg9UKG8is/3Dh6/4ZeEu7Np1EampqQKnIyIiImUIUpDq2rUrJBIJnJ2dlSpGAcCjR48AABYWFipMRkSUtzQ0xXC6fVlmW8BBD0R9/iJwIiKiwqNR/UpYNK83GjewkNkeG5uANU4ecHLyQExMvLDhiIiIKEcEKUhVrlwZAHDt2jWlx0RHRwMADAwMVJKJiEgV5BWjACDI84SASYiICg/zcsaYP7sH+vRoKLfPzp3n8esvu/HuHZ/cTEREVBAIUpDS0tICACQlJSk9pkSJEgDSzpMiIsrv9IyK43+XT8tt37dgKSCRCBeIiKgQMDQoBruJHTFuVFsU09GS2cfP71842G/HzZtPBU5HREREP0KQgtTHjx9RqVIlVK5cGXfu3FFqTMuWLQEAb9/yAEoiyv8UFaMA4NPzl8IEISIqBMRiDQwb0BxzpnVFqZKyV8s/ffoe8+a64ejRqwKnIyIiorwgyFP2rly5AgD4+eefleqvq6sLW1tbSCQS+Pv7qzIaEZHKuQyxUXcEIqICo0ObmjjtMQcrlwyQWYwKD/+G2bO2oraVHYtRREREBZggBamdO3dCJBJh2LBh+OmnnxT21dfXx6FDh1CxYkUAwPbt21WSqXTp0ujZsyeWLVsGb29vfP78GRKJBBKJBG5ubkrNMWbMGOmY7H6NGTMm2/l0dXXh4OCAoKAghIWFITo6GiEhIXByckKFChWU/t6srKywefNmPHnyBLGxsQgNDYWfnx8mTZoEsVis9DxEpBzn4EC5bSxGEREpp0a1MtjtOhG7tkxAdcsyWdqTkpKxbu0xVLOchHXrjiMpKVkNKYmIiCivCLJl7/z58zh69Cj69euH48eP46+//sLhw4el7SYmJmjWrBm6dOkCW1tblClTBhKJBLt27VJ6i19OhYaGqmTe3KpSpQq8vLxQs2bNTNdr1aqFWrVqYcKECRg+fDh8fHwUzjNu3Dhs3LgRxYoVk17T1dVF6dKl0a5dO9jY2KBXr174+vWrSr4PoqJGrCn/j9HYqCgBkxARFUylShrAflpXDB3QHGKx7J+VHj16FY7z3PDkyXuB0xEREZGqCFKQAoCRI0fi5MmT6NChA+bMmYM5c+ZA8v8H/Pr5+Un7iUQiAGlFLFtbW0GyvX79Gg8ePEDXrl1zPUeXLl3w/r38N0mKzsLS19fHyZMnpcUoV1dXHDhwAHFxcbC2tsaCBQtQokQJHD58GC1btkRwcLDcDK6urhCLxfj48SNWrFiBoKAgmJiYYOLEiRgwYABatmwJDw8PWFtbS3//iSj3rMeNlHk9NjIKWybNEDgNEVHBoaOjiQmj2sFuUkcY6BeT2ef27Wewn7Mdvr6y3/sQERFRwSVYQSouLg6dO3fG7NmzMWfOHJQtW1Zmv69fv2LNmjVYvXq1Sgsmy5Ytw/Xr13H9+nWEhoaiUqVKePnyZa7ne/z4MV69epWrsQ4ODqhVqxYAYO7cuVizZo207erVq7h48SL8/f2hr6+PtWvXolOnTlnmEIvF2LBhA8RiMSIjI9G6dWs8f/5c2n769Gls2LABdnZ2aN++PUaOHIndu3fnKi8RpTEyK43u0ydnuR4bxWIUEZEifXo0wPzZPWFezlhm+/v3Yfhl4W7s3n0RqampAqcjIiIiIYgACL5MRiwWo1mzZmjSpAlMTU0hFosRFhaG27dv4/Lly0hMTBQ6UqaClLu7O8aOHZvtmDFjxsDd3R0AYGFhkauClKamJkJDQ2FsbIyQkBDUqVNHZiFu8+bN0hVjjRs3xq1btzK1Dxw4ULoNcv78+fjjjz+yzKGrq4u3b9/CxMQEwcHBqFevXo7zKlK+fHnpSjBzc3O8e/cuT+cnyk94bhQRUc41aWiBRfP6oGG9ijLbY2LiscbJA05OHoiNTRA4HVHhw/fnRJSfCbZCKqOUlBQEBgYiMFD+B7qiokOHDjA2Tvvp4M6dO+WuCnN3d5cWpPr375+lINWvX79MfWWJi4vDoUOHYGtri7p168LS0hJPnz798W+CqIgZ95eTuiMQERUoFc1NMH92T/TqVl9me2pqKnbuvIBFv+7G+/c855KIiKgoEOQpeyRf27Ztpa8znqX1vRs3buDbt28AgDZt2sid5+HDh/j06ZPceTLeQ9Y8RKSYhliM2h3k/7+zebydgGmIiPI3Q4NiWGjfE+dPzJNbjLp48R6aNpmD8ePWsRhFRERUhKhlhVRh5O7ujlq1asHY2BhRUVF4+vQpzp07h82bNys87Dz97CggrZgkT0pKCp49e4b69etnGgOkHYpubm6e7Rzft38/T3bKly+vsF3euWBEhUWjnl0w4vdlctvfP3qC+G8xAiYiIsqfxGINDB/UAnPsuqCkiYHMPo8fv8O8uW44fjxI4HRERESUHwhekDIxMUHLli1RpUoVGBoaQiwWZzvmf//7nwDJfkyHDh2kr0uVKoVSpUqhRYsWsLe3x6xZs+Dq6ipzXIUKFQAA3759Q2RkpMJ7vHnzBvXr14epqSm0tbWlZ22Zm5tDQyNtsZuip/mlz/H9vZWV3dxEhVXP2VPRcdwohX1e3fsXHivWKOxDRFQUWLetiV/n9ka1qmYy279+jcZvy/Zj82YfJCUlC5yOiIiI8gvBClJmZmb4888/MWDAAGhq5uy2+bkg9ezZM3h4eCAwMFBa7KlSpQoGDBiAgQMHQldXF1u2bIFEIsHWrVuzjDc0NAQA6XY8RWJi/lt5YWBggK9fv2aaQ5l5vp+DiBTr/4sDWg8doLDP2mHjIOFToIioiKtZvSx+deiFdq1ryGxPSkrGhr9OYvnygwgPz/59DxERERVughSkSpUqhYCAAFSqVAkikUiIWwrC09MTO3fuzHL9xo0bOHToEHr27AkPDw9oa2vDxcUFx48fz3K+U7FixQBAqScLJiT897QZXV3dLHMoM4+8OZSRvi1QnrJly+L69es5mpMoPxNraWVbjALAYhQRFWmlSxnCYXpXDP65GcRi2ceTengEYL6jO54+/SBwOiIiIsqvBClILVu2DBYWFgCAw4cPY/Pmzbh79y4iIiKEuL3KREVFKWz38vLCsmXLsGLFCujr62P8+PFYuXJlpj7x8fEAAG1t7Wzvp6OjI30dFxeXZQ5l5pE3hzL4mFgqSsRaWlh9yz/bfi5DbFQfhogoH9LR0cTEMe0xdYI1DPSLyexz8+ZT2M/ZDn//fwVOR0RERPmdIE/Z69WrFyQSCXbt2oWhQ4fCz8+vwBejlLV161ak/v/qifbt22dpj46OBqDc9jl9fX3p64xb89LnUGYeeXMQ0X+KGRooVYwiIiqKRCIR+vVsCF8vR8yb2V1mMert2y8YM/pPNGs6h8UoIiIikkmQFVKlS5cGAOzYsUOI2+Urnz9/xpcvX2BqairzKXXpB4UbGBjAyMhI4cHm6YeQh4aGZtqal/Gw8ey21WU8yDzjAedE9J8VAWez7RMTEQnXyTMFSENElH80bWSBRfP6oEHdijLbY2LisfqPI3B29kRsbILMPkRERESAQAWp9+/fw8LCItOB2kWJonOzQkJCpK9r1qyJoCDZjz4Wi8WoWrUqAODBgweZ2mJiYvD69WtUrFgRNWvWVJglY/v38xBR9rhFj4iKokoVSmLBnJ7o0aWezPbU1FS4u53DokV78eHDV4HTERERUUEkyJY9f/+0rS9169YV4nb5SunSpVGyZEkAaYW5712+fFn6WtaWvnRNmjSRbse7cuWK3Hlq1qwJMzPZj1n+/h6y5iEqCrraTYRzcCDmeu7N0uYcHCh33PoRE1QZi4go3yluWAy/OPTC+RNz5RajLly4i8aNZmHChL9YjCIiIiKlCVKQWrNmDRITE2Fvb5/pUO2iYNKkSdDQSPtt9vPzy9Lu6+srPU9rzJgxcuexsbGRvvb09MzSfvToUZl9M9LV1cXgwYMBAPfv38eTJ0+ySU9U+DgHB6KL7TgAQBnLKnAODkTZ6pZYEXhObjEqITYOLkNskJKcLGRUIiK10dTUwJjhreHvswCTx3aAtlbWRfWPHr1Fn96/oXOnX3H37gvhQxIREVGBJkhBKiQkBOPGjUONGjVw+vRpVKtWTYjbqlSlSpXQoEEDhX169uyJRYsWAUh7op2bm1uWPklJSVi/fj0AwMrKCg4ODln6tGjRAuPHjweQVsC6ceNGlj6enp54+vQpAGDBggWoUqVKlj5OTk4wMTGRviYqarR1dWVedziyG8UM9GW2AcCmsVNUFYmIKN/p1L4Wzng64H+//AwT46x/NoaFRWHG9C2oW2caTp68roaEREREVBgIcoYUABw4cABPnjyBl5cXQkJCcO/ePTx+/BixsbEKx0kkEkyYkPfbZFq3bg1LS0vp16VKlZK+trS0zLJaaefOnZm+trCwgK+vLwICAnDixAncuXMHoaGhEIlEqFKlCgYOHIiBAwdKV0c5ODjI3LIHpBWHhgwZgho1asDJyQmWlpY4cOAA4uLiYG1tjYULF0JLSwuxsbGYNWuWzDmSk5MxY8YMnDhxAkZGRrhy5QqWL1+Oa9euwdjYGBMnTsTAgQMBAJcuXcLu3btz/HtGVFBNdduEqk0a4uv7Dzke++HJMxUkIiLKf2rVKItFc3ujTcvqMtsTE5Ow4a+TWL78ICIiiua5oERERJR3RAAkQtyoWrVq2LZtG1q3bq30GJFIBIlEAk3NvK+bubm5yd3aJi9LRu3bt4evr2+242JiYjB79mxs3bpVYb+qVavC29sb1avLfhMYGRmJESNGwMvLS+E8EyZMwIYNG+RujQwKCkLPnj0RFhaWbfacKl++vPSJf+bm5nj37l2e34MopxSdCaUMHmJORIWdaSlDOMzohsE/N5X+IO17R44EYL6jO549y3lhn4jUh+/PiSg/E2SFVIUKFeDv74/SpUtLCztRUVGIjIxEamqqEBHy3M2bNzFixAi0bNkSTZo0QdmyZVGqVCloamoiPDwc9+/fx/nz57Ft2zZ8/vw52/mePXuGhg0bws7ODoMGDYKlpSW0tbXx5s0beHt7Y926dXj9+nW282zbtg2BgYGYMWMGOnXqhHLlyiEmJgYPHjzA3r17sW3bNqSkpOTFbwFRvqfoCZfKYDGKiAqzYsW0MMmmPaaMt4a+nuwfZF2//gT2c7bh8uUQme1EREREuSXICqmtW7di3LhxSE1NhbOzMzZt2qRUcYUKFv4EhvITrWI6+P26b67HsxhFRIWVSCTCz70awnFWD5QtU0JmnzdvPmPhgl3Yt88PEokgi+mJSAX4/pyI8jNBVkh16tQJEokE69atw/z584W4JREVcbkpRn189hx+O/fj/SM+gZKICqdmjatg8bzeqFengsz2b9/i8Mfv/+DPP48hLi5B4HRERERUlAhSkDIzMwMAHDlyRIjbEVERpKEpRp2O7dFx/ChUsKqZ4/FcEUVEhVmliiWxYE5P9Pipnsz21NRUuO04h0WL9uDjx3CB0xEREVFRJEhB6sOHD7CwsEBiYqIQtyOiIqZqk4aY6rYpR2NOumzEk6t8XDkRFW5GxXUxw7YzxgxvDW0t2W/7zp27Awf77bh376Ww4YiIiKhIk/0olTx29uxZAEDTpk2FuB0RFSFlq1dVqhj14ckz6WtX21ksRhFRoaapqYGxI9rA32c+Jo5pL7MY9eDBG/TquQxdflrEYhQREREJTpBDzatWrYpbt27h69evaNSoEcLDuRS8MOKhiaQOzsGBSvXjljwiKip+srbCQvteqFrZVGb7ly9RWLpkL1xdTyM5mU/eJSrM+P6ciPIzQVZIPXv2DD///DMMDQ1x5coVdO7cWYjbElEhZ2BirFS/mIhIFSchIlK/2jXLYf8OW2zfME5mMSohIQlrnDxQzXISNm3yZjGKiIiI1EqQM6TOnz8PAPjy5Qtq1KiBU6dOISIiAk+ePEFsbKzCsRKJhAUsIpJpmZ+3Uv2229mrOAkRkfqYlS4OhxndMKhfE2hoyP5Z4+HDl7Fg/k48f/5R4HREREREsglSkOrQoQMkkv92BopEIhgbG6NZs2Zyx0gkEohEokzjiIjSZbdV7965i3j/8AkeXAoQKBERkbB0dbUx2aY9bMdZQ09PW2afa9cew37Odly5EiJwOiIiIiLFBClI+fv7s7BERHnGpHxZuW1rh42DJDVVwDRERMISiUQY0Kcx5s3sjjJmRjL7vH79GQvm78SBA3wPRkRERPmTIAUpa2trIW5DREVEn7kzZV6/ftSLxSgiKtRaNK2KRXN7o25tc5nt0dGx+H3VP3BxOYb4+ESB0xEREREpT5CCFBHRjxKJRFhzT/H2u8v7DwuUhohIWBYVS2GhfU9061xXZntKSgp2bD+LxYv34tOnCGHDEREREeUCC1JEVCBkV4xyGWIjTBAiIgEZGelipu1PGDOsNbS0xDL7nD17Gw72OxAc/FLYcEREREQ/QG0FqXLlyqFMmTLQ09PDjRs3EB8fr64oRERERPmKlpYYo4e2wswpP6GEkZ7MPiEhrzHXYQd8fG4KnI6IiIjoxwlakDIwMICDgwPGjRuHcuXKSa/XrVsXDx48kH49ZMgQ9O/fH5GRkZg0aZKQEYkoH2rY/SeF7Vf/OSpMECIiAXTpWBu/OPRC5UqlZbZ//hyJJYv3YuvW00hJ4bl5REREVDAJVpCqWrUqfHx8UKVKFYhEIul1WU9+CQwMxO7du6GhoYGdO3fiypUrQsUkonxGpKGBkat/U9jn6pHjAqUhIlKdOrXKY9G8PmjZrKrM9oSEJKxbewwrVx5GVFSswOmIiIiI8paGEDfR1taGl5cXqlatitjYWKxevRq9evWS2//169e4ePEiAKBPnz5CRCSifGrNXfkF6Vf3/sXm8XZ8sh4RFWhmpsXhvGIITh6aKbcYdfDgJdSqOQXz5+9kMYqIiIgKBUFWSNna2qJatWqIiYlB27Ztcffu3WzH+Pj4oHPnzmjZsqUACYmooOEh5kRU0OnqasN2bAfYjusAXV1tmX2uXn0I+znbERj4UNhwRERERComSEGqf//+kEgkWLdunVLFKAC4d+8eAKBatWqqjEZE+ZiBibHM64eX/S5wEiKivKOhIcKAPk0wb2Y3mJkayezz6lUo5ju64+DBSwKnIyIiIhKGIAUpKysrAMCZM2eUHhMWFgYAKFGihCoiEVE+N2nLWtRo1TzL9Re37+JtCFcKEFHB1LJZVSya1wd1apWX2R4VFYtVKw9j3brjiI9PFDgdERERkXAEKUgZGhoCACIjI5UeU6xYMQBAUlKSSjIRUf7lHBwot+3o7y4CJiEiyhtVLEpjoX1PdOlYR2Z7SkoKtm09gyVL9iE0NELYcERERERqIEhBKiwsDGXKlIGZmZnSY+rWrQsA+PTpk6piEVE+lPEpnEREBV0JIz3MmvITRg1tBS0tscw+p07dxFyHHbh//7XA6YiIiIjUR5Cn7N25cwcA0KlTJ6XHjBs3DhKJBEFBQSpKRUT50awDbuqOQET0w7S0xBg/ui0u+czHuFFtZRaj7t9/je7dlqBH96UsRhEREVGRI0hBysPDAyKRCJMnT0bFihWz7b948WI0b552dszBgwdVHY+I8hFzqxpy29aPnChgEiKi3OnWuQ7OH5+LJY59YWSkl6U9NDQCU2w3okH96Th9+pYaEhIRERGpnyAFKXd3dzx48ACGhobw8/NDjx49MrVLJBKIRCK0adMGx48fx+LFiyGRSHD9+nWcOHFCiIhElM+9vBOMFJ4pR0T5WL3a5ji8cypc19nAomKpLO3x8Yn4fdVhVLOchC1bTiElJVUNKYmIiIjyB0HOkEpNTUWfPn1w5coVVKxYEcePH0dsbKy0/cSJEzAzM4OeXtpPEUUiEd6/f49BgwYJEY+I1EikoYE1d68o7LNv4TJ8evZCoERERDlTxswIjjO7Y0DfJnL77N/vh4ULduHVq1ABkxERERHlX4KskAKA58+fo0GDBvDy8oJIJIK+vj6AtOJTlSpVoK+vD5FIBJFIhDNnzqBp06Z4+/atUPGISE2WXFC8CnL7NAcWo4goX9LT08acaV3h5+UotxgVGPgQrVo6YMTwNSxGEREREWUgyAqpdJ8+fUKfPn1gZWWFvn37okmTJjA1NYVYLEZYWBhu376NY8eO4ebNm0LGIiI1MixporA96vMXgZIQESlHQ0OEgX2bYN7M7jAtXVxmn5cvP2G+ozsOHboscDoiIiKigkGQglSFChUAAN++fUN4eDhCQkIQEhIixK2JKB+z3fqXuiMQEeVI6+aWWOTYB1Y1yslsj4qKxcoVh7Bu3XEkJPDcOyIiIiJ5BClIvXz5EhKJBNOnT8fmzZuFuCURFQDVWsg/bwUAXIbYCBOEiCgbVSuXxi8OvdG5g5XM9pSUFLhuOY2lS/fh8+dIgdMRERERFTyCFKTi4uJQrFgxXL9+XYjbEVE+p6EphtNtxdtY1g4dK1AaIiL5jEvoYdbULhg1pCU0NcUy+3h738C8uW4ICXktcDoiIiKigkuQgtS7d+9QtWpViMWy38gRUdEhEonkFqM+PX+Jg4tXICWJ21yISL20tcQYM7wNZth2hlFxXZl9/v33FRzst+PMmdsCpyMiIiIq+AR5yt6ZM2fwf+3dd3QVVdfH8d9NCAkQeu/SpFdDk96l+ogU0UeRIqCACAlVOioqoiAoIL0pSFNpFpAuXUWkCQjSe01I57x/8OY+YOYmAZK5Kd/PWmetmzl7zuw7J4SbnZkzklSzZk07DgcgERu7a4PLvlUfT6YYBcDtmjYqq/Xf9dewAS0ti1EXL15X926TVbHCmxSjAAAAHpEtBamJEycqODhYAQEBypPHehFQACmDl4+3yz6eqAfAncqVya+l897QtAkdVbBAtmj9ISFheu/dr/Vkse6aPv0HRUbedUOWAAAAyYMtBaljx47pxRdfVNq0abVjxw516NBBXl5edhwaAAAgRnlyZ9KE9zto1eI+qvJUYcuYhQs3qkTxHho6dL5u3w62OUMAAIDkx5Y1pNavXy9Junz5sgoVKqT58+dr5syZOnr0qK5fv67IyEiX+xpj1LBhQzvSBJDAxu/fbrn9rx27tfqTz2zOBkBKly6tt17vWk/dOtaRj4/1H8q2bTso/34ztWvXXzZnBwAAkLzZUpCqW7eujDHOrx0Oh7y9vVWmTBmX+xhj5HA4HtgPQNJU+KkK6jlnimXf9Df6KfDqNZszApCSeXg41O65ygp48xnlyJbBMubvvy9o0MA5Wrp0m83ZAQAApAy2FKQ2b95MYQlIwVwVoyRRjAJgq1rVi2lo/5YqWdx6TcubN4P07juLNWnSKoWG8pAFAACAhGJLQapevXp2HAZAIpQucyaXfQc2brEvEQApWtHCOfR2QEs1qFPSsj8iIlLTpq7VqFFf6cqVWzZnBwAAkPLYUpACkHI91fIZl30bZi2wMRMAKVGWzOnUt2djvdS2mlKl8rSMWbVqtwb0n6XDh8/YnB0AAEDKRUEKQILKkC36o9MlacILnbiVF0CCSe3lqU7/raXe3RsoQ/o0ljF//HFCAf6ztG7d7/YmBwAAAApSABJOjkIFVa/TS9G2f9L+VfuTAZBiNG9cToP7NVeB/Fkt+y9cuK5hQxdo9ux1unv3rs3ZAQAAQJI87DhIhQoVFBERoTt37ihPHutFRO+XJ08eBQcHKzw8XCVLWq/1ACDxG/jdInenACAFqVC2gJYv6Kkpn7xiWYwKDg7Vu+8s1pPFumvmzB8pRgEAALiRLQWp9u3by+FwaNWqVTp37lys8efOndN3330nDw8PvfDCCzZkCCC+pE6TRuP3b9f4/dvdnQqAFCJv7sz69MMX9d2iN+VXsZBlzIIFG1Si+OsaNmyBAgODbc4QAAAA/2ZLQapu3boyxmjt2rVx3mf16tWSpIYNGyZUWgASwNhdP8fYv3j4OzZlAiC5803nrQF9mmrD6gH6T/NKljFbthxQlcr99MrLH+v06cs2ZwgAAABXbFlDKn/+/JKkgwcPxnmfI0eOSJLy5cuXIDkBiH8Oj9hr3OeOHLMhEwDJmaenh9q3riz/Xs8oe7b0ljHHj5/XwAFztHz5LzZnBwAAgLiwpSCVNeu9dRxCQkLivE9oaKgkKUeOHAmSE4D4V6l5kxj7WcwcwOOq9fSTGta/pUo8mduy/8aNQL0zZrEmT16lsLAIm7MDAABAXNlSkLp+/bpy5MihAgUKaN++fXHaJ+rKqFu3biVkagDi0YvvDXfZt3DQCBszAZDcFCuSU28HtFD92tYPO4mIiNTUKWs0atQiXb3KZwcAAIDEzpaC1MGDB5UjRw61atVKK1eujNM+zz33nKT/3boHIPFyeHjoo33bXPYf3rpdl078Y2NGAJKLrFl81a9nY3VoU1WpUnlaxnz33U4NHDBHR46csTk7AAAAPCpbClJr1qxRvXr19Morr2ju3LnaunVrjPG1atXSyy+/LGOMVq1aZUeKAB5DTMUobtMD8Ci8U6dS55drqVe3Bkrv62MZ8/vvfyvAf6Z+/vkPm7MDAADA47LlKXvTpk3TlStX5OnpqTVr1qhXr17y9vaOFuft7a3evXtr9erV8vT01PXr1zVlyhQ7UgSQAH7/fp27UwCQBLV8prx+XjVAg/s1tyxGnT9/TV06T5TfU30pRgEAACRRtlwhFRQUpBdffFFr1qxR2rRpNWHCBL333nvas2ePzp8/L2OM8uTJIz8/P6VNm1YOh0Ph4eHq0KGDbt++bUeKAB5R+mxZXfZtmL3AxkwAJHUVyxXQ8IGt9FSFJyz779wJ1fiPVujDD5cpKCjuD0oBAABA4mNLQUqS1q9fryZNmmjBggXKnTu30qVLp9q1az8Q43A4JElnz57Vyy+/rE2bNtmVHoBHNHKD9W21P8+cb3MmAJKqfHkya2DfZnq2WUWXMfPm/ayhb8/XmTNXbMwMAAAACcW2gpQkbdy4UUWKFNErr7yi5s2bq2LFisqWLZsk6cqVK/r111+1cuVKLViwQGFhYXamBuARpMmQ3nI760YBiAvfdN7q1a2BOr9cSz7eXpYxmzb9qQD/mdq795jN2QEAACAh2VqQkqTQ0FBNnz5d06dPt/vQAOKJd7q0em/HenenASCJ8vT0UIfnq6pfrybKltXXMubYsXMa0H+2vvlmh83ZAQAAwA62F6QAJG2ZcubQsHXfuuzfNH+RjdkASGrq1CyuYf1b6smiuSz7r18P1JjRi/TZZ6sVHh5hc3YAAACwCwUpAA8lpmKUJP266nubMgGQlDxZNJeG9m+hujVLWPaHh0doyudrNHr0Il27xgNNAAAAkju3FaTSp0+v9OnTy9PTM9bY06dP25ARgNgUq+rn7hQAJDHZsvqqX88m6tCmqjw9PSxjvv12hwYOmKO//jprc3YAAABwF1sLUg0bNtQbb7yhWrVqKXPmzHHaxxgjLy/rhU4B2KvHjEkx9rOYOYAo3t6p1PXl2nrjtfpK7+tjGfPbb8fl32+mNm7cb3N2AAAAcDfbClITJ05Uz549JUkOh8OuwwKIJ6Xq1HTZ93nnngoNCrIxGwCJWatmFTSob3Ply2P9x6dz567q7SHzNX/+Bt29e9fm7AAAAJAY2FKQ6tChg3r16iVJCgkJ0TfffKO9e/fq2rVrfBAFkoguk8dZbueqKABRnqpQUMMGtFKl8gUt++/cCdW4D5fpo49WKCgoxObsAAAAkJjYUpDq3r27pHtrQdWvX19///23HYcFEE88XKz1dv7ocZszAZAY5c+bRYP6NlPLphVcxsyZs15D356nc+eu2ZcYAAAAEi1bClLlypWTMUajRo2iGAUkQeN+32q5fdHQMTZnAiAxSe/ro17dGqjzy7Xkndr6I8XGjfsV4D9Tv/5KARsAAAD/Y0tBKmpR8t9++82OwwGIR2kzZrDcPq1bH5szAZBYeHp66MW21dSvZ2NlzeJrGXP06Dn1D5il777baXN2AAAASApsKUidPHlSJUuWlK+v9YdWAIlXz7lTLbffuXnT5kwAJAb1apXQ0P4tVaxITsv+a9dua/SorzRlylqFh0fYnB0AAACSCg87DrJ8+XJJUoMGDew4HIB4FBYc7O4UACQCxYvl0oIvXtPcqV0ti1Hh4RGa8Mm3Kla0mz79dCXFKAAAAMTIloLU+PHjderUKb311lsqXry4HYcEEE8KlCkVbduXQ0a5IRMA7pA9W3q9P7KNvl/WT7VrWP8fvmLFdpUp3VP9+s3Q9euBNmcIAACApMiWgtStW7f0zDPP6OLFi9q2bZtef/11ZcqUyY5DA3hE3unSavz+7ZZ9F4+fsDkbAHbz9k6lnq/V16Y1A/Vi22ry9Iz+keHXX4+rXt3Ber71ezp69JwbsgQAAEBSZcsaUseP33uyTtq0aZU5c2ZNmjRJn376qa5cuaI7d+7EuK8xRkWLFrUjTQD3eW/Hesvt544ctTkTAHZyOBx6tlkFDezbTHlzZ7aMOXv2qt4eMk/z52+QMcbmDAEAAJAc2FKQeuKJJx742uFwyOFwKEeOHLHum1AfdLNnz64qVaqoSpUqqly5sipXrqxs2bJJkubMmaNOnTo91HhNmjRRt27dVKVKFWXPnl2XL1/Wrl279MUXX+iHH36I0xhp0qRRz5491bZtWxUtWlSpU6fW6dOntXr1an366ac6ffp0nMYpVaqUevfurYYNGypv3rwKDAzUoUOHtHDhQs2cOVORkZEP9d6A+3094j13pwAggVSu9ISGDWilCmULWPYHBYXoww+Wafz4FbpzJ9Tm7AAAAJCc2FKQmjt3rh2HeSiXLl2Kt7GmTp2q7t27P7AtX758ypcvn1q3bq1p06apR48eMY5RuHBhrV69WiVKlHhge8mSJVWyZEl17dpVL774otauXRvjOJ07d9Znn30mHx8f57Y0adIoe/bsql27tl599VW1aNFC165de8h3iZTE1a16UsIViQG4T4F8WTS4X3M1b1Lesv/u3buaO2e9hg5doPPn+f8DAAAAj8+WglTnzp3tOMwjO3XqlA4dOqQmTZo89L7vvPOOsxj166+/6sMPP9Tx48dVpEgRDRgwQJUqVVL37t11+fJlDRs2zHKMdOnSadWqVc5i1BdffKFFixYpODhY9erV0+DBg5UpUyYtWbJE1atX1/79+y3Hady4sb744gt5enrqwoULevfdd7Vz505lyZJFr732mp5//nlVr15dy5cvV7169SgswNIT5cu67Puk/av2JQIgwWVI76Ne3Rqq039ryju19UeCn3/epwD/Wfr9979tzg4AAADJmUNSiqxKjBw5Urt379bu3bt16dIlFSxYUCdPnpQU91v2ihQpokOHDsnLy0u7d+9W7dq1FRIS4uxPkyaNNm3apMqVKys8PFwlSpTQ339H/0A/YsQIjRw5UpLUv39/ffTRRw/0V6tWTZs3b5aXl5d+/vlnNWjQINoYnp6eOnTokIoVK6abN2+qUqVK0Y41efJk9ezZU5L0yiuvaP78+bG+x4eRN29enTlzRtK9K8TOnj0br+PDHq6ujprWrY/u3LxpczYAEkKqVB56sW019evZRFkyp7OM+euvs+ofMEsrV+6yOTsAQHzh8zmAxMyWp+wlRiNHjtTq1asf69a9vn37ysvLS5LUu3fvB4pRkhQcHKzevXtLkry8vPTWW29FGyNVqlTq06ePJOngwYMaP358tJgdO3Zo5syZkqT69eurUqVK0WKee+45FStWTJI0duxYy8JX//79nbfq9e/fP65vEylI+qxZLLevmz6HYhSQTDSoU1I/rgjQO0NbWxajrl69pT5vfqGyZXpRjAIAAECCSbEFqfjw7LPPSpIOHTqknTt3Wsbs3LlThw8fliT95z//idZft25dZc587ylGc+fOdXkb3Zw5c5yvW7duHa3//rHvj71fcHCwvv76a0lS2bJleXohohm5cbXl9v3rNtqbCIB4V7J4bn05o5tmf95FRQtHf6hIWFi4Ph6/QsWKdtOkSSsVHh7hhiwBAACQUsTrGlK1atVyvt6yZYvl9kdx/1iJRaFChZQvXz5J0qZNm2KM3bRpk0qUKKH8+fPriSeecN4aKD14bmIaZ8+ePQoMDJSvr69q1qwZrT9qnMOHD+vixYsx5hK1wHrNmjV17NixGHNHylH75Rcst8/sFWBzJgDiU45s6eXf+xm1b11ZHh7Wf4datuwXDRo4R8ePn7c5OwAAAKRU8VqQ2rhxo4wxMsY4b2W7f/uj+PdYiUXJkiWdr6OugHLl/v6SJUs+UJCK6ziRkZE6fvy4ypcv/8A+0r1F0aOKYw+by8PImzdvjP25c+d+qPGQePhmzaxnB/Sx7Lt1+YrN2QCIDz4+XnqtY2290bW+0qX1tozZs+eo/PvN1JYtB2zODgAAACldvD9lz+FwPNT2pCp//vzO11ELBbpy+vRpy/3u/zowMFA3Y1mj5/Tp0ypfvrxy5Mih1KlTKywsTNK9BQqj/ur9OLnEJraxkXSN2rjGcjtXRwFJj8Ph0H+aV9TAt5opT+5MljFnzlzRkMHztHDho//BCAAAAHgc8VqQqlev3kNtT8rSp0/vfB0YGBhjbFBQkPO1r6+v5TixjWE1TtQC5fGVC1KmLPnyuOzj6iggaalSqZCGDWyl8mWs/+AQGBisD95fqo8//lbBwaE2ZwcAAAD8T7wWpDZv3vxQ25MyHx8f5+uoK5VcCQ3934f+NGnSWI4T2xgxjRNfucQm6rZAV3Lnzq3du3c/1JhwrwzZs+nttcss+6Z06WlzNgAeVcECWTW4X3M1a1TOsv/u3buaPWudhg1boAsXrtucHQAAABBdvN+yl1KEhIQ4X6dOnTrGWG/v/63dERwcbDlObGPENE585RKbs2fPPlQ8ErfMeXJp6A8rLPv+WLdBIYFBln0AEo+MGdKod/eGevWlGkrtZf1f+vr1++Tfb4b++OOkvckBAAAAMaAg9Yhu377tfB3brW/p0qVzvv73LXVR48Tl9jlX48RXLkg50mRI77IYJUnrp8+1MRsADytVKg/9t1119e3ZWJkzpbOMOXz4jPoHzNLq1Vy5CgAAgMSHgtQjun+B79huZbt/8fD7FxW/fxxfX19lzJgxxoXNo8a5dOnSA7fmxVcuSDne2fajy77VEz63MRMAD6tRvVIa4t9CRQrlsOy/cuWWRo38UtOmfa+IiEibswMAAADihoLUIzp48KDzdYkSJWKMvb//0KFDMY6zc+dOyzE8PT1VpEgRyzGCgoJ06tQpFShQ4LFyAWa92V83L152dxoALJQqkUfDBrRSjapFLfvDwsL16cSVevfdr3XzJrfcAgAAIHHzcHcCSdWJEyecayrVqVMnxtjatWtLuncl08mTJx/o27p1q/N1TOP4+fk5b8fbtm1btP6ocUqUKKGcOXO6HOf+Y1iNg+SvUY/OltvXz5xHMQpIhHJmz6BxY9ppzZK3XBajlizZqlIl39CAAbMpRgEAACBJoCD1GL799ltJUsmSJVW1alXLmKpVq6pkyZIPxN9v48aNunHjhiSpY8eOLo/16quvOl+vWBF97Z9vvvnGMvZ+adKkUbt27SRJBw4c0NGjR10eD8nXMz1fs9z+x48/25wJgJikSZNafV5vpE1rBql96yry8Ij+X/auXX+pVs2Bat/uA/399wU3ZAkAAAA8GgpSj2HChAkKDw+XJE2aNEk+Pj4P9Pv4+GjSpEmSpPDwcE2YMCHaGOHh4fr0008lSaVKlVJAQEC0mGrVqqlLly6S7hWw9uzZEy1mxYoVOnbsmCRp8ODBKly4cLSYcePGKUuWLM7XSFnKNaqn8fu3W/Z98kInm7MB4IrD4dDzrZ7SxlUD5N+ridKmjf701NOnL+u/L32k6tUCtG3bQYtRAAAAgMQtxa4hVaNGDRUt+r9bH7Jly+Z8XbRo0WhXK82dG/2pY0ePHtVHH32kwYMHq3Llytq2bZs++OADHT9+XEWKFNHAgQNVqVIlSfcKQFEFo38bN26c2rdvr+LFi2vcuHEqWrSoFi1apODgYNWrV09DhgyRl5eX7ty5o7feestyjIiICL355ptauXKlMmbMqG3btumdd97Rrl27lDlzZr322mtq06aNJGnLli2aP3/+Q50vJH0dP37Pdacx9iUCwKWqfoU1fEArlS1t/YCKwMBgvT92qT755FsFB4fanB0AAAAQfxySUuRvorNnz3Z5a5sVh8Phcvv06dOdVzBZmTFjhrp16yYTwy/9RYoU0Zo1a/Tkk09a9t+8eVMvvfSSVq9eHWOeXbt21eTJk+Xt7W3Zv3PnTjVv3lxXr16NcZxHkTdvXucT//Lly+dcYwvu1+C1jmr2Zg/LvildeiokkDVnAHd6okA2DfFvrmcalrXsj4yM1OxZ6zRs2AJdvHjD3uQAAEkWn88BJGbxeoXU3r17ZYxRmzZtHli8O3/+/JKks2fP6u7du/F5SLczxqhr165atmyZunXrpsqVKytbtmy6cuWKdu/erWnTpun777+PdZzjx4+rYsWK6tmzp9q2bauiRYsqderUOn36tNasWaOJEyfq1KlTsY4zY8YMbd++XW+++aYaNGigPHnyKCgoSIcOHdLChQs1Y8YMRUbyGPCUxlUxShLFKMCNMmZMoz49Gqljhxry8vK0jPnpp98U4D9L+/eftDc5AAAAIAHF6xVSkZGRMsaobNmyOnTo0APb7969q3Llyj2wHckLf4FJnPKVKq6+i+dY9u1c/p1+Wbzc3oQAyMvLUy+3r6633misTBnTWsYcOnRaAf4ztXbtXpuzAwAkF3w+B5CYxesVUlG3pFk9CcjVLW8AEo6rRcyl/1/InLWjANs1qldabwe0UOEnslv2X758UyNHfKnp039QRARXtAIAACB5iteC1M2bN5UxY0blz59fBw4ciM+hATyEolWe0uszJ7vs//6zLyhGATYrUzKvhg5oqaerFLXsDw0N16cTv9N77y3RzZvcSgsAAIDkLV4LUvv371fNmjU1dOhQnThxQkePHn1gzaiYFvUGED8qNW+sl94fFWPMoc2/2JQNgJw5MmhAn6Z6vtVTllcQS9LixVs0eNBcnTx50ebsAAAAAPeI14LUjBkzVKtWLVWrVi3aFVIOh0N//vnnQ49pjJGXl1d8pQgke7EVowDYI02a1OrRqa66d6qrtGlTW8bs3HlE/v1m6pdfWF8RAAAAKUu8FqQWLFigsmXLqm/fvvL0jP60INaRAhJW/jKlYo2Z22+IDZkAKZfD4dDzzz6lAW82Va6cGS1j/vnnkgYPmqvFi7dw9TAAAABSpHgtSEnSwIED9emnn6pevXrKmzevvL29NWLECBljNHXqVF26dCm+Dwng/7311UyXfdfPXdDcfoP55RdIQNWrFNHQ/i1VtlQ+y/7bt+9o7HtLNGHCdwoJCbM5OwAAACDxcEhK8N9OIyMjZYxR2bJldegQtyUkVzxW1j18s2bWqI1rYowJCQzUlC69bMoISHkKFcymIf4t1KRBGcv+yMhIzZzxk4YPX6hLl27YmxwAIMXi8zmAxCzer5CycurUKRljFBbGX4OB+JSz8BMa8O1XMcYse3ecTv3BUy+BhJApY1q99XojvfzC0/Lyin6ruiT98MOvCvCfqQMHTtmcHQAAAJB42VKQKlSokB2HAVIUj1SesRajJFGMAhKAl5enXunwtN7q0UgZM6a1jDlw4JQC/Gfqhx9+tTk7AAAAIPGzpSAFIP6N+21rrDGftH814RMBUpgmDcpoiH9zFSqY3bL/0qUbGjF8oWbM+FGRkXdtzg4AAABIGmwvSGXOnFmdOnVSw4YNVaZMGWXJkkWSdO3aNf35559at26dZs+erevXr9udGpBkxOWJlas+nmxDJkDKUbZUXg0b0ErVKhex7A8NDdeET77V2LFLdOvWHZuzAwAAAJIWWwtS3bp100cffaS0ae/d3nD/L9V58+ZVnjx51LhxY40cOVL+/v6aPn26nekBiV7xp6uq27QJcYo9unNPwiYDpBC5cmbUgD5N1eZZP5cxixZt1pDB83Ty5EUbMwMAAACSLtsKUgMHDtS7777rLELdvHlTv/32my5cuCCHw6GcOXOqYsWKypgxo9KlS6cpU6YoU6ZMGjdunF0pAomaw8MjzsUoAI8vbdrU6tG5nrq/Wkdp0qS2jNm+/bD8+83Qjh1HbM4OAAAASNpsKUiVLl1aY8aMkcPh0Pnz59W/f38tWbJEERERD8R5enqqbdu2GjdunPLkyaN33nlHq1ev1sGDB+1IE0jU6nd+Ocb+tZOmqWnv7vp51nzt+2G9TVkByY+Hh0NtnvXTgD5NlSN7BsuYkycvatDAOfr669jXcgMAAAAQnS0FqV69esnT01OXL19W9erVdfr0acu4yMhILVq0SFu3btXu3buVPXt29erVS2+88YYdaQKJWrM+PWLsP7x1uw5v3W5TNkDy9HTVoho2oKVKl8hr2X/r1h299+7XmjjxO4WGhtucHQAAAJB8eNhxkPr168sYo7Fjx7osRt3vzJkz+uCDD+RwONSgQQMbMgQSt/H7Yy40fTlklE2ZAMlT4Seya+bkTlo0q4dlMSoyMlJTp6xRsaLd9OGHyyhGAQAAAI/Jliuk8ua99+H+l19+ifM+27ZtkyTlyZMnQXICkovfv1+ni8dPuDsNIEnKlDGt+r7RSP9t/7S8vDwtY9au3av+AbN08OApm7MDAAAAki9bClKRkZH3DpYq7oeLir17926C5AQkFRWaWF8lOKVLT0WEhSsiLMzmjICkL7WXpzq+WFNvdm+gjBnTWsb8+ec/CvCfqR9//M3m7AAAAIDkz5aC1KlTp1SyZEk1aNBA27fHbY2bqFv1Tp3iL9JIuRwOh17+6B3LvpDAIJuzAZKHpo3Kaki/5ipYIJtl/8WL1zV82ELNmvWTIiP5owgAAACQEGxZQ+qnn36Sw+FQQECAypQpE2t82bJl1b9/fxlj9OOPP9qQIZA4VWzWyHL7J+1ftTcRIBkoVya/ls57Q9MmdLQsRoWEhGnse1/ryWLdNX36DxSjAAAAgARkS0FqwoQJCg0Nla+vr7Zu3aqAgABlzZo1WlzWrFnVv39/bd68Wb6+vgoNDdWECRPsSBFIlF56n8XKgceVO1dGTRjbQasW91GVpwpbxnz55SaVKN5Db789X7dvB9ucIQAAAJDy2HbLXvfu3TV79mz5+vrq/fff19ixY3Xy5EldvHhRxhjlypVLTzzxhBwOhxwOh4wx6t69e5yeygckRw6Hw3L7Xzt225wJkDSlS+utHl3qqvurdeXj42UZ88svh+Tfb6Z27jxic3YAAABAymZLQUqS5s+fr6tXr2rq1KnKmzevHA6HChcurEKFCkl68Jfvc+fOqVu3blq7dq1d6QGJTo+Zky23r5nwuc2ZAEmLh4dD7Z6rrIA3n1GObBksY06cuKhBA+doyZKtNmcHAAAAQLKxICVJa9asUaFChfTcc8+pYcOGKlOmjLJkySJJunbtmv7880+tW7dOK1ascD6ZD0ipilauFG3b1Nd6yxjjhmyApKFmtWIaOqClShXPY9l/82aQ3n1nsSZNWqXQ0HCbswMAAAAQxdaClCRFRkZq6dKlWrp0qd2HBpK84Fu33Z0CkCgVLZxDb/u3UIO6pSz7IyIi9cW07zVy5Je6cuWWzdkBAAAA+DfbC1IAYlfE4uqoC8f+dkMmQOKWJXM6vfVGY/23XTWlSuVpGbN69W4N6D9bhw6xJiEAAACQWFCQAhKhN2Z9Fm3b1yPec0MmQOKU2stTr75UU727N1TGDGksY/bvP6kA/1n66affbM4OAAAAQGwoSAFJRGREhLtTABKF5o3LaXC/5iqQP6tl/4UL1zV82ALNmrVOd+/etTk7AAAAAHFBQQpIZDLmzO7uFIBEqXzZ/BoxsJX8Khay7A8ODtXH47/RBx8sU2BgsM3ZAQAAAHgYFKSARGb4uu+ibft6JLfrIeXKkzuTBr7VTM+1iL62WpQFCzbo7SHzdfr0ZRszAwAAAPCoKEgBScDF4yfdnQJgu3RpvfVG13p6rWMd+fh4WcZs3XpQ/v1maPfuozZnBwAAAOBxUJACEpF0mTJabo8IC7M5E8B9PDwcat+6igJ6P6Ps2dJbxvz99wUNHDBby5b9YnN2AAAAAOIDBSkgESnToE60bTuWfuuGTAD3qPX0kxoa0EIli+ex7L9xI1DvjFmsyZNXKSyMhf4BAACApIqCFJCItBs5ONq27Uu/sT8RwGbFiuTU2wEtVL92Scv+iIhITZ2yRqNGLdLVq7dszg4AAABAfLOlIDVz5kxJ0tq1a7V06VI7DgkkOeP3b7fuMMbeRAAbZc3iq749G+vFNlWVKpWnZczKlbs0oP9sHTlyxubsAAAAACQUWwpSHTt2lCQtXrzYjsMBSU6+UiXcnQJgK+/UqdTpvzXVq1sDZUifxjJm374TCvCfqfXr99mcHQAAAICEZktB6vLly8qePbsuXrxox+GAJKfv4tmW27/54BObMwESXstnymtg3+YqkC+LZf/589c0bOgCzZmzXnfv3rU5OwAAAAB2sKUgdfDgQdWpU0cFCxbUvn38pRuIqxO/8u8FyUfFcgU0bEAr+VV8wrL/zp1Qjf9ohT78cJmCgkLsTQ4AAACArTzsOMiCBQvkcDict+4B+B8PT+t1cya80MnmTICEkS9PZk0a95K+/epNl8WoefN+VoniPTRixEKKUQAAAEAKYEtBavbs2Vq/fr2effZZDR8+3I5DAknGqxPej7Zt1pv9ZVjMHEmcbzpvDXyrqX5eNUDPNqtoGbN585+q7NdXr3b8RGfOXLE5QwAAAADuYsste7Vq1dJHH32k7Nmza/jw4XrhhRe0ePFi/fHHH7p+/boiIyNj3H/Lli12pAm4Rem6NaNtu3nxshsyAeKHp6eHXmhdRf69n1G2rL6WMceOndPAAXO0YoWLp0sCAAAASNZsKUht3Ljxgas9nnzySQ0bNixO+xpj5OXllVCpAQDiUe0aT2pY/1YqXiyXZf+NG4EaM3qRPvtstcLCImzODgAAAEBiYUtBSpIcDoddhwKSjPTZskbbdvvqNTdkAjyeJ4vm0tD+LVS3ZgnL/vDwCE2dslajRy/S1au3bM4OAAAAQGJjS0GqXr16dhwGSFKy5M2tt79fHm37nLcGuSEb4NFky+qrfj2bqEObqvL0tF6W8Ntvd2jggDn666+zNmcHAAAAILGypSC1efNmOw4DJClWxShJiggLszkT4OF5p06lLq/UUs/XGii9r49lzG+/HVeA/yxt2PCHzdkBAAAASOxsu2UPwP+ky5zJ3SkAj6xl0woa1LeZ8ufNYtl/7txVDX17vubN26C7d+/anB0AAACApICCFOAGozevdXcKwEOrVL6ghg1oqacqPGHZf+dOqD4at1zjxi1XUFCIvckBAAAASFJsL0ilT59ebdq0UfXq1ZUrVy6lTZtWnTt31qlTp5wxuXPnVqZMmRQSEqITJ07YnSKQoJ6sXtll34QXOtmYCRA3+fNm0cC3mqpVs4ouY+bMWa9hQ+fr7NmrNmYGAAAAIKmytSD1xhtv6N1331X69Okl3XvynjFG6dKleyCuTp06WrhwoUJCQpQvXz5dv37dzjSBBNX9i08tt09+pbuMMTZnA7iW3tdHvbo1UOeXa8k7tfV/Fxs37leA/0z9+utxm7MDAAAAkJRZPxIpAYwYMUKffvqpMmTIoLCwMO3du9dl7OLFi3X+/Hl5e3vr+eeftytFIMGlSp3acvv8/kMVHhpqczaANU9PD/23fXVtXjtIr3epZ1mMOnr0nJ77z7uqX28IxSgAAAAAD82WglSFChU0bNgwSdKCBQuUK1cuVa1a1WW8MUZLliyRw+FQo0aN7EgRsEW2Avkst185dcbmTABrdWuW0I8r/PXe8OeVNYtvtP5r126r71vTVaZ0T3377Q43ZAgAAAAgObDllr3evXvL4XDol19+UceOHeO0z/bt2/Xmm2+qbNmyCZwdYJ/yjetH2/ZFj7fsTwT4l+LFcmlo/5aqU6O4ZX94eIQ+/2y1Ro9epOvXA23ODgAAAEByY0tBqk6dOjLGaPLkyXHe5+TJk5KkvHnzJlBWgP0av94l2rag6zfsTwT4f9mzpZd/ryZq37qKPD2tL5pdsWK7Bg2co6NHz9mcHQAAAIDkypaCVO7cuSVJR44cifM+of+/no63t3eC5AQAKZm3dyp1faW2er5WX77pfCxjfv31uPz7zdCmTX/anB0AAACA5M6WglRYWJi8vb3l5eUV532iilg3btxIoKwAe+UvU8rdKQByOBxq1ayCBvVtpry5M1vGnD17VW8Pmaf58zfw5EcAAAAACcKWgtSZM2dUsmRJlS5dWrt3747TPo0bN5YkHTt2LCFTA2zz1lczo237cvBI+xNBiuVX8QkNG9BKFcsVsOwPCgrRuA+X6aOPVujOHZ76CAAAACDh2PKUvZ9//lkOh0OdOnWKU3yhQoXUpUsXGWP0008/JXB2QMKr+WIby+0X/z5pbyJIkQrky6LPx7+s5Qt6WRaj7t69q9mz16n4k901evQiilEAAAAAEpwtBanJkycrIiJCNWrU0IgRI2KMfeqpp/Tjjz/K19dXoaGhmjZtmh0pAgnqucH+7k4BKVCG9D4a4t9C61cOUItnylvGbNjwhyr79VOXzhN17tw1mzMEAAAAkFLZcsve0aNHNWbMGI0aNUrDhg1T06ZNtWzZMmf/M888o5YtW6px48aqW7euJMkYo0GDBunChQt2pAgkGO+0aS23/zxzvs2ZIKVIlcpDL7atpn49myhL5nSWMX/9dVb9A2Zp5cpdNmcHAAAAADYVpCTpnXfekZeXl4YMGaLKlSvLz8/PuVjuuHHjnHEOh0PGGI0ePVqTJk2yKz0gwfScM8Vy+74f19ucCVKC+rVL6u2AFipWJKdl/7VrtzVq5FeaOnWtwsMjbM4OAAAAAO6x5Za9KCNGjFC1atW0fPlyBQcHy+FwPNDCw8O1du1a1apVS6NHj7YzNSDB5C35ZLRtn7wQt/XUgLgq8WRuLZzeTXOmdLEsRoWFheuTj79RsaLdNGnSSopRAAAAANzKtiukouzdu1dt27aVp6enSpUqpRw5csjT01NXr17VgQMHFBISYndKgP3+/+pA4HFlz5ZeAb2bqH3rKvLwsP4bw/Llv2jQwDk6duy8zdkBAAAAgDXbC1JRIiMjtX//fncdHrBFS//e0baFh/IEMzw+Hx8vvdaxtt7oWl/p0npbxuzZc1T+/WZqy5YDNmcHAAAAADFzW0EKSAnqvvpitG3L3hlnEQnEjcPh0H+aV9TAt5opT+5MljFnzlzRkMHztHDhRudafQAAAACQmLilIJUjRw7VrVtXZcqUUZYsWSRJ165d059//qmNGzfq0qVL7kgLiFdZ8+ez3H7+r2M2Z4LkokqlQho2sJXKl8lv2R8UFKIP3l+q8eO/UXAwV+IBAAAASLxsLUjly5dPH330kf7zn/8oVSrrQ0dGRmrFihXq37+/Tp8+bWd6QLwasmaJu1NAMlEwf1YN7tdczRqXs+y/e/eu5sxep2HDFur8+Ws2ZwcAAAAAD8+2glTNmjW1cuVKpU+fXg6Hw3VCqVKpTZs2atKkiVq0aKFt27bZlSIQbxwuFpf+4bPpNmeCpCxjhjTq1a2BOv23plJ7Wf+4Xr9+nwL8Z2rfvhM2ZwcAAAAAj876t+Z4lidPHq1cuVIZMmSQw+HQ2rVr1bZtWxUsWFA+Pj7y8fFRwYIF1aZNG61Zs0YOh0MZMmTQypUrlTt3bjtSBOJVvU4vWW4/uJkCK2KXKpWHXn2xhjavHaTunepaFqMOHz6jVi1Hq1HDoRSjAAAAACQ5tlwhNWjQIGXIkEGRkZHq1KmTFi5cGC3mzJkzOnPmjFasWKEOHTpo3rx5ypAhgwYNGqQ+ffrYkSYQb5q/9Ua0bZNe6eaGTJDUNKxbSkP8W6ho4RyW/Vev3tLIEV9q2rTvFRERaXN2AAAAABA/bLlCqlmzZjLGaPr06ZbFqH/76quv9MUXX8jhcKh58+Y2ZPjojDFxahs2bIh1rCZNmmjZsmU6ffq0QkJCdPr0aS1btkxNmjSJcz5p0qRRQECAdu7cqatXr+r27ds6ePCgxo0bp/z5rRdChj0iQsPcnQISsVIl8uirmd0167POlsWosLBwjf9ohYoV7abPPltNMQoAAABAkmbLFVJ58uSRJC1ZEvdFnpcsWaIePXo4903upk6dqu7duz+wLV++fMqXL59at26tadOmqUePHjGOUbhwYa1evVolSpR4YHvJkiVVsmRJde3aVS+++KLWrl0b7/njf2JaIw34t5zZM8i/dxO1e66yPFysPbZ06TYNGjhHf/99websAAAAACBh2FKQun79unLmzKmbN2/GeZ+o2OvXrydUWvHq888/1+eff+6yPygoyGXfO++84yxG/frrr/rwww91/PhxFSlSRAMGDFClSpXUvXt3Xb58WcOGDbMcI126dFq1apWzGPXFF19o0aJFCg4OVr169TR48GBlypRJS5YsUfXq1bV///7HeLdwpUDZUurz5cxo21k7Cv/m4+Olbq/W0etd6ildWm/LmN27j8q/3wxt3XrQ5uwAAAAAIGHZUpDas2ePmjdvrrJly+q3336L0z5ly5Z17psUXLp0SQcOHHjo/aKKTpK0e/du1a5dWyEhIZLuvffvvvtOmzZtUuXKlTVw4EDNnj1bf//9d7RxAgICVLJkSUlS//799dFHHzn7duzYoQ0bNmjz5s1Kly6dJkyYoAYNGjzK20QsrIpRkvTjFOvtSHkcDodat6ykAX2aKneuTJYxp09f1pDB8/Tll5tkjLE3QQAAAACwgS1rSH366adyOBwaMGCA0qRJE2t8mjRpNHDgQBljNGnSJBsydJ++ffvKy8tLktS7d29nMSpKcHCwevfuLUny8vLSW2+9FW2MVKlSORd+P3jwoMaPHx8tZseOHZo5815RpH79+qpUqVJ8vg1IGr9/u8s+c/eujZkgsarqV1irFvfRJ2M7WBajAgODNfTt+SpR/HUtXLiRYhQAAACAZMuWgtT69es1atQolSxZUhs3blT58uVdxpYrV04bNmxQ8eLFNWrUKK1bt86OFN3m2WeflSQdOnRIO3futIzZuXOnDh8+LEn6z3/+E62/bt26ypw5syRp7ty5Ln+JnTNnjvN169atHyNr/FuBcqVd9u1Y+o19iSBRKlggq6ZN6Kglc99Q2dL5ovXfvXtXM6b/oCeLddd7732t4OBQN2QJAAAAAPaJ11v2XK1vJN17Gt2ePXvk5+envXv3av/+/dq9e7cuXbokY4xy5sypypUrR7tVb9iwYRozZkx8pploFCpUSPny3fvldNOmTTHGbtq0SSVKlFD+/Pn1xBNP6OTJk86+WrVqPRDnyp49exQYGChfX1/VrFnz8ZLHA/osnOGyb/uSb+xLBIlKxgxp9GaPhur4Yg2l9rL+cbtu3e8K8J+pP/44aW9yAAAAAOBG8VqQGjlyZKy3mBhj5HA4VLZsWWfx6X4Oh0PGGPn5+cnPz0+SkkRBqm3bturQoYMKFCigiIgIXbhwQb/88ovmzJmjjRs3Wu4TteaTJOcVUK7c31+yZMkHClJxHScyMlLHjx9X+fLlH9gnrvLmzRtjf+7cuR96zOTgpfdHWm6/evac5vUbYm8ySBRSpfLQy+2f1ltvNFLmTOksYw4dOq3+AbO0Zk3SWCcPAAAAAOJTvC9qHtdH3scUF9cxEpPSpR+8ZSt9+vQqVqyYOnbsqBUrVujVV1/VrVu3HojJnz+/8/WZM2diHP/06dOW+93/dWBgYKxPMjx9+rTKly+vHDlyKHXq1AoLC4sx/n6x5ZhSVWrexHL7fP+3bc4EiUGjeqU1xL+5ihTKYdl/5cotjRi+UNOn/6CIiEibswMAAACAxCFeC1Kenp7xOVySEBQUpO+++07r16/X4cOHFRgYqOzZs6tOnTrq0aOHsmXLpueee06ZM2dWo0aNFBER4dw3ffr0zteBgYGxHieKr6/vA31R48Q2htU4165di3UfuFa2QR3L7asnfM6C1ClM6RJ5NGxgKz1dpahlf2houD6d+J3ee2+Jbt4MsowBAAAAgJQi3q+QSmny5s1reVXSunXrNGnSJK1du1aVKlVS3bp19frrrz/w1EAfHx/n69iuVAoN/d8ix/9+UmHUOHG52immcWITtd6VK7lz59bu3bsfasyk7tUJ71tu/2v7LpszgbvkzJFB/d9sqjbPPiUPD+vnRHz99VYNHjRHJ05ctDk7AAAAAEicKEg9pphukbt06ZLatGmjQ4cOydvbW717936gIBUSEuJ8nTp16hiP4+3t7XwdHBz8QF/UOLGNEds4sTl79uxDxadUk17p5u4UYIM0aVKr+6t11KNzPaVNa/1vb+fOI/LvN1O//HLI5uwAAAAAIHGjIJXATpw4oZ9++kktWrRQsWLFlDt3bp0/f16SdPv2bWfcv2/D+7d06f63MPK/b82LGie2MWIbBw9n/P7tltsjQuO+LheSHofDoeeffUoD3myqXDkzWsacOnVZgwfN1aJFm7l1EwAAAAAsuK0glT59eqVPnz5O607dv6B3UnTw4EG1aNFC0r1b/KIKUvcvEh7b7XD3L2T+7/MRNY6vr68yZswY41VbUeNcunTpoRY0x4NK1Kxmuf2HKTNszgR2qla5iIYNaKmypaz/vd6+fUdj31uiCRO+U0gI/74AAAAAwBVbC1KNGjXS66+/rlq1ailz5sxx2scYIy8vrwTOLGG5emrgwYMHna9LlCgR4xj39x869ODtP/8eZ+fOnZZjeHp6qkiRIpZj4OG8NuUTy+0HN261ORPYoVDBbBri30JNGpSx7I+MjNTMGT9p+PCFunTphr3JAQAAAEASZFtBasqUKXrttdckuS7QJFelSpVyvj537pzz9YkTJ3T27FnlzZtXdepYP60tSu3atSXduxrq5MmTD/Rt3fq/IkidOnVcFqT8/Pyct/Vt27btod4DYrdj2XfuTgHxLGPGNHrr9UZ65YUa8vKyvprzxx9/U4D/TP355z82ZwcAAAAASZctBalu3bqpW7d7Cz3fvn1bK1as0L59+3Tjxg3dvXvXjhTcplChQmrUqJEk6fjx4w8UpCTp22+/1RtvvKGSJUuqatWqlsWkqlWrqmTJks74f9u4caNu3LihTJkyqWPHjvrwww8tc3n11Vedr1esWPGobynFS5Mhg+X27V8vtzkTJBQvL0+98sLT6vN6I2XKmNYy5uDBUwrwn6Xvv99rc3YAAAAAkPTZVpCS7t0mVr9+fV26dMmOwya4Fi1aaO3atYqMjLTsz5Ejh5YuXep8+t1nn30WLWbChAl67bXX5OXlpUmTJql27doPPH3Px8fH+WS+8PBwTZgwIdoY4eHh+vTTTzV8+HCVKlVKAQEB+uijjx6IqVatmrp06SLpXgFrz549j/SeIb2z7Ydo225fueqGTJAQGtcvrbcDWqhQweyW/Zcv39SI4Qs1ffoPioxM3gV1AAAAAEgoDkkJ/giowMBA+fj46IUXXtDSpUsT+nC2OXHihLy8vLRs2TJt375dJ0+eVHBwsLJly6a6deuqR48eypYtmyRpy5YtatiwoeVC4u+9954GDx4sSfr111/1wQcf6Pjx4ypSpIgGDhyoSpUqOePefvtty1x8fX21Z88eFS9eXJI0bdo0LVq0SMHBwapXr56GDBmi9OnT686dO3r66ae1b9++eD8fefPmdS6wni9fPp09ezbej+FuDodDH/3xS7TtE1/sorsuCpNIGsqWyqthA1qpWuUilv2hoeGaOOFbvffeEt26dcfm7AAAAB5eSvh8DiDpsqUgdeHCBWXLlk1PPfVUghRC3OXEiRN64oknYo1bunSpunbt6vLpdw6HQ9OnT3dewWRlxowZ6tatW4yPkC9SpIjWrFmjJ5980rL/5s2beumll7R69epYc34UKeE/vG7TJqj401Wjbf+k/av2J4N4kStnRg3o01RtnvVzGbNo0WYNGTxPJ09etDEzAACAx5MSPp8DSLpsuWXv0KFDqlWrlnLnzp2sClIdO3ZUnTp1VL16dRUuXFjZsmVThgwZFBgYqNOnT+uXX37R3LlztWPHjhjHMcaoa9euWrZsmbp166bKlSsrW7ZsunLlinbv3q1p06bp+++/jzWf48ePq2LFiurZs6fatm2rokWLKnXq1Dp9+rTWrFmjiRMn6tSpU/H19lMcD09Py2IUkqa0aVOrR6e66t6prtKkSW0Zs2PHYfn3m6nt2w/bmhsAAAAAJHe2XCHVoUMHLViwQAsWLFDHjh0T+nBwk+T+F5jSdWuq86Rx0bZvW7xMu5avdENGeBQeHg4938pPA/o8o5w5MlrG/PPPJQ0aOEeLF2+xOTsAAID4k9w/nwNI2my5Quqrr77Sc889p5deekm7du2yXNwbSOysilGStHvFKpszwaN6umpRDe3fUmVK5rXsv3Xrjsa+t0QTJ36nkJDo670BAAAAAOKHLQUpSXrppZf00UcfacKECXrhhRf09ddf66+//tKdO7EvDrxlC1cpwL2avtnDcvuXg0fGuK4XEofCT2TXEP/maly/jGV/ZGSkpn/xg0aM+FKXL1uv9QYAAAAAiD+2FaTCw8O1b98+Xbt2TdWrV1f16tXjtJ8xRl5eXgmcHRCzhq9Z32p68e+T9iaCh5IpY1r1faOR/tv+aXl5eVrGrF27VwP6z9KBA6yvBgAAAAB2saUg5enpqa+++kqtW7eWdO+pckBSUbFZY8vtP3w+w+ZMEFepvTz1yos11Kd7Q2XMmNYy5s8//1H/gFn64Ydfbc4OAAAAAGBLQapHjx56/vnnJUknT57U3Llz9ccff+jGjRu6e/euHSkAj2TQysXK/kQBy76Dm7banA3i4pmGZTTEv4WeKJDNsv/SpRsaPmyhZs78UZGR/PwBAAAAAHewpSDVtWtXSdKOHTvUoEEDhYSE2HFY4LE4HA6XxaiJL3axORvEplzpfBo2oJWq+hW27A8JCdOET77V2LFLdPt2sM3ZAQAAAADuZ0tBqmjRojLGaOzYsRSjkGRUad3SZd/dyEgbM0FMcufKqIF9mql1q6dcxnz55Sa9PWSe/vnnko2ZAQAAAABcsaUgFRQUpDRp0uj06dN2HA54bB6enmo3crBl35FfdtqcDaykTZtar3epp+6v1pWPj/WDD3755ZD8+83Uzp1HbM4OAAAAABATDzsOsm/fPklSwYIF7Tgc8NjG/e56faj1M+bamAn+zcPDofatq2jzmkHq06ORZTHqxImLat/uA9WsMYBiFAAAAAAkQrYUpKZOnSqHw6EuXVh3B4lb2owZNH7/dpf966bPUWjQHRszwv1qViumNUv7atyYdsqRPUO0/ps3gzRwwGyVKvm6lixh0XkAAAAASKxsuWVvxYoVmjp1qnr06KFx48Zp4MCBPF0PidKYrT+47Puk/av2JYIHFCmUXW8HtFTDuqUs+yMjI/XFtB80cuSXunz5ps3ZAQAAAAAeli0FqZdfflk7duxQxYoV1bdvX7Vp00bLly/XX3/9pTt3Yr/aZP78+TZkiZSuZUBvl33z+w+1MRNEyZwprfr2bKL/tqumVKk8LWPWrNmjAf1n6+DBUzZnBwAAAAB4VA5JJqEPEhkZKWMe7TDGGHl5WS9YjMQlb968OnPmjCQpX758Onv2rJszejgx3arH1VH2Su3lqVdfqqne3RsqY4Y0ljH7959UgP8s/fTTbzZnBwAAkDQk9c/nAJI3W66QkiSHw2HXoYCH9kzvbu5OAf+vWeNyGtKvuQrkz2rZf/HidQ0bukCzZ69TZCS3/gIAAABAUmRLQapQoUJ2HAZ4ZI26dXLZN/31vjZmknKVL5tfwwe0UuVK1j8vQkLC9PH4b/T++0sVGBhsc3YAAAAAgPhkS0Hq1CnWdkHS8/fe37Xui9kKusEi2QkpT+5MGtCnqVq3fMplzMKFG/X2kHk6deqyjZkBAAAAABKKbbfsAYmVl4+35fZvP5xgbyIpTLq03nqjaz291rGOfHys14nbuvWg/PvN0O7dR23ODgAAAACQkChIIcXLnDtXtG3T3+jnhkxSBg8Ph9q3riL/3k2UI1sGy5i//76ggQNma9myX2zODgAAAABgBwpSSPGq/KdFtG2BV6+5IZPkr1b1Yhrav6VKFs9j2X/zZpDeGbNYkyatVFhYhM3ZAQAAAADsYktB6vjx44+8rzFGRYsWjcdsgAfV6/xfd6eQ7BUrklND/FuoQZ2Slv0REZGaNnWtRo36Sleu3LI5OwAAAACA3WwpSD3xxBNxjjXGyOFwPPA1gKQpS+Z06tuzsV5qW02pUnlaxqxatVsD+s/S4cNnbM4OAAAAAOAuthSk5s6dG2tMunTpVLx4cZUtW1bGGP3222/av3+/DdkhJcuaP5+7U0iWvFOnUqf/1lSvbg2UIX0ay5h9+04owH+m1q/fZ3N2AAAAAAB3s6Ug1blz5zjHli5dWrNmzVLp0qU1duxYLV++PAEzQ0o3ZM2SaNvWfDrVDZkkHy2alNegfs1VIF8Wy/4LF65r6NvzNWfOet29e9fm7AAAAAAAiUGiW9T8wIEDql+/vn7//XfNmzdPBw4c0JEjR9ydFpKh1Gmsr9w5sm2HzZkkDxXKFtDwga3kV/EJy/7g4FCN/2iFPvxwuQIDg+1NDgAAAACQqHi4OwErQUFB+vjjj5UmTRr179/f3ekgmRq762d3p5As5M2dWZ9++KK+W/Smy2LU/PkbVPzJHho+fCHFKAAAAABA4rtCKsru3bslSQ0aNHBzJkhuHB4e+mjfNnenkeT5pvPWG13rq2vH2vLx9rKM2bLlgPz7zdSePUdtzg4AAAAAkJgl2oJU1JP2cubM6eZMkNzEVIya0CHu652lVJ6eHnqhdRX169VE2bOlt4w5fvy8Bg6Yo+XLf7E5OwAAAABAUpBoC1JNmjSRJN28edPNmSA5yZAju8u+LwePlGGR7RjVrvGkhvVvpeLFcln237gRqDGjF+mzz1YrLCzC5uwAAAAAAElFoixItW/fXoMHD5YxRlu3bnV3OkhGRqz/zmXfxb9P2pdIEvNkkZx6u39L1atVwrI/IiJSUz5fo9GjF+nq1Vs2ZwcAAAAASGpsKUjNnDkz1hgPDw9lzpxZlSpVUp48eeRwOBQREaH333/fhgyREnimcv3tPi/gbRszSTqyZvFVv56N9WLbavL0tH4Gwnff7dTAAXN05MgZm7MDAAAAACRVthSkXn31VRlj4hQbtXbUrVu31LVrV+3duzchU0MKUql5Y8vtn73aQ2HBITZnk7h5p06lzi/XUq9uDZTe18cy5vff/1aA/0z9/PMfNmcHAAAAAEjqbClInTp1KtaC1N27d3X79m2dOHFCmzZt0oIFC3T16lU70kMKUbZhvWjbJr3cTRFhYW7IJvFq2bSCBvVtpvx5s1j2nz9/TW8Pmad58zboLmtuAQAAAAAegS0FqUKFCtlxGCBGpevWjLaNYtT/VCpfUMMGtNRTFZ6w7L9zJ1QfjVuuceOWKyiIK8oAAAAAAI8uUS5qDsA++fJk1qC+zdSqWUWXMXPnrtfQt+fr7FmuWgQAAAAAPD4KUkAKld7XRz1fq6/OL9eSj7eXZcymTX8qwH+m9u49ZnN2AAAAAIDkjIIUUgSHR/QnxB3b/asbMnE/T08PdXi+qvr1aqJsWX0tY44dO6cB/Wfrm2922JwdAAAAACAloCCFFKHL5HHRtu1dudYNmbhX3ZolNLR/Cz1ZNJdl//XrgRo96it9/vkahYdH2JwdAAAAACCliPeCVERE/P4Sa4yRl5f17URAXJWs9XS0bef+Sjm3oRUvlktD+7dUnRrFLfvDwyP0+WerNWbMYl27dtvm7AAAAAAAKU28F6QcDkd8DwkkDGPcnUGCy5bVV/69muiF56vK0zP6bYuS9M03OzRwwGwdPXrO5uwAAAAAAClVvBek5s6d+1j7OxwONWvWTFmzZqW4hXgxfv92d6dgO2/vVOr6cm317FZfvul8LGN+++24/PvN1MaN+23ODgAAAACQ0sV7Qapz586PvO+zzz6rUaNGKWvWrM5tp0+fjo+0gAdsmveVu1NIMK2aVdCgvs2VL09my/5z567q7SHzNX/+Bt29e9fm7AAAAAAASCSLmjdt2lSjR49WxYoVJd27Sur8+fN67733NH36dDdnh6QsfdYsltt/Xf2DzZkkPL+KT2jYgFaqWK6AZX9QUIg+Grdc48Yt1507oTZnBwAAAADA/7i1INWwYUONHj1aVapUkXSvEHXp0iV98MEHmjJlikJD+aUZj6ewX0V3p5DgCuTLokF9m6vFM+Ut++/evau5c3/WsKHzde7cNZuzAwAAAAAgOrcUpGrXrq0xY8aoRo0aku4Voq5evapx48Zp8uTJCg4OdkdaSIbqd3452rZJr3RzQybxL72vj3p3b6BO/60l79TW/5Q3bPhDAf6z9Ntvx23ODgAAAAAA12wtSFWvXl2jR49WvXr1JN0rRN24cUMff/yxJkyYoKCgIDvTQQpw7shR5StV/IFtEaFhbsomfnh6eujFttXUr2djZc3iaxnz119nNaD/bH333U6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     },
     "metadata": {
      "image/png": {
       "width": 594,
       "height": 340
      }
     }
    }
   ]
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "id": "Y_CGhXjOMjRt",
    "outputId": "a11612cc-f3ea-4f41-dbfc-23583fecc247",
    "trusted": true
   },
   "cell_type": "code",
   "source": "plot_qini_curve(test[y],uplift_test,test[T],perfect=False)",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "text/plain": "<sklift.viz.base.UpliftCurveDisplay at 0x150eb7220>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 480x360 with 1 Axes>",
      "image/png": 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W4bLaGpe2SKfItruM5lJkC15u51JHLEgRERERERVQv82eiuZ2fTNts3PCdIGyAb76+ed6jOCAL3hx7RZ+aZ1y9fn9k2fgdfIMukyegIp1a6Vrq6Wjg2otm+LFtVu5npdkSSQSpR7wXZgYGRmhR48eAABTU1OFtnD17NkThoaGMsWJtKtn9PX1FZo/tdjx3215X79+xY8fP1CiRAnUqlULGhoaghxsXrx4cZQoUSJXY8THx8PPzy/b/dIeZG5paZlp27QHmac94Dy7cxkaGsLY2DjTg81T5/rx40eBONAcYEGKiIiIiKhAEmloZFmMAoDEfPgPmwubd+DusZNIiItDbETKP8bPrHHDpIM7ZdqOWL8a02o0ETpFomzp16+fwsWjVAYGBujTpw92796d7n1wcLD0uWTJknj58mWm42hra8PExESmb6obN26gT58+MDQ0RKtWrXD1as63/ipq/PjxKrtl782bN0hMTISmpiasra0zbZs2ntXvszy+vr7pxvLy8pLbTiwWw8rKKsfzqCsWpIiIiIiICiDTUgV7e01EYFC6zzndAkikDlK363358gVTp07Nsv3KlStRrlw5DB06VKYg9fTpU+lz3bp1sywg1apVC5qamjJ9U7m7u6NPnz4AgMmTJwtSkFKlhIQE3L9/H02bNkWTJk2gpaWFhAzOu2vVKmX7cGxsLHx8fLI9161b/1u92apVqwwLUvXr15du67t9+3a251FXGqpOgIiIiIiI8t7c8yeybPPXkpUCZKJ6RkXNVJ0CUYbKly+P5s1TtqAeP34cR44cyfLXsWPHAKQUMdJuGwNSVjSlFlAGDhyY5fyDBw+WPl++fFkm7uHhgUePHgEAunfvjkGDBin8tf3222/ZXvkFAIsXL4ZIJMrVr5ysjkp16tQpAICxsTF69eolt42FhQXat28PIOX3LSfnOl27dg2hoaEAgGHDhmXYzt7eXvp88uTJbM+jrliQIiIiIiIqYLR0dbJss23sZHx6UXC2fgDAOruRct8vupaz2wGJhDBkyBBoaKT80/yvv/5SqE9qOw0NDQwZMiRd7Pv379J4vXr1MGvWrAzHadOmDcaOHQsgZYvbmTNn5LYbPHiw9Hwpd3d3jBs3TnoQujxFixaFm5sbTpw4AS0tLYW+JnWyY8cOaaFoxYoVMDNLX9TW0NDApk2bpCvL1qxZI3ccd3d3SCQSSCQS6WqqtBISEuDm5gYAqFatGqZPlz3Tr3Hjxhg5MuXPtmvXruVoJZa64pY9IiIiIqICpHGfHui7MON/gALApxevCuRh1MlJSdji4IixO9bLxJyf3cXshm0RX0CuS6eCI7Wg9P37d9y8eVOhPl5eXvj06RPKlCmDIUOGYNmyZeniU6dORdu2bWFubo7ly5ejdevW2L9/v/R8JEtLS3Tr1g3Dhg2DlpYWkpKSMHz4cCRlsPXV19cXffr0wdGjR2FkZIRNmzZh3LhxOHLkCLy9vfHz508YGBigbNmy6NixI3r27AljY+Pc/caoUEhICJycnLB161aUL18eXl5e+PPPP/Hs2TOULl0akydPRtu2bQEABw8exLVr13I81+rVq9G/f39UrVoVq1evRqVKlXD48GHExMSgTZs2mDNnDrS0tBAdHY3JkydnOI6trS1Kliwp/Zz2fKvatWunW4EVGRmJ48eP5zjnvMKCFBERERFRAdG0fy/0njcjy3aPz18SIBvViElzZft/Lb9/BRuGjkHAqzcY7rYKBsbG2DZuMiKDQgTMkOh/mjZtisqVKwNI2YolkUgU7nvixAlMmjQJ1tbWaNiwIe7fvy+Nffv2DS1btsTJkydRrVo12NrawtbWVu44ISEhGDx4cJZFlfPnz6NZs2bYtGkTmjdvjho1aqBGjRoZtg8MDMTixYsRHh6u8NekTrZt24bSpUtj/vz5qFSpEtzd3WXanDt3DiNGjMjVPJGRkejSpQs8PDxQpUoVjBkzBmPGjEnXJiwsDIMGDcKTJ08yHGfWrFlo3bq13FjPnj3Rs2dP6ef379+rRUGKW/aIiIiIiAoIRYpRAPDv/YKz5UOe63sPZRizd12B5fevokrjBrCwqYLF1zzQZvggiPPhtiLK/1IPMweQ7QJB2vZpx0n15s0b1KxZE4MGDcKxY8fw/v17REVFIS4uDl+/fsWlS5cwffp0lC9fHh4eHgrN+ezZM7Ro0QJt27bFunXr8OjRI3z//h0JCQkICwvDq1evsH//fgwcOBCWlpbYsGFDtops6mbRokVo3rw5Dhw4gI8fPyIuLg7fv3/HhQsXMHDgQHTt2hVxcXG5nsff3x916tTBzJkz4e3tjZCQEERFReHVq1dYu3YtatasiXPnCt7WYxGA/Pu/DlIrFhYW+Pz5MwDA0tISAQEBKs6IiIiIqGDRMdCHlq4OYsIikJSYmC5WsrIVZpzYn+UYH54+x4k/5Z93UpBMObI7W+1vHz6O4M9f8K/3Q3z2fZWtvuZWFTDz1EEAwMbh4/HW51G2+itLbr4/X7duHUxNTRESEoJJkyYpK0UiyieU8WcCt+wREREREeUDNdq1gr3rCunni9vccX79NunnzIpRm0dOQFJCIhLj4/P1aoXsOLLgT/RfMlfh9s0G9E73eVqNJuk+a4jFKF6uDCo3ro/Xd+7j5/uPAAAdfX1pMQoAJrhvwozazZGcwVk8RESUggUpIiIiIqJ8IG0xCgA6jB6Oi1vckZSQAJ1MrlU/u3YDYiOjlJ2e2vny2i9X/as0aYA3d70BAMXLl8WsM0fSxe+fOosj8/9Ej5myKwVWP74FPXEyqkS/gF3nmfjxIzRXuRARFUQ8Q4qIiIiISM1VbtxA7nsT8xLQEIuxzOuy3PjeaXPg51Wwz4vKzL2//s5x3zHb3KTP/y1GAUDDnl2hIRajUe/uMrEKRvEYUjkEbeuVwh2/w6jfvXOO8yAiKqhYkCIiIiIiUnNjt7vJfd/wt66wHe8gNxby5RuCPn9RZlpq7/6ps7nqbzthFKq3bZVhfPXjW+k+a4okaFc6Ej3LhcNAM2VrZMUiCRgysFmu8iAiKoi4ZY+IiIiISE2ZVyyPmX9nfGNc+9H2Gcb2Tlf8/KSCKikhAe6TnTDcdWWO+nccq/h17iX1EvCrZQRMdZJlYoN71sR0LU0kJCTK6UlEVDippCClo6ODevXqoWTJktDX18fff/+NiIgIVaRCRERERKSWdPT1My1GZYWHaqcI/fodLv3toWNggPG7Nub5+CJI0KhENBoVj4GGSDbuH64Nx0k7WIwiIvoPQQtSlpaWWLp0Kfr37w8tLS3p+xo1auDly5fSzyNGjMCYMWMQFhaGjh07CpkiEREREZFaGLF+VY775narWkEUFxUFl/720NTRRmJcPLR0dFClSUMUL1cGdTrn7N8cJtpJ+NUyAqX0ZYtN8UnA9W+GeB6ig/O7j+U2fSKiAkewglSDBg3g4eEBU1NTiET/+9GBvGtnT58+jY0bN0JLSwsdO3bEhQsXhEqTiIiIiEgtVGpYL0f9vr99j9uH/srjbAqOxLh4AEBCXBxeXLsJALix/wi0dHQw3n2TgqNIUMM0Fq1KRUFLzqm8X6I1cf6zEW6eu4F9M+bnUeZERAWLIIeaFylSBH///TfMzMzw7ds3jB8/HjVq1MiwfWBgIDw9PQEAXbp0ESJFIiIiIiK1IU6zmyC7Ds5ZnIeZFA7JSUmIi45WqK2+OBk9yoajvYVsMSoxMQlr1p9H36GbMb1Vb+yZOodbJ4mIMiBIQcrR0RHm5uYIDAxEkyZNsHXrVvj6+mba5+LFixCJRGjYsKFScipevDi6dOmCxYsXw8PDAz9//oREIoFEIoG7u3u2x7O1tcXx48fx6dMnxMbG4tOnTzh+/DhsbW0VHkNPTw/Tp0+Hl5cXgoKCEBERAV9fX6xevRplypRReJxq1aph8+bN8PPzQ3R0NH78+IHr169j9OjREIvF2f7aiIiIiEhYqx7eyHlnOTsQSDG3slhZVtEoDkMqh6BikQSZ2OvXn9G0yQzM/H0j7h4/jdBv35WVJhFRgSDIlr1u3bpBIpFg7dq1+PTpk0J9Xrx4AQCwsrJSSk4/fvzIs7G2bNmCMWPGpHtnaWkJS0tL9OrVC1u3bsXYsWMzHaNixYo4d+4crK2t0723sbGBjY0NHBwcYGdnJ105lpERI0Zg48aN0NXVlb7T09ND8eLF0bJlS9jb26Nr164IDg7O5ldJREREREIoUrxYpvE3d++jShP5P7Q947xBGSkVGg/P/QM9I0OUq1UdxcpYSt9raUjQqmQkapjFye23eZMHZszYheho+XEiIpIlyAqpypUrAwBu3FD8Jz2hoaEAUrb7KdvHjx/xzz//5Kjv0qVLpcWohw8fYsCAAWjQoAEGDBiAhw8fAgDGjBmDP/74I8MxDAwMcPbsWWkxatu2bWjbti2aNGmCOXPmICIiAiYmJjh27FimWx07duyIbdu2QVdXF9++fYOjoyMaNmyIX3/9FcePHwcANGnSBCdOnEh3jhcRERERqY8GPTI+suL63kM457oJx5askBv/+sZPWWkVCkkJCbix7zD2TZ8Hl/72cOlvj+vLFqKr4Ru5xajAoEh06bwIEyZsZjGKiCibBFkhpaenBwCIiopSuI+hoSEAIDY2Vik5LV68GN7e3vD29saPHz9Qrlw5vH//PltjWFlZYebMmQAAb29vtGzZUpqvj48PTp8+jevXr6NBgwZwcnKCu7s73r59KzPO9OnTYWNjAwCYMWMG1qxZI43du3cPV69exY0bN2BgYABXV1e0a9dOZgyxWIwNGzZALBYjLCwMzZo1SzfXP//8gw0bNmDChAlo1aoVBg8ejH379mXr6yUiIiIi5TIqaobOk+SvrHfpby99/vzilUw8LjoaUaFhykqt0NHU1MDvYzvAcXQ7iMWyP8c/efIuxozegMDAcBVkR0SU/wmyQurnz58AkK1zkOrVS7lV5OvXr0rJadGiRTh37lyutu5NmTIFWv9/4KSjo6NM8SwmJgaOjo4AAC0tLUyePFlmDE1NTUyaNAkA4OvrC2dnZ5k29+7dw86dOwEAbdu2Rd26dWXa/Pbbb9KVaMuXL5db+JoxY4Z0q96MGTMU/TKJiIiISElKVqqIQSsWoeuUCajZoQ0WXTuncF+3waMQ8uUbACA8MAhnnNcrK81Cp2L54jixfyImj+sgU4yKiIjGyBHr0LvXMhajiIhyQZCC1P379wEAnTp1Uqi9hoYGRo8eDYlEglu3bikztVzp0aMHAODly5fw8vKS28bLywuvXqX8BKtnz54y8datW8PU1BQAsGfPHkgyOIRy9+7d0udevXrJxNOOnbZtWjExMTh69CgAoEaNGqhUqZLcdkRERESU93QNDdBu1DB0GDsCuoYG0DU0wIyTB1C3iy3ajBiMYWuXZdh30/DxMu+SEhKwe8osuPS3x84J0/Dp+Utlpl9oDO7fBJ5/TUHtGmVlYrdv+6J2rd/h7n5JBZkRERUsghSkDh06BJFIhBEjRqB27dqZthWJRNiyZQuqVasGANi/f78AGWZfhQoVYGmZctDh9evXM22bGi9TpgzKly+fLtaiRQuZdvL4+PggMjISANC8eXOZeOo4r169wvfvGd/okXYOeeMQERERUd4qVq4MnJ/dxZ93L6Hz72Px64RR+PPuJYzZ5qZQ/4TYOMRFRys5SypezAi7N4/EsgW9oaennS6WkJCIeXP3oXWr2Xj3jrfnERHlBUHOkDpx4gTu3LmDpk2b4vLly5g/fz6OHTsmjUskEpQoUQIdO3bElClTUKtWLUgkEpw/fz7LYo+qpJ75BEC6AiojaeM2NjbpzqpSdJykpCT4+/ujVq1a6foAKYeipxbHsptLdlhYWGQaL1WqVLbGIyIiIiroNLW1MfvsUbmxsjWqKTTGkQV/5mVKJIdtu+pYubgvzEwNZGKvXn3GkMHOePDgXxVkRkRUcAlSkAJStpTduHED1tbWcHNzg5ubm3R72sOHD6Gt/b+fQohEIjx79gyDBg0SKr1sS3se1ufPnzNt++nTJ7n90n6OjIxEWFjmh1B++vQJtWrVQokSJaCtrY34+HgAgKWlJTQ0NHKdS1ayGpuIiIiI0lv5IHc/XPW7542fHz7mUTb0Xwb6Olg0uwf692ooN75xw1nMnLkbMTG8QY+IKK8JsmUPAIKCglC/fn1s3LgRcXFxEIlE0l86OjrS58TERGzbtg1NmzbNskCjSkZGRtLn1K10GUl7u2Dq7YH/HSerMTIbJ69yISIiIqK802ehU676b3FwxFmXjXmUDf1X/Trl8c+JqXKLUV+/BqPTrwvh6LiVxSgiIiURbIUUkHKo9u+//45FixbB1tYW9evXR4kSJSAWixEUFIRHjx7B09NTaTfr5SVdXV3pc+pKpYzExf3vLzE9PT2542Q1Rmbj5FUuWUndFpiRUqVKwdvbO1tjEhERERVE2np6aNKnZ67GiImIyJtkKB1NTQ1MHt8RExzaytygBwDHj9/B2DEbERTEG/SIhJK6e2rRokVYvHixirMhoQhakEoVHByMQ4cO4dChQ6qYPk/ExsZKn9NuN5RHR0dH+hwTEyN3nKzGyGycvMolKwEBAdlqT0RERFRYzfvnhKpTIDkqVSwB1+UDUbO67NEV4eHRmPT7NuzZc1kFmREBrVq1wrVr1+TGYmJiEBQUhGfPnuHMmTPYu3dvut0vRPmRYFv2CpqIND+xymrrm4HB/w5H/O+WutRxFNk+l9E4eZULEREREeVel8njYGBqkqsxHnlezJtkSGqYXTN4HJsitxh165Yvatf6ncUoUlt6enqwtLREp06dsGnTJjx//hw1a9ZUdVpEuaKSFVIFQdoDvrPaypb28PC0h4qnHcfQ0BDGxsaZnpuVOs6PHz/Sbc3Lq1yIiIiIKPfajhwq931wwFccnLMYE/dskYlFBAbh2OIVqFivNn5++ITPvpnfnEyKMy9eBKuX9kPr5tYysfj4BCxccBCrV59AcnKyCrIjkm/Tpk3YtGmT9HOxYsVQtWpVTJ06FVWrVkX58uXh6emJqlWrcqEB5VuCF6REIhGqVauGihUrwsjICGKxOMs++/btEyCz7PH19ZU+W1vL/uWWVtr4y5cvMx3Hy8tL7hhisRhWVlZyx4iKisLHjx9RtmzZXOVCRERERNlnaGYK2wmjULpKJZSvXSPDdnumzs4wtnvKbCTGx3NlVB7r1KEGVizqA1MTA5mYr+9HDB7kjMeP36ogM6LM/fjxAy9evEj37vr163B3d4enpyfatWuH0qVLY/To0Vi7dq2KsiTKHcEKUnp6epg3bx4cHBxQtGhRhftJJBK1LEi9e/cOAQEBsLCwQKtWrTJt27JlSwApK5nev3+fLnbr1i3pc6tWrTIsSNWvX1+6He/27dsy8Vu3bsHOzg7W1tYwNzfH9+/f5Y6TNld54xARERGR4oqVtcTsc8dyPU6iAhfckOIMDXSweE5P9O3ZQG7cbd1pzJq1B7Gx/H2n/CUhIQGLFi1Cu3btAAAdOnRgQYryLUHOkDIwMMD169fh5OSEYsWKQSQSZeuXuvr7778BADY2NmjUqJHcNo0aNYKNjU269mldu3YNoaGhAIBhw4ZlOJe9vb30+eTJkzLxU6dOyW2blp6eHvr16wcAePHiBfz8/DKcj4iIiKgg0dAUY+XDG3B+dhfOz+5CM81FL7kx6dBOhdq5DR4lfT62ZEW62KmVLnmSC6VoWLcCzp+YJrcY9eVLEGw7LsDkydtZjKJ86+HDh9LntEeypBKJRGjTpg1Wr16NW7du4efPn4iPj0dISAgePXqE1atXy+2X1tWrVyGRSHD16lUAQOnSpeHs7Aw/Pz9ER0cjMDAQ58+fx6+//qpQznZ2drh69SqCg4MRERGBZ8+eYdGiRTA2Nlb469bS0sK4ceNw5coV/PjxA3Fxcfj69SvOnTuHQYMGZVo7cHd3h0Qiwbt37wAA5ubmWL16NV6/fo2oqCh8/vwZR44cQbVq1dL1K1euHNatW4fXr18jOjoa3759w/79+1GxYkWF86aMCbJCat68eahXrx4A4N69e9i2bRuePHmC0NDQfL1X29XVFaNGjYKWlhbWr1+Pli1bprvxTldXF+vXrweQUsl2dXWVGSMhIQFubm5YsGABqlWrhunTp2PNmjXp2jRu3BgjR44EkFLA8vHxkRnn5MmT+Pfff1GpUiXMnj0bx44dw9u36Zcfr169GmZmZtJnIiIiosJixPrV0NTSkn5e6XMN02o0yfW4+kWKZNlm/dDRSEpIkH7+/OIV3Cc5wcyyNAJevkEcb8rKE1paYkyd0BHjRraBhobsz92PHr2F8eM2ITg4Qk5vovwjKSlJ+pyYmCgTX7BgARYtWiTz3sTEBLVr10bt2rUxbtw4DB48ON3Chow0a9YMp06dQrFixaTv9PT0YGtrC1tbW0yfPh3Ozs5y+4rFYhw+fBh9+vRJ97569eqoXr06Bg0ahA4dOmSZQ9myZeHp6SlTMCpZsiQ6d+6Mzp07Y8yYMejRowdCQkIyHatmzZo4f/48SpUqJX2nr6+Pfv36oUuXLrC1tcXt27fRpk0bnDhxAiYmJum+7kGDBqFTp05o0aJFuiN4KPsEKUj16dMHEokEHh4e6NGjByQSiRDTZqpZs2aoVKmS9HPa/3NVqlRJZrXSnj17ZMbw8/PDmjVrMHv2bDRo0AC3b9/GypUr4e/vDysrKzg5OaFu3boAUgpA//77r9xcVq9ejf79+6Nq1apYvXo1KlWqhMOHDyMmJgZt2rTBnDlzoKWlhejoaEyePFnuGImJifj9999x5swZGBsb4/bt21i6dCnu378PU1NTjBo1SvqHwM2bN9VyGyQRERFRXhJraqJSo/qICAyCTfPcF58AoGgZS8zxSNmi9+LarSxap0iMk12JE/rtO0K/yT9igbKvspU5XFcMRI1qshf8hIVFwXHiVuzff1UFmRVsIpEIRYsaqToNQQUFRaj837NpizL/PRIGADQ1NfHlyxecPHkSd+/exdu3bxEbG4syZcqgadOmGD9+PIyMjHDw4EHUrVsXr15lfIlCqVKlcPLkSSQlJcHJyQm3bt1CfHw8mjdvjgULFsDU1BTLly+Hp6en3OKMi4uL9N+hr169wqpVq/D06VMYGxujb9++GDVqFI4cOZLp12tgYIArV65Iz1Q+efIkdu3ahS9fvqBChQqYOHEiWrdujRYtWuDs2bNo0aJFhgtf9PX1cfLkSWhra2P27Nm4fv06kpKS8Ouvv2LOnDkwMDDAvn370KFDB5w8eRLh4eFYsGABvLy8oKmpid69e2Py5MkwMzPDzp070aRJ3vzdUlgJUpCysLAAALi5uan8/7ypHBwcMtza1rx5czRv3jzdO3kFKQCYO3cuSpQogZEjR6Ju3bpy/8+0Y8cOzJs3L8NcIiMj0aVLF3h4eKBKlSoYM2YMxowZk65NWFgYBg0ahCdPnmQ4jqenJ8aOHYsNGzagZMmS2LBhg0wbLy8v/Pbbb/l6ZRoRERFRVkQiEVY9uplpG+dndzNdJaVXpAgsq1XFNz9/RAQFQ1tPV1qMAoBfWjfPsC8JQyQSwd6uGWZP7QJdXS2Z+PXrzzFs6Fp8/PhTBdkVfEWLGuHHzwOqTkNQJYoPQmBguEpzmD59uvT5r7/+konv2LEDixcvllk99ejRI5w+fRrr16/HvXv3YGlpiTlz5mDoUPk3gwJA1apV8f79ezRr1gxfvnyRvvfx8YG3tzdu3LgBLS0tjB49WmbxRI0aNTB+/HgAwIMHD9CqVStEpVkReuXKFdy5cwd79+7N9OtduHChtBj1xx9/YMGCBdLYw4cPcfz4cezbtw+DBw9G06ZNMXr0aGzZInubKQCUKFECIpEIDRs2TLej6P79+/j58yc2bdqEChUq4M6dO/j+/TuaNWuGwMBAabs7d+4gMTERM2fOROPGjVG7dm08fvw40/wpY4KcIfXjxw8ASPdfZEEhkUjg4OCAzp0749SpUwgICEBcXBwCAgJw6tQpdOrUCaNGjcqyEOfv7486depg5syZ8Pb2RkhICKKiovDq1SusXbsWNWvWxLlz57LMZ8eOHahXrx62bdsGf39/xMTEIDAwEDdv3sTYsWPRrFkzBAUF5dWXT0RERKSWGvftqVA752d3UaR4MZn3JuYlsPT2Pxi73Q2Lrp2D87O7WH4/+ytsXPrbZ7sPKca8RBHs2+qAxXN6yhSj4uMT4DTTHe3azmUxigoEMzMzNGvWDOfOnYOdnR2AlOLI4cOHZdp++PBB7la+VAEBAdIjXLp3757l3I6OjumKUalu374tvZSrRYsWMvGxY8dCLBYDAEaPHp2uGJVq37598PDwyHBubW1tODg4AEg5B1neVkQAGD9+vLTeMHHixEy/nvnz58scbwOknDMVExMDIKVw5ejoKLeGsXnzZumzvK+bFCfICqn79+/D0tISVatWVZvq4fDhwzF8+PA8G8/T0xOenp65GiM6OhqrV6/O9flOL168kFlhRURERFSY9Jk/U+G2C6+cAQBc2rYbnuu3AgDmX5K9jEYRPmc8oaGhgcfnLyHsBwshytKlY00sX9QHJsb6MrHnzz9gyGBnPHnyTgWZEeWNRYsWZVh8SUhIwOHDhzFhwoRMC0+pjIyMULRoUejr60sP/o6OjgYAGBsbo0KFCtLDvv8rJCQk04URDx48QJMmTeQe8t2+fXsAwNOnT9MdxP5fu3btQufOneXG6tWrB1NTUwDA7t27M9zpExERgaNHj2L8+PH45ZdfULJkSXz79k2mXXJyMo4ePSp3jNjYWPj5+aFmzZoIDg7GhQsX5LZ7//49wsPDUaRIER5unkuCrJBycUm5OSSrSiURERERkaq0H20PM8vSuRrj5v4juL73EItRSmJkqAvX5QOx2WWo3GKUq8vfaFB/CotRVKC9efMGq1atQkRExgf0ly1bFm5ubnj37h3Cw8Px7t07vHjxAs+fP8fz58+xfft2adu05yn/l5+fX6a7fYKDgwGkFL3S0tHRkZ7Z7O3tnenXc//+/Qxj1atXlz6nrsbKSNp42n5pBQYGZnroeWhoKABkeP7zf9v99+um7BFkhdTdu3fh5OSEVatW4fDhwxgzZgzCwsKEmJqIiIiIChldQ4Mc953reRyLWnfJUd9tYybleF7KWsN6FeG6YiAsS5vKxD5/DsRwe1dcvpzxeauU94KCIlCi+CBVpyGooCBhbmnctGkTNm3aBCDlkPLSpUuje/fuGDFiBH755Rdcu3YNTZs2xZs3b2T6/vrrr/jrr79gYKDYn4V6enoZxlJXUmUkdcVS6ta8VCYmJtLbLlOP8MnI9+8ZX/CQekt8Vu0ApFsRlbZfWop+PTn9uil7BClIAYCzszP+/fdf7NixA58+fcLFixfx5s2bLP+LBlIOLiMiIiIiUsTi6xmfR6KIRdeyPrdTnqhQ/sBVGbS1xJjm+CvGDG8l/QduWocP38CE8ZsREhKpguwKN4lEovIDvguqHz9+4MWLF9LPT548gaenJ86cOYPTp0+jaNGiOHjwIBo2bJhuG5uZmRkOHjwIAwMDREREYM2aNfjnn3/g7++PsLAwJCQkAADatGmDK1euAIB0G19eSjtmXl1sltU4yvg6SLkEK0gVL14cvXr1grGxMTQ0NNCjRw+F+7IgRURERESK6D1/JjS1tQWfN0mBc1wo+6pYmWPdKjv8Ym0hEwsNjcTECVtw8OB1FWRGpBoeHh7YsmULJkyYgHr16sHe3h67du2Sxvv27Ss9c6lXr164dOmS3HFS2yhL2m1x5ubmmbbNLJ66JRAASpYsCT8/P4XGSduP1JcgZ0iZmZnhxo0bGDRoEMRiMUQiUbZ+EREREREpomm/3zKN75gwDdd25/019VscHPN8zMJMJBJh5JAWOHtsstxi1NWrT1Gr5u8sRlGhtHjxYkRGpqwIXLhwIbS0/nfL5C+//AIACAoKyrAYBQD169dXao5xcXHS7YQNGjTItG1m8efPn0ufGzVqlOk4DRs2lNuP1JcgBak5c+agSpUqEIlEOHbsGNq0aYOiRYtCLBYr9IuIiIiIKDcOz/sDboNHISIwCI88L8J1QPZvW44OD8eGoWPg0t8em0ZMwOUde3B6jRvWDxmN+P+/Kpxyr6S5MQ5sH4WFs3pAV0crXSwuLgEzpu9C+3bz8OkTD46nwunnz5/YujXlRtCyZcti2LBh0pimZsomKB0dnQwXd+jp6WHo0KFKzzO1IFazZk3Url07w3YjRozIMPbgwQPpaqthw4bJ3bYLAIaGhujXrx+AlFvn5d2wR+pHkIJU9+7dIZFIsG/fPgwYMAA3btyQnkpPRERERKRMd46cwFc/fyT9/9kpQMpZJG6DR+Htg8fwOeOZaf9Nw8fDpb89to76HQlxcQCAuKgoPL14Ff7eD5EYH6/U/AuTbp1q4+LJaWjepIpM7Nmz92jYYCqcnU/m2Zk0RPnV6tWrEfP/hfBZs2ZJCzWpW9oMDQ3Rp08fmX4aGhrYsWMHLCxkVx7mta1bt0rPt9q2bRv09WVvxrSzs0OXLhlfJBEfH48dO3YASLk5b+HChXLbbdiwAcWLF5c+U/4gSEEq9X/safe2EhERERHlJW09XbnvvU6clvs+KSEBf69yxc39RxAbGZXhuHEKXMJDuVPESBfrVtph45rBMDaW/UfrWueTaNhgKp49ey98ckRq6Pv379i5cycAwMrKCnZ2dgCAo0ePIjY2FgCwe/du/Pnnn2jTpg3q1auHoUOHwsvLC3Z2drh165bSc3z69Ck2btwIIGVbno+PD4YNG4a6deuiTZs22LRpE/bu3Qtvb+9Mx1myZAn8/f0BAAsWLMDx48fRpUsX1KlTB7169cKVK1ekq8Tu3LmDbdu2KfcLozwjyKHmgYGBsLCwQESEMFdkEhEREVHh09pe9vr5LaMUO9tpz9TZGLPNTeb93unzcp0XZa5JQyusXTYAFqVkD1n+9Okn7Ie54urVpyrIjEi9rVy5EqNGjYKOjg7mzJmDAwcOICAgAOPGjcOOHTugr6+POXPmYM6cOen6HT58GNu3b8fly5eVnuPUqVNRunRp9O7dGzY2Nti9e3e6+Nu3b9GvXz+8e/cuwzEiIyPRrl07eHp6wsbGBr169UKvXr1k2t26dQvdu3dPd+sgqTdBVkjdvHkTQMoSOyIiIiKivGBU1Ax9F85CjXatYF6xPGzHO8i0yWzlU1rRYeHS7XipXAYMR9Cnz3mSK8nS1hJj7vSuOLRzjNxi1MGD11GrpiOLUUQZ+Pz5M/bs2QMAsLGxQe/evQGkrIxq0aIFTp48iR8/fiA+Ph5fvnyBp6cn+vXrh4EDByIpKUmQHBMTE9GnTx8MHjxYenRPVFQUfH198eeff6JevXp4//59luN8+PABtWrVwoQJE3Dt2jUEBgYiPj4e3759g6enJwYPHoyWLVumu92P1J8IgNI3YNepUwd37tzBmzdv0LBhQ8T95y97KhgsLCzw+XPKN22WlpYICAhQcUZERERUEFWsXwdtRwyGTYumWbZ16W+frbHNLEojKTERYd9/5DA7UkTVyiXhttIONlVLy8RCQiIxYfxmHD58QwWZFSy5+f583bp1MDU1RUhICCZNmqSsFIkon1DGnwmCbNl79OgRHBwcsGPHDly4cAEODg7Sw9aIiIiIiBTl/OyuUscPDvii1PELO5FIBIehLTFzcifoaMv+U+Ty5ScYbu+Kz58DVZAdEREJSZCCVOpha76+vmjevDlevnyJJ0+e4M2bN4jO4pBIiUQCBwfZ5ddEREREVLiIMrjuOyMebluUlAnlRKmSxli7bCCaNaokE4uNjcec2Xuxbt1p3qBHRFRICFKQsre3l/7FIpFIIBKJUKtWLdSqVSvTfiKRiAUpIiIiokJOpKGBDqPtYTthVLb6vb59T0kZUXb16FIHS+f1gnERPZnYkyfvMHjQGrx48VEFmRERkaoIUpD6+PEjf9JBRERERNmmIRZj9ePsX0++6/eZSsiGssu4iB6Wzu+FHp3ryMSSk5PhvOYk5s/fj/j4RBVkR0REqiRIQapChQpCTENEREREBczKh9k/2NplwHCAPwxVuWaNKsH5zwEoXcpEJvbhww/YD3PB9evPhU+MiIjUgiAFKSIiIiIiRXSZPA5tRw7N3SAsRqmUjrYmZk7uhFHDWsmN79t3FY4TtyA8PPOzZImIqGBjQYqIiIiIVEqkoYH63TthwB/zstXvyYUrqNWxbbp3m0dOyMvUKJtsqpbCuhV2sK5SSiYWHByBcWM34dix7G/BJCKigkelBSmxWAxTU1MAQEhICJKSklSZDhEREVGBUapKJWhqa+PTc19Vp5KlAX/MQ/3unbLd78rOvbh/8gwG/rkAhmamOOO8HrGRUUrIkLKioSHCqGGtMP33X6GjLftPjIsXH2G4vSu+fAlWQXZERKSOBC9IWVtbY/z48Wjfvj0qV64MkUgEIOX2PT8/P1y8eBFbtmzBy5cvhU6NiIiIqECYd+EkTEuVlH7+Z+N23Dl6EpHBISrMKmM5KUadcd4AAIgMDsH2cVPyOiXKBotSpli7bACaNLSSicXGxmOW026sX3+WlxwREVE6ghakli1bhunTp0NDQ0NaiEolEolQtWpVVKlSBePGjcPq1asxd+5cIdMjIiIiyve0dHXSFaMAwHbCKNhOGAUAWN61HwI/fFJFalJGxYqi/x9zUaaaNXzOeGa7/6F5f+Cbn78SMqPs6tWtLpbM/Q1FjPRkYo8e+WPI4LXw9f2ogswor2hoaKg6BSJSA8r4s0CwgpSbmxvGjx8vLUS9fPkSXl5e+PbtG0QiEczNzdGwYUNUq1YNYrEYTk5OMDAwwOTJk4VKkYiIiCjfm3RwZ6bx2WePAgDWDXLAx6cvhEgpHU1tbSy6elb6ufUwO4X7Hl20HAEvXysjLcomY2M9LF/QB11/rSUTS05OxupVJ7BgwQEkJCSqIDvKC9HR0TA1NYW+vj40NDSQnJys6pSISEVEIhH09FJ+8BATE5Nn4wpSkGratCkmTJgAiUQCX19fjB49Gnfv3pXbtnHjxtiyZQtq1KiBiRMn4siRIxm2JSIiIqL0SlWW3TYlz6QDO/DXklUoWakCmtv1BQD89ccq3D16UpnpYcT6VTnqd3jeH/jKVVFqoXnjyli7bABKmhvLxN6//45hQ11w86bwxU7KW1+/foWFhQW0tLRgbW0NX1/1P4+OiJSjTJky0NbWBgD8+PEjz8YVZP3lmDFjAADv3r1Ds2bNMi0w3bt3Dy1btsTbt28BAGPHjhUiRSIiIqJCp8+CmdJiFAD0mT8T5WvXVOqcVZs2Uqjd8aWrpc8PPS6wGKUGdHQ0sXBWdxzcOUZuMWrPnsuoXet3FqMKCC8vL+nzr7/+KnPkChEVHk2aNJE+P336NM/GFWSFVIsWLSCRSLBixQqEh4dn2T48PBwrV67E1q1b0aJFCwEyJCIiIiIAcNy3FV7HT6NR7+4AgITYOMxq0FrQHK7tOYiPz17Apb+9oPNSxn6xLo11K+1QpVJJmVhQUDjGjtmI48fvqCAzUpbHjx8jISEBWlpaqF27NqZMmYLz58/j1atX3L5HVEjo6+ujTZs26Ny5s/Tds2fP8mx8QQpSJUum/MX16NEjhfs8fPgQAGBubq6UnIiIiIhIvtRiFJBySHrVZo3x+va9XI9raGaaZZuDsxfh+9v3uZ6L8oaGhghjR7TG1Im20NaS/afDP/88xIjh6/D1a7AKsiNliouLw4YNG/D7779DLBajdu3aqF27NhISEhAdHc2iFFEBJxaLYWRklG515PHjx/H9+/c8m0OQglRsbCy0tbVhYGCgcB9DQ0MAKX8QEhEREVHWNMRimXdf/fwVPlcqI6O3uGBajSZZN8zC4usemcZdBwyHRCLJ9TyUNyxLm8Jl+UA0ql9RJhYTE4eZM9yxceM5FWRGQnn06BHc3NwwceJEaGlpAQC0tLRgbCy7ZZOICrZbt27h77//ztMxBSlIvXv3DrVq1UL37t1x8+ZNhfp069YNAKRnSRERERGRfIZFTbH4mvxiz4MznvDz8sGUI7vzfN7i5cvCrHQp+N33QXJiUq7HYzFKffTpUR+L5/SEkaGuTOzhQ38MHrQGr159VkFmJLRHjx5h/PjxqF27Nho1aoRSpUpBX19f1WkRkZJJJBJERkbi9evXuHr1Kj5/zvs/8wUpSHl4eKB27dqYOHEiPD09ceXKlUzbt23bFo6OjpBIJPDwyPwnaURERESFWTuHYeg8KeNLYN4+fAIAcOlvD6NiReGw0TlH82jq6CAxzcr1JTc8YWBqkq5NblZR7Zw4Pcd9Ke+YGOtjxaI+6NxR9nD7pKQkrFp5HIsWHUJCQqIKsiNViYuLg5eXV7qDzomIckuQW/ZcXV0RHh4OLS0teHp6YsOGDahbt266vYgikQh169bFxo0b4enpCS0tLYSHh8PV1VWIFImIiIjyHdPSJTMtRgFAUkKC9DkiMAin17jlaK6VPtfQoGcX6Ojrw3bCKJliFAA4P8v4JuU6nTtmGDuxzBnhPwNzlBflnZbNquDiqWlyi1Hv3n1H61ZzMHfuPhajiIgoT4gACLI2ukOHDjh9+jS0tbWly7Hj4+MRHBwMiUSCokWLQltbOyUpkQjx8fHo2rUrLl++LER6lAcsLCyky/gsLS0REBCg4oyIiIgKtswKQKnk3VT3S+sW6DhupBIySjGzTgvYtGyK2Mgo/Hv/AQD5uR5duAwBr94oLQ9SjK6uFmZP7YLhg5rLjbvvuojJk7cjIiJG4Mwot/j9ORGpM0G27AHAxYsX0bhxY2zbtg3169cHAOjo6KBUqVIybb29vTF69Gg8ffpUqPSIiIiIChx5xSgAeHHtJoyKmaFJ39+UMu+qR+nPDPX3kX/TcsBrP6XMT4qrbmOBdSvtUNlK9mbrwMBwjBm9ASdPZl34JCIiyi7BClIA8OTJEzRq1Aj169dH+/btUb16dZiZmQEAgoOD8fz5c1y6dAk+Pj5CpkVERERU6Nz762/c+yv9bTm6hgYYt3Njns9lVb+O/AAPMVcZDQ0Rxo1sg6kTbKGlJXs7o6fnA4wcsQ7fvoWoIDsiIioMBC1IpfLx8WHRiYiIiCiHipQoDoeNazJts8XBMdvjxkZGSVdVKeNWvrTuHjup1PEpY2UtzeCyfCAa1K0gE4uOjsOM6buweTMvFiIiIuVSSUGKiIiIiHJG18gQCy+flhvz93mE06vX5ck8e6fPxdA1f+bJWPI8v3oz60aU5/r2bIDFc3rA0EBXJubj44chg9fi9eu8v9qbiIjovwS5ZY+IiIiI8safdy5mGPNYtznP5gn58k3htu6TnbI9fmRQcLb7UM6Zmuhjq+swOP/ZX6YYlZSUhKV/HEbTJjNYjCIiIsEIUpCqXbs2EhMTER0djdKlS2fZvnTp0oiJiUFCQgJsbGwEyJCIiIgo/0uMj8+zsZKTkmTeebhtkXn35bUfQr9+z9bYj89fynFelH2tm1vj4qnp6NShhkzM3/8rWraYhQULDiAxUfa/cyIiImURpCDVv39/iEQinD17Fl++fMmy/ZcvX3D69GloaGhgwIABAmRIRERElL89u3w9z8d0HTAc946fxsdnL3Bg1kK8vn0PLv3tcXHrLgDAnaMncGRByra+4C9fFR73qvv+PM+VZOnqauGPeb9h71YHlCheRCa+c8cF1Kk9CXfvvlJBdkREVNgJcoZU69atIZFI4OnpqXCfc+fOoU+fPmjfvj0WLlyoxOyIiIiI8od6XX/NMKaMIo9EIsHdoydk3j+/cgPPr9xI927PlNkKHYQeFRqWV+lRJmpWL4N1KwbCqkIJmdjPn2EYPWoD/v77ngoyIyIiSiFIQapMmTIAAF9fX4X7vH79GgBgaWmplJyIiIiI8hu75bI/pPv+9j2OLlqGpIQEFWSUnsuA4TCvWB4x4REI/xkIk1LmKFbGEmYWpdFsQG98fO6LUytcVJ1mgSYWa2DCqLaYNLYDtLTEMvGzZ70xysEN37+HCp8cERFRGoIUpIoWLQoAiI2NVbhPXFwcAKBECdmf6hAREREVNmYWpeS+Pzh7kbCJZEYiwXf/d9KPoV+////5Ug9w/+QZ1eVVSJQrUxQuyweifp3yMrGoqFhMm7oT27adFz4xIiIiOQQ5QyokJAQAULZsWYX7pK6MCg8PV0pORERERPmFtp4u5p6X3TpHlGpA74Y4f2Kq3GLU/ftvULfOJBajiIhIrQhSkErdqte9e3eF+/z2228A/rd1j4iIiKiwWn7/qqpTIDVlZmqA7W72WLWkHwz0ddLFEhOTsGTxITRvNhN+fllfLERERCQkQQpSHh4eEIlEGDp0KJo3b55l+xYtWmDIkCGQSCQ4e/asABkSERERqZdGvbvD+dldOD+7m2GbO0e4aqowa9vSBhdPTYdtu+oysX///YIWzZ2waNFBJCYmqSA7IiKizAlSkNq6dSsCAwMhFovh4eGBiRMnQkdHR6adjo4OHB0dce7cOYjFYoSEhGDz5s1CpEhERESkFvSKFEGpKlbot2h2lm29TpwWICNSN3p62li2oDd2bx6J4sWMZOLbtp5HndqT4OXFnQZERKS+BDnUPCoqCnZ2dvDw8IC+vj5cXV2xbNky+Pj44OvXr5BIJChdujTq168PfX19iEQiJCQkYODAgYiIiBAiRSIiIiKV0itSBEtv/6Nw+92TZykxG1JXtWqUwboVdqhYvrhM7MePUDiMdMPZs94qyIyIiCh7BClIAcDly5dha2uL/fv3o1SpUjAwMEDLli3TtRGJRACAgIAADBkyBNevXxcqPSIiIiJBaenqwG7ZQsREROKfTdux4JLiq51c+tsrLzFSS2KxBhxHt8PvY9tDU1MsEz992gujHNbj588wFWRHRESUfYIVpADg2rVrsLKywtChQ9GlSxfUqVMHxYoVAwAEBgbi4cOHOHPmDPbv34/4+HghUyMiIiISjK6RIf68c1H6uVGvbirMhtRd+bLF4LpiIOrWKicTi4qKxZTJ27FjxwUVZEZERJRzghakACAuLg7bt2/H9u3bhZ6aiIiISC0ocj5URm7sP5KHmZC6s+vbCAtm9oC+vrZM7N69Vxg6ZC3+/ferCjIjIiLKHcELUkRERESFXa2ObXPU7+OzF3hwxjOPsyF1VKyoIVYt6Yf2ravJxBITk/DHksNYtuwokpKSVZAdERFR7glSkHr79i2Sk5Nha2sLf39/hfqUKVMG165dg0QiQaVKlZScIREREZH6SkxIwN8rXfDxma+qUyEBtG9dDauW9EOxooYysTdvAjBksDO8vf1UkBkREVHeEaQgVa5cOUgkEmhryy41zoiWlhbKly8PiUSixMyIiIiIhOX87G622p9YtgYfnjxXUjakTvT1tbFgZnfY9W0sN75lswemT9+F6Og4gTMjIiLKe9yyR0RERCQQk5LmCre9tucgHnnwoOrCok7NsnBdMRAVyhWXiX3/HoKRI9zg4eGjgsyIiIiUQ20LUsbGxgCA6OhoFWdCRERElHOW1apiypHdCrcP/fYD7pNmKi8hUiuamhr4fUx7TBzdDpqaYpn4qVP3MHrUegQGhqsgOyIiIuVR24LU4MGDAQAfPnxQcSZEREREOdN25FB0mTxO4fZ3jpyA14nTSsyI1EmFcsWwbqUdatcoKxOLjIzB5EnbsWvXRRVkRkREpHxKKUhdvnxZ7nt3d3dERUVl2ldHRwcVK1ZEiRIlIJFIcOECl6oTERFR/qOpra1QMeroomUIePlGgIxInQzu1wTzZ3aDnp7sGat37rzE0CFr8fbtNxVkRkREJAylFKRat24NiUQCkUgkfScSidCgQYNsjfP27VssX748r9MjIiIiUiotXR2s8L6mUFsWowqX4sWMsGpxX7RrXU0mlpCQiCWLD2HFir+QlJSsguyIiIiEo5SC1I0bN9LdjteqVStIJBI8ePAg0xVSEokEsbGx+Pr1K+7cuYPDhw/zDCkiIiLKV0QikcLFqG//vlVuMqRWOrT5BauW9EVRM0OZ2KtXnzFksDMePPhXBZkREREJTykFqTZt2qT7nJSUBACwt7fHy5cvlTElERERkcqVrfkLJh3YoXB7kVhDidmQujDQ18ECp+4Y2KeR3PimjecwY4Y7YmLiBM6MiIhIdQQ51Hzv3r2QSCQICQkRYjoiIiIilchOMQoA/lq8UkmZkLqoV7scXJcPRLmyxWRiX78GY+QIN5w//0AFmREREamWIAWp4cOHCzENERERkcoUK2uZaXyX4wyMWL9a+vn1HS/Ex8QoOy1SEU1NDUwe3xETHNpCLGcl3PHjdzB2zEYEBYWrIDsiIiLVE6QgRURERFQQaevpYvn9q1m22zLKETHhEXDpb6/8pEjlrCoUx7oVdqhZvYxMLCIiGr87bsOePfJvpSYiIiosBClIlSkj+5dxdnz69CmPMiEiIiLKPZOS5ihevizGbndTqH1MeISSMyJ1MXRgU8yd1hV6etoysVu3fDFs6Fq8e/ddBZkRERGpF0EKUu/evctxX4lEAi0trTzMhoiIiCjn6nfvjIF/zle4/Z6ps5WYDamLEsWMsHppf7RpYS0TS0hIxKKFB7Fy5XEkJyerIDsiIiL1I0hBSiQSCTENERERkVJpiMXZKkZ9fO6L4ICvSsyI1MGv7atjxaK+MDM1kIn5+n7EkMFr8eiRvwoyIyIiUl9qc6i5gYEBqlatit69e6N06dK4c+cOtm/fLkB2RERERFlT9LyoVK4DR0DC1TAFmqGBDhbN7ol+vzWQG1/vdgZOTrsRGxsvcGZERETqT5CC1N69exVuO336dLi5uWH06NG4c+cOnJyclJgZERERUeZ0jQwx68wRGBU1U7jPxa3uLEYVcA3qlofLcjuUtZT938WXL0EYMXwdLlx4pILMiIiI8ge1u2UvMTER48ePh7W1NaZNm4bLly/jwoULqk6LiIiICqk/71zMVvvdk2ch5Os3JWVDqqalJcaU8R0xbmQbiMUaMvFjx25h3NhNCA7mQfZERESZUbuCVKrNmzejVatWcHR0ZEGKiIiIVEJDLFao3Y/3H3HAaYGSsyFVq1SxBNatsEONXyxlYuHh0XCcuAX79im+rZOIiKgwU9uClJ+fHwCgfv36Ks6EiIiICqvVj29l2WbH+KmICAoWIBtSFZFIhGEDm2LOtK7Q1ZW9/fnGjecYNtQFHz78UEF2RERE+ZPsOmM1YWxsnO4/1Z2WlhZGjBgBT09PfPnyBbGxsYiIiMCrV6+wc+dONGrUSKFxbG1tcfz4cXz69AmxsbH49OkTjh8/DltbW4Vz0dPTw/Tp0+Hl5YWgoCBERETA19cXq1evRpkyZXL6JRIRERUq3aY5ZhrfO30eXPrbsxhVwJkXL4K9Wx2wZO5vMsWo+PgEzHLajbZt5rIYRURElE0iABJVJyHPrl27MGzYMLx//x5WVlaqTidTlpaWOHfuHGrWrJlpu7Vr12LatGkZxrds2YIxY8ZkGN+6dSvGjh2b6RwVK1bEuXPnYG1tLTceGhoKOzs7eHp6ZjpOTlhYWODz588AUn5PAgIC8nwOIiIiZei/ZC4a/tYVh+cvhfepcwAA52d3M2y/acR4xEVFC5UeqUjnjjWxfGFvmJoYyMRevPiIIYOd8fjxWxVkRqQYfn9OROpM7VZIVapUCZs3b8awYcMgkUjg4eGh6pQyJRaL0xWjnjx5gmHDhqFx48bo0KEDFi9ejMjISADA1KlTMX36dLnjLF26VFqMevjwIQYMGIAGDRpgwIABePjwIQBgzJgx+OOPPzLMxcDAAGfPnpUWo7Zt24a2bduiSZMmmDNnDiIiImBiYoJjx46hRo0aefZ7QERElF/pFTGC87O7aPhbVwDAgD/mwfnZXQxasSjDPrsnz2IxqoAzMtTF2mUDsMVlqNxi1DrXv9Gg/hQWo4iIiHJBkBVS/v7+WbbR0NCAiYkJjIyMAKTs1f/+/Tvq1q2Lb9/U96aaXr164fjx4wCAO3fuoEWLFkj+zzXPdevWxd27d6GtrY3g4GCUKFECSUlJ0riVlRVevnwJLS0teHt7o2XLloiNjZXG9fT0cP36dTRo0AAJCQmwtrbG27ey3wAtXLgQixYtAgDMmDEDa9asSRdv3Lgxbty4AS0tLVy5cgXt2rXLq98GAPwJDBER5R8GJsZYcvN8tvv5nPbEzQNHlJARqYuG9SrCZfkAlLEwk4kFBARhuL0rLl16LHxiRDnA78+JSJ0JskKqfPnyWf4qW7YsihQpApFIBJFIhHv37qF169ZqXYwCgGbNmkmfly9fLlOMAlJWPJ09exYAYGZmJrOdbsqUKdDSSjmTwNHRMV0xCgBiYmLg6JhyjoWWlhYmT54sM4empiYmTZoEAPD19YWzs7NMm3v37mHnzp0AgLZt26Ju3bqKfplERET5VoW6teD87C6cn91F95mT8EubFjkqRh1bvILFqAJMW0uM2VO74OjusXKLUUeO3ETNGhNZjCIiIsojgtyyt2fPnizbJCcnIyIiAu/evcP169fx5MkTATLLPW1tbemzvFVLqdKuEtPR0UkX69GjBwDg5cuX8PLyktvfy8sLr169grW1NXr27Inff/89Xbx169YwNTUFkPL7LZHIX/i2e/du6TlUvXr1km4HJCIiKoi09fQwcc8W6edWQwag1ZABORrrs++rvEqL1EwVK3OsW2WHX6wtZGJhYVGYOGELDhy4JnxiREREBZggBakRI0YIMY1KvHnzRvpcsWJF+Pr6ym2XejB7cnIy/Pz8pO8rVKgAS0tLAMD169cznev69euwtrZGmTJlUL58ebx//14aa9GiRbp2GfHx8UFkZCQMDQ3RvHnzTOcjIiLK75bfv5In4/jeuJ0n45B6EYlEGD6oGWZN7QJdHS2Z+LVrz2A/zAUfP/5UQXZEREQFm9odap7fHDp0CGFhYQAAJycnaGjI/pbWrl0bXbp0AQAcPnwYERER0piNjY30+dWrzH/ymjaetl92xklKSpKu1vrvGFmxsLDI9FepUqWyNR4REVF+cXP/UVWnQHmspLkx9m8bhUWze8oUo+LjEzBzxi60bzePxSgiIiIlEWSFVEEWGBgIe3t7HDhwAM2bN4e3tzdcXV3x5s0bGBoaolmzZpg2bRp0dHTw6NEjTJ06NV3/MmXKSJ9TDxzMyKdPn+T2S/s5MjJSWiDLbJxatWqhRIkS0NbWRnx8vEJfa1b5ERERqZqmtjaqt2mBL2/+ReDH7P29dXTRcvRbNFtuLDqLv1spf+n2ay0sW9Abxsb6MrHnzz9g8KA1ePr0vfCJERERFSIqKUhpamqibt26qF69OszMUg6NDA4OxvPnz/Hw4UMkJiaqIq0cO3XqFOrXr4+pU6dixIgR2Lt3b7r4t2/fsHDhQmzbtg3R0emviU69VRBIKSZlJioqSvpsaGgod5ysxpA3TnBwcJZ9iIiI1F2R4sWw8MqZHPU9sWwNAl6+xoZhY9OdOQUALv3t8yA7UgdFjHSxZO5v6NWtntz4WueTmDt3H+LiEgTOjIiIqPARtCClp6eH+fPnY9SoUdIDuP8rJCQE27Ztw9KlSxETEyNkejmmqakJOzs7dOvWTe6WvZIlS2LgwIF48+YNPDw80sV0dXWlz1mtVIqLi5M+6+npyR1HkdVOmY2TmdSzrjJSqlQpeHt7KzweERFRbhQrVwZxUdHoNWcaanZok6Mx7v31N+4eOyn9nBAbiz1T56BWx7aICArGw3P/5FW6pGKNG1jBZfkAWJSS/R7006efsB/miqtXn6ogMyIiosJJsIJUmTJlcOnSJVhZWUEkEmXYzszMDE5OTujduzfatWuHgIAAoVLMEX19fXh4eKBVq1ZITEzEypUr4e7ujrdv30JXVxeNGjXCggUL0KJFC5w5cwZTpkyBm5ubtH9sbKz0Oe2NffKkvZ3vv8W61HGyGiOrcTKj7v9dEBFR4eH87G6O+h1dtAzWzZvAuHhx+JzxxMdnL2TaBAd8wVX3/blNkdSEtpYY03/vhNH2LeX+4PDgweuYOGEzQkOj5PQmIiIiZRGkIKWpqQlPT09UqlQJQMqh2+7u7vDy8sK3b98gEolgbm6Ohg0bwt7eHtWqVUPlypXh6emJOnXqICkpSYg0c2Tx4sVo1aoVAGDkyJHptuslJCTg0qVLuHr1Ki5cuIC2bdti7dq1uHr1Kp49ewYA6Q44/+82vP8yMDCQPv93a17qOFmNkdU4RERE6qpp/17oPW9GrsYIePkGAS/fZN2QCoSqlUti3Uo7VKtaWiYWGhqJCeO34NChzG85JiIiIuUQ5JY9BwcH2NjYQCKR4M8//0T16tWxZs0a3Lx5E35+fnjz5g1u3rwJZ2dn1KxZE0uXLgUAVKtWDQ4ODkKkmGPDhw8HALx+/Vrm7KhUSUlJmD9/PgBALBZL+wDpDwrPaktc2oPM0x5wnnYcQ0NDGBsbKzTOjx8/FD7QnIiISJXaDB+U62IUFR4ikQgOw1ri7NHJcotRV648Qc0ajixGERERqZAgBam+fftCIpHg1KlTWLBgASQSSYZtJRIJFi5ciJMnT0IkEqFv375CpJgj5ubmKFq0KADg0aNHmbZ98OCB9Nna2lr67OvrK/e9PGnjL1++TBdTdByxWAwrKyu5YxAREamjyo0boOvUibkeZ+/0uXmQDam7UiWNcXDHaCyY2R062uk3A8TFJWDa1B3o0H4+Pn8OVFGGREREBAhUkKpevToAYNeuXQr32blzJwCgRo0aSskpL6S9DVBTM/Pdj1paWnL7vXv3Tno2U+rWv4y0bNkSQMpqqPfv36eL3bp1S/qc2Tj169eXbuu7fft2pvMRERGpg7Hb3bJupICgTzwLsaDr3rk2LpyYhmaNK8vEnj59hwb1p8DF5e9MfzhKREREwhCkIJW6hezLly8K9/n69SsAoEiRIkrJKS8EBwcjLCwMANCkSROIxeIM26YtEr179y5d7O+//wYA2NjYoFGjRnL7N2rUCDY2Nunap3Xt2jWEhoYCAIYNG5ZhHvb29tLnkydPZtiOiIiIKL8wLqIHt1V22LB6MIyN9dPFkpOTsWb1CTRsMBXPn39QUYZERET0X4IUpIKDgwEAFSpUULhPxYoV0/VVRxKJBOfOnQMAWFhYYO5c+VsBTExMsHLlSunns2fPpou7uroiISEBALB+/Xro6uqmi+vq6mL9+vUAUg5Kd3V1lZkjISFBentftWrVMH36dJk2jRs3xsiRIwGkFLB8fHwU+TKJiIjU2pfXfrh54CjuHDmB+6fOyr0h7+CcxSrIjITQtFEl/HNiGnp2qSsT+/jxJ9q1nYuZM90RH58opzcRERGpigiA0tcsnz17Fp06dcLVq1fRvn17hfpcvnwZrVq1wvnz59G1a1clZ5hzVatWxYMHD6Q3150+fRp79uzB27dvoauri8aNG2Py5MkoV64cAODSpUvo0KGDzDjLli3D7NmzAQAPHz7EypUr4e/vDysrKzg5OaFu3brSdhkVvgwNDeHj44OqVasCALZu3YrDhw8jJiYGbdq0wZw5c2BkZITo6Gg0bdoUT548ydPfCwsLC+nh6paWltKtiERERDlVtkY1TDq4M9M2Lv3tZd6JRCLUsm2HstWr4Yr7fkQGqe8PuChndLQ1MXNyJ4waJv+ogv37r8Jx4laEhUUJnBmR+uD350SkzgQpSA0aNAh79+6FRCLBnj174OjoiOjoaLlt9fX1sX79etjb20MikWDIkCE4dOiQslPMlXbt2uHQoUMoXrx4pu0uX76MPn36SLfWpSUSibB9+3bpCiZ5duzYgdGjR2d67oGVlRU8PDxQpUoVufGwsDAMGjRIurIrL/EvPCIiyktmlqUx1/N4hvHPL1/j1AoXJMTGCpgVqQPrKqXgttIO1lVKycRCQiIxbuxGHD16S05PosKF358TkToTpCAFADdv3kTTpk0hkUgQGBiIo0ePwsvLC9+/f4dEIkHJkiXRqFEj9O3bF8WLF4dIJMKtW7eyPOhbXZiZmWHkyJHo1KkTfvnlF5iYmCAxMRHfvn2Dt7c3Dh48iNOnT2c5TqdOnTB69Gg0aNAAxYoVQ2BgILy9vbF161acP39eoVz09fUxYcIE9O3bF5UqVYK2tjY+ffoEDw8PrFu3Dh8/fsztlysX/8IjIqK85Pzsrtz35zduw8sbdwTOhtSBSCTCqGEtMWNSJ5kb9ADg0qXHGG7vioCAIBVkR6R++P05EakzwQpSJiYmOHfuHBo3bgwAGa7yEYlEAIC7d++ia9euclcTkXriX3hERJRXVj+5DQ0N+UddytuiRwVf6VImWLtsAJo2rCQTi42Nx+xZe+DmdoY36BGlwe/PiUidCXKoOQCEhoaiefPmcHR0xMuXLyESieT+evnyJSZOnIgWLVqwGEVERFRIZVSMosKpZ5c6+OfENLnFqMeP36JB/SlYt+40i1FERET5iOxaZyWSSCTYtGkTNm3ahJIlS6J69eowMzMDkHKb3vPnz/Ht2zchUyIiIqJ8xH2Sk6pTIAEZG+th2fze6NaptkwsOTkZa1afwIIFB3iDHhERUT4kaEEqrW/fvrH4RERERDKKly+bYSz023cBMyFVat64Mpz/7I9SJU1kYu/ff8ewoS64efOF8IkRERFRnlBZQYqIiIjovzI6yBwAXAeOEDATUhUdHU3MmtIZI4e0lBvfu/cKfnfcivBw+Tc2ExERUf7AghQRERGphWnH92UYWz9kNCTJyQJmQ6pQzbo03FbaoUqlkjKx4OAIjB2zEX/9dVsFmREREVFeY0GKiIiI1ELpKrIHVqdKjI8XMBMSmoaGCGOGt8Y0R1toa8l+e3rhwiOMGO6KL1+ChU+OiIiIlIIFKSIiIiJSGcvSpnBZPhCN6leUicXExMFp5m5s3HiON+gREREVMCxIERERkUoVKVEcCy+fVnUapAK9u9fDkrm/wchQVyb28KE/hgx2xsuXn1SQGRERESkbC1JERESkMjoG+lkWo4ICvgiUDQnFxFgfyxf2RhfbWjKx5ORkrFzxFxYtOoSEhEQVZEdERERCYEGKiIiIVGbZvcuZxoM+BWDvjHkCZUNCaNG0Ctb+2R/mJYxlYu/efcfQIWtx+7avCjIjIiIiIbEgRUREREpjWrokxFpaCP78BclJSeliJuYlMu3r0t9eiZmR0HR0NDFnahcMH9xCbtzd/RImT9qGiIgYgTMjIiIiVWBBioiIiJTC+dldue/jY2Kxb8Z8jNywOsO+UaFhykqLVKC6jQXWrbRDZStzmVhgYDjGjtmIEyfuqCAzIiIiUhUWpIiIiCjPmVtVyDCmraebaTEKAA7NXZLXKZEKaGiIMG5EG0ydaAstLbFM3NPzAUaOWIdv30JUkB0RERGpEgtSRERElKeKlSuDmacO5mqMiMCgPMqGVKWMhRlclg9Aw3oVZWIxMXGYMX0XNm3yUEFmREREpA7ytCC1c+fOvBwOACCRSODg4JDn4xIREVHWRBoaMLMojdBv35GUkKBQn9lnj+Z4vuAvX7F36pwc9yf10KdnfSye3RNGhroyMR8fPwwZvBavX39WQWZERESkLvK0IGVvbw+JRJJn44lEIhakiIiIVMTMohTmnj+R7t3HZ75YZzdSKfPxEPP8z9REH8sX9UHnDjVlYklJSVix/C8sWXIYCQmJKsiOiIiI1EmeFqQ+fvyYpwUpIiIiEk6FurUwcc8WAIDPaU/U795Jpk3ZGtXQd+EsHFu8Ik/n3jFhWp6OR8Jr3dwaa5b2Q4niRWRi/v5fMWyoC+7ceamCzIiIiEgd5WlBqkKFjA8wJSIiIvWWWowCILcYlapxnx4ZFqSqt22Zo7l5ZlT+paurhbnTu2LYwGZy47t2XsDkyTsQGRkjcGZERESkzjRUnQARERGpXrFyZbLVvt9i+ec8DV+3Uu77fTPnZziWsrYAkvLVqGYBj2NT5Bajfv4Mw289/4SDw3oWo4iIiEgGb9kjIiIi1GzfOlvtG/XqhuN/rEJS4v/OAjItVVJu29SzodbZjYSuoQEa9OiCul1scefoCdw/cYbb/fMhsVgD40a2wZTxHaGlJZaJe3j4YOSIdfj+PVT45IiIiChfYEGKiIiIYGFTNdt9Vj26iZXdB+DHuw8oU70aJh/K/Lbd5KQkRIeF4/reQ7i+91BOUyUVK1emKFyWD0D9OrJHNURHx2H6tJ3YssVTBZkRERFRfsKCFBERUSH3q+No1LZtl6O+TqcPI/Tbd5iUNJcbv7TNPTepkZrp36shFs7qDkMDXZnY/ftvMHTIWrx5E6CCzIiIiCi/EbQgpaWlhUGDBqFnz56oVasWihUrBj09vUz7SCQSaGlpCZQhERFR4SLW1ESH0cNzNUZGxSgAeHb5eq7GJvVgZmqAFYv64Nf2NWRiSUlJ+HPpUSxdegSJiUkqyI6IiIjyI8EKUpUrV8apU6dQtWpViEQioaYlIiKi//frxNHoMCal+OTv8wi7J8/CH7f+UaivS397TDmyO1vz+Xs/zG6KpIbatLDG6qX9UKJYEZnYv/9+wZDBa+Hl9VoFmREREVF+JkhBSl9fH56enqhQoQKSk5Px999/4+fPnxg1ahQkEgmWLl0KU1NT1K9fH40bN4ZEIsHdu3dx8eJFIdIjIiIq8LT1dKXFKACwql9H4WLUlV37cjRnXHR0jvqRetDT08a86V0xZEBTufEd2//BlCk7EBUVK3BmREREVBAIUpAaO3YsKlSogKSkJNja2uLq1auoVq0aRo0aBQBYtGiRtG2tWrWwf/9+NG7cGIcPH8bGjRuFSJGIiKjA0hCLsfz+1Wz1celvDw2xGBKJBJLkZADA2wePUbFebYXH4Ha9/KtWjTJYt8IOFcsXl4n9+BGKUQ7rcebMfRVkRkRERAWFhhCTdOvWDRKJBEePHsXVq5l/Q/zkyRO0adMGP378wNq1a1G3bl0hUiQiIiqwJuzZnKN+yUlJ0mIUAPy9yhWXt+9RuP+X1345mpdURyzWwKRxHXBy/0S5xagzZ+6jZg1HFqOIiIgo1wQpSFWrVg0AcPLkSYXaBwYGYu3atdDU1MTEiROVmRoREVGBVqVJA5SvJXsQdWZOLHPOMPb00lX8s2lHpv3POK+HS3/7bM1JqleubFH8tXc8pk20haamOF0sKioWY0ZvQI/uf+DHj1DVJEhEREQFiiBb9kxMTAAAHz58kL6Li4uTPhsaGiIyMjJdn9u3bwMAWrVqpfwEiYiICqhha5dnq/21PQfx4cmzTNv4Xr+FD0+fY/QWV5mY2yAHJCUmZmtOUr2BfRphgVN3GOjryMTu3XuFoUPW4t9/v6ogMyIiIiqoBFkhFf3/h5pKJBLpu9DQUOlz2bJlZfqkti1ZsqRykyMiIiqgNHV0oGtokK0+jzwuKNQuKiQULgOGp3sXHR7OYlQ+U9TMEDvW22Pl4r4yxajExCQsWngALZo7sRhFREREeU6QFVLv3r1DzZo1Ubp0aTx48AAAEBQUhODgYJiamqJZs2bw9fVN16devXoAgPj4eCFSJCIiKnC6Thmfrfbuk5yyN4FEApf+9jAtVRLxsbGICgnNXn9Sqfatq2HVkn4oVtRQJubn9wVDBjvj/v03KsiMiIiICgNBClI+Pj6oWbMm6tevjzNnzkjfX758GX379sWMGTNw/PhxBAcHAwDKlSsHJycnSCQSPH78WIgUiYiIChSxpiZaDOonNxYXHYMd46fA0sYa3/zfITosLFdzhXz9lqv+JCx9fW3Mn9ENg/o1kRvfusUT06btRHR0nNw4ERERUV4QZMvexYsXIRKJ0L1793Tv3dzcAAAVK1bEmzdvcPToUZw9exZPnjyBpaUlAGDbtm1CpEhERFQgiLW00KBnF6x6dDPDNpuGj0N8TCzePnyc62IU5S+1a5SF519T5Bajvn8PQbeuSzBu3CYWo4iIiEjpBFkhdfbsWdy4cQNisRgVK1bE27dvAQB37tzBkiVLsGDBApiamqJXr14AAJFIBABwd3fHoUOHhEiRiIioQJjjcQwmJc0zjPP2u8JJU1MDjqPbw3FMO5kb9ADg77/vYfSoDfj5kwVKIiIiEoYgBamYmBi0adNGbmzx4sW4efMmHBwc8Msvv0BTUxN+fn7Yu3cvTpw4IUR6RERE+VbJylao2a4VAl77ITYiMtNiFBVOFcoVg+sKO9SpKXuJTGRkDKZM3oGdOxU7zJ6IiIgorwhSkMrKlStXcOXKFVWnQURElK+YWZTCjBP7FW7vNniUErMhdTS4XxPMm9EN+vraMrG7d19h6JC18PfnDXpEREQkPLUoSBEREVH2OD+7m632r+94ISkhQUnZkLopVtQQq5f0Q7vW1WRiiYlJWLzoIFas+AtJSckqyI6IiIhIoILU27dvkZycDFtbW/j7+yvUp0yZMrh27RokEgkqVaqk5AyJiIjUm6aODgxNjBH6/Qd6zZ2erb5HFy5DwKs3SsqM1E2HNr9g1ZK+KGpmKBN7/fozhgxeCx8fPxVkRkRERPQ/ghSkypUrB4lEAm1t2eXiGdHS0kL58uUhkUiUmBkREZH6K1+7Jhz3bc1xfxajCgd9fW0sdOqBgX0ayY1v3uSBGTN28QY9IiIiUgvcskdERKTmclOM2jp6Uh5mQuqqbq1yWLdiIMqVLSYT+/YtBCNHrIOn5wMVZEZEREQkn9oWpIyNjQEA0dHRKs6EiIhIdYqWscxxX5f+9nmXCKklTU0NTBrXARNHtYNYrCETP3nyLsaM3oDAwHAVZEdERESUMbUtSA0ePBgA8OHDBxVnQkREpDpzPI7lqJ/XidN5nAmpm4rli2PdSjvUql5GJhYREY1Jv2/D7t2XVZAZERERUdaUUpC6fFn+Nz/u7u6IiorKtK+Ojg4qVqyIEiVKQCKR4MKFC8pIkYiISKpul44YtGJxuncvrt3CLscZKsooe9wGOeD3AzvSvbtz5ISKsiEhDBnQFPOmd4Wenuz5nLdu+WLY0LV49+67CjIjIiIiUowIQJ6fGp6UlASJRAKRSJSrcd6+fYsmTZogMDAwjzIjZbKwsMDnz58BAJaWlggICFBxRkREWStevixmnTmSYXxx224I/6mav4d0DPSx7F7WK1xSt+ZVbtwAUSGh+PKaN6gVVMWLGWH1H/3QtqWNTCwhIRGLFh7EypXHkZycrILsiEjd8PtzIlJnSlkhdePGjXS347Vq1QoSiQQPHjzIdIWURCJBbGwsvn79ijt37uDw4cM8Q4qIiJQqs2IUACy8cgbTazZVya2vihSj0vK7562kTEgd2LarjpWL+8LM1EAm9vLlJwwZ7IyHD/1VkBkRERFR9imlINWmTZt0n5OSkgAA9vb2ePnypTKmJCIiyrYqTRoq1G7N0zuICY/A/lkL8erm3VzPW7lRfYg0NPDm7v0M2zg/U2wet0EOuc6H1JuBvg4Wze6B/r3k/+91w/ozcHLag5iYOIEzIyIiIso5QQ4137t3LyQSCUJCQoSYjoiIKFMW1lUw9diebPXRK2KEUZvW4t/7D7B55MQcz/3fQtO0Gk2y1d914AhIuB2r0Khfpzxclw9E2TJFZWJfvgRhxPB1uHDhkQoyIyIiIsodQQpSw4cPF2IaIiKiLNm7rkCNdq1y3L9Sw3o57mtauqTMu6pNG+H1Ha907/SKFMlwDBajCgctLTEmj+uA8Q5tIRZryMT/+us2xo7ZiODgCBVkR0RERJR7st/hEBERFUAammI4P7ubq2JUqgY9OueoX8shA2Tejd7qKvtui4vc/he27MrRvJS/VKpYAqcOOMJxTHuZYlR4eDTsh7mgX98VLEYRERFRvibICqn/MjQ0RIUKFWBkZASxWJxl+5s3bwqQFRERFWTz/jmZZ2MNWDofX/38Ub52TQQHfIXv9VuZtrdu0QSjNq1VaOzMzo56cfVGtvKk/GeYXTPMndYVurpaMrGbN19g2FAXvH//XQWZEREREeUtQQtSDg4OGD9+PGrWrKlwH4lEAi0t2W/KiIiIssO4RPEs2xyYtRA/3n1A+1H2qNG+daZtpxzZLX0+vdoN1/cektvOslpVhYtRY7atyzDm0t9eoTEofzIvXgRr/uyPVs2qysTi4xOwcMFBrF59AsncsklEREQFhCAFKQ0NDRw/fhzdunUDAIhEIiGmJSIiAgBoKLAa1+vEGfx49wEAcGn7btw9dhKjt2ZcIEqr+4zfZQpSRkXNsOjaOYX617Jthyf/XFb41j8qWDp3rInlC3vD1MRAJubr+xGDBznj8eO3KsiMiIiISHkEKUiNHTsW3bt3BwB8//4d7u7uePDgAYKDg/mTPiIiUhqRSARzqwpZHkR+dNEyBLx8k+5dVGgYXAYMh3WzxujkOCbLuZbcPI8FLX4FkHIouaLFKAAYumYpsGZphvH9MxcoPBblH4YGOlg8pyf69mwgN+627jRmzdqD2Nh4gTMjIiIiUj5BClJDhw4FAPj6+qJFixYIDQ0VYloiIirENLW1sfLB9UzbXHXfj8fnL2XcQCLBq1t38ebufUw6uDPTsQxMjKXPS2//k61cs/Lzw8c8HY9Ur2HdCli7fCDKWprJxAICgjDc3hWXLj0WPjEiIiIigQhyy56NjQ0kEgn++OMPFqOIiEjpxJqaWRajnl26lnkxKo3kpCRsGjE+y3bzL/0NTR0dhcZU1MHZi/J0PFItLS0xnCZ3wtE94+QWo44evYWaNSayGEVEREQFnqCHmr9+/VrI6YiIqJCq/Wv7LNtc2r47W2PGRUVn2cbEvARW+lzLtI2/90NYNair0Jzuk50Q+pU3qhUUVazM4brSDtVtLGRiYWFRcJy4Ffv3X1VBZkRERETCE2SFlJ+fHwDAzEz2J4FERER5zW75wkzjqYeXZ9eXN//mqF9ap53XY+fE6Qq1ZTGqYBCJRBgxuDnOHpsstxh17doz1KrpyGIUERERFSqCFKQOHz4MkUiErl27CjEdERFRpk4sW5OjfscWLc/95BIJwn8GZtlsnd3I3M9FKmdeogj2bXXAotk9oaujlS4WH5+AmTN2oX27efj48aeKMiQiIiJSDUG27Lm5uWHQoEEYN24cTp48iVu3bgkxLRERFUJizYz/anv78AnOrHFDclJSjsZOTkqCS397GJuXgKa2Nqq3bYm6nTsq3N99kpP0OT42Ftq6unLbufS3z1F+pF662tbCsoW9YWKsLxN7/vwDhgx2xpMn71SQGREREZHqCVKQio+PR8eOHXHixAlcvHgRbm5uOHjwIF69eoW4uDghUiAiokKiSIlict/nZZEn7PsPAMD1PQezLEg9vXQVl7fvkXl//I9VGPjngnTvsrz1j/IFI0Nd/DH3N/TqXk9u3NXlb8yevQdxcQkCZ0ZERESkPkQAJMqeJDEx8X8TikSQSBSfUiKRQEtLK+uGpHIWFhb4/PkzAMDS0hIBAQEqzoiICiPnZ3dl3rkMGA5k4++e7PildQt0HJfx9rqN9mMRHxMrN1auVg1UrFcbX169wes7XkrJj4TVqH5FuCwfCMvSpjKxz58DYT/MBVeuPFVBZkRUGPH7cyJSZ4KskBKJRJl+JiIiUiolFaMA4MW1m/Dz8kaVJg3RYcyIdLFtYydnWIwCgA9PnuHDk2dKy42Eo60lxvTff8Vo+1bQ0JA9ovPQoeuYMH4zQkOjVJAdERERkfoRpCC1ePFiIaYhIiJSifiYWDy/cgO+12+jePmyiAgMRnRYmKrTIoFUqVQS61YOxC/WsjfohYZGYsL4LTh06LoKMiMiIiJSX4IUpJYsWSLENERERCqVnJSE7/48pLqwEIlEGDGkOZwmd5a5QQ8Arl59Cvthrvj0iTfoEREREf2XIAUpIiIiooKkpLkx1v7ZH82bVJGJxcUlYO6cvXBx+Ttb52YSERERFSYsSBERUYEhlnMJRmRwiAoyoYKsW6faWDa/F4yN9WViT5++w5DBa/Hs2XvhEyMiIiLKR1RSkKpQoQKaNGmCkiVLQl9fH5s3b0ZQUJAqUiEiogJiwu7NqFivtsz7oM+8UYjyRhEjXfwxrxd+61pXJpacnAyXtX9j3rx9iItLUEF2RERERPmLoAWp2rVrw9XVFc2bN0/3/vjx4+kKUuPHj8fChQsRFhaGatWqITExUcg0iYgoH9E1NMCfdy9lGL9z5ISA2VBB1aShFdYuGwCLUqYysY8ff8J+mAuuXeONiURERESKkr2XWEk6d+6MO3fuoHnz5hCJRNJf8uzZswd6enqoWLEiunbtKlSKRESUD2VWjAKAb/++FSgTKoh0tDUxd3pXHNo5Rm4x6sCBa6hV05HFKCIiIqJsEqQgZW5ujkOHDkFHRwe+vr7o1KkTjIyMMmwfFRWFU6dOAQA6deokRIpERERE6VhXKYXTh3/HmOGtoaGR/lumkJBIDBywCkMGOyMsLEo1CRIRERHlY4IUpKZMmQJDQ0N8+PABLVq0wIULFxAdHZ1pn2vXrkEkEqFevXpCpJgnihYtihkzZuDWrVv4+vUrYmNjERAQgHv37mHVqlVo3LhxlmPY2tri+PHj+PTpE2JjY/Hp0yccP34ctra2Cuehp6eH6dOnw8vLC0FBQYiIiICvry9Wr16NMmXK5OZLJCJSOZGGBto5DMPSOxfRZcp4VadDBZBIJMJo+1Y4c2QSbKqWlolfvvwEtWo64siRmyrIjoiIiKhgEOQMKVtbW0gkEjg7OyMsLEyhPq9fvwYAlC9fXomZ5Z0+ffpg8+bNKFasWLr3pUuXRunSpdGoUSNUrlwZv/32W4ZjbNmyBWPGjEn3ztLSEpaWlujVqxe2bt2KsWPHZppHxYoVce7cOVhbW6d7b2NjAxsbGzg4OMDOzg6enp7Z/AqJiFRPQ1OM1Y9uST+3HTEk0/YbhmX+ZybRf5UuZYK1ywagacNKMrHY2HjMmb0X69adhkQiUUF2RERERAWHIAWpChUqAADu37+vcJ+IiAgAgKGhoVJyyktDhgyBu7s7xGIxvn//js2bN+PWrVsIDg5GyZIlYWVlhW7duiEhIeNbd5YuXSotRj18+BCrVq2Cv78/rKysMHPmTNStWxdjxozBz58/MX/+fLljGBgY4OzZs9Ji1LZt23D48GHExMSgTZs2mD17NkxMTHDs2DE0adIEz57xvAsiyl/SFqOysstxBhJiY5WYDRU0PbrUwdJ5vWBcRE8m9uTJOwwetAYvXnxUQWZEREREBY8gBSktLS0AyLQg818mJiYAUs6TUmfW1tbYtm0bxGIxbty4gW7duiE8PFym3YYNG6S/D/+VWnQCAG9vb7Rs2RKx//+PKB8fH5w+fRrXr19HgwYN4OTkBHd3d7x9K3tI7/Tp02FjYwMAmDFjBtasWSON3bt3D1evXsWNGzdgYGAAV1dXtGvXLtdfPxGRUDTEYoXbbhs7GVEhocpLhgoU4yJ6WDq/F3p0riMTS05OhvOak5g/fz/i43nrLxEREVFeEeQMqW/fvgH430opRTRp0gQA8PnzZ6XklFfWr18PXV1d/Pz5E7169ZJbjEqVUUFuypQp0mKVo6OjtBiVKiYmBo6OjgBSinuTJ0+WGUNTUxOTJk0CAPj6+sLZ2Vmmzb1797Bz504AQNu2bVG3bt2sv0AiIjVhWrqUQu12T57FYhQprFmjSrhwcprcYtSHDz/Qts1cODntZjGKiIiIKI8JUpC6ffs2AGR6flJaenp6GDt2LCQSCW7cuKHM1HKlatWqaN++PYCUFVBBQUE5GqdHjx4AgJcvX8LLy0tuGy8vL7x69QoA0LNnT5l469atYWqach31nj17MjzbYvfu3dLnXr165ShfIiJVGLxysULtQr5+U3ImVBDoaGtigVN3HNo1FqVKmsjE9+69glo1HXHjxnPhkyMiIiIqBAQpSO3ZswcikQgDBw5Ehw4dMm1rYGCAo0ePomzZsgAgXdGjjvr27St9PnbsmPTZxMQElSpVgpmZWZZjVKhQAZaWlgCA69evZ9o2NV6mTBmZw95btGgh004eHx8fREZGAgCaN2+eZX5EROqibI1qqk6BCgibqqVw9uhkOAxtKRMLDo5Av74rYD/MBeHhmd8ITEREREQ5J0hB6vLlyzh16hQ0NDRw+vRprFq1Cg0aNJDGzczM0LBhQ8ybNw+vX79Gp06dIJFIsHfvXjx+/FiIFHOkcePGAIDQ0FC8fPkSdnZ2ePz4MUJCQuDn54egoCD4+/tjwYIFMDAwkDtG6plPAKQroDKSNp62X3bGSUpKgr+/v9wxsmJhYZHpr1KlFNtOQ0REpAoaGiKMHdEaZ45MQtXKJWXiFy48Qs0aE/HXX7dVkB0RERFR4SLIoeYAMHjwYJw9exatW7fG1KlTMXXqVOm2srQrekQiEYCUItbYsep9XXe1aik/rX///j3Wr1+PiRMnyrSpWLEiFi9ejD59+sDW1hZfv35NFy9Tpoz0Oavzsj59+iS3X9rPkZGRCAsLy3KcWrVqoUSJEtDW1kZ8fHym7RXNj4hI1a6671d1CqSmLEqZwmX5ADRuYCUTi42Nh9PM3diw4WyGW96JiIiIKG8JskIKSDmYu3379pgxYwa+ffsGkUgk91dwcDDmzJkDW1tbhQslqpK6Jc/a2hoTJ05ESEgIxowZg+LFi0NHRwf169eHh4cHAKBGjRo4duyYtOCWysjISPqcupUuI2lvHDQ0NJQ7TlZjZDUOEZE6qm0r/1bQyOAQ6fORBX/i8flLQqVE+UivbnXxz8mpcotRjx75o17dyVi//gyLUUREREQCEmyFFABIJBKsXbsW69atQ8OGDVG/fn2UKFECYrEYQUFBePToEW7duqX2hahUqdvwdHV1kZiYiE6dOqU7lPzBgwfo2rUrzp49i86dO6NZs2bo1asXjh8/Lm2jq6srfc7q646Li5M+6+nppYuljqPI711m42Qm9ayrjJQqVQre3t4Kj0dEJE/F+nVgYV0Fz69cR8iXb9DU1saQNUtl2gV/+Yo9U2arIEPKL4yN9bB8QR90/bWWTCw5ORmrVh7HwoUHkZDAG/SIiIiIhCZoQSpVUlIS7t69i7t376pi+jwTGxsrXWF07NgxuTfkSSQSzJgxA507dwYADBw4MF1BKjY2Vvqsra2d6Xw6OjrS55iYGJlcFBkjq3EyExAQoHBbIqKcqGXbDkP/v/jU02ky/H0ewap+HbltT61wETI1ymdaNKkM5z8HoKS5sUzs/fvvGDpkLW7d8lVBZkREREQEqKggVVBERERIC1Kenp4ZtvP19cXnz59haWmZ7jD31DFSZbV9Lu3B6P/dmpc6jiJb8DIbh4hIldqPGpbuc0bFKAAI+/5D2elQPqSjo4nZU7pgxJAWcuO7d1/GpN+3IiJC8R/IEBEREVHeE+wMqYIo7SHjih5IXqJEiXTv0/bLaktc2oPM086ddhxDQ0MYG8v+NFjeOD9+/Mg32yOJqHAoXbWyqlOgfOwX69I4d3Sy3GJUUFA4+vRejhHDXVmMIiIiIlIDgq+QMjMzQ5MmTVCxYkUYGRlBLBZn2eePP/4QILPse/HiBRo2bAgAWX4dqfHExPTnVPj6/m+7gLW1daZjpI2/fPky03HkbR9MzcPKykruGEREQtMQi5GclAQAqFCnpsL9XPrbKykjyo80NEQYN6INpkzsCG0t2W9tzp9/gJEj3PD1a7AKsiMiIiIieQQrSJmbm2Pt2rXo3bs3NDWzN626FqRu3LiB4cOHAwCsrKxw6VLGtztVrFgRgOw5TO/evUNAQAAsLCzQqlWrTOdr2bIlgJTVUO/fv08Xu3XrlvS5VatWGRak6tevL93Wd/v27UznIyJSFgMTYyy5eV76eUW3/hi+bqVCfV0HDFdWWpQPlbEwg8vyAWhYr6JMLCYmDjNnuGPjxnMqyIyIiIiIMiPIlr1ixYrhzp076N+/P7S0tCASibL1S12dPn1auuWtV69eGbZr2bIlihUrBgC4efOmTPzvv/8GANjY2KBRo0Zyx2jUqBFsbGzStU/r2rVrCA0NBQAMGzZMJp7K3t5e+nzy5MkM2xERKYtYSytdMQoAZp05AgNTkyz7bhg6BhKJREmZUX7Tp0d9nD8xVW4x6sGDf1Gv7mQWo4iIiIjUlCAFqcWLF6N8+fIQiUQ4duwY2rZti6JFi0IsFiv0S10FBwdjx44dAICOHTuif//+Mm0MDQ3h6uoq/bx161aZNq6urkhISAAArF+/Hrq6uuniurq6WL9+PQAgISEh3XipEhIS4ObmBgCoVq0apk+fLtOmcePGGDlyJICUApaPj48CXyURUd6o2rQRnJ/dxaqHN3LUf9fvM5AQF5fHWVF+ZGKsjy0uQ7F22QAYGab/OzMpKQnL/jyKpk1m4NWrzM93JCIiIiLVEQFQ+o+aP3z4AAsLC+zbt0+6xa2gKFasGHx8fFCuXDkkJCRgy5YtOHHiBMLDw1GjRg04OTlJVzZt2rQJEyZMkDvOsmXLMHv2bADAw4cPsXLlSvj7+8PKygpOTk6oW7eutN3cuXPljmFoaAgfHx9UrVoVQErx6/Dhw4iJiUGbNm0wZ84cGBkZITo6Gk2bNsWTJ0/y9PfCwsJCeri6paWlzPZEIircnJ/dzXHf0G8/4D5pZh5mQ/lVq+ZVseaPfjAvIXuBx9u33zB0yFrcucMzEomIAH5/TkTqTZCCVHR0NLS1tdGmTRu5W9byO2tra5w+fRqVK2d8O9TOnTsxduxYmUPNU4lEImzfvl26gkmeHTt2YPTo0ZluV7GysoKHhweqVKkiNx4WFoZBgwbh3Lm838LAv/CIKCPzL/0NE/MSWTdM45zLRtRo3xqSZAluHjiKnx8+Kik7yg90dbUwZ1pX2Ns1kxvftfMCpkzZwRv0iIjS4PfnRKTOBDnU/MuXLyhfvjyioqKEmE5wr169Qu3atTFu3Dj06dMHlStXhqGhIX78+IHbt29j69atuHbtWqZjSCQSODg44Pjx4xg9ejQaNGiAYsWKITAwEN7e3ti6dSvOnz+f6RgA4O/vjzp16mDChAno27cvKlWqBG1tbXz69AkeHh5Yt24dPn7kP+qISDgdxo7IdjEKAN7c88abe95KyIjymxrVLOC6wg6VrcxlYoGB4Rg9aj1OnbqngsyIiIiIKKcEWSG1a9cuDB06FCNHjsSePXuUPR2pCH8CQ0Ty5HSrnkt/+7xNhPIdDQ0Rxju0xZTxHaGlJXumpIeHDxxGuuHbtxAVZEdEpP74/TkRqTNBDjVfs2YN4uPjMW3aNOjo6AgxJRER5WNbR/+u6hRIxcpamuGvveMxc1InmWJUdHQcJozfjK5dFrMYRURERJRPCVKQ8vX1xYgRI1C1alX8888/mZ61REREhds5l42IDgtXdRqkQv1+a4DzJ6aifp0KMjFvbz/UrTMJmzd7qCAzIiIiIsorgpwhBQCHDx+Gn58fzp07B19fXzx9+hRv3rxBdHR0pv1Sz1YiIqL8R1tPL8OY330fnHXegC5TJqBK4wYAgB/vP8Lv/gOh0iM1Y2ZqgBWL+uDX9jVkYklJSVj25zH88cdhJCYmqSA7IiIiIspLghWkKleujLVr16JYsWIAgFq1aqFWrVqZ9hGJRCxIERHlY+0chsq8u3f8NO4ePSH9fH7DNgS8fA0tXR08v3IDkuRkIVMkNdGmhTVWL+2HEsWKyMT+/fcLhg5Zi3v3XqsgMyIiIiJSBkEKUmXKlMGNGzdQvHhxiEQiAEB4eDjCwsKQzH94EBEVSO1GDUP70fYy79MWowAgKSEBj89fEigrUje6ulqYN6Mbhg5oKje+Y/s/mDJlB6KiYgXOjIiIiIiUSZCC1IIFC1CiRAkkJydjzZo12LRpEz5+/CjE1EREpAJ6RYqg8+9jVZ0Gqbma1ctg3YqBsKpQQib282cYRjmsx+nTXirIjIiIiIiUTZCCVLt27SCRSLBu3TrMmjVLiCmJiEiFJh3YruoUSI2JxRqYMKotJo3tIHODHgCcPesNh5Fu+PEjVPjkiIiIiEgQghSkzM3NAQDHjx8XYjoiIlKh0VtcULx8Wbmxo4uWCZwNqZtyZYrCdcVA1KtdXiYWFRWLaVN3Ytu288InRkRERESCEqQg9fXrV5QvXx7x8fFCTEdERCpgWrokzCuWR9VmjTNsE/DyjYAZkboZ0LshFs7qAQN9HZmYl9drDB2yFn5+X1SQGREREREJTUOISS5evAgAaNCggRDTERGRwCrUqYl5/5zEqM0uGbb57PtKwIxInRQ1M8SO9fZYtaSfTDEqMTEJixcdRIvmTixGERERERUighSk1qxZg6ioKDg5OcHU1FSIKYmISEAT927Nss2xxSsEyITUTduWNrhwcho6tq0uE/Pz+4LmzWZi8eJDSExMUkF2RERERKQqghSk/P398dtvv8HIyAi3b99G+/bthZiWiIgEoCGWPZT6v9YPGS1AJqRO9PS0sWxBb+zePBLFixnJxLdtPY+6dSbh/n1u4yQiIiIqjAQ5Q+ry5csAgMDAQFStWhXnz59HaGgo/Pz8EB0dnWlfiUTCAhYRkRpb/fhWpvH1Q0cjkWcIFiq1a5TFupUDUaFccZnY9+8hGOWwHmfPeqsgMyIiIiJSF4IUpFq3bg2JRCL9LBKJYGpqioYNG2bYRyKRQCQSpetHRET5T2Ici1GFhVisgd/HtIfjmHbQ1JRdOXf6tBdGOazHz59hKsiOiIiIiNSJIAWpGzdusLBERFQI3TlyQtUpkEDKly2GdSvtUKdmWZlYZGQMpk7ZgR07LqggMyIiIiJSR4IUpNq0aSPENEREJCDnZ3czjQe8egOvE6cFyoZUya5vIyyY2QP6+toysXv3XmHI4LXw9/+qgsyIiIiISF0JUpAiIqKCZcLuzRnGbuw7jKeXriEhNlbAjEgVihU1xKol/dC+dTWZWGJiEv5YchjLlh1FUlKyCrIjIiIiInXGghQREWVbxXq1M4w9OHteuERIZTq0qYaVi/uhWFFDmdibNwEYMtgZ3t5+Ksjs/9q77+goq62P479JJQm9Q+hB6VWqdFAQsSJV6SCogIqAKFekqiC9KE0FBC8qXQERRYogXUSkKCAdpAdIQvp5/+DNXOJMGiTPZJLvZ62z1uTZp+yZg3HYPAUAAADuwGUFqcKFC6tgwYLy9/fXnj17FM6/pAOAW6j93FOuTgEu5O/vo+FDnlLHNnWcxmd+vFaDB3+msLAIizMDAACAO7G0IJU1a1YNGjRIPXr0UOHChe3HK1WqpMOHD9t/bt++vVq3bq0bN26od+/eVqYIAEhCuxFvuzoFuEj1KsU1ZWxHlSiW1yH2zz/X1bPHVH333V4XZAYAAAB3Y1lBKigoSN99951KlSolm81mP+7s6Xvbt2/XwoUL5eHhoQULFmjbtm1WpQkAuA/L3hvv6hSQBry8PPTqS4+qf+9m8vT0cIivXLlDvV+critXbrogOwAAALgjx2+VacDHx0dr1qxRUFCQwsLC9OGHH+qJJ55IsP/p06e1ceNGSdJTT3FpCAC4i9O/H3R1CkhlpUrk0/JF/fT6y486FKNCQm6rV89pav3sexSjAAAAkCKWnCH10ksv6YEHHlBoaKgaNGig/fv3Jznmu+++0yOPPKK6detakCEAIDmCalRLMHbh6HELM4EVOrWvq2GDn5Sfn49DbNu2Q+raZbL+/vsfF2QGAAAAd2dJQap169Yyxmjq1KnJKkZJ0u+//y5JeuCBB9IyNQBAMpWoWlmvzPvY4fifv+zUr2vX6x8KUhlGvrzZ9OGodmrWqJxDLCoqWiNHLNa4cUsVExPrguwAAACQEVhSkCpfvrwkaf369ckec/XqVUlSzpw50yIlAEAKFK9SUf0XznYaWzt1psXZIC01b1pB40a2VZ7cWR1iR46cVedOE7V37zEXZAYAAICMxJKCVLZs2SRJN27cSPaYLFmySJKioqLSJCcAQPK9umiuq1NAGgvw99Xwt55Sh+dqO41/NGO13nxzvm7fjrA4MwAAAGRElhSkrl69qoIFC6pAgQLJHlOpUiVJ0sWLF9MqLQBAMlRs2ijB2I9z51uXCNJMjWolNOWDjipWNI9D7MKFa+rRfaq+//5XF2QGAACAjMqSp+z99ttvkqRmzZole0yPHj1kjNHOnTvTKCsAQHJ0nzo2wdiBHzdZlwhSnZeXhwa9+piWLHjFaTFq2bJfVLlSf4pRAAAASHWWFKSWL18um82mPn36qFixYkn2f/fdd1W79p1LBr766qu0Tg8AcA/mvvKGq1PAfQgqmU8rv+ivV/s8Ik/P+F8Hbt4MU/duU9S2zQe6evWmizIEAABARmZJQWr+/Pk6fPiwsmXLps2bN+vxxx+PFzfGyGazqX79+vrmm2/07rvvyhij3bt369tvv7UiRQDAv9hsNtVu/aTT2OT23RRy9ZrFGSG1dOn4sNYuGaDKFYs6xLZuPaSqVV7VggUbXJAZAAAAMgtL7iEVGxurp556Stu2bVOxYsX0zTffKCwszB7/9ttvVaBAAfn7+0u685eg8+fPq23btlakBwD4F/8c2TV66/euTgOprEC+7Bo/pp0a1y/rEIuKitbwd7/Qhx8uV2xsrAuyAwAAQGZiyRlSkvT333+ratWqWrNmjWw2mwICAiTdKT6VKlVKAQEBstlsstlsWr9+vWrWrKmzZ89alR4AZHoeXp566MmWmnhge6LFqPmvv2VhVkgtLR+tpPUrBzotRh06dFp1ag/S2LFLKUYBAADAEpacIRXn4sWLeuqpp1S+fHk9/fTTqlGjhvLnzy9PT09dvXpV+/bt06pVq7R3714r0wKATM/Dy1Pj921NVt/rF/5J42yQmrIG+Grk0GfU9pmaTuPTpn6jt95aoPDwSIszAwAAQGZmSUGqaNE796gICQnR9evXdejQIR06dMiKpQEAyZDcYhTcS63qJTXpg44qViS3Q+z8+avq3m2qfvhhnwsyAwAAQGZnySV7J0+e1IkTJ9ShQwcrlgMApED2/PmS3ffr4e+nYSZILd7enhryekt9veBlp8WoJUu2qnKl/hSjAAAA4DKWnCF1+/ZtZcmSRbt377ZiOQBACgzf8E2y+5478lcaZoLU8EBQAU0Z21GVyhdxiN28Gab+/WZp4cKNLsgMAAAA+B9LClLnzp1TUFCQPD09rVgOAJBMHl7J/738/Udz0zAT3C+bzaZuz9fT22+0UpYs3g7xLVv+UNcuk3Xq1CUXZAcAAADEZ8kle+vXr5ck1a9f34rlAADJ1HVSwpfgbZy3SLdv3pIkfTtxug5t2WZVWkihAvmza+HsXho59BmHYlRkZJSGvDlPTZv8h2IUAAAA0g2bJJPWi5QuXVr79u1TSEiIHnroIZ0/fz6tl4QLBAYG6uzZs5KkIkWK6Ny5cy7OCEBSJh7Y7vT45PbdrE0E96xV88r6YEQb5czh7xD7449T6txpovbvP+GCzAAArsb3cwDpmSVnSB07dkzPP/+8/P39tWPHDnXs2FHe3o6XEwAArJM9X16nx2f26mdxJrgX2bJm0eT3O2jm5C5Oi1FTJq9SzRoDKEYBAAAgXbLkHlIbNmyQJF2+fFklS5bUwoUL9emnn+ro0aO6fv26YmJiEhxrjNEjjzxiRZoAkKl0mTDG6fHwWyEWZ4KUqvVQKU3+oIOKBjo+Qe/s2Svq3m2KNmzY74LMAAAAgOSxpCDVuHFjGfO/KwNtNpt8fX1VsWLFBMcYY2Sz2eKNAwCknpLVqzgc++/QkS7IBMnl4+2pgf0fU5/ujeTh4XiS85dfblHfV2bq+nWKigAAAEjfLClIbdmyhcISAKQjjbu94PT4xeNc3pVePRhUQFM/fF4VygY6xIKDQ9Sv7yz997+bXZAZAAAAkHKWFKSaNGlixTIAgGR6ciD3iXIXNptNPTrV15ABjyuLr+P9Fzdu/F3duk7RmTOXXZAdAAAAcG8sKUgBANIP3wDHG2AjfSpYIIcmvdde9es+6BCLiIjSO/9ZqEmTVnIWMgAAANwOBSkAyGSCalR3enzZe+MtzgSJefKxKnr/3eeUw8kT9A4cOKlOL0zUgQMnrU8MAAAASAUUpAAgk+k5w3nh6dzhvyzOBM5kz5ZFo99prWefcF44nDRxhf7zn4WKiIiyODMAAAAg9Tg+oicNVK1aVdHR0QoLC1PhwoWT7F+4cGHdvn1bUVFRKleunAUZAkDGljVPLk08sF0TD2x3Gt+xdJVioihwuFrdWkH6fsVAp8WoM2cuq1nT/2jQoM8oRgEAAMDtWVKQat++vWw2m1avXq3z588n2f/8+fP65ptv5OHhoQ4dOliQIQBkbCM3rU00vn3JCosygTM+3p4aOvAJLf60jwIL5XKI//e/m1Wlcn9t3Pi7C7IDAAAAUp8lBanGjRvLGKPvvvsu2WPWrFkjSXrkkUfSKi0AyBS8fHwSjV8+dcaiTOBMmQcK6puvXtNLPRrLwyP+/5aDg0P0fMfx6vTCBAUHh7ooQwAAACD1WXIPqaJFi0qSDh06lOwxf/75pySpSJEiaZITAGQWAblyJBpf/J9RFmWCu9lsNvXs0kBDXn9cvj6O/zv+6af96tZ1is6eveKC7AAAAIC0ZUlBKk+ePJKk8PDwZI+JiIiQJOXPnz9NcgKAzKJg6aBE49w7ynqFCubQpPc7ql7t0g6x8PBIDX37c02d+o2MMS7IDgAAAEh7llyyd/36dUlSsWLFkj0m7syomzdvpklOAJBZ9J41OcHYoS3bLMwEkvR0q2pav2KQ02LU/v0nVLPGAE2ZsopiFAAAADI0SwpScZfqPfXUU8ke8+yzz0r636V7AICU8/HzSzT+/UdzLcoEObL7afr4FzT9wxeUI3v8fYmNjdX4D5epdq03dPDgaRdlCAAAAFjHkoLU2rVrZbPZ1KVLF9WvXz/J/g0aNFDnzp1ljNHq1astyBAAMqYPdv2UYGz3qjUWZpK5PVy7tL5fPlBPP17NIXb69GU1a/ofDRkyX5GR0S7IDgAAALCeJQWp2bNn68qVK/L09NTatWvVr18/+fr6OvTz9fVV//79tWbNGnl6eur69euaOXOmFSkCQIZTvlHC/wAw7/Uh2vrfJRZmkzn5+nhp2JtP6svPXlLhQjkd4osWbVSVyv21efMf1icHAAAAuJAlNzUPDQ3V888/r7Vr18rf319TpkzR+++/rz179ujChQsyxqhw4cKqUaOG/P39ZbPZFBUVpY4dO+rWrVtWpAgAGU7PGeOdHr986rSCL1y0OJvMp1yZQpo69nmVfbCQQ+zatVt65eWP9fXXW12QGQAAAOB6lhSkJGnDhg1q0aKFFi1apEKFCikgIEANGzaM18dms0mSzp07p86dO2vz5s1WpQcAmcaiIcNdnUKG5uFh04tdG2nQq4/J18fxf7M//LBP3btN0fnz11yQHQAAAJA+WFaQkqRNmzYpKChIXbp0UatWrVStWjXlzZtXknTlyhX9+uuv+vbbb7Vo0SJFRkZamRoAZCg5C+R3evzYrr0ST29LM4GFcmnS+x1Ut1aQQyw8PFJvDZmv6dNX8wQ9AAAAZHqWFqQkKSIiQnPnztXcuTzZCQDSynPD3nR6/NuJ0y3OJPN49onqGv3Os8qezfHJhvv2HVfnTpN06BBP0AMAAAAkFxSkAABpr3yjeg7HPu7xigsyyfhy5PDT+8Oe05MtqzrEYmNjNf7D5Ro+/AueoAcAAADchYIUAGQSEaFhrk4hw6lf5wFNer+DChbI4RA7efKiunaZrJ9/PuiCzAAAAID0zWUFqWzZsilbtmzy9PRMsu+ZM2csyAgAMoaE7h+F1OPr66W3Bjyunp0bOo0vWLBBr706RzdvUgQEAAAAnLG0IPXII4/olVdeUYMGDZQrV65kjTHGyNvbO40zA4CMI1u+vA7HVn04xfpEMqjyZQtr2rjn9WDpgg6xq1dv6qU+H2nZsl9ckBkAAADgPiwrSE2dOlV9+/aVJNlsNquWBYBMJ2/RQIdjf+/9zfpEMhgPD5v6dG+sgf1byMfb8X+f33//q3p0n6oLF65ZnxwAAADgZiwpSHXs2FH9+vWTJIWHh2vlypXau3evrl27ptjYWCtSAIBMIXv+fOr04ShXp5HhFCmcS5M/6KjaNUo5xG7fjtCQN+fro4/WyBjjguwAAAAA92NJQapPnz6S7twLqmnTpvr777+tWBYAMpVGXTrqqcGvujqNDKfN0zU0cugzypY1i0Ps11+Pq3OniTp8mHsdAgAAAClhSUGqcuXKMsZo5MiRFKMAIA1MPLDd1SlkODlz+GvsiDZ6vHllh1hMTIw+HLdMI0YsVlRUtAuyAwAAANybJQWpuJuS79u3z4rlACBTKVHVsWBytzVTZ1qUScbRsN6DmjimvQrkz+EQO3Hiorp0nqRt2w65IDMAAAAgY/CwYpGTJ09KkrJmzWrFcunCuHHjZIyxt0aNGiU5pkWLFlq2bJnOnDmj8PBwnTlzRsuWLVOLFi2Sva6fn58GDRqknTt36urVq7p165YOHTqk8ePHq2jRovfzlgCkU/0Xzk40/tcvOy3KxP1lyeKtkUOf0aI5vZ0Wo+bN+1FVq/SnGAUAAADcJ0sKUsuXL5ckNWvWzIrlXK5y5coaMGBAisbMmjVL69atU+vWrVWkSBH5+vqqSJEiat26tdatW6dZs2YlOUepUqX066+/avz48apVq5Zy586trFmzqly5cho0aJB+//13tWzZ8l7fFgA3FHLtuqtTcBsVywVqzdevq/sL9R1iV67c1HOt31fPHlN169ZtF2QHAAAAZCyWFKQmTpyo06dP6/XXX1eZMmWsWNJlbDab5s6dK29vb128eDFZY8aMGWO/8fuvv/6qDh06qGbNmurQoYN+/fVXSXduDD969OgE5wgICNDq1atVtmxZSdKcOXPUtGlT1a1bV0OHDtWtW7eUM2dOLVmyRJUqVbrPdwkgvRixaU2i8U/7DbIoE/fl4WFT3xebatXiV/VAUAGH+Hff7VXlSv20YgX36QIAAABSiyX3kLp586Yee+wxffPNN9q2bZuGDRumxYsXKzg42IrlLfXqq6+qVq1aOnz4sFasWKGhQ4cm2j8oKEhvvvmmJGn37t1q2LChwsPDJUl79uzRN998o82bN6tmzZoaMmSI5s2b5/TG8IMGDVK5cuUkSYMHD9aECRPssR07dmjjxo3asmWLAgICNGXKlExzthqQUQTkzKFRP6+z/3x463Yd+GGjsuXJ7bT/5PbdLMrMvRUNzK0pYzuqZvWSDrGwsAgNHvSZZs5c64LMAAAAgIzNJsmk9SLHjx+XJPn7+yt//vz2+ypduXJFYWFhiY41xqh06dJpnWKqKFKkiA4dOqRs2bKpcePGaty4sUaMGCFJaty4sTZv3uwwZsaMGerbt68kqU6dOtq50/FeL7Vr19aOHTskSdOnT9err8Z/rLuXl5cuXbqkXLly6dChQ6pYsaKMcdzWmTNn6qWXXpIkPfTQQ/azr1JLYGCgzp49K+nOZ3Hu3LlUnR/IrGw2myb8/kuy+3/Sd6BuXbmahhllDG2fqamRQ59W1oAsDrE9e46qc6dJ+vPPsy7IDACA1MH3cwDpmSVnSJUoUSLezzabTTabTfnz509yrLPCSnr18ccfK1u2bJo/f742b96sxo0bJznm6aefliQdPnzYaTFKknbu3KkjR46obNmyeuaZZxwKUo0bN1auXLkkSQsWLEjwM5s/f769INW6detUL0gBSBuPv/ZSivpTjEpcrpz+GjuirVo+6nj5ckxMjD54f4lGjfpS0dExLsgOAAAAyBwsKUgtWLDAimVcqm3btnryySd19epVDR48OFljSpYsqSJFikiS07On7rZ582aVLVtWRYsWVYkSJexPLpSkBg0axOuXkD179igkJERZs2ZV/fqON+0FkH7kL1lcxStXUPEqlVS37TPJHvftxBlpl1QG0Lh+WU0Y007582V3iB0/fkFdOk/S9u1HXJAZAAAAkLlYUpDq0aOHFcu4TI4cOTR16lRJ0pAhQ3TlypVkjYu755MkHTmS+F+A7o6XK1cuXkEqufPExMTo+PHjqlKlSrwxANKXYpUr6LUvPrmnscd2703lbDKGLFm89Z9BT6hrx3pO459+sl4DBnyikBCeoAcAAABYwZKCVEb34YcfqlChQtq2bZs+/fTTZI8rWrSo/XXctd0JOXPmjNNxd/8cEhKiGzduJDlPlSpVlD9/fvn4+CgyMjLZ+QYGBiYaL1SoULLnApCwey1GrRw3WXKjy5ytUrlCEU0Z+7xKl3K8TPzy5Rvq/eIMrVq1wwWZAQAAAJkXBan7VK9ePfXq1UtRUVH2+zMlV7Zs2eyvQ0JCEu0bGhpqf501a1an8yQ1h7N5rl27lqxcpaSLZgDun4en5z2Ni46M1Ilf96dyNu7N09NDr/Rqotdfbi5vb8fPdc2a3erVc5ouXgy2PjkAAAAgk6MgdR+8vb01Z84ceXh4aMKECfrjjz9SND5Llv892SmpM5UiIiLsr/38/JzOk5yznRKbB4DrlahWOcVjvpkwTcd385CCuxUvmkeTP+ioGtVKOMTCwiI08I1PNHv2OusTAwAAACAplQtSd99c++eff3Z6/F7cPVd6MnToUJUvX16nTp3SyJEjUzw+PDzc/trHxyfRvr6+vvbXt2/Hv8dJ3DxJzZHUPEmJuwF7QgoVKqTdu3enaE4A/2Oz2dR33sfJ7n/20BEtHf2hTGxsGmblfjo8V0vD33paAf6+DrFdu/5S504TdfToeRdkBgAAACBOqhakNm3aJGOMjDHy9vZ2OH4v/j1XelGmTBm9/fbbkqT+/fsrLCwsxXPcunXL/vrfl+H9W0BAgP31vy/Ni5snqTmSmicp586dS1F/AI48vb1lTKxio2PiHc+aJ5dGblqb5Pj5A97W9fMX0io9t5Y7V4DGjWyrFs0qOsSio2P0/ntfa8yYrxT9r88eAAAAgPVS/ZI9m82WouPuasCAAfL19dXx48fl7++v9u3bO/SpWPF/fylq2rSpChYsKEn69ttvFRYWFu+eTEmdfXT3jczvvsG59L97O2XNmlU5cuRI9MbmcfNcunQpRTc0B3D/Jh7YHu/ngZXq2l8npxg1uX231E4pw2jasJw+HN1W+fNmd4gdO3ZenTtN0s6df7ogMwAAAADOpGpBqkmTJik67s7iLn0LCgrSl19+mWT/d9991/66RIkSOnXqlA4dOmQ/VrZs2UTH3x0/fPhwvNi/59m5c6fTOTw9PRUUFOR0DgBpq9mLXR2OTTywPV5RKiEHftykH+fOT4Os3J+fn4/eGfSEOnd42Gl87px1euONTxUaGu40DgAAAMA1UrUgtWXLlhQdz+xOnDihc+fOKTAwUI0aNUq0b8OGDSXdORvq5MmT8WJbt261v27UqFGCBakaNWrYL+vbtm3bfWQOIDFZc+dS/4Vz9OcvO7X8vQny9ffX46+m7CmccTgrKmFVKhXV1LHPq1SJfA6xS5eC9WKv6fr2210uyAwAAABAUjxcnYC76t69u2w2W6JtxIgR9v6NGze2Hz916pT9+KpVqyRJ5cqVU+3atZ2uVbt2bZUrVy5e/7tt2rRJwcHBkqSuXR3PwojTrVs3++sVK1Yk960CSAEvHx+N3LxWeYsVUb0Oz2nige16f+eGBPsXLvOAHqxb08IM3Z+np4def/lRrVjUz2kx6ttvd6lypf4UowAAAIB0jIKUi02ZMkVRUVGSpOnTpytLlizx4lmyZNH06dMlSVFRUZoyZYrDHFFRUZo2bZokqXz58ho0aJBDnzp16qhnz56S7hSw9uzZk5pvA4Akm4eHxu3dnKIxA5d+rj5zpjmNTX2+Z2qklaGUKJZXyxb21Rv9WsjLyzNeLDQ0XL1fnK6nnxqtS5eCXZMgAAAAgGShIOViR48e1YQJEyRJNWvW1LZt29SuXTs99NBDateunbZt26aaNe+cPTF+/HgdO3bM6Tzjx4/Xn3/+aX89a9YsNW7cWLVr19Zbb72l9evXy9vbW2FhYXr99dcteW9AZtOgU7tUm2ty+26KjeFpcHd7vm1trVv2hqpXKe4Q27HjiKpVfVWffLLeBZkBAAAASKlUf8oeUu4///mP8ufPr549e6p69er66quvHPp88skneueddxKcIyQkRK1atdLatWv14IMPqk+fPurTp0+8Pjdu3NALL7yg/fv3p/p7ACA9Pfg1V6eQIeXJnVUfjmqrR5tUcIhFR8dozOgv9d57XysmJtYF2QEAAAC4FxSk0gFjjHr16qVly5apd+/eqlmzpvLmzasrV65o9+7dmj17ttatW5fkPMePH1e1atXUt29ftW3bVqVLl5aPj4/OnDmjtWvXaurUqTp9+rQF7wjA/Ti2a6+rU0g3HmlcXh+Oaqe8ebI6xP7665y6dJ6kXbv+ckFmAAAAAO6HTZJxdRLIGAIDA3X27FlJUpEiRXTu3DkXZwRYp0SVSuq/aE6qzZfZn67n7++jd998Ss+3reM0PmvmWg0a9JnCwiIszgwAAPfB93MA6Rn3kAKAe1DowdKaeGC7Jh7Yrqotmimw3IPJGjfnpdd1+dSZNM7OvVWrXEzfLR3gtBh18eJ1PdFqpF55ZSbFKAAAAMCNcckeAKTQxAPb4/3cecKYZI3bMHeBQq8Ha9Gbw5SveDF1+nCU0367V6257xzdkZeXh17t84j69W7m8AQ9SVq5cod6vzhdV67cdEF2AAAAAFITZ0gBQAr8uxiVXFfPndfvP260/3z51GnN7v2q077bl6y8pzXcWcniebV8UT+9/kpzh2JUSMht9eo5Ta2ffY9iFAAAAJBBpOoZUnv37pUxRm3atNHJkyftx4sWLSpJOnfunGJjeQoSAPdUpp7z+xkl5JO+A1WleVP9c+xvpzcqD7txU5Pbd1Obd4eoaIVykqQj23YoJioqVfJ1F53a1dU7g5+Uv7+PQ+yXXw6rS+dJ+vvvf1yQGQAAAIC0kqoFqapVq8oYIz8/v3jHT548qdjYWFWuXFmHDx9OzSUBwDK9Z01Odt8bly7r1pWr2vrfJUn2XTpq3P2k5bby5c2mD0e2VbPG5R1iUVHRGjVyscaOXaqYGP4hAwAAAMhoUrUgZcydB/Z5eDheCWiz2VJzKQBI1xYMeNvVKaRrjzapoA9HtVWe3FkdYkeOnFXnThO1d+8xF2QGAAAAwAqpeg+pGzduSPrfJXoAkFGk9N5RMdHRaZSJewvw99W4kW316YzuTotRH3+0Rg9Vf51iFAAAAJDBpWpB6sCBA5Kkd955R2XKlHE4UyruDCoAyMhm937N1SmkS9WrFNe6ZQPUsU1th9iFC9f0eMsR6tdvlm7fjnBBdgAAAACslKoFqU8++UQ2m0116tTRwYMHFRkZqej/P0vAZrPpjz/+UHR0dIpaVCa7uS8A93Jka/wzp76dOENh/3+2KO7w8vLQwP4ttGxhXxUvltchvnz5L6pcqb/WrXO88TsAAACAjClV7yG1aNEiVapUSQMGDJCnp6dDnPtIAXA3Ng8P1W79pNPY1Od7KjYmRt9Nn21xVu4jqGQ+TR37vCpXdLyU+9atML326hzNn7/BBZkBAAAAcKVULUhJ0pAhQzRt2jQ1adJEgYGB8vX11fDhw2WM0axZs3Tp0qXUXhIA0szQtUuVO7CQ01hsTIzF2biXLh0f1n8GPiE/Px+H2Nath9S1yySdOHHRBZkBAAAAcDWbpDS/sVNMTIyMMapUqZIOHz6c1svBRQIDA3X27FlJUpEiRXTu3DkXZwTcHy8fH43bu9lpbHKH7hL3xXMqf95sGj+mvZo0KOsQi4qK1ojh/9W4ccsUGxvrguwAAMg8+H4OID1L9TOknDl9+rSMMYqMjLRiOQBIFbWefSLhIMUop1o0q6hxI9sqd64Ah9jhw2fUudNE/frrcRdkBgAAACA9saQgVbJkSSuWAYBU9dw7g12dgtvIGuCr4W89rfatazmNz5j+rYYMWcAT9AAAAABIsqggBQDuxsvH8b5HcK5m9RKa/MHzKlYkt0Ps/Pmr6tF9qtav3+eCzAAAAACkV5YXpHLlyqXu3bvrkUceUcWKFZU7952/wFy7dk1//PGHfvzxR82bN0/Xr1+3OjUAkHTnyXoJ3TsK/+Pt7akBrzTXyz2byNPTwyG+ZMlWvfzSx7p27ZYLsgMAAACQnllakOrdu7cmTJggf39/SZLNZrPHAgMDVbhwYTVv3lwjRozQwIEDNXfuXCvTAwBJUt/5MxONz+jSx6JM0q/SpfJr6tjnValCEYfYzZth6t9vlhYu3OiCzAAAAAC4A8sKUkOGDNF7771nL0LduHFD+/bt0z///CObzaYCBQqoWrVqypEjhwICAjRz5kzlzJlT48ePtypFAJCHl6dKVqvsNDZ/wNu6fv6CxRmlLzabTV07PqyhA59QlizeDvGffz6oLp0n6dSpSy7IDgAAAIC7sKQgVaFCBY0ePVo2m00XLlzQ4MGDtWTJEkVHR8fr5+npqbZt22r8+PEqXLiwxowZozVr1ujQoUNWpAkAGr9va4KxzF6MKpAvuya8116N6pVxiEVGRmn4u//V+PHLFRsb64LsAAAAALgTx5t+pIF+/frJ09NTly9fVt26dbV48WKHYpQkxcTE6Msvv1TdunV16dIleXp6ql+/flakCCCT88ueXRMPbHd1GunW480ra/3KgU6LUQcPnlad2oM0btxSilEAAAAAksWSglTTpk1ljNEHH3ygM2fOJNn/7NmzGjdunGw2m5o1a2ZBhgAyKw8vTz379hsas+37RPvFxsRYlFH6ki1rFk16v4NmTe6iXDkDHOJTp6xSzRoD9Ntvf7sgOwAAAADuypJL9gIDAyVJv/zyS7LHbNu2TZJUuHDhNMkJACSpzbAhqt36yST7TXuhlwXZpC+1qpfU5LEdVTQwt0Ps3Lmr6t5tin788TfrEwMAAADg9iwpSMX8/5kFXl7JXy6uL5d/AEhLSRWjJrfvZk0i6YiPt6fe6NdCL/VoLA8PxxNpv/rqZ73y8se6fj3EBdkBAAAAyAgsuWTv9OnTkpSiy+/i+saNBYDU5Ovvr2eHDky0z49z51uTTDryYFABrfryVb3Sq6lDMerGjVB17jRRHTt8SDEKAAAAwH2xpCD1ww8/yGazadCgQapYsWKS/StVqqTBgwfLGKP169dbkCGAzKTgA0F6f+cG1e/YJsE+V86c1YEfN1mXlIvZbDb16FRfq5e8rgplAx3imzYdUJXK/fXFF5usTw4AAABAhmNJQWrKlCmKiIhQ1qxZtXXrVg0aNEh58uRx6JcnTx4NHjxYW7ZsUdasWRUREaEpU6ZYkSKATGTw8kVJ9lk46B0LMkkfCuTPrkVzXtSIt59RFl/veLHIyCi9OfgzPdLsHZ0+fdlFGQIAAADIaCy5h9Tp06fVp08fzZs3T1mzZtXYsWP1wQcf6OTJk7p48aKMMSpYsKBKlCghm80mm80mY4z69OmTrKfyAUBq+qz/YFenYJknWlTR+8OfU84c/g6xP/44pU4vTNDvv5+0PjEAAAAAGZolBSlJWrhwoa5evapZs2YpMDBQNptNpUqVUsmSJSXduVwkzvnz59W7d2999913VqUHIJPoPH50ovGpz/dU7P8/iCEjy54ti0YNfVatn3rIaXzypJUaOvRzRUREWZwZAAAAgMzAsoKUJK1du1YlS5bUs88+q0ceeUQVK1ZU7tx3Hid+7do1/fHHH/rxxx+1YsUK+5P5ACA1VX3skQRj0zv3zhTFqDo1gzT5gw4KLJTLIXbmzGV17zZFP/30uwsyAwAAAJBZWFqQkqSYmBgtXbpUS5cutXppAEjQ9x/NVXRkpKvTSFM+3p4a9GpL9e7W0OEJepL03/9uVr++MxUcHOqC7AAAAABkJpYXpADAFYqUL6sBX81zGvtr+y4d2rLN4oysVeaBgpo67nmVL1PYIRYcHKK+r8zS4sWbXZAZAAAAgMyIghSADK98o/rqOWO809iulau1bXHGPWPTZrOpZ5cGGvL64/L1cfyV/9NP+9W921SdOcMT9AAAAABYh4IUgAzNw9MzwWKUJP3xU8Y9K6hQwRya9F4H1avzgEMsIiJK/xn6uSZPXiVjjAuyAwAAAJCZUZACkKHVbv1UovEbFzPmmUFPPV5V773TWjly+DvEfv/9hDq9MFF//HHKBZkBAAAAAAUpABmYdxZftXn3zQTjh3/+xcJsrJEju59Gv/OsnmlV3SEWGxurSRNX6p13FioyMtoF2QEAAADAHRSkAGRIOQsW0LAfViba5/uP5lqTjEUerl1ak97roMKFcjrETp++rK5dJmnz5j+sTwwAAAAA/oWCFIAMp1zDeur10YRE+2z4ZEGGuXeSr4+XBr/WUr27NXIaX7Roo/r3m60bN0ItzgwAAAAAnKMgBSBDqPZ4c3UaNzLJftGRkZr32hCFXLtuQVZpr+yDhTR1bEeVK1PYIXb9eohefukjff31VhdkBgAAAAAJoyAFwO017vaCnhzYL1l9p3funcbZWMNms+nFrg01+LWW8vVx/FX+44+/qXu3KTp37qoLsgMAAACAxFlSkPr0008lSd99952WLl1qxZIAMpHkFqPCQ0LSOBNrFC6UU5Pe76CHa5V2iIWHR+rttxZo2rRvM8wliQAAAAAyHksKUl27dpUkffXVV1YsByAT6T1rcrL7zuyZvMJVevZMq2oa/U5r5cju5xD77be/1bnTRB08eNoFmQEAAABA8llSkLp8+bLy5cunixcvWrEcgAwmqEY1vTLvY/vPH3V7WX/v/U02m01l6tVJcvyMLn0UFRGRlimmuRzZ/fTesNZ66vFqDrHY2FhNGL9c7777hSIjo12QHQAAAACkjIcVixw6dEiSVLx4cSuWA5BBeHh6qljlCvGKUZLUd/5MTTywXZUeaZzkHKHBN9y+GFW/zgNav2Kg02LUqVOX1LTJf/TWWwsoRgEAAABwG5YUpBYtWiSbzWa/dA8AkpK7SGEN+3GVXvvikwT7dJ30fpLzLBz0TmqmZSlfXy8Nf+sp/ffTPipUMKdD/PPPf1KVyv21Zcsf1icHAAAAAPfBkkv25s2bp44dO+rpp5/Wu+++q1GjRlmxLAA39p/vlt3TuMXvjFbhMg8oOjJSf27bqYjQ0FTOzBrlyxbWtHHP68HSBR1i167d0kt9PtLSpdtckBkAAAAA3D9LClINGjTQhAkTlC9fPr377rvq0KGDvvrqK/3++++6fv26YmJiEh3/888/W5EmgHTCw9Pznsf+c/S4/jl6PBWzsZaHh029uzXSoFcfk4+346/o9ev3qUf3KTp//poLsgMAAACA1GFJQWrTpk3xHj/+4IMPatiwYckaa4yRt7d3WqUGIB0a/9tWV6fgEoGFcmnyBx1Up2aQQ+z27Qi9NWSBZsxYHe/3KQAAAAC4I0sKUpJks9msWgqAGytZrfI9j13+/oRUzMRazz31kEYOfUbZs/k5xPbtO65OL0zU4cNnXJAZAAAAAKQ+SwpSTZo0sWIZAG4sd2Ah/Wfd8vua49R+97u5d84c/vpg+HNq1aKKQyw2Nlbjxi7ViBGLFRXFE/QAAAAAZByWFKS2bNlixTIA3FhyilFLR49Tq9dekV/2bBZklPYaPPygJr3XXgXy53CInThxUV06T9K2bYdckBkAAAAApC3LLtkDgIRMPLA90fjWxUv09979unrmrGa92F/PvPWGw6V9c156PQ0zTF2+vl4a+kYrde/UwGl83rwf9fprc3Tr1m2LMwMAAAAAa1CQAuBSNg+PJPvsXrkm3s8rx05S7sBC6jrpA0nSR91eUuTt8DTJL7VVLBeoqeOe1wNBBRxiV6/eVJ/eH2n58l9ckBkAAAAAWMfyglS2bNnUpk0b1a1bVwULFpS/v7969Oih06dP2/sUKlRIOXPmVHh4uE6cOGF1igAslL9EsUTju1etcXr82rkLmty+WxpklDY8PGx6uUcTDejXXD7ejr96163bq549punChWsuyA4AAAAArGVpQeqVV17Re++9p2zZ7tz/xWazyRijgICAeP0aNWqkL774QuHh4SpSpIiuX79uZZoAUpmXj4/G7d0c79iGTz7XloVf6s1VixMd+++zo9xR0cDcmvxBB9V6qJRD7PbtCA0e9Jk+/nitCzIDAAAAANdI+lqZVDJ8+HBNmzZN2bNnV2RkpPbu3Ztg36+++koXLlyQr6+vnnvuOatSBJBG/l2MkqRmvbpo5ObEizAf93hFEWFhaZWWJdo8U0Prlr/htBi1Z89RVa/2OsUoAAAAAJmOJQWpqlWratiwYZKkRYsWqWDBgqpdu3aC/Y0xWrJkiWw2mx599FErUgSQjkxu302T23dTRKj7FqNy5fTXrCldNOm9DsqWNUu8WExMjN4b85XqPfym/vzzrIsyBAAAAADXsaQg1b9/f9lsNm3fvl1du3bVzZs3kxyzffudp25VqlQprdMDkI7cuHjJ1Snct0b1y+iHlYP0+KOVHWJ///2PGjV8W8OGLVJUVLQLsgMAAAAA17PkHlKNGjWSMUYzZsxI9piTJ09KkgIDA9MoKwDp0U+fLXJ1CvcsSxZvDR34hLo9X89p/LNP1+v11z9RSMhtizMDAAAAgPTFkoJUoUKFJEl//vlnssdERERIknx9fdMkJwDWaDN8SIr6nz5wMI0ySVuVygdq6rgXVLpUfofY5cs31Kf3DK1cucMFmQEAAABA+mNJQSoyMlK+vr7y9vZO9pi4IlZwcHAaZQUgrXln8VXdNs8ku//Vc+cVGxOTdgmlAU9PD73cs4kGvNJc3t6eDvG1a/eoZ4+pungx2PrkAAAAACCdsuQeUmfP3rlpb4UKFZI9pnnz5pKkY8eOpUlOANLe2N2bkt33xqUrWjR4WNolkwaKFcmtJQte1puvtXQoRoWFReiVlz/WE61GUowCAAAAgH+xpCD1008/yWazqXv37snqX7JkSfXs2VPGGP3www9pnB2AtGDzSPjXy/71P8X7efPni/Xft4e71dlR7Z6tqXXL31CNaiUdYrt3H1X1aq9p1qzvXJAZAAAAAKR/llyyN2PGDL300kuqV6+ehg8frpEjRybY96GHHtKXX36prFmzKjw8XLNnz7YiRQCprNvk950e/3zgUF09e14/ffq5xRmljty5AjR2RBs99ojjE0BjYmL0/ntLNHr0l4qOdp/iGgAAAABYzZKC1NGjRzV69GiNHDlSw4YNU8uWLbVs2TJ7/LHHHtOTTz6p5s2bq3HjxpIkY4zeeust/fPPP1akCCCVVWzayOHYhaPHdfXseRdkkzqaNCir8WPaKX/e7A6xY8fOq0vnSdqxI/kPbwAAAACAzMqSgpQkjRkzRt7e3ho6dKhq1qypGjVqyBgjSRo/fry9n81mkzFGo0aN0vTp061KD0Aq8vLxcXp85bjJFmeSOvz8fPTOoCfUucPDTuOfzP1eAwZ8otDQcIszAwAAAAD3ZMk9pOIMHz5cderU0fLly3X79m3ZbLZ4LSoqSt99950aNGigUaNGWZkagFQ0bu9mp8fDb4VYnMn9q1yxqNYued1pMerSpWA9/dRo9e49g2IUAAAAAKSAZWdIxdm7d6/atm0rT09PlS9fXvnz55enp6euXr2qgwcPKjycv9QB7ixb3jxOj2+ct8jiTO6Pp6eH+vVuptdeekReXp4O8dWrd6tXz2m6dCnY+uQAAAAAwM1ZXpCKExMTowMHDrhqeQBpZMTG1U6P//vJeulZ8WJ5NOWDjnqoagmHWGhouN4Y8Inmzv3e+sQAAAAAIINwWUEKQMZTutZDTo8f27VXJjbW4mzuTcc2tfXukKcU4O/rENux44i6dJ6kY8cuuCAzAAAAAMg4XFKQyp8/vxo3bqyKFSsqd+7ckqRr167pjz/+0KZNm3Tp0iVXpAXgPnj5+urlT2c4jbnD5Xp5cmfVuJFt1LxpRYdYdHSMxoz+Uu+997ViYtyjsAYAAAAA6ZmlBakiRYpowoQJeuaZZ+Tl5XzpmJgYrVixQoMHD9aZM2esTO+eVKtWTY899pgaNGigihUrKn/+/IqKitL58+f1yy+/6NNPP9XWrVuTPV+LFi3Uu3dv1apVS/ny5dPly5e1a9cuzZkzR99/n7xLhPz8/NS3b1+1bdtWpUuXlo+Pj86cOaM1a9Zo2rRpbvG5wv2M27PJ6fGT+w8o5Np1a5NJoWaNyunDUe2UL282h9jRo+fVudNE7dr1lwsyAwAAAICMySbJWLFQ/fr19e233ypbtmyy2WyJ9jXG6NatW3riiSe0bds2K9K7J5s2bVKjRo2S7Pf555+rV69eioqKSrTfrFmz1KdPnwTjs2fP1ksvvZToHKVKldKaNWtUtmxZp/Hg4GA9//zz+u6775LMO6UCAwN19uxZSXeKj+fOnUv1NZA+TTywPcHYtBd6KSY62sJsks/f30fDBj+pF9rVdRqfPes7DRz4qcLCIizODAAA4P7x/RxAeuZhxSKFCxfWt99+q+zZs8tms+m7775T27ZtVbx4cWXJkkVZsmRR8eLF1aZNG61du1Y2m03Zs2fXt99+q0KFClmR4j0JDAyUJJ07d05TpkzRc889p5o1a6pOnToaMGCA/Zd/ly5dNH/+/ETnGjNmjL0Y9euvv6pDhw6qWbOmOnTooF9//VWS1KdPH40ePTrBOQICArR69Wp7MWrOnDlq2rSp6tatq6FDh+rWrVvKmTOnlixZokqVKt3v2weSJb0Wo6pWKqbvlg5wWoy6ePG6nnxilF5++WOKUQAAAACQBiw5Q2ratGnq27evYmJi1L17d33xxReJ9u/YsaM+//xz2Ww2ffTRR3rttdfSOsV78u233+rzzz/XsmXLFOvkhs158uTRtm3bVKZMGUlSgwYNnF6+FxQUpMOHD8vb21u7d+9Ww4YNFR4ebo/7+flp8+bNqlmzpqKiolS2bFn9/fffDvMMHz5cI0aMkCQNHjxYEyZMiBevU6eOtmzZIm9vb/30009q1qzZ/bx9B/wLTObk6++v93ducBqb+nxPxcbEWJxR4ry8PNS/9yPq36eZvLw8HeKrVu1Q7xdn6PLlGy7IDgAAIPXw/RxAembJGVKPP/64jDGaO3duksUoSVq8eLHmzJkjm82mVq1aWZDhvXnyySe1ZMkSp8UoSbp69aoGDhxo/7lNmzZO+w0YMEDe3t6SpP79+8crRknS7du31b9/f0mSt7e3Xn/9dYc5vLy87IW7Q4cOaeLEiQ59duzYoU8//VSS1LRpU1WvXj2JdwgkLaFi1HfTZ6e7YlSJYnm1bGE/Dejb3KEYFRJyWy/2mq5nn3mPYhQAAAAApDHLLtmTpCVLliR7TFzfuLHuatOmTfbXQUFBTvs8/fTTkqTDhw9r586dTvvs3LlTR44ckSQ988wzDvHGjRsrV65ckqQFCxbIGOcnvt196WDr1q2TSh9IVEL3g9uy8Esd2ZrwfaVc4YW2dbRu2RuqVrmYQ2z79iOqVvU1ffrpehdkBgAAAACZjyUFqevX7zxh68aN5J91ENc3bqy78vHxsb92diZVyZIlVaRIEUnS5s2bE50rLl60aFGVKFEiXqxBgwYO/ZzZs2ePQkJCJN250TxwPx6oU8Pp8esX/rE4k4TlzZNVn33UQx+MaCN/f594sejoGL07bJEaNhii48cvuChDAAAAAMh8vKxYZM+ePWrVqpUqVaqkffv2JWtM3E239+zZk5appbm7n8IXd4bT3cqVK5do/G53x8uVK6eTJ0+meJ6YmBgdP35cVapUiTcmOeJu4p6Q9HwDeqS+oBrV1GfONKexk78dsDgb5x5tUkEfjmqrPLmzOsT+/POsOneapD17jrogMwAAAADI3CwpSE2bNk1PPPGE3nzzTS1ZskS3b99OtL+fn5+GDBkiY4ymT59uRYppwmaz6a233rL//PXXXzv0KVq0qP113A0HE3LmzBmn4+7+OSQkJMkz0c6cOaMqVaoof/788vHxUWRkZKL9k5sfMo/qrZrrhbEjE4y7+t5R/v4+Gj7kaXVsU9tpfObHazV48Gc8QQ8AAAAAXMSSS/Y2bNigkSNHqly5ctq0aZOqVKmSYN/KlStr48aNKlOmjEaOHKkff/zRihTTxIABA1S79p2/EC9fvlx79+516JMtWzb767hL6RISGhpqf501a/wzPuLmSWqOpOYBkvLCuJGJFqP2rXXtfZiqVymudcvecFqM+uef62r1+Aj17TuTYhQAAAAAuFCqniE1bNiwBGPGGO3Zs0c1atTQ3r17deDAAe3evVuXLl2SMUYFChRQzZo1HS7VGzZsmEaPHp2aaVqiYcOGGjt2rCTp4sWLevnll532y5Ili/11UmcqRUT87y/Qfn5+TudJztlOic2TmLh7XSWkUKFC2r17d7Lng3uq/njzBGM3Ll3W9qUrrUvmLl5eHnr1pUfVv3czeXo61tpXrNiuPr1n6MqVmy7IDgAAAABwt1QtSI0YMSLBp7vFMcbIZrOpUqVK9uLT3Ww2m4wxqlGjhmrUuHPDZHcrSJUvX14rVqyQt7e3wsPD1a5dO126dMlp3/DwcPvru2+A7oyvr6/99b8ve4ybJ6k5kponMefOnUt2X2RMCT1VL85n/QdblEl8pUrk09Rxz6tKxaIOsVu3wvT6a3M1b577nm0JAAAAABlNqt9DKqm/sCanX3LnSI9KlCih9evXK3fu3IqOjlbHjh21ZcuWBPvfunXL/jqpy+cCAgLsr/99aV7cPMm5BC+xeYDE9F84J8HYnD6vWZjJ/3Tu8LDeGfSE/Pwci7Hbth1Sl86TdOLERRdkBgAAAABISKoWpDw9PVNzOrdTqFAh/fjjjwoMDFRsbKx69OihlStXJjrm7huFJ3VJ3N03Mr/7Bud3z5M1a1blyJEj0Rubx81z6dKlZN/QHPDPkV3Fq1RMMB4anPjN9FNbvrzZNH50OzVt6Pi0yKioaI0Y/l+NG7dMsbGxluYFAAAAAEiaJU/Zywzy5MmjH374QUFBQZKk/v37a+HChUmOO3TokP112bJlE+17d/zw4cOJzrNz506nc3h6etpz/PccgDNjtq2XX/ZsifbZunipRdnc0aJZRY0b2Va5cwU4xA4fPqPOnSbq11+PW5oTAAAAACD5LHnKXkaXPXt2ff/996pQoYIkaciQIfr444+TNfbEiRP2ezM1atQo0b4NGzaUdOdsqJMnT8aLbd261f46sXlq1Khhv6xv27ZtycoRmVft1k8mWYySpN0rV1uQjRTg76vxo9tp7rRuTotRH81YrRoPDaAYBQAAAADpHAWp++Tn56c1a9booYcekiSNGTNGH374YYrmWLVqlSSpXLlyql3b8VH1klS7dm2VK1cuXv+7bdq0ScHBwZKkrl27JrhWt27d7K9XrFiRojyR+bQbOTTR+I5l32jq8z0tyaVGtRL6fvkbat+6lkPswoVravnYcPXvP1u3b0c4GQ0AAAAASE9cVpDKli2bChcurKJFiybZ0itvb2+tWLFC9evXlyRNmTJFw4YNS/E8U6ZMUVRUlCRp+vTpypIlS7x4lixZNH36dElSVFSUpkyZ4jBHVFSUpk2bJunOU/4GDRrk0KdOnTrq2fNO8WDTpk3as2dPinNF5lG5edMk+2z/erliY2LSNA9vb08NfvUxLVnwiooVzeMQX7bsF1Wu1F/ff/9rmuYBAAAAAEg9lt5D6tFHH9XLL7+sBg0aKFeuXMkaY4yRt7d3Gmd2bxYvXqwWLVpIkjZs2KBPP/3UftmeM5GRkTp69KjD8aNHj2rChAl6++23VbNmTW3btk3jxo3T8ePHFRQUpCFDhqh69eqSpPHjx+vYsWNO5x8/frzat2+vMmXKaPz48SpdurS+/PJL3b59W02aNNHQoUPl7e2tsLAwvf766/f/ASDDKlK+rLpOfM/Vaah0qfyaOvZ5VargeMP/mzfD9Gr/2fr8859ckBkAAAAA4H7YJBkrFpo5c6ZefPHFO4vabMkeZ4yRl1f6vPe6MSn76E6ePKmSJUs6jdlsNs2dO9d+BpMzn3zyiXr37p3oukFBQVq7dq0efPBBp/EbN27ohRde0Jo1a1KUe3IEBgban/ZXpEgR+72x4B4KPVhaT77RV2Xq1UlW/88H/UdXz6TdHnd9vp7+M/AJZcniWJD++eeD6tplsk6evJhm6wMAALg7vp8DSM8sqfT07t1bvXv3liTdunVLK1as0P79+xUcHMwj2f+fMUa9evXSsmXL1Lt3b9WsWVN58+bVlStXtHv3bs2ePVvr1q1Lcp7jx4+rWrVq6tu3r9q2bavSpUvLx8dHZ86c0dq1azV16lSdPn3agncEdzNoWdJPhYxz49LlNCtGFciXXePHtFPj+o5PnYyMjNLwd/+r8eOX87sDAAAAANyYJWdI7dmzR9WqVdPhw4fVtGlTXbp0Ka2XhAvwLzDuK2+xInp7zZIk+33co688vTwVduNmmuTR8tFKGjuijXLldHyC3qFDp9XphYn67be/02RtAACAjIbv5wDSM0vOkCpbtqyMMRoxYgTFKCAdKt+ofpJ9JrfvlmbrZw3w1cihz6jtMzWdxqdN/UZvvbVA4eGRaZYDAAAAAMA6lhSkQkJClCVLFqc39Abgek+/+ZrL1q5VvaQmfdBRxYrkdoidO3dV3btN0Y8//mZ9YgAAAACANGNJQerw4cNq0KCBChUqpP3791uxJIAEDFq+SIUeCErRmIiw26meh7e3p97o21wv92wiDw8Ph/jXX2/Vyy99pOvXQ1J9bQAAAACAazn+LTANzJkzRzabTR07drRiOQAJqNvu2RQXoyRp3YzZqZrHA0EFtPK//dX3xWYOxagbN0LVpfMkdWg/jmIUAAAAAGRQlpwhtXjxYj377LN64YUXtGvXLn300UdWLAvg//llz65yDeuqzbA3Uzz25pWr+nvvb6mSh81mU7fn6+ntN1opSxZvh/jmzX+oa5dJOn36cqqsBwAAAABInywpSEnSCy+8oAkTJmjKlCnq0KGDvv76a/31118KCwtLcuzPP/9sQYZAxlT9iRZ64YMRKRoz68X+8g3wl7ePry6fOp0qeRTIn10Tx7RXw3plHGKRkVEa9s4iTZy4UrGxsamyHgAAAAAg/bKsIBUVFaX9+/fr2rVrqlu3rurWrZusccYYeXs7nkkBIHlSWoySpNs3b+n2zVuplsMTLaro/eHPKWcOf4fYH3+cUudOE7V//4lUWw8AAAAAkL5ZUpDy9PTU4sWL1bp1a0l3LtsBkPYKBJVM8ZjNC79MtfWzZc2i0f95Vq2feshpfMrkVXr77QWKiIhKtTUBAAAAAOmfJQWpl156Sc8995wk6eTJk1qwYIF+//13BQcHc3kOkIbeXPnfFPX/59jf+nX1ulRZu9ZDpTRlbEcVKZzLIXb27BV17zZFGzbw1E0AAAAAyIwsKUj16tVLkrRjxw41a9ZM4eHhViwLZGg2m03GmATjHl6eKZrvzMEjWjpq7P2mJR9vTw169TH17tbI4Ql6kvTll1vU95WZPEEPAAAAADIxSwpSpUuXljFGH3zwAcUo4D51+nCUqrV81P7zm9UbKiYq/iVvlZo1UrcpSReXJrfvJk8vLxljFBsTc9+5PVi6oKaO66gKZQMdYsHBIer7yiwtXrz5vtcBAAAAALg3x9MX0kBoaKgk6cyZM1YsB2RYOQrki1eMkqQPf90S7+fs+fImqxgVJyY6+r6LUTabTT27NNDqr19zWozauPF3Van8KsUoAAAAAIAki86Q2r9/v5o1a6bixYtr/37uGQOklF/2bOo7f6YKPRDkNN5vwSyd+v2gzv91TM+//26y5rxx6XKq5FawQA5Neq+96td90CEWERGl/wz9XJMnr0r08kIAAAAAQOZiSUFq1qxZeuSRR9SzZ0998803ViwJZChjtq1PNF6yehWVrF4lRXOuGjf5flKSJD3ZsqreH9ZaOXL4O8QOHDipTi9M1IEDJ+97HQAAAABAxmLJJXsrVqzQrFmz9MQTT2j8+PFOb3QMwLkSVSunyjyhwTfsr29cuqKrZ8/f81zZs2XR1HHP66MJnZwWoyZNXKFaNd+gGAUAAAAAcMqSM6Q6d+6sHTt2qFq1ahowYIDatGmj5cuX66+//lJYWFiS4xcuXGhBlkD61H/h7PueY+nocQq+cFG1Wz+l2NgY7Vqx+p7nqlsrSJPe76DAQrkcYqdPX1b3blO0cePv95MuAAAAACCDs0lK8xu7xMTE3PP9Y4wx8vb2TuWMkBYCAwN19uxZSVKRIkV07tw5F2fk/rx8fTVuz6b7nmdy+273PYePt6cGv9ZSL3Zt6PQsxy++2KR+fWfpxo3Q+14LAAAA94/v5wDSM0vOkJLuPIULQMoM+Wbxfc9xdNee+56jzAMFNW3c8ypXprBD7Pr1EL3y8sf66quf73sdAAAAAEDmYElBqmTJklYsA2Q4uQsXuu85Vk+ccc9jbTabenVpqDdfbylfH8dfFxs27Ff3blN09uyV+0kRAAAAAJDJWFKQOn36tBXLABlKQmcVfvbqYIVcC9ari+YmOcdnr755z+sXLpRTk97voIdrlXaIhYdHaujbn2vq1G/u+XJcAAAAAEDmZdklewBSJkeB/E6P37h4WZL096/7Vap6FYf42cN/asmID+5r7adbVdOYd1orR3Y/h9j+/SfU6YUJOniQQjMAAAAA4N5QkALSoVyFC+qd71c4HP+03yD7642fLYxXkJr32hAF/3PxvtbNkd1PY4a11tOPV3OIxcbGauKEFRo2bJEiI6Pvax0AAAAAQOZGQQpIh5wVoyTp5uUr8V5/3OMV5SkSqKtnzysi9P6eblevdmlNfK+DChfK6RA7deqSunaZrC1b/rivNQAAAAAAkCwqSB0/fvyexxpjVLq04z1sgIyqQaf2ye4bERqm838eva/1fH289ObrLfVi10ZO4wsXblT/frN082bYfa0DAAAAAEAcSwpSJUqUSHZfY0y8mzlzw2RkNs8Med2ytcqVKaRp415QmQcKOsSuXbull1/6WEuWbLUsHwAAAABA5mBJQWrBggVJ9gkICFCZMmVUqVIlGWO0b98+HThwwILsgPQjVyHHwlBa8PCwqXe3Rhr06mPy8Xb8NbB+/T716D5F589fsyQfAAAAAEDmYklBqkePHsnuW6FCBX322WeqUKGCPvjgAy1fvjwNMwPSl3fWO793lCStmzEnVdYILJRLkz/ooDo1gxxi4eGRGvLmfM2YsZqzEwEAAAAAacbD1Qn828GDB9W0aVNduHBBn3/+ucqUKePqlACXu3LmrA7//Mt9z9P6yer6fsUbTotR+/YdV42HBmj69G8pRgEAAAAA0lS6K0hJUmhoqCZNmiQ/Pz8NHjzY1ekAlgjIldPp8Tkvva6Fg965r7lz5PDTxxM7a8rY55U9m1+8WGxsrMaNXao6tQfp0KHT97UOAAAAAADJYckle/di9+7dkqRmzZq5OBPAGqO2fOdw7PatEIVeD76veRvUfUAT3+ugggVyOMROnryoLp0naevWQ/e1BgAAAAAAKZFuC1JxT9orUKCAizMB0l6Zh2s7Pb501Nh7ntPX10tvD2ilHp0bOI3Pn79Br706W7du3b7nNQAAAAAAuBfptiDVokULSdKNGzdcnAmQ9nrPnuL0+LVzF+5pvgplC2vquOf1YGnHp/ZdvXpTfXp/pOXL7/+eVAAAAAAA3It0WZBq37693n77bRljtHXrVlenA7hMbExMivp7eNj0Uo/GeqNfC/l4O/7nvW7dXvXsMU0XLlxLrRQBAAAAAEgxSwpSn376aZJ9PDw8lCtXLlWvXl2FCxeWzWZTdHS0xo6990uWAHdg83D+bIFvJ85I0TxFCufS5A86qnaNUg6x27cj9ObgefroozX3lCMAAAAAAKnJkoJUt27dkv0Y+bh7R928eVO9evXS3r170zI14J7558iumOhoRYSGJat/YNkHVfOZVtq9co3OHfnLfvy1xc4Ltsd27Ul2Lm2erqGRQ59RtqxZHGJ79x5T504TdeTI2WTPBwAAAABAWrKkIHX69OkkC1KxsbG6deuWTpw4oc2bN2vRokW6evWqFekBKdZtylhVatZIkvT18Pe1c/m3ifafeGC7/XWDF9pp0Zvvat93P6jUQ1VVtHxZh/7fTZ+drDxy5vDX2BFt9Hjzyg6xmJgYjRu7TCNHLlZUVHSy5gMAAAAAwAqWFKRKlixpxTKAJfKXLG4vRklSu5FDdWTbDt24eNlp/4fbt3Y41unDUdr33Q/qO3+m0zF/bd+VZB4N6z2oiWPaq0D+HA6xv//+R106T9IvvxxOch4AAAAAAKyWLm9qDqRXNptNQ7750uH4uz9+o6G1myki7H+X72XPl1eP9O6meh2eczpXYLkHE1wnsZuZZ8nirbffaKXuL9R3Gp/32Q96/fW5unXrdoJzAAAAAADgShSkgBSo0rxpgrH3d26wvz60ZZsCcuRQ8SoVE+xfqnpVp8fnD3g7wTGVygdqytjn9UBQAYfYlSs31af3DK1Ysd3JSAAAAAAA0g8KUkAKtB2RcLHobuUb1kuyzzNvDXB6/OblKw7HPDxseqVXUw14pbm8vT0d4mvX7lGvntP0zz/Xk5UfAAAAAACuREEKSIEsWQPSfI2YqKh4PxcrkltTxnZUjWqO92ILC4vQ4EGfaebMtWmeFwAAAAAAqSXVC1LR0an7NC9jjLy9vVN1TuBeFAhK+5vzr5n8Ubyf2z5TUyOHPq2sAVkc+u7efVSdO03UX3+dS/O8AAAAAABITalekLLZbKk9JeByHcYMU82nH0/TNa6eO6+/duyWJOXK6a+xI9qq5aOVHPrFxMTog/eXaNSoLxUdnfDNzwEAAAAASK9SvSC1YMGC+xpvs9n0+OOPK0+ePBS3kG6kdTFKkvIEFpYkNWlQVuPHtFP+vNkd+hw/fkFdOk/S9u1H0jwfAAAAAADSSqoXpHr06HHPY59++mmNHDlSefLksR87c+ZMaqQF3LNeMyfd9xybP1+s/d9v0KtffJJgn4Prf9CYYa3VpcPDTuOfzP1eb7zxqUJCbt93PgAAAAAAuJKHqxOQpJYtW2r37t1atmyZKlasKJvNpn/++Uf9+/fXgw8+6Or0kIn1XzhH5erXTTA+pUN3/f7DxiTn+XXN94pJ5P5qBfyi9J82xZwWoy5fvqFnnh6j3r1nUIwCAAAAAGQILn3K3iOPPKJRo0apVq1aku5crnfp0iWNGzdOM2fOVEREhCvTQyaXt3hRlajqeA+nONFRUTLGaMMnC7Thk/9dqlrz6Vaq/3xb+8/TXuhlf/35oHfUZcIY+882GdXKd1s189ySt9f/zgyMs3r1bvXqOU2XLgXf57sBAAAAACD9cElBqmHDhho9erTq1asn6U4h6urVqxo/frxmzJih27c5CwSu9/bqrxONT+/0otPju1et0e5v1srDw0OxMfFvOn71zFn76xw+MXqsyC0V9o+W5BmvX2houAa+8anmzFl3b8kDAAAAAJCOWVqQqlu3rkaNGqUmTZpIulOICg4O1qRJkzRlyhSFhoZamQ5wT/atXa/Nny9OvJMxDsWoONM6vaixc4fq2XLZ5OPpGN+16y917jRRR4+eT4VsAQAAAABIfywpSNWoUUOjRo1S8+bNJd0pRN26dUtTpkzRpEmTdPPmTSvSAO5b8D+XtGnBf+95fJ7cWTVuZBs1r5nNIRYdHaP3xnyl9977WtHRzotZAAAAAABkBGlakKpSpYpGjhypJ554QtKdQlRoaKhmzJih8ePH6/r162m5PJDq5r325j2PbdqwnMaPbqd8eR2LUUePnlfnThO1a9df95MeAAAAAABuIU0KUuXLl9fIkSP17LPPSrpTiLp9+7ZmzpypsWPH6urVq2mxLJBq8pcs7nBs+fsT72kuPz8fDRv8pDq1d/60vjmz12ngwE8VGhp+T/MDAAAAAOBuUr0g9cUXX6hdu3ay2Wyy2WyKiIjQ7NmzNXbsWF28eDG1lwPSRLNeXR2OXT9/IcXzVK1UTFPGdlSpEvkcYpcuBatXz2lavXr3PeUIAAAAAIC7SvWCVIcOHeyvL1++rKlTp+rs2bP2+0fdi4ULF6ZGakCy1XiqpcOxm5evJHu8p6eHXu3ziPr3aSYvL8c7l3/zzU692Gu6Ll++cV95AgAAAADgjtLkkj1jjCQpb968Gj169H3PRUEK7qREsbyaOu55VatczCEWEnJbbwz4RJ98st4FmQEAAAAAkD6kSUHKZrOlxbSAJTy97v0/i+fb1ta7bz4tf38fh9iOHUfUudMkHT+e8kv/AAAAAADISFK9INWkSZPUnhKwVLFK5R2O7Vj2TaJj8ubJqg9HtdMjjR3HRkfHaPSoL/X++18rJiY21fIEAAAAAMBdpXpBasuWLak9JWCpfp/Pdji2/evlCfZ/tEl5jRvZTnnzZHWI/fXXOXXuNFG7dx9N1RwBAAAAAHBnaXLJHuCusubJley+/v4+Gj7kKXVsU8dpfNbMtRo06DOFhUWkVnoAAAAAAGQIFKSAuxSvXDFZ/apXKa4pYzuqRLG8DrF//rmuXj2nae3aPamdHgAAAAAAGQIFKeAuz38wPNG4l5eHXn3pUfXv3Uyenh4O8ZUrd6j3i9N15crNtEoRAAAAAAC3R0EKuEuWgACHYyvHTpYklSyeV1PHPa+qlYo59AkJua3XX5urzz77Ic1zBAAAAADA3VGQAv7f+N+2Oj1+Yt9+dWpfV8MGPyk/Px+H+C+/HFaXzpP099//pHWKAAAAAABkCBSkAEl+2bPLw9PT4fitU8c17+OeataonEMsKipaI0cs1rhxSxUTE2tFmgAAAAAAZAgUpABJY7Z973AsKFuEGjTwUa7sjsWoI0fOqnOnidq795gV6QEAAAAAkKFQkEKmlqdIoOq2ezbeMW+PWDUuFKqKuSIk+TmM+WjGar355nzdvh1hUZYAAAAAAGQsFKSQab374zfKUSBfvGOF/KL0WNFbyunjeAnehQvX1KP7VH3//a9WpQgAAAAAQIZEQQqZkqe3d7xilIeM6uQPU818t+Vhc+y/bNkveqnPR7p69aaFWQIAAAAAkDFRkEKm9MGun+yvc/lEq2XREBXwi3bod/NmmF57dY4WLNhgZXoAAAAAAGRoFKSQ6fgG+MvTy0uSUZXc4WpQMFTeHo79tm49pC6dJ+nkyYuW5wgAAAAAQEbm5K/hyAiKFi2q8ePH69ChQwoJCdHVq1e1c+dODRw4UH5+jjfqzixsNpve37FBAV6xeqb4TTUt7FiMioqO0dC3F6hxo7cpRgEAAAAAkAY4QyoDevzxx/XFF18oZ86c9mMBAQGqVauWatWqpV69eunxxx/XiRMnXJekizxQp6ZKZ4/QI4VD5OdlHOIXgqP0RNPB2rfvuAuyAwAAAAAgc+AMqQymcuXK+vrrr5UzZ07dunVLQ4cOVd26ddW0aVPNmTNHklS2bFmtWbNGAQEBLs7WWtlzBGjZmjF6stgtp8WobzafUFChDhSjAAAAAABIY5whlcFMmTJFAQEBioqKUvPmzbVjxw57bOPGjTp69KjGjx+vcuXK6Y033tDo0aNdmK116tcvr6XfjlL+nBEOsZAoD7V54h2tX7/PBZkBAAAAAJD52CQ5nioCt1SjRg3t3r1bkjRr1iy9/PLLDn1sNpv++OMPlS9fXteuXVOBAgUUHe34dLl7ERgYqLNnz0qSihQponPnzqXKvPfikT7d1bJfb3nYjLIe+E7d29WUh4fNod9fN3y04XxW9S1fzwVZAgAAAGknPX0/B4B/45K9DOSZZ56xv543b57TPsYYff7555Kk3Llzq3HjxhZkZq1yDR5Wy369lds3Wh1KBatnh1oOxaiIGJvWnc2qNWeyaed3m1yTKAAAAAAAmRQFqQykQYMGkqSQkBDt3bs3wX6bN2+2v65fv36a52UlX39/9fp4gqrmvq0XgoJVwC/Goc/ZUC8tOpZTh4OzSLLp6xEfWJ8oAAAAAACZGPeQykDKlSsnSTp27JhiYhwLMXGOHDniMCY5AgMDE40XKlQo2XOllTot6uvZ4jdVIluUQywmVvrlkr/2XvGT0Z0zpgZWqmt1igAAAAAAZHoUpDIIX19f5cuXT5Ls14knJDg4WCEhIcqaNauKFi2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\n"
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     "metadata": {
      "image/png": {
       "width": 594,
       "height": 340
      }
     }
    }
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.4",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}