Lung-Cancer-Project / Git / [60ffcc] /Lung Cancer Prediction.ipynb

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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n"
   ]
  },
  {
   "cell_type": "markdown",
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     "name": "#%% md\n"
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   },
   "source": [
    "# **Getting Started** \n",
    "\n",
    "**Title : Lung Cancer Prediction**\n",
    "  \n",
    "  **Lung Cancer Status :**\n",
    "\n",
    "  0 -- > Yes \n",
    "\n",
    "  1 -- > NO\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68b21a1",
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    "tags": [],
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     "name": "#%% md\n"
    }
   },
   "source": [
    "### **DataFraming**\n",
    "\n",
    "**Read .csv file into pandas**"
   ]
  },
  {
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   "id": "878078a5",
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       "\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>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SMOKING</th>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <th>ANXIETY</th>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <th>CHRONIC DISEASE</th>\n",
       "      <th>FATIGUE</th>\n",
       "      <th>ALLERGY</th>\n",
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       "      <th>ALCOHOL CONSUMING</th>\n",
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       "      <th>SHORTNESS OF BREATH</th>\n",
       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <th>CHEST PAIN</th>\n",
       "      <th>LUNG_CANCER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
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       "  GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0      M   69        1               2        2              1   \n",
       "1      M   74        2               1        1              1   \n",
       "2      F   59        1               1        1              2   \n",
       "3      M   63        2               2        2              1   \n",
       "4      F   63        1               2        1              1   \n",
       "\n",
       "   CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                1         2         1         2                  2         2   \n",
       "1                2         2         2         1                  1         1   \n",
       "2                1         2         1         2                  1         2   \n",
       "3                1         1         1         1                  2         1   \n",
       "4                1         1         1         2                  1         2   \n",
       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN LUNG_CANCER  \n",
       "0                    2                      2           2         YES  \n",
       "1                    2                      2           2         YES  \n",
       "2                    2                      1           2          NO  \n",
       "3                    1                      2           2          NO  \n",
       "4                    2                      1           1          NO  "
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     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data  = pd.read_csv('D:\\\\DC Universe\\\\Ucsc\\\\Third Year\\\\SCS 3201 Machine Learning\\\\CCPP\\\\Csv Files\\\\survey lung cancer.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13039c27",
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    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
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   },
   "source": [
    "## **Exploratory data analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f0fcdbc",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(309, 16)\n",
      "GENDER                   object\n",
      "AGE                       int64\n",
      "SMOKING                   int64\n",
      "YELLOW_FINGERS            int64\n",
      "ANXIETY                   int64\n",
      "PEER_PRESSURE             int64\n",
      "CHRONIC DISEASE           int64\n",
      "FATIGUE                   int64\n",
      "ALLERGY                   int64\n",
      "WHEEZING                  int64\n",
      "ALCOHOL CONSUMING         int64\n",
      "COUGHING                  int64\n",
      "SHORTNESS OF BREATH       int64\n",
      "SWALLOWING DIFFICULTY     int64\n",
      "CHEST PAIN                int64\n",
      "LUNG_CANCER              object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Shape of data \n",
    "print(data.shape)\n",
    "#dtypes of data \n",
    "print(data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "16813a45",
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 16 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   GENDER                 309 non-null    object\n",
      " 1   AGE                    309 non-null    int64 \n",
      " 2   SMOKING                309 non-null    int64 \n",
      " 3   YELLOW_FINGERS         309 non-null    int64 \n",
      " 4   ANXIETY                309 non-null    int64 \n",
      " 5   PEER_PRESSURE          309 non-null    int64 \n",
      " 6   CHRONIC DISEASE        309 non-null    int64 \n",
      " 7   FATIGUE                309 non-null    int64 \n",
      " 8   ALLERGY                309 non-null    int64 \n",
      " 9   WHEEZING               309 non-null    int64 \n",
      " 10  ALCOHOL CONSUMING      309 non-null    int64 \n",
      " 11  COUGHING               309 non-null    int64 \n",
      " 12  SHORTNESS OF BREATH    309 non-null    int64 \n",
      " 13  SWALLOWING DIFFICULTY  309 non-null    int64 \n",
      " 14  CHEST PAIN             309 non-null    int64 \n",
      " 15  LUNG_CANCER            309 non-null    object\n",
      "dtypes: int64(14), object(2)\n",
      "memory usage: 38.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Info of data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6e6157b2",
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     "status": "completed"
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    "tags": [],
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GENDER                   0\n",
       "AGE                      0\n",
       "SMOKING                  0\n",
       "YELLOW_FINGERS           0\n",
       "ANXIETY                  0\n",
       "PEER_PRESSURE            0\n",
       "CHRONIC DISEASE          0\n",
       "FATIGUE                  0\n",
       "ALLERGY                  0\n",
       "WHEEZING                 0\n",
       "ALCOHOL CONSUMING        0\n",
       "COUGHING                 0\n",
       "SHORTNESS OF BREATH      0\n",
       "SWALLOWING DIFFICULTY    0\n",
       "CHEST PAIN               0\n",
       "LUNG_CANCER              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7731ed0",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### **Checking Duplicates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df56d2a0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39cf3a7",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### **Dropping Duplicates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf7ac645",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data = data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64f9090b",
   "metadata": {
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     "duration": 0.063232,
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   },
   "source": [
    "# **Transformation**"
   ]
  },
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       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0      M   69        1               2        2              1   \n",
       "1      M   74        2               1        1              1   \n",
       "2      F   59        1               1        1              2   \n",
       "3      M   63        2               2        2              1   \n",
       "4      F   63        1               2        1              1   \n",
       "\n",
       "   CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                1         2         1         2                  2         2   \n",
       "1                2         2         2         1                  1         1   \n",
       "2                1         2         1         2                  1         2   \n",
       "3                1         1         1         1                  2         1   \n",
       "4                1         1         1         2                  1         2   \n",
       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  LUNG_CANCER  \n",
       "0                    2                      2           2            0  \n",
       "1                    2                      2           2            0  \n",
       "2                    2                      1           2            1  \n",
       "3                    1                      2           2            1  \n",
       "4                    2                      1           1            1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label encoding\n",
    "data.replace({\"LUNG_CANCER\":{'YES':0,'NO':1}},inplace=True)\n",
    "# printing the first 5 rows of the dataframe\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd2ef0eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:12.315487Z",
     "iopub.status.busy": "2021-10-02T11:31:12.314839Z",
     "iopub.status.idle": "2021-10-02T11:31:12.321554Z",
     "shell.execute_reply": "2021-10-02T11:31:12.321045Z",
     "shell.execute_reply.started": "2021-10-02T11:29:06.564905Z"
    },
    "papermill": {
     "duration": 0.074691,
     "end_time": "2021-10-02T11:31:12.321686",
     "exception": false,
     "start_time": "2021-10-02T11:31:12.246995",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    238\n",
       "1     38\n",
       "Name: LUNG_CANCER, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value_counts of Lung Cancer\n",
    "data['LUNG_CANCER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6017b9e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:12.456064Z",
     "iopub.status.busy": "2021-10-02T11:31:12.455433Z",
     "iopub.status.idle": "2021-10-02T11:31:12.469498Z",
     "shell.execute_reply": "2021-10-02T11:31:12.468851Z",
     "shell.execute_reply.started": "2021-10-02T11:29:06.575685Z"
    },
    "papermill": {
     "duration": 0.083068,
     "end_time": "2021-10-02T11:31:12.469633",
     "exception": false,
     "start_time": "2021-10-02T11:31:12.386565",
     "status": "completed"
    },
    "scrolled": false,
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "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>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SMOKING</th>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <th>ANXIETY</th>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <th>CHRONIC DISEASE</th>\n",
       "      <th>FATIGUE</th>\n",
       "      <th>ALLERGY</th>\n",
       "      <th>WHEEZING</th>\n",
       "      <th>ALCOHOL CONSUMING</th>\n",
       "      <th>COUGHING</th>\n",
       "      <th>SHORTNESS OF BREATH</th>\n",
       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <th>CHEST PAIN</th>\n",
       "      <th>LUNG_CANCER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0       0   69        1               2        2              1   \n",
       "1       0   74        2               1        1              1   \n",
       "2       1   59        1               1        1              2   \n",
       "3       0   63        2               2        2              1   \n",
       "4       1   63        1               2        1              1   \n",
       "\n",
       "   CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                1         2         1         2                  2         2   \n",
       "1                2         2         2         1                  1         1   \n",
       "2                1         2         1         2                  1         2   \n",
       "3                1         1         1         1                  2         1   \n",
       "4                1         1         1         2                  1         2   \n",
       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  LUNG_CANCER  \n",
       "0                    2                      2           2            0  \n",
       "1                    2                      2           2            0  \n",
       "2                    2                      1           2            1  \n",
       "3                    1                      2           2            1  \n",
       "4                    2                      1           1            1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label encoding\n",
    "data.replace({\"GENDER\":{'M':0,'F':1}},inplace=True)\n",
    "# printing the first 5 rows of the dataframe\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26fd7566",
   "metadata": {
    "papermill": {
     "duration": 0.064546,
     "end_time": "2021-10-02T11:31:12.599224",
     "exception": false,
     "start_time": "2021-10-02T11:31:12.534678",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "201ab113",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:12.735363Z",
     "iopub.status.busy": "2021-10-02T11:31:12.734580Z",
     "iopub.status.idle": "2021-10-02T11:31:13.781258Z",
     "shell.execute_reply": "2021-10-02T11:31:13.781694Z",
     "shell.execute_reply.started": "2021-10-02T11:29:06.595300Z"
    },
    "papermill": {
     "duration": 1.116112,
     "end_time": "2021-10-02T11:31:13.781860",
     "exception": false,
     "start_time": "2021-10-02T11:31:12.665748",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LUNG_CANCER', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lung Cancer & Lung Cancer Count\n",
    "import seaborn as sns\n",
    "sns.countplot(x='LUNG_CANCER',hue='LUNG_CANCER',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e668bad1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:13.922963Z",
     "iopub.status.busy": "2021-10-02T11:31:13.922332Z",
     "iopub.status.idle": "2021-10-02T11:31:14.094105Z",
     "shell.execute_reply": "2021-10-02T11:31:14.093121Z",
     "shell.execute_reply.started": "2021-10-02T11:29:06.798751Z"
    },
    "papermill": {
     "duration": 0.24632,
     "end_time": "2021-10-02T11:31:14.094263",
     "exception": false,
     "start_time": "2021-10-02T11:31:13.847943",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender & Lung Cancer\n",
    "import seaborn as sns\n",
    "sns.countplot(x='GENDER',hue='LUNG_CANCER',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "afb3ec5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:14.233917Z",
     "iopub.status.busy": "2021-10-02T11:31:14.232962Z",
     "iopub.status.idle": "2021-10-02T11:31:15.972026Z",
     "shell.execute_reply": "2021-10-02T11:31:15.972516Z",
     "shell.execute_reply.started": "2021-10-02T11:29:07.022033Z"
    },
    "papermill": {
     "duration": 1.810272,
     "end_time": "2021-10-02T11:31:15.972694",
     "exception": false,
     "start_time": "2021-10-02T11:31:14.162422",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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c1k1ERESrVLXtb6shIgEADwP47UW3eQBYlFLx0t8/AuDfaBQiERERkebC81n4XTbYrdW5Hvj1t4aglILDasEL58dhtdz5eT51b19V4iAiIiJjqVpSSUS+AeARAK0iMgLgXwKwA4BS6kulw34FwI+VUovrrNsBfK+0LtcG4OtKqR9VK04iIiIivQvPZ9DsqV6VEgCICFq9DkzPpav6PERERGQeVUsqKaWeWsUxXwXw1VtuGwBwsDpRERERERnPbCKDbUFP1Z+nxevEaCRZ9echovr09beGtA5hSay+JFo/PcxUIiIiIqJlZPMFxJJZNFW5UgkAgj4nZuczyOULVX8uIiIiMj4mlYiIiIh0bDaRgQLQ7K5+UqnV64BCsd2OiIiIaCVMKhERERHp2GwpwVPtmUoA0Op1AgDnKhEREdGqMKlEREREpGNhTZJKrFQiIiKilTGpRERERKRj4fkM7FaB11m1/SoLXHYrvE4bK5WIiIhoVZhUIiIiItKxcCKLZo8DIlKT52v1OphUIiIiolVhUomIiIhIx2bnMzUZ0l3W6nViiu1vREREtApMKhERkWZE5HER6ReRayLy+SXuFxH5Qun+syJy5Jb7rSLyroj8oHZRE9WOUgrh+UxN5imVtXqdmE/nkMzka/acREREZExMKhERkSZExArgiwCeALAXwFMisveWw54AsKP08TSAv7zl/j8EcKnKoRJpZi6dQyZfQFMNk0ptvuKw7ql4qmbPSURERMbEpBIREWnlGIBrSqkBpVQGwDcBPHnLMU8C+JoqOg6gUUQ6AUBEegB8DMBf1TJoolqareHmt7I2vwsAMBnnXCUiIiK6MyaViIhIK90Ahhd9PlK6bbXH/D8A/jmAwnJPICJPi8hJETk5NTW14YCJai2cqH1SqdFth90qTCoRERHRiphUIiIirSy1ykqt5hgR+TiASaXUO3d6AqXUs0qpo0qpo8FgcL1xEmkmXKpUaqrhoG6LCIJeJyZibH8jIiKiO2NSiYiItDICoHfR5z0AxlZ5zIMAPiEigyi2zT0mIv+9eqESaSM8n4XfZYPdWtuXbG1+FyuViIiIaEVMKhERkVZOANghIltExAHgkwCeu+WY5wB8urQF7j4AUaVUSCn1L5RSPUqpzaWve1kp9ds1jZ6oBsLz6Zq2vpW1+ZyIJrNIZbkBjoiIiJbHpBIREWlCKZUD8DkAL6K4we1bSqkLIvKMiDxTOux5AAMArgH4MoA/0CRYIo2E5zOaJZUAYIrVSkRERHQHNq0DICKi+qWUeh7FxNHi27606O8KwGdXeIyfAfhZFcIj0lQ2X0AslUOTJkml9zbA9Ta7a/78REREZAysVCIiIiLSodnS5rcWDZJKTR4HrBbBJId1ExER0R0wqURERESkQ7OlzW/NNdz8Vma1CNp9TowzqURERER3wKQSERERkQ6FS0klLdrfAKAj0ICxKJNKREREtDwmlYiIiIh0KJLIwmYReJ3ajMDsDLgwn84hnspq8vxERESkf0wqEREREenQbDKLRrcdIqLJ83cGisO6Q6xWIiIiomUwqURERESkQ5FEBo0N2rS+AUBnoAEAk0pERES0PCaViIiIiHQomihWKmmlwWFFoMGOUDSpWQxERESkb0wqEREREelMNl9APJ3TNKkEFFvgxlmpRERERMtgUomIiIhIZ6LJ4nDsRrd27W9AMak0FU8jmy9oGgcRERHpE5NKRERERDoTSZSSSg3aVip1N7qhAIxF2AJHREREt2NSiYiIiEhnIokMAO0rlXqbi8O6h8IJTeMgIiIifWJSiYiIiEhnIsksBEBA40oln8uOJrcdw7OsVCIiIqLbMalEREREpDORRAY+lw1Wi2gdCnqb3RhmpRIREREtgUklIiIiIp2JJLKat76V9Ta5EU1mESsNDyciIiIqY1KJiIiISGciySwa3dq2vpX1NrsBAMOzrFYiIiKi92NSiYiIiEhHCkohmsiisUEflUpdAResFsHQDJNKRERE9H5MKhERERHpyFwqh7xSuqlUslkt6Gt24/r0nNahEBERkc4wqURERESkI5FEBgDQpJOkEgBsC3oRiqQwn85pHQoRERHpCJNKRERERDoyWxqIrZdB3QCwvc0LBeD6FKuViIiI6D1MKhERERHpSDRRSio16KdSqbuxAS67BdcmmVQiIiKi9zCpRERERKQjs4kMGuxWOO1WrUNZYLUItrZ6cW1qDkoprcMhIiIinahaUklEviIikyJyfpn7HxGRqIicLn38+aL7HheRfhG5JiKfr1aMRERERHoTSWR1M6R7se1tXkQSWUzE01qHQkRERDpRzUqlrwJ4fIVjXlVKHSp9/BsAEBErgC8CeALAXgBPicjeKsZJREREpBvRZFZXrW9l+7r8EADnRiJah0JEREQ6YavWAyulXhGRzev40mMArimlBgBARL4J4EkAFysYHhEREZEuzSYy2NLq0TqM2/hcdmwNenB2JIq/OX4TIrLqr/3UvX1VjIyIiIi0ovVMpftF5IyIvCAi+0q3dQMYXnTMSOk2IiIiIlNLZvJI5wq6bH8DgAM9jZiZz2AsmtI6FCIiItIBLZNKpwBsUkodBPAfAfx96falLnstOxFSRJ4WkZMicnJqaqryURIRERHVSCSZAQA0uh0aR7K0fV1+WAQ4PTSrdShERESkA5ollZRSMaXUXOnvzwOwi0gripVJvYsO7QEwdofHeVYpdVQpdTQYDFY1ZiIiIqJqiiSyAKDLmUoA4HbYcFd3ACdvziKVzWsdDhEREWlMs6SSiHRIqRlfRI6VYpkBcALADhHZIiIOAJ8E8JxWcRIRERHVSiRZSirptP0NAD64PYh0roATg2GtQyEiIiKNVW1Qt4h8A8AjAFpFZATAvwRgBwCl1JcA/DqA/0lEcgCSAD6plFIAciLyOQAvArAC+IpS6kK14iQiIiLSi2giC6tF4HFW7SXahnU3NWBLqwdvXJ/B/dtaYLNoPaKTiIiItFLN7W9PrXD/XwD4i2Xuex7A89WIi4iIiEivYqks/C4bLGvYrKaFR3YG8V/eGMQb12bw0E6OHyAiIqpXvLREREREpBPRZBYBnc5TWmxHuw+72n34af8k4qms1uEQERGRRphUIiIiItKJaDILvwGSSgDwsQOdyOUVfngupHUoREREpBEmlYiIiIh0QCmFmEEqlQCg1evEo7vbcHYkinOjUa3DISIiIg0wqURERESkA4lMHrmCMkxSCQAe3hlET1MD/uH0KNvgiIiI6hCTSkREREQ6EE0WkzJ+l3GSSlaL4NeP9CCTK+Dv3x1FcZEvERER1QsmlYiIiIh0IFZKKhmpUgkA2vwufGRvOy6Nx/HuUETrcIiIiKiGmFQiIiIi0oFoyphJJQB4YHsrNre48f2zY4gkMlqHQyYnIl8RkUkROb/M/Y+ISFRETpc+/rzWMRIR1QsmlYiIiIh0IJrMwiKA12XTOpQ1s4jg1470QCngu6fYBkdV91UAj69wzKtKqUOlj39Tg5iIiOoSk0pEREREOhBLZuFz2WER0TqUdWnxOvGL+9pxbWoO/RNxrcMhE1NKvQIgrHUcRETEpBIRERGRLkSTWUO2vi12bEsLmtx2vHRpktVKpLX7ReSMiLwgIvu0DoaIyKyYVCIiIiLSgWgyB7/Bk0pWi+DRXW0YjSRZrURaOgVgk1LqIID/CODvlztQRJ4WkZMicnJqaqpW8RERmQaTSkREREQaU0ohlswiYMB5Src63NeEQIMdb1yf0ToUqlNKqZhSaq709+cB2EWkdZljn1VKHVVKHQ0GgzWNk4jIDJhUIiIiItJYKltAJl8wfPsbUKxWuntTE65PzmF2npvgqPZEpEOkOJxMRI6h+J6HWU4ioipgUomIiIhIY9FUFgAM3/5WdvemJgDAO0OzGkdCZiQi3wDwJoBdIjIiIp8RkWdE5JnSIb8O4LyInAHwBQCfVBzyRURUFcavsSYiIiIyuFiymFQyQ6USADS5Hdje5sU7N2fx2O42rcMhk1FKPbXC/X8B4C9qFA4RUV1jpRIRERGRxqImSyoBxdlK0WQWI+GE1qEQERFRlTCpRERERKSxaDILAeBzmSeptKvdB4sAF0PcAkdERGRWTCoRERERaSyWzMLrssFqEa1DqZgGhxVbWj24NB7TOhQiIiKqEiaViIiIiDQWTWZN1fpWtqfTj6l4Gjem57UOhYiIiKqASSUiIiIijUWTWfhN1PpWtqfTDwD4ycVxjSMhIiKiamBSiYiIiEhjsZQ5K5Wa3A60+Zx4/dqM1qEQERFRFTCpRERERKShdDaPVLZgyqQSAGxp9eDEYBjZfEHrUIiIiKjCmFQiIiIi0lA0lQUA+E2aVNoa9CKRyePcaFTrUIiIiKjCmFQiIiIi0lAsmQMAU1cqAcDxAbbAERERmQ2TSkREREQaiiaLlUpmTSp5nTbsbPfizetMKhEREZkNk0pEREREGoqV2t98LpvGkVTP/VtbcHJwlnOViIiITIZJJSIiIiINxZJZNNitsFvN+7Ls6OZmJLN59I/HtQ6FiIiIKsi8r16IiIiIDCCeysHfYN4qJQA41NsIAHh3OKJpHERERFRZTCoRERERaSiWysLvMuc8pbKepga0eBw4w6QSERGRqTCpRERERKShWNL8SSURwcHeRpxmUomIiMhUmFQiIiIi0khBKcylc/CZvP0NKLbAXZ+aWxhMTkRERMbHpBIRERGRRubTORQUTF+pBBSTSkoB50aiWodCREREFcKkEhEREZFGYqkcAMDvMn+l0sGeRgBgCxwREZGJMKlEREREpJF4stgK5quDSqWA245NLW5cGGOlEhERkVkwqURERESkkYVKpQbzJ5UAYG+nHxfGYlqHQURERBXCpBIRERGRRmKpLASA12n+9jegmFS6OZNAnMO6iYiITIFJJSIiIiKNxJJZeJw2WC2idSg1sa/bDwC4PB7XOBIiIiKqBCaViIiIiDQST+Xgb6iPKiUA2NsZAABcZAscERFVQC5fwI3pebx8eQKpbF7rcOpS1ZJKIvIVEZkUkfPL3P9bInK29PGGiBxcdN+giJwTkdMicrJaMdLqTMRS+PIrA3jp0gSuTsShlNI6JCIyCRF5XET6ReSaiHx+iftFRL5Quv+siBwp3e4SkbdF5IyIXBCRf1376Ik2LpbKwl8HQ7rL2v1ONHscTCoREdGGXZucw7/90WV8+dUB/P5XT+LI//ET/M1bN7UOq+5U89LYVwH8BYCvLXP/DQAPK6VmReQJAM8CuHfR/Y8qpaarGB+twt+eGML//r3zyBUUBIACsK/Lj1870gOX3ap1eERkYCJiBfBFAL8AYATACRF5Til1cdFhTwDYUfq4F8Bflv5MA3hMKTUnInYAr4nIC0qp4zX9jyDaoFgqh54mt9Zh1IyIYG+nHxdDTCoREdH6nRmO4NvvDCPoc+JXDnfjF/d14MuvDuB//955zM5n8LnHdmgdYt2oWqWSUuoVAOE73P+GUmq29OlxAD3VioXW578dv4k//c45PLC9FS//8cP484/vxRN3deBSKIb/8voN5AusWCKiDTkG4JpSakAplQHwTQBP3nLMkwC+poqOA2gUkc7S53OlY+ylD/5SIkPJFxTm0zn4XfXT/gYAe7v86B+PI5svaB0KEREZ0HQ8je++O4K+Zjf+2UPbsK8rgId2BvGV370Hv3K4G//ux1fwypUprcOsG3qZqfQZAC8s+lwB+LGIvCMiT9/pC0XkaRE5KSInp6b4jVMpl0Ix/OvnLuCx3W348qfvxtagF067FR/cEcQ/OdqL4dkkfnxxXOswicjYugEML/p8pHTbqo4REauInAYwCeAnSqm3bn0CniNIz8ob0Oqp/Q0AdrX7kMkXcHNmXutQiIjIYApK4W9PDsNmseA37+l7X/eM3WrB//Wr+7Et6MGffucsokluGq0FzZNKIvIoikmlP11084NKqSMotj18VkQeWu7rlVLPKqWOKqWOBoPBKkdbH7L5Av7k786g0W3Hv/+Ng3Da3t/mdqCnEfduacarV6f5gpCINmKpdVe3Vhste4xSKq+UOoRipesxEbnrtgN5jiAdi6VyAABfHQ3qBoCd7T4AwNWJuRWOJCIier8zwxGMRpL4xKEuBBpuvyjjslvx7//JIUzEUvhPP72mQYT1R9OkkogcAPBXAJ5USs2Ub1dKjZX+nATwPRRbJKhGvn1yBOdHY/g3T96FJo9jyWOeuKsTPqcNL14Y5+BuIlqvEQC9iz7vATC21mOUUhEAPwPweMUjJKqiWLI+K5W2tXkAAFcnmVQiIqLVyxcUXro8ia6ACwe6A8sed6i3EZ842IWvvXkTM3PpGkZYnzRLKolIH4DvAvgdpdSVRbd7RMRX/juAjwBYcoMcVV42X8B/+tk1HOxtxBN3dSx7nMNmwaO72zA4k+CLQiJarxMAdojIFhFxAPgkgOduOeY5AJ8ubYG7D0BUKRUSkaCINAKAiDQA+DCAyzWMnWjDFtrflrjSamZuhw29zQ24MhHXOhQiIjKQd4dmEZ7P4EN72iGyVDH7ez732Hakcnn89Ws3ahRd/apaUklEvgHgTQC7RGRERD4jIs+IyDOlQ/4cQAuA/yQip0XkZOn2dhS3+JwB8DaAHyqlflStOOn9njs9hpHZJP7nR7ev+IN6dHMTmtx2vHx5skbREZGZKKVyAD4H4EUAlwB8Syl14ZZzxfMABgBcA/BlAH9Qur0TwE9F5CyKyamfKKV+UNP/AKINiqVysAjgdtTfNtUdbT5c40UpIiJaJaUU3hyYQWfAhd0dvhWP397mw0f3d+Jrb95EIpOrQYT1q2pN/Eqpp1a4/58C+KdL3D4A4GC14qLlKaXw7CsD2N3hw4f2tK14vM1iwf3bWvH8uRDGIkl0NTbUIEoiMhOl1PMoJo4W3/alRX9XAD67xNedBXC46gESVVE8lYXPZYdlhYs4ZrSj3YvXrk4jly/AZtV8xCcREencyGwSoWgKTx7qWrH4oex3H9iMH54N4YdnQ/iNo70rfwGtC8/itODMSBT9E3H8zv2bVv2DendfE+xWwVs3wlWOjoiIyFxiqRz8rvoa0l22o620AS6c0DoUIiIygLcHw3BYLTjY07jqrzm6qQlbgx586+TwygfTujGpRAv+9sQwXHYLfulg16q/psFhxYHuRpwZjiCVzVcxOiIiInOJJYuVSvVoR5sXADfAERHRytLZPM6ORHCwNwCXffUt4yKCT97TixODs2y5riImlQgAkMjk8P0zY/jo/s41b6E5tqUZmXwBF8ZiVYqOiIjIfOKpHPwN9VmptH0hqcRh3UREdGeXxuPI5hUO9zat+Wt/9UgPrBbB994dqUJkBDCpRCU/uTiBuXQO/2QdvaY9TQ1octtxbjRS+cCIiIhMKJXNI5nNr/lCjll4nDZ0NzZwgywREa3o/GgUfpcNfS3uNX9tq9eJ+7Y244Vz4yiO6qRKY1KJAAA/Oj+ONp8TxzY3r/lrRQT7uxtxbXIOiTQn69PqJTN5XJmI41snhhGKJrUOh4ioZiZiKQCo26QSAOxs9+IKK5WIiOgO0tni+4V93YF1L7Z44q5ODEzP4wpbrquCSSVCMpPHz/qn8Iv7OmCxrO8H9UBPAAUFtsDRql0KxfDvftyPr74xiH/+nbN44N++jD/9u7PI5Apah0ZEVHUTsTQAwFen7W8AsKPdh4HpeeTy/L1PRERLuzQeR66gsL8rsO7H+Mi+dogAL5wPVTAyKmNSifDzK5NIZvN44q6OdT9GZ8CFFo8D58aiFYyMzOqdm7P4b8dvotFtx+8/uAU/+qMP4vce2IK/PTmMz/zXExz6TkSmx0ql4rDuTK6A4VlWqhIR0dIuhmLwOdfX+lbW5nPhns3FFjiqPCaVCC+cH0eT245jW9be+lYmItjT6ceN6Xmkc0wI0PJGZhP4h9Oj2Bb04H96eBu2t3mxu8OPP/+lvfj//Np+vHp1Gv/+x/1ah0lEVFVMKhUrlQCwBY6IiJaULyhcm4xjZ4dv3a1vZR/Z247+iThGI7yQUWlMKtW5XL6An/VP4bHd7bBZN/btsKvDh3xB4TqHbtIycvkC/vbEMLxOGz55T99t33O/eU8ffvu+PvzVazfwxvVpjaIkIqq+yXgaNovAZa/fl2LlDXBc80xEREsZCieQyhawq3QRYiMe2RUEAPy8f2rDj0Xvt6pXMiLyHRH5mIjU7ysfkzozEkU0mV34IduIzS0eOG0WXB7nFUda2vGBGczMZ/DLh7vhcS49R+TPProHfc1u/Mt/uIB8gRsajILnCaK1mYil4G+wQzZ45dXIvKUNcKxUqm88fxDRcq5MxGGR9y5CbMS2oBfdjQ34+ZXJCkRGi632l/dfAvgUgKsi8m9FZHcVY6Ia+vmVKVgE+OCO1g0/ltUi2NHuw5WJONc10m0SmRxe7p/EjjYvdt7haoPbYcOf/OIuXJ2cw/fPjNUwQtognieI1mA8moLPVb9Dusu2t3lxldt46h3PH0S0pP7xODa1eOCyWzf8WCKCh3cF8fq1GS4GqrBVJZWUUv+olPotAEcADAL4iYi8ISK/JyL1OwzABH5+ZQqHehvR6HZU5PF2t/sQS+UQiqYq8nhkHm8OzCCVLeCJuzpXPPajd3Vid4cP/88/XuFWIIPgeYJobSbj6bqep1S2s92L61NzrEytYzx/ENFSYqksxmOpO16MXquHdwYxl87h1NBsxR6T1jBTSURaAPwugH8K4F0A/z8Uf/n/pCqRUdXNzKVxdiSCh3e2Vewxy6WJ16d41ZHek8sX8PZAGDvbvegIuFY83mIR/NGHd2BwJoGfXJyoQYRUCTxPEK2OUqrY/sZKJWxv8yKdK2CUG+DqGs8fRHSrgal5AMD24MZb38oe2NYCq0Xw6lXOVaqk1c5U+i6AVwG4AfySUuoTSqm/VUr9zwAq93+ZauqN6zNQCvjgzo23vpX5G+wIep1MKtH7nBuNIp7O4YFtq/9e+4W9HegKuPDf37pZxcioUnieIFq9uXQOiUwe/gYWYWwL8mJUveP5g4iWMjA1B5fdgs7GlS9Ir5bPZcf+7gCOD4Qr9pi0+kqlv1JK7VVK/V9KqRAAiIgTAJRSR6sWHVXVmwMz8DptONAdqOjjbmvzYHA6wVJ2WvDmwAyCPid2rGHIntUieOpYH16/NoMBvtkwAp4niFZpIpYGUHxxW++YVCLw/EFESxiYnseWVi8sFV5ocf+2FpwZjiCRyVX0cevZapNK/+8lbnuzkoFQ7R0fmME9m5tuW+u+UVtbvcjkCxiZTVT0ccmYJuMpjMwmcc/m5jVvOfrNY72wWQRff2uoStFRBfE8QbRKk7Hi3EG2vwFNHgea3HZcL7U5UF3i+YOI3mc2kUF4PoOtrZ6KP/b9W1uQKyicHORcpUq546sZEekA0A2gQUQOAyi/I/SjWKJKBjUZS2Fgah6/ebS34o+9NeiBoHjVcVNL5X8RkLGcHo5AABzsWXtFXJvPhUd3t+G5M2P4Fx/dA6ulfldv6xXPE0RrNxEvJ5VYqQQUq5VYqVR/eP4gouWU5yltq+A8pbK7NzXBZhEcH5jBQzuDFX/8erTSJbJfRHFoXg+A/7Do9jiAP6tSTFQDbw7MACiW/1Wa22FDZ6ML16fm8RiXwta1glI4PRzB9jbvuts8PnGwCz+5OIG3b4Sr8v1KG8bzBNEajUdL7W8NrFQCim8aXrrMpQx1iOcPIlrSjel5uB1WtPmdFX9sj9OGg72NC++HaePu+GpGKfVfAfxXEfk1pdR3ahQT1cDxgTB8Thv2dvqr8vhbWjx4ezCMXKEAm6Wy7XVkHDdnEogksvjI3vZ1P8aH9rShwW7F98+OMamkQzxPEK3dRCwFn9MGp82qdSi6sDXowd+ezCCayCLgZvVWveD5g4iWc3NmHptaPBWfp1R275ZmPPvKAJKZPBocPBdv1B3f7YvIb5f+ullE/tdbP2oQH1XJWzdmcLQK85TKNrV4kM0rhCKpqjw+GcPFsShsFsGejvUnL90OGz68tx0/Oj+ObL5QweioEnieIFq7yXiqKldfjWphWPc0W+DqCc8fRLSUeCqLmfkMNrdUrwv26OYm5ArFjgrauJUyCuWBOF4AviU+yIDC8xkMTM3j6Obmqj3HptIvgcEZDt6sV0opXAzFsC3ohdO+sSsAHz/QifB8Bm/f4PpPHeJ5gmiNJmJptPsrtyLZ6LaVNoNen2RSqc7w/EFEt7k5U1z2tKm5ekmlI31NAIBTQxzWXQkrtb/959Kf/7o24VAtnLpZ/OE5uqmpas/hc9nR4nHg5kwCH9xRtachHRuPpTCbyOKRnW0bfqwP7miFw2bBS5cm8eD21gpER5XC8wTR2k3EUrinihd2jKa3qQF2q2Bgmhei6gnPH0S0lKFwAjaLoKuxoWrP0eh2YHubFycHecG6ElbV+yQi/18R8YuIXUReEpHpRSWrZDDvDM3CZhEc7G2s6vNsavFgcGYeSqmqPg/p06VQDAJgd+fGLza6HTY8sK0FL12e4PeTTvE8QbQ6SilMxtJsf1vEZrVgU4uHlUp1iucPIlpscGYePU0NVRvTUnZ0UxPeuTmLQoHvLTZqtf+nPqKUigH4OIARADsB/EnVoqKqemdwFvu6A3BtsCVpJZtb3Ehk8piaS1f1eUifLoXi6GlqWPfWt1t9aHcbbs4keCVbv3ieIFqFSCKLTL6Adh/b3xbbFvTg+hSTSnWK5w8iAgBk8wWMRZLY1OJZ+eANOrKpCbFUDtd47tmw1SaVyu8KPwrgG0op1okZVCZXwJmRSFVb38p6S32wI+Fk1Z+L9GUuncNoJIldGxjQfavH9hQ3yL18abJij0kVxfME0SqMx4oLLDhT6f22Bb0YCie4kKE+8fxBRACAsUgSBQX0NlVvnlJZ+f3wOzc5V2mjVptU+r6IXAZwFMBLIhIEwLVeBnRhLIp0rlCTpFLQ54TTZsHwbKLqz0X6Um5h2Nnurdhjdjc2YHeHDz/tZ1JJp3ieIFqFiVJSqSPA9rfFtga9yOYVhsN8zVCHeP4gIgDAyGyxGKGnuXrzlMq2tHoQaLDj7Eik6s9ldqtKKimlPg/gfgBHlVJZAPMAnqxmYFQdZ0prEw/1NVb9uSwi6G5swGiElUr15upkHA12a8UH7H1wRytODs4imclX9HFp43ieIFqdyVixJbyN7W/vsy1YbHW4PsUW53rD8wcRlY3MJuB32eCv0PiMOxERHOgJ4MxwtOrPZXZ33P52iz0ANovI4q/5WoXjoSo7OxJF0OdER43K7nuaGvD6tRnk8oWqD1sjfVBK4erkHLa3eWERqehjP7i9FV9+9QZO3gzjgzuCFX1sqgieJ4hWUK5U4qDu99saLFa2Xp+awy+gXeNoSAM8fxARRmaT6KlB61vZ/u4A/vMrA0hl81WfN2xmq0oqich/A7ANwGkA5RIBBf6yN5wzIxEc7AlAKvxmfzk9TW7k1TRC0dTCjCUyt4lYGvFUDjvaKtf6VnZsSzPsVsFrV6eZVNIZnieIVmcinkKT2w6njS9eFws02NHqdWKAA1PrDs8f5tc/HsdXXruBt27MIF9Q2N7mw8M7g3DYeMGZ3pPM5DEzn8HdNRjTUnagpxH5gsKFsVhNn9dsVlupdBTAXsVd3oYWT2UxMD2PJw911+w5e5qK7U8jswkmlepEeXvP9iokldwOG470NeG1a9MVf2zaMJ4niFZhIpbmkO5lFDfAsf2tDvH8YWLfeWcEf/a9c3BYLbhvWwuuTMTx0/5JnBuN4Lfu3cTfh7SgPDKlu6n685TKDvYGAADnRiJMKm3AatPD5wF0VDMQqr7zozEoBRzoCdTsOQMNdvictoWha2R+N6bn0exxoNHtqMrjf2B7Ky6MxTAzl67K49O68TxBtAqTsRTa+CZqSdvavAsXJqiu8PxhUj86H8Iff/sMjvQ14eX/7RF8+dNH8c8e2obff3ALUtkCvvrGIOKprNZhkk6MlJY79TTWrhChw+9C0OfE2RHOVdqI1VYqtQK4KCJvA1h4J6eU+kRVoqKqKE+2P9DTWLPnFBH0NDVgmEmlulBQCjem57G3y1+153hgewv+/U+AE4NhPH5XZ9Weh9aM5wmiVZiIpbGz3ad1GDX39beGVjwmmsgiksjiy68MwONcy9hP4FP39q03NNIezx8mdHUijv/1W2dwuK8RX/39e97X8ru9zYv/4YHNePaV6/jvx2/i6Ye2wWqpzWgO0q+R2SRaPA40OGrXHi4iONAdwBlugNuQ1Z6x/1U1g6DaODsSRU9TA5o91akgWU53kxuXxuMcgFYHJmIpJLN5bG31VO059nc3wmmz4K0bTCrpzL/SOgAivcsXFKbm2P62nFZvcXj5VDy95qQSGdq/0joAqqxCQeFPv3MWDXYrvvTbdy85Q667sQG/eqQHf3tiGMcHZvDg9lYNIiU9GZlNYEsV30Ms50BPI17un0Q8lYWvBlvnzGhV7W9KqZ8DGARgL/39BIBTVYyLqqA4pLux5s/buzBXidVKZjdQmoVRzROCw2bBkb4mvH0jXLXnoLXjeYJoZTNzaeQLCu0BJpWWEvSVkkpsb64rPH+Yz9+fHsWpoQg+/8TuOybRD3QHsLPdi3+8NIEY2+DqWiyVRSyVq+nmt7IDvQEoVRwVQ+uzqqSSiPyPAP4OwH8u3dQN4O+rFBNVwcxcGiOzyZrOUyrrXjSsm8yt2vOUyu7d2oyLoRhfgOgIzxNEK5uIFZMl7aXkCb1fo9sOm0UwHWdSqZ7w/GEuqWwe//aFyzjY24hfO9Jzx2NFBL90oAu5gsJLlyZrFCHp0Wip+KCnhkO6yw50F98fn2UL3LqtdlD3ZwE8CCAGAEqpqwDaqhUUVd7Z0eLwsVrOUypzO2xo8ThYqWRySikMzsxjc0v1rzAc29IMpYB3Bmer/ly0ajxPEK1gIpYCALa/LcMiglavk5VK9YfnDxP59slhTMbT+Pzju2FZxZykFq8Td/c14dTQLGJJXiysV8OzCVgE6AzUPqnU4nWiu7Fh4f0yrd1qk0pppVSm/ImI2ABw7aeBnBuJQgS4q7t6A5TvpLfZzUolk5uZzyCRyaOvufq90Id7m2C3Co7fmKn6c9Gq8TxBtIKJeDGp1OZnpdJyWn1OTLFSqd7w/GESuXwBX371Bg71NuK+rc2r/rqHdgahlMJr16arGB3p2ehsEu1+Fxy21aYnKutgb4CVShuw2v9rPxeRPwPQICK/AODbAL5/py8Qka+IyKSInF/mfhGRL4jINRE5KyJHFt33uIj0l+77/Gr/Y2h5Z0ci2Nrq0Wz4WHdjA2KpHKK8AmFaQ+Fi0rCvufqVSg0OK+7qDuDUTVYq6ciazxNE9WYimoJFgKCXSaXlBL1OzCYyyOULWodCtcPzh0n86MI4hsIJPPPwNoisfptbs8eBAz2NePtGGKlsvooRkh4ppTAym0R3Y+2rlMoO9DRiOJxEeD6z8sF0m9UmlT4PYArAOQD/DMDzAP5fK3zNVwE8fof7nwCwo/TxNIC/BAARsQL4Yun+vQCeEpG9q4yTlqCUwpmRqCZDusvK/bFjEbbAmdVQOAGnzVKzK/BH+ppwdiSKLN946MV6zhNEdWU8lkKr1wmbVZsrsUYQ9DlQUMXqV6obPH+YxH8/fhN9zW58ZG/7mr/2/q0tyOQLXO1eh2YTWSSz+YU5vFoozx1mtdL6rHb7WwHFgXl/oJT6daXUl5VSdyxLVUq9AuBO65meBPA1VXQcQKOIdAI4BuCaUmqgVAr7zdKxtE7jsRSm4mlNhnSXdQRcEABjUSaVzGo4nEBvkxuWNVyZ2ojDfY1I5wq4HIrX5PnoztZzniCqN+OxNDq4+e2Ogt7ivw9b4OoHzx/mMDg9j+MDYfzmPb2rmqV0q56mBnQGXDgxyO2+9SZUen/YpcE8pbK7SsO6z3Ou0rrcMalUalH7VyIyDeAygH4RmRKRP6/Ac3cDGF70+UjptuVup3U6N1L84divYaWS02ZFi9eBUCSlWQxUPelsHuPRFHpr0PpWdrivCQDw7jBb4LRU5fMEkalMRFMc0r2CVl9xe+g0h3WbHs8f5vKtk8OwCPDrd99549tyRARHNzdjLJJa2ARG9SEUTUGg7RILv8uOvmY3LvFi9bqsVKn0RyhuY7hHKdWilGoGcC+AB0Xkf9ngcy+VwlZ3uH3pBxF5WkROisjJqampDYZkThdDMYgAezp9msbRGWhYyESTuYxEklCozTylsq6AC20+J94ditTsOWlJf4TqnSeITGU8lkIHk0p35LRZ4XfZWKlUH/4IPH+YQr6g8HfvjODRXW0bSgwc6mmEzSJ4Z4gXDOtJKJJEq8+p2ZDusr2dflwMxTSNwahW+j/3aQBPKaVulG9QSg0A+O3SfRsxAqB30ec9AMbucPuSlFLPKqWOKqWOBoPBDYZkThfHYtjS6oHbYdM0jq6Aq9gzm+EAPrOp5ZDuMhHB4b5GvMsXHlqr5nmCyDRS2TyiySzb31Yh6HNiipVK9YDnD5N4a2AGk/E0fm2dVUplDQ4rdnX4cH40igI7IOtGKJpCpw7OjXu7/Bicmcd8Oqd1KIazUlLJrpS6bbejUmoKwEbXiD0H4NOl0tf7AESVUiEAJwDsEJEtIuIA8MnSsbROF0Mx7O30ax0GOksT/VmtZD5DMwkEvU40OKw1fd7DfU0YnElwU4O2qnmeIDKN8Wix/ZvtbysL+pyYiqfBsTqmx/OHSfzgXAhuhxWP7mrb8GMd6GnEXDqHG9PzFYiM9C6RySGSzGo6T6lsb6cfSgGXx9kCt1YrJZXu9E7tju/iROQbAN4EsEtERkTkMyLyjIg8UzrkeQADAK4B+DKAPwAApVQOwOcAvAjgEoBvKaUurPhfQkuKJrMYmU1ib5cOkkqlDHQoyrlKZqKUwvBsoqZVSmWHexsBAKc5V0lL6z5PENWT8Vg5qVSbDZlGFvQ6kc4VMMerxWbH84cJ5PIF/Oj8OD60p70iFxd3tfvgsFm4hatOlN8X6qFSaU/p/TJb4NZupX6ogyKy1L+qALjj/3ml1FMr3K8AfHaZ+55HMelEG3Sp9EOhh0oln8sOn9OGsQgrlcxkZi6DRCavSVJpf08AVovg3aEIHtu99vW1VBHrPk8Q1ZOJUlKJM5VW1uorJt6m4mn4XCxYMTGeP0zg+EAY4fkMPra/syKP57BZsKfDh/OjMXzioIJ1HZvkyDjKSSU9tIZ3BVwINNhxcYxJpbW6Y1JJKVXbXhaquPIPhR4qlQCgs9HFSiWTKc9T6m2pfVLJ7bBhd4ePw7o1xPME0eqUk0rtOnjhrHdBbympNJfG1qBX42ioWnj+MIcXzhdb3x7ZVbnZtvu6AjgzEsXNmXn+DjC5UCQJn8umiwsIIsJh3euk7Yh1qrpLoRhavU60+fTxIrYz0IDJeAq5fEHrUKhChsIJOG0WtPm0aek43NeI08MR5AucvUFE+jUeTcPtsMLn1HZphhH4G+xwWC2Y5gY4WoaIfEVEJkXk/DL3i4h8QUSuichZETlS6xjrgVIKL1+exAd3tMJlr1yOcEe7F1aLcLZNHdDLkO6yvV1+9I/H+L5ijZhUMrmLoRj2dPq0DmNBZ8CFggIm+ELRNIZnE+htdsMi2pQnH+ptwlw6h+tTc5o8PxHRakzEUujwuyAa/a40EosIWr0OboCjO/kqgMfvcP8TAHaUPp4G8Jc1iKnuXBiLIRRN4UN7KjuCwGmzYlvQg4uhGAf2m1guX8BkPIVOHQzpLtvb6UcqW+Cg+DViUsnEMrkCrk7M6ab1DQC6yhvgOFfJFLL5AiZiKfQ0aXcyONzXCAB4d4jDuolIv8ZjKW5+W4PW0gY4oqUopV4BEL7DIU8C+JoqOg6gUUQqM/SHFrx0aRIiwGO7N7717Va7O/wIz2f4e8DEJuNpFJQ+hnSX7eWw7nVhUsnErk/NIZMv6GJId1mzxwGHzYIxzlUyhVA0hYICuhu1SyptafEg0GDnXCUi0rXxaEoXg0iNIuhzIpLIIst2eVqfbgDDiz4fKd12GxF5WkROisjJqampmgRnFi9dnsCh3ka0eis/AmFP6f3LJbbAmVYoWiwy6NJRpdK2oBd2q3BY9xoxqWRi5R+GfTqqVLKIoNPvWvglQsY2Wqo40zKpZLEIDvQEcHYkqlkMRER3UigoTMZZqbQWQa8TCsA0W+BofZbqM12yj0op9axS6qhS6mgwWLlh02Y3FU/j7EgUj+2qfJUSAAQa7Ojwu3B1kkklsxqLpuCwWtDsdWgdygKHzYIdbb6FDeq0OkwqmdjFUAwuuwVbWvW1NaG8Aa7AHmnDG5tNwuOwItCg7caGu7oDuDIRRzqX1zQOIqKlhBMZZPMKHX5tFhoYUbC0/IGtL7ROIwB6F33eA2BMo1hM6fVr0wCAhyu49e1WO9q8uDmTQCbHikUzCkWKFbxazWVdzt4uboBbKyaVTOziWAy7OvywWvT1g9oVaEAmV0B4PqN1KLRBo5EkupsaNB88u787gFxBoZ8l0kSkQ+Ollm9WKq1ei8cJATism9brOQCfLm2Buw9AVCkV0jooM3nl6hSa3Hbs6wpU7Tl2tPuQLygMTHMZi9kopRCKJnU1T6lsb6cfU/E0JuMc17JaTCqZlFIKF0MxXc1TKivPlBjnXCVDy5Y2NmjZ+la2v7v4gubcKFvgiEh/JmKlpJIOXzzrlcNmQaPbjmlWKtESROQbAN4EsEtERkTkMyLyjIg8UzrkeQADAK4B+DKAP9AoVFNSSuHVq9N4cHtrVS9eb2pxw24VXJ1gUslsIoks0rmCLmcNlod1XwrxYvVq2bQOgKpjLJpCNJnV1ea3sjafC4LiJpy7uqt3dYOqSw9Dust6mhoQaLDjPJNKRKRDE7FiYqSDlUpr0up1slKJlqSUemqF+xWAz9YonLrTPxHHVDyNh3ZUdwaV3WrBllYP5yqZ0HjpYosez4t7OspJpRge3sk5a6vBSiWTulQa0r2306dxJLdz2Cxo9jgWrtySMY3OJgAAXTpIKokI7ur24/wo+5+JSH/GYymIvDcniFYn6HNiOp6B4gxGIl159UpxntIHdrRW/bm2t/kwPZdBNJmt+nNR7SxU8OowqRRw29EZcHGsxhowqWRS/RPFH4Kd7fpLKgHFFji2vxnbaCQFj9Om+ZDusru6A+gfj3OYIxHpzkQ0hVavE3YrX3atRdDnRCZfQCyV0zoUIlrk+MAMtrR6anJhcUurBwBwY3q+6s9FtTMeS6HRbYfLbtU6lCXt6vDhMpNKq8ZXNyZ1eTyO7sYG+Fz6eMN/qw6/C+H5DBMABjYWSaK70aX5kO6y/d0BZPIFXJngCYCI9GU8ltJlib/etXq5AY5Ib/IFhbcHw7h3S3NNnq8z4ILLbmFSyWQmdH5e3NXhw/XJOWTzfK+6GkwqmVT/eAy7O/RZpQQUSx0VwKn6BpXJFTAR08eQ7rK7ujism4j0aSKW0mWJv96V2wWn+FqBSDcuj8cQT+Vw79baJJUsItjU7GFSyURyhQKm4mldnxd3d/iQyRcwyO+7VWFSyYQyuQIGpuaxS8dJpU5ugDO08WgSCkB3o1vrUBZsanHD57JxWDcR6c54LIWOAOcprZXPaYPTZsHUXEbrUIio5K2BMADg3i0tNXvOrUEPpufSiKU4V8kMpuMZFJQ+5ymV7WovDutmC9zqMKlkQten5pArKF0nlZo8DtitsjD5n4xlJJIEAHQ36adSSURwV1eASSUi0pVUNo9IIqvrMn+9EpHSsG62vxHpxVs3ZtDb3FDTRS3luUqsGjEHPW9+K9vW5oHVIhzWvUpMKplQ+Zt/d2kdoh5ZRNDudzGpZFBjkSS8Thv8LpvWobzP/p4ALo3H2f9MRLpR3nDTpuMXz3oW9DoxNcekEpEeFAoKb98I17RKCQA6Aw1w2jhXySwmYilYBGj1ObQOZVlOmxVbWj0Ly6/ozphUMqHL43HYrYKtQY/WodxRh7+4AY6rgo1nNJJEd2ODboZ0l+3r8iOT47BuItKPcpu3nq/I6lnQ50Q0mUU6l9c6FKK6d3VyDrOJLI7VaEh3mdUi2NTixgCTSqYwEUsh6HPCZtF3KmJXh4+VSquk7/+TtC794zFsC3p1v7q43e9CIpPHXJqrgo0kmy8O1+ts1N8bpP3dxWHdF0ZjGkdCRFS0UOYf0N/vTCMob4Cb5lwlIs29fWMGAHBfjSuVAGBLqxdT8TTfN5jAuEGWV+xu92EonMA8v+dWpO+sA61L/3hc1/OUysovsNkCZyyT8TQKqliKrDebWzzwOm3cAEdEulFufzPCC2g9em8DHFvgiLR2/EYYnQEXeptr/xqwPFeJLXDGZqQ5g+X30+yAWBmTSiYTTWYxFk0ZIqlUfoE9wQ1whhIqDenu1OFVd4tFsLfLjwtjTCoRkT6MRVLwOm0INNi1DsWQWjwOCJhUItKaUgpvDYRx75ZmTcYfdDc2wGG14Mb0XM2fmyrHSBdayvOJ2QK3MiaVTKacSd1tgKSS12mDz2ljpZLBhKIpOKwWNHv0OVxvb6cfl8fjKBQ4q4uItDcWSaJLh+3CRmErnW+mOaybSFMD0/OYnkvj3q21b30D3purxEolYzPC5reynqYGuB1WXGZSaUVMKplM+Zt+l443vy3WHuAGOKMJRZPoCLhg0dmQ7rK9nX4kMnncDCe0DoVWQUQeF5F+EbkmIp9f4n4RkS+U7j8rIkdKt/eKyE9F5JKIXBCRP6x99EQrG4sma7p624xavU5WKhFp7K2BMADUfEj3YptbPZiIpZHIcMaNUU3E0nDYLAi49V+9a7EIdrZzWPdqMKlkMv3jMfhcNnTpsDVpKR1+FyZjaeRZVWIISimEoildtr6V7e0qJlQvjnFYt96JiBXAFwE8AWAvgKdEZO8thz0BYEfp42kAf1m6PQfgj5VSewDcB+CzS3wtkeZCkZQuZ9AZSdDnxPRcGgVuiyXSzMmbYbR6Hdjaqt126b5mNwBgOJzULAbamIlYCu0+p24vTt9qd4cP/RNxbitfAZNKJnNlfA672n26W/W+nA6/C7mCwsw8r0AawWwii3SuoOs3SNvbvLBZBBdDnKtkAMcAXFNKDSilMgC+CeDJW455EsDXVNFxAI0i0qmUCimlTgGAUioO4BKA7loGT7SSVDaPmfkMutn+tiFtPidyBYXZeW6AI9LKu0MRHO5r0vQ9Rk9TAwTAEKvRDUkphfFoylDbUHe2+xCez2CKLdh3xKSSiSilcHk8Zogh3WXtpV8qEzH+oBpBKKrfId1lLrsV29u8rFQyhm4Aw4s+H8HtiaEVjxGRzQAOA3jr1icQkadF5KSInJyamqpEzESrNraw2EC/iXgjaCttgJtkCxyRJsLzGdyYnsfhvkZN43DarOgIuDDMpJIhxVM5JLN5QwzpLivPKWYL3J0xqWQi47EUYqmcIYZ0l7X5nBAA49wAZwihaAoC/W9s2Nvpx6UQf/kbwFKXO2+tL77jMSLiBfAdAH+klLotk6iUelYpdVQpdTQYDG4oWKK1CpXObZyptDFtpXPOJGcwEmni9PAsAOBIX5PGkRRb4IZnE2yHNSAjDeku28Wk0qowqWQiRhvSDQB2qwWtXieHdRtEKJJEq88Jh03fvzr2dvkxHkthhqWqejcCoHfR5z0AxlZ7jIjYUUwo/Y1S6rtVjJNoXUZLlUrdTCptiMtuRaDBjglWKhFp4tTNCKwWwYGegNahoK/ZjXSusLCanoyj/P9M7xenF2vxOtHqdTKptAJ9vzOkNSl/s+9qN06lElBsgeOJwRhCMX0P6S7b01lMrLJaSfdOANghIltExAHgkwCeu+WY5wB8urQF7j4AUaVUSIpDHf4awCWl1H+obdhEq1Nuf2sPODWOxPjafE5WKhFp5NTQLPZ0+uB22LQOZWFYN+cqGc9ELAWf0waPU/vvo7UoD+um5TGpZCL943F0BlyGWNG4WIffhfB8BulcXutQ6A6SmTwiiawhZoOUk0oc1q1vSqkcgM8BeBHFQdvfUkpdEJFnROSZ0mHPAxgAcA3AlwH8Qen2BwH8DoDHROR06eOjtf0vILqzUCSFoM8Jp82qdSiG1+53YTLODXBEtZYvKJwZjuii9Q0Amj0OuB1WzlUyoPFYamGerpHs6vDhykSc28rvwFhpQrqjy+Nx7DRYlRLwXl/tZCyN3tLVB9IfIwzpLmv2ONAZcHFYtwEopZ5HMXG0+LYvLfq7AvDZJb7uNSw9b4lIN8aiSc5TqpDFG+BavKz8IqqV/vE45jN53SSVRASbmt2sVDKYglKYjKVx31av1qGs2a4OH1LZAobCCWxp9Wgdji6xUskksvkCrk/OGWpId1l5rSSHdetbeeCsEZJKAId1E5H2RiNJdBnkd6belWdwcFssUW2dGtLPkO6yvmY3pucymE/ntA6FVmlmLoNcQRlqnlLZexvgeLF6OUwqmcTg9Dwy+cLChHojaXTb4bBZOKxb50LRJLxOG3wuY7RX7u3y49rUHFJZtlUSUe0ppRCKpFipVCFtvmJ10mScrxWIaunU0CxavQ70Nuvnd1lvS7GzgS1wxjG+MKTbeJWmO9p8EHlvKRbdjkklk3hv85vxkkoWEbT7uAFO70JRYwzpLtvT6Ue+oHB1Yk7rUIioDkUSWSSzeUP93tQzp92KxgY7F3sQ1djpoQgO9zWhuB9DH3oa3bAIh3UbyUQsBQHQ5jPeObHBYcWmZjc3wN0Bk0om0T8eh9Ui2N5mvD5VoNgCNxFLQXEApy7lCgVMxtKGGNJdtpfDuolIQ6OlzW/drFSqmDa/E5Nxtr8R1crsfAYD0/M43NeodSjv47BZ0BFwMalkIBOxFJo9Djhsxkw/7OrwMal0B8b8v0q3uTwex5ZWj2E3zLT7XUhk8oizN1qXpuJp5JVCZ6Nxri70NbvhcVg5V4mINFGeQ8f2t8pp97kwxQ1wRDXz7rD+5imV9TV7MDKb5EYugxiPphbm6BrRrg4/BmfmOVZjGUwqmUT/RMyQrW9l5Q1wExzWrUuhiLGGdAOAxSLY0+nnBjgi0sRYqVLJSMl4vWvzu5ArKITnMlqHQlQX3h2KwGoRHOgJaB3KbXqbGpDJFzDF6kXdy+QKCM9nDDmku2x3hw8FBY7VWEZVk0oi8riI9IvINRH5/BL3/4mInC59nBeRvIg0l+4bFJFzpftOVjNOo5tL5zAcTmJ3u/GTSpyrpE+haBJ2q6DVYGuc93b5cTEUQ4FXsYioxsYiSTisFrR6jPV7U8/KA14nOKybqCZOD0ews90Ht8OmdSi36WkqDusemWULnN5NxlNQgKGTSuXijcvcALekqiWVRMQK4IsAngCwF8BTIrJ38TFKqf9bKXVIKXUIwL8A8HOlVHjRIY+W7j9arTjN4MqEcYd0l7mdNvhcNg7g1KlQNIV2vwsWHQ1pXI09nX7MpXMYmU1qHQoR1ZmxaAqdjS5YLMb6valnwdIGuIkYKxOIqk0phXOjURzq1V+VEgC0eB1w2S18jWcA5d/ZHQZOKm1u8cBpsyy876b3q2al0jEA15RSA0qpDIBvAnjyDsc/BeAbVYzHtMpDw3Z3+DWOZGM6/C5WKumQUqq0+c14c0E4rJuItDIWSRqqZdgInDYrmtx2TLJSiajqhsIJRBJZHOhp1DqUJVlE0N3YgJEIK5X0biKWgs0iaPE6tA5l3awWwY5278LGdXq/aiaVugEML/p8pHTbbUTEDeBxAN9ZdLMC8GMReUdEnq5alCbQPx6H22FFT5Px3vQv1u53YTLGAZx6E00ady32rg4fLAJc5LBuIqqxsUiSQ7qroM1XfK1ARNV1ejgCALqcp1TW0+TGeDSFbL6gdSh0B+OxFNr8TsN1PNxqV7ufG+CWUc2k0lLfNctlC34JwOu3tL49qJQ6gmL73GdF5KEln0TkaRE5KSInp6amNhaxQV0ej2Fnu8/wJfbtpQGcMxzAqSvlDUZGTCq57FZsC3o5rJuIaiqXL2AilkKXASs89a7N78TUXJobn4iq7OxIFE6bBTt1PLO1p6kBBQWEImyB07OJaMrQrW9luzt8mIynMTvP96q3qmZSaQRA76LPewCMLXPsJ3FL65tSaqz05ySA76HYTncbpdSzSqmjSqmjwWBww0EbjVIK/eNx7DbwPKUyDuvWp1A0CYFx+6D3dvlxKcSkEhHVzkQ8jYICK5WqoN3nQr6gEOaLeqKqOjsSwV3dAdit+l0WvjCsm0kl3ZpP5xBP5ww9pLts58KwblYr3aqavyVOANghIltExIFi4ui5Ww8SkQCAhwH8w6LbPCLiK/8dwEcAnK9irIY1NZfGbCJr6CHdZW1+JwTgsG6dCUVTaPY44LRbtQ5lXfZ2+jEaSSKS4BsQIqqN8lXzrkbjv4jWm7byBji+ViCqmly+gPOjMV23vgFAoMEOv8vGYd06Vi4WMENSqVzE0c8NcLepWlJJKZUD8DkALwK4BOBbSqkLIvKMiDyz6NBfAfBjpdT8otvaAbwmImcAvA3gh0qpH1UrViMr93WaIalkt1rQ4nVgPMoXinoSiqbQaeCr7XsWhnXzBEBEtTG6kFQy7u9OvWrzFd+YcFg3UfVcnZxDMpvHQZ0O6V6su8mNkVkO69ar8gUAo3Y8LNbmc6LRbUc/N8DdxlbNB1dKPQ/g+Vtu+9Itn38VwFdvuW0AwMFqxmYWl0Pm2PxW1u53MamkI6lsHuH5DO7e1KR1KOtWTipdCsXxwLZWjaMhonrApFL1OGwWNLntCyuqiajyzo5EAOh7SHdZb1MDLoViSGbyaHAYs6rezMajKTTYrfC5qpp2qAkRwa52H9vflqDfJllalcvjcbT5nGj2GHdF42IdfhfC8xlkctzioAdGHtJdFvQ50eZzclg3EdXMcDiJJrcdXqfxX0TrUbvfxUoloio6MxKF32XD5haP1qGsqLu0/XqUc5V0aSKWQkfABTH45rey3R0+XBmPo8BlEe/DpJLB9U/ETNH6Vtbud0GBZe16MR4tnqA7Db7BaG+Xn+1vRFQzI7MJ9Da7tQ7DtNp8LkzHM9wAR1QlZ4YjONDTaIjN0j2NpWHdbIHTnYJSmIilTTFPqWxXhx/zmTyTmLdgUsnAcvkCrkzMmWLzW1lHqSKGLXD6EIqm4HZY4Td4yereTj+uTcZZAUdENTEcZlKpmtr9TuSVwswcW+CIKi2VzaN/PG6I1jcAaHBY0eJxYJjDunUnksgiky+YYp5S2a6FYd1sgVuMSSUDG5xJIJMrmGaeEgA0exywW4VbXXQiFE2h0wQlq3s6/cjmFa5O8gRARNWVLyiMRpLobWJSqVraSm9QJuJMKhFV2sVQDLmCwgEDDOku6212Y5SVSrpTLhLoKG3tNIOFpBKHdb8Pk0oGZqbNb2UWEbT5XAvrJ0k7+YLCRCxl+NY3oNj+BoBzlYio6iZiKWTzCr3Nxv/dqVdBrxMCYJKvFYgq7uxwBABwqLdR0zjWoqepAbFUDtFkVutQaJHy+zkztb95nTb0NDVwWPctmFQysMvjMVgtgu1tXq1DqagOvwvj3Oqiuam5NHIFZegh3WWbWzxosFtxKcQTABFV13C4eLWclUrV47BZ0ORxsFKJqArOjETR5nMujKQwgp7Spk1WK+nLRCyFJrcdTru5tvLtavehf5wXqhdjUsnALo/HsaXVA5fJflDbAy7Mp3OYS+e0DqWuLQzpNsFKbKtFsLvTh4uhqNahEJHJled6cKZSdbX7nKxUIqqCMyMRQ7W+AcXXqhYB5yrpzHgsZaoqpbJdHT4MTM1zVusiTCoZWP943FStb2XlYW4c1q2tUCQFm0UQ9JqjD3pvpx8Xx2JQituCiKh6hsMJiABdjeZ7Ia0nbX4XpufSyBX4op6oUmKpLAam5nHQIEO6y+xWCzr8LowyqaQbuXwBM3NpUw3pLtvV4UOuoHB9ak7rUHSDSSWDmkvnMBROYHe7+ZJK7aVhbhzWra1QtHh1wWqAdbKrsafTj1gqxxWgRFRVw7MJdPhdcNrMVUWsN+1+JwoKmJnLaB0KkWmcHylWdB8w0Dylsp4mN0YiCRR48VAXJuNpFFSxA8VsykuyuAHuPUwqGdSV0sT53Z3m2fxW5nPZ4XFYOaxbQ0opjEWTppinVMZh3URUCyNhbn6rhTZf8fw0yblKRBVzeiQCAIarVAKKw7pT2QITzTpRLg4wY6XS1qAHdqtwWPciTCoZVDkzutuE7W9AMavNSiXtxFI5JDJ5UyWVdnf4IFJclUtEVC3Dswn0cPNb1QV9xQ1wfK1AVDlnh6PY1OJGo9uhdShr1lNK5o9wWLcujMdSsIqg1SRjNBazWy3YFvRyWPciTCoZ1OVQDB6HFd0mGKK8lA5/ManEElZthEpDujsC5vn+cjts2NLqwSUmlYioSlLZPELRFDY1e7QOxfTsVguaPQ4O6yaqoLMGHNJd1uZ3wmG1YIRzlXRhIpZC0Oc0zRiNW+3q8LH9bREmlQzqcmlIt8WkP6gdfheyeYXZeZawaiFUGpJupkoloDSsm0klIqqS4XDxCvnmVra/1UKb34UJtr8RVcRkPIWxaMqQrW8AYBFBV6OLlUo6MR5NocNk7yMW29Xhw1g0hVgqq3UousCkkgEppUpJJfPNUyorr5/kXCVthKIpNHsccNnNNWh2b5cfw+EkokmeAIio8gZnim9mNrWwUqkW2n1OzHADHFFFnB0uDuk+aMAh3WU9TW6EoinkC+x00FIyk0cslTPlPKWy8giaK6xWAsCkkiFNxNKIJrOmnacEFJNKAiaVtBKKmGtId9me0mD7y6xWIqIquDkzDwDY3MJKpVpo97tQUMA0B/MSbdjZkQgsAuzrMu5F656mBuQKiu8fNFb+9283cVJpZ2kDO4d1FzGpZECXS0PBzJxUctgsaPI4MBHlSaHW0tk8wvMZUyaV9pWSSmyBI6JqGJyZR6DBbsght0ZUfsPCYd1EG3dmJIqd7T64HTatQ1k3DuvWh3JSycztb92NDfA5bZyrVMKkkgFdXtj8ZtwrCavR4XdhPMZZCbU2HktBAeg00ZDusqDPiVavAxfHmFQiosq7OZNglVINtXodsAiTSkQbpZQqDek25jylsia3HW6HFaMc1q2piWgKLrsFfpdxE5QrERHs5LDuBUwqGVD/eBwdfhcCbrvWoVRVu9+Fmbk0snnOSqglsw7pBoongD2dflziClAiqoLBmXnOU6ohm9WCFq8Tk7wARbQhw+EkZhNZQ89TAoqv83qaGrgBTmPjsRQ6/C6ImHOhVNmuDh8uj8eguK2cSSUjujwex+5O87a+lXUEXFAAJrnZpaZC0SQa7FYEGsyZtNzb5ceV8TkmK4moojK5AkZnk6xUqrF2v4uVSkQbdGYkAgA42NOoaRyV0N3oxkQshUyOr/O0oJTCRCxl6nlKZbs7fIilcpzhBSaVDCebL+DaZBy7TDxPqazd7wQAzlWqsVA0hc6Aea8u7O30I5Mv4PrUnNahEJGJjMwmUFDc/FZr7T4nwvMZJDN5rUMhMqyzIxE4bJaF4cNG1tPUAAVgLMJqJS1EklmkcwVTz1Mq28Vh3QuYVDKYG9PzyOaVqYd0l7V4nLBZhNnfGsoXFMZLSSWz2lse1s25SkRUQTdnioNhN7eyUqmW2v3FquZrk7xQQLReZ4aj2Nflh8Nm/LeGPU3FmaAc1q2NcjFAR11UKhXfU3CuEpNKhlMvQ7oBwGoRtPmcLGuvoZm5NHIFhc5G8w3pLtvS6oHTZmFSiYgq6sb0PABWKtVaucXiygRf1BOtRy5fwLnRqCla3wDA57Ij0GDHCCuVNFEuBqiH9reA244Ov4tJJTCpZDiXQzHYLIJtQa/WodRER6BhYXA0VZ+Zh3SX2awW7O7wcVg3EVXUwPQc/C4bWjwOrUOpK80eB2wWYVKJaJ2uTc0hmc3jkMGHdC/GYd3aGY+l0Nhgh8tu1TqUmigO6+b5h0klg+kfj2Nr0GOK8tTV6Ay4MJfOIZ7Kah1KXQhFk7BaBEGfU+tQqmpvlx8Xx7itgYgq5/rkPLa1eU07j06vyuesfiaViNblzHAEAHCgJ6BtIBXU0+RGeD6DRDqndSh1J2TyMRq32t3hw/VJLgCyaR0Arc2lUAxHNzdrHUbNdDYWfymFoin4XObcRqYnoWgK7T4nbBZzJy33dPrxjbeHMR5LoTNg3lY/Iqqd61Nz6Gly4+tvDWkdSt1p97twhVeKidblzEgUfpcNm03UurswVymSNMXwcaPI5guYjqexv9s8CcqV7O16bwFQPYynWY653zmazOx8BmPRFPZ11c83bKe/eFJgC1xtjEXrI8nCYd1EVEnxVBaT8TSCXra+aaHd58RYNMWqZqJ1ODMcwcHeRlgs5qmy7G7ksG4tTMRSUKiPId1lfE9RxKSSgVwKFb9Z99ZRUqnBYUWj245QlH3R1RZPZTGfzi1Uh5nZbp4AiKiCBqaKQ7rN3jqsV20Lw7q5AY5oLVLZPC6Px03V+gYALrsVQa+Tc5VqLBQpFgF0mXjhz622tBbH0tT7ewomlQzkYimptKezfpJKANAZaFj4JUXVM1b6N+6ogz5or9OGzS3uhZ8pIqKNuD5VTGa0MqmkCW6AI1qfC2NR5AvKNJvfFisP6+b8zNoZiybhtFnQ6K6fkSVcAFTEpJKBXByLod3vRKu3vl60dgZcmJ5LI5Or7wFo1TZWqgbrqoP2N6BY8XeJSSUiqoCBqXlYLYJmbn7TRKPbDrfDyqQS0RqdGY4CgKk2v5V1NzVgLp1DNMm22FoZj6bQEXDBUmcLK/Z2cgEQk0oGcjEUw74uc5WnrkZnwAWFYp8uVc9YJIkWj6NuVoDu6fBjcCaBOW4GIaINuj41h03NbtMvOdAriwh2tPuYVCJaozMjEXT4XQstpGbS0+QGALbA1UhBKYTqdAHO3i4/ZhNZjNfxe1W++jGIVDaPa5NzC8PA6kn5lxOHdVfXaCRZVz3Q+7o5V4mIKuP61By2Br1ah1HXdrZ50T/OmUpEa1Ec0m3OC9adARcsUnx9S9U3O59BJldAZx2M0bjVHs5qZVLJKK5NziFXUHU1pLusyW2H02bhsO4qSqRziCSyC9sy6sFdpXWn50ajGkdCREaWzRcwOJ3AtjbzrOM2ol0dPkzPpRGez2gdCpEhRBIZDM4kcNCErW8AYLda0BFwYZgb4GqifPG/HpNKuzt8AFDXYzWYVDKIcuazHiuVRASdARcrlapotDxPqY6SSm0+F9r9TpxnUomINmBweh6ZfGHhRSVpY2d78d+fLXBEq3N2pPj6x4xDust6Gt0YnU2iUMezbmolFE1B8N7ihHric9mxqc4XADGpZBAXQzF4HFb0Nbu1DkUTnYEGjEdTPClUydjCCtD6OhHs7w6wUomINqS/lMQoJzVIG0wqEa3NmeEIAGB/jznb34DiBrh0roCZOVYwVlsomkTQ54TdWp/phfKw7npVn//XDejiWAx7Ov2wWOprmn5ZZ8CFTL7AsvYqGY0k0eS2w+2waR1KTd3VHcD1qTnMc1g3Ea3TlfE4LAJs40wlTbX7nfC7bOgfZ1KJaDXOjESxLeiB32Xe9e/vDetmC1y1lTe/1au9nX7cDNfvAiAmlQygUFC4GIrV5TylMg7rrq6xOhvSXba/OwClUNflqkS0Mf0TcWxu9dTN5ky9EhHs6vDh6gSHdROtRCmF08MR085TKitWzgg3wFVZIpNDJJlFVx1ufivb0+mHUkD/eH2+p2BSyQCGZ4tZz3qcp1TW5nfCIuCw7ipIZvIIz2fqakh32f7ysO4RtsAR0fpcmZjjPCWd2NnuQ/9EHIqt8kR3FIqmMD2XNvU8JQCwWgTdjQ2sVKqy8dJF/7quVOqq7w1wVU0qicjjItIvItdE5PNL3P+IiERF5HTp489X+7X1pPzNua/LvD3PK7FbLQj6nAhFWKlUaWN1OKS7rM3vQpuPw7qJaH1S2TwGZ+Y5T0kndrb7EE1mMRlPax0Kka6dHYkAgOkrlYBiC1womkKuUNA6FNOq581vZZ0BFxrd9rrtfqhaUklErAC+COAJAHsBPCUie5c49FWl1KHSx79Z49fWhYuhGKwWwY72+p7X0BVowFiElUqVVv43rcekEsBh3US0ftcm56AUsItJJV0oJ/c4V4nozk4PR2G3CvZ0mv93V3dTA3IFhYkYk83VMhZJwue0wWfi+VwrEZHisO5QfZ5/qlmpdAzANaXUgFIqA+CbAJ6swdeazsWxGLYHvXU/r6G7qQHxdA6xZFbrUExlNJJEoMEOr7O+hnSXlYd1JzL1OViPiNbvcil5sZPtb7qws3TxjRvgiO7szHAEezr9cNrM/96il8O6q260Tmez3mpvpx+XQzHk8vVXFVfNpFI3gOFFn4+UbrvV/SJyRkReEJF9a/zaunB+LIp9dTyku6w882eU1UoVVa9Dusv2dwdQUPXbA01E63dxLAaX3YLNLR6tQyEALV4nWr0OJpWI7qBQUDg3GjX9PKWy4nZjK4bDfP9QDZlcAVPxNLqb6ve9RNn+ngDSuQKuTtbfwohqJpVkidtunZx4CsAmpdRBAP8RwN+v4WuLB4o8LSInReTk1NTUemPVrYlYChOxNPb31O88pbLOQAMETCpVUiqbx/RcBt2N9dsDXf7ZYgscEa3VhbEo9nT6YbUs9bKFtFAc1l1/L+iJVuvq5Bzm0jkcqoN5SkCxLamv2Y2hMCuVqiEUTUIBdbnw51Z31fECoGomlUYA9C76vAfA2OIDlFIxpdRc6e/PA7CLSOtqvnbRYzyrlDqqlDoaDAYrGb8ulL8pDzCpBIetOKx7lGtBK6Y8WK+eK5Xa/S4EfU4mlYhoTQoFhYtjMVYS68zOdh+uTcRRKHADnNltZCFQPTs1NAsAOLKpSeNIaqev2Y3puTRHHVRB+WI/k0rAlhYPvE5bXb6nqGZS6QSAHSKyRUQcAD4J4LnFB4hIh4hI6e/HSvHMrOZr68XZ0SgsAuztZFIJKP7C4rDuyin3l9f7iWB/d4Ab4IhoTYZnE4inc3W9mVWPdnX4MJ/Js6rZ5DayEKjenbo5i2aPA5tb3FqHUjO9zcX/VrbAVd7obHFIt7+hfod0l1ksgn1dfiaVKkkplQPwOQAvArgE4FtKqQsi8oyIPFM67NcBnBeRMwC+AOCTqmjJr61WrHp2biSCHW0+NDjMP0hvNTisu7JGZpNobLDX9bYGoFiuem2Sw7qJaPXOjxbnsN3FpJKucFh33eBSn3V6dziCw72NKF3Xrws9TcURGmyBqzwO6X6//d0BXAzFkK2zYd3VrFSCUup5pdROpdQ2pdT/WbrtS0qpL5X+/hdKqX1KqYNKqfuUUm/c6WvrjVLFQXqcp/QeDuuurJHZBHqa6+dK1XIOlIZ1X+CwbiJapQtjUdgsgp0dXq1DoUV2tBc38fUzqWR2G1kIVLeiiSyuTc7hcF+j1qHUlNNmRUfAhWFugKsoDum+3f6eADK5Aq7W2Wy/qiaVaGNC0RSm5zKcp7QIh3VXzlw6h9lEFr08EeBgaVjlmeGIpnEQkXFcGIthe5u3LlZyG4nfZUdXwFV3L+jr0EYWAr3/gUy+9Gexd4dL85T66meeUllvsxvD4QQKivPWKoVDum+3vzysezSibSA1xqSSjpX7McvfnPTesG7OVdq48jylniZWKgV9TnQ3NuBdJpWIaBWUUrgwFuU8JZ3a0e5D/zgrlUxuIwuBcMtxpl76s9ipoQgs8t7FtHrS1+RGulRZQ5XBId2329ziga8Oh3UzqaRj50aKpfV7OrlZZrHuxgZugKuA4XASAqCr0aV1KLpwqK8Rp4ciWodBRAYwMpvE9FwGh3qZVNKjXR0+XJuaQ54b4MxsIwuB6ta7Q7PY1eGHx2nTOpSa6yuNe+BcpcrhkO7bWSyCfd3+hQ3u9YJJJR07OxrFjnYfXHaW1i/W1chh3ZUwMptAu9/F1o2Sw72NGI0keQWLiFZ0ulTVeKi3/lpIjGBnuw+ZXAE3Z+a1DoWqZCMLgbSJWHuFgsLpoQiO1Nk8pbIWrwMNdiuTShXEId1L298dwKXxeF0N62ZSSaeUUjg3EsEBtr7dhsO6N04phZHZJHo4T2lBuRT8NFvgiGgFp4cjcNos2N3p0zoUWsKu0rBuboAzt40sBKpHVyfnEE/n6nKeEgCICPpKc5Vo4zike3n7exqRyRXq6hzEpJJOjcwmMZvIcvPbEjobXRzWvUHh+QyS2Tx6OU9pwV1dAVgtgtOlIZZERMs5PRzBXd0B2K18GaVH29u8EAH6xzmsm6js3aHi65t62/y2WG+zG5PxNJKZvNahGB6HdC9vYVh3HbXA8dWQTpWHe3Hz2+2cNiuCPifnKm3AcOnfrqeZJ4KyBocVuzt8rFQiojvK5gs4PxrFoTocdGsUDQ4r+prddXWVmGglp4Zm0eS2Y0urR+tQNFOeq1ReVkPrxyHdy9vU7IbPVV/DuplU0qmzI1HYrYJdHSytX0pvkxvDswnUcWv8hgzPJmC3Ctp8HNK92KHeRpwdjqLA4a41IyKPi0i/iFwTkc8vcb+IyBdK958VkSOL7vuKiEyKyPnaRk317HIojnSuUNdX+41gZ7uPSSWiRU4NRXC4rwml2eV1qaepAQLgJlvgNoxDupdnsQju6gowqUTaOzcawc52H4coL6O32Y1EJo/wfEbrUAxpJJxAd2MDrJb6fWGxlEO9jYinc7g+xZaJWhARK4AvAngCwF4AT4nI3lsOewLAjtLH0wD+ctF9XwXwePUjJXrPu6UWWVYq6dvOdi9uTM8jnWObC1EkkcG1ybm6HdJd5rJb0RlwYZBD/DdsKJxAbzPHaCznQG8Al0IxpLL1cQ5iUkmH8gWFM8NRXgW9g95S2xY3OKxdrlBAKJpCD+cp3ab8M8cWuJo5BuCaUmpAKZUB8E0AT95yzJMAvqaKjgNoFJFOAFBKvQIgXNOIqe69fSOMzoCLJf86t7Pdh1xB4cY03zwSnRgsJsPv2dyscSTa29TiwXA4gTyr0tdtPp3DzHxmoZ2Qbne4twnZvMKFsZjWodQEk0o6dHUyjrk63s6wGu1+FxxWC4bZE71mE9E0cgXFzW9L2Nrqhc9pY1KpdroBDC/6fKR021qPWZaIPC0iJ0Xk5NTU1LoDJQKKmzNPDIZxz+bmum4hMYLy+ID+cbbAEZ0YDMNhtSxsuq1nm1s9yOYVxrjwZ93K779YqbS8clVgeUC+2TGppEOnbkYAgEmlO7CIoKepAcNhnhDWaihcvGrLE8HtLBbBgd4Ak0q1s9S78lsvHa7mmGUppZ5VSh1VSh0NBoNrCo7oVkPhBCZiaRzbwqv9ere11QubRThXiQjFCsuDvQG47Byrsbml+PqXLXDrNxxOwCIc0n0nbf5iRfMpJpVIK6eGZtHscWBTC9/030lfsxuhaJJrQddocCaBQIMdjRyst6RDvY24PB7n91VtjADoXfR5D4CxdRxDVBNv3Sh2WzKppH8OmwVbgx5cDjGpRPUtkcnh/GiUrW8lPpcdLR4HBmfY7bBew+EkOgIuOGxMJdzJkU1NeHcoonUYNcHvBB06NTSLI32NLK1fQW+zGwUFnB+rn8n6lTAUTqCv2c3vr2Uc7m0qzjUbiWgdSj04AWCHiGwREQeATwJ47pZjngPw6dIWuPsARJVSoVoHSgQAJ26E0eS2Y3vQq3UotAp7Ov24FKqPeRZEyzk9FEGuoHAPk+ELNrd4cHNmHgVukV6zfEFheDaBXs5mXdGRvkaEoimEoubvrGFSSWciiQwGpuZxmK1vKyq3b9VLr2olRBIZRJPZhdJfut3dm4o/eycHOf+52pRSOQCfA/AigEsAvqWUuiAiz4jIM6XDngcwAOAagC8D+IPy14vINwC8CWCXiIyIyGdq+h9Adeft0jwlCzdnGsKeTj/GoilEEtwUS/XrrRthiLz3+oaAza3FLdJT8bTWoRjOtck5pHMFDulehfL7+fJoGzOzaR0AvV+5RI7zlFbmddrQ7HHUxQ9qpZRLfTe1eDSORL+aPA7sbPcubEqh6lJKPY9i4mjxbV9a9HcF4LPLfO1T1Y2O6D3D4QRuziTwP9y/WetQaJX2dPoBAJdCcdy/rUXjaIi0cWIwjL2dfvhdHHtQVn4dPDgzj3a/S+NojKV8MZ+zWVe2t9MPp82Cd4dm8bEDnVqHU1WsVNKZU0OzsFoEB3sDWodiCL1NxQFoiuWrq3JzZh5Om4Un0BUc3dyMUzdnuW6WiBa8cX0aAPDBHa0aR0KrtaezuAGOLXBUr7L5At4dinCe0i1aPA54nTbc5FylNXt3KAK3w4oWj0PrUHTPYbNgf3egLoZ1M6mkM6eGZrG7wwe3g0Vkq9HX7MZkPI1QNKV1KIZwc6Y4T8nK1o07Ora5GfF0DpfH+UaEiIpevTqNNp8T29s4T8kogl4nWjwOJpWobp0fjSKZzXO5wC1EBJtb3Bic5ga4tTo1NIveJs5mXa3DfY04PxpDOmfuBUBMKulIvqBweijC1rc1eG+uUkTbQAwglspiIpZCH+cprejo5vJcJfNfWSCilRUKCm9cn8EHtrfyhbSBiEhxWDcvEFCderu0sZKVSrfb3OpBJJnlzLU1iCazuDo5x9a3NTjS14RMvoCLY+Y+DzGppCNXJuKYz+Q5SG8NOgKuhV5VurNTN2ehUNx4QXfW0+RGV8CFtzmsm4gAXBqPITyfwYPb2fpmNHs6fbgyMYdcvqB1KEQ1d2IwjK2tHgR9Tq1D0Z3Ni+Yq0eqcLW1G5pDu1TtSel9/yuQFEEwq6Ui535KVSqtns1hwoCeAEzeZVFrJOzdnYRFwBegqHd3cjJODYc7rIiK8erU4T+kDnKdkOHs6/cjkCrjBNheqM4WCwonBWVYpLaMj4ILLbsHAFH83rNapmxGIAD1NDVqHYhjtfhe6Ai7Tz1ViUklHTt2MoNXrQG8zf1DX4tiWZpwfjWIundM6FF07MRhGZ6ABDht/7Ffjni3NmIilMRxOah0KEWns5cuT2NPp55IDAypvgLvIuUpUZy6GYogms7hvG5NKS7GIYEurF9en5rQOxTDeHpzBrnYfXHar1qEYyuFNTXjX5AUQfHepIydvhnGkr4nzGtbo3i0tyBcU3jH5D+tGZPMFnB6OYBPnKa3aPaW5SifYAkdU16KJLN65OYvHdge1DoXWYVvQC7tVcCkU1zoUopp6/VqxwvKBbaywXM62oAeziSzC85yrtJJMroB3bs7ivq0tWodiOMc2N2MsmsJw2LzbBplU0omxSBI3ZxL8QV2Huzc1wWoRvDUwo3UounVhLIZUtoBNnKe0ajvbfPC7bDh5k0klonr2ytUp5AsKj+1u0zoUWgeHzYLtbT5ugKO68/r1Gexo87LC8g62BYvbPFmttLJzoxGksgXct5WVb2tVfn9/3MTvVZlU0onyNxmTSmvncdqwvzuwsOGCblf+/trMSqVVs1gERzc38/uKqM79tH8SjW47DvVy3qFR7elkUonqSzqXx9s3ZrhcYAVtPid8LhuTSqtwfKD4evjYFr5XXasdbV40ue0L/4ZmxKSSThwfmEGj247dHT6tQzGke7c248xIBMlMXutQdOn1a9PY2e6Fz2XXOhRDuWdzM65PzWNmLq11KESkgVy+gJ/1T+HhnUFYLWxNN6o9HX5MxtP8XU51492hYlUJk0p3JiLYFvTi+tQ8F7Os4PjADHa2e9HscWgdiuFYLIJ7t7TgrRusVKIqOz4Qxr1bmmHhi9Z1uW9LC7J5hXdNPll/PdK5PE4MhtlTvw7lEl8zX1kgouW9PRhGeD6Dx/d1aB0KbUB5WDfnKlG9eP3aNCxSvOhKd7Yt6MF8Oof+Cf5+WE42X5yndC+rlNbtvq3NGJlNmnauEpNKOjAaSWIozHlKG3F0cxMsAhxnq9JtTpeuVj2wjd9fa7W/OwCfy4bXSsMuiai+vHBuHA12Kx7ZxXlKRrans1gFzhY4qhevX5vGwd5G+FmhvqLyXKXXr5m3imSjzo9GkcjkmaTcgHtL7/PfMul7VSaVdOD4dc5T2iify459XQEO617C69dnSler+P21VjarBfdvbVnYoEJE9SNfUPjRhXE8ujuIBgfXJxtZi9eJDr8L58eiWodCVHXxVBZnRqJ4kBXqq9LodqDF48AbfK23rHIihJVK67er3YdGt92071WZVNKB8jylXe2cp7QR925pxrvDEaSynKu02BvXprG/pxGBBl6tWo8P7GjFUDiBoRlzlqsS0dLeuTmLqXgaT9zVqXUoVAEHegI4O8KkEpnfWwNh5AsKD2xnAmC1tgW9eOtGGLl8QetQdOn4wAy2BT0I+pxah2JYxblKzThu0rlKTCrpwPEbM5ynVAH3bm1BJlfAmeGI1qHoxnw6h9PDETzI1rd1Kw+5ZAscUX15/lwITpsFj+5m65sZHOgJ4Mb0PKLJrNahEFXVa9em4bJbcKSPGytXa1ubF3PpHM4w8XybXL6Ak4Oz7HiogPu2tmA4nMTIrPkuVDOppLGR2QSGw0nczx/UDTu2uRki5u1VXY+3b4SRKygO6d6Ara0edAZcbIEjqiOFgsKPzo/j4Z1BeJ02rcOhCjjQ0wigOBuEyMzeuD6NezY3w2Vn2+5qbW31QAR47Spf693qYiiGuXQO927hPKWNKrcPvmXCBUBMKmmsvFXqPlaSbFjAbceeDj/euM4TQtnr16bhsFlwdDOvVq2XiODB7a14/fo0CgWumyWqB+8ORzAeS+Gj+9n6ZhYHegIAgDMjEW0DIaqiUDSJKxNzC1XWtDoepw0Hehrx0/5JrUPRneMDnP1bKbs7fAg02PGWCVvgmFTS2PGBGTS57djZxnlKlfDBna145+Ys5tI5rUPRhdevz+DuviZerdqgD2xvRSSRxUVuDiKqCy+cC8FhteCxPWx9M4tGtwObWtw4O8xKJTKvly8XkyIfYtvumn1odxvOjEQwPZfWOhRdefXqNLYGPWj3u7QOxfDKc5XeNOGwbiaVNKSUwmtXp3Hf1hbOU6qQh3cGkc0rvHndfD+saxWez+BSKIYHOahxw8rDLjlXicj8CgWFF86P4wM7WrmO22T2dwdwju1vZGI/vTyJ3uYGbG/zah2K4Ty2uw1KAT/rn9I6FN1IZvJ460YYj+xkkrJSPrCjFcPhJG5Mz2sdSkUxqaShy+NxjMdSeHQXf1Ar5eimZngcVvyM5asLM4Du5zylDWvzubCr3ce5SkR14K0bYYxGknjyUJfWoVCFHexpxGgkyUoEMqVUNo/Xrk3jQ7vbIcKL1Wu1r8uPNp8TP73M9xBlxwdmkMkV8PCuoNahmEY5QWe27zMmlTRU7tvlD2rlOGwWPLC9FT+/MgWl6nv+zcuXJ9HktuNQb6PWoZjCg9tb8faNMFLZvNahEFEVfffUCDwOKz6yt0PrUKjCynOVznKuEpnQm9dnkMoWuLFynUQEj+5qwytXppDNF7QORxd+fmUKLruFQ7orqK/Fja1BD352xVwVcUwqaehnl6ewt9PPHtUKe3hnECOzSQyYrKxwLfIFhZ/2T+LRXW2wsrWyIj64oxXpXIHbBYlMLJnJ4/lzIXx0fycaHJxFZzZ3dQdgEeAM5yqRCb10eQJuh5UJgA14bE8b4ukcTgzytR4A/Kx/EvdvbeFs1gp7ZGcbjg/MIJkxz4XqqiaVRORxEekXkWsi8vkl7v8tETlb+nhDRA4uum9QRM6JyGkROVnNOLUQTWbxztAsHt3NKqVKe3hn8d/053XcE31qaBaRRJZDZivo/m0taLBb8Y8XJ7QOhYiq5McXxzGfyePX7u7ROhSqAo/Thu1tXs5VItNRSuHlS5P4wPZWJgA24APbW+GwWkzXmrQeg9PzGJxJLLyvosp5ZFcQmVxhYbOeGVQtqSQiVgBfBPAEgL0AnhKRvbccdgPAw0qpAwD+DwDP3nL/o0qpQ0qpo9WKUyuvXZ1GvqA4T6kKepuLZYU/N1lZ4Vq8dGkSNovgIZ4IKsZlt+Khna34x0sTdd9aSWRWf/fOCLobG3BsM6/0m9X+7kacHYnw9ziZSv9EHGPRFD7Ei4kb4nHacO/W5oUtevWs/D7qEb5XrbhjW5rRYDfXDOBqViodA3BNKTWglMoA+CaAJxcfoJR6Qyk1W/r0OIC6uTT40/5JBBo476ZaHt4ZxPGBmbqdf/PSpQkc29LMzUUV9uE97QhFU7gwFtM6FCKqsPFoCq9fm8avHunmRlYTO9gbwPRcBmPRlNahEFXMS5eKb055sXrjHtvdhutT87g5U79jNIBiUmlTixubWz1ah2I6LrsVD2xrwU/7zTMDuJpJpW4Aw4s+HyndtpzPAHhh0ecKwI9F5B0ReXq5LxKRp0XkpIicnJoyRmVKoaDws/4pfHBHK2xWjrWqhkd2tSFtsrLC1RqaSeDq5Bw+tKdd61BM57HdbbAI8BO2wBGZzj+cHkVBAb9y+E4vVcjoDvQ0AgDODkc0jYOokl6+PIn93QG0cU7rhj1WGnReTtTVo1Q2jzeuT+MRdjxUzSO7ghgKJ3DDJDOAq5nRWOoy35KpOBF5FMWk0p8uuvlBpdQRFNvnPisiDy31tUqpZ5VSR5VSR4NBY3zjXwzFMD2X5tWEKrp3SzOcNgt+VodzlV6+XEx4fIjbPyquxevE3Zua8I+XmFQiMhOlFL5zagRH+hqxNejVOhyqoj2dPtitgtPcAEcmMRlP4dTQ7EIyhDZmU4sHuzt8eP5cSOtQNHNiMIxUtsDWtyoq/9ua5b1qNZNKIwB6F33eA2Ds1oNE5ACAvwLwpFJqoaxEKTVW+nMSwPdQbKczhfLwt4d3GSMJZkTlssKXLtff/JuXLk9iW9DDctUq+fCedlwYi2EsktQ6FCKqkFNDs7gyMYdfv7t35YPJ0Jw2K+7qDuDUzdmVDyYygBfOjUMp4OMHOrUOxTQ+fqATJ2/OIhStz9d6P708BYfNgvu2tmgdimmVZwD/zCQzgKuZVDoBYIeIbBERB4BPAnhu8QEi0gfguwB+Ryl1ZdHtHhHxlf8O4CMAzlcx1pp66fIkDvQE0Op1ah2KqX1kXweGw0lcCsW1DqVm5tI5HB+YYetbFX14b/Hf9iVWKxGZxtffGobHYcUnDnVpHQrVwD2bm3FmOFq3cxfJXH5wdgy72n3Y0e7TOhTT+Oj+YoLuh2frr1pJKYUXL4zjA9tb0eDgJsFqemxXG45fn8FcOqd1KBtmq9YDK6VyIvI5AC8CsAL4ilLqgog8U7r/SwD+HEALgP8kIgCQK216awfwvdJtNgBfV0r9qFqx1lIomsTp4Qj+t4/s1DoU0/vwnnb8mZzDixfGsbfLr3U4NfHz/ilk84ol0FW0LejF1lYPfnJpEr9z/2atwyGiDYomsvjB2TH82t098Drf/7Lo628NaRQVVdPRTU149pUBnB+N4ig3/ZGBjUWSODE4iz/+Bb6vqKStQS/2dvrxw3Mh/NMPbtU6nJq6MBbDaCSJP/zQDq1DMb1fvKsDf/XaDfz08iR+6aCxL2pVdUq0Uup5pdROpdQ2pdT/WbrtS6WEEpRS/1Qp1aSUOlT6OFq6fUApdbD0sa/8tWbw4vlxAMDjd7FEtdqCPieObmrCixfGtQ6lZp4/F0Kr14F7+CK5qj68tx1vXp9GPJXVOhQi2qDvvjuCdK6ATx3r0zoUqpG7NzUBAE4MsgWOjK089+fjBn9DqkcfP9iJd4ciGJlNaB1KTb14YRwWAT60hxeoq+1IXxNavU78yATvVbl6rMZeOD+OHW1ebG/jINBa+MV9Hbg8Hq+LtaCJTA4vXZ7A43d1wMp12FX1i/vakc0rDuwmMjilFL7x9hAO9gRwV3dA63CoRlq8TmwLenByMKx1KEQb8v2zIdzV7ccWztGsuI+VWuDqbWD3ixfGcc/mZrRwTEvVWS2Cj+xrx08vTxq+HZtJpRqankvjxGAYT9zVoXUodePx0r/1D+vghPDy5UmksgV8bD+vVlXb4d4mdDc24PtnzP99RWRm79wsDuh+ilVKdeeezc04eXMWhUJ9LfMg8xgOJ3BmOIKPH+DrvmrY1OLB/u5AXc1VujYZx5WJuYX3T1R9j+/rQCKTx6tXp7UOZUOYVKqhF86Po6CAJ/az9a1WeprcuHtTU128+f/h2RBavU4c28LWt2qzWAQfP9CJV69OIZLIaB0OEa3T198agtdpM/wsA1q7Y1uaEU1mcWk8pnUoROvyg1Ky42N8X1E1Hz/QiTMjUQyH66MF7rkzIViE31O1dP+2FjS57fj+mTGtQ9kQJpVq6LnTo9jZ7sXuDm5nqKVfOtCJS6EYrk2adwtcNJnFS5cn8bH9bH2rlV862IVsXtXVzC4iM5meS+MH50L45cNd8DirtreEdOqBba0AgDeuzWgcCdH6fP/MGA71NqK32a11KKZV3gL3nMHf8K+GUgrfPzOG+7a2oM3v0jqcumG3WvDE/k785OIEEhnjboFjUqlGRmYTODE4iycPdaO01Y5q5KMHOmGRYvbdrF44F0ImV8CvHunROpS6sa+rOMPge++Oah0KEa3D198aQiZXwO8+sEXrUEgDHQEXtgY9eP26sVsOqD71j8dxMRTDJ1hlWVW9zW7cs7kJ33lnBEqZu1X2wlgMN6bnWbmrgScPdiGZzeMfL01qHcq6MalUI+X2K/7yr702nwv3b2vB3787atoTwndPjWJr0IMDPRw0Wysigl893I3jA+G6KYsmMot0Lo+vvXkTj+wKcnFGHXtwWyvevhFGNl/QOhSiNfn2yWHYrYInD/F9RbX9xt29GJiex6khc2+L/IfTo7BZhLN/NXDP5mZ0Blx47rRxL1QzqVQDSil8790RHO5jiapWfu1ID4bCCVOuDx4OJ/D2YBi/ephVcLX2K0e6AYDVSkQG84MzIUzPpfH7D7JKqZ49sK0FiUweZ4YjWodCtGqZXAHfe3cUH97Tzg1dNfDRA51osFvx7ZMjWodSNdl8Ad97dwyP7W5Do9uhdTh1x2IRfOJgF37WP4WZubTW4awLk0o1cHo4gisTc/iNu3u1DqVuPX5XBzwOK/7unWGtQ6m4b78zAhHglw93ax1K3elpcuP+rS34uzooiyYyg6+/NYS/OX4T/+7H/WjzOTEcTuDrbw0t+0Hmdv+2FogAr11jCxwZx8uXJzEzn8E/Ocr3FbXgddrwsQOd+MHZEObTxp15cyc/75/C9Fwav8HvKc38+t09yBWUYS9UM6lUA986OQKX3YKPH+Qkfa24HcUTwg/Phgw9BO1WuXwB3z45jId2BNHTxCo4Lfz63cUquDcHOOyVyAhuTM8jFE3hwW2trO6sc41uBw72NOJn/VNah0K0al9/ewgdfhc+uKNV61DqxlPH+jCXzuHvDdyedCfffmcYrV4HHtkV1DqUurWj3YdDvY341slhQ16oZlKpyhKZHL5/Zgwf3d8Jv8uudTh17TeO9mI+kzf8ysbFfn5lCqFoCk8d69M6lLr1sQOd8LtsrGogMojXr03D7bDiUF+j1qGQDjy2uw1nRiKYihuz5YDqy43pebxyZQqfurcPNivfxtXKkb5G7On047+9edOQb/jvZCqexsuXJ/HLh7ph5/eUpv7J0V5cmZjDmZGo1qGsGb9zquwHZ0KYS+dYoqoDRzc1YWe7F//tuHlOCN94ewhBnxMf2tOmdSh1y2W34tfv7sWLF8b5poRI58ZjKVwaj+PeLS188UwAikklpYCf9Rt36w7Vj/9+/CZsFsEnj/F9RS2JCH7nvk24PB7HOzfNNZ/1WyeHkc0rPHUvL1Br7eMHi/O7vv7WTa1DWTO+oqoipRS++sYgdrX7cO+WZq3DqXvlE8L50ZghM8C3ujkzj5cuT+KT9/TyzZHGfuu+PmTzCt86ab6ZXURm8vLlSThtFjy4vUXrUEgn9nX50e534qdMKpHOzadz+PbJYTx+VwfafC6tw6k7Tx7qgs9lw395Y1DrUComX1D4m+M38eD2FmwLchOq1vwuO375cDf+4fQYIomM1uGsCd+JVtGpoVlcDMXw6Qc2cW6DTvzy4W64HVZ87c1BrUPZsP/6RvFq1W/ft0nrUOretqAXH9jeiq+9OYhMjqupifSofzyO86NRPLCtBW6HTetwSCdEBI/tbsMrV6aRzuW1DodoWd8+OYxYKoff49ZKTXicNnzq3j68cC6E4XBC63Aq4uXLkxiLpvA7923WOhQq+fT9m5DOFQy3bZBJpSr66hs34XPZ8MuHuJVLL3wuO37j7h58/8wYJmIprcNZt3gqi2+dHMbH9nei3c+rVXrwmQ9swUQsjR+eM8/MLiIz+cJLV0tVShxuS+/3kb0dmEvn8NpVboEjfcrlC/ir127g7k1NuHtTk9bh1K3ffWAzLCL4yus3tA6lIv76tQF0Blz4MMdo6MaeTj+ObW7G144PIl8wzrgWJpWqZDicwPPnQvjkPb3wOHlFVE9+/wNbkC8o/JfXB7UOZd2+8fYQ5tK8WqUnD+8MYnubF3/16g3TzOwiMov+8Th+eC6E+1mlREt4cHsrAg12/OBsSOtQiJb0wvlxjMwm8fRDW7UOpa51BhrwiYNd+NsTwwjPG6s96VZnhiM4PhDG7z+4hUPfdeb3P7AZw+EkfnR+XOtQVo2vrKrky68OwCLAZz7AX/56s6nFg8fv6sDfvHUTn3tsO7wGS/qlsnl8+dUb+MD2VhzsbdQ6HCqxWASf+cAW/IvvnsOrV6fx0E6uZSXSiy+8dBVepw0f2MYqJbNa6wbOTy0aSuuwWfD4vg788FwIqWweLru10uERrVuhoPDFn17D1qAHH97TrnU4de+ZR7bhe6dH8devDeBPfnG31uGs27OvDMDnsnHouw79wt4ObGn14Es/v46P7u8wxBgdpiWrYGYujW+dHMavHO5GR4CtSXr0zx7ahngqh/9qwGF73zo5jKl4Gp99dLvWodAtfvVINzoDLnzhpausViLSiVNDs/jhuRB+78HNcBvsIgLVzscPdmIuncPP+qe0DoXofV68MI7L43H84Yd2wGrR/5tLs/v/s3fn8W1ddf7/X0eSd8v7HjtxkiZxkjZp03SjpbRQStsBOqzTFigwMIVh+Q7MyvIbttmYYYYZtoEpTCkww761MC2UtaV7kjZJszjN5iSO91Xe5EU6vz8kpca1E8uWdK+k9/Px8CO27tXVJ/K1zr2fc87nrK/1c9P59Xzt0RNpV0w55mjvKPfv6+QNl63Cn5/jdDgyh9djuOPqNTxzephHj/Y7Hc6iKKmUBP/10DGmZsLccfVap0ORBWxtKuPaDdV8+XfHGAlOOx3OogWnQ3zxt0e5eFU5l6/RioJuk+fz8qfXrGXniUEeS5NGQCSTWWv5xE8OUO3P4x0vUpssC7tiTSVVxbn88Kn0Ko4qmS0ctnzmV4dZU13Ey7c0OB2ORL33JecxOjnDnQ8dczqUJYnUGPTy9heqjIZbveqiFdSW5PEfv3w2LTqqlVRKsJ5AkK892sYfXriC82q0NKObve+69QyNT6fVaKVvPHaCzuEgf3n9hrQYCpmNXr+9idqSPP71gUNp0QiIZLIfPX2a3aeG+KuXbUi7qc6SWj6vh9dc3MivWnvoSeOFPCSz/Ojp07R2jfC+69ZrlJKLtNSVcPOFDfz3w8fpHJ5wOpy4HOkZ4d49Hbz5Bc1UFec5HY4sID/Hy3tevI4dbYP8Lg0WkVBSKcE+/5sjzIQtf3bdOqdDkXPY2lTGdRtr+a8Hj9E/Oul0OOcUCE7zhd8e4er11VyxttLpcGQB+Tle3nfdep46OcTP93c7HY5I1uofneTvfnqAC5vKeO22RqfDkTRwyyUrCYUt39ul0UrivOB0iH974BBbGkt5+QX1Tocjc/zl9RuwFj79wLNOhxKXT/38EIU5XhV9TwN/tL2JFWUF/FsadFQrqZRAR3pG+eYTJ/mjS5pYVVnkdDiyCB+4sYXx6RD/8cvDTodyTp/95WGGJ6b565dtcDoUOYfXXdzIeTXF/PPPWpkOhZ0ORyQr/d1PDzA6OcO/vHYLHvXwyyKsririijWVfHvHybRaylky01d+d4yO4SAfvHGjPsNcqKmikNuvWMX3n2pnz6khp8NZlMeO9vPz/d2869rzqCjKdTocOYdcn4f3v3Q9e9qHuXdPh9PhnJWSSgn09/93gIJcL3/x0vVOhyKLdF5NMW+4bCXffPIkrV0Bp8NZ0LPdI3z10TZuuaSJ81eUOh2OnIPP6+FDN7VwvG+Mrz5y3OlwRLLOPbtP8+PdHbzrmvNYX+t3OhxJI2+6YhWnBiZ4YH/6LOUsmefUwDif/80RXra5VqPTXez/XbeOquI8/vaefa5PRIfClr//vwOsKCvgbVepllK6ePVFK7hgRSmfvL+V8akZp8NZkJJKCfKLA9389lAvf/aSdVRqfmpaef916yktyOFDP3yGsAsbhHDY8rc/3kdxni+tly7NNi9uqeW6jTX8xy8P0zGUXvPtRdLZ8b4xPvyjfWxfVc57X6xVMiU+L9tcR3NlIf/526Oun24gmclay8d/sh+PMXz0FZudDkfOoiQ/hw/ftJG97cP87xMnnA7nrL72aBv7OwJ84MYW8nO8Tocji+TxGD7yik10Dgf5wm+OOB3OgpRUSoBAcJq//fE+Wur83H5Fs9PhSJzKi3L58E0beerkkCsbhP994gRPHB/ggze2aKhqmvnoKzYTtpaP3LNfNyciKTA4NsXb7t6Bz2v4zK0X4fPqMkfi4/UY3vGitTxzepiHj7i/OKpknnv3dPDLgz2877p1NJQVOB2OnMPNFzbwwnVVfPL+Vk72jzsdzrxOD03wrw8c4poN1bx8i+pzpZtLmit4zbZG/uvBYxzsdOfMGi2FkgCfvL+V7pEgX3rTxeT6dAGbjl69bQU/evo0/3hfK1etq2Z1lTtqYp3sH+ef7m/lheuq+KNLmpwOR+LUVFHIX16/gb//v4P88KnTvOZiFQsWOZtvPnFyyc+dnA5x96NttA9N8LYrV/Pgod4ERiaZ5mzn2kwoTGlBDh/4wTP86TVr8RjDbZetTGF0kq26A0E+cs9+LlpZxtuuUiHldGCM4Z9fs4WX/ftD/OX39vDNP7nMVR0a4bDlb76/F2vh724+X6tHp6n/7w828ttDPXzgB3v5/p++gBwXnWOgkUrL9osD3XzziZO8/arVXNhU5nQ4skTGGD71ui3k+jy879tPu6K48uRMiPd86yl8HsMnX7NFjUCaeuuVq7m0uYKP3bufUwPu7MESSXcTUyHueuQ4pwbHef32Jppd0jEg6cnn9XD9plpOD02w++SQ0+FIlgiFLe//zm4mZ0L82+u24lVx7rTRUFbAx2/ezJNtA3zmV+5a/OcrDx/j4SN9fOQVm2iqKHQ6HFmi8qJcPnHz+expH+ZzLjvHQEmlZekcnuCvv7+HzQ0l/KVW5Ep79aUF/NOrL2BP+zB//9MDTofDP/zfQfa2D/Op121lhYY/py2vx/Bvr98KBt79zaeYnAk5HZJIRjk9OMHnf3OYjqEgt166kgu0mIEkwNamMhrLC/j5gS4mpvS5Lcn3mV8+y6NH+/nEzeezprrY6XAkTq/e1sjrtzfyuV8f4TetPU6HA8CuEwN86ueHeNnmWm7RjIe09wdb6nnNtkY+/5sjPHa03+lwfo+SSksUnA7xjm/sYmomzGduuYg8nwqeZYKbLqjn7Vet5muPneC7O045Fsc3Hmvj64+d4O1XreZlm+sci0MSo6mikH993Vb2tg/zsXtVX0kkESamQtz3TCdffPAIYQt/cvUaNjcooSSJ4TGGV25tYGxyhnv2nHY6HMlw9z3TyWd/fYTXXtzI67fr5j9dffyV57OpvoT3futpx2vfdA5P8I5vPMWKsgL+5TVbNeMhQ3z85s00VxXxnm8+xWkXLQSkpNIShMOWv/r+Xva2D/Pvf3Qh59WoNyGTfODGFl64rooP/ugZft3anfLXf2B/Fx+9dz8vaanhgzdtTPnrS3K8bHMd7752Ld968hR3PnTM6XBE0pK1lvbBcX6yp4N//lkrjxzp4+JVFbz32vNYqWH9kmCN5YW8ZGMte9uH+d5O5zqaJLM9fXKQ939nN9tWlvH3f3i+0+HIMhTkernrLZdQnOfjj+/e4VjZg+Hxad5y1w4mpma48/btlBbmOBKHJF5xno8v376dqZkwf/K1nYwEp50OCVBSKW7WWj7x0wP8ZE8Hf/WyDVyvUSQZx+f18MU3Xsym+hLe9b9P8bvDqSv2+uvWbt79zae4oLGMz956kebTZ5i/eOkGXr6lnn+6v5Xv6gZF5JystfSPTvLk8QG+9eRJ/vH+Vv7zt0d5sm2ATQ0lvOfF5/Gqi1ZQmKd1RyQ5XrS+mjVVRXzoR8/wiFaDkwTb3zHMm+96ktqSfL58+3Yt9Z4B6krz+epbL2F8KsStX36c9sHUJpYCwWneeveTHO8b487bt7O+1p/S15fkW1tdzGdvu4hD3SO84xu7XFFaQ0mlOFhr+af7W7n70TbedtVq3nXNWqdDkiQpzvNx91svobmyiLfdvZOf7etM+mv+8Kl27vj6LjbU+fn6H19KkW6SMo7HY/jX123lheuq+Jsf7OX7u9qdDknEdSZnQhzsDPDjp0/zqQcO8W+/eJYf7z7Nif4x1tUU85ptjXzoxo28fnsT9aWqNyfJ5TGGN1y2ijVVxdzx9Z08fFiJJUmMve1DvOm/n6Q4z8f/vv0yKovznA5JEmRjfQn/87bLCExM89ovPkZrV2qmwvWPTnLblx9nb/swn731Iq48ryolryupd+2GGj712i08erSfd3xjF8FpZxNLSiot0nQozId+9Ax3PnSMN1+xig/ftFFzUzNcZXEe377jcjavKOGd//MUn/3VYcLhxNfCmQmF+eeftfLn393Dpasr+OafXE5pgYapZqr8HC9fvn07L1hbyV9+bw//9eBR1ViSrGet5cnjA3x/1yn+8b6DfOPxE+xuH6KhtIBXbm3g/det529uaOH125u4eFU5BbnqzZfUKcj18rU/vpSmikLe8tUnufuR40m5HpDs8eCzvdx65+MU5nr55p9crlW5MtAFjaV8951XYLG89ouP8bN9XUl9vQMdAW7+wiMc7h7ly7dv54bzNZsm0716WyOffPUFPPhsL2++60mGxqcci0VDIRahf3SS937raR492s97rj2Pv7h+vRJKWaKsMJdv/cnlfPCHz/DpXzzLY0f7+dTrttBYnpjG/0jPKH/zg73sOjHIrZeu5OOv3EyuT7neTJefE5lz/+ff3cM/3d/Ks92j/MOrztewd3mebz5xMq79b7tsZZIiSY5Q2PLTvR18/tdHONwzSp7Pw4VNZWxpLGNVZSE+jz4PxR1+3drD67c38Z0dp/jYTw7wtcdOcMPmugWTAen2tyipEQ5bvvy7Y/zzz1pZX+vna398KbUl+U6HJUnSUlfCj951JX/6P7t45//s4k2Xr+IDN7YkdDZCOGz5xuMn+Kf7D1JWkMt33nEFFzaVJez44m63XLqSglwvf/W9vbzqPx/ly7dfzHk1qZ/yqKTSOfzucC9/8d09DI1P82+v28prLm50OiRJsfwcL59+/VYuX1PBJ35ygOs+/SDvuHotb3vhakrylzaiaHBsii8+eJS7H2mjINfLZ265kJsvXJHgyMXN8nxePnfLRZxXXcxnfnWYfaeH+fQfbdXqVZIVQmHLT/Z08LlfH+Zo7xjra4v5l9duYXwypMS6uFZ+jpfbr1jFzhOD/Hx/F1988CjNlYVsW1nOulq/RhnLWXUOT/DX39/L7w738QcX1POp122hMFe3YpmuoayA777zCv7lZ4e465Hj/OpgN399Qwuv3NqAZ5m1U/e2D/F3Pz3AjrZBrtlQzb+8dgs1fiUps83NF66goayAd35jF6/43CP87cs3ccslTcs+v+KhT7IFdA0H+dTPD/GDp9pZW13E3W+9lE0NJU6HJQ4xxvBHl6zkqnXV/NN9B/nMrw5z18PHef0lTbx62wo21Zecc/SatZanTw3xg13t/PCp0wRnQrz6okY+cGML1X7No89GHo/h/S9dz0Ury/jr7+/l5s8/whsvX8X/e8k6KopynQ5PJOFmQmF+sreDz/3qCMf6xmip8/Ofb9jGDZvr8HhM3COzRFLNGMMlzRVsWVHKE8cH2NE2wA+fPg1AtT+PtdXFrKoo5Or1VawoK9DIdmFiKsSdDx3jiw8eAeDv//B83nDZSp0bWSTP5+VvX76Jmy6o46P37ud939nN5359mLe/cA0v31KPP45O6lDY8ujRPr76SBu/bu2hqjiXf3nNFl63vVHnVBa7pLmC+//shbz/u7v50I+e4Xu7TvHxV25mS2NZSl5fSaU5ekaCfPG3R/nfJ04SDlvefe1a3vvidZqWIgCsKCvg87dt450vGuaLDx7l64+18d8PH6e+NJ9LmitoqffTUFpAcXRYayA4TfvgBK1dAZ48Pkjf6CS5Pg9/eGEDb3/hGq3IIABcs6GGn7/vaj71wCG+/lgbP9jVzruuPY/bLlupnu8sMzQ+xamBCUYnZxifmmF/xzAFuV4Kc30U5/nOfLakm5lQmHt2d/D53xzheDSZ9KU3buP6TXUp7UkTSZS8HC9Xr6/mheuq6AoEOdIzypGeUXadGODxY/18Z+cpavx5bFtZzsWrytm2qozNDaW6nswi41Mz/Ojp03zh10foGA7yBxfU84EbW1Q/KYtdvKqCe999FT/Z28GXHjzGB3/4DB//yX6uXFvFledVsaWxlOaqIiqLcs8kiMYmZ2gfnOBA5zBPHBvgN4d66A5MUl6Yw5+/dD1vubJ5yTMnJLPUlOTzjT++jB8+fZpP3n+Qm7/wCK/d1sgdV69hXZLvOdPz6jTBQmHLY0f7+f6uU9y/r4uZsOU121bw3hev0we/zOv8FaV84bZtDI5N8fP9XTx0uJddJwa5d0/HvPs3lhdw1XmVXL2+mus21erDX56nvCiXf3zVBbz1Bc380/2t/PPPWvnsrw7zyq0NvOHylVywolQ9UBlmaHyKHW2D7GgbYPepIY72jNI/dvYii0V5PupL8qkrzWdlRSErKwopcXHicXhimm8/eZKvPdpGx3CQTfUlfOmNF3P9plolkyQjGGOoLy2gvrSAF66rJhS2dAWC1JbksevEIE+dHORn+yMFenO9HrY2lXLFmkquWFvFRSvLlGTKMNZaDnWP8INd7XxnxykCwRm2Npby7390IZetqXQ6PHEBj8dw84UreOXWBnafGuLHT5/mN4d6+VVrz3P7mMjopplwmOnQc4sCFOf5uOq8Kl6+tZ7rNtbq80Oex+MxvPbiRq7fXMtnfnmY/3n8BN/b1c4L11Xx5iuaedGGanK8iS8zkNSkkjHmBuAzgBf4irX2k3O2m+j2m4Bx4C3W2qcW89xEefL4AO/79tN0DAfx5/t47cWNvP2Fa1hdVZSMl5MMU16Uyy2XruSWSyMFOUcnZ+gOBBmfDGGxlOTnUFuSr5WKZNHW1fq56y2XsO/0MP/z+Anu2d3Bd3aeYlVlIbdf0czbrlrtdIgJlQ7tRCJYa2kfnOCpk5Ek0o7jgxzqHgEiN5qbV5Tw0k21rK0uZmVlIf58H4W5Pn55oJuJ6RBjkzMEgpHPl67hII8f6+fhI5GlzcsKc1hZUciqikK2NJbSUufHl4QLhsWaDoV5+EgfP93Tyf37OhmfCnHFmkr+7g/P58UtNUqOSkbzegwrygq47bKV3H5FMxAZBf/0ySF2nRjkiWP9fP43R/jsr4+Q6/Nw8cpyXrC2kivWVrKlsUw1xeKwnPYj0Q50BPjx7tP8fH8XJ/rH8XoMN55fx1uvbGbbynJ97snzGGO4aGU5F60s5+NESq/s7ximfXCC3pFJJmdC5Hg9+PNzaCjLp6WuhLXVRY6275I+SvJz+NuXb+Ld157HN584wdcfO8Hbv76TssIcPv7KzQmv5Zu0pJIxxgt8AXgp0A7sMMbca609MGu3G4F10a/LgC8Cly3yuQmxqrKQDXV+PvQHG5XxlWUrzvNRXF3sdBiSAc5fUconX7OFD960kf/bG7k5HzzHKJZ0ky7tRLxCYUvH0ARHe0fZd3qY3aeG2H1qiL7RyO+vKNfLxc0VvGJrPZc0V7C1aeHRCgc6AvM+PhMO0zkU5OTAOCcGxmnrG2Nv+zA/2dtJQY6XrU2lXLwqMu2mpa6EupL8pI0MCk6HONw9ys4Tkfoyjx7tZ2h8Gn++j5dvqef2K5o5f4UK0Ev2qvHn87LNdbxsc2SJ7+GJaXYcj/ytPHasn3/7xbPwCyjI8XLJ6goubCxlY30JLfUlNJUX6CZyHstpP5IRz28O9fDVR47zgrVVvOPqtVy3qUYFkyUudaWRUcgiiVRRlMt7XryOO65ey+8O93Lvng5WlBUk/HWSOVLpUuCItfYYgDHm28DNwOwP+5uBr1trLfC4MabMGFMPNC/iuQlRW5LPV996aaIPKyKSEKUFOdx22Upuu2wlkY/KjJIW7UTn8AQHOgJMzoSZnAkxOR0+8/34VIj+0Sn6xybpG5mib3SS9qEJpmbCZ56/prqIq9dXc1FTGRc2lbOxfvkjiXweD00VhTRVFHJl9LGh8SmaKgrPTLn50oPHCIUj50yuz0NTeQG1JfmUF+ZSXpRDeWEupQU55Hg9+LyGHK+HnOi/XmMIWctMyDITtsyEwoxOzhCYmGZoYprhiWn6Ridp6xunY3iC2Km5oqyAF2+o4aYL6nnh+iryfOqoEZmrtCCH6zbVct2mWgAGxqZ44lgkwfT4sX4+f7iX6J8uXo+hvjSfFWUFVBXnUVKQQ2n0y5/vI9fnIc/nIcfrIdfrYXV1EWuzo3Nrye2HtbYz0cG88bJVvOmKVSpvICKulOvz8JKNtbxkY21Sjp/MpNIK4NSsn9t5fu/AfPusWORzRUSySgYOn0+LduKRI/385ff2LLi9rDCHyqJcqorzaKn3c92mWtZUFbG6qoiWuhJKC1Nzk1FWmMsrtjbwiq0NQKRI7DPtwxzpHeVk/zgn+sfpHZ3kYFeAofFphsanzty4LpYxnLmhLS/M5ZLmcpqrGllbXczFq8ppSELvl0imqyjK5cYL6rnxgnogslrY4Z4RWjtHODkwTvvgOKeHJjjYFSAQTerOrrMy27uuWctf39CSyvCdspz2I+FJpVR9zouIuFEyk0rz3f3MbQEX2mcxz40cwJg7gDuiP44aYw4tOsKIKqAvzuckmxtjApfG9QaXxoU743JjTPodxm8pca1KRiDLlPR2IgFtxDmdSOzhlnXOvSGBgbiYW/8u3ULvz7kl/T1y6m/xb/4Z/mbpT3djO7GQ5bQfv79TZt5LgOKKi0uvRd0YE7g0Lpf+DiGz4pq3nUhmUqkdaJr1cyMwd2mshfbJXcRzAbDW3gncudQgjTE7rbXbl/r8ZHBjTKC44uXGuNwYEyiueLk1riVIejux3DYi1TLod5s0eo/OTu/Puek9ygjLaT9+TybeS4Diipcb43JjTKC44pUNcSWz8t8OYJ0xZrUxJhe4Bbh3zj73ArebiMuB4eg858U8V0RE0pvaCRERWYrltB8iIpJASRupZK2dMca8B/g5kaU+77LW7jfGvDO6/UvAfUSW+TxCZKnPt57tucmKVUREUk/thIiILMVy2g8REUmsZE5/w1p7H5EP9NmPfWnW9xZ492KfmyRunBbhxphAccXLjXG5MSZQXPFya1xxS5N2IpUy5nebRHqPzk7vz7npPcoAy2k/Esyt55Piio8b43JjTKC44pXxcZkMXKJaRERERERERESSLJk1lUREREREREREJENlfFLJGFNhjPmFMeZw9N/yBfZrM8Y8Y4zZbYzZGe/zkxGXMabJGPMbY8xBY8x+Y8yfzdr2MWPM6Wi8u40xNy0znhuMMYeMMUeMMR+YZ7sxxnw2un2vMWbbYp+bxJjeEI1lrzHmUWPM1lnb5v19piiua4wxw7N+Nx9Z7HOTHNdfzYppnzEmZIypiG5LyvtljLnLGNNjjNm3wPaUn1eLjMupc+tccTlybklyLPQZn6x2J10ZY7zGmKeNMT+N/qz3ZxZjTJkx5vvGmNbouXSF3qPnGGPeH/372meM+ZYxJl/vjyyWG6/53Hi9Fz22rvkSF5NT9xLnisupc2vBe+JZ+6T0/FpkTE6cW4uJK/Hnl7U2o7+AfwE+EP3+A8A/L7BfG1C11OcnIy6gHtgW/d4PPAtsiv78MeAvExSLFzgKrCGyTPee2OvM2ucm4H7AAJcDTyz2uUmM6QVAefT7G2Mxne33maK4rgF+upTnJjOuOfu/Avh1Ct6vq4FtwL4Ftqf0vIojrpSfW4uMK+Xnlr6S97XQZzxJanfS9Qv4c+CbsXNf78/z3p+vAW+Pfp8LlOk9OvPerACOAwXRn78LvEXvj74W8+XGaz63Xu9Fj61rvsTF5Mj13rnicvDcWvCe2Knza5ExOXFuLSauhJ9fGT9SCbiZyAUX0X//MMXPX/JxrbWd1tqnot+PAAeJXCAl2qXAEWvtMWvtFPDtaHxz4/26jXgcKDPG1C/yuUmJyVr7qLV2MPrj40BjAl532XEl6bmJPvatwLcS9NoLstY+BAycZZdUn1eLisuhc2sx79dCkvp+SXKc5TM+We1O2jHGNAJ/AHxl1sN6f6KMMSVEbgL+G8BaO2WtHULv0Ww+oMAY4wMKgQ70/sjiuPGaz5XXe6BrvkTGdBaOvldzpPLcWsw9cUrPr8XE5NC5tZz8wZLfq2xIKtVaazsh8iYDNQvsZ4EHjDG7jDF3LOH5yYoLAGNMM3AR8MSsh98THU53l1ne0O0VwKlZP7fz/JNvoX0W89xkxTTb24hkp2MW+n2mKq4rjDF7jDH3G2M2x/ncZMaFMaYQuAH4wayHk/V+nUuqz6ulSNW5tVipPrckBeZ8xier3UlH/wH8NRCe9Zjen+esAXqBr5rIFMGvGGOK0HsEgLX2NPCvwEmgExi21j6A3h9ZHDde86Xr9R7omi9err3ec/LcWuCeGBw8v84S02wpP7fOEVdCzy/fcgJ1C2PML4G6eTZ9OI7DXGmt7TDG1AC/MMa0RrO1TseFMaaYyB/t+6y1gejDXwT+jsgJ+XfAvwF/vNRQ53ls7rKAC+2zmOcuxaKPa4y5lsgf6lWzHk747zOOuJ4CVllrR02k1tWPgXWLfG4y44p5BfCItXZ2T0Sy3q9zSfV5FZcUn1uL4cS5JUk29zPemPl+ndnHGPNyoMdau8sYc43D4biVj8hUhfdaa58wxnyGyHQuAaIdbjcDq4Eh4HvGmDc6GpSkEzde86Xr9R7omi8ebr/ec+TcWuCe+MzmeZ6S9PPrHDHF9kn5uXWOuBJ+fmXESCVr7XXW2vPn+boH6I4OfSP6b88Cx+iI/tsD/IjI8C8W+/xkxWWMySFyQvyvtfaHs47dba0NWWvDwJdnxbsU7UDTrJ8biQwPX8w+i3lusmLCGLOFyLSIm621/bHHz/L7THpc1tqAtXY0+v19QI4xpmoxz01mXLPcwpzhqkl8v84l1efVojlwbp2TQ+eWJNECn/FLbncyzJXAK40xbUSGYL/YGPM/6P2ZrR1ot9bGeiG/TyTJpPco4jrguLW211o7DfyQSI0LvT+yGG685kvX6z3QNd+ipcH1XsrPrYXuiWdJ+fm1iJgcObfOFVcyzq+MSCqdw73Am6Pfvxm4Z+4OxpgiY4w/9j1wPbBvsc9PYlyGSJ2Eg9baT8/ZVj/rx1fNincpdgDrjDGrjTG5RD4o7p0n3ttNxOVEhpB3LvK5SYnJGLOSyAXim6y1z856/Gy/z1TEVRf93WGMuZTI31n/Yp6bzLii8ZQCL2LW+Zbk9+tcUn1eLYpD59Zi4nLi3JIkOctnfLLanbRirf2gtbbRWttM5Jz+tbX2jej9OcNa2wWcMsZsiD70EuAAeo9iTgKXG2MKo39vLyFSX0LvjyyGG6/50vV6D3TNF09Mrr3ec+LcOts98SwpPb8WE5MT59Yi40r8+WWTUKHdTV9AJfAr4HD034ro4w3AfdHv1xCpbr4H2A98+FzPT1FcVxEZcrYX2B39uim67RvAM9Ft9wL1y4znJiLV4Y/G/v/AO4F3Rr83wBei258Btp/tuQl6j84V01eAwVnvzc5z/T5TFNd7oq+7h0hRthck+71aTFzRn98CfHvO85L2fhHpxegEpolkv9/m9Hm1yLicOrfOFZcj55a+kvPFAp/xJKndSecvZq1Uovfnee/NhcDO6Hn0Y6Bc79HvvT8fB1qJXLB/A8jT+6OvxX6d69rKiXb5XDFFf34LKbzeix5f13yJi8mpe4mzxuXgubXQ9ZJj59ciY3Li3FpMXAk/v0z0ySIiIiIiIiIiIouWDdPfREREREREREQkwZRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJckaxpg/NMY8YIzpN8ZMGWNOG2O+bYy5ctY+bcYYO8/XzKx93hJ97IAxxjPnNf7VGNM26+fmOccZM8YcNcb8rzHmhfPEePcCr2+NMVctcMxRY8weY8zbk/LGiYjIOZmI49HP5fMW2Oec7VB0v3O2RSIikjzR6/1dxpgRY8ygMeZpY8yn5+wT+2x+wzzPf1Ns+zzbVhpj/jvaBkxGP/M/Y4ypmrNf7Jr/5XMef0W0Dbkr2vbE7k2K54ntijnPPT/6+DVzHs8zxrzfGLMj+n8OGmMOG2P+3RizOp73TrKPkkqSFYwx/w78ADgNvB24DvgA4AceNsasnbX7N4Er5nz93gV/1EbgNYsM4S+jx7kJ+DugEnjIGPPRefZtnef1rwD2LHDMV0W3fdkY88ZFxiMiIol1BdAc/f6WuRvjbIdg8W2RiIgkkDHmg8BXgJ8DrwZuB+4BXjnP7qPArfM8fkt029xjbwZ2AZcDHwauB/6JyPX8E8aYhnPEdj3wvejX2621z0tazfH/nWM7xphC4FfAx4j8n18L3Ah8FngJ8NC5jiHZzed0ACLJZoy5GXgf8FZr7d1zNn/DGPMKYGLWY53W2scXcejfAh8i8qF+LodmHfNB4G5jzCeAjxljHrTW/nbWvmOLfP0zxzTG/BLYTqTR+59FPFdERBLrVmAM2Bf9/u9jG5bQDsHi2yIREUms9wD/Za390KzHfmKM+fg8+/4EeK0xptxaOwhgjKkAXkrkHuG22I7GGEPkOn0QuMJaG4huetAY81NgL/CfwB/OF1R0dNGPo695u7U2fI7/x2+Bm4wxF1lrnz7Lfv8AXAhcZq3dP+vx3xhjvgC87RyvI1lOI5UkG7wP2DHPhTwA1tqfWGs7lnDcvwcunDskNQ4fBzqAdy7x+WdEeymeAZqWeywREYmPMcYLvA64F7gL2GSM2TJrl/eRnHZIREQSrwzomvvgAqOCHiNyPT979sJriIxKfWzOvlcTSd78/ayEUuzYp4mMDHqlMaZ57osYY15AJJn0S+A2a21oEf+PHwIHiIyImld0lNIdwH/OSSjF4gpba7+8iNeSLKakkmQ0Y4yPyJSBB+J7mvHN+fLOs98TRD7YF/ygPptoY/BrIsNfnxf33K9FHHIlcHwpsYiIyLK8GKgFvg18H5gmOh1iie1Q9KmLaotERCSxngLea4x5szGm8hz7WuA7/P4UuFujj811dfTfexY41o8BA1w15/HtwH3AI8DrrLXT54hpdmz/CLzaGLNpgX0uBgqJv40SOUNJJcl0lUAecGr2g9GidrMv1M2szX9O5IZg9tevFjj+PwCXG2NessT42onciMx28TyvP1/j4YnGXm6MeR+wDfjkEuMQEZGluxUYAn5mrR0AfgHcEm1bltIOQXxtkYiIJM67idRDuhvoNcbsN8Z8whhTssD+3wauMcbUGWPqgBdFH5trBTBkrR1e4DgnZu0320eJTK9+lbV2Mo7/Ryy2Y8AHF9geq+E0t43ymPg6tyWLKakkmS52kT53uOpf8PsX6u+ete1/gEvmfL1jvoNHayE9wiKK4J0jvtkOzvP6l8yz3z1EYh8A/h34K2utCumJiKSQMSaPSIHVH1lrp6IPf4tI0e7LWVo7BHG0RSIikjjW2r1EFuR5JZEaRwb4W2Dn7BXWZu3/NHCYyDTo1wOHrbW7l/LSCzz+AJHkz0KJoYUPGJkZ8Ung1nkWhICF26h7mdVGGWPOj/e1JXso6yiZrg+YBBrnPP4NIsXrAHbM2dZtrd0Zx2v8A3BfdK5zvFYA3XMeG1/k678feBioITIF71+jRb/nrhInIiLJcyOR+hv3GWPKoo/9lkjbcyuREUfxtkMQf1skIiIJEh0R9JPoF8aYtxFZEe5twGfmecp3iKz4Zph/lBJE6iyVGWNK5tZUimqetd9snyOy0vPfGmO6rLX/Gcd/BeDrwEeAvyFSt2luTBBpo56d9fj7iKwGdzHwpThfT7KMRipJRrPWzhApknf9nMe7rbU7E3HBbq29n8jSoHGNVooOJX0xzy/it1hHov+H+4CXAwE0/U1EJNVidTS+R2RFn0Ei0wjyiPRYW5LcDomISHJZa/+byOyAlgV2+TaR+nmXs3BSKTaj4JULbH8lkTbjd/Ns+xsiyaHPGWNet5iYY6KjaD8FvJnnd3DsAsZ5fht1JNo+HYrntSQ7Kakk2eA/gMuMMW9K4mv8A5He6m1xPOcjRIayLjv7H13C9J+BG4wxW5d7PBERObfoNIiXE5nudu2crz8nUjPvWlLTDomISAIYY2rmeawaKOX5MwwAsNYeBO4E7rTWti5w6IeA3URGHPnnHL8e+DPgHmvtiblPjK489zbgZ8A3jDHXLvo/FPFlIp0efz3nuOPRuN9tjNkY5zFFAE1/kyxgrb3HGPMfwN3RD+CfEJkWVwm8NLrb6Kyn1BtjnrciG/DUrHoZc/0Y2E/k5uF5DQGwwRjTB+QCq4kMj70B+Ji19sE5+xYt8PpHrLV9C7w+wBeBDwB/CejGRUQk+W4msmrOZ6y1T8zeYIx5hMjU5FuttW+Lsx2CpbVFIiKyfM8YY+4hUsuoB1hF5Pp6HPjaQk+y1r7zbAe11tpo58JvgMeMMf8CtBEZ/fRhYJjn19eb/fyZ6CilXwI/NsZcE63ndE7W2qAx5tNEOqHn+jBwaTSmzxMZKRUkUqbjzUAo+rPIvJRUkqxgrX2/MeYh4F3AfwN+oJfIlISbolPYYm6Lfs3VRGS1tvmOb40x/wj87wIh/Gv03yDQGX3dq6218w1vbWH+KXFvIlK4dV7W2lFjzGeI9H582Fp7cqF9RUQkIW4lUpD1ibkbrLXTxpjvEimO+q442yFYQlskIiIJ8QkinQafBSqALuBR4I+stceXc2Br7T5jzMVEVnT7JFBF5N7gx8DfnaMDGWvtuDHm5UQSP/cbY66M4+X/k8g0uop5jvliIgmt24jUU/IRmcr9K2CrtfZIHK8jWcZERtKJiIiIiIiIiIgsnmoqiYiIiIiIiIhI3JRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJRERERERERERiZuSSiIiIiIiIiIiEjcllUREREREREREJG5KKomIiIiIiIiISNyUVBIRERERERERkbgpqSQiIiIiIiIiInFTUklEREREREREROKmpJKIiIiIiIiIiMRNSSUREREREREREYmbkkoiIiIiIiIiIhI3JZVERERERERERCRuSiqJiIiIiIiIiEjclFQSEREREREREZG4KakkIiIiIiIiIiJxU1JJRERERERERETipqSSiIiIiIiIiIjETUklERERERERERGJm5JKIiIiIiIiIiISNyWVREREREREREQkbkoqiYiIiIiIiIhI3JRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJRERERERERERiZuSSiIiIiIiIiIiEjcllUREREREREREJG5KKomIiIiIiIiISNyUVBIRERERERERkbgpqSQiIiIiIiIiInFTUklEREREREREROKmpJKIiIiIiIiIiMRNSSUREREREREREYmbkkoiIiIiIiIiIhI3JZVERERERERERCRuSiqJiIiIiIiIiEjclFQSEREREREREZG4KakkIiIiIiIiIiJxU1JJRERERERERETipqSSiIiIiIiIiIjEzed0AIlUVVVlm5ubnQ5DRMR1du3a1WetrXY6DiepjRARWZjaCbUTIiJns1A7kVFJpebmZnbu3Ol0GCIirmOMOeF0DE5TGyEisjC1E2onRETOZqF2QtPfREREREREREQkbkoqiYiIiIiIiIhI3JRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJRERERERERERiZuSSiIiIiIiIiIiEjcllUREREREREREJG5KKomIiIiIiIiISNyUVBIRERERERERkbgpqSQiIiIiIiIiInFTUklEREREREREROKmpJKIiIiIiIiIiMRNSSUREREREREREYmbkkoiIiIiIiIiIhI3JZVERERERERERCRuPqcDEJHU+uYTJ50OYV63XbbS6RAkxYwxTcDXgTogDNxprf3MnH0M8BngJmAceIu19qnothui27zAV6y1n0xh+DIPfb6IiIhkNrX1MpdGKomIiFNmgL+w1m4ELgfebYzZNGefG4F10a87gC8CGGO8wBei2zcBt87zXBERERERSSIllURExBHW2s7YqCNr7QhwEFgxZ7ebga/biMeBMmNMPXApcMRae8xaOwV8O7qviIhkCGNMkzHmN8aYg8aY/caYP5tnH2OM+awx5ogxZq8xZtusbTcYYw5Ft30gtdGLiGQHJZVERMRxxphm4CLgiTmbVgCnZv3cHn1socfnHvcOY8xOY8zO3t7ehMYsIiJJpxGtIiIup6SSiIg4yhhTDPwAeJ+1NjB38zxPsWd5/PcfsPZOa+12a+326urq5QcrIiIpoxGtIiLup6SSiIg4xhiTQySh9L/W2h/Os0s70DTr50ag4yyPi4hIBkrWiFYREVkerf4mIiKOiK7s9t/AQWvtpxfY7V7gPcaYbwOXAcPW2k5jTC+wzhizGjgN3ALcloq4RTKZVvURN0rmiFZjzB1Eps2xcqXOMxGReCmpJCIiTrkSeBPwjDFmd/SxDwErAay1XwLuA24CjgDjwFuj22aMMe8Bfg54gbustftTGr2IiCTdMka05i7w+O+x1t4J3Amwffv25yWdRETk7JRUEhERR1hrH2b+nuTZ+1jg3Qtsu49I0klERDKQRrSKiLifkkoiIiIiIuJGGtEqIuJySiqJiIiIiIjraESriIj7KakkIiIiIiIiSafFAEQyj8fpAEREREREREREJP0oqSQiIiIiIiIiInFTUklEREREREREROKmpJKIiIiIiIiIiMRNSSUREREREREREYmbkkoiIiIiIiIiIhI3JZVERERERERERCRuSiqJiIiIiIiIiEjclFQSEREREREREZG4KakkIiIiIiIiIiJxU1JJRERERERERETipqSSiIiIiIiIiIjETUklERERERERERGJm5JKIiIiIiIiIiISNyWVREREREREREQkbkoqiYiIiIiIiIhI3JRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJRERERERERERiZuSSiIiIiIiIiIiEjcllUREREREREREJG5KKomIiIiIiIiISNyUVBIRERERERERkbgpqSQiIiIiIiIiInFTUklEREREREREROKmpJKIiIiIiIiIiMRNSSUREREREREREYmbkkoiIiIiIiIiIhI3JZVERERERERERCRuSiqJiIiIiIiIiEjclFQSEREREREREZG4KakkIiIiIiIiIiJxU1JJRERERERERETilvKkkjGmyRjzG2PMQWPMfmPMn82zjzHGfNYYc8QYs9cYsy3VcYqISHIZY+4yxvQYY/YtsP2vjDG7o1/7jDEhY0xFdFubMeaZ6LadqY1cRERERETAmZFKM8BfWGs3ApcD7zbGbJqzz43AuujXHcAXUxuiiIikwN3ADQtttNZ+ylp7obX2QuCDwIPW2oFZu1wb3b49uWGKiIiIiMh8Up5UstZ2Wmufin4/AhwEVszZ7Wbg6zbicaDMGFOf4lBFRCSJrLUPAQPn3DHiVuBbSQxHRERcRiNaRUTcz9GaSsaYZuAi4Ik5m1YAp2b93M7zE08iIpIFjDGFREY0/WDWwxZ4wBizyxhzx1mee4cxZqcxZmdvb2+yQxURkcS6G41oFRFxNceSSsaYYiI3CO+z1gbmbp7nKXaB4+iGQUQks70CeGTOjcKV1tptRKZLv9sYc/V8T7TW3mmt3W6t3V5dXZ2KWEVEJEE0olVExP0cSSoZY3KIJJT+11r7w3l2aQeaZv3cCHTMdyzdMIiIZLxbmHOjYK3tiP7bA/wIuNSBuERExAWWM6JVRESWx4nV3wzw38BBa+2nF9jtXuD26CpwlwPD1trOlAUpIiKuYIwpBV4E3DPrsSJjjD/2PXA9MG+9DRERyQpLHtGqWQ8iIsvjc+A1rwTeBDxjjNkdfexDwEoAa+2XgPuAm4AjwDjw1tSHKSIiyWSM+RZwDVBljGkHPgrkwJm2AOBVwAPW2rFZT60FfhTpo8AHfNNa+7NUxS0iIq5z1hGtxpjYiNaH5j7RWnsncCfA9u3b5y23ISIiC0t5Usla+zDz10yavY8F3p2aiERExAnW2lsXsc/dRAq1zn7sGLA1OVGJiEg6mTWi9Y2zHisCPNbakVkjWj/hUIgiIhnNiZFKIiIiIiIiZ6URrSIi7qekkoiIiIiIuI5GtIqIuJ8jq7+JiIiIiIiIiEh6U1JJRERERERERETipqSSiIiIiIiIiIjETUklERERERERERGJm5JKIiIiIiIiIiISNyWVREREREREREQkbkoqiYiIiIiIiIhI3JRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJRERERERERERiZuSSiIiIiIiIiIiEjcllUREREREREREJG5KKomIiIiIiIiISNyUVBIRERERERERkbgpqSQiIiIiIiIiInFTUklEREREREREROKmpJKIiIiIiIiIiMRNSSUREREREREREYmbkkoiIiIiIiIiIhI3JZVERERERERERCRuSiqJiIiIiIiIiEjclFQSEREREREREZG4KakkIiIiIiIiIiJxU1JJRERERERERETipqSSiIiIiIiIiIjETUklERERERERERGJm5JKIiIiIiIiIiISNyWVREREREREREQkbkoqiYiIiIiIiIhI3JRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJRERERERERERiZuSSiIi4ghjzF3GmB5jzL4Ftl9jjBk2xuyOfn1k1rYbjDGHjDFHjDEfSF3UIiIiIiISo6SSiIg45W7ghnPs8ztr7YXRr08AGGO8wBeAG4FNwK3GmE1JjVRERERERJ5HSSUREXGEtfYhYGAJT70UOGKtPWatnQK+Ddyc0OBERMRxGtEqIuJ+SiqJiIibXWGM2WOMud8Yszn62Arg1Kx92qOPiYhIZrkbjWgVEXE1JZVERMStngJWWWu3Ap8Dfhx93Myzr53vAMaYO4wxO40xO3t7e5MTpYiIJIVGtIqIuJ+SSiIi4krW2oC1djT6/X1AjjGmisjIpKZZuzYCHQsc405r7XZr7fbq6uqkxywiIimnEa0iIg5SUklERFzJGFNnjDHR7y8l0mb1AzuAdcaY1caYXOAW4F7nIhUREYdoRKuIiMN8Tgcg6eObT5x0OoR53XbZSqdDEJElMMZ8C7gGqDLGtAMfBXIArLVfAl4L/KkxZgaYAG6x1lpgxhjzHuDngBe4y1q734H/goiIOMhaG5j1/X3GmP9cyohW4E6A7du3z5t4EhGRhSmpJCIijrDW3nqO7Z8HPr/AtvuA+5IRl4iIpAdjTB3Qba21c0a0DhEd0QqcJjKi9TbHAhURyWBKKomIiIiIiOtoRKuIiPspqSQiIiIiIq6jEa0iIu6nQt0iIiIiIiIiIhI3JZVERERERERERCRuSiqJiIiIiIiIiEjclFQSEREREREREZG4KakkIiIiIiIiIiJxU1JJRERERERERETipqSSiIiIiIiIiIjETUklERERERERERGJm5JKIiIiIiIiIiISNyWVREREREREREQkbkoqiYiIiIiIiIhI3JRUEhERERERERGRuCmpJCIiIiIiIiIicVNSSURERERERERE4qakkoiIiIiIiIiIxE1JJRERERERERERiZuSSiIiIiIiIiIiEjcllUREREREREREJG5KKomIiIiIiIiISNyUVBIRERERERERkbgpqSQiIiIiIiIiInFTUklEREREREREROKmpJKIiIiIiIiIiMRNSSUREREREREREYmbkkoiIiIiIiIiIhI3R5JKxpi7jDE9xph9C2y/xhgzbIzZHf36SKpjFBERERERERGRhfkcet27gc8DXz/LPr+z1r48NeGIiIiIiIiIiEg8HBmpZK19CBhw4rVFRERERERERGT53FxT6QpjzB5jzP3GmM0L7WSMucMYs9MYs7O3tzeV8YmIiIiIiIiIZC23JpWeAlZZa7cCnwN+vNCO1to7rbXbrbXbq6urUxWfiIiIiIiIiEhWc2VSyVobsNaORr+/D8gxxlQ5HJaIiIiIiIiIiES5MqlkjKkzxpjo95cSibPf2ahERERERERERCTGkdXfjDHfAq4Bqowx7cBHgRwAa+2XgNcCf2qMmQEmgFustdaJWEVERERERERE5PkcSSpZa289x/bPA59PUTgiIiIiIiIiIhInV05/ExERERERERERd1NSSURERERERERE4qakkoiIOMIYc5cxpscYs2+B7W8wxuyNfj1qjNk6a1ubMeYZY8xuY8zO1EUtIiIiIiIxSiqJiIhT7gZuOMv248CLrLVbgL8D7pyz/Vpr7YXW2u1Jik9ERERERM5CSSUREXGEtfYhYOAs2x+11g5Gf3wcaExJYCIi4goa0Soi4n6OrP4mIgIQClue7R6hrW+MHK/hJRtrqSjKdToscae3AffP+tkCDxhjLPBf1tq5o5hERCT93U1kReivL7A9NqJ10BhzI5ERrZfN2n6ttbYvuSGKiGQ3JZVExBGBiWnueuQ4PSOTGOB3R/qo9ufxpTdezMWryp0OT1zEGHMtkaTSVbMevtJa22GMqQF+YYxpjY58mvvcO4A7AFauXJmSeEVEJDGstQ8ZY5rPsv3RWT9qRKuIiAM0/U1EUi4wMc2dvzvG0MQ0t166ko+/cjM/fNcLKMjxcuudj/P0ycFzH0SygjFmC/AV4GZrbX/scWttR/TfHuBHwKXzPd9ae6e1dru1dnt1dXUqQhYREWcsNKJ1V7SDQUREkkBJJRFJKWstP3r6NCPBad525WouWFGKz+th28py7nn3lVT78/izb+9mdHLG6VDFYcaYlcAPgTdZa5+d9XiRMcYf+x64Hpi33oYk39RMmO/uOMVtX36cux4+zi8PdjM1E3Y6LBHJIrNGtP7NrIevtNZuA24E3m2MuXqB595hjNlpjNnZ29ubgmhFJFGmZsL88mA3X/7dMV77xUf5xmNtBKdDToeVdZRUEpGU2tM+zKHuEV66qY6misLf21ZelMt/3HIh7YPj/ON9Bx2KUFLFGPMt4DFggzGm3RjzNmPMO40x74zu8hGgEvjPOYVWa4GHjTF7gCeB/7PW/izl/wFhYirE2762g7/+wV66AkEmpkP8prWHLz14lP7RSafDE5EsoBGtItlpYGyKLz14lN+09jAdCjM6OcPf3rOf2//7SUaC006Hl1VUU0lEUmY6FOb+fZ00lhfwgrWV8+5zSXMFt1/RzDceP8EdL1xDc1VRiqOUVLHW3nqO7W8H3j7P48eArc9/hqRSKGz5k6/v5JGjfXzy1RfwR5c08a0nT/Fs9wjf2XGKrz3WxruvPY88n9fpUEUkQ51tRCvgsdaOzBrR+gmHwhSRBJuaCfP1x9oYCc7w5hc0s77Wz22XreTePR38+Xd285av7uDbd1xOjldjaFJB77KIpMxTJwcZCc7wss11eIxZcL93XbuWHK/hs786nMLoRCQeX3u0jYeP9PEPf3gBt1y6EhP9m15f6+cNl62kf3SKe3Z3OBylpLuh8Sn2nBriG4+10R0IOh2OpJhGtIrIfH6yt4PekUluvXQl62v9Zx5/5dYG/u31W9l1YpA7HzrmYITZRSOVRCQlQmHLQ8/20lRewJpzjD6q8edz+xXNfOV3x/iz69axqlKjlUTc5GT/OJ/6+SGu3VDNrZc2PW/7mupiXtxSw69ae9i2spzzaoodiFLSmbWWJ9sG+OneTkJhy3d2wkfv3c87XrSWv37ZhjNJTMlsGtEqInMd6xtl14lBrllfPe/1xc0XruCBA9185peHuX5TLetmJZ0kOTRSSURSYn/HMIPj07xofc2ibgbedtVqjDF868lTKYhOROLxH796FovlH151wYJ/zy9aX01ZQQ4PHOjCWpviCCXd/eZQD/fs7mBNVRHvffF5/PLPX8SrtzXyxd8e5cM/3qdzSkQkC1lreWB/NyX5Pq5tqVlwv4+/cjN5OR4+/YtnF9xHEkdJJRFJiV0nBikryKGlfnG9BbUl+bykpYbv7zqllaREXORk/zj37O7gDZetoqGsYMH9fF4PL9lYQ/vgBPs7AimMUNLdkZ5RfnWwhwubynjzC5qpLy3gvJpiPvXaLbzj6jV884mT/OCp006HKSJxah8cZ3xKq/vK0rV2jXByYJwXt9SetV5SVXEeb3lBM/fv6+Jw90gKI8xOSiqJSNINjU9xpGeUi1aWn7WW0ly3XraSvtEpfnGgO4nRiUg8vvjgUbzGcMfVa86574VN5VQW5fK7w1qmWxZncjrE93aeotqfxx9euOL32gxjDH9zQwuXNlfw8Z/sp2tYNZZE0sGjR/t45ecf5qp//g1//38H+dyvD9M5POF0WJKGfne4l/LCHC5eVX7Ofd965WoKcrx84TdHUhBZdlNSSUSS7ulTQ1hYVAMw29XrqllRVsD3dmkKnIgbDI9P84On2nnNxY3UluSfc3+vx3D5mkpODU5wekg3EHJuDx/pY2RyhtdsayTX9/zLVI/H8C+v3cJ0KMw//6zVgQhFJB73P9PJW+7awfDENB++aSMv21zH6OQMX/ztUQ52ahSrLF5XIEhb/ziXra7E6zl3J3VFUS63XrqSn+7tpHdkMgURZi8llUQkqay1PH1ykNVVRVQU5cb1XK/H8PIt9TxypI/h8ekkRSgii3XPntNMzYR5w2UrF/2cbSvLyfEanjjWn8TIJBOMTs7wuyN9bG4ooamicMH9mquKeNPlq7hn92na+sZSGKGIxGPf6WHe+62nOX9FCfe++yr+5Oo1vGh9Ne998TpqS/L57s5T9I3qZl8W58nj/fg8Jq5O6tsuW8lM2PLDp9qTGJkoqSQiSdUzMknf6BQXrChd0vNvuqCe6ZDlgQNdCY5MROL1nR2n2FRfwvlx/D0X5HrZ2ljGnvYhgtOhJEYn6e6RI31Mz4R56abac+77J1evIcfr0bQGEZeanAnx59/dTWVxLl99y6WUFuac2Vac5+MNl63EYwzfevIkobAK78vZTc2EefrkEBesKKUob/EL2J9XU8zFq8r5zs5TWuAhiZRUEpGk2t8RwACbGkqW9PwtjaU0lhdw3zOdiQ1MROKy7/Qw+zsC/NElTXE/d/uqcqZDVlMdZEHToTA72gZoqS+hxn/uqZU1/nxuvXQlP3z6NN0B1VYScZsv/fYYz3aP8snXbPm9hFJMWWEur7poBZ3DQXadGHQgQkknrV0BJmfCbIuzlAbAH21v4ljvmM6zJFJSSUSS6kDHME0VhZTkP/+CYjGMMdx0QT0PH+ljeEJT4ESc8pM9Hfg8hpsvbIj7uU0VhZQV5LC3fTgJkUkm2Hd6mPGpEJevqVj0c978gmZCYcv3d2lag4ibDE9M85WHj3H9plqu3bDwsu+bG0pYWVHIrw52a6VfOau97cP483ysriqK+7l/sKWe/BwP9+7pSEJkAkoqiUgSDY5N0TEcZPMSRynFvGxzLdMhqxWkRBxireX+fV284Lwqygrjq40GkeTwBStKOdwzouWkZV6PH+unqjiXtdXFi37O6qoiLl9TwXd3niKs6TMirvHVR44zEpzhz65bd9b9jDHceH4dI5MzPK66e7KA4HSIZ7tHOL+xNK5VpGOK8nxcs76Gn+3rUluRJEoqiUjStHZFprpsql9eUunCpnJKC3L4TauSSiJOONAZ4OTAODeeX7fkY2xpLCNs4UCHpsDJ7+sbmeTU4ASXNFfEfcNwyyUrOdE/zuPHdUMq4gbjUzPc9fBxrt9Uy+aGc9ffW1VZxJrqIh4/1q/aSjKvg50BZsKWLUuszwpw4wV19IxM8vQpTYFLBiWVRCRpDveMUlGUS2Vx3rKO4/UYrl5fzYPP9qqHQcQBP9vXhcfA9YsooLyQhrJ8Kopy2a+kksyx5/QQhkjiMV43nF+HP9/HD3adTnhcIhK/n+7pJBCc4e0vXLPo51yxppKhiekznZEis+3vCFCS7zvrqqDncm1LDTlew8/2aeGfZFBSSUSSYiYc5ljvGOtqFj+V4WyuWV9N3+gkB1ToVyTlfravi0tXVywrQWyMYUOdn6O9o6qdIWdYa9l7apjmqiJKC+KvvZef4+X6TXX84kCXzisRF/jWjpOcV1PMJc2LL6jcUldCWUEOjx3ViEP5fTOhMEd6R2mpK1nS1LeYkvwcrjqvip/t79IqcEmgpJKIJMXJ/nGmQmHW1/oTcryr11cD8NtDPQk5nogszqmBcQ73jHLdxqWPUoppqfUzE7Yc6xtNQGSSCTqHg/SOTrKlcenTGm66oI5AcIZHj/YlMDIRidfBzgBPnxzilkuaMHEkALwew6WrKzjWN8bA2FQSI5R009Y/ztRMmA11y7+fuG5TLacGJjjaO5aAyGS2ZSeVjDE/MMb8gTFGCSoROeNwzygew5JWaZhPtT+P81eU8NBh3TS4kdqCzPVQtED+NWdZwWexVlcVkev1cKhrZNnHksywr2MYj4HzF1F7ZSFXrauiOM/H/c9oWoObqZ3IfN/f1U6u18NrtjXG/dyt0emve9uHEhuUpLVDXQF8HhPXIg4LuXpdpIP6wWdVozXREvGh/kXgNuCwMeaTxpiWBBxTRNLc4Z4RVlYUkZ/jTdgxX7C2it0nhwhOhxJ2TEkYtQUZ6sFDvawoK2Bt9fITxD6vh7U1xRzqGtHwcwGgtTPSVhTl+ZZ8jDyfl+s21vDzA13MhDQFzsXUTmSwcNhy3zOdXL2+mvKi+FcJLS/KZVVFIXuUVJJZWrtGWFNdRK5v+WmLpopC1lYXKamUBMv+7Vhrf2mtfQOwDWgDfmGMedQY81ZjTPyT40Uk7U1MhegcCibkJnS2K9ZUMhUKs+uEVm5wG7UFmWlqJsyjR/t50YbquKYynM2GWj9DE9P0jkwm5HiSvobGp+gKBNlYv/xpDddvrmNofJqnTw0tPzBJCrUTme3pU4N0Dgf5gy1LXyV0a1MZ3YFJuoaDCYxM0tXA2BT9Y1NsSFApDYAXra/hiWP96qBOsIQMPzXGVAJvAd4OPA18hkiD8YtEHF9E0suJ/jEsiZv6FrO9uRyvx6iQo0upLcg8T50cZHRyhmuiNc0S4bxo8f6jvaqrlO1ao9MgE1Er48rzqvB6jOruuZzaicz1f3u7yPV6eMky6u+dv6IUj9EUOIk42hO5TliboEV/AF60oZrJmTCPHdO9RCIloqbSD4HfAYXAK6y1r7TWfsda+14gcWeAiKSN431jeD1mWUt/zsefn8P5K0p5XA2B66gtyEyPHOnD6zFcsbYyYcesKMqlvDBHhTKF1q4AFUW5VC9jVcGY0oIcLl5Zzm8PaVqDW6mdyFzWWu7fF5n6VpK/9EFnxXk+VlYUnUk4S3Y70juKP9+XkDYi5rLVFeT6PDx6RDVaEykRI5W+Yq3dZK39J2ttJ4AxJg/AWrs9AccXkTRzvH+MpvICcryJr8V5xZpKdp8aYnxqJuHHlmVRW5CBHjvazwUrSvEv4yZhPmuriznWN0pYdZWy1nQozLHeMVrq/AmbWvmiDdXs7wjQE9DUGZdSO5Gh9ncE6BwO8rLNy18ldGO9n65AkEGtApfVrLUc6x1lbXVxwtoIgPwcLxc1lWmkUoIl4o7v7+d57LEEHFdE0lBwOkTH0ETCp77FXL6mgpmw5emTQ0k5viyZ2oIMMz41w572IS5fk7hRSjFrq4sJTofpHNLNf7Y60T/OTNiemQ6ZCNdGVyj8rYqwupXaiQwVm3aaiFVCN9aVAHCwK7DsY0n66g5MMjYVSnh9VoAr1layvyPA8MR0wo+drZacVDLG1BljLgYKjDEXGWO2Rb+uITKsVUSy0MmBccIWVlclZyT7tlXlGAM721Ss2w3UFmSuXScGmQ5ZLl9TkfBjr4leJKquUvY62juKx8DqysTdMGys91Ptz+Phw5rW4CZqJzLfr1t72NpYSrV/+dOUqvx5VBXnagpclotdH6ypTvz9xOVrKrEWdhwfSPixs9XS12+FlxEptNcIfHrW4yPAh5ZxXBFJYycHxjFAU0VBUo5fkp/Dhlo/O0+oIXAJtQUZ6vFj/Xg9hu3NiU8q+fNzqPbncbxvjKsTWARc0sfR3lGaygvJy/Em7JjGGK5YU8ljx/qx1iZ0yoQsi9qJDDYwNsXTp4b4fy9el7BjttSV8NjRfiZnQuT5EvcZIenjeN8Y5YU5lBfmJvzYFzaVkefz8Nixfq7btPwpm7KMpJK19mvA14wxr7HW/iCBMYlIGjs1ME5tSX5SLwIuXlXOPbs7CIUtXo9uGpyktiBzPX5sgC2NpRTnLaf/aWHNlUU8c3qIsLV4dPOfVSamQpwenODaluVPlZnrBWsruXdPB0d7RzmvJnHLUMvSqZ3IbL873Iu1JPTveV1tMQ8f6aOtbzwhq0NKerHWcqJ/jPW1yfnd5+d42bayXKtJJ9Bypr+9MfptszHmz+d+JSg+EUkjYWs5NTietFFKMdubyxmdnOHZbg2NdpragswUnA6xt32IS1cnfpRSTHNlIcHpMN0qqpx1jvWNYonU1kq02EqFullwD7UTme2hZ/soL8xhy4rShB2zubIIn8dwpEfXedmob3SKsakQzUmqzwpw2ZoKWrsCjARVVykRllOoO/ZbLgb883yJSJbpH50iOB2mqTy5JRK2r4rc6O48obpKLqC2IAPtbR9mOmTP/K0lQ3O0ls6J/vGkvYa407G+MXK8JikdECsrCmkozdfKPu6idiJDWWt57GgfV6ytxJPAkeM5Xg+rKgs52juWsGNK+jjRH/m9r6pM3v3ExavKCVu08E+CLGf6239F//144sIRkXR2ajByc9hUkdykUmN5ATX+PHa1DfCmy1cl9bXk7NQWZKZYzbKLV5Un7TXKCnMoyffR1j+WlBXmxL1O9I3RVF6Iz5OIRYh/nzGGK9ZW8evWbsJhm9AbXVkatROZ60T/OB3DQd61tirhxz6vxs/P93cxEpzGn5+T8OOLe7X1j1OY66W6ePmF3xdy0cpyPCbSQa3ajsu37NbcGPMvxpgSY0yOMeZXxpi+WcNcRSSLnBoYJ8/nScjqH2djjOHCpjL2tg8n9XVk8dQWZJanTgyypqqIiqLEF8iMMcawqrKIE/3jWGuT9jriLsHpEJ3DwaROa7h8TQWD49NaXdBl1E5knkeORlZafMHaxHcMnBedHnukR3/H2aatf4zmyqKkLrZQnOejpa6EpzTrISES0UV0vbU2ALwcaAfWA3+VgOOKSJo5NThOY3lBSorubmks5VjfGMMTmgvtEmoLMoS1ll0nBpM6SimmubKQ4Ylp/R1nkRP941iem/6YDLEVC3e06WbBZdROZJhHj/ZTV5LP6iQkievL8inI8XK8T1PgsslIcJqBsamkTn2LuXhVOU+fHGQmFE76a2W6RCSVYuMRbwK+Za3VOt8iWWhqJkzXcDDpU99itjSWAbD/tEYruYTaggxxrG+MwfFptjcnP6kU+7w4NTiR9NcSd2jrH8NjIrWPkqW5spCq4twz0zjFNdROZBBrLY8f7ecF51UmZUSJxxiaKwuVVMoy7dHrgWTXZ4XIwj9jUyFau1QQfrkSkVT6iTGmFdgO/MoYUw1oKReRLNMxNEHYpqYRALggusrIHk2Bcwu1BRliV3QoeCpGKtWV5OP1GE4Pqlh3tmjrH6OhrIBcX+LrKcUYY7h4VTk7NVLJbdROZJCjvaP0j01x+erk1cRrriqif2yKgFboyhrtgxN4DDSUJXclaYBtKyPXOU+dVFuxXMtu0a21HwCuALZba6eBMeDm5R5XRNJLqop0x5QX5bKyopC97UMpeT05O7UFmWPPqSH8eT7WVCV+ufe5fF4P9aX5GqmUJWZCYdoHJ5I69S3mkuYKTg6M0xNQzsIt1E5kliePR27EL1mdvFVCY9Pq2jRaKWu0D45TW5Kf1I6HmMbyAiqLctlzSh3Uy7Xk1d/m2Ag0G2NmH+/rCTq2iKSBUwPjlBfmUJyXqI+Vc9vSWKqlQN1FbUEG2Ns+zAWNpSlbNauxvJCnTg4StjYl9djEOV2BIKGwTUnnQ2yk3c4Tg9x0QX3SX08WTe1EhtjZNkBVcR7NSax9U19aQK7XQ1v/2JmyB5K5rLW0D05w/oqSlLyeMYYtjaXqoE6ARKz+9g3gX4GrgEuiX9uXe1wRSS+nBidSNkopZktjKaeHJugfnUzp68rzqS3IDJMzIVq7Aim9eG8sL2BqJkzviP6OM92pgeiI1vLkT2vY3FBKfo5HU+BcRO1EZnmybYBLmsuTukKX12NYVVlIW5+mSGeDgbEpJqZDNJal7n5iS2MZR3pHGZ2cSdlrZqJEDCnYDmyyWg9YJGsFoqs3paqeUkzsxndv+zDXttSk9LXledQWZIDWzhGmQ5YtjaUpe83GaIIhNuRdMlf74AT+PB+lBTnn3nmZcn0ezm9QD7TLqJ3IEJ3DE7QPTvDHV65O+mutqizilwe7mZgKUZDrTfrriXNiU+EbK5Lf8RCztakUayML/1y2Jnn1wTJdIiYr7gPqEnAcEUlT7SmupxRz/opSjIkklcRxagsyQOwGPJVJpariPPJ8HtVVygKnBsdprChM6siG2bY0lrGvY1jLRbuH2okMsSM6AvCS5uTVU4qJrRTZPqTRSpnu9OA4OV5DjT91HUwXrCgDdC+xXIkYqVQFHDDGPAmcGbturX1lAo4tImng9FAQj4H60tSOMijO87G2ulg90e6gtiAD7GkfprIolxUpWHUlxmMMjeUFZ5LTkpkmpkL0jU6dWW0nFbY2lXLXI2Ge7R5lU0NqanTIWamdyBBPnRikMNfLxnp/0l+rsbwAA5wamGBdTfJfT5xzanCChrICvCmq6QhQ7c+joTSfPbqXWJZEJJU+loBjiEga6xiaoKo4jxxv8ldqmGtLYykPPduHtTZlvd8yr485HYAs3972IbY0lqb8b6mxvJDfHe5lOhR25HNEki+WNGxM4TTprdEp0nvah5RUcoePOR2AJMbuU0NcsKIUXwo+r/NzvFT7887UZJPMFApbOoYmuNyBKWhbGss0UmmZlv1JYK19EGgDcqLf7wCeWu5xRSR9dA5HehacsGVFKX2jk3Rp2WhHLaUtMMbcZYzpMcbsW2C7McZ81hhzxBiz1xizbda2G4wxh6LbPpDA/0rWGpuc4UjPqCMr7DSVFxC20DmkKXCZ6tTgOIbnamilwqrKQkoLcthzaihlrykL0z1DZpicCXGgI8CFTWUpe82m8kJODY6jclyZqzsQZCZsU9pGxGxpKuXkwDhD41Mpf+1MkYjV3/4E+D7wX9GHVgA/Xu5xRSQ9jE7OEAjO0JDiqW8xW6IXNXtOqYfBSUtsC+4GbjjL9huBddGvO4AvRl/LC3whun0TcKsxZtMSQ5eo/R0BwjYyZSjVYqNXVFcpc7UPTlDlzyM/J3WFdo0xbG0qY496oF1hKe2EOh/cp7VzhKlQmK0pTCo1VhQwPhViYEw3/ZnqlAOjWWO2zlr4R5YmEWMW3w1cCQQArLWHAS3DJJIlOqIjC+odGqm0qb4En8eorpLz4m4LrLUPAQNn2eVm4Os24nGgzBhTD1wKHLHWHrPWTgHfju4ryxD7G4oVrUylkoIcSvJ9qquUoay1nBoYT/kKoQBbG0t5tnuEialQyl9bnmcp9wx3o84HV4nVnknlSKVYsW51PGSu9sEJCnO9lBcmf3XQuc5fEelM073E0iUiqTQZvagHwBjjAzQ2USRLxKarNJQ6k1TKz/GyrtbPM6fVu+CwZLQFK4BTs35ujz620OPPY4y5wxiz0xizs7e3d5nhZLY97cM0lOZT7c9z5PUbywtp1w1DRhocn2ZsKkRTCpeJjtnaWEYobNnfoTbCBeJuJ9T54D67Tw5R7c9L6eIsNf58crzmzGgWyTynByciRdkdqI9aWpDD6qoijWpdhkQklR40xnwIKDDGvBT4HvCTBBxXRNJAx3CQ8sIcCnJTN6Vhrk31JRzsHHHs9QVITlsw35WFPcvjz3/Q2juttduttdurq6uXGU5mixTpLnPs9RvLC+gfm2J8asaxGCQ5nCjSHbMlOp1zt+oquUEy2olldz5IfHa3D7G1sSylN/9ej6GxvFDFujPU1EyY7kDQkTYiZktjKc8oqbRkiUgqfQDoBZ4B3gHcB/x/CTiuiKSBjqEJ6h0apRSzqaGEvtFJekZUrNtByWgL2oGmWT83Ah1neVyWaGh8ihP942duwJ2wIjqFtnNYf8eZ5tTAOD6Poa4k9bX3avz5NJTmq1aGOySjnVh254NGtC7e8MQ0x3rHuGhlWcpfu6m8gM7hIDOhcMpfW5Kra3gCy3PXAU7Y0lhGVyBIjxb+WRLfcg9grQ0bY34M/Nhaq09ikSwyOR2if2zKkYuL2TbW+wE42DlCjd+ZguHZLkltwb3Ae4wx3wYuA4attZ3GmF5gnTFmNXAauAW4LUGvmZViN9xbHRypFKvL1jk0wdrqYsfikMQ7NTjBirICvJ7UT2uAyM3CHtXKcFyS2omFOhlyF3h8vrjuBO4E2L59u0p4nEWs5owTbUVjeSGhcB8dw8EzNZYkM3REO5NSOaVyrq2NkU61Pe3DvHST7iXiteSRStHVFj5mjOkDWoFDxpheY8xHEheeiLhZbESBU/WUYjbVlwBwsDPgaBzZaDltgTHmW8BjwAZjTLsx5m3GmHcaY94Z3eU+4BhwBPgy8C4Aa+0M8B7g58BB4LvW2v0J/89lkX3RejPnNzg3Uqk4z0dJvk8jlTJMKGzpHJ5wZJnomK1NZZzo13LRTknyPcO9wO3R17icaOcDsINo54MxJpdI58O9CXi9rLYnOo30gsbUtxVninVrClzG6RiKFOkuLUh9ke6YzQ2leAyq0bpEyxmp9D4iKzhcYq09DmCMWQN80RjzfmvtvycgPhFxsY5hZ1d+iykrzKWhNJ8DHUoqOeB9LLEtsNbeerYDW2stkdWC5tt2H5GkkyTAgY4AjeUFlDqw6sps9aUFZz5XJDP0jU4yHbI0ONhObG16rgf6RetVW80B72OJ7US08+EaoMoY0w58FMgBsNZ+iUg7cBORzodx4K3RbTPGmFjngxe4S50Py7f71DBrqoscufkvKcihtCBHxbozUOdwkPrSfEeKdMcU5HpZXVWkDuolWk5S6XbgpdbavtgD1tpjxpg3Ag8ASiqJZLjOoSBFuV5K8pc9k3bZNjWUqCFwhtqCDHCgM3BmxJ+T6svyOdwzwnQoTI43EWUfxWkdsRVCHUwqXbCiFGNg76khJZWcseR2Qp0P7mGtZfepIa5eX+VYDE3lBRqplGFCYUt3IMgVayqdDoVNDaU8dWLQ6TDS0nKu2HJmNw4x0TnSznZ1ikhKdAxP0FDmzPKfc22sL+FY3xjB6ZDToWQbtQVpbnxqhuN9Y2xqcEFSqbSAsIVuFcrMGB1DE/g8hqriPMdi8OfnsLqy6Mw0T0k5tRMZoGM4SN/oJBc2lTkWQ2N5IYPj04xPapXQTNE7OslM2FJf5nwdo031JZwemmB4fNrpUNLOcpJKZ5uYrknrIhluJhRZ/tPJ3ufZNtWXEApbnu0ecTqUbKO2IM0d6hrBWlwxUqkhWqSzc0hJpUzREZ3W4FSR7phNDSXs1xRpp6idyAC7Tw4Bzi7oELvm7FDtvYzRGR3N6vRK0sCZzrUDmvkQt+UklbYaYwLzfI0AFyQqQBFxp+6RScLW2ZUaZtuoYt1OUVuQ5mIXTxtdkFQqL8olz+dRXaUMYW2kSLfTdfcgcrPQPjjB8IR6oB2gdiID7G0fItfroSW64q4TGqKjWWLTaiX9dQ4HHR/NGhPrXFNSKX5LLoRirfUmMhARSS9d0Zs+p1d+i1lZUUhRrlfFulNMbUH6O9ARwJ/vc3R1rhiPMdSX5msFuAwxOD5NcDrsinZic3RlwwMdAa5Y63ztjmyidiIz7O8IsL6umDyfc7/Owlwf5YU5nFZSKWN0DE1Q54LRrADV/jyq/XnqoF4CVcEUkSXpDkzi8xgqinOdDgUAj8fQUl/CwU5NfxOJR6xItxtqo0FkCHzXcJCwtU6HIsv0XJFu50e0xnqg96uukkjcrLXs6xjm/Ghy1kn1pQUaqZQhrLV0DE+4YupbzKb6EnVQL4GSSiKyJF2BIDUleXhcciMKkYbgYGcAq5tRkUUJhS2tnSOuKNId01CWz1QoTP+oSq2ku47hCTwGakucTypV+/Oo8edpWoPIEnQMBxkan2azC9qKhrIC+semtDBLBhiaiI5mdUHHQ8ymhhIO94wwNRN2OpS0oqSSiCxJdyBInQtuFGbbWF/CyOQM7YPqwRJZjLb+MSamQ64o0h0T67HsVF2ltNcxNEGNP58crzsuNzc3qAdaZCn2n46M8NvkgpFKK6IJCE2TTn9uKtIds6m+hOmQ5UjPqNOhpBV3tPLiasHpEPs7hhkYU6+xRIxPzjASnHFF7/NssdEWWuFHZHFiN9huGqlUU5KH1xg6tAJc2uscCrpmMQeI9UCPaoSDSJz2dQTwGNjoYJHumDMrwGkKXNrrGA5iwFWd1FoBbmmWXKhbMl8gOM1//OIwX3+sjZlwZDpRZVEuL9/SwIY65xsVcU7XSORmz21JpQ21fjwmsgLcDefXOR2OiOsd6AyQ4zWsq3HPZ7rP46GmJE8jldJcIDjNyOTMmRtAN9jcUEoobDncPcoFjc6PuBBJFwc6hllTXUxhrvO3jv78HPz5PiWVMkDn0ARVxXnk+twzzqW5soiCnOjCPxc7HU36cM9vUFylOxDk5s8/wlcfPc5rtjXy+dsu4hVbG/B6DF97rI0HDnQ5HaI4qHvYnUmlglwvzVVFWrVBZJEOdAQ4r8bvqgs6iBZiHQ6qPloaOzOtwUW1MjY3qFi3yFLs7wi4op5STENpgVaAywCdw0FXtREAXo+hpd7PgU61E/FwPt0srjM4NsVtX36cnkCQb//J5Vy2JrL0bmBihu2ryrl3Twe/PdRLYa6Pq86rcjhacUJ3YJKCHC8l+e77CGmp82v6m8giHegMcPW6aqfDeJ6GsnyeOjnIyOQMJfk5TocjS9AR7XxocFGtjKbyQorzfGojROLQPzpJ53DQFSu/xTSUFfBsd6SYsts6RWRxxqdmGJqY5nIXtRExm+pL+MmeDqy1rlkZ1+30Vyi/x1rLB364l1MDE9z1lkvOJJRicrweXnXRCjY3lHD/M50c7VURs2zUFQhSW5Lnyg/aDbUlnBwYZ3xqxulQRFytZyRI78ikq+opxcRGQXarEGva6hiaoKIol/wcr9OhnOHxmMhy0RrNKrJosSSsm0YqrSjLxxK5HpX0FPvd1bmo7l7MpoYSAsEZjYaLg5JK8nt+8NRpfr6/m7+4fv3zEkoxHmN43cVNVBTl8qOnT2vJxSxjraU7EHTd1LeYDXXFWAuHu5XwFDmbg50jAK5a+S0mVrRTNwzpq3M4SINLbxYOdgYIhTW1UmQxnksquWukEqhYdzpzaykNeO66SKuFLp4jSSVjzF3GmB5jzL4FthtjzGeNMUeMMXuNMdtSHWM2CgSn+Yf/O8AlzeW8/YVrzrpvri8yYmlgbIpftXanKEJxg+GJaSZnwq5sBAA21EUagkPdIw5HIuJusdpjbljNZ66iPB/+fB9dGqmUloLTIQbGpqh3UZHumE0NJYxPhWjrH3M6FJG0sK9jmMbyAkoL3TMVubQgh8Jcr5JKaazLxaU0NtT5Mea5zjc5N6dGKt0N3HCW7TcC66JfdwBfTEFMWe9Lvz3K4Pg0H33FZryec09rWlNdzLaV5Tx2tJ/hiekURChu0B0brurSpNLKikLyczw826WGQORsDnWNUFeST1lhrtOhzKuuJP/M542kFze3E88V61YPtMhiHHBZkW4AYwwNZQVKKqWx2KwHN5bSKMz1saqikEPdaicWy5GkkrX2IWDgLLvcDHzdRjwOlBlj6lMTXXbqDgS565Hj/OGFDZy/YvHDW1/SUoO18JtDPUmMTtykOzAJuHO4KkRWbVhX49dIJZFzaO0aYUOd+0YpxdSV5NMzMqlpSmnIzbUy1tX4yfEaTWsQWYSR4DTH+8ZcVaQ7pqE0n+7AJDNhleFIN7FSGnWleU6HsqANdX5a1UG9aG6tqbQCODXr5/boY5Ikdz18nOmQ5c9fuiGu55UX5bK9uZydbQMMjk0lKTpxk65AkJJ8HwW57im+Otf6Wj+H1BCILGg6FOZoz6irk0q1pfnMhC39o5NOhyJx6hoOkufzUFbgnukyMbk+D2uri2ntUlJJ5Fxi0382r3DXSCWAutICQtbSO6I2It0Mjbu7lAZEymm09Y0RnA45HUpacGtSab5xcPN2VRpj7jDG7DTG7Ozt7U1yWJlpJDjNN584yY3n17GysjDu579ofWQ56seP9Sc6NHGhSM+CexsBiBTr7hmZVKJTZAFtfWNMhcJsqHVvUknFutNXV7SdcOO0BoCN9SXqeBBZhP0dwwCuHKlUH70WVe299NPl4inSMS11fsIWjvRo4Z/FcGtSqR1omvVzI9Ax347W2juttduttdurq6tTElym+c6OU4xMznDH1Wcvzr2QssJcNjeUsuPEgFaCy3ChcKRHqNbv3kYAVKxb5FxifxtuHqlU48/DY5RUSjfWWrqGg66/WegcDjI0ro4HkbM52BmgsiiXGhf+PVcV5+HzGDqVVEo7sXbd3SOVItdHmgK3OG5NKt0L3B5dBe5yYNha2+l0UJkoHLZ8/bETXNpcwZbGsiUf5wVrKwlOh3n61GDighPX6R+bZCZsqXX7SKXo6Av1RIvM71DXCF6P4byaYqdDWZDP66GyOO/MssOSHoaiK4S6eUSrbhZEFqe1a4SN9e6b+gaRGpo1JXnqeEhDXcNBygtzyM9xbymN5soi8nweDmmq9KI4klQyxnwLeAzYYIxpN8a8zRjzTmPMO6O73AccA44AXwbe5USc2eDxY/2cHBjnDZevXNZxVlYUsqKsgCeODWCtiqpmKrcX6Y6pLcmjtCBHI5VEFtDaNUJzZaGrL+ggMjReNwzpJTYVxc0jlWI3ya2dulkQWchMKMyhrhE21rt3RGtdSYFGKqWh2Mpvbub1GNbVFqvzYZF8TryotfbWc2y3wLtTFE5W+87OU5Tk+3jZ5rplHccYw8Wryrl3Twedw0EaygoSFKG4SXcgiCEyLcXNjDFsULFukQUd6hrhgjhW+nRKXWk+z5weZnI6RJ7LE2AS0Z0G0xpq/HmUF6rjQeRs2vrHmZwJ01LnzpFKEKmr9NTJQUaC0/jz3bcwgDzfTChM3+gkm1w6Am62DbUlPHRYNZsXw63T3yQFhsenuX9fF6+6aEVCequ3Npbh9Rh2ndQUuEzVHQhSWZxLjtf9Hx0b6vw82zWikXMic4xNznByYNzV9ZRiYqNdujVaKW10psG0BmMMLXUlZ1a2EpHni62Q2OLmkUoq1p12ekcnCVtcX0oDYGO9n96RSQa08M85uf/OUJLmp890MDUT5nXbm8698yIU5HrZVF/CnlNDzIRVsDsTdQ27f7hqzPo6PyOTM3ToQkPk9zybBkW6Y55bAU5LRqeLroC7i3THbKiLjGYNh9XxIDKfg50BfC6vvVcf/azRFLj0kQ5TpGOeq7+nqdLnoqRSFvvJng7WVhexuSFxww+3rSxjfCrEs11afjHTTIfCDIxNpU1SKVas+1lNgRP5PbFpoS1pkFQqK8whz+ehKzDhdCiyCNOhMP2jk64u0h2zsd7PxHSIkwPjToci4kqtnSOsrS4mz+feUYeFeT5K8n2qvZdGugNBvMZQVezuUhrwXFJJ5TTOTUmlLNUdCPLE8QFesbUBY0zCjntejZ+CHC/7OoYTdkxxh57AJBZ318mYLZZUUoE9kd/X2jVCYa6XpvJCp0M5J2MMtSX5dA1rpFI66B2JTGuoK3V/XcVYnRi1ESLza+0acfXUt5j60gJNf0sjXYEg1f48vJ7E3X8mS3VxHhVFuUoqLYKSSlnq//Z2Yi28fEtDQo/r9Rg2N5RwsDPAdEhT4DJJrKZJOgxXBSgtzKGuJP/MVB8RiTjUNcK6Wj+eNLigg8hnTncgqPpoaSB2Y1db4v4e6PW1fozRtAaR+QxPTHN6aMLVRbpj6krz6RkJMqP7jrTQHUiP0azw3MI/6nw4NyWVstRP93awsb4kKfOkL1hRyuRMmMPdmgKXSboCQXweQ0VRrtOhLNqGOjUEIrNZaznUPUJLrft7n2NqS/OZmA4RCM44HYqcQ6ydqCxyf1KpINdLc2URrSrWLfI8rZ3uL9IdU1eaT9hCz4hGtLrdxFSI4YnptJn1ANGFf7pVf+9clFTKQj2BIE+dHOKm8+uScvw11cUU5np55vRQUo4vzugOBKlJk+GqMRvq/BztGVXvlUhU72hkFZN0KNIdc6ZYt6Y3uF5sMYd0aSda6vwaqSQyj1iHXDos+15/ZkEHtRFu15Vmsx4g0k6MT4U4Naj6e2ejpFIW+sXBbgCu35ycpJLXY9hYV8Kh7hFCyupmjO5A+qz8FrOh1s9UKExbvxoCEUivIt0xsYvPbt0wuF66rPwW01JXwomBccanNApOZLbWrgDlhTnU+N0/6rCyOA+fx6jjIQ2cSSqlyfQ3mL0CnEa1no2SSlnogf3drKosZH1t8pYI3VjvJzgdpq1/LGmvIakzMRWZepJ2SSWt2iDye2J/C+k0Uqkg10tpQY56oV1uJDjN6ORMWt0stNT7sRae1XR9kd9zoHOElrqShC7mkyxeT2RBh85hrRLqdt3DQfJzPJTk+5wOZdHW1+peYjGUVMoyI8FpHj3ax8s21yW1oTivxo/PYzjYqWHlmSB2M5duSaXzaorxGDikYt0iQKSnrao4j8o0WMp3trqSfPVCu1x3IFLPJJ3aidiIvVZdq4icEQpbnk2Tld9i6krz6RzWgg5uFxvNmg7JypiiPB8rKwqVVDoHJZWyzO8O9zEdsrx0U21SXyfX5+G8mmIOdgb0AZ8ButNwuCpAfk6kEOsh1cwQASI9bek09S2mtiSf3pFJTal2sa7oKIF0aieaygspzPVqWoPILCcHxpmYDrExDeopxdSX5jM+FWJkUlNZ3cpam5alNCC28I/uJc5GSaUs8+ChXvz5Pi5qKkv6a22sK2FwfPpM76Wkr+5A+g1XjVlf69fUBhGivc/dI2k19S2mrjSPkLX0jqo9cauuwCT+PB/FeenTTng8RjcLInPERu5trEufpJIWdHC/oYlpJmfCadXxELOxzk9b/zjB6ZDTobiWkkpZxFrLg8/28sJ1Vfi8yf/Vr4/euBzuUQ9guusKBKn1p9dw1ZgNdX7a+seYmFJDINntRP8YkzPh9EwqlRQAkXoM4k5dgYm0vFloqSuhtWtEo6pFog52BvAYWJfE2quJVl8aaSOUVHKvWPudTos5xGyoKyEUthzpUSf1QpRUyiKHe0bpCgS5el11Sl6vtCCyasRh/QGmtTPDVdPwZgEiSSVrldwUSceV32Kq/Ll4jJaMdqtQ2NITmEzLaQ0tdX6GNKpa5IyDXSOsqS4mP8frdCiLFlvQQcW63Std67OCFv5ZDCWVssiDh3oBuHp9apJKAOtqimnrG2M6FE7Za0piBYIzBKfDadkIwHMNgabASbZr7RrBGFhXk35JJZ/HQ7U/T73QLtU/OslM2KbpSKXI38NBTYETAaC1K5CWnQ91JZFi3eJOXYEgZQU5aZWsjGmuLCTX59FU6bNQUimLPHS4l3U1xTSUFaTsNdfV+pkJW473jaXsNSWxzhTpTtOk0qqKSEOgYt3uZIy5wRhzyBhzxBjzgXm2/5UxZnf0a58xJmSMqYhuazPGPBPdtjP10aeXQ10jNFcWUZCbfhd0EOnd7NZIJVfqSuN2oiVaN0Y90CKRVaJPDUykVZHumPrSfPpGJ5lRR7YrpWuRbgCf18O6mmIt6nAWSipliYmpEE8cH+BFKRylBNBcWYTPYzQHNY11nxmuml5LkMf4vB7Oqy7mkEYquY4xxgt8AbgR2ATcaozZNHsfa+2nrLUXWmsvBD4IPGitHZi1y7XR7dtTFXe6OtQ9woba9Ot9jqkryWdoYlqFMl2oKxDEY6DGn37tRGlhDg2l+WeKE4v7qPMhddJ5mnRdaT5hCz0jmsrqNjPhML0jk2k5mjVmQ51fnQ9noaRSlnj8eD9TM+GUTn0DyPV5aK4sUj2bNNY1HKQk30dhbvqs6DNXS52fZ9UQuNGlwBFr7TFr7RTwbeDms+x/K/CtlESWYSamQrT1j6Vlke6Y2CgYjVZyn67hIFXFeSlZBCQZIivAqY1wI3U+pNbB6N9BOo5UiiUsNAXOffpGpgjb9BzNGtNS56dnZJLBsSmnQ3Gl9Gz9JW4PHuolP8fDpasrUv7a59UU0x2YZHhiOuWvLcvXPZK+w1Vj1tf56QoEGR7XOegyK4BTs35ujz72PMaYQuAG4AezHrbAA8aYXcaYOxZ43h3GmJ3GmJ29vb0JCjv9HO4Zwdr07H2OiS0WoGLd7tMVCKZ1D3RLfQlHe0eZmtG0GRdS50MKtXYGKMn3UZ+Gf89VxXnkeA1dKtbtOl2ByO8kXRf9gcgKcIA6IBagpFKWeOhwL5etrnSkOFpsSVJNgUs/YZu+K/rMFpvyc6hbDYHLmHkeW2hd71cAj8zpfb7SWruNSA/2u40xVz/vYNbeaa3dbq3dXl2d2pGabhK7CErnkUplBTnk+Twq1u0ywekQQ+PTad8DPR2yHOvTdYoLqfMhhQ52BmipL8GY+Zpnd/MYQ21JvjoeXKhreBKvMVQXp98U6ZiNZ1aA01Tp+SiplAVODYxzrHcs5fWUYupK8inO82kKXBoaGJ1iJmzTP6lUp6SSS7UDTbN+bgQ6Ftj3Fub0PltrO6L/9gA/ItKjLfM41DVCfo6HVZVFToeyZCZ6w6Dpb+5yZjGHNO6Bjk31ae1UG+FC6nxIkXDYcqhr5MzNczqKrQBn7UKniDihOxCk2p+H15N+ycqYan8e5YU5updYgJJKWeCRI30AvHBdlSOvb4xhXU0xR3pGCetDPq2k84o+s9WX5uPP86mukvvsANYZY1YbY3KJJI7unbuTMaYUeBFwz6zHiowx/tj3wPXAvpREnYYOdY2wrsaf1hd0EPks6grohsFNYvVL0rmdWF1VRK7Xw0H1QLuROh9SpH1wgrGpUFrWU4qpK81nfCrEyOSM06HILF2BYNou+BNjjGFDnZ+D6nyYl5JKWeDxY/1UFedxXk2xYzGsqy1mfCpE55B6mNNJdyCIIZKdT2fGGNZr1QbXsdbOAO8Bfg4cBL5rrd1vjHmnMeads3Z9FfCAtXZs1mO1wMPGmD3Ak8D/WWt/lqrY001r10haT32LqS3NJzgdJhDUDYNbdAWC5Od4KC3IcTqUJcvxejivplgjldxJnQ8pciC6AmJLOieVosltTZN2j+B0iOGJ9J4iHdNSV8Kz3SOEw+rYmit9l3OSRbHW8tixfi5fU+Ho/Og1VZGE1rG+UVaUFzgWh8SnOxCkoiiXXF/655/X1/q575lOrLVpWSsgU1lr7wPum/PYl+b8fDdw95zHjgFbkxxeRugfnaRvdDKti3THzL5hSOckRibpHg5SV5Kf9p+rLXV+Hjna53QYMoe1dsYYE+t88AJ3xTofottj7cVCnQ8/ip6bPuCb6nxYWGtXAGNgfa1zndDLFZuG2zUcZH1t+rd5mSCW4EvnIt0xG+r8jE+FaB+cYGVlodPhuEr63ynKWbX1j9MdmOSKtZWOxlFSkENVcS7H+8bOvbO4RlcGFOmOaanzMzwxTc/IpNOhiKRUbP5/JoxUiiWVVFfJHay10WkN6d9OtNT76Q5ouWg3stbeZ61db61da639h+hjX5rdAWGtvdtae8uc5x2z1m6Nfm2OPVfm19o5wurKIgpz03fMQWGuj9KCHBXrdpFMKaUBz11HtWqq9PMoqZThHjvaD8Dla5xNKgGsrirmeN+Y6iqlielQmP7RzEkqxXqsNAVOss2hDFj5LaYg10tJvk83DC4xND7N5EyY+tL0H4HcouWiJcu1dgVoqU//dqKuJF/T31ykOwOmSMds0L3EgpRUynCPH+unxp/HmirnV/xZU1XE5ExYdZXSRO/IJJb0XtFntjMrwKkhkCzT2jlCRVFuWi/lO1tdqVaAc4uuDFj5LSZ2M60eaMlGY5MznBgYP5NcTWd1pfn0jASZCYedDkWITH+rzYAp0gBFeT5WVhSq82EeSiplsOfqKVW64g95dTSxdbxv1OFIZDFiN221aV6kO6aiKJdqf56WApWs09o9Qkud3xXtQCLUluTTMzJJSIUyHRdb+S0T2onq4jwqi3JVrFuy0qHuEawlM2rvleYTtpHOUXGWtZbukWBGTH2L2VDnV+fDPJRUymBHe8foHXG+nlJMrK7SMdVVSgtdgSBej6EyQ0Y3QGTY6rNKKkkWCYctz2bIym8xdSX5hMKWvlHdMDitK7qYQ16O1+lQls0YQ0u9bhYkO8WSqRvTeOW3GK0A5x7DE9MEp8MZU0oDIonXtv5xgtMhp0NxFSWVMtjjxyL1lK5wQT2lmNVVxbT1q65SOugOBKnx5+H1ZMboBojUVXq2e0QjHCRrnBwYZ2I6lBG9zzG1KtbtGrGV3zLFhtoSDqmNkCx0sDNAcZ6PxgxYobmqOA+fxyip5AKxKdL1GTBFOmZDnZ9Q2HKkRzNvZlNSKYM9dqyfupJ8VrloycM1VUUEp8NnhsyLe3Vn0MpvMRvqiglOhzk1MO50KCIpEZv3nwl1MmJq/Hl4DCrW7bDpUJi+0cmMqKcU01LvJzgd5kS/RlRLdjnYGciYadJej6GmJI9OtRGO645Nkc6g+4kW1Widl5JKGcpayxPH+rlirTvqKcWcqavUq+yum01MhRiemM6oRgBgQ/TGWnWVJFu0dgUw5rnVDzOBz+uhsjjvzMWqOKMnEF3MIYPaiY2xNkI3C5JFrLW0do1kxNS3mLqSAo1UcoHOQJCyghzyM2CKdExzZRG5Po/uJeZQUilDHekZpW90isvXVDgdyu8pKcihskh1ldyuZyTWs5A59ZQA1tUUA/CsbhgkSxzqGmFVRSEFuZlzQQfRJaPVC+2orsAEkBkrv8Wsqy3GY+Cg2gjJIu2DE4xOzmRWUqk0n9HJGUaC006HktW6A8GM66D2eT2sqynmYKfq782mpFKGerJtAIDLVrunnlLMmuoi1VVyuTPLRGdYQ1CU56OpooBW9S5IljjUNZJRU99iakvyGRyfZlKFMh3TNRwkx2uoKMp1OpSEyc/xsrqqiFbdLEgWORA93zfWZ86I1lgNH3U+OGcmHKZ3JLOmSMdsqPNrROscSiplqB3HB6j257mqnlLM6qpi1VVyua7hIPk5HkoLcpwOJeE21JZopJJkhYmpEMf7xzJq5beYWMK7W0tGO6Yz2gPtcdEU+0RoqS85U4tMJBsc7IxMk86ktqJWK8A5rm9kirDNrHpKMS11fnpGJhkcm3I6FNdQUilD7Wgb5JLmclfVU4qJ1VVSIUz36oreLLjx/FmuDXXFHO8bY3JGIxwksx3uGcHazOp9jon1fKqukjOstXRl2MpvMS21fk4OjDM6OeN0KCIp0do5QnNlEYW5PqdDSZjiPB/+fJ+SSg46M+shI0cqRUaAqwPiOUoqZaCOoQlOD02wfZW76inFlBbkUFaYQ1u/VuByo0y+WYBIweKZsOVYr5KaktlaOyMXOxsycPpbWWEOuV6PpjY4ZGRyhvGpUEbeLLTUq1i3ZJeDXYGM7HyoL1XtPSd1DQfxGkN1cWbVZwXYeGYFOE2VjlFSKQPtPDEIwCXN7kwqQaRy/on+MazqKrnO0MQ0kzPhjLxZgOeWVn9WdZUkw7V2jZCf42FlhfumQS+XxxhqS/J0w+CQ2AixTOx8iC0X3aqbBckCo5MznOgfP7PyYSapK8mnJzBJKKx7DSd0B4JU+/PwejJv1kO1P4/ywhyNVJpFSaUMtLNtgKJcr6t7HVZVFjISnGFwXKsyuE0m3yxAZPqlz2PUCy0Z71B3gA21jC/8BgABAABJREFU/oy8oINInYbuQFCdEw7I1MUcABrLCyjO86mNkKwQO89bMmjlt5i60nxC1tI7qtp7ToiU0si8UUoAxhg21PmVVJpFSaUMtKNtkG2ryvF53fvrXVUZqavUprpKrhO7WcjEwnoAuT4Pa6qLNFJJMl5r50hGFV6dq640n/GpECOqfZNyXcNBSvJ9FOZlTg2WGGMMLXX+M9NHRTLZwQxc+S2mrrQAULFuJ0xMhRiemM7IjoeYlroSnu0eIayRcICSShlneGKa1q6Aa+spxdT488jP8ahYtwt1BYKUF+aQn+N1OpSkWV+r3gXJbL0jk/SPTWVkPaWYWOJbxbpTrysQzNgp0gAt9X4OdgU0Ck4y3sHOACX5PlaUFTgdSsJVF+fhNYau4QmnQ8k6mVykO2ZDnZ/xqRDtgzq/QEmljPPUyUGshUuay50O5aw8xrCqokjFul0ok4t0x7TU+WkfnNDqPpKxYlMaNmbySKXYktGqq5RSobClJzCZ0e3EhroSRoIzdChhKRmutWuElvqSjFzt1+sx1Kj2niO6M3zWA3BmJLjq70UoqZRhdrYN4PUYLlxZ5nQo59RcWUjvyCRjurF3jZlQmL7RSWozuGcBIiOVAA5rCpxkqNhFTiZPfyvK8+HP8525eJXU6B2dJGRtRvdAx5KxrZ26WZDMFQ5bWjsDGd/5oOlvqdcVCJKf46G0IMfpUJJmQ21sBTjdS4CSShlnR9sg5zeUUJjr/joHsbpKJwc0WsktekYmCdvMLL46W+xGW3WVJFO1do1QVZxHZQYu5TtbrZaMTrmuM4s5ZN50mZj1Z3qg1UZI5jo1OM7YVIiNGVikO6auNJ9AcEYd2CnWPRyktiQ/I0fAxRTl+VhZUah2IkpJpQwyORNiz6khLml2dz2lmBXlBXg9RsW6XaQ7g1f0ma2pvJCCHK8aAslYrV2BjCy8Oldsyeiwat+kTHcgiNcYqvy5ToeSNCX5OTSWF5wpYiySiZ4r0p3ZSSXQNOlUstZG6u5l+L0EEF0BTu0EKKmUUfadDjA5E2Z7miSVcrweGssKOKG6Sq7RNRzE5zEZP7rB4zGsry3WSCXJSKGw5XD36Jmh2ZmstiSfmbClf3TK6VCyRtdwkGp/Hj5PZl9CttSVaFqDZLSDnSN4zHMlATLRmdp7mgKXMkMT00zOhDN6inRMS52ftv5xgtMhp0NxXGZfEWSZHW0DAGx3eZHu2VZVFnF6cILpUNjpUIRIT06NPw+vJ3OHq8asr/VzqGvU6TBEEq6tf4zJmTAtGdz7HKNi3amX6Su/xWys93Osb0w3C5KxDnYGaK4qoiA3c1f79efnUJzno1NJpZTpHs6OWQ8QGakUCluO9Oh+QkmlDLKzbYA1VUVUpdEok1WVhYSs1XKMLpEtNwsQaQj6RifpH510OhSRhGrtjIyuaMng4qsxNSV5GFCx7hQZn5pheGJaNwsiGeBgVyCjp77F1JXm0xXQfUaqdGXBym8xsessjWpVUiljhMOWnScG02qUEsCqikIATqiukuPGJmcYCc5kRSMAkakNoEKsknkOdQXwGDivptjpUJIux+uhsjhPUxtSJHazkA2dD2ojJJONBKc5NTDBpmxIKkVr74XCqr2XCl2BIGWFOeTnZO4IuJjmyiJyfR7VVUJJpYxxtHeUofHptKmnFFOY56PGn6di3S6QTTcLAC3RIsYqxCqZ5mDXCKurirLigg6gvjSfzmH1QqdCLHmXDZ0PzZWF5Pk8tKqNkAwUG1mRDSNa60sjtff6NDI9JTqHgtSXZu7qoLP5vB7W1RSr8wEllTLGjrZBgLRZ+W22VZVFnBwY1+o9DuvKojnQAFXFedT48zigGwbJMIe6Rs6MssgG9aX5DI5PMzGl2jfJ1jkcpCjXS0m+z+lQks7n9bC+1q+bBclI2bDyW4xWgEudqZkwfaOT1GdJBzVEpkpr+puSShljZ9sAVcV5NFcWOh1K3JorCwlOh1UTw2HdgcjNgj8/x+lQUmZjfQkHO9UQSOYIBKc5OTDOxvrM732OifWI6oYh+TqHJ6gvK8CYzF/MASKjOJRUkkx0oHOE0oKcrLj5r/bn4TWGziG1EcnWHQhiISvOq5hN9SX0jExm/Ug4JZUyxI4TA1zSXJ6WF3qrKosAONE/7nAk2S2binTHbKwv4UjPCFMzWn1QMkOsSPfmhlKHI0md2MWrpsAlVyhs6Q5kVw90S30JfaOT9IzoZlQyS2tXgI31/rS8b4iXz+OhpiRPbUQKxFbZy5bpb8CZumQHOrJ75oOSShmgazjIqYGJtKunFFNemENJvk91lRwUtpbuQDBrpr7FbKz3Mx2yHO3V6j6SGQ50DAOwqSHzpzTE+PN9FOV6tWR0kvWMBAmFLQ26WRBJa+GwzcJp0gV0DE1gVWojqTqHJ8jzeSgvzJ5ZD7HrrWwvp6GkUgbY0TYAwCVptvJbjDGGVZVFGqnkoIGxKaZDNutGKsVuGFSsWzLFgc4AlUW51PjznA4lZYwx1JcVqBc6yZ7rgc6ediJ2s7BfSSXJIMf7xxifCrE5izofGsryGZsKEQjOOB1KRuscDlJfmp8VI+BiygpzWVFWkPWdD0oqZYCdbQMU5nrTelnQVZWFDE9MMzQ+5XQoWSmbVvSZbXVVZClQJZUkUxzoDLCpoSSrLugA6kvy6daS0UnVOTRBjtdQlUUJy9KCHJoqdLMgmSWWJM2madKxEZadQ+p8SJawtXQNZ8/Kb7NtrC/RSCWnA5Dl29E2yEUry/B50/fX2Rytq9Sm0UqO6AoEMUCNP7uSSj6vhw21fhXrlowwHQrzbNdoWncwLFV9WT6hsKU3ywtlJlPHcGSKtCfLEpab60vZH51WKpIJ9p8eJtfrYV1tsdOhpEx9aT4G6NCI1qQZHJtiKhTOqtGsMZsaSjjWO5rVq9CmbxZCgMhKP61dAbavSs96SjG1Jfnk+TycUF0lR3QHglQW55Lry76PhI31fg52BjTPXtLekZ5RpkLhrKqnFFOvXuikstZGVn7Lwh7ozQ0ltPWPMxKcdjoUkYTY3xFgQ52fnDTujI5XXo6XiqJcOrQCXNJ0ZGGR7phN9SWELRzqzt5O6uz5NMlQT50YJGzhkjQt0h3j9RhWVhSqrpJDOoezr0h3zMb6EvrHpugd0QgHSW8HzkxpyL6kUlVxHj6PUbHuJBkanyY4Haa+LPvaiViStrUre28WJHNYa9nXMcz5K7KvnWhQ7b2k6hyewGOgpiR7pkjHxK67snmqtJJKaW5n2yBej+GilWVOh7JsqyoL6Q4Es3rooBOC0yEGxqZoKMu+ngWIJJVAqzZI+jvQGSA/x8PqquyZ0hDj9RhqS/J1w5Aksfc1m1Z+i4nVndl/WlPgJP11DAcZGp9mUxbVU4ppKM1ncHxa9xlJ0jUcpKo4L6tGwMU0lhfgz/NxoDN724ns+61nmCfbBtjcUEJRns/pUJZtVWURFjgxoClwqdSZxcNVATbWxVaAUy+0pLcDHQE21JXg9WRXzZuY+tJ8OoeDmsqaBB3Dkbp72baYA0BtSR6VRblaAU4ywr5ocvT8LBzRWh/tPFVdpeToHA5mbQe1MYaNDSUaqSTpaXImxJ5TQ2k/9S2mqbwQrzGaApdiHdEaJA1ZOK0BoLQwhxVlBVoBTtKatTay8lsWFumOqSvNZ1xLRidF59AEVcV5WVl3zxjDpoYSJZUkI+zvCOAx0FKXfW1FLOGh2nuJNz45w/DEdNaW0oBIXaXWrpGsXYU2+64OMsi+0wEmZ8Jc0lzudCgJkevz0FCWT1ufRiqlUsfQBP48H/78HKdDcUysWLeknjHmBmPMIWPMEWPMB+bZfo0xZtgYszv69ZHFPjebdAwHGZ6Yzsoi3TFninWrFzrhOoeDWVlPKWZzQymHe0aYmgk7HYrIsuw/Pcx5NcUU5HqdDiXlivN8lOT7zhSUlsTpDERnPWRxO7GpoYTxqVDWLjqlpFIa29E2AMD2DBmpBNBcWUT70ATTIV24pUq23yxApK7Ssb4xgtOaZ59Kxhgv8AXgRmATcKsxZtM8u/7OWnth9OsTcT43K8TqvWTzSKXYMsZdumFIqPGpmf+fvfsOj/Mq8z7+vdWL1SUXNffeE8clPSSkEgJLAgltYYFQd2GXLSwsLLALyy4sbQOEvBBCKAFCCgmkQ7pjO+69ykXFtnrv0nn/mJGjKJKtkWbmGc38Ptely9LMM8/cGo/mPOc+59yHxo6emKynNGBRYSY9fY5D1Vom7QUNPgTPnqrmM3XCYtG0rNQzM/QleAZmf8VqKQ14rVh3rM5qVVJpAtt8rJ5Z+enkT4qeKvsz8tPp63dUNOgDPxx6+vqpbumM6c4C+Driff2OA9rdJ9xWA4edc2XOuW7gN8BNYXhs1Nl7shkz36y7WJXi3zK6Uh2GoHqt7l7sDj7EemfBSxp8CJ7a1i5ONXfG5A6hAwqzU6ht7dLgdZBVNXWSmZLApCio8TtWcydnkBhvMbvxj5JKE1R/v+PVYw1RU09pwPTcNICYnToYbqebO+l3rxUvjFVLinyjdrurYnfXBo8UAeWDfq7w3zbUOjPbYWaPm9niAB8bE/ZWNTMzP520pNi9oANfzQyNQgfXwOsZy+3EzLx00pLiY7oIq4c0+BAkA0nRWJ6pVJidSr/TjNZgq2zsiNki3QOSEuKYMzkjZtsJJZUmqMM1rTR19LAqSuopDUhLTmByRjLHlFQKi5ONvka1MIZHoMG3FWhWaiK7K2OzIfDQcNuUDa1wuBWY7pxbDvwf8HAAj8XMbjezzWa2uaamZjyxRrQ9Vc0sjOGlbwOKslNpaO+hvUvFuoOlsrGDrNTEmB6BjoszFk7LZI8GHrwQ8sGHWGknBnZ+i+XaewMz87UDXPB09fZR29JFUYwnlcC38mFPVXNM7kKrpNIEtemor57S6pnRNVMJfHWVjte10x+Df5DhVtXUQUpiHLnpSV6H4ikzY0lR5pkLLgmbCqBk0M/FQNXgA5xzzc65Vv/3jwGJZpY/msf6H3OXc26Vc25VQUFBsOOPCPVt3VQ2drCsKHZHnwcMXNRWqsMQNJUNHeos4FsCt7eqmf4Y3dnHQyEffIiFdgJ8M1pLc9PISo3djVmy0xJJTYzXjNYgqmrsxIHaCWBJUSa1rV2cbu7yOpSwU1Jpgtp8rJ6CjGRK/cvFosmM/DS6evs1NTUMqho7mJaVitlw112xZUlRFgdOaXefMHsVmGtmM80sCbgVeGTwAWY21fxvUDNbja/dqhvNY2PFLn8ydKmSShT6Nx2oUl2+oOjo7qOurZviHHUWlhRm0dbdx1HNpA63kA8+xIrdVU0sKYrdWUrgG0QszklV7dYgGkjQFaqdYFmx7zpsVwwOUiupNEH56inlRGUyYHpeOoCWwIVYv3Ocau6M+aVvA5YUZtHd18/B0yrWHS7OuV7gU8CTwD7gd865PWb2MTP7mP+wm4HdZrYD+D5wq/MZ9rHh/y28NzDDbrGSSqQlJZCTlkiFRqGDYmCJiEagYelAZ6Ei9joLHtPgQxA0dfRwvK49puspDSjKSeV0c6eKdQdJZWMHmSkJZKbE7gy4AYumZRFnsKui0etQwi52F8hPYFWNHVQ2dvDhS2Z6HUpI5KQlkZWayLG6di6c7XU00aumpYuePhfTxVcHG5jlsbuy6Uzhbgk9/6jyY0Nuu3PQ93cAd4z2sbFoZ0UjM/PTY3pJw2BFOWlUNrR7HUZUqGxQUmnA3MmTSEmMY0dFI29bGbN7AoSdc67XzAYGEOKBuwcGH/z334lv8OHjZtYLdOAffACGfawnv4jHBpKhy4uzvQ0kAhT7i3WfbOqMyhUf4aYi3a9JTYpn3pQMdsbgTCUllSagV4/56ilF285vg83IS6Ostg3nXFTOxooEJ/0j0ANFC2NdaW4aGckJ2gFOJpxdFU2siuL2IFBF2ansrmyivauXtBguLh0MlY0d5KQl6nUEEuLjWFKYpZlKHtDgw/jt8M+cGJhxF8uKcnyJpIqGdiWVxmmgSLdqOr5maVEWf9lfHXN9WC1/m4BePVbPpOQEFkzN8DqUkJmel05LZy/1bd1ehxK1qho7SYgzCjKSvQ4lIsTFGYuLMtmlHeBkAqlp6aKqqfPMOn5Rse5gqmzsONMBE1+HfHdVE71aNiMTzI5yzWgdkJWaSEZKwpmZmDJ2J1Wk+w2WFmdR19ZNVYzVBlZSaQLafKyBlaXZJMRH73/fjPyBukpawhAqVU0dTMlMIT4udrLo57KkMIt9J5u1zl4mjIF6Slqy+RoV6w6O9m7fwE6xOgtnLC/OprOnn0PVrV6HIhKQnRVNLNfgwxlF2amqvRcElSrS/QYD5TRira6SJ1kJM7vWzA6Y2WEz+9ww919uZk1mtt3/9SUv4oxETe09HDjdwuooX+owOSOZ1MR4jqtYd0j0O0eV1kC/wdLiLLp7+zmsDoNMEDsrmjDzbXcuPgPFuivVYRiXM50FtRNnqFi3TESnmzs51dzJMtVTOqM4J5Xali66evq8DmVCq1KR7jdYOC2ThDiLuR3gwp5UMrN44AfAdcAi4DYzWzTMoS8651b4v74a1iAj2JYT9ThH1NfPiDNjel6adoALkbrWbjp7+inRyMLrDMz2iLWGQCauXZVNzMpPJ0MXdK9TlJOmpNI4qUj3G83MSycjOeFMfRqRiWBHeSMAy0s0U2lAUXYaDtROjFOFBqjfICXRX6w7xgYfvJiptBo47Jwrc851A78BbvIgjglp09EGEuONFSXZXocScjPy0qlt7aals8frUKJOhX9npGLVynidmXnppCfFs0dJJZkgdlU2avR5GEXZqTS099De1et1KBNWZWMHeelJpCbFex1KxIiLM5YWZ2ngQSaUnRVNxMcZi6YpqTSg2D+oqqTS2A0U6dbAwxst87cTvk0oY4MXSaUioHzQzxX+24ZaZ2Y7zOxxM1scntAi36vH6llSlBUTF3kz8nwJj+OqqxR0FQ0dJMXHMTlTRboHi4szFheqwyATw+nmTk43d51Zvy+vUbHu8ats6KBIs1nfYGmxr/ZeV6+WzcjEsKOikflTMmKi7zBa6cm+ZdIVqr03ZirSPbIlRVk0tvfE1PvLi6TScFWBh6bxtgLTnXPLgf8DHh7xZGa3m9lmM9tcU1MTvCgjUHt3LzsrGlkzM8/rUMKiMCeVhDhTXaUQqGhopzA7lbgY2upytBYXZbL3ZDN9/bEzuiAT00BdF20R/UYq1j0+rV29NHb0qLMwjOXF2fT0OfafbPE6FJFzcs75inRr6dsbFGWnnpm5L4FTke6RDezIG0tL4LxIKlUAJYN+LgaqBh/gnGt2zrX6v38MSDSz/OFO5py7yzm3yjm3qqCgIFQxR4Stxxvp6XOsnRXd9ZQGJMTFUZKbxtFaJZWCqbe/n5NNnWem/srrLS3KorNHxbol8u2sbCLOYNE0FekeKi0pgdz0JMqVVBqTKn9nQTOV3uhMZ0EzWmUCOF7XTlNHj5ZJD6M4J03LpMehUkW6RzR/agaJ8bFVrNuLpNKrwFwzm2lmScCtwCODDzCzqWa+KRRmthpfnHVhjzTCbCirIz7Oor5I92Cz8tM52dRJR7emmQfL6aYuevudkkojGKhXtr28wdtARM5hd2UTcyZPIj05wetQIlJpbhrlDe0xVdMgWMob2jGgMEvtxFBF2ankpiex01/8WCSSDRSVX66k0hsMJM0rVFdpTE7Ut1OSq9qsw0lOiGfB1Ex2VTZ6HUrYhD2p5JzrBT4FPAnsA37nnNtjZh8zs4/5D7sZ2G1mO4DvA7c6XRWyoayOpUVZTIqhDsSsgkk40C5wQVTun+pboiLdw5qZn05WaiLbTjR6HYrIiHxLGhpZWpTtdSgRqyQnlZbOXpo6tNlDoMrr25mSmUJKomqwDGVmZ4qwikS6HeVNpCTGMW/KJK9DiThF2akYaAncGLR29VLf1k2pkkojWlqcxc6KJvpjpJyGFzOVcM495pyb55yb7Zz7mv+2O51zd/q/v8M5t9g5t9w5t9Y5t96LOCNJe3cvOyoaWTsrNuopDSj211Uqq9FSpGCpbOggLSme7DRNVx2OmW93RSWVJJKdqG+ntrWblaXZXocSsQZGULUELjD9zvlHoDVLaSTLirM5eLqFNi2bkQi3s6KRxYVZJMR70uWLaCmJ8RRkJHOiXkmlQJXXa4D6XFaWZNPS2cvhGOnD6hNmghiop7QmRuopDUiMj6M0N40y1VUKmvKGdkpy0jAV6R7RytJsDla30KoOg0SorSd8yzPPK83xOJLINTUrhYQ444RmugaktqWLzp5+jUCfxcrSbPrda0uLRCJRT18/u6uaztQBkzeanpfGifp2+rUgJiDl9e3EGRRqM4cRnTfdd3229XhslNNQUmmC2HjUX09peux1IGYVpHOqqZP2bnXwx6u1q5eali4VXz2HlaU5OIdqZkjE2nq8kfSkeOZPzfA6lIiVEBdHUXaqZioFaGDUXrUyRnZeSWx1FmRi2neymc6efg0+nEVpbjqdPf3UtnR5HcqEcqKhnWlZqSQlKJUwkln56WSnJZ4ZBIx2eidMEBvK6lhSlEVGDFbYn5nvr6uk2UrjtruyCYev1oiMbIW/oOU2JZUkQm090cDykmzi4zTj8GxKctOoauygq1ebPYzWifp2UhPjyZ+U7HUoESsrLZG5kyexRUkliWCbj/nen6tmKKk0kun+5LmWwI1eX7+joqFDS6TPwcxYWZLN1hgpp6Gk0gTQ0d3H9vJG1sbY0rcBJTmpJMYbR5RUGrcd/iRJkdZAn1VWWiKzCtJVV0kiUnt3L/tPtXB+DM5cDVRJbhq9/Y59J1u8DmXCGKinFKcl0md1/vQctp5ojJkirDLxbDneQFF2KtO0i+OI8iYlkZYUz3EllUbtUHUL3b39qqc0CueV5nC4upWm9ujfMERJpQlg64kGevpczBXpHpDgr6t0tEZJpfHaWdFETlpiTO0gOFYrS3LYXt6g7cgl4uwob6Kv32lJwygM1AXaFiPTz8erubOHmpYu1VMahfOm59DU0UNZbWwUYZWJxTnH5uP1Gnw4BzOjNDeNE3VKKo3WwICr2olzG6irtK08+q9BlFSaADaUxW49pQEz8ydxqrmTdhVOHpdtJxoo1sjCqKwszaa2tZsK1WORCDOwPl87v51bVmoimSkJmnU4SttPNOLw1RmRsxvorGsJnESiysYOTjd3aenbKJTmplHT2kVDW7fXoUwIW483kJYUT256ktehRLzlJdnEGTGxBE5JpQnglSN1LCnMjMl6SgNmF/gucLUL3NhVNnZQ1dTJ9DwllUZjRUk2QMwU2JOJY9uJBmYVpJOdpgu60SjNTdPf8ShtPdGAAcWqu3dOA0VYlVSSSDTwvtSM1nMr9V8Xx8JskmDYVt5Iaa52kR6NSckJzJ+aGROzpZVUinAtnT1sK2/kojn5XofiqSJ/XSUllcZu87F6AGbkaQR6NBZMzSAlMY7tKtYtEcQ5x9YTjeooBKAkN42Khg6qWzq9DiXibT3RyJTMFFIS470OJeKZGeeX5iipJBFp87EG0pPiWaAdQs+pODvNN5vkeKPXoUS8po4eDle3anfQAJxXms32E430RXn9PSWVItzGsnr6+h0Xx3hSKSEujhl56RypUe2Csdp8rIFJyQlMyUzxOpQJISE+jmXF2Vo2IxHleF079W3dSioFYKDuw3b9LZ9Vf79j24kGdRYCcN70HI7UtGnZjESczccbWFmaQ0K8unrnkpQQx7SsVCWIR2Fgwx8V6R6980pzaOnq5VB1dG8Yok+aCPfS4VpSEuM4X2uimTN5EjUtXTR1RH8F/VB49Vg9K0u1BXkgVpZks7eqWduRS8QYWMZ13vRsbwOZQAqzU0mIs5ioaTAeR2paaensVfHVAAwkd7VsRiJJS2cPB041q0h3AErz0the3khvX7/XoUS0rScaMNMS6UAMFOuO9plwSipFuJcO17J6Zh7JCZqKPmfyJAAOR3mmNxSaOno4cLqFVdNzvQ5lQllZmkN3Xz+7Kpq8DkUE8F3QZSQnMHeyljSMVmJ8HEuKsnjVvwRYhjeQsFRSafSWl2QRH2dR31mQiWV7eSP9DhXpDkBpbhodPX3sP6U+xtm8eqyeBVMztUQ6ADPy0shNT4r62o5KKkWwk00dHK5u5ZIYX/o2YGpmCpOSEzhUrSVwgdp2ogHn4AJdYARk9UxfEm7jUXVGJTJsPd7ICs04DNiaWbnsrGiko1uzDkeysayevPQk8iepAPxopSUlsGhappbNSETZfKyBOHttwxE5t+n+ZPpmDT6MqLu3ny3HG1gzUwPUgTAzVpZkK6kk3nnpUC0AF89VUgl8f5RzJk/icHUr/S66i50F2+ZjDcTHGSu0BXlActOTmDdlkpJKEhGaOnrYpyUNY7J2Zh49fS7qL+rGyjnHhrI61s7K044+ATp/eg7byxvp0bIZiRBbjjcwf2ps7xodqOy0JIqyU3W9dxa7Khvp7Oln7SwllQJ1wcxcymraqGnp8jqUkFFSKYK9fLiW/ElJzJ+iZQ4D5kyeRHt3H6eatItPIF49Vs+SwkzSkhK8DmXCWT0zly3H6rXOXjy36Wg9zsG6WXlehzLhrJqRQ5zBxrI6r0OJSOX1HVQ1dbJGnYWAXTAjl46ePnZqmbREgN6+fradaGCVBh8CtnZWHhvK6uiP8l26xmpDmS/htnqmrkECtdZ/3bbxaPRegyipFKGcc7x0uI6L5uQTp2UOZwzUVdISuNHr7u1ne3kjq2aoszAWa2bm0dbdx56qZq9DkRi3oayO5IQ4zTgcg4yURBYXZrFBo9DD2uC/0F2rhGXABkbtNyhhKRFgV2UTbd19ShCPwbrZeTS0+2qQyhttPFrPvCmTyE3XEulALSnMJD0pnleORG87oaRShNp/qoXa1i4uVj2l18lMSWRKZnLUb8sYTHuqmujq7deo1RitOVNXKXobApkYNpTVcf70HG3cMEZrZuayvbyRzh7VVRpqQ1kduelJzPUP3Mjo5U1KZv6UjKjuLMjEsf6IEsRjtW627zXT3/Ib9fT1s+VYPWs0S2lMEuLjuGBmblQPPiipFKFePqx6SiOZOzmD43XtdPdqOdJobD7mqyFyvop0j8nkzBRm5qezSTMcxEON7d3sPdmsjsI4rJmVd2bmprzGOcfGsnrWzspVPaUxWjc7j83H6+nqVcJSvPXKkToWTM0gf1Ky16FMOEXZqZTmpkV1x3+sdmsG3Litm5XHkZo2qluis4SLkkoR6sVDtcwuSGdaVqrXoUScOZMn0dfvOFbX5nUoE8Krx+qZkZfG5IwUr0OZsNbMzGXT0Xr6tM5ePDJQT0lJpbFbPSMXM98uZ/KaioYOKhs79N4ah3Wz8+js6WdHueoqiXe6evt49Vj9mRk3Erh1s/LYeLRedZWGGChgvlo7v43ZmbpKUXoNoqRSBGrv7uWVsjoumzfZ61Ai0oy8dOLjjENa83xOff2OTcfquUD1lMZl9cxcmjt72X9KdZXEGxvK6klOiGN5SZbXoUxYWWmJLJiaqaWsQ7xSpuUy47V2Zh5msP5IrdehSAzbdqKRrt5+LpqtVQ5jtW52Hk0dPew9qeu9wTaW1TGrIF0D1OOwuDCTSckJUTsTTkmlCPTy4Tq6e/u5aqGSSsNJSohjZl46B0+rWPe57K1qprG9R8sox2mNv7OlJXDilVfK6lg1Q/WUxmvtrFy2nmjQ8ulBVE9p/LLSElk0LVO1WMRT64/UEWewWkuUxmwguR6tHf+x6Ot3bD7WoHpK45QQH8cFM3LODOREGyWVItBf9p9mUnKCdus6i/lTM6hp7aKutcvrUCLaS/7aXBdq1GpcirJTKc5JjdopqxLZGtu72X+qmbW6oBu3NTN9y5R2VjR6HUpEUD2l4Fk3K49tJ1QIXryz/nAtS4uzyUxJ9DqUCWtqlq+OphLEr9lb1UxLV++ZnS5l7NbNzqOspo3q5uirq6SkUoRxzvGX/dVcOi+fpAT994xk4bRMwLdLnozs5cO1LJiaQUGGCjaO1+qZuWw6Vo9zWmcv4bVxoJ6S6mSM2+ozuzkqQQyqpxRM62bn0d3Xz9bjDV6HIjGorauX7eWNXKh2YtzWzspj09F6evs0oxVe2/1YM5XG78xMuCi8BlHWIsLsqWrmdHMXV8zX0rezyU1PYnJGMvtU42ZEnT19bDpWz8VzNEspGNbOzKO+rZsDquUlYfbKkTpSEuNYVqx6SuOVm57EwmmZvHRItW/gta2zlVQav9Uzc4mPs6hd2iCR7dVj9fT2OyWVgmDd7DxaunpVV8nvpcO1zMxPZ2qW6imN16JpmWREaV0lJZUizF/2V2MGlyupdE4LpmZyrLaN5s4er0OJSJuP+eqGXKR6SkExUJfqxYPqjEp4bSirY9X0XNVTCpLL5hWw+Xg9rV29XofiuecP1VCQkax6SkGQkZLIkqIs1mvZjHjglSN1JMYbq6ZridJ4DSzzevmw/pY7e/rYUFbHZfMKvA4lKiTEx3HBzNyoXF6ppFKE+fP+apYXZ2u50igsmJpBv4PnD9R4HUpEeulwLYnxxmrV5gqKwuxU5k2ZxPMH9X6T8DnV1Mn+Uy1cOEejz8Fy2bwCevoc6w/HdoK4t6+fFw/WcPm8AtVTCpJ1s/LYUd6ohKWE3fojdawszSE1SYMP4zU5I4WF0zJ57kC116F4buPRejp7+rlsvpJKwXLxnHyO1rZxoq7d61CCSkmlCFLT0sXOikbetECzlEajNC+NtKR4/rJfH/rDeflwLStLc0hPTvA6lKhx6dwCNh2tp71bHQYJj+cP+j7f1C4Ez/nTc0hPio/5BPH28kaaO3s1MzqILptXQG+/0/JKCav6tm52VzVxkTZlCZor5hew+XhDzK+GeP5ADUkJcdooJIiu8F/PPXcwuvqvSipFkOcOVOOcOg+jFWfG/CkZPHugWsX0hmjwX2ConlJwXTa/gO6+/qhcC+0VM7vWzA6Y2WEz+9ww97/HzHb6v9ab2fJB9x0zs11mtt3MNoc38vB4dn8N07JSmD8lw+tQokZSQhwXzsnn+YM1MV14/7kDNcTH2ZmlvTJ+q2bkkJGcwLMa7JIwUv8h+N60YDJ9ShDz/MFq1szM1Qy4IJqZn870vDSei7KVNkoqRZC/7K9mSmYyiwszvQ5lwlgwLZPG9h62nmj0OpSI8kpZHc7BRUoqBdUFM3JJSYzTkssgMbN44AfAdcAi4DYzWzTksKPAZc65ZcB/AHcNuf8K59wK59yqkAccZt29/bx0uJbL52t5UrBdNq+AioYOymrbvA7FM88eqOa80myyUrX9eLAkxsdxybx8nj1QHdMJy2DT4MPZ/Xl/NZMz1H8IphUlvs/GWF4NUV7fzpGaNtVTCoEr5k9m/ZFaOnv6vA4laJRUihAd3X08d6CGqxZOUechAHMnTyIx3vjzvtNehxJRXjxUS0ZyAsu1W1RQpSTGs25WXswvmwmi1cBh51yZc64b+A1w0+ADnHPrnXMDe3RvAIrDHKNnthxvoLVLy5NCYeAiOVYTxNUtneypatZ7KwSumD+Z6pYu9lRp56hg0ODD2fX09fPCgRretGAycXHqPwRLQnwcl84r4LkDNfT3x2aC+IVDvvZR7UTwXT6/gM6e6Fr5oKRShHj2QDUdPX3csHSa16FMKCmJ8aydlccTe05pVNDPOceLh2pYOzuPhHj9iQfbZfMKOFbXzrEYnuEQREVA+aCfK/y3jeRDwOODfnbAU2a2xcxuD0F8nnruQDWJ8aYZhyFQkpvGrIL0mE0QDyTTLlfx1aAb6IDF8gyHINPgw1m8eqyelq5eLX0LgSvmF1DbGrsJ4ucP1FCUncrsgnSvQ4k6a2flkZIYF1VL4NTjjBB/2nWSvPQkVs/UTl2BumHpNI7Xtcfsh/5Qh6pbqWjo0AVGiFzm7zAMjODIuAw3rDpsdtjMrsCXVPqXQTdf5Jw7D98I9ifN7NJhHne7mW02s801NRPr/+zZA9WsnpnLJBXbD4nL5hWwoawuqqafj9ZzB2uYnJHMomlaLhNsBRnJLC/OUlIpeEI++DCR24m/7KsmKSFOgw8hcNm8AsxiM0Hc3dvP+iN1XKbl9yGRkhjPhbN9S6WjhZJKEaCju4+/7Kvm2iVTNbNkDK5ZPJWEOOPRnVVehxIRnvEvBbxC01VDYkZeGiW5qbwQozMcgqwCKBn0czHwhj9kM1sG/AS4yTl3Zq6wc67K/2818BC+Ee3Xcc7d5Zxb5ZxbVVAwcWZlVDS0c/B0K5fP099xqFw+fzJdvdE1/Xw0evv6efFgjb/DpM5CKFyxYDI7Khqpa+3yOpRoEPLBh4naToAv4bFuVp52+g2BvEnJLC/OjqqO/2gNLL9XPaXQuWJ+Acfr2jkaJSsflMGIAFr6Nj456UlcNCefP+08qSVw+EatlhRlMjUrxetQopKZcdm8AtYfqaOrN/ZmOATZq8BcM5tpZknArcAjgw8ws1LgQeB9zrmDg25PN7OMge+Bq4HdYYs8xAamRF+xQBd0obJmZi7JCdE1/Xw0tpU30tzZe2ZbYwm+Ny2YjHPE7PLKIAv54MNEVVbTSlltG1cu1N9yqFwxPzYTxM8drCYhzrhwdp7XoUStgaXS0bJbqJJKEUBL38bvLcumUdHQwY6KJq9D8VR9WzdbTzTwpgVTvA4lql0+bzLt3X1sKKv3OpQJzTnXC3wKeBLYB/zOObfHzD5mZh/zH/YlIA/44ZDde6YAL5nZDmAT8Cfn3BNh/hVC5rkD1RTnpDK7YJLXoUStlMR4Lpmbz9N7T8fUgMSf9/k6C1ouEzpLCrPIn5Qck8tmQkCDDyMYeH9pZnroXLGgIOYSxM45nt5zmrWz8shI0e6goVKSm8bsgvSomQmnpJLHBpa+XaOlb+Ny9eKpJMYbf4rxJXDP7q+m38GVGoEOqYvn5pOWFM+Te055HcqE55x7zDk3zzk32zn3Nf9tdzrn7vR//2HnXI5/554zu/f4i7Yu938tHnhsNGjv7uWlw7VcMX+ylieF2NWLplLZ2BEzNfmcczyx+yTrZueRlarOQqjExRlXzC/ghYM19PT1ex3OhKbBh5H9ZX8186dkUJKb5nUoUWtJYRZTMpNj6nrvcLVvBtw1izVAHWpvWjCZjWX1NHf2eB3KuCmL4bHntPQtKLJSE7l0bgF/2nkyZrf+BHh89ymmZaWwtCjL61CiWkpiPJfNK+Dpvadj+v0mofH8gRo6e/q5dslUr0OJelcunEycETMdhgOnWzhW1673Vhi8edEUmjt7Y65mVyho8OGNmtp72HS0njdp6VtIxcUZ1y6eynMHamjr6vU6nLAYaA/fvEjtRKhdu2Qq3X39UbEETkklj/1p10ly05NYo6Vv4/aW5dOoaupkW3nDuQ+OQm1dvbxwqIZrFk8lLk6zG0LtmsVTqWnpYlt5o9ehSJR5fPcpctIS1S6EQd6kZC6YkRszSaUndp/CzJfwkNC6dF4B6UnxPLYrNt5bEl5P7j1Fb7/jOiWIQ+7aJdPo6u2Pmfp7T+45zYqSbNVmDYOVJTlMyUzm8ShoJ5RU8lBTRw9P7z3NDUunaelbEFy1cApJCXE8uuOk16F44tkD1XT3anZDuFyxYDIJcRYznVEJj67ePv6yv5qrF2lJdLhcs3gqB0+3cri61etQQu6J3adYNT2HyRnqLIRaSmI8b1o4hSf3nKJXS+AkyB7bdZKS3FTNTA+D1TNzyUtP4vHd0d+/KK9vZ1dlE9csVl8iHOLijGsWT+W5g9W0d0/smXC6YvXQozuq6Ort552rSs59sJxTRkoiVy6YzCM7qujujb0LuCd2nyJ/UhIXzNDshnDISk3k4rnadVCC66VDtbR29XLtUl3Qhcv1S6dhBn+M8pp8h6tb2H+qheuWaLl9uNywdCr1bd1sOqpNHSR4Gtu7eelQrf+zSzPTQy0+zrh68VT+sr+aju7o3vX3jzt9ibO3LFM7ES7XLp5KZ8/EnwmnpJKH7t9czoKpGSwpyvQ6lKjxzgtKqG/r5pl9p70OJazau3v5875qrl48lXgtfQubG5cVUtnYoSVwEjSP7qgiKzWRi2ZrZ65wmZqVwuoZuTy6oyqqE8SPbK8iztRZCKfL5k0mLSmeR3dG/wwHCZ+n9p6mt9+pHmsY3bh8Gu3dfTwd5f2LP+6sYkVJtoq/h9HqmbnkT0ri0R0Te2BLSSWPHDjVwo6KJm5ZVaJRhiC6dG4B07JS+O2r5V6HElZP7z1NR08fNy0v9DqUmPLmxb4ll3+M0SWXElzt3b08tfc01y+dRlKCmudwunF5IUdq2th/qsXrUELCOccjO6pYOyuPyZla+hYuqUnxXL1oCo/tOhmTM6glNP6wvZLpeWla+hZGa2bmMTUzhUe2V3odSsgcqWllT1UzN6ovEVYJ8XG8ZVkhf95fPaF3gdNVq0fu31xOYrzxthX6ww2m+DjjllUlvHCohsrGDq/DCZtHtlcxLStFS9/CLDMlkSvmF/DHnVX0aRc4Gaen956mvbuPm9QuhN11S3yzPP+wfWKPFI5kV2UTx+ra9d7ywE0ri2jq6OG5AxN/dx/x3smmDtYfqeNtK4o0KB1G8XHGjcun8dyBGhraur0OJyQe3VGFGZoB54GbVhTS3dvPk7snbp1WJZU80N3bz0PbKrlq4RTyJiV7HU7UueX8YsCXuIsFDW3dPH+whrcuL9Subx64cXkh1S1d2jZaxm0gObxayeGwy5uUzGXzCnh4W2VUJogf3FpJUnwc1y5WZyHcLpmTT156Eg9H8QwHCZ8/bK/COXj7yiKvQ4k5N60oorff8cdd0Tc7vb/f8cDWCtbNytOubx5YUZJNaW7ahB7YUlLJA3/ZX01dWze3rCr2OpSoVJKbxsVz8rl/c0VUdg6G+uPOKnr7HW/VCLQnrlo4hYyUBB7YUuF1KDKBVbd08tzBGt66Qslhr9x8fjGnmjt5+XCt16EEVVdvHw9vr+TNi6eQlZbodTgxJyE+jhuXF/LMvmqa2ifu0gbxnnOOh7ZWcl5pNjPy070OJ+YsLsxk/pQMfh+F13uvHqunvL5DfVOPmBlvW1nEy0dqqZqgK22UVPLA7zaXMzkjmUvnFngdStR61wUlVDZ28FKUdQ6G89vN5SyalsniQq2t90JKYjw3Li/ksd0naZnAa6HFWw9t9c2QueV87QbqlSsXTiYrNTHqOgx/3ldNY3vPmVm8En43n19Md28/f9ih2UoydjsrmjhwuoW/Ok9/y14wM25ZVcyO8kYORFn9vd9vqWBScgLXLNbOs1655fxinGPCDlIrqRRmx+vaePZANe+6oISEeL38ofLmRVPISUvkvo0nvA4lpPZUNbG7spl3XaCOqJduPr+Yzp5+Ht81cddCi3ecc/x2czmrpucwZ/Ikr8OJWckJ8dy0opAn95yKqhklv9tcztTMFC7RQJZnlhRlsaQok/s2lUf1DoMSWvdtOkFqYrxqo3nor84rJjHeompDoLauXh7bdZIblk4jLSnB63BiVkluGhfOzuN3W8rpn4ArbZTVCLN7XzlOvBnvXTvd61CiWnJCPO+8oISn9p6ioqHd63BC5v7NFSQlxOkCw2MrS7KZXZDOfa9GdxJTQmPL8QbKatp45yolh732rgtK6Ort5/dbJ+ZI4VAVDe28cLCGm88vJl7LKj31rgtK2XeymV2VTV6HIhNQa1cvj+yo4sbl08hI0TJWr+SmJ/HmRVN4aFsFnT19XocTFA9vr6Stu493rdY1iNfedUEJ5fUdE7JOq5JKYdTW1cvvNpdz3dJpTNGWviH31+tmYGbc+8pxr0MJifbuXh7cWsE1i6eSnZbkdTgxzcx495rpbDvRyG51GCRAv9xwnEnJCdywTEWUvba4MIuVpdn8asPxqJhR8iv/bN3b1pR6HIm8dXkhKYlx/DrKZ1BLaPxheyXt3X3culp/y157z5rpNLT38MedE79gt3OOX7xynEXTMllZku11ODHP16dL5JcbJ17fVUmlMPrd5nJaOnv5wIUzvA4lJhRmp3Ldkqnct+kEbV29XocTdA9vq6K5s5f3r9Ost0hw83nFpCTG8asJ2BCId2pauvjTrpPcfH4x6cmadh4J3rtmOmW1baw/MvFGCgfr7Onjt6+Wc9XCKRRlp3odTszLSk3k7SuLeHh7ZdRuSS6h4Zzj5+uPqeMfIS6cncecyZP4+fpjE37wYeuJBvafauG9a6djptmsXktJjOddq0p4cs9pTjZNrILdSiqFSU9fPz958SgXzMjh/Ok5XocTMz508UxaOnu5b1N0jQw657j3lWMsnJbJKr2fIkJWWiJvXV7Iw9uqaOqInnosElq/2XSCnj7H+5Qcjhg3LJtGTloiP3v5mNehjMufdp6kvq1b760I8tcXzqCzp5/fbo6eeiwSeuuP1HHwdCsfvGiGOv4RwMz463XT2VXZxLbyRq/DGZd71h8nIzlBZTQiyHvXTqffuQk3q1VJpTB5dEcVlY0dfOyy2V6HElNWluZw4ew87nqhLGrWPgNsKKtn/6kWPnChRhYiyfvXzaCjp4/fRFkSU0Kjq7ePX248ziVz85ldoALdkSIlMZ73rp3On/ef5khNq9fhjIlzjv/3YhlzJ0/iotn5XocjfgumZrJuVh6/eOU4vX39XocjE8TdLx0lLz2JG5er4x8p/uq8YjJSEvjJi2VehzJmFQ3tPLbrJLetKdVM6QhSkpvGlQsm8+uNJyZU31VJpTDo63f86LkjzJsyiSvmT/Y6nJjzySvmUN3SFVXbRP/4hSPkpSfx1uVFXocigywpyuKiOXn87OVjdPeqwyBn94dtVZxu7uIjl8zyOhQZ4v3rZpAYH8dPXzrqdShj8vzBGvafauH2S2cRpwLdEeVDF8+ksrEjKuqxSOgdOt3CXw5U856100lJjPc6HPFLT07gfWun8/juUxytbfM6nDG5+6VjGPDBi2Z4HYoM8eFLZlHX1s39E6jvqqRSGPxxZxWHqlv5uyvn6uLOAxfOzmNFSTY/fPYwXb0TJ+M7kn0nm3nuQA0fvGgGqUm6wIg0H7lkFqeaO3l0R5XXoUgE6+933PnCERYXZnLJXM0kiTQFGcm847wifr+lguqWTq/DCdiPny9jSmYyN63QwEOkedOCycybMokfPnd4Qm4bLeH1w+eOkJIQr3qsEeiDF80kMT6Ou1444nUoAWto6+a3r57grcsLmZalmnuRZs3MXFaUZHPXC0cmzKxWJZVCrLevn+8+c4gFUzO4fol29vGCmfGPV8+nqqlzwq1PHc6PnjtCelI871s7w+tQZBiXzStgwdQMfvjcYfrUYZARPLnnFGU1bXz0stlawhqhPnrpbPr6HT9+fmItb9hYVscrZXV85JJZJCXoMi/SxMUZH798NgdPt/LMvtNehyMR7ERdO4/sqOI9a0rJTdcuv5GmICOZd64q5oEtlVQ2Tqyiyj95qYz2nj4+drnKskQiM187UV4/cWa16mojxH6/pYKjtW185qp5mqXkoYvm5LFuVh4/ePbwhN4J7uDpFh7dWcV7100nKy3R63BkGGbG3105lyM1bfxxp2YryRv19Tu+88xBZuWnc/2SqV6HIyOYkZ/O21YU8csNx6lunhizlZxzfPvpgxRkJPOeNSrQHaluXFZIaW4a333mkGYryYjuePYQ8WZ85FItkY5Un7h8DgDff+aQx5GMXn1bN/e8fIwblk5j3pQMr8OREbx54RQWTM3ge38+NCFmKympFEJtXb3879MHOX96DtcsnuJ1ODHNzPina+dT29rNnc9PvGmqA7791EHSkxL42KUaWYhk1y6eeqYh0GwlGeqPO6s4eLqVv3/zPBLi1QxHsr+7cg69/Y4fPHvY61BGZf2ROjYereeTl8/W8ugIlhAfx9+/eS57Tzbz2O6JMQot4XW4uoXfb6ngfeumMyUzxetwZASF2am8e00pv99aMWFqK/34hSO09/Tx6Svneh2KnEVcnPH3b57H0do2HtxW6XU456Sr2RD68fNHqGnp4gs3LNTyhghwXmkON60o5McvlFFe3+51OAHbWdHIE3tO8aGLZ5KjadARLS7O+MxVcymraeN+bR0tg3T39vOdpw+yYGoGNyzVkuhINz0vnXddUMKvNp6gLMJ3guvrd3ztT/soyk7l1tWlXocj5/DW5UXMmzKJbz91kJ4JMAot4fWtJw+SlpTAJ7Q8KeJ98oo5JMXH8d+P7/c6lHMqr2/nZy8d4+0ri5irWUoR7+pFU1hWnMX3njkU8TvBKakUIkdr27jzhTLeuryQ80pzvA5H/D533QLizfjPP+31OpSAOOf4yqN7yZ+UxIcvmel1ODIK1yyeyvnTc/jWUwdpncBLLiW4fr7+GMfq2vncdQu0JHqC+Pur5pGSGM9/RXiH4fdbytl7spnPXbdAu0RNAPFxxj9fs4Cy2jbufeW41+FIBNlQVscTe07xkUtmkTcp2etw5BwKMpL5xOWzeWLPKdYfqfU6nLP6nycPEBcH/3TNfK9DkVEwMz533QIqGzv4yYuRXd9RSaUQcM7xpT/sJjk+jn+7YaHX4cgg07JS+dsr5/DkntM8ueeU1+GM2iM7qthyvIF/umY+GSmqpTQRmBn/dsNCalu7+NFzE2PpjIRWbWsX3//zIa6YX8Dl8yd7HY6MUkFGMp+4YjZP7z3NsweqvQ5nWE3tPXzzSd9y+7cs0wy4ieLKhZO5ZG4+333mIHWtXV6HIxGgt6+fLz+yh6LsVG5XLaUJ4yOXzqIoO5WvPro3YmcevnKkjkd3VHH7pbO149sEcuHsfK5ZPIUfPneE0xFc31FJpRB4cGslLx6q5bNXz2Oy1kFHnI9cMouF0zL50h9209TR43U459TU0cPX/rSPpUVZ3HJ+idfhSABWlubw9pVF3PVCGYerW7wORzz2tT/to7O3jy/csMjrUCRAH7p4JrML0vniw7tp7468mYdff2wfDe3dfOWti7XcfgIxM/79xkV0dPfx9ccieyachMfPXznO/lMtfPEtC1UXbQJJSYznSzcuYv+pFu56IfJmlHT29PGFh3ZRmpumJZUT0BeuX0Rvv+Mrj+7xOpQRKakUZFWNHXz5kT1cMCOH962b4XU4MozE+Dj++x1LqW3t5kt/2O11OOf09T/to66tm6+/famWy0xAX7hhIWlJCXz+wd3a5SeGPXegmoe2VfLxy+cwZ/Ikr8ORACUnxPP1ty+loqGD/33qoNfhvM76w7X8dnM5H754JkuKsrwORwI0Z3IGH71sFg9sreD5gzVehyMeOl7XxreePMAV8wu4ZrF2Bp1orlk8leuWTOV7fz7E4erIqsF3x18OU1bbxtfevkTLoyeg0rw0Pn3lXB7bdSpiV9ooqRREff2Of7x/B33O8b+3rCBeCYCItaw4m09fOZc/bK/i4QiuqP/8wRp+u7mcj1wyi6XF6ixMRPmTkvnC9QvZdKyee9Yf8zoc8UBTew+ff3AXcyZP4pNXaIRwolozK4/3rZ3OT186ysuHI6NuRmN7N5+9fwcz89P5zFXzvA5Hxuhv3zSX2QXpfP7BXRNiBrUEX1+/459/v5OEOOPrf7VUMw4nqK/ctJjUxHg+89ttdPdGxjK4V4/V88PnDnPz+cVcMrfA63BkjG6/dBYLpmbwbw/vjsjl0koqBdH3njnI+iN1fPmtiynNS/M6HDmHT1w+mwtm5PD5h3Zx4FTkLU2qbu7kH367nXlTJvGZq7Tt50R2y6pirlwwmW88sT8i32sSOs45/vWhnVS3dPG/tywnOUEjhBPZ569fyOyCdP7hd9up9fiizjnH5x7Y5avVdetKLZWZwFIS4/nWLcs51dzJ5x/chXOa1RprfvTcYTYereeLNy5SvZsJbHJGCv9z8zJ2VzbzP094v6S1sb2bz/xmO8U5aXz5rYu9DkfGITE+jv9953Ka2nv4lwd2Rlw7oaRSkDyz9zT/96wvC/zOVap7MxEkxMdxx7vPIz05gY/+YjON7d1eh3RGT18/n/7Ndtq6e/nBu8/TVNUJzsz475uXkZmSwCd/vVW7wcWQX248wWO7TvHZq+ezvCTb63BknFKT4vn+bStpbO/hE7/a6mlB1h8+d4Qn9pzin69ZoJmsUWBlaQ7/ePV8/rTrJL/ceMLrcCSMNpTV8e2nD3LTikJuOb/Y63BknK5ZPJW/Xjedn7x0lD9s9241RF+/42/v20ZNSxffu3UFk5ITPItFgmNxYRafu24Bz+yr5qcvHfU6nNdRUikIdlc28Xe/2caSwiz+46YlXocjAZiSmcKP3nMeVY2dfOTezXT29HkdEgBffXQvr5TV8bW3LWXulAyvw5EgyJ+UzPdvW8nR2jb+8Xc7VF8pBmwoq+Mrj+zh8vkFfFS7+ESNxYVZ/Pc7lrHpaD1f+sNuT0YLn9xzim89dYC3Li/kw5fMDPvzS2h89NJZXDG/gK88socNZXVehyNhcKKunY//cgsz8tL52tu17C1afOGGRayekcs//34n2040hP35nXP8xx/38uKhWr5602JWluaEPQYJjQ9eNINrF0/l64/t46VDkbEUH5RUGrejtW188J5XyUpN5Kd/vUrTzyegVTNy+fa7lvPqsQY+9Wvv10Df+fwRfrHhOB+9bBbv0IhVVLlwdj7/et0Cnthzim9EwLRoCZ3D1S18/JdbKM1L4/u3rVSR/SjztpVFfPKK2dy3qTzshbvXH67lb3+9jeXF2fz3O5apExpF4uKM7922kul5aXzsl1u0a2iUa2jr5kM/f5V+Bz/9wAWaSRJFkhLi+NF7z2NKZgof+Nmr7D/VHNbn/+FzR7hn/TH+5qKZ3Lq6NKzPLaFlZvzvO5czd3IGH//VFvadDO97ayRKKo3Dibp23vP/NtDX77j3b1YzOTPF65BkjN6yrJD/uGkxz+w7zSd+tcWzGUv3vHyUbzy+nxuXF/LP1yzwJAYJrQ9dPJP3r5vOXS+UcefzR7wOR0KgvL6d9/10E/FxcfzsAxeQmZLodUgSAv949XxuW13CHc8e5ltPHgjLjKUXD9XwoZ9vZmZ+Ovd88AINZEWhzJRE7v7ABSTExfHen2yivL7d65AkBFo6e/jrn23ieH07d773fGbmp3sdkgRZ3qRkfvXhNaQmxvPen2xkT1VTyJ/TOccdfznEN588wNtWFPJvNywM+XNK+KUnJ/DTD6wiPSmB99+9iWO1bV6HpKTSWO0/1cw77lxPe08fv/jQai1RigLvWzeD/3jbEp7ZV817f7KRhrbw1VhyzvG9Zw7x5Uf38uZFU/j2O5dr98AoZWb8+42LecuyaXzj8f388LnDXockQVRW08q7fvwKbV293Ps3q5mep45CtDIz/vNtS88klj7/0K6QznR9aFsFf3PPq0zPS+MXH15NdlpSyJ5LvDU9L51ffng1HT19vPPHr3CkJrK2J5fxqW/r5r0/2cjeqmZ+9J7zWDc7z+uQJERKctP49UfWkBQfx60/3sCLh2pC9ly9ff185dG9fOupg7x9ZRHfumW5ZklHseKcNH7xodX09vXzzh+/wqHT3s5sVVJpDJ7Ze5qbf/QK8Wbc/9F1LC5Ugcxo8b6107nj3SvZWdnEjXe8xM6KxpA/Z0d3H//wux1855mDvOO8Yn74nvNIjNefZjSLjzO++64V3LSikP954gBffmQPfaqxNOFtPlbPLXe+QldvP7+5fR2LCjO9DklCLD7O+Prbl55ZCveen2ygsrEjqM/R2dPHVx7dw9//dgfnlebw29vXMTlDM6Oj3YKpmdz3kbX09PVzy52vsOlovdchSRAcrW3jljvXs/9UC3e+93yuXDjF65AkxGYVTOKBT1xIYXYqf333Jn7w7OGgX/Odbu7k/XdvOrPk7X9vWU6C+hJRb+6UDH770XU44JYfv8IrR7yrxad3WwB6+vr5nyf285Ff+KaeP/iJCzVDKQq9ZVkhv719Lf39jnf8aD3f//OhkO3ws6uiibf94GUe3l7J3181j2/evEwJpRiREB/Ht9+5gg9dPJN71h/jAz/b5PkW5TI2zjl+seE47/5/G8lISeB3H1NCKZaYGf90zQK+f9tK9lY1c+13XuBXG48HpdOw6Wg9b/m/l/jZy8f44EUz+OWH15CVpuWUsWJRYSa/++g6slITec9PNvDz9ccibhtpGb1n9p7mpjteor6tm3v/ZjVXLVJCKVZMy0rlwU9cyPVLp/HNJw9wy53rOXBq/DNL+vsd928u55rvvsDWEw188+ZlfOnGRZqhFEPmTcnggY9dSP6kZN73043c/dJRT9oJ9V5HaVdFE2//4cv88LkjvGtVCfd/bB2F2alehyUhsrI0hz/93SVcs3gq3376INd970We2Xs6aH+kDW3dfPmRPbzthy/T2NHNPR9czaevmqtGIMbExxlffMsivvFXS9l0tJ7rvvcij+6oUqdhAjnV1MlH7t3CFx/ezbrZeTz8yYuYXTDJ67DEA29dXsgTn7mUxUWZfOGh3dzw/Rd5ZEcVvWMYlNh2ooGP3LuZd/74Fdq7evn536zm329crEGHGDSrYBIPf+IiLp6Tz78/socP/Xyz6ixNMM2dPfzrg7v48L2bKc5J45FPXcyaWVryFmvSkxP4v9tW8t13raCsto3rvvcC/3j/jjEV5O/vdzy55xQ3/eBl/un3O5mVn85jf3cJt6wqCUHkEulK89J48BMXcvn8Ar76x7184Gevhr3OkrYZOIfjdW1878+HeHhbJXmTkrnzvedx7ZJpXoclYZCTnsQd7z6Pt688zdf+tI8P37uZRdMy+eBFM7h+6TTSx7BLR1lNK7/aeIL7Np2gs6ePW1eX8i/XLNDIc4y7dXUpy0uy+cf7d/C3923jvk0n+OpNi5kzWTMhI1VrVy/3vHyUO58vo6evn3+7YSF/c9FMJYZjXEluGvd9ZC2P7z7Ft546wN/dt43/yEjmxmWFXDa/gPNKs8kYpnB7d28/+0818+KhWv648yT7TjaTkZLAP7x5Hh+5ZJYKcse4rDRf8e571h/jf544wJXffp7bL5nFJ66YTVqSLuUjVXdvPw9sreDbTx+krrWL2y+dxWevnkdygv6eY5WZ8baVRVw+v4D/+8thfrnhOL/fUsHqGbncsGwaF83JY1b+pGGvJTp7+the3sizB6p5dHsVVU2dlOam8Z13Leem5UW6/ohxmSmJ/L/3r+LeV47zP0/s5+rvvMBHLp3JJ6+YE5Z2wqJpRHzVqlVu8+bNQTnXnqom7l1/nN9vrSAhznj/uul86k1zyUqN3c7/rzee8DqEYb17Tei3yuzp6+ehrZXc9WIZh6tbSU2M54KZuVw0O481s/KYnptGdlri67Z2ds5R19bN3qpmNh9v4Nn91eyqbCI+zrhx2TQ+ccUc5nmwfDKW/x8jXV+/49ebTvDNJ/bT1t3HdUum8uFLZrGiJHvc5zazLc65VeOPcuIKRhvR1N7Drzed4K4XjtDQ3sNVCyfzxbcsUkFuP32+vKa/3/Hn/dXcv7mc5w7U0O2fsTQtK4XJGckkJ8bT1++oa+2ioqGDXv9yueUl2fzVyiLecX6xJ1uM6/8wsp1q6uQbj+/j4e1VFGQk876107ltdSkFGcnjPrfaieC0E509fdy/pYIfPXuYqqZOVpZm89W3LmFpsWqwgj5jBqtr7eI3r5bz8LZKDlX7CvKnJcVTmptG3qQk4uPi6Ozuo7qlkxP17fQ73yz3S+bmc8v5JVyzeIontZP0fxjZTjd38o3H9/PQtkryJyXx3rXTec+a6SFtJ5RUGqSioZ1n9p7m/i0V7KlqJikhjnevLuUTl89mcqaKYuoDxJco2nK8gUd3VPHykToOV7+2I0tKYhwFGcn090NXbx9tXX109PQBYAYrS7K5ZvFU3r6yyNP3k/4fI19daxd3Pn+E32wqp6Wrl/NKs3nH+cVcv2QaOelj2/FJnYWxtxG9ff28eKiW32+t4Om9p+nu7efy+QV85qp5QUn4RRN9vgyvo7uPTcfq2V3ZxJGaVmpbu+nq6SM+zshJT2J6bhoLp2WyZlau50W49X84MWw5Xs/3/3yY5w/WkBQfx/VLp3LTiiIumpNPUsLYOplqJ8beTvT3OzYereehbRU8vuvUmbb701fN49K5+a8bdIx1+ox5I+cc5fUdbCirY9+pZsrr26lv66bfQXKCr38xq2ASSwozWTs7j8xhZryGk/4PJ4Ytx+u54y+HefbA69uJi+fmj3k5/UjthCdzZs3sWuB7QDzwE+fcN4bcb/77rwfagQ8457aGIpajtW384pXjPH+wmiM1vrWHiwsz+fKNi7hpRdGYO3ASncyMVTNyWTUjF4Dq5k62nmigsrGTk40d1LZ2ER8XR1JCHKmJ8RTnpDJvSgZLi7NiepabBCZvUjJfuGERn75qHvdvLueXG47zhYd28+z+Gn7y19F1vT+e9uBcjw2WX244zpcf3UtOWiLvXl3KzecXs6RII84yeqlJ8Vw2r4DL5hV4HYpEifOn5/Lzv1nNkZpW7l1/jAe3VfLw9io+cOEMvvzWxV6HF1QToZ347yf38+Pny0hPiue6pdO4+fxi1szMVTJJRsXMKM1LozQvzetQJIqcPz2Xn33Q1078fP0xHva3E/963QI+etnsoD5X2JNKZhYP/AB4M1ABvGpmjzjn9g467Dpgrv9rDfAj/79B19TRwy83HmfNzFxuW13K5fMLVMdERm1yZopqbEnITEpO4IMXzeQDF85g78lmomhiKTC+9mCUjw2KtywvZGpWKm9aMHnMMwBEREJhdsEkvnLTEj5/w0JePFhLcW50bSIzUdqJt68sYtG0TK5eNFU10EQkoswumMRXb1rCF/ztRCgGRr2YqbQaOOycKwMws98ANwGDP+BvAu51vrV5G8ws28ymOedOBjuYZUVZ7PjS1WoARCRimRmLC6NyZsyY2wNgxigeGxT5k5K5dsnUYJ9WRCRokhPio3WL+gnRTiyYmsmCqZnBPq2ISNCEsp3wIqlUBJQP+rmCN85CGu6YIuANSSUzux243f9jq5kdCCCWfKA2gOPDRXEF4D0RGheKKyD6fwxYoHFND1Ug4zCe9mA0jx1vGzEgEt8DkRgTRGhc+nwJSCTGpP/DwI0lLrUTY2snouk9EA4RGVeEfsZEYkwQoXFF6P8hRGZcY41p2HbCi6TScIuLhy7qGM0xvhuduwu4a0yBmG2OxIKEiiswiiswiiswiiukxtMejKqdGE8bcSaACHytIzEmUFyBisS4IjEmUFyBitS4xiDi24lIfa0VV2AiMa5IjAkUV6AiMa5gx+RFUqkCKBn0czFQNYZjRERkYhtPe5A0iseKiMjEpnZCRCTCeVFx9FVgrpnNNLMk4FbgkSHHPAK833zWAk2hqKckIiKeGk97MJrHiojIxKZ2QkQkwoV9ppJzrtfMPgU8iW97z7udc3vM7GP+++8EHsO3LehhfFuDfjBE4YxrSUQIKa7AKK7AKK7AKK4QGU97MNJjQxRqJL7WkRgTKK5ARWJckRgTKK5ARWpcAZkg7USkvtaKKzCRGFckxgSKK1CRGFdQYzIXbXtUi4iIiIiIiIhIyHmx/E1ERERERERERCY4JZVERERERERERCRgUZlUMrNrzeyAmR02s88Nc//lZtZkZtv9X18a7WNDHNc/DYppt5n1mVmu/75jZrbLf9/mIMd1t5lVm9nuEe43M/u+P+6dZnbeaH+nEMf1Hn88O81svZktH3Sfl6+XV++vc8UV9veXmZWY2bNmts/M9pjZp4c5Juzvr1HGFfb31yjj8uT9FY3UVgQUk9qJ4MYV9vdWJLYR/nNHXDuhNkIGqJ0IKCa1E8GLSX2J1z+v2ongxhX895dzLqq+8BXiOwLMwreV6A5g0ZBjLgf+OJbHhjKuIcffCPxl0M/HgPwQvWaXAucBu0e4/3rgccCAtcDGUL9eo4zrQiDH//11A3FFwOsV9vfXaOLy4v0FTAPO83+fARwc5u8x7O+vUcYV9vfXKOPy5P0VbV+jeb28eK0DPXcY/5bVTgQ3Li/eWxHXRvjPHXHtxChjUhsR5V+jec08+ltWOxHcuLz4W464NmI0cXnxvvKfW+1EcOMK+vsrGmcqrQYOO+fKnHPdwG+Am8Lw2GCf+zbgviA991k5514A6s9yyE3Avc5nA5BtZtMI7et1zricc+udcw3+HzcAxcF67vHEdRaevl5DhOX95Zw76Zzb6v++BdgHFA05LOzvr9HE5cX7a5Sv10hC+v6KQmorAqB2IrhxnUUoP/ciro2AyGwn1EaIn9qJAKidCF5MZxFzfQlQOxHsuM5izK9VNCaVioDyQT9XMPwLuc7MdpjZ42a2OMDHhjIuzCwNuBZ4YNDNDnjKzLaY2e1Bimm0Roo9lK9XoD6ELzs9wMvXC8L//ho1r95fZjYDWAlsHHKXp++vs8Q1WNjfX+eIK2LfXxOI2orgUjsRuIj8O/byfRWJ7YTaiJimdiK41E4EJmL/jtVOjDqmwaK6nUgYT6ARyoa5zQ35eSsw3TnXambXAw8Dc0f52FDGNeBG4GXn3OBM8UXOuSozmww8bWb7/dnkcBgp9lC+XqNmZlfg+0O9eNDNXr5eXry/AhH295eZTcLX8HzGOdc89O5hHhKW99c54ho4Juzvr3PEFenvr4lCbUVwqZ0ITCT/HXvyvorEdkJtRMxTOxFcaidGL9L/jtVOjC6mgWOivp2IxplKFUDJoJ+LgarBBzjnmp1zrf7vHwMSzSx/NI8NZVyD3MqQ6YTOuSr/v9XAQ/imp4XLSLGH8vUaFTNbBvwEuMk5Vzdwu5evl0fvr0CE9f1lZon4PtR+5Zx7cJhDPHl/jSIuT95f54prAry/Jgq1FcGldiIAEf53HPb3VSS2E2ojBLUTwaZ2YpQmwN+x2onRxRQ77YQLQTEtL7/wzb4qA2byWoGpxUOOmQqY//vVwAl8mblzPjaUcfmPy8K3ljV90G3pQMag79cD1wb5dZvByMXibuD1hc82BfI7hTCuUuAwcOGQ271+vcL+/hpNXF68v/y/973Ad89yTNjfX6OMK+zvr1HG5dn7K5q+RvN6efFaj/bc4f5b9p/zbJ97aicCi8uTv+OzxeTh+yri2olRxqQ2Isq/RvOaefF6j/bcHv09n+1zT+3E6GNSX+L1z6l2IrhxBf39FXXL35xzvWb2KeBJfBXM73bO7TGzj/nvvxO4Gfi4mfUCHcCtzveqDvvYMMYF8HbgKedc26CHTwEeMjPw/Wf/2jn3RDDiAjCz+/BVgc83swrg34HEQXE9hq+i/mGgHfjg2X6nMMb1JSAP+KH/tel1zq3C+9cr7O+vUcYF4X9/XQS8D9hlZtv9t30e34esl++v0cTlxftrNHF58v6KNmorAqN2Iuhxhf29FaFtBERmO6E2QtROBEjtRFBjUl/i9dROBDeuoL+/BjJUIiIiIiIiIiIioxaNNZVERERERERERCTElFQSEREREREREZGAKakkIiIiIiIiIiIBU1JJREREREREREQCpqSSiIiIiIiIiIgETEklCSoze9DMDptZyjD3PWlm+8zsdjNzI3z926Djj5nZt87yXF82s9pzxJNgZp8xsx1m1mFmDWb2mJldPOS4X5jZ4WEev9EfV/GQ279mZm1mlnC25/cf+4ERftfDg455zsx+P8xj9ppZ3JDzfcvMjg3zPLPM7C7/69ZlZk1m9rKZfcTMUgcd9+WzvP7vHXTc4Ns7/P93/zL0dzazRDP7BzPbbWbtZlbrf90+d67XRkQkUpjPUf9n3pwh913uv73WzCYNue9TZuYG/XyZ/9hbhhwXb2bbzOwl8+8jfJbP/mHbx3PcP/B1wsxeHfw8g86f4P+sfiaYr52ISDgMcw1bZWYPmNnsEe4fzTXuSNfmQ891ysz+aGbLAoz5nkHn6Dez42b2MzObcpZjKszsPjObMeRcl58l9p8MOi7dzP7DzA74r+FPm9nzZvahIee72MyeNrMa8/VrDvljKR50jDPfNvNDf69J/vs+MOi2Y4Pi6faf77/NLP0c/4/D/j/JxHHODrFIgP4O2Af8K/DvAzea2c3A1cAVwAz/zW8COoY8vjxYgZhZPPCw/3n+F/gLkAl8GHjOzN7vnPu1//D1wHvNbLJzrtr/+BRgJdAOXAj8btDpLwQ2Oed6Awhp6O/bOYrHLATeAdx/toPMlyR7DDgMfA04CEzitd+9FPjioIc0AdcOc6qhibX/BX4PpAJvAb4BJAL/OeiYO4D3+J93I5ANrAVu9B8vIjIRrOO19ulWXv85NyAP+DjwzZFO4px73sx+CXzHzJ5wzrX47/pbYAlwnnPOjfR4v5Hax25/nANuBj475LYufIOGm4APAD8bdN9ngDnAX53j+UVEItXga9hZwH8AfzazxcPcP9hI17iDDb02H3yuGcBXgafNbKFzrj6AmPcDH8T32bwY3zXzIjNb55zrH+aYBf5jHjOzFc657iHnew9QNuS26kHfP4CvD/OfwG5gMnApcD3wUzjTd3gOX1/pQ/janIXAu4HpQEUAv99gvwb+D0gCLsPX/8jD1/8abLT/TzIBKKkkQeWcqzCzLwNfM7NfOOcO+7PT3wHudc49Nyij/apzrjWE4fwtcANwnXPuiUG3/8HMfgPcZWbPO+cq8SWVwJcsetj//Sp8F+cPMyipZL6ZOhcA3w0wnrH8vs8Bn+csSSXzzUL6LbABuME51zPo7j+Zb7bXBUMe1uuc2zCK5z826Lhn/Q32+/F3tswsDV8D+AXn3OBO1oNDR8hFRCLcbUAbvgvw2xg+qfQc8Fkz+z/n3NkGBv4RXwfhK8A/mFkhvs7I951zu0YRy9nai5qBb8xsFcBwn+dm9kPgv83sYedcg5kV4Rvs+W/n3MFRxCAiEokGX8NuMLMTwIv4EiZD7z+bY6M4buhzHQNewZcM+fWIj3qjtkHnWW9mHcAvgPOBV0c4ph24D19/ZD2vt9M5t3u4JzKzucA1wDudc4P7D78dcm3+cXwTAW4ZNNDxNPD9cV7Dnxz0e7zgb3s+YGa3D0qgwej/n2QC0PI3CYXvAQfwZanBdxGbhu8iO5w+DTw7JKE04AtACr7MPMAuoBlf8mjAhfhGel8acvtyIJ03fsCHwn8CK8zsLWc55p1AIfD3QxJKADjnTjrnHglSPDuAkkE/p+ObuXRqmOc910i8iEhE8M9svQV4BLgb3wjycEsc/gfI4Y0jrq/jnDsN/Bvwt/7zfBdfG/PvZ3tckH0B6AX+y//zd/B9Vv/XiI8QEZl4tvj/nRGG59rh/7fkrEed22hiHutzZfv/Pde1eTZQPdz1epCv4XcAyUBBEM8pEUZJJQk6/5KwjwPXmNkX8U23/5xzrmbIofHmq+8w+Csos1vMrATfB/XDI8R4BF8i6VL/z/34lm4NTh6twzca8Qq+xM5AXaILAee/PRBDf9/R/K4bgWfwdQ5GcilQ6ZzbE0gww7z2o5m5WAocHfjB/39aDnzZzP7KzDICiUFEJEK8CZgC/AbfcogefLOVhioH7gX+2cwSz3HOH+G7mP4DvoTV3wcwW3Xc7aNzrhn4B+AjZvav/hg+cY4ZViIiE80M/79nkiijvMaNG+a4c/WNS/3/Hj3rUWOIOcDnOlsbcQDfrNvvmtnVNkydW7+twBVm9kUzmxXoLxCAUqAFeEMd3DH2RSQCKakkIeGcW49vze5X8U3r/MkwhzXiu3Af/HVZkEIo8v97/CzHHB90HPhmHp1vZkn+nweSSrvxrbEeWEJ2IbDXOdcQYEyNvP53/dBZj37N14C1ZnblCPcXMkwtqiEf0vFD7s7jja99jw0pCMhrDW6Gmd2Grw7HV4cc8wEgA9/67UYz22xm/zjodRQRiXS34fuMfsJfJ+Np4NYREjnfwPe5+/6zndA/WPFlfJ2HF4YsQziXRoLQPjrnfoOvnuDXgd84554O9BwiIpFm0PXtPOCH+JIWAxsQjPYa93vDHHf3WZ5rNr46otvxDRaMJeYkM1uBb9ZrOa/NWBp8TKJ/hus38LVJm4Y53fZhYv9rODOg8BFgPvAk0GxmL5hv457Bbdo38S3p/ipwxHxFz+/0v6bjYf7fI83MrgU+BnzNOdc35LjR/j/JBKBsoITSN/EtEfjfEaZRXsobC5EeCHlUI3sZ35K488ysBt+o9QbnXL+ZvYovmfSC/98nx3D+ob/vsdE8yF+H6mV8Syn+PMwhhm/m1Gs3mOUzqO4GvkZr1aCfm4CrhjlX1ZCfv+f/GvAdfydlcHx/8Te0NwBX+s/7TeBtZnbpkPXTIiIRxcySgbcDDw0qhnofvnoXaxkyK9U5d8R8dfk+Z2b3nOP0t+P7fF5iZnnOubpRhhXM9vGbvPa5LCIy0Q0kIwacAN7lnDvpz5mM9hr3m7x+Ex5442yaoc9VB1zgnOsKMObzh5xnN3Czc679LMeU4dvgaDi3AkeG3DZ4JcF9ZvYU8Fb/Oa4G7sI3K/c2/zHN/gHrtfg247kUX7/tvf7r960B/Yav+Qf/14CHnHP/Pcxxo/1/kglASSUJpe4h/w61LYSFuiv9/04/yzHTBx0HvkLX/fiSRtXAgUEdgFeAC81XbLUUXwIqUOP5fQd2gLhwmPsqgUVDbmvktZlV/w5MG3J/r3Nu8yied6DBzcK3jPHvzewZ59xjgw/y7270G+A3/lGQr+Db7eFGxjCaIyISRtfhqy3xmJll+297Dt9GDbcx/FLnr+PrFLxrpJOa2Y34PgNvw1dj8Bv4Ro9HI5jt47naYhGRiWQgGeHwLR+rGjJ4Pdpr3BOjOG7gueLx1VT9FvBrM7sowEHTffhmt/bhK1lRfZZjEoGL8bUzP2b4dmbPSIW6B/j7MD8DfuZfrv1j4INm9g3n3A7/MQPlPF4B8M+iegHfNfzb/afqw/f7DzVw29CdsH+Jb0A6Hd/sqQ+a2cedcz8actxo/59kAtDyN4lKzrlyfDOB3jrc/WY2E9/Wzi8MekwLvk7Chf6vwR2JV/Ath7vI//NYkkpj5px7HN9so38b5u4XgBIzWzjo+F7n3Gb/h/VoR8aHc8J/nj/jW/p2APjm2Wp7+BuogRHxBeN4bhGRcBionXQ/0OD/KsdXWPSdwywfxjm3F3gI3+6cb/g89Nfg+z5wv392578CHzKz1SH5DUREYsfANe4W51xliDeGGXiujc65u4BP4pvZc0uA52n3n2fbCAmlwce84t9R+av42qA144gfAP9GPt/x/zjitblzbju+5d+Dj6kBpg5z+MCA9dDf57T/93jeOfc3+JZgf9V8u4FLlFJSSaLZ94ArzezqYe77T3yj0D8dcvvL+BJKA/WUBmwA8vHVD6p2zh0OerTn9jV8I+rnDbn9d/imin5nFIVjx8zfIH0R36yoGwH8676zhzl8rv/f06GKR0RkvMxsEr5p//fhWyIw+Osf8C2DHmn5wX8Ci3ltNHewL+JrM/7e//NP8e0m+sNRFIIVEZHI9EtgD/AvYXiu/8W3HC+g5/LXQU0d5q7XXZub2eRhHmvAbF5//f4icOMwbddN+PpSr54jpH/F1x6OtpasTEBa/iZeusDMhtaMqHbOlQ36eZ6Z3TzkmDb/zB2ApGHuB3ge33KDq4CHzOxb+JYzZOD7UHsL8D7nXOWQx63Ht3PdVAYllZxzdWZ2EF9Sx6vlXA/ja8iuYFABcudch5ndCvwJ2GBmd+KbUZQCLMVX52jouusEM1s7zHOUD/OaDPYAsB/4J3xbb2cBB83s58Cz+KYJz8fXgFTiG8kXEYlUNwFpwPeccxsH3+GvZfcFfDOZfjH0gc65bWb2OL52YfDjFgCfBb4w8HnqnHNm9gl8F98fxbcz3NmMpn0UEZE3Gu017oxhjnND24Khd5rZ14FfmdmV/pn8IeGcazez7wD/YWbznHMHB929zD8oMliTc24fvuvwR8zsbnz9mnZgBb72bDvwkv/4n/gTRQ/g6yfkAB/Et8xv8Eysr+MbXH/SzH4MNOPbOOKfgG+fa+Mi59wmM3saXwmNHwwq2D3WvohEICWVxEt/Gea2n+IrEjdgoCbFYMd5bSvODHxLFoa6wl/g+m3A3+L7kPxnfLu4bQAuc869NMzjBpa1teJL4Az2CjAP3wd02A1uyIa570UzWwl8Dt8SuWn4irzuwTdj684hD8li+DohX8Q3+j5SDP1m9l/Az81sHb4lef8DXA+8G8jEl0x6EvhP51xTQL+kiEh43QYcGq4T4ZzrMbPf+Y8ZWsx1wH8yJKkE/AA4BHx3yPm2+pP+XzOz+51zb9heeZDRtI8iIvJGo73G/az/a7A+zt0//i2+nT3/meE30AmmO/Albz6Lb0BiwBv6Av5YrsKXIPoJcA2+gfJUfMXM7wb+2zk3UAPph/hWYHwJX7+hEV+/4Rrn3FMDJ3XObTezy/Atx7sb39LwI/hmUH13lL/Hf+Ib8H8nvpnBMMa+iEQmC+0yVBERERERERERiUZa1y8iIiIiIiIiIgHT8jeRcfCvRT5bcrYvxLtSiIiIiIhImPl3Bz3bjsi9I90nEk00U0lkfL4E9Jzl6zLvQhMRERERkRD5M2fvB4jEBNVUEhkHMysECs9yyAHnXEu44hERERERkdAzs/n4Ng0alnNucxjDEfGMkkoiIiIiIiIiIhIwLX8TEREREREREZGAKakkIiIiIiIiIiIBU1JJREREREREREQCpqSSiIiIiIiIiIgETEklEREREREREREJmJJKIiIiIiIiIiISMCWVREREREREREQkYEoqiYiIiIiIiIhIwJRUEhERERERERGRgCmpJCIiIiIiIiIiAVNSSUREREREREREAqakkoiIiIiIiIiIBExJJRERERERERERCZiSSiIiIiIiIiIiEjAllUREREREREREJGBKKomIiIiIiIiISMCUVBIRERERERERkYApqSQiIiIiIiIiIgFTUklERERERERERAKmpJKIiIiIiIiIiARMSSUREREREREREQmYkkoiIiIiIiIiIhIwJZVERERERERERCRgSiqJiIiIiIiIiEjAlFQSEREREREREZGAKakkIiIiIiIiIiIBU1JJREREREREREQCpqSSiIiIiIiIiIgETEklEREREREREREJmJJKIiIiIiIiIiISMCWVREREREREREQkYEoqiYiIiIiIiIhIwJRUEhERERERERGRgCmpJCIiIiIiIiIiAVNSSUREREREREREAqakkoiIiIiIiIiIBExJJRERERERERERCZiSSiIiIiIiIiIiEjAllUREREREREREJGBKKomIiIiIiIiISMASvA4gmPLz892MGTO8DkNEJOJs2bKl1jlX4HUcXlIbISIyMrUTaidERM5mpHYiqpJKM2bMYPPmzV6HISIScczsuNcxeE1thIjIyNROqJ0QETmbkdoJLX8TEREREREREZGAKakkIiIiIiIiIiIBU1JJREREREREREQCpqSSiIiIiIiIiIgETEklEREREREREREJmJJKIiIiIiIiIiISMCWVREREREREREQkYEoqiYiIiIiIiIhIwJRUEhERERERERGRgCmpJCIiIiIiIiIiAVNSSUREREREREREAqakkoiIiIiIiIiIBExJJRERERERERERCZiSSiIiIiIiIiIiEjAllUREREREREREJGBKKomIiIiIiIiISMCUVBIRERERERERkYAleB2AiITPrzee8DqEYb17TanXIYiIiF+kthWg9kJExGuR2kaoffCOZiqJiIiIiIiIiEjAlFQSEREREREREZGAKakkIiIiIiIiIiIBU1JJREREREREREQCpqSSiIiIiIiIiIgETEklEREREREREREJmJJKIiIiIiIiIiISMCWVREREREREREQkYEoqiYiIiIiIiIhIwJRUEhERERERERGRgCmpJCIiIiIiIiIiAQtZUsnMSszsWTPbZ2Z7zOzTwxxjZvZ9MztsZjvN7LxB911rZgf8930uVHGKiIiIiIiIiEjgQjlTqRf4rHNuIbAW+KSZLRpyzHXAXP/X7cCPAMwsHviB//5FwG3DPFZERERERERERDwSsqSSc+6kc26r//sWYB9QNOSwm4B7nc8GINvMpgGrgcPOuTLnXDfwG/+xIiIiIiIiIiISAcJSU8nMZgArgY1D7ioCygf9XOG/baTbRUREREREREQkAoQ8qWRmk4AHgM8455qH3j3MQ9xZbh/u/Leb2WYz21xTUzO+YEVEREREREREZFRCmlQys0R8CaVfOeceHOaQCqBk0M/FQNVZbn8D59xdzrlVzrlVBQUFwQlcRERERERERETOKpS7vxnwU2Cfc+7bIxz2CPB+/y5wa4Em59xJ4FVgrpnNNLMk4Fb/sSIiEuVGuXvo5WbWZGbb/V9f8iJWEREREZFYlhDCc18EvA/YZWbb/bd9HigFcM7dCTwGXA8cBtqBD/rv6zWzTwFPAvHA3c65PSGMVUREIsfA7qFbzSwD2GJmTzvn9g457kXn3Fs8iE9ERERERAhhUsk59xLD10YafIwDPjnCfY/hSzqJiEgM8c9YPen/vsXMBnYPHZpUEhERERERD4Vl9zcREZGxOMvuoQDrzGyHmT1uZovDG5mIiIiIiIRy+ZuIiMiYnWP30K3AdOdcq5ldDzwMzB3mHLcDtwOUlpaGNmARERERkRijmUoiIhJxzrV7qHOu2TnX6v/+MSDRzPKHOU47hIqIiIiIhIiSSiIiElFGs3uomU31H4eZrcbXntWFL0oREREREdHyNxERiTSj2T30ZuDjZtYLdAC3+jd/EBERERGRMFFSSUREIsoodw+9A7gjPBGJiIiIiMhwtPxNREREREREREQCpqSSiIiIiIiIiIgETEklERERERGZMMysxMyeNbN9ZrbHzD49zDFmZt83s8NmttPMzvMiVhGRaKeaSiIiIiIiMpH0Ap91zm01swxgi5k97ZzbO+iY64C5/q81wI/8/4qISBBpppKIiIiIiEwYzrmTzrmt/u9bgH1A0ZDDbgLudT4bgGwzmxbmUEVEop5mKsmo/XrjCa9DGNa715R6HYKIiIiIeMDMZgArgY1D7ioCygf9XOG/7WR4IhMRiQ2aqSQiIiIiIhOOmU0CHgA+45xrHnr3MA9xw5zjdjPbbGaba2pqQhGmiEhUU1JJREREREQmFDNLxJdQ+pVz7sFhDqkASgb9XAxUDT3IOXeXc26Vc25VQUFBaIIVEYliSiqJiIiIiMiEYWYG/BTY55z79giHPQK8378L3FqgyTmnpW8iIkGmmkoiIiIiIjKRXAS8D9hlZtv9t30eKAVwzt0JPAZcDxwG2oEPhj9MEZHop6SSiIiIiIhMGM65lxi+ZtLgYxzwyfBEJCISu7T8TUREREREREREAqakkoiIiIiIiIiIBExJJRERERERERERCZiSSiIiIiIiIiIiEjAllUREREREREREJGBKKomIiIiIiIiISMCUVBIRERERERERkYApqSQiIiIiIiIiIgFTUklERERERERERAKmpJKIiIiIiIiIiARMSSUREREREREREQmYkkoiIiIiIiIiIhIwJZVERERERERERCRgSiqJiIiIiIiIiEjAlFQSEREREREREZGAKakkIiIiIiIiIiIBU1JJREREREREREQCpqSSiIiIiIiIiIgETEklEREREREREREJmJJKIiIiIiIiIiISsIRQndjM7gbeAlQ755YMc/8/Ae8ZFMdCoMA5V29mx4AWoA/odc6tClWcIiIiIiIiIiISuFDOVLoHuHakO51z33TOrXDOrQD+FXjeOVc/6JAr/PcroSQiIiIiIiIiEmFCllRyzr0A1J/zQJ/bgPtCFYuIiIiIiIiIiASX5zWVzCwN34ymBwbd7ICnzGyLmd3uTWQiIiIiIiIiIjKSkNVUCsCNwMtDlr5d5JyrMrPJwNNmtt8/8+kN/Emn2wFKS0tDH62IiIiIiIiIiHg/Uwm4lSFL35xzVf5/q4GHgNUjPdg5d5dzbpVzblVBQUFIAxURERERERERER9Pk0pmlgVcBvxh0G3pZpYx8D1wNbDbmwhFRERERERERGQ4IVv+Zmb3AZcD+WZWAfw7kAjgnLvTf9jbgaecc22DHjoFeMjMBuL7tXPuiVDFKSIiIiIiIiIigQtZUsk5d9sojrkHuGfIbWXA8tBEJSIiIiIiIl749cYTXocwrHevUW1ekbGKhJpKIiIiIiIiIiIywSipJCIiIiIiIiIiAVNSSUREREREREREAqakkoiIiIiIiIiIBExJJRERERERERERCZiSSiIiIiIiIiIiEjAllUREREREREREJGBKKomIiIiIiIiISMCUVBIRERERERERkYApqSQiIiIiIiIiIgFTUklERERERERERAKmpJKIiIiIiIiIiARMSSUREREREREREQmYkkoiIiIiIiIiIhIwJZVERERERERERCRgSiqJiIiIiIiIiEjAlFQSEREREREREZGAKakkIiIiIiIiIiIBU1JJREREREREREQCpqSSiIiIiIiIiIgETEklEREREREREREJmJJKIiIiIiIiIiISMCWVREREREREREQkYEoqiYhIRDGzEjN71sz2mdkeM/v0MMeYmX3fzA6b2U4zO8+LWEVEREREYlmC1wGIiIgM0Qt81jm31cwygC1m9rRzbu+gY64D5vq/1gA/8v8rIiIiIiJhoplKIiISUZxzJ51zW/3ftwD7gKIhh90E3Ot8NgDZZjYtzKGKiIiIiMQ0JZVERCRimdkMYCWwcchdRUD5oJ8reGPiSUREREREQkhJJRERiUhmNgl4APiMc6556N3DPMQNc47bzWyzmW2uqakJRZgiIiIiIjFLSSUREYk4ZpaIL6H0K+fcg8McUgGUDPq5GKgaepBz7i7n3Crn3KqCgoLQBCsiIiIiEqOUVBIRkYhiZgb8FNjnnPv2CIc9ArzfvwvcWqDJOXcybEGKiIiIiIh2fxMRkYhzEfA+YJeZbfff9nmgFMA5dyfwGHA9cBhoBz4Y/jBFRERERGKbkkoiIhJRnHMvMXzNpMHHOOCT4YlIRERERESGo+VvIiIiIiIiIiISMCWVREREREREREQkYEoqiYiIiIiIiIhIwJRUEhERERERERGRgCmpJCIiIiIiIiIiAQtZUsnM7jazajPbPcL9l5tZk5lt9399adB915rZATM7bGafC1WMIiIiIiIiIiIyNqGcqXQPcO05jnnRObfC//VVADOLB34AXAcsAm4zs0UhjFNERERERERERAIUsqSSc+4FoH4MD10NHHbOlTnnuoHfADcFNTgRERERERERERkXr2sqrTOzHWb2uJkt9t9WBJQPOqbCf9uwzOx2M9tsZptrampCGauIiIiIiIiIiPh5mVTaCkx3zi0H/g942H+7DXOsG+kkzrm7nHOrnHOrCgoKgh+liIiIiIiIiIi8gWdJJedcs3Ou1f/9Y0CimeXjm5lUMujQYqDKgxBFRERERERERGQEniWVzGyqmZn/+9X+WOqAV4G5ZjbTzJKAW4FHvIpTRERERERERETeKCFUJzaz+4DLgXwzqwD+HUgEcM7dCdwMfNzMeoEO4FbnnAN6zexTwJNAPHC3c25PqOIUEREREREREZHAhSyp5Jy77Rz33wHcMcJ9jwGPhSIuEREREREREREZP693fxMRERERERERkQlISSUREREREREREQmYkkoiIiIiIiIiIhIwJZVERERERGTCMLO7zazazHaPcP/lZtZkZtv9X18Kd4wiIrEiZIW6RUREREREQuAefBv+3HuWY150zr0lPOGIiMQuzVQSEREREZEJwzn3AlDvdRwiIqKkkoiIiIiIRJ91ZrbDzB43s8VeByMiEq20/E1ERERERKLJVmC6c67VzK4HHgbmDnegmd0O3A5QWloatgBFRKKFZiqJiIiIiEjUcM41O+da/d8/BiSaWf4Ix97lnFvlnFtVUFAQ1jhFRKKBkkoiIiIiIhI1zGyqmZn/+9X4+jx13kYlIhKdtPxNREREREQmDDO7D7gcyDezCuDfgUQA59ydwM3Ax82sF+gAbnXOOY/CFRGJakoqiYiIiIjIhOGcu+0c998B3BGmcEREYpqWv4mIiIiIiIiISMCUVBIRERERERERkYApqSQiIiIiIiIiIgFTUklERERERERERAKmpJKIiIiIiIiIiARMSSUREREREREREQmYkkoiIiIiIiIiIhIwJZVERERERERERCRgCV4HICIiIiJyNnWtXWwoq+NITSvnT8/huiVTMTOvwxIREYl5SiqJiIiISMRaf6SWP+08SVycseVEAz996ShvW1HIN96xjJTEeK/DExERiWlKKomIiIhIRNpR0cgfd55k4dQM3rayiA9fMosfPXeYbz11kN5+xx3vPs/rEEVERGKakkoiIiIiEnEa27t5cGsFM/LSuHV1KYnxccTHGZ9601wAvvXUQa5eXMVblxd6HKmIiEjsUqFuEREREYk4T+w5hXPwzlUlJMa//pL1Y5fNZmVpNl98eDdN7T0eRSgiIiJKKomIiIhIRDlR18bOiiYumVtAdlrSG+5PiI/ja29bSlNHDz9/5Vj4AxQRERFASSURERERiTDPH6olLSmeS+flj3jMosJMrlo4mbtfPkpbV28YoxMREZEBSiqJiIiISMRoaOtm/8lmVs/IJTnh7Lu7ffKKOTS293DfphNhik5EREQGU1JJRERERCLGxqN1mMHqmbnnPHZlaQ7nT8/hN6+W45wLQ3QiIiIymJJKIiIiIhIRevv72Xy8gYXTMoetpTScm88v5nB1KzsqmkIcnYiIiAylpJKIiIiIRIQj1a20d/dxfmnOqB9zw7JpJCfE8cCWihBGJiIiIsNRUklEREREIsLOiiZSEuOYM2XSqB+TmZLItUum8oftlXT19oUwOhERERlKSSURERER8VxPXz97TzazeFoWCXGBXaK+dXkhzZ29bCirD1F0IiISqVo6ezhc3araeh5RUklEPNPT18/R2jYe2lZBbWuX1+GIiIiHDle30tXbz9LirIAfe9GcfFIT43l676kQRCYiIpGopqWLHzx7mP96fD9Xfft5rvjWc/xl/2mvw4o5CV4HICKx6UR9O7/eeJzmzl4AEuKM96+bwb/dsJC4OPM4OhERCbd9J5tJSYxjdsHol74NSEmM59J5+Tyzt5r/uMlhpnZERCSaHa9r4+evHCM+Lo5rF0/l4rn5/HLDcT5y7xb+66+W8s5VJV6HGDM0U0lEwu7Q6Rb+3wtlxMcZ711TyiOfuoibzy/m7peP8tn7d9DXr6mrIiKxxDnHwdMtzJmcQfwYBxauXjSVU82d7KrULnAiItGsrauXX288QXpSAp+4bDaXzivgvWun88DHL+TC2Xn864O72F7e6HWYMSNkSSUzu9vMqs1s9wj3v8fMdvq/1pvZ8kH3HTOzXWa23cw2hypGEQm/5o4efre5nPyMJD55xRwWFWaxrDibb7xjGZ998zwe2lbJPeuPeR2miIiE0anmTpo7e5kfQIHuod60YDJxBs/s1dIHEZFo5ZzjoW2VtPf08e41peSkJ525Lz05gTvefR6TM5L57O+209mjzRvCIZQzle4Brj3L/UeBy5xzy4D/AO4acv8VzrkVzrlVIYpPRDzwwNYKuvv6ue2CUtKSXr8C91NvmsOVCybzzSf3c6y2zaMIRUQk3A6cagFg7pSMMZ8jJz2JFSXZvHi4NlhhiYhIhDlc08rek828eeEUpmWlvuH+rNREvnnzco7UtHHXC2UeRBh7QpZUcs69AIy4BYdzbr1zrsH/4wagOFSxiEhkOFzdyqHqVq5eNJXJmSlvuN/M+PpfLSUpPo4vP7rHgwhFRMQLB0+3UJiVQmZK4rjOc/GcfHaUN9Lc2ROkyEREJFI453hm72myUhO5cHbeiMddPDefqxZO4acvHaVF7UHIRUpNpQ8Bjw/62QFPmdkWM7vdo5hEJIicczy19xTZqYmsmZk74nFTMlP46GWzee5ADTsrGsMXoIiIeKKrt48T9e3jmqU04KI5+fQ72HCkLgiRiYhIJDlU3Up5QwdXzJ9MQvzZUxmfvnIuTR093PvK8TBFF7s8TyqZ2RX4kkr/Mujmi5xz5wHXAZ80s0vP8vjbzWyzmW2uqakJcbQiMlYHTrdQ0dDBlQvP3Qi8f910MlMSuOMvh8MUnYiIeOV4XTv9DmYVpI/7XCtLc0hNjOclLYETEYk6Lx2uJSs1kfOmZ5/z2KXFWbxpwWR+8mIZXb2qrRRKniaVzGwZ8BPgJufcmSEl51yV/99q4CFg9UjncM7d5Zxb5ZxbVVBQEOqQRWSMNpTVkZmSwIqSnHMem5GSyAcvmslTe09zuLo1DNGJiIhXymraiDdjeu74k0pJCXGsmZWrpJKISJSpb+vmcHUrF8zIISFudGmMD1w4g4b2Hp7aow0cQsmzpJKZlQIPAu9zzh0cdHu6mWUMfA9cDQy7g5yITAz1bd0cOt3Kqhm5o94q+r1rp5MQZ/xuc3mIoxMRES8drW2lOCeVpITgXJZeNDufspo2Tjd3BuV8IiLivVeP1RNncP70kctoDHXxnHyKslP57avqT4RSyJJKZnYf8Aow38wqzOxDZvYxM/uY/5AvAXnAD81su5lt9t8+BXjJzHYAm4A/OeeeCFWcIhJ6m47WYwYXzBh9I1CQkcybFkzmwa0V9PT1hzA6ERHxSldPH5WNHcwMwtK3ARf46/a9emzE/WJERGQC6et3bDnewPypmWSljn5Dh7g4410XlPDS4VrK69tDGGFsC+Xub7c556Y55xKdc8XOuZ865+50zt3pv//Dzrkc59wK/9cq/+1lzrnl/q/FzrmvhSpGEQm9fufYdiLwRgDgXReUUNvazZ/3VYcoOhER8dLxen89pfxJQTvn4sJMUhPj2Xys4dwHi4hIxCuraaW1q5fzS89dRmOoW1YVYwYPbq0MQWQCEVCoW0Si29HaNlq6ellRkh3wYy+bV8CUzGQe2FoR/MBERMRzR2vbiDMozU0L2jkT4+NYWZqtmUoiIlFiV2UTyQlxzJ0S+ADEtKxUVk3P4fHdJ0MQmYCSSiISYrsqmkiKj2P+GLaKToiP47ol03j+YA2tXb0hiE5ERLx0or6daVnBq6c0YNWMXPadbKalsyeo5xURkfDq63fsqWpm4bRMEs+xg/RIrlsyjf2nWjha2xbk6ASUVBKREOrrd+yuamLBtIwxdxiuWzKV7t5+nt2vJXAiItGkr99R0dAe1FlKA1bPyKXfwdYTjUE/t4iIhE9ZTSsdPX0sKcwc8zmuXTIVQLOVQkRJJREJmaO1bbR397GsKGvM51g1I5f8SUk8sftUECMTERGvnWrupKfPUZoX/KTSytJs4uOMzVoCJyIyoe052UxSQhxzx7DqYUBhdiorSrJ5fJf6E6GgpJKIhMy+U80kxtu4GoH4OOPqxVN59kA1Hd19QYxOIpmZ3W1m1Wa2e4T7LzezJv/uodvN7EvhjlFExudEnW8ZQihmKqUnJzB/SgbbyxuDfm4REQkP5xwHT7Uwp2DSmJe+Dbh68RR2VTZxurkzSNHJACWVRCQknHMcONXCrPzxNwLXLp5Ke3cfG8rqghSdTAD3ANee45gXB+0g+tUwxCQiQXSivp3MlASyA9wZdLSWl2SzvbyR/n4XkvOLiEhoVbd00djRM6barENdPm8yAM8frBn3ueT1RtXTM7MHzOwGM1MSSkRGpba1m/q2buZPHX8jsHpmLimJcWoEJqCxth/OuRcArVsRiWIn6tspyU3DzEJy/pUl2bR09nK0ToVZI5n6GSIykgOnWgCYF4T+xMJpGUzOSFZ/IgRG++H9I+DdwCEz+4aZLQhhTCISBQ6cagYIyshCSmI862blqRGYmELZfqwzsx1m9riZLQ7ieUUkxNq6emlo76EkJ/hL3wasKM0GYLuKdUc69TNEZFgHT7cwNTOFrCDMaDUzLptXwIsHa+jt6w9CdDJgVEkl59wzzrn3AOcBx4CnzWy9mX3QzEIzZ1kiQmtXL9995iBv/vbzfPPJ/dy36QS1LV1ehyUTwIHTLUzOSCYnPSko57tsXgFHa9s4rhHnCSWE7cdWYLpzbjnwf8DDwx1kZreb2WYz21xTo6SkSKSoauwAfMVTQ2V2wSTSk+LZUdEYsueQ8VM/Q0SG09XTx7G6NuZNmRS0c14+fzLNnb1qF4Js1NNMzSwP+ADwYWAb8D18H/5PhyQy8dyppk7eeecrfO/Ph8iflExxThqHqlu449nD7NQfopxFT18/x+ramReEWUoDLpvvWwf9gmYrTTihaD+cc83OuVb/948BiWaWP8xxdznnVjnnVhUUFIz16UQkyAaSSkUhTCrFxxnLirNVrHsCUD9DRIY6VtdOv4M5k4PXn7h4Tj5xBi8crA3aOQUSRnOQmT0ILAB+AdzonDvpv+u3ZrY5VMGJd9q7e3nPTzZwqqmTn33gAi6fP5lfbzxBU0cP9206wf2bK8hKTWR6XrrXoUoEOl7XTl+/Y3ZB8N4fM/LSKM1N4/mDtbxv3YygnVdCK1Tth5lNBU4755yZrcY3SKJK7iITRGVjB7npSaQmxYf0eZaXZPPTl8ro7OkjJTG0zyVjo36GiAynrKaVeLOg7hCalZbI4sIsbf4TZKOdqfQT59wi59x/DXzQm1kygHNuVciiE8989dG9lNW28f/ev4rL/TNEALJSE/nrdTPITkvkVxtP0NrV62GUEqnKalqJM5gRxKSjmXHh7Dw2Ha2jTzv5TCRjaj/M7D7gFWC+mVWY2YfM7GNm9jH/ITcDu81sB/B94FbnnN4YIhNEVVMnhVkpIX+epUVZ9PQ5Dp1uDflzyZipnyEib1BW20ZJbhpJCcGt4b92Vi7bTjTS2dMX1PPGstH+D/3nMLe9EsxAJHI8d6Ca37xazscvm82Fc96wmoTUpHjes3Y67d29PL33lAcRSqQ7UtNKcU4ayUEeFV47K4/mzl72nWwO6nklpMbUfjjnbnPOTXPOJTrnip1zP3XO3emcu9N//x3OucXOueXOubXOufVBj1xEQqKju4/6tu6QLn0bsLgwE4A9VU0hfy4ZM/UzROR1Orr7qGrsYFYQVz0MWDc7j+6+fraeaAj6uWPVWZe/+ZcXFAGpZrYSGNjzNRMI3XYd4pn+fsd/P3GA0tw0PnPVvBGPm5qZwrpZeaw/UseamXkhLbQpE0tnTx8VDR1cPj/49WvWzsoDYENZHUuKsoJ+fgketR8iMpLKgSLdOaG/dijNTWNScgJ7qjQYEWnUTojISI7VteEgJEmlVTNyiTPYcKSOC2e/cQKFBO5cNZWuwVc0rxj49qDbW4DPhygm8dCjO6vYd7KZ79264pxTDd+0YArbyht5au8pPnDhzDBFKJHuWO1AIxC8nRoGTM1KYWZ+OhvK6vjwJbOCfn4JKrUfIjKsM0W6s0KfVIqLMxZNy2SvZrhGIrUTIjKssppWEuKM0pzg55czUxJZWpTFhrL6oJ87Vp01qeSc+znwczN7h3PugTDFJB5xzvH9Px9iwdQMblxWeM7jU5PiuXhOPk/tPc2p5k6mZoa+NoJEvmN1bUEvqjfY2ll5/HFnFX39jvg4O/cDxBNqP0RkJJWNHWSnJZKWPKr9YsZtUWEmv9tcrnYjwqidEJGRHK9vpyQ3jYT44NZTGrB2Vh53v3xUmzgEyVn/l8zsvf5vZ5jZPwz9CkN8EkYvH67jSE0bt186i7hRXnStnpFLYrzx8mFtyyg+x+raKcpJJTFkjUAuLaqrFPHUfojISKoaOygMwyylAYsLM2nv7uNYXVvYnlPOTe2EiAynu7efqsYOpodogBrg/Ok59PQ5dleq3l4wnKvXN7CIcRKQMcyXRJF7XzlGXnoS1y+dNurHpCUncF5pDtvLG2np7AlhdDIR9PT1U9nQwfS80DUCq2bkArD5mKasRji1HyLyBp09fdS1dVMUhnpKAxadKdatwYgIo3ZCRN6gvKGdfkdI+xPnT88BYPNxFesOhnMtf/ux/9+vhCcc8UplYwfP7DvNxy+fHfAUwHWz89h4tJ7t5Y1cMjf4xZll4qho6KDPOWbkBb+o3oCi7FSmZaWw5UQjH7goZE8j46T2Q0SGU9Xkr6cUxg0+5k7OIDHe2FPVxFuXn3t5v4SH2gkRGc7xunYASnND15/Im5TMzPx0Nh9rgMtC9jQxY1TrU8zsf8ws08wSzezPZlY7aMqqRIGHtlbQ7+C21aUBP3ZyRgolOalsO9EY/MBkQjnuX1oQyumq4Btd2KKZShOC2g8RGayqwb/zWxiTSkkJccybksFezVSKSGonRGSw43VtTMlMJjUptLWOzp+ew9YTDTjnQvo8sWC0RU+uds41A28BKoB5wD+FLCoJK+ccD2+vYvWMXIrHWGF/ZWkOp5o7z+zoIrHpeF07kzOSQ1589fzpOVQ16f02Qaj9EJEzKhs7yEpNZFKYinQPWFyYyZ6qZnUeIpPaCREBoN85TtS3Mz2Es5QGnD89h/q2bspqVW9vvEabVEr0/3s9cJ9zTlMEosieqmYOV7dy08qxTwlfVpxFfJyx7YTWpcYq528EQrXr22CrpvvrKmkd9ESg9kNEzqhq7AzrLKUBiwuzqG/r5nRzV9ifW85J7YSIAFDT0kVXb3+Y+hO+ukpb1J8Yt9EmlR41s/3AKuDPZlYAdIYuLAmnR3ZUkRhvXL9k9AW6h0pLSmD+lAx2VTbRr1HAmFTf1k1HTx8lY5ztFoiF0zJITYzXEriJQe2HiAC+HX1qW7uYlpUS9ud+rVi3dvqJQGonRATw1WcFKA7DZg6zCyaRkZzAjvLGkD9XtBtVUsk59zlgHbDKOdcDtAE3hTIwCQ/nHH/aeZJL5haQk540rnMtLsykubOXygYtSYpFZxqB3NA3AgnxcSwrzmJ7hToHkU7th4gMqG7pxAFTM8OfVFo4LRMz7QAXidROiMiAioZ2khPiyM9IDvlzxcUZS4qy2FWp/sR4BbKgfSEww8wGP+beIMcjYbbvZAuVjR383ZVzxn2uBVMzifNfsJWEYcqiRJaKhnYS443JGeHpLCwvyeael4/R3dtPUsJoJ12KR9R+iAinm32TT6Z6MFNpUnICM/LSNVMpcqmdEBEqGzsozE4lziwsz7esJIu7XzpKV28fyQmhLQwezUaVVDKzXwCzge1An/9mhz7sJ7yn957GDN60YMq4z5WaFM+sgknsqWrimsVTsDB9GEhkKG/ooDArlfi4MDUCxVl09/Vz8HQLS4qywvKcEji1HyIy4FRTJ4nxRu44Z0aP1aLCTC1ziEBqJ0QEoLe/n5NNnVw4Oy9sz7msKJuePsf+ky0sL8kO2/NGm9HOVFoFLHLaMiPqPL3vFOeV5lAQpCmGi6Zl8siOKqpbupjiwfR28UZfv+NkUwerZ+SG7TmXF2cDsKOiUUmlyKb2Q0QAON3cxeSMlLCNQA+1uDCTP+08SVNHD1mpied+gISL2gkR4VRTJ339bsy7kY/FsmJfH2JnRaOSSuMw2jUju4GpoQxEwu9kUwe7K5t586Lxz1IasGBqBgAHT7cE7ZwS+U43d9LT5ygO47LH4pxUctIS2VmupQwRTu2HiABwqrnTk3pKAwauUQ7pGiXSqJ0Qkdfqs4Zxh9DinFRy05PYqTqt4zLamUr5wF4z2wSc2YvVOffWkEQlYfHs/hoArlwwOWjnzE5LYnJGMgdPt3DJ3IKgnVcimxeNgJmxtDibHRWNYXtOGRO1HyJCa1cvrV29TPGgntKAeVN8SaUDp1tYFcaZtXJOaiei3IFTLdyz/iibjtbT1w9zJk/ikrn5JMarJqa8prKhg7SkeLLTwjeT1MxYVpylpNI4jTap9OVQBiHeePFQDYVZKcyZPCmo550/JYP1ZXUqeBZDKhraSU2MD3udjOXFWfzwuVo6uvtITdJ7LUJ92esAJPR+vfGE1yGM6N1rSr0OQfAtawBvdn4bUJSdSnpSPAdPaaZShPmy1wFI6Pz21RN86Q97SIyPY/XMXA6ebuGZfafZWdHIe9ZMD1oJDpn4yhvaKc5JDXtd3mVFWbxwsIb27l7SkgLZx0wGjCo97Jx7HjgGJPq/fxXYGsK4JMR6+/p5+XAtl8wtCPof7twpGfT1O47WtAX1vBK5Kho6KMn1oBEozqav37H3pEYXIpXaDxGB13Z+m5LpXQfSzJg3NYMDWv4WUdRORK8/bK/kXx7YxeqZuTz3T5dz9wcu4BOXz+EDF86grauXe9YfpbWr1+swJQJ09fZR09IV1npKA5YVZ9PvfDuYy9iMKqlkZh8Bfg/82H9TEfBwiGKSMNhR0URzZy+XzMsP+rln5KWRFB+ni7YY0d3bz+nmToqyvWgEfMX1dqiuUsRS+yEi4KunlJ6cQEaKtwWy50/J4MCpFlQTOnKonYhOuyqa+Off72T1jFx++tcXkD/ptYTyvCkZ/PWFM2jt6uWXG47T16+/x1hX1diJw1fjKNyWlQz0JxrD/tzRYrQLWT8JXAQ0AzjnDgHBK8QjYffioRrM4KLZwU8qJcTHMTM/nSM1rUE/t0SeqsYOHFDiQSMwJTOFKZnJ7FRdpUim9kNEON3cyVQPZykNmDclg4b2Hmpbu70ORV6jdiLK9Pb1888P7CQnLYkfvfc8khLe2OUszknj7SuLOVHfzoayOg+ilEhS0dAO4MlMpckZKUzLSlFdpXEYbVKpyzl3pvU1swRAKeUJ7MVDtSwryiInRDVwZhekU9vaTVNHT0jOL5FjoBEo8iCpBL4pqzsr1QhEMLUfIjGu3zl/Usm7ekoD5muX2kikdiLK/HrTCfadbOaLb1lE3qSRk8nLi7OYO3kSz+w7TbP6DDGtoqGD7NREJiV7U9NoaVEWu9SfGLPRJpWeN7PPA6lm9mbgfuDR0IUlodTe3cuO8kYunBP8WUoDZhX4in+XabZS1Cv3NwJeLWlYXpxFWU0bzZ26GIlQaj9EYlxDWzc9fY4pEZBUOrMDnIp1RxK1E1GkubOHbz15gIvm5HH90qlnPdbMeOvyQvr6HX/efzpMEUokqmzs8GyAGmB5STZHa9s0IWKMRptU+hxQA+wCPgo8BvxbqIKS0NpyvIHefsfaWXkhe46pWSmkJsZTVqti3dHO60ZgWXE2ALs1ZTVSqf0QiXGn/EW6p2Z5n1QqyEgmLz1JSaXIonYiivxyw3GaO3v51+sWjmoDl7xJyZw/PYetxxvVoY9R7d291Ld1U5ztZX/CV1dpl/oTYzKq+WXOuX4zexh42DlXE9qQJNQ2lNURH2esmp4TsueIM2NWQbpmKkW5zp4+6tu6Q/peOpelRf7iehVNIZ19J2Oj9kNETjV3YvjqVkSCeVO0A1wkUTsRPTp7+rj7paNcOq+AJf7rs9G4dG4Brx6r58VDNbxlWWEII5RIdLLJN/BQ6GVSqSgbgJ2VjVw8V/2JQJ11ppL5fNnMaoH9wAEzqzGzL53rxGZ2t5lVm9nus5z7+2Z22Mx2mtl5g+671swO+O/7XKC/lJzdhrJ6lhVnkR7iNauz8tNpaO+hvk3FMKPVQCMwzcPR55z0JEpz09hV2ehZDPJG42k/RCS6nG7qJDc9adhivV6YPzWDQ6db6NeOU55SOxF97t9SQW1rN5+4fHZAj8tJT2JFSTavHquno7svRNFJpBroT3g5mzUrLZGi7FT2ndSAw1icq3X/DL7dGC5wzuU553KBNcBF/7+9+45v9KoS//+5Kpa75d49vXl6STIppEBIIQmhBEjoNRvqwpYvLPuDZRd2YdllaQsEFliWlhB6EpJMCqRnMpme8fTq3i3JTZIl3d8fkiaO45mxx5KeovN+vfyaseqx/UhXz7n3nqOU+tQ57vsT4LqzXH89sCTxdTvwPQCllBP4TuL6ZuA2pVTzOZ5LzFCynlI6t74lLaiI11U6NSBb4Oyqyz8OQK2BMwsAK+uKZRAwn09y/uOHEMJGugMhU9RTSlpaXcRoOEqHb9zoULLdJ5Fxwja01vxi6ylW1hVz0YKyWd//4kUVTEQ1u9qG0hCdMLMu3zhFuS7D6rMmragt5kBXwNAYrOpcSaV3A7dprU8kL9BaHwfembjujLTWTwKDZ7nJzcBPddxWwKuUqgUuBI5qrY8nOkHcnbitSIFkPaXzebOfrapiDx6Xg1MDY2l/LmGMLl+QAo+LIoM6NSQ11xZzcmCUkVDE0DjEy5z3+CGEsI9INMbAiLmSSstq4pNe0gHOcDJO2Miedj8Hu4e57cKmGdVSmqrem0e9N49tJwbRWlYRZpMuf9DQXQ9JzXXFHO8bkdVy5+FcSSW31rp/6oWJ/c5zTSXWA22Tvm9PXHamy6ellLpdKbVdKbW9r0+2YZ/LjlNDKAUbM1ADx6EUTWX5tA5KUsmuuvzj1JXknteHh1RqritGazjULbMLJpLO8UMIYRH9I2E08Ykms1iS7AAnSSWjyThhI3dvayXP7eTmdedfE+nCBWX0Dofk3CGLRKIxeoeD1JYYu+sB4pPUMS1jw/k4V1LpbMVw5looZ7qzUH2Wy6eltf6B1nqT1npTZWXlHEOyv52tPpZVF2VseeG88nx6AkHJ+NpQJBajJxAyxyBQVwzA/k5JKplIOscPIYRF9A7Ha2VUFZknqVSc66auJJfD0gHOaDJO2MRYOMK9ezq5cU3tnM4x1jSUkONysOOUbIHLFr3DIWLa2PqsSSvlfOK8nWvPylql1HS/VQXM9S/fDjRO+r4B6ARyznC5mKNYTLPr1BA3zWEGYbaaygrQQNvQGEsTM4PCHvqGQ0S1ptZr/CBQU5xLab6b/bIP2kzSOX4IISyidziEAioKzZNUAlhaU8ShHulQazAZJ2zisQO9jIWjvGlDw5wex+Ny0lxbTEtngNevi+FymKO4v0ifZH3WOhNMUjeU5lHkcbG/y290KJZz1leq1tqptS6e5qtIaz3XpS73Au9OdH7YDPi11l3AC8ASpdQCpVQOcGvitmKOjvSOMByKsKEpc+3fG8vyUCB1lWyo05do/2mCQUApRXNdscwsmEiaxw8hhEX0DYcoLcjB7TTXyeGy6iKO9Y4QicaMDiVryThhH/fv7aSyyMOFKajZuqa+hPGJKEcl6ZsVOv1BcpwOygpzjA4FpVSiWLesYp2ttI3wSqm7gOeAZUqpdqXUB5RSdyil7kjc5AHgOHAU+B/gIwBa6wjwMWALcAC4R2vdkq44s8nO1vhS0kzUU0ryuJzUluRyalA6wNlNl38ct1NRboJBAOL7oA92D8sJghBCmEjvcNBUW9+SllQXEY7GOCW1W4SYk+HgBH851McNq2txOuZeY3NxdSF5bid7O2S1SDbo8gWpKcnFYXB91qTmungHuFhMisXPRtpaNmmtbzvH9Rr46Bmue4B40kmk0I5TQ5QV5DC/PD+jz9tUXsDOU0NEYzolg40wh05fkJpicw0CoUiME/2jp4uwCiGEME40pukfCbPMhO/Ji6viHeCO9o6wqLLQ4GjEbCmlfgzcCPRqrVdNc70Cvgm8DhgD3qu13pnZKLPDI/t7CEdi3LS2NiWP53I4WFVfzJ42PxPRmOlWOYrU0VrT5R9nXaPX6FBOa64tZiwc5dTgGAsqCowOxzLkVZpFdrYOsaHJm/FOXfPK8wlHY3QHghl9XpE+yUGgzmv81rekFbWJ4npSV0kIIUxhaDRMNKapKjJfeZzJSSVhST8BrjvL9dcDSxJftwPfy0BMWWlLSzc1xbmsb0zdToiVdSWEozGO9cnr086GxiYIRWKmaPqTJM1/zo8klbLE0GiY432jbMjg1rekeWXxlVGtA7IFzi7MOAgsqiwkx+mQQUAIIUyidzgEQKUJt78VelzUluRKUsmitNZPAoNnucnNwE913FbAq5RKzVIacVpwIsqTh/u5urkKRwp3IyysKMDjckhtG5vrThTpNkPnt6TFVYW4HEqKdc+SJJWyxK62eD2lTBbpTvLm51CS55a6BTbS6Ut0ajBB57ckt9PB0ppCWakkhBAm0TscX6FsxqQSxE8eJKlkW/VA26Tv2xOXiRR67tgA4xNRrl5RndLHdTkdLKku4mBXgJiW2jZ21ekPooDqYvOcT+S6nSyqLJSE5ixJUilL7Dg1hNOhWNvgNeT5m8rypQOcjXSZcBCA+D7o/Z0BtHwAEUIIw/UNhyjJc5PrdhodyrSSSSUpyGpL0y2bmfYPrZS6XSm1XSm1va+vL81h2csjB3ooyHFy8aLylD92c20Rw6EIHUPjKX9sYQ5d/iAVhR5yXOZKSUhH6dkz119QpM3OUz5W1hWTl2PMB7t55fn4xyfwjYUNeX6RWl3+cSqLPKYrnthcW8zAaPj0lgshhBDG6R0OmXaVEsCSqiLGJ6J0+uWk1YbagcZJ3zcAndPdUGv9A631Jq31psrKyowEZwdaax470MMVyyrxuFJ/frGsuhiHggPdcnJvV13+cWpNtOshqbm2mO5AkIEROZ+YKXOdEYq0iERj7G7zGbL1LakpUVepXWYbbKHLHzTV/uek5roSQIrrCSGE0WJa0zccosrESaVkse4jsgXOju4F3q3iNgN+rXWX0UHZyYGuYXoCIa5aVpWWx8/LcdJYls+RHnl92tF4OIpvbIJak+16gJeKdcsWuJmTpFIWONg9zPhE1JAi3Uk1xbk4lZKkkg2Mh6P4xyeoMeEgsLw23rZa6ioJIYSxAuMThKMxk69UiieVjklSyXKUUncBzwHLlFLtSqkPKKXuUErdkbjJA8Bx4CjwP8BHDArVtp48Et8qePnS9K3uWlJVRIdvnJFQJG3PIYyR7ApeY8JJ6mRH6QNyPjFjLqMDEOm3p90HwPpGr2ExuJwOakpyafdJXSWr60kMAtUmHASKc900luXJSiUhhDBYchtyVZH5xoqk0oIcygtyZCWEBWmtbzvH9Rr4aIbCyUpPHu5jeU1RWutrLq0u5NEDPRztHWZdo3GT4yL1Tp9PmHCSuqwgh8oiDwe7ZaXSTMlKpSzwYruf0nw3DaXGtn+vL82jY2hcujhY3OmZBRMOAgDLa4o5KPvvhRDCUC8llcy7UgkSxbr7JKkkxGyMhiJsPzmU1lVKAHXePPJznJL4taHuQJBct4OSPLfRoUxreU0Rh3rkfGKmJKmUBfa0+1nd4EWp6RphZE6DN49QJMbAiBTrtrIeCwwCJwfGCE5EjQ5FCCGyVm8gSEGOkwKPuRfFL64q5EjPsHQNFWIWth4fIByNcfmS9CaVHEqxpKqQw70j8hq1mZ5AkOriXMPPT89kWXURR3pGiEp30BmRpJLNBSeiHO4ZZk19idGh0FCaLNYtW+CsrDsQpLrIxINATRHRmOao1MgQQgjD9A2HqDTx1rekxVWFBIIR+qTLjxAz9vTRfjwuB5vmp39L2uKqQkZDEensayNa69NJJbNaVlNEKBLj5MCo0aFYgiSVbG5/V4BoTLO6wfikUrwFvaLDJ8W6rer0IGDCekpJy2vixboPyT5oIYQwhNaaXpN3fktaUhUfM2QiQoiZ23p8kI3zSsl1O9P+XAsq4gX1j/fLyb1d+McnCE7ETFtKA+LlNEDOJ2ZKkko2t7fNB8DaBq+hcQA4HYq6kjzpAGdhVhgE5pcXkONycKhHBgEhhDDCSCjC+ESUqmLzJ5UWJzrASVJJiJnxjYU52B1g88LyjDxfab4bb56bE1L7zDbMXKQ7aUl1IUpJUmmmJKlkc3s7/FQWeag2yQe7htI8uvzjsj/VosxepBvinQYXVxZKxwYhhDBIX2KbSqUFVipVF3so8rgkqSTEDG07MYjWZCyppJRiQUUBx/tHpa6STfQE4mOEmc8nct1O5pcXSFJphiSpZHMvtvtZU19imvo39aX5TEQ1vcNBo0MR56HHb/6ZBUh0bJAOcEIIYYiXOr+Ze6yA+AnroqpC6S4lxAxtPT6Ix+VgbWPmSmssrCxkLByVuko20R0IUpzrIi8n/dsn52JZdZHsfJghSSrZ2GgowtG+EVPUU0pqKM0DkC1wFtUdCFKS5zb/IFBTRE8ghG9MOg0KIUSm9Q6H8LgcFOeau/Nb0pKqQo7K1hohZmTr8QE2NJXicWXus+DCigJA6irZRU8gSI2J67MmLasp4uTAKONh6Sh9LpJUsrF9HX60hjUmSiqVF+SQ63ZIUsmiegIhUy9VTVqWKNYtW+CEECLzeoeDVBV5TLNK+lwWVxXSNxzCPzZhdChCmJp/bIIDGaynlFRakIM3381xSf5aXjQWb+Rg9l0PEN/5oDUc6ZXziXORpJKNvdjhB2B1vdfYQCZRSlHvzaNjaMzoUMQsRWOaPssMAtKxQQghjNI3HKLSAlvfkpZUJ4p198mYIcTZbDuZrKdUlvHnXlhRwIn+UWJSV8nSBkZCRGPaEucTMkk9c5JUsrG97X7qSnJNVyizoTSf7kCQiWjM6FDELPSNhIhqTU2JuY6n6VQXeyjJc8sgIIQQGTYejjIcjFBlss8eZ7O4Mn7iIHWVhDi7rccHyHE5WNvozfhzL6iQukp2YIWmP0nzygvwuBwclvOJc5Kkko3tbfeZqp5SUr03j5iGbr8U67YSqxTphviKuGVSrFsIITKuL9GIw0pJpfrSPHLdDukAJ8Q5xOspecl1Z762ZrKu0gnZAmdpPYEgDmWN7qBOh2JJdaEU654BSSrZlH9sgpMDY6xp8BodyivUe+PFujv9UlfJSrotNAhAfB/04Z4RaT8rhBAZlFxFYJWxAuInDgsrpFi3EGfjH59gf1fm6yklna6rJMW6La07EKK8wIPbaY00xLLqYtn5MAPW+GuKWdvXmaynZL6VSt58N3luJ50+SSpZSU8gSEWhB5fDGm8by2qKGAlFpCi8EEJkUO9wCJdDUVqQY3Qos7K4qlC2vwlxFi+cSNZTMiapBLCwopAT/aMyYWhhPYEg1cXWmXRYXlNE33CIwVHpKH021jg7FLO2L1Gke5UJk0pKKWq9uXT6ZPublXRbpP1n0vJEcT0p1i2EEJkTL9LtwWGRzm9JS6oK6fCNMxaOGB2KEKa07eQgOU4H6wyop5Q0vzyfsXCU/hE5wbeicCTG0GiYagudT7xUrFtKapyNJJVsqqUzQG1JLmUmnSmsK8mjJxAkGpOZBisITkTxjU1Yoqhe0tLqRFJJ9kELIUTG9A4HLbX1LWlxVbwD3LFe2VojxHR2nBpidUOJIfWUkprK8gFoHZTXqRX1BIJorFGkO0kmqWdGkko2tb8rwMq6YqPDOKM6bx6RmKZ3WFYrWUFPwDpFupOKct00lObJPmghhMiQcCSGb2zCUkW6k5ZUx5NKR/tkzBBiqlAkyovtfjbOKzU0jooiD3luJ6cGxgyNQ5yfHgt1fkuqLPJQmu/msExSn5UklWxoPBzleN8IzXXm2/qWVJdY9tglW+AswUrtPydbLh3ghBAiY/pGQmigsshaYwXEW0e7HErqKgkxjZbOAOFojA1NXkPjcChFU1k+rYOSVLKinkAQt9NaNfeSHaVlkvrsJKlkQwe6A8Q0pl6pVFHkwe1U0gHOInoCQTwuB958t9GhzMqymiKO940SjsSMDkUIIWyvL7H62IorldxOB/MrCjjaK0klIabaeWoIgA1Nxq5UAmgqz6d3OMR4OGp0KGKWugNBqopyLVdzb1l1EYe7h4lJ2ZYzkqSSDbV0xldmmDmp5FCK2pI86QBnEd3+ENXFuSirDQI1xURimmPSJloIIdKuNxDCoaC80Dqz0JMtriyUpJIQ09hxaojGsjyqTLBi/aW6SrJayWq6AyHL7XqA+PnEaDhKh5y3npEklWxof6efkjw39d48o0M5q9qSXLr8QWLSFtTUtNaJ9p/WGwSkuJ4QQmRO73CI8gIPLoc1P14uqS7k1OAYoYisgBAiSWvNztYhU6xSAmgozcOhpFi31YyEIoyGIpbq/Jb0Ugc4OZ84E2uO+uKsWjrjRbrNvqqkzptHKNFaUphXIBhhfCJKTbH1tjMsqCjA7VQyCAghRAb0Docs2fktaXFVIdGY5mS/rIAQIqnDN05PIGR4ke4kj8tJTUkup2SlkqW81PTHemPEstOT1FKn9UwkqWQzE9EYB7uHTb31LakusZJKlhKa2+lBwIIzC26ng0WVhTIIWJBS6sdKqV6l1L4zXK+UUt9SSh1VSu1VSm3IdIxCiJdEYjEGR0NUWfCEIWlxVaIDnGyBE+K0HSaqp5TUVFZA++A4UalxYxndfms2/QEo9LhoKM3jkDRyOCNJKtnMsb4RwpEYK03c+S2pusiDQ0GXXzrAmZmVBwFIdoCTlUoW9BPgurNcfz2wJPF1O/C9DMQkhDiDgZEwMW3NIt1JiyoLUQqO9MqYIUTSrlYf+TnO0yUFzGBeWT7haOz0xKcwv55AkPwcJ4Uel9GhnBfpKH12klSymZYO8xfpTnI5HVQX50qxbpPrCQQpznWRn2PNQWBZTTGd/iD+sQmjQxGzoLV+Ehg8y01uBn6q47YCXqVUbWaiE0JM1TscAqCyyJoTEAC5bieNpfkckZVKQpy249QQaxu8uJzmOW1sKpdi3VbTnajPavbyLGciHaXPzjzvDiIlWjoD5LodLKwsNDqUGalLdIDTUqzbtLotWqQ76XSx7h6ZebaZeqBt0vfticteRil1u1Jqu1Jqe19fX8aCEyLb9A0HUUBloXVXKgEsqSrkmCSVhABgLBxhf1fANPWUkrx5bopzXZJUsoiY1vRatPNb0tLqIukofRaSVLKZlk4/y2uKcTqskQWu9eYyGo4SCEaMDkVMIxrT9A1bexCQ4nq2Nd2b3Cuy01rrH2itN2mtN1VWVmYgLCGyU+9wCG++mxyXtT9aLq4q5HjfKJGozEYLsbfdTzSmTZdUUkrRVJbPqQHpAGcFvrEJwtGYpc8nltfEdwFJSY3pWXvkFy+jtWZ/V8ASW9+S6krixbq7ZAucKQ2MhIjEtCWLdCfVluRSlOuSDnD20w40Tvq+Aeg0KBYhsl7fcIgqC299S1pcVUg4GqNtSD6XCJEs0r2+yWtsINNoLMtnaGyC4aCUNzC7ZH1WK59PLKyUjtJnk9akklLqOqXUoUR3ns9Mc/3fK6V2J772KaWiSqmyxHUnlVIvJq7bns447aJtcJzhYMQSRbqTaktyUUCHXz68mVF3wNpFuiE+myXFum3pXuDdiS5wmwG/1rrL6KCEyEYxHV/VWmnhIt1JyQ5wR2TLtBDsafOxsKIAb36O0aG8QmNpvK5SuySATS95PlFt4TFCOkqfXdqSSkopJ/Ad4h16moHblFLNk2+jtf4PrfU6rfU64B+AJ7TWkwuzXpW4flO64rSTlk4/YI0i3Uket5Pywhy6fNK9wYx6AkEcCsufKCyrKeJQz7DU7rIQpdRdwHPAMqVUu1LqA0qpO5RSdyRu8gBwHDgK/A/wEYNCFSLrDY2GicS0pTu/JSWTSkelboYQ7Gn3sbbRa3QY06rz5uFQ0D4kdZXMricQpDTfjcftNDqUOVlWU8ThHhkbppPOdk4XAke11scBlFJ3E+/Ws/8Mt78NuCuN8dheS2cAp0OdriFjFbUlebTJgGBK3YEQ5QUe3Cbq+HE+ltcU8/NgK53+IPXePKPDETOgtb7tHNdr4KMZCkcIcRbJzm92SCoV5bqpKc7lqJw4iCzX7Q/SEwixtsGcOyByXPEu0rJSyfx6LN70J2lZTRF/3N1JIDhBca7b6HBMJZ1nijPqzAOglMoHrgN+O+liDTyslNqhlLo9bVHaSEunn8WVheRaLAtc783DNzbBWEiKdZtNTyBo6f3PSculWLcQQqRNXyKpVGmDmkoAS6oLZaWSyHq723wApl2pBNBQGp+YlpXo5hWJxugfsXbTn6Rl1fHzicNSUuMV0plUmlFnnoSbgGembH27VGu9gfj2uY8qpS6f9kmkXfRpLZ3WKtKdVOuNv8l0+mULnJmEIlEGR8O2GASWJpJKUlxPCCFSr3c4SHGui7wca01qncmiykKO9o4Qi8mJqshee9p9uJ2KFbXmPbdoLM0nOBFjYCRsdCjiDPpGQsQ0tlmpBHI+MZ10JpVm05nnVqZsfdNadyb+7QV+T3w73StIu+i4vuEQvcMhmi2YVDrdAU6KdZtKbyA+82yHpFJxrpt6bx4Hu2QQEEKIVOu1See3pCXVhYyFo3TK5xKRxfa0+VhRW2zqHRANiWLdUkbDvOzQ+S2p3ptHkcclzX+mkc6k0gvAEqXUAqVUDvHE0b1Tb6SUKgGuAP446bICpVRR8v/ANcC+NMZqeS8V6TbnvuezKfC4KMlz0+GTD29mkhwEamwwCECiWLcMAkIIkVJaa3oDISqLrV9PKWlJVXw2+mivbIET2SkW0+xt97O2wWt0KGdVVewhx+mQukom1hMI4lSKykLrjxFKKZbK+cS00pZU0lpHgI8BW4ADwD1a65Yp3XsA3gg8rLUenXRZNfC0UmoPsA34k9b6oXTFagctnfFaMVZcqQRQV5IrHeBMpjsQJMfpwJtvj0J0y2qKONY3QjgSMzoUIYSwDd/4BOFozBZFupNOd4CTpJLIUsf7RxgJRUxdTwnAoRT1pdLwx8y6A0Eqizw4HdNVxrEe6Sg9vXR2f0Nr/QDxts+TL7tzyvc/AX4y5bLjwNp0xmY3+zsDNJblUZJnzQRArTePg93DhCMxclzW7jRmF92BINXFHhzKHoPA8poiIjHN8f4RltdYM/kqhBBmk9wqbaftb2UFOZQX5EhSSWSt3W3xHRDrGs2/A6KhNI9njw0QicZwWbxbsR31BELML883OoyUWV5TxC+fb6UnELLNbo5UkFeeTbR0+llZa/43/jOpK8lDA91Sv8AUtNa2af+ZtOx0BzhZsiqEEKnSO5yol2GjlUoAi6oKOSJJJZGl9rT5KPS4WFhRaHQo59RQmk80pukOyI4HsxkPR/GPT9iiPmvS0upksW7pKD2ZJJVsYDg4wcmBMUt2fkuqkw5wpjIcijAWjtoqA7+wohCXQ0nHBiGESKHe4RCFHhf5nrQufs+4JVXxDnCyxUFkoz3tPtY0lOCwwJalxtJ4w582qatkOslEnx2KdCctl0nqaUlSyQYOJDparay3blKpJM9NnttJpxTrNoWeZJFuG80s5LgcLKoslEFACCFSqDcQtFU9paTFVYX4xyfoGwkZHYoQGRWciHKgK2D6ekpJJXluijwu2gelrpLZ9ATsdz7hzc+hutgj5xNTSFLJBqzc+S1JKUWdN5cuWalkCt02HAQAltdKxwYhhEgVrTW9wyGqbNT5LUk6wIlsdaArwERUm77zW5JSiobSPFmpZELdgSC5bodla/6eybKaYtn5MIUklWygpTNARWGO5WcK60ry6A4EicZkqbnRuv1BinLtt51hWU0RHb5xAsEJo0MRQgjLCwQjhCIxWxXpTpIOcCJb7WnzAbDOIiuVABrK8ukfCTEejhodipikxx+kuigXZZOmP0nLa4o42jdCJCodpZMkqWQD+zsDNNeVWP4FW+vNIxrTp4t+CuP0BIK2W6UEsg9aiFTr9gd59mg/h7qHGQlFjA5HZFhvYlWrHVcqVRd7KPK4ONIjSSWRXfa0+6ku9liqrmZDoq5Su0+2wJmF1pqe4aCt6iklLasuIhyJcXJAjrckey1DyELhSIwjvcNcvrTS6FDmrC7xptPlC1JbkmdwNNkrntgLsbnS/B0/ZmtZTbzu2MHuYS6YX2ZwNEJY1+GeYf79wYP8+VAvk+sYL6su4vpVNVTZMCktXql3OF5vyI4rlZRSLEoU6xYim+xp81lm61tSgzfesr59aPz01lVhLP/4BMGJmC0nqSd3lE6uas12slLJ4g73DDMR1Zbu/JZUUeTB7VR0+mVPtJEGRkNEYtqWg0BdSS5FuS4OSRtQIc7br7e38fr/fpqdrUN8/KrF3PWhzXzwVQt49fIqTg2O8u0/H2V3YvuEsLfe4SD5OU4KbbZVOmlJVSFHJKkksoh/bILj/aOWKdKdlJfjpKLQI8W6TeR05zcbnk8srirEoZDziUns+Skgi+zvjB/MdkgqOZSipjiXTp9sfzNSTyA+82ylZc8zpZRiWbUU6xbifN21rZV/+N2LXLKonG/cuu70CpUT/aMsrChk88Jy7trWyj3b2whORNm8sNzgiEU69QZCtlyllLS4qpBf72jHNxbGm59jdDhCpN3eDh9grXpKSY2leRzpHUFrbfmSIHZgx07SSbluJ/MrCqRY9ySyUsni9nX6KfS4mF9eYHQoKVHnzaPLP05MS7Fuo3T7x3EoqLR44fczWVZTxMHuYbQcY0LMysMt3Xz29y9y5bJKfvK+C6dNJhR6XLzvkvksrynivj2dHO6RD1x2ZefOb0lLqqVYt8guySLdqxus11G6oTSPkVAE/7g0YzGD7kCQkjw3eTlOo0NJi+U1RRySzzinSVLJ4lo6AzTXFuNw2CMjX+fNIxSJMTQaNjqUrNUdCFFe4MHttOfbw/KaIoaDEbr8siJOiJlqHxrj7369h1V1Jdz5zo3kuM78/uByOnjbBY3UlORy17ZWeT+3qZFQhPGJqOU7z55NsjaLJJVEttjd5mdhZQHFudZrAd9YFq+r1DYkZTTMoCcQotrGkw7LqotpHRxjLCxNSkCSSpYWjelE5zfrb31LqksU6O6UE37D9ATs2akhKVmsW7bACTEzsZjmU7/aTUzDf799Pbnuc886elxO3nnRPAB+u6tdVp/aUHKrtB3rZSTVe/PIdTukrpLIClprdrf5WGexIt1JNcW5OB1K6iqZQDSm6RsO2XLrW9KymiK0hsPSIRSQpJKlnegfZXwiyqp66y1RPZPqYg8OBZ0+mWUwQigSZXA0TI2dZxYSHRtkH7QQM3PP9jZeODnEF16/knmz2GpdWpDD61bVcrxvlBdODqYxQmGE3uH45I+dVyo5HIpFldIBTmSHLn+Q/pGQ5Yp0J7mcDmpLcmmXcwjD9Y+EiGpt60mH5YnzicNyPgFIUsnSWjr9gD2KdCe5nA6qinLpkg5whuhNFum28SBQkuemriSXg9KxQYhzGhwN85WHDnLhgjLevKF+1vffNL+UhRUFPNzSw3g4moYIhVF6h0Pkue3b+S1pcZUklUR2SNZTsmpSCaChNJ+OIanNarRk5zc7Nv1JaizLJ9ftkEnqBEkqWVhLZ4Acl4PFVYVGh5JSdV7pAGeUlwaBPIMjSa9lNdIBToiZ+NZjRxgORvjSG1adVzcdpRQ3rKklOBHlL4d60xChMEq885vH9l2WllQV0uEbZzQkdTOEve1u9+F2KlbUFhkdynlrLM0jHI3ROxwyOpSs1uMPxpv+FNp3JavToVhaXcShHpmkBkkqWdq+Dj/La4psV1C5tiTevSEQlO4NmdbtD5LjdODNt16BxtlYVlPMsb4RJqIxo0MRwrQ6fOP88vlW3rKxgaXV53+SUVuSx8Z5pTx3bECKdttI73DQ1p3fkpITd8f6ZLWSsLc9bT6aa4vxuKzbrauhNF6sW+oqGas7EKSi0IPLZueoUy2rlknqJHv/pW1Ma01LZ8BWW9+S6rzxVTJdsic647oDwURdK3vPPC+vKWIiqjneN2p0KEKY1rcePQLAJ16zZM6P9ZoV1aDgicN9c34sYbyRUISxcJSqIvtubUhanOgAd0SKsQobi8Y0L7b7Lb31DaC8MIdct4N26QBnqJ5A0Nb1lJKW1RTRPxKmf0RWxklSyaLah8bxj0+wss4+RbqTahP7b6UDXGZprbNqEACkrpIQZ9A+NMZvdrbz9ouaTif656Ikz82meaXsODWEb0xWK1ldb2KrdDasVJpXno/bqaQDnLC1Y30jjIajrLVo57ckh1I0lObTNiQrlYwSmogyNDZh63pKSculo/RpklSyqJbO+MmwHVcq5bqdlBXkSAe4DBtOzDxnwyCwqLIQl0PJICDEGfzo6RMo4PbLF6bsMa9YWgnAU0f6U/aYwhjJeiXZsFLJ7XSwsKKQwz0yXgj72m2DIt1JDaV59ASChCNS4sAIPcn6rFk1SS3jgySVLKql04/ToVhRa7+kEkBdSS5dslIpo3oSv+9sWKmU43KwsLJAkkpCTGNoNMzd29p4/bq6lKxSSvLm57CmoYQdp4akE5zF9Q4H8bgcFOfau/NbkjR3EHa3u81HkcfFwooCo0OZs8bSfGIa6SRtkO5EJ+lsOJ+oKMyhrCCHwzI+SFLJqlo6AyyqLCDXbd1iemdT581jcDRMcEJOPDIlmcSrzYJBAOLFumVmQYhX+uW2VsYnoildpZR06eIKwtEYL5wcTPlji8zJls5vSctqiujwjTMsDUSETe1p87G20YvDYf3XdH1pfDKkTeoqGaI7ECTHZf+mPxDvcLusuoiDspJVkkpWta/Dzyob1lNKqk20tO+UWYaM6fKPU5LnJt+THTPPyxMnCdJlUIiXRKIxfvl8K5cuLj9dKyCV6rx5LKgo4LnjA0RjOuWPLzKjdzhEVZZMQEC8ww/AYSnWLWwoOBHlYPcwaxvtcV5RnOumJM9Nu9RVMkSXf5za4lzbN/1JWl5bxOHu4az/TCNJJQvqHQ7SOxyi2Yb1lJLqvPEPq10+2QKXKV3+4Oki6dlgRW1iH3SXzC4IkfTYwV46fOO8a/P8tD3HpYvK8Y9PSI0aixoJRRgJRbJia0NSsm6GbIETdrSvw080pi1fpHuyhtI86QBngJjWdPuDWVGfNWlFbTHjE1FODWR3R2lJKllQskj3qnp7zChMpyjXTZHHJcW6M2QiGqN/JJRVSaXm2vjr50CXdIATIulnz52iriSXq1dUpe05ltUUU+RxsV22wFlStz97irAm1XvzKMhxSiJU2FKySPc6GxTpTmoszWdwNMxoKGJ0KFnFNzZBKBKjriR19RjNrjlR33h/lp9PSFLJgvYnkkp2XqkEUOuVYt2Z0hsIEdNQk0WDQHWxh7KCnNOvJyGyXdvgGE8f7efWC5twOdP38cDpUGyYV8qhnmHZfmpByc4+1cUegyPJHIdDsaS6iIPdMl4I+9nd5qOuJNdWW1obEnWVZLVSZiWLo2fTSqUl1fGO0tl+PiFJJQva1+FnXnk+xbn2LoBWV5JH73CQiai0BE235CBQl0WDgFKK5trirJ9ZECLptzvbUQrevLEh7c+1cV4pMQ27Tg2l/blEanUHghR4XBTZ/DPIVMsTHeC0zu66GcJ+9rT7WNfkNTqMlKovzUOB1FXKsC5/EEV2dH5L8ricLK4qzPqdD5JUsqCWzgArbb5KCeIFXWP6pVlRkT6d/ninhtKCHKNDyajmumIO9QxL4lJkvVhM89ud7VyyqJx6b/pXLFYUelhQUcALp4bkJN1iegJBarJolVLS0uoihsYm6BsJGR2KECkzMBKibXDcVvWUIH6iX1XskZVKGdblD1JR6CHHlV0pBpmklqSS5fjHJ2gdHGOljTu/JdUlTmw6pVh32nX7x6nJok4NSc21xYQjMY73ZXdxPSG2nRykbXCct2xszNhzbppXyuBomBP98vqzipjWiaRS9sxCJyWLdR/ulg5wwj72tPsAe9VTSmoozadtaEwmLjKoyz9OrTf7xofmumJ6AiEGsnjSQZJKFpPcr5kNK5VK893kuh10+mWWIZ201lnX+S0pWZdsf5ff4EiEMNZvdrRT6HFx7cqajD3nyroSct0OtssWOMsYHA0zEdVZtbUh6XQHOCnWLWxkd5sfh7Jn85+G0jzGwlGGxqR2XyaMh6P4xiaozcLxYUWiWPeBLO4oLUkli2npjJ/8ZsNKJaUUtSV50gEuzYYSnRpqs6hId9LCigJyXI6sL64nsttoKMIDL3Zx45pa8nKcGXveHJeDtQ1e9nX4GQ9HM/a84vyd7vyWhZMQFYUeygtyOCTFuoWN7G7zsbS6iAKPy+hQUq6xNB+ANqmrlBFdgfj5Wm0GttCbzYpamaSWpJLFtHQGqC72UFmUHfUMGrx5dPuDRGJS8yZdkkW6s3GlksvpYHlNUdbvgxbZ7cF93YyFo9ySgQLdU21oKiUS0/IatIieQLwIa1VR9o0XEF+tdKhHtr8Je9Bas6fNZ8utbxAvFu1yKNoHJamUCV2+7J10KCvIobYkN6snqSWpZDH7OvxZsUopqa40j0hM0xvI3j2q6ZaNnRoma64tZn9nQPbci6z1mx1tzC/PZ+O80ow/d0NpHmUFOexN1PUQ5tYdCFJWkJN1RViTllYXcaRnmFhMxgthfacGxvCPT7DWpkklp0NR582TYt0Z0u1PdAa14aq3mVhRWyzb34Q1jIQiHO0bYU1D9iSVGhJLKDtkC1zaZGunhqTmumKGxiboli6DIgt1+4M8f2KQN65vQBlQqF8pxZr6Eo71jTASimT8+cXsdPuDWTkLnbS8poixcFROUoUt7G7zAfYs0p3UWJpHp3+cqCSC067LP05dSa4hnyXMoLm2mKN9IwQnsnM7f3aeRVpUS4cfrcmqpFJZQQ65bgcd8gEubbK1U0NSc3IfdBYvWRXZ608vdqE13LS21rAY1jR6iWl4sSN7axFYQTgSY3A0nLWrWgGWJop1H5S6SsIGdrf5yHM7WVJVaHQoadNQms9ENN61UqRPNKbpGQ5l9aRDc10x0ZjmSJZukZakkoUkP3CvrvcaG0gGKaWo9+bJSqU0Od2pIQuLdCctl6SSyGL37+2kubaYhZXGnVTUFOdSVeRhb2LWXJhT73AQTfzvla2WVic6wHVn7xYHYR+723ysri/B5bTv6WBjWbxYt6wuTK++4RDRmM7q84nm0x3gsvN8wr7vIja0p91PXUlu1hTpTqr35seLdUelWHeqne7UkMUzC4UeF/PL86VQsMg67UNj7Gr1caOBq5SS1jZ6OTU4hm8sbHQo4gyyufNbUqHHRVNZPgclqSQsLhyJsb8zwLomr9GhpFVpvpv8HCft0gEurbK56U9SU1k+BTnOrD2fkKSShbzY7mN1Fm19S6ovzSOqtdS8SYPORKeGbB4EIL5kNVsHAZG9HnixC4AbV9cZHAmsqY+PbXvbZQucWfUEgridirKCHKNDMdTKumJaOuU4FdZ2sDtAOBpjbYPX6FDSSilFQ6kU6063Ln8Ql0NRUZhdCx8mczgUyxPNf7JRWpNKSqnrlFKHlFJHlVKfmeb6K5VSfqXU7sTX52d632zjH5vg5MAYa2z+5j8dKdadPp2+cYpzXRTluo0OxVDNtcWcGhhjODhhdChCZMz9e7tY01BCU3m+0aFQXuihoTRPusCZWHcgSFVRLo4sLcKa1FxbzMmBMSksLyztdJFum69UgnhdpZ5AkFAkOwsoZ0KHb5yaklycDhkf9ncFsrJDaNqSSkopJ/Ad4HqgGbhNKdU8zU2f0lqvS3z9yyzvmzWS9ZSyqUh3kjexdFWKdadex9A49d7s3f+ctLIu/rrK1tkFkX1ODYyyt93PjWuM3/qWtKbBS6c/SP9wyOhQxBRa63jntyyup5TUXBevm3FQVrcKC9vd5qOi0ENdFqxUbyzNQ/PS6nyRWjGt6fTJ+QTAqvpiRkIRTg6MGh1KxqVzpdKFwFGt9XGtdRi4G7g5A/e1pb0dPgDWZFGR7iQp1p0eoYko/SMh6kplEFiV2Hoj3adEtrh/b3zr2w1rjN/6lrQqcbLeIifrpjMcjDAajmZ1p9CkZFJJtkwLK9vd5mNdozcr2r/Xl8ZX47YNSl2ldBgcDROKxCSpxEvNtLLxfCKdSaV6oG3S9+2Jy6a6WCm1Ryn1oFJq5SzvmzX2tvmZV55PSX52blOqL82jJxBkQop1p0ynP97JRwYBqCzyUFuSm5WDgMhOf9rbxYYmr6le/978HOq9eVKvxoQ6E5M6dVnc2SeppjiX0ny3rGwVluUfm+B43yjrGrNj90Ohx0VZQQ6tklRKi+Skf52JPk8YZUl1ITkuB/uy8HwinUml6VLfUzcY7gTmaa3XAt8G/jCL+8ZvqNTtSqntSqntfX195xur6b3Y4Wd1fXa8+U+nwZtHTMcLwYnUSA4CZjqpNNLq+hJelCLBIgu0DoyxvyvA61abZ+tb0sq6YtqHxqULnMl0+sdRSFMHiK+eXllXQosklQw3l9qt2Wxn2xAAG+aVGhxJ5swry6dtcAyts6/WTbp1DI3jciiqZXs0bqeDFbXFWdl0JJ1JpXagcdL3DUDn5BtorQNa65HE/x8A3Eqpipncd9Jj/EBrvUlrvamysjKV8ZtG/0iIDt+47Ts0nE1y6WqHtARNGSnS/XJrGko43j9KQIp1C5vb0tINwLUrawyO5JVO1zeTrUWm0ukLUlaQg8ftNDoUU2iuK+ZQz7CsnjbQXGq3Zrtdp4ZwKLLqvKKxLJ/hUIShMfmMl2pSpPvl1tTHJx2yrVh3OpNKLwBLlFILlFI5wK3AvZNvoJSqUYnNvEqpCxPxDMzkvtkkuXpidRYW6U4qznVR6HFJS9AUkiLdL5esq9TSISezwt4eaummubaYxjLju75NVVnkoarII6tATKbLPy5bGyZpri0mHIlxvC/7irGaiNRfPU87WodYUVtMgcdldCgZ05QY72QLXGpJke5XWl1fwkgowoksK9adtqSS1joCfAzYAhwA7tFatyil7lBK3ZG42S3APqXUHuBbwK06btr7pitWs9vb7kepl056s5FSiobSPEkqpYgU6X6l1aeLdfuMDUSINOodDrKzdciUq5SSVtaVcLJ/VFq2m8RYOD67nw1dombqpWLd2bfFwUTmUrs1a0Vjmt2tPjY0Zc/WN4Dq4lxyXA5aB7PrRD/dpEj3KyXP17OtrlI6VyqhtX5Aa71Ua71Ia/2vicvu1Frfmfj/f2utV2qt12qtN2utnz3bfbPV3nYfiyoLKcyiGYXpNJbl0zcSYjwcNToUy5Mi3a9UXuih3puXlfugRfZ4ZH8PWsO1q6qNDuWMVtYVo4EDsgXOFJK1DGWl0ksWVhTgcTmkWLex5lK79eUPlCX1WQEOdQ8zGo6yMYvqKQE4HfHJaVmplFpSpPuVllQX4nE5sq5Oa1qTSmLutNbsbvOxJou3viU1JuoqtUtdpTmTIt3TW9NQIh3ghK1taelhXnk+y6qLjA7ljGpL4t21pAucOSQ7v9XKeHGay+lgeU2R1P4y1lxqtzLldravz5q0ozVepDvbkkoQL9bd7Q8SjkgttFTplCLdr3C6WHeWnU9IUsnk2gbHGRgNZ90y1ek0lOahgFZJKs2ZFOme3uqGEk4NjOGXQo7ChvzjEzx7tJ/rVtaQKGdoSkopVtWVcKx3VFammkCXP3i6rqF4SXNdMS2dAekmZZy51G7NWrtODVFR6KEhC8sfNJXlE9MyOZ1KUqR7eqvrS9ifZcW6JalkcjsTMwqSVIJct5PKIg9tsnR1zjqGpOjqdJJ1lfbJCglhQ3852EskprnGxPWUklbWFRPVmkM9shLEaB0+GS+m01xbjG9s4vT2QJFZc6ndakzE5rCjdYiN87ymnlhIl0Yp1p1SMa3pkCLd01rdkH3FuiWpZHK7WofIz3GytLrQ6FBMoaksn7bBcZkZnINkkW4ZBF4pmVSSukrCjra0dFNV5GF9o9foUM6poSyfolyXdIEzWDgSo384JEmlaZwu1i3HqGHmUrs1G/WPhDg1MJa1E9X5OS4qCz2SVEoRKdJ9Zqeb/2TR+YQklUxuZ6uPtQ1eXE75U0F8lmF8IsrAaNjoUCzrdJHuLFz6fC7e/ByayvKzrmODsL/gRJTHD/VxzcpqHBZYpu5QiubaYg73DEv9CwN1B+LjhXR+e6XlNcUohSQ+hWXsPJW99ZSSmsryaR0ck8npFJAi3We2pCpRrDuLzickU2Fi4+EoB7oCrG/yGh2KaSSXrsoWuPOX3EsuMwvTW91Qwt4On9FhCJFSTx7uY3wiyrUW2PqWtKq+hImo5nDPsNGhZC0p0n1mBR4XiyoLeVHGC2ERO1qHcDvV6Zbn2aipPJ+xcJSBEZmcnisp0n1mLqeD5rpiSSoJc9jX6ScS01m7THU6VUUePC6HLF2dg7bBMUrz3VKk+wzW1JfQNjjOkKyGEzaypaWH4lwXmxeWGx3KjM0vLyA/xykdtgzU6Rsnz+3EmyfjxXTWNJSwu80vqx6EJew65WNlXQm5bqfRoRimKTE5fUrOI+asdWiMOm+eFOk+gzX1Jezr8BPNkmLdklQyseQy1XWyUuk0h1I0lObRJp0bzlvr4NjpFV/ildY0eAHY3eYzNA4hUiUSjfHYwR5es6Iat4W2UjsdiuU1xRzsDhCJyRY4I3T5g9R6c7OyqO9MrG3w0j8SkmLdwvTCkRh72n1ZP1FdWeQhz+3kVBYVUE6HaEzTMTROo5TSOKP1TaWMhaMc6s6O1dbW+XSZhXa1+phXnk9FocfoUEylsTSfbn9Q6mycB//4BIFg5PRMjXiltY0lOB3qdOdFIaxu24lBfGMTltr6lrSyrpjgRIwTfXICkGmRaIzuQJD6EjlpOJO1iaL3e9t9hsYhxLm82OEjFIlx4YLsTio5lGJeeT4nJak0J13+cSIxTVN5gdGhmFayfM2utuw4n5CkkklprdnZOmSJLj2Z1liWT0y/VOtBzFxy26Aklc4sP8fF8poiSSoJ23iopZtct4MrllYaHcqsLa4qJMfpkGLIBujyB4nGNA0yXpzRitoi3E7F7rbsqZshrOn5E4MAXDC/zOBIjLegooD+kTDDwQmjQ7GsZG1bWal0Zk1l+ZQX5LDzlM/oUDJCkkom1ekP0jscYkMWd2g4k9PFumUL3Ky1DY7hcihqpJPPWW1oKmV3qy9r9kGbkVLqOqXUIaXUUaXUZ6a5/kqllF8ptTvx9Xkj4jS7WEzzcEsPly+pJC/HenU03E4HS6sLOdAVICZ1azIqOcbKScOZeVxOltcUy0olYXrbTgyypKqQctn9wPzE6pqTA3Iecb7ahsYpznVRIvX2zkgpxfomr6xUEsZK1lNa3yhJpakKPS7KCnKkWPd5aBuMF9VzOeSlfzYb5nkZDUel65RBlFJO4DvA9UAzcJtSqnmamz6ltV6X+PqXjAZpEXs7/HQHgpbc+pbUXFfCcCgiXT8zrH1onCKPnDScy9rGEl5s9xOTSQhhUtGYZvvJIS5cIKuUAOq8ebidihP9sgXufCXrs0q9vbNb31TK8b5RfGP2b/4jZ5YmtavVR67bwfLaIqNDMaV5ZfmcHBiTjiuzEI7E6PCNy9a3GUgWspQtcIa5EDiqtT6utQ4DdwM3GxyTJW1p6cblULxmRZXRoZy35TVFOJViv2yBy6i2wTEa5KThnNY0eBkORTguJ6jCpA50BRgJRSSplOB0KOaVFUix7vPUPxJicDQs5xMzkDyf2NXqMzaQDJCkkkntaB1iTb3XUp16Mml+eQGjoQgDI/bP/KbKga4AkZiWzm8zkG37oE2oHmib9H174rKpLlZK7VFKPaiUWpmZ0KxDa82Wfd1sXliONz/H6HDOW67byaKqAlq6AjKRkCFj4QgDo2HZ+jYDaxMdQ2ULnDCrZD0lSSq9ZH5FvOnPeDhqdCiWszuRIGkslfOJc1nTUIJDwa4smKSWjIUJjYYitHT4uSDLOzSczbyK+BvZCZllmLHkG5rMLJxbfB90aVYMAiY13dKIqdmEncA8rfVa4NvAH6Z9IKVuV0ptV0pt7+vrS22UJnekd4Tj/aNcu8q6W9+SmmtLGBwN0x2Q1u2Z0D4Ub4QhkxDntriqkPwcJ3vafEaHIsS0tp0YoKksn1rp5Hja/IoCNMhqpfOwq20Ih4pvIxRnV+BxsbymmJ2yUkkYYVerj0hMc+GCcqNDMa3KQg8FOU5OynLzGdvV5pOierOwvsnL8f5RhkZlNZwB2oHGSd83AJ2Tb6C1DmitRxL/fwBwK6Uqpj6Q1voHWutNWutNlZXW6342Fw/t60YpuLa52uhQ5mxFbREKZAtchrQNjqGAejlpOCenQ7GqvoQ97dIBTpiP1pptJwZlldIUjaX5OB1KJqfPw85TPmpL8shxSRphJtY3edndZv/mP3I0mNC2EwM4HYqN0vntjJRSzK8o4KQMBjO2q9Uns86zcHofdJZ0bTCZF4AlSqkFSqkc4Fbg3sk3UErVqESxF6XUhcTHs4GMR2piD+3rZkNTKVXF1u/2WJTrpqk8nxZJKmVE29AYlUUect3W6xhohLUNJezvChCOxIwORYiXOdo7wtDYhCSVpnA7HTR482RyepaiMc2edh+NZTLhMFMbmkoZCUU42jtidChpJUklE9p6YpBVdcUUelxGh2Jq88sLGBqboMs/bnQoptc/EqJ1cEy2vs3C2sYSnA4ldZUMoLWOAB8DtgAHgHu01i1KqTuUUnckbnYLsE8ptQf4FnCrloI7p7UOjLG/K8B1Fu76NtXK2mK6A0EGZfVgWmmtaR8al0mIWVjb6CUciXGoWzqGCnNJ1lO6SJJKrzC/ooAO37gkg2fhcM8wY+GonE/MwvomL2D/ukqSVDKZ4ESU3W0+mVGYgfnlBQBsSwyY4sx2SVG9WcvPcbG8pkg6wBlEa/2A1nqp1nqR1vpfE5fdqbW+M/H//9Zar9Rar9Vab9ZaP2tsxOaypaUbgOtsUE8pqbmuBICWTtlmlE6nBsYYC0dlvJiFZLHuPVKsW5jMthODVBd7JAkwjQUVBcQ0tA6OGR2KZcj5xOwtqCigNN9t+/MJSSqZzN52P+FITOopzUBNSS45LgcvnJSk0rk8f3yAHJeDeunkMysbmkrZkwX7oIX9PNTSzcq6YlutNikryKG2JFfqKqVZMjEi2xtmrqE0L9Ex1N4nDcJaXqqnVE5it7iYpKksH4eCE/323paUSttPDlJekENZgXU7ymZasvmP3Yt1S1LJZLadiJcEuWC+1FM6F6dDMa8sX1YqzcDzJwZZ3+jF7ZSX/GxsnFfKaDjKgS45iRXW0RMIsuPUkK22viU11xXTOjjGcHDC6FBsa1erD7dTUVVk/VpcmaKUYtP8Ul44JZ9HhHmc6B+lOxCUrW9nkOt2Uu/N41if1FWaqecTRd8lSTk7G+eVcrR3hIGRkNGhpI2cYZrM8ycGWV5ThDdfMsAzMb+igMM9I9Kh6ywCwQlaOv1ctFBWv81Wchvq85K4FBbysA23viWtrC1BA/sl0Zs2u9t81HvjnZHEzF0wv4y2wXG6/UGjQxECgKeP9gPwqiWvaIwqEhZXFdI+NEZwImp0KKbXNjhGh2+czXI+MWvJ35mdF0JIUslEItEYO04NST2lWUjWVdouS87PaMfJIWIaNstxNWt13jzmleez9bg0FRPW8VBLN4sqC1hSXWR0KClXXeyhvCBHtsClSXAiyv7OAE2y9W3WLpgfH2NlS74wi6eP9NNYlse8xGdl8UqLqgqJ6fiqLnF2p4u+L5Tzidla01BCnttp60lqSSqZSEtngLFwVJJKs9BQmkeOU+oqnc3WEwO4nfH9vGL2Ni8oZ9uJQWJSV0lYwNBomK3HB225Sgni24ya64o53jfKeFhmllNtV6uPcDTG/Ao5CZ2tlXXF5Oc42S6fR4QJRKIxnjs+wGWLZZXS2TSV5uN2Ktu3e0+FrccH8Oa7WVplvwmrdHM7HWyaX2rrSWpJKpnI84l6ShfOl6TSTLmdDtY1ennumH1fpHP1/PFB1jZ4yctxGh2KJW1eVIZ/fIID3bIyQpjfowd6iMY0162sNTqUtFlZW0xUaw71yGsy1Z4/MYBSMK9Mkkqz5XI6WN/kZdtJWTktjLe3w89wMMKlklQ6K5fTwYKKAo72SVLpXJ4/McCF88twyNbo83LRgjIOdg8zaNOSLZJUMpFtJwZZUFFAVbEUx5yNSxdXsK/TL3WVpjEaivBih1+Wqs7BRYlOjFuPy+yzML8tLd3Ue/NYVV9sdChp01CWT1Gui30dklRKtW0nBmmuLZZJiPN0wfwyDnYH8I9LIXlhrGeO9KMUXLJIkkrnsqiykL7hkNRDO4sO3zhtg1JPaS7sXldJkkomMRGNsfX4oLxYz8NlSyrQGp6V1UqvsOPUENGYPp0YEbOXrKskq+GE2Y2EIjx5pJ9rV9bYujOLQylW1ZVwuGeYkBRXTZlwJMbOVqnrOBcXzC9Da9jZKquVhLGePtrPyrpiaf0+A4urCgF4JlHYXLzS84ltWzJJff7WNHjJdTtsuwVOkkomsbvNx0gowhVLZUZhttY2lFDkcfH00T6jQzGd508M4HQoNs6TekpzcfHCcradGCAqdZWEif35YC/hSIzrV9uzntJkaxpKiMQ0B7qHjQ7FNva2+whOxGQSYg7WN3lxORQv2HQmWljDWDjCztYh2fo2Q9XFuRTkOCWpdBbPHx+kONfF8hr7roJOtxyXg43zSm1brFuSSibx1OE+HAoulmWqs+ZyOti8qJynjvSjtZz0T/b88UFW15dQ4HEZHYqlbV5YTiAY4YC0MRcm9qe9nVQVediQBUX5G8vyKc518WKH3+hQbCP5QVdWKp2//BwXK+tL2C51lYSBnj8xyERUS5HuGXIoxaKqQp4+KucRZ/L8iQEuXFCGU+opzcnmBeUc7A7gG7NfyRZJKpnEk0f6WdfopSTPbXQolvSqJRW0D41zamDM6FBMYzwcZU+7T5aqpkDyd2jXJavC+oaDE/zlUB83rKnNig99DqVYXR/fAheULXAp8fyJQZZWF8p2mTm6YF4pu9t9hCJyXApjPHOknxyXgwuk8c+MLa4spHc4JF3gptHtD3JyYExWsabA5kXlaI0tVytJUskEfGNh9rb7eNWSSqNDsazkbMxTsnT1tJ2tQ0xENZtlEJiz2pI85pfnS7FuYVqP7O8hHIlx45o6o0PJmNX1JURjWlYQpkAkGmPHyUFZpZQCFywoIxyJsbddVtEJYzx9tJ8L5peS65aC+zO1KFFX6akjch4xVbI7udT9nbs1DSV4XA6et+H5hCSVTODZYwPENFwu9ZTO24KKAuq9eTwjg8FpTx7pw+1UXCAnCSmxWeoqCRO7b08n9d48NjR5jQ4lYxrK8inJc8sWuBRo6QwwGo7KTHQKbErUMLRrhx9hbj2BIAe7h7lssUxUz0Zpfg6LKgv4y6Feo0MxnWePDlDkcdFcJ/WU5srjcrJxXinP2XDngySVTOCpI30UeVysbfAaHYplKaW4dHE5zx7rl5P+hCcO9bFpXhmFUk8pJS5eFK+rtE9OYIXJDI2GeepIPzeuqbV117epklvgjvSMMB6WrUZzkUyAXCSTEHNWXuhhWXURzx6TSS6ReY8diCdFXrOiyuBIrOfqFdVsPT7ASChidCimobXmicN9XLq4Iiu21mfCJYvKOdAVoG84ZHQoKSVJJYNprXnycD+XLC7H5ZQ/x1xctqSSQDDC3naf0aEYLjlTdcUymalKlcsWV6AUPH5IugwKc9nS0k0kprlpbfZsfUtaXV9CVMsWuLl6/sQACyoKqCrONToUW3jVkgpeODEkyU6RcY8d6KGxLI8lie1cYuZevbyKiajmqcPyOS/pUM8w3YEgV8r5RMpcuSye8H3SZseZZDEMdqJ/lA7fuNRTSoFLF8WX7T8tW+B4IpH4uGKpHFepUl7oYU2Dl8cPy9JoYS737e1kQUUBK7NwaXpDaR7efDd7ZDLhvEVjmm0nBmWVUgpdvrSScDTG1hP22+IgzGs8HOXpo/28Znl1Vq1aTZWN80opyXPz2EH5nJd0+nxCkkop01xbTEWhh8clqSRSKVkQ7nJJKs1ZeaGHVfXFPGGzF+n5eOJwH9XFHpbXFBkdiq1ctayS3W0+Bkft1wpUWFPfcIjnjg1k3da3JKUU6xq8HO0dIRCcMDocS9rb7iMQjHCptB9PmQsXlJHjcvDUYZnkEpnzzNF+QpEYV6+oNjoUS3I5HVy5rJK/HOyVUhoJjx/qY1l1EbUleUaHYhsOh+KKpZU8daTPVseZJJUM9uThPuaV59NUnm90KLbw6uXV7GwdYmDEXvtUZyMSjfHUkT6uWFqZlSeZ6XTVsiq0tt+SVWFdf9rbSUyTVV3fplrX5EWDdNs6T08d6UcpJKmUQrluJxctKOOpIzJWiMx57GAPRR6XdHGcg1cvr2JgNCyrX4GRUITtpwZl61saXLW8Et/YBLvbfEaHkjJpTSoppa5TSh1SSh1VSn1mmuvfoZTam/h6Vim1dtJ1J5VSLyqldiultqczTqOMhSM8fbSfq5ZJMb1Uuaa5mpgmq5eu7knMOl+xVI6rVFtdX0J5QQ6PS3cQYRK/3dlBc20xy7J4VWJVUS713jx2tw4ZHYolPXWkj9X1JZQV5Bgdiq28akkFR3pH6PKPGx2KyAKxmOaxA71cvrSSHJesGThfVy6twulQPHagx+hQDPfs0X4molq2vqXBqxZX4lDY6nwibe86Sikn8B3geqAZuE0p1TzlZieAK7TWa4AvAj+Ycv1VWut1WutN6YrTSE8eji9TvWalLFNNlZV1xdSV5PLI/uwdDJ441IdDxQtLi9RKLll94rC9lqwKazrcM8yLHX7evLHB6FAMt67RS6c/SE8gaHQoljIcnGBnq49XLZHxItWStTKfkjqPIgP2dfrpHQ7x6uUyoTgXJfluNs0rPd1FL5s9friPghwnm+bJyrdUK8l3s6Gp1FbNf9KZyr4QOKq1Pq61DgN3AzdPvoHW+lmtdXJqcSuQVZ+MH97fTUmemwvny4s1VZRSXN1czVNH+rK268rjh/tY31RKSb7b6FBs6crlVQyNTcjSaGG43+5sx+VQ3Lwue7e+Ja1t9OJQ2GopeSZsPT5INKalWUgaLK8poqLQI0klkRGPHujFoeAqSSrN2WtWVHGwe5gOX/auMtRa88ShPi5ZXCEr39LkymWVvNjhp2/YHiVb0nmU1ANtk75vT1x2Jh8AHpz0vQYeVkrtUErdfqY7KaVuV0ptV0pt7+uzTrZvIhrjsQO9vGZFFS6nvFhT6bXN1QQnYjxzNPs+yPWPhNjb7udK6fqWNpcvqUgsWbXO+42wn2hM84ddHVy5rJKKQo/R4Riu0ONiSVURu9t8xLSsIpypvxzqpSDHyYamUqNDsR2lFJcvqeDpI33EZGWrSLPHDvSwoalUtrGmwGsShc6zeQvcsb4ROnzj0kU6ja5MlL+xS53WdGYzpqsQPO2oqpS6inhS6dOTLr5Ua72B+Pa5jyqlLp/uvlrrH2itN2mtN1VWWufAf+HEIP7xCa5dWWN0KLZz0YJyijyurNwCJ60/08+bn8P6plJb7YMW1vP00X56AiHevCGrFvie1bpGL/7xCU70jxodiiVorfmz1GBJq8uXVjI0NkFLZ8DoUISNtQ2O0dIZ4OpmKaeRCgsrClhcVcj9e7uMDsUwyYlTKdKdPs21xVQUenhckkrn1A40Tvq+AeiceiOl1Brgh8DNWuuB5OVa687Ev73A74lvp7ONLS3d5LodXC5LzlMux+XgyuVVPHawJ+vq3mxp6aamOJdVdSVGh2JrVy2rZG+7n95hqd8ijPHbHe2U5Ll59QrZ6pC0orYYj8vBLinYPSP7uwJ0B4KyXSaNkh31njgskxAife7bGz+9umF1rcGR2INSipvW1PHCyUG6/dn5Oe+R/T0srS6koVS6k6eLw6G4clklTx7uYyIaMzqcOUtnUukFYIlSaoFSKge4Fbh38g2UUk3A74B3aa0PT7q8QClVlPw/cA2wL42xZpTWmof393D5kkrycpxGh2NLV6+oon8kzO627Dm5GA9HefJIH9esrMbhmG6hoEiV5GxgNq6GE8YLBCfY0tLNTWtr8bhkDEnKcTlY0+DlxQ5/1tbUm40/JwrRSgfa9Kks8rCmoYRHpeivSKP79nSxoclLY5kkAFLlxrW1aA1/ejH7VisNjIR44eQg18lumrS7prka//gE204MGh3KnKUtqaS1jgAfA7YAB4B7tNYtSqk7lFJ3JG72eaAc+K5SardSanvi8mrgaaXUHmAb8Cet9UPpijXT9nUE6PIHuUZerGlz5bIqXA7Fw1l00v/E4T6CEzHZUpkBy6qLmF+ez0P7uo0ORWShP+3tIhSJyda3aVw4v4yJqGa3FNI/p8cO9rK20UtlkdTkSqfXrqhmd5uPXulMKNLgaO8wB7oC3LRWGjak0qLKQppri7lvzys22djeowd6iGnkPDUDLl9aSZ7baYvzibRuotdaP6C1Xqq1XqS1/tfEZXdqre9M/P+DWutSrfW6xNemxOXHtdZrE18rk/e1iy0t3TgditfIkvO0Kclzc/Gich58sRudJUVbH25JdBNcIN0E000pxXWrannu2AD+sQmjwxFZRGvNz7eeYnlNEesavUaHYzr1pXnUeXN54cRg1rz3n4+eQJDdbT75HJIBr12ZWNmaxUV/Rfrct6cLh5Ktb+lw09o6drf5aBscMzqUjHpoXzcNpXmsrCs2OhTby3U7uWp5JVtaui3f0EEqM2aY1poH93Vx4fwySqVDQ1rdtLaO1sEx9rb7jQ4l7UKRKI/s7+HqFdW4pZtgRly/qoZITMuJgsio3W0+WjoDvGPzPJSSba7TuWB+Gd2BIO1D2dsO+lwebonPil6/Smai021ZdRFNZfmyXVqknNaa+/Z0snlhOVXFuUaHYzs3rokn6rKpYHcgOMEzRwe4dmWNfMbIkGtX1tA7HGKnxetBytlnhrV0BjjWN8rr18ky1XS7dmUNbqfi3ixYuvrEoT6GQxFuWiszVZmypqGEem8eD2ThfnthnJ9vbaUgx8kb19cbHYpprW3wkuN0sO2k9WsUpMuD+7pZVFnAkuoio0OxPaUUr22u5tmjAwSCsrJVpE5LZ4Dj/aOy9S1NGsvyWdfozaotcA+39BCOxnidrHzLmFcvryLH5bB8/S5JKmXYH3d34HYqmR3MgJI8N1csreL+vZ2WX1J4Lvft7aI0332604xIP6UUN66p5cnDfQyNho0OR2QB31iY+/d28ob19RR6XEaHY1q5bidrGkrY2+4jOCEFu6caHA3z/IlBrl8lJw2Z8rrVNYSjMR6V1Uoihe7b24nLoaSgchrdtLaO/V0BjvWNGB1KRty7p5OG0jw2NHmNDiVrFOW6uWpZJffv7bJ013JJKmVQNKa5d08nVyytwpsvW98y4fXr6ugJhNh6fMDoUNJmLBzh0f09XL+6Vra+Zdjr19URiWke2Gft2QVhDb/Z0U4oEuOdm+cZHYrpXbggUbC7zWd0KKbzyP5uojHNdTK5lTHrG0upK8nlT1m0jUakVyymuX9PF5cvrZRyGml0w+palII/7uowOpS0GxgJ8czRfm5aWydb3zLs5nX19A1b+3xVzkAz6PnjA/QEQtwsW98y5rUrqinyuPjNznajQ0mbR/b3MD4R5aY1clxlWnNtMYurCvnj7uxZGi2MEYtpfvF8KxvnlbKiVopnnku9N16w+7ljA7ZfqTpbf9zdyfzyfCnCmkEOh+J1q2t58kifNHcQKbH1+AAdvnE5p0izmpJcLltcwW92tFt6FclMPLAvPuHwetlOmXGvXl5FocfFvRY+n5CkUgbds72NolwXr22uNjqUrJGX4+TGtbU8+GI3I6GI0eGkxW92tFPvzeMi6fqWcUopbl5bx7YTg7QPZVd3EJFZzxzr50T/KO/c3GR0KJaglOKyxRX0jYR4/HCv0eGYRk8gyHPHB7h5Xb3MRGfYjWvrmIhqtuy3futoYby7X2ijJM/NtbL1Le3edkEjnf4gzxztNzqUtPrdznaWVheyvEZq7WVartvJNSureWBfl2W37UtSKUP84xM8uK+bm9fVket2Gh1OVrllYwPjE1FbFlTu8o/z9NF+3ryhHodDThCM8MYN9SgFv91h/6XRwjg/ePI4FYUeqYMzC6vrvZTkufmfJ08YHYpp3LenE62R1Q0GWNtQwrzyfP6QBdtoRHr5xsI81NLNG+ScIiNe21yNN9/Nr7a3GR1K2hztHWZXq4+3bGyUCQeD3LKhgeFghC0t1px4kKRShty3p5NQJMZbNzUaHUrW2dBUysKKAn71gv0Gg9/t7EBrePPGBqNDyVoNpflcuqiCX+9ok202Ii32dfh56kg/77t0vpxAzILTobh4YTnPHR9gX4ff6HBM4Q+7O1jTUMLCykKjQ8k6SinetL6B544PyMpWMSe/39VBOBLjrRfIOUUmeFzxjquPtPTQPxIyOpy0+PWOdpwOxRuks6xhNi8sp6E0j19vt2bJFkkqZYDWmrtfaGV5TRGr60uMDifrKKW49cJGdpwa4mB3wOhwUiYW0/x6exsXzi9jXnmB0eFktbdsaqB9aJznLFxgT5jX9588TqHHJQW6z8OFC8oo9Lj44VPHjQ7FcPs7A+zrCPCGdXLSYJQ3bahHa/j9TlmtJM6P1pqfbT3F2oYSVtbJOUWmvOOiJsLRmC0nqCPRGL/f2cFVy6qoLPIYHU7WcjgUt2xs4Jlj/ZaceJCkUgbsavOxryPAOy5qkiWFBnnLxkZyXA5+sbXV6FBS5plj/ZwcGOPtF0mNFaNdu7KGkjw3d22zz/ElzOHUwCh/2tvJOy5qoiTPbXQ4lpPrdvK2Cxq5f28XXf5xo8Mx1D3b28hxOnijzEQbprEsn4sWlPHbne1oLStbxew9c3SA432jvOeS+UaHklUWVxVx6eJyfvl8q+0Kdj96oIfe4RBv3SS7Hox2S2LnyT0WTF5KUikDfvbcKQo9Lt64QV6sRiktyOHG1bX8fleHbQp2/3zrKcoKcrh+tRRpNFqu28ktGxt4aF83vYGg0eEIG/mfp47jcjh4/2ULjA7Fst536Xw08KOnsre2UnAiyu93dXDtqhppP26wt25q5OTAmKxsFefl/547SVlBDq9bLfX1Mu1dm+fT4Rvn0QM9RoeSUj/beop6bx6vWSGNpIzWUJrPVcuq+OW2NsKRmNHhzIokldKsfyTEn/Z28eYN9RR6XEaHk9XedfE8RkIRfm2DQntd/nEe2d/DWzc14nFJjRUzeNfmeURiml/KaiWRIv0jIX69vZ03rq+nujjX6HAsq6E0n5vX1fGzraeyNum7paUb//gEt0oNFsPdsKaWkjy3rVZOi8w4NTDKYwd6uPWCRqmvZ4CrV1RR782z1QTF0d4Rnjk6wNsvasIpDX9M4V0Xz6N/JMRDFivYLUmlNPvZc6cIR2O86+L5RoeS9dY3lbJxXik/fuaE5Zeu/t+zp4D4Hm9hDvMrCrhyWSW/eL7VcrMLwpzufPwY4WiM269YaHQolvfJ1ywlGtP891+OGh2KIX7y7EkWVBRw8cJyo0PJerluJ2/Z2MCWFlnZKmbnh0+dwOVwyNY3g7icDj5w2QK2nRxkZ+uQ0eGkxE+fO4nbqXibTDiYxhVLKplXns//PXvS6FBmRZJKaTQWjvDT505y9YpqFldJpxUz+NCrFtA2OG7Zdo0AI6EIv3j+FNevrqWxLN/ocMQk77lkPn3DIe7b02l0KMLi2ofG+Olzp3jzhgYWSaeuOWsqz+etFzRy17ZWSxbAnIvdbT52tfp4z8XzcMhMtCm8/aImIjHNXdusv3JaZMbASIh7trfJylWDve2CRopzXXz/iWNGhzJng6Nh7tnexhvW1VNRKAW6zcLhULz74vnsODVkqeSlJJXS6Nfb2xkam+AOmWU2jdc21zCvPJ87nzhm2SKZd29rZTgY4fZXyXFlNlcurWRZdRHff/IYMYuvhhPG+vojR0DBp1671OhQbOPjr16MUopvPXbE6FAy6ifPnKDQ4+LNG6Wuo1ksrCzkymWV/GzrSYITUaPDERbwv8+cJBSJ8aHL5bOfkQo8Lt518Twe3t/D4Z5ho8OZk589d4rgRIzb5ZgynVsvaKQkz80PnrBO51pJKqVJKBLlB08eZ0OTl03zy4wORyQ4HYqPXLmIve1+Hj/UZ3Q4sxaKRPnR0ye4cEEZaxu9RocjplBKcceVCzncM8JfDvUaHY6wqIPdAX63q533XjKfem+e0eHYRm1JHu+8aB6/3dnB8b4Ro8PJiA7fOPfv7eKWjQ0U5Ur3QDO5/fKF9I+E+d3ODqNDESY3NBrmf585wQ2ra2Xngwl88LKF5LudfNPCExRj4Qj/99xJXr28iiXVRUaHI6Yo8Lh41+Z5bNnfbZnPK5JUSpN7trfT4Rvnk1fLLLPZvGlDAw2leXzj0cOWW630qxfa6PIH+cSrlxgdijiDG9fUUe/N41t/Pmq540uYw1cfOkShx8VHrlxkdCi28+ErF+FxOfj3hw4aHUpG/CCxRUNWN5jPxQvLWV1fwg+fOm75Oo8ivf7nqeOMTUT566vls58ZlBbk8N5L5/PAi10c6rbmaqWfPXeKwdGwfM4wsfdcMh+Py2GZWpCSVEqD4ESU7/z5KJvmlfKqJRVGhyOmcDsdfPSqxexp9/PoAeusJglORPnuX45xwfxSLl0sxVbNyu108PFXL2ZPm48/H7TO8SXMYevxAf58sJePXLkYb760fk+1yiIPH71qMVtaeviLzV+ffcMh7n6hjTdtqJcVbyaklOLDVy7ieP8o9++VOnxien3DIX7y7EluWF3LUllRYhofetVCCnNc/McW601QjIQi3PnEMS5fWim7aUysssjDuzbP4w+7Ojjaa/7VSpJUSoOfPneS7kCQv3ntUpSSophmdMvGBhZWFPCVBw8QiVqjU9fPt56iOxDkU1fLcWV2b97YwLzyfP7z4cNSW0nM2EQ0xuf/uI96bx7vle4+afOhVy1kUWUB/3Rvi63r2Xz/iWNMRGN8+MrFRocizuC6lTUsrynim48escxnEZFZ//XIYcKRGH97zTKjQxGTePNz+PBVi3j0QC/PHus3OpxZ+d+nTzA0NsHfSM1G07vjikXkWmSrpSSVUmxwNMy3/3yUq5ZVcsliWaVkVm6ng09fv5xjfaP8arv5u68Mjob55mNHuGKpHFdW4HY6+NTVSznQFeCPe6RehpiZHz99gsM9I/zz61eSl+M0OhzbynE5+OLNq2gdHOO7j1u/g890kt0Db9nYwIKKAqPDEWfgcCg+efUSjveP8sfdslpJvNzhnmF+9UIr77p4nryOTej9ly6griSXf3vggGW2sPYOB7nziWNc01zNOqnNanrlhR7ef+kC7tvTyZ42n9HhnJUklVLsG48eZiwc5bOvW2F0KOIcrmmu5sIFZfznlkMMjYaNDuesvvnoYUZDEf7xBjmurOL1a+tY21DCvz94iLFwxOhwhMmd7B/l648e5uoV1VzdXG10OLZ3yeIKbl5Xx51PHONE/6jR4aRcsnug1HU0v2uaa1hdX8J/PnyI8bB9V86J2dFa8/k/7qPQ45I6miaV63by6euXs68jwC+fP2V0ODPytS2HCUdjcp5qIX91xUIqCj38y/37TV2rVZJKKbSnzcfPtp7inRc1SSV9C1BK8S83ryQQjJi6aOuL7X5+tvUUt13YJPvpLcThUHz+pma6A0G++xd7roYQqRGLaf7fb/bidjr40htWGR1O1vjH163A43Tw6d/utcws80zsbvPxu13tvO+S+dRJLSXTczgUn7uxmS5/kB88aZ320SK9frezg63HB/nM9SsoLZD6emb1+rV1XLq4nK8+dIje4aDR4ZzVrtYh7tnRxnsuns98WflmGUW5bv7+2qXsODXEvXvMu6JVkkopEonG+OzvX6Sy0MPfXiv7nq1ieU0xH7hsAXe/0MZzxwaMDucVItEY//D7vZQXevh/1y03OhwxSxvnlfHG9fV8/8ljlu0QItLvR0+fYNvJQT53YzM1JblGh5M1qopz+fxNzWw7MciPnrbHyXw0Fl/dUFno4WOvllpKVnHhgjJet7qGO584RtvgmNHhCIP1j4T41wcOsL7Jy60XNBodjjgLpRRfvHkVoWiM/+/3+0y7kiQcifGZ375ITXEun5RaSpZzy8ZG1jaU8MX79+MbM+fuGkkqpcidTxyjpTPAF16/kuJct9HhiFn45NVLmF+ez9/9eg/DwQmjw3mZ7z95nH0dAf7ppmZK8uS4sqLP3dhMUa7bdqshRGrsbffx1S0Huaa5mrdsbDA6nKxzy8YGrl1ZzX9uOczedp/R4czZL54/xd52P/94wwqK5LOIpfzjDc04FPzjH8x7YirST2vNP/zuRUaCEb7ypjU4HNKYxewWVhbyd9cs5eH9PfxupznraH7v8WMc6hnmX25eRaHHZXQ4YpacDsWX37SGobEJ/vVPB4wOZ1qSVEqBve0+vvHoEW5aW8frVtcaHY6YpfwcF1976zq6/OP8070tpvkwt7fdx9cfOcwNa2q5QY4ryyoryOGfbmpmd5uP7z1+1OhwhIn4xsJ87Je7qCz08NVb1khXRwMopfjKm9ZQWeThwz/fadoZwJk42T/Klx84yKuWVPD6tXVGhyNmqd6bx99du4wnD/eZ9sRUpN+vt7fzyP4e/v7aZSyrkZIHVvGByxZy4YIy/uneFtPV6dtxaohv/fkIN6+r47VSs9GymuuKueOKhfx6RztbWrqNDucVJKk0R4HgBH99924qizx86WaphWFVG+eV8onXLOF3Ozu4a5vx3eD8YxN84q5dVBZ5+Lc3rJaTTYt7/do6Xr+2jq8/eoQdp4aMDkeYQCQa4+N37aLbH+Tbb9+AN19qZhiltCCH/377enqHg3z8rl1MWLC1eyQa4+9+vQeXU0mC0sLeffF8Ns0r5Z/ubaF1QLbBZZv9nQE+98d9XLKonPdftsDocMQsOB2Kr79tHW6n4sM/32Gaovv+sQk++atd1Jbk8kWp2Wh5f/2apayuL+HTv91Lt99cNbwkqTQHsZjmb361h7bBMb5123pK8mWpuZV9/NVLuHxpJV+4t4XtJwcNiyMa03zi7l10+Mb5thxXtqCU4ktvXEVtSS4f++VO+oZDRockDKS15p/v289TR/r54htWsnFeqdEhZb31TaX86xtW89SRflOtWJ2p/9hyiO2nhvjSG1ZRWyLFua3K6VB849Z1KAWfuHsX4Yj1Epzi/AyNhvnwL3ZQkufmm7euxynb3iyn3pvHN25dz6GeYf7217uJGVzyIBKN8bG7dtLtD/LNW9dLeRYbyHE5+Oat6whHYnz4FzsIRcyRvARJKs3JV7cc4tEDPfzjDSu4YH6Z0eGIOXI6FN982zrqS/P44E+3c6xvJOMxaK35wr0tPHG4jy+8fiWb5LiyjeJcN99/10aGxsJ8+Oc7CE6YZyAQmfXffz7Kz7ae4q8uX8jbLmgyOhyR8NYLGrnjikX88vlWvvHoEaPDmbE/7e3i+08e552bm7h5Xb3R4Yg5aijN59/fvIbdbT4+/0epr5QNQpEof/WzHXT5g3zvnRuoLPIYHZI4T1csreQfX7eCB17s5ssPHjDs9au15ov3Jyavbl4lk1c2srCykK+9ZS27Wn2mKg4vSaXz9L/PnODOJ47xjouaeO8l840OR6RIaUEO//e+C3E5FO/84fOczOC+aK01//XIYX629RS3X76Qd1w0L2PPLTJjZV0J//mWtWw/NcQn794thbuz0PefOMbXHjnMG9fX82np6Gg6/+/aZbx1UwPffOwI37VADbQdpwb51D272dDk5XM3NhsdjkiR162u5aNXLeLuF9r40dMnjA5HpNFENMYn7trFtpOD/Odb1rJxnkwmWt0HLlvAey+Zz/88dYKvGzRB8Y1Hj/B/z53ig5ct4NYLZfLKbq5fXcsnXrOEX+9o5+uPHDY6HECSSuflZ8+d5J/v2881zdX8y82rpHaBzTSV5/PT919EcCLKrT/YyuGe9LeC11rzlQcP8u0/H+Wtmxr4h+vlZNOublxTx+dvbOahlm7+/jd7JLGUJbTWfPPRI3z5wYPcuKaW/7hFuvqYkSPRYeXmdXV89aFD/MeWg6aZBZxqX4ef9/9kO/XePH74ngvwuJxGhyRS6G9fu4zXra7hS386wD3bja/1KFJvIhrjU7/azZaWHj5/Y7MU2LcJpRSfv7GZt21q5FuPHeHLDxzI2FY4rTX/seUg33zsCG/Z2MA/3rAiI88rMu9TVy/hrZsa+Nafj/L9J44ZHQ7SU3AWtNZ874ljfPWhQ1y9oopvv132PNtVc10xv/zQZt794228+bvP8t/v2MAVSyvT8lzj4Sif/u1e7t3TybsvnscXblopiUqbe/9lCxgJRfivRw4TisT4r7eulRNCGwtHYvzTvfu4a1sbb9pQz1ffvAaXU+Z0zMrpUPzXW9eRn+PkO385RsfQOF958xpy3eZ5je5u8/GeH2+j0OPip++/kLICKfRuN45E4d/h4HY+/du9RGOa22TFgW2MhSN8+Oc7eeJwH5993XIpzG0z8QmK1eS4HHz/yeN0+Mb56i1ryM9J36l3KBLlc3/Yxz3b27ntwka+JI1+bE0pxb+9cTVj4ShffvAgY+Eon7x6iWF/c0kqzVBwIsoX7m3h7hfauGltHf/5ljVyEmhzK2qL+cNHL+UDP3mB9/7vNj58xSI+efVSclypOxk82B3gk3fv5lDPMP/vumV8+IpFMgBkiU+8Zgm5bgf/9sBBegNB7nznRsoLpY6C3XT6xvn4XbvYcWqIj161iL+7Zpm8xi3A6Yh/WGsozec/thzicM8I37h1HUurjW/x/dC+Lj75q3jX2V9+cDONZflGhyTSxONy8oN3beIjv9jBP/zuRXoCQT7x6iWyytHiWgfGuP1n2zncM8xX3rRatifZlMOh+JebV1Jfmse/P3SQQ93DfPvt61leU5zy5zo1MMpf372b3W0+Pv7qxfzNa5fKZ40s4HI6+Oat68lzO/nmY0doGxrjy29abUiOQqZKZ+BY3whvufM57n6hjY9etYhvvm2dJJSyRL03j99/5FJuvaCR7z5+jNd96ymePtI/58cdDUX4zy2HeP23n6F/JMSP33sBH7lysQwAWeb2yxfx7dvWs7fdzw3feprnjg0YHZJIEa01d29r5ZqvP8n+zgDfvm09f3/tcnmNW4hSio9etZgfvWcTPYEgN377aX709AnDOvqEIzH+9U/7uePnO1leU8zvP3KpJJSyQF6Ok++/axNv2lDPNx49wkd+sRP/2ITRYYnzdO+eTm789lN0+YP87/sulISSzSmluOOKRfz8AxcxNBbmxm89zdcePsRoKJKSx5+Ixvjx0ye4/ptPcaxvhO+9YwN/K5NXWcXpUHz1ljX8zWuX8rudHbzpu89y3IBmU7JS6SxCkSg/fvok33j0MLluJ//z7k28trna6LBEhuXlOPnym9ZwTXMNn/vjPt75o+fZvLCMD162kKuWV81qC2TfcIh7trfx46dPMDAa5g3r6vjcjc2yQiWL3bS2jgUVBXzirl28/YdbedumRv7+2mVyTFjY0d4R/vm+Fp460s/mhWV89c1raSqXk3+res2Kah765OV85rd7+eL9+7l3dwefvm45lyyuyFgMO04N8tnf7eNQzzDvvngen33dClNtxxPpleNy8LW3rKW5tpivPHiQa7/xJF98wyquXlElJ48WcbxvhH974ACPHuhlfZOXb75tvYwLWeTSxRU88qkr+OL9+/n2n49y9wtt/NXlC3nrBY0U57pn/XjhSIx793Ty3b8c5Xj/KFcsreTLb1pNnTcvDdELs1NK8YnXLKG5tpi/+80erv/mU3zkysX81RULM/ZZQZJK00i+UP/7z0c4OTDGa5ur+dc3rKKqONfo0ISBrlpexaOLruCXz7fy/SeP8cGfbqei0MNrm6vZvLCMVfUlNJbmn94ep7UmEIxwtHeE3W0+njjcxzNH+4nGNFcuq+QTr1nChiZp8SlgVX0J9338Mr7+yGF+8uxJ/vRiF5+6eilvv6hJThwt5ET/KN967Ah/3N1BntvJF9+windc2CRbVWygssjDD9+zid/t7OBrDx/i7T98nlctqeAjVy5m88KytJ3YH+oe5puPHeaBF7upLcnlR+/ZxGtWyORWNlJK8cFXLeTCBWX8zT17+NBPt3PJonL+8YYVrKwrMTo8cQYDIyG+85dj/PS5k+S6nfzD9cv5wGULpK5eFiotyOG/3raOd148j68+dJAv/ekAX3v4MNeurOaalTVcvLCc0rPUxxsLR9h+cog/H+zl3j2dDI6GWVFbzI/es4lXL5cEs4Crm6t56K8v50t/2s/XHz3Mb3a28dnrV3DNypq014FWZu1qcj42bdqkt2/fft73HxwN8/tdHfzoqeN0+oMsryniM9cv58plVSmM0rp++Xyr0SFM6+0XZX7p8EQ0xmMHerhvbxePH+xlNBw9fV2Rx4XTqQhORAlOxE5fvrCygGuaa7hlYz2Lq4ypyyF/Q/M72jvMP9+3n6eO9FOa7+btFzXxrs3zqSmZW1JbKbVDa70pRWFa0lzHiOlMRGM8fqiPX73Qxl8O9eJ2Kt5z8Xxuv3xhVq02M+t7C6T+/SU4EeXnW0/x3cePMTgaZkFFAW/Z1MBNa+pSsh3NPzbBlpZu/ring2eODpCf4+RDr1rIhy5fSKEnM3OB2fT3tKKJaIxfPt/KNx49jG98gmuaq3nHRfO4bHHFnJLYMk6kbpzY0+bj/547yf17uojEYrztgkb+5rXLqCzKnnHhTMz6/pLp95a97T7u2tbGAy924R+Pb2mt9+YxrzyfyiIPHpeDcCSGb3yCtsExTvSPEtPxlYtXr6jibRc0cfmSCkOSSfI3NL9nj/XzT39s4UjvCPPK83nvJfN5y6bGOX+OONM4kdakklLqOuCbgBP4odb6K1OuV4nrXweMAe/VWu+cyX2ncz4DQSA4wZZ93dy/t4unE6tILlpQxh1XLuLKpZWS9Z1E3kCmF4nGONg9zKHuYdqGxvCPTxCLaTxuJ5WFHuZXFLCqvpjaEuOXpMrf0Bq01mw9Psj/PnOCRw704FCKSxaVc+sFTdywpva8HtNqJwtzGT/OJFUnC8PBCZ452s/jh/p49EAv/SMhKgo93LKxgfdfNp+qouxb1WrW9xZI3/vLeDjKg/u6uPuFNradGASgqSyfy5ZUsL7Ry9LqIpZUF561208spukfDdHSGWB3q4+drUNsPT7ARFTTWJbHWzc28s7N8846e50O2fj3tCL/+AQ/ePIYd21rY3A0zLzyfD519VLesL7+vB5PxonzHydiMc3udh+P7O/h4ZZujvWNUpDj5JaNDbzr4vksriqc9WPalVnfX4x6b4lEY+xp97HtxBAHugK0D40xMBomNBHD7VIU57qp9+axvKaI9U2lbF5YTl6OsavY5W9oDZFojC0tPfz4mRPsODVEntvJ1c3VvP/S+aw/z90yZxon0jblpZRyAt8BXgu0Ay8ope7VWu+fdLPrgSWJr4uA7wEXzfC+KbG71cff/2YvDaV53H75Qm5aU0dzXeqr8gv7cjkdrKovYVW9LD8XqaGU4uJF5Vy8qJzWgTHufqGV+/d2sfX4wHknlaxkLuNHOuLp8I3z1OE+9rT72dvu41D3MJGYpsjj4lVLK3jT+gauWFaJW7YzZJW8HCdv2tDAmzY0cLJ/lMcP9fL00X7+uKvjZR+4vfluSvNz8Oa7cTscRGIxIjHNwEiY3uEgE9H45J5SsLSqiPdcPJ+b1taxpqFEJrbEWZXkufn7a5fzidcs4aF93fxiayuj4dQUADY7s40T/77lIN9/4jguh+KihWW899IFvGFdHUXnUS9HZBeX08HGeWVsnFdmdCjCZlxOBzesqeWGNbXsbvNxz/Y2Hnyxi9c2V593UumMz5XSR3u5C4GjWuvjAEqpu4Gbgclv9jcDP9Xx5VJblVJepVQtMH8G902JSxaV84ePXjXh2DsAABcaSURBVMpa+fAmhDChpvJ8/t91y/n7a5cxPhE99x3s4bzHD611V6qDee7YAJ/53YsU5bpY01DCX12xkFctqWTjvFJJJAkA5lcU8N6KBbz30gVEojFaB8c43DPCkZ5heodDDI2F8Y1NEI1pPG4XTodiUWUh1cW51BR7WFZTzOqGkoxtbxP24nE5uXldPTevq8dOZS3OwVTjxM1r61lRU8xVy6ooyZdEkhDCXNY1elnX6OWfX78yLY+fzk8v9UDbpO/beeXswHS3qZ/hfVPC5XSwrtGbjocWQoiUUUqddRuNzcxl/Ej5ycJrV1Tz57+9gvnlBVJ0W5yTy+lgYWUhCysLuW5VjdHhiCyTRROkphonmuuKZaeDEML00jUZms4zlOlGtanTJ2e6zUzuG38ApW4Hbk98O6KUOjTjCOMqgP5Z3ifdzBgTmDSud5g0LswZlxljkr/h7J1PXPPSEUiazGX8ePmNZj9GmPVvDhLbeTHx+wuYNzazxiV/z/M3k9hknLDPuQRIXLNi0vcWM8YEJo3LpH9DsFdc044T6UwqtQONk75vADpneJucGdwXAK31D4AfnG+QSqntZitKaMaYQOKaLTPGZcaYQOKaLbPGlUJzGT9eZrZjhJl/txLb+ZHYZs+scYHEdr7MHNt5MmycmMqsv1uJa3bMGJcZYwKJa7ayIa50FoN4AViilFqglMoBbgXunXKbe4F3q7jNgD+xz3km9xVCCGFPcxk/hBBC2J+ME0IIYRJpW6mktY4opT4GbCHe6vPHWusWpdQdievvBB4g3ubzKPFWn+87233TFasQQgjzmMv4IYQQwv5knBBCCPNIa9VXrfUDxN/QJ19256T/a+CjM71vmpz3ctc0MmNMIHHNlhnjMmNMIHHNllnjSpm5jB9zZObfrcR2fiS22TNrXCCxnS8zx3ZeDBwnpjLr71bimh0zxmXGmEDimi3bx6WyqPWoEEIIIYQQQgghhEiRdNZUEkIIIYQQQgghhBA2ZdukklLqOqXUIaXUUaXUZ6a5/kqllF8ptTvx9fmZ3jfNcf39pJj2KaWiSqmyxHUnlVIvJq7bnsKYfqyU6lVK7TvD9Uop9a1EzHuVUhtm+vOkOa53JOLZq5R6Vim1dtJ1afldzTCujB9bM4gp48dV4rEblVJ/UUodUEq1KKX+eprbZPz4mmFcGT2+ZhiTIe9bdmHWcWGGsRn1Gjbl+DDD2AwZI2YYm1GfQUw5ViQe35TjxSxiy/jxJuNG+plx3JDxIuVxGfHalTFidnGZbnyQcWEKrbXtvogX7DsGLARygD1A85TbXAncfz73TWdcU25/E/DnSd+fBCrS8Pu6HNgA7DvD9a8DHgQUsBl4Pt2/qxnGdQlQmvj/9cm40vm7mmFcRhxbZ43JiOMq8di1wIbE/4uAw9O8FjN+fM0wroweXzOMKePHll2+ZvI7Mur3O9vHz/Br2JTjwwxjM2SMmGFsRh1rphwrEo9vyvFiFrFl/HibYVyGHGt2+JrJ7yjTv9/ZPm4mX8czeN+T84mZx2TI6/ZccRl4bJlufJhhTFkzLth1pdKFwFGt9XGtdRi4G7g5A/dN9WPfBtyVouc+I631k8DgWW5yM/BTHbcV8Cqlaknv7+qccWmtn9VaDyW+3Qo0pOq55xLXWaTt9zXLmDJyXAForbu01jsT/x8GDgD1U26W8eNrJnFl+via4e/qTNL6WrQJs44L5/P4mXwNm3J8mElsRo0Riec23ThxHnFl7DgD844XM43NiONNxo20M+O4IeNFiuMy6LUrY8QsmHF8kHHh5eyaVKoH2iZ93870v8yLlVJ7lFIPKqVWzvK+6YwLpVQ+cB3w20kXa+BhpdQOpdTtKYppJs4Udzp/V7P1AeLZ6SSjfldJmT62ZsTI40opNR9YDzw/5SpDj6+zxDVZRo+vc8RkymPLAsw6Lszq8U02NoA1xgcw3xgBJn4tG32cmXW8OEdsk2X8eJNxIy3MOG7IeJFeZhorTPu6lfOJGcc0ma3HBddcAjUxNc1lesr3O4F5WusRpdTrgD8AS2Z433TGlXQT8IzWenK2+FKtdadSqgp4RCl1MJFRTrczxZ3O39WMKaWuIv5CvWzSxUb9rsCYY2umDDmulFKFxAeeT2qtA1OvnuYuGTm+zhFX8jYZPb7OEZOZjy2zM+u4MNPYksw0NoDJxwcw5RgB5n8tG3acmXW8mEFsydtk/HiTcSNtzDhuyHiRJiYbK8z+upXziZnFlLyN7ccFu65UagcaJ33fAHROvoHWOqC1Hkn8/wHArZSqmMl90xnXJLcyZUmh1roz8W8v8HviS9Qy4Uxxp/N3NSNKqTXAD4GbtdYDycsN/F0ZdWzNVMaPK6WUm/ib2i+01r+b5iaGHF8ziCvjx9e5YjL5sWV2Zh0XZhTbJGYaG8DE4wOYc4xIPK/ZX8uGHGdmHS9mGJshx5uMG2llxnFDxos0MNtYYYHXrZxPzCym7BkXdBqKaRn9RXwF1nFgAS8VmVo55TY1gEr8/0KglXh27pz3TWdciduVEN/PWjDpsgKgaNL/nwWuS+HvbD5nLhZ3Ay8vfLZtNj9PGuNqAo4Cl0y5PK2/qxnElfFj61wxGXhcKeCnwDfOcpuMH18zjCujx9cMYzLk2LLD10x+Rwa+dk07NiQe92zvd4aNDzOIzbAxYgaxGfZaPltcBh9nphwvZhFbxo+3GcYl48b5/35NN27M9HENfB2f7X1PzidmHpOMES9/TtONDzOMKWvGBVtuf9NaR5RSHwO2EK9i/mOtdYtS6o7E9XcCtwAfVkpFgHHgVh3/zU573wzGBfBG4GGt9eiku1cDv1dKQfwP/kut9UOpiEspdRfxKvAVSql24J8A96SYHiBeUf8oMAa872w/TypimmFcnwfKge8mfi8RrfUm0vi7mmFcGT+2ZhATZPi4SrgUeBfwolJqd+KyzxJ/kzXy+JpJXJk+vmYSU8aPLbsw67gwi9jAgNewWceHGcZmyBgxw9gMOdZMPFaAeceLmcZmxPEm40YamXHckPEiLXFl/LUrY8SsmXF8kHFhkmSGSgghhBBCCCGEEEKIGbNrTSUhhBBCCCGEEEIIkUaSVBJCCCGEEEIIIYQQsyZJJSGEEEIIIYQQQggxa5JUEkIIIYQQQgghhBCzJkklIYQQQgghhBBCCDFrklQSKaWUepNS6s9KKZ9SKqSUOqyU+pJSqmLSbXSiXeHU+85PXHfjpMseT1ymlVIRpdRJpdT3lVKVZ3j+m5RSf1FKBZRSo0qpbUqp96lEz8ZJt/tC4jG3TPMYv1FKPT7p+ysTt1015XbFSql/UUrtV0qNK6WGlVJPKqXeqpQ642tLKfWTKT/TgFLqaaXUZ5RSJTP4nRQopb6olDqUeN4epdQTSqkPTBPzdF8/nCamBYnrWqf+ribd5r1KqR2Jn3NIKbVLKfVfU25zpuc8eqbfhxBCGGHSODD169Ept/u/xOWT32N/cpb3u+TXTxK3PamU+s9pnv8NSqmHE2NAWCnVoZS6Wyl16aTbvGIMmHTdqsR1V066TN6DhRAiRVTcicT76OJprp/2HGHKbaYdAyZdf6axSCul3jnpdpMvH1dKHVBKfVop5ZrmMR0qfv7zlIqfk4UTcfxQKbUmcS7RqpT6xTT39SbOLV5xviDEmbziIBTifCmlvgZ8Evhf4OtAAGgG7gBWAm88z4f+C/BZ4sfrBuBLwCLg6inP/2ngK8DPgf8AwsCNwP8AFwIfnuaxr1FKXaC1fmE2ASmlqoDHAS/wX8AOwAO8GvghEAL+eJaHOAi8D1BAGbAZ+DvgdqXUq7XWJ89y398C64n/HvYBVcDlwOuAH0257TuA41Mu653mMW9L/NsIXAo8PflKpdQ/AF8Evgp8BsgFNgLvBP5mymN9DfjNlMuCZ/l5hBDCKH7gumkuA0AplQu8IfHtbbz0HvtF4M5J9/k34uPBRyZd1nemJ1VKfR34BPBT4HvAADAPuBV4Wim1WGt9bHY/ymnyHiyEEKlxMTA/8f9biX/2TofpxiKAqRMCyff3POLnOF8B3JPjUvGJ7XsS138f+HdgGFgKvAfYQ/yc6hPA75VSP9Ra/2XSc3yJ+MKTT8/5pxJZQ5JKIiWUUjcRTy58QGv940lXPaGU+gFwzRweflBrvTXx/6eVUvnAl5VSdVrrzsTzbyT+of7ftdafmXTfR5VSh4DvKqW2aK3/MPlxgXbgH3nppGGmvgeUApu01h2TLn9IKfXfQMn0dzttdNLPBPAnpdT3gW3Ek3JXTXcnpdQS4FrgrVrrX0+66ldnWGG0V2u97xyxQPxkaSuwOvH/p6dc/zHg+1rrz0667D6l1D9P81gnp/xsQghhVpFzvF/dABQDjwFXKaVqtNbdiYTP6aSPUmoQcMzkvU8pdTPxCZj3aa1/MuXqnyXG0/HZ/RgvI+/BQgiRGrcBo8QncW8jfUmlc41FSZPf3/+ilFoJvHtKXB8nPpF/jdb6sUmXPwH8j1LqgwBa6z8ope4HvqOUWqu1nlBKbSA+Cf9BrfXAXH8okT1k+5tIlU8BO6cklADQWke11g+m8Ln2JP5tnHTZx4hn4f9tmtv/gPiH/09MDS1x+9crpVbP9MmVUvOIv1n/25SEUvxBtW7VWr8408ebdL924F+AK5VSy89wM2/i3+5p7q9n+5wAiQFpFfFk1r3AW6ZZSutN5XMKIYRF3AZ0EB9jHMBbU/CYnwRemCahBIDW+r7khIkQQghjKKWcwFuIfzb+MdCslFpjbFSvsIeXnw9BfIz57ZSE0mla6x9qraOJbz9OfJXs3yYmp78DPAP8JC3RCtuSpJKYM6WUG7gEeGgWd3MopVyTvwDnDO/bBMSAU5Muuxz4s9Y6MPXGiTfO+4BLpkmW/Bo4THy10kxdTnzb2mx+3pl6JPHv5jNcf4j4jMk3lFLXJLZmnI1z6u95mhVNbwcixLfV3QVUAq+ZcpudwMeVUu9RSpWf4zlf8bdVZ6kxJYQQRjrTe6RSqoj4SqV7tNYHib8P3na2x5rJcxHfTvHwXOM+C3kPFkKIuXs1UA3cTXzL2QRzHAPOZpr37ZnsKGoCTkx6jEbi2/VmNMYkym18Efgc8M/ES1vcIZPGYrbkQ4ZIhXLi9YRaZ3GfbxJ/c578daZCoirx5upRSl0C/APwA6315JUz9bw8yTTVqUSML0uIaK1jxPcjv0UptXSGsdcn/p3NzztT7Yl/q6e7MpE0+xCwDNgCBFS8OPiHzrD9bTev/D2/Z8ptbgUeSSxz3QIM8cpB86PACPGZiz6lVIuKFykvnuY5p/vbvmIFmxBCmEA5r3y/SibV30i8ftzdie/vBjYrpRbM8fk8QNvkC1Xc2ZL/syHvwUIIMXe3AT7gIa31IPGJ31vn+P58JtONRRNKqflTbpecNChSSt0GvIn4LoekusS/U8eYqZMNk3+GrwEniSeWvqa13p+qH0pkD0kqiVSaTVb7P4ALpny9/gy3fRPxN9cg8SWZPbxyK9tc/Jx4gugfZnm/dGTxzzlQaa3vIr5U9f3ET3KWEt/i98tpbn4rr/w933f6yZS6CFiYeBy01mHgd8AbJ6+C0lrvBVYQ/xt9NxHn54DtSqnCKc853d/2C+f6uYQQwgB+Xvl+9XziutuA41rrbYnv7yb+vn/rHJ4v+R4/dfz4W15+MvHROTyHvAcLIcQcKKU8xCcWfp/4bAzx1fzzOfNugrmYbiy6AJi6FTo5aRAg/rn/O1rruyddf6Yx5lu8fIy5IXmF1noi8bgQHz+EmDUp1C1SYYB4t7OmWdynVWu9ffIF02Tjk/5MvAOBB7gp8f8v8fKuBB3EEy1nMi8R4yuKzmmtI0qprwLfUkp9YQaxJ+soNXHm1VXnK7kKqudsN0qsKvpf4H8T2w+/D7xPKfUVrfWeSTdtOUeh7tuIDy6PK6W8icv+BHyAeDe53016zhDxhNR9ACreXvuHidt+86WHfOXfVgghTCoy3fuVUqqCeIfR70x6bxwGXiC+ZfjL5/l8/cTHooYpl/+MeEdREs9xOr7Ev9NtD3dOuU2SvAcLIcTcXE+8nugDk8aAx4m/f98GPJfi55t2LJrGfxDv7FZCvHbSp5RSj2qtH0hcnzxHmTrGfJX4boNa4jWipgpP+VeIWZGVSmLOEhnuZ4h3JUuHIa31dq31M4nObj8GPpnYN5z0JPEC10VT75yoJXED8KzWeuqH76QfA73MrH3mk8RnANLx8ya75M14sEr8/r+e+PZMBb5fIfF7eSvxVqSniG97G+KlRNJZ941rrX9EvIPejJ9TCCEs4i3EJ97+mpfeG4eAC4FVSqlV5/OgiTHoOaZ0RNVa9yTGuaknFQPEawjWTPNwtYl/e88nFiGEEGeU/Az8a156/28jPsH91kQRbyO0JsaKx4jv5DgE/EdyO5vWuo34VrapY0xysmHWjYSEmAlJKolU+QawSSk1tV5Pch/vdSl8rn9K/PupSZf9N/Gs/XRb2D4ILOHlq2leJrEK5z+JbymrPdPtErc9Bfwe+KxS6hW3VUo1zqab3KT7NRDfUvYXrfWhM9ymSCmVN81VSxL/nnWF0xRXEv9ZPw1cNeXrp8CNySSdUqpqmlgqif/OZ/OcQghhBbcBB3jle+N1xFd3zmUL3DeAi5RS7zrXDbXW48AO4OZprr6ZeFfOVK+YFUKIrJUo63Aj8e1uU8eAvyFe9/QqwwJMSEwqfw5oJr6TI+kbwC1KqSszH5XIVrL9TaSE1vo+pdR/AT9SSl0K/JF4YeflwB3Es+Yp6ZamtW5XSv0f8CGl1L9orX1a6x1Kqc8CX1FK1ROvfREmPih8DLhTa/3Hczz094HPEu9k98Q5bvvhxG22J37uHcRnL64gXgvj3Zx9NqBAKbWZ+N5nb+I57yC+veJ9Z7nfMuBepdSPgWeBMWAd8e51u4Gnp9x+zTQ1j/xa6wO8VIDwm4mk2mlKqUDiZ3gD8W0ZLyql/ki8m0Qv8e2Ef5d4/v+b8vjzEz/bZFpr/TxCCGFyiQT/ZcA/aK0fn+b6h4i/f/5/5/P4Wus/KqW+AfxEKXUV8S3F/cQLtb42cbORSXf5AnC/Uupu4ic5E8THtr8CPpJoODGZvAcLIcT5uxnIJ/75+GXvm0qpZ4h/5r4NeHTSVa9VSk1dub9/UtHrpUqpW6ZcP6q1fjDxf9c079sAbVrrjmkuT/otcBD4e17a1vZt4p2qH1RKfZ94gfFhoApIxjB6lscUYtYkqSRSRmv9t0qpZ4kncX4J5BFPJt1LfBVQKn2FePLlwyRqW2it/10ptZ94wdN7iNeb2AfcTnwf8bniH1NKfR341xnctjfx5v93xLux/SvxD/q7iK+guv8cD7Gc+BaIGPHifAeId1/4ntbaf5b7HSNex+ha4j97HvEi4z8G/n2a7X2/mOYxHlNKXQ+8Gfj11IRS4ufbmfhdvp14UulfiA+y3wLKiM+OPwu8TWt9Ysrd/zbxNVkUeb8RQljDrcS3OE/3/gnx5g6/UkpddL6JGq31p5RSTwIfAX4EFAF9xMeF10060UBr/YBS6gbiJzK/JD627Qfeo7X+2TQPL+/BQghx/m4Djkz3/q61nlBK3QPcppT6yKSr/muax/lnXmqScBMvX00E8dIT8xP/L2H60hefI15Hdlpa65hS6svA/ymlLtZaP5e47C3Ae4nvwHg/8U6mncBTwCVa61TXhBJZTmmdjgZWQgghhBBCCCGEEMLOpKaSEEIIIYQQQgghhJg1SSoJIYQQQgghhBBCiFmTpJIQQgghhBBCCCGEmDVJKgkhhBBCCCGEEEKIWZOkkhBCCCGEEEIIIYSYNUkqCSGEEEIIIYQQQohZk6SSEEIIIYQQQgghhJg1SSoJIYQQQgghhBBCiFmTpJIQQgghhBBCCCGEmLX/H8IuC8Z+G0RxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1800 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how data is distributed for every column\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (20, 25))\n",
    "plotnumber = 1\n",
    "for column in data:\n",
    "    if plotnumber <= 9:\n",
    "        ax = plt.subplot(3, 3, plotnumber)\n",
    "        sns.distplot(data[column])\n",
    "        plt.xlabel(column, fontsize = 15)\n",
    "        \n",
    "    plotnumber += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8db6571",
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.histplot(data[['AGE', 'LUNG_CANCER']], x = \"AGE\", hue=\"LUNG_CANCER\")\n",
    "plt.title(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a578b8ca",
   "metadata": {
    "papermill": {
     "duration": 0.073622,
     "end_time": "2021-10-02T11:31:16.121349",
     "exception": false,
     "start_time": "2021-10-02T11:31:16.047727",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**We can see that the distribution of data is normal ! lets move for the Model preparation.** 🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132910ad",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Find Correlations in Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38395b8f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(data.corr(), annot=True);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569358c0",
   "metadata": {
    "papermill": {
     "duration": 0.07183,
     "end_time": "2021-10-02T11:31:16.266208",
     "exception": false,
     "start_time": "2021-10-02T11:31:16.194378",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# **Model Preparation**\n",
    "\n",
    "Spilt into X & Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "667ce9a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:16.417274Z",
     "iopub.status.busy": "2021-10-02T11:31:16.416635Z",
     "iopub.status.idle": "2021-10-02T11:31:16.419581Z",
     "shell.execute_reply": "2021-10-02T11:31:16.419047Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.482791Z"
    },
    "papermill": {
     "duration": 0.080964,
     "end_time": "2021-10-02T11:31:16.419712",
     "exception": false,
     "start_time": "2021-10-02T11:31:16.338748",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# separating the data and target\n",
    "X = data.drop(columns=['LUNG_CANCER'],axis=1)\n",
    "y = data['LUNG_CANCER']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "811f1830",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:16.571471Z",
     "iopub.status.busy": "2021-10-02T11:31:16.570856Z",
     "iopub.status.idle": "2021-10-02T11:31:16.574088Z",
     "shell.execute_reply": "2021-10-02T11:31:16.573555Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.490495Z"
    },
    "papermill": {
     "duration": 0.081128,
     "end_time": "2021-10-02T11:31:16.574218",
     "exception": false,
     "start_time": "2021-10-02T11:31:16.493090",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is  (276, 15)\n",
      "The shape of Y is  (276,)\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of X is \" , X.shape)\n",
    "print(\"The shape of Y is \" , y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "341593ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:16.728310Z",
     "iopub.status.busy": "2021-10-02T11:31:16.724947Z",
     "iopub.status.idle": "2021-10-02T11:31:16.894127Z",
     "shell.execute_reply": "2021-10-02T11:31:16.894720Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.504204Z"
    },
    "papermill": {
     "duration": 0.24632,
     "end_time": "2021-10-02T11:31:16.894893",
     "exception": false,
     "start_time": "2021-10-02T11:31:16.648573",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train is  (220, 15)\n",
      "Shape of X_test  is  (56, 15)\n",
      "Shape of Y_train is  (220,)\n",
      "Shape of Y_test  is  (56,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# separating into train and testing\n",
    "X_train, X_test,Y_train,Y_test = train_test_split(X,y,test_size=0.2,stratify=y,random_state=42)\n",
    "print(\"Shape of X_train is \" ,X_train.shape)\n",
    "print(\"Shape of X_test  is \" ,X_test.shape)\n",
    "print(\"Shape of Y_train is \" ,Y_train.shape)\n",
    "print(\"Shape of Y_test  is \" ,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3f4d29cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:17.050617Z",
     "iopub.status.busy": "2021-10-02T11:31:17.046678Z",
     "iopub.status.idle": "2021-10-02T11:31:17.053869Z",
     "shell.execute_reply": "2021-10-02T11:31:17.054337Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.519679Z"
    },
    "papermill": {
     "duration": 0.085183,
     "end_time": "2021-10-02T11:31:17.054504",
     "exception": false,
     "start_time": "2021-10-02T11:31:16.969321",
     "status": "completed"
    },
    "scrolled": true,
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    190\n",
      "1     30\n",
      "Name: LUNG_CANCER, dtype: int64\n",
      "0    48\n",
      "1     8\n",
      "Name: LUNG_CANCER, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# After statify Y train & test values\n",
    "print(Y_train.value_counts())\n",
    "print(Y_test.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e89f8d0",
   "metadata": {
    "papermill": {
     "duration": 0.07314,
     "end_time": "2021-10-02T11:31:17.202612",
     "exception": false,
     "start_time": "2021-10-02T11:31:17.129472",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# **Feature Scalilng**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3f862af7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:17.361087Z",
     "iopub.status.busy": "2021-10-02T11:31:17.360435Z",
     "iopub.status.idle": "2021-10-02T11:31:17.362740Z",
     "shell.execute_reply": "2021-10-02T11:31:17.362152Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.529817Z"
    },
    "papermill": {
     "duration": 0.086397,
     "end_time": "2021-10-02T11:31:17.362873",
     "exception": false,
     "start_time": "2021-10-02T11:31:17.276476",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# scaling the data \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "68a7ad80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:17.517096Z",
     "iopub.status.busy": "2021-10-02T11:31:17.514074Z",
     "iopub.status.idle": "2021-10-02T11:31:17.519484Z",
     "shell.execute_reply": "2021-10-02T11:31:17.520043Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.544209Z"
    },
    "papermill": {
     "duration": 0.083302,
     "end_time": "2021-10-02T11:31:17.520223",
     "exception": false,
     "start_time": "2021-10-02T11:31:17.436921",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.04653624,  0.48957626,  0.94686415, ..., -1.26025208,\n",
       "        -0.94686415, -1.08545743],\n",
       "       [-0.95553309,  1.21082701, -1.05611771, ...,  0.79349205,\n",
       "         1.05611771,  0.92127059],\n",
       "       [ 1.04653624, -0.23167448,  0.94686415, ...,  0.79349205,\n",
       "         1.05611771,  0.92127059],\n",
       "       ...,\n",
       "       [ 1.04653624,  1.09061855,  0.94686415, ...,  0.79349205,\n",
       "         1.05611771,  0.92127059],\n",
       "       [ 1.04653624, -0.71250831, -1.05611771, ..., -1.26025208,\n",
       "        -0.94686415, -1.08545743],\n",
       "       [ 1.04653624, -1.19334214, -1.05611771, ...,  0.79349205,\n",
       "        -0.94686415, -1.08545743]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ad766a79",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "randf=RandomForestClassifier(n_estimators=100)\n",
    "randf.fit(X_train,Y_train)\n",
    "y_pred=randf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3eb0f63f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05188234, 0.2028285 , 0.03905459, 0.0610416 , 0.04305452,\n",
       "       0.0822048 , 0.05833999, 0.06249451, 0.08548712, 0.0476663 ,\n",
       "       0.08213085, 0.05652442, 0.04079609, 0.05496677, 0.0315276 ])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f2d37f65",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "features = data.columns.values.tolist()[0:15] #Dropping Dependent Variable Lung Cancer \n",
    "feature_pro = pd.Series(randf.feature_importances_,index=features).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "81119afe",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGE                      0.202829\n",
       "ALLERGY                  0.085487\n",
       "PEER_PRESSURE            0.082205\n",
       "ALCOHOL CONSUMING        0.082131\n",
       "FATIGUE                  0.062495\n",
       "YELLOW_FINGERS           0.061042\n",
       "CHRONIC DISEASE          0.058340\n",
       "COUGHING                 0.056524\n",
       "SWALLOWING DIFFICULTY    0.054967\n",
       "GENDER                   0.051882\n",
       "WHEEZING                 0.047666\n",
       "ANXIETY                  0.043055\n",
       "SHORTNESS OF BREATH      0.040796\n",
       "SMOKING                  0.039055\n",
       "CHEST PAIN               0.031528\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_pro "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67bb27c1",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Feature Importances Vizualizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a1d3d09a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# Creating a bar plot\n",
    "sns.barplot(x=feature_pro, y=feature_pro.index)\n",
    "# Add labels to your graph\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizing Important Features\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3bc047",
   "metadata": {
    "papermill": {
     "duration": 0.0797,
     "end_time": "2021-10-02T11:31:17.675506",
     "exception": false,
     "start_time": "2021-10-02T11:31:17.595806",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# **Model Training**\n",
    "\n",
    "**We will train different model after the evaluation of model we will select out best model for production.**\n",
    "\n",
    "1.   Logistic Regression\n",
    "2.   KNN\n",
    "3.   SVC\n",
    "3.   Decision Tree\n",
    "4.   Random Forest Regressor\n",
    "5.   XgBoost\n",
    "6.   Gradient Boosting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab607e9",
   "metadata": {
    "papermill": {
     "duration": 0.074265,
     "end_time": "2021-10-02T11:31:17.824394",
     "exception": false,
     "start_time": "2021-10-02T11:31:17.750129",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a86e1fc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:17.977401Z",
     "iopub.status.busy": "2021-10-02T11:31:17.976743Z",
     "iopub.status.idle": "2021-10-02T11:31:18.064538Z",
     "shell.execute_reply": "2021-10-02T11:31:18.064003Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.555842Z"
    },
    "papermill": {
     "duration": 0.165392,
     "end_time": "2021-10-02T11:31:18.064687",
     "exception": false,
     "start_time": "2021-10-02T11:31:17.899295",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of Logistic Regression Model is 0.9363636363636364\n",
      "Test Accuracy of Logistic Regression Model is 0.9464285714285714\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, Y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "lr_train_acc = accuracy_score(Y_train, lr.predict(X_train))\n",
    "lr_test_acc = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "print(f\"Training Accuracy of Logistic Regression Model is {lr_train_acc}\")\n",
    "print(f\"Test Accuracy of Logistic Regression Model is {lr_test_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "07112652",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:18.217580Z",
     "iopub.status.busy": "2021-10-02T11:31:18.216611Z",
     "iopub.status.idle": "2021-10-02T11:31:18.224120Z",
     "shell.execute_reply": "2021-10-02T11:31:18.224594Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.575791Z"
    },
    "papermill": {
     "duration": 0.084961,
     "end_time": "2021-10-02T11:31:18.224750",
     "exception": false,
     "start_time": "2021-10-02T11:31:18.139789",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[48,  0],\n",
       "       [ 3,  5]], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix \n",
    "confusion_matrix(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e0913cce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:18.376811Z",
     "iopub.status.busy": "2021-10-02T11:31:18.375880Z",
     "iopub.status.idle": "2021-10-02T11:31:18.385208Z",
     "shell.execute_reply": "2021-10-02T11:31:18.384591Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.582515Z"
    },
    "papermill": {
     "duration": 0.08672,
     "end_time": "2021-10-02T11:31:18.385344",
     "exception": false,
     "start_time": "2021-10-02T11:31:18.298624",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97        48\n",
      "           1       1.00      0.62      0.77         8\n",
      "\n",
      "    accuracy                           0.95        56\n",
      "   macro avg       0.97      0.81      0.87        56\n",
      "weighted avg       0.95      0.95      0.94        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "print(classification_report(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074d426c",
   "metadata": {
    "papermill": {
     "duration": 0.07577,
     "end_time": "2021-10-02T11:31:19.255964",
     "exception": false,
     "start_time": "2021-10-02T11:31:19.180194",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **SVC**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7064820b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:19.415159Z",
     "iopub.status.busy": "2021-10-02T11:31:19.414512Z",
     "iopub.status.idle": "2021-10-02T11:31:19.421981Z",
     "shell.execute_reply": "2021-10-02T11:31:19.422631Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.645843Z"
    },
    "papermill": {
     "duration": 0.090449,
     "end_time": "2021-10-02T11:31:19.422865",
     "exception": false,
     "start_time": "2021-10-02T11:31:19.332416",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of SVC Model is 0.9545454545454546\n",
      "Test Accuracy of SVC Model is 0.9107142857142857\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "\n",
    "y_pred = svc.predict(X_test)\n",
    "\n",
    "svc_train_acc = accuracy_score(Y_train, svc.predict(X_train))\n",
    "svc_test_acc = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "print(f\"Training Accuracy of SVC Model is {svc_train_acc}\")\n",
    "print(f\"Test Accuracy of SVC Model is {svc_test_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a0df5176",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:19.584364Z",
     "iopub.status.busy": "2021-10-02T11:31:19.583689Z",
     "iopub.status.idle": "2021-10-02T11:31:19.586516Z",
     "shell.execute_reply": "2021-10-02T11:31:19.587027Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.658337Z"
    },
    "papermill": {
     "duration": 0.086247,
     "end_time": "2021-10-02T11:31:19.587191",
     "exception": false,
     "start_time": "2021-10-02T11:31:19.500944",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[46,  2],\n",
       "       [ 3,  5]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "confusion_matrix(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9159dacc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:19.745907Z",
     "iopub.status.busy": "2021-10-02T11:31:19.745306Z",
     "iopub.status.idle": "2021-10-02T11:31:19.754757Z",
     "shell.execute_reply": "2021-10-02T11:31:19.754009Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.669119Z"
    },
    "papermill": {
     "duration": 0.090064,
     "end_time": "2021-10-02T11:31:19.754922",
     "exception": false,
     "start_time": "2021-10-02T11:31:19.664858",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.96      0.95        48\n",
      "           1       0.71      0.62      0.67         8\n",
      "\n",
      "    accuracy                           0.91        56\n",
      "   macro avg       0.83      0.79      0.81        56\n",
      "weighted avg       0.91      0.91      0.91        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "print(classification_report(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca028f4",
   "metadata": {
    "papermill": {
     "duration": 0.077376,
     "end_time": "2021-10-02T11:31:19.909996",
     "exception": false,
     "start_time": "2021-10-02T11:31:19.832620",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **DecisionTreeClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2e683bb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:20.071381Z",
     "iopub.status.busy": "2021-10-02T11:31:20.070710Z",
     "iopub.status.idle": "2021-10-02T11:31:20.102875Z",
     "shell.execute_reply": "2021-10-02T11:31:20.103443Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.681571Z"
    },
    "papermill": {
     "duration": 0.115986,
     "end_time": "2021-10-02T11:31:20.103618",
     "exception": false,
     "start_time": "2021-10-02T11:31:19.987632",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of Decision Tree Model is 0.9954545454545455\n",
      "Test Accuracy of Decision Tree Model is 0.8928571428571429\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(X_train, Y_train)\n",
    "\n",
    "y_pred = dtc.predict(X_test)\n",
    "\n",
    "dtc_train_acc = accuracy_score(Y_train, dtc.predict(X_train))\n",
    "dtc_test_acc = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "print(f\"Training Accuracy of Decision Tree Model is {dtc_train_acc}\")\n",
    "print(f\"Test Accuracy of Decision Tree Model is {dtc_test_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aac1ad98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:20.261640Z",
     "iopub.status.busy": "2021-10-02T11:31:20.261055Z",
     "iopub.status.idle": "2021-10-02T11:31:20.266467Z",
     "shell.execute_reply": "2021-10-02T11:31:20.266982Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.691492Z"
    },
    "papermill": {
     "duration": 0.086122,
     "end_time": "2021-10-02T11:31:20.267160",
     "exception": false,
     "start_time": "2021-10-02T11:31:20.181038",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[44,  4],\n",
       "       [ 2,  6]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "confusion_matrix(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d08d9323",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:20.425804Z",
     "iopub.status.busy": "2021-10-02T11:31:20.425214Z",
     "iopub.status.idle": "2021-10-02T11:31:20.433326Z",
     "shell.execute_reply": "2021-10-02T11:31:20.433818Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.701860Z"
    },
    "papermill": {
     "duration": 0.089446,
     "end_time": "2021-10-02T11:31:20.434006",
     "exception": false,
     "start_time": "2021-10-02T11:31:20.344560",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.92      0.94        48\n",
      "           1       0.60      0.75      0.67         8\n",
      "\n",
      "    accuracy                           0.89        56\n",
      "   macro avg       0.78      0.83      0.80        56\n",
      "weighted avg       0.91      0.89      0.90        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "print(classification_report(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe964f2",
   "metadata": {
    "papermill": {
     "duration": 0.078879,
     "end_time": "2021-10-02T11:31:20.591732",
     "exception": false,
     "start_time": "2021-10-02T11:31:20.512853",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **Hyper parameter tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a621acca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:20.757308Z",
     "iopub.status.busy": "2021-10-02T11:31:20.753241Z",
     "iopub.status.idle": "2021-10-02T11:31:25.625608Z",
     "shell.execute_reply": "2021-10-02T11:31:25.626099Z",
     "shell.execute_reply.started": "2021-10-02T11:29:08.713809Z"
    },
    "papermill": {
     "duration": 4.954645,
     "end_time": "2021-10-02T11:31:25.626276",
     "exception": false,
     "start_time": "2021-10-02T11:31:20.671631",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 512 candidates, totalling 2560 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [3, 5, 7, 10],\n",
       "                         'min_samples_leaf': range(2, 10),\n",
       "                         'min_samples_split': range(2, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hyper parameter tuning\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "grid_params = {\n",
    "    'criterion' : ['gini', 'entropy'],\n",
    "    'max_depth' : [3, 5, 7, 10],\n",
    "    'min_samples_split' : range(2, 10, 1),\n",
    "    'min_samples_leaf' : range(2, 10, 1)\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(dtc, grid_params, cv = 5, n_jobs = -1, verbose = 1)\n",
    "grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7be53270",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:25.792604Z",
     "iopub.status.busy": "2021-10-02T11:31:25.790322Z",
     "iopub.status.idle": "2021-10-02T11:31:25.795880Z",
     "shell.execute_reply": "2021-10-02T11:31:25.796587Z",
     "shell.execute_reply.started": "2021-10-02T11:29:10.684282Z"
    },
    "papermill": {
     "duration": 0.089268,
     "end_time": "2021-10-02T11:31:25.796807",
     "exception": false,
     "start_time": "2021-10-02T11:31:25.707539",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 5, 'min_samples_leaf': 6, 'min_samples_split': 3}\n",
      "0.8863636363636364\n"
     ]
    }
   ],
   "source": [
    "# best parameters and best score\n",
    "\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "761a5e27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:25.967299Z",
     "iopub.status.busy": "2021-10-02T11:31:25.966511Z",
     "iopub.status.idle": "2021-10-02T11:31:25.969662Z",
     "shell.execute_reply": "2021-10-02T11:31:25.970408Z",
     "shell.execute_reply.started": "2021-10-02T11:29:10.690391Z"
    },
    "papermill": {
     "duration": 0.092374,
     "end_time": "2021-10-02T11:31:25.970627",
     "exception": false,
     "start_time": "2021-10-02T11:31:25.878253",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of Decesion Tree Model is 0.9181818181818182\n",
      "Test Accuracy of Decesion Tree Model is 0.8928571428571429\n"
     ]
    }
   ],
   "source": [
    "dtc = grid_search.best_estimator_\n",
    "y_pred = dtc.predict(X_test)\n",
    "dtc_train_acc = accuracy_score(Y_train, dtc.predict(X_train))\n",
    "dtc_test_acc = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "print(f\"Training Accuracy of Decesion Tree Model is {dtc_train_acc}\")\n",
    "print(f\"Test Accuracy of Decesion Tree Model is {dtc_test_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4627663b",
   "metadata": {
    "papermill": {
     "duration": 0.081609,
     "end_time": "2021-10-02T11:31:26.135313",
     "exception": false,
     "start_time": "2021-10-02T11:31:26.053704",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**Visualization the DTC tree.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1a0fdc3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:26.368686Z",
     "iopub.status.busy": "2021-10-02T11:31:26.368018Z",
     "iopub.status.idle": "2021-10-02T11:31:28.017448Z",
     "shell.execute_reply": "2021-10-02T11:31:28.017970Z",
     "shell.execute_reply.started": "2021-10-02T11:29:10.702591Z"
    },
    "papermill": {
     "duration": 1.80162,
     "end_time": "2021-10-02T11:31:28.018149",
     "exception": false,
     "start_time": "2021-10-02T11:31:26.216529",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.66875, 0.9166666666666666, 'X[8] <= -0.073\\ngini = 0.236\\nsamples = 220\\nvalue = [190, 30]'),\n",
       " Text(0.4375, 0.75, 'X[13] <= 0.055\\ngini = 0.38\\nsamples = 102\\nvalue = [76, 26]'),\n",
       " Text(0.225, 0.5833333333333334, 'X[7] <= -0.283\\ngini = 0.487\\nsamples = 50\\nvalue = [29, 21]'),\n",
       " Text(0.1, 0.4166666666666667, 'X[6] <= -0.055\\ngini = 0.475\\nsamples = 18\\nvalue = [7, 11]'),\n",
       " Text(0.05, 0.25, 'gini = 0.346\\nsamples = 9\\nvalue = [2, 7]'),\n",
       " Text(0.15, 0.25, 'gini = 0.494\\nsamples = 9\\nvalue = [5, 4]'),\n",
       " Text(0.35, 0.4166666666666667, 'X[3] <= -0.175\\ngini = 0.43\\nsamples = 32\\nvalue = [22, 10]'),\n",
       " Text(0.25, 0.25, 'X[10] <= -0.138\\ngini = 0.499\\nsamples = 19\\nvalue = [10, 9]'),\n",
       " Text(0.2, 0.08333333333333333, 'gini = 0.426\\nsamples = 13\\nvalue = [4, 9]'),\n",
       " Text(0.3, 0.08333333333333333, 'gini = 0.0\\nsamples = 6\\nvalue = [6, 0]'),\n",
       " Text(0.45, 0.25, 'X[1] <= 0.249\\ngini = 0.142\\nsamples = 13\\nvalue = [12, 1]'),\n",
       " Text(0.4, 0.08333333333333333, 'gini = 0.278\\nsamples = 6\\nvalue = [5, 1]'),\n",
       " Text(0.5, 0.08333333333333333, 'gini = 0.0\\nsamples = 7\\nvalue = [7, 0]'),\n",
       " Text(0.65, 0.5833333333333334, 'X[5] <= -0.009\\ngini = 0.174\\nsamples = 52\\nvalue = [47, 5]'),\n",
       " Text(0.6, 0.4166666666666667, 'X[9] <= -0.128\\ngini = 0.415\\nsamples = 17\\nvalue = [12, 5]'),\n",
       " Text(0.55, 0.25, 'gini = 0.444\\nsamples = 6\\nvalue = [2, 4]'),\n",
       " Text(0.65, 0.25, 'gini = 0.165\\nsamples = 11\\nvalue = [10, 1]'),\n",
       " Text(0.7, 0.4166666666666667, 'gini = 0.0\\nsamples = 35\\nvalue = [35, 0]'),\n",
       " Text(0.9, 0.75, 'X[3] <= -0.175\\ngini = 0.065\\nsamples = 118\\nvalue = [114, 4]'),\n",
       " Text(0.85, 0.5833333333333334, 'X[2] <= -0.055\\ngini = 0.126\\nsamples = 59\\nvalue = [55, 4]'),\n",
       " Text(0.8, 0.4166666666666667, 'gini = 0.0\\nsamples = 23\\nvalue = [23, 0]'),\n",
       " Text(0.9, 0.4166666666666667, 'X[1] <= 0.79\\ngini = 0.198\\nsamples = 36\\nvalue = [32, 4]'),\n",
       " Text(0.85, 0.25, 'X[1] <= 0.069\\ngini = 0.26\\nsamples = 26\\nvalue = [22, 4]'),\n",
       " Text(0.8, 0.08333333333333333, 'gini = 0.18\\nsamples = 20\\nvalue = [18, 2]'),\n",
       " Text(0.9, 0.08333333333333333, 'gini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(0.95, 0.25, 'gini = 0.0\\nsamples = 10\\nvalue = [10, 0]'),\n",
       " Text(0.95, 0.5833333333333334, 'gini = 0.0\\nsamples = 59\\nvalue = [59, 0]')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "plt.figure(figsize=(15,10))\n",
    "tree.plot_tree(dtc,filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ea6d6947",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY',\n",
       "       'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE ', 'ALLERGY ', 'WHEEZING',\n",
       "       'ALCOHOL CONSUMING', 'COUGHING', 'SHORTNESS OF BREATH',\n",
       "       'SWALLOWING DIFFICULTY', 'CHEST PAIN', 'LUNG_CANCER'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea0ba11",
   "metadata": {
    "papermill": {
     "duration": 0.084498,
     "end_time": "2021-10-02T11:31:28.187573",
     "exception": false,
     "start_time": "2021-10-02T11:31:28.103075",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "973d674a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:28.363686Z",
     "iopub.status.busy": "2021-10-02T11:31:28.362882Z",
     "iopub.status.idle": "2021-10-02T11:31:28.743171Z",
     "shell.execute_reply": "2021-10-02T11:31:28.743904Z",
     "shell.execute_reply.started": "2021-10-02T11:29:12.173982Z"
    },
    "papermill": {
     "duration": 0.471868,
     "end_time": "2021-10-02T11:31:28.744148",
     "exception": false,
     "start_time": "2021-10-02T11:31:28.272280",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of Random Forest Model is 0.8636363636363636\n",
      "Test Accuracy of Random Forest Model is 0.8571428571428571\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rand_clf = RandomForestClassifier(criterion = 'gini', max_depth = 3, max_features = 'sqrt', min_samples_leaf = 2, min_samples_split = 4, n_estimators = 180)\n",
    "rand_clf.fit(X_train, Y_train)\n",
    "\n",
    "y_pred = rand_clf.predict(X_test)\n",
    "\n",
    "rand_clf_train_acc = accuracy_score(Y_train, rand_clf.predict(X_train))\n",
    "rand_clf_test_acc = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "print(f\"Training Accuracy of Random Forest Model is {rand_clf_train_acc}\")\n",
    "print(f\"Test Accuracy of Random Forest Model is {rand_clf_test_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4372b6c1",
   "metadata": {
    "papermill": {
     "duration": 0.084613,
     "end_time": "2021-10-02T11:31:28.914226",
     "exception": false,
     "start_time": "2021-10-02T11:31:28.829613",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **KNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "12b18a3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:29.096136Z",
     "iopub.status.busy": "2021-10-02T11:31:29.095224Z",
     "iopub.status.idle": "2021-10-02T11:31:29.115767Z",
     "shell.execute_reply": "2021-10-02T11:31:29.116646Z",
     "shell.execute_reply.started": "2021-10-02T11:29:12.447549Z"
    },
    "papermill": {
     "duration": 0.116222,
     "end_time": "2021-10-02T11:31:29.116965",
     "exception": false,
     "start_time": "2021-10-02T11:31:29.000743",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data  :  0.8863636363636364\n",
      "Accuracy on test data      :  0.8928571428571429\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k_model = KNeighborsClassifier(n_neighbors=16)\n",
    "kfitModel = k_model.fit(X_train, Y_train)\n",
    "\n",
    "# accuracy score on training data\n",
    "\n",
    "kX_train_prediction = kfitModel.predict(X_train)\n",
    "training_data_accuray = accuracy_score(kX_train_prediction,Y_train)\n",
    "print('Accuracy on training data  : ', training_data_accuray)\n",
    "\n",
    "# accuracy score on testing data\n",
    "kX_test_prediction = kfitModel.predict(X_test)\n",
    "kx_lgr_test_data_accuray = accuracy_score(kX_test_prediction,Y_test)\n",
    "print('Accuracy on test data      : ', kx_lgr_test_data_accuray)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb053f68",
   "metadata": {
    "papermill": {
     "duration": 0.090846,
     "end_time": "2021-10-02T11:31:55.714844",
     "exception": false,
     "start_time": "2021-10-02T11:31:55.623998",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **GradientBoostingClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "658f1c0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:31:55.904493Z",
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     "iopub.status.idle": "2021-10-02T11:32:03.863098Z",
     "shell.execute_reply": "2021-10-02T11:32:03.863549Z",
     "shell.execute_reply.started": "2021-10-02T11:29:34.469690Z"
    },
    "papermill": {
     "duration": 8.057705,
     "end_time": "2021-10-02T11:32:03.863723",
     "exception": false,
     "start_time": "2021-10-02T11:31:55.806018",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.001, 0.1, 1, 10],\n",
       "                         'loss': ['deviance', 'exponential'],\n",
       "                         'n_estimators': [100, 150, 180, 200]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gradient Boosting Classifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "parameters = {\n",
    "    'loss': ['deviance', 'exponential'],\n",
    "    'learning_rate': [0.001, 0.1, 1, 10],\n",
    "    'n_estimators': [100, 150, 180, 200]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(gb, parameters, cv = 5, n_jobs = -1, verbose = 1)\n",
    "grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "26a024cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:32:04.055683Z",
     "iopub.status.busy": "2021-10-02T11:32:04.054909Z",
     "iopub.status.idle": "2021-10-02T11:32:04.058712Z",
     "shell.execute_reply": "2021-10-02T11:32:04.058223Z",
     "shell.execute_reply.started": "2021-10-02T11:29:41.731406Z"
    },
    "papermill": {
     "duration": 0.102348,
     "end_time": "2021-10-02T11:32:04.058848",
     "exception": false,
     "start_time": "2021-10-02T11:32:03.956500",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'loss': 'exponential', 'n_estimators': 100}\n",
      "0.8727272727272727\n"
     ]
    }
   ],
   "source": [
    "# best parameter and best score\n",
    "\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "63c57a7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:32:04.252226Z",
     "iopub.status.busy": "2021-10-02T11:32:04.251103Z",
     "iopub.status.idle": "2021-10-02T11:32:04.423453Z",
     "shell.execute_reply": "2021-10-02T11:32:04.422809Z",
     "shell.execute_reply.started": "2021-10-02T11:29:41.738062Z"
    },
    "papermill": {
     "duration": 0.272347,
     "end_time": "2021-10-02T11:32:04.423589",
     "exception": false,
     "start_time": "2021-10-02T11:32:04.151242",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of Gradient Boosting Classifier Model is 0.9954545454545455\n",
      "Test Accuracy of Gradient Boosting Classifier Model is 0.9107142857142857\n"
     ]
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier(learning_rate = 1, loss = 'exponential', n_estimators = 180)\n",
    "gb.fit(X_train, Y_train)\n",
    "\n",
    "y_pred = gb.predict(X_test)\n",
    "\n",
    "gb_train_acc = accuracy_score(Y_train, gb.predict(X_train))\n",
    "gb_test_acc = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "print(f\"Training Accuracy of Gradient Boosting Classifier Model is {gb_train_acc}\")\n",
    "print(f\"Test Accuracy of Gradient Boosting Classifier Model is {gb_test_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "54d1d576",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:32:04.613696Z",
     "iopub.status.busy": "2021-10-02T11:32:04.612852Z",
     "iopub.status.idle": "2021-10-02T11:32:04.616576Z",
     "shell.execute_reply": "2021-10-02T11:32:04.615992Z",
     "shell.execute_reply.started": "2021-10-02T11:29:41.886551Z"
    },
    "papermill": {
     "duration": 0.100659,
     "end_time": "2021-10-02T11:32:04.616706",
     "exception": false,
     "start_time": "2021-10-02T11:32:04.516047",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[47,  1],\n",
       "       [ 4,  4]], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "\n",
    "confusion_matrix(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "221dbe1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:32:04.810101Z",
     "iopub.status.busy": "2021-10-02T11:32:04.808478Z",
     "iopub.status.idle": "2021-10-02T11:32:04.817021Z",
     "shell.execute_reply": "2021-10-02T11:32:04.817908Z",
     "shell.execute_reply.started": "2021-10-02T11:29:41.894838Z"
    },
    "papermill": {
     "duration": 0.107779,
     "end_time": "2021-10-02T11:32:04.818207",
     "exception": false,
     "start_time": "2021-10-02T11:32:04.710428",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.98      0.95        48\n",
      "           1       0.80      0.50      0.62         8\n",
      "\n",
      "    accuracy                           0.91        56\n",
      "   macro avg       0.86      0.74      0.78        56\n",
      "weighted avg       0.90      0.91      0.90        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "\n",
    "print(classification_report(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29acc25d",
   "metadata": {
    "papermill": {
     "duration": 0.094352,
     "end_time": "2021-10-02T11:32:06.829186",
     "exception": false,
     "start_time": "2021-10-02T11:32:06.734834",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## **XGB Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4b18200f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:32:07.027709Z",
     "iopub.status.busy": "2021-10-02T11:32:07.027060Z",
     "iopub.status.idle": "2021-10-02T11:32:07.135651Z",
     "shell.execute_reply": "2021-10-02T11:32:07.137127Z",
     "shell.execute_reply.started": "2021-10-02T11:29:42.088476Z"
    },
    "papermill": {
     "duration": 0.212621,
     "end_time": "2021-10-02T11:32:07.137371",
     "exception": false,
     "start_time": "2021-10-02T11:32:06.924750",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:18:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Training Accuracy of XGB Model is 0.9363636363636364\n",
      "Test Accuracy of XGB Model is 0.9285714285714286\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb = XGBClassifier(booster = 'gblinear', learning_rate = 1, n_estimators = 10)\n",
    "xgb.fit(X_train, Y_train)\n",
    "\n",
    "y_pred = xgb.predict(X_test)\n",
    "\n",
    "xgb_train_acc = accuracy_score(Y_train, xgb.predict(X_train))\n",
    "xgb_test_acc = accuracy_score(Y_test, y_pred)\n",
    "\n",
    "print(f\"Training Accuracy of XGB Model is {xgb_train_acc}\")\n",
    "print(f\"Test Accuracy of XGB Model is {xgb_test_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f72d0991",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:32:10.273645Z",
     "iopub.status.busy": "2021-10-02T11:32:10.273002Z",
     "iopub.status.idle": "2021-10-02T11:32:10.292150Z",
     "shell.execute_reply": "2021-10-02T11:32:10.292626Z",
     "shell.execute_reply.started": "2021-10-02T11:29:42.409354Z"
    },
    "papermill": {
     "duration": 0.122791,
     "end_time": "2021-10-02T11:32:10.292791",
     "exception": false,
     "start_time": "2021-10-02T11:32:10.170000",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.946429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XgBoost</td>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.910714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.910714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model     Score\n",
       "0  Logistic Regression  0.946429\n",
       "6              XgBoost  0.928571\n",
       "2                  SVC  0.910714\n",
       "5    Gradient Boosting  0.910714\n",
       "1                  KNN  0.892857\n",
       "3        Decision Tree  0.892857\n",
       "4        Random Forest  0.857143"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = ['Logistic Regression', 'KNN', 'SVC', 'Decision Tree', 'Random Forest', 'Gradient Boosting', 'XgBoost']\n",
    "scores = [lr_test_acc, knn_test_acc, svc_test_acc, dtc_test_acc, rand_clf_test_acc, gb_test_acc, xgb_test_acc]\n",
    "\n",
    "models = pd.DataFrame({'Model' : models, 'Score' : scores})\n",
    "\n",
    "\n",
    "models.sort_values(by = 'Score', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3155b684",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T11:32:10.496956Z",
     "iopub.status.busy": "2021-10-02T11:32:10.496256Z",
     "iopub.status.idle": "2021-10-02T11:32:10.722890Z",
     "shell.execute_reply": "2021-10-02T11:32:10.722396Z",
     "shell.execute_reply.started": "2021-10-02T11:29:42.423665Z"
    },
    "papermill": {
     "duration": 0.330856,
     "end_time": "2021-10-02T11:32:10.723072",
     "exception": false,
     "start_time": "2021-10-02T11:32:10.392216",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18, 8))\n",
    "\n",
    "sns.barplot(x = 'Model', y = 'Score', data = models)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95503ccf",
   "metadata": {
    "papermill": {
     "duration": 0.098307,
     "end_time": "2021-10-02T11:32:10.921409",
     "exception": false,
     "start_time": "2021-10-02T11:32:10.823102",
     "status": "completed"
    },
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### ***Logistic Regression gives us the best result so we will save this model for production.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d2e6af5f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bc8c4b10",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# save the model to disk\n",
    "filename = 'LungCancer_model.sav'\n",
    "pickle.dump(models, open(filename, 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "50b525b0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.946429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.910714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.910714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XgBoost</td>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model     Score\n",
       "0  Logistic Regression  0.946429\n",
       "1                  KNN  0.892857\n",
       "2                  SVC  0.910714\n",
       "3        Decision Tree  0.892857\n",
       "4        Random Forest  0.857143\n",
       "5    Gradient Boosting  0.910714\n",
       "6              XgBoost  0.928571"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "loaded_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c192307e",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Create Pickle File for Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bbcf863d",
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('Lung_Cancer.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(lr, file)  #Selecting Logistic Regression as the Model because of Giving highest Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c5a781c1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#Reading the Pickle file for to certify model was imported Successfully\n",
    "model = open('Lung_Cancer.pkl','rb')\n",
    "logistic= pickle.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ff028558",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Prediction Parameters testing using the imported Model\n",
    "y_prediction = logistic.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c6ba43cf",
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lr_acc = accuracy_score(Y_test, y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "827d18bb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9464285714285714"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcf162f",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**So this will Verify that Our Imported Pickle file provides the desired out put for the Lung Cancer Dataset for Prdiction Purpose**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d9d68f73",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "## Example for Predicting the Lung Cancer\n",
    "import numpy as np\n",
    "\n",
    "X_New = [0,45,0,1,1,0,0,0,1,0,0,1,1,0,0] #Non Lung Cancer Patinet \n",
    "X_New = np.reshape(X_New, (1, -1))\n",
    "\n",
    "y_New = logistic.predict(X_New)\n",
    "\n",
    "\n",
    "#Lung Cancer Patinet \n",
    "X_2 = [0,55,0,1,1,1,1,2,1,1,2,1,1,2,2] \n",
    "X_2 = np.reshape(X_2, (1, -1))\n",
    "\n",
    "y_2 = logistic.predict(X_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c6933519",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patinet Doesn't Have Lung Cancer\n"
     ]
    }
   ],
   "source": [
    "if y_New == 0:\n",
    "    print(\"Patinet Has Lung Cancer\")\n",
    "else:\n",
    "    print(\"Patinet Doesn't Got Lung Cancer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "58d8cfa5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patinet Have Lung Cancer\n"
     ]
    }
   ],
   "source": [
    "if y_2 == 0:\n",
    "    print(\"Patinet Have Lung Cancer\")\n",
    "else:\n",
    "    print(\"Patinet Doesn't Got Lung Cancer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13825006",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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