review-to-rating-using-encoders.ipynb

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     "text": [
      "/kaggle/input/flipkart-product-review-dataset/flipkart_product.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "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",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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   "source": [
    "df=pd.read_csv(\"/kaggle/input/flipkart-product-review-dataset/flipkart_product.csv\",encoding=\"unicode_escape\")"
   ]
  },
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       "                                         ProductName     Price Rate  \\\n",
       "0  Candes 12 L Room/Personal Air Cooler?ÿ?ÿ(White...  ??3,999    5   \n",
       "1  Candes 12 L Room/Personal Air Cooler?ÿ?ÿ(White...  ??3,999    5   \n",
       "2  Candes 12 L Room/Personal Air Cooler?ÿ?ÿ(White...  ??3,999    3   \n",
       "3  Candes 12 L Room/Personal Air Cooler?ÿ?ÿ(White...  ??3,999    1   \n",
       "4  Candes 12 L Room/Personal Air Cooler?ÿ?ÿ(White...  ??3,999    3   \n",
       "\n",
       "            Review                                            Summary  \n",
       "0           Super!  Great cooler.. excellent air flow and for this...  \n",
       "1          Awesome             Best budget 2 fit cooler. Nice cooling  \n",
       "2             Fair  The quality is good but the power of air is de...  \n",
       "3  Useless product                 Very bad product it's a only a fan  \n",
       "4             Fair                                      Ok ok product  "
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    "df.head()"
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    "df = df.drop(['ProductName','Price','Review'], axis = 1)"
   ]
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       "         Total   Percent\n",
       "Summary     14  0.000074\n",
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    "# Checking for the Missing values\n",
    "# Using isnull fuction to count the total null values in each field\n",
    "total = df.isnull().sum().sort_values(ascending=False) \n",
    "# Percent of missing values is estimated by dividing total missing and the original total\n",
    "percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
    "# Concatenating the Total and Percent fields sing pandas concat fucntion\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "# Displays top 20 from our max sorted list\n",
    "missing_data.head()"
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    "df = df.dropna(subset=['Summary'])"
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   "source": [
    "df = df.dropna(subset=['Rate'])"
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       "         Total  Percent\n",
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    "# Checking for the Missing values\n",
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    "# Percent of missing values is estimated by dividing total missing and the original total\n",
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       "  Rate                                            Summary\n",
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       "3    1                 Very bad product it's a only a fan\n",
       "4    3                                      Ok ok product"
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    "train = df"
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    "train1=train[train['Rate']=='1']\n",
    "train2=train[train['Rate']=='2']\n",
    "train3=train[train['Rate']=='3']\n",
    "train4=train[train['Rate']=='4']\n",
    "train5=train[train['Rate']=='5']"
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   "cell_type": "code",
   "execution_count": 13,
   "id": "ef6d804b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.429055Z",
     "iopub.status.busy": "2023-07-07T05:08:32.428618Z",
     "iopub.status.idle": "2023-07-07T05:08:32.435031Z",
     "shell.execute_reply": "2023-07-07T05:08:32.433998Z"
    },
    "papermill": {
     "duration": 0.024069,
     "end_time": "2023-07-07T05:08:32.437067",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.412998",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train1=train1[:int(train1.shape[0]/100)]\n",
    "train2=train2[:int(train2.shape[0]/100)]\n",
    "train3=train3[:int(train3.shape[0]/100)]\n",
    "train4=train4[:int(train4.shape[0]/100)]\n",
    "train5=train5[:int(train5.shape[0]/100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6fcb7fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.468330Z",
     "iopub.status.busy": "2023-07-07T05:08:32.467942Z",
     "iopub.status.idle": "2023-07-07T05:08:32.475894Z",
     "shell.execute_reply": "2023-07-07T05:08:32.474828Z"
    },
    "papermill": {
     "duration": 0.026087,
     "end_time": "2023-07-07T05:08:32.477973",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.451886",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((196, 2), (62, 2), (156, 2), (396, 2), (1086, 2))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1.shape, train2.shape, train3.shape, train4.shape, train5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0098fd9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.508477Z",
     "iopub.status.busy": "2023-07-07T05:08:32.508107Z",
     "iopub.status.idle": "2023-07-07T05:08:32.515704Z",
     "shell.execute_reply": "2023-07-07T05:08:32.513748Z"
    },
    "papermill": {
     "duration": 0.025946,
     "end_time": "2023-07-07T05:08:32.518382",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.492436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train=pd.concat([train1,train2,train3,train4,train5],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "758db48c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.550084Z",
     "iopub.status.busy": "2023-07-07T05:08:32.549713Z",
     "iopub.status.idle": "2023-07-07T05:08:32.556500Z",
     "shell.execute_reply": "2023-07-07T05:08:32.555281Z"
    },
    "papermill": {
     "duration": 0.02572,
     "end_time": "2023-07-07T05:08:32.559081",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.533361",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1896, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "338c8b7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.590302Z",
     "iopub.status.busy": "2023-07-07T05:08:32.589937Z",
     "iopub.status.idle": "2023-07-07T05:08:32.597686Z",
     "shell.execute_reply": "2023-07-07T05:08:32.596514Z"
    },
    "papermill": {
     "duration": 0.026533,
     "end_time": "2023-07-07T05:08:32.600338",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.573805",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rate', 'Summary'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3584dbea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.631550Z",
     "iopub.status.busy": "2023-07-07T05:08:32.631159Z",
     "iopub.status.idle": "2023-07-07T05:08:32.641722Z",
     "shell.execute_reply": "2023-07-07T05:08:32.640523Z"
    },
    "papermill": {
     "duration": 0.029083,
     "end_time": "2023-07-07T05:08:32.644242",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.615159",
     "status": "completed"
    },
    "tags": []
   },
   "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>Rate</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Very bad product it's a only a fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Very bad cooler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>Small wire and moter capacity is very low fan ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "      <td>The product doesn't cool as rated by other cus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1</td>\n",
       "      <td>Cooler not provide cool air... Because 1year e...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rate                                            Summary\n",
       "3     1                 Very bad product it's a only a fan\n",
       "8     1                                    Very bad cooler\n",
       "48    1  Small wire and moter capacity is very low fan ...\n",
       "59    1  The product doesn't cool as rated by other cus...\n",
       "67    1  Cooler not provide cool air... Because 1year e..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f8964bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.676247Z",
     "iopub.status.busy": "2023-07-07T05:08:32.675906Z",
     "iopub.status.idle": "2023-07-07T05:08:32.679292Z",
     "shell.execute_reply": "2023-07-07T05:08:32.678632Z"
    },
    "papermill": {
     "duration": 0.021981,
     "end_time": "2023-07-07T05:08:32.681348",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.659367",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebd5913",
   "metadata": {
    "papermill": {
     "duration": 0.01495,
     "end_time": "2023-07-07T05:08:32.711455",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.696505",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Remove punctuations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e110f791",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.743724Z",
     "iopub.status.busy": "2023-07-07T05:08:32.743112Z",
     "iopub.status.idle": "2023-07-07T05:08:32.748787Z",
     "shell.execute_reply": "2023-07-07T05:08:32.747586Z"
    },
    "papermill": {
     "duration": 0.025372,
     "end_time": "2023-07-07T05:08:32.751815",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.726443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import string\n",
    "def rmv_punc(text):\n",
    "  punct = string.punctuation\n",
    "  return text.translate(str.maketrans('', '', punct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c8d9885",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.782857Z",
     "iopub.status.busy": "2023-07-07T05:08:32.782357Z",
     "iopub.status.idle": "2023-07-07T05:08:32.795010Z",
     "shell.execute_reply": "2023-07-07T05:08:32.794168Z"
    },
    "papermill": {
     "duration": 0.030295,
     "end_time": "2023-07-07T05:08:32.796975",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.766680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Summary'] = df['Summary'].apply(lambda x: rmv_punc(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a18cc39",
   "metadata": {
    "papermill": {
     "duration": 0.014303,
     "end_time": "2023-07-07T05:08:32.825880",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.811577",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# remove character with undefined name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "16b6538c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.857467Z",
     "iopub.status.busy": "2023-07-07T05:08:32.856966Z",
     "iopub.status.idle": "2023-07-07T05:08:32.862449Z",
     "shell.execute_reply": "2023-07-07T05:08:32.861399Z"
    },
    "papermill": {
     "duration": 0.024612,
     "end_time": "2023-07-07T05:08:32.865401",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.840789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "def rmv_char(text):\n",
    "  text = re.sub('[^a-zA-Z0-9(/)]',' ', text)\n",
    "  text = re.sub('\\s+',' ', text) #remove whitespace\n",
    "  return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72d10012",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.896886Z",
     "iopub.status.busy": "2023-07-07T05:08:32.896301Z",
     "iopub.status.idle": "2023-07-07T05:08:32.921560Z",
     "shell.execute_reply": "2023-07-07T05:08:32.919811Z"
    },
    "papermill": {
     "duration": 0.043784,
     "end_time": "2023-07-07T05:08:32.924165",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.880381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Summary'] = df['Summary'].apply(lambda x: rmv_char(x).lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "25fcadee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.956293Z",
     "iopub.status.busy": "2023-07-07T05:08:32.955832Z",
     "iopub.status.idle": "2023-07-07T05:08:32.965240Z",
     "shell.execute_reply": "2023-07-07T05:08:32.964202Z"
    },
    "papermill": {
     "duration": 0.028575,
     "end_time": "2023-07-07T05:08:32.967807",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.939232",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', '4', '5'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "02154fca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:32.999660Z",
     "iopub.status.busy": "2023-07-07T05:08:32.999046Z",
     "iopub.status.idle": "2023-07-07T05:08:33.010338Z",
     "shell.execute_reply": "2023-07-07T05:08:33.009380Z"
    },
    "papermill": {
     "duration": 0.029565,
     "end_time": "2023-07-07T05:08:33.012373",
     "exception": false,
     "start_time": "2023-07-07T05:08:32.982808",
     "status": "completed"
    },
    "tags": []
   },
   "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>Rate</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Rate, Summary]\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = ['5', '3', '1', '4', '2'] \n",
    "    \n",
    "# selecting rows based on condition \n",
    "df[~df['Rate'].isin(num)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "50ae9345",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:33.044995Z",
     "iopub.status.busy": "2023-07-07T05:08:33.044577Z",
     "iopub.status.idle": "2023-07-07T05:08:33.054831Z",
     "shell.execute_reply": "2023-07-07T05:08:33.053331Z"
    },
    "papermill": {
     "duration": 0.029063,
     "end_time": "2023-07-07T05:08:33.057024",
     "exception": false,
     "start_time": "2023-07-07T05:08:33.027961",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', '4', '5'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[~df['Rate'].isin(num)].index, inplace = True)\n",
    "df['Rate'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1289916",
   "metadata": {
    "papermill": {
     "duration": 0.014752,
     "end_time": "2023-07-07T05:08:33.087030",
     "exception": false,
     "start_time": "2023-07-07T05:08:33.072278",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "78b260e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:33.119526Z",
     "iopub.status.busy": "2023-07-07T05:08:33.118576Z",
     "iopub.status.idle": "2023-07-07T05:08:35.109904Z",
     "shell.execute_reply": "2023-07-07T05:08:35.108396Z"
    },
    "papermill": {
     "duration": 2.010735,
     "end_time": "2023-07-07T05:08:35.112730",
     "exception": false,
     "start_time": "2023-07-07T05:08:33.101995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns #visualize\n",
    "from textblob import TextBlob\n",
    "import nltk\n",
    "from nltk import word_tokenize, FreqDist\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "import wordcloud\n",
    "from PIL import Image\n",
    "from wordcloud import WordCloud "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d462ecdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:35.145222Z",
     "iopub.status.busy": "2023-07-07T05:08:35.144861Z",
     "iopub.status.idle": "2023-07-07T05:08:35.156718Z",
     "shell.execute_reply": "2023-07-07T05:08:35.155584Z"
    },
    "papermill": {
     "duration": 0.031021,
     "end_time": "2023-07-07T05:08:35.159288",
     "exception": false,
     "start_time": "2023-07-07T05:08:35.128267",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ignored_words = set(stopwords.words(\"english\"))\n",
    "ignored_words.add(\"bad\")\n",
    "ignored_words.add(\"good\")\n",
    "ignored_words.add(\"flipkart\")\n",
    "ignored_words.add(\"also\")\n",
    "ignored_words.add(\"like\")\n",
    "ignored_words.add(\"item\")\n",
    "ignored_words.add(\"one\")\n",
    "ignored_words.add(\"much\")\n",
    "ignored_words.add(\"even\")\n",
    "ignored_words.add(\"product\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8095db1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:35.191789Z",
     "iopub.status.busy": "2023-07-07T05:08:35.191068Z",
     "iopub.status.idle": "2023-07-07T05:08:35.965756Z",
     "shell.execute_reply": "2023-07-07T05:08:35.964808Z"
    },
    "papermill": {
     "duration": 0.793178,
     "end_time": "2023-07-07T05:08:35.967983",
     "exception": false,
     "start_time": "2023-07-07T05:08:35.174805",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5BktXe+x6oEJnV0R1Tx6vZNOeC6lf6xN20ixvD2Bk+dXCU48jmy1Ao6Wi+/zLAFz5ZiNzaDQIU2A5BmmkWDZ1ZKpZuNxDpToLf2lNEkhe+IWrRL2U+o2AfNWhOulx5XgTme7cK9kOOr0ZivkJXKeEY+dpdq7QbF8l5w9jme5KWD/nDWGaFpbpYBqZ5dpoXybvj2EYNjoNSNI+rpOF4271xgwhKBUmEcKg2bm67jy0ljQ7VxgfepBURnSDeTq9GVrd60wMP4LrFOkG8zTb1zBNh8mRx3DdIu3uNHHcIecPUcxPIISg2b5GGLdotC9Tyk8iVYqUMamMcMivTPYAKo1Jgza54T0YjodOE7TMcvSm44OWG7Jag7BOFLWyVAIao2kgVYoQBgKBVAkXrv4hqYyyqhadXePM/HHGRx4iijtorbL0SfAu5Pr0wMgZXIpqd4kuXMHZM4mOE1QYreT+Cx95CtlsE5w6h+z0SOtNCs9+EKtWpffKawjThKHa8oPb9JDp3ALCsbFGR9BJguoF6DTN+AKDCRkgnV/ArJQpPvtB7IkxgnOX39KljuYPUXLHiGWfvqpT/sA4hVGffj0kWAqJ+ylO0c68nEShtV6T63RLDn7VJT/skYQpYTPGLTporQlbMYvnGsDmE/DDjzs8+0mPo/da/Ng/atBqKh570uGpZ1wOHs6+HUHmWQnBugjFmt+Afl9z8VxCp6W4fDElTTTf+WdzPPyYQ23I4F//z1vzaJextKD43/9Fiz2/YPHoEw7/5d8trPlda4gizT/5+w0OHbX4M9+f4+i9Nv/1366zbBM3GpJeR3PqjYSlBcUjH3Do9zTXrmRGr23Do0+5vPx8xJuvr0a/VCJRSYpWCmEZg8ChQJjG4E/2raTN3mrYXggM30H2olVugM5ONG31UWECSmNVC5SfPoJdyZE2+6AUOpZE0w3mf/tVKh88Rnh9CWEIDNcmaXTRiaT1ygWSxc6W71+aaJZuhHzxP93g3mcqTBzMkaYK2zOIexKZKAxjNSpkWgLDEBhm9rdSmk49Jg4ybpllC2zbIOynWboh1RuOOVmk0UTpOEuJCoO3Vmkv8MsOTs7i9/+/30QrzTN/4x7KEzlq+wu4eZux+2q8+HNn6MwFVHbneeQHDjL1zTJLlzrMnmwQNCN6SyH5YW/Ne2w6Bkc/MclLP3uWpSsdom6KSrNnVxjz2PvECL2liM/9v7+BEIJP/rcPMXK0jIwVvfrG3IiNsC1jIOqlzJ3vYLt9+vWYiy8u0JwOVsL0QTPmxqkW/UbMzOkWi5e7xIFEpYo0VqthC61RYUQ8PYtsNNFJsoa0koRrB4eNLiczDtai38wsKZlItIpQqSIJ1VsKUW4H3d4sUdzBspYQCOKkRypjoqS7wgXQWhInXdrdGfpBfSUf1enNEic9BAZSxiglCaMGcdIlTddWJCy1LgJiU76A1oogXKKBQKOIkx5KpXS60zh2nm5gESddkjQYLCvQDUyiuEMqIzSa2cUTpGlAGLdIZUSnN0OS9FFaEkYt5CCfHxld9MD7VDIl7rfRi1MYzbksAqAUGhCGiUwiNgrRaC2RN6U8Npoa4qS7blkqQ1qdqWwfaMK4tYWn9Nag4oR0qY7odNAyO1MVBMTXriPbXbRMka02Ok5I5haydz1OUGGYcTWuXsco5EmmZ5HNFqrfR/UDtJTIVhsVhIiFJYRloqVaWaaVQhgmyWIdLSUqComvXgch0FISXbyM6nRJwoi01cHI54ivz6Ba7ZXz3C4cM4dvlXBMH6liDGFk16M0pmOSG/bJm4LWVIfK3hKtqQ5aZQbAMoQhsH0rG7w7Brkhn+5sH9MxyQ97zJ+6vfd1+GhGuHvjeMLr34iJoiyEXhsyufeB2+fxN0KroTh3OuHKxZT6okIqzdhExHd8r8++g9svrpISFucVpinZu3/ja9EaZqcl+YKg09HEsebq5ZRbA2T9vuK1V2Luuc/m4rmUa1dSDAMOHrFxbMH0lGTqagpYCCHIHZnA3VVFGAbBlXnihTbFh/eT7KphVfLowYSBXB17VZwSXa9TfuoIwjZJlrqr4efBetn3amB6Du54FWFbGI6FYZvY5RzeZLZ/s9HNUg/XFskdnUBYJvFCe2U8uBnCMHGKVapHHwchaJz7JnGngWFICjWb0X0+pWGHqC+pz6RcP9PjgY/WCHvZTZo+36PXSrn/IzWGdnsUqvbKuC5vmvC7jZTGXMT9H66ycDXEyRkY2sazS4NowBAFdxipEqK0y0jxCKmKCOMOSdpHqpQ46TFaOkYnnCdKOlsmRqehpF8P6cz20QqifoowBF4p4wEEjYjOXEBnNsByDKJ2QmV3nsZUj6SfEnUSok6SGQM37zdSnP78FKWJHMWJHN2FgIWzLfr1iOKoT3lXntJkDpkoBGD7Fm7exnTNLZ33Mrb19se9lHpvNUR740Qz+58mK/n/8GIHNPSbN93AW7kBGnQYkczMkS7V0UmaEc/eRiShohVuYEYIgeXmSMPemsWG5WA6HmkUZN7sDug9UdIhSm6ximU0mMhWb0KS9ge5bmBgDMRJlzhZPic9WLb2HJfR6c1ARu/bFEka0OpOrTluGLcI47V5+42WpWlAvXVh7bXFCVHcWbPeuglaK2QcIOPthafeCrr9OSwr+3iS5F04rpTI9tpnrJM0SxE022u8e9XuELU7a2rJ0jCCemN1vX6Qbbe8TXf9M1+zbKm+uq+FpZXFyfQgRRKEcMsxdwZBzq5gGS6GyAYVrTTtGz3CdgwKDNvAzll0Zvr4FZd+IxzkR1cHs/5iADrLm8a9hNyQR9xLsJXGMI2VfOpmGJ80qVQNvvgHIY169q2cO52w76C1IwJeHGtkKvArgnvutzEtKJYMCgUD1xPvadmfkvDS10J+4AfzjE+Y2A6g4YFHbBbnJTPTKa2mxsh4dQjXwjR80maP4Noish/h7a5h+A4qjAmvLaISSThdR8WZo6STlGimgYoSTN9F5RJUGBPNtTLyodYkSx1CyyRe6mAWPWQ3JFUa2eojbBNhm0QzDZKlbjb5JxJ3ooqKEnSUoJP1xqdhu7jVUYbufwYhBMHiNDIKQPawbAPbMVi8HtKYjWjMRMSh4sGP1jBtg/p0SHspQSvN+MEcftGiORshU0V7MSHqS9CwdD3MosbXAkb3+vhFk8WpkNZCknn+WhOnPYQwCJMOqQqp5vYihEk3nCNKu2it6QRzeHYR07DuMMquRRJJgma84u/IWKK0xrANDMvIeGuDd0srkInCdMw7HkHGkrNfuMGhj0xQGPXI11z8skv9ahfTMjAdA9M2KI54aA1zZ5r06iFyg+dwO7x9OgP6lr83+u0WLBsAZs7HLBWJr09vunthiCwEdoc7p+5AFDRMi8LEYTrT51BpglYyy5WXR8jVJunNXyXuNQds+LcTa2/Cxvmo7YxCm1K57rDPbTygHa/37kFrRZJsgeDzbmCzWeTW5W9lttnqtm9xRhMIyu4EplgdIpTUzJ5YvHVF0NCe7q68Ho0rq8bN9Gvza9ZDwMjRKjKWhK3bf2OGCfmCwHEE87OrA1uvq+m0FMlWKpTE2sxBtWbwxNMuH3zW5fARC883sOws0nDxfIIwYJtSFm8bpIQXvxbyN/5egV17TGpDJu2m4slnXF7/ZszczPKJabRSdI5fIZ5vI4N45XnPTdfhljLq+hffXP2HBhXEzH/mZTDFunUBut+6Spcs/dg9ObWu1DCaa2XHu4kQPv9br2y6PwDT9XGKVSw/o/A5xRphfYao2R0YAZlzpFazwXz5F6YZzOEry6bP99Bq49f7+B+uvpuzl/prtgM2LBfsBOt5Rgud8wMtl23ybfTG5yVjSfN6l3s/vRu3ZBN2E7yyQ2nS59Tv9ZCpwrANLM/EdMyVqr0kSDMCooa4Lzn5u9ewfJMjH5/kge/dx+u/don2bJ/2TJ/WdJ9v/cZltM6M7CSUK6mEreI9FR1y9uxCtrsY+Rz2nkniGzMb300BlWPDFCaLeFUPwzbXs5cHuPhbZ0j7ycY/AqaTY9cHvgeVhDSvvEHr+lnSoMOeZ76f7uwl8uMHaV8/Q/Pyt96mq7yLu/jjByEENX/PCpF1U2zkBNzO3tSwcHZj4uvGJ7K83dqdKsWmJXw3wzQFtrs6WPytf1Bi/0GLK5cS/uF/VWdhTnHsXou/9l8Vmdy7vbDqO4FGXfONlxKqNYMPPO3wwlcjPvRRl1/6uS43rmXGgFaaaKZF2o9R6S0lENtJiW5l3Y00BzbTIbjN/tJ+h3BphrjTBK3pL0yR9FbTeps9y1t9pk2qk++43XbxdpYTJoFk6VKb68eX+OjffwC3YBG2E6ZeW2TmZB0h4P7v2ceRj09QGs/h5C1KEznO/OENbry+hIwlf/7ffHBA1Nd0FyNO/0GWHm3PBJz/ygz7nxrlz//4B9FSIwzBV/71CebPtdZVJdwO76kxEF68gntoP8Ky1oVYlyEMwUN/5wPs+tA+7LyNShUy2nwUuPr5C7c1BkAj44CrX/vPVPY/gFcepdNvo9KY+RNfYezhT2CY70thxru4i3cJAtNwqHiTayID7zaUhE5LE0WaXXsteDGLJOTygnLFwPNXJ/k41CgJnreabqzWBJWKwBrM8Y4jOHjYYnFB8oXPBSwtZAP+yLhJofTO13UruTpJmcbmE9tzXwz45Hf6fOjjHq2W5sLZlOkbkiDIxkedSNqvXcpIgbeWa79PodKY3tw1zn/m/wRARv1BOvZPBprXunTmAixb4OcEuZzgzV+7gGECSmLbgqtfusrlL17DsrMwWachGRo26Pc0s69Os3giq0LRQqASRRLKjIyrNb/5X7+0ciwtNelNEfDrry0ye7KBaa+++2E73pYhAO+BMWBV8tgjJYLzMxi+h+G6CNfZvARKwPiTu2ldbtA4t0T3eisrSdoESff2oUetFWnQIe7UM7EY18P2MgawjMMtm5SHqh/Et0sITKROOLP4pZW6f1PY+HaZsjtBzq5gGw5CmGg0qYzopU260QJB2tpaDesGGM0fpubvwTYy0ZkL9ecJ07UymwVniIIzgm+VsE0fe5BslDohVTGJDOglDdrRPIkMtmUNi8GEUfbGydtDuGYOy3AwhAUCpEyIVZ8w7dKPG7TiuW1LCW8E2/DJ2WUKzjB5u4ZlughE9lx1RD9u0oxmCJIWUmdGodqmm7C/8gQ5u4opbEDTjua50XlzU9WyjZC3a4zkD1F0RgBIVcRScI353vltncsyHDOPbxXx7crgnfIwDQsDMyMRajl4piGxCojSLr2kQZA0N00oLT9D3y7jmUVcK49j5nBMH9cqYJsey665a+WZKNyzcj23Q6ICzi4997Y87yuXEvbsN/nwJzxefiGi29U88pjNox9w1kQgFhcUo+Mmjz7h8MoLGeH2I5/weeARd8XHSGVG3vM8wdCwiRAwudvkwx/12H/Qol5/Z+uP2y1Fv6dwPcEDjziceiPGdrNyxPimV+vMqYSnP+Jy+JjLB55xePmFiKC/VntAx9lE6lRz2JUcZt4hXuxiV3IYtoWMEtJulJW5TZRJmkFWxeXZCNsgXuwRzrc3zO+/U9AyJe1vr1rjjwuU1Kh+ip0XPPlhlzTNoh29niKODI4es9BK4LgA2bO0j1qMjLncmEppNhRLCwndxY2fR/82VQEyVm+LWuE7YgwYrsVmyX1npIR/ZILg/Ax6wMw2yyWEtVmITmBYBr2ZDnPfuMHSm29NYU4rTRJ0KIwfwLRdhGHgD+1Ca4VbqiEsZ9Nzvxkj+QMUnVFMwyaRERfrLxKrPp5ZpOAMU/YmqHqT5OwqtuGulM2lMqKXNOg4Q3SiBdrRPJ14ge3m44vOCOP5Y3h2EYCp9uvEMkBpnU3S7jhVfxcldwzPKuGYuXXGQCwDevESWmta0cyWJzvHzOPbJQr2MFV/FwVnCMfMYxvuTcZATCz7BGmHXlLHDYt0ojnCNNM92D4ERWeYojtK0Rmh5I4OjAFvYAxIUh3Rixv4dplWNEM3XiJV8baPV/P3UfV3YRsuWits02O2e5Z0w7qWjeFaeYb8vYzkDwIQyR6JCrdtDDhmjrxdI+8Mkber5J1q9k6ZHqawMcjEmJSWpCrKjAHZJ5QdenGdhd5lekl9xTC6GabhkLOrjBeO4llFPKuYGQOGj21lRuYygcoyXMreOGVv/I7nHCRtzi09j75NyeBWcfFcyuhYwke/zedT3+nRamrGJ02qVYMoWv1mzp9JqA0Z7D9g8clP+0gJB49YeJ6g3RqUvko4eyph1x6TRx53UCojDzquIE3X+gGHj1kMDZuMT5rc+4CN5wk+9DGP+pKiWVdcuZzSaigOHbMYHjGZ3GVyz/02liV49pMeva5maUFyY0quEB+7Xc3UtZT5WclHP+UxsctEqeycLl9YfUeXFhRzM5kK330POvz7H+8QRxuPD85wAbuSQ0tFbm8Nu5onaQUYnoU7lDk5djWHVfAQloFhm0RLXXJ7qsSN3rZJZndxe9i2YP8hmyDQJLHmxlQW2dq910JKyBcM1EDkyrIEhZJASpN+L1v/vcQ7Ygy4k7UBmWT9b96+YfwDowAks3OkwsAaHcYerm28M61ZOD6D5dv4I3m8mp9FBja5b3Enuv28qiVRZ4nRBz5G2FpAGCb50f0kvTbFyaOYtrNSGmNZrChLyTR7gBtBCIFvlRBSMFY4wlj+CFV/902XoFfWswwHzy4ylNtLP2my1L/KhcaLROnWa3M3gm34maeoLUruKPcMf5y8M5SVg90CExvHzJGzK5TdcfpJi268uKXJzjJcav5uxvJHGMkfwhT2SkfLmz1Qw/JxrBwFd5hhvY/J4r1caX6Due55grS1qXriZnDNPHvKjzCc20/Orqz5TWsNwsQSLp5VYii3j2Y4w1zvPAu9i6QqHqyzrUO+x8jelZq/l73lRyg4wzimv+napjAwDRvXWq1z11pjCofr7Tfop81129iGR9kdZ3/liXW/aa0HctSr/2YDzYuN8HbmW8+dTkhTGJs0+Ys/VCAMNV//asTUNcn+Q6vHefXFCKUyEaAf+dEi/UDx5c+HvPF6zPikSZoCAj7/uwEf/3afZz/u8vFP+1y7nPIr/7FHnGhGRle/lY9+yufxJx127zXxcoIkgb/xd4uEoebN12N+9Rd6tBqKj37S48lnXCZ3m3ieoNfV/N1/XCIMNN94KeL3fiugUc+ilVGoOf5qjOcJvvf7c3z7d/vMz0n+83/orTEGtIarlyTXrkgeecLhW69FxJsEPJ1aPhN6u7TA2LfdhzAM2qemsfIelUf2YHoWwY0mdtnH9GzSXkTz+BS7vu8RjNMzSLKyP9PNAZo07IPOyliFZWd/D0RfMm0NlRGv7xjiFxi2g2E5G353MgoGJa87mACFgWFaiExGcjD+iDXnqJVEpckWiLQCw7YRxqDHxwBaa7RMUXL7fT2WfcleV7EwJ1mcVxw+qvH8ZQK8II41i/Mp/XMa14P5mZTF+fe2J8TOjAGRiTsIIUji9RPz7h/9NCpJSVvrQ+BWObcSBfCOHc4U14RAtjcOH2mlOfUfX+fJf/YsTzz7DAhBsNRHxRsL2Dz/T75AWN+8xEzGIbPHv8Ds61+8yRUQmLZD7cgHWDj1daL2Il5OcPg+D9uB4TGbS2ciLp7Z2NAQCPJOlX25x6l4k3hW8ZZzG+T6blokRFa65Rbz5J0hXpv5zYFnvjPr0DFzeFaJkjvK0aFnV9IHd25dqakHV7cUFTAwOTr00cGEXF7Z/83HWJ4sBOKmiUPgmDmODj1L2Z1gunOKud65LV+bbbg8NPbdVLyJFZGmO93fsjuOb5WoeXtpRTe2fKz3CzyryN7SIxyoPrmmffhWWpHejIX+RaJNUlFKSxIV0IkWbvkl+7ZvTgkoLUlkQKz6dzyHWPbeOoNrACkzr/9f/vMWsEo4+9i3eXz821dLGDttzVe+EPKVL9zSJVSA7RhURm0sx2DquuY//Eyf//vf93B9E79o4BcsvvmTAY251Rn3Z3+yw8/+5J3P7+f+XZef+3frNTA2w8VzKRfPpfzCz25cNrwMw4JWU/LclwKi2zQ+TXsx3liR6hP7iet9crurVB/dh4wS4kYPnUjMnEMw1cAqehjO+ghsbnQPh/7MjwJw5lf/FTLo4Q9NUj32BIVdh7FzRbRSyLBH2JilfvYbtC6/eVuDwPLzjD76CUYe+sgaSezld/nyH/wHutMXkOHt78NG8GpjlPbdR35sH251DCdfAmGg4pA07JP0WvRmr1A/902SbmPz8xQGppdj5MGPUNx9BKdYw3QyufO4U6d99TStSycIlqa3zG9IU8309ZQv/l6QGaADfO4zfYSAD37UY3Y65fL5FK3fhgrgtxE7MwYGJ3/0yTLVMZfFGyFzlwMas9mE0nzpLL3TNwiv3jrIQO7IBKUnj5B75AHssdGBxybWDHg3Q5iCp/75xyjuLdOb69K93ibuxpsSZ9QWw15OsYZpOWuswaVzr6BkFi80XUG3LamNmJw/FbI0t3lrT0OYHKo9g2vm0Wg68Tzz3QssBlcI0w5KSwxh4ph5av4e9lcewzHzGMLEEBYVb4KD1Se51nqdMN1ZTs0feMQldwzb8FCkzHUv0I5m6ScZN0HpFNvw8O0yBXuIojtM3qnRjuY3DCPfDIHB0eGPMZY/vOKhaq0J0jY32idoDfL0iYoQg8m/5I5R8/cwnDuAO2gIMpI7gGVkyovTnZN3vC7PKrKn/AhVfxJBNpAt64xfab5GPbhGLHsr9zhnVxnJH2I0dxDfLlPzd1P2xlZq5f84oOxNMl44yt7SI2uWS5UQpm3mehfoRJlmutQJSitswyVnV8g7NQrOCAWnRiJDOvEiUm/sVkayx3zvAkv9tSqWy8/vQ3t/+KZ1u9zovMm15vE7nr9Go96GFMHbgfH9HnuO5pi9GuL4Jg9+pIzjGfQ7krArWZrJDHzjPX49hMgikZYNBw/bfOLbPSo1g5/4324/HnROz9A5NztI52jGPv0AzdeuEc1nmiBaZ5K/KlWrLqvWzPzeiaxd8crxBRgmhfED5CcOUNxzDMvLr8xWhmVhFCpY+RL58QN41XEa575J1Fo/xgOoNKE/d5XGxRKWm8N0PCy/gFse3vE9MmyX0Uc+Tu3eJzFdfzVqMbgu089jejmc8hC5sb20Lr9Jssm8YuVKFCYPMf6B78AuZA3isnJvjRAmXnUMtzRM+eBDtK+dYf74l0hvqn7YDP2e5st/sNYQWIbW8OoLIeqm0sj3iyEAOzQGhAGWbZArWSxNZ0IPfsFkuWCo9fWzpO0A1V/vbYZXF1BRSjTXwSyXiKduZF3aioV162YHExQmi0w/f42F12dpX22ib5MmSILbW3CG7TJ07CmcQhXkzdEFzcxrf7Di0WgNSaLpdxXliknQU3RbapOHJ/CsQqY01j3DQu8SnXieKO0h9bKAkSCWAYnMyF17y49SdEewDAe0wVjhKPXgGonaWSvgscIRbNPDwKQTL3C1+RqdeJFE9klVgtISjcIQJt14iYZxA9t0sQx3cI6bwxQOJW+M0fwh7EE75lTFdOMlLtZfpJcsEcsga208CBEnKiKWfTrxAvXgOgerT5Kzs14MRXeUCRTN8Ab9pHnb4+btIcYLxxCD3LhUCe14nqnW6zSCG0QDQ2D5HqcqJpI9utEiI/kDjOaPYAt32/fzvYJrFhjO7Wc0f2Q1CoKmFc5Q719jKbhGmHZIVJg908H7KoRBN17CDq9jGS6W4WEKE6luX1mjtFyXshGIldDwcoxXa4VUKYm6jZv6PoQQAss1KA/b5MsWfsEkiTVRT9JrpXSWEkb3erj+e2sNeL7gyD02P/Q385imoFFXfPWPQq5fvb1RpaUCuToctt+cJq731kz0coNXQG1SkTX8wIcwbIek16J++mWCpRm0UlheDn9oktK+e3DLI1SPPEqwNE3Sb6OS9eO8ShO6M5eJ2ktZGsLx8Ud2Mfn0d2/5ntwMu1ChtPdeqkcexc6ViDsNwvo0/fkpkm4TpRSm42L5RZxCBcsvEG8SFTBsl/K++6geexynWCVqLtC89AZxewkZh5nuTGWE2rEnsPwipd1HQUqmX/rdO0a8tGbTlA5kDbPer9iRMWBagnzFQg7aAseBJOyvvrTxbHPTbbVUqCDKFNta7UFukqzX+4YbaOpnFwnrAWG9T3+ui4zTTZ+JvoPQgmHaFCcO01+YQibhSj5Is9ZKUyozBEYnLPJFk8ZSumkAXwiBwKIZzbDUv8JScI103aCpUTolSFsk/ZC8U8M0bEruKAjI2RWK7ihB0qan6re9ho2Qd2porejFdWa7Z5nrnd8w9K90SkpEJLuQwHp5yPWwTY/h3P6MFyEESqvMQ+2eY7F/ZUOimNIpkUyJZJ8w7ZCzK+yrPIaJjW16FJ0Rav5egqS1aR7atQoU3ZE1HIF+0qQeTLHQu7TBxKRJVUQaR6QywhAGeWeIorNzb+TdRskdo+yOr6RhADrRAgu9Syz0L9GONmnGpFl9rgNYwnlb8/fvJ8xMS77wewHT1+WmZe8A/U7K/LXMYVFSc+20Jg4UvVZK2JN06gmOZ6wZv5bhTZZxhwqZ/r5j0Tk/R9qNEJaJO1zAGy1mhOR2QNzokzQCXDO/5hlsFVpDHGlaTU0YKE6diDn5RkLQ357rGMw0N53otwJ/aJLe3BU6187QuvwmYXOe5d4iUWsBjWLkwWdxysN4lVHCpZmNowNakfbbK9UDhuOticJuB8KwcMsjlA8+iFsZIWot0r56mt7Mpcwg6bbQWmHYLpafx86Vsfw8Mupv6Hr7tQnykwfxh3chk4jG+ddoXT5J0m0MxOYETqmGaTuUDzyIU6xR3HsU9/QQUWvx3XfnhYFfGMHL1UjjPlHYJA5WoxSuX8Hxy1i2TxS06LdndnSYHRkDlm1QHnHod1IKVZvGbER95qaJxxCbMvLtah7/4Bjh1QXSRgtn9yTCtkgbG4dgtNLceP4qEx/YhTDAzjtErWDTGsrG2cXbqxAKgen4zJ98bkOLdhkyhU5TMjxmZ21QB61Yb4f57nna0dwGhsBapCpivncR2/QpOsMDLwwq7gS9eIlesn1jwBAmQdqlHkxxtXV809DwetyZYOOaecbyh1eWSBXTjhaYar+xBca4JpYB11qvMVE4imeXMLCwDZeJwj3MdE5tGpnI21Uq3sQKo11rTTOcZqF38Y4eaiS7NMMZ8v0rf6yMgeHcfgrO0KBCIgu4z3bPMNe9QC9Z2mCLrHvh+k6QkOoYYZgDQuz7KB75NuDsyYSzJ+/sZrUWkkyO1tjcqes0N04BFg6NUHlwF8KycEfyXP+N4/QuL2IVXGpPHqBwaBiVSHqXl+icnUU2Yyr2OAvqClovt3te5tAMSHg38WhuPmgYaM6cTPgf/7vWTXyb7T8zFbw111PJhPbV09TPvEoarJKaZRzQn59ChgHD930QYTk4pSHsYnXTVMHbBcvL4dXGKe4+itaa9rUzLJ16kbA+u2Y9KVNk2CNq3L7irLj3HvzhXQCEjTkW3njulgiCJm4vsfjmC9jFGk55GKdYo7DrCHGnid4o1PIOwhAGxepeRnY/QtBdoDl/jvpNxoBfHKUycoR8aZzW0hWCztwmCre3x46MASk1QVfi+CZTp7q0FuI1760zUtq014C3b4T8sUkaX34TrbLQNVptytgUpsFDf/MJvKHcul7RG+Gzf+6X6c/dhpSiNWnYzZiot2FvZDk8weJswtGHfQolc1MnejnVsNC/TLDFnH87mqUUjZCoEMfMAVD2xmmE17e0/UbnsNi/wnzv/DYMgTvDMXMUnBqFmybUVjTHUnD1jkbPTWc3yE9fZKxwDN8uYRo2NX8PrlUkSNobGhV5u0rZXW31mqqIVjhDM9xctvpmBGmT+d5F9pUfB9iUl/J+gSkcqv5u/JuiAv2kyUzn9Kbvlen5OMUawcLG7403PEnUXEDFf7zC+283bjs2bjLnLn7tAotfu4CwDIaePkjx2Bgqkdhln9rjeznxY7+1Zn1LuJScEbpp1oAsVRGxysjMRXuIWAUkKkJpiWPkiFQXS7hoFBqFIKsI8YwCkewRqe2T694qerNXCBZvrDEElqFlShK0idpLeNUxLC+H5W5e4fJ2wRveRW50LwiBSmMW3niOuLN9hwmydFph12HcyghJt0nz/GubkgOTXpuoPkc8soRTrFHae4zG+dfedWNAqZT5a6+i0Tju+nR6c/4ccdimNHQQy97589h5aaGGQtVm3wNFzrzY4Oqbq6GxPX/vu9CJJO2sZ/VblRyGk+VC7fFxwtMXMFwHa2Ro48NIxcv/4rnVzlp3QNi486AnhMnR7/47RJ16lioY5AiuPf+rKx6WaUKuYFIZtmgupnTb8jaGekaky0h6WydOhWmHVji7UovumkVcs4Ap7DsS+jY6h3Y0RzOavfOq24BvlSjcIjTTjRdph9s/zmL/CrXcXnxKgyWCireLRIYkau27YgoL1yrgWcWVZa1olnAbIdhUxfSTJonqYxu5bZ/vu42qP4k14AlAVqI33TlJOuCQGI6HWx3FqY6ATIkaCwjDwB0aJ1i4TmHvEcLFWTBMvKFRwsVZvJEJ0n4byy/gj+5G2HbmOSmF6WWEK50mtC++yZ+06MFbgenZDD97hNI946goxRkqEM63cao5DMsguLG5pHLRHqJgD9FLG9Sj6wy7e4lkn2F3L520Ti9tMuEf4WLnVWruLhIVEakujuEz5h+hHk0RyXffEAAIlqZJg9t8Y1qTBj10RSOWy/veYbilGm55CK0k/blrWQXCNsv9VvZVGcXy8gjDRCURwcLtK43SoEva7+KWh3HLoytR3O3Ayw9x8ME/y6mXf56JA8/g+mWifoPZq69wz5N/lYuv/wauX6E8fIjS0H4Qgm7jOovTb9Bvv73j+e2wI2NApZpeM+HKGx0asxGt+bWeaO/cNOHlOcLr6603/8AouaOTAMhmC2f3BMK20PEmk5+G+unVMJRhG7gVDzvnkAYJcScivYk0eKdqApXGNC4dx7BdtEzWhFBXQisCXN/gwD0OhVJWZ+z6xuaRAbKJfbv52VTGhDfpCyzrEDhmjiDdXjveMO0ORIfeXolPx/TXTMhAJmqTbj8v2ksa6whtBbtK3bC4NbPjmHksw13jzYdpZ2Vi3Cq0lgRJB9v1eX8LDQhydhVDWGvSIu1ofuWZGpaNU6oiyNpFqzjMasHNbEAWpo1dqmYcFsNEhv2VCJjhuBiOR/fqGfJ7Dg+8oUFtttpZl87NYFlwYL/Jd3y7x2d/N+TqtfdHZcF2UDg0gpVz6F+r0/zWdYaeOYThmMh+DDkHp7YJ4RlBM57NWpHrBNtw8cwiC+FVDGFm4mZGbkX/wxA318lDL6nTSZbeM6KmjIKstn6reBc+KcPO3l20Jum30W9Bgtn0coiBNoFTGmLXh78vU57dBHahgp0rZell19uSIN1G0Fphmg62m8fxiiiZYJo2aI3tFihUdmPaHtfO/BGgmTjwQXLFcZK4TxK+O6qNO48MGAKZKNJYravy67x2iWimQVpfP2GoMEaF2YCu4xiFhq5CBZtrAyTdGG8oR2lfmeLeMrmRPKZvI8OEsBHSm+nSurhEb+bOE5SSKb35K2sXCgPTdlfHQ52lQnodhRCZ5rnapBvX8gaJirZdAy71ena2IayswmCbyNjlb7/Wt2nY2OYqGz9jlcfbkuZdRiKDdZETx8xlkrq3wDIczFvKARO5/WvUaBIV3sSLf//CMXOD3PIyNGHaXpVTHpR/GbZD0m2iYYVBbfoFkl4LrzaO1oqoPodh2Vh+AcsvYgz64SbdJqbjImNW0mQyfHtbPxsGlEoG995r86WvbP89eT9AS4Xh2pg5G2cov7JchgkqSolbfYaeOYROFUk7IFrsopaySTSWfVIVoUX21iUqxDeL2IZLpLKqm1iFVJxxfLO0Ri47SyWE71lpppbp1vsdvEsflDDMAflQZ0JCb8FwNcxVdVzDdla4A3eCStMdGyFaSeKgjZerYgwiKcKwcHNV4qCJ5eTw8kO4fgXXz1KElpPDdnJYlsu7lZR4C6WFgvKIQ3HYod+Ra9LvvZNTgxVFJoHpDSR+BehUElzOPH1hmSQzc6h+cFuGpumaDD84xq6P7GX4wbGsYdGgO5OKJZ1rLaa+dJkrf3gha2J0WwVCRdxdG+ITpo09tPaliELNtQsR+484pMmdpSLVbUu4NjkV1DrynBDGnTvFbQCpkm1r8G8FmRbCauhaoVbKFLeLVMcZsUrrFY/fNDaWfzYMC3GLMZDV1G9/kMyiEe//ELglnHX902MZrpLOtAats0iA1hiWjTGQ1Lb8PFF9ntzEfnSaEDUWsPJFMAwM1wOtkIPwrwyDrNWpZYMhVhQ3326YJgzVDCYnDIQBaQrz81l5bi4nKBYEli0wBAShpttVxDG4rqBSFmgyw0JrCENNo6FxXSjkDZxBawLLEgSBptNWaMD3BZYlaDSy43hudqwkhU5n6+9AMNuicGwMf3eF8oO70IkibYek3Yi0H9M+Oc3QUwdQUUp/KouABkt1YhWggVRn6ndSJbSTRYr2CLbhEsouoWzTT3OU7VEsIxNOUloidYrUyZZUHt9ZvNfHvxXLjpjYUZh+zZ5ummdUmmR8mq0ICmmd8W622jbxJiiZEvQWKFR2o7UmjTPjO1+epN9dwDBsLMfHy9eojd+HJltHptt3MN8KdmQMpLGmvZhw7zMVZLr6wd4Kw7dxJ2qUHj+IcFcnFNmLWPjMS1hDVXQqkU43ixJ0N8iTCRi6b5SH/tbjGJZJ80Kd+eMzhM0At+xRu2eY6rFhHv77T9Gb7zL/2gwq3t4Ds9wcI/d9iP7i1ApnwDAglzeRUuPnTWxHbKEKb3sPTg9kM9derrGhp3zHfbFDac87wMBc29N+hSW9MyjkgCyVXaNlOLd4wxlMrHUfvtLptlmyWut3JGLydkOwsWG0qlOReTJ2sUJv6hy5yYN0p87TG/xZRv1bL6z8f9KqM//S59cdq3n2NSr3PIFKY9Jem/zugwTzU297yVSlbPAjP5zHNMH3BHPzkn/837aIInjqSYfv/V6P3btMfF9w/HjC534v5OSphIcesvkbP5wjijXlskkca44fT/jxn+hyzz023/kdLocPWaQSJiZMXnkl5td+PSBONB982mH3Lov/8992iCK47z6bjz7rcmNa8hufCTaVFL8VSTNg+rc3b2M+/6WzzH/p7LrlV7uZKFMjXs1FB7JNM57h5u9zIbzCuooCuUVJ8veTbN27AJXEqDQCIbDzxbdEBE6jfkZc15qoMc+VP/xPxO3Ft/Fs10OphH57jomDz9BcOI9MImy3wND4fUxfep4o7BD1m6RxwJWTv4fWKqsQGkgrZ1h2E1ZTSmtxk3CfuHPl20bYsc5AoWKz+1iBJFbMXw2A9aFG/8AYY3/+KepfPEH5g8eIZhpoqbByWRg8XWpgVctYI0OoMCR888y6fQhDcO8PPUznepurf3CB689dycI1g7ivEILqsWHu+2uPcN9ffYTG2UWiDYyB8t770UrSm7/K6AMfW3s9jktuaNeagVhJ6HUkvm+QJJo7RacyL3Z7L+lGUQA98LzfSdxf+QSBbHOp8407rqu0XJPnN4W5znvdDgxhrpn8UxVtGGVQrJ/4DbHeQLgjxMp/3lVkzPCtf14aUHr9S2YKC0lMVu5Up37i6zjFKo2TLw9CpjtD68LrmF4Ow3Lov/78OzK5jI8b/Pf/Q4eXX4kZHzf4V/9rmUMHLS5dTvna8xEvvRwjpcY04Wf+fZV77rE4dTpBCNi7z+Jv/e0GN6Yljz3q8I/+UYHPfs7EsmBoyOTGjOJ//pdthICf+5kap04nvPhSzPMvxPzkT+T4v35aEEWayUmTo8csfurf97ZsCNwMYVrYboG433yLd2Oj+7vNe26a5I7eQ/Xj38bS536bcOrqjol0f5wQdxrE7Tr+8G5yY/swXA+ZRDt6Z6P6LDIKQMlBmmDyHTcGZBrTbU1THj7I7JWXCLqLFCq7KQ19jHOv/SoyjWjMn6U8fJj7nv7rK5HT6Usv0Fq8iGV57Lvv0+SKYwjTJo0DysMHuXHxa8gkYHz/05SHDuD4ZYQw8QvDzF59mX5rFplunXuyMwKh0kT9lBNfrTO23ycJN34htVKk3YjOiWvk7t1N981rGI5F4YG92e9SIXw/IxBuJs0kBJVDNS7+zlnaU611GgIaTbDYY+6bN7jvrz2KYW08WSxbhMK0yI/to3X1xKBRBqg0ty5UZJjg5w2uXYqZmUpoLd1+JLmV7LYVGMJcxw+4dfJ9J2AIc8vRB6lT0ptKFQVG1i1PmNs2WmzDW1ESXEaqog29fanSdXlT07C3LSssBg1/3m0IYWKK7R13I96JY+ZIZJhFfrRGJTFRawmdvjXSn05TZL+HFO9cX/mr1ySNhiIINGGoaXc0uZzAdQUPPWjz1JM2tiNIEs2+vRaeF69EGWdmJP2+Jgyh3VbMzigmJ03iGJpNRbujiAZ0hBvTEt8XlIoGCwuS116Luf8+i3Y7MzTOn08Jw+xeeeVRbL+EYdokYYc07OGVR1FpjIz6aMDNVwg7i6DBKVQojBxg6fJrCGHg5CsIwyDutQhbmwhAvZMQBsKy7lAr+ScLQX0Gb3Ga8sGHMG2P2tEnaJw/TtzeSHfj9tBK0pu9gunmsPwitaOP0756+h0uF9SkSZ+TX/8Z+t0FlExIk4CTL/0cMs360fTas0T9JvWZkyx7unHYQsmURPW5fu7LGKZN1q5codKYJOqgtWLh+us05s4MKjsESsbEYRu5zWvaGWdACEzbwDDEwDjTG4fQpUZFCbITZA1DagWMnItZzGohjXyOtN7AcOzblqiYrkkaJKho48lHpYqkG2N5m7dOjjv1lbB83G3QW5haGQQtv0Bh4tAaS1MPSIRezqBUMQn7mjDY+PgCgWvm2K4Hagp7RWNgGW+H5KvAoOyM4ZtFBIJUxwSySydZ35DGMTyG3D0049lM2veWCThVEXG62uhGCIFlujiGv60yP8gUBW+dzLOWxuvvaybLvPZldgx/TcpiK8h09v1tRjP0LV7H9iMLprBu22VwI8Rp7xbDSJCzywRJE7l8j7RGv4WIwM3YSKjo7USvp0nT5UqdTN7bMGB8zOTQIZPxcZOXXo6xrMzBXRaoEwJyvmBZCsQ0Bb4vCAONMMBxMoNiOVqe8wVSQpxoglDz3PMxDz3o0A8yQ+S11+KVx2n7JfzyGKbtErYttJKYlovl5lCOTxx0cPJVol4TlUZoKRGmiUpjiqMHMGwP0AjDeveNAaVIFudpv/oSaav1pyZVkPY7hPUZwqVpvKFJyvsfGEzqV4lai8iwl/FLTAvTdjFdH7tQzfQSovVliN3r53CKVYq7j+KP7GHonifpzV0h6bWQcQRaIkwH03YwvTxWrojl5elMnc1UDXcCreg0rq1eU5zSjVfT4iqNiNOIOFxfRaa1JOhtHr2Iw9aG220XOzQGwHIMapMujm/g5k28nEnYWzu4qDAmnm8CINshzngF03dQUTaYmfk88fUb4LlYQzWMYgHV6697eEkvwSm5WDl7Q6PDdC28Wo6kG2/6gST97GYZlkP7+lmCpeurrYq9fMYXuMUYSBPN0KhJdcgkTTWt+maDpxjoAwx6Hm/RY7MMd03Z3kpPerVzdrdAYBsuo95+LOEhRObdh2mXftpYISwKka1Xskeoubvop+1MIOWWiTkrI1xb2uKaeVyruG1jIG9X19TRA/TT5oY5/URmPRqWGxABeFYJy9hen4GsQVSB7Uzo+hZexHY5HAJzpUR069D009YaYyzrHDhKI7i+A92J9x56kwKcSlVQrRoIASfeTPA8QbujkHJ17UrFYM+gLfDu3SaODbNzitFRg1xO4PsG+/dlaYNcTtDpaFqtLFrwjW/EfPLjLkqZHH894Y0Tq+9X1jTHxzAtLL+IFQdoLTEtH8NyiMNOVnpmmsggyqIHUQ+ZhDiFalbSmSZZ9dG7Da1J5udI5t+DiMQ2IAYVL4bjZUa4YWA6HnZ+VUzLLpRxy8Okrp8ZuFpl3IAkWhepUklE2JindeUkdqGCP7oHYZo4pWF6s5eJ2/WMUOt4WF4Op1DBG97F/OtfRiYR+pZy5N7sFdzKCE5pCK86xvDDH8G6UCJszJL2O2iZYjp+1vSoOIRXHcUulAkWryPj25Pd/zhjx2mCOJS4eRPTEkwczAOCC99Ya52E1xYJr2ehnIXPvkruyAQIQTSVWTk6CnGPHMTwXIRpksv5BMdPILs3WXNaUz+zyMjD4/SmO7SvNJFxusIZMGyTwq4Su57dx+KphdtLEZPpDDQvv75mWRr2mD3+h2tCb4aRiQ6hoVgxyeUNDGPjFJ0QAsfy8axS1rBnCwqAAoFn5ddI5UZpd6UD305hDSb4ojXMtxqfJ9UxQ+4edufupZkM04ozqUpbeFSdCXbl7uVk8yskKtiQGBimHbrJ0oqhlLVerlJyR2lF29PArvq717RW1gOhpI0mOqmTTIUt7eHbmUhRyR3GtfK3tEfeHIawsM08vl2847prj30zXyF7ttvhKvh2Ed8urzQa2ipa4RypildyhgLBaOEQ052T72rdeSaEO/Do0YOoitiQ6LnpPnQWFbipDxhplulg6qrkyGHF4Y+6/H/+eYn5eUm3q6nXV59pp6P4kR/Ok88bRJHm5Vdirl+XDA0ZhKHmyGGLf/L/KmbRhZdizp1PaTSy7dttTaeTVR70+4p6ffWjNS2XuNcgCbvkh/bgV8dRSYQQIosCIEjjPl5pBJXEIMBy/Cxa0K1j2T4qjQnbt5e83RIMg4z4tHqP0GrjycYw1kY91SbrreyX1XDLTd0KV/68g7DzJXJj+yntuwfDcjEcN/PYvdUSzZEHP0x65FFkHKGTCJlG9Geu0rlxnqi5/t5GzXkWvvUcdq5E5fAjeLUJvKFJhu57eoMz0GilWDr59ey53vKrjMMszdBtMfHUd+GWhxh7/JNs7DBkFm0abl9X5Y8bdmQMGIbA9UxqEy5prDn3SouLx9cLIzhjZXL37qb5lZOoIKZ7YtAqdfB0+m+cZN0DuOVF1VJz8ueP8+Q/e5YH//YHOPID97N0cp64GeKUPSqHauQni6hE8ur/8nzW3njbEAjTQd804GoFQU+RK2Tex8F7XBxXcPzrm3vt44WjpCqiE995oKh4uyh7kyulRQCN8AbdeGcym8uwhINvFWmnC6x2EAzppU0KVo1WPI8hTKrOJEW7xnx4ZVCHv0lERYVZl8Nwiqq3B4CyN0GqIua654nVVsJmWVfH8fyxlVbGSqc0w2mCpLWp8dOL6zTD6RVjwDJ8yu4kXa9OM7y9chhkzZ/GC4fvuN6tiGWPVMW4ZOdasIdxzdyWDbVhfz81f8+2j5uoPq1wGttwB5LEgpIzxnjxGPPdCzvqWbEzaJRKbzIEwDSsledwJ8QxHH895VtvtFZIe7Nzir///2yuzGG/9MsBv/wra78lpSCfz1KPM7OS/+F/6jA/v9pFdBnNluLLXwn5mZ/rr/x2q5E+MiJ4/usxp8+s9TIXLy6TZjXtmXMrTsVgEaDpL91YaWcLMP3GF0ErovbCilG4E+33FZgWdq1G9dmP4+zeg1ksZp1Aux26x79B++UX1+mu7P67/xB7eCRLWYQhc7/+ywTnz3ErK1J4HuN/5a+RNhu0Xnye8gc/TO6++xGGQXR9it6bJ2i/8uLOz30LsPwi+fH9VI8+zmYROac0hFO6WXVWZ6mb1vyGxgCAjPpMffXXqJ/9BqW995Ab3ZMpCvqZAJSMA9KgR9Jp0J25RFCf2ZRkmwZdWpffpHvjPNWjT1DccxS3MpJpclg2KgpIgi5Rc55gcZruzKX3pknRu4gdRwaCXsrrf7TExMEccbCxVK9Z8MkdGKX5lUHf+lvX0RstXI/2lSbf+slXGH9yF8MPjTPx9B4My0Cniv5ij+tfvcL1r16hM9W6Y9dCyy9x8Nt+hLnXv0B37jIy6mP5BcYf/TZuvPTbK3lUpaDfVXzhM22EkfEH7qQ1MFY4SpC2SVV0WwVBx/SZLN7HkL9vYLlm+60HU3Tj7ZNibobUCWHaZdjdt+LJWYaLb5WoRzdY9vsWo2vMBufZk3+AYXcvjXiGVG8sEBOlPa63T6wYA6awKLljHB76EGcWv3zb0j2BwLUKHBt6FttcVQFMVMj19onbaiN040Ua4RQTxXuyfQnBcG4fGkU/qRPLzQ0zx8xT9XYzXrj3NndrY/TiOmV3nDzVwXENJkv3M90+SesOcs81bw+j+cOU3LFtHxdgtnsOx8zj2+UVouXe0qPYhs9c7xytcOvRGFNYCGEhVbxtXQiFIkq7uFYBIUxsw6Pq7cI2vC1FKTLOzdplN/97KyR4KdfvY2X7DfY/OWHwxBMOn/qUR6upOHc2ZXr61h3c9A2vNJW/9dxv2eamd/QtGQE37U/FMTLo03rxeVS/n5XNjYxQfvpDJEtLhFcuIzurDtbcr/wChuOQO3oP5Wc/dvv9mwbegUNgGCTzcyx85lcxHA+dJqTt9eNSsDjN2V//1wjEIG++8fO1yj75g2PE3mlmfud3SXu9jJl/6/7qMyTHv0T99Mvbui0yDknDO8gwa02wMEXUWsCwnIEksrHym1YSLSUqjak9vZfig7uRvYjOmRnqL5y/ZV9qECV4jfaVk6v7GpBRsn1laSGVbJ6Cfj/A37Wf3L7D9C6cIpxfW8a6Vew4MuB4Br1mQqeRkER649JXpdCpwiz6yG6wY/KziiWNMwskvZjm+SW8oRymYyJjSdQM6c10aF6sb0lfQAgwbQd/aBKtNWFzDq1SnEJl/XEVNDflCaxiOeTtmjlG84ewDYd6cJ1OvEgi+wMPK2tCkrerjOYPUfV3DUiHgIZmeINuvPiW+AKQ6fH3ZItI9pnIHQOtsE2PSPbopc0VwyNRIZ1kkYXwCjV3N4mO6Cb1DQ0CqWOa4TT14BoVbxLTyIiPQ/4+9leeoBFcJ0hba+SQTeHgWUXyTo2KN0nV35OVFQpBmHZpBtM0wmXjZGNEsk8nWqQZzlB2x7N0zGCS31d+jPneJXpJfTDZZV6sZXrk7Ro1fzc1fy+emUeqZFCWuDXeQDuao+rvpqLViic45O9DqRTb9OlE80Syz7JbaRk2jpmn5I4ylj9CwR3BECZSJdtOFXTiBRrhDXy7nLW3BjyrwEjuAK6Zo+mM0Y0XBsTLZKD7kHEjbMPFNj1s08czC/h2hcX+JdrR/A4UIzMp5CHTHwhPmXhWkd2lh1noXyRKu1lKY2BkGJiDag9r8Iy3WDN/C+JYc/Wq5Dd/K6TbXf9uzMxIvva1iGiDAGCvp7l8JeW55yJmZyU3puX7s3+81qigT+/0KdJ2Ex1FgCBt1Ck/+UHs2hDx3OwaYyBZmAfTxB4Z3epBUFFE/+I50kYzq0AQZLmaW6DSmHDpzkamilKihRbN8CzB/Bx6k97ROk1I0hZJ762T2jY8jzTZUllt/3odb08N07WwS97GK2mNDHtZv4M/xjBcD7tYQdjOdmhra7AzOWIBpmXg5U3SZC3x52bobJSicP8e4qXOijugE0l4bRHhuggn02fWcbx5fwIg7sTUTy1QP72AaZuYnoWMUlSitiUTqQcKhCpN8cojCMMYdOhaO1EYBni5jCdg2YIwUISb9hbXBEkLx/Qpu+N4VhHPKlEPpgjTzoAEZ2APfp8s3oc9GGS1VqQqZq53bhAyf2tlXgpJmHZoxDOU7BEMYRCrgGY8t9IFrZs2iGSPVMfMBRcp2SOYwsYUJht1hlZaEiQtZrqnccwcObuCadjk7Ar7yo/imQU68cKa3gG26VOwa5S9Car+rhUyXSJDOtE8872L64iJ64+b0k+azPfOk7drmUSxYZF3quw2H8IQNq1whkQFqMHE7VoFKu4kQ7m9eFaBREWEaXvgqW/NGOjGdbrxEmHaWekguNxW2LUK1M0C/bSZiYMIE9v0yFllhnL7qPp7UCpduRfLE/pWEcs+jeA6juHhmvlMolgYFN0RfLtC2RtnqX+NftIkVVEmxMRy9UJupblTzq5QcIboJw16cZ2U7RkDWmvqwRRlbwJT2AhhYBkOu0sPYgiDXtIgkQFSpwgEpmEPJKRttFZMd09t63gr1x/DjRuSGzc2NsLn59VK6uBWtNqaEydSTpx4n4tMaY1OUtL6EobvI/wcGFnJoEoSzHwew9mgLHUb+X7V7xHPThPfuHM6DQAB3kQFw7EQhkArTdqLiBcyo87Mu1hFD9O1iBv9NWRrYRn4u2ukvQjDsTBsEy1V1szpTmOzITBzDu5oaSVlo8KEcLYFSuMMFTBzDsLOVDfTTki81MsIg56NXfYx8xmZU/Zj0k6I7GXveu/8HO5IEW+isu6wpu9gFlysggdisG0rQAYxGILcniHSfoRhmxhONk32ry1teD1WsZyRJQcRCp0mxI1FhO1gev5AATRr7KTiCBn0UFE4uK9FTD83aEOuUHFE2m5mt8b1su2d7BxVFJH2Olk1kTCwimVMz0crhVUorfQo2Sl21sI40XTqMceeLiPTgSGyUZrAs/H3DFN6/BAqTlde5qTR48q//Az25Dj2+Ag6Tkhm50lu3MY6FQLDFFi+hT+axyl5JJ2IsB4Qd2N0ukWjQGcGweLpFyjtvge/OoE1eXgdQczxBAeOOTiuoDZicflsxKWz8YYWl9KKme4pyu5ENmhbJXKlB9hVeiA75CC0eOsx9ECutBXOcL11Yk09/1uBQjITnGUmWK+QBqwRG1JIzrZf2HC9NeeK5nr7BK5ZYLRwhKIzjMDAMfPsrTy6ss7NuLnhThY9UTTDGWa6p5nunt7StUSyx1TrDYb8fdnERKY14FoFDlQ/MNj/+vurtSJIWzSCGyz1r3H/6LdhbpEEKHVMPbiGY/rsLT+apVsE5J0aeafGZPE+IDOSxC0SqUor2tEcrXCWRIXbNgYAWlFm4Fimx67iAxhkRDPLsCk4w2vaSd8Ob0XKVKOY6Z5honBPNsmTGQR5p8qRoQ8P9q9QqJVqD4FAqZRuvLRjY+C9gBBgWpm08Z2wXHL8lqo7hYGZy1F84km8vfuxymWE52Ul2/nCwLt7a7K7st8nXdq6mI4wDSb+7GO44yVMz0EFMZ3zc1z/xYxfUDg0SuWJ/RTv30X71A2u/+JLqDC7CVbBY99/+SytE1N4ExWcah7Zj7n4E19ABbe/UWbOoXT/Lia/73G0UgjLpHdxnmv/8QVUlDL80WMUjoxjV3KoVNJ+8zozv3McFab4u6sMP5v9DtC9OEfrtas0j1+9o2ec2z9M+dG9lO7fhTAMepcXWPraOTpnpjEci31/41nap27g1PK4w0XQcO5/+70Nr6d8/2N4Y7sxPB8tJUm7wdwf/TZOZYj8gWP4u/ej4hi7WCZamKFz7gT9axfBMCkefZD8viMYrouKQqKFaRa//kXQGm98D7m9h/EndoMhiOZu0DrxTaLFOQzXpfLwU+R27UeGAcI033IKa8eNitJY89Jv3Z4o1ztzg0v/4tc3/d2ZGMteAMfGnhglmZ7d1PIdun+U/d9xiMkP7yU3utoxLGqGLJ1a4MJnTjHz4tQWzny5TlvTvPIGbnmU8t77GDry1OADHHAGJHTbiqFRk3NvRtTnb9/zwBQ2pxa+wFj+KOPFY1S8yZt+3XiQ6SdNFvoXOV9//o9N6djFxovUgylGC0fYVbwf29gk/HYLEhVypfkNZjqnt9mRUZOogOOzv8XR2rMM5w+Qsyu3rLP+/taDKWa6Z5ntnCHnVLYtodwMpwnTDomMOVR7ekOdglvZ9VprpjsnmWp9izDtUvEmtnXMm9FPmpxe+CKN8Dr7yo+Tt2trGkZtFctEwJ0glj1OLX6BveXHGM0f3kA3QexIOvv9htFJix/8OzW+5y/emSA5P5Py5c91+On/ZefcHqtSYei7vhdnbIzGl/6I8NpVZLsNQjD5t//eihjaW4FWCpVuPUKiU8WV/99XMyE4AeWH93DgRz/B9V96ETS03pginG0RXG/g7aluvJNEceOXXyZaGET87vTaCcjtrrH/b36Mk//s10jqvdXUgxAU752gcM8kc59/g9bxq1gFjyP/5Lvpnskk5/OHR7GHCpz8sV8DYO9f+zDF+3cRN3r0L29uCDnDBYoP7sKu5jj1z38DNBz+bz5N/tgYSbufRR4G1zP7O68TLHff3eR6iscepvHaC/SunCftDsa2wTxmej4oxcznfhkQTH7PX8Yb30PSbWMYgtI9DzP9uV8hbTfxJvcw/My34Y3tRkUB+f2H0VIx9as/A8CuP/dDeBN7sn4jjkvh4D1c+5WfQsUx5fsfo/zQk3e44bfHztMEpkCprHTIdrOWkPEtojyGZ+NMVBn+zseI55q0XjyLThXeniHa37xEPD2De2AvwjBI5vubGgK1e4Z5+EefwC17tK+0uPr5i0TtEKfoUtpfobCrxKP/4GmSfpZKuF15YRr1uf7Sb63knOLOEkvnXqU7c3FNfatSWbMi1zN45IMO594IOXsi2vCFECLT2E9VwvXOCZaCa1S8cYZzByi6I1lKABOtFLEKaUdzNMLrtKM5evHSO6o4aNgGwsx0q7XSWK6FShXCEJiuieVbOHmb1rX2mlbQt0MrmqWXNJjpnGI0f4iKO4lvl7BNP9PX15pERoSyQz9p0onmWepfGZArdxb9SFXMhcbXme2dpeSOUfV2UXTHcEwPgYHSkkQFtKN5FnqXaEWzWahex/ST1o685Cjtca31Go1wipHcQQrOML5dxjVzWIaD1AmpigjTLt14kYXeZZrh9ID3IQjukAa5EzSK+e5FmsENCs4wRXeEojOSvVOGv0aRUWpJMijFDNMO3XiJZjhNK5rd8T0H6MZLXKi/wEz3DEP+XqreLny7gmU4gzSXRqqYRIVESZcgbdOO3oayu3cRQjBoCHnnyEDGL3tr8taG6+LvP0jrpa8TXrmckfq0xnDdLEpgvLWowLYhwHBtdv2FJzFcKwvPjxQxcg4YAuRqmentDMvuxTnSXrjlfLXp2Ri+Q9qLMkPgJvK3MAXerirhTJO0HYLK2tMHV5dwx8topRGWQTBVZ5kbG821sUo+3nj5tsaAO1LEH69QvG+Sw//4OwFwqnkMe2klJQDQu7xA0r4z123+S5/F372f4Wc+iVaKztk36E9dAkBFQWYg6Iy4nbQb2fHKNYRlYZdrjH3ie1FJMuB1ZO3GTd/HLg/hVIexyzWALBVh2ph+HsPziOsLmeGoFTLsk7beYiXaTjYyLUGxajN20EfJ7CPqt1OmTq2txbSHS5QeP0T/7DS5oxMYnoMwDfxD47S/eQnD89CpBEOv1sbeAmEI9nz8ABiCmZevM/eNaYKFHjKSGK6JV/WoHhtmz8cPsudjB2ldamTMz80w6Im9+k+FjPuEzUz/fRlKanodiWULGguSXkfd5qUQGMICNKmK6CX1rJwvaeKaeUzDGtTGZzXsUdolTNuDVqc7G6gX+pcJ0vaK1G6YdjYsPascKONVPXSqCRohw8dqJGGKjCUq1aT9JDMYjK0PcEqnxDIlVSFKS5rhDJbhYgrrpskpJVUxiQyJZY8gaaJ20OnwZsSyTzuShGmXdjSPa2VCT1maSg+0Cfr04wax6q+UAUoVc3bpKyscjdtVIdwMjSJRwWBCjXDM3OA6s0k46yORXWcs+/Tj5k2lloIgaXFq4Y8G55DQjbevgS51TJDGpCqmnzRpWtM4Zg7bLeLWRkBDND+DRiFVMpiYM+GqmzkcO4XS2f3OrrFHM7iBZXqDHhNiZZ3l+5CqaMv3908ttEalKVaxiLBtDM/DLBTxDx3GsO1NVVRXrBbE22owGK6Nv6eGN16m/uIFZJhQODpG+eE9W9b0AFDh9tr8qlShpcRwLQzXRsq1zlbaCrDv8zIDhWwusKs55OkIFUtM38Eur0arrIKHYRmkvdvzY9J+TNqLiBY61L9+ngEDl2iuvRoVAFSUbEqSvBnR4gwqjbHyRaximcojTxPcuJKds2VnnIEBq890fWTYH4T2DVQS0b18DtkfzJ1aEzcWsfJ5VBwSN5fonHsTyLRH4sYiWqbYRg3Tz6+8K8KyBsfZOXamQAiYtmD8YG6lo2N9Zn05ilX08faNMP2lE+Tv35O1M3Yd7KFMBMYsFbKKA6U3f7kFjD25i9bFRmYMvLKWENMCgoU+hYkSYx+Y5PQvvH77/s/CwC3WsHPlm3pba2QS0Zu7yvLbqAGZZhKqM9cSmnfoTbA2b5wSpu07EuTeCtrRLO07lLkBFCcLFHcVScMUO28zdM8QYTMkasdEnQgZpNg5G7EFr+hWKC3pxos7muQ2hBAYjodTqhE15tFpgmE7OJVh4nYDFYdoxyL1bQIXArqoNCHpNFFRkJVn5ctYwxVcUUXLFBmFJO0619tv7Pi0pIppR3dWfTPdHLnqPqJmJpGaqIBrreM7Pu7NSFQ4MDAzg89Mc+SGDqFRdNsnMVwPw3bRgxa4wjSQcYyZKwwkdY0BgSnO7tW2kBm5nWiBDrdKWr81GAYcOmxSLhs4jsA0s2WphMUFxeyMpNXKegzs228yPGJg2wKZZj0LLl1MCd89Paa3BSqOCa9ewaoNkTt8BBkEGL6PO7GLtN1amyYQArs2jFWpIBwHdzLLcbu79oDKKgZku0WyDX7ArRCGwPQdrLyTEel6EWknWuEEIMAZLpLbN4w3Vsap5ckfHCGcbq6ZeDWbSE5uAp1K0k5INNeidP8u0k6YGfX9iHCmRTjTBA3+ripaKkzXRpgG4VwbFSQZCfDAMIV7s3ScVfJIGj2ihU5GAtw3lHEYhguo0CV3YIRwukHS6hPONBG2mUUkVPa9xPUeaSdcMT4Gzvwd4VRHgKw1uOG42JUhllOXwrQw/Tzu2C4My0LYNioKSbutTPthcZa008qqGQbHSntttEyJ6wuYXo60184IiIaxQiA0LAfQ+JN7UVGIXapmPcPfAnZGIEw1nXpC1JNImaUK4mC9BaWlQoUJspt9rcK2ENZqv2Mjl8uYtbfLkQlBcXeJq394kf7sxipQcTti4cQs+7/rMMK8vcVs2i61o09S2XMvlp+JfSgZE3ebnP+9n0KrLFSeKRAKWnXJ4mxK0NuOV5uRyoTICFWbI1tn/XoCw7QRCKRcG7HYNkQm4qIV+EM+cS8mWAoI6iFRO6I/36ewK49xh/v2bkCYNt7QGEOPPMvs879L0q5jFyuMPPFJll5/jmD+Bv74Xgr7MoEQDSTdBs2TrxLMXsOwbIqHHiC/+xDCspBRQLg4w9I3v/KO1wgLw8QfmmD8me9h9qXfp3v93Dt6PBn2UWmS9fQwTNyRCexSlbTfBZW1QO1dvYA/sRcVhxkzOV8kri8QXL/81u7HzZ7rZvu5udZ48C0Iw1hTEmYYUCoJ/vqP5Ln3fotq1SCfNyiVM3nhP/hcyGd+I+CN1xPKFcFf/qs5PvRhl3JZ0OtpzpxK+Hc/0eP8uXRHHQnfaSw78rcWAchuh9bXvkLtO7+b8rMfB6VJ6ov03nwD2euik3Sl8kqYJvmHHqH02BPZeGkIUJKhZz9CGknSdovuG6/T/MoXs51rjY4idBxvuaOhilLCG3WEZTL2XQ9lbHxhEF5rZt1YDag8to/KY/txhgsYns3Ydz/M0lfP0j03i9Y6Mxw2qCozBz2V1EYTq4ZovsPs77zOxJ9/HKQCIehfWuD6f36ZYKpO9/wcpft3MfyRYxmB8MR1gqtLqCgFQ+BNVNj7g88A0L0wR+/CAtFsC9NzGP/uh/F2VTF9B51KrEqOG7/yMvFSl86ZGQzXYvdf/mBWFYRg7vMnaLxyObsn4XJU4A7fiRAMPfVxhGllSZQ0oXPmjRUyn05TDMuh9oGPYBVKhDNTRAszpJ0WCIPOuZOUH3gMw3EzcnuSMP17v4Lsd+lfv0Ju70GGn/lU9gIJQf2VrxDcuELcWqJ/4wpDT30cGfTRSpG0GgNlyi099nXYmc6AJciVLWzPoDsTc+Ncj8bs+tCMihJUN2Tyhz+Bf2CU0e97kmSxQzTTzH4PAoTjZKPCbTxTlSoMy9jcezUEpmOiNylxvBnCtClOHuHc7/47hu55EhkF9OauMPrgx1ibJsgIhE993GH3fodzb4ZcPb+1kKshTBynQDm/h8XmOYzlblIqGUz6AsvyEMLANB1KuUnm6m+uHN/zqxw+9j14fo1Tb/wi/d7OvbGrX15tjrFZ/Wnv81e2Fd57L+GP7cbKl5h94XNEizMro62wHZzqMKUjD3Htsz+PivoU9h2j+sDT9EZ2Ey5cf8cNAiB7cd6K8bYDCMPAm9hD8/iL5A8ewy6USXttvIndCNvCHzsISpL2Onhju7IQ5hbvhekOQpFaoWWKsBwsz8ewPVQaE7eXMN1cZnDYTmZ8Konl5kl6LYRpIwaEp9zIHpoXX185dm3I4C/8JZ/7HrD4sX/S5uIFyaHDJv/NPy1wfUrx4/9Hl6VFxYGDJn/pr+T4yEcc/sf/vs3UNcnkpMF3fY/HT/50hb/0/XUWF9X7ThNmaNhgdMxg+oak2Vg9OZ0kRDemmPmZn9pwu+XGTZBNJs0vf4Hml7+w8rvtwIeedXnhuYhbM6I6ipj9jz+3rfPUUhEv9Tj1Y7+xssx3q4wOP8Dk8OMs1E+z8Ienmf/8m5vu4+y/+Oy6ZbYjePQjeeavJyzOpnRb6y022YtofvMKzW9e2XC/c7//BnO/v3FUL7i6xPWrGxM5ZRBz6d9+cdPz7V9aoH9pgZnffG3dbzqVG17PhtCa67/585v+rJKIYHaKha98boNtFZ3Tx+mc3jh6GE5fJZy+Sp0vI0wjM06MgfOYRCy98AVWrv5t6NK+I2NAS00SKnIlmyRU2O7GXmV0fYm533wJ/+A40Y0lknqXeLZJNJORKJK5BexdEwjL3FxjQGvqpxcZe2KS/nyP7lQbdTPRxDIoTBbZ+8mDLL45j0ru5CJotEzQWiIGOWSZxiuSlsswTMjlDZJYky+ZeL7YspiD0ilx0kMAk8OP4Nh5pIzpR3Wkisi5Qzh2kW4wR7Nz9d2ZpGDTc9++ISBw3BLF8i6W5t/pEjKReUMDtM68hje6m9qDT2P5BZpnXiOYzQwepzKCU66x61M/MGhXbQM6s7p3qsSxRWgl6c1d5crv//ymCm5vJ+xyDXdoFGFaJO0Gst+jcOhehGkRLc6RdBqMPvtdzH3xt1FxjFUoAYJwZntGUWHyAE6xhkoS+gvXqB58mO7sJVSa4uTLlPYcw8lXCOqzxJ0l4m6mYOcNTSAsi+LEIeJug+70pXXH9TzBsXssXn89odFQRJGm2VC8/lrCB550sO3MT9i12+TT3+3y4/+qyxvfSuj3NHOzkrl5xae+w+PZj7k8/1zE3Nz7p63v3n0m+w+Y1IZMOm1NqynZvdcEBa2Wot3W1GoC1zMwzUxsaX5OUakKvvf7fGZnJGdPp1y7un48syzBgw/aTF2V9LqKfk/T7WqGhg0WF1Qm6UyW5ixXBHOzCtcDy8yihP1N9VJWEUYtbsy+Ss4fYWzofizTI0kD2t0pWt3rW7oHUmqUhMMP+thuxPk37nJJdgJhm5SfOkz/zDRaKqxyDrtWoPPmFKZro5IUu1bEHS3RvzyXSVt3gi3xHW7GjiMDXsGi10woVG3c3MbGgLurRuWZe0jqHYJLcySLHdJOgAoyc9YsFkluzCAcB8PbuGxKK83VL1zkyH9xPwe+6yiVI0N0rjVJ+wlWziY/XqR0oEJurMDJnz9+Z0a8VhmBUAi0lniVMZxcGdNanjBWViNJwLazqgm1hYjRyv0RFq6Vx7GLaJ0QxV2kipAqwbVLaDRR3EHKCNv0cZ0sXbFOBvV9CtvJUyztYnTsobfZGMiIVYbrDhjbAmHZWIXyyhpJr42evUraa2PmCpSPPoKKI4KFG8h+FxmFtM4eH0zImUcbN+ZZlpTNTx6iuPcYrUsnKO69BztXytpat5doXXqTpJsZqqbj41ZHKR94gIU3nqNy6GGc8hCG5ZCGPRZe+zIyDjFsG394N+XDD2NaDkorlt78OuHierEX0/VxyyMU9h7DKVSz600iotYijbPfRCVZdM2tjOCP7CE3thfDckh6LXqzVwgXb5AGWapMBj26l88ONO1bmYjJIHyv4giVRDRefwkZ9AjnbmDUFweCLptX7Wz4RJRCpynCMPEqYwjLJo2CrPOf46FlSjToK5+GPVQSYXk53GINIUxML4cZBVh+ATtfXvOeS6lpNTUT4yauKzAMcD3B+ITJ/IJCyqxXQa1mUCwanD2TEgZ6Raa4WVfMzyl27zHJ5d8G1+htwmNP2Hi+YGLSYmTM4Pg3BR942sEwoFgUoOHrL8R8+KMujYamWs1aMH/xD0McRzA8YtBu6815hIBtw8SESW3YZnFBcvrNhI981OWzvxVw4LCFVtCoKx54yOKlr8ccu8ei19Ncv7a1MWa5csBzS7h2EakSNBLXKTNczbHUvHjH8Uop6DQltTGLZXqW4wme+GgR0xLMT8fMTSWEfcXTnyqigdlrMXM3EqK+4qlPFdEablyOmLue0K5LsDIVU62zigNhmehEYvjuoKwr+122uhg5d2WuwRDoKMEs5lD9MCOuv8NOmBmkRFevkoq3JoIlDIEzVITD4wRXF9BaY3gOTq1A4cG9pO0+hmliFj3Mko9dytF85UIm9LcNg2DHOgMCmL8aUJv0SDfT7DdExhT1HKxyDmGZmEWftNUjWeyw0gbwdnkODfPHZxi6f5TKkSHGn9xF9egQMkwxXROn6KI11E8vMP/azB0liZVM6UxfQCtF1FpACHOlhTE3sd0zy1pjWtnHu933RgNR0iZOeiiVZB+TzkLISqUolZLKCKUlUdzm3Q4tvxW4bol8cZx8YWf6+5tBq6xEBq3xx/dgFco4pdpqPb8Au1TNWqHqjIlsejmEZWUSqN0m0eIMOk0yHooApMzy6MvnXhmmfOhhtFLYfgHT9TFsF7tQQSvJ0skXs7SDZeOWh6kceZT+4nXsYhXTy2NYNsK6WWI4i1wYloNTHsIbmqBz5eQ6Y0CYFl5tnOLee/Gqo4NwusJwXNRNJa1WvkRubB/5iQOYTsZCNl0vYwvbDp0rp9BKouKIeHGV1CiD9Q2j+lczLXbZ6yBZVtlc+55ZeYfhRyZonV8irPfX9faIOw1kHGGYFkIYBIvXSXoZyU2rlFgpdJqVRSX9NipNMCybuNtExVnr2TToZFU8t8jTBn3Nt76V8Bf/ks+HPuxy+IikVjMolQy+/nxMGGhsR+B5maHQauk1aXCpoNNW5AsC237/GAPH7rO5djklCFYJdffeZ3HmdIppCipVg2rN4OBhi2++GuN6Br4v8H1BHGXDYdBXG8oxL0MICCNNsSjQyqRckRw4ZGIYMDRkZL1VBjyn/QcsjhyzuXYl5cqlrRkDpmHju1UEgl64SJL0SGUIGBRyIysT8m2hIQrVCsncdgS79jvsPuQiU41fMMgVTK6cDbn38Rw3LkcszgpMC3JFk/sezzF1IWLeEZlhZBjYEwPBLZV1JkRKVKePOVRZIdmhNarbxxqqrHjHWmt0GGFVS8RTc4OSvC3dih3BEBa2dEiabaK4iTloaZ51JZUDRU83a1kvQ25/MgLDsXBGy0QLbYQQWHkXd7yCv2+EZKGdkYQdGzPvYuZchGVuOxa6szSBgjiQTJ/v0akntJc2zqWHVxeZ/dWvU3hwH6UnDmGVc8h2n97Zaep/9Aay0cQeH83IXu3NW0T2Z7uc+cU3mHh6N+NP7aZyuJbJEYcp7astFl6f4dofXSSYv7O+tEoi6udfRRgm3bkrxN0Gdq486FGwdiBctswtS2xW+bjxMXRKGDcJ6811v/XC9azfINqsPlQjhIVpuVnp4iB3K+Uy92D9oxbCxDTtjFg28IyVliiZbmjJL/MWhDBXL1hrtJZImazZZvk88oUx8vlRhDBwnOLKeWitkCpFyR2WsylJ0mkSt5YoHXkYLVN0mhAtzQwaSAnyuw9T3HfPCkGnf/0SSbuBlilJp0X70puU730MYWSvtgx6BPM31oTMTMejfOghbnz5V4k7DfyR3ZQOPMDIIx+jce41VDzgvxgGll+gcvAhlk69RNRcBDROoZL1NSd7n3o3LtKfuUJx7zF2Pfv9G16anS9R3HOM6tHHWDzxPK2L38rKy3LFzCAYRAUKk4fI7zqMYZrMfuMPSXsdSgfuz9q2Vkbpz14l7e+0SmX9+5IbL/LYP/0Yb/ybF5h7+RrJLV0/w/rGqqBJt7npUeJOnbiz/p2O2mvf/XZb86U/CvnLP+jzQz/skyTQaCgunEv57G8HdLuacgXksp7JLW0ehBhE7uSWuXLvCioVg28uKDodzbKqcLFkMD8rabcUSprUapnU+bkzKe2WZtfuLBU5M51t12ho2u3bXJSAq5dTCvls8swXBEkCfk7geZmscxBoXnkp5s/+eZ9iyeDyxax/w1ZgWR7FwiSd7jS9YJ7VFuYGpmnvSLfDLxg8+HSBbitlcTZl9wGHoUd8pq/EKKU59Vqf2WsJaaypjlhIqTn5jT5z12OCrkJYNrkHD6NTiZYK2e6SzCwBAfZYbdXjT2VWhTE2hFEpgFSoKAapMPI+yewibCFV8laQNYcrg9YYooNvVyj54/SiJbSWmYS5U6UbLdEKpu8gQ58RNDVZGajhZFEAWwhkN0D2o4FCr0FS7yL7EWm7/86nCYSAfMXi2FMVNDC61+fUCw16zQ0ak2iNCmLar5ynf/YGxYf3U3zsIEPf8Sj1P3oDFQSE5y9hFouYxcL67W9Cb6bDhd88zYXfzGRsxaBr4bbP37QojB2gOHF44OFlE2Bx1zGmX/3dla6F6IwBazmCfMHA9d59z0PJlEr1APniM1SHDuM4RaKwwY2pF6kvniPorx1cLcujVN7L+K4nqNQOYZoOUdik2bjE3My3aDUurVlfGCb5/Ch7D36CQnES285hCIMkCWi3rnHj2gv0unOkaYgQJoeOfjeV6kFcr4QxaMDzwY/+s5X99XsLzE5/k6krX935RWvFzJd/c9OfG298ncYbX994U5nSPvct2ue+ddtDyDikef44vflroBRp0EVGfapHHiU/foDeTHafBBkdfOnUy/RmrwyUK28/EW6G0r77cCojdGcusvD6V1aWZ30xVlE5/AhJr0393GtE9czzb5x5Fa86Rn7iAIVdh2ief3vKFd9rLIe6R8dM/tHfa/LmiYToFh5yq6VZmFfEERw+ajMzI7PKAZF5mvsOmHz2d0J626r2eWfxrdeyFMDuPSZoePXlhOPfiPnwx1xGRgw6bc3zz/X55Le7G3rXlgmPPGrje4LnvrKemK3JBNH+zJ/zGRs3OHc25ZWXEi6el/zQj+SZmDQ5/WbCm28kNBuaoWGTSxdT5rfBqZAyptefp9vPypd9L4vQ9cNFFhsby5zfCYYh8POChWlNHCnefLVPuymZnYr59Z9a5Nt+oEp9PuXCmwGXToX8+k8t8um/XGVuKuHCmyFXLiuE56L7ATpKkc0OycwC9mgNe3yI4OQlEGANlTHbXazxIWSjjez00HGKWcxlhoTaXgnkThDLXlbiKwSW4TJcOMCVpVcAzXD+IGV/kiBpUnRH6YRztzUGVJQy/9lvrlnWef3q237O248MCAh7kkuvd5CpYvp8n/bCxp6gPVSk8NA+Sk8cRhiC/sVZGs+dJLiSsePt3ZMYOT8zGjq9teVId8BODAHIcsETj3+auRNfybyxlePpNdrOWkPQU3zul1oIkekNvNvw/CoTuz9Au3mVa5e/gtaSkdEH2Hfg47huibnp1+j1MrU3y/LYe+Bj1IaPobXi6qUvkqYhxdJuCsVJjt775zh/5jdpNa4MrlPg+zXue/gHicIm01MvkUQdhGHgeBXKlb1IGa+UPGqtmJ56ibnp49SGj1KtHcR2ipw99RsrH5ZUMXG0s2517ya0kiTt+sqz10oi45Ak6GSdv+ZvqtfVmqg++5YlYu1CBQEES7fRhjAM7EKZ4p5jVI4+uuaYpu0Sd5tZiuRPCLTOQt2eBz/1s1W0yvgAQV/z6qsxP/FvuszMKKauSX7zMwE/+vdztFqK69dSxidMvu07XOIYvvrliIVNmhe9F3j15ZjXj8cYhkCrzEOfvgHHX4uzfLaEMND81L/tEYaa+pLi9MmEZfXg//R/9zGMzds3B33Nz/50H8PIJpv/P3v/HS/blZ514t+1c+WqUyenm6OucmpJrdBWZ3e3I04YGIOBD8YYGJgAfPgRhg8DM6SxZzBmMGMbB4yzOydJHZSlK+nmfE/Op3LVznv9/th10j2pztWV2oHHyObW2bX3rh3Wetf7Pu/z+IHEc+GrX7L55vMxeTDwJZ4XE6EDX/LOaY/JLfgCvcMP0Dv8ALqZ4a1v/DtouwXpepJcephaIy53ZVKDKELFdssdcZs0TdA7pPPkJ7L0jRgUejQUpcWLX6rx6T9fpLSoMz/loU/76Do8/KEMmYJKZTnASimMHjV56Jk0qayKUP04yel6VD//rfgU5UqZIMKfX6b6hW/HWQEE3o1pZBjG20axL4qaSpC45zAgUSyTyHbB3zwBp3NDjB77CIl0L2df/PnbHs8SRp602Y2uWkQywvYqjBTuo+4sggDbr6EIjZozuyqO9p3GnoMBGcXqfGEQMXgkRTKrEfoRjfLmbgDF0FCTJuUXzhM2bYJqi6Bur+oORPUG0o7rJZGzjV+0gH0fPoRdsqlPVjsqBex8/iFOeQ4jVSD0WmuZgC03jiPw7xQ0zWJq4ttUyzdxnApSSuzmAkeOfy/pzCBuV3U1GCgUD5PJDhEEDjOTr1CtjCNlSKM2Q1f3MQaGHqJ/8EFqlUmkjFAUDV1PYVl5FmbfoVK6Hj/4AlTVoFK6jm2XiVavj4wzEUIhme7BDxxU1aJeW1cbb5ckvhPYd0DjsadMXn3RZfxmwE63dUvIeLUQi19tzAJFgf+uyUar+90xnx3rUzTnx6lPXsGrbWybinx302ednwAYWYvhZw/RdaofGUbUxyoEtr+hA8fIWaRH8vQ+MkxqMItQBH7NpTZWYu7lCez5BumRHIPPHEIGITd+9xyhu/b9zP4CvQ8PA3Dz989vKw2uqjA8ovI3/maa3/pNm7EbIbYdk+ZSKcFnvs/ig0+avPySx/xcxGd/36ZQUPgLP5FcLdk5juTn/l3cfrgHGf73HL5P2zp5XatytPkzu52qvnVdY9srKfntj7Gyzfr9eV68aFl5VA0TjhzVeO1Vj6WlaEs7Z0XV0YwkupFa/Sxh5smlR8hn962qDypCwXbKHZvhhKGkvBTwzc/XMBMKrUZIrRTiOhFf/s0ygS9xWhF2KyLwJW+80EDTBc1aSKsREQaSN56PP2tUQ+rttkTpbvEjIrnh85UhaP1nYcPGvnAzFuOqNra0coa4DKLpifh6vAuzKC9oMle7SCQD/NDBDeqoioEfOggEilhCCAU/dP74BgMQt1KHgaTQb6CbKpqx9UWL/JCgZiN0FS2bRLEMFEPD9QIixyfyPFRNQ0axhfFWEIpg6On9LJ2Zw63Y7zoYWJHyVHUjJqG1Z424Bvbetp/tFZEMqVUmqNdmiNr+BZ5bo9lcJJkskkytOeJlc6NoepJmY4Hy8lV8v9Xevo5hZvHcBrnCfhRFbe9LEkUhvt/CNDOYVo4w9PC8Bp5bx25tnnTCNhcgCn1kFMYSuMEfDfm3bF5w4m6dS+e9WJdlh22FEKhmgtX7LQRC1VA0PeYLyAjWGfC8G/e/FYS+hw4o5q1mP+sgJaHv4Tcq2ItTNKaubrXRbR3fyFkUjvXQ//g+AjsgDCISPSlUS0XR136roinoaQOrmIzTqZHE6kmipXVkKBn77EWiUJLdn0dN6My9MkH9ZtyBgYDkQIa+x0YpnZ3bcTZLpgRDIyoPP6LzN36qxbWrcaeAokA2F2sIjO5TuXBeMD4muXol4Lmvudx3v04qJXAcyeREyDeec3G+gwH7e4nbeew2fEfGAcjVywGtDrkC8T4iwtDFcatEoYcEnKCF7ZTp9PlbyayOX9lc5rh+3qHN81s938mWuyqOtgJ7i89uG2FIuLwXg7R3ebjIp7lOmfU2E9momolh5YhCH9eu8F7OT7dHIJQS34tQNYUolNv2qUeOR1Bp0PXhe9ByKcKWi31zHt68gX1jHsUwUAs5pBcQ2dtMKkLQe/8A9fHKHVHJE4qKVejHLs0RBf5apLvHN88NGmiKharooEq8sHXb7nDbIQjs1W6E9Wi1FkkkixhmZvWzZJvQF4YuqmqirnO4U1WdwG+RK+xr1/pdoijAdWtUyzcpdB9F05PUqpPUa9O0mvMEvrPpuHuFqoFpxmxwCahKzAB3bLm6KkomBaYlUGK7cnxPUq/Ff9P1uNVsha/hexLblqtCK5mswDBjVnMmq6B26K8gFBU9W4wn/8BHNcyYyKcb+I1yW75X331He4DfqGBki5i5bhTDavtnyFjGWojYJEtGePUSQlExs0Wayo34cyFiQqhglbewV6RHcvQ/sQ+rJ83pf/4cbtmh61Qfw88eQs+sPSuhF2AvNFh4dZLS+XmiIKLvsVH6Hh1l3yePMfbZi7RmajRnauQOFSneM7AaDKimhtWdItWf4dJ/fmPHUl46JejpVtA0wdXLAc1WvKJVlbjnvtGI36YVUyDPgy9/0eHLX/yjEXz+cYDnxQTFvcLxqviBTctepunEJV0h7qw75a0Jsq0yeXvO7v0JhGHl6O4/hdNaZsmtryrkvhe4PZ0BRWAlVTwnJJnV4/a7LRbV1kg3xY/ex8x/+Qb+Ug0tkyB9z366P/Ugkz/7BfTenjUL474e/KmZLSdl1VCwSzZe492ZrgDIKKC5OIXXrBK6zdWLK2/VDN0Fb839YXxuKZPEUJ7G0u7a9XtFGLhbhsVh4MQe7O0uAClDNM3CSnSRSvczOPzotvuMvxP3entulYtn/xt9gw/Q2383I/s+iKqZtJqLjF37KpXyzdg85DYxPKLx0GMGT32XRRBIurpVyssRL3zV5g9/20YI+NinEjz2tElXUcGxJefe8fmPP1sniuIWrceetHjkCQNVhXPv+Hz18zZn3/YxTPizfzHFQx+wcB1JFMmOXOcgdv/KH7mX+vgF3Ooyyb4Rcgfuxm/WaM6OI6MAxdij6ceGVfDm86hPXMJI5+k6+SjFU09QufImMgrRUzlUI0FrYYLId6lcfYuuYw9TOPYQXqNMa24MxbSwCv0IIahPXb2tgCA9nKPrVB/jn7tI9doyMpTMv+oS2j4DHzywup1f9/DrJWo31roBSmfmMHNWnP5vv+elCwvoaZOBx0cZ+4NYayK7v0BqIINbcSid2/l9qNclS0sR6bTg8Q8avPqqh2tLursVHn3c4IknDb78RYe5uf8+I3wnEEbeaiAAkEr0IIRCvTnzHTyrP32wEnm6h+5hYfJ03M75Hh7r9soEkcRphpgJFd+Laz5bnaWMIiLXx1+sgiTmDFSbyHaN0pucxjxyILYwnmtsPRlLSflqCTNnYmRNmjPvkqAmYwXCZPcw609aSklzcaItot2GgOITh1EtA79mr9pZJke7aE2WCao2SlInub+bxpV5EvuKWH1ZZCRx56pYg3kqp8dJHexBhhHN63uTFV6RLN78eQIkhKG7SuYJAge7tUyzMc/C/HZseonn1TYQgKQMmZ85zcLc25hmlnRmkP7Bhzh5z49w5eLvU17hEtwmsjmFXEHhZ36ihB9IfvrvZhka0dh/UCMIJM9+0uLn/02dG9cChkdV/tbfy3HilE69JnngEZOuboW/+ZdKSODv/dMcJ+8xcB1AkTzxtMXf/1tllpciHn3C5Cf/ema30wEgdG0qV0/T+9BHMLPFmEeyPMvkc/91z5F37vC95PafItm/H81MopgmI8/+CJHn4jerzL/+FepTV/EbFUqX38BrlOk6/gi99z6FRBLYTVrzE9htXYL6xGUizyWz7zgDj30KPZWN3ctqJepTV6lP3obngRL3JRsZi8ZkZfU1C5o+znKLcB1nQLU00iM5Rj96lK57+tGSOoqqoCWNWBJciWVRS+fnSfSkGHzmIMn+DK2FBoW7+tDTJrPfHtv1lBoNydl3fP7xP6zzV/9amn/4TxSEiNXxZqdD/tE/qPH8c+5qlui/4z3CLYsgQ0+haUl0LYllZDCNLADJRDe2U/pTEgz80XjmND2JlezCSna9P8e7nS8pqiCR0Zi93iJV0AkDuWUjgAwjZBjR9ew9eO3MgNGbi/siAXSdyHZRkhbKNq2FMpJc+90LDD+9H6fk0Jpv4JZvP1UY+i5Ll14GxMr/W8MmcpfA7M1SOT2BnkuQ2t+NDCWVt8bJ3T1Ms819UIw4hSaEQE3oyCAiCiKEqpDcV0TLJghqe5fi1PQkhpnFsSvrVuiCVLqv3V601m/ebMyi60kE0KzPEgZbZ1HCcPNkJ2WIDEMcu0IQOLhujULxL5NM9tCsz28KBiIZ8wUUZffHp9WMB/d6PdaObzZiZbl0VpBKqQwOa/yZH09RKUdYidi1LplSSKYkg8Mqd92r81f+ZjzJd3WrmFZAJiewLIW52bgnu9WUVCsR8x2uImUU4laXqU9cQmn3TIeejVdd40mETpPa5CW8L/wiUbC9WVRz5gZuZRHVTK6m/JERCiqKFLSWp0BGmFoGHJ/6+CWiSpVi7gjl1nisROnaRL6zcnL45SXqzhnq4xcRqk5Cy+C6dZzm8s6E1x0goF2O2Pg7pJQbDGZ6Hxqm//F9aCmDK7/6Fn7TwyokKN47wMhHj65uFzQ9WrN16uNleh8ZZuprV8nuL6CYKotvblZf3Ar1uuQbz7tcvxZgWrGwTBhIbBtmZkIaDUlXUUHXYW62s6KrYcJ9Dxi8+Zr3vpgXCaGQ7dpPJj9CMtOHbqbaAfw6NVMkCxNvUF68StDm8ghFo2fgbtKFEQwzg6LqRKFHozrD8tx5XLtCFN6SARIKVqJArvsg2cI+dDPdzvA1aNXmWZo9i79F4K4oOl39J0jnh7GSXSiKhtMqU5o7j24kNywOwjZHQFMNTCNLrTGz+jv9YOcxrHf4ATKFUZq1GUrzl+kevIdsYRRF0Qh8m1ZjkeW5c9iNjYsiIRR6Rx4iUxhmaeYMzeos2a4DFPqOYZhpoijCc6rUyxMsTr+9/ptoukVX3wmyXfvRzTQCQRA4tGpzLMy8je80NnU/CEUjUxgh13WAZHYAVTNwWxVqyzfju3XLO2YmCnT1HqU4cDc3z38Ou7W86d7073uUdH6YKPC4cX6jr4EQKoaVpXvgbhKZnnicVtRYtMttUCuNUy+P47RKCKFSHLiLbNd+rGQBK1VEVQ369z9Koe/Y6iQbhi6l2QssTL99x5Rrb0+BUELoSzRdWf33VkNlUGnSOD+BlktjDhQQikJYt7HH44dBTSfj1kJi06LtELghakKn575+jKxBc6ZO4Gy9gpt/fXoDu/lWxOSxJFauN1aTEyKeDNwWTnVxY0QjiIUcKi20tImaMAhdHzVpxMIPhoqiCox8Cj2fjB0TI4nQFPSshTtXJXv3MPZMBb96O7rckq7uoyiKht1aQsqIZKqPZKoHu1WiUV+L0ivlMZKpXgwzQ6HrMLXaFFHgxSIhmoGqmURRSL0a64qrqolhZkgku3Gdyqo7oqLo6HoqvseRvyXTNfAdAt9B0yxy+f24bkzMkVIShi6Bv/ZbfV/SaqwxnIMgDvAUReB5sXb58mK42ho2di1gcSEkkxEEgcS1YW4mREqYnw25cS2gWonQu1UMcy07ryixwUtnl1US+R6t+YltS0MyCgmaNRrNOOCytAxB5BLJCFNLE0YuhpYi9H1ktYEUsRaDH9pIGWFoaUw9h+JHIHQSeo4gcmk0l2g4DjknQ7N2Az+00RSTlF7ED2LeSUKk0TydWjVuOzISQ/h+GT9oYKopdMXCi2xUoa2KUfmhgxc02TpFJ4n8kMgLMLLmakVPqALV0FYNwIQiyOwrkDtcZO6VCZbemsFvemT3F0iPFjbuMpTYS00qlxbpuX+I8oV5VEvDq7s0p9eIWvmCIJ1W0A1BvR4RBfFnQQithsR1JS1bMjMTxvLfGuQKsSBPIiHo6495BfNzEUMjKrPTIbm8gmkKfE9S7FGolCOiCAxDUOhSOHRY46033p9gINd1kOLAScxkV9vnxMewMiQzfSiqju82qC3fJAicVX6SZiQp9ByjZ/g+VM1avUfoFl1WDlW3KC9cplmdiUuF8d0hkx8mVzxEvvsgqmbGrXUCdCNNIllE0y3mJl4n8JobWP9d/SfpHrwbK5EHoRAGLlayi+LAKQwzg6oaq9uGUYCUEa6o07QXaLVLBYqiEO3C5LNSRXLdh7CSBRRFI1sYQdUtVNXETORJpHvQjATT17+F7zXXlUAFiXQPhe4j+G4DTUtQ6DuOYaYRio5m6CiK2ibQrTtesotc9yG6B06h6daG62sluxCqzvLsWZxmaQP/Kd99mELvMTL5YRRVJ/BtrGQeRT2CgLY0/RpUzSCR7iXfcwRNT2yZrU2me8h27Sf0nVu+a2Ilu+geuJtcz+H43WuzIiUmRiKH7zZo1ddajoWitV1vlfax2tvLaPWaSRndcY7abVsYN6s+ZkohDCRhsHU04C/Vqb58mdTJUYyeLH6tjjtbxp2MV2BKwkJNpwibLaL61gqEQgi6TvSAhOLJHvofGYqzA5WtswPL5xZ2DAYUzaBw4F6SXYOxPrwQhJ4TC9GMnUOuF3+Q4JWbsRWz6+NXWwRNl8RwAb9qI0OJahmgCPRcIraRrDuolo6WNqmdm2bge++ncW0eb3HvqXbXrZLL70dTTZrNBaSMKHYfQ1F0GvVpapU14YlqeYxMZpBC8Qj9Qw9hmBl8rxW3EBpJND2J61Rp1GdiVznNIp3pp2/gfuq1GXw/HkA0zSKTHcZ1a7QaC/FLews8t4rdWkYi6Rt4gEZ9Ok55By52c5HGumAgfo43PxxBIFmcj3vGX3vJjQ1ZZDypz82EFLoUZqZCNM3nm885RGHcjlarRqwkN9JpweCQimUJuntUMtn3zoY5aRZx/BpRFJAxe/Ejm5TRhdee/HU1QSgD/NDGDx2Sep5sYoCmu0SAh6mlEUE8DQehu/oiq4qBpefIWL3YXgU/tEmbvZhaipZXIYxcLD2DFzbRFIOUEfcu66GNoVooir7avlQOtiGxSvCbHm7FIbOvgGpqhF6IkTNJ9KVXuwmEIjAyJqqpUbm0gN/0UDQFqydNsndz5s4t2yyfnWPw6YMUTvYR2D7N6doGFcOBQZW+fhVdF0xOhGTSgqFRFdeRLC9FNJuSA4c0xm4ECAGFgsLAkMLEWMjYzZCuYhwMqCocO65Rq8ZeBF1dCo4j2X9QZXIiIgolhinI5eKAYKe2vDsHQe9IvBp27TIL02ex64sks330jz6CmcjjtEpMXP06Tqscc1FUnUSqh6FDT2ImcpQXrtCqzxP4NrqRpNh/ioHRR1CESug7NGuxAqRmJCj230VX/0lU1WB57jyt+jyKqpNM9ZDt2sfw4adp1uaolcbaGQiBqpsMHnicRKqI3VymVr6BXV/ETGQp9B5Db2cl1h4bSSRDPL9Bte61JYih2VrcudexDVXVSeeGUFQDu7FIZfEaimaQzg2RKYwyeOBxqks3qJVurgt02t/VTDKFfRhmBsPKUlm8SuDbGGYahIK3LhOqatbq/gwry9LsWezGAjKKMKwsXb3HGD74QQKvRRT6OK2YA6NoJr3D95Mt7EPKkNLCZZrVWQwrTaYwSjLdi7IuOHq3MBN5Cn3HGTr0VCwAt3gNp1kiDBwUVSedH8b3W6st3FJGtOpz+G4N08qT6z6ImchTWx5jeeHCqnS5lBFuq9Jxq2cnuG1vgtCXvPWV3XuepR/ReGdsy78F5SoIgZpKYQwP4l7fYjsBA48MoWgKQcsnaPkIIbAKW7doiV0Y5ULVSfUd4PoXf4Hi8Ue3tTCOT16y8LVY8bBRd2hcXdhyn9Wz61y8Vl4YAYnBPPWLs3il1p6dAaWMWJw7i91aIpvfz9DIYxhmBscuMzX+LUrLVza0/4Why9TES1QqY/QN3M/g8KPoZpoo9PHcOq3GPMuLl9bSTJGH5zURis7gyKNomoWUkiCwaTWXuHbpD6iUx7YkEDYb80RRiKaZ9PbfS9/gvURRQK06zez0a6sZi7Y2yAYahowkkYyFUGanQ77yOZvv+5EUXV1K7AcRwv/0UyWWlyJOv+6h64J/8i/zcQVHwG//WpMXvuYwORbw1usef/vvZ2k1JI4bBxEdXlz2WhdUUEjouVWNhu7EIVpeCVNNoSo6EslM6XWG8/dT8sdpestoiknLL7evhbylbzk2YlKEGqs+hjZZq5/Z2nma3jJB6GK3vxtrIYo4GDCLTJbfZCR/P7pq0fRKLDauM5y/n0pratvBoTFVpXRhntGPH2X+9Smc5RbFU30MP3t49dmMggiv6SKjiMKJPhZPT5MezjHwxD56PzCyaZ9exaF8YQHVVBl6+iCLb81QubwxBZzLK7iu5PLFgLnZkL/xP6aZmwvRdUFPr46mwS//YpMwhCefMbjrXp3pyZDDRzXOn/PxPRXLaksPa3DkmEY6LejtVxgYVLl5I+TAQRXDEFy7GvD8111++MfuLPN9O8Qp6uM0qrMsTL/DwmSsElevTKDpCboH78a0sgS+vcpFSWb66R44RTo3yPilLzM38Tq+u7YQWph6i1OP/kWK/SdBCG6e/xwAxf67yHcfBhkxff0bzNxcU+FUVIN0boBTH/jLjBz9Lm6c/QPqlRaqqpMvHiaTH6GyeJW5iddZnluzIS4vXGHo0JMUB+7GdzcG/bqaIJMeZLkSt7caehohRNtHZWcEgUujMsXNC2u2vYaZId9zhKP3/xCDBx7HbixsCgYQColUN3Z9gQuv/fKO/J1s8QBdfccxE3mmrr3A9I1vrab3hVCYn3id+576Gfr3PYqUIXPjrwKCfPEQmcIovtdiafYsU9eeX91nIt1D98Ap9h3/+OZzuy0I8t2HGdz/GJKIS2/+OnZ9YVXIbWtImtV4/LRS3RhWDiklrXZgtal0dAdx28HAnYB0vXbLlCDaRmdAhpKv/9TnOt7nbkZFIIkCDymjuG6zamHcGflsV6yknYUg8kMWvn6xrYzVORy7xIUzvwFt57D52XdWXfxoKyXeOugne0fQ03Eqd7r0FhOzL5Ma2B+bykQSLZFC9HWRNPYRhSGalURJZpiqv4M3/XWsQj9R4ONWl7CXptv7337CtFtL3Lz2Fcauf23deW1UcZwcC5ieCPjs76597z//h3jgWynLfelzNl/9gr2BvLHSOnj5vM/Viz6//ktrg2UYrFE7/sO/q8f9yqydahBu35ds3XWEpjtH+bf/XTxw7KF7pNSaYCh/L6aWYmz51bjcJAzsoAoILC27uq2UEkVRMbUUSSMm/2TMHoLIJQhtIhmRMXtoWssoKFhGrl2rja+fJnQsPb3huyBpeWWCyGEge5JA+shQEHZYL6xeXcavuVj5BB/4Fx9DehHLZ+epXF5Ez67J4s5+8yahHXDw+09x6M/cQ2umyvTzN7j+22c58iP3btpv4ARMfukqo999jLHPX6RyZbP3RhCAY8eckatXAtJpQbkUcaMUUOhS+dE/l+TyRR8QzEyFWBacedunu1vl8FGNfEFhYjzkG897/P/+WZYvfs7h1Zc97rnPIJWCC+d8enpVuooKz37Uel+kw4VQsJLF9oq1iu9snCTdVgXfaWAYGQwz255sJcl0D7nuQ0RhwOzYqxtKagCuXaG0cJmuvhOks4OYVg7XqVLoPYZhZamXx5mbeGPDd6LQw24ssTx3Lq5Lp7uxm0sIRSXfcxghBMsLF2lUN1oP1ytTNGtz5IqHNv0+TUuQy4ysBgOWmUdRNGrNOXZW8QCnsURpYaNksefWaVRXjncATd+6W6eydI2lmTO7EnmzhRHSuUEC32Z27OUNdX4po1VORKH3OKlMH4aZwfMa5HsOoWkmlcUrlOY3Oq7ajUXq5cm2Euu7by1OZnpIZnoRisbi1Nu0anN3dCV/p/GeBANGSiNdtLCyOuoWgkShFzJzroLW3YX0PMIwQG4lj9XG7hN855BRhN+stMlUIcnuYZJdg6j6Rgvjd41I4pXWOif2fJ7rGf+Eu85baiJN4DQJmjUCu4GRLRI4DbRECkU3kJGkevMsmZFja0JLYUDouST698cENl1DS2eRixMdnuPmoGTj39uSqusuwa38xSjcvp945fvb1X73qjqn9xUJa01kuHdd70gGLNTblsGRx1LjBgoqYbuspIi45rdQv0IQuXhBEy9o4YUxYWy6cgZJ1C4RwM3ll/GCuL1ScebiH9uu/VftGeruIn4YTxQT5TcII59QBjS90lrNHxGbUMmQ+frFVX6Hqsb6DglTUG9EJBJtnYZ6i6u/+iaTnz1PKilwax5O08c7N8bSlUac5p+tM/X8dZbPzqHoCqEb4lVspJQsnZ7Z2vxEwPKZWeyF5gY1Q4DzZ2KDlRXFvJe/7aHrMZfE90DTAxIJQbMRk5A1XaBpcbdBFEq+8sUITYNKWeI4kl/4vxuUShGOI6lVXXQ9Nt+5cT12BFwxL9phOLkjkEjCwAYkqm5uqjNreiKu68sIz6uzEq2qmoVupPC9RjvrtvnFdu0qYeCiqDpGIg4GDCsLSPx22vtWRFGI04wzSbqRRtMTRFGAYWXi58qpbwo8QBIEDr7fijuUbvkbQDF/hCB0SSW6CUKX3QIBiEmIWxEZozDEc2qkMn2omoWiaJtWyb7bxO2ge0k3UiiqgefWtyFLy7g0I0NUPYFuZvC8JoaZRSgqgW9vyMisnnvk47kNzERhi33uDYaZQdMTyCik1Vi8I+Jl7yXuaDAgFCjuT3PkmQEyPRa6pW6Ztm9VXGbOVYia8UCpWBZo7129dz2iwKMydgYZhrQWJ4h8B81KUxk/e8ejttsNBG4HiqoR2A0CNx4sjGwBoagIzUCoOjJyCVp1FN2IBW9EbMIT1svtFbJAhv6qe96fRMiYJh2bnTh7/51usDZ4rEzUt8IL19Kt6wcpJ9i4cmx5ZbaDHzkQrXFibH+NkBeydQZt/bl1FxWOHdGRbWfwrryC40oWFiNsp0lPwqFYUGhZkuVSSKNk41WDuMssDHGXW7jLmy2RnaX2Z4qIMyOWSrI/Q/54D7PfvImz1Nw0t9XrGz+olKNNnUe1qlz3742dSY6z8Z0cu7n2Tnnurft6HwdbKfG9Jk6zhGHlSBdGaDUW8b0mhpkmXRiONTsaCxtIZUJREIra1hDZRqytTeITQlk1BFMUbTVztN3vXCHJKYoWZ1yjcLXjR0bhluObXKfCuh5+YFOpTaBpsbCa7VZwvc54T9vtE2L7dlaIcVtwEGQUdJQKF4oa66Vs494KtG3jZfs6asQuyBoQ24dHW52jlLuk8bc9o9jYbMM5am1Nl+j2nVzfR9zRYEDRFA4+3sepT8Ta5L4TEG5h8GMk45qeP7+AMAzUbAbFMjdt915Ahj7V8fMANBfG8RoltEQWt7a4fX75jwFC1ybyHGT7RRKqHk/sTpNQUVZteQO7Eb9Iqrb66DrleRTdIHRat+XI98cFMghRUkmMA8OElbXJOaq3iBrvVub6jw6KBZV7TxlUKhHFLpV8XlCpRmTSIVJKRod1CgVBvSGZmAw4c97bkwCnnjJI9KSwelKkh3NYxQTLZ+c6bvnd1IK8y7/3sq/3E4HvUFm6RqHveMwkD1yc5jJWskAy3Uvgt6gsbazzrkyUyopl+Bbnr6harOcg5Wq6PJ6gxJZMdqDd+RQHDpEMV+3Yo/b/FYqy5XeFUNq+GRvhBza1xiRd+cOoqknLWcZ2trNav3WfYst9gogn45Vy4hY3T7aZ87tBRlE7YNqeH6IoK91iEVIGSGhnz+Qu57jVPtfKv1shnvg3fi8OwOJjddKG/Z3GHT1DVVd4+EcPcuZzk1z66gyL12s7ButhOV7xBPM7i/GstP/daUgZ4TWreM33T7P6vUJj+tqGf1eunt5yu+r1M+QP3xf32tfLpPr3s/jON9e9mH+0U1nvBlpXFuvYQZSnHo7LUu2f2njxTRovvPqdPbk7iJYtmZ4NWV4O8XzJjTFJrR4RhHDiqM78YsD1m7EPQLMluXRlbyuh7KEujvzofRRP9eEsNbn8K29RH69sKhH8acDUtRdQtZhIOHLkQ/GYYlepLN+gPH+J8uJGf4mV9HTcemgQRrFR23qYVhZVNfHcOq4Tj02eXcFKFNCNZFuTYOPqWREqVqIACHy3QeDbCKXNwJcSzUyjatYmYpymmeh6ctO8rKo6SatI0uqO+RFmjqaeZqG0sc6+FRTVRDcywEbCtaKomFaOKPIJA+c2V+AxPLdOGLqr5YLNK29BIpGPszC+g+c0AInn1JFRFJcOjDSBtzEDpqpa3L2wDlLK1SyCULQtDIxEu2PLwvfWZQ+9BkFgIxQNK1VkL6687SMDMqYavw/tMXsOBhQ1bv+KwpjIlcyo+G6E78m4T7nX4spzs5QmGndsXslaA3GvvVfB8Xdns/537IzqjbOoVhJFN1k6+60/1hmRvaD6h89RFc9v/sMf8VreXjE2ETA+uTbQrjeFeenV2PzlVqOYvWD5ndk1IyIp3xd/+D+qKPQeJ50bpF6eYPzSV3DaHT7xCnfzRWnV5ykvXiWdH2LwwOPMjb+6oXZtJvIUeo8jhEqzOrvaW788dxEzUSCR7qFv9BFmb764+h1FNUikuikO3EWzPo/dXCLwW6iaSWX5Gv2jD1PsO4HvNijNrS180rkhkpl+NCO1qZvAMvIM9NxPtTFFELpYRo5OOVXJdA/F/pNUl6+vfmZYGdL5IZLZPsoLVwj8d+cxUSuNYyW76Oo7weDBJ5i+/q3VLIoQCmYiR1f/XXEnVX0Bz60BgsrStZicmR+mq/8E09fWApZEqkg6P4xuZjcETVHo4drxdcsURnBay4Tr+BeF3qMkUt0o2saWxGZ9gVZ9kWKfpG/4QWbHX9mTcFgYeO3zEBiJ3KYyxJ3GnoOBREqlZ1Bn37FErDyowLWzNrNjLjKSLN2okx9O0lh2aC7dmfqzE9Tpz50kaRSwvSpSRpRbnZHc9gJVhcFRnf5hnd5Bjd4hjZ4+jUxOJZFUMCyBrgtUTRCGMvYMdyXNekilFDI3FbAw4zM7GTB5w6NW+aM5ycooJLCbCGcXC+cO0T+sMziq0Tes0zugUexVKXZrmEkFwxQxKUwVeF6E50ocO6JRi1heCJmf9pm47jE74bM0H7ynxC8lm8Y8NIp5aJSgVKHx/KuYh0aJGk382b1JRd8udB36hnQOnjDoH9IpdKvkCiqFooaVEhiGQFVjaUzfk7hORKMaUS1HVJYDluYDrl/0GLvm4rTkto7I6+eh9QTMlc93FeQRCj37HySKAuzqHE5jKbZyhlg7Itx7e+ZekEgJDh03Gd6v0zuk092r0d2/9h6qqkBGMafAbsZ2ueWlkMW5gBuXXa6ed3Ed2VGcG+6tsWQD0vmhtsKeQxC4u/KO7MYipfkL5LsPMLD/MaxkV6wz4NloZoJi/yl0M8Pi9FssTL+1+r3ywmWSmR66+k4wfPBJrGSeVm0BRdVItAVvpJRMXX0euxkHJGHgUV26TqM2Qzo/wpBmkcmPYDcW0c30aoeC59Q2p7hlTHatN2fIZUYxjQxSBggU5C4kQk1PkCmMcvieH6BRmUBRdVLZQTJd+4iigNmxV7bUL9kL6uUJdD1BMt3N4IEnMMwMdmMxFvwyMxR6j0G7xbCytJI1lVSXr9OoTpHJD9M/EuuxNKszaEayrTPQh9sqo+prhErfa1IrjxH4dqwfYeVo1ueIAg8zWaDYfwJVNTdlGZAR1eXraLrF8OFnOP7Aj1FZvIrTKhGGXqw5kSwSRQHLcxdoVCY3fD3wWrh2Bd9r0j14N77XjINDGfdZtxqLcYfIHVrM7DkYCHyJ70usZCw4hIjd6SDuU77x8iJHn+4n02OxdLOBW/cJb3EvC/2I5ZtbiwxthTD0cfzY3yDOlty5CCmTU+gd1Nl/xGD0kE53n0auoJLJqWTyCumMgpmIFdRWxE8UNc6KRGEsnuM5ErsVUauE1KsR1XJIZTnEbr03wcCldxyunPdYXggwLAVVF+iGgl0PCfwIVRf4jkQ3RDzoi3gl6DsRyayG24q3u92EQCan0N2vMXLAYN8Rg95+jXyxfb2yKumMQjKttAMnUFWBosRtf2Eg25OcpNmIqFdCykvx9SotBUyP+1y74DIz7seD+R2cb6wj+1DzWYJyDX2oD4RAH+4jWCy9p8FAz4DG8H6dgRGdvnaA2d2vkcmrJNMKiYQgkYqfMVWLr5UgnqQCX+I4EU5L0mpGNOsR9z8WBwXzMz5zkwFTNz0mb/rbBga3BSmxa/NoRhIz1YWZLoKUtKrzuM3Se5JNynep9A1pHDhmcvCoQc/AynOlkkorpLPt91CPuwaQ4AcS342vTauxEmQmmJvymRrzmboZ/7c0v31KOvDawmm3AbuxSLawj3R+iJEjH1pNV0dRSBi4eE6VWmlsdYUfhT52Y4mZsZfpHXqARKoYa8/LKHanRGFh6jTl+UsbZHsDv0Vp/hJh4JHvPkwmP0o6OwhtS/YwcJm5+SK18vg6wqIk8Gzmxl6m2H8Kw8pS6DlKJj9CGLrYzSVcu4Jp5Uhm+jf8rphAOIbj1chEAX7QxPObHaneOa1lqss3SKR7KA7cjaoZcUkkcJkbf5VGZepd98uHgUO9Msnc+Ot0D95NOjdEKtMP6/Q85sZfobJ0bbXUAhD6DotTbxF4LdK5IfLFg6Qy/YShR+C3qJfH0Y0Uue7Dq9+JwgC3VWFu7GUKfcdJ5QZIpLuJwhApQ2rL423Vw+Lma9EsUZ6/hKqZ5LoOkCmMks4PrXEmhEK9PLk1f0LGXQgLk29SHDhFvvsIMgqQMlxVgRWIO6ZEeFvBgOdEBF68wrObIU5zRSIRnLrPiY8OkhtMUhpv0iw5hO7GgcNpBHsKBiIZULfnY9INcCeCgVxBodirMbRf5/BJi3setjh+b9yjrHRghauq8X+6IUgkIdel0j98Z21vt8Nnf6OK7QiCSMFMxQptZkKlmQ7w7Gh1oNRNZaVRACHArgcU+gwWJlzCerhJp343ZPMKxT6N4QMGB44anLjH4tTDFqm00tE105W47ctKQCYH3X1rf4siid2MuHHZ481vt7jwtsP8dEBlOaBWie5IUKAP9RHZLu7lm5iHRgHQClmi1p23xdUNQTqr0N2ncfxei+P3mBw6YTJy0CCV7qxzRll5vlIK3DLOhKFkZsJn7IrHxbcdzr5hMzcdUCuHuM6dGBwkjdIkqcIw6eIIZqprXStphG/X75gASjKt0Dugsf+owbFTFvc8muDEPSZCEbuK3qmawLIgk1tb2UopCQO4ccXlylmX86cdLp9xmJ30cbbIFnju7QUDmp6ImeJRQCKRo6vv2GqyJJIhMgzwvAaqZrAweXq1Rh74LZZmzoKEXNcBDCuLouqEgUuzepOl2TM4rfKmOni9PInnNvC9JvniIXQzXq17dp1WbZ7F2XfwnFv5T5LFmTNEUUQmP4KV6kJRNXy3yfLsOYRQSWX62p0va9cgDB0a9gKmnkZVNHw/tlPvJBtkN0uU5i6QbXsoRFGA69Sw6wsszZ5pp+w3nqPTWqZWHsdtlTew/I1CAhlKQjcgcjcGdE6r1P5tAfnuw5ipLCDwnQat+jzzU2/iObVN2c/y4mUi6RGELZLJPhTNQKo+tcUx7HqJZLon9ixZ5TRIAt9mbuJ1pIywUt1tnQSBa1eYn3yDRKqbVG5wTV565ToGDo3aDL5vE/g2yXQvmhH7mERRgOeUaVSmtmzFBNrKlm8hFJVEuqctHS2IomDVR+JOQcgOmXkrBIZESqFnyODEg0nmJn0uvdnAbfcQmymNn/ytD+06VdfmHX7lJ77V3nG8alVX7GfXfVlGcfCx8TwUNMXAD29vABciHmCf/Giaj35/hqOnTArdf/SZnuvx2d+ocvFCyOJSvNJ2mrHnfbaoEwWS5VmX/Xel8JyIZFYFBE4zJPAiUjmNt75eZmna7XiCVRQwLMEHnknxqR/NcuiESaH43l6z8nLA6ZdsXvpag1eea2G3one98s1+9zOEtQbOxesUfvDjLP/S75D75NN4U/O0XjtzR85btDNl/cM69z6a4DN/NsfhE+ba8/0eIAoly0shf/BfKrz89SbTYz6u++6zKqqRJD9wjES2F4TAqS2AlOiJDJXZy9jVd2fbvaIrcPdDCX7gJ3Icv8ei2HvnnyvHjrj4tsN/+lfLjF/zaDU2PkuqCj/5d7v5kb+a35WotTDj89znGvzCvyxT6D3Owbs+hd1YolWfw2kttycygaZZpLL9ZLv2oxkp3vrGv4nJa39M+DmqapJK9DDYcx9CqDRa81QbU9QaU9t+Z/TYR+gbeZDq8hjjl7+M29q+fbYjCBj+xAm8mktrqkJjvIRQxEqWfDVQjC+pxCymkUGI1/aBEYpoi422VTkUVtU29WwC1VBxlmJuW/ZID+5yE7fUivetKG0uzJ8MMkwn0/ye3zzfk4S+JJ3XyNsrHvLxgdxmwL//7q/ufmLr/v+9gzp3PZzkgafS5IvahmBgdizgF/7xElKG6GpsEGFoSQqpUcaX9s7+thKC4QM6f/V/7ea+R5No789C/j1BbdnHbghGT6Z4/QvLHH4gg6IKSnMek5dajB5Pkcyo2PWIVj3AaYZkunQCPxZz6fQZzxVUjt9r8aN/tcA9D1ubibTvEfJdKt/1qTSPP5tietzjZ//REpfOOHju7b+cYa2OPtiLeXgUrb+brj/7GcJqnah+59oKB4Z1vvfP5XjkmRSjh/T3RSNfKNDdq/KX/scin/mzOV7/Zos//LUql8++C86OUCiO3kOrMk917irBOpJbfvBE28nxXexeQO+gxvf/D3m+/y/kOzeZug2YluC+DyT4v/7rMJ/7zRrPf7bOO6+tEcDCEFw3wrEliWRnN0zVLA7f/b20arNMXvk6tfJmIatEqpuu/pMcOPlJkpl+At8lDO58Fuq9gGVk6e06yZXxLxFFEQM995BO9lFrTLNbdiDWSn2XD74iyBwokj3SS+XcLAhB+kCRwqkBGjeW0bMWetpAKAqNqTKRF9L3+AG8msPy6SlkJCncM4g9UyVoeSiaSmIgS/XSPKqpUbh7EKOQYPa5q/h1l8yBLqIgJGh6JPozFB8YoXplgeZkBb/2x+OevVvcBoFQwUwqlBcCeod1VG3jTZcyzhB0H8qQH0qhmQpn/nACGYGii3abIER+HCF//EcK5LpUrrxtszizMe3oNBWSRh5FUckmBogfMYWUubk2sxsGRnU+8EyST/5wjpEDOpr+/rRrvFeoLvlcOecydr6JXQ95+/kyyDanw4t45fNLtHVH2iYuCnc/lQdinQfdFPi7TKwPP5nkme9O8+ATSQpFlVgn5P25ZivHMS0YPWTwD/5tH1/7gzrf+FKDq+c6z2qsR+uNc2iFHFpvEX96gWBuEW96/o4EA4Yh+OBHU/zIXy3QN6STTHVWOrkTWL0nAoo9Gk9/Is3J+yy+/Hs1fu+Xq7dZNpA49SXs6hxh4KBqJmaqi1Z1ltr89V3lYneCmRA88WyKj35/lrsesNqBwO4lgdvFypijavDhz6Q5eMzgtW80+c3/t7wqfe3aMe8gkdw92hWAIhQMM009DLZ09oRYJS+Z7gHilsI/LlkBiDkPftAilegligI0NTaIsMwcYeTj+y3e0xaSSNKcKNMcL9GYKOOVWyT6s/gVm8zhblRTozVdpXJpnr7HDzDz3FWakxW8moO9UCc1XMCvOiSHcnhlG7/moKUMQjfAXW5h5BIEDZfmVGVV/VMoAj2foHD3INNfvUzkBUTBH5979m6xd85AIKmXA26cb1GvGATeugdCQLYvwQM/uJ/8UJJEwURGknOfnyKMIor70hRGUhgJjXNfiNNN6axKoxbx9ktNmtWNL1UYCtwgQFU0aq1ZJLGaVGzV2jn6RzQ+8j0ZHn46yeghHdN8n5a37yHCUOLZEZ4dP6x2feO1u/XfgSe5caaBqinUlrwda6SqCk99Is0zn0hz7B6L3kHtfZvYboWixB0cvYMKT3wkRSqr8MZAi9e+0dpzlkDaLkFYJmo5BPNLRC0HNZcGKQlLt6c1sbLCffCJJB/9vgz7DhsYpviOBZqqJkhlVExL4dlPZwhDeOlrTeam/E1S0DtBCAUjmadZmgIJmpkk1T2CXZt/V2pqVlLw7GcyPP5dKU7ca26o9b+XWLkd6azKwWMGui6wmxGf/Y0qnhfzBpxmBD2d7S+SEc3GPMlsL8W+k7FLqNtAItE0EyNRIFMYIZMfWW0RfDcB1PuNSAb4vk0q0bNB3CeTGsDzGtQC+z2X1438cNV6WzFUkoNZECJ22RTx38OWj2KoRF4AikBN6Bi5BKnh3Oq2oq1uG3kByYEs1auLSCSKqaFnYrE7qyeNX3MQokHkh+SO9GDP13FLTULnj859yxzrIzGYRyiC+a9fuqP73nMwoOkxm9dpxeTBaJ0lnaIKRh8sMnxfF4EbYiRVukZSq6l/I6nRezhL96HMajDg2BG1UsDynE+rsVUU5qMIFcerrwYDjtf5wG0lBI88leKJj6Y4dNxE0zofpKWMme+tRrzaXmHDyygedFUVNENgJRSSqXZy7D2YA2LRi3jAch1JoxZtDMI6QBhI5m7unu6yEoL9R00++n0Z7nk4QSrz/gzWO2Hlmu4/YpJMKWSyKs16xJnX7Y4mOGFsrAdFLZuoFaeJrZOHEEuV2w4G+oY07n0kwYe/J8N9H0je1j7uNFZ4MYdPxiQ835W89XIr7jjooJNUKCpGIkuqMIjbKBH6NrqVQbcyvBvyrqLAXfcn+NB3pzl2t/W+BQK3Ip1VOXBM8NHvz3LtgsuVcy6eG2G3OnunJHFXwPLseboHTpEpjLR96Ztxd5VqYCZy6EaaMPQpzV+MA4U/ZpkBL2iSSqxFR2E7CHw/q+j2QuyZISNJ5AWEzhqR0C21iMIIe66ODGXsoRHFejeRHxI6PqHj49dc/IYDcwoQqz76DQ+vaqNo8TPoVezV4KM5XcEspFAqrW1UCr9zSI4UyN8du/h+R4MBRYFin8HgAQMBDB4yGbvk4DnxCKMZKg/+8AGuf3ueS1+fpedgho///XtWv1+ZbuE2A7oPZEi0GdWNaohuKBy6y+L6eWfDgxaFEteO/bUVoaEQ918HW9jqbgVVheH9Oj/21wp092kdBQJSytWWQd+XLM8HjF3xKC+FNBpx+1LgS5IphURKIdelMjgStybq7Z56VaXjlXQcXMeT/Uq3ycrkH38mCcM4aCovhcxPBVy/5FKr3Hm1N02Pyyk//te7ePSZ5J6zAet/y8q537p4ECIm9igiZsvHn3V+nN5BnQ98SKV3QOOf/s1ZSgvhzj3zqorW18127EN9dGjP9tIrsJKCpz6e5kOfynDi3q1d2HbCyr2WK9dLbrxesVFl+1op7LlMI4Tg8AmTH/pLBbp6NH7vVyqUl3Z/blTNJNU1QjI/iKIYsWKc7+DUFjYYaO0FigLpnMKf/5kujpw0SXbYUbGClVVoFMaW2Lc+W7HVRhyMt41Qd7xWVkLhyF0mf+6nu/h//tkSYcA2i5GtEYUeU1dfwHeb5LoPks4NxeqAik4QOLh2mVZ9Ars+Rm3pApmMJAxFbNDkr/HSkklBFMXvjN4eO6Rkdbv1z7amxf/p7dbKMATPkwTBNjy3NUer+Pu6QFHFajZR1USbZCc3ycbHmYEGItm7KmNsO3UWSxd3uCY+ge/ELPc7lDVYen1NT2b+2zfXftM6zH/7RrztG+1tJbSmq5u3HS+v+qnXLi9Qu7Kw+vfJz53fuNMtjvMnHXvLDAhYnPGoLvuEIYxddlbbCgGECn1Hc3zhf3uH8mSTnoMbbYG9VlyDGTho8W9/7wAQT5qprMqP/kwPlSUfx5arD9LEVZd//lNTmFqW0e6HSBoFFKESyZB3Jn5n19PN5lX+13/VR3efhtrhIsTz4Oo5h9e+0eK5z9aZGvd3fygEJJMKj34oydOfSHPXAxY9/Z2zE6MIJq57LMwELC8ELC+GsXjRRMDiXEBpKaBRu31dgE7x6NMpPv1jOR595vZXuL4P41c9psY8SoshzUaE58TsbSshSGdV8sV4Mj9wzGxnVPaGZFpw8n6L/+0/DPBPfnqehRl/24BAzabo+ekfxxufWdVrXw9jsHeDT8Fe8JN/p5vHP5xiYOT22G9SQnkpZG7KZ3bSZ2E2xLGjWMxLgJlQyBUU+od0BvfFGhhWYu/Xa2BU49M/mmXfYZ3//e/M49g7P9CB16I0eRansYxdnb8j6e1Ct8pf+V+6OfWAxZbS7x0gCODaBYfZyYDSYkijFuI5Et0AK6nG+hd9KvuPmPQOartm6YSABz+Y5If+cgHflXsuO0WRz+zYS8yOvbTpb4oKDz9k8AM/luAjHymSzQrOnQ/47GdtPvc5h7m5+Fn8xf9UYGIy5MoVn+/5TIJTpzQWFyVf+KLNF7/o8NprazyqJ580+PjHLJ591iKbFVy8GPBrv97ipZc8pqZDdCv285YR6JaCbqm0Kj7JfDwWjdyTp2sowSv/bQIkWGmd4mgCzVS59vLyhvO3jBz9PfdRrU8ShA6WkWe3rNDUtReYuvbCnq7hnrHTLbr1b7f++9agfy/7+lOAPY1iK2S0ZEbjwMkE6ZxKZblMUG1fubbOQKpgUJ/b/MYXRlIku0xmrjT5rX81A7SZp+0VT3RLtO+0RXssI025OU7LLWFoydVywU5pt4ERnSc/lmbfERNV3X1FJaXk5hWPX//3JS6+41JZjgfmjh4KCXYr4qWvNTn9UosDR02+61NpPv1j+fg3djB+/8rPlrh01qFRi1avc2zfG2cq3usOlyc/luYj35vh/scSe653Xz3v8M5rNhfecrh2wY3LGMHaea9kDGJzkLVWUsOMV673PZbg7gcTHDxmYFgdELiEQFElh45b/OTf7eL3fqXKuTe3LoFI16P12hnqX38ZuYXnceaZRwkWl7f45vbQDcF3/3CWpz+ZptCt7ul6uU7EtYsu3/h8g/NvO5QWAhw77nNfzQ6t/s61a6UZcS/9vkMGjz+b4tFnkqSz6iYC71YQQpArqNz7SIKf/oc9/F//eGGVOLc9JHZ1brVHWzOSJHJ91BdvdvxbV9A3pPHQk0me+ni6vWrv/HqVlwKuX/J46WtN3vhWi0Y9vOXZkrFfXNsET1Vjoau+IZ27HrS495EEpx5MkO/aPB6tnMczn0zHBMM7WLX47k9aPPWUyUC/yt/6W1UqlYgPfZfJIw8b3HVS53/6n6uxBbeADz1jsn+fyi/+YpMrVwOeecbkqSdN+npVLl+uUq1KPvIRk09+wkJK+Ft/u8LSUsRHPmLx/d+X4NgxjX//Czb99xZplT2MhIqixlmA/ECCfL/FhecXcBsBrarP0Sd6aJU9KrPOajbyVkgZ4Qc21cYkXbmDWGa2YwXCP+k48lNPg4CJ3zqNu1Bn5M88QPHh/ZTfnmL8118DASf+l49RPj1B7eIcge0z+Im76H78EGpCx11qUDs/y/TnzuIuxvoCRleS3D1DDH78FJf/3XPs//FHSB/sRrF03IU6F/7Fl/CrWzulJvcV6X3qMIX7Rxj7L69Sfvv2VAlvS3QoDCWprLoqbrOCMJDceHGBkx8fJt1rketPIlSF7oMZ0t0Wh57oJVkwuPrtBcav7J7qX+EjOF4NX3VjIQ8iNMXcMQ2lqDAwovHUx1Po+u4Dj+dFTN/0+eWfLXH+TZvKcsgW88aOkBJcJ67pXz3v4jkREvj4D2Qxd5nghIAnPpLmxmWXZu3d99PvFSMHdZ75ZJqT91u7nusKPC+WgP36H9Y596bD/LRPeSmkWg53qOPfWjOIA765aZ+3X7bZf8TgkWdS3P2QtWuJQgiBbsC9jyaZnQzwPbllK13kejRfeZuwWt/yBbHPXSFqbv2SbQUrKdh3yOATfyZLvkvtmINSr4Zcu+jy1ks2F952mBn3KC3FK9tO73e1FCs1zk8HvPVKi3seSXLiXov9R4xdv6uocVbmoSeTPP5smtMvtahXNx9YUXVUzUTRTXQzhWokEUKgmSkSmZ49BwO6AUfvNvnw92Q6YurDWpnstW+0ePuVFlfPu8xM+CzOBttkgDbf12Y9orIccuWMy8vPNXnkqRSPP5tsqxduvGednlenUBR47DGDXFbw3PMu75zxcBxwXEkqleDBBwzuv1/njTfiVf/SUsiFCz6vvuZRKkV4nuTEcZ3BQYXBQZVqNeDDHzaxbcnb7/icPetj25IvfdHhvnt1RkdUjh/XsBMq2e4M5WkbVVPI9lqEQURp2sauBzG/qalhpVSyPRmcZkAyr2MktE0eOl7QpFS5iuvVCUMPx63hevU/9YEAxNdJtXTM7hTuQp3UgW6EpiLUuBvBrzqkD/VQem0MszdDYbhA7tQgCy9cIXIDjGIKo5hi9Icf5NrPfxMZRqAo6GmT9KFu+j96gsb1RWqX51E0BaEphK2NGeqVe2X1Zxn42AmMrhTl05O0Jsu3vXK87e7erYbAKIi48sIsJz42xNDdXaS7TTRD4cRHBknkDDK9FuWpJjdfWcRulxcKPRqBL6nfUgNPphWSaRXXDvCCJmrkQ1vtS1VMdlqy57tURg8ZHDy+uy1yGEpq5Yhvf7XBK881cV35rlNEzXrElXMuml7nxL0WIweNbQeclXHpgScSvPg1k1o5orz8/rm/qWpcHjhxX+eCL41ayMyEzzuvOXz19+tMXPf2xFRfhYRqOaJa9pi47nH9kkujHqEocPC4SSK5uxpkd5/G/Y8lqFdDJm9uQUINI/zptjiOrqFmM2hdOYShIV2fsFon7LC1UAjo6lF59JkkR+4yO1rhSilZXgg596bD6ZdanH6pxdTN21Pu8724rFBeChm/5rI0H1JaDHDs+HyUXTgFmi7oG9J45pNpZiZ9XMfbIjUey9sqqk4yPxCL6EiJbqbQE5kt97sTBkZ0jt9jcfyezjgVUkoCH86+afP85+ucfd1mdirY8zvp2LFC4+ykz/VLLvVKhJkQHL/HIpNT3lMRqFRKsH+fRq0W8eabHpVKfPJXrwZMTYY89CCcOK7z5pvxczA3F3HpUrBaOrhxI6RcjhgdVenqUhACTt2lMzcfMbAQ8cEPro1ruZxASsHQkMob1xzSRYPqvINmxOqjdtXHc0May278WQRWSiPVbeDbIa2qj+dsnuBlFOD5TaLIp+UsY2gpvKC1abv3A4phoqUyaJkcrYnrW/49MXQARdMBSejYtCZv2U4IFN0kOXwAoWogI4JWE3tmbM/n45WbmEoKsxi7Gxr5JF65SegGmMU0YctDz1gEDRezO03hvmH8msPit64SND2SIwUKD4zS96FjWH1ZnPm4TCkUBTVhoKUMll6+gbtQRygCLWPF3RIraE/2Wsqg+NhBrIEc9lQl/s5S58q+t2LPwYCqxRr9vidJZRTEusE6CiRXXpgDAUefGaBrJIXb8Lnve/dRnWtx+blZbr6yyOz5yup3jt2XoFENOffaxgdtYJ9B34jBS1+qIZEEkUvgubS83VWtDhw1OXrKIpnaPeK3WxET1zx++z9X8AMtrmfKFcJSFBMh1pUjZBTGNpYyjubiHx5tKlkEAZx53eGbX2rw0e/LMnJQ33agFkLQ1a3xwONJyosh5eXOV6rvBooC2YLKZ/5sju4+taNyhudG3Ljk8e2vNvj9X63i3RHp23iim5sK+MNfqzI76fMXfqbIgaMGidTuaeXj91o4tuTsGw4X3tq+Y0Ir5DCPHSBx12GUTJqoWqf52jvIsRlCZ/dMlZUQHDhi8t0/nGsT1HbeXsq4Dn36xRa/9f9VuHnZ26SouR4CgSI0REyVRSIJpb+l9nirKXn9my2uX3S5ftHlr/39Hrp6VBRF7nq9nvx4mrdftalXYnOt9YhCn8CNiMIAVbdozF0lClyMZA6xx2K/EPDAE0lOPZjoaPUtJQQ+LM4F/NK/Lb1rkamVfTZqEd/8UoPZSY+/9He6OXmfRSavvGftn11dsd/E/IJkfn4tsI8iqNUiqtWIwcG161GtSRaX1o0xMiYwRxIsKx5vu7tVjhzReORhg+AWGfHLlwJUIm68XtpIfBPVNQJh+7PKbPv9aH+2NL71BL+iQNhylqk1psmmBnnfFMdugZbOkj54gszRuxn/9f9n4x8VBS2Voefxj6BnCwhFxS3NM/4b/37DZkJR0DJZep78OFoqixCC1vQYU7//S3s+H2+5iZY0MLvTCFWgGiqtyQaRF5AYyOGV4q6S0PbJHEmT2lfk6s9/E3s2vh/VWtz1MPDRE+TvHWLhG+170J5LF1+8TnNsGdm2BPfKG+9R7DoqSB/uYeT77mP2KxdYfvkmjevvzl/lthQIPVdiJgSuE6vZ3Yorz89x5fk4KFC12HUl9LZOL516JMnirL8pGDhw3OLuD6R46Ut7J3cdvdvkxL27ZwWklFw77/GF/1ajWo4oHn4AzUwSBT6+08RvVbHyfbi1pVieMgxpLI6RGz6O36qip/KomoFTXaS5tLWL4u/+fxUOHTfpG9YwzZ0Hnw98KMXkDY+3X31/goFsXuUH/2KewVEdrYNyipSSN7/d4nd/pcob32q9JzwGKeP0cHk55Af/hzzPfiazq1KkqgqOnjL5i3+7yP/8E9Pbts+lHrufyHUp/+YXCctV1O4C2Y88gZKwaL789q7ndv9jST78PRn6hjojhzq25MWvNvi3/3Cho7a1pJpjOHGClJqP22nDOjdb79AKa9umZ0uLId/8UoPlhZB/9HP95Is7B3VCCDQNfuAn8ggF/uBXq7cQUyVRFBB5AbOX1+ytPbuG26p09LtXkOtSeOLZFCfv77TTQjJ50+Pn//clzrx+59+Bq+c9fv6fL/E9P57j0z+aQ9+9unJbWHkv2s0gG7DS8bAeQSDxdmgVVtp8iF/91Ra///sOly5vzCxJua5ZZv1u5Baf3fq3baBpFtn0EIvluH3NNONnstaYge9AqSDutNniuFGEV15i7Nd+juS+w2SP3ou53vRk5fthiLe8wM1f/rekD54gd/IBxG1K0DrzNYxiisRQHqs/h1ex4/Q8ktSBIn7NpjleQk3oaLkEUkqaE8sb7kdoe9gzVZIjXbFuwurvkbTGlpHB9tlhoQrM3gwn/94nKL0xzuK3rtEaL93Wb1mPPYd6VjIm6Fw9Y1MrtRkwK60rlsrH/8G9HHm6n0yvFTuv+dGmQEAokO1S6d9nkM6q5AoaA/uM1f8O320xuN+IDVr2en6WYN9Bg+EDu7/pri25fsnllRea7fPSYptWKdHMJMniEOWbb2Ok85jpLlQzSbJrGKGoZAePYiSzCKGQKPRve4xWS/LWK/aOK9YVFHtUDhw1OHRi53NXE0lS+4+QPXk/iaH9CGP3wOdWGKZgYETju384t+o6uROiSHL1gssv/psS77xq31YgoFr6hkzSTrhx0eWzv1Hld3+50tH2qUzcKvbUx1LoxtbHkGFI1LRXuwfC5QrSceOa3S7IZBUeeCLJI0+nOjqfajnkzGs2P/dPl3Zl70OcFTiaeoSKN8+VxqtcqH2LGecqR1OPYig7T6aBD+fftPmP/8cS1y911nY7tE/nvkcTPPBYYvuN1g2+iqpjJvMd7XsFz346Q/+w3rHU8IW3Hb70OzXeeum9S0dP3vR47rN1/uDXKu/ZMebmQqrViGRKYf/+tR+vKNBVFBS7Va7fCDt+h1xXMj0dkMko9PUrsUjSuv9ubUG8I5ASiaS3eIqu3GHSyT50Pcl3IhDwyktU3nmFqd/9pff92FvBWajH6f6hPKkD3TQnlmncXCLyI1L7iySG8rTGS4SOz0oEsGUWaovpTRKLKe0EI5cgOZRn6aXrFB/dT+7EAEaxs3FpJ+w5M+A6sRBP90C8mpRyrcYuiKWIH/qRA7jNgNqczcKVKtPvlClNNlf7uVVNMDBq8NAzaQ4ctxg+JMmtM76RkaRZj3j9+a2dnHbCwKhOtqB0tNKdHveZmdhYZ47LAzEFPgpCUt0jCMCtLyOjkK4D97J4+SUUVUOoOpGM8Js7C9aMXfWYuuFz/wd2Ph9FFRR7Y2vg6xe3p3tHvk/oOmiZLMn9h0nuO0T5jW8TeZ1r0Q+O6jz8VIpMrpP0baz38Nv/ucLspH/bqdsDP/YgM1+5RGuqsuu2YQg3r3hYiRZ3P5Tg+L071+gVRZBICT72/VnefNHG32KlJT0frStP8sFThLU6aiGH0HWkvzvh4aEnkxw8anSkXR+GkhuX4mCmVu580I+IaIYVWmENkATSI6KzUT4I4I1vtTh8MhZmGtq3S0Cpxp0cjz2b4vRLa8GdohkYVgYr27the91MYWV6qC3c6Oh8FAUe/VCqnanY/Zo16yFnX3d4+etNgts1Q+ygNzwK4/fx1RdaPP5sioGR7ct3twvfh9de87jrLp1PfcpiaiqkVo34wAfiToJGPeLVV709BdRf+YrLQw8ZfNeHTGZmIm7eDEgmYWREw3Ek164F+HfGRBIA16uzsHyehFVEU01q9ck9ib1tBT1XoPuJj7H4rS8RNGtYPQMkhw9i9gww++XfAilJHzqBnivizIzjVUt0f+BZtFQGhCB0Wsx99Xfv0C+8fXiVFqHtoeeTJAdz2DNV3KUGif4setbC7Eljz1Rwl5sEjXgct/oyMTegXbZRTA2zK83i4g1kGCFWswO7PxRBy6N5c5npz55FMTS6HhpFaApzX7tE5Nz+Q7D3bgJPYjciqssBYbhRrCIKJeNvLtF7JEuyYJLptdBNhdxAkuXxBvV5h8ayg1vzcFoRlaUg7hV2JaUFf8Mxpsc8bl7cejW9k4dz35BOMq10VP+em4p9zldSbE51Yd1fZdxWJRSc2jJ+qwIIvGYFr1mlVZ5F1S1kFOK3dn5J5qd9FmaDFQnsHZHrUhkc3SV9JQRC1RC6gaLpcS13T2I0cTBw3wcSHRGpHFsydtXj9Iuxe+DtInuom4VU57nZZj1i/JrHS19vcPSUiaLuXA/XdMHxey0GR3XGr3mbVuT+1BxaTxdqPoOStBCmgTczT7C0Mw9FUeC+DyQYGNXbxlw7Y3E24PJZl3NvOrsO+IaSwFSSbWMXQUrLowoNkGjC2BN7e2k+5O1XbIo9GgMjekfky6N3WfQP68xOxu+fpicwEjl0K73BjEjVLTSzs9WHosYeCfsOd57du3nF4+p5l9nJAKEJFFUlCkKEGvMnoiBC0RVkKFcXFXG9ViNwfMx8nD3xGx4gCN3tA7xGLWLiusc7r9n0D995Mykp4dXXPDQd7j6l8+lPWTiO5OBBjTCEl1/2mJ7e21L+xRddisW4u+BjHzNZWtJRVUinBTduhNy8eWeDgTDyaLYWAYGiaLhu9V0TCBXdJDm0Hz2TJ7Sb6LkiyeGD6F3dKGaCyLUxuwewegdx56fjBZnnQiaHkS+imjtksd5HRG0VRKEIrIEc1YtzBA2XyI8QqoLVk2Hx6iJ+3cFdqOEuNSjcN0JzrETo+hiFFOn93SiGSvP6IpEXolh70KXxQrxyk+bNJRa/dZWBj99F5mgfzlyN0hvjt/27bquboFkLufD6ZgZ26Ee8/Tvj8Qr3QJq+YzmG7i5w/CODhL5k/nKF+UtV5i5WGX+nxPgVF90QVBYDXvjsTtyAtbdVEQqaam77YPYOah0PQPMzAaXFtUGjMb9x1SMUdZMX9sLFbyM0A7s0s+68JG1/zC2PU1qIWd+BL7dNYa8gV1DpH9n5wVAMAy2bQ02k8GsVwlYDuYc8oZUUDO3XO1LNk1JSLYe8+LUGSwvhu+q0cCt2rD5oahtS83IHM5Dycty++AM/kSeTU3fsBVcUQb6ocvfDFpVSiDN9i//5pRvolTrmoRHUYh5/bgnn4nWi+s4M3FRG4a4HEnT37f66SCm58LbDuTfiFtXdkFSzdOmDrJhw9Rj78COHiBBTSRDJaOvAd5tV8Duv2hR7NR56KkkmuzPhz0oq9A5p3PuoxdyUHxOTNB2harjNMr699k7qiSaK1lk5StdjDkc2v3vr5Yrq4hvfanHzskuEwCpYaKaG3/IRatwKGPoRekIjdANCL4xlZ1WFZG8Kp2STP1gAIWjM1lE1ldpEBbkFn2kF9WrEC19o8uynM+jGnTfgOn3ax/clpin41KcTpJJw7XrA17/u8txz7mpaf3ExolyKcG5Z91QqkoWFELsd0J45G6DpDh/8oMFTT5n096nU65IbNwPm5ztsRxZxpwiKguwgcpBENFpze/zl2yMKArzKMno2j7s8j2olUEwLb3keI5vHLXmoVgKh6XjVZUKnxeKLX8bqGSRz7B5yJ+6/Y+fybhF5AaHtkRzO4yzUidxgVS45MZjHmasSOT6Nm8uYfbMUHzlA7fI8ftUmfaiH3F2DOAsNahfndi0L7ITlV8dIjhTI3TVE/0dPUr8yf9sui++JcWgUShav1Vm8Vufc56dQNIXhewsM31/k8NP9PPhDB/iF738OgG9+rrYDw1qgKTqqorMy8RpakkJ6HxNLr2/5jWKvipXoLBhYnA22lWcVmoFV7Meev4UYKBSyB++iPnZhXVpeYGTyePXKlgGB70vq1YjSQkDf8M4TfSYXq/PtDIkMAmQYIDQNNZneU2bg2N0Wh06YHQVNUQQLMwGf+6/Vd91yWb+6SO54H2Z3OpYLbS+bWzPVVb3xWxH4sDAb8MIXGnz4MxnSu0xwEAsoXT7jMn9LMKBk0xgHh0k+eAqlkMVcrhA1mnhj00SNrYNLRYWHn0pS6FZ3DeSkjKVhX32hyemXO1tF+ZFDI4gzE82wvKV+Rig3D9xGQiX0JWGwURirXo24dsHlleebfOR7srseP5NT+dCnMnz19+qxlW+jhNsob5IddlsVvA796U1L8PBTqY41GFxH8to3Wkxc9zC7kuz/6CH8ZvybW/NNoiAkM5ojtMNYOtcNaC00qU9UyR8qoJ7sQTVUFEOl554+Ej1JzvyHN3Br7rYBQasRcfrFJqXFMJYqv8N25lLCmTMBZ840+D//z+2DzZ/5mcqWn//c/735O6dP+5w+7fOzP3t7LptqKo1eKKBmsjQvnLutfbwbyNDHXZrHyBcRqha3AkpwFmYxu/sJ7BaKbkAUETRuTxX0/ULQcHHmamTvGiSoO8ggwq85uIt1ep8+gjNXI/JC6pfn8UpNVEPl+N/5MFrSwFmoU35nmkv/+qsbA4F2hboTrB8mpj97lrDlM/jpexj90Ye58Z9e7IgHdSv2HAwk0wrpvEoqq3Hzws6MX6GAkdLY/0gPR57qY+juLjRLZeHKWlq9vBhs+/sNLcFA/tQGi1BNMUlZ21sY5woqxi6s/RW0mhGuLVF0k8y+4wjdwK+XCVpxbSfRPYg9P4nZ1YuR7UI1E8gowsx1oxy5n6BRIXCagKDr5COULryGW14g8jZHZoEvaTZ3v0GGJUhldp6kw2aT1vg13IVZtFQGZ3EOubuk3CrufSTBkZOdrfImrnucec2mUnr3xKH8qQFUS4/JSeu4Jld/8WWaE9tPNGEIX/qtGo99KNVBMCC46/4EIwcNxq56G4R10h98AOkHlH7js4SVOmpXjsyTD6MkE7ReO7Pl3lRV8Mwn0h1nm958scXkDZ9WU4KioCTXpJ2l7yPdjbyOZlhtcwQEQ9YRFvwJ/Gjj87MhMyAg023y2I/tozxtM32hyszF2oZBZOqmxwufb3QUDCRTgnsfSdA7oLE4FxCsZmkEiWwvmd6DqLqFEBCFAXOXv7XrPg1L4eGnkx0RU6WEV55vUloKCAIwhEDRVPS0wCnZNKZrhF5IeiiLnjGoXFnGKiZJdKcIbJ/MaI7WfBPVVOM2wpk6leslvKa/Y2YA4ufqxa81efYzmS0VCv+kQS8WSew/FGcFblUZeh8gfR93aY7U8AGMQhGEwK+VcJfnSQ7sI2g1iXwfr7o3RdDvBKoX56hfW0QogtCOA9fapTka1xa49gvfWv0MwF1qcPOXX2Hs1+IFrCJVMqKLE/qjnObLAHjLDWY+f5a5r17c8N1bsfDCFRa/eZX12fLIDZj9ygXmn7uMjKLbCgTgdloL/ZgBMXzIJJ1XuXy6uaHXXDUU+o/n6D2apftghuK+dCy8crPJG//1BrV5m9rcWhCR61LZf9zi8N2JTQN9ZRG+/hvj+KHDyminKgYJJ7/t+Rmm6FhW1HXilh6hqOiZPNVrZ0j0DhP5Ln6zhtBiq0w9lUUxrDilGfogoDV7Ez2VRUuksRem8JtV/EaFaJtJOQhkR/V2VQXDiDs2thPyUUyLxOAoWjZP2GqSu/shqmdfR3ZAIFQ1GDlo0DvY2a2fuulz8R1nw2Qj2q45Mori9lHdXC1TCEUhCgJU3SD0vQ269jd/480tuwmchTqGpWClNcy0yuLYxiBTRjB+3WN2KiCTU3c0uREiJqiOHtIZHNU3qhIKBel4BMtVCALCpTIyCLdPEYtYevjYPVbHAeZbL7coLcUiOULXMPsHURMJhG7gLS3gTmyu6a1M9mmti0Vvals+THtj3GaA0/BRNIFhqZtWE416XBOfm/bpHdjNflqg6XDifov6N1o0avEzKoSga+QUvtvEd2ooioZq7O5ZYRiCQpdKd29nuhUygjOv26siZJEf0Vpssvj2HKEb4jU8ZCSZeXESoSr4DRdlvAISAifg5hevEbpB3J4lJZEfxQ53HaReZQQX33Z44sMpuI1gQOg6mXvux+gfRE0m4+fL81j66ueJWi30QhFzaAih6WjpLHqxG2EYBNUK5Re+SuS6aLk8iQOHsEb2oVgJIs+lef4M7uwMCEHiwCHUdBqzb4CgWiFstRCGgTkwRPWVb+GXSph9A6RO3o09do3EwSMITcdfWqB14xru5DhKMkXm7vtIHDiE0d2DDAKM/gGQksaFs7SuXIqFpYrdZO59AC2bR6gqoevQePtNnNlpQGANj5K9/0FaVy6ROHQUxbIIG3WciTEa57cOptcjCgO88hK5E/djFLpjHY7SIn5lGeOuB/FrJaLAJ3S+M+JGe0IkN2czI0nkheDd8uxJNnweIqiJRa65b27YRgYRYbDzok4G0dadokEUZwnfBfYcDAhix6+BUZOobeW7OlEbCg/+0H5yQykMS0XRBI0ll/Jkk/nLVZZv1mksu/j22sW66+EkI4dNrKRKFMgN0oZBENLyaoSRjxAqqtCRMqLuzG97fooqOm5fi0IZSx4LQFHwG1WS/aMouomqm2iJDFoijVB1FFVDyojAsZFhQNCsoSXTKKpK6DkgI4SIhUy21vrellKw8fwVgaLGpLXthjPFtBDr0mmJ4f2xDkIHv7nYp9HVs/OEuoIokszP+Ixf2/iAaokMmpGM1SDNFHoijVcvIWWEkS7g23Wk7xGF1Q3BQP36EnrGREuZKIaK33DxSvGLr1gKmqHQuy92S1yesjfYNNtNyeQNj/4hbddzFwKG9hn0j2wMBqQfIBIWxkg/ke2gpJKgiG17enVd0DOgke/e3ehqRUL36vlY7Q5ARhGRY6N3FRG63lZI2/HM0YSOj7pjF4GiCkqTLZx6QG1xcwAYBtCoRty84tHdq7GTC+uKeNLRUxbvvGazmp0VAs1M0yrPgqKgWiaqulKu2/5JS6QEvUNaR7LWK74V1867uO0FRWD7lK+WaMzUNxymtdDc8tCN1harqA66ClaOf+NSfOxOyL0boKokDhzGHBwh8j38ctznLRR1telfsUyM7j7MwSHs8TGCagWhaYStdmeVopI8dhK9UED6Pn6riVBVkkeOx9eiXkcvdGH09BHUKug9veiAt7iA0DTMoVEiz0crdJE8chRvaYGgUkZJJNByedLH78Kbn4MoImzUiWwbGYSErRZ+KV59R3YceAtdJ3PvAyim1eYgRQhVJX3P/QSNOpHjoOVyJI8cx19eIqhVUFwLNZUmefwkretXiZydM8UyDPBrZbRECj1bIGw18BpVgmYdLZlGS+eIfI+gufcusvcSAgVdGBT1Eea9G0SEKKiYSpKM1sWiN4FEogkdU0mRUYvtaUxgR3UaYYVAxu9pQkljKWlMJRnrNoRL644j0IRBlz5IIH1UoaGgIgkp+bP47X2o6FhqkrTaBYAuTCIinLBBKZjlduu5ew4GFFVgJgT5bo3JqxvTmaqu8OAPHaBZ9pi/WGHmzQrTZ8uUxrevmd33eBrNELz+fIOFqY2TjutEcSCAwNTS6GoCRSi4wQ6Erz3UXYQSk5NkFBE043au0IuzEIphghCoiRQrHrJC0u4eqCFlROT7beKgJAoCFMNEuK1NpENoG850ZKHcwXmrGpFjEzk2QaNG2Gx0nPLbd8gg3aEcq+NIlubDTbV3LZHBzHYDYGa60Kw0Lc1AyohU7z6cyjz24uSm0VXLmKT3d2H1ZlATBm6pSfXCHH7dicWrBPTsS5DIqNSXPAJv43FvXHY5fNKkfxfeBcDgPp2BW7YLqzX0ni6sE4cIq3FroXQ9wubWKxErIdh32EDXdyeYSRmrWU7d9FdXuQQB3sIC5uAw0nGIvJ2j/lAGq4JDQbS2rRM117oKBOiWSm3Bpbbg0ixtnQ3yfcnlMw4PPp5ga/HwNQgRC3VZlsJqCColXquKXV/ESnej6taa+s0Oz1o6qzK8v7MC/IpC4+SNtXbVwA6oXNkmTdzpGLeHsXB63KfVjIhC2dH7uQJF08nc9yCRY9M4+w7u7DTICDWdWZ1goZ0dGhhk+etfxl9aBOJMpPQ91GSK9F334M5OUXvrdbz5ObRcnv4f/DGiVhNnajLeh6pSeeVFup79GHoujzs5TlAuk9h3AG9uBqFpKGaCoFalefEcaipN9v6HSN91L7W3XsdfWqRx/kzMM5ISf3mRykvfXLuPioKaSJF98FGWvvgH2OM3iew4iB388b9E68pFvIV5hKqhWgkiz6P25msIVSFzz/3kHnsSLZvDc52dx6EoImjWEbqOns0TNGr41RKR5yBUDS2VwV2a+yPHF1BQsNQ0BxP3seRPEMkQTRik1QIj5kmWvWlCAkwlRVEfolsfJpIhQiiU/FncyF4NBiwlTUHvJ6/1kVRzzLhXV48jULGUFAcT91EJFlDR0BUTAXiRQyVYQBJhKkmK+jB5rQ+Q5LQ+Qumx6E1RDuZ2zizugD0HA54bUa+ElBZ8EumNy6XACfniP3uHqTNlArczhmQUwdQNj5e+vP0DoKtJ+nMniGRAGHn0mye4PPf1Lbe1W7FjXiewEgLTFNTKDpVLccqmMX55bV/z8cuY7N9H6NpETovkwH6W3vpG++9r5MKVz7aDpokO7XpjR7adKAB+eQktlSYxOELqwBGaEze3dOTbCodPGKQ69JKfGfNZng829ez7zRqqbpLsHsJv1XAqC6hmEt1KEQUeke9hV+aRt9Q5hj56nNzxPmQY4VZt+p48SPlwN9NfvAhNm3RBp/9wiovfdLZ8nG9c9Cg/2dlzNbRPZ2BkowFL65V30Hq6MPYNxt0EU3O418a3JQ8mUgpHThq7TKUxwhBuXvbiiWW17C5QLQujfwBvdhq5QwO9ANJanh5zFKCtthaf+FvVr+JE7QBYgl3zOfSBIt37U5QmW4y9uZlv4XmSi287HYnRCBGTSq11GgpSRiyNvUHgtlCNBG6rQhT6u6a30lmlI8EviHk0c9M+9Vr4vptzrcB1JEvzPkP7dLL5zksFQlVJHjzM3G/+Cv7SAiuyl2F94zgmfR9nYhxvcX5VGUgSvxd6bz9Iib+0FK/ggaBawZmZRBgGRl/8d295Eel5SMchEFW8xQUMRUHoelyyA6Tv0bpyMc4C1Gt4y0sE9Spm/yD+8tKOk7SiG+i9fUSehzM1RViPV+Z+uYQ9OYbRP0jYit+RKPCpvf0GMvCRQZy9COt11HQalhZ2X5TIiNBuoWeL2NPj+NVSXPKxm+i5Luz5KfzauudZCFBWzKXWaYBvOI5o/ynOzIpdtlv5mxDbbXd7SKsFuvRBrtlvUguWUdqqQtG69uByMEctXKamLXM89djmnQhBRi1yo/UW5WAOVej0GqMMW8epNZcJZURG62LAPMTp2lfwpcOR5EMALPjj78pIas/BgGkpmJZCoxqS71E3iPtEoWT8jSXMlM7QqQJd+9Jolsobv3EdGYGqxyl8GcVtiADz0x4COHjS4saFrVsiTD1Nw12g5VWIohBdS25rYdyoRVsKzmyFVEYh0cEEXbl0Gi2RRKgazdmxjvZ9KwxLkM3vfrntlqRa2X0Ed+an8cpLKLoFMorr9x1g/9HOugggrtOvb71cgWqYICW1qSs4lYV2WrZ9HYVcy//egq4HR7j5629QvbwQ19RVwQP/7FMsfOsGrVKTesmnPOtipbf2or952aWy3FnQY1qCXJdKz4DGwkz8HevkISLHpfXmOSCOEhL3HiOs1PHGpjftw0oIDhw1d1tYA7Fj55VzLuH6QFRKwlaTsFIGoewofyqRvF39WltvYCO2KhlUZm36j2TI9FhbpsUDT3LlrLulXPhWSKYEvQMa89MBjVpc8irufxAr1YWixZN76NmUp87uuJ9URmFgl9bYFbhOrF/xPvPYNmFmPGD/4XBPwcBaV/HuHSbbZ4TaJcrdnq/177aUa23EYt2XtzTLEHtYI3Z4LpJNJNiVY3V6GGdxlsTgPlBVQscGRcVdnCN3/D4EENSrCE2n98mPkzp0Ei2RRtENhKpy9K//YyLPpfzOKzSuXyRoVOl9+pOk9h1FTSQRuhGref71f0zoOSy//HWa41eRSHoe+wjpA8dQ2u2LAla3W/zGF2hN33xXWYlGWKbiz3Eq9TRz7g0W/XFaUR1kZwuY+PpIXNmiGi7iSQcdaIY1uvXR1bFBIFCkujrx7+TguxfsORgIA0kQSIxEnGo+dn+Smxcdlmd9hAJdo2me/qnjZPoTmCmdMIg4/Vs3Cb2IvuN5+o5msTI6L//SNSA2yrnr4RRPfirH0oyP6649+POTPr/yrxdoeSUSRo6u1D4kkpZb2lqnGliYWZem3QX9wxpdPRoT13fuuZWhT9BqgBCbVrudIJ1VKBTVjtT+Wo1o23bHFajpLMnh/XGqrb0SCZr1Hfv1VzA0qrfTwbtjcTZYJZSth9eoxKWSKIpXiuufxR2eS6EIoiBC+is1dVYHMM0Q6KZCq+aTzOuxp8UtaDQi6pWIVjPa1YRKCEE6ozA4oq8GA/pQP2G1jnd9cvVE9cG+OP29RTBgWgpD+zqb2KIQZsb9jb4IQqAkkvjlMmomg2LtrOtgqSn6jIMIIWiGFRbccbqNEcreDCFrz51QwEpreE6IXfO2vOZRFNsm16sRprWzIudKCaR/OBZratRi463l8bdjpU2hYCbzZHr273odEkmF7v7OJlXfk7HY0Q7PzNF7E5x6OMm+Yyb/5V8vUFoI7ngWobwc0upwzFiBDEOcyQlSJ+4idG28uVkglgoPXacjfWB/KRY50wtF9N4+/IV51GwOc2AI+8Y1vKUFzL6BjgZ7oekkjxyPywTJJHpXETWdwVuYWw3MZRRCFKKmN7pPRr6Pv7SIoumYA4Mx38dx0HIFrKERGmfeImzeXjvjJsiI5deeRzHMNW5AFFJ+60Vql98haLdny1BSOv0i1YtvbRRVk7Jd1q0TOi1kGLD82guU33m1TWzeuJ1fr6xyGZZfe57KmVc3Bk4r21VLRG7n/fmxKsjG59wO68zKG9SCJbJaN/sT91ALlljwJrCjzoOMUAbr7vnmhVUzrLLkT3Ey9SRO1EATOtVggUZQ6fgYW2HPwUAUgedK6uWAxLBBoxquWmAqmsKx7xpA0RRmz1VIFk0OPNK9+t3QC0nkDIbu6QLiYOD6eYcwgFRWwWlGBP5axaO0EA+AYeTj+HH7lRAKTXf71pO56aDjF7t/SO/YtncrHkCnKPaqFHu1jiSS69WQxbmdAw5FVUEocRtOpbRygrvue8WCV++QGV9e2vpaytDfU7C7Ame+Tv5kP2YxRWgHmF0J7PkaMghJ5XW6Bk1UVcTXaYtTjMKYKd+ohh05UlpJheI6oSAlaSHdjas0JZlAMbZOa+uGoKvD5yOKJIvzmycqGQZEYQCNOpG9E0ta0GOMEhFiiARJJYdAkNf7qPoLhDJ+JoSAdDEm74Z+tOPKPwhiy+Ncl9rRs9fVs5GcGQUuMgoQQhBFaVTN3FFcC+IMWK7QWTAQBpLSYrjj6rVRCWnWI7p6NAyzM2XRvaJeCTvyj1gPGQTUz75F8uBh0idOEe0/jCQWQqq9+fquZDqIyXutq5dRUykyd91LeKiFYpj4y8u487NxyaFvoIOTkQhAzxfIPfwBFCuBlsniTI4R1tbauINaDX95icTBw+Q/8EEi38eZGsebmyVsNWmcP4M5NIpR7EGGIcIw4hLH0uKW7dK3C7+yefz2a+WN5QEp8auluIxwC26lrXjlpU3bbIVOt7sVEtnmCejtACAm+iXV7KbtgsilEi0QEZJUcxhKgqSa2VMwsFvwJ4kIpU8ofZphmUD6NMMqIe9OgnLPwYCU4HsRtXJIcUAyfcOlXo5nBlVTOP6RIc59YZJr35yj72huQzBQX3Dw7JDcwJqs5JmXm1x8c+tBckWMSKDghy5QQ6BsEkRZj9lJn3o1IgzlriS5gRGd3sFYcOS29dA7wNA+fVdVwRVUyyGzE7tkKqKIyGkhfZegUQVkR2UCTYd0TunYOKZWDt+V/PCtqJyfJXu0l+RQnqDpYfWkKJ+bJWh5pLMqqbyOlVLjLoJtXohWI17t9g7ufjzTijMyK5CejzB0tP7uuLNAj+/JVuqNigqmKUhnO8uiRBHtVevGMoH0fVTLImy2iHawSRZAUR/iZusd0lpxVaI4JhQqa6tnRZDuMgjcEK8l4mu1A3u+vBQwtF8nsXtXILkudZ3VsMBI5lA1EyEUjER215SzEHFrYSrdWTAQhFAtBTtmBmbGPaykwokHN0rRmpYg26WiKAKhQL0cIhRIpFQWZ3yGDhjUKyFBIFEUsep3shXq1QjX3ntmwL5xFaOnB3N4FNW0iFyXyHOov32aeO0YoUQOQaW85fMsw5DGuXdIHjlGYt9BrGSSyPeov3OaYHkOhCBo1BGqipQRYbOG9F1kFBI5DkG5tFqCkFFI5DoU7j5BEKk4M7M0r14iWidtGFTLONOTWCP7SR45RuR5hI063tws0vOovv4y2Yc+gDE8iqJphI5N7Y3XCCoVVM1E8SXB0sYJNXJs/KXFuK159Te2idmdtE91CF1n1RckkRRUyhF7WMi/K0giAukR4pPRutCjJgkli6WmN56jMLHUNKH08SMPO2yAYEMGwRRJdMVqv98KSSVHSEAQuR2XdBQ0NMWgGVapBktIolVSYyA715u5FXtvLRRxy1Uqq1KaCzZ4EwgVug+kGXt1keqsTd/R3Ibv+m6IjCKM5NrFqZbCOGujrNo5x5ac61rxDC1JX/YYYZtAmDGPb0sgnB73WZyLV7S7ybEOHzA4eMygb1Bnevy9iQaEgFMPJjh+T2fSv0tzATd2cZ6TQYBiJTB7BkgMHwBg+eXndkxzKUpsWayqgk6lV6vlCKcD691OMfOVy9QuL5AYyKGnTRZfukn18jxIKLcUVF0wciLDUtnZ1oa4WY+oVTtLS6zwBlbg3pgicdcRun78ewkWl9H7u7HPXiFY2LxSMS1BMtNZ1wW0g4HFYOOiWQgU08LoG0AIsFWBv7y953ggQ9zIJiHjDhqx1SwvJU4jwEhqVGZt6ovujpNpaSnsmEOTzStr6p1CkMj2YyRyCEUh8FqUJs+s/m2ryU03BKaldKzJEPpy15LYVhACRo6YfPSHCiRTCrqp8I0/rGBaCqceSfFz/2CGv/kvBnn+96ssL/iksyqtRsRrX69vWWJoNcK9m28JgVbspjV+A3d5gaBaxb15c8MmmtpCDyaYe2Uyvl5bXJagUqb2+ivU3nhlZceApHC8By2hs/jWm6zc4Na512Lxp5aNMzGGMzGGYpiYQ8PIMKT2xqsU3XOUxhzsehjz5VYSOQKk6+BOjjE3MbbhFgoRHyEoLVL68me3jMMz+QMYpYjqb//BhvO0x25gj91gfUSqaiaaZuI61c07ug0IBXr6FO59wEBGsP+gxle+4HDj2t5LtrcDicSLXKacSxxJPEJESCuq0gyrJJS1gCCrdTNqncRUknHXhnSY98Yo+2uSziPWCdJaF5aSQldMjqceoxmWmPNuUg9K7Wa4jRLksv0/q9dDCHRh0mOO0m8cQBFKXFb0Jpj1rt3279xzMGAkFMykimdH9B81UddLtEqwqx6pLnODsNAKivszpIoW9YW1SctMKNz9aJKnP53jrofi5cvVszYvfaXO68/XadUjDC1N3V3AdstEMsTYgUAoI7h0xmHkgN6R3eyxuy2++0ey/Md/+d6oXt31oMVdDyYYHN39UjeqEWNXPS6f2zkYCO0m9YtnqF9aJ/SxS2pJUaHYq+1Jg71RD3GdO1igFdCYKNNYURvcgvS2PO1QW3S3FdBwbNkxJ8RMCArd64KBS9fxbkyipBJoXVmCUpWo3tqyEyOZVjpOdwPISFKrRBtvg5RErkv9rTcxBgYJ7e2DNQk4UZ19yVMk1Ay6MDHVBM2wskGBU0qwqwH3faqIZirMXKzxzudmtt1voxZ23F2TL675egjASGRI5vtXNTYyPfvpjSSt2hyVmUs49Y2BTTKtdKRfsYIwjFfle+U/HTxhsv+YyeKMz+/8xyV6BnU+8oMFRo+YVJcDUhmVxZmA7kGdfLdGGEq+8YdVom2O02rI23LilI6N0DN4c3OrDPz10NMm+cNFEsUktbEKURhh5i30pEF9soLVlWTpzCxWMUWyL03Q9MjuzxN4IVY+gWpqpAYy1CYqRG5I70NDIAQz3xqjMVHZcCxFhXyvzujxJNVFH00X9I1a9I6Y3DjXxDAVct06RkJh+qpN3z6LK2/WyffqdPWbNKoBh+5OMXPDYfaGTXVpbXGUTPeSzg4ihEKjPksi2U2usA/XrRH4NgiFRLJIvTqFIlTS2UGSmT7mp07Tam6vCdMpBFApS06/5hNFkssXAxbm7rRn886ICBhzzjHurMk4S1ZCoPjZWfInWfanNnzv1ja/a/bpdTHh+m/H/7sRlni5+rurn/rSZTmYplSfQSLJqT3ktB586fFS9XcAUITKoHGEYevY+xsMeHaE0wxRVTj/WhN3XRo59CPOf3Gah3/sIOOvL5HIGyiawuCpAvmhJEef6UdRBRe/tjZ4PfOZLD1DBlfP2nzx18sIAftPWIweMSn2afzu/7tM010iYRyimDmIlJKmu7xjCurSOw4DIzoPPZnaVXClf1jj8WdTXDjt8NLXm3eMnCQEZPIKf/6nuzhw1OhoEj79covrF91tV8UbIXdcEW4+n9jityNVuPZ+XVve0fLJsb/yBFNfvLAmPSzg6E8+xuRnzxFWm2iGoHvEIt2lM/ZODaex+UK4ruw4W6FpYl3aO4b0PMIgIGq2YrGhbW64rgtMq7PAaaV5wm7eMrEJgWIaGH19SNclau1EwpKM2+dIq3kSahZVaLTCGrVgcWPqT4JnB1z59iLF0SRuY+cb1KzLbZUsb4WVEOjr6BNCVZm/9jKhZ6MnMmR7D7F4802i0CP0NweshiHQO+AmrCCK5Ab78E6R7YpFjRamPKIQlmYDTEugG4J6NeTeJ1Kcf71J34hOvqhRr0Ysz29PVHTsaAd/lG3Q7hIQnkfYaGzQFljdJIrwqi7T3xxj+EMHAaiNlamPV+h7ZBgQKBcWMDIGqYEMftMjCiWhExAFEX6zxfxrUwx8cB8z3xyjMVVDKILW7ObAIwqhXgpQtbhsAnGAkMxqBK5E0yWaITATCr4b0TVgoOqCZFZj8JCFYSlUl3z69pnUS/6GYMBulWg1FxFCwWktk0h1E/gOpplDVS1ct4amJYiiAN9vxCUFVcexNy6wtL4uin/h06i57b1UGs+/QfP184SVtd+4wlOz1YjDR3WOn9J48RsurbHd7cGN0X6SDxzHOnmQqOWw/F++QFipwa6SvVvV3lamaHHLvzdvAWvlNjaI39z6nS3KRzt8pggNpV0qX/nMEElUoeNH7zNnIAwkjWrI+GWHejXcoBIX+pJLX5vh5MeH6D6UoTCUQjNVHv7RgwgFAi9i4UqVsVfX6k6D+w18T3LmlSazY/GgV6+GPPJdGQb3xyNTJAOa7lJbbEjg7SQ6BCzNB9y87DJ21eXgsZ01+A1ToXdQ5xM/lKVWibh6wcFuvrvUuK4LCj0qH/v+DEfuMnft65dS4jqSd161N6n93SkIAbrR6apNEobxyu1Otn1Z/VlUc+2RE0KQOdyDmtDxS+A0QmauNMn1bX/PwkC2JbF3h6KyYXJbRRQhvZ0HA1UTHZHuIL5/K9drPYSmoff0oqbSsTDUrpFYRELNkFSzKCLOStT8hY1DgwDNUHCbAXNX6ji1nQcA35MbeQw7QNfFmkWzEGh6IhbXCn2kjFA0E69VYbtZVdVER34EK4j5R3t/wDwnDnBWdE6spCAMY45LdTnk1MNJXv5qjXyPRrYgINo5qA2CDuaGLU4+cl30/n50RSGoVAgrlQ2bCCFQDJXUQCbuRIoi9KSOLCYhitPBmX15Ej2p+B1o+ugpA3uhSZDyCb0Qr+agmhqhF7+Miq6iJTS8ttxyFAY4E2NUvv0cyazK/5+9/46z7LruO9HvPvnmWzl2TuiARiODJEiCURKpRAWa45EVbEse2yP7eWw9P48946SxLVvPQQ5yFCXZpmSKpiiSFiSSIgASOQPd6ByqqytX3Vs3n7z3/HFupa50q7sBiJZ++ACounXuPvvsc87ea6/1W79V7Lfo6reoVyJAEAbJwh8Fsh3CEvQM2SipGN7v0DVoYTmJgaDpglY1WiaEL1+qjJBtFVipJE66Bw0NTTPRtDgpMS0jHKdAM/SSZ0Vo6LqNXKVAKgwDs78bY7Bn082RW8whNpD7VO1teFfPisprJ3OTfXAXztF92Ad3ofwQ+8Ao7pnLqNZ6L52uW+i6jaYZmGYa1y0jZQio5NpljKYZWFYWpSS+X13+XhwH7Q2qQNdtCsU9NBuzyTOgm3huuX1cuPwdy87Ras61nxUNECgVo+lmsuhretvAWuHUebKBIzNkzCK77KMAGMJGCMFCeFNRvR3ilgiEXksycXX9zkBJxdSZRay0zp6H+kgVLKbOLJLtdajPu1x/eYHJN8osXF2x+lIZnWYt5MZlf3kHM3nNp1FLk0oni4KhWfhhAzeoAGAZW7v/3aZi4lrIK8+02HPQagunbT4Rp9KChz+YYW4ywjBhYiykVol3HC/X9USBrX/Y4PAJm098ukC+S+8o7jx2KeD8Gx5z029PHEyIZKfcaTpwHKk7kr8qDA0jbeH0ZzHzNtl9PYh22qBmaG3paJHE2HxJaSIpc7xZmKCdHdURNK3zBX39d+mYL6AUxBuw+oUQ6Kk0KooQmrbhJLcaBaMfR8uiCzNR3dTSFMwBym2Fs+W+GQI7a9As+TQrWxsDUaQ6NugME/Qle1EposDFcvJJDNhOI7fRTdd1tqmDcBMUW4YwdCOpqtg7bJDJavSPmAS+pFmPaVRjdh+22XXAoqvfpFGNqS1GlOdC3vtdOb78ayVatZigS982BCClSuSBdwgVxZhdXdDVnWj532QMRG5IUPOwu1K0puvIOAYEhmPQmKohNDBSJkhFWPMQumjLAGsEdZ+wmWgwuPPNpCpeMxl/zTaA9vwbx/hTE4SzE3T1W7j1GM1IPICtekxpKsAwBW5DUSsnZdQNS1CaCrDTOjKG8nRArRzipHXmbng0a+vnoCh02wtWQo2MIh+pYqLIJ4o8PHcRIZJaLnEcEIYemm6wmuCu/ADv0jhmtQ66nrwTtonRXUCk7G3n6KERnXQmIQ92GtYxh3oxeototoUyDayRPvwLYxuKfRuGg2Xl0A2LVCohvgdBo82TyxLFAZrQcFJdSYYQCqkkhm7j+1XiOEji+WaKfH4E36ui6QaWmcH3a9hOF1IGiLbB4KS6iCOPOA4wzTSaZhLFXvKM6BaabhJF3k3GQJOWrJJVRbJGIkeslKQelygF69Ojd4KdEwhXKZIquTGXaOzFBcZeXEiYlLqG0CDeZCcWhRLDEnT1GlTLyS3Kd+vYKbG8A0zbPRiajRtUkMT0F+7iRumlLRerqfGQr32xzsc/lSdX2DolSYikWMunfqLIkXtsXnyqxUtPtbh01k8miiUrdMkz37ZSBcn/NZHk0OeLOve+J8V7PpzhvR/NrHNRbwSlkl3LF3+twuT18G3NatjJwhjHrN0AbpNSdrOS1xKnw8w5FO4aYM+P3EPhyADpkSIyiBIXuqEz/8wVYjfEtDSy3SYDBzKUbribum3jWG248G4ETQPT7DyGvea7eue7XKVYKzbUhowi/JkpdDuFnsu1CzttTL4TCPakTnDdPUM9KiNVTErPcSjzILVogXhph6XAb8T0jKbp2Z2mNN7i+gYKhEuIwp0YA9qyZ0ApydzVF+kaPkYq30cYtChdf42tYlOantTV6BSbGVFLSGd1Tr0vy33vzzK42+JDP1jgrVdavPlck8kxn6MPpPnRP9+LZWk8+eUqYxc98l0GhR6DaimmWo5J52Jq24h4xbHalE+wKYRAWCZ6oYCmG8SN9d7K+vUK9euV9R7n1b+3f071Zdj10YPMvTJJ9/F+ymdmqVxK3OyTT1wFYP61dnh1g77KGErTAY//yvSaz6cur0TCltLrVxQy17a11Ster67Ewudn1hclajXmlhuLQpdWYz1XIFqosPDv/jtoIknpTdlYuwYofO8HsA/v3vjEJO/x6C6Dj33CYexKlBgDHTpQVZwUrlJtVrqK4k3XDV23MIwUQhOYVoZ8YZRWq4Rh2BS79uP7daLIRcmkDdspEMchSsWEYYs4DkgyKXRct0wQNrDIosxkaNKZPiwzQ+J5jVAqpth1gFZrnlxuGNsp4LZKNOrTGIazbFituR4k1WiearQ5EflWsWNj4MDJLMP7HWaue1x8pcGuw2kqCwG1jZThFMhVO7x8t4GU0KisHPvkl6s8+j0Ffv4/7+X6xcR1M7LX4sxLLR7/XDLJhZFHPjeIAELp4xi5bXetvqe4fjngl/+fBX72b/eRyXW2EB69x+HoSYfP/EwXU9dDzrzqcu1CQHk+plGTtJoJ8ziV0UhnNPJdOsO7TY7eY3P0VIpMrvPUPUjY8U/+boMnvtLo2P19q9jJTn+18WRYGQq9ByhNbVyZzLRz5Hr2omTM4sw5TCdHz/DdVGYv4C0uMP/cNeafH+PEX/sQ4186Tf1aaalDKKkQAoYPZdh9d55M0SBwrU135aJz50b7FLc4pqrzRVSwSQRASqJKhdQ9Sbw4bjW39G16skk9Ki9LD0dRgC9bayVGBTh5g4HDORYnXGJfbplauJPxunms4sBlYeyVTY7e5Fw7dMRsZV/WKzFPfrnKU19ZYaWv7uJ//PmZdZ/PjIf8+Y9fRil44neqW47N6j7sOAtOKZQf0Hjtdeyhwa01JNYHltf97C40ufLFt0gP5bj++EXijeTcb+FRvkm88CaC603N3xZfagedkwrZaCEbLYRlIltbazJICdeuRvzW51qkUoJqRVLfQAxtI7ReOYeedtDTDtFijcazbyCbG58vCJtohk02NYTvVwn8BpaVw7bzybvY/swwHHTdQMoY152nu+cIjcY0YdhEKUkUuQRBE8vKkkr34NhFPLdMOt3TnlcShc8wTDhEudwwppkhjkOi2Kden8R2CmSyg5hmFrh9EmYn2JEx0DdqMXooRc+QxfVzLfI9JsMHHKIokQAeacegSlMBYSCxUxq5LpOJSy0sR+f4e/KEgeT0M1UW2kWJrp3zKc+VeP7rdQZ2mSiZSBQvTEfL0rNeWKXhLeBYeWxyzNbOd9TfIFB86/cbHLnH5r0fzjAwsn2uv2jPnra9Uuo3/HgSE07cie0sIQ00keyEDFNg2cm/24UkVmNuOuKNF1r82r8ovwOGAIQ7YEzrBsuriKYbOJkeekfuwW8tEoUumm7iZHvxGgu49XlUHLbdiIrQb6yfWZRi4nfP0ZqprVNKVAqq8wHjp2vYGYNcj8WyNOpNXdZ1gd6hhyOOk2fgVhDHdEwqE5sVoRICPZMhLC8QLi5uQyBMWMF70ycJpYdUEltPY2tpRpwkrdaN68wH1/FqIfV5D6UUhq1tOQ+b1gqhbDuEYedel40QR6qjWgjLEDsv3pU5MpiEF6ou/uzGqWtrbRqBnrHIHhrEm14kKDdRHZQ33hZCJFUJBwfRslniDQiEO4KCOIhoTlSRsbylhf87E51eaLJpSGcEkzckYYce1GBsisXZEpWvfhsVS+LF+qak4Sj0aMWzeK0SannR1ts1D0DKuP3ZyuQkZYzvVYmipbC5IgxbVCtjKCXx3DJCaMRxyMSN5ygW9xL4NVy3hJRL5cJXlBOXqrwGfj3J1HgHH4QdGQPNakx9MUlbKU0HhL7EtDV0I2HyFnpNauWIynyIk9Fw0jr5bgMloVmJ8FoxoS9prcoTDwNFtRwTRwG+LxEKatXkONMShL5CIWkFJYK4hUBsXbVwNVQi7/u1L9bRBNz73jS793dWRCVxFYFp7bzOeScYvxLwyjMtvvV7DUpz70y+bBh2noGg62JV/DfRJmjVZkjl+tB0EymTqlzp/CBuYz5RX1v68iZbjMZ4GcMxcXoy6I5JUHFxZxJlLq8RY1gR3cPOygKxQV81nW3LCa90Q63RwTD6ulFhlLCJlz7r7UYGAbK29pmSsdrQ9b8RhKCt37B+46+iGD2dAaERaYLoprjy8nFALWqXlVVtV2bcJJI+kQzauceJF8CwdbxGhOnoiWbHFrtfw+wsgwQgDjvnY2z4/ZiOayFAe9w6rRSoCdJ7eknv6sFfqBNWOq1532ZhOwbpA/3ErQmim4yBW/FoLNUI0FMOmmGgZ3NYQ0ME09Pbf3eLrsrbrEn/PyuWDPvhER1dh7GrEbXq9s+aCiPiMIJaJ3LKCimjNaRHOlD1Wx3TX2knbPd75VmLwhb1+iRxFBBF25R7VnJdiODtxo6MgVY9TtJXTI1GJcJOaaQyOumcQeBJLEejfMGlMh/QrVtt0QuBk9GZmfdo1WNkrNbsuIb3WhR7k/xm3UjmtZ7BpFvNuuTMi8lAB1GLIOp0AliLc697ZPNacl4FI3vNHe3g7xSUSioSToyFvPBkk+efaPLGC+626YyZtCCX1UinBFevR1gWDA8aGLrg8rXOTGSlEhZ2h8sbmgaGQTs1M7GAW/VZsl2jCE0nCn2UklhOAd1wknQioaMbNgqFbtjopo3QjGVr10hb5Pb3YHdn0EydqBUgDIE31yAKY/xmTBRI/JZcs4ivhmEmhmcnuNkzYA73I1vuGmPA3DVIXKkR3GQMRNHWlSM3HC/zpu8ohYpChGlhZbOg5KbGACRZM0tSQ0IIJDGxipe1BpbqmWuGQLVL7qbyJtkem8bCxtoUibeqs/EKg84NoI2Q8Dk6P35JwGxbaAIj65A7MYpVSBE1vGRuMXXsgTxCCKKaR+wFSSy3P/ksrLnErQAVS6QfkdrTS8OaWde8bogtU5A3gwpDomoV0XLb97kzldE/xi1AJWvD7r0Gti2Ym4k7Mgb+sOFOCTG9Hdh5amGc7LaEgK5+k2KfSaNqtRd5SeDGy2S7yJe49YihNsdANwROWqPQYzA/mcyaH/pUgaP3pjEtQeumvPLp6+GyMXC7eOlbLSbHAiavh/yvf6GbXEHDaNepf7ttAqWS8EIYKmqVmC/+WoVnvt5gYbazmXN02ODUCYt9eyx+4ZcW6Sro/NAnsmSzgr/3i5uTx1ZDSkWjEXfkGVgaDyetYVoCqSRx25KVMsYw0+iGQxwFyDjAcrJYdg6hGdipLhQxpp3FdgqEXoOg/QIMfvAA2X09yFASVF267xslNZxn8nfP4s01cOsR557Z+nosW6yo5G2DOGprEgiBMHTM0UHiWoNgrE3EEmAf2EVwY4bg2lqxkMBXHUsxLyloprM6tcX1uc9xo46eG0ZzUhs3kHSFLnNwlQsy+VQowax/FV+uvAeRLzEdHd3USBdNRo7lufCtjQlF6YzWsSfFcyVRlNT/EJswAWW8ufEZBckc0Ck0TXRUNVSzDZzBAvkTo0Q1Fz3rYGQdUIq+j55AaILG+WnciTIyiJY/q5+dpHV1nmCxSVTb3OVqWqJzD8UqqDCkdfbs9gfqOkJvS6yKFQVQpdqeunYwX8WyowJHyzASRv7qdtVSe7JdyTS6RVePEKC3M2C0m/usIJaJjPctZGHcCnQdMlmNXXt0Jm8kNUBu9sQJ00jGZIsJXfrBzvJIdQ1h6G1JaIVy20a3ADQ9yYzStI3HJ7qF3GwhEKZOe7fa8YZVSQVxjApv3cu8Y2PgyhtNIHG5zFz3+fw/m9jwuIWpgNJ0sIa9+sLjS0V1Vo5LpTVefrLB7/xq6TYJLNtjajzid/5LleefaPG//G9FHnksS8/AxuVy7ySkbHsDnmjy+f9UoTQb3dH8/Y76EENpNkbu4MTZvIbtCGqVOrNjLwKwMPH6qiNW/NPN6lr36NiZr65rr/u+XVz+tReonUtya9EED/yTH2D2W1dgroHQwEol6pabpXo5Ka3jEsyeq1hcVBh93eQeexjr4G6UH2LtTgobCNvCyGUIZ9YXMGk1JNXFzidSTQjyRY16NV5TxEkIDT2bRbqtLTkDCsX5+nPLL78mDFJ6nh5zBKnWEggNSyNdTOoTzF9rcfnZzQuw5AqdFSkCqJRiIpmhMDRK966ToBSanlChlZJEgcv1V39n0+83G5LWDjQ6ND15xjZJsFiGdEOaV+epvjaGP12ldX0BPW3R/d5DhOUm3myF/N27KJzajTu1SFBq4M9WKdy7ByOfYuGJc1v2w9mmquMtQySFsNL3HMY5eQhrdAC9kEVzLDCNhETXdInKNcKZEv6lcbwzl4kbre0XWcsk++gpnCN7sYb7MHoKiLSD8gLicpVgYg7vwhiNZ99YWcA6haFj9HWRvu8uUicPYQ32omVSIATS84nmyrhnrtB65Szh1DwqePvDnGEEtZpkZkqSSicy4Tc/M10/8lHSDxzFGOjZuJFYMvdLv4F79mrHY5I+dYTse+8h/eBxonKVqb/1b5CNFnpXAefIHjIPn8DaO4yeTYMmUF5AuFDBO32Z5gunCWdLKL8z762wTIy+LnIfegDn0G707gJaxkmMnG0WqXBiluaLb1H54jc7OtdG2LExsBNsx14FOPeKy6n3ZfiF39hLeTZaY7TNTgT8+i/O3dE+xTHMToZ87pcrKAkPvD/N8O7OeAQ7O49idiLkxW+1eO3ZFmOXA0pzMa3GzuVXb4Z2K6xtlUzWnqtIpbcv4gRQ7NFJZTRqlc2stFu4EHXTL+3dkZXS6Nud5oHvG+Di84tce72G11g/yWTzGoWuzoyBwJdUFkKihRr1J18gi0IFEf6FJFVLSUm8WCOureegBL6i1ZR4rsR2trfQNR36Bg2mb6wqYywEwrbRTBMtlSKytxbA8pW7PD4CCKTLsH0oIRjdNNTVWY84lNsqEPYO6B2HVWoVSa3coDq7QLN8g/4DD7M4eZbQb2KlChQGD7EVQcH31bKaXyeLq2EIuno6nYLWnlPoWsI7KTdQQUzlxauYxTT2UJGw3ECFMYvPXiZYWK/WdzOWjN47CXOwh/R9d5F5z0mMvi6EaSIMfYWgIEAvZNFzGYz+bpxDu8k8eIzyr32F1puX11XXXIKWTWHtG6H7T3wco7eIsMy216G9k8w4aCkbY7CH1N0HyX/0Ycq/+TX8S9eRGwjtrOv3niFy77+X1MlD6MUcwjSWvQMAumWgZ1JYowNk33cK79w16k+9gn/x+h0dv5uR+MsU9brESbFh2q8MI1QUt1O+199PtZQLvqMTt9NxNIHRlUdzbDIPHiN17104h3cjLCu5r+3xEaaBnXawhnpJP3iM5vOnaXz7NaL5bTyee4ZInTpC7kMPoGfTSZt6+55ucj3L1+36xLUWcUe8iM3xthoDnWDXIRul4Nmv1ajMr90x1zssSLMT6AaM7Db5wR8vcvz+1Kb68zJWuG5CbszkBJatYZqJO1Gp5O9RpAgDhdtUNGoxiwsxi6WY0nzE3GTEzGTI3FTEwkxEoxZ3zIC9GUux2FR7wurt1Umnb8Gt2a6sl81p6KkOjIFuveNdeCdoji/S9/Ae8of6iZoBqf4szfFFYi9ERorQTzglmaKxab56NqeRL3bm9/ZcxeJ8BFISL1bx3rqECiPCqbaBqRTSCzZ1zYa+ojQXd1RXQmjQO2C04/MrD7GKIoRpIXQDI1/AGhommN64lkC/tWelPSHQMTE0c930pemCfJ9NoxQsF6HZzC5LSv926hmIcBsRKo6IlJtULNQMhNDRdAPD2qbWh0qMqEY9pti9/ZjpemJwdpL+dzPCmkvz8hz5k6NolkkwXyOstIi9kPyJETTLxJ+voqVM7P48+ROjpPb04t4o05KScHEl7JLJadgdjlEnMHcNkHvsAZyj+zAHexCWCXFMtLBIXG0iXQ8VSbSMjZ7LJN6ClIMII4KJuQ1rZQDoXTmcu/aRfex+zJF+hGkQL9aJShWihQrKCxCWgdFbxOjtQu/KoaVsCt/7KPVvmHiXxonLm5TSFWAO9VH4nvfiHNqD0VMAXSOcqa9j0gABAABJREFUKSW7/1aSvaLZFsZgD9boAEZPgdTdB9DSNpU4Jrg2+baGDZRKbJ50euP07daLZ/CvTqBn02i2iXCsxHOyaxCjt3hb5xaA0jVyH7of+9BuzJF+0HX8KxPE5cry2OjFHPa+EUTKxjS7Sd97BJSi8qUnN3V/6T0F0vcfJfPQcYzuAiqKaL12nnCmlHiJohg9n8U5cQBzuA897aDimLhco/r4M8T1FnGtQbRQua1rfNeNgXQmIfZNXQtYXFhrDOy0rOh2cNKCwRGTD3x3lg99MkuuqCeqfDeh1ZDMz0ScP+2xMBMlk4WzZAyQpAHFiXpa6CeFc2pVSXk+YnEhZmE2YnYqorYDN/NWqDcUtYakt1vnwXtt9owaFPIa1Q5zbVdjbjpicMRki/D1Mrp6DdJ30Bgovz5J/lAfqYEc0o8wcjblNyaJmgEIQRQq5sddNrPeTUuQyXVeDMdryeVMDRVGRPOLqDBCNlqg6+0JQiB9f8PYahAo5qajjowBTRP0DRlriWjtEsbhYhlhGKhwKf1yIwgcPdumD7Y/ERrVaH5NoSKAOJLoVlLlUdvCw2PZSehio2d8I5QXYppLtQKUwm+WMZ0chuWgmw6ht31N9sBX1BYlxe7tz6ebgu4+oyNbQCmFP1MlrLSQQYQMY1pj8ziDBaKaS9TwCStJvQmnP09UTz6TQVIiOW4F+NPVJI570y4r8QzcmedcmAbp++4ifeoIRn93YoiWqvjXJgmn5okrdeKmB3GMlnHQ8xn0rgJ6PgOxJJxf3DimrWnY+0ZI33uE1LH9CCHwr0/jX5kgnJwjmq8gPT9R9OstYo32Y+0dwd43jHN0H/FiDRmEePXWhnFlYZqk7z+aeARyGZTrE0zO4Z65Qjgxl3gVlEQ4FuZQH/GxBtbuIfSuPKnjBwjnykSzJWTT23mcvJNxbWfsxNGSc2X9Mx2Mz8D4DGjasjEgTAO9mLttY2AplpW+/yhaNo1sevhnruBfuE5UWgSlELaN0Z0nrjZInTyMMA3M4T4cL0B/6pVNDTF73wjO4T1YowMoKfEvXKf5/GmCyTnietsYKGSJGy0yD9+N2DWAMBMOQ+uNi4kRcKvckFV4142B2YmAgV0W+4+lKM+Fa5Tv6pWYG3dIq98wYHi3yaMfy/Djf6kbXd/4gfI9yfUrAS882eQLn63QqN55IoOGzs1lKrfCQilmciommxH8qR/NsVCO0TWo30KRl6nrIYeP2+Q7qMjXP2SQK9w5Y2Dhxev48w1SQ3nMrM3im1PULs8TuUkqqqYLpi80KA62yz3ftErkixrZgt4RgVApRbMhmZ1cJePr2EgtIUbpXTlS99xFODFDOD1PvLj+RfU9xeRYwKmHty8/reswvMdc59FQUUjrwtYx6/aRtOLqmmdCoaiGs8QqWn0YkS8TnQEJurWxzoCmQ1ePTjqrbUuOWxIbmp+KaLQNTKUkpRtv0j16EtPOEAYtKlPn2W7Z9lqK0mzE7gPbh95MU9A/ZHTmuY0V1dfXaq8HCw1mvvo66KJd8zz5fPmzVWmO3tTmbtpCt94RkXFb6Bp6T5H8x96TLO4oZNOl9doFKr/9zWRi3yh1yDTQ0g5GIbvppK5lU6TvP0r63ruWY/e1rz2P++alDZ9dvZDFPrKXnh//JHoxR/rB48RNl3BynmiufNPBOnoxS+F73oeWTUMcE0zOUf3Kt2m9utGzew73zBWKP/BBnGP70dIO+Y8+TOulswQ3ZjcNcdwOEjVRQctVaHpCiN6UayIl0vXB9YnrLiq4M7KuQgis0QGCqQWaL56h9vvPIes3ueY1jebLZxn8/w1g9BXRHBujt4h9cBetl97a8PVJnzqMOdSbcEn9kOpXv4138foao002XcKpebS0g5a2sUb6Mfq6MLoLxNVGYuTeJt51Y6DVkHhNiaZB7+Da1JxO3Zud4MhJh4/+QI4f+LHipmlESim++dUGX/9SjVefuU0RkU1gYjMk9lBSM7g0kGy/oMcS3roQ8BP/++3zJ65fDjpmye/ab9HVe+cekeKxQQrHBrG70ghdkN3fQ//793P9v7+BO1PDNCMOPVhgmbB+04uz54BFscOywmGgqFfiNbUeUvcdI16s4bo+xR/4GMG1CTLvewDvrUs0n311XRu+Kxm75CciU9s8irouOHjUbnMxbm1nFEqfRlRGoUjpOfqs3fhxk1ZcW1Yh1HTB4OEc89eaZLvtxDOwwSlNU3DwmL2l52A1fFcxPxMuGwMAhpXGyfZiOhl0v4HfLOPWtlZDazZipicj7u3gnJYt2HPQ2mkUdz020jbYgd5B36BBNn/7eiKaY5P/6EPo2RRC1wjny7hvXKL0q1/e+othhKw2CKqb66dkHjyOtWcILe0g/YD6U6/SfOE0ytt44Y2rDbwzl6n+zpN0/9gn0SwT59Bu5GKdypefWnOsXsiQee8ptHwGIQTuhTGaz5/exBBIEFybpP7Nl1BeQObRUwjbIvfhB6l+9duEU3deKjeKoNWSpFKCwE9+f6dJ2KjEVK//wQs0Xzyz3hCAZU9Q/ZsvkP+u96L1daHZFvaBXbReOstGc4O1bxi9mIMoSuant65s2gXvwnWMniLWSD8A9oFRwtkS8U5JohvgXTcGnn68tvnifIc25YO7DH74J4s89MH0ppO6Uorf+0KNL/xKnckxhdkuyRnLJPdb10yE0IllgGk4RLG/7PKVMsI28/hhHU1L4ghRvPHNCfGRxAg0BBogEWjkKBAQEOITE6Gho2Ng4xDgE5CQf3J0ARDgERIgkWTIExG0f9/aQnzrVY96h96O4d0m/UOJBoTbvP2bceAnHmLu6auUXh5HrZqsw5pHOm+Q77eIAkXvrhTGBjUFDp9w6O7vbNKeGAuZHF8bdlJRnLgQcxm0lEPjudfIF3Obxmibdclbr3T2kukG7DtsU+jScZuSTZrcFEltgru51HyRnNFD0RzElw0OZh7gfOPZ5dRCpRReIyJr2yxcb1Jf8De0PSxbcOKBVEdphUrB+Tc9WqsKcwmh07v3fmYvP0sUtLCz3XSNnKA6c3HLWbhRk0xc62xnaNmCXQdMnLTWGbH2pvoXdwqj+8yEu3CbEFbial+q9uRfmaD29edvu12A1MlDGD1FAFQQ0Xjy5W1Z/NL1aTz3JsUf/ghaJpW4rI8fgK98a80Y6tk0mfvuWv7dvzSO+8albfvkXRjD6C2Sfuh4cu333kXj6dcJpxfu+D0yTchmNeIIMtnk9+2yUO40FOBfuUEwPkNc2Vr4Lrg+s2Ko6Vri9dlon2AaaLYNuoZsBYSbcTrakPUmsrnCd9FybbLhHcC7bgzsRCL3VvHdP5Tn4DGbdHbjgkVRpFhciPnqb9YoTdtkzF6cTB43qBDLANvIYhpphNDwggpeWEfXLDJOL6aRwgtqWEYmKcxjpPDDBqXaFaTqZEUQmJho6PSKIRpUqakSKTJ0i35aNIhUiIFJjiIaOlmtSEUuoFBkyKFjkhL9VNQCTWpbGgSzkyGluYhWU27LBzBMQf+wwa59JhfP3L7l2RgrJxUMB3LE3orrThg6URDRKIeMna5RXQjw3fXXcPC4TXdfZ4/s5FjI9I217kHlB1i7hzAHemm9/hbK97esJhj4iumJkEopptC9XfVJgWkp9t9l06hJKuVbcNsJgS5MTM1BIFgIJiiaw2uvQUJ12qVRCohDSbSRhj0Jv+L4fU5HWSNKwfnTPp67NoVR0w2EbrblWHV0w9rW6dGoSm5c7TCVSoDd9g5cOefje5s0LpKF1t43RDBVWs5xV0GEnk8TN1w0M3kuVCwRlo6wTDTLTJjW1c1Z1l29OvmiftteSGEa6IUsRjEpVxw3WkTzFaK5znRAtoRhYA70oKVtVBQjGy3CucVNZXWXoRTS9QlnSli7BhGWgZZLo/cWiZfY7ZqGSDuYg0k6nmx5RIs14ur2WRgqCImrDaL5RcyR/oQD0Z1Hy6QSXs4dhGzrtDipxOt14JCJUnD92p0nmW+FcGIuqW2wjRUS1xqJDgO0s4o2EaSK4mU9AqFrSdrpFhCWuUbcSgXRHSNt7tgYGOjXGRnS6e1KWMDNluTKtYjSYkx3UeeuQybPv+zhB9Dbo3HkoMmFyyFCwK4Rg1x7AUqnBDNzMZevhdQbCtOEvbsN9owm6oDNlmJ+IebilWRiefDeJC0rCBRdRQ3HETz3kk+9LjcdCyEgX9R54NH0Kqb3egSe4tzrHpfP+jh6D+lUN7aZJYhbWEYW03AwtOQmSSNL0yvjWAXSdjeG7qCUwtSd5ck8KUPZGTQENiksYZMVeUKVVL4yhImBSaTCdvnapG0bBxMrYZsrg6woEhNh42Bgtr0Nm78gbksxMxGxuBCRzmzz4AkYGDHYe9i6I8aA0DR0x0APzTV+d6ELokDRqkY0qyFeIya8qaZ6vktjeI9JNt8Zh2FqPGR2Yu2iFE7MgJQI00xSoaQinJwlKlU2bEPKJIw1fiXgaNbZMgNjaZdy5KTNtYv+LRkDUkV0W0No6DTjCm5c39CF7jdjaG7evmkJunp0RveZndUlUHDpjI/vruIrKEWrMo2d6cJK5dENC6++wHbWQKspmZsK8T2JZW+dkilEovx36LjNxFiwqTEgbAtzsBtrsHtFACaMiCqNRHehO4eWSSEgyQ4hSRsVbQNhM2NAiMQrkAgz3b4xoOXSSeYAIOtJqtediFdrKQst7SB0HekHxI1W53F5qYjKNcyhXrQ2oc4oZpeNAWHoaCkbLZ3wYuKmi2x5nYnXqGS8o8U61uhAwj3IpdFS9h03BuIYWk3FtSsRui6o1+XO6mDcqX6UEyLmdlDBShxDCNGWct3ANaAUcb2J4RcRRnv8sulNDQ69K49ezK70p1K/LaGh1dixMXDymMXHH0sxOmwQxYrp2ZgvfrWJ6ykOHzT4cz+Z48z5AL8sObDX5Kf+ZI5//+t1dA1+8BMZhgZ1KlXJQK/O2I2Iz36uzuVrIcW8xvd8JM29d9sYBtTqknMXQ8bGQ4IQPvOpDAqYW4jZNWLQ16Nz6UpIsyWRm4xF4rq12HPI2pKB3mpKnv56UjUwZyU0ez9s4Pk1sqk+grCJKytJkQpNww0WyaUGUEoSxomymWxXq4riAD+obeoVEIhEtlfpaOhIICWy2KTQMdewyRvUaFIjIkTHIMCjW/Tj4xKpEAFY2IQIQhEgVdxR/PXaRZ/9RyxG9mxP8hrebXLkbodv/E79tnTrAbJ7uqiem0G6EXL1AywVMlbLuvb1myY6TYMDR2x6BzojD4aBYmIsZGZy7T3wLlzDvzaBsMzl0IB75tKmYQJIUkjffNlj/xEbu4MMjJMPpHjxqRYTY+GOwlwKcGWdHnOExXCGxXAahSIi7JhouoRcQWPvYZvuDvgeSioCX3HxjLfGGEBJKtMX6dt3P6aTI/TqbQLh1gh8RXk+SbHtH9pe0EtocOJ+h+efaFJb3HjA9IyDc3AEzbFwiln0bAoVxoQzZfwbc6SO78UoZpNaFPUWsuESzi8ihIbUN39ehICj96Q6VrTc8jpMAz29QjSN601UB3n92zcsknbb4iIqjJGNnfCZFLLeXN6lCl1LBHLa65KwTDTHXjbO46a7IxEhFYZr3dYpB7HN7vZWICU0GopvP3H7m5LbQdx0O2fud/jaBhOzGD0FzP5u9EIWa/8IwZUb7bTn9juhaWgpG3vPUEI2VCpJk56cTVQV7wB2bAwcO2Kyd7fB3//FRU6fDaG9Iyp0sGPr7dG4ei3k7/6TCkLA7/63QY4dMYli6O/V+eTH0vzAn5rF8xSf+aEMH/tgihNHLV47nVxsFMH5SyH/+JeqHfXVtATv+UhmW9EV35OcfimpEbBQu7zmb3V348Ijc9VkYrTNHD25fYBisXGDMGquSwVbgkCQEXlydGFqFotqnqaq4ZBef7Ba/s8yNAQmFj4uAogIWWSOIv1IFRETEXVQWOPNFz12H7B44P3pbcV0hnZZHL1H0jdorGHm3wpql+dpTdfw5hvEbrh8eXGw9cul64KPfH+uozRHpRRjl3xuXA2olNa2q3flcY4fInX8EOFcieqXvo69b5S4Wie4Prlhe3EMT/1unY//YI5cUdtmvATH70uxe7/F2MVgRwqGoLjSXF8u+EL92ba3qHMM7zZ538e20QRow/cUV877TF0P1+20DMvBtHOYdsKMtzLFbQmEkIg9vf68y0e+L4e2zbogBDz0wQy/9R8rzE9HG3q+VVteV6Rs4lKVcKaMgnZOfZ641iScryTt6TpxrUk0V8XeM5Ao521x7gffn+o4VXVL6NqaRVAF0ZZG5o5gWWv4Ejttd40rWVvqZ2INCF1f9qAAyQO/k0B8rFCr3l9hGXcshv2HETIIE5nnO4jmc29idBcw+rsRtkXfn/4BFr/4TfxL40SlKiqOMboL5D76MJkHj2P0d6HCCP/SOP7VqTuWvbFjY+C3fqfJxcshP/OTeYYHDD733xs8/7JHvbH+AVrKDV3CzFzM7Hy8rEx46UpINqNxYK/BUL/O/r0mn/t3fUiZxIXKFUlm1QJwYzLi7PnOL9wwBIdO2NumVsURLC50ptt/M/ywzvTiGUCgNjEClqBQNFSVc+rl9m/JPzfU5cQjoFhOOaxSbr+uSadMLLoZ5IJ8nR4xgCVsIhWyqOapUkrSUjrITACYvhFy5VzA+JWQPQe3DxX0Dxv88E8V+eX/Z+G2CDvSj8iMFkkPF1g92O5cnbi18X01rSRU8eHvz3a8g3vy8QZzUxuoF773XlQUU3/iebIfeBAQWPtGCadnNzUGlIJrF4IkhJTWKHZvPtEJAWjw6MczeK7k61/aPu66HZaKE3WKfJfGkbtt3vvRzoyBWiVOvD433Veh6fTte4CZS88Q+Q2cXC/du05Sm7mE2sbl4XmKZ7/R4IPfk+1I/TBf1Hj4Q2mCUDF2cf1zEC/WqT31JkIXiUz1cl/bKjRySXmp/ZkClCJcqG66sBkmDI6Y3P1g6s6oD8ZyjeysMO/coqg8f+U6NLHjgkjCWq441k67C1gaRBVFa3aWwlh1bCfQRdL+Ul/98M6UiP4jBP/yDerffAnp+mQfPYXeU6DnJ79/pb6EVEl4rK1KKBsu/pUJSr/yJVRw59I4d2wMlBclL7/uMz4Z0det89M/kcf3FU884+L7UCzoaO0QSS6rsWt45YXo6dJptVTyrCkYGTb41nMe07OJgbBQivnFf13F8xWaSPSob0yuMMKl3HlVtGK3znZF25Y00r0NSGudYLvJ8WbcTPBLDIB1ra75LCSgrOYoil4kMZ5q4eOhUMTbZBCs7y/cuBrw/BPNjoyBfFHnPR/K8OXPVZkZD3fMlF/C5O+dQ2xwM4LFzeOL3X0G3/uZPI6zMflzNZRMJIRffdqlvLC+kwqBdD3icpWlYLrQBdsF1qWE577ZpNijU+zeOlYgRJLGOj8dcfpll5mJd6Y89RIe+kCG+x9Nd6SoFwaS0lzMS99qbRjSEJqOblrIyELTDHTdWtYk2LJdP+HgNGox1jZFgJY8Le/5cIap8YjxK8H6cJQiKcKy0WO+VdGZLSaLfFHnw9+3ZKzcvjGgomiN3K+WyyDSHcSVtm1YJa77SKKUSsIR+c4MvQQikRVuGyYqlsT15rJBJYMQ5fpJkSRNoOUzaJuR3TZq3TLX9Ee2vMR4+WN0DqnwL99IpJSFIPO+e9BMHaU0xJJt64eEU3OE47P4VyfxLl0nqtRvNYt5Q+zYGDh8wOTwQZMoUmRSAq0dJggCxUIpJggU3/2RNJWa5NB+k9VcC01LSITf/ZEUmbRGvSGZmYuZmokIAsUrb/oMDxqEUSIoUa+rNZ4AxQ6vXbRLpG7zrlu24PDdDuX55rYE3XcLkpgmVdLkiFSIj7dtGuFWmJ0KefNFl+/+4Tz5rq3d36YlGBgx+eB3Z/kf/61Gtby+Ml8naE12Ft5ZQrFb59Bxm4c+kEF0UHI6iuDsax7TE+Ha+HcbyvMRto21ayiJQx87kMRhOyAEnXnF5fh9DvsOW2RyW+/4CkWdg8dtHn4sw1d/s0r8DtkDo3tNTj2S4uBRu6NqZ/MzMRfPeMxu4EVRStEsT2BnerDTXWi6Sas6QydvoJSJmuHV8wHOvRq5wvY75N37LY7cbXP1gs+1C29vHXc7JRgcNXj4scwt1fnYCCqIVgiDpoGeT6MXMgjbum03rvIDZK2ByidpZFo2legNdMJJ0LWkhoFhoKRMMgBWixRFcVvbvoFezCWiNplUZ/0WAi3lJBK67Up9ca1JfCe4En/EICwDPeOg5dIoP6D15qVETKjNrVJBRFSpEc2WCWdKRPPlO2oIwA6NASHgrsMm3/PRNFGYLO7Pvexx7XpIEMD0bMzZiwGPPerQaimCUPHGWwGum7j7XVcx0K/zkQ+k6O/VefFVn7HxkIWSxPMUX3vC5ZMfTS9v1CYmY555wSWMYHY+plKRhJvUud8QKkkb3G7Q0hmN93w4w7nXPWqL8bvCUt0OCkVIQFWV7kh71bLk8lmfS+c87nskjdhivhYicat+4kcLnH3N49Jb/hpxmrcDliU4cNTiwfdn2H/X1gV+IPEauS3JU483Ns1Zj8oVzME+7EN7QNdJ3X2YuFztKI3q+uWQS2d9Dh23OXR868VNaLBrn8XHfiDHq8+0mLwevu1Gpu0IHvpghuP3OvQPb7+zi+OEW/Hy0y2ijd4pJWmUblAcPoZuWoReg0ZpHNPJEYceUm6t+hJH8MozLUbaGSDbGSfZgs7x+xwWZmOmroebpxneJoRIRIaOnnK46x7nTjgFAFBhYgzE1QZ6TwE9k0pqBPR3Ed7YnmexJWJJODWP3pXD6C6gpR3M4T78a5Nbe0Y0LSmE1FNM5Gv9kLjWIF5c9bwrhXQ9wonZxBiwLYzuPEZPnnBq82qYAFrKRi/m0LvzSTcbzYTd/sfGwI5h7Rki/cAxUncfJJyYpfr4M4ma4x0QE+oUOzIGlILPf6nJ57+0caqO6yn+z7+/cV7tA/faVGqSM+cD/sOvr598G03Flx9v8eXHN3YZ/+N/ubNdJbR3KPMRo3sttpq+MzmdT3w6z8xEyDe+VGd+JlpfcfF/QlQXY/7bv69w9/0prG123pomGNlr8hN/qZsvfLbC899s3nK4oBPsO2LxXT+U58Pfl+vo+MBXzEyGfP136gSbaFe4r7xFUMhhDPVjTs4SziwQ3Jju2K35yrdbZDIaB49tv/PO5DQOHrf5C3+rj3/4V2eo1+TbVqLbNOHoPQ4/9he7OhbPqS7GvPWaxwtPbvy+CaFRHD5KFDSpzV0mDj0yXcN0HXk/89dewa3OEAVbp489+T8aPPLhDEO7zY7Ej46eSuE4Gpfe8nj9efdtef9SGcEjH8rwwz9ZvO10wpuhgoDW6Uvk3n8fytCx9wyT+8B9lD/3+G3v4twzlzEGe9C78gjTJPv+ewkm57ZcLLSUTeahY2ipJFsgnC3hXbi+bmKTjRat1y/gHD+IQmEd3IWzUNnWGLAP7cY5tGuZgOievnzblfP+SMI0SJ08TOahE6gwpv7EywTjM5uqS75duHPC838IEUeKC2/6xNH2b6KmwU/9f3r4Z78xyv/9Lwf50T9T5P73pRjabZDJaZjWtqHl7zh4ruLVZ1p88yt1qh3mxZ98MMVP/ZUefvwv95DO3tnJFJKd28OPpfm5fzTAY5/MbViqdCNcOevzK///0obhgSXo3QWcowfIfeABMu+9l+wHHsDeN5rosXeAG9dCnn+yxVOPNzqKnSe79TR/858PcuI+547Kay+hu1/nA5/I8vf/3RDFHr1jl/cXf7XCM19rbmo4IQTp/ABKxmR7dtG7736yPXu49vJvk+kexkoXtj3H3HTEs99ocu71znaKQsCeQxb/5z8d4PjbMF7ZgsbP/PU+PvXjRQZH7rzemnR9ao8/k+jiK4Ux2EP20Xvp+bFPJlkNm5GXhECkHewDo8kuewMCX/Ols/hXJpC1JsIyyD12P7lH702qC24AvZgjdfIQxR/6aKKIGEv8S+M0n3lj3bFxtUnj2TcT13MscQ7uJveB+8g8cvem12ofGCX/8UdI338MSDwj9a+/QLRwB0SW/ohBTzlJeqehI3SN1D2HsYYTEadtCW93EO+YAuH5iwFz8zH+O6A4uIQwVLz87Rbf9ycLbFVqJplABYa5pFOe5q6TDm5LEviKOFJIuUNPgYJYKryWIvAUvi/xPUW9GjPZzoGfn44ozUVvu8t9K8QxfPHXqgyMmNx1j7Zt+p6mC0b2mHz0+3Ps2mfyxV+tcP5Nj/A2jVjTEvQNGnzwE1k+/qkcQ7vMxADrYHW7esHn5WdanHll60Un/cAJhGnSeP51VLOFls3gHD+EcGzc185uex6lYHo85Ld/vcrJB1IUerZWJUxEdRTH73X46Z/r5ZVnWzz3jSYX37p91186o3Hve1I88IE0DzyaJpPTNq3mtvYaFK8+4/Lqsy5TN7bmSkgZIjQdK91FKt+PpiWljIVmdBxof+GJJl29Cfdju8qAQiTaIF09Bn/p7/Txld+o8tqzLpPXw9vyElh2EnL6zM90c/QeJzGa3o5JNpZE8xWqv/s0uccewBzoRss4pB+5G3PXIOHELNFiLYn1S4mWSaFlUuhdeYzuPFrKpvy530M2Wqhg7ZyggpDm86dBKfIffQhhGOQ/8T6co/sIJmYJZ0rIloewTMyhHqxdA9h7htHSSYit+dwbtF47T7RBUSOUQjY9Kl/4A7o+/TH07jzmcB/FH/owzl37CMYmiRtukt7pWFi7B0kdP4AxkJRnVq5H7WvPE04vbK9R0FbjW1LSM3q6EM5KGFDLpZPUOVTClQiihAzUqcpeO1VSs0yEbS7zNtonx+jJY/R2JdoLYZSkgN5qbfk7hLjZIpwtEc0vYvR1YR/aTe9P/2By7VKu8SqpKEJ6AXG1QXB9mtbLZxNC6Fbhog7xjhkDjaai0XxnmdVxBGOXAsYuBey/yyKT3d5XaVoC00okSm8HSiUGRBgoolARhcnPbktSmo+olmKqlZjKQszk9ZAr532mb4S4TdXRxKc5DmgaspW4aoVtJ/nHt0B4uH454OmvNdF1wfH7nW1L3jopjf5hQTqbJgoU+4/YjF8LmJ0IWZiNN99tboCuXp2RPSaj+yz2H7E4+VCKvQetjifrajnmladbvPzt1rZGlZZykK5HMDaJarlo2TTO0QNoduciKW5LcvW8z9e+VOcTn86TzWubKltCsjhn8zqHTiRy2L39Om+95nHlXMDMZEirITsmGJpWUmFvz0GbI3fb3HXSYf9dFqN7O+t/HCkq5ZivfanG5PWQYIu4vFKS2vw1ALz6PKFbRdMt8v0HCFpV4qCz3f7sVMT5NzxOv+zxwKPbe2CEEOiG4sBRm498X47BUZNLb/mMXQqYuBp0HJpaMuwHR0127bc4dq/DqUdSZHPrqziGgVouRtVJKuRWUGFSi15LO6SO7cfcPYheyCYcgr4isuGi/AClVKIIaFvo2TRaOlEyFba5saGlFOHkHK3XLqClHTIPHMPo60JzbMyhXqLFGioIExW7Yg6jK4+Wz6LiGO/0ZZovnSW4PrO5YE4c4569ivnM66TuPoQ50o851JsYF8N9KN9HySSbwegrYg4k8sXRwiLe+TGaL51Frk6BXA1dwz6wC+fwbjAMhKkjDAOh6+j5DGZf1/Kh9v4RhGUmBlEYJVK9cUzcdIlmSrinL69vH8h96IGE+LiU0tk+j7WnXQQIQAhSp45gjg60BX0SKWAVxcSLNfxL40ldhXcasSQcn8G/fCO5b2kHPTO04aFLMtyy5WEN92F05Wm+cjYxxG6TX3BHjIFUViOT03HSGjM3AqLg1sx4TYf+UYvSTHhHahYolbCaX/pWi2xeY9c+DcN8Z9wuQgh0nUTC9qYMo9XlXQNfcv1ywLPfaHLmFY/pG4kWfqO+HTEojzAMglYLYZqYvX3E9XpiHAgQloUKw0R3X0sIXCqOke569bLAVzz9tQapjKB3yGB4l7F8DZvBMATFbp2PfSrPXacczr7qcf60x9XzAZVSROCrxKCP1TL/Ykl3QjeSugeOI9hzyOLEAymOnXLYe8jquK68Uoo4SgrsvPDUFq5oIdDz2bZxLZKcXdNAZDNgJLK2agcGlJTQqEu++ptV7jrpcOCo1RFBLpVOuAa7D5qceCDFM19vcP5Nn4XZiGZdLhuNqz1QmpaMl2EJLEuQK2iM7DV54NEMD3wgTW6DhW3zficlnc+84vL07zdoNde+X8I2E5102b5ZSlGbvUQq30/oNgiDJrph07P3Pmozl7blCywh8BVXzgU8/fsN7jpptz0YW/d56d059UiavYcsxi4HvPztFq8/r1GrxMlYRQkJUknaHpHEa2UYCfk0X9Q5cMzmyEmbo6cc9h5cT0JdGuelGhaZvEZPh7UvtkI4MUfjmTeI6y0ycZwQ+NpkO6OnsBJvbOeRqzBKZIAbrSTEsMkuWLY8/Cs3kC0vWURH+hM2/0A35khfW3NBtReMkKhcIS7XqH/zJbwLY9tmH8SVOvUnX0H6IekgxOjvRs+mMbryK+5qpVBhTFxvEVcbSejhuTcJxqY2bVcYOqnj+yl8/2No1tbja+8bwd43ctOFS6JSldZrFzY1BvLf9V7Mge6Ev7DJ8yU0Qfqew2s/bD8E/rVJKi3vnTcGhMDoSbIx4mpbKTIIkUvv4hqyiUBoIjH48hn07jzWniFUHNOKJMH4xgJ5neKOGAOH70nz0MfyHHsgyz/+i9eZnfB3LF0rNEhndX76/x7h1/7RNOOX7hwj9Yu/VqG7VyeT07asUfBuwLI1Dh13OHTcodWQvPxMi29+uc63v9bYdMeop9OY3d0gNIKZacy+fvL3PYh7/Rrh3CzCtLBHRgnmZjGyWbR0GmEYxI06jTffZKObMzcd8dTjDXxP8TP/3x5MS3RUuhcS5vyufRYf/6E8caQYvxowMRZQno9p1iWBp4giheVoZHOJcE/fkMHeg1YHqn7roVSyCCzMRnz2n5cZu+RvumMUjk32sYdQCszhPoRhYO0ZIVqsJi5KXSO4sbOXSEmYuBbyX/5NmU//2SL3vieFYYiOxsqyNPYdttl32EYpqJQjZicjpm+EzE1HeC25zO63bI18l87AsMHwbpPeAZ10B96tdf1ViUfq2gWff/PzCzSb6zNsnN29ROUmUcNdFs+JQ59G6cbyMTIKmDn/1I7PPzUe8vQ3mtz3aJr3fiSDrquO73mxx+BUj8E9D6WI44QbMjMZUl6IqVckvicxDIHtCDI5ja5eg5G9Jnv2W+jmdvck0S35wmcXSWc17n0kTc9jd8ZZGk7MEk7N03jmddL3HSV9fD/GcB/6ch5/UswobrSIFyqEE3N4F64TXJnYsja98gKCa5PM/vPPkX3kJM7RfZgj/Rg9haSQkRcQlauEU/N4l8ZpPn96R/LF0fwi1a9+m+bzp0nfczjZSQ/1LsevlRsQzZVxz16l9dp5wqmFzlInRXIvVJjk0u9Um0UptVwNcsPmdS0R6OlUKvhmaNqG8XmBQrQNNiAx1DrZp6q2Udb2bqhYstEXtUyK4g9/hNSx/ejdeaQX4L11hWiujPQCVLQyTsLQ0dJ2Il08OoA51IdwLHKPPQBSEdyYuS3Wu1CdMKHYepcoBOw7luJ7f7KX3/pXc7dsDGRyOn/ln+6+48YAQK6o8dAH0/zIT3Vx9J6tGATvHpRKdtFJ6VyXf/G355mfiTZc6Jz9B9Ech9bZMwhNI3v/g3jXx5Kf776HcGEekUot7/JQCiUl9Vdf3kaMReORD2X4uX80gLHtZHpz/2FZ2WyVUbvWtl36z4qhsVNjIPAlc1MRf+tnppkY29p1LCyT1InDa0+4pFLX7kw4PUc4NbejPiw1d/SUzYe/N8cP/UQX2g7X6Y3G6+aXcWm8bnWsIFEZfOKrdT7/HypMjm8QHxWC4gdP4N2YJ665SD9E+gHKj7BHexKhmoZHXE8WFXtXb7I7bHrEDQ+9kEZPWahYIltBEl++CZoG2ZzGP/71EfYetHDSO2PjLk9TS2OkNniu2j+IVb9sNVxxrPiNf7vIb//nCiN7LD70ySyf+vHilv2Ymwr55lcb/Nt/uIMdpBCrbuRNf1t9MTudyDdr93baXNf+Jn3eabqVEOi6Rd7oYXf6BBcbL+LGW5frXQelNucObGEodNY2iZV/U/On7jMxDI2XX2m/N3IH161pq4Qx1/ddGDr9f+l/wTm2HwwN/8J1yv/ldwkm5zc/R/t+68UcfT/zqeS7mkbz+dNUfvtJwsmN01g7WebviBmsFDfJhCYYPWDznu8qkMrq6Ca8/M06Y+dcZKz4nh/rJZXVMUwYO+/x5rMN/FUlVPfe5XDgRBoB/MF/L992mlGzJnnpWy0WZmJ+/Ge7uefh1Lax8Xcaom09Z7Iadz+Y4q//4gC/+s/KXDrr01wVNtDzBczeXvR0mnCuj3BhHj2VwsgXkJ5HuLgIpkkwO4uRz6NZVscPcKMmee6bTf71z8/zI3+6yOCI2XFoRaxa6e+EkMtGqC7GnH3V5QufTRa27WLIKoxwz13Z9phbgVJw5VyA79ZYXIj5sZ/txnG2rtS3Gm/3eCmlmJuK+MJnK7z87Raz01sQpQSYxSz2UKJ7XnvlCrkHDiIMHbM3h3QDai9dIn14BGHqmL154ppL480xiu8/jj9dwhooEpUb1F+5jPTXjulSeOVf/b15fuqv9HDkbptsvnPraXlMt9cQ6wj1aswLT7b40n+uUinFZLLxhuJLW3dqaQFaZaFsBLXN328Vb1e7a9q/Q20rlYShhERE7R38nRLf0MS2BLp0RnD8uMHLL4UcP5EU0FIS+vp1Ll2MMC0YHrZwbMH0TCKqtmuXzuCgzuycQgg9WVCFQreTkJOMQjTDJA489FQmkXoOfDTbIW41trw+YSZlqZ279iJsE//SDVqvXiCcLm09Lu37LV0P9/Rl7CN70fSk8qTeld3UGOgEbxuBsHvAYHB3sgM4/2qTXJfOroM2+aLO/FRItqhz4dUWdkbDSWnc874sL36jhpSw+4iDEOBkNC6/2bqtR10IGNlrsnu/xeCoychek74h453M2NgxNF2QzmocPuHwXT+cx7TrvPGiu0z4koGPPzmBZprLHAB37Bpxs5FUEQt8hGkS1+vE9VrCG1jCNi+glFCvSl58qoWdEjz0gcyOJ+63CxNjAa8+k5AFz7/pE3bCTVEK5b59Iii+p5gcD3n2m00yeY2Pfn+Orl4d03p381CjUHHjWsDjv1XjxaeazExERNuQplP7B4jrLt6NZNfr7O4jdn00x0KFMZpl4uzpI276aI6JbPloloFRTNM4M4aRT4OuoWUcpN9Y176UcOWcz+99oUa9muHeR9IUtqj18HZhbirk7Osej3+hRmku0RSpVWJKs50ZA5qmYVgZsj17aJTHMcwUSsWEXh1Nt4gjH7EUv1cxuukQhx5Cay8oKDTdRGgGmm4QBy5xlJC/NHSK5iA6BpEKaMkalrDxZItIBRjCIqt3IZFYwiYmSoiHQhCreOUzVEICjRboMgcRaATKxYsbxCqiYPajoROpAF+2cOWK9ktW7yKl5xBCJ6VlacYVbC1FrGKq0TxKSYpmP5VwFk820THot/dRCWfwZQtNaDh6jqI5AEoh0KhHJRpxknaoC5M+ew+xChEIQhUw61/dYsBFMp5AUmnRSAz4dmaC0VsgmFxIPJ6beA4sM1ncX34pZHBQJwwVngcDAxqXLsKxYyaZjEBKSKUTzsrYtZh8XmA4Key+LNL3EYaZlNCOIlQUkhraRbC4gOakEEJDoTDSWWoX3kRtkWIlTAO9t4hIOQhNEC3WCOdKnRehkirxwK021m5zR/G2GQO9QxbFPpPF+YgXv1Gj0GvwfT/Ry+gBh4uvt6gvxrz8RA07pfHAh/KceCQxBkxbcPzBDAvTAVNjARdeu7W62KYlyBc1+ocM7n4oxfF7U+w9bDG0y8S6TcbwOwEhBOmM4L0fy1CvxpTmIq62pZmV5xFMTqw53h+/vvxzXKslD4ZSxLWdizVBEuf9xpfqtOqSwJccPOrQO6iznev1TkNJRRjBzI2Ql77V5KnHG5x7o0ND4B2C11JcvxTw5f9aJV/QOXLSZmDEJFfoPO//TiGOFM26ZGYi5IWnmjz+WzWaddnRJkxzTKK6iwwjNF1H6CJx/y8Vt1EqKYGbbocEvHDZ/ajCKPEGSLkycW8At6V45hsNolCh64Jj9zl09+q3FP7YCZZ4JnMzEadfcnn2D5q88vTK3NKqSyqlhJxomNuFRXUMO022e5RWdQo7XUDKmDj0sTNdxFGQLAwyQsYhmp4oQlqpAkpJZByA0NANG003CZRaNgZ0YdBtJEzylqwSKJec0YOMJFLG2FqKHnOYWMUIAVLJhHApDGK5+jMNhSRQHkWjHxD4qoUhLBpRmW5zmFD6eLJBTMTqGmcZvUjRHEChyJu9WGEKW0sTqxBJTCBd+u09uHEjMQaEwYC9F182CJWPo2XoMUfIGF1IFSHQCZRHI64AYGo2ab2AQmIIE12YLAQ3iNXG1qqeTbeFk0jGLZtKiJYCtGwaoyefSCpX2nLQG3gzNE2QyWj09Wn09GhUqxLfV+g6OCkYHNSIImg2Fek05PMar74a0mppaBkbu7sfGYWoOEYzzIQPEEekhvcgdCMxEpYyJOwUQtNQ7Tl444vS0HMr2TXKD3aWDaAJtLSzbACoKL5tkaK3zRjIFnQsW6M0m9zgainCtDW6+kxyXQYL04lEa6MaE8eKXFfSlf4Ri55Bk2d/t8LVM7dgCAgwdBgcNXjksQw/+ONFBkeNjtXGVsdWbjfctvLzrU90haLOIx/KICX8+19Y6Nyzdgfce6W5mN/9fI1Xn3P5xKfzfOpPFbHsJOtjKaTxdmEpNTPwFAtzEb/6z0s890QTt/mHxwhYDSlhZiLiX/ydOT74PTne/91ZHvpAGssSbS7B2zdeS89sHCU73NMvuzz+hRrPf7Pz90fFisrT57BHukntHyRaqNE4M07mrlGEJpBhRFRt0jhzncxduxAkRW5kw0UBufsOYHRl8ccXCBfXewVWo9VQfOv3G1x6y+NH/kwX3/3DeUyrHWJ9GwZJKYWMkzDF//jNGs9+o8GVm6qfBoGiXouplCN6B7aZFoVACA2vUSLyW8h03P5YkMr1Yzp5ZByiZIyMA9zaDLphUxw+hgC8Zhm3NotuOgiStpagCxNN06mGcyyGM0QqpM/cjY6JQKAJA0fPIVXElH8JR8uS0rPowsTSU0z6F0lreRw9iykc+qzdBNLHVy3Sep5ec5RmXMXRskx45/HkxvcqUgGNeBFftjCESSlINh95o5eF4MaG3wEQCApmP/32Xt6s/gG+WnkGdQwEYAqHSe889ahMzujiSPa9pPQczaiyYeVVe98w5khvos2QdjC688QNN5mDHBN/bIbU0b203rxMvAmZMY4VtZrkve+zKBQEjXpiONeqisOHDWZmYhxH4PtwYzzmwEG4/36Tri6NhWZyz52eAepXz+P0DaLnCsg4Jg48lJQIKZG+jwx9/GAaGQY7moOFcVMp6S0PFmi2hXNkb2J0yCRsEG+kIbEDvG3GwOJ8SLHXYHhvEl/pHTLxXcnsDZ/STMC+oyk0HXJFA8MQLM4lRsP0dZ+vfHaB0f0WP/IXBvhPPz+J1+p8UHsHDL7vM3ke/XiWfUesHU/AcZxUXfNcidtSHakXLkOspIOZlsBJa6TTtx/k3LXf5KEozYtPpXj12c6ZwXcCcQyTYyH/6RdLfO2LdX7wTxW4/9E0o3t3PrY7QbMueeNFl2e/kXgD3k1hpp0gDOAbv1Nfrgj5Qz9R5P3flcXsXMpgx1AKGtUk3fHJxxuMXw7wtlBi3KiB6rfOAOBdW4k5huUG7oXJlbC0UrTOTuCeX/lsqcLd4tffIPZ8Oq0mFkcwMRbxS397nme+1uRH/kyRwydsunru/JTkNhWvPNPk3/2jEnPT0aY6GF5LMXYp2NYYUDImClpEfhM73UUq149SMTIKcHJ9IGM0TSOOfGSssNJdWKkimtCJQ4/Ib9FcnMBKd5Hr3o1urhCaPdngeus0vdYoo85d1KJ5FApNCATa8j8xEqkkMvkJgUCqGKkkMTEKiYZIwg34SBVRaRsXAL5sbVnyPPEA+JiaQ6wiIhUkhsqmwu7JZGBqNprQCZW/xhBYHjugHpWJZFJGWSpJKD0MkRg7G90ZYRkI20S6PlGpRjAxn5RNVolHSktZSfigXeJ3I/5Atar4nS95aNoKFSJJdV7hHa2q8swbb4Srjm0BM8veVm92YuViUOSPnkJhEtbKuDM3kL63rSGg/IBgfKnwl8A+vIe40cI9P5aU690MQmDuGiD7npOkHzgKQDhbIrgxR7RQ2fKc2+G237xMXuPEw1lOPZpj//Eko+D8q02unHGZvOqz/1iKP/M3h7HTGqefb3DtrIvXkhx7MMOP/9wQpq0xdc3n659vF+BRUFkIKc0EtBqSH/urQ3z2H0xtq6UjtKRq2//+f/Vx4KhNvmtrMRgA35dMXQ+58KbHxbd8xq+ElOaSyULJFXb/TrGkBLckYpLNJyliPX06o/ssjp5KUsuy+c7cyEIIevoMPvHpAq899/Zotm8HKWHyesCv/VKZ3/18jT0HLe55OMXDj6Xp6e/c87J5+wq3Kbl8LuCFJ5q8+ZLL/ExEoyppNd8dQ0DoOnoqQ9TYucXdrEsuveXzr39+nq/8ZpV7Hkxx7F6HQ8dtuu9ALnscq0Ss6pzPW694vPJsi/J8TKshNy48dItQG8RgV3+mopjm6etttbSdty8lvPmyy/XLPnsP2xy71+GBR9McPeWgabfmTVFKEfhw4bTH6ZdcXn/B5ep5n2o53pJw6nuSG1dDHnh0u/ZjQr9Jbf4qSsUEXhWUQsoYv7VIcfAIjcVJQq+esNTaKQ5CaCglUe1Uq9Ctsjh7fo3ORVor0G0OkdLzSOL24lli0DpApIKkmnObJ7AdIhUyH4wzah/B11w82aAV1wlVB+7k9kK3xEe4uV1D2Ghtj4YmDDJGERCEMkApiSlsTOEQqo34Ojf3fetraZ2+gjg31uZgrM4EYs3PKgi3JRKu9qwqlYgbLi3yN3td13lhlzNa1va3ceUctHVcVLx1Aa/lpoKIcLaMd+YK9pE9GN15Mg/djTnQS+uVs8uphSgFhoGeTWEM9mDvHcYc7sPoLSYNhRGNp17Fff3CtufcDrc9KwW+YuKKj+8pzr/apFqOKc+EVBZCGtWI536/imVraDpMXPVZnEukRZ99vIplCzRdUJmPmB73iQLF4/+1xMJUSBwpoqBJeS7a1jVumtAzYPAn/7du7jrpkM1vLsay5H5+9ZkWb73mceNqyNxUSGkuproY47bkjtMit4IQiRyqnRKk0hrn3/R54wWX0X0mR085nHwwRU//9rchndW466RNrqC9rUVvtkIUQqWULDjlhSQ3/vRLLoOjJn2DBl19OsUenUJRJ1fUsKxE5Elv64AsicUEvsRtSeoVSaUcszCTyDKX5mLmpiMmx0LmppMyxG+n4WMWe9CdNCqOkIGHkgqz2E3cbCCjEN1JkRreQ+PyW2hOiqhZR8UxZqELFUXoqTTS90FJNNtB6AZRs07UqKHCgMBXlOdjPNejUoo5+7pH36DBwIjJyB6T7j6dXCER7MrmNCxHrKRzqqTiZhgkRlKzIWnUkzGbmQiZmUie2YXZRNZ6ZjJ8x0olr4aKJd7YXDuP+tbgu4o5Nxmn+emIS2/57NpnLWsrFHt0cgWdfJeObQt0Uyzv2sJA4XuKRi2mUU2ep5mJkPGrIbMTITOTIXNTEfXq9v2rlmO++ZU641c2XyzdpmTyeghLsX8gXjVhREFEvTRO6NWQ8daMTaUkKlwbJw6URy1aoBlXkcT4soVUMSIUSCWTHX+koZTCkw1C5eHGdXRhJiEI2SBSPm5cQwiNVlxjNhhDEhOqgEj5RDJgLhhr7853BoXCj5tEKqDX2k3O6EWgEUq//XdJI6pQCWfZlTpGpAIEglq0QCMqL7eyo3N6wfbfEBqaboCReG5U2whL+BoJmVLTzSTuryS6YaKZCWfDcLL49QVkFCbfaWcICF1PDDUh0A2LKHDZaOKVwa0p/6kgoPYHL1CwTMxdA+j5NPbBXQkBt+ku600ITUNYRiJdXcwlSou6TrxYo/nsm7inL9+2VwDukM7Au43eAZ2HPpjhZ/92X5LatYlHQEqF7younvH4vS/UePVZl4W56F2ZRAtdOicecHjsEzke/XgGJ7V1SppSyc7lZ390grFLwS0T6AzNbrsTY+5ISpIm6B0wGRg26B826Bs2KRYF3QMJUdNs/yvjmNCHMEjKVbfqksVSwuCevhEyOxWxOB/vrET1pl0yVl2fwDJS6JqNH9aRauVmp/ceRjNNYs9F+h5mroCRKy4v6JppkD10gsWXn8buH8JfmEUGPundB5C+i9BNomYdoWlY3X2JB0EIvJkJ4ubGZZF1Hbp7DfYesegfNih2J9LXhS4NO6VhWmsXusBPCIH1akxtMRmz8SsBk2MhntsZMfA7Eemsxp4DFoO7DHoHDIo9Ot29Ok5Kw3IMDNNC123qlUVaLUmtHFMpx5TmY25cCbh2MViWGf5Og2hvddWq/ybb39vKq+r4+0VjAFtP04wq2HoWAYTSQxMGKS3HlH+R3akT2FpCgItVhEAwG1zDjWrowiBrdNNn70nIjAgq4TSL4QyWlmbA3suEd55AujhahiHnEHP+GK24tmXoYivY+T4MO42SMXHgJsRN08G008g4WiOfLeMQ3bASY8CwcQr9uItTBK0qSImZKaCiMJF7b5NBddOhWZ7cULDtdiAsg9xHHsY5sgdzsAetTZZcVlJcIiHGEhmEKD9Eej6y3iQYn6H2jReI5hYTr8gWeMd0Bt5NWLZg32GbP/HTxS0XVKXaXoyxkH/5dxe4esF/VyfS6mLM899sMnEtZN8Rk/1H7C1doktyq4fvtpkaD2/JGBAIMqk+/LBOGLnEMmDjSWK1Dw4EGrpmEssAddOxum3QiCxqV2IuXQnQLEnUCDALNlErBAW6peMtvHOlTR0rjx82iGWAJnQK6VFyqQGmF8/gBovLxxnpDP7CLFGtkhR+OXoPQWkOI5snalQJK2XCapmwViG9e/9yfrGRzSN1HXd6gqhewcgV0O1dVN98keK9jxCU54g3udw4hvnZiPkO09j+qKLVkJx7w+PcG0ufJOllSikMwyaV6SNbGGD6+rnkb22XtSIRjkl+VytaDqI9Ib6dLjUBpqOjJMRhfMunuvkdW/r09tD59yvRLLQfz6V0wCUskqh1jrtnNv1+rCLK4RTlcL1EcRj7XG2ttOnJJtdar3fctyWYKR2EIA5iZKQo7jqGlSkS+S38egkZhTiFfsxUljhw8WolvOosTqEfGUfIKEDJGCE0DDtFunskMSRCD6cwgJXOE/suUsbJ5lIImqXNiZO3ChVE1B5/BveNi9j7R3Du2os1OohWzCbSzUKgIonyfMJShWhukXByDv/KBP7lO9uf73hj4OSDDh/+viy7D1jb7qxfe97l13+pxOVzt1817k4gjpOc51/9Z2X+7381hLUNyUzT4PBxh2e/3mSnS6sQOvn0IPn0MLXWFLFM3GUZp48gqhNELTShY+gOumYAAi+oIoRG2u5hsPsEc5Xz1FszK7trTZAeyTPykcOEDZ/WdA1vvknPd42QGsgy/9I4QtPI7evh4q+8cCtDtNOrxDLSjHSfYnrxLRre9gIcKgpRcZKX7S/Molk2wcIsUa2KMAyMTA6ruxcZhVi9Q2imhW7ZSLeJCpMJRUmJjN7Z2uN/lKDpJk6qi0LvQRrVSdzGPFG4Qk5L5wbI5AYAgeeWiUKXfPc+fDdZdEwri6abuI05mrXp5TS+O41U0eZjf/NemiWP137jCuWxjb1Df4zbg5nS+d5/+DBO3uKVz13i4jcmCVo1/MYiceihZEyqOEjo1vBq8wghkJFPqzSBjEKy/fsIQp/Yb5LqGSVsVQm9JKvCShdxct349RKh10g8CIbd3qG/fdcUTs0n8tVPv/72nWQbfEcbA7oBJx5I8eD7M9uGMc697vLSU00ubVM+VugamaEsqd4Mzek6rdmt06RuF56reOMFF7cZY+g62pYlcWFgxEDf4V1LWUXymRGkDMmm+mh5C2TsHmwrTxz79BYOMVc5i21myaUGaAUVurJ7mJh/mTD20DUDQ7eRKlqzaxG6oHh0AIDYj/AXXepXS2R3FWlNVTEyNlbeQYabu9buGv0ers89TyE9TE/uABOlV7DNPFJGOFaelFVE1y00oXNj4RVafomUVWR338M0vDlsI4cXVnH9RYKoyWjPfeRSg9hmnro7S9ObByCXGsSxCm3GsmSy9Ab1S28h43aZ0FBQv3gahA5xlJC8hKD0wpPIwCesLi677YQQiREQxaAkYbVM9cyrAFTPvJqQiL4D8PE/0cV7Pl7gK7+2wGvfbmzrUtd0OHh3ih/66T5+/zfLvPbtW383zEI3eiaLNzUOgDOym6hRJ6ourj/WypDv2cfcjVeSZ1BKTGslR7ur7zAyDpAyJp0boFmdJgqaOKludMMijgLmJ1+nb/QUbrP0thkDgvaaEcmO3LJ/jDuH6viZlbx+pWgtTLDkYxGwXAvBTGWXQwKR18CrlxJhMrVUIUzQKk2gVIxSikzvbuxcCgHolkMcJNlcuqlhpQ1SXTaL4/V3hcN1p/G2GgNLIY9bdcebFgwM6pRLEtdV6wZ894GkfGsnKmZnXvE4+7q3rQobSiE0DTNn0VXopTXXeFstQqWg1ZTMTsbYjoaT2sIYgG1L5m4EXbexzRxzi2fJpvqIVYSh29hGhtnGOLaVx9AdLCMDaDRas2SdPjRhoJQkjH2iOMDzq2sKjKhYUX5jiur5eWI/Iqx7RG5I6fVJVCwRpo5m6Ygtxs/UHXQtWex1zSTj9GEbWdyggiYMwthjoXYJy8jQk9vXnnB10nY3s5W3qMkpotgnin2kiqm0JtF1i3L9KnV3llgGZJw+FIrFxnWi2MM28/TmD1CbmWKFAq/adc1XPSBKIb3k5VerHuJ1lyMlqk3G2kp17A8bUhmdrj4DO9W5WqJpCfLdBpZ9exwiPZvD6u5bNgbMYg9IRVStcPMIKyWJo4B89168VgkZh1ipAnaqiOUUiSIXITSkjIgjj1S2D1AILRHJUkoSxz5C6Mvx+LcDfjPk1d+4TNiKaS68faqXtw6xLEb0HUmm2AI3kzWVXDHIV1+pV51DxvFymEBtwAGIV33Xq84StqqAaBMME6S7bQaPd5MfSlOdbBL/T0DeuWPGgK4nMo5SJipjmgb5gkb/gM7MdEStmtwSyxI4KYHnSXwvqTuu66ItOMJyaVXbhmK3zp79Br4f4vtqXdbInoMWPf36tjXIfU8ydikhXa2GVXQwUyaapaNbOs3pOpEbIsMYFSsyA1kAhKHhdKWwCjZCEzQnk+PsrhR2IXEh+RWPoOZhd6WwcjZKKty5JmEr2NaYkBIWFyKGdxvryh2vgQDbEYidKt0qiVIxhu6wxAdQSiJVjKEvlXZNrGipYtygShi13bBKtUMKyfej2F/xDkhFc7yy7nStqVrHfKU4DrDNTOKmj+qkrCKmniKMXZSK8cImi81xTD3NkcJhmt4CYeyiCZ26O0cQNZdPJIRG018g6/RRd2epe7NJ2pPTRxi71NwZ/KBONtVHd27f2+76+45Bh2OgFNTKMaefb3Qs3bsZNNPCyOYwCl1Jhka+i7jZ2PC5iaMAr7mAaeeSHGIhkFGA1yojhKBVn0XXLeI4JApdUunu5OcgMRLiOPEEeM0SUm63G7h1xIHkxsvvcAncDiE0HcNMY5opaI/Zdyxu450N3Z2FbiKvSeStD8qmu22G7u7GzhqbEta/03BHjAFdh1xeY3hUx3UV42MRjiPYd9DgfY85PPk1l7NnQgRQ6NIYGtGp1yRjVyKKXRrpjLZch/ziuRDDgJ4+nd37DPYfMrlxPUZblNxsw+05sL1XQCnFYilJX7s5vaiwv5vsSB4rZ2HlbKaeHad6pUzkRYSNlR2e4RgUD/fQc7wfI21y/fcv07hRpftoH11HelGRonRujtrYIn2nhsjv7UJGktmXJiifne8o7arV6Cyl0TB2vrcJIpeWv0hv4RCOVUATGn7UACHoyR3ANNOU62NowkDXEuJCLCMUsr3wK6SKKGZ3MVdpEcsO3KwdvrBeWCPj9BHFHg13jkJmF7owMI0UYdQijJKdeRi76JqNphkQJ/0Jopvc1GoVa/amQfKDOlJGKGKkjNDEd3SE7M6hTUzNdent0rKKOFR4Lbms7SEEGJYgndGoV2J+97+UaTU2flgNUyynSGqrKt5JqQj9JE0SSLwwUpHetR/pueipdCJjvMGONY486pUbrLYUfLfS/ow2N2CF9Oo2Nq5AuTi/dS620AV2xiAOkz5qpoauJ5XnZKwIWxFxtL6ynZUxMOwVzZAokIStCBlv/RKYaQPd1JLQYPvSZKwImiHyJrEzoQsMW185HohDSejFyHD7+UXXHdLZPjL5QTTdxHMX2ztdhaYLrIxJ3A7n6aaGZmjL5whaEWqDa7GzJgiIvIRkZ6b05YVRRhK/Ea3VqRCgGxpm2li+BiXV8jk2mjN0U8OwdTRTS7zMsUra3GAStNIGwhCoWBE01xqrmiFwChZBIyQK1t9DM5WMrdA12tzT5J57EfGq462sgW5oFEez9B8p0ip5ZHpsIj8ZOxkr3MrOvYNCF2jtc0fenc1Y6BR3ZEbcvddg3yGT/gGNKxcjJsejRPPZEWSyAt9PhCIyeY1sTlAsavzoj2X4+b9R4b0fcJZz5++53+Jf/kKNex+0cFIapgWH7jL51h9s7HLbe8ii2EGI4Nr5gEZ9/QtjFxziIGbulSlqY4uc+tn3cOE33yRsrt09hI2AhTdmaE7X6Trcy+BDo9T6s2iGYP6NGeZenkz688kjGCmDxkSVsBFw6EdP8PIvfIuoA9GcqDOtCoJbSJcKogal2mVKtSusfgsa7hwlri5/5vplFhtJjYPp8pvLx8VBwNXpbyHQbjn1ZzM0vHn6C0eoudNUm5Pk0yPEKsTzqxi6TcoqAOCYecKoRRy3PS2bjYGS6JqJ4Gb3yR+7ADaCaQmG91l86mf2MrLPxm1Izr/a4gv/do6rZ5P3LpPXOPVojj/7t4bIFnSiQPFv/q9JvvWVtXUvDAPuujfFxz7dzd2PZMnkNSxHQ8ZQWYh47mtVPvsPp4kjcCfH8BdmSe/ah7BsFl96mqi13a5tq3t4+/e3Z3+OD//cKcZfTErIjtzby8DRIoZjUL5W47l/f47pM2Va5bXG8Af+0gmOfHSUVHfiZbv0zUme/bfnmL+0SV0QAVZK56E/fRf73jtAfjiN6RhEbkTpWp0n/umblK7WCFsrC1rX7izHP7mbve8bpHtvDhlJrj8/x+ufv8r0mTKhu7WnZskjoukWQmj0DBxjce4CUejScyDPR/76Ka49PYNmaoze10v/kSK6pTHxygLf+qXTVCaa6xapj/yNU1gpg9e/cJVcX4oH/tQhcoNpZCSZu1jl9/7Oy9SmWysLadpg5J5e3vMzR+nel0No0Jj3uP7cLE//8lvJAn7Tbdz9UB/Hv28Po/f24uQsSmN1nvm3ZxND5Ca8588dpe9ggfJYnW/+k+U0FHRLY+jubv7Ev/sAv/d3X+HKU1N4tZU53rA1Tn36AHseHqD3YJ5U0SJyY8rX67z6G1e49twMXnuB//DP3cPIPb1k+1NJRgNw7Ht3L7dVHqvz2R/+2o45BF17C3QfLOJ0O5z5jfM7+/Idwh0xBiZvRCBgaMTh6N0mZ08H1GuKhXlJeUFy5WJEHMPwiM6uvQaDw4nIypJ87/RExJuvBqTSgkxWY2jE4Pq1kEvnI9JpbdPFL1dMhFq2Q70ab5qKp2JJHMTEYYwwtLVFBdrof2CE9EAWTRf4ix5C1zBSBpEbErVWeRBSBkoqIi8iaPhc+q0zxB1aefmiti0xUKlEWnWzkt7b4/ZSlu60IQBQd2cZ6TmFJnS8sE4YtxAIFpvj5NODdOf2c2zX96ILg8nyG9TdWTJ296b9a3rzmHqKPX2P0PDmqbWmt+2D3TVAZmAPTtcgVqEHK1ukdv0sbnmG7qMPYWWKTD33FVpzN4i9Jppp03fPBynsOYbupIkDH3f+BrOvfJ2gUQGlKOw7Qd89jzH3+hN03/UgTrEfhIZfK1F661lq188uW3+6k6Hr0L10H3kQ3UoReQ1aczconX0OrzxDqneEnhPvQzNMJp76rUT3vI1U7wg9xx4BYOq5ryLDnZHjckWDx36gyK/8g2kW5yMOn0pz8j0Z/vzfG+ZvfOYaUaRo1iQvfL3GWy82OfmeDD/2fwxs2Nap9+c49WiWTF7nr3/6CnGs+OE/10f/iMWNSx6f/+X5NZoe0ndpXrsACJzBUYSmEVbLG7b9TkEzBPf8yD4asy7jL81x5itjCCG47zMHeOyvneTZXz7HxT+YJA5W3utv/dIZnv9P5ykMZ/jo37h3Q9XG1SiOZvnwX72HgWNFzn9tgoX/VsWtBVhpg/4jReozrZXFXUB+MM0n/v4DyFAx9XqJF/7TeQxb577PHOSRn76Ly09O8ernLm99XZqGYaWxnDwyDijNvLUmzq4ZGvf+iQPMnqtw/flZ3vziNVIFiwd//DAf/rlTvPAr57n+wnqPS9eeLHf/wF4MR+fFX72A34xIFS1Mx6Ax5y5PL7mBFLsf6uex/+MkV789zRv//SpxKOnaneXIx0f55GiGb/yD16jPJp5AoUH33hwf+mv34FZ8zv/+DSZfL5PutnnPn72L7r25zY2tHcCwk8yE/iMFpk+XefGz56lOtbDSBr2HClQmGvirDIcXfuUChn2ZPQ/3c/JT+yhdq/Hyf75E5CdzYxzcWkrp4liV6o3ahuvPO4U7Ygxkchr5QuLGGRjUV2k6Kyxb8OB7bV5+3qfQpVHs1jDbKmtLlx3HJJyAKMkLnpqIKBQ13v8hmz37DZ779sbnTaWTtraD56pNXXbp/izpvgy9JwdZvLiAbuqkD3TTfayf7HCOrsO9mGkTq2Bj5Wx0xwClqI0tUtjXTf7BLjKDeRrTNRYvlug63Et+bxfegkX9RrWjugRLWQLbkrIULJa2dz9+J8EP61ydfbodEmgxvXgaEARRk0pzEred3igQtPwyUezT9Etcnnlyw/ZiFTE2/xwCQSSD5TBD0y8RRA2kjGgFi1ybfXqZDCk0DSOVIbfrMPNvPEVu9xHSA3uwCr3Uxt4iO3KIzNB+Iq9JqOvkdx8lO3yAypU3iPwWup3CKQ4w+OB3M/nMl4h9F6EbWLku+k6+n/qNi9Sun8WwM9hdA/SfeozWzBiR38LKdpEZ3Etu912Uz79IHPiYmTxWvpuBBz7OxJOfJ6gvIgMXK1MgPbCHxsSl5GKFwMjkSQ/sYeH00xuSobaD15Kce7XFpdOJTHirEaOk4n/9KwPsuctm/GJSITIMFLXFiGY93tQ47x8xSWd1pq4FlGZDlITrF3yclEYmr+M1JXoqg7BsdMdBd9JoTkKSSQ3vpjV26V03BgTg5E2e/w/nmTpdolnyEJpANzU+8JfvpjCSJtPrUJtaiSMHzYigFSE0QehtPj4A2T6HoRNdDN7dxRtfuMqVb03TmHeJA4lmaMyer+BVV3hGuqlx5GOjOHmLt746zpWnpmjMecthg+Pft4eh4110781tmcooZYzbXEBoOraTFNm5Gbqtc/bxcebOV/AbIYajY+dM7vmR/fQdKlC6WqMxv9ZLa2VMnLzJc//hPJUbDeJIops6uiHWhDr6DhXY994BGvMur/23K9TnXJRULFyu4lYD3vfnjjF8soeJVxdoljw0Q+Pgh0bQTY2rT89w+clpWmUfw9ZwChbHc+ZtC+FZaYOBo0VGTvVw9nfHuf78LAtXEo+MZghmz1doLnhrjLvaTCuRh9+XIwpivFpI6Vp9OUyw0U7Nzlvsef8oTpeNU7AxUgZ+zef6UxMsXFhk8N5+eo90YWVMKuN1rn498c527S+QG86gGTpjTyYhsaF7+3GKNq2Sx+yb8ww/MEDP4S7snI2MJNXxGlf/YHxbg3Qj3BFjYKki2NiViFpFLjP2a1XFhbMrMqnlkkQTEaYlcFuKKFZcH4toNRRhCGNXIpoNybUrEb39Gr19OlcuhjTqG8fTDVN0xKxvk4o36btEBpLYj6ldKxM2A3THwJ1rJGTCSNKaayJjiZW1lwmBzek6RsoETSCjGBVJGjMNdFvH6U4T1n38qr+t71/ToasnkVzdTEJ5CUpB+V1STHy7IFVEtblSjrnprRCwgqixnhdAwh+oNMc3bbPWWi92srqdKPaottaWgEZoaJZNY+oKupMhv/sIhpOhMXEZw05jdw1iOFlQivzeY4TNKrXx80StGrqTQQ779N/3EexCH97iTNKkEOhm0mZQK6HbKTKBS/HgKYxMPil5W+glM7iX2HOpXT9H7Lcws13kdh2i79RjWPkevPIsfrWEmSmSHdq/bAzodhoznUczLZoz19dkPHSKwJeMX/SoV5IdzfxkyOTVxLswuMti8mrnapdL9TiWM7xY+Xm5awKEJjAybfJgW8pVc1IIc73rd7ltXWP4vaOYWfuOEbaUUtTHq1Qul4m95KVSgFcLmXxjgfJYHdneoEy+nqSgpQoWTt6kdvMjpkBGattJONPj0Hsgj5U2uPzEFKVrteVdJbAuC0HTBbvu78WrhsxfrDB/qbq88xx/aY4jHxsl25+iuCuzja6BWM6mCP3meqKmArfqM3tukcpEMxFu0gRXn5nlvj+ZuP8zvc46Y0BGErcSMPnawuZTnYD8cJqe/XnmL1aZPVdZHie/FhIHkg/+pRP0Hkpc/M2Sh6YLhk500Sr7LFyqUbqyUh9k4tV59r9vcMtx7gRm2qDvcBErazD+4hxTp8uJIdZGs7Teyxa371UUJJLwMpJEXrxiDGwAK2ex6z3DzLw5n3hDDmTJDWW49s1kgZeRxEgZZAcza55tpRSp7hS9R7oTY0BA79FurKxFqzSNmTYZun8AGUoiL0IzNIbvH2Dq1Tm8irchz2Mr3JYxYPbmiZse1XpErRYxPhaxOrNqdjrm6//DxTCSmPjFsyEX26GBpcX9tRdXvvDy88nPi2XJ+LXt0xKXJpztkM1v7EFQsaQ5VadyqZSkELYf0NZsg/LZm1xi7fKqqwd4/rVp5l+fRmgrn8+9NLk84XVyMyxbcPiEQzqjb1vsRymYvhERbVFJURgmeipN7LXaqXJ/jI4gY6JWnaBRwa/MEXQPIDQdv7qAXy2R7t+NbtoITSM7uJ/r3/iv+NV5VBwReU3iwKX//o+SGdhD1GYsyziiPnkZd2EyOc5tLOsUmJkiQb2CXewj1beLxYuvAKDbKWToEdQW0TSddN8ofq2EV57ByhbJDO1PfKhKYueTkEbUrOFXbo0dHkeKanklVhvHSa2AVl2S7zbRt6fkLKM0G7LroM3QHovBUYsoUgzuNpPFtG1gxJ6HCkNktgAyxp1MjDo9nSFubS6lpds6J/7s/RT2daGZO+jUFlBScfm3z3H211/HXTIGJFQmmvj1FRKfUuBWfJRUGI6B6dz6tJnqsskPpQmaEfOXq+uIgjdDaIKeA3lKV+vopk5ucEVfQbSzYay0SX7V5xtB0wxSmR4yhWHq5THWp28qalOtxDBp/0lJRflaDRlKUkWLdJe9rl2vGlC6Wt9yz6PpGqmiTapgMX+xuubccSipz7q0KgHFkQxOwVq+7uJwhoWrdbzaWkJe5UaToBVhpW9vL2s4Ot37coStiNK12hpD4I5BS8LH+d05nvvnr6DbOnEQk+lPU75cAWDu9EJCejzWTaprpYJl7UaD3HCW4r48RspACMgNZwmbIeXLi2T6Mwye7OON/3KW0sVFMr1pHv7Ze8mPZonccA3npBPc8mgK2+DIL/1pZj73NLVXrhBML7JZivWaSmGqM3nnpTKTW8FzFVGosLZJLewfMrA3yN/3ym6SOudHG7p31kBuIhOqWL/ob/TZJsjldb73M4UO+AIKKRUXzviblmAFyOw7zMDHfpC5P/gKjctn76wAztI2704d94cISqnleLuSMTIMEnZ7+3eh6RipNDKOEZqGX5lbqz0QRwTVBcxcF5ppL3/Pq8yvHQupkHGEbtpouo5up0kP7CY9sJuR939qbZ9kjJkpIISGuzCJmcnTc/w9WLkugvoiqd4RjHSO6vWzt3HlG0h4L6vnbCyMuxlefaqOpsGP/vk+/skXD9BqSCau+rzw9Rrf+nKlff1x4rKeuIY7ObZs7dcvnLnlgi93Fgq/Fq7b4a/OUtlxau8qGLaG4RgJCbGjV0ng5C32PzrI/vdvvBsuXa2R2mChXo048gn9BlHQRDfsDc8d3Mz+J0mXjIIY09GxMus9N1Eo8epbL6KGk6RuKwVePdzg3Aq/FmCmdXSrPbgCnKJF6EbL2R3L/WwlHtudYKOQgqYLnKxJq+y/faFXCWE9YOFsiff93AMAVMZqjD89uf1XI0lrwaV8qcLoQ0MIDbyKT22yjowVhd05ckMZHvjpkyscEwVm6tbSHb+j86vmpyNG95qkM1u9nYJDJxyG91hcOR/QrK08REu7/3dLLWxw1ODhD2Z4+LH0ci3tzSBjaNQkF950eTfmTGEY7Pmxv8jkl/4zYWXzuK7upNn1mZ/hxuf/w5Y7vT+MWPscbOB2Wvpd0BEXJKk3u5V3JnGrB7USU89+lebM1Zs7hIzCZaljv1qiOX2V4sF7Kb31LKmeIXQ7TfXq6Q46szFMSzC4y1p2GxuWIJPXKfbqzE4GxNvsXFcj323QO2QiJfyVH7hMoxojY0Ucsa6sspHNo6XSBHMJwVN3UiAlEveWr+VOQa3PPLtjiIKEsJzqtjp6hpRS+PWQyddLXHlqmoVV7vIlhF6EW9l6UtCNxFgIgyaBV2ejKzQz6xcR3dIwLJ3QjfAbt+ZpDN0ocaMLSBXMDbQkkrS/hav1JI2P5O9uJVhO+VsNJ2ut+2wNbhpX0zFwcusNGRUrglZEutveUvn1diEMDStv8dqvnKE+1STyO+d91aeaXHviBic+cxgUXPvmDWZPLyAjSWWsRmO2xav/8TTzZ0soEgMn8uMdG0twJ4yBTrbwbxNmJkIaNZvejcnNQDtH2hDc85BDaTbipW+taJrfCsniTmFgxOC9H8nwyU8XMDogQTZqkteea/GueP41DT2dxeoZQGzlwtB19EwWu3dgeVf9PxPi0EOGASqOsXI9hM3asoaE0HSsXBf18XOr6hSoLQ1NpSRR4BEHLmY6S+S1NniXVn6P3Dr1G5coHDiJO5/EG8P6ImHz1lnV6ZzGfR/I8sI3apTnQvYdTXH/B3PUyhFX3/IIt/BC3Yy4PcH1DJr85V8YJW6XYK4sRFw96/H13yq32xPomRxWVw/BfMKvcAZHCcrzRM0G/1Nou26CVtmnMtHk4IeG6T9SZOFybct4s5KKufOLpLod3IrP/KXKunVcSbU89htDkM72Yae70A0H3Uxx84oshKB7T65dAIhlzkDfkSK6qdEs+7esqqhiRXPBozHvMXiim+Ua3STGRmEkTbrLZvF6HXdxyTunWJxo0LU7uxw6WELX3ixWxlj3mITNCAXrjk932xR3Z9f1K3Rj5q9UOfH9e+g9kMevhx1rBCzpI+iWvqVRJ3SBbulohsZdP3iIyI1Ag1bJ4/Lj13DLLke+/yC9R7rJDmYwUjqnQsm1J27QnG0StkLqUw2sjLkcrmqVXFSsaMw2GX9mktFHhtj1vuFlks6r//HNd8cY0NIWmWOjZI7tQk9ZxA2PxtkbRJUWKowQlkH3h07QPDdBMFdFeiurWffH7sG7Pk9YaaJZBrmTeyj/wZvIIFqzC8udStTi/MkywWxl+fsT1wJqi9vHHISAo/c4lGZjxi8HzE69uwy8vYcsHvpgmoc/mGHPoW2qE5GsD7VKzMtPtzqbJ4VAc1LkjtyNZiU7gqjVxJ24RtxaIdIJ3cDuG8TuG0IYBiqKiJq1pARvqwlCYPcP4QyMYBa60Cybwon7kwkbhYoiKq+/kFj8w7uxewcxu3oQpkXxnoeJ/WTykL5P9fRLy+fV01msrl6snn6EYSA9l2CxRLA4vyz/qzkp7N5BdCeFOzlGes9B9FQGlCKsVWiNX0nKjL6DUHFM2KrjlqbI7b6LoF4mchvolkO6bxShG7ilaWK/w92tUgS1Mn5ljszwAWoTF4laNVCJQp/uZPCr88uu9Nh3ac3doO/kB8gM7U9U+CrztxUKCnzF1FjAgx/KoRT0DpnkigbPf71GrRwhJQztsRjea9M3YrLvLod0VufU+7Kksjq+Kzn7cpPFuYi77k3TM2By7uUm1XKi7S5E4mk4en+aq2ddLrzWQs8VkueufygZKwVaKs2y2ssmY+WVXeyuFEbKQDMSIRpN/0NgdArQNIFuJYJAmt7+2RDJDnDVJTXnPeYvVfHrIUe/ZxeXn5ymPtMi8mM0UyPb61AeqxM0k92jjBTXnp3l1KcP0H9XkVbFpzbVIg4kZtog02MTNKNtiyJJFROFbrtS3warl0iyKPY83I+VNpZ35Uc+OkLQiqhONmnchsRy5UaD2XOL7Hmonz0P9VO50UBKRabXYe8jAwmH4kKFZlvDQUaKiVcWGPiThxi+uwd3MaA+08JI6ex+uJ90t01zYa03pD7n0u/FFEezDB7vojHnYmVMhk92M3x39//L3n+HWZad9b34Z+14cqicq3PuSZrRjDSSRholBCKIaAy2MdhgbPBN5tp+7AsXY/u5tu914GJwBJsLNiAQSlZCeUaTQ09P51g5njr5nJ3X+v2xT1V3dcVOkuDH93kktersc3Ze613v+32/3w1Plt8OWDxXxakF7HvnILqpx/emGaCbGqnuBM1lB6fsbShVBE6EW/MojmYojKRxbuoAuTloSnYlyA6maS60qM/GvLTscIZMX4rscAan7OCUXcpXqzTmm2i6RnvZIfLirh4VKdyax+TXZ1EKGnPNNZGpyIuYe2WR4r48VsZc2/+dljzuOhiw+wskx3oxu7MYhTSy7YGm0XxzkqBUR09aDP6lp5j//75G1PLWBQODf/GdlD5/itaFWfS0xeBPvIfGqev4S/XYBAZA0+j+0CNEdYfqN86vCwauX/QpL4dEkdqRfDd+wKbVUMxOBDz7xSbNmvymJjR0HeykIF/QeccH0nzg+3MMj5s7HjeA50iW5kJef353k4wQGlZXL3Z3H0Ymj9A1wmYdlKR59TxIidB1jGye7OEHSI3tQ5gWKgjwqysI3Yi3E4JE3xC5ow9hFXsAyB17CNUhgUjfo/rGi6BpJAbHyB48hlnojpUmTzy61uoWNuvU3nwFUAjTwu4dIHvwOImhcTTTJHJaOLOTtK5fpj19FZTCSGXIHn4Au3cAGfjkjz2MWehGoXBmJ3Hnp4i+2cGAUnEXwcQ5Cgcfxl2ZI2jVMFNZMkMH8OsrtEuza0HQbuBVlmjNX6fnxDvIDB/Aqy6BUuh2EivbRdCsIDu+BzLw8KpLyCggM7iXxvRF3JWNnRO7RaMacvWMw5f+sMIH/2IXA6MWriO5fs7lC79XWSPvDoxZPPTODIceSJJMa9TKIftPJBk9mKBeCVme8wk8xRPvz2Hags/8bpmLp9ooqUikdB5/X453fjjPicfTXD7dxsjkMHJ5jFye1Oh+QOFXVoic5pZZRhkpVs4s4zd9rKyNkTAwkga6Hae2N/uPnjBiqfF7RDjcDHbWJFmwsdIG2b4kZsogkbPo2pNFyThF3iq5ayn2Vtll8UKVudNl9r97CIGgPNWIW/lsg+59WU5/7Dqh30ZGEVEoufr1BQ4+PczgyS4SeYuFcxXCdkSyaFEYy1CZbFCfb22YsG5A4TlVoijAtFKYZmpNbfJmBF7E/ncOkira1BccEjmTA+8eonSlRulqfW3VfidYuV5n6sUl9jzRx/EPjzH7RhkZSnJDKfa9c5DZN1ZYvFBdK3fISDLx3CLHPzzO8EPdmCmDxXMVrIxJ/+ECRkLfsPpduV6n71CenkN5jnxwlMXzFVJFm+79OXJDqRsliNXzdSKWL9WYPVVi5OEerJRBdiBJa9lFT+h0jWeZeGGRxdZG3oJT9Vi51uDoh8YYf6I/bkGMJIETce3ZhbXtksU4GGgvO0x+bRqlYOCBXvJjWRJ5O26//fotnU23IPIizn700qaflS6UKV24N+24dx0M5B49wMoXTrH0sReJWi59P/R2Bn7kSebDkOo3dqcDHVZb+AsVwnKT7MP7qH7jAmE1bn/RLIPMyTHmf+vLeDMr6753+bzH1PWAejWi2L39qQgBxx5KMLLHJN+l84nfqeI6t6/md7sQnfpyV5/ByUcT/OBfLXL4AXtXQQDEE9CV8x7PfbnJ4uzuVoDCMMgde5iFz/4BfnkZq6uX/LFH6H//9+FMXyNyHcxiD9kDx8gcPM78//h9wkYVq6efzIGjDH7oh7j2m/+SqN2k9uYrNC6+SWJwhLG/+LPMfPQ38Uq3MNejiMorz1A78yrJ4XFGf/ivMfm7vx4HILcgNbqPzIFjWF29LHzuDwkbNTIHj5E9dILU6D5mPvZf1rgGmmWR6Buk+MiTrLz4VfxqGSOdASGInPaG375brBsclVqTYo75A/FnQbPKyrkX0JMZBh77DoxkurNin2LqK39A5LZZ4xus/d6GvO4aGdWrLRN6bZRS9D3wFGYmj5KSsN2gvTxN+fx662cZBVSvnab76OP4F17GKd15MPDlj1X58seqALz54tb8jtefae7oUHjkkSSZvM7CtM+bL9z4rcCPWFkMqCyHpDLxKt6dmyKoV9ATSfzS5tLBt0L6EWf+86sb/q4ndOxcAjNrYabj/1g5Cztn031igOLhbnLjhV3tAxWnf5XcvE0pXuWvL/0cfv8ID//wfnoO5m5suDfLyCNx8NwquTz7G+c4+6nJmFSsoDbT5HP/5yu87a8fZfzxPo59eAzd1vGbAUsXa2hC3ChhKmguO3zhn7zGkQ+Osvft/Rz5wAiGbdBacVi6WKN0ubZNIBAj8FpYiRzJdDcyCjcEAipSTL6wRHPJYeSRHnoPFtAMwcyrJZ75t2epzWx8PpRky2t1K9orHtefW8St+bz9Z4/xjrcPIHRBs+Qw9eIy3/iNc3jNG6trJWM1v2/8+lmOfdcYB58e4sT3jlO+3uTZXzvD8e8eiyWTb9r33BvlmKMSKQ69d5iHfnAflZkmV748x0svXOLJv3kcdQspNnBCPvtLr/DoXzzI2Ft7eewvHyKRs/CaActX6sy9sYK8WU6+U+EoX2/wxseukxtI8dafOIRuaniNgPkz5XXBQHOhhZ23efgnTjDwYC9CF4RuRPlqldmXF/h2wl0HA7UXLtF47RrOxCIgmP/tr5L713+V5L4B3OkSYWV3JLKo5VH+yhnybz9C88wUYbWFlrTJnhxHtjxal+bwS+snFyXh9efaFLt1vvtH8zvvRECuqPFTf6eb931vli99qsHrz7e5dsG756Q804K9h2wefnuKh9+WZM9Bi+7e2H74drQyyssRz36hyef/aPcGGyoMqL/5Cs7MBNL3CBt1wlaDPcceJjm6j/b0dRL9w2QOHGPlha/gLs6AlIStBtJ1yB9/lMzewzQun1lL298rZA+fRBgmlde+gbc0Dyhqb76KnkyTP/Eo6T2HqZ97HYjFgKJ2i+rrz+HMTcWdH60Gu2PvbYSZyKJkROivDyQsI4MR6Mw/+wlQksbMJZqzHUU3pahdP0N94tyaSJFSEsNVhNMzOG6L5vIEK5Ovo2TE0PH30SxN0pq7ysU/PBNnR24aeL3aMmf/6z+KXdU6f5e+Q2PiPN09R7l86r/gNcusBiCbCQlpukFj6jx+bfmOhIbuB5ZnA5SCoT02J59Ic/alFnZScPCBFG/7QI4DJ5P8h1++kW24WVI6NX4ANA2/XCK8TdGhyI1ouy1Y7owzouPdIcBv+JgZc9fBQOlqnT/6W9+If/dWBnsz5D9/5POoSK3rFHrzExOc/fTkNgI4sZjazd9RErxGwNd/9c04i7H6XSEwBgfwZtqoW/Zfn2/zym9f4tXfudzpZuis7DUtdntLpMHbboxQtGrztOoLrLv4N0E3NU79wVWe+3fejX1ItakfA8Dn/9Gra34Bu0HghEy/tsxHf/aZzhgYn4OSassWy+vPLTDx4lLsT9XZXoaSqVeXEWzc98LZCksXqjzza2fWto8DPLjytblNtSCCdsiLv3WBl/7rxbXzhvg7a14IHTz2U8dZOF2idKVKdbrJ//g/Xl4jf29Gn/PqPvOvLrBwan3Qu1oC+HbCXQcDQblB1F5tk1FELZeo7qAlTIxcatfBgHR9ai9dpve7HsXqy+Mv1dBTFpkHxmm8do2o6W76QF4+65HN67zlHUkGR7ZXpVp9AA1DMbrX5Pt+PM/TH85QXYm4fsln8qpPeTmiVo5oVCOctiTwwe8ITEDM1jQ7Zix2QpBMaaSzGvmiTk+/QVevTne/Qd+gQa6gk8lpZHIadnJ3aomrUErh+/BHv1Xlxa+2aTdvgxCiFEGtsjZRqChEug6R08LI5NEsCz2RxO4boufJ91F85G1rT7Fm2mi2jZHJdSxg7yE0DSOTIzk0RnJwhO7H3732kZGNW+iMbO6muT62DfVWbmnPuwOut24lyQ0cInAb1BfWp9xa5Wmc+hKh13lWlUKpmybZW/6/EBqZ7jFmTn+ewG0QBe7atS5dfyUmGob+Orvnm6FutVtVnayEiu/VrZ8jNISmoZk2iWI/+b0nmH/xs3j19ZmybyVq5YivfLzK0bek+Mhf6+FHfq6PKFR4rqK8GPCp/1riypvO2ntkFbuxeweI3DbJ0b0xMTWKCOuVOyMkryVhbqz8drtqvfk3NqywRSxRLAO1Jjiz7ivRTuS9rRFPgDd9V9dJnziBv1RBBbdkAVVn4rtlX8LU0RImWjJBVNuFt8NW17ajCCsjtWOWYe34O9sVCxr/xy8UaTQkUzMh07MRUzMhE1MBvUMm3f0G7aak1ZD0j5gkkoLlhZB2U2IYguG9FnMTPu2WpG8o/nxxNkQqxeg+i0Y1Ynk+ZOkmrteWAluqc103CS6ibcSzNru2m+H8p68TtIK4lU/F12CnqxVnUL79SbF3HQyojt3vGmQcuQlNIHYg94iOIxiACiP8hSpBtYk9WMBfqKKA1OEhlj/+EtLZnOXZrEuuXfD4yqea/NBPFTCtzXtK1+1XCOykoH9Yo3fQIPAVg2MmB4/bNOuSdlPitCW+pwjDuDVq1SlLCIFugGkKDEt0LJk1UhmNbF4jk9PJ5jVyRf22Jv+boVTMwv7yJ5u88mybhZlgW/Glzb4vo/WmH0pKVBShGQZC09YmendhlqBeYd3G187jzk/fe4KeEGuEQXd+hqC5ngUvPRd3YWbdgKWkRPrra/CGnSbdNYKmmwhNw22U8FpVZOiTHzwcK+HpJoFbx6kugBDkBw6R699P6LUQQuA1y7iNZZL5fhLZXjTdpLZwidCLB5xEtgc704OmGwhNp1WeIQo9dMMmVRzCzhRJZLuJVxASIRzSXSMkC4O0ytO4jRVUGKebsr170a0kQjNQMqBZmiL0WmR6xjHsTOd+aFsmPKxMgVT/OImufnQzgVOaw1mZ2z1RETDNNLadIwjaeN7da7rfijBQXDzVplmLGDtok8nphGEsXrQ06zN9xVsX0GqmhVnoIr33EEGlhNXdD98OZMBbUBxKcux9/ZSut1i+3qJZ8vCde5iNMXTMnm70fB4VRej5HMIyMTK96PkcKAgWFlGui9HXi57Ngozw55eQrRZmfy9aNnNDYMwwsIYG0FKxCJFst1G+j7BthG2BgrC0QlSpbjyWO+yuCwLF6bM+URTfwn17DE4eMzGMFJ/8qkt3n4GmRwQhDIyaXDvv0jdk0mpE+K4ilRaMH7TwO66WlVKE70pOPJbEtGO3TClZFwzcipHH+mM3REMgfcnShQqtZYfew0VS3THHRGiC2deWcGs+Xfty5EcyuDUfO2PSXumMMSrOYJSv1xGaYPChHjRNMHdqmb4jXXQfzLN4tkxtponscKe69ubIDacxkgaRF9FYaFO6XKX30Ob7vjUjYI/1xh45TYeodnvlTy1pI71ge3W+XeKugwFhGoib69+aiA1/pIrbrlZrpzebERBHtMLUb0zcKs4OtM7PYA11Ya80CastrO4srYuzRO7WE9PSfMgX/rjBo+9KseeAhWXvHBCsHa4Wr/BH9liM7NmZ2X+/EUXxCzF93ecTv1Nl8oqP597eykMIgWaY6+sRQkPoerzyVAqlJJHTonH+FM1r5ze2wEm5fhWx7uMNjcJsseFGRBHeyhK1M6/SmrrFXGXTNlW1Iao2kzm6xx8i8Nqx0qOScd1dRmS6R0HTMBNZ/FZs0xr58URtp4voZoKU30bJELdRwkoVyA0cxErlaVXn1rIDqeIQ+cHDhH6sTe87NYSrYSTSpAqDaLpFqjAEQkPKiMBrkMj20rv/MZSKCNwmMvTQDIvc4CE0TY+DASWRMqK1PEmu/wBmMku01oq4+TOr20lSfSOk+saJ3Bals8+jXBddWGCYSBkPSrpuImW0dg+EMFAqvt/JZBe53AiNxhxh6CCEjlISpWRHRlhHqSj2gRAacQpXImUQy9gKLbZ+1gyEEEgZdTIfCl23iSKf8gJUFgPOvSSJonCN+LgZZOAjfR+rZ4DqK8+CpiO9bwfRofVId1kcemcvhcEEqYJJZcahueLhtiKcjpTu3UDPZjGHBjB6e5CtOFDVsxmM3h7M/l6IIoRhEMzOYY+PYvb3EdVqhLU6ynHQi3mMQoGoGXM69Ewaa3QYo6sISsXbeR5awkZYFkoptFQSp1q9kwTbpnBcxRe/6pDPaRzYZ3Kk3+TEUZO94yaf/rqLZsSW1rYtyOZ15qdDxg/aWLagXonwPcXAiBGr1L7psTzfKTmNmyzPh1h2vODaDgfeN4IM44Vo0Aqoz7XwWwGDD3aT6U91LJYN/HbA/KkS/ce6GXtigPnTyyTzdjz56yJWlxRQvl7HzlkMP9ILCuZPlyiMZTn63Xs7EsweQTtE6ILhx/rIDqTQTZ2gFSA0QX22ueW+b1YGFJZB6sgoUdPBn1tBeSHC1FFBGE+bmkAYOtIPEUZsEa2kWiPY2yPd+EvVOHMe3d2zeNfBgNmTRUt11K8E6GkbI5cicgPCWhuFInICNNtErGqb6hpmbx5h6OsnLBVzEHq/73GEELSvLuBOlwhWGtsqBPqeYvq6z7/5xSX+3v89wMCwsasMwbcLVuc/pRSNquTiaZf/8M9LXL1wh/KYQmAWuhCaFree6jp6IomeShM0akjPI3LaRO0WVncfXD0HNxuX3KIgGNfdOg+a0LZRGLxpO03fuF0UETbr6Mk0ZqELrseTyVb73Qq6mSCR62X6md8m9Frr6uYzb34BgEz3GMl8H5nuURYvfYOVidcJB1u4jRLlqTfWtq/NX0RoOtnevev2YSbzaIbJwunPEbgt1vLbjWXalXkKw8eYP/9VAvdGarY08SrpnlGiwO1cKoN0cQjDsFm6+hKBUyeZ72fg6LtYjCI03aK+cIXawiUy3WOk8psrzDmlWZzSesWynu4jGGYKKQM8r4aMIrLZITy/Thh6aJqObedxnBJRFJBK9ZBIFKlUrpNO95NIFIkiH99vousmiUQB161iGAksK4uuW/h+g1ZrEdsuYBg2jlMhmSyiaSa+38D3mygZki/soV6fBSUxrTQJO0+rvUytNsVWM447P4O3soRuJwhbTdzZSSLX2fT+C9HxF1E34sVOVfK+Y+5cnd//hTc48lQvx57upzCYxGkEXHupzLmvLFKeahMFnc6kOziexIF9SM/DeeMsUbOJfXA/iSMHkZ5PWK4gW20yT7yF2hcaCMsiqtdpvfoGUT1uQfWnZlFBhNkbO3mag/1ErTZRsxUHEpaJXiwQLpcIFpYBRe6978Y5fXbtgJWK1e7upDcdIJ/T+IWfz9PdpeO4ipm5kE99ts1rbwRoGY12U5LKaBS6dJIpwePvSaGkIp3VyBY0nJYkCKE0FzK8x2RwzGT6qs/0VZ9UVmdpNmBhepsspYDuAwVe/S/nmTu1HOsNSMXAyW4SxQTzp1eYfmmB3FCGJ37mBNXJBnbWRDME178+R302XgBYaZOBk90c/8g+zvzRVQZOduPVfZpLsZHUhc9M0He8C7/TGSI0YnXIdw/zwm+cYfHMytozsN2+14IBIbAGu7FHe3CvLYCukzw4hNmbx18oQ6TQUjZ6PoU/u4LZX0CzTaTjE5YbSD+k8N6Hab58CefaPGFle5LvTrjrYCD/xEGiehvlh0RNl74ffBsqCHE6E7mWsHAuz1N48jBBqY6SEiOXZOAvvCMuE9zy8jffmKD3ux4hdWgILWFR/sqZXU0QMoJzpzx+6Wfn+aGfKvDE02m6ev70CCwqBS9+tcWXPtng+S+1ad0OR+AWCMMkf+ItONPX8FaWsIo95I49TFiv0Z6+jnTbuAsz1C+cpvvxd+OVl3Dnp0FG6KkMyYFR6udPIYMAUKgoJKiXUVFEet9hAIJ6Fc0wCVs3SJ0q8AnrVVCSzIGjtCcuEbXbCF0nbMWTZv3cKbJHH6L4yJME9Sru7CSaZWEVe9ASSZpXL+xYnnBqC8ye/gJ7Hvt+It9lZeJVmivTaIbN6IMfIgxcDCuJkiFOfXds9VtRnTlL6DbjfXguc+e+hNeqcDuEPaFp2NleMr3jWJnuDo8gwq2XsNIFAr+1Fsy4zZXbmkxsO0e1NolSkt6eo+hGAqddImHnUZYiDB1qtUkcp0NGjI+IVmuRkeEnWC6dJWEXKOTH8P0Wtdo0nlclkxnEsmKbW00z0XUbKUOUssnnx9A0g9nZF8jlRink9yBlxOzsi4Civ/9BbDuH7zXJZoao12fWcy9ugmbbWN19JMf2dTIR0J64jNPeOKAdecDmr/9CF42K5MoFj8krAdMTAXOTwW1nzW4XMlK0Kz6vf2KWU5+aI5E16D+Y5fj7+vkbv/MEy9daXHpmmSvPrzB5qnLbAYEwDfA8pO8hfT9u+7UscD2U7yMdl/oXv0awsEhUrWGNDJF96kncS1dwL1/b8HvetUly73sKPZPGn5mj9eobZN72KLLlIB0nNoNaVdDsHGvpco0/+BvPsNpNcbtQCoIQvCAuq4Yh+AF4vmTmbMDVcz4KRb6o89R3ZfnyJxvx2kPRKb2yFkytSj1LCRdPx0GglDdi8c0PAL7xq6c49P4xjnznOLW5Fq/91wsUxrL0Hysy+GA3Bz8wCkB1pknkS6JQ4jWCtUAAwG8FuHUfoQmSXTZ73zXE1AuLzL9R2nS3QtfIDqWpTTUJWutllrfb980XzptcxL2+iDuxSFhrYw910b4wjb9YJfPAHoSu0XjhIsUPvQVh6NSfvxAf38Ehal99E29qEXd66a4DAbgHwUDpf7yGkU8x9BPviXUGvJDFjz5P++JszB/wApY+/iL9P/IkfR95PG4La7k0z0zHRke3cAGUVDTOzJB7dD9Wb47GqYndv2AKZiYCfvfXK7z2nMO7vyvD29+bWXNS+3aDUuC5kktvunzq9+pcPuNRWgxpt+4u3aMCn9JzXyL3wKOY2QJCM4icJktf/uSa/ntQLdO4eBrdsuh6yzvQbBuUQnoeQa1M7fwp1i68lETtFivPfYnMgaPkjj6ICkOCeoW5T/63G/vtrPxLz/4J+eOPUHjgMWQQ4JeXWfjsRwFwZidj7X+3TfcT70GzE6gwIGq38BbnaF69sOP5RYFLozSB2yhRGDqCne3BSGSRYYDv1Fi+9jKZrlHsTNdN1zr2F9itMqLv1KjOXaBZnqZr+DjZ/v2wdB33NoILJSV+q4JTX6Z0/VXcRqlDRpQkcn3kMj0xl0DoWMn8bdVsNd0klxshinyCwMEPWghNx/XqgEITBlEUB3M3HRGgCMI22ewImtAJwziLEUU3Ex7j9jmBIJ3uQ8qIDke/ExjEvxnJgDB0Geh/kGZrkSgKCAIHoWk0Wwts9+LavQMkx/YT1KqowMMsdG/5kl675PP//MNlLEtjbJ/JweMW7/veDKm0xt/5K/O7v2h3ASOhs/fRLvY8UqRrNIkQgi/+v5dxGiF9BzI8+F2DjD1c4JnfvH5bv+tNTGHvHSPz9seJajUIQryLVzAG+rBGR4iqdYKFBYyeLsyBfsyBvjiAQKAX8iT2jWOODKOnktj7FwmWS2ipJFomjZ7PYQ0N7mrwuxtme70h+Zf/tkY+p3Fwv8mxwyY/8D1pfuFv53n6e+bxOiqWzbrk5a+1Yq+aNZbnLU/ozbxd2LVDa+lilcZ8m669OboP5Nn3nmEa822q003K1+pMPR8/J1EgcSvejfL1LXAqLrOvLrHvqWESeZugHWwwSrpxrLGyYnYghZFYT7ZuzLe23vfGX+ocSvx+SjeIx2InwOhNkn5gD1GtjZ5JxBlcEWddVSTR0gnM7hzS8Ykad9f5dcfBgAolC7/3LM03pzALadzJZfS0TdhwYsGhVSJEJHGnVyh/8TRmVwZh6kg3oH11AX+xijdX3hDVSNcnrDSRYURYvz1CReArFmbiFUOrIZmbDDj5WJLhcZNMTtuV5fH9hFIKt62Ymw6YnQiYvOJz5bzH+Tck1bIg8OI0vKbrawY5dNL0Ctlpl9k6WPBKi5Se+xKt65eQnoueygCKqN2kPTOx5hKlopCwUaNx6Uy8yrcTgEIGAWGzgbrF71yFEY2Lb3a2jY1OImdjp4gMfBqX3iRs1hGmFbcsths3fe7hLS/Exj6VEloigYoipOsS1Ctr+43aLeoX3kBPptYdi9B0rFSBbN9eVBRhZ7pwGyVCLxassdJFMt1jmIlMzFDv1NMDp4nQdFLFYWQU4tQW8dtV0l0jZHrGSeR6yQ8cRDds3EaJRK6HZLYPpSRWuoC3PIHcymdACHQjQbZvL4lMDzIM0HQTp7aEW18mcOoksr2d1sYQr1nGa5Sgbx+p4hC6mUAzrNtaVUoZ4vsNPK/e4YAoNM0gDB1Wg4HViR7obNcxBWrMra3GVwfEMHTjZ9OtxhyEzt+Fpq99TxM6CoWUIe12Cd9vxkI2ZpIgaBFFfodToMUlhO2Wc50yUlBZjrNCth3fr01KRam0xr7DNtlcnGoGwexkgHOXQfNukO2x2fvWLrrHU8hQ0a76VOYc2hWfhcuxxXFt3mHsoQJDR3M7/+AtCCtVhK6jVeso1yVcqeAvLBI5DuHyCsr3iWp1pO8Tllbi1b2mxZ85LsHCElGrjTAMolqdxP49+DNzKN9HeT72vnHcq9eIKjWU56GCkPapNzctu2qmjjC0Na6XMDSkH6FZ+g3ZbUMnaq+fHJMJwXe+P0UkFZqIg4NTb3q82SEVrp1roCgthLf1nO8IAYats/89I/GxdNkkiwmqU02qkw2ai23sjMnQQ70opahONfAbW2cevbrP4rkyb/krR6nPtXCrceBgZUyGH+klP5Ih8iJ0W6d8vU51skH5Wp3BB3vo2pcndCMaCy1qM80t9y1vGVudq/OE1RbKC3GvLxG1XZAKf6lC1HI770QFYRmEdQehCZwrsb5I+0ws7if9u1fVvfPMQCQpffIVAHbSW5OuT+2FjQpKzpWbRBc6Tml60sLsyhA2HJzJ5TsiRUQRlBZDyssh1y95rCyFHH0owcCISa4Qs/1TGQ1Nu/+8AikVvqtwXYnTigOUxbmQy2dcLp7xuHzGpbQYkcz1Y9gGQo/b0qxUgShwEUKLV9IyREYhMnC3HWSDSolqJU5rBdXtW89UFOIuzuIu7uygBQpveR5veYeVmJR4S/MdDYHNESsOxqqDW27jtmldPb/xAyHQzQTJXD8yClBK4TXLOLVFhG4Q+S52uoiMQkK3GXu3A6HXxG9VsTNdJLI9BE6dwGlgJuOWxsh3MKx0TDxsV7ESORL5vrgtU4a49SVCrxOYKkm7Mtsh68X9z0I3sDPdBF4cdBh2GsNK0a7M0q4uYKcLWMksMgqIQp9WZRa3uYKdLmKni4R+m3ZtHoXseHxv/9x7bp12axnHrdx8cdhqpA2CFkEQX4t2e3nL3425ADtrWrg37ddxbt3vdgTTGNJ1CMolgsoKdv/wtiSAfFHjsXckyRd12i3J9PWAics+1y/eB8vZW5DIGYw/XCTbYzH5epWpU1VKEy2c+o0JpTLroBmCnj0b9e93gnJc/OnZDUGQbDQJYN2zIJutG9t1tvXb6xdLyQeO4c/OE1VrCNPE2jOGPzlz0/MU4J67uPFABCQGsmiGHneIKbVGXNMsHRXKmLim2BAMmKbg+BETqWC5FDE9G/LK6wFnzgfrHGvjTOi9L+tohkZhPLvW7eU3A0qXqjQX25Sv1SmOZymOx3LbfjOgOtmgtexsWM1DrDlQnWwgI8n86RKtFRchQLd0csNp3KqH0EXHktmm5FaZfmmRvqNFUt1J/FZA5EcsnatsuW9uSQ54kzfeR2/mRkkiLDcJy5un/1ezAO3z0/fgCsYQapeWffd10tQEwjLQExbJff0M/Og7aJ2fYflTrxAsb1SxuxMk04LRvRYPvDXJw29LcuyhBImUhq7TYVN3uHGw1nO7Pm27yQB3E5FpLeu0RnKKRStcVzE/HTB11efKOY+zr7lcPO2ut3UGevc+jp0q4LVW8J06xaHj+E6dKHDiSU+GRIFHbeny1ivUP8e3P1YH81trV53XUC8UUJ6HdJz129/ymppmiijy1zoJ/rRBmCZCN5CeS/boA6DAW5rHX9lYhhnbZ/Lhv5AjX9DxfMncZMj0NZ/JKz4zk1uf//gH9jP+gQMMPTm26edKKq788XnO/fYpnKXN9VCyvTZjDxW48NUlomDrobI4kmTgUJbzX74zjsq9gjk0gL1/L3omjXRc3MtXCebmd1yNC11j/IceutG1pUAGYUxY84I4G9HyaU1VaFzdWEM3DHjLgzZ7xgyCQDE5HfLGWX/DOPfn+NZgN9P8t0UwYPbmyD28l+Gf+QBRw6H02deovXQF9/r9ebGEiK1bh8bM+D/jJoOjJgMjBoVug0xWI5WOswemBboh1rS8o1ARRXEHg+tInLai3YyoVyQrSyHlUkSlFLKyEDJxJWB+Kn4hdmJAF4eOYdgZNM1A0wzMRBanvtDxII9bwdLFYRauPHtfrsmf4/7D6OrCKBZxr14leeRI3PttGqAUYWkFFYZkHn4If2kJf2YGYVqYA/0Ei0sEpRKyefckoW9XJEb2EDXrW9pjCw0yWY0Tb0nwyNuTnHhLgu4enR9+59SWv3kvgoH/f4EwNPb+6CMgIXIDZCTRTB3phWiWQeQEtKYrVE5vlL/OZgU/+WNZ3vFEgsnpkIQt6O7SePk1j1/9D/U/Dwi+DbCbaf7bgm4flJtUnr1A88xULH/ZctcZGt1rKBVP5jMTPssNm8vBIF0HnmDll/87mgzXygdap4tuXYbgpizAatpMdmQroyjWCZARsX1roNitbk9t8Upcx+3sUAiBkmFcs04VMO0sjZWt0+p/jm9/CNtGz8V1ZaNYQJgm/vw84UqZ5NGjtF55BX9pmWB5majtkDoyTuvUKVInjiM9789EMGBk8yQGR9HtxLq/J4bHaV49v2kwcOCoxY/+TIF2U7IwG3L1gs8rz7RZXvjWSTGbCY23fP8Ir/zRDOEmyoR/6qAU7mKDyhtzhG1/rcddRRKha7H41xbKhAlb8NSTCf73XypTq0uEgNFhg3/wvxX4tf90bzK7f477j2+LYIBIItseXvveiY6YQ70kHzxE/XPPbdmaGAagPIHyDUwyrCyGG2VAv0mQ0db1z1jy1ov17P8cf3ohJeg69vg4WjKJ0A2MXD6OLqVEBnE6VrNsNMtCyQizr7dTKvgzMOEAZr6I0DRk4K9rIVVRuOV7urIc8fXPtyh0xZyBqas+y/PRXXfd3A00Q2PkRJ7XPzG3q2Bg1Z35m+mUejtQUlE9u4C70kLdpt6AJgTplGBhMaLZik8wYUekkuJ2GmT+HJtBaGiWhd3Vh5ZI4i3NrbVpCy0m9N4L9UG4y2BAt5KY6TyB0yBy7y7NJnSDRKEPv1kl8t27G/x0DXOwl/STD1P//HPb1stUEBJVGrgXJu6vfvRNCoC3OyJEgbsmZLMVNNOOuQT3+hwE6KaOkTYx01bsJW/qaIYWs487drFAp+tBrZlwREGEDCKkL4m8kKAdELaDOxY3udfQLR0jY2EmTfSkgWEb6LaO0Dvn1KnXr52LHxF5EUE7wK97hE6wYy32ZkjXJarVELZNWK1h5PNgxO2OYakUt3A2G0jfj50LV8rxtpUK0rlzL3nN0NCTBmaqcw871r6aocV8HU3EE1UkkVG8Aoy8kKDp4zd8Qje87QliSygIGtW4hz64EQzo6SzRJhoDAE5LMnHJ59AJGyshcFpx98S+IxbnXr8/qoXDx/OYia3bUO20Qd/+DNoO7qNCA8PUYoGatEHgSdx2iK4JokjdsBK+g0DBSJmYaRMjaWIkYjtnzbrpnRSsycNHfvz8hl5E0PLxax4yjNa1+LVn70ymOpKKlbLkgeMW5YpE02B4SKe08s2ziReawMxY8TVJ3Xif195l4ndZRioekwJJ5IdEbojf8Anawb17xu8RhGFiZPMk+0dI9A9jZHJUPIew3YyF5fJFtEQKv7yEvA3b9K1w58GAECSK/RQPP0rt2ps0ZjZhqN7OgSTS9D38XlbOPke7NIMM7uwlF7aFlkmh9xQwunLo6eRaW4wKI5TfGYAECMuMPainF6hMLWzsXOgM1GtkL6VQQYiwOi1QYRTLQq4+8YYeqyyuTiRSovwQpEQzLYxUlrBVv+Nz2w52sRe/Udm03e9OoBkawtAwkgaJYpLCgS4KB7rJjORIFJNYeRsrZ3cGobjdS4YSGUSEbkjYDnArDl7Fxa04OItNqlcrNCareHUvfiEjtcZS/qZBE2i6QDN10gMZCoe6ye0pkBnOke7PkOrLYKRWgwINFUpCN8QtO7grDq3FJvWJCqXTi9QnqkReGDuh7aLrJarV4l7yDlInTxDW6vhTN+re7TNn1/4dljYXO9n1qRoamqmR6E6RGcmR31ekcKCLzHCOZHcKI3VjEpGhjAfGpo9XdWgvtqhcLLFybpnmTA2v6nbu790NmM789I1AX4i4dVZAe+ralg6ZvQMGH/rBLKP7LBJJQWkhoqff4CN/Kcf/8TcXN/3O3eL9P3+QXK+9pQmRpgu6RlPbBgO6IbCSOqm8QehJRo9laZR9lqcc7KSO144QmkDK2BzId3aTYYjl3nVLo7C/m/yBLrKjeTLDWdL9GezuZBzsdaRrpR/hN/34+S21aS00qV5ZYenUAm65TeRFd/0O+r7ipVc9/tZfyzE9G2LbgmJe44WX3W3XJpqpxcHLZnw0pWIhn11cD83UMLM2XYd7yO8vkhsvkhnJku7Pxu+yFb/LMpCEboBXdXHLDk6pTWu2zsqFErWrZfyGh/SjXb/P9xtGNk/24Am6H30XKoowc0WaV8/jLMyg6Qap0X2k9x6l9PwX8ZZ20xG2w/7u+he+nfJehk7fz/8o1p5BtHQKDJ2RX/v7ax83n32dlf/0x6AUeiFH91/+buwje+OgIAiY+fn/a61MoGWSpB45irV/FKOniNGdJ6q3qH38KxR+8P0Y3TmaX3uF5nNvEC7ELXzZdz1C6tETmGMDaKaBN71A9Q+/iD81j5kokBneT2P6En6tdM+vW+HQI9SuvknbuT3Rkw3otNGMvncfQ0+O0XW4h/RAJg5wdoCu6+i2jpmJPR6yY/kN26hI4VYc6pM1ll+fY/brkzSma0Te/a//Ck2Q21Og/63DjL13H93HencUZBGWjmXpWDmb3J7Cus+a0zUWXp5j4cUZ5l+aQd7mObhXrm7Qc7gnEGBmLEae2sOeDx2keLB77Z5sBc3UYvOgrEV6MEPX0V5G3r0nPs6VNtUrZWafmeL6Zy4RuXdRruq0Y+qpDHbfINnjDyN0HWfiCs7MxKbBbDavsfeQxd/9qQV+8V/3xZNnpLZTKL9ruM2Ac19e5OoLm7fn2hmD7/kHx7e17+0aTjB6LEPoS978ygqBHzF4MM3Y8SyBJ0nlDaJQ0SjFAcL0ue05IXrCIL+3yMhTexh73z5SA5kbWbmtvpM0SCYNkr0pONy99nclFZXzy8y/MMP8izOsnLlzsna9ofjX/67OYw9b7B03cX3F9cmAN89uP5kf+L6jHPyh42RG1uszqEjhlFp86iO/t+33jaRBZiTHwe8/zuj792EmzW2Fu3RdR0/o2IXEhndZhZLa9SqlM0ssPD/F7LNbE1O/WcgfeZBE3zArL36F8qvPcOjnfnntMxmFtKeu0vPE+zbwb+4Ud+9NkMzQfeStFA48hG4laC9MUDr/AjLwsHM95PceJ9U3DoCzMk9r/hrN+asAdB9/O+nesbX0uZnMgIDM0AGMZBoVRVQuvwpA8eBb0HQDr75Cc+7K5gcTRiz/+z/EHOgm9ZajZJ56lPlf/PW19L9y/bVJOKo1KP3mH2P2d5M4spfch9+18fc0QerhI1Q/+idohQyJw3vp+ssfpvJ7nyNx4gBaPkvyxAEayxVSjx0n9fhJnDNXqH7iKyg/IPXIUbr/0ndR/diXUfNNNNOicOghhNBpzlzGr62AEOT3nUCYNn51Ga+6jAoD8gceQLMS1K+djQf3dA4jlcVI52jNXMWrldDtJPl9JwjbTTTLvqv7KHSNwsEuBp8YYey9+0j2puO042oq+V51k+iQ6Epi52y6jnRz4CPHKJ1ZYuJzl5n92sS92ccmGHhsmD3fdYji4R6S3Ul0O87u3M15pYdy7PlQmuF3jtOab3Dpo2eY/frkrlfP0nXveVBYPNzNwOMjjLx7L5mhLEbSjLM8d3GeiWKSvocH6Tray77vOcyVj51j4aVZ2gt3Tmi0ewew+4Yofe2zqEhSeOhxzHwRv7IxUF4l6a6ewqpXwS7FJO8Il54psXCpQWV282xFImvQWHG3XVG7zZDGis/eB/MsXG3TN57CSur4TkQyZ2CnddxGSGMlYGV2mzSvJtjzwQMMv2uc4qEe7JyN3nHCu5P7qpRCaILCoR6ye4rs+Y6DlC+WOPOfX6M5XbvtMp4QkEoK3jjrc/ZCbDIU3oai4YZz0MFMxSn/TUtxAgbeOszwO8YZfuc4ZjbOUK7qDNwRDI383gJG0iB0/PsSDFj7RwmmF25kp3c6pEwBBdQvnd74oVJEnhOLdd2jF+GugwHNtGnOX6e1OIFuJeg69Cj16QvIMMDu6sfO97Fy/kUA0oN7SfYM4zcryCgkM7CP5twV/EYFw07S9/DTCAR+fQUrncMuDqwFA8meYbzaMv4OHu6y0SJK2si2G6vfrVQ3NzmSCtloE9k2srWFjKOKA4hgbhlRbWDkc5h9XXjXZtGyaez9o+i5DMLQST/+AP7EPN6lKfypeYgkrSAk/fYHMYf7kL6BkpKgWsJvVLGLfWimhV8vYxd7KZ9/hchtdWxpBe2lGRJdA9hdfZ0HXBC2W7ileVL9o5jZApHr4K4s4NfLJHoG7/hFyIzkGHlqL93He9dS5pp5dxPIVojthQXoWjwhZyDywrtbbW4FTWDlbMbfv5/hd4yT31fAyiXQrY1iI3f084aGZmgYto6ZNjn0wyfIjuaZfWaSxlRt56DgHgYCmqWz54MH6H90iMKBLtJDuXiAvAcQuoauxyldI2Gw//uOUjjQxeKrcyy8OHtH9051XDF1O4kKQzTDREsk0VMZVBgib7JnbtYl1y76/NjPFhjeY/L0hzMszoacO3X/XA6vvVzGrQdbrvy9Zsg3fnsC3906s+M0QhautmnXIxorPpdeqqJpcaeRbmhoOkSBolUN8Fobf0cYGomuJAe+7wh9bxkmO5rDytlod2n1vPpe65Ye82YSBkbS4IG/8SgTn7vCytml22q1zGU1/vbP5Pjnv1qj7dz9My1EXOe38wkiL1wnlSxMjf3ffZiBt45QONhNsje9Y3Zk1/s0dWQoac7chw4ITZB+4kHq5RrRLoOB1XdE6BvfY6FpWIUepOuu47ppQkdu4QeyE+56tFBRhFddojl7Bc206D3+JGYqh1Jq7X9X+QSJrgF0O4Wd6yb0HXTLpl2axVmexsp2r6VMg1aN0HNIaDpGKouKIjRdJ/LaBK1778W+HWTLIWo5aGFIVGsgHRfZaCHrrfhG2RZC17EPjCIbbazRAfT8DSWy2DUshyj7qHqE36jgLM2QGhgHAX6jEmvKL8+iZIRmJTCSGWTgEzSrmLmuWCHPaxM0qzhL02T3HEVPpPFUifbSNEFt5Y7Ij5qhkR7KdtKO+8mM5DBT5r28fNtilThVv1ahvXRv2+Y0UyPVn6HnZD/jHzhA19Geux5Et4LQNcy0Rc/Jfsx0nI5ffGWO6pXy/Qlybt63JjBSJn0PDzL2gf0UD3Vj5+5N2nDDvoRAtw26j/aSKCawCwmUVCy9Mkfo3N55qtBHhT5mV2zTq1BodgKr2EPktvFvCgZq1YjTLzu85ckUS/NhLOblKE6/cnda7NuhOrf9b0ehYuKVyrbbhL6isRLQKMerW6dxk1rgDiKNesIgPZCh79Ehxj9wgFR/Js7S3QdohobdlWToyTFkKDESBstvLNCa21mJEsC2BW97zMbQN4jr3TkE2IUEznIrZswDuq3T+/AgY+/bT+FAF1b27rKht0IpRdD0qVzeZMGpaZjDfQjLjAWzDB2UxJ9ZQtYa6F159FwakYiPKao1iErVjmV0Is5AH92Hd3WKaKWGdDyCmYWN+7kJYbOGnkiQGt1HK4pimXo7gZktoCdSZPYfw1tZRHouAoGuWWTNbpywjh85SG4vKLjrYCD02sjQZ5UOG7QbaFYSoWkITSdoVW9s67bQdBMjnQdNJ2g3UR01PSVDgvaqzrrEb5Rxk2nSfeNEnoNXXyFo1rbXO78PkF5cWlBKxSsWt3OuUsYRqaEhDB09nyH9xAlSjx7dkIlY9aReffs1w+oMBgqkInTbaw+8lS2Q7B/FSGbxqstrOgdKSiLf2eCaF19nrUPKuo3UnKGR6Emx54MHOPLjD963TMBOCJ2Q8oXlXQ88u4KAZE+KoSdHOfRDJ8gM355mvOrcF0VnFbWWnt6BXyAEhf1dJLqSZEfyXPyDM9SulO8fQVKAkTYpHOjioZ97K+nh3G0FPDdY7Oq2zhMgPZDFzifI7SnySvlZKpdXkP4uBh9NQ1gGCon0Payu7rijpxXLOFtdvQT1Kn7pBjGwXpG89HWHySsBQ6MmtWpEeTmiVvnW6QwAWCkdv72LY9js9m/zSAhdkBnOMfT2MY7++AOYWWtX92RNWEbd+PfqfRXcuL+b7rOTsRt7eh+pnhR2IcHVj5/fVZAnI8VySZKwBX4QK6+unt4dU2KEIFFMrq36dVsnM5Tl5E8/SvFg930JjGQg8SoutasbgzxhmWSeeixWys1l4gWf0Kh+9PN4bZfEsf1Y+0bQi/FY40/M0vrGKZTnx51tb3sIo7dI+m0PIdsO4XyJ2g7BgLMwhZ5MUTj51thkTTewu/sRQsMq9pA9/ACl579I2GqgCZ2knmUwc5QVZ5KyO7Mml75b3HUwYGWK6HaqU8jTsAt98aQeBWimRXpgD6tPoZ3pQjMtvNklZOCRyPegmXZcu9UN7FzP2kPvrMyjopDek+8i8h0ql17FrW2tqb41dtZJ3xa3pnI3pHZvyMqu/PancS9cJ6o2NnwnUewnO36U/IEHEbqBszyDW97IhI4HZg0z14VmmHEQsskb5ZYXiHyX3ofehV9bQWjGbU063cd62ffhw+z77sO7/s4uxSo3xVaD0cKLM7QWmveUQJjoSnH8Jx9h6MkxEsXktttudk5hOyBo+chAYqRMrJwdC7DctO12g2uimGT0vXvJ7y/yjX/4pTjQuQ/xgJmxGHr7GA/8zKOkB7M7bn/ruUo/bpMMmj5G2sTK2miGtuvzNJImhf1Fnvyn7+WZv/cn1K9Xd8yEmMN9ZN/1MGGpint5Ck1r4p65vOP1UQoWZkMWZuPfT6YFgyMGs1PfGu0NK6nzjr+yh2f+ywSBc2+DkvyeAkd+7CQj79mLkdg6U7fZsyu9CL/lI70IBVg5GzNlooRad423Cy56Hhgg0ZPCzFic+Y+v7ni8nq945ZTLD3xvmrMXfKpV1RFmU1y4FNxRNUxogmRvKi4pCug60svxn3yY7qO9m25/p2PTzdehNd+gdn2TbJ4ALWGROHGAxX/871FBSPLho6SffBj37BWsfaNY+0YIZpao/PYnQdPo/fkfI5xbxrs6jXdxAu/6DInjB6j+wecIZndH1mxPXiGolgkaNbofezeaYdL16DuRvodfXmbp65+lfuFUh5Qr8KWDGzYwNIs7UXi462DAb5TJjR4hN3oY3U7SnL2EV10i8uNahpXtYu93/ER8sLUVmvNXaS/PANCcv0rfA08BxCfYKK/dVBUFhF6byI/TdYHb3H1LnlSd1bmIWfD3MZmglEL6AeFyFaOvC+36LFG4yeS9sohXLXXqYToyCuNVvoKll/9kTR/Aqyzj18toV99EdXQ8VSfrsloKWHr5T9b+3Z69Hv9bsGvPguF3jcd19HeO3955BpLa1TK16xWas3WcFYeg4eE3fGQQt0npCQMrY5EezJLsT5MeyJIdyZEd6Yjr3KLmOPnFq3iVu++RXYVm6Tz6C0/Sc7IfO79zujxutSpTPrtE6c1FGjN1ZBCtBVarK6ZEV4r83iLFw910H++j52T/thOlbsfM7yd/5b08879/Abfi3JVN7IbzNHUOfv8xxt6/n1RfesftvbpLc6bO4qvzLL48S2u+QegEqCjWhVg9TzNjkerL0H28l/7Hhuk+1rdGztoUQpDsSfPWv/8u3vz3r7D4yuy2gV36sWN4k/PIegs9l8Ya6cc7f33tWd8tRvdavO97Mvz6P92eQ3S/oBmCkZMFdEMQAFo6gT3SS+6dx1l1/EOIWHPDC1j6nS/tynQt2ZPk4f/l7RQPdcecmi0QZyolpTOLlM+XqFxaoXKhhFd3QbKWaRQibr3L7SmQ31ekeKSXobeNxgHuNgFBqj/Dge8/Sn2iwuwzU9sGeQlb8ORbkyjgnU8k1iZ/KeGn/+dldlkiXwfRye4JTTD8jnHG3r+f3ocHt7wWrfm4ZbIxVaM51yBo+PgNj8gLEXrcIm1lbbJjebKjeXJ7C6QHs3EHQgf1ySrli5s8TyrOEHuXJin+hQ+BEESNFu3n3wDA6O9C1hpElU4JW0n8mQW0XBotmyYq33lpO6hXqL7xAvWzryKMVVdPiQxDVBCsdeeAIlIh7aCMpadvuJLeBu48GFAKt7rE8pln4hRGh+IbOS1k6MUXpFGmcvk1GjNxDT3yHEKnudZnXL78GkYnq6A6NRG/XkKuKZMJdCtBY/oi0t99NUo6LlHbAU2QODSOd20GocXWnMq7105nCsKI1gtvkDy6D9loxSn9Sh1hW1jDfQTzJaJaExUGmy6AZHDTMSmJCiXRNjrGN28f7dbwu4PCgS7G3ref3ocGtx1sVhG0fFoLTRZfnmX59CJ+zY2FaJyAyIuQYSzgEU8ocf1c6+gT6AkDIxELoyR6UuT3Fcnvi/uiU70p/IZP+dwSgXNvpKd1W2ff9xyh62jv2mp+MyipCJyAqS9cZfHVOVrzDfyah1d1CNqbCwkFDR93pU3lUonZZyfpOdHP+AcPxAPKJjyLeBDWye0pcOiHT3D1Exdozt47YtL4B/bT/9gwmaEsYovSgFKK0AlYfGmW5TcWKV9cXtNLWA0EboVf93DLDq35OkuvzdN1tJeBx4bpOtZDoiu16XkiIDuaY/wD+xGGYPZr28hmaxpawgapEKaOXsxuWfp79B1J5qYCfE/xyNvWZ3iG95jsPbR9u+Td4D0/s59M99Y1acPSGDiUXdMZkF6AP7dC7StvkHn0EN7kImGthTAN0if3bpuiX4Vm6Rz58QfJ7y1ipswtJ2sZRJTOLDH9pWvUJiq4Kw5+w+sICW1yLUVcjmtM11l+Y5H5Z6cYfe8++h8d2rLurukaVtbm4A+eoHqlTHO2sWUZqNaQ/Kvf2HzCu2NfAiGwi0lye4sMvX2UvocH0c0bxF+lFG7ZoXqlzPxzU9Snavh1j6DlEzohMpDIMEJFceeE0AWaobNydinO9mUs7GKS7EiOngf76TrcS2OqRn1iax6IZpm0T10gqjaQrTayGXNAZK2J2DOMlll9RgVGT4FwoYRyO/OWItaguY1SrJ7KYHf1khgYRU+mENpNQflNmZDq6RcJKmU0oSOETjusEanbv/C3FwyITo1caKgoIvId3Kq/doBKSoSuY/TkkW0X6Xp49RJ+uxxrXK+2QOgaSIVXW8bbJL1jpvOY6Tx2vgclJe3SbIeXsDtIzycsVfGn5kk9chRzsAcVRAQLJbxL8UBl7R1CL+YxeotYY4MIwyD9tgdRfkC4Ut26w2ATqCjCOXURc6AXa6QfbbWbQdPQ00milkPUaN3XDMVuoJkaw+8cp/tYL8nujQP7KlZrybVrFWrXKlQvr7D0+jzlC6WOZrl2I4BbdeDTtPjfm2UnRJxSrl2rkNtTJjuSJ9WfRoUSt+zcE+UvzdJJD2QZfXoviWJiy5qiDCLcisv8C9NMffEq5Yslwl2Im0R+RFR2cMsOTIKz1Aal6H/rCIUDXZuWI1YzJUPvGKN8YZmg5eNV7zILIuIyyPA7xjqtUJunkZWMA4GZr08w/41pyheWac7uzMuQoUQ2fYKmT3O2QXuphVd1cCtt+h8bJtWf2XSSMhImPQ/041VdqpfKtOY335c/vYCeSaGl46xNsLCyebcPkCtqrCwJkimNR96eYurajTGg2K2TSN4/jsvIyTwyVDSWt1iE3NrGFkZEjTZRo0364QMElSbBYgUtacVZgh2igVUNgaG3j2FlrU0DPKUUft2jdrXCxOevMPeNSbyat/P7oyDo3NP2YpP6RBXZySZ2H+0l2Zfe9J4KTdB1pIe+R4aI3Omt76kPL77qkcsKTFOsm+/ulC4jNEF6IMPwO8boOta3brwK3ZDq5RUql1ZYObvE4iuzOKX2rkpx7k0Lf902SA2kaS00qV+vsnxqAWe5vfFLojORmwZa0kb5QcwdyKaRrke4XEa1XfRinsSDhxHEng7hSm0tYEAqwnIV+8AYejGHrLfwJzeaPt2M5OAYyaFxjHT2xji76bXSEUJDFyYCDUOY979MIBIWWtJGGDrKC1B+gJZKgKHHaewwQrMM7AOjhMuV+GIEIbptxUp9ejxhKD/2AFC3Kvh1YOe6yQwdwC704q7M4dVvU6QnjAhmlmh+4xTZ97yVxMmDyHqL9qvn1oKBxPEDJI7uxSjmEZZB1GiR+853onwf58wV3HPXkK5HVGvGLJhQQzqd/w+oICBqtmORIqnwLk/Ryr5O8qHDJB85ipZOohwPf3I+fpDuoKYlbDNWPFSKjprmWqo97jHubKcJoiA2CNlK1lTocTp3z3ceItmX3nJsWg0EnFKbyS9cYe65aWrXK2tvtZZMxYxaIVBhiIoihBGza1UYEjU2GTBUXIevXChRuVBC6AIjaZLoSt4zaWK7kKDnwQH6Hto8lQirgkcupTcWOP3vXr7z8oSC5myd8797GmfFYey9++h7dAhjk0yLEIL83iIDj4/gN3wWX5m9K/6A0DV6Hxyg96FB7MLmZRAlFZEXUr1S5sx/fA1nuXXH17m92GTyT+JSipIw/sEDsTrjJgNTeiBLz8l+aterXP3E+U3P0z13jcShcfR8BhWEuJcmt3w3Fmcjmg1FT59Gd7/ON754I2sjgP6h+2et0qr4TLxa4dpLm5chEhmT3r1p5CaznRBg5NOgFJptIqztxXAQHZ7Je/aSGcltmREI3ZDq1TJXP36BqT+5eienBQoiN2TmKxMETR/54cMMPTm21gWz/jwEuqUz/oH9tOYbtJeam2aTNA26ixr795kkEwK9s4BXCr7yzPYqhFtBaILc3gKFw91YGbsz/sWlyvpklSsfvxAHAXfhNhl5IY3JGo3JGpNfuBqXBjd5FoVhoGdSqDAkcWRf3EVmxXyu8I+/SDi7hD85h31gjMw7HgHAuzhBOL98Y1EpJe6bl0kcPwBhhD85t2MwkB4/SHJwjNbkZdyF6S07xsJ2Ew0dXRiE0idtFtHE/G1fj9t6mxKHRtES8UMjbAvleOi5NHohg2YaRG0X5XiIdAI9m8Qa649r9l6AVsjEAYQXIIMQ6Xg4Z6/DJvX15vw1mvPXbvtkbkZUrtH6+mu0vv7app/XP/116p/++o6/037xzNq/g7llml99BQD3wgTuhYn12752nvZr5+/8oG+GEOTeehD37ARaFKAbGr4Tp700U5DImFhJPdY8zxosXm6gmxpKglPfuNI10xaHfvg4qb70unTbzVAdb4Gg4fPSP/k6pTcXY9GPm5A8fDjubfV99Hwe2WxiDQ2hooiwVMK5dhXlb5/FUZFaW6ncK+T3FjjyF09uu41Xd5n60jUu/O7pe8NTUDDxuSs05xoIXTDw+AiwOTlr7P37486Jc0u7klndCrqtc/wnH95WUTDyQmrXqjz3i1/GWW7dPXmxkyV6/f99EbuYYOCtI1sGBPkDXRz8oWNMf+kafmPjqtreP4J7YQLZchCpBInDewiXypse49nX4nukCfjqZ5o884UbA/+RJZvewXujF7EZLj1bYulKk+rc5s9JIhvSWPE2tVApf/Zl0g/tJ3VsHBVEVD7z0qbj3Cp02yC3p8DBHzy25TZKKRZfmmXyC1eY/vL12z6fzbD48lzHkRDGP7h/yyCk94EBek7005yp05jaWA7oKmj8i1/pZqBPo9FU+H58M6WEZ55z8e8wGMgM3egCUh2PkOrVMi/88ldpzW9dtrgTbCc/rHfFK37/0iTNZ15F+QHWnmGSDx/FHh8inC/hnLqAc+rCNjtQ1D7x5ds6psht45XmaVw5i7swve22Gjpu1MCNGjSDFQJ5+223txUMhKUa9p5BtHQC99I0yeN7CMsNVBiipxKIpA2pBFG1iWw6IARGIQPpJFGlgWw6RC03jrAOj+G8cfmeCq/cdefANtAzCaz+Asn9AzhX50HTMIoZhK4RLNewR3sx0jZR20N6HUnjhIU/t0LYctEzSaz+At5MCc3QMYoZVBARVprIKCJ1cBB/oYb0fIRpYA92YXZlGO1xSWghCHAbAa2KT3mqRWEoQbpo4TVDFPDIR0bJ9iaozLY59elZgptannRbJz2UZe93H0YztyeWNGfrvPb/PMfia/Obph+V7xOWV5C+j9nVhTk+TlirIVstolYLYZo7BgOancDMF0kO78GZn0YQ18c0y8avlrB7BtDtBDLwCdtNwkaNxOAY/soSfnmZqLU++1A83EP/W4bJjua33e/Vj19g+qvXccubpALvFCqWdT31ay/x9NHeuAa7yZhqJAy6j/cy/sEDXPnYnQWMdjHJwGPD5PYUthVaWTm3zIXfPX1vAoGbELZ9Xv0X3+Cpf/MhsiP5TcWbNEMj1ZPm4A8e4/zvnt4wYNuHxglmlpAtB80ysfcM4L55ZVsC4dJ8yOc/tv6eXz7jMXn5/tmcn/vS4vbqgo2QP/z7pwncje+IWciQGOsjanu4l2Ywe/L4c2W2uhk9J/sZfXof+jYiUZWLJa5/5hLzz28/Kdwull6dJ/Iieh/oJzW4eQkIoP/RIdyKs2kwoBuCoQGdH/vpJVotte4s74Q8uBlkKGnPN3n+l75Ca65+T8m4OyGqNfGvTJH//vdjH+nwP6QiqjXiAOB+SIoDjYtvkj14gtGP/FXCdn2DwNAqlr7+GeRymZSRRxM6CTNLqe3jq9sb524zGKgiWy5aOoGeS+FdnydYKINSGPk01tgAzoXJOHUexe0Owox1Q1UYQRihpWz0rhze9fl7ckMNPYFpJNE1E9vM0nAWiSIPEGiaQRh56JqJUlGcAu/EC/I2CRbWQAGzJ0/jtWtEbZfsI/sJSnWkH5I6MBhH2EohTAPDNFBA45UrZB7ai1iuxzcxikju6ydqeoTVFmG5iQwj8m8/TNTyMPvysbmI49M4dZ3iU8cRQmAmdXRTEAURg0dyOFWfTLeNnTFYmWozcChL4EaUp1tUZh2iW0LxVF+G/keH1zFnN0P9epW5b0yxcm55yzqkOzkRT/ZK0b50EWFZKN9HRVGn7LPz229kcljdfTSvnkd6Lqmx/UjPxatXSI7s6XBLFELXMbN57K5eItfBKnYTtZsbgoGuoz10n+zfmjCoFNVLKyy/MU9zunbP48UoiGgvNZn4zGX2f+Topsp/Qgiyo3kGnhjl2qcu3pHhT7I7yfC7xreVoW3M1Fg5s8jK2aV7HxcrcCsO01+6xtj79pPfW9ywiRACI2kw8vReLv/ROfwgWn8cUsb92D0FtISFXszvKJiVK2ocOGpz+mWHVe5sFIHTvn8kHBnufPE2CwQAUif3EpTqRPUWImlhDffQPjsZj4G3QBgahQNd9D08sC27f+pPrlK7VrlDo6itF0kyiHCWWsw+N8XBH9g6M5EbL1A82B1LBLfXv+NSKmo1ie+D46r7YlfT6JQG2gublyruJ5Tv488tUf3Dz6+VS5VSqCC4KzfRnZDZd4TU6D7cpVnchZmYWK/UhjsZuW1AIonQhEnTLxOp24/CbisYUF5A5AVrUr/S9eMaulQo1481qZe2ZmMC8QshtFi85x4ICBm6jWWm0TSTVLI7viSRj5QhSkUk7AIaN1bDCkkQOjheldsS6dE0NNtAzyZRYYQwNDTbjDUSjHiFtJZqMnV0y0BL2WimgVFIxwGSEOipBCqIiNoeYaO9Vv9HgPJCsIyYnNIx3ShPtwizCjttUFtwaVUCnHpAdc5BNzUaJQ8rpaMkRIHEqW2UUE32puh7eHDb1WTYDihfLDH33NS26XvZupGqjep3yI4XGppuoCfTEEUI3UCYJpppoenGjTZKRVyQFB3CYhBsmDiMlEluT5Hc6PbCQgsvz9KcaexeKc/Q0ZIJ9GKOYGo+VpLMZ0EIwqVb6sgqZmvPfG2Csffvi01/NiGAmRmL7GiO7FiB2rXNU+NbQbd0kr1purbos15F5eIK1fNlaGhk9C4C6SKJ0IWBjkmgPEBhCAupIiJCDGHhyAYJkSYkIFRb338VKeZfmKHrSA+ZkdymJSdhaGRH8mTHC9SulNeVmrzL0wjbxMilQQi8qzM7ZgeTKY39RywGho1Ya2AmoLwc4bTv/6QgNDBtne7xFIalb8j6zJ6tbQgcjHyaoFQHBHo2hdGd25IzkO7PkB3JkezdvD1UyZg1v/zmYkxe3ewYhY6hWwihE4RtRGe8Uyg0oWFbeTy/TiRX78P64/XqLouvzHHgI0dB27zMZWYskn1pMkNZqlfKADx00qK/Tyeb0dB1+JGPpFlcjvC8zoSl4E++6ty1q7rf8Khdr7Lw0gwy+BYITUkV87+u7+wMKETcaqlpcWB0N+euJ1MoJWleu4A7Px0vaDfZLnJaaAqkipAqvCPyINxha6HyA4L59QOidDz8ye0VlVa3k8690xTXhI6mmejCjAMCuwuAIHRw/Qq59AhShRi6BQiiyKfpLHWCgd0jrLfRs0mSe/twlCKsteNBLWESlOoI27xBskvZ6OkEiZHuuL3IMmPVtSAirLeJWh7S9ZFhhADcmRX0pIW/UI0zC7kk9lA3su2xcLFOMy1J5i1q807MB1BQW7gRkbar/voe/pvFRQyNZF+arqM9255fe6kV99qfvj+WsDdDdjy5E/2DuEoRuU4cNGVyBPUKmmHdpKYmCaII3bLwKyWkuz71leoMUIktuiNWa41zz03jNfz4PgmtY0VtxgFSJ/iICbBmHLAaOnohS+LIvrVgwBzpB13fGAwAKpQsn16gtdjCTFtoyY3BwGq7Vu+D/dQnKre1wrHyNumhLJmhrcWFZChZObtE43KDrF4kpeVoiwa+cjCFTULL4MomoQpIalkC5eErh6TI4kuHtJ7Hka1tgwGA8vll6lO1DSzvVcSyxTo9J/txFpvrggH3wgSJQ2MxhyiMcC9O7KpUqBuCpz6UYeqqz8U3PaavB5QWQ0qL93dysJI6PXvS7H2sm2yvHdvbKoVhaQRuxOKlBv4tK/6w3ooD+nxqjQG+VcmhcLCL9FB2S78MJRXVyys0p+sbVuSr0HUL285jaBYtJ8LQk3HwrCI0TSeTHoAmeEGDKAq4NRgI2wHlc0tEfhSTYDeZS2KvAJvCwa61YODRh20eOmHFE5+n+OGPpJmdD2k21Zq51JefuftgoDXfoHopvgbrjkmAbQl6ezRsuyNW5yjqDUmj+c3NHqxC12MyZcLWmJkPcb07P47IbRPUKgTVFUKn2RkTN/6eiiIUgkiGGJpJwsjghLXbzg7cPzrurYIsm1Lcb3nq1Np/7e73gSBy0aMEyVQRz2/ghy0MzcIP2zTby2RTQxi6ha5ZcVYgimg5y1vvR4hNj9VfqOIvVG9k3Ca2PjJrsEj65Di15y/ueBYKaLx8ZVu6Q60Otflt0lHqlv+9Cen+NNmR3JZKfKuT7sLLs1Qur9w/6dybEDZqhI0at8fx2HzbngdjtbStoKTCKcX6AGTz2GNFtGSCYHoe6+A4WiqB8mPfiXBhGfvQPsLFEsH8ctwWtCqg0myjPB+R2kbRUMHyq3MkCgkyw5uXZIykuVYqWGfAIjSUkmiagVJyQ+99dixO0265axVbQ9cmKkRLkm5jgLask9YKHREaSaRCcnoP5XABXZiEyo9NTQTkjJ5Oa9IuoKB2pUz18sq2Lar9jw2z+Mos7ZsY39a+YRLH96Hn4pVw8vh+Sr/5iW170BZmQv77v6/y+/+xymPvSPKWd6R4+rsz+K7il37u/gavvfsyfOB/OsTMmzX2PFJk4VKDKJD0H8oy+drmWdDaV94g/dB+zL4CKgipfPalLQWHeh7oJz20dVZLhpL5F2eItiHLaZqBaaQQQMIu0F04iGEkCEOHIHRQKPK5MZqteRqtjSxzFcUti83pGrk9xS0DEzufoPtoHxOfjV1jv/qswxtntl7YKQW3KYOyyW8olk8tsPjaRuZ9Ji04csjkV/5BkeNHTKJI8eKrHn/wxy3+6JP33rdisyrOrR1/tiU4fNCiu6hRbUTrggFx02JtN6Ne2G6RGjvA8Id/DL9eRvre5pyBr30ad2GGUHl0W6PfOqOizZDu20PX/kfIDOzDSsWiIssXXqB89TW8mySFj33/38FM5dE0ndB3qFx/g9mXPr1Bf/9WCE1nzzv/AoHXonL9DRqLEzRbC2yspijmll9DoMVynLBldAWQGznC8KMfoj57mYU3vkzkb0LA2MVdDFYaNF66vPOGt/m7d4L8gW5ym9R21+06lCy8MEN1M4OO+4rbOektyFcP9G87IUVuyMwzk6hQxi2aho5IWEjXj93A/JA1wynbRtYbscuk5xFVbr8MMvuNKbofGNjSD8FIGPQ9NIBuGbF2Rue0NE2np+9BlFI0G3O0W+slS7PjeQqHtg4GAOafn8FdbhPIgJaso2NQlyVSWg5N6HGQQSwOFSofUyRAg5VwlpPpp7jivEZb7u6cV86XyAznGXxidMttek/2r+nLKxnzddJvOUrja68RLq7cKKvdRgC6uumqZ8f9hm4IdFPji//2Mpkeizc/N8/EaxUy3RYf+Ucn0YyNB5E8Nk77wjTRSxfj+7vV0lhA1+Fe0v1bK0iqSLL40vaqjlHk4QcN8plRUsmemCsVOrheFT9oY5kxMVCgbelqF2cgymSGclsGA2bWJrfvxlhy9XoY3wJx0xrqllt5t2uLyAspd1qSb8XxIyY/8WMZwlDy3u9dJPAlQQjN1r0fTJMJwY//cJY9IwaVqiQIFWGk+IOPN/nrfylPd5fGhSsBL756Y9F2YI/JA0c1LBNeeNXj5/5aDtvWOH3O45VTHtcnt4+UguoKjUunaVw+c9MCdeO5hc0GujBI6BnyVj/LzuQmc+HOuOfBgKabDDzwHjTDor00yfLKTKzFX54ndNf3hE587ffQrQT54SPkx47FCku7gkAYBsK/EWptdfLxJ9Gu5h0hNIRudkQcdnkom+0zCAkb95jcJDR0w0K30wStyq4Nm9KDGVJ9mW23qVws4ZRahPfZYe9+ID9ewMpu3WYnA0nlwnKsRCYEhBJCiTU2CEKt6Spolok1OhCz2oVAS6cQhoHZ34XRH4tfGb3daOkkQW8X4XJ50/3VrlcIGrHP/aYcjY4AU3YsT+16hcgNMYwE2fwoQugI5KY1v1RPmswO/gPVqyv4TR9POqwEMwg0QhXQFrWbflN0/tZY+5utpWhEK4TK2/Ug4iw1aS3UYzGxLRQQjWTsvJfoSq6JwshmG6MYi6jERFQIl7fnGfUO6rz96TQPPJZgaT7k2kWf577corz0TagfC4GmC1QUlwZ0S0Mp8NsRibSxaX09MdaHP1Mi2kF+ODOSx8rbaFu0+spQxuJPc/VtW9+iKMBxq4SBg0KgCQ2F6vCmJELEvy9lsOWqUSloLTSQ2+zH6DgpxhLvcSlAAYWcxi/8fJ5f+RfVtZVwT7fG3/zJHP/0X1XvXIUQaEzV8GvuphnLdFqjt0fnjTMBcwshnqc65PA7399W0HU4tM/ka885dBV0+np1bEtQrUk+/+U2hw+Y5LIab3nQZmomZM+oyfCgwZvnfC5dC/jge1PUm4qVSZ+RQYOeLp3f+K3tA293aRavsoxmmKRG9mHmiwghOtL9VZyZCZSMkK4DShFKHy9yvjmiQztDYKSyJIuDNJcmqM9dorU8hdB0It/d4C3QLk3HTmWZItno4K73opSkcv00MnAJmtV7ewr3Aopd6ZDfDnTDItk9jJ3tojLxJirYHYs12Z0i0b29WU99sralDO+dQOgmhbHjtJYm8G9yrbzXMDMWdj6x5UoGQEaS5mwdJRWy2SZUiqgeS2JHzdiGOuZbCIQeCycJQydqOqAk7vlrKNeL7U1nFxCWGTtZboGg5ePXY7nmrYRc0CAznKU5Wydy4wFbRj5CCMJw44C9apZkprfvBmnNxX4Dkgj/pt/YrHa4+rdVxbKVYJ7wNmqMoRviN3z8hr+p+NGqTHGyNx1b0ZbiLFtUa6LnM7GSW0dwLCxVtn32DCNWIbx81mNuKmTqms/yQkTrXgfcm8BvR6xMtTCTOitTDsPH8nSPpmIb5UawqUGOMHRSR0cJh7tRfjwTts/eIq4kIDOURbeNLYm9MpS4VXcXJl4KKQM8eRd9fCrWF9muTCh0gZ40MFMmQetGVss04chBE+2m1zCZEDz6iI22fSfzjmjO1PFvITT/8EdS9HTrnDhmcmCfgWkK/tZfyxJFcPZCwLPPuzSaioF+jQdPWAwOxCRHTcDyiuTFVzzmFyLajsIw4K//lQwvvOzT060xMqxTyGs0m3HJYWIypN5Qa/yEpVKEZQq6uzSSaY1DByyKRY1WW5JKCYp5namZkN5uHU2DC5cD2o5iZFCnWlOUqxKpYnOnnaCkxExlSY8fINE3zFoaBoXV1YeZzlG7cAqAhJEha/YSyDaRCr8NMgNCxGl/08KtLtFamtxxMlAyQkW3efBKUrn2+t0d658yaFaCzMA+zFSO2vR5dvXeC0h0Jbc37FHQmK4RefcuK6BpOpn+PbEU9f0KBjRBojuJnjC21eaXoaS92IqJhM02stWOc5q7XD6EizdKJ169ufMXJHhVF7/ubRoMrOJm0piUIU67TDoziFTRhqyPXUhgZqwtV5BrqpHLrR1dAzf5NpEKdl0euOlrhE6IU2pvqYQIsfGOddPzF1YbmENxR4Ry/Zvy/VvfD9dRTF3zOfOaRxhIPFcRhVvSe+4p2lWfay+VMSyNqVMVjry7j/FHivjtkNkzGzsJAIKVOmZ/EbO/iGx7oKB9bmrDwaYHs+jb6H5EfoS7cu9r31shdMNtL6gQAk3TsHI2YTvAtiCbFezbY5LJaBw9ZOI4Ck2DgT4dy7xTXvsNNOcaG0S63v64zd5xg+FBg74enURCo5CPMzaW5fDqKY9GU9Hfq/PUkwkOHzTJ5+JgoO0oTAOefcHj6vUQ0xD8r38rx//3+y0KeY09YwbdXRoygp4ejU99xuHcxdh5MQgUUQRBqIgkWJbgwWMWo8MG80shmiZYjeukUgQ+JBKCrqJGq62wzDigmJ0PWVzeOatl5oqkx/aTP/YIkecQ1OKMsG4lMIsFkoOj+NUVnLlJzChJyszhhA38yNl15vhm3KNgQHTSaQZmIhMzWWXc1y864eJOPIDd7Uasc2OKB8FtTlrE/eqrx7ia0tvxe1v8lhCiQ+BQm3wW76NzZJ362eb7EFpcv12TFl53TvKWFUR83IadItM3jt+uIXR97brC1tdWT8ROXZsZ6dyM2kR1lyUC0TmetdPc5KETRKHPzEuf2rx//JZirxBi8/shtPXNEbd8LgRkBrKxxekWUFIRuSHtxeZ6kuV9nkGclTZuxdnaVljEE4Fmd94NpYhkgGEkkDJE09bfr1Rvesd7GAURznL7tq2gFQpP3dmEE7YDWvN1Cge6ttwm0Z1aF4xG5TrJo3sJV2p412fRMil2SklVShEvf73NweM2ybTG5BWfWjnCtATt+1Afvhm1BZfXPh63lF3+Ron5C3VSBQvNECxc3Fyrv/HiBTTTWB+tyFufX0FmOLtlgAcg/RB3pbWjUNi9gNA0Ij/a+dUQMZGwvdhk77jBe96V4Ae+O83J4xb/9dd7kTEtB9dVfOEetBW6K86GAPd//ntxWemD703wc389yzPPe/yb36hza8KuVpc895LH//hCm2sTIVIK/uEv5PnxH8kQRTHnYRU/+eMZ/vsftfhn/7rGxFTID38kzf/0szmWS5KpmZCOTEzcJaEgihRRqPADRX+fTjIp8DxF21FIqXj5dY/5xZCRIYN3P5nks3/S5u/8rSI93SHXJ+NswdoF3eL5zxw4RmpwjObV8yw/94V145aeTNP9+NN0veWdLLXqtBYXCWQbTRgE0v0mGBVtga4Db6Fr/0Okuocx7DQIjeFHv5OhRz+0dgKXPvPrOJX59Q59t4m+4++i79g7MFMxOas+c5Glc8/SmLu0YdvMwH4GHnyaxtxlhGaQGzlMIh+vSFqLE0y/9CmCVg0ld75ouaFD9Bx+ArvQx+Lpr1C+Gnt8a6ZFojBA176HyQ0fxEzlUVFI0K7TWp5i7vUvxC6Nt9zs4z/wd1k69yxeYwU710Pv4Scw03lCp8nKlZcpXXyRoB2v1IYf/U6ygwewc93oVjxwdh98bO26Rr7Lm7/3y5tOvMmuFLq9s2RrfTMP71shBPmRo/QefTuGHVtkShky9fwf41bmUTLCTBfIDR2ie/8jWJkiU89/jPrs+o6K/uNPke7bg2HHpQsr00Vt+jzVidM0FmKWcqp3lL6j78TOFgFB6DnMvPhx/OYNroQQgkRvalvthMgNcVZ2Z2ByL+FVnB0NiZK9N2ShhaZj21lcp0Ii2YVpri/r2MUERnIbO1upcJbu3H/gThG6Aa3F7bXh7UIS8yZOR/rxE3jX54iabfR8Bmu0H+/c9hbGw+Mm3/EDWY4+GDvsffQ/1/B9xff9WI5f+vn73wp7M5orPs2V7ccwsytL7l0niZoO7TMT6JkUYa21oUyQ7E1vO9EnulPs/a7D7PmO3ZdQ7wqa2PZ9AmKtFDt23zt/KeDilYA/+mSbf/Z/dvG//IMVHKfDJZDxKvpuq6VBy79jbYGJqYjJ6TjQXWX9/87vt/i1f1Gkp3v9df/MFxw+9VmHl16N9XJ+4z83+OmfyDA8qDM8ZHD+YsDf/8crSAWvv3mjM0Ap+PQX2oShWks2rg+ovDgRKeFv/4NldF3EfhaRScHoJ6d304hWaEU1fLV+zLCLfSA0Kqee37CAidw25Ve+xr6f+N/QE2mSdh89iTFaQRUvalNTC4Ty9lr470kw0Ji/gldfRrdSpHtH6T/xLirX36A+d4XIj0/QrS4h74ZJApSvvEpj9jJGMs3oE98LSm6t2iVAN226D70VpzxHbfoc5asuRiJD9/5HGHn0O5k/9UWcyvaGDpmBfQw8+DSh51A6/xy16XNrnykpO6v2JCuXXyV0m+imTbI4SHb4IEMKZl759AauhNA0skMHSXnDKCVZPPs1VBRhZbtol2bXES3LV16jNn2BZHGA/pNP4VaXWLnyGpHfechltKWCm1VIbLvyiH8g9gnYqeddCJ2eg2+lfO0UXm2JKPAQQsNrltcyE6HToDZ1Fre6wPg7f2TTXpyVK69QmTiNphsYySyjj38vjbnLONX4Pmi6ycDJpyldehG/WUUzTNK9Y/Q/8DRzr36W0G2uHhB2PrHt4CVDeQdp87tH5EdEOyjFJQqJG+UNJQn8NtXqdSynhO+tX3FaWXtbXkScsr9/0rxb7jZSO15fM21i3hTIKCnRs6lYqMvQ0btyOyoQZnIa4wdM/tUvLvPTv9CN7Hho3IFl+23DsDRyAwme/Et7QMCpT84yd75OMm8x+kCeS88sEwW3pP8fPkCwUCGst9AzSeyxXtrnJlC3TGp2MbFliQtu8C5uzgJ+qyEA3dTX1rNSQq0m+a3/1qDRkHdFFtwMQTu44yC3kNf4zg8kefCEydiIQSIpyKQ0RkcMdIN1fIbJ6YhqTa7NuWEInqcwDUGi4/J8c2Bz89y8JrK0BVa3jaI4owBgCYO0nickIKnn8JWHH60PBlRHE0JLJKB1SxlPaOjJFDIMUDKi5ZeJpI+CWGhsF4vcW3FPgoHQaRB5DkLX0U0LJSVeo0JreWptYovbqO4uTAzdJqHbQm/ZRL67KXlnHYSGkUhTn71EuzRDFLgYdgrDTlEYP0Gi0Iffqm3SQqhA00jk++g98jZC36Exf4X63KW1SRjiidhvVqhMnMZrlJG+i9ANQreFmSmSGdy35YtsZYq0WlXqsxfjjEkUYSTShG5rXdrfa6xAswIoojAgcBq0S1MEzmr9eutrYCSM7dPoKo7iIy/ahb6AwmuukOoaRAgNpzKPVy+ty/QoGRF6bURT2zIVvzqZG4kMhbETVCfP4FTnCT0HoemYqRypriGygwcI2g00PSaYWqn8+msp4vPbru1DSXVPzUx2CxlI1A7BgJ64lTgmSCS7QG108dRto2ODuxVU3Id+vwvot+5Vqh3LErqlrwtI3fMTGH3FWN9BSryLkzset6bF9dnZyZAojFsUWV9tum/I9tocf18/Ti1g7OEiybyJit1/OfqePq69uEJ0yySvp+y4ddU00BJWrEC4Ccyk+U0JaO45bgnA/UBx5px/XyT6ZbCbsWkjhIAf/cE0Rw8ZNFqKZ1/wCALFyLDB/r1xF8jNQ0erLQluCeqkjE9V2yFbcidvnULiS5e0nifYQuTLr5XRk2lyRx6ifuEUUSsWHtJMEyNbIHfoJN7yPNJziFRIJEOKiWEa/jKBcOOx5DZwT4KBeHUaQaQRhX5M3IoCZOAhd8l6v429IaNwdwQJpZCBR236PKETr7aCVo2Kdoqew49j53owy/MbgwGhYSQy5IYPk+waYvn8c9RnL8VWyrf8ftCuU5s6t/7rQiPRNUhmYM86PsCt8OolqlNn1wbDYBOynQzjByUKPFASJaNOZ8bO11U3tR3TfjKUu4q8lZLUZy5S3Psgya5BNNNCt5K0lic3ZD52PC47RbLQT6p3lPnXv4DfqsVZHs3ESGTQDAs72x2XnOK941YXUDcpmAhA66xQtjzmSG0r1nK/IIMIuY1LHXRWV51HQwgNy06TyQzge3V8f33qXTO1jl/D5lgL6O76yG8P8fXdfgWimdq61a93eQpQ6LkMKoqtxncKBoJAUa9GHDhqkcpqDI2ZNOsRtcr9v7fpbov9T3Tz9f90jT1v6ULTNWSkiELJ8PE8mqEB648jWK7FCoS5WP8iam3eGreV8+OfNkgJK+X7U6JSkdp50bcJdA3+wg+kuDYR8szzDl/4skO9oXjn222+9zs3dleF4d1JB98upIrwlUNe9HaCgY3n6C3NYaQypEb2ocKAoF5FKYVu2ZjZAsmhceoXTxO2mx39EEHW6iWQHu2w/u2kQPith1ISt1bqpFs6f5MRzspsp20jh5HIQG29wEtM1tvD0CMfZP71L1C5/saN9PRmEKuDdYc9KwTSc9B0a8tB3Ksu4TXK93U1p5k7DDZSEe02vazUWqtoum+M3PBheg89zuTzH6O9PLV7gqgQpLpHKIwdpzp1Dqe6uD5jpBSB02Tutc/hVrepBwsQhth2dajkvQ4GRCc9uv09i4Jow2rxVmimtpbV0HWbTHZ4SwKh0MX2K0i1ygTf4qh1ET9m97gBW0q5YxeKpms3hHmEQIXhmvW3lkyQODRGML+87fKqVo4485rH3/h73QyMGHzXD2ZjrYGv3EP3ya2OXxMYps61l8q866du3NOY+Lr5d+ovnif9wD7MnjzSD6h9+dQmxXMRZ0z+DAQD324QIs4kHTlo8tE/bvPaGz61etxGuGfMwLK+Ha65QEPHk2009DU/iZvRmrpC2G6C0Oh5/GmEEY8LSkZErSbV0y9SPf1ixxxO4EsXN2qgi+0XSVvhz3QwAAoZOBteWhmFSBmgmzaaeUv7l1IUxo6TGzkciw9tV68TAitTpDB2gsL4ccx0EcNOonWEi7ZDFLhrq/77hZ1U2uKan9p1nstIZgndFvXZS/jNKkpK0j1jOOW5XQcDuaHDpHpGiHyH8pWX130moxC3toRuJUh1jxAFLkErli02EmlC7xYS1o6Vjd2f22bQMcnoeWpRCUskKRh9CKGx6F/f+cs7V13WEMkA11nB9+pYdhbt1gBS7eL3toDdnabrkRG8lRbl12Z23N7I2GiWjr8bm+ddHpew7bg0kElh9neh57NrKXRrbID2qUvblhCXFyI+/Xt1XnvOYWSPSXUlYnkhZGUX7Vl3CykVgRuSyJprWbZU0WTkRB6/HW26as2/50EEAm9qCefyLLK1SRZPsG22589x51AKXE+xvBJx5LDJhcsBy6WIwwdNfv6nc/T2fOs5GLowSOo5pr1t7MyVwlueZ+mrn2b52c9iZAsxcdv3iNqNW7g2Cj9qc732yh0f05/xYECgGfaG4FtoOppmEAX+xu4GIWguXqc2fR4jkabv2DvwWzUa85c7E9MN5IYPUxg/QaZ/L6Xzz+FUFwl9ByORJjd0kP6T77nP57c9okhum3iI7WbNXdVehaYz/Mh3YCTjdjmlJDIMWb74QmytCSSLg3QffAwzncdKF+g//hS5oUO41UVKl14EoGv/I6S6hwndJnuf+jEA2iuzNBau0i5NE4UeUy/8McXxExT3PAAi5jZUrp2iNnN+rSShVFyb33YyEmIdZyKhZUhqWSwtgSMbBNIjoxdjkR7polCktCwtWSNUPoawSBtd1KISgXJjhz8sBIKiOUQ1WCCl5xAIGtENRUKhiW25GhBnD27QjyVh4GLaJs3mAr67niwkQ7lj3VTbYn9+pb1GXBOGRqI3Q/5IP+5ig8gNOn/L0rhWQrN08kf6sQopFp+5ijNX2/Q3185TiG0JcBDfI+l4RPUW0gtIHtuHNzGHbLnomWRsHLVDRKFpYNmChZnYnOhmItb9RmXG4ZWPzfCD/+QkAwezvOdn9uM1Q4Qh+Op/uErgbAxIal9+A7M3jzXYRfEDj4Cms/Lxb2xgoMlg+77+KIgIGn4s8PNtgqDpE7rBN70kdbuQEn7ln9f4oe9L8yv/sICUMLcQ8U//ZY1f/Lv5b9rzszUUCsmofZRIRdTCRVpyq/dNocKQoFamMyDeNf9uM/yZDgaEEFjZrnUyx0LTSeTjFV7oNAi9jSugoF2juXANBGQHD1AYP4FSEfXp82vdEQCJfC+JfC9efYX63CUCp4EMA+x877ZcgTuCUqgoijMOu0wtykBunxoWqzXdnX9PKUll8gy6acfPo5TIwCdo19YezMBpUJ+5gGbaNOYuE/kuodeOV/QdVK6/TmPuMjL01lZVgVOPf6dznu3lSZBRZ1+xVoRTmV/HGYBYt3y7eqKmi3Us/IxexNbi9syUnqelqnG9HgtDj218LT1FWzaQSiKR6CJ+dtaXBgQ6Bkk9i6UlN/T0aqaOZuyQGfJvrCqljPC8OmHoEkU+UbR+8Jd+tMGW+mYIEZMMNwvqlLzhLy+EwEhbcVvbUI6g7hK5IVZXCq4qgqZH2A7QEwFBY2dOitDFjq2rURiXTJQfokKJe/46YbkW26HXbIRh7FgqGxgxePJ9aT76mzXCTUR+7ifcRsD06SqGpTF3oR6b+jgR7WrA7NlaTGi8BcoLYuJgMYPRlYuFhzaBDLYP1v2ax8JLs8y/MH2vTgcNDUtLktYKNKMygfKQ7G5iEYAMFM2Z7eWRv1k4dzHg3/1Wk7n5qNPFsL5n/xsv+jRa0NOloeuxdPALL3n80/+7xqWrAVLGfJRf/mc1Tp32WS6tD+z+xa/WmZ4JmZq59x1JoQqoBUsYwsLSEkRsDCrtviH0RJKgXiOolkBK7L4hEv3D2N0DSLdN7dxrhK066h6wN7/1wYDQ4nR8RxRIaPrmwj43vtDxDtA6jNCO3/1mkZLQMJNZUj0juNVFZOCjWQlyI4eRoY/XrKybqFYRdSa50HOoXD9N96FHyQ7sJ3SaNOavrA1emplAMyy8RpmgYzFpJNIkcj0kCn337hoRT8ah18ZM5TGT2Tgtr2LVok0Nldi5SyBWFFutXW5dc+4cAPWZbVJaAqKgRat0hcjfeqCoTZ/v7BvMlIHf2viixaWIHRwflYrb6baLdQwNI3Gj/m5oFkbHrU9Dx9ZiIpEmDAxh4sgmAoEuDLTO82UKC0NYsfKXMDGEiS4MfOWSN3oIlI8n119/3dK3bwUkVu+7cW8UYegQhpuL/4RuGEv3bnmicYZnM703PWFipExUJDEyFlYxhYokViG91nopBOgpC2+uRthwMZLmhja4TXerr7++myHyIqQXxUs1KQnLNaJGG8IIPB/v+g7ZHSBf1HnL25J89De3z1TcD0SBor7o8ebnF8j3J9AMQeBEtCr+hpbCVRi9efR0AumF+PNlols1BjrYSfEvdALq1ytM/cnVdX/X0GLzNVRnIo9/w8AkDmFX751Ye54jFa/mTWFTNPppC4+VYA5PteOgF6Pzaze+q6GjCY1Q+QhEPHGJJKHandfL/cb0TMT0TPzOCN3AyuWQUUTkNBG6ztwiLFTj+URFIbplE7QVf/jpIF5cCY0Iwe/8fiuux8v1ZNCPfvz+cFI0dHRhIJH4ysUiuem7mxwYxcwWaE5cJKiW0Cyb9NgBUiP7MDI5VBTi1yu0Ji4RtXehjroDvmXBgNANdMNGM+1YxEYz0M0EVipPGDjIMIjTz6siM5qO0E00w8SwU2i6gTAtjGQGK5VHhj5R4G6sXSvoO/ZO6jMX8Ns1zGSW3iNvx6ks4FYW1roMNodi+cJzJIp9pLtH0K0k7fI8UYdMGLpNosAj2TVIqmcEGQUk8n2k+8ZJFAbu6fWSgY9bXaBr/yNkBw/g1mNipBDalhNnLNixQwQvBGbKImzdeT8vxKtwM6FjpUwaSzsr2umWRvf+HAtvlu+MQ6li2d/tgh3N0Nbp+btRa63u24oq5M3+zqpeEXWUHwPpktDSneBUw9JSJLUsUkXYWhJT2CS0FPVwmdHEYea9q7Sj9ZOUkTTRE9u/Wn7NRe1ylevXd9CnF8RmTZsko+yeNHZXGt02SA7kSA3nCZtevCoNJSqSBHWX9EgBd6kBuoaeMrG60juWCW69vpshaAXrNBASB8dwzl9HNtoQSaLqdu9f5zcCRa0iMS0Rp3dXqyvcl2zpOohO9izyJZXZ3Sk1Jg+PElUbtM9cJ1iqxnWOTZ5Tv+5tn/ExtHWCTcBaEKsJA6UUnmwTEaKjk9LzhJ3gNM5qxcGrLZK0oioKRUJL02+OM+1fwFcuCokhTFJallAFODJmphsY2FoaQ5g0ojIaOhmtSI85wlIwST0q3ZH+/f2CnkiT3XOU0GnSnL6Ckc4QuQ7JvmEizyFst0j2D9OcuIieSqPCEBmFcbnYa2Nk8kSuQ+S1t3aZvEcwhU1Kz5PW84AgoxeIVIgj178Ldk8/RiaPvPxm/P+7B8jsO4pSkvKrz5DoH6Z48q345eU/vcGAbiUZeOA99B1/V2wj20Gi0Edx30MAVCffZPn8czTmYivg4t4H6D70VrKDN9S4ksTqgAAyDJh58ROUr762RsxTUUht+ix+u0bX/oexOwqEzYVrTL/4yU1b+TZASeZf/Rx9x5+isPckY098L9e/+t8ARXXiNEG7Qf+Jd7H/fX8VlKS1MkNj9jITX//vHPrOv3nX12oVfqvK4umvYKZy9J94Cs20kaGPW13cMhhwV5wdW78AMiNZvLp7V8GAbmhYaZNkwd5VMGBnLR78gX0snqui7mC/Silai61tgwEjacQmTZ3sYTVcoHrT5/Voo2VzIyqjbkqbVoIbolQtd/XbgpSWoxIs4katDYOinbexc/a2x++stHfsOLixrUvgbH0fhSZI9qbQNqnft2eqXPvdG6Si2vnFG5bCq9/XtbW0b/m1acqvTe9q5WckdJK9W9vvQhz03FzztvYO412fjYOBXaJZl1y96PFdP5zlwmkPz43T61EE09fur9hScSTJ0af7eeF3JzctCWyG+lffWPu3SJikjo3Tev3qBvJru9Ta9tnXDW3Dc5TScvRbe6mHJbqsQRb8a0QqIKt3g4C83se8f5VAOaS0HL3mOIvBJKsvgUDE638VAYqs3kVaK4CAgt7PlHcWgU5Kz5LSsmtcGCFAEzqGZm0oKwgB6ZS4L9bBu4VmmAStGqHTQugaiZ4hapdOxZO+7xE5sSS5UhK70IuV60IzLULPRYUBup2kNXeNyN1eUfNewFNt/NClHdXwVJukliVQG0tJmpVACI2gGt+DwgOPEbbqtKevUr9wivbkZfb+lf8V3bLjm7AqdWiYEG2fddoMdx0MFB4exZ2v4S7UQUkac5c5//F/GbPlt5AejnyXhdNfZun8c/FBJE20pLmOwazCgCi8cYGqk2eoz15eFzyshyLynI0MfU1n+fxzLJ19dq2Or2RI6DkblhX1uUu0P/MbyChcxw0IPYfFN7/C8vlnkTJ+iQBCt0199iKtpQmErscPm4yQUYCKIt78/X9M4La41VHlwqd+Ne5ouJ1uAiUJ3CZTz30MTTdYfbm3U2/zai5BKyAKojXp282Q399F7VqFsLXzwLr/3UOETkjkS9LdNk49oDzZYPBYkXRPgsaSw9LFKnve1k+mL0myYKGbGlMvLVGZbHLg6SFSRTsOPJQikTPZ/85B0r0J3LrPxPOLDJ7spj7XwkzomCkDrxFQutbAqXhrk5iSiuZ0bfsARgg02yDZm8ZZbu1qglO7qp/GCdpF//qmGuB2MYm1jXkPCpozjf8fe/8dJlmWnveBv3N9+Ij0rrK86Wpb0z093eMdBgPCkAQIUpShwSNppWellVZarkRpRa20Ky650orapfisSNECoAEIwhAYgAOMn+lpX91dXV3epnfhI64/5+wfNyqzsjKzXFf3NLB656npqogb1997vvN97/e+99326K90SXp313IwbBNvOE/Sj+8pBHTnO2Jb/fcB3h+mZ5Mfv7s9drDhb5NmVmFE6SsvQpKiwgiUpv21H9z1xVWqGDz5rIfWZP8dfN7vKP7Gf73T5/5RojDkcuzTo7z0j27cdTnh2pilHGYxhz1awSwXELaJ4Tp4Rybxz1zfVurRQH+xe9eA0PQs8hM7/S00ika6jG24CGGQEyWKZo1I+UTaBxQCA4mkma7QSldRZPdprANiHdKXLRQSR+QomFViFRLpPiDImQVMYbKa3CBSAQq52Rcfq4CebG4LgEeGDP7n/36Ev/i/X79Nc//DhYoD0iArx1iFCm51BKc6glMbxXC9rIxQHcXKL+JUhgFB3G0ho4zwrdWA0PshCHd5RgHXyJPqlEj6hINszJ3QSYK2Hcx8Zqde2H+M5plX8BeybiatdUbgHQQCRi5P7tAR0Irg2hWU/2BljocKBgzHxB0r444Uye8bzmrPro1d9ojWu2il8Wo1hClIWgFaKZxagbjtAwKnmkPYFnG9h04V7qSHN16h8UaHeGP3dMdm2eABIRDI+P7Ej7RMSXYtG2hkEiJ3rEOjZUIqd9+vJOziTkyS9rqoMMhIHgNTGpVED56O0hoZ+btQTfaA0kStgLgdkhvZYwYnoDRbyQho94HSaCYBLGOJU7ApjGk2rrRpL/sIU5CvZYNgcdRDpYrG9S5pLJn9xBiFEY+4m9BbC0Frxo5X2f/COH4zorXQQxiCI5+bwsqZ2K6BYRlYrkmaKJbfa24nC2qIuxFxJ8Kt7m5jLITAsAxKM2XCun9PyeUHQawCJDsDAWEIvOE83t2cIoH+Uue+1RGTXkzciUj6ya5p+VtmT8WpUuZcuEswIGwHM5dDWA5WsUjSrCOjLMAwHAcVBAjLGhCRNIbrIX1/7zy8yKSGHzQYCM5exSxkXI1bFsbbX8A7jVtWF1N+45d2uireqRj3QUErjWkbd5WY1lJmqoNCYJ/cT1LvoMIYI+fiTA3tfNnrjIh3t3vAdC0KUyUM29hW7hMY1OwJckaJvmwR6j591cYzCmgUqU4HhDSN0vI2HgADUzC9ObuPtE+gurhGHo1C6phA9cgbZYasKSLt00hWUGRkXYFB1Rqnla5uBs5CgGPfXfPjg4aMQ6LmQC9GQ+fae6R+l/78lYEAXkx38Flv7nJ2DtIErRXFmSPZcQxK0XqP9/m9IFwbe2yI8peexfAcVJzgv3UZ/63t3jm28CiaQ4OSYwHQ2XVU27MSUWONXO4Aw5/4Usa38XtEG6sk3XYmjFesIKOMiG0Wi9gjYxiuA0o9FIH94YIB1ya3bwgdpxiuhTNSQpgGKlXkZ4cQhoFZdDPSSdEjbvrk9tU2I5ncTI1ovUt+uka43sUquDgjxT8SalybEAZ2bRhnZAxhO8h+F52mGK6HVSwRrSwh/d2JRY8SYT0gbN0lGADK+6tZi+F9wLQN7LyFSi0MS2y+JPv1EK9sUxjNXvSWZ9JdDWjOdYl6KY/9xCwq1ay816A138cwM1OUkcMlLn9rieZcD69kc/InZ+mth3jVrCXUMA2snCYNd5KWVJLZE+dG83sS9gzLoDRbZePd1UcaDOwWCAA4FRe34u55PvVAV7+31L1vh0GVKsJmSNgI7lqjL86UaV6u72qSZNg2ZrGUqTuOjGfE3XYTlcTY1SF0MR2IAmWkTDOfJwzm9rw97YKDW/EyrsIex4keBAPtrf2RjTbCttBxgur5YA6umxCYTg5hWcgwm9FolXXPdHuC069ECNNEy3Qrw6cVwrR2dJk8SsSBpLUccvDjNdrLIfKODoDWcpDFS1KhghhSSdroEi/VUf0Qw3MIK/mdnAEN3YXOZlfJbu8+wzZwqy5uLUe44W8v7SCIVJ9EhyQ6Qks2Z/BZh4AkViZSbGUyARQpXdnc/CxSPl0aSJ2ikCQ6IVUpAgPLdBBsuYemOqEvWxh3kFNSCQvLKbMzFusbkiTZCn263Q8pYJMSGWwNprfS/VF02703+D5q3PZ8CEHSbSJMa5vU/EPDEAjHxp4awaqVSDfaO4IBqVNSHeMYt4iDuwvChKuLGI5HfuYAGAa96xeJG2voJEaYFla+QLS+hIpChGFgWJk8u0qSh1JtfMhgwCI3VWH5d87gTVawyx7Sj2ifm2fyJ57AKrjELR8VJjgjReKmj+FkO2rYFqZr0zo9x9RPP03/Zp1wNQsIovr7J0F8VCBMk9z+gxhO1j6lilkHgDM6jlUqI30fFYYf6IsMwF/rEaz71I4M77lM9cgw7sDU6F4OYWkkBwOvRkkwTJ2x0e1sFm9aRubRLiAJUmI/zVL5gwmfMMRmIKCkznTeDYHlGJiuiUx11rutswElm8GIPbkB7esNSrPlbTa5t8OwDWrHh7nxex9OoFneX8MpuXu3a2qQYZIFA/fB57iFYK1Pb7FDad/uOveQlXtWXl/c9TthWpiOB4aJ4Xm4o+OgFEmnhTM8ilUso6Jw4B4oELZNuHBzz23lRgvkJ0oDOd7dIaOUYK1H1NoqcZhDFfLPHMssjK8tYpYLpCt1DNvFqY1i5fOE9VUMwyQNelnd1DCRYZBlMOI4EwoToOIYK5cnatc/MNJX2E1Yu9LlM3/xINdfbxD1Um7nKL/5mwuk0aAjQiqUVHRfv7ipKSAsE//dG7sG/b2lDkkvQqcKsUsZL8tsmVQPD7HaCtGxRKOROmE9mdu2bCIb9G7TugBId9G8T3TMSnJt89+xDollSEfeVm7R0JV1undwakLdYznJOm5uz3SkqebmXMKPfT7HxSsJ7Y7a1Pt683T0qIUvHy20pr947d7L3c+qooT45grrf+e3KH7ySSpf/cSuy/mqTRj3yZlN+rK15/qCpRsk3RZxawPDtuldeQ95K7gRgBB0Lp4h6bVRcUjS2MCqDaHC8KGeh4cKBrRUpL2I0c8dxy7nSLshhmMx/OIh4mafqNHHdKzsZbDcAgPSXoQ3UcF0LNRtL0GtNIZt4tQK5KaqBIvNj0TbyvtHVs8XjouQKabtYObyJK0G8foqabf1gQcCAO1rTbo3W+gXZnadfQghcMsuY89MErdDWlcau6xlC921LAoVBgStiKGDJUrjeSafGGLi8Rq5qkP9+lgWENyBGy+vcvRLUxz5/BRhOyYNJRe+vsCxL02Rq7mE7Zhr31/GzlkUhjySIEWmitJ4brfsMQAbZ1YZfnyM4kx51+MzHYuJj08PxHE+eMW6sY9N4tV2ap/fgoxS6u+tI8O7t0Xeic5ci9blOhOfmN4zgzZ2apKbX7+y63cqCkn7PbyZ/aS9Dqnvo6XEKlWwayMkjXVSv49hWhhuDuTd21Krh2rUjg7ddZ8b59cJW+E2TkLhuZPEN1eQ3T5muYAzO0H47lWKs0exC2WEaZEbncEu1QjWMsVEYZioNCHaWCJ/9BB2sYoQYmAIVmX15X+VybZ+AK0Fbt5ieH+ejRt9SqMupdHthL5dxZ5u52CYBtZYhXi5wZ0XXEWSxsU63lCe4vTuQZ5hGcx89gD1s2vEcVa3b6R3d1r9oLGDMOsKXvx4Fox//lNbQbnW8K//u2vEHx3NpI8MFOldA4FbSLstuhd3LqfTlP6N2zIOhpFlnvM5rLhIfA8xsN3wUMFA0vJZ//aF2xiMOjNKQGymJ4Qhstnd4MHwbzYGqbKtG2nl6++hUknc6NG7tp4FCY8oEOiv3uDqN/5BNrt8QLOkam6G0cIhPKvE2ZXfvWcLjUBQyU1xaPhF5pqn2ehfQ6cpvXffytqKtlRfQKlsbR+Exdcu6Nxo07nRQkYyc/nbA1Of2kd/pXvPYODmK6ub7Xlaa4zvCdJY0ZzrcuHr81ngFynm31jPlPNkdva+9dffJgklzfnegE+Z3S9JJHn9FzO+wKaqoGAw4GWCObfupd2w9vYyM58/wPDjY7vzBkxBfqLIyBNj1M+tk/Q+wDeTIZj61Cy50fyei6RBytIP5x/Yia1zo0X9/Doykrsa3AghyI/mqR0bprfYobdwh4phHBHX10ha9azH+rZafbS6BFqhtcYdn8LN5UBoDC+HioKds1pDMHRylOHH766lsfzq4o6ShZYSs1JEODbCMrGGK1s23IYJhknSbWf1X2FmWQCtUXGIv7aAXR4i7XXQaAzTIqqvInchAz8qrN/o8/W/cWnP72M/e44Nz8YaLpN//MC27w3XJndsmuDszV21ItbfWqa0r7J3MGAb7PviAS7987OkYUKSRKTyozW6btQVv/Af7kbk1D/SQMA9OkP5c6dwD05iVotopUlXGtT/6R8QL6yh4xTh2NR+5lPknzmKWS6gooTo+jKNf/4t0np7s7xj5D2qf+wFCs+dwCjkkP2A6Moizd/8frbcB1XuNUzMXJ7CvsNYxTK9a+eJmxugNcKys+d4UE4TjotZLGO4LuL6NeDBOiMerptAsyvxRe/xd2BXxapbGQKt9EO1l90Nmbvfw9WADGFgGg6WeXcS2O0QGFiGg3EbcUM/anPvh4CMU/orPRrn1xk7NbnncqV9FUaeHKd9rUnj/Pre67uDRHXrLkilhtuY+OoODs4tcaHE33lOdpN0vV+k/YT29Sz7UT26sxQihAADZj53gP5K7wMLBgzLYPSZSfLjhT35C1pp4l7E6huLDxwMZCn3PvX31hj72O7XUZgGo09P0F/p7QgGgCwQ3SV9qNXWOUnq68h+b1B7jHZ9yY08MUZ5f/WufAGVKtZOLxG3twcD/hvncWYnMgtjKem/fg60JlidI2qsbvIWtFLkJw9kfJsoJFhfAqWylO4gEBJCbArKfFDQUm8O+HeDkXOxhyvIfojqBds+d6aH95xQ1M9lz6V8ZmJPEq9dcJj65D7mvyvpzXc+Uv39kN0i7c72+8oyYXTEpN358F1DMQRmpcjQz38R2erSP32JtNEBpbFqJWS7h04lZrlA/mPH8B47QP+NC8iuj+G52PvGGPr5L1L/5a8ju32s4Qr5Z47inTxA9wdnUH6EUcrjTA4z9Ke/yMY/+BoqeDDn1vuBmS+QmzpA5eTHsAolzFyBqLFG3KojTIvC7GGckUm6F9/JSgV+QHjtCvbwyP25+t6BH70C4R8BaCCWfdZ7VwmSXV7CP0poCNb7bJxZuWswYBcdhk6M4K/O0LnRJL1LX/tHCVpp2lcaNA/WqRwZ2jOFPvrMJEsvzxM2gkcfEAiw8jYzn92PXXD21OuPuxHduTa9xc6DZ8A0hI2A1TeWGDs1yS3L0jtROVRj6MQIa28uETYePBhWUYiK9s6kCctg8hMzFKfLmXLlbruaqs3jvJMkKZsdxOGZbBYWxqhO1vKZ9rvA9k6euFNHBr1MEbTbAiDtf8SerwF0KpH9gLTTR/XDzQyp4TmY5dyesuDhhk9nrk1vqUvlYG3H90IIMAUTL+yjt9glboXE3Y9WZmA35HKCTz7v8ptf83caNn7AEI6Nd3QGZ3KE9pkr+O9dJ11vgdbYY0PIbgBKY1YKFD7+GGmzg//OFdJ6ByPvkvNDaj/3BeypEfScxBqpkD91lLTeof/2ZVS7n2UaTh6g+tOfxp4YIl7aQEePVvPCHZkkN7UfK1cgWl+h+tTzGe/nFgyD4sHj+HNXSIMewrZIux3MYumhHEo/srZZluHgmoVNa0fTcHDMAo65lYK1zRyW4WIIc/AbD9cq4VllPKuEY+bvsIYUGMLCtUoIDGzTw7WKeFZ58NneJDNBli3wrBKGsDY/NYWNZxWRKmW5cx4/bu74rW3mcMw8prBxzcLm/tlm7rZ1ZTCEhW3m8DaPozzYvyKWcf+Zitvhr/dZO72cGYzcJZ1VOVRj5vMHKB+s3ZUY9lFD81Kd+ntrewYwQggqh2qMPj1BabayWeZ4VDA9i+J0iX1fPLh3VkBr+std1k4v33cXwZ0ImwHLr8zfVYa5MFli6OQoI0+OP/LjREB+vMD0Z/ZTmNy9pVBrTRqmLP1wjqSf7MiAWCNVjLwLlolwbazJkT29NsK1RfqL1wjXFtAfsMPn+4XsBYQ3VolurJI0uqggRvkRyUYb//zcnmlklSraVxqsv70y8JHYfbmxU5OMPTdF5fAQ4iPybOZygpwnMARUK8a2P9OTFn/ypwubzSIfJgzPIf+x48TLG/hvXyG+vozqBah+SHR9CR0nmdJmpUj+5AF6L71LvLSB7PRJVhr0Xj+PMMA9PI01VMYaruAd3UfvpTMkKw1k1yeeX6N/+hLCMHCP7sMoPNy7+W7IzxzEqY3QPnealW/8Oireyj5oKYnWV3CqwxiOg+G4mKVyRrKNwrvqz+yFj2xmYKbyNOOlE7y7/Dv4SZOZylOMFY+idMqbC78GaE6MfZEgabPRv0E3WuPQ8AtMlI7jmHkSFdEOlri0/h38pA1obNOl4k3y2PhXeGfxN5mpPcNwfj+uVSRJfV6b/6eEye7yqDm7wkjxEEeGP8XZld+l3r+JECYjhQM8Nv5lLMNDI3lv5essd85t++3x0S/gmB5rvStMV56i5I2TqpB6/ybL7ffY8DMRCYHJcOEAU+WTVL1pbDOHaVgorQjTDmvdy1xc//YDn8ukG9O8XGf51QWmPzULprFHK5NJ+UCNF/7KF/jBX/59egvde3YXfBQQNgIaFzZYeW2Rmc/tB9j1+A7/zHFM28Bf6T3UrHkvVI8McfLPncIb3psroKWmeam+Q2f+QSDDlM6NFosvzTHzmf2YnrXrcQ6dGOXxv3iKxvl1/PsUW7ofmJ7F0//+8xT33UWXQmUyu1f+xbmd946AwotP0f3W6ySrdcxamcpPfprgzXP35YXwkcagmwDAnR6m/NmnkL0A/93rmKU88UKdvS5E/b01DEsw+6VDO+SHb0EYgv0/fgSv5hF3ItrXdk46PjDsQd79sc/niGPNK29E/Kf/QWVbIFMqGjx+4pbt84db1hCWiT0xRHR5ARXuHkQaeQ+zUgTLIlnc2C4IlUripTrWcAVrtJoJSBkG8eLGNnKojlPipQ3s8RqG6zxyerJdqgLQuXx2l281KgoyfoCRtdzqOEI7NtHcDfRdsnt74aGCAWGb2ENF0raPiu6YpVgGdjmH7EeoaOdMzcg5WCWPtBOgwr3TKrH0idIuBXcIP2lScGpoLQmSFjm7SpA0ydtVetEGAsGhoRcYKRzk/No3CZM2rlVirHiEZ6b/JG/M/yqxvEWmEHhWkcMjn2K1e5GF1tukKqbgDBEm3dsU6LbIjkVnmH21U5S9Sc6vfoP13rVsOZ2w2r1EvX+DocIBjo99fs/jqXhT5OwaV+o/oBOuMpSfZbR4mCOjn6U9v0KiAsZLxxgpHADg5Zv/CI3m+OjnsUyXdrjCzcbr932NdpzPbszZv3ua0acnM4nTPSaNhm1Qminxuf/xq5z9u6dZe3OJ/soH0/JpWAYjT0/QX+oQNjMHvYdF+1qT87/4FhPPTWHt0Ytvl1xmf+ww+ckSb/4PLxGsv08jEgH7v3KE2R87zOQnZu666MJ3rrP0gzmC+vvbpoxS3v3bbzB0fITiVGnXljTTNSkfrPLpv/ZjvPx//Rb95d69PSruBgHlAzWO/fzjzHx2/11npu3rTW7+/hX8tV2CEA1ps4M1XEW4TjabEmCN1FBRnKXXo492BuB+UHrhJPH8Omk765jwDo7jv3cTnex+f8tY0rre4uzff5NT/4cX93w27bzN1CdnqR4Z5u3/+VWWX55/f9f1LjBdk/x4kX1fOMjkC/s4/8vvsPTS9nbGpZWUNAHPFXz6BZdf/Gd94ji76LWqwXPPbCeMf5gQYm/S8RZuI3bfCeM+RZQedfbtNtwy1rOLJaJw+3tDmBa5qQOk3cwpVwUBcRjC2i2L4w9JZ8AsuBRPztC/uES82t5GDjQdC7OUywiGuwQDdjVP/ugk/uVlopW9WZiJDInSPjmrAghMwyFR2WcFp0Ys+5nJhEowDYvR4hGW2mfphMskMiJMuqQqYqx4lFp+hqY/j0YhEBjCpN6/QStYIkp7KBRJGuyQotVakbOrHBh6HoRgufMedf/GtuU0ilTFSJXc9QJEsk8rWKThz5HKiI3+NTyrRKkySt6u0I4C8k4VEPSjOrEMAE0nWqPiTuAYOZSWO6SN7xdaKnoLHW783mX2ffEghT2U44QQaANyw3lO/BtPMfnCDI3z6yy9vEDnxvtr+3TKLoWpEsXpMuXZCrXjI+THC7z3999i493V9xUMyCilt9jl8r94j2N/+oldRX+EEDhlj5Enx3nuL32KS//8HI3zayS9B6z1CciPFTn0M8cZf3aK8oHqnmUVrTX+ap/F78+x8d7a+383Djgg137nEgd+/AjVIzvb+271p5cPVDn1H3+S+W9cZf3M6u6kwnvAG8kz8fwMky/MMPrMBMLaPasEWRmj/t4a89+6vudxynYXc7iCOVzJhMrafdyjs6TNDsnSOvKPQDCAACPvYpKJrFlD5Xs+s3ErZPH7c8x89iBDJ0d37fwRQmA42SD9xL/9LGMfm2T19SWal+sEa+9PU9+wDUqzFSoHahT3lSnOlCntq5AbzuOUHKz8zufp0pXMBtiyBN99KeSb3w02g4HxMZNPPu9+4EZSu0FLRVpvY08MIZzdhzgdRMiOj05TrLEaabO7OY4J08AeqWQ8gmYHo+ChpcIeHyJtdDc7wYRtYo9U6b95ISs9PGKEqwvk9x1i+Pkv0Dr7BsI0sStDFPYdxq4MUXniOXrXzyP7g2z2QwYBt/BwmQHTxK4VyB8exx2rkDR7JK0+sh+TPzKBME1kN0QamUxx/tAYwjJRQYxVzuEMFeHIBM5YhWSjO/jtdjZmokIi2ce1S9immzl0yT6x9MnbVfykiVQJGoVluHh2iVa4SCxDtJYondKPmyQypGDX6BqrJCocRHuCTrRKlPaQOruIid5FwlUYTJZP4tllGv4c9f5NYvlwM7tUhvSiDRKZpaejtEcyWJdt5SASm0pkQmzN9jI+hN4MBOyJMdJWO5tl3KoLaY1wXUjTTZXHHTenztLMC9+9TnGqiGmbeEO798MLIRC2SeVgDa+WozBZIjdapH2jQbgRDDwPMg18FctNjX1hCISZSQCbORs7Z2HlbKycjVvxyI0VyI3myY8WyE8UMxlkx7y7SM/9QmcujQvfvcHoM5NUDg/hFHemXA3LwK14jD07TdJPKO+v0J3v4K/1Ces+cTfadSAzHBOn7A7OR5Ghx0aZ+cx+8pMl7F1elDBg1SeKhe9cp3Fxg6j5aEoTKlGsvDJPYbKIXbApTO7UrxeGwPQsxk5NIESmTti60qC70MFf7WX1/F06eIRlZAZPtRzFmTK14yOMPjVB9egQubuUQVQiWX97hZXXF+kt38WJUKpMjTPJ2rqMnEtab6N6/n2/ULP7zMCwMvttw8qkq8Xgv4ZlYNgGxenynmn3W/CGc1QPD+FWXHSadUFkf+Rma6xKFSrJPrufYC68toxVLWIWPNAQ3Vy9Zw1XpQp/tceNr1/GrXoUpkp7BgSmY1I9MoSdt8mNFBh6bJTeYoewERBu+KRRiorlYL8lGALDNBBmdt5M28AuutiFzN7azts4FY/idInCZIn8WIHcSGHz/aASuaueQmegLug6mt//VsDicrrZMe0Hit/++odPHgTQcUJ4YY7yVz6Oe2gKnaTIAVHVrBaR7f7mZ9H1ZfJPHyHdaCHbPYTr4B6YRNg28cIastlFFnLE82vknz5CstpA9nzMQg7vyAzCNolvrn4g3QTBygLCNMnvO0L56BNZB8H+Izi1EQzLRgY+vWsXMo0N00RYNsK2UL2Hy+Q+NGdACEFudgQQxBsdwvkN/Gtr5A9PIHsh0Uozq90MFag8ewjDsUhafVQsEZax+dtouYl/bZWgv72dLZEhsexTc2ZxrRKpigjTDokMqeamySUNwrSH1lkwIDCI0u3CI1orYtnHMfM7iHph2ssG2L2Ob0AwnCw/Rj9u0o/qu5ID7xdSJcTp9uhdD/53a9+CpEPeqZF3qhSdYZSW5O3M3tJPmiAE7oFZmFtA+UHWuigVGGBVKqgozgIDxyZZWds1Stx4Z5WlA1VM12L0mcm7ag8AuFUPt+oxdHKUuBPRulynO9fGX+sT9yKSXkLSTwCNMAWmY2F5Jm4thzeUw63myA3nKc1WyI3kHz2p7TZoqWmc32DhO9cx3SyY2a22LQyBnbc58NWjjH1siualDern1mldqtNb6mSKiWqgcSCymYJT8ShOlagcrDH81BhjT0/epoewy74MAoHefJur//Ii/nL3kWZMW1caLL88v/ki3y0gEUJgeTaTL+xj+PExektdVl5boH52DX+1RxqkmQ6EzlQkhSGw8g7eUI7KwSoTL8ww/Pg4Vm53bsKt40RDb7HL/Levs/rG4t2ZzCqbtSk/yNq4RqqE567teq86FQ/LNbNshCEGzHowbRPDsTLFy5yN7dmYnonpWpieieVlQWj18BD5u7kqCijPVpj57H6idmYTnYYpaZgigxQZpcg4RYYpaSBJwySzHB4Q/fTA2CZqR1lAbJiYBZdoYQOzlMeqFlCpJHhn/r5mbFpqbn79CrVjw4yb0xSnSnt2bAghKE6XKU6XSaOUqBnSulKndam+ZVAWpiR+kgXnjonhmJiuiZWzyI+XyI8W8IZzeMM58uPFLKB6CEn4KIYfvrZ9MOz2NL/6G/0PS05lG1QY45+5QuUnPkHu8YMYtk28vA4a7MkRgveuZYN8u0f/tfOUPvMU8cIaab2NUciRf/Iw8VqD6PoysuOTbrTw37pE8VNPEt1YRrZ6mMNlco8dIF5tEl1fRgURwrUxCh6G62DVSgjHxiwXsCeHQWnSVhcd37+bYNKq46tM/6Ny/BniVh27XMP08iTtBq13X8NfuJZ5EQy8R6xqjTiVWUfQh+VaqJWmd26B/uUVqp84SuHYFL3zS7Rev0L12UMYro1V9PBmhvGvraGiBGGbWAUPcOhfWaF/YYmxn/wYuQNjBDe2BwOx9AmTHvlyhaIzQpj26EV1lEopOsP04zph0iaWAbZ5a4a7u8Lebm9hrdVd+3UNw6LkjrHYPsN48Rh+foZevEE/vrsoz57nC72nnv0trPcuYZsu+6qneH72XydRWTZhuXOO9f61LP1YLuEdPoiKY3Qco4IQs1zCyOWymZbnIWyL1u/8PjraPVq9/juXiZoRpmMyOmg3vNdLQAiBW/EYf26a8eemH+ocfFi4+CtnQWSErKGTo3c9tvxYgfxYgelP789e8FITtTL+gpIKwzZwyi523rnvQObWeoK1Pm/8Dy9lCpAfgCbr0ktzRK0QK28z/Zm9iZMATsll6LjL0PERIJuJpkFC0ssyPIZtYJccLM/esyPiTtwKBGSY8s7fepX1d1eJ23efIUXXlyh97lnM4Qqq69N75d09X1pHf+4kI0+Ok58oYOVs7IKD5VnZDPcR+JhkXSZDVA7dXUnxdshYDoKGhNRPSPyE87/8DvWLTUS1QuULzwAaYWXnUCuNM15j5e/+HtyHloqMJGf+9hsca4Yc+OoRitO7K2veDsu1sCaKFCaKTH96/30fy6OEEJm+mmGIbSV4+Qj9QO4bUpGsNFj/e1+j/OXnqP3sZzErRbSUxPNrxPOryHYP2erR/uYbmJUCtT/+mWyZKCa8tsj6//KbyHYPtCZZbdL+1mmMYp7hf+3HMIo5VN8nvLzA+t/+TZQfggbv6D7KX3qO3OMHMPJeNomYHqX8hY+h+iFrf+e3CK8soPcgNd4Jd3QS0/Xo37xC++wbCMOEgWDbnVGWlilaSpzxCexaDf/SBWT/wxAdguwktfoDAqHelUiRtH38a6vM/rtfxr+6Suu1K8SWSfHYJGnLR8VpNhzvcq8nMiBKu7hWgZI7Qjdax4+bAzGgHLlBqaCfNMlpidYpJXeUKO2hB7ayhmGSt4dZ6V4kVQ9Wi5QqoekvcGn9e/hxk+HCQY6MfIZzK1/Pyg0fAByrgGeVSKTPmwu/SiLDLHegFaDBMJDNFrLXx3CdzKlqYox0o5GpuPVjZLOVZQ3uIv2lpWLph3O0rzV48t95ln1fOvT+0/QfJWi4/GvnaF1pcOxPnWT6swfu+6fCFHjDue3x4wOeGhmmrLy2yMV/+i7rZ1Y+OA6Vhsa5dd746y8hg5SZzx+4b/dJYQrsooNddLb27wGPU6cKf8Pnjb/2fdbeXrkvF0ZrNDMsUz0fFYQ4UyPE1xZ3DQiKMyUqh6qZydZH5PY07KwEYRftzfOWG8qhw1WCc3MEFxeo/eTz9M9cI1ltYeQcKl985oG2kXRjLv6Td9l4d5XH/8KpjKvxETZxq5QF//4vlPn5P16gWjWwLUGcaC5dSfiT/+YqPyoaSHDuBspXm66c8fIy6IHA3a37LUlp/Nq3af76dze7JrTWmfvSbdBBSOPXvkXz17+z53LBe9cJLtzMrtWdl0uz5dJ5B6yxMVS/j7pj8C7MHqF07EmsYgXDNAk3VoibGyTtBnFzg6ixStLcQEuZeY8UijhT0wSXLjwUdeD9tRbeRhY1bBNnvEztE0fIHRxHBln9T8UpVjmHVcnjTdY2a2e3ZhV3g9KSVEUU3GE2+teJpY9nWEgVU3BqNPw5kjRAAKu9y+yrnSJKe4RpB8csMFI4hEbS6M8RSx/TeLDDzQZiyUr3EoawGCrMcnjkU1xc+9YHogJ2SzWq4Axzavpn0VqhdEqQdGiHSyx2zhKcu4iWalA3NRGug3NwP0JDsrpB2migw92V47Zta1CjPPv3TtNb6LD/K4fvaTzzhwkqkTTOrXH2H0T4a30O/uQxTNe66+x+2wv3Id69t7QErn/tEiuvLdK++v4Il/e1TaWJ2gFn/96b+Ot9Zj57IJtN3iO4e7/HGjR8Ns6scv13LlF/b+2+7Zh1Kun/8EzGGbAt7KkR9jpJYiB3/kGWlh4Uu5+3zdEhU7Yr5rAqRVSUYHgO9khlTy2FvZCGCY331nn7//MKh37mBLNfPpQJWn2EzsUtuI7gxY+7/Ef/RZ2f+WqeXl9z9nzMT/14/oM2Zb37fs3OYo+OoXo9ZK+fpeh3g1S7KuQ+8HKD4OBBDzl/8gTR9ZtEdwQD7XOn6d24OPAL8TBzBUw3h+G4eGNTFA4cw6kMsfa93yVcX0KFAbLXRTjOA99v8JDBgAwi+heXSFo+Win8G2vEGx1UkBDc2CBp9olW2hiOhTczROuHl4hW29jVPCqW9C+vkLZ9QNO/sIgMdg8dlZaEaQ/PKm8y9pWShGmXsjdOLANSFaETyWr3EvuqzzBZfgypU0xhYRs5ljrnCNMOSqeYDxn7xLJPI5jHMGxGi4cYzu+nESyQs8sU3RFydpWiM4JleowWDuOaBVKd0OjfGGgc3BuGMBkpHMQyHOr+DaIBv0AgcK0iFW+KXrRBs5eZt2zecIaBcN2MtNlqI3v9+/Y9UEmmFDf/nRskfsLwyVEqh4co7avctR7+qKC1Jg2yuuyjtBe+haSf0L7a4OYfXEVJzegzExSnSjgl994/fgBolQntNM6vs/TDeVZfW8jsaR+yO6JUFDi2oN7cevGMjxnYtmBhcRcZcKnpzneY//Z14m7MyJNjDJ0Yuatt9cPgFg+icWGDjXdXWX9nhfV3VgackftcR5JijQ0hOz1U1//Dry+wC8IrSxg5B3ffGMIQhNeWH1wRbkCIbV5pcPMPrpD0YoZOjlKarZD/EDIlt0pdvcUOrSsN+st7k9KEISjkDd55N+a5Z1w26oq3zsT8zFcf7f133zAMzEIB79AhDM8lUQph2wjHwd23D2HbyHYb2e9j5HLZH9dB9nqk7c5OAp4QCMfBO3AA0CSN5sCO3s3+eC7JRj2b2QtwpqdBGKT1OtL3EYaBMzUJhkG6voEMAoRt405NoaIoq/M7O82n0l4bGfQw3Bx2uYZVKGMVSljFMmaugOE4g9KB2Dy+tFHPfAo+rGBA+TG981tWqcG1tc2/t9/csoP09g3jTlRpvXoF6UdUP34YlaT0Ly5tLtN7b2HP7UiV0AqWqHgTpCpGo5A6phOukrPLxNJH6Z3tpTEAAFGjSURBVBSlUxr+HHm7ykjxIJbhIVVML9pgvvkW6S2hfMNAm1knAaaJkAZ6wNK33DxKpqg0RhmQmpJI+2iyns5enBm8lN0xiu4orXAZzypR8aaoeBOYhk2YdikVJykURgmCBr1og1D1CdMOqQpJ5fZ6aix9etEGqYqwDZex0nGitMN862064Qpag2U6TFeeZCg/SzU3TTO443wpRbKwxPtB63Kd3lJmUjTxiRnGn03xhnLYBQfDMTEewgFrLyiZMbNlmBD3YvpLXYJ6sNmR8KihEsXGmVWC9T5RM2Dk6QnK+yu4ZW9Tj+Bhgx6ZSFI/Ie5E9Fd63Pj6Fea/ee2BggDLymZWjgO+r1EaRkdNioUsGMjnBa4rOHrExnWg3lDkc4Iw0kSh3pbNbJxbx1/p0bnZJOnHDJ8cw616WDn7fdXZVaqQsSTpZcc59wfXWH51IWtVfMCpn+E6uEdmkI0O8fwKwrbYU9XmDyn6Z66ROz6DVSuhE0n/rSs8LK1ep4r1t1bozrWZ/OQ+xp+donZsBLfqZc/ngFz5fpEZ3uhNAmUaJETtiI23V1h+dYHOXGvv3ypNt6c2CbeFgmBs1KRaMX405R0hELkc1vDQpviOMAyEZeHMTGOWSiTr66TNJma5jJnLY7g2aa+PcFaJ7gwGDAMjlyP/xOPITifzw0gl9sgwwnEy4R/HIW21QUm8I0dAK+JcjrTZBPTWZ65H2mohLAv3wH5ku42wdh+GrXIVq1DG9DysQgW7VMH08ggzc/FMe23iZp2038WwHQwvR9puYeYKHz1vgqTRo/vuHMOfO4mWiv6lZYK5vU1w7kQs+1yr/3DbZ1Ha42bzdW42twvwKJ0y1zrNXOv0HmsTiFyepOLx8o1/SH50H0Y7RMYS08lRO3SKoLmMX18k9jTNsk/YvgGAVxklDft0/FXeXvrNzV7/un+Tun9zc/1Wrkhx/CBaSdqL7wHglIa41noNtYuc6mr3EqvdzBGtltuHZTi0kg6tYCvQyrgTPRIZYBp3b5N6P0j7MSuvLrD6xiLeUI5DP3mMiU/so7SvjFN2swds4KE9ONx7Y/B+3ywJaU3ci+ktdmhe2GDppTlW3lh65CZVu6G/3OPcP3qb3FiBkSfHOfDVI4w/O4VhG2hx20B5t+MaHIMGUBp/pcfaW8usvLbIyisLJP0HL44ODxkcPGCxf5/Jm28l+IFmdsakUDA4ey7hiZM2hw5ajA4b+IGm1VY885TDpSsJ5y+mbGxsP3dhI2DxuzdZfnmB0acnOPInTjD8xDhu1ctKS8bgIO91nGxdt6gV0L7eYvnlea799sXsOB9y7M49dZR4bgXZ8zFKeezpUcTbl3Y19doMHD+gQPFRIesq2Pq3CmL6b1/b+wcPgbAecP23LzH/jWsUpssc/MljTL24j9xIPuOJ3J7Ju99n89a9fNuz2Ti3TvNyncb5ddbfXiHu3LtlLknh+lyKIQRRqDn1hMPnPunhencTeM/O261Ac89l7qOcvANSkq6tEd24gex0SNaygR8h8M+8izM9hVWr4c7OopOEeGmZeGGB/BOP487OEl2/sW11wjQwPBdh2/TeeQfZamEND2O4HtHCAvHiIqVPfRJ3fza4y26XtN4gd+I4Ws1mPK/BZ/nHH0PHMcnaOv7Z90jqdWoTE7sGdCOf+CKFA8eJNlYIVxaI6it0rrxH2mujgu0t7kYuh44jcB2i5YU9yeN3wwcaDMh+RP/iMv3Lt0hUD3FhHwFyIzO45RHsXAkhDKx8mdzQFInfBWGSG5okPzJDb+0mTnGI3PA0XnmU/vo8Vr6EV5sgbK2RRj5OcYjS1FGC+lIWoSUxfn2B8sxxuktXUGk8SN0Y5IYmGT72CbqLl/A35kn8vUsGftJCCIOiM0ItN0MrXMLAopafYax4lJxdYaH17gd+rrTUBOs+7/3iO5z7pXeynueJEpWjQ9SODFOcKuEN5TKGfdHJ6vCmGPRjZ5oDKlJEnZCw7me9z/WA3mKH+rl1+svdTW39h3rQ3yeCtT7z37rGwneu4w3lGHpslOrhIUqzFQpTWZ+1nbcxbBNhCFQqSYOUoJ71cPurPdo3mmycWaV9rYmK5fs6jlvc23LZoO9rNjYk+2ZMKpWMnf2J5xz+8a/4PP2kzceecfjxL+e4ci3l+DGb1VW1Ixi4BRVLVt9YZO3NJZyym2kGHBumdmKU0r4y+bEidsHBdM0sIEoVaZgSdSKiZoi/2qN5MWu57M63iZrBjkHvoY43lZjVIsJzEKaJNVpFq90Hg9f/6vezge0jTJ6DjJD7Yd3HaZDSvtLgnb/5Ku/8rVcpzVQGYkFVyoeGKEwV8Wr5TEPAszAdE63YapOMJImf0F/p4S/3CNb7+Gs9OnNtWlcam8fyIPd0s6X4S/9VA6ng7//jHvtmTKanLM5dSO5KHrz86+e48pvn73p9H9W5NfI5co89hlkskrZaaCkxDAMNqF4XFWdt2btFLzpOSFbXaH399ym9+ALx6hrINOOUpRmLXxjGZqpettvoNKF3+jTW0BD20FCWUUgTuq+9gT06guF5mx1hWqldE2z+/HWEaeFUhigdfYLc5D7ixjpxa4OouU60voL0syyGTlO00uQfexwdx3Re+SFp68Fa4T8cb4IPoK3qQZAbmiLYWCDptcgNTyHDXnbxBMg4IGqvEbbXiNpraKWwnBxoRdRez8oIiIHYR47ixEFa199GpQn50VmsXCFTtive4ZinFVFnPfvT3SAN7iLEAsRpn7nGm1TzMxwe+TSW4aC1IlUR/bjBavcSnXBnXelRoVQRHDhsU6mZ/OCbmauXBqJmSNKL6c63WX5pPmNT3xIwMQQYW9H/rRfIrXpjJtqiNkVb0jDNRFA+pNshP12hcmyMjTfnSTq3dYDoLOgJGwGrby6x8e5a1odtG4MgYGuWpQezpi0xGoWKJWmQPLTh0O0wBoKS3a7mqSdsrt0QHDpgMT1tcuSQRbujefETDhNjJkmqWVmT5DzBlSsprfY9MiqDa3G7PsTiD+ayY72VXh5kfG5lO7TU2Yw8VVl//eCaPSpOR+/7b+Eem8UsF9BJSvdbb+z5fthqx/yjU0J4VNBKg4LeYgd/vc/Gu6uYjpXdw3dcW7hVBmBTH+GWINGmoFIi75sEuhtuVUGiSHNzPmVpRRKG97hug+fwQ7m+g+fYqtUQdlYe3GkKtQeR1XGwajW8o0cyfpYh0EpgOA75J58gd+I4st8nXllBRzH5p58kLRZJ1jeQrTaq75N/4vHss7V10lYbw4sovfAC6cY6hmVtCcjdht71C/iL1zOiuDAxCwWsfAkzXyA/fYDKiVOYhTIbL/0rwo0VlN8nvHoFqzb0R8uo6FFCAJZXxLBshGFiOjnsfAU7X0HGIUommb5zmoBWKBmjZIKWCZZXwM6XUGmEkilaKdzqGEm/jTBNTNslNzyN6XhYuVKWfTAtrFyJNOgihIFhOQjLRid7p240imawQCx9ck51WzAQJG38uLmplngLluFScIeJ0z5hst3n3DIciu4o/bhBqiIsw6XojtDyF9BoKjWD8UkTyxY0NyS9nsKyBeVKxg8YGTMZGTOJY02vq0jihKkZTXNDkaYaS0Ahb7C2IrEsQbVmYJjQairWln/0aV1hGhRna0x87jDtC6sk3XDHs66lJu0npA9AgHvU6PY0cwuSVkchJbRairPnEm7OpzSaijdOx4BmeUUihKDXV3iuYH1DbSrA3QtaaWSU9cdzH2nfDxJpo424toiRc9FSkqw+nG7H/4YM2UCufqT38O3QQByzKUv8o0S0kKXLZRBAmhIvLqKCYCDOlrUXaqlIO11QimhhEcPZWYrVUqJ8n2RpmXR9g7TTwSzkMctl0rU1lO8PCIk+KEVw/gI6ipG9HioIQAgCy0RHMWm3i46ijCcgBMr3SRsNko2NndtV2T4alo2ZK+LUhrErQxl3IF/EsF20TMnEB7L/CMtCy91bGO+FH00wYIBbdimMFjA9k6gT0V/pP5KZ1m6Ie80Bw9IgDXqZlGMcZkx8w0LJhKSfORsCqCQiDbL0izBtVBJvntyos4FdqKDiCBVHpIDlFkj8DsIwUSpFkF1AgDTsAzorHdwDYdohTDtwH4rHQpi4dolafpaWv4BU6UD/QA8UGaHsTRBLH6liLMOh4k3RDpbQWlIbMjl83KHXVVRqBmdPxyTxlureoWM245MmQaBpbkhaTcWhow6NqqTXVVg2jE9Z+P2IyWmLkdFsRr22kn4kggG76FDYV6V6cgLDtR7a0+GDRt/X9H3J8srW5GD9ttT/2rraseuGsetE4g8HUkmydP+8oQ8EpoGRc7EnRsA0QKpMjrbV3blcIYc9NrS5XLJaR3Xfp8HV/4YPBcnyyvZ/r66RrG/s+fAkK6u7r0hKZLebifjc0v8fG0MNhUQ350gbjW3rDM5f2PGQBp3Ojs/SZvOu7yR3ZBx3aCxrK/RymF4ew/GycSaKSLotkmYdGQw6z4wse8BAnOhB8SMJBkzHZPyZcR77+ceoHamx+MoiZ/7+Gdo3768N70HRXbq847O1d7+z7d+tG2c2/x621ghbWYdE0m+xceHlO369N/s5aq9t+3fjyhsPurv3BcfMbUoXb/SuUc5N0A3XAUXRG6Peu7aZKVBakshgIH6cwc0JHBde/u2An/8LJa5eTDZVxEwTPvYJj+XFlGIJPM8iihM6LcW+gxZLcylBoMnlBGmseeKUS7+nCAPF9KzFmy9ns0/DNkAItFSbaeZb+ugIgU5vSz8P9NMRbKUrxYAFbN6Wzr5FfFJ6T219YQiKB4cp7KtmWuyuhelZm+lorfSeKVFxe4qVQXZRqj1Jjlm5xMjObaJ2/l7r+yqN3G1wv/N98Yc2EPiIwPBc3CP7GP5zP52VK6KY1m9+m84fvLp9uUKO3BNHqP38lzFLBXQUUf/Fr9F/dTdL2Q8AQiBs6wMxwbljQ1m9atuNtdtn94ZBNulRDITSHuS3wuT2UUzpu3flCDL55LvJyu/A+3l4bvutiiLSVivjGuy2zvv57B6Tk8rJUxQOHCdu1Yk3VghXFzPRoU6TtNdh2/k1DG5J8Ru2jRAP3gH2IwkGZCiZ+84cc9+d4zP/9WeIH4KF/aPFj36GGaU9uuEanlmiF61R8SYJjDYaSc4u3/P3tg2jYxZf/ZMFeh3N6LjJwWMO+w/ZHDqWcO1yTL5oUF/LBvknnnHpdVR25IMAt9fVPPWcy/JCiusJwkBz89rWi+uJ/9MXyU2UWf7WZeZ+KyM/Tv/4Y0x+8SjF/UNc/Ds/ZPmbl9BKM/biAaa+dAy75PLGf/7baA2VY2MMf2yakedmKe6vYRddZJDir3RovLXA5V98HRluWWibOZvD/9ZzjH3iALmJUuZcaAg++b/8aW6/Zr25Ft//i/9kR63acC0O/plTjH/yAPmJMoZrEbcCVr5/lRu/9g5Rvb+jdj77008w9Mw0aS/mvb/5PY79hecZfm4fuYkywhAEqx3e+e++QX+htaul9/+GDx+qHxC8fYmFM3+DwotPUvnqJ3dfrtOn/8N38F9/j8InnqDy05/9UPfTPTbL0J/9Kiv/t//1/kRxHgKGZeOUhynOHqdx7hVUHGNYNm51hML0ERrnX0XF919aemr0qwDM996lHszdY+kt5K0aT43+OAWrBkIQpl1eWvqlPZcv2kOM5g8x5M3y5uqv3/d2HhVku41sfzCT11tY/eZvsUXoYXvHgdj8vyyoUIq006H9wx889PZ+tJwBzQei5Pf/T7h19lIVUfRGMYWFZXjk7RoFd5gw6QICy3AouCMUnWH8uEkcadZWUr77dR+lIYk1N64m2Lag31Ms3EwwLZApKAkXzg5kp4UgTbJygu1k30OWmdIa4mjreoYbfZxanvzUVnCSny7jDuUBTfnICMvfvgxK41Q83OECnctrWcCsNKXDwww/M0PSjVj42jnSMMEbLVHcX2P6q48hY8n1X32LdBBMqkSy8epNuhfXqZwcp/bkJMXZGlf/6Wmijf7m4J+1xm2/77zxIkf+rY8z+vx+GmcW2Xh9HpVIvLES018+Tn6ywty/PMvGG3PbY0ExcOas5njiP/k8hm2y8fo8aRBjeTalQyNE9X6WHfhDiTuzYLvlH/+QPsP3afmqB8ZEHyaE52DVylmJ4n5gGDjlEbRMSf3MqtpwPEzHzQRweu2s08m0cMpDyCggDfqoNMlKplIiTBtEjEpjZByB2vrs/kts2f1y96bCnQjSNm+vfY2KO86Qt4+h3L57b2eQJfyjCqtQJj9zkMrjz2LXRhDGLrN9KVn6+j8nWLzx/rf3IAuX95WpHalhWAbX/+D65ufDJ4YpThVJeglLry0xdHyI4RPDFMYKGfNSapZeX6J1rUXcvXcWwLANpj4xRfVglbO/tJWSGz81TnlfmdW3VunMd3DKDiMnR6gerOIUHbTWrJ9Zp3GlQdj4YPwDPkpIZEArmAegE65giIwXYQiTRAasdi4SJt1NwaOVzjmitI/SkvqG4OK5hG5n62HKBvLBgBnrbePAFjN4a/l7GWNF9T5qXxVvtJh9IAS50RJo6M+1KMzWBq1jYBWyVkV/sb25ifb5VZJOZhoUNXxUIrEKDtXHxvFGigx/bIabv3kGBiqeWip6Nxr0rTaGZ1GYrqImy7TPrdJfbG2y0+9sV3JqOSrHxxl5dh/rr95k9aXrhBs9tFQ45Uy0p3JijMqJMfoLLYLlzrbjtHI2ucky/lKH5e9dItzoI6MUwzJonFnKgpUfcUfNw8ASDlV3kiDtEMoeprDQKDyzvMlPsY0c3WR3DoD32EGcA1OY5QIIgWx18d88T9rsgFSY1RLFz5wiWV4nfO9aZvhimljVIuWvfor+K2dIljeyz4XAGhsi98RhrKEywrGzck+nT/f7p1G9YODeZuEe3493bD+G66DihLTeIjxzhbTxwc3kzGoJ5+AU7uF9GK6N7PZJFtaIri1uchFKX/x41qa20cI9NIM1VEaFMfH8KvHNJdKVOhgGpc99DHtiBHvfOEbeY+jf/GObrdnRzRV63zu97cEzHA+nVMOpjpL2WsjIxy6UyY/vz/hRkU/s5NAqRVg2ppsRqLtzF0iD7qb3yZ3dULcs1T8MaBSh7OKmeRJ170xEJPvUwzl6yR9dAmpu+gD52cOZzPnV81QefxZ/8Tppv4tVKJGfOUT7/FtI/8EMifbCAwUDpmcy8vgIueEc8z+YJw1SEDD82DDDJ4ZZfTsjYHg1j+JEkcJEJptp2iZmziTuxvcXDFgGo0+MMvPJmW3BQO1IjYlnJ+guduksdBh9fJShY0MUJgqYTtYX7hSdgTJc1hb1hw2eKOAIl466902eqSxmL+Igae34vulvT9M1btO+7jQh6N+7Ne2uX9/j+6jhI8OU3ER5MOA72JUcaT+mc2WDsRf3IwwDI2diF10Mx8qCgcGGu9fqdK/Vd25XKkY/Pkv18UkM6zZipoa4nQWBSSdERpnMcdT0CW/LDNwJb7RI9cQY7kiBpW9eon1hdVNFUBgCb6TI0KlpivuHKExXdgQDhmNil1xa51dYf31uOx/hyk6W8N1QquxDypg46pAmwQP99kFQLE+jtSIK26TJ7oQ4y3ApWjWkTkl1jGsWCNIOOau8afaSs8o7gwFDYFbL5J45hjVURbh2lu0cHwIp6b95PvOOd2ycmXGcA5MkKw1UsoGRc/FOHiR/6jjB2SsZcU8IjLxH/rnHcA/NYLgOGAMuynAF8fI7Gb/EsbFGa+RPncAariBME6019lgNYRj0fvDW3vr07wNGMY8zO0H+6WOYtTLCNLDGhjArRYTr0H/tLEhF/tRxMAziuRXMWgWzlEPYFtZwBbPg0V1vARqzWsIaq2HVyptB0K2HLb2T5EjGqzEcD6dQQaCJOw0sr4A3PAlaE7UkdrGSBQJO1sXhDY3jr95ERg9PhvTMEq6ZxzJcNJog7WAIc0et3zULOGYe2/AQCFIVE6QdEhWhebCMmSFMKs74wPZdkN5hGmdgYps5ivYQjXABzfZ3XNEeQZAFE7HKnq+cVcY1i5jCRAOpivGTJqnOxioxyKwWnRG68TquWcQxchjCQmtJO15BYOBaBRwjRyta3pbxNoRFwaoC0E+b981x8MansctD9Ocu0b92geKhE/SuXSRcW8AuVUFp4sbaroJ2D4MHCgaaV5pEL0bUDtcozZRoXm5i52wq+yt4VY+lVzNZ3O58l85cZ7OdqThd5It//YusvbVG61rrkey4YRqc+FMnWH9vnUu/cYnuUhen4PD5v/Z5wkZIUA/ozHXuvaKPGEbMaUbMKd6OvvOBbidJIEl2GRyFQAgDIQyUTHk/KeBoo0fqx3gjBYRlUpiuYLomwWqH9sVVDvzsk5iehVPJYZc9dKroz7e2NjkgEBqWAbfJ6QrTIGoG2GX3kbgt5sZLlI+OZr4Zc02EZWIVsyBDCOgvtdGpwhspkp+qAPM71qHilJXvXn3f5YCDx3+CwK+ztvQW7cajVbG7HfuPfYU09lldfJNW/cquyxiYhLJHqiJMbPJmhU68DmiUTtFa7ZoMFo5D4dnHyB0/SPv3Xya6PIcGis+dpPonvkCy1iT0byAbbdpf+z4Tf/kvErx7BZ0kmMUCpS+/QP+Nc0TXFlBdH+E62JMj1H7uS3S+/jLBO5dIVhpZV9KBKWSnD1JhjQ+TP3UC7/gBNv7hvyTdaGGP1sg/c5zaz3+Z4N3LpI3OQ0sD7wX30Ay5p49ij49Q/ye/h2x2yD1+mPxzj1H5Y58iPHcts8Ml21+zkKf5L75BdG2R3DPHKX7yaYqfehr/zXPIdp/Wb3wba6RK/rmTVH/2i6z9j798V86AjELi1jr5sX241TGC9UWUTJFRHxXHBOuL2MUKdq6I5RWQYT8TXNMaBs86hrmpc5+1qRlZB5Zxi9S3/T1gCpuJ/FFGcgcoOcNInbDQO49t5LKOqFv3AgYjuf2MeAcou2OYwqafNJnvvksrWiKUd9df2Q6BbeQ4PvQZPLOMKSxC2eWlpV/eXMI2PYa8GR4f/hLfW/iHJMrfHJgFgkOV59BoVvqXWA+uYwqbycIJxnKHcK08Siv8pMW19hu042WUlpjCpuSM8sTIj/Huxu8znj9CzZ3GM4skKuLNtd/ENhwmCycYye3ntZV/QaK2AnnXzHOo8jxSJ1xpvXLfx2zlCugkpn32TWTQR0lJ0mtlaoRryyTtJhNf+TnC1UXS7vvPej2ojR+NCw2cvMOhHz/Em1feZOoTUwA0Lm2l5ouTRWY/P0vtcJYG1lqTG81ls3ch3nf9TQhBYbxA5UCFsafHOP6zx7d97w175IZzf+iCARuXnCjiifdv8GFYLqbtZlGjEBimjYwDDNvL0oVCIIRJmgRYTh6ZxtnAa9oIoDh2iN7aNZKov3W99lCK2wvBSpdwvYfhmBRmKpSPj5H0IrrX6/Tnm2AYlA8OY5U97KJD0g3xV7aumTtcoHJ0lInPHaZ8fAynksP0LAzbxLDMrC55yy70fdxSdtHFGy1iFRy++Ou/sOdyUaOPmbN3fK6lIvXj7cJGfwSQqJBIBQgMPKtIwa5RSKsUrCGUlmg0eavKnRfAyLmUvvICnd/5HuG5q8hWNhC2/9UPKf+xT+Me3odsdYlvLhPPrdD+2g8ofe5Z8k8eQXZ8hGnS+vVvbbLordEqpc89S3xtkfbvvYTqbGW4/MbW/WJPjZL/+EnaX/se8fVFkIqo1UV2+5S+9Dze40fwT5/f9vtHgfypYxilAp0/eJlkPmtn67/6Lkbexdk/hffkUfo/fBuAeHEN/62LBO9mAVj/pbexp0fJP30Me2oU2X7wfTNdD7c6CoBdrGV1/l0QtTaI2CA/PotOY2QUYNgO3tAE+fEsS9e8dBohDLzhSfIT+8EwaF16kzTwN9nqADOlJxnLHyZI2lxc/R6JDjla+TQ5q0yitwbCycIJJguP0Y5WuNp+lUj6zBSf4EjtBZZ655jrvvMA9vKaSPZ4ZflXKDvjTBSOMZrbv/0YZZ9OtEqqIkZys6wHNzYHZkNYjOT2c6X1Cr2kgWPkGc8fYbJwgkvN79ON69iGy3j+CKfGforXV3+NfpKp+AkErlHgUPl5lvsXme++SyJDSu4IYdohQFO0h6m6U4znD7PYe28QhAgcI8dwbj9n1r82KK/d59FqhUYjzEGHRhJhOC6Gl0OFAVF9Bac6gmE/GuO1ByYQNi41sAs2p/69U7z9d95m5tMz9JZ6LL+eqeO5ZZdn/4Nnufntm1z6zUuEzRCn5PAT/+tPPNiGdnm5m46JdcuvXWTEnrO/dJarv3d12/K3zDZuh0BQNoaZsA5QMUaxhINJJkcZ65ANucT15AyuKDBuzlIyalxNznDUPkXBrACajqxzKT5NQlbTqhqjDJvTjJiTWMKhrzqspjdpqlVCnT3URVFlzJqlYozgiQICga87LKZXaasNYh1gYPKk+xkKRhlX5DCx+EzuZzcP6WZylvn00n2fOsO0GTn0LErGaK2RcUAS9slNHkepBNPKbh6tFabt4RSqRN0Nol6TqN8k6qyD1hRHD6BkShL28JtLD5zSi5o+UcNHS0V+qkL5yChxKyBY6ZL2Y/z5JsVDw1jFbH9u5wuUj4+x7ydPMvrcPsKNPvP/8izB2iDTMFRg4nOHGf/s4Qfanz0xuJfiVsDlf/Tabcp32xE3g0EZ4w5sKqllMG1j0xhIK42VMzON/VShUo0wQO2WlfmIIdER7WgleyEh6MRrSJ0yl57J7gUNrWiFbQ+fEAjHwh6rUfuzP0H1T315W3nGrBQxa0WMvJd9oDWdb76OMzuBe3gG2e7R+Ce/h062ulKE42COVAmvL+7wmd+EITAKHu7+CYb//E8z9Ge/urVbpoGR9zKugXVvvY8HgmlgVkrknj5G7vHD29oAhedkGYuh8qbkrur6pBut7etIJUiFcB/upS7DAH9tnqi1lglMJSECQdRaG9ybKWFjlVvKNP7KjcwefSCC1lu8gr82l4nrDFLOadCnv3xt67M7JnDj+UP0kwbr/g16SQON5lrndfJ2ZRt5cKb0BPXgJhvhTfpJE41mrvsOo/mD5K0qZWecRrgz0/Z+kOiIteAaI7lZWtEyiQowhU3Vm0ZrTTteJUy7FJ1hpoqPMdd5m1a0QqpCItkj6QXMlp+m6k6SqphEZgO4ISyW+xdohAtEso9G0wwXNzMP/bRFK1piPH+Exd55QJKzypScUVIV0oyWdwjH3fU4Oi2EZZObnKV7+Szh8hylQydxa2P4SzfITcwgwz5aPprW0wcOBuJeTH+tT9gMmXx+kuJUkY33NjKNAJHxCtyKS9JPMiGhWDLx7MQDOd/JWJJGGR+hPFumM9ehOFWkOFnErWQPTNjMSgFuJRMvWn8vq1vmR/IZ+/eOFrCSMUzNnMATBeaS8whhMGHuxxYevuqwIRc2X3quyFExR5jUB2mqVRpqBVNYpDpGkgUZZWOYYXMKT+RZTm8gSSmICsPmJK7wmEsvoUhRKCId0FSrpDoBNEPmJKPmNAYGq/ImCsVCeomiqDJiTZMXJa4kb2/ue1c+IElmIJ1sKBuZRCRphyRoUxiaxhAOhukMBkCJlilRr4FSEplEpGEPK1fCyZcJ2msYloPtFffUj78btNQkvYio4ZMbLVKYqdA6v0LUyNj1vfkW+akKVsFBK02wtpU+Gz41Q/nIKGmQcP1X36Z7bYPUTzLZ1NkUdV+p3vsbcGWYkvYicuMl6m8uoOJ0Vz6ESuSujoSa7WzzkaMV8sMeKlH06yETTwzhb4SE3YS4l9Ca76Lusm+m5VIsT1EdPoLrlYnjHu3GdXrtRZJ4kG72qpRqsxSKY9hOESEMorBNq34Vv7e2xQMQgtrIUYrlabxcDaVSmuuXMC13c117Q6MGAWDW4JH9XerbZnK71D/FQLCi98N3SFfqmeLbbUhWNkhvVx5MU4RjY+RcVBhva6e6tT5hmXf3ih+Ut3Sc0v3eadLljR1BXTy3jOo/Yh6GyPrx0/UG/umLJKvbOS46SUkW1zZ7zLWUe+sGPDRZT2ddBOGW8pwG9DYtja2/yzsGDy1TpEzv+CzZsdxtO4pnlmhFK0Syt1mXD9I2Usebz4LAJG9XMYVFxR3fRgws2FVSFeKYuYc75LtAqoR6MM/R2ovYhsetbqqaO0k7Xt3UXDGFTcEeYrJ4gpo3vXkcBiaWYeOZRUxhcftZ6CdNEhVuLnt7/T9IO7SjFSYLx3HNPJHs45kl8naNRriIvIduwp0IludIOk2SQQmge/k9yic/RmH2MPnZQxiWTefCOyS9R5MBf+BgQKWKsBlSv1hn/+f3IyOJv+ETtaNMMCZR9JZ65EZyTDw7gZKK0kwpM7CRt0QRDIaPD2PlLPIjedJiyuhTo+SGczQuNzIjjdU+/dU++z69j9aNFk7JwS27yDAzhkn8hJXTKzhFh4nnJnCrLghwSy4b5zfoLnS3vQwKRpm8KCFJWZNzaDSuyFEUNSId0FFbD7EQBqawsYTNSnqDWIdYwkYgNl+OQ8Y4nigQaZ81OUeiI6rmOLPWMSrmKJ5cwNcdUmI6qo7WemCJrLCEw6g5Q6j9wTOqqcslQuGTN8rYhsNy+hD1YkNgD5fJH5lBNRNiv0UaB8R+iyTsEXY3staiXDmTOg77yCTcbDmK/RYyDrByJWK/jUyyiFhrtS1FCGAWPdx9o8SrLdJWb09yXuonhBt9vJEiufESay/fIGoG2UC50KKwr4rp2YTrPYK1rcEpP1XBLrkEq13WX7u5ScoThkCYAnf47qUUlarsfhMic3W7C+J2QLDapfLYOIZtEqx03pdOe2k8T2WmQBpK7JzF8KEyWmpMxyRXdmjevPvD67gl8qXxjLhk53BzVQzDQgiDxtp5IAsYPK+Km6thGlla2K1WyXJggnYz6/bJFUapjRwllx9BAzKNKNf2Y9s5Ih6dNfXt0Eqjo5j45jLBu5c36+VbC2xv6XMPTiEcm7TeRrZ7eMf3E126iU7SwfoUOooxCrks27LrRjVaSmQ/JL6+hP/m+UyW9c7tPuquDq3RSUra6BBemSN4Z5cM3m0GT5k3wANyFu43RviQ2uyEEBiGjdLptgEus5OXWzV6IbCEQzzocDLFVvmiGS7RTTaI5aMnySot6cRrWMLBs4r4aQtT2FTcCRrhAqlKEGRkRMtwEIBp2Ntsf9f86/hpe8cAnqhwTwJgIkP6aQutNUV7iFTHeFaRnFVm3b/Og9Yyo41VknYjU8AFwrVF7MoQKIlVKJFETXpXz22aFb1fPJTOQNSJWHx5kRf+0gtc/dpVeouDndHZjH3++/NMPz/B4c9PQ5jy7u9cp329TTLQz7bzNkd+5gjF8SKFsQJaa4781BGidsTbf+dtOvMd6ufqOEWHIz91BC019Qv1zayEShV5T7D+7StMf3qWmVMTHPjSAYQp6C33CJoB3cXtJA1HeBjCwFe9gToWhKpPziziiO3RqUAgdcJaOoevsxd3qrfPbobNaQLdpaMaKCSmsOiqOrGOyBlFysYQvuwQ65BUJxgYmWIWBl3VYNzcjy0e0pLYGKjeab2NDCUsi8KTB5j8iz/Oyl/+XVrLFwba1Rk6y9mLqjh6AJlEBK3djY8Sv03j5jvkh6bRMiWJdtYxnckhRv7Up2n9wVt0T1/Zk6Wd9iOCtS756QrecIG4FRA3fLTS9OaajHx8FtMxCZY7BLfxBbRWm0ZHwjIwBhoHdskjP1WhOFu76ylK/RjpJwhT4I0X6c03Mwb8oLx9+2AfrHRpXVhl/DOHmfjcYRa/fiErbwwGDmEIDNtExikqTuFeTRgaVKqRicKrOETtmO5KgGELchUHeY8SgZcfQqYhN698gyhoMbHvearDR7CdIq2NKyiVINOQXmeJXmcJv7eGUin7Dn2e6tAhhNgKBkYnnqRYnqHXWWRl/jXSJGT/0S9j2wWMPWrL7wtaD1zeGtiTI0SX57JgQGWBmbBM9G2KjMK1KX3peXScZAN4nFD60vP0f/gOyXoTZBYIJOtNnH0TGHkvyx7cGlAHMsEojeoHyFYHZ3oM/+2LkKaDzPhgux+Ekp9UyGYXw3NxJkcIzlzeeiYNY9Ba/eAdDFqpzfUIx0HLW7Nq/aBjyiOH3gzmxA49gcHHt3/Ccu8C68F1+js6nm7XRH2E+4ciSNuEaZeCXSOU2fhUdsa40nyZVMebW1Vacr39Bo1oCanuvD/05uTt9nXvtc8aRSx9mtESQ7l9BLKLZ5VwzQL1hyiFSL+7rTCr4oj2e2/QPvdmpjh4Z7D7PvFQwUDSS1h6ZYlf/7ndlZ/O/bNzBC9f4pPPu3z6Ex6/9mt1Lv7axc3vo3bEy3/1Tonf7WjfbNO+2ebCP7+wc6ct+N/9hRJ/4o8VuX5zjW98+ya/+lt3J97EOkDrKiVRwyCTjy0YVSxh01M768AKuevncItMkmdITDBtHd3xfV+3cQcBho3LhLWfmjlB0ahiYiEQOMKjrR6s7ewWnKlh7OESKk4Jzu/CbJcpzYW9JVN76zfuazt+Y/Gh9u92JN2IcLXL9I8dR0Yp4XqPpBth2Cady+vkJ8sYtol6Y57+bfX4zsU1SgeGKR8Z4dgvfIKN1+ZxhwuMPj9L6fAIcTvEqe6dYvQXWvQXWhiOxbFfeAG3ViD1YwzLQCWSpW9szeD8xTZrL99g+OlpjvybH6d8ZIT2pXWi9R4YgvxUhZFn97Hwe+dZ+ualexIFr35n53kbO1HFck1aC/eO4vudZTZW36PXztazeOMlXK9KsTxJqTpLu3GVKGwThdvvz/WVMxSr0zjelsjTyMQTtOpXqa+ew+9lUtk3r3yTYnnqnvvxsFBBROf3fkjtz3wF5YcZi3+9hVnwcE8eInj3KrLZQbg2uccPk3vmBOt/61cIL93EqpVxj+1n6M9+lY1/+C+RzS5pvU3vB28z+Vf+HUpfeh7/7YuDnnyBe3Ca8NJNlB8SL6zRf+Us1T/5BeKFVaKrC1mHQrmAc2SW/stn0OGDCOjcH/zTFyh84glKX/g48dI60ZV5DMfO2gurJfpvnocHDERULyBtd0FA/tnHNjMOWmlU70ftjaCJZB/b8HDMPLfy6LaRwzKdzdZCpSVB0sE1C9iG98Cco/eL1eAqBWsIPIikT6pCOvHaZoo/VfEmd6AZLT6S/UtVzHL/AsdqnyJMu5jCop/UiR6oa+Ie0PqRBwLwo1YgfEikKfz9X+7R7WnGhu+PELSeLoIFs9ZjvJj7aVISIhVQl8usyhs7ls/kd/aaAmYR8VJ6lZX0Bj3VuuO3apNbcML5OJawaas6V+K3SXRE2RziqP2x+z/gO1B4fBYz7xItf/QFN5JuSG+uiV3yqJ9eIPW31AL7c82sZco0SLpRNvgOsPztK6R+wvhnDjH+qUNMffk4cTOgeXaZ67/6Fkk34tR/89U9txt3QtZ+eB0Zpcz+zJMc+fMfR5AFJ62La9uCAYBwpcvZ/+HbtC+sMfzcPqa/cgIrb6MTRdTyaV9cozfXyOSPHwLrl9tZKfw+MsRpEpBEWy8PJWOSpI9SKV6uQhuwrBy1seNUhw6RL45hWk5mXpWrEgUtGNhuO26ZJO5v4wfEYQcp421p0UcJHcX0X30XkXfJP3WU4mdOYXgOKoiI51aIriyg+hbOzBi1P/MV2r/9XeKbS+ggIk2bNH/tm0z+Z3+B/MefIHjnEulqnWRuhfX/769R+swp8s8+huE66FSSrDWI5lbAj0jXm/R++A6YBuWvfhKzWgQEqh8QL67RfzmTxPaeOELxxadwj+7DLBcwch7Vn/sSpa9+EtULaPzj3yW+vkTumePknzuJe3Aas5LHyHkM/fmfpvrzX0a2ezT/2e8TXVsgvHwTFYTITo/az30Js1JExylpq0t0bSELBh70HMYJ8fUl2r/1PSo/81lqf+pLyK6P/9ZFWv/im4/4ij04Vv3LDHmzjOcFoeyRyIiDlWfJWRV6ya2Sq+Zm9y2mS49jGBagNxn7ZXcMP+3Qj+ubpddHvo/9qzw+fICcVaITr7Hav7ptVh/KLnPddzhYeZZQ9miHK8QqxDZcqu4k68F10vsQQLodUic0wnkMLEZzB+kmG6wHNx7xkX0weOhgwLbhv/xPqvzN/7XDkYM2J487pKnml361x3/871W4uZBgW4KJMYv/+N8rUy0bdPuad85GfOO72czqc5/0eOpxh1rVIEng8rWE3/haHynhwKzFU487PP24g2HAtZspr5+OuHA5exkHoSYINOl9+qwbwsAkS4tejd8hIUbqhFAHJHr3C37XdJAOuKXaFe1hMygwKJvD1OUSLblOoDOyjYG5q1ynZksBUGBsD0ZMA6uUo/Zjpyg8czhLW/dCik8eBGDtn303S9VrjbAMSp84RuHkfoy8S9rqE1xeovtalp3xDk2QOzKFd3A8S59KRf/MdYIryyTrbQzPYfzPfRH/wgJWpYAzWUNYJv2zN+m+dhEV7GwFsoZK5A5PkH9sH41/dZpkvQ1ak/oJ7YtrnP4rv7uDja8SyTt/9RsIQ9BfaG0jfcowoXFmCX+pzVItj2EbyEgSN33idohKFaf/q98lbvpbKWfTonL0FP3FK6R+l3Cjz/orN+kvtDOfAkClkmQXG1+tNHE3ZPk7l2m8u0Rx9iCFqX2YboGwEVJ/+xIq9qgdfx6nMsb66W+RBj3WX71B5+oGhnP3oFTL+0+I6t3Sp3pAUhwYkEztfxGvMEyaBCzdeIlURuTyI0zsez47F7c0GYSx9dvbt6Af3EjmQaCTFP/0BeIbSxg5L7vPUonqB6QbTXScYPR7VOqX6Fy7hg6ya6LTlHStzvrf/Q2SlQ1ku7u5vvDcNWSjg5FzB4JCCh1EqM6gDKGzUoH/+nvEN5cQTkaUJZXIfoCOs6xAMr9CNwjpv/7epmkWg1q+TiXJ8gY6lURXF5CtDkGlyPTnDzL69CTLZ+eov7tC1PBJVgYOeBqS1Tq9l88QXp7HcO0BzyFB9nwYcB9av/XdrA213tp2rvqvvEt4/voON0fZ6tJ/7Szx3PLm+UsbH2y7tFWuoqIQFd09+7XqX8EyXEr2MMdrn0GqmH7aJEjbJHLrfbgR3MA2PQrWEPvLH0Mg0CgSFbHmX6VPHYHBvtKTFO0RclaZnFXCNYs8NfJVpJZsBDdoR8vEKmCycIKqO4FnlshZFVyzwFMjP4HUCev+dTrx6mZZIJRdItmn7IxSske43nmT2+/5REasB9fJ21VGcwcZzx0BBsZuOqQe3r+3wu2QWtKIFqk4Y3TiNTrRHm6IHzE8dDBgGIKPn/IoFXsc3G/x8VMucaL5ld/o8fQTDn0/s16tVgyiKLNqPX7ERkrNG2/FSKX5zIseaZp959iCz77o8YNXQvxAc+ywzceecul2FcKAZ55w6HQUyyuSdvfBZzQCA1OYWMLGFi4GJhoPR3hE2stIfg/wcuyqBjYeZWOYrmoS6xADA1s4g3bFLFjItqNRZAI+Nu5ma+OdyLoPJIYwyIkige5uBgVZmU6joiSrQ2qNihNkOBiYb3vZC8PAnR5BJSkiMbFHyliVAv2zN1B+lPWtGgKdpOgkxXAdCk8eyJjPG22EbVJ+8QQ6TpF+hAoTzKJJ5cUTBJeXSG6r/WmlskDg0ATuvhFQbCNI6VQRNwNWv78LIVLD+is3dj/BGuKGT9zYOyW62zq1TDfPhYol4XqPcP0+CTYa/KUO/lIHFYwhewlR8xJJv0Nvbh27NISzzyY3Op319gb9zeUfJQzDwjS37g8hDAzTQhgGMo0AQW30KHHUpdO4TmPjMjINUcMSOagt3xr8lYwxTHsbP+AWGfH+mWn3hnAcdJpuc2aTGy3knW10t0F2enRfO0+yUs9+C1lJPEoI3r64Y3nV6RPdSyNAKdL1Jul6c+/ttns7SY27LddoIxttYlPQc3pM7QN98ybRe/OEje3ENx3GpCv1rHyxB6Iru9eNk6X1XW2ddZKSrjVI1x4yA2gYWKUKabvF/QZ+7sQ0cX3trsGAMEBZHVrxHIkKyVlllJK0ouWMEGjGGK6PmUCY9tgIbhI7AUV7CNtwkTolSDvE0t+c8EidInWmTBjJHkTZoHzrz5YDa5opGOoOoewjAKWzTKxCbnuHK52yEdwglF2kSukl28uyGkmQdlj1r1Bzp3DNAgYGUif4aTsT1kKjUISyz2Lv3EAT4V7nUhOmXfJWhVgGRPIu96wQCNPKurV2I5YaBoZlo9Jk5/dCICwbnSaPpPT18GUCrak3JaWCoFw0yOcFTgK1qkmnI4kiTbFoYBjw9365Q5zAX/lLVUaGTKamLKTUvPhxj//pf2lz5r2YyQmT/+6/rHHogEWrrTl53GF22uIv/98baA3/7X9e5fBBm4tXkocKBixhD2bkBgfsx7NDQBHriK5qEiQ9Yn3/ghDr6QKT1iGqxiiB0aWnm5jY5ESJVMfU9TIKSaB7OMKjaFRJdIQnilSNESyxk7yV6IhIh2g0I+YUdbWMgUGkAmIVIts+9X/5Kla1CFoTXFmi8/IWp0I49kCER6DilPrvvIawTCqfeZzhn3oee7hEFMQkzS7ybET/zHVUEOOMV5n4ha+QNnt038zEUKxS1qLZfeMy4Y1V7KESh/9f/zbOeDVTfLu1TdOk8NgsuWNTyI7Pxm+/+lDCKfcLw3YG5inZICmjIGONG9lD1V+6Nui9vWWDLLC8AkrKzPVLa5RMswdo1w0YGJaLUx7GdHK0Lp1GyxSVJESNFYRh4I1Ob/uJMEwMxx20hglUEmWFJMtChj5WvoRKsrT8LdGtPbcP2HYe16sijOwl4ThFbDuPAOKwjRAGrlel216g111GpiFCmOTyI5jm7b3qmjBsYzsFXLeML1bRKNxcFdNyEfFWoCUsKyPjDeyhIVN6vKV8Jwb3lVYZ+e+WaYqWMjvHw0PIdgcVBJsvJtMxMT1r021NGIKkFyMTiWFlGh8b76wQt6NtVtaWm/0Osu4kFUtkLLFLzpbNdapI/WRHC/G2S+mYmI6ZWWnrrGVZhimGbWT7NRCsSvpZy6owBHbBGVhQZy2nMspstpd+OM/ki7OkwfbrZhedTU0JNMT9GJ2qzc+11AhTZDoTYbrV4SQEhuttnkOt5KaIllZySw2QwbkWBlom2fnWeuu3aTq41/W29aEVhuNSOHiU7qX30HG82R5sOAOdkTTJrq8hss+UIjd7EJ3EJPXd/Say+1NQrgnazXnW2nMIwaZrKdykUjOoDZv4yqDXUXTjdbrx3usDWOy9t3XdDLAdQRTuvLbL/Yss93cGinthqX9+07sEwPQsZJTelk2ErrlKs7Ow5xivdEo/aXC2/vv3sUWBIUxsw6UTr9EfaDDsubRhYuYKqCjYdIcUtr2ps2HYDnapStJto6Jgx2+tXIHU7931fXK/eOhgQCl4972Yx447DNVM+v1s9vrCcy7XbqQ0WxKl4eKVZJM70+0pCnmDckkwXLXYv8/i//KfVun7gx5cDfmcQbkIB2ctXnjO5R/8zZHNbV6fS/G8B5/NmFgctB/HwuFi/Dp1uTw4eJsxa5YJ6wBT5mFupue2fnSPQKuulpFpyqg5w2HnaTxRICWhr9qspfNsqEya+Wr8Dvvs4xyyn8ZybPqqw+XkNIfFUzs4CZKUjXQRgWC//ThHxClC7XMjeY+ldHfJ2N2g45TWN99Gx2lWu2z0SJs9rFqRaLGBPVSieOoQhScPbPZ0uzMj9N+9sc0Zq/v6RaKbayAVabtPvNbGLHoYbhbICMMg/9g+yi+eYP1Xvkf7B+cypvd94eFkA8uHnqI0ewxhWpi5Aquv/B5hfQUrX6J65BmqJ55l4Zu/QlhfRsUhlldg+kt/hnB9EStXREYBwfo87Stndl2/UxmhdvRjFPcdwcoXmfzkT9Nfvkb76pk9rVzd4UmGH38Bw81hmBatC28gLJvC9GEWv/3P2f8Tf57mxTdJ+m1MN4eKYzrXz+4ZzRdKEwgh6LYXCP06k/tfxMuPEPQbdFrzgCZJfByvQq4wit9bo1AcZ3Lfx8nlh+h1tgiM9ZWzDI9nwW8UtUlin5lDn8Nxy4T+1ozTPXwQq1ZFhRGy34dUYk+MkaxmwjWG52GUiiRra6A0VqWMVop0fQMch9Innye8cJno5hyynWVKRk9NMvniPnJDORDgDec594tvUz+7Rnl/lenP7mf6M/t5+a98i85cGxVL3IrL1KdmmfncQXSqaF9rsn5mhfW3ljnxbzxFYbyElbPo3Ghx/fcu0b25twzr6NPjjD4zSe3YCCqWrLy2wOL3b1I7NszM5w5gFRxMx+TSr5yldbWBU3Y58WefxC65mLbJ4vdvsvLaAsHG3tmpIz93ktJ0GbvokIYpF375Hdo3Whz/s0+SHy0Q1H3yYwU23l1l8Xs3s4zCIBCovfh5hGURrSwSr69hOA6G7RC3GtjVGlaxhJYSq1zFKpUJ5q4TrSyikoTq859GmCbBzatEK0uoJKb2wucwLJtodYl0IFhTefYFrHKFcHmBuL4OCCqnngfDoH/5PEmzgZnLUTn1PGmnjendve//1sBfHTLw+4ow0Lie4JNfzPODb/SJYxifsnj8GZe3Xw/pd9UDT1prwyaPn3L5wTf9BxU9vTsETH7qAKuvzQ9IzAbecIHpLxzixu9cIOk+GD9g+6ozWWfLsCnZo0wWj3Ou/m3a8d1LBIZtY+XzJDKFOALDpHT4JN2rF9BJlBkS7T+KP3eFqB5tZQeEwLBtzHwhmxD9KIMBqeD0mZif/+MFFpZTXjsd4XmCf+1ni/zjX+tRbyrKZWNXGfAk0Vy8mrCwlPL/+J9avPl2hNZgWYIgUIyPWZw4lvLKmxH/0V/eQCqwTEGaauKHUG4rGlUsXELdZ0NuKUbFg5l7qHxskUXLge5yKX5zm6bAXmipDTqqzrXkLKbjYlgOSidIEoRlYlh5uvicV6cxdJaVSMIuCslpub5rxNjTLfykzVxynlsDprpXL9ud0HrXuj4IzJLH6J/6NLLjs/y3/xXxegurlGf2P/v5HUurMEHvUHwb2AxCNourFQlvrJI7sQ+tNK1vvXPP3ct7wxRyo7R7C6QyxDQcTMPGNB1Mw6HT37uLoXX5LdpX30EYJuX9J8hP7EdGIVFjlfrZH+LWRrcLJBkGbnmY1Ve/TtJpoNK7E+fi1jprb34DGQd4w5MsfvfXQas9RZe8kSm84UmSbov1H/wWTrFK9djHcIfGkaGPYbskvRZ2sZJpjStNe/ntu6b16mvn6XdXOHjiJ8gXRonCNuvL71BfPc+tAOrGxa8zNn2KA0e/wuETP0Xg15m78i2mD3562/Etz72KMExqI8d44uO/gExjVuZfpeeWiMJBeUMIrKEaztQEsu9jdLoI06T78qsUn/sYCAPZ6RJfv0GyUceeGAfbRkiJDEN0vUGyvEK8srIZCNyCEILV08vMfeMqpX0VjvzsY/QXuzTOrxN1QnIj+c2nwHRNSrMVZr98iB/+l99EST1QcLSoPTaCnbd5+2+9imkbDJ0c4/ifeZI3/p+7+7c75Syo8Nf6vPxXvjUQIlMMPTZKfqJI62qDa79zifxEkSN/4jGGHx/DX+0RNgLe+puvkhvJM/2Z/Rz4iaOc/6Wd97QwBbXjIzglhyu/cR5/tUdpX4XH/8IpXv2r3yM3nCfqRlz/2iWC9T5a6k2dFTOXp3TyaWSvS9pr4wyP4VSHabz8HUa/9BMUbYfehfeIlhbIHzpGuDBHvLFC8fgTVD72Amm7hex2SDpNcjMH8CZnCBbnSRt1uuffQaVZqcwqlojXV2m+9gNUHOFNTONNzZJ22iTtBqUTT6CimLixTufd0ySNDYY/82MgDEYnTD7+6Rw3riRMzljEkaaxIWnWJV/6qSKriymNDQnC4OhJh5/7c2WmZi0un4vxcoJCyeArf6KI4wjefi1k7moCAn7sZwoIAS99M8CywM0Jbl5NWFuS/LGfK9JqKgpFwU/9mRJT+20unIm4cTmm1dj9mTU9i/EX9mHlHIK1Xpb5cU1yowV682200rgVDytv01/qUJgpM108RLjWp7/cpb/Y3rz/vJE8hekKxZkKnRsNCpNl7ELmhhvWfVZeurnnM1t0hpkqnGCqcIJEJVxuvkwzWiRRIWYuT/HoE0QbK9jlalbK8vsknSaVxz9G0m0j4xidJrijE1Sf/TTO0Bjh2lKWBbIsKidPgWURLM0RrS0h/R7VZ14kaTdIum3sXAFvYh9WqYJdruDPX8s8C9p7l8vuxEMHA1rDhcsxJ45VuTGf8s57MdWywf/5P6zy1/7fLdJUby14B6SExeWUf/Utny9/NsePfyGXeWYIwV/9G03W65LTZyLyecF/8X+sIZXGEIJvfi/gpVcDcp7Bv/PnSjz7tEO5bNJuS8bHTf7Zr/eoN9SO0kqsszprJjJUpaubGJgUjAo1Y4y8UWI9md/qPb1rELA9M6HQCENRnj2apZNlikpjVJpgFyrYuRIqTVAyQcUh0WImhnSr22CXMztQp9t7wFJxglnwMIveHmvYG4ZrY7g2SSpJm110mFB4cT9GYfd13RVS4b93k3BujeKzR8kdn0b2Q7qv3j2NZxoOnlsdpMwHmgI6RQ08E/aCMC2KM0dwh8bRUuLWRpFROMhm6ExRcZe6m1aZwYeMg7sOwtnCmZpblg5X95T6NL0ChuUQ95roNCHuNjFsB8OyiaOAwvRh+is3sQtlLK+IjHyS3h6zWSG4dvF3kWmMlBHd9jym6aBUQhR2SKKtOne3PU8UdbDtHEKYSBkT9DeIwjZKZUqXAFLGrC29TXPjMpblZi+2oEFz4wpKJptdBkYhTzy/iHAcDNdF9n28w4cynQehQStUFDF4UNFpirAsnPExwstXEY6LWSqhen2Uv5XODJs+UTMgDVOidoidtzPOi9KoRA3spLN9tQsOdt4m2PBJ/GTzRjZKDsXJMpVDQxz5k4+BygKHWw6Vu8EbymXbbIXbUvvecCZcFKxnjpr95S5uJcsEJH5CfyUbUPy1PoZl4A3tPlMWpkFxskTlYA3jx03idoTpmoTNYHAPKZJuhL/W3ylgJQwMN0fa76KThGh1CRVFaKVQqcRyzezvA30CncaoeOAx4rgYrkva76GThGDhRpYlsx1k6G+rLWutBqTIAZfDMBG2gwyy3/avX8YqlhGmhQrDLLAbEEsdVzA2ZWJagkJRZGJfAm5cjjl7OuTQMQfHFTQ3JMvzKfW1lB9+K6CxIdl3wGJ8ymL5XMz89YRTL3jMHLBorCle/0FAu6l49kUPxxU01iWrixKQTMxYbKxHLC9K1pZTfvAHPp2WxL+Lu6owBLmRIssv3aB6bITcWIGkF9O72aK32Gbk6SmSfoy/2mP4yQm01DTOruLWchRmKvTmt57F0myV0sEhZJBQ3l/DsM1BaZFNAvJe8JM2C72zbAQ3MrOjtLUpYywME7tUybg/jpuVNC2HcHWRYPEmzvA4hu2QJAlxq0Ha69C7dp6028YqlnGGRgnXloma6+SmZnFHJuhdPU+weAN3dBJhWhi2g5nLk7Yb+HNXyM8eRsXxhxcMbDQUX/t9n9PvRMzNp2zkBb/6W32WljMp4QuXE5LbJqhvnYlxHMHahiQINN/5QcjJ4w7lkhi8DwRSQhRpLl/NHuDHjzsonckEtzsSqcCQmuXVlFdPZ8TDONasrEnSdPd3faJDOmqDvFFi3NpPVY8hENjCxRYeXdWgqxrcevsYmBgiEwgyhJmRRsRAYENnlpyx8jcVtSIR4pSGskE/iQazSIFGY7o5TDeXpVTD/iPhbEVzG+QfmyF3ZBqtMqJR59WL95V1V1FCtNzAcG0qn34cFSZYw+WM6f2A9q5aa9JWn/D6amYxe3ya4jOHiBbrxEv1PdXeMkKkQggLx/ZAQ5R0UTLBsvZOUzrlIezyEKbjEdZXcSrDg66Mu5zUQbDxqEg2O6AydcZbZiLCyGxzZRSShn0KkwfpLVzG8grZMiIToRFWxnvQKhNwEaaJXavhd+voOMlUNqWPlnJAMFIgNMK2M4EdWxCEdXxfZTK8gzRhb4cdsSYKmkTB9pdC1n64hXhxCdlsIdxssFFBAIaxObDrOM6CAUD1fVKtEZa1KagTLy6jev0dwZiMsnr/rQHyVj1+N2il0QoM27zj86zzRKWKsJ4FFiqRdw0GtFQYhtghg66SLRVUyOzSs8BEZr8ZdIUYZvZOUntxEnQmKqVSTdQK8dcyi+y4m3W63Ap2dlOy1DIlaW5gD40AGtnrZpOHag0Z+Ci/j2HbOLVhMAyckXEMLwfCIGlukHY72LVhUJK010X6mbmTNzlDbno/0u+T+lmHhez3yO3bT9JqgJLIXgerUkVLSdrtkMoWhufhTk5juN5m94kQYJqCUtnAssG0BJYjCENNY11y7KTAMARpCn5P4fcVK4spfl8xNmGSJprVpZTrl2Ne/EKOoVETJVNOv5JSX5N86ScL2I6g21aYVkZK9PICpTR+T9HvKpYXUmR6j2fWEJk1esHBdC0M20IYCVEzIOnGIMB0LaychWEbmSpizsb0bCzXwqm4uBUPt+phevYmvyUNU2zb3Ho+jbu/uKWO6SfxprHRNgiRcQO83ECIysyenTQh6XVwRyezE6BV1skRRySdJtLvZzwQrUk6TeKNVfIzBzFcD60kab+LN7Evk+s2smNLe12ijRWKhx/DfECvi/elMxBFmv/+b25FVo0W/OX/dqsOub4R8+bbW9HAd17a/vC+/W7M2+/uXmNeXpUsr0q+98OdD3ySaP7pv7h/klpKwrqcp6bHGTanGDXySC2JdEBX1WnIFXydtTBZwslMjISFIQws4eDrzKf7VmBQsoZpJymemfUxRyqLqJVMkEmUyRm7OcLWGjpNsPKl7IUtkweXIt0F/sUFnLEK+Sf2U/38kyg/pPf2NVQQZ3Ksd+gPqDD+/7V3Lj1yVFcc/91b767unvfYY5sxjiGDDUYEEhEFxCqKsokUFpGSKFKyyBZlwbfIF0jyFaIskCIUgghIiSBBAQJ4DCax8YyHmZ5XT0+/632zqOqel8ceI5sE5v5W3bfrXbdvnTr3nP8h2mjmqU7tPt3LC/iPnaX6zEWyMKb9z38T3Fgl3mwxkGwNP9vcqyqoFPH6NmkvRKUZWZQQb7ZI+yEqSel+cAMVJYx970mc0xPEq41DXetJGhKEDeIkAKpIYZIkAVHSxbbLh563VR5FGhZJr0NvbRHLr2C4fqFdb2F6ZaRpY7o+iePt7P9e2ABCIC0H0/WRpoXh+RhhnzTskUYBzugkpj+C5VdzD1AcknRbVB+8wNbHb+NGMxipn7twAaNaRRgGWRShkgSzXMY5M4uoLZO026gsw/B9sn4/T6OLcwNbeiXSdgtrbJy020UlKdJ1iDdvH6B1J4KPj14IK202SVvFdEBhYAVXD/EG3eLaCykwS1YxAJs4ox7hdkAWZcTdCNMxKZ0o5/0szhCGoLvaoVdrs/7eCnE3QqUqr19yCOF27g20qw7eCR8yRRIkBFs93HEPb8rHmyrhTfv0N3v017uE2wFjc5N4Uz7uhEeWpPQ3ughDYI/kx2pVHOwRhyzJ6Nba9Gpt6vNrtBa3cy2YILljGmkWhfSXFrBPzCAdlywI8j5VdYgbdZJmA7NSxRqbyLUiJqexxiaI6huEtU3SfhenWFf0e3nwWRDgP3wR99QZokYdpTLSTptwvYZ7ajZPf2xtE67XsKemMRyXtNshaTcRYZ/y3GMYjpcbI1FIJskDAkVe7nxwI01LUK5I3JLAKwksO+8CYaAYGcs9dKYlUBl0Oxm5jSpIE0hiRaksSRJFkkCaKrIsjxFoNjIMsyjmlEIcKcYm8gDEKFKHxg4IQJiC8gMjpGFC0o9J+zuF6sJGH3vExZ0o0VvrUJoGbzrvW1E7xK7kD0yr4hB3Inq1NtI2aC80KD8wuitz6/MHLyil8qJQIj+5Qd8YGAjCspG2nQfxkgd2Gp5fvAjsvFwMgkeFlLk3wHF31jXM3ECNgl2CRHf35vmlFB36PLSzBu2swc3koKLhbkasKSzhkKmMRMWFFrrClh6mtNlKlwFFpAIs5WKI/CZE7W2aN6+g4oPGjX/yHCrL6K0fPud0NyT1Fpsv/R1eOqji2H77kwNu+u7lBbqXF4bfW29+ROvNj/Ys0/jze8PPaafPtV/9bs/vKk5Z+vUfdo5hu0PtNy/v3c+HN+h+eOOOxx+E2wThNgD7M7x6wW1Ss+qr+CcfpDQ7hzt5CpWlZGGAMG28qTOMzj2JOznD+MWn6a0v0avdINzeLB5Yd2kR7MvNNxyPytkLVM4+gjsxw+Tjz9Jdvk7ns2uEjXX8U+eYefYHSMOicfUdgvoKplfGrk6QdNuk/S6pXSItIoJLX3sIc2ycuL5J0mziX3yMtNNCWhZxYwsyRWnuAvHGGuFqDRXHmNUR3HPnabz+KtbUCaTXRtoO7plZ6q++fNiZ3B+O4mUpruHu65jHAFiceOoUp587y+jDEzz0/AXq82usvbNCZ6XNyls3eerF75CGCe3FJpvza6y9W6O30ePiz7+BNCX9eo/N+TWWXrt1DY+oHbL27jJTT8zwzRefIQ1i1v+1ymd/XcCuupyem+SJF76NYRt88vt5mtfqWBWH6adO8cQLTyMtyfLfFtl4fxVv0mfuJ5cYf2QSb7LEyNlRNq+ss/SXT+mud5j97nks3yYNE9beW+Hmq9ch26/tsIssI2k3qb/xSvFGeOv+aY2OU557lNYH7xBvb+2RNd58/U/Dt8lh22t/PNDWvvI+SGNP+fFwbeXAcsHS4p5jCKcNNmop49OSjdWUiUkDzxOcnrV47vslzp636bQz0hRWlmI+/STi+Z9VmH83HMg27LnnC9dibl6P+NEvqgT9jI8vRzQ2Uqqjkh/+tMLi9RjLyo2GXjdjeSnhx7+s8o83Aq5dDQ+NGVCZIm6GrL61SBoefGBvzRcBfLvjlffFLl/57dt7Vyp+b316b0TdVJoQtbaxKlWSdgvDLSENE2tknMrXL2GPT+fLJTHh+grhxiqjl75FsL6cTw/t6RsKadnYY5NU5h7HmThB2mvnLxV7Lrq6a0+oUIf22H0Lfu6KWl8ufGOEijmJLT02oiXGrZO0kjq+OYItPOrxMjPOedajRXxjNFciDP5TdKDDLuXg2t0/kZdjQ+EOG1xKhRpOR+QpWIWADGr4hxCGyV3rwwtZlCDeNcAUlfGG+1CqGFDFniyMvLCTytsNI9+3kDuDkMooPfIoZrkMhokwTcxKhWhtdegaNCrVvG21RvfqFeyTp3HPPIB0XbZeewX/4iWMko/0PKRlsfX6UdKevmCKeWalGN4jaUmyJCtcm3nFv0E6oyoK+ghDDN2yKlPD9YcpfLu2eVi56Z39i4FO0041U7kzBw55miL57UIYctiu0sE9BmHKYWrq7n3vPiYUw/MQZpGmeURRtFth+GXc07OEtWWSTuv+THPdBmnsDGuDv5zKwDDy7jxIjVcqb7McQRzlUz1C7PotFx8cfhaC4ZSuFHka4SDGrMiKHG4vCtQt0+8HCClwxjyCrR3xsf9LitTcPcUbVDYcz9SwiJbKxx7THKaIFvOJxXaKcSRTw/6tBuvtXk7KPQbBUR7z2hjYhyQXJhJCEmchlnRIVIQknzaIVYQtXGIVYggDlCgEhjSaoyO9UqF+J4s5RYmKo0Ke2UAYxrAt7fWQjoOwc+U9aVlYk9PEjS3SbiefK2xu/69PSXOvkXnAWRZH90WL/l6z3yPwRW1vtx6G5tZoY0Cj+cohMKsjCNchbRciPxqNRnMbtDGg0Wg0Gs0x5yiPeXnHJTQajUaj0XylOXI2wREdCBqNRqPRaL5kaM+ARqPRaDTHHG0MaDQajUZzzNHGgEaj0Wg0xxxtDGg0Go1Gc8zRxoBGo9FoNMccbQxoNBqNRnPM0caARqPRaDTHHG0MaDQajUZzzNHGgEaj0Wg0x5z/AmFkQqxMOSKXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text3 = ' '.join(df['Summary'])\n",
    "wordcloud2 = WordCloud().generate(text3)\n",
    "# Generate plot\n",
    "plt.imshow(wordcloud2)\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('WordCloud.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "906aaa90",
   "metadata": {
    "papermill": {
     "duration": 0.024145,
     "end_time": "2023-07-07T05:08:36.015202",
     "exception": false,
     "start_time": "2023-07-07T05:08:35.991057",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ENCODER CODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f12f5d79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:08:36.051457Z",
     "iopub.status.busy": "2023-07-07T05:08:36.050903Z",
     "iopub.status.idle": "2023-07-07T05:09:00.614125Z",
     "shell.execute_reply": "2023-07-07T05:09:00.612148Z"
    },
    "papermill": {
     "duration": 24.584158,
     "end_time": "2023-07-07T05:09:00.617172",
     "exception": false,
     "start_time": "2023-07-07T05:08:36.033014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting evaluate\r\n",
      "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: xxhash in /opt/conda/lib/python3.7/site-packages (from evaluate) (3.2.0)\r\n",
      "Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.7/site-packages (from evaluate) (0.18.0)\r\n",
      "Requirement already satisfied: fsspec[http]>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from evaluate) (2023.1.0)\r\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from evaluate) (23.0)\r\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from evaluate) (4.11.4)\r\n",
      "Requirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from evaluate) (0.3.6)\r\n",
      "Requirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.7/site-packages (from evaluate) (4.64.1)\r\n",
      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from evaluate) (0.70.14)\r\n",
      "Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from evaluate) (2.28.2)\r\n",
      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from evaluate) (1.21.6)\r\n",
      "Requirement already satisfied: datasets>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from evaluate) (2.1.0)\r\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from evaluate) (1.3.5)\r\n",
      "Requirement already satisfied: huggingface-hub>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from evaluate) (0.12.1)\r\n",
      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.7/site-packages (from datasets>=2.0.0->evaluate) (3.8.3)\r\n",
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      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.7.0->evaluate) (4.4.0)\r\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->evaluate) (2022.12.7)\r\n",
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      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->evaluate) (3.11.0)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->evaluate) (2022.7.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->evaluate) (2.8.2)\r\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\r\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.3)\r\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (22.2.0)\r\n",
      "Requirement already satisfied: asynctest==0.13.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (0.13.0)\r\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.8.2)\r\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.2)\r\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.7/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.4)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->evaluate) (1.16.0)\r\n",
      "Installing collected packages: evaluate\r\n",
      "Successfully installed evaluate-0.4.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from datasets import Dataset\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import AutoModelForSequenceClassification\n",
    "from transformers import TrainingArguments\n",
    "import numpy as np\n",
    "!pip install evaluate\n",
    "import evaluate\n",
    "from transformers import TrainingArguments, Trainer\n",
    "#Loading libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "553f5b8a",
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    "execution": {
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   "outputs": [
    {
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       "   Rate                                            Summary\n",
       "3     1                  very bad product its a only a fan\n",
       "8     1                                    very bad cooler\n",
       "48    1  small wire and moter capacity is very low fan ...\n",
       "59    1  the product doesnt cool as rated by other cust...\n",
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     "metadata": {},
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    "df.head()"
   ]
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   "id": "767ef29e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:00.709694Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "\n",
    "df = df.rename(columns={'Summary': 'text', 'Rate': 'label'})\n",
    "\n",
    "# Split the dataset into train and validation sets\n",
    "train, validation = train_test_split(df, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e6995e2d",
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    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:00.763703Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', '4', '5'], dtype=object)"
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     "execution_count": 33,
     "metadata": {},
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   "source": [
    "df[\"label\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8d3fd478",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:00.811999Z",
     "iopub.status.busy": "2023-07-07T05:09:00.811594Z",
     "iopub.status.idle": "2023-07-07T05:09:00.896103Z",
     "shell.execute_reply": "2023-07-07T05:09:00.894903Z"
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     "status": "completed"
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    {
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     "execution_count": 34,
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   "source": [
    "class_df = df.groupby('label').count()['text'].reset_index().sort_values(by='text',ascending=False)\n",
    "class_df.style.background_gradient(cmap='winter')"
   ]
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent_class=class_df.text\n",
    "labels= class_df.label\n",
    "\n",
    "my_pie,_,_ = plt.pie(percent_class,radius = 1.2,labels=labels,autopct=\"%.1f%%\")\n",
    "\n",
    "plt.setp(my_pie, width=0.6, edgecolor='white') \n",
    "plt.savefig('Label Distribution.pdf', format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4e0efa4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:01.224610Z",
     "iopub.status.busy": "2023-07-07T05:09:01.223343Z",
     "iopub.status.idle": "2023-07-07T05:09:01.268690Z",
     "shell.execute_reply": "2023-07-07T05:09:01.267254Z"
    },
    "papermill": {
     "duration": 0.073888,
     "end_time": "2023-07-07T05:09:01.271220",
     "exception": false,
     "start_time": "2023-07-07T05:09:01.197332",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset = Dataset.from_pandas(train)\n",
    "test_dataset = Dataset.from_pandas(validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478c0437",
   "metadata": {
    "papermill": {
     "duration": 0.018113,
     "end_time": "2023-07-07T05:09:01.307712",
     "exception": false,
     "start_time": "2023-07-07T05:09:01.289599",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7d52ee17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:01.346248Z",
     "iopub.status.busy": "2023-07-07T05:09:01.345082Z",
     "iopub.status.idle": "2023-07-07T05:09:01.349944Z",
     "shell.execute_reply": "2023-07-07T05:09:01.349284Z"
    },
    "papermill": {
     "duration": 0.025892,
     "end_time": "2023-07-07T05:09:01.351756",
     "exception": false,
     "start_time": "2023-07-07T05:09:01.325864",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import re \n",
    "import string\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "78af6287",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:01.390123Z",
     "iopub.status.busy": "2023-07-07T05:09:01.389315Z",
     "iopub.status.idle": "2023-07-07T05:09:01.907458Z",
     "shell.execute_reply": "2023-07-07T05:09:01.906485Z"
    },
    "papermill": {
     "duration": 0.540269,
     "end_time": "2023-07-07T05:09:01.910081",
     "exception": false,
     "start_time": "2023-07-07T05:09:01.369812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2baa1b1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:01.950540Z",
     "iopub.status.busy": "2023-07-07T05:09:01.950132Z",
     "iopub.status.idle": "2023-07-07T05:09:11.224242Z",
     "shell.execute_reply": "2023-07-07T05:09:11.223061Z"
    },
    "papermill": {
     "duration": 9.297315,
     "end_time": "2023-07-07T05:09:11.226886",
     "exception": false,
     "start_time": "2023-07-07T05:09:01.929571",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tokenization\r\n",
      "  Downloading tokenization-1.0.7-py3-none-any.whl (10 kB)\r\n",
      "Requirement already satisfied: regex in /opt/conda/lib/python3.7/site-packages (from tokenization) (2021.11.10)\r\n",
      "Installing collected packages: tokenization\r\n",
      "Successfully installed tokenization-1.0.7\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "600840fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:11.267892Z",
     "iopub.status.busy": "2023-07-07T05:09:11.267509Z",
     "iopub.status.idle": "2023-07-07T05:09:11.578653Z",
     "shell.execute_reply": "2023-07-07T05:09:11.577223Z"
    },
    "papermill": {
     "duration": 0.334377,
     "end_time": "2023-07-07T05:09:11.580960",
     "exception": false,
     "start_time": "2023-07-07T05:09:11.246583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tokenization\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e70c2dab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:11.622432Z",
     "iopub.status.busy": "2023-07-07T05:09:11.622026Z",
     "iopub.status.idle": "2023-07-07T05:09:11.633769Z",
     "shell.execute_reply": "2023-07-07T05:09:11.632692Z"
    },
    "papermill": {
     "duration": 0.034334,
     "end_time": "2023-07-07T05:09:11.635716",
     "exception": false,
     "start_time": "2023-07-07T05:09:11.601382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>very bad product its a only a fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>very bad cooler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>small wire and moter capacity is very low fan ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "      <td>the product doesnt cool as rated by other cust...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1</td>\n",
       "      <td>cooler not provide cool air because 1year expe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                               text\n",
       "3      1                  very bad product its a only a fan\n",
       "8      1                                    very bad cooler\n",
       "48     1  small wire and moter capacity is very low fan ...\n",
       "59     1  the product doesnt cool as rated by other cust...\n",
       "67     1  cooler not provide cool air because 1year expe..."
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "efa8e2e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:11.676881Z",
     "iopub.status.busy": "2023-07-07T05:09:11.676511Z",
     "iopub.status.idle": "2023-07-07T05:09:11.682499Z",
     "shell.execute_reply": "2023-07-07T05:09:11.681834Z"
    },
    "papermill": {
     "duration": 0.02874,
     "end_time": "2023-07-07T05:09:11.684363",
     "exception": false,
     "start_time": "2023-07-07T05:09:11.655623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# splitting the training and testing part from the data\n",
    "X_temp, X_test, y_temp, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a53ac989",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:11.724304Z",
     "iopub.status.busy": "2023-07-07T05:09:11.723680Z",
     "iopub.status.idle": "2023-07-07T05:09:11.729514Z",
     "shell.execute_reply": "2023-07-07T05:09:11.728866Z"
    },
    "papermill": {
     "duration": 0.027718,
     "end_time": "2023-07-07T05:09:11.731217",
     "exception": false,
     "start_time": "2023-07-07T05:09:11.703499",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1516,), (1516,), (380,), (380,))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X = X_temp\n",
    "train_y = y_temp\n",
    "test_x = X_test\n",
    "test_y = y_test\n",
    "\n",
    "train_X.shape, train_y.shape, test_x.shape , test_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c362f043",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:11.772087Z",
     "iopub.status.busy": "2023-07-07T05:09:11.771446Z",
     "iopub.status.idle": "2023-07-07T05:09:11.780867Z",
     "shell.execute_reply": "2023-07-07T05:09:11.779686Z"
    },
    "papermill": {
     "duration": 0.032124,
     "end_time": "2023-07-07T05:09:11.782622",
     "exception": false,
     "start_time": "2023-07-07T05:09:11.750498",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262     1\n",
      "1613    4\n",
      "215     4\n",
      "871     4\n",
      "750     5\n",
      "Name: label, dtype: object\n"
     ]
    }
   ],
   "source": [
    "label = preprocessing.LabelEncoder()\n",
    "y = label.fit_transform(train_X)\n",
    "y = to_categorical(train_y)\n",
    "print(train_y[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c271274d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:11.823343Z",
     "iopub.status.busy": "2023-07-07T05:09:11.822683Z",
     "iopub.status.idle": "2023-07-07T05:09:26.507718Z",
     "shell.execute_reply": "2023-07-07T05:09:26.506200Z"
    },
    "papermill": {
     "duration": 14.708609,
     "end_time": "2023-07-07T05:09:26.510350",
     "exception": false,
     "start_time": "2023-07-07T05:09:11.801741",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "m_url = 'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2'\n",
    "bert_layer = hub.KerasLayer(m_url, trainable=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "933d6f39",
   "metadata": {
    "papermill": {
     "duration": 0.020092,
     "end_time": "2023-07-07T05:09:26.550146",
     "exception": false,
     "start_time": "2023-07-07T05:09:26.530054",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### ENcoding Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "39b657b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:09:26.589717Z",
     "iopub.status.busy": "2023-07-07T05:09:26.589300Z",
     "iopub.status.idle": "2023-07-07T05:10:11.395050Z",
     "shell.execute_reply": "2023-07-07T05:10:11.393526Z"
    },
    "papermill": {
     "duration": 44.8282,
     "end_time": "2023-07-07T05:10:11.397194",
     "exception": false,
     "start_time": "2023-07-07T05:09:26.568994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow==2.0\r\n",
      "  Downloading tensorflow-2.0.0-cp37-cp37m-manylinux2010_x86_64.whl (86.3 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.3/86.3 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: protobuf>=3.6.1 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (3.20.3)\r\n",
      "Collecting keras-applications>=1.0.8\r\n",
      "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.7/50.7 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hCollecting gast==0.2.2\r\n",
      "  Downloading gast-0.2.2.tar.gz (10 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (0.2.0)\r\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (1.51.1)\r\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (1.4.0)\r\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (3.3.0)\r\n",
      "Requirement already satisfied: six>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (1.16.0)\r\n",
      "Requirement already satisfied: wheel>=0.26 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (0.38.4)\r\n",
      "Collecting keras-preprocessing>=1.0.5\r\n",
      "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m325.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (2.2.0)\r\n",
      "Collecting tensorboard<2.1.0,>=2.0.0\r\n",
      "  Downloading tensorboard-2.0.2-py3-none-any.whl (3.8 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (1.14.1)\r\n",
      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0) (1.21.6)\r\n",
      "Collecting astor>=0.6.0\r\n",
      "  Downloading astor-0.8.1-py2.py3-none-any.whl (27 kB)\r\n",
      "Collecting tensorflow-estimator<2.1.0,>=2.0.0\r\n",
      "  Downloading tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m449.9/449.9 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from keras-applications>=1.0.8->tensorflow==2.0) (3.8.0)\r\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (59.8.0)\r\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2.28.2)\r\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (1.35.0)\r\n",
      "Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.4.1)\r\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (0.4.6)\r\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2.2.3)\r\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (4.9)\r\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (0.2.8)\r\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (4.2.4)\r\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (1.3.1)\r\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /opt/conda/lib/python3.7/site-packages (from markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (4.11.4)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.4)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (1.26.14)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2.1.1)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2022.12.7)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.7/site-packages (from werkzeug>=0.11.15->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2.1.1)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.11.0)\r\n",
      "Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (4.4.0)\r\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (0.4.8)\r\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.2.2)\r\n",
      "Building wheels for collected packages: gast\r\n",
      "  Building wheel for gast (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7554 sha256=767608460c1022dca61305fca8fdda210d9807d4f1e65a519d70a3de2974767d\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/cf/87/6f/3f34218ef184368cec9ee65bdfd65baf117811f0a0ce1263ff\r\n",
      "Successfully built gast\r\n",
      "Installing collected packages: tensorflow-estimator, keras-preprocessing, gast, astor, keras-applications, tensorboard, tensorflow\r\n",
      "  Attempting uninstall: tensorflow-estimator\r\n",
      "    Found existing installation: tensorflow-estimator 2.11.0\r\n",
      "    Uninstalling tensorflow-estimator-2.11.0:\r\n",
      "      Successfully uninstalled tensorflow-estimator-2.11.0\r\n",
      "  Attempting uninstall: gast\r\n",
      "    Found existing installation: gast 0.4.0\r\n",
      "    Uninstalling gast-0.4.0:\r\n",
      "      Successfully uninstalled gast-0.4.0\r\n",
      "  Attempting uninstall: tensorboard\r\n",
      "    Found existing installation: tensorboard 2.11.2\r\n",
      "    Uninstalling tensorboard-2.11.2:\r\n",
      "      Successfully uninstalled tensorboard-2.11.2\r\n",
      "  Attempting uninstall: tensorflow\r\n",
      "    Found existing installation: tensorflow 2.11.0\r\n",
      "    Uninstalling tensorflow-2.11.0:\r\n",
      "      Successfully uninstalled tensorflow-2.11.0\r\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "tfx-bsl 1.12.0 requires google-api-python-client<2,>=1.7.11, but you have google-api-python-client 2.79.0 which is incompatible.\r\n",
      "tfx-bsl 1.12.0 requires tensorflow<3,>=2.11, but you have tensorflow 2.0.0 which is incompatible.\r\n",
      "tensorflow-transform 1.12.0 requires tensorflow<2.12,>=2.11.0, but you have tensorflow 2.0.0 which is incompatible.\r\n",
      "tensorflow-text 2.11.0 requires tensorflow<2.12,>=2.11.0; platform_machine != \"arm64\" or platform_system != \"Darwin\", but you have tensorflow 2.0.0 which is incompatible.\r\n",
      "tensorflow-serving-api 2.11.0 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.\r\n",
      "tensorflow-serving-api 2.11.0 requires tensorflow<3,>=2.11.0, but you have tensorflow 2.0.0 which is incompatible.\r\n",
      "tensorflow-probability 0.19.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\r\n",
      "tensorflow-decision-forests 1.2.0 requires tensorflow~=2.11.0, but you have tensorflow 2.0.0 which is incompatible.\r\n",
      "tensorflow-cloud 0.1.16 requires tensorboard>=2.3.0, but you have tensorboard 2.0.2 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mSuccessfully installed astor-0.8.1 gast-0.2.2 keras-applications-1.0.8 keras-preprocessing-1.1.2 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m2.11.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow==2.0\n",
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2232acf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:11.446124Z",
     "iopub.status.busy": "2023-07-07T05:10:11.445743Z",
     "iopub.status.idle": "2023-07-07T05:10:12.661022Z",
     "shell.execute_reply": "2023-07-07T05:10:12.659568Z"
    },
    "papermill": {
     "duration": 1.242387,
     "end_time": "2023-07-07T05:10:12.663568",
     "exception": false,
     "start_time": "2023-07-07T05:10:11.421181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading collection 'popular'\n",
      "[nltk_data]    | \n",
      "[nltk_data]    | Downloading package cmudict to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package cmudict is already up-to-date!\n",
      "[nltk_data]    | Downloading package gazetteers to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package gazetteers is already up-to-date!\n",
      "[nltk_data]    | Downloading package genesis to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package genesis is already up-to-date!\n",
      "[nltk_data]    | Downloading package gutenberg to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package gutenberg is already up-to-date!\n",
      "[nltk_data]    | Downloading package inaugural to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package inaugural is already up-to-date!\n",
      "[nltk_data]    | Downloading package movie_reviews to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package movie_reviews is already up-to-date!\n",
      "[nltk_data]    | Downloading package names to /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package names is already up-to-date!\n",
      "[nltk_data]    | Downloading package shakespeare to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package shakespeare is already up-to-date!\n",
      "[nltk_data]    | Downloading package stopwords to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package stopwords is already up-to-date!\n",
      "[nltk_data]    | Downloading package treebank to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package treebank is already up-to-date!\n",
      "[nltk_data]    | Downloading package twitter_samples to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package twitter_samples is already up-to-date!\n",
      "[nltk_data]    | Downloading package omw to /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package omw is already up-to-date!\n",
      "[nltk_data]    | Downloading package omw-1.4 to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    | Downloading package wordnet to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package wordnet is already up-to-date!\n",
      "[nltk_data]    | Downloading package wordnet2021 to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    | Downloading package wordnet31 to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    | Downloading package wordnet_ic to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package wordnet_ic is already up-to-date!\n",
      "[nltk_data]    | Downloading package words to /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package words is already up-to-date!\n",
      "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package maxent_ne_chunker is already up-to-date!\n",
      "[nltk_data]    | Downloading package punkt to /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package punkt is already up-to-date!\n",
      "[nltk_data]    | Downloading package snowball_data to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package snowball_data is already up-to-date!\n",
      "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package averaged_perceptron_tagger is already up-\n",
      "[nltk_data]    |       to-date!\n",
      "[nltk_data]    | \n",
      "[nltk_data]  Done downloading collection popular\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download(\"popular\")\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1ece9432",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:12.714391Z",
     "iopub.status.busy": "2023-07-07T05:10:12.713337Z",
     "iopub.status.idle": "2023-07-07T05:10:27.664061Z",
     "shell.execute_reply": "2023-07-07T05:10:27.658842Z"
    },
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     "duration": 14.983582,
     "end_time": "2023-07-07T05:10:27.671760",
     "exception": false,
     "start_time": "2023-07-07T05:10:12.688178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bert-for-tf2\r\n",
      "  Downloading bert-for-tf2-0.14.9.tar.gz (41 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting py-params>=0.9.6\r\n",
      "  Downloading py-params-0.10.2.tar.gz (7.4 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting params-flow>=0.8.0\r\n",
      "  Downloading params-flow-0.8.2.tar.gz (22 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from params-flow>=0.8.0->bert-for-tf2) (1.21.6)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from params-flow>=0.8.0->bert-for-tf2) (4.64.1)\r\n",
      "Building wheels for collected packages: bert-for-tf2, params-flow, py-params\r\n",
      "  Building wheel for bert-for-tf2 (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for bert-for-tf2: filename=bert_for_tf2-0.14.9-py3-none-any.whl size=30534 sha256=76b51cd4ca67e79c01d53823a442ae04ec371e487b66bce8ebbd01e24bbada9f\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/d3/b3/5f/1ba04fd4e099213b9c9a3f5b1ac5548fa2e599cb8fc9a868cc\r\n",
      "  Building wheel for params-flow (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for params-flow: filename=params_flow-0.8.2-py3-none-any.whl size=19472 sha256=3de56fd0f9775da7128c9f8379c5b14c40172b5ece51341537dc50cbcc13a7bf\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/91/17/7a/d8dc86bae260c349990d0c36b60685b2c4cd601866d083d471\r\n",
      "  Building wheel for py-params (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for py-params: filename=py_params-0.10.2-py3-none-any.whl size=7911 sha256=b73718705d38a8daac670c672feeb28bcc22a3908c6b584d05e7f916798c52f4\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/c4/94/74/551bca501c76c25b5f8512e8dc531f9a24dd221cdbbd3e6321\r\n",
      "Successfully built bert-for-tf2 params-flow py-params\r\n",
      "Installing collected packages: py-params, params-flow, bert-for-tf2\r\n",
      "Successfully installed bert-for-tf2-0.14.9 params-flow-0.8.2 py-params-0.10.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install bert-for-tf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "adbca162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:27.744860Z",
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     "shell.execute_reply": "2023-07-07T05:10:27.789703Z"
    },
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     "duration": 0.083686,
     "end_time": "2023-07-07T05:10:27.793302",
     "exception": false,
     "start_time": "2023-07-07T05:10:27.709616",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from bert import tokenization\n",
    "from bert.tokenization.bert_tokenization import FullTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ad7c7cf8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:27.857517Z",
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     "shell.execute_reply": "2023-07-07T05:10:28.025105Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\n",
    "do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\n",
    "tokenizer = FullTokenizer(vocab_file, do_lower_case)\n",
    "\n",
    "def bert_encode(texts, tokenizer, max_len=512):\n",
    "    all_tokens = []\n",
    "    all_masks = []\n",
    "    all_segments = []\n",
    "    \n",
    "    for text in texts:\n",
    "        text = tokenizer.tokenize(text)\n",
    "        \n",
    "        text = text[:max_len-2]\n",
    "        input_sequence = [\"[CLS]\"] + text + [\"[SEP]\"]\n",
    "        pad_len = max_len-len(input_sequence)\n",
    "        \n",
    "        tokens = tokenizer.convert_tokens_to_ids(input_sequence) + [0] * pad_len\n",
    "        pad_masks = [1] * len(input_sequence) + [0] * pad_len\n",
    "        segment_ids = [0] * max_len\n",
    "        \n",
    "        all_tokens.append(tokens)\n",
    "        all_masks.append(pad_masks)\n",
    "        all_segments.append(segment_ids)\n",
    "        \n",
    "    return np.array(all_tokens), np.array(all_masks), np.array(all_segments)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a78b7d",
   "metadata": {
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     "start_time": "2023-07-07T05:10:28.059692",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fd33047e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:28.199260Z",
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     "shell.execute_reply": "2023-07-07T05:10:28.206701Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def build_model(bert_layer, max_len=512):\n",
    "    input_word_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n",
    "    input_mask = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_mask\")\n",
    "    segment_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"segment_ids\")\n",
    "    \n",
    "    pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])\n",
    "    \n",
    "    clf_output = sequence_output[:, 0, :]\n",
    "    \n",
    "    lay = tf.keras.layers.Dense(64, activation='relu')(clf_output)\n",
    "    lay = tf.keras.layers.Dropout(0.2)(lay)\n",
    "    lay = tf.keras.layers.Dense(32, activation='relu')(lay)\n",
    "    lay = tf.keras.layers.Dropout(0.2)(lay)\n",
    "    out = tf.keras.layers.Dense(5, activation='softmax')(lay)\n",
    "    \n",
    "    model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)\n",
    "    model.compile(tf.keras.optimizers.Adam(lr=2e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e9fee791",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:28.273627Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "max_len = 250\n",
    "train_input = bert_encode(train_X.values, tokenizer, max_len=max_len)\n",
    "test_input = bert_encode(test_x.values, tokenizer, max_len=max_len)\n",
    "train_labels = train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ef27e08f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:29.008930Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' heavy noise front fins quality is worst road side cooler front fin quality is much better than this cooler not wort it '\n",
      " ' very nice product '\n",
      " '1 the fan speed was too good 2 cooling is very nice and in summer too it had given immense cooling effect355 litres capacity so half day sufficient for usagein summerin winter more hours it will run with that water4 there is valve at bottom so we can drain out the water and can keep it aside in non usage period like winter5 shape was too good go for it blindly'\n",
      " ...\n",
      " 'yes its quite goodyou can go for it nicely deliveredthank you flipkart'\n",
      " 'you can also buy without any hesitation ' 'yup satisfied ']\n"
     ]
    }
   ],
   "source": [
    "labels = label.classes_\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e4cbabf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:29.089903Z",
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     "shell.execute_reply": "2023-07-07T05:10:31.058050Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_word_ids (InputLayer)    [(None, 250)]        0           []                               \n",
      "                                                                                                  \n",
      " input_mask (InputLayer)        [(None, 250)]        0           []                               \n",
      "                                                                                                  \n",
      " segment_ids (InputLayer)       [(None, 250)]        0           []                               \n",
      "                                                                                                  \n",
      " keras_layer (KerasLayer)       [(None, 768),        109482241   ['input_word_ids[0][0]',         \n",
      "                                 (None, 250, 768)]                'input_mask[0][0]',             \n",
      "                                                                  'segment_ids[0][0]']            \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem (Slic  (None, 768)         0           ['keras_layer[0][1]']            \n",
      " ingOpLambda)                                                                                     \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 64)           49216       ['tf.__operators__.getitem[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 64)           0           ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 32)           2080        ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 32)           0           ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 5)            165         ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 109,533,702\n",
      "Trainable params: 109,533,701\n",
      "Non-trainable params: 1\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model(bert_layer, max_len=max_len)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0b11961d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:31.141525Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "label = preprocessing.LabelEncoder()\n",
    "y = label.fit_transform(train_labels)\n",
    "y = to_categorical(y)\n",
    "print(y[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ea83188e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T05:10:31.216607Z",
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    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "38/38 [==============================] - ETA: 0s - loss: 1.3292 - accuracy: 0.5322 \n",
      "Epoch 1: val_accuracy improved from -inf to 0.55592, saving model to model.h5\n",
      "38/38 [==============================] - 1496s 39s/step - loss: 1.3292 - accuracy: 0.5322 - val_loss: 1.2475 - val_accuracy: 0.5559\n",
      "Epoch 2/3\n",
      "38/38 [==============================] - ETA: 0s - loss: 1.2645 - accuracy: 0.5594 \n",
      "Epoch 2: val_accuracy did not improve from 0.55592\n",
      "38/38 [==============================] - 1484s 39s/step - loss: 1.2645 - accuracy: 0.5594 - val_loss: 1.2504 - val_accuracy: 0.5559\n",
      "Epoch 3/3\n",
      "38/38 [==============================] - ETA: 0s - loss: 1.2405 - accuracy: 0.5743 \n",
      "Epoch 3: val_accuracy did not improve from 0.55592\n",
      "38/38 [==============================] - 1497s 40s/step - loss: 1.2405 - accuracy: 0.5743 - val_loss: 1.2490 - val_accuracy: 0.5559\n"
     ]
    }
   ],
   "source": [
    "checkpoint = tf.keras.callbacks.ModelCheckpoint('model.h5', monitor='val_accuracy', save_best_only=True, verbose=1)\n",
    "earlystopping = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=5, verbose=1)\n",
    "\n",
    "train_sh = model.fit(\n",
    "    train_input, y,\n",
    "    validation_split=0.2,\n",
    "    epochs=3,\n",
    "    callbacks=[checkpoint, earlystopping],\n",
    "    batch_size=32,\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cc33d9",
   "metadata": {
    "papermill": {
     "duration": 0.034284,
     "end_time": "2023-07-07T06:25:19.691891",
     "exception": false,
     "start_time": "2023-07-07T06:25:19.657607",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Evaluating the Bert Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0eee67a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T06:25:19.763848Z",
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     "exception": false,
     "start_time": "2023-07-07T06:25:19.726716",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 146s 12s/step\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f19ab957",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T06:27:45.523538Z",
     "iopub.status.busy": "2023-07-07T06:27:45.522914Z",
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     "shell.execute_reply": "2023-07-07T06:27:45.531001Z"
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     "duration": 0.113054,
     "end_time": "2023-07-07T06:27:45.534909",
     "exception": false,
     "start_time": "2023-07-07T06:27:45.421855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08764995, 0.03387451, 0.1103043 , 0.2361243 , 0.53204685],\n",
       "       [0.08764995, 0.03387451, 0.11030432, 0.23612429, 0.53204685],\n",
       "       [0.08764995, 0.03387451, 0.1103043 , 0.23612428, 0.53204685],\n",
       "       ...,\n",
       "       [0.08764996, 0.03387451, 0.11030433, 0.23612432, 0.5320469 ],\n",
       "       [0.08764996, 0.0338745 , 0.11030432, 0.23612434, 0.5320469 ],\n",
       "       [0.08764998, 0.03387452, 0.11030433, 0.23612429, 0.53204685]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9f312d4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T06:27:45.608098Z",
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     "shell.execute_reply": "2023-07-07T06:27:45.613298Z"
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     "exception": false,
     "start_time": "2023-07-07T06:27:45.570799",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(380, 5)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fb8be642",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T06:27:45.689796Z",
     "iopub.status.busy": "2023-07-07T06:27:45.689051Z",
     "iopub.status.idle": "2023-07-07T06:27:45.693281Z",
     "shell.execute_reply": "2023-07-07T06:27:45.692654Z"
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     "duration": 0.043006,
     "end_time": "2023-07-07T06:27:45.695140",
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     "start_time": "2023-07-07T06:27:45.652134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred = np.argmax(prediction, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "486450ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T06:27:45.766909Z",
     "iopub.status.busy": "2023-07-07T06:27:45.766487Z",
     "iopub.status.idle": "2023-07-07T06:27:45.772682Z",
     "shell.execute_reply": "2023-07-07T06:27:45.771494Z"
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     "duration": 0.04543,
     "end_time": "2023-07-07T06:27:45.775262",
     "exception": false,
     "start_time": "2023-07-07T06:27:45.729832",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(380,)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4d3dc04d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T06:27:45.848113Z",
     "iopub.status.busy": "2023-07-07T06:27:45.847627Z",
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     "shell.execute_reply": "2023-07-07T06:27:45.854414Z"
    },
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     "duration": 0.047082,
     "end_time": "2023-07-07T06:27:45.857666",
     "exception": false,
     "start_time": "2023-07-07T06:27:45.810584",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "41438940",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T06:27:45.930286Z",
     "iopub.status.busy": "2023-07-07T06:27:45.929885Z",
     "iopub.status.idle": "2023-07-07T06:27:45.938666Z",
     "shell.execute_reply": "2023-07-07T06:27:45.937448Z"
    },
    "papermill": {
     "duration": 0.048478,
     "end_time": "2023-07-07T06:27:45.941161",
     "exception": false,
     "start_time": "2023-07-07T06:27:45.892683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(test_y, pred)"
   ]
  }
 ],
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