firsttryforreal.ipynb

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    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import h5py\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from pathlib import Path\n",
    "import cv2\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "from torch.utils.data import DataLoader,Dataset\n",
    "\n",
    "from tqdm import tqdm\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import timm\n",
    "from torchvision import transforms"
   ]
  },
  {
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   "source": [
    "root = \"/kaggle/input\"\n",
    "root = os.path.join(root, \"isic-2024-challenge\")\n",
    "train_img_dir=os.path.join(root,\"train-image\",'image')\n",
    "\n",
    "train_metadata_dir=os.path.join(root,'train-metadata.csv')\n",
    "test_metadata_dir=os.path.join(root,'test-metadata.csv')\n",
    "train_img_hd5py_dir=os.path.join(root,'train-image.hdf5')\n",
    "test_img_hdf5py_dir=os.path.join(root,'test-image.hdf5')\n",
    "\n",
    "sample_sub_path=os.path.join(root,'sample_submission.csv')"
   ]
  },
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       "      <td>ISIC_0015657</td>\n",
       "      <td>0.3</td>\n",
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       "      <th>1</th>\n",
       "      <td>ISIC_0015729</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ISIC_0015740</td>\n",
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       "        isic_id  target\n",
       "0  ISIC_0015657     0.3\n",
       "1  ISIC_0015729     0.3\n",
       "2  ISIC_0015740     0.3"
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   "source": [
    "sample_sub=pd.read_csv(sample_sub_path)\n",
    "sample_sub"
   ]
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     "text": [
      "/tmp/ipykernel_25/2637033960.py:1: DtypeWarning: Columns (51,52) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  train_df=pd.read_csv(train_metadata_dir)\n"
     ]
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   ],
   "source": [
    "train_df=pd.read_csv(train_metadata_dir)\n",
    "test_df=pd.read_csv(test_metadata_dir)"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isic_id</th>\n",
       "      <th>target</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>age_approx</th>\n",
       "      <th>sex</th>\n",
       "      <th>anatom_site_general</th>\n",
       "      <th>clin_size_long_diam_mm</th>\n",
       "      <th>image_type</th>\n",
       "      <th>tbp_tile_type</th>\n",
       "      <th>tbp_lv_A</th>\n",
       "      <th>...</th>\n",
       "      <th>lesion_id</th>\n",
       "      <th>iddx_full</th>\n",
       "      <th>iddx_1</th>\n",
       "      <th>iddx_2</th>\n",
       "      <th>iddx_3</th>\n",
       "      <th>iddx_4</th>\n",
       "      <th>iddx_5</th>\n",
       "      <th>mel_mitotic_index</th>\n",
       "      <th>mel_thick_mm</th>\n",
       "      <th>tbp_lv_dnn_lesion_confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ISIC_0015670</td>\n",
       "      <td>0</td>\n",
       "      <td>IP_1235828</td>\n",
       "      <td>60.0</td>\n",
       "      <td>male</td>\n",
       "      <td>lower extremity</td>\n",
       "      <td>3.04</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: white</td>\n",
       "      <td>20.244422</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Benign</td>\n",
       "      <td>Benign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.517282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ISIC_0015845</td>\n",
       "      <td>0</td>\n",
       "      <td>IP_8170065</td>\n",
       "      <td>60.0</td>\n",
       "      <td>male</td>\n",
       "      <td>head/neck</td>\n",
       "      <td>1.10</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: white</td>\n",
       "      <td>31.712570</td>\n",
       "      <td>...</td>\n",
       "      <td>IL_6727506</td>\n",
       "      <td>Benign</td>\n",
       "      <td>Benign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.141455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ISIC_0015864</td>\n",
       "      <td>0</td>\n",
       "      <td>IP_6724798</td>\n",
       "      <td>60.0</td>\n",
       "      <td>male</td>\n",
       "      <td>posterior torso</td>\n",
       "      <td>3.40</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: XP</td>\n",
       "      <td>22.575830</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Benign</td>\n",
       "      <td>Benign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.804040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ISIC_0015902</td>\n",
       "      <td>0</td>\n",
       "      <td>IP_4111386</td>\n",
       "      <td>65.0</td>\n",
       "      <td>male</td>\n",
       "      <td>anterior torso</td>\n",
       "      <td>3.22</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: XP</td>\n",
       "      <td>14.242329</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Benign</td>\n",
       "      <td>Benign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.989998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ISIC_0024200</td>\n",
       "      <td>0</td>\n",
       "      <td>IP_8313778</td>\n",
       "      <td>55.0</td>\n",
       "      <td>male</td>\n",
       "      <td>anterior torso</td>\n",
       "      <td>2.73</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: white</td>\n",
       "      <td>24.725520</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Benign</td>\n",
       "      <td>Benign</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.442510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        isic_id  target  patient_id  age_approx   sex anatom_site_general  \\\n",
       "0  ISIC_0015670       0  IP_1235828        60.0  male     lower extremity   \n",
       "1  ISIC_0015845       0  IP_8170065        60.0  male           head/neck   \n",
       "2  ISIC_0015864       0  IP_6724798        60.0  male     posterior torso   \n",
       "3  ISIC_0015902       0  IP_4111386        65.0  male      anterior torso   \n",
       "4  ISIC_0024200       0  IP_8313778        55.0  male      anterior torso   \n",
       "\n",
       "   clin_size_long_diam_mm          image_type tbp_tile_type   tbp_lv_A  ...  \\\n",
       "0                    3.04  TBP tile: close-up     3D: white  20.244422  ...   \n",
       "1                    1.10  TBP tile: close-up     3D: white  31.712570  ...   \n",
       "2                    3.40  TBP tile: close-up        3D: XP  22.575830  ...   \n",
       "3                    3.22  TBP tile: close-up        3D: XP  14.242329  ...   \n",
       "4                    2.73  TBP tile: close-up     3D: white  24.725520  ...   \n",
       "\n",
       "    lesion_id  iddx_full  iddx_1  iddx_2  iddx_3  iddx_4  iddx_5  \\\n",
       "0         NaN     Benign  Benign     NaN     NaN     NaN     NaN   \n",
       "1  IL_6727506     Benign  Benign     NaN     NaN     NaN     NaN   \n",
       "2         NaN     Benign  Benign     NaN     NaN     NaN     NaN   \n",
       "3         NaN     Benign  Benign     NaN     NaN     NaN     NaN   \n",
       "4         NaN     Benign  Benign     NaN     NaN     NaN     NaN   \n",
       "\n",
       "   mel_mitotic_index  mel_thick_mm  tbp_lv_dnn_lesion_confidence  \n",
       "0                NaN           NaN                     97.517282  \n",
       "1                NaN           NaN                      3.141455  \n",
       "2                NaN           NaN                     99.804040  \n",
       "3                NaN           NaN                     99.989998  \n",
       "4                NaN           NaN                     70.442510  \n",
       "\n",
       "[5 rows x 55 columns]"
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       "      <th>attribution</th>\n",
       "      <th>copyright_license</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ISIC_0015657</td>\n",
       "      <td>IP_6074337</td>\n",
       "      <td>45.0</td>\n",
       "      <td>male</td>\n",
       "      <td>posterior torso</td>\n",
       "      <td>2.70</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: XP</td>\n",
       "      <td>22.80433</td>\n",
       "      <td>20.007270</td>\n",
       "      <td>...</td>\n",
       "      <td>0.304827</td>\n",
       "      <td>1.281532</td>\n",
       "      <td>2.299935</td>\n",
       "      <td>0.479339</td>\n",
       "      <td>20</td>\n",
       "      <td>-155.06510</td>\n",
       "      <td>1511.222000</td>\n",
       "      <td>113.980100</td>\n",
       "      <td>Memorial Sloan Kettering Cancer Center</td>\n",
       "      <td>CC-BY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ISIC_0015729</td>\n",
       "      <td>IP_1664139</td>\n",
       "      <td>35.0</td>\n",
       "      <td>female</td>\n",
       "      <td>lower extremity</td>\n",
       "      <td>2.52</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: XP</td>\n",
       "      <td>16.64867</td>\n",
       "      <td>9.657964</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.271940</td>\n",
       "      <td>2.011223</td>\n",
       "      <td>0.426230</td>\n",
       "      <td>25</td>\n",
       "      <td>-112.36924</td>\n",
       "      <td>629.535889</td>\n",
       "      <td>-15.019287</td>\n",
       "      <td>Frazer Institute, The University of Queensland...</td>\n",
       "      <td>CC-BY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ISIC_0015740</td>\n",
       "      <td>IP_7142616</td>\n",
       "      <td>65.0</td>\n",
       "      <td>male</td>\n",
       "      <td>posterior torso</td>\n",
       "      <td>3.16</td>\n",
       "      <td>TBP tile: close-up</td>\n",
       "      <td>3D: XP</td>\n",
       "      <td>24.25384</td>\n",
       "      <td>19.937380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.230742</td>\n",
       "      <td>1.080308</td>\n",
       "      <td>2.705857</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>110</td>\n",
       "      <td>-84.29282</td>\n",
       "      <td>1303.978000</td>\n",
       "      <td>-28.576050</td>\n",
       "      <td>FNQH Cairns</td>\n",
       "      <td>CC-BY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        isic_id  patient_id  age_approx     sex anatom_site_general  \\\n",
       "0  ISIC_0015657  IP_6074337        45.0    male     posterior torso   \n",
       "1  ISIC_0015729  IP_1664139        35.0  female     lower extremity   \n",
       "2  ISIC_0015740  IP_7142616        65.0    male     posterior torso   \n",
       "\n",
       "   clin_size_long_diam_mm          image_type tbp_tile_type  tbp_lv_A  \\\n",
       "0                    2.70  TBP tile: close-up        3D: XP  22.80433   \n",
       "1                    2.52  TBP tile: close-up        3D: XP  16.64867   \n",
       "2                    3.16  TBP tile: close-up        3D: XP  24.25384   \n",
       "\n",
       "   tbp_lv_Aext  ...  tbp_lv_radial_color_std_max  tbp_lv_stdL  tbp_lv_stdLExt  \\\n",
       "0    20.007270  ...                     0.304827     1.281532        2.299935   \n",
       "1     9.657964  ...                     0.000000     1.271940        2.011223   \n",
       "2    19.937380  ...                     0.230742     1.080308        2.705857   \n",
       "\n",
       "   tbp_lv_symm_2axis  tbp_lv_symm_2axis_angle   tbp_lv_x     tbp_lv_y  \\\n",
       "0           0.479339                       20 -155.06510  1511.222000   \n",
       "1           0.426230                       25 -112.36924   629.535889   \n",
       "2           0.366071                      110  -84.29282  1303.978000   \n",
       "\n",
       "     tbp_lv_z                                        attribution  \\\n",
       "0  113.980100             Memorial Sloan Kettering Cancer Center   \n",
       "1  -15.019287  Frazer Institute, The University of Queensland...   \n",
       "2  -28.576050                                        FNQH Cairns   \n",
       "\n",
       "   copyright_license  \n",
       "0              CC-BY  \n",
       "1              CC-BY  \n",
       "2              CC-BY  \n",
       "\n",
       "[3 rows x 44 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0224dcbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:07.917725Z",
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     "shell.execute_reply": "2024-08-02T21:36:12.722064Z"
    },
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     "duration": 4.82018,
     "end_time": "2024-08-02T21:36:12.725105",
     "exception": false,
     "start_time": "2024-08-02T21:36:07.904925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_images=list(Path(train_img_dir).glob('*.jpg'))\n",
    "img=Image.open(train_images[99])\n",
    "trans=transforms.Compose([transforms.PILToTensor()]) \n",
    "img=trans(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b24695c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:12.749983Z",
     "iopub.status.busy": "2024-08-02T21:36:12.749642Z",
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     "shell.execute_reply": "2024-08-02T21:36:12.754299Z"
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     "exception": false,
     "start_time": "2024-08-02T21:36:12.736853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 133, 133])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "395e97d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:12.782033Z",
     "iopub.status.busy": "2024-08-02T21:36:12.781721Z",
     "iopub.status.idle": "2024-08-02T21:36:14.915777Z",
     "shell.execute_reply": "2024-08-02T21:36:14.914829Z"
    },
    "papermill": {
     "duration": 2.149555,
     "end_time": "2024-08-02T21:36:14.918291",
     "exception": false,
     "start_time": "2024-08-02T21:36:12.768736",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_img_dir=train_img_dir\n",
    "targets=train_df['target']\n",
    "images=list(Path(train_img_dir).glob('*.jpg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a73b7b75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:14.943761Z",
     "iopub.status.busy": "2024-08-02T21:36:14.942959Z",
     "iopub.status.idle": "2024-08-02T21:36:14.949493Z",
     "shell.execute_reply": "2024-08-02T21:36:14.948644Z"
    },
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     "end_time": "2024-08-02T21:36:14.951664",
     "exception": false,
     "start_time": "2024-08-02T21:36:14.930193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ISICDataset(Dataset):\n",
    "    def __init__(self,train_img_dir,targets,transform=None):\n",
    "        self.transform=transform\n",
    "    def __len__(self):\n",
    "        return len(target)\n",
    "    def __getitem__(self,idx):\n",
    "        img=Image.open(images[idx])\n",
    "        if self.transform!=None:\n",
    "            img=self.transform(img)\n",
    "        tg=targets[idx]\n",
    "        return img,tg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e230f17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:14.977027Z",
     "iopub.status.busy": "2024-08-02T21:36:14.976701Z",
     "iopub.status.idle": "2024-08-02T21:36:14.982879Z",
     "shell.execute_reply": "2024-08-02T21:36:14.982033Z"
    },
    "papermill": {
     "duration": 0.021351,
     "end_time": "2024-08-02T21:36:14.984841",
     "exception": false,
     "start_time": "2024-08-02T21:36:14.963490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_transform=transforms.Compose([\n",
    "                transforms.RandomResizedCrop(size=224, scale=(0.4, 1.0)),\n",
    "                transforms.RandomHorizontalFlip(p=0.5),\n",
    "                transforms.RandomVerticalFlip(p=0.5),\n",
    "                transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "                ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24212b53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:15.009545Z",
     "iopub.status.busy": "2024-08-02T21:36:15.009145Z",
     "iopub.status.idle": "2024-08-02T21:36:15.013632Z",
     "shell.execute_reply": "2024-08-02T21:36:15.012725Z"
    },
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     "end_time": "2024-08-02T21:36:15.015524",
     "exception": false,
     "start_time": "2024-08-02T21:36:14.996411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset=ISICDataset(train_img_dir,train_df['target'],train_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a993cfb8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:15.040646Z",
     "iopub.status.busy": "2024-08-02T21:36:15.040305Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class StratifiedBatchSampler:\n",
    "    \"\"\"Stratified batch sampling\n",
    "    Provides equal representation of target classes in each batch\n",
    "    \"\"\"\n",
    "    def __init__(self, y, batch_size, shuffle=True):\n",
    "        if torch.is_tensor(y):\n",
    "            y = y.cpu().numpy()\n",
    "        assert len(y.shape) == 1, 'label array must be 1D'\n",
    "        n_batches = int(len(y) / batch_size)\n",
    "        self.skf = StratifiedKFold(n_splits=n_batches, shuffle=shuffle)\n",
    "        self.X = torch.randn(len(y),1).numpy()\n",
    "        self.y = y\n",
    "        self.shuffle = shuffle\n",
    "\n",
    "    def __iter__(self):\n",
    "        if self.shuffle:\n",
    "            self.skf.random_state = torch.randint(0,int(1e8),size=()).item()\n",
    "        for train_idx, test_idx in self.skf.split(self.X, self.y):\n",
    "            yield test_idx\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "283a2a91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:15.075914Z",
     "iopub.status.busy": "2024-08-02T21:36:15.075132Z",
     "iopub.status.idle": "2024-08-02T21:36:15.099548Z",
     "shell.execute_reply": "2024-08-02T21:36:15.098802Z"
    },
    "papermill": {
     "duration": 0.039218,
     "end_time": "2024-08-02T21:36:15.101636",
     "exception": false,
     "start_time": "2024-08-02T21:36:15.062418",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataloader=DataLoader(train_dataset,batch_sampler=StratifiedBatchSampler(train_df['target'], batch_size=1023),pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae8314de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:15.126691Z",
     "iopub.status.busy": "2024-08-02T21:36:15.126102Z",
     "iopub.status.idle": "2024-08-02T21:36:30.568988Z",
     "shell.execute_reply": "2024-08-02T21:36:30.568015Z"
    },
    "papermill": {
     "duration": 15.457903,
     "end_time": "2024-08-02T21:36:30.571281",
     "exception": false,
     "start_time": "2024-08-02T21:36:15.113378",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22 s, sys: 1.66 s, total: 23.7 s\n",
      "Wall time: 15.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "img, lb=next(iter(train_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1fc5b343",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:30.596171Z",
     "iopub.status.busy": "2024-08-02T21:36:30.595874Z",
     "iopub.status.idle": "2024-08-02T21:36:30.603570Z",
     "shell.execute_reply": "2024-08-02T21:36:30.602657Z"
    },
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     "duration": 0.022551,
     "end_time": "2024-08-02T21:36:30.605681",
     "exception": false,
     "start_time": "2024-08-02T21:36:30.583130",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 0, 0,  ..., 0, 0, 0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85f48628",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:30.630742Z",
     "iopub.status.busy": "2024-08-02T21:36:30.630378Z",
     "iopub.status.idle": "2024-08-02T21:36:30.642158Z",
     "shell.execute_reply": "2024-08-02T21:36:30.641278Z"
    },
    "papermill": {
     "duration": 0.026653,
     "end_time": "2024-08-02T21:36:30.644287",
     "exception": false,
     "start_time": "2024-08-02T21:36:30.617634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 224, 224, 3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.permute(img[lb==1],(0,2,3,1)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c346e3ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:30.670417Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7b95f313b8e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.squeeze(np.asarray(torch.permute(img[lb==1],(0,2,3,1)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ad9be76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:30.969114Z",
     "iopub.status.busy": "2024-08-02T21:36:30.968438Z",
     "iopub.status.idle": "2024-08-02T21:36:45.059307Z",
     "shell.execute_reply": "2024-08-02T21:36:45.058498Z"
    },
    "papermill": {
     "duration": 14.107419,
     "end_time": "2024-08-02T21:36:45.061755",
     "exception": false,
     "start_time": "2024-08-02T21:36:30.954336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_2,lb_2=next(iter(train_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "071f8228",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:45.090231Z",
     "iopub.status.busy": "2024-08-02T21:36:45.089595Z",
     "iopub.status.idle": "2024-08-02T21:36:45.096042Z",
     "shell.execute_reply": "2024-08-02T21:36:45.095185Z"
    },
    "papermill": {
     "duration": 0.022563,
     "end_time": "2024-08-02T21:36:45.097910",
     "exception": false,
     "start_time": "2024-08-02T21:36:45.075347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 0, 0,  ..., 0, 0, 0])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b05a4337",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:45.125175Z",
     "iopub.status.busy": "2024-08-02T21:36:45.124906Z",
     "iopub.status.idle": "2024-08-02T21:36:45.466510Z",
     "shell.execute_reply": "2024-08-02T21:36:45.465623Z"
    },
    "papermill": {
     "duration": 0.358173,
     "end_time": "2024-08-02T21:36:45.469278",
     "exception": false,
     "start_time": "2024-08-02T21:36:45.111105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7b95f4d51c00>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mowuGlGmuYYeqHOa9UHbbHlwJElTqgqlXNordo99n+80hMGr7r0GYHtEabUncVHDqQNmgPqena2gPc/bXaky9n3vlqP/d10C/Dq8pgHtEJEY219URUZelWrMtWKX5mqb6a65/l9qf42YxDkwcubORlUVzYGassJ7GpFQ7bNnaFu0b7Lmt7xqE11ZetGuobSi1jWmD+EZmDuOBaTowHg6M80oac8BypUAJeC/X565rhjWzLoWc+9EqlOLgX+2LMtCCJ+p7SjCMkAspVZE/+DVfzFnh4/iuitB7LaR6i0BO8QNP4Tcn1858Eege7Y2SBfXYfecWlxhFv3B8McJeiAra0nVuSfXleGDB1A1tLOJMHxnVW4UKbOgDFZzE5L0Ovs/IYSL1Ub8vS+89LCXm7HuHXMfsLSmP3uuFufebh8L782RJva6f7+yaA/t5o/7RvX2wxsX6xq8P5h81V0SdtisosENBEHsLp5ErQ9cs+R6iUx+70qR9bK6YaB2oXyWcfOyjDwYO3PARR26ZWTlVxv48+4gyn9qZpZY3MhCB3nur8Za0WbcR8fW0XPX7tX2GqrO3JRSPRGFmqQHvA7kKj7WuDJiq1TPVFxvjv9kJ2yMzhw0uHDlyrOsr7ORULS8F4csbvLKGYKgtDvBrrc+bqkgTnUlkjoxkRtaNgwg2DyF1ZKri9MghHTkOR46vP2aYj3B8BcMJxjMcjyGkzg0snI4TrFDOcLmcWZaV07rGdcCynFnq52mcYS6wFkqaGcc7WgAFbLNXC/YMrAnGCfL6hWWUlDy5G96F3nsh5XtctCgf2S96sXYtq4GAv+QLkGYpLc73rviGXo9fcoapKDfVA/vsWiVc1xbdUnFLUMJF+6OmTX+L5Rj3xp4pf24PtQnSciuqZ0Oy4noh6RZgb+2IWaq/3bpyxq/4Jt8TNtkzrgVj6DsXZF5epm1G7oWiGJhvQvbf1Md6ntomAaU6yLLxABIJsubfU7Bt6w+FfIvJqq97q8wtBX/Hri/dtX1ASqbta/NQad3bW1n67lBBpBumypxnLjW67Cnf2W93aN4FsdWy9Qe0OQltDs1EGMHESAtOXp5Y/SpZ9/m87H8LIOxABHUoSD6AOMFlU/USDWZrxxoo1W7B7KuJebtfISQKdjlVhSjCJrR/THbbuFvlufbKSK62adl+GaptBuEnkiWunWy6ckRBF2kYSdMYoeJjguFCmoBxhrkKOfdPLYUyQCqQ8kgqhWnLfhfCteRS65NY8sowGL6UUjwHICdY6igkiZeXyFdwfJbv1tf6F6H3WkgJxhE5c+k1U3i60VWMTsxH3SotWBkN5H9xjLwPILgGz0kg9NYANObl76qfNEVN+3Dj9kIqVf0tXmMnqEQ9xPecX6C3gPZwyp4puvYvYe797FCcNg9rE7N8hu4Dckbswg72G389u4iTW4C95aa+dMtU1rYzfLVJvkeHeQ9dORIGgnocGpbQvhZ16FBsPzev0bX7PYBEL6+X5qqH5UtIz3ZP5M2I6LcjMxMz6xMvmJ6rkXHSqMRvDiFrXxiET26mbEJxrtZJ3qyQ62zL5+C4K9th/ShPQirGeGLgDkX5SewM9a6whhqCEZZugHvDdp32Vi11/RYUbDBYjUKQRchJCPdoc6miqWzW0rABfmu19dz32gBFF1K2UsYpfEHjUDvgEaY5XvNc/UbVU1oKnE6kpVTNK6C6aSxEwMNIKQNrkpCCnDNTKptcYkgwV062FjivUHwGvUQOYsfnkbKt9+86IeU4d6ExB/kEHDKBfSSRGMpHNGamSfM5scjvrSz4Yjjqu5Br8qn77pog0TbBaEu4eQ+VnZ43l/pTSrSUP2LoEoxOvUXytrZ6RgvV2YWhrBlZUfpOTP+aP8/ZYS8o3afSv1xJ6OHFBg3t0ysJFpbVo+jEc/fdl2gC9lpgisrw0PszT4NIfOm6Jf40BKa1t+8bV4zkd7qvv/v+Lwku9x44zHmsQurAVPsrkiEtVdP+O6EZBVfrmanCa1O1RBKKWBPzf4n2QRL6LqEsHrKZBNPNKC9LgHYSUnr2RPPpNo+QtuhOMN2FZTIWuJxhuTDsVKc2K1MteaiQe6N1e+Ye7puqIr3W0npVpK2ilBLTdGS8OTLeHOA4wzTAcQzhNE0hwHYLNcFwC5cF1gc4ZBhKW6TfipR4llytVOsfCQ7abxz51um9FlLOpKB1mWub/XJzv4gEkBhcD8NdWzquMeq+PhDBf3PIT9Q7w/tpKgHS2lXMToonJhQsrCQp12GSfoG7tXfNwnOB2dfTndluTYlp+72qy7WADRcqfZmyhvv+7CFML7sXUi6gvN3XlAFn+oqu63PqCc71ufFc/Xsrx20TzbdrQsp9l6LnFqeXn9hbfj4m6iv3c+kZBQU/yKMWLenna9lde428R6J0zUw9f6isvM3VfXofZeLw+VG692Y96XMTAFP13zRBOOye7ImEBvubrca+cmrwQFIQwYIAwQZ0ttVWNjjQM3S0AJSBfSiKbM5mMynfh+zyKH/7NqWoxzDULu5WRgJ2aYVS9RdRLa9EGhLZFkBGgRMvqKEVDiylQMnGAPL2kic8cjeuLGjX5qXOKb3KE776dtWk0XstpJwZadE/0vJx9QLAw5F9IQtdh9ZxCjMWk3oecthrran7vffluI/Cv/PniqkdCQ0+ysosrHUBHio7ad4B+Wro+uPW2ub1Eum5Xode4ZLg1+uR0N7dX6d7Xft3IdkHS2jSSkB4YttPaRaRrndB6IEvHXvZ2rrYfdo/JL+vnqs+kvX0CHzT2qU5oq0GN9Yvet7JvusFhMZBwkNtdHjRrdoeLn5ucerZeo73oZ7pFvHJrmv3npjryB2ZmZlqHwdA5f2zUjg/a11dGMhMzJso8lYI3JKd4xSRcisHHrf544JWPjStrwOBfEQy3VsO3HLkYw7cEkl/X1ULrdlHoXy2tDxtu2zLqNGoivpL7eVxhKxRa+C7t0C9NKIkWK3smbmK0XbPGYW8ryjnyMKJwoVll/ciDCDWleGyMg1rjNBYhUteII+Q7mAYo65OwwDHI8OywLLwyBqIXRpZLxfy0mZfAspoImMtsNbxzhmWE+QB1oH1fE8pkfpZmRAX7lk48cCZx5qGa+GBC6f6L+8UtH6dvI3eayEFT+ESN0DFIETue9Ii1NQbrlwnYVbYd5T7bXqmfI2uWQW95SBGdy36y4WHi974tGzwjJaQt6m3YlwgC4pygeSfJXzcoHdfTW/IX9OMnrNe3IpUOW4N7Nu835flQu+aBXLNQnWnbS+kPErRNwi/zaoWuSC6Bmw4TNf7wCScsr2L+kXsLFJ6fe5eYj0ezOPluVIyVB04M6NAnKe4ApuV9RyV7c64Z6gCKzFtc7D5Uj3jYZALVN8i4LCsIlNvSHWLbKQHuiX8TgMzGW1EbrPe50CqvdwCFOQ7aiMgX1QIghXKNVY6hKVVFrQlJPL2pV0b0xNcI9X+Uu/Gp7SpV5GFIvKF1JLyiTXHFqWpDAFY5kLJI2md4NnjAJP9r3GCkjI5FfJQx6yUK0OrEYpxX5hCWOULi2EOFx6qMPqUhUjbu/DAygNxZa7idx9K4XP1Xei9FlKuRYs0AVxqi/pOcoYwdJ9dSEGPhz/1l3xR8oFyIdUzfRdS8XcTvZGc8oKcz+5s7tHzXsiK4bll1z9fz3YG7Ey8799+QejdITExURcSvQC8Bg14IIAUDQWX9HRtEYz2fS80HNrzdDxe9kv+OVkyXv61a1wpuaZJOux3jRL7bQFujfa+rmt+VB/bYMtKCQojvZq293H29breH9HCVMMkWjBC2JxKQtqX42up9znKktIROzeMHKtYOjJwu/l8phokPjBWG7yQdhnjhios5A8KZU7BSOt2z9Z7z/L/MawXWRtE6MXeps4mpBQP2notFIaoxVQFUwgpnW0QoN9aTiwZLhnmMpFKYlxXGOYXmY+Lme27UgG4RFhVhLWW1n1zt3uK0kZNYT3lhbVcKFwonDjzOSc+48Q3K86zsvKGONJj5ULmTHnWivqugPt8oWt6uF/CfSu9httDdg736bNbXL4nx2Eo9404HPgSuSBViHmAeE+PxBBzDSjnXNHtE5mJcHS3fVJuhfRQm1t/nnlAUJNbnd5fYoJ+vpMfJ+E+Jvd/SaDd2zPEYD1UXfV2N6srBqqDh2P7HiBnC7Bn1t4WVyywa11YwX7fm+iOBpuqPyW8PUNE3yYXvl4nhzBfCpx4jtTX3nYJWSVeVdvdkljYKzCJwomFoc6kkbH2V+z8yYycOKO9QPs6tAg+7R9SyMJYD/WIgAIdUaFDMu5RstKllq01JfJ+hNjvdiBxy8Qttxw4cscNt7ziju8h1VCN6IuhKjRrbX8Ttg/VSyT4LZG2LC6yuwQHStw98aoOdljPzZfCP3NZGPKnVSQ2KFZhKFQrNQNnLjXIqSCfVfR7tFp22W77by6sy8r5fCLnicNhYpjsCu8sgDEECqdH8npmoWZDySvrZWE9nyk52plLBJ8sJRIfO0Wi9MKyrFzWC+c1zvzKPHLmV6sldSLzQKkbGDJnMgsnHuunp8Lpuwruk2bqjl4xYTFCN7Z76oMEesd//xq7V19G//4SuTbfDtXbZ0ZwmC7am20Bpx0D9JfXydvaW38ODapODiH1TNeP4nDrT+X1Fpnf51avCxCHD52xu3/vGuz5HBT3XP01F1RPt0D6uaO+0bUaH4dO39ZfvcbYWzE+Hs/Nm2sgWz8ne0u1txYdQvR6jNvfysuQraz9v4xc316Poftb3o1xE0KKe1OgdvTNavUNp7r3hQc5awxif91QIxEPHDhy4Ia5ppWVBTRupXk7Wr0119dNuJatNi27XemuT1u7SGMEJYimCXKJFyMpN7VqQEmFqTURuNd2RYWYT9XyU/3bqHuuilKKEku03kojKLACfW9UVnLJrDlTciavmbwu7biOsqIDZEoOoVSGxhFySeRSWPOFS1mqGFpYOfPII2ceuGzpDi4oaCJXlUTpZl1xfA7JeoneayElJuhmqnbgr/adL+prJMYyvnCNBMeBlxnLFyE9d6JlRvB9UmKM+k6bRkNYBOrrDn4xRDFSMWb5oVzQQgu0cI2mh6UkSHxjc388Rh+I4cJScIui3MSMemjqYp9PVmbnDn5CDiE6rNX7tlQ3t8JkDeo5GmMXvgNtU7Bbuq4Y9daT/Fsn9oK3h5dlWbrw9PY6VOX11PXOktwyH3lqNUEDnUZaMILbhOtW6oF9D/aqusht3RhxBZuPdRZPZOYqmgK2VVaIfVpgRyScEhEOMTFx4I7bakO95svM3DBu+UQCphuqsGlj+7YZJNq3TwJVu50YPoZxrvuRUh2sVB89weUjWCPEJpUzY35DOzrzU/YqVmHljPJg3A6vSUyUHMEIa7V/EzCNUxwKmGAcB8ZpZBwnmI4wHkNobht5rQfrol/uM+fTwnI+k5ewolaqXM33TBwZuWM9jyx5YEzrBlOuDFxK4f78GTqbbOWehTfc8ykP/CqPfJMWZnTZ+k4Ig4KhrinUfVTzc/ReC6neAnBtTIywFzy9xilyxuTMUy9t3vQABC8Pu9+f5Zq8nlNolkOmbXj92NrQZ2ZIdo+njbmW683r+ZzG4vV3gdH7iXL3+RqW3Ped2J77TMQwVS+3JL3+gglhn0ao9xm6/83HxC02t0T6QBe3ICe7rxdQYugaI2V0ULv63SD6zv12fb/5/OmhWGf76lv1W2HfBqd+Xme7rrfcHOpueQsVJhx+qmXTmpv2r9IUAgHLZjPtaxH5UdrzwlpbWa7ORY1fX199jnwYR44cua021IEjIzp1d6RsltvKypmpWjF98HwiMRvbq4dU1Ho220ezJzFDqurEJpiG8EkNqWo+iVTWMHNydQ6kODe5kKDckuqMUP+21g0M40QaZsZ1DulR8+qllBiGgXEYGYaBob4zDmHFzTOkOeoy+qiGlchwpJQTOedtMQwZWuxmIrNw4SEiJMtMXmvWw7xwWc8s+cKF+wrlLUTu+DMn7rmvR8NLnfV5eW1d9HzqXem3hJDyhexD1TuiNflfElL67MxGnS3fkZNDLXS/5+5d1ztDLjQG+JG16VqOORearqFcE1Kqq/we/aRwK6Bnbv6ca0KrZzQOp+r52uuk8sbuOm+TaO2ul2XR+5Iccu2Zdu9074XUaN9rPkzsn+HfyeJxxUH9eq1fnusvZ0veFldEeiHlCoQE+3P+qx5m1DNdiHvf6G9vp4RUQeerLrWcPWg2bLVZiBi75mks24wb8f1PkQJp2ea8qIfXvf/118ShiqUjR2ZuiAPa0w6cnepzYuNtZGdpkKKoZW5J252tX8K3Rm0JrOGDSjetYgORjWG3iXaM8LtIOR5/J22fL5uFBRdaMHwUmBgZpplhmBlTRNGlte6XSmkTTP6KYzpGIuy8zp7JR5KoyxBbVZQCaSCql7YZMqLMhDoRK+cUR8GvFy7nBy7lTASwr1V50WnPjzzyyJuKqfiY+by99nJl7V3ovRZSnrJIJqbvd1aHSBtW5oNX9XXHXvA4k75mlqrz3U/U+49gD2n5gPTBFlqYdzQYSc+UFi7gRTCY+4ZUNxeqYkyaJKqr4DfXeXunP+wtNe0B6mGs3ky/BqjoeSLV0X/3vnHmqUCFj4hx6hm7nqnvPJJNbevv0fgoS4MyNvj4yQJP3Xee709zQBnf3Tcnl/prWj97mz1AZ6Bl0Vdd1dciZ+YOF6oM3yjsAk4n/XigiuaA1oNDqwOXDX4Tmz6gw9Ah3OFr/V01K7VeUbMA9JYKNT6ic5Zij1XhzAMrlw1yVD16RhUpjsbqpxEbv2HYWq2cJU9nV9QkATdM1c46ckRH82mF+Om5rT1nUjpCuquTK1VLpdbyfA5r5XiMoyumCQ5j8wfNA5xnWBdY18j4cLnE57JASVCOjFyYKBH6kW74KN1xvL1jnA8cCyzLDZdl4XK+kEvNiV6zma8jpAxjWSDfR52OwKzsmNYnlxVWeHXzitvhwOmze1YuXA6JZSmsOWbFdhTIOjCRmcbPICWmKZHWsLxOWRZ2+KMWHjnxhlN1Nzjv82ChHkGQ8qy0aDe8G73XQsohpUxEkr0hNvOKUYkxqFM0nHp5BJ1DUG7pODylaDyHl3ohJaare/S3rnUmqrpJk/WB1T3LlVfvh/K/oU0MZ7zY9c9p3t5Of06f0kftdMHnWpK/q739s93KmewlhULw2jWNy/t4te/6Z7pLWYJNbez9dG6daVzcNygLqg/y0LNmu0b9JEGSrpR/pPX7tfHx+eBKhxQAV8T2YE8IH59jsmrVT6uVpXCB6NPw6cR1MSs1/91KVjn93qMoO9SbtcJbAAstEVC7dz9GjWnqqHI2gaLDN9qxKMqIN1QrrXARfFYjClMaYBzDIkmwiyvNCdaB2AdV65UOMMxhCE0DHG6g1OwMQwoLZoxkr8wj3BybPygB8wTLBZYl0uSVFVImlyECH9aJIR2Z08gw3HAYjszjLeN8IE0zYzlCWklphjyQ15W8ZpacWfPKclkhxz6psSSGkiBdSCWxZZko1PYVKAPj4YZhnMmPK0NJpGXhxJl16ytlxYBUwhPFMEIamIaRUiKDh0Y51ZkfFuoeU+kRqJV2EoDmp/zvNww17vIaALyn91pI3dMW2gL8DUJAfZPm6P4eQiu/BT6h+X70nY4vcCe3x/W7r0JMwQ/VUzDFc74ChzCcSY1dGQOhnbs1l2jWjAsUQX3XfCG6JhFW5YZQWDuhWVJuHakffc+QC6l+A68LlcIefuytrd735UJKjPUjYky+bP0qAdtrXb1PyMl9Ser//iRljUdv6Wp8ZNm+6spWGL6f/+RCRxbCXNsiS19+TY25jhJ5wz7IwmHcPshDwmzlqUNaQlW/qT976FeC5my/aTzC8opDGuNw9QGd++zXPeCW4rrNL/VlONOfwo6wn/97iHOsVtwBhYSLMgMrqXpETowUJm6rJypVaBI+Z2KugenjcCANM7x6FVbO7CJ8hccz3D/C+T4wsPHYYLQDcf3dXQidywLjGvDaOEbuvJsJPvmE7eTB8zmsqOUEjyf45ucwJspy4TwskDNlXTmMt9yMA4fjRJoOMB8rVAesE+O4Mh4yIwfW84XT+Z5zfuRU7uF+IlXx8tEruL2bYL3AVGApFgm0RJm3t3HhNHI4JUiPsMDlIj/SYetjh50ZR5iOjMuRiQuPa6RdzqyMXBjrqpOF6vNUvENr5PM6z29pCNYPAB9zZGEmgkpepvdaSEmD9ugw2Gvm/cF3YhKelVfwjzN/Z9YeFaUF534LeDu+6lChkzRct+I8YtF9aIuVc83x2PvHsPs+pTE4Xds79h068mwbYmZeH9iHZff6kFsM6i9nlD023fuXBrtfSoK3sW+/92vv5/F69pCrWwYit8CjHsr1ljlQNli2txp7y6f3abp1K6tLzF+RfOsz5XkkoujcXT+wH8veuu5hXVlgxf4OKCrsHh0jsVB2CovPFT23r1tvOeu7HuIMgaQzcQPo02HpopnQ5mcSM7fM3BkEqBCJVBW+kTHNpMORNB9C0MxjCCqGEEjLGdYc35c59gyV6PGSMoxTO0p9KC0ufpucI4w1yIHMdpR7qXhcKlWgFZSxoqQE48gwho8pjZHdPI1aHbLmEqXU4rbDBWNFZk5oL9a6juTLgWFYA0pMK1yqsMpLCK4UMziNA6zrdtDhcZjrMfB1haSRwzAyyShcFspayBfIaz+bQqVy5Ehj60rHNaXd/cLNY/l2eq+FlGuW7htwGG22z2I8noW72P0SVD2TUYoZhzu0MHtH/DXqoa/S/eZ/S0D6wvZp4tf2z9PfzkDUtjfd9b1l4859Z+xeD4f2dJ3DbnS/9ULKIS2vi/ukesh04mk7Xah4vXSdCxhZrL5AsGtdqHsb9pq+UoSWquiUzZLystw53O/9cqE8sF/Q7nD267w8txj1vN4n9ZyTOlt53m/iu5rT6quJvJsb8l05q/Ix6YWUxsTnjt4deh5oWe7GTkhJnIXCEJ6qOJ/2holbhprlnE2oJQ6sDGliGOawUA6HsCY2IQXkDI8FpgWmAdZao7X2UMmQRkqKnktD2WOsCSL57BhlySeVUu3M2uvDEAIu1Rdx3zAMDOMIQ5wTtcGFgulqmiPllG09PJLrgYnRjIW8ZIaxjvSQYdFrqdp09YUNA6w50imROKSZcahtTiMMB+ZhaFVZV8grZYFcngL/iWVbbz62vo6vCbFh9/JIypfpvRZSPcP2k0cFHX0PARW5b0GNFvO4EExcAklnCq3sgzMk4HwBZprR3FsTvtD9O69zT/09slKcecHeX+Xf9RE2DwQzc+irv9cFlpdza9/1Qmhkf5xF3y4JBhdSIikX/jx974y/9/WJxC9Ge/dnOLTnAQQuVP08K5Xp46fgA2iaXxw7HglfBKl5cIkE1z0BO39GC7CQNSQId6Adn6Ln99YI7H2HElS9MHOLUcqS5rmjBf4Mr4vuP9W++oi9sBUMqfos9gzqvfc0lMLHpWdaOi4QBsaaNiksqRiRiLtLVSzF9a95zczEoW7nDbs28JDINhHerMNwG9bT8Q4++ghubuDj76vaaobLGAEFp79FU2VihNb1XDevDownSKunww3/TkB9EEleV1jXEFTLBcoFygKnE5wXWFYu54V8WaqwGEJIjTPjMIYPiRQCxdVPJeo7Vato5/1seTnykljyEj22EFbRWoXuaY125kfix1TrB4xH5nFkSrlO7IBCU0oVB14p65l1eeBcVhYWFj4l81BHOVSZ3o/rCqLzxFc8DVCqLeBd/FEq870ll97qrGswn7Rql/zOnHoYw/UG14S12MuVv1+ypHr4zaGv/h6HeFRffw52z7Xn9e3Ts8VY/F4v85q1d00o6t6RPdPzsRCMNLIXDPrNmRv0GtbTer7UVhc6vSUlQSRozZ+v77B73YK9BinqeAnNsz4ST/NCVpTDqX2/Pi27taVXSPya3moVhKZ5Ll+s/K1iEH09ff7re5/vvSV1LerQ53xvJQbjaodgxHEakSA1np82q0n581L9Pa4bNi9V7JOaqoCa6gi00lPticQYUJx8UPMUnycCestDQHSC5+RjKqs1pFBKicSruUDST3VmbhPRraQK62W9imkSYR3F+FbYuG7OJZV6FMa6WVCRyDXXDBM95mIrQ4O4ZkoaYC2kNUdR6wVyIeIqg/sNrKQ0VOttjKAS0iakWuaMlVwKubRdUS1/hLhJyzCf9rV68p2vaV8fsQvv3ei9FlIO3wzsLQNPMdRLe13TC6hrL2mR15jGu5CeI1q733pdwvWLnjG7BfFFSdDOSwZ2DwE6nOQba3uSReI+H4fbXEBo8va+lIH9mL0bWt2uu7HPuv8VT8ffYS23KP0Ij5dIOevmColdUzL8AESHoV8ir2dvlUroFvYWsX4XSjATQUE3RPCJgj/6+mluPzenXWHr/Vr+XHiqICp6NlK+Ns3/yMfIz5Qr02t3HrY5IOAvhFOcGHysaWTHWtLTEHQjTaTjAIcBjtVHxFo1kxKBD3kOS6jcQhoZLxcS10+33rfUaKSWfQoLaJV1VbY7IL5KaWAcD6RrXOhyDuFkpJ1qb6W8wlqhu8sKy8p6/pzMhfV0rl7UMdCE4cA0fxSh82Pt+6kKqXMVsK1W9diNE2dO1Uf5dj6oXnoOJRJSNXP+7hBSzhS1WMUEBfHpN++4a1i/CyT3UXm8/3Tlfp9avdBxa8YXujPh5ywpZ9xuUYl6iOwlptOTrDnXqlf2grt39vd+Moc8HWrS7+5zUd/LSvD6ugWlMetz5fm49W0UA9e5Ww4RHq6U0ZghG3B0IYKjPVHPHjLMdZ4lIhlr3sqVRay5pHOp+s3Wqn/Pdnr/lPrSx9eP3dB88PkjYXRDRLAqkkpCX9f2wUWar96//fg7iuB17v0PN8AtiRvGCsu190g1e4NyVSycWTnTDp0frZwjCkKf6iH3EfGn3pnp8wZuI3UY4TDFPiZZUayQJkgz5AXSGt8vqYYkTiFolpmUM6M24+56eICpMnQIgXRZYbi0ztuwWM2IlWGYSMMNZVwY0sg4jqR5jGCJw1j3Uq0sj0v4gYyWy5mcL1zyG5aiMJamFimH3mU9MOTClN/U3HwR+L+QeeCRhUcykZz3ptzxar2F8UA6TLG/KlW/WKmhO6cI78+sRLa+pbYmdsvpoExXXnyOaa74+nDlV+6BgdIlxnqe3msh5Ro6tDBYdZxHpDks4ZCaFmKx73J3XQ+R9L859Vqzwyg6PPElzbofZGf47sORVXINPromrPr6X2tL74fo2+dCya29a5pTz3ivwXdufcm6ceHgzLD3Y/lzdJ/2NWle+D4oUbLfAoBKTBV68Ou8DnHikGZHO71Wz4H9fOpTwriy0o9VX5bqpvZqHNQWt1ChCWPlfnzFfo+ZB8DopTr39fBrvD29da+Xw+stCezIgSNTzRBxYOK4HZ0RNKKDKFt6Wfmf4qBAQX1xTtTAVIWUsz5f0QnSwLaXaRhhTPG3aj5M4ZdJJYTNNISwWseA9qa5WiU5ythO5q2fx8EGu0SAwriwBU4spWoAA4rwS8NEGgeGYtkixgGmqFsBWDLrZalRdI2W5UwuF5YSG6LjHNyWcDazksvCwshUCqwruSaUDWV75YEzZ1Yiw0VEQ77Ka9R5HEOYazaNF0pOUHJknCCjxFg60iWjdLxKkdv21/m6dsjelSHxQe3h+64QUr0l5SHaznS8g3SdmIkWoUMWK41ZKBTb90b5tf7cHsYSw7/nKWzii/85Jj/b5+fM6GsWop5zplkTHkbv9/awp2c6dyZ2BezY+kPPdg0cGiPrmZxPOvWxH1HizE/M05m/YAKvjyeA7YVUT2KIye7V9Rq/YLyKNmuhB3GG177+3nc983ZLyoNIirXFlQLvG0F/Fxqc7f42b+cdLUPHTX13S9atZWhj5M/zPuz9Vit7C9U3xR8ICy5yk7/mY15zrEIqALt5Y5crK7E1N7FyRoeQR2xfBFHoFN9x80G5Xe1kakBZa0RKgtMUwmBZqwVUiH1OpQ7QCPNNhfy+CUOG+S4ObVpywIHDEBYYtSMu57Yo5gLTGi2XkNoWywhDIh0HSgqhN41TwIyjri1wOlFOZ9bTiYfTiTWvHIZxmxvn/DlrWVk5sHJhraErmrXnspLKA4fhY2BiyANKTnvPmTNxLq6E1ARMZeSxvOFwmRi3iMM6quclLLvzwloWTjVkIrKExNm6K0tFCBZOnFi3mf0yafV8Vkfwoc6Z80s3Gb3XQso1bafnLAm9O9zkEJXDFw5nSWDstevrvpMeQuuZt5iDwyjOTHto70Jjmr1P6bnPXg+3iNzqccHmdfE699RrSd7+wn48VvbP6cdE1/n+tcleg5XXW37P1avXtVvSm3ZXlJtoe5/CdV8ou4i/Ng9az4Y9JcDjeXjVtchrWqba0VsrsJ9PLqRVL99S0fuC+jnqc7X3hV6rr5cnAemQoPeznil4NsLD5+0QjQMHdKz7wFyj9mI2x+mzhVT1dHfHp/o+bMEUfe8pk6C0+lLvyJALaYV0meEyh6DJa+3oalkl2OK7p0JLzJojmGDKME3NIitRbnDUUjukwFpC8Kg3dWRGaqMeGZUSNWFenQgFJaItS8BzazmHQCrNg7gWHR4SbewT5bZZrTwcpa7hAfmz4py5tdpAhypyLkzrwrgsIaxTne1LRCSWsuLHiSjdsM4abr89rdFz5IiC5rGgv3eh915I+fs1rbmnwvMSXAtVsKGsscTeT6Jpre+uPeMapOiWmwsGP221h3yE9/cWyLuSsmR42HCvXXt/POcoF7lOK4GiNkvbzuyPmbhG7suQVu6wn0NpL1EvnMJy1OHlY62XHMI6nLslEo0yUhUI5UnZTjoWTi7tl/TIHmb0ukrwaFz6I0N0/4Em7DVGgvFcSDVBse+La0rUc36xXvi4/6qHZbVGmiWVOHDklhtecctttaPGal8ljjUm7xFFiEW82YWliq6+RqMFU+x7tK2qTOFcR2VghPLAcFkYHya2BLDrOWqbU1hIYhIaiMtcG+qjWS2xwxj595Yl1H/RSgiptFp5Y4PR3IldVrZoPwCWyoRWyrKyritruWdl5Zwne8REeKEW8luEgRICx6EoM03MwKVuzL4hTnx64IHDcstcBGnWVXs+wxIWW1huIeJCzF2quBo3APCLkO8/le9aPtt3ofdaSCnAQFaKOqJfoH3gQnM/Xoe0hKP6AWzumHdm4M+APSSlz9orI6vIIUmHrtzPICvjlsaAxLx1rSyuXiP3qDxNBqVIEoMZ2Z8Jpb5wq0XPcItH2TrueOoZcAhVfXfN8Q4NMnLmKktAKZAcNu0hyV4I+DOk56Vq+Vzs6dK+I51PaOoRT6YkpM1GCuV3pRDJaM4s6Dhs7Z/z9FSyVg/2m0POasfZrlWQxUP9260hz+nomRrU11g51Gu1Z82tOD1XyXB8U3Dzz7U5ee24b1n8qpeHuYcAUkDD3jZsdtBQmZxr1srd8ZRdRX8O9c6ETlITk7xUp/4jJ+JcqTkyUawXyuMbxqkGuz8cYF7hUGKfVBqIDa4poK5DCivLUgRBtbTGA5wznC9sYeKXFLDhWD8PqXbgFNDiUEeqAMsI6wKlZrhYMiyRMJbzSr6EkPpO0UBAsHmzgUL8nJYHyroyr6fq/ZvJ+UwuC+cK8q0bZhAUaybXHOgLK2OdP/kJH3Q/lbsjHG5OPOU/z9F7L6R65uiwk1tW0lwlgHrdpIf9+mf08EoPv7lmLAF1Zi+sBGn3Phb5ntwidOGg5/XarHxg7gNyBoU9x4WULDRde03b9r5wxuT5Cq/1X7a/fUuAC6neb9P3qwfA6H63Tv0ZdNf4Z0FJ7kMSAJjJpO5fuybvlmccpZ6IwyaenhfVL76he3n7JTA0xlIiLuzHvVdaZPl6f/oYq14aS2cAquOj9V8P2fbW/7XPLtR8b1ZYodqv1GAiqQs+Uv4LjE/W4b59ibyVGzNKB4iciAMjHnioGnoBbihlZVhODJdLWFOXC2lIkMc64WUXUi0fKkRXe2EbyGpp5FRz0OYQUmve4ilYbRQSYX1NtmMvlXjGklF6pEhsG/eWuh8qstG+Cw70rqQN0THCUctQ3BYy53KGkkl5IZLxxhlcufbtstlhPia52ms6jF5Q+Z6HOO/pIW9f0/BdYkkJylKGCAmo3nfzEvU+mF5D1+d+g2j/DDGCe5pg653iqrNbBCeaxuz+AA8o6AVVfzqwa+fuyNdvCt4QNAT7SdXDUs+Rw4W6130smowuXKAJb4+281Nu/bPanruyVI5bLG6Nlu46eS16Pb35W0r9PzPaMtBBest2BVW7zNwTjP6epyHaPbmF4v0He4tXFtkjTVik7rPDs+rXvo/1nb+LpKh8bt8LYnb4zvvTM030gsSt37a1NiCrRyJCDwYmPqXZYGdK3W8jKyjq/TyUlaueHmMR1u9atfz7un/nkTdkUq3/ibmKwWG9xCGCLFUwpNg3NaYIqBCNM1uARF6gVJWmZFhP7e9z7dUD1UKqn/MAjyOMGYa1wX4QId7jGBkgulFb6ucIE3nFy+DxFyFB2QFrXyp0FwFAAR9+xhsiZ8cR7UqT5ytE0KUKon4WnVi5J3PeITntlOfma3JeJoXWs7PAXnl8id4F9v9C9PWvf50f/dEf5fXr13z/938/f+SP/BF+6Zd+aXfN7//9v5+U0u71J//kn/zCzxKT97xmsj6uCZJeG3/p1UNfor7cvkyHM156+c5+PcO1715T9QADj8LqN8C6Jj913/WO794qc43nWruuhaU7E3Xfhv/mioPDev5y6K8PUBm6Mq6NheorViDhdC0cvLWtbDZThFIIsooS5TK+GMTne598rqi91yypvu7en2v3+dpcKt0r8/K8c8uq31jcP69X5rz/9sBde763p9Q+jFiwCxceeeCRex554FTPkfqcM4+c6r9zBYwW2lm9rT0R7yfNvR3It6LoQO0RypWRtvYHLLswhlgr2TrLZu1Q2ktPLxlyDutILx1kKAurhp5HQlq7LucQWqvbzDYTEhUWrMEYaYzHkxjGgXmYmdPMlIbtFYe3ryjEp+U5VG6OiTEdiPD8qcJ21PviYMcjt3UbwGETUlLNFjJnztuYnGpEoAItZElFmcqnGC8lrZoZOTIx13jMa3P+Gu9yvvMu9G23pP7CX/gL/MRP/AQ/+qM/yrIs/Fv/1r/FP/vP/rP87//7/86rV+3gg3/1X/1X+ff//X9/+/vu7u4LP+sNbCi4Fo+c8c85jaWB91qta+z9wnXz9VrHXmMq2Hf+W4/7+5RWnWXxuBCa2ENtEkyCjLK9++To/RtDd7+e6fXuobpEC7yQz89J8FwP6elZF7vP/Wyqx9G+cwtPz3fhN1pdvH29MO1T+7jw3Ou0hYlCBJvfEBp/3B3+rLIdpfGGp2Pp80bWjo9jL7RhL0x9O4RsjtW+48qzJCAc/8eu9TmR2SeHFanssbtf88gP1nSYWFDx3gchK+dUI8gKE2cyMwdWjnXHzpkLb7ivUNG49Ymc6SGaLrWeqz2hzS9ZYSuP5HqNLK7wk0QewLAUM5NPADdNRUvt2XUNWO7S+Yiy2e+lxO/KXLGkWl6KfVKD6mureXteCvhwmWANo4uU4DByLIWcy2ZdAnXf1MpYM0ZkCpFGN0ZrSjfMQ93oXKgi4lJFdIipA7dceGDhwiMnFO7fxuyEklGx1Xra7FV12MjMso3+DYnExJnYNqDu1PaCveXv22iknHseyHehb7uQ+nN/7s/t/v6zf/bP8v3f//384i/+Ir/v9/2+7fu7uzt+8Ad/8O/oWb1Gf435OvVaL+wzSvh1rhH3QsrLkXl7DR5xIeQwjJcDewbcv/y5ap8Oa5Tz0a0hb4POOpIQcOEnYad+0pEk3heLlaU2a4L1kX3qf93ju9AVwScLzgXwLcpWADrUO9M2zKp83dvj2t5e9b+sazHqQoM5n1sYwSIetjtCOBUeaEl63Yq6ZmXrN/WzhLsEgubTtfq/jTQu76p9vo1cs/Wja2BvvbmAvz5PU1VeSrV5FhKfM3KiMGxbek8V6juhY/CCooyxWrBj/TVm/LCVDQqAydtsu0HnGSXTz9OQGMeVNMjL56PkMYuitVpFmECqt12I3yAi/ZYKFS65WU06xn3IAQ1+/hB+qcMRhqpCHW5jD9JagfdU4HDLUBZSWRnmOBRxMI41nD9hyRcey+e8FE03zHVP2Toy5BOlXMg5TvyKNXogMyF/FLQQcmq/Ftb67FQt16erJNSAM2NVGyMTiNSDpmqPdUxcYYv72xwW/Peuwuc77pP65je/CcCXv/zl3ff/2X/2n/Gf/qf/KT/4gz/IH/7Df5h/59/5d561pk6nE6dTcwV/+umnwFOYpYedeqjPfRki3wDs5EEM18ihF4dXHB5xhibqISGH2bjyG/a32ifrge7+HoKUFuxWmASGvlPZEswXK6fvE/9d714PCRH5xqTtH+q7R/F5xKIrF7FQ2kkzEubXghGcJEgV0CBhpTH3PlN9ndydH/yp7KAyt3x7i9EtULfoZelpPLzubkHrPXXf7ev3dB711/o86C3RYt/BU7h1tut6n5aX7wKr+SQF/Mn+1M6ahw2kO1XHvLYD+I4or3XEocVZQ4XWv210QnQFAFaqQhOju5UyAMlVK1+tfa+VEBrFeljJZXP8ngDGECSsCSWhjWKjhmnJIajOC5Qh9ltp39RwiErVQIo0JLaTdHOJ/VQ2XlBgjWyU4/q5zY3+U2IYEkMaSRxIqUBOzDnUUyXqjT2AMYNXwldVaBsulPejjWJvn0OqIzLUHhmqEhGjkXe16yFv/db8mF+MvqNCKufMv/av/Wv84//4P84/8A/8A9v3/+K/+C/yIz/yI/zwD/8wf/kv/2X+jX/j3+CXfumX+K//6//6ajlf//rX+dmf/dkn3/fBDK697+rBU8Hkn68tyJ7xuGbpMFLvX/DyXGA5rOZLRcxTZU92vQdLuA9JZav+fXCGM1VZTJ/Q+sr3fGkqTvWez9jDTIL+vN7ShAb77AJL9ZV1o7DoO2vDR4SQ/NjqBCOxNyTvrEf1k8Nd2Gc9S5aTTgUVLCgLr7dMRZ7IU7tDPqUduaEx971fo5XjUGPufvNgEH2veaKAH5XXjznWhxoDL1ttke/R/Y0H9nNK5eieO5oVpVDyfh6v7BmNrHjd26ygtOnf8gVmMm/4JgcSRwb8sPGJAzNHxm1GHCvTGzhyZGB6wpjC1grVKvInnOq4nQPyYmLkTMQCHlmHyJE37YBP9dAd28wf76FciNGuPXs57X1YpDibasqQTi0zxbpWc2GBc4UCdcpuItIOjbXX0gmmBIcM6RxnWo0pkrsCUBhtNiUSaYF8/picT6zlzMS0rdexJpyamEnDUDH3Wyi3HO6Jto5xGEwpK3fnR04ELBvbAdIWuhLlCccINVHnHTdDYGTiWBXOmMUSWs1bWHb8TsqqnnLNTfAu9B0VUj/xEz/BX/krf4X/6X/6n3bf/4k/8Se2z//gP/gP8kM/9EP80//0P80v//Iv8/f8PX/Pk3J++qd/mq997Wvb359++ilf+cpXdhq5hID0JddMXyLpWr1wewkv1TMcHnOntws4J2kYrlWortj9/at0764f6m9BXXLwC/LSc515ueBTOb2V5haWyrhmtfXkerGEnCsRDvm1c77GGggLvqG218jcGfucYtEHB6iMHuodujubj6hsglVWlPe5a4qyFEW9zybRLFjfGC5/k+oKTQD52PSRmt7mvi5uuUkZ8XXg84wr1x5o51xpLrh3RXNH0ODMwIE4PgMSp6qeuRUfIGpo4HuYNMIbIqjfRyIEUUsmG7Qfq4nCzA1sPpzETARcuw3Y4woX2rb4Zjc0yT6GX6lktvRFubBlqSDFvtc0skW9rUtckxZKKnFA4rJEjYcVhgqOjzPboYjTCGUOYZYSaXCHQNp6Kp0Lg2evsD4CwoKaRrbMGFMENJSSAmosAwxTrKcC03Ig50QkVdbmDAWmB9QYgRXZbLWeSrV85SdUTr8WauGvfuwcdfgisPd3TEj95E/+JP/df/ff8T/+j/8jv/23//YXr/3qV78KwF/9q3/1qpA6Ho8cj/2hA7FodByDutR9AD5Nn6Nr2vlTY3f/mxbhxT7766Xn9TBWtu/6cq5ZbOnKd2rzIy1E2jfwupbtAt3hnZ7R6e+mub0Mp15rn8qXVq+yD4QuGxkLIl/bwA2lRmz15fV+kGv+IGhCyoMRnEmLgvHuWZ+CJBQwcE9YOo/dM1wo6JmtjL3lLItFY6l+c2gY+66HQmf7rQOodv6pgWblSAmRkBppypS3eLaXfISuVPQogAJe7rZ7Rm645cCBAnxu/jxHLqL/m5AKURZbo4NltlpF307VYnpeSCUOjAws23URcrM8y/Z69dGeONQnzDVcnUsw+FQi7FxReYnYvLvN/CqQSoZ1gjFTxgznC6kkSGO9bIHhkzZxpgqEz3NcM7oKMlRBszIMPbDc1XwcGOY4Gj5NY4S7M8Sz812FIccYiZKYzqEurXmteRTXqiREFOVY2/W8/8s50FBrHHGCsqFcUXfe5jxE5Arw2+jbLqRKKfzUT/0U/81/89/wP/wP/wN/99/9d7/1nv/tf/vfAPihH/qhL/QsLUplPxBU9RmNoXlU20vkWql3nhjSNWHkAuLaxk5RPzi95SQN44EmIPXZrQZBax735Ce/Xght+MHek92j+rpw6tupPjvaddhvr+pvr9lHGIqcifdM0s89Cq08ceCjyqikyYWAbRk2UmVDZQdZuUam9klIySelwJGRNkbxt7JMHEgMnLmnVEZ6ogl6WVIiCV61IdECZXoN0hUC9b0Lbu0l+ZwG+wnelIV5tLb4OHr7/btemPucVt9g3ye7diLGVoLdLa+5/vYaeE3iS3zEkYlbJhK3xFEQjww1Eem1uiroJMYp/o8dUHMV4NGrgu6uq0A69gMSB2bWaqUOFUk4QBlZ15Xz+kgeLxw5wnSEu5uaGHYJK0etvtxCWkNYDGtkp3i8ED6tJeC4aQhraFrr8cuZSCFUw0XWwpATaR0Yz1P4tMoQ96n3xxmm15G9Ii3wutD2ZRnXWFPd4T9CKUynCFAYWRi5bEjDyCtGpvBt6fDGXGfiwUD4LbT2ljFl7lg55ESEuMSuMu09awETLfWRMrLEWQEjN0RqpCg2SnnkUrdq7OFiaPzOld5rKMRL9G0XUj/xEz/Bz//8z/Pf/rf/La9fv+Yb3/gGAJ988gm3t7f88i//Mj//8z/PH/pDf4jv/d7v5S//5b/Mn/pTf4rf9/t+H7/n9/yeL/QsLXpf/MLEHTBwzReeTn91mMrQUF2D1fxzL6h6n5QzCf3tUFBfvi9qCR2dJTRa2cnuu3Svxd6vwYbevuf8buozzyohZuZh8desqR4OU/k9Q4zPqWLhQ32mdoK8G7Sosvv2edv8upV9f+tkVx9L70d9Z+DQ1saBp+3s6+wL0y1a0ZHGvNWfHswwXCnTn9X7sTwk3+e7Q7p9/f0lZuBCGJrgbNGAI0ciR7mnLqKy0F5gO1ytGjRIKdVrokV5K4Xu6gir1snI6knlZ8z1r5TC6knJRjoBY7WOyFUAWW+UoX43EHunElsS2qF+TvV+HQFSv1Oa1ZJKPLPoldn2XpU1nuG9Pg5R5qYpttmViGenIZGGkbEMjGVkZN3GROEjrW6Vo5RSPxtAWKiWXWJMhZQiL+BQtBst4FpBfaXWpyWR1WgNaKNw8JdStxzEjrVr0Lgrv1rpLSIU3kVUfduF1H/8H//HAPz+3//7d9//mT/zZ/iX/+V/mcPhwJ//83+eP/2n/zRv3rzhK1/5Cj/+4z/Ov/1v/9tf+FnSDmUxyHp4QxMGJ9qiE7mjGfbmaC+gpOFec2ZLW5c1s7B3rsu5LL3Nw+M9ss6hO/cFXWpbJHhVd4cK5Tv5Ji1UWsKst3KukcpRG3SsCDQotY8Ee5tV2pOWpltp4UWIkqSt3XDDxFT1tGAyj3Xy33PdWpXvzSey+llh4M60o5zCmUvVTFuQxZnobw/HV/0lQHqM3eHHj2jWL7y8o173+onCWB/5vFCboI3/oT5PkLcEicqWH8xhRe8jT46s+ayyBemp/jdEgIus6AhQyMCBhTMnCvfcc8+6ZeXoAy9ut/Ij08GxBk7EwYZSEAtveGO9E3cfmYiTqo5bXwXCkFiZKnxY4vSqaeTmbmQ8HMJn4yvgsgZkNwlATbA+hEWzrLBcaqj4WCfY0vBjnb7LWp2qE+P6JcZtgKrFdXMTkN5hgrVAvkD61RBS6wicw3paV1gv8fszToI0JKbDxHE5MC4FP2d5HEyFdAhhkd1qbV5zLT6wgmGeo1fPa/XrXYgclaDEU8rgV56o3qGYrCw8cOKex6vbeLTmNC9j3d8Q4TERNh9hHb/6pN09fUfgvpfoK1/5Cn/hL/yFb8uzxGAlwd/Q/AhuZQmfNzR5Z9H4984w3FdzYR89qIWtlwSEkiULClLYt+9Fkf9AkJZnKu8ttUf2TMSFVKlt7jNvOGO+Bsl5/8l6UP0lHmDvt/Bwaq0JgTKuNfmrt9D2v7dd9HEK60AiTnC65ZZL3fwp2O1EY9ouQPpxdAvaIw8dUpVyoT5xK1hpikReZyeNk4SX+ryHhXWtW3Dqe90/E4KhhzA9O7qnztK8kV/vY1qUnuaF2uBWjLfFYXApQQ4BJrvGAyYmCmOdsQuFExceiQPhz5QtK4f7wRrDGrnhruZAODBzS0TlxegoBWqptS11tktMjWRy1cclBBXskUgcxolpGpmmkWGaqq9mCqsFoIxhUWSbRedTFRig49cja3m1vTeTMG8Tf/M53VZ7Mw0VchvYjvnYVkCugq/ApQSkWOp3ZaHkmryWsNLSUC21Wu4wT7R9SEAp5FUBDKrXynbUxpIp6wNbcMeSa+R7ne1DjTiseQmHXBjypdo27XDDAPIu1bpaiIi/gYHMUOHAGP89iuHowUTihqme1jxyrB5o/Ysa/gYIqV9P0vwRc35DE1QupKSnOFN1BtMM2qcRJ7pGjMxhRYfZtOnT9UDX4P1dTmi9HK8/sWfK7mORhuxhnZ/Z7yqnt1yuDbK34bHWu990KmEqX5LC1GV1wF74jewZYQ8h7uHZ9q8JqZGBmduq1T1w2cbTLVQXUtme1wsqja0HoVig8a4fJFxcMPg49kLeGbCepe+V2+/pERSNfA6qr3vI17PZe9SfhMYrQlB9iRbUIKXpU/ZCWH3jUX2+PrI9w605XSuYN+67oJOKHrnwSNnmoIJ23AoNpTBxYOIVn3Bk5sgU/iJSPT4vSjzxWLV3yBwoTDUiLVSA0OLnqu0HkxRicZhmpmlknscITJjGKjQ02lMIqtXAyPMpwsmXsb7X30odjc2ScvWPEFJ3t80fdHWWVIG0rCGUHi/19hL5/EqdJWu1WY8TzEMVcomUxohghxrpByUX1nWpQqrOok3bDCG7npQbpdE0TDDMpPE2+mVIkBNpydUabAfDx7/LJqRGFgN1FZXZ0oW5WeKow8zAHQduuGFm5oYbs6NGrm0avkbvtZDS1NBie01bvK5Ra3E7nq/3axaGppuXrfv7wAkFKzis0vsL+gSMYoaySNwn9UhjytL3XNg57AghXCQ0eohKDGio5YrBKf2Q+11UjrfZAw/cwpOw1DKR5YrdL1+O+4L0Wzy3LQdwYRJpSC/krQy1362Phs3v4dh+z5D7kbJ99vnxQFMQdL0LLtcQNSbAkyXm8Jzgxmvk10kLdUtO1NfHBWom5rnG3tvrQhnrD0+xpQwkvZ9KQtaVG0cP3EL08RGsox1IrgSGJR5Hwcf9MfZnVgYmjtyxMG6Mt9TZotwFE0eGKorytmog8s9FZos4wxfWtZBPC3MZGVZZQ2tYT/PhqUm8rM2SyubyVwqkbCpfSmGZiTJhdS2+mcDJhOFShdVKjFIhBMVQLbCU4rwrwYbUslkZMqSleopSgWMTWs9RbPQ1h8Z0F9aT2gxQDzjs1+Naob6WemqoYx2q08rCWEOeZo5ceIN2SImnHjigE5bT9u27OCCe0nstpHrHr9IFiQk7I/MhdYfuNVnu0FWvIzmT8FdfltfLn+8CyYWkw8p9lIx8Wif7rHv8OJDeWe3v0GBLQSRe//OVe6VESjDBvk91rZ6tHH5Dd20Pd8WrQQu6WkLKY43cSurrAE/nQP967hovV3XUONL1zcC+Hx2287pccxpfIx8b7xdH/12BUdkqVxGaHrhj7HUTaD182O+LciGl+qicZPc8HbumgIDCGspmgfmcFVSs1EfKERGnzpZ671Thn7D1otymgo4ogepQf4sdOmKpI8nGroQRpIwROSwGVmASjAf7AAd9X7bPJZcKCypAYahCJXXaVxU4PkO278zXJAhxx3DCWopssyksnHFkOzVYhyUOYwSE1BN+N+juJdquG6LuScIi6lJQ29spu0//KdmVGqx0wqEWHCryMRK7C1u6ZuqI+cnKtVLfbULKaSCyKrytC8SQV54yZ5FDdY6x6np34GtR92W8jTR9XTB4MIY7813I9YxFgqvX47RoXXCJnAGJnDFJI1cYvB9C6FamrArVsdCY0nPTMdc6R96AlZV7SvUeKRR25YT2S6n/v53k1pn+9vpJYOh9vPK7C2ORC5WXyP1ovSXlvrfene71VL9PNKXDFYf+eaHdtig9Wfeu8Mhyc0tJFqrmmFvJQRJjl531qDnX/FnBtOBErhbVSPhWInNChEUs9duwmqCQahaKuUJEsrYfuFSdf352hgyEx65XrQjr6XJhn1mi0llwn1bFELn7ngiHa9YTUeb5md921RurYFphLAH3aSTn6g8712uOY4UI3w0ia89QDsFulpcM58gur6NQrpGCU9pRK6EijXxsAT5HMmfWbUzytxRg9Rz9lhBSWmyKINKQ9Jq8GIz2pbj14Yzc/SqCxxTxdEMTSspJd6T5vPw+aa1+ZpJvlm36SWP4/YF6fjaLwzqaAGpDjwtj3zvk5haANPKeSTv0ozo4zJbs8zUrp7dAVV93/sduGB1EEHe/tPycAbuV0D+3p+e+d+vQqVc83JKSYPL2Xnjax17Pa8Ja5cof6BGigh6fE1KeSUJbLU5XntFvQdB4asx7RUIWsRQkn2Ni1apX+F2jxfGdbKq9LzDWgY5zOFSbaOKG23qMxG31TH2EVs689doRZVIcuSFVz6V0+bj+wokHJhJTSozDEBaHr66cI6puHALmSlV4rOtmKVFSfF7WODW3hC8pL5k0loqSDbWzbISXM23TrHGQXPZ/PyG36X3FVatnGKOMocCUSWtNpTsVUoZhLaShWlxpYAtvr50/XOqaGg4wTXHtmik5/GBhLEaGxRbHp8SzPYUtJavqUmfQkYW0WcRxZxyY2A6Y1+E3qQrzCydiy0lkwijPCfmOfssIKQUjKN2OQ3SuHcvvIu1T3/lGRxdSKu8VDSbxzala3M70Xbi509kj/Hr40QMxPGrQs2f0Qkr+nX5iOeMRuf9J17i1KMEuhub1UnvdwuzL6KFVD7d3Z73uUbY1F1IvkffXNZi2h3RFep7/LRZyrZ80Di4o3OJxy9qhOPVtXy8tMH+WrtWWCQWuyDrSs/sQdldqBtp8UOrlXnC7X8rreeHJYembJaUNzOpPhxH1jGi/21x7aNH3W0WGuTje3YXUK15z2OK+XpF2q0JeM63c4/Zby11xJHMiNrlWxW8YSClUwG1G5Rx+pXUIAZVK+37VrCb8UTrivcSZUXnJDKlCcVvdjLEu54ASN3X1XfEUzco6mslWtIRPqYJvynBJIXynkZJT+NrGgaTNwqXOyhEYCsNQyxuOEeE4pBYskk/kPJJLQsdPhv/36Wm8WxfWX1cy56qyTFV9jni/gF4vVYgp88doqzICMMYKFU7wwvN6eq+FlISI9m98D2HxfMR1IaXwYzl6tVClQXq5KlvCRQLGj6MQPKJ9OisWaUQTUgrkEBwiPa/3J/ghfa6dw164uDB8m/EvxuQRZypLbfI9W1rwE41R3tt3HiBxjcSkPLXPgbQxtlieqYI3HurwrVHvW/pWqPft9JaU971bcm5drDx/im0PWWoc7mn7sjyVlQfiqDwPDkn1ms/Zz72V/bz3ukgIuU/J/bfQrCgJPbFSCa5HYq64peUCW8+TxXxkZuLAYdv+O3HLLXfcccsNiY9IWl3pJl7a5MoQlkRSTep31dd0WCGXE8dSmBNMQxz0F/4cSElqklq/hqB60judcNGC3vHPer8GnSu/LStxfMe1388wTjAdrvyOaXFTBE1MQxvQXYjoEJDjkdZHz838IQVEmDRCS0CFq0IktCcqAiVeOiEZwmaOoxHv0egeKBwYOPHAyrkex6JjKyHyWuS65WAi1ZOTJbxe2kfo9F4LKXfMSpgowkhD02vOIw2+0GKHpzqQmK3v4ncYq9/g2ltgvrfIrSeHWFwDd03cNeL+Huy3/vtek+7JtXhp40N3n1tX6ju3Mt2B39fTLS130svtHeemKjtBa0mpxr/SVLoDNj3zEvlS7V8OETpdK8f7qIeHNXd6SFh94/CdX+P94uQWag/x+p63Xkj5fFEQjCwqPa+HurHP7ksS/OcQpoS0w8ele/X78Bzi9rI1zs1SDysq3O1hVcWW7hnSEcZbGG9CyOjJA7TTc6OUVN0pKRXGnJmWA+MAw5hIw1QbXyAJ+jOhtJsI10a/9lwd0FKP7Cg5Q167gAUrTJXasj2orGvgtKynKoBTulI3u2UbjBeESEohlEp96VFDqsKstqu0HBJaby1ESZCt/5O1l8h2PcTpa+FlzCyb/aTV62tG67nYUxo8/C70XgupI21Doz4rx9g1TU+ZnkfaviYP+fVpIC1TQs8tCb1k5Luxv9hv/blN7oCGvaNcr54R+glbvrflGrx1DW7SFL2jHZnhznrYCxXPr9eXK+BF7VebZT1qLPyMqAND1aQnJk4bEl0q1h3JXuBSAQM5ciWmpCyof3o92C3APuBFiW37/HvOZNWvvVbn4+HIuZat6GzXPHTXJrvH97bJ6vI9cfpbFlUPs6m/tV8uPDJR3me0TeF91J7a6kJVMJ7XTwqbntnPEWrdhvo89bnmuB/D4m0Pt/uJW15X/1MAgBs+kW7h8CU43sHhJnxFxcWxj/TBJP7CeJm5K684HArjmGrgQZ2VY6oLxG3Lt1FTY0sprKeVlM8tlH2wctycntaAC1OJIIfdaof9qjmHUBsJq3Ec2Q5RTGPAfGtYPKy55QpUWbnEmVVz+KigwnnzeMUCJPZdpQEe23iq6icUbL5Y60cKE5mFkRsKKwunbe7E/ZnCPbGtd6gWVIRfPO3N8D1H6w9MzCgD4/ikstfpvRZSnlW7HSEgCCu8HftccBE35n4XJad1fUdTzIMc3tXn4UxSLx8KD9YQY7rmKPdnqf6TvV+rj+AnuvbwzPV0v4uZSsioXkLuPUtBz/hcf2zCIvZRjNX75CRn7MJ9FVIt9LzpXdf7vtdNvyipjm4NOrnFqbb1gRbpynWuZOhaQbd+r28x6CG5t5ELTwkppYy60IKHPEhFZbuAv9jfapvzOLXN9/FJOGqzvOaKhLyjGqLYrh3ZziNXY1hPSfhCGqsfxph4CQf/NtKDjf44hHGwJFKqMPKoTbApzlZKZql8SzNEVMhrZimXyEpehZTsDIahWVfjWDXCKhSGsRNqsnImWg5AopeX2rvDEAJ6HCI7Rdbqe5eZ8Z2gtAkabbp37GWtquaKwtIbyXq+4cCRedvMOzHWvyL04l3ovRdSWhS+UTEWTzjmo4sFO5yYgUtdihlq/qin2raYcg9/XSOHzLpYnU1wOFziPqiT/e6QUj/gsijcaujhrB6iWbsyHIDoSYzb+1R9IkjILUmvX289NEE2VM3J9mhs9wg6WHe+smuWoGvo3r53pefa+5yQgj0kp/G95ofz63olw5UGv/e5IIt05b2/pveTuQUvK0jRdT3058qSKxaaex6EI8EkS7G3NHu0QFb2DXu2GmrihE5xHWp0107VGmx1lUgXxLq2TqhJY4Fg/GOCBVKp6W3HMQIE3IjZzcqe3o05FmDNK2teGcdMSu1M4JQGOA5tIYzmYBhSCJrBft8GbayLLXq4kOtm4ARj3fGVxooj65pCCz2v0GAp8VWCPiHvt05t222yWdhEVD2zqs6kOJJePC/t5ngcuDLVLI2H6p08ViE1cuCO5UW12ct6j0kQn0N+8jkVdCCDZPcBeGDgwsc8VJQ1hISgLd+EqPgiwVfODDzXW7+3BdoiFYMT09CidwGl5/eM/0BbSm4xurByIbXWvxf2Ifb6Ha4zR1lmYjgO/eleMWgxJoE16pNbq5f6MZbdysD9Zk2txOEMkZevbLCX+sNJ9VCSWNhbIK5jOlPv/YQOHGUa3DvydEuAk48v9nvzseyvcxjXx1OZHXq2KAtb96qdgievwX2970jfHdlHBmo+e4LdnlxgCfob7G+sLr3Fr+Aj9wV/P00w6qiVsa6/+Of1sJaXC6wnyBc4JTif2Ta9qsHTGEx9rgEKC8HYs7UiAXeHuvDW3dO2is+yhvoABp8hCqJeWKvHulB4eHzDwMTN8CXGeaSMhVGBGrNy+FH3U9WVkixMaRcEUXt2zbCs5NO5KUHrAHmEMtTLVvLlTFlihZQcMOSUJ8a1NEahs61eFM5PKYSLr3b5q8T3Lpw4MTOSecW57oSamAzl+J5qCMjminTAIaTuOJDrmnzFgVte8aXKl78L4L7egnrKDNKmB6SqCYxkFKk/UXZ7nRxWm66UKUHTa/K9O9XhIb9HTFKCTYtf9/mWOzENh896n1ivCYvZuCXldZXWc02D18uhSvcvuDWnuuh52X53zV3O2bZ/XTkmyiZE1R8enNG3va+j97Xe+/F46Tp3J/jz+vrrnn4se1jV3+n6r7d2n7tWdZBV0ltRDh07BOt+tYnGB12A9227Zke4pVjox7GN0Ur4Ngf2CoNHjXqdx7ry0g6LUK1qS1YDFi1CruRAyFKux2Dk8mTQ4lSKVGHDqUFk28BYL26TyVSbJ1kgIttEzitrORNBPYklr0TmunWb8EqRumWjSCki9ARhZtWjRgaOqQskDEgv5xp0kdcIClnU/rLt3cpLtV5KZi0LQxkYSiblTEkJcibVo0FKyaQy1EjIQkkZSuxzUlCSw+W+T1F/i3Oycc+xrv2RRNkA/ATMNTRqYtrE1KGiKEfumCmV394xc8PMDSMT8zvG973XQsqtqGvWCCbdIeCnUvW70ALXzQqQ1XNm7+bsGVZviQzsDyP0bvcACZE2bfbhvg4T9kEAzc92He5zyOnSfecJUz1TBuwHX8xGQt/b7yiKrCgJ99XaMNr9PSOMvotTa8To1H6V41aK+nXawIzvLC6v56m/JISfo2s6oPeXFKdr18l67KFA1cGht164qX7KNiGlSgE+uk5zzmFrPeMl36rKk0DWdx6FqEz/Gm/NZ7WnzdG1KoIj+31QurOE/+kiM8NtSsglk0thYoGc2GeTHUlLYVwgDv6TTVft7QQKuW4dX/1Ek6lzW6BGo/WysixnzvkzVuZqB8wkBnI+cbMeOKS9Uhnh41PAftMQFtUZWAZYL3Vwxy7FTcz8nFdKjgwsETRRgy5qkES5LCzLUsc2s3JG50lNl2rBjdW3tays60oqiVFJbIcBlgdKPrFwIqOTqV5eUzH2iZHjlnXxyIHMWMc0Zk4cuzLWd4GDKzMzt7xmZq6wfzt0ZWTm/N0C97lPSgsqGG6pXo8Wx+R7AXrm63nnfDf+Na1T9/ah5xJ0ukaWixzc+qxF3e/FEaN2IeGZK9wZrrq7Q9815kzLZtBDUg6RSfCqDspwAXsfmDR1wZ83dp/6yS3bmXR1Cup5Dtm5H86Fsvqh0MZHjFNlqa+vjZMLAn+W+qe3NFwp6ZUF9bVTb/Hpb8FtfRZ0nwPOlj2QB/bzwPvK7/fjRlwYOhSpulwLAOotam+D6q466LNHMrqC1Pdlg4tbL0Zao3lnT7Xei5CNYk/OHFhRrr5DMNqR8PWMqUatpYC5jke4uYW7V3A51w5Tba1na8JWVpsta7VyGGpS8kwuijUdKw6w1trWI9cL5NzN7mGIus1jwJPzDOXEdphiTgHjXcEzcs7kNbNeVrQPN9qgsG+dewu5rFzKG9KSGMrIOiVSgSHngATXCwuFVBbS8oZUJtKQWPIDa85kZi68vJE2rKjIpRif5JXN3PKKKKVl+DtW4RNuA23fDsF0wx0thK35poM3v3ROQKP3Wki5r0ZCCtSdGaXsgDgUrV+Uvmh9YftivcYANWQe7ebf+3NcAIkxOXTizFML3YWnC4jewuqtPK+LC0TBi88xdTE/1VXTt3+++yAkQHyqS6C2+l0THXuG2zM4Pc+FsqBQMUcfu+fIhWHfRhdSz0FiDnu64HdyIdXPJYcwRa4MaC6oTg4Lqs0S0FIs3CfqPjyfu7e0cVZdNFYuqK5lKnFm0M9Bn1c+xx0C7YVUst6T432/R06l7vGHloBWe+hqgtWJEFD13KYEkZV8PsDhEIIqDfBYDxYsnZAq1VJJNpJK4spYtbmVXJTTTnXN9f+oWS6HCtFZtXVCruo3jvG5FFjqb7JeZLnV+0vOlFIoa0BzaUiBEOb9+U5Rh4WlnBjXI2s5ksYcfVoPUSz5zEKcITWsK0MeSQku+UxGx5woaMlpzx0HwqNMFS4a5SM3FAojC9oDdWCuo7sSJ4Xd8oo7Yldc7/8TLiID4u30XgsppSt6TSSnDCsqczFjdmFFyTjkExEDcad4n3DT/SEiMUv3CYjpyGqB5xmU/FCuHYtUBw3INQHVs3xnpr3g0W/uT3BNX0EJYmDXxIk06Vu755V9Vp8oqeh1kfQyOUQmK+xLzEwVtV55YH3HySyGDs16zPbe09VtJeyF+bug5h5EIGHm9VC9POBG9fK6923xJMMetOC5HfV8PedjK0MCQ8pDv9hdKZFQ7C1JzSv3Rfp1gqE9CMXXUXvKu8G1cWV4bxUJuJtXcwgoGEMYHKtAeFLQGALpdN77pTaoowrQQvx+WshrCKdz/pxTZfbtzDP5tGem4cA0TvsJn0+wnMMftc5R5vkUUN+yhn8qj1V4rpGAtgqrYRxJQ2E8jpEVPQ0Rd5Hz04iiSkWBHWcYykLJD9UCvHDPufKqIZx6BbShduWh2jx7FW+tAmdBGSliVoR6cURbs6OntWqCo0xk4rywL3PLbQ07v0Fha1FfiJN+r7tBXqL3Wki1DaOBkcYpkgCZ5gzMdeJLdkeqjrUuBWdIWpj+Nzx1JLsG7Usw0VLjuAa/dC9nDFjZDju5BqvfeysN+16f/XsPQXZHu1tFbg3296suDjmqTuXKfU/ZkAOLT6lv84HEkWHDthPDNqJvI2n0GovBPj/3cqjULShZQuovv96tL+we9ZPAK33nWw6uWdB9G5x6q8t9jA7dYmW7Zao6+pjr997CvOb/EvXz3iFODyb3z62clklkeCKsVFrr0WSzSbt02r6nIV5DfeVCw8dSRMvlHMxfm2G3oIT63CGF/2arR/19XSnrwpovLKXlshNo3QKvJoY0koYOJ1BQx1rVj8sJlhOsC0rlVHR2VY7cgPK2jmOo0am2LaUhrk/RHxtEVhZyWTeFO5UTpWSGMkAZ0UlQFy6V3+2Ft8ZiomV9yShlb97+KWeEjkFMCAJszoxUAT8o1d8UEdQzE9p2Mtj4ik+pZOz9bfReC6m77TVxYEKJOWab6KuBPsEgCg8sV6OR9hpgkGuZfp1IC14aqxQfj7RTsIRgmp45uWByH5trrc5Y3oUGK0MWgxip/EofsReI7lxXe3QseR9BKehNjKqvX9u78bKQcQb/mpEbZu74hMRK2Wzit5MzX4cCHfZz62XprhHTlT9SOfEO7AWFmLnDYRIAN+wVCmhZPjwv47vaFRJuniW9D8xR3XWdBJVbQW7tONzsfaPPycq+JlJceTqwn6uyuvf+tcKZlZEzicS07UyE1tttNg07O62OwFjiNY1hqTCG70dQXRqhDOGPknC4nGso+hKC45JbI5J7W2so+2llzY+c8wPnkrgwsHKpjLZaUEwcxpFpHBlHryfE4YgDpLrH63yG9SEsJ44R1HA5h4WXE+uyMgwlTuu4sRCYcYjAizOQw3YR7HjJ90SM3oFSFtYS+XJGDhz4EpeKIp0583Ly1vAOgdwiyhcRnyRK4oD4YopHcIgIprglc0/mxMwnzBy441B3RR2qBaU+jvIUKyBk5F0RkvdaSMXC0PawmcLZpk109sV0/tj9UHbwSQ+79STm9UUEhMM7fU42h54cw5dVow2RvvDFbJ5zM7pWK1+OfFJyvisC8oYQTn4c+DUNGPaBDD3M4w50MTOvX4MXM4kzA8t2zLgnUYU+I3ckn9VGwS9C1ywRt0ayXeeQnFPvC5x5eX709yRaewZa2q3nLLneEtV3KvMlv9vfCUlYSWlyZUzz/W3roq/nNV9pZuHMAxO3FGYiG/a5atrN65iqaF/qSbseaznniZInWFPN/J3glENI6fiNMsBwqKfpHmA9b1ZLhKXX0S4poLaxVBlVr+EM5QL5UkGvgLwUZn6cZqY0MY0j0zBtwFdQDb5Yy34SbojepU64kbxGLsA1h0owjO7tzKStT5q3er9ZN7NP5jbVtf6pQXXnesdEOzxU5w0MhG9tNcuIrvznVof4RGJiYOWWwqEGSQQX1mGHTVVrqt1cQUT13eGtK6s9872lsXbWWGNLhq1jQ7dspiyEkAqhJUHxHOzi5GBED/X0VOzdndzu7HbtdLB3FzC+98sZ+HOC0qEq3ecbiT24RJaRfHA9jNe7td0Pob5wi0Xat4QiV+6PBbHuotLcbydITW2Jffe9l+1l6selQQv7lwuoa744hzLVdncLXLOAnGEP3XUelSgrSizI79U9pSvreSj1ZbrWez4/HYJ0+Fk6/bV18Rwc7O9PhW4ohhFzFp+mTZxdkM9J0NESbn/U84nElG/iGKh1oOSJlIda6Qrr6SiNMVWNJKDAUs+I2jHdkkLYJdARFqUKsVJWSlkM+IrVmkjM48Q0zIxjiNe0ZcGoo5uJOng8xSqYMYRoyUMcAVJfQ2qe4yaIJPwidGQbmVLqmMqB0Ho+FLqHqoyFZynXEVLKoqneHbucikGZZRvPgq8afe+4QKzN4Lth9WbiOJYJZeRTIjp2/V5qTVtARmL8brCkbkgcmThyy1xTbujkk5W8dVFYMQvnqsn73py3kRadO65934lI2rpvUv2c/XEMHmGnssUoZ8LpfUcIEc/+0DOM56BHnaOlfUsO/VDL/ZKV78lIxTzdwnIYUMJPsNUbu1ZtV4LVHg47VN34DfvjKNT/bo2BhKz3zvPkfSMmqb97ZURzwRUO9bGeK6GrYBDBfoU2fh4ZJyEvOE1tUe1f08ajAc/NH6Y6O2QqoXjsrnc/kpelevgz3BqU4PF575kkdISNEin7frp+vk7dS/uzfN40yHCpMWArCyMj94x8zpFPOPBJZXSKAAvxfM9Sxy9UnkRiPB8Yx8xYTmGanGtryxqBCek12wy9nOD0GeQFBS+UslQVCVIaGA93bIEV9YBDauR6oB3h1znxpnpZRo63dzVYohPVucDJt6I3ymvekmKUvLLmC0pntK4LQ2pCAoBxqhrL0vxm68SwvGHKb4DEwrj1aoP0Su3tCFaPvVDBAyOMfuCu7mUqHGpPSwjV/VmspDpiUhUbb1nqPE0c6y6naVsh4W4JMXVL2nqxX7uxqqR+wA3Tdub3y/ReC6mxhkyENibXe64ddUNhoFR8Vrl+3XrqncXXSPCLO5jb8/fMSgLhmhHda7bJXr1jfWGvgeulpVC6MgZ796nhFoGYiEMyKh+a5eUnAasul67sgX14uCxE99l4PZXM9JpPZu+UVxTVUDXGYecP8r1eHiThgqhv214vbOQ+NLdm3HJ2OLbQ9j95sITukyU52DU9BKbvvH/ch9XX2QW+B2WorSpP6cE+JoTia5q1LBg3WZm+P8/nnAdllO6a0j1P/qeb7jn7NRU9JQd6OPPjLNiBlYFLbX8Dkw+MNId6jNAwrGG5lIG2rwlKWSFfoCwBk6Vcpf4QVlIZUOaHVOJwvlIyw3qhJMMCSrQ2lMWRW+Zao1teDbfcDncM4xyJbJ1kSeWwdPb59eL7VGp9UmEc4qZSCimtcaBiDfAoFJhWUh5DWOUacFHXwpgGYYcVwm+pmDVaZRM2ZQe45drDQ7WCBgoKSUrbFSuJvK28ggtAP94jyhm2ZxQSl2qbhRiNslpIlgd/tBW/kLYV+DK950LqjoG72iHS7WIprozEWZH3LDxyZnmyCOE6A+tJDM2tGVk/EgTQnMnKbeYv15QFq4jkGH9DYyRijs3tGBaImJbqoGc73KI6i5H5PqfRfnd4ThaEhGahJd91S0MC6pXV9Z59v/YQlb73TAsOeTZrLf6FsxjCowFnCo/sfXxitn39+wntwkj9JFJdVF8tbO9fCXjNrGt+GLXtTGPi3i6fH162U2/hKLPDSlMIVuBTWl9LWHwMfJnIn/cDhJD6Ms0ql3Xjwl6szYX/45V6Qetv9a8E0yuaMFReyT3biYADpRoNFhYaP6zMPJKI/AWanR/tRugxajddasWOpMVtyNpjpapWw1yx7ViZCSjzTFoS49LOQEiL+3QaBdwe5wWHYvuK4/Qxh8Nr0uRnXVWaCMjxvEJeUQRhKoWyBqhFSm3jce2TbXUuFzhf4HSKciZgOsCsoyWjzWNKjMOBdT1tvRPI0LLr78QFnVQcTxqYK288ouhE4Sa9gIjwqggoXyrm1Ejr6FJnzh2ZmbVeHZBuPDO2YE/bqpFqLI4m0ix/O73XQmrYAC6PTVPUSqoMbuVM3hZ/L6R6SCPubd+5Zi5y/4+XcQ2OEWMb7bOXi72L8QoSk5ASQ1F4u5iOW3LPkTPePviht0Rc08+06D9nZF5/t5RcQLmQ8qXQW0AqRwJ0qssoKMDVM3mLkPPXXs9r9eqXXt8/Q/db33bRtVl1y37sexj4oZbxJZrP7zlSOb2lLAjO/ZcjIRgKocionodap+8lhNKXgd9GjNsn7JMu9/13zfIUBCurup9jmj8upNxC37PwBhnNZAoXymYTwl5l+VIw8QTMd8QeJiAfw3pIzctRcj3KI/6KVuz2QaVIP8QIQyGVkTKUsLKWsGLOxBaUlUwcIhKHiaThwDCODNNEGRNlSgzTkTTNsVmYREtqW+JzGWAcQ0hpJEsKH9o0hGBbR7aovW3W3cBSKOsjS36g5AvzBco6k86ndu98hmGFAYY8QAlBk7ZZon5oGxBCqZmYuauK/MxM7HlKTJzrCbpxp3aOiuudGaqAj98LFxMmF3JFrW7QFu0zjywsnDmh8PWbbTPvTe1dqTcAyh/4XSGkDmiTWYv8d9O0VCNYO9jb3iiHXHrYz+ENZ4Aih25Uht7F9PzVw3LSSDUR9Nnr4hq+11MMrYelejiR7nfX6veO7b2/QwzKGZtDQj5hdL+nHxq6d+/Hvq8dEtvXq2wjF+72p/f34+ef+3547u/eivbrPHhBv/meMsFv8vPIKnFLVNS32Z/ncF8Pwfk9UojcYpYv7BXNatKrpadqdbgGf3qwg8b6ZN/7PG0+xiacPMjHx1A1jzEWNKVDcxJp19oKBiWC4acq3tOAIvOivhICL8BEKYVAGCs8OA6knCh2tlOuAupS4S31Rkojw3CMDBbTGIcoDhUyvLZheHNFmSpTCiWn6hhMUBLkIc65YqRlu6g9ljNrPlPKmamkSBhb7eQ0jjAFpFZS2eaK4LaA5wTNXbZ+jDky1H1LM5GSat0gt4vNyEg6u2w8s/U1NBixCeaIehzqkwOjydUUOPGwQZBKrTDvOIGrdvBUpbxO77WQOjBz5IYjtzVi6MxaHZ6XitqO1dUXnRoaxMnKUJe5TiKLRYz9mqXlqYbOBAxzD/xtQtu9ry8dm6HJoz1HOslU8JD0jJt6zcdcD85QvWa7R/V8tOskODwljr97W9z492tcACnowZmWoLdTV0ZiD3mJufvmYl2nvUURWBJnfY6cquM6IjRdo3cB7nudnJ4yy30fusJQrK4ehi+92L/rmbfoyF4YCxrUlgNXVhxupfussXXFwmFAaMJRkKsSLMuakU4t4Sk+2itPbiHquW6h9miCB/J4ZhRta1DwhPoSK2etNU81Bkyb72OeJWLm1ivXsVlSu04qsN7DcAfTK3aUrgiQZymOpggGvxJHTlybLVWVzFXYQVhpk2zTVAcox54oclh957Xu03oDl0PUbZ5gmiDPAUUOtbfrYOXySOaRlWNl/2sdjBGW0SbIA4mFiZUjZevB2FE4V4sl0rhSW7dw5sKDAXIhhqK3lwrWXWp3lw5APNV+eL19s9bDOu55w8rIhcQbfpVl2zwQ/6jHIU27s8UFiF9btc/Tey2klHYjMhTMnDcdwZPNT3WRxRAE02jp6l37vuZkv6b563stajEFz+ytz2IuEkgeCeZh56P95lnd9SxBM6W7R0zUX251uTXlU6PX6Hty66ePLIO9ILzYNd5Xqr8LJxeSLiyizIWFxJkzCxeWunP+Wt166tvmS8F9ieqPyb7v+xCe9ml/3WTXad7M9l1vHfWt6GFRf9Y1i1j+RRf4ElB9cIT0ap/bPh98jBwKVnSjP9fJ6zd09/X77dwqlqLRDs5RFr8IZg6qTD4vtEMOK/C0BSVUi+dZ27hel3Nsnl3z00bs7krbu45k3M+e+rc6jxTWkXpuGurEH3aMoZSahKgslLIyrJmUMuOS2BLRjqm2N1IZRdj4zBbyXfYe3qQ+IsLBptqDpVpVIXzHakHNW4+fubBwJttKakfmaF9V75l1mxt7l6UVWwoSI7ELckGJlDxjyNPOb7MwavddYEmNzBwYueUVEwc+57PN4I05oyUokOFS0de8CRjXFp2hPMe43afjqWoU+PBonxXaq4UrJ7wsJgkrdyve0bB+h6XkD5L10YINngZPiCn1vp/nrIu3kaaS949viL6270zC1C1R1aHXe1X/pYqnROyqumwY93VSO3th54JE/SCS1eD7z2a7V3XrBZSu0zh6fyoC8tiV4ULqWt39Os2/58ZJEK8H7X7E3orSdQvNp+lWk1tKTvp95uV8agP7/nFI0aE/908pHdm0MdKWBy9toR9UQVRVnqFaKWrZ+i7hTbWFuYQ1c1m3+166UyJgvLrxwka/7zgFRGSgjHBpO53ko1wrJjMuE1OeGUqK+3KFD0uG8shSEaCZA9sG23KhCK7sHh19H1xlIY5DaTVORM5LyBU3unCPs/pcvXJrDfePpqkEbSrxw1fc6x6C7VKP/EgkTqZMXt8g7KPXVN533a7/XgupiWTQwcCBlUziUu0mkDwPPeLMKxYWbnjgxIUTlxr3VzYYxbXFnhm5D+FdSIyy5Ri8/nIh5Qylf46YpENprt1rwvVlY8/omaDaKUYtISpY0q0R18Y9jPnavhqHp8QoZT0+F4G31Cdk7pFH8ZoPzIWca/XvIqTcr6L7b9mPcaFBdXTPe6kPJRR6Ye3KjQunZNc4ycJ2iFAv34Tt88rskR306grXNfTA26Y5so8Y2/er+k4vzRVn8fIURyiy7h0r1HeLcrvNtLPeSCOkGQ5TY+Sq0ThEYMJyhuGKyFnWan2dYS0hoE7nGsywUJYLeTmx1o26suMSkXFuSFNE1U1zQHPHA4wTHI72kDXqcTAxnOQsUM8VuJxZ8onPeeDEI2uFFOd84Hg5cVwPjGlifjxQlkfidKjY4HpkRdtpRvX4OgY8mEYY5hDkRarvxFi9l21OJqgOjkiDJWtV8ZcDl82zBOGr39vvEVaiHYWu9sJa0yGcOdOC1SXBtX1EQfCtxHdPJ/uU3mshFYumhU1MpqldOyZiIPL7pbpEI+boguL/e/+TC4DnKD3zcn+Ww3q9b8gFjSaawztOLmhco/fPDsE4Y3wOStLfHjGo+vb3uNHvARfudxEN7Bm2rAWV7bS/vyVE8r5saZbau8bM+87bqsl9TRP16+fuGlmtA/sxpfvck37zvrkmDJyuafm9IPP55IKpnz9ceXa2cvpn6F0QsUPZXr9r8KCPpW8Aid9aGJPKENQ3bvF01FVqIjBNYUWlIayTUtdxslpd67C1QFqrY63URK41hx4LZb2Q86Uy39YDMcZTCKlxIk0j7fDCKYInjEGHhTey5f4r/UotlLywloUTZx45sbBUgbzE8R55ZWZiWldyiUzraxXna2XtufZP0kwtUwjwNJJYoTTVLMC+YuOZgInCSvh3ldOj3VO20dGuUudwEZCxbu3aq1LBNQMwjICLFcGkEQ6jcDWJsFAT07YS2qFJL6EkTi/x32+J/r1/798jpbR7/c7f+Tu33x8fH/mJn/gJvvd7v5ePPvqIH//xH+dXfuVX/o6fG+GQrxnr/udDTXbo/2654Y4jR2645YZXmzcrbUOohea+oyN7oXLofrvp/tbGyo/wJLjx6gWVf/aMBddew5V7b2lOa0+ldKBnHC8Pdt9eP8bDLQcXSn668CMBcSpoxIMqNBW9v7xs1eti5SgY4xpPcibZM8x+HG552i4xeDn8p6ro9NaOynUGLyvSX9cSB7ugGGgQ7ti9XrLK3c/jc05jrpcHMsiy9bHQy+eS96sL9HclWXVeh8hgH4fejTXDwf7oS/lNFFbSq4R1BaQqDIY6AisRwPAkLKPSUvca9a/zI8v5DZf1ZEHXHc1zWEzHIxxv6vsRbvzv+n6oPT3dwfHjakntZ8SZMw88cM8bHnnDiTfc8ymf800+5W/zq/wav8Y3ecy/yn15w6dEJhz5tdsewDjbds9h7iC9gvHjsKy2UB6fKeJQUy2l1F/O9bVyIHOoYzEz2FovjJwYyUQOicREqfddmFg2aBFig8gb7pH4LzUQY6kAZqBVJy68YeFTFh658MiJ++31LvQdsaR+9+/+3fz5P//n20Om9pg/9af+FP/9f//f81/9V/8Vn3zyCT/5kz/JP//P//P8z//z//yFn1NItVtCf1yqXhJRfeOOAdSj0wC5bqXFlSeotRjnQGOm0iU8Yg6a8Pio/q29MrpWSV4n9ni99BPPIpG634CdPjN2103smbxDgN4G1Wffd+qXPeSn5dAnn5WGLoDDHfKeyaOH3J5j9nTX9paDWzGyRnuoqrea+n5xS9bbrXGdt5mQt1kgIdy7jntyC8Pnhr7zNknfnrrr++dku1d1FI14vWOc+j5We5/zgQnA6S27lwI8nHolyX1SMb+Hmv86arugLBMRs1a2NRcjlVHWggqUlgdYa0Qda42lyJAfw39DjvBusw9TSmHhlASnFXKmrHGkRUBpSrsqn4kOsq9jP46keWwReNtrhtk9gDY6pcBqEX3LQjlfKKczD+WxMuZz3T90qSjCQCYSvoYtNdBy6B0YNns+sd+AkAPey76nKIWlWdWTtI1iG9H4LiL+pg3jcEdGrk6RlrEv5nDLWKE27w8njDFstlRm5FJXUbti3fxiSt9UWHnEs6A//EYGTkzTxA/+4A8++f6b3/wm/8l/8p/w8z//8/xT/9Q/BcCf+TN/hr//7//7+Ut/6S/xj/1j/9jV8k6nE6dTCxz/9NNPAWmNERtTqjNvqWmQSjWCY9gzYwWHStXlUjVArzmT3UEuaAWa9SBLQN8p88SB0G3EMEOXCXLt3KEZRWEJvnJ4UfVTXXyDqOrm5Tkj6yPWnmO2ye45sA8l9kzsYmJenoSMJyp14ag6qj7PkTPkdOXaPk7IIbReSKkfom/cGxMkiDCYfCQmjvzsTalwmPK5ZXTNyhNrcSHpQSWqmz/HBZQrDrrfSe3U3igntdl9cH39HMaUDxar30tC2Z+huaI50r4bOBK57kKB1BxP6ISh1sJhY14jM6VcSOURLrJQLpQsaOvzrWY5Z0qO2ZJSYpS/hgEewhkfmWYiDu2+ixCNw/nCGzYxB6Q3j2FRzXMVTvJPefi00flNhJmzQF7gcoHHE/nxkTfcc899tRgeiLwQ0XexKX5lZK7K60RkWLmtAF/rsb2auhBZNaB5pWVlgVSe/SxPwA0j5ye2Z6mrbdwFTkCmcOLCQLFvSp1Pqd4TCa3OKDdgYeCEu1cC9rutUOJa51bhxOcotyrA5+8I931HhNT/+X/+n/zwD/8wNzc3/NiP/Rhf//rX+R2/43fwi7/4i1wuF/7AH/gD27W/83f+Tn7H7/gd/MW/+BefFVJf//rX+dmf/dkn37/h15hrwvepMptcgaa86S+x1ENDPhFJWR5qyMSJdVsqewr3YyRPvGHkUsucKDshJV+JLI6PaLqQmDrsmelLfqWX4J9eax5pTn/Puedwpdpyjfy5fbSh1xk8uGHPxL2/JEh72K0Pp792v9fnwLRpmeuTkWnPc2HoTFd+kXFLXHrenn2od4enIBMbEfMmOK61z8flJUYuS6a3UlRHF+aibN+7JeXWJPa9KxB9f/gzNR9Un956ciHrqZL8We6j7N81xu27OKxh2kY80RJ5tXoFk9YG0Qfi6I7XrZfyPc1TGOqJA3VlE9Nq9RgBFcVPiEsouLqF48C469HaA/n2qSaS1zrZP9t3bM4hkNYFlgXuH+D0CJ9/k/vlcx555JE3XHjDymcbg46IuFTFxafVjhlYuCVzxytmIggsxPte1YGXwXrNVmWODKxD25XXDUxsXG6tfBAGVlYuKGFtMbGl8VEMoD8xc+LeWhdh8QND3UUVASAXli04PaNN+REJAPDwGyWkvvrVr/Jn/+yf5e/7+/4+/t//9//lZ3/2Z/kn/8l/kr/yV/4K3/jGNzgcDnzpS1/a3fMDP/ADfOMb33i2zJ/+6Z/ma1/72vb3p59+yle+8hUeueeRlUJiqpBBHFEcBz9TzVYNe65218pjBQJ01FcjdygHsw9jPCZ+2ZaPH6zo8NrM3nq4ph27kOoFluv+WlzOMK7BaHTX+TVOzoT8Oke1HY70uri23UNKzlDFwHqUvGf0LgSSvYaq4SqHvdDu/jnO/Pu+E4g3MNBOa9Zvqn9CsU0SvtcSEHvd/P6nNtoeguyfp2u9TIdZ/LNDcn3deyjzObqmdO34rV3Tj62u7+fH0L3afIxIuZboeXryfDnRMwtyqa8bQ12stNWe3mBYr1crNEEZ6kRy537DSGInUev56Ac9o8J12luV11peHZ2l7DtuXcPXtSyUdYX7N5TzI+v5DSfueeSx4joXMmeaI6EFEuTNStJRF8faWgWAiZot3QId+j51oLatEwU0tP1XvZBaNiG1oGD0OOAwkB/leTlTcLCU2hodwLKfx6k+NZO5bHuzcuXJpXrIZCnC6cUZ3OjbLqT+uX/un9s+/57f83v46le/yo/8yI/wX/6X/yW3t7cv3Pk8HY9Hjsfjk+9/lb/BwsgrPuFQ91m36axYGeUDiMQdF86c+FucKFv2MNdgoWewmZE43isTOnkEW4z1ntARnDFLy+yzYruQ8kXvGQ9E7uBW2W6VuCarJf1Q73VLy8mhIF3XBxx4Hd13ogAB2DM1WZwqTwL6jv0ZVge7t7ClDgV7/szEgZk7voeVhQuPZN5s8IDqpmfreer3VHtaMJ58aU8UZQpnyxvmkJzDfK7PuoDtha33Z2+xwFNFxQWiC0iV54LBy5alqsCP5wJLEm1O9daYyM/I0nO1HlxZ0fM0nhP7OrR55raz+42CQru+cOKeGZ3+dqzr41OGyrSn7WzfdWO/Hrwcni5B/2N9TFUdh5FUHhnLZzv4d6mWRsxv+VDMvi0XOJXwhWXZ5SM8jHus+rzAr30Gj49wObOubziVE9/kcx55w5lTZe8nVk4kIgGrU2vTwlg3GszMNYSrpx7A3veoZlFBe57i31LDlUJ4fE7ejs4IeuDEUsOaQk0bTUgtZB7I3POcGnQNvnbAMcTx59t61KpfUDKsuO7N1dKf0ncE7nP60pe+xN/79/69/NW/+lf5Z/6Zf4bz+cyv/dqv7aypX/mVX7nqw3obnYjTHQceWFi4peX6bYERCon0tPZlm6LVZbvT6vdCKvZ3ZGMJMZwxTBciQ7cWuH/WhHTtV4LA/V59gk7BNdmu6cN9db20GD+e3K0dt4p6jdT9Rm5B6V4P6uhhsD3jaACNB4n0DB67tz8K3RdoQsG3L4Gfz1FTfXtrwduRn3lXe2WpuZUmPd+v02+9BSXyUHDdr7l1TYD4d73Vcs3KfRt53bx+zjqfs57ku3LYVv4ozd9Ua6ag6YXSjaW3qR1QHr+JZeWtj1Yu9ft5Wz9ex+damYYE00QpR1hWxnKqoF/Urh3753eVLUydlOKI+ZyJTcBDCC1l8oE4/v3xm6yXM+t64U35lBNnHrjnxJvqD6dCbKXCi0N9ljJatH4IPjXV3mt7xp5CvT3wXmovD+iQw7Xmlgh47QEFT8S+Jp+1cKmIUqSJS9W2vdDO4bvsbKcelOvXkefW1LWBZSWDL4NX+jq7nov+KX3HhdTnn3/OL//yL/PH/tgf4/f+3t/LPM/8wi/8Aj/+4z8OwC/90i/xf/1f/xc/9mM/9oXLjmzhhcwDMyfiKK6h7tvWXqlYBH2EykDZFl0MZqMmEDSFJjzjnwxo1eGedsTCAy2UVIPnwkD+LN+Qq212TgrdfglKlLWz0lIw9cLEoaNrPqQe5oOn8JMLXA8pd/hRmn8P9fWMWGX4fnZoEYRev+esgHclF1IueNU/qoO3bw8bPoVohf5LmF+DV12I9YJQ4+iZxr3NDjerTzU/vC7vBpS0dvRCyhUWv66HlK8JKbfoA+KLnHGhXGlD6r4d8czmXwoBr7PffFfOCR29s5KeMMjrDRxgSDDPpDxS8sCYF4aihD8uILo5tSwhjIYB1hQbgTnv+maDdtcTPP4qcUD7yq/xa5zrvwizjgACbW6dDMAL21HWUFhbkcVQdqWO0UjbWLRTeFsvthjUGKWlcraVE4+boHpT7aPo2YI8QkEupNqhHCcUd9cjBP023N6vLD73VDEvG4JSeHrY7LsFoH8HhNS//q//6/zhP/yH+ZEf+RH++l//6/zMz/wM4zjyR//oH+WTTz7hj//xP87XvvY1vvzlL/Pxxx/zUz/1U/zYj/3Ys0ETL5HOv4nTcgt3nDkwMHK7WVMAOtIr6MCBj0k8MnLaNFvXnG+JaKQjNxyqO3PlfsekhaxKMH1KdPqv0vYNOTOXQNB+GQ+g8Cg6Mc0D+wFNdp8zDd+PJNjooT5fU1mQnMN9Ay3JrQspZ7AStirLJ6QHW/j+LBd8exiuWXu+H0TPGgi9Lup1Qrmgvwgz7qnlKNvva5KAUj16n1EPI97YZ1mB8mrcEJsTdR9WvrYfFJoC4XVwC9vJrRgJBrfEpVR5H7p/0q1kf+8X+7UgECEJyiLhWxKUYV05A8VSS2Vv2m14qb+M3JG4MNTDcgoDZ85MFdhVy8/EGURp1xOxWyd3vXPNWmWaIoT89V0InZTg/DEpXzjkT1GYhsast+4jMaylmM73UMRym4DIJbzZb3jDAw98yt+soeZnHomU1jcot8bEsR5XIUtzsLYsXLjhljvuuOEjZo47kdzq57k8av2A2GpzYeHEwolHThVuPLNwT2y5GevoFJSiKZ59rtbS47bOlM/UBY2e9pyQ6tGbPkJUvM3vc572wLvRt11I/T//z//DH/2jf5S/9bf+Ft/3fd/HP/FP/BP8pb/0l/i+7/s+AP6D/+A/YBgGfvzHf5zT6cQf/IN/kP/oP/qPvqVnNZehtMvormT/IDSp+D0m21ST9SQWDhuQV7bpGAwqVVtMzr89g3KI7Wyvk30Pe5+Ua8Puk3Kfk/QmIbm+KK8FNrhfo//sloSmuNfHmeQ1S0oM1fP0uVvby35JW3fLzMtxgRl9Wyqwqk0F76RHXyEd9pF3DMoXUV+Hvs4+No1NaOOCC//mlpeys9CAZQ+tdw+D93nPeB2iS93L6+kMYbDP7hd77n4f6/55bsG5n7XPz+flRfxaRqEQsfpGIunOVOdirnUe0RaRst3V505oQRO9Na3sBZvPYxjZjtPIJY7FSBEhqn1waSvL92oZ+TlRq6sTOoOWCoSFWDptAiGSIa81CEv11ynT42ZptmRModBMHDhyqIndhqqiJmt/W2GaJQ7MtsOIMtodutbXguBVastbIHhBwR1KCtv7Yt390QspzWspSS6k3NUgpMHRgm8VFfm2C6n//D//z1/8/ebmhp/7uZ/j537u5/6On7VntIXEmafBudTlIr2imeKZIyNvWOs/15BDMIUDtACf1U+fsWcOElLKvCCN2ckDHTRoruVq4d/RNlxCM5tFEmxOvYD0YzMWGpzZQ4pumUlI+nN75u1tGWnZM54L0nArSWPkk//aM94QUOrEZ1aXLy6ocmUwcTrz02S46puXrDRZLQoYCMViZkAB642aV2GsrPBxW6hi5u73U59rvB0+7MmtXxeG19zobnWnK9fAXig681hpYfwrbb7e2ftH9rePd+TFXDfYiioUpk31uqXlvBCphhJ9DenoZ/m4XT8SGQ/W+DZVAZXqDPQJtrX2jthwILVRM19stifVS0Iheizm0cIbPq05+c4mQsdq862Vz+hgwvh35Ig2EY81syHAkRvuuGMcDkSqJVNdV+8vB5vfhi3EjJFivVbOKCfFullQe2jvJf/saVfy3s8rV8O1ICL2LdqUuOd4y3P0HfdJfSepdywrqkv5JNp1TXuKpKWR469UCysE1IWhOh6l051YN431gbJlOHfLQOlM3M/Ss9Xen7Cv8/53t7T66egOfGk4l+7lQQkOVmjZYd95slIJMxewLrTcZzN2947dtbCHDQQ0uCXTO1oTDVaMBZ9whnqNeji0aY+qw7oT2C6kMteXew+TzaS6PXXkwAx1X1VC51wNKORZB+j5GKtfvXyxyN6/6NfIMlK73OJZ7d01Xtdc5fPqQ8fd39ZrthJqmT0ELTHjGdcVHVeYKyQnrf5M4UB4gdZ65cBQWx2qXrBQHUUa9w61VzM6ySCRtrFovr82ozd4MI0wzmxnPQ0ZUssXJ4wk+jh6MT3xnEIkb92HBEW/5urruVTbnK1dYStpTcbmXD1JLVes6YE4qzYsTBg5MqY70jSTlA9w04B9VmgkJKiczWsVtJmdWyGGJoTPqWy7ovZrtN9G/C5CyhEXV9x7SLXnMe8WDNPovRZSe6avw4xblEwzjpN9NzCTNlYyMhJ4+onEiYGI51/JdWNaDMw9MViOo7p/Q6+e+bnV1L9S99lhl94yUXt7a2fpXhf77NiwT3kXNG7NOTPE/nYh5dDkNebnUMBI88dcE1Jqh+7TIgkLueysz558gcgf9FRIlZ1muNj1PSwh8jGJfkvGXsJSWrhUEEXnEMUsaczJ798LBod1PRDGx9vBnWvBLqJeq5USMdR+1OJ2wevPcCgWu14wpfsaXUjFeIegyHVmlXpn4lSF1MBI7IxpQQGh0imAQgcPhpCSMCm1nKZoen/GSvcZLbhvgukAZahCyjGNhMRbGw1n/BJSiwmp6G0lU1233DahOqTaKtV3rX0w7YJGdKJdpIu64aZaVVHvlI4w3ES9ladwyUT2duV5UV2nWqZiiMUvFHzSHBEeKLZu9T/X1bBsfMuFi/hcbxW5kII9AqHRcl7jPSxyJMl/e1eM5L0WUm0PjrJ/nTlxX5dFm+Qjw7avCYTm6tiv2GNw4qGmP4x8FLKQPBjhTCRokZbpUS4OD0FjCNKctRwc43VkfLKyen+NUy+kPJLwkZbcVYzbXa7ub3AmOrM/rkL183RQmpiCv16zx6clYLB7eu3JGSLs+8/740zT3nW/BzhoUTjD9n0/dNe5gNJ1snJ0r/vXWr9FdOeE8tEFC1jIlSWEji2vhYSeINdc2xBlJhRjJSEl5p8R1LkXNB6Iof5Rv3pgTQ/RuDWrtqttUihcgUlWzh5Cb5/9PXFTWfhKQJup2pGRsjSc8Gut68SRIw9XR6hRAIXyBA+7sY1n1pWUDBobRhjm8CctJzhPsZfpdILlAbJW8BWdPYXfivHAdn7VMsAyVSER6qeOHGnBEEcGLtV+/qjykBBg2p4sihMWJLgScRihA8F1dQ83YQmuK4wp6nMeTIMr8YqKo0CMROHAmaEq09DCjYKn3XMi046LjzIe2Afw+AGZveCSlSWSz9z3yTlJoZBS8wmKaE4ceV0t5xB7844bPk/vtZDaa7sh19sm0HZcR+m6M6GUoi6mlupOLNtGU+150kuRbk5ivr1F4kLKHc0Oc/UO95H95HBymAeeCrReuPkycAvNLbgeXtQ92a6VFqQyPZN5D51dC0JQvQf7rr+mdL+pHOmSqlN/fV/OtXK9r2EPoXlfuIt6H0jSbETNGdUxXg3eu5C39np9oswA8MZqIfYRkFqI6qu+Xv37NbhOdM1R3VuL6Zm/0zOvVo4OxSl1FalXUm2h7KFidW1wu8LB2ym9YriDXWNBESh1adpK2qynNMJQe6nkEFCXSwisHOcelTr6LZOnt6eEXysNdcKPMMZqTmWAsiLAUHWd6moNQZ9YUQYNhSF4X3kwQ971yd62rW1KdSNGysS5WaVOYscg2gyOPoyURArqUWCPNuaeKU8QHvE3F1KaI85HXFiploKRVXtfj85XZEDEAa8DB+IE9WR9sHw3CCkxk5uKBEegQySJn2yqLzX1JcTQKkO6ptaFlXsum29JVtM9YTnJhakBU9c6/CYL5SP2WrAYe28RScPVBJFw67Hd5+iahSCD331Xvg+qf+2Tg8Y9Cp1X0EbBj0trjvPXNF+cppkmt1tx6ps+CMTJF4cLaYVv/51Qj6NDE8buotfvCrfeHySolDMRwwX7/td4v6matI99vLc5F6k3Q9890vpPc6RXMnrBfY003q6Y+DhfIzGYL+LEVkj4wGFbW5GdQL6/6JUD5cl8jzYlxgp3jYzccmCuh+o8BSSh2eIGOBWgpIDIxtQamAt8fg/LIyyfolnkmRh0IvAIIdTKahYKMI0w1gCMdYVLQStUgRAhMiOab6gqcQgBpRpyihW5csM+AMhtEdhOHp5ehVWYMxyWeD+5B9JXW6zusZano+NbN4UHSn70expf8RyVPbTX8yV9p/UvvucwtlokhXamBdm8Bm44cuDIK14TOQw/AiKU413yTrzXQkrM9FTDHQ6UChjMXQcm08YK2aAHuRW1R8nhFn9pYE/bffvoOFkbbrlo+nuUlVtJvcXkmrMLqt7PJRN8AT5jf56R9vUM9vlA7M/qrSp3lKp892lBgzGdCQp+6ye7C2dBiu4L87bKypIgcsuy0M6BEqN1geXjdE2Qa8zUf8nukcDW3qeIvmq9q/oG7FE4V79K2ubOuusb9clkNQlhU0wTDXd16LkjA8sGsaqsj61PJVSvWTy9z6kPuvCIS/+92YN7v6DDtU7uW1Tk6FiFcPjkopcVQnLisun22iofYOlawXNQOPYNM7fMTHwvA0fG6RiMurgHcyAjH4tO+7La5UvVFtVThPVUAgCP480VGC5rL4KqImij+qTXhSGPsA4wHOK1rbAT8j0OvCL8P6cqqC/M1UOtiNxeIfBMEhLpTY3MIWzzAJcF1gTpPuDL+QDzbVh5dzkE5rLAKcN6hvWEQtlDcdBMTrRQ92Frhc8r+dAdCnYg1uE+zR1XZt3K1/XQEKNXxLwO4ZQ4MnDLgQM3RDLwSDAbOTPeTUX6LSGkzlWTC6YWuZj7k3lLnRyhYbTszOomLWrXGvylAfcsKRI+vn/JGadbFb3/xevvEI7+7gWZw11iHhfC0tOEUxkKD/djvZ9mPnxq3ut5Ej5aeL1TX2a/B2ioneovjypzP1pPzwmafSTZ3qnfC6CedL100JG9NaXNqgoS9kMO9IxHqF6Gwkh+8px9aG3aqUV+Rk/TVJtYld/nluYP0HgKWvbxHNn3k+ahR1g6zOLXPweDus7v88uvcyYXmzvKJnTkNVLAw5lLZV7atBqiZalAejsWPQ4ffc0R+B7ScKxReRfKckEe43WzBWTLu32awgrKpfqPNCPacZuXmp9usRUX4Q0RGiUacpxtwDqSxmqhbaMWqz38ZOE9jFCKmDczjwZEqq9dWfFQroRHM0qtK4UQQKnAnEnjCNMEt6/CqgNYLgFj5hq2tV6258TsFDDdUitLLXfLSPPm3H0vF0ZvWcuX3h/yCs0XLmVJc/oG36qQODJxZCYOw5zIFYKN4+u/C4SUOto17LEOTcTwxeDFlI94vZVSM2wFaTH2aXquPatfyH2AgxhPL6R6n9FLEJab31z5rLr2mSYkpGVq3xJajWeBUFu9nGyfn/P3qH1ufYmx9lbXNWHdt6/vR2lhvR/MNbfeke7+uefo6Y451W9gqslNQ6hLecnkenyLFuGDtUn3u1IS/Ruu/vBKlF1SHYdgZXdI0CrQRm2ONF/PCynYR0qKMcii12dd70LyXaA9H7Nrvs+AeiJ7Qd4sqZEFLB1p5iPWGmjd5ujMx8zMvOIVx/QxDJ/Ax1+GwwGOEzyc4PEEb86kfGZioRBnF+1omqq1kyAv8dpauhAxufd8zuc1iXDZmLc8NxeUi6TwCQsHDhy4hTzDMoYwKJrddbTnQwjGhSoIIqkRxJm1iThJd+R2U5AFL95Vh8SBCMXZh8TUsP0yMt5/VKOFpuiXubLnyxC+t+kYVtWGryiky+Pv9uTBNXJhPNAUjzaurUYONTuMdyQCIQD+Nm3bjdaCgp0iI8nEDQc+5hUHXjFxw0QyhXrcoQ8v0XstpMR0Bedc6reRdnFAQa1yXi5VXGlIXcN3c9id1G7VONzynHO6d0L3Dm8v12FBMX0Xhm5W65kGFiC/ja6br7zcMe8Mq39+DyleI7/PYT6v7zVB1z+v7zsXThL2biH0FlMH/DwZLx+znuRyhuailzN+qOIjoIiWKPXdltLer+bfqU7aVul9pnZLiXD/nn53+NKFc9+X/bx0Db/vE4eW+3kmpuS+zP3zBH02QE7KYkDBOoQDViLD9kTL+AAjqUzhA9JrHknrRGSKiDubYKlHgAwzEY13CAsqueoEsmEF4SvIRXwg1c+t7pGdIQRuHBmSygWKb15oh2zsbaU4HqhQuGFl5JaViYEbE1KxzfvIoX6eaQdyuP+o1r+s9VWhz1xHKNfPWSHybeWV3Wxqn33M/dVvWenXic+j/qW58dw9DjOP6PCWieYvi74c6ki1wxVfpvdaSDnMVQjoa6TwyMKBZeswN94vhDbh08QhNIevVvvbF6ovXmceenct1C0XJ4EUGnRBd069haG2uCU1Wj+oTnL+uzv6YPf22tK70DWm71qakwsktU9w4HXsfu+MFZMMqDLACydtH4W9JQf7cbhGUb5Gd6ztalcrMfFtVXnUxy5Qn0K6oRjpUJF+UblAGtjv3fL2Z/ZCoadivzuct9o97s8SaZyvKQ992T1s+DToJmwID73W/FN591w4EynHAi6LI8wTkWluLEtjyOqxcYDDCKnZvyM6zfc2BNN8C4cbGCY4KT2prt8HLSg4AyK8faYlcD3UFiuyV7kZKKfaCLVIKyVViG3fg+FZO3LHDRde7aDFqNmhPnMPDsMYwjaXEEibiD9DPsNyhvOpQpnAaYHTGU6fwqJ01grKKPZUAXplU3h6S7hXpBwh6Od47++E/Tj3ATpP54sChhT/GEI1RkV75N5O77WQcv1GGmpCR3i0ThN5GPk1IeWD6FaNM/WBPe7fD2JDi9v97qS8JhQkCMWk93rW3spI7AMx5Ch3BqfotBv2mlDvJ3ENy+vlmrXomqXl9+s+Uc8ovexe63em6I7agIyErz9nG71MvQURgSKChtYK3cxbfYZq08zEfqiZvCkK3k7vz7WKgJYxo7VRcxJ7d7+QMwth+s+R+ro/qwwcSXhquaptnpFalpT7Ot0HIaYlpiPf5tCNXqad6uqwe0KpkiTOTgQ8GCcewTnOZVoLlAHWvC2QsDResc26cSaOc5cl5SLZ41GV8VGh4zrtdqj6/FjvagItb/W7oFx7KmfhzFg1f8o9qbTWpSr6lc1iZGLo5uiw9aJC5139EB5xqvVN8ex8gcsjXI5EpN8Cj/fxWpa6CIUHSWA9DePW066tQUcu9LdnpNHvnvneUY3S3ac5c4OL4yPDtolE4Vnqv7239m30Xgsp2DMgLU7HWd2prOGUIOmFlJvE0hjEnN1Z72HdPbR3zYfk5rVounId3XNUN/0txuLWydTdqzZ7xKH3zTUB9Zz57vScJu5C7hoESve9QwN6hvenJr62derIb5055GU+ZwX2EKALl2a9xLdxrk+IlRjPYCgTa61XE1LXdnT0mL/6Vm30fvGx7IX7NVjz2rM0j/rs0hJ6g13rUOE1FunwYqYpN340qQsp5fJ2G9zBtb598gQHy78QiWbj20JYByknGKfWccU9dpW5jVO8pqkGE4y1owSSy3ZY0Tb9gJZCSLVkryNtv9Qe9ltYiC3J61bz+K6CweVR4mqbKRLZ+mY/Iv3fnVpcxC3iePco+yaE1HKuAinHnq/TZ/D4GWTFjGq2aceT2zcNwHZFEPusmmhuanx9Drpi0uXl2EiK1WTXSZHRESR7Z4r64jnOc53eayHlUJubt4Lt3ASd7XdoAsWtJdcE+4AIh6o81qiHf5yR+aY5PRO7VuU6RKjlr0kkWNAnm9f1mrPb2+1BEr6Bz6FM7H5Nxn5CNsbzdAn29ZEgdcHjgmnsPs80C6IpAcKzD1u7lEhn5ER/QphTryz037tVGG0+VU17rt/Ffp6ZM0eW3Xi5DqgytLdOOz6UZUJzcg8N7oWFa609+XUuDFf2rnIvV9GnfV+rX0XtvOqm54bDO/FqEyaZiZtazsrITdWOtZX7EeqJAtRINwnHQmLhyMpU12ImYv3CqjrxhsMF0vpIBCnMYVGVU63tq9YD000IqKlaGvlUmbxa1TZDlBpPOFUBIr/QUD9FQHusuoV7zmb/lK2nAwqMTOcLA0N1H0SCo7ZC6ywftOmjqjJlgeLb/p1DEfXOyjGi4AeNeBUv6wFygvvP4PJ5vT7CpJYtSduKjoqPUPvgTjHm4w56m2hhIFrnEdDSAh56xVsKvicUVkvEW9buGRJyofCUbfSh+S9X+/cu9F4LqR5e6i0Ftz56uEqfrxmevfUi7V6D636D3upwLf5a2e8CgblG7Yh1tneV6w7ra/f3/jbf29Qz7L5OQ/e36qNy+2ddg7WcXLN3q0kamO4dcT+UH9cQT3ubxfGSn01l7cfJ7YFh08ShXJ1j/Vh4n8JeUfG6mAdmN7day/Z97glfxRzcWu7huh5a7K1Nh4x1v5SgFcGsianaEy03ofRzd/onns6AnlqOTIFxCxcuXJg4MzJRSmFYImkUjGE97OzWFAKpUN/z/mUrQpkdWvu1a0sWoIKpUu2zyMmncdaZ3jFWaw1gj51tggoHJsoG380w1Feq7D3XESv9LPPPsulzLbPZpZuvbqm7l9YI5NBMiz1G5/qK/UbtYJg2g9wicjTJ+VahQXVag46++EiLnF/4WnaB2GaFr7CWn0SBbN8VQqon1zRfYlT9PdfQ0V4gSVeCp8zFNd5eoNB9voYTu7O6Z4rXAhNUpmvUbrI7yZX6YJ9farNPCM/g7dag7vWJX+xzXweV3VtPHj7dbJiKzVdadxkBv72kcQmPwIqOzE6VSfa2Wo+mZ/YHOGrePdCCV3wudkHNu0we0PxxUQe2Y/DOjYVtDOFAs9I05xwlUHnYZ2dGLqRU/7sqoMY6kmV393Mj+zaKlsoL+GApmhOJuSwcLovVTKPysF3FegtpIMKvK63nsKps1Z85s+xUBB3RE2Im2rSHmC48sCAXfrL5vvLAeYMKAzoszN6DwysYKgQ5jpASnAotSa1DWj5CezVu8n4tBdYFHqtdviqAIqDN8PSdWXkg8whMzyqZWmeC6zRvVJosJM+wcs2id7rGyw7syxif3JPrmlloPsDYEPQu9FtCSDnD1WLUoOgEUTFSZ9DeRX3HaiB8D4pf+5yQch+E+zIcCulhyo9oGo1bStjnTCzbHm6S6S1zvSch14IeeyGldjr02MNFrlENdq+WorJBuBblfdRbUP3CUBSQenRkpuU9a8sivkkoosndsU699neNXKhIDI71WUN9Wjta+/rc8X5QtKX6wT0HPbSc7Rq3qFzIRx+OJOIwDO2vcnhUe+TcR9kzEdgrBvFqPpSwzqKdc4XE4mSAM0r0MxK+nUgJOpKIHOYxgkqn+zJpHM9166gyc0d2+VdoEypE+qIbFmTXhZ/GSyuQHyHH1tRc06Fdap3F/KIvla1CvaASAnaKMchk3oBZYisLj7xh3tQNt1TqzJ5nGOcmoACGIWDL2srwyumIj4y29Q477MC8huUS7S1jG6ByomygcpwHFWx+ZdlQgAvaZCOoVuvsBrb0BT4PoSmIR3vvenpThvR5D5XvBaHP37CeBhRBqd1pF3IFfr8LhFTpXvB0wXvnaZGLmbjjumdq+r0/RqF013fu0N1nZxJXpvg2adxqU50k2Nw6vOd6lgdpyK6z6eW7zMVMPaBD7W8Ta98nqpsYq36DfYAAXT/1oNDQvfYO2YQ2XusQBLEK7XzR82Jcmvv4GvWwWU8ucKAJ5mBY0XPKetBndu+FVO6+c3KLyxUVFybSy72/W9+lrX9X+23qytT86sEl7wspbQpI0TPbOijVilJaI6UoDbEdx9tErFtGaWSv4QL7fta7YKnEhQvaebQwMaH8FYLoJkZjlglypFxiraNaSrWiAmQNthdMetnBSAqDUU+2lVi2MRZFaAe17ZGV74SHsgfZkR+yoJQZopQQUtktpQA5lWWBzVsGoCTY/lrDEtt8bvI0RmxyQWmw1yqalET3bGKgKVuynuRDcp4ETQH3vZVOvs60bnqo333P+7UXQiqj4HOdG6yavhve9V4LKdeGXXsfaMc8+J4had8n2oLucfuezB26uYpveCq86OrQM3mh0NeWtE8EMR9nbhIuHsvj1Mf3SIP3AFXP1+XMVvVbu/Jc4MK+r3tyiErkuqtHAPUY9tsmYD+ZC2/PDvISaR70kXoNjFmqnpe3o0/8mh7AubYhcl//9vLsE5o7yvvWJyGOdl52FplnBpBQ1dzU/ILGODT/4zVw5MhIpPY51lm51v8jOdDRAC/hEQpkeBs1Ual6qM09WLjWpEURlJ6Y+ByttEicFBsAjiwcuGHMQDlC+ch6x2eiVopyICigw0HRpg7tk7GqthIGvndK5zZlRj7uhFWp8OPQhNQzFOMdgUBpA3l9BktlOdWrj+wPkHmOBFZLMEcyr6jzZRNQmRYoo8hmt7Z7VeMapK3v1JsSfC6gnqe46syJtdrnz7kxnrv7vaXm3g5yBujm64HEgcRS2Z3DMQNPp0KP6esZLkCc0UAbZLeGRLJy+oF0n5W3pdfOryVzdeoFrUNQziRz9xLT6/S4rcy1K7N30sPTPnrOcnIL10Nah62UZqdJGyz4GaON/LveCvbnXCONt5i893cInLCnnOU5edt7i+w50kLX+EnDra733Zhce9Zq93rQg96VHLlfB/7SaU9jtZXGmsk8+i8AmLGKyoDy2mwL66lCbwyMG8Ar+3omcWGgBaJHdF9hqgw/skdQy2/WcTz9XD+FPzKzMm2sPWLrFPmXSLWhDdhs7vhLtaoipjCCICQmW6xrJFtSsMS+12MeX6rOH5npEgdGVoZ+lHKGlGl5BHP4yorjFc2m3R8V4ivAgPE0QpoCztwJr8dqkS2UjdnHzAirymeY+udpUINDw/s27wVTpsHpParjvmtvgSMyXm5DP2RJXUcenqP3Xkg5DTS8XnnrwqcUe95PFegTwuzmr5PrXrAXUGf2jkh/tjMQFwzOCKWVeLnrCy9loPaTf0U9fKgym49lf4SHW1A+CZ3O1i793jPRZnU8hS5dSFxjlkp8u4f6BFrKVxAbHBu6vief4HvxtrfcevIxlfXoAqrBU7Extz+J+Rpd2zvV0wrV47H3BUjA9ELGl3nMkWEL4/DTlN1X4HNDftlmuWrPmY7/nJh5hc6rHrmvTO4VpdpaTTWCSCur3VOy+zJKcxsA3dP8cRciKdLMhZE71Puh+et4vnbQR0COsSEg4upiVR04BECYJRRliQioDO/MmYdt1miutREMIRUHGF6IU4Ohgcdsd+SaSWPlAYV0z1yYahmlivEtj95a4LwSp+q+wdO4po0DaNS06o/dd8SoDTXU/rxCOdf2vQHe1LV7YeVNtUca1hJ1ldBme+ZU1S6tCwVO9GtZ68l9qJ7bz9eJ+GwfXOYvUZStWL5AKC5W/rvQey2kROZ2NM3RGWgsi4iN2TNu1yJEvSGer9zDC8/rgyn0uRdOKkfnV12LDBT7ds3fn/OaPazpz3IhJKGwdmW5fwmeTsxeM9Lfbsm4kPS6O/z4Nlg17l3Q1kpp5Ne8Htcs2C9CbsGIXPCq73wLor6/5r8U+Tx8W70kWPp9a2cSc2XPZ9K2yN1Kmqtuf66z0S1YtempJRUUCpvOtkoV+JJldEOullaEqCRyzUWnNpVaHyom0eb9gVzj69jKDlaUKbyqwubAq9qWZiW7kIr5FCVeWHjgzA3nqnh4SzQL1q0/PdRGO+lit1WcYuTsrq3R2eaaLM25CvWl3rnwhs84cK5r8UKiBm6UEfJYrake/+itKXcGuMqo054uUdallkemZXaXgAoOpr1fVDtq2WZHZKSP/JMjcWh8eXbtad4rC89o32lueiJqIVHPIQjRosKFz1G2+LWOzdmsqHfzRgX9lhBSz8FLro/qYGWHzHompXeHEHvro4e19o7up9CTQ0tO/gzPGaj73SkpjVhCzCE6+cdcc1bZvofqGqznOlzuPkuw9BClYCrVsfddFfvOLch3mZQaJfdB9XUoV777otT7Dr3esLdufV5Ii3wOSuxd4C+RQ4U9vOvekr7/ZBmFPbTu6tlbuL1/NOoY9lOD+nR1eLFaxo2A/abKIlp/C6qLUWinwUZQheqSaJBeoXBk3hirwhVK/a3t88nkCgdC2AcXLkTmhwACC+302Z6UnCk0/rguahobiX3Gaw+VpzmN2kXgTuRjjPeFlROP9btjFb7VMisDrIISewC/9Uars3rd1VGzg8sQgm+Hdyx1bSjedGWgcZ+yifWhtjr6s43O86sv27uUHJ+LUtAze/7k68WpoUOXrWyV4xbZF6HfEkJK5JrjNUegYDPPvPA2DaOH43pyq0mfHfp6F5L1Il3P9w/p5c+RMBrYH3DoARrS5Yrdo7Zg12W71q0jaIcdwh6+6wMiniMX6u8L+YKSUgNtTNWed7GWvuhz4xVMLeZo2bG6IHlulItDnp69O15wlysV15/6QJtx2qfm/1wP14i+zDZaKH7ZQsIPLAzP7Ol5ShFXONTQn8fq9j9u6t60XfdSy1ItJ9aO4wLBwKdqE6h9CrXw/l44s3BhrUDkkRNHRg6MHPgYJVy6Rm2OPLf7SDsYNatONCC45eTLnMi8oezC/TVDD3U+TDRPmwD/l8fJhYbq6ll7NP89eKjfy/kcSclVK1XWt8IL3msh1RvRbtG4vycGomzCyUO4Ra4B91aVhIC+84gWc3lu5fT3qx4+KVwrwa5zS6Fvk/tT3N/TW46uDTkM1FshHjTizFDXLna9nuXJR93fBvs+df+ULBDXztT+l60SaYrqo9AMR5ZdW5/rb/9OZep6t0CuLbreItK9bj33OrFf38+tvp3q037MZHsMW+v31MY8QKkGvzXFQ3O2zZPGSGWrDizVpuqT4ci6GSjo2IwItRAcN2+pi2Eg/DwTZ1Zy9fVEiNJe65ZW715GQXytbG1HUJi2zvmSX2PYmLhO141dX8oqsW6Yidp6qi0JEFM9qvwZbk/OzExMDJzQ9l4FZTwS6ZEigDryZRyq1aWTkfzglwhQaOEqzeLUOXcrIZQU8ODcLJIYlXpNPP1M5lz7rmXNoIqmcZspGQVHZDLK+aG0SBoPn289iuNnTbl174rptXnb89R+Pfa/v6tF9VtGSPVLzZdBRLqV3REX15iImKoGzJmbLJuBFjnoz7vGUHxw+j1KzsSd9J2f59NfN3Tfu7Oyn4C9P0z3690FjPtGpIupbzx1kVtyvc/G29sLtQt7YfWSRubJbFr5A5mBGe0F2edGxOrTt8vb0gupa4ugdwLLeuqVBy1+10b9+RKCB1qfQgtuGOz6vbVejIk2QTRTKpsba/JUqnBo8WsNvkoVGpxRVoO1tj52Q00kbknGPuS/jVSrMyvnyohvax8mjryyeoU7/J4LEQWYKLwh70YlahN5PcRogc3KkaWxbl6hAN6iRpdamzOZOB24ECt6JlUhpdRFS7Xd9Mwz9wRsHuH3gt1aqE5THyaOTNwQkXyyYsLjcyKSzS6MPNbkqSOn+tTDNjci32Sq8/7AUJ8citUJuCVy7J3sdW8j76s98J+VE5eKA2lLt84HgKX27MClCtSZ2PAeR1OWbf1erNSeLzkpiL/3vysozbff9JB1L6T89x6J+q4QUvLZXHMQaz+AawPXk9rvNWNoTnXXwt2SkJDyfT/XmPY1es7hKAbqjNF9SbrXyX0QvabultG7wI66/mwvkZif/F6FFt/lk1D3lO6lMZLg2ruwX6Y4KE4MMbRRhzWfa4sLCb/umoX1NvK54YqB+6cSz0craRzfJRLQ6UDzv2gDbGzcDhZZmKt2L6shIur0WVr+FwEmm+VSquC9QVuKp3o+UrLywhab+IhbTpv991j9Se8O7sh2aimMnAIHWWt87spUBUyuqufTFSX/VCTcLcysu3Fsz5XIDy/fUC2kXEVfiODCQ33OebOZBg5V/ES05LR9Hhk4MVUesVaFa6zln2odlFDrTEB5Z1YuVfhMdSzipOjIp7HsVoJU6ZEBJW31tfEupPXeR7D27g3NcwVnfatC47vSkhJdC5boYR1pDddw0V4YSNg0s3kPufk+nx7+cXJzt3R/63esDA9t7+Ek2PtEsl3rUFQ/CXqo7zlys9zxY/dJ+STr+1kuXi9PcKLy1Xn5TUikZ+um7BOCgzSSKvcaRAtP++C5xdA/Mz3zcsuzh1mfg0+vPauHD6/Vwa/fW2Zxd8wNwUilWmnhOYrnt83PUd/onbL9Hywt4LzWM82OUk7tANQEYsV4ioE2i0RtOjBRKhi21Jq38Igm1hTE1P61oIKWDHbfKyE20yZmIgPfhAIivM37toZio3O5Z4P71Mtq08QBHXip86cGU3sVN7hfszofK29PjmfHuc6pfr/WaDuNZvwf2SMihrGldho6IbXWLd0LKwPzpgY0AJXtfdiNikDW/ZzqIWlHXuj+Fv/wOd9nl/ByrkF9Pd/7rhNSDhv1TnzHW2XSugOvZzh39p1QYh9gP+hQ1GvTYshulcjx6MzfJ4QsDWkprrWkrrzP2FtOIxGCrn1fTqqLlob6yevqpLp5/XwSKV2KLElZUrKgeger4Ew5WjMtoaWeE59bMK1THK1ww4HbCvqdKji0Vv9CW6QCT9y/9pwltX/GUwEEDd7thZ9bzg536N5rOQJcI9X9eoajAPt6pSfw9aHaT1O1aJQdIjMwMRMHVFyqCNHeo4CLzkRA+cRMQQmojhXeak59pWh9wwOPRKA11Sa45SMi2u9QGXr4o6gpegRBUcGuC7GRVzRXln6qweG5Co4QcFJplIe89UhYQw91DSeOvOZQocehioMzJ5YKhem4QvXyiZXEIyMXDsQBl6D5deTAHc2rFFzkyGsKMyMLE5dNYR1q61T2uyoYC6eqRuTKVwryNa3cc677AoOUE6fRWsUU1dI7cmAgM9bxHUgcOVT0qNSAEMi008l7xKltbY65rgTUfTCFeOMtbd3rfuwe5x+a41p7rmQ7T3lXZOG9FlKu6e59F9dfDavfv1zYpO47dZAPbv/ciBG6rkX32oRr/qqXmJc2yPUWoYSsIvD8fofbnHpB6JPnGvx3zaf2NrDG2+ATUN97n/qCbn0ffpHQHkEu3giqTbUfmnO7pbSZ6gJ4Os2v+QW9Lb0l6OR9Lx/UtYCHobvuGnR47fve8r6mkbZxbKHDg/WCvo/0t6UqAO1JcvRTbRTlwtvP2VThLQdhEwpPkNWQ0S4XWbLq77HWRLVNJM4MLIysG4DmoeKRmtbd9Gu1hCJTYGP98mw1vT9XFSs8RaGOFC4buHbivkbhaScOVSzHKcAL8kcvtbyh9muAl+0gx3H7dqrg3LJZWjOROcN7e61jlog9fmHlhp0ZQfB5N5ay8SLNauS0SDX9bKbZy7KAwWeQ/EtDFeRaP+3XUIkOW36dgBtzXVm9JdRTbwk5j+utqYn9WuotMCnI/ffOp74rLKkeeoGnEN81IXUt6KD/TkwK9ilA+lMqk13/tk53Jugai8OITirTNyCrXg7lXRNSblUUWtBCb/KLekGv+r4kqPq+7i0W9VcPX+77e+b/J+/dYm5bz7rw3zjMMef81mHvrkLZ3UmRisZwAUUgNiRGW1uhG1MVqqZaYxHSEk2j0gtIDRhaSUqUGAISemNQA8Qr0xguMCBgL6wNhTREQwxtKsRIQSm7a6/v++ZhHP4Xz/Mbz+995pjf+lbt1v+i78pc3zyM8Y73+Bx+z+HlKUWTL/0Gli1a20PQiBvT2lUyKdUUc9GNtMSQtc0h159ep2sEOC+gLK0H9j/bMrUEFGqgXDPfqZCcETl+NyBiy/RMJRvDZhYUbNzt25XDW+quHBpO6GFNIX7ocaIKWJrGBRxR4+j54mqX6CcfpwlV2pn13EYdXzP2626JhLa1a017z/9gQc3XOHg01Q7DzDh6H0+z5uxhqW3ZV8xjwROzLPXu5GLTCHOH6NGiwQordD7mB5lvJo6tfB1M80oe5j6QLbDYThnR44gRe9QubpguEkeDkE3FrrKDQGnhMhsagUG7buejtYblmjCmPolGde6U3VyyEM579KiXJZgw27PYA7WR60jcpnzBmdRXfMVX4Ld+67dOvv+7f/fv4sd//Mfxhje8Af/xP/7H4rfv+q7vwoc+9KEnfhaDWLcwJX6PaVaWqXWoRMsJIpRjG5eHSU9QoktmoFkcdMnVLi2GFaA8PlvvpY1LobFLeR8EISab3jXXco0yMy6UC5xuAxaF1g5YdsqAP+8OIlELNTdmD9P7+B1QwgMcLy7smJuo+57/vQtgi7vosEaLLZgTm/2f3OOswgCzOZjWlLU2daBQyFMzmFEPgPyeGSXf6+nNS5uNdSgJZQrTPr2nUKFCyJLmTqZY2huNOcWcjWhwRI3enSnUxkO7C8Cj5SpwjYy+NlYwh4AIqK1cMzKty4Aiy/zd44A94E4BHQa0OKLBles8NQ44zpouHMbae0qiwbUNI/OHeT13qJ1trRAZ3KzU8+pWa1QE3zYSnRNrYPC9PuAalzjggGvYabeWXNV6OuAaFXqYz+FDrNBgg/WsnZ4e82erqoGtU/OPXMMcNxqMaHF0J4dhnrEd9l5fh2tnziuscAGee2ta0IDGWWuHu6hwiR4PcUSH0QWHDpOfW3UfhD0tTuqIFS5gJwNfoJkFmGh7i61rsuZzucYOF1g5iGvxWI9QMhOuVaCkIxMifZnC1NxPjf9ORwruQ6YTU8FIEQWuddLC25QvOJP6lV/5FQxyONl/+S//BX/+z/95/NW/+lfn7971rnfhAx/4wPz54uLi83pW1oQ4+IqTspCotAC2ILIcJnlLyKPpMcloTHqZ5DtKX+ZhRa+qETRk83n8qxoP23Et1yjx1D6Q4eq97LdKRSrXsr5sIFWYUIksmQu/02zc9JDU+geEfS/HV6iGQA9IBkiu5dWhRocOLTbup6UJR5XVEL4q8xJwtNQpQDfXEsyWx0Yhu6yNn6sHC9/pJlRQjJuxkusyk8rPU2kzyLYyqjCS61wqaqDroUZk4mt8vdLBmyNK2JDaR9iKWr+Hmu0AHmPCnTLNjG5AxPCMPq5Wo62PCnmfjicrdmnECZbplaFP8p2dJHXAHgeYk3iPcGCnk0jv3oZMrcvWaAYLwoz2neVVNF3f2Dt8hNg7IMbRSu1k3Ni5CQfUBIkEMFelefoxSwTdRka0mNChQz3nErRvm1kc7/wujg/FbXMpqV0jwyzoTWhxXewRRWBoV9USgs4yJM3fNUxZ169mv9EZzgzyNuULzqS+9Eu/tPj8Qz/0Q/jKr/xK/Nk/+2fn7y4uLvDcc8/dus79fo/9PoyJDx8+BKA2olILOmcfWqHCBi3u4d68WeHwyBWYspHswfxzLrBBhxUOoFPuAEbwtGgRh6ftYUGN5cTkcg3mNA6CphLMUa6hMwIJqxos1d6Tyzk391yUWCoUSS2OzhF8Xpb4ea86fLDOO4iEljS8mia1whZbdLiDBlu0WIM5rAHM42kAUY0BV2BwZhB2EwfMKdcsAu18bwgTWBgHlfDg728bRa9F49i0PmqiKlCQabMsbTplaKoFxriG5KzaucK66l4f2i8l+LKHTPFqtpzBoSeLYuoBbLBDB0sCFGstrKimWeww4Bo8L0kFO+pEjG2z70YAV9CMf1xTFCspPEZPuUvIqktvugED9tjjgD32uHZSTTDKyKWx3f3Mstcudjbz0zU4YoSKuCvUuEDj+iRDoccTYYRzUfmomnBlZyntHajrZn1/QuxwwrUjgA3oOtJh6/2gzjIAcxC1ilnlrHLF1wDWCM2azDdfrU49uoYpCGc6M6TrGCcYgKPRMMaV8hgfzjARmgGGJt2mvKw2qcPhgJ/6qZ/Ce9/7XlRVbJKf/umfxk/91E/hueeew1vf+lZ8//d//43a1Ac/+EG8//3vX/zNlpQNI1Pwq1s2wIGusHWWs8U93+hGai1jGNPz9+iw841eY4Ot4/fXYMwEMWNaB7hp1NahGggJvBL3e/I9NaJsQ8twU4azsoGSL/UIpNSjXjnKnLRN+kyVaZeMnSSEDPDL8Jku0kh+26HBGjW2LmWqwzGJFjWmoehTIxuSGqyl3aEVhsh+jBj7oYDOUtSQfuY4UOpj4SbUsVPNSO1tWUjIY70kQCiTYpuWhC06pGh92ka9jlpv5aNS4RrM+TaAee5614UiayKRgnAJn2Cik4kx9fyEWJ/MBFHJ+NdSg0FuZvSH3B+Rbyy9P6OWJ4UFd4DqUJhbGxk6THw0TQaIFRb39GAuCKudGmAFuufbkR96zm3oQbX8G07mRyFbOrH0OHq8VuUamfXdfPZCRx1d5ykjMelOlTENGir0CFf+Fnav0j3/fFGhStEfyHulS+oxracl0MGL96ivwFHuW4LSz5WXlUl9+MMfxosvvohv//Zvn7/7G3/jb+CP/JE/gueffx6//uu/ju/93u/Ff/tv/w3/9t/+27P1vO9978N73/ve+fPDhw/xmte8RuQgRo+MLiPBF5wVm+oKW9zBBhfY4j6YvoUOsOvZNdYknwmTI+gXaLBGgwlHPwyAG2hw5Z82KaCEkPQYezUyctA5kZrlXLXBJW8YEl1lUtlWo39JVLdYtokoI+eSV6g0E9UM7629boVzapwyqTUqNNi4bH6B2r3LapdnYxsxnDRIofW59jGzs5A4gpwFa3MJIFCj0UMZKZuf2yAUKvjcpfr0Optf5j0Y5/EDQtvh+yXmx8JxzYwK8j0Zl9qylsgPn8H2Tk6ANZJN4wWzAZxHe9SgawEtCPZkJfoExxrJeheyPhlNDVogIuM6NWeeT8UyCDOjzZe+qVMxL9SZyTIIa5oNbvD5ydZa0wEjh3iQX1r2Rj+v6Qg6f4+o5jsiRq1ZYFLRi3CIP87QtWXcYDaLA0YcEUl5rW8rABfg4Solw9LCcc2HwGDui3kQUkuL+cprTNeZjpbCzplJ0RRRoTwmRk+KGKU+1R3J1HKmi3PlZWVS/+Jf/Au88MILeP755+fv3v3ud8/vv/qrvxqvfvWr8aY3vQmf+tSn8JVf+ZWL9azXa6zXp0kayaVt4CpMWMtAR/qVxmWrzmX5OHigRo21M5j9LI9ZHEYFppGMyH2TqIIp0QY1zBNNArKCQV62vKxVlgvNpnSPwHTPQU3siy4QIIydGVLiAjrIe57io5Dc0vMULlpqg8J5dNi463/jpKDYTtTc1gA2qBw66rDC2rWpBkgERI/qsA22xwHXmDCgxRYMtoxed6hxQOTVDntkDkbmll67EGPCzVRIe4+T7PQ0Usq4xqC5wvYumLS4hM34QRw+yr4uBz9rIXSs2nJ26liC+xSa0XWSyzktzOrjmUrqAVcyeu1TM397SgQJqhuDuATDDuLJe3CGguF0iJBhXtfDRp1atzGUgBrrYnffvpT4A1M97RFWavav8Z0WIejntRTa9xgHZce9h858dFvaJOJPBRLloB5Y2CulLn8A8HD+ZXB6ZMCmjdIWhhXdwzRHYVG72SKYVRaodf9Y3SEsHaSOSX7PKALL45ySzpWXjUn91m/9Fn7hF37hRg0JAF7/+tcDAD75yU+eZVLnShjzeIZruT0Ya0HmU8/vyFwo6Y0gtECDc6RKoUzZ+n0VeMB0DZpO4/ADxprbacDMURyGWotgODXWV2deqi7zrxog1SFYg+pUowJKDSqXkOJO7WkKNKjDRid/CfcBsaVI0G0ManRowVDUIHslDDM66MT5pLcZMxk0CGy9jNHhGjjtN/sUmk/ATb3fp9fll2o42VHHxoJWBIuqsdY34BmqZlfj+5BKz2lzKtVmJpKNzqpVZCb1eIDn9Hmxe+JoG7pS89gKugnY/8Em6VjE/cR6GPcU7i9MbsUUr4Td7V0L5qVr535N8z7js6v5meHuVMmq4siUrGpa/Dcir/74NjI5wHti4Ccj+cq9qnNlbeepXD3CLhRiEU8SDgqC+bpqFoUUpF8qDMvlk8ONo3YHC/NPXGODo79iHeWaVdjVsuQ8QcRA8QsdybzG80jfdo2+bEzqJ3/yJ/GqV70Kf+Ev/IUbr/vEJz4BAHj1q1/9xM8ITeoAgm2l1FrBoupHtBidUdRoHCFmDRZ5s5ZB43WaOHKL0RNuHsHEMeZLw9DF1tlEhwZ3sMLWgQcd5IO3Yocyhx0ZgRJAtkQ3gWqPZWhlGC2BWCAKeZ7ToJS56V+VSCnTXaB0hlgDeCb1A1C4r8YGF+iwQYM74MmwRtgBcyLmpokM2YPrUY8crqhw5YZnPkv1BThLG+bDDJQ5035mzr9bJ04mwfbCpFTCU+9KzTZCeNNOfjbHGs4yiXqDFhOOsPiUNSyYlOltlr1PORcqHOj4sw+qJed7lTHfRkpV4YMCCGtgxE84GFCDMs2Hx6uzvQdcweKJ7kBzrJi3nV01gDoTPfDuokGDDQDTcTtHLGoMaFxqn9zpQeO3+rmVg6xyW1PUzJY0dYoQ5khi+dsP3sccpWildfEDMGa6B8+6nfxp49w/1a1MALJe2Hc8kzZwicjXbvTIsBbGl2n7qbNrufJ+ruU6280D9j6W99DiHhqMeBYdWjzEgP+NyWu7gNGM7MBAOG+pZKedrNGrO0deh7d16MrlZWFS4zjiJ3/yJ/HOd74TbRuP+NSnPoWf+Zmfwbd8y7fgla98JX79138d3/3d340/82f+DL7ma77miZ9DyTY+x7OoP608WLR1bYihg0sE+3ESKANJzRw6znVNvrgm8LA0le7LfNAHREpMndgKpa1J7RkaJ8XrqMlkI74ukFNZsixLdpdcqEHQQYJZkNf+nrESTbqHGkZ4UbJMYC4DSmQcT0IiFqp6nPWoHsC1s6ERE1YyjzxmvseygXhKn2hIHp1kqW1GpTuurXPB2/Xs0B09rTxd0IjWA0HpVUbQ7Enkx9L+x7HiutAMIkt9VuLA9aebXddJ1tiH+V2MCcNWaxDqVtCb9/ewFEQU4NQtYYlAsYc5W5+Wm1awYgCfX6mdLehoGtxIB3LTnkqkppqvsz1HQamcE9vXpzpWrEKK1lzPTwZS3lxojrDV2mGNDdbYYoU7YOb00KSU0TDwn3vjHGPhulG6qaaB3CPC16dC0c3lZWFSv/ALv4Df/u3fxnd8x3cU33ddh1/4hV/Aj/zIj+Dy8hKvec1r8La3vQ3f933f93k9R4lyBR49YMNC8rgGj4NeOUHRZfRki4JJfHpH2JtZ1gQYXc/jt+GfMvZKJrUkfRAemxCunVz4JEqZSfF+yl1cUAp4LPUyb++lbV5af0JzUiaVU/fzPiPhZgnUXwn5ENawPsaZQYT5emdRx5lJAXSRGJ3oWz8pYU9nN5OWUf7xnFPtu9oWR5Ru5iX02c6vZiZlZkcZ0GCFFh0G7HGNEaMzsScjqJlJKVFgG7Ndktfqk/TYFf7N2rlq6Ef5XQUJCwqwdc0VznVshGRwhrxsbD/teTAplnOCYmmb0lG5rc649B0hR5tlTaRrYbtkV8un29au/TRSYzk/GnZd9mOU1crzsJZbfG4HP66PnDljwB3WGLDGBTocMc6MlQiJOj/oXr5J+8lKAu89Z/vmnlLB9zblZWFS3/RN34RpOp3U17zmNSfZJv5PyhprrOetX82aElOLMMadTKpDO3uJNW4jiQ1ShqUZg9ljciJkd1dYoXYdqgVw7cS1RZxoatJmg1E26WGW3nfCpDj56mzACVYokNfSvLzFabJS1qMGToIL59Bs9SLUtEtkmOqpFxCXve765y2ADVqRhW02WqwR/lCAEbToFc9Hol1gmIFUM1tf44hHGGffJUJFVwDW6EUQyBJ/EEbtN2HNxiPe9hjn03wGlJpohTIxLsfQ/K4sbmYD8+hr0xYya8IadtyLAYqmUx1BMFrBGd0ledNnPH9CGKtHlMHcymi0cEwywVDwiMIRs5swWS+1d9Yd0HfJMthWbYN6IKodQsMbao9dMttIB6Y5sqwWvHcqRBDq28AwrxoTAMzia5CZzRqPJNTe1z5nzM8XqXxDFGl8h63nGbDaW0xo3RVBGSQ1R2PW8ax8rIm1n60n2hO7mOJOM4+gW3ybDmicpI8j0B/te3SwHdEDfnqxwZBrRLDyBZhLZYU7uIcH6PH7qDwDhZ5GzHbxrGB11Ir2x/xqsH4WeM6xVV0D55CAXJ7q3H1c2MP8mclQQrplyv3a9SzKRrSL6LYiuWP2MJPZaA6OKInGt0HrqLa5uwehNq0OYOAfE0gqU6A82KSXbmhqTaohkYAuLYTwr4L3pFS7Vf5Euk6BqHOOAlyY1KRUy9Bin8mEVBIj2SsdXUl4zFl3wNHBvuy9xr80Y5fPK/ui/eDGOHoNZHjqLq7SMKTvSsLoqdnMa0h93shwNUFWHKVgok1s4gl5FE5hN+3PkL7jOPD3c4IISxDGsl4dr9Ca4lo65ujYZBelrI2fk7x1LVTyjeYSqWREOXZVMaYqnMQ9enwJj4ovMxdaMSbFueO/2CGMtmKN8VTNGEEXLNqjm7kfEbFlGln0JyKzqpOnETBepTXlK2IagUmtzUu+dyGuVB46HbMRB5aQ2ef9nWu7idFwB6sT1ZJ2z9YifWbL/n/hgv5ylxW2bgWwgVnP3mOMwbGsEMwlZlJShQtczNOp56MSOjJfrKEYnDD0D66P2XEJvUdPtbB8wzV2mOY4iAN6HN2JOghlMNVw3+ZLiU6GSLgYzhEkJQ4TypM0tS+sl8/LgIli1RpaSNlujZDPrJ2DSIxsxfX8be3JZdo5MDG8kXoB+fa4Ro8jLnE917JBSfzo+sp+qJSvmwzSPzoqWN+muQ5qDjk6XrUG1VTsGkriTNAaW3JwQrDH5FrahD2uYCcsDR4hZKwlwx05RQzbzflZgst0vm9aF9G+GK9s0M6OMyRSpeZz+jzWo+NFRsdsKtonZZSPKxVGmJMD29Kj9v3aw07KtVjGjc/JKbluF5jUFmvQVf1mrCG3x9Ca1kXJ3mmCHZJo/9pENThqFI4B1fItfq3DBh3WuMA9rE5Elz0w9sB4lDo1j4PiJzaDDZ6BOa0cYa7pI/Z4iD2ucImHuMZ+PuP3SYuaJQj3ayq1mxwncrnt859qJtXIxFfgMdkr8Kwdc5wgstw4s7LrQrOgw2c7s6fRUdu+0GuAIN9WQgIKWWSFletXMT3c+JopQieTBEHhHoUCWdSDjq3KHmFZhVYiq8SBddyGYOi954yiZc0mV5ZgkDIzzXdAa9TRPeD6QsIiCEsoMhNvlfqq9JlFXV/VO47vV/LdEiE2d3rToOwYiqGoPxxkDqATPQ+iKO1dlQtOdLPn/aUWrCN326La1W2ZQL5XC8fjcUyFz1NWoHVxF2QN8clKnuEq/Wo6lOX+Jo7CHIIxo6N/Z3sigiCetCgeU81tGp2pKn0IJkVNzPacrfyI3uTZx5WvsA1C32b/uUqBsBrmUeXZveGz23sy3Gsc/CD6qYDEb7LjKsNfQn6oSVFYZQ9VW78J0vt/apP6v1UChKMtwOScFXiwWYVuXghrNNj7wJfd5nU8/GBAK5KtygIqP07QFCmUS1r3YRnnBVzNBKlHSKZLdgS1Mam2tVSWDJpLTCrbWoBy6Z9bRCVxjXur9DlbJ5YhHyPK1cwGCPBZUp7RQT510+YGYbAzNxQhKGUqS7AFtziZGa9nLkQgIt8148dSv1dgRgk6SMeVkRqLOQqmmVzR+TjG2xhdxPWd2naWGAbbkgl8lf6SKCxdu1RU0FGNSX9T6FmfxaICVq5XX0uw8PkWna7UaVGX46/GMtZYwc7YZcaJGElbB9ZLhe7zaE0nc3IqyhkMyH1vNIHxfapdNwLeaTH7tB0KYhltDrJHW0SOvnhiKQJEBo5ynELn5HjZqtxjh6OfuBX7ITtX5dfjmJTC/2SyNjPTyUwuldvpr085k7LIGJ6/UqF3HcaiLJZlffMtYxwFAwZNrWaciy7UMJEOvrQwE1jWZ5obySttEA1G9/ZqsJuZTid/+bpEeaihmmtzBPhSUUKzJBEbFBqjofFAKgWToGt+LQ0WrhERMHSoWPmYsW4l/KF9WUjrgJf8lzugBWfA3pnUHjxyYvB+3wFwF1snQNfYYcQ16Ol3Oruq52pfNB0L268sQl1jM5MyeIPEhs65DcY5zio8Bnm+6h577DG5u0yQfktvajE5Nl9qpSwZr5Ik/U5FJjJXOtEoydX+qedWJd/pnLVyH1ASqJxNLiT/GC0VJHT8Mw4BBHGiq3uDAaOfBbX3p4fXJ+ZAaC2tg2w1BmyxwgYdXoFnQL9K5ukf3A1gQmjjJQSoK8l6MDhEu0cPWp9sfVRYo3WgccCAZm4Z0RSte4lJkcbAwUIb49rFH7omsS3EDcq7w60qg6nBeuimdYVrXOMal7iCna21F4E61pdq8mQ+dKbiiQ014vgOmhEYtB8ISzOPjgmmweqzRfr/qQv6/62ikpp9NpuSSVH1zFDgwEs9X2WkO1LdI8ld+ozJmRLV9vL0KMoOlKAYPU9JSyEFlVDMtlNhg2ZOncNjObSQcD5OOj4HES3BMfm3DI0pA6PmcpRXjSBySoBICDLsFDAmHU0iGHP0aKI4Ewnz7JgObHDI6AmAzZ+rJKhLGoZqCZQ9ybCyLeccg+fvpaRtgIZlIxj8PX0SdayY7CakS+oBJA7aziUGGf5m8V4hX43hUqiY88D3SoxYcl8z1FkvvNQ2l20PMVZl0Xk4D0VGjpFaRAj7P+8efopsemYhrrHyoOp6JrMVJgGKCTKfxi6FVhz+hHqUZMyj9ZtO4/V8ReztcMlQJjWBYhkAjDMxJwoUsKEyzDxaB/BgIIBZV1SXPaWGPCFsmPP4TSfrLIvzuia53jj/9CzWtUchrkF4VffowQjG/9PyVDMpLYw+NyKynxfIAUfXqlYzVzf9i86vxK1PcwMCmKGoYc5IUQ56AIB2Sk0k8zT9Lqu0mrnBMrJvsMMjMJp+qW6+X441ubmooTNL4k36TGmHmRZYIrVomQmDhvE7/p0ymfycGsAGEyzbwMGfN2LAbibsmtvLMieuscHWXV8m0GGcLtJ85rkxUY2RfWJ0vSadJXhyrq7BCUaLlfcgXA8G1wNG2LFyfA5zKjTgOa+HWWyBCzpM/pklWRZlBEsMQrOIZKbBFtJIT6hTx4aFTF9DIBTaCQ9HO1vN1lIJi50zkKt2pYz09DobkXauie7pfFI4DLBtYYtqsAEDqlUUoH5jI2EzdZMpn/ots5cMWDnJVhMBvQOPHhQbJdzIu9Se8O81gTjcxG9qi5YJkUwWCPchJiVbp6tNFzRr1DV67GbNVIvOs2q63N8TjFZxfamTBO/f+LEi5sRivd7DAPwJDz8vuqXlqWZSB+zRwcAsy6c1uvxqhsWV/7PhfmlmLxN60CWUaf3t6GtbVOfgtcrvGFyqByh3hWI7umegQRE0tgehVEcGtUHdVBTGoVSq36nsNaX7SEDYHyXcqkGMC59VK+LziJaTQBIE07oV59b202G3nre+6Y9TIh12PTXYo2u5lASjPVkzUBiP7Vc7oGoitEHlojFFAZtNvml79+uDJwqdZq1qcL2KbbAkOPZUIwQ12jntD6HqoRj7LOGSIdFKobFxS2C2WigokFCL5NxR29Pn8rPOa94DAUuRtOroxBjntaVrT2G+EGTMfle5ZkpRje+Ggt2GEzgPemG8o4mkPLSQ2os9xXYo8yrSgYfZ8lRHNN/E1lMNbbxl6kg+zb23ZLOa5cS022ZumWpsIyYc5j5ZbnSbS3OIYN9tJCaEEwQQIDj1MJ37I5hOwGq2GbXjRgA7FdiMD1Xh58o1FIE6Nu9GwUzbM8ypBY89URsxTy+oZlugzQUFsX5WF6gTq/BTIY50fFx5qpmUmQKZ/LJxTQoYcHTmVMFcVplch8NksnPli8KkRZ4aU8+DbyUWPSEGpogJtkF0XYGKEQpJqEecbqPSVLtcFCYcz3yXoRT+BpQEZ4khKTHJvwMBYdUIGxXvocSFVEf4NZVwEDfwMH9TRsJEuyPJLGBHm1MWXYKNFNpT4pztTySUGV5jUdy8FAosh73VYag7RzSPG0mjnT02zuuqcVnzXLvz3NLqYMHTVeF4nNdNtgcpaVdBhlYOPpfC04Dwtmpw2h7CXHE+E2vWqLDT9UOyqDBlQLr8zNPZmEDWiHokuuJeI/GvZ2JIsmgoRtYTFL6zlFoBxutqi94Z6zHa0TkjKTVU7lzGRJWrKA4OKV2SmFrX+k5WYKvMTAgGGwfdYX4PIFbzqZAaFmEdebOX2hiTne/8/mB2JvQwPqt1yhhslEwLCBZuWnQF5lacUM0+1S00As2eZUJZ7yJcjDjVg9scIv9UM6k9rj2rOL1qYppWWPmSZOSUhp6aj1XYpGoHDSonDoEXTyDUwxNeWmdvk09oBreIsFuS0Vx45RWA0clDj1Oo7wtdCN8NN7yUqPNaG8HyGi4a9pznyug9eu9yIWlcwyBAw9lDrjKX7p3Xfel5/A5yZy4BCJYaghY6CJwrQQ6CIJDhrmHQ34CDu6IDpl9NsxikhLkHEmO5XVGplTFpz+BZrLGGJneNTAx9kpFLhrGGjdkjBCMixKf2RtquMvRIhknIr8EFjJzxfOlpfr6uJ3VSAQLm1lMAohVm5Rjmq5f9AcM+Zi4SG08gDRyxx0uzts5D4gcfLwqwN5eIDqxxxBoH0MY14ADatzmu3cLqbtEutDoXA+MqZ5eEfXs8dOByC7p0A0A4ahnVMrs7neBV8DbsYO/OSDGONRrcQ4UD6OpEndB8oTfofLz2OKDCAaPv6soZPDX6LdYwM8oBNJqsfSzstG2bgQNeAlBjjTUMCemhmlSDzmv9Q86kjBjaIg8JDD6RPDGXRlH68BOCIfRkxGYF5qawXwaMOIhfkR0AbTLH0bfqND83zJG0S/GU38EJiG7WkCStxUeUxDRrROyrakdq3IS850ZU/J/QF98rw9HTNRUG4vMULsyScpaItV0qhaP4zcbLtqfNSDMDJwPCYE5vPGNHjO/QIypGed6AklFmCI3jod6S2d6j7dRn8Ltjup56RI+SSGfobqnwEItpHpFTxmDm/wYbtOiwxsrPSxvd1d2cg6hvlCZxrgeFhnjcIBlR1qSpGZNR52ta1won6eXSeuBaOsh3OmbaRgCuR9heC1AIPrr2r3JdI/JF8JQ3k9RNzz7CAnjjrOxIPUwYC0nLKV1Twj2iQu2pvUgrtEUh6palkv/Vs5NaIvNUMqjbsuVXvh9NP2kxgFBkVeysslDMJh2J85Xp3FU7czH40HSu4/xs3h1zAFDfi9AKupNwjYy+c1vXvlrYSRNrNJ5awRhghxYTVjjAgFg7NpbjZpT08ewceMqZFDcWHH8N2wPPa2UUOhXMA8zi1Po/kytb1Nii82DgFr37plz5ANeo3TRYe3broy/9awzyz7YYEzUdcMQOB9cGKP1TbmjAAwUiuA44tVUp9KLLVJmeTrXCWXzfA67sl+N28O8fwaTe7LzA+yl/ASVRu8mupu0NRkJ2QseWxgkhCcVhHscR5vr6SCTgrLMqM1r6XovGdpCAkhzmLO6sQ/tCwk30n71TqFQDhR/PpGoM6DDggDj4JYq1q8IGa9zFfWxw10EVC7AIoWPCHjUqHF1StpIdl9UxhNAe205BRc8ZYl84NjZeBKVIGHuc06AOCE/KEaGjnI6DaRWYWQiP4FOhzmbDbCNGDGmL6tD5ujU3AfOcZYvLsvd6muJwVHUt4oq2ntjqtNprdL6vbQQrr+lmrZziB21sg6/w0e8dMOAI4AITalz7bNRgLkJG5p0ranawuigYj96DlY/bHp2zH8vhQUckOpwNfkgs77baehBwJaO6xNHtsnexcsFpjftosXKdysZrhXsAOmxxAKHBEZ/1Uetce6sB/MEN/bPyVDMpPTWmnt+3zsX19M8o5tqsZxqZwssklbGlzy+/OEtnkkmOw/rMaYLurssunpp9u8Ly01Si16JS6ZIrOEsmtFqv1h0Ou0xFGU4eKoVnF2c1O7Moys96DQ4AVu4o3GADak1xDq9mFgtiT1gqa4x5HLSfqgFp39lvZf78PYjWclmaR9VsOXY6X4SMR1SeXvYAevlZVnHTxLONjWvaUogyiHiFDiu0sIP0mFOdFi9z7GfElmUsf5xGZwQ9VjxTljL1FTPtq0auY3kbjZFFhZuMGmRgz+oOWG2aIbTyaQwPOWKPIw7Y4RoVKvfnjSx7en3lKAs/b9xxB66lERCu5p1BrcKgTdMWNEOjrgLVRHpEYDfHmN6GZUaaEH8q9Kixw+C6hyE4EwjOjaB+VkltdIuxlGx7P13PdKrDTK0OzoKIZlj/aPdS3ZJek423Q8dd10DtLE9/Jy1igrkBDQ6ei/PaaWaFHi0OuPpi0KQ0ADZcZ+lFpenvSxmVDIxqZ+vGRdqgbItTHZ/mv2HaDVfW2FARPVX6B1bzUiAxUgiNGkuGy1gUxuJf7RWXaSaw2oKlQiITsFJJ/NUtdYKmPgkrAqEzLcqgWK8RPRtr5vEbXIJWYDRDogrv5bOQqvTSe3ScOda56DVcP8rE9Dr+Zd/0uQ1O57X2O6p5TbEfJswwv1uNEQyOjfTGkb+6Q+W2r7A/NG5uDijOYJsetE0xjW6Y0yecriv2h4yVNrCYrzLlDWSs2NLMbJZekHszY+M1dVEL6x7n9RAuI9n2C8TRLpaWmAyAtuN29lfkHZgFSpuvg4966dJRpZ6Mrj3V7k9c7sZGBMUetFZbJCZdJEafQwVl2XvuRJ6oNjhkWfko0yihd/G0b7aBmh698sa5vsOsHynV4krXWK0akeOUCFS5ciYfrWhLCeAqhEtGZZFaBw/Xn5x19bi8pYjzVDOpHUIaZEdWaHAHaw/sM9gvyIX5uazBDF8rdJ6WtnHgrUePaz804oA9ju7xv0eDHjWOLhkMLv2U+ed4zLn51DTeigvs5ulWe5DCVGpPySUTfjXGa6DmDgHzaIZv1quajxrD1wDupWeyrUrkuWVHxLEhqsVC3pPQ3QFwBxussUbjERcDKuxwRA/gCnscMc1O/VmTooyZx4F/meSS11572w6IQwzOmWa5TdlPhbc0TEC/YxB2lgFNq51mJmlkpsXBZdmdCyp2lHfj67TDysGXBnuYbYIszqwIK4dRKJ2uQb+oi1mDv8LkItcKFR6hxwF7jAV8e0QZZbNFHFrJVDl3fM7uIuL5ljTzvdsWWJRZ0xTO36mVA7FuuH45pq18ouw+oMXRwbFujimivm8WM9t7DBu1dxXiLGEbqSBxDGXt551R4RJ7tGiwRedWlgbtnFeBeqY9gVFZMTo550cFYD9rCsAlRj9ghrpJDcv70GOHChvUPgOm4V24qHaJwSmI6XYTWifsth4ZrZbdcmxW15jQ4IARD3HA/8IVHiFOveJMDKC1ygTrDRrU2CAYTOviD527egxoPUOi0TFzeRpgWEED4hwNDngRO1zjCi/iEj12GPAHmGbFApiKuL2bylPNpJRg0eOHSyG885S/ZzWdMltVSGSHeVIsUrt3sMaWqTG+OFuz1LToRVS7FDSBPnwl5EGiqBmnVfrXck7aX5JYkZ61ZHbNkvVSzA0QXl5LUCGX45JmQWJuknicYctcZ5XUxtHTcQHOawAsfBYBGtaZnSB4rbaZxFT7z7qa9L5O77Wu3Datj38Hf5IJS6xhdALUYeUktXUQyfyuTIThCrV1aZDXcZbIm5lJhSZPsSnaom1URsLf2DdlUlsYM61l9RC6Vg18ac3ovDBsQcc3UiuVwgBbOviOoLM5QTLaoNp5NQVSEn22OmLkGA5MID8coUKTMr3L9BkeYWgaRT2PQINmzrWwRXXCpGyVVFXlg029+OBiRzjIqC5CF3rzCKwxYT2jNBXoyKGjo7PBc6VZAoQ0ON7C5MvzreNKxpvZfBrrtiOK6DpmELNFcDFOKlYPRxozFSTt6702o6HHWYiIPc6W3DYXxVPNpBSOMO1lPUN9y2gnjZixUY/uymtY9h69x2fTmHh0ZXl0o6xtaJM9NYcfAF8cPPWSE2gSLCV6Sq1qsNbcV9kJgK2u5PsliJAEfkLAoJopItfHeyqUBFjLko1GGWA+S4bwEYNPeUAHrX9say6ZefO7m2weoRWafcZMwqf2DfaPY6eaXjbqq8ak2RfyWB9mQres+QasM2DEaoZfIszzCHOS3joTr9DOQc0DBjyagZkRPY7YOUFpcensaiVMyrS1EcG0TltVw5jPklMJIfNnAZHPlX0wXg3epvOCDRBEJR/QSM1abV8c3xBVjDl0nkNiAj3BzERPrz66TLM9dMoJWHEQBIVMqjw8kScmN64NrJ2CWADJCnaE4goVOqxwgXCi5yoX8lk7w5qs50b+O1AgoYMD4W3T9OyQxwvcm7UT1Txr/z1KP68ROCvglfD1QEhvhz0e4hrXsz66m/dSh62gR2QvBxef7sxiAbDG4G0P/0YtYTU2xyHLFzji4LTOoL5wcPv8ylPNpCj1rR0Uues+eCxBiCcHmcy5k0bLcFStnFnZ64DKdapH2Lty3mF0RriaZVeysvhnC6iHJRtlG/R0WWpVhObU408Jfo7xUSJJhlWnOg4omeE1gtSozakcm9NnsKjknbUIAwuiEBajwX07b/8OjO2wMS9zqK2wckLcz0yjRyk5AiUTzu3W9jO1aBC/aB/7pIxKNaVOfl+L3sN+HxHuuHxlKJHjYCc5bwBcu4xqlo0KwAYX2GCLzoGbyPfYY8Clry+LLTEY5drHrkXlVqf9vGZJmGzFju6RRaKgMCw9NLNtiP50F6icJNeuVZguuOQpqZ6drJNjxbXG8algcPIGwaRUYyMzMhuMueys0GHwndth7eLOdsZJSJQPvj8H37nW13AoUDdrdQknk4JrTxEVSSYGNLiG2aAugOpZoF7Zq2mBhitHywRc7YGJwLExqWb24jwKnTCwusMz2OAZ2Im/msMyGBmLtbHH6CeC25o8zvpL7yzBdKjBkyJd4egCUOwni6MyL0lDOkZfORV2rrV2s9ZpYzq5KFj7GjC7X4MOI/ZgTCi1pms88liqWDM0y/BU6C+KBLOmcFceR2LeTxVKvUHj2JkDYjzZdlbowtk7kzpgP3umMGuxRokEzDLO/3j+JSdnhDGkIb12KDUaSqcZ1ot+xO+MYeH7XDclsl7uy9oUUGqUS/CNQmO5jvyZGstqftHI38yQQgAEEdPDGCmVzEt/oeg/n3MTBKhtPTXMl0xKoSeCN8GkWKMVdcXVOQu/rKjfxqdxL1LLwq0QJ889o8ZfIYA683OsvO7RpduDszKL3Gdd1KBszMJpIkO5CvexV0P6nqZ30+pMpAtPvulkbejoKIPSZyqkp1n/FQExfcRAMXN6Mli0QzszyrVrUo3ELfVg7kN601HDKMs5CZ77lXuKqZIYI2lO2r33fAU0d51JNcCqdiYlYtA0AKNafuNF6K6kF/DfWjRYw/JC8ighy7BSGiZirAnBWfjBEQNG7NE7pTqix5XTosndScpRGObZt+czl4rpVEfX2vlsdROJ9jAFla1LUjymv50cgTrOa0hRiWZux+3KU82kLlDjDja4g/tuXI1caFEGn1RT6iefUJMfpnSl+Zld4SX3SDli55Pcw+wGvdxDFB2zDjbOsd561MVLgJ/UGvfRwN4gJIvbFIX2aKReYlAcgyV4Z6kQ3tIFoQ4W+l0Nk4aBU8bWosIalUMzNIYPPm573zglI385C8c42lfazFosa1INaD1rZVPfXJbirawN1azh84DNEjyZ5jGy3BumHewRbiqdZ0EDVEAo1+K5QoatyWk1sxyDY619tjoZGgqUdgQg7Kjq9ZdtrgPiiBSNpdL2WizW5ECXrWjK9p3nz6vQ+FraosUDv3eCnThrkNe+GIdwjDgHRz5ZqYGqBZ69B1Qr4Ki94PwMwP4IHHtgoohQ9joYyeCa3x57XOIKf4Ctu7AMfJ5rsJjnm2mWLDy2RQdz6N7B3OZL6w69Prmm8j4LDz+LZKtnUTIX2w0NWkTKpraohe9MSDCHnQMG7BD5TVnyyeJfFMfHG2yyxhobh+EOs1bDAxWsMAgxPPwotSoJZyjbDgdnUr3DfYNLnxa9EEctEiAg1lwe10DY7REiuJGFkmaGXlgUZor2lZqEEoUljWqUuh5npOTioSaXNS91IlD7mToQ0HmFYZYmZZlkqlH/vQsT0bcSWrttoYamTFhl2BYxFmHDCsZEab40g3N+bSSYFYLkWsd9TM+MMYr10fgn62WczVsa/XkalbnsHH1lHoDiyarZLtneVGtUzZTX0nZrp1OHjyKN6HZGFmN7IptEZlLUwEk8aAfNY6MZOJacOQI2tb1q49i5HmXebaZpbFF7alO6PBOOMo/GHrGPdFVBaECpjfOvkegSEdG8DTUJc9uZJjUdgXG0F/zvMABDD4yaGEvt0jQEBOLSY0SD3oP99xh8rXHmmP+dcxMHNNos0pVlhRom/O18XiLPPo8UzZB0JaNUzX2N9M8TWh8X04krcW9nqRFu8YQwj752h3k0mVQu1kvomY3bPh8vqj7VTGqLe47u28F4e4RdCKA3ChBDwxNYNGQ1JBYyGjM2Hh3sYxYEq++IBhr6y0KztZ67dAmD9V6U79gaEkvdzFpIbPT7JY0o27r4PqdaetxSIHNSLyxlkCTiyqTo4qywYutu/StswDSsBx9D6+ckSLUVig5lTumbC6V3So2sRz3GuClIVNWxYyXv1/N3jOUya5+5wASTYpJMJUM6l/bcqgDyWqyh0Gbt27MG81bEquvdwkIX+j2mYjxOtaGSKel3ytDa+V46bljGANXmJkzYO8Q0OuZA/zGuKY47ww+69B3bmrWqKb3PoRcDJrcMjWhwFw3W6CSmztLrRsbICpM7Mlj2l8rFv/BxjLqreZZMm5l8HKJQX+Rs1zhiALPWcb6wWjuTAnA8CpPqgd0BGI/GwMAkY4d59AYPpA3XlsEhOLM5PsK1e3nGcRuhR2paJtV/LZhhxB4NLjHiRfDsBZt7g+4OGItsMxwXekSapXPE4GHjzOvBMxyihMXY1nwLntlnHnw9rlxUmXyseYdCfVZMDDHP12s8rjzVTMrSDsVhWwxYA3CyXBmmFr5eJUQQNiUevEFtQdkFw/QAWgZ477VLLHRYeAhjTteIRJuRRvM8NPS4wk3O94T7NP8ek6wqK2Av9F6VbpX5sK1dum+QNpNJKQFT54YwP/euIVh0zQETrjDNHn8b2Cm1PLU2p2EBTu1Kg/zN/eRTOX9qJ6HXXpNefN6A8BMDmE1wcJhmnJnHQZ7BeTSPRpN7jWjvwFNQrf3mcm6puExHMmvcFexUWiMojPPqEd5x7BMQJ6PymaotqTt+MDPTRYzYblDJPxa6Wg/YuJh1jRplwlzNw0fXco67zhXnRZnUiIhZ23sf+KKgxrZv3M3Izm/jGdc6EqVfJxPNRvh+FuW0n8a2Gtl5jd/Z+dyYe0YnNtUemK6A3//fNsrD3hmSj8rUAwNdloKQT9g7VSB4eypi0lX7Ei+hwbWLN/YaXd+2I1rUab8GRTq6t5vD+ivR4BFa7ADcQYUO5txkEZ5aIsxCwwxOyzgLZaEFxkkTsYaY6tjWc+hPeQ/CW97OguDtRNKnmklFCB89x8IgHxANwMM3NGdElRYNmU3p0QIwCWoYE8dZETe4ILBfajA7mBb1EiK4VJVlk+ZuO0XaxlMmpU4Sqk2pbU4N3JmYaBtquU5tX/pifcq8aKPQa2yZWi2EO8I13vQS/mNGhYjciHbcNA55PHIhBKbOEvpeITHWE95gjRPa0eG3qRjnJScNQml2raWkIZhmziEWETWBEJCNyoj9/AzVTnV+1BFnkmcqzKfu8tTqSMpr8CQsHR39ZOysQhwzyCsyw+Hfc/Oj3o+cIwpT3AtAOAJVcm0HJsnqUWGFEnTUiDUm72FiaFs/I84TXT0Jl5/LXB5xOlUrNQIHYHdpLR33wDxTTHvME3PpYGKsfJxTEZUiM0u4yuzBLIgtRveObOdruFsUyOZny6c4YMQGK088EMfSMx12WfLaOVfU6WxCUEjrIeHRGnTiUNGnAqHC/Gwb8ScpTzWTOqLHHpdYwSKw+1mhP18M1mA+gMfjoTGcJucP6B0MMWXYpKFptjk99Nf/AvBZnDKpByiPYH6SotuV9anUTUKg2SyyJBPjsJyRAghbmcJaeaEr1KQZBspnMBB1wBHTPBZ7ZGcMy8JwfKzl7MlLhgDVk69sq5q4je0Z++jnMb5EOa6EuSLex0ZpjwF71DjM+uKECQe0UAZM25w57V4j7JY6jyxcqUxHlfunDiCqZxhR2KJ24ntbq5+5hZvDO+deoem8frRmzRLCwkM9mAmEpxjTAYdr2/Sn0e3AxDEoBgER6NCjRoU17mOFazTYo0XkPwe4/pb3OJ0R2pmBW/peywbC41AJv+2B8X97C9nyIyxcgEljDYFZI/IMEoruZU1pGcD4M56KtwYjufQ0qXKUuXojw2Y1W4EvMKHFHpcwh5Q1Np7tpgz7/T8rnPtqpkTlbFcA1ugWKfHn48zyVDMpw3lblA7gp5L14JoTEd0BdLAd04RROjEZIBI7GrhBqTCcGgK355LZ+YvHiO8RG5SEMBv6KcUf5LolaZR2JmUe6kG1xKQ0BQ2L1p97r99lpkjX9wolo+T31g4jL3bwtoFYe4xzO7MEXrm0Su1A5+PcZlINiM/Xck6TJAymEF825jf+6eDaMRmHgWBxPVeF3t85qz9gwgHmhhA5DxtnSQdUsxMAT+cNR2pdX5D6lzQmhfaCSRnxXRXHTNB5Y5lEUEamF2KF9UxiLZh0xAbT/GwF31jyvGWtW08BaGVMc+yWEb8RtV85gedbUxPiiI7gsTs1Iv+m9imyPETRjHd6YM/SvwC/Tcw0+nIJnsy991MQlAkx0IX16yySKdpamYS28MxvHh/Iw9iz6EEKEjq3WbcO2OGAndvSd+LCYIJJN6NJmGsjI7bTIBoZhQiSDv0p3Od7Z1Jm6SXaQBiV9mV1V2LS3Zu03JvKU82kmE3KPJ/UhywXw7YNuptcQrSry/xkthDaeZIa3wa0WBAjp2GScQHhzXeFkkntUHr1Za8wSuR9ug449ZZSW9OS/UlztfG+JdKUGaD2Xgm7EnvWTzKgz6Kca+Ngbra149MH7HGNcY4VKxlUBT2Th16TlcME55a0MrOscSCNAXDqgq6wpo6vaowPXQYfUNpTsmYGhJ2m9W1owsnkXlXMNdG5XWIPOiyYUKBeX9G/zAQyg9Lceq2DQ0a+Dbha4+4NI1gWEnQjLjzn5xIVeljUVAWDopYDrZdK3oMqQDHYWvNLBvm1uyvsYP5vdN+nO0dk6iRkb2R27USyKjSoEsA/FXzClST0zmBSFE4Hb+MRBzxyN/IBl9hDHbW4Z0jkNfdNpBoCDErOfqgNgM3MNPjiTE/z7iQrjwNK9tjhEXbY+dFAV9i56HycmV0ruyRCSyKHKRlXpIICxuIfndIiwCU8AIEAXHVXVLAMlGTwnx+7ecqZlKXuHLDB6UHOUUa3GB08Yn0Fi51m0kiVrm5TGJiqruZ0kHjkf9XIHjJFXA8EweOyu8KpnUQJ6DVKLUox/oNcq5qSAiXR/lOjNwkj4SQSkRpwuV83YdkG1RKPmDxu5RqjaxT5WiOytW8N1mwO4zYm/f8xJMHCdp2r75yG+gihoe5RMimSlRqhGTD1KZkUTekNpsWVOThUbc4+vWx9K1wbcb2VEtqjvauZmVeDDWpvTRwKmPtsQZ5Talczi0yAsb7BexDMcZK/wM22KS1k5FcIhxPV0DmutOb02DlkaXk2VuhgkZE7VPOKZat4MlqH0XVYTRGk7SPM2s79ZXsaT1FlzhRxyKFZC5nP8xp7WNDsgCOOM/mG10Qba40aB0yuH6lfG3MSstfqFKHj1WBYXLW2Ynk8yZWzqUe4hiXE7v0cvBFr0AevdubOVpLGhBUp2nWuUH8K8Y+pp1ZuOxxwcM9eOy/KWmqoRGiaEcp8m7OSgaecSVkp5bolQ3oFGu8NXDC3TibCn6AmvuxiAf8c9cf3Wa7RlzKMDOGwUI5SZqRaQmZSqjUp1KaGdnWqWIIMz5Vzmkt2UFhypCBjVULfYCjan6VZ5tzjZqFMW83zcftyWweL3G62S8cyHIhLW1/WpJTxcT44hqcOLJFIVw3QpnOG9l9qmSUz4Bpa0oz1YAked67EcqkMWLaTlHWWnoAKm2aYVEvui64d/e3me5l47DBb82i5tDHgiVkUQmMGQvZfPlOLu17TJOmYqnOFnrIdB2lQr4hzue2+SCJLzCaSUKsjFs7gBaWLAp2yyrkyNh5s6uiBC8Y4marN2tOCPorlGGTxJHZfJdeU1HAE5h6NohRMWGGae6MnJjMvYA+zmzGEI6jqFwGTMi+WDRi5ZFO0FIdjcfwAUPuQrsD4AKLb7UzQ9/PSKBcIbVD2PiTmPUKTIsMidKcu23oAYCufqQmdS12/BPcpg8zOFPlehcS4ldUYTpiLGgJfehgAr+MiY32aTmiAybPc3hNCw9OxsPeR19oA2AErJ0ZKnJdKua2X4b7sHUe5lTE+LGRSZEg7lA4MtCtSSNCxMwNx6XCi4QU0WNe+VS073Yi9W7doy8h9qKU+ljyGvbd+QnZCqR3uW88A1rkSwpa6F9tMZ7KoWqY6RpyazcNdnffypU4eqhEq5D4Uf230B+yxwhpbTFjhDixhEkWca+yx9xgvzIT94HtbHcPDqYTH0ZsTVIUeexCoa9DgAMtNZz2IOa886Wy4wIxC2CtnC5nU72HH93TzTFl29vAxDvEv4MMJRzQ4osUWW9HWjFpcObhHbRzznTyyxNItbdDBThOPMszeiFEIdxqwqvAes5GybmsrTSI2NgNa1FjjAmt35ofrZWtUmLCBYVUPQevVMK+qx5cnRlU+8pGP4K1vfSuef/55VFWFD3/4w8Xv0zThH/2jf4RXv/rV2G63ePOb34zf/M3fLK757Gc/i3e84x24f/8+nn32WXznd34nHj169KRNkVLKlrZYmUePJ1Py7JkDelzi4LkkDi6R7LDHDjtc+9QfwMx9PfTE06y9LGlOSuyZKVxzljEB55Tq2S+8juk5PU7tQbkNS69zhW3V4NZw8j19saiEyr6pZkGNRIkY87QZCt46Jh7ym4UQnqL1GsB604LV8Sxj/kubXWZgSzY6vU+1YfZP4dYdTMC4lBcTCEc9Bo4MaGHnktX+tyralduhNjBlWnHNkvWqLOoMkE/CYnJWkqXB7RyDy8sM8dAMKj2W94Cu0fw7xzGDW1nj1/mJ+23XXvteNVDLcm1f49LjkGxUIgNMiUJomK19F/kl2PtsgzENLXYPbajxr0mvUs/VrJ6DPMHsf0wtPDrgO/qaGmZK1buuvccB1zNl6sF8faUu0orurFY1hnjoHjIQUl/VfMVyWRIay0wYgLr3xHgwk0WD1hWK2OO3Yz9PzKQuLy/xute9Dj/+4z+++Ps/+Sf/BD/6oz+KD33oQ/jYxz6GO3fu4Ju/+Zux20Xc8zve8Q781//6X/HzP//z+Nmf/Vl85CMfwbvf/e4nbcpJ0U2rS4OygBn1DzjiEgdHl/m6xh5XfkyXMay9q9P05YnzeNVZIhMYIJBmEn49iluJ7pTqyQzqsPAcZVBLhPfzYVLarnzeDxe2xhUpbEP7AgN/qZlo0Gs5JgxZjLOBaGy1xDBjQZTVv0njgJYKmZQybdUkdfwUulSCpt/p2JFZVCgZGLWvaxhzeoRgUtEGEis7i5dMyhhVddIulTEJ8WlsS4bNHleUeFUFER1nJqXZ1I/OpAbUsyMMNU0KTkvr8qZXZlJK2pSZ6F6KeseZSe3AA3WY5fvRzKQi0ex0UqfurwHnnALG9P8RpRsWyS3/P/UMBHQNaY6JEZEyaHQmZVbewWnMON9z9H/M8mdMag8ew8rEYiy2iwn1NnMbmejAZj72EA/OjNfNO2u5lI4Wyqi41siKeAb6Fq0zqWZmcI8vTwz3vfDCC3jhhRcWf5umCT/yIz+C7/u+78Nf+kt/CQDwr//1v8aXfdmX4cMf/jDe/va34zd+4zfwcz/3c/iVX/kVfMM3fAMA4Md+7MfwLd/yLfjhH/5hPP/880/aJNDn5NplrAOYDdq2sbmJM58fCd+A1iN81C4xYprlFPr00GKlzg+0Udi5mwETATbdTNiajeFMssjYoglB0AiVkSnQvJqhPSWyZE5HlMScRJWMY+l0WrbFfHDs/q3ct0EJyZSekPGZz8ted3pdA+DCo1BWnmuR2ymnsqGUrYRsWYtYtispFKVlSt+RcCrh5dgSnlW7iTI0zbahtizOGe/r/G8DZT2mWe38SraBAoMyJYXZOJYcm2G+pp+l1qViZHsCMyZa1BZTVZlOwpN/jJiSpFZg7jc6j2QIbUkw2uMU7uOaZuSRCg1XKL0lK5Q9sXnqMeFzODgbOuIKdjT6USDoad6XO5QMjzZE7gUyhBXoG2wJluhw0sy7y3apat4rdxMfHLaiY4BmrMnJVcnezBZWgcdYDKhgJ31POIBuTjU6mIfsgD0GbDBiPX9nWTLGGfYkraLQ07kIeIGt+wsaLBp6lyX70vk5X2zXxDGG0wyKkk1bUa8+2sVoJW0RJ58b8zriAJpgHle+oDapT3/60/jMZz6DN7/5zfN3zzzzDF7/+tfjox/9KN7+9rfjox/9KJ599tmZQQHAm9/8ZtR1jY997GP41m/91pN69/s99vtw0H748OH8fnJ5kHhuOPPqlCjD4cQYJsxvSztPwAXqHMCNtgRn6GQTplIDM5/DzUiCSg8xEgBupEpeCgHl+rQoI1VCB5SGfch16ltHaJL9VPlKSSDvURsMx7WS9/ochfmYdIYO51XRsrIf/PamfivzyDa7fK2SjqyV6LUcgxxwzesprHBctW6V3ofiZRKkkT8Dk/jcI0qGrHPDOvN6Klt1ahqPyCsewciVPaHHQcjqcWZNzH5uYxipkfKYZm0za63ZnqWERueLDD5ri7ye698cEWzUjFhangVmm5lkLLmfc7uB2HvxnToHxIiVBn6bL4VgI7tFmWIqQEQ9L4EOEyPCIjbNDKuM6aKVykbaWKiJ1KNowpGppQVT0xpOMbqm0jh7IiWK1ZOdRLRMi69IFsu1T+2cOEjl/RvnNutYqchR+Z6afI7/L+fu+8xnPgMA+LIv+7Li+y/7si+bf/vMZz6DV73qVWUj2hYPHjyYr8nlgx/8IN7//veffG847g4H9KhnkmllmBdXCZ8AsTkoTaiETalWN94EeDrL0w1AyU0N20uGf96nwbCKweumBk43K+T7FiVxzHajRv7qs/U7FhJinjuaYUklnEo4KBWvAdz3Z1yhJMxqKLc8ASYDbgRM7Ite5S1judiondxUlODlxLdAMFISxAwdZg3upo0xITKJ6DzzMzWsGuFssYbBVhPCJV/DBiiC7RCONaqB6HxnOxXlc23h4IEA9vsd2NlDFlY9gtnkCEeVHquKAeS1fpOpO+8N1qeSuv5+jWDm7JseGaPCEglmPe/MZpbvSzDxfMl04DaF6Mo1DjObWjmAtS/GcAT9EUc3Lqj7Cb3eYndGMlm6/vcnu9N0kgp2xtPgbjot9nM9jMSykJG76LHFgD2YhsAOeeU+y1jCaVGYNM972HIr0NWiQ8RV2fEjDXqsipOtA2a2qMgGKwxYw/Cvh3hceSq8+973vvfhve997/z54cOHeM1rXjPLHZavOkymjRCCc9J0hnGOCAlvj2AgrIMSdSPXKdwBnGonOeBTGSHrOKLcxEvaEBbuU/tQfl4tdbDoM9QRoYVtF80Onu1RNU7rJkRoJ6x2Lm0RZi3HI2xRPOzP4ngstdToMmfAA9SvIsbEtIBz2mS2KeXPQDAsZbSqpajGrO91LJRYrhBMidL+CqU2RTsIta5ars12Tc0sznnleKv9z4SNys+qHVzI4Gm17KnqLysEvrCH+b0dQWDK8gyalEwxYSWCQRaA2C4Was9L1ylx45gfEYxH7XYshK3JqFUgs5hCI9qAHQzIU7fMs26a6+Z+PUeSQygJ1wKLoSwhU+Z0uMa1M33L1245GtXhhHqogaZm/WaKVszwnOlftqq42lee2E3h5chlSai4B6PxRlm9TBLLA1kt4Ho198zGl21b1sMBZV8j6Mahup3Su8pXSe3RZXYybxxASajT9k+PbrY/b10c6nF7t4kvMJN67rnnAAC/+7u/i1e/+tXz97/7u7+Lr/3ar52v+b3f+73ivr7v8dnPfna+P5f1eo31en3yfRB+pjeyTwrjKAOBfJ/rUYakBwlyE21RMpwlCE7tM4TB8j3ZZTxrCJnZ8H5lUNofJRzKFJc0KRIUtXspk1KIT+1iS9+R4HewQ7DNzHwsiHFI/JWbdRm10Xq7mAZogpK7OEGHMfq0OCzPXYb2poXvBvleoVSk6/R7hUA5TmrfWKFM97NKdVKzI0SsWjQ1CLVHZmCG462MYPRWrtA4AESSqUyqgrG9DSYPtDR4mwyKOl0JS3KulRlrTFiDUyZ1TptXosa+5322l2tZT06VlOeH6ZxNk+rdthGhqBQm2Ie91A2pN9YA15xpR20xlpEFf+cHXnR+9AqP8lAmNc1Mqnf2Oc772+hBPYN9AH3sAPOus5URNK0GkwwQIAZ4nlm0jxlBGLILrxmuSzHgmHbJc2WUd/RsLM0j+rKVUmGDGhvQC5LPgTM3Y5pMxw20WM1r20bwfHu0fEGZ1Gtf+1o899xz+A//4T/MTOnhw4f42Mc+hr/zd/4OAOAbv/Eb8eKLL+JXf/VX8fVf//UAgF/8xV/EOI54/etf/0TPU+2Eg6wESiEcyO9LRZkH453IrCYAd/w6blASbnq2EWJifrcsqd+kQpMBkKmpLUaLQoTn7ChASP/nEtjyuAqm6tRzorRww29TP8m8WQ64mjeAahwt4GeOrt2YewQzkjFq37wnebzkDkcnl5Z0tkXnQJ3FwRhR0NxxOib8ToFD1dF4PcfocYVzRocWatjUgAnf6inFJEoBT9l1l7C1wWt6xBEuA4JgX6NkiirsBJRsrgP3MTkA1M8CBo9SMEJRu24BJx4Eppd3QXZ11zHSFEa6ntVmlb0Ul+pupQ4KWEO6jiULRfYc5qzb4YgJu5lsmoYVgo896wKl1jqhzPiyLUTZKGQ35k3Hs6FqX0vMKW9OC6ZR7WYSzbEw7U6BORJpY3CAnhBezW1q5xHit6QKj7y2u1CjgqV5U3edR15757vFjBJquzI/WswMKRglQOpW44DK29GAp0kffI560BvAVpRBmKZHXs6OYQ0mBycrrHHl82h3PDqr55bliZnUo0eP8MlPfnL+/OlPfxqf+MQn8ODBA3z5l385/sE/+Af4wR/8QfzxP/7H8drXvhbf//3fj+effx5/+S//ZQDAV33VV+Etb3kL3vWud+FDH/oQjscj3vOe9+Dtb3/7E3v2ZcmYU6RwXjb287sspan2pS613JiEKQi1KVxG6IeEW421S4bl/D2ZFD27lEEpM8oMTiVQtV+p0X9Ja9DD/pRB6RhA6jJtKQJXGXMSYxUm6KyBrV3W41m01JpoBA+5M440sH5Vvq03AOx0HCMHhKpKaC8za51vHc8pvar0e75OmV/WgjlOtM/Fhi1tYtTOtb0UaEb5vUa5JviXQE8LdTKIRDcN4kgEJp2hP59mEJzSSClMN6KENnX/nNPol4QuHZusnaoApvN1DjJU6DAEkQCMqIFGz+McODJ4FRyysBhQGLUMEn0+Y3DL0lCMWmRVoPOGfcueMhFWHFgYkULMSBFZLI6gC/gkuErpjqFGBl2VNHKECGa759r7GLAdW1b7TNNhw7S3I8J5aZifbrqZiZvh3l+ntaanbFudEQbOQ2ksyzuPX6TL2n5BOFgqT8ykPv7xj+ONb3zj/Jm2one+8534l//yX+J7vud7cHl5iXe/+9148cUX8af/9J/Gz/3cz2Gz2cz3/PRP/zTe85734E1vehPqusbb3vY2/OiP/uiTNmVebEAsRtWulPCToSixpmwJBDEgdEM7AW1GzAaxhhFsaiEkynRBOniqrAABAABJREFUv0QZO6NEiUyPDIfEnnDbFiXBVPsY25WdA9QNXG1KdIZQMy2JYZd+Z9+VEVbSvy1MdrvwcbhCnJVFcElhRhKIFSpssZKcaEwnu8eAawywIM0djrh2l9wgsHZ0wQavRI0WEzaocY0BVzjiEozdIZFXOFKN7oR8WGhHyva2aP04j4NCYaqp67pTbWcrz7tG5Fpkbscc06ZhARxHZRTqdEJtlhod+6EhAybpj3N9NSxnAVyCVX8z9SDkt3HkSMmAcxSNaibqmaq2Nh2vHOuWhYYB5WGgtHVGvvNTBsg4wp3Uw2NTLgBsUWHtTzJWHcmgue97ABewNEsb2IkKAMC4qSuPlbQMNBEftcEKa08fa6KWBf2XVlFg667qQERWHcC85ZcYcUDtrgaVw5csZc5FtYqSGtl3ZWjJJQxziHrsNALLPlGDmXfIlHnQiKXB5hlQkXTWLMk9Nqi9rar3jvP9ASSO83MJHFI8O+Ial0WPdoWocr48MZN6wxvegGk6X3lVVfjABz6AD3zgA2evefDgAX7mZ37mSR/92KIOA3yt0ue5nSjxcD0ufSPXcllQk1il15JUSI+/HH/DzasyEe/hBtI+ALFJqQEB5VEJWVPgOGSCkI3YWXI+p00slTzG2vdTzUTJ3c1P4CcagtW0GtJ4VWgZcc8pXKljDLluqa+3Kar90iNNN9BtDcFaj36nhfOqUj+1WK5b1fiNEYzF+gko7XQmFbbUuD8KOmO6TouONRmrQs9qh9XnsR+6ppX0qr2P/T2n6ULuzW7yS0XtsIAKdXYe7wqWFnXtu2ycZ4D+pRAdgn0acdP5ddRyzBJlYHWZxy/qWbac2VW0l61gIQwr8IDKZq6BINpY3L9c2OqSucRuobM5g+xLp4tYtdSatJ7lp47znZGl4van6T0V3n23LU16X6e/S4WTSjsKNYhsVM+u2Wqc5bQpLKEBkBpPxbIknat9aonhkjg/riwxEG7kSAtT2iCUoeXtsvRZx1b7rlBqaRE6D6zpc21rEiJRI3Z1Mh7KoJeYj8LBU7rutoxKocEM00LqZ91azjF+rYdlaZRYtxJ4MiSul16+o+ah43OOVI0LdRDC1fHSdipsl/us854FQO0L9Q0ytkbe69gSJRhTPUjPaaVOhe4h1+h7Ff5sD/PQ+BU6WAyftddWogUImwMFIbFYEwptnRaDH0cfm1HmYyrG8LQWbTUPZ6zRuq7XnVgPTZyhS0Z8p7Ovu5JuDij6orNuPoSVf9IDkMbFek6fwjaEIab8bckKvlz+0DCpCqXbqk4zv1sqhLoopdK4mnN93ZH6CfWRWGg+M546SshHPQUzg1EiAb9Xk7qSEXAxhTHSihrp9S9hkzvy3EcI5smlo5LzRupVqIzPq+V9dlvXsVYbjhEl8/CxVnTOtvbzom89Xn8NTaOk6Wc5A41vVvOwqmBRGWxThkmVsDao5m3WAHJPkAkWtWdm+5Myeq4ZXt9ITerRxfZUKNeCxhwpK4d8x3vZlhVsjfC7GqXdisLUOWFG15JCjlynjdzPMczHvbNPLWx90TsR8r1qKwprV3KNhjpMCCSD8Db7pExQoT8VNsJppBS4KlTosIUd/cJUT2Ub1thggy3uY40Od9Diwq+YsMIAC0Hf4eAH6bRYY0QzQ8jlyil9dW2sLWaN7bS1ZB6ZAGarLMerRQeeCGztb9B6Wy7wwPdFKZJZxgwTrRt0AF5CJHFjK1/CNDNc0w4jdWxZ6KVIL8a+2NVl/zKLVsZXQU83hveJ8WJLdy+Xp5pJ5WHTRasS8HDmemDZ0Mu68mfWofUq5MJ8dTmvmRK+JclOpU21sWTNQSVtJaIq4yxJz1kTmNL3QOl0oe3LWod+1rFA6icJCbFpns5pkmic+WlG3SlpczRD83fAvLcmh3hqxBEryxrUUh8yRHWqVU0n9+f5VscXMh4SH9avdid18FCmR4EBco+2SyFbrUOFB7VRkVBT81JBQf+yHcpwdf0S+s6MuV+oVzXqAKeirfp3aU5YxoWXjneD5XnUtUvG26brzQpKR4Dw/AuomzF51NzbuXaeVMwEXpNfPc6tBphElkyx8vvigFUj+rX33OZyQlNoGJP/yqgnug3xMEue89D6q5p3UGR50ADicJGYCmqhghnhSrp+aGHUVSX3s42N7AnNSRJ1qs6kR73YZ+b0yxT2fHmqmdRNfFi9rHRjLg3LiDL6n4yGhm8gpDpubMj3TC56LS8efJinX2E4dehQHH9AGORV+lUFXwkeJfrsYUVJOTOPAadEl4SGUKfaq3KZUBJOLjd17aXdzBwoetDNtnKyFok347oOCr9a60esXfq6QoPe67NROEpbbi5T0Y8aoa1qn4B8gEH5e05zxO8ynMx116fPOXnwI/md86j15DCH1p/XIRwGNF8g17rCfgoIqfyqmj9tp5eItZ2ZWT5Uk0Uz+qvgNKV78lgqhKeaEd+rFkUtX7U4tpHjRkalyY4hfdaSwbIoOmKTP8tczTtsRdgN7YPOA1av6X8GuzWIs2yn2brEp6xn3S9CXxtcgIG+DPDtMGCNC3S450KIsS8GY/fuhnHAS+AZVwPWmIS0Mx6J9w6I04vN8SEcGuw6c9UhWytHaDuv1xFMi6tFGVQk4g173ukYP6481UzqpqLSqhJ03SCqEajWQwhDszeTGGmsEBCb+9rv43Hxej8lXE6PSrf8TEK2xBRUk8pF+0Kp/g5Kz7QMK+k4qBaUCdpt7TasW6Vg2hOMgNi2aOeejL65TlmCjZMlc8kgaWgXBpHc1kYX9ZZairY9ZM2b+8jrNF/jko0rawU9Yn0QXtul388xqR2C8dxFua7ItNg+IJx61CeMf9kHClAKa5Ns6l7IdqJsf80ZU56k8F4NgSCTOgcGqe0r25yVQTNDJEGnDhUOPuIxNqWUH71Shx1L/rPCRnQoogJAOELYOVJmuyKjDfxg8jtr/9XikmxGrE9G0pmz3NLX1ljhAi060fQAnutgpzs/8k/NHLY7uqceM63fvI+n4h3Xs+0T2qVOXZLGOXKRzLfycXj8E5+0/KFkUirJKqFWF3XVaEi8c0Zr9cajVxLrZtGD8RTuUxLLTbjk0MCiEqj2gyVDHHofiSQJDfse2Z5LKZfPUfhEjc4ZXgTOM6wMZenzKE0fYXapCT3o5hoxHFFPQElWC9P8EhTBDEAYqr7kBHFONlPmlOeA/Wc/bwIilNGfWiFOi645rhU9WFHHLjMp/Y4CiMJ/mlVB+8JnKhSnnpDAKRyd+6SHVXKOlUll+JLXLcHNLEvQo4JRGZZeKkt7J5gCGXuFFgwbx0zeeZps7MOY6YCfWYt9WznzaB0E7cGgct2tEQ1FeIvPykJx6BM8HFFXb+TOsORLQIsNmE0jNE8yqSOOuPYWdeJcfvQ2V67d8UmnpyznFa/jT7DvFBSfYPkfK/SoZg2PPYvDO3RsQyXg2cS3LX+omJRqCNRmyEAovXIDZkKlRDwf006CoNAObQGsTzUpbloNml3yNtQNSomS9+gmzZIsYUuFuiZ5doM4hI9SI4nZldzDuCxK7IzQpyR+4a+N1KOF7dbAXiUwDUqX4xZH0GsvMm7HVtely6zdPR65hNpjnEcijlxZ0hQVHmXhmGsMVbYH0p4DxLxnIq6n9uY5yUXbMwD4HBjJEvCfXqtrkhCeEnZqGgyupiZDJxn2Ozve9MhkpkznxL7EWUvlX9W2eQ+/45yrYKf2LNZBuDLHAqod7qbC+aZFQwufZ/tshQ5rt+7w96NHPEXgqe21EXu3xD1Ehw0mbLGbmRsQotIW9Zx3woLQ96DHX+0aTz3rbgf/3vQoIiSc4+gTEyMFFFdjQIMeF3jGgUbmFJlwwDUsP8sj7DF5e46+j444YOfwnTHpO5icVTMzYYcOdzDiJQAHWAzX2sW+K5wDu80ex6yDdo3R1AnXmNAhzoAjtEeYb5rvJ3jIwz1u59kHPOVMqlp4AafSPaEVPQZcjbysS7UmtddQI6gQOdZqBNNTqTRLgWrfUTvP0hRlxsWiEqsS5CpdT8n7Wq6lFyKJleZpU9hEGaSeJqxHoVfpb5amVfNku4HIn2YarJl0I5ywHKuom/5Hlc9ND5qKTYuaEBLxqfS+JM0rwTcCTpP66TU6//qexFe1bn5eeiYFCtoud+keLRkOXmo/BZERYavZyb1qt7lJe1TmqmurSp9VINL9pEISmaWur6wRUUDIBGcJKoXcn8eU+kpZh42aec0ZUY7gBerhoSmU9TMzPQ9It3pKuZ9QHL+juwI1fBJgdc6InmWEJNpNHc9cHcIsEJqZgYt0jbBQ4B16HDHBsjAayDfAvPB6fw/QhYEIBDUpO98JsIwaNod2GKe1pD8ZXXUHAdQ1HQUDxvyt6mz1rEmVx/LchFeU5almUpnwq+TMASSDeoRlJjVIXSxKWLhRSPgnhGZBgpGlax16EkR1u13alPqdQir8y+dk549afue9BCwocVPSVqmZkjq1vA3KTBqWcy++4/hkrzMlZvlkGD5DoRgyBxI3LTGXTHwTIJxl7zZrVQs7tK1H2ZbbLfkQHFb+rF36nYIKma4KLbThkNnweBJCrLkNyuh4OKauLy06tmQWud2Ej3cIJ5dLaV8tf1k47uoMnDUgFVyIGqhdSud66SgbIPbBUtH1/7iSIVUtdcEurDTYokLrLIbPoDi1DMiGgGQMwE7VCqtPPI+hD5tZi4hEqkbsNZy18dwWKkwSgeFnlkiWNKDCiHamAoQCTUM64go9duidrRgaYm3Y++GPw4xOBEB+bk+Yq/2AEZ+V6zbOsF9aFKDp9KFpntVMQiTCvrdfMp2LU7i/iBwnuKE15oIbjSeikiHpb5y8LMmzKFxEDUthLS5PlUazyzFQ2nX0WQFDlMRB26IEj89QOIabUSEy1kMCS4izS3Wwb8rg7cgNe/GcKGVc9NGZfJnekXYSWtX8dEDMjT5T7SQsWZqOWK4RlesbcRwd5d8Yi1yWnEyUkZMM8ZpzxFjtk9lWSc2c8B1/y5COaiJ0sOF3urE5F2QkS9o255Zt+Zy/71B6E25QumOrl6iuNTLgHOvE9aLjwDnuvR8jyr2kY3zOyQfpN9W4dK3zr85HCGNxdWQvsF9qUIOxd1mbq+VvCTEao+mhweNWzK7DgzksJfI0r2ZrRRzZbr6Yo7N9e07l6Vw5D3p15IywPgcF2OMRzIVo5c+1vvfocT07LZg2ZYxiPIvQVKiwwh2HDwkvMgMh22+st8EFmOslQupVL7VZ13Wl9JcOKY30lIw+AEGKEbezSz31TIpaCuEEIKRBSvHKwPi7ailAual0UXPJq3SnkEh2GODG4328V42niufT2YHP1d/4XiX1vOlyv4CSwPAzJWwlRgqvKcynfwnRRJJMywa2RukGvQR3cqzoJk1jfmYsZLhLsFvJuknuSweNXNdS4RxkmBLps84ltfGlV9ao6BDBtaetZ6sJ92WNSzU7Pe4jM+8p3ct8knwunz0g9oMSEB0fzhUZHnNIapYHtYWqUxADirV/WnKAN9c811ouuge5ZrLAwPXL1KhASPcByukT7XuN2lEmpc5BtjYMVqvT6mS0EL3p4jzlan4S3SUMLmvAzOH8rp3bqxBf6QcX0CHH5ACzFTH3fxwCcsD+xOnliEg0lM0fAGYGVc87zWLAzIsw4MoJnTC707wvJqiyB5PQh3BGobbbpHe0WFETvo1WDTzlTIrQ1F2UZ9Bw0ib/XTeaEqYlyIV1ALE5uEF086o7r24mxgrphtA0No3UzY3PJKFsj7ZVCScLCZoyZfZZmRk1GI19oZdYtkdRc7pAaFJ8hhG5FjXWGHzbrDEVfaG0rcQt20fYf0rqag/MsO3jZKxMGFUyXmJeLCTyiqNnQqzMKGsTnBcyJsJ9RxikPCLg0UruvZb6lvqiWh5zR+ZrJ8QxMhoftUY4TgDBCFXIaGBzy/VMrVwhYDpcMJiXv+9g56dybKhJnSuqTWYYHijXiAp2qpHpfuD6tLonWStMRUz2WgKpEybscQUa7VfCEqxdKuJakHmfRr13VgE06HHEDpe4WVeMYh52ExqMi4JYzDtxnyiMebJTqQjtXaLHcAIxA6UQ0qHys6s4DxUarGddr5l1vC9Fg0eY8AeusRnLpvYEkD4oiGu1r3EPd7CHHecRz26xAo84jANNGteuKOgatT7cuIqiPNVMiul8OljUgabUV2LHBXIqPZWQQ5VeE04lEr0vfz5HhLL2wm1E6ZtMj95wCu1xIytcxv6Fk0EQ3QnLfSQUx0Wt0CTHqkVJ1LKUN4HeTBXouKDPoWSetSmFKPValaC17Qph6ridK4r3UyAovahOtWiF2lQrG9NnJZ5TqmM48yLzUI1bLWzniq7T3N8sEFEIUccJ3kcBgUKBCjSsn4xJNeDMlJVxa+yfOomooMHX0l5SpsTx0zySvI7PVyGQ8C8Zcwlx0wKUDf5wLUpTwJquQAaloabhrFClOsx1YZiBtUwN2Aq6YKg+QSJvrS7pD9PTZnGtbLvpeEfwMBtzmChpjQp5DSo/u62dd+nku1kd5YGw7zE7BkRHUial2iXH0/wHR6wTc2V+DtWgIhdn+f3qi4FJUYLcwg7Q3vs5KsApQc8edtwcGoy4BA0uwRMq+WYtJi/jESgM89yEZFBXiFNdeSYRoSSF7NhfJTZkKGwDN7oaatk31XJIDGhjIyHTzAY6BoP/b0ykcolsnMdYCbZKd3y+2vEyodVCmGjJA+wmaEDnSOd3qQzpL8dC4d9z7dNyjknl/mct4aZSo9Rez7WbhJ4w3wVKpk8movZGDaGgVkuhhfUReqbGrTCmeiXmdE6QZ9GJBYj5JMOkdsg1QFd8XeMKUXIsFEZVYcaKsbEBA9oiNfTymFs+PB5byIRHaj8pdzyP7djjEqUtCohd1zi7GTFi5dreHg0MLFwhwMjGWYfVCpQib551GynLKLEvvEK5DrhWbP9aYqWNa03GXlYYkpt743WTqTXzrNkxHWTbAJzFlW5lNq6ds8GyzWT+7Gkzt6QC4UaGRy+7tJyWp5pJBRQVS0wzCpvqG1Kiph8CbAnoRuEmo2svwYBcloiXEq18XbZRDIh0NJSCtZ1ciCplKjxFAztzt/E5TNK5ZNRUbYK/ZYY0Is49IqGP49PHGVun/JeJu6bB0XFWAro0blkaf5zNSrXIbF9QmxPrV0J8rpBwqqCiR1aoxjrCIGYSfp7cS4eFHMJwm7IkIOXfsyOA2pQULqXGoUIM51PHClhmemQM6rWqsKCu8SwUZmFQ+0RGyDW+X6iPbclu7ksvglalUNijn51tJvcJNaiJJVwWjHLYgXwDAkwE6GZu7hIWuspxDKsSSfLqhLkBQO36TDjAm6ZRyYjwAA+7fihaeVPhPJKRGwg3+X6Of5H+6Og12v9MXmvpj2rs8RCnQRHBJlkG2IEwHM8ctUYXcxsbjjIjtVoY2Lfyb5Zwp9PyVDMpbrbSA7+al0SpBp9uUKC0//B34uHc4EBJ4Ja+Uyaltg7+pgZrtQVQawICVddYE0I3Cj0qPMd+sv1kOnpNLdcBp8RE4bKj1FXJZzs6rUxXqf3NUJlqcufgM/7Ootqglvw596mW7wj3LRn2Vfs9V3R8JwTjVzi1RwSl8qBDwlfKJG+jPbHk9bnEqDj2LEtEnGTmKPcAsQY556qVqCZVITQnBsNnI73Oma7LJcEhQ+zKpLTOXN9R6rxpDdUyWmQswaRYN6HpnHkh3kXcE+a6IvkQ/esiGS1ZlRFj6owEzehaYYS6hZohONOE81Q7zDs12kiIXQVWFUSYaUMdFpjdZXR2W2awpHZjbLPHNSw17alYFa20g0cmRHKn5cIWsyfsR/xybo0vlaeaSS0VEk8l0nrSp0Ip2RgO+b6DSctqW8hEhxt2RGhG6vkElFoAJcQBpaRKgkhYRAmMZkhgf7K0TUiHi11Bj2xbGlDCeqyPUjn7X8Y1hTNFJ9/xSJJztjjaTXIf2Ba9TrVfJdjabi06HjyNlX3hvJAAMqZNUxEp8VTvSqBkdgevm8zpGnYqMddUBXMkeOT3UKu5lnY/TpuqEZk+FO5aYrBN+syidlF4m+g0xHFRprskpyucp7C0fsd1ogId15+iD1mTYhvV8WOPct0BMYbUVglNcj6UMDeuq1i7pjlmiKfwcuxsbCZ3aClJ4zCzJ2ApYerR7UFXOKBCjS02MLtWA3PXtlNvTec6eltM/wrtIcRdZtez42pslK1fNcx4YaPIrBe2fjdu67nEgBErxNEzPPqww4SVnxm8xsaFDx7uPmCPl1BjxHY+isQK57DDBSp07mgSu9PcRtb+3lo7ueissOBS4Z5pYKdyAWFrW6H74oD7QhILx1xyaNWG1KuMuHvWINRGsWRDOCcVKyEioaDkrdJrhrSyxKwSrf6mBF7bnc23fK5+nyUtEgQyG9VItB9qF4M/k5qfujQvuVPfVFSiPlc4Dmy/js+5OpUoEjalZlMh5n9J+2oQGlDIw2V7VQvW7+gFp9KtJm7NEOFSWdI+QtY+HY8ay+OT11WpFZxq4nX6TbWVY3qvkKAWHc9zmhTXrWrcSy+Wc9/nsbJ6G7dDkdhPJ3XpX74fYa7mRuoZnkqNQ/M0YGY9O5iX3ho9JnH2LjOWlEcHUtMq97jtKmonZq1qUYGBHjReTKCTh2EYLZhXwhwpmAdC58D+Z/t5WCHdOnQkTrWY87pNaIdhTCkjvPRaBTdZM1fl4OuwQn3iR3m+PNVMioSzwRGt82UyBb4nITkioJkOkb9P4SuWJYJ4E3HNRvTMeDRuhMQn2yCUUSp8pdCdZrcucfjz2ajpAXkh12kslEq5avhXyBEId3+VlrOn1+NKZq7nivbjnI1Gf2c/WkROOxU6cs7FSu5rEVkSlNkApWAAr5NBzx0iC0nn39UIjVpd/W+yyS3Zcep0r5IPar28VxnFOUhbf8/XqPCkULRqgEuabN5nS21QJONJ4c9ctG7bC5Wfp3sHlkEOWLKpLJXRZ6QGD4sBLKHQ6HBhZInUAOYVJlzMsFkmnae9q8Dzn2IMLQufMVTOQY01Kt/ZcdjnHkzEOrhF2A50P2Byf8e+EDWiMOB5PCtePHmJo+IH79lyCa/EfH/tdjGzERrtuF27nmomxWMPzF1imolQBwuCU0JP1ZNMKkdJa9HvScw1o7gWQlqKr3Pz60YlAVzC9LUNahBVSIuxIoS3uDzzUSQZNruAgQjPImDGUPHLhLZH2GbcyYt1br0uxnQpgeKWzceY1CidM7RdZIDKJDkeHOdMBEMeK4mu9kXHrMapPSUzfYV1eV3WVDje1JSu/fPGn8Nkvi3iTCa1FfUylnlb6inPHEPVpLJ8S1vYEQHBEsrOzO5xL44D+6zBupwLLLxXrZRzy3kmDKrfZUFs6aV1c25VQ15B03RVWOMuOqzReYaHqdh5ZVGkwZwpJtToUc+6xjRDhCOmIvdlbqPOYQTHmsPGMDPJYe6LedtZRNceI2g9G/xpIYhW6LxH5p13b9bRCBA2OOKIBhPuo8IOtUcamea1QdigIjMmocxB/unRGktFNSaWSAVl42xZNc2BRLOAMOBZCw+MtLqbed8fvhiYlEJkABBeN3Z+pg3zOGPauuiXDLtACV3kF5957jptV37lOpagiAxnKINqUbaXfR9SfUAwDdog9Jj3lVyjhIMEmh6HPLhRCbxqhtoe9oPt1nZkTUhhIR0XHStlWDreGc5U6K1KdfH3+obr2I6s8ai2q1qtzl8v9ynzBEwo0LgidfdWgsfNqmOp2rWOnULHtbzP63rJHpTXel7vhBB1TeXx13ng38zM8z7Q9ZXRhizfq6aYoe0Mg1r7Y6dH/oNpsU5tP0DXAOstD92gB6juBWVILejykFMFEV6z0WKyWH26ZaI4BTFjvYfOTPjQzoKqvU5Le2t7usYKK1hc2OA2c8umHusznsWn0S43YEAcElLJvPIfM3qouxfS2LKvhBj110yNSvDZRAJaym7n+/pUMykmTt2gwgo11rgAA/QoNexxBTvPiINa2mJow1BpjkSKBmOVANUuQ+l9DYvIbxButepMoDYRGqeVGdyDSYh35Tq2cY3YoNR2dOND+sSiiUSpJb0k97GPlyiN2DXCwYAxXLyeGqNqcypRU/tTLY/t4mbfpDaSGGVoJScpzdCSOn3wWRobpLFKnIcgNrH1yDzYT4W69B51yuCc8DkNAgKkEDABuI9g9NROmGD2JWnXEoPhGKojhGbCUObGMc+aLsee/dX6OeYsHHfIfToXGT6lZrNBuaZUI8tMVgOGWbIQQM1/jdgTnGt4W+zzhAHXODoAZnNlqV2pOVCAYAkGWrnWYaPJAyUs30TMSawXsxitcYE4zn2DFp2zCJoZajToAOydiTbOXiZcoYdFO9Exg6frVn7gxw4VDo507MBDNcxbb496/mdu4zV6HHEHPe5CExicK3R+6FFjjz3M0d6AxYCOV97PAYwNG9DNGlA1Q6HGmNZoUUuaJUJ8Fu7feJ0MmI59+vmUp5pJxSLnvxVaH/6DGyiZn6ou2FApRav3U1aCKVOodMWNz8Ej4aIUro4UE8L+1SA2PxOUEq4DTqVHfa/t0fqBkimwXXzP63bQjRrtUk2K2gA1APV6VGmTmzjDPnEq6rLmqIGf1PBoJxwQgc3n4ppU+1ENTrWArK2oM4oyNaRr1R6njI39HBGMmd6RGl8HBNGmc8odBPy3lfHRYy1UM+LYcAz5bLVzalFoWDPeq+CwZJtiUc2V41Sl+zimB7k/a5BsN3/PaAPrUflcmRjXbIOSMZWaU27vKITRoDZ1WkJ6Bhb+agn7UIkGdFhjjQ53cBfNrNlY6yz/w3kWwQM9bJ3Q147ecbHujW1YtJId2GjPsezrowsb06w3driAnTs1znPHI+FVq9NC1/wDGEaiWSI5ZjU63EHvIjQzH46uewWrBAwuNf1SNXFqmJW/X6GDpUiqpB09JigFu7k81UwqNvQKK3QeJGaf6KVj3B4oA+ViQdKx4hynVylfNzNQQmfUEuhNqFAc61cpnxnKqQFQCs/ayRKhYd15ink9iWmDMO7TFqVJKAlJ8TolKLyHxJ7tGhBJUJVZ0A2cDIiETKEdMkKO0xamcajNACgTpmoh0dExUiY1oZwnSB2N/OV3qpWSaRxRMmTOHZkzBRJqI5w72u0U+hsRTOpK2sx553woUSUzVQ00Q6Z6Lcd7jRj/JSJ8rmSbC+sFSsavziVk2not0QfWeZN0r+3W/vKgTYWo83wHUY7EqHacxR5jeqrtKdWhlkvEPp2WLTbY4h4e4D5qR2hMkJqgXsXxxPhL2wxtXcameOg6wFRKO/+9wm7u+wSLYephDu41Il/EGndxxDUa39WmkTGPuTGBXOz7AXtci+7YFDSmQY017qPCJUYc0aCH+UA2IBhogdAjavRonJFybw0I2NDmrkWH9cyoAOCIHhOufRRvV55qJkViTK+TweUVizm3+IYDdkXWrRa2ERgzwySs6hhBYqTMSze/4v0sS79x45Ook3gRslIXXxKyrC3o2Vf6PKC0F9D+xDgT1dxGBDNmHJdqjdmWMKZr1cjOozyAIC50IMi2ENat2TNqhAaicFLO1EAin21BCoEN6TMJnjLwAcaAVdhQrQ4ooShlatnOkgk671GNilCoxpopxKpwm0KPuajGwvljuwgv3oet5WcREOS5+lRIUOai7aIWm5mMogacAwo5XB9EGMh4ss2La2ST6lTYvEVA3qyL2jYTSce1kcJohTUqNNjOoHY8Q0ekxRoR5ApkVlrm++Z3FXoMeORxUtQXLGPDdq4jnINqEWp5XCEA9E6FtNhsrHDqD2d7qHKEiON0d4bXlgrbz0MGuX8IL7LO07ONSfvs/Coy13CfN8wlzsNuRY+7wn72h1TRpXFYccCI3ZyG1kwwBnIeT8ZjuTzVTCqW2ugqf5Bx9WbJUAY3gEqsSky4kUkgFArgc/NGzJuSLxIjpGsz8SXDYmnlfsXuM0OhFK/tVaiKL7VxZMN1lnpZx7Xcm2EphX+0X5mwL8FLGWZSmG5Mrwx15nrYVoWZVOPNfc79Vi1OtbfcD16v9hmtb5LrVdPIrxqn83+uqLZE+I9kgjahDUKzWxorpO90Tetzcj/zGCxprgoFq2ap2iF3JJn3hTyLa0o1J4WM+T3h1dCuLPdeeQ6uaUM3a3Aq91fzffH7ae4+wHyHjzgi0qTyhNtwQbB22wgPPkoWy0TILJ5jV8UoMx4q72ttYTW372aSra4XQDg3hJvH8qqL9R87hE4RlX+nDHACHVWGWYOjqGZ31d7vEqxWZSJ7EJ4rTzWT4kY54OBEOEzug6vSlJJZKoQkqgGrhg3jRAnl0GsWh3MnjCocpTCFOjIs2VpUk2LJDhR8T8lTr8vLTqV9LTFeJaQ1pt9HqYNtVU1uQBy3MQIphv12ZUkjGdKLUvpNhdqPQqqZ8eVsCbx+CTJ9XKGbPokyww9YyESG9F12XLidDFnCrFlIehbBpDIEeq5+jsHSuuHY8RkaOqC2VhaOYWY693C6RzYwG92XyHf6/Gwfpls9IcCI0+N5RVtUoIPUYQF6u11R7clcyjNJtF4ccOmMsEOFDRo02IIZJVbOriYAdzCgxwrXuMYBPCqxHDNNMPvylMq1MP3m8xmfskxocIQ5hKxFSGOPBocDmfHDkihpiVirc1TqtDzVTEqVe2M+dgSzBQ/m6O8SZlEJjbYM4HSjZs2oQhmTkqVz1Too/SqsQ+hLJSeV9lW6JzzIZ6vhOTtYqBm0l3pIjLJ8qJqMEsAlLU/Hgtcz3RHbT4afNa4lpsx6M8NUu1DWfpfu1US4vFYZrUKpHA+ODYn147QK9kFhxUru1YwMSy/tg64N/XxOo1JhJMOuhM5IzHWNLNnz8rOVsbBwPrOQop9HhIbNa1XjU4RCY6fuIOxNS9qpMtm859T2GUGl+7kmGvhbdKDdZ6kMDkvVMyUYES7ssUvD+E/bTYUeR9fEVlihQYcaa2xgzgNr0clsd9DmYuaHvRggetgJu0GfavmrfV0uzC0eq8MOawzxuhHWUGGAZTIf0aJGi423vinGn04dPXYYcCi0HNUuG3SY3Bo1LkKYeeVN6dPko8KUTY8vTz2TUldvc0efTiR7LgAuTTIoBkIqRLECFpc4NyEJhMJxSoiVkLCs0vtsDGe6HXW0AEomxesz/KhEi31Vo392Fec1kGsUmsp2tqUlRyal3my08eX4qZsketZDYq/JRzPkpM/n0lYmo5udfcru08qkeD+hKpYlpqFwLO9VzWxJC8xaxxJzygyDhX3uUDJ71cRoq9E4KdV21EEEC8/W5/A6helYj4YIqA1Qx0H3F1NureQ9tSh6OGpRLZhjoUwvj5Gt1QlHXM+6S+VXt1iD6WCXSo+jaxjcWeMMgjXyFMZCmV8w9/cIy9hnPsRrNFhj6yJxh0gTZL56Na7BJLVHHNDjODMq+vcpRGx9rry/08maYCHQ2DqDtO8OaHAF2o0mrBFHgwyoYfkqrO0Xrg3W8/jb+raWHnGNiB6z2kMjq9DhAhMad84YnJk9iYZmMz7N9z6+PNVM6hHiqASVroFY3CqN6gYljEAnA5Xm8wavEJ5TCuOdG2JCfJxmtoHEQ19kPCT02RCvCV3JTJUpaVGHBI7F4NffledoX8mY1COQREKhP+0btzhflOHIpJZsGSxkTCSIWk/WIrPUrUUhu0r+BvkpNcoMAXK8yaQ4DgpTqX2vlu9ygC41QA1+VqbLdUIiTg/Qc4XX5UTBE4JxEbZu03XAqZCkgk7WdFnIFMmYdAx1LQ2IhLtAzD0ZUCcvxm3dQTg9MFQjC3c3jYPCjkCQuRF2AOC5PTFhOnESt3kM/9F8HlJoKjxXN2wnBve16LBChzWqwp+SI7rG5CBfPwNhexxdnzpgmteeQqo2PxMskSyjI29XbO2aI0OcADGiwRG033W4h1N7G8ADSW5ypQeABmu0WKPFxkejB6OghoJCfOHL7UfCy0c+8hG89a1vxfPPP4+qqvDhD394/u14POJ7v/d78dVf/dW4c+cOnn/+efytv/W38D//5/8s6viKr/gKVFVVvH7oh37oiRuvQafMmabEYcmukrWPJcPsSl5demkKmuwgQKKsEq++MpMrYYwyToeEL8fuaH+WoLlsX8p1ZSlfiTOlXgaG6ospaZRgZCiIz9NnKSFS5qXMY1p4ad/yi0XhSmU8+XXEqaam36lX2xJkp/1ipvvrhb+0aeoZTDreeR3qmsmvvE44P+fcs/N96tat1+dnZ4FCNaC8dvMzGqlfNSjdK3TuyHvgnPDBkrWpPP/luqNcbqwLICx4Oqc2l3FlJJVlYll1aCDoF2fYts6oGnHhyM4IhCTpc2xJakfsMc0exaRViprYKw5az+lr8wjRkYNHsturlTkKm5m5exDis/5ayw7Swh50kTDHh6BQ1ODiEPgy5wdfOltV+i2uid9vU55Yk7q8vMTrXvc6fMd3fAe+7du+rfjt6uoKv/Zrv4bv//7vx+te9zr8wR/8Af7+3//7+It/8S/i4x//eHHtBz7wAbzrXe+aP9+7d+9Jm4KXYN19BCMAjFNRQzKhNLUTqWazQUj0GieT4Tsypy3irB2F5lj3hdQDhMRLwjxKvWwLp4oEVV2qVcPYy7XalyVJQ9s2AvgcgtDqibwkZJR078h3ChsqkVYNQxkE20etRolok56nsJLCjyTmNJ5nKTr3L8NOS0wyy3n8jlncSWy3qU61m6nmwKwiCvep1sa1RBd0rUfDA3R8OJc5D59qUnlL69hkqBa4jX0j7uV64Dhcosz80aCcVxbuNY1vosv4PQSjIqNcI4SCm9qjDE3fc37KzCBmJalwPY8Rs/npfLKQJtjYmDO17c8VGkyglzBTBJiNZ8IKLS5gsT9N4aeoZcAEi0g64Bo77PAIR1xjmgVptosOVWthH1FslZhOp+5N/JUjAkQKJRuLPR7CjmJ8RloY78wiNmKPh4iAaKYqWmOSGa4xoU07qEaFC9e37e4VxuQOxAMO2wLLAhjabKz4plUQ5YmZ1AsvvIAXXnhh8bdnnnkGP//zP19898//+T/Hn/pTfwq//du/jS//8i+fv7937x6ee+65J318UVQS1+/UBrEkeS9J9yolLkEnGodC6EXjf1T6zwGatEOpcVQJdm43UBK83Odzhc/X56jDgGpW2akiE0lKviMizQ1QSrAZcjvKM+mQApSMVd/nNi7Za7I9jW3QMVI4L2vSS5DStPAbbVjAqQMB51ltbxo0zDHINrElBplh5CUtSrUO1SjITPSZrIelRbkOTyGw0jY3pnuzlqzCCceV60GDulWzolalc7ekKaowsLQull4Es+h8rvYs2mTOFa5Zrms++3HF9rx5/lmOPI21ijL5yJqrxOBWqB57TCcONk9ixbldUTyF3yyPxYA9aLubHBg07WlCdmbgd5VEcrLvdCShZ+Xt9KJo7W2vf2K470nL5z73OVRVhWeffbb4/od+6Ifwyle+En/yT/5J/NN/+k/R9+e56n6/x8OHD4sXEBtOixKsc+q+yiVLUKDCHAr7aUBlm+rVemjjyYcLLm1UhWNyyX15XMkEXokmpfjsdq5MioxKX8qwtB+5fRluy/YYto+MKjOhmxwL8mvJkUK1RA3UVjtfhvp07dBWdg4aJMx36a+X/HWJgPuY6kqhHF0bS4SJQk/ue5ve57FfgiVHqU9z+umLaYcy81fthfWTQXM8tX9kckvt5tpZEkrOzXN2mDgHjQfMdHrfebJsRRl71sTj2+VSuV5AyOs86TTiv8c4e/NloeZ27gLL5XG2o3zt0r8BB/TYgXnNe1iSJQboLv0zlrvDiAPom2hQo/oul0/n//r6fMrL6jix2+3wvd/7vfjrf/2v4/79+/P3f+/v/T183dd9HR48eID/9J/+E973vvfhd37nd/DP/tk/W6zngx/8IN7//veffE/vJmoqHC5diEAsCoWm8l8lqKpNKaNRWC4nklV4MBcyNBICEoMVTqViblJqJ7lkW5MWJQoK9Wn/9FmEo7RuOlNU6R6NOTrKdYReWqmTrugblNon26j2C36n/VXC1eB0TFU7ycZ9piHid5T4b1tU+1EifYQxJTKwR/L9km2H8wyUc6JatxJirmPV2Lt0DVA6bADluHLcCNtyjJWxKZys/cwa81V6vYiScdMBgu3OsFyNcg0qhKnAEP9yvJhg9j6CoW5AS40dOziiQYNrDBjQYpgtHx0uwAx15wphcxZjphN69Fgt7l4rdKWIHqlfMcBRvsIVrnCJK1zigAMstWzQFt2jnO/WATa1ZWuhW/0Bj1DfcjWbu/3DM78dYTGknTNN68fk46DFjuJYY8KAGj2AR7MmNcwidtlarTGnv72tR5+Wl41JHY9H/LW/9tcwTRN+4id+ovjtve997/z+a77ma9B1Hb7ru74LH/zgB7Fer3NVeN/73lfc8/DhQ7zmNa+ZiQGJPVBCGFmtVoKbHQqWuL1KdUsG/3Hhnir9hdyr73UzZ9jlcapw1gyndM85SEmvO6dx6hLN8IRqi2TaKhnyXgoKS04eqq3qlqdUruOSjeUZPuR3ynzVyUTtkXkOGpQMOdeZtcRsl1Mniawh6qZS4UbHcGk8lODzO72GRRkpYVvWyfqzow5wasMDSgFA9wD7nLVShTa1viXNiG3l36U1vgS3ZycLPa+Wv/KQU2NSjbMwq8H+8STZ6FtoXGU77O/yjqM+wOis0eG8cdbnWItpHEfscYRlO2cklO4/wpxKA/JY5JbEehz8mePCVactnzCc/WWad1w4jDCGqby2FtSl9jZYT6aT+piNkKcenzIlZrRYPhpxubwsTIoM6rd+67fwi7/4i4UWtVRe//rXo+97/Pf//t/xJ/7Enzj5fb1eLzIvhTaUWGUNQ4nECINmVCLm9bqRWK9u8gwRqIS01K5zhVIyJVvWfZuiBGRAHMCnC17bxc2u0jdQ2nAalFI060O6Hghb0wZlNu8RpRNHHhv2mWNKOArSLv2sUrdK5efGQ2G+zMAHGRvVWtS7StuZ26+MagdbM3Q3p7OIMgQVAjJEp0UJsnpVKsRcy7UZgtPncSOz3Vmz0f4pwQRiDaqjCZk9+8oM9crcKNzxc2gGpZODrle9h+1ZcpAoYU5LB2TxPRbxQzepET1W2KLxjIm1Q3LANWp3rWaflnQQexYTpy7ZmXjU3x4jGnd8scMvWjlIZ8QOPfa4xks4YFf0b4tSIOZzqSk/iS3n5SqRVb7cEYz1Mm+/FphPDL6Qe+NULsKDrV+V99eIab7utq7rX3AmRQb1m7/5m/ilX/olvPKVr3zsPZ/4xCdQ1zVe9apXPdGzsm2JGxhYRow5BTzjhzFWS4RKiT03F98T5uEm5KY/x2hUY1Hmp1qgGqIzfEimocRXmVsmkApd1lI3iapm+ob0idCd2gFU+8jaAn+79r+MWUG6J9vg7uA0v9uIUhLnS6FayHudmwzZLgkPJIZ6srAyP9Wesh2TBIbPUkcChfs0zmiJ8KgwwWdqTBG94Oi8ol6o2jaasFnXhBh37bsKcbnwPo49pXvVgLW/O7mObWI9ylQ1j2BGJwi16TOW9kxmbIPPQgXLn2DjYFYf66eZ/2usZvm99YBVc/ymkHZq0SGTikMoykIHddOOBlyhnjWE3rW4Giv02KHHAUdU4HkLjLhaY0CLqUgUEPu/gqYOYt8tt3kFTY1d+TgciwRqp2OnvWSw7Wk82PK9p/uGTMrqHNC61nmU+yIIoHeA0wKa42TgcjyPYFb425QnZlKPHj3CJz/5yfnzpz/9aXziE5/AgwcP8OpXvxp/5a/8Ffzar/0afvZnfxbDMOAzn/kMAODBgwfoug4f/ehH8bGPfQxvfOMbce/ePXz0ox/Fd3/3d+Nv/s2/iVe84hVP1BaFooBS08kQj9py6HpMiTTbg8g0lFBlIkWCSCZ1k6Sv9WRngRVCi6G7Mutkm8gclIDS8K9Mg9eqxkRCtZbvlZFVUp8ytsyIyFD5HGpUqm1kKVnr0jo1RED7u/TivOlyVqLN90txTpnB0k6mY6tQpjLrDIWeY4qnVonTMcjt0OvIpPQcJXVYyVo752qHUzivwelYqVa/BM/lF4symAwD57oVqmQf2J4sSB7Sdzo+Wso5YHrWHpY7ARjnA98BzmI9s14DBI2pHYQ+nEJMEb0T8U7KMgLo651tVGAK1RaDs6ERRxw8n0QwKcKUHZgjfCroUy115X7X85MDCB/dZeV2jtvag8kZ4eN1tiXzRTUzzNGhVVKQwAzKyLDe+08fTK0/NKjMwM6VJ2ZSH//4x/HGN75x/kxb0Tvf+U78wA/8AP7dv/t3AICv/dqvLe77pV/6JbzhDW/Aer3Gv/k3/wY/8AM/gP1+j9e+9rX47u/+7sLmdNuy5DygTIubKNsttOgGflxRRnc7RXW5ZIakBx8ulUwsCbXUMMn0JuiAzgskGLcpZMSU4ZQQ1ghpn23ic9dynfaTLw0SVWhyqSjj4WcsvD9XlIAyoTDT85BBsq5rhMMHE+dCnk9NhJkyGOuTYdG1/M4gVs2akbVlanbUPngvP2vRoGMVDKidcKzpxEKnmNusU7WjkVlRCGLdFOZIYO/CnBvY5zvSbsb+ZURgCf7hPN2EfAwI0M0+VQsy+mmfPt9CMtqjcV8905qM5DPGqEeDDis02DisaH05uFbRumY9+t6bTmjTk2gTLLYvevAAwi90yYLFTcUCnJv5gFltIzOdWwyb2u+evDwxk3rDG96AaTq/PG76DQC+7uu+Dv/5P//nJ33sYuEiVk2DvF2x9ww7UOJUo+0o9ej32eg9pFdeeOcMn1n6Vygm2w1yH7UeMjUSExK7kM5KoqhQDuT7JWN8Jffksc0SuBq3eY0yyXPGcL2H11Xpvdp0loqOZ37PethWjf+KTNol5Ja93YBSo879ILyVV3rOUqKhBZO8JyNs5Jqc7YRCjEJvqr1D2pedRfLaVK0yl7zWeZ32YwODI6kptwimn0MUFK7VkveoPl+fnTVNrsO4x5hU3hdWF098sv/tWL8WtJPkNpXrx95RYwAq8BD1OFZiwoAKlvAodLbGnx2IgNm46GpAkGuQJ0+yast5Gp0RWjZxc0TgERmhmVRzm6PvSyW+r4rvKu9hNX82rW6af5uKu9S5gr/bvAUF0JTeI8a5zhEBaU4+KpBnPK68rC7oL3chTEYtIhO1pSHg9VwUauhWGE5tPEAQgBzUuIRx56JEQ7M9UAOisT+XLHlSw6GzgxLOLn2ncGKAIFbUFsTvqTlp/BdQEkTey+cNcg/rq+Q9XxqzQ1hLi9atWt85LSBDb/rS/pEp3UVkDL+LUyY1yOeD3AuUQgBtToS/GpTuzPoMJd5AgCO5f2RK1LwYz0QtiPFJmg1FNSm2mfNyQEDIatMqQSUrCgVO8n7wtozeFs4pIWFqpncRKbM0tq5JdWspsz1Y0fWqcOE5mDH3JOIX6XE2OGOrsMIWPQ4YsQePZSdjCAjOdCfLFh5M64Dw6RuKGTxgmF8dJj+MkKdNGSRe+VzZ6bXKpkwf2ztLmNDP5NqyDTY4osVdALXbd8iQaA+y2Zh8dGj7WipL2lbt7Ag4OpNZ+XhXLpgOyEdsTCizlnfo0cyBzQ0mt0VRv9VzpJp5xMu2Nicrcrk81UzqHmyT0HaiUqRKv5TQNMaE8Ai1EXVH1jimVr6nPUDd1m8qGaZTO5IOPAmIxvdoHcCpZqFSPttEyZZaIZfJAXGMPBmrah01SjtRJc9ge+kgQNsWCadqW016ZYap79mnBiXxzcx6iXkrk1INTx0J2FYyRTIDrhdlFBRa1CEFCK871s/nHb1uHSsgHEKWNCkNIs5EmQycxJ+EHgjYTJkw71M7otpN1ZNMNRHVwth2hfpUc1EhZ0S5tjSrBEngkoan9lQWhff4IkxIDY3vK5xjdiXOQWl+cHjJCLu9jh60CtBxIiT6QBgmHB1CM2bVuq2onbWDlT+PNhqAqZgAYIcygVKFAY3PB9PgllrZEcOsl1Dva2bmUe56Mk3NJaiFmfjKwkDb+KzvQhPiiq6d2bDHlVwfMxCO/pYmqsUKOxeVJ6xQuQbK9jKxk2apoJb5RcGkLhA2BuB8JHyGEIDYnCoVs5DYkYkp5PckNimF95TILEFjaoM5x5hYstFZbRzqFNDAmAnrZ/uVGefrWSeJsmoOSjDVPqIkQ/+ee2U4UiHGJW04L+UM82mbFTIkY1KYLrtIUxPM48cxURtV530mI9J1QQ2dmoAG5nI95jYDpQaizJTjj4X7OHfKpDm3Ol8K+eW1pPAaiwpeysCoPalNSpMsI7Uvz1ceU0UAyHAV7lRN9xRCPwWXmX+CNhDG6jCJajDP0nFCx8Qin0YnwQYZxjrVozxiNsyjEIAfumpZzCtnPGyb6Ri0qU3u2RZzV0kPuCq1jN4egyGXM5mfBs1W6VNVzBZ7aO2q0WNEBzqQxFwSnKuLmkJjbJxRNbCDQFqEo0aknqXvJACELU1Pvbq5/KFiUix6tHmGxSacGv0VjlPbTIb8blNIlJTRqNut1pftAEp0tNw0lUqA6E5PCVcl40fyTBIEhWceV0j49umZfN6StNymv59PWTK4cwzPeTllIqh9bVGJ48Y016dbP0vvWwTzUS1A416AILJ0IiDcpu1fqps2H7WZ5THM8NgaZZwXvzslYeeL7g9luFq4llSTBEo4k/1RgS/vGf2O9/GgC+1jpEpdLjXWYCoeSupkHpHbYZjX5rmiAhHXktGHcfbjM52KOpI9YZyPfJwQoGikfrZ4q2oWNGpU6J3VW83M8EdBIFtS84w8vlibS+eFel6h9qnBPVSJ8tja5SoyrMQOjuSJx+dGsEHkNAEadL4Gemf0AV5qaYUanwcoT8tTzaRUeuSyofTLjaWu2SqVcZEq3LfkCMHnkNDRW06N2EvSPlC2ifVljUI/q8sx71e7UYYY8zN1SWXcX+1Q+dl5O6jMqM/idlTokhudbSfhU+k424DUUNxLXUBJOHURZ62CDH+UaznWSwZ83nMt7eF36jHHtqvE3chnZdZsD9vEfhxlPHR8+Z3O6VpeOefdiFhzFEC4BtjvrI1Qy2tQrnPVXtkWjjUJKtukjjlaVPvS5+bUYUvwbkYyOJb8jv3RdamF0jtzT9h3DVZYQY361J94HAcjfJY0bm2TCqd2nekuGvUEB+xIfGkxajC65jSiEY3E7rIj1Gl/0r7Hnqzm3pX6aDlCdBgxWLKEVwlFWrtWiGM+2FOOToCcdvzi6OPDXo1eRxzEwXZxJI3dTs4ce48gA0zEyBphnH3MT9SsbitQPdVM6oiwtZCRkMcDJaSlm4UQBg3EhK/IpNR2xU2ubsKEuvTYCt5LTQMIYnwumDJDHxuUmziXS5TEMkNHml+PhRj/hTyLY5PdwVl0rNT2oxqZFs3FRyHhDiJ7An8j0dT0OoRos5SemRT7rFCjMgjW36MkmtwIx/leugpHIeMFwgaVtTTOI8eEtiUVcK5QEtgacUwLxyVvuDsITUrhyUysaSdiOyn5tyiPV2F9a7l+kHoUIeAY87wntpnByjr/qgmq3e0uIlMG15BCrJnhqJ1sJ/eEoDbNzI/Pm1ChRY0OlomcMniLFbbYgu7OvTuLH3DAAUf0DqvxeUtHv+haBGJ/GSMzKjCid7iPli5bD4cZ5gN6HNChwYQ7aNGiQ+fsYEQzayUlkwI4h6ZXmBamlnUFU0dnhY2zzQzeNrPm1uC+36nYDd1D2JIKe1zP0KOtB+uVneBr5w/bScY8rctcRSwS6ugsu4bmjKnRyXzrTjNMqfL5pW3rNuWpZlJcfGQWNO5mBsC/S2mOuCR0Q1JqVYmX9XCD0kuJWkWGfoCSWLEsKdBsHw34NxX+rkwx27G0qBOJwjMkNgppZikZXq9Conpshdru1FbRoSRkfFFOVOakWpRqHaxLCR0Z8zVOHUBUm1o6KoIMXX20dIxyG/Qv1wXXGPtAgk7tiUeVQK7huKgjAH+n8KNrVucjOyoMMEGFdZABKqSZ504ZaovTvUCIcYXIxMJ7h/TiPFC7o9dk3nOqyenc5bWpGjXbNcr1mPs3OZltHDJqYQb4DvWsZ9J2wyPbwzU/28oUTeAckexT+1WI1tp4ALPhqVDM+bbsExMO2KGaLYb2K7USHctSXwpjQ+V/awyIoFmWHSyEWFtei1ZoY9MUI6d59biOzZnk4MzmgIP76dVosXZNqEKHO7CTgif0uMYBV773J4w4eOAyUM89DAsUZ6/2WisX2RjgXDm7vU15qpmUaiwZ6sqbngyG35FgcoJJfEM5jt+BU/ye93BxD/KbtuOmdrMoczjHpCaUTIzPJJE8BzmyKENRjVLHo0rXcTxYt2pUdHVWgEK1wvxSpqCQnRIjHTvep1K5OldUKG19fD4hsk6+4zOVSSkEpuOmMFBIyUHQ+Hw6VKzkd61DwRqtS9uj7ti5//keXcdZ+4Dcr3NF4ULvUTguz09moKqtZzhWs0tkoVD7mRnz0kvXQDCIAKzsOnMQt790BLDXJE9gnoYeE7KGrn3IcHfuo0KAVgYh8jHGce2EOIeXDhzRNh1T0pXQk+zp1fwXiEiwwdsUGkuOrCrZbyN1Vw53BgO3dvauF01+AIfhC8b6La4MsBizFh0sYqt3DTZGmChCCzrds53BnBtnUrWvmvDuA162Qw///1RaxOmWlKCXjHGE/ViUoTHbgEJ2es8S4VetBCiNsGxPg1Mogd8DAXuQUKm0XEqRZf3aB7aRi3JYuE61NA0UVSi0Qen6q5oXGZJu4kbuV3fkRj7T/VvhJy2UwPdYLqw7xqFyzXWa27qX39k+pj1aggpVmFiSprVku5jOCRCnQJMZqLs5/3ITAwEXqkv6tfzG76j18Dl63HjOOLFDrPtermv9t7uwMA3OjTIkFRyWzovSc6NGxHzk+V3hdKzzGmbflDGrxqjjd4HKSeMWDczNJRirWYi4byoccYWHqMEwV0tNtMM0tx2IuSfT1rnQQGhCwh1KZpuhUu4R3T/RnyMsF/q+2P9M4kQGpfuB2quxYD6vL4SRau4BR5Hl4HsxVqw5fnAumDZ2cMZyxAA7P2qPK+zQ42oekwktDrCA5RZ7fBYjOqxxz/WgNdawk4LVrNEhXPabmQ11CBAzkvc2qGY3i3GRWp+Wp5pJqUZAWCVLo1r0N5XilIBlgqUwUtaulurPC1s1BpVKKTFCrueGz/CT1q/S3m2KQlf6UkKlUnGNsv1AMIsMien9unGVAeiLdWg9nLvsqKDajN1fzX+ZPYBERrWTx41NRvKpfS2tjaydqvRNBk5IjqSD6zD3P2tBldyThZFBnrXkGJQha41X41joYY+KGCiRVQZChkp7lDJD9oFMTp1i8lrR8bvpO21TaGEVOqzRosUanUjgmGefsUZwbWXE4O2w6KT4tRTudD7V2pNzPiqz0rGCjF99wwug3XUqxlX3NMcya6sqbKlGj/k+7oI8sqffEeCjM34kgY38hxwto3uTowaDr8VaRrmfNTvNdRiaaD23zOC9lf81hlX53Fgfmrkft02V9FQzKZXiuIFvS7wzY+JiygooCYIafB/XJtVeuCk0wDJvGBI9OrRmZpkJ27mSmQGkLVr4HZmTntbK38lEVeOCtGst16kXH4nYuaJYP/zeLHmro0kJb9m/I46oMM22QdVWbhqf/GwVQFhusu+xfRw7SuSsi8QmQ3QKWyohA0qC2KBkomTCKoHrmlK7ns4FoWhm6+e9uS1KqPV8rEuUufr0Xq6X9UI9LFmbP1dKiNBsFWvcRYcVOjAMtPY6LUB2xA4Mix1wxBHXxfo7YjzZ08pAJkTWFzJ4RVLUZSFryNzTCotnTQoI9EGdNvKYECbOEDYL0ywFwzFL3DJWdFpomxtmXQr+zcH7WDmDUueSCUcMaDBhdG3LwMM4yCf624D2w/AChDOojb9nfJla7E0fV9jvceWpZlLcNFlKp2Jco1TdIb9zA2uUwE2bS6ERPaojL2peq7CaMik1TpPYsI2q2XEBs136LP6eN9cSkWb6JvXeIuEhbMbNp0uGBHHJXsCypFnS4/KePFsTpiq0pRtYbSfj4vs4nI3pazjOylSW4FFlTEta05A+8zptH/uqzh4cI95Hz0CuyQqRIkmdCUg46XyiDE7HgUKJMjt64ikzupa2ajYVdXho0ovMTu16O9j6fITSoSgLNZoVg+2AtFmdJLJGkJ0sQqBrXIeik3mHSsRQ8wrr0btIdcTncMQBexxc6q/QoMcB06xFcl7Znh3CREAasMSg2G7OK4UAClS6l3Tv1KhwxFTsV76/SUPSjCucnyPohVqBdrgGPLq+ES105eAa3WhMy2H48ISDQ3yMHWv80yRu+gHpN5j8+msMWKFHjWsMoFPE5D03JhUu7aGhUkfD/M7Wc4PBZ2pwMeHyi8EmxYkiM1CIQjdAlqQyI1ANKRNd3Xj8XVX4JUOoSp0kRiQ86u7MhUvmQMKSNaYMTfYomVQ2bmtRDVHhNbYzEwwtqsXkutUGljURHVclArldeSOTiJMxKczSzq0oRYnMPDPE+DjY6abv9IlqxlYtNxN+tTfwPYl7XoeqiWj8E+/l2CqT0fRVamfJLvm6JvKY1HK/ElQyYD0rLXsE5iBe/n5Ogw1IqNTKlbFZ32q3aBDYa5wgt34dY5aMbDO9kIFXTGAaxDGvSxVGqAXzuwzJs1213MPx17WiYxlw5LS4tnW9LGmeCvNS0AqYO5IYxbdApB1q0RQ5QTTfRRyiMXo9I5hlL7Q0rhv7y5E1Jmb9tBwco68o+vDRjSVKhUg1S13QZpV50Ufv9b5IQna+PNVMiiXjwSR4mchlexNQnielBlYOO/H3K7knMyktJFbMhJGhAcYwZYeBSq7PRD0zJsIVSpzOFZUoqZWp0VgN2DcVNbSzrWzPkvMDtdM9IpYt/86xW9qknFOFQ62N03y/ElclCCy3AxPKku0Yw8J7FrZVNcVaPqsjSibO7JsyUmUYFcrNqc4pvO6zKA+cpLfp42BvQl58ltqh2EfWQeZEl/MtSgcKMmZNtqzjzjEgcz09X5sk73HFRtYY0ogeLYaCyPFAxPOg75JQlWMitf/Z2WoJ6ue9zE/H4FiFaZdi7s6tzajv9qaL06K7K+NIVri+GG9HOqdtOODgvVmDrvG9j4I5eVhGjgYXzqhM42IOxEBNDn6nRd81/qTD4oielqeaSZ2DFSh95helkyXmkhevEst83dIrS6klYQ1iRMggExJdNBl+Yrs09uMm7UmL3qvQo2o4p/pJea+2kX2kNJq1Hn6nzzmk+4FT20+Weim9j/Je78+MO6TAUota6sNNBDHDfWrvylAQ50zhSULMGtysGj+ZE9eC9l+JJDUpHXMg3P6ByAyx5AKf+8S+ZNvJEiKgMjuZlAZsL7mu69ywsM9kchbV1CEy6/Ec3YitGXx3NOhRwc6tNQ1q8r5ZC03vah3sambdwSwpQ6H16IvjwbYqc+Lvuoe1T3ndlOttmvUMfU6mNZke6BxrnY1cnddsaDEWalvNM2pUzvQfpU4TmDeidsht9PtbTPO8mg9e41fFIYY9DrO2xGS91t/NPAOh2xEWjHAA0+IMZCRsb2N/k4ElylPNpBSWAMqJXPLC4cLVnGfAMpGuUOLtel22V6jmpvCPSs+U2G5iUmpf4fMUz87H1t+2KK6usA6Pil9i2ueKQj+qeSqTonTeIKR0So9LTCoXjo9qFfk5I0J7yDY+1cKW2n8bCZVE/VzdQMmk1J6gaY60qPcmUDJAPo8MvZbrm1QHCT/tI8wcd25N6Jxr/zmG2ZNPYXIiA8qk9KV22Fw01u0uKqzR4AJ30DuYtHe5XOfp6AlYzbNv4+xIQ76tZTVWaNFjMxNJ22U2H0OhIXHuFDHRseEezrRC/y71sRRsxxMBOJsSWMgkea0yId7bCusqBYcKzNZg7QsPSF5px7PH0y3rxQa0HBkjNzZjwuaEbmZ9nbeDRz+ypVEMgByxwhYtNm4rK0docpsVwAiyozNPUiNzcrlNeaqZFDc1JW6F1z5/VXm5cKPzPKmMObMooeAi5/fqnv3/olCyVWL6pAtAmXDO8q0HCy6lAALKDck5WrKHZduKjiPHXedCCUK3UB83OevScVBNRm1ieR2phqf9Ua2DzIXMRAlZdl5okAmdlSXiRsKWiSahNI6rMhwgBAb2f0QEYvN3Pp8ak6bQImO6QJlfMGcr0TFUQs/jR9bYYOVPYDyQGfUnTy5kr9atU0+yOuk8UDs5VC2JzhHsex5X9l+ZEudOPVjJJjXkAThlLnn/Q35fYo7nSuhk9Tx3tlYtGJcHIY7Yuz7ViPYT+fjGmfGcimzsc3jvmijM+Tyix4AazewSBABHFxzuo0OLO04FVNMafZUNsLis2jXbPD63heOfeialLsgqBS6VJbX/tkUhrJvgPn0O27MEPy7BI0jfL12jUNaTlCWPKv0ut1//AmWbdNOoxrlkWF/qD8fkJialcKYyEKC0R2XD9zl4he+137pJtX5lonmcc1t1zeXgzjrVsQRB6zhoWYKCMsPUPtEhhvUpbKm2KjIR3qswn0J7mUktwXzaRzJcyDiYraPyvOUrmMde6+2zfAzAhFpmQx0mli1VintQt6gcuuIBfAZXWf8qMJ3R0vrWuVEhI++PrFGzz2yF/mVRdGHpuTqHeW6jh4RGAZ7SS/Zj1xz9m4DolB6G1TDEoRCsqKmFmKHZKcy9gr60bOERTLzUovJMfZE6V8eGJ2vR7SOS2Jbj87jyVDOpK4Qk3CKSx2dJlxLQEnG7LbEngyLkds5YT0Nk9uhRrFkl+TwBQ7o/S87a3ttCdBXiSAjNuq1ESZdhiWZbWWIktLmxTxeImCv1atM4FiXiLEt1UwOgQV7HIxv9CZFl6FXbB5TzoYf2LcGnFH5yBgik90q49aiMpZg7FoVLl3I+sjB7hMJqOg6qwXIMuXZorwKAh2A2hxIqVG2R6zAzIB1L1ZQ1Hi4TYXWS2GCNCz+8xHJJ3JmlffNIm9DIrEXOAo3Oy6NCVmsjX6NHixEdLjChQ4cGLa4xYECLdo6s0sTLGaLVXI8amsGidETtk5mZ6X7W7/Ic6+cMYyt9MnuSsdoBB0xo0fgYsgfWngEanGuAaI3GmYRpX2wXkzU1Dh6aa9jkI9aDiZMAYEBTuI2NmNDN6+ECwIDr+bkVGhcPzIGiR++MFsg7rf1igPvOMZpshOTfJ7HncEFyAWUjsxpbdahVradUUafvswahW1ElEiDiWSZExnbaL0ikl2xqLJSCecYRM1afS2mTx+BxpUKZdke1B9WCwhh8vp78WYkuGY6OJ+S7m9qqEi21Bo7h0nP1+QppqcbJeeMZVQ1qdKh8Qw5onbQcMZ60LWszKqmrpq4aWrb76XyrZ2GPTMJLjUf7QOKs2sISnKXadi7Vwm8KZ0YuAoJJB8ShEbUzWouMMoLZIrz9ljCKpaJPMvbYwtIKDRixgjkHUODRsVNkgQxc80XqfufaUWHutkU156Ve8Hu1g4b+E5n7zX9xL2vB3E+YMXCApYRqMGILc4ww1/GgRuFYYQxrRFDKEeEmHn1mPkHOI7P3WXpassceGgJgwdaDewiyZh4J+STlqWZSyijOFdVyKIOx3HRfmPfifQ5AzRg3TYy64ICAQjJDVS0kQ2JcUgoJ6LPYnsv0zFzImO6izF5NTefJlsty0dNouRFVIlQo6LZFmdQ5CZXXNTivjVQo+6hMipK11p3nQol0m96b9hgJYFoHDnvRUy4X2JTOL5kD71CtUfun7dJrFG4dEdBlhh5V2ldtnuuADhiKBHDOVOPK0JYyP22vPauGMiizoGjAgrUykpDaAYNV0UPqi6q/5BK6ebgVHJ0A72cQi6EQS0yKMJ9q2BSqMqzP/mRBid8vMXO28iZ6RaFTc1JSc6uc2ViOwt3M2Hv0bteL+KwrxPyPmNDO4gvZHPUe+F1H6dNQoE3WLgJ+0TNzhThij50LzCMO2LsuN/q49TjKfI8YZjHiScpTzaR2KJPKqqmVi0EZijIZdV3ORfFrMhZNurlkDwEoKcYC5/NJOFQT6+S7c4Rx8Od1/peLnMuqR8BG/D7bIu7CNKdXIpKCUotSQsZ+Lm2ivKh4HQkdmZQScRJdtiW3L9e3pA2rZkbTL5kRn6G2KxJ/ZSIZWtT2aJ8z4+MzlmBKalCEtFowKeoGdiTDGkzcQ2RfGRXnnqfr9v4dM4OoM4jaimqEIwAT1bB92d6hgpP2hddTs6ZWvUaFPaY5g4W2gWPLe7nnSlgwDrOziKUKa2zQYI0JHY44oEePR7iC5dteoUaHFh0u8AqYf16FDitn+jFeJqv36OdTrrgTbHQabGApaQ18sjDRHSqHpiocMGGHtdSpGicdQ0yLqtBhmvW6CeOcaom0guPNvctxZ6uW4OusbWanGDJRha855rEvJxcgLFaMOtERJqyq4Mp1dIEeHSo088qgZSi0orBUAT0sY8cOgTRs5/6NHhRg0OMVHqHHEYN7E/bzKuf/tGfxm9Ala7cg3qY81Uwq206WIKYl43rOgrBk2NS/ajtYcpxQqUoZoGpiU7q+Sa8MWan8qBAg61eJ9RTHDgJHG8JG/mr8jkqA52DTDHPpexJQldTzhmRblHAqU87PVViFjErjg9jPOr0yHJPr1H5qsCXHmgRHtQMlxMr4VjAXgBV4RNwKazdSm5OvIfsdBl8PqkdXvgaY1NOIfIs4gI5riO3gnCmzPwfDqRYPuVbtoOoQYayE2kdczzaoEJDhTnV3oAMDwMwRASjzoLw9DmhgR4y3qDF5HrdwmaBmGuNU+aiyp9GGCjwFqfZsf/a7kVPre+OaZY+VQ2LhGlBqlYw4ijUeOp0KDedI602wexZCdd3nNakaGvc233PdKz3R/a+20EiHRk3IeqNaej0zsGgDaZcKmgER1y5CmJ5kNR5FN9NCcS5GoaRpN2FZUZ5qJhWLq1TN1fX03Im7S0UnPBP+K5h0ofUt3ctCCUjz5gGxEEncB5TaX9bigFKKzX3S04E1ewZgEhBPyL2DcGRgGdL1ZHxqQ6pQpuLRv3TGoMuyFt5LaXOS79UOosxNx4glPK0qb18kxGS9uulIwDUGKWuIHCtuRHUEUEcC9fJq5DrTATpYtu41WnRY+Tvrs50Re/SZHpw4l24PbEmM2B4TNuhxidDY2WaOGYm2EticxYFQpva3Rulark4QW2ywwRrAi7Cj0ENrU+hWC8fIjmnIs7+GHc63dkl9jwN67DHgCkDnumYzt5xn0xLQ4tq0nHxmxq+9tTaOdlKT6WtrXKBDhwEH1DDXggbP+pMH141a3MUV1hg8dMHEiLD5MSsej9QwkH8P2/tXCO03oxCqTS3RBo6XCjx6LE+2d+eSTQpNMfs9akw+0gH1ci/okSVsJdcVaUrn33PdaOyhas9r1OiwBQ+6Hxxq7DEtrpGgUFEa3zu2J74I0iIpvKTbhAxJNSZdBCqhLklGHHA9cVezGtxUWL8GsJLxUBpSe4t6hPEaMlMgCDHbqMwqMON4Rl4o9OR7nJOElmyL0czuK/ldgzofB29A+pTHkHXxt6UxtrEJ0IBb9JyVgiVrvZwfnu6rGvCE0juU/eR3FiO0QocOHTbOpFbosHYmZaGRe1yDRxP03toVVg5/jIgzklbyJMtuZulkpllrIuHhlZp3Ls+Trq9sZ1NbGhmyrQtjC5ZVfgM7Ju8Ahl2SwKkNTTWZ2jWfcsxp+4gre1zhiN7d4acTGTvupT0D3qoKPAjevrO6W6wxYUSLwcdRd0rrLTI9DV5XLy4J1rIxCUFcWUwTewpVU6jMY69oSV7DqqVjbuEKFcLZ4CZbFUv8zhl9fFFkRbVCZVKKKCmd1P1bu766dkj76KHYgwtk4bx+WmpfZSzm2M7V+PjyVDOpjItTwsxMStVXLjourKUBUOmGRxick3KW7l1iUnmhcoo4dRnqU4Kunm2UlNR1mQuPhE3r0cDaxy1r1UgUalO7jAbKUtu4Td2sV+tXOJFEeMnBxNoW35yDFc/1RzViaqKPELkMaTNUCI3tJGM2o3rt7GiLNS7QoMUajR8Gt0bnhJMzM2F0u0bYEuw7noHK1Ufma2ZzC9Us1yvXTittY1EmpJq4jqFK8gr3tTOoNWKFDSpYAGbrPSGcrOPO+uJcoVL8sdia2hld4wxicA2Te2E6mWO7d0yab4V+ltWttwbyrb2fA+LIhxD/IjlPsOcDLgGMrjPZOUnqel7NI0NdKrQTFRjYbkUD9Hddwxz3cp1WzqR4Qm6p7Zxb0wQ7qxOWR++7ZYEbOIXxlpiU3qMIj/W18kg3E6uPHqx78JDf5dm0YuMbAtk4z8EXgU3qwl96jDVQumc/QhAptUFQogRKeI1Q0AEmbV8jGI4uVhZuKC1coAyipCaj2bFz7AuJpxpnSaiygZwLiMRrCe7jdSQqNMae0zxUNstalGYaIMMEwuh+HyVmnpm5QhzUSMLN14Iwuxk951EK5tS6d3iCIYktwmngnDzJ+afQwrHli2uDY0FAQl2PL2Bw5j2YA0DrDKnDFmvcxRYrrFBjjRYrbLCaHX4HrP0AicExe3q1MReArQMbUdM3iNBPaLBG53fr2lWCp7YP/Y7r6Jw2SgShgu0L0+RHbLHDBgc0DrWFSftULAtmV/u8dTOz5ZF69n8PYI3e+7LDMMecrVwDDZuPyeKHWeuxHh4w4gjmTZicqa7RuN5q1sA1elxjwB4HBwhXYPBwgwZrNDiixh4DnnWPtD2YOzwLgMwdOAHYY8Alphnu4/obZTQiTZFZu3YILzuGh1DQmUCbcIUNmlmrXPusZbifY829twZDojswkWvv7L/F9Sy8QtYMESCNLVStsJZ7clFbLenAPQAEjxsc0aJ3tOCU4TAguJLfuW5ujiQsy1PNpNQQnLUStUWoQTFLl9XCfbrISNgqLG/+zLCA0thJ+K6W37PUo6p11qz0uvx81s3fVL7S+5WoKXFb6oeOCcdVYT1lUrRxkYFkpxLWp3asqK8qiF0zW/HUQWDyQEz1RzrVpCjt871KvKo98VC/7OyiUm8JZdazY4Q5RzQz2Ne5PrRyD7XGR2d0okXbSrwYf2J/rZ0KFEWPVOue0iuvWZ2/MV27VBTyBAgdW5yNQZF0YYj5y6/QqHhKa+U9tF/NSmFkl8IGpf1wjqAp3841oosEMyoAPQ6w8FXM99nqq2Y9tMMaG/BkpNG1LIaq2pW2riYMaLBysJXPMo81K+aVOM6wYpkbUtEXHXOOSD3PzjT/ljWqYR4Fnsdkz7eEuKfaUGZWcTT7CnTub1xM5ZrhSlIGlR0rMsS9VCr5LfYbHUsqdLCMgKOP8oTqpC7dtbF3q3ms6vnbm8tTzaRISDVP3JIBT21MmTABpfETci2dJZayDpwrKgmppqTPynAS71lyasjt0npze1SeOceMeH/+XqFTMhIey6CZCvReOk1c+Ofs0KH1qkRmEEszk5oGLVqsQF2yRYsDauxRO5uK5U8tlBIgJVT+ViGgUHWe0e/Yrjx2KvRsAWzRYo0tOmzRYoULl2I3aJxJrbDGPZQsPFwAgFPnFH4X8xTgSo+68NzUwvlU6ZafFRbUZ2jJGhfbQAjIiKj5wnXydN7TpNepBqugNl3vD9I7goLG8kmkzS9s7+TOwqBpwt/PNQArrNHiGSfPcOBpg3u4cNbfoBIxdHCtaoMOg7O4vQcSV3jWV9cVYqWucfTRMJP+gGvXotQJhSUEIMscbgla7ezgzMi45iwwnzjCGnbWcI0OkW0vP6OkHdQQL3BzrpJynlVIz1rTkkCj6yuETPuWXqgX83y3xfor+04BQ5nUiAYHNNjgsGiVPC1PNZMi0bMEllbUO0839tL3KoFnuCprUhobQmZAyE29oPIiUHuB2qGUoSgBz5IwPQC1L0v9yRqMStYq/SqzUVd0TZPUyvsGcRosCTjby6SjjKNYShWlWiU/G6Q0ilY24YgRzDowonXIaCgWPe0gncudapvbu0MsyQ6/n6Sf6t2oc89+qEZeMvHB226uDcfZVbtCjWtULsbY9u2xx9EzADDzmdkgaidG1k8GmlK/egm99JrjRyKhMLAawVUz0s+qyavtRD+rA0rr48kIHLjUzDWgEC0zj7fuiD/NLAauj9FHECAYRhvS2megwuS5CEYAj7ytlY9y5ValsGQYUzu6vc7moEGDa/RgiGiLrbTCEgL1Xt8KK9zFK1w7n1wQihisI66xwxE7HBAn14ZQtEXsn4AGLWVRj951zwY1+nncKgSEFyKMwZcR4l3D3Odr1O6wkgUMYy5kacA44wHj3E7azokWKINSgVVpT7Zh8XcV1DbzurEVx302+Hyt0IDHb9i4REIlyL1WN4FY2+t0anlcud1VUj7ykY/grW99K55//nlUVYUPf/jDxe/f/u3fjqqqitdb3vKW4prPfvazeMc73oH79+/j2WefxXd+53fi0aNHT9qUYjAv0ku9tFT1plRxjsDn68io1LCuGC2JGyd/qa7MpDIh1DpV62D/lqTq275YVOvc+Jg9A+BZAA8AfAmAL4UF/b7CXwwE1rx/9Aq7gzL2Ssc7j6Hag3gI4h4WJc/Nbku7hvlrtbBQRSsBezDgs8UGK9xFh2fQ4RVY4RWo8AwiWJnt4Rhq5g32i+8p5JCZBaxDFmnb3cAgcwA4zn93OOIKR1xiwCV6XGMPOqETALNgX9MhzGnCNvRhruuASxxwPW9x1Q7zWlFJV+ea65VrVrOkaH0qbNm6qOaXpdCpQV83jt2deR3Y+BPmJABEPztrS4tpBnVXqLHBCnewxj3cxX2scYEKHQZUrrFc4Qo7XGI/M3izZBik16JDjcZH/4geB/QeHrxDHMPXYoPad+SIDiNW6J18mib8DO7gFdjiwjXkDWoH//a4xhWu8RJ2uMQ4B9bCx4DrShEbW7ujz6CBbrWLIBZDF3B42HPtzh48FpBZ9gjhVQW9smdU6FHJ+B5A65/ao3eIMJkjTumAMiEVtrXoNdzrdh1zrh99fdvVlick6Fo1t+aACkcwiMD8XGvXBluHzl8muO/y8hKve93r8B3f8R34tm/7tsVr3vKWt+Anf/In58/rdRnJ8Y53vAO/8zu/g5//+Z/H8XjE3/7bfxvvfve78TM/8zNP2hwAJVRDDUhtVYwVOIe/snCjK26r33FIqY0s2XXUcYFlKbOFGsCV8CiBH1EuKHWM0GDUpUJNLycEjaMTjCFRwmN/lvBwjZOiRL2Vtq3QOvHrizFRjY+Fba5nKTTGAijHHchSlD2tdlndQBb2dYMGAw7Yz1odPRGZBYTtz1qdasmt3NNgxBo71OjmK032N3N1gxp7ATM62IF+L+HK2U9ZIk2QXW/rwsIg97MGFSwlQ8NqhCcx0fO6boJ4ow06b2TO5l4MZ8N7XBdQjY0bbW0VIkvcyud2Qg8mNx0xYQPz7uNs79E4iW/RYkCPvb+MSR1AR4AOA1o0mNBhjS3olFHPz7deKoxF9+jWV8BxBgkdRK9WQLVG07SoMaA5XrqYsMaAFgdUeAk7XDlz0j1FQr71z4pQXIHB0KF1Tb4mjWBbrgrLn2ftGXz0m1kPg99tO55ChJ4rxhVORgisZo3F2PYwowe3oQks55youLb0UE0mPOrxottnn5n3VFlIjfWbdmbeHSrccVaVaeK58sRM6oUXXsALL7xw4zXr9RrPPffc4m+/8Ru/gZ/7uZ/Dr/zKr+AbvuEbAAA/9mM/hm/5lm/BD//wD+P5559/ovaoxE41PC8yfQGhESlRzobO/HcJk87XqStpna7R15I9SPvD9mWIL0f/Q/qQJW7+pu7jqk1p/JS6kWcmVcvv4bpc4QLtvMVscy5vjWz4zzDlMEMB0S8apCtf2NE/g3UMPmCGgzDq1qiKeSXTJ2FWuIZjDZTjA/ltnMnA4E/rQVhKD0tgqWE+XuFu3c9QSGlVi/WUjy8ozfGnzNq0bY7CeDJu3AO6DvIeKF8VmO9hctl+TK2tQSjOiumUFi3DpzKXwyj95XfWzgF2KEc/60IHf9+LlD2B/o6xCpii1trBmaalxA4rrzChqgwwH6d2vn9CjalqMNUtSuO+zWRooREkvmSj4ZpSD2FaZWI9R84Nm48BdNqIlFGxyxhbFLuo3MekA+GcYuMz+VyNPlcc70wbopclnePfTNvyfTyHLDLnGNRq7TqAe3SaZx2pRtZFZ5cQNuzf7crLYpP65V/+ZbzqVa/CK17xCvy5P/fn8IM/+IN45StfCQD46Ec/imeffXZmUADw5je/GXVd42Mf+xi+9Vu/9aS+/X6P/T4ilx8+fAggFsqV/1V9LefmoixM4qXZBLj4VDob0gsLdUN+p5rNpPYqmW9RnoQLlAtiSStj3dcIgqKSOV1htX066eynRshTciZ0o0wqZzKvF+qxF6GTe+hxxBE99i5nLWmMuahBt4aFjRpsUC5ZLmba6ejd1Dh5vk0hU6IDitplzo2Xtu+ICQcfdSbHnNCJhhsEFLAM36Zj9Dhghx12s/bxuGKuFuGOzmco46xgc9fCvAzX2OEAC0jlsR4kmKdaXBnUzfYTeGnQuON8Sca1DeMs91Nk2iHEoBi9AR0sJPhqhiDNeeKAPfbYYY9r7HGJ3czo6ds2zVaocmxs7gbUs9uKRax16ABcokKPpruHYVih6msMDiYPTYuqblHXDTAcMI3HeYzYJ4ohbEl2dMqrTW2CFYAD6hkqZoSWra0OleuNXM2NXwHoWjP7mTFdY0HqsMJQB2bOGHwk8v7XOVPGo+hEdkHn/Oi+IK0gKsVUavzOgNo/wMoBc4YQDPMaycIbXWWACAtY/b9jUm95y1vwbd/2bXjta1+LT33qU/iH//Af4oUXXsBHP/pRNE2Dz3zmM3jVq15VNqJt8eDBA3zmM59ZrPODH/wg3v/+9598zwFmMkS6igNhPFdpWQ3HLcqJ0ZJtSBHMWaYAyjYf3dBZe8t4sG7FJUme9VeI5LL0wlqqP9ejDJJt38DiHO7KZ+0fJTiODZ8fLugVzNxpR1SfjzG/fTESURcwqUmRI5jJ2r6jUV0PMigL25rJptr2zgkEuj50A++8jSabX7vFzFoyoJ51OhsT26xHjDi6lxjdogl5ZIbF7yavdZrRfsypbuw6Y9wr3/ArmB9e7aSEc6f2U4UAT8edPnjmWjCi1CHIxJiFz4Qkk5j3xZWDtz4whan4dXBBpnG0w5Ik7XAAfdpCW6DPp4oUFM/MdrOaw6cv7KyqagO0DVBPwKpDXx0xDk7mqwp1XQPVhAFHTLXbQMcYnwMO2MMOEFnSppaM9tRMghybWGDWvK2zIzrKDKgQvqzUp0KkOCLyCZLJVFj5KJrvK0dlctZ0W/K+XLi+ab8kfVF6QnoDmOBNBYBzb/uoR4Wdx7JRK4ws6KFXUmQim9vh8eJslC84k3r7298+v//qr/5qfM3XfA2+8iu/Er/8y7+MN73pTZ9Xne973/vw3ve+d/788OFDvOY1r5kJCW1O6lmnudnUwy0bjnPJzERtW7TFAKV3FXBq0M7PqHDKQPh9k+5lmdL9ZGzUDBq5hwtH29uhZFJ0HNiitFVl13LWoe7QoTlYL4y48cnl+Onf/F77ZiQtzq/hdYHW9zOTskIirqBdFBU+MqzFPp1jUpyfeJLaIe2MKIAWI7OK1S7zEm+fZtx+8BRARvZIdEu9i88lk2tBtsFi2j9tQ0bczPnCyN0I5os4FvAN4/I4pjpDGYI2Mtq7DB+RLWY1qNwmVM13qjcra6xcD6nmzwH5jA59WqAtAAwz1Kden5FDvfHx0lk8+u8WymqhvBusqzU2zRpYrYEG6BvrQd03aKYaUwXUdQNCb0MFjDXAU43MatS7/TAYlDKpJZ2dEGvJ1gdfGTwVy7Qoc57hGVnNzJBtdo8OldHXL6Dt1keldR0kwNBz4PByWTJVqGlCER5FGjTIeYdTrdq0rwG1YyGhrVH04NpuYGAqax0RFrfblZfdBf2P/tE/ii/5ki/BJz/5SbzpTW/Cc889h9/7vd8rrun7Hp/97GfP2rHW6/WJ8wVQpi0CSulGUyJxUjjACmnpAHBCKeOsEYTqPoy430/PoMTB+i5QMjp6BtG7ZyvvCbMtEXYgplPlLk3XqHYXdXOld+MG5ZEMZFR0frhAyeiCSZSOGTGWI1rsi3bamAVpVQafcXxIfZFdw2qITTCiwR41jjB33srbNLnWNaKZ9T4+sULjmQ3UK0ndsNmmrdxJIqMMmto32zymejocsAaPPzCZvkUrMEyPPT7nvn7AFuZe0GGPaTbvN6BVhSNgbhqqaVVYJ7HFPBvXM2MbfeUPqLEGM92FcMZ253k8IOBd8wAcsPaxs7FssfFETw3uuXZoDv499rjC7gRFACbQg9FmJHLS2XOv/NuwwzVFHeb1dYF7MIeYNSpYmKtBph1aPIMOd9Bhg2frB1h1HdqLDmhHoBrRVAOavkZTA4exxjidxhHVY40B99B76lnr+R4Drk/WPWRtBGg7JeYEv8J22Br3EbksBl/PeW9PoAY1oMEBj9wR52K+grrXFnYMpAkoahukr2BVQLQUnvkNGYgK8owfvJLPKvbxcFUK5NSejjAkZgJDOSxdldHYaRaxLHMKmXHt4uyItc9kOQ6PLy87k/of/+N/4Pd///fx6le/GgDwjd/4jXjxxRfxq7/6q/j6r/96AMAv/uIvYhxHvP71r3+iupUZZeN8flErUYawZGAk4xphhI4ayhZhx2GdlKg2COLcp3pauY+vMmFplGz0VFy5RjBjPUFVmQHro2s4s5/zPTUrMkk9dlzHj8/WzUgTeQ6spYytxH1M9eV6ceb3eGmOCZKyg9/H7/PslRH7HDMKHRQYNPcgHVF0XWTvKLU9RJ9H0HmimZ8C16UG8CC6EgBT6TKMybwvDpAI0q2JW6sZdiM8qH0vhQMKZLwmawZLe4WiQthOOn9ZItTB+wy5Bwt1kjSxvmm+x8ZAUzOx9ZEDkBkVDKjVNoX/3gotOqyaFk3bApsNquqIqRp8fEY0U4t2WGEYRTeeJozTgKkiXMa20WG8XDssoYm3qOYeRAmw19oWOUoI95UjxV5N8+gQBK3AU4mtDcakVq5BqbNQjBzXQTgQxdjH3CidVJhPv9O+5/1b4XTdhHA6znXomidDZ1wYQKuf6oPnNUAtT8ykHj16hE9+8pPz509/+tP4xCc+gQcPHuDBgwd4//vfj7e97W147rnn8KlPfQrf8z3fgz/2x/4YvvmbvxkA8FVf9VV4y1vegne961340Ic+hOPxiPe85z14+9vf/sSefVzwekgbsMyfScRvQnO50fVQwAvYpN1H5K4iIabZmB4wajMiE1r5vWQe96RuvrQ/igtTYtHA1AYhCaknIRC59Bjb9AxCi7sjbVAnCRb2SY2wSozUOaNK77lIGQuVg3pJ+E9N4jcVzgaly9vl+Qqgq2Q+tKupJqVrhmVpfWSC3Pu7ERMqHNBjdOCvwYhxjv86bVkNJoCygFUekjABs4aqTErbFTkblgq17QwDq0Xg8YWJnrZosXVmxfQ7puvRxM+iDPy0RQEQqbFeoW0edEhYK7y+DmCsXDBNs/V0WKFZAfV2heruXWDYAZPtkrqp0FQjMDRoJqcE4wgMAw7DFbJHZjM7jpTrnTQkYp02rrOMCGiTdju2q3NWtcIWWwwz2WaNPNxiQo/W18w1GKlmybaYFisyshgcSOpwOtqkbWy/QrxkUKpdE31aYlKZIZRwf1lo19S8gHpfvpJAvgVv3N6u9sRM6uMf/zje+MY3zp9pK3rnO9+Jn/iJn8Cv//qv41/9q3+FF198Ec8//zy+6Zu+Cf/4H//jAq776Z/+abznPe/Bm970JtR1jbe97W340R/90SdtykwUL1EScmo5aqNY8tIBYlIVk2Ud1JomxMmdG5TEjcSPk94u1HcPp6fiaqAfIUnVjoAgiFyaHEFqNfm9OkfoWVKqQVkwJl27V2CAJEk78xmrVqSaqloKSPx08XO8VLPi/VmTpZZTg7mgCVwcXHLeYDXrmt1c+yhbIN41/n/tMOEwjyOFARMcmDvM2qN4OvvCkjUQ/cyofiNYZjRvnYzpuWNawhVgJRKx9cL8w7gKeP1KxuvUgmr1Va7t2EjT+VqZlELCS/0kETOnDGYnXPn/G9cM4XJ97wBO2HAoqBgbZW51YIUGR4TT9ejPCbuhAUAdamdTq5lVWSEL2KB2za6r1tjUW1Rdh2q1AroOGAZUk/d2qNDUwHQ8oBp9ZscRaBqsDiOq6YjD7IMLtNhihQE1LuZ1Q72v9j50aHGBC1ADOoL5NWy9rdy1wRweVtigwx2sMKGd1wsdSGp3P7EEWB1G3EeHz6HGCEu4RZ2xm7U0MqkKtPfZSlScASjt4ArvKuyq+QhVmOT+zHZcfU/HIzINCh0ZXua+Mo2KIc7AAUfXGGNN3qY8MZN6wxvegGlaIvdW/v2///ePrePBgwefd+CuFm5plQy4GTkQhOzOKZbsSSagSO8Z8Nmm+nRhUErUxVNDGURAbbSb0CaVmSXbpkbaCCktnULYBw3W3aC0QdH9uJuZlG1B882Kmip59hJ0qmOmjJKLld/rXHATqNTN91F3GN7rmeStwMSz/TyiIQsrpAWEL1GNoYA/CPHFXIWsqRuKn7V+JeYcD2onoR2yB2FH1H7HumKUT+mWTngnnsO4oPPu64TJpplJWZ9aB2Qz8QKUcER/ovWEmFSjMeDKWOQBTFulTGeU93QboBhgmkJVrGfuTRvfFRhU0DiDrOY7OAoB980RNlUDNPJCC5AmVUBVTainCdPoszeOwDCiaVaYRgBVBUw063do3K2dOhLZryXoat3dfwNgckJrocsAV2kLeuCR1a4TbnB0x32zJRpJb9yK13gQTTtrYsa6OXIDGF8VIlWNOLWY+0AZTEDN5T4ms8pZd3StZLeVzLhUiFZBtJffuMZMcGdkl/nxMmj9thr+y26TejlLVkVvD2uUk0OSpRPBuknc1QCZbRaazULvVYbDorCZEnXI9zntIusg8VMmpffdQ6Qr2sr7PMmj3EMY6YAdavQzQadGuASHsY08xuQKpdRW6gPnSyZaX6jC9pMRqgSoJF+1xKVC+GPpGvbzUups0m9aTHvo0OAOLDWSMVNbhzzSwOwSPI8pRicInmn6o0vjG5hB3czXI45o8Qi9S92Ulmlj1TnRLBsmPFXg4RY2NpZXA7jyVj4+Z4u5tvRo5l3AUQybIQU9Gw+eBWXnRk+w7A900TYX/mtYZrsRPTrspkfAeMB6es6Yz24HNDVQOc4wTUB/RDOJ2FlNvglGTPWEdr9G249ohgEd7FykNegevsbgIlaLPe7gAvdwgbu4D8vVuMUBn8Pg2pilIL6PDTqs0ZrXITqssXa4MiygE444+PNijAe0uO9Iyd1ZNCvHdThZT+fn4BR6u03hnlHbuZol6CGcPWD5zJvbMjgk3sASoF3iCkc8umUrn3omlV2OJ/lNDejZOWBK9fA6vidpoLazQymZZE2DRWE+bRcQUieJfzbUZibJonCV2rJWUqe2NS+0rFoz43J4C0XKm/DPGWeATceIvlaWw3lwn7qSSU0nzyth2MwowrGA9o8wpEdRo7O5tI7ei0lqzGN/TmDQzazfVzgVLFTjoNbAa5Xw66ZVKbd25kO5We0O9IrSrAHVXEvZLquvxoS8YqM/EzYurR5haXrNYV4FCGrjIXA0bqDnGiAbNEbF99P8KvcBnx+QNWtmCtIOtQcK6/yHw4QB4BMaHGBWCwAOKkbWD7qz91ONoT8CxxbN8QhUK2NUraUkmpojMI3AGNoVANStkf+2PaIde7RDaI0tVqB/Wu0zZBnKL9DhLloH282qsgYDMMyStHZodIUOF1jVHapmi2qqUE8wxjnVaCd3kfeVba7/0ywYRG5HfmPsqfY2ReBzOBbVrqOodpyRj3L9lNqWfpe1KNIZ/S0/R5+X14Ouy3rWP5mppXfn9ceXp5pJKeGmqllqCeWLA5aLMgleS688um4P6aUOBUq0NEmpMixlTEr0pnSdvhQaGxAaVg+TfrXNqjkxo8RdhHSvrusM3OQzDLZaOxEZUOOICj3i2IF67hOTfdpRcte4xmF2Y12CD9h+Hfsm/caDHSiBG8hXEk1exZSaAc91M9TFDUc4laEB5emrJZbew+ZX10Auk9yXbTu5qPE52lOjw9bJWOMb3wQD6zdPo61doz+1aFnbFWwrywie09Q5YbsEI7b27u5MmKdHHL9iNpXOnThMbDFYxrwIBzAnH7PERaZutUNwPK3/nTMem9MODXp8DgRtY6xr1+QtE2SPGnvPDB/9rtyFoodFWNU4TA0Oux26CaibBlUDoO2AzR2gb1B5eCmGciXWzQWqesC2GzEOI47HI3iWmTlG7J1BGvs0LekV2OIZDwcwcLzBXQy+G1u02GKDDbZYY4279SvQrNbAdoN6GGanjbo/oOrDuabHCpMf1Lia58GcVWqsnCWOPh5kS7QF2o6y9cWcLDUimq8UGIHS1q70MJstNNu9eiJnBqZ7XW3XKpCzhGfwYaZXgCExtylPNZNiMCoJOGEnNc5qwGrWXIBTz60M7SjmOqbv9H4SJDIpbsZztrClos9infoMgj7UWuhMQbtLmfKGGonGIFlp3VPKzJoGQzB/hBGx0bMmsN/j7Owxwk5wrbDGAdMcNK0OHCxqw8kMQhkWT/AcnDBasGgP4BrmNzdiBx7LN51AkkYkj97TDgyu5RHlekJqsLx4MViRY50ZLSXEzISzxsg5o8bIzRyv0dkSXTfM28tShR7ABK2aCa3ChNr1EzOiU78dEHIufBThWmY/67iDS6/cF7QVxho2BmAMKRigQYbWQvb9CntPDHtKBA9zb65gETG0PZiuaemMejTYgR51E1r30L0EIc4+raQKFSxxL3Pj2yy81D/ECntscMAGz6Dtt0DdAkMPHEZUY16NPkpVhaZbYTWs0Q09Nv0OmIAjnsHRswnSFb7DGhfYYoMOG2wAF+NqrLxflgy3wxoX1V1s6i3ai7uo2tacOsikDgdMo5JvszBXzsRX2GD0JzJwd3Tb14gDmGXfQo7pGLNyQecagB6OEesw2yUVXs9CNO3lpCN62jnt5hRE1N6b61myWXHNKf0dYadD36Y81UyKEjM5eMANsX052EsdPUdU83cKk/C7rNbqhKnUftuSIcQ6fQ+Epkimy5IhrXgFk1JgiACHFR73TSmsmqVtMqlenjOBZyINTg6Xs7xrvxTyXO5jwF0knvSaowx59PaNqT7mOTPCTkN7JCRVCU8NyspwMkOC9DlrUZlJZdubwiHlPXEyLxPR2safEIHK/BcsqvbtzZSywR6ot0zFOqOOQ61zlHE7ZVAcw9EZTDPXRmDV8rMbA9uhx0G0KB03Y1h00eZB8NWsFVWzHQ6upa0wgQl59yCoNTghjvGssMcBludxcl20wW68xtDbU9vV2pwpNgOqacBEDSo7eFVOJOsGddOibVZo+xVWGLD2sNnebWqmAXZua1qhqzaYMKKfjJ00vvIb17i6eouu2aJer1HRoaOqzHmjr4FKdwBAt3NzVIlYqxo8MJOphgaMfijJgIiLoot6JCTSNRBrkU9dcoDQ6yiEEkVq0rWQdcM9oU4b2SavReFAtm/EF4kmRUMfEEY+nlmUE6Yqd1f19FwhAQrPpdOy5BygxnnNi7AENZKZ3tSGrLIDQajJRNSGpsZOu77ye0vyc+55pleFxySZ1IjQUC1PmwERjGRfcoAg9KVM5fHF86y53EzyHse8WTs5bgpdhGuLjTQFmHwQJa9aipPieCtBV1hUr1taQ1V6v4c5SJiGESNhbL7xsTPdZ8ABB+zAxDImLdvZTBOY4ZA2osHJOv3juF7sd+unMcAcEzPCUIdwMBkQh25Yu/Y44IhIF8RA8iUXimnuJ+di59pfAw3RpR20nmsxXdcSFNfOqPYnTOoKLQwUO6ISr7fjeMThMKLZt0A9Yo07mFnnALNJDZLEqYv8LnVdoWlatFWFcapxB/d97I6OQFjS4211gW29wUV3H9M0ojqsME4DxmkA8BAtVtjW97G9uIvVeg1sWqCtgVXjqquviGkQzL0C6g7VVKGdandsMWbHTCS9pA7qMWKPHtPsuhAzrsyGc6FgMekBaZXpcFGI/mhsKOnmkpDN/aF0S/e9Ighq1uCa033zRaFJsXBAVcIlcVxiUpSo1aFCDYi8LofPqXcSr5mkzlquqxfqU9htJS+V0LMxX7UUtblkxqVqN59nhyPQfK2HHDAnMaGYcjmqRkMmlTWEGjzY4XSMIW1m2/KCP9U4iLXTgdne8zeNgSshWMt9FjkYppNnKDOpUh2Z4JIJqgu9Yvf5Or034/EVIi+aZSsfvI9wQquHgRw8j9wIhoDawecmU1cuZkygC4GdX8xQAuqPHC86owSEWx6Ex/Zy7VqJM6H2LipoeEFmwBk2D1vf4Ou0AT0Wec+EzjWG1TzT09xqAs/qGlI582gxoQNzhA/zvT32/RH14Yhub1DhNIwYjyMwTb7irf5qmKTR1TwPhiuYhsTxaNBgXW+waTbomjVWXYNpsuRMw1BjHGvU1QXaeoVutUW9XqHqGqCpgBqYqgEYB2AYMRz3GAae+Gvw6TT1Pjv13E87pIRHlRjCYYmKBz8hizuR2p45tFMYUZvjgKAXeT9nO7rSq+ySk4XLauEeLdkpQ9efJiEYUNrJbyp/KJgUibPinkCosdmzC4jNmnP7qdR8lHsnqY91K5ykz1BjfSZulPzVE4+TR0Kgi0tzfuf6lhYIrzHNZ4Id8A2sZ1cEAzLgUivlsWomM6XdiARK4Ssj7tPcNsaiKZP6/9p791jbruo8/Jvrsfc5576MMfaFBBOo0lDKQ0CJY0WtgmLxEI2SQqUU0Za0KLTUpAqkUUXVhjT6taZBTaVWlPzThvSPhJQ/CCoJkSgEU4IhDQW1gdYKFSlJY5sUY997z9mP9Zi/P8b81vzW2Gufcy4YfA/e42rfvc/ea801n+PxjTHHPCkEXYNCrL0mGqg82AJej6w4jgEFjuUca8HM5NsAVvUfqp9I/TRK3trmvDqJVLFgbVbpfYmIGi1KrFGhkvb3SUQt0aDHMvVrn37lpuSAOYo0Ztb3drxemWwt2isaiMIAB812rdaoBhp1Q22yn24t93hfxhhiHEPltjm8G4JsZqiT3WQwGYVUNzwRQwmr5JuzgHDzqxqL1vOga/SYweDINRZNA/RrnD9cAkXq03WH2APVvAaCjWLZjmExW1OWb7wecoGnSLtQ42L9JNR1jaquMJvD9ocWM6ybDl1XYV7O7bf9fYACCgCK1GNth7hu0SyvYt2uUuZ3m8llXII7A2lbl2iS6lGBJxDbgS9mX2eVybI12gZnU1rWMFSD48bxJU9RHsOXkgoc77bw13nXgpLyS64jJl1YYnzS+RFOR2daSKlGqHCMOvemOpQDp8KNzHJKY1YtnAvbQz9Ti3eK1LlomSByzTloXPC+Pozj4YmZUxCiMv8jWISfCWq6ZLkXpkA3qokJCfp4tC0+uqdEwB5m4CHY6hgleU2MAvc4v5//zqdzUebqHcMnEe/V8vSl16mVxahRto3t4nfbYEzeSyt/AaBGA4vtMsjKcmPwKHRjNAuMcwoSxmWyXZ8NjkSLgL1bSrvGlieS+B9b5Vk5yIrJOtWbDIdw0P7QH/Sd5OsUHrWZVWIfc5j/qU8h5tYygpcGKTYwvxjnUs6lza28dmqvtdTalXx5vVkR6/UaMfRo+xW6tkOMEWVfoSxKVEUFVAGhGEPdRVGgjBVijChCgRACyrpGXc2wv38eZVmhLAqExDXKGTCvI2IE6qpGKEqgroFZyEIqNkC7Rt816LoWCxCsZVIp1oH25iIpZcaO2Y8W/nINSxxiIR6cEhGW5c+UNDvd2ITAKo0Fx4FjQVheoV/WIM+h8TpX+E+vgftb10T+NvczBRXT1zHY6gkhpHSox8EBY7OUf2vY5XEWif89uu/VpPW/R/eaElosx6DDHNyg5ZFJ+XBmtVi8Z0mfp1ZAbnOujf1dpjKos9J9PY7SCamezAdQocQe5kMk4ErgGd9O75ObClJQTXzK9+ODHHywAxmmHs2uY6HPaCfK0es0RRWFpBdSlXw+TkhxMRbD5z6BPYw/zPF9GoRCfVmtR2bkGLebOSwI5dKRzr+zZTz1UoHMntu0mMfQMudfgTwjLQgkj1pelzZX6sTqrGYNYuo55kwo0ue8/2c8Jvns4F6enZ8WERFjj67r0McWTbdG23Xptx4oaoQUqm7pk6JZRQFAUSDEAkVXoggliqJAVc1R1TNU87kJqBCANsG0JbduBJT1HlAUQFki0BHcR6ALQARi7NHHDm3kHGaq2AIZ5CtT349XMxMvEfBbJ9snoyT0S/XD3FHkQ61fYJwFnePOssZ9Pa3gKwX3PlZIw+g3SF3UilvhCQL3EVZhJ9EZDmTGpo51drg/9MNrplOwhn7mwvW5sUiqeXptf8o/o/uMWI4KCc8kWX/vhVEIk0LBtFRzzFvW7hLAAiHBfvQFlUOw/lHSe+MA4wFAhT3U2E/76ivMMcM+ruAA17DGEi3i0G8KyQFjZ6u3uGhtmA0Qh+/UIiSz1PJyf9CnMD5MISJvSdC+4WeWybqpBcFUW1zUSnQya7JiXdAsZwHLRkGLxjRT69cDrNM42J6mFnE4NkG1XNatT/eSAhjZuoe8AZSprtj/c9iG3mbodxX2OsePC2pRjTtHflmOujrt1jMf1rXRuJsvys59OodzoJDiMRBWny4dOmiB7RXWqJJNYVsIAE3UxCwVFTpY+A7VpxYxrrBaLdHHHk3Xoe3lMJgioosdUM5Q9AHocgRgX8/QlWXCjiugqlHO56hmNaqDuUycDqHvUA0NDBaIUZZAWZl5WUbgiLDhHD3sFOCua9H1DBPfsxEqKhShQhEroD8Phqdw/dpuxRKHAA7R40iElM0pS9F0gBZMMcSXznsSjxnUdanrkZlpNNcpo4gZbsK5zflHGitrNk8VSmabGIxFq+8JETihmi4ZuUp5dQYCm5o8yVtKQb7v5V2FlLdSeI3CKHymD6LQ51J39ALHW22l+z4Lj7EVAGmjWjEZPtSU/wXUwW46/jxBVBFzsflq7KFKAbm2P98Sj7ZYo0oRWep49RbRcRTdZ0JPHk6bskypmfZJryzSdwrJaoCAWk8acNPLNSpwfN18G3WOKRbvHdVURMyPZjF0EU2Cu2zRquWisGjAOIgnv/TwDLWVmR/QZsSURlxMvLbRlMVu86ZLlkAYBRPx4Arms1MLt0gs1oBLbo/IYRBZeNqBf7ZZtsY+asyxhzrl+0viBwb4rdEiYt3NEGNA15ulFIi+pbLb2KMIAQgRXbDQhBgiUASEqkJRVSjqCvWeWVKoq7HJ2XdAKx7Hus7h5gWSmUYvZIVQ1CjKiLJsEYqIIvbpzoC6ZB7EErGvEfsO69ZU1T4plcz2AfhtBnZVg4hD5NOSl8g+qbyBdhpq5/rUNeF5o843uM8q7BShgvyuPjBFJvS0hNPQmRZSdMaR2SgO7yEr4HTO/Cl4yg/elNWjlhX/JrPV9CJKXOxTpPX3+7zYzojNAVR4RidVbkOGGUy75T4aouIHSeu3Q/eKpBXaCTkVSsxh2cUO0GKNBkvMRCc6rWA6jlhXtp2LaYqY+j/DXhyzOLSWc0CtzICMuXtYi2Xw+WxXh800MYV77931CnVwvpIhr5LmTP+hhzA5xkC2LvVZua850/K9+Zjy3IdqFfq6HyekPHVJyHYpXNsLKWYGrwdQU8eC/h1TL6liEO6qhzIwZGE/jxkOMMc5HKDGHgrUiXUzUalhKEVroPS2Q2E69OhCAEqg73NGPPqhiqpGWVeY7++jms1MCA0raGZCai2nytY1oEIqKQsDnFlZsEvdhFFS7hAC5mW2/MuuRN+16NorqZbrtLbM5wsYQKjqp/n3zCphpNxRejFAgb4g7+/lWFhJ42M+/DVT88LPJc6fyv3O+a4b/v13p6EzLaQ0PJbaZ4V84qx3DPPlO4cMi4xFSY9d4GTQ49bJyCigFvI9fzuHPNgaqsBIs+juKWEBDzOE5HQGAAvPNS0rY9DAWBAq5GZn9RgrsI2axpbo3ehwlPovgrnI9sFj5So8GTdjjn3s4TzC4JWyzZaWcGcPS+xjloRdL/XSI8bV6mQfT8GeSL/z5NgZgAOcg0Wt2aFpBSKWaQ/JHJalwMbHtjqusR6WNscUyJociZu86S9SHxIVDdZbA3Mo+P1ufCALPO7fO8BYOSHkwZ1c2kdkfRlSGwtCzhPNN9FjAcsvx82ddkBeRISdctUNZUUYIqXrQOec+idIATmkhkKT0X4VetRoMEMHzU14DnaqkiU8CmixwFWsUGMPduJvmeof0eBIEq72opCU2MN5nMMM+5jjSXhKyv1wkJh1RIclIlbocIQG5xK8uYBFT5aYl3sp+OEARWEj1BTcbpzHNhSFBU8UBeZ7e9jb20O9t4eiqgB/GniM4++KAkx7ZAPUp8kUga5HESsUocT5S+fyLKkroDD/1+C/6Vr0qyVmq2tY9VfQxqtY4/+hxQIlgAPMMYcdq9PD4v4MOutxmMbkCnLCZ85jCi7P73ICrMz7uG69C8OvUwpFRR+YB5L7ryIMylvBIO8FNs+vuh5l9kwLqSnHry48aor8nUzSW1SqBQBj6I2DTVN1AVvseq4Kn0FGp3XogZRQJS92ZXpTjsuMBYeUYAZJALTgUe1kuqpdB/c3ncwe/qKzmZF5DcxvFUAw0FiMMQbLA51Z5zp5UZZJVy8EdhpDXV5I6RiwrfpOypZUSIcXVMlashgpO0TBwgPMFd+nvmW82BjWmHqxjylAPBRG4USBRSiPZaoSpFqpjjEFlFqz/vA5kvrZPLRLAdG73yycwg6dt/cuMXob25y3ItdHn+OhHLjv2U6FHxX+VjuWqXPtHCpmQyBbJcTcI2KOHpnd9smG1TrauJeDF5TnW9VD9Js9l2do8ZAUKioRMW3WLVHVOdy/TWqdZjcJISCEgKIsUVUV6tpgv1BVJoTSHZMqVYxACEDfW3KL2JuA6iNiH4EYEEKBqp5b8AWQ/FgVUKSZEwPQLi0SMZjKY1GzK/RYIyAO0GmbWs3wmxbGjxYArmJsQZEXUWhp7Wn5qKWvfEjXsPYAMC1suEY0S8uRe77fI/WEEVLaWWQ8U6RQHLVZJWqvGrVHTZeaCoMyuGuaYQYcHA4Cd/LT2iIzosbCxbhGtvK0HeNAC7bKhpU4NEM5GXRQIEeRUT+j9dUldtqiAw+AUHvR9uZ0qR/K9NQiLQs7aG7sYbNd+av0b41VslzGSUfZ7zqZydDZb8rclUyAVOnIgzoJKfaUWYcWVWhgT5/E1pRlRjjDf0ftTwWDCoQy9ekSY6tX4cOpCKjoftMQXi5WIDMY324/h30UIn1TGgwEMDFwN/S9RjFOkc4zL9RVK2ZZGhWmAnMNRnyq6mSW9Wo4O8C+MfvL/JgtVoiJ7VKZY3vMErVURXNcQoUWNRoXMGQ2JKHdOKwoo6IsTEiV5SAg1l0Aoq1IposqARQhoKwqlIUFZmBWmyABgL61V6VpiBOt1xhSL7UN0LZouxbo4rAIEro47vmiAg7mQKyArgZ62rEleJpa3iFn59hWaWZyOzcjN6+l18MY7/lr0vfc0uCV9hb5UNepDe0dMoqk3x3J/ZwPU5v5l8h8kaHxX8MY0XhC+KS40NX5R0ajfgx12tFJDYxhGt30S6HhwyV5PzUTZcL6DDI6LvR+4tXJb6phKCRpp72ukq4Yh42efJH5s86q8edJY0vYLIM+2WVBoDeGEI9FhQWBtCjQoAYTW0asscQaa1zDNSxwhFVyWrNOysT8mEw57hUy1d9sEzJ3dZlWbuVlttujG+qvuq5aCGTs25i1Xq8BE1PWkCoykHu8FaLBCGpB6tykP8GXqeT9WxwzterIdNR/F6TNCuFwfLwWq+Ojn7dBstSwaZcAZDgUAExYayK1QIkV2hR83sMyMa7TrAYK2EEZPOCyRJlyxncosEAAT3Rj4LWtjIiQEkYBlquhSLDjHHWYoypnKIpyCKAIsQX6CESzoIoE9RVFgbooUJQl+qpCEdsUos6e7BBXi41UgGh7oO/Rdx36tkXfdShiSKZI5EMR2hahKBBCCXTpWI1FBEIFhA7oGqBv0cY1+hhRDCM5toR5fD0hfyo9Cu8RQlZ+p9lCsgILV/amW4RITS/lHGJzGwfnsJ67p7yWvHMp93i/1nF0poUU4TdqYsqoCdfogHFA6TciY6BA4T26qFfy0mhCTiGFctQ/puTNamrkZEJr91v2V0QUWA4DS62I9VAYDRgLWRVSpPydwXqqyVTCjqidLlNSn33MBojmKo6wxApX8SgOU15sps8ZHweSX8q0veVC68G7us0DVoDxYTbGfQKPMqSlz9F2qiA5DXnhwnI0w4hqo3rPVDkq8NR3yPHhPGTOtG1CShUhIAsp7VsVUOqDU4WHz5yCWXzwhO8H34fKzEyoxfQ8xqSplWzpjLpBdQMsuDpvICnTQRgki+ibpwwdhzDvnolbC5GxXH6WNT0mBmsjVKJGjX1UxR7qYoayLEzYxJisnh7ogFAEFNEEk232LRAqE1IhNgh9awER6IHYo1sss+CRmvZ9j269RtPZpuK9am4QXjoVOADoQ2vCsixtv1XXA+vGQt7rFujWiH2DprdjSvLxG/0wHnkt90nMj3kThQD5lPKjFmMrxwupKataffPkR0sYrOi3dXCu8zBWDYdXfxT9mVrmaehMCylaRITccvrFscNZtQr+rdookEPHC2RhQEaiA89raTl4vxIXKBky4T4Oog6MX/wKD21jrsq0PPObuo6fydBqVzI3gdrhCRoH12GJK7DsyxYm0WCNQxxhjRWu4AquYYkjrLHA5lEBbJ+OhQYZaMZlE0jjqThP2a6RvAwtVqAPbQnLg6aBDioAPcNVnx2hjTUy5Kd1VasKwGS7TkNaBsci+0bGUIf2kwZNKKkgVmGkMJ3OAVV61B+wjdTfptYXhaJGgKmlbrkz+HTmgAipDjzAcIly+A7JvzQfgnpqGQVLqluMIFRPIYk988XNR36+x4xiBLoOfRfR9xELMOQi0/6p2afOfOFQXUjadQl0DLfJHCLAfNJU1iqUmDqjt0eO6qMfSP3BU8Q5x31QTNOmwD75Gy2iBYBHMIbr1DBQ3ynnD+vSIx+JowFNp6EzLaQa5MgU1YR1IbGT1e+ji13L4v1T0V4eroM8Rx2GZJgKE/mIGa/5K7ykFoe+gO2CC9i8XuEuL7DGsBhhkhyybG3rE0BjvoQm+Z8OcYjVkKxljQW6AUbS+qlvRrV91o0MyvwZBWZuKvIoAisrIjvb4xC6oVrb1OZCWqv0dfBvXUzjQIQx3KWQsVLc8lnL8AETOudYZx9EUmBzDkS5z/uOqHhMQaseWp6C+UhaJokCylu+OjfzTp8AzTmiFiD3KKkGXcCyF4Y0xtUgwgA7BFHB51wrBjywPjEJOMK91v60K2sbVgkAIe0UHN6RrC1Y8ENMHp9oAqrrerR9hz72yDvRbI1sO7cq1zcg9tY/sYgIwyCl5yGaD6u32cY0wWFYj/mUalulnXvGGNalQs6xixMvD+nqyQnsd4/qLOVFhV2FFOQ+Wu8UdKyn8tjTIhzAGRdSS2xaTFnDGwsJMjTtfFLAuANpllJQUbeZMlHVx8WFaaHTmSnQpWu5+jIMRNIowVKutYljeyKC/KaCViEozYJA0gickGpZDEvNchWYpmZbL+0eOxrAGFOPJdZYpH9fwzUs0eGq9CNhPg8bsc5si1q6s/S8KsUuzRzgx0XJM5EsJ0GDFVocoR/gAw+hKmnf+OAGzhn+Hif+Du4e7cspKuWdY0UFRuupSVuju8cHW5C0jcpwtK+3WVInWRnqg/Df+XpYXanYMBc30GExtMWiRbc/s4ClUC1wLnmQaDMDYWMUCSjaAYoxhR+VsDDuspih7xvE2AGoUAagLFpUdYFytolJhABUZYk+BHR9QFWWKMrSoLe+B1ZrdJiB5z+t2jXWzRqr1RIxanBRQIcVimPYbYsefYwI6wpFyfFNPrIyVQawAIx1Y74ymN+4QgXL+dKCmwtmKakuiXNRhRPDzoGxAsa5xh5RiO4A4ywqVKQoZL6GHEXIkHYq8N4657xTHqo0c++noTMtpBT6ItavWiSnj7dg/OLx02xK+9ymmaqQ4nVA1kwixs+eghrV8tnUuuNoEvogAIWnFFDQ38lwKxSJIYTUTwWYdDY/n+HcFpZgn3ocYoFDLHGIftjdrlDnlE+HAioLKYoehizT0W05LJS4ydPaa3tDWuSDFqcSxXq4z1tHrJO3WDYBlLFv0/eljp3va0+cIxSoCqPp/JqCbHt3j87dbVCYWvn6LH43Nf+nNNuxEkSLyQTUHHuDmAppNq8S0MfjG3XeR3mGKWBF2h5udhMh52JiNDhTI+iRbNM3FfoY0fUtutik3wKKWKLvC3RdRNlGoKDHukfVB8RoByuOoiAiTEAFG4W+jYjBvGtN06RktZv9VZSVZbAAgLJEFYGqqC2JbQxADClxLVCUJYq6QuDMKor83LZFaJu00SKfpG1zhKpi7gUgr3lVfj3iw6ZlK3M8D3R+aeYe/kZfl569dlL+Pd7LpFRclx6d2DZ/p+jMCyntbAormpskP2ieKSlE5oXKlJDy/q2pzcErIGU2G9/rhZQy96m6MijBB0t4IacwzBTTMR+QaWPqbyHcl/tmDEZy4+4hlriK1aBJ+cmpsB7bQuuJcMIMelwIXeAmprxPinarjUcHS7QZR/2hkINvayWtCO43tawUEtT+VKHGvlGYkNedtNg4N7dpniqkPFzqhRQX/HHPVHhX/Qosb6oO25gG/QeWgbzEDBbMcoBzCEMvEswzAdKnLefM+KEMkjB4jTL5pXgWLUCviw+IJvzFMBnbKwhYuHaPLtp3JsIM+utjEjRFD1QZEC37YGmTNjhAtGCGogBigb7lebhrdE2HvhtfTwFRVBWKUAABYHL1ChUQIhALhK5A7HuTSVWBstLNwaUJKADoOqBtUcM2qwMMZLJ9UZnP5QS82p8UUuw5zlOvnKsLQPmm8hfyJvMkZh+XX2/kAX4bhfI5ls+1RqX9OH/jFJ1pITWlRfOzapCY+LxNc/Xl9u53hnm27h3Ikzcih2OqMNPPvJ6wEweP9fP7E6bqcxwRa84WA3Nll/ACLJMJJArrZcresE4QG9OurAA8iux05f4vDadWiG+eNmLWSW+mmKI/gtF71icG5Vg/Edwc9wHHkgtsKkpIx6LFtkQ5x9OUQODc4ueTSMdeU8Fo/Y67V+/vcX0LVoUctVvtO1WUSGRgHFeznEpcwBwHOI859rCHS2DP2jN62ElRduDGEg+Do0KGl2GmfGg6s1SUKb/+eDWMiXBfN4jztayNfKLYNLFsZkg8jpLAip3teTpugACLEmTmiT6t4iICCd6LaUNwKBTEDSlyECagulkSWHMEHCXxX8AyGXLzegk93IJMny/ueaLQIsLDsO9tpEoUiZY/I/O4N0rnokYWTinzqixpNhzSccFhns60kGJneKtH/QeephBvlkWasqRUG/XWm5/2/THX0dpT81e1eXWY05+mE0mvp3YU5DPkO31WLWKZBytMnWZL57SxoG5gPN6CU4tN4TXq1/nkYUYoVWB4Rv6XTwvO1mzOj0HLjno593t5qEKFvo6fQnMeptv2nfqg1LqemktT5fn6+ZBcCikf1OPvU2tIf4snvHq5188/3R7hrSmvZVcYj7VGYlbixWvTONn4290Mo/ABQIVcNwawuBHb/g7okZMg61zjcR5dui8M7ethG2E5X/rYousL9F03+H76mPfUxSjvMZrvqI8IfY8+9uj7HrHtZIyjlRNge6x0zjIzRbCeCH1ELCIQIwJxwiA9EdIrwrKx9xGxAzpZfZYvP8i48iDLbpib2TIdb+MIGO9ZZBnKW5R3ekWP1hOTBqgPdSoITeeSKtKqoOh60vlwGjrzQorvHrefElLeb8NB9pqkhnDyOxVGava2GAupgE2riYETGh6q2rjWi5ONwIC2SQMUyIDm0lY1q0nnYfqZZXgz0WXIN0vfbCeBGhNP/Wji0bmq8J46/PXE4RohZbA2cMeId7G93VBaZmBHMJgRaLAAz+dVxs/FQ21R+1ADTNgvumBJ/E6JfTcVRHCSNavjzdc15P0lCrVp30UYXKMChD5WVVAoPHSBe5iSe+5U8HFLhZ7Mq6Cawi+cj1mpYgriVWKUK8QUFtQNmfc6tDhCgyXWOIJttc3tLJAztFgG+HXqrwqWkfLaMH481nCeFBqzuLOFQPbaYQWL9AS6ZGl0OJfEW42yAdp+jRBymNCqbNEV1pouxgHGK5IgqWK0bOQAuq5Du1a1sEQoAjBDikY1cDKEZBWVEpLQRUP8AGRnFnuY+EYHtB1wtEa3WqFpVzhK+w7tCJOAFmWCMk1NXOAIdph8HrfzyO6FAjbHDtL47qVrGJHHsVUUiJYW0YYSec+V399EHsR3dXt4SwoYK0uqwOq8Pw2daSHlSU3SKSHVYxP7JzyoHeuDE7aRMi8gaylT9aIWzWnPqeqf7Z+rjFGFFJ9TY3NiqHVInJm169Okpx5LjFu1msyQx1Fa2zQiL6B44nCNAnuwQ66rwX8RJvqWLd+MldI+noI++Z0fU1/v6yVaOtsgY8jvCqHxxSgrTfipcB+DSsgwqLyoBaLaMBlF7a7xVlTnrudnHw2pVhXHkQqIn8e2HaFNmQs6rPCnqb1dYmQRCxyla3Jef40kI9xj9bbtqB0W6FJPt8kCiwkSrlAjpP1F4wMVO3A3Ftl3xBwRZVK/rAf6PqILPVbNYsgu0cUOfYiIlRtdP8ixN/it0y/7xBwCUCWhFPKsG83bGICesHovV6RyEYG+QVyvgdUSq/4IKyyxStlc8jGH9uqTBdWgHVANzhUKbyYz5pzh+xxjnqDIAgUXx7uQMgjzaUi7X4dTvI5rkvxE/dTX64sifVsJKUputar87xxYhcaAcadPZY2YIoVhtKyA8aRVC0B1M7UpvHXlGZ8XUiyf2wS1rQr5NCB0w91GDZh61FuACgWoJq66IAWVMn5izhrJt5eE1Ax5MyLrtklkGEEWFIXqJnw6ZdmooNJrvp5FQcbqLS0/rnyW1yLJJJgBwO+7A8bWnvo1qWVSO1ZBQ8ZSyL05LCDXVS0vfubc07mkY6Hjr0pQhoR6lEl3DngY3Ka7huWOWGAxYmLAZqRn/ttWW5sOLhn3nnmrevCEsmK0FvvRP8sDaEdCxuFbwEK/uy5iHZaW+iiWdmBugIWbR9PvAxuvI9snYdKPn2xWUUiNGc+EkSCIAZaPzwvBDpYLMKkg6zXieoVVf4Qllil7yzpFspqAWg9p0UxIMZ2uKgIzZKuJvnBaTPRVqZBhlRQtYOok3sfcfw3Ga4D3skzPR1NPjaBeH03L658QlhQZJc1Uj/17fxEZwD5y51E4kUl4uI+DMbVLupJrkMpkNJtqpBQWzDxBxkSmrtTKPSROrgUyE6FgIHPmpjrVcjowmCGmZJ9mPenRI2zTfuoPv4eHE9czexVSPfKeC+632MMMFaoUTWhiipm5241pT7JNjOUAYjYpxCJCYyjZfxoaCxwf2koB64WMJ71GrcXefad9p4LdO5UppIjxU9HQjBsBOSea+g70jCnep9ov9wJq23XeE45hP6mQmjq/ipYUx5vrgnVaIIL+pA4hld+MmBbrwnWm85Rz0iw7ikoV/TH1TZ/G3KyoDocImKMQ8dmmMuzQCssdwr5ZoULRU3zPUBYRsWzRlTbDyhVQlAWKao6yLFGWBWazCiFmwRRjtCPoZdGHJJjW6woxdijLCmXfo1ilA3xiAXRroIvACuiT1det8qru1iug61F2BVZdg2W3xiG+igZHWOHRBGWXOMISayyxSi2NCfjz65aC6RzGWdB1zdbI8/AIWUAdYYzEsMwlbD5qmInC++oPyzGe47FXwTLHeD8W/z4NWgWccSGlklpDitUi8AtxKjxbfTEeCuHgbWN+6h/QgdPrVYvRJJ9ad+8Xm7IMVEtV5qmakgIZ4/viMDk5kTXgoJP7PMzlmTYjB3kdBWSetONd8uoEZzhEHC0Lizy0bcaqn1HX3dzi6evq6+xpygo67tpt7Z6CkHVs1MJTB7MGULAsD1/SouKi51zhc3S8aVF52M8/u534rnNlQ+oTXXlqjZHsOXGUkYVlcG4EKcvvFxyXNB5voE9QZ4cStpHV9kYV6FLOdbO4uWPOyuApUTG9TEAUKGKDPpYIfTqYvohAYVBcCAFlUaEsChRlAXSa0wKD5TQkqE3/W/xECmuINM8sAANtC7RA3wFNv0bXd2hXfa5XswT6DkVXYR0bLLDECkfJp7dCiwIdAvLJvPn0uKn5rdtJiKrQwo7I22A4llRklcdQwedoMAUSrXEVUBogpUqc5xf6O5VxzWyhW0ROojMtpLwjThnlcQxpSoh4zXMKCpkqkwPCVw7d3TSB6ROgI5mQGg9phLtePyu0xTors+J1fuA906R2pHqs9t1JRDjvALm/IjRLhgW5m8N7m8ju0GOBPolJyxxIwOLGI7XQ1MpVeJl2ngqBtXxeIGu6qn1GbAoOMgIfwEPLH8jjqgEYhZRFiFGd25pz0pcd5F4tX9EAkpZHSAiweb8H4ILUxZhUAR6qqcpQJv3LUsZaEugmCbl6gPFKnENAjRbXkDPiq6i3J3MrOvolqriPMs6BrgVCBObVMO/rok5HeiBBhOMVVDDEHEAIBcqqRF0GzEqu8QJ2am9v5a/X6NqIZg0s1tewbtdYrZHqo6EzJTos0OAqVvga2gT32algRfJM2Yr1lioFDfucc4mMn8oQeZAGV3Feev+SfqaQovVOvxY/c63z2TqX2EJuRymR0RVaUBRa2/AUT9cN2X/84x/HD/3QD+FpT3saQgj49V//9dHvPETMv975zncO13zXd33Xxu/veMc7rrcqQyQcX1ysjI6bu7/35DsKEF3kQGYWfHlNWE1t3qvHfGgdWB6j0DQaTf8mQ/ERc2QcivGrQKGp7jVj/Y6T0h/j7DM2aFQahafe32G6b7xQNO8TfQQWwN5jDYsU5OclbGtm9i3YdxYrRphkG3ls+3qcslzEhOAIhU31ibZNLdN+4h6W7ceLdazdi+Kb46jwsFrFqgB5FEDHWTcLb9NQNWSZTEZf7BsGfGh/qJWo6Xf0yPIjWBTj4cZ3/UZ5fh2NUQPbE7XAGlexTMBXgyU6rLBAg2vgyNs/+svML5VPZ47oUaOLHZr+KkJs0rUWyVd3PWboUMNObqrKEvWsxl5ZYl6WqOsaVVWhTJ9nsxp7dY1ZXaOu63ROVQT6BoiMQO3Q9Q3WzRFWzQqrdo0VrmGJK1jhESzwKI7wCK7iYVzDFRxiiWtocYg+jZ9tIy4RE0g+Q4U6ZefYXP/Kb6b44Mx9p8KCPJEvpi3zllMSwziARRNeSK9z6XUhfe9/5+tAnrHv7j0NXbcldXh4iBe84AX423/7b+PVr371xu8PPPDA6O8PfehDeMMb3oDXvOY1o+9/7ud+Dj/+4z8+/H3hwoXrrcqw6KYGj/AINQoVJLooCveumgXkHgoRouIK82l9PCarGi/rq3VgHQts1k2ZlndS8lrV5j0E2EnZFGIqaHu5R5kGMNbw+Rv7ARiHR6tmzA3DOX1LC+6GypGFmpMAiKmUkDw05pHYLqQUttW+CO6aKYhQdW8gQyC89jhbToXUFLP1ELPWUdNkaTLPKetfmfdJ7Ynu85RDWvuJf09B4YRuGc6ggT1aNxVUFD5cJw2252fT/ld0Qn2pWdHqsYLFAO6hSSBgPv0sg8p2dUwrnWmZTNGpkkjqgLhAGWYoUNshhzGiinZHESIQzFIqQrAjNWJEVVWprgFlVaIsCszKEnVVo6ysZ0IfgWi+q4iIPvbo+hZtu0TTNVj3DVY4EuWsBc+DYpTiIommKrUFqdZZvbHvLKt8P8whrkflC7omlS96Zdwr2ooIqGIUkYXbPkzgUOlXeFfXBO/xVj4DPA7SZ5+pZxtdt5B65StfiVe+8pVbf798+fLo7w984AN46Utfimc961mj7y9cuLBx7TZarVZYrXIinitXrmxcUyA3foYMQ3ihwOtIZNyqiauQUn9UdO+QMjkY5zBmLj3y+VUcbGVSTASTXcK5Dlyw6pDnBGilvBKZOSi+DEwnK2UbKMAixgxF/R7aF2xDh8xYFIZgYEiRWmAH3EVElDBn+9j56y2OYhDnJxPrwquVyevi/FYAiCrQe4xxdypItIxUaVKNlgmJ/cJVBUvxf4VzlDyw6qE6jpmfb0BWVg7lWj7Dow19qjcd8Sspo0QOuGBOOf5GZkXIqITt42sGz4tC42zBlXSc/AJ72Et2he37s/XAVWon1lbYBz2gQAvzjM7BjOfzskRZFSjq0o6JrypgPkv+pA7lbIYYCpSrFWZFhVlRoTyYIxTJQ1rOYDkBpVOaHmg6dGvzQXUrCyppYfufLD3yMp0bYHlcqIhFNENLMkqwhziw/iq18gg9GqxxNPyiKIdCduQTlZQwNbeo1PMaVVrp45pjbBXpPk6dD9uElPqi9mA8sp6ozza6brjveuihhx7Cb/zGb+ANb3jDxm/veMc78OQnPxkvfOEL8c53vhNt206UYHTPPffg0qVLw+vpT3/68JsKCXbeAWxx0ISdTby8aeyDLvTVu1e2GsaavHcuko6Dyny5+kx9NjUdjV5cT3zH7wnx6bOmggym3n29FA5k+bqZbxw8wAwRGczr3D9Nmpufw7N/+alL+uaYleo4qWWMie+8dar3alu1zdvtt+3EezTwoHQvtZpyLsOxQPPwm0KDhG783GK7/Tz0UGGFzWf59QBkeFph3+ieoWXqfFcri5FkhP0ogLSMnGi2TqBWOYylzukFehyhSyeZWWh2O0DDeb6xtuM5wiCeCkUo3atAKO0wwlBVCGUFFAUQCoSiQJWOny/LCmVZoahqhPkcYVYj1BVCXQF1DdQzuw/m8kLsErzdpn/c+cRdUGuscYQGC7SDdWVZ+mzs7HQASyQ2S+8V6rQpPtmDaf/hWCGaQmSUX03NDz8358hW07n0zpeH+vzvB3LvgZTB6/YwVtJPQ9/UwIlf/uVfxoULFzZgwb//9/8+XvSiF+Hmm2/GJz/5SbztbW/DAw88gF/4hV+YLOdtb3sb3vrWtw5/X7lyBU9/+tOz8xPZEtABoN+HxEFh+KNGrizld4/7A9Mwitcy/f2dvCsjY51pKqsmBOTF7hc9tVOFYHSy8Tdu3lwjMzktb8qXhInf+VlDl7kYaE3x+dnxbmCLMYmQ2m8tszzmEWvEoS3ZKRvBrOsBXORLRLGFfFtnyHVULV2Vj6mX+j+A8Xh/PUKKpFaK7hsDxvtQyAyoSPE6woJKaoEXyGPrrVsP5/A3Wsqc61PBNQDtlRwM4UPHte90b95MrlG4UOHAFsbY1Ec8A3AupRwuUaFFhwo9FlggZ5LIVlWJHrOU9aJHiznWsK0N+6mtBnCNI8vsvCnLtV6jLGAh55iB+dtRz4B5Bcysh0MfENsOIQCz2QxVUaMsKqCaAVWFcOBBzNTzjXkqjfG2WOPaIJTWab/TEgu0WCOiQYtrCQTPM9P+t+/K0UiS9mCHyy8STlGgT/3FNQDk9Qnkdcz5oHCfZo8p5V4GwLB1VPzPA7gF09tm1F/ZIcOBqpT5ebrdLBnTN1VI/ft//+/xute9Dnt7e6PvVeA8//nPx2w2w9/5O38H99xzD+bzuS8G8/l88vsVDJqI2HRGU9P3lg+wPapEB4sLjvezbF8Lr9keuufoJGEdVIMBMrR2KN9reLhG8qhvCsjWTIFxUlqF7LixlEKI5bFfaCn1GE+4ldzvtXffFgqpZbJ/jEEGlEnMGLPsh/JOEgYFKsxwLi3ZDsu0qVHrogx/Jn9PaWhsExl3JZ+pgHjmrZon/yZEofODTEF9PZw3xPiDXM/60pmsJ6JCruOzAjJkzPFukIXxnrt/aqz0N2V/2r619AX9Z5w3wHicyZC8X0v9pL4PdB1VANYJ5DMIyw6zXAvsp75QUoMWS/TJ07lCwBJ7OIcKszTbTNkphlXBQ83XKPoZig5SoxJo08jFxj63PULsEPoeRQcUVYkwmwF7e0BZ2UspAuijwX/lDOgD+j5ijS7ZfUss8cggsDqsYGHlpuZRqAVE1NhHDTsXgCdsmTCywCMA6BHQwdLPBnTYQ3YFULDr1gDyC/IOwnrA2KLX5qjCXMCUKbWclEdScVV0BnKNWnWqVB83Rz1904TUf/kv/wX3338/fu3Xfu3Ea++44w60bYs//MM/xPd8z/ec+hlcVOx4LtYaGa5QAaHWxHGQieL+CkkRfvGkZa3kb/qGdCFPQYIqFPg976ncZ5L3N0z5F1gHarVari+HDlgVrIp3+8mlDJzf98j9bswlwqKUxgxOBbCHH61OEba/Ku1nSfBGm8rTsWL/eOjWt08hPV0sBabroG2s5G9gDIH5wBgKevqhZnKfapk+wq6QMtWCVoVCGUiDsdUIjJmBt/y9osbrvYXEa9lm9RuoT8oLIu0v3+dkohTmGZruBvtnjTgoRaoc0gIkWexbL/N+jZBUkzq1zuC/NoFnWV0LsRqYO0Jh0XldhAU+BKDvgL4HYm9Cqk9oQFkAVW2+q9Kx2Njb/aEAQgn0QIwRPF6mxSpBe22KOKT3rUchIt2s6DKBn3USUhaObmFGptpZ6iie3hsHfjR1aoKOja4TjpXCex5NUH+xwna06HU9Qe7364/l8X0Krj6JvmlC6t/9u3+HF7/4xXjBC15w4rWf+9znUBQFbr311ut6xlcwxtlvQl70ajEoDEPIRHFcIEMqZGKEGa6l95swZkZqBSkz8L4ohUpoRnOvATVKLkwgDz7rqoyMmTKADP9dRYb2lCi0mcnA+0fUagSyxuwtKfYhLRV1eqtDtENOVqlWS4lNc99PYk7CFtyw2KFGiTkqRJSYoUeHiwhYoMNi0NyADJmpnwrIAtEvEEjbbccNsEp6rSo2hKa4e4tl0yrV5JscP44dszl0MIiE+0Z0nlTI++NYL/Y/x5Znd6lvEXLtPnL+vz1kCIeC1StaqhzwOo5PCeAishAhErGAzSHvN+U4eitLFR0NpriSvjuHcVYTm5Nx8KfyWAgVfOpTY5tyXrmIFY5wgA5Pxs3oUaBNs7lDkxiwbYuYzWrU1cyi9pgUlimQWlFHOwB9j6oF0LVAt7bWFjUw3wfqEijTSmxa4NoSqFqgWgOhRY81WhyiwRWssMAKLRrYudKcKWZZ2uaLMtVxjouosYd6OKs4YoVFuqJEh6uwxLqLwU9l11lv+7GjkhnSXCFsx7HSCFOF4ryPy0dPR+S9Vsqr6L5QvqoCkxD/9dJ1C6lr167hi1/84vD3l770JXzuc5/DzTffjNtvvx2A+Yze97734V/+y3+5cf99992HT3/603jpS1+KCxcu4L777sNb3vIW/PW//tfxpCc96brqolYENTvVaH2HqHY39XlTo9/UTDxU6LV2tVRUy5hy5HvHptZn6vdtmqqa55BrFTahJsuJ6WEfD1P5/vPPI9NT7X4lnwm2VHKPTwU0bUkx9Q6vsVAK0zbj0Db/rtaV71OvRLBefhymrGst3zuc2W8q8DXARQW1r4ufD0reCqHlQQVqmw9N55/OWe+H8vNJmZNaiyzHrxX+rv2u4wz5btsa5LxhuZqRg/Of5Ptfn5t/t8Mt9IALPfKFlQ49gC4ihGh/BH1S6qkesGwTvTS+hx1OmFrYB7umaYGmAZo10K7RNEu0vUF6PZgBgwdw9OD81kws1iY9n7hIgF5MbQyp73g2sR1KEsXzpP2sUKlHh7xvyAspH0Qx9TvLodKjAmyGMTKgyvfXS9ctpH7v934PL33pS4e/6V96/etfj/e85z0AgPe+972IMeK1r33txv3z+Rzvfe978bM/+7NYrVZ45jOfibe85S0jP9Vpyft6VEjB/TblhgTGTN6byMB2YbCt06csKw+rTJFCd8eRwiyde3mib6uEacMef74e8hCUbwc1JT0qgpbgPjImTkZ4XF+Mx9Ba2idPxDbSBTaloOh1tB6mfFcnEevO+eQhYGUQ/LtH3vXv4dYpeETL8kqS+tLUOvZQpN7rhdRUW7RPaJFR8OiWBoWBvAauCov66lhv307tD90sz37ZVlf6WFieRbtZWlrbrEuwjHelK9veIL3QIXYRoekRq1IElfaUrsgS6Bur4AIYMqB3nQmoxQJxcYi4OsLR8goW/TV0g3dtuuetxkH6sdhop7+TQUkNbANzka5g4ATHXvNasI+U0auFpOtGkQZaRV5AjVthz8lbCXI0NZCVtNPwtePouoXUD/zAD1iOqmPojW98I974xjdO/vaiF70In/rUp673sacmDvJxjAoYa32qNfp9BxRSdNgzMlAFn7foVKP3jOCkepM8DERLBdgMB+e7MgIKDeZRC8jwzZR2rROT9dcNeyyvlbKUGozPTCLEtZe+IyTHZ7JsTWpaIqJCTFCUxXjxOAaDAvsRg1ZSRq24t/a/wqd83mkElS5kKiy6OVzrQGbB56sPSRUeD5UAp9M2OUYaOq5lqCXMOa5JYqfmpgpa7z+jY17bxnqoYFOrn1CQ+nNV6FGZ5PNUqE8peVxTCjdTS7fvKsxQw06ViklI2XlUHTqE9HfXr9HFDuU6AGWNWCThM2hLY/W0Q4eibGxzb1EBZQ+UXRZq3RpYt8DhCt3hIZrVAou4xgoNGJ/ajMQ2wMM+syDtUYP+NK7YaTI7ikfb29HyHCvNJsN+V0Gkyo1P2+YtU1pFHgLkOlKFtZMxoa+V7haWR+WDfHlKbB9H39Tovm81TVlAx1ku6rNix6vTFtgOS3HRb4OTiom/p5yGJzEmZXj0O7Ge3pJSKES1Wt0srIxD7RJOwLl89vAP7w/uXtbtGvLEVcZGgUJB5Zmd9k3+bM5vc0DbfhgPlWnfTfWtL1/x9inYaGr+kIJ7cXzVB6aCkAxDhTyJfaj1A6aZhn+mh1X4zKlx0nGmFeuFsrfI2HbWl0oLr/VjMJP7eC/HWJkS66qCmv3ifW1+HLTtWaBaWI1ZfRYPZ3UiaBbSOmBmB6BHmw47bBFiAQv162Cbm8azh0d/hLZDbFqgbBG6CBT98DuaJbBuEY9WaBZHWK+XWEceqJH3BfLpNo481TdA90UVQ6/Z6mCIBGvPfYc8qqNFBvlUMdRxV2hb/U8abKOkQkqtVo41ywHGgVYUUvRHTc0TnefKW09D3xZCih1K6a7kG6g4qS5sPVqBU5ZJYANsn4dqE35AWZ5+5rtG0SiTUChHNUnVsNUk17ZMQUf8WzUg9Rsxyo/PmhJS3FFOeI5MEVKOhya5OOj0Zn+WyEJribwnqpB2A5sLyZ5huugCdvorXc56PT97xjtlRfMetq9KUAtbtg1uVScwMBb8fiHSUtDx5vVeOCsT0cVLAcT5F+TaiBxhxYCOAuPoLO3/Xj6TKCh4SJ4G8HANMdxd/UvbIHQP9xHa1Lmi/ahW31R/c20RMgqpzXuwwA4KpBJzRFRoUwBBgQoB5xBQpblhpbdp1hhSV6JBRNd3KPprKNACnfZiJoayz3ugWMfEFELSFHsg9sCVQ7TdCsvuGq7GJRZY4xBNCpY4TAcZrlMdLKu7lU0Lj3PL4mAtiGKV1m1INThCg2tDeYYqWClq9frjYQjZUvFk/1OQeAHh4XcdX91PNZPf2GMqAMd9mOc+g6t0zI9DlpTOtJDyDIodoq8pZzNJHbi6wx5yPTVJH53m8Vve4zX6bZ+BMSQyBQVRI2GZ2ha95zhS/wRhQt6np/lw8mqUICeuwp6caHodBaBmFmAEpcILPrz4OLK0s03aIcLsfnYX+0UFuLdyPXmLdsp+0jJ0HNSZPwVXeGHj4UG1uoE8T6fmivcRkEmwfoT55u5abYmH+yjc1ZKask4UJmJZnAN+7vp6EoLTvtE56i0+ra8vVz9TgLKOdkqZ5VuIsBPLAiqESdZrNdKAAwtGWGHw6fRcVR4bsLvK1iydsLIZ361bdLFFHzv07RG6vsEyLnGUEuGusEj7o+hBsl4YZ07xOE+QXwzMs/6zDIaW61JtsnFJGpE75TcfIxSbEDPHZ2qM1YpXhU23Zeg6PAnO07n0hLCktOPpN9LFRa18SsqTuIg1zFenE5mCD9ecsqSAzeecBDd6N62HwlS/4zJUy+YkYlkUHpqJ+hC5D5k7ThmUQj3KsDuY1aT1piVF6CEdAzeCDek38308RWZHrUfZs7VfgLHS4K1RT56pbiMyVrV8lHlPBan4he+FDOSd7DBisx6qvfZyjwpibgA+cPd6wa+BE0QXfLQWrTY6u1V4sj6ad1BJyzlufSkyMCUKPOlzVIDaq8AcM9SDi54tmCVLZGq1MY2r1YQpiBhDZyf0enszU2gDQl+iDD362GG9PsKyb9DEFi1W6NCl1LEmolZ4GGv0iZ/wudOQ8hTZ5uY21byDJaVtksDL46oKTyvfbxMSqkTpmlQf4ZQlpfd4XxYpZUUEUpuv1+d0Ep1pIUXhoaaowjbHYZ/qh1LGo9qC+qFo8npHtQqN455FZzInxwJ58fGMJz3nBchCihOMu72VSU45s8louO9LIU4lD2MpXEnmTE2efam72kksX4+LYFsWyH26SO8MpigwtuysTj16LMBEtOxbXUh7CDK2GZfXAAW2QYU0+071ViAOB7yxbWoR6GLlPjCti8LMqixpeT6IYBujYnkUJuflfvbnOenP01ikFDSKBnjYTcnDnlNMCZgWUsehFcdp7L5czl3dH7iPCnMcoEpxfEVisba1F7DDOkz8ACUKzMGkQ/1gg/h4tRLMLa5xr1ZeRI8GTVxi0beYrSL6GLHs8zHuFB5rNFjgUTRYYgXL+28HNVqARJ59Ybi3wRFq2Fb3OZbpOrVJWBPAzgXoUKZWqK9Px4kvHe9i4ne1jrxV4+cUP1PRKZLNquEdJ1lE2xSa00b9nWkh5TeJUktn+LNCF5uu0TwgPlBC4STVBHXvgAqpbVqkajsK5/kJQZhMN8MiPUM34rK92ha1INSU9pPBt13bqfVVXwLbrTCSF6Qk9jv39FAoeqeuQhP8m/XO1kY71KEYfc/rlL1aTVXDVCGl0KhqmtyxTwE75WtindSSYN0553r3O8vzyXe95byNlOFTyeD4qbLkx+448oxJYRwlnaNKUz4pX842SF3n6XGk81aZWp73BSxXX5mYP/ce2chZv9v+I/Pw1AnSswRKNtpV6rPMoq2MvKI4fkxx3KNBETvEzs44WyKCR4FYDok2JTziqVdzWIpkRqIOZ/iCoLeFBLVJkNgsChuqEWvDvo1S682+U/hW/bX+Gs8jlB8B47Hv3XUUmWyXKnvHzUWttz7rCeGTuoCxCboNRlLG4gd4Cr5R5sDB0ag3Lec4LUIhMpJez3N7aIFo4AGQLSlNEkvNku2earNqUD4w4yTSoBIyQ+6N8tCkkjpv6d9TXwchQE0KS9+VZ/RIGiOgGLjFQgWUmGGGYri6RUzuZC4VhSVV0OjzZrKwVIAqHNpj3A/AWGhYvTZ1X7Uotd9570mWFD/riai8byoQiO9euGi9TkMKHfl6+ecep5xNrTOFlrb5olQ4qSU1R0iZ+cyq8C3tkDcsBNSI2EOJBRgxV2COcgC0PdHezfNwDY2hs91MLUpE8NBFJjk6RJuE1BItGmHjCqdp2ZydNr846x6bU6kZBk4XgY+8PC2pYqVCz4jmwAwFetgRPP6c5+2k8+YJIaQ0OolSWi0Ar+1tc4wrHOM1coUFo1zjHY9KfJ4KOUxcz0WpiVw93EeNmdCX+irYjinLgfg/n+/9XcDYZC/kXrX4prRo7Rdl9P5kX+1bFUzecmGfEapjtJqNoYE7doCdCaoZ6kFIMT9FBdN0K/fsgHEfqBWnPjdvUfsy/PcKKfs5pvtVvM13mqWsc4vEsr1GqgqNRwE8+fWiVh/nC19K2n4+Sy25bfNfSRnfNkUSmM5yYOPVo0keD6trMayLBi0alFjCLI4edoxFiZDGOI8881DwCT0aRPBweoZ5t+DJ0iEJqZCgwXy0RgtLFmtlB3AjMetvgjOjKBYKZLbXeH5pL8RUGob6YLChymT7+75XxIF+yynrn2Pj/bce3dHvgvuuST1TpHaXad0xNETXkP9b5yhwegXqTAspalrqh1HoaMpqUuGjzNtbC96S8maqMjeveU45830d9DqmE9ompGgFeoe6J2r42k62wQcfUFNX4vVkXFNtUtL+oiWV9VJlMOO2qbVCwaSCPUdzGcQTUaHAfGAH8wT5WB1K9KhQY4mI8dEnXLSqWKgVpEEvXvDyfl2skLJ8HjLOOQopMns/V05DWhapcJ/JqLyQJU35DHQvC+9hPnDOEb58fXTe+LoB4zUwZWXpvJ+y+jzEWsirHcTpEiW6JJ50PtVoUOEIJUISApbzsUCHCsVo5vXohhCeCjwl2gRHk4RPC56DxhRFHSpYMM9RsrMsCwphvICIEj3miOme0ilmHTqshrAI5S25L7sRP+H+Lksoa0H2QBjar4q3BtnwoElVrHWd8Trlg+qL9miBrh0m9y1lBFjPKUVY58UTUkgx8k4jh9ShTaanRDjGCylgzJzVeuJi1oUEd52SZ+iKI3ufB8tQDZ+TmGWQ4fB3Gtde4yU0yPbXGEfbeStON7V6K8LDhD5YRNvHQAEKW2pxUcqZsqA0kIHfZ1yd2rCFCpjGVqFHDUX5uySuZqjTYu0mJ7+26TioyhPb4IUU+5J7irwPUC3wr5e21XGb76d0v+vY8d3D1Uo6Vjpnp9rhtWOlKWvMw54e+mO9/F5CXt+mVZjHPd9rPqEWKxQo0Sb7yM5EsPCJVVqTtDF5ZrTZNhRI3IC7xhUw5NvuC6hwPomZq6n+2XoAOvAkK011RCqTUtUlS0mVx220aWVZL4aEFnBOav/xeiYdBsawN/tMXQXANF/TPub9IbW7QJdgvgybe37kEyKzjt5vdho600JqatEpxAeMtQ2PD6vkV0EBjDtdhZTXIKe0QtUCvVU31pzGZXhmzsAJNbs9TOa1bWpLXqtfy2/aJxGbfeSFN5+jmpBqYipYeX/hnqFM28MdWo9cvkVGVcMemGw1mcZpd9ExXSRgRrXwKW1dn+efexy86eERbY+/x0MeJA+Dnday0vv9u857LXNKUG+L6uvdSy3hKWHm+8/TSYJZ57DvV0Z9qqDMMGb2I47XaUwxcz0KhGQRzZAzTZg4iknsWL1DWlN5H5V9ttg7C4vQuW47n+h5Gs8tK43JYtnPfE6BnGWiQNzo1yAj6dchrwAKeUYcjTPXpn6m0NLN/EBW5HkdySMGUzC1CkY7NmcTnYJ8jq5MPpMW/WkVuDMtpLi/I8jfHCzNH6YO8YjxUQH0B+l+HCBDOryORwNs0yiVOEn3sZkZYUqLVWuHdVXhwXyBmtFAAyOyRmmke7rIELhxt0B2SlMAknrkvU4e8kG6VnecE97jPd768sywk+tpHVbyyseYWN6AEnPsg0cn7oOod4cysZOsu1E7Y2CGKiQkL1SUGeuhcMcxep0zU5DXcdCeD7DYtkjJoFQzVS1UrSbP5FWj9qRBHjru6kskbOsjZ7dZdVMW4zZLjddpcA3rr/kDuRb1KBidz4S3lBnac419mr9xH3Zy1CwxSgt9iEmgmbAowA23ZkM1ybYycbYatepRWMB7Nzm2swQL2jwMmKFKIqVLATBFQgGaVE/dQlOjxCx5wLivKzP/AiWqBM5bgPtyFEFLK0mtpQyVjpVxziu1WlWh0/5WYjnkbSxDea0nzlM9VboGcC71lQW2THGaMZ1pIaU+hYDx5lFgLFSmtEWV/FOar9532kwJvAfIR9KrgOKAKXntSesyVU9OFg9n6u/bhJQykCXGvjzINb6d1Ij0PCm1mhQ+hLRVX8r0tM4khfrK9BrrlzGNRTsIqewfsIBhby150jlAJunnhr9varymyGujx9GUxaZlTEHG/kWBpAxiShNWC/i4unmrz1syvv7b7u8xnk8KJ6svspHv1YfnhZCfzwTtSJx3FlFn4FyNIoV3swZlmh2mApoHqUyCyQSUhTXYO0+FUuoRhwg+uD5ph2v4GnuTKsQBrEayySyZUykg4Xjt5/EY97YiEdv8W34dqBXE3zzkH4bvDMFgSYRGzcrM7aQ1rHNVlQjWmmt+L5VdpwzwJp6+zYWULp6ArAVQm56CaDocz5D84ovuntOQLkZgHLHEieGv13oqTdWR7QjuehVoqiF5IaVQncITvj+VVMgy1FW19+ju0fx1fGkCyikml4UdE4bqVO9gMVRMO2sLPWvB3GC5CVX6vjutkOL7aQUPmfNp/F1T1/XymxcMKpgU2tF6atl6rzrV+ftJpIznenx4wHhOqjBVeJgWG8eKlj33ARZy7ZQlpYKL6MA8Wd1AjRmYchaghzZv6u2G7xih12E1hJabwOyxQjPqW28xaiImy7VnPhoCiNkiN6ZcJzsIKXyiRok6ZSMsRiPmKdfCCyb97P1cOpe9EPXQb1Z+ClSoMR/CqgilNmnt9UMZ5EGaO5J9pH3D58xhASV2qCPSrrXFMe02OtNCikIJyJ2leLCHnxQL7+VvZQ7qzKdZ+42S4sBTzFPPYPIMh1om9xhNTWWWq3ClLmqFjtRsZ3/x6IcSpu14X5qKikrupSDyMBktrhqWvuc8MpSnaXgIHaqgNKhvhhIHKHEO+Xi3Bk1aJAscgftT6L6mgNLTdbUvfF9nKCXDd/qbtlWtG9/vXkmIcq0yeY2228bw2e9kAmTYvEct8RJjmMX7BLyF7X222yA5YybjYAsqfkrbLH8qPr5shSG59hbYhJGA7D/tkBVODznq3LRUUQUu4FZYxF6FiBq2p65KPh/mxGNOPdoG7WArrGEpjowRM+LPwD9rn82/7NGaCSO22WR5Jiq0Q8+ViRk3MP9UkcqeJViwQJ0Eag9uSrZa+i2y/eAXG+891LnOfuPvxwlZzgtbx3uo0sqr0z9eU4Ih9/mwH8uVMd7GAYzh++C+K7GPCiVmiesY776Ck+hMCyklvyhU2ncTn9UUJrPSe5XRHKfjnIbixMvXtdvyG+9XZuAd/X4iskzgeCiAzJNaa15W+XOGUcaWWC1latg560fLice772F8vITHvXN7LJ9ASLWwvolpiXRYo8Uy6bxrgQq2aZZwzyDRH1dgHDLuBY1CeAqXKGzitVXI/RlCGY+frw+wfT4Cm2Oi1pR3TnvhqeVPEdulFpx+nloDfp75+vu2aR/yWq809rC5RE+FavrRlaHz0dLNmvYfUaNHhTa1opBWG+PvRAzR/rZrzCPViwgjxNWlcaa3yOapJWGymhBqpg0VXY3NhrDc+z16VGmGV0lwhdSb3PeXy4DUnwd25H7zyIEiP55fbONB1o9WvxlmKa98PZTYokSXSiiSqsDTrLxC4mFeKzsM8H323OVtJCfRt42QIrPUXG8+GEEd/DpI1B6nHO7+MLhvBnmL7zjiwqQwUYbmTX2dwP64BjpVudedS2q80z9bTbq/hjRHPpLjOCF1Lv1NK03rr8TvK5Qgcs/zpI5whMN0YMER8l79KeIY614m9ZtxkarG7veGqa9TrQ7viPaf/RjmNm0KZd9+tZo8KaylGirrRNoGd6pwUPhPLRJCwj5gYkpJU9TBExmkb8cU3LxtW0Lp7mPfaPY9IgD7OMAB9jFHjYgZOuylOR1haZTt6lXa9otBSPVYoQU33W624iiVEtGAm4Bj6rMIO323QJmCvs2itxlmOSoKMGTeeMxcyu4Tw64RhhqsYRGs9cZc4nxlfzXub82lqRaUAp4B22FwUzwDLuAANaqRJWVW7xEW6FBhhuhCyMag/Fg4mkiqwTjdQoC/0wqfbxshBeQO6jHOnbaGGam6WVElvcJbCg1q53+jQkoXJTBehNs0322/q1bN3xSv1/pyKmngR8aIM4MIUu4cBtPxzCE9j4b1Mfw9bwgmow5SNoXUAbLw8058hfuKhIgXqBET/GEHHzS4giUWaLFAHBLytlIXklpGmnRW26zQElFxCj09Ql37sZBr+PIWMKMy9Xv67jTnY7YAxuQtKO0j3s8IT0JgauUoejC1GVvncDnxvULSagGqz5Jw5DaoU9vifVN8rrf6OI4BWZliEmLOc97LuaKKlDHVGQ7wJPSIWCUgtE89avDaCg0WaNPmXfZVg3X6nWHkAHvZ+iOmvo0ba5RiKyTIsAASGFegRQ1gnawIC+MIQ8QfA+HtbkKOHZivndYc0rM1AnG8cVaFFkm3rXjoeQr6tb4lzGfW1CwBvDav7aQrE55L9Ek0M3S+F7E4fqatZ56SbAJ5KnzqeDrTQkrhAyBPeDIoMhKedaS+H++I9lqfaqOKs5/G2pmi44TUVHCEv1eZJpkHITdeo0JWYZeMc2enZokxZKcMbwYTLBeweXYRy6azm0JK93ap9UW4b1uaf3Xg8viEAhU62ObHBdZYYI1rWGKFzXFUBy6Zv54O3CJnX1cBybr0sBOF2Te6bUD722P9+mySWjEaIqy/aRCACqkpmEyvVX8kWcI2IaXzjOTnuEKaeo36Z1VIeYvSM+xtMDbL4rt/sT0k7pPy/awOfvWT2Twrk5C6BEtbdIh+AKhsG3iTgOI2xewxiVGbPJ2WnDYM8F1A3gfEtmwKKfslpBLtGrYAKLBGQMQMZao3GTZ9dCqO+H+Xvtfcki0aNNDzpHScPapAIaVry689tWrNejaIr0qiapZWNmHPWfL1UYnIEYmFiPzo1jKTAud1beESuQdOQ2deSJEhFsjZxKcCEEhk2LwfyIxZFz1hDzr/W5jx/8g3UF812QGFKzIznaq3WiT7sPbuIzuzVSgDY0bpseecciiXQ6uJZRfpfp6xtZL7+a7HATxJnqGO0ymoy2txdHznTAOWxrNBwArAUTqPVH1GPFpDLSAuwimoSiHbyv0GmDCmwPFh9H5xU2CUcs8UAyODp5A+kM88csWnJ/KQqSdafVS6VJv2/jCf8kqZkgpohYG5DlT5UQvZ10XffWCF91dQGVKLS+cQ68c5eAFZ0aFFr79fhB2AuI+9FMnXYY2viMeGpy2v5Du+LFN6SJwigpvALSjcBEI/ZOZThQgYK4OmwPRDn7ZYJ2jcQhEsBGg+WFMGZcd0bUCbVkibamtBHQEh7cjqhlHYrsayDxV29uOtwt0+B5zDHDPMsId9nMO5ZEdVCcpcpfGlhRRSYMWlZDnZmQ0MWOJV8ySSLKiE9lZWUc3fZj6u6RO8NunMCynCUdS+vKbhh9YvFGqkOqgaJk0t+gAZVjvJ8jmO1KLi4uS7Ov3VctLQ+hpjvxEZMLUnSNuV6SnDUEuK5WlQAxnYGhk+U2If6J4plk8G7oWBt3hVgBDKypM456C2gInxURwawsyM4wqBzly5agF7CxoYZ7338JdnzmpRkHl38ptep4yB85RM1gcFTFkmSnzWtrOqvB9qyh/krWEvaPwxND7AyJfHfp3yr/k6KAyqzF41/hIWCUpLnv2mfiiO7wwWuTcbUskGdAm6i4PItkwTPqsED5tgDge2hnkh1sl+0HyeUzq/hy0BsyuKxIaNqRfYTxZKmYLkLczCjhKxz9z2WyQBYXAgIUCGUGwLevCWqfr0vEVskbUmjEzAzzHHAeaYw2IMaR/2o+eGJHJqzGB+sy71LQUpM3gUyZ7KISEMgmJQSJdGptmYVdN0poVUhRziTO2GC4HMS4nWxRHGvqeAHHRRw7Q0FQZk1GW6l9rs10s+yGNP3hfIFgLb6K2eC8gLlwxfhdRUxI8eXMh2qXVGLV+d8ZqZg6R+q4upLjw6hOUel6mASsKmj8gmdwvLApDzAdoR8qwLkAUUx2KJXC8yOAZsUAArPEKLgUTf3janspIyVfbTVACHF1Jz2Dy9iPG+EvVfqb7vly8VsKPUL7SkfPCG1nGq3iQ+Qy1BHqioh2VOHXIJZCVH+/044vM0EtTnmawA3IxxTk5vCdicDTjAAWaYYR/7KNNBHmZ7NOhS4teYLIIclmAB5nM0sOM79pJOn4G7DsAhIhrE0enTcHWZmtsY6m7hG/uIOEDAk7GPGQ5Q4yKQarNCiSb9q1CkIJ5y8Et1SdjyzDNd096qU18hkDdJU3nn/LM1X+BJeBL2cQ5VCjuvB1UXsKASPktjDC1A5BwMmFyjwgpHWGExAKsMGekSsBlgAedGAV3q7SZZjIutoU9jOtNCSvcPVRhr1t43oNaJ1yanoAlOBv62TZv6ekmZkvdDMFABUNw9Lwomi61gsUtkWmy7+pn4LHWGl+6lm2y1btpm9fl4n8A5jC07E6w1/MFoqnmp09zqZrqrxVq1WAC4iohH0Q/nVHEv01WMz65ShYHtx0T9PTbPMfdt9ZaN+kS0PzvkFD55B8mYmfFa/k4otZDP9Jt6IaWWDD+rkGrc71qu98Ep7Mm2e1QhQ1Z53XAdLDFeI1SOlFF7a43XcS56q1rbNbXOVIDpvC0Ga7sdYKliCE4w/T2mJ9QJCOvQgEceGshnALuxTV7N1MX90EckhSu9Ba11nacS9mTFGQvv00w2i67BkbS7Tu1apV+LYR5wnPUUg16e5/lXiQwnH4D+4JDWZinrkgCdeeZsq7Gpb3Gw4rqhn/N6pbdM7W32cZfKNoXTgp8w1I57z6w9FspyGjrzQkqZlB4256NfPLMiHSekSHSgPpZCis/WScdncQMjfQsUwqw395JE5LB6bfuUkALGTESZmc8CwQnpYUe9h5YTIwFr+b5GwDyZ+7mtcZjydkBdHp9Mpmu1CDgE8CgiriL78Zhp/VA+s93q81KISuuv8JR+xzarkNJ6+YAGIAtNzXVIa2NqUzkVKh1nwqYKg6lV1LrvyLRUKVFrWX1qc3mOQj6kbdYjn+uDT9YYa+3az6QpRYDt8/5e/Y31VKsScr3u12NMGdlojzV4HGIxvBfDmMUkHiw1sbFX2wBr3hTvc2S9PJ/YBnsqFamf6lHLsqe1TzPE9metYKEaJkIsaMMAdjJ3RYSUj2nJWke1lunr3U9PsHVeJgAy210WadiiHCLvci6ZnNGFpxSzP7V3TF2JCajMcJ95/fJTQgII4yCkmieCJaVaH53AqnV7GITkd/J78guXDOYbgfimiBNRF4r6oDjpKAzIZNnmCNuvrUEFx/nLWF418Zpy1E8RBRG1NDq5LcHmXODAelRmDhqx2iuMwYW3kKuXMGH0NYzP22rS91y4S2S4D9gM0fbjOzXeSiosWC8dH9aZkC/nBecIn6nJj3kPlYupukW5js9WuJUCSYXUNsVJfW3qm9DFrt/z+fR1XcGmoGRwiodKp0j9baw/x2WKuokX26HWvsHR2ZOmQpbIggpCWwsVqjTrdP34gBtgzPQp6Mc7gnKb6LNVPlLCxmy+YaOyhm1Sw/L+QkMkahQAjlLovPqtOe/XUopS6T7TwrVErsA+9mEHlrA/89ZhJTsYZ44Sc+Qtw3YWl/XgGg3WAzCa8RwVi6ZGxNRG8meOvGWJ6XCUEJPVRs9O05kWUspgFfJQhu21j+DePXnLhhrZ8fE1Xx8R5uEwe0c9hcpUlBTb5rUs1nebE1+tIf37NEJKLS/NUM1Emfai1quxa/bOc067YcQ2x4xXK3SpsJdaRr6PFIpUv5i3nPV+tbim4DUyaa/9e4t9ylpXC1XHq5ffgyvb+x5Yrp54DIznjNJx46sWoQ+a4DOWMAGs60ZfahEx+MezYh0nb2n5Ovl5p5Ya+4hIgg+EsT7iJ0ufReWH20YtkIHnO4eN+6fWCl8UrB765fhoWipND9anluey9CAPZrHoh9oUyfazwI28h2pqTfrvionvTUiFFF04G3xOVjYQ4FdFrmOZeiyAGTRC6teQ+pZwH4G8AtzRleFSs7w0DwdSyzUM5LT89EwLqQNk/FWZgN+JPZVlYoo44TzkQCF1Orl/eqI2FzDec0TSwALWi++qbfu6kfnpNNQQe81Mrr6u44iMT1Mc0VooU2SQBdoSktkf1TaiR5VsUQ8HaUg1x0C1R9aPS4MWSu/qQgvvHMy6YyCI3k/GpBDulLChkPDWs7ds9BWlbMKhQJ4/tLpYJw0OmCpbLanDVB+2c8/1lxLnjVprasEVGG9PYB9cgQmoRzC9XlhXheV8/Wl5Rvcd4WeOlQp59W+posE+ZEBHKTOa++ga9NgfrBTNN1cl1m/2bcQMc6wQkc/T9UJqyqLyvlPyEwZgsf+JKuiYmJDluWg1CtTosADPpiIcbj6pEpavcokSq1E/0OKbIo2iVapR4zwuDNGPuTUd8sofU0h1LVNP2OnCHVbDaDJEvkv1NuFvQpe+ZubQNGGlAsZ+D0N9qlPiN2daSHGRe9hkynoqMb2gp0ghM/VTfLOIyKymvJnC8Ln/awoeIWyoVoVu1g3u+xpjBrPNsc12k2nsyWuc3T2zqDCwQdWJAdvQZ2BAABPDbgoIhVgUiiQDVBuN1/rADe97U5oCYzxp8I2S+pBUOCkV0le6Z08tYD5DARMKbM2Yob64Hln4sJ1Tbcj+m00rUuvg4TyNlvRWK4UTy/bj1sh16iP0fhy1jniPCqkpn+kUK2MAc48I5pkrcIQuMc4eLYqh9ODuzXWioCqkbwr5jiXoWPix1z62ehfJx+Nn3jTxqpzfbnMteyGU17OhE3Nh5VbObMjDV6bVmXOzTwsH+o0ZOD51hVKZxFqXsJHTtTWgTmJwCnacojMtpBRO8Q5w1eCmYIXjSOEd0jdTSFHDVdJABT7fCykPg6lIIDNX7VYd0V4gKVMgqUasmq1aYNmZrkZ+AS7/IHYdI69CghNs2YzbQEbNPuAY6zUMXValw28i3iakFM7bRr6P/W++vsrMFaaikjA1RpB7SOp304AFRnjx+Sx/G1EpUyGl80cFlTLdJaZ9r9636NcYhRyftc3C0znN/gHy/Jwat/E6jMP/3RAqHcFzowJWSUDVsNQ9RVoDPBFpPB7AeF1z3U+tffX7TinB47VnZ0hlRj9+n4Y4+W/c9m2KVhZexvT3BkgvJGiTm4e5uVbHJAzXjnvV+srK1/N/sXEnwBOHKdDiqXiscQTjFuVE+VN05oXUNjN4SkPnArse/xKZsPoJHmuBNWV8U9umqa+LSqO+gMyUgnwmTOItI3OHjp37/Fs3RlOzVPiKzljew/vILAhe0qozjZIUR1CB0jZYiji/truBpTGik58RfpqVgG3RRaPM1H+nFpNCWcfRNuuM/XISw1Zome/MOqJCioJKn8kx8fXR39Wa1DpomzXpsqYO0z5SS2ZKs2ef8juuMd8/Wl+WwawpnHd+q4XCrvRisP4KhS6wBo/QmyGkXJE1LBFPCdsQ3uAqLCWs9i37C1J3hgSo0nCEPC9oZVPY7iFv0ib8bHPRgL4K8yQuDUrrEQbxgWF8elgoQT50kWcAT8N5BmjOwDOseTawlW0RguNM4xSdJtoqzJM47VCkrR8WzqF2URxGuE8llEMZHJFqxCU3qQCDNfLvJYpTxvZtzqVj6Z577sFLXvISXLhwAbfeeit+5Ed+BPfff//omuVyibvvvhtPfvKTcf78ebzmNa/BQw89NLrmy1/+Ml71qlfh4OAAt956K376p38abXvaKo+J5rjXaIFNpzgn/JRDeErLmrJm9PfHilQTmyLPTD0Uo9q7Zlzg4mcqHkbkTVkcpdzLe7jJVyP5fAQVoMyjQIeQNM6YXv3wsmvGLVU/hK8Lha1/sX5kbP7UX695qubv/z5Os/a0zXLiZ32uLm0PD+r+Iw1fZ25CQm4qNNQC9EJP5890YMsY3vVKi59XXoNXKNiPvd7vt37oy0Ojak35bRYa2KAZNjR6V5NFrwAsEHGIiEP0uIYe19DiEA0O0eAIDRawLb6M/J2yBNtReSaYDtOLY6JnjrH+iipo33BMcrCBie8cLG+KW4Ml1liihWbu2xzjvDZM+Foei0r+mVAs0z891zqXEUa/aqhDPqiklVc3rF2Ah+gE0OfHZ+Q5qEApfyuGWockOJnN7zR0XZbUvffei7vvvhsveclL0LYt/tE/+kd42ctehi984Qs4d+4cAOAtb3kLfuM3fgPve9/7cOnSJbz5zW/Gq1/9avzO7/wOAKDrOrzqVa/C5cuX8clPfhIPPPAA/ubf/Juo6xr//J//8+upzkDUNinmPI7NCU/Y5CRxqM5IMj9dON9M6G+K+NwpK06tLDJxzRzAbBDc/cAF753VZP7nMT6Ww2vl2jfs4+wEno0sEvqfchtKAQXsjFJ/kCPdutkitJJbxBEDaDFmvF7LV8XCQ3fsB/39NGPqYR5PKqS4eVcDMUhaV2WSa2QrkeNNYj8xU4mWp4tYlRMlQlkKpepvPvDDW1AaBKL9q9GPxcS9/vnaB7QONE0U5xcDR3x0pS+TgoyCsIT14Qxd6occ/6eZXKZCy0m695CCKSdWzYKJfllVkqb8gB0aYdklAmawU3p7rLGC5V6w1LcNxtnwlGx+hWSb8fRhE0ozGaFS1pkP5LA6WkZ2YCU2UYkugYe+j9dDT5Wo0aWVOAPPtwqjmTo1g0ycZ7txPfTEaSjEGL9unvunf/qnuPXWW3HvvffiL/2lv4RHH30UT3nKU/Arv/Ir+Kt/9a8CAP7X//pf+HN/7s/hvvvuw/d93/fhQx/6EP7yX/7L+JM/+RPcdtttAIBf/MVfxD/8h/8Qf/qnf4rZ7KRdGMCVK1dw6dIl/H/Ik3IFi0paIjuAmYSUGio1sKkDi1VD1FNnGRzwVeR9O4+lNRWQBQN9LQrbaG8wElAFSCn30II6J2WcR7aKuPAJZ1Co7aXr9mFpaTQnoIcS+ax8LHzAReyjAIN/qSmW4GHeWn+yY4tximB2Z9W4mTPQID/Tzez3OGi6bAvTSJG5UTATOvJWNbNCQPpIWYLuR9IAgylhx++USljqI8I+GoDhocgSGa4kI7yCrIwo4+eG6anTlP0ZYFXqg6xY1KntlvObkCk3RnONkIlz42526I/ng0YlqgWnbfPsR1mWt9JU+dC5xjHRcYSUweuofB4iC2iFsIFNZcRbUuQNawCPIvMOjjfryfUyB3BJ/uYY3AqkBEgHuIAL2Mc+bsalZPPMBmhvgYfRwCLnVliiRZNOnF6jQYMlLDO7JhK2Pi5QY54Cy7OQmgunsIMVOffMitNc5CX2EFJARYbzkGyqcwkDaaTf+sE6s2u4Nlr0aHAND6cMEjkow+IZA/ZRoMQBwqA2NehTwqlD9HgF/giPPvooLl68iG30DfmkHn30UQDAzTffDAD4zGc+g6ZpcNdddw3XPPvZz8btt98+CKn77rsPz3ve8wYBBQAvf/nL8aY3vQmf//zn8cIXvnDjOavVCqtVzpl75coVAHnSUQgp81Fm7xmMt6S8eU7y0NE3G+6b+qxau0Il/F0jHBXfB8ZRbxoeq1FMCuco81N9ShkM/Q2EDjj1eVKp2U9554evf0y156Fn3TCKeR87NefMKIM800pgPXwbPGSkFDEOa1ffydT1ep8KLO/fVPL+Sx8lyDFlG1Rj1z1KhJPUimWdNTReoSatgzrWZ+AB5plNKyxISyQn+h3Pey+kxmDOOChjm26sQSMKT6vSpZAr+5b9rmsCGFv07GtaXkAO5DiJwfE5CvE9jCzA+UwqPUAOiPHWY56v+UxfO2bDamXhQuUgOCztrSVQNnBtnfxRLXKGh1yHPA+YCjeHIflovAwNWykB3CdWSjl1AhzzCHap1l1aKblUeph51AZA3KPEVeh+KLtPZ23GZnLGCgzvJ9HXLaT6vsdP/uRP4vu///vx3Oc+FwDw4IMPYjab4aabbhpde9ttt+HBBx8crlEBxd/52xTdc889+Kf/9J9ufO934Z8kPKhdrNy1tAxUsH2z6XTDs504/NQaqdFpaHKHsU+KC5dwivoELiAnmSX0N1XXFsooDHaokr2jUAHdq0rl8P8MAT3shJoKPC9njT6JujgSWFMQJ9/JbA8wZrzsG2Bs/bBvPCDhITnex7lCZcj7cDwRrtQy+X7cYtM6smyOLS3dOcZ7AI+b7ybg7DBAi3g7OfrKB9+o5eMd96r4aT2mLB5l5Arv6vdqrVIRoPD0/rZK7h+zxrHiEHEyg9MAkmswwfT/0vtX5XnnYONwM3LfULDrsy15dYcah6gRwIzsc8xwgD0AlsPuKAmvBg1aHKFBgxVWyTaJo/2P7ONyC4eKiGhl9lYyepaIqEOJOWrsocbBIOTKJHA6FElQduhwhC6tTiArwtnrdQCgSDCshaSscQE9VqgGnKKXo3d8Xfuhru1W4HxMX7eQuvvuu/H7v//7+MQnPvH1FnFqetvb3oa3vvWtw99XrlzB05/+9NEgTjl+PdSjpr5qKNQjCkwvfO/f+kbIW2tTARAa6bbtuXotBY1mIGc/cJERJlKNmcJN4SINNSdYx13iticlk1lDs7TvoRhNyvEekSlRY3pdLtkO0VbrJluu3L2fhZc6rqNrk76Qvm8xZlreQua7CkZvPflNv1NjAuRNt2S+3oLQvzUgQYUCBan6P/bSb9xfpH6QqaCRMLDsImnteV+a+nMoDNRSH5czJkUUdN15S5/3clxoQdZyXeGupw+KEKgKKSqTWicvSNWanhKuvh18HwdhmJC6grw+kOrFQzEr5I3Z5B0UXGtELNCjwjrNqTLN4Zx3sEn2kgmlPkF/FCnjhLLsW5uHBsaVYFZCm7E5S54FMMX0FwMZ8qcixT2aTcXsLx2qAQ2hBUZLzY4ZsXjCeVIBC0TYacY9KsxgOdvb9K2iLGOvs82ZmPrgmyik3vzmN+ODH/wgPv7xj+M7v/M7h+8vX76M9XqNRx55ZGRNPfTQQ7h8+fJwze/+7u+OymP0H6/xNJ/PMZ97V/BYm1NIRRduwOZiUgZDzZobEL30P2miXy957T2633T/yJQfxC9KzUTunbbAeNe+JuPt5X5aTxRWdiyIbUgECnTJoWuZx3K97HSZPewlq0ktkTGkummnlJjBEsDYbyZIjJEWcgfDcNlvXoAHbAZQkLHPBAZZJkBiD9MwYK7XZkSeBjIohOfLISNWKI5CSq03rSsVBWYZZznUYNVneIDsw9Ls/1PRjUZWmy4JKfr+tD2EGVcY12sq+IGkgnsKQp+6VyNDyfSnnsX+Y70aTJenCiXnBZWsPblmm4eb9efYUEAtYVn2H4FZVIxw5bhraqaltJdERMfQgGWCzeJgM6kVRgFlV/U4HFZDttpJJnwi2mG9MLJO9yWyn/idBeHngwu55i1w3frIZnKBOQr0sOPh++SHsl1XNc4NYuo89hBQ4ggdmlSfPcyT2LJ8Hj0izCc9Q4FzG/1u86//5uTuizHiJ37iJ/D+978fH/vYx/DMZz5z9PuLX/xi1HWNj3zkI3jNa14DALj//vvx5S9/GXfeeScA4M4778Q/+2f/DF/5yldw6623AgA+/OEP4+LFi3jOc55zPdXZoCnG4RcCB8pDFBqWrTQFAz2WpBo7naNAXgg1xokmfb1UiKkPgcTFnSGDDG1SOPHk2BwGbL3WJK1oDWZC9v6+Dh3WWCcgopNlVWz05HbqEIflvB3Kyn4puEVXuPc5bCvjHHtgxucSLWbocJjAhtPpcJtEAc0cfJ6mIQ5dnJtChQyWmjrbRgZ5CRluUmuRDNNvrM6KmJ2L1GMF7sHy6X10Dn09ypjOQ+0Dzjtv3Uzd68vp3Xfsp21rFMj9pZBigSywWJ7CgV7BPc1rLa+FfE/ruYcGQcUEYS/QJfuhTFaNQWtI62qNPol53w9eUJla14KHNc6STUXBNZ0pwkiVXlsVPNnJatdvXKk9ZiNpO70ssdFmngnlspyV43pF5ICi08636xJSd999N37lV34FH/jAB3DhwoXBh3Tp0iXs7+/j0qVLeMMb3oC3vvWtuPnmm3Hx4kX8xE/8BO6880583/d9HwDgZS97GZ7znOfgb/yNv4Gf//mfx4MPPoh//I//Me6+++5Ja+k48li0Qg4K/XmYxS8WCildXFru18vQTksaZeQhF42WUkYQ3X1qafGljmyWrQxd9xoRLtSggw48ViNvolSHNZLOxQOviTXb87WkDD5o/RVGbBIWv02A8N4w3Dcey/G2AdvkOEu2FEs07ZfsO9fDQ7D5mZvkYUC91sPICjd38q6/KRym/ixaUnqaL3/z8N4UZKhzwna7jIUSrcKpueehNN/+qf7wSp9a/CqQPYRNa8bPZV3PLM/v0fP1VOGt++wUhvWCamod+ed7y5pW1RrjOlJR5Fgv0xNWSahYdFw5rBVCsP0wQtPPI3Gs7bh6OwG4SndYdO30TFYBkWFfPYqDexgJrivX4xq2GhDEs7CoIOUzgS5Hp0zXE2xkkirb42V9dTrnyXUJqXe/+90AgB/4gR8Yff9Lv/RL+LEf+zEAwL/6V/8KRVHgNa95DVarFV7+8pfj3/7bfztcW5YlPvjBD+JNb3oT7rzzTpw7dw6vf/3r8XM/93PXUxUA48Wmk041Ht07RY3Oi0LVyD1xwnpN9euhbToOsylrFJVG6hEvZ320Xi0yLk4/iAZOIH3mWUa6iJk9Yg/ABeQERoCl61ygHy1GEpmBLa8WK6wR0KNNoaU2aS+md9YiIKeuBPphgXTgWaqaoofMW528IS2xvCzyAYp2Nqu1fw9zzLCHA8yTRVdgiSMUWOIc2kEg8nnKQNmv7FOSWlB0anMsvB9RfUWEwlbyO9s4kzKYMUOVlIvIm6l52rDOb51PKpQ43p7Z6bEiR8jwFoUX6zeOVMs0beHmNpMU1tTgHFUoSB7S1lBw1okwtKbjGvtGxxYp4VGb52FYs7blweA8v9l4KguFWn8Kj5bpb7XsrqXnXYSN1ZOQBVYF89dY2ibb+dcib5g139SYl2n71SeaFRrzapWIKVDCsgX6UctWjOUk77DCGhEF9BRgWlR98isdhzNlW7aAbiG2nVa9pAvgVpQypWiyzzNYuBTP354OllO6brjvJNrb28O73vUuvOtd79p6zTOe8Qz85m/+5vU8epLILHQHv2p2qhlycNV5rjSl5VEAeD/QtsV6Euk9ylSmtLiMH2ciI/SOa53c3vLj5Fafm/pDcj4+EwMN6NYc+2C8dWY+AS4x2wBAyMIcsEswG1mFnOcrO3PZx33yRW0uRrY2ppZQV1THP/tB22wLrkUrgq1J4b2WFiduHcMsHDeDEfib7tsJ8rtmTaBvRbMkZMtmDHeopawWHseUbWUQgVdU1LpT8paArgfNm8c2ES7zyYd5j44Pn8d6a//MXTkazKPRoXy2zjWFfIHNuarWlPYB+4SKgcKXbLdaQZoKaum+4zj4Z7NPVFEgusC6rJAVHIXsiBxkS4X9GsGzbFlXVS5UiBPpoNJcIKb1yjVnuSpqMI8EEIe1ZiphgO2VK1CImtiiH1ZKhtItQ0QFni9lqEqLgG4Ilu9SCYQzA5g6yUTYDLNUToEaNbpkRRnv8Fkip+kb2if1eBMDHhVfB8YCCsgMn42dYZpBKdGZCoyj5WbIwvHrJbVygPHC5+SkUN3DGN/XScuFwogvtUJI1AyXyIuN/UEN1yLHKhQDDGGwgC5cH1RSJHR8jQ4rSeZPmuPqoDntJV0qp0MpUz/EFJDRb7SJykGfrqzQD3WgkFGt1xYeGZ8dqlikJd3hCOv0HDqnt5Fq5C02gwnYf95C1/tYD24sX2Gc+43KR4u8z41BF94aKpGFHeQ6tT70fRuphQbkvHm6T0pPFNZgBVXO/FxluxRqUxSAY8b6HmIspFgvDVzQdrBPafEDqiTlcoCxYGM/aY4T3Y/G17XUBv1OhRTbRPtAhdQSObqU/cK2r+XzNqJyYvn8AIox7wfT6EsK4Ry80WCGfvAU2eHzebVmQZdTM7GfWhFTdlRj4RSOGQJq5Hi/DkcpS8UKKxDqN5uuSeVYuAaPqt9LgRMBAXPMYackl0MNTkNnWkgxuuYQORyUNCWkyOQ1guskUgtFYcFvREhtoyk/R+lenhnRyqHwJENRRqDaq4Zq274oc76a/wYwr03cgEtVaye+blv+Iq4gLxy+GkRU6LCHFaoU/XMa4oLnMRZkDB3ydkHF6j2siXStwStLWFYyC+9dp3YBp5v4tI5Yvga1eF+Y+l4CMmO5BlOmHkWeS/QxAdkXSMVJx5YaOcOie2TLmtd6IeX9QyT1VapFwOeSKcNdo/dr//N5uu9MrR3OL/o7H0WOIpyygjRXIeeSB5iouJCJqqCOyPOVbYkw4aKQK+f1YarTVeRNvJor0cOf6udi2ZoySTfNz6Qd24nq5AwFmpEl5X1fmple+yEA6Rzdk3EdKp7MxJejPduEOZQJGt8WzmC9uEo9zLN7c45OpTCIKabYLVHhoJijjy3WsUOPGi1Ox0XPtJBS+IMLhhTcy0Nd+hswXtjq8p8SGvr79RKFnZKvo9bJQzX83jNsfWmZ/jm+HzZ3q+dUmLyndPerAFTNmkpAlLK6BDJ04AmfPEfKXoQYvPXLdvI01Qxc5Lpg4h6924DE8ULqJ/qF5UxBxGqBq6+M4+8hQdW0gU14ifOU1i/vZ738nGXddCM268/2MNKQ468CR/vLr49i4jv9zZeRlZRxfdkPnpmrBcR8eH5+b4OsFT6celcoU/04rBct3pVcowcW0mrTEHxFZPy61zorDOdh2ilXQ77f5n4G1sze1zXu17t+7+eolc8zf8NQfpdmC9cd1xGJYsp2NfUpE0kLhpznEnnYPFdtnyJ+s0VmfZtT0bJG1sqQ6loM/ywTe5nsqamVuElnWkhxwXAPhgYMEEahICNz8AKBDFjxYyDDiDphSphT9DTZLbbRlO7A/TBPQtbIiG8/jLFGpftciId38j0FBa0AtpEa7Tlka2o1tNQmmE1zA/JKRFySdmaGbZPQzha1X/ex2R90m7aYoRnshRVK2P72w2TdHGKBFjmIgdo9tfJ9zNJkXiOHTYwPS6QlaYwyJP2Nom0cMTjHmAEovKaaOYUTA0wIv6mDf0rgUaBQEaK/5wCZAWqKHRKfpxns1YLuMA6+ADJTPcJ4/1ZIz1NBwzWgzyPsqHClvnshpcmZ2XavqCgzZX3Zt/RXKU35lKkYcV1oAINaTXrcPYUcoTnN48f28Jor7nUE2xPFnJ8aYMJ6HU30B/uR+xD5N5+ryX7tGEYgYo2AgC6FKDSIuCrP02NAOEYsay5/n0fAAQIu4gDML05P7CM4RE4Ca60/SGhGkXxCLWKC7Gztr7FEiRYHqFBjDxX2cYCDJEoMKl9ilRLiGoZipx4UOMIyPXsFBkl0qFAn4bWHDjVmWPVUOudp7E+DZX0bCClgvKjUUqGGB7nOa8gqpFST49/KyIKU8VhQNtnzyztnVcOkxjxlVU1pcB4yqd07oAymH7Qyal5Mx0/9qgSdsVmjCogD/h9HPZXZHJ24bVogJnJsmmvY+VggBlQosIcZyoSV00lbJNvLC1DmMSMcYe07HRTir2KZCq+R0ZNRc67oGHhSXxXvn4qc0+tUSKnVCowzMJBRU0hxviqDU6FDaLiAavd5nnD+zeT3Ke2e0JOuNR15FVy6LvksuHu29b9vB59J65GwnSptXCcUbD4IpEOGYJmfjy8mK/blaZ+zDkCG3+mjZHvYn2N8gnPR1AgKKA3cYJ/5NUyi0mTCu8Aclli2BH2vZkUt0SZlM6Q1aiIyJEgvhykhKX7MUWG1tNNzmZfCrD0GXixSQicDDC2knqdfUW0N6LGCHStv0bkzMOgppp4L8tdJdKaFFDVej6Iq7KSDTCcnMI7YAcYOSk5Kv5hZ9jcC9ykRgtFD0zysoNq7wgpKKqS0bC6WKQGlQiof3xAHZm8amx3WxlzLFYp0nECLVQo3B2jBxoF5GymwY6W3Yj8ssUpHvI39bNkPxxNH7UCCFbi0OaYmqDi+BCkYkkHh5AX3FG0TLppwNSL7iMjsffDC1Nio8CGsx+/9dVP7fNQH5IUyLYlrUhdgbIHReunlNwqXQr5jdB+zyLPNGszC+/SYE1LvPut4qpAq5bMK922wur60bIaDs+3R/e7LUZ/0ITLMdyWVQd/UwpXH8Varmb8TweFc4Bj5ucO2EnZmuZrUdlucm0LtGs5vAqpOa7RI/h0D3BYJSOc6MmHaiFWtKpVB8tVISCEJKW4iKVL9OxzhKpoBqLaZailyCSIaR7G53g3gXlZWbEbk4ImT6UwLKVoIOqm8U7tGFkB6nRc26m9QwUfB1cuLWHvOy359dS6Qo4XIHMiY9DkK8RDi8Fg3P5fImSO8w9lro7qQPWTFHMdmkTQo0aGDpYK1vA3dhplu39AiMhuH2cowCKLxMmxTHgu2TaOK7Dtz8i5THWLaGVRijgqLdGcQ5kQ8fpMUQqKgUP/IlCV0HPyr2i6w6e8BtjPdbcTeUY2fSgutgm1+RmVk+q7QGL9TbZzPooDcRzXMbRKjPZlch+gEx4zMWOvgLRc9eoV7w1So6Dqemqc+MAbyvSoKvEfXus5vCpxHka2nryJHYU4FntDCYts0eIPrStcZBRYDRGJ6nh9/wvMLmJBUq5P3ETpWfkAlYYkWARFzrFGBB9Zb7c6nGV+mkKgCJWYJgAeAHGHbwbaIWO1jWu0tmAKtSNAkE6Otk2JYYo4LKSBqliBA7rQy28yeUaUxoZ3VJjWYffAECJxQp2R0f3NRE88vMJ7M3oGr7ySFMLKTMAuX0/imPFPhhCbOrPCOMiEVVjphleFqW9WHUk68fHvV8e3rm/dJMeUr7w/DJFTKmSMoLJAAAgP6po6M1/NH1drxpbYDdGFexXxIQRjaQChlCnIL7rMh7ON9UsXEfayTMkgdj4DN+afMRV98xnHEOap7xMjAx+DpNDQ29Sz+vc2q8evFMr3ZvjYdVaoC3rrxcBRcuaxDJ98pI9d2KSypxH7XNqrw8jC3WloUWPydwRFfgwkfWlCETaeIqIrCvYpEUMArrKiQZ4e8AVjHhcKb1rAKfK88eYuT0Le5Adph7Hji7zyVVKY9StxSq0odiUESesouU9fm9cs134Pw/yzZcWWKCmYGC4oqK7uQ8iI6jMH3aZVyk860kNIJ7U17ThQg4+weFlCnuZbBzyoAgDyxmMx1ieMj/XVicvLp4XVMe8Pr/CZADiEtQWaN8Kek1si53ebpGdz/NLXwt0FTY03cRIQJzwKMz7PFPyVSMEAZ/hdu9FMyFPtkwLRBgx5h5CfJcUHdhlWozEbnA9u1jyotvn6w5TxzIakl60mFxri1232Z28pRYah+IO6R8/4pfz19I9pmrY+G3Ws9WDYP9bYjyUvMk0bdJw2aIrgYIKQMf7LPtjESChG/NsnwWWeNxhszsk1FgYEMuqdJn6ebggml6UGPRzDhdAWbyskUqRJLJZWK5j7smJsLsDVICBDIgvgI01Azw9evpmt53EyB6f5kedlP3aPDGhboVA+ipgJ3TRmnCMNqoZXZgGEctKQMVC/TvRhsKVvzNgIlKuzjPIACBziHGnNUKVktLakGzO+eiYEawHoQpSXKFM5+Mp1pIcUBVSYFjDUf/u1hHdUC9VqWo4LL+7W40ZOQCS0cDzmqdqVwhwYyeA1sivn5OqjlRIjvnHtRSJE4wT384a0YHwHJ76yfxzan5t6Lw3eb8F2YYAXjXfdT/oeQ6tzL/4QiqRXyudkVPOUnyYw+oEaFHiGJN54LPPYn8J19rKSLL7rfabWr1rvN2tAy9FrVlqfmoV5PYl11bL2PiHVXK4PtzMHGmgiUWd2AnI1g3KdqlU1bw+PffBuVPBNX5UMzXPCl0Xf+WVPPnoIHTyOgPHEN78NSIJ0HcBNylvpzyMd5eGVB+07dDFP9OoVy+HaxT2zsexlD5nfJtnI72ENxECi2Rovkb6ayci5ZYAV6NMJDLR/6fhKAc+xjBjsfuECdBNQS9EGtE15hed6zFcUjUnlcyWnoTAspNY39glZSi0gZwtSC9kzACwcgCynNR+ahRIbPktEppJFdjvnlobip+itkov4sLo7z8s6FxHpx47NCZFOLlcJFhaVnULmvuDkwA4AKh7HuAREeIlR2ouNjn7No60QIccOjgVKc+v3w+3FwrUUsFbCj1BmlaCyLS4Uav777UHH2HclbSBr9R5jqJFJ/Az9T+VJYid9FedffVOnSOqrV5f2zFFI8WDxDPvROsKfHofxeSLH+wLSw0vumFAoltZ4I2ek60zZsn1X5ebqm+y33nYao7BCtuAlZSFE4ncd4i4EKcW9Z+3rpb2o5Kyyr7cz3GqRepjGs3N22F4snGXTDviaDk0MSMyFZUufSPCiQU0sTZq9wkEDhEvuYo8YMFgDfogVPzbZDOywsaoUGhaiFQXjJE0JI+cEEsknuB9k7lz3TbeXlLQt/PaMELyCHwmpaJo3Q8rChHqugUCAhySUyfKJMKMAWQIkM580A3JL+vgg70oGwAx3g2yIWgTGsyH0/CstkxmguVfakaVWW6U9hG0/eopr6fRuz2ganHUfHPY/O4irFrgU0iCkBbZn0vgbZ90LYyTMzWsjAJoxHq9rDtepXmVJGCMlFbD//iETn/8kZDbLAVNiY9VayrPHcP2MJsVYwN/kR8p40WjScSz4CkeM4dYyJCgbtn+PawLmxwGa/TVlRSn5TMRXUErkPfdDVSVTDBNLNsLV2G2zd3YIcGcn9aao4e9+1+ubUFcG6cm+YIh6aootkZa8REDHbMnMYcbfGEVawM4AZ+1oksWaepXMosYc5DpKSo6tpDiY0m2GWwLo56rJGVVTo2h6IFQIugml81ynIwpTYBdZJTWa2mgDg8NgRzHSmhdQ2xkdNXq0PD+95WEjhBC2bzNJr24qj86WWlB57Afk8JVhViKlwmIIaFQsn1McM2fwuHylPSCwzU98HfPnd/t7Syk50qxmT8BuqvXk0ufcreFagEBD7gO/5peeKZmxd81RkyG+TwsaLwfVACcvFbPWwFLfUBLOFMS0ota4e7uPYM6JN27qN/FzYpjkrdDW1Z06tPIWDlBV4GHE8ToRlbA8bHfsUUKq9q4LlSYWv1p2/6dhPoQbB3adpvbTcqT5SAQCMIyNVSGl03WlYJceVcPp5mIC6CFMMeTabJu7VMfBl8V2FqAY9TcH7fj7aeDPEYfyP/UHQbZVSLNuJBSosuCrqwb9ESDCrILTPSszDPIm2GmVRoSgrhNbSOvUCw/fJY9bLKDMBbZ/WYPNEEFJeg9PvSD7UNrjruAB1P5J2it9LBeSJpJkRdC/KFKkQYnlcgAwK8JF5hBZIhBF4EN759H4A0+4uIu+3qlFgHxWuoYWduDmGZpRpUEBp+9g/ede9WVO2WEyTYq5l7p7HaHEoxLopRnQ3PxcqyZ4RksbGffLctUHHbw5qpa/MW8zeWY/hNwuinSeo4xARQAND0jfhGCVlFAU2MyjQ0iUTZH+wr6cYFh3lfuuAz7TAseIGXl8OfWLef+OFlFp4FgzUoxqCZMy7cIhxwlcNI9e2av2V1Pejz2vdNb5/1TL1MBiJSkTprmdQg8+iQaHOZL9fg62dfQAP4eQAKMDG9DzMaroFtt5uRxZY9ENxTqs1qevNW95z+U7dAkq+nbw38xETKgXaUT/ZuDVo0OARHKFBizUs/ZEBdvsJrEOK1tvHHDxEo0NelRWqFM13qbqAsqhs7IoCXVEAoR0amDNYLNCkvDRaR+UER08EIbUN81ct2Ed4Kf5PrVkd5AHjg9KUURAWq939LNMzNdWmVDv2MJdCHwrxaVQXJ/QMprnR/3QB2ZqiQJ1Kq6Pwoe8nuN/Uv5TrpPYevUZlqqe1qE3iaNonOP6W54lucw4zLLaCZb2wOtKZz42D0/4NtULH8yOixSEslwVBW7WLNmlqjnnLXJ/rIV2vUXufjr+X465Cm/MHGOcCnLK2x63N9wPHRRjav3UC9DTPnWY80TJpmShScByp9eT7g8LXQ+1TbSFplCBpnIYojwWF1AxmObFdK+SUXicJKUbiEo7TMfYCEcjCysOSPrrSw8dTs3BqnHP77EwnQC3hZoBuaUHxtAGdz7YK5pjjPGocoMYcdeJuzKvp64qQM312fY+279HFRhTGNq3PmCD0rK60zqRoTwSsWc8zTFMTmgOtWp43nyHXUEhxonTYtL44STjpgM3ILy44hQW9Jqmkw0PDmkOoUCWFJGG+ObL1xPDXvfTKGc5td0TeAQFU8kQyDF1UfnFsMn/6ozLr55EAzKhXpqt9iLpZReMndMjZIjYXJndvlIOQ4rEC/bAI2L/jkAzvA1SyiKIFCnEtj8/o9ddvBtnkNm1CL1RaqCAUGDNlbxlomTpXObdUoSGp4OAzjhezJ/uu2NtNSndDps6X1lsVNu/3PY5UGGl5arEz2P0k0jGu3PfMo8d+nEvZXE/MXn6EHFx0eMLz1FfEV+FeU34zD8tqfT1POq7tiggoX+ChgwDAVGBd+rxGh0UKCm8wPiLeyihQY4YZLmCGPVQpWi8iJF6UD0AceFIEYmpo3/doug5dtO2/FFUdePROTILIUtl2CW8hPSGE1FQINSeMRtbx5aEQ1QIP5Pfa/VZgzDiAsUZZyL1H2IwiYj29dsVF6SPKStjioSDVSL49mPP2AjLsRw1yD3XSrEyYdEk0VOjR4cow4agFsg8icsYD72PJvqwaFfbBfH6a1qTEAUo06HAIOmrz2TIlZqhHC8nyBFqoSSnCKufey1FK+5gjIOBa8hvZwYX9iOEpw8xnGIURMwuwjcELWKwTd5XYUd4r8Ewr9fuodu/9S94KUiZCKHiOrHhM7Z1if1fIljAd8LqnSdt5FfkQxZlco1AnLX71Jel8ViFq5fdYDcwlbmQLJ9ynFgPnKp+tTJtBOOo/01OXVdlTZW4bk1Ylz2fMoCXCejGqVa0qtmMOE0aE/bi3cNtGXqUpRZdtY5BNifHJwtyXpciNKicK6UbkoClFZthmII+3za06eZBsBdvRNBa1aiBbP+THbEfixlIezdMup/lgS/HTAZZokCMCrfftc4u2bxH6Dtf6JbpYokWJBVZoUrDEEjxe1Gy3Np07ZXbZGEQ/Hdh3xoXUtkm9zeE85axVeI0Qhk4mnZwKcagGqb4AMiRmDlDoDNgM1VWhqWWT4WlWij15cR+Ud7YWqVa2XRUJfAtDXf0poP5ez3zz4jJ7M0wCLQE5eSV/jcMv3LfBMhWX9pbAuKbMLxGG+k5BRXD353vyk4iGN0PPtOBOEm4sVh+Oj/b0FppfYFNWj/aSzkF/Hy15f2aRlsm6aOi1lrkNKlKYWZUthZLpatetBlMBDr58ChrWk/NH/TEajUim7v1ivs88TQVXqMVJFMGCFwL2UKTtBkCNmHxu/SB8dc57GH4bbVuznCtc6xrdqdd5aytg89m9K5NEIcbP1m7LDOLHj/uPGG7urSfrO57yVKNCLYqn7rMaj8Rw0HxvNmkTm0GpYaq0nL0vn+7t1+64zNPRmRZSxxmLXhCp5q2ak2osqo2OtfIcFQSMs4h7IcUDz+hwVuEDKd8zAA0ioHZIjZEw3wFsIV5A3rDLiW/32RKxvS08lKyQSWr2SZdsOPpMVDP2mrkxswLFUBvzbnUuF0M3lKR3U0+eJu3v05AP8lB4dJPiIJjUirFIsR4tlsOzqbn6w8oAABrpSURBVPnTYqC/ghkBSGTu9HOoD4q/eS14G9H6oLDYx1hw5FZYecyUPRVscNwzOApEFzSZsQYYnJZYH890lJFQIAFZSNEX5DN4+DmwLdOCPh/Ia4Sbag8APAUBe5hhD3uwdD0FgBYLLHEVhziCWVG+j1mP0zBNtQxpXera0YAgLY/Q41QEH8eVR/HodhYfNKECWnkJYMpWC26kHY9QVkQLzJLNNMMF1DhAmULMAaArga4HujiuP9MddfEaADvBu0GBFgXy2dpjMtV1lj5twn2npTMvpLwfR3/zPgUu8Ea+myIfcKGCYCp6qEJ2NE/5D/YwhtO81s12aJJRZYBkLBeQGY1q3Bm2yYa0MeUOFlhtE5YJSarBmLfgVcJ/EdNRdgXmYC5l2iQ6KRXis+wEtKhU/PMTaxE2LCH6ssrRd22yYBtwC+JJ8FCPOIx3xHgucM+NCgRCMg1ypJfCNpxbVCL0sELCL5o49bRCSoMk1NHPuek1ay+k1DryPlK1xDh3NUxa66/Z2abqzeeoYsXv9XkkPQKedWe/+j1DFDYKmwe51/tG+Z22+yJq3IwZnoGnYIY5Ztgf8II1ruEQ1wAAcyxQox/GXf2x2j7fdhUGqiAtkPuXKpoKKfIeXcv68r4rPTmYvCBvJ2E5YYD17WyoPllPfQLYOjTIYehA9iTb2q+xj9mQL6Ia+Wd7rPpraGI/CJ6cNJa5NDtEhBRfy2RptJwYx8tdVbZ1vkg8oRiu6FO9TkdnXkipleKFlCf1EU051oE8qVRAqSCgAzVbGTlqiJi0h3Y4ITtkIbUN9uPvOrG5L+oAmwfBKYSQmT6ZzjhmiedFVcm6Yk7iChkt9haiCak6me3GHvLh0UY5qMGS88fRSGgNrWQmbbHJmxeTh354yijD3H1E3zZSC1UVGeL+AWN/jmqwqvHzO6+wtPBM3mud48AA9ifks4fflOn6cpQ58m+9X8tRC88jBJw7msi0SLUkLDQFhQNjv+tJkXDMKA7kMaCFOiWkVMFU5dFDj/qdzv1zqHET9nEZT8UMeyhTQrAePY7AIzPXmGGNCv3wrCkExSsY7FcP2/bIuTSp2GkfaZ11bJVveFiZlvtaymaZWdkImCWPakggY4+YrChmk8jPLsFcEfQPz7AHO9bQoL5C+rvHOq6Sss1V3g/eSku9ZEJKz9xSkJ4OBm4ZsTaYShwGwRdTv2wzE8Z0poWUOoE9w1cGReJCP8I4bVFmyOMy1SxX5kEMXBk6tVZOPE423YPFOquWD4xP3OSmXEJ8N2OcF0whItWW26H1ntWrds+zNGPS+nowzHs/6Ui+TbnnqvSpTMJjlaDEgDUOB3yad1v9zGMQ01IpZaTMkpqlWlDT6tIitoPZVlgN5/Eukf1GU9i2x/Ijpp31FFK0piPGR4yrkNKgDC8Q9LPCkBHjpKbUqFUA8bNCuTp/tE1eGOtWAgZbKBPlXPL9VMFgsSl/pkKhCj37QBK+1nLdlPVFK0AF5ZSvhe1s5VoFiRWCzUw3Wyycq3T9V3gSCtl5FNDjADHlFjnEPP1KZIIbcznXfTaPDuN0R+w7DTziwYsdsjKqSgHHj3VVtEXbp3OO95qVmDPM2Mv2+FkW9B5rrNHCTrnWyE/2VxzKs3/nsIdzuIg9HKBKOwUNTjbhY7wqbyfBUFcLWurSdVHSXJdJ1DGlLe/L6Ar3Ts2SpbdOd3awnWrH05kWUsBYK1WNVq0Tr416uK90ZQV4Jj3W+PzBgeoX0edqEkzV2tTJ7BmfQopcUPQfbHOsH0djLZ+m9iYizz6Y9k/kUtRgp1bEqduNrjf93A4qzC5qOlKLpF/xNx5bz3OBmVWCuQHbCU1fSS0XjoFmS9DAA996vU7HTJUcllu4z2RuQB4PFVA67nz5Tbuq2et4boOfdN5660vnlj5TM+9r9CDb4/tvShHwkJvCWr7erKu+e/WJ/TX1PEi5Ou79xPU8NTpgjhDmQFEBMSLEHiE2sHOV6hRskCFWvhNCZ6ok9S1SaeT608MMfXAE+YP2u64nhYIpePV+hVH5LD2GPivkIa0JbmuPozm7OXZUEXlwh6WUDckVQPWyT6vV+7NyOcxEYRYcg7L4f05QzBO+mRaJjoACJdoEU3aoTrTJjc68kALGMAxJtT4yDV1cfOcC8qHXOrnUWekXOSfelLObfg1PCtVQ01GfBPd6qAanx3ooUzppmMeMnTXbBDpP7zxvwHSWTdpVrlaHPrdEh4CjJIjGU02tU2W6XOTUvhbwoOU0KcPwm1D9ceCE/dQn1U5cp21iH3qFQv2LZBC6EVZJGQ3nkoaan0QKS6mPdOp+/qYbvNVCnyPHXXnBf9KmWmWsXFfaVh1HBXvVUuR1un58O2hlaX34XKUKPWboEYo9oDoHzPaAtgP6Flj3KHCUsnYXQ349rqk9eZZaPiQNZWd4O2ey74eIzQwk2hYVZh7h8c8ltK9BLpwvANCiwRrLFGKerb8pJY7wW4UKsySiitTbtpbX6FCiT7bNcWRHx1PRNNjRnhGGkvOBh1GgwphyFHZYY4215PM7ic60kFLnMxelMgsuHC4UOsHpe/G7xikIKilXmQow3umvFpruK1kgn+jZYrxglajxUmvSxTPH2NHtd7lH94L7nHWc6Oraj4RCSD1VTrAlYza2LdASH9mk6mFhvXZyzPgIb7ZVGXCXpiaDZk3jalCCOzisxgZDBhRJQFmiouyD8NatEpklx0BhG29Razk+uICkjFO1Yu8H9KSQLzVzasbqE/JChM/W+cvPCp/p8Svqy/LEs8S8w96eHzBHDe4d4wioRq+beD1kqp9V+dM+ImPnHNCIP0/UzRVx0Lr4wAlvebSo0WCOGFdAG4C+AWIBRNq13dBfahHNYEogAzemhIUKKW9JqcDRTbtBvud8UGiY61TnBMeWfUghxfycJqy437AZ2D8VrKk9o0b27SZMHmHJjwwO7VDDgqwA7oOkZVSA2/FniLDcftl+LZKwKs1aDSXKUKCqZkAE6pbWlPm7rJ41AoC9U6rGZ1pIAXmxe+3GQo0zM1H7wTus+VIhRQbFSepxeYVoVEgxOoc+KT7XWw2smzKrKaHkBaX3I2g9dDErBLoJRfbC9COYIHZKiFrfZY+X72MGG6jPTX2EGRbUZLF2Pm5IAax9WkgM7CAIoeHmHKttwoE+FT8GChMpnKLCB/K974fjft8GielcoVWh0BKZkrc49D7WVS0HjYSDu1/7m/OWv2uOSe6vqZOqYzFahGfH/txWyvR14+85ojTXQddTJa8pVEHn1BTU6YWUr6P1kW0qjbFBjMEsqCGcwYRURBzqpTxjL33nM2uQdCsI7/HwrFroXG9aT/Kiwn1HAaSbdFk/+r1VUNXgEfA5iGjbuVokrnGmZFbiUTfMEMGj3ZnFglvr2VYC+Dloynq0GIDECmUoURUFqroGYoEQk5IaO/SdzShTaMLg1TqJzrSQmsvLCyll4GRyuj9KBUTlyqIjWzVDRiwppgzkxXsEs56YmHPKUawCUbVfPa1XsyyzLt45zrLYBg+jZBofqe4hBxV2U/AO+5GLbFxythqvYmy1lsjOXhMGlj3sIE1noMR6SKKSx6wQXZA+LmWCyiA8lXKdMtApPwoZ6FSfedhS26uMiG1VCxKY7kd1pGtkpgpz1lWfrcdxqIWi5W6jyr2yf4OH3FUTQN/paIopet+qohtk8qo4ajSf7xtg05fsLboVcjTjEkc4QolDHGIPHeZDHokeHVZYYYlDHOEQHRZSX2YE4ThOKWqqVG6C1puQsApxXT9eiWQfct4GZD5WIQsvy8tZ4AAVAg5gvtZraBCHzCN+PDT3aO5D/jPbuUihJ6ZgWxC7lTIb+qDEXhKKJppK0LtkdpMFPFGoVSiLEmVdo6oqzOZ7KEKBcr/Eqlli3a5RLsxXaIrR6V0MZ1pI0QzmYldNW2GZ3Oljx7IKDGUY3scEjDeETmlKPkcXJ7Q6ygkXlBgLHaZxUYHERa+TPEo5bJt9Dggy5LYXfAwzEPLrpDyNFIO0i21TuIzXKTNaIB/jrUKKbaef0JhLhJ4O2iTtTdvG51YT1p1qdFPMmWOr2egJufgsAOq4VgFjfbepXPTuWn3x2bqQ9DqdK7QkdAsEmXYl15FU+2a5qjCQManCw2f6sPYcIGH68zq5yHM4cJ6TKkj4DNZZr9MAghLj9cT55mEoD5l6y4b9oHunVBBo27mvbYEOh1jjEEdpnpkdbsHTC1zBVVzBIY7QY4kMB/t5rW3gd5w3fr4Edz37zAtT1tMTx1rnJvvN9wdzZFocX8QSHZYJ6tOgn22wdEA+I3eJHjNYxnSGKtkWE7sjDAmXZtCMm7ntNpOKYdalAzNDiaKoEIoKKNIMCQExlIgRiH0vfcOQq5M8YEZnWkg9BWYKE25S7ZgaigofLlwyez8x1VFLItPScoBNhzsnCycxTXgKAy5GFUgk3bCnmQDoR1OqpIycz6/A+VRiBPAoFlgjDnu3OJ0YYk2hogKHUIMurK9hDJv1GDt0aTlexViTU0tRQ3Ytb16EHS+dFxf7P1t4ceRPUcY9NRbsF8I3c4whV1p6ClepVewVFIW6AGzUVYMeGArNMR37+4woFDmWOh/4fg6b84uZLVS4qqtZ4UzSBYznkEJ/NkY8BG8c1qHzBMhJWMnQW7kOyP1Xu++VdO4ysEBz7qkSp3AokK0kKoVcZ1ynGqX5NbQoscJFfC1t2N3DGmt0aNDiUVzFNTyMR/EwelwD8ChMudJAGS8YOMa0Vq4h+1o5Zt6fyHop7Eyrj9cDYwSE16nyyc9mXVogQocC13ANK/RYIOIItpbJ+6iQUqiz/pxjzEYe0aHEEj2APZQo0s4pOzK+Qok5KuyhxgE6XEVMnKJPNefh7xkOtKMQS1QoyxmKsgbKCl0P9DEAVYm269CtGzTR/F/rJDLXp8qaeMaFFJm134dEa4QTfIo4UbywAsaOawoixaZVu2rlvXNlszyd2BpQoKGw/Jt7pcjwlEkR4lBGaAEWAftpqVhQg9ECY61Ky2EQiTqTSYRYFhgvBApx1o3lMMOAaoIaRMI6M3qKQpB9xjHjZw1kIANQZqzjqr491e5ZjhdCmjtOfYZ+nijcRK2bfUYFiO1Sh7lCXSXGFrhqz+rzVGGl71MwbCflTKVJ4jgeSH9oeb7vVOO2uhSwjNhrrJBzv5Fh93I9GS0w7j9ts7abc8A/Wy0+WlCqMM7kOz5PM6Yv0GIJOwnLIt5WKcKywxGu4BArXEE/WP2aHSZKXdSqpQLYIfMXnVsa9KJzQBUg+qm5BtTCVph+lb7jmFlfhPQy0UVYjsqnBgcphO/nVUy9bQFTLSwRbJMAupgyV5xLIRkl9qpzMP8S0PYVYgzoY5/aFxLMN7b3i1CiqGpgf8+sqGCrNgLouw5djEk8LsFMFvn0tpPpTAqpGK3rqd2plkrSQAYfgq77Zgq5XvFpTiDmTKNFALnX59ryPhRI+UPdkRmM+jE4XBqQAIytGM07xueYlhaxTGzCtPY4CAjVWpepDC5WIFsAXDhk3ktkS2khdeDvBQi1ZCGmAp8pY9RKUt1dI8W4qBpkbZL9q4twilSYsQ1em9VgCs2pyIXutx+octBhLNRU4KsloFb0XMrRuaGOeZajChW/02wYU8EU/G4KmmTd9zEWcsFdp33H59oct1Bi+i78pnS/72+K1PL0PlGtA/tLf1e/I8vwVjDHhWN9DR32EHAFLUp0SU+3vPxXscQh7BDHaxhD1BQ+GpShddC1yPWq0b261lmvpdyzlHcKKXUt8B6O/34qyxQGOwfbIDjzLl9N6/oq8tpQXzmJApTPqFMNS3Ro0KBCgxYlWnSYIWIPBRpE1OgRCgwldrFDjH2aCZZ2aT2oLXbMYo/eog5DgaKIKENOFoC+R9f3aPo12tjgEMukSFgY+lUGZMXjZhMQ4klX3ID0x3/8x3j605/+eFdjRzva0Y529A3SH/3RH+E7v/M7t/5+JoVU3/e4//778ZznPAd/9Ed/hIsXLz7eVTqzdOXKFTz96U/f9eNjQLu+fGxo14+PHd3IfRljxNWrV/G0pz0NReExp0xnEu4rigLf8R3fAQC4ePHiDdf5Z5F2/fjY0a4vHxva9eNjRzdqX166dOnEa7aLrx3taEc72tGOHmfaCakd7WhHO9rRDUtnVkjN53O8/e1vx3y+LaXjjk5Du3587GjXl48N7frxsaNvh748k4ETO9rRjna0oycGnVlLakc72tGOdvTtTzshtaMd7WhHO7phaSekdrSjHe1oRzcs7YTUjna0ox3t6IalnZDa0Y52tKMd3bB0JoXUu971LnzXd30X9vb2cMcdd+B3f/d3H+8q3fD0sz/7swghjF7Pfvazh9+XyyXuvvtuPPnJT8b58+fxmte8Bg899NDjWOMbgz7+8Y/jh37oh/C0pz0NIQT8+q//+uj3GCN+5md+Bk996lOxv7+Pu+66C3/wB38wuubhhx/G6173Oly8eBE33XQT3vCGN+DatWvfwlbcGHRSX/7Yj/3Yxhx9xSteMbpm15fAPffcg5e85CW4cOECbr31VvzIj/wI7r///tE1p1nPX/7yl/GqV70KBwcHuPXWW/HTP/3TaFufhvjxpzMnpH7t134Nb33rW/H2t78d/+2//Te84AUvwMtf/nJ85StfebyrdsPTn//zfx4PPPDA8PrEJz4x/PaWt7wF/+k//Se8733vw7333os/+ZM/watf/erHsbY3Bh0eHuIFL3gB3vWud03+/vM///P41//6X+MXf/EX8elPfxrnzp3Dy1/+ciyX+eSn173udfj85z+PD3/4w/jgBz+Ij3/843jjG9/4rWrCDUMn9SUAvOIVrxjN0V/91V8d/b7rS+Dee+/F3XffjU996lP48Ic/jKZp8LKXvQyHh4fDNSet567r8KpXvQrr9Rqf/OQn8cu//Mt4z3veg5/5mZ95PJp0PMUzRt/7vd8b77777uHvruvi0572tHjPPfc8jrW68entb397fMELXjD52yOPPBLruo7ve9/7hu/+5//8nxFAvO+++75FNbzxCUB8//vfP/zd9328fPlyfOc73zl898gjj8T5fB5/9Vd/NcYY4xe+8IUIIP7X//pfh2s+9KEPxRBC/L//9/9+y+p+o5HvyxhjfP3rXx9/+Id/eOs9u76cpq985SsRQLz33ntjjKdbz7/5m78Zi6KIDz744HDNu9/97njx4sW4Wq2+tQ04gc6UJbVer/GZz3wGd9111/BdURS46667cN999z2ONTsb9Ad/8Ad42tOehmc961l43etehy9/+csAgM985jNommbUr89+9rNx++237/r1GPrSl76EBx98cNRvly5dwh133DH023333YebbroJf+Ev/IXhmrvuugtFUeDTn/70t7zONzp97GMfw6233orv+Z7vwZve9CZ89atfHX7b9eU0PfroowCAm2++GcDp1vN9992H5z3vebjtttuGa17+8pfjypUr+PznP/8trP3JdKaE1P/7f/8PXdeNOhYAbrvtNjz44IOPU63OBt1xxx14z3veg9/6rd/Cu9/9bnzpS1/CX/yLfxFXr17Fgw8+iNlshptuuml0z65fjyf2zXHz8cEHH8Stt946+r2qKtx88827vnX0ile8Av/hP/wHfOQjH8G/+Bf/Avfeey9e+cpXouvsSL9dX25S3/f4yZ/8SXz/938/nvvc5wLAqdbzgw8+ODlv+duNRGfyqI4dXT+98pWvHD4///nPxx133IFnPOMZ+I//8T9if3//mDt3tKNvDf21v/bXhs/Pe97z8PznPx9/5s/8GXzsYx/DD/7gDz6ONbtx6e6778bv//7vj/zL3250piypW265BWVZbkSpPPTQQ7h8+fLjVKuzSTfddBP+7J/9s/jiF7+Iy5cvY71e45FHHhlds+vX44l9c9x8vHz58kZQT9u2ePjhh3d9ewI961nPwi233IIvfvGLAHZ96enNb34zPvjBD+K3f/u3RyfbnmY9X758eXLe8rcbic6UkJrNZnjxi1+Mj3zkI8N3fd/jIx/5CO68887HsWZnj65du4b//b//N5761KfixS9+Meq6HvXr/fffjy9/+cu7fj2GnvnMZ+Ly5cujfrty5Qo+/elPD/1255134pFHHsFnPvOZ4ZqPfvSj6Psed9xxx7e8zmeJ/viP/xhf/epX8dSnPhXAri9JMUa8+c1vxvvf/3589KMfxTOf+czR76dZz3feeSf+x//4HyOh/+EPfxgXL17Ec57znG9NQ05Lj3fkxvXSe9/73jifz+N73vOe+IUvfCG+8Y1vjDfddNMoSmVHm/RTP/VT8WMf+1j80pe+FH/nd34n3nXXXfGWW26JX/nKV2KMMf7dv/t34+233x4/+tGPxt/7vd+Ld955Z7zzzjsf51o//nT16tX42c9+Nn72s5+NAOIv/MIvxM9+9rPx//yf/xNjjPEd73hHvOmmm+IHPvCB+N//+3+PP/zDPxyf+cxnxsViMZTxile8Ir7whS+Mn/70p+MnPvGJ+N3f/d3xta997ePVpMeNjuvLq1evxn/wD/5BvO++++KXvvSl+J//83+OL3rRi+J3f/d3x+VyOZSx68sY3/SmN8VLly7Fj33sY/GBBx4YXkdHR8M1J63ntm3jc5/73Piyl70sfu5zn4u/9Vu/FZ/ylKfEt73tbY9Hk46lMyekYozx3/ybfxNvv/32OJvN4vd+7/fGT33qU493lW54+tEf/dH41Kc+Nc5ms/gd3/Ed8Ud/9EfjF7/4xeH3xWIR/97f+3vxSU96Ujw4OIh/5a/8lfjAAw88jjW+Mei3f/u3I4CN1+tf//oYo4Wh/5N/8k/ibbfdFufzefzBH/zBeP/994/K+OpXvxpf+9rXxvPnz8eLFy/Gv/W3/la8evXq49Cax5eO68ujo6P4spe9LD7lKU+JdV3HZzzjGfHHf/zHN5TPXV/GyT4EEH/pl35puOY06/kP//AP4ytf+cq4v78fb7nllvhTP/VTsWmab3FrTqbdeVI72tGOdrSjG5bOlE9qRzva0Y529MSinZDa0Y52tKMd3bC0E1I72tGOdrSjG5Z2QmpHO9rRjnZ0w9JOSO1oRzva0Y5uWNoJqR3taEc72tENSzshtaMd7WhHO7phaSekdrSjHe1oRzcs7YTUjna0ox3t6IalnZDa0Y52tKMd3bC0E1I72tGOdrSjG5b+f1u23hA3QzToAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.squeeze(np.asarray(torch.permute(img_2[lb_2==1],(0,2,3,1)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "649f1288",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:45.502503Z",
     "iopub.status.busy": "2024-08-02T21:36:45.501671Z",
     "iopub.status.idle": "2024-08-02T21:36:45.507723Z",
     "shell.execute_reply": "2024-08-02T21:36:45.506913Z"
    },
    "papermill": {
     "duration": 0.024809,
     "end_time": "2024-08-02T21:36:45.509550",
     "exception": false,
     "start_time": "2024-08-02T21:36:45.484741",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.float32, torch.int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img[0].dtype,lb[0].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02bf2252",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:45.541816Z",
     "iopub.status.busy": "2024-08-02T21:36:45.541268Z",
     "iopub.status.idle": "2024-08-02T21:36:45.547976Z",
     "shell.execute_reply": "2024-08-02T21:36:45.547119Z"
    },
    "papermill": {
     "duration": 0.024884,
     "end_time": "2024-08-02T21:36:45.549944",
     "exception": false,
     "start_time": "2024-08-02T21:36:45.525060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1024, 3, 224, 224])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2f27dc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:45.582905Z",
     "iopub.status.busy": "2024-08-02T21:36:45.582322Z",
     "iopub.status.idle": "2024-08-02T21:36:45.589195Z",
     "shell.execute_reply": "2024-08-02T21:36:45.588280Z"
    },
    "papermill": {
     "duration": 0.025868,
     "end_time": "2024-08-02T21:36:45.591422",
     "exception": false,
     "start_time": "2024-08-02T21:36:45.565554",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df_p=train_df[train_df['target']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8e19814",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:45.625419Z",
     "iopub.status.busy": "2024-08-02T21:36:45.625086Z",
     "iopub.status.idle": "2024-08-02T21:36:45.935307Z",
     "shell.execute_reply": "2024-08-02T21:36:45.934445Z"
    },
    "papermill": {
     "duration": 0.329193,
     "end_time": "2024-08-02T21:36:45.937508",
     "exception": false,
     "start_time": "2024-08-02T21:36:45.608315",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age_approx', ylabel='count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df_p,x='age_approx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2552d0b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:45.971417Z",
     "iopub.status.busy": "2024-08-02T21:36:45.970876Z",
     "iopub.status.idle": "2024-08-02T21:36:49.242105Z",
     "shell.execute_reply": "2024-08-02T21:36:49.241253Z"
    },
    "papermill": {
     "duration": 3.29094,
     "end_time": "2024-08-02T21:36:49.244665",
     "exception": false,
     "start_time": "2024-08-02T21:36:45.953725",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "854aa86d41cb4a5682184a17e96de219",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/21.4M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = timm.create_model('efficientnet_b0', pretrained=True, num_classes=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bbc4d05c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:49.279700Z",
     "iopub.status.busy": "2024-08-02T21:36:49.279342Z",
     "iopub.status.idle": "2024-08-02T21:36:49.284643Z",
     "shell.execute_reply": "2024-08-02T21:36:49.283793Z"
    },
    "papermill": {
     "duration": 0.024711,
     "end_time": "2024-08-02T21:36:49.286560",
     "exception": false,
     "start_time": "2024-08-02T21:36:49.261849",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def freeze_pretrained_layers(model):\n",
    "    '''Freeze all layers except the last layer(fc or classifier)'''\n",
    "    for param in model.parameters():\n",
    "            param.requires_grad = False\n",
    "    model.classifier.weight.requires_grad = True\n",
    "    model.classifier.bias.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6a916fb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:49.322206Z",
     "iopub.status.busy": "2024-08-02T21:36:49.321348Z",
     "iopub.status.idle": "2024-08-02T21:36:49.329280Z",
     "shell.execute_reply": "2024-08-02T21:36:49.328377Z"
    },
    "papermill": {
     "duration": 0.027966,
     "end_time": "2024-08-02T21:36:49.331390",
     "exception": false,
     "start_time": "2024-08-02T21:36:49.303424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freeze_pretrained_layers(model)\n",
    "model.classifier.weight.requires_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ecd071fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:49.369495Z",
     "iopub.status.busy": "2024-08-02T21:36:49.368645Z",
     "iopub.status.idle": "2024-08-02T21:36:49.373759Z",
     "shell.execute_reply": "2024-08-02T21:36:49.372816Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear(in_features=1280, out_features=1, bias=True)\n"
     ]
    }
   ],
   "source": [
    "print(model.classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2ebb92fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:49.462459Z",
     "iopub.status.busy": "2024-08-02T21:36:49.461595Z",
     "iopub.status.idle": "2024-08-02T21:36:49.468843Z",
     "shell.execute_reply": "2024-08-02T21:36:49.467982Z"
    },
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     "end_time": "2024-08-02T21:36:49.470782",
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     "start_time": "2024-08-02T21:36:49.392757",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loss_fn=nn.BCEWithLogitsLoss()\n",
    "optimizer=torch.optim.Adam(params=model.parameters(), lr=1e-3)\n",
    "LR_SCHEDULER=torch.optim.lr_scheduler.StepLR(optimizer,step_size=5,gamma=0.1,last_epoch=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e80bc607",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:49.506173Z",
     "iopub.status.busy": "2024-08-02T21:36:49.505579Z",
     "iopub.status.idle": "2024-08-02T21:36:50.982987Z",
     "shell.execute_reply": "2024-08-02T21:36:50.981961Z"
    },
    "papermill": {
     "duration": 1.498232,
     "end_time": "2024-08-02T21:36:50.985498",
     "exception": false,
     "start_time": "2024-08-02T21:36:49.487266",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "a=torch.rand(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "48cf83c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:51.019962Z",
     "iopub.status.busy": "2024-08-02T21:36:51.019567Z",
     "iopub.status.idle": "2024-08-02T21:36:51.024112Z",
     "shell.execute_reply": "2024-08-02T21:36:51.023260Z"
    },
    "papermill": {
     "duration": 0.023868,
     "end_time": "2024-08-02T21:36:51.025981",
     "exception": false,
     "start_time": "2024-08-02T21:36:51.002113",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "device='cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7f039c4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:51.059543Z",
     "iopub.status.busy": "2024-08-02T21:36:51.059212Z",
     "iopub.status.idle": "2024-08-02T21:36:51.111941Z",
     "shell.execute_reply": "2024-08-02T21:36:51.110978Z"
    },
    "papermill": {
     "duration": 0.072177,
     "end_time": "2024-08-02T21:36:51.114338",
     "exception": false,
     "start_time": "2024-08-02T21:36:51.042161",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EfficientNet(\n",
       "  (conv_stem): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (bn1): BatchNormAct2d(\n",
       "    32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "    (drop): Identity()\n",
       "    (act): SiLU(inplace=True)\n",
       "  )\n",
       "  (blocks): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): DepthwiseSeparableConv(\n",
       "        (conv_dw): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pw): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): InvertedResidual(\n",
       "        (conv_pw): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (1): InvertedResidual(\n",
       "        (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): InvertedResidual(\n",
       "        (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(144, 144, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=144, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (1): InvertedResidual(\n",
       "        (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): InvertedResidual(\n",
       "        (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (1): InvertedResidual(\n",
       "        (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (2): InvertedResidual(\n",
       "        (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "    )\n",
       "    (4): Sequential(\n",
       "      (0): InvertedResidual(\n",
       "        (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(480, 480, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=480, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (1): InvertedResidual(\n",
       "        (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (2): InvertedResidual(\n",
       "        (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): InvertedResidual(\n",
       "        (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (1): InvertedResidual(\n",
       "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (2): InvertedResidual(\n",
       "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "      (3): InvertedResidual(\n",
       "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): InvertedResidual(\n",
       "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (conv_dw): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)\n",
       "        (bn2): BatchNormAct2d(\n",
       "          1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (aa): Identity()\n",
       "        (se): SqueezeExcite(\n",
       "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act1): SiLU(inplace=True)\n",
       "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (gate): Sigmoid()\n",
       "        )\n",
       "        (conv_pwl): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNormAct2d(\n",
       "          320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "          (drop): Identity()\n",
       "          (act): Identity()\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (conv_head): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (bn2): BatchNormAct2d(\n",
       "    1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "    (drop): Identity()\n",
       "    (act): SiLU(inplace=True)\n",
       "  )\n",
       "  (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
       "  (classifier): Linear(in_features=1280, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "81b41373",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:51.150123Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "a.shape\n",
    "a=a.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3b72eb48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:51.360975Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1024, 1])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=model(a)\n",
    "p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "098949b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:52.754062Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "def pauc(y_true, y_pred):\n",
    "    y_true=y_true.cpu()\n",
    "    y_pred=y_true.cpu().detach()\n",
    "    min_tpr=0.8\n",
    "    v_gt = abs(np.asarray(y_true)-1)    \n",
    "    v_pred = np.array([1.0 - x for x in y_pred])\n",
    "    max_fpr = abs(1-min_tpr)   \n",
    "    partial_auc_scaled = roc_auc_score(v_gt, v_pred, max_fpr=max_fpr)\n",
    "    partial_auc = 0.5 * max_fpr**2 + (max_fpr - 0.5 * max_fpr**2) / (1.0 - 0.5) * (partial_auc_scaled - 0.5)   \n",
    "    return partial_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "978cf4e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:52.797882Z",
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   "outputs": [],
   "source": [
    "def train_model(model,device,train_dataloader,loss_fn,optimizer):\n",
    "    \n",
    "    model=model.to(device)\n",
    "    \n",
    "    train_loss=0\n",
    "    train_score=0\n",
    "    for X,y in train_dataloader:\n",
    "        X=X.to(device)\n",
    "        y=y.to(device).reshape(-1,1).to(torch.float32)\n",
    "        optimizer.zero_grad()\n",
    "        with torch.set_grad_enabled(True):\n",
    "            pred=model(X)\n",
    "            loss=loss_fn(pred,y)\n",
    "        train_loss+=loss\n",
    "        optimizer.step()\n",
    "        loss.backward()\n",
    "        y_pred=torch.round(torch.nn.functional.sigmoid(pred))\n",
    "        train_score+=pauc(y,y_pred)\n",
    "    train_loss/=len(train_dataloader)\n",
    "    train_score/=len(train_dataloader)\n",
    "    print(f'train_loss={train_loss} | train_score={train_score}')\n",
    "    return train_loss,train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "258137e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:52.842459Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e3678c10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T21:36:52.885781Z",
     "iopub.status.busy": "2024-08-02T21:36:52.884988Z",
     "iopub.status.idle": "2024-08-02T23:07:26.211059Z",
     "shell.execute_reply": "2024-08-02T23:07:26.210102Z"
    },
    "papermill": {
     "duration": 5433.366576,
     "end_time": "2024-08-02T23:07:26.233264",
     "exception": false,
     "start_time": "2024-08-02T21:36:52.866688",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [1:30:33<00:00, 5433.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_loss=0.004009579308331013 | train_score=0.00019548246018665725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epochs=1\n",
    "train_loss=[]\n",
    "train_score=[]\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    l,s=train_model(model,device,train_dataloader,loss_fn,optimizer)\n",
    "    train_loss.append(l)\n",
    "    train_score.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ba50a2",
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