{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d848f74",
   "metadata": {},
   "source": [
    "## **2. Cross-sample analysis**\n",
    "\n",
    "In this tutorial, we demonstrate how to perform prediction and evaluation on cross-sample scenario. We train XChrom model using one sample and then using another independent sample as the test set to evaluate the model’s transferability to different samples, thereby illustrating the model's performance in handling data with inherent batch effects.\n",
    "\n",
    "We utilized a publicly available dataset of single-cell multiome data collected from bone marrow mononuclear cells (BMMCs) of 12 healthy human donors. The dataset includes multiple batches arising from donor sources and sequencing platforms. All samples were prepared using a standardized protocol. The data was subsequently annotated to identify cell types and remove doublets.\n",
    "\n",
    "- **Data Preprocessing**                                       \n",
    "    - As an example, we extracted two samples labeled `s1d1` and `s2d1` (where `s` denotes sequencing site and `d` denotes donor). We used `s1d1` as the training set and `s2d1` as the test set. For the training scATAC-seq data, we performed partitioning by cells and peaks to generate training/validation data for model input, following the same procedure described in the `1_Within-sample analysis` tutorial. For the test scATAC-seq data, we used the entire test set for evaluation.                                                 \n",
    "    - For the scRNA-seq data, we first performed batch effect correction on both `s1d1` and `s2d1` samples using Harmony (though alternative methods such as Scanorama, scVI, or ingest could be employed). The batch-corrected cell embeddings-typically named `'X_pca_harmony'`-were stored in the respective scRNA-seq datasets of both samples and served as input for model training and prediction.\n",
    "\n",
    "- **Model Training**                                    \n",
    "XChrom takes two inputs: ① DNA sequences (one-hot encoded, 1 × 1344bp) and ② cell embeddings (cell number × 32). The model is trained to predict chromatin accessibility probabilities for each genomic region across all cells. Importantly, the cell embeddings used here are derived from batch-corrected scRNA-seq data from both samples.\n",
    "\n",
    "- **Prediction and Evaluation**                   \n",
    "After training, we evaluate the preprocessed test sample from two perspectives:                              \n",
    "    - **Binary classification metrics:** auROC and auPRC, as primary evaluation metrics, which were summarized at the overall, per-cell and per-peak levels.\n",
    "    - **Cell state fidelity:** Using the neighbor score (NS) to quantify cross-modality neighborhood concordance by comparing scATAC-seq and scRNA-seq data, and the label score (LS) to evaluate the consistency of cell-type labels within local neighborhoods. Both metrics depend on a parameter k, the number of nearest neighbors in the cell-cell graph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257eccff-77f6-4260-9177-ce21cb5c3229",
   "metadata": {},
   "source": [
    "### 1. Download Data (BMMCs: s1d1-s2d1)  \n",
    "- The original data is sourced from [GEO accession GSE194122](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122). We extracted samples s1d1 and s2d1 from this dataset and made them available at: https://doi.org/10.5281/zenodo.16959682.  \n",
    "- After downloading, save the data as `'./data/2_cross_samples/s1d1_s2d1.h5ad'`.  \n",
    "- Download the human genome file from:  \n",
    "  [https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz](https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz)  \n",
    "  and decompress it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3416d2b2-7282-4750-9d60-d3376f75dcd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"3,2,1,0\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "529f57e2-7f94-4cb2-b730-6b680a5461de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xchrom as xc\n",
    "import scanpy as sc\n",
    "import scanpy.external as sce"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7151f2e9-331d-4351-9968-82742191189f",
   "metadata": {},
   "source": [
    "### 2. Read and filter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13921364-ebd3-4b32-b915-62e4d3c5d8c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 10444 × 129921\n",
       "    obs: 'cell_type', 'batch', 'Samplename'\n",
       "    var: 'feature_types', 'gene_id'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = sc.read_h5ad('./data/2_cross_samples/s1d1_s2d1.h5ad')\n",
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eefa6547-aebc-4948-ad68-85f7b989227f",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_rna = adata[:, adata.var['feature_types']=='GEX']\n",
    "adata_atac = adata[:, adata.var['feature_types']=='ATAC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73fa4504-006b-4504-8db0-8aa582d6ed65",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_61297/2766228937.py:7: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n",
      "  adata_atac.var['chr'] = chromosome_start_end_df['chr']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     chr   start     end\n",
      "chr1-9776-10668     chr1    9776   10668\n",
      "chr1-180726-181005  chr1  180726  181005\n",
      "chr1-181117-181803  chr1  181117  181803\n",
      "chr1-191133-192055  chr1  191133  192055\n",
      "chr1-267562-268456  chr1  267562  268456\n"
     ]
    }
   ],
   "source": [
    "## Break down the features in var into chr, start, and end\n",
    "features = adata_atac.var.index\n",
    "chromosome_start_end = features.str.split(\"-\", n=2, expand=True)\n",
    "chromosome_start_end_df = chromosome_start_end.to_frame(index=False)\n",
    "chromosome_start_end_df.columns = [\"chr\", \"start\", \"end\"]\n",
    "chromosome_start_end_df.index = adata_atac.var.index\n",
    "adata_atac.var['chr'] = chromosome_start_end_df['chr']\n",
    "adata_atac.var['start'] = chromosome_start_end_df['start'].astype(int)\n",
    "adata_atac.var['end'] = chromosome_start_end_df['end'].astype(int)\n",
    "print(adata_atac.var[['chr', 'start', 'end']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53a19888-af89-4227-bc44-10541d5826ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 10444 × 13431\n",
       "    obs: 'cell_type', 'batch', 'Samplename'\n",
       "    var: 'feature_types', 'gene_id'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_rna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb517f85-44bc-49b9-9616-00747a6e91b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_rna = adata_rna[adata_rna.obs['Samplename'] == 'site1_donor1_multiome']\n",
    "train_atac = adata_atac[adata_atac.obs['Samplename'] == 'site1_donor1_multiome']\n",
    "test_rna = adata_rna[adata_rna.obs['Samplename'] == 'site2_donor1_multiome']\n",
    "test_atac = adata_atac[adata_atac.obs['Samplename'] == 'site2_donor1_multiome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a405eea0-19ef-435c-868c-1b66b8d40ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(View of AnnData object with n_obs × n_vars = 6224 × 13431\n",
       "     obs: 'cell_type', 'batch', 'Samplename'\n",
       "     var: 'feature_types', 'gene_id',\n",
       " View of AnnData object with n_obs × n_vars = 6224 × 116490\n",
       "     obs: 'cell_type', 'batch', 'Samplename'\n",
       "     var: 'feature_types', 'gene_id', 'chr', 'start', 'end',\n",
       " View of AnnData object with n_obs × n_vars = 4220 × 13431\n",
       "     obs: 'cell_type', 'batch', 'Samplename'\n",
       "     var: 'feature_types', 'gene_id',\n",
       " View of AnnData object with n_obs × n_vars = 4220 × 116490\n",
       "     obs: 'cell_type', 'batch', 'Samplename'\n",
       "     var: 'feature_types', 'gene_id', 'chr', 'start', 'end')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rna,train_atac,test_rna,test_atac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63e15bcb-5219-4a47-892c-e201b0258140",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNA data after filtering: View of AnnData object with n_obs × n_vars = 6224 × 12228\n",
      "    obs: 'cell_type', 'batch', 'Samplename', 'n_genes'\n",
      "    var: 'feature_types', 'gene_id', 'n_cells'\n",
      "ATAC data after filtering: View of AnnData object with n_obs × n_vars = 6224 × 51571\n",
      "    obs: 'cell_type', 'batch', 'Samplename', 'n_genes'\n",
      "    var: 'feature_types', 'gene_id', 'chr', 'start', 'end', 'n_cells'\n"
     ]
    }
   ],
   "source": [
    "### Filter data\n",
    "train_rna, train_atac = xc.pp.filter_multiome_data(\n",
    "    ad_rna = train_rna,\n",
    "    ad_atac = train_atac,\n",
    "    species = 'human',\n",
    "    filter_ratio = 0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "052964e7-4ef9-4a9f-8bc9-5c65949d8df8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNA data after filtering: View of AnnData object with n_obs × n_vars = 4220 × 12210\n",
      "    obs: 'cell_type', 'batch', 'Samplename', 'n_genes'\n",
      "    var: 'feature_types', 'gene_id', 'n_cells'\n",
      "ATAC data after filtering: View of AnnData object with n_obs × n_vars = 4220 × 56439\n",
      "    obs: 'cell_type', 'batch', 'Samplename', 'n_genes'\n",
      "    var: 'feature_types', 'gene_id', 'chr', 'start', 'end', 'n_cells'\n"
     ]
    }
   ],
   "source": [
    "test_rna, test_atac = xc.pp.filter_multiome_data(\n",
    "    ad_rna = test_rna,\n",
    "    ad_atac = test_atac,\n",
    "    species = 'human',\n",
    "    filter_ratio = 0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ae0c17b-f209-4b91-b77e-e7cb00aaf8f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/miaoyuanyuan/miniconda3/envs/py3.8_tf2.6.0/lib/python3.8/site-packages/anndata/_core/anndata.py:1230: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n",
      "  df[key] = c\n",
      "/home/miaoyuanyuan/miniconda3/envs/py3.8_tf2.6.0/lib/python3.8/site-packages/anndata/_core/anndata.py:1230: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    }
   ],
   "source": [
    "train_atac.write('./data/2_cross_samples/train_atac.h5ad')\n",
    "test_atac.write('./data/2_cross_samples/test_atac.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b99ffd7-e43f-4b6c-a924-6c97a89ccf0e",
   "metadata": {},
   "source": [
    "Filtering was performed on the scATAC data of both the training and testing samples separately. We removed peaks accessible in fewer than 1% of cells and excluded sequences not belonging to chromosomes chr1-22, X, or Y. The filtered scATAC data was saved in `'./data/2_cross_samples'`.\n",
    "\n",
    "Similarly, the scRNA data of both training and testing samples were filtered to remove genes expressed in fewer than 1% of cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee23a923-0a3c-431d-b46b-5580bff5549d",
   "metadata": {},
   "source": [
    "### 3. Process scRNA-seq data to generate raw cell embeddings (2 samples batch correction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3a4728-67e7-4e28-a2e7-15a9b1646f00",
   "metadata": {},
   "source": [
    "We use Harmony to correct batch effects between the two samples. After filtering, we concatenate the samples, perform PCA (e.g., 32 components) to embed the transcriptomic profiles, and then run Harmony on the PCs to iteratively remove dataset-specific effects. This yields batch-corrected embeddings stored as `RNA.obsm['X_pca_harmony']`, which represent each cell and serve as the raw cell embeddings for subsequent model input.\n",
    "- Alternatively, latent embeddings from other integration methods—such as scVI or biology foundation models—can also be used as raw cell embeddings. Save the resulting array in `RNA.obsm[cellembed_raw]` (shape: n_cells × d) so the model can access it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "632612a1-a4f9-45bb-808c-db98560ab31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/miaoyuanyuan/miniconda3/envs/py3.8_tf2.6.0/lib/python3.8/site-packages/anndata/_core/anndata.py:1763: FutureWarning: The AnnData.concatenate method is deprecated in favour of the anndata.concat function. Please use anndata.concat instead.\n",
      "\n",
      "See the tutorial for concat at: https://anndata.readthedocs.io/en/latest/concatenation.html\n",
      "  warnings.warn(\n",
      "/home/miaoyuanyuan/miniconda3/envs/py3.8_tf2.6.0/lib/python3.8/site-packages/scanpy/preprocessing/_simple.py:842: UserWarning: Received a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1455.6x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rna = train_rna.concatenate(test_rna, batch_key=\"batchid\")\n",
    "rna = xc.tl.calc_pca(rna, n_comps=32, max_value=10)\n",
    "# Calculate the neighbors based on the PCA space before batch correction\n",
    "sc.pp.neighbors(rna, use_rep='X_pca')\n",
    "sc.tl.umap(rna)\n",
    "sc.pl.umap(rna, color=['batch','cell_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7baf296-bb59-4ebf-b675-c893044c02b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-17 15:28:50,221 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans...\n",
      "2025-08-17 15:28:52,369 - harmonypy - INFO - sklearn.KMeans initialization complete.\n",
      "2025-08-17 15:28:52,435 - harmonypy - INFO - Iteration 1 of 10\n",
      "2025-08-17 15:28:54,739 - harmonypy - INFO - Iteration 2 of 10\n",
      "2025-08-17 15:28:57,068 - harmonypy - INFO - Iteration 3 of 10\n",
      "2025-08-17 15:28:59,430 - harmonypy - INFO - Converged after 3 iterations\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Batch correction\n",
    "sce.pp.harmony_integrate(rna, 'batchid')\n",
    "'X_pca_harmony' in rna.obsm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92b48f69-772a-49a6-b2b5-b558fcf46c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9UpIdlBSlsHVnM4NL07nkvLHfQsuFEOLYIX+zhRDi+8/8bTdAiK/Fnrdg0bVgT4RAX3SZvw8uXMhrb5dh0ESZVswDcbdT4t5CsuJmsHcrnWU+IlYNsxqi7uE5bE06lfnX3YPZJINRhBBCCCH+L5avOMiO3c1YLSqhcDRbPinRwaxpA/jrk2sASE+Loavbx+GUop5ePz29zQB093h58C+fMWZkNqefNOxb6YMQQgghhBBfN4lCiu+n9MEQmwFZozg8hCTS2whahAVTCjGrCv6wzlZ9CL1GPDPUcjr0RPYYxZwVuJeAKY7i0AEGtS3BWbcbtPC32h0hhBBCiO+6vJxE7DYzKclHRjC63AEAJo7NB6Cr24fNavrCvjEOC719fkJhjV3lLXR2e7+ZRgshhBBCCPENk4C9+M7aXt9Da5//qGV/+fgAI+/9iLWeHLi1krZz3uaZ8DwAzA1robeOffv2kGGPTmTW0hfgTNMmitV2MtQ+5pm2UUA7Dm8zQcPMFn0IaS/PYuVfFrC+qou73ytnyN0fsqG66xvvrxBCCCHEsUzTdKpquwiFj0wYaxgGzy3cyl+fXMvg0nTu/PnxTBiT279+3aY6whGNhmYnihKtahgIfnHC2VBYQ9OiaRihkM4/nlnPE89toKfXxxPPbeQvT6zB6wt9/Z0UQgghhBDiayYlcX6Attf38o8VlVx3XAlTS1K/7eb8f1lX2cWCf20mN8nB+tuP71++q9GJOxChrNFJhyvAixvq2a0toIFMzhqZzpC2Ks7fcCED9EGczz2oClRO/QMZkW1k2kPs2F7D2f4NPBz+EZ1GAr+2vA5AvUtn48Z6uj1BAmGd2i4v00rSvq3uCyGEEOJ7pqW1h+4eD4NKs7HZLN92c/6/rFhXzdqNtYwdmcM5p40AQNMMmtv6CId12jvdbN3ZyJ59bf37zJk5gPeW7aWisrN/mdmsMnv6ALzeEOGIxo7dLXzZtFttHR4qKjvo7vES0XT8gTCxMdavv6NCCCGEEEJ8jSRg/wP0+pYGVh3oJM5mPipgH9H071CtdgMFaHX62dHQy7iCZHY09HLTnFIumpjPE6uqKW9xAZAcY2FPynlU9amYX13DS1YFu81KYXwMz142gb61/6Rn9+u8mH8b1/ifI0NxUmXK5VI+JUXx0mYkc1/kMgrbXLx1/TTKW/qYPSj92+2+EEIIIb5X6hu6CATDpKbEkZOd0r9cNwxURfkWW/Y/p0WidekrDnYQmBvGajNTU9/NgvPG4fEEeH1RWX/2fYzDQlFBMqvWVeMPRABw2C1kpMdx2QXjeOzZDfQ6/QwbnI6uR4P1qgq6fvQ5e10Bfnz5ZMJhnbSUWIQQQgghhPiuk4D9D8z7ZS1srevhhKEZ3Dx3YP/yd3c28cs3y/jZ3IHccsKg/uX7W108uaqay6cVMb4w+dto8hfUdXn55+oaDMAA3tzaiK4b/OifGzGbFKaXpPUH608dkcX4wmQeXLofu1kloI9kSuAfvDHdw+rtl0Pzg3Q2LyNdrePT+s941HwJs7SN3GR6B58RzdDyG1YMVJJjrXS6g9hMKsp35MFZCCGEEMe+yuo2FFUhOyuJjPTE/uUVB5tpa+9j1IgCUpLj+pe3d/TR0+uhpDgTq/XYuJ2vb+qlbG8LAP5ghJr6Hto73axcV0NKkgOTSSUU1jCbVCaNz8fnC7OrvAX1UBkck6owbHAGB6u7aGt30+uMlj3cd6ATk0lB04wvBOsBkuJtuNxBEhPs32R3hRBCCCGE+Np8V9KpxVdk+d426rp95CQ5GJgZ37+8vtuHbkBt19ETeL24oY4lZS08vab6m27qf3Th05tYV9VFZoINgM3btlD/r0uZoFQQ1gxWHYwOqb5rRA9PnhTDxKIU4u1m5g+LZU3KgzyX8SYF/goI9EHDJt7L/hnPRU5mtFLFfcYT7NBLCRsmmm2lAKSpHu6dm8XTY+vJfmooS5//HeXNfdR3e79QQ18IIYQQ4n+rvd2J3x8iNSUes/nIhKs+XwjDMAgEwkdtX1PXTlu7k47Ovm+6qV+qs9vLc69sxe+PEBcbTXh4a3EZK9fVANDj9PdPEjtpfD4zJheRm52A2awybnQuVouJkgGp1Df24vYE6ez2HhWAP1y7HuifkNZhN3P+WSOprOnm5Td38PSLmwFoaXMRDEa+kX4LIYQQQgjxdTg2UnLE1y6i6TzwwT5c/jB5yQ6OH5Jx1Pqb5pQyJCueg+0eajo9DEiPZnFdNaOYkKZz+dSib6HVUcGIxnPratENmD8ul9H5iUTqdZ6/YiLnPbmBS41PONe0jjRcXB6+HYBbhnu5puomqI9l9K9r2XPvyezc8CkFB/dhqBYis56hyVJEQdlfWRBegsPspklPw6QY1BtZzFae59ZTxpHat5jk1XdxadklBNy9xCoBZtqq0XSDKx55G6cljfWXpxNjs0LOmCONPrAceutg0o+j47eFEEIIIf5NIBimuqYNh8OKRdNJTIg5av3I4fl0druJhDUiEa0/mF9SnEVPr4eMjMQvO+w3wtnnZ8vORpIT7QwqzSA5yYHDYeHk2YN47rVtaF+SDT9uVA4bttTT3unh8gvGM3l8AS+/uYNQWCMYiHDuGSPZsrORxR/u/dJzFuQmMWv6ALbsaORAVSefrKqi91DyRHKSg117mlm0dC/FBclMn1xEZnp8f+DfMAw2b28kKdHOkIEZX3p8IYQQQgghjgUSsP8e+de6Wh79rJKHzh3FKSOyjqzY/gKhTc+zrel89hpFAPzstZ2UZsRxwrBMZpSmMSovib0tLv6xoorNtd0svGYKAIMy43nk/DH9hzr4xm8YUPEUpjP/jjL2km+kX0t2tfDQ8gMAfLyvncU/mQ5AQ7ePQETnFU4g3eTh5cic/n3y8gdAZx7+mGyeXV3PwMx4/rnTQU7oZ8weN4z9a7qo2ejlX5Y+LGp0Yrc3tdl8FJnMAT0XU0jhljd3868TSzlOseD1B0lSApQZpUy58RmUuo9YafkZm9SxOF7aDYrKwunLeX1fgGtmFHDGkktQjQhkDIEBs7+0X+XNfSwpa+GaGcVkyDBuIYQQ4nupfF8DbneAMaOKcDiOTIh64GALvX1e/P5Q/7Ltu2qw2yxkpCeQmpKA3W6hta0Xl8uPYRgUFkTn0MlITyAjPQGIBqLXbjhAV4+fk+YMJS7um7mn+GxNFWV7WwGY2RfklutnArBjd3P/Nqqq9NefB8jPTaJ8fzsOu5lV66opKkyhsqYLgIlj83h3aTkdh0Z7ms0qkUM18ZVDZXMamp28+vZOxo7KBaIvDSBaD/+Gq6ay8K0dAPT2+XnlrZ1kZcSTnRmPsy/AgMJkPltbjaoo3P2ruZj+w7xN5fvb6OrxMXNqESZJuhBCCCGEEN8CCdh/Txxoc6Ot/COPRcp5bsWv+dPyBNLibOSnOLih5hlKA+X8ZuBk1mQfzwcbdlEcqmNtw0h2NDj5MweYWpLClAFpDMtO4ORhWdz4ynaGZsfT2Otn5sB0zhidg6Yb+MqXYTaF8Sy9i7gR82HTk1A4DQqmfG19mzIglWHZ8dR0eSlMieHGe//Aj83LKB/yM85Qd/Ejywb8cx+kYoWHPJuFJqcfc2IW/GIvc37/GW2fVAJw4cR8/Knz6UmLJ75hJ89Z/kwAK2eFfs+gZDMr+7IYlhPPpd6tnBxZyb8sF7PSNZw7/X/hX/a/06472DH174xOy4S26H+dyTGtKEoGoYCH41ZfyDPhX7Nt2Qos4XEMSlEpzR5NzcoXSNz+GGXDfo03dzqnj8qmocfHH5btZ311N4oCd5w69Gu7fkIIIYT4drS1O+nscgOwZ28DiqJgNqtYLWY6uqLz7aSlxmO1mGlp6yUQCBMIhHH2+bBaO4mLtZOcGIuuG9hsZsr21JOWGkefy09BXhpxcXY6Ol3sr+ymuc1LOKJw5inDaGt3kpOdjM1m+dr6NnJYFrUNPfj9YfyBMPc89DFxsVbSD038mhhvp3RAKnsr2tB10HWD0uI0rr8ymUefXg+0w7pqZk4pwusLEQhqeLxHXl7EOswEghEURSU/N5G2Dg+RiEZ+ThLNrdFSQIdfBVx4zmhURSExwQGASVWxmFU6uzy0dUSvf11DDwClA1IxmVReemM7PU4/I4ZkMnRQBhnpcXi9Id5esgfdMMjNTmDggLSv7foJIYQQQgjxn0jA/nvAF4pw3UtbeDuyjDSTi4/7trPSO4maLi9b6qBcWcBcdQfN6jz+dsoQrt1/Jamu/byT+mN+2TwbA9hQ3cP+Vjc7f3sSN726g2XlbSwrbwNg0Y5mNlR309jjw6ZcxBPGQ3gyxhFX9jp8dh8kF8PNu/rbU9PpISXWSlKM9Uvb+7+VnxLDspuPA+DTfe3o+z5hDLuJ3X0P881dxBDkxfWv4vLPI9SfiaUQCGsMzY6n0x1AM6Crz8UZGV3cstzEQLObW0wGsUqQm5W3OMOziSY1lQUNd3JB7JuMiOyl15rF6vBgTiqNZURTFbqqkjkmmtHFiHOhcRvK5ifoThqJWQ+Rr3Zyj+UV5oR3oZsVNh+/hlJHMg1rX2W2Xkndhre5PxJDWaOTZ9fVMm9EFjMHpnHm6Jyv5DoJIYQQ4tjh9QbYf+BItrnXF/zS7RITY0lPjaelrRcAq9VMKBQhFNLoCXlRFYWJ40pYt7GCcFijp9dz6PhBLBYTiqL2V9/Lzkygpq6djk4XwVCEIYOi9xiGYeDxBoiNsaF+RVnjg0rS+dVPZgHw4acVGAa4PSHcnmjQvc8dYMfuZgwDzCYVlGjQXgFSk2Po7vUBYLdbaGhysmN3y1HH73MfDt7rVNZ09y/v7fNTWpyCbhi0d3iw28wU5icDcPpJQ2ltd9Hc6iI/N4nGZidwJENfVRXOOHkY3b0+qmqjx1yzsZb9BztwOCw0NjsZNSybSEQnPzfpK7lOQgghhBBC/G9JwP477M1tjdjMKnlxCs95bkRRDJpG3oQ7OA92d5EVqzAzsJJdxmCe0M6GCi+Ji8u5wOklVYVRrtWkMpYcpZs9xoD+MjonFsAVFfeyVy/knsiVlKTH8tqWhkNnHc4wnuepGZPJyvJhFE7DW3gCsYaBoihsqunioqc3k58Sw5rb5vzHtv//OmFYJm9Pu4NXtr3MfONDYpQQy7SJvBw+HoBAWMdhUbn59V2MzE0kv/Uj7siP48GGEZxY9zBnNawkxjqRFj2JZ7VTmW6txZGcDW7IU7q5zbaIh3ync64tiz93TKW5o5m9953FrofHMia8k/2LfseWwbdx8ogsSqb8hE1l+3m7Yyg98YOZmtbLHGUntO1Ci80kLT0TgJpxd1K//z22Js7jrIR07Jbog3JhaiwocLDdzfCcb68GrRBCCCG+Gpqm09TcTVJSLG3tzv7lA0uzaWjsJBiMYLWYCIW1/nU1te309roxDt1L9bmCOOxHJp7NykoCICbGRl+fr3+5okCvM1o+pqQwkQGFiUwen4/bE8AfCJFyKNMdoLa+g/qGLrKzkhgyKPcr7/cJswfS1umhsclJ5EuK10c0HUWBvz+9DofDjNcbJjc7geZWFyvXVfeXvjnMbjejRXTC/7bcbFbp7PbiD4T56bXT+cPfVhIIRnhv2V7SU2MZMzKX6ZOKWLR0D43NTnKzE1BVha5uL/5AhOKCZBQlmv1fmJeEzx/GZjUxfEgW+w52gBHNwG9s7qOr20ue3J8JIYQQQohvgQTsv6OqOtwsfGcRf7c8TtrkC7CbnahGhNTjr+XGUCoh4yCXmj5hasXT7NULOS30e8Yo1XywXaMicjkPW/7JCn8pb1gfoFRtYXXhz5g1PpqddEZ6J6p6kKFKPR/m/5JNtT3EWE3cor7FXH0dvwjfiCc4jnojg7Mbf07vgTDXH/wXF5hW85fOkxmoeJiidYExm2fX1aIoClfPKP7K+n7eqSfCqSfy4p9/zqWe5zFQueXMqfx+2X5a+gL4w9GHu/hgO09YH4UOqMp+hLl9u0GH49VtmFSDf5guZdhdr+Gt7ebmZ7P5teV15inr2aEU8Unpb2grbwMDZj+8iocLTyJQvY+Nvjwe/bSSv35ayTM5H3Ccfy0TLWsp9w9gcFIS7q7oqIR1rgwan/wJD5X+kmevmA2nz+Yyw8Dj9fPhS3/gqqJSxuSP47pXdmAxKZwzNo9AWGNtZRfTS1OJscp/TSGEEOK7prW9l5q6DhQF8vNSAbDZzOTlpGC1mOjscuHxBo4K2BuGgbPPRyAYXVbf5GJwSTKKopCfl0JsbLQmfVysnb4+H4ZhkJIcR6/Ti6ocLgujoBDNIjcMA7c7wN59TXSkufD5gv3Z/eFDk9fWNXSSnBRLakr8V9Jvi9nElRdNAOCeP36MAVjMKqfOHcRHKysJhrT+timKAkBHp4fkREf/pLGHDShM5oqLJrLs0wo2bWs4at3wwRmU7W3D4w3x4uvbSUly0OcOUFbegm7Ax6sqSYi3EYlEr0pLm4uRQ7NpbI6W0Kmu6+Hhx9dw8fwxXL1gEgCartPU3Mfm7Q1MGJtLS5ubLTsa6e71cfkF43G5A3R0eSgtlvI4QgghhBDimyFRwe+CjgpIKqDTG8ZqBElsXkNC0iimWaspUtrp2fsBcdkjYOwCSC5iEPDEJeN5Y0krWXomG0wTuc70AXdYXuM9bRq/Ma4mQ3FyrWkZO/RSQoaJ4+ofhecfg5+X8+fqfPrCV1NjZKOoCiYFTsv1cW3rOyiqwfHm3ZRmXM7DHx+k1xcGYHbbCxSzh5MjYc60biQj6OSZZ5P4XXU0UH/C0IxoRjlEnya1EJht/2W3y5v72NvSx3nj8zF1V0J8FtgT+tdn5Q5APWiQYQkwbEgGp4/O4d0dTfz8zTIAHr3mJFY8OhUtHOKUYitpO7oxDDAp0Ye4tGAjb3y6nvnBJcTYVrAjPJCgqYHmhLFcOCGfdleQbfW9dLqD7Mycjz9lCE+vgXvNLzBF3ceQniZQQDMUxioHUdshZEpDA+aYdgPw3IFTmPGQjxW/mM21L28jpWYxfzU/TouRQp39NGKsJlJjrei6wb3v7+X1LY2cPiqbxy4e9xV+gYQQQgjxVdJ1A58vSFycHbfbj8ms4nL5o6VfiN7q9Pb6iI93MKgkG4CM9EQy0hPZvDU6t47JpKIdykbXdQOrxYTJpFBanNQf1G5s6qG7x8vkCaXUN/YQDkew20z965NT4unujtZot1pNmEwq+w809bezo9N1qBxMNFDe3eNm+64afL4QHZ0upk0+ErDXdB0FBVVV/su+7z/YEW3ngDTa291kZsQftY8jxoLPFyY5ycH4MfmMGZnH8hUH2Lm7mcyMWE6YNYgXXttOOKIzoDiV7buajjp+TX0vu/e10tHp6V+mqpCVkcCMycUcrOnG7w/T0ubi/LNHUVbewoGqrv5tXe4jpYcMA/bsb+0viXPYq4t2MXxIBqedOJR/Pr8Jjy+ErhvsKm/llOOHoKoKMfZo/f/nXt1GT6+Pc+YN75/sVgghhBBCiK+TBOyPZfsWE/jgduy+Fg7ETcburiNF6QAMlIQR/Ct4K90mK9fYq0lpXgFJBTDhyv7d84ZN5pOkD3FYTHS9/wQAloRMju/ZjqJAh5HE/uOeZNyGk1EMCGNi8cInsKZMJ5U+PtYn0F3dTVKMhZ/GfwItBl5sPBs+iaef2sTFkwtIcliwWVSco37Gos0v8op2AsmKh7kxNbxSE534qyQ9lgNt7iMB+4XnQd16uOpDyBn7H7t/3cvbaXb6UatX8KOKmyF7DFy3GkI+9CU/Ja2ulstDt1GrZ3HH49czft7VJO9fxSzVYLU+mn+srmeHcTH3KX/H01bGzjEPsG3bRnKVTryGnTylixnrT0MBRgCqWsic4F8gCMaWRvY0O5mp7qa0pJRpzS8wqe5J/miZxvHqThKUaDaYYUSHpL9oXcCVoVdI1aIPjEHDwl+NC6kzsqE3wJgHPsEX0rhNbSCCmXDuFBIcFmJD3dzJS2jbG6hoLQWgptP7lX6NhBBCCPHVqa5tp6m5B13XsdstBALh/nUO+5H5ewKBEOGIhsfrJ+HQZKiaplNUmEEoHMbrC9La6gQ4KuBtNqnk5abQ1BydJNXnC1JxsBkFcNijt+49vR6Sk2Lp6ztyzxAKaewsq0FRzKhqhJgYG15vCF3X+gP8NpsFny+EooDDbsHrCxIbY8PnD/H4vzZiNqv85OppWC1HSvJ8Xnevj9cW7QKgpDiV6tpupk8u4uQ5g+jq9vLusnKCgQgAHV1enn1lC/NOHEJldRcGBnlZMehapP94ifE2sjLiaOvwHHWed97fc1SAXdej2fK7ylvw+6PXe9bUYt5fvg9/IMKXURRw2Cz4Pvf5RK919Hh7Kzqoqevp319RYNqEQoKhCLpuUF3XTVNLH25PAIDObrk/E0IIIYQQ3wwJ2B+jFu9qZvC6Vxjia8EwIMW9HxsRTBgYQOzg2ZyZV8RJSisDD7wM6UNgzm/6939tSwN3LNqDSVV4Zq7KGakrMdwwY+6ZHL/kOmyEiTXpvH0gSGvoPM61baZEr6Ow7WPGauWUWFaRori5L3I51yuLKDj4GijwTmQmbuIgpNG4/g3uMu3ghBue5Pr3GtkUvpG0WCu/9N5ArN/EdSeUUNXhYUlZC3e9V85Jw6M18nE2QMQPns6j+tzpDtLuCjAiN1ov9Oe5+2nW63ijLIuzrSa8up0kgKYtqOVvMx5YYDI4qOZxqmsJexduYbapnukWE4ODL5KZYGe0Vs44tYqWVg+nNv+NyXoPb+izqCKfl6x/QAG69HjqjUweilwEwKi8RDrcAaaYDvKi+Y8YjRBS7BgGhAwTz+un49KtTFf3YiXM4rgLmXfWhXzwkUKM5saelMm/quIYNvsCrgiEeX1rI/5QdJj7AttKzEaEYHsldd1eXj2ui4FbNqF/spcpvtPZxRlcMjk/ekEiIaheAYXTjhpZIIQQQohvnmEY1Dd00twSDdYDRwXrAbKzk3C7A+i6Tq/TS3JyLBkZR+qg7z/QRGeXG7PZRGZ6PIfi6JQUZ1JZHS2rZ7Wa6Ox0HXXc1jYnZvPRWeIeb+ALtd9r6p34/BHi4qyce8Zg1m86iGEceRmgoFCQn4bL5cPZ56O+oZNhQ/IIhTR8vhCqSSES0Y8K2Ht9QUyqgt1uJS7GSkqyA03TqW+MvlAwm6LH31Xe0l965rDG5j6eemFz/+8dnT4GDzwy6e3uvW109RwdCD9c2uffDS5No7qup//31Rtrj1pvMilo2pEdRw/PJi0lhi07moiPt+Hzh3E6/Zx3xih27mmmsqabYOjzpYlgf2UH804YQn5uIo3Nfbz6zk7CYR2TqjB+dO6h6x6ktd1NaXFq/4sQIYQQQgghvkoSsD8GGYbBL98sI0M/nd8mKJwS+giLAvuKr2ZsTCe2IScTM2I+DwN8sggiAeis4OXla5h+chYD0uPIiI+Wm0nXu5i15mf9ZWDiNj2MCyt2I4RF8zKq9S3e0Wbyqu94LjCtojF1OrMTnLg7G1muTWJAWiynDsiE3fCJNp7JP36ca3Z3s3hXC7eG3qBEbUWvWkxFayl2gtweeYZKUxZLw1M4ed3tpBpD2J163dE17K9YCs5GaNwMzzwEZz1Ok6WAeX9fixZw8/L5BYwbPozzau4CQ2ecbTy/Dl1DZfBU3gc+dBUT1KYxRdnHh9ok9lPEaKOORdo0fqMuJFVxc7K6jbS4MUyZMQJtg0KbvZhfmFdwuftpABaazubH3l/whPVRjld3so7xWApmENPg5PhByQxf+1PSVRc6oAIWImioxBLkZtNbvGU+mVnGbkzoDBwVYO5rO3jFWM8YtQYmP0jrwJmoa++niSyumn4tT6yqBuDT1EuZ0vkGC/1Tef3Vzfxq7nEMHHEuavk73G5+jSy6yK+eAFNuhzV/gjV/ZnPs8bya/1seOX8Mpv9mmLoQQgghvh59fT5q66PJBodLrJhUhZSUOFRVJT8vlfi4aCb95m1V6LpBb6+X2rpOBhRlYDKpWA7NUROJaDQfyq4HaGzq7v859Lkg8mGZ6Ql093qJRKLr0lPj8XgDhMMaZrPKhLEDqDjQgscbJjM9BgCPJ9BfcucwfyBEY1N0NGBcnJ2c7Oj8RUmJDm68aiqKqlBX304kojN8aB6dXS72H2hGUWDi+BKaWtz09PoxqQoJ8VYK8+IozIuW1enu9eOwmfAfqsVvsaiEw0efv6M3iNsbpqggkbqGPtJS7HT3evsD9Ha7mcDnMuaTE+1omoHZomIY0NbhxqQqaPoXI/qaZhw1oW9RfjLvfbgPALc3yIIfjWNXeQvvfbiXuBgrw4dksLeig4S4aDA/oul0dHl54fXtnHvGCAzdoKk1+uJE0w3qG3tJTYnltUVlNDY7SU50MGlcPtMnF32hLUIIIYQQQvxfSMD+GLO3pY/1VV3cOKeEsqZUrj+QxkRlMq9kvc6Umn/AvIdhxPwjO8y+Axq30trWxOPlZt7q3cWZY3K4ZuYAVv9qNgfrG3C+H08SHjY45jCj4zOSgQ+tJ3Jq+BN+bXmdBywv8JE+iYWFD/LShSV41z2OUtfG73O2s3TgWZy0agrj4p4gnFDE63mZuLd00OkJ8X7ujxnp24QzMpXkmD5ygzs5T1mJblZoiqQz2Kgl1ehmjn8XOVVD2VvwHPcs2s4Z40u5fNp4eP9n0F4O1Sv4U91UXIEIi6x/YNySKoh7C8ZfiV6/gZmd2ylxtLFiyk94b3stOzauYGH4BjSOZH/dGXsvw3OS2HVwC3OVHYDBbe/s5tqYcsZh4PF48GRmQLTMK0PtXWTYksn0ezB0qIykYwB77z+ZyKo/YzZtRwFWKZMZE9eLc8qvCDnb2b61ntlGGftNJfw9EM9V5g/J3Pw7TgzfQLOjiNFGI8ondzHTlEWGFs2UO7vyTGapZSSpft62nc1uPcB91ueZpB3EmvsSHP80xGWx/UAtV/Qug8pPOPHhCbw2vZhURSXOXc2KXVX0nDaM9EMvYnyhCJc8uxmLSeXlqydhM3/50PXPC0V02voCFKTGfDVfViGEEOIHorPLRSikkZmRSCAYpq/Ph6pGA7mdXW4mTSglNubI3DzDh+SyZ18jgUCYpuZufP4gudkpDC7NISs9ic6uPhoPlbyxWEwEgtFM/X/PEgcYUJxBanJ89LwuPwV5qbg80WC9xaKSmZ6E3W5l1aYmXO4Ax08vwGxSCIW/vFSMYUCPM8D6rW14/QoDi5Nobu1hcGkOjhgre8qjWfJ+f4jK6tb+fbbvrGXUiEJKi1Pp7vHR2+cnMcFGUlIsK9dVU13bRTB45GVDOKwzsDiFjDQH67c2A+DzhVj0QTlWSzTLvqXNRUK8jT5XtO58TlYCnV0e3J4QAL19AU6YNZDjphbz5PMbgeg1t5gVUlPimDg2j9Z2F9t2NR8665EXBIeD9RaLCii8/OaO/nU9IT+WQ6MINN0gNzuB+iZn//ZF+SkU5afwyeqDVFZ34Q9EWPbpAfZUtJOeGktTi5PePj879zQfFbBvbXfx2qJdDBucySnHD/7S6//vtHAYQzcw26z//cZCCCGEEOIHQQL2x5jb3t7N3hYXd502FKtqYrKyn6dsf8MWOwzUUuithYeK4OwnYfCpYHEQvOwDfvvqDs5vfAPaVvO7pWcze3AGpRlxpLx9CyYjwD36Vdzje55dDGRReDrX6Ms4oBYwmAYAThiUzEnzB2I8MozYSLQ+e37bx7zpvJZAWGeTMwm918n5/9zAzkYnCXgp7fqMtZEB1C5fzvvWx9hiHkifEcub2iz8A+Zxa00Aw1B4WHkKo8GF/sKZvBLaz2+2PAzTroCzHoeaVTD+cgpcjaTjJAkPBipBLKwquo37N63iZl4jbug8Tmv+G47dL3O2EmawfR5rQ4PYp+fzhu13WL1hXq04ngnWaj7MvIHM7PO5qew55pk2c5PnJgqUDga1reDX+lX81vIKIwM7WHN6C8buHJTaSqqNXGq7vCghL5Z1f+r/PAZplXzQN4HSj/7KFHU/saarWRcayYLwIsqVQmKJXqsbzEv4U8FLnNZ6LgScJMXFsMV2Pp7YfH4/o5AhL5+Nis7JNTm4TNGAeWZGBhOGZQJwYMwdXLzqU35rDtJuzqayK8iOhLmcZLIx3Kjn+Vn+/mA9QJc7xK5GJwrQ5w+ToTrh3evoSRjCq3GXs2BKIb6QRkqsFbOq8O7OZl5aV0V9Wwc+JY7MRDsWs8pb10076rhCCCGEOFo4HKF8XyMAE8eVsGtPHRANYsfF2jGZVKpr2vD6gowdVRQtHRPnYMzIIvbsayAQCNPT48HjCZCWOhiHw0pzay8QzdQPhzVUVUHXjS8G64syiI9zsHVHdf+yhqbu/u3RoLm1h85uFz5fCE0z2FbWRmFefP9LgM+zWFSCwQh9rhDBkEZDcw+GFr2X6ehyMbAki+FD89E0jbg4O6DS2e3FH4hQXJiIphmUFqdSVduNosDMqQN48fWd9LkCX3rtGpr7cHv8OOxmcrITiHFYOFDZdajAI7i94aPK3/Q6/Zx3xkhefWdXf7marm4vdY09tLa7j3wmEYO2Djfvf7T/qPOFwl/MvJ8yvoC1m6KfWUlRCi53kML8ZOJirbR3evD6Qnh9of7tp08qIjHBDkBSgqO/xn0orFFb38OCH41lx+7oC4J5Jw456lwtbS6cfQGqarsP9d/JxysPMm5wCkVZDpJys+mLuEiyJKKFw/S1tdNaWYehaZjNCqrZjCMhgYIxI7/0egohhBBCiB8GCdgfY84Zm0uM5mZC71LObHqSFaZSknBD81Z2n/I2rmX3MkPpxajfhDL4VAC21/eyZV8Nz9ifAQsYpSdgNSm4A2Ec/lbMSogBlj4sukYpzZw/YxiFW16gxUjljsjVhBU7JvNZDNlQzwURA4cBXksKf/HPQ7PqWEwKYc1AUeBAuwfdgBnqHk5X1jHXuoO/h84kDi+TLHXEaV40TKyo7AFmA9ARSuLC6RMYuPYW7GqYWN3Nh3taOWXEaJScMYf64OQ28+sMUNsIDZ3PL7cmsnTPdiCevzOf+QfKODVlI6oSffi8sNDNJfV/o9uIJ0VxowCXmT4hUfcxSGliYbuL5+NWYOmrpcA8hluVN1EU6I0kYFJULJoPFt+IctM23thSz75yE389byRggCUWw/DSrceSo/awgI+p1rPxY6XayOM69RXilCBZahcWdAwgNy2Rv104hopnJpIe2knN9Mf4+24Tv5ozmGF5CTBkHgfrm2gMZJCYP5pN0y9nyrCB/Z97gsNMRLFyV+RqHr9gHP9y1XF846Mw927wdDDmuPPocAfIiI8+QBakxvDSVZMwqyoZfeVQ9SlUfYLDWM3DwaksL2+jvMWF3aIyNCuBnY1OXrc+wDjbQS4M3c0O5yAAWpx+CdgLIYQQ/4VovflEfP4Q9Y2d/WVpDCM6sWxubgr1DdEyMz5/CPuhiWdb23vxeoP9x8nPTcXrC6JANNj+OaqqEOOw4fEeCXzbbGbchzLpP7+drhvExzlo83RgMWzYVBvBYISJYzKorHHS3uXHZjOTnPjFv+/hsI6mGRTmxROO6AwbmILbHQ3Yu9wBGpqdFOQmHeqfQSQSobK2j2BIY8jgHJ59ZQteX/ReLD7OSmV1J1pEIzbGjNcXIS0lhq4eX//5giGNzp4AmmagRXRCoUh/yZrD1xCOjCzodfpZvHw/P/3xdBYv3YvbG+SU4wfR0RWdlNZuMxEIfrFk0OGJZP/duNG5TBybz5YdjZjNKoNK0qip7+XE2QMJBiMcqOqkq8dHOKwxfWIhpaVplBSm9u+flGg/dF4z808fQVePj9qGHo6bWkxigp2CvCTCEQ3LoZGOY0fmYjabyMtOYP/BDvYdaKehyUnE7cIxyMLuzj3ssFcxJJLPIE8Wrj4fS3ZHr+dZoy3YjQjenl4Mw5D6+EIIIYQQP2ASsD/GXDMukR99dg0x2wNYFI3pGfl4exzEGn5iyp7HMNygwD82dPDxgbW8dd1U1ld1Y4pJZmn8xTi721nakcoTf1lNYWoMJxU8xpbucrSYNK4IvkW84mPk2GmUmx7hF6vCHDTyGa9Wcdnee3lKO4MNBS+xo6GXUYXFrK7sYkZmPLR7aHUFyE108OwVEzjzH+tYoY1lddqFzJh5Ai++YaMulEWDns4MRy2vRqYAUJKo8IrlQWwWE8sd87kgdDe/Mr/B5c7HuelVE88XjuGmOaWsqeqiIMVBe2Qu7c567i4rxF8SfXi52rSUBaZPKdbb8fQmEQcw72GcO94lyVBIVdxUKwUMUFpI1H0YJitPtQ/lJ6FfYDHVopWcQKfpYvZWrKOEFpZoU3gmcgpv2H5HVrwFS3wWF8wbyHmTanC+cg5tA+aS9bOdrDvYStKii0lTnfgMKyVqK9v0QSwLDOaX1hTilFYshhbND1OgftI9lJgUzmm/Gn9Yx/6+i0BYp6ZzBxvvmMsLBQ/yZHU1EwclcOvJg/sn1gX4y8cHKG/uY91tc4h3WIizW2DRvbD7dRh1Acx/mhtf2sYn+9t55tIJnHAoK3/mwHQIuuFPp4IWYk3K+TzXVkiczUx5S7TmaiCss7PRiRmNIUoDVkUjR+lkhzEIm1llVN6RdgghhBDiixRFISk5hvbOaLY4QHycHbcnQETT6e09Mmlq2Z568nNTD03s6sdiNqGo0br0Xd0uqmvbyclOJsZhIRCM9AfuNU1nzOgidu+px3UogB6J6HR2uYiPt2M2q2gRnaTEGHp6vaSlxrNK+wTFHmYIYxmdOoQDB1spyk8gIz2BUcOzaGnp6m+Xqijoh6LjmZkJ9Pb6mDwu71Cg26CiuhevrwOPN8zk8XmMGZ7Fjj1t5GTGMqAwiaraXj5acYC4WBsQvUcbPSwVswkmjE5HURRKS7JZ+HZZ/zntNjOBYARNM0hJdlDX6OxfN3VCAfsrO3D2BQ71P9o2k0khKcFOQpydSy8Yz649LTz+3EZOmj2QX9wwk5Xrqti5p/ULn9GXBetVFcaPysXri44mCIY0PvzsIABLP97PeWeOYsSQdKpqu0lJjmXmtAHEOCz9n8f+A80kxJm5/eY52KwmTCaVhx9fjcsd5Ox5wxk9PJtNWyvRNZ1JE0qx2SyoqsLo4dlU1nTx2qJd2G0mRhXayYvXUC1mPCE32AFvmLA/gMtvcPjdjTeoY7eoOOLjJVgvhBBCCPEDJwH7Y42hYTI0TESzh5LiE3i/YxIXmldj87WjjDkHf83brOweQnWHl/EPfILv0IRem0f/hHedzUzISKLO2Ultp5eFbjO+SB7XhT5BVaCbBHq6/TzZNYZqpR0Mg7tSPmOsZwOjksM4x/6G+XWxnF1zD7+11PAv/+1MmXocT6yq5pIpBTgsJo4blE5LZxeJx/8C07DBPBfXzWtb8thX1so+fzGZCTZsmkEyvWR49qMqMH9oDH59AqesvosUrYtxaiUL6wq57Pmt/V1/9doF3LF2OisqOrgsLRbN08ndvQsB6DJl0B2yMNDkorm1jfy29aDAp9oY3tNm8pj1HxgoNBupPKT9CeVQSfe32nMJ9S6l9by3CaekMu+th8jr+4B8pRM8wIZ/EJ55G3s3fsgY524at7UyasdUlsQ8QKFaB4BdCVGlFvOy5UIIwSp9NEVKGwpwYfguukwZVL+ncWHzXhxmuEJfQqOWzgdMpbUvQH23l0c/q6LHGyIjIXhUsB7g2bW1+MMa5a1uTjwUjGfcpeBuhbGXAuAPaxgG7G5y8vtl+7lm5gAunlwAlhjCuZOpqa/j5tYTuOz4sfxmVDYn/20Nn0/ey1a6SFKiAYXfWZ7ng+B0Xrt2sjwQCiGEEP8D/z55ajT7Pfqz3W7GwEEoGCYYitDZ7aKx+cgksvl5qTQ192CzWQA/rW29R5WBgWgWf1ubE/Vzf5eNQxspKGRmJNHc0kPPoZcD4XCEPAbQ7mth9KBB2HUr8fEO4mINhg/PJ8ZuJTHeRl1DJ35/CN0wMJkUrFYLvYcmeQ0GQ4waWUhEg/bOlv7ztrY56e52U1XXh8Nu4ZbrZ/Do0+uw20wcf1wp7y3bRzis09XtJ+HQKD2rRWXbjkZ8/jCqAroBgWC0lIyigNcbJD3FTmdPNEC/bVcTADdfN52q2m4+XnGQcCSa/V/X2Et1XTeF+cms3lCD2xPknQ/KSUq04/YcGbEA0RI/JlXtP9dR68wmnnl5C6efNOQL62rqovMHfLa2Fl03GDUsuz9YD+DxBujsiiY/HJ4wGGDG5CKqarspLU7FMAx03UA3DDZtb6BsbxvnnTGSnKwEUlNiSIy3kmiJMCbfSkpRPharFW1PmKxAIjFadBRGX+jI513eojNrEBSMlXI4QgghhBA/dBKwP9bEZbDr7M8YsvRc0iJthIecyf0HHJxvWk2+cwucfS9PpS5g54cVZMeYae2LPrjE2cw4/WG23nUCdouJM/6xlj3NLuJtZp5XH2Csvo9eEkhSfKS+fSLO0B1o+kjsFpWm4T8mv16nuG0tvg8v5BemkznLHJ3Ya3j3x0wacT4R3SA70cGdi/awrbqFT2y3k/9OLzu875GYO5ITh2WxpKwVVYnWVH/7+qlc+PRmLgn9hp+fNJiJyfn8yPE6CVoXAVM8iROu4mxPhI/2tgIKmZFmnlpxgMsbf8MfbDVcvek2fnX+iXyy4QpKEyL45zxA2gvTUCM62XufRjdAVeB4Uxl3hq9lx5xX+PPKBu6KPEVe9JmKiGojXWvn75YXqd/Tw7O9Y3nA80+MQ8H8iGLBvPohbl4RwVc4h1GmK1kTLMZlRMgyKjj8zKwCZeE8FvuiD3wPRC7jscjZxCt+2tQsIpHoA/Xqg50MDu7h19bX0VBpsI3hNH0VycHB/Pi4ATy3rpafzCml3RUg81BtVIDHLxnLgTYPcwanH/keFM2I/jvk6Usn0NLn5+3tTdR0efl0fzsXT8wF1YR26WKueWQ1ISPE8UMyGJhqZU3R83zcGsPy4GjONq1li3Lk4S9e8fP7c0YwrjDlK/ziCiGEEN9fhflpBPwBWtr6UBRITIylqzsase/odDN75jDK9tQTDEUIBI7UjrdaTVjMJmbPHIam6XR1u9F1A4tFJRzWUZRoWZhwWKOqJjpZvaKAyaSSn5tKU0sPLrf/qFI5EJ0E94yRM2lq7SVWiWFneV1/gH9XWR0lxRkkJ8fidHrx+0OH2mJhUGk2ZXvqMZkUBpZk4bBbqWuMBqYz02MZNigdk0nB6/VT1+TCHwizZPle/P4wJhWam7u45LzRrFpXQ15eGmaTiQ8/OwCA2Ry9cdI/V+Zm5tRiVq2rYfLYTKrqoufJSIuls8uLAeza08LmHY2EI0deiOi6wUtvbMcwYNTQLJx9fjTd6M/G/7xwWCfMkesIR8rjaIcacvjlAIDNakLXITbWSjisMWpYFu2dbgYUpeHzh4hxRAPpCfEOBpZkYbGYMR8qdwMwZUIhUyYU9v8+eUIphmHwwuvb6er2UtvQQ1ZGPClJMfzksgnUbdsJqkJSdha9fUHW1KoUJVoIazrugIFhHOm3N2KicPwYVLM8ngkhhBBC/NDJHeExaL87hl/77uLqYbCzejC/nOpk6cH5jE8OkJYxivWf7QNgWkkane4g6yq78AQjGLvfpqvuWiz2WB7p6aTSkssy92RizX2YVI1kXGAceqCxxjHF4WaKazknb1mKt2Au1UYOTkcBJ+rbAWjQ03g4dCaRJzbQ5w9jI8SnMXeRZ2vChxXDMLhj0W4azG52/fZEHrt4LPcu3kuvP4yqqCz92QxuezuBCz7q4Se1r7Ghpoc/WopZHRzGig0bWZzzAgPUEaQoHhaYllIfmUu6so8YJcgi62/xvPcHZgX/RkJSKuub34JIK4ZiwhxygQJ6fC7Vvhhesv6R4i0RXqKT5ebjyLdGsDri6HJ5ScsrQW9IgrxJvLsvntmWsXhwsFEfxkRTJScnNrC7q4B4LzSYi+gimv1+QeguXrD8iWTVi8+wUp9xAsP0eOp7fHiDGr0k4CKB3542jHuW7AUg0NfB7+zP0ackEhhxEQuV9cTvfhnWOrn+gle4flYJd79XzvWv7GBYdgKPXTyWAelxHD8kk+OHZP6X3wmH1URJehw3zi4hK8HO6ebN8MBJMOPn2Of+lnGFySze1cKLG+p4pGQnea2fcKUBYy27GavWoitxfBCZzHBzC8Un38TFkwv/y/MJIYQQ4mjBUHRy2LTUeNzuAKkpcbhcftLTEwgGI/gD0cB4YX4anV0ufP4QoZBGTV0HzS09RDQNXY/WJj+csf/vmfZWq4lIRCcS0amt7yQxIYa+sA+b1Yz/cy8C/IEw23bWEInoNDV1H3WMSERjX0UzycmxDB+aj91hobauE03TSU6KZfyYYrbtrGPn7nosVhvhUIDYGAtpyTbsVjDQsduiLxoikQgebwjdAJcnzIZtbezZ382E0RmkJdvYVd75ufNGOxMXayU12UZGmoPtuxpRgF5XiILcePrcYfrcAYqLUujs8uD1hQkEjs6Ot9lMWC1m3J4gVpuJ/BSFnASFTTU6/175ZsSQTGobetA0oz/LPiMtjpTkGPYd6ACgrSP6YsVqMTFregk7yppo7/SwY3cz80+PJjT845n1dPf4GDwwjfPOGIXFYiIvN5X/jtUafZQ69/SR1NT30Nbh5oGHP+Xi88YycEAaqtkcLX3T3sn7qxtp6QrQ2gWHP/aJhWbS4jQwWTjztFHEJEmpQiGEEEIIIQH7Y9LeFhfNegoPlMOfLQ9wnmlNdMUJT/BxrZc1lV3YzCp/Pm80Xd4g037/GboBZ5o2kheoJOJXyVR1BtLMOHsLJ7l/y5nqeu6zvISiGDxvnMZedRAv+u5gtKUKDFCCbbw26QUWVQS4YVQTDdUfcHfvqXSTCP4wFpNCsuYmT29CUSCGEH0nP8EvPn6DtSnnYo24OX2gg+m/mIXLH6RQ6aDVnENlu4sT1O3c2vAX9qsFXG75K2nJNs4KfYitay+n0oeD6CgBpbeexdo0LjKvxABMaDxkeZr0kRdAOFrPdWukhEq1iCK9ienufZRkj0VtrcTwqSjonKavIOC34gu4ycPFx3XdjP5NPfvL2wiadnB1+FbMKkR0+GncCuI89Tx7TjYuZxOTNtxDkzWNJ7Vz+J35GfZq+STjJUYJMbpnOQvV4XiDGheZPiMFN6bjfsHQ7ATUQ1ld2UoPA2gloFuYtXMKI4xK/pkxDGfOKeQd+mwDhyZa29fq4t2dzfzypMFHffa6bqCqSv+2Ln+YjM9l48fbLVw+rQjWvgOGDl2VAFw8qYBeX5iLJhdAcgYGCopisFsrpcVIJ3nOj/nlSi/zhmTzyJQxX8v3VgghhPg+c7kD6LpBR6erf5mqKuTlptLY1EUgECY5KZYBxZnExdnZu/9IZncwdCQobRhG/+Sxn2c2K0TCen+teYD4eDuqSUGL6NjtVvpcvv79Ioey0kMhDavV1N+enKwkmlp6iYuxo+s6BXlpZKYngRJtRzii0+cO0NHlp7nNS152LKfMKcbrC0a3CURr6/sPBdJ7nf6j2mkyRV84pKbEEY60kZ5qR1WiZX2a27x4vCGmjMsiHI7g90cwgD37j36pYDGbuPWm2SxZvu8L1zkS0TGpOhefO4ZV62to6dLpcIFqUtC1o69ZV7e3fxLcw3505ih272+DQwH7w0JhjU9WHcRqMZGRHkd6atzn1kXQDYP9BztpbOljwL+NQtQNo79ckRYOg6Jg+lwmfFpqLGmpsbzw+jY03aDXGZ14N62oAF+vk7jUFAaVBKip78UA8pIUbFYTE8YXUdrYSPbQQSTlyMhHIYQQQggRJQH7Y9Cdpw3h431thCI6OclxcOi5sKGuksknn8+pI7IYnpMQDewagAIY8EBkATv1Uir0PK7LOsjkXCuBbi+zvLtZYPoMk2Lgt6ZRVXQDzt0u1pmHkWnq5m1O4DrfFm7cehLDtZGkhSwMC+5kom0Nz0ZOpd7IojtuMCd6l/IWcznBVEZCcjqWulWcrK3m5JQY+Mf9oIdJvmk7yVv+DFue4uXIhfTpZ9FjxBM0LLQYqTx28TgeXLqfv3ZNwp5hUDLxRIy6daRX/Z19gRQ+KLiVPYEz+LTFxvmmlfzK8jbU9sGcOwDIVJxcHL6S+9Snmcp+1vsLWRcexKmmrYxRa1CAGCWEqqg8GT6DyPgr6fOFeWp1NWHNYN6ILCxmlcW7WnCHVQxUhmSn0JmaQXCDBYfJ4KxcP7TAMLWR97UptBtJ6GMv47aCwfztg238QfkXAE90HM8fqwv6h37vM4pYELoDW1wyZj2OLf6RTOsZhWupxhW9e7n3zOH8fv5IppWksrPRyYIpR2e5b6rp5rLntnDOmFweOm8UFz+zibKmPl69ZjKTB/xbltf0WyBnLOROAGDygFQmD0hleXkr16xu4f5T/kVO7xbmjrkFpx7DiNxE5s0xpGa9EEII8f9pYEkW+yqaMJtVMCCi6dHAtj9EZmYSXl+wPyv7P/29TUyMwWo24XT50HXtyPIEB/5AmEjkSGDfYlFpbunpz8JXFKW/7A1Eg951jU5Uk0pCnI3YGDMZ6Ym0d7owDAPDMNi46SAJCTGMG1PMlm1V0aA8kJRgw+WOjghw2M0MGZQbnUBVN4iLs1NYkEZzu5fs9Bjqm91kZ8ZTW99LKKyRnxOPxaLS6/QyZng63T3RxILu3gDNbdEa++UV3aiqTuTfAuyHS9dMn1SAs8/Pzt3NAJx+0lDWbKzB5Q5GRyGoCgOKUqmq7aKlzYXVbiMSiICmHXW8CePycfb5Wbeprn/Zxm0NtLT2AZAWqzAi18T2+ghmhx2XK4jZbKKj08OLr2/jsovGU1KYyo1XTWPbriZCIY3C/KSjzrF6Qw0r1lZxzmkjGDEwjepNW1AUlZJpk44K2gOcf9ZoWttd/QH/lPxcUvJz6aypoyguwAkzS0BVmTg8DbPVgsVuJ2tgsdyfCSGEEEKIo0jA/hiUEW9n5a9mEwzrFCSfzK1Pv0tdfT2zEk7nphgLTy4Yf2TbBDuf/XI29y3ZS2lmMTfOvQzb83OxtO0CtZSi7ir+ajVRQy4AlmAXPz1wOa/xKE+aLuGNmCu5zP00Flc9ALNMezCiCVYkKx5utbwFQJ+pgERzAwAl3pe5c84I3v3wE260tnLChB9jrd8IukZEi+DZ/hZJQC6dnMdnXGxZwV/0CyhItDKj433iyCWEhfs6ZpC0IsymecXYqiMMMeqp6wly9Vmn8+qL23hVO4ErB/pIalyJ+v4toKg44pO4OsPP/Jp1qBj4uhu42lxDwLCwUp3KHH0jWONQL3yTqm2xLN/Wxj+3foY3FH3A+6yigwfPHsGWmh7Oct1FgbmXD8teJ10Pg6Jh07uJn3U29RsDFNa9TYmlmxy9jwd31HFge5i/XjKVtz84j0S9j03+XHY09PKU5a/kKF1cFrqdnZYxvH7ZFG59ezddnhBmNVpQf3eTM3r9TSrnjMvjnHGHc+6PaOzxEYroVHa4gWjtVd0w0P59vDyAaoKS44loOt2uACmxVt7Y2sgza6qp7/Hz2X47P5pwMa3LGihOi2VEbqI8DAohhBD/B5kZicTF2bFaTIDCuo0VAFgsJhLiHYwZVdS/bXpaAiOHF9DQ2EVuTjLxcXa27aylr89HQryDcFg7nG8BQJ/rSBa72ayioBAOHwlOG4ZBdX0fJYVHSqaEIxpZmfGY1Gj2OEBcrJ22dic2m4WkxBiaWnrQDYPuHg8eb7B/fp4D1b34AhpTxmaSlBzDweouFEUhEIxwsKYNf0BnxOAM+lw+7DYTMQ4rI4dlsb2smY4uPwV5ibS09tLU6qW908uAohTaOo/UmG/r9Pb/bLeZCQQjpCbHcO6ZI1j2yQFefmsnkYje/zJi/eZapk8qYsXaKoIhjfhYC+8uLcduiz6q9LmDXH/FFF5/t6w/499mNfHBR/tJSXJw6txBfLa2mqREB7v2NPe/KOjyGiQ6VKYPT2D4lNH87an1eH2h/mvf1NxHSWEqDruFmVOKv/Rz7+qOTtLb1ePFIDVaXpIvuTcDYhwWSopS0SMauqFj6AbO1ja6aqP32elA+oAi2g5UkpyXQ1J2ltyfCSGEEEKIL5CA/THq85OS/uKi01hf1c2w7HjuXbKXS6cWUmI0En7jCt4LjGV9/nVUdng42OEhZcPvuM68K7pjzljqrKW80xDHi9pJ3GBezPWmpcTjozDZQX1vAE/QzxvKHPKUTvLoYISpAV2xoBrhQw8zKiFLElZfBxpmHg2fiY6JsflJPGIq5He2X3BC8WxqLlxNeWMvie0m8sJWEhWY6GjmovAKVAxGUwceHZbBP8dexxneU7D1HuCgP49PV63idMAbV8B5Y3PJS4nh1pMHMyg+xMIV5fxMW4ah+VGADO9BUivfxmEOoxkKGdlFJLeXEVZM5OmbogMOSk/AZERYtKMZg+jktAAKOqfo61i/oYeLJ0/iL58c5CqWYN62EoCKgosYkmrC2PAYD7edSHr6FO4yL0Rt3ckrlvvwGA5OWfgXevX5ACwYksz12fHM3rYLmxKhSOkgL1zO2mffpjJwJmDi4skFFKTGMndotEb9/vo2Kt+6i6zhs5h06qW0uwL85t09zChN4/JpReQkORicFQ/Aaz+eQrcnRH5KzH/8ntz8xi6W7m7lsqmFvLSxHoclOiTeAPa3utnT3Memmm7uOWM4JlUeCIUQQoj/i9gYW//P48cOIBgMEw5rVNe0UVSYQWeXi5radlAgOSkOnz9IdW0HweCRsi2ZGYkEAqH+IPvn2azmo8rnHKZpOi3tPrzeMMOHpGC1mNF1HVM0LwCrxYTNZumvqZ6UGEN6eiJDNB2TSWXnnhbUQ8MyNQ1a230MKEwgHNEJ+IN8uqaeU48fRGVtC81tXtyeFoYPTsZui76MGDY4g5SUOBwOC3nZibS0dmJSFUxqtLb9rvJ24mKOTM6alGjvnyQ2EIxgs5qYM6MQrzdIU0vfF/rX2xegqaWPEUMz2F7WSluHl7aOaNB/5LAsVEVh6ccV+HwhRo/Ipqy8leChZIzePj8ffnYQgI5OD3OPK+VAVQdNLdEhqnU9GnubXaw7uBlNi5YRuui80bjdIcaMzAFg7aZaNm9v4PQThzJkUAY19T2s2VjD7GkDOP3koYwans2AwhRMJpWSqZO+UBLn83Rdp3rTVrRImLjUFNwdXSiqiqFHz+3tdRJwuelrbScpO+tLjyGEEEIIIX7YJGD/HZCd6OC88Xnc8vpO3tvVgssf5pGCDVi6Kxiju7m97AwOjzgeZqlHATzmZN63nsszHgc1WvSBp97IRlEgYEvHGuxBJYZ/Oh5nhL6fy4K348HBAzxPR/oUnmwdxCLLPWSofaxKOoupHW+goLBaH82DSUsY89njbDn3OkwpWVi9LVz7wiZu0Z5HGX4yOWYXig4JWi8Vej5DlQYURSeIjbBqJ27QTN63byRu45/YoZXyWO/ZTLAk81rfCF75rIonV1Wz+c4TSH52InN9dezUBrAx43xuzKujqWYfS32ncam+Egd+xnYs4q8Jv+KDrkx+Z3mOQXYXKfveQ636lEWJQ7i878fEJ6by5nXT2PrBs5xd/QRN3WmcvPpxYqwmdmsDOE9Zx85IMd7afWQ31pJouJgZMbjHdyN3/PgRNr3/NOPb3yQJD+hhwMGY/CSum1VCfkoMH6jPsHRDGXso4S3LvZjR6Ugs5C3/RF7aWE9RWizp8TZe29yAqfwNfu55C+fWT+DUS1l1oINP93ewr8XFFdOLmV6aRlufn0ue3USPJ8RP5w7sD9g3dPuo7/Eyc2A6AGsOdrL2YHSyt/zkGIZlJzAwM46N1d1MHZDKfWcN5/2yFvJSYiRYL4QQQnzFEuIdEO9g/aYDhEIRYmJsdHW7+gPube3OQ1seCczbrGZ6+3xfmlUd47Di84e+9Fxms4mTZxXhdPkwqSqapmMyKWifKzljNqm0tvcyZlS05F6fy0fFwRYAcrOSaG2LHlvXNZISrBTkRhMEAsFINDN8QBq7djcxodBMuytMW6ePrHQHB6p7aGjqY+qEXMaPzuPRp9ehKDCwOIn8nGRCEYVgMExmhoMdu6P3Jc6+AHExFmJNYbwhSEmLpbvbhccbwWE34w9EGDU8mxlTinjlzR243EH27G8jPzehvz+Hy+fs2dfW/zOAxxPi/LNHsW3Dfmo6wpgViBjR7UcPz2b65CJmTi3mmRc309zmorzFQNOj+6WlxtDd4+O9pfvIyognPs7Gweoudu9tJRTW+HRtFUMGZbCjrImauh4S4uzML0hh4IA0PD29tB+oxOpwkF5ShNlqAaCx2YnFbCIrM3o9u+saiIRC0TmfkpMIerzY4uPw9ThJyc8lKTcbZ0sr8elp/+PvmhBCCCGE+GGRgP2xqqMCXj4bBsyGc/4JwMWTC3H6w1xd6oFl92Ik5PFpzu/Rdh3Z7dfGTbyo38egSAvObW9TE5zPKcMzWb63nde1Ofw+5nXSgw28Z/yEE/gTJ+gbUBWDa81LqTFy+MSYxJTpP6HprTKe005lNmXM6VuEVfHxVMqtPNb9GHmBTmiAWD2M0bQV3ZHCzVopZ5g242lowpaQjt/Zyo3Bm9ihlXBD3Bpui/wTG0GuCvyCh7Lm8tEbb3E1MM5URYOWzSztCYKHHjqzEu3YLSZwpKD01jHWVMPY2QNh5B3kte9jcaCPXdWPkrDuQewRN0MKs3k1UMiF7rvJDXfzJ8vTTA2VM5ZtPGYJsi88gnuWJBLjymSUnsU6fSRmi8IFE/JZXDaPstizOS62idubbgADAlkTKZ50N29lDuKDDjd/6TyV1byOSTHIVzrpNRLITbLz548qcFjNBELFTDhlDBelOWh6K5c8rZk7g39ni/oX9ofSONju4ScLd6AbkMJAZqXNImPk8SQBZ4zOobHHz6TiaK3TsKZzyt/X4jw0gdqNC3fw2MVjOX1UDhc8vZHWvgDPXzGROUMy+GB3C65AhBOGZHDtcQO49rgBPLOmhsW7WqjscFPe7KKsyckjnxzkpasmMzLvyDB6IYQQQvzvtbU7qaxupaQ4i5zsZAAKC9JwOn2EQhG6uj0kJsSg6zpuT+AL+wdDEYJd0czv2FgbXm+wf91/CtZDtF59ZkYiPv+R7Q8H681mhUjEwOsL4vUFURWF7h4PsbF2PN4wcbEWwuFI/0uCUFhn4IAkLBYT4bCG3WZm5uQcMHSSrGEGZpjITVJZXOaiszuAxxvGbjXhsFsxm1QsZhOhsMaBaidnzRvNcdMHUl3XTWyMFb9fobm1l6yMWCaPK8DbUIMnYLC1PojX56Cytrd/MtuGZiebtjbgch/pk9ViYUBRCp1dXtJTY+nq8eFyBzAMGDcqh6L8FHJzEjlQ2YEeib4ICevgsCj4wwYxsVbeXrwbq81MZkY8Y0fnEgxG+GRVJRAN2hsG+Pxhaup7aGjs4dDcvSQnOjjhuFIA5swoIS7WxqRx+QD4XS4ad+6OXj+fH09PL6XTJ9PnifDsy1swmVRu/eksHHYLfa3tYBiklwwgJS+XlLxcGnbtQQuHCQeDBNweXO2duNo6KZowFtV8ZGSCEEIIIYQQIAH7Y1fXQXC3Qv2G/kWTilOYVDwJGreAFkJRTVx6zmn8q2olZ/vfZYJ6kKcSfsZ9/p9xtnUb2dN/ym9dblLT4lm7t46PHXeiqAoRQ0VH4XH7k6gYOJVEPtQm8oLtYQBOXzGLO04dQmVHHk+4gzxnfRjayhhvqiFP6UQ3IDj4LBwTLsH/+pXU+mI5w7QZgNgJF9M3/iZOePhTuvVoWR/TxKvY0GqmoWovDTEj+NfaWl6NXIgJP2Z7LCdPmc7jq2qYVpLCYxePJ8lhQe3Yizd5KE82DCQJF0X6OE4I++HZuRD2cUfwD5xsmsAt5nfI6niOab9Yzb5mF9e8tJUrtTt5ZVovjVsWcy4fMSNSzp/3z0Mjjo8tfyMQ0TlxYAp3njaMNZVd7Gt1M3/eJFY3jCGPNnom/ZGJJVksrHDym3fLAZUd1lLsisbgrHgOdqm4AxHWVHb1fzaLy1oYHOPhI70RFAgZKsUpdsYWFGBSFbZv38wfjEdZbkzjAcevqdroYfE4DwMaF/Gr1AgMugoAVVFIi7MRjOgMSo+lpstHYUosACNzEwmENfJTHAD8/MRBaDqsPtjBP1dXc/2sEi6ZUoBmGFS1e1jwr80kOiz0+cNUdbolYC+EEEL8H7ncfiIRHZfb1x+wz8tJJS8nlfqGaHa5w24lJyeZHbtq+/eLi7Ph84Vw2K2kp8ejKCo+X7A/YK+qCpqmo2lGdFJbOCqrPBzRcPZ5GViaRWenC4fDSq/Ti9Vqxu0+8mIgNycFm81CV7ebvQc6iIuxEAxpjB8zgMzMJPbtb0JVFTbtaOey88fR1t5FOKzR2R3E5WmmL6RS2xXGkZREfm6YxuY+ZkwuZPb0UqxWE3sr2sjLSaCmvhdFAV03qK3v4cXXt2M2q0QiOhNGZRAfZwEFBk+bQE1dD727d7O/qpfhQzLYuaeVYFDD6fSzwxmddNZiVglHdAaXpjFyWDYPPboKtyfIaScOZuknBwA4fkYpqknhg48r2Hegvb/P8Q6VzLRYmjv9VFZ30dF1pH7+jt3NWCxq/++BYIQxI3PQNZ2EeBue5kb2teoUZzto7I7w2ZoqigtS2L2vjYElaaQkR0c5qiYziknFZI72S1VNmMxmYmIUkhId2O1mLIfKEuYMH0JXbT09DU3YE+KJTU4ia1AJro5EXO2dOJtbo41RQIuEJWAvhBBCCCG+QAL2x6qhZ8CFr0H64OjvzdvxLrub33Ueh2PUmdw9dgHsfJm4HU/zwfBmUsrewEqET73bGX3KVdz6XgHzP36bR0z/wAD2xlxHht5NJKDjw8EmfQjD1XpQIKZ4MkVxp7KyYhuVvljKuw3KP6zg2pnFPPyj0cCb7Nm/j/zPfgVAlZFL9+S/sq6qk1f1ZygOHWCR7T4A1iWfxSMLyzl/+nB6vCHaXAFumF1Cn/8u2l+5j8VdN/C7TReCejx/Vq8m0WShbXUNADaziUSHhVUHOshYfjcj+lYwgOn8IvITbmgJsbyigj+kDcPf00RfIIaLTJ9iKCrq2AUkOizUdnsIhDU0A27dZOOBiWfyxCY73dY89GD0YcisKpw1JpuIZjDpd59yx6lDaXb6uHBSIXPX3U2nO8hP33mCiZZ3GM4QsrmRMWo1E9VKwobKn3tv4dwRv+Ed9WROHJbBZ/s70A0437SSoaEGfqb/hBkFNh5tLCHbms/SzQ38yLKep5TXyTN1M4xGJvecgzsQYffePQxY9VMA6hMmcMm7XUwrSeXDm2ei6UZ0lMHnPH3ZhP6fl+1p5ZY3dlGSFkOXJ8T6qi6uty4npn0f15/6EK+VWXhnZxMXTcpnQmEKc4dmfI1fViGEEOKHYUBxBokJMaSmxAFEJ15t7iYcjlBakoXDYaW9w0laWjxxcXY8h7LsVUUlOSmO7h43gaZQf3a8qijohnFoAlaD2kYXA4uTACgsSMft8tHjjE562uv04uzzMnJ4Aakp0fIr9Y2deL3B/uMVFaaze08j67a0kpcTR1qKA1Bwe/y0tvVSUpxJW4eLM05OonRAGinJdvbsbURVQmwv6yA3K47tTUGG2BUMLZoJn5EeR0TTqDrQybtLywmFoynpCgqbtjfg94eJjbX2T5JbXd9HUX4Ck8YnYTabOFgTTXBwe0K0d7gZPTyTLTtacDgs+P3REYWJCXYK8pLYXtbMrvJWzjltOJpmkJudgMmkoOsGjzy5Bt2IXrPPc/t1PE1uxozIQQFsNjONzUfq5IfDerSEz4AUDlb30N3jpbG571C5wGi5nNaeEH6/hqYbfPhZBTt2t5AQb+OkOYP48NMKTpw9iDEzp6EoKsrnygzGOEz8/IaZ/b+3HThIb3MbFrudSCiEz9mHq70Di81GWnEhQY+XkM9L5sBS7PFxWOx2hBBCCCGE+HcSsD9WKQqUngCLrgEUiMsktnk9J2oeHt0/DGzro9tte46s7sr+3cz5ExmZm4iigFkLgInow0uwmzP0B5ms7uM+y0tMM+3nj+ELGKNUM3vOgxQ1WPnT9lO5z/QCH1jv4JfhG3huHSTFWFkwMoai148nXvHTnD6ThrG/ZWdVJ0+srMYAXJRSXng5a1oV/v5GJTeYFnNJ52r2RfK4PHw7FW1uRuclktmxlhTVxR8tz7I6OBZzQgYtfUeywq6bNYB/ravh98sq+K3ZwggzZCm9vHrtJO5dso+D7R4GzXuasYHNfLz+FqxoYLLiKTyebRUd3Pluef+x7tf+znFbd9Od/RP+1DAdExoaJjxBjcW7Wkmwm3EFIqgq3DQ5hfCbF3KH38etxvXYlOjD4xgqWGy7m1Tc1OkZKCYzhUYLb5V1847exGmjslh280y8u95j3OZ/oRg6G0LD8Y28jJVXZeN//9e80atxXGAleWo3IcNMpZHD+47foE+4iCu3jSYUmcX4gngq/Uk09Tby2f4OLpjoZHxhyhe+Eptqunl9SwM/mzuQx1ZUEYro1Pf4yUywceHEAoz35qHoEeozjueiaefyo/F5mE3qF44jhBBCiP8/ZlN0Etbd5Q2kpMTR0dmH1xfNku/sdBEMhqMTv1c0ox2aZBQgKyORQChMdw9H1Z3XD6XQq6oCKJQWRUfDhcI6BXlpvLV4N719HoaURLP5DQOqa9oJhSKEIxo1tR0ApCTHkpOVzN79jbg9PiaNzWRneQcjh2bi8fg5WBXN6j5cpudwzNvjDaFpGg67mTHDUvH6NYaWJtHS7sZmNTGkNInhQ7J4fVEZlTVdWA9lq+dlx3HK3KE8+8pWAK6/YgobtlRjNcP23Z00qF7CEYNN22vYubul/5x5WTGYzQppKQ66evz916Grx0dXjw8FUBSFkqJUnH0B3lq8u/96Hb5qh68ZRO9xOdSXnXui5zn71OHMnl7Clh2NHKiKjno4YbCZiScMwRtSeHfZ3v7RAId39wU04uNsHDetmA8+rgBg5LBsGpqceH1hdu1pobggmeSkmC98J3qbWgh6vGQMHEBvcxsYBrquYXHYscY46KqpAyAuLYXcEUMx9CFHBf2FEEIIIYT4dxKwP5a5W2DfYgD2nbaImrge/tE9niviy6GthpA1GWvaYOiupFNJIVR6KusabCx/bgsnDcukzTkXvesZVAUylR46TenMPfFMtq/dzfjILn5tfh1NMRP/7BTM8Zdyk7qfSep+AC5L2cvfggP580cHGBgIcZLixwDiZt3EmKLR9D00n6XWeszofKaP5R7/jbSpQc43L+UW87tgwATVS4YdLnx6I3eeNpSF4St4y3oPWGIIBC0kfC5Daoq6j/G7lxPJvxqrSaWMgejGx+QNHkdBSTo/mzuQNQc7mZ/nJemlGzArELQkody8lQUvVrKrsRpVAd2AaSWpxDESo62CoSPGcXbXbv6s/ZlXtbl4Y/OZENrKmzm3MnDQcM4cnQMvzMNSv4GzTaBN/wUDhv6Nf76eyQjPRkar1ZgUnaBuJf2iZ2ne9yEfbBobbXNxKuXNLmZtugcFndakcVx24uUkJyVhad2BZc8LXAtco/2KWep+3tOncz1vM9y/g8jWP3L3oNtweVW8HfVE/G6GZMVxoM3DBU9tYutvTiA51nrU1+HxlVWsrewiJdbGj48r5q8fV5Bih52tQbbW9bAhci2FegMPLzHxa72Wq2cUfyNf08Mimi4vCIQQQnzv9Tq99Ll8BIJhigrSOFAZDYZ7fAF03SA5KSYaGNdBVSEzI4nqug5MqkJ2VhJt7U4+F3PGYbeQnp5IQ2NXf515q0Vlzfr9hMMhSgqOlLRLSHDgcvmpONhCVmZS//Khg/Po7HLhdPro7PHT0ubFYTNjGAYmVcEgWr7mMKvVzJr1+0lNSTzUFgNFNVG2r52crDi6eqKB/YR4GyvWVpOeGkt1XTdWi0oorDNhbAF5uUnMPa6EYEijfH8bHV1ekhNsnHZiKYNKs/jHsxvQtCMvLSaPy8NuU1BVhYlj81m+4iCGEb1GFosJw4ABhSlMGJ2Hw27hb0+tIxLRMZtVzjp1GGmpcTz1wqajPouBWWZGThzKzj3N1NT1oKoK2VnxrNtc1x+sHzU0g0GTCvBFVHbva6WhyUlivI0+d5D4OCvZMRFqu3VcniCfrK6ipDCZ3q4+uls7GTcii+CAWMpqennjvd1cf8WUL3wf2g5WgWEQm5pMUk4W7h4nYb8fFQh5j5Tnqd2yg+JJ47DHx/9vvm7/J8ahL9qXTXIshBBCCCGOXRKwP5YlF8H8Z3hqTS1/eCfA5OKrqerp5ff1afhNp1BhGsGfzv0ltO4iPX8KD39SSdOeKgA+2ttOnNVEk5pJHu0sMK/gBHaTuaqHiBG9aXcoYSz4QYGiwAFiTvwZDStupYBW8vwHuXZ2MZ/u72BwsY2Hd1xPWTiHh/JmYcfgNNMW7EQnRxuoNvP3hnPRTXbSR8+lo2kTsaYIsb0V3G57ndsD5zJ65VUUmzX+Muwd0pMT8Kyow/CH+dP8Efx6UTm3Wt7CsusAM+IzWHbzzznxEZ01xng+O/s0fv1OGW9ta+LKaUU8t7WNXxoKKAYHQymUlfvZ1egE4OEfjcblD1Pe3Me5O07DYTmDnROPY/7BzViaNIbbu5kQUwnBWt6tW8VH4VSum1VCb9xAEtjEK5G5PLg2zIJQKyf/6HYeW1HNbZnbGLn9TgabmtCX3UCKs5ZP5zxAYMyVrNzfStXq1zgQPoWRai2LLT8jbuUWvC0VnP2jK8jOv5wlNfCpNo6V+li2Wq8jRfEAYNaD5DV9QL6+G4uh8bsly6nRS8iyG+RnZRFri/7XfHljHSmxNk4blc2EvDicvjALphQwID2Os3dei96yk/dmvciUqcWcvecE+vxhQugkOixf6VdxRUU7z62r49aTBzM8J4H7P9jHrsZeRuQkcd9Zw/nT8gr+ta6WJy4Zxykjsr/ScwshhBDHkszMRMLhCPWNnRyobCUmxorfH+4v72IYMGl8KaFwhPg4Bzt21R6qTw+tbU6sluikrYeFwhEaGo/Mi/P52vVDB6VhNlvo7Y1OVIthkJmRiK7rJMTbaWuH2BgrVqu5Pzje1OrF2RekMC8erzdAcoINuxGmN6QSjhhomk5vX5AYu4mubieqqlBYkM6m7dF68iaTiRmTC9i9txWXO8j6zXVcceF4UlNi+GjFAQYOSGHU8By2bK1EMTQUxURtQw9tHT7aOnw0tXtp7/QTDmtYLCYuPGckLneAvtY20lQ/ptgE8kaUsGJtNcGQRk5WIk0t0RI2FZWdZKTHUTIglRi7BZcnSCSi88FH+/nRqYOYf9pwdu9rQwuHqW1y0dCtcfD9PQCcecowivKTqDrQxJ59bf3XMy0tjqcW7sDQDa66ZCJVB1vQQwH6ALvZ4GDHkZcKwWAEm8mgx6vj9HmpbKzGboFYu4nigugoBy0Soaehibi0VOzxccQkJ6MoEJuSDI54XvyoGZtF5dIzBuKIi4G6xmhGvQGK+tXWq++qrSfg9pA9dBC6ptN2oJJIMERCZjopBXnUbd1B2B+geNJ4LA4pvyOEEEII8V0hAftj3ajzaazZA00NbK3rYUJhClvqDP6gX87FowrAbIOGjRysquTpNUfqlJuJsMBYil0PoB5Kes6kC8UcS7uRxl99p3DcxDGcXvYTTOhkK93cVZHO3PTLuabzj9hik1hb2cWNQ3wUvDGPK414poWfIBDW6HBHeDB4O2NN1dyRsho9bTDnJQzEYTHz97WVPMKdHDz1IKy8lwavmRK7h3HhHeiqwu/313AwkIQCWFWDZWX1GMBT+tmca1rDI6vy+csQjZMHJaBXfcbpj1joDJrQDXh+Qx26AXOsA5moHOSgloMrEH1pcFNeNfMzs7n6E43PKjoOXQUF6xsXMrNpDX8MX8C+9PN46bQYOstX0NQ0k5+U9MKzJ1KRfh4LAi+iYQIMyjZ+wvrdRfxqSgx7OxyMBMKGiZrs0zD7PuWD3gG0r69l0I4HuM/8MdtMAzkvdB8FHjtv+m4ky9rLupZizqs8GRMRJtmbKB05BVdVETH+KrYYg2jWUnm19yTS1FM5sxgONo5gmXYLufQQPn0VVrPKrkYndy/eC4C/ysf1ZTdQoo+j2/s6A9IBdwtqxM/8ITGQFMOaXx2H+Z0r6GxtZGHdgwRHZ2P7iiYyW7ipgXVVXeSnxNDu8rOiIpq1trvJBQp0uoPoBjR+bni7EEII8X1kUlUK8tNobXfi94cIhzVMqkJEM1BVhbzcFBRFoaOjj6bmHvpcvqP2j3wu6xyOLpEDkJmRSFt7NICtazqKWcdkUtE0HZPJRCgUIS7OzsGqtqP21w+V4Bk2KJXWdi8Tx+ahKgbxhh93SwdZCfH0xafQ0+vhQFUPwwelYLVG7xPqGzqJRKKlfaxWE1t2NBEK62RnxNLjDFDf0MHMaYPwed3ousH6jRXouoGzL8jOvV1Htd/tDmE+dP8xcEAamRnxvP3+HjJiDXJLzPT5wrzz1Lr+lxaHs+rrm5309PpAh+df3UZWRhwuT7RNwZDGilUVHDcui/h4G05ntK+BsEFhfjIdnR7cngCLlpTR1H4kq11VFZxNrf3lb6rrumns8JNgh+R4C6NG5LN2Uw2qqhJv1Ul0KDS1uXDYTEwZk8WqLc34QpAYq3DSnEFAtAROV209PU0t1HkdrC/rZGKhmfzRKqGwRiAYQdNNxGdmYLOayRk3jp0b95GdYODp7sEW+8WyOv+/uuoaMHQdR1IivU0tKFv2ofqCdIwpwWK3E/IH0CMRIuGwBOyFEEIIIb5DJGD/HfDg2SPZ09zH+a2PMN3VzvOjHuC1vQHe31bJuNA2ztp7PyWoaNqLQPQB6UR1O7ebFuJXHOwZfjsjK59ECfZBUgGPZz/Doi0NLNthYrN2Bb+3Pke21srGmi6SRpzENZdewcZNTtauqCHR52e2JYbE+Cw+vmg2hWlxLN7VzDZjCN60iai3PIEKPAhsXL2UX9mv5mDSDO5ovwdnYj41fQatARO3m67l4jnjaFuXAmiAzmv6r8lp7uFUfs/+uClc1zsagA3P3MLtbKLI2sKSyHReNE5kwQXn09Tjo6rTw76Ct3jvg79xhfljNjSuY/2CUnLfvhvjeTtjJ3/GmoPREQTPXj6eno9CpKAwavwMrj1pFsTZSC+awcsAH98FTVuYYkugMO0marp8XGH5lHtNz9EaTiZ7XS+GASHFSqORRmNviEZfIf/YpQF1VNg+BaDVSKEwJYb6Hh+bLUM5zlbJsBHjid/ayH3/j737Do+juhf//57Z3nfVe5dsSbbk3isdTDO9dwIBEkgCIYQ0kpBAAoQbCL2H3gmmG9wL7pZsy7J6Lytt77sz8/tjjYhD7v3m/i43N2Vez+PnkWdnz86cPbZmPvM5nyM+zUr5E8j5CcQKob2FwprZLDn0JLmynysSN7OisZF3T3Og+30EnZLgu69s4/rzCqjJtVKSYabXE+GzHU2cpU/SYPagcRiJJFJEz1vF+l37ePBNiVmlTdytexQOvUc+0LZzDdsa6lhc7gDtkaV1/v84oyxG6FAL27osdLjTN8I5eJkpHuLDvSLrfnAMzf1+5lVk/o8/S6VSqVSqf3SCIDC9sYwtnx8imZQwGXWHa9oH6OweRafVfiVQ/wVZVtBqRfJyXfQPjANQVJjB+HiQWDw5EawHiMYShCNxqivzyM6ys7+lH38gguZwCbqcHAfVlXkT+wJUlGZywtFTJtrYuaudgC6TDK2R3sEQI+4Q4uFsDp1OQ2lxFu2dIwiHi8F3dHnQaAT0OpFAKE48ITHuDbNh88GJNkPhBDqdhuWLJyGjw+kw0TfgpbPHC4DNpmfR3DI2ft5NJJKgotRFllOHWycybVoFH+xKr8V08rGTmTmtCI1GZEZjIQAPP72FoZEgyxZW0NHjQZIURBEG/Qovrxn6Sn+GQnGisSRrNnYeXgvgSxpRwK5LYjcKWGwmCnIsaDQCMUkgGU9SWOhCb9AjywoOh4n2gTCCoCAgMG16BTlOI69+1EE8keKzT5uZv3AytqxMxjq7kZNJRobSiQopg5VUIkGG08RV5zUS83rp3baDrPIS3l7dSddQhCkFGqbGOnAVFiAIwtdSx95otxP1+fD0DZCKRjF6w2DSo2/pJRxXKD9hMVIyicn+9yvDo1KpVCqVSqX6n1MD9v8knrtiLtZ7NqGJxOjat5VKUwWvSN8luM/AamEuPlMpk10ugrEUfd4IO+RJtCol1NBLfuuz3Ou4hZtKN6M54Vf8ypZP7sHn8Iai7GUSP3P9mu64nUsay7hkfhnYrVyyIIOYBCdOyYfcNrQaHXuaR3l6cw+LqrLQigIVOVYA3tkzQCCaZO+abcwngc7fzRvDA3yk/ylVmmE6jYVU0MfLY9O4+fjJvNs0yNB4kNy4F4sSwSpEmV+bi0tyc3AwwDfcbwKgIHCyYQ+nipsgYx5Mnz/RH207Oqj2DLC29WPey6zj6owKdgUd/H5tLzaTDk84wVgwwYPCbfTGujlaM4OTrAYAwvEUe/p8zJt/Ixq9lb784xl9cQSAAcmJJIIkGtNTlwWILf4huQf/RNXwY6CFbealjCT0rE01UCy4ack9lTuOrefdPYO8FbmDkqOq8EVTBGJdePR6EAGDHeIBUCQqs63IhwSCipHlNdmUZZopGv6Uz+2zuc67jJ3uLKIft/LM5XP41lFV3PJ6E21ZxxA//QTQFXP0fevIthmozbPx8QEZCJMM+0F+HoAmuZxt+jk81HInvPgcnPciTDqBpCTzXtMQs8szKHSa/usBpyhfrkinKBy14XxW6INcFb0Tp5DAp8ngV+LDzBUP8ouUh1jyKBZUZX1t412lUqlUqn90Br2O+slF7GvpJxpLkkylM8YjkQSQQKsVEQUBg1FHJJI4op57KiUzPOzD4TBjMuipLM8lJ9vBrj1dAGg0AhqNBoNeR0aGhbw8J1qNhqrKPNxuP8VFWdROEhA1Ims3dqDXaynITWduW8wGZFmmf8CDVivS2esjK8NES6eXzt4Ac6fnIooCKUkmHk/R3R+gsMCFRqujqzdISpIRRYEFs/LIzc1k1B0iGk2Xu+nsDVBT4aSrL0AiKbN8SR1nnjIVgHgixa/vX4MsK3y8pp0Tj67BZNTSN+AjK9NMfrYBQRTx+WOIAqQUsFoNEw8f/IEYoXCcU0+oo61znPoSE0aflo/2J/li/V5RBFlOX6IsXVjB7qZBxr0RBCH98EEUIBZPfw8LZpeQk22jr3eMuY0iM+dNItjfx9nTdXxySMYfTvdzMikhKwoWhwUGwtTna5g5qxKzTmYkJFNToKfQrrBx9zAGm51Fc8uwZmcRHHWzbFYBC+05uHRJ2jduxZGfS9g9hnx4LPiHRrCJcbQiuMwCRruN7u07SSWSlM+dic5gIBVPEPZ4seVkIWr+65mRiqJM1KOP+gNEfT4ApGQSBIH4gjoUMd1Rmt4xtDodejWzXqVSqVQqleqfjhqw/yfhMOnoP+WPfLh+M86sE3jkKDvaR+IoKLzICj7zVpFliTEWTmdXhXUZBMuOR+h5nMzEEGeO/p5FgYfIeL6fl6r/yE2Jx0APisZAo/txAiktb0wroOpwED7TauC2mmE49DrkfIdeX5KfvLMffzTJ9BIne396HGa9hj5PhBtf3nP4KOfQI/yEmTPmUNqVwhJTEGSZYmUQEZlVTQMcXSjz2jULiCUlXvv0VV5Y10yrUsyPU+tZ1PQDwhUnIrkFNChIjlKC+mycwTbu2uBhy7sbeeLSWeTYjFRf8hDbP3iWD4dmcKdzBMXThU4uJyEp3HJcDVqNyG8/bqXfG+X0aY18++hqAIKxJMf/bj2D/hjfPbaGbx/9A7bu6CMUH0QjCuyTy1FEHXmCl5Oid2K22XndJYB7F1GNne7SM3n4nEtQ7q1BIJ05d7P7dqY/m8dRjVWsbXWzsW2M2eUZ1ObZeCR4GQ2n/4TZUyfjqT6T9zds54/7TJjMM2jyaMgeDnDWI1vYn/FD5kYGeGDZMm7vy+aSBWUAnD2rmDOV1Yjtz0PmPSTiDiRZIZGSKc2woBGgsdjJz0+thzVHE3N3cLn7ViwOA77BdjIVieh4DybguS09/GLVAWaVunj9mwuOGF+/+fAgfd4od50xFcvW38HaX8PKR4hNPoO7nn2DS5IWbILAOZP0HLf/dryKlY32FfhDA3SI5Sz97Ro++94y8hzqTaFKpVKp/n1kZdkpKszA4wmRl+dCIwq0dXxZPz2RlNBoxYlgvcNhJhKJk0xKpCSJQCBCSIyh6RTx+r4s5WIy6gmF46RSEjOnl08Eae02E8FglNExPwV5GbS2u1m7qROAW25YSklRJlqthqFhLx1d6WSETJeRlnYv1RXZBMIpRFEgmZIRgJQss3FDB9dcMo+aqgIa6ot5f/VBunvHaW0bJzfLBkoCi8VI88Fx4gkJSdEgy5DhNLG3uRuTUU/d5CIMei2XXzCLjz47hCjA2HiYaCwFgM8fo3ZyEf0DAZ58YTuiAMcsqWJydbqcYygUY836FnoHg5x20lSWLaxgYH8LLrNIjkNkNCCjKGAw6IhGk9RW5+DxRPAHYhiNWmZNK6KyLJNnX9450Yebt/ditxkwmXTsHA2xetsQS+cU4xnTYDLpOP/sBnKybVxw5nT8g0PYhCgVBgNGLSSHe2nq6Wbt3vR1dUlNNRUpH/WTcgEomFLL+58IyIMKK6a4CAyl+1qRZPQmE7FQGHtuDpmlxUxP7qexQiYVi6PV6wl7vSiSjJRIoDMYGDp4iNDYOFmRErIryyeOX47FGX/7U3RZGTiPmUfPrr3EgiHKZk1HliT6m/dP7GuwmIkFgihaEVGjQUmmiOa76N61h4o5M7+Gka5SqVQqlUql+ntSA/b/4LzhBC983kOGWc8P304Cs/nWZAvmvCquy3qcW0Z/wFPCT7hUvJV14UYmCb1cafiUlrILOevgcn6tbeNc7TqyDTLuYIzxcJJ73H7uEAEBhOLZPLt8MSPBBDNLM4788HdvBF8P+8I2Tt5cTU2ulaNrczjaNoDF68HvmEymVc+Khnw2tY8RiqW4/PzzOWlqAT8AWHMRvoFWnO1vE1e0DJjrWTYpB2QZYyrIxScspKJqEnd9cJC3dnSzSA97+vz8MvUbnpk/xi/ayljV5+DXK+t5btVBokk/rcNBcmxGcBYz+/wf8Qawe8MqZEUghg4B+M1HrXgjSXQaAZdZxzVLK8k6nF2/s8fLoD8GQKErHVw+tbGAIX+MZzZ1EYkYGJctOKxWFjfMZ0Z1IdQ46ert5ZbPjexsmUyb1oQ2v5HISDumuBsFAUGWeGfPAIVOIwO+GF3uMDNLXbQMB7n6zV4+Ki3jh+908OnBFBBkTnkOdx9VzMGmz3k1rOPz8hvQ96zFWXoSTx9TQSie4rUdfSQlmXM2/w7R3wNVR1M1+yrW3bIcs05k45YNKIpMKJ5iSpETeagJY8TNYqGJ94NLWO67hFphGUMbqli/ABqKHOTaDSyoPLJsjcfrwbPhcbZLjTTNKWH+2CFQJGhZxdJ3bLyZvI1CcZw/WG/giiVHET/kZEQoovL83/LArpvY9nkvsqKQlCRUKpVKpfp3EAxF8XhCxBMpBgY9ANitRpxOC+2dwygKE3XTv1iMVhQFFFkm+WcLzhqNeqLRBF5vmEg0PrE9K8tOaYkRo0E7EaxPt5XgUHu6LMze/SN8vrOfkiIn5SUuEokEep1IPCHhdFiw2YyEw3FEUcOFZ83EYTeRSkm0dQxxsG2cfQfdIChkZ1rJzrKQTEmYTTrOP2Mae5oGMHq68bnHQDTS3ukBBI5aVEkyGSUnI5v6umIOtPQTCsWRZQWNRqC0yMU3LpmLLCu88PouABw2Pf5ggrfeO8DQSBC9TkOGy8z82aUTJWyGR3047HqKsWIxp0v5ZVdU4I0o+CP9E4vwFuTZcDnMzJtVgl6nwReI0dvvo3/Az+xpRdisBpLJ1ESWfSicIBZLYrMaCIbieEMp2oaTRGMpWnYdJOvYWbz45h6WVwoYLCK5JfkY7DY693fhsMLsOhvJeIL5MwpZPK+cRCTKaM8A7SMJtu3qA2DB7FKyCvMxOx1o9ToG9rWAoqDRahGARCRdNicCMDY+8V16+4fIr7VhdjmIBgKYHPYjxpjvUBfeVBzdxp04jppLIhxBTqUIDI8y1t0zsZ/N6cRWlM/gvhb0ZhOFDVNwt3cSGhufyPRXqVQqlUqlUv1zUQP2/+AeXd/JI+s6qD6c+Q4wKbYXBkL89upTGXrwYWJBL+OKDY0ocKP2bU5iKweiRp4TL+BnXMNpM2qwmK28VrMIgLsfPoBggJg+A+Nl7zH9r3zuukNuGhqvwTW4lkBmIwZ8FLvM3Be4Bf64DRmR45KPUFRUzH+cN41Fd68BoG0kxCXbtzHDEeam5rtxAuFjf8NF78XojQt8cmCYzN47cHWtgvNeYqHewq+k+7mJ41l77Pt8vG49RjnKwNRvkpkawOEfpCbPzvNXzma89wCzSpw88GkbsqLw1KZuSjNNNPULZPEHJIMNi0FLts2AAuTZjbz/7cWIMS8M7ITCmSyqyuI7x9RQmmnm9OmFrDk4yjV/3ElSkknfC1pZGP89z1wwh2t6XmBkZz+Bsru4038cYxkhbp5VTEoRiF24irMe3gyB/UiCBh9WUOD65dVUZlsodJmQZIWP9g/jiybpcIeYUuhgS8cYBU4T27q83JS1g7N7f8wZjtnc4fkF28ZKOb85xJ2VCtf8cQeb2tM3deuMF/GH+UG0U88BoMBpgnW/5eSNv6Rq0gUIJ/4GgNWZF6AEN7FZmYpJL+KXLHyu1FKt0/DOngEMGg1aUSQuyby+sx+zXsNJU/PpeuX73KV7jY36Gby2o4Hchb+geLAZXcs7nJY0kqdNH0e7R2L1sJnc83dy8VPb0T26hVD8yxvBP+0d4vrlVV/j6FepVCqV6h9Ta9sQwWAUq/XLmWWBYBSTSU9jQxlNzT3I8pGLycqyMrHNaNRhs5rIzrKh0WiIxRITmfnZWTbKS3O+8pmJpERnj4fcHAeiKNDZm651X1uTg5SM0rSvF4A1mwdYuqCCuppMDh4aBEVieCTAK283Ma0+l3A4THamgRmNhXy+sw+PN8LOvf2s39JFMilxw1ULyc4yMRS04ZCC5BcXsXbLAAa9lvlzSmje14skyWS4rNRUFZBMyfiDMXY3DeIPRGlpc5OdaaF/MH18NquRQDCBw25kZDTE9IZCVhw7Ob1gbyqF2WSguDATFJg+zYbTbmHzti4+/KztiPN3WQTOOWUqb31wgG27+pg7s4RkUiIvx8rCuWXYrXq+cfFs7n8sXR9/Ya2FDKuGrR1xzjl9Bv5gjNIiF50dDg7ubiNTE0ZAIT/XRvNohGXVOgJDwzQfDNPcGmLuZAc1ziiyJBH2+LBmuujeuQcpkaC1M0lJrom6KSVkZVqAdJZ7z669RLw+HAV5ZFeUImo0bDRrGZJTJICLEiLKF/V9UPANDjNg9rIl9yDLtTkY+xScTgs2q5HRcTdKfgYUZuHu6iavbhL9e5qPCNajKCTf3YDlu5eRUVqMp6ePvj1NpGLphz/JaJSo/6sPA1QqlUqlUqlU/9jUgP0/uOPqc1l/aJT20TBmvYZ3z8+j8pVlsFdH27nbON19IwaSxNHzq/pBjosqhGNT2DkQwaRE2XRNLeYnHwNg+szLwFXKd84/mfVDZSyZOwfW3gWHPoIzn6BdyuGKZ3ZQlmVm/aExSjLqWH/RQuY9cQwf2UoZWfIxPJ+eahwXDfgkPZZwgnyHiWWTslnb6uZ3q9M3V+tRyHedS55FYM6syzktvJtvdb3OTz8aoUHfxUJBhpgPtj3GVN+nfLSgAm35HJZ9cheyyYCYdzUzTpvCHacdXjRt7V2w9tcc6L6cew8ci14jkJAUmvrTWWtjOMgzGHj/+oUICGRZ9WhEIZ2V9vyZMLgLznkObd1p3HhMujxOIJbkmj/uICGlb57NOhFJgXhKy56OfhZt+wUZwNOrFrC6JX0zduaMQo793Tp84SQV2WaaKcFm0EI0RUWGnvNnFyKIX9YfPWtmEa9s76PfE+U7x9bwnWNrWPnQJhyE8PS2AFAX3s5M/VYmzzsGl0XPDS/tmgjWA3wUq2dLxRwWG//sZkuXDhBMLsqhUyNwwv3rmZS3kv3Ooxh1h7HICk9dNgujEqPbl+LGl/eQbTPgDsZZfWCER9d1IgjwzIJxFg+/Rgw9a6VG3tw9wPv7hlgrDpMrwDKxGY0AHXI+n8rTmRNPsbZ1lHhKJpGCUxsK8EUTtI2GmFHi+trGvUqlUqlU/8jyc52kkhKhUAyrxUheroP2zhHc40GyMm0TgXlBgMwMG7F4kkQiRSgcx24zkZvjoK1jmGAoyvw5NSiKQkqS0wHkvAya96eD7/W1RbjHArR3jhAMS2ze3s/MxkJmNuRh0vuYPS2PyjIXLQfT5XQSycNZ/bEkGS4rRqOOWCzJlh3dZDq19PSPodVoyM2xc9SiYmRZpm/Az6qPDyKKAoIAqZREe+cIiqKltG4qhzq9KApkZVow6HXMmlE50Q8fftZGb7+P0mIX3X3eie0DQ4GJn3OyrZx7RiNWczqhQqsRSSRSbNvZDsC82dUYDDqqDi+eO+4JcrDty7JC+TkWgv4I5ZkiTc19HGxzA6DVigyNBMnNtlKaa6Jtw2Z0JjOVOXry7VBgTQEpjppdRG6Ojdyc9MKrk2qL2bS9n/17I2Q3hrnigtkA7Pl4PbGEhD+Ynom545Cf6qMLcNmMxMNhult70KcSyLJCOKFQm5Fk/sySI8aFcHgxX3tONmGPj9GOLuL5WawNerjZk8DscuLIzUFj1OMfGGaopZWwM0XYFOHQWBeWwQIsJj15gREU0t+lJMuMd/f+9YEYjoJeB1rtRFkeOSVhy84iGYujKDI6tYa9SqVSqVQq1T8dNWD/D25GiYsXr57H0feuQxQFcvKLIHsyGO1kulw4TDr8UQFQWHToLrSim7jWycXafRhyqnEUroSFN4Ko5Y8tMpaxtzhj9xWQWQXHb4e9L4G3G3o206I5ml5PhJFALL2obJYFOtYiyEl0KQ8vfN7L3LOf48Calzi793Ti6BEFAY0o8IcLZvCdV/awpnWUpKQAArd6TwMv/OixuykaXctRmh1oTHP5kfYH/GyBjrXdBdww8yYyrblo518LRifk1CPa8kD7FzcXQjoIXpRhY0FlJnX5dizdH3Fh7GVu9qxkvTyVbyypYMlv1iArsP6WZRS6zFz61DYuG9KyVNQRFh3YDje3sc1NhsXAPZbnWZ74jOfKfssfOrNJJCXOmlHIuYtr2Tx6PT29PWTXzOPX5WA1aHGa9YRiKWIpiYpsG9GB/ZxodbNRX8Uz0ZuRHi5F+82NeKMpHvysjZe3p6dL/3zVfj7aP8xDF82godDBTUM/YKm/iYSjDL2/m3Om2vBWF3HGQ5snTrnAbuD4qfloRIG55UeWsWHBt2DKmWDLZ8/uAQ4OBwknUmz4/lG8u3eQkgwzjYYhePwopjur+KnmNjyhOLeeMIkphQ6+/3oTt+heI2vnNkTAnTGTxqXf58LOcT7YN4wogQCU2oEwvCgfi6x3kJJlLpxbSsuhNr4Zf4Iar51J1zwPWsPXPvZVKpVKpfpHVViQgdmkZ29zDwaDFrvdjE6nwWk3YzF/+TtRUWBsPHjEex12MznZDoLBKE6nhY6uEaLRBO6xAAV5LmRZnnhPPJHC54scDvbHEATIybLiHksHxIOhGANDQcpKsxka8tLVly7PI5Cu995QX8L6ze2EwwlKC630DgTp6PHgcoZIJuK0d47h9cfRakWyMy0smVeC1xeiujKPcCROZoaNopiMzWqguuKri8sLh0vaVJZnICsKJUVO9rWMIMsyep1AaZGNmdPz2bW7E71ey8J5kwiF4zz+x23U1zgxGrR8MQ9BkWWC7jFEnZHyYjslBTYMRhOpZAydJoPJBWYs+fls2DFIKiUxbUoBJqOOqvJMZFlCkRUUWWJ+jZnuoRAe2Ua/O8L+7R3MG0ty0rGTScbjPPnSLgaHQwA8/eIO5s4sYfmiSjb3CgyPJYEkep1ISpJxFhWhhHyMd/cQi8q4YwoZeVmsXKrD7HRMnP8XihvqkZIptAY9gwcOkoxGOSPm4Lq6OfhHRrFmZhD2eOjb1YTZ6QDAHtFzXPlysqMuRulHHw0SCYbAZsKVm4vGbCSVSOIbGERBQRZBRECQAasZ+agZREJBcidVMdB8ADmVQqPTUtRQ/z8Z4iqVSqVSqVSq/0NqwP6fgNOsZ8OtyxEQMOk1cP1WAIqBvT89Hm84waurN1GyO51x9JGwkGK5jTeDdSwNJcg99ueMBmL8+FefcqnmM87QyTDWhiKl+EPGD3Gyh/c+L6HDs5/ZZS62d6czpLJserZt/oQ5wGDmgnS5k4waFNtCrM/tJBKIYzZo8IQTrD4wwreOquLTlhFEAQxaDUlJ4gxXJ1eN30NS1NAkl7HLeTT+4SD1a39AkWziKe0r3LLyYQDe2TPAJ84H+fHJdeQezlCaMP0iyKhArD6VF8O98Oyp6e2hfq7ObGK9eyp9nsjhhwVw14cH6RgNM+CLcnn0RkxCgoy3ZTb9AF7a1sujb33MXM0h7ioZRhiMcm19ind9ZsZDcW49sZZsm4F982/itoPbyXqvlasWVyAKYAx0s6nhA/xTLiXmrMbVdibO4Bip1PlYNGGi4720D3o57ZFtaP/sJi4Ul/j04Ch9nihnzSri8+2FzFf20zfjB1RK3ZSvvxmXeydw8sR7BgNxcmwGLplfhl77F/0BYC/AH01yamMB8ZRMQ1H6xu+UxgI2tY/x6cF2jk5GMcU95NqNDPiiNBY7uP+TNob8MVYY38RAnN/qr+eJwdnc7I9x58qp/PCkWrSfXgjbHkQIDfOhNIv6k6/nuykDP357P/PKnLwm3IpFMw5uuO2xN9kQzOX1axeoi86qVCqV6t+Gy2Vl4fzJaLUigiCwaP7kideWL6knHI7RcmiAYDCGKAoTWffBcBSdTkPt5CKGR3z09o3xxRXDuDdIeVkOZrMeUdTw8NNbyXSZEZDo7gugKGA2iXi96YBzhstK/eQ8tBoBvU7LuC/G2HgMnU5DKBwjHI6Tk+1gaDSIJCvkZlvo7gtQlGdGlhUmVTo52OHD748TiyUYdfsAaJxSSmFBBoqi0NYxRv2kXJYuqPhKHxy/vIZ4PEVJkZPMDAvvfngASVZIJCRsVj27m8coKkjPwEsmUzzz0g4kScbri7JlZwxZVhj3SaxcMYW+pn10dY0j6wwIDicaDdRPyqFpfy8Wq5Gs8lIAptbmsmlbD+s3d7C03obNqNA/lqQrlcWyxgrGR7ysW7cP8Ewc5+iwF9/gEEMthyi1KAwe3h6JJtm+u4/liyqpnZRHMNBFUoLLz5/NZxva+cMz25g1NQd7QiYQlZEUMHm9OGqnYnF9dWahIIpodDrklEROVQVGmxV7bg6iVoOrMB/f4BARX/phi6jTgiCglUVK9aX0HWhGm0yk8+qtJnRDHrwolM2egcluJ7uilPe6PmAgMUxRxEV1KJfKBXMZaWtnoPkAzsL8ieMIjLgJe3zozSZKpjf8N0e2SqVSqVQqler/mhqw/ydh1v/nX5XLouea05aD68ekknFG2mys7atkazCLQyNBHCYdO3u8nD6tgED0VOTYbhKuKn73URuP7rcCi4D0jd9Rk3PxRZKUZ1vwR1Lc7DuTaxzFnLF4GSbvOnj0Uioc5RQEL2KGxsvN+i4u/o9h9gdMlGaakRTQiALPXTmbpzf1sLMzxg65BiWjCt+xv2PNmnYq7b1kJENYNTHOmJrFweEAHaNh7v/kEF3jEWaWurh8YTkAkUSKZzZ3c8XuczD6O7gp+T2Oqi3kgkA/mDLhqB+xfWwuuINMje1kU+4zfJJzBQ93GRgJxPnxilosBi0//dN+7CYdAF3uEL/XPUCD2A01t8PSW3iyO5uZntf4jNlEE+m67OWZFvIdRsqzLDz2wTYe1d+HlJnC7O3EmPRzS+Jq7k55UYATj17OyR9PJaCYuKXtEJKs4LLoefzSWVz65OckZciw6ChymTBoRS7WXcEvoxdxwXgpP/a+hAkFR9cqVk6/Bl8kwZrW9MOXuz9s5elNXay75aj0w5o/c9cHB3lkXQe/WjmVC+Z+OSU7Jclc9vQ2kpLIn85+n4ZJVTwWMNA2GqJnPMq2bi8aUaB1yX9QIwwQDRxLfEs/X6xpZzFoYcG1xEKj5Bx4gxO0O3inez1ux2Ky9Ql+Frsbi1YmldLxUPJk3hhwkpCiDPqjasBepVKpVP9WdDrNf/qaxWJk2tQyOrtGMBp19A94iCdS+P1RZEUhlUgB4HSYMei1+ANRcvPsNB/oJRJJoCgKkUiCYDDOUUsq8fqTTK7JIRAIoyggihpmTitgaNhDd48bk1GH1axlwax84kmJp17Yhj+YYM60fMqL7ZhMeux2Oz5/kt6BACajhvxcB1Pqinn1nWbyc20T52R3mBj3BInHU2zY2gXAzGmF5Gan9xn3Rti+q5etO/sQgPkzcwnHBKKxFBWlGdRUZrOrqZ9QKEHfUJihYT/zZxbRuWkAATj/jEaGRoKs3dSJ0Zi+PhsPSnzams5wv/LCydiset5+/wA9/T6ysyxMb0xfGxYWODAatGSa4eD+Xnb2dROXRKKxJDaHmT3NA0D6enTF0hLCg4PYTDGCwXRd99rqbCZNd7Fx3X46xxQK89LnNHNKHnmJQRRFwZgM0DfkR1FgZDzBmNZGiVOgQJ++Xu7b04wtO+uvZrF379xNPBSmbPZ0MoqLJraHPV6GWg4BUDpzGkabjZDHg0arxd3ZTSqeQNRqcRXlozeZ8Vj7IRKBw49ztHo90wtnwNAuCsfNAHj7+tHodCAIhD0+AARBQJYkZElCSiZRFOWIhYtVKpVKpVKpVP/41ID9P7tUHOQU6C2w5Ga2Nx3kmvXzEHUKxyxfztzyTO79uJVH13ei0wgkJYWVV7zJdS/sJhTvnGimschBhzvMZQvLuOvMdCZO+2iIc3o87A0e4MI/XcO4sYRMJYXJ18YDxodB1FE02Me8pJX9rECWFTItesbDCb7/WhNd4xFEjOw66WUuqxXpj8k09fsRBAe+b3xKht1GZWYhs+9cjTsY54wZhTjMek6oT9cwZctDdGx4mybfPHaZM5mtGWQonsFmcToXnP8yKDKULeaihJ5PerdR3v8EhYFdmMbtXHvi/Zh0Gs6eVYxGFDipIR+jNn1THYilkEj/LAcGiC/4HpmvnMc3xI95NdVCMH4sAGVZFrbcdjTJva/hG/0F2ckBUl4tLZa5ZE66gLdeHONSfQlFuiBTZi/nRNlPw/67WbruO4zaL+f40y+lpDKLXy53IK67m3diiwlEl5BjN/LkZbN5ZnMXL27rZTjzbB6xdSLEvPyucA1PC6fT54ngiSTwR5IkJIXxcJwCrQnxcNa+L5Lgs4PpWqUfNA9xbF0u7zUN8tj6Tu46s4HTGgvY3evjR1tkousOMBKIE4iluG55uu6sJCtctz2Xfq+dH62wsf6W5ZRkmifGw6N749yzdyWnCxlU08e9e7KJ00nz5RnYXlqHhMgZpqdpiuo4uSGHc2eXqDXsVSqVSqU6LJWSEEURrVZDTXUBTft7iSdSCEK6Lr1GFNm9v4tgKF0vXavVUN+YS+toK5JBA5jRaEQa63MwGbUsmFPG4nnpDPf+QQ8fr2nFbNQiy31oNOlZeNFYkmAogc2qRxQgFpNIJmWiMRmDXiQaTdDX1c6YO4JNr6W8LI+yYif7Do4QCMYZM+o44ehJWMwG4rEkTft6EYR0RnsyJZOTZSWVknnvkxaaDgyTTEro9Rr0OhFRFJjRkEd5aSY2q4Hy0gyqyl20dw6zdnM/sXiK1vZRzjltKnq9lprKbGprcpk7swTz4YSKiNaMRhSQZIVINIXRqKO7zweA15d+yCEKAlMm5zFlch6trf1s3RHBFw5h0ENuthWdVsQ9HgGgKNfCjFlVDB9UeG39MJ69hzhxVga5pWW4nGb2Nvcju32MeaMA2F02lPrJePuHGG3vZH6tk89bfIyNhzj7tAbMcQ8BdxJkCTmVQpYkUokkWr1u4nuPBUMkwhEUWcY3OExuVSWD+1uIhyMUTJmM0W4DBQb2t6A3GYl4/QiiiPlwtr7PEmNzdD1lI5nMqV2E3mxGZ/iyxJLQ7qNm3JleHAEFT98AWr2erLISxrp6QABBq0VJJsksK8Gem6MG61UqlUqlUqn+CakB+39mqTg8OBtifrhuK9jzmTm5gkMZS9HLMW5f4+fJg5upzbdj0sJ3jH9iRLKTYV2EIIBZr6GhyMHiqiyuXVZFLCmls6sPq8qx8t3jJtGxrQs5YGRNrIYP42fyI8cHvBpspF0p4Bz7fpTJZ/O6ZjuVWRY2Z57BvZ+0srQ6m67N3cjAia4B9A+dhtNQxKzSR7liURkei40HNvdy2YIwS2uy2dHtYXP7GMOBOE9t6sJl1vHN9T9iqiLxiP5zHqp5gflnnsDP+4PU5duh80N4+QKw5DA299e0DJu4RXsq38nI5lHPIo7yRbntxFo0okDLUICqHCu6wze01y+vondsEQx18GK7jl/8/GOemnk0403b+FypJT+cAGBr5zgWgwbn5hcpTg4QLV7M5R1L2Rqr4wVzIxfPH+Hs7b/mlmWTWBQycsZMGwUxJ3hgWngTJS8/DUf/hHPFMGjXs8I1grm3EmpOZKb7bewz5rClQ8+wvow3x2s4T/yU4OYnuMNTA8Cc8gymFznZ2jXOorvXsKQ6i6NqMrgk9ARdIykWjsWZqzfxx/ZlnPnwZhRFYdAfY1uXh6WTcnh918BXhow7ED/ctottXenSR0P+KOPhOIIAH+8f5ixnKxWrf0OlfA5vCUtJHZ7Cb9SJ3Hcwk2zdpTiifUT9Y8wsrePO0xtwmHVf+SyVSqVSqf4dBUNRdu3pwmoxMnN6OsheUpRJIBBBr9ey70AfBfkuNBpxYnabXqeZ+Nmg12FzWSjIz8DptCAIApo/KxVYmO8iK9OBQDpDX5LSi5PGExIdPQHsVh15OTYap+Rht+qpLM8mEIjg9YcJGQyMBsNYTTJWi5ZtOzuIxmVqKrI4Zlk1w6Nh+gcHWTinFKs1PWtuzaYOFKC1bZhwJMnOvV9eX1xw5jSKCxxEogkcdjP7Dx5k685e8nNtzJ9VSDyeZMbUHEbHwkgyKEBNZXrNIJ8/Sk6WdaKt2smF7NrnZtwb5a33mjGZ9BQXOugb8CMKItFoEpNBS8TnQxBFht0BCvJtmEx6SvVBYqk4s6YX4x0axUqU6QtriYcj5NZUImx0gwLrmjx8sGMTV140h9FA+vrGaTfSO+DDZjHQPpSgLCebeChEuVNkhyIRjCts+7ydadnpa6jMshLiwRDJeJy2DZtx5OeC2c6mpjFy9FFydOmZmt6+AcIeL4lIFBQFKZHEaLXiGxwCIBVLt4cgkIqlH9xEbApeKYTeIJKMxtHqDUSifuKRCOExD6HxdJkfUaNBTqW/f41BT9jjRdCIKJKMIss48nPJqSz/nw1klUqlUqlUKtX/GTVg/89MTqWD9YkwpNLZQXq9nsk3vsMnB4YJ/XEnXe4we/v9fGeyl290vwRAwvFDsq0GOsfCbO30sLXTw8cHRmgZDvLGtQuYWuQgEEuyrdPD5Dwbd7pLGZn0HpcsKGfhUIBk5Xd58sFNxCUZS/lp/O5YJ/z+dABWfPtEVnxvGQAZVj2JlMzHLQe4RNEwHBHY4fOyuDqL95qGea95iEhc4p6zGwF4bH0H6w65eWPXAJ5wghkzfkjJgUfwCk6uWrEIQaNlZqkr/aBivB0QIDxK3d5f8/3jXyHX0ciKGZfznds/4IkNXSysyqJ1OMhdHxzkvNnFEzMHijPMFF/zewj+kKcePUg8FYapZ/HNkUa2dXs4Np7iw33DXPv8Tix6DRnJU7izehINp3ybXffvARTu/fgQz181l0hC4rWd/fzyvRYcQohfOYYpnvYzHMFu6DgEGgNMPwM8XZgPvAOvX0E/WRQxRrWoY1nlO7ze7GPEVAAKaM0uGkwORoNxTqzP445VBya+7k3tY7jbd3KZ4RGmA9MPx8j3u+axy5OeMP3L0+sZDcZZf8hNnt1AIJYikpCozjLyG81DiIMib3AJCyqyOKY2F4NG5NENnTy5sZtsmwF3MM4s530cq9nFkJBL3RUPUZZloX00RKHTxMqHNnFKVOY63WdMkbs5reeXNPX70GlF5lX8xcK4KpVKpVL9G5IkGVlWSCaliW1Oh4WF8ybR0TVCOBxnZMSHJCtkZdoZGw+g0YgYBDPJYRuxpAKECYZibNw2hF6n4VtXL0Sn0+DxRvAHY8yYmktbxzDVlXloNOlM/sGRENv2jODzxzlqySQMepHWtkHaO4dZMDedDJCcKmGyHcJmN9HSOoJBD2PjETq6/Zy+opZnXtpPJJokJ8vK7BmVSJJM70CEVEpiYGgcFKiryqCj20NepomK0vTvfodOSygcJ364zM/QSJCe/iCFeVamlmQSjiR58oXttHZ4mFyVw8tv7qG9a5yzTp1KQ1269nqGy8o3r1jAqDvE4899jqIkuOy8WTz01GZkOd2no+0dePvTFegdgg57cQmuegM7NjSzqSOFlNnJrAV1rF57iOT6ZvJtCr4oZFo0LJ9dwfpdw8Q8UTSiwPJFFWza1kNHt4eO7s8xaAXiKXA5jKyYoiXq82M2CgTjCg6XFa0BNFotqURiInAO4B8a4eDwAM19Eg3FenLyBESNBlGrJRGOYLBYyCgpIjDiRlGUdLBdkhBEEVGnRRQ1JBPpZJGGrCkYUjbKyGdoX+tfHV+iVkvlwrkoqRSxUBiNTkfPjt0TryuSRNjjI+Lzo9FpMVgsX9PIVqlUKpVKpVL9vagB+39meks6sz4VhYwvFwL7044ONrz1CMcXLeLDPtBrRUrqFoDzIrDmMJ5KB+sByrMszCx1sa51hERKZsAXYWqRg9vebOa9piG+We3DmoR2t4l5FZnMq8jk1++3EE/JrJiSx+/OnY4ipRBmXAqDu+HpE+HsZ6FkHvkWgd2fvsEzgWoe5AEipDO1fre6jQKnEdfhrOx4SsKg1fCNJZV8Y0kl969u5fUdA9znWcC2WC1aUaD80b2cN6eEKxeVw+qfwdaHYMpZIKfwlx7L/e+2gQKLq7K4YlE5rcMBphc76T88zfnPZw4MDvRi/vhmnDULee3aa+n1RHhtZz+La7K4fUUtd77XQiSRwqgVKc204AmX8IhUx9XjWh65eBZXPLOdttEQ27s8vLazf6LdU8QtrIi9S6B1D/bvN4P/ZnAWM+yP8brxWq41rkMbHadQGUtH1+Ukc1K72OKsY/bK2yC6AFPJfP6Ukc6I6vNEuPujgwgI/PzUOlqGg7QOZxAp+R76XU+jjXtot83mje+czhMbexgLxUlKCr//tB2AVd9aRKEzvdjslNBWeGk1AGtWXkLJnGoEQWDL+o9w+dsZpAzd4XI7H+ZcRWPuNC5Z8G1wZQCQZU1Px77tpFpGW/0o7i1sC87BJmi47oVdBOMp7jm7gSXV2eTY1Tr2KpVKpfr35XRYmDOzEv1frD+0v6Uf91gAu81IIBhDqxXJz3cCCjnZDgKBKFISQMBmNWKzmSnI9SEIAilJRqfT8NhznxONJjnx6PR1XySaoKYqHfB+/d39AJx+Uj3lpZnEYgkyXBZisSTbdrYzvaEMrUZDvS3Brg93sU2bxZJ5hSRTMoV5Fu59aCMmow671UA4mg4gazQiZ54yFVmW2bG7iwOHxsk1y5wx3UBKq+eBJzZx6vF1lBa7ePblnYy4QyyaW8q4J0qmy8KfPmolL8fGlRfNZnJ1Ng67CZ1Og16vQSA9s+ALbYcG2bijn8XzK7n+qgUEgzE+WXuIk4+vxWzS8+QL2ynLMzHJCkabFV0yiS7qw1JUiSa7ELm9h+HRIClJpqXNjceuIX+SluaBJP3eOCZHmG9cOp94IoXVYqCn34vDbkSLTAqReFIBQcDrjzEczSDDpOP8cybR3R9kSm3uxPfpHx7FPziM1mAgf3I13v5BGjM1SLYUHe3DuIxaKqqyKa6rxtPTB0AsHMY3OISo1TBp2SLi4QiCKDLa3kFwdAyAirkzMVitzJNz6N29d6JfBFFEkWWchflotFqyKsoQRRG0WnTG9DVXRkkxyViMqD9AKpFAFEV6du5B0IgUN0zB5LAjav7z9RZUKpVKpVKpVP9Y1ID9Pzt7/sSPv3q/hfWH3PzYtorf6h5jj387H3IL8yoyWTmnAvgDAPnAHy6YzoGhAN86qhqjTsOsX34CQCCazoyqy7cTPrCaW/vu5MbsUl6Y/Q7vNQ2R7zTy/r70VN76Igffemk3qw+M8OLVdzB97HwYHoaBnVAyD+97P+NO8V3mGxdxY+I6JBnKMs10j0cY9KWn/r6yo4/FNVmc3FAwcR5TCpzc72un35cOthc6jbSNhrjno1be3NXPQ1U5lAIUzoQ5V2NadTM/MgR5WXsqJr2GH5w4mfbREOvbxrhwTglTC+28sbOfz1pGWT45m98/8RR3KR8R7d/Cb4eXs3xyDi9+3gvA2tZRunt6uEf3CIbyOSy48h7u++QQv/+0jWhS4q3rFvLqNfPJsuopzjDzzWWVFLlMzChxMTZUQuumPsrmr0zXFnUWs2/Az9XP7eCm8O/RaseRRS1xDBhIIlcew0+bC4gSQ9AaidSdw0ggzhcTmIszzPxoRS0/ens/t7+9n/e+vYjqXBvRxBy2bN9MnbKPW3xn8Zao4cyZRcz65Sccrl7D0poscm0Gvv/iJkw9a7j03POYqbdCIkRpYAfvNs3k9dUbeCpwDW/qNSxJPcQTly7GG0kwuywDtOdPfB9fLFYmywo/fnsfkYSRKVd8yP3P7ySSkCbq6t/8WhN5diNbf3j01zW6VSqVSqX6p2SxpAOpkiTTvL8XQRAIBNPXNYnDi9uXFGeTlWEjKyO96KmsKMTjSbRaDYUFGYQjcQaHvsjkTv+Cz8u1oRVlotE4ToeFYXcUi8UPikRZkYVAMIrVauD+RzagANdcMpdtO9tRFIV4PEVKijB+32uUDnmJzJmFIBRSUWKnqcWDJCmEDpcF/GB1Kw11+VjMegBEUWTcm6Szx0cn4C/UMR6J4fbGeenNPRQXOLBa9Ix7RWon5WKzGHjzvX0IAjhsBgx6LRecOZ3O7nG6ejycc1ojB9tGaescIyvDglWvsHnDAbrcMmLqICctLqKtJ86+gyOMjoWQZQWPN4IvEMF1fAFLG6bQvWM3Ifc4BouFpYurKShwUVLkIpmUiMdTTJ2UQ16WkZRzkF0Hx5k3uxydToNOp8E3NMzrb+3HH5YAkUL3MCPZuWj0GiZVZbF61wiiIPDTRTbsVmN63aTDHHk5BEbdhNxjDB1so3L+bESNhpyKEA91jLC3X6K0OIZWp8NoszGw78vZks6CfJLxOPs27sIfk5k1v24iYJ9KpvB3dOHpG0CR0mNEazBQNnMayUQcs8NxxBj74vos4vfj6e0DQaBk2lR6dzeRTKTL7SiyQu/uJux5ORTW134NI1ulUqlUKpVK9fegBuz/hbzfPES/N4pSN5eE513q553OhvrlZNsMX9l3RUMBK/4sSP6zU+vZ0jHO8VPSC75ev7wKak+EJ3+HbC/mF4dLs5wzq4g+T5SlNdlct6yK5fesJZqU+OTACNPPeAx6tkD9SkYDMcYsNcSjBo49+niqdthoGwly5oxCanJt3PzaXoJxiSKnifmHS6m0Dgcx6kTmVWaycnohH+4bIpqU6fGkb3CjSYn9gwFuG05wtu00Vjaex77dm5my+xkuAS6R3qD37auQT7uLq5/bQddYmE1tY7zXPEgoLvH8573s/vGxNNsW8ntPN/tTlXy0vY855Rlcvbicfm+UD/YNc46lk+XSXgK9h3AH7+S82cUM+6OcPDXdX3PKMyb67dYTJtM7HiGeklgwbQqaGW+gKApPbOgky2qgZSjAkD9Gk7Ge0437aJ5yK2dvzKfApmXThSfyvQ+2Yur6lMdWmznklRnwxXjskhkcW5uHIAjMLc9ErxFJSDLbuj3k2I0898bbXJfcihsnSxYvB8Bh0uEw6fBGkgCMt23nk/94kIs1fpZo1/Hcq4eoNNtxEoKWd/numrkYZZEefS4pQc9pc2sodJqoK7Dzi1UHeHZzFzqthkumOdDsfpbc2Wdw6anH8SfTHVSKB4gcuJLPZjtxHnie6+PXszk5CQTItX91rKlUKpVK9e8qkUjh9aVnNRYVZjA66mfypAIMeh1m85G/M0VBoLQke+LvZpOespJsBEHAZEwHzi87bxYDgx4OtQ8RjiRZva6LvfuHmDstF6NBwzmnTiEry4YvEEsHuX0RpjeWkUpJWK1GOnvCRHIyMXvDHHPeQnaHkkiSwsI5Zej0Gt54dx8AtdU5E4vBdvd5yc22MqOxkKHRIN29XloGkocfIUAkmqS1Ix10Lil0UpTv4I13m+nu9WLQazjUOcbaTR001ufz7Cs7URRoqMun6UA6AaSr18s3L55JfYEOrZiiMiPBWGc3MxqnE40mGXGH6B/0Y7SIxMIyW/d2sbRhCtkVZfiHR3EW5KERRSZX56QPyKRjxbGTSUbT6/NMnVnD1Jnp41y9ro1J1dmk+geoyhQ4KGnR6DQ4qqcy0OamriqbU06oI56QMWklunfsJhYMEUlA41Hz0Oh0CIKAxeUk5B4jFY+TiscRRA2ffbSbUFyhLFMkvyo9A8Jo+7IcjSCK7N7dQ6LZTUtPlHBcQZL2UeRIJz54+wcmgveQLn3jKipAZzKiNRjo2rYzXQJHq8VotxP2eCiaWofeZEq/QVGQJAmjzUoqkSQlxdEZDCRjsSMWrlWpVCqVSqVS/eNTA/b/Qh69eCb7+z3Mm1mK9rgzAdD6owSiSYyHpxy7g3Hu+6SVY+tyOWpy7sR7T24oOCLLHWDYVI3x220YDUaOfXkPZr2Gm46pIdtmYOX0QiC9eGnXWJj9gwFwTgZnCQAb2vp51DuTN20vsnrmUlrf+xiAez9pY055BmaDlmBcIpqSyLQa6B4Lc8J/rEcjCGy4dTm/O3caWzrGiCbjNBY7GPTFcAfjaEX4mfZZauIDJHbOYuWHlXyTs7iq0ou991M27evg4d4NzK/MJJ6S2NjuJhSX0IgCjUUObEYduRlO7hs9G7NO5MK5hRxXn8cZM4oIxJJU5VhZXDGT1Z8F8dsnc7xewz3vthJLSlz6zDZuO3Ey31hSCWPtEPXwox0mnj+cnX/p/FLuOG0Ku3p9/PK9FgQBPvveUgBOmrqQO3ddQEt/AAUveRnpLKmLBn6OcXQTs+VCTkzeA8ANL+whKclMLXLwzOVzePv6hezo8bByWiFH3bsOa9DLFXo9GksWw4EYU3/2ERfNLeXnp03h+6/vJZ6S+YH2RRZJ+0kWLyfcbWKPVMYn8rH8R/EHbM27mCrFSsuQwlGJ+xAFkDcPkuNycNXiCpoH/KRkSCUkMpse4xuaN2lpOUTzjLlMjh9CAKI9u+gajzFPHOGBZRLR2Udh1msmFvZVqVQqlUoFJpOeqfXFAGRl2qmuTM+MDIdjSJKM5vDvTb8/wuCwl9KSLMymdHBVEATKy3K+0qbLaWHxgkn4/HFa2j3U1uQyqToHjzdEcVEmWq0Gp8OIxxulu8/LorlfLj66p3mIPfn1NBx9NILZwbpP9iIIIMuDLFv4ZXlFUSMgCAI79/TzzocHcDqMfPebSzj9xHp+//gmBKC4yEFvvx/5i+l9QO+Aj227+2g6MIxGFMhwmRkaCfLZhg56B3wU5jtIJqWJYL0owuSqbDR6PXarjhkloDEYyCgqICvLzmkn1jPqDrFn/yBllTY+29vEzCkVjLpDrN3US2O+QmDrDkqmN2B2Ooj4/Gj1eg7taEJMxBAEKJxahz0nm+27+1i/pYuO7nEuPr2W+RnjnOBwEvjsc1p0GgZMApVFVvQ6DR3d46RSMhGPSFmWhlBMYe+aLZh1Ao7cHPLrJqEzGlBkBVmS6dq6A42SzorPzM9i+FA7UjJB/uQanIX5+AaGkCWJLZ0pFAKUFjmQRvzY9AoGuw1BAXOGi9DYOMrh/pRTKdwdXVizMtEbjcTDkXRQPpkk7PGAouAbHEZvNk30/1hXD/FgCICixilYXM7D36daDkelUqlUKpXqn4kasP8XUn/gfuo33U/S+ATNGceQbdNz1D3r0GoENt56FA6Tjjd39fPStj529fiOCNgDyLLCS9t7qcq2kucwcuzv1mMzaNl821E8fsmsif1uOX4yAE39PjSiwMkN+Rw1+cgbylNMzUzLf5hsZQz7R9P4WeNSPo1UsLvXR0Ohg/JMC+81D3HBnHSA32pMD8WUrLC2dZTz55QSiqdvfL65tIq3dvezumWUlKzwSOoULnE101B/Gks7R1kbvJwrz53D5l3r+Ml7EZKeKFctdrCnz0/rcJCj8hM8VHeAxNQLuOrZ7WxqHwcgkpQ5d3YJw/4o7aNhTpiSx/eOm5Q+gaq7Afhw3zCv7OhDI4CiQNdYGKQkPL4c4gGU7AeBdMb9WCiBoijUa/p4Mu9NmoovxGbU8YMTJ7Onz8dzW3om+qehyMHnneP09Gg5RwN5JplLZ5fy1KZuEpJ8uH/9bOkYZ0VDPnUFdlKSjEYU6BaKmB1/mOqMDPbuSNfQf3hdBy9/Yy6JlIyswB+l45ARec2zks3idXxb9wyXpB7jus4b+aBNy69XlpHnNLK2dYRnN/cyX9zPWTt/CRnfZ1JeFdu60lPwfYXL6XE34Zh9Hnu2vMZUUgSx8Kcp9/P4J01cVz7ExYtuwKTV/w9GrkqlUqlU/7p6eseIRBM0TtWi1WgIR+LsO9CH02FmemM6mN7d68bjDaHViFRX5R/x/mQyxdCwj+xsO35/hJbWAbIyrUytL+Xqi+dO7OdwmAEYGfUzZ3oBo2NRaquzj2irblImUirByFgInU5LVUUmsgR9Az6yM63MbMznUPs4s6cVAaAczqP3+WP4/FFi8dREgP7Mk6fy+B+3EY+niB8u81Ne4qK6PIucbCsFuTZWHFfLG39q5mC7m/bOca69bB471u4kW6dBsGeSl+dg2qQMurfvQkqkZwlKiThZZSV0do+j1WkoKXRy3LL0ork1xem+WfVxC/sOjlBhMmHRyiRjcSJ+Pz079yBqtYQjEjadgoJAKpYuD1OZoyNRZyWrtBC9yUROVQX+dduJNLeRO6+Wk6boKSq2M9bdi1EjE0qBxmzFJ2ipzEzPkkBR8A+PkFNTiS07C4BYKIQAVOZoKXIIOHNFIuPp/YdaDpE3qQrfwBCCIFBfoMEfVVhSoyeVbwBBIBGOoEgysYNtlM2agSxLDLW0koymy0cOHWglv24SWoOBZDQ969TksBMPhckoKWZwf3oWrEavQ6NNX0+7igqwZWX+d4eqSqVSqVQqleofhBqw/1cy3g6KzCcbt3Jdr5HTphWgEUGnEfnZn/Zx0bxSTptWSMtQgOPr877y9s8OjnL7W/uwGrR855hqEimZ8VSC8x7dylvXL5zYb0vHOLe/vpN74j/nzFSM68WfsKppiGybgcXV2fijSeR3vkdlfCD9Bl8Ll2Xt4rIbtgHw1u5+vvPKXiqyLRMB8v2DAU5vLAABTpySvhl78ILpHBwOUpNrZcQf44zphfR6IrzZtQTH5EuYllHKE5eWArB25z5GdBXcdbaBtYfcnFCfx+PrOwG4IvkSxk0fERg8xJrWcxBJr/kK4DTpOefRLQwHYjx28UyO0+wCXy+78s9h/1CQldMKuXxhGQ6TjkFfjJuOqQFRC5nV4O3itrMWYdsZY1PHGO81DzF3awaXdN3J0b4PcRo1zPpllMsWlPHjk+u4blkFz23pIRyXOKWhAElRuE26lgyzjtnCAXZt/QyHsQZ/LMXySVlMKXQSSSRZ9ts1/PjkOo6uzeXj7yxhe5eHH7zZzLzqPIZC/YyF0vVm+71R7j1nGvevPsRqz2w+Ss6GUYAkhfphBEXmKnEVv9M+yC833UF0zkl866gaWgZDfCOyH6fvAOx7g87gDQCY9Rqe6cvmocTPuShQwg/nQ+yQE0oWo+jtFJVVs+zss0AN1qtUKpVK9VcpikI0miCVkmja10syKZGbbQdAkhVaDw1SVpZNSXEWWq1IQUHGV9ro7nXTP+DBH4ggHX6oPzYeorNrhIryL5MvevvG6O0fI5lMB88dVoH9LX0smFuDVquhb9DL0JCHrAwD+w+NMzIaYs6MYk4+Ll3bvKV1ALtF5LQTaqgoy0RRFCqyNMyckovVbsHpMOF0mFhx7GTMJh2RQJCj601kF+by0kfdBENx5s0qJcNl5oYrFyDLCus2dzJnZjGZmRZkSSYry0JphoheI/Lufi/NB0eRQznkCGEkNERiSZx2A2PjYZ59eSeiKHDzDUtp2j+Ey2kimZTQ6TTMn1VCMimRUWzBopWx52aTjMVQRC0JjZGKWTW0HRoiIzHCSFsHlgwXob5uiiwyhvgobRs6KZxah3VGHbFAkIhJg6jVYM3MIB4Kc3ydjg3dIh2DYQTAVi6SYxex5+VgcTlZN7aZwegQpxWdhN1qp2rRPPxDI4z39mHNcBEZ9wIgyxJag4HMilK8fQNMTU9QJRUMfDFAUKQvZyiMHGoju6qC/NpJDLe2ISWSxIJBwh4PqcN16QVBIOoPpLPtEwlcRQW4O3vIKC4iHg5jy84it6bq6xvEKpVKpVKpVKq/O7V+xb+S0/4AF7/FruJLAHhnzyCxpMwxtTm8tXuQ+1e3kecwcv950zlx6pfZW4qisPrACDk2A2WZZmxGLXv6/ROvHxwO8LM/7SccSxFNSHxyYJiAd4xGeT/TxXbyNH70WgGrQcs9Hx3kJ2/v487QyfTI2QQxEXDW8Sv3Qh56Zx3+9+9goOsg+XYjCyvTmUmjgRiXPbWNt/YMUj/8Ds5nlkHPFpZNyuHapZVc9cx2wgP7eXNXL89fOYdD5ffxk4Onw1gbANHWz1j8p0UUvXcx06Kf88BppeTYjbx67XzeK3+DecYeyJ1K1rwL+fHJdeQ5jSjA9csrKck0s6Ayk0KniaosI7xyEXx4K48++yw/fnsfaw+N8tNT6tnaOc6rO/rSi9MKAtQcB1EP1qan+cFJtTQWO9EIAnl2I8y6kh7nPH7aMxWAQCyJRhT4/gm1lGVZMOs1OMw6Zpdl8NNT6qiiD0dskJ9qnua0qVm8sDzCU74r+J59Dc9u7qF7PMLaVjcAFoOW2gI7Jr2GQyNBNv3gaI6ty0VW0ou+3vTKHrrHI5j0Xz6LM+lEfip+m/MTtyMJAkYhSWq8c6LOfjwWJtu3m6Aukx/7T+W6ZZVUZluIJCSiCZlpRQ7OnVWCuWgqxtt7MJz/HPd+cojt3R5+9u7+9Ie0rII/zIOD7/8vDW6VSqVSqf75CILAzBkVzJxWjkGvQxBgxB3AYjEgSzKDw16GR3y4nBbqa4ux/Fld+1RKYtQdwOW0oNNqiMeTxOLJiddH3D4GBseJxpJIssyI2z8RrBeE9B+tViSRSHGobYiPPmujdyBILC5RUWrHbjOwq2mApv0D7GnuZXA4iFYr4nCk664HRtyMtLZRaQowMhrgiee3EY4kmDuzhPLSDB57cQ/vfO4j5B7nyotmYbXoWbOxg1Qq/VDhjXebWbOxgz++uovSIifHHzUJvVZDUcMUNvRqsNmMlBa7qJ9Wib2wkI/2RVnVnMRSMRmrRU9OtpXCAgeDwwE++LSVl9/cy2t/aualN/ZgMGg5/cQ64sN9ePr6ifr86E0mtgzqeX3jON5AjPkLqtGZjIhaLaJWS1Z5CRqddqJkjCxJaGwWMo5ZgKjVoDWkM96zyktx5OfhC8aZVSRQngFZVRX0KVm8st5NQDbiGxwiHgwxEktfn2n1egxWCyjphV4nLVuERqdFkWR6du5hvLMHOZlCo9NNfH9a05elbL4QDQTxDw1jsFpIRGNIySQanY5YMET+pBo0Ol16sVlRwJadiSXDSVZZKbVHLcHksBEYHiXoHsM3mC45NNreSdf2XSQOZ+arVCqVSqVS/W+47LLLOP300/+vD+Nfipph/6/EaIfKo7i9QuHShZVc98IuLAYNVywsIxhLTdSd/0tv7hrge6/tpS7fTrbNwPZuL4UOI/efO41oMsVtb+7jmc3dvLazD6NWQ1KSCODgdsvPGfIG2SNnMK3YzvYuDw+u6QDAoF3ODeKfsBHl/tTxPBafzVOtD+GIfEiJtIUVc+/jRyfXAeA065mUZ+PgcJBG/2cg7+ez915k8TVz0WlEvqN9lVMML7Kn5FJ0Y2UwtAOA7Z++zkucyLdLApQJClN1/dhXXwHbCuG7B8i3GcgfXQVSnE9nP8bM4mVcOUmPOxjjj1t6eOHzXgpdZu47d9qXnTHnavB0UmNbxHBvnIXed+CVzZxU/i32DWjwR9PZ7DJi+mmXkK4JurAyi+e39vLJgRGOO/s41nsmse/tfSyqzOQnh88T4J3rF5GU5Ik1Bf64pYdXAxezyvAjposdTJklodvyFvj7UT76IZckF/F9rqE4I31TNxaK8+G+IbrGwvR5Inyyf4AL5hRT4DTx9KZuAHJseu6eMki8cwPustNYLOzi9t5ZbBmq55uJImYbB9ism8pvT6kHYGR0lMm6XoQkfNYZIZExSIc7PZVbAfyxFFOLHLy9e4B4SuIXq1qIHJ76vunQKIqiEGv+EyZ3C5HmP2GefNL/7yGsUqlUKtW/GpNRj8moZ+b0cvyBCC2tg2S6rNjtZkZG/WRn2v/q+9o6hhke8VFUkEEyJZEMSdROKkQUBcbHAwyPBti2q4e9B8YpLnRQUZwOtLd3+XE59YTCKRbMKePgoUH8gQhGnUL/cITiAitFeRb0ikQ0lGR83A8oHOrwctSSSeTmpNfZMdmtoNXTM56itWMMRYH2/V00zp6EViOiICArCjFzJrv3DhIKJwiFE7z78QEsJj0aTXo+oyAIvPjGHhrq8zjrlAZCSZH+kQiiIHDqibVkZFoRMq1k7/aQGg3yyp/2cd7KaVx/5QIAorEk1RVZuJwmxr0R9FqRzzZ2oBVF6jMdxAJBUofL6YiHp1CKYroGv9npwDcwRMTrI6uslFgwRHB0DGdRAc789GxTnclI9aIFCEL6Pal4gqh7lKMn6XCaRUqzBGqnlrJuyzqy9EmCB5uoE/KRkKmoT8/0jIfCBN1jSMkkvsFhtHo9hVPqcXd1E/Wlk2CMNis6o5F4OITFlYGsyAQSCRQpfU2FIKAzGsgqL03Xq5fTDz6kZJLA8CiKLCMlkyAIKJKMqNUhiCKevgEUWWK0vWti7ATd4zjy8/APjZBKJIh4fV8uTqtSqVT/gC677DKeffbZib9nZGQwe/ZsfvOb39DQ0DCx/c477+S9995jz5496PV6fD7f/7PtZcuWsW7dOiD9Oyk7O5slS5Zwzz33UFpa+rWfi0r1v+2yyy7D5/Px9ttv/18fiup/kRqw/xckCALFGWbe/dYivOEET2/qYt+An09bRnj3W4uZlGc7Yv+qHCtOs47GYifH1+eyoW2Mq5dUkGk1kEjJ/PajVjzhZLo+ugyZVj2SkmCPrpGDchCATneYpzd34xDCLNC0sjrVwM/tP8Tl38/G6DxAokj0kNKYaDHNm6h5v6HNTVmmhQ9vWsL3X9/LzTsv5RhxJ6/2LuGyT9s4J/QCKzJHwA+dXomfvebhldqVhLp2MeXAvSyW13PRnnO5ZtFbXBx7CZpfBa0xfWKiCBe+xmufbOCWDRbOjx/k12c08M6eQcIJiXBCYv0hN+cfrqMPwInp2vXfA6a1jMDrl4A0js9WQSg+h2c397CkOpuXPolxr86CTufCBLy2M11Lvt+bzmC6eF4pLUN+Xvy8jzvfb6Eiy4IoCFyztBKNqIF4CJpe4RfLZ/FB73LC2fcSHB/kiSYzt0SCGABBkZip68IkaDj68HoDVz6znb39fkQBrHKAGW8sJoqBG7+3nV29Xvb2+UnJCssP3gHRcbztu3CFO/iG3Ej9okc4c0YRp/xhE5Is02Dzw+bXueOkmSifahGBo0t1VHU+SS6zGTlcm7/Gs4bhX1zL6uhZrJLnHznWRJFr/riT3QeWsFIjsr3zON76eoaxSqVSqVT/UkRRxOW0smBuDcFQlKFhH2PjAQLBCPPm1CAKwhH726xG3G4Rm81EVUUekiyTm+NAEAQsZiPDowEkSUGWFWKxFEOjEYrz7WS6jHT0+gmGkoz7WqkocZCZDJM17uNQVEdXX4hoLEm5LUlShOaWcRDAatVTkGdHliTCHi8Wl4vJS+ax+onNKAoUuwQEv5s1mzS43WGyMsy4x8Os3tRNcaGTkkIH4+MhdjcNTpzDyhX1fLahHX8gjl6Xvu0oLXZx+on1fLzuEG+/fwCdVsPk6hw6enzpivlRif5BHwV56QcZJqOOi8+ZMdFmW1Mbf3w/HZzObTRg1sNYTy8Rnw9D3I9BJ6DTalBkmZA7vW5RMp5OuCicUkfn5zvw9Q9itJiJR6JYXM6JevSJSISw109OZTmZkgRKelHZ0c5u5hQpSLJm4qGA0WhG1GhIJZJ0bd+FcjjAnoxGGWppRdRpqZo/l/ZNW5ElCVGjJegeO/w5h/tIEMivrUEQRAYPHERAIBmLk4xGsWRlEB7zoNHpMFgtBEfT70VJl9BZv32AwdV9LKzQ4LR9mbkP6br6Xdt2kkqkzzviC+AsOHJtBJVKpfqvSJLMhiY3Q54o+RkmFjdkTyyU/r/lhBNO4OmnnwZgeHiYH/3oR5x88sn09vZO7JNIJDj77LOZP38+Tz755N/c9tVXX83Pf/5zFEWhp6eHm266iYsuuogNGzZ87eeh+vejSDLhpnZSngDaDDuWhiqE/+V/L/+uEokEev2/R1lmNWD/L+6Zzd38/rN2BNLZ0s0Dvq8E7BuLnez5yXG0Dgc55YGN5DmMnDQ1j0FfjLs/aPkiwYeL5pZSV2DnltebcJh0OM3pm4Ncu4FIXGLIH2NT5RsUDnzIptIrKDrjlxS7LuaCx7cy0uWhONyEVolx9tIZXPpmE41FTlY1DVGSYebT7y3likXlBKIpJMdcjo+l6F/7FMX6RwAInfky339JISWHeGPOHaw8MBOTkGSlZjNZBNhtfIb4cQ9jmH0lFEz/8uQqlqKfW0WB5yCV2Vauf3EX8ysy+fjACPMrMrjj1Pr/tO8e39CJJXoZ3ygdIVFwKpqxES6cV8KmjjEahXbshPnksz/xXPtc+jwRABqK7JBKQMyHw5junzd3DSAdXqDt2LpcntjYxWmdP2du8GPmZVQy79u7gClc+dgWtnb2kDf3Oi4tmcebfSakwrm0HLcQon7Y8xJLrXBQ62RasZOBbjeZBEgJWuLJGJNybezt85OIx0mlvGgF8LtqcYU7WCru5WcbNiJIs3n3hkW8sasPy4unQnyY4yedBNZMgpE4swb/yKmaLZi0XdySujZ9zPpm8qQhlmn2ssu2nF+eVsvNr+/DE0kSTcrsHwzgxsVj0ik0WBwM+2M4zbqJWQQqlUqlUqmO1NY+jD+QvnaIx1PpgK72yMvyosJMigozGRj00N45TF6uE483RDKpsGNPD1azSKbLyCXnTKelbYztu/sQRB352QaicQtj+gTjngh7D7g5Z+MnyIEwx15+JvPOnY2oEfjDI+sYC8hAFI0oMGdGCY899zmLpjixJ704C/LIqalm2aJKDrQOM7vCTHNPhJ070msEaUSB006s5+3399PT5+WYmdm8PeA/4hxcDhPXXjYfry9CUYFzYvuMxkKGRgJ0dI8TjiR5+/391FY68XrCNEwpYmZj0V/tN0VRSLkHmVakIaMoH5c5SiIcIausBPeBQ7hDCrGkwp4NuzHOKZ0IWJtddqRU6nA5mfQN9HBrO5Au/WOy2xlsaWV3q4cD/XGOW1jMzHm1KIrCwc/WA1BaW4aclIj4A2SWFWPLyiQVTxAYdaPV61EUBVlRkA9/JrKCRqdFo9MiSxIR/5d9I+q0yMkUKAo9LQfImTaJ4sYp+EdG6d21F4Cs8lLC415kKUXE6/tKX/R6JUIxhfGwSH6RHVtONsMthwCQ4gmQv6yNr9XrSESjapa9SqX6m7y5vo/vPLCTfndkYltRtpnffWsmZywp/l/7XIPBQF5eevZTXl4et956K0uWLMHtdpOdnV5E/Y477gDgmWee+W+1bTabJ9rOz8/n+uuv59prr/36Dl71b8u/fg9DD7xGyu2b2KbNdpL/rbNxLJn2dzsORVGorq7m2muv5eabb57Yvm/fPhoaGmhra6OyshJBEHjkkUd49913+eyzzygtLeWpp54iOzubq666iu3bt9PQ0MDzzz9PZWUlAD/72c94++23+eY3v8kvf/lLxsfHWbFiBY8//jhOp/OI47jnnnu49957SSQSnHfeedx///3odEcmFnzhb2n3i9kEc+fO5YEHHkCv19Pd3U1zczM33ngjW7ZswWw2c+aZZ3LfffdhtVoBSKVSfPe73+W5555Do9Fw1VVXMTw8jN/v/6eZmaAG7P/FHV+fx5bOcXrGw4yFEtTk2r6yTzwl8fj6TnQaEUVRiCRSnPnwFnQageThhbBOqM/lBydNZjyUoDbfzrRiB9csqWRt6yhnzyqmzxuhbSREgf8gqaE1vNBlJW9zNz88qZbj6/MQRYGmmqeJjrTzdlcRfZ4hjDoNTrMOk16k+vYPuHPlFB65eCaQrmv/m6F6Uj4dCb2T54fyOW+OBodRx54+HwVyLbM1hwjIJnqVHM489H3u+yyXxkyF98dXYWw4k2Pq8zi6NpfTphVy2rRCfrHqAO81DbG4Oot9dxz//+y7W46fzDt7bJQuq8IVS7JnJMXMUhcCcEtqJf3aUj6J12J0h7lkfim//uAgr+8c4LaRm5F7t7I79kOgdiJYf0J9Lha9lhc/76VWKzNXC/F4DMMHP4CpZ3PZgjL0Wg2L5k9m1eBUbl29F1p9nKP9Ndr1dwHwXVMGN133Nn/8cAM/VcpZkfgVCbRc2xHjxmOq0WkEXtrWx4fCHEqEYUp9Lcg6M5uket433E7/jmxWmd7msfVdnK7XUScCtnwUKYVZDrJb08ASh5cli6+A19P9cGfsbJo1ReyxL+eB8K1Mem0Qe+zneMhnTnn6ocev3m9hQ9sYeXYDC+76lFllGbx6zfy/2q8qlUqlUv27K8h3ISsyoVAcnVYzEUT+c7F4kqb9g1jNOhJJiVF3kOERH7IMFpOIokBNVT6FBRnICrR3jTGpKpvyEieTa5Lk5Tpp6xxDp9OAuxXvvi42d/jJ6fFQVOCgelI+rvEIFSVOTHKE/e2j+AMxRvwSDqvA+9vGGXq/n6sumsO5p09LH5TDw8EuH+FIApvNgKzI1NbkMHVyLus3HkKvAUkBSQaLWcebq5rxB+JkZhjxeGMcf/QkcrOsVJRlsuLwYrcPPbWF4dEgxyyt5ryz5/yX/SYIAvm1k8gsi5JdXsZ4ZJzRoBut04LNF2epyc94lou8bBEpkcSS6SI87mW8p4+I148iyxOZ8F/IKismND5OeNzDkCdJNAntAxFGP21lyfxysspKSESiZBQXMbp+G2zai6duGG9ZPpHD5W5sOVnYcrIZbevki9ZlSSIWDlM0bSpDBw4SC4QmPlORFQSNBkWS0KNl76Ft1BZMITI8OrGPRqdLZ9MLIoJGRG8yYsl04elJz+pcVKnFE1EozzMQHvcS8fgm3usqKiCjtJiuz3cip1IkojE6Nm8jq7yU7Iqyv2GEqlSqf1dvru/jnJ9sQPmL7QPuCOf8ZAOv/nzx/2rQ/guhUIgXXniBqqoqMjMzv9a2PR4Pr732GnPnzv1a21X9+/Gv30PfTx7/yvaU25fe/vOr/25Be0EQuOKKK3j66aePCNg/9dRTLF68eCL4DvCLX/yC++67j/vuu49bb72VCy64gIqKCm677TZKSkq44ooruOGGG/jggw8m3tPe3s6rr77Ku+++SyAQ4Morr+T666/nhRdemNhnzZo15Ofns2bNGtrb2zn33HOZNm0aV1999X963H9Lu59++il2u51PPvkkHbOMRDjhhBOYN28e27dvZ3R0lKuuuoobbrhh4mHe3XffzQsvvMDTTz9NbW0t//Ef/8Hbb7/N8uXLv47u/rtQA/b/4uoK7Lx6zXwiiRShWIocu5FN7WP89qNWvnVUFfMqMjjvsa00DwQAWFqTxbpDY1gNGhRAkiUUBa5aXIFBq6HAaeKDGxcD8MSGTh5d30l5tpWlNdnk2owkDd/h9pFjeH9nP+WtbkY9Aaa1/QfH5tZhqvw2536gAEP87NQ6ltXkUJZl4bY3m2kdDtHr+fIJfo7dyD03XkIwdj5Tf/YxfDYw8drSmmy+b/wpN8+3ctrGlZzHRjQjCW7VghiAk3Twq2YP1+5ewb3zopy5dBa4yrhsQRmhWIqzZ/31rK2/NLPURZZVzx3v7kcjCmxsHyOalHj92vkYdfOZnHc8M7vGmVOegUYUeLdpkFmlGTASR1BkdEIKp1GHL5quq1qeZeH3n6UXyv156hJ2ydUspJ2zPn8YRvZxwmWrOGFKPnvff5yTtv+Ice0yXhJPQRtJ37yljJkItacgPLOCSxNBPhRvZ4tcj8Oopd0d4gdvNmMzaJEV+JH2uyhRHzuD1yIIMkVyDzohhUWI8uymLm4wvI+LMJ8WXENpfz/FYR+CoHD1eWfhnPwbAH4QbueuD1rxYudZ6XiyfAGm69sRFYV8PEgZFTx28UycZj3TS5zYjTpObsjnk5bRiYcUKpVKpVKpviov10lerpNEIoUoCmhEkd6+McY8QWprClEUhW07O1AUhab9AXyBBMFQnMlVLkbcUSpL7dhtevLzXADUVGZTU5nOPmze30skEsfltFBdkUU8kcTw2xt48+ENhP1RDrSO8N4nLfj8MebMKGZyvpbR9nGmF5ipnVzH1No8DAYtHz66kURSIhiOk0c64aOiNINbv72MlkOjvPTmHt79oAUFONThpqI0g2hKoKEunw1bu4hEkygKWAywtFQmkq/jg9WtAJxwdA0zGoowGrQcu6yalkOjTJ9a8Df1nbMgj9Z2N5+9vZdJs0RiUoSx8DhFJy7C0tpNfUUhQZ8PZ34eYa+PZDSGJSMjHdA+XE5GEASUwz9r9XrGe9NB8DllWoYCMu0jfva3jWMy6li2sAJFUdi9fhemVAJdlgNTJEHscCa9RqfDYLEwuK/liOM02qwMHWglFghObNPo9Uh/Xrf+sNyYndjA+MTfrdlZjHX1AKDIMpOWLUbUiCiyTCwQIuL14TKLuMxAKpXe7/D5WDKc5E2qRkqmsGZloNXrEQ6XW/piH5VKpfprJEnmOw/s/EqwHtKz9QXguw/u5LSFhf8r5XFWrVo1kR0bDofJz89n1apViH/lofZ/10MPPcQTTzwxEeyrqanho48++h+3q/r3pUgyQw+89l/uM/zg69gXNvzdyuNcfvnl/OQnP2Hbtm3MmTOHZDLJ888/z29/+9uv7HfOOecAcOuttzJ//nx+/OMfc/zx6cTWG2+8kcsvv/yI98RiMZ599lmKitLxtAceeIAVK1Zw7733TsxecblcPPjgg2g0GiZPnsyKFSv49NNP/8uA/d/SrsVi4YknnpgohfP4448TjUZ57rnnsFjSazg9+OCDnHLKKdx9993k5ubywAMPcNttt7Fy5cqJ199///3//537f0AN2P+bMOu1mPXpr/vdvYPs6fPx9p5B7vrgIG2j6YwfjQAC6Qv6c2cX8+TGbrSiwLYfHU2GxfCVNjd3jOMOxtnZ48Vu1HLOo1uYWuigPMsy0V6kdTVX6T9A8nxMMueHnDatAJdZz6XzyyZuHn56Sh2nNOQzuzxdM12WFe7+6CCr9g4hyzIXzyuhZzzC+rYxRGTWHXID8Op2D+doBGSNmXjlSuTurRjCPQgKxA1ZPKh5iJP3bEQ5lI3w/XaKM8zcfVbDV87jv/LmrgE+2DfMlAI71y6tZHFVFoIgcFx9Hk9u7OIXqw5w+rQC7j9vOqu+lX6QQfwdhPAod4v56ESRH7zZxKcHR3l37xD9vnSN+yRa3pIXk1k6m+5ghD+Nz+Hs579B/uyV7N62jkZiXKX9kFOVrbRM20mnsJRvbdDQ0OfirUov/a076JbT/3n5Yyn6Pel2g/H0Tdu9+Z+xdPAxtBN5XgJnJX7KpYbPWKtcRtyUR27Mg7b/JbLFAIe/dvzbXsRWNh2bUcflC8t5aWsvPYfr8tvEBCkEEPT0GKoZ9ET5cN8wDUVOfv9pelr5HafVs+H7y8n8K+NFpVKpVCrVkfT6Ly/FB4Y8xGJJ3GN+OrvdKIqSLrMiK+h1GrQaEZPJjMfnxW43cdJxDYjikXXvFUXB6w0jyTKRaIIRt5+OzhFychyYTVp8fhhxh/D5YwD0Dfg4blEpYY8Pe24OzoK8ibauvHA2vkCU4sOlbELhOKs+aqGr10N2tpUptXl4fREGhtK19Nu7xlEUGBhKZ53nZltxOUx4Rr1oNWDUCRRlGRj0xPnw00P0D/g55/RGqiuyqK7I+m/128atXfT0+8jOL6ayJp9MSwaiXo+loYa+vc2ExjyIooizIB9bVjoz056dhYKSzrJXFPp2N5OMxRjr7iMeSl8Lm/QCFVkaXHkZtHT6yEyN0d8cBUc2UX8Ak0UkWVNISoHKGdMY2H+AqNdPMh5HbzaRiEQnjjEWCmOwmI84bo1Wg5T46vkYZR1yNAGCAIpC6HCt+y/4BgZxFRUiiCI51RV079h9RMkbAFGrRU6lCHt8JKJRgu5xAsOjGCxmyufOwpGfh96slsRRqVT/uQ1N7iPK4PwlBegbjbChyc2y6blf++cvX76chx9+GEhnwT/00EOceOKJbNu27X+8OOyFF17I7bffDsDIyAi/+tWvOO6449i5cyc221erEKhU/y/hpvYjyuD8NclRL+GmdqzTa/4ux5Sfn8+KFSt46qmnmDNnDqtWrSIWi3H22Wcfsd+fL+Scm5v+tzx16tQjtsViMQKBAHZ7ek2hkpKSiaA6wPz585FlmdbW1onAen19PRrNl6WR8/PzaW5u/i+P+W9pd+rUqUfUrW9paaGxsXEiWA+wcOHCifcZjUZGRkaYM+fLmZsajYaZM2ci/8VMy39k6ioI/4ZuOqaGm46p5vvHT0Kn/XIISArEEjG2f7OS20+q4ycn1/HoxTP/arAe4K4zpnLv2Y1ct6ySWFImJSsMB2K8sWsAu1HLgxfMYL++gZdSy1lb8m2Mei3/cd50frZiEsKbV8Orl0AqjlGnYUFVFrrDTx07x8I8uq6TAV+UoUCcM2YU8cOTajlTXE+74WKu07wNgM5VwuiVn7Moeh/fbanBFO5BAL6dvJ6N0VJOFjYCsDfs5K3d/f+/+urCuSVcNK+EH66oxWbUcuGTn/PY+g4A7Mb0TbbDlK7H5Q0neOnzHjxJPWRUUOA0kW03cMaMQgCiyRTTip0sqkw/mMgw69gaymHZ4PX4PcPkt78Cn/yUl60X87PkpYQx0SvkY7OYiebNQkZkT5+fNY2/5fnZbzLEl1MDL5lfygn16f9oMy16ljrdE8H6uM7BhcKd3KJ9lZWswylEkPIauUNzPUnS/5mOynYGTdX8tLWEZfes5eVtvbQOBSeC9QCl5jgjphqeMF7Kyvm1LK7OYnFNNlU5Vs6YUcil80vJtOgpcpkx6dX69SqVSqVS/XfUTiqioiyH7CwHX6w/KwgCOVkm5tbl8J3zp3HssmqWL6rk5GNr/2p2oyAITJ9WxpS6YlxOC1IqfS2wu2mYweEglaUZLJlfPrH/1No89GYzJdMbwOLgqRe2s3ZT+jrHZjVMBOsBDrSOcODQKNFYipHREGedMpWKsoyJ179I3i4ryeDqc+pZUpxAL0UZ8adY25qebShIyYm1kTp7PAyPfJl9/t9x9NJqZk8vYlZ9OTtCu3mi41kGI8MAaLTp67Iv1gSIh8L4hobR6HXoDAb0JhMGsxmzK31uGp0WvdmM3pq+6TO7HLgkLwtKZLSpKMHRMYSgB7tZM5EGoTHo0Rn0iGL6esc/OEzZ7BkYbNYvvwugaEodOmP6OtrsdCIdzob/4nP/kj035yvbNHo9I20ddO/cQ9jjxT84ckSwvj9qZE2bRFQwYs/LwZGfi85gxJadiSXThau4CEEQMFjME8kyKpVK9dcMeaL/753+G/v9d1ksFqqqqqiqqmLOnDk8+eSThMNhHn/8qyVH/rscDsdE2wsXLuTJJ5+kra2NV1555Ws4ctW/o5Qn8LXu93W56qqrePnll4lGozz99NOce+65mM1HJhD8eU35L64N/tq2/yq4/cU+f35t8Ze16gVB+G8HyP9au38emId0gsp/dk3z59v/cp9/tpmGaob9v6E8h5GbjqkhmpA4rjaXy+aX8sLnvXSNhVnZfy/ZT6+FFfdxxaIr/9M2vOEE27o9rGjIx6jTML8yk0+/uxRJUfjhm83MLM1gcr6dy5dP4acfX8Nvp/9ZZntoBJoPTx066ieQVXVE25XZFm46phpfJMnyydm8vWeADIuB783UIDYrnJgfonbxdE5uyGescw/X8DpvKcchFc5BM7CNW7SvMkgWzyaPxa9xcF/iDHhlLxa9luPq8/hbRBMSq1tGWFydxS9PTz9pXH0gXZpmzUE327q8/OqMKdx7diOvbO9ld6+Xuz88yNZODz98ax+FLhPvfXsxdqOW+RWZ3HN2I5PzbEiywnmPbQHAE0nisugx6kTk2jMZTYa5r6+K3oTCQel4ll14GwtqctFrRc6aaea1Hb183uXlg+Zh7jqzgYVVmTy+vpPiDDPvNg3y4f4RAMbDCbrn/YKqkjnQ9ArBqjMZ/NjMbm0VU5UuXhOP576DJxMXzWxX8rlN+yKzxYMcimZxrvAp7tgufvDm+TQUOb5I9ALg2uxmigcP0iBp8Dku4yJnH/nW6aAVue+caRN9l5Jknt3Sw2gwxi3HTUKrro7+LyEUT5GSZJzmf48V2VUqlervzekw43SYiUYTFOZnYLEY6ewewWFVMN77NN3BMFWP/YDliyr/0zbCkTiJRIrsrHQ2VFlpNtlZdopHQwTDSWZNL6ZuUi7VFVkMDPmZXPNlgLi7z0t3nxePL8KyhV/9jCm1eYy4Q1gteooLnbyxqpn6SblkuMx4vBEqSjM4dlk1hfkOdm5vo31Qoq7KRMdIgpFggtUtCWKpiSRyItEkjzy7he9/axlm09/2uyUZixENBCktyqKsOF0OKOKOIssyofYBhvRB8mpr0Oh1+IeGMTkddO3YjSJJDB1oxZaTRdHUehRZJru8FJPdhi07i6B7jOHWdNnCqD+IRqsBQcCS4SIRiRIcdaMV0gvWVi6Yg/ZwlldB/WQ6t2xHSiYJjXsond5IaGwcT/8AzoJ8evfuIxmLp9sNBqmYOxPfwBBhjw+zy4Gn98iEksDwyFfO+YsgfywQoG9PM1qj8YjX24YTDPtStPf4qKwtQ5ZSCKKA3mSiZNqX199SMoW7sxujzYKzIP9v6m/VP77IqBe93YzWqM5uVf3P5Wf8bbNw/tb9/qcEQUAURaLRr/8BwRdZwP8bbav+PWgz7F/rfl+Xk046CYvFwsMPP8wHH3zA+vXrv5Z2e3t7GRwcpKAgXcJwy5YtiKJITc3/bPbA/5926+rqePbZZwmHwxPB/E2bNk28z+FwkJuby7Zt21i8OF0JQ5Ikdu/ezbRp0/5Hx/v3pAbs/429sauf+z9toyLLwmc3L+MPa9oR1vxtmTe3v93M+83D3LC8ipuPnwRARXY6q+i1axdM7Hft0kquXfoXN32OQjjjCZASXwnWQ/oX803H1CDLCr/+oIVnN6dreBadeRVnNh5NfsZ06pwuBEHAvvW3XCq8z/xCE5rG85EGtlMkuCkR3BTPOZVFGxsn2q3Qe2HfZqg9FTT/xdBPJXjooyYe2DTCqY0F/P786QD88KTJnNyYz7V/3MloMM4Jh/L4oHmIbd1efvT2PvYPpp+cKsCgL0ogmuT+1Yd4elM395zdyJRCBw+v7SCaTD9htBo0dLjDHFeXw08vmM29HxfycksHJh28+o25zPmL6eHH1+fzeZeXQCyJRhRYXJ3N4up0vdrvvrLncJtajq/PJSMzmxVv11NguI3Hwh9w31SBjdrv8lPlO8QlheSBYVKSwgGhAocQRi/ITKGTKdpOAN40n01Tf3pKe3WOlYYiB3NP+gkjqx0ErMvIXX8r+ZEN7Gv/Jp+XXsNRk3Moz7Kws8fDBY9/TvxwRt9xdbnMLM1A9c+tyx3i9Ic2Y5KDfGz6EXIsRNspbzJ75uz/60NTqVSqfzkdXSO4xwIU5LtYOG8Se5q6JkrX/b/s2tNFKiUxbWopLpcVQRCwWo1UWY1U/dl1xcXnzPjKe6dMziMUjlP0Z1n1f85s0nPK8XVEoklefXsvnT0e9h0Y5upL5hJPpsjNsmI9PCvzgw29JBISeVV2ivLhYLubYDpuzZIF5azf3AWA0aBjdCyELClUlP3XiwqmJIm+vfuJh0LkTa7BVZgOOp9dthKffxzf7nZ8hMgqK8HbP4giy/Tu2ntEvfhYMAxA1/ZdJKJRymfPRGvQE/yzEjSKLCPJMnmTq3Hk5dHftA9FljFYLBROrUNn+DIwqtXpMDnshMbGkRJJNDotjvxcHPm5KIoyUYde1GrIqawgFYvjHxnF7HKSjMexZmb+f+ydd3gU1duG79mebem9N5JQQu9dEBBEUBEERBEU/dl7712xYe8NFQUbotIR6b0TII30XjbJ9jbfH4sLERBUEOGb+7qi7MyZM+dMdrMzz3nf50WhVgICVlMTLrv9qKK4HPlaJsN1SFxS6bQExUQzpq2BXTuL6dounLJdewBwOZ3IEAiKiUamkFNfXEpNvu8eT5AJkmB/jrB39i8svf55ApNjaC6qRKaQM3XPHAxxR2dqSEicDP2zw4kL11Jeaz2mj70AxEVo6Z8dflrO73A4qKryZUs1NjbyxhtvYDabGT16tL9NSUkJDQ0NlJSU4PF42LFjBwBpaWl+//tjYbVa/X1XV1fz1FNPodFoGDZs2GmZi8S5jy47DUV40J/a4igjgtFlH615nQqampr87//fCQkJISEhgalTp3L//feTlpZG7969T8n5NBoNV111FS+++CLNzc3ccsstjB8/3m9b82/2O3nyZB599FGuuuoqHnvsMWpra7n55puZMmWK3+Ln5ptv5tlnnyUtLY3MzExef/11Ghsbz6psQ0mw/39M//QweiSHcH6W7w194+A0GPANNJVCyKF0abcDfrkbAuNg4D3+YzvHB7M6t472sYF/7+TZl52wydJ91by/+iAy4VDmr0zFDRuC+GXPesZ2iuGR0e34qr4DHTzFfF7bnXfXvoIcEQTYq+3BDv2FiJQCMKxtBGkrb4LyLTDyRehx/KIXvDeQW+uL+E3zDJ0T2vo3K/DQRV3J8+OyWbGvhtigAO67IJOYIA2zN5QA0DEukKv6JJEWoSc+REvZoUK6Ly85wHsLN/FB6mrKorP4ojIWlUKGzmHmzsq7aP4kmjf3TwZkRLtL6TTnWohuBy4rBKfARbO4um8SHeMDyYxqvUK7sbCeBbsqGNAmjM+m+Srdb8kto66iiI7y7QiVH9FXFsFb+vbMubYn8xf8wIMBTxHmqsAi6NgnS8fr9Y3foo2nruN1dLOkkLupBEGAT67uTmWzndm7m4lKv4lbv9jG9bJo2stVbDBH8tRPOSzLqWbOjF5UNtlxuL1EGtVc0D6a7Ligk3xDSPxXEUWRi95YQ4vDQ7ysGqNQAcDOpV9Igr2EhITEaSAqMhCHw0VEuO8eq1N2Mp7PH8Vrc6AM9W2z2ZwUHKwmMiLQH00PEGjUYjbb0Gj+ejaUQiGjX8/kE7b7dU0BhcUNCMLv0Y/w85L91NVbGDuyHZHher+3flllMwfya/3HZrUJp7i00f96cP9UPp2zFY9X5OZr+hAedmyxxWZzsnlbAXICCFM60OiPSI12ydHIgolMT8XlcCB6vcR2aEtTRZVPiBcEjJHh6END0AYFIooiLrsdGlooX74WT6gBbWCg3wP+d+oOFtNSW4+lwTdeh8VC4YbNBEZHYjU1ExgdSVhiPLHtsrCbzQQEtr4/qy8uwe1wEJ6SRFiyz3u5ePd+ftnWQodYK9GBcgS5HH1IMFEZ6RTv2HncVG2lRkNYShK1hUW4PR5kCgXJ3btgrm9A7rbRLSOQutxcf/umymqcZgtej4ew5ESch0R+tU5HUKwk1p8LNJVUsuSaZwAwl9fidbnxutxsfO5Thr5x9xkencTZilwu45WbuzL+kdUI0Eq0/13ievmmrqel4CzAokWLiI72/Y0yGAxkZmYyb948Bg0a5G/zyCOP8Omnn/pfd+7sC6779ddfW7X7I++//77fWic4OJjs7Gx++eUXMjIyTv1EJP5fIMhlRN98GaWPHN+yKeqmcaet4OzKlSv97//fueqqq/jkk0+YPn06zzzzDNOmTTtl50tLS+OSSy5h5MiRNDQ0MHLkSN56660z0q9Wq2Xx4sXceuutdO/eHa1Wy6WXXsrLL7/sb3PvvfdSVVXFlVdeiVwuZ8aMGQwfPryVx/5/HUE8AyY+zc3NBAYG0tTU5C9gIPEfpWgtfDLS9+8HKkGl/fP2p5D8mhYmvLsetVLOlLhaLhvSm26zfJFDvVJCuEG9mP6Fr1AqRLH/st8Y1vQt4vJHET1uFimH8FDLeBrQI5cJeLwii9stI6NqAUz4AhJ6HvukoggvZ/lse679FWI6Hd43byrs/R5vj+vptOk8mu1uwvVqnr2kAweqW3C4vdw+NL3Vit3d3+xk3hZfuvMN8h+4RzkXkzGDrUPm8N3uJgr2bmSR+j48yJgeMItYajDo9dxXey8WmQGd95C3qyEGbt0JiqMfwH/aVcFNX25Hq5KTEWVgzrW90LzfD1f1AaY572KsYh1rxGy+d/fl/Su7EvfVULJkpf7j3/NcyGxhDG2ijRiDQilrcrG56PDD9KQeCXy5qQQZXr42vIrcYeIq5310TEtgSu9E7pq3k1EdohnbOZZXluYyNCuSi7vEEqaXUnPPBXaVmbjojbUABChlTPN+R7KsgoAxrzKqW/oZHp3E6Ub6vv7/g/S7Prs4WFRDUUktRkMAXTun/Kvn3rKjlMUrcjHo1aQlhxEdaeD7X/YCMHJoBstX5+NweAjQKJg2uQcrVuWzL68GQfAtCng8Xrxe/D79MVFGXC4P06/oQYBGecxzmi12tmwtQKGU06dXBrIj7rVeeH0lZouTy0dnIlQdBEFAGaAhtn1bTOWVaIMCCYxqHXGc99ta3G43ON1wqPCvNiSI4NhYqvPzcdt86QCCTIZMLkemkCOTy3GYLchF8Bw6vSEinLgObTkWlftzMZVXIsjlBEVHEpoQT/6GzdjsHtYWuhiUrkQhF5ApFESkpVC1P7fV8TKVCq/TSUCQEbVOj7muDrfjcMVaY2QEzdU1/nH+HpkfnpKE2+mkqbKaqMw2eJxOWurqCYqJwhgRjiCTrArPBTY8/THrn/gQgMDUOJoKykAuMOPgfHSRUnbruc7p/s7+blUpt7++tVUB2vgILS/f1JVLBsSf8vNJSJzNNK3aQeXr81pF2isjgom6aRyBAzqdkTGtXbuWQYMGUVZW5o84/yc89thj/PDDD0dF9P9X+z0WXq+XrKwsxo8fz5NPPnnaz3cqkCLsJf6c+J7Q/04wxv6rYn2d2cEj8/dSb3ExWfYL/8v/GE9lFPeO+IWSegv3XZBJw0s3IAgQRw0J7aKgYRTCskcQEBnpWoZNYedF7e30SA7l592VlHe/j4zMV/78xIIA160GuwnC/iBG1vuKsMkKV5AacRF7ypuoNTu4+5udrL9/CBrl0St1l3dPYG1+HU63l1+dfejv2UXXpjy6L7yQa00v4CWBm5w304KW272v0klWwIEOT3Nl7RMUWAN5vVMpnXJn4fV6Ufwe41C6CfGLcTSkjCF0/Ot0TQzmki6xfLetnJ2lJppsLjSHptKMjlf1dzDzso4E7qni+UUHGCd0I0MsBcFXdfpq+SKesU+itNgDxTX+sWuVMhJCdXRPDubrzaXcoFhAN9cWBBk80FdP/34d+HFnBS12N7vKmrC5PGw82ECEUcO1A/5d8UDi9NEm0kD/9DBW5/nex530tQxgN+rQSkAS7CUkJCTOBDHRwThdbn8U/r9FaYWJtRuLcTg9OBqs1DWUkNUmgm6d4ggyakhPDeeXZQcAX6RmWKiWdpmR7MurQRTBdcgWMDM9jJpaC3aHm8njOvutdI6HXqehR7c0FAp5K7EewG73RcUXlrbQRqvE43LhstqozS/0FdM9BqFJidTvywVBwC2TgdeLtcGEy2b3i/Vw2B7H43IRFhkJa3cjmG0EXNgXc6MJUTxsV1NfUkZtwUEiUpMJSYjDGBmB02rD2mjC0mAiNCEeAVAroHOcguDocAKjIjFVVNFQVn7UGL1OnzhvMzVjMx0uWCfI5WiDA1EFHPayF71eBJmMiNRkgmKjKdu1F6/Hg72lhebqWtwOB8bICEmsP4fInHA+ObMX0nSwAkeTGYVWjSYkEEF29qT6S/x3uWRAPGP6xrJ6Vy2VDTaiQwLonx1+2iLrJSTOZgIHdMLYNxvLrnzcDc0oQozostNOW2T9n+FwOCgtLeXhhx9m/Pjxp0SsP1spLi5myZIlDBw4EIfDwRtvvMHBgweZNGnSmR7aSSP9xZX4c+QKGPIIdD9+AdrTwbdby1hXUI8AXCb/zTcUSxVThZ/pnhxCvdXJVkcsAAJexC8ug9c6stZ4AU1qX7GKDrICOplXsavMBIh8vGLvyZ1cH95KrPd6RRxuD4x9G1fyYNam3sEnV/fg6+t6IeArxml1eo7ZVdfEYFRyGXVmJzeOG878uLsAAbvDQbJQRaqsip+8vdmt6cZObwrNopYHVztZZU+jXhHJW/YR9LC+xgDLc6A49DBbux/B3kTZnjXM3VzK8wv38922cgZnRDDn2l5EGjVwzTJebvM5Y+TrmKTdQK+UUApqzeTXmNmaehN9VfPY601CBNYbhvvHq5Qfvsm3ury4PF50KgUp4TquNW5AAL70DGGXK5aSeisXZkfTKT6IizvHsCq3FrkAk3pIURfnEhqlnNnTe/LOxYlMEJZzvutX1K4mqNx1pocmISEh8f8WtVpJRnoMwUG6Ezc+hfy6poD6Rmurbftya0hNCiUsVEd5hcm/3e5w8fbHG/hmwW6SEoJbHbM/r46gIA1Wm4v1m4tP6txarRqV6nCskej1+mzbLmhLWlIw2WmBpPXrRegh+xmPy3XcvvQRYbiVctwqBYldOqI4VHtHbGg57jF1VVV4jVq8CZF4DiUoe92H7/9sTc2IXi/VeQU4rVYq9uzD2mgiOC6G+I7tUAZoSOvbE1WAmlC9DIVagz4sFEtDA06zBUNEWOtaBccR10WPB5lMhiCXo9IeLvwoyGS47HbcThdBMVGoDXpUWi1uhwOZQnFUloHE2U1QWhxX7/uatDEDsNeZcFsdmMtqMFfUnfhgCYmTQC6XMahzJBOHJDGoc6Qk1ktI/AmCXIa+cxuChnRD37nNGRHrAebMmUNGRgZNTU288MILZ2QM/xVkMhmffPIJ3bt3p2/fvuzevZtly5aRlZV1pod20kiWOBL/SZbtq+bTdUUYvM1cV34/7RSVKNxmvo+6jduLejClVyIXh5TQacVkZHgR8T3jiCLUh3QmtHG7/5nnQ9mlFDv0PKH8FAY9AIPuPelxtNhdDHnpN+rMDowaJTIBGqwuLu8ez3OXZrOz1IRSLqNtzPHfx7d9tZ1VeXU8NCqT+77bTbC7HoPcxRLNfbjcXvo5XiU5OYVNBxsR8EXGByjlWJwegrVKGq0uVHKBoW0jcXlE3pjYkS8+e48P8g08esUwRBFeXZbLvSMyGZx5+GHs/bdf4trqJ7AKWjo6P2RclzjyasxkRhn5eksJKo+VMKGJlNQ2XFv5GHGuIp5xT2aRtyf908JYk1+HCPRMDmbjwUZGqrbzjOwd9orJTHbcR5RRg1IuUNpoIyVcR2WTHZvTw4OjsuiaGEybSAN6tZTEc05ga8T7aifsdityPDQGtSfqluUgb21dkFPRjFcU/bUtPC017N+fQ2qn/sfMQJH47yN9X///QfpdS5wIURRZtf4gxWUN1NdbMTXb/V71Wo2SFouT8WOy2bmnggMFR4uGEWF6aurM/tfBgRoam+wIAlx1eTdS/kKR+qaGJip27gJRRCaX4xVEcHmIaZeFMTIcS0Mjar2uVYHYI/G43RRt3o4gk2GMCKNx9RYUpbXIEqOxRgcBvodvuVyB+1CkO4Lgs57xeJArfZH8Kp0WuUKBLiSY4NgYCjdtwevxktqrO41l5VgbTcS2b4vyUDS81+Mhd9U6RK8XmVyOKHoJSYjHUt+ALjSY+qLDloVBsdGYyiv9rwW5HIVahcvq86OXqZR4nS5kSgVel9tvi6Mx6HHZHXhcLgwRYbTU+H4XyT264PV4CQg0nlUF1ySOT8mKLXw78nbfAxCQdcUFjPjwwaPaFS/fTEhGor8YbeWmvZgrakkfO+jfHK7EKUT6zpaQkJA495HUNIn/FFanm9oWBzd/to7zFLtIMEBH8nCJAXD7Xthj4oeqq4krNRK2axccIdaDT+yub6wnFN+9q1cQmO79lpY2F8BBoKUCzDXgcUFg7AnH883WMmpafKnRw5xLeFDxBc8op5Icdg0rD9QwKOPE0UqvXu4rBDJ/RzlOt4g2LI5Zl6Rj/fo5LB4XdtRsOujzjBcPjfvSrnGkhOloHy4nb+uvKNMHctc3vgyByiYnV0+7gUvtbgIDlFD4GyPiP4fw+4DD49mm7Mw3ngHs8Kbi8oj8eqCGqmYHW4obuSS4gEus83jFeTEpsmr6eLaADN5UzuLudr/x3TZfanaoTkWXhBA2Hmyk0qUnSN1CT0Uuw5JCqGhxs6eimRCdkvHd4tlVZuKX3VV8saGYp3/eR0qYjp4pIdwwKI34kH/PTkni1NFkdXHJ22uJUjn43GVGK7jh4veICk07Sqyvqy5jx9u3cJFsLXXjPmedtx2d559PO28ZL295ljZ9x3JhdswZmomEhISExN/F6xXxeL3s2lvJ8lX5hAQF0NhkByAuOpAJF3fku592YyluZNO2UoqOKCp7JA1/iMxvbLITHqqjtt6CxeqkvNZFkF6OLuDEUWklJbXIvV4EwOt2++4FA5Q4TE20eDwYT1BYVa5QkNrbVzi9Yt8B3DGh6GKi0GekYD1UvFX0eHF7DnvGI4pEpaficbtRaNS01NSiNuipKyjCabMTnpJEet9eiIcWERRqNRqDAbmq9felIAiI+MR7AEtDI/YWM067wye6iyKIInazudVxhvBQmqt81oUBgUa8Hg8Op8sf5a9Qq1EFaLA2NSN6PCjUaoJiorGamvA4XRRv3YnX4yEgKBBtoJGwlCRkkkXOWUn5ul38eOl9RPVq71u0UimYsPIdNIFHF23e/tY8Vt4+C5lSwbUHv2fj05+w4+1vAWg/fTTtpowkpneHf3sKEhISEhISEidAEuwl/hIbCut58qccZgxIYUynEwvef4Vnf9nHu6sKeXJMOx7TfcsE9wJydBcw2305AwcOJs4Qy+j6mSjIhfpDBwkyOOQfut8by1pPO/aLSVyi3kRvdrDPm0BWfCSGi56HltsgJBXe6AZuB9y89YSi/ZDMSBbuqSTcoOG8omICnVauS23kyg3FlDXaeP/Kbpzf9uR8wcZ0ikWtkJEdF0RMUAA93bOotztxt/oYilyvXkLllhCiJlxHt10P0u3At1Tq7kBOF+5Sf09iUS3X/tyeXWUm3rmiC11WvwgHV5Fv0/K5cQZ3Dc9Ar1bw8pUDWfqrm69+tWHQKHjt8i5sL23k3VWF3GN9iSjBRI84L/eXjqDJq8UoWHEKGkJKl5GmTSTfqsVrqePTVWZAxXYxnWmue6l2GGmjDWDWqDQufmstZrubXikhTO2TxNhOtcyYvRWAwjoLhXUWTFYXSWE6rh+Y6ltgkDhrqLM4UNfnYBVEvNFtkNflwG/PQUMhTPgcskZD2VaYdxWh5moul7uQIaJoLuD51XKecwURJqthZZnIa19ux+XxcnHnuDM9LQkJCYlzjpKyOmpqm8lqE4NOpznxAX+B9z7bSF2DhdHDshCABpONiFAddoebYYPboNEoqa234vWKlJSbjtuP23PY710mE2ibEcmo8zMxNdmoN6u57plqkqKVvHHPie+rouMjKXN7CNXJaKmqQPBCYFg4uz6ehyCT0f+BW1DpTi5YIKpNGtqgIAIjw33R9LnHaSgIVOcXktyjC0VbtuNxupArFL/voqW2juq8AuRKFXHZbanOzQd80fwag57QxHhkcjnJPbtSnVuIua4OZYCGmHZZ1BeX0FRR5T9VQJCxlW89goC1scl3IlHE1nTEvkPR1S6bjci0ZPThYYfOLaINDiKlZzcq9+dirvXdPNtMTdhMTXg9HpQBGkITJCvDs426PYXYG5ppLqpErlEhyOXMPe8GAKbnfoMuMoQdb3/Lhqc/wVbrW0DzutzUbM/1i/UAez5cwL4vFnPl9tkEpZza5zoJCQkJCQmJf4YUViHxl1iaU83eimYW7Kw8ceO/SFWzL1qrxeFmwoWjQBFA226D6T/jJX52dWPka6t5ZqvMF5WEDGRKCIxHlKvJ90azQBjEVcoVzFS9h7Xj1TzlnsIU5/2sG/QlBCdCQi9f4VyFBhQqkJ14vSohVMvc6/rw5qQuDL/9fSwXvkPKxJl0ig8iVKciMfTww+Ds9UV0e2oZS/ZWHbOvjYX13Pjldm7/egcNFicKpRo3Co6sDdVNOMB9wqe8LHuVtfvLqFPF4UHggz0iXYRc/id8i/DTrewrrqKmxcElb69nSt4gmtqM4/aCrnyyrohlOdUAKDe+zkXrL2NvzDNsHXaQHt904zpxLpO6RhMq+B70VLV7mOl9iWHOF1gi9CAAO1c2vcMy7zVMl//MBvXNLFDeD4BMgI3yLuwVk9EoZNS0OJDLBJwekbFvrqO62U6HOCOKQz74aoWMzglB7Kto5u2VBczbUorE2UWq1s5Pmkf5TvUw8t7XgSbYt0AmV4EuHOxN2H6+H5pKweNEdqgwclXsMG4bms6N8ocZHzSHmKxeBGuV3P71Tu6Yu+PMTkpCQkLiHKSq2kRLi40Gk+WU991iduByeggJ0dI5OxaNWsHYUe25eFQ7CovqeeG1lVgsvmxEr1dEp1WhVMpQKn2PGUaDCvBlQ2a1CQd89xQXDstCp1URGx2ISikgE0CjPjmrlrBQA526ZRCflU5Kj27EtM8iLC4ObWgw+qhwFGqVv235nn3kr9uE02Y7Zl8NJWVU5uynrqgEj8t9eMcfbWNEEa/bjctmRybz2by1HBLB3Q4ntQVFuGx27M3N5K/ZgEypwBARTnN1DTX5hbgcvmtUuS8Xc10d2uAgdCHBFG/djiE8HI3hcHR0K7H+0LndDodfnG/FEVHyXlFEoVQcau8kd9VaFCoVuuCgw80VcnShITSWVVCTV4i9xXx0nxL/aQLCDlkP2hx0uv4S5CoFygA1AWFBKALUmCvrWPPQO36xHkCpCyC2X0faXX0hMrWSzEnDiOjcBkEm4+OsCRyYt/xMTUdCQkJCQkLiGEgR9hJ/iRsHpxGiU3Fh9p+nGp8s+6uaUSvkJIfpeO6SbKb0SqRLQjDI0qDTRERRZNKj39DihAdUcxgvrEAAPCK4vXBV881Mj9jP0KoPGO5dzyJvN/pH2Bky7CLercqgqbgB4cgKXiod3LINvB7Q/DW/v1/ybOTUd+YWuZY3JnU5av+GwgbqzD7LmWHtoo7a7/R48YoidpeHh37YTbnJRqf4QApqLLQ43Bg1CnKdSSz2dKOSUBbsbeBrZ288nm547HLGyNZgRou+3QW826c/T/2cw4bCBla7M9nS+QompTnYXNTA4EM2PUvyzIwE1A37YfXzYGvA8tvrdL18Ot6wVxGXPYQ3IIhNzmQaXUHsybyT2Jx7MeJ72NfiQIaX8AAQXCIhOg3f/a83kz7YyOKcKuZsLiVcdzhifsneKpbuq8HtEXn64vZM7ukr+jZ3cykfrilkb3kTj87fw13DMzBopEj7swKVDlloii8jpXov2Bt9Pz1mQMEK+GgEARwWD+oIIc8Tyfo8NxPi65ni/pY5dSP4bMYAbvpiCxeVPE94jgWcP/oWzyQkJCQkTglZGbE0NlqIiQo+ceOTwNpoQqXXoVAquX5qLyxWJ1ERBuJjghg7sh31jVbe+2zjMY+12VxkJeowWTyU19pobnEiCNAuM4pR52dysHgNgtBaD89MUjP7iWi0mr8WSyR6RRybc5DpAlB1j6DPXf87qo2loQGPy43TYkUVEHDU/t+taUSvl+JtOwHQh4dhrj3kwy+TgdeXHSDIZJTu3MPvJcD8hW0FgaC4aJRqNZX7cvG4fFY1URlpKNQqZHK530//d4Hc2mjCZmpCFEUq9u4jqXsX6oqKaamuRa5U4vW48XpF1HotjuajRXWVTovTYsUYFoo+PIzK/blU5OwH7+HvZdHjxeV0UpN/EICUXt1R67SIokjV/jysTU3Ul5ah1moJS0r4S9de4swRlBqHOlBPRJcMytftwtHoK5Y86q17+OnyBylZvsXf9vdaBy6Ljeot+5CrVXgdLhpzS5m84SPeDBsGwLrHPiDjsiFnZD4SEhISEhISRyMJ9hJ/iRCdihsHp52SvkobrFz42hqUchkbHhhCYICSbkm+gmNvrcznvVWFDInxsES4GYdagUrwIEdkuyeVO13X04KOWoLYWRTCFXILq73Z7CeReaN7000bwmfTe9BgcRIT9IeHM5Xub433rnk7sbk8dIgNZET7oxcsnhjTjsGZEYzscLRYD9A/PZxf7xxEuEHNV5tLWVdQz/8GpTFz8QFaasw0292Ahuu8d6BWCDhsvigvuSAnLlDDQMsu9FhBY+DOuTvJq2nh0QvbkhFtoE9qGGz+gImlL2IrfJFdQf1oaDuFGfluHo1YQ2zvCVSveJ13zANY9VMOBbVhKOXv0kZv4KmJ7dkUpuPHBT/QXlYEwBjHE+wU0/jO059XJw7kDaea3eVN1JodlDX6ItTkeBjuXM5OIZl9QgpjO8dS0mBjZ6mJpNDD17hHcgj3fLuLA9W+h812sYGM7yalX/+X2VPexOQPNqKQCdwzfB4DNs4getN7+JKyvFC8Dqr3gO8VMnyRk2E0ECJvJDO2jOA1z3K3YhNTuiYSrLuUV8ckEfr2Sl9aV30+RGefqelJSEhInHMY9AEY9EeL0X8HU2UVlTkHCAg0ktStMwa9GoPeJzbPnruNmjozmj8pKu8VReK0DsJVUF7r26bVKLloRFs0agW3Xd8PEAj4w+J9oP6vFyd3lldjWrYeAF37dGQBRxeZTejcEafNhj4s9Jh9hKcmY4yKQK3T4bTasLeYCU9NPizYe4+w8lEq8DiOKEIrlyO63b7Id9EXzS/I5cS0y0Kt16FQqfC4XFjqG9CHhqBQKdEGB2GurSMg0Ig2JJj6ohK8bjel23fhdjoQvSKGiDCM4WEEBAaSt2b90YMWILFLR0wVlchkcjxOJ+KhhYffx4YoIlerUCgUqLQBeFxu5MrfLXwENEY9popKnBZfbYHAyAh/cVyJ/ybbXp/LmofeJTw7jS63T2Tn299irW7wFx7e9Nxn1O7Ma3XM7wtjigA12uhQdr/3AwA97r0SgM63TGDj0x9jqTq6ULSEhISEhITEmUMS7CX+FluL6li6cSdThvch9o+C+EmiUysI0anQqxWoFTLKGq08+VMOIztEs6WoEZPVxa78EjRqOzpEvKJAPXqUgpuXAj7ictu9hOtVuDxK3rONBuCSTrF+0V+jlB8t1v8Dbh2azvqCeg7WWbA63WhVrT8+oXo147r+uT93UphPyJ7eL5np/ZIBSA7TsWxfNQt2VrCvsgWFTKBrYggHqlqotzjxiGBzeXjWNZFiVRtuP+9BarZvQxShfWwg20oauenL7SyKWUpESyUrFv/AjbUCL13WkbeefASF3Bet5s64AvX6YpJrWiioteDyiFSYbFz81jo6xQUS4lFS7RpPsNCCHF8ht/GxDXzw8yoq1ansKmui0eJics8ENhc1kFm7mKfkH+I1RiBMW4xg0PDk2PbcMCiVg/UWLntnHS6PyKfTutMrJYRmm4v0SANDs07O81/izLE0p5ommy9qsHn+PUQrfxcLfKKFt3ovMsCtNKBwtYBSC0Mfg62fIqvZy5r5H9C2wwWkNhYRZfQ9/IcHGcEYBy2V4HX7IvQX3gf974COl//7k5SQkJA4B9nZsJt6RwMDo/ohF/66AA6gVKsRBAGV1ncPlVdYx6ZtpQzum0pxWSNOp4emYxz3uzAoilBmVhAfKJIY4sErivQemOUX+bUBqmMc/fdQRYeh65IFgoCjroGA+KMDKjQGfSu7maPHLaDR+/bHZbfzb4/r2B6n1UZdYZEvCl8QMIaH0VhW4Z+oQqnA5XYTYDQQEGhA9HoRBAFDeBjle3JwWCz+aPuS7bsQRZHU3t2J69AW4dAF0wUHYW1oxFRRhXgoOt5c30BjaTnBcbH+KH4vXlyCG5VMhUKhomx3DjaT7zcRkhhPUFwMpiPGpg0OJq5D8xX/iwABAABJREFUFoJMRkrPbthbzNiaWqjOzUcXGkJIfAwqrRaZQoEuOBCF5ujFDon/FjmfL8Rjd1C1aS9Vm/b6t3sPWTnV7S0EQBMWhL3ORFB6PH2fmMGiqU/itjn48dL7SLqgN7U7clEHGQBIvqAPm2d+jlytwm13sG3W1xyYt5wLPnmEsPap//4kJSQkJCQkJADJw17ib9Iw9xbu23cJu354BQCr001pg/VPj8mrbqHf8yt44PvdgC9af/39Q1h+50A0Sjm/7K5k8d5q3vq1gLRwPREGNTODf0AhiIiCgFUVylWeR8iSldLRu58kjZlasxOTzcUr4zvRNTGYyb0TT8t8zQ43s9cXs76gjucXHeDjtUWnpl+7i/u/282OEhOTevpSkaf2SeLLa3vROzUUpVygjaqOwWnBhEYl0NzpGiyKIBqtLkR8WdqbixposDj5Luo2GPMmv0VfjUwAs8PFrOV57C4zYXW6iQ0K4L4LMnmzt5lfLlbyy6396JMSilwm8D/P53zUOBWdYOMaxUK+Uj3JCNkGbq1/nGebH8Dm9HBRTBONexbxxcYSLu4cS3avIbjDMpFZ6xDe6QsWn4/r5e9vYNL7G9lS1MiOUhM3fbmdDYUNVDbZmXV5Z0J0p+5BXeL0MK1vMpd3j0cmgFPwCSziEcKPDBGPiE+sN8bB1b9Ap0mQPYElxnHYLS1YCtaBpQbWvgIeF7zVC7G5HFH04lnxNGx4B+oOwL4FZ2qaEhISEucUoiiyvOo3djTuZk9jDgAWtxWr+9je7b/TVFlN7qp1NFX6auDoQoLJGNSPmLaZAGzYUsKB/Fq27yknOtJISJAGufywn43RoD50ft+P0aBhb4mNfY0a4tLjUQeHkJwQcjqmjKvZjEsh4BK8mLbuxtlgOiX9WpuaqckrQPR60IX6xh7XoS3hqcm+KHRBAJkMfXgYygANwfFxuA9F3nu9XgSZgLXRhMtmJzItlZi2mShUKuRyOdbGJupLyrCbzYiiiC44iLCUJEKTE4jt0I647PbI5L7v3pbaw1HPMmSoRRUCAh6HE5upCV1IMEqtlobiUkxlFURltiEwOhKFSoW1sZHSHb57bqfNxsHNWynfvReX3U5zTQ2V+/NwWq0oNWoi0lL8CwgS/11GfPgQcYNa23IKysMBRKLbAzIBe52JhCHdGL/sDSI6tqHrbZcjKBU0HijBXF6LubyW7a99Te2uPOYO/h9epwuP3cGiaU+R8/lC6nYXULZqx788OwkJiX/CY489RqdOnc70MI7LoEGDuO222870MCQkziokwV7ipPlxZwX3fLOTRouTdlG+qKvzqz+EnB+Z+N4GBsz8lXUFf0intNT57DOAfVUtlDXaWJN3uI1cJvgfEC7rGs/VfZN4ZHRbVuXVUtPiIE7ls1GRdZyA4b4DLHhyBs6BD1GtTePxQcGM7xbH3cMzuLhLLN/+rw9dE0+Nd+sfyatuobrFjkeE5FAtfdPC/nGfdpeHi99ax9biRpbkVFPf4kQmQFSgmr0VTaRF6Jmq/pUlslvolfMEoQoHIztEo1MreOmyjtx3QSZdEoJ59pJsZl3eianDekDnK3h+Yh92PjqMxXureX1FPqPfWMvE9zb4TlpfgPrLi2m7eCJNtZX8vKcKr1ekb7wvClpAhiOsHWX6bC69YDhlRLLO255hmcG81ngj7/I0OwOuZ2rVM3Tv3JXf+nwKugjQhoHC98CeHKZDp5Lz7CUdMGgUrM6rQ6OQ0Tnh9PxuJE49gVolz12azRfX9KL7eZcCIIieVm38Wo3oBYcZlj0Gyx5hQFANlylWkd24FDqM90XeiyJ4XLgQqPAGIy9Y5rPFGf4MjHjuX52bhISExLmEKIoUFtVQWOQT2zUy33fxtvqdlFsq+Sh/Nh/nf47D42h1XFm1i5IqX+S21dSEx+XCajL59wtHFDIdMiCNHl3i6dQ+hpKyRhpMdn/t03Gj23PXjQN59O6hpCWHYtCrGX5eOm3bRNCvVzLDB2dw5fiu6LSnZ7He2tiEV/QiKBQoQ4JQ6P+e7eGRuBqaqPrqZ1y1jTTkHUT0epHJ5SAIuJ0uVNoAX3UkrxdTeSXKgAB0wUHoQ0MIS04ktn0WgiCQ2LUTcR3bExQbTWB0JKm9u5PatyeV+w9Qm1/IwY1bqS3wecubKqqoPpBP3cFibE1NOFpaCAgKhEN1YuQqJTKVAplBQ2jCYa/5gCAjLuuhgBkBRNGLITyMsOQEEAQUGt/9nUyhQKFSoQzQEJGWgtfl9kXnCxBgNPzjaybx7xDWPpVxi2Yx4uOHyZzo854XjyyUDP4aBuogA6bCchaMf4BNz39GVLcs326Xi/RLBtHl1gl43R7EQ5ZPLoudvG9/JTw7ncGv3E776aP/vYlJSByiqqqKm2++mZSUFNRqNfHx8YwePZrlyw8XRU5KSkIQfBpCQEAASUlJjB8/nhUrVhy33/r6euLi4hAEAdMR33X/hMcee8w/juP9FBUVnZJz/RFBEPjhhx9abbvrrrtaXadTgSSy/3eZOnUqgiBw/fXXH7XvhhtuQBAEpk6d2qr92LFjW7X75ptv0Gg0vPDCC622P/bYY1x+uS8D/sjP25E/zz0nPcP/G0iWOBInzfML91NusmLUKDmv11PEqB0o9i2AAwtRyKcgEwTkh8T3/JoW4oK1aL64DCq2waUfMjr7UmQCZEYdu9hrsE7Fo6PbYbI6MdvdxAUHEDD5c6jZBlljQK5AAALqdhNgyyO6aRG9xr1x2ufdYncx8f0NeL0iM/qn8O7qQpbmVNEpPugf9fv8ov3k1RwuIpZbY8YrwlsrC3nq5/2oFTKe4AAo4ALZRkbVTuLnb8bT3b2ASy+YCQMnABBuUDOmU6y/H0EQMGiUTOgeT4PFSW51C8pDtjgotaBQgSAnOzGcizvbSQnT4e01mKsOZFOiSyawewoPfL+HK+oCeSfyE4wBSt4b0gY2+363gWIzYslvhOzrhtMbzPYJ39H5wKuQuwg6jOPdKV2pbXEQF6zl9RX5tNjd3H5+G64bKKXVnhVY6mH5Y2CMo/eAu6Dp6EJ3rUjuD59eCIl9QaVHowuGvrf5Cjt3nQphh2pe3LCepQvmMmrfvYiAMORRaDfm9M5FQkJC4hzHZndSXFKLiIhSKefy2Al8U/ktjS4TNY4aZAjIBF+lEY/oocHRiModzM0vVuP1wocPRxGRlkJAoBFD+LGDEWKijMREGSkubUSpkJGUEMKgvqk4nG5Sk3y+8HK5jIqqZqw2F6IXLr+k02mfu7mugcqiIhQhBsICdNjLq3E1NqGO/PtBFaIoUvPzSuSltahrmxB0Gpw9tXg9Hsp27QVEjqi1jujxYG1opGTHLjwuFwmds1HrfIsGf7TiEWQy5DIZIfFxmOvqcVpt/qAVf3S7KBIYFYnDYiEoJhpBkFG6aw+6kGAEQaCpshpZhAyVVos+Isx/Lt+xYCqvwmH2fW/Hd+yAqbIKe4sZjUFPco9u/D74mnyfdUpyz+5odFIR+LOBqq372frKl6RfPJisScPJn7/quG3lGhXm8lrmDr6B+MHdUBq0JA7tTmhWEkEpsXSYfhGaEN/z2FW7vuCHsfdgyitFExpI70euISRDKkAsAV6vSHFpIy0WBwadmsT4YGSy05eJU1RURN++fQkKCuKFF14gOzsbl8vF4sWLufHGG9m/f7+/7RNPPMG1116L0+mkqKiIzz//nKFDh/Lkk0/y4IMPHtX39OnTyc7Opry8/E/H8Mknn/DJJ5+wcuXKE473rrvuaiWWdu/enRkzZnDttdf6t4WHh5/EzE8Ner0evf749m8SpxeP6GF9806qXXVEKsPobez4t60JT5b4+Hi++uorXnnlFQICfAG1drudOXPmkJDw53/HP/jgA2688UbefPNNrrnmmlb7fvzxR+6++27/698/b0diMJzaxX6Xy4VSqTxxw/9nSBH2EifNw0Oi2aa/g4mbLmH6x+sp6/M0DHsahj7GF9f0ZP1959HTvIJ1S+Yy9OVVXP/5VghOArkajLEIgsCF2TGkRfz5F0m9xUl5k42aFgduQxy0vxTkR6wtDX4Q+twMA+46vRM+xI5SE3aXL/rkUA0vShuOTi//cmMJry/Pw+sVj9p3LOZsLAEgOzaQF8Zl0yHWSEJIAAmhWpRyAYfbi+iL4UIuE1ALLs4LLAd7E5RvOWafbo+XerMvim5Mp1gW3TaADfcP4fNrevoaeN3gcYPHhU7u4ZUJnbh5SDqPLDjA6qZIuieHMGeTb1wbDtazpbiRFftreP71WSB6sImH/ogm9CZeqKW7PBfH6tdg91xY8SQA1362lX7P/0rXJ5fy5Jh2vHNFV67tn3JS10TiP8CWj2DbZ7DyGdjxBc1ZE6jBlx3h1f/BG3jMOxDbzffvko3gNMO++aAJgvWvw7sDwNboi8DXhjCqexai2uh7V7vt/+asJCQkJM5JAjQq9LGwK2wVy6tXciCnijHxoxgSNZDs4PZMS7+SKSmXk9O0j1/KljC78Cv2tOwi2CgnyCBHo5IhVyoIionyFyQ9HjV1ZpwuLza7i7iYQL9Y/zsXj2rPoL4pZGVEnM4p+7Ht3A9uD4LVgdfuBFHEY2v93eL1eqnJL6S5uuak+rQ3t2DWKfEE69FmJhM5ZigqnRa1XodMIUeQHfvh2+104XY4cViObT/k9XjwuH2R0JHpqaT27kFqn56Ep/rqGfkFewHUeh3xHTugCwmmKjcPQSZDQMDSaAKguboGp9VKQ1EJ9UUlrc4jHCGm1RYV01JTS2NZOR63m8INm8hbvZ6S7buI75xNYtdOklh/FvHbXa+RO28FP096GFNBORmXn+/fZ0yO8f9bZdQxae37GBOjAChbswNXi5XNMz9HEaBmzUPvsGDiQziazHhcboLT4smaNByZUo7LbEWmOL0Ck8TZQc6Bal5+exUfz9nCNz/u5uM5W3j57VXkHKg+bef8PSp406ZNjBs3jjZt2tCuXTvuuOMONmzY0KqtwWAgKiqKhIQEBgwYwHvvvcfDDz/MI488woEDB1q1ffvttzGZTNx116nVDvR6PVFRUf4fuVzuH9eR2/6Ix+Nh+vTpJCcnExAQQEZGBrNmzTqq3UcffUS7du1Qq9VER0dz0003Ab6IZ4CLL74YQRD8r4+0xFm8eDEajeaobIJbbrmFgQMHAr6sg4kTJxIXF4dWq6VDhw7MmTPH33bq1Kn89ttvzJo166iMgZycHEaOHIlerycyMpIpU6ZQV3fYRcFisXDllVei1+uJjo7mpZde+juX+KxhQf1KOm67hItybuTavEe5KOdGOm67hAX1K0/rebt06UJCQgLfffedf9t3331HfHw8nTt3Pu5xL7zwAjfddBNffvnlUWJ9aWkpe/bs4YILLvBv++P7OioqCp3u+BmNSUlJPPnkk0yaNAm9Xk9MTAyvv/56qzaCIPDOO+8wZswYdDodTz31FOD7vKampqJSqcjIyGD27Nmtjtu/fz/9+vVDo9HQtm1bli1bdsyMk3MFKcJe4qQZka5DXFiPXibQIVxJniWAuD6+Lw4NoKndBd9dQy9Bjl54D61KDpd97POvlp/8allquJ6vZ/RGq5ITGHCM48LSYdhTp2hWJyZEp0IlF2gfG8hdwzMZ0CaCjvGBrdo0211+b/6+6WF0OQn7l9vPb8Pu8iaevrgDgQFKRr++hpIGG1f0TOStSV3o89wKnnVPIkeewZUTJhJlVBMSGgt5S6HNCMAXCbaj1ERWtBGNq4nNb83gx8ZEhk25l8GZvgfmiENFPwEIiofpS33m93rf/j3lTazKq8MLzN1SBkCUUU1+jYXoQDVpLVu42vwBHk0wjTY5WuqoC+mCx3AQa1Md3wvnY9W3YI4YwEWA51Bqbb3FSXWLg4k9pCids4p2Y2Hz+4jmWpYuW0htvorJNAIgWOtAkPlWrcIzYcv7ULkLAoLB1ogoHio6uOLQ59NlhTd7g9sKN2zE+uuLaB3Nvn1/sNiRkJCQkPjrCIKALlSOt8KLQ2lFpnOikCnoGNIBgAC5nM2N21hdsw6t3CfO6tVKPngwChFfQMDJ0rVjHDqtiriYwGPuz0gLJyPt34sm1Oj1BPy0haCB3TH26Ii72YwqrPX9l7mugfriUgSZDGPkiRcS1AY9hsxUFNmZRKSl4HG5Ma8qACCxW2dsh7ztAdR6PcqoVCIjNIiiF6fF6s9S8Lo9OKwWAoxGrI0mSnbsAkFGWu8eKNQ+eyBVwOH7M2NUBDKlAs0Rlj5NVdW47A4QRZqqfCKZQqPGYbagDNDgstlxmC0IMpnf1iQoJprqFp83vlKtxqVUotbrfYkBh3yMHGYLKo3GX1RY4uyg042XUrPtAB6Xi+/H3IlKf2ixRYDmgxUgE5DJ5SQN78ncITei1Pl+v79b5njsTna8+Q0AdbvzeTd+NOEd05m4+j02PPMxosuDIPP630sS/3/JOVDNV9/vPGp7c4uDr77fyeUXd6RtRuQpPWdDQwOLFi3i6aefPqYQGBQUdMI+br31Vp588knmz5/PPffcA/iE5SeeeIKNGzdSWFh4Ssf8d/F6vcTFxTF37lzCwsJYt24dM2bMIDo6mvHjxwM+0fKOO+7gueee44ILLqCpqYm1a9cCsHnzZiIiIvj4448ZMWLEMRcFhg4dSlBQEN9++y3Tp08HfAsFc+fO5YknngB8kdhdu3bl3nvvxWg08vPPPzNlyhRSUlLo2bMns2bNIjc3l/bt2/uPCQ8Pp7KykoEDB3Lttdfy8ssvY7PZuPfee1vZEt199938+uuvfP/990RFRfHAAw+wdevW/7TH/t9lQf1Kpubezx9DNiudNUzNvZ9P2jzL6NBBp+38V199NR9//DGTJ08GfAs906ZNO26WyH333cebb77JTz/9xNChQ4/a/+OPPzJgwICT+sz9GTNnzuSBBx7gscceY/Hixdx+++1kZmZy/vmHF5sfffRRnn32WV555RXkcjnff/89t956K6+++ipDhw7lp59+4uqrryYuLo7Bgwfj9XoZO3YsCQkJbNy4kZaWFu68885/NM7/OpJgL3FSbDrYgFJuoPPVi1DV51P6s4qrP9nM59N70s+2Avb+AIMfgMR+yPQR/Dp8FME6n4/qn4r1ogj7foTI9hB62DKlR/LpKU72d2gXE8j2R4ahVsiQywR6p4Ye1caoUXLDoFTqzA7axRzb8ueP/NEiZuZl2azJq2Nyz0QCVHLaxxjZUwHfiIP57NMKOsYFMv+mttBhHABr8urYVFTPa8vzuTA7mjfa5dLbvJR0hZGNzj+JIIjr2urlx2uLaLA46Z0agkwQ2FFiYlBGOOsKGlDKBQYp9hBDLbWOUGIF38q5fu2zFM/IpbzZScdmO9O+D0DfomCA1UmkUcP1A1LIijEyqkP0sUYg8V8mLB363Y6w6D5izHvpkLMZABEBwes63M4YA6Ub8HpdvGUbwQz5j9jdHtR4UUdmIVbtwqsIQGau9EXUW+sRWioB+IzRDIofg9BgJT5Eiu6TkJCQ+DuIokh+SyFx2hhGxQ7H6XWxtHIFGwsVXJ8xjQ21m7F7HLQPyiJUHUK7wEwyAzPQK0/s8262etmcY6dnew1ajS8hVyYTTrlI808w9MxGl52BLMB3v/lHsR5AFxyEMTIczUmmbstkMmLaZvhfy5UK4jq2x+tyow00otZqqSsswuvx0GJ2sf23SpLaRDOoVxBqrc86x9LQSGN5BZb6RqIy07E2NiF6RcDjF83/iCAIGMJa31/WHSwBUSQ4LgZzfQMelxt9SIj/33BIhD+iz6oDuaT36429uYWm6hpfbYJGEwGBRrRBQWgDjWiMBkmsPwvJGDeE/XOWUvjTGkx5Zf7tcrUaj90BXhGv143dZMbZYsXl9hA5dhA1P61GdHuQq5UoA/XYaxqRK5XYHc3U7z2Irb4J0eMT6WP6d8RltWNvbEYTfHLPMxLnFl6vyC/L9v9pm1+W7SczPeKU2uPk5+cjiiKZmZl/u4+QkBAiIiL8UeAOh4OJEycyc+ZMEhIS/jOCvVKp5PHHH/e/Tk5OZt26dcydO9cv2D/11FPceeed3Hrrrf523bt3Bw7b7AQFBREVFXXMc8jlciZMmMCXX37pF+yXL19OY2Mjl112GQCxsbGtsg5uvvlmFi1axLx58+jZsyeBgYGoVCq0Wm2r87z99tt06dKFZ555xr/to48+Ij4+ntzcXGJiYvjwww/57LPP/OLsp59+Slxc3N+/aP9RPKKH+4teOUqsB58BnQA8UPQKI0P6nzZ7nClTpnD//fdTVFSEIAisXbuWr7766piC/cKFC5k/fz7Lly/nvPPOO2Z/8+fPZ8yY1ra19957Lw899FCrbT/99BODBg067rj69u3LfffdB0CbNm1Yu3Ytr7zySivBftKkSUybNq3V66lTp3LDDTcA+LNrXnzxRQYPHsySJUsoKChg5cqV/vfk008/3arPcw1JsJc4ISX1Via8tx65ILCn03docuZxV+BVPOm5kOggDSx+EeoOgDEOrv4ZgJOOr9o9D767FkLT4eZj27ycCdyHblwVchm7y5p49Mc9TOqZyLiux/+iuWfE37/BAJ+3/5H+/j/d0p8tRQ2Me2e9byxHFIBbuLuS/32xjUijGkGAML0aMkfh6jYDV3BnRmWfvFB+7QBfOvbVfZNoHxvIzMX7efPXAvRqBWaHm1cZQ4M2nAhHCVcoliEDNDgJsuSTmdWdW+ZsA2Bo2wiW5FTz3bZywvRqtozM+kfXQ+IMsv9nvGojGY4K5HhBkCF0vxa2fw7aYJ84UOArarRO3p0XLaOJGf8Q3ZNCCNGpUDfkIL47gBaXgFpQoFGpEQJjCWhzHu4dNYxMC+GpV19gEb1YffcgwnXK1rZXEhISEhInZK9pH0sqVxCuCcPmtmF2WwiQB2BQ6nF6XGyp3w5AlrENV6VO+kt9fzDfxNKNVkb21XHjZf+dovGiyw0KOYIg0LJlL5atewm5aDCq6GPfecqVCmLbt/1H5zxSSJcrFaQP6ENjWTk1eYV0i67BGnC4jlBNXiGN5RWotNpD7ZWEJfkyDQOjI1Bq1Cd93sj0VCyNjYSnJBGVkU7R5m2YKqtaCfQyhRyv+4iMNdF3Tn1YKBU5PlsIQ3gYDaVlmOvqkcnlhCZJmY9nI+byWqq37ScgIhhbTSPIZSg0atpeMYLdHy8gKDkWe0MTJUs3ARA98QIixg/n4k8exlZcRXCbeLa/8Q2/3f0alqp6AMKzU1HqA4jsnEFLeS1KnZYvelxNZNdMJqx8G5lScdiuSeL/BcWljTS3OP60TXOLg+LSRpITT12A3e+Lmf/0/SaKor+P+++/n6ysLK644orjti8pKaFt28PfEW63G5fL1coL/oorruCdd975R+P6I++88w4ffPABxcXF2Gw2nE6nP/q8pqaGiooKhgwZ8o/OMXnyZHr37k1FRQUxMTF88cUXjBw5kuBg33e6x+Phueee4+uvv6a8vByHw4HD4fhTqxOArVu38uuvvx7TL7+goMA/n969e/u3h4SEkJGRcVT7s531zTupcB7fck8Eyp01rG/eSb/ALqdlDGFhYYwaNYpPP/0UURQZNWoUYWHHruWTnZ1NXV0djzzyCN27dz/Kh765uZnffvuN999/v9X2u+++u1UBW/At+PwZR/7+f3/96quvttrWrVu3Vq/37dvHjBkzWm3r27ev3zLqwIEDxMfHt1pA6tGjx5+O42xH8rCXOCHBOiUZkQbaxwaiCE5ARKBRHs6M/snEeSvAfehLfe834LRCxY6T7zyiLRhiIKnfaRn736HZ7qL/C78y4IVfabG7WJpTxbYSE99sLf3Xx9ItKYSYQA0yAe4efvhLLj5Ei16toHtSCDsfHcZjF7UDtQHlhTOJ7vvXHsozo4y8NL4j7WN9Ke6NVl8UtcXpi95qQcs2dXcmKlfRIAQjCjIEAaLWPgpAUpgeQYAB6eGMaB/F5J4JPHyhJNaftXg9ULkTmaMZJS5keGDKfFAbwWXxfcYnfE49QRR6owiMa0tOyN30sKxkXUEdaoUM1AbsQgBVYgjn2V8m77KVPtucDuNQuFoIy/mUl+WzyPDkI3+7B7ycCeaT8xeWkJCQkPARpglFK9cSq43BoDQgF+QEqwLpEdqVJlfToWKzsLVhBxa3hUan6aT7bpusxqCVkZWsOk2j/+vYiysoeeodauf8AoBlWw6O0iqs+w/+q+OQyWToQ0MRBAGFUk639ofFDbVeB4JAcFwMbQb2xRgRjlqvI7Z9FvrQozM0/wxjZDjRmW2QHyrC5nG5W4n1AArVoQWAI0SuxrIKAFQ6LTK5HI3RQGhCPIFRkYQmxv+dKUv8B2gpr8FSUYfT5CsqLFcqmL5/Ls3FlYhON2HtU+j0v0v97VU19Ry88Wnyv1yMqbAcQSZDF334PShTKrj4p5dRqFWkXzIIa1U9Rb+sA6A+p4g3w4fz7Yjb/tU5Spx5Wix/Ltb/1XYnS3p6OoIgsG/fvr/dR319PbW1tSQn+4LRVqxYwbx581AoFCgUCr8AHhYWxqOP+p5jY2Ji2LFjh//niSeeoFu3bkdtO5XMnTuX22+/nWnTprFkyRJ27NjB1VdfjdPpBPAXD/2n9OjRg9TUVL766itsNhvff/99q8WLl156iVdeeYV77rmHFStWsGPHDoYPH+4fx/Hwer2MHj261TXasWMHeXl5DBgw4LiZZOci1a66Ezf6C+3+LtOmTeOTTz7h008/bRWx/kdiY2P57bffqKysZMSIEbS0tLTav3DhQrKyskhMTGy1PSwsjLS0tFY/f+d9+scFuWMtDv2xzZGLcEf++/8LUkijxAkxaJR8OLU7u0pNfNAQxSxHB2wVKqjIZcqa/6H2NPkaxvf0Rcvv/4mSPk9zxY52TOgez4j2UYTp1cf2o49qD3f+/S/m04Hd5aHe4jz0by/T+6WglMsY1u7YKWenG61agVcE1xGeku1jA9nz+PDTcr7HRrdjQrd4FHKBqz/ZRE2zk3KTHY8KlDhoEvQEy2yQ5vM8u+P8NtxyXhoKuU8YePriDqdlXBL/EjK5r85BYzF8O823IFe2EVbPhKBEuOAFMEQTpBYIcVRB8WwEvOxZ+QEF9jV8aL6dTolhtLsrj3d/3M/l4UbCYpK46I019FcXcDcCqI14XQ6+VT6M26YCwQ2OFn9dBQkJCQmJExMVEMmk6MvxeEUO1ldRyUIqbFVUlC9q1S5RF89nBXNweJx0C+vM/qZczo8eTKQYglqna1Wo9HeG9dIxrNeJrXP+TTxmK3i8eEy+WighFw3Guq8QY6+O//pYZIfueQTw2d0cynQPjoshOC7m+Af+AxK7d8bjcGJraaEy5wAIAk6r1bfTX0Tm0KIBkNS1E6JX9P9+Y9r9s0xQiTNLdI92XLpoFk2F5Sy/aSbasCBW3PYyRYs2ENMnmz6PXIPpYLm/pkHpwnV4nC6W3fACAKO/eZaw9qlMXPs+G575mA5Xj6Z2Zx6Lr3mG4HRfBnFAeDC22kbch4o3NxVVnLH5SpwZDLqTywI62XYnS0hICMOHD+fNN9/klltuOUrIM5lMJ/TUnjVrFjKZjLFjxwLw7bffYrMdLgS+efNmpk2bxurVq0lN9VnTKhQK0tLS/G0iIiIICAhote1Us3r1avr06eO3/QBfZPrvGAwGkpKSWL58OYMHDz5mH0qlEo/nxPXAJk2axBdffEFcXBwymYxRo0a1GseYMWP8Ir7X6yUvL4+srMOBdyqV6qjzdOnShW+//ZakpCQUiqPlxLS0NJRKJRs2bCAhwZfR1djYSG5urr/g7blCpPLYkex/t93fZcSIEf6FluHD/1wjSkhI4LfffmPw4MEMGzaMxYsXYzT6XB7mz5/PRRdddErG9MdC0Rs2bDih5VVWVhZr1qzhyiuv9G9bt26d/z2ZmZlJSUkJ1dXVREb6LBo3b958Ssb7X0US7CVOitc+ns2Kah3h0QnYRBUKmYDbK7KXNHrqShGmLYLQVBrmXEcwAitL3JQ0WJm/o5wXlxwgPULPktvPjj/QXi+0jTKSHR9IuMF3M3LzkPQzNp551/WmqtlOVvS/4yWpUsjoGB8EwJLbBrK3oonrP9/GtcEf86TzRZIt21lgvJIPd/Xk064uArVKv1gvcY5Ql+vL4XP6orhQB/mKzUa2h4hMeLs3coevEC0yOcT2pGPZDnoot3PryliedXThhkGpDMiMYuWBWraXNLKrrIn98ghuvmM7Gl0IsvcH4zaVU3L+h7RJTmpVw0JCQkJC4sTYnQ5W5mxD5zSilCsJ1cTQrK7DJXOiFBSEakKZkHgJbtHDpnqffV1xSwnNrhZyy/dhLdMQHBdLVMbpEyZOKdGhePtno23ruydTRYWhijq9D8HHQ6nRkNKrO4JMQK78dx6nFEolCqUStV6HNigIS30DVQfy0IeFYq6rB1FEGaChqbIKXXAQwDEXYyTOTtw2B00HK3A0mQ/VRABNkAEEgcwJQ2nILeanyx/yF42Vq5UkDOvJwZ/WALBg3P0gCFy+9j1Cs5IpW70DdZCBpsJyDPERTN0zB6/bw5e9p6MyaBn2wYOEZ58lfxskThmJ8cEYDeo/tcUxGtQkxp96q7S33nqLPn360KNHD5544gmys7Nxu90sXbqUt99+u1X0fUtLC1VVVbhcLg4ePMjnn3/OBx98wLPPPusX238X5X+nrs4X5ZyVlfWPC2r+E9LS0vjss89YvHgxycnJzJ49m82bN/szAwAee+wxrr/+eiIiIrjgggtoaWlh7dq13HzzzQB+Qb9v376o1Wq/zc0fmTx5Mo8//jhPP/0048aNQ6M5XOw8LS2Nb7/9lnXr1hEcHMzLL79MVVVVK8E+KSmJjRs3UlRUhF6vJyQkhBtvvJH333+fiRMncvfddxMWFkZ+fj5fffUV77//Pnq9nunTp3P33XcTGhpKZGQkDz74IDLZuacX9DZ2JEYVQaWz5pg+9gIQo4qgt/H0BhbI5XL/5+NYRYj/SFxcHCtXrmwl2ut0OhYuXMiyZcuOav/75+1ItFqtX+g/FmvXruWFF15g7NixLF26lHnz5vHzzz//6bjuvvtuxo8fT5cuXRgyZAgLFizgu+++84/p/PPPJzU1lauuuooXXniBlpYWHnzwQeCf22n9V5EEe4kTc2ARzzfdzUFNLAeH/0qL3U2n+CAGzlzJVMtNzHK+SczXj7Mq82Fm7hxNIOdBeRDPXZJFoFbJLXO2Y9D8SeHZ/xC/7q/hznk7abA4OVhvpqDWzM3npdMr5eg0ZlEUcbi9aJSnp4DI7wTrVATrzkxKepBWRd+0cLY/fD5ymYDQ2A7HvoU8/EskJo+JkgYrHbSBZ2RsEqeJsq0wdwrIVXDxO1icIi2NNUSJXmgogIOrwHZIrBeU4HVB5XbU/W6Cim3kl7YlwVFNhz3fsnZbKj81d2CksZBfuu3D0ukaNKG+TBXZ/9ah8jhpoz7a/1BCQkJC4sSsr99EvnEn0a4ERiYNoa07jmp5OYsrluERPVTZqvmteg0NThMWtwWAAIWWoSGDiGjS0UgJMsXpvYc5VZiWb6Bpyx4cGbHs3F1G9YYqho9oh9GgOart74KlcJrFgTNZuFUVoEEVF0NQTBSCTIal0YS5rp6GkjJanC74Z7b9Ev9Btr0xl7UPvUtsv2wGvnAzurhw9ny4AESRhv3FlK7e7i8eqw424GhswWt3kjZmAEp9APu+WgoeL7/d9TqV63YB0OuR6bSbeiE977uSwGRfZsh1pQuQq5XIVWfHs5vEqUUmExg5NJOvvt953DYjh2ae0oKzv5OcnMy2bdt4+umnufPOO6msrCQ8PJyuXbvy9ttvt2r7yCOP8Mgjj6BSqYiKiqJXr15/GpH+X+L6669nx44dTJgwAUEQmDhxIjfccAMLFy70t7nqqquw2+288sor3HXXXYSFhTFu3Dj//pdeeok77riD999/n9jYWH+h3T+Snp5O9+7d2bx581H+4Q8//DAHDx5k+PDhaLVaZsyYwdixY2lqavK3ueuuu7jqqqto27YtNpuNgwcPkpSUxNq1a7n33nsZPnw4DoeDxMRERowY4RflZ86cidls5qKLLsJgMHDnnXe26vdcQS7IeTbpdqbm3u/LuDti3++fkGeSbj9tBWeP5M/E82Pxuz3O4MGDOf/887n33nvR6/V07dr1qLa/f96O5LrrrvvT2g533nknW7du5fHHH8dgMPDSSy+dMPp/7NixzJo1i5kzZ3LLLbeQnJzMxx9/7C9uK5fL+eGHH7jmmmvo3r07KSkpzJw5k9GjR7dajDqXEMQzYDLV3NxMYGAgTU1Nf/mNJfHvU7pnLbE/XIoseQBMnguA1+PhzWdupdnu4UHll3iBDcaR3FYzih5BLQxvG87oi3xfKiarE51agfIsiMJ++ucc3l99kIxIPeUmG2aHh/QIPUvvODo74LrZW1ixv4bPp/ek5zEE/XOZDYX1NFqcXNDh5IvbSpwlWBvg9a6+9PqbNjPig31Yq/P5IWEeId0uhU5XwJpXQG2AsAyoz/XVokgdDHV5mCwOtJ8OReX1paBuCruYbrZ1yCzVMPBe2PQeRHX02ey0uwTOewiMMZQ2WPnfF1vpmxrG/SOzcHu8bC81kRFlwHiWLPidi0jf1/9/kH7XZx8rq1azrWEnAyP70jW0M17RS7Olhp/z5mGSizgUMtQyFTJBhs1jJ1BhpHdED9oG+VKS3Q4nCvV/x6P+z6h8cw7OyloU3dvy9V4bAV437Xu3of+Qdq3aedxuCjdsAURSenb/16Lf/wuIokhzdQ2qgAACAqXP8LlG4S9r+enyhwhtm8yYb5/no7YTQISIzm0Y8NyN6GPC2frqV0T3bIdSp6Fu30HaXDyYoLQ4arYfoGLDblbePsvfX9LwXhQt9lkW9HxgKtvfmEd4dhr1ew/S5dbxdL1jEgq1ioIFa1jz4Nv0efxa0i8ehN3UTN2uAmL7dTzti2ISx+d0f2fnHKjml2X7W0XaGw1qRg7NpG1G5Ck/n4TE2cyC+pXcX/RKqwK0saoInkm6ndGhg87cwP4Ct9xyC263m7feeusf95WUlMRtt93Gbbfd9s8HdgLWrl1Lv379yM/PPyqr5lzg/89drMTfoqbFzrQ5B0gRbmBKj//Rp2g9v375PKtcWTwuzsajFHAojLiVOvo0/8ItGoHJjkWI20Rm2gPp1bkT/dPDz/Q0jsLl8fLlxhI6xAUSrlcz/t31tIsx8sqETiSG6thf1UzxljIAShutuDxe/4LDr/treG1FHs02Fy6P6Pe7///EsTIOJM4Rds8DW4Pv3wd+JtyQxcbaaIpGfUlIwqF0y8H3+0T7L8f5bHJGz4Jlj8PaVwj6wxJwjzZxII6HojWQ86MvOv/gKsALO77wFZu94hu2lTSyp7yZuhYnYzvHMuaNNTg9Iv3Tw5g9vee/egkkJCQk/uvkNuezrWEneoWedGMaG2u3sL52EwEyFRa1gMoroBZUOLy+e5QgZSAmVxOry9eirdMTEROOVntqPYhPBZ4WC+Yd+9F1zMBZVk3dt0sJHNyDsPEjsBWWYt2Tx0X2KpReD4pigMOCfd3BYsz1DXhcLkDE6/Ug/3/0qCMIAoFRkpB2LiJ6vex6fz4eh4uanfnYWyyojTpkKiWX/PwyKr0WgMGv3MZ3o+6geNkmUkb3J33MIOZffC8HF/qKySII/B4G2n76RXg9HgS5jO1vzMPZbKFi415El5t1j32AXK2i2x2TKPxlLQ0HiilYsBp7YwvL/vc8AP2evp7ud11xnBFLnO20zYgkMz2C4tJGWiwODDqfDc7piKyXkDjbGR06iJEh/VnfvJNqVx2RyjB6Gzv+K5H1p4r27dvTu3fvMz2ME/L999+j1+tJT08nPz+fW2+9lb59+56TYj1Igr3ECdCqFHyhfIoI6pn6sYysxE0Mcf5GtUegOWU4uvpdqM2VuNJHsja/mPqky9idux+Z6OGjbc28vW0Tr03qTO+UUEL1x38w/GF7Oc/8so8HR2UxplPsaZ/Xz7sqefTHvUQZNbw8viOVTXZsLg8GjZIreiXS59nl2N1etCo5KeE6FEfcnHy/vZztJSbGdIrhpfGd6HTI711C4pxg8wf+fx5c+Rk18seYf1NfXw2FA4tgxRNgrvUViQWo3gOfXgQuy+E+FAFw02aQKXzWOrM6gssGohsAL14+cl3AZQGbCUwZBMCoDtG02N20jw2kzuzA6fEp/3HBZ85yQEJCQuK/yu+2L2a3mU/zv8AjevHixSG6idZEUWWvRhSdJOkSqLBVEhUQidnRQrBdQ1VdLpWWJsKSNaQakpEJx4+SrV/wK/bcYiKmXIQyIuS0z6tx6Xos23JwVdcjDzQgOpw4S6sI7NcFZAKNC1aiBBAEAqJb+9c3lJXjcbqISE/BEBaKUv3fW5CQkPg7WKoaOPjLIdHd62X+mLsJ79SGUXOeRKXXsuHpj9n/9TJsdSacLb5CxIULVlO5YTe2WpO/n4hO6Vy2/E1cZhu1O/MoWbYZmUaF1+5b2JPr1IQP7o1p7R6ie/oWw/o+PoOQjEQyJgxlx5vf+PsyxEuLQ+c6MplAcuLp/7svIXEuIBfk9AvscqaH8beZMWPGmR7CSdHS0sI999xDaWkpYWFhDB06lJdeeulMD+u0IQn2En+KXq1Al9WPhgOrqZRF09j1JjS5IXTNvonXirQsyt3E892a6Tvmeto64KaXVvKG60m8gAcwahTc9OV2BmWE88nVPY57ng2F9dS0ONhQWH9aBfsdpSYaLU66JgbTMT6Ivqmh9EkL4+0rurB/7y42vjSOssQxvDNlNB+vLaLcZOV/A9NaFbG4e3gGTo+XkR2iJbFe4txj9Guw62uo3c9HFb05YG4hr8ZMVrQR1/YvUVbvPdzWEAst5UeI9YfCtrpPB104KDXYmhsJ8Nj9Yj1AkxDCU54pPGWewvz4vnSsPYBCGcAVvRL9bb67oQ/hejXxIdp/Z94SEhISZxEJhgQClUaaXS1o5Bq6hnamzFpOn/CeLCxfioDAoMh+dA7tSLG5lO9KfiTIoyPKHsgBYzEudRnNZc0MiOhDt7DjP2Dac4txNzbjrKo7rYK9NacARbARbdtUnOXVaNumEpCeiEyrwV5QStkLHxE4qDth40dg2XUAr8eDsVfrIm4RWenkmPYRE2lEpZa+OyTOHfQxYQx7/wFKf9tGY24JVZtyaCmpxm22odZr2fba1zhMZn/7wNQ4mgrK/NsEpRzR5aHHA1NRaFQotRq/3/3vYj1A+OAudJh1M1snPE5s345UrN9NaNtkut52OQC9H72G+MFdieySiSbY8C9eAQkJCQmJs4Xj1VQ4FVx55ZVceeWVp63//xqSYC9xQoQJnxHoFfnO5cHidLM/vDtdE4MxH9hFmRhOrVwLhb/ijh6Ew+1FrVTw/KUdeH1FPoMzw5m9voTMKAMtdtdxi8/ef0EWXRKCGd4u6rTNw+7yMP7d9TjdXhbc1I87zm/DZ+uK2FvRRH61GdXuz+mpWIqwq4TZYjbfby8HYHvJFtbcex6lDVaW7K3mt9waDlSb+XV/DTlPjEAupQZKnEsk9vb9ABMLi7ls08e0i04HYLZ2Kl63ncnKVQRoAuDSD+GTEb7jLvsEwrPg68mw/k3YNY9vu37KomWLeUfl5ciEwIBJH9P/twCcHg+p8kp4qy+odHDnAVD5RJYuv9vvSEhISEgcRYBcw/T0K3F53cgEAbfFTruAdNQaLU6vE1H0EryxFlt6Ke4oNyIiWqMBVUwkihYLsZoI9jfnosOI2+NFcZw6QxFTLsJZVYe2ffppm4utoJTaL39G0KhIeOh69oY1ssNewBBZHLb9hTgO+u7HGhetQZOWgG3/QQCqPvqOuDunYt6+D2dTM00eMweCC6msauTSxDGnbbwSEmeCdleOpN2VIwHY/sY8anfno9T5shD1cRE+cV4QiOyaSbspF7Di1peRKRWMW/IaHoeLHy+9j58ue4C0MQNwWmyUrNjSqn+VUcew527mt9vfIi67Dbs/WsCy/z1P4tAeXPLzywDIlQoSh3T/dycuISEhISHx/xRJsJc4KeQyAZ1awUVvrKGg1sJHU7vx5Nj2XNUlkMzZXWCHm/AbNvDrXYMQgAijhlHZMQBcPzCN815cydebS1lx5yCCdUcXOAvUKhnfPf60zkGtkNE7JZSqJjuxwQG8tPQAKw/UEh+iZUB6OI94ziNKaOQ7Tz/Wbi1DJoBXBJdHxGR1cdMXW6hqcQEQoJRzXlaEJNZLnNO0Pfgp7H8FLOvBZWGovhN36i5hmmMpOL0QlgqXvO/zpQ9K9EXVN1cAIjhaaLftES5VbaJOGUuYyoWlzz1sKHfQsbGW2VHrYMgj4LKDQgNqvc9CR0JCQkLipFHKFLjsdgo3bUUQBNL79WJSynga12yn4YXPsQUbyPr+OaamTkav1KGSqegV7hPcUt0d+fTDrSTGN3L1pGOLcMqIkNNuhaMMDUIRFoQqymdxs652Ix7RQ5vANAJ1h23RRKcL24GD/tcehxNXbSN13y5FANTZqaSpQ9ElJp3W8UpInGkOzF1G5ca9eF1uKjfuJSQzEVtNI9aaRtx2J+2nX4Sj2UJIRiJytQqPw4XLYgPAWt9E5bpd4BVRGnSEZCQQP6gLbruTA58tJLpdKr0evJrc71aCIKA0StkqEhISEhISZwJJHZH4SySEaKkw2YkwaFDKZWQlxELyALCZwBhDpEZz1DGiKOJ0e/GKIm6veHSn/4Bmu4u3VxbQPz2MPqlhf9pWEAQ+neaz5XF7vNxyXjoJIVqu6Z/M/K8+IBiBO13/4xLlBpYbnmSLNZJ60Uh5+hW4tn/FOtc9fBUyifysG7ikSxztYwNP6VwkJP5ztBmBaedP1HpjSK/6Dp14kBfFxSAHPA5YOwucZijdBAvvwRTUnkZPOMGRURitZWiaS3Ahxz7kCeg1jpcW5PDRtoNsyb0d3NUQ2R53eFsULguix4nQUAg/3Q4ZF0DfW8707CUkJCTOCmRyOQqVEkEmQ5DJCJArkaenY0mIJKB9CgAh6qOzltyH7s1cbu8pH5Ozuh7LthwMvTuhCPpz6wxFkIHY23zpzV6Xm+FR51HnbCDeE0p1QdnhhiolXpUMzA4EwNA5i+qiYmyDsjE0WNG3S6NvehIy1dGBIRIS5xKZE87HYWrB3tCMKb8MU36Zz5UQqN9TQMH8VZSu3MbeT3/GlF+GPjYchU6DIS6CltJqRK8IAly1/TMM8ZHM7nYVdbsL/P1nXDaE2u0HQBSx15ko+HE1m2bOpu8T15EwuOsZmrWEhISEhMT/LyTBXuKkcLg9KGQyPr66B9XNdnIqmvF4RX7eU01e5HPcfF46KsWx06mDtCqW3zUQRAg3nNoCYN9tLePtlQUs3lvFijsHAeDxijy/aD9BWiU3DEo76hiPV2TUK8spb/H4CuEWr+eJhoeZoZJzvuN5ZspeQ+6AVPk+AD7Js7M8T097hZex8Raur7WwpahBEuwlzk0cZvj8EnDZqOn3OMqmcoxN1bykvBabzcJDyi8Rf193q8uFvCX+Qw2mHILw4qypQiY6SBLAgRJ99kUADMmKYG1+HQdT7yDMvR3ajuHZXw5wgbcNhvAUMorXQsk6MFdLgr2EhITESeDyulEqlaT17UWDo5EqRw3RAVFsUx7E8NplpAdlHffY9JQwbp3RF73+1BdnNS1bj21fIV6Xm9CLBgNgdplZW7OBNGMqqYbko+dS20DFO3MxGLTorHYaAosQbfbDDZwuZIfttjGv3Y6jWxswBKDo1IbGFjMKixWtJNhLnIPU7Mjlp8sfIiw7jeTzutCQW4Klsu5wg0P3ZoJcxrY35lG5fjeCQg6CgLm8FoDG3BJ/O214sL9wbObl53NAJiOiSwYBIUaC0uPJ+WIRAGHZ6ez7aglVm3LI/WaFJNhLSEhISEj8S0iCvcQJKW2wMuq11cQEBbDw1v7c/c0uVuXWctewDF5ccgCAzCgDw9pF8caKfFLCdUcVjo0wHB15f0IcZlCoQX5s3/v5O8p5ZVkemVEGruqT5N++p7yJ91YVAjCxe0IrC57l+6rZufonFpjvYZG3O7fU38xi1DymkaEUPDylns0CsS9jhbV4EWhRhFARPpQdQluuGzyRH+vjWfVTIUV1Fqb2PfphU0LirKelCko3AhC+YCoOwYlc9LIndDiDEtWw6UtfENfEeTDnssPHdZqM1QmK3J/xdL8G1foXARnqLhNRv94GhjxC327TWHz7AGCA/zCVoY613nZc27wGQm+AIY9CXHfYtwBSBoFaKmomISEhcSxym/P5uWwxnUI60D+iL18WzcPlddE9tDOb67cDkG5MxeFxsrV+O5mBbYgKiGzVR2iI7i+f1+5xoJEfX+SvX/ArjuIKVPFR6DsfXjDIaTrA3qb9VNtrjxLsm37bTGNjI7Y+WajyK1DUmXA6nOzoKlCaCH1/EwlplIHXiyiAoAtAoVGjlavRds6moawCq6mJpqpqtMFBf3lOEhL/dSo35dB0sIKmgxU+oV4ER7OV9ldfiNNiI3fucnTRofR7+n8snvYUCCC6PZz35t1se+UrbPUm0i4exN6PFqCNDCG2bzbvJo5h7A8v0P2uK+h+1xWtzqeNCsVcUUfRog2MnP0YoW2TSR7Rm8Jf1pF8QW8EQbIFlZCQkJCQOJ0cOyRaQuIIWuxuzA43pY1WCmrNZEYZUClkxARpkAsCMgE6xAayNr+OWcvzuGPuTkTx5KxvqpvtrM6rPXpHbS682AbeG3RoDC5eX57H7rImwBclv6WokSabiw6xgUzskeA/1OH2IhOgTYiC4CW3wNJHAcivMfPkTzkUFRWgws3QsAaGybfRWVaADF86eNeUCIoHvMpk10PYURHorueBQRHMvaE/mqxhXNQjnVuGpPPcpR3+wRWVkPgPE5YGF70G2jCEpH7cHPoBQxwz6Z6VzFXDekLqkEM/A2HY05DU33ecKGIY/xYBDxWj738jBCdBSDLs+d7ncV+6GawN8NtMqD3gP929IzK5IaEMraMWqveAXOWz2vn6Clh4H3hcMGcSfDIK7E1n5ppISEhI/Acxu8yIiJRbK3F73YSqQtAptGgVPs9pnUKLSqZie8NOtjXsZE3N+pPuu8ZeS43t6PuzvaZ9vHXgfVZWrQag3tHA+tpNWN0+f2yv6MVeUIbXasfQMxt1fJT/WKfHFx4fZzdSM/tHLLtyAbAdKMK0dD3OFjMAqshDFoeiSH04ODUCjOmBOt13ryeIgNlG1IzLCB89GF1IMBFpyYQmxhOWlPgXrqCExNlD+6svpOP1l6DUBZByYT/kGiXIBDrdOI7eD00nrEMqbaeMJP3iQXS8/hLCO/iyjJUaFVfvncMNVQtpO3kEKoOWoNRYChaswVpVT/3eQmp25rHp+c9wNFv857vkJ1/RWlN+KS6rHa/LzcKrHmf+xfewf84Smkuq+Pai29n06mdn6IpISEhISEic20gR9hInpG2MkTuHtWHm4lyum72V5XcO4oGRvoipjvFBKGQCCaE6QvRqRneMIT1C/+dRF3lLKa43s8iRzZxNJRTVW3n4wiym90s53MZpRnRZcTTVoBFFvtxYwvtLtxO8ZSXC0Elc+n0LQ7MieOmyjgzNah0tZnG48YrQQV4MO+cAcEvFEDaWu6hutlNEX8odoXzV/CLvKV/kZufNeMKyaJEZ0Kp1HFzzFWs9XZmluJz7tAsQVr8E6cNBpUWrUnDH+W1O+TWWkPgvYKnKp+mLqZiiB5B2Rx4qhQz1l9toqKyibbTRl/Ey5bvDB/S5yfdTsw9CUg9v14bArTth9Uuw/AnftjYjYN1rsOYV2PsdBCZAr/9B6iCU4z+Ckg1giIZPL/S1V2ggqgMU/goHfvZt2/k19JwBQJ3Zwe1f76BTfBB3Dsv4F66OhISExH+LziEdKbKUUmQuZm3tBialHM56ig6IJlgdBEBWYAZ1jgY6Brc/bl+iV6Spqgqv24NL5uHL5p8AkampV/j7AfzCfL2jEYDfqtZQZCmhyFxCoi6eDXWbGX5RL5IsRrTt0ludwyN6AAgptGI7UImr3oR5617shT6felVeOd7yOrxmnw2OzKCj305oSTEQXVuFI7fEt10fgOh0Y1q6ntAx5wGg1mqJSEtBQuJcZOd737P+8Q/pdvdkbqxbjMfhYs+HP+Ky2NFGhKCLDGHKlk/97c+bdQduu4Omg5WEZiX5t8f168iNdUuYN/wWvC43MpWShCHdWXDZ/VRt3kfR0k0EpcbQ58kZ6CPCGL/sdRwmM2W/bWPTc58REB6MOkiPMTGaba99TcnizdTtyqP9FReiDfMVpzYVlZK3cAWJ/XsS0T7z375UEhL/b0hKSuK2227jtttuO9NDkZCQOE1IEfYSJ8XgjEiiAzVHFXZNDdeTGOpLp9arFbw+sTO3DEk/Vhc+TKXwxTjiF01lw6IvabD4oq02FjYAYHW6ueebnbx+QM8I50z6mR7n591VJIVquVG3gitsXxCz9kEcbi/bSkyE6FUEaltb5gzOjOCXW/rz8PVXwpBH+DXzMYr3b2ecawFJQQoGZ4SzVcykSJlGrSwcQ5t+XB0wizfK01Ht/4EbvV8B4EwfhWgzIVbv9UUIS0ic48z/YS4xLbsx7J9LxkMLefKnHF67vDM7HjmfQRkR4HFD2Vbf/50WWPIw7P8ZIrJAoYJ9P8HcK6HhoK/D6M6HO194N7idIMihuRLyFsHsMT4BPygBEnr7hP+MkdDrBnioGta8gjhnEvbANAjPgrZjfH0Vr6d26SxUBYtxrp7F+ryaf/9iSUhISJxhBEGgjSGVALmGGG1Uq30x2igC5D47wnBNGJckjD6mb/zvNFVXU3hgD+srNlCZl4/BpUEEKqyVADQ4GllQuhCX1wVApbUKS6OJpIAEZMiotFVR0HIQtU3EvfsgqqhwBFnr4I3+kX24PGkc7QaPxNi3MzKNGntBKYJahSouCkWQAZnZjhCgAbUSQ5e2aG0CYWsr/WI9gDopDtHpwl5Qeiouo4TEf57f7noNW52JNQ+8TdWC5bhNTUzdM4dr8r9FFxmCvbGZuj2+orGW2nr2z1+EvanZL9ZveOYTlt3wAm6H77krrK3vb4HX6WLx9KeIG9GJdjcOoyG3EKenmQ2vvEdTSTnRPdsTnBVPWOdk4gd1ZcDzN3LZsjf4ZsSt7JuzhLihnWh79QUEhPiKWud8sYjt735DU3EZ+39YiN0kZUZK/DWqqqq4+eabSUlJQa1WEx8fz+jRo1m+fLm/TVJSEoIgIAgCAQEBJCUlMX78eFasWHHcfuvr64mLi0MQBEwm0ykZ62OPPeYfx/F+ioqKTsm5jsXmzZuZMWPGaetf4r/N1KlTEQSB5557rtX2H374wR88u3LlyqPe8xUVFbRv355+/fqdss+CxOlDirCXOCGiKFJcb+HTaT1oE3l8P+nSBisRRjVqhdy/rdnu4vrZWwk3qJnSK5G3lhXxhjEZbfNBntTOYc3wq1iyt4rEkAA6P7EEpVygpsWJTIAhWZ1Rmmzc+tU2RBHS6UgX5Vbadr+Su2w+//wbPt/GvidHYLI6MWqUyA49HLaNMQKwLvoqqtVWPikcRrCzkuoWFXUR44gTakm270EmU1BvF1hVVEcO/eiqreY7SzYAH+9xs1t4iKxoA08Gxh49YQmJcwxn20t5sqyCMmUynyqfRbE/GtmouRg0hxbFlj0K69+APjdDRDtfxPzOryBzlG//mpehfKtv36B78Vc2A18h2V1fgeiBsHRwO6BqJ8gOfQ0tuBUKlsPA+2Dw/TD/ZjBXIQCapnwaEkawrRReXrqaH6xXkeWo512VAgVuHvouhd733vVvXioJCQmJM47T40QQBKamTiZAEXDMNl7RS5OzuVWUPEC1rYYlFStoF5SFXJBTYSnBFNxIpaoRt05Gl8gulDsqsRaU8EPjaipjBWw4sLgthKlDCcFI8bYdFIRV41V6UQgKBkcNwL17O8KWAhqbVxN+xWi8VjtynW9sMkFGjDYKURRRRoUhaANwllUj2h04q2pRhATiiQzG1jYBlUKJdXchHlOLb8AKObh9Efq2PXkAaNIl+xuJ/x9E9+5A2arthHdIZvF979F20nB6PHyNf/+3I26jZkcuY+fPxNpcR+W2XbisdjpMHIvTbGX94x8AkDV5OLF9O6LUHf57UbJ8C4HtIpCp5GTfcDH25kZEPD4PfK+Xre9/jtflZsjbdxKcksA78Rfidbqw1zcRmBVB4pBuFCxbRdnqrWx/4Tu0McGkTuiF02ylcMVa2l4y8l+/XhKnBlEUMTVZcTrdqFQKggK1p7V2QVFREX379iUoKIgXXniB7OxsXC4Xixcv5sYbb2T//v3+tk888QTXXnstTqeToqIiPv/8c4YOHcqTTz7Jgw8+eFTf06dPJzs7m/Ly8j8dwyeffMInn3zCypUrTzjeu+66i+uvv97/unv37syYMYNrr73Wvy08PPwkZv73OJ19S/x1vKKXcmsFFrcVnUJLrDYGmXB646M1Gg3PP/881113HcHBwSdsX1BQwPnnn09mZibffPMNWq32tI5P4p8jCfYSh9n0vu9n9CxI7O3fvHJnAYvmfshObW9WPnTRMQ/9YXs5t329gwClnMRQLXOv64nxt8extthZXzAYmUyOXBBYkd/ME8kPcTcv4U4eweXZgUTkz+WuNbE0YvT398DILK7pn4K5dBelH95CrUfL855JrOg9m+69M5lkcbKpqIF2MUYW7anif19s5eJOsbw8oRM0lfHaxmYSqpexeH8DCz3dCYwdRpZlE5scmdQXNSAXtVR4Q3GKKlYWWYkJMnJx51RSOo6maf4esuwu9lW2UGHsxPVDj59CLiFxLjF1QAYMeANv6RZkHz6BaMkBlxVUh4oSakMP/z/9fGg7FpL6He5g6GOw9wfoehUAFquVJmNHNIk9CMmbB2EZULLOV1D6f2uhqcTndQ8Q1w3KNoHhUJSo4XC0qCiCTKlGN38qmuZhzFYPYHp8JVZdIsp933Ol9WPWHZhISZOHZ37ZxwXto3l+XPbpvFQSEhIS/xqLypfS6GxibPyoVsL88qpV7GvaT4YhnVHxw4957JKKFeQ07afjbjkd5akEjR5EdV4+VUoTtZ46DjTnYXFbaHa1kByciN7hJCkqnXhdHLtLcyjzNtAlV0S0ClRkqBkQ2ZcYbTQN67ZTvW0D1u5u1JFqhsUOIU4Xg72LgKnagq5zFo2/rKJl/U5CLx6KrnMWzuo6mjbtIDfMjGFDKUEmAWVUGK66RnB7cNc0IARqwePBU9WAq6oOZVQoxgHdEWQCTau2ILrcuGsbUcWEY+zd6V/6DUhInFkuW/waABuf/YR1j31A4bItrQR7TWggMqUCdaCe4Kx4XFYrcb26AKDSaznvtTsxV9QS3cv3TBOUFUWHW0bhqndTuGAN9dtKMVfWkDpqIAOfvh233Y7a6AvS0oWGYa6sQa5UAaCPicBWYyIgOghBLqNmez67P5xPZJ90QjskEdYujYCQIGwNJmp27yMqO4vGwhKKV28gc+wFxHSV7s/OBmrrmsnLr8ThdPu3qVUK0tOiCQ8z/smRf58bbrgBQRDYtGkTOt3hgujt2rVj2rRprdoaDAaionzPCgkJCQwYMIDo6GgeeeQRxo0bR0bGYbvMt99+G5PJxCOPPMLChQtP2Xj1ej16vd7/Wi6XtxrX8Vi5ciWDBw9m2bJl3HvvveTk5NCpUyc+/vhj/7gLCgq444472LBhAxaLhaysLJ599lmGDh3q7+dIS5yJEyciiiJfffWVf7/L5SI6OpqZM2dy9dVXI4oiM2fO5J133qGyspI2bdrw8MMPM27cuFN2Tf6/ktdcwK9VqzG7zf5teoWewVH9STem/smR/4yhQ4eSn5/Ps88+ywsvvPCnbXft2sXw4cMZNGgQn332GUql8k/bS/w3kAT7/2dsLW5ALpPRKT7Iv83jFREA2b4FUHeAuXM/IzfaRH/XWtrEhtGj4CcGqzazTFWEyToCjVJObnULHWID/avsszcUA2BzeUiuWYZ5Vx3GDW8SBSQK7fnfxUPplRJKkFZJ0pan2O1VcktNJnduu5ar5It5RNmH21w3AfDU2PZc0csXNaUv/Y0sCsmSQ6B6DhlDfavYIToVn03rAcDcLaWIIphsLsj5EeZOoYunHf3ke7lIKdDT8wb/Kx/OjP7X83K7KK6bvQWZ04lMEKkXDQTp9bw9uSsd44PYWtzADYNSGZQRQb3ZQbBW5Y/al5A4J3DZfZHxcd0hdfAxm8jiu8FFbyDoIw6L9QD974CuU30e9QDjP219YPIA388hala8RXLzTtbkh9Lv4nchMA6+uxbaXQIy2WGxHsDeDI4W2PoJdJwI5z0Iu74GUzGuHtcT5Gyit2MdtyjMvBP9AtOv7IR2xTMIgpNEsZops9dR7fZFCewsM/3jyyQhISHxbyGKIgUtB4nQhGFUHRZCPKIHAYH9zXl4RS8/lv6CgIBRZUCGnMKWQgDsXruv2KvHjt3jIEQd7O83t8kXje5xuchxFZBR3Ybm6loEuRNVlIphMefR4mphr2k/B5p9bZdX/YaAgIhIuBmiKiHEpqL3sDFEBfjqBrnzS9E2uYgph5qEQP8DqSYhmqjrxgNg2+cbn9dup/GX32jZsIuCNNiRLsPQR8awn7246hoJvWgwcoOemk9/oCLQyuaYvYQpYKg5iogrRiPXBWDZk0fYJeejjAzFa7Eh10tRYRLnFk1Flez+YD7tp40mKOXYmb2dbxqPXK0iaXivVtsvXvAiLosdtdF3z9Zp6oRW+zted3Gr15U79yDIBWQqGLfkNer2FLL5hdkkD+uFXKVErjos5Bz47DeaiyvQBkfQ+77pjFsyi18ffAGlXkP25EuZP+Z+zOW1aMKNDHjxBiI6ZLF33gJsDSY8DgfbPvgSQSFHdHswV1afikslcZqprWtmT87RlmMOp5s9OaW0bxt/ykX7hoYGFi1axNNPP91KrP+doKCgE/Zx66238uSTTzJ//nzuueceAHJycnjiiSfYuHEjhYWFp3TM/5QHH3yQl156ifDwcK6//nqmTZvG2rVrATCbzYwcOZKnnnoKjUbDp59+yujRozlw4AAJCQlH9TV58mTGjx+P2Wz2LyIsXrwYi8XCpZdeCsBDDz3Ed999x9tvv016ejqrVq3iiiuuIDw8nIEDB/57Ez/HyGsuYEHZ0QtBZreZBWULGR13wWkT7eVyOc888wyTJk3illtuIS4u7pjt1q1bx+TJk5k0aRKvv/46MpnkjH62IAn25zgzF+9nQ2EDr03sjNcrMu7t9YjApZ1jeX5cNpameu5562tyVG354MInWZr/CvPqu/JA84sMlG8Bnw6PQ67jF1MCX3y9g8AAJT/sqODBkVlcO8BX4CszysDW4kYu0OzibWbByhDM/R7k9V8LKRIjCRKsrPl1E2pdWyYJi1HIPWR7DrLOk8VIxRbWetujkAk8NbY9l3eNgYX3+oTCfndAUxns+4mOUbEg8wLyVnMcH1XDxcG3ks8F7CzpQkcgxKhjjaULLR45LnUwQXIF760+SGKYDr1aicHaSJxQRzAtzLw0k47xQTTZXFz+3gZcHpHFtw0gI+r49j8SEmcte7+HX5/2FXi9c//x23WZcuztv4v1J4Fr8MP8sOg9RjkWwpwVMPUXuGE9pQ1WXpiznbGdYhjye9HoThNh349QucNnvdP9Wl+UfWAcquxxWDZ/jg4YqNjDgClZsP1zFOtfw22Mp/T8D4hZ7UHeZOeeCzLpkXTyY5SQkJD4t3F53fxctgiFTMHI2GHsazrA4orlqAQlfSN60zk0m32mXBZXLCXZkEzP0G6sr9tEuc3nJ4+v7isCAnJBTrGllK31O9jWsBOr28qk5MuIDIgAfBFeiRuasIWo2JPixizuJzbMQI6nCKfXidvrZlfjXvSKQ1GCou8/ogByQU5DqJvKzoF06jIEsyOM5+fW06tDAH1G9scULqcxqJp+jUnHnGfpoEjWtS8mzdhI+xILANEuI0UNZmJLvMiDjTQ7m7H/soykqy8HmYyKWC+iTKAuAiL7X4JMqcC6/yB1Xy1Epgsg/v5rJbFe4pxkw1MfkTN7IS3ltVzw8cPHbKMyaOl2x6Sjtsvkcr9YfzIExSRSumYrxT9vJferldxQs4h2Uy6gPreA3V9+T+qwgf4ist3umEjZts1YTFXU7M0F0UtQWgK6sBAM8TEIh6xQLUWNJA3qw87Z39BUVEpgUjyJ/Xuy//uF6KMjiOvZhdCM0xdpKnFqEEWRvPzKP22TV1BJWKjhlNrj5OfnI4oimZl/v1BxSEgIERERft94h8PBxIkTmTlzJgkJCf85wf7pp5/2C+X33Xcfo0aNwm63o9Fo6NixIx07dvS3feqpp/j+++/58ccfuemmm47qa/jw4eh0Or7//numTPE9w3355ZeMHj0ao9GIxWLh5ZdfZsWKFfTu7XNSSElJYc2aNbz77ruSYP9/7J11mN1U+sc/Sa7buLu2U3d3p0gpDkW6uC8UWGzxxRZ3d4pTpBQobanRlrrbzHTc/d65Lsnvj0xvmW3ZZX/bwi7cz/PMM3OTk5OTZJKb8z3v+b7/T2RFZnnD6n9aZkXDavKsOcfMHmf27NkMGDCAu+66i9dee+1ny5x55pk899xzx6QNEY4dEcH+d0xth4ePNlbT7PSzubKdiT0SSI8xUt3u4dOttVwyLpfsr//ES8HV3Oy5hCvfKWSx/ksu1C/no9jLaG6vJyGg+rx9oTsBwStwUfBDlunOAiDGrE6NbHP52VLVjlkncfv5s2HRJ5A2mE9NZ7AivpK/prczfvM1GOo3sCLUj/vk8zFIIdbIvVEQWaeMIj3BgMnu5cmlJUxPaCFm/YvqQQy8AKbeC+tfBEcNVK07FMErh2DXp1C2Aq2nmUDZKmbtm0Gm8ASN3hj8gg6ly0Lbold/G7USd8/qze0LZO4wPUxhdibnFWUAYNZJDMuJpd0VIDnK8OtcpAgRfm1yx0Pe5G6R8Iqi8NfPd9Hq9PP4mf0x6Y7OV0Nh/1HE5A5G+PJ8aC2GWDXJ2WebytHvep8PmoczucfJauGU/tD3dFj7NK6N8wlueJ8oRU1WFnjjeG72XsIzOhC0JgSNjqqYYQQMvZFzT6DA4OSznuthzPWsr/Fw3mvr+dPonPBMnQgRIkT4b0FWZOrc9ZQ5KwCYmDyOOH0sEhJ+JcCqpjX0iSniu/plyCgc6CyjrFNN5B2ni8Uv+3GHPIQU1c9dK2gJKSE6A53oRR0+wYdW1CIrMs3eFtyyh5pRsYxOHEFtwwpSrak0apoJubUMsw3hg4pPuyL5Ia1CJqvFSF2Uh4p8gWhdFH4pwPoiN7XiFtg6jlVbPeyp6mT8Hdlk5PVG//YBEDagDBoWFm88QQ+lneXYd+wlNuRjd9oe8koUtICp0sHkrmAQe6yHJSeKaEMil8RZsY0bzJAdu9EmG+mRNRBRq34XaRNj0cRFoc9I+TUvVYQIvypFc2ZgL6+j15wZ4WXu5nYWX/g3Egf1YPQ9Ry+55NDrziNjzBAc++8jKjslHFF/4LtVOGrq0EdZyZ8+EVEj0evc43A01+JubWH7Wx+BAoIk4Gpooq2sAtGg3veWVNVP2xQXg8ZoIH34ILwdDnKnjiN9+CAqV/3Ihmdep9fpJxKVkXrUjiXC0aXD7u5mg3MkfL4gHXY3MdG/fJDoX6F0ddr/00EARVHCddx6660UFRVx7rnn/mz5qqoqevXqFf4cDAYJBALdrG7OPfdcXnzxxf+oXUeiX79D9lApKer3W1NTE5mZmbhcLu655x6++uor6urqCAaDeDweqqqqjliXVqvl9NNPZ/78+Zx33nm4XC6++OIL3nvvPUCdaeD1epk6dWq37fx+PwMHDjzqx/ZHodZd180G50h0Bp3UuuvIMB85+v1o8PDDDzNp0iRuuOGGI66fNWsWn332GatXr2bs2LHHrB0Rjj4Rwf5/lA63n4pWF/3To4/4xfbVjjrqP7qBFyxt7J/xCCk2PbIMj585gNNfXIdBI5KfaEETn0WoZi1tioW/a15GUGTagjrqaiup0pjZKI4jMVTPGZ4POUmjxVAbYGWDBo04jme/L2Hh9lpWFregEQVCskK7LoX0azYD8O1D7zHP9RrT7ZvAnIiMwARpBx70XOG/nkGZUWypsuPwBtnToD7oHJ4grfpMwikz7JUQm0XT8FuI8TegTR8WPsbAlvfQfnU1CiIC8EFoEnEWHbWuZEIoXDk+jy+31dHi8pESZaCyzU2PZCuKAvUOL1/6c9l8wiEfOI0kMv/i7lNMI0T43WFLhfMWdFvU6Qvy3oYqFAUub8zrZpn171LZ6uKvn+9ieu9khmTHcNIza0iLuYLll/eC0qXQ62Tmapdg075Ep7AOnrwTBAFM8VC/FUXSow860SHi10ej83eglX20EsUlaV/w6twRoDXwwQE9z3fczrDGWD468CforIfYHFY3DuZAs4vv9jRGBPsIESL86nT47ciKHLal+UeW1C9nd8deYnWxjEgYQruvg1RTMv1j+7ClbTvx+lhEQcSmtWL3O5CRUboSeNsDDoJKEE3X67uCglf2ArCtfScAOkHL4rpluAIuHEE1Yas75CbXmk2+7UIcfgffN6wCoLW1Db2oI6SEUIDabJG2eC/xliSgmVZ/W7jdLb5WRvWyk1ZSSUJmI4qShSYrGWlAHtbklG7vot9XLWe/t4zcToWRmxSWTwUl1gJNLpAkbCP64/hxOzqDEY3Gj16vR9TpkAx6pDY3o/cmkTj4kICijY0i7foLjs4FihDhv5TMiYPJnDi427L6dbuo+G49tWt3/MeCffOeYqp+2EDe9AlULt7E99c8St+LZzH89vNp2rWPxD49ocsG1NPWwYq7HyEmN4uOiloaNxTjqXWQcXxfgh4/WqsRJRgi6PaSOXMAmfdfRsHxkwBoL68i6PFir6qlZt0mAOJ75NO0ax+uphbayyojgv1/Mf5/Idb/u+V+KQUFBQiCwN69ezn55JP/X3W0trbS3NxMTo4aIPT999+zc+dOPvnkE+DQoEB8fDy3334799xzD6mpqWzbti1cx4IFC/j000+ZP39+eJnNdmw8+3/qIX7wO1SWZQBuuukmFi9ezKOPPkp+fj5Go5HTTjsNv9//s/XNmTOH8ePH09TUxJIlSzAYDBx33HHd6l20aBFpad0tt/R6/VE9rj8SrqD7qJb7/zJu3DimT5/Obbfdxty5cw9b/9JLL3HzzTdz3HHHsWjRosiMiv8hIoL9/xg17W72N3Ry6TubCckK86YWcm3fEHw8F/qcAuNVvzY5GOBP0rdovDK7a7dy+rfVROkVruinXnK9VkQjiTDrOTjuMU5Zv5XBy54AIEno4HxpCelCK+t7nk6VkETP4ruwm3OQJR1rO/oRkmXMbXv4sSMLEAjKCoMyo7n0nU2MyIkjNcbA3eZP6eHdhKzAMncey6zXcYX0Je81TQBgS5Udi14iGFLwBmWsBg0TeyQQF2WjdPjfyNO0IkRl8uWmMq5d2Y9xhZN5W3so8n1lZyqFcgJO0UpheiJT+1/AXQP7otdIePwhjDqJ66cW4gvK6CQRTyBElFFLTbubaKOW9BgTUsSfPsIfjc5G+OYmyJ0IQ/4EgM2g5emzBtLm8ofFel8wxMebahiWE0th0i+3h1q2t4nVJS0MaV5AetwuMpRT8YWy8X7+ZwylX0NLMbYeM2FbJtbCcbDhZXVDu+qVKRij2Tf2RQqW/gm9vwOAkM5KuTeZhgMumryQWL2Ky+XleIfOYObgAqi/BspXQd4kLi+KJ96iO2S1EyFChAi/Ak2eZqrdNaxsVP1nz8g6hQ5/BxtbtzA5eTyZFnU2nyugWsO0+dvY3LKNRl8TifoEQqgR8wbJgCRIzM2bgyvkYXXDD+x1FAOHxIYgQUYlDKfKVU2duwGb1opW1NLsa8GvBKj3NITblemMIzEuhZeK36BvdC+0ohYRERm18x6SZZINyXR42/AqPjwWgTpfE1GyDrsxAIBVY2FAbF8KsHD2ZB/xMb3oCNjZ0LqZ3b3KGZuYwtCfnIu4MjeGaIVEhw5Tz1TOTO2HaXwWiqyAHELQaIiaMBRBksgSQgiCiEaU0MRGgSigjYs+ZtcpQoT/Vmp+2M6mR+cz7JbzSe1KDpszcyRjH7iSuN454XIhjxdvXRPGjBRE3S9PGli7cRvtZZXs/XQRQacfQRIJON1seultQj4/Ay86h+T+vfHZHZgS4mjevR9nQzMhv4+4/pnIGQJRaVnYaytRgurzSme14O900lpygEJxCjXrt2DLTMOWlkLWuOGE/H5EjYTeZqXX6SfSVlpO6uD+/6KlEX5LdL9wlu0vLfdLiY2NZfr06Tz33HNce+21h/nYd3R0/Esf+6eeegpRFMOC/6efforH4wmv37hxIxdeeCGrV68mL0+1Z9JoNOTn54fLJCYmYjQauy37LVi9ejVz585l9mw1/4TT6Qxb/fwco0aNIiMjgw8//JBvvvmG008/HZ1OdUTo1asXer2eqqqqiFh7FDFrfplF3y8t95/w0EMPMWDAAAoLCw9bJwgCL730EpIkMXPmTBYtWsSECROOeZsi/OdEBPv/Idz+IMc9uRqXP4gctnrRwNK/QvNeWPn3sGB/0qAsyl3P88F3a3h5p+pBeFvoFc7cuQKDZiqxspfqpx4j4cynMRz4Dq1+Ii9wGpcrn6DFjzU5j0alJ0XTLsC69mMswXY8DpGhnme4cVoh1+x6khlt77Ircw5/Vy5gf4ODTm+QeruXz7apNjqWvCm45ApeC8xgoHSAUFsFnms+5vxWN5s/3IbbH8LpC6HXiJi0Ip3eIF9ur6euw8umylxeHhHN1KcH0MM4AFG4CZuh+79rv8GjuaXyfUbmxtFrXC4/TZ/pCYTo9AZItBnQSqpfmE6j/k6PMbHpr1MjYn2EPybF38CeL6Bmc1iwBzixf/dop482VnPHF7spSrHxzZ9/+dS504ek0+H2c+meuzDWlrBg2gxO2yzy3n6F8yzxaDNGQMYwuF6NBmXQ+TQ7Axg++xMWZznCWR/QJ30QVE1U/fYLZxDqcyYLFt6FV7IR15kBb5+EDYU7J9sgezhkXKra/FiTsQBzR+ccsW2KouALyhi00hHXR4gQIcL/h2pXLR9XfoZeVKPUBAS0oobVTWvxhLwsb/yBCyxnAzAr43i+rv2Oks4DNPqaAGjyNYfrqnXX80nl54xOGEGjpwmDaAyvCxEiRhdNjDaaIlshFc4qZGQ6Aqp92OyME1hY8y1BJUifqF4EnR4MkkBxqBJPyMOGVnUGZII+nmZfC6CK/43eRq7vdRWra39gs30HJk8Ie3QAg6xFlgQ6g042NGwk6bON5Pr9lP1JYEnDciwaVVDRS7pu52NA3giyv99A1IShGPMywssFUSBodyOaDIh6dRvdT/ISmXrlkXnXVQhSJBlahD8eO15aQPk3azHGR4UFe1GjYcgN3T3rHTv24a1vIuT2YOvb4xfXX3DcJAzRUeGo9xM+vJdvL3yQzOP7E9crG3NCLHEFOWSOVoffEnrm04lC8esfoNPrGf23K5G0Wn54+Fm87XYyx41AazRQtmQVWrOJilU/Uvr1MgCGXXMhxphoCk+YStDjRRAFzAlxmBPijtg2OaQOAIhS5P3styY6yoRep/mntjh6vYboqKMvQD7//POMGjWKYcOGce+999KvXz+CwSBLlizhhRdeYO/eveGynZ2dNDQ0EAgEKC8v59133+XVV1/lwQcfDIvtB0X5g7S0qN97RUVFvyiJ7W9Jfn4+CxYs4MQTT0QQBO64445wlPzPIQgC55xzDi+++CLFxcUsX748vM5qtXLjjTdy/fXXI8syY8aMweFwsHbtWiwWCxdcEJnF9v8hzZSKRWP5p7Y4Vo2FNNOxn1XUt29f5syZwzPPPHPE9YIg8PzzzyNJEscffzwLFy5k0qRJx7xdEf4zIoL9/xAaUSTeqqefq4w7J8aR0LaZ2LbFMHoeofqdKIUz0QDVbW6u/3Ab1e0JtIZmoqCgEWFwqh6a4HxpKQoKUjtUf3wDGa0/0BKaiF+JQdBAuxRHdNMGogD30wO53nMH12iHs0buQ7RRy8QeiWzaqL5Q+SULFwzL5qK3NtHYeWiKlk4SWFmv4WKxgtu075MmthJEQpP8CFpJ5LyRWQSCMq+vqSA9xkhZsxpxFm/WsaWqHVGABKva8W1x+hmeG8dTZ3X3VzPoJF6fO5R/xO0PMuXxlXj8IZbeMJ60aONhZSJifYQ/LH1OhdZSyP7nIvyQ7FgKkywc1yf536reatAyb1oP6PksHPieqJFzmb3+Xi6UvqEjcTrRPWZ03yChBxd9+AO7mu7k4ZMKOD29K9lU9lhVsC9dhq74W1IBghJ8fRNdBqqQP1kt+9X1sPUdGH0dDL8MOqphwSUw+AIYe8jL79J3NrNyfzPzLxnO0Ehi2ggRIhwlDJIejaDBrDExMkaNfK911zE0fjCbWrYwIn4IAOWdlaxs/AG/HAhva9VY8YY8BBRVHAkqQapcNTR7v8IT8nbbj01jpd3fQbu/g/IDlWGrHIBYXQzxhjiCXfWkmJJoEpvZFtoFQdCLenyyD4Oo72Z1A2CSVOEl3ZaBpNXSHqhhP/VE6aNoDKgCh15rpMPkIErSh5NMOoMuhscPpV9Mn3BdiqIgZiaSfNEph50nb2Udja9+ijYpjtSrD0+cCUTE+gh/WIbfNhdDbBQDrjztn5bTpyQQ6HSiTzyy+P1zmBPj6TlrOrb0FEJ+P1Gp6aRNKsKUHEXasIEYoqO6lTdmpPJZxX7k82ZyanoeUpd9hy09FZ/dQdWqH0EUQZbxOTopW6JabWnNJswJ8QBseOY1PO0d9DrtBJL6FlG7YRtVazbQ69QTiM3PBiDo9bHuiZdAgZHzLkVjiOQR+y0RBIGC/BR27an+2TIFeSlHNeHsQXJyctiyZQv3338/N9xwA/X19SQkJDB48GBeeOGFbmXvvPNO7rzzTnQ6HcnJyYwYMYJly5YxceLEn6n9f4snnniCCy+8kFGjRhEfH8/NN9+Mw+H4l9vNmTOHBx54gKysLEaPHt1t3X333UdiYiIPPvggZWVlREdHM2jQIG677bZjdRi/e0RBZGLyWBbWfPOzZSYkjz1mCWf/kfvuu4+PPvroZ9cLgsCzzz6LJEmccMIJfPnll0yZMuVny0f47RGUg/Nrf0UcDgdRUVHY7fZj5gn2u2D9y1C2XLWuaCmGKXexfsM6hiw/F0n2hYuVzv6K4z7sJDfBwuLrx3HJ25tYsqcRPX7m254jV2pif/IsrtxbxBu6R0gQXaTRSACJV4IzmSxuxakYyRYb2CUU8KZ/MvPy6+lb8wEhOcQs/98o1+bj8oUQBRiYEc0pzS8wR1lI7bC/ssE6mXmLalEQuWl6j67ErX6eefdjPtPdSZsmEW9IIZNGOPEprtrXj0U76zlraAa3HNeTJ5YU89a6Svqk2shPtPD5tjrOHJrOxWNyuW/+YuqDVm6akss015dQMA2S+/LOj5Xc8fkurpmUzw3TukeXePwhxj+yHLc/xNJ54yMJZCNE+I3xbf8U3WcXIQy9GKLSYO0zMPtlKJhCu8vP6Ie/xxsI8e7FwxiVl/CTDTvhowugaS901qnLep8KuxfASc+Ctx1i89Tn5IaXAQHFGENw+FVoV9wHmSMpPv5jnlpawpwRmTyzaBPt9eVcN+dkZvSJJC/8JUS+r/84RK71L0NWZJbVrwTAKBnQS3oGxPTju7ql7O8sDZfTizpGJgxnReNqBsb2Y3TCCN4p+wB7QO1wG0Q9kiBh1Vpp8DYCamS+ghL+fSQSDQk0edWIfBERSZAIKOoAQO+oInbb1QjE0zJnsaFlM1XuGrSilhmpU9CKWurc9fzYshEASZC6Es4KnJ93Nh9WLMAb8nJc6lSyLZl8VPkZrb42Cm35tHhbafO3MzNlKggCq5rWYNKYGJUwnGZvC/1i+2CUDCys/obSzjJOyTyRLEtmt7b7qutpeOVTdCnxpFxx1tG6JBEiRPh/suv9L2jYvou+c05h3R1v0F5cxelLn8WSGk9LaTlbX/+AQEEmk88/C31X9LuiKLhbWtn2xocginha2jAlxKHIMv5OFwMvPpvWfQdI6lfEzvc+w93SiiIrxBcVIAeDtJWUkzN5LJbkBJp27Sdr/Ag2PvcmAGNuuRq91fJPWhzhIMf6O7u5xUFJaX23SHu9XkNBXgoJ8ZF3hAgRfkqJ4wDLG1Z3i7S3aixMSB5LgS3vn2wZIcI/JxJh/19AWbOTzFgTGkc12FLp8Cmc9uI6PnQ+RJzcCmUrIODmo71eTut8lyASlUIaHwXGcPrgdP70jZ+ArNDh9vHIt/vYv3cHGuI4JU9hSK3aKRtR/jSv6Aq4h8v4RHsvBEBDiCs1C5kRepK3xbuIEzp5wz+VgKClrrKYVYYLmOt9lzd0j9L8px+Z91kx+xo6aXD4iA/WgwQpWx5ndvBvRMUdx/KCv9Iz2cpX2+u4fmohp554As/UFlHhkhh74DEypUaw1/AXfSlXWlfwetstXPu+hw3lbeQlmDllUDon9U9lRp9kxhYkMH99JauajPRONali/bJ7advyOaf476VfejQALU7fYefTqJNYdsN4QrJCtEl32PoIESL8uuj7nwo9p4PeAvNPB3cr1G6Ggik4fUH8QRm9RqJ3SrS6QWcDWJJAb1UT5O74CL69FYpOhBOegBMeh9pN8OVVIGrh9kawJKGseJBar56TVxTw3eTHiO09mfd+qGLRznpc/iDv6B5Eo98G2mwgIthHiBDh5wnKITqDnURpbTgCnUTroqh11/F51SJ8cvd3jw0tm/DJh2YZCggMjRvEqsYfAHD5PXxV/S32gAMRgQxTOpVuNXrRFVITkRlFA56uBLJHEus1goaQEqLN1x72oDdqjExOHsey+pW4Qm7a/e3h8ourl9KpuLBprYxMGIbdbyeohBgWPxif7EMSJPa078OtqP6+zoATs8aEiMBe+342tW6jw2dnsCeTIcFcQkWjafW2kWPNYkHVQpxBFz1sBWxp206Vq5qSzgMoioIkSCgoh80OANBnpJD+lwvDdjgRIkT4belz9ix6nnIcokaiZtVWAi4P9vJaLKnx+O2dCLJMgjeIXpJUQd7lRm+1YE6IZ/RfrmLPp4sIeb3kTBpNUt9eKHKIsmU/ULlyHfbqWoZdcyE75y+gZV8pLftKMSXEUXTKTJIH9GHDc2/gamzGFB8TjtY+FlHbEf5/JMTbiI+z0mF34/cH0elUG5zINYoQ4XAKbHnkWXOoddfhCroxa0ykmVJ/tcj6CL9fIoL9b8zb6yq484vdXB69kVu8TyD3mk3rkNspa3Jwo3gxj41wEZucxfYNK3irticzdAYqlGTES1cgb6/nL5Vt1HR0ICLj9HhpXv0qq/Sv8FloNCXxt0OtBF2JzEDguqwKpGoXSHqEkI9GJZovTffi8GvYHsrltMRa+neuJCNUzRTvBkTAp2jZ9srlPCBU87ZhFo/ErWFVynjk8k2IXRmv4zR+NILM3Z9to9oRJDfBwuOLi3H4gtgMGnLlOJCAVY+QpbdBwEGgfC2rZHWq1oFmF/d+tYf3NlTx3mQv5gYbx/cbwM4aO3NGZPLQygZO1/VkvWYSFfVuFAWum1LAFROOPGJpNfzyJEwRIvxRsHsCvLTyABN6JDIs51e2hNF3RUyd9KyaILbXSQBkxJr46tox6CSRKJMWnh8FTbuh/1kw+yV1m63vgrsFJB0IAhijIW0wbWmT+LzWwtCX5tKn6SsEZD4VJtPm09CQdwaxsTYuGlTFyQdexZw0kfJ9BnLQ8NSqBl599xsGZMYws28K543IOqy5wZCsJuaOECHCH5KvaxdT2lmGQTLgDXmZnDweT8iLT/ahF/UMjx+CM+hkr70YT0gVvSUkTs+ezda27RR3HkDuEt4bvQ3Yg50AyCjE6KPDgv1BrDorHq+3W5Q9HBLv04zJVLprwpY3AK6giy+7pmHbNFYkQUOMNpr2QAexnQa0WjCboqh11bPLvgeAJEMSW9t2dNu3gsKCqoXhfVW4qrpWKGR8WUFLqILqk3OIzS+gw29nYEw/DJKeoqhCDtQsxiyZsPsd+GQfiYYEZqZOo2fU4UnPACTLsU+8FiHC/xoBeyee6nrMeZlIxl93ZrCmawDtjOXP46prIW20mhQ2dXA/jDFRmJMS8DtdrPn7c4T8AfqecwpJ/YoAaNi2GzkQQNLpEDUSIJHUtyeN23fTXl7N2keex+foijhVFIJuD6lDBiCIAqlD+lG9ZiNil9WOgsKO+QtwNjQRnZ1B1tjhxOQe/n4mh2TEyPvZr4IgCMREm/91wQgRIiAKIhnm9N+6GRF+Z0QE+9+Yg17qDQ4f6KC6bD95e4bwuGEi13kvYcIWDY+e3p+VSf3YW9PMYN9LBBE5+YdyihudCPXbyMDE67pHiRedfB1zNrRDWoyZIcJ3HBTrnboEEr3tyI4tMPA86HcW98z/lnWuVL4W78AmK8SLrfTrqEA42MUUNdQp0ZzlvZ3VhusB6Kt5CW2tEy1+3gxO50LNYl4KHs+TjafyRftZXCPYuS/vFfbWOXD41E6lwxvkjNi94O466LxJBJP6Mq1cpk98FPvdVsx6ifc3VOFvPkDCZ/NAEHkq+yMW7vWhAIuKTbyo3Mn7Zw5nxtpKvt3dwFc76vnz5ALeXFOO3RPkmkn5iBFv+ggRDmNbdQdp0Ua+2FbL+pVfw3YHw26++7dpjDUJ+p3ebVHP5J9MrW0rU387myHggQ/mQPWP6rLkvurv3Z/B8gfYmHol9x5IZFnrXxGQWaf04aQLbsJf7GVlcTO9Um1kNK0gw/49rs2bmOO+gVLlYpwHTCTTzLM1l+Cr1iL33YJojgk3YXt1B2e+vI5xBQm8fP6QY3k2IkSI8F/Kwagob1ek+JrmH/GG1Mh6n+yj2l1DvjUXm8YaFuxDhHAFXex3lHSryyv7MUpGPCEP6aY0at314XVaQUNACWIU9QyI6UuaMZVFdYuB7pH2le6af9peR7ATR9egwIC2TBJ9FtbHlVPtrqEaNeq/T3Qv1jb9GN5Gg0Sw6z1RQWFgbH8Mop5qdy3pxlQ6g050PZ201FWyRVNBsK6SBH08nUEn3pAXo2SgI2DHJBk5Neskvqv7niZvM7WeenKs2fzYvIEkY+LPivcRIvyRUWQZf2sHuthoOveUULxgBdFFufS88OTfpD2J/Qugf0G3ZQcF8866BkJ+1Xor6PPhbGxmx7ufIgcCIAhEZaQBULxoKe1llRhiovF2OMJivc5ipu95p9GwdRfNe4tJ6FVIZ20D3g4H5UtXIwe7+ozVdcjBIC17S+ioqGLCXTd2a0/Vmo0UL/yOwhOnhZPjRogQIUKECL9XIoL9b4gsK7iaKvky62P2xU9j7JYnGR/Yzt+0e7BK6ktRvMZHw/tXc5e0nNnGIk7z3Eo8dmzl32B1mvhAfxd2rOhEGW3IwzbDcM645kpe+6qR0vXbeVBTiB0z/pjhHN/4Ioq9Bbaup7Pn6bzhHAnA7ckvclrNQwyWShGiUkGQEDoqqYkaxIUNp1JNImuEgQyzttLiUUjByYpgP5bZZvFG5ynUBK3cGbeUPGcdoJDgLublA4c6mQaNiDLpr1D+GWSPgQHnoFn3LCdU3A/iRDj/cwCm9Upm+Y4DBJv6odWbcGECfGypbOeFOYNx+4OMzIunZ7KNJJueiT0TOf6ZH9hTp/rBTuiRQP+M6F/xCkaI8N/Piv1NXPLGOu40fERuTg/e0T2LyeODA+Mg778wM/z0B9QI++kPQkcVHFimLo/JhqxRoCiw8XVoKWZq5lZemHMrneI7XPz+FywN9ufsrQ7e36BGrp7Z/BSxe96F3ImYylbwke5evhQmkJY/ACU2n7gNnSAADVu7nYsGhxdvQKai1fWrH36ECBF+e9xBN8FQkDRjKrIcot7XGBbrdYIWvxKgxdtKubMSUO1qDka+b23dHq5HJ+rwy358so8xCSNJN6fybd1SOvx2QBXRTZIJe9BBpbuGOm8jKaaUrjolREEMW+3E6mLwhLx4Qh7MkilspaMVtFi1Ftq67HAEBGqSXOz21aJIgtpeOYCCQrOvFU9jI2O2KFTkCrgKrfSJ7kWrr5VCWz651hw+rvicGnct2ZZMpidNhrOhuW0HPb0tVDiryDSns89RDIA36GVi8lgSDQkkG5OYnjqZnR276WHL5+XiNwgoATSCJiLYR4hwBDr3lNK0bht7vvqR2Lw0Nj73GaJWQ/7Zx6Ex6n/r5nXDnJRI+qhhGGNspA0dQM2Pm3E3tyJqNURlpqM1GQl6vdRt2k7Q46Xn7OPIGDUEZ2Mz5UtX43e6aN61j9ofN9O4fTeGKBteu4OozDTsVbUASHo96SMG0lZaQWdtA0GPDzkY6orcV/G0tnX7HSFChAgRIvyeiQj2vzIfbazm290N/HlyAYIA3vWv0U/zOTFtW/lSOJuPhelUGgaypTOa0wancZHyGUV7lgDQR9nPioTHcPpl+ni38jwn4lBMlCsJPGK+hda2dmR3PDPfrqS40clwwcFgsYStSgGnVY5jpRjiWs0C0sRWrBue4a/DL+aAkkZv7xYGS6WEBA3fFdxDYloug3+4jKzWDSw1bKBVl459+tN8tr+CR3boOcG8D2fAR02bEx1a7tO/zSyxBElQkBX4c9sDfGV6jTq3Gp229pZJxFr0MOSk8HnYEMinUIrjgNSPr7/aw7yphYwpiGdMQTywCoDrmzqpde7g+L4pzOiTHN42xqzjnll9kNurWdG+iAMMZ3yvDIpS/nkCnKZOL8GQQmq08Shf1QgR/vtQFIVHv9tPi9PHEHE/5/EVlH/FJ6GxjLK1kJrY+9dtkKsVXpkIpji46DuQjmBb1VEFi+apf4+5HuLz4fQ3QWNQB/taSmDLW1CxCqKzECf9leMsibDyHV4wPMft0nVMKRqKWaehzu5Bby8HJQS2VBRbGvWdCqcpy6B0GdxWD+IuUEJ4M8by53c2MUP5gdnJrUwfejGfXD6SzLgu64aKNVC/nY4+FzB/Uz3TeyeTnxhJihYhwu8FRVFY3rgaf8jPmMQR7LUXU+aqAFSbG1CTxAoIBOQgYxNGsaV9W3j7kBIiXhdHq7+NGk9deLlf9mPTWHEEOylzlrO1fQeu4KGBQAUFe9AR/hyQAzR5msi35JJuTmNj6xZ8XdH5eZYczFozG1s34+qyI4zWRjE6cWQ4av7gwEFLoBVE0KEjIAfC9Td4GuhXI5HUECRWtJJz3NlIwiEx7CAaQUObt53NrVsZHDeQgbH9uq1PNiaxsXULfWJ6dZv+nWRMJMmYyJ72fQSUAKIgMiph+L88/4GWdiSLCdHw3yVSRohwLJB9fhy7i5H9AQ4s28KBbzfQUZhGXGE6MT2ykQy/bo6Heo+LJY01FFqjGRGXdMQyDVt3UrN2A6b4WLLGDid1SH8URSE6KwOt2Yi3w86BJSsJerzE9cgjdUh/REmidv0WJJ0WfZSN5IF98HY40JpMNGzbScjnx5QUj7OxmVAgQMjno6O8mkEXz2Hvp4uIK8zF1dLGvi++RWcyEpObSfaEUcQXFRCToya0rtu8A0mnxRQfS+v+A6SPHIxGH3mORIgQIUKE3wcRwf5X5uXVZZQ2Ofl+XxOXj89ls2kqPT3VTGYLb+seYlrgMVZ3qOL0J5trqTSkca9UyEZvJudrl5Lu2MqH4kwylf2sk3vzWPAMQojQKjAiN5Mfy9roK5Rxm2YdxZoeCIqaXBbgPM0S0sVWtSFly7hYWAF/3sY1821MUOJpMffgih+M/Fn/MoOFYhRFtYuO99dgW3QOebKXWUYDfsGEVdtGktVIFrWc7v0WxQE7zKPIDezHKcWw9NopLFi5gaLgHmJ9NTSE0rjx4+2MyI3l6kkF/G1vAjtcz2DcJ+EJlNM71cYpg7p7fuUnWvn8qtGE5MOTrwGIX13L3cr3XDHaRNKJs//peXd4A0x+bCWBkMzyGyeQEhUR7SP8vilvcfHc8gMAXDxyKktLdzGgX3808WdRH2PC4ZLoaf0VG+RuUQV5ZyNejwu9OerwxFXmRMgZpz54tCZY9Qhkj4O0QfD2bKhcDTnjVS97OQiPFcGZ7+AuW48p6OLv4wQoSqIoxcb4R5YzQTiflSP7Ydr4LALwN/88Rol7OHXCEGy7PoUhF0J8Ps9/t5/Fuxt5Qv8AlPlg7dMMmfUcZJ+rtuvjC8DVzMoDIR7ZlcuO0kpeumTyr3jyIkSIcCxxh9xs6/J132Pfx4zUKeF1IUKIiHh/kmh2dfNa4nWxuLq8/hQUWv1tR0wW6wh2EquLpc7TAHCYN/0/srltG3H6WE6IncGKxtUAmDVGNrZtwSQZcXfZ7wB0BOwsqv0WUAcWDkb5GwUjiqDg7UpiG6uNpj1gJ8WYxISZx3Eg4Qc8WdEE5RC1njrWNq9nePxQ0kwpVHdZ7+xx7AMHFNoKsGq7D1D2iCqgR1QBsiIf1n5ZkVnSsByAmanTKIzKP+JxHsRzoJqmNz9DmxRP6tXn/NOyESL8HvDUNeKpqkM06Ck6ayo+T4Bec08kfUQvBI2E7PMj/YqDV+0BHwFFpsXnIeQPIOkOD6iIykzDnBhPfFEB7pY26rfuJH34IBBE1jzyPLLfT3wv1U6ns66B1Q88zbCr/kRHZS1yIECv007AlppMYu8e7Prgc8xJ8Rhjo6nfuB1BFEFWkPQ6MkYNoXHHHnqddgIag54fn3oFZ30TAM2791Py9TJG/PkSRI0GZ2Mzez5eCAJYUpJw1jUiB4PkThn3q527CBEiRIgQ4VgSEex/Ze6d1ZvHvytmU2U7Ll+IW04fz1VvitiEp7GKfhpCUeGyogCt1kJ2jv+UOz/ZyTJlMEaDkTLLIB7rOIN7xOcZJu5nSdLF3CS+Sy+xmTNtc7nV8x6jpD3szUvmedOnvLCpAwCnaEURJFp6zyW0+wvMElib9nCL7TuetLzGhdFbeG33G0z2LWGrks9Tgdn0EcqJFlzkCvVMlLajV7zoAl5kBMydBzhd+xV+RUInhOjjWkutJpOM2zfDge85d32XiF7yDD9OXMIPpS3sa+jk6kkF3DKjJ1/vqict2si+hk4m9kgMH/e+Bge2QDPa7fN5vn0o7+xVmDM8k3tm9el+MvMmQUsJbXGDeeTj7VwzKZ+suCMnxpEEAZNOwhsQ0EYSFUX4A5ATb+bayQV4/SFKmzvR9r6NKUNNDPdqGffMOgQE1twyiQTrr9QpTOgBF37L7pYQsx9cx4QeR/CH1xrggi/Vvze8At//DeIKwJIIlWvU5eUr1d8OdQq1v6mYaeVn0i/Uh3kFF5IPmPUaEix6MoUOjBufQ0F1vnkgdxcVQ+7A5vwevrwaUvrDZas4Id2Dzvgln5nOYGZgKTH+eqrWfEjmvkVw0jMw6AKoXEvu4MncU/ciF9S+BWvug9HX/uzhLt/XRGq0kR7Jv+aoSIQIEf4/mDVmpqRMYGPLFuwBBxpRw4j4YfzYsgFQbWd8iq/bNsmmZKJ00Rxwqnk3YrTR+BU/rqA7LMZnmjLoDHZi01qw++2ECKGgMDNlGutbNtIaUG1sREQkQSLdlEq5qxIJiV0de8k0p2OWTPjlIB0+O+6QByGkoEgCYlfGoYNJakNdwRkAPsXXTUxvC3SQpE/krJzTWFG/mi1JJeCFxkbVX7rO08Ae+z5yrFlMSh6PM+DEJ/vRilosGvW9SlEUat31BJUge+37cfqd1HjqOD59OoW2Q6K8KIjkWLLo8NuxBxwsb1jN2MSRaMQjdzlErQZEEVH/60YVR4jwW2FMSyLQ4UAy6pGa25j6/E24HV6CXj+d2/egibKSMHHkr9aeImsMFo0Wz6ZdLH96/hH94c2J8YycdxkA29/5hObd+/F22GneU4LsV227WvaouTv8neosotbiMuRAAK3JQHSWGpSlj7IiaiREjZaWvaWA6uWfMqQ/ib17ULthGy17i/G0tlMwczJRWRm4mlowxsXgbmpFCcns+XQRtrRk8mZMJL6oAI1ehy0zjQOLV1L+/RriCvOIykw74rGGAkGa9+wnNi8bnSWSTDVChAgRIvx3ExHsfyUaHV7uX7SXyUWJfHz5SHbXOcjWOdC9NJzvNQGm+h7BEJ+N06u+5IjAI6f25f6v9/PwN/sRBFgp9ydB1CE2VbFQdydpmjYUoDHqHMaUf4igyBwfsNEhWlgV6sMPviFQ/i0p5FNKOkMzbQhVIZrdcILnSabFtfLiRxeQFnBzb3oVxi0/0LOrvbtDWeyzjmBt50D8IUiJMrCSS9H5WmjFRg2J7NINwMdStik5DGcvogCpcj3IMiCERTIsicxMc1E7vQcDMqL5YEMVmyvbOWNoBq+tLuf8UVnEmNWO2k0fb+fjzTU8qnuF08Tl9A+N4g35aspauqaQO5tU8Q5g1DUw6hrue+VH1h6o4biOD8gKrIDZL6pC3E8w6zWsuHEiIUXBoo/820f4/VLT7qau3UvPVCsC8OoPZcgKtO7/EWX93fiVROBxbjItJGbh23Dyc2CK/XUalzmCakc9/tAW6u3ew1YrisK5r62ntt3Dp2eOIS5rDKT2h/UvhcuE9DGIvnZ8phQMk29B6DcH8+Z1bHDFYuvqfEUZtfxw8yQ2l9ZQ924saV0zixJqlpBQ9z0YoyE6G3In0vTsNNLa93G1Yoe+V3G3+302bFzDl233QosPliXASU8DUBSSadK5wA3Bzsaf/QJ9ZPF+nlteitWgYefd04/mGYwQIcJRpsZVy9a2HYxIGMp5eWfh8HdS564Pi/UA8YZYaj1qolibxkbPqEK2t+9AVlRhXkCgrUt8P4iAgEVrospdTbu/o9u6Nn9bWKzXi3r8sp+AEiCgqPY1BsnA0no1Sj3ZkEyDtyG8bUa9BslooDzaCZJAgj6eJl/zT/YLZq2JzoCz2z79XV748k8i+0UEBsT2x6Ix0yuqBysbfkAraok3xHGgs5xh8YMRBAFZkXmj9F3sAQeSIBFSDg0OdPjthJQQfjmAUTIAcFLGTBRF4Ym9zwHQ6GlEFCROzjgendRdmNdnppB+88WI+iPYpEWI8DvCX9eEIolo42MIdrrwVKqBBxs+Ws6WlxeSN2Uwg+bOYMMTHxO1aCPjH7nm8JmIxwBBEMg0WdnXrubW8Nkdh5XxdjjY/PI7mBPjSR06AH+nE0GUCHp+8i4nCiArWFKS6HHiVHQWM5JejyX5kM1OTE4mE+75C/sXLqaz9lDybXtlNfWbtmPLSMUQHYU+ysrK+54g4HaDAoUzJ7N/4RI8re3YK2uwV9YQ37OAARecAYCnrYPSb5ajyDIBz6FZSD9FDgbZ9uaHtB+oIKFXIf3PP/1onL4IESJEiBDhmBFRLn8lvtlZz5fb69hVa2fWgDQkUeC9Z27jUtGFBgFRkDnQ4uL2mUU8sng//pDMbZ/vxhdUI6QOvq4lRRmYFGsnrakNWYEgErH7P2DpwIfJ1bRw2dYnMAl+rg7N4+KYGgbUvMwsbRbzYp5BM+0+gts/ZB2zuLaglD9XXwddHbeQNZWtSgGVYiYb404iPn8oHw/LZvwjKwCot3u5K+NursmspNhpYPjJV/OYRkOPO3oBcLL4A4/rXkAqOh5EEXvqGM4NPMQgdnNP3Tto35zBVX9Ro9D63/Mddk+ABoeX1SUtBEIyo/LiAShu7ARguWYMhaEqvgyN4soJeVw2Lg9W/h2W3488+W48w67BH5S55O1NlNY187LuacY1l4K/Ayp+OEywBzDqDvdpjRDh90QgJDPzqdU4vEGiDBr6pEVx0FHKoNeBIOELiTwlPc304Eak4iBUrIZeswCoaHGxdG8jZwzNwGY4NuLJjD4pfH7VaJbuaeS4p1Zz5/FF1Nq9nNAvBVEQ2FLZgScQolZMIe5Pi+Cd2ar9jSkeBp7L/pJSejV9xefKBM4aPBctsPj6cZR/dBuWd/4Gk26Cqh8Ry1bSDw2Pas/k1tCLiMl9kPOnEvjhWZrdGpzHPUbC8htI9FSgHBxdbC3hrvqz8KanoGnuiqat3RRu+6bKNi5tmEV/oQ+PDbqQHMAbCGHQStS2u0mOMtLi9PHccjVqrHfKoej6pk4vZp0Gc2TAMEKE/yo2t23jQGc5Ro2RKSkTqPPUs7RhRbcyTd4WRsYPY13LBpwhJxtaDz0XJCRChMgyZ9Lqa8XZ5VGvoLDHvp8hcQOpctaERXWbxsr+ztLw9r2ie5JkSKTB04gAuIMeqtzV4fW6rsj0aK0Njahl4IgR2AMOyrvscpp8zfSwFiAIAkaNkQlJY6hwVfFZ1cJuxzAobgAAWeZ0trXvQCNo2NmxB5/s54T0GTR5m9nctk3dly6KDr+dZGMSg+MGEJSD4QEAi8aMPaAKeiemHUeeLYdPKr+gzl3PqZmzSDEl0+Rt4tvapViCeno5UtkYW44C2AMOEqT4w66BZDL8G1csQoT/PXzVDTS89BFoJMxThxPoEsUVWUH0q1ZWQZ+f7+97m9b91bB4AyNun4shRs3P5WtuJeTyYMxKO2YifuHxU0no3ZPqtRvZ8tr75EweQ8DlJrF3DzxtHXjaOvC7PPS/4AwSigpZ9bcnALCmpZAysA+l3y5HloOYE+OIyc0CYMzNV7Ll1ffZ+sYHJA/og6O6jl2vfIUhNhpDmlH1wc9OxxQfh7u5DZ/DSfKA3pR8/T1K6NDAYO2mHciBIDqbBb9DfRa1FpcRV5gLQNWaDciBABqzkfge+SiygiLLCKKI127HGBNN4869tB+oACAq65ANq6etA0O0TbXmiRAhQoQIEf6LiCgHvxJFKTZO6JfC7IHqFL0nlhRTGxjBYO0eFgtjqFbU6IM2t49BWdFsKG8Li/VFyVZunNGD0t3bmN45H7mjCp+iYY3cm0nSdi7QfMfZlefy6txzcWx9Gb0SQG+Lo+ewsdTve5famCnMGZDFlStb6ZVyCY9+V8wUsRJBp+BXNNzLRfxt3ysMFGT297yO+Vtj6CO3sbykHVkBm0HD0OxYviyXuKTtMSaEymFxOd6WCm4ZdBmlQhapUXPZFn8KA5eeTfUDg5kX9QRCSh92O2y0+L6gRckKR+/fc1JvtlS1c9bQDNJjKjl1UDqnPr+GshYXSYYAJ/cw8eA519HqvpK8tRVYDVpkRWHjvgqGAos27uOGb5dwxwlFbKpsp1CoZ5p+E4ofmHyX6ksdIcIfEEkQSLQZcHidnKos4fKCfD7MncTjS0qoMRTC1XvQ7N3IzEVnI8vwrvZUzi08Lrz9HV/sYnVJCx3uADdO73HM2jkgI5pbPt3BvoZO7lq4m+JGJ1WtLuZN68EnV4ykzeWnX3o0DXYvNm8AE0D6UGjcRbZZ7aj276l6pVKylOAnFxPr9WES3MgfnMPBLpceuLwwn6uDi7lsWDS99z/LPiWdq7zXcNaXb3O1poJ9cjo5YhN6/AQb9qBxNmB0HopmZfJd4T8XbKklhEQofSjpcVZmP7eGrdUdJFj0NDt9FCZZ+ObP44gxaWl3Bzh5oNoh3Fvv4IRnVqPXSCy6diw58ZFp2BEi/LeQbc5CK2gZHNsfT8jLxpYt4XUH7WYCSgCzxoRZY+6WNLZ3VE/yrDns7tiHiIgnePjMoaASoiiqB01NqmCfa83GpDGxvmUTvaOKkJUQjd4m2n3tVP5EqAeI1cVQ5a7BKBkxSEYavI0Ud5ZS4lDzk8Tr4tFptOzvLAlv45f92P0OCqx5ROlsiIKIRTKzvGEVa5t/JMOUjkVjRkEhGAwSlFWxMEEfz/D4IehEHXH6WMqdlaQYk3h5/xsggFVrId+ay/jkMdS4atlr348gCLR4W2nztSMjs7DmazSihqKonnQE7GT54ojzWRjh6klsYRYJhsPF+ggR/giIRgOCTktQltn+5jfkzp6EzSjg/GErRdOG0f/2i9jx0gIqH30PY5yNwVfMDov1iqLQtm6L6vNuNqJPiDs2bdRIWJMTaNlbDAp0VFQjBwIMvvRcYnKzGPCnMzFE2RAEAUddPUG/OiPInBRH24EKtBYTPnsnSX3VYK4D362kYvWPKAH1GdO6X31uBfxetIKH5P7DkOUgacMGUvzVEgB8TieVK9cd1rbm3fsJJ1cDBI1E2ohBP1lfDEDqoH54Oxz8+NSrBL0eNHo9Qa+PzLHDSRnUNxwBl9S3CIDqdZvY/8ViTAlxDL/2IiRtZKZPhAgR/nhMmDCBAQMG8OSTT/7WTYnwD0SGkn8FGuxezn1tPYt3N9A3PQpfMMSyfU3sVrI51X8vtYXnh8u+sKKMDpef4Tnqy9gt5q942vwGQ1KN9Kt4jeyK98ntWINeCNKqqC9ycUIndwwJkhlnIj4mBkEQuGpMBgZnNSliG9MS2nl2eSlf72wIR7DnCnWEENhQOI8RJ19FuXUIsqTnzL1XM8pQzq46B3sb1OiPyUVJ5CVa0PjsLA4OQInNJVi3HUPNDwR3fsb1I2zMG2pgUJoZ3C3E+GrZWd3KA7P7cf6MUXgUPZnefWBXE5mdPDCNe07qTa/UKLLizDy1rITd1c1c4X+D912X8LeKs/h67SYE4JXV5Tz87T4eX7KfM8uP48bYZ3hJOgd/SCY12shdJ/ai14Dh3CNcwbsZ98DYeaCNJJSN8MdEFAW+u24cC89J4U7hFRJX3MyevbsBuO24ngiWBHKHHId90FVsMwxFO+R80ByyJzi+bwqTExxMyLfx9c56Xl1dhqIcOTHif8oTZw7gnpN6c8rANBKsetaVtXLTx9spSrYxtiABf1DmuKdWMa5sDnWTn4Xib6B0KaZytVNXZPND9QbY/CYaXzs1UgbvCSeAAkE0YE0BwFG1k1klt9Dng+Fotr1Nf+EAd42QkBW11+a05qIjQJMSxTZvsprsNr7noYa+dyYVL5/N0PuXsmRPI33TbNw+sxfL9zWxtboDgGanGo3v9AaRRIFRefGIAhxoVqPAFEV1CnP7Q7y5pvyYndMIESL8e+y3l7CsYQWtvjZi9DE0eprC0eMAuZbs8N9LG1aQa87GrDEBYJZMBJUQOZZsypwVlDgPdPORB1Ub6hfdmzxbDiIiGkFD76gi3EEPoiBi0hjZ3r6LrW3bCXQljD3IwJj+9InuhV7U4w15afG1ICGx174/XGZYwiB8XYMEBlFPijGZ3R17qXHXUu9poE90L4bEDUQRFGRkPCEvla4qLi38E8mGZACavOpAgiAIjE4cwZC4gWRZMvDLftY1b8QZcuEMurAHHGxu20abr50Ov52dHXtYWr+c5Y2rcYfc9IrqQUAJ4g356BfTiyGxA9ElRFNqa8aWmkiPqIKjdt0iRPhfQxsfTfrNF9Fui2bH24tZevlDBAJ+tL1ziJ4xGltmMoPnzaH/VbMYeO2J9L7oxPC2giCgT0tG1mvRWC04S8rxdiViPdroLGb6n38Gfc+ZTVxhLsbYaEq++Z7qtRuJ75GPJTkRe3UdG559HY1OR+7U8TRs2UXL3hLVHkdR0Bj1NGzfTfPeYpRAEEGSwkK7pNcR0ysdU3I0jbv20Lh9N1teeRdnfSMIAgm9DgWLWFKTw39rDDpEnRa9TU2ArQRD/PjEy2x88W1W3vcEoUCAuJ75ZI0bQcXKtQQ9HvV90Kdagfk7nZji4zBE2RAlDe7WNkC1yQFwN7fSWlx2TM5phH+PhoYGrrnmGnJzc9Hr9WRkZHDiiSeybNmycJns7GwEQVBnlhmNZGdnc8YZZ/D9998fVt/GjRuZPHky0dHRxMTEMG3aNLZt23ZU2nr33XeH2/FzPxUVFUdlXxEi/CNz585FEAQeeuihbss///zzf3sm1oIFC7jvvvuOZvOOyNG8v7dv387ZZ59NRkYGRqORoqIinnrqqcP2uXPnTsaPH4/RaCQtLY177733qPbFV6xY0e2eNxqN9O7dm5dffvmo1B+JsP8VsBg0pEQZcfuDrN26m5PlJbw5axL3rfVQ3OhkV52DcYXxrC5uQQFKm10EG52c3Deey0veg1poeH4bqYKeDtlItKh68x1QUlkSdy5TMwXGjpkIgOay7/l+0fuMXzwXGRCRYe9Cnp10DsU1HUw5bhItTj/5lXVIKIwJ/MhjywVub7qYRbaHSQpVYfcJaCWBohQbj57WD40k8srT97Hd8CKrYk9HuHYroarNvP/2c3wrTOLKd8dD0AtXb2TR8Pk8uqqeHtEyfTbeSp/8qQRNQaRAkH01LTzwaR2pNh2fb67gntkD2LPiIy4MfMMzxiqi5XYUBfxoyY/Tc+6r6zFqJY7vl8LZw7IobXIxashA/tozkZp2D33Sojj31fX8UNpCXyGN3S2JnNd1zh//bj8VrW4ePKVvxIIiwu+aL7fXEWXUMr4wAYCnvy/hmaU1PKIbR1AWKPdHoeBBELu+uAUBT1stg3wbGeR+D5gQruss3Q+c1XkFnnXHc8buOcgK9EmLYkTu0Y/mKkqxUZSiDjpO7JnE9CdXsbGinb/M6EmCVY8kCsRZ9Pw99ATJK6ogYyQ4asCWBtU/8vmWCmb+cDK6kAvyJtP7xKcRW0P8+JVExuizyeg9Aja+RnzZenIqvlPNv6zpMO5Gpg46l8zmFVALg+TdKIg4MDMk0GVz0VH+k5YqZNd9TZpvBNuUfLSSyLCcWNpcfsYXJuD2BzlzSAYIMLlIza8xNDuGRTvrWbaviduP70WvVBsXj83hldXlvLWukk5fkMfPGHDUz2mECBH+PWxaKxISCgo72nbhCXkZnTCCDS2bCChq9LlNY8URVIMddtp3oxU0FFrzKe4sZb+jhBpXLSLiYWJ9siGJHlH5xBvU5+eFBefxacUXzK/4KFymtLOM3lE9MWvM9IgqYGHNN3T4VR9pBYW1zesJKkEkQSLY5RsvINA3uheD4gbQ4Gmk1a964feKLmJC8hg2NG9mbfN6DKKBNw/MJ1YXwwV557Cvo4R6bwNJhkS+qV1CgS2XA84yNIKGXe17KO0swx/y0+htZlLKuPDAwMHktgf37Ql5+b5+FSbJyIiEYeglHQICg+MGMiJhGAICZo2Zbe07CcpB4jRmjHILvVGj/5fULSdGF82oxOHH7sJGiPAbo4RCuCtr0SXEorWqInP7hu1E2XQk98slZWABSiiEYNCFrVgMURY0MRo6O9roqGvE9BPBuv5AOfVbduJVZEz+EAgCySdNOSb2OAlF6uBaUr9eVK3ZSPHC7wj5A2SMUhPRavQ6RI2GgNdD9ZoNmFOT1Ch6RcHta2PPp4vwtnUAkDpsMD1OnELl6vU0bN1J//NOQ5Flmnbto3bDNnwBNUo/Ji+LrHEjiSvIZUVxGSGfj6D30IyloEcNjPB1RfUfPMf2ikOzkiStBr3VQsrAvjiq69BaLaQNHUjQ4yF1cD8ArKnJNO8ppnn3fuIKcskaO4LG7Xtw1NSz451P6HXaCaQOOdxW9Y+Koii4O+wEfX40eh2m6KhjmlehoqKC0aNHEx0dzd///nf69etHIBBg8eLFXHXVVezbty9c9t577+WSSy7B7/dTUVHBu+++y5QpU7jvvvu4/fbbAejs7GT69OnMmjWL559/nmAwyF133cX06dOpqalBe4QZFW+++SZvvvkmK1as+JftvfHGG7n88svDn4cOHcqll17KJZdcEl6WkJDwH5yRCP9LKIqCw+sgEAqglbTYDLZjnofEYDDw8MMPc9lllxETE/P/ric29tjnszva9/fmzZtJSEjg3XffJSMjg7Vr13LppZciSRJXX301AA6Hg6lTpzJx4kQ2btxIcXExc+fOxWw2c8MNNxyxnXfffTcVFRW8+eab/9bx7d+/H5vNhsfjYeHChVxxxRXk5eUxefLk/98J6yKiZP4KWPQa5l88nLF/X07ndw+AZilji3YzJPsvFDc6qWx109zpQwGG5cRS2+Gmtt3L5ztb+EviAFIc20j2qqP+EwKP8rjmRQZJpVwzOgnTttdQtnlYn3gGg4aOQdewk6jd7yKh2unIlmSW6qcxedlZDFVCXNmoYbRjNbN1G0EGKlZxA6vI145iyaj5fL+jnN11CvmxJqb3TuaL7XX0SY3CLHeCBIMS1A6jPj6HOVE7Ocu3DtHrB40BNHpy+o9Bs3cbN8Utg23zoWYTmuGXQ0wm80s0rCqu5WvjHdyuq2P71jN5WnlT/S+UoUaOY411JplTLqF3YS8aP1nG8fJybiycTHJqf96/dET4nEab1KjgYTmxCHWbeUf+K0ElhkXbR3P9Rzvwh9TO5VlDMxiVH5mCHeH3ye46O9e+vxVJFNj614nYVtzFiFofTzKNef7LMWhEPj1rMHqNiM2gRVEUlu1t4rXi3txoKEWbcALvL9jB9VMKEQQBmyKhB5aXdCAAPZOtRBmPwvTggBf8LjAfWfjvkWzl/tl9MOs0JHx+FrSWIv3pW765dgyaB8sRQn46W2vQXrcVg1bDJ999z43LPeis7cxUlsGBZfBkb4pSB0L7VlhfDPkfQdM+rBXfASAM/hP0PhV2fACZI+mRGgu1IHpaYcbD5MtB+O52goKOPbmX0a/uY0gbDPu/QpYMTC+0UFohccaQDAj6Ma28lwEtIlFGHaeueh9hxoNgOgn5m1s4fv8atubczPRRhyLFJhcl8foPFYQUhVan/z8/pxEiRPiPSTElMyllHEvql/N9wypkZCYmjcOkMWEPOLpZ1BTZCtnfWUpACVLjVEV6GRlXyI1W0HZLxDosbjAbW7fQ7u8gQR9PpiWDA45y2gMd4TJRWhv+YIDd9n2YNCZ2duxBQUEjaAgqQba370RBQURkUvJ4VjeuxSt7VRsdZGrd9ZQ7Dw0uxhvUzlaqKQWdpMMn+xAAvaRHEAT6xvTC0+rBJ/upstcgCiIDYvrSK6oH39Yto81/KGnuttad4b8PivWJ+kSmp07CFXITJIikSGRZMojRRVMU1d1CTVEU0kwpmFsUsjpiMIkxNO9eRUX5DvaPV4X/EQlDEYXIRN8Iv09c5dV07ipGG21Dn5/LqlueI7EglfT+OUy4/VxEo4G4MaoATldARefeEmLj4/DYrBh0Ohy7S7D2yifk8apR6oC3uh5Tagq6+GhV8Nf8Z135gNuDqJGQdLojrk8d0p+g14s1NZk1f38OY1wMgy46h4EXns3ml94h4PZgSU1i8MVzCPn9bHvrY9oPVKC1mAk4XdRt2Ezdhs2YE+NxN7dStnQVRaeeQPW6zQRcbgRJpPCEqUg6HS37SojOTkdrMhDy+fC2dTDkigsoW7aatuIyJIMeW2oynrYOdFYLjupatGYTxrgYXE0tpI8YjLfDTsWKtXg77GoQ3DfLGDD3TII+P9ve/BC/00VsYS6ZYw8NGMYXFeKoURPg+l3u/+h8/p5wNDXTWFwanqUA6mBNUmE+tsRjI0JfeeWVCILAhg0bMJsP2Uf27t2bCy/sbnlrtVpJTlYHtTIzMxk3bhwpKSnceeednHbaafTo0YP9+/fT3t7OvffeS0ZGBgB33XUX/fr1o6qqiry8vP+ovRaLBYvFEv4sSVK3dkX449DqaqOitQJ/6ND9opN0ZMdlE2c+dmL4lClTKC0t5cEHH+Tvf//7kdvW2srVV1/N6tWraWtrIy8vj9tuu42zzz47XOanlji33nory5cv58cff+xWT79+/Zg9ezb33HMPAG+88QZ///vfKS8vJzs7m2uvvZYrr7zyZ9t6tO/vf9wmNzeXdevWsWDBgrBgP3/+fLxeL2+++SZ6vZ4+ffpQXFzM448/zrx5847qgEpiYiLR0dEAXHvttTz11FNs2bIlItj/r5AcZeCEfim0uY9HVjrw9zqN2o1ueiRZsBq0bKpsxySF0FWuJCFhEO14uUfzJhp7OYIAAV00b8T8GUugJ3eEHuX+0RLbDtQx1+9CAR79ciOanVqurb2dUcJuAohokVmoP57jW17Hj5YGKZU19QKztPvQSm5kBOS4AuTWctISojH/eBd3uXdwjeZGijKG8MhiNbqqMMlCcWgmP8pFuCoLWRyU0djrEFuLEQ4mazztdbAm09sKS+eNh86esKwVLEmw8kHQmrnsylJCskL89lbMePHroggg0ajE4FF0vJh0D7fPPZlYs/rSeHVuI1dWvEjo89coTyrhnDe2kB5j5P1LRqCR1E7etZMLYKCGzmfup9wTyzUfbEdWVKFxZt8Uhh+DyOAIEX4rVhU388nmGm6YVkhWnJmsODMjcmOJNeuwdBTDhpcYAbx/9mVc9GkVbn+IbdUdhGSFO7/YzUVjcjhzaAb7jQN5OWsSlZvd7GuoRq8Rmb++CgErOboXKfWaEUSBfQ2d/OWTHSy8Zky4De0uPzHmI3fsfpbXpkLTXrh4KaQOOGKR0wdncPcXOzi+fC1a2Uvr1i+5tbQnZw95nr4bbyXeXc36d25h+EWPMWP8WKKanqQwfjRs/Mn0uPYKiMmB5r3wZN+wLQ4Am9+AzW8CXR6oo65R2xSVDoYoaDtApyGV+c5BlOzyc3+0gqH/GdBnNuKnF3FFxxNccc8uta7SpRg2PMO1isDCzpEIUg2+0pV80tmfE9a/TQIuPE2beJM4ZvZV2zAiN45d90ynpLGT7Hgzty7YQYvTz3kjMhlXmPjvnc8IESIcNTLNGaSZUtGJWvxyAJvWgk7UkWZMCVvBAOx1FGOUDHhCXtyKJ7y9VWvBprHiDLqI0UXTw1bAxtYtKCj4ZB+fVH1BkiGRRm93CwutoKUl1AqAN+hF7gq0OEiMLpo2fzv5llyW1KvPOQ0SGlFiW/tOdrTvJtmQFC7f4G6kT3Qvql21eENeDsamnpalJhXvE9OLPjG9qHXXsaN9N7Iis61jJ45AJ5NSxrO+eRPVbtW6UPoHIb1PdC+mpU4CIAHVfscr+/i65jsGxPZjRcNq+sX0ZmzSKEC18Dg1axZN2mpaOg6wz3cAW1k1yfUwyJ1JUkFRRKyP8LvCWVpJyOXG1qcQQZLQx8fisVkwpCWx/5PvKf54GS1F2RSdM43O3SXIHi8oMp27S/DWNxEzfACaKBvx6amY8rJxFZfhA1AUXKUVREta7LHRNFZWE52ciL+xFef+Mmy9CwE1eW3Q60Vr+uW2oN4OO+sefxmNUc/om65EPIL4r9HrSBnUj90ffoGnrQNvh4PaDVtp2LaL9JGDqVm3mfbSCqrWbCRz9FDypo3nwBKwJMVTveZQcm5ZlpH0ehp37KV5Xylyl6+9Iivs/2JxuFx0VjqFx0+hYsVa4osKsFfVYk6Ip/1AJSGvj/aySkzxsfQ58yQqVqylbtN24nvmM+zKuQCUfPM9zXtUP3u/yw0KOOsbqd24nY7yKkC1v2nYuovcKeMAyJ08hrThg/C2t6O3Wdn6xgcYomxkjB6KJemPGR3taGqmdueew5YHfX51ed9eR120b2tr49tvv+X+++/vJuYd5KAQ9s/485//zH333ccXX3zBX/7yF3r06EF8fDyvvfYat912G6FQiNdee43evXuTlZV1VNsf4Y9Lq6uN4qbiw5b7Q36Km4opTCw8ZqK9JEk88MADnHPOOVx77bWkp6cfVsbr9TJ48GBuvvlmbDYbixYt4rzzziM3N5fhww+f7ThnzhweeughDhw4EB7U2r17Nzt37uSTTz4B4JVXXuGuu+7i2WefZeDAgWzdupVLLrkEs9nMBRdccFidx+L+PhJ2u73bbIF169Yxfvx49Hp9eNn06dO59dZbqaioICcn51/u999FURQWL15MdXX1Ec/vv0tEsP+V0Eoiz54zCBgEXMhf3t/CymJ1NP/eWb3ZVNnOtcJHXK5dyB7TyTwu5XO6ZhUBReKpmFt4pqEPQYdIss1Pg8PLyQsArFRLc+ibncr2iiJ6+oI84T+Z6IwMPmwrwOdxUlKXyol6gYA+ji3uLO7QvMNdwbkkCu0MEkvxaGMwU8zg9sUISghEuE15nT/vyyLVGMTj8VDcCLFmHbtcueg6ZbZVt/PFNoUK/60MFEqYPSCVPHM87PwEUgbgjcrBYE2Ck5/D29FIx/51WLIHkx5j4paZPZm1/SEM/nbqq/PBW8QWwxWIgsJjpxZB535otrNL25cPKywMkwvpNGXw5tfF1Nu9NDq8uAMhbNJPOnqxOZxoeIuKdnW6ZEqUgfcuGREW/iNE+L3w7PelbKhoIy3GyM0zemLRa/jg0pHqSkWBCbeC3srI/r34MC6NDRVtmHUaFu9Wk6i6/SEKk6xsuWMqkx5bQUWLi3EF8YzNT+CtdZUoCuz3qDY1J/RJZmtVB2MLDs1QOfGZH9hZa2fO8Ezun933Z9u5r8HBoh31zB2VTZxFDwEPKCEIqVOZPf4QJz7zA81OH8/PGcRo/1p8a19l04GZfC/1ZrpmM9ofn+Zlbx3ttTYkvRW8kJikjrRbKpYw9cD9cOAfduxpV/d1kE71GUtyf2jYDnSNMDbvh9WPU5Z5CpoNL5Cx4wM1Z4bpOlaEYrhQsxiDuwHKV8PIKyGpL+RNUOtqKQGvg5bMGbx3wMBboRmsk3vT23Y+60tbWeC7kcu0XzNS2kdT2qlUtbqpt3sYnhuHUSfRLyOa2g4P729QI3eX7Glk4dVj6Jse9e/+O0SIEOEoEKWzcWb2KeHPz+97Ba/sQ0BgYGx/trRtC6+L08VS46kLf84yZ1LpqqIz4MQoGahwVVHhUkUhvajHojXjDDhp74pez7PkcKArKt7RNRAQrY2moyvyXi/q8MlqdJZFa6HN306991AS7CAhSh1l6AQtfiVAi68VSZAIKSFqPfV0Bpzs6dgbLj8xaSw1rjp8so8scwZaUUeaKZU0UyrFjgPUuevpYSsk05yOiEh1pSrYu+VDz1GDqGda6iQqnFXYtFZafW14ZfV9y6Ix8339SgJKgNLOsrBgfxBtvJUlyXtAFBBHCgz1ZjN+0PFhC5AIEX4PKLJM5y41yMmQkog+MQ5ttI2ESer90OvcGOxlteTMHIWlIAfJZEIJhfA2NBPyqENrSkjGlJmKLi6a5mVrETQSxvQUBJ06y1EA/G4PsiyjSYwDuxNdbDQAIa+Puq+/x9VuJ27U4LClzZHw1jURdLkx52chh2QUOYQcDIX9fDvrGtn00jvorWYGX3YeNes207y3GGeXZ74iy+xf+B1yIIi9qg5Ro0EOBjEnqAJJ4/Y9tJdW0F5aobZbq0UJBPC0tCF1HYv8E1sbY1w0njY7yDKiTkvV2k3YUpOxpiVTtuwHNQEQoLWYCDjV6Hd3SxvOhibie+bTUVFNfM98ANrLKrGmJqOPtuHrcNA1OQhDbDSO6trwPgVRxJKShL2qFo1BjzkxHr3FhN5iom7T9nBy3MYde5lw95EtE37PKIpCY3HpPy3TWHwAa0L8UY1OLS0tRVEUevbs+a8L/wyxsbEkJiaGfeOtVisrVqxg1qxZYX/uwsJCFi9ejOY/nJ0SIQKo90tFa8U/LVPRWkGsKeaY2ePMnj2bAQMGcNddd/Haa68dtj4tLY0bb7wx/Pmaa67h22+/5eOPPz6ioNynTx/69evHe++9xx133AGokepDhw6lsFAdJL7vvvt47LHHOOUU9f05JyeHPXv28NJLLx1RsD8W9/c/sm7dOj766CMWLVoUXtbQ0EB2dna3cklJSeF1R1OwPzhY4vP5kGWZe++9l3Hjxv3H9UaeVL8R+YlWBOo5bUg65wzLZNneRlpr01FCAj17D2J1RRYvBk+kRE7DGz+NYH0DApARY6TBob7c9Uy28lrD8QypLGdfr7dxDbmCzXuiaam1siQ0iLqQhuuTtyN2yFi99ZwqqeLVU6FT+bjwUfqXnY65YT2u+P681ZDJdM028qhmtLgHp9vJl/obiTc4CGisNPr1vKo5kQ+C4znr5R+Z3juZNXJf1tCXE/NtagQt4BPN9HK/xEkD0vnT6BxWFjt4vPpqxhrieQewGbTMv/5kbluwE73Hz8xxOTjrZmMMOtjaomPo5+MRgh5uCTxEZSiTczX3YglKDNNpsBm0XD4+F5tBq1psbH0HcsZBQg8ePXMgF7y+kcIkK2/8aWjYMidChN8T100t4LMttZwzLPPwlYIAE24Jf+ybHkVqtIEh9y9FUeCFcwcxpehQNGab04+swKCsGP6+eB8Hc6/EmXWMyI3j+qmF5CUcmub55bZadtaq3sq17aqYoygKbn/oUJ6IgBd+fI6Pd9h4rTqVoKxw84yecMkycLdBrPql2OkNcKDZiQI8tbSYBPujFHq287e8HNpTroKNF2LzqqJYjOIArwPfeV+RkzcWgj6IK4CkPiiNuzjs1Ufs/rWmICAoCoy6DtY+CVoD1GyAmg3k8gGhg7OEgHnuJ5mnh1o5ltsCF5LG6VxVsgRmPAg5Y9VCb82CzlriEbjcHIU0eB6N1Xom98/nuMESq3OMTPtOnSqIoYghL4ymxenj1EFp/P20/kiiQFq0kftP7sMTS4vxBkJEm46C7VCECBGOCnH6OBq9jUxMHkemOYMyZznuoIeQEiLTkhEW7EUEpK7Ol4iARWvBE/IiAFG6KDr8dkyKkSxLJgNi+rK+ZRPOoCu8nyhdFM2+lrBYD6hJbM2ZlLuqqHHVYNPYcAQdYfsdAGdIrUP1tQ+ioKAXdbT62vi48jP8XYK/hERIkfmseiEABslAUA4yILYPg+MGsq1tB/aAA2dQTY6dbk7l1MxZ/NC0DpNkpHdUT8qclcTpY9netotlDSu6DShoBS0tvlaSjUm0+lqZmqJG4DsCnZQ6yugd3ZMYXTQjEoeypnk9vZP6MjRlLEIksj7C7wxBFLH160nI5UEXf7iHsDE+mklPHxJ+jWlJeGob6NxVjKDVkDB5NBqrGnGoyDLIMgoiVVISxj27OBiL2GPsSCSzkfih/cMWOQCOnfvQiBLWmGg12SogB0OAEo6a97R1ULtpG9oOJzqdDm2UFVNiHKO6IuulLi9vV3MrIZ8Pt89H1Q8bqVy5FoCE3j0QBGjatb+r7q6ErZLI+LtuQGs0EPT6iCvMpXHnHvyd6nNKCRwS57VmE6GuHB0AgkZCZzYTnZNJ/cbtaI0GHFW1OKoOiesHOSjWHyQUCuFt62DghWdjjI3G1dzK5pffDa+3pCRhTU1GDgaxJCUw6OJzqN+6i6rV61EUhZA/wMYX3kQQRXqefBxpQwcAqm+/o7aBuk3b0FlNP3PFf98c9Kz/ZwR9Ptwddswx0UdtvwcHjf5TUVNRlHAdHo+HCy+8kNGjR/P+++8TCoV49NFHmTlzJhs3bsRoNFJVVUWvXr3C2weDQQKBQDerm3PPPZcXX3zxP2pXhN8nDq+jmw3OkfCH/Di8DqKMxy446+GHH2bSpElH9GUPhUI89NBDfPjhh9TW1uLz+fD5fEeMdD/InDlzeP3117njjjtQFIX333+f6667DoDm5maqq6u56KKLuuVrCAaDREUd+RiPxf39U3bv3s2sWbO48847mTp1ard1/1j+H9uyevVqjjvuuPB6v9+Poijh2QQAt912G7fddts/bdvq1auxWq34fD42bNjA1VdfTWxsLFdcccW/d5D/QESw/424dnIBfxqVzTUfbOXWBTsZV5DAfcVjWR4/laVjJvNMTANvrUtmTWkr2j2NWHDjxsDeBgc9ki34AjKyopASpedqaRVSyWJsWj0D960gWu7gFq2DgVl+0kNqpJcgwPJQf76Rh1GjJPD+Ljd3pPfC1LIdR0stTwu38k30hXzWcxmv7ZVQGkV0BNEKIQzBVqwiPCS+xNpQT5o1qWTHqS8xogBzPyzne0sURtlFrT4X2S3y+bY61pe3MTo/Hotew5if+MinRhvZWt2B3RPAH1S49M9vcO6r61n7bjE/JvdEcjfS4FVvdm9QxuuUWVHczJ57Zxw6gRtfhe9up9rQA/GylVz45iZc/hAlTZ1Em3Tc8NF2qtvdvHze4Ih4H+F3w6i8eEbl/fKcDDEmHbMHptHpDTIyNw656wuq1enD4VU7UU8uLQmL3tN7JYEAi3bWo9eK3RKjftsVpd8rxcrjZ6oJua5+fyuLdzXwxp+GMrYgAXZ9Asvu5QZjCjuz32Bmny5LGkOU+tNFos3AU2cN4P0N1YwrjOfGxadxgpTOWUIVtu3XoSroCsQVEGw9QL0cS+tHd5E38QKsy29XRfuLlhJ4ZSo6xXcwbl797Xd2PwmKAo07oGU/XPgdfHGVap0jB1AQkQQZty0Po6MMoSscK01sY1OgBz0bN8LG2/FJZvZfsJ1+mQmQ3Ac6awEFncHMVZ6XoeZdlG03I0y8jVNGFUHNybDnc/yb3qHDrXrUfrqlljOGZIRtuuaMyOKuL3cRlOGC1zdQlGrjvll9IjODIkT4jTkz5xSaPM0srV9BSJGJ08XS4S+nX3RvRiQMBUWgpPMAzb5mypyVgOrz7g16MYsmjFrVksIoGnAH3RQ7SonRxlDpUmfVxOiiMWtMNLibDtt3SAnR7GtDK2gJKAGCivqcLorqQUgJUeasCAvyIgKhrmeWX1bL6UQdep0ej9dLiBCrmtYgdD3hZUUmqATZ1LqNzoCLoBzAIBlIMR4ayI3Vx4StexKNiZyWNYsXi19nV8cejJKhm09/QAlgDwSI1cVweY+Lwsu/r1tBmauSvfZ9HJc6jTXN6wFwh9z4Qn6+rP6aWH1M2GInQoTfA+bcIwRS/BN0sdHo4mPQxcYgGvRhESLQoSa4RpZZvrYds5TEzMQaTNnpeGsbCHa68NQ2YMpMC9fla2pDURQUm4XkgX0J+f2se/wl5GCIEdddgs5i5sCSlTRs3UV8bhaZ/XqjjVZnUxqibN3aldSviM66BlyNLXja2tSFgmorE5K77n9FwRgXi6e1DUIyPz75CtkTRrH/y8UYY6LoderxbHvzI/4Rb7u922clGMJeWYOiKPQ640T2ff4tgkZCCR56zmjNJgJH8JWv37SdtpJyajduY9iVc9FZzOisFvyd6jugJTkRR3UtrqYW0oYNIDYvG1N8LC17S3C3tFG5ch0o6syGvQu+JnVIfwRBQNJpSR7Qm5p1m3A3t7HppXewpaVQcPzkY5488r+FfyXW/7vlfikFBQUIgsDevXs5+eST/191tLa20tzcHI6afe+996ioqGDdunWIXTO73nvvPWJiYvjiiy8466yzSE1NZdu2beE6FixYwKeffsr8+fPDy2y27vdJhAgHCYQC/7rQv1Hu/8u4ceOYPn06t912G3Pnzu227rHHHuOJJ57gySefpG/fvpjNZq677jr8/p+/h8855xxuueUWtmzZgsfjobq6mrPOOgtQLc5AtcX5xwh96SeDyT/lWNzfB9mzZw+TJk3ikksu4a9//Wu3dcnJyTQ0NHRb1tSkvucejLQfMmRIt2fA008/TW1tLQ8//HB42S9JypuTkxO29unduzfr16/n/vvvjwj2/8uUtbhYsb8ZUYDvrh/Ht7sb6HAH2FjRxnd7GllT2kqfNBtj9KXcUHcjG+nNOd6b2d9wSJCKMWkZd9HfYEM6DL2I9ytTOKXzPU4MfI3QGJ4NyOpQby4P3oBPUS+5AOwZ9gDZi85mg9yD4/um0Ojwkr9iFPmJZk4dGsvNZffzku9mJI0EqUNxa60cZxnCiYOyePdHtZOqKDIPa1+mnWiMN2wlVRfN07sbeHpZMQlWA59sVqdXT+qpejSvKW3hxZUH6JlkYUt1Byf1TwUgPcaIIErsmvERr33yBUFETDpJjd7VSfz1hF6w9G7kpv0stycxqfENAJY7M3jvrY14AyHShSbmWdZx/mN2fmi2IAPFjU6G5Rz7rNcRDhGS1f86SfxjvNj+t7K9uoPtNR08fGo/XL4gUx5fhUYUWDJvHN6gjCgISIJ6nUx6De9fMpzmTj8ri5vomWxl1oBUWPUoVK+HWc9x28wiDBqJBVtrufzdLXx02UiaHT6CskKbq+sLP2c8ZI/FlD+Zj8aMpKrV3c3zXlEUfEEZg1bipAFpnDQgje92N7BDyaNGX8TFjhvA7zh0EK0laIAoXGT4NlP8bRNWujqzQQ+vDF3EV6vWc+6EAZwzOIHHnn6c3hzgOGljuIpw/ypjOKQOgtaSrgUiQlfEqslVDShq8uxBFyBHZfBg+pn0sblxvPgMNl8DDa+fx1PZ9/HszIcwtpWhtJZQ5zeRum0+AvDS6ioWbFtJsk2P6D+PV4YksC7Yk+CPClpJQK8RyY7rHsmQYDVQb/dS0eqirMXF93ubGJkXx+tzhx7V/4UIESL8e1S4qmjwNkIHjEkaSbu/g1ZfO50BJ7vte+gMOEkzpuAIdNLZFaF+8LfLp4pLOeYs+sf2ocpVQ7+Y3qxvVZ9L7f4O2v0dP7tvWQlRYMtlj30/fjlAv5g+1LsbaPa1kGlORytoafY04wg5idPHohd1xOnjMEh6hsQP4r2y7kJZgj6ec/POxBlwsc9RzNbW7fhDfhq8TRgkA+lmVfjb1LqValctFo0FX8hHvjUHURCxaiy4Q26OT5vOJ1VfAIQj/q0aC0PiB/Jl9dcYJQNljnK8QS8GRUuTp5n1LYeexc3eVj6u+IxWfxsNnkampkz8wwhg/y38XGRahF8XT20DCAJxY4bia26j8evl6JPiiR0xMPzOYhetBJHISJZImDwGf1MrSiBA0OlGGxPFzvc/R2PQ03PWDGKG96fk2+U0rN+EH5mM0cMIuL3IoZAaBQ8kD+iDp7WdjImjiC3IJWDvRJAkhC6LUaVLgBFEkYLj1MG0fV3e8tbUFDrrGvjplEZPa1v4b5/dwf4vvgXA2+EgvmcBltRknHUNDLjwbNwtrRR/+d3Pno/k/r3xO93drHIOEvSqs8qNcTFEZWcSX5iDKS6GgMdLW2kF3rYO1j76ImnDB9Lz5BnsXfA1AZebtrIK/Hb1fXHr6++T2KcIZ0MT1rQUonMy8XbYoQEQRUxx3a0qbGkp6sujotBRXkVHeRW1G7eSO3ksWeNG/OLr/L+KRv/LAkd+ablfSmxsLNOnT+e5557j2muvPSz6t6Oj41/6XD/11FOIohgWBN1uN6Iodru+Bz8fFB01Gg35+fnh9YmJiRiNxm7LIkT4ObTSL5sl/UvL/Sc89NBDDBgwIGxbc5DVq1cza9Yszj33XEAV3EtKSigqKvrZutLT0xk3bhzz58/H4/EwZcqUsMCdlJREWloaZWVlzJkz5xe17Vjc36BG1k+aNIkLLriA+++//7BtRo4cyW233Ybf70fXlVz9u+++IzU1NWyV84/3e2xsLA6H4z9+BkiShMfj+dcF/wURwf43pH9GNA+d0pdYs478RCuJVgMbK9r5dHMNnkCIk8S1/L1zPluFKWgJkmvycvOEHmyqaGfZPnVkaG7gI4QPt9F5/IuI0T1YZDieEZ5VJMlqFIMAfBQcx8tR12H2hLAKYNBpEAWB1fZEFg7+hrfWViJtqwsLraVNLhQFHK0Q0AcQBJH2Gc9z3Es7MOtbGZafxN0n9eaUQekEPZ2M/GgXQlCh5tHRGNN680TblTh8MqXNbUiiQEhW2NfQSUGSlfc2VLG6pIUzh2Tw4eWHvE6b7S5CsoJz19fMD95IR2Ih/otX89a6Ck7ol0pRkgX+9gyiHEQf6o3QNXiXL9Rxsm4jE6+5BOmLK8iv/wolVMsqruQZw0sMW3gHnPtp2IojwrHhqx11fLOzHotew8riZkRB4Nvrx6n2RRF+E656bws17R7MOg1jC+Jx+gLkUIfrw3dJm3QlS+eNp6LVxdw3NuIN+rAatBz/9A8Eu54Dy/c1M37vs6ovfPkq0vuexuxBaXy+rRZLlwXOq3OHUNHiol96tLrT6AyY+xUAJSX7ePDNBewxD2ftLZMQRYGr39/Kd7sbePvC4YzMUyPNp/VO5qyhGXywsZp3Y8/gvPiFBAN+tO4miMtDadqDXgnyVnAKn4fGMEXcwtD8RIZljuCqTLio4kZ0P95BR8bHtA68ilcbnQyflUyszYbyzCDw2QEB4cQnYeE1h05QXH6XeK9AyA9aI/Q9HSbcgvjpJQz22iF3AnqfOiofDAXRFn+F1PQ+uBsRgFRPCX5Rj2xJ5e8tJ6L1uyluVEW7/SfdwZgUG/clVXLHF7sJhEL8WN7CrAHp6gwBn5OHT+3L/V/vY1KPBD7ZUktzp4/l+5r4ZHM1pw3OOMb/IREiRPg5BsSoOTpyLFkkGOJxhzy0+dupdtUQVILIyLT77QQVVQzLMKWRYc6g1HGAJl8zAHWeBvytAWakTsYn+zBJRjwhL0o4lILwtnWeBiySGY/sJdGQSJQuimxzJhWuKna07wqXrXLVYJJMuEPqoIBB1DM0fjBfVn9NpjkdZ8DJWTmn0RnoZEfbLnbZ99Lka+aZvS+SYc6g2dtMZ9AZHlzwhrx4gh6MGiMbWzbjCXk5Lm0qRVE9AFXgdQZc+BU/uzrUBIT51lyGxQ/mQGc5g+MG0OxtobSzTG2gAoiQ64ih1NpElDaKkzNO4JvaJdgD6nupRhYZ29GT6m07yOjfDyEyuH/MUBSFFrcTTzCIRhCw+7xYdHoyog63bYnw6xB0uenYuAMA3bRxyIEAKAptFdXUV1RRcMIU4sYPx9LUytlyCaLRTNBhxb51d7gOe1k1jdvVz3lTx6GPj8XQ5SEv6fVojQZGXHcJiixjiFZnN8b3yCO+h5o8sHH9VlwlFcT0zCdmSF/kYJCW79eiyAoJk0YhdnnNFx4/hfbyKjpr67FlpSP7/HjaO5B0OnQWM876xsOOL2+a6tfb/7xTWff4y+x89xOG/fliorMz0EdHkT99Au72DrZ22dforBYyRw9l9YNPh+tI6N2D5t1qTgAlJCPpdeTPmIghykbxoqVkjRuhJpDtmjUa9Hqp+mEDoiQR6ooYPSjWW1KScNY30lFRhc/eiae1jUl/uwW/y82eT75SI+6bWw/NcPB4ESWR3MljadlXQkxeNpWrfiTk81O56keistKJzjo8qePvCVN0FBq97p9G0Gv0ekzRR9/e4/nnn2fUqFEMGzaMe++9l379+hEMBlmyZAkvvPACe/ceytHS2dlJQ0MDgUCA8vJy3n33XV599VUefPDBsNA2depUbrrpJq666iquueYaZFnmoYceQqPRMHHixKPe/gh/PGwGGzpJ909tcXSSDpvh2M/S6Nu3L3PmzOGZZ57ptjw/P59PP/2UtWvXEhMTw+OPP05DQ8M/FexBtcW5++678fv9PPHEE93W3X333Vx77bXYbDaOO+44fD4fmzZtor29nXnz5h2xvqN9f+/evZuJEycybdo05s2bF46klySJhAQ1KfY555zDPffcw9y5c7ntttsoKSnhgQce4M477zzqAQxNTU14vd6wJc4777zDaaed9h/XGxHsf2PO+okX9bxphaTHGDl3eAZJbRtoCTZhKGsn4HEw3fcQJ48cxtvrKqm3e8P2D5OFTQit5Tz+2lvszvBjrl+PR6Owf9TDhNa9AIrMA8E5XKbfyHOteYgGG1EaEW8gxFPLSki06ilItNDq9OF1dzJO3MEa+nPXicO45G0PE3yPMzA5jpsUE+1uP60uPxe9tYlv/jyWYXE+MCWjaE0QcJGu1BOsbaHBcw4WaxTpMUYm90ykvMVNq8vHV9vruGpCPik2A+ePzGZvvQNBgJ5LL+TpylWcLtyFU0giKGjZZTdw/xsbAIEzszy0vDUXY3x/9LKb+owbWRFoYIK5ilHrX2Ck8jEkzeP6tiHMkUv4IqR6TY9UtkKbQ00wGRHsjxmfb63h+g+387DmJU6RVvF48DReUU7B4w9FBPvfkNkD01ixv5nBWTHsrnNw6Zgsemx5i+Tyb2hY1Eb25V+QHW/mRusSLve/Rf3mh5jZdxjbqzuobneTl2CGolegbhsUnQjA2IIENv11KjaD+tVhM2gPifX/QOa3c3ldu5f7xBsQBDVaq67DQyCkUNbiZEdNByf2TyU12sj4wgQWbK1lhLINoWUfnwQnYjz1K04usiI8N5yAz8+BvHmM99UR6/Ux4Jzrw/vptLcSr8gYPjmXB+/qShQbaABT9qEvYoMNXpkIhp+0NXME9Jip+toLGjVZbUsp7PoUDiyFA8ugdKlaVmtCmv0K9y+bg87RCOZEgn437pDEh/3e5PENTnSSyP2z+3DLgp2cJKwm7sOH+Yt0IZMmTmNEbiwOT5BpvZI548V13NV4Nb2ESsZdtJgx147l5gU7MGhE8hPNlDa5uPHjHSzd08jc0TmM6LLQiRAhwq+HTtIxLH5w+POM1Ck0eBrVxK3J41nesApn0IVGUJ+Fg2MG8EXt193EeJ/so9Zdx2ul71BgycMd8qARNPSK6smOjkMivF/2E1JCiKKISTTR4GmkwlVJijEZi8aCP+jHj9oRtGltjE4YwTd1arRqrjUHR6ATGVmdFVDZxOWFFwIKfWJ6s8uudn4CSpBat5p81qqxYNaYsemsCAgc6KzAqjUzPXUy9Z5G8q15XQMDRj6u/By/ou7brFGjoSqd1TR5m0k1pbC1dQebWreSoI9HI0pEaaOIF6OpMtaheMCn+IjWReGX/QgIKCgYQloMPgmX344cCiGJka7IsUBRFOo7HXT4ukd3+UOhn9kiwq+BZDCgT4oHQUDQa5G9Pkz52ZR8uhC/x4s5MZ7sSaMRNRp2f/4Nbkcng3Iy0MbYCPn8KL4AUVlpFJ44DY1Bj86i3pf50yeSMWooeqvqu23sSkj7j/gcnez64hsUBfoftPGRZUI+PygKjpo6HHWNpI8YhKjREJuXhae1HXdjM0GvD0GSGH7t+fg7nWx+5V10ZjMpg/vRWlKGJSWRrHEjATVSX+7yr9/2+oeMuuEyGnfuQwmF0OoORWZrTUaW3f4QGoM+vCy5f29cLW24G5sRtVpCPj/OhmZq12/BXlnDnk8XhQf6LClJJA/sQ+k33xMKhdBH2/A7nIhaDcmD+lK7bjP6aCtZ40ZQ/OUSEATWPPI8WpOJ/BkTcDW1EJWRhs/uYP0zrxNwe9BbLYy66QrShg9k53ufYYyNJhQI4Hc42fTi26QPH0j2xNGH2Qn9XhAEgaTCfGp37vnZMkmFecdktk5OTg5btmzh/vvv54YbbqC+vp6EhAQGDx7MCy+80K3snXfeyZ133olOpyM5OZkRI0awbNmybkJ8z549WbhwIffccw8jR45EFEUGDhzIt99+S0pKylFvf4Q/HoIgkB2XTXFT8c+WyY7L/tVmt91333189FH3mZZ33HEH5eXlTJ8+HZPJxKWXXsrJJ5+M3W7/mVpUTj/9dK655hokSTrMxubiiy/GZDLxyCOP8Je//AWz2Uzfvn3DPvdH4mjf3x9//DHNzc3Mnz+/m4VVVlZWODFtVFQUS5Ys4aqrrmLIkCHExMQwb968nx1U+E/o0UMNdtFoNGRkZHDZZZdx9913/8f1CspB1/1fEYfDQVRUFHa7PeIJdiTWPgPf/ZUfGMQn/hFs1A2h1mvgr8cXsXB7Hdtr7BQkWihpcpIn1HJdfhM3lPTBj5bXo15jkm8Zy5QhTBY2AeBKH4u5ZjUOUxYD2x9UkyyiWmFEGbVhO4u3dA8xXtzB+uiZDL/ufb7ZWc9Ty0q4bWYR4woT2Flj556Fu3H6giwYug/Tkptg2KUw4goOVNWAo5b09EwWtGQwMjeO7Hj1JfLCNzfyfdeMgMvG5XLraBuNfj1jntwAwAbrTcR4a7hGvI2brrqG+z/fyHclDkBEAR7K28VZtQ/gRY8BHz/En0H/i5/HGuyAFQ9Aj5nsNg3jivlbqGl30xUgzDBjHfNnx6Lte8pPfDEiHE28gRC97vwWs+Jmp+FiAHyKht2xU9Gm9aPPnscQknrzf+ydd3gVVfrHPzO31/ReIZCEACH0KkWaIFgQxS66YsHed/3Zde1t7V2xrNgQOyK9904gJKT3fnuf+f0xMRgBRRddde/nefI8ycyZc86U3Hvme97zfZmzAkQRf1Ci3e0n3qr/73b8fwhZlhlw+4d8rf07oiCwVepB2kk30HeEklzl63/OZGrgOypy/kbGOU92HvMfDyw+vRJ5/5f4z/0UXcYgANpcfkqbXSzYVs17GyuZ2jeRmyblHEpuu/EV2hc/zO2e8xhz+hwEQeDzDYX8Y3o/emckwuM54KxXEsvmTIUR17Jn+xr6bLtH8a+f/gx8cR0IIq2Xb+fVZfsYnSwzPNYLH88GYyzEZkPlOsg/C2a8CnW7oHgxHFyubEvuD6+MAVlZKotKS9CSyk5/Kv3dqzs+lQBBBFlihu8edpCNJEOsScv5w9IZsmo2I1SFPBc8lR09r+W1iwaxsbSFNSVNvLGmnE+5kZ5iLVdpH0DTbQSf7VQSgo/MimFTeSshSUaSoV9qBJ9dPeo/uw//IeHv6/8dwvf651lQ+QXlHb71P+TMjNNYWPUVQSmIQWXAHXIjIhCji6HJ1wyAWlAR/IEHvFltIiAF8Uk+orVRtPrbOvfpBC0++fBoraExAxmZMJxV9Wup8dRxcupkLGoz+23FrG3aQJIhARmZA/YSpqdOwaw20eRrxhvykWJMotnbSq/IbLSiFkmWeL7oVQId/venp08nUmOl3tPIN7XfYVGbcQZdyMjE6+I4p9tMXih6lUDHqgKDyoAsy3glb2f/CqL6MjbxBOo9jexpL2RAdD42v4MvaxYhy3Jn4ty+QhYjk0b8JhGaYRQcPh9V9rbDthvUGkRBwBXwE6U3kGRR7kGowx5CJYaTAv9e+FvaqPxqGRX7ijBYzGgNenpfOBNDTDRBl4e1jz5PwOej77kzSMhXIiH/0/FZyO9n03NvIksSg6++BE2HUB50ukGW2fTy27ibW+l58gQS8nt1itK73ltA4579gMzwG6+gau1mXE3N5J83E7/TyfonX+5MJpQ5bgTJgwrY9uq7eNvtIAr0nDKe4q+WYEqII++Mkyldupq04QOp21FIw449xOZlYyuvIuD20OfsU4nv04v2iioa9xbhamgma/JYWkvKKF28svNcVDptRyLbAAGn6/CT/T66DSVqP+D2KJH5HWRPm0j6qCHUbN6Bq7GZytVKvg1Ro0ZjNBLZLY2GHcpKhsT+fajfvgdBpUIOhUgfPYzsqeN/9X04HvzW39n2xiYaDpR0ibRX63QkZGdhjY877u2FCfNnpsXVSnlLeZdIe61KS2ZMJjGmsD1zmF9POKzlj0hUJggqmvQZLPSOQuWDGf1TOKVfMqlRRm76cAeBjoF1lZjKU+3Z+FEGKvfaTyY9L5XXqwdR70/gjAGpmBq2A2ByV2OQ3QwUS/Cmj+KSE3pysMnJ+xsr0GlUDPNVgw8GRyrLCKf0TWJK30Ozz31TI/j4yg4bm3XKck5aSiC6O1nR3TvLnXPoVz7bUUNNmxu1KCAIMMxQDf86G6s5DYPmEXxBicnt/+D6QTqenXkmdm+A807oxda6nTQ7/Vw4PIOsvkO45x0bWp2Obs7tvFwznMDTq1l16zikKU/w1tpy/vn1GuItOgwaFYMzo4kyaThv6HA0meEPyN8SvUbF+cMyqG33UK06h+Ti+QAMaPuGQOu3CIIEdTsJtlVy8+IW8g68SENAz9iL7laSlIb5zWl0+DALfmKwIQtqxt34HgZfIyz6B2jNjLzyeZZ+8hSqwnVs/fxTZvRPQYjKBHP8Uev8rrCBZfsbuWVyDtEmLf6gxC0f78SgUTFrUBrf7WtgzqR/EXX6i+h+cFyUSctAkxaHN8DWijZanH7GP7GSB07rw/nDMqBsJZGhFh4cYCdycDq3P/M6r7fcSv2nA+H6xZB7MhR9DY462P8l7PuCPnM34C98Dq23mcCSB9AkF4Co5osiDy/uCrGo1sTylLeVDnhtymcWUFNaSNLCqxBDPtj9EUR3hy+uhYn3wcx58NEFyjEhP+r2UgZSigctho5IVwrO57598WzzZpObYKGloYrpmnLmFt0F6hLWmyfxifNUrs1PpLTJydmvbECLn6fS1xLs/QATFts46I3Hsr+RmQNTKGlw8X8n55GXbKWixcWNH+5ke2UbN3ywA0mWmdIniZP6JB735yNMmDDHTpQ2kkoqEQWRoBzCqrbS09qdFGMyfSLz2NtW2JkUNkoT1SnWAwTlEDnWnhTZizGo9AyPG8qK+lUAOALKuEuFim7mDIbFDWZTy1bq3PWY1CbFTx/Qq5SEtqMTR3bpV6/IbHpFKp6lCyo+B6DN105PaxZJxkOfGylGJW+QLMusb9qEVtQSkkJoRS3Vrlo+bfmCeH0cImKnbc7k5An0jszF7rczLnE0i+uWATApaRy1nnq2t+5CI6jxSF52tO3GJ/mZnDyeJEMCC6u+pMxZgV7UIYgCKYZkDBoDI2KHYtQYj+OdCfNjDBoNJo0WrUqN3ech1BGf5Qke8glv83qINZqpsrXiDYUQgR4x8ajDov3vQsjrI+j3I8syok7LwOsupaW4lPJVG7CmJjPoigspXvgNFYuWY7CYaS9rIHlk/k96h5evWEfIH6D7xNEIgkDQ5ca2oxB9UjwOux2f3cmwGy47TPRXm5X/x8T+fWjcW0TFqg2ULFrG0Gv+hjkxntbiUpBlck+fgj4yguoNWwEoWbSMnidPICIjFU9rO36Hk/Ll66jdsovhN1zGyvueBEmmeX8JuggL0VkZlC1fS0vRQfSRVqVewFHTQMCtrAYpW7me9soabJW1OKprMURHsvXltxl0+YV4Wtqp27pTuX4+v7Iy4EdkjBlG5ZpNyCEJfXQk3tZ2Qn4/jpo6BFFEa7UgSxJxednUbN7Bvk++AiAqK5OIjFTKl63BZ7MT9HiJ6p6BSqcl55TJ9Jl1Kk37iin+agmVqzYgqlR4WtroPuEETPGx/+HT8MfDGh+HJS4Wd7uNoM+PWqfFGBkRzoMRJswRiDFFE22Mwu61EwgF0Kg0WPXW8P9LmP+YsGD/R6TXdLijkckhOOfLffRKsnDh8EwAiuoduPwhepv1VLd6UIsiFS1uUiP1DMiIot4ejfmU8zhxVy3/91UMTYaeXDs9RP2L0ykLxbNVPxcdfhpSbyGht2Id883uenbX2FjZ7wYmHXwQsXw1FC+BnhOO3scsxeKCg8tZv+cAm+oFLh/THb2ma2boRxcVUdOuDMDmjs1iXK4PVgu0uILYvUEm9Iqn3m4md0gfAC57ewsbSls5d2g6Ll+Qq0/sQUmjk7c8IxE88Mw5l1Lz4U7igdlvbmJLeRt/O0Gxu2ly+JABZ1MVBw44WbqvkS+vHUV6dNekFmGOL/edqtw7f3AgO55tYoBtCTbZQISg3Pc22czyCjUzCq9ltGoPqKFsRyQk3wCmsN3Hb43NE6BajuY06VFevXAo1a0iQz4/B6FdiRKNiMshyV1EnmonpSUvwLZNEJcLV208ap0Pfr2PsmYXPePNXDKqG+UtLj7bUQvA7hob6fXfUVtWTtSFTx3xHo/N0DO2x6fMr0tkI33QqJTBTPOIO9nenkhkzhwGA/8wfYGuNUhaoFw5cNqTcPITcHAZfHeXIsCbYtktZDOQZsVv/7ZiXL4gNUuKGZsTx03Ra4GOySEpwCahL/HSbjKde2BHhy2FIIKzseOC1cCyB5TiMogCyB0JagORWdiDMp6ILDJPeYZE+79Z5b6OzZozObnHLvTVa8CrhHRZ/Y2UefS88fkycjUNaFR9mCmsYWrjawS8izkYegiA207K5fyOz3dCAWivIjUqleRIPVsrYEdVO2XNLg40OMOCfZgw/2XGJZ7AmISRtPttrG/aRH5Ub9JMip9xk7cJvxygmymDMlcFtqAdtaAiVheLRtRg1pg4KXkCfilAhbOSCK2VgTED2NC8qdMLP0SIgph84g1xTEwax/NFr+IIOulu7kaps4w1TevpE5WHTnV0wS7NlEq5q5IDjhK0Ki1qQUWfqLwuZWwBOxubt3T+fXLqZFp8ShLJRq/iwZ9l7oZfCtDdkokv5OPt0vcJSiH6RvZGK2robulGvbeRoBwkTh9LP1MaG5o3E5SCvFr8FhpRQ5xOEbG8kg+AWncdbslDnauO87PORhTCwvBvhVoUyYhUglZijCZKWpsOKyMA7V433g6bHAlo93qIMRjDIsPvgOTzExEbgz46ksSxw7FX1LDjzQ8AqNm4nRP+7zraausJBYJseupddr/8FQVXnsG4p284Yn1em52SRcsBSMjvhTkxHl99M/6mVgJON3tWrKZ9QC7V5QcZkXlkS5P43jm4m1tpc7oQBAGhY/Km24QTaNy1j5ie3VFp1IhaDZI/QNDnQ63TMvjKiwj6fDTs2kfJN8uwJCegMRq69O2Ef1yLq6mF8hVricnJQlSr0VpMBFxufDYbWosZv8OJq64RV11j5+pov9uDHJJoPnCwU6z/MdHZ3bFV1pA8MJ+eU8ZTs3knQbeHhL69qFq7mdbiss6yvnbFBmL9ky9hjFXGqIJKRdvB8k4PfEEUyT1tCoYoZQVK0OvD73QRkZaMqFbkk4bdhXia29BZzWRPm3gst/xPhyAImKIi/9vdCBPmT4EgCEQYwisHwxxfwoL9HxWVGqMKHprRt8vmK8dmkZ1gZmj3GARgxovrKGt2YdSq2VbRzmdXjyTGrKPdrUTQtDj9eCJzOdf0Krc4H0eHH0mGenUyCR113jI5h69311EwcTx8tR4qNyjJI3+KyHTIPAH0EVy/sIwGZ5D0GAOn9++aiOf2qb14Y20ZVa1uBmdGQ1I83LCXXQccjN7Zym0n5dIzwdJZPt6iRyUKTO6dyJhsRWSLNem47aRcUqMMTO+XzNDuMRg0Kk55bi2eQIjBmdFs+MeJXP3vbTQ0NvKG+yq0uiBjvU/yt7e28MncEWEv9d8BrVpkQHo07AYdAdaFejFc3EeLbOXmj3ZSrFWWlYYQ6bb3WTB64eTHu1by1c3QXglnvKb4jof5j8lOsPDF1aPQqAROfnkD7Z5K/p02lBFGFyTkQbcx5JpfQW4T6aZzgEoH5kR4qo+y2qcjiewPuXFiNsv2NzKtX1JnG/ef2hu9RkWDw8sZbfNJamiAvSfAkDmHd+rAItjyBrP0kQy9eS9VrR58wRCPbvLxYfmJ9Au18K8kF6nRyVAF4vC5h44VBOgxXvnpQHvqUzR9MoNIgwrpu3s4uGkZ3zovom+kn74V9yiCfHQWtB6kvD3ANsNkrgi8o9joaM3Ifiey382lwduIah7J44aF4Gjg+1yIHl0cel8DRXGTeHSPlRnta1A/P50z2vYTIzaR1Pwqmqg0xarHFAuOevICe7ltbBLT1l9Hmr+J66e9RmyPq+DL/WiyJ/FaXTWjih5Er7oDb+AS6mxeui2/GvYuYMfAR/hiZxpalUB5s4vECD2jesQwb105F43IPF6PRpgwYX4FoiASrYvi5NTJXbZPTZlMnaeeLEs3GjxNfFD+CaIg4gg4iNHHMCVFEXS8IS8SEt6QlwxzGltbtxOUgnzv3aAVlPGKTqVjbMIoXEE3A6ILeL+8Gb1Kj1rsGhjxYzLN6RTa9pNqTGZZvWIfkWFOx6Ixd5aJ1EYwPG4IB2wlSLJEtC6SDHMaWZZu7GzdjSPoZFLyiWhEpS8BKYhepScgBBkZPxSjWonG7R+djyRLZFt7kGCIJy8yF1mWmXfw3/iFAOdkjmVo7GA+q/oKb8iLW1Im8pv9rSyrW8m4pNGohJ8+nzD/OVqVCrUoEuxYnfs9MtDk7mol0uhyoFWpsOoOWReGJIlqezs6tZpEc3hsdrwwdktDbTEpNn6rNiFJEpbkRHwOB3F5OYpgrlIhyDLqaAMqnQZDUgTL736c9FGDyZo4pkt9+ggr3U4cScgfxNRhWWJIT0by+9HFx2C1tVIxKAcbAXoH/ERqdYf1qXLNJuq37yEuL5vuk8bgd7kxBEPUbtmJq76J6o3bSOzfB43RgC8QJP2EoZ3HqnU6UgYXkDK4oHNb2glDqV6/Fa3JyM63P6K56CByKIQ1LZmqtZuxpiZ12sz4Hc7OiQAAndWCz2ZHlmUQRSWaXhA6k80CyqwTAsjKioWqtZsJuD0EO6L1a7fsRFCJiIIaUa0m6PFijIvBkpxAw85CnPWNDL/xctzNLZSvWE/m2BGULlmNp60dv9OFSqsh5Pez5aW3Cbg9xPXqibO+EVGrwdPchjVVGQc3Fx3sTOobJkyYMGHCHC/CHvZ/cpqdPkoanJz96gYAHj0jn7MGpxGSZHZVt9MnJYLr5m/n69313D0untPNuyiKGsvQvOM3qHhp5UHWH2zh0Zn5JPyH3uSyLOP2hzDpfn4uqdHupabdQ//0KIrqHUx+ehVGvCzT3YSOABN8j9FKBMmRBlbfOg5RDEcL/ebYawm9dQotlhweNd+Cs7oQny6GjXUy00PfMUu1nAGqg8hAuyWbqBs2gqcVlt4Pag1seg2Q4eJvIGPEf/ts/nQs399IWrSRHvHmw/a1ufwM+ucSQpLMNSf24KZJOZ37vE8NQG87qPxRcB4MvBhen6AI3TfsA+vRI7ur29xYDZrOSbHhDy1lgGMFf+9RRdqsxxUB+8d4bbD4Tkgbwj1V/XlrXTmTesWxcV8ZPo2Fp3L28epekWBif5LkBnr16c/1E7IPHR/0sfrTlykz9+eCtGaEmO7wyRxoLlJEeOC7YH/6m9uI0oOq+2gYeT1ta17nX/YTmD4wi4E77oToboS6T+Did3bgDgpskXMx4GXt7Hgi3p+OSpCplmM5NfQwicE6PtPfi5ogIVlAJcg4E4dhrt+AJ7InhvbizrYltYFGVRLtFy4hbsn16KrX4Dr7UxKyCjpPoe7Dm0gqfI3tERM4t3UOnkCIhfGvUmBfzs3BuSzRKEl12j0Bok3azlwjN0/KZnBmNEN/p2S04e/r/x3C9/r40uprY69tP5ubFeuI63PnIooivpAPm99OnD6Wlw68gSfk4aTkCTgDLjJN6cQbj49dXEgOsbh2GSpBxcSkcf9xxHRIDiHLMupjSBLb7G1BJaqI0kayrWUnKxpWH7FcXkQOJ6X8NSNT/2g4fF5qHTYsOh3eQBAEkCQZv3R4ElqrVkdqRBSegJ9mtwtRELD5lFwFvWITwtH3vxA5FMJb14QuPgZRe3gAkbeukbaNOwCIGTMUbUdUt7uljfVPvITcMdHS64yTcTe3UrFyPVqzidF3XP+T7bb5fURotIiCgLu5lXWPv0j7oF6kjhrK0ITkI95HR10DFSvXkzZiMIWffIWroYnYXj1p3leMLsKCzmrBXlVLbK+euJpayJk2kdjcHp3Hu5tbKfr8W+L65CIIAtaUJDY+89ph7WjMJtQ6LRmjh6GPjKBu2y4l2Wl+HhWrNxLXqycqnY79n37d5ZiEvrlUr9/6k+ctiCKiRk3I5yeiWxq2sqpDO0URU1w0BbNnsfmFeQiCwLAbLkNjUN5fZVlm7aPP422zoY+KUDz4ZRm1Qa8k/ZWkzpUAANa0ZOxVtYgaNdnTJxGdlYkxJuon+3e8CH9nhwkTJsxfn3CE/Z+cWLOOGJOWAemR1Nm8nNhL8Z1WiQL905UBQ1KEAUGA2MQUIvsNZuhPVfgruGJMFleMOT4TAIIgHJNYDxBv1XcmL81OMHPL5Bz0apErtr7GgXob6YlxOJpdqDr888P8DliTUV27hXhAiZ0vAGB7ZSunvxDiA+lE9okXYRACRDkOYH/nXFxNlSQ5leh7WVAhCCIcabl/3U4I+iBtyO90Mn8uVh5o4uK3NhNr1rLljsMFkCiTlq+vPQGHN8CgzGgkScblD7L+YAu3N9zMrOhibsltgeFXQ0QqiBqQAuCsO6pgv6u6ndNfWEdalIH3LxuGRiUyvlc86w6OJ3jKYDAdsqNy+oKc88oGDBoV7146FK3WBKufoCDnUQQBbmp/gJ66lbyjm8WUkg84UavhQv/LfNti5iznjbBlN/6Z89DGZ+PcMp8T9t7LAFmHsNEH+kg49wPY8gbCrg9wYGaiejt4odljZUnU5Zwdk0XUqQ9yz/cdahoEa59BteFFhqfcT7PdydvuSxBFkcAHArfJc5mdWM03tQZcmLnP9DHqkGJb8VFoDLvkLKaNv40R1mYM8XmUfvsC76zex3DVfiYFN6MNNPL2sp3cXLcDjz/IU19s4qDezfUjohn23Rm02UQeDV7OqsYBeDoSUd6vvY4rJt7Gx1+0khdn4KXzB/L62lJmDUzj7Fc3YPcGeXzxATQqge13TcJ8jJ+VYcKE+f2J1kVRENWXwvb9RGsjETusJXQqHfEGRZQ3a0xIcohkYxKR2uO7jFolqDqj+o9XfRzjWCpWf2hCsVdkDraAnXh9PEvrlhOUg6QaU6l2VyOGo+t/Nyw6PTm6w4Nqau3ttPu8XbbZ/T4qbW24/D5+GNWlEkQkWUb1o0G10+9Dq1KhVYW/k46EY/9BXMXl6FMSiRqcf9h+fVI8USMGojbqUZtNyB0WRYGaBrL65uFXq5D1OhLy82gvr6Ji5XpCgcBh9fyQ3bYWNrU2kmOJpL+kQW00EJGRSnS7h4LYxC5ivb26jp3vfExCfi+yJo3B226n+JtlGKIjcbe00nqwHACVVou9SrE/9LS24WluZc/8hegirPSedSrG6Eh2vbcAZ10DLQcUb/r4PrnknTmd+u17aC0p64yQDzhdBJwurKnJWFMSO6PTA24PDbsKKf56qSKuJ8QS8Pjw2x0EnC6qN2xDbTSgNRlxN7V0etT/EBkYcdOVhPx+tBYTO9/+mLaD5ah0OkI+H66GZqrXbyXgciNLErv/vQBkSBrUj8KPvui8/t42W2edKUMH4G2z0bBzLwl9c4nJ7UFrcRnxfXLY8uLbSIEg+xd8TUR6CoPnzj7mZyNMmDBhwoT5KcIjq78AgiCwYO7Io+6/c1qeImZr/rovRoIgcNU4JcLjwhGZ1LR5yIw10eL0YdCqwtFA/2X6p0ez8paxLN3XQFvyaso/uZY2H4wo+wYryupWQQBBDoEcgtZSSB1ESJJxeANEyg549USQQnDNVoj56Qmiwlo7S/Y1cNGITCIM/xt2SJkxRpIi9PRLjTxqmZzEQ/ZTV7y7laX7G7nz5F7Y1VEUJZwMpww6VPiSb8HdAsn9Ye9C2PUhoYn34zCmEWlUJlRUooBKUCbExj62AqNWxZrbTjzipFuzw8eeWhuiIGD3Bog98C20lnJaYgvTbhqK6vnVCAJUONW06CKJoZ27VW/yQOLljAqWgbOdwDuz0Ah+3NZe6AQtJnzIugiE7MmQPoxdYi6LCyO4IfAqHkGPKEAsdgZu+wec8AVsfFmZ9EnsC8sf6OzbleovYOAJsNqLLKvQyBKPi89DA/RRwaiBw3BukpFFkNU6PtefyV5fPNe3roL178Gk++l+0lVMcz9Nr/I1+OR0aoPxXGVZRbS3EgQY5FjB/PoUlusbGeGoobuo4TtpMJGRMeSbtJh0Kv51Tn/iLXqG7lnHxrI2FhfWkxRhYOqzaxjZI4a1JS0A9Ig3Y/wLf56HCfNXwaIxc3n2xUfdf363WYRk6Wdtbv7MGFR6xiUqOZOyLJkEpQBmjRlHwIFJHc4x9N8m2RpJhN+HLxRElmUaXU4EQcDp9x1WNiRLimAPSLIMyDh8PmocNtSiSHbM0ZPVf4/d5yUohYjS/+/45GsirQhqdWfk/JHQxyuTXFIwSNOStSDLGLulojMYiOuTg6lHBgCxOVnkn38GhuhIAA4uXonP7qDH1PGIKlVnQtrvJ1Xk/aWs/3IFMTlZDL7yoiO27aitx2ez01pSRvrIwbRXKBHpo267hpbig+z7RIly97S2K5N2MsghCUtKIo7aBoINTWx65jUElYghJrqzXrVBT3SPTJIH5iNLkiL8fz/g71jgX7tlB6a4EylduobIjFTaK6qp36EE8SBJJA3sR9Oe/fjtSmJuZJlgh+WNSqslunsmta07lOtsMhIKBDHFRVO1fjPu5lbyzphGvwtnsu319wm4XAREAbXBgKuxqXPlQntFNZI/gNZsVMT6jv5FZqYR8HiISEsha+JopKBEa3EpVeu2kDQgH1dDE9vWb8UUH4ursRkEMCUcnxVSYcKECRMmDIQF+/8Z/spi/Y/RqEQyY5WXwBjz4f6MYf47ZMSYuGRUdwCSb/mOKU+v4pSml7lUswiNEOwsJwOCoEaWZSY+tZKyJhffjtxPthRULFp+lKSuxenjtk920z89snPS5s7P9rC1og0BuGZ8TxYuXUXTnmWsNU3gibMH/yWfi4wYE+v/Mf7nC3bQ6vITkmSsBg277p6ETv2j5H+pAw/9vu4ZqNnKN01xXFs3idcuGsSJuQn0To5g/T9OxOkLMu2ZNQgInPb8Wi4e2Y1zh6aDuxUaC6myDuDhb/Zz9qA0ZgxMJVYPxQPuQB+0kdb3TNTtFcgqDS5Zz5e6kynxpfK89W3mt3TnX+qr0ApO3rBewam29zDhJd6+ly+iLmR6QhtC79Pg82vhm9uYvXk8J/lCqDQybllDDA4kBNa0RRFfX0bEN7cq56Pqev+l6q3c1jiJW3QZxPsqkM1JysoCQQVyiMjNT/B55PmMcezCEdLy7/87F0QR20tToH4drH6CA0P/SXLtYoz2MnZZx5HvWQ47lKSOK0N9ecI/jcm9E7hgai8EzxI0agMPN0bR7PRzz+d7EQCrXsPuahsby9oAEAWBunY3ADq1yMisGNYebGFfnYM7Fu5maPcYTi1I+UXPSZgwYf44CIKA+n8oylyv0nV+/lo0lp8pHeb3wqTVYUK5LxF6IwdaGo9aVpZlfMEgpW3NCIBOrQRFSLKMLMtdRHh3wE+jy0mMwYRFp0OSZart7QDo1Rr0ajW1dhveYBCjVkuS2fqXFPENKYkYUo4xabwkIweDyCEJQ1oy5h7dENRdPyPi++QCEPT5KVu2BoCmfcXIksTwGy9HZzGTZ40m3WjB7S5llwA+u4MNT79K3syTsaYm425uRQoG8dodNOzeT+bYESQPLkBUa0gfNRRLSiL6SCtix6oJtdHQ6QuvMRpwNytJqtUmA0GvD0ISckjC3dhMZLd0zAlxaM1GDny5BESR/QsOWdv80IO+vbyaht37qVi5nhq9TqnrB1SsXE8oGEQQRUVgV4kQkhDVKkJ+P7Vbd6LS6wh5fah0WsbceQNBr58V9z4Osow1JRFjfCyO6jpkSUIfacXb2oa3ta2zDZVWS9KAvvQ8aTyJBX0wREdir6mjaV8x7eVViCoVokrV6Y8PIIgC3nY7UjCIKTEOlVaDvbqOttIK9i34mpQh/Tv97cOECRMmTJhfS1iwDxMmzH+Ff87oy8qiu7H3e5ymN88n16OImwIgLbiMQtMQSpuUZGjVuh5kI4AswRuTIXkAnDsfgA2lrSzZ18DGshbOGJCKQavCHwyRFKFnQk8r/meHMbW5GK0QpKaxhR1VPRnfK+Fo3fqf4Y2LB3P+axu57ZNdpJoFhuy4HYwxcPITHOYhNfE+KPycb6rGIckSDm8QXzCETq0ixqwjpmUbW0Zu4M7GE/lwj53nl5fw4ZYqnvbcTqZzBy9LV7DIPxqDRsVDkxMJPZxBz6CH24KXc3ufRvYUVfKM/mXig3X8X/IBvhUmMts5hln5LvS7FkMwwBRTCRE2B22ykacDZ3Bv29vQBogiBFyEDixmjujmldAIfMYETvQsYZR6HyFZ4l15EtP1MaDWIwf9YIhC8LYTDEmoZT8BBB7zP0A5KaAxo55wD9TvhtielKx6jzdb+jFS3woOsOKAj2azMPshXq04mcdNbfTa+ymhwt3opDp26frT2Nra5dv1keAsTDiYfPABMnz30GLIoOXtixhp0bEk9gJAR2KEnr21NhKseqKMGmLNOpIi9JzSL5mUKAMPfr0fgOFZMUiSzL83VfHJthpO6p2IVi3+JUWOMGHChAnz+6IWRVIsEYRkCa2oorJDYP+e8rZmoowmlPh60KnUeIIBJFnmQEsjMUYTsUYlj47N68Ed8CMKYNRo8IdCqEURtSgiSzL7mw9NDPi9HhLNFoRj9V36iyJqNcSMGkTL2m00r9mCoW82JV8uIbGgNylD+ncpq9Zp6TVjKu7WNqrXb0MOBQkFgkiyjCgImNUa7B4v3SeMpnbzDrztdkoWr8Tb2o633YYUkjrFc1GtwtXUws63PgDAnBhPRGoytqpa9NGRSMEgyYP74W5uQ2M0ANC8vwSNToeAQMDl7uxXe1kljpo6dBFWpGCQxt37O/dpzCYCzkOJjgWViM6iPC+yJCGoVIgqkVBH4tnv61VpNWiMZnJPm0LT/hLMCXGUfLOUkD+AqFIRAryt7VRv2o69skbxndfrKFm0orMtlU6neNL/iIDTRUtRKT0mjcXvcrP34y+J6ZGJt60dAF2EFVdjM4boSARRID4/D2+7nX4XnkXF6g3UbNwGQFT3DEKBADWbtuNqbGLApecjqv93JmTDhAkTJszxJyzYhwkT5r/CgPQoBnTkWbBd8hl1z/UhkTYEAUQ5gOHDM1kQn8wXyddx4qTRkPw67PkU9n8BZSshFITdHzJl7TM8Pug6nipJYOQjy/j7lBx219jRqkR6mZzQsg8EcMp6TtCXs3rFN6R6s8gpGHW4MP0/RGGtncpWN4GQzANvLeBz7UJlx/g7wXAoYdbWilau+8DPOUMu49HZmVzT5ubdDRXc+OFOXj5/IBPyEuDLG9A17ePkbjIfMhiNSmBHVTtbtZGkimpi5RYSVA5uOmUkOBtQBZUIJYteg+X96YxsKaYtNJRJ4la0pUG262+j3GbljMa7UMkSjLyOpLVPgwCRuBkW50W2CQjIYKsDQGwr5UpKGZnWRl/7SgS1vWO1Bnw8rIIolQeCXgTgY/0ZzCzQ4S9airppJ35DAlpPLTHdC6DiO1h4Baj1YI4nuufpzO2hJZ1GaFKuibd4GaPdN1KsjiQnuA8JgWy5FJUgERnYhagOIQMh1LwemMxL5tcxyF7ignWwxspK1RRm2NaCDWbWrKC+z8t8WGvgjBfXc8fJvTh/aDrPLj/Ile9toyAtkh1V7UQZ1IzKjueK0d057YW1CIAvKNH33sX0T4vkg8uH/z4PTpgwYcKE+UsToTd0/h5nMNHkOSSwBgGb14tGFInWG4k2mlCrRNyBAO6AH5ffT6wR6hx2PMEAVp0eu8/LwbZmLFodQUlCp1LjDHSNpFYhUNXeRpzZglFzhDxG/0P4GlsJhQKsyopFt2kT1tIKgl7vYYJ95ZpNVK7ZSN7MaaSPGEwwGOIbdyseRyOnp3RD4/VT+PGXAJiT4qHdTsDlxt3ciqBSJvplWUZjNpE1cQxN+w501q0xGVn/1Cudfu4AtZt3dmlfHxWJKSGO5n3FygZRRK3TEvR4kYKhzmj01mLFzz6uTy5Ne4u61JF72km0FJcBEPIHyJoyDkd1HbaKanx2J7rICHw2OxEZabSWlLHznY9AFDDHxZE0qB9qrYamwuJOYb98+Ro0BuX5/XG0vhQ6tJr3e9sblU5HKBDA29ZOw65CylesJ+B0Ub9jL2q9nvi+vWgqLKJ5fzHDb7wCa3oqDTv20rBjLxEZqdgqqjFER5FY0Bt9VAT7PvkKUFYOLLvjYbpPGE33CScc450PEyZMmDBhuiL+fJEwYcKE+W3pHmcm5s5S/t3rBZplC15ZTZa3kAH2JdzseQqqt0DOVDj7XThnPlz8NajUUPgZYtM+Zlr3YtEr848FaVGcmBtPQXoE+c8WI3W0IQsC40OruKLuHnI+mwbrnv3vnfAfgHs+30u7O0BKpJ5dUne25f2DBms+hQ+N4fM12zvLbSlvo7rNw4qiRkw6NbmJVqpaPYQkmTqb8jLGsCsg60SGTTmfx2bmM+/iIUzoFc9Tput4LDCT61UfsTjz35w1OA0S+/Jy5tNcFLidcWffgGhOwIeWceIOtEIQSaXjoqHJvBHzb1RyCLRmxZrGoPiiCiodU2wfKWI9QM0mmtQJnTF52d5dCD4lgqrDapW/rwlR9/ndned0QvO/kdc8ibFpJyQVYPHUIEy4F0ugBUJ+5aigB9oriN76NJnbHkX02fFH9UCSQQy4ia5YxE1RaxCB7aEsasz5IIiIuSeDMRZh9K38X+Z8SkghPVBGXLAOWR/JYnkYiWaRTyMvIiRqEWSJaxIL8fqVl+JtlW38e1NVZ1+TIhSbgnZPkBsm9GRNSTOBkIwM6NUC/qBEVeuhyLYwYcKECRPmeBFnttAzOg71D+wI/VKIgCQRkCR8wSDxJgvpEVEkWyJItig+7e1eD95gEJNGi4CALEOk3oherQYZWjxdv7dCyLiCAcrbW/H8TELVvzKSz4+jsBgJgYBOgy03k7jxIzk4uoD5y5fhbGjqLNty4CDedjttZZXorBZ0kVbcoQB+KYRfktCYjKQOG0BiQR/yz59J3sxp5M44mchuacghqdPDPW3YQCzJCaSPGorWakZtNJAzfSIqbdccUBqTkZ7TJnYmn9aajQiCiKrDN19nNRP0KAmMZUki6O2azLjlwMEudjgAO96YT82mQ2POg4uW07h7Pz67E5Vej89mp/8lZ2OvqQNZVvodCOGorad63RbKV6wnKiujsw/eNjuO2gbU+kMJlkWtBkElktC3F1qLibyZJ6MxGUEQCPl8IEkYYqJpLa0kLj8XrVWJ+A96vSQO6IMckkCG1pIybOUd4zNBQGNQxmcBj4esSWOoWre5s011xyoEz48S4oYJEybM/zJvvfUWkZGRP1nmnnvuoaCg4Hfpz5+BsGAfJkyYPwRatch5Z59H3Zw97BayleXWMpgqlsFr4+FfBUrBnCnQuB9emwADLoRJD8AJN7Fg7gjW3nYifZIjWLa/kU1lbdi9IV4yzSVoSkKQZZyyjgZtqlLPD6LI/1cob3axvVLx7bz0hO7MzahhueZ6Vo89wICZtxJpLyJPrMBWtbfzmNkjM3n0jHyeOLOgc9uz5/bntIIUHvx6P8uLGmHgbLjgU7RxWQRCMov21rNkXyPVbR4OyGl4ZQ2LmpVEXCWNTvxpo1hPX5bsa4CLPufeXl8wTzUTADHkI2PHkyR6S5TGrCmw/lnwtIIhBuYspU5ULI2CHYvE4oIN+GUVVeoMys0d/dRZQWNit6Yft6r+jaVmdWf/E1DyG/jVVkgZAAhw8Duo2qAUmP6Mcjzgl1WUienIAy5kXuZjCAJoBCWCXgx0+LlmjSbq6iVwcwlM/ifcehBO/D/uP2MgN8yaCuZ4QKQyaihjCu9kxPrLOH38aFRTHwF9BOgjeezMfkzoFc/1E7K5dXIOho68I/mpkXSPM2E1aDDr1PSIN3eex9bBy9iXdC+fnZv8HzwVYcKECRMmzNHRqFRkx8YTb+iaJLjV66a0vYUqWxuiIBCpN9DicVHR3kqqNYIks5VIvYGeMbH0iI4lKIXwBoO4gn4AtKIa8QcrHYWOH/F/cPVjo9eDPeBH0GowZWVQm5WMTqVidGoG8cMG4Ikw4UqNx93e3nlMrzNOpteMqWSOUVbYqUWRU5My6fX1BvY8/jJ+h5Pc06bQ5+xT0ZoM+J0u6jbvoL2sClFzaJG9o74RKRjC227DFBeLFAjgabMx/KYrFH/2DjE84HLTUlzK9/ESolZLU2ERIZ8fc0oifc85rcs5ySGp83dthBW1ThG41Ua9IpgDAbcH3/cJZaGz7oj0VPQRFlQaDfsXLiLo9mCMjabbhDFdVsdqLSZShw3EEBPZpe2QX3nG0scMZ9y9tzD8xivImT6J0f93PcmDChjwt3PoOeVERI0yKSFLEo27CqlatZEhV19CQr/eaIwGdCYTPU4aR2L/PiT270vSoPyOfsokDshHpdVg7Ei0a4rrCCzRaDAnxGFNTSJ7+sSfvfdhulJfX88111xD9+7d0el0pKWlMX36dJYuXdpZJjMzE0EQEAQBg8FAZmYmZ511FsuWLTusvuuuu46BAwei0+l+VgQsKSnBYrH8rKD4S1mxYgWCIBAVFYX3RxNZmzZt6jyXMGF+CbNnz0YQBB5++OEu2xcuXBh+nn5AZmYmTz/99H+7G7+asGAfJkyYPxR9UyMZfPc6dp38JcEfRHQFnM28s75c+WPdM1C9GWq2wYhrwBCFUasmMUKPQaviouEZjM+N5/lz+3P6ZXfRmDoRs+DFLPjod8KpcGsZDLjgv3OCvyOyLCNJyttPICRx5XMf8/RLL7K9so0Tc+O5NacJraOStOa17KlzcLbvdq4OXEv+yGmc8twaHl20H51axVmD00iPMXbWa9VraHX78QRCFDc4aHL4uPXjnbywvITbP93Nw9/s55R+SRSkRbBGGECubx6LI89m6/wHuOWp13h1dSn+kMyCbTUgqnhw1jDmxu8+1HFbJfSYCCoNNO9XfgfwtNA2/0qSpAYAmk58nKaoAciAVghR79PyXSW4/7YG/l4J/1dLH2MbWWIdZnc1AJLaiBfl5VMVdCKVLIcbCqG+UGkjZQB8cytYFRHch4bnct+hKnIor66voeNyKtH7nlbkaU+TP/P/sCy+GZ7pB88MgCZlybf29bEkfzoDnI34UaGzxKITgspk1KeXQ8ky8Npg3b/I8u/n5fbLyD7wKjZvAEGAk/smsa/OQU2bB5snwKert1HQ8AnRohOzToWh5CsMbUXE2feyYFs160qaj/9DFCZMmDBhwgCxZgu5MfFYtV0Ttzv9PtwBPyEpRKvHjSvgRxQEogxGJbGyqEIlipi0WkwaLVF6I6nWSLpHx3RxrE+PiCI7Jh6d+q/v2Cr9INK80evhi7pyFtaUEZAkDL170hRlwhkK4g4F8W8vJHbxBjLW7MJRVM+8gvMp+WwV+ggrKUP6o9IeshAyi2q8Dc347A58diftFdXs/fBzihetoGTRcloPlhOT3R1DTLQifHdEim96/k02PPUKbaUVSIEg1Ru2ojObGHTZBUjBQ1Yy7sZmDLGKMN1+sBxTYjwAzpp6dr79sVJIEOg965ROMRzAb7MjqERG3DKXsXfdxOC5sw9djA5h//uJAQBbZTXWlCT6XXQmnhYl0MQUH0vZkpWYkw7loeo5ZTy2imqctV0TJRtiouh77gxSBvVj+xvzWff4i2x85nUAXE0tbHruDQ5+twopEEBt0COoDr1vbHnxbWwV1QTcHkqXrCbg9mCrqMZRXUfA5UFjNJAxZjhlS9cghSTs1bXUbNqGSqdE9VuTEmgvq8ReU4/f5aZy7ebOJL1/NmRJovVgBfU79tJ6sKJzVcZvRXl5OQMHDmTZsmU8+uij7N69m0WLFjFu3DiuuuqqLmXvu+8+6urqKCoq4u233yYyMpIJEybwz3/+s+s5yDKXXHIJs2bN+sm2A4EA55xzDiec8PMWRvfccw+zZ8/+xednsVj49NNPu2x74403SE9P/8V1hfnjIcsyfqcNb3szfqcN+Ucrin4L9Ho9jzzyCG1tbT9f+Bjxd0x4hvljEBbsw4QJ88dDEOg3+AR2xZ0CwBKGMMN3N+9trISSJdBYSEDU0Zg+hZV7y2lyHPKp9AZCfLilmpX7azlhy9UkLZxF8tS/U5M7G1v+32DIpWCM/m+d2fHBawPv4Ymzfsjf5m2m++1fM+yhpdi9AdQBB+8LdzJP+wiLF7zJ4H8u4RXpFL7KuA3HpMfJiDGi7zYMuffplDQ72FVtY+H2mqPW//SsAl69cBCXjOzGwu01fLilmmeXl2DVq5GBsmYXO6psBEIymVobz9Sfx8D9j/GC9mkGZUZzR+IGtqougu3vwoHFEPAQEtQsDI2kInoU1G6HSQ9Q2esyBuw9g91RE9kk5VLdqkRiydZU9gbTGFl3LY9q5yJljGKwqphrNJ/hrt2nvIh62hD7zICTHlYslQAx6EaHMhARZFmJ3N/+NnhaFN/6llIIepGblCRpFsHLbOtW0mOM9I3XICHiR48n+xTuYS5T1mQRqtoI294Cn0NJjLz4Tua9+SLldpmArOJ56TTUcoDEA++xODSQFsmKJGohaxwATsy888H7qNpK4cAiSktLGBncSF/bMmaW/J2EkOLTP6bsKWJW/IOPspez6PrRPJtwPy9H3sgycTg3friTi97chC8YIkyYMGHChPktEEWR1IioLkK7DNh9Xprdis2NWhCRJQm719NFsPAEArgCftq9bppcThpdDtIiojBqNMQaTBg1WlTin/vV1B0MEvoJkcYXCvF+5QHeLN/Pd/WKvYo9oIxJArLEh1UlfFhVQpzWQKbRQi9LFFFZGSR4gvTM6s7BL1fTuq+cg1+uOWL9olrF4KtmM/DyC7CmJlG2bA1123bTuKsQQSXibmrB3dyGq75RWcYqy9Ru3omzTgmESB7cD63ZhL2qFlt1LXXbd6PSahHUKhAERJUalVpNj6njicrKxNXQhC7CovS/w0c+Ii2Z5v0HkQIBLMmJnft97fZOmx0pGCR12AD6njcDXaRioxTydRWJPG1tVKxcr5yXRkNraQUAztr6zjKG6Eji++QiqJRVifroKKzpKbibW2grraB6wzbFQ1+WkYJBdsz7kJ1vf4QsyZ3ic9DjxfMDQV2lURPdsxsAXrudilUb8LS201ZaTltpBQG3h7bSClwNTcihEIIocuCrZdRu3kHGmOHknTUNc2I80T27Ub9jDwe+WMy+BV8f9Zn4o9K4Zz9rHnmOba++y575C9n26ruseeQ5Gvfs//mDfyVz585FEAQ2bdrEzJkzyc7Opnfv3tx4441s2LChS1mLxUJiYiLp6emMHj2aV155hTvvvJO77rqLoqJDuRKeeeYZrrrqKrp37/6Tbd9xxx3k5uZy1lln/SbnBnDRRRfxxhtvdP7t8XiYP38+F1100WFlP/nkE3r37o1OpyMzM5Mnnniiy/7MzEwefPBBLrnkEiwWC+np6bzyyitdyuzevZsTTzwRg8FATEwMl112GU6n87c5uf9xfLYWWou2YSsvxFFdjK28kNaibfhsLb9puxMmTCAxMZGHHnroqGWO5Vl64IEHmD17NhEREcyZM6fTuubLL78kJycHo9HIzJkzcblczJs3j8zMTKKiorjmmmsI/SDXSVtbGxdeeCFRUVEYjUamTJlCcXHxYX1auHAh2dnZ6PV6Jk6cSFVV1WFlvmfz5s1MnDiR2NhYIiIiGDNmDNu2betS5p577iE9PR2dTkdycjLXXnstAGPHjqWiooIbbrjhT7uS5a8fwhAmTJg/LX3nvMbKLZcz5/M2ZODZcVkQE6RZk4LR30Tcu+MJyRFcnjifBXNHAKBVieSnRuBrr8NSuQyQIeAh5ex/AYqg/9TX+8hLtnJqQcp/7+R+La5meHYgiGq4dptiqXIEDtTZeEX9BDE+Ox7711jX3EmkbEPWGKgRkwhJMs+tqcXu7ccdPSQuPUHD+5cNAyAYknD7QuSnRh61G9EmLRPzlCgng0ZEqxLwBiQsOg0QpMGuTKKoRYGBKUa0dSFkQYVl4Dk8M7EA9Vdvot7thjVPQYtif6MaNpdT+p2L+MoYkENQvYWN0Rdxgfwmr+ovxtRDy0OlinWOYK+mv28zH5m+oK9/P8Lg16it2I9W8uOtPwAL54LaAFteU6x0vIciD2RAkgUqrQPp5tgCRYvAkgSOOgh6CahNqIOHkuzl7XoY9M28FniZfUPuI9vgQrP+GW6WJRrbFiBvygWVAUIeRbAv/pbzWcxI77/wosOEh7m6hYqNzoQ7+dgfy5wRqaDVs37zRp6tymIr2QhaC7eeejkPfHoFau0mqutjSRWbeXPgUMwTz2HXNyUYG7fz75YsUgsbeGqPARjE6A3VDM6MIjXKiE6t+nXPVU9XfdoAAQAASURBVJgwYcKECXOMZEXH0up20+p1IyAQazThCQRo8UBQlqh02ACIDSke96BY66hFEZUg4gsFCfkkEs1WMiNjAPCHgrS4XUTqjRg0mqO2/Uel2u1kcUMVyQYTJyUeOWLWL4Vwd4gbdV5F4N7cpkSHW1QaPKEgyDJ7HIqAPDAYIDIliWHXzwEgNrsn0bmZ5J4z6aj9MCfEdf6u0mgQRJGA24M+MgK/y4WnVRkPCWo1hkgr7uZWVFoN2dMnEt+nF60HyvC229jx5gedIny/i87C226n6LNFnW007tmvWMMU9KFhVyHeNuWe2ypriMxMB0HA3dJGxphhlC5eCUD58nWodFqa9hXjqm+kcU8RfqeLH2OMi6G9rIqY7CzUep2SQDZAZ7LYzmu+cRt+pwud1UzigL4EnC5qNiqe+LVbdxLft9eha+90HUqQC10S6ooaNZljRqC1mknq3xdvu53GPftx1Ss5A0zxsaQMHUj5SkU0tlfVApBz6mQS8vPY+K/XCPl8BNweij77Fmd9I9Q3Yk1JwpQQR3yfnKPerz8ijXv2s+vdTw7b7rM52PXuJ+SffwbxfXKPa5utra0sWrSIf/7zn5hMpsP2H4tNzXXXXcf999/PZ599xq233nrMbS9btoyPPvqIHTt2sGDBgl/S7V/EBRdcwGOPPUZlZSXp6el88sknZGZmMmDAgC7ltm7dyllnncU999zDrFmzWLduHXPnziUmJqZLZP8TTzzB/fffz+23387HH3/MlVdeyejRo8nNzcXtdnPSSScxbNgwNm/eTGNjI5deeilXX301b7311m92jv+L+Gwt2KsOHLZdCvqxVx3ASja6iJjfpG2VSsWDDz7Iueeey7XXXktqamqX/cf6LD322GPceeed3HHHHQCsWbMGt9vNM888w/z583E4HMyYMYMZM2YQGRnJ119/TWlpKWeccQajRo3qXMEye/ZsiouL+fzzz7Fardx2221MnTqVwsJCNB3f6263m3/+85/MmzcPrVbL3LlzOfvss1m7du0Rz9HhcHDRRRfxzDPPAMpzP3XqVIqLi7FYLHz88cc89dRTzJ8/n969e1NfX8/OnUqS9AULFtCvXz8uu+wy5syZc1yv/e9FWLAPEybMHxatVsOYESN4OaIelz/I9H6KwL502BvMWj0ZgCTBRorWwW0f72JMThxT+ybxweWKnycHzBD0QmyPzjpXFDXx8qpS9BrxzynYyxJIwY7IqKMvTf3g4nwSX9qJKAchVA+JfQnsXsDfvX/jjDMn8HfbeqK/uoi31CchSfd2OVatErlgeOYxd2lbZTv+kExqlIF/ndqNgpavKIwYzdJ6PZee0B2zTg2t+aA2YLYmMfHJlaib+nCWahKjQ26yKMEbkUXrziUkbngRGQHBEA2jbmTmykcRNJ/i1zSirdpKQNCikf2URI2mR90qYsRyCIWo2LmcxpxL0PcYSd8Vc8DV2GFrIyjR899fPrWeBr+eJLGdVKfyZS7XbUcAfMYkmjwSPr9AqlZEF3QgA6rIVFj5MIIskbf7MfDakAGLABY8UFYH1lSwV/O9AasKmUfGmSgzD8SsU1MY/JCcpEgmZAxlwved2fgywxs/IEqTxkn+R3jZPYbpvlj6pA3CU1fIa/4pFJhaOe2kq8FsIGfiHO73j2RUj1ge+1aJHhIFGNUzlstGZx3z/QoTJkyYMGH+E7QqNQlmC1q1Cr1ag1pUYdGp0IgigR/aZshQY28nxmhCr9aQHROPLMu0ez3o1V1F+Ra3mzavh4AkkR7x58szJKEkhP+pCHuLRsvQ6Hg2tjZ2WrFHa3V4PEGCssQpKZnsbG/moEtZTfjjWEBTYgyDbjz3mPtkq6xBliQiMlJJnzWdJlEmsqwW2esjbfggAOw19Rhjo/E7Xax+6BlkWcaYkUygqQ0EAX1UBHs/+Jyg14ugUmGMjSKmZ3f2fvCZ0kZVLX7HIdE9Ni+bxr37QQBZlnDWN5KQ34vY3J7s/fBzvM0O6lcewJgaQfzQQ+NzQaXqFNH9DiUKuOXAQQC0VjN+u/OwpLWuphYc1coqxLotu/DZDq0+lQJB6rftRlCJXfz0AfqedwbO+gasaSm4G5uI79MLQ3Rk5/6Sb5Z2JtAF8NkdaM1GLMkJOGrqkSUJa1oyqUMHIIgivWZMpXrjNixJcdRu3tF5PrE5WfSYPPaY79cfAVmSKPpi8U+WKfriO+LyshGO44qYkpISZFkmN/fXTwRER0cTHx9PeXn5MR/T0tLC7Nmzeffdd7Farb+67WMhPj6eKVOm8NZbb3HXXXfxxhtvcMkllxxW7sknn2T8+PHceeedAGRnZ1NYWMhjjz3WRWSdOnUqc+fOBeC2227jqaeeYsWKFeTm5vLee+/h8Xh4++23OydAnnvuOaZPn84jjzxCQkLCYe2G+eXIsoyzrvwnyzjry9Fao3+z6O7TTz+dgoIC7r77bl5//fUu+471WTrxxBO5+eabO/9es2YNgUCAF198kaws5R1z5syZvPPOOzQ0NGA2m8nLy2PcuHEsX76cWbNmdQr1a9euZcQIJZDyvffeIy0tjYULF3LmmWcCiv3Uc889x9ChQwGYN28evXr1YtOmTQwZMuSw8zvxxBO7/P3yyy8TFRXFypUrmTZtGpWVlSQmJjJhwgQ0Gg3p6emd9URHR6NSqTpX5PwZ+XOvOwwTJsz/BJN6J3J6/0MzxjPGDmFp/IVUEcfLwalExCbzwZYqbv1kJ1P+tYoP1hbBRxcrEdN5p3Spa0SPGE7vn8LNk/5c0S6dmOPhmm1wzdYjJs6tt3kZ9uBSrl94EOGCT+CstyEpH0ZczczYhXwSGEGry0dyoBI9AUZb6zl9QOoRGjp2/jG1F8mRBqrbPHiXP4bqu/+j7877uX5CNqIATl8Qorvz5AY7gx74jla3n0c0r3CxejE7tf3hqk184R9IsucAkizgUVu4U76cm1cFEPrNgpRBaHUGCLjRyMqS6R6DT4LKdRBwIQMZxW8zuOhR+nw9A7w2QqjAXgsdL9Ag4up3MQvi5hIrKi/CDVIEoejszvPYK3cjpDKSJdajEpRjBOiI6OpYOo2aBjmSJjkCCRUk5sOAixVrHVCi+gF0VsYMyOfsIensrbOznVzUGUO7XrjuY5GTBxA38iJumZzDFWOyyEuywkkPseL0LbwtT6Gq+9lUVVfw2Lf7OdjkYHVxE/M3V+L0BTFpVWy5Y2JYrA8TJkyYML87giAQ3WFl8z3pEdFdXi69wQA2n5cqWxulbc20ezxU2dvRqdWHRdFHGQxYtDqiDUb+jKQbLZyZmsXkhLQj7q9yO5lXvh9vKMTo2OTOKPwxccmIgoBXCqEWVPg7JjwSdAasP7i2v4bes04FQcBWUc2atgY2tjXS1j2ZtOGDCLg9SKEQlqR4dr3zEdvfeB8pEEQOBHFX1NJt4miGXT8Hb2s7wY5EmWqDHr/TTe2WHaQOH0RUVibuppZOn3tRo8GakqT4zksykj9A4659NOzaR+GnXyNqNLhqWnFUNtJepAjtgkokc9wotJZDUdUyAsIPchlYUpI6f//h9u+j+gEE8QcJjDVqIrtnkDyo32FivSkhlpjs7iQNyKdx9z6sqSldxHqAhH55mBPj6TVzGilDB5A9fRKiSsWQqy4mZegAECAmJ4v6nXspW74Wd0srzfuKqdu+t6O/iZx4/21EZh75Wfgj01ZWhc/m+MkyPpudtrKjW1j8Gr63z/pPRU1Zln9RHXPmzOHcc89l9OjRRy2zevVqzGZz58+DDz7Ie++9d9i2Y+GSSy7hrbfeorS0lPXr13PeeecdVmbfvn2MHDmyy7aRI0dSXFzcxX4kPz+/83dBEEhMTKSxsbGzjn79+nVZrTBy5EgkSepiGRTmPyPgsiMFf9rzXQr4Cbh+2sr2P+WRRx5h3rx5FBYWdtl+rM/SoEGDDqvTaDR2ivUACQkJZGZmYjabu2z74TOnVqs7hXiAmJgYcnJy2LdvX+c2tVrdpb3c3FwiIyO7lPkhjY2NXHHFFWRnZxMREUFERAROp5PKykoAzjzzTDweD927d2fOnDl8+umnBH+Qe+XPTjjCPkyYMH86NCqR8XOfpdXlZ0SbB18wxHsbKnF6Q9zUfBcnfLcHCMDeTyGhN6Qe+lKw6jU8Navgv9b3/5RWl5+yVg0DMw734W/d+Q18eQsjXCfzjWccB80j6dHd0rn/jYsGcaDByfCsGHCfC1EZ5GWMBKPusLp+Dl8wxJx5WyhtdjEtP4lxOXG8v6mSZ6u6URDXE1OvU3D5gox7fAW+oMSSG8ewobSVZqefmyZmE9d+EraiBZh6jECOzYYhlxJa+RFqQUIdtHFH4HHO2Z8EZ14EOVPAayf49W2ojTEQnQErH+nsS5dhuRyCUIgAGvyygBoJjSABEs1VRZzUshWNoAxQNITwX76e+rXv0rL2DRZIYznN+zEZalAHOl5WznwLorrBon9Aw15Ukp+7op9iSb2W3bo5GOt3KZH8ATdEpsOs9+CVMeCzw4sj2T51EW+urUcU4Lyh6bj9IQwaFaIoQFwOwmXLiQG6pNLa/xVTvr2B/ZMvR7vqYeQiP9OkNJ6seQlvQMIbCHH/qb3pnRJBtOk/e5kPEyZMmDBhjhc6tZrcuER8wSCCAG0eD86An4AkEZAkap2KwOoO+OkRFYtadcjGTa/WkPYnjKz/HlvAhyyD+ghRx6uaail12QnJMjUeF30jYtB977uuUnNacjckZCwaDUOiE8gJ+EgzWn6VeOluaWPnvA8RVCoSC/KwJCXgqK1H3LyXqJH9SdKbaK+oZuvL7xCRnkLBxWfTXl6FFAyRf8GZlC5Zhd/pRG+1YoqPxZqW3GkBE+iwr2k+UEq/888AwFHfSMmi5cTlZeOsa6B8xZFtDeRAEBmI6p2KLIMpRbnXckiifudufO2HBC1RJTDmzpvZ/vr72KpqaSkuPVRPMIguwkLfc2fgamymZuM27HWNCKIKjclIwOVGDgSxV1Z3Xr/Egt4Y42Io/W4VroZmNj37BrG9elC3dRfedjsDu59H0OtFrVcSxyb2601iv94ApAzq19l28TfLaCosovuEMZR+t7Jze2xuTwBMcTHE98klsaB3lwmEPxPfr244XuWOlZ49eyIIAvv27eO00077VXW0tLTQ1NREt27djvmYZcuW8fnnn/P4448DiuAvSRJqtZpXXnmFSy65hEGDBrFjx47OY5555hlqamp45JFD7yLR0ceWH23q1Klcfvnl/O1vf2P69OnExBxulXKkSYcjJTDV/GjSUxAEpI4Jv5+auPgz+nj/UZGCgeNa7tcyevRoJk+ezO23394lcv5Yn6Uj2VAd6fn6uWfuSBypD0d6Bo/2XM6ePZumpiaefvppMjIy0Ol0DB8+vDM5blpaGkVFRXz33XcsWbKEuXPn8thjj7Fy5crD+vtn5BcL9jt37uSLL74gOjqas846i9jY2M59drud66+/vksyjTBhwoT5rYg2aYk2adldrbwAigIM1pShk77/UpTh7VPh2u3w/rmKNc6Jd8CWN6DgPIg5cmTy/V8W0uL08fAZ+eg1fwxPcHfldp5f38xn5Sqq2zw807+WioP7sY65motGdqOyxU39uk8ZEqjiJM12FvhGc8aL69lx18TOL8AYs47hZh00HYCXR4MlAf625Ff152Cji1XFzQC8tLKUYd2jOaFnHNsq8qk59wqyEyyEvAE8gRCBkERQknj67AI2l7cyNT2I5rtqbvPO5INlMm9mNHHmmMGwKQp8dmStGZ23nXfi3wcu4qtdddyxcDdWzyCuntSHM9fNAZfiK4rGqIjlHQgAaj36oJcvI8+ll1RMN08h4tA5aN1gbF3XWdYf2weDq5KDdpgQ2klBcDdqdcegA3AJFvbsLkLd+gWDGtd11n/fFBMrPhHYLPZjtGo3woFFkDUepj4OMd3h5Cfhm1tBhgH6Os4bmkGPeDPrSpqZ/dZmRvWI5c2Lh+Dxh3hueTGDMqIZ1TOWa/69nUBI4uXEdaidDWjrtyv3yFZFrljFk3WzOTh7EckpqcRb9L/qvoX56xIen4UJE+aPgq4jCvpI4jWAJMtU2dtJMFuotduINhjRqTW4A35ijCbEI7y4hySJWocNvVpDnMl8hFp/f2RZpthpo8LloNrjRAaS9UbaAn7Gx6cSq9NjD/g7xfrmeh9SvMya5jrGJxxa3RipVQInDjjaWd1cR6bRQorBfMTr8HM0FR7A1aiMz0rqGsgYMxy/04WprpWhSZmIahWtgQCyLBH0+VDrtAy8/AKCHh8IoNZpcda52P3eJ4y95yZyTp3MlhfmIWrUyCEJKRjEGKOI7Qe+XELNpu2EQkHSRw5m/w+Sq/7Q4qZzmygiqqH7KSNwNTSh0mjImjSG8lVdE4omDszH09qO12ZHChwudAVcHhp276N+224Cbg8AnpZWes2Yyr4FX6PS6Qj5fLQdLCd1+CB6nDQWlVaLs76Jxt37kEJBYnN74LM7SRrQl9JlayhdvJKeU8eTMXoY7uZWqjdsJWXoAESVit3vf0pMz260lpThszmQAv4u5+e12Rlw6blEZKSh0vy54yG1lmP73zrWcsdKdHQ0kydP5vnnn+faa689TEBsb2//WR/7f/3rX4ii+IsE//Xr13eJNP7ss8945JFHWLduHSkpinWqwWCgR49D9k3R0dHY7fYu244VlUrFBRdcwKOPPso333xzxDJ5eXmsWdM1sfS6devIzs5GpTq299K8vDzmzZuHy+XqvJZr165FFEWys7N/5ugwx4qoPjZB+FjL/Sc8/PDDFBQUdLm/x+NZOlby8vIIBoNs3Lix0xKnpaWFAwcO0KvXoXwiwWCQLVu2dNrWFBUV0d7eflQ7rNWrV/PCCy8wdepUAKqqqmhubu5SxmAwcMopp3DKKadw1VVXkZuby+7duxkwYABarbbL//ifjV/0jbJ48WKmT59Oz549cTgc3H333Xz44YeMGzcOUDJdz5s3L/xCGOaPx6ZXYcVDMO1pyD4JRJXyE+YvQd/UCJbdPJYIg4YI7zJor1TscBbOBUGlJBWt2Qw1m3EFwFT4Pu76YoznvXNYXXZvgNfXlAEwe2Q3CtIif+ezOZyFixZxyoazmSMbeT3wEiaVwMn7bkNFiAe2Z/O+Zgr/WLCb0WkzyJX0fO4fhEmrIi/JeuTZ6qAXQj5oK4eXToCb9sEvfCnMS7Zy2ejurCxqos7mIT81ktun9uqcRbd5Aqwtaeara0ahVokkRShWMacWpMD652HfF9xoyKAwfioRejWfL5zPdE8rAiB420FrxqZN4P7XPqdVm4zFU8132luQ1hhh1NWw8iGwJCu2QM56cLcCMohaSBuCXLWJaTlW2LwVZIlQ7Q6MZZsVr3pZhUaANNtmeKY/o1JGUqnrQaQ6iMVVSWjQJayv8nFCwzvk738SQZYOhfFHZpCQM4x1N8psrXuH2vK3SNn+JBxcqkT99z8fqjZByA+iGt3H5/HP5AGs2RniPf9oAtJwNpS2cPbL65mQl8Dzyw8SZ6nmi6tHsWhvPQA1k64jI7E39JgIpliEkqX43jsbo7+ZpsYGLvukHKcvyJuzBzO0+2+TxCjMn4vw+CzMn5Wqe9/Avb+cbk9cizrGiqgLrxr6KxFjVCxzdGo13mAAAWjxuLH7vKgEgWa3E78UosntQhAgKEmIgkCM8fBoP1fAj8Pvw+n3/yEEe0mW+aqugkafIhirBAGtIFDTkUy20uWgyNHGAaeNKFnLzsp2Xnu5nCuu7MagvnFHrPN7//9ytwNVcx1j4395rqWUwf1oL6/C1dCE3+0hqls6PU4a1zkedDW1IIUkht94BboOG5qINKWdHW99QHt5FVqzichu6bRX1lC6eCWyJBHyKdGMWrMJW2U1u9//lPayKkIdUY4li5YT3SOT1pJyTAlxhAIBAi5353GiTosxOhK/w4XOYsZeWUPI56etrApPc2uXc6jdtIOqVRuJzu6Oz+5EVKuQZZm0EYOpWreZkNdH1ZpNXY5RfPJ7MOzGK7BX11C/fQ+tJeVUr9+CWq8jeWA+tsoaQLHS2fb6v4nqlsGON+d31lG+cj3O+kZkGeq378bvdBGTk4W9qhaf3cnAy87HXlVDfN88uk8Yy/7PvqF28w6cdQ1IwRAr730CjcnA0GsvRWv6c1o7RXVLQxdh+UlbHF2Elahux9/u54UXXmDEiBEMGTKE++67j/z8fILBIN999x0vvvhiF8sMh8NBfX09gUCAsrIy3n33XV577TUeeuihLkJ6SUkJTqeT+vp6PB5PZ6R8Xl4eWq22i5AIsGXLFkRRpE+fPsf9/L7n/vvv55ZbbjlidD3ATTfdxODBg7n//vuZNWsW69ev57nnnuOFF1445jbOO+887r77bi666CLuuecempqauOaaa7jgggvC/vXHEY3JiqjW/qQtjqjRojH9tvkRAPr27ct5553Hs88+27nteDxLx0rPnj059dRTmTNnDi+//DIWi4W///3vpKSkcOqpp3aW02g0XHPNNTzzzDNoNBquvvpqhg0bdkT/eoAePXrwzjvvMGjQIOx2O7fccgsGg6Fz/1tvvUUoFGLo0KEYjUbeeecdDAYDGRkZAGRmZrJq1SrOPvtsdDpdl4CmPwO/yMP+nnvu4eabb2bPnj2Ul5dz6623csopp7Bo0aLfqn9hwhxOy0FYeBVUbjz2YyrWgbsFSlfAk7nw3GD4/oPV7/rJQ8P8OegWa1LsQWKyIGscFJyr+LxfvRkyR4KoAVMc77qHsEnK4R3PCEVYfTwbvru7sx6rXsPjZ/bjH1Ny6Zca8dt33NUMjft/ssjzG1qwy0ZsmnjuO60f2+6dijTwEvaahvF5Yxz/WLAbAL3exKw8A1f0tLPilnG8f9mwI1eYlA/nfaL4rZtifpFY/8nWak59fi3bK9u4fWovJvVOwO4N8uXOWs57bQPfFTYAcNdne5j73jbe21RJcqShayX9zsEz4DLuDVxAm8vPs8tKeHVzi+I7/z1pQ0iuWMjfKm9hysF7udKyCo0goQs5YO+nHDztSxrGPAyNe5X/bWS+/0pblXktI1yPUbVnVYf3vIDQWEgzynJVNSFAUiYuAH3NWtLPeAjraY8jTH6AhXFXYqs5QCMxCGo9BiGAI2kkzHwTLvgMnsgl6vletL5/OTPWZSJ//1myaz7yvGm8VRZBKCYb9B3PT+02RrGThzWvEym6GR9awxO155FT9SGDMqK4YGgGTQ4fL50/gGfP6U9Gcrzy/JrjlHvTcwJfpd3IAXUOsRoPjQ4fbn+IJfsajvm+hflrEx6fhfkj4Fi/h5rH/02w9di9Wp3biwjUtdD+3SYKp9xE9SPKRLosy0i+n/aFDfPnwKDRIAoCRo0Wg0ZLqjWSbpExpEZEYe6ILDdoNEgdy+klWabF7aKouQGH71DiT4tWR5zRTIr1dxibAbaAH9dP2Bi0+r2dYr1FreGkhHRmpGSRqDdiUKnYZW+huMMCKC3SQoxaz6Un9+D/RvWnf9SRBfveEdEMiFTEBL3ql0VqF33+Ldtefx9Zkul3wUw0RiNBt4eSRcvZ9c4nOBuUlYlbX3mHHW/Ox9vW3mkB8z3dJ44mNrcHfpcLQRTZ9fZHnSL39xiio2gvq6JhZyE+hwN1hzDtqKlHbTTQe9ZpRGak4m1tJ+TzozYobWhNRqypSfidLlqKSpTKBAFHXf1h5xLy+gCwlVcx4pYryZo4ht4zp+FqaFb2qVWdx8fk9mDodZcSm9uD1Q8+w+bn36Dwwy/w2hydSWrLl69l3RMvoTHoMSV0XHtJpu1geZd2Ay43ddt2o4+yYklNIjYvG0tyIjmnTCL/vBkYY6JILOiDqBIR1SLZJ09AYzGhNZuwV9chBYP4bA6cdX/e8ZkgiuRMn/STZXKmTzyuCWe/p1u3bmzbto1x48Zx00030adPHyZOnMjSpUt58cUXu5S96667SEpKokePHlxwwQXYbDaWLl3Kbbfd1qXcpZdeSv/+/Xn55Zc5cOAA/fv3p3///tTW1h73/h8rWq2W2NjYo1qADBgwgA8//JD58+fTp08f7rrrLu67774uVic/h9Fo5Ntvv6W1tZXBgwczc+ZMxo8fz3PPPXecziIMKDYu5qTMnyxjTsz83WyI7r///i7WNMfjWfolvPnmmwwcOJBp06YxfPhwZFnm66+/7mJNYzQaue222zj33HMZPnw4BoOB+fPnH7XON954g7a2Nvr3788FF1zAtddeS3x8fOf+yMhIXn31VUaOHEl+fj5Lly7liy++6JwQu++++ygvLycrK4u4uCN/9/6REeSjmQ0dgYiICLZt29Yl+cD777/PnDlzeP/99xkyZAjJyck/u+TAbrcTERGBzWb7zbNxh/kL8s1tsPEl6DYGzpp3xMSbh+FqgZIlip/5K2MUG40b98GW1+G7u2DyQzB87m/f9zC/G7urbZz76gbG94rn6bP7K8K4WseaSh9PfFfE9PxkmpY9z23Sq5AyCOYs/cn6mp0+Ys2/3Ov9Z3mqL9iq4G+LIe3IM8tf765jd2Uz103MRa899IV35kvr2FzeBsDZg9O4P2s/moVzlP+J28p/vm2fE9Q6UB19mZ7DG+C5ZSUMz4phbE4857+2kTUlzVw1LotbJudy7isbWFfa0uWYHXdN5JNtNTy5uIiHz8hner/kw+qtanVz4hMr0KlV3D41l3c2VPLEgGbyls4GjQku+JTQ26eiCnoOO1YGArKKA6TTWyhDAGRBhSxLiMg0EE2c3IosCIiAIKhADhIcfi3q9c8A4EWDnkMv4gG1GbXkRZCC7Ol5BX2KX1J2jLgGkgfC7g/BUQ91OzoT0AI8H3kzV/Zo4+DeTWS59yAgM9X/IHkFI7nD8jmRm55ARkBEZoPYn1wqMEt21AQ5GDOW8TWXEWfW0eT08cgZfemTEkFOggW16kcvQvPPg/1fIg+8hJvcF+HyBXnsrH5Y9b/tEsvw9/WxMXbsWAoKCnj66af/K+0fj/FZ+F6H+U8pvvif+MpqiT1vMvEXTUHU/vznk7esFn9NE0Gbi9rH3sM0IIduT15L+a3P49p+gG7P3ICxV+Zv3/kwvxutHjcNTjuJZitRBiOBUAi1KNLiceH0+zFqtDS7FX/sGIOJBLPlqHXJsowky6iOs3jY7vexoKYUraji7LQeR7T2kWWZnbYW1IJAn4hDkbKSLPNW+X5klMV5w6ITaPH7OOBsJ9No6WKFczR8oVCnx/3RcDY0UbNxG2kjBmOIjmLZnQ8jhyQGXHouUd0zWXHPY4T8h8Y5+qgIRt12NTvf/ghbVS2DLr8AY+zhvtt123ez94PPieqegTkxHkdtPebkBKrXbSEmJ4uY7O4c+OK7nz4BAWWwBqh02s4o+87doqiISrKMSqvBkpxIe/mRk5hqjAbF9kYQ0EdG4G1rB6DXGScjiCK1W3bibbfhszu7WPAkDeqHr91Oa2kFSIfGbX3OOY3SZWtxNzQpQRGy3Gmh8/3fxrgY3E0tiBoNshRiyFWXIKhEzAmHiz3L736ckM9HrxlTqd+xB2tqMj2mnPibC3S/9Xd24579FH2xuEukvS7CSs70icT3ObJ1RZgw/6v4bC0468q7RNqLGi3mxEx0EeHV0GF+Pb9o6l6n09He3t5l2znnnIMoipx99tk88cQTx7NvYcIcmUGXKJYnpSvg2YFw7Q7Q/8xAxRQD/WYpv1+xFjQG0JnB3jG7bq85+rFh/pRUtLpw+ILsre2I9DMpEUujeloY1TOW+ZsqecU9Gik6kvGDplEQlNCqf/BCVrwEDi6DMbfywsZmHl1UxE0Ts7lmfE/l+aveDL1OhV8YAQVwoMHBpfO2cGJuPPeYYhQ/du3Rl3dP7ZvE1KhaWPu4MrHUEbk9qXdip2A/f3MVk7v1YVyv6ZA29Kh1dUH380vKF+6o5eVVpXy9p47Vt57IPafk8c3uei4YriwzG5QZxabyVlKjDJS3KMvALXoNfxvVjb+NOnrSp7RoI4tvGINWLZISaeDcoRnIssw69yskJGeQlT6UmtRppJd/BCjvfUJiPgy+DGnFgzQ5ggiiFjrewQQ51Olak0Brh4WNTECG/eah9B19Buo+p+Pdvwhd2wE0HMoeH0KFJuikRE4hhnasggdffD80KpHVseey5+MnuYpDvqykDgVRBASuanwV/y6RWF8AWZBxYULWRXBj4Rm4kYkSYH5wLJ6M8SwtdfIv7YuotBoY/X+UaE5EqK0h0qihxeVjTXEzt32ym9kjMrnnlN5dL9i42yEijc0JZ7HgwxpEAeZvquSy0UfOwxDm17F3717uuusutm7dSkVFBU899RTXX3/9L65nwYIFvPzyy2zdupWWlha2b99OQUHBce/v94THZ2H+CMRffDItC1bQ/N63eA9UkvnY1T97jL5bMvpuyqSuPiMRXWYSAIGmduRAkFD78U1sGOa/jy8YQAa8QeV7WNMhTMcazcQaodreDoBRo8GkPdwiqcXtQkYm1mim0taGK+AnIyIak1aLO+BHkuXOyP1fSrHDxpa2RvIjYtCIIjpRPKqPvCAIFETGctBpo9DeSp5VEb9FQSDFYKLa40IGNrQ2MCkhDVcoQK712JLq/pxYD1C6ZBWNu/cT8gfImzmNfheciae1jagsJZrTkpyIq7GZUCCIFAgQ7BDN+1145k/Wm9S/L6a4GIyxMaj1ynX0OZzIwSBpIwZjSoij9LtVBDui4AES8vMwJcZRtnQNciiEIKqQpRDIHCbWA8gdAro1LYWeU8chqjVse/XdLhMM3/O9Rz2yTELfXBp2FWJNT8HV1ELl6g2dEwMA1vRUVBo1QY+X5sIDyjl3tCWoVKh0Wva8v/DQylJZxpKahKO6DgBjbDSZY4bTXlGFu7kFtV6LFAix79OvsVfVkn/+GYeJ1b3PnIa9ph5XUwttpZW4GluI7tGNmOzuP3md/+jE98klLi+btrIq/A4nWouZqG5pv0lkfZgwf3Z0ETFordEEXHakYABRrUFjOoo1bZgwv4BfpDQVFBSwfPlyBg4c2GX7rFmzkCSJiy666Lh2LkyYIxKXo3jRPztQiXSVJXhrmiKi/u07JUnjTxH/g4HWxPuh1ykQ3wu2v6f425vCs6B/BablJ2PVa+gRf2RR+sxBaZj1ar7dk8pZH9RweZ2Of0z9gY/hN7dC60GIysThHQ0o/vYAfHQx1GyhbuQDJE285vDKy1bBhpdg7G2Q1O+w3XtrbVS2ull5oAluWKJYs/yceP7VDVC3E7RGGHkdAOcNTaeu3cu+OhvNTj89MjNhwLtdjwv6Fb963dEj1H6Kib0SWH2giXG5ytKzHvEWrhl/qK4bJ+VwQs9YHlm0nyanj+HdY1CJhwYnIUmmtt3DRW9uIt6i455TepPr2Yl32SOEokeSUPM5jL4B8s9icWEDn64s5Drtw3y18yKSJBMLM54lO1bP2IJs9GkFAKh6TiRpxUMkpw1BqNwI2+d16bOMwBJhOIND24kUPfR1roXF26DfWej1il+rDy1GfNjSxrEq7jzWbd3BTPVaeoRqiDowD3pMgvM/YseSYj7yjeI0zTICqNBNe4SkCCNUrqe99gCRXhtaQCuAhIA6Op37C2JIWdaMEx27Yk9G3ejm4po7mK0T2DxtMbG5mWCOZzKwf1Bfrvn3dryBEJmxSt9MOlWX6/fQ1/vQa9TcPOVherj85CQ6KKp38siiIuac0P0vMRgMSTKbylppdHiJt+gZ0i26y3P0e+F2u+nevTtnnnkmN9xww6+ux+VyMXLkSM4880zmzJlzHHt4ZMLjszB/BCJGFyCoRCp3FCPLMv76FsqufQpdZhKZj171s8cb+xwSuLo9dS2B+lZEs4H2JZuJGDcQ4ccrj8L8KUkwWzBrdZiOIqonmq2YtTqaXE4qbW2k/8A6JxAK0eBSIn4tWj2hH9johCSJ8nbFCz3REkG03nBY3S1uF95ggESz9YhR+XVeF+5QkPaAj3PSeiIIwk8mfvWFQqxoUoJ/4nUGYnVKm0OjE9C1t9Dq92BSa0g2mEg1dh3n+aUQKkFE9Su/w1OHDSTkD5AypD8AsbldE2AOuuJCqtZvoXrDdjytbXQbN7LLfikYorWknMJPviCuVzbdJ46mYWchjXv2o4uw4mpqps9Zp2BOjKd8+VpqNu2gcU8R5qREIjJSkUIhorqlk1DQG1OMMlkR1S2duq27iOyWTv2OvbQWl3ZpU9SokQKHAibsVTXs/+xbhlx18WFifdqIwdiqa3E3tRD0KNZIFas2kHPqSaQNH8iWl985JNaLIkOvvoTWg+UIgkDZsjWHhP4OYnp2Q9RoaNy9D5VWg6jVEnA4cVTXoTYayD/vDKypSah1WpIH9aP7+BPY9vq/0cVbEdVqEARUP5hA8jmcFH+1lNhePegxeSzt5VXUbVN87ytWbfjTC/agrISIzsr4b3cjTJg/BYIgoDX/PpZtYf53+EWC/ZVXXsmqVauOuO+cc84B4JVXXvnPexUmzM9hSYDrdoAgKtHyNVsh4AZH7c8L9t8jy6DWKv7mX96o2OMk5MOVq3/Trof5/RidfXSfMpUoMC0/mZo2D1/uriMj5kcJzk64CQ58A72mcYs5iZP7JtEryUpJo5M28ughlHDVsiBXJNczqXdi12M3vARFXynP4slPghTsYjtzar8UREGgd3KEEqGvUl7iFu2pw6hVd+233wXzTsHntFFnLsAc1Z9YKYQsiBi1au6angdVm8GaDhE/SnAly/DSKGUFyeWrFH//X0hihJ5XLhz0k2X++fV+dlS1kxlj5LrxPblz4R7aPQEendGX015YR3WbG5c/RGmTi7OfXcrGfl+hr1qNWFGMWqzHv+tTtPlnkZto4RzjJnoFS9EeeIkssY683h4Wp91Nrxd28LeRWu6Ylgc730Pc9hYUfgpeG2SdiMMvY6lajsOYhiUmmfwB12D9TPlekgEh5IM1T+Poez4GQY024MVtTiZi1mtM11uZfsqZBHcvhE8UYVMu+Q7h6X5co9Zxlb4cQQ6wWcine+5UeLUAHHWYZQEEsEf1xdq2mxo5hgn1d+D7WmLFjPdIq/6S/F3vk6/VQ1AJ+h+68Vqw3A05UwDQqkTWH2zB4QsyIiuWC4dnEmc5JGKUNDp5rSMJ8nnD0kmKMPD51aN4cvEBsuLNfwmxftGeOu79opA62yGv4qQIPXdPz+OkPkm/SZsff/wx9957LyUlJRiNRvr3789nn33G4MGDGTx4MAB///vfj3isy+XiyiuvZMGCBVgsFm6++ebDylxwwQUAlJeX/yb9/zHh8VmYPwrWkflkv38f6mgrnv3lBBrbCLm8nQnJjwVZllFHWlBHWig6724CNc14SqpIumLGb9z7ML8HoiBi0emPul8tikTqDdh9HkIBqYsdjVoUiTYYkWXQqlRkREQRlCR0ajV2rxeVKOIIBviqvpKZaVkYfrQKssntRJJlLDo9Zq1OSXT/g+dyaHQCiXojGUZLZ7shWeKAw0aS3kjkDyYZWnxevmuowqRSoxFFtIJSXpZlIrU6RsclUed1kaAzHib62wI+FtaUYVZrOCP1162Ui87KJDor86j7g14vJYuWE/L5icrKJDa3Bzve+gBrWgqJ/fLY+OwbqLQa/A4XNZu2Y6uqwe9w4Xceyu3VerACc2I8Ud0zqFq/lYDbQ9tBZUySd+Z02sur2PDES+SfP5O4vGzKlq6htaSMhl2FhPwBkgcX0FRYRMDlQRdhxZqahCE6ksrVh/KQ+R0uGnfvJ2lgPq7mVgIuDzE9u5E9bSJCx8T9jnkf0ryvGICizxZRu2UHfpcbQRSQJRlDpBVRo6b4qyVdroGg1SD7A6BS0by/BI3JSO9Zp1CxcgPO+sbOcnIoRMmiZfQ953TUHYmv/U4X7qZWvO12Rt9xA1Iw2CWRbMOufdTv2IO9upbEfr2JzExjyFUXU75iHUkD83/VPQ0TJkyYMGF+yC8S7E8//XROP/30o+4/55xzOl8Mw4T5zTH9IMPz375TEk8m9z96eVkGKaQIpJtfh69vgSmPwJA50FahlGnY/dv2OcwfhmBI4s7P9qLXiBy4/yQ06h8tP+5/nvKDksq0T4oyY/7AV4WsOHgSaVGnU+fzYjUcwaN3zK2KWD/8apg3HWq3K89oQp5SnyhwakFKl0OK6h1c8e42VKLA1jsmEGnsiOJxNSHXbEWNwFP+KTz14an4ek5hdMUlRBq0fDlNQvPeqRCRBjfs6doPWQJPGwQ8ys9vwObyVnZUtQNQ3uLm8cVFrDzQDMAlIzNpcHjxBELcOjmHzcs+4VXxYTzOcSw1nMIw9zK8sgbfsOvQAhkxJjKuepHgzo94bqmDc6RFRHt1TPusL1vF86m1dUQJ954Bqx4Hv4tgVBYfSxPYahnOFt80ntS+R/+qjSTUX0J9ymQSa75V3HGMcbD6cZ6WL2Wi4GEYe1A37VYmNK7bCYKAmHsyIURUSAjI0F6OyKHs7H2k/eyprCZ+1I34Vz6BxlWPDPis6dzZMIB1ch9yUyPx+iUi+wxDFWuGmi1gjEGu2kBA0KNt2gf7vuwU7AVB4MMrhlPb7qEgLZJbPt5JolWvTEzIMtnFr3FnYgv/Dk1A0xFdqlOruq4G+ROzaE8dV7677Ycr2gGot3m58t1tvHj+gOMu2tfV1XHOOefw6KOPcvrpp+NwOFi9ejXHmtLnlltuYfny5Xz66ackJiZy++23s3Xr1t/U8ubnCI/PwvyR0CYpKxVN/XqS8djVaOIif1Ksl4MhUIkIgkDVvW/g2LCHbk9fjyEnnZBNEQ9t320JC/b/IwRCIeqddixaPWnWqC7PjiAIJJoP2WCqBKEzUr7G0U5AlinyulCJAiKHP3NJZiveYACVSsX7lcXoVCpOT+neGeWuU6nItkR2OWa/vZ0NrQ3EaPWclnLI6q/F78UV6ogWD8FHNaXkWaPYb28n2xKBTlSx09ZCT3MEo+O65vIJSjIhWcYvSb9oMuuXULlms2JHI0DbwXIOfruC5v0ltB6sICa7GyG/D1GtIm3kEKrWbsJZ10jSwHzqt+9BliQElYqUIQWAYo8y4NLzaNi9j5oNWwFo2FlIS3EpyDI+u7LqIb5vDq0Hy0EUMcXHEgoEMERFEnB5CLhcNO0tIjIrE5VWS8iv9C3gcrP3w8+BDm97SaK6uQU5FKTXjJMBiOnZvVOwByXB7Q/xtLaj1htIHlJA7ZZdig2OAMaYKFx1jQiCgC4qgoj0FJL698XbbkeWZYJeLz6bg1AgiL2qlvaKKgzRkQBYU5MpuPhstCYjPpudoi8WkzSgL0n9+xJwe3A2NKK1mjFER3XeQ0N0JL1mTD3u9zJMmDBhwvxv8ovNlz/66CMWLlxIIBBgwoQJXHbZZb9Fv8KE+Wm8diVqOL5DtErs89PlJQleGQ22arjwc9izAOQQNOxV9udMgYMdURk+x6+2Dwnz56G02cX7myoBuHx0FokRP+8XCnDGgFTsngD/mNqL3EQLliMl/UwuUH4A2srB71R86n+C9Na13Be3nNUxZ3atMyqTJYNe4vV1VeRqGhCR0JQuxeY+E7vHQMCQikZrgegj+MWLKrhyHfjsvyq6/ljISbQQbdLS6vLTLdZEovVQ1NwdC/dwy+QcusWYGJwZRelyO2pBQvI6sI+8H/G7pSAIPL2iiqULPPx7zlBSozJRj72FZN9+Hi6ZxPvuexDlEFf1DmCe2WEvFJEK+khwt2D3Bplaej/zhQeZZd5FhkWGFpCNcSTOngevTVTOP3cagfL11NdFM4wfTGx425XJPEFA1GhoSxhCVMOGQ/sFFcghJEGFWq1j2KfDYfozaAMOEEDWRxBX8RU3J+azfcSljB10KKrqhYpUtlpe5F/6VzCzgY8Cw1HFdOfs8bd0uYa9kqz0SrKytaKVL3cpPqpzx2YR7diPsPQe/ga84M1j3cEWTjlCAt8/KyFJ5t4vCg8T64HOZH33flHIxLzE42qPU1dXRzAYZMaMGWRkKEu9+/bte0zHOp1OXn/9dd5++20mTpwIwLx580hN/fkkgr814fFZmD8C/sY2BEATr3h1Wwb/9OSir6aJg5c/gi41npS/X4BzxwEkjw9/XTOGnHR03ZLw7C5FOoK3dZi/Jg6/F4ffhy8UJMpg/PkDOog2mPAGA8yMiUMliEdMFBuhNxCBAVvAh08KEfo+ae1PCOauUBCjSk2Gseu7QQ9zBPvsbTT7vQg+EVknUXTQjRQn0x7w07PDGsGqOdyHP0an54zU7mhF1W+2Ui4mJ4vSpatBlonqno6rqUXZIcsc+HIJfc89A2NMFD6nk6q1mwDQWS1Y01OwlVeh0qrZ+Mzr6COs9L/kHKKzMohIT8bXbkMQBdrLq0GW6T5hNKnDFDu2iLQUBEFArdPhbm7B1diMxmREazF3eOiH0FstZF92HjvmfYgpMR61RoPP4cReVdvpbQ/gabN3/p7QL48DXy9FDh6y0/le3P8+qe3Gf71C3/NmULtpx/cFcNU1Yk1LJmf6ZCLSlfFTKBDA3dxKRHoyLQc6LHskidi8bBLy87pcw9gcZexcvGgFrcVluBqbSerfl9otOzvbabE7CbjcaM0/WqkbJkyYMGHC/If8IsH+lVde4YorrqBnz57o9Xo++eQTysrKeOihh36r/oUJc2TeOU2xwTnnA8g56efLyyFor1LsM765FSrXK9vjchQxv/jb7wvClzfB6S91JJUM81clO8HC7VNz0WtUJJpVSgJile5ncxhM75fM9F8imv7tO6Xu1IE/WcywYDYXBj1cOG08/EicTB4whcai7UzKlmDbG4iCyILLBqE1RWGMN8PfKxRx/kiY45Sf40yD3cuFr28iwarDH5QwalXcd2pvPtla3Vlmb62d9zZUUFjnICXSQI1/BEVCIifoosnf8gwPqa7gIekprqy+hXneZylvdpMapbyc3zI5F/pshVc3AxB/8h3QXgytZZA7FeauB7+TiJdGIwheRgXXcQUfQcf7qGSrQVW3C9KHwZY3YMMLaLRm7hg+lead3fCHINlfhhTwIu7/iqrE8WjVIurTXyPwUgFqgggIEJsNQQ9iZAa6mq3g9+P57AYMsrJiQRDUIKiIaN3F2MVT8JdOpjLrQvaqemJf8hiDBBfzh1zCuHEn8vq6SEZnpYPlRxZKQR9seIFecQWdmyY8uYrP5w5GnXUmnxe5kQwxlDU5eXVVKXNG//l9UQE2lbV2scH5MTJQZ/OyqayV4VnHL7dIv379GD9+PH379mXy5MlMmjSJmTNnEhX188kADx48iN/vZ/jw4Z3boqOjycnJOW79+zWEx2dh/ggE2xyUXHgfANnz70Md+fPBDyG7C8npwVfbTOmVjyF5lESW+m7JBFvtBGqUFVuSw419zS6so8JWE391InQGAqEQRo1WyYUQCqFRqX7SSx4Ub/xjbkOj47SUbqgFEc1PjPcbvR5225SBRe+Irt8RoiDQyxrFjvZm4hyRbN7iINFkYGLfBGK0OvQqNT3MEUftd4Tm1yXG/TlaDpRS+MlXRGamKZ7KVjN9zj6Njc++AYAkSdgqqin1+nA1NGFOVMaIolZLa0kZQa8PUaMm6PER9PjwttmQgkFUWg0qjYaC2bMoX7GOpsJi9NGRdJ9wAi3Fpaj1eiLSkhl521V4WtvZ+vI7CKJIwOXu0j9Pazvu5jYiM9Jo3FsEsow+MoLE/n1p2ncAOSQhBQI4G5pwt7QS8gUwxESRPnIwFSvXd9YTlZWJo6aOuD651G7ajt/pYtc7H3fu1xoN+J0u7FW17Jn/KdE9uxHftxe1m3fQsLMQgLyZ02ivrKal6CCJ/Xoj/ijZr7fdRs3mHWhMSm4Cn83BlpffIe/MaVRv3IanpY2IzFTKlq0lJrv7YXkEwoQJEyZMmP+EXyTYP/vss/zf//0f999/PwBvvfUW11xzTfiFMMzvjz4SRPXPJ+r8HpUGrlit2INseEkR7NUGSBmkiKnFiw+V3bsAKtfBlWtB///snWd4FdXahu/Zfe9kp/feqYHQe+9FQQQFEQt2PfZj5YDYO3Y9diwoChYElN5b6KEkJJDee9u9zHw/JiREQMEPLJzc15UL9syamTWzy6x51/s+T5txyKXMuM6hiF9Ow71uL0pE0HrJsjLq043K/hAuuxxIP1uwvuwwZG+EXjdB39uh9BBE9GpZv+u/iGmLSY94hJen9qFHtB/02QEqLR39IlvanS1YfxHJrjSRWd5IbrUZh0skNsCDQYmBdI/yxe4SSYn0xuGSqDTZSS9tpLjOStcIb9KK4rgn71VGKffh6zkCW6MaSePJ+9N6MTDxFJmr9GVQng4p14AhgI8O2blq4xi8xHre1t9Bb0cqPxsu464JHxOodjC0oBZb6iq0rkYEQFLp4ZPRrTvtaMS4+3U8RZOskAUo3HbE72/hM9s02quKGBNiRRk7EGHMs/D+EKjMgCs/geQr+emr9xif+Tg57kDiNHXo3Q1grQaPQDBXgcOEJv07EtK/4zvDAzyqXgyAdGgtx6/aSE59Lrk787hvZGKL5BHIFT/r5qPxCOaZyWuYu+woNRYHOTUOnD2f5eVj+4j31vHaOrkcfEKXUMJ8LtBn9C+kovHswfo/0u5cUSqVrF27lh07drBmzZrmsU1qaiqxsWeoVDmFc5XN+bNpG5+18XdAUCkR9E264Mpzuy8ZOsQQ//4jKH08yb3vDUSrHXWwLyp/b2pX7cRV05JlW/DEh3j1Tybq6bbqkUsZpUKBQa2muLEeAXBLEp4aLVHevz+peq7Y7CIeggat+szB+nxzIw7RTYyHF1EGTwxKFZqmsZYkSWypKsXkdBKs0zMhNBqPSDWJgUb8fdXodS37/L1JhotBXV4B9voG6vILkUSRgPYJaL2MpNxwFdlrthDYuR0ui5XS/bIMqKW6Fr2/L9bqWhoKZQNdjacBh9OFMTyUDleMRamRKz8lUSR/ayooFAR0SCSoc/tmfXlBoUCp0yI6XXiFh9Lt5pm47TZK9h+hKj1LrmYEbA0NHFn8Y6s+2+rqKTvQWpbU0dDI/o+/xlZTh9bbiEKlIrxfD/wT4zj0+RJqjucwdP6DqHQ6Ko9m4jRbZKNZQQBJwmEyo9LpcNlsWGvqKE49QHHqAfSBLQkABdt3E9q9CyW7D5K/eQchXVtn2Gev2ULp/kMEdkoisFM7Ko9mUl9QhEqrJbhrJ/I2bAMJCnfsoSrzRFvAvo022mijjQvKeQXsc3JyuPHGG5tfz5o1i1tvvZWysjJCQkJ+Y8s22rjAXPMtOBrlwP3WBaDxgD63/fY2PlHyn2+T231UX4jsJUvgnIrohMYycFjaAvaXKke+w739LT4rSmamcBylwoak0iGotHAGzVMAk93FnB8O0yHUi9uHnKO8zGeXQ9EeuH65bG78a5bdBaVpsnnyyPmnr0/7GkXpQbIKfuLNo1q2Pjwcgtqf82leTPrHB/DuzO6EeeuoNDkoq7fR//n13DcqiftHJfHcygwOFdXx7szumO0uInz19I0L4KOtOWTbpxBS5eTlmkFkijPw13my6VTjXlsDfHs9IEHSOJj8Hp++sIEQZwf66/OZaP6OGEUFvRr2IywFlGp6iG4mOJ7nI+1rhIllqHwioOqYPDHnsiOH58FDNJFNNPGC7FshKrW4jeH8x/W5fOwyObMbhRrUOlnOyFIFJQfpEapBnSXioZbkYD2A3h+6XA0j57PqnfsYU/0lElAtGXnHdRk3KVehFV1Equr5xftFzNpAjJqxvLI6E4AHRydBzED2CZ3ZVN+Rjh5aWaEHWLgjjw3HZCmlrDITCgGu6R1FqPfZzfr+SQQZz+08zrXd+SAIAgMGDGDAgAHMmzeP6OhofvjhBx544IHf3C4hIQG1Ws2uXbuIiooCoLa2lqysLIYMGXLB+3mutI3P2vg7oDQaaPe1nGEvOpyUvr0Uzz6dflcWR99O/i6pg3xwllbhd/lglJ56VIE+rRu6RezFvy0v18Y/m3JTA412OyXlTvwDhGa5mN8KfNtcTirMJvz0Bjw1v5+1bra4ef/rEhQKgTtmhqHVtA7a291u1lXI1YJ+Gh2jgiNbrXeIItmmeiSgzG7B6nYzKDCUiNCLkzF/vsQM7Y/O1wefmAgaS8qpLyhh+8vv0mXmlUT2687xXzaABJ2unkTOui2E9eiKvbGRyozj2OsasNU14DDJWfGGQD+8IlqqSquOneDELxsA6Dx9El6R4aQvWQ6AuimjHeRJgwMfLQJApdM2Sw8iSbImfG09aoMBp6Ul+16hViM6nae8VuGyyhP29nr5Wa1k90HCe6Y0t7HVNSK66/AIDqAupwC1hx6nWa6A1HoZaTd5LH5x0Wx+akGz3I5ar+Okq5PDZEFr9EShVqHz8cZhtnD85/X4J8YRktIJ7+hwSvcfouZEPgljhlJ5NBOtl5HUNz7E3tjkr5FfhEqnJXb4wP//m9dGG2200UYbp3BeAXur1YqnZ0tGs1KpRKvVYrFYfmOrNtq4CChVoPeVNejXPykv63A5eJ2DOWH/e+RgXNFeKDsCbgco9eBuGr51uRoGPnBu+2rj78/ye+VM9ulftUiRHPoWZekB/qM8QKXkzSvhb/Hvay9rCdKegc925LLsYAk/HSzhtsFx56Y56rbLxq/iWbR3u1wtB+vjh515/aR3KNq7kg2Hkxmf/Pf7PI5PDsXmdPPBtwfJKjNRUm9j+4kqPtiSw4kKEwBzfjxMdqWFgQkBvLspG6db4pH7r2fCm3E4RQmtSsGNg+VJCIdL5M31x0kK9uTyqD5QsEv2k3A7+TppE4cdk/gubCzShqeYLS1DgShPr2iMuF0OEu0FpLljCRPKwDsMukyDHjeA2kB+xm48Vt2LvzWfeC8nP3s9yKocK8ljb+GW1DHy8RV6NGLT78DaeXKwHuDYz/DLw0R0mgKjniJy1wdkqwZhdFQQZM6EnW9DYDuGjLsaFn2JALzofJlSlSeNGNGJNeiPr6SDPQ2cKvIra3h74wmmKLbgPrIC1fgXeED/DCUOK1225jD/so5E+BpYlJrffK2NejUGjZKHx7W/aHq3fza9Y/0I9dZRVm87o469AIR46+gd63dBj5uamsr69esZPXo0QUFBpKamUllZSYcOHXA4HKSny6XyDoeD4uJiDh48iKenJwkJCXh6enLTTTfx0EMP4e/vT3BwMHPmzEHxK0mFmpoaCgoKKCmRsxUzM+UJmpCQkIsSQG8bn7Xxd0Ghk6uHalZsp3rpRhpT0zF+Me+cto14dBZV36zHll+KaLUj2l2t1gffcQW+o3pf8D638efjbrSQP+e/qAN8iJh7Y/N9rcZqRULC21ugukqifbQnvjrDGfXoT1La2IDV5cThdpHg9/sSgBLgdjf9/ww3H41CQbyHF3bRfUb9ea1SyYigCE6Y6qm0W4n2OMdq3z8JpUZDeK8UzJXVFO3aj6WyGofJTPWJXE78sp6TN9z0pcsxlVagNugp3XcIjacHKTdcxe63PwVA7WEgaoD8fbPW1lOwdRcBHRPReHrgMJmRJAmFSolfQizeUWGYK6qbJW5OIqhUcqAempd7BAcQ3rMrYb1ScDsc5KzbSlHqfkSnk6AuHag5kY/LYqHLzCkcXPht047kYL+gUHDg02+a93/sp1XU5RSQdNkodF5e1GTnoff3w2EyY29o5NDnS+j3wG1E9u9JwTZZp99SVdOShd9oor6wGNHpwmmxUnHkGKX7DlF+OIPcDdtInDAKQalEEORJiMSJo/CODCPts28BuT8I4BMbRViPNrmuNtpoo402LiznbTr70UcftXoodLlcLFy4kICAFimDe+6558L0ro02fo+AdtBzNqgNp+tC/xpRhKxVENoFjiyFinQoPQimMjmoGtgeet8G3WaC6u+RJXNRMVfLhpsXyYz0b8OhJeA0w4FF4B+Hq/0kbEPn4+kwQ95WApRmrh7Zh4/21nJVr0i8zrKbIKP8mYj2N5x7wPT6FbLZ7JkMYQH63SX/nY2QzrxtF8muL6Tzb2h9/1k4XCKXv72NRpuLFXcPxNdDQ361hZ8PlwHw+PgOTOkezqfbc8muMMnZZ/U2lAqBKD8DblGizuok2EvHLYPi+GF/MaUNNp75OYOZfaPZnFXJ2xtPoFdKXH75FOh5E3SaAtnriTr0BlFKDbNtvdhgm8bxHvdzby8DEWXroeQAqkOLeU37PsNsL1Gt8+fa7BWQvUGW1FGo+GbrEVbX3clq3X9QCUqcgpo3Na9SvnsTJIyEnI1oxr6IffvbqL0CEY6tbDnxkgMAnKiyEZ35ImqXmVCpHBdKJKGpJsPeiN52CCYsgDVzEUUnYVSw1t2dQTGd0fW+GYI7gWcQUSEBzBloZMKx7agaiyjev4qCmvFECJVUFpZRFOXLDQNiGZgYQHGdFV+DhoJqC9M/2MkD36Tx0fU9/+y3/qKgVAg8cVlH7vhyvyxjdMq6k9+wJy7reEENZwG8vLzYsmULr7/+Og0NDURHR/Pqq68ybtw48vLy6NatW3PbV155hVdeeYUhQ4awadMmAF5++WVMJhOXX345RqORBx98kPr6+lbH+Omnn1plvE+fPl0+nyeeYP78+Rf0fE7SNj5r4++E1+AULIdOYBzw+0Esd6MF075jGPt1pnbldiSnG0dJFdYjsiGkR68OBEwdhrFPp4vd7b8FuXYrngolgWcIFl8q2AvLsRzKBoWAR0oiuoQIdB1iiPDy5nhJPTpP8PNWolepMTvt+OjObj6rViqwukCvUp/TsT0NSu64NgwB0GlPnwgQBIGhQeG/uY9oDyOpNeWY3S6sJ6P/fyGm8kr2/vdzfGIiSbn+KkDWsq/LLUDv50PHaZcR1LkDFYeP0VBYDIDodIMAxrAQ6nIL8I6OwCM4CP+kOBrLKnE0NJK9eiPdb55JwbZUCnfsxVRWQUS/nvjERuEXF0XWinXUnMgFQaAurxAkiYSxw/CODqcmO5+GwhKqM7NRG/Q4LVa03l6U7TtMfV4REX17YDVZKNyxtzmYr1AqkZquZ/GeNIzhISBJRA7oTcH2VFRaHXW5Bc3nba+Tqx3LD2VQn1/EmbDW1qP2MBAzfAB5G7bjssiJGQq1iqDO7YkdPgi9rw/+SXFoPD2ozjxBzYk8zBVVFKfuQ3K7cbndlB/KILxPd3yiI+j3wG24HA40Bj3Fe9I4/vM6CrbtJmpg24RiG2208fdg06ZNDBs2jNraWnx8fP7q7vypCILADz/8wOTJk8nLyyM2NpYDBw6QkpLyV3ftvBGk8xBkjYmJ+d1AlSAI5OTk/GabhoYGvL29qa+vx8vrbOGxNtq4QIhuqM2TdeuX3QUhXSC8O+xbKGflH1shZzlftxxi+v/Vvf3zWNAJGkvglg0Q1u332/9Tyd8JZYeQVj2GILmZystonQ2M7ByG/ehKbletoFQVTj/Ty9w9PIEHR5/dPHJvXg3xgZ74evx5D9Gbsyp5ZXUm94xIZFTH4D/tuBuOlfPGuuM8OLodg5PkjLV6q5Pez67D6RZZ/+BQYgM8APgqtQAPrZJJKeHMW3aEzVmVdAjxIq2ojicv78T+/Fo2ZVXy9jXdSQhqCSjWWRzc9dV+YgM8eGZyMg02J3N+OMIE1W7GHn0YSe/HI/E/onY2clXpK0REx+FZl8lhz4HUdL6RL3bl83HN9WjMpRCQhDtqAO973k73EC19fxoqT0jN/A72fgyZP/OjaizGMXMYkRyDuPU1FNsXNPflkEd/Nokp3CEuJkvTgU6N2yFuOBTuAqcFl8pAg1OBnyBPRKxzpTBKdRA7OrQpV8q/IQe+AJ9ouG0L/Hgn7uNrWdvjXcZOmEajzUlNdSXRy6aCzkv21cjdAkEdkSozeZVruVP6BqXk5mafD3h59ng+3JLDTYNiCfPRsza9nFs+30tikCdrH/jzpFf+jPv1qiOlPLk8vZUBbai3jicu68jYzn+/ypK/IxdifNY2Nmvjz8ZtsSGabZR98CP1a/fgf9UIGncdwVFYjs/4/tSt3IHSx5PEz+ai8v57ZTFfLDJtZgYc24evSkV6p76ohbNnlv/TqVu7G0dxFRULV4LRgOKTB1EIAnqlikaHA8Upk7XR3r54nEXuRpQkrE4HBrXmT61AO1hXRaHFxJDAsDNm4l8sctZtpSY7j85XT0LnI/9WV2We4OCn36Dz8Wbgo/8CwGV3ULBtN/6JsXiGBLL/46+RJJDcLkSXmy6zriRz2WoEQaDLrGko1S15fPUFxRxbtorIfj0J69mVxpIyTqzehCAIVB07gX+7eASFAoVaTX1+EcFdOlBzIo/ATkko1Wqqj+dSeyIXAENQAOE9u2A3WTGGBXP0m2UoVEoGPvIvdrzyHi6bHa23F52nT8I7KoLUNz7EXFGFoFQguUU0Rg+UGk2TJr0We30jwSmdKD94FACFVoNod5x2nbReXvi3j8NSXUtddj6h3ZOJGzmYve9/jstqo9PVkwjq1A5bfQONpeVkLF1JSEoninbtR3S5MAT6Y62tQxIlOekLiB7cF/928dScyCVu1BAUCgWZy9dQuH0PEf160n7SmIv63p/KpXrPLisr49lnn2XlypUUFxcTFBRESkoK9913HyNGjADkMU9+vlyFqtPpCA4Opnfv3tx+++0MHz681f7uvfdetm3bxpEjR+jQoQMHDx487ZiSJPHqq6/ywQcfkJ+fT1BQEHfccQePP/74BTmnk4FTHx8fSktL0elaqrl3795Nnz59mvvRRhvnyg033MBnn30GgEqlIjIykilTpvDkk0/i4eHRFrC/RAL255Vhn5eXd5G60UYbF5FVj8Hu9+XseZ2PbDx7YBEgQO9bYfQzcha0d+Tv7emSoWz/zxgbatEjoDi05NIO2Ef3g8g+uLK3sP/YCeqdblZrnkE8JnC9+xFmqTfiCOhEOw8jQ5J+u5S6Z8yFleagsQxWPw6JY2hsN4W9ebUMTAxArWx5QB+SFMiQaB3s/hDKRkFI8oXtw1lYdrCEtKJ6VhwqaQ7Ye+vVLL97IFaHuzlYD3BNn6jm//+UVkKdxcn1/WL47yzZbPe5nzPIq7awP7+WhCBPJEli3rIj1FudLLyxd/P5eunUvDWjGzQEQ923OGuL6HjwGea7bmARt/CKPpWp5dvo6VvMPbYBDMh9nzJPHyKVdVB1HGVNDnd6rAYk8E+A4n2IkkS5TU0oMMm5im/X6MBjCorhc8BWC+ZKpGMrCGjM4B7FDgAirPtBAeRvB0nO9FK5LPgKcha40PsOSn1uw7a2HzrJCgcXtVy4unwo3I2p5BieooNRYbKkw9Xv78Jefox1mnQEhQoGPQjVOeAThVCRzr99N+Oy6DE7RManxDB74R7SSxtYk17G1keGM6qdH2suc+LTvvNFeb//SsZ2DmVUxxB259ZQ0WgjyCjL4FzozPpLmbbxWRv/RHLufAV7YTn+Vw5F0Kqp27AHd3UDCk8Dof+aSsCVw3BbbM0yO/8LvFCaiwsJs9vN2oYaxnsH/P5G/1B8RvXGUVFL3bY0HPFylawoSZhdzuZg/cmsee1vZM8rBOGswfw/SqnVQnpDDV19AtAoFFjdLoJ/leWf4hNAjMFIjrmBDkZftOdotPz/pSh1H45GM7W5BYR2k8cEAe0S6H7LTPR+Lca8Kq2GuBGyrrqlqqY5A33AI/9C7+uNra6emuNyUN3RaELv54PDbOHINz/hFxdFn7tvat6XMSyEbjdOp/p4LubKaurzi3HZbAgKBZIoUpudj6m0HK/wEMoPpeN2OFHqtEhuEUtFFflbUnHZbPjERKHUaBAEcNntzXI59voGstduIW7EILrNnk7mirW4bQ5qTuTiaDQDsl68uykwX34oo7lvzcF6AbrOuoriPQeoyjiOvaGBkt0HUajlz075kWMEdmon6+ELAl7hodjqG9jxynsggehyUZdXiH9SHNaaOpw2O5LLjVdkGA2FJSi1GgI7JrHvgy+RRBFHo5mOUycSPbgvWi8jUQN6Xei3+i9HdLsp3paGuawajxB/wgd2RXERP+d5eXkMGDAAHx8fXnrpJbp06YLT6WT16tXcddddHDt2rLntU089xS233ILD4SAvL48vv/ySkSNH8vTTTzNnzpzmdpIkMXv2bFJTUzl06NAZj3vvvfeyZs0aXnnlFZKTk6mvr6eqquqs/Zw/fz55eXksXLjwvM7PaDTyww8/MGPGjOZln3zyCVFRURQUFPzGlm38E5AkEXdtMaLdjELrgdI3HOEiT7qPHTuWTz/9FKfTydatW7n55psxm8289957F/W4bfx5XNBPUHV1Na+//vqF3GUbbVw4Dn4lG9XWFzZpikuwdDZYa+CjUfBmChxc/Ff38k8h6Kdr8MCMAjfsege2vf5Xd+niolCgvmYRphk/8ULnUhAECqQgCj274Ho4n+jbl7D6/sEXPiD/e6T/BEe+g80v8Oj3h7lx4R7e2XiieXVBtYV5y45Qvv4t2ath2d3w88Nw6NuL3rWHxrTjvpGJ3D8qqdXypGAjXSN9ANiRXUX3p9fy6prM5vWeWnke+OScQ1m9jbGdQ5gzvgNXdA/ncH4F+z65n8bdX/FTWil3LdrHrZ/vpd56is6/VxjS4IfQWMqYrt7KHUPimdTJn3ZjbkPyiYbaPOYbf+J21QoibRkIbitIEpLoAlM5mJpkiK79ju+LvfHN+7nZ7+wy11r49jrY9ylc9jpMX0S+IZkwRW3z4fWKpoc/t10u01ZqWKkcTpHYFDjZ/R7X+R5Fd9VHYPA//eJ9dTXqhgIedNzK/joD/HcQT9bPwSZqODD0M5i9CuuAh/l28CrKx7wHSg3U5qGy1+IdlcyMYd3pFSM/dMefrEjY9DxJa68naMt//tgb+jdHqRDoF+/PpJRw+sX7twXrLzBt47M2/raIEjXLtyPZnbirGuTAWaOF4he/xHwwi9x/vcqJ2c9hPZb/+/v6h5NqqueH+moALJLITXkZbDfV/bWdushognxJ/PBRwu67Gs0pgUAPtYYOAcHE+voT6+v/mxr2F4OjDTXkWRo51ljL8pI8VpTmU2Zr8QQpspjYVV3Ojuoy9tVWsqemgq2VJVTZrb+x1wtD8owrSBg3nODk1mbOfvEx6H29AcjduJ3NTy2gKlMeUypOyZ4/aexaZQHN0HEkX3slej8filL3s/utT6jJyubEqo0cWvQ9WSvXtTqGf2IsfomxuGw2NEZPogb1ISSlE/FjhqJQqag4kolHSBAAbpu9+VgOswXR5cZcWU1I1070/tdsDn31Q7OpLEBdbgH7P/oSl91B12un0vX6q89+EUQRhVqNQnVK7qEEhxZ9R8dplxE9tH/zZMDJPohuN4e+WNrUVqIs7Sj7PvwS0eVCQqLjtMtInnklSRNHETWoD0kTRgLQUCh70UQP6YdPTCSGQHncZwyXJ5kOL/qeE79soCwt/ez9/Qdy/MfNfJw4laWj7+GX655k6eh7+DhxKsd/3HzRjnnnnXciCAK7d+9m6tSpJCUl0alTJx544AF27drVqq3RaCQkJISoqCgGDx7MBx98wNy5c5k3b16zbxDAm2++yV133UVcXNwZj5mRkcF7773HsmXLuPzyy4mNjSUlJYWRI0de8PO7/vrr+eSTT5pfW61WFi9ezPXXX39a2++++45OnTqh1WqJiYnh1VdfbbU+JiaG5557jtmzZ2M0GomKiuKDDz5o1ebw4cMMHz4cvV6Pv78/t956KyaT6YKfVxvgLD9O45aPMO9dgvXwz5j3LqFxy0c4y49f1ONqtVpCQkKIjIzkmmuuYebMmfz4449nbFtdXc2MGTOIiIjAYDCQnJzM119/3arN0qVLSU5Obv7MjBw5ErNZnjC94YYbmDx5Ms899xzBwcH4+Pjw5JNP4nK5eOihh/Dz8yMiIqLVZxzgkUceISkpCYPBQFxcHHPnzsXpPIu/XxNFRUVMnz4dPz8/PDw86NmzJ6mpqc3rly9fTo8ePdDpdMTFxTX341Lk/z36kSSJ1atXc9VVVxEWFsazzz57IfrVRhsXhsLdsqmsMUwO1otuQACvCFBq5cGcSg8qDTgtsOzOMztQXSJUNNq4/K1tuJCzTQpPBiBT3/8Le3WRqMyEyqxWi0Z0CKaHphAFIqvFXtQ5lXjrf1/zNKu8sXVA+UKRPBV63UJq0r9Zc7QMhQDxgS1l/x9szebznfm8VxQP4T0guKNcLfLzQxe+L78iwtfAfSOTCPXWn7VNekkDNWYHe/Jqmpe9dnUK949MYlpPuWLlqRVH+e/mHErrbaiVCj787BN6Fn7Ky+r3AYk16RWsSS/nkaVpzPhgF4eL6kEU2W4K43HnTVxv+zfJjgO8kj2BnIW3sLtGjwRUenTAFjuyWfv8NecVWDyj5Yx4jQEOL4GNzzEy/XF0Qst7V+sZJwfZAztA3nbY/SEhGtn01KH1A5UeDS16tA5DELgd2LX+vM3UlpM//C10mAj3HKRRK8u22BUesjQOIlrByW3G7SQUfQ9lh+glpjFTuZZfzIkQ0ZMPNqTz8NI0HluWJXtonMQ7AoAnJ3Um46mxLLyxSQ/VLx4QLn3PiTYuGG3jszb+ztT+shNDlwR5stVqlxcKAtp2TdWOooiySQbHUVxJ2QfL/qKe/jmsaajm9vyMVsusksjn1WV/UY8uHubD2bjqGptfC4KAn172uTmJQhB+V95GkiSsTudFkZFI8fGnndEHpyhiF92oBQUGZUtweFdNOUcbatArVQRodFjcTrJM9aTVVV/wvvwa37hoYob0Q6E6e6ZzfX4RTouVhqJSAHTeXnScdhntrxiHR5A89v/i2/2sO1hFrdYPU1kFx374BVtdkx+LIFBxOIOCramkL11B2udLcFptuJ1OnGZLc5vanHzKDh7l4KeL5cC3JOKfEIMh4JRkBqWi+dnKabVSvHs/mSvWYquua9VnQ6AfhgA/NJ4elOw7RMXRDNnUFdAH+LWY1zYhOp2ILhcKjRqhabJHcrsxlZSTOHYYvf/V4iMjKBStnu+M4aHkbtyOtaoWJJBcbnTeXuh9vTn89Y+kL1lObXZeq+PpfeTJkH7338qwpx4msp/sJ2QI8EOhUjZPllwKHP9xMyumz8FUXNlquamkkhXT51yUoH1NTQ2rVq3irrvuwsPD47T15yLpce+99yJJEsuWnfv9Yvny5cTFxbFixQpiY2OJiYnh5ptvpqam5vc3Pk9mzZrF1q1bm7Ppv/vuO2JiYujevXurdvv27eOqq65i+vTpHD58mPnz5zN37tzTMvpfffVVevbsyYEDB7jzzju54447mqsQLBYLY8eOxdfXlz179rBkyRLWrVvHv/71rwt+Xv/rOMuPY0lbjmRvPRki2U1Y0pZf9KD9qej1+rMGw202Gz169GDFihUcOXKEW2+9lVmzZjUHwktLS5kxYwazZ88mIyODTZs2MWXKlFb32A0bNlBSUsKWLVtYsGAB8+fPZ+LEifj6+pKamsrtt9/O7bffTmFhYfM2RqORhQsXkp6ezhtvvMGHH37Ia6+9dtZzMJlMDBkyhJKSEn766SfS0tJ4+OGHEZukyVavXs21117LPffcQ3p6Ou+//z4LFy68ZJ9zztt09iR5eXl88sknLFy4kOLiYmbOnMnKlSsZNmzYhexfG/8ruF1yQF3v+/ttz4alBo7+IOvSezZJm2x4BnI3Q+Jo8AqFHjdA5yvBaYWKDCjZD1tehls2wZaXIKjjaQPCS4Ltb+AqOcTz1us5VNxIV/5LhFBJsRTIfZpl3HLZTC6ps24sg/8OBAS4/wh4BrWsm/Aqu3UDeGO7P5O6/b4+9qbMCm74dA+dwrxYec+gC9tPgx9MeIUNP2fgdOcwqWsYl3UNa149vVcUpXU2Rg7oQ4aiL/raTGIAIv4eZbfX948hxFtHr1MqE3rF+LV6Pbx9MNuOV/Hp9lzyq81EdRzGiiPbOOIKR6dSMqpjMMsPlbLtRDUmu4tlacX4rHuFgXnfsVS6kyx9F3albWU8LsKoZJbjMa5P1vP+RpEOoffxS9/2sOdD7grPRhsxWNaR1xohuj/kbsHHaUE0hpFh9aOT6wihthz59+G72eCwgKMRXdOnX3Plf2HxTLnjChWILjY2hNGlw0gmjX+U0QuvRKiTVzsDOsjTXifW4mmXH4jVohU4GXwXSEruywafK+l2Yi2+gpkkoZBbtubyQGcr/9oznv76BNISv4BRv8iTB+3HQ2CLh4JeIz+AFtZYePJQO8ZNTOPKntEX4Z1s41KibXzWxoXELcoTmErFH5dBsOWWYD2Wj8+o3ggqJZIkUfzyIhAljIO64q4zEXz7FRjaReEoq8ZZVUf9hn3YjhcS88b9VH21Br9JF/j++zfAJoo8VHQcH6WKb2rLqXC1fsgOVWl4KDjqLFv/M6nftJ/C+R+jS4wk4cNHW62L8fGjtLEei8uJl1Z3lj20UGE2UW0146c3EOJ5YTW8A7R6Bmr1LCvORQL6+AW10qnv6u1PvsVEL78gSq1mJEClUNDJ60+u1DwLHa6cQG1OPkGdWsYUYT1aG0B3ah9EVlou2Z99gXZkP4xhwTSWVoAk4RkShNtux1pTR8m+NJCg6tgJjq9ch6MpYK/39T7N6NW/XQK5G7YTNbA3tXkaTKUVhHTtRH1hEZbKGrzCQxCUSqqPyZn/+kB/rJXyJIdar6expIyDn31LQ0Gx/DzWFChKmjCKtM++AWiW4UGhIGpAbyL6dWfHS+8290HrJU/0Fe8+2LxMEk9JjBAEogf3pWTPAWpO5DUvzlqxlvA+3WgoLEap1RCS0gmf2EicZitBye3QGo3NbZUaOdmnKjMbh9lCj9uuwzuyZfz+T0Z0u9n0wOtyNsyvkQABNj34BvGXDbyg8jgnTpxAkiTat2//h/fh5+dHUFDQeckE5uTkkJ+fz5IlS/j8889xu93cf//9TJ06lQ0bNvzhvpyJoKAgxo0bx8KFC5k3bx6ffPIJs2fPPq3dggULGDFiBHPnzgUgKSmJ9PR0Xn75ZW644YbmduPHj+fOO+8E5Czm1157jU2bNtG+fXsWLVqE1Wrl888/b54Aefvtt7nssst48cUXCQ7+83zRLmUkScR6bONvtrEe24QqKP6iy+Ps3r2br776qtnr4deEh4fz73//u/n13XffzapVq1iyZAl9+vShtLQUl8vFlClTiI6WnzeTk1tL8fr5+fHmm2+iUCho164dL730EhaLpdnv4bHHHuOFF15g+/btTJ8+HYD//KelOjwmJoYHH3yQb775hocffviM/fzqq6+orKxkz549+PnJ99SEhITm9c8++yyPPvpoc2VKXFwcTz/9NA8//DBPPPHEeV2zfwLnFbC32+18//33fPTRR+zYsYNx48axYMECZsyYwaOPPkrHjh0vVj/buNT5YjIU7IIbVkJUnzO3Ob5WlgIZ/h/wPUPQasPTsPcTOat+SlPGeP97ZIPH4XNbBcJ4p48sjaPSgssOfnGQv+OSzV6V1j+DSnSgVCXgqe1G96gIrI5Qcgrr+Mp4I7cmXWKBHLUBPIMBAdRyhviqI6V8uauAx8d3YId+MDaOY3O6f3s/yBIvaqWAj+H3M/H/KPePSqJrpA8DElpr1XYO9+bjG3pRWm+l9tXhxCjyqbviS3y6XnbR+nI+qJUKJnaRH1CW7iti3rIjPDa+A6M6BHP7l/voFuXDE5d14uNtOWSUNrL+WAVf3tSb+47dS5XNAYjMmdABPw8NHcO8yKu2IEkSB7OLiVTCyARPpg3uTqMtmZt+CCbNGcSchFwmmTewRjODxKAwijN3E+62o608hEPnhQYQ9b7UVJTirTSgdlpQWGvoRC1oPFHo/aC+WM5q7zoDTqyVZXRUetj+VpNcFkiim0r8WCkNIHTwg4R66jHodNgUBtwKHR6JTaWy9cUIgEMXCL1uQqMEafNLVPr3xnvsywxRKDhw/Bp65H/ESNVBnucDCnOuJslto5eQTq/c+8F/Ngy6v9W1fXP9cdZllPP61SlsOFbBuoxyCmrMbQH7Ns5I2/isjYuB0+3kYFEagiDQLSLlrEH7qsXrcFTUEHLHlFbSGycpmPchjsIKAHzH9UMQBIJvnYQ9v4ywe65CoZf1x+2F5Ry//mlQKMAl358FrRpbVgHCJSiTddDSyBc1cgZ9O40Bm0JkuNGXLJuVdLuZsd7+JPxKN/2fjjrAB0GtQhPWMt6pMDdid7kIM3pzMntEPIes+ZPSacqLGAQZERxBtd1GlKG16XGi0YdEow/p9TXsrCkHYGZUIjrlH86Fu6BojZ6EdO0EQPrSFVSmZ9Ft9gycFitZK9cRO6w/E0d1YOOWn5BEkbwN2+k8YzJpn8uSi+aKSnrcOouyA0fwDA3CabFSvPsADpMZBAju2omEscMo3LmXoh17QQCfmEisVTUgCKiNHjQWl4EkUX44A42nAQQBp8WG6HajUKkQXS7sdQ1ojB44TBZZDsspL/NLiKW+oBi3w4FHcCBHv/mx+dwUGjVumx2Nh5740YOx1TWg9jDgtFrReXuhMcrv1clqAa23kYQxwyg7eITqrBzCenYhpGtH/OKj2fXmxzga5GoPU3kldXlyVqigUJC7cTtJE0Y2VySAHMw+vOh7JEmiy8wpFO3cS3VmNgZ/30smYF+8Le20zPpWSGAqqqB4WxqRQ7qfvd15cjKL9/9rHC1J0nntQxRF7HY7n3/+OUlJsgzoxx9/TI8ePcjMzKRdu3Zs3bqVcePGNW/jcDiQJImlS5c2L3v88cfPyaR29uzZ3HvvvVx77bXs3LmTJUuWsHXr1lZtMjIymDRpUqtlAwYM4PXXX8ftdqNsmijp0qVlEk4QBEJCQqioqGjeR9euXVtVKwwYMABRFMnMzGwL2F8g3LXFp2XW/xrJ3oi7thiV34X3S1yxYgWenp64XC6cTieTJk3irbfeOnNf3W5eeOEFvvnmG4qLi7Hb7djt9ubPSNeuXRkxYgTJycmMGTOG0aNHM3XqVHx9WxJqO3XqhOIUibrg4GA6d27xV1Mqlfj7+zd/DkGW2Xn99dc5ceIEJpMJl8v1m0bZBw8epFu3bs3B+l+zb98+9uzZ0yqj3u12Y7PZsFgsGAyX1rjpvEYV4eHhdOzYkWuvvZalS5c2v3mnGme00cYfwlYnB8ocJjlTvngfxA/HIQp8tiOPLhHe9Nn8IhTtAd8YGD7n9H3EDYMT6yDhlFnFxJHy36/RNH2RXXY5617jCaYyyN16ett/CrYGWec8si90mda8WJIkdnd9mv17tuNLNW9EbMLd9z5Gt/Nj8f4yQrx/P4vpH4fOC+5NAwT5wR/4Ylc+209Uc3TTt9ybO48res0mYOL8391Vzxg/9v5nFB6a38giyd8BZYeh503wBx7WdGol45PPnu1v1KkpUPvjcBejMzaVGWeskH0ZRj0JAYnnfcwLzZHieiwON0eK6gkyajlYWEdulZknLuvEy9O6Mvnt7bhFiVkf725O2tGpBEK89Tw5qeVGP3LBZj5x3sHW0Bt5afb05uVjOzeVcD4fCfYGNiSo+KnDMK4+NJN7VAaiKaFP4TYAhPKjBDQdRULArvFDZykBQQmOU0yd2o2D3C0tr5uC9cSPYH2ene+t3QjRWHnmp8M8G7WPxLKDcjb+Pang05T12GEi7HwbTURv6DET69a30Usi9RWFrFy/h8n6gzTm7kFQSHKWnvoExuAwJLUHgssGOZvA5YB246GxjB9yBTZnVrK/oI6CGgupuTVM7RFBeYONYe1OqRRpo41TaBuftXExkCQJURIRJEGWH3FYcYkujDojdpeDysYK/DXelP33BwC8B3fDI+X0+5HXgC40bD+Mvn3LhGPg9FGntRO0GgSVCsnhROntQeC1YzEfzMJV24glPQ9j33+m6Xa61cxbFYXcHhhOV0NLlm57nYGeBiN7LY3YJDe9DF48GhpDjFrH6xWFXO176f3mGzrH0fGXBQhNci6SJFFlkfVxS00N2Fwuggye+Op//4E7wOCJj87wm/r2h4+Z0GgUtIv7Yw/wnio1nr9heHsy614lCCiagoQ567Zira2j/eSxKNUXL9njXKkvLMFpsWKpqqa+oBhzeSUVRzMJSelMzIhB5K7djNvpIO2zFn8kr4hQfKIj8ImWZfqcVhvZa2QZlNjhg4gfNRiApPEjSBo/AofZwpanZYmD9leMk3XfmwKwotOJrVYOnpvLWwLBglKJ5HbhtIggSdQXFgOgUCkJ7tqRmiZJmpOTgAqNGu/I8GapGkmEQ19+j9vpwGm2oPXyZOAjLXIfIV07UZdfRPTgvhgC/HA1mdOW7EnDJzqShuLS5mA9gNqgI6hTe8rT0nG7XFRnZlMeGUbUwN64XW4KtqbistqoTJflNm31jcSPHoLez5eoQWdJNvsHYi47N1mnc213riQmJiIIAhkZGUyePPkP7aO6uprKykpiY2PPeZvQ0FBUKlVzsB6gQwfZH6KgoIB27drRs2dPDh482Lz+zTffpLi4mBdffLF52dmCi79m/Pjx3Hbbbdx0001cdtll+Puf7oV1pkmHM0l/qX/1+yIIQrNsyG9NXPx/J0XaaEG0my9ou/Nl2LBhvPfee6jVasLCwk77TJzKq6++ymuvvcbrr79OcnIyHh4e3HfffTgc8m+jUqlk7dq17NixgzVr1vDWW28xZ84cUlNTm79TZ/rM/dbncNeuXUyfPp0nn3ySMWPG4O3tzeLFi0/zZDgVvf7scrwgT7I9+eSTTJky5bR1Ot2lF9c6r8iS2+1GaNIUVF5Eh/A2/ge5fjnHs9JJrYpgyM8ziazZAWOe5xf9JJ79OYMgo5bd0x+Hw99Bj9ONWTj2s6zrnTQaulz1+8e7dSusnStL8Ax7DNxO8A6HyH/wgCvzZ9jzERz9sVXA/tPteTy1M5Lxsdfwbul0KAbrtx+C4MLf3Z333ePY3G8Cj45rj/Y39DD/cfwqE/CxcR1YnlbCOM0RhCwz0bYs0J7bT+Dv6twvnimbF3sGQ6fJf7DDZ8dTq6LPnPWyz4K2Kctrx1tQuEvWtR/+1xuRPjK2PX1i/RiUFIgoSsT4exDspcUtSnQO82bxLX245qPdONzyDXxAvD+PjGspe60y2bE63IzoEMR3FidXTxjSvC6tsI7iOqs8qZE4Go5+j9BxMr1i/AiPaYfaHk+fmk2ISj2KqN6Qu4UCIQxfGtjlSmS79mrmD3DC+qfkyRzfWAjrJpvPSk1VFjofmPWDPHF3+DtGZj/DIPUutLj4sKSIhPJfQIBqfSwvrqvlycvD0TfkwPY3wFROfcYGak9MIsaVw6eu0fzXdTkrCxfgW7QaP+KokTww+gYTW5fDppXvMdf6MuNilNzqu5eAPtNhyQ1wbAXrnfewwt2X24fEE+ylZUr3cLQqJb1j/bnrq/3cNSyBmwed2Tirjf9d2sZnbVwMNCoNXcO7UGepp9pcQ251LhISXcKSKWsoo8JUidXDRujdU3GU12LofPpvU9GuPTTsPULoDePQxf52BqomyJeET+dQ8elKvAZ2wXtod3xG9MQ05BheQ1Iu0llefN6uLOTr2nLsksgnMS3VLncUZLLX0kg/vZGd1kbynXbWH9vbvH5BeT4vRyZxvf/vy/f9kxBOGWsKgkCElw92lwuL04F4ntmxvxWsr6hysHyDrEN9343hGPQX/rcxwuDJDTFyBa9SUCC6XOSskxMBQrsn4xcfc8GPeb50u/FqTGUV+LdLQO3pQfmhDPR+Tcb2IwYiCJCzpkWTPKx3t+aAPIClqgalVoNPTCQuq42ogb2b11UczUTj6YFPdAQ6Xx8cjSZ846LR+/pgKqvAWluP02xBqdNiDAuhLicfQSlL2khuNwEdk9D5elO0fQ96P19UOi2+cdFkfLey+Rh6X2+63XA1kiRx+Osfm5c7zWaqm0x1AQS1mrxNO4gZ2p/qrBzKDh7BbbOTtXxtK3kdhVpF5oq1uG32Vts6zVZOrJIlUEK6dkIAQnt0Ycer78uVBSfleS4bjdbogcHft2l/6ex551O6XHslPjEXPoP2z8Yj5PQA8v+n3bni5+fHmDFjeOedd7jnnntO07Gvq6v7XR37N954A4VCcV4B/wEDBuByucjOziY+Xq60z8qSJ2VOyoLo9fpWkhx+fn40NDS0WnauKJVKZs2axUsvvcQvv/xyxjYdO3Zk27ZtrZbt2LGDpKSkcx7jdezYkc8++wyz2dx8Lbdv345CoWg1OdHG/w+F9nS/hf9Pu/PFw8PjnD+HW7duZdKkSVx77bWAHPg+fvx48wQVyPfkAQMGMGDAAObNm0d0dDQ//PADDzzwwB/q3/bt24mOjmbOnJZk2/z8/N/cpkuXLnz00UfU1NSccSKse/fuZGZm/qHv3z+R8wrYl5aW8t133/Hxxx9z7733Mm7cOK699tq2Wbo2zonPduSxLqOcF4d6EKZzQHh3iuusPPdzBnNNzxFdspFX7HcRoHIQpFBT59LTOcybO0MymahKBfOVMOntM2rMSxueRTCVyRnHl5+5DIjafPAKA6Ua1FoY/1LLOqVaNgD9pyJJkLkKPENg0IPNiw8X1aNUCAgCdHZnnpQeRC/ILtqjlPvpqcii2/ZODIgPYGTHS7c8rnO4N53DvcGdAOGdIKrfhdt59+ugMPXiassrFC3BeoCR8+Ho93JW/98AvUbJuKYqgfSSBvKqzRTWWjDZXXhqVcz+bG9zsB7AZHcx9b87efHKZMYnhzLmtS002Jysvm8wj41rGTiIosiMD3dhcbj56pY+9L/ifag8BptfJKz9BL65rR8UqmDZVhTdrwedF0LuZqJi25E/YRHiood5ouZR2KCRM+jtJtm7ov1E2XzW3FSyZ66QJ7zih8lVOceW47aYMJmqsAr+CBJIag/8rTncfehKHOXR6A1ayN9Omq43nay70TvzkIzB+Le/hgXhiQRsfAGAGKEUb8GKVJfDEeNgnq0aTqHkwxf5Sj44MYz/totg7MnMGUmiV4wvtw+Jw8fQopd7sLCWWouTPXk1bQH7Nk6jbXzWxh9FkiRyqnIRJTfBxmA0Kg06tY46az1l9WU02htxia5W2zjdTnz1vtRa6nC4HWgm9MRfZzzjvis2pqI5UUbVqp34jjj9Him53DgrapslUrThgUT+54bm9So/L3xG9z5tu38K5U4HRywm4jR67gyUs5VFSWKrqQ5/pRoBqHSf2SDOAdxbmMVUnyA8LuGJOC+tDrTgEt1YnU48NdoLsl9fbxXxUTq0WgV63cWTzTlVkkehUtHhygnYauvxifl7+A/ofLzRNRmmmkorcJjMzVnqDUUlcrBeAJCD2g1FJex67QN63n4dLrudPe9+ht7XmwEP39Vqv3X5RRz6YikKlZKh8/9Nt9nT2fPuQjJ/Wk33m67BPymOwp17Kdyxlw5TxlOcuh+A+FFD8AgOIH3JCqrSs1A3yRdYq+XJlfBeKU3Z93JCRWNJBSV70wjrlUJoSiecFgu2ugYkt4gkiUguN4JCga26lhOrNmJvNFG0c5+sW39KoN4jOAD/dol4hQdz9Nvlrc5FqVQgKTRYa+oAKNt/GEGpIGb4QHk/TfuI6NeTyP49W91b63ILcJjMNJaUXRIB+/CBXfEMD8RUUnlmHXsBPMODCB/Y9YIf+91336V///707t2bp556ii5duuByuVi7di3vvfceGRktxtyNjY2UlZXhdDrJzc3lyy+/5KOPPuL5559vFcg7KcNRVlaG1WptzpTv2LEjGo2GkSNH0r17d2bPns3rr7+OKIrcddddjBo16qIFtp9++mkeeuihM2bXAzz44IP06tWLp59+mquvvpqdO3fy9ttv8+67756x/ZmYOXMmTzzxBNdffz3z58+nsrKSu+++m1mzZrXJ4VxAlL7hCFrP35TFEbRGlL7hf2KvzkxCQgLfffcdO3bswNfXlwULFlBWVtYcsE9NTWX9+vWMHj2aoKAgUlNTqaysbBXQ/yPHLCgoYPHixfTq1YuVK1fyww8//OY2M2bM4LnnnmPy5Mk8//zzhIaGcuDAAcLCwujXrx/z5s1j4sSJREZGMm3aNBQKBYcOHeLw4cM888wzf7ivf1fOK2Cv0+mYOXMmM2fOJDs7m08//ZR77rkHl8vFs88+yw033MDw4cPbsrvaaGZndjUfb8vh7uGJfLo9l6TazYQVyiWT9UOf4ZXyQaw8VMpt2hxCBBf/US8iUlHFPjGBHuvv48T6hTzEQXkc+f0q3D/cibv95WgmvS5nyjaxgJmMEd9H0+c2znhrPbQEvr8ZvCMhoqecpZtyzZ9wBS4ekiSx7UQVHUO98Fc7IeMnOVs4ogcgB02PvH89PZQFHLrlQzw+m4kgwA/q8ayztWNYiIuRwm7WNMYyOj6IPnF/D6Osi45SLcuYXEhGPXlh93cuRPeT//6GdAzz4u1ruuGlU+OtV1NabyXAqMXqdON0S8zqG0WDzUVaUT3HShuZ2CUMg1aJzelGo2p58M2tMnPFu9vRa5RE+hqID/QEtx1qcuVqA0sNeAaR6orjJeUb3OUXz/D2weAThSWgC5GeBqIVewCQ3A75d0SpAe8w6re8i7erqqXTsYPkqpvDXeD2rdDlagxr/gOSyF3CYuoFI4JvEsaKfUQoqqC6Dro/CfZGkgISUR3ZjQk9wvjXuNxcRuHhN6GhCAmoanctHscXopScmDtdg/lgELckh3Iwp5RbK14iZV8kXyfMp1C4jnZB8bw+JB6VsnVw4e7hiSQFGxn4K5+DNtqAtvFZG+dPaVMwPtw7jAqTPHFZZZYlDjqFdCS7MhuH23HGbTNK0lFYXYieGpw2J0dL0xEEgXDvMCJ8I5rbiZKIc0IXJIOW6MvHnnFfJQsWU/vzDhR3jkQ9tDMhXiEYzxD8/yfR4HaRaq5nmNGPA5ZGDtnMeCqU9PSQx62vlRfwdFkeISoNDwZF8kqFrJvtLSgwSyLjvQLYb20EJK7yDb6kg/WnolIoMWov3Lmq1QqunvjnywqF90r50495rkT07YFCpWzO/HeYzCi0GiSnC0kU6Th1ItlrN+O0WLHW1qP18kShVKDUaFrtp76sgqKMY+j8fTEGB6JQqbDXN+Ky2jCVtWgXiy43Wi9PdL7etJ8yntDuXfCKCAMknBYrAE6LbGKr8TaiUCg5tmxVc4AcQOdj5MSqjTjMFhLHDafiSCbmshZpHYVahUKlwmWzy9I6+UWE9uiCtboWa20dttp6NJ4eRPbric7XhyOLf0RyuxGUSoyhQTQUleJ2uQhN6UxNdj6R/XqQs34LotNN6b5DhHRPxt5oIjApntDurQ0YAZKvuYL6gmICO/1xs9S/EwqlkqEL7mPF9DnyRM6pQfumeYqhr957QQ1nTxIbG8v+/ft59tlnefDBByktLSUwMJAePXrw3nvvtWo7b9485s2bh0ajISQkhL59+7J+/XqGDWvty3bzzTezeXNLBUm3bt0AyM3NJSYmBoVCwfLly7n77rsZPHgwHh4ejBs37jclO/6/aDQaAgLOPp7v3r073377LfPmzePpp58mNDSUp556qpXh7O9hMBhYvXo19957L7169cJgMHDllVeyYMGCC3AGbZxEEBTo2w/Dkrb8rG307YdedMPZc2Hu3Lnk5uYyZswYDAYDt956K5MnT6a+XpYs8/LyYsuWLbz++us0NDQQHR3Nq6++2sq/4XyZNGkS999/P//617+w2+1MmDCBuXPnMn/+/LNuo9FoWLNmDQ8++CDjx4/H5XLRsWNH3nnnHQDGjBnDihUreOqpp3jppZdQq9W0b9+em2+++Q/38++MIJ1JEOs8EEWR1atX8/HHH7N8+XKMRiNVVVW/uU1DQwPe3t7U19f/puFAG/9sMkobuObDXdRanEzvFcnYziEsXfQ+byteAeAx1634D7qZtzeeoLOXnVjTfoaoDjNFtROLRwSejdnN+3JJChow4CfIs5c1w17kUMgUhjZpOvd4Zi3VJgfTe0XywKgkvA3q1vIuJwP2JxEUMLfqNNmUfwQ/PwwHF7Eu5U1u3qKjd6wf397WD7JWg7kKus0EoKTOivfrMXhgQ5y+mJyfX6PRbGW6+QHsaIjw1RPqrWNPXi13DYvnoTGXxkDzf4qyI5C+DPrcDh4XtjT1QjLxra0cKW7gjsFxjOgYTI9oX0x2F9tPVNE/PgAvvRqb043DLeKlU3OkuJ5rPtxFuxAje/Jq8ffQsG/uKXrHlVkUlpXzS00YM/tEM+b1LRTVWWXprEkNSEtns1gcwc/RD3N7Qj09tt+G1lEDEjRqQ/A0GlFUH6dOMuBKGEtAUBgkXw0r7oHkadDvLnirJ1QfB4+g5iz8rSE3MGjGw1Cwk0ONRsTIPqRE+lCz5mX8djzDbncS3RQnUAsiL7iuYYJiB1uCruWufz2E6/3hqEr38d+IF3nhRCQD4v2pyjnAau2jSAj0sL1HDV6olQJ75oykwerC7HDRIfSvvUe23a/PjaFDh5KSksLrr7/+V3elmfMdn7W91/87VJoqOVEpj7ESAxMwOyyU1Jc0r1cr1HhoDNTZ6tEqtdjddoSmSI3kEkGJHMQ5gxFsYmACOrUeT60HTpeTvYX7AOgQ0gGdSotO3VpftPilRdT+vAPz/PGI0f546bzoFPrPM0kWJZEjJUdxiS4+dKn5ur6ax0KieSg4moXVpSRqDQwy+gDwZkUB80pyUQC/JKQwJecQOkFBtVuuZJjpF8yiJiPTZfFdGGL0PctR2/i7UnE0E2tNHVEDev9tDZOdVhtbnn4NSRRJufFqdN5eeIYEYamuxVJVjX9iPIJCwGmxotSoUahU5G9NJXvNZkJ6dUXw0OEVFEh4csv3tTa3AGtNLZIoEpKSzMa5ssZ3cNeOqPU6inY1ZdmPHkJDcZmsB98UivBLiqMmK6d5XyEpnfGNi0KhVlGwNZWkiaMxBPiy9bk3AVnTXnTI1SmxIwYS3rsbFUeyEASJwI5J6Hy82bHgfSwVZ77vRQ/pR9SA3mx/Wc5aVmk1OExmAtonUHVMltpRatS4m47hFRlG77tupL6wRJ6E8L60x2fHf9zMpgdeb2VA6xkRxNBX7yVx8pDf2LKNNv73cJYfx3psY6tMe0FrRN9+KOrgv95rro1/Lv9vK3uFQsG4ceMYN24clZWVfPHFFxeiX21cAqzPKKfW4iTAU8NtQ+IJNGq5X9WHka638NCqCXJl81jBQ5RGzOBK9yr6O35EVGpQuB3NwXpJkqsZf3L35W3VDdyhXsGwEBu37gpkf+1uPpscRN9IPYuV81io7ItBdwt9n19Pz2g/vr39lOzjLtMQfaKp3fAGJ3JyKPTrw9RzDNbXW5x4Gy6+aZTN6WZXTjV94/zRqX+jb2WHwGGic/UqtKrJJAU3yaQkjWnVLMxHj3j1pzjXzOPwxu8wjf2Y59fkkuQj0CXCh5m9IzEXpvGFMYhpPc6jhDNtMez9FMa9IOuAX6JsPS4PUAclBv61HakrhB9uh7ghMORhvtlTwLxlR5mQHMqr1v8g5G0lrbCauKtfxKj7a83NRFEiq6KRdsFGMkob8ffUEOylo8l3hj35tXSN8iXlqbXcPTyBRan5lNXbWHXfYKL9PZo/90v2FtJgc7Evv5Zvb+tHsJeWuxbtp6zBxifX90IwxnL1x4WU1B3j271FXNMnkj0bvuMx51e4fragQsJXqqe41srMX5x48BJ7dHdjEKykO0PpO+ph7N/fiY+jFikkGkbNh9JDMOw/LSbVk9+VDWkTRuP8egY1kpGUvsNh1WOQsYx9rjE8L4nsvS8Z3/3vIAG9lVnUSJ40YORr11D+y0TirRq0W3N4LfdOgoVaLkfggPE+yv2uZ2pRL97zvp+o0GBqDnqhUgh0CvNGQGD8m1uxOFysuX8ICUGeZ7zelxSiWzZvNpXLXhDR/f+SCdUPP/yQzz//nCNHjgDQo0cPnnvuOXr3Pj9Jju+//57333+fffv2UV1dzYEDB0hJSbkIPT47beOzNs5GRaN8f/PUeOBr8EWj1FBSX4JGqcHhdiBKIiISepUelyQHkaWTaZaqk8HHlnwftUKFJEl46b04XikHupJDO2NytDy41phrKG8sJ8InnEjfljFH2APT0U7tR5GmAavThlZ1bnIooiQiSRLKP+F3wuFyYHPa8NKfPSgmSiIWpwVFRin9ND4s8VeQqDWgEARmB7TW7r8nKIoGl4uva8t5s6KQp0PjeKeyiE46D7oZjNwUEI6fUo1JdNPHw/uc+7mruowGp5MhgWFoL9GsfEmSqLfb0KvUaFX/78fY/xdFFhP7aivp7htIpMGTo9/+RMXRTOJHD+H4ynVIooTDZCZx3PC/tJ8ALrsdR6MZvZ8v9QVFGMNCUGrUzXryjcXl1JzIo3T/YdpNGkP6kuXovL3o9+DtqA0tBoDFuw8gOp3UHsumy+yrUSmV7H77EzxDgug4dSIqnY70JSsAMJVX4ZcYS82JPCqOZqI6JUu/NreAmuO5qAwGXE0Z9i6rjegh/cjfugtEidDuyfgnxVGVeYKOUy/DGCbLeHS6ehKiy4WgVJG57BcMAX7ofX059OV3stktUHrgCNGD+501WA/yhEXG9ysRnXJAXh8egtvhQN+kTe+XEIvdZMZcVoGgVOIdFU5tTj77PvgSrZeRQY/fcwHfob8fiZOHEH/ZQIq3pWEuq8YjxJ/wgV0vSmZ9G23801EHJ6IKisddW4xoN6PQeshyOX+DzPo2/tlc0JFOYGDgHzYkaOPSY1a/GEQJhrcPIjZANtpY/+BQNhzO5ZufVvCc4Uu8iku5UzxGhFAFAijcDkQEFE0PgseFGJLI4wrVDsZPnImu/QKOZGXh3iHxhPV9hqz6iUPuWLooc7lKaef+9MsRJVkf+/OdeWzJquLZKzoT7KVj1hqRndnXIEoQ6zRwJsX6oloLh4vqGd0pBKVC4JkV6Xy0LZeXruzCVb0uri7hcz9n8PnOfG7oH8P8yzudveG0z+DDoYRkf8uxaSMQuraUZ1ocLpbsLWJwUiC5lSYiKhpIqj1ON+k4x7/dTYbteXoLGYTHDSTkwOv47X2NXn3ugIBf6cqWHAC/+FayQ80c+FI2O81YcckG7IvrrFz3yW4Atj8ynDCf33Yrv6jkb4f8bVCTg63/g3y1uwC7S+T7A8WM6TqaQKqYeyyOIZuyeXjsX1sl8eLqY7y/OYepPSL4bn8RAZ5adj8+gqcnd+LFXzK5eVAsBwvrqLc6Wbq3kNwq+SEto6SRaH8PXG6RN9cfx8+gxkOjJCbAg8eX7OUN+1zuclqY5niCA4W1fLkrn5I6m3xQAe4clsiuratJEvPBArXtrsKv21wWB3nz/veryVfHsiNlB99/8wk6rwA67/0aT0etvLnWKJtOfzJGltm5aR1E9oLI3vIfYLr9AGl5NQxbPgBs8sNfgEGgq583nvveRbDVIkly+MpPMOErWAkNDqG35QjvWp8ke38/nuFWciQ9/dXb8HVW4Czbzhez76Jb9BhcbpF+Dbsx2V18fH1PDFolod46aswOvHR/bUDiTyH9J1j1CDS0ZPjiFQZjX4SOl/+pXdm0aRMzZsygf//+6HQ6XnrpJUaPHs3Ro0cJDz93/Umz2cyAAQOYNm0at9xyy0Xs8bnRNj5r41Ri/WOoNtcQ4hWMUqHES+9Ft4gUCmsLqTJX45bcNNgafnsnp2g4dw7tjIhIjbkWk92ES3RzuPRIS1MEqk2y3I5bdJNdlYMAxPrH4hJEchU1uJ2yXrXiLL4LjXYTkig2B80PlxzB5rSRHNYZg8bw/7gav096WQZWp5V2QUn4eZxZPlClUNFBFUnhy1/QHShZ/BQa35a2eXYrqxtqmOITwPqGWoqdjqa/alY2yNemwuVkhJcfN+VnsMfSwKLYTuhOMVV1iSI1DjuBWt1p/hSiJJHeUIsEVDtshOkvjsHdX0293UZJYz0apZIEv782oSLH3ECVw0aOuYEAm4uK9CzcdgdZK9biHRVBfX4R+Zt34pcQi39i7F/a1/0fLqKhuJSwnl0p2ZNGcNeOJM+4gsTxI6g4kklwlw4c+WYZTrOFvE07EZ0uLNW1SKKEoBSw1TeQv3knPnFRWGvqMIYHs+/tT3E7XUhuN40l5bSbPJa9//285aCSRMcrJ7DthbdlrwqXlciBfQjsmIRCpcRhMhPYIQlrXQNl+w8hSRJlB4+AKD8DKjVq6vIKOfjpNyg1GobO/zeCQiC0W+fmQ3hHhFBfUEz60hY5CpVOi29sFNmnGOmeRO/vi6BQ4DCZKdl9AK23LL8lKBSIDiduhxNbbT0DHrkLva8P5ooq0j5fgjEilHaXjcZcUYVSq2kO6l/qKJRKIod0/6u70UYb/wgEQYHK75/vY9HG34vzigTExZ2b0V1OTs7vN2rjkqDaZMdDqzo9I9xSg7fel1Hqw3z2VRE9h09hao8I/Dw09Dw0n6man7G55SyqeEV5c56WKMEc5/XcrvqZaEUFTlHEpPLCU2xAd2Qx9k0v0NlUiNY+l6QwDVRDsiKXLHcYL7muJqfagh4b4Qo3E1bPpKvox6aMH7i6Tyz5VWZESWK2chVzrIth3yvQbVarLM47vtyPf+lmEhLySLz6eWossn5rldnOt3sLSQ73PneJCrdLfqA9x+yv+EBPFALEBZ75IWtvXg1VJgdjO4dAu/FYjqxkV10wJ/N2bE43Yxds5AbTR2zb5MXchskoMbA0bgZdSxYTqHUxW72bec7XOZgazze6QdwBWBV6ft5XxNjOIXhoVZD2DfxwK8QOhuvPoMc27kXIWA69bz2363AKZrsLl1v6UyoW/j/4e2joFe0HAvh5aH5/g4tJpylgqoDwHvx4oJi0wnq0KoHkIA3ZFSYW2K+jVBvPkBgP2P4GxA+HkNM1Nv8MdE0yVF46FR4aFaHeclChxykVL33j/Hl/czbHyuXMS0+tku8PFuGSRHz0Gt7ccKJ5f+klDfhRT0ftMRCgjyIDXYmOG6xr2KMYzF1ju3F1zyjsTicdyUZEIDvsch481JlPMgehVUrMddfLnhWWYZR16ci1x+7AfeKUTht8AQG0XiBJpJv07NiUybW9w9AZ5Ae5fy9JY/2xChb5dqaXtJVSZTiXPbSQy9R6yJMgbTEmfQhVHa4n1p2PENiO96J7svCj7aiddtqpyll2fTwJcQl4KIaTsTaS2Vs8qf84lY3/HoqXTsWevBpcosTxChN94/xZfd9gREk6Tcv+kiP9J/j2Ok5zNmsolZdf9flFCdovXbqUJ598khMnTmAwGOjWrRvLli1j0aJFrdp9+OGHLF26lPXr13PdddcBcjD+jjvu4Pvvv8doNPLvf//7tP3PmjULgLy8vAve9zPRNj5r49fYXXY0Sk2rwK4kSbhFN3q1HqvTwpHSoyQFJuKh9UCn1lFtrvlDx6owVVB8iqSOSqFClFpMxiWk5kx9q8NGna0OgDDvMASE5nUg6+gHegZh1LVUFjndTo6WHEVCItgYTKx/DC63C1ESsbscNNga8PfwR608t7GFKIkoziPrTa/Wy9dTdebxwA+1FURrdaQEBKBrF025084BlUifpvXHbGaGHtuHDYl3KgopcNpRAdFqHflOG3EaHRUuJ42imydKcwlWaVAAVU4ny+oqmeQjB6Z3VJdx3FRPD99AUnxa6x8rBIERQRE0upyE6s5/AsNVZ0Kh16LQ/r3HZzqVCo1SecGMaf8/dPcNxKhSk2j04cSSlbhtdlR6Ld7RkVQfzwXkALFSoyZ343bCe3dD43FxJ5fOhkKlQhAENEZPEGg2oY3s15PIfj0BiBrYmyNf/4ippAwAj0B/Dn/1PfGjBlN28CiFO/Y2768uv0jWjG9CEkWKUw+g1GoQFAIdrpxAYIckqjKbBlyCgHdUGIXbUincvrtpIwnP0GCCkztQm51LY1Fpqz57RYTRWFqGQqVE7aGn4ugxXDYbod27omiqDNj9zkLcdrtcKeAW8QgJot998iS5UqMmf9tu9H6+hHZPxlxWQVjPrtgbTc2ms75x0YT37oZ3ZDj2RhMZ3/9MZXoW9kYTPW+bhbmyBktVDba6ekS3iEdQAEPmPYCguMTHZm200UYbbfwtOK+AfV5eHtHR0VxzzTUEBf35Jj5t/L04VFTHle/tIMrPwD0jErm8a5j8YHjwK/jxDhrjJ9Au+2eeleCGLZHUWRzcPCiO6JgkpJKf0Un25mx6UZL/lYB4oYwsKRyDZGWXcSzWrt2IPvwmglONv6kQSYKn1Z/yfflgeijVaHGSpCzhMmknYVIVL6s/oKQxFn/q8VY04B/tAU4bT4bsYLD1NY5L4ShFJ9LKBxH2fUbjdWvIr7bQOdybnjG+zKxeTEJBAaR14bkr7uT6fjHklNfx/NLNqH1C8dJr6MVR5vWSUPW5BZTy12hffi2vrM7k1sFxDItSwbt9Qa2HO3fJ//4O1/ePYVbfaBRn0Lq0Od1c82EqDrfIsrsGkKq6Bo/GbLas3kxMh16E+eqpszjxrD/ObO0qcMDbwjDKJW/cff+FYvkyfJU25k0bgvj1exSIobzYMBqvkTPZXaVm2cY09hfUkhzuzUSDEU9BKWt3n4ngTvJfE402J0dLGugT69ccGKizOLj1831E+hl49aquzecwcsFmGm0u1tw/+P+Vte50i6w+WkbvGD+CvHS/v8F5olMrW0sq/ZWoNDBALrvV1hYBEO5r4GPLnXjbirlDCz+n/JfEwm9h+1Nw5Hu47fSsoj+D+0clMb13JKHeeh4Z1x71GR5ovPRqhrUPYl2GrAs/KDGQX46UkV1hol2wkfbBns3B/Cu6hXG01Eh5nT9B1DK2TzK9059BUZnB/gmJKAdMpdbsoHjvz8SJ9UgSvJifSJDCRAB1SC5kg6zja+D4GvqM+AjzMT0qlQqlywQqHcQOhW+uBZP8gJr+3fOMdqTCFjPHej9Bex+J0W4zgapKuknH0AhuIsUicNnl77W9EazVGK3VGLc/DONfgR7X4/76YR6zfsJzruk8bN1C16WDMBvjONogoAmK5QG9m4ets7hv8QH0GhUuUaJLhBd942QvAoVCQMHfU/f2giG65cz6XwfroWmZAKsehfYTLqg8TmlpKTNmzOCll17iiiuuoLGxka1bt3ImSx+LxYLT6cTPryVT9qGHHmLjxo388MMPhISE8Pjjj7Nv374/XfLmVNrGZ22cSkl9Kfk1+fgafAn1CsW7KSs9uyqbSlMV3jov6psy6PNrCgjxCsHPwxeNSoPdZf+tXTfjo/dGQIHVaWmW2FFmVaD+5SiOiZ0h/szZz/U22dxMQECr0uISXagEVXPQ3i26OVJ6hDDvMEK8gpEkCY1Kg0FjwOwwU95YTrAxiC7hyTjdLorqiqg2V1NnqcPkMGNQ6wn2CsH/lEz4soZyaiw1xPnHYnfZySg7hr+HP4lBCed0ru2Ck5Ak6bSsdoCNjTXcmJ+Bp0JJUZeBzLsyhQ4bDrFx7x5WjByNVlBwyGLC1vQ71yDKlQRahYLHQqO5vSCTeK2eWf6hPFWaiwSUuxz8FN+VW/IzKHc5eDk8Hm+lmpim30H9WSQpoj1am/UWOGzUuVx0MbRMfuw213Nf4XFuCgjjpiapHmtmATl3vYI2LoyEDx49p2tyNpzV9VgOncA4sCsK9YWvENOp1H95Zv1JPFVquvm27otvXEwrXfaYof3JWbuZmhN5uKw2EseP+Cu6SvebZ+Ky2dB4ehA7bABK9ekTM75x0ej9fLDW1CEoFagNOiqPZoIAbrsDtacHTpMZgNhhAzj+y0ZEhwOlRk3cmCFkLV8DQK9/zcYrLBi3w0Hu+q3yziUJU3lV8/9PUrb/MDXHc/CNi6E87SiCUokkutH7+uB2Okj7bAmiy42ttp7Di74H4MQvGwnv2wPPkCCUahWSJDZr2Z+6b0t1LW6bHVNJGcfLyunzr5vwDA1iyzOvyzI4KiXlhzKozc7D5XAiCKDx9ERQCDQUlpC/ZVfzJEXkgN4omhIo2iRh2mijjTba+LM4r5HU4sWL+fTTT1mwYAHjxo1j9uzZjB8/HkXbLPP/Dhuehe2vw7SFuAz9cYsSeVVm7l18EINGxaiOwbLmNmDI/oWDYjyCWk9qucDWlRlE+xnoMWAO9u2fESrUIiEgIVGEP9FCFaIkcLN6FQAFYgAzrIt4fIc3y22Ps6x+LgEKQIB2QhG3CivQCU5ECWyCnh/d/emlyARAsjeAACokxG9vAD8DI/LWggDFUiDLXAN5VPUVUmMZV763g6xyE2/N6MYtg+L4JGMGE1W7SQlsj06tpGukD8KS69it28wTPMpnpV34WvsUqjVm8AnD3f5yVh4uZV16GTtzqjHqVNSUOJhkqkap1iC4bOcUsAfOGKwH0KoUDGkXSGm9lSg/A35pdxOpOsokdjHl016UWJSsvX8I/5k9jfxDxUSHBLOl99U02FwEStXyRUOAyN4oHi8hd2Mu4XsLaZ+YCMYGtp+oIq/azKLUAg70jOTFR/JAazxjX37N/d+ksS6jnLkTO3LTQLnkN6vcxO68Gg4U1vL8lGQ0Kvk3wuEScbpF3OL5e13nVpn5z4+HGdfel925Nfx0tIaBCQF8eXOf39/4H843ewo4WtJAj2i5BFfhsuGwNIBCDmt+klrKW4KB78O6Yug6/U/p0/K0EkK9dfSMaS0PEOotf9ZPmj673CJHShpIDvdG2fT5/uj6XuRUmlApFBi0SroKxxHEWp4/4ou3vuW2lBBspNLkYFTZi9za24+7J42AfXWQvgxl+3FkvXcN+SWlVBvbEYWASpCYr/4c7w7DOVY5BmPVQcIoh4AkpMAOJHbpB/0LQdF0jJPBl6qslv5HJxCZvQLcELfzMRBErgauVoHbLm+nCO8Geh95A2MYCEqQ3K2uQ5w7B4XgRABE5GthqDtGewmUZRkkAi8xCYFA8qrkB2APzf+A/M2p5O9oLYNzGhI0FMvtYgddsMOWlpbicrmYMmUK0dHRACQnn7kq5dFHHyU8PJyRI2VvA5PJxMcff8znn3/OqFGyEfJnn31GRETEBevfH6FtfNbGkZKjWJ1WuoQnIzVlt9daaqmz1NEzqgcqpQqLwwrQHKxXKVTU2+qpt9XTN6YPIV4h5Nfkn9PxGmyNrbLoAdQbMlEfLAIPDbazBOxPauFLSBTUFFBhqmyVYa8QFIiSiNVh5WBhGiIi3SO7EekbwfGK4+g1BgRBgVqpRoGCarMsJ2N2WHC6ndS7ndTbGugZ1bP5GpTUl2B32akx11JrrUVCwu6yneulBThjsB6gndaDjjoPOuk9sIsiqRqRLRO7MPFIGbGHtpGk82Bzux40ul1sM9cz3TeYPh5eKASBjY21TecscH9wFHcFRjAz9ygVLgdd9B4MNfqSaq7njYoiipx2Poxuz6zoJDTnMIHplEQGZ+6j3u1ic1KP5qD9+oZa0m1mfqyrbA7YSy4Xkigi2RzndU1O0rA1jaqlG/GbMpiKj1fgKCgn+JbLCZw55vc3/gcjukWyV29E4+nRrPPeUFTaKmic8d1KlFoNxrBgAjsmXfQ+uR0OSvcfxj8pHr2fT/NyhUqJxlOu4D0ZrHdarNgbGvEMkSd5tUZPBjx8F/UFxRgC/DBXVpOzbiv2BhMNBcUo9S1VDV6RYUgu+Xvb4coJBHfpiLWyFpfdjsagY/PTr+Gy2lB7tlQUSJJIYOd2NBSWYK9vBMAYEYpPdCQJY4bQadpEBKWy+btmranDYTY3nYACg58PlqoanBYreRu2nfH8w/v1aP6/IdC/9UpBrgRwO+TPuUJQyMF+t4i7qVrAZW2ZsFSoVDiaJii0xv8BL6E22mijjTb+dpxXdOCqq67iqquuori4mIULF3L//fdz6623ct1113HTTTeRmNjmgHzJk/ULuB2w/U263zSBzQ8N47W1WaTm1qA8+TDT53ZIfR9JgkWhrzBtYDLSx7tBlFi6vwjdvg/opzCx2Ck7zF+l2ky0UIUEqAWJzIhpxCvLiMrfCiK8Ir3MA5oApjme4FvN00QrKqhVBlDh2x3/qjXgEcC80E/YddTEbncHssQI3te+3tzlQD9vUDRlaw55mFrV5RxMb2R31+nsKzRh2H8QSMDHoCazvJGParrSV7cRFk2Fia9Bz9k47fJDLm55kHc4eDKD9HkQ0ZsfDhTz7yVpxPgbuG1IHNN6RDDtvzt51/48j4xMZrTely935bPxWAVPT+58fpnl5elQdggheRofXic/fC7eXUAHu0Ak4ImVmY2f8YTrej7alsvciR0h8VkAtECgWgmEwb1pcoaqVh5w3jsykXtHyt/XHtG+zOwTzbKDxZyoMNEv3v/M2vVnIdJPj0KAcJ+WTPfesX68Mq0rod665mC9Tq1k7QNDcLhEQrzPPyt+fUY5Xjk/M7nwAy5H4lWtk02Ke4F/UMD+54eg+gRM/QT0565/+cRPR3E4XYwKqGVoYgAT8p4hUCk/7FSrQjjsSECvcFMwdQXtQ377vVt1pIw6i4PpvaP+0Cm8se44aYV1bMisQKNSkP7kmN+UbXnhl2N8tC2Xy7qEcKCwngHxAbw4tQtxgU0PP6ZKbs++C0kSaUxZjCYwlo+35dI/3p/r+kbTPsTImzYXVSpvbE43uh43QI8boLaAxPKfSVJKYNkvS54KEEEFZCymPfCdayBXqsrJrIP4yuVIuVux3pPO/V/twMto5JWrZA+GH5PfIVoqoXv/UQxQKLG+9Ck6Zz1FUhDhQjVanNhR05yPVpkFFRkQ1AE2v9ASrO91C/S+BSqOoRg+F7ulnntdmWhm/AxqHab8NOavzMRYn0l4kD+NVX7MHhhLmI+eFYdKuHnQucmaXDKYyi9su3Oka9eujBgxguTkZMaMGcPo0aOZOnUqvr6tv5MvvfQSX3/9NZs2bUKnk3+zsrOzcTgc9OvXUoHj5+dHu3btLmgfz5e28dn/NpIkYbKbkJCoMlUT7hOOl86LnKpcOTtWdKNSqojyjSSj/Bh6tR6DxoCfwbfZJLakvoRKU+VvHsfP4IfVYcXqsp4WrAdwTOyMwkOHOKojAJ4aT2wuGy7RdVpbAK1KiyAIKAQFcf6xNNpMWJ1WAo0BlNSVIiJL1ygEBfW2BtySiNlu5lDxIbqEd0F1SuDa0TQ+89B4oNfoUSmUnKjMpspcha/BlwDPAIw6T/Jr5QmJOP84JEkitzoPCYk4/9izBuXPxPoGedw71OjLjvby+OxIxQlsnlrMAixuuscetpnZY2ngpsBwbgps7YMxySeQgx16E6qWA6EahYIl8S2Th+9Hy740DxUdZ11DDcl6z3MK1gMoEYho2q/PKeas/wqKwFupYox3y2S7oVMcSYvmo/T6Y7r3tSt3YEk7jiXtePMyQfcXywmeB1abyI9rq/DzVjFm8Jk9Cs5EY3Ep+Vt2AdD91llUHD2Gvb7F+0GhUSM6nKgNenrddeNvZmZLokjhzn14BgfilxBz3ufgsjvIXLYae0MjNSdy8UuMpftN1/zmNvs+XISptJzQnl2pycohbtRgwnul4B0lf07tDSZqjueg9TIS1rMrSo2akn2HiR7UG6+wEOJGDabyaCYumw1BEGg/eSwARan7cVnlCTGnydJ8PNHupPJIZqs+NBaX0VhUiq22jriRg0lfupzQHl2JGtALlV5HeJ/u+MZEEtgxCVN5JXveWXjaeQgqJZJLHoeV7NpPWLdkVFoN+Zt3NjUQ6Hz1JIyhwdTlF5E8cwqZy1bjFRFGu8tHo1ApKdyxj5wNW8Etovf3xe1wEtylI/qmSYLI/r1OO24bbbTRRhttXGz+UDpfeHg4c+bMYc6cOWzevJn58+fz8ssvU1VVddoDbxuXGO3GQ9lh8JWzEiONAvd7b+KGegP/+tpB2hOjZRmMWzagMgbzalNm+c2DY3lvUw4HCut42v4RKsHJFartaHFR69UeX4MWoSyNvWI75mV352ftYwBUiUYCFI1EUEWDYORJ1yw+1ryKr7uKDeV2OihBYalifvAOxmWvY4F9EuulHuQFj8ZDJRI49hH0J41RzZUsPGxn/vJ0AO6v0fGu9T/cqc3hu7hnGJQ4AUmSeHN6Cn1SNVAK6Hx4Z+MJTkQ+TX2Mm6vjOhNxoprufcaAVgW2BuL3PsAUhT9H1eN5bFwHAJ67Ipn9BRGo/P15Z+MJ3lh/HIdLZNixCkZ1DOaOL/fRLdyDucIncqbuqKfOfL2/ng51+XIGb5dpAKw4VMoz5vv4KH4bnct+ZI9DztjpHC4Hap1ukTu+3I/TLfL+rB6yv4BnU6abKMJZMi4npYQzKeUspoqHvoXsjTD6GfBonbHyxGWdeGxch+bA/Emm9jg94/SkHnx+tZkgow695vcfPMsbbKw+WsaooHpuUL+FShBxSQpUgsiIgOrf3f5vg9sFez8B0QWlhyBuyDlv+lZ/CwP33IN+rYlqV38mq3bgkgQaBS/MDoljmuvkGoq6r+D4MQjqCCfWckzVgVfLumLUq3nuimQcbpE7Fu1DkqBTmDfJEd7ndQrOvV9wzdbHyXZeR1zAKDqGeZ0xWL8lq5LCWgvX9I7C1mQkuPJwGaIEy9KKeeaKzqhPbqc1QkgyDpuVe8eloDYGMqJ9MFe8t4MJb21j07+H8sh3hzlYWEdKpA9XdIvA7nKjXXYnAhIicv1InT4KP7VTfhWQSIMulMPiDMZXVJFYm4kVNQqND6XHdvF24TT2SUmY7JvJKG3k/tU1KBV60gd7oFUp0Y15AmHlA8QKZTj8O0BNFipB4E7r7SzQfYTB0SibL495FrrNxG2p4V3nRNblppDw7re8Unk7glKHVrTL77e5AiJ68klZNKU+RsK6jiKrwcbaWYlE+snZZx3Dzn2S7JLBM/jCtjtHlEola9euZceOHaxZs4a33nqLOXPmkJqaSmysXCX0yiuv8Nxzz7Fu3Tq6dOnSvO2ZZHP+TrSNz/43EQQBD60HJrsJTZOOu1qpRqPSUGetI7s6h44hHTBoDXSLTEGr1DYHp0vqSzE7zBTXleD+VaWQp8YDh9vZHAyvsfy2xr0Y4YtlVi+UTVVHJoeJAA9/qpqy4BVNmfFeOi9CvUPx0BgIMAYgSbJcT61Fzji3OC3NQf4YvxjUSjURPuFoFGqK60sQRRGQKKgtJMgjEB8PHwRBgcvtJMgoZwub7WbqmvanU2mJ8o1EkiQifSNRCAqsLitF9cXNGfph3qHYXQ7yqvMIMgY16+IHegbwa/LsVqbmHEYAjnbqS6haK0+amKt5z1vLIlHHOnMjbiR0ggL/pvckz27lhrx0hhl9eSJMnqCN0crj5LNJ7gC8HHHmCTdJkij/6CcAgm++vNX2CkFga7seuAHVKcuNShV3Bp0+PtOEBiCJIrb8MrRRwec0eWE5lo89rxTBcLqevNr3n3NPK692kFtoI78YRg30PWu1668xhofiHRNJfX4R+z/4otW6kyamAE6zlcbScqrSj+MdHUF5WjoaTwMNJWX4xccQO2wAlRnHyVq+BqVWy7AnT/dF+T0OfbmUmpO6+X6+BLQ7Xe5JkiSKdx9A5+NNQLv45kzz0r1pAFQcziC0e3LzxIJHkD8aTw+8IkKbAtsqDEEBZP64CltdA9GD+5K9ehMNRaUEdkhC4+mJ02rl+M/r5QrGpvulIdgfZ6MFlV6PzseIztsLl8NOTVYu7qZrpDboyV6zmcaSchzmnUQN6EXh9t0U79qPuaySkJTOeEeGE9gpicqjclWkd7Rs7KvSanG65IkBU1kFdbkFBLRPIGpgH2qycxEdLvK3pFK4fQ/1BcXoA/yw1dbjNFvQGqfgdjgxV1YR0D4RD39fJFEkYewwFCoVOp9/zue4jTbaaKONS48/XH9vs9lYunQpn3zyCampqUybNg2D4a8x0mnjT8Jai1UwsDT6OfxiJjABYM/HRO56grf1iTwX/g7LDpbQd8UIgqQqPuv0KbdMuxwcZtpr5Yc8h1PkoJjAKOU+tLiokrzYGns/l4+/HCn9J+78ViIIWeNQREDZJPlRqI7jhYE+fLIlkBO+A0ms3cZk5WYKFeFEUI66YBPD2UsfzSEG2d/g06JQwgL9uPb7e8lo1BHpzOVE6EQcHe8HICHQk66R3pQcDiBZKKRju0Q5CKhScrlmL5RuA+9IXB0m88qiX5AkmNq3Dx3DvOkYdkqQ8+NRdKs8Rooa3jW2A+Qg7LjkUGICPBj3xlYE5HPoEGpkcrdw9h0+SnDRatIrgoDPAHD2vRu7xofZn+4l0s/Ay1O7oHBZIXoAZosF3Y93oKzKhOH/4fmBSlYlJNOx72QmvzuFBnMhazzmk1Q8CLotoNrkYP2xciQJyuptxAQ0ZUw1lsP7g8AQALdtadbePyfWPQkNRRDZG1PyLDy1rbdtFax32SF3K0T3A83p2VobjpUze+Fe+sT68c1tp+vE11udvPrO21zr+IaEXmPJOZzBgorJqDVLmaEQaZAMfJXwKhMSdUR2/weVWytVMP1reQImdvB5bTpKSgVR1nXvpsjCjgIpcRy1di1xhd+3NMxaBfs/A6032OsJkjxZa/8AgBm9o+gZ7cuV3SOoNtnPam78W6iLdhIoNDA7spTOtw4+Y7DeLUrc9Nke9O5G4r2VzYFoUZLNaG8aGEvnJ1bh76klJdKH6/rFcKTDJ6z4eTnfvtaJ4149mVhxNwoBXG4JSYIbB8RgSFvIpJ9vZun++3kkuzO7YjUEICAhoEDE114MNjdOhRa1pRqvfncxf+RQ+NgHaiWqRW/WKSYy2xsQnHTxtuGpU9M+1MjgpEAGqTLQPh8KQR0Qbl6Hu64Q5fbX0FZnwKCH2GyJoUfqSgySBQLby5VEAO0ncNhjAK++sx2opxQHTk8dGpdZlssZ8wxE9GTRrnxeWytnH2ZVNFJlcuDnoeGx8R3O+324ZIjuD15hssHsGXXsBXl9dP8LfmhBEBgwYAADBgxg3rx5REdH88MPP/DAAw/w8ssv88wzz7B69Wp69uzZaruEhATUajW7du0iKkrOoK2trSUrK4shQ859Eu5i0jY++9/D4rDgqfFAJSgxaOT3Orc6jzprHQICXlojWRXHm4PTiQEJBBgDcLgdqJrGAmeajArzCcNT60lZQzkl9WeWr/LUeKJQKGiwNaBSqHCJLtySGwEBpULZHKwHmkxi7bjcLtJL0xFFERGRMO+w5uN767xwul2ytr1ChbYpS1ylUGFz2XGJLgI95b6frAiIC4xrFWB2up0cKjnc/FqrkvchCAIRPuGU1peSVdGSDR7oGYBeraewtgir00pFYwVWpxWrw0qgZwANtgZyq/MI8gwi1DsEf5OdIW41+9UiKempfBXbmf6e3qh8Y+ijFLjMM5CgtK24AQ+Fgt2WBhJ0BnabGzhoNVHlcjYH7AHyTPn8WLCSZN9OjAg9998RR3ElVYtk3XDvcf2QwvwxnJKBLwhCqwc9Z3U99vwyPLufuSKo7L8/Uv3teoJunEDQ9eNPW285mkveQ2+jMOrxGtCVmuXbwOmCkwFulQLvMX3w6tMZ78Ep53wefzXRYVrGDPbF26g652A9gEKpkKVUfvXdiRs1mPzNO3E75CoUt9NJ3qadVB451qwRf5K63EJihw3AOyoc37hovCJC/9A5mCvk71lEvx60nzT2jG1qcwo49sMvIAgMe+phdD5eWKubJrV8vPCKCGXDnBfwDAnCKyKUuJGDMIaHUJmexcZ5L6MxejZnzotuEb2fLyEpnagvKGbXGx+h0mlxWm0o1WpEp6v5O22trEUSRQSFgvr8eqKvnYp3VDhbn3+r5VqqVRgC5OoGn+hIAALaJ1KVmY1So2H9488T2qsrnadPZs97n2EqKcdSVUPUoN6Yq2qpzpC/z4Edk/BLkCfe40cNRqlWcWLVxlbXwVpVg298NPGjhwJw+OsfqGravkllH7/EOALaxf+h96KNNtpoo402LhTnHbBPTU3l448/5ptvviE+Pp7Zs2fz3XfftWVu/S+w7TX0298gzN2Nx4qTmGD9CXI2AdAhLpovru3DrI9T6eUWUQlurjxyB43unzA25nF58V481N3Y5OzOSOV+nChBgtedU/hmj5HjBe8xo52SuVddyUPfH2Gm4zFu8j/K8EY5c+hT//uZs3M64xVOvnVeSySpaCUn/u5KBMHFNnU/hpCKAQfvqd+gj/IYYp1sZJvSlI1tLP+OAbe/zYCEAGL9PdBrlJSMWsabu07wxg+F9NqfSqCXjt6eWm4whkGsHJD8ttcJ/At+IdQziYoGz9Ympyr5/4IAt4VmkVZYx91fH+CKbuFc3z8GPw8NNocbi9ON2ebCU6ticNojDNHsJL/3E+woup6koh+of/tyngt9k4K84/Qu3ELtwDn4r7oDqWAXK12DuEq1BWfJIdTb3yRy7Vxu6X4d6N7iX8MS2LB8J0nuLDhcBhMXEOKt4/1re+ASpZZgvdsJa+bI0hIOM7jt5xywz6408R3XEsNevtseQurS1cy/rCOZ5SbUSoH5l3Vq/YCz4WnY8RZ0mwWT3j5tfyqFAoXAaRn5AMfLGymqtdK9fg1Jygyk3fn0c1rYpN2Cj2DBjRJ7j1uZNOSKZq30fxRJo8+5aYPNyTsbTjAwMYBBOS0mslGKKtmK1F5FXGRvKAS33p+Mvq/QKaU3Qm0eRPRmTeoB1prjCPHSMq1HBD2ifBEEgVemdZWz/TNXQmQfMIace/9HPwPRA0jpMBGagvUlddZWMk9KhcCdnSXuzLob1epwUu7Yw5J9RRi1Kj6+oRcbj1Vgd0mU1NkoqSvjcFE9Nw2MxVdoRIMTobEECbhxYCz/1v4IHzzJnRNfg7oaOGjBr/YgDykOomgowSap0QsO3IIGpeSgQdKz35nIUOUhKE2D42shaQwU7iJKWcnA2h8g/mm4fRuexjCw1OC1bQGfj5gA+3fK34vSg/DxaGa75vC0tIhgDyXarS8TKkbiFdQVqQYESzW8NwBmfAUxA0lRF/FZrwIOGAbR3lQMnT6AJbPk/bW/jLwqM6uPyqa2eo2S24fEc7Cw7g/LEl0yKJQw9kX49jponto8SdNvytgXLqjhLMjjmPXr1zN69GiCgoJITU2lsrKSDh068NJLLzF37ly++uorYmJiKCuT3zdPT8/mv5tuuomHHnoIf39/goODmTNnzmla8TU1NRQUFFBSIgc5MzNlGYCQkBBCQs7jO3ee59U2Pvvf5ERlNmaHrLWsqCtGp9Zic8qBtWi/KEK8QtiVl9rc/njVCexuB8V1xc3SNiKnS9xkVRzHS+uFv6cfgR6BVJrlAPnJwDyAQWOgwiSbiCuElu+BhIRbcuOh9sDsNLfab521rlnLHqDKVEVKRApWpxUPjQFJknCKTjLLs8goy8DP4IcoiehUWpQKJUadF0atEQ+NB4Ig4BSdKFA0Tz6AbGp78hheOi/KGsoorishLiAOnVqPgIAgCIiSiCTJskE1Zjm5JNI7gmPV2XxuMpGWd5DOCjdOt5OC2gL8RQOF1z3NE5LEyKcvw61UkGW38N/KYtY21vBRdAemCgLX+oewpLaCareLlfVVXOMXwhTfIBpFN91OMYEtcth4oKQEP9GTCEfteb3vqz0F0iam0Oh2s6Ehj6LqTL6I6cgn1aWM9fbnev/Wwd/8x97DllVIxH9uwGfk6RIfglr+rRVUp48PzYdOYDmcjWixIVps1Hy/qWWlKIFaSfgj12Ls3QnVH5TW+asQBIEenc/NtwnkLO7iPQcJ6doJU/npMlIaoyf6AD9MJeUYw4KJHjoAtV6Ho6ER//YJ5KzdApKER0gQscMGALJGeo9br8VhMlN+KIPAjkkoVOd+7+s2ezrm8iqCkmUZJdHlxmm1ttJeN4YGYQj0x1JZTdbyNSRNHMWhL78jpGsn4kcPIfOnNc3nZyqrQEI2mz2Jw2QGSaLT9MmUpx0l7YsldLhiHHvf/wKn2YLLZkdyu1Eb9Eii/HuiUKtlg1fAZbcjutzUFRSh1GjwjY2kOisHgIojmQx67G6CktvjFR5KQ3EpZQeP0nXWVHa/sxBJFClJPYBSpcJSJX9PVFoNBVt3y9fPy4i90URtTj47F/yXXnfegMbTA72/L6E9klHptCDJuvT5W3YhSWAMC8FaW9/cB62PF+G9umFvaMA37n98fNZGG2200cbfgvMK2Hfq1ImKigquueYatm7d2qpMvI1LE6dbZMHaLGL9PbgqcTTuzDVUacbwXAKwdm5LQ4cJ2zuDecU3maXd3ueOtCvww4R0bAkFnl2IkCRGKvcTKNYiKtSoJXkA+Ih6MT/aB/BgzdMod0n810fPFWImM5QbsNepQAluCX4p8+Q/fuGYGhvoa9+JDicIUCn58i/73RzOiONO5TQeVn+LXgV2UYVWcFGHN6+7JvFv9bc4veORjvzCjcvUKASBtQ8MJtzXQJy6hgmKXfyc3xsJBT8DU5883JxF3qvsG6g7yleLP2RO+RAW3tibIUlNEjNXfwU/3QWNpaiGPswP64roWLeJ2Ts+xstrLnWWCE76qxbUWpn342HGOuPpq88kumMfjlu9CCj5jAB7OiWZu/m3ehVTlVsgVSUbYwoCpRHj+Uw/geumXAVHm7Kp1fLD0ORu4UxOeQQOhENAi5nV6E6/CgiVH4HDS+T/z/rxjJnvZ2PN0XLeregCdMGrVgGIHK8w8fXuAgBuGxJP+Km6/AFJIChw+yfx0LcHUQgCL0xJbs7GHpwUyK7HRuBjaK1vWlRrYfybW1EqBJb0GIn7yAGUiaMhfRk+glzqqsTN4WPHuGnnBj69oRdD2wWd83n8k6g1O/j+QBF7tq4iY78H/bv0Q1mXDy4rpxS8Q987obGcBQUJvPOLlq6H87hj6AeM7RzCovzd7Myu5uuZPZrNasnbBtXZYG+ENXOwa/3RPnCo2dvgdzH4QbeZzS/fWn+cV9dmcc+IRB4Y1fL5u394DBx3g8uKUqXg/9g77/AoqvWPf2a272Y3m94LJRBC772jFAUR7A0UK4rXXq7XXq+Keu2VIvYCiCCC0nuXACEB0nvPbra3+f0xYSECth9Y9/M8PGRnzpw5Mzuzc+Z73vN9F93QD75/BLbtYPSAm5k+KI1NR+rx+gNUNDl5fNlBhrQ7i2eFdiwukq+TXilm/Cs/QbQVE3h3NPVh7dGc/RKD3PWMWvc4NIMkQLamF20m3IGhZBVfBiYQltgBDNmw7S05D0XPq2hOGcWhykZ8nWVbKeJbPII3zIbNr+DLW4Fdl0Zw7oy7mW621Xzj78fAoVeTvvYW6hzhDG1YKq9vEa32bV9Dl7TB+N+fwnBHNT07TMV06Ev81oEgBcBRzzvfbObJzXaGZURz7ZA2zBja5q852HSmyJoEF70P397bOgGtKVEW67MmnfZdmkwm1q9fz0svvYTVaiUtLY3Zs2czfvx4brrpJjweDxdccEGrbR5++GEeeeQRAJ577jlsNhuTJk3CaDRy5513YrFYWpVfsmQJV199dfDzJZdcckI9p5NQ/+yfh9vnprypnOiwGKLDovBb/WiUatRKNRWWymA5h8fJ7tI9xBvjsHns2NzybK06W90JFjgnw+q2YnVbUQrHXhmO96R3eB1yJLeoDFrnHEWSpBPEeqCVWA9yBHyDvZ78ugLMejOZcR3RiJrgAMBRKx6D2kC/NFlo9vq9wUGKH0r3ggA9k3ugUqhQiko6JXSisK4ArUqLTq0jv7YAj9/DoZpDpEWkIiEFI4AbHA2oGpWolSoEQcSkN7G1Gua6/YR7LHzW8psdkAI0eC0IoogKGKTQkhkVzfXRSWyxWRAAbUubX0zpwMMJbfiooZqJZtlWRykIwUSvR1nYVMtqh4cMdQavJPX42e/jeObVV7J6jPzsNfk8uKQA3zc3sNxaT67LfoJgr0mLx11cRWNUGFfnZ3O2KZKbYo7Z48RdO4nISUNRx7X2cW/6bjtlT85H17kN2sw03EWV6Lu1x74951ghr5+K2Z8gKEQ6fPAISvPfM0mn29rMkRVrqTt4mOaqGoyJcdir6wj4jt0TgijS4ZyzKN28nbrcfPZ/vJCoDu3IuuBc1GEGyrbsRBBF+t50FUqNPPujYlc2GmMYxRu20XC4gLDEOAbceu0vbldYXAxhcccSPe+Z8zGNhSX0mnFZ0BNfpdeRMqgPeV+twO/xYEyIo/d1V5D/3Trq8vJJGz4AZ2MTjrp6Av4AlTv3otCoMbdNQ6FWUZ97pGVf0RxoiUjf+MyrGBPjaT9hDOVbd9GYX4TX4UQQRXRREaQNH4i1tBwkgehO7fHaHRz+dg1FqzfR9qzhOOob8dgcZEwYjSCKmNPk6/HI8jU0HCnE3Ww7lngW8NgcBLxeBKWSlCH9OLT0OwhIuK1yXiefy43P5aYu7whh8bHs+3AhgigSFh9Lc0UVMV3kAQ1bZTW753yCpbCYpP690BgNpI8c/JN5BkL8/XjkkUdYvHgxP/zww2mve8SIEfTo0YOXXnrplGXS09O57bbbuO222077/kOE+CsQugd/nl8l2B88eBCDwcD777/PggULTlmuoeGnPS5D/HXYkl/PG2vzAeh31wjSb9nKxQA+D+yKlb2ZAQthhNduxVN7mBsefAUx5SX49j68hnjG191NVuAQ1yq/pbLtVLqfNw7mjkey1VLhj8aJliWBIfQQDhMTm8DExpdoJ8jCjSTBy7qbSY9I4Hz3/9hva+LLsOeD7dsuZbJPkqcVv+6fTETmUL6uMPGO41/E0kSeP555/nE0SWG81PQ6riUzsThfByScXj9GrYrzDvyL89RFPK65k2zzGCb1TGpt+XLO83B4Jd8fGYwkefD6WqLQsj+DhddBnxlw1WLW5tUwb3Mx9ymPEC41U5ezHonLiDKo0apEyptcLNhawvtM4MFz72JGehvGpA4kkPNvRL+bftE+zh03EzZaoPMUaDscwdHAv0zHvXD1vRY6ntM6KloQoNeVJ/8CS7fL4mKH8TDsHjnRacqvS5x0Wb9UbC4vBq2SszrFYXV5qba4+WhbCT1SzSSZddjcPnYUNjC4fTTqXldBjyuo3/IhM/Zfydu+c6gcfcyrG2g1S+GzHaU0Ojxc1CeZSIMarUpBJ1UNCp8TRCXf9XkLR30p7+Wq6CiWUWUYA42+H89A/tuws6iBS97eyph4O59rHsPnFXmFpdx2/3Ny0tqSrVB7EFIH8MoOO+8duJDuyWYUtXUoynfw9pJS+qRPYe5VvXA5m9Ebj4uu/eAC8Dlh1ENIgMZdT/b21XQb+utF0X3F1by65giJ1HLFpllYdmn5ZuAnPL6qgh4pZmZftYmE2FgKG5y4jmyk0xZ5tsVTlX2Zt0OedGzWq/D4JTR4+LflYdQqNRsjb+ecjHjGZMXzTdnTNG2ex1XKlUQ3H6JpzTNor3ofNihBqUWY9AptDm1EtWgGIm46D+hNv75nw/I3oXij3FClFmNad3rbFsPef9PUtg26zmPRKBWQNZma9e8SW3+Idf4oEgwD6TvxRkgbzB3PZyAoJbBl4Ok3jaEbnpElJkm+5b7yDeCu3d2YqsimuzWLoWolL+2P4iGVjnmFcVRp76LR4WXtDjlh9ZaCet6f8RdKkPx7kjUJMs+B4s3yLKCwONkG5zRH1h+lU6dOfPvttyddV1RU9LPbh4WFsWDBglb9oLvvvrtVmenTpzN9+vT/TzN/FaH+2T+PKksV1c01NDga6ZXSk8RwWQi2ue1UWauC5awuCx6/hzp7PX1SewcTsf7YozzZnIRebeBwzeETBHUAg0aPxWVttUzXEqmuFJR4/d6fbG+iKYEKa2WrZUej4JvdzUiShIREY7MT4sDlddHslkU4lajCoDGQGH6sP6RSqGgX3RaPz0OFtTK4PUBBXQE1tlo6xGYQZYiiuKE4OHAQkAI0tHjbGzQGnB45gW5lyznrltQVlULF5PgMlhbto4tCJNIQiVapxeK0YI6IIeajRwH42nisXzMnPYsGn5dY1bFgBLNSdVK/eADL2t2o46O4tF0CRW4n54RHo1P+usHcxxLbkllfRVuNjjGmSJoDfubUyv3nSeGyeFvsdlHudTEozEzKA9OR7g+w6vn5XLK/gPlXDeamMcfaJwhCUKwPeLzUfrgCXfsUlNFmBJUSdUI07sIKJJcH06Cu8jUkgG3rARSRRiSPr8Ue5u/ZQStcs4n8FWsxtSRlbcovpuc1l6CPieLw8tXU5x7B7/GiDTdRtHYTzvpGtOZwnE1N1Oflk+vz0/OaSxl4x43yIFeLWN9UXEbO518jiGJQXLdVVONzueXI8F9J8cZtNBbIyZV3v/cR4alJaMON1B8uJLZLR/rNuoawuBiaisuo3L2Pyl3ZNFdWI4gizWXyPWqIkweZ/G4PrsYmFGo1ppREkvr3wpgQhzEpgeZyuWxzRRVlm3cQ0SaVxvwi9LHRdDhnNAXfb+DgF3KgQ79brsGUnMDWl97Ba7MjKBRIfh9RHdpRvn0PuYuWY4iLwZQo56xJ7NuDhiOFVO89EGxPx4lnE/AHqN57AMnnQ23QY0qMx1rW+ncF4ODCbwiLbwnqEQSaK+T7uzZH9r/3OV1YS8pa2l9Np5un/+rzHOLMMH36dObPn3/C8rFjx56y7/ZLEASBRYsWMXny5P9H6/68rF27lpEjR9LY2IjZbP6jmxPid+D4e0WpVJKSksKUKVN49NFHMRj+fDPdQvfgb+NXCfZz5849bTsO8RegeAvNeRWIgpaABOsO1R6zWFGqZauJHz7GP+R2bjzYn2RvW5Kp49a1T0JiD/C5UFmKiAtUsUfI4vN2wzlY1cyLrxzimSlrKKlrZt2hes4pXkqqUEEbsYppgUXc5L2GR5TzSBVqKJDiMfW/lG0r5GjuVJWVd8QLOduvI1fRiY2msdzTqy3PrjyMDhf7lV3oaZnLFrETHykns90r+yBWEEVeIAkh4wL+16knt368hzs+3ctdYzvS3RCD0FTCg6MTod8xr+QGu4enlx3gvMhahgy7m9eGaam2HucJ72yZuuyUBZDoMA0apcibwoW06TiAt8rTkSRI9xzmf+p5fGocQXnGZTS7fIzr0iK4iyJiUm+o2MOjl42EhG7Qefyx78B0Ei/Lky07GV4nzDsH/B64bjWMeuCXbfcjwvUq7h6X2WrZR9tK5NwCDQ4eWXIAi9PLoj3l3DyyHXePzQRRJHbrE8SKVTynfpfPD99EYriOpAgdHeKOTTtucni458tsAAa1i2bTvaMQBQHR2xeSetCUNILrZu8GOvHYpM60jw2jV1oEdTY3yRE/48lcsg0UKkjq9ZuO+4/C5Q3gC0g0SEas+lTKbALJsbFIopJr6y/H4jqLORMdmLpNZN27u2lyeIk2qnlnqINR2x6hxm3mwjfj+S7mf2iLN+Gf9jWKtAFy5d0vgZoc6HUl8w8G8NcXMr7zqF/fyLzldP7kch5kBFEqC7HUE3DCocJiHB6Rzfn1zE0K526zk9tfWUiFW8M6jRpRpeWjHeWA/AJqdXp54/JerNq0hY5VWwgANa6pZA3PQC25Oa+jgV2Zr7ItdyW9tt+OUh8OhlgQRFnMFVUYs9+T9QEBqg/vpn7IRUTFd8UraviI8UzbIXv4S4Akqrj502wGx+xg5vU3Q1Q71hvGckHTXMw084DlSq6x9+KSsBiEsx6FvZ/AphdRxMoR+Z/5R5KvaEu6WMOjvikMEg+gcYdzv+86YnUaagJuvnCPAGBy+0TWHqjG6fUTpVczOuvvORvktCEqoM3QP7oVf1lC/bN/DpIk0eBowNoiZnv9XlxeV9C7Xq/WoVPp8Pq9JJuTKG0sC25Xa6tFarHACVroCCKR+kiqrNWIgkiXxC5UW6tpcjThCRyLmFccjbD3BRCsTogMQ6NU0+SUZ5eIiAiCEPSvN+lMKAUF9S3R8U6vPHh5vPnV8QMDyeYk3llSyv1v7Wf2LUpmnJuOQlQQCATonJiFTnVMzLa5bZQ1lROpiyA5Ipk4UxySJKFWyGK5t2UGgM8v/3/Uw14URGKNsVRbq+VyPnmQQaPQEKYxoFAog/tJ0Rp41qhHISpoF90WURBJiWgRt1Unfi9KQWgl1v8Utp25lD7yHqJeS9Y3s3khpcPPb3QSuujCeCq5dXJRS8uxL7bUkqnT81hlIZVeD1+168ZwYwS+eivx3+wkHrjmsx1s7N+fap+XEcYIopTHDqx58z5q5y9HNGjJWjabTt/MRlQpcZfX4swtloX6bQcQ9VqSH5iGrmMainADSPxkdL0kSdg8brRKFaq/WDTz0SSt+khzUNjWmIwIooi9qpbwtGTShvYnom0qe+d/SsDnp9sVUzm8fDXO+kYa84vY99FCmopKQYDBd89EqdFgiI3G3CYVrTmcjAmj2Pa/d1EbDSg0v+x6Op5Dy76nZMMx+yskCWtpBc4GPT6ni4ode4ntkkl1TR0HPl0SLOZzuXEd560f8PnpNGUCBas34Wo8NoOs+5UX4LY20378SESFgsPLV2MtKUcXaUYXJQeIaMNNNOYXYy09NmuuYnc2YfGxGJPisdfUIaqVFK7eJK8UBFAq2P7yu8R2yaTLpecT0ykDQRSD1jr26rqgxU1Svx7UHjzC/o8Xo42Q50YqNGr8Hi9KrQaf04UkSQT88raCKBCcTBQIENu1EzX7DiL5/ajDwkjq1+NXn+d/Cv6AxIF8Nw3WAJEmkc7tNCh+RZ6H38q4ceNO6NdoNCcfvPJ6vahUJ/lRPkN4PB7U6l9/b4b4+yMF/PgqNyE5KhH0CSgTBiOcoeCjoxy9V7xeLxs2bODaa6/FbrfzxhtvtCp3Ju+T0D14ZvlVgv20adPOVDtC/NmwVsC8cxgvScRJ/yO1TQZTeyfLXug5X0FKf+zjXmJl0l2kx4az5bvNwHDytVchbPDDJR9Rr2+L29ZEQBI5r2ciSRF6vs+VI/K/3lvBN/urSKSOj7THfM7z8w9RFhhOpii/YGZRwrK9K4FMRou7eEd8gVXOnlznvQu8gMtP3dYS5qieZaT4A98c6Ms56h0A7PRm0hSXxXn173CjcikKApAQgb/kS0ZL1WwsHczk1zYxTy8wQgqA7VhEGj43P6z5gri9qxmi+gIap6C7cC7p0Qbsbh/3fJlNcsQw7r9+HcTIybu6JIWz/9GxOL1+Jr2ykaJ62cbljjYlJJfkMSwgoR74b7qnmFuf6+lL5USt6tOcFFCphXaj5WSx5vTTWvVl/VMBiX8v2s+8zUWc1yMRAfh2fxXTB7UhpnoDmJKxu/184OjHB2vzKW10Eq5TsffB0bBgMtjrCJ++jEv7pVDe6KRDfNixJKaaMA4oM1m5o5q+KUYs5Qcx6XrQJsbAd5+9zoTiZ2HMQ9DvupM3sLEI5o4DQQF35oEh6rQe/5lkSEY0q+8cToxRg1G7l/DirXR15uLxt2HjkTrcPsiPGUOi85jYUWVx8dYhB70lPUcCiRTWOShyFNE+4OHQ4qfocNMn8vU18aXgfqbfcCwi2FW8A0d4ByLN4T9uzsmxlCFKfsYne1joHUV1fT7vcw5duvZgZrydg5VW9pc14H99AguFRm5XzESJHzHgYkyGma2VAWptbu4f34nxXRNY/3UeAUnOW/eN8WnC9y1mz0Y9PeuX0rnfLHQTnoCRE9GodKDUwM3bqHUKPL66ihkJF0L1ATID+XxcGc+Lb25h9V2Xc/Wu9uw/Ushk8w60rmrUkg8CXkaJu5lhWQHLi+GCOZx782w2vutlWPUHPCW8x3N7ujOoXTSejGsortIyyvo0ij7TKDL3J9ITzb0f7EGrFLlat5L7pDnUlbRhrTALm5TKAxMyaRdrQK1QMLh9NN0fXYnTC/eM70hKhJ5r5u3gX6MzTvwNCBHi/0mof/bPoc5ez5HaI8HP8aZ49Go9Hr8Hi9NKpD6SjrEdsLlt+AP+oO2NX/KTX1dAl4TOQREdoH1MOyotVUGbm5KGEiyu1hZPAA0twQnatzag2lmC86ah2PofE4sDBIJKvISE3WXHJx2zCWl0NrWsk9Eo1LiPs9ARRZHICDcd09R072Jjf+X+YAS+x+cJCuken4fihmKsrmYaHY1ISMSZ5Khch8dBUX0x0WFRpJiTMWgMwXMUa4zF7rZzsOogElIrD3u3302vuJ6tjletVNM7pRcIrb35Twea1Dg06Qlo2yWd1noBXkvtiEFUsKChin+X59Neo6PW6+WdugqGhZmxrN6JOi2e2uZm5g1pi6OykN2OZiaFR/OuNo7CO19Bn5lGwr8uwtCrQ7CNokp+ZVTHR2FdtwfJH+ChO8ZSrlXw8bCuxOWVU/bUfDyV9aT9dyb6zLSTts/idlLRbEWrVNI2Ivq0H/+ZpN1ZI4jrmkVYfAxdLplM1d4cAj4fjrpG7DV1uJqsRF7TFmtZBQqtloDNjqOuIZjYFaD2QF7w77wl39H5wnNR6bT0ueHYTNlh/7kNAL/PR1NBMeb0FBS/UJw4Kq7HdsmkIb8In9OFUqeh80UTqd6bg7vZRsGqjVhLyltvKEFYYhyOugaQJLpeOhmVXs/Bhd8Eiyi1Gko2bqd0y04CXh89r7mUfjOn47HZURn0CIJAWHwMHpud4vVb0cdG4ahrQBAEyjbvhECATudPoHpvDn6nG1GlJOCVZ2UEWoT5mv25VO7aS1K/ngy+92a2vPi2nNgXqNiZTfrIQcR0zsTZaMGGROeLz8PdbMNZ20D+yrVoTEZ8LhcEJLw2eVAyKqMtxpQEzKnJKLVaBFGgZt9BAPrfeg1Ve3PY99FCOp43DrUhlKD9KJv2Onl7URN1Tces06LNCq4/38zg7mfW2lGj0Zwy348gCLzxxhssX76c77//nrvuuosPP/yQG2+8kbvuuitYbv/+/XTr1o3Dhw8zevRoAM4//3wA0tLSWs2oXLBgAQ8++CCNjY2MHz+ed955B6NRDjAbMWIEXbp0Qa1W8/7779O5c2fWrVvHunXruPvuu9m7dy+RkZFMmzaNJ554AuVxOUB8Ph+33HILH3zwAQqFgptuuonHH3/8hBluR3nhhReYO3cuBQUFREZGMnHiRJ599lnCwuRB0OLiYm655RY2btyIx+MhPT2d5557jqysLEaOHAkQzFs0bdo05s2b9xvOfojfgqdgMc5NdyHZj/22CoYkdIOfR9128hnb7/H3ymWXXcaaNWtYvHgxcXFxLF68mFtvvZUnnniCoqIi/H4/paWlzJo1i1WrViGKIuPGjeOVV14hLi4uWOfXX3/NI488woEDB0hMTGTatGk88MADwWs7dA/+vvfgrxLsRVE86cGZTCY6duzIPffcw5QpU/7fjQrxx2FxeClpcNA1LgISutPsdGNsVDGSXZz/ioMb9Gu4oOYVSBnAfPMdePZ8worEqYRplNjcfr6KmM6U5Ga+c3agwtqeacqVzNG+iPmcPaw6WI1GIdI+1kB2udyhrMHMN/6+jBJ3oxH8FJFEDWbyAsl0FMsQBbiz8XEGqLpQHDsasV4iQ1HFEJOdof160ynBxGc7S+lyqBgBGKX4AQBJoeaKdgpmndcX9QdPoqgJgFIH+mg6fX8Lr6sFPotTsrOsmbudV/P9+dMJ73ns2i3+9C5GHX6fVHMfJLuAEH5syvC+cgvLsisRBLh9zDi0KgVU7YfmSlQZZ1HS4KCo3oEowI3D2zF45DCyF+spNg5kavJJBFFRcfrFepAjVi775Ddv/t6GAt7fUsy/xmQwpdeJU7ov7ZfKsuxKNuXXU2VxoVMrsNaWs6+8iVGbZ0P5TjZFXs5LzoncOSiduZuKyEo0gdch27r4PQjWCnYVN3Ko2sbavFp6p0Xwzb5KfPuXcE35g9T7u5KpS2C8eiXvfX+ISziXKyybUSitULbj1IK9LlL20ldqf9af/egsgbGd47mob8pvPl+nk7YxLW32OhHfnwh+D+rr1vDpDQOpbXbTMzWCmR/sZHdxA50SwrlnbCZb8mPo+e3bJJgN0OTkAsf9bNbcQofGdXB4JXSeDMCa3BoS7QeIlupxtT+HpE0Pot3xDh5JR870HWS1OfU5WLi7jDcXfc/1wztxwdXLiYrrzBViGPd+OYEleyt4ImcF92jWc7d7Eo6SH/CqHGgFiasUK1AKfoSAn5eG+HAnD8LpdhOlFWDXfGZ28SPulmfSR3orkQoqkQIZIEKxFEcmgM4sN6L2EHxzF4cMY1hyoB2HDGdxi7eU96UZ7BS6cpbeAgvOZ669lIaUGEx2LwI+kGfuM3VQV6Td6xBarB20KgVDJl+P48u9LK7sw7bCBoY/t4Yks46ypmQeGvYB/bwWprxfTkJ4PfOu7ov+8BJs23bhFQWiXYXMN73F5lGfc1n/1gLFveMz2VXUyNjO8dz7ZTarc2uIM2lDgn2I006of/b352gSWb1ah1qhRiEq8Pg8uL0udpXsRikqcHidJJmTqLPV4fa5MWuP9TkMGgNmnZlaW11wmUqhIlIfSYO9EZvbhlFjxNYSuX9Kjibm8UtBQV6n0uH0OhEQ0Kv1xBpjMaj1HK49gtvnbrX50Sj3BGM8e8p/CLbD5rLRuaOfdx9si1IpDzSoFCraRrchXCcfhyRJZFfsw+v3BhPfqpXHhMx6ez0Wl4WAFKBLojyrqcnRhEohW+rYPXb8UgCFqKB9dDs0Sg3FDSXEhJ1cOP5xMunThSo2gox5//lN2wakAA+X7mWNw8tbaVl01rXu4+hEBY8ltuV7awOVPg9ZWgO7my1srCjHlpRB1RuLAHjz1rPI6xDLvyPiKHA76Wcw4S6sxltRh83uQnJ7cWTn49hXQMxlYynTKlDNWY5jVx7uAlmQuDVcR0SzC8uSHNwltfitskDqLqw4pWCvVihRCAI65c8L0M3bDlD/xRriZkxEd4r6fk8EUcDYYtlSf7iA/R8vQqFRM/LRu+l2xVS05nAEQWDPnE/wOV1EZ2aQ2LcHdYfyaSooQW0Kw2O1ISiVSD4flbv20uGc0aj0OgI+P9X7cuQocY2a6Mz2bH35PVwNTQiiyMjH7kY8STLgoxxcuIyqH3LofOl5pAzqQ3haClIgwKb/vorHZqdyz35EhYLGwhICnuPsq0QRAgFcjU2MuO0ufE43olKJx2bDUlKGIT4We5UcbOVzuSnZvAPJJwu4R+161GHywFj1voOUbdkFokBjfrEc/R6Q0JhNuJos+Nxedr75Piq9DkNsNA1HCoPNkLw+orM60HCoAK9D/q3ThpvocM4Y8leuw9Nso2TjNkq370ZqEfk7nDeW2gN5lGzYRkznjnS9fAqlm3YGRwU9Njv66EhShw4g4ri+rRQIkDKoLwqNCo3JSOGqDfhcbmI6ZxLfPev/eZX8Pdi018lTc+tPWF7X5OepufX8++qoMy7a/xQPP/wwTz/9NC+++CIKhQKNRsPcuXNbiYVz5sxh6NChtGvXjh07dhAbG8vcuXMZN24ciuNm9+Tn57N48WKWLl1KY2MjF110Ec888wxPPvlksMz8+fO56aab2LRpE5IkUV5ezoQJE5g+fTrvv/8+ubm5XHfddWi12lZ5iubPn8+MGTPYtm0bO3fu5PrrryctLY3rrjv5+6soirz88sukp6dTWFjIzJkzueeee3j99dcBuPnmm/F4PKxfvx6DwUBOTg5hYWGkpKTw5ZdfMnXqVPLy8jCZTOh0oXxZvxeegsU4Vl7Gj+3gJHuFvPzsj86oaH88Op0Ob0ui7yNHjvDZZ5/x5ZdfBq/5yZMnYzAYWLduHT6fj5kzZ3LxxRezdu1aAFasWMEVV1zByy+/zNChQ8nPz+f6668H5PvuKKF78Pe7B3+VYL9w4cKTvhA2NTWxfft2rrjiCubPn8+FF154WhoX4vfnqjnbiK5YzYsxS6ntdj3v5yq4w/8e4yp3YPNeyCf1nZgaGYeQPpjR+16lo3I9SyxuvnVfjl6tYL5iCg9n27mybgN3KVYCEG3UYqreTKcNL7JMcYjLK/9NNZGoFAJdxXJGiT8AIn7gPfdIPKi4SvM/vrkgjFUfPMVFinUMV+zjmxodH0qjuFy5mpfNHxE5/CJATmL6vw9fZHDuE/RRHMGCgXC/nYzST1F8cQDf6Dvh00tk726NCTpOQBBVXHxwNherYP/53xOeHAl538KqR3FFZvJObjyPKQW87c9GOHcpHDcVu196JPeOyyQpQieL9YEAzBkLHhtc/S3t0gbywYz+6NSKYLLPbhc/xF8pBWBpg4PHl8nRJ4v2lDOlVzKSJPHvRfvYVtjA4+d1YXD7aGZf1IO31+Zxs/VFtOa9GKwFBMrvhZH/huxPmZs3DLfXR5qygY1xs/Hpk7n5ixsY0vktLu0WDvFdaBO9k9IGB5UWJ0P/+wNun58RgoXpagGfqKbWKYECCpv8FPsdfBN3DQM7n0WXoScXn7YXNpAepSf25m0nXf9jVhyoYlVuDSUNjj+NYB9EqYX2Y8BaDhHp9NCb5eU+N89UXcejuiYsqdfQfv6FdJ/wPDc+cyVrcmu4et4OlGGRCCMfg5q90E4e8d1fbmHGvG3s18xAL7i50v8wb/dwogNMgpOkb6ZDRDSMeggOfAk9Lof9X8CueTDoVkoqOvG1eBfCJgHi3uC90gQeX7qRWSPbMbBtFJ0KngJpP1cl6eiqlgeLJAnSBXn2ioTAVUsaeNreiwSljfVCT4Z5N5LSazqVxq5EWg+iwYcA9BIP09v7DrdH9qeVIdOyO6FoPf3jmrhm8JtcU/sMySVbGaM4wBM334Jmxd2QtxoVEMdhDsZOIDOwGcHngnajMHcYBNufhyPfwRczoPc0aDMMzcwNOL/YC7vLkYBU6w4GiHWcs/0L4mhgcOBuDtSns397Dbfk3wkKsEkaApIPo7eeJ7/aw95SC/+9oBsur5+iejuX90/j8hYR/7YxHYg3ablmSJszftmE+OcR6p/9vfH6vewtzyYgBTCo9cQaY6hoqiRAIBi57g8oUIpKjJowyptkQdXqksV3pajE6/PSYG9oJaCH68KxuqzU2+uRkLC6j3nUH41u//HfmtsnEuFQUKRuCpY9OpggIRFtiCK+JeK9V0pPdhTtbBVpH5ACNDmacHldpEWkUdxYjNfvJVwXTpizEa1SS529DoWooHNCFl6/lyprFaWNZZh0pqBXfofYDIwaYytRPd4UT0CSiDLIPuzNrmYOVuciCiJ90/oQZ4xDKSoJ04ShVcm5dLISOv0/v53flxxLLp83VlMl6VjX3ERnXRiNPi9XFeXgDPiZn96ZJLWGd9M7sepIARe/uJyrSqpQNzQjvJBOwu0XU19axcpUEwqnm1ElFoa+vg7nuL5M6xXHLfddwqAO7RD1WpRR8kDJsvIy/lN+hI8/Xx1sh6BSEN7sQgQU+4vwKxXoOrfFPLYf5rEn5msJuL3Y9x7G0D2DjtFxJ6w/GQ2L12PbcRB1cuyfQrA/HkNsNIa4GIwJ8rHEHk1mWlWD3+2R7WwE2DL7TXpffwVh8bHkfvUtZVt2EZXRBnWYAY0pDJVefr8o2bSdI8uPnV9jUjyBFmFcCgTY9c6HGJPiiOuaRWN+ESmD+3Lg0yXYa+rocslk6g4V4Pd4yF34DT2vvpTsD76g4XAB7caNpGLnXqp27wNkCx/38YJ9S2S7QqNm/eMvEfD5UBvD8DqcSH4/Hc8by6Gvvwta00g+P4JCgaAQEY4TXLxOF3lLVuJptpHQuxvJA3pR0zKbQKHVMOTft7L9lffwNMuDOn6PB110JJ5mOQl2m1FD8Llc1OUconLPPuw1tbQbO4Kkvj2I7tiO3e99jL26Fulo20WBQ1+tCO6/9kAegijQVFTS6nvyuT3sevt9Ol90Hgk9u+ButhHw+ug46exgmU5Tz8VaVkFMVsavvg7+jvgDEm8vavrJMm8vamJAV+0Zs8dZunRpMKL1KPfeey8PPvggIEcSX3PNNcF1V199NQ899BDbt2+nX79+eL1ePvjgA5577jkAYmLknB5ms/mEyP1AIMC8efOC0bxXXnklq1ataiUWtm/fnmeffTb4+YEHHiAlJYVXX30VQRDIzMykoqKCe++9l4ceeij4XEpJSeHFF19EEAQ6duzIvn37ePHFF08pFh6f+LJNmzY8/vjj3HTTTUGxsKSkhKlTp9K1q2zV2bZt22D5yEj5uRcbGxvysP8dkQJ+nJvu4uS5W2TPVuemu1GlTzzj9jjbt2/no48+CkazezweFixYELz+v/vuO7KzsyksLCQlRdY8FixYQOfOndmxYwd9+/blySef5L777gvO3G3bti2PP/4499xzTyvBPnQP/n734K8S7H8qQcC0adPIysri+eefD70Q/oU5S9zJZaq3MVmakdY/yKOCA49CSQCBen0bOnUeizD5TgAWZz/HWYEK6lIn0K7KQHKEjp1FDdg9AZoqDiMK8s+UsTkf3j+PTggoRIneYfX0HjGYrAQTL7+3H63CS2EgjoVd38ZdDNTaUStFlKm9eUK8gVJfDJPFTRzwp3GL+ivckpLmmN4E8rbwcVk0m/Pr2VeuZ4HiEW7QrkJtK2Oa8jsCPg+Kki28U7iAEQOfoJOiXPbdt9fA9reg3ShQh9Elqyu8kBn0pFfZ6vhYepMP3aN5OK4LnVStR8dEUeCmEe2OXyAnSKw7BOZUQLY1+SsTaVCTlWjE4vDy8EQ52uRAhZWPt5cCkL/iDQZLA4nPGMNDQ43w8qLgtos27OKrtWqeVSznWmUJD0VYaf/tXgQhgEJQ8p1zPCtELVJSeyYkenj1sl4MfOp7HlmSg1IEnUrBWk8vRgXe4ObAh/QVD/KI4QFuMaznAh10n/7iKacxrTxQxfULdpERG8Z3dwz/Rcd6YZ8UapvdDO8Y85PlXliyjZ77n6L7gDFEjpr1i+r+fyMIcOnH8t++Y9YB+FyY3FWAmxhvPvhcUL0fgDiTFr1aQZc4NbpuU8B4U3CzRLOOlMgwtrl6kBKopEadgmf8LES1gcrsVaTU7IRaCOSvRfS7yM/dS1ztFsKwI333MDdNWwG5Yag8jfDlDLydPwQEjtTa2VJQzzPCFM5XxjJl8n1Ib3+F0OJZ7FfoEQI2QKLS4sKgcCD43GzyJtNNF405pQ8Ju+eBAH4JFAK4BR31fgMHKo9LcthcDUXrAVhjnsLg9lEYa+WXP62/GV7OgnD5HvSkDuWF/ERGVO5had/nmTip5bmU+w3++O6UODS02f+F/HugNqAoXM/s7lnc4lzCI2V9eNfzX1SCX/a+l+BS837G2J/HW9zyAiGI+NBgkNyYA/UYA1ZKG2UbrFs+2s33B2t47oJuXNhH7hB1SjDx6HldTstlESLEjwn1z/7eHP/Ms3sc2D2OVuuVopL0yHRijNFBawmAWGMMDY5GwnXh1Npqwd9qM+psddQdF3Fv1BiJDosCCQobioLL401x1DTX4pf8aHQ69NFRUNUUXH+8F70gitTb6nF4HdQ01yIIAqIkIopi0HbH5XPh8rmwOC3EG+PQqXWEaWUR3elxEqYJw6wz4w/42V95IDhg4DzuuLVK7QkR8CqFirTI1OBnjVKDVqlFo9IEbW2iTxFN/1chVhPDpZo9NKmSmR4t5zT6oKGKDbYm9C4v+zYuI2r0IAanxdOpzEnFnsMcdX6+IfcHYssbmbzpCPfb29N302Gq3F4Mbh+1q2B1xmB8STp6HSlDlRZH4mt3csmK5fj27cWaEsGa0Z0ZuUpOAip5/agAQ79OeEpqMA3vQcJNp57FU/XWIhoWriPy/OEk/uuiX3SscddOQp0UQ9SFp8614/UG+PCrGiw2H5NGR9Em5feJKtWGmxh4uxx1GPD5EZWyEOO22pACAVR6HfbqWrwOJ476RsLiY4NJUMPiY0kfPrBVQtnwlCQUWg2Sz0fA50dt0NP7+qv4Ye7HNJWUYykuw1JcRvk22Y6oOjsXe00tADlfLqPdmOHkfPE1nmY7e+Z8jNoYRsDnx1pagb1KLqfQaOh88SR2v/NhcL8akxG3tRlRocDrlvuZXrtDPgaDHo/DGRTrjyIIclJiR10DpiRZeKnLPSKL74JAWEI8mjA9FbvkHFX2qho2Pft6S0JiiGiXRmN+MT6Xm943XkVEutxPOrJiLWEJcbgam6isrkUfHYk+OhK/x0tS3+5U78vFUixbpgZn+tDiX+/2UHtATih7dNYAgMduBwlcjU34vT62vvQOPpebgbdfjz5aFlfiumYS17V1rq5/Mgfy3a1scE5GXZOfA/luumVoz0gbRo4ceYIH91ExDKBPnz6t1iUkJHDOOecwZ84c+vXrx9KlS3G5XL+oz5Oenh4UCo/WVVNT06rMj/d38OBBBg4c2OrZPHjwYGw2G2VlZaSmys+hAQMGtCozcOBAZs+ejd/vbxVhfJQ1a9bw1FNPkZOTg9Vqxefz4XK5sNvtGAwGbr31Vm666SZWrlzJmDFjmDp1Kt26/ZVCAv9++Co3tbLBOREJyV6Gr3ITqqRhp33/Rwe3fD4fXq+X8847j1deeYXXX3+dtLS0oFAO8nWbkpISFOsBsrKyMJvNHDx4kL59+7Jr1y527NjRSiz3+/24XC4cDgd6vewKEboHf7978FcJ9j/H2WefzX/+89umeIb4c3Bz5E6Emmbc0V0Q6grwI6DGR50ygcum3YIgyKJojFFDrz6DaL/+Ldp4l/FY7RXktwhnAJ96hnClaSOd3NmAREASUAgSRwIJPHbbTRjUSnRqBfeY+zCu8RnMNJOzs5oOacncNbU9A2s+Ijz7CNv6FXNL6aW8UjSFp3UL0EkeAkDigbdR/fA8K91PcEjRHrcvwNvXDiTCMJzXX3mGi6W15Ks7870tnfm+s3l3VzSr77yOcJUKds6BusPQaRKMfkhucHxXvJU5VPe5m5vWQnfpMP9SLUTnugP4BRGxl39+Br6NPw6DRsk3t7Z+qHSIM3JO1wQM1Tu4qvZ5+FgJ/66AyLZ8HnMro2rm4kXJo+6LuEb5LfHUEe9bh8unQSUEmOMby9CzL+CLg1+zxx7NvxdJvLOhkOJ6e7DfrVYquGVke/67Io/rxMVcKG4A4D/CHJQ1lcSzEXzPsOqIhfmbi7iw+iXOirejvXQ+6CKwuuTo7Gqri4+2lbR47f804ToV90/4+Qi7+r0rGOlfi3fzTjjTgn3Vfvh8umxjM+o/cGAxfHE1DJgJY5+UZ4oMvxcM0ZA1GbpOlfMVAFmJJn546GxU742AFw/A1d9CSl9AHohJitBxdf6/mDEkna/GZqJVKbCf/RR3lW1nWNlbzFItRvS78EgKqmtrUAW0KAQPOr8bzebn4c79+D+5HIXbwowJg4lrZye3qpmJ3RL4Oht6Bw5R//ltxE18DcXCa3AIOsY6HuPT7nvJqfdRUBbDMuP5jAyvIKb9TBx9XsJs1kH5HmwlP7A09jouzr0dZUp//t0mk4+2lfDst7ncMy4TjHEQ3xWpch/f7ivny707SVVOZl3YTgRXk3zuLCXYdEkU2Q1M02wgIVCBZ/eNPNwYxvD+vRj15QwUXgdWfxolQgz+6BGo5k4n2SdHZbUB3mpvpdk1ksjyNSD5EQQ4y74cQQCN2HKxJnQnzFqNYLPSMPgh/tfuHDLjTQCEaZQIgvx/iBB/BkL9s782SlEpv3BIslDt8rmC6zRKDV0Tu2JzN9PsasYX8BFjiKbOXo/H78Xj99DoOOahrRJUeCXvCfsQEemckIWEhC/gayXYV1qriDPGolFq8Pi92Fw2EsMTqbBUnFBPUX1Rq8+iINInrTdV1moqP/4W9apcAtePwJkRiYSEw+ukTXQbAoFAcPCgc3wWJp0Jj8+DSqFCISgwaU3U2I69wPmlnxaUQPah75nS42fL/ZWI1cXwcOblrZZNDI/mq6ZapqzNJnnxDir2FtP2f7djHtOX9zZvY/S2QvZkxLCsUxyvfneAqEYHY5Zn4xcEfAqB+eO7cNOQAXz46QZUVY3kl9ZxXaQLu93BvPe3ovb6+O8DkxmZlAwckBXbFvHVvisP/AEce48g+fw0fLWB+q834m9qxjS8J0m3XwKAv0mOpG7edgBHTiH6rJ/vW2vbJZFwywU/WcZq91NRIwvN2/Y2n3HBvmLnXgpXb6TjpLOJzswg96tvKd+2m25XXEBMVgfCUxNJGdyXuO6d0YQZcNTVE9VBDvJJ7t+LyPbpbPvfe1Ts/IHBd9+MQi0n6jOnpwS92rtdMYWYrI4IokjWheey9X/vEvC0RLi3JFI9KtYD2Ktr8Xvc9L/tOvZ98CXhaclkTBhN1Q/7cVuaMcTFyGXcbqr35pDYrycV2/cgKBR4XW7iumdRlyvnxVCbwjAmxBOV0YbkAb2QAgGaCkqCkfe1+3NJGdyPgN/P4eWrUKiUxGR1IKpD26An/eGl8gxrfWwUjlrZE1/yyYN1Co0GpfaYyLvnvY+I6tCOpP69KFqzqdW5lhDY99GiVstSBvej5kAu7qZjwRz+loEGQalA8gRI7NOdih0/IAjQffqliAqBiDapSAEJhVpFwOf7SXuhfzoN1sDPF/oV5X4LBoOB9u3b/+T6H3Pttddy5ZVX8uKLLzJ37lwuvvjioLj4U/w4WaYgCK0Gvk+2P0mSTggek1p+E08VVPZzFBcXM2HCBG688UYef/xxIiMj2bhxIzNmzAhanFx77bWMHTuWZcuWsXLlSp5++mlmz57NrFm/UyBZiBOQHJWntdyv5ejglkqlIjExsdX1/Euu2x8vDwQCPProoye10dQe99sdugd/v3vwtD6tnE5nqy8yxF8P4axHIb4LmtLtaOr2IwFvJDzFR0U6+m0uZN+BfRxyR9JTOMLT6vcwCbU4KndwWb/7OVBpJbZiDReKa/iv7xLKks+lU342NVI4T6juYJpnAVFY8bzUi9H2R3jhqmG8f01/3nx7B//1PMX+QDrnFj/FZYFlRNS+BqISXcDHnV307K3rxxFnJKggoI/GF5aErSafBsnIHWdl8OG2EmZ9vJsZ+g3cqlyEVvAiSH7may6lwecFu4f82mZ6pUVCY7F8sNHHpj7uHbWA817bRLf1lTytehOUAbqQT6B+CXD+H/Jd/NlQK0Veu7wXONvCZ0tBFwWLb4Ks8zAOn0nvD+Qp0ApR4C3fuXQVChip2IsWNz5RQ9dr3yTDuxtWf0FXBN4yjUEhCMcHyeDw+HF4/Sz/11Bi3r4FJGjWp6CP7UjAVkVV1xvxWPzMmL8TJT7e06xAVeKHqn3QZhgHKixIgNXl4+31+b9IsP+lXHzFdeSsr6Vjz18Wuf//onQb1B+G3G9YoL6IXhtforMUgOocaK6C6gPw/cOgMrDJOI6Zn2m4ckApd52VAcvvRu33gt8HUgB+JGp0iAtjc349FqcPrUqB2+XkkScepcrfkdzOt2LrPR3XlzcR46tkENkgyjNlfNpolJFtWbCnngcPXs8dZ3Ugq9jD0uxKVuXWcMWAVNrFGLjRuhRzjY3iusnM8VzFfqkNYxS7WR91KZ36ptJzZR4XVH+K3unmkv6XE2buLjfs3BcIAy4BCivPIspswriviraNGxm+cy3k1sL5byGFxSMI+3hCNZf17h6U+CK5w/A0d3qeIDJQjw4XYc5yOjvKqY/oCk0VqPDxUMGlPFP3H0ZFpENNDt0V8u/Amm1L2OgfxlWKFaQoGhEFAW1aX7QqPXSbgPDNHUhAQBBQIMHg20FrhA7jUCrUYKsiJrEnx8/PmH1RDx48N4uoMA0hQvwZCPXP/vp0iO2Aw2OnvEkWyVWiCr/kx+1zU9lUQbn1mHh+NCLd5XURqY/E6XEGo9u9kheDWvZzP1qPN+AlIAXYUrAdpUKkR3J3OsR24FDNoWCd1c016NV6HMdFuceExVBvryPQ8pJk1Bixe+wEpACiIJJiTqasqZxV+39gy74mRm0pQVtnR13QhCsjEgnZtgbA4T1Wr04ti65qpZoEUzwljaVIztbTzZtdzejPRP6fvyDpGh3fd+iFIxBFxf5KdO2SKXn4XWKvPoeyG89hzAUVBEQRFQJPXt6PF19fR1KDHYUkYc9K4+bbpqOd+y1pW/JQRofjSIujSgnn7SrG6PYRAA57nDRMHUK3NkmUPToHgLDB3XDsO0LA7iJq6kgal2+h8pVjASyNSzeROOtCUIi4imSxwltRR+Pyrb9IsP8lRJlVnDUkgvxiJyP6m09LnT9FXV4+zoYm6g8VULnnAHUHDyMFJJqKy4hsn07hms2UbtqBs74RU0oSJRu20eWS8zAmxZO7aDnGxHgkSUIKSNBqZopAWEIstsoaAgF5pkrFrmxyPv8aRJE2Y4YiKpXkf7umdYMEAXWYHk24icJVG3A2Wmg/fhTWsgryV67H73bT8byxHF62ioDPR/n2PSQPkiMVJb8fye8nZVBfTEkJlG7dhauhiXrrEbpeel5Q1O59nTxAJAUkLKXlhKcksu+TxbibrBxcuIzC1RvpevlUhB/NeHHUNmBMiqe5oioYEe93u6k9kIdCrcLv8RLw+qg9kIdSr0NUKgn4jtlnFaxc26o+lUGPLjKcjPGjqM05TPXeA63Wd7t8Ks6GRhL7dCd1UB8UajW6SPNx5xgG3nEDkj/QanZDiNZEmn5Z7o5fWu73YsKECRgMhmAyzPXr17dar1Kp8Pt/fqD3l5CVlcWXX37ZSjTcvHkzRqORpKRjycS3bt3aarutW7eSkZFx0sjenTt34vP5mD17dnD22GeffXZCuZSUFG688UZuvPFG7r//ft555x1mzZqFuiUx9ek6xhC/DEGfcFrL/Vp+bnDreLKysigpKaG0tDQYZZ+Tk4PFYqFTJzl4sVevXuTl5f3iOo8ndA+emXvwtAr277zzDj179jydVYb4vYlqByPug4J12Ip2sd7TgT31Ah3NEmdVv8ds4X1eUFxAvMpGJsU0RXRlStU0ClqsUj5WfcNARQ6FJJHlki/WSJq5lGX0Fo/IATleeETxHjVNvemRYubJy4bin69CNMZBPViarXh0caid1QBkduzEFyMG8dUaCVfNfrRZ41GMuI9PNhVwu05Dm2gDTy+XfRIn+D4lXaxmkW8wGRc+w6q4OO585X0O0pZ24S0jhyMfoCZ3M76Y4YS7vBi1KhQKAaUoMEDYRxfpMMUkwoCZiL2n/+5fwZ8enRmmfQ0bX4TvH4HaQ4y7aSO5j4/H7Q3w3IpcPthWQj0RiEhgiEXZ/RL6pkeCbyj0vwkhOoPNfc+iyeHh2/2VPLksF6NOyeX9U7liQDrhOhUNox+kfvcCzJe8yYJDCl7M2YWwP4ItE7X0bxOJ2xdgW5tXGBztQIjvhvP5LsxwStRlvY1OH8ak7kk/dyQ/Te0h2Pc59J0Bxni6tUmANq+cjjP48/S6CkQlpPQn953PudK7Bw8qxMINKF7rj3D9GkjsCXFdyKmwYnF6UeQtgf1zoLlFtLl+HeijwJxCk8PDrI/3cLXnE85T+5nPWTQ5vLy57gjhe9/lOeUb7BIzmLr/URLMbXgwLhHKj0UCCIDSVYd/y+ssiR0HyDNtXvjuEH3TI+ibHsGk7kk8Mbkr/q8vgl1zSN35BI+q6ygOxJIm1hBwOhHbv0qv1Ajef+tfxNkP0yVmCD/uDiz5oZzbP9nF1Khinrx5Ghd9+z8UPi80AR9fgl+hR5JEVPhRIUdVRVdtIFlVTr1kRC/IkaeCANFN+4LtVwgS9zU/BbNK4aNLoGg9Tk00GoebcqJIEesQJUl+f17zFAS8cqJqQEjuj2L0f6BkGwy6uVVOC0wndsAUohAS60P8qQj1z/76mHXhmHXh+AJ+ypvK8Qa8KAUlSlFJrb2uVVkJCY1Cg8PraCWEgxzxfryPvTfQEm0vgCBI+CU/Pr+PcK2JpPBEyi0VaJQa3D43Xt+xZK8A6ZFpRBmiqLZW4fK5STInEqYJo9JahVlnps5WJ9cX8HD2IANChyG4c6rodPFU6jwWCuoL0Kl1SJKEXq0nJiwaf0C2Ijsq+v+Q5+SOV8t46z/JaNSgVqiJDov+y1vbnAn0mWm0f/d+iu5+FduOg6hiI/jfzVN5Jrk9Vr+fc4/8wOEoCTUCggSq+Eg6nz+KcI0Oz6Sh+JpsRIwbgKFHBhvcDhryHAjqg1iHZDGrbx+GmiJgRATW1bvwNlhJfegaiu9/A/vuPJq+2078DZNRp8ShSY9HFRuBsW8nyut8fLSwkuS2QxjatgCFQUf0JWP+X8eZX+yktsFLv+5GRFGgbzcjfbsZf37D00DHiWcR0TaV8NRktr/yHiBHjRev24Ktspq0YQOoPZBHdFYHag/k4XO5KFyzCWdDI55mO9ayCgbfMxNRoUChVmMpLSd30XIUanWLiA/W0nLs1bWUbNwu7zQQoPD7DXS76kIEUWxtUSNJeJrt5C1ZgahUIvn9HF6+GmddA9GZ7ZECAWK7ZJLcvzdbXnwLR2091dkHWx2Tq7GJtGEDiMxow+53P0JjNCD3nFqT8+XXVO7aR0yXjiT07EpN9kE8Ngcem4PNz7+BKkwfjHY/2rbmMrkv+eN2+z2tZ/nYKqsZ+u9b2fT86/gcLpR6HT6Hs9VsDp/LxaGvvwNAVMkSRtrIwRiiIpAkieiOx+xKj9oP/RiFSgWqk64K0ULndhqizYqftMWJNivo3O7M9XPdbjdVVVWtlimVSqKjT/27r1AomD59Ovfffz/t27dn4MCBrdanp6ezatUqBg8ejEajISIi4je3b+bMmbz00kvMmjWLW265hby8PB5++GHuuOOOVlZtpaWl3HHHHdxwww3s3r2bV155hdmzZ5+0znbt2uHz+XjllVeYOHEimzZt4s0332xV5rbbbmP8+PF06NCBxsZGVq9eHRRa09LSEASBpUuXMmHCBHQ63Ql5AEKcfpQJgxEMSUj2Ck7uYy8gGJJQJgz+vZt2AmPGjKFbt25cfvnlvPTSS8Gks8OHDw9azjz00EOce+65pKSkcOGFFyKKItnZ2ezbt48nnnjiJ+sP3YNn5h78VYL9HXfccdLlFouFnTt3kp+fz4YNG/7fjQrxJ6DtcN7uv5Lv13zPN577kSRY2DwYlKAUfGiG30FTc1tuzM6gQDqWPOo530VcIG7mhutnwdxz8CPyqX84B6X2xFFMG6EaQYDzlVuYX5VD768O8p9zOjHjroN8uryYBJ+Fy5wfoxZ8kNwXuyKcEZ9DTNRuvpkSAe/txbahgEHrevD5jUPoGG9kxYEqVAqB+HAtC4230dm1G+OIe+nSOQPmT2KOsA5JqUV41wwzvuNzcRz3HkpHU/ADMYKVx89OIK3qO4yBznzkHU5atBJ9x1GkjTvnjzv/fwV6XC7PVuh0LgBalQKtSsGDE7NQKUSSkx8FS3/ocRlEtCQLU2pg/DPBKsx6NZf0S+OSficmE4sccg0MkZOZqAqLsRCG0u1Dp1bw6flm+OgikM6GfrPBUobaVkGMJBCp9PLoBd1/2zGVboelt0O/6yFvORxaDm4rjP/vb6vvt6JQyclQD63kEf/L+FDwqX8kk8SNqEU9pY4wplbcj7PIT0J4IS9e3J1xhd9CdgX15q54Op1PQmIPWHIrZH9OIK4froKhjNLIUXFTUwbyfVED3x+sppcQxzB1FAe0vYjXavlkewk9UoYx0VQN9lrwuyGpDw5Bz4IiIzuKm3jp4h4EJIn7vtzHiI6x3DyyPdhqyftuDpX78hkBCC4LkqBAldwdyZqNmD4UAJ1aAd0u4bblufRccphFM1u/VM3fUsws5UJusy+C116GPtdA0UY5aXRDEUqaQZBf+lboHmTN4A8o8V5PjiUak8qPtO91hB91mOSUP6DAL89OSOoFRevRxbRhUNkOBugqEDtfDkdWgdcF8V2gaAO0iFJ0vQDaDJP/hQjxJyTUP/vnkBqRgsVhweaxyclcf/R+2C6qLRa3tZU3/VFUChWpESnk1xW0Wu5yB9Bq5BccAYGC+gJsbjtdEjsTaYiiuKEYj88TFPePJnvdUbKTeGMcYVojjY1N5FUfQqvS0i2pK6IgUt8ykNA2Ppq9+Y3EmqPpcskAUCkpqioCwOFxcKjmEOlR6QQkiQZHIw0lu9AoNMSb4qhqasTpDrBio5sLzjKSHiUPEoQ4NbHTJ6CKjSDqfHlGoE5UoBMVfNa2Cy9Ul5L2wDRMuWVETR2JqJHVS3ViNMn3XRmso51GT7uLx8HF8iD9kOPqT338+uDfoqYloszmQNsuiZgrx1L9xiLCb56KcUAXig/Z8SHSiI6wvllEnCQh7S9h575m9h60MWFEFAtX1OH1SUSalXRo8/vOstCYjKQM7EPeUlk4RhDwu+UBMIVahb22AWdDE7mLlhPTKYOul03hyIo1eJrtGOJiaDNyEEqtlh2vzcXdbENjMmKrlAOUFBo1ppRESjZsa73PCBMEYP/HizClJOK2NuNqtCCIIjGdO9JcXoWzoRG1MYy+M6dTuXsfFZZmUocOILJdGs2V1Rz5dg3Ohia5QklCEEWMyQl4bXbCWhLnGhPiMCXFU3+ogKJ1m2k/dmSrdlT9kANA7f48PM12ojtl4GxoQvL7cdQ1EHAfE+HVRgOJfXogKhVYSyvwe3005hed8rw2l1eh0GjQR0ZgdVSiDjPgczjRRZrRR0fSVFyGLsKM1+nE3WQl4JX7Z0l9uqOP+u2iT4gTUYgC159v5qm59acsc/355jOWcBbg22+/JSGhdUBMx44dyc3N/cntZsyYwVNPPdUqGeZRZs+ezR133ME777xDUlISRUVFv7l9SUlJfPPNN9x99910796dyMhIZsyYcYL14FVXXYXT6aRfv34oFApmzZrF9ddff9I6e/TowQsvvMB///tf7r//foYNG8bTTz/NVVddFSzj9/u5+eabKSsrw2QyMW7cOF588cVgmx599FHuu+8+rr76aq666irmzZv3m48xxC9DEBXoBj+PY+VlyG+bx3fK5HtEN/i5M55w9pcgCAKLFy9m1qxZDBs2DFEUGTduHK+8ciwgcezYsSxdupTHHnuMZ599FpVKRWZmJtdee+0v2kfoHjz996AgHTX7+QWMHDnypMtNJhOZmZnMnDmTtLQThbcfY7VaCQ8Px2KxYDKZfnlrQ/zuzHxzKS9XXolSCPBFxxcYP3QAhwOJVFpc3PThbsw6JZ3de7hHs5A1kReTV+PAlTaCCMHJU5XXotbq8LqdBHSRfN7vc86PLML2w2JWlsDHmouoqK7m/Ewtj5TOwOpTMsD9Kt+q7yNNrIER97M24Rqmz90BwIaRBaRs+Q9NGOnheou50/syMjOWsS+uI6/aRkyYmlqbB4Nawd67eiKpw1Ctegh2zpMTECk14G7Gq4lgrPNJLmc5M8SvORJIoL1YyZu+ifyQeRtvXtHnp09KiN8dt8/Py6sOkxlvYmL3RMj+HBZeC5Ft4dY9AFTlbmN5Ti1jRowiJfI3vsCtfgLWPwdtR0CfGbDlNTjrMUj9bS+Yv4Wc3RtJOfAmxhG3wobZcOhbAGanvUmDLp0H+kL9pzdT64LHvVeRTQar7hyO3t9M9ZZPmLY1HrUplu/vHI5mzmhU1T8A8JX6XPKdBrrGqbmubByxRi0Wp5dEfznRWNghZaJoSfraMc7IituHyV76jnpoOxxJkvjvt3lISNw3LvNEf7iPL4O8ZbziO492sSYmjBwJ7UbIUf5HaSyC9yfjczRS4g1n7eAFXDO69eDKR9tKqPvuBW71zZUXtD9b7u8cXgkxnaBWjgwLICISwDX6CbQdRkNcluz7f2ARVkmHSXDCwFnM2BxOX98eZqhWgtaI6sb1ULUfN0oeXVvH41UzURCQBwbOffFYQxoK5ETUNbnQ7WJQ/DP9TkPP678Gp6N/Fvqu/zr4A352FO8MJntNi0jFqDWiFJVUWquobq5GRCCAhCiIqBUqXD438aZ4LE4LTq8Tg1qP3eMgXBtOdFgUDpcPq7sej99NQJIj7aMMkdTbG07Yf9fELlRaKqmz1yMgYNKasLgsgPxC2CelNxISO0t2AfJAgdfvJUIfQVpEKiqFigOVObh9bjmRbUsE/9FyP0aSIFyZSufUxDN4VkP8FjyV9dR9toqICQPRZaRQ/uyHNH6zmYiJg0m68zIAclYfQl9aQuolw4MDBL+W9xdWUVblYUT/cDw+iapaDxNHR2HQ/T4iiBQIcOTb1TgbrWSeN5ZNz76O3yNHk8f16ILWZEQfE8nBhd8EI8INcTH0vv4KHHUNHFmxlqaCYuJ7diV9xEC2vfweUsu0eUGpRBAF1HodriYramOYnCz1OM9IpU6Lz+kidUg/Opx7FvWHCtCEGwmLi8Fjs3No2Soi26eT2Lt18jtJklj7yOzgoEJE+3TShw/CnJ6CQnWsX1NzII+DXy7D7/EiKBR0v+pCItu1fmYc+PxrqvfmBG1rsi44l9zF3xLw+dCEm3BbjvnKCwqRzhedR0TbVDTGMDY8/TJuS3NwffrIQRSt2Rz8bExJpNc1l1Kfl4+gVJC3ZAUeq5z3oNuVFxDbuWPweOzVtbisNpACRHf89bYNfxfO9DN7014nby9qahVpH21WcP35ZgZ3/32SO/9aNm3axIgRIygrKyMuLu7nNwgR4jThKViMc9NdrRLQCoZkdIOfQ9128h/XsN+Z0D14+vlVgv3pIvRS+Ndgf7mFc1/ZSLTCxldXppOUeUy0XHWwmmvf38nozFjeMr6LIvsTMKdBUzGHM67lrH2jMCs8/DCrPbw1HK9ST9fmlxFUelIjdeRV27hR9z33SXNY5e/OaMVeJKC/61XMgp1LTXtRqzR06juC+7eqcYk6VunuQ1mXi18bwa7z1tCvQzJvrs5l3uq9nK36gS89AxgSbeP2qG20L/6EIpKIuns3ETolBVsWkrxnNuo6OTqEc1/CVboH7d75LPX1p6u6gvudV5Kj7ckto9pzxYA0tKo/fiQ0xCkIBODAQojvBjEdWq3aX26hbYwBvbq1wFrW6ODrvZVc2CeZ6FPZlbgssGs+ZJ4j20P9GJ8bNrwACd3kMidBkiRyKq10iDOiUvw6f8fcKiv7XrucCxXr8WdOQpE2AFb8m1pFHP5bs4kP18LaZ2Dt0wBs8HfhJvEh3D4f/gC8cmlP5qzcyagELx+VRiDYKlk8tJxo60H+VdifW20vYY6I4n/pr3Jer3RumruejdLVqAU/M3XPstPXjppmN6M7xfLetL6/qu1sfoXAhhdZ2uY/9M9MJW7hBXKeiFt2HCtzaCV81JItXhBh1m6IPImPbeVeeKslmr331bKY/sNHoNbLgxhxXVgqDKN3xSfEiw0IohruyoWv/wUHv6YxcThhOjWqyt04PT4uV73E7kYNlyXX8lT6Xtg5B1d8b4YWXcMmzb9QiRLCtd/LkfchWhF6Xv9zCH3Xfx0qLBUUN5SgVqjJiu8U9HwHKKovptJaSXpkGhWWSjx+DwpRgT/gx6QxYXVb0av0xJniKKwvRK1Q4/F7EAURjVKD0+tEIShOmdRVISjQqXSEaQzUNNdi0hppahHrw9QG2ka3RavScrAql2b3MYFOp9TJvvo+FypRRZ+03gQCAQrqCmhyWoLR+z2Se3CwKge3z9Nqv2EaAwmmhJAVzp8cf7MD68a9mIZ0R2E8FjwRcHtwF1aiyzxx4NDR2ITLZiciOfGUyeJqG7wUlDjp2TkMterEvpWl2cfO7Ga6dTIQE6k+aR0ebwBLs++U63+Kkk07OPS1nEw1c/I4ag4couFwAbpIM4PuuglBFNn51gKaCuUE9oJSGUy0qtRpyTxvLIeWrSKhZxdKNu1AbdAT36MzLouV6uwckCCqYzt0kWYSendnx6vyjEhBFFBo5D6rz+miw6SzSR306/pne9//HGtZJfE9uyCIAkVrNpM8oDeZk8cd7EjcAAEAAElEQVQFyxxa9n0wsl8XaWbwPTeftK6idZs5slz20e92xVSkQABbVQ3WskrqDxcS0TYNa3llMIGuMSmevjOns+7xF/G73IS3SUGhVNFYUIRSowFRxGuzE9c9C6/DScPhQqI6tqM+Lx+QI/WH3HtLKEnsSfg9ntn+gMSBfDcN1gCRJpHO7TRnNLL+t+J2uyktLeX6668nISGBDz/88I9uUoh/IFLAj69yE5KjEkGfINvl/Aki638PQvfgmSP09AtxShLCtWTEhhEfHk1Ch35ytOmC8yEsjtGXfMy2+0ezfH8Vw5YM5JWOBnqlhsPm11AndoF90ORX835BGOb2jzCm9DWuUyzjFe8U8qpttIk2kOBqBh9kiuXUEk4MFq5Rfsszvstw2TdytfIjWDOXZwNtcU3/HmXdtbDuWRT2GvqtPB/PShVXNZXRSdmO4eI+LkkspFPD9whyQAaJUg2NXj8FdTbyln/AZcocSOgJbYZC1wvR9rwCBlzLmLzv0a59lGtUK7jF140nlh1Ep1Zwef+fny3yl6ShEArXyVHDqj9nhMTPIoqyTcmP+Hh7Cc8t3ETvFCPv3Hxuq3VPf5PLsn2VvL+liIAk8eVNg0iO+FEkvjYcBt8KgM3tQ6MUW4vuecth3TOgMcH9pSdt2utr83luRR6X9kvl6Sldf/IwKpqcFNbZGZykgHnn0t4foL0ihxJFCtsMF9E+cRDWHhq8sd0YE96SMLLnlfizv0DRcJgog5qpzct4ULmAh3xX4w/04EvtE3A4jyOqh1gmZdHUcxLRsUYeLj5AxNz7ECyVPBZ4A9Le5uJUK+oSWZR5ffow1lmiefqbg5zf8zf4/w+ahThoFpMAKvbItj66H01T7nA2XLQAfC4ITzm5WA+Q0B0mvSr7+He/RPYwTWiJGhswEwzRaFd/R0LV6/KygAeebQthcSAoiKjeKlv5ADpRyXPnd+TxdQ08WX4F1AVAoUWb0ouHB41im2MpQzNiTz5AEyJEiBB/QgxqAyqFilhjLDq1jmZXM4drjxBrjCUtMpWE8HjZxsbvITYsFo/fg81tQ6vSYnVb8QV8+PxeTBpTUFQPSAGcXidapTbocW+x+TFoRZTKYwKNX/Jj89iweeTOVmZ8Jodrj2BxWrB57JQ1lVNvb5StyVo206l0OL3OYB0+SRYyKywVQf/9cG04EXozOpWWbond8Pi9FNUXBSP3bW47h2uPYNAY0P1V+y4/gz37CL7GZsKH/3XzTSiMeiLGDzxhedkzC6jcnk/ilCGkzJjQet2+A/i9PtbWb8IbruCi9CknCPcxkSpiIuXofL/XK3uRH8eW3VZ2H7DRaPVxwfgYTsYXy+soKnMx+awosjIMP3kc1rJKBIWI3+Nl34dfomhJJqcKN1MYSKbThDYoV60heVCfYLLVNuPPZu/b8wj4fOijIrBX1wKy0G5MjMfrcFK6ZSeSFEBUKmg/fhSCIKCPiqRw9Ubq8/LpfPEkjPGxKDRq/G4PgkLJiIfvpGDVRmpz8ohq/+uT9Xa/6sLg30d98VX61knI244ZRlhcDIIoEp566j5g8oA+IIEpJZHIdukAxHXLIuAP4He7Uel1bH9tLtZSOZ9Sc0U1q//zDAqVCoVGg6XwWN9ZpdfRdsxQitZtoXqvHFAlKBTE9+hMVId2KLUaojPbh8T6PxCFKNAt48+fsP7jjz9mxowZ9OjRgwULFvzRzQnxD0UQFaiS/pn2qaF78MwRegKGOCVRYRq+u2P4sQWNxbIQJ6rA6yDWFEZ5k5NyYvjCfDW9RneF0Q+TKkncGTjCJztKeWhJDjMUuUxSVTFS8QOv+KeQFK5DpRB4xHYeGmUtlyjXUh6Iwq81Et1+MOyHzUIPzglsJUmoQ1QbeHX1ERb7zTwc3QWNfTU0FuJCh0Fyokrrh9TcSFafEfDdGpACSEj4248hyazD7fWzQDGFVLWXI/7BNDCVOzQtCSASuqFtKgZBwdD+A5ipbc+q3BoGtP0b+6N+dTMUb5IHYIbc/ke35rRiaaxjteZO1DU+qsq2Ep/cNrhuQtcECupsFNfZcXgDVFpcJwr2LRyqbua8VzfRNsbAsluHHlvRZhhkngtJvU/ZBqNW/lk1aX/+5/WKd7dRUGfnw0lhDK7ej0JQIAD16iTu3iSSfGAvZU0JCEItL+jK0NfuZey2q1HEdoI2w8gacjuzdnyJMjfAUHM9IzvHQ3YCNBXzxGVDuT+qG7EmuaMdmdYZBs2CzS+DvQaAO6ZfhmtNOVpzAsR1YngcDO9w8hfdX0ViT7j7CKhOcn6zJv2yOnpdefLlhmjweRijOwQRbaCxEBCBADgtIPmh+xVw5Ht5WvoFc2iX1pl5KU3w35YJZVd8AW2GIg/phCwWQoQI8dciXBdOn9Rjz6FmdzNun5smRxPJ5iQ0Sg0urztYNjpM7tN4/V4kJOpsdZQ2lbWqU0BAq9Ti9MnCut3pJzxMQXW9j+RYHWqFGrvHjiiIBCQ5j4hWqWVfxT5UCjVH80nW2xqQkLDYAyRGmJEEHxH6CJyWY4J9WkQqABqV/HxSK9T4A37USjmSWKlQolQo0al1WF1W2ka3odHRFEym+3ck4PJQdMfLSD4/qjfvQX+SSPS/MmUWkbUTbiC8ppYfx26HJ8RjbaynUtFAwC0QIICCk0clNlVUUnnwEJGpycRlHBto75ppoNHio2fnUyd406gFBIGTRugfj7PRwo7X54Ig0nb0ENxWW1CwbzS2JecHN1UlDUTsz8Ve10BS3x7ku2LYkifSo9e5JFj2kXneeHa8MTfo9a4y6FHptIgqJb2uvQy1wRAclGg7ZigNBUVYispwW5oRlQoG3nEDRWs3E9c1Sy4zeghtRw85ZZt/KalD+hHfswtqQ+v+mVKjJrHPz+d/UmrUpI8YdMJyUSEi6nW4LFaiOrYLCvZH7YH8Xh+iUkFs10wa84vRR0fSfdpFqA16Av4AOZ9/jaBQMPrJ+/7fxxjin8f06dOZPn36H92MECH+sYTuwTNHSLAP8ctJ7AGXfCRHzbYI3ned3ZHhHWLonXYsklYQBGaNzuDTnXIUxaHUS1iiSeP5PFkInNAtnsHto3nwq/3MbrgIfZiJs1TZKJqLOMe5lHe1l1Jh7MPl/rc5O03BBYM7s+HVbbyneg2tQvYrlyQwCi68gpKtcZfxaPNknk/tTtdbJyK90hch4OGx5kk86vHRNiaMJbeNRPnyvxhYvYnhpcn0TougW7KZCIMaOk2EB2vRiApuzV/Nrdn/goJbIOaG4DE1u7woRVFOmPlXJ+NssFZA6okd7r867WKNeFEiImHSyS/1kiQhCALndEvgnG4J5NfaqLa46Jseecp67G4fLp+fRnvrKfnoI+GSn57iddXAdMZ3SSA6TA0BP+yaB3FdTuqDn5lgpLbZjTm9Bzv6zKbQrmbh3moKpfb0SYtgZMdYssubCNMoufOzvQwWshmrdsvWPNO+BiA6ZQAUncf4NsNApYArFoHPhUGt54T4sbMflwccYjMBORJAO/oMvRxpw89MvQA5i2HFv0F39DsMQNeLYOidEPBC/ElmNujMMH0ZuJvlWTYhQoQI8Tch3hSPUlRi0h6zRegUn4nT62y1TKVQkWROpNZWi4BAdFg0Tc5GvH4fEhIpkclIksSR2nwMOgUOu5K4KAGv34u2RVw3qsNw+JwkmhIRBChqKAYcwX34/AFUKhFrs0SbWBV+SSDJnIhOpSO/Tra5cLVE8MeERSNJAfLrCvD4PVRZqhAFEbMuHEEQaBOVTnpkGoIg4PF5qLHVYvfYMWqNwf15/V6UovKUVip/FQSNCuPALnhrm1An/v1sfwxZKQguqdVsjaP9s7iMdsTRDp09Ca1Ci0I4dV/b55Gtk/ye1rkOkuI0XDop9mSbBDn/7Ghc7gB6nQK708/+PDud2usxhbV+HVbpNGjN4YhKJQl9umGpacTvsNN4KB+dXkWCSU3XTA22phQMcTEc+nolZZG9ISwLZUwCPad0BqDfLdfgqGsMesEPue8WEARERevjE0SRXtdcirW8CnNaCgDacBOZ543jTPBjsf50UvDdeip27sUQG429pg6VXkdSv54k9O6OqFSgizixb5jQqyuiUoEh5m8cLBUiRIgQIUL8BkIe9iHOGPU2N3U2Dx11VkocKv771nt85+qEJCoxCQ7mnBvB6ioteWXVzK6biV5wIwBv+87hc/9wlp3rR93valDpeO2LlQzOvpckhYVIqZGC6FFkBApAF8EFnofZWWpjULsoPpjRn+aindzw7jq2Bjrx5Y0DKWtycnaGCe2Hk6ixung59SU+2V3NqLZh3HVODwo/u4e27bPoMPEOWPU4bHgeMsbC5Z8BUGVxcdaL6zBqlKy+a0TI2/5Pzvp9+aSaNRyyKvlufwVfZVdz/4RMPtpWgiAIzJne55SR9cdzpKaZCL2aqKN+9ysewFO8g4YJbxGf/AumJG95DYo2Qt43oI+Ge/JPWfTggWzSPxtNE2F8NewbuqbFMLj9sRd2SZK454tsam1uXh+jQR+VIg8e/FNpKoWF18kzHlR6CPhg2F1/dKv+loSe1/8cQt/1PwfZnkZAIYo02BsprC8MrlOLauLC4/D6vFQ3VwcT2x5Fq9SSEpFMdFg0/oCffRX75fokOTLabTcRZnIQbYimurkagGRzMikRyZQ0lFBuqUClUGGQ2pNfbmNkHzMHq3JRKVQoRSVWl5UUczJb9rppcFUxvEs7MpPj2VexH5vbRlpkGonhCQA02BvIqzlElCGKDrEZv9v5C/HrCXi81O0rxNAuhfwqH+Irc1BWVBJ16VnUf74GXUYyqY9dh6D86T62JEm4mpvRhIUhiiIeb4CFK+pQKSUmjY5CpfrpWDSvN8CarU3U1HspqXDTqb2e888+9QDJ3tU/UB6IRm2toq3JSXzPLuijjgUpuZtt7P94MWGJ8YT1GUp8jBqF4q89ePT/oS4vn/yVa0kbNhBLSRkRbVKJ7ZL5Rzfrb0nomR0iRIgQf39CEfYhzhhRYRqi9rwKqx4jNa4rr7GPlfEXI1hKGSNtRlgB3c1pPCpdi0Fw4xD05Bn64kq+jI/Kn0D93UHZ6WLgTK6L3INaLGCvvy1ni6+w/tqzQSv7V95bWM+Fb21lc349OZVWurTtyw3T0rjK42fu5iKWZldyw/C23H/9GuKAs/JqmLL/FrpVFvH8nKu53/857ALGzYShd4A5FdqPCR6H1x/A7QugEP0Efv/xrV+GzyN7q8dkQreL/ujW/KEM69qOjYfryPn4bp5RLOJCsQOJ3zUyz30fxVI8e0qaflawv/itLewvt/D6Fb2DFjH+3R+gdjfxxFvvc+/td+MPSKRHn8ID1WWVI8ABojtCxlk/ub/kSC0KAfQKgRuGtUFQtfaLFASB5y78+anK/xjMKXDNt390K0KECBHiL4lOpSOn8iAWlwWVKL8K6FV6HF4HnoCH0sZSog3RQbFeIYiolRq0Ki2NjkYO1x5Bp9ZhUB+z9UCQMGvD6dw2K7gfhSBSYa2k0lpJSkQyKREpaFRa9Go9GRevoLbJzTfPjmBsP9nep7CuCKvLSmljJetz6rlsvJnC+hIyk+PJiGmP1WVtlXTW32LN4w+cPEHun4Emi5NN24ro0TWRpIQzOPPsT46oVhHbuwNrtzaxebcVOk0iLKURzUEHA+ss+Gob8Tc7UEYYT1mHv9lB7l2v4jcb6fTE9YgiNFp8FJS4AIkdEcX06JZOIABhhpML/wWlLnbusyGKEB2pomObn86HoI+NgSoJrTmctqP6nLBeYwyj9/VX/Kpz8XcmumM7ojvKVkXx3bN+pnSIECFChAgR4qcICfYhzixel/y/NhxEJWdHViP5ixGaW9ZrwrjrmpmU7Uni6iV1HHYm8/Hk/sTYTeA140vuT2Ozm6i+V/Pt7oPMbeiCT9166nPfNlH8a3QGzS4fnRLkCIORHeVpsdVWFxsO1yECPR9bSZxJy7yr+xEXE0Co97DfEcn7irPwGpOZodLy9PKD5NdkMTsrlqOvVSmRetbcNQK1QkSv/pPeMgVrYMNsOUln1wvlJJ3Amrwa9pY2cePwdv+omQHp0XrcmhoUfokssZgwXLQXyklu25nxXeJ/cttGu5vthQ1IwBtrj/DE0hxsHh/jwv6N33aAVUIf6r/MZkt+PXPOT2BU2evgbOKbTs9w39f53DW2I1cNTIfRD4OtGs5+Qk7A+iN2FDVQXO9gaq8kjAkd4M79sn+v6s+f3ClEiBAhQvy1OepDr1FqCHgllAolglcIivQapZqeST2osFRQbavB6XXSKS4Tm9uGQhBRiSp8AR8p5mQO1x6hpsGD3+CHhGP7SIpIwhvwYWyxURQEgTij3D8b1j2WHbn1xMbaWb53K2u3wsPTu1PdXI0vEGD5pmbio5RMGJCCP+CnqKEYrVKDKBzzH48Ji8ag1qNR/nl97bfuLGHb7lLqGhxMu+RY3oGy8noUCpGE+Iif2PrvR1K87AWPqMBmisbu9+NTqki5etxPivUAlsOlSHnFCKLIlu1V7D5oRa+2olIm4PUJNNkVvPVRJV5fgAsnxLBhh5XkeDVev8TBIw4umhBDmxQtPbLCiItW0bvLyfd34JAdg15BerKWjC5JJLb1odP+c/rQIUKECBEiRIg/ByFLnBBnFkmCmoNy5HdjIbzSS15+9pOyuNztIpg7HhQqFnd/m64776NtpBbh8AoAHkl6m/kFYcyZ1pdEs45xL61HoxLZ8+DZsp+8JMHye8BeC41Fsmf4Nd+CunXk8yNLDjBvcxEA/x6fyfV9I8BWwxelBpbvq+LOszuSGW+kw3+W4wtIvH9NP4adjuSbvxfuZlh6B8R2oqb7TJ5enstZWXF89+U7qLxWuk28hSsGpP/Rrfx9cTdD6Xb+t82CtfwQ1cln89DELGKtB8CYCKaEk242Y94OVuXW0CE2jJpmF01OHwBqhYDHL3HvuI7sKm6kx+FXuUW5OLjdax3nUbd/FcPjXIyY+Too1cF1To+fnEoLvVIjgoNNnR78FqfXz4IZ/Ria8Re61kL8owg9r/85hL7rfxb+gB+P34tOpaXKUkVhQxFqhZokcyIqUYVGpeFgVS6Revm51eS0EKY2UO9oCHrGB6QAPZK78/ZXhfzr5V2M7BnL9y/KMxS9fi/5tfnoBRFz7lqU0enoO599Qjs2H9mFoPDy7LxaXrxxJPHRSqQAvLvsMOnJAuN7Z2L3OMipygGgf3q/VqL9n53qmma+X3+Evj2TaRvpxlO6F19cd2r2bcYhmugwaDRa7YmD+n9nLM0+LNU2yucuR3Q6SG0fQez151Fa7SUpTo1KeeL3K0kSefU1uJbtQBsZycLSJEDA46xErUtAEGDa1Fg+X1aHwxVo2Qa0GhGjQaC2wU+XDnomjWltf2Oz+3G4/MRGyX228io38xdWI4pw57XJJ21LiBB/BkLP7BAhQoT4+/MnDRcO8bdBECCuZUpkVDs463H570G3yP83lUDdIRAUTE62w4p1UA+MeRREJYdz0pCkelxePyadkniTBovLx6RXN/LFTYMIl5ph+9stOxNBAHtTDY+tb6ZrcjhXDJATPc0a1Z4PtxbjDUj0SDHL/t/6SC6IhQt6ywme8Pv4ut9+DgltW/mHn3FcVvB7wHCKfR7+DkxJx87jydAYYeo7ACzZUMCiPeUUlJSySHoBUSVx+coMrhhww6m3/zuiMUL70fyr/XHLCtbB+5Pwm1LJuWgjXZNb5lF4nbDxRdBH0ylhOBuP1HHPuEyeXJZDk9PHuC7x7C+3YHF6GZoRzcqcarrGqqAB6DyF96rb89o+gRz1AmiATWvP4c7tJu4b35HJPZO558tsvt5bwf3jMxndKZbvcmrI0DRwSArnUHVzSLAPESJEiBC/KwpRgU6Uo4ajjdG4fG7CdSYi9HLEd21zLb6AD5vHTrwxjurmGgQEksxJ6FU6CuoLkSQJSYLMVBNatcjOvAYuf3wTHz44GKvLSqOzCQsQ7rbhbyxjf0ETTyzYz83nd2BoNznSXvQm8NT8XazdYSflIT0KhSyQzppyLHm4VqnBrDMToTf/rmJ9wG1DUGoQTjJLLuD20rwpG0OvDijNp44Mj4s1cvkFPQGw716Er64QHE3EBKqRAlBR1I62mf8s//1wo5Jwo5nUJy8NLlu7rYnNu6x0SRYZ10cbTLzbZPWxPdtK+1QdGqMS8dx+JOjMCPOr8Pu9DB+YwvZ9PmKj1Hi8oNWKiKJAs91PzywDuflOLM2ygH/gsAONup78EhcXnxtLZLiS9z6rxO4McPUFcXh9EvvybKiUIIoCDU0+4qLVJz2GECFChAgRIkSIM01IsA/x+zL41tafzalwzUoQFZDUC6a+J//d+XwA3u3jp8Li5Lucam76cDePjEunaNU7rKvrQm1zb8JjI2Hym+BshKTeIPlZV63l0505fJ1dERTso8I0rLh9GI0OD25vgHEvreeG4W05v2fysbbkLKbTD0/SyRAL4gXBxfvLLewpaeSSfqmoFKf5RdHnhtf6gcsCM7dCRFrr9UUb4cMLQGOC+0qCVjc/xeSeSQi539C3fA42tIRJTu6Q5gP/MMH+ZBiiQWUg2x7O+a9uPDaT4rNp0DKr466ZW7lr7HgAtCqRJ785yNjO8bx0cQ/8AYnthQ3sKWniZvUU9t98O8RmseyNzTgCTeR2u5dMbRPvFcdQZbXw9PJcJvdIYqJ9IQmKWi7Z/gC61SVcA9wk+PmQUaw68m9mDGn7B56UECFChAjxT0YpKkmPat3/iA6LRqFQYlDrUSlU+KUAJq2RsBZ7m3BdOJIkkV+XjzG6mS+f6s173+Sx7WAtABH6CFLMyejVevT6KMSwaN54+zCfrynB6fYHBfsBnRK59yIVs2/Q8MJnuXy6uph59w+kS1tzsC1llnKanE0oRAXxpmO2dos3lCIB5w9NOe3nxG+txrbtY0R9BMbB005YX/vRCmrnL8c4uBtpT/6y/pW6TT889gYstmpMAVCIYLLsA/5Zgv3JMBvlV1LfivUceX0HHT9/EsGg471PK3F7JfbnObhjxrE++7C+4RwqdNC5YxQDeytRKQXWbm2ivtFH21Qtl0+ORasW2X/IQUCS6NrRQEKsmnXbmnB7JNZvb2LcsEgARBE+WFyD13f8pHOJojJXSLAPESJEiBAhfiHp6encdttt3Hbbbacs88gjj7B48WJ++OGH361dACNGjKBHjx689NJLv+t+/7+EBPsQfzwpfY/93fWCVqscHh8LthRT0uAAIP3IAqYr5mJJ6Euz6jIeXXKA3Kp0eqb25J6BmQCM8PiYPiidLkmtk3u1jZFfMh9ZcoDcqmaWZVcFBfsGuwd9Yl+aI3qibjuE47e85aPdFNU70CgVXNT39L8UIkkt/wInrjOnyYMasVmnFOtdXj9Ojx+H10+Su5CmD27lYms2YYIbWjZpo6w//e3+KxLXGe4rZvacXWhLGok0qNlXZsHXqKEnQOoAiGwXLL6jqJGDlc18sLWY83smATBCX8jsiakkJyRCXBQA867pR3mjk8yEcwC4eFs25vyvKQk7C0q2cnbZy5ylAsEOCPLXDZARLjFsUuff8QSECBEiRIgQP48gCETqj/mrJ4a3tpHbc8jCghWFXDlZQUAKkJDg5I4ro9ESzdo91Xz0fSEHi63864JMLhghC9K3Tu2Iy+PnhkntW9U1uGsMkiTx6eoi9hxuYvXuKrq0NRMISDTZPAQ8epxOBWFmc3Cbkmo7Ux/cAED+x5NITwg7rcd/zDH05M6huo5pKIx69F1OPeAued3U2QKIoojRUYDzwHeIAR8RAC3xHy5fyHIFoEdWGJ3iAhRem40QaUJQK8k5YkcQ5e5vj06trS4LSl1U1no5mO9geD8zXl+A1CQtBr1IZjsDZpP8invDpQlIyFH9AAUlTo4UuzAblezcZ8PulPvegUDr77lDGy29upzeaypEiBA/T1VVFU8//TTLli2jrKyM8PBwMjIyuOKKK7jqqqvQ6/Wtyrdp04Y33ngDrVbLyJEjMZvNVFZWotUeywm2fft2+vfvDxz/2x4ixF8X4WeCOKdNm8a8efN+n8Ycx44dOzAYjj2vBUFg0aJFTJ48ObjsrrvuYtasWb97236OefPmcfXVVwc/GwwGOnbsyAMPPMCUKVP+sHaFBPsQf2o+2VHKvM1FdEow8uG1/emvioavVxHe83yeXn2ET3aUArCrpIl7xsmC/Z6SJtw+P0OOt7XxeeCHD3m7OI75OyTO65HIXWd3BODj7SXcv3Af/dtEsq3ybnqrI/jyuDac2y2Bz3eVcbi2GY8vgPp0+lkqNXDzNjnS3hh34npzCty276Sburx+/AGJMS+so7bZjT8gsaXzYto376AKMwhK1ko9GCHtwBSwQN1hiA5FcaFQseDa/rh9AbQqBZe+vZUtZRdzx+BbuFWzFD6YIs/0MMbRNtbAeT0SuWZwG3nbvZ8gLLqBqamDYPDyYJUmrQpTwrEp82ML/8tY9VIqoh18MbeCsZIGo+AmIEERCTT69fRW5tM3IxEhUv/jFoYIESJEiBB/av7zbjardlVhNnbknss7Y/PYqbJW0S46lt4PrKWgwgaAUZ/PBSNSAfh+ZyVxEVp6ZUQG6ymvdbB4Qyk9ujh55f5o9u5PY9pYWdC/4onNfL62hB4ZEezOa+CBK9U8NkO2kIuP1DK6dxyFFXa2HKg97YK9Mjwe49AZCKdIaGsa1BXT18+dsDwQkPD5A4j2aorXfkGfN6NRqNQcuduKEPAhAU6fSE6VQK8kP0bJghTwI4ihpKaaKBMdP38SAEkU+XpVFZIEk8+KYl+enaWr6zlnZCSBAKQnaYiOUNKnq2xHtGJ9I9m5dob0MQXFegCTsfWrbmmlG4BGi5fcAtcp29KpnSHkXx/iH08gIFFa6cbm8BOmV5CSoEEUf36292+loKCAwYMHYzabeeqpp+jatSs+n49Dhw4xZ84cEhMTmTRpUrB8dnY29fX1jBw5ki1btgBgNBpZtGgRl156zHJrzpw5pKamUlJSctrb7PV6Uan+WXlIQpwcvySx2Wah2uchTqlmUFg4il/gjvBbqKysDP796aef8tBDD5GXlxdcptPpWpX/va7TmJift/kNCwsjLOzPOSBuMpmC57G5uZm5c+dy0UUXceDAATp27PiHtCnUEwnxp2ZS90TGdo5j5oj2DG4fjTKtP9yyAwbezJReyfRvE8nFfZJ5/bJeNLu8BAISz67I47PtRXyx9diPFnveh6W3MXLfvUhAcb2DaquLsS+ux7NjPq+pXiJS6SE1Us/IjjHgbsZyaBNFr51Phu8Q1VY376wv5J0NBaf/IHXmk4v1P8ETS3Po9NC3fL23gmaXD39AQhAgrF4W97+X+vMv942MELLRq0QUfie210ay5cAZaP9fEEEQ0Krkl+OrBqYxNCOGs/t2gp1zoWgD7332OZvza/nXxz+wLLuStKgWUT0sTk6WHJ586sqdTZQTQ60UTsWhHVzA9xgFNyWBGN43TGeUezZ5KReBSo+Q2PN3ONoQIUKECBHi9HLbhR2ZODiJi0e2w6g1kmCKp2dyD0xaE/+6oCMjesRyy/kZPHdTT5qaPdidPm59eRf//SiHDftqgvXc/uoubn15F/e/XsTlDxQjCALvLs2n54xvWPtDFYGAROc0E+nxBgZ3jcXh8vHGVzlc9sRaIsLUFFTauOKJLWzLqTvtxyhqjQjKX2eJ0u+Gb4mfvJAjZVaQ/IBEmDoAriYALvokgre26+iWJCAIINnrKVixgCaL87S3/6+IoFQgKBWIosDAXiYy2+kx6BXkl7jIzrWzYkMju/Y3s2GnlapaLwad3JczhR39/9SxaE1WHzqt/Op7vFgvinBUg0yOV6PViESaQzFtIf7Z5OY7eG1BBR9+VcNX39Xz4Vc1vLaggtx8xxnb58yZM1EqlezcuZOLLrqITp060bVrV6ZOncqyZcuYOHFiq/JfffUVY8eORaM5NrA6bdo05syZE/zsdDr55JNPmDbtRGuzHyMIAm+88Qbjx49Hp9PRpk0bPv/88+D6oqIiBEHgs88+Y8SIEWi1Wj744AMCgQCPPfYYycnJaDQaevTowbffftuq7s2bN9OjRw+0Wi19+vRh8eLFCILwu9uChDgzLGmqpWvOVibm7+Xa4oNMzN9L15ytLGmqPSP7i4+PD/4LDw9HEITgZ5fLhdlsPuE6ra+v59JLLyU5ORm9Xk/Xrl35+OOPW9U7YsQIbr31Vu655x4iIyOJj4/nkUceaVXmkUceITU1FY1GQ2JiIrfeeszyOj09PWg5k56eDsD555+PIAjBz4888gg9evQIbvNz98/R+27hwoWMHDkSvV5P9+7dg4N0wC86tl/C8ecxIyODJ554AlEUyc7O/tV1nS5Cgn2IPzUpkXreurIPE7snnrCuX5tIPr1hIP9N3sKIRb144InHuPmj3dwysj1rwx9j5o6xUJPbUtEAmgxtWebrA0BeVTPrDtXiq8nlytrZnKPYzms8w4dX9+Iyx0fwdDLWRXeSXrsa9S45mataIdAtOfyEdvwRVFicSBIU19uZ2juJr8/1k9PxXRoTBlMf2YMPfKPoJR4mTGqmTJmGXdLiD/h5b8ORP7rpfyq2FtTTMzWCBTP6kxlvgks+5G39DTx5OI1NR+oZlhhgadiTmNY8IG/QbiTcXxZM8AvQ7PLy6urD7C+3yF43H11MUu5cnvJexjqfbHcTkOAG/91Ej72XTgkmUkddCw9UQr/r/ojDDhEiRIgQIf5fTBiQxOInh7fymj/KLVM6suqlMcy8JJwaVy7dr13M+ysKuPPiTqiVIjNn78Dnk61IzhmYhEGrZMcBB8WVXhauq+KzNcVk5zdR3SBHQ18xLp6988fw5IJ9xEz6kv+8u49F66pZubMCAYiN0JAWZzihHb83gYBEWa0Dq8PLmiMinzQMJu+5NH64V4UyPIFaKZotpSpGtfOgFnwcrNPKor3Hzg/7K39+B/8QfD6J/GIng3ubmDI2mrQkLWcNke2Zdu+3oVYJGHQiVpuP/Xl2AIb1M3P39cn0yAoLWl7UNXjZuNOCw+nH7vDzzicVWKz+E/aXHK+mX3cjyfEazj87mjtmJJMQe/KZFSFC/BPIzXewcEUdzfbW90uz3c/CFXVnRLSvr69n5cqV3Hzzza0sNY7nxzYgS5Ys4bzzzmu17Morr2TDhg3BaPovv/yS9PR0evXq9Yva8eCDDzJ16lT27t3LFVdcwaWXXsrBgwdblbn33nu59dZbOXjwIGPHjuV///sfs2fP5vnnnyc7O5uxY8cyadIkDh8+DMiRuhMnTqRr167s3r2bxx9/nHvvvfcXtSfEn58lTbVMK8qhwutptbzS62FaUc4ZE+1/jh9fpy6Xi969e7N06VL279/P9ddfz5VXXsm2bdtabTd//nwMBgPbtv0fe3cdJ0X9P3D8Ndsd18k1ccDR3SWCARaKiYGd2PKzu+NrtygmEqKAIALSIN0c3HHdtZ0zvz8WDhFUDDzAeT4e6O3sxOezd7s785735/1ZzTPPPMMjjzzCggULAJg2bRovvvgib731Fvn5+cycOZOOHTse8fhr164F4IMPPqCioqL58a/90fvngMmTJ3PHHXewceNGWrduzfjx4wmFQgBH3bc/IxwO89FHHwEc9efHsSCnD8hOWIt3VfO/H/fwpn45sUEX7dnLQleAOLMWXaAeQfJBIDIkm4QOqG5eS3jJXhJ+LqXO4eKMyte4VTsFBRISoChZwfv/e4Q2UiEXKEGR1ImZBcm8FxwJwJUDMhmQ88fDfI4JMQw7v4tMrGtN5tlzOzGhbwYfr9zH7M0VDDC8QQdxBStCIveE7+KNi7rStkoJS78hKlDFe6FT2S5kM/GUbi3T/uPQD9uruGrKz2TGGPnxjsGRhZmDyBnTjrM3V3Bx7zQadFtou2gr4vo9MOrpSBqWWk+TN8jp/1uKVqXkzE5JvLBgN5u2buYd/WtQtg4EBbbYZKZUdeE65WwMgp+XR8XRulMSpx/h5pNMJpPJZCeLJz/ZxqINVTx6kwWdDlLi1FQ3+NBrlQRCIlUNPsKihAq47NRMerWL5uVpO/lwXiFLN1dxRr9Y3r4/kZuerkSvFRB0FWwpr6a42hGpgT+2FTuKG1iwygkCfP3IQBKi9X/YrmNBDHoJ1RWjjs1CoVTx89ujqHP4GXXnIqoafFwUU41WLxIG4ow2Pn5gBG0166FpL8nWyE0Lr2Cma558bnDAolWNrN3spEt7E6MGRcon9cgzEw5LuD1h8tqa2LrbQ3G5n007XXRoEwnuqVUKSir8fD67mtxsAw5XmMJSHzV1AYrK/QRDoNMKBIMS4f3TRgkCjOgfJU8uK5PtJ4oSC5Y1/O46PyxvoHWG/h8tj7Nnzx4kSTqs7ERMTAw+X2REzA033MDTTz8NQFlZGZs2bWL06NGHrB8XF8eoUaP48MMPeeCBB3j//fe54oorjrod5513HldddRUAjz76KAsWLOB///sfr7/+evM6t9566yE1rZ977jnuvvtuLrjgAgCefvppFi1axEsvvcRrr73G1KlTEQSBd955B51OR25uLmVlZUycKCdunejCksQ9ZXuOONuNRGQ6wXvL9nKaNeaYlcf5Lb/+O4VI/fgDbrrpJubNm8dXX33VPMcDQF5eHg8++CAAOTk5vPrqqyxcuJARI0ZQXFxMQkICw4cPR61W06pVK3r27HnE4x8oj2Oz2UhISPjNdv7R++eXbT/ttMh8gQ8//DDt27dnz549tG3bluTk5KPq2x9pampqLtfj9XpRq9W8/fbbZGVl/cGWx46cYS87Yc3YUMa6ogb+p7sGzv2A/hNf4OlzOvLkd1u5w3cVd0a9CimRjPo6l58ap5/bT2lDbpKF01RraF3wEUokQpKCz0JDEW1pTFDOIV7ZSO35s7Gd+wp3Ba9iSzAy2eg3G8tbrrPrPoQvL4EvLwXAqFXRMyOKc7qlkB1n5CnvWN4Pncrr4TH0SI9icJs40ruPglZ90Wf15hb1TP5n/YxemdEt14fjTIJVh1GjJCvu0BpqQ9rE8dx5nQiFJa5abuP10JmEwyH4eGzzOg5vkB6OBbzXNJGLiv6P8xJreFR6NRKsBxhwJ6nte6M0RhGI6wQKFa1Tf/uLSiaTyWSyk8Wr03excF0l+wqtZEfn8MJ1AzilZyJT5kXK8l0xOhOtJlLCxBv0kZVi4OlruxJl1oAkMG1RNV/Od3D7JTE8PymFKLMBSYL/3ZXCsldHMLZfDvNXOZGIDGx7b87eFuurb/tCvJu/w1+wCoCUOAOdsu3cdE4bUuMM3PCthY826Klwa1DHZDJ2QCr2rE4oLfFExUTOyTLTorCYdb93mP+UaLsKQYDoX5Wl6d3FwrB+dorK/BSXR0ZfFJX5Wb6uqXmdxqYgwZDE1t1uBEEiNkrFzgIvnv2Ty/Ye6Sc5RYnVrESnETDoFYfUvJfJ/utKKvyHZdb/msMVbp4P4p/26yz6NWvWsHHjRtq3b4/ff/CY33zzDf369SMqKurXu+CKK67gww8/pKCggJUrV3LRRRcd9fH79Olz2ONfZ9h37969+WeHw0F5eTn9+vU7ZJ1+/fo1b7dr1y7y8vIOmQj3t4KcshPLClfTYZn1vyQBZUE/K1xNv7nOsfLLv1OIZIw//vjj5OXlER0djclkYv78+YfN7ZCXl3fI48TERKqrI6UMzzvvPLxeL5mZmUycOJEZM2Y0Z7n/FUfz/jlSuxITEwGa23W0ffsjZrOZjRs3snHjRjZs2MATTzzBNddcw+zZs/9K9/4R8hmK7IR1xyltSLUbuKBnKtgNtAce/247/Uvf5kbtLBqTrwAuRpIkRr+ylBqnn29u7M/bl3TDVRMHc36msq6J/xNuZKJ5FQqhibTGdSRINeR9VE9Q+IFbhuXw7rJC/CGRC3umtlxn49uDPgpSf/HlHvIzuPIj+p+Wx2slQ9hY05O7OyQwumPkAwxrClwxF2VDEcx0oskaesguRVE6phMHHe86JFvZ8tDI33wNnpm3i3JHgJWKXK7nG7x1pZRVO8mOM5MaZeDB7AKs+6qgeB7PapZBwEWxGMsMsR83L3ueKwwfccXk7SANBr8TDIefUMpkMplMdrKZMrkva3bUMbZfJDAfmwvdJ86luDpSRqFbm0ig2hPwsKlsM2qlmm6pXdk99Ux+XF/JPW9vIBRU8NPaIEO6xqBUqBAEP/sqvYx9dgFRFg2Xjkzny0XFaNVKJozKbLG+Kq0JBOv2obTENS8TvQ5u61XPZYN68vi0Ujb4wgzq2BZ9dqSsizomHXVMOsGaAvx7V6JJ7nDIPiVJOixo9V/Stb2ZLrmm33wNvv2xjrx131Mb24ryVu0orwo0T4rZoY2ReT81EAxJFJREgnsHdlOUtILTS17mrNbDea/1o/gDIoIAGrWcvyaTHeDy/H6w/s+ud7Sys7MRBIGdO3cesjwzM/L5/utJNI9UDueA0aNHc80113DllVdyxhlnEB399xLWfv1ZdKSSPb9e55ef40f6TD9Qukt2YqsK/Xaw/q+s90/69d/p888/z4svvshLL71Ex44dMRqN3HrrrQQCh7bt15PTCoKAKEZueqemprJr1y4WLFjADz/8wPXXX8+zzz7LkiVL/taktr/3/jlSuw48d6BdR9u3P6JQKMjOzm5+nJeXx/z583n66acPm0Pj3yKfochOWKlRBu4Y2YYUu6F52el5SSRZI3evbcFIvTBBELDq1WhVSvQaJSqlAtGciNRUToJ7B+/GfE6vqs+gfB0LYy5mfGAyftSIEnRV7eO18KP0FdfxztJC1hTWt0hfadUb7i6EU588uGznt7DoMVQzr+GW4a15ZXwXfMHw4W20p8Hlc6jtehOfrCqiyRNkT7WTTo/M58J3Vv27/TjO/N4Ni52VDgCWinmc5n+CftV3Uv3qSGoebc3t785FGvkEdLkU0vqBLQ1JbeD7vJdI6D8BQakBjREQQKmWg/UymUwm+88Y1i2Bey9u35xFD3DNmBzMBhUC4PIFAVAIChSCAqUisp5Rr6J7myh2FztZvL6Jod3imbGkipomJ+/PaOLJ9yKZVA3OAOW1XnwBkSZ3kJteWktto++wdvwbtOndsQ69EXV86+ZlvoLV+AvXYq1fx2u39eTlm7uzbHM1+ypch2yrjs3E1PsiFBoDgfLtSJJEoHw7jgUv4Sv463VXTwa/Faz3+cO4XAFaFWwmM/9nkCTy93n530dlPP9uCYtWNnL+abG0ztCT2UqHTiNgNSvp2MZAhzaR6wWTMvJ/rUYhB+tlsl8xGZR/vNKfWO9oRUdHM2LECF599VXcbvfvrutyuVi0aBFnnnnmEZ9XKpVccsklLF68+E+VwwFYtWrVYY/btm37m+tbLBaSkpJYtmzZIctXrFhBu3btAGjbti2bN28+ZITAzz///KfaJTs+xR/lxPRHu96xtHTpUsaMGcPFF19Mp06dyMzMPKxO/NHQ6/WceeaZvPLKKyxevJiVK1eyZcuWI66rVqsJh3/75t7RvH+Oxj/VtyNRKpV4vd5/ZF9/hZxhLzspLNxRxcq9ddwyPIdOfXLhB6BxX/Pz3940AH8ojFmnZml+DZe9v4b5ZiPZQMjbhEoM4TKm0dTnTgq/WskE5Tx2RQ1hgGcJKLcgKNVc4u3CuqIGemYcJ8HX9AGQM7I5637FnlomfbkJhQBzbhkQmUT1Fx7/bgczNpSxs9LB6A6JeH0+hKItOFwdsJhMRzrCf06Vw8e7Swuw6NTkV7vIUxVzk+IrPgydwi4plU7CXoxhH3v37mbgWwoW3v4csWYtiCJC2M9E9f7sj0E7QKWL1LyXyWQymew/6oUvIkOaJ53fjq8XF7Pg50p2FTsB0Kl1dG/VDUEQEASB+9/dxBOfbEOjUhAIiXz1YwU7ihz4vUaqarVU1Ucu+m47rw1zVkUmaRUE2FLQRGGlmxjb8VFWRpOUi+hpQJMUyZx/9rPtPPHJNh7/eBv5n56JUX/o5Zd7wwwkvxtBoSLsrAEkAiWb0GUefd3Vk12ooYxgzV5+KG6LJChZcvpEgpKiOX1eksAfkFi10UlZVYBLzoqPbBeWUAgHEjTO5PxAf2LUtpbriEx2nEtN1GI2Kn+3LI7FpCQ18Z+fmPn111+nX79+dO/enYceeoi8vDwUCgVr165l586ddOsWmYtt3rx55OTkNGffH8mjjz7KnXfe+aez67/66iu6d+9O//79mTp1KmvWrOG999773W3uvPNOHnzwQbKysujcuTMffPABGzduZOrUqQBceOGFTJ48mauvvpp77rmH4uJinnvuOeC3b07KTgx9TVaS1BoqgoEj1rEXgCS1lr4m67/dtMNkZ2fz9ddfs2LFCux2Oy+88AKVlZV/KjD+4YcfEg6H6dWrFwaDgY8//hi9Xk9aWtoR109PT2fhwoX069cPrVaL3W4/bJ0/ev/8W32DSGZ/ZWUlEKlhv2DBAr7//nseeOCBP7Wff5IcsJedFB6YtY2yRi+ZsSYu7HopeOvZFz2At6dvYUyXJHplRKNRRYKnnkAYUYLbjU/T5C4iu97NHTj5qakdV8zpRXttDG2EUhwJTuj3GFsr3cwP9OfBjrlc2KtVC/f0F0xxcNGXzQ9bJ5gxaJR4AmEWbKuibYKF7eUOtpY3cU7XFAZkR7G7oID+2TFYDWpuV33FdarZ7Pt6N5bL3m7BjrSQcAg2fw7J3SGuLax6k4KNm3l/36m0ijEDcBaLGKFYh1oVZvTo8dRZv+Xnon3sWGZCDIYJHJi1TKEAxS+Gaupt/35/ZDKZTCY7juwtc3LnGxsAOLNfCk9d04WOmYVkp5i55eWfueOCdqTGHxyy7fRGMu+Hdotn3uoKlPtHwVU3NXLPxFgWbRDw+CTapdsY2TOJO17fQGaSictOzaBH2+Nnjh6VPRlTj3HNj/t1jEy8VtXgY9PeBvp2iCVYUwCCgDomA1VMJqGGEhSWOKT6EgAkv4tA+XY0Sbkt0oeW5PaE2VXooV2WEZUKFq9qwtywg3bazTgaEgAdzv3nWXqdwIj+dhSCwK4CDzv2enH/olSHSnloMCxOc5wk3chkxymFIvKemv597W+uM7yf/ZiUVc3KymquG33vvfdSWlqKVqslNzeXO+64g+uvvx6AWbNm/WY5nAM0Gg0xMTF/ug0PP/wwn3/+Oddffz0JCQlMnTqV3Nzf/xy++eabcTgc3H777VRXV5Obm8s333xDTk4OEMkinj17Ntdddx2dO3emY8eOPPDAA1x44YWH1LWXnXiUgsBTydlctm87AhwStD/wDnkyOetfn3D2SO6//34KCwsZOXIkBoOBq6++mrFjx9LUdPT19W02G0899RSTJk0iHA7TsWNHZs+e/Zs3xp5//nkmTZrEO++8Q3JyMvv27TtsnT96//xbfYNITf0D9fG1Wi1paWk88sgj3H333X9qP/8kQWqBAloOhwOr1UpTUxMWi+WPN5DJ/sDna4r5Kb+Gh85sz/T1Zfy4oxqtWsHS/MjJxsdX9mRATuSCaUtpE1UOHwlWHWe/sYJ2JjenxzfgrNjLpMAbBKNaI4khNMMmQ8dz6fLIfBo8QV4c14l1xQ04fSGeOTcPreqfHQr4T9ixdQNRc6/B2nEUulMfpv/TP1Lb0Mj0zhvIFfNh91w442Wa2ozj/Vce5LbA24TanY0qqWMkY9+aApb9k3g4fNz42Qa6pNq4d/SfuzvZUr7dXM6+WjfXDspCpfyD7Pb1H8M3N0JMG7h6ETyRBMDXlkuI63MRU3ar2bFzCzcrpyN0uYQ71xpRCDAgJ4Y1+eVMVz9AG209ihEPsSrmbGZtLOfmYdkkWvW/f1yZ7AQif1//d8i/a9k/TRQlJr0WmYz9+eu7srduLyDRa8ImHJ4gBq2Soq/GEmXRIkkS81aXo9Mo2Vfp5qpnVjOyZyIl1W5uvtBGhxwV707zsnqrm68eGUAwJNLlyrkAbHhvFJPf2UTfDrHce3H7Fuzxb1v0w3Iyg1tJ7DoMlTkW57JIxqa6VVdC1XuQ/G6MfS5GDHrx/jwNJBF1cgdUUa1QaAwobYkIykj91lBdMd7dP6HL6Ik6ofXvHfa4IIkSjqU/o7JZMHZq84frz1pQy7Z8D906mMhspeOrObUoBImE7h8xMv1yvvpOgcsTRhCgXZaO7Xt8aNQC6Sk6isu8ZKUoECWB3t1iKC2rpqHJy7AB2Sj/6LxQJjuBHOvv7J17PSxY1nBIpr3FpGR4Pzttswy/s+WxFQ6HiYuLY+7cuf/4xK2CIDBjxgzGjh37j+73SKZOncrll19OU1PTYfX5ZSeebxpruKdszyET0CartTyZnMWZttgWbJnsRCdn2MtOChf0bMUFPSPZ75+vKWZfnYcr+2ewpayJJk8QYf89zn21bsa8tgyVQmB8r1bcNDibq3ZOQF+0Fca+SUD3JU57LtHxByeYfem0RBw7fqRraj9u+3ITANcMzCI3qWUDGk/O3cG3myp48+JudEyJDLNqF9oB7l2Ed3ipbDeeb8PXUWvQkb1zH6gjJ1finLuwzr6FW894BdoXo3gqDXZMj+zU1gpu3gQhH+uLHWworCah4kcYcj3oDx/CdDwRRYlbPt9IWJTomGJjUOs/+HJM7gbR2dDm1Ei9+WEPQsESzin8GNatZsBpL+CM2o1xwyqC+/ZxF08gSgqW7K5loKWOtoFihCBIPz7KZyYLs0pNbCltJL/ayQeX96Rv1p/P6pDJZDKZ7GShUAi8dFN3APwhP3XuOgBuPT+HJz/egfoX9cPf+24v1zy3htYpZs4Z3Iq7LmzHS1/uIixKjMgbiFLt56XrrZiNB+vA3nxua4IhiR1FDuasKmfV9toWD9gHgmFG3bkIty/EgheGYTZEguy94p0EK9w0FO1CG+tHEBSgUBIsXg+CAiSRxhWfokDE2udiwq5avFvmECzbCoA6uT36dsMAgWD1HkRnNb6idajic477kgr+fWU0LlgJAhjaZyP8QcJLZqqO0ko/6Sk60pJ1dM41sVhcyDOhuVzp1HPziOvZvc/D6k1eqveXSQoEJXYXerEYoaRKRKuGOYvrKSrYCUjsKgQUBq4cl4hBf/wl3Mhkx5u2WQZaZ+gpqfA3T+qcmqg9Jpn1f0ZdXR233XYbPXr0aNF2/FlTpkwhMzOT5ORkNm3axN133824cePkYP1J4kxbLKdZY1jhaqIqFCBepaGvyXpcZNbLTmxywF520nl+XGfW7qtnQt907hzZhgZPoDnr2aJTYdAo8QZEPlpRBEBbSwLD1QUELOmMmxNme8UWPr/ahFWv4puN5YzaeBuDPGuhVZDnzrsAly/Y4sF6gGX5tZQ1etlS1tQcsKfjeQR9bibMD6N+5xs+VFViVJtx2vMw67VQsQkxHIzMNj37ZnDXkC+k00YqRALCAR/5zw4jx7cFa+zZfN7GTLeid2H2Lhg3pQV7+8cUCoEbBmexp8ZF11a2P94gPhduWnfw8YBJkHMKfHo+ZA6GaZdjdteAoESrkHjnku4szq8l3qzjemMpwjwISgrUvibuTvyeUMcbWbyrGn9IYsb6MtomWIgytvwEMzKZTCaTtTStSkt2bDZIEn0ui+WmsyIj96IskTrIMVYtggC7S508+cm2/c9pMOvVeH0Sfa9cjEmvYuN7o9i0t5F5q8t5bXo+YVHi/CGteOSKPLq2aflyJ03uIEs31yBKEpX13uaAva71AErcegY/UsI9Q6qZ0FFE0ETK7wkaI2JTOQoxiFIBrpUfo+8w6pD9hp21OBa+FpnLPjodQWdBbKogULoZbWqnf72ff4YmOQ5DxxxUdssfBusBOrY10bHtwbmVRg+Oor5aR0yRnT7qnmzZ0QCASgnxMWq6tjdSXRskLVnHjr0edhcemBwuSF7HHKSwi90lGgRBZE+Rh/Y5JpRKOYgik/0RhUIgLfn4KtkSFxfH//3f/7V0M/60yspKHnjgASorK0lMTOS8887j8ccfb+lmyf5BSkFggNnW0s2QnWTkkjiyk16DO8ATc3bQLzuGTqk2hjy3uPk5tVJgYE4sC3dWo1IIJNv0lDR4aJdoYUeFA1GC65UzuUI1D+W572LrcAoufwhRAqte3XKdIjJaYGNJI6fnJR5S/sUfCjP0uSU0eYN8PczBjXPruV09jVMVayDvfLYUVtDR+RMAztiu6Ko3ohYitdgLogYQri0gR1EGQKM5B5t7Hwy+Gwbe+a/3sUUtehKKlsOIRyAq89C69NtmsXfBW+TX+ekb5cZy3muQ0o1hzy9mb40bgIwYI4vuGNwiTZfJ/iny9/V/h/y7lv3bft5Zxwtf7uSOC9qxdmcd17+wtvk5o1ZJr/Yx/Li+ijibFocniF6jQqEQaHQFCIuRyxe9Rsmat08lN91KVb0Xm0mDVtOyGdQ/barGFwhzSo/EQ5ZvyK+n7/Xz6ZplZsFN6v3Z8/ur3qb3p37nCqJ0kfMxhSUe0VF1cGOtGfzO5oeCzoIU8GDsOhZV1HE0v9IxFg5LbNxah0qpIDnZhN2ibg6+i6LEtnwnPyxrQqmUiLZrOGtkLKGQxBuflCNKkclpu7Q3MWpQy9/ckcn+Dvk7WyaTyU5+coa97OQjSfDxWGgshsvnMW+Hn6/WlbI0v5ZV9w3j5qHZfLOpnNIGL9ZwI001PkBLSJSocvp4OuknTqmfyiThavbY+1OYdC1P627kq6mljOu2mR93VRMIiSy8fTCxZm2LdTM9xkh6jPGw5VqVkh8mDSIoiigFAdPW1WwO+TjFpELoNoGtcXaqC2dhadjK2+I5PMqtGAhhufQz1IZ23PjBUi52T6G3cieZznxQ6aDXdS3QwxY25N5DHq4prOeTVUXc0dFLq2mXkgVkKkFoAlSRv4Np1/Zlye4a7v56MzEmObteJpPJZLIDSqrdDLnlB9qlWZn91GBe+HInX/xYhFoVKZ2zbHM1SzZWU1brxe0PU1IVuQFe3ejHalTziXUvQlk116o6EpURR9t0K42uAHmXf8cDl3XgsSnb6J0bw0+vjmjRfg7sFHfE5V1yoiiddhYmvQqVr45QfQkKrQlBpSEc14ZpKyQGJVbT1lCNFIpMwIvagKnbWYRd9Xi3/QBSEAQFks+Bwhz7nwrWAyiVAt06HVpycP02J5U1QXJaqdi4pQmvX8JihBhLGIMucvNm4vhEtu32sHRtE0a9XMteJpPJZDLZ8U8O2MtOPuEgFK2EsB8KfmR0h3PZUtZEhyQr109dR0GNm311Hiy4Wai9Ha1H5MP+03lqWSO+oIilZh1WhZPLEksYaHkJ1Da+tl7FV8D87ZU4fCG0KiUtMDjlqOk1SvRELlJmXN8P6AfFvSn99gm+LBnCBqkj0BGA3iPncl6PVChbjum76wg4RnCfNJFedh+fua9DEfLB9lnQ5aKW69Bx4M0le/lxZzW7CwN8qEomXqxCRIlS9CNt+AThlEexGzWM7ZLMiNx4dGq5RqpMJpPJZAfsq3RTWOGmpNpDg8PPnePboVErGNI5josfXc6G/HqqGvzN6/uCIgM7xfLTphqa3EGs7jLMhJk0JJ6PiwWyk0ysy69HkuD5z3cSFiV8gfDvtKDlRVv3J3po4rAMuBIAf8lmln87nee+VPFjUpDPxzXhDwtYup+LJjqJwL71BCt3RYL1Kg1oTeCuR3TWIHodKPT/7ezaH5Y3EgpJVFQpMGgjIxYCfpH1OyUy093kZBiJsqoZ0MNKjzwzOq0csJfJZDKZTHb8k89YZCcflYZgfB4AO9cvw2pQ88RZHVEIMGdLJTsrI0OKRQQCqPFLKhbtqkMhgFoh8HnUtXwf7oahZiMULoEdszhn+RncHLWaRm+kHM7gNjHEmFouu/7Pyq9ysmfOi6RUL+Zi1QIAmqt3qnTc8MV2Gha+iL1kAa+n/EB7YxPvOK4nKBKZnDXnFADWFzdQ7w4c8RjHo8/WFDPurZXkVzmP+Pyy/Foe+mbbEfs0b2sFszaWNT++YUg2I9rFs9OhobfrWYom7uSNwKmRJ1e/AZ+Nb17XqFWhbOFJmWQymUwmO54MyItDqYBQWOLtb/fQJSeKD+/tw/yfK5m3puKQYD1AkyvA3rLI93ecXct12i7cQDsm/dDAul31PP7xNn5cV4XVqMblC6FSClxxWmZLdO0vC9YU4stfSq+4Bq7t4UEhSMzP13D1N3Z8TTV4NszGv2c5oqsGZVQqhALgrgeFClVCGwSdGX8gzOINVQRDYkt356hIkkRBbQG7qnYTFo98g+Xdymk8W/r+Yckx4bDE2s1OSisP/q2MHGAnNlpJVZ1IYWWQazo70VeUkxCtIH9vAwVFB88B5WC9TCaTyWSyE4WcYS87KS3LuZvC4nf5srgf8/YvO71TEntrXNS7A2wvd3B+9/YM+fYFBCScvkg2tChJ3JlVQW7jOpyCCbRW0NmhcR91TQdP+OdurWJNYT29s6JboHd/3h3TNmMpzeN/tu3U6XrRtrKYS9Q/sth+Di8u2IUrIPKT0cwYoHXNfEYkjsBQHkCFSKBsC2e9t5Oc+FJmbiynfZKF724e0NJdOiqfri5mS1kTC3dWkxNvbl7+2ZpitCoFb/9UwM5KJ4lWHdcMymp+vtrh49pP1gPQIdlKVqyJbml2nhvXiU4Pzwdg0V4XXxgvRhM2cnXoUwora0kMhiOZ9Y3FkQB+ak84/cV/t9MymUwmkx2nBnSKY/GGan7eWd+8bPIlHbCbNRSWu/AFwrRNs/DmrD34AmEcnhAQCfIbs5JYtr0Ou1ENQqQCossboskdbF7n5pd/5toxrVukb3+WFPLj2TATkBD0Vm7t50CBhIhAj84xCAVLCAMoNRAOEHbWNf+MGCJQXcAjz81jW62euavLmTSuLc9e37VlO3UUwlKYKmc1AN5gEiZtZILZQFBkzSYn1gQfd5U8D8AIW186m9o2b7tzr4cFyxowGZXcfFkyAJ3amcivqueDBXupcrgYlNcbk86BzVMLUbGUVvrJTIucA1bVeNmxu5HsDAspSYeXlZTJZDKZTCY7XsgBe9lJqWffIfzkSuKGVvbmZSatirFdknn02+3sqHSyu9pJQGUgEJK4aWg2erWSWLOWjIw8Fv88HbPopKtvD7u6PUS5vjWLlvqhyQdEhqakRRtaqHd/3hl5iVgce7B5i7g0aiW5Jon+wZXo6zyMEYJsVmVyRnhB8/r28h8ZE3iEOzXTWRvKZluFg20VDtTKyMS8J4rJp7XjnaUFjGgX37wsv8rJvdO3APDomPYs31PHaXmHTgwXZdQwumMC/qB4SH+tejXPnJNHnTvAFz+XUNIU5HX9WXzm70alz87lC/O569S2ULkFqraCswJOf5Emb5Dbv9yEQaOgR0Y0F/VshULOwJfJZDLZf8yrt/bgxS93cv3Yg0H13HQrZw1IZcx9S/D6w8014A06FY9c1B6fP8zAznGs313Pqu11NLmDqFUKPr2/L2t21vH8FzsI7U/UToo5cc7NUGpQJ7QmWFOA5G1Cm9SeYPk2FEjY3bv3ryREAvQAQc8vNhZQiEG8NaUs2RwpiZOWcGIEoFUKFWarnQJvMRr1wdGqm3e6+WlNE9E2Fbf1v5SGkIP2huxDtk1O0JIcryEtWXfI8jP6JrOloJFO2dn8tMmLZE6mKiCg2CfiC3hITfKRkaqjrt6Pxxumps5HSpKRimo/C1c0Em1Xk5OmJzv9xDnHlclkMplMdnKTA/ayk5JRq+LW4a2p3B9gByht8HDGK8sQgb5Z0UwcmMnFvdOod/sp27WO/nteJ3nYdQRtI3nC+hD3OB5BQOKLpdv4IBAJ+LYRigkp9TRok9lY0kjiiRC8/n4yV23/Bs56BrYp0YWDdBKrWFbRlRJFIrcovmKksB5BAqekQ1Ab2KzuyzZ/Jg8Y76e80UdqlI7+2bFMPq0tJq26pXt01L5cW8LCHdWoFAJvXdIdgFbRBs7olIROpeDCXmlc0if9sO1USgWvX9TtiPvsmmbjgrdXY9RGRmV0SLYQb+nO3K2VdEi2RlZqPQrGvgmxbVhX1MC6onp+2FEFgHHrJ5QUq0k753FQyh/BMplMJvvvaJdmZfIlHVD8ojLJB3P3ctXTqzHplFw+KpM7x+fSu30MWrWSl77aSZ3Dz4Uj0om1aXn4wy24PCH8QZH/e28zO4oczfuxmdSolAr2lDrJTjEf4ejHD0kM4f75a5DC6NufSqhqF6LfDXob+Bwg7S9vI+wfSgAIWhNSwAtSGIUlnrCjipi4KJ6Y2IGJp2ej0544c+c8vGk628M7qFI0MjHxPACyWulIS9bSOsNAj1bXHXE7m0XFZeckHLZcq1FyXr8s5v1Uj1GvwOUR6d7JxL5SPw5XGJsl8trkZFowGlTExerYXeghf5+X4nI/xeV+NmxzcepAO107HN9/OzKZTCaTyf4b5GiR7KSyu8rJs9/v4sKerXj2+11sr3Dw1VgTPVbeiDd6MCKRmuPuQIiMmEgm0v/N2ELKuo9JVi2h8Ds3D9qiKWv0YjdpwQN1YT33qqbST9hKe2UxXkFHN/drvLIwnwSrji6/yOI/Lm39OpLp7amBM16CJ5IwA21EM3ocvBc6lTNt+4h17WRDx/vpcvo1PK9Tc6/Lj0WnRqM6cet9zt1aCUCy7WDGnVal5H/ju/zlfe6r9VDr8qNWaPn86t503fsGmvXv8fyEDyEzEZxV8OFoMMRQPW4W5z/5I2FR4qr+6Xy8LJ8n1e/BdqjLHU50hxF/t4symUwmkx33vlxUxBc/FnHnBbkMu20hKqXAY1d14ulPt9MqPvId7fKFSYjWY9CpGNY1gZ7XzGPT3kYAHvloMz+uryY+SofX7yYUltj1i2A9RMrjNLqCTHptHe/e1Zs4u+7XzThuBDw+gg3lCIKEwRKHoFTuL4/zKwoliKDN6Y8uvTuSJCIFPCj2l5F54JR/t93/hIpqP112XESeEKRr92DzcrtVzUVj4n9nyz/Yb02AQFAiO01Pl/ZGKitdpCdCn+4JaDVKaut8bNxaT3KigcJSPzPn12EzK2mToWdXoReA739qoH1rA1rNiXPzQyaTyWQy2cnpxI3EHWfqXH6enLODjSWNAGwobuDpeTtp+NVklpIk8eTcHTwwayuhsEhg/wRR+VVOapz+X+9W9idNW1fKgu1VvLesELtRjVopoK7eDE3FJNevQqUQSLBouXV4ZCh2MCwydXUxH4ZG8kFoJE8HxrG32oU7EKbWGcnObyfs4xrVd3RQFiFK4BK1dFBXsKMycnPgeCGKEq8t2sPna4oPLvQ2RIL1QIUmHTRGaB25aRGrcNJNsYcSKQ5dTBokdWXgGRMw6yIZ9DEm7YkbrHdV46zMp32SGbVSwKhR8vIP+Vzy3mqqnT6W7K5hxobSP9yNNxDm1R/zWVd0sNbu8Nx4Pri8B59d3YfemdFoyleDtx5p+kR4uTNUb4O6PVCyipgP+9Mm3oRWpeD7bVW0SY7hLdWFfB4awuiv/QTDJ8YEcTKZTHaiCnpcuCqLEEORwKCvsQZPbflhk1mKoSCOkt146yqRJKn5X9DjRBLlz+q/69nPtjNzaSlzVpVhNqiwGNV8s7yUijovCiFSIq5raztXjI7MJ/PjhqrmYD2A0xOist5HYbmLUDjyu/v1byUUljDqlHy3spx3v93zb3TrqIgBL95dSwjVHzzv+HmvgsdXD+L59YNApUEVnQb7g/AHCQh6K+rEtujSI6MEBUHRHKw/EdU1BgmGJYx6Aa1Sjd3Riunf1zJnUR1hMcyUqm9Y6dj4h/tpaAry05pGnO5Q87LBvaycNTKGUwdFkZKgo6EpgMcTYvHyStZvrqO+0U8gKFJY7MLj9qHXCri9YZyeMCaDgE4rIAGzF9b/9oFlMtkxNWHCBMaOHXvY8sWLFyMIAo2NjQC89dZbdOrUCaPRiM1mo0uXLjz99NOHbONwOJg8eTJt27ZFp9ORkJDA8OHDmT59+mHnADLZyUYQhOZ/RqORnJwcJkyYwLp16w5bV5Ik3n77bXr16oXJZMJms9G9e3deeuklPJ5IOb533nmHAQMGYLfbsdvtDB8+nDVr1vzb3frPkTPs/yFTVhbx4U87YddcOl8/kYdmb2dTSSMGtZKbhuXgCYQ4/61VhEWR7RWRyUunripCEATO7ZrC5z+XIAgw64Z+5KXYWrYzJ7AJfdPxBsKc3TWZjslWHAufI7T8VV4Ln0n/wTezOTML3/xHeGnGGu6TetEl1cZpHRMpb7TycMll0Agzb+hCfpWTW6bfTEK4lirRRk9LA4nKJr6vT+By9QKeU7zMaM3/uKJfekt3udmm0sbmGwij8xIxalT4FGb0af1xFW3gxs828Hz3H0kqXIUaOFBB/ULlQkxFFZHh1w37IKFDS3XhL9tc2shri/Zw9cBMuiWbkF7vg97TQIP/KYJSMisL6thT46LRE2Tp7hru+noLYVEiM8ZEp1Tbb+532vpSnpu/m8wYIz/eMbh5+ZA2cfiDYSa8vwbJeRWTuw8jZ+2DSAIIQR8YYsBTi8JRynWj07jxi62UNHi5aVgOfbOeZ9TLS0kw61AKch17mUwmO5bclfsIepyIQT+mxEycpZFArtpgRm0wE/S6cRTtQKHRE/I48DvqcVUUIihVqPQmgq5GBIWS6DbdEJRy1u1f9cx1XZm5tIQbz27DPRe1p98N8/lxfRUqpcD0Rweyt8LF5Hc20vPqucTYdIzqncTQrvHUNfrYVNDEtsIm1rw1kk/mF/LSV7tQKiAsQmaikQZXkAZnJEHG7QuTkWDk/KFpLdzjgwKlWwgUrSNUX4K5z8X4/GHatlIxvuNebIpaHEvXgCURIeBG4uD5GUhIrlpCflfLNf5v2rzTRUGJj+H97DicIT6aXsUv42S7Cn3s3BsJBoTabefWwicxKQ0U91z4u/tdtKqRnXu9uL0iowZFAaBWK2iXZcDlDrB6XR3Rdi1NrjABf5DKai9dOkYhABIQDoukp+rZscdDeVWAGy5Jorw6wIzvazEZ5fe5TAaRIF59gx9/QESrURBl1yIcB9cu7733HpMmTeKVV15h0KBB+P1+Nm/ezPbt25vXaWxspH///jQ1NfHYY4/Ro0cPVCoVS5Ys4a677mLo0KHYbLaW64TspCNKElU+D55wCINSRbzO0JyQ0FI++OADTj31VHw+H7t3724Oyr///vtceumlzetdcsklTJ8+nf/7v//j1VdfJTY2lk2bNvHSSy+Rnp7O2LFjWbx4MePHj6dv377odDqeeeYZTjnlFLZt20ZycnIL9vLkJgfsj1Jwfza8UfuLl2zbTDDGQno/Ts9LJHvj05zRNI3l/1tFx/S7aav0MKpjpM5ivTvAlrImAC7okcLGkiZ2VjpBkvj85xIgUqKyoMZNXoqNL9eWkGzX0y875t/u6gktyabn0bEHA86+/MUkCU0MV6wnvuJ7lP5korZ+wLVSFFP8nahoqmSQYhO3pNXSkJdDWB9D51Q77RIt3Dt9KwViIm3iTQRGvk7ihjs4N0aJt9iCOhSik6KAIdYE4PBami0hN8nCud1SiDZpsOjUjH97JasL65lvqiYbN32FTaRv+vqQbTyShq1tbyWnRwb4HSdksB7g45VF7N62HlvZLTDyejyCAQk3fjRolAKPjm3P9PVlNHiCnJ6XxJLdtdQ4/WTEHpygbWl+DXFmHW0SDtYuHZgTQ6+MKEbkHjpEOyxKnPryUgpr3YCKIkcac1GjlsLs+u412ntqWafqjGXM8wzJSebU9rWoVQrGdE5Cq1KydvJw3IEQMplMJvt7JDEMCAj7i6JLYhhfYy0aoxWlVocuOoGg142/qY5wIIDabEMAVLpIGZaQx4kYCiKGQujs8fiaakCSkMIhgq7G5n2K4RACEr6GajRmOyrtCTCHzXFkSJd4hnSJfJeGwyLbCpuan1u5rYbXZu5mycZqABpcQfZO34UowoRRGVhMGi4Ynk6XnCjKary89NUuBAEGd47jq1ttKH++jaXOIZw1pS2iBOmJJtTH0QhBdUIO4aZy1AltqGn0kXvptyjEADtvqkQhQL1LICpcABwM1nuCYO98CgpJQmGKbrnG/00/rWlilWoxL2xZxGOJdyKg50C8PsqqpFcvNUvMP9Bf15vuUe3oXtOBTsaDExIHxCBzGn6in6ULseqo5uUdWhtxusLkZh86ybDLHWTZ6mpEERyuMEUVIpnJCsIS/LypHqUCAiGBtjk2stLB5Q6TkarDalZhNavImphCOCxn3spkldVetu9qxOcPNy/TaZXktrGRENey33+zZ89m3LhxXHnllc3L2rdvf8g69913H/v27WP37t0kJSU1L2/dujXjx49Hpzt+S6bJTjz73A5W1VXhDh+8vjcqVfSOjifdaDlmxxVFkWeffZZ33nmHkpIS4uPjueaaa5g8eTIANpuNhIRIrCo9PZ1TTjmFyy67jBtvvJEzzjgDu93Ol19+ydSpU5k5cyZjxoxp3nd6ejpnnnkmDkek/ODUqVMPOfY777zDtGnTWLhw4SHBf9k/Sw7YH6UzX13Ovlo3s2/qR3acGcrWw1eXgULNN6NW8NmGei6IbYffpWZhQxw9mv6PMcoV1O14BuKuIcVuID3awL46D4lWPfeOyuW7LeVY9RpeW7SHJLuOcd1SGZEbz8q9ddz19WYEINasZfZN/Ym3yF8qf4V53Jus+PJx+tZ8TnjNizyTO53OwkB+DLZFq1RwRkaYe0peJ6bCCRWAQk341JEIahMCoMfHrip4/rPZTFPOx4iCAAqShBAfKh5B+Y4Prl0KCR1buqtoVUqeO69T8+Nqpx9RgmtcE/l4QAPu7QrCTlAKUBCOIU1Zi09hYkZjJgMTBxBj0rZg6/+eawZlckf+xcT7ignPvo2unreIMyj433UDSbDqKG/08c7SQpQKgUfHdKBtopmsWBPm/Tfg1hXVc8l7azBqlGx+aCRKReSSOS3ayBfX9DnseGFHJee5P+VLoTcTTh/CRyuLGF33FGlWJVJdBTepqnnBM5r0fC1PdFDx5iXdIOgl/MVFNIlqZmY8yIPf7iI30cKcWwb8q6+VTCaTnSzCAR8NezajUKmx53RGEAS8dZW4q4pRGcyo9SbCQT+CUokUEgl5nc3bhvxe1HoTGosdKvYBElpbNPqYRALuJgRBgae6FI3Zjs4Wg1KjxV1VjKemDHdlMWqjGVtG+99sm+y3KZUKpkzuw71vb6S4ysP1L65lRI+DyQ9p8Xp0WhW7ip18OLcQgGBI5OrTszHqI9/boTAs3ljN1I+/5pLYn4n3NiFK9wCwaEMVPa+ZR8WMs4+LbFClwY6xy1gAfNVuHO4goTBcOTuWt67L4rVPt3P/oIP1+CVAVGgR60vRdTy1ZRr9DxnR387DlW8QFsPcXvo4Z0mPkZOuo3cXC3HRGl6tnsLb/nfYpFnNDOkNbm98mlb6g9c8r5R/whMlbzPS3o/P2j7XvLx1hoHWGYbDjtfQGEAUQaWELnnRbCgqprHCg7v9WrLzT8WkEyiuEmmdHSA328glZ8XjcgdZuqqKKLuOLbv9FJT46NnJzPB+x/kcVTLZMVJZ7WX95rrDlvv8YdZvrqNrXnSLBu0TEhJYsmQJRUVFpKUdPppKFEU+//xzLrrookOC9QeYTCduWTHZ8Wef28HC6rLDlrvDIRZWlzEsjmMWtL/33nt55513ePHFF+nfvz8VFRXs3Lnzd7e57bbbmDJlCgsWLGDcuHFMnTqVNm3aHBKsP0AQBKxW6xH34/F4CAaDREVFHfF52T9DDtgfpQZ3AH8ojCew/y5zVAYkdwNzIl+vLqCgtIZ71O35IHQ/D+k/xxuKXCDsrPXTD3D6gvTPicEbrOLFH/LJiTdzYa/IF8xpeYlApI79hpJG2iaY6Z5mZ2NJI9VOP7UuP3aD5sStJ96CzLGt6HvtqzhnqPFtnoVp0/sYL3uP76as4xLVQv6v9B2klB6ACGXrCEhKOj72E+9c1ovvBxaQuWoyn4aG8LjiWn7Ivpcvt/twYmB8SgNn1L4NggI0x+eX/munRvFTiQFzVEeSVo3hAVcBRVIsaUINGYpakMAu1bOvpJht5Q4GtY5t6SYftWBY5P6ZW1lZUMdjYzqg2/MdqZowBMCtthNtszAgRUGXiq/AdgbWpjWMzNBhsidQ7wnwzLxI6aDT8hLIjjOTZNOTbNOTGWtsDtb/Hs2KF7he+oJL2zkw9bsCi17FfdO95KUnMGtjHEsCnUiwaKndV8+2okqy93yAJjodZf5czJLAosbzACVFde5j/ErJZDLZyUsSxf3Z7wcKXQiojRaUGh0ak43iveUYtSJ6zf6MWUHgQD2OAzXtpXAYjdlGwNlA074dRLXuiiEqEjzW2+MACLiaCAd8aMx2fI21iEE/IZ+nuQ0HsvtlR++CYekMyItj5B0/sqPIwXcry/nwvt5MeGIV3oBEUZWTAXmx1Db52FHkZOOeRlLPncH6d0fRs100a3ZEgknf1Y0kFBL5YFMmJr2KvEwrK7bVodhft/V4kxxjYN6DHflxp58hXeNQVMxk8qAQkhT584RIlr1J6SdYuRMpdxiCUt2ibf4z/IEgBdsK0ATd2Nun82jgadQKJWEpjFltQK9TkJqoATFMEw1oFRo6GnI4J2YEuws9rNviYudeD+32Z853MrbBqjTT3XR0I0ALiyM35TLTLSTG6dEMXMOb9Z9wn+4WNIkhqp0SiYk6mhrd7HbV8UXtHE4Pn4nTJVJTF6C6VkCjjkxgK5P9F0mSxPZdjb+7zvZdjcTH6o7ZZ+y33357WFA9HD6Y6f/ggw9y9tlnk56eTuvWrenTpw+jR4/m3HPPRaFQUFtbS0NDA23btj0m7ZPJDhAliVV1Vb+7zqq6KloZzP94eRyn08nLL7/Mq6++ymWXXQZAVlYW/fv3/93tDrwv9u3bB0B+fj5t2rT508e/5557SE5OZvjw4X96W9nRkwP2R+nbm/vT6AmSHbf/y0Nvh4k/QsjPu8/nImqbGBF4hivtG+js2U5j6hDeT36OC4b3BGDmxnI+WVWMXq3gfc1z9J9bwbzGD9kbiuG6QVn4QyJjXluONxhm3i0DmXZdX/ZUu1i7r446V4CzXvueU9rH8+qFXVvwVThBKdVU6XPIVjgYqdxEyKhlYOs4zjLFwyYQ9FYqggYSWYdb0OMPC5Q0eBioiQzXvlC1iLNZhmZPkPek+1BmDeLMMXZ47Q1Q6SJlkY4za1cspNv3Z9MGKFOn4Q+VowV2S8mkUosgSAhAg2Dl+rOGMzDnxCq99OXPJXy+NlJK6oMVhTxe+DBaoZ6vwoN5vukcLkjdyqU1n8Oe7bBxKoaKjbyVcwrhU16k/O2hvGRrw9YOd5IZE3k/J1r1LL9n6KEH2bcMYtqA6Qi/39wxUPozpu7jAZi+vhxfSCTZpueuU9tQ0ehjc2kDm0odLP3qFdq7XqNESOTz4DjKpSgMUclQVkmfrBN3mLtMJpO1NJXOEMmsVygRhEjQXG0wE9W6C7uLA9z1pUScJcRT51UiKJRIYhiNOQq10YzWHMmedVeXEHA2NAfzHcW7UJusqHVGtNZoAq4mmvZtR1CpiWnbneg2XfE7GyEcwl1diqe6BFNSJvqo+N9pqexIkmMNiGLkBorFoCbKrGVM/xTc3iA/rPPRvW00n8w/mGFf3einwRkgEDo43eyCjR4WMAiAJya2x2LSsGJbHW1SzYcf8Djw1SfTODWhhLxWUFqsJWBTogQUkohaOhiQEjRG9B1GnlDBeoA9eyupaAwheRVMK3yROc6fAOhVMZY4TwYZ7V1UNwaor9OwqOJb3gt+xCNpN2FVmjm9aQzdOwzkwdjbm/c3wt6Xwp7zmx97wj5WOzfTz9IFjeLw1yY1yUhltZeEOD2SJNGpdAivS0PolhVNJS68rRzU75GorPHzmP9lVipWsM/9E0MbHqbOraVTWw1ud5D2bYyH7Vsm+y+ob/AfUgbnSHz+MPUNfqKjjk0FgCFDhvDGG28csmz16tVcfPHFACQmJrJy5Uq2bt3KkiVLWLFiBZdddhnvvvsu8+bNa55Q9ni8aSs7uVT5PIeUwTkSdzhElc9Dov6f/V7ZsWMHfr+fYcOG/antfv3+kCTpT79XnnnmGT777DMWL14sl5c6xuSA/W8orvPw/IJdnNM1haI6N5+tKeGpcw4veyIBarWOgNLNyA5JDB/1OGzIJFi0g14b7qS+ujWGzqcwot1ZLMuvYXCbOIYsKkTwNDBl7k+sENuTE29iyop9mLQqLDo1UUYNAKsK6vi/mVtpn2QhEBYpqff8y6/CyaNEjKIg3JViKZbWH17EWseFPJLRny+GTIZFjyNJUSCAWqnkqwsz6N6xFev3XYN70Rz6KraiJYggwDW5Ibqc0xUM6sgNG40JtMdfhn1M6Q8cSBRPCRVRIdrJjz+VmFEPoojXgKuKYNCH3ZLIeEtiyzb2z8hfALNuYFjHa3lG34EmbxABgV3qXKyhddSJJtooSrim+iX0QoAq0YoqoT/RtfmQ2Jn89Ytp69mOUSqme7/XUfxWNv3mr2D6VZDUBa5efHB51TZQaiC9P1y9qHnx2C7JOHxBBuTE8Oh3O9hR3shNyXtJS8/ik6IcOqjaUyTFc6f6SzaLGZy9LRJcWF/UQFGdm7Ro+cJQJpPJjobf2YC/sRZjfCru6lLEoB9Lq8MzgxSChEIhoFYrMcSlojFZ8TfV43fUEfQ4CLodGGJT0EclgCiisUThKi8g5HUT8rrwCgJWlQpXZRGCQtlc8x7A31CN31GHSh/5/g8HfP9a/08mkiShUUdutOyrdHPJYytocge5/7L2aNRKXvzy4LDuzCQjnz7Qj7ZpVvp3iGFjfsMh+3piYifuujAXgKwkE11yjs9yJrmmyMgAlQLSTX4KnXrEvFF0iosCBCSFEsndgMqedEIF658ofpupNd/ybPTdFJcmUe9Ukpc0gOksQhPWcpr/LERJQOOMBNIrFGXEWPWY6g20M2TySNHruEQPa7RL6NTuwd88zj37XuCT6tncnHQxD6Xd0Ly8rsGP2agiI81MRtrBmzWtko24PCHCKJi5vpSfTT8wNqML0o525IkDqbPWM5gbsdv1WCwSVbUBTHqBskoPyUkGVEp59Izsv8UfEP94pT+x3l9hNBrJzs4+ZFlpaelh63Xo0IEOHTpwww03sGzZMgYMGMCSJUsYNGgQdrudHTt2HLM2ymQAnj8I1v/Z9f4Mvf6vlaU68L7IyMgAIvM6/Jn3ynPPPccTTzzBDz/8QF5e3l9qg+zoyQH73/DFz8XM2lhOeaOXRk+Q/GoX09eXkZdia15n+vpS7py2mcu7f8DczSX01yahj0qmoeed2H5KJlYIw76tUPkjCfeM561LujNjQyk3659ECJaywpfLKXFNVNY5WLanDptBzcYHTmnef4xJg0KAvBQrD53ZnmxzGN4aBIYouGgaKJQt8MqcQCSJxumTqK6tZXDldwjKcGTIcQDuyuhNq+EjAR9B1CwJd+I85RJMwVpSapci/DgN8/b1TAjezLPqtxipXIdH0JN9+m1YDfsvoJKPr9EOu6uc3DB1PSPbJ3DHgAvwFM/GbcmB8nUkKhrYq7fSJXN/cN4YzYlzGXjQumXz6OaqQl+2gsmjz+OurzeTHmNksAQUeblW/S3X8C0fSqfRUVPD58n38tjoQTDmcdYV1XPDx2sZHrycGkM2b9kjwZfl+TW0Lf0KpS2JXdYB9MyIQrCmREZPRP/iZLGhCN4aCAo13L4jMspmv3O7pXButxR2VznZXu5gnHIJk+rewWNIpqfiSe4xPUa2dwvjpCU4JAOh/RmF9Z4g93y9hc+u7v1vvowymUx2wvJUlxLyulCotfgbawAIeVxozLbmdZxle7E6annj+jgkVxVKdRZqg5mgx4EY9AM0Z9VbW7VBY7LiqihEqdUT9nsBUBus+BpqCfvcaCxRWH9xU0ChinyD6qLiUapbgSBQt2sdOlscxvjUf+mVOHFVN/g45/6f0KoVbCk4OPlskzvIOYNSueq0HAT2MGdVefNz+aUuYixaxj24lGVbqg/ZX1K0nlvPa9ucIXZKj+MrEWHqgkIe+XALz93QlZE9+1G37SdCxiQsnkIy9F50Kg9K4y/ON/THboK6Y0GURN6r+pqGkIPdqr1kJrahweVhgKUHSp+CvmJ/MmMN+MUgDo9EmW0boYwy7k69klvEC5hbv5QtnnwAhlojI5P9YoBZdT+yx1tER2MbsnSp5BqzyNa1QkAgU3fwfVZa7mbz9gbsVg19esQd0rb2bSPnams3O1E7ohlrPoPEGhPECtTmd+HWcE8shv3lsQBbkgKbWaCuJMC+IhfZmSfW70Im+7u0mqO7SXW06/1bcnMjN2zdbjcKhYLzzz+fjz/+mAcffPCwOvZutxutVotKJYfCZH+PQXl0f0NHu96fkZOTg16vZ+HChVx11VVHvd1LL72ExWJpLmVz4YUXcsEFFzBr1qzD6thLkoTD4WiuY//ss8/y2GOP8f3339O9e/d/rjOy3yR/Sv2G8T1bMXNDGeWNPqz6yIXZr0eK7Kl2ERYlNlaFKPPp2FIWuegIoeB7qRenCyvwGxL5Pu5yfvpqE4+f1YFp60pZXm4hNao7l6i+5VHHuyxeOoi7T32RDskW/KEwbyzeS26ihVM7JLLloZEYNMrIRUjVdqjYiKRQc8uUpURHx/LgGfKEZ7/F11CObcv72IDtlgHkOFbRJGlZKPTj/MtuBp0FGMLCczbx1rzdjHStJQoXsXEJMO1ucpBYo1tBSFCCBE8ELiC4cA9Pn3t83klcsaeW/GoXgXA5dygWYVCIGE77P9Zu3EDJ1q9JG3Tiz979rGc0SQEVnVPO5tIeqZzSPh6bQQP1r8JPz8HGTxC0Fi6/awooVRz4Glm0YiUbv3sHjfIUPgmPoE9sNEgSy3eV88JHX/C19mFCKLjU9z4fmV6ll7iJurjeaHvdTnOeltYMxjhQ6yPB/CNoHW/mpfM7o60MIa15H4OnjIuZw5sNZ1JKNrdYH6O/uI7TNbuY785EUGkx61T8b2E+vTKjUSsFftpdy5UDMjBp5Y9nmUwm+zV9TCKusgJCXtdvrhPyeUAS0eMmpJQI+dxALAr1wc9uhUaPGArhqSlDH5OEt64KkPaXzhEJuhtRGcwYYpPR2WIJB/x46yvQ2eIwJWVgiG+FQhlJnPDUVSAGA/gd9QTdTejs8ejsx1+5vOPFZwv3sWJrLQBZySb2lkV+l21bWfjy4chE7A9enoc/GOaDOQVUN0ZusqzdWcfXS0qa96NRKQiERMrrvHz9UwkXDk//dztylH7euJeB8dUsXlvMEHUpJosVU4/RuNdNRwx4USe0bukm/i1u0YsjFPkd9rd2pduwaE4ZEIVOq2BGzCts3lMN9ZBgN+4PqB8Mtk/YNZn5jctRoyJIiF6WPIJiiKdK3uHl8k8OOc7F4UvoFezLR1FvcUpUu+blep0ShQAGw2+fN3XONRISw/gF8NQAkohWDQWlQRJiFCTFgFmvwhYjoTUocNZKhCWJXXuaaJVipL4xQCgkkpZy/I2qlcn+SVF2LTqt8nfL4ui0SqLs2n+xVYe67rrrSEpKYujQoaSkpFBRUcFjjz1GbGwsffr0AeCJJ55g8eLF9OrVi8cff5zu3bujVqtZunQpTz75JGvXrsVms7VYH2Qnh3idAaNS9btlcYxKFfG6wydJ/7t0Oh133303d911FxqNhn79+lFTU8O2bdu48sorAWhsbKSyshK/38/u3bt56623mDlzJlOmTGn++x83bhwzZsxg/Pjx3H///YwYMYLY2Fi2bNnCiy++yE033cTYsWN55plnuP/++/n0009JT0+nsrISiEziLE/kfOz85yNCDl+QNxfvpUe6nfnbq+meZsfpC7JmXwO1Tj+mcCMXZsfQIz2NqwdmsnhXNbM3lXP3qLak2PVY9Sou6JnKDUOzaZsQCe3FmrV0PX0iTT8UUN/+Gm5e2hoo5aocN8+2L+Gr9K6c3zMFz+o9sBx0Oj3XDc4CYO6WCl76IR+TVsXWhxNYtKuaBneA95cXcmHPNCZe+BXb60S+meVGIbi5Z1RbtCo50/5IHllSjxS8EiM+Pmk4g7cvfJcv1pVw9aAc0Fn4vxmb+Xp9OQNbx/D8uM58uPQ1EoP7OCe1D5E8G9DhB0HFqjMWs3utj0ldk1u2U7+j0hEZkh9v1kHBYnCUQflGepx2FZx29Hddj2dPjOvF8r3ZnNctBYDp68vYVNrIY2M7YB77GvS4AtQmmHUDtD4FOpwDQN725xii/oHOVpGcCa8TZ9bBJ2fTp2gFdt29FKhbYww1kiFU0jG0FYEgMZVL2Tr1Jjrc/UPk4IYouG1b5M7d79R5G9slGUgG02T4+QN65J7K7A06qp1+rgx9TrfwJi5kGnMMQ7jeM5GFO6qYv72KtGgDerWSnZVOjFolVw3IPNYvp0wmkx23Qj4PvsYaNCYLvoZadFEJ+BtrkMQQkhgm6G5CH5uCgITaZMFTU4YYCmJMSEOp1hIO+jEmpiOFAmhMkcwgnTUab0M0YU8jWosdb205IZ+L7TVRaIRssuMDaO3xNBVuI+xzo9ToMMa3AsBZXoCvvoqw34c5JQd/Uy1hvw+/sx5TUgbm5GyCXhe++kokkAP2v+PV6buaf46365h0XhtWba/j+Ru64fIEGXXXIjbmN/DolXm8eUdPnpq6jd65sYR/VYEhM8nI+GHprNxey5Aux+8cAg/0rUThdiAmhwmXl4MkIvrdmHqNb+mm/SPMSiNf575MfaiJ7uYOhKQQk0tfwKYyc3/adfTqLNHYFEBCYsOWOrIyLFhMkWSoxU1rABgfO5pbUy7FrDSSt34sIiJqVOiVWlxhLyIiHfxdsEsxUAPTdq7iwo6RifWio3ScMiT5t8scAmqVgj6dbUiSlXWb6vD4QnRsq2F7vpt4O4gieL1hvPlgtoHfI1FS6iIQlPD6QpRXRkbe2K0aLGbNsX1BZbIWJAgCuW1srN9c95vr5LaxtWh9+OHDh/P+++/zxhtvUFdXR0xMDH369GHhwoVER0fmB7Pb7axatYqnnnqKxx57jKKiIux2Ox07duTZZ59tzhiWyf4OhSDQOzqehdVlv7lO7+j4f3zC2QPuv/9+VCoVDzzwAOXl5SQmJnLttdc2P3/55ZcDkeB+cnIy/fv3Z82aNXTterBShCAIfPrpp7z99tu8//77PPbYY6hUKnJycrj00ksZOXIkAK+//jqBQIBzzz33kDY8+OCDPPTQQ8ekfzIQpAOzDvyLDgyraGpqwmJpuaGGU1bu45WF+dS6AiRYdFQ6Itn0CgEaPEH6Zlh5ufxCogQnO8bMoUOXPuQ+MA9PIMw5XZORJJi+oYzL+qTx8Gmt4avLAAHanY4083reDJ3OZ5YrMOtUbCt3ssN4Lfqwg09jbuWhyt58elUvuti8KC0JoFASFiUe/24HMzaUYtOrmXxaO66asq65vblJZubcPBBJknh/+T6SbTpO7XCUw35DAVj9Jqh1kNYf4nOPzYt6HHlzyV6emrsTi06Fwxci2aZn2d1DaJxxJ8s27+Z23xUEflEYxoaTSappZPcZQ9+i16B6By51DE8qrua8i6+hc6qt5TpzFFbsreX/ZmzlygEZXJQjQunP0P4sOAZDsFrSgYlRKpt89H5yISY8TOwdxy1jI3XhWfUmzLsbnzGZnYaupPU+i6fnbOfC8Exanf88tnaDI+u92gNqd8NFX8PqN2DPD1S1u5zHC7M4x/0F8UIjBa2vZPTFt1FS78GsU0Wy+f+CaoePvk/9yCjFSp6wzsLsLuK90Che015BvTvIRdYt3KaezoY2k3i3PI0nzu5IVqx8p1rW8o6X72vZsXc8/a4dJfkEnA1IYhilRk844EVltBByOwAQVGqkUBAEBbasjgiCQEP+RgAsrdrgKi9EDAWwtGqNUmvAWZqPxmznvR9NLFjt4pZTaunRTk/A3UhxrYrn58XSM8vDeb096NRgy8pDEkVUmkgGYTgYwFlWQNDThMZgRanT4609WK7FEJuMMb4VYiiIt64SrSUK1VFOLlbTEOL7VW5i7Up6tddjM5/8SRij7lzEko1VCAL4AiKXjszgueu7MvquRazfXc/+qnGolAKh8MHLlLnPDObcB5bh8YUwGVRkJ5n54cVh2I7zAKq/cA2B8u3oO5wKYhhJDKGOTmvpZv3jDpyfTan6hlsLnsQm2vgi9zl62COjgX/eWEN1rZ8qcyFT9O9xY+JFXL3nQSxKI6s7f0GcJooSfwXdN4xDI6jZ0m0mbX8+nYAU5MbEC5lduoIzg2cRK8bRpp2ZEUnd2O7eQ7Y+7YgT0B6N8koPG7fWo1YJiBKE9/+9qVQC4bCEWiWg0Sgxm1RIkkCn9lEolfJElrKWd6y/syurvWzf1XhIpr1OqyS3jY2EuL9WO1smO1ntcztYVVd1SKa9Uamid3Q86Ub5+kn2151ckbw/sGB7Fd9tLOae3joSMjsye1M5ta4AMSYN1wzKZHeVk3htkNFbbkGrV/B+9PM4ygzo8XP9lzu4S5lGnFnDvjovc7dWMv26vvTOimZUhwRwlsOuOZEDRWchIHG2chlv1Z9OTlYCOaoV+Ewp6Ju2M7D2cwKhnlQ7/SjTk6FiM8TksLHcx/vLC4HIDYN1xY3NNxIA7j8tcsIrCAJX9s/4c53fNh0W3B/5WW2Eu/ZGSnucxK4dlMUV/TIoqnNz6stLqXX5mbtmC6M3v8MZwNvCCLKEctKEKl4Nj2WMcgWXqhYQyt8Lmf2hegefa89ham0H0grrjvuAfd+sGH68Y/DBBVF/8m/kBDBzQxl3fLWJSae05uwuydj1KmaJk0nZXM81e16gSp/FtE5BVECj20dn92waFmzkC/fTfE5XFsZ0w3ZgZxPmQFNJZC4Cgx3s6exNuIiCajf30Z53L+vO6EQL28qbOPPV5cSbtSy/Z+hfyiiJs+iYdl1f6l3d+KruCopKivlok5M4faT+412hN7H6G2i/7Rkeu3yxHKyXyWT/Ke7qUsIBP4bYZJRqDf6mOkBCqdFjiEsm4GxEEgQQFKj0RsL769AjiTTu2Yw9K++QfVnT2xHye9CYo/A11hDyuhHDISpqtYiSwLzNZjql1qDQ6IizBrlqcAMdk71IEoghASQJpUpN0OtGrTfia6gm6IpMchpwNWC2xYKgAEkEQUAfE6mPq1Cp/3QN+0/mOvhhjQeAzq29PH79yZ+ZP/fZIQRDIm99k88tr6xj6eZqJj6zkp931R+y3i+D9QAzlpaSEKWjqMqNxxdiw54GCipcdDVH/ZvN/9O0GT3RZvRs6WYcU7fsfZJptd/zVbsXsSpNpElpPOx9ktotCtroTuPs2OGMES8CYK13E5vEXbxc/jESEhIQp4n8DlO1iSzt9DEaQY1NZeF/WZPZ4dmLQWkAg59V6gXMyP0fBqWONyu+4L59L3FB7Chez37gL7U7MV6PUhmNxydR1xjC6/HgdIYQRZAkCAQlAsEQggA9u8bKwXrZf0ZCnJ74WB31DX78ARGtRkGUXduimfUy2fEq3WihlcFMlc+DJxzCsL8MzrHKrJf9d/ynAvavLMxnQvWTJOxeBkldGJb1Mmv3NXCmuJCxyyaxut19zN4VpLVvCwDjql7g4dAlrBdb48LAd5srmHVDf/o9vQh1oJHEJXfSVisg5j7Jxzsl2nV8kI5JZrS9roTVbxEfbuA06z5usa4iTvUBQZ+dYjGWWWJvPr6yJwNyYmHdhzD7Fmh7Oh3P/ZihbWNZsrsWg0bJpb3TuKR3Gld/vI4uqVb6ZEUf2qHCn6C+ELpeGinR4amHoAesKQfXqdwK22dBuzMgpQfU7ILYtqBsubpz/yaNSkFOvBmzTkWjJ8jq7z5itALKpGi2S62Ypb0fBRI71O24+Zo7CC51oW47KpKZ3u9WhkkJ6PfWck7XlD8+mOyYy692EhIlSgt2MbWhigZvELVBgYDAvnoPBUITNd2TiJMEVobbYjVoaBUo4kPNU1wfvp05myvJTfIwrF08mGIj/wCSu0FyN+54ciHlTT6eOrsj7RIjd8M1SgUqhYBBq/pbJ6mdU23c/NkGvtlUzsW9WzHjujyem7+LaqefzeYBdGv8nmcdI5jx4lKuGZDBvaed/KNgZDKZTAyH8FRH6pL7G6vR2eNRG80E3Q4kScRVsQ9jfCtc5QUAhDxOBPUvM6ql/dn0bXEU7yQc8OGuKkalMyIZQ4QDfjSWKHT2eM4brmPznlp2VmgJKE3ohTA6VZiOKQGQQKnWYM3IRanW4CjZjb+pDlNiOjpbDL7GGsSAD6VWj84WjUKpwFmxD0NcMopfjWRbsNpNtFVJ17aRmvlVdSGMegUmw8FJ+lZu9lJZF6JfZz3bC/3UNobJSjkRp4P/a9QqBYM6R8rYFFa4KaxwA6BRSGhVEs5A5LUa1i2e68a0ZuqCQq4dk8PT13bB4Q6SX+akrslP19bHd7D+v2KHpwCv6Oebuh/Z4NpJQAqiEBSEpBC1oUZWOzfTVzkMARvlyjLsSgu7PJEkpURNDM+UvMcZ0UNoZ8iktT69eb/nxY6kwFtC943jAJqD9QBGhf6Q//8VgiAQH6vn5Q9KcXtFxp4STdtsBV98W0coLJKbqSIYEnG6Qrw5tYzThkSTnX50o2dkshOdIAhERx153i6ZTHYohSCQeJSjK2Wyo3XyBuwlCRzlYD1Yc/zOkW0wzjFBI1C+AVVwDm+nVtIutBN7QzFFa2bTc/TjVP2YSHyoguyaH5iimcO9itv4zNOD+zzPYp1aw/dXf4Rh+ZNYt38OwG5dJ55ZEsNy7bOwOYC0+GGENqMgoSOP97uFtXPeJxZQ+xsIaNtg6XAhA7yLQTwHtBZAAJ0VzY7p3Ff7OqXSOFq37kmivxD0dr69qf+R+/fJuRD2gy0V0gfA633AWw/XrYCYnMh6398bCeyLIbjqh2P8oh+/PpjQg23lTSz+oZD6oBk3epZqb2Ol/UxqamvwJnYjOrEVjHv/4EbRWWQAGTEnyAdvQxHYWv1uffUT2YbiBj5eWcSQWAcPFd2Is8jAxoyprMibRf3OZbzaOA1NZn+MHe/kzvzvqPQoEJtK+dQ/kWyFxFND47lp3m5UCoFdj41CeYRapzcMzWbp7lqGtotrXpYTb2bNfcPRqhWHrf9nDc+NZ2t5E0PaxPHod9tZX9zI2AyRvnWLCAkKrEIkYPHW0kI+WV3M6snD5clnZTLZSUUSRSQxjEIVCU4rlCoM8WmRoL0k4muoQmWwoNQZCPu8gISnuhRddAK+usgEV1IwcMg+XVVFqHRGbFkdaSraScDZQMDZQDgUwN9QDUDA0UCqoGDCqenExJhIaptMw54t+xsVRlBp0NljEYMB0OoR9gfhBYUKb31lZDmgi0pge6GfzCQr0a27HNa/bQV+XvqsAaUCvn4mmaKKILe9WE2sXcn790fKGIZFiSc+rEMU4bHrYnhn8lGWNzzJdMy08dmD/aiq93Lbq+tRCpBgDmM3CBQ71TS4ggzoGMdZA1M5a+DBkQsWo5qUuH9+ArdjIRiScLhFoq0nb6mjtyq+ZJs7nxxdK96unIZW0DAgqhth+z7ervqCjHAy42NG09EezU3CE6QZEllV6yVICI2gZpC1B0+VvstK5yZm5L5y2P7TdElcFX8OgiDQSnvwvXJJ/JmcYu9HrNr+t/uQm2OkuNxHrF3N+k01xNgkbFYdoVDkZp7DIxEIwqZt9ZSWuxjc9/idM0Emk8lkMtnJ4aSNBAXm3odmzesUdvs/vlSfwZwtFbx+UVe293ua72bFY8BLm8pFnKJcjdjmdObrJuJOGsfVfdIREt7EMfMOdjcI5CqKyMzO5aF935FY/j0gkhwuQ8wdQcP2mewMpzKjth12owNvSIOaEILfCd4GGHgHTd4g5y1P4VN1Lj0Uu/ifezj3broZNkQuOsk7DzIHg84GH4wi27WOJ9t0JbV7V3ijLxii4Y7dbCl38dh327mkTxqn5yVFgrKdx0cy5hPyAAFUGhCUsPQF6HIxpPeDrpdBOAS5Y1rul3Ec6NLKTpdWdsZ0vga3/0qML3UiSaoj2OUUvq3N4+YTPYN+ybOw6DEYcAcMu7+lW3NM7K5y4vCF2O3zE9Qq0ePn5X4BVrsDtC98gRxFKWxYwY2bNDx2xy2c88YK9tYZmKiYRN/MKM7t2Z1BBRvIjDUeMVgPcFGvNC7qdXhdWavhn8l6PLNTEmd2ipRO+OrnUlRKgeqGRoSACy0SD6g/5sPwKADcgTB7qpx0bmWH0vWREkeGv39RKpPJZC2pYe9mwn4v5pTW+BqqkCQRW1o7gl4XQUdkkruQJ1KrXmOLJexzo49ORG+PQ6FU46kpay5FozZaCboaCfs8kclgk7NQGywE9u/nwE2BCAmkMKO7BjDGGfA76gj7PQefDQUi+64pI6pNN0yJGRjjUhEUSpzb9wCRYP33mw28O6uGfp303Hd5NN6GavyNNZgSM1DpDLRKUNMxS0NclAq1SkClFFAIkcO/+XUj5w4zE2NTMmaQibLqEK1bHd/114+1cUMi37nnDU6jrrKcTjesoswp8Mn/9WTRhiquOiO7hVv49zzwVi1b9/h55NoYurQ5OTNVlzvW45X85PuKARCReCv7QW7a+wQrPRsAuLvoBb5tWMLMjq+QsGogYSIzCN+RcjlDbb3Y4NrB+NjRR9y/UlDyTOYdR3wuXhN9xOV/1oj+kfOrUEhErVYQZQUIIhK55DLpwWoCvVbA7Q4SFiWQJJqcQWwWze9OeCuTyWQymUz2V5y0AfuCijraAls3rWYW7Sl3STz3/W40SoHvQyNJi9LTOqOIujIP+vxFDFMKdD79Rmpdfm5ZqKNn3qeIosS7lU7KtqxktnYqAFXdbifenoaiVS/GLbCTX+WCbW4WThrCmNdfoXWMlilDA+zVt8fs8BFr1tIrI4qryh4kL9HC6n0NdA3s5jyrGkNCx0hjDfuH8576JOz4hm59bgS/A7RmPNoYbvtkPUadmtWF9eyrc5MVa6JdooUHxImscdQzcEkNl/Q2kHr9Klj8DKx4CWp2wtWLoOO5kX+/tncRfHkZ9LwKhv21uo8nIrNOjVmnJnDttwSqt5HWYSxPngwZ6eL+CU7EYMu24xgqqY8EVsqIIV9MorOyEMN3V9M9ayyxilKCgpYSMYo9wShCooRRE8lmW6PuSbvUDBQK+OiK46CGrLcB3hrEG2o993V8hc82SrykPJtJ6q8pMHQCH5i0Sq6QZrDm7amIHTvQdcfTkXrJF0+HrCEt3QOZTCb7yw7Un/fUlDYHzH1NtQTdTQCoDGYUSjVBj4NAYw1KrR6tJQq/q4mAswFzUgYhn4eAq5Ggq7F5v6bEdJBETAlp1DsbQBIJOBsxp+bgLMlHG5WAxmhGoVIjiWHUBgsKlQZRFFEoFc0Z9JHjR0qgCfsD/uaUHMSgH31MElGVXgQBbDofjpJ8Qn4PYZ8HZ9lerOntMGoF7jmtBglw13hJS0hkysOJPPpeLbOXutBqBC4/w8pVY2xHfH0+nedgxmInd10aRY/ck3uuoV9KiNaTEJ3Fgud0qDUq+ufFM27oiT8xazgcqdEuii3dkmOn2FdxyOOgFOTaPY+w1ZMPgAolIcI4Q26UghK1oCIsBcjRphGjttHVlMv3Hd9piaYfor7Rz9r1tSTG66mu9RIIHpxDQaNRkBAtEgqDWgXzF5Vhs2hoaAqgUgoM6Z+I+h8YiSmTyWQymUx2wEkbsDeNfY6l7zdwhns+9SF4kMtRKQRKG70AFNV7MZ05mgHrLCzV3oIYDnL268tR25MorPUQKFjOF1nzqehwDQ8Gu7AzNAa7XkXc+pcIrHsZpyGdedfN5YaZxdS6A9iNWlY9FMli31LaxJmvLSPGVMia+4ah1yjxBMJIgoAEfBl7Mxfd2B+UvzqxS+4a+SeGYfVbMOQ+7tzTje+3VpNo1ZEWpWdY03QaPn8fbniHbzaV0+gJsrPSSVmjl9fGpsOmqZHJZHtd8/svUNVW8DdB6c///It/AtDEt4b41i3djH/O4HugwzkQfQJlokkShHxHPflxfrULgAdVU+isLEQSFAiZA3myIJM7RTuJigaKxVjmKSfBRidTrryebo8twOkP8+qiPeg1Sm4Ychy8Pj4HNJUiKFQ0qR2crfiJGmxcYP6IKTefzhpfiAmvzmOS/3NQwLaa/TdjJJGyr+7g51O/YUzn5N8/hkwmkx2nLMnZOEryD81uF8OR0YBAyOvGnJxJwBmZgDTs99KwZzPi/kC/s9yN2mBBZ48j4KhHDIdRaLS4ygtwlRegMliw53TCUbwblU6P1hKNrkMMAJ7a8kjw3hodCcKHQ5Fs/f3fQ1p7PJbkzMParLNFtheDAbomVDL1/6IIVufjbwqh1Br2t9uFs3QPxrhUgvtHCIQ8DhRKFcX1NnYVBYmyKBjW4/dLuewqDuDxSRSUBf9TAfsDhnQ/ub7fHrk2hkanSEL0iXPJJYoSksRRT7Ba4q9s/lkAlCgZZOnBgsYVAIQIA7DVm89q52ZeyZrM1XseJN9fxKSCZ+hgaE13c/t/vB9/lscTIixKON1BgqGDwfrUJAO5bWy43CGWr6lG2v9UOBy5CxMKS8xbUkWPTtEkxP63R8zIZDKZTCb755w4Z49/Ukq0hZSBw2HufMqJ4aah2VzVP5N1RfU8+M02rAY1j3+7A4PJwpnBl9ApoVY0MiTeTGGth7PVq1CWriLFFMN7V04F+kN9If7X5qEOuYn27GHK3CVsLLFT6fBz6xcbmDggk2ijFm8wjEGtJMYUmUn98bM6smhnNQoB1uyrZ2i7eEoavGwpa+K0jomHl+coXQs/PQPATVfsZGOpi7JGL6M6JDDZ8ynKpjAULmXKuAxCPzzOTH9X+uZ1g42fRiaeVWog55Tff4F6XQe2NEjtdWx+AbJ/lyBA7Al2A2L6RNg2Ay7+OlIW6giCYZFL31tDrctPn6xobhySxehyDRSBcMpj0OcGYuZsZ97KZVzOXLoqItlcwcYyAiGRT67szcyNpeyucjG0bdwRj/Gvs6fBxIWg1NBhdR3Xl78JwMi6NnR/fCGzb+rP7Wf14ZUvLuRi6Vva134P3S7Hu3km291mXp69mjGdz27hTshkMtlfo7VGo2msIeBsAEGBKSkDnS0WMRTC31iNUmvAXVUMCmUk+hcON2e7S6EgSBB0N6G12LFldgD2Z+g7G4BIkNzXUE3Y5ybscwMChthkxFAQQREZeaVQaRAEAVtGLuGAn4CrEb/fh84SRcDdBBJoTNbD2u5tqMZXX4VK78KUlIGzZA9hvweN2d5cN9+SmkOZlMUPazyc0cWNSWtm9k8uRBHi7CpaJfx+ibVJF9rZutdPz/b/vWD9yUinUZAQfeJkXgeDIu98UUkwKDLxgkQM+iPX3t/rLeH8nZNI1sQzPnY00Sobb1R+Tn3IwQ9579FOn8XM+oX87NravE1IClPgLWG0fSCvZ93PrPpFSJJIW0PGv9W935WcaECrVWIxqVm2uhp/IHKjobTCQ5MzSL+ecWS0MlFYHEkgcbhCJCfoKa30UlsXZNWGJsaeEtuSXZDJZDKZTHYSOWkD9gD0uoZQp4uYGFITY9ICMLR1FENv7clXWxq4c9pm0qMNLLj3bKqavLy3bB9r9kXqnr4SOJM6lYFB7W6igygxc2MZqwqc2PJm00Yq4Pu125m/3sDl/RL4cm0pP+2uZVelk2qnnyijhjYJZsw6NaGwSLJNz8VdIjUWz+l2KmFRot9TP9LgCRIIiTR6AqzdV88TZ3Uk2qSFpC7Q9VIc6liS42J4/aKufLq6mGsGZaLMfTlS7iZzMHmrXoPaWXRNKITsy+GpyZF+n/X2wTI7v0Wpgtwz4b2RUF8AE3+MTF4rk/1bmkojpXycVb+5ytfrSllZEHlP5le7uKBHKvEXvwvV2yOjUYD7Ruci5d0J787FrAghXTyDM2dJ5K/4kRnX9+Ppczr9K935U5IiExVef3qYhduG0cO3gk/Uj3G1fxLuPQp6dx7CjaEziZEqGKNdh7HrJSjdjYzYOYOM1AWAHLCXyWQnLkurNkjhEAgCiv2TuxrjkjHGpeAs30vQ3YTOHocxIZ2w34O3tiISSAcgkt6qtcYihoL4GmsIepwYYlPwOxsI+9x4a8rQ2mLwN9bib6wh7PcQ8rpRm6woNNrmwL3aYEap0UUy7pMyCXqdNBVuB8Ce3RlvbRmCUokxIR1BENBZYwj53KgNZrSWaEiWCPlcGOJS8dSUoVRH9v3VUoGNu1XYYpLJVQks3+xFo4IHr/7jettWk5KcVhomPFxBUoyKZ285Tm42y/4TwiK4PWHConRIlvkvBcUQz5a8T4GvlAJfKUsd63gv51GWd55KY8hJjj5Symh+x3d4puQ9nip9ly7GttyZciVX5d/PY8Vvsr7r11wQd+Sa9S1FEARioyPzDHTvHM3SVdUcqJrpdAVxuUPExuiaA/ZarYL2be1U1fqIj1aQkaZtqabLZDKZTCY7CZ10Afu3luxl+d46njknjwSrDpXORMwvV3h7MNQXcNaVPxI+uyN5KTYueW81qwvqOXBa2jnVikUfw8pwDpe27sTszeVM+nJT8y7O65bD/LCBOLOW8T3T+HB5EQAJFh317gB2vZr1xY0oFQJOXwi74IJXe+APS1yif41ubTNo8ARRCFDa4OGVhfmIEgxtG0deio12iRY2dnmUc95YQdr25fx4+2A6pdoiB4+95GBfOo6Dqu3Qfiyry0O4bOfSPTqItWwNOErA74TsEZDS7eA2kgSLnwSFCgbcDtU7IqVxXFVywF727xr/GdTmQ+qR68o/9t12Oqy6nSWaPVwSvJcKRQKntI8Hta45WH+AkNINxn+OYIiG1J4EWYKEH1E68sXmcUOhpKL/4wg/DCEaB19qHkE9V4Sk+XzWbRf3rB7JZM9EtsR0wtRuFFRvIrvPf3sCaZlMduKRJBFn6V4EQcCUnHVIfXiAcMBHw57NKFRqrBm5qPUm1EYr9bvXRwL7+6kMFpBEVHojCpUKZ+kefI01AIRULgTl/kC80YLGaMXfWAsISGLku0ASRcSAn4CzAWN8KgFXE037tqNQa1CoNaCMtElQqnBVFhF0RbL2tbZYFEo1Sq0Otd6Eu7KIcMCHOSkTiGTUmhIO1ls/d5gZs0HByN5GtMFaeuSESU0x89G3DtpnqimtDnPmQBM288HsZYc7zEffOujaVkeMTUmjUyQYDBIOS0ddmkQm+7t0WgVXnZ9IWJSwmo98mdh/00XNE8wCRKts9DB3JFYdRaz60IShW5IvIVWbQH9rV0RJQkJCJPL/45nVoiHarqahKTI3lCTBirVVDOqTgM2iptERRKdVolIpSE4wUN8YID3l98tdyWQymUwmk/0ZJ13A/oPl+6h0+Fi+p5ZzuqUc+qQkUV9biTXso6GpkQt6tgPA7Q+jw8cb6pcRtWb6XjUDnfZgDcL2SRbSow1EGTUMbhPHWdo1dEjcTtKIm2kdb+bTib0pb/Ry+1eRoH5ZoxerTsWL53fBbtSAKwxBL+FgmPymRso9xTybshxFYkde3ViGKEFeipXP1pRw99dbyIo18sy5eSgE0Pyqzn2100dJvZduafZIgP3c9wB4d8rPLKg8m/uiG7h6xQ0HN1j1Otxz8KSa2nxY8nTk57zzIxPTumsgpfs/9BuQyY6S3n7kYH0ogCMo8d7SQtZotxIrOBge08ikGy/FpP2dj6w2o5p/nHVDPxy+IInW47+kQEZiLGcEHucbzWQsghdJUCLMuYvOFRuZZoln+eDPIv3udH7kn0wmk51gwgE//qZaAAzxqSjVh2aihgI+JDFMOCCiUKnRRycSDgaQfjFTp1JnwJ55aJ1rtclKwNWIUmtormkvCArMSZkoNDpQKAj5PHhryhCUKkIeJ0qdAXNqDrC/dj6R2vRiMIDKYEGp0aGxRuOtLQcimfyNBVtBktDa41Dtr1kv/GrC+pDPA4KASqunSxsdXdpEMnVrt5dww6Aw8/ZmMH1liB/WQCgMu4sDPHbdwfIZP633Mm+lmw27fbx/fyLP3hyL1ayQg/Wyf53deuRzLW/YxzLH+kOC9RPixvJM5u2ohCNvo1VoGB93WvPj9V2+RqfQoFMc/9noFrOmOWAPkYmD122uo8kRxKBX0jY7UjarfVt7SzVRJpP9DYsXL2bIkCE0NDRgs9mOuM5DDz3EzJkz2bhx47/atqMhCAIzZsxg7NixR7X+hAkTaGxsZObMmb+5zuDBg+ncuTMvvfTSP9JGmUz295x0Afvnx3VifVED/XNi+GrlbsbsmYxGb4a88xFj2nBW6ElUgUae0bUhFvCHwtw0NJtsSU3mtE0QEiDkBG00xXUevt1SzvndU1l855DIAXwOeOoGLkMCy1lAPH2yopEkiaJ6D75gmCkr96FUKuidtT/LxBRLwQWLWVlQR9OP9bxp+IBetXPw1Jv4zncttqS+vHJRV8b8bzkABbVuUuwGVt477LAA5UVvLWdy08PUx2uIunoWaIwA3DQ0myiDhuH9e4P9SqjLh8KfQB8VycKPz43sIDob+t0aySKztYrUPo/OOva/GJnsD7y3rJBw7R4m7rgCsz2DDilPcFPjw7xyipn/634uil/P9fA7jFoVxt8L7h8n7p62mcW7q6mSErgjdB0vdirDuP0LqNgM1lRMTSWMXH059Nnc0k2VyWSyv0yl1WNKykQQBKRwGHdjGQFHHRqzHZXW0JwZj9D8HwQBLCnZhPwePNWlh+wv4Goi7Peii4pHZ4sEvf2OevyOOhAUKLWRm7U6awyiMRipfa9Q4qurQKnSoNr/vMZsw5LaGm9DDUFXA2LAhxgKRIL1koTWFovWGou/aX8Wv9uBJTkLrSUqkpG/XzjgZ++mrUxfZyU+MZorzjo4ttOUlEHI62Z4vJ3yRhd7y4KUVYfQqAUanGHs+7Ps+3fWs7s4QLd2kUB/bubxH9CUnfw8YR+PFr9BQAzwYfUsLow9jWiVjTh1NE9lTGKAtesf7+QX4jV/XBaqpfkDIp9+U43VKKJTg0olEGXVUF3nJxQUUakEPN4wZZUeoqN0Ld1cmeyk8euA8oQJE/joo48AUKlUREVFkZeXx/jx45kwYQIKRSSxsb6+ngcffJD58+dTUlJCTEwMY8eO5dFHH8VqPXw+mj/jjjvu4Kabbvpb+zhevPzyy0jH++hz2T9m8ODBLFmyBACNRkNMTAxdu3bl8ssv5+yzDy+vu2jRIp599llWr16N1+slPT2dUaNGMWnSJJKTk//t5sv2O/4jWn9Sv+wY+mXHMHnGFtasWcF52vmRJ7Z+jSIqk3ev/4lqp59uaZFg+pNzdvLhin1M6JvOA2e9jaK+EMJ+AB79bjsLtlexp9pF11Z2zu2WgqA0UJN9AQmKJlRx7ZqPKwgCk0ZEJv28sn8GurodGHbPgvZnM2drJfdNXc//bJ+zt382wvZ1kY00Jt4Xn2OV6nwMX77E66EarhDu4r2rBhJvOfIJ4DBjIYOcmxBqgdo9kBSpz52XYiPvXFtkpdNfiPx/5xz4/MJIGaCz3+I7d1s2Vni4bdT9GDQn3a9edgJr8gZ59Nvt5Al7mah1IjjKmH1HX1D0b+mmHVOLdlVT7fRzdtdkTuvYHWO7eNgwCEzxYEmMzDERk9PSzZTJZLK/TR8VD0D97g2EAz4Awv5IZr3OHoc1PReFSt2cud5YuJ2w34s5JQdDXCqCUoEkiggKBY7iXUhiGDEcRKU1oLVGo1RrUemNqI22Q46rUKkxJ2c1tyHs8xJwO9AYLbgq9uGrr0JQazHEpRy8MXDgglahwFm6GwClVo+lVZvIz5pDg+mCQoE7oOKqgfVsKPbCL4ox6myxYIvFBNx9mZZAUOKLBQ4+n++kwVHLxaMsZJhrUKmVTLpIviCSHV8WNq7ircovUQsqJCRcoof8HnNbulnHlNMdpqI6QJ0aRg200CrZiF6npLjMTVyMjobGAFt2NGAy/P4E0jLZiU6SJAK1DYh+PwqtFk2M/bDRZcfaqaeeygcffEA4HKaqqop58+Zxyy23MG3aNL755htUKhXl5eWUl5fz3HPPkZubS1FREddeey3l5eVMmzbtbx3fZDJhMpn+od60rL978+KvCgQCaDSaP17xBCdJEp5ggJAoolIoMKg1//r75dcmTpzII488QjAYpKysjBkzZnDBBRcwYcIE3n777eb13nrrLa6//nouu+wyvv76a9LT0ykuLmbKlCk8//zzvPDCCy3Yi/82xR+vcmLqmxVDlEmHiAJJpY+U30jqQk68mX7ZkQupeneAROvBwPid0zbDkidh6nn4ynfQTlFMTpyJxbtq+L+ZW3lryV6en7+b/lvP4B71Pc3Z7b8Wb9FhnTYOpl0Bu+ZiFrws0t7OAN8ihLXvwCmPQqfxuJVmgpKCTS4zUZXL6K3Ywbx28+mjLfrNft1z/lBQ68GSBIl5R1ynuM7D+LdX8Ul5IlhTQWeBrybQb84wJq0/lTWrlh5552IY6vYe5Sssk/1zrHo1tw/P5qm4BUgJeTBhDihO2o+nZlOu7Mn/xnfh+fM6MaxdJJhFl4sgZzjEt4+Us7r465ZtpEwmk/2DNGY77J/0VVCpImVk9EY0JisqXaTcTDjoR6HWgiAQ9DjwVJfgrijCXVWMr6kWpVaPQmfEU12Ko2Q3Qa8LR+keQl53c/b8EUkSjpJdNBVuRwyHEPfXx5eCfvxNdRgT0tBaYzgw02TY69k/Oa4CtcGEQqk84m4VKjVt26cgSdA287cv7P2OBtwl22ifGkKrEahpCPPAW3Vc8SLc+loAvy9wxO28fpHaxtARn5PJjqXBth6MjRpGnDqK0+wDeTXr/1q6ScdcjF3N+afHMu60ONq3sWE2qVGpFGSmmTEZ1aQmGxk1LJnMdHNLN1UmO2a85VVUf/8T9ct/pvHnLdQv/5nq73/CW171r7ZDq9WSkJBAcnIyXbt25b777mPWrFnMnTuXDz/8EIAOHTrw9ddfc8YZZ5CVlcXQoUN5/PHHmT17NqHQH393rlu3ju7du2MwGOjbty+7du1qfu6hhx6ic+fOv7nt4sWLEQSB77//ni5duqDX6xk6dCjV1dXMnTuXdu3aYbFYGD9+PB6PB4ApU6YQHR2N3+8/ZF/nnHMOl156afPj2bNn061bN3Q6HZmZmTz88MO/258tW7YwdOhQ9Ho90dHRXH311bhcrubnJ0yYcEj5HLfbzaWXXorJZCIxMfH/2TvLMDmqtA3fVdXePT3uGpu4uwvEISRAWIIFd18su7hbFhaCBwjubiEJRCAh7j6ZZNyl3avq+1HDJEMS5FtkF+q+rrlm+tSpOqequqdPPec9z8vcuXN/8lp9fz2eeeYZcnNzsdlszJw5E5fLdVg79913H1lZWRQWFv6s/sViMa688koSEhJITk7mxhtvZPbs2T/b8uePxBMOUdRUT6m7mUqvm1J3M0VN9XjCod+szTFjxnD55Zdz+eWXt16zm2++uc0qCpvNRkZGBrm5uQwZMoQHHniAZ555hueee44lS5YAUFFRwZVXXsmVV17JCy+8wJgxYygoKGDUqFHMnz+fW2+99Tc7B52f5k+liL27oYIl63fCwjlMdRbz1mXjUI12yqLxnJX0Kpz8QmvdkgY/w+//mneXb2D1nHH0zUtgXyyNgGCFlEIM80dzRdH5dLK48AQ1/8JgOMarazTfxsL0n5hp7TRBs59Jas/I+EbirQZUQYS0btBlCigyqf4i9qWOZ9CpNxM99W1i3WeSt/8N+Pw6WDUPnhkFNds5UO8luGYBVG6ApPYIN+yHq7a2PlD+kOVF9Xy3v5HnN7rhmm0w4R4A4vFjFSIMyDjKbV84Bx7vBysf+2UXXkfnP2HFQ/DGLK7oa6SbZyVizRZQjixa/NnokuHk+N5ZR599/wtMWujo6Py5URWZQH0lweY6fNUHsKZk4myJeFciYWypOViTMlrrB5vradqjJZtN7jrwYLQ7WvSSt7yIWNCHwXJQmI/4PcjhACAgmY9uUSEaTRgsdox2J6qiYI5rsS4UBAwWG7aULKJ+N6gqJkcCzrxO2DPbYTBbCTXX46+vwlO+F1fJTpRYjIjfTaChGjkawZaURkq3geR16XjU9kOuOmIBL52S3Lz/YDbd2msRZ1FZwBcxoB7FB/zaR+o4584a9pT+Nb4bdf54QkqYC4tu456yZzk5ZQKVkTpWeTdjl/77cwP9GnTIs5KXdfT/JX901KSOzm9JsKoW19otKKG2grISCuNau+V3F+1/yLhx4+jduzfvv//+Ueu43W6cTicGw0+7Cvzzn/9k7ty5rF+/HoPBwLnnnvuL+3T77bczb948Vq1aRXl5OaeccgqPPvoor7/+Op999hmLFy/m8ccfB2DmzJnIsszHH3/cun9DQwOffvop55xzDgBffvklZ5xxBldeeSU7d+7kmWeeYcGCBdxzzz1HbD8QCDBp0iQSExNZt24d77zzDkuWLOHyyy8/ap+vv/56li5dygcffMCiRYtYtmwZGzZs+Mlz3bdvH2+//TaffPIJCxcuZPPmzVx22WVt6nz11Vfs2rWLxYsX8+mnn/6s/j3wwAO89tprvPjii6xcuRKPx/Ojfvv/LXjCISo8LmKH5F0CiCkKFR7Xbyrav/TSSxgMBtasWcNjjz3GI488wvz58390n9mzZ5OYmNj6+XnnnXeIRCLccMMNR6x/tPwOOr8PfxpflN01Hq57ZwuXGj7iWMNbUPIti0e9y9XefxNFIrUxAkEXhL2UK8lMm/ct1yvPc66yiA9fPIfpJ86i6zHxBIaUYjPJxMo34vb6KMzNYHWjh+nWLdy4/kwmUsBs8z1M651NOCZjNhw52orpT2i/3zoTdn2MeOxdsORWqNupCe/DrwRRouuQSwkkxzHqYZFE3yDuMO+i0jCJaVvfRqrZQsm6z3hwdYCnjI+CPY3Q1bt57OsyeucmMLF7xhGbPqlfNs3+CEM7tPhE9jgJvr4LwV8HJ7+Eo3D0kfsstIiDqvz/vg86OkdjXUkTcRYDXTKcbTd8+2+IeKH3qXD6OxByQ0bPP6aTOjo6Ojq/KsGmOvy1ZdoYQ1X43qP+e5RICDkSRhBFon4Pvsp9AMSCPgI1ZRhtcUgmK9bkDGLhAKHmWkBAMphBEBFNZgI12spEg9WOZLaiquoRBTVRMpDYsReqotC4V5sUsKXnEagtI+xuRM1RsWcUEPV7sGfkIYeD+KtLtJ0FAVVVCbsbAfDXlhJqrmvtqz09l2BjNZbE9NaVAj/EkZ5HyGzF0mIPdNrEeNZsD5HkNPDglalYzEeepBUFAQFQFN17VufXRVVkYmVfIKUNRLSlt5bvCuzn3QbNVvSGnHN4rMM/6GJt/0d1U0dH53dCVVU8W3f/aB3Ptt1YMtP+0ImrLl26sHXrkXN8NTY2ctddd3HRRRf9rGPdc889jB6t6SM33XQTU6dOJRQKYbH8/BwVd999N8OHDwfgvPPOY86cORQXF9O+vfZ/8+STT2bp0qXceOONWK1WTjvtNF588UVmzpwJwGuvvUZOTg5jxoxp7dNNN93E7NmzAWjfvj133XUXN9xwA7fddtth7b/22msEg0Fefvll7HbNBWLevHkcf/zxPPDAA6Snp7ep7/P5eP7553n55ZcZP348oIm/OTk5P3muoVCoTd3HH3+cqVOnMnfuXDIyNH3Kbrczf/78Viuc55577if79/jjjzNnzhxmzJjRuv3zzz//OZf/D0NVVWp8nh+tU+PzEGcy/yafl9zcXB555BEEQaBz585s27aNRx55hAsuuOCo+4iiSGFhISUlJQAUFRXhdDrJzMz81fun85/zpxHsC5LtPJy5lB7hYtTMYxB6z2JdSRN+LBSmOxjcLpnwE8MxB2oInvAx06Ofca5RG4h2NlbBy9MojIWgY0fI6oflms1YVJUuO+toWrmBpOguBFGmr1hMh/Auxj4soKgqA/ITeWhmb7IStIiTOk+Ia97eTP/8JM3T3tQSiR+fpUX4N5dAwUgQJebF/53qJ57GLkZolCdTo2RzSvAfsBeUUb05qXcxzekz2PndYvYb2pPcYSx3fLCN9zdWEm81HlWwt5kMXHnMIb7XkoH6M5cRiwTJzMo9+kWceC/IEVhyO3iqYcqDv8Kd0dGBPTVeZj79HRajyMZbxmOrXAXvXwiDLoQTn4Xa7VA4GQx/fn87HR0dnb8SRnscgmRAlIyIJhOWxFQ8lZr9njEuAQWVpqJNiJIBS1LbcY0sywQri0EQsCSnYzDbSOk2GEEQ8FYdAFVBOSRyKRb00bhrPYIkYXYmtSa6BS1Rrb+2DFtqNqa4RARBRBUEzM4kBEBseZiyJKQQbKqlcde6tieiQripBntGAaLBgBLVot0FSUI0mvBW7CMa8CJHI8S3eN3/EMlsxZ6e1/q6IMvI83MSsdmM2GxHH5LPvTqVv/+7jpvm1TPn7GSG9PxrRDnr/PZEdjxDcOXfMWSNwjHtS0KbHiK87Sm6j3uBu/KvJMngJMmYwBlpx//RXdXR0fkdiDQ0HxZZ/0OUYJhIQzPm1KTfqVeHc7SJeY/Hw9SpU+nWrVsbYXvy5Ml8841mC5yfn8+OHTtat/XqddBm+HvRsq6ujry8g9/XP8Whx0hPT8dms7WK9d+XrV27tvX1BRdcwMCBA6msrCQ7O5sXX3yRs88+u/WcNmzYwLp169pE1MuyTCgUIhAIYLO1DQzYtWsXvXv3bhXDAYYPH46iKOzZs+cwwb64uJhIJMLQoUNby5KSkujc+cjjl0PJy8trI+wPHTq0tZ3vBfuePXu28a3/qf5ZLBZqa2sZNGhQ63ZJkujfvz/KDyLX/5v43rP+x4gpCoFoBPsP8h/9GgwZMqTN52Do0KHMnTsXWf7xANxDPz9H+yzp/HfwpxHsLUaJk4Nva9G5fV+EHiey6MulAERiCq+sLuVCm0quIFGYkcjFw7JgHSCIdD3pVvj2EUK1e6le8xntdk2FbifAKS8zsXs6J/TJ4l+bp9PF2cRodT3z1UeYFL6PehJZWdzILR9tJy/Jxg0TO3Pzh9tZua+RnVUeTbA/YR4MPA82vwb9zoIeBzMyV5aXcJ9BW7JSlTiYhHa9WbKzlrQ4M70H9IW0SfQFPvjnLBzmMznj+TUUHdjFc3FvYeo0Bpjws66NNxRl3LwNRGIKX1+XQnbCUR70RBEai7S/182HyQ8c1XZHR+fH2FLu4ryX1nNcr0xun9adFIeJnEQrwy0HsD7ZF5Lag7ca9i+DkddqNlE6Ojo6On9CBFQ5hizHSGjfHQQROegHIBYMosZahAFBxJqSRdjdgBwOIkgGHBl5eCIBBEHCtX8HcsiPPbMdtuQM7Ol5RP1ura7BiBqLtrSnosoxQs11KLKMyeHE5EjEV30AORwk7GrA7EwiqVNvwl4XocYabGk5iIaDCSRjwYOequbENORwCCUSRLI6sCalIbR48JsTUhElAw071wIqgmTAaP/5Sd0iPjdqzU5CZiu2Tn2OWs9iFqlplInJ8NZijy7Y6/y/ea6+kntqSng0t5DpCamISd3AGAeCiPvVQgRLMmqgGqVuLZf1O/LyeB0dnT8vSvjHxfpfWu+3YteuXbRr165NmdfrZdKkSTgcDj744AOMxoPf6/PnzycYDAK0Kf/h6++Fy18qEv/wGD9sQxCENsfs27cvvXv35uWXX2bixIls27aNTz75pHW7oijccccdnHjiifyQI0X+/5joeqTyQ33O/1O+P/6h7RwqzP+S/v2wzq/Zz9+CnxLrf2m93wNZlikqKmLgwIEAFBYW4na7qa6u1qPs/wv50wj2AEx/Gqq3QJepAHhCWlKOksYA/fMTWdvvQ3J7JIM9mayp3aDvZEjIpTxk4XnTtbxXX8E91Y/QzgDs/IgXFq1DtaVww6QumoVq90cwfDaCBMGHUwjgFpOY3i+bt9dVAJCTaGPRTs3T7ZT+udS4Q2TEW2DrW7D+BXBXwulvA+ALx7jp5JGUvX86zW4XHlM+Vw4t4O7ph1uBJNm12ckpPTJocn3K+OBS1PLdELsUir+G/KFgieftdeV8tq2aO6Z1IyvBhslTCpZ4JIMTu8mAKMiYpJ/wxJ7xDLwxC3IGHi7W+xtgzxfaZIbFeeT9dXTQLKoafGHWlTQBkOww8+2N47TcDIvKwJ6mfV7bjfqDe6qjo6Oj81titNqxZ+RrUfYGI3LkYES8GotgtMdjTc3CZItDEEUSO/QkFg5iMNuIBrxIJithd0PrPsGGSpRICJMjHkdmAaHmOoxxifgq9rXWEQxGTI4Ewq56It5mrMnhVmHfYHOgKgqCKBGoK0cOBxFNZmwpWQAocoyEgq74akpRFQUUmYT8LghHSDgrGbXxmTk+mYjPhSrHiAW8KPFJxIJ+jI4EAPzVJSiKjCOzAEGUkMMBJJMVQRRBEBCknx6Oz5mdxPwPXUwbdXgOpQNVESpqY4zoY9WjpHR+lLUBDy45xnq/h+kJqRizx5BwXh2+z09E9ZVjLDgOQ8/LMXY46Y/uqo6Ozh+AaP55UcA/t95vwddff822bdu45pprWss8Hg8TJ07EbDbz8ccfHyZqZ2dn/97d/EnOP/98HnnkESorKzn22GPJzT3ohNCvXz/27NlDx45Hz4tzKN26deOll17C7/e3iuUrV65stT/5IR07dsRoNLJ69erWlQTNzc3s3bu31R7oaJSVlVFVVUVWljZu+u67747azs/tX3x8fOsqhJEjRwKasLxp06YfTfr7R2P4mfnmfm69X8rq1asPe92pUyekI4xZv+ell16iubmZk07SvudPPvlkbrrpJh588EEeeeSRw+q7XC7dx/4P5M8l2HeZ0iZS9/bju/Pwoj3kJFqZP3sAsqLy6LcHGFUo0i8vEbJ6A/Dc4u28/F0pqQ4TX0iTOV5Zi2xJ5M6v64A6xnZO5bFZfQGIZK7goueXUU82fx/XkY83V3H64DziLEYm90hnR6WbOm+IZ77Zz65aLy+fOwi175k0VR/A3O9CHMCyPXWc99J6pvTM5PEzn+S6p1exdn8z72+s5KbJXdqcUnG9jw2lzczom83Zw9tBv+v57NH9fO3pzC1f3kPCukehx8lw8vM8s6KY4no/Ux/7lkJlP+8ZbkaIz8R05TYWXzsKURCwm49+y+s8IV77bANX1WxHdFdoFjmHWpR8cSNsf1fz4Z90369113T+hEzsnoHTYqRHpgNKV0FWXzBaYdAFYE0gkj2UD0tNDIjEozui6ujo6Py5+V4MB5BMFizJGYTdTZjjk4jLbEcsFCBQX4k1KQPRaMJo1UTp76PiBcmACiDHQBAJNlYT9jSRVNgXU4soLqjgrdyHwWLHYIsjFvRhSUjFaHcimqzI4SByJIy/phRUFVtqNtbkTMKuekxObVm/r6aMYEMljqz2JLTvQcOO1cjhAJaENExxCW3OKeJzo6oK5rhEnLmdCHub8JTuJRrw4S7dQyzow5HVHrMziWBTDQBhdyOCgJbwNiGVuKz2JHXuj/gjD1YAUb+H8pJGyuuMrNwaZOyAtpFrNz/VgMurcKsxmcE99Oh7nSOjqioPpKUz0ZnMJLNErGY1howhANhGPU60fBGGzFHEqpaBEgF+vn+zjo7OnwNTSiKixfyjtjii1YwpJfF36U84HKampgZZlqmtrWXhwoXcd999HHfccZx11lmAFlk/YcIEAoEAr776Kh6PB49H8xVPTU39UfHyj+T000/nuuuu47nnnuPll19us+3WW2/luOOOIzc3l5kzZyKKIlu3bmXbtm3cfffdRzzWbbfdxuzZs7n99tupr6/niiuu4MwzzzzMDgfA4XBw3nnncf3115OcnEx6ejr//Oc/EX+GsGyxWJg9ezYPP/wwHo+HK6+8klNOOaXVDudo5/pT/bviiiu477776NixI126dOHxxx+nubn5vzoQwWY0YRDFH42gN4giNuNvY/tbXl7Otddey0UXXcTGjRt5/PHHmTt3buv2QCBATU0NsViMyspK3n//fR555BEuueQSxo4dCxz0wb/88svxeDycddZZFBQUUFFRwcsvv4zD4WhzTJ3fl99mque/hGl9slhxw1hev2AINpOBt9aV8+iSIv7x/rY29Wb2z2Vs51TmntKH27pWIKgyUrCB2QPScJgNzHhyFXUeLSLMlNqep6+bzep/HMP6kmZ2VHkwGyT65SUw4oGlVLlDnBN5nT3m2fRu/hLkGG+UJdC/6Bwu/FZ7AG0ORJAVlQZvmFpPiF3VXuIsBs4Y8gOftBenkvBkTx5992veXFcOgGqJ5zb5XN6PDMRt7wCSCdK7tZyH5iUWiMr4ZImIKuJVzIybu5yxDy8jFJU1C5LFt2rWQT/grXXlvLe1kRBGLYJe+MHbo90oiMuE/GH/6a3R+bOy82NWf/gUfe5czKZyF7k7noIXJ8OnLREQBjP0PYM3iyVueG8rV7656Y/tr46Ojo7O705cZjtSuvQnLlNbzu6vLSVQX0mgoapNPVtKFqa4RBLa9UBssaFR5RgGuxMlGsF14KAHrSUxleSug0jo0JOwq45Y0IfJmUQsFMB9YLsW2R7VBIhYSFsWH/W7iQa8hF1aBL8a03zplViUiLcZAMlsw+g4aHMjR8M07F6Pu2QnntLdxMLasSSDGVBRlZiWdFYQkcwWRIPxoE2OqmhR+2iR/A271uEtL0IQRIKNNYed//f468oxxdwIqMTZDh+6D+hqITvVQF6G8Qh76/zVUeUo4W1P4PtsGuLLuZzgWQ9LTsP34Vgie14FQHRkY+56DqE1/yS44nJCG/TAHB2dvyKCIODs1eVH6zh7dvndRNSFCxeSmZlJQUEBkyZNYunSpTz22GN89NFHrUL8hg0bWLNmDdu2baNjx45kZma2/pSXl/8u/fz/4HQ6Oemkk3A4HEyfPr3NtokTJ/Lpp5+yePFiBg4cyJAhQ/jXv/5Ffn7+EY9ls9n48ssvaWpqYuDAgZx88skcc8wxzJs376jtP/TQQ4waNYpp06Zx7LHHMmLECPr37/+T/e7YsSMnnngiU6ZMYcKECfTo0YMnn3zyR/f5Of278cYbmTVrFmeddRZDhw7F4XAwceLEX5T89/dGEAQyHD/uPJHhcP5mn5ezzjqLYDDIoEGDuOyyy7jiiiu48MILW7c/99xzZGZm0qFDB2bMmMHOnTt56623Drtfl156KYsWLaKyspIZM2bQpUsXzj//fJxOJ9ddd91v0nedn4eg/gHGUB6Ph/j4eNxuN07n72etsr/exz8+2MbUXlmcOeQH/+zq97BrxXtcti6F2xwfMfqYqfj7XcjQ+74iFFX46u+jyU06mNyjvCnArmoP++p9/G1ALkt21XLje9pEwHPGuYyXNiAjIhUMZ8WwFzl3wVpSHBZyk6x0SHUwvW8W3bPiafBFGP+v5VhNEmv+cQw2U0sEvCLDAwUQ9nCp5X4uPfM0emRrD3w17hBN/gjdsg6/du9tqGB7pYtPt1aTagwx9/TBnPTsRgrlIl7vtg6hYi3WYDV1I+5ifmQ8s4cVtHralzcFuH/hbmZ0c3Jsj1xtMsBbA07dy0rnZ+CthbnaUrhjwg/Ru88g7mm3BevnV7Ix/zx6nHYfpogL4tLZVuHm8jc2ckLvLK6d8NPJbXR0dP4Y/qjva53fnz/yXofdjQQaq3Fk5GO0xbXZFvE246uvRA54EY0m4rI7ggDuAzuRTBaSCvu2qR8N+pDDQVRZxpKUjrdiXxtLne+xpeWCqhCor0QwmDCYLRhtTkxxCRisDkKuenyVxRgd8SQUdDt4/IAX1/7tAEgWO4nturfa5cRCfgTRgPSDxGKKLBNsqiEW8BH1u5EsVqwp2XjL9iBIRox2BxGPNkFgzyxAlWPYUrI1y5yWaxBsrkOJyyE+3kZMhlBEwWn/74wa1PnvIrxrAcHll4BgBDWKdcyzRA98SKz0C8wDb8Pc/UIQBERzAuGdzxPa+AC2kY9hzJ/0R3ddR0fnKPzW39nBqlo8W3e3ibQXrWacPbtgzTo8Ylvn/8f48ePp2rUrjz322B/dlZ/F7bffzocffsjmzZt/87YURaFr166ccsop3HXXXb95e/8JnnCIGp+nTaS9QRTJcDhxmn+bCYcxY8bQp08fHn300d/k+Dr/HfylBPsf5fkJUL4GVYXNjhH0vPYTDJJIrSdEOKqQl3xQrN9Q2szZL67FG4rx8eXD6ZWT0Fo+b2kRtqibK5zL6bLrcUgo4EDBKXxmP4GHvyptPcaTp/djSk9NCN9f72NntYeK5iDnDC+gvCnA9CdWcXLsMzplp3D6pbf+7NO44o1NfLJFi9Ca1D2Dp8/sT1ljgMSFlxBX9CEblY64hHheT76cJVVm8hJtLLp2FBbjwYe+9SVNvLmunFuMrxG/+RmY/CAMvqhNO698V8KinbXcM71nm2uj8xfAUw17PoOeM8GiTSLFAm7EkhWIb5+BisCWM7bSJT+bjzZXctt76wlh5kXjg4yRtiCc9jYU/ryEyTo6On8s/5Xf1zq/Cf+N91pV5JaErhqmuCSceYUIgkAsFEA0GFuTxaqqSrCxGn9NKYJkILnLAARBQFVkIl4X/voKDBY7cjhELOjFaI9HMplRVZWwq761jeQuA1qPGQ34iAV9CJKEJSGVUHM93krNK9+anIEjs23Cu6Oeh6rSuGdDa2JcR2Y7rMkZRIM+3CW7UGUt55IgGVv+VjE5k4jPazuhHWyqRQ4HmPOqlbKaGP+6Jo0OOQeXWSuKyrx3XESiKledmojR8N+7jFzn1ydWtx7FVYSx06mt0Xyyt4Lw5oeJ7HgGwZGLfcIbGNL64/viZGKln7HY6WSM14fRGIdz1lZEa9offBY6Ojo/h9/jO1tVVSINzSjhMKJZs8H5b7Yn+V+iqamJRYsWcfrpp7Nz5046d/7fCGD7LQX70tJSFi1axOjRowmHw8ybN48XX3yRLVu20LVr11+9vV8bVVUJRCPEFKXVBue3/Lzogv1fgz+Xh/1/gNz9RGrL95Ml1GMlTJdbFvLsWf0Z16XtDHJJg5+ZT69CVcFpNZBoO/ig1D8/kRfPHtTyajzUnY78yom02/wgJVE3V4w7j/KmAF/vrmNrhYv8ZBsCAt2ynJz23BpqPCFSHWZS4sz0im7mdtNL+OpswM8X7LPiLQjAiE4pXDNei3bOS7bBuL+zxxvjH6Uj2EseSsuq67LmAPvqfK3R+wD//qqIb4oaODWnjgEA0QAAd3y8na921fFqxhuk7S9hbfhSlu5JZ/awgl92sXX+t/niBtj1MTQdgIn30Oj2EX2kHwl4aCQdA1EWr9lKnxSBGV+PY5Q5xn3R0zARRUAFOfJHn4GOjo6Ozv8CgojRkUDU5wK06PbG3etJ7Nhbs505hFBzneZPD0gmc+tDkiBKmOOTMccnA9oDVdjThLd8L1E/IBmwpuYQ9buRQwGiAR+CKCCZbQiShK/6AABGm5NYONDaXtjT/LMFe0EQkAwmYrKMOSEZc0KKdkyrA0dmO3w1JaixKKocbd0n4nUddhxf9QFQVUKhPBQFojEVVZZxle7G5VP418IU9lfJAJxybJxukfMXw//ZNNRwM3ZzAsb8yYR2PEfomysBEQQDqq8SuWELqr+KWNmX+EWR+SmpDPL5iZcj2upeHR0dnRYEQcCcmvRHd+NPSb9+/WhubuaBBx74nxHrf2tEUWTBggVcd911qKpKjx49WLJkyf+EWA/a58Vu+uOSMev8OdEF++8ZdBGzvumMw7ufREc7Yo1BdlZ5qHQFqXWH6ZBmZ0bfHBJsRnKTbDiMIu9cOACb7Ueiy9O6woBz2b3qI/ab+vL+0n3MHJCDJxTj481VzP/mAIIAK28ax+lD8lixt54hHZLJTrDiOHUK3kVvYMjs+YtOY86Urlw9Jh+rxQKHJg3J7MXrmXPYXVKKQRQAFQGY3DODTmkOGn1hkh3aP5jzRrSjtDHAF3nXMGDmDa0e+a+vKccie8gJvEOeoFIonsCJfY/7eR1b9gCseAhOfh66nfCLzknnKMhREA3we0c6dJoANVuh/RgAfOEoFjWGgIpbNdNNrCWt7FPwpGIK1pMpwG3GlxgefpybhyRwRtdxv29/dXR0dHT+JxEEAUdGPs37XAgGEygyqiIT9XsIy1GUSBhzfDJGWxyS2YogSpicycRlHz2duSAImOMSCTsSiAa8AISaqjFYHKiKTKChkljAi2Sxkdi+J6a4JM0uxGjCnpbbUr8Wa9IvswRI6NATVZYRDW2H3paEFAL1Fcixg2I9gogtLQdVllGhNSmt15jHK0uinDElns4FFjKSDUSDPmIBD++tSmgV63PSfr6f/U3z6imtjvLItWlkJOuPBb8GqhxGkH7/h3Zju+nE6tcjJffQ+hGoadmigKot04+WL8KQ0hvUGHYVLq2v456sbJ7o8wyiXbfA1NHR0fk9KCkp+aO78P/i9ttv5/bbb/9Njp2bm8vKlSt/k2P/GVm2bNkf3QWd3wF9ZN6CJAp8efUowrERoMKOKjfbvnqN70ob+FwZgiDAlJ6ZJNhMLL9+LDwzCv59AC5cBskdjn7c0dfTZfT1ZL2+kY1bq1FaHIjqvGGyEiyAgMNs4Ju9DWwsc1HRFCA7wUr/Xj2h1+ZffiINRVifHQuphXDB1wfL1zzDre6FeNqdwwcHJKxGkdGFaXy6tYYt5cupdAV54eyBVDQHKarzUtYU4PlVAW6YMonvHznumN6dB77YzVWhy8g0h/j77FOIs/7M6K3a7aBEoX7PLz+nvziqqh6+nKqhCJ4bB6ld4PzFR97xwDfQuA/6n/3/E/Vrd4AzC6yJbcv7nan9tJCflkhj39NRd75JU94prPNVMm7qHMjNR7GlIgbqcdhsLBlZS86xJ/7yfujo6Ojo/GUxWGwkdx2IIIgo0QiyHMV9YOdBATLoI7F9D0x2J4kde9G8byvNQR+JHXsddSmyIIrEF2gRWw271qHKMoLUYq2jyFrCWJNVmxwIuAHNWkeUDDgy8nFkHDnp248RdjfgrdiHNSWrdX9VVfFV7UcyWzXBXo4hWezIkRCB2jKCDZWAQGKHnoSa69hdIbF2r4QvGmB0fwegRembE9OwWDRbncwUiZvPS/5ZfVJVlZKqKN6AQpNb1gX7X8iRxmeRvW8Q+Po8zP1uwDro9iPuF9nzKoItHWPu+F/epiIj129ASu2HILa9X7YxbZPIWQfeQrTkU1R/NWL6YISoF9vY51ADtYTW3QnAGCGVyV3vxpgz5hf3RUdHR0dHR0dH57dDH5kfgsUotXq5D0uLMKz6VjDBiVInBvQbgNmgbatvaMRRsxeLGkQIuVr3j8oKcsiLpXEX5A5uI5Lef1IvZvVOoHftB/gysxFTCwlHFb7aXcuqfY1sqXAhKypLdtUyuP3Pe9A6IkEXRLzgrgDg7k938sX2GhYKjxEXrCCR9giM4WbmM7iklCbhNNY1d0UFKpsD3PzhDgBOG5QLCCzcXsMJfbIBOHVgHh1SHby2JpWTR3ekU7r2sPjiyv18vq2G+0/qRYdUrcwbijLz6e+wmiTevHAI5mmPQ98zoIMeXX0okZhCRFZwmI/8Ufz3kr089vU+LhvbkWtbLI4ACDRB2AOuUqjfC4kFYDC13fn1UzQ7o/hc6HTsL+vYviXw6kmQ3gMuWakllN2/DLpNA6P1sOrJ21+AWBBzxSpmui7nllKF83JBHHA2bHoNs7eK2Jr5LLBO4tSMSko/n8sXCWcw4ZjxR0yerKOjo6Oj8z2ipH1HSmYLUZe3VawXjSasyRmt9UKuRk3QjAQBFdDGYaoia4K4ImOw2NscO7FDT6ItyWAFgwmLM4VgUzVRn4to0Icqa1HrciSMaP3/D5vliJa4T4mGURUFd8ku5FgEJRJqU6+0OsqqIgfju3tJtGuTB2FPE4H6SgZmGjluWHsm9PYTDXhbE/Q6sztw9nG1TBwUIrdjFmajgKqqeCuLURUZZ06n1gS2e0oj3Dm/gdH9bFw4I4FHrk2j0S3Trb2+jPtQAiEFo0E4Yh4AVVXwfz6DWOUy7JPfx5h7TOs2xVsKqMjuYuTmPUiJba0OYrVrCCy9AASJ+HPrEIy/LA9UaO2thDf/C3PPy7EOf4hYwxZUfyXG/CmH1Y25S1AatwIgly8CJYLqOYCY2BlD/hTk2nWonv0EV92AaE2ltu479pW9z4qu07mi3SUkGPTxmY6Ojo6Ojo7OH4Uu2B8NeypK4RSW76pkRyiO6zprSZj21nqxfnAhqWqQUjWd/Oz+AMiKyqRHV3Cr9w5GsxHsaXDJKnCkAuAwG+hT9AT2zfN5QrIgXlbOhMe/Q1VBDTbxcsorfODuxPS+I/6zfucO5Ithb7G8Cm7yR1i6p45KV5ALxdnc09dFQ2Ay2XsOcLr0Ncjwqule5gqzqe82m2O6ZrC9youiqFw6tiMjHljK62vL6JWTQLsU7QH31o92sKvaQ1a8lbfWlZMRb2FXtQdFhaW761oF+0ZfhD21XgyigD8sY7YnQOHE/+zc/lep2wXrnkcZdBEfV9jonuWkU7r2kD31sW+oaA7y6ZUjWq8dNdvhqztp7nYmjy4RUYGt5S5Ae59JogB5g7XVHftXwBMDofuJMPPFtu32nqWtbMjs9fP7Gmji5VXFROubOFcyIdhaJo8+uQr2fgFNxWxofzGzX1hHYbqD9y8drm3vPBmKv2Jl5hkIbthR6dbKx90Mw65g2zv3cOvOLBpWHmCM4wk6N37NhlqVOwJZvHXR0P/XZdXR0dHR+ethtMUhmSzIkRBKNILZmYyqqsSCPgL1WrCCIBkRBE2gjoWDNO/bAi0rHI12JwnturceTzJZcO3fgRKLIJltmBPT8NdXgKqiKgqCKCFIBgxmy3/Ub2tKFrGgH0GSUOQo0YCndZstNYeQux4lEubttU62lluJygJnjgpgdiZjciYT9bsx2hM4Z7yMt7ICT1kdyV20MagSixKs2U8CsGO3gztfCnL1tCg9krVkunI4iMGqjeNKqqK4vArbi7UJhMwUA5kpf83HgciBj5Br1mDqcSmxisUY209HNCdSVR/jiodqSUmQeOqmdERRE+0jRW8SKX4PQ+YIYuWLAFDcRZB7DKoSQxANmPtej5QxjMiOZ/C+1QfrqHmYu53X2qaU0AVD1mgEexYYDg+AOBpy0x5C2x5DDTUCIFhTtRUaH42HqBfHtMXEqr4htPkhzD0uxTrkbiRbCmJ8R23VCCqqrwK5eTdSSi8ck99DbtqBf9HpKK49RPa+BvveoHcszPOGZp61pHBD7nk/3ikdHR0dHR0dHZ3fjL/mCP3nIBkRT3uD2rVlnNcUYFC7JL4rbuC059bwosVFLmBxprZWlxWVJn+ELKUGRMBfB80HWgV7gEWeXKarEFUUXG4ve2t9AHRq+IoC12cMdmZD1h2HdWVDaRN5SXZS49pGP9360XY2lDbz7FkDyE44OOi/bKmMokLZ6xt55sz+/P2dLext6osyegiPpcUx7+sMXlk9k1nCYkxhF1OMGzhh47HkJto4b0Q7TntuNb5wjCk9M4jE1BbrHo1zhxfw6dZqembH8+SyYkJRrS2bSeJvA3Nb6xWk2HnjgiHIikogEiPJfjD6+9Ele1EUlWvGF/41Ms0vfxB2vE95bQNX753JJc6V3Ni+FGXSA3hCUcIxmWDkkERfW96Aoi9xyhF6516HJxjjgZN6Udro5/jHv6V9qoMPLxsOWX216HoAwxGEhOP+Bb46sMRDyAOBBkg6uq8vIQ/q4/05MRBgXHguvc9ex4BMAyz8ByVyCtmWFIw5A/lgYyW+cIyNZS5OevgT5pw4hLvrzicu9WLGdkhD3bWT3TXeg8e1xNMlzcLcpkU0HHscBuE6Fn4o8pnleGYNyvt1rrGOjo6Ozl8CyWQhsWNv/HXlmm+9IOCvLSNQr9nHgCbKf48qx1rFetAS1/7QykRtSbapyjHCnoaWCH6BiEeL2Dc5kxBEqU0/VEVpjXIXDskZpMgyntLdCJKEM69zazuxoI+ItwkAg9lBXF4h/qoDiEYTttRsbGk5uEt3M7ZbmKgsMbRDACUaIdhYjcmZxMb6fJ58qpmzJhkYkevA5IhvbVOQDFiTM1DkGPt3S8Rk8PmjkAwGi71VrAc4drANu1WgQ5aAEosiGowt/Y7hrynFaI/H0pIU989OcPkVqKF6YnXrkau/Ibx1HobM4UTaP0AkpuILKqiH1A9vfhS5cQtiXAHYszCk9MHU/UIi+94h8NXZmPv8HevgOzFmjyK69zWAw7zsBXM8jmkLUbxlCIKA4qvUnjmsaUftZ6zqG3wfTwIUEA04z65Ead5FcOV1CI48hJgf0VlAZNlFEAsS3jqPWPVKrEPuBlXG1OkUlOZdRL0lyPUboNMpAIhx+Yj2bASDDXO/myiNT2VnyTtUZfThpqTRv/LV1tHR0dHR0dHR+SXogv1PcGqLoOgORrnk1Y2owB3GK/k0sJKdseF83lLPZBD54qpRsPUmWHU79DkNcgcBUNYY4K31ZYzp2JsHvPcxbPAwBjkTkAQBWVVpyptEQWh3axLPQ1m0o4YLX9lA5/Q4vrxmVGt5pSvIx1uqcAWibK90txHsj+uVxcdbquiXl0gwovDKeYNxmkQoXwvRvlw+rhOMmw9hH/KmV7ltsQNJFBjcPomSBj913jCbyppZNeeYw/ozc0AuMwdowvx7lwwl2W7GF46R7DARZ2nrZz+kfTKTHl3B3lov71w8lP75SZQ3BXh0SREA0/pk0zHN8f+8M/9DDDwfogHEHmeT36hyfuwj2F2B2G40n105G08wSvvUQ67DkEtAiSH1PpUPs/q2Fm8sa8YTilHWFDgoNvT+G7QbBY4jPOh9+29Ychvk9IdoWIu2n/0JtBt55H4KAoIgYjIYmNUnn9ysDN54/CJmRT8kpmQxgzmcvs3HiXl+elZ8S2LTZo71rqLos2PYXHkekijw0MxeWIwSg9od4nuvqhjXPkV7OUJ70wHoNJ6cbu8w6Ve6vDo6Ojo6fy0EUWz1gY/43C1iPYhGI0o00kaMN9riNIG/vpKoz4Ujq33r9ojPTcTvxpyYRtTvwZndntj3FjUCWJIzAQFb8uHJOP21pQQba7AmZ+DIbNdaHvW5WqPnVUVGaLHzMVodiCYLSiSEaDIhSgaSOvdHVWRiIT9GWxwJBV2ZUADjxgUJNRsINlQiSBJGq52ici+BkEpRpcrxY3q2vR6C0NqHE7NU2mWZ6ZyfiZl0jJa24yxJFBjaTaKpaDNNjSLJnfshiBJhdyOh5joi3ua/jGBvGTCHWPVKDFmjCTbvQWneSaR5J7k9LuHpOR2xW0RtVWML1uEPES35BHO/G7ENf6i1XPGUgKqguIsP1h39JJZBtyHasw5r1/fZCcTKF2HqcRmR3S8iSGacp+9GMB3FgkYQtDGaJQ3LwFuQG7fj/3QKqC3BHmlDCK7+J6Y+NxDacDf4K5BrVxPa+BCK5wCxskXYJ72NIXssxk6nth5WbthCrPJrEEREWxrD+t7JsL53cv5/dll1dHR0dHR0dHR+BXTB/ueigqKq2Iwij5w1mrmLMjipcxosux//hjf5p+E6Tpo8kZEjZ8Ow02HdfCj5FgpG8M8Pt1G7bxN/N91EL4MNY69iRKPE2xcPYdmeejrk50PXeUdsNt1pwWIUyUs+6HFZ3hTgmH8txyQJ3D29B+O7prfZ57FZfXloZi/+tWgvx8/7lhS7iX/aP2SG51X2ZM2g84ULANjZqHDDuu7si/gAlbQ4Cx07OHjh7AG0Szn4gNfgC3P3pzsZ3TmVGX1zWsv75yf95GWTRAFR0H4AchKtXDmuI1FZoUOq/Sf2/i+negtUbYIeJ0PT/jbWM6qqcs9nu/BHYtx5wlCMpw0nF1jeC9j3byheCr3/RorFTIrjB76x8Tkw+YHDmuuXl8hHlw0n2WFquzLBebiQAMDaZwEVxVON6MwGUQLDIW1FgxAL4VYdLC+qZ3zXdKxXbMCoyASX13Hf57vY7RvCANM6OomVPCXOJXtLNdGtRvoR0Y4hQMfQDqb0zGBohxQynFZO65MIxkPurSDAKS9Dw149h4GOjo6Ozq+MFgMtWew40vMI1Fdgjk/BVbKTWCiIaDQTn1dIfG4nlFiEUHM9ciSEZLLgLt3d6ocvmW0YrA4MVgcoCoosY7Q6MOV2OmKrosnS5jdAyFWPt2IfBosNe3p+q/c+aJMMyYV9URWF5uJtyOEABlscsaAfVAVbajb2dC1IJBbwEvG0WJ8IEoIocdZUJ10LTPTtfLC9aMBLoKEKW2oOxpYoekkUGNDt+yCOg1H4bRC+14APjiXMziRiAS8G+/++b3n0wCcgSkhpA1EjXqT4g6sL3T6ZJ95x0b29iRNGX4K5xyUAmLtfQHjbk6hyGCmpK9lHOK4haySGrMODHsx9rkVKG4AhbUBrmSBKmu3ND1D81QftdIK1IBpBMoNwyCqNcDOCZEHxlqF4ijHmT8F5xl5URSa05maoXK6J9YIEqozatJVo3WqipQshetBqSQUMuRMxD5iD6MjB1GkWgvnge0LKGIZl2IOI9iyEX2DPo6Ojo6Ojo6Oj89ujC/Y/k3ibka+vG4OiasL2pWM6khlvgQUvYPfVMkF+iTkfZPHtjeOoX/8+qQtvxEUc0+2vIAgCAdVOE3HURROp3edibDcrq/Y18vjX+9hb6+WJ0/phkLTB+qurS1m+t55/TulKKCqz5dYJbChrps4bIi3OglESMRtE0k1hZu25CrEul0/y5/D8twe4bEwHihv8zOibzc4qzUe8wR9hbcjMDCPs8lr5Pv3V8r31bK/0IAraavGorD20juvSdgJg4fYaPtxcxYay5jaC/fcs3VPHVW9s4rwR7bnq2LYPtu9dMgxPKEpanPaAKQgCpw3O57jHv2HZnnqGdUymV04CJ/TJ5kCDD4fZeJj1z38tb50BrjLUja8iVK6jZNDtfJM4g0+3VnPdxM7M//YAAGcNLaBrppPtlW6+3l3H2cNH4+z4C5LAyjF46TjwN9D7vEVgO3qCsn11Xi5/fRPds50Mijub1OZPeFO4jGfPng5hL9hbouZUFZ4eCe4KXs68j+D+7ygZcBpXzhhDcZ2PZ1fsB2DuzOMRTH1g+VXYs0fTtO09YuYk4jI6YK/8BiJ+dtkH8vm2GqpcIc5M3Q+vnQxdpmoi/cvToaGIihM/4Iadicjb13LGkHyO7334Q6yOjo6Ojs4vxeRIIKlTX83aRRCwqllIJjNRnzYGkmMR/LXlOHM64K06QMTThL+uHNFoRjQYUKLaBLQcPrh6LeSqJ+r3IAhgScpAELQkrr5q7XvdmpKN0eYkuetAzWJHURBEsdU2R5CM+OvKkKNh5EiQWDCANSUTJRLCkpSOEtX842MBLWACQIlGW88p5G5Abon0V1QFVVWxmERG9m37/R9oqCLiaUKUDBizOxx2bfy1ZQQba3DmFWJyJLSWS0YzSYX9tKjtlj6LBiMmZzKesj24m328uz6RSUPtdMw1sa88TE66EYtJPKyN/zZkzwH8X2qWL1jSUEKNRI79hrfW5xMIqQzsZmHlliDb9oU5YbSWT2jllgDNXoXjRlz6y9pyF+P7eCKG9IHYJ7zxo3Uj+94mtP4eDDkTwNEOfAcQ7NlaZL1oQDBqgTKyqwjvu4MRbVnsceWzz5XHCadKWNtNILLzBaL73kawpmOb+CaKt5TI9mdRk/ujHHgHydkOBAmlYQsgo3jLUF27kFJ6ESv7kvCG+7COfAxjwVS8H4xFcrbD3OsKQhvuJ7LnVayD7kBK+QV5j3R0dHR0dHR0dH4zdMH+F/B9JPTq/Y3Mem41SXYTG7uNhm1vU60mc6v8BIHH/smFtWcwR+rCASUDmoo5oGZyzbGDGbzkSWREntJ0cTqlx2EyiFiMEl1uWcipg3K5e3pPnl5eTEVzkAZvmE3lLqb2zOSzbdV0zXTyxVUjyYi3sOqmcVjfPAnpwHIoEfm04W9sLndz56c7KW8OsrfGy+3TejDjyZVkJZgpNCZBPUzM9Leez1lD8xGAN9eWEQx4SAyWAL0IRGKc/NR3gCa4H9crkz01XkZ0artEOhCJYZREdlZ58IRibCpvPuyaWYwSFmNb31dXMEKjP4IvHGNXjReH2UBFc4CHvtyLURLYdOsEHOZf96359PJitlW6uXd6T+JtxiPWUVWVhdtrKMyIO5gA9lC8tVC5HgonadHq3aZD8dfUiKlkAs+urWdDfBl7ar1srXBz1wnd8YVlumZq0Wo3f7idzeUuJFHgsrEdf37nY0Go3AhyGHy1YPvBygZVhaJFkN6D9SUyu2u8eOrKuMn4GHVCIm5LlhZZ3xJdv6q4gYw4E+2jAVCinBD6gDzjcioafWx88SPu2ZvLsV1G0jc/kZP65wA50HMt97y9hfcCYyAAyRETG255C2p3UvD5LbyV+DRVgx+HwHpQYpp3vqJA5QYIe9i0bTurirX2DzT4dcFeR0dHR+dXQ2pJCPu9l73ZmYxoNLcK4xFfM01Fm5HDQW0HVdWsaQxGDI4EYj6XVtxiYWOw2IkGfET8XnzVJcTldsJodRBqqgUg7G5ClaOY4hKJeJuxJmfiyCzA7EwiqbAvzcXbUOUYIeqIhQKgKkSDXmhJYmvPyMdXtR+D3alNFMSibXzmHZntCDXVEmyqRTIYUWJRJKOJaNCPu2QXZmcicdkdsKXmIEoGrD+w7PEHFWwWgWjA12K5E2gj2AOt3vWHIkdDgEpdnY8vVplo9si4vDK7S6O0yzIy74b0w/b5T4jGVOa93Uy8Q+TcaQlHrecPKqzZHmRQdysO2+GTBrH6LahRL8asEYi2TAw5x6Ai4qouxqh4eWNJlCW7AwDMGOvg5GPi6JKv5VYKhhXuW9CEqkLHHCNdCn5+0IjiLkb1VxKrjh5xeyCksHlvmP5dLMRKPkNx7SXi2tu6XTQ6EM3aeatyhGjZQgRrKihR1FiAJ8rvoT6YQtLuMoZ81w3VX4Wh/cmYOp6Eqd0JAJh6XMH5d9cw2pDCCTyJscNJxM34mmjVKkLfXI7gbI+pw8mEdzyj9TlQg+KrRPWVEQs3oe54Drl+PQAhcwL2Y1782eevo6Oj82tz9tln43K5+PDDD9v8fTQ2bdrEvffey4oVK3C73eTl5TF69Giuv/56CgsLf7+O6+jo6PwG6IL9/4PMeAvJdhPdspww/Uk2pc/g3k9jbI1cgK0phFN1c6t4BZ+IVzFNWsVM+/PE7XiN6wz7eMN5PjUuLWpqUo8M7p3Rkw2lzcQUlZKGANTu4NExBhY3tUdVYHOFi/apdqxGqdXvvdkf4f1NlZzlqwdgYfwpdMpOoVteOk8tK0JEISvBgs0s0S3TyeoDTdQkaA8TVvPB5K92s4GLx3TgglHtEV6YgPjyWpj1Fp6MMeyp1ZKGuoNRMuIt3DW9R5trUN4UYPK/vyHdaeazK0bSLsXOwIKftsgB6JLh5OPLRiAI8Py3B+iVE8+LK0sAUFQ4xC70V+PfS4oIRmWO75XJpB5HtpD5bFs1l7++ibwkGytuGNtaLisqMUXB/MRACLmh81SY9TpMuAu4i9cW7uSNomlcaV/CFeGrWTzsAU4cmHvYpMOpA3MxiALHdP2B33xjMUgmSMjliJjj4LwvtQj5tK6Hb9/6FnxwEWT05MTzVxBTVFwlm0ne7SXJpPDyOf2o84RYX9qM02rgjPlrcVoNzOz2HCNyTSi1OxHcFSTGOcjZ9QJ3G/M4eX83SpoCXNA5iMmRAs5M1JYoQLtJap2EQDTgKF3CYCDSwQSJJ2sTA6oCogjnLQZvNaOzR3CesJd9dX6m6WK9jo6Ojs5vgMFiB0FAsthwZLXDV1NC2NWAGosix7RxkGA0o7YI+RhMxPzaeMdgdRANeDHHJWJPz0U0mgh7tUAEJRJGlozY0nIQRANhdwNyWEYyW8HbrP0GYiE/YU+zlugWQFWxpeWgxKKEGqsBzbpHlWNIJgsxvxvRZNa+XQ+xpzGYrTgyC7AmZ9K8bwvNRZtIKuyniftylGhA67PRaj8ssn7FxgAPvtLEpKF2Lp3RkWjAi8mZyM/BmpSBwWzD02RkaM8Ak4bZuf1ZzZonHFF/Yu9fTnFFhCVrNSH9lGOdRxTjAV78xM0Xq/xMHBLhylMPnosaCyF7DuB7byigYh33IubCU3Ec9ykAd/6rjMqqZq4uuIlJveBAx1fokm+mS/5BUd5iEpg81E6jRyY/s+0kxt6yCDlpBmyWI/fLmDcB++T3EZ3tj7j9mfddLFkbYMYYB+eOvw8ptR+R3S+jNO9EyhyBdeDN7K+M4PUrFLofIrzhXiIdLsE1aAkZOVmMDqxia12Uju7HUL0lAMRKPkQwOhBt6UjJvUGyoqpQEe6MKpqQknshSGZU914Uz34EexZSSi+sI/6FlNIHKa0/htR+2I//AtGaCkDIkoQacWPudcVP3zQdHZ2/DKqiEC6pQvb6keLsmAuy2iRZ/6P59NNPOemkk5g4cSKvvfYaHTp0oK6ujnfeeYdbbrmFt95664/uoo6Ojs5/hC7Y/z/IT7az4Zbxra/7jpjCEwk11Mtv8ca337GtsT9vntkZ/2sJuCISFU1+zjE/imCAUlc6d3x6DENyzSTsfoMnlsVzQM3kmTP6MTRdhad7019V2D34Y/r06sF5I9rxyJK93D2jOyf10wTdR5fs5aXvStnX9U6mdHJz8VIrKb5y1t88Hql+B+fuuRipeihPrn2A1QeaAHjGNYiukxYxfWT/w85HEgUwOzQvTKOVjHgLb180FICMeMth9QECEZlgVMYViDL/2/18sb2GGyZ2ISPeQueMuJ+8hj1zNA/NR/7WByo30mHPYq7xDeKOkwZiM7W8LctWgxzRkqr+hzw8sze7qj2M7aKJ5aWNfhasKuH0wXnsrfXx2upSzh3RjuwEK90y47j94x2cM7yAQETmzOfXEI7KbIp3Ygi5wdTWe39/Y5BGnIyMfkumXMPM9EqsZgORmMJdn+7EaTXw4aYqshOsvH3RUKKKwvZKNz2y46G5BJ4YDEYrXLtTE+fDPmjYA9EQvH8hDDwPRl7b2p4rECHeamz1nt1WG6IDVvyKk1RinDEkH/KaoK4ddJuOZLFz9SurWVXcyGVjOpDiMJNgM/D8BhcLi62EY9k0+O7iWtFAH3knn6tDCcoyzuYdGJ/9B9iS4boiHjypF1cfU9gmnwKphSwURpItl1Oxq4bJw7Lg8xvAWwWnvwudxkNaF5zALcd1/4/vo46Ojo6OztEwxyeTGp/c+jouqz0mewKKHCVQX4VkNGFNzcZbrkU5K6GDqw5jQR/eiiKk9j3x15QS8TZr/vcF3VDkGO6SnYgmM9bEdJx5nZGjYUKNNcS374HJpo17POVFyOEg1tRsYqEAUW8zktmCI6s9EU8TSjRMxOci6m0+aHkTCRNf0A2T43C/eUGSWoR8oeX8UhBECYPl6NZ4Hr+CqoLHJ+OtPoCqyJS57CQnGEmKl466H2i2hSZHPB0ccPN5NlZsDJCZLBEMq9x3mbbKUlVVVm0Nkp9hJCf9yCsWfy6d802cNdVJgkNqFes37AqxpSjEqROcvLPEQ3WDTN8uZlZuETEZYcEnbmZNdLJp834qlz3M+IL1IBpAiWrBD4dQ3mDASIRCwzKIQGEfP5BCbWOMBZ+6ycsw8t7XXo4f5eCyUxJx+2SaPFGyU418vc7P3Nea6d3JzL2XacJ2g0smHFFYvT3EJyt8XHN6Ir07TW69Lr6ASpz9oJhl8azDKHbCEHMh2guw9L6KZYFz+WhvHZeOiadzVOW6R+sJR1XmnjqIDIOVGvtkGj0OYlUeZnA2M9LAYB9DzA3Ys8BfRaxqKdE9CzC2PxH7hNd44sZ0QuHzSIy/sLVtMW2g1i9/FbK3HGIBgt9cBZKJ+LNKMWaPaa1rP+aF/+g+6ujo/PkI7NhH02crkD2+1jLJ6SBp6ihs3X/BSvHfiEAgwDnnnMOUKVP44IMPWsvbtWvH4MGDcblcf1zndHR0dH4ldMH+V2JSjwwgg/N7j+H8ljJ1zh7q6/0Yn19HfSSeBNVPevfRTBOz6Fj8Msbv7uM+Y1dOjdyCKAg4452QkIfXH+SBpVXk7lY4bXAeb64rZ8mu2lbBvjAjjoJkG4N7FdK/ezoXK/von5/IhtImrKF6bASpKd/JJw2V3FBQTInUjsXVJvLaF8IhCdDWlzSRaDdp9i+nvY0acvPlgShdGvz0zz9yNNb3/q6dM+JYdM0oatwhbv5wO66Gai54xYuswJJrR9Mu5ecllK12B4ksOI9R0f1smJILvVvSfPnq4cUpWqT2MbdCXCb0mXXEY8iKyoq99Qxsl3RUK52pnSxMFb8DNQ+w8sTSfby9voJNZc1sq3Ajq7C53EW/lvNesKqEUFRmX52PBl8EAdg361u6GOshqW00270zejKlRyZL9z5ItbyHdh1PZH+VG18oxiurS1vrNQci+CMxbv1oBx9squTmqV05v69DE+nNcVriMYB3z9EsbrocB54KKP5aE+yX3U/Tls+ZXnM2wwYM4P6TeoG3hsJV12FQY9jqVsKLkyEuQ0uA27SfojVfcE/5ZPrnJ9JQdYATxG+4/sYzqQ/BCf9eSmd5L2cPSGKvz8KJk49lniWXbsk2nk+y0k7KR3jHgepI58udtQwsSGoj1m8qayY1zswIwy4cagPZ6lagH+T0Ry6NsqjSzNgC+TBLJB0dHR0dnd8DQZSwJGpiqy3l4Oouk2MQUb8bT9meg2VxCRiscfiq9hP1a4k7lWgYg9WOHAkhiCKqouCvLUOJRZEjISItEfgmWxyqqmK0xaEClvgUSBAIGs1YElKJBv2oigxAsL4S0WxFsjpQ5BiiILSxw1FVlYjPhdHqQDQYSS7sh6LEiPhcmOISMTt/fDXjcSMddM43kW71EK7VgjYefLeamGDhxduOkqT+CHy7OcADL2v7P3FDOimJ2vhq5ZYg9y1oIi1RYuJQO706munW/sg2Mm6vTHFFhL5dLG0S3H6PIAgM72WlujHWWjbv7WbqmmV2l0TYsV/LL7BpT5Bpo+L4+BsfvoBKboaBR98woaj/JMdxKQPO+xrCTUjO/DbHf+iqVPZXJLB054t0LTDS6MnEKcdYstbPik1BUhMjBMMqe0ojxGSFKx6qo9kj88i1aTjtIqIIiU5NgI9EVS57sIZgSKUwz0i9S2bXgQi92osEFp9BcaXMzdse5toz0xnT38bSdX4+3d0dUHnnW4HtFXV072Bmf2WEskYz974SZtJQN4X5Jqy+TaTHR0g4vwmq9xD3+ZkE4gZj7DQLKaETxo5/I7zpIQI5ZyAg4wjvJfjNlUSMGWzZGmRwd0vrKgBViRGrXA6GQyZ1VAXBmqqtBBCNRCu+xtRhxs9+L+jo6Py1COzYR/0bnx9WLnt81L/xOamzpvzhov2XX35JQ0MDN9xwwxG3JyQk/L4d0tHR0fkN0AX73xBBMtApI54vrxrFde++S//8JK753ru8PIa6+yO+rBoEQERWWLq9lLymMIb4XPIzU6nxhnlksRYFlmQzsXB7DXd+soM6t49Cq4/pfccSfOciLtz3DR9E/s0F30VIsObzWfh26sRU+gWXcKlvHo229rwduJvvihvpF+8HayLb6mKc/PR32EwSa/5xDHEWIx8Xhbnqzc20T7Xz9d/HtD2Z+r1c+84WPq108OZFQwiEZR77qohg6XoeM86np6WED8QJ3KacT+qGR6BuA8x4Bhw/sH/5Aav2NbI9MJwZJiO9Oow7uMHihOz+EPbAV3doZQUj2tjGfLmjhkZfhBVr1rK2Kkq7/Hzeu2TYEdtRP7sOYfu71Pe8iDPLj2Nw+2RGF6ayrqQJWQVJEPBHZDIOfMC1HaoxdjyDUwflsavagz8S47IxHemSmQAkHDzotnep+e5Nvsi+gkF9+vDvrxKY0H0alz+xGlcwykn9sslOsNAvL4ELEzfw9FaVYfd/zWV5Zdxh+JIE8RZwtIdrd8HGBfBgO5g6l6A5BTMioc7TsXWeDHlDibx+GsK+r0hSQgwSd+MK9Nb6YLTiFuIxqwHsYgypcgMCKisTTyC17z+5ZHUCxXvquWJcR/5e+w6s+oqIoRF/r8u5PPwsp0lfw2oYZbTB9BJuPb4bx/5rOfvrfbx7yTBMF27lkte3seXVjYzrksYLZ2sRWxtKmzjpqe9ItptYP+tZKF9L0qC/wQ4twuFK20N8ttDLHKGEizKL4ePLYcQ1MOSSH30/6Ojo6Ojo/NaIkoTZmUR8u+4E6sqxpmRhjtMm7IONBuRIuNX/HgQCDVWoioLR6kARJULNdaiqZhEjSAZ8NaVamRzDYItDNJpwFW9DVRTkaJio92COH1WRkYNaxKJoMCLHYsihAIrBiGSyEGyowl9bhsEWR2L7HgiSRKD6ACFXPZakdOKy2tqvhL3NeMr3IYoCiZ36EGquIzlUx5L1AlvKkzl9SDNju/qp9ku4y/YgSgYcWe2PKKAfyprtmt9/SoJEZsrBx4W8DCOpCRLJ8RKvfO4hK8XAczdntG6XFZXPvvGRnWbg7ucbicTgrKlO/jbeecR2bny8HpdPYdooO5v3hhne28q+8gjbijWx3mzUPOwTi2/nH31MfO6/ngFdLUwY4qC21kW3vz2F9H3gQwsLPnGxpSjMlOF2JFHgs9LRiHkOnptbh8Mq0jHPQEayxPBeFuqaZdbtDHH13HoURUUQwWSEAd2svHVvFsqK2bgXLMcydSFWcyLRmML50+Mpr5XpmGvktke2cK3jE/KABMON+AItE0RWke8TCtssArtKIuwuiXDL+Uk0exQOVEV572sfHz2cgfuFU2F5EJ/9K0orVW7f/ioTEl8hO20+as4xWPrPwdvzcS6+vwabWeSFW0dSJkznoTcjNHkaOX96PDPGaOcf3vQwoXV3YOxwEtYRj4BoQnLmE1x7B4bMEUT2vk5g8WmIzpW8sa4Di9cEmHN2El3b/Xzvfh0dnT8vqqLQ9NmKH63T9PkKrF3b/6H2OEVFRQB06dLlD+uDjo6Ozm+NLtj/DiQ5TLxw9qC2hbkDES79jvKX1pFf56NffiKfLFzDWLWcqLuGJ2aIRN69koXKQOZyCjWeEM+uKKbKHeIx41NMU79D2SwR2/sVSdF6dm5ZC/QhO9HKDcedxabSZqR6O5SkUG7vDk2Q0LAB9ZGLqbN3IuG8ZcRZDHhDMf7+9maePWsghelxZDgtDGmf3Lav3hrUp4dzX0xlhfwY60uaWHugmbUlTdxr+IqeYgkAppgHTySGdeOzEHZD6SroPp1d1R5ufG8rM/pmc87wdm0OPbVXJjWeq/HmJLBgr5e8xlrGdUnXfNDPXwyxCM1vnEdTUCHPns73i6994RiXvLqBDlTwhXkOTeY4rje+ceQbULOdwK7FGFQD79SksrvGi9Uk8cGlw3l7XTkby5r559SurC9pZvjHf8dUWk22MZuqr9ZzQum/seRew3G9j2DL8+2jZNRuo6Y0hS/9Eajfz9JtIbISbMiKyhfbavBHZNp7FtHTdD8PYaFb6AVmNT9DvKEIGAn0BqMFarZDNADVW7g+fD7LQuOYWdGN247vDg37MO39DIBHYycx4sSLmdQrH3z1qILI8/0/ojkQ4cFT+vHFm0+xfts21hdl8e7FJ5NYspgngq/Q1x1ELRhJ84FNXLEEmrZuZKp6iHWRILYuJTdKIpIoYBAFNi56ldn1C7lfOI2onEK/uxbzjyld8FUX8YHlDnZZhyAkXgWfXAVNxdCwF6q3cGZBHpsTjtVWahSvAV8t8v4VSLpgr6Ojo6PzX4LJ7sTUrq1dmzU5A7MzCdeBHYgmM6IktUbcy9EIRquDsLuhtX4sEkIOeFAVRXsd8BIN+A5a3vi1yHpzYhrWpDRCjbVaFL23qcXuRiXYXEvY1YDREd+aQDYW8BJsrMaanInB7kTwNmO0tbUcDLsb8Xxv76OAKssE6spRFYVPNmdQ5zHSJTPM+O5eBClIxKNFstvT8xAMmu3Lh8t9XHRiAt1/ECV/5pR4UhMNDO9t4f2lXob3tpKXYSQvw8iC2zOpaYxx53MNdMpra0OzYVeIZz5wYxAhpl2So3rTL17jx+XTKm3bF6asJsaQngL3XJbKi5+4sVtETj4mjrXfbaP7jqehCRrqT6Tyo3lMdy+jou+HxCW3tXr0+hXe+UqbENlb5qJ3JxPVDTJ7y8LYzALxDpFNu7XJgPeWHrREqmuO4m/JSRxpySFrs4h46tejhuoRfcWEwv0IR1RcPpXxg+18vtLHhopMHo97BItZ5KbLetOtnZmaxhgdc0wM72mla3sz00Y7uPDeGmoaZHYdiHLtaYnc9mwDNovI/iqF9PShhCtXsuqNuSw2PklEjVIa6oZfdlLm6sVQWoZpooAkae+Zsq/vZ5I5xLuGG1i5Ocin3/h4YFYZlpo1WucFCcGSQvCbq1CjXsIb7wdAyhgGsSBiXAGb94ZpdGvXRhfsdXR0AM2z/hAbnCMhu32ES6qwtM/5nXp1ON9Pmuvo6Oj8mdEF+z+Y+bMHtv4964TjWG5/kl6F7cn37AKxkqxEG/Xt80mPM/PQor2IAmTHm8EHTy/fx8jJr7B81bdsCQ2EgJ8dVR784RgPLtqDqpqYctp8etV9wzbzc/jjRiKrUO8NU1/j5r4TOnP5Wzto9Efof9cibj2+Oyf0zWL5nnr21/ton6olucVoJWxOxotM++QUzhpawPCOKXTwrcdcHWVV3EQGjv8br69MoUtYIDT+OezNO6HLVABW7K1na4UbgF458WQlWMmM1xK0WYwSl43tyJKdtdz+yU6MksCeuyYjfp951mDiuMpzqHQFmbu1jpP6awMDu0liZv9caIog1pmIs8az4NzBeENRAhEZbyhKx7Q4TnnmO7rXfMRtqpu9ajaN+VN5qHMTAwsSYcubnGIzc8pJ2rLgsV3SQL6fbxZ/wOs1fSgofgObFCRctqH1Hm0sa+as59cwuWcGD42+nvXLPqFGnMoj+87hGrMPwaeype8dBELLyI2DxcaxbCiXqXd0wZ7XhwmhdG7ePZGrsvLo2P3Eg2+ESfdpfu8dxzNsUz3f7W9i0PdJfFM6snXg/Tz/XSUfxYbyUlwcllADPN4PRTDwuvthvNi4/JgAg48/D7P/Di6yv4mRgbw7phk++AQWriJ0TREDv+iMrKgUKire4XPYaJ5GvxXngzUJf0Rma4Wb9y4eij8ikxpnpmvt8xilA/QYOI6HXJ0ZEVzGpiWbiXpqOdu4hz7GMDRMBnc5kX1h7jZfzXkdejBkxjWsdKTy1LJiXigdg0MOUFwxiPd+7Q+Qjo6Ojo7Or4xoNJFU2Lf1dXxBV0KNtVhTs/DXaFZ3prhERMkAokTM50IwGLVks6pKqLkWa0oWEZ8bucUnP+xqwGhzEnLVIxqMOLI6EPG7kSMh5KgmIEd9bpw5nTAnpBJ21RN2N+KvryI+vwsGq52QqwGzM7k1qlGQJEAAQcAcn4xkMuPI7oCvuoTTh7rY3ZTKhNGZ4CrDaIvDaItDkCREgxb+sGxDgOKKKGu3B+iUFtS2i5qNXVqSgbOmxvPqZ828sdjP9uIw91ya2npNVBVKa2KU1sQ4Y7KT1BbLnMI8E707mTEYYMOuMEN7Wpg0xE4oolDbKBNnFxEFlavm1hMMKa3HO/mYOPaVRzlmgI0Pl/kY3dfWOhkwfGQvQtZbeHdpgN2N2RgS1hJnbKBk9w6GtuRm+mi5lxc/cXPZzHjOmBzH0vUBctIk1uyIIIrw3bYwg7qb2FoUpU+hCbdPpao+isMmMWGwjcVr/PiDCkN7WijIOujNbz/+cxRXEca8CfTv0sSukjDtWpLTHjPQzpK1flaXHoc5LHBJupHV24PcNb+RnDQDFXUxiiqizBgbx01nWPGuuIGC9B4k5VxJvy4WlqwN8M4SL38/5hpilV/TL24ZB1KNdMwzIjCJC7/dwPhBNgb7ykmSG3nh1p4YJAFjpJIRhicgGcadehF/nx9liOUNxKWPEpO195tgsCPXrkENNxHd9zaCswPG3PFYR8wFOULwm6u4LFdmV9jLSDUD+Pev8MnR0dH5X0f2+n+60i+o91tRWFgIwO7duxk6dOgf2hcdHR2d3wpdsP8vIs5iZPRxp2sv5AEgSNhzB3FnWldeXV1C+1Q7ZoPIqdXncrGUwozGV/D6+3D55ddzTLWb2S+sBWDRzhqGtE/GapTI/vxkhJCLOGB1qQcDPRkubqfLF8diiPkZedpT7PvkFh4PHsPra+KocgcpbwqyudzVKth/ssfPVU0P0SXdwedXjsUbivL8Nwe4xv0suYZiHmyeRULKeF675NDlzhNb/zpzaD6CAHaTgZOe+o7sBCsrbzpof7N0dx0XvbKBdKeZyT0yD4r1Lczom83yvfVtfPUFQeCBk3sBvSA0Dqtk5q7P9/D5t+uxmiRKoglcc2wn1h5oYgPDUGwS34bz6V65n5urz4BvBUB7UJRzhiA6M4gpKsbu0zFaRxKYv4bnrOfyja8HjVmjObWl3SW7avGFZfybP0Td/ihN4kg+Dyo8kJaGRY6AHKZ092amBZdCAM5hKWcbTAhXHACzg3af7+IZZRjDBl1Eh/gc1P3fIGZ0B1sSdDsBgE1lLhr9Ef79VRGTe2qRdr2mXsIto0IM31OvTTZEmkAQESUDZw9vR8zoIDfJihALM658ntbZ8r9B4UTofRrkDcESqOat0zvg2fIxQ3OtWEdeDnSFnj3BlsIN723l862VPDLYz/Qpx1HrUVGG3UJm/bcUtuvAo2WfYd8/DyUoMtX8NPeG/ZT4ezKorjPnn/o6z2xReXmTwOJgF5b7mzC+dBymmvZ8Hj0DSRhJhmJpzYGgo6Ojo6Pzv4LRYseYrVnRODLbEbHHY05IQRBEfDUliEYzCAJqTAvNjniaSCrshyMjn0BjNf7aMiSThajfjcFiR7LY8FYUHbGtxj0bMFjsODIL8FWXAFokfdSnBT4ochRJNLeWg4o1KR1HZjtiIb9mzROL0js3Su+CKpJS+yOkpxyxrYtOSmDVliDHdG7CXVKNJSGVuJyDvsS+mlIm5lcRHZJKfoe2uY1SEiQGdLMgCpAQdzBXTUKc1Jqk1eWVcdpFrny4joq6KNGY5gmfkyrR4JLbHO/LVT62Fkf5fJWPcAQSHCKv3Z2FLKsIAlgG/AOl1g2VXp5qWoA9UkSnodq4SVVVlq4PEI3Bcx948Ie0fVw+BYtJICarhCMq32zSbI627YsgK9C1wMTDV2vWjet2hfAGVM6YEk8oorCvPErPjmYkZzskp7Y6dOeBMHXNMh+t8HHhjATMJoF/XZPOvvIItU0xHDYRUZs/ISleojDPRL8uFgAKhNX4A6/AJhF14BUcP9JBMKwyfYyDauNoLIMX8PbmbgzuamVwDyuqqnLsIDsFmQa8r/fF7wtQP2gxvQb0pqw5g7jet2ORIljC6/jXiDVYyl9GMCShGiwQbiRauxbroNuxpfUnuPZuVG8xiq8L324O8eKHtfzNXsdg50KGx4NBPfOI7w8dHZ2/HlLcz8tD93Pr/VZMmDCBlJQUHnzwwTZJZ7/H5XLpPvY6Ojr/8+iC/X8rkhH6zwagrDHAzR/uAOCuE7pz60deBoh7aS/WoEY2Acfx+poy6rwREqxG3lpXwYh2Dl441g7rp8CWNwmrJh6PTedu4wuYBBk12ARymPh9H9A/uoF7DQd4OXUstxzXn22Vbqb1zoKGfbDiIbrmnoTNbCI/NY799T6W7q7n/U2VKIYpTDet5WN5CL2bA3TLautPumpfA4GIzLHd0rlwVAf21HixmyTap7b9gq/3hZFVlUZfhJP7t11aV+sJ4Q1Fue34bhQcLZGtJR6AUHMVX5mvQxUEJhjnsaKogaHiDnpYGng7OIBs6jGUrkAwqYDKarkLdSQyRI3j7y+sZX1JM+9dMowh7ZPZcedEdlR6uPPTJGYP05KYbatw8+TSYgASpDACKvFSEMlgouzUr0kyRvjky0UY2w/l3aL+VFVVMC6wkLjELPI/ugyqtzBn1ltcOrg/ZmcqD99/M9eH5xHJGYbp/C8OO61bm+bQdGc1ZTM+ok/Pnly04Dt2Vzby8eYcXj1WhrRuCEMv5e/dBhzcyWiByQ9RU17ESa8HmDGgjutmPAXNpfBYPwYYrRBywV5AVKgLiyyyHc/MAQnkJNZxjeFdpm/5EKVhAJNrbsIVsHDDyFO56J2J2FHBloxYMJJru43kglcd4IFFn+1i9j2TGRvnZfHmlyhxZ+BZ9w0p9bs5zdZAY9/b+NuAXBIdJl2s19HR0dH5n0YymrAma57twaZaQk21iEYzJkcCoaaa1npyJIRkMhNsqAZFQYlGCIcCml++M4lY0IccDh7egKqiRCMEm2pbiwxWO868zoCAZDQT9jQR8TQhmsyAgCAZUWIRfDWlRH1uBMmIKkdBllEVpTVqXju8StjdgGS2kZ1qZ+axRoLNIXxukMzWNl0JBCLc8l46gsHErBltt+0ti5CRLPG3Y50YDUf+bk+Ik1BVlfrmGNGYJmTbLSK1TZpYn54kUtukBU9sLdYmO8KRln4Cbp/MZQ/WYjWLPHFDOmdNjedv4518tS6Br9Z1YHQ/LbHqh8t8FJVr+5uMAv6QikGEeLvE49ensaUoyNfrg3TOM7F+V5gtRZpwn5Escen9NSQ4JR68UCAmxFPrVpn1z2oUBU6bGMfpk+Nbz+f7Icwn3/j4aq2fp+akYzGJ3DSvjmAYLj5RZkLiKzwxYjUZ4+7GnJh38B7mjMXc83K21Rfw71uruerURP5xTjJL1wd4+NV62mWN4UBVlFVFTcw81oksq2SnGSjMMxF25HL/7n9QXJTC+H1NLFkbpEviUO4c8z6hjfdiAQR7LuYeF6EEqolsewK1eQfhLY8QN/0rQo3F7Fn1GR1r1vPBbi+1biObEq9izOhpGDKGaclodXR0dABzQRaS0/GjtjhSvANzQdZRt/+auN1uNm/e3KYsKSmJvLw85s+fz8yZM5k2bRpXXnklHTt2pKGhgbfffpuysjLefPPN36WPOjo6Or8VumD/P0BGvIWpPTMRBJjUI4MEq5Gisjvp7thBynBN1M9wahE8rmCUIe2TuM/0PMx/BwZfCpIJRbBQFMrmrMhNdLU08sbl0yDiA5MDddcnZEWbuCZ7D49uTeTLHbV0zoij38ILoGojHd0VbLv1Q0Y8sJRj/7Ucu0l78KtMHsaA9hGeToynR/eMNn1u9kc484W1yIrKkmtH0T7FwYEGH59eMZJ2PxDsTxmQy+NfFVHeHKS43keP7IMPR6+uLuWl70rZUeXh3UMSyl7++kZ2Vnt49bzBZCVoD5G3nNAH6UkHBoOB4zvm8/TqOvZYHsKsRDjHuZis8H7uVs7Go1pQEfky52r6DRlNWoKD8qYAwahMo197iBMQWLSzltMG5zGjrzaJYDWJWI0SmfEWrrrwNqK+0xB9CXyXnUai3czjXxUxd1siN1c9yRmjOrN8yHWc/+54musj7AhcjCHsxvXMJKyKn30zFrIr4CQiSnxQYsK8qZLCdAff7mvgkjEd6JcbT+8vD2BX/LyzZSt9unfnofpLSDM3M6vyISKb1mAqXw3OzNbI/FYGX8jC2AEq1+/km6J6rpvYWZsAkkwQC0N6D0jpBItvJQ2Vt8MKUfl45kzuihy0wlYQGveRZDfhDUZ5aEUtF1hEJLRJAk55ifHAZ1faeXNtOZ3SHRglkR5Ni/nYfAu1CX1JKfG1XDMTNwxPhN1vQI+TaZO0V0dHR0dH538YkyMegy0Oc1wiJmcSBouNaMCH0e7A5NDGMqLBiBINIwgiBns8YXcDwYYqrKnZBOsrESSDZqUDGOxO4jIKEAxGIj4XvkotSEAympn3np+KBoF/nCtTv+cAybYI1rQ8Ejr0wFW8nWBDFaqiCeE1kWSW77ZzwghLq/3N90Q8TXgr9iEajCR3GYAixxAEgeQuAxENbR8LhIR8qtx1CIAvqGI5xOb8xU/c7DoQwWEVOXOKdq5un8w/n2wgOV7i9guTEQQBQRC4/G+JPPhSE307m3H5FCrqNJG+vllBEDR7HYMEsZag+4lDbEwc6iAQUvH4FIIhlWhMxWQUcPtk9ldGOf+E+Fbrmu898sf0t3LRiQmUVkeRROiUZ8ZoEPhweYCtRWHqmmTOmhpPr05m3l7iYUtRiCaPSnltlIZXBhGVkqkZspaWdAS897WXwT2sBMNaxP39V6Tw0XIfHy7z4wuqbNsXRhIFgi35ibcUhRllmUe89wBUj4LE8w9eS8mMdfhDfP5sAy5viF0lEQZ1t2IxCWSbiujGLsTs6RRkWXjhY3frfk/caCT/xG/w7aiGoEJVvYzZKHBR8qVEdha31rP0ux5z9wtQlRiG7HFED3yIqfA0ABYcuJAvy2bzt6wG9m3TJjak5J6UO4ZRti/K2AH646COjo6GIIokTR1F/RufH7VO0pRRv1vC2WXLltG3b982ZbNnz2bBggWccMIJrFq1ivvuu4/TTjsNj8dDbm4u48aN4+677/5d+qejo6PzW6KP0P4HMBlEnji9HwCnz1/Nyn2N3DW9BylDxrbWuXRsR4wGkaJaH+9sqGBHpoFcwFVdxJ3SHHr0H85XA7uzrqSZrlKFFiaU1hUAoXASNO7jjJWprKvbD8Dq/Y30a9L+Jqc/wqO9eC8WY4L4AJ6wGbtJ4tGuu3CsfoYeyV/BqBlt+hxnMTCiYwreUJSMeCsfbankmre20CnNweJrRx92jm9cOIQ9NV7GdUlrUz69bzZ7aryHRd4v31OPNxxjf72/VbC3OFNouHAdl7+xGVdJiESbibKcGXSinCxzHOwrZc6Z0/jylV1MEddwiXUxab1n0eSPMH/2ALZUuHl7fQUJVhMN/jBPLy9GEgVmVtyLULmRjqe/w7qbj8UkiZgMIrcvE1mwaisXjW7PnMldOb53FnUHtnF+xXz4AkZfPQmbyUCNJ8zKES/z+hdLucP4EjaiLNtWzJwrLmXqywMoagiRu3gP5U1apN2q4kYWnDOI8qTP+WTzdrI6j2HZ7hp6CAEsaBF6s3aP5L3hKZDVD946g/2F55HSdQROi/bwGlO0RDyJ9pYHdWeWZo+z/V2o3Q5T5oIlnuKSEuRIFwa2+OVLE+8BgxGhcBJfdBrJ59uqeXDhHhocg0n37oApD7feg+5Z8dw1/eDkyi6PiUJBIiUzH1I7wTdzofep8OUc2P4e1O+FKQ/+2FtdR0dHR0fnfwbJZCGxfQ9URaZx93pURSGxY28MFltrnfj8LgQaqogGfET9bs0+B7REtpIRR1Y7DBYbEZ8b0WBCNFkQJQlTXCKSxY5kNOE6sJ3T+6i8sTqebfscLPnWSf+CAMOTTAT3b0eLR/++T1beX+5gzY4wCEYuO6Vtnw1WO5LZitGurYr0V5cQctVjScogLqtdm7qpSSYevkobl6UkSG22nTQ2jq8cfsYNOHiudc0yB6qiVNRFiUTB3JKPdmQfGyYJXvi4GUGQSHKKJMdLZKcZ2F4cIRpTufLURO6a3whAt3ZmOuebqGuO8ci1aWwpCvPcBy7OOyGBT77x8cUqP+W1USJRFadD4tbzkxncw0KcWNqqRgAAhadJREFUTUQQBJ56t57Smhh3XpRC/64Wpo924PbJ7C2LMu/tZu69NJU3vvQQDMO0UTbWfLcDs+CjOpBHKCLz4BUpzHmigXAU/vlUPf6giqqCosIF0xNJjDPQ6IoRiar4Agetfb7bFqJT/weY1n8zqCrexedQlX83HTtmIbXYPcot47MEhyZ2De1lpV3Xy7CED/BMjcq50y6mySNT2xQjySmRlighCAI3nZ3Cp9/4mHlsHKmJEvL6acQOfIAa9SPa0jB1PRcAQTRgKpiCqWDKwfdgbDfQjpTcjhTWBigqizBxiJ3bnmnA5VOwmkWG9mq7gkJHR+evi617R1JnTaHpsxVtIu2leAdJU0Zh697xR/b+z1iwYEGbvw99fSQGDBjAe+/pWdJ0dHT+nOiC/f8Y7VLsrD3QRG5i24G1IAiUNwV5Z0MFAFfXn8DuMZkkfPsvTlXKuOC7bjyy8hsWTLHR8YtpeAU7RbM30i9Jhh3vA6BSCWjC+Mn9cqDgNajeAp2nIq96kgRVYN7MrpSHbfTKTiA7rge41kPnKfwQgyTy0rmDAGjwhVm4vYZ4q5FB7ZKOeF45iTZyEm1tytbsb2T2i2uZ0TebCT+I4H/zoiHsq/PRJy+htey74kbOmL8GWVWBEABNY+6D9smgKBDxIlniKTjBSPmW58mdeB31niDT536OV3CgAt5QjKpGL6+c1oGZ/XPomOZAWH0pBJuhbheOwoPLm1PjtIfuVIf2uyDFzl1nHwdfnK1FsztzePvidNz71tChaQVX051LIlfxlvkeJu25hdfiuuG0WYEQJulglEKHltwBuYV9ONaZxLPz7ubD2HAE8X4sapBSNYMU2URg9C3sevI0+ru+ZOv2Rr5Id/PA7GOJd9hJjTNjEGTykg5ZzdBlChR9CZl9ILsv5A+hA/DZoRfWngzTHgfACJzQJ5sT+mQD4wi/dS57n57NQ0m38+Klk1ofPr/nwpUOGoLPcVvBAE4dnA/jbtY2bHoNKtZB+zFHvPc6Ojo6Ojr/0wgCotGMIse0RLSHIBqMRLzNB+1vRAmDLY5YwAuAt3IfktGM0ebE33wAQTKQ1KkPUZ8LOeRHiYZA1TzZTx7kJbu3jWgsC4tZwGkL4G1SQRCJb9eNqN+DJSGF41UFRRWYMPRwK0HJZCGpUx8AogEvsXAQQZQw2hxHPLXO+abDyt5e4uHVzz1cc1oS2WkHI/g75Zq47YJkDJLAocGXCz5x8c5X34s+MQwSvHxHJoIgEIlqYrjZJHDcCDuNHplhva2s2hrgnheayM80UFYTQ1UhIyHC+L4iNY1WeheaeepdFwYpSjii4rQf4qPvlKhqiLVG3g/uYaV9toGn3nMzqLuF9GQD825IZ+PuEGU1MWqj7bisaBU+JRFK3cw5O4kWXR1V1X4EATrmaud68jFxvL3Yw6NvuHC2HbpSogzD13EywTc74RRq+PCbXsT1nM2ZUxKIs4utEx+HToDY2k+iefdC+gwZQkKcxN2XpPJDCvNMXHv6IWPoYffCsHtRgnX4PpqIe34i5p6XYx16b5v9ZE8pJ/gnMKGTnbSe65gy/OCkzJCeVnYdCNMuu+0qDB0dHR1b945Yu7YnXFKF7PUjxdkxF2T9bpH1Ojo6Ojq6YP8/x93Te3L78d0xSId/WfrDsda/sxOt0G0aSvFSYonHYy0WqXGHeXlTMx1VC1VKAh9sqqbfjN4w+iaI+Lil80mc/Nxa4q1G4ixGKBih/QBz0p9ibamb4+tE/j4hv6WVeDj1tR/tb1Gtl7s+3cmKogY6pNq5cXIXzluwjtwkG7dP6/6j+5Y2BghFFXbXeA/b1i7FzrkL1vGP97fx9Bn9GVmYyrZKV4tYD8M6JJNoN9ExzcEtH27jrfUVzB6azzXj7TgKBpDbfxQAcW+dxwre49LYtXwZ6w/AJa6HsT22nIdOfhF6nAidPoTGfdBpQps+XDa2I2cOzW+NamfPF/DWGTD4Yph4DwApDjMp6+6AyvU80v5iXmnsihRUsAgRFFXl/pN68vXuOmalFLNg4VoeqR/E62vKeH9jBQXJNm5vup5/GHYy2bCeGaHbgXiGOGo5P/YGl92xnir1GM4RwxSp2TzTMJvtD+XzRI/HuMn+GdOsL0CXV6l2d2DRsmXMKv8XpuFXsa/LxVz47+84tms6/5jSte2Fjfhh8W2Q2Rv6HZKELBbBtOdjeipRYrW7CcfGYzO1/fdx1pAClu6xMbzTDx40+56u/ejo6Ojo6PwJEQSRxI69W/5uO5mtqmqrVQ2AyR6HwWInKMsYLHbC7nrkSAilJWGtKseI+j2Y4hKxJKRisDuRw0GCDVXY47Wo+PGD7S3HtmrJa1UFVZaxpWiewn07Q9/Olh/tc9jbjL+mFDkcxJKUTqUvnmdermPSUDvjBv54MsHS6hiyAmW10cO2Oe0iNz5eT3qSgfsuTyU5XmLnfs2YXhSgVyczXQpM1DTKPPFOM9uLw9w4O5nO+SZmTXSSECcRjak88Y4LgIpaTayXiDCyciz2ahc3zVyP5EwmJUHCYRWxWdqOie+6OIVIVMVq1sqD383BuG0ec8a/jrHd8YAmmD/3oRtVhcI8Iy5vMr5mzQtHEgVuvyCZBreM16/w5pcejk14Ccuit5j72YOUh9pRUmcETHgDWpuCAFYzfLctyIpNQQbG/ZOutrWs9JyAvDLAZysDXDMrgT2lEZx2kc75ZnbsD/PJCh8l1Tdw4Yx7MfsVzrmjmvOnxzO8d9uZALl5D6FND2Hudj6GjCEHyxt3oLh2AxCrW3/Y/RAd2Rg7zcKIimhvu1r1ir8lHlZfR0dH53sEUcTSPuenK+ro6Ojo/Cbogv3/IEcS6wHuP6kXZw8voLQxwNAOyeAw88GAV/n7O1sY0TGO+2b0wiiJnPnZa+Qkx/HPsYXajmPnANAb2H7HRERBwPiDNi6bOYUuu+qYOeDwL+33NlTwxNJ93Hp8N8Z0bmtpc+enO/mmqIFOaQ6uPraQHZUevtpdhyQK/GNKV0yGo8/SzxyQQ0a8ha6ZzsO2yYqKLxzDH5E5/+V13D29J+9vrGRc51SO753FjH5aP59cto9XVpfRW9hHrLSGWc81s7XCxSvnDmZEpxQsURcIKnHqweV+mTYVPEC0JRouq4/2cwRaxXqApv2gxKBh78Gy4q8hqy8Yrey2j+KrfXBd+5e4eko/xgaMvLCyhNMHZhD34mlcqUQpFS/kFGEF74ZH8U75GEwmL4jQVShHFOCDYaVkb3yYFBoxiwHOjP6DBal/p139EgB6iKV02XkSm+lEf2IozWU8uq0I46YvMRm3wzaZzfZZ7K/3s1itbRXsgxGZu194h795XqRXYDUYbW0Fe4MJ4cz3KS4u4qr209usCPieC0a154JReuIyHR0dHZ2/HkdLqi4IAokdehELB1FjUUzOJARBIBrwEnbVY05Iw5qcQdTnIthcjykuHlNcIoIoEpdz0HbAnpYDgnjYseMLuqFEw62e+YfirdpPLODDmdcZyXTQgF5VVTxle0BVMdrisCZlsOrrIDv2RxAEflKwv2xmAqP7WY84KRCOqsRkqKyPMffVJvIzjYSjKkN7WjhzipP8TC1i/+an6tm0RzN/37grxNxXmxBFeP6WTIwGiMkqogCyoonhAipmMQxyBFQtQGVIjyPbuEiigNV88H7Irr2gxJA9xRgBRVH5dnOQEb2tKCpU1kWpa1aYNtLOcSMdbNgVotmrkJ4sseBTDwCi7OeZijvINO+kp+Fr9isXA2AyadaVfQrNfLP5YBLhLaHJ7IpNRj7ErujJ91ytSXV9QYW5rzVR26hN5qzdESIQUqhrltm0J9wq2MtNO/B/cTKqEkH1V6GGm3FMPmj/YMwZi3XMMyj+KgI5sw+7FoJowH7MCz9yN3V0dHR0dHR0dP4b0QX7PxEmg0ivnAR65SS0ltV4NGuYb/c1kJ9s454ZPRlx9bijHsNskI5Ynp9s59wR7Y647avdtexv8LN8b/1hgv3J/XMIRGRuOa4bfXITUFWVW4/rRnai9UfFetAeREcVHr4sGCDOYuS9i4dx4cvryUmywfIHOau5nNtrzsYdirUK9sM6pDAweRtv+u9ArFe5NGUB2wUjBqnlQW7mS3y7Zg3ffqvS2WrkvhN70TljLHjKIbXzj/bvMAZfouUFyGpJjBNogldPBlWGGc/yt+3vU5w/mWOHjyA9PZVp936FOxjlg00V3MUwBsW76eCqZbC4mxSryPBJV/HakvO43XcnG5T2fHrlCLp9MQ/URprju/Gc51SIag+yXyiDWZZyGqMbXkdEZlOsgBWGflzWZzanGDazd3+EfR2upqPFxwzbVtSTe7V5n+ys8jCu8ml6SZuIJXfBMPTiw8+v3ShqlW6cOX8Nwzum8HKL5ZGOjo6Ojo7O0RENRkw/SP6qRDXlNuyqwxyXgC01G1tq9lGPIYhHHp8dSaj/nrC7EVWOEQv52wj2giBgTcpADgeJy+mIaDAyfbSCJAoM7fXjkfkANovIoO5HFst7d7JwzawE5n/kplOekfe/9qGosK8iSn6mkTNbBPuhPa2UVEVp9ips3BNCkjShXRTAYhJ54oZ0XlvoYeWWIIO7WzhlfDpJzo0gBxEduT/Zxzb9Hfc8ct0GDDla7qfV20M8+kYz8Q6RF89YTMn29bwZfz3HjXIQDCk888HBpK9GCeLjJBa6ziGiWInLG8TgTgcYu3YhS+qmIQqw4LYMZt5UBUD7bAMHqmIoskoUbayZniziavIyPfE5tnqHEowfRkGmkV4dzGxRQgzsZkFFZdJQG53zTYzqezC6PlL0Noq3BAApcziWPn8/7PzMXc7itS/cvP6Kl3OneTlpXNwvuj46Ojo6Ojo6Ojr/feiC/Z+cS0Z3wBuK8erqUvrkJvwmbdx6XHcGFSQxo+/h0fcHPdA1BEE4qvD/k3x8BdTuhFNfh7h0umQ6WXHjOOSQD+n+l8EAHykj6Z7dqXWXPrkJvHP1JHihB8gxHp09Go9ibfWfx+zgvEURwjGFe2b0pF9+y/LgXyrWA4gidDhkMsQSr9nohNz8H3t3HV5Hmf5//D1zXJITt8YaaVNvU3enQikFihWKu7svsPjiLou7FuqFUnd3TZvG3Y/rzO+PKQldWP8t9n1e19WL5GTkOXNSOnPPPZ+HPZ8RW7yCpwbGQo+ZbDjaSJsvhIzCXw3PE620Et3/Qs5b/xyVoXg2db0Lo8/NykgfhsojSVNr8H99A4H6razQjeb5phk8bniLSfpkijrdicMaR2Wvu3hmbQK3eV/kMv0SAD55NkBWoIhzpT0QPBn2LkK3+zPOtMZDck849xMA5uyspDoygXirTN8znum46fA3fMGI9nSD/6ePwQuCIAiC8K+JzuyCq+KIVkw3/W8m/HRkFRAJ+DBG/TT6xJ6afcL3UTaZWZN/+kTjP+PyKjz8ViMJMTruuCAegAmD7UwYbOebla72LHidDLnpHXn4J4+w0z3HyH2vNdIjx8QVp8Ugy7TH29S3RFi6yYskwTVnxh6Pt0n4t8cHIJtikDPGt3+fl26gc5qB7jlGfGtvIjni5+6TxiPHncpXy7UYRrMJAgFwWHy8Pu1L9q1dwBf1tzJ5+FQ27DFzIJILRJBlmP1ADYoKMXY4VhVGkiCswMQhFpraFHrlGalc9yHTY19hsH0htxQv47z7qmh1q+3vdeuBAEfKg1Q3RAiEVE4fqxXdg8Vfae8hthu2yV8im34+xsbr17bl8Sn/0TESBEEQBEEQfltEwf4PTpYl7ppSwF1TCv5n+0hxmLlo+L9fhD9a72Z7WTOnF6b/JILnZ+35EsI+qNsHUcntL8smGy9ZrkZyVbNV6cIDA/6m88pggSvXAGAC/rZn/4pRORyscdI/6+9kee79CopXwsSHtElZf8aC3dVkxVtP6FpH1sGsz7SvS9eDJRYGXsYzSw/Tsvo13k524h9yIyOXbkciAqvvBSBa1jGz/CFMe45yJHQuk4wb0ROkqDGESQpxMJBAslpLobqffvr9cHQ7vis28vzzj3Gb/AlI4DQmYwvUsd+fwJZwEvkJCkkDLgFUsMbBzo/A19o+1BF5CTxyaCglky+ib9rfzyoc3y2ZZbeMIsXxvykuCIIgCML/BbJOjyO72z9f8L9gsEZhsP773dY7D/sJhlQG/53ImR+rrAux/1gQvQ4CQRWTsSOKZkB3M+8uaCOiaIX4Yb1P3F7nNCMfP5z2s9vNSTMwvI+FBIeuPYv+x1RV5cPFWlzN7KnRPxtJ5AsorN7hZWB3C/GOjicUkuK0SWcBAvv/QqRxN4aMCdz2Uj1FZSEKsg0M7WXh3QVO/P4wgW0Pk6BPwKNL49WvWml2Kj/ahxaxA9Dq/mFs2n/LasKcPTGKB/7aRIJ+DH0sIzkY0ho7nB5toa5ZBqaNsBMIQYxd5nBZiAMlgfaCvTF7GqGyxdgmfvR3i/UAl5zqYNxAK53TxASygiAIgiAIfwSiYC/8Yupdfi58Zys5iTZemVXI9Z/u5GCNk2BEZfaQrBOWPVjjJMqkZ+HeGrok2xlXkAyzv9Fy4nNPjPSRJInr73yC9zeWcmcwQve0f69D7NaT/qabfvv7Wlf88Bu075c9CG0VkN4f+l+sZdD8yJqiBq7/dCd2k559f5708zvJHq79AVo9u3hI/x5ym0qV/jRe6vQXzrTtJbX4C8ifhHRwLqbWowD0jHITPO0L9N5qFu+RyTl2DZfrFzE48AoPhc7nLtMcjGE/5qL5XGJaiTGsELCmEn3RXDa54jnfaqK82UtcwaNae1v+BAAiBdPRxWg3NtyBMM8vKyLWZmRKz9R/erzyksSj1oIgCILwRxF0t+GqKsYSn0rInMT9rzeiqPDG3cmkJ3cUgBVF5WBpkNgomWVbvIzpb6VbZxO3nR9HbLR8QrEeICPZwGePpfHlMhddsox/u9t/yGySuefijiaJYEjl8++d5GUYGdrLQm1ThM+/17rhxw+ykpag/0nR/rOlLr5a7mJITz9/uuznu/NNPa5o/7qx1YmiwqHSENNH2SjINnBu2pfgdlAU/QBlxZlABJAoLDAxup+FxFg9j77biKcjvh5J0or2Hp8W9wPQGO7EN9KHPHxDAoWlAWKj9fgDKr3ytCc+B3S3EAypDOrppfD43ABF5UEeWHQjw/vcxXVx/3iCWJ0snfAEgyAIgiAIgvD7Jgr2wi/mWIOHgzVOShrdRBSVid2SqGrx8sSSg/RIi6YwU7sY2V3RyoxX12PQyQTDCma9zKE7+rBj51Y+dPXnvNgWYm1GchPtALy9roSFe6p5ambvf6uYXO/0k2A3IcsShAOUfHEXrfpE+h34i7ZAbDZ0n6511h9bBY5MeDQVup8Kp7/Rvp38ZDtdku10/5nJcX/O/dN7U2v5E6nhKt6t6sRbR2W2d+nD+zl1cGg+HxnOYr0nhSbVwYWnzsTaNRuAiztVEHozGqMlhnxPgA89J9E5IZlTnJ/j+O4uUgAGXo7ppIfBYGHI8ekE/nbS3q2lzZz/ocLkngFeOAdavUGO1ruRJQmXP4zZ8PM5uT8rHNQm1s0aqkUACYIgCILwuxLyOFFCAYLuFuyxKfTJN3KwNMSf32ri+VuSsFm0DvevV7p5d0EbZqOEP6hyrCrIeZMdrNvlJSlWh8UokRyvJyZKR0RRee6TFrx+hTsviP9JMf/viSgqbW6FuGjtXKS6IcQzHzeTnqhn2VYfBj18+mgaKfE6Zk2KQgXiq16h7et7sYx+FVNBx8SrvfJMrN7hpV/BP8/lB3j2pmTemt9Klwwjn3/vpqwmzNHuN/Hc3ssIhVQkIsiEuSL9QYad+y5xx7v2LzjZwetz2uibb2RvcZBwBAqyDRyrDFFWq02Qe8usWMYP0ibzHdTD+rP7n7/GzXsL27j+rFgmDbVRXhvC6VE4VBr8l8b/Yy2uCMWVIfoXmP7uhMiCIAiCIAjCb5co2Au/mCE58bwyq5DUGDM6WeKWk7qy8nADe6vaOFrvbi/YR1sM2Ix6vEHtIicn0U5w7g0UHvuedeEZnH3gLCIqxFoNLLh+BF9uq+BQrYs1RY0/Kdh/u6+GOJuJQZ3j2l9beaieHWUtvLTyKLMGZ/LYab1wH1pO56J3iKgSgZhcTG3FcGCeVrDvebr254dInoaDJ+wj1WFh6c2j//GbV1UoXQvJPTFY40ibrE0adn6jh7aQjnMGZcJKD6gKA9PNvF4+kJSEBBq8avsmHEkZcOcRKPqWuV9cQHXmaKrdEWL8lSgqKAYrrb0uoe3Fk4mOTyPhwg/hwFz45ioYfQeM1PbZXFPKFGUN5Q0nAZAea+XLq4ahk6WObP9/UXDlkxjXPwUF06BTf+gxA+Jy/q1tCIIgCILw67EmpiEbjBjtMeh0ErdfEM8FD9RQ2xjG6VHaC/YJMTokwB/Uzk3iHTrueLGe46drzF/rQZYgN93An69MYNV2L6oKNY1hsn8U1RIMqazc5qV3vonUBO1SJBRWWbvLy/rdPjbt9XPXhXGM7GflrXltHCoNUVQewqCHUBjW7fIxcbCN86ZojQLeVYdBVVBaDp3wvgZ0M/PeA//4yUGvX+FwWZDeeSbiY3TceTyHv1OSnk37/Izoa+HrFS5CEZUeOSYaGgPsjXmYTrWh9oL9tBFRTBhk44OFTnYWBRnWx8zGPf72aJzMFD0RVeWce6s5ZYSd86ZE8/qcVpZt8fDA5QntXfYHSrR1qhq0eYLGD7Rit8rkdPr3Y24ee7uWA6UqZ0+MwmaROXmEDbPxX4ifFARBEARBEH4TRMFe+EWd3PvEC6e/XtCfvZVtDMzuKKh3TrCx7b4JrD/ayONLDnGgxsl3lu70V3ewVenI4m/xhnh9dTFPzezD+uJGzhl0Ynb9nspWrvpoBwadxO4HTsJq1LO7opWL39uKXta6jXzBCADWvFFscpxMmZzBjGnTYdMLMOgKWPUEHFwAp74Mvc8EeyIk/ouZs+56WHATZI+Aqm2wbw6k9IWrVrcvkp1g46kz+/D9gTqKxr9N/rqb6Xr4A16KrOPqspt5sKyFmQMysJuO/1VVQuCqQUIlLUrPhTVnMFlJIZA3mTvPncLBzx5jhGs7EdcOCLi0SXrDfqjZ077PSUf+zCTjKr4M+3lwfhIPTu/x9/P7/wF/KMIjmyM8oOoItdRiPfRnqNjSkdsvCIIgCMJvniTrsMR1zA3ksOt45qYkvH6F2OiOIu+Y/laG9DKzdJOXd+a18u1GL1mpespqwu3LKCocqQhR3RDmrgvj8AXUE4r1oHWSv7ugjR45Rp68QXsccO4qF+8tdBJt1/b3w02B08ZEcbAkQO98M73zTOw5GmBANxMPv92IP6Bwz8UJWIc/jSFnBvpOY/6l97uvOMBnS52cOSEK38rLCbuq+br7+5w5pWMOn0E9LPTON7Fxj5+nbkzk3tca2XcsBFio2wf1zjb6de3o3Pf6VTx+7ZwyNkqHqmrROOdNjmLm+Chm31+Dy6uyZIOb86ZEU1wZxBdQqagL0SvPRCissvOw1km//aCfzBQPEwbZGPIvzCPwt8L120lrW85R+QxWbZepa1bQyTBjjIg0FARBEARB+L0QBXvhV5XqsHD/vP1c/fEO3rt4ICPztSlhzQYd47sls6W0maP1bv5UMxxfZAiB4xN7pcWY8QQiTOmZSq90B73SfxrHkhlnpXe6g+RoM5bjMS/psRa6JNtJi7Fw1+QCco7H6shmO0Nu/oQhP6ycOxwqtsKqx7XvjyyDtH6QM+Yfv6GGIq0wb44GUzQcXgSVWyHQBkA4HPjJX7o1RQ1c/sE2LAYd5+lSuRuZfrpjfCM/wM1pn3QU6wE+mgnlG1CmvYjc52yervVT3TqRgVmxvL/xGJeUvgLAX0LnckarRNdRt0NaX8ga3r4JX6dh6Gr2801DCpvqS7lrSoEWgxMO4nnvdFwuF/bL5mOP+scRN6oK30UGMF3NI6G2isyk7uh7nv6Pj48gCIIgCL95aYl6rnq8llAYXr87GYddO48yG2Wmj7Lz/WY3x6rCxEXJlNV0rJcQI6PXSaQk6OnW+eef2uueYyQlXseA7h0F765ZRuKiZSYPszOyr4XMFK3I3yvPxKePdmpf7uQRdj79zsmmvVo2fFltiO6dbRgy/84cQsftKvITrFxN3yw3SzeNZefhABadj6si36CzRZCMR4H0E9b59DstAz8jWU/LjyaaBRj4o7E7PRGufKwWVYUnb0igR46ZPvlm4h0yOp3Eq1+24jr+xGQorKCqKndfHM/RiiADumnb0eugW7aRkuoQpTVhvtuoFexB67i/55VG0hL1PH5t4j98nwAoQU6K+ZDtrnE4PWbyMwz07fqvxQIJgiAIgiAIvw2iYC/86tq8ISKKitsf/snPdpe3ANDq6/hZcrSJu6Z0Y3qftH+43RirkfnXjTjhtXi76Z/H1wA0FcOaJ0HWgy0RBl8JigIBJ1hi/v56n58PjYe1r6e9oMXQpA9E/eoSAC5pOIennH6So818u6+Gu77ey4VDs5AAXyjCO6ExXGieRzoNVCoJbCppZvrL67g3ZQuDY1ygN6FKMlcsqCe8dw/vnZZKz28vwf/1fjYHruBSo3ZBuFIezCy9DHojFJzMxuImDteW0D0tmlnLe9Ev8wP6944lxekncvwmCP5WzJXrsaHw0dodnD917E/eXpsvxOXvbyPZYebFc/ry5YXdSX/vCDIq7tPmE53W5Z8fW0EQBEEQftMiitY1rigQjpz4s2BIobZJe3FnUUe+ek4nA7fPjmsvtv893TubePtPJz5x2TvfzIcP/ePzOoDN+3xs2a/N8Nqvq4nunbXu9Iii/t3Il8bWCH96rQFV7cZzOeM4a/oK7NZounc28MyXb+DQN1DjKWD4eG35t+a2snK7lxF9tCJ3RZ12DvrDZLIAn3zr4kh5EKtFpmuWEb1OIhhSufPFRi6e7qBvlgfHqpHUttlYXvI1oE0I64jSJseNi9YxqIeF7zd7sFtkqhrC7D4SYOa4KOpbw6QndlyiHSoJ0tgaobE1wqHSAAXZP70RUlQe5PlPmpk4xMZpY4ayK/MbWkrMGFB5/tbknywvCIIgCIIg/LaJgr3wywi4YOvb0GUSJHVDVVU+3lxOpxgLb180gKpWHwUpJ06OurqogU0lLT/Z1AcXD6LrvzjB639s2ztwZCkN8QO5L+phRuxsZnbN7bD3Czj7I9g7B458B2d9AHnjO9bLnwjuOohOg5zRENcZfK0Q8mpFedXYfrG3cHcNrd4Qe6vaOLVvJxbuqSZBFyBdakZS4VnD5dyrfoSzxsrgxq+0lc6fw4Y+j7Ps02NkNLjhrQno3bXYgTA65jEGozWKI80JvLXuGI/M6AXArg/vYKqyhvV9nySsGPEEImw61syuila6pERz1ehcsCexYsDrrD9czcw+hYAWe3P9pztJNPh45LR+1B3YSteKuXxRPgHfGb3Izu6M+9x5oIREsV4QBEEQfmcq6kJs2utjyjA7dquM26uweL2bIb0svH53MoqiZdX/2EeLnXj96gmv6XXw0u3/+8Lwh4vbKKkOM6i7Vqjfc8TPC5+14PIqvHhrMg/8VYvKee6W5PaM+WibTPccI6Gmw8TnDSU2I5Ursg1s3ONjt0dr4siL7piYde0uL60uhcQYHZnJOirqI8RFyzS1KURZJVxeFRXYciAAwOodPj59NJVPvnUyf42HQ6UBFn97lKc6V5NoMGKUAxitZnx+har6MDsO+SksMFNUHuD5T7Xz3JNHaN30/qDCup0+FFWL5cnPNDKmv4XvNnmIRNT2myHltSFe+KyFgd1MnDkhmrmrXJTVhlm/28dpY6KYNSOXmEQP3Tob/+efiSAIgiAIgvD/nyjYC7+Mza/Dikfg8BK49Ds2lzRz39x96GWJAw9N/tli/RNLDmLSy8RajeQkWqlo9uGwGshLjsLpD/Hkt4cY1Dn+n3ba/1OKAs3FEJ+ntU8BB9PP5EBkN1/UjGFzVTP7an3MTm8EVYEld0FbubbuwUUnFuwnPar9+TGzA6nv+YRcDbzVqxOO2pXgmEKt04+BMGP1ezlj2kxWFdXj9Bu4znA3g9KNdAtZuLx2MQA7jP3pa2tB/uZKhk98mB1d5xMZcQusy8PnaePe4IVs1A2g25AzmbOjCvBzrMGD0x9iR1kLp1r3k+atJzauksG3XkxilImvt1cSUVRG5ie0D3XitLOZOK1j6CWNHvYcOMj3pttpe8pGtknPw4ZazhvWC6tR+9+Hveuo/+74C4IgCILwq3jtq1Z2HwngD6rMnupg7moXn37nYschP09cn/ST5b/43snm/VocTadEHZkpevYVhxhVqGWtl1QH+Walm1NH28lN/++Kxb6AgsujkBTXcbmS08lASXWY/SVBPD6VmCgPLo+Cx6/y1EdNVNZr3fA1TeH2gr3RIPGX65OBZKDjnKVrtpE++SbSEnXkphsprgySnWqgqVWLv1GRuO7sOO54sQF/UCvWF2QbqagLtz9hkBgrEwrDva82Mm6Alb5dTJxzUhT3H8vhzxVfEVLNSMYo7rgghvtebQagtilETaOeptYIEqACkwZbmTTERlaqHl9ApcUZoVOS9r51Ork96/8HOw75OVQa5FBpkEXrPTQfj+y58GTtfFqSJE4eYf+vjr8gCIIgCILw6xEFe+GXkTcRDi7UJm4FuqVGMzI/gYw4K0b9Tx9h/nRzGQdrXAzuHMvmkhZqnX7mXD2sfXLU7/bV8tGmcr7dV/ffF+y//xNsfBnG3guj7wAgPbcHD2feR6LVwNVxVkamKhCYBEdXaMX62M7QUgLxudo2lAjU7tFy7n+gqrD5DbAnwYxXMLgbcDzTBVSF1SctYWhuPFeFP2LC0Y/h5YfY2Pt0TLve4Wb/NTxwZAQyCqea8ugjHSU1PhY52AaeRqq+f5lOnv0QZWP/wEdZX/k6jSmj8FUpvL66mM4JNmYNyqBrSjR3fLWHb/fVcs/gh7giq47o3mdjQsdba0v4ZmcV14zJ5WCNi24p0chyR3cZfieEfHRLTea+KfnYV/oh7GOLbRK9ZA/WzS9w/sEkenftwh2TCxAEQRAE4fdndH8rbp/Snsk+uIeF7Qf9jB1o+9nlv17pxuVVGDvAwsptPhpaI3zzVEf2+9cr3KzY5iUcUbnjgvj/amx3vtRASVWIx69LpGeuFgNzyqgojlWFGdjdRCAIvfNNRNtl5q32UFkfxmzUomlsZu3c0u1VaHWFSU/uuHng9irMW+1iUE8Lj12byNqdXp54v5l4h44zJ0Rx6hgbK7b6eG+hk7wMA9mpekprwoDK1uNd9T8Y1c/KNyvduDwK78xvI6JAcWWY4X3NrNzWG7sFvC0q97/ezMDuZpLjdcRG67jluXqcHoVLp0fTKclAboYJf1DhifebcXkUJg+1cbgseMLEtgCKpwbJYGfyUBt7iwNs2uun2amQEi9T36zwlw+aibbKzJ7qYGjvf3/CWkEQBEEQBOG3QRTshV9GWl+4cnX7tw6LgQ8vHfx3F4+3axdmbb4wD8/oSYMrQL+MmPafT+yezMz+6QzJ+Q8vBiu3wfZ3YcQtWk49dPwXiDIb+OTyIR3Lf3wmHFkKfWdB15Oh6FutYB/2aj9/YxTU7YPuM+Cs9zv28e2dgKRF5VhiIHccbpeTq+bX4KOF7VP7w4qPwd+KpXY7AI9Gz2NNcCzuQJhn4+7jmtZnye15CnQbTdG+rTy05CjvGQ8SObCQyNFSrohsI7rFyRrOI6JCcYOHsKryyZYKxnTVJid7enuEU8bN5Put1dw/bz8pDjO1bX7um7sPbzBCtFnPST1SONbgJivWjO61YeCuh6vXc8roIYS2pGDw1BA38CzkZSvJlMoJ1RbxtccmCvaCIAiC8Ds1aYiNSUM6ivP5mUaeu+XvR9s4bBIuL9gtEmeMs9M57cQu+umj7YTCKjPGRP1H41m2xcOxyhAXTItGJwMSyD/q68jPMPLyHdr4IorK2XdX4wuonH1SFEN7WXj8vSbqmyN4AwrhiMr591cTCsOdF8QyqlB7n4vWu/nkOxfbDvp57pZk8jKMZKXoMRklXp/TSlKsji6ZBrYdDFBSFUJ3PBEop5Oe0uowUTYJVVUJhSXGFFoZ2svCN6tcrN+tPXnw/qI22twKqgry8Sc3IwocqQiy9aDCgrUe0pP0OD0K32/x8tpdKTz2bhOb9/lQFFBU2FccRJbh00fSMBokmp0REjmEa85wZHsnos7dzyXTHWze60eSYHgfK3NWuGlxKrQ4FTbs8YmCvSAIgiAIwu+YKNgLv0mL99YAMDwvgdlDsn7y8xirkafP7POf72D1k1oGvcEKU56EAZdA7E/3A0D9IXDWgN4EqX2g2zStAD/gYkjTst4JHS/cB5wd6yV3h4JpEJUCpuMXrufP4d5Pd+IrqyYtxox92EXQuTfU7Ia1TwNg81fx5/HxLK2A+P3vMdiwh8CqP8HwCtIGp3Ns2Vyc2AhHdHzgLOQmh4uRky5mVkkKn2wup0+Gg5H5iczZXtk+FPV4cH5FszbOgdmxDOocz4HqNjYWN1HvCvDG6mIeX3KIi4akc0cwjC6ssGB7JTMn5WO4fCm0lNKl8yi2y1GEmso4OfEkenVy/OefgSAIgiAIvxtV9UGqGrUomNGFNrp1/unkp/kZRu666D/vrH/1y1YCIZWeeSaeuC4Jj09pj7b5W5v3+TEbJWQZRva10DnNyHM3J9HmVshMMRyfjFZb1h/qyNwf3NPCjkN+xh9/iiA1Qc+rd6Vw3n1VAOSmG7jv0gTW7fLQ0KLw/sI2AMpqwjxyTRwvf95GdWMEUHlzXiuPX5uEy6u0F+xbXcrx7eq4bXYsb81t42BJiLEDLJRWh6htjmAyaIV8RdHO0WqbwoQjcO5JUaQk6Fm6yUMgpFJeG+SzpS62Hwrw0NkBshWV+qYw1aUBCjqbefaWJIwGicxkPW6fQmq8HlmWGNPf+h9/BoIgCIIgCMKvTxTshd8ERVG5+YtdtPlCnNwrlRZvCIC0GPM/WfM/NPwGVIOF192jaVp0kHumdkMG8LWAzgRG7ULn/k9WcnfR2Vg4/gj03jm8HZpEKOjjqmH57Zn39DgD1j4Fxh89Qm60wTkf/2TXDqsBq1HH9WPzMel1kDEQfM3gqkEFipR07lhURlg2cRraxbAc8vDQe3O5vfpm1pv8HBn1MqcuAh9mzF0u4JG+vXi4t0pVs5cDNU7irUaq2/xUt/kZJB3kRdsHfP3mKXzqGkW0SU/PNAezh2SxYHc1X2yr5L65+7homHbDQtLpeaXbh3yxoYge1TbefG4N5w3J5IKho6B0Hf033gADL2XI0Oz/zWcjCIIgCMJvQmNrhL+830S3zkZKq0Mcv/9PWuL/5hLikukO9pcEWLnNg8enMHGwdl7V7IzgsMvoZAmnJ8LtLzS059UDrNzmZbnqJbeTgRF9tXM4g14iL91AUXmIYLBjH9mpBv7yN/n8qqqSEKsnooa5aJrWjDCir42XPm8mpN2jIKLAn99sJvCjbRVXhrj+qVoq6sLERcsM72NmwVqtOeKK02IoyDJz14V67n21ka0HAvTIMbHjcBAJbaNun8LZd1djNEpE22QKC8x0yTJypDzIwnUe7nutid75JiQJQlHdecaznsNVBoat9/DiF63ccHYs2alGvlruYst+P7eeF/eTGB1BEARBEATh90cU7IVfze6KVu76ei+zBmUwrXca83dXo6q0d23nJ9m5ZHjnf7yRxiPgbYLMIf94ub+VPYISW1/+8sxqoIQLh2WTIdXDq8OIWOLZPO07BuemsO1gMSY5gCqBBLgTelO+5Fku0n2HssGJfNU6SMiD1F6gM0JyTwCCYYWvtlcyIDuWLsknPha+paQZbzDCjooWzh2cqb1o0yZ+lYAzg/fjx0SGw0KPQRexcFM1DfYCWos2YjFqE5bpDi8gPmY2qhIh5+h7fPRhN1o7jWb1kUZGy7sxbZyHmXF0NzXxcJc6Uo6UcXngNfaGdCxRBvPV9ko+21pBSaPn+LG2cf24fC4dkUOnGAvBiELv3Az2VLay6nADi/bUEG8zMaBqA8nuWihZ0573LwiCIAjCH8eS9W4Wrfdw3VkxNLZGOFASpLI+TK88Lf5m4iArDvvPd73/YF9xgNgomU5Jhn9r39NG2pFleOXLVoorQ0wcbGP1Di9PftDM4B5mZk2OIhjmhGK9BPj8Cos3aIXyz5a6eP3uZCRJoiDbRFlNuH0C16a2CBv2+BjT30qUtSNrx+lROFqhNYscrQySnqyNW/fj+X2AQBAKC0xkJOn5fouXlDg9xVXaes1OhUAQLCZIjtPxwqfNnDzCTllNqH28Hr/WfZ+erI2nok573etXUYG/ftOKy6tQ2xRBJ8PgnmZuOS+ONneEhBg9eendKakOMmeFi7KaMJv3+amoC7P9kJ8Wp8Kh0p/m3guCIAiCIAi/P6JgL/xq1hQ1cLDGyYLdNcwems1fTu/NzooWiuvdXDMml+vH5SNJ0t/fQCQMb47TYmguXwGd+v9b+89JtHPNmFzcgTAZ1jC4Q6iRIM1tbVz4zlauGlvAQ5eezppjaYzZcjX4W7FVb+TPhv2EJCNSJAJhn7ax7qdqf477YlsF983dR9fkKL67edQJ+71pQheu+mg7X26r5MKh2XSKsRAblcYC/STkQAsrHI9wpfMSjrmzKdzzKj1j62nOKeR74zgafJtIbN3FsbJSzhufxYQjD5NfNZfgUT1d9n+ADDxneIU4j5sx5iWk0ojq7kcDsSRKLfSRi/lOHcyRejcASVEmenVy8Mp5hZgNOuKBRxYeYN3RRl49r5AReQnE2UxYDDLXfrKDZGtPNp/2V8ge/m8da0EQBEEQfh/W7PJRUh1i+0E/55wUzczxUUQUlfKaEDecHcOkofZ/uP6h0gB3vtSAzSzx6WNpPyl6/zOj+lnZf0zrRvf4FAJBra1/834/m/f7+cv1idx2fiwVtWG+WOZCBbYd9Lev7/ErKCroJLjy9BiuPD2m/Wd//aaVdbt8VDeET3jdYdcxaYiV7zZ5eXd+G73zzNgsMoN6mFm0XmtuiImSaXUplFSHqG+KkJmip1euCYMeymrD+AIqSzd7eer6eB54sxmvX+XTpS4UBWRJy6ZvblOQJK1Qn9Op4zJMBawmiSPHbxokx+kY3d/KhSdrTSyx0TrueaUBnQ7+dGkCaQl6th7wU9sU5vlPWxjc08yt58W2P10gCIIgCIIg/L6Jgr3wq7l4RGcsOpUxBdrEqJ9vq2B7WQsAiVGmfz6ZqayDpO7a5K/24xOkVe+Edc/DsOshfQBEwkSW/ZkFZToOZpzN3VO68c3OSrLjbfTLjGXR3ho6t6xH3fUMUs+ZPGu/mYKWVaRJjSREGRmQHQfZUyHqT7D6SVw5U7GGAxgGXgJdJkN8Lkv21nDjZ7u4ZmwuN03oAkD/rFjyk+xM6pnSPtwPN5XyxdYKchJsZMVbibcZmf3WJlyBMPujb+CUcCMtsp3YgJvrOlfxXqmP3i3fAxBTu58N/g/4sP+f6Bb5hOcaB1G14iiT2QgyVNh7MSy0jw1KD+ZHhnGObgWpUiMAUs1OEoGHpStYYRtLhs5C2fEs+zirgVfOK8Sklzn4/CnEeMuY77yDemJZsLuaGyd04dIRnalo9nJv9BL6WWqh4N2OTH5BEARBEP5Qrp0Zw8a9Pk4eYccXUJm32kXoeEN7lE3+pwX7uGgdMXaZTkn69mL9iq0edh4OcNkMBw67jsbWMG/Na6OxJcy0kVEM721m0Xo3owtt+IMqa3f6WLvTxytftnLFaQ50Mu159HaLTM9cLSqnriVCUXmQHjlGDpYGuWhaNN2yTehkiTe+buW7jR7+dFl8e9f5wO5mjlWGKCzQIgcjisorX7RQXhvCEaUj3iHTJdvIBQ/UYLdJuL0d2fetLoWeuUb2FQdpQYEGOFIeIqLAuZOimLPCRTAEd7/aRDjyw1glnB4VpWMz7bFCx6rC6GRwRMn4AyppCXqOVmoF+34FJi482UF5bZBbnmvAYpJodmoHoLYpTGaKgU5JBtbs9LJ0kwdFURk38EexjIIgCIIgCMLvmijYC7+KF5YdYf7mgyw23olxh8wF5ufZUR5EL0OPNAfnDsr85xuRJLj0uxNf2/wGHJhLBIk7uJlhnuWcUfYiM4A7j/UhyWbkgyWrKFNTyIi1kBZjIcXlQVIVcNcxyX+Ynrrd5OZ1p2DYpR3bHXgZlXmzGPPUKqKM/VjffxJWo/bX50i9m2BE4XCtq33xbqnRfH/LaO0bZzUoER5bdBBfSGFvlTYx7U3j87n5i92YCaD4tXVjJTdbzcO4/tggBnZOYE3jCMwRD3NCQ0i0G/m6WObSgjN5pu0eFimDuSVyDWPNRVwTV8onnsd4LTydC3Tf48XI3Mgg3ghN451BlaQmp3J23sWMdwUYlpfA/qo2Hpi/j21lrVz54XZeOLs3mS2bsUkB+tlb2BRJ4uyB2mdwtN5NszvA5aFPIBiBso3Q5aT/6HMXBEEQBOG3SVFUHvhrIzUNYRrbIuw9GuBYVZBQWCs8Z6UYOG3MP79hnxSn5+NH0k547f1FThpbI3RK0nOwJIjLG+FwmVac9gddLN3kZveRIG/Pc9Kni4lou1bE9gVUWpzh9mL99WfFkJ3WEbNz++w4lm7y8MJnLfTOMzC0V0eHeUl1iEBIpaohTL+u2msTBtmYMEgrbJfXhnB6Iny3yXt8jRDxDpkEhw4VcHl+VGVHO+3cVxykW7aB0mqtUK/TgcUks3GPjx45JnYeDrQX6/Mz9JTVRn72GJkMMLyPlVGFFgx6idgoHVmpBlbv8PDREiffbvCSlmAgFFbwHT8ORj1kphjITNHe/77iAOGwSiAEWw8ECARVTMZ/72kGQRAEQRAE4bdJFOyFX8X6o404XS50lkbCwIH6BlQcxNtM7K5sw2Ks4px/pWj/t4bdAJJMac75zPmkkpmGL0AHm+jBpHwHyoqHWG2ay3OhMzjUlslDfazEXfowvLMJStbQ3ZGOu/NMCuKj4PURcNpfIbk7AJuPNfOM7iUmq1ugYi47DT1p9gS5ekwuPTtF0z8r7qfj8TbDy4NACfPguPk8t9FJaoyF/pmxRJm1C67XTC+1T2qrIDHQv4F3zE5ibYPJr1nHx+HxfB4eyaBONraUtmCv3kQv+Rhd9HWMDY2lqsdY1u2/jeGqjmI1DbdkpcmQyjZ1KK933k5p/p28dDTA19+uwx9SeOW8fkQUuGxEDtvKdiADKjIfFLzKzr37Sek3lt2naln84YjCaa+sxxUIs3rS02QpVZA79j/5yAVBEARB+A0LhlT2HOkoOB8oCeILaEVru1Vmf0mQrQf85GUY/+1tXz7Dwe4jAVBPjK+JtoEsqew9qhXvFVXb783nxtIl08ANT9fz1XIP3TobyUjSYmDW7/Zx76XxmI1aBv363VrBfV9xCJdXYe/RAMlxOu66MI6jFUEKC36a6b79oJ/732gkN93AmP4W9h0NkJqoZ9wAG+W12lgkqaMbHrSvHXaJhlYF3/GJZ6UwRNskSmvC/JDiaDJoD4EO623mSIUWQWgxgS8AsXYJRYXCbmYG9TTx7UYPm/b6sZgk7r88nhi7joHdLMxrcBOOqJw83M7yLV6qGyPcNjuO4X20GxI/xA5ZTXDOSVEkx+lFsV4QBEEQBOEPRBTshV/Fc+f05YF5ei47fAOX6xbTVz5K0oAZDM9L4LHFhxjVJfE/27CrBo6tIjelF/dOnYSn9FQOFXsI6228WHEaSyOFoIPzBqWTuOtFpE0RyB8IreWAiuxpwH7GS/DWBKjfD2Xr2wv2hVmxePQNmNQwQWc1Z89xEwwrzLt2OOMKkqF8E6x/AUbeqsXxAMh6IgYrbq+PogY/m+6ZAGiFcFmSmHvtcAo2zIGDOwEVCe3KcBD7IP0UOAoVaiIvG15gYt0uHuz0FN8ZJ3K67q+YIh4KI/vYut3J48YthNCxNNKf5uShHKhuY67lEVLK6/mg2MwnkZORJbAbddz3zX5avEHuO7kbs4dksnR/HQMf/Z60mHjKlQHkHNGidCKKymdbK/CHI0Sb9Rj6ngOxIhtVEARBEP6IzCaZv1yfyNMfNVPTGMEXULFbJE4fGwUSLNngoXvOv1+sByivDbP1gJ/rzopl5vgoNu/zUVEXJhyWKK7S8nYkCSYPsbJkozbJ7Iu3JeH2aedFeh1cOM3B+ffXoKpQ3xwhM0Ur2A/rbWHbQa3xYcdBH09+2ILFJPHVXzoxoLuFxevd7D0a4KozYtony7WYJGQJKmpDnDLSzu2z4wEIhVUkSZtY9qXPW2lsi6AoHe9DVSXyMww0tUVQVS173uVVyErRkxQnU1INgRAQgvcXudvX8wUgKVaivkV7Pyu3+Vi5zdf+vh02iXteaURV4U+XxlLbZOLzpU4WrnXj8mgD2H0kwPA+VhRF5ctlLmQZ0pMNzJ7q+I8+E0EQBEEQBOG3S/61ByD839QpxkJJo5vB8mGG6w5wqX4Js4dmce/cfTgsBk637aPiqWFU7Vjy7224fBM4q6B4JZePymHCBXfjuWQ1/TkIQHa3/qyZ9C1ft+VRLHdmn2UASmo/tk74gq9SbmLu4M841hqB3HHQ6yzoNxs8jTRWFhFRVKwXzaFixtcY+p7FiLwEClKiSI+1aPve8lc4vBi2vt0xHnM0ayYvZYjveb463lFW2+bn+kefZfXj0+lpbsJ85ltwezEMvBQJrct+rXEk1cZsam+owBSdyAR5ByY1QM+6eSw76mJFr7/wQvh0VkoDmBhViixBGD0SCs83X8sq060cjmj5+f3lIs6NPcRz+pdJo44Wb5AYnZ9Pl67lw03l1LkChBVw+7WOsooWLw8vPEDX+5bw1HeHCEVU3jE9Q9rrBVB34L/63AVBEARB+O3KyzBS3/KjGBcJMlIMfLTEybDeFr7f7OWiP9dQURf6t7a747CfhpYIFbUhLj7Fwet3p3DJ9Gi8xzv4Lzs1mpnj7JTWhtDroCDbQOdORqaPstIjx8iQXmYCIZVhvc2cNzmKzBQDNY1hDpYEGNTTwu2zY3n8ugR65JpIT9LTv1tHV/3HS5ys2elj24GOzv7uOSYmD7URDMPW469v2e/jtNuruP6pOnrmmnn97hTefyCFbtnaTQqDDsxGiRlj7Nx5YSzG48k8Xr9KWW2YLfuDDOimZeObjBIW04kd7z8U63/MapZQVWh1K6gq2Mzw8NstbN4fIBCCQEgldPzjqKwLcsPTdZz3pxo27fOjKFDdEOaaJ2oJhX+6bUEQBEEQBOH3S3TYC7+aaeHltKh2PlJOYm5kBOMO1dPmC6EoKtVr36fQs58Nqz6gU+GUf3mbd9aNJ8UaYPbYS0g4/tpba0u4N2wkTYKUATM46Z1m5hqfI08+xvPuM+mss/PERg/mKgu9Kj5j6vqZwPHZ1UbdifreZByeFs4K/IUKOQ0JibU5Ad65aCCeQJivtlcwNDeeLiNvA1M0DLnmhDGN6ZnFwoLHSQqUQVtPHp5fyUXhLxmsHCK8432Y9BDY4iFzKMgGHt8Xw23uZzAtXcvqhme52fcSHL/m26x04/zBmYybPpXyhFF4Fh7kSCgJRTIgpfXj7TEjsM2zE/RDOHsq/qMHOSh35eG4b9H7tlBHCo8xk/nmP9MpUs456oNsDeczuHMsd0/uxvPLjzC+ezIrDtbRXz1AdEwWy3xWuukqwdsGbZXtTxwIgiAIgvDH0uKMEPlRvV5RVI6UB1EUqGnUuuRVFeascHHTuT8TBfgznJ4I/oBCfoaBk0d0TIz62VJtTh+jAeIcOt6a52z/mdevEgiqzF+jxd3sPxbkzW+0nxeVhSjsZua25xu0IrdFwuNTyU418MqdybxxTwr1zWHmrXYxboCNa86M5cCxAMP6WE4Y18zxUew5GkCvg8bWMK/NaUFVtacB9hUHKCwwo6gyw3qbyUrV8+1GL/UtEf70ehOZyTqCP7pnYTHCFafHMHGwjcfebWLDHj9ZKXrKasNMG2EjK9XA63NasVtlbBaJ6oYI2ak6Smu0g+0/HrHj0x4UwGiAYAhmjLaTGKtnzQ4vZ4yP4tG3G/EGIDZamxDX41cJhsMEgioGvYjEEQRBEARB+KMQBXvh19FSphWiDfBI7ids2w9dWv0suG4E8b4STOutrIhMJXrCPf/yJlVVZcHBVrzBwSj7AvRtq2N810SmZgQ5p+hxpmTruTl7CDbjMt4NT+Ysw1pGn341NpOeZ5KWkN3wKtVKHEbCNClRxMsu1GX3g8FKCDc6g5EEiwlFVZn+8jq6p0bjDoTZWtqCTpbYdf9Eok55nlWH6zl66BgXD++MTpaQVJXciq8hEoSXC7k2lMLD4VmcZVhP57SZ9AUoXgFzLgPZwK32TOrjCkkzeOg/YhJ7Kk7G5QvwcXMBzxjfYOWOzSzJ/YBTeqex/GA90VFpNDf2I6F6Exkln3N9wpvMGpTO+B45bCu9ksi3XxNuWY7UaRBfVmqRPM6QRKqs45pxXbl4qUKLN8Tpr29gSq9UZg/O5JyimzGYlqMGEwle9Com3YugN0Hnkf+TXwdBEARBEH59Ow9rFWOHXcagl2hsjdA5Tc9j1ybQ4ozQ0BImGFI596Tof3mb1Q1hSqrD6GRYvN5Dny4mUhP09Mk3s+2Qn/MnR5Oa0DGRrN0icccF8ZiMEokxOhpaT5y4taE1wpZ9fnQyRBSItsqEwwomo8TMO6s4e2IUny114Q+qLF7v4Y17Uhjex8LCtS7iHHqG9ba0b6eyPkxVQ5g1O3yoP9p/Vqp2ifT5UhdfLndht0qkJ+nwBlR65piYNNTG85+24PUpePwqviC8/EUrffLNTBtpp6k1Ql6GgdqmCIvWe7jw5Gj6FZi4YkYMnZIMLFnvZuE6Nw67TE4nQ/txV1QtrmdUoYXvNnrZcSjAwVIX150ZQ3aqAYtZxhtQ6NHZxJBeFhx2HfEOHXareGhaEARBEAThj0QU7IVfRS3xbGY0MipD+/UmKy/CtN5pxNqMMPevULKQcT1Oh969fnZ9RdEyRiWpo5tIkiQ+umww20qbeWzxISQJDgxezim73mZ75AI+K53GTSqsun0sba88Sq5/L5KlBoAMfxEAfgxcE74VveLnGeMbGFJ60Xby68hKiHlWB2a9jo3HmjjvrU1cGvwIX1hiK2cQUWDhynUMrv+Cxw71oUjNYMWheu6Z2o2enRww63OU4tWw4QU6yS3Uxgzg5uYe3Ndqh4pWXl3i5unoLkT7KjA7i0nRVbMr9yr6L7yGnt0GoY69l8I3J2GpCTFMPsC3/jBJ0WZePyOHqU9/i2o4BBJUF+1gcVUBjcE63tnaRCAcYWjlGsz6GsIp3SkJxQIqZwQfZECqno/HTWFpdxdXfbQdRYVGVwACLgzHlgMQNsZg+uxMVKMd6a7y/+0vhSAIgiAIvyr1eNlaluCmc2Npaoswoq8VWZa46MEaGlojXHdWDMnxP38JEVFUdPKJnd4F2SbuuiiOQyVB3pzbRlaqHlWFirowqgob9vo5Y3w0D14ez0NvNREMq8REaQVob0D5yT5kGXrlmRg/0IrdImGz6pCAT75zcrgsyNcrtWI9QENLmL9+08LeowGOHc/KP2mwlUumx9Az18T1Z8ew+0iANTt8JMToaHVFcPtUWl0KW/a72XLAh8Mu0+ZWcHsjWM0QEy3xwSInl53qYFhvC6ffUUUorN08CIVV+uSbmTgkzMtftLaPefF6D/UtEWKjXDS1RqhpDFPTpN2IiIvWnfD+pgyzcdE0B0N7WXjivSYAXD6FQ2VBmtq041HfEuHpj1oY2N3Mg1ckIAiCIAiCIPyxiIK98MsrWUv8N9eQrpi4LXQVjtXlzL12OKsO17PuaCN9jFOZlN+KcfCVP7t6kzvAlBfWEm0xsPiGkRj1x7uKgl4K02x0S4lm1eEGQhGFdUfqmAjEmCVcnjCfb63gknwfiWqJNpSlr7G6qQddks4i+cg+XoucSnXqOK4fn8/XrivZVtbKV4+tJd5uZNPd45FlieF5Cbx8UhTT1nwDeugllfBoeBbS+kXk6FfxZ/1OLgndzobiJh5auJ8rR+XiCXbli3IrjYEUWlQ7T53Ti9VFDSzYXc2u8laWVuq4rssrPDlNJvGzqRgiPvodfg4kcJVsZ+rWYSwbdz5qWzHeAbdzzqBMUCJEvTuaVcZ6vrdMYYJhD4ljr+KC4lgMOom315WSFW+lsc81VEb1I33waSRWFVPT5ueGST2ZNSgLgPQ4C5Wt2sRnFoOO7XVh+p/1IXgbuXhtLI8pt2N0dCVF1v3cxyEIgiAIwh/AnBUuvvhei51pcSnsPRrggpMdzF/jps0dobDAhC+gMqSX5WfX33nYz4N/bWT8QBs3nBPb/rrHpzCij4X0JD07D/tJitWz92gASQJVhUOlQUqrQ3j9KqqqRcH85f1mzpwQRWq8nqOVWq79gG5mzpoYxZ4jAT5a3MahshDDe5u55xKtYH3OSdHsPxZg79Fg+74DIZi32nPCOJdu9hJRYOIQG6GQSlGZ1t0eCCo8eUMS81a7eHteG23uCGU1YS6cFk0gpPDZd268fpi/WovpeeL9Zob2sjC60MqOQ35uPDeW9GQDJdVBXvuqFZ0OYu0S0TYd502JZveRIBV1IXYeDjC4h5kuWUay0wwM7mFhxVYvOh3ccUEcQ3pa0OkkdLKEPwgmA9Q0hBk/0MrlMxzEO3Q8+WEzABnJ4lJOEARBEAThj0ic5Qm/vCPfYXCW01+G2+LWkZith68/4PX9A9nkSQUk7usxicvqD0DGYPhRF31NZQmHm8I0eYK4/GEC4YhWsG8th9eGgz2J5SO+YeOxJnIT7FzZdA4XdTubzLxeTNx1gDF5Dph3MQRcqJLMW3Vd+HjBAZbfcjLPNuazq6KVqso2lh2s49MtFUTjBqy4A2GONri58J0tDOocTzhs41h4BrN0Kxin20kLdj4MT2SwvoihuoO8anqH27mJrSUtbCnZ9qM3n8m1Y3IZlRtDytdncLLXw1dxz3PHiDg+2ONkyDsBekr38ajhXeotuQwcMISb1ig0uAK4Cs4i0mcWzY0e0gCQkPQm9HoDqZNupOvn9RSu1fHV1T3ZUNzIu+tLqWn1U+q2sT3vLM58/RBXjc4hPymKYXkd3VhWo56/zu7PnxfsZ1VRA5IE7148nXBEYeeCpYwKvsDrowqZ/Mv8dgiCIAiC8CtYvcOL26d1pmel6PAFFJ78oInVO3zty0wdbqO4MsSAbifexN+0z0tZTZhwBKoaOsLd1+3y8sT7zZw0xIYsaV31LS4Ff1DlopOjqagP0eZWiImSue7JZlRAr4PdRwK0uiPccHYsH33rZOehAJv2+Ylz6Fi8vqMA39imsGq7l1e/bOHCaQ7Ka39+MlxZBkWBKJuEXpZYvtXL2l3e9hx6q1ni1vPjsJkl1u70oahw3uQoCrIjfLzESfhHqTw9cgzERutYt8tPTWOYl+9IprE1TOj49Ec6WUKng4QYPWMKLXzynYsjFSGuPD2Gt+e1svNwgF1H/Jw1IZrmNoVH323iroviSE8ykJ3WEQ1UWGDm0unRfP69i6WbvWSkGDh9bBRFx+cU0Ovg3En/ejSRIAiCIAiC8PshCvbCL2/krQTa6gmVb2Ps0OFYl94IwF/iq7gm+k6avUEuKr0Tin1gjoGepwMQri8i7q2hDFD1fDT2FZL7TibKfPzCJuiFoAe8zTS6fKgqdIq1MK5bEmcUplOzYxEXNl5N7ef9UT1HkWQ94dPfZtiGbSSnxpObZOeV8wrZUNzIl9sqibcZuaHTYW5qepj98Sfxou1GQq+OZo7k5LJjT5Kdmc2z4bM4Rb+FBJw0KVHsUvMo73k9WXvv4ZA/DluknNui17PINIXdrmiCYYXMeCu3J22FZ6bSxd8EMqQVXUwyDTgiE7iXS2iV49ivZLHPOJr66OlccZGN280G5uyo4v0NJdQ6Azx9Zm+m9U7Df9EqYgwK3moFlXpCioo/FOFYgwdFhWBEYcPRJiqbvdS0+fl6ZxXzrh2hHc+Iwvsby+iaHMXqogZKGr0kR5u4ekweACpaMT8YVshPifqlf0sEQRAEQfgF3TIrjhc/a8YbUOjX1cLc1W4AumQaCARVfAEtE37JBg+fP5aKzaIV7T9a3ManS13IEtw2O4b+BR0d+E6PgqpCq0ureCsqjC60YDXLDOtj4Y2vA2w/FODJD7Tol2ibzJWnO5izws3pY+0UZJt45KpEFq5zc6Q8SJRVIiZKptWl0LerkYbmME8d7zY/VBo4YcLcHyvIMnKgJIjLowIqdotEWqKe8towobDKyH4WNu31s3Kbdv6k08HH37qQoD3b/gdJsXp65pmYNsKO0SDxzvxW5q5yowJv35dCbLSON+9JwW6T+XKZC4BIBFpcEQLHo3oCQdh+0MeRihChMJTVhhjR1wpoy327wcPoQivLt/pw+1TyMwyMKdR+LkvaDYh4hw6rWWTXC4IgCIIg/BGJgr3wy7PEsr7MzTh3MVtWfkG+aseih6wpN7MobySrixoomtOV7pFdsOnV9oK9TqdDRcUu+Rmw/3EME2d2bDOpAK7dAkYbF9btZ0b+25imPoElpQuPLjpA8fojjDEq1DS1kKgPodcZMez9nJOrF0MnP6WNvbAYdQzLTWDuzipeXlnMEzlBZBQa62vYEKrmRVMlJkLcMTKB/gN7c+aAdPTB11i/8SteKR1JjNXAJ75B7A2fQprBybtJ35DTvJZzOjVyS8ZDfL2jivJmL8F98zF6tQvTI46h5LdtBGCWYRWJ575C4bE3SNi2kmPBWsZ9k8O4giSG5MTxl28PYcHPNbpv0VeHmbqqGKmljM9H1DBs5OWsuX0s28qaGfXkSupdASZ1T+a7A3Uk2I0cbdC60eKtRvhoJrSUsnbYuzy8sJwok573LhnI3so2LhmRzeE6FxFFZWhuPEtvGoU3FKFTzM8//i4IgiAIwh9DpyQ9RRUhVBXqm7RifVy0zN0Xx5MUq+eTb518scxJKAxzV7k5b4oDAMvxorGiwqGSEGP729u3OXW4nfwMIxnJer5e5SISUbn4lBhMRombn62jqFxrcd99RIuxSYnX8cbXbTg9CkaDzNGKIOlJeqaNsHPJQzXUNUfolm2g1aWw54jWaf6DK0+P5dxJCo2tYfYfC/D9Fi91TRFy0w00tWn7iXPIuL0Kbp/KsN4W2twe6poj1DWF2X8s2N4lbzWBy6sV6zunGbjydAevftVKeW2YHYcCrNzu47JTHew47GfHoUD7GEqqgzzxfgs2i8TZE6OYNSmKMf2tLN/i4ZKHagiGoG8XI7uKgkiS1L4/m0Xi8kdrSU/SkxynZ8FaN8WVQaYMt7Fup5eLT3GwfKuHEX2t5GUYefPeFGwWUawXBEEQBEH4oxIFe+FXUZc+ib0HD1LUaQYXFN/MI1N7MzMvHYD3N5QQcE3hfdM+9FEpULYRsoYixeeiu+x7lOV/xnC8iP9j9cZ0oi0GzF8NJCbgIvjtHTTN/JyPNpXjUwqZbX2NHS0mxiXLvDSrECq3QlMxz5Vl88LaVUSb9Sy/dTRrihow6iSsA87l8R2ZfHDUREpCHDOaHibeECSu0o43poHDtS4uXeHhvMFXcn13G2cNyuDSpz7iWv18JGCfuy8AwYajVEdrj5MrCkwrns6IcBL7lM48ef4VrJv3CAPdyzENvYKcBDsf7hvKZXnTOWodh3WXjp6dotl0TOseu9axkesCX6CW7eGhtntZo7sD+yY/hOvJmPYsN3++i3qXduHosBqQAJtJj1kv4w8r1La6UD3rkMI++tjbGJYbT+/0GPpnxfHV1cNYfrCOP83dic2oY/9Dk4m1GYn9yZEWBEEQBOGPRidDvEOmxaWQn22krDrEI1cnkhSrXS58tdzZXmCORFSq6kN0SjJwxrgobGaJ1Tu8jBto+8l2o20yXr/Cx0u0bvN5q110zzG2F+vjHTJNbQqnjLRx/hQHn3zrZN+xAK981UybS2VANxPnToqm2Rkhyipz+QwH973WiDcAsVESLS4VuwUWrXOTEKNj3W4f2w74OX9qNDFRMl0zjVz7ZD0AzW0KxuNXP8VVQVpcWsV/z9Eg+uMpPzFREvddmsDznzSj10lcfpoDX0AlI1lPepIej0/hSEWI3HQDny11Hn+PEk6PypodPoIhlWBI5fU5bSTF6iksMPPVCjeqCgY9/BD06LB3FNyLykJUN4RpcUaYcZmdw2UBxvS3MqKvlWkj7Lz5TStzV7s5WBrk/ssSSPk7k/4KgiAIgiAIfwzibE/4VZx7zoXAhfQCzgf8oQi3f7CC05rfIbatCxsMvagZ/xIZa26Hgwvg8pXQqZD3SuOoin+Cu/oW4POH+GRzORO6JWFZ8yiLdlewOOkK5ib3RC3fhKt0B589dS0Tu1/LuiONNJnS8aguDrok2PsFdB4N123h1XsXAyqKqtLmC1PvChAr+zh55WSmSTInXTiPOV9/zhI1h8PBaKQ9tSzcU8sY81FWGF/FuitIhZrA/pR53HDKEDzz7OiUIC+5RjNWF4M1YxpvZB1hjXcLd9aOoSgSRxFT6Z8ZQ4rcSnbtB6A3Qd9ZvDL3KN/sDFHW9xaaW0N4gw2sPdJIs1vrPMsfNgOK9yN1O4VxRXZMx0KoQCBtMAFviHtP7sbCPTWcNziThXtqUIEEu4muKVEs3FPD4Xo/vqsWYQ02E5c/gk+6nvi5xFgN5CbaGZmfgCAIgiAI/3fIssT7D6ad8FpFXYgbnqolGNYmg02Jkzl5hI2357tYuN7DZ4+mEY5AaU2Ykf2sdM0yUl4bYst+P6P7mfnzW02UVIeZPsqG3SLhDai8v8hJepKOvHQDjW0RYqK0gn2zM8K81S5mjo9i/CALNzzdAGhTGWnRNZAUJ3HHS4307WJmbH8Lz33SAoDbB9+sduPyKFhMEooKHyxyopPhiyfSGD/AwvJtWvNE8PhNhwEFZuwWHet2eXF5VdTj9fPJQ2zUN0eorI9Q2NVEn3wzFzxQTVObwo3nxPLKly2EI/DREieyLGE2alnyq7b7mDTUxtaDPjw+LWM+OV6HqsIts2KpbQozfbSdR97WmjCyUg0crQxR3xyhpjHMA5fHE+/QkZtu5LlbzCd8DklxOlITdIw8HpsjCIIgCIIg/LGJgr3wm3C03o3p8HyGGRaQoSYyXUklY9ke2gxJBFQ9R480MHjVOTQfsPN25DR0ssThWherixpo3fghd/lf4wod7A+Oh+u/xbf6BeJX3s8oaQdDhmWyYE81swOf8mx8BdlxJlixlnDsZ+hv3M6o/ERWHK7n6TP7kJdk58urhmENNKD7vAkkiR6lH9A/9AJjDYV83PkJxubH0bj8RfpxiBy5FhWIl9rYGfJwxdxyLgxN4TbDl9zlWMoXfd7jisGJOF7M4RRgkGkBI0KvElJgZ0UrY54pZY1Vj1FvBklm1uBMWrxBzhuShaKoxFkNLNlXSyCs8MI5fZnUtxOMXgLAIwMiHNyzgLdXF7FkTjQ6eRnLbx3DLRO70OoLcdnIzqTJzQzq3YvMeCvTetcSYzVgzYoHVYU1T4Epmg3xZ7B4Xw03jM/nrjl7KW5wc+3Y3F/190EQBEEQhF/f1gN+iqvC7d/XNiu8Pd+FLIM/oLJxj5dPvnNRWhNGkqDFGWHdbh/ltWG++N6Jx69ltutkiU8eTeOOF+s5VBrC61c5f0oUT7zfTKtLIStFT1VDmPW7/TS2RrjmzFhio2X8AZXrzoolLlqHwy7T3Bbh5S9baXNH+HK5i/DxSJypw21YTCrfrPQSCnekzut1EtsO+Fn5o4lzs1P1jB9kw2aRWbKhYwJb9fhqX65wo5O0TniTUeuHP2tiNHuOBBjQ3cwVp8Wwq8jPhj1+AD78cwpxDj3TR2nz/bx5byoL17pZuM7NdU/Wk51q4OU7kml2RlAUuPbMGDbu8TF9lJ2zJkSzYpuH3nlm0hL1OD0R3vi6lUE9zFQd77ifNMTGW3PbAOiRa/z/+wELgiAIgiAIv0miYC/8+ra+Tc/WcvpMmEXF0TpSaleREdlDRIWPoy5lUuN79F05G50U4AazlX2dLueva44BkJ9kJ87qgFpo0CXz2DXnAWAdfjWteju3LJE58tomescEuNk/BzyAB0rlTL5s6MforZt468LBeIIR7Cbtr0P/rFgo2goFU2HItZi9jUS2v0lezxG8N3MwHFwIfEBYlUGC8rjhpE2/H0/AgcsfZpluIBcmlNO5cy/u3D4abLeBPRncdVgJEFYUQEYnS7giJj4Nj+Xc6DqMnkYGZmfx3sWD2FvZxmurj3LR0GySok3E+CqYvmYalA2HU18GwGzQYcvsw7z6VkCBCKw4VMdHm8o5VOtizaDNzNzzAsj3wejbmdwzpf2Qu8t3Yl/xCACvx6Wxplqmts1P3wwH7kCYgpToX+jDFwRBEAThtyYUVvlgURtJcTomDbFyrCrEkYpQ+88Nem3i1Mffb0FVITlOh80i88l3Lox6yMsw0NgaAb9KpyQdl0x3IMsS91ycwGdLnSxe7+GJ95vR6yAcgbLajpsCq3Z4mTHGznsPpBKJdBTNB/e08PVKF+MHWbnklGi+XummutHNFafFMGWYnRc+a0ZRQTk+8Wz/AhPXnhnLwnVuFAUsJon0JD1piXo+XuLk1vNODP2LHC/+RyIQQYvbkYBwRGXaCDvTRthZvsXDgZIg50+JwqCXsJplbnymngtOdjBxsBYH5LDriI3W4fRodwBKa0IcLA1w98sN6PWQk2bkYGmQ7jkmeuaamDy0I/N/wRo389e42XHIT2W9dkyCIZW8DAOgxQsJgiAIgiAIf3yiYC/8avZXt/HxuiM8duAWACZMyCLW6IGIFxVQjXau8r+FS/ZhUMMczJpFt2HTidmpdRdFm/U8elpPeqQOZ8eh0UQnZvDasmJO75dGz/oF2MJOmqTOAGRmZPL4gXMZKe+hjhju91/Cd6Y76bToK5Y0foA1ZwhjuiZ1DG7Zg1C/n/kNybwcmAq2z1DL4ZtAGLurloApnipDNnp3Ja/UdmNkWyan9Eni1fMKyY4fwYf7xzKm+i0Kw348x7YQiRixW+L4MOEeTrGmc/24PFxeP3kf9CNKdSFVw4ENC+h+8nUcPHKU+XO/5Pum7hyudVHc4OHi2D1IvmO4PW6MJysY9TJt3hC5iXbmTw3x7o5WvqlNRCfLGFuLicLOrr17yAQIebX3tG8OlKwjGPDw1j4jaUxkTJ88TsvuR8Xyoyw7WM+UnilsvHv8L/Y7IAiCIAjCb8uaHV5W7/CyaZ8fSYKbzo1hz5GOiVXNBnDYZOqCCqoKJw2xctqYKB54oxGA3HQDV5weS2q8jq0H/CTE6HhnQRvTRthYvcNHXFRH0XlobzNrd/pP2H8wBLe/2MCM0XZG9rOSkawVq9vcEd6ep3WaHy4LEhulw2yUOHAsyOShWm68xSRht0o0tChsPxTAZJQ4b3I0mcl6umYbWbDWw/7iAP6gysrtXsxGiLbpiHNIDO5hYXhfKyu2evlsqYsWl8qGvX7Ka0LkpBv5ZqWTj7514g/A/mN+GloUstP0NDsVvt/sYeJgG4qi4guojB9opaQ6xIqtHoIhCAYVQmEIhaGkSpssNxRWUVWVT79z4fUrHK0MsfdogGibxMTBVgJBlYXrPHy90s3VM2OYNsKOIAiCIAiC8H+DKNgLv5pXVxWzaE8dQ9Mvx99QQsz3nzBR2gpAABPmkBtCbnaGe7Kn171cM3My6GSGLbiOXvpGHvPP4qw3NnFyr1ReOa+QRxYe4J31JThKF9Oz6WEMwPrBV7M+50bGWIrZ1f9Olhz1cWjzUlaab8cqBYlIRl5YU8WRNVu5Z2oB6482o6oqowzncFHP3bywtw8FoWUgwWJlCKsO1zNg2QukBJv4xDOV2Y4QT/IWR2vycB7cwbyDeVTEDeVAjYsv6cELvW5BV7qG/qEKVOCaC2aDwcwz3x/ii40lrJOCSCrsj2RyV1FXFpwM/i+v4N7gdpKiZ/NtYBiDpGpWMBi/fDPbnSlct7+WJneAPy84wNjoGt4N3sqzeguXXb2bHpFDnM3NVBgTyVLrCdtT0Y/7k3bAl9wJngaMwPWqRA/lE4JbVDKOHuGBU7pz3zf7GJmf+Gv9OgiCIAiC8Bvw2pxWnB6F7FQ9pTVhXvi0FeV4XIwE+EPgb1HQyXD5aQ5OGRmF16/Q0Kq1theVh7j52XquPTOGqcPtXP9UHceqQhw8FuBQmdalf80ZMaQl6pFlGNHHytJNHvYVBwiEQJa0LvePv3Xx/WYvk4fZOHAsgD+g0q+riSirzJqdPqrqw6gq7C0OMHeVi1XbtdgbvU5FJ4MKrN/tZdV2HwdKgvTtYmJXUYCYaIkx/c2s2u4/vrzCuw90IhBUefy9Rg6VBU84Hiu2e0mO1/PWPG2C2cxkHTWN2ntNjNVRWh1m/7EgLa4IL33ewtb9fqJsMm1uhW6djdwyK45lWzqid7wBGNbLTL+uZspqQnz8rfOE/ZmMMu8ucDJpqI2pw22s2u6la5aIwhEEQRAEQfi/RBTshV/NJcOziURUkobfzeUfbKN7cA+JlgB9InswE+CYkky63MQY/T5C1XPQ66aCp5Gz3B+CHgxqkPsjl5JgN8K867nj8FL82Q8ysN8IWBUHQTeW7EFM8C6BL26m0BzHmuhbuCq3iaTSZlBhvdqPw2omAI8tPtR+QbqKXCbcfhkfWl8ibcuLAIz1Z7Bv7gqKA/2JlrrxVXgEp6mHiaBj57pFnKlfy9VqDpc6+2GRgiwy3kNMkQdZUvFgpkSfy/vzDnP+4CxeWl4MwKn6R3m5bzmX7upJbV2Qz+fNZaxaiw8zddYuvNZyN0mmVorHfsL8tllct+sv5C35jLekGwEju502ysxpZKXn0iM9HlatQZJ1RBSte01217KpqJJe2WlsTL2S3pE9LDvmZ3c4i5FdElld1IhRJzOuIJkNdyf/4r8DgiAIgiD8tlxwcjQHjgUZXWjhwTebUAGHXaLNraL+aLmIAtUNWmzL+t2+9gz4H6JlrGaJG5+pw+VVGNDdRGFXM8VVbZgNEv0KzHywqI21u3wkxuqIscukJmg3CBQV/EFtY/UtET5c7GzfdlKsjncfSKWkupaKOm3fjS2R9mI6gMsLNrM2we2rX7X96HVtYK1Otb1Y/0NO/UeLtfifrQe0Jwk6JelIitGxsyjIvFVuPL4IOhl0MlQ1RIgo2o2Fy091IOOkqDzItX+pRZZAUaHNrWDQQ+88I1FWmbKajjghgD1HAxypCGIxSnTJNBBrl9l8fN+DuptYtN6LQQ+zpzqYPdXxX32egiAIgiAIwu+PKNgLv5r+WXH0nx0HgEGWKFOSMYVakGQI2DMomryMw+ueZ0rta8Sqxy+4/B0XXmdbtzHj1s+xG/VEHvkMoxLkkcxFtKbdx+LIIPYkTebOHqchHVkKSMj+Zgo9X/KdbhgjjXYIutHFpJHkNiLLMioqdU4/H0a9Qnebi/hgBr4DH7bvb6HpXmxKkIheokfgHfyYeKPTY6SY/Czcdoz8RCtFMSN5ZUQhl727EQ8WrARANtAQ1YvL686nZlslpQ0eeqRFs7/ayYFwGjV9T2OCvoaPNpUTdeAzkkJVLFMHsCLSm1PMOcRxjNycrtyckIe6ZTFSyEticC9Pn3kxdc4AlYmLyFg2A+n5nkiuGjy6GL4f/B6DN1+Lx5jIee/t4aTu9Sw90J20mEISEkzsqWpjulHPxrvHYTOJ/w0IgiAIgqCZMszOlGFQVR9qL5S7juex98wxcumMGO56qZ5ASJt4FqCprSODvrDAyN0XJVBZG+Lo8dz7y091UNccRidLTBluIy1RT1KcDoCGlggNLRFSE2QkieOZ+DJ6nYTTo2LUQ5NTwWrWMui3HfBT3dixPyQ44U4CcM3MGD7+1kldc4TUBB298kzkZxh58fPW9mVsFgmDHpraFD5d6mJMfwt2C7h92pievyWZm5+tp7YpzLLNPhRVG1tuhoGy6hCdkgykxBs4+6RobnmuHgC7ReKhK+M5VBokNUHPy1+2smaHj5qmCLnpenSyRFF5iEBI5aZn6umVZ6SoPMS4AWbMRgl/UKUg28RZE6OJi9b9f/xUBUEQBEEQhN8TUakTfhNuPqkrobXf0c1TAYAa3YlRXRJYEb6eZQ2j6FU4jLFPr6Sq2cNmayKx4QZM4+7EZDbw3voSPIFJzDJtINYUjePtwUwFYqvKWXdkGiO7TILrdxDc+Dq7Dhh5xPsSSlBm07gveLUohvraRpJoIYiO3TkfE129EalVhXnXYXFXEFZldJKCTQpSp09lSyCL83XLGWmvZPCwezCWb+LmvgfZc6CMM5u/o8JUyqq7HuO1ZV+w+kA182KfJ7tuM3cn5fKI73S2lbVg0MkMyIplf7WTrLrvebjkEW6afhdfN1zBxzvhw+Bo6tr8FNy/DL1eu2B7ddVRyg13MqOzm1MKLmBGYQZN7gCzn53DfKUSWdZxROnExnB3po/oS+rJO3hiySHkNcXHJ7qFGIuRDy4dxLf7apncM4UYq3jEWhAEQRCEn0pJ0DN5qIV1u/y4fVpFvH83MzFRMlecFoPLq1BYYOaMO6qIRFRkCSQJrj4jFqtZ5ovlLgAcdpk5K10UlYVQge82eZk42M4l02Pok2/i+80edhzyU9OoEGWVuPGcWJ77tAV/UCESAbsVEmJ0NLZG2HcsyP6SRiKRjnGqKu2T1wJ0StTRLcdIlywjuekqa3f5qaz3MvRKK2/em8zTHzUTCiscq9JW6JSkw+NTWbXdR04nPfo2BatF5sPFThRF5eGrEnjzmzYq6kOEwhBllfnm6XQAIorKl8udpCXo6N7ZxKShNrrnmJAkifvfaERVweP/4aaGwscPpwFw87N1VDWEaXYqx4+RnuducVBcGWJ0oQVZlv7XH68gCIIgCILwGyYK9sJvwtojDexsG0wf3VoKpcNspxuLFh3kk83lZMZZ+aCfnrImL4oqMdj3EkWPTmlfNyvexhXMoqHv3VxQ+xg5QKslkx2uPFo+eJAutz9NcnwOe3vdxQfrvuMSk4VoycfWpZ+iy7ma7nI580334TclYKuuQ0LlT8ELOTc9n24NBwlJFnRhrbP/Se907jZ8xim6TRAAtkTBocVY1Ag9jQ6kMNjrt3Gs0cOfZhTypxmFUGRg67yXea6hPw+c04Pnlh/BZtLzWe+dtHYJY1n3OpKvmoS6Dby4awbuwGzOGpDOy6NyMOk7uqsW761hX3M+XYZ155JCLcYn0lBEeTCKs3WP895FQ/m+2IpJL2PUaZE4d00p4NaTurB4bw2rDjcwoVsSMVYj5wzK/KU+WkEQBEEQfocCQZXth4JaB/txRgPc/XIDtU0RhvYyk5ZoaI+vyU038OJtHfF66UkGZNnP1Wc4eP7TVi1ax6bFxdz9Sj3vP5hG/24WjlSEWLtLi6hxeVVe/LwFi0kmFI4QiUB8tJ6yWq2jPhRWGdLDzJYDfmRZm8QVOor1oEXWzF3lZtV2H9KPxv79ZjcXnRLDszdrY/zk2za+Wu6izaVw6/lxPPdJC12zTNgsEpGIyoK1bsIRLSP/WLX2pMCl06MZP8jWvk2vT2XzXj+KCvdcYqdzmpFQWKW8Vns6ITtVz10XxbNkvYde+SY8PgWbRebZm5NQFG2+gLqmMP27mclMMZCZYvj/9fEJgiAIgiAIv2OiYC/8JuyubKM+bOPF3JfZf/gINCQT424CoLzZS2mjl5kD0vliayVhRSGiqOhkCXytjLWWMP/aETR6Aly051yejhxhgO8wV+vKkSUI1JwKS96nMD6fSyedz/NrLuD0yHesivRlcGo0L44bhv5DGbvFwpERb/PMkv0sZwCz+o/gTv8oLt5zPt3kNuqH3MeRo0Np0lUgN21jWzCTrLzL6NptOjQewdb7bJrXvsGkzX1pfXMT2+6biMNiYK6nJzc1XQVABFh2y2io3Quv30PC8fcfkQzoTnqEEe4Svt1fxxfbKkmONnPrSV3bj9FzZ/Vl47EmzhqQob2w7V2SFt7E5h6n45z6KlEOC9dmwr3f7KX/I8t49qw+nF6YjkEnc2rfTkzrnaYdM0EQBEEQhH/C41NoatMq4T9MQPv9Fi8er/baxr1+7r4onpQ4mdpmhUBQaV+3rinMuIFWeucbCYY6Mund2rywNLUpHK0I8PZ8J2P6WzhpiJWlm7wAOD0q918WQ01jmDfnttEly0hqop5Ne/3ERcvccWE8lz1SQ1ObgiTBrElRrN3lIxBUaWyLYNDBtJF2oqw6EmN1xEXrmL/Gxdpdfuqam3juFq1gX9MYIRCCcEQlO83Ap4+m8c0qF2/N7YhfTE/SMWOMnW/Xe2h2Kbw938mA7hYcdq2hIsom88AVCfj8Cp3TtKcWn/mombW7fMyaHMVpY6KwmmUuP83BVY/X0dAa4aXbkuiUZECng+vOiuXqmTHi/EwQBEEQBEE4gSjYC78JTe7A8a9UGogBV4AGV6D953qdRPmhnXSR/JTImRQ3uEl1mFE/mEV0zXreD13OZ5Gx3DGpgNh1EeQILFf6YjJZGOGsgEMLkYBZ41KY5ZjGO7Wj0cvwUK80HOkOuGkfGG3km+y8PuIMVFVFkiTCEZXzgvfQ1djABxOuxXtgHee4LybOdiXFbg+vGrvStdeY9nHqJj9GcuUm0mWZ4noXhVlxtHiDAIzMT2B6nzQiisqWtjjchjHoTXaKvHbGjRlHvi2eAdltfLu/DoC/rinmwmHZJNhNAOQnR5GfHNVx0HTahaHNYsXmsLS/7PRr7WYu/4/yXUFcDAqCIAiC8C9raIlwPFGPYEgruJdWn3hu0eyMUNusLRQKg9ev0NgW5uZnGgiEVFQVDHo496QoPl3qap+QNqeTge82edlzJMCBYwGuOdPBlv0yrS6F2CiJ3vkmBve0MKSXhcRY3d89h+maZWBEXytfr3TTKVFPY2uEcAQsRplZk6Pbl7NZJMpqmklL0FPfHCYpTo/Xrw3mvMnRJMXqCYVV2twRzEawWWTCEbjxnDhsZh3J8TqaXdryT7zfxKt3prRve0A38wljMui1saYm6LGatSceFVU7NqGwSjB8YuC+OD8TBEEQBEEQ/pakqqr6zxf7/8vpdOJwOGhrayM6OvqfryD84c3ZXsnhOhdmvcyLK44SYzVg1MkUpEbRM0HmVvUD2PUJiqoyyv88A/v0wukPMbX4Ec7Qr+PS4K2sVvqy8bbBxL5cgIkQ5+uf4dHcA2Qdfht0JogEaLFk0a/lcSQgLcbCytvGYNTLf3dcqqqy+nAD3dOiibYYGPToMlz+MJIEY7sm8fZFA3+yTqs3yKDHlhOOKKy6bSyZ8Vb2VLbSJTmKQFhh8vNrcAfCuPxhuiTZCasqF8Tu57SKJwgNu4VP5Wk8t6wIRYXFN4wkxWHmrjl76JcZy9Vjck/cWVsVRKWA3BGd4w9FONbgoXua+LslCMJ/R/x7/X+H+KyFvxVRVD5e4iTOoWPdLh/7igPE2GVknUqXDBOd0wwcOBZg15Fg+zrXnBHDe4ta8fq1Qn0oDNE2mTsuiOO+1xoB6JtvpMmpUFkXbp8rNt4hU6n4sbgMjB1g47bz4/7h2HyBCLsOB+lXYOJYZYjbX2zAbJQIBFUuOiWameN/+ju8eZ+Ph95qIjlexzt/SsUfUKioD5OfYWRfcYB7X23AYpJweVXyMwyAisun0tQa4a6L4vh+k5dN+/xEWWU+eyyNQ6UBPlri5LQxUfT/UdE+oqi0OCMkxJzYF9XmjuD1q6QmiH4pQRD+O+LfbEEQhD8+ccYo/Cac0T+9/evpfdOY+8EL1LY4mXNkNHlVG9FFPgBJR8SWzIDMDM4dlMEX2yq5M3wlRQMeYM3mWow6GbPZhj+xD83N1SR1ymb/gc/J0gE5Y8BZhTriAVIW6Kh1+jm9sNM/LNYDSJLEmIKk9u8X3ziSL7ZV8OLyo4SUjntdDa4Aq4saqHf6+Wp7JanRZhRUHBYti7R3egwArd4QTe4giqIwOj+BylYfxxo8hF1bcOCk5vBSru9UxPSZM6iNHUD3tGgW7alh6YE6Nh5r+mnB3tHpJ2M2G3SiWC8IgiAIwn9FJ0tccLIDgClDbWw/5OfJD5rxBVQ2tfo5WhGkoVVBliErVU96ooGe+UZiovQoSpiJg60sWOslPkYmJV6P1Sxh0EvUtUSoadRidUb2M9PQonD6OBunho9CQOK+Hr3+6dgsJh1De2tPF3bPMfHibUl8vMTJ5v1+fhy6f6wqSE1jmDU7fVQ3hLGate5+ALNJJj9De1qxue14Z75JoltnAzsPB9vz8QHW7fQRZZW5fXYseenaOsu3eNl5OIDZJJ1QsNfJ0k+K9QAOuw6H/d/4AARBEARBEIT/s0TBXvjN2F7WzOy3tzA2NcQr7qfAANsDXfja05Mruk4npdc4jIUX8pIsc9qr69lb2YpBJ/PWplqMOolAROGV1SXce+1yHED8ogNEH/MAUG/OwjnxHfKS7PTbvZ3Jh+9j3JbDRPos4sblPlz+MK+dX4jVqP/JmDLirCRFaRdi6bFWbpnYlVH5iXRJ6YinuW/uXr7bX0eKw0xtmx+dLFGYGYPD2jF52BdbK3hh+RHOH5LBO+vLWH1E6zQbkhNHj1EPs2rPAEZYS2HbW2S1VZB10UIAJnRP4obx+fTq5PgfHn1BEARBEISf+nqli/cXtpESr8MX0JoVVLTJYwsLTJwzMYoeuWaa2yJc9UQtOhn8QVi6WQusL6kKU9MY5ssntCaD65+qbd92VoqRq8+w4bDrGP5SOqFiA28n+ci+wsbTH7XQI8fIJdNjThhPKKyyrzhAz1xTe/xMbrqROy6I42hliO6dje3L3vVSAx6/iiyDooBOhs6pJ07s+sJnzRwqDTKyr5m1u/zUt2hPDMwYbSclXkdlfZhDpQGOVoZJjtczpr+2/pkTojAZJU4aYkMQBEEQBEEQ/n/6x+3FgvALKmn04g1GqAhFcTBhEiVJE6iTEmgliinHTkcNuKB+PwBNbR7CCnRNiSIn0cZFw7IBSI42s+5IIweqndwztRvdzn+a1l4Xc+q2Xkx/eR2+YASDTmKsvJOocBOBPXNZtLeG1UUNlDd7TxjP9wfqOOO1jZz35uafjHVAdhzR5o4LviE58STYTdwwLo+seCsRRWVXRSsA3+6rYcWhOtYcaaCq1UedM4BOkpAl0MsSZw3IYGhBJmPOuh79sGuh11kw6rb2bZv0Om6Z2IWJ3ZP/Px9xQRAEQRCEf6y0OkQ4ArnpBuIdMr3ytIJ4MAzBsMqBkiBOTwS3N4w/oBIKqyTH6ejXxUSPHCN6HZgMEmt3eWl2Rnj25mSuOdNB/wITHy1x8pcPmgGIqjchIVFTH2HzPh+HSoN8v9n7k/G8O7+N+15r5K15rSe8bjbJ9Mw1If8oE76wwExaop5bz49FJ0NEgW0H/YTCKovWuSmtDrF5n5/y2jD648V/WQabWWLSUBunjIri6pmxnDfFwbgBViYMsrZvOylOz2UzYshMOfEGgCAIgiAIgiD8t0SHvfCbcUZhJxKjTHRLiSIpegwACxvd3PjpTi7TL0H6/hXY14fi6CGs8v+V+6w38tj1f25f/9qx+eyqaOH8tzdj1Mns+/Mk4rsOoyGtP8rhtWTbTHy8uYz5u2u40JhOf+kIVquVN87vjycYpiAlmnm7qvhsSwV/mtadpCgTZoOMw2Kg4E9LSIk2c9eUAgZkx5FgN+Hyh7hv7j66JEdx7dg8Lh7eGYAReYk8s/QwZw5Mp6TRw1Uf7UCSYPH1IxiWm8C0PqnMHtzK/hoX5w3JwmzoyJ8nLgfOePOXPOyCIAiCIAh/11VnxDC8j4XCAnN7R/vGvV7eme/E5VF4b6ETp0dhxVYvEQW6Zhl59BotTjCiqASCKu8uaGXxei89cow8eUMSJw+PIjXewJ6jAbJSDDz41wZaXB1Rg92yTVx9Rgydj8fXvD2vlZqmMLfMiiPleAZ8TWOY6bdUMryPhVH9rAzsYUavkyipDvL+Qicnj7Bx10Xx7du0W2RWbvNy8SkOlm328OpXrWSl6Hn4qgQq6sKMLrTQJ9+NySAzqvDErvlBPSwM6mH5nx5nQRAEQRAEQfiBKNgLvxmSJDG6S+IJr+Uk2Flw/Uioi4N5G9gXfxJy5TZkSSUmWIPLHyLqeKe7w2pgw7EmAAx6CX8ozJYSJ0Nz49l093gkSeK+uXvRE6ZSl0FvqweDCif1SGnf38eby9lS0sySfTXcelJX9j44iScWH2JbWQulTV6u+mgHI/MT+PDSwWwpaWbermoMOolrx+bhD0VYU9TAsLwEXji3H8GwwsebyyjMjCHWaiQvOYoWb4jPX7qXSz1/ZWvoVPaWZzFw5h1gEqGmgiAIgiD89ljNMoN7nlisHtrLytBeVlZs9fD1Sjf+oILbpwBQWtMR/q6TJaxmiYMlIUCLpalrCtPmUSgsMPPNU52QJIkrH9NicixmiLbK+EMq00Zq50bhiMrcVW4UFY6MDDJ9lJ2pw21c+2QdEQU27vWxZqePC6dFc9aEaJZt9rL1gJ9IRGVgdwuNrRFKq4P072ZmQDcLzc4INc1hMlP0jOhrJTfdyK6iAOfcU43bp900qGyIMGuSmA9IEARBEARB+HWIgr3w+5Dcg9LTFzHt6VVY6UofeQiblG6c4w0SZTaw8nA9b609xnmDs5jcI4UhOXFc8/FO1h1t5K4pBVw1Wpus9doxeVjLV3Nq8wrwQGDtC4SCYeyFZ+E0p3D16FwmpPiYFbsdIjkYdAYuGJbF/N1VpDjMHGvwtGfJj8xP5PpxeeQl2TnW4Gbm6xtp9gQ5s386T52SzftfL+HRPVEYdBJ7HpiEQSezYE8N3duKkfUq5xtXEXukFbbGwYibfr1jKwiCIAiC8B8YN9BGMKzy0uetWE0QjmgF9h98/r2TorIgM8fbWbfLx4h+Fm54ug63T+W5W5LokqnF61x6qoNXvmihsU3B51d4e14ro/pZmTrcjtevcMM5sRypCBIKa9vW6yTOHGfnzblt5HQycKQiRPbxbPoZY+woKowbaGXdLi9Pf9RMKAy3nh9Lj84m7ny5gYaWCJnJemZN1ory327wtBfrAT5e4mRgNzP5mR15+IIgCIIgCILwSxEFe+F3IzXGzIRuycgSlDUlkhWOsLeyjc3Hmlm6v5b1R5vISbDz2Om9GPbEcoJhBZ0kkRXXkTe67mgjH1an0ts8nLhICwP9h7CveYjg+mcZr75NozfC/ownse7cBaoHdeBlfLa1nAZ3EKc/zLJbRrNgTzVN7gDxdhO3ntQVgK+2V9LsCTJIOsgZoV3w+SYuL1mDTT+OpfrRyPIkAG6akM8XtgdoSLyYRH857PsS8sb/CkdTEARBEAThv9cr10SXTAP5GUbW7PTRLdvI6h1e7BaZr5a78PpVxg20MnGwjYfeasJoALtVwmHrmEpr7U4fjW0KRr2WjV9cGaa40snGPV6KysOYjBAMwaJ1Hl67K5mEGB2rd/hw+1SMRpn7L4unrDZMRFFJjNVz5ekxACxY4yYUBknSInRe+6oVr18rzNutHfu/+bxYtuz30SvXxOodPlpdEdKTxWWSIAiCIAiC8OsQZ6LC74ZJr+OtCwcA4A9FGPToMq79ZCcAd0zqSk6inQuHZWPSy+gkCb0ss/TmkWQndMTNTO6Zwv7qArypr3Ngz1pSq58kI1yG05xCQ1MYkNgq9WJ0VD2k9WPFoXpeW3WMS43LuKSLj8fnR7HwUBu1bX7umdqtPX9+Rt80gmGFs1Zci76oBZK6gyQzS7+Cc1mB1DYd4nNIjjZz/aTeQG9tQMOu+SUPoSAIgiAIwv9XnZIMPHdLMgBj+lu5/cUGthzwI0lww9kxNLYqDOphYX9xAEmCmCgdb92Xgu5Hk8POHB+FQS/RM8/Il8ucVNRGUNE69hUVfAHolm0gEIJ4h46PlzjZcThAlFUiM0XPw2834fWrOGwSw/tY0em0bV883UFMlMycFW4+/dZFXLRMOKwSDENpTQhVVZEkie6dTXTvbAKgfzeRVS8IgiAIgiD8uuR/vogg/PboZIl4uwmTXiY5ysSO8hbOHphBWowFk14mEFYIRhQSPxoHD8bAlr+ytbSZQFjhwek9OGtgJnc03E3nSCnVk94i4bZtdIqxIEtgmvRnuPUgpA+gS3IUnRNs3CO/T6ejnzI7pYxuqVF8v7+OQY8uo6rVB4BeJzNrcCb6/heAzgj1B2DSEyDpkAB8zb/m4RIEQRAEQfifs1pkTAYJi0kiLVFPUUWQ08fZMeglJAlUFRpbIpx5ZxXTb62krCbEzsN+0hL13HBOLFkpBsprI8gyPH9LInddFI9BDzYzPHpNEi/dnozNItMz10SUVcLlVfl6hZvxA63kpRt47tMWbn2+vn08sVE6Zk2OpmeOERVodiqce1IUoEX3KMqvdKAEQRAEQRAE4R8QHfbC75JBJ7P05lFEFJUrP9zOsoP1pMdaeXB6D/Q6mTdm96ei2Yt1ZTmgUrV3NWce6cTNcRu4MbcOJv8Fup8KVTvJ7DEEJIn1d/00miYjzsrK28YQmncBukAzg8efztwJRgY/thxPMIIvGD5xhZAHIkEwWCF7ONywAzyNkD7glzkwgiAIgiAIv5LsVAOfPJKKwSBxxh1VLFkfpn9XC0N7W+idb+LK02Mw6lVe+qINgHcXtLL1QICcTgb6dTVx1oQoCrKNxNhlctONyLLE3KfTf7IfbXspPPpOM906G5k91cGh0gC3vdBAq+vEKrzZKNNy/LXUBB2njommT1cL0Ta5vRNfEARBEARBEH5LRMFe+N0y6GQMOrh6TC4Oi4Hzh2S1/2x8N+3RbLIXw6FF1OVeialkOxcFPoG9zWyWetNl0jPE2n46mVgoomDQaQ+f1Dn9bNuwjJN3vktYMqA/TYvm+fbGUXiCYXIT7SeubLQdH8D9kNJT+zo2+//3WxcEQRAEQfhNMpu0c6jLZ8RQUh2iX1ctakaSJKaP0s6bTEYZX0AlEFLZeiDAsaoQx6pCxDl0PHlD4glxOT8IR1T0xwvsNY1hHn2nkZLqMG2eCLOnOijINvHyHck47D99gNhqltDr4I4L4zEZJbpmiclkBUEQBEEQhN8uSVVV9ZfeqdPpxOFw0NbWRnR09C+9e+H/qFBEwXB4AetXLOCKyslM6Z9Pv8wY4qxGpvRKBaDxq1sp3buOjzIe4qJJg5n52gayLH6eD/0Z2dGJHrcs0mYu+3tUFdx1EJXyC70rQRCE/x3x7/X/HeKzFn4tobDCnBVu5q124/QoXH9WDK1uhQHdzORlGPH6FW55rp6KujCXTo9GReKd+W1E2yScHpURfS3cfVH8P9mHii+gEG3T/ULvShAE4X9H/JstCILwxyc67IX/Mww6GbqfSkt4IFELD5IRZ+He/9feHb7WVd5xAP+eLOmapElq26w2Yg3YWi3BKhsbsjctZaXVISIzliRUqaOKZUMctS8KQ8R/oCCCLxJvLTgi6oK+Ul+sxWFg6sjsGNtA0dka1jCmTdZYm/TuRbG0y9VFaM1J8vlAIDzPczjPQ7g5v/u95z7nt39OUSQjv96WtsaGLP/rb/KD4j8Z+tefUq3+MNUk32lemTN3/y5d17R9fVifnO8X1gMAzEpDfV12bmvNqYlz+cNfJvPxyakMHZnI8LHJHPzV6pw4OZWP/3l+C8KPT07lmvaGJMmPNzXlJz9qntXd8g31RRrqhfUAAMwPAnsWnZ/e3JGf3tyR019M5djxU1m1bElal55/KdT3DuYff/tjfnnbz/O9tub8fv+WtC5tSPN3vVQAAK6UPXcvz567l+ej0bP5+0dfZPP3m5Ik69cuyS+6l+ff49P52dbWNNQXue3mpVmzsj51NbbOAQCA+c6WOABATa7Xi4e/NQDMD67ZAAvfzKcyAQAAAAAA3zqBPQAAAAAAlIDAHgAAAAAASkBgDwAAAAAAJSCwBwAAAACAEhDYAwAAAABACQjsAQAAAACgBAT2AAAAAABQAgJ7AAAAAAAoAYE9AAAAAACUgMAeAAAAAABKQGAPAAAAAAAlILAHAAAAAIASqJ+Lk1ar1STJqVOn5uL0AMAsfHmd/vK6zcKlNgOA+UF9BrDwzUlgPz4+niS59tpr5+L0AMA3MD4+nra2trmeBleQ2gwA5hf1GcDCVVTn4GPZc+fO5ZNPPklLS0uKovi2Tw8AzEK1Ws34+Hg6OjpSV2cXvYVMbQYA84P6DGDhm5PAHgAAAAAAuJSPYwEAAAAAoAQE9gAAAAAAUAICewAAAAAAKAGBPQAAAAAAlIDAHhaZzZs355FHHpnRPjQ0lKIokiSVSiVFUeSmm26aMe6FF15IURTp7Oyc0Tc5OZmrrroqK1asyOTk5Iz+zs7OFEWRoijS1NSUrq6uPPPMMxf6R0dH09PTkw0bNqSurq7mPAEAFhK1GQAAFxPYAzU1Nzfn5MmTGR4evqR9YGAga9eurXnMSy+9lK6urmzcuDEvv/xyzTFPPPFERkdH89577+Wuu+7KQw89lMHBwSTJmTNn0t7engMHDmTTpk2Xd0EAAPOY2gwAYHEQ2AM11dfXp6enJwMDAxfajh8/niNHjqSnp6fmMf39/enr60tfX1/6+/trjmlpacnVV1+ddevW5cknn8z69eszNDSU5PxdXgcPHsyuXbvS1tZ22dcEADBfqc0AABYHgT3wlR544IEMDg7m9OnTSc5/HXv79u1ZvXr1jLHvv/9+hoeH093dne7u7rz11lv54IMP/u85li5dmrNnz172uQMALDRqMwCAhU9gD3ylW265Jddff31efPHFVKvVVCqV7N69u+bYgYGB7Nix48I+qdu3b7/kDrD/NTU1lUqlkmPHjmXr1q1XagkAAAuG2gwAYOET2ANfa/fu3Xn22Wdz9OjRTExM5Pbbb58xZnp6OocOHUpfX9+Ftr6+vhw6dCjT09OXjN2/f3+WLVuWxsbG7N27N/v27cuDDz54xdcBALAQqM0AABa2+rmeAPDtam1tzWeffTaj/dNPP01ra+uM9t7e3jz22GN5/PHHs2vXrtTXz/y38dprr+XEiRO59957L2mfnp7O66+/nh07dlxo27dvX+6///40NTVlzZo1KYriMqwKAGB+UpsBAHAxd9jDInPjjTfmnXfemdH+9ttvZ8OGDTPaV6xYkTvvvDNHjx79yq9c9/f3Z+fOnRkZGbnkp7e3d8YDzlatWpV169alo6PDG0IAYNFTmwEAcDF32MMi8/DDD+epp57K3r17s2fPnjQ2NuaNN95If39/Dh8+XPOYSqWSp59+OitXrpzRNzY2lldffTWvvPJKurq6Lum77777cscdd2RsbCzt7e2zmt/IyEiSZGJiImNjYxkZGcmSJUuycePGb7ZQAIB5QG0GAMDFBPawyHR2dubNN9/MgQMHsm3btnz++ee54YYbUqlUcs8999Q8prGxMY2NjTX7nnvuuTQ3N9d8ONmWLVvS0tKSw4cP59FHH53V/G699dYLv7/77rt5/vnnc9111+XDDz+c1fEAAPOJ2gwAgIsV1Wq1OteTAAAAAACAxc4e9gAAAAAAUAICewAAAAAAKAGBPQAAAAAAlIDAHgAAAAAASkBgDwAAAAAAJSCwBwAAAACAEhDYAwAAAABACQjsAQAAAACgBAT2AAAAAABQAgJ7AAAAAAAoAYE9AAAAAACUgMAeAAAAAABK4L+vtdm4nGP9YwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1455.6x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the neighbors based on the PCA space after batch correction\n",
    "sc.pp.neighbors(rna, use_rep='X_pca_harmony')\n",
    "sc.tl.umap(rna)\n",
    "sc.pl.umap(rna, color=['batch','cell_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d6f5ac09-1175-4f44-9d66-e5abeae86db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "rna.obs.index = rna.obs.index.str.replace(r'-\\d+$', '', regex=True)\n",
    "train_rna = rna[rna.obs['batch']=='s1d1']\n",
    "test_rna = rna[rna.obs['batch']=='s2d1']\n",
    "train_rna.write('./data/2_cross_samples/train_rna_harmony.h5ad')\n",
    "test_rna.write('./data/2_cross_samples/test_rna_harmony.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b94cf1e-bc2a-4249-bdc8-84c8603d15b4",
   "metadata": {},
   "source": [
    "Now we have raw cell embeddings in `RNA.obsm['X_pca_harmony']`, and cell types in `RNA.obs['cell_type']` as labels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61ed101-7cc4-466f-8495-40bdda0f716e",
   "metadata": {},
   "source": [
    "### 4. Training/test data prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8965f71b-3b54-46f7-9566-223c9b1c6bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train/test data is saved in:  data/2_cross_samples/train_data\n",
      "successful writing bed file.\n",
      "successful writing train/test split file.\n",
      "successful writing train/test anndata file.\n",
      "successful writing sparse m.\n",
      "Successfully saving all sequence h5 file...\n",
      "Successfully saving trainval sequence h5 file...\n",
      "Successfully saving test sequence h5 file...\n"
     ]
    }
   ],
   "source": [
    "## Generate data for model training\n",
    "train_folder = './data/2_cross_samples/train_data/'\n",
    "input_fasta = '/picb/bigdata/project/miaoyuanyuan/hg38.fa'\n",
    "train_atac = sc.read_h5ad('./data/2_cross_samples/train_atac.h5ad')\n",
    "dict = xc.pp.process_train_test_single(\n",
    "    ad_atac=train_atac, ## can be a str, Path, or anndata.AnnData object\n",
    "    input_fasta=input_fasta,\n",
    "    output_path=train_folder\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0fa7f2bc-5a9d-46c5-833c-95bf0af62e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train/test data is saved in:  data/2_cross_samples/test_data\n",
      "successful writing bed file.\n",
      "successful writing sparse m.\n",
      "start saving all sequence h5 file...\n"
     ]
    }
   ],
   "source": [
    "## Generate data for model evaluation\n",
    "test_folder = './data/2_cross_samples/test_data/'\n",
    "test_atac = sc.read_h5ad('./data/2_cross_samples/test_atac.h5ad')\n",
    "dict = xc.pp.process_test_dual(\n",
    "    ad_atac=test_atac, ## can be a str, Path, or anndata.AnnData object\n",
    "    input_fasta=input_fasta,\n",
    "    output_path=test_folder\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118f2cdc-92ba-41e5-b83d-f9a193dc46d4",
   "metadata": {},
   "source": [
    "We performed preprocessing on the filtered scATAC data of s1d1 and s2d1 samples separately:  \n",
    "\n",
    "**For the s1d1 training sample:**  \n",
    "Following the same procedure as in the `1_Within-sample analysis` tutorial, we partitioned the data using 90% of cells and 90% of peaks to generate train_val sets. This produced:  \n",
    "- `ad_trainval.h5ad`: containing training cells and training peaks  \n",
    "- `m_trainval.npz`: corresponding count matrix  \n",
    "- `trainval_seqs.h5`: base sequence file for training peaks  \n",
    "\n",
    "All files were saved in `'./data/2_cross_samples/train_data/'`.  \n",
    "\n",
    "**For the s2d1 test sample:**  \n",
    "No partitioning was applied. The filtered scATAC data was directly converted into the required model input files:  \n",
    "- `ad.h5ad`  \n",
    "- `m.npz`  \n",
    "- `all_seqs.h5`  \n",
    "\n",
    "These files were saved in `'./data/2_cross_samples/test_data/'`.  \n",
    "\n",
    "Note: The `input_fasta` parameter should be replaced with the path to the genome file corresponding to the species used in your dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef04773-2828-4975-b0f6-4ba80ed0f295",
   "metadata": {},
   "source": [
    "### 5. Train XChrom"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bdb0d3f-ce05-4c6c-b881-59635ef9fcc9",
   "metadata": {},
   "source": [
    "- We use the preprocessed data from the s1d1 training set obtained above as sequence inputs for the model. The `input_folder` should contain all files generated during preprocessing. The batch-corrected `X_pca_harmony` embeddings derived from scRNA data are used as raw cell embeddings and serve as cell identity inputs to the model. If using dimensionality reduction results from other methods, they must be stored in `cell_embedding_ad.obsm` under the key `cellembed_raw` for extraction.                      \n",
    "\n",
    "- If you want to compute NS(k=100) and LS(k=100) for monitoring the XChrom training process, you need to set `trackscore = True` and specify the `celltype` in either the RNA or ATAC H5AD file.                           \n",
    "\n",
    "- The model is set to train for 1000 epochs by default, but an early stopping mechanism will be triggered if the increase in training auROC remains below 1e-6 for 50 consecutive epochs.                               \n",
    "        \n",
    "- The `save_freq` parameter determines the frequency of saving model parameters, with a default value of 1000 (meaning intermediate model parameters are not saved during training)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cc56e30f-d087-4f1a-98df-9fc023b23b8c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Start training XChrom model ===\n",
      "Input folder: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/2_cross_samples/train_data\n",
      "Cell embedding file: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/2_cross_samples/train_rna_harmony.h5ad\n",
      "Raw cell embedding key: X_pca_harmony\n",
      "Output path: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/2_cross_samples/train_out\n",
      "Model parameters: bottleneck=32, batch_size=128, lr=0.01\n",
      "1. Load raw cell embedding and make z-score normalization...\n",
      "Raw cell embedding saved to: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/2_cross_samples/train_rna_harmony.h5ad.obsm['X_pca_harmony']\n",
      "Initial cell embedding saved to: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/2_cross_samples/train_rna_harmony.h5ad.obsm['zscore32_perpc']\n",
      "Initial cell embedding shape: (6224, 32)\n",
      "2. Load training data...\n",
      "3. Prepare train/val data split...\n",
      "Training peak number: 41772, Validation peak number: 4642\n",
      "4. Create TensorFlow dataset...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-17 15:34:12.915082: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-08-17 15:34:14.522287: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21971 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:b1:00.0, compute capability: 8.6\n",
      "2025-08-17 15:34:14.522987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 21971 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:98:00.0, compute capability: 8.6\n",
      "2025-08-17 15:34:14.523430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 21971 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:4b:00.0, compute capability: 8.6\n",
      "2025-08-17 15:34:14.523840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 193 MB memory:  -> device: 3, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:31:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. Build and compile model...\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "sequence (InputLayer)           [(None, 1344, 4)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "stochastic_reverse_complement ( ((None, 1344, 4), () 0           sequence[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "stochastic_shift (StochasticShi (None, 1344, 4)      0           stochastic_reverse_complement[0][\n",
      "__________________________________________________________________________________________________\n",
      "gelu (GELU)                     (None, 1344, 4)      0           stochastic_shift[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, 1344, 288)    19584       gelu[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 1344, 288)    1152        conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D)    (None, 448, 288)     0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "gelu_1 (GELU)                   (None, 448, 288)     0           max_pooling1d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 448, 288)     414720      gelu_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 448, 288)     1152        conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 224, 288)     0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "gelu_2 (GELU)                   (None, 224, 288)     0           max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 224, 323)     465120      gelu_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 224, 323)     1292        conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 112, 323)     0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "gelu_3 (GELU)                   (None, 112, 323)     0           max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 112, 363)     586245      gelu_3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 112, 363)     1452        conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 56, 363)      0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "gelu_4 (GELU)                   (None, 56, 363)      0           max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 56, 407)      738705      gelu_4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 56, 407)      1628        conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 28, 407)      0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "gelu_5 (GELU)                   (None, 28, 407)      0           max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 28, 456)      927960      gelu_5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 28, 456)      1824        conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 14, 456)      0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "gelu_6 (GELU)                   (None, 14, 456)      0           max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 14, 512)      1167360     gelu_6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 14, 512)      2048        conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1D)  (None, 7, 512)       0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "gelu_7 (GELU)                   (None, 7, 512)       0           max_pooling1d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 7, 256)       131072      gelu_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 7, 256)       1024        conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "gelu_8 (GELU)                   (None, 7, 256)       0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 1, 1792)      0           gelu_8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 1, 32)        57344       reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "cell_embed (InputLayer)         [(None, 5602, 32)]   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 1, 32)        128         dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 5602, 32)     0           cell_embed[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 1, 32)        0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization (LayerNorma (None, 5602, 32)     64          lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gelu_9 (GELU)                   (None, 1, 32)        0           dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 5602, 64)     2112        layer_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.compat.v1.squeeze (TFOpLambd (None, 32)           0           gelu_9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "sequencing_depth (InputLayer)   [(None, 5602)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "final_cellembed (Dense)         (None, 5602, 32)     2080        dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims (TFOpLambda)     (None, 32, 1)        0           tf.compat.v1.squeeze[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_1 (TFOpLambda)   (None, 5602, 1)      0           sequencing_depth[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.linalg.matmul (TFOpLambda)   (None, 5602, 1)      0           final_cellembed[0][0]            \n",
      "                                                                 tf.expand_dims[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 5602, 1)      2           tf.expand_dims_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.compat.v1.squeeze_2 (TFOpLam (None, 5602)         0           tf.linalg.matmul[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.compat.v1.squeeze_1 (TFOpLam (None, 5602)         0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add (TFOpLambd (None, 5602)         0           tf.compat.v1.squeeze_2[0][0]     \n",
      "                                                                 tf.compat.v1.squeeze_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid (TFOpLambda)    (None, 5602)         0           tf.__operators__.add[0][0]       \n",
      "==================================================================================================\n",
      "Total params: 4,524,068\n",
      "Trainable params: 4,518,218\n",
      "Non-trainable params: 5,850\n",
      "__________________________________________________________________________________________________\n",
      "6. Set training callbacks...\n",
      "7. Start training...\n",
      "Model will be saved to: data/2_cross_samples/train_out/E1000best_model.h5\n",
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-17 15:34:18.467559: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2025-08-17 15:34:22.797225: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8800\n",
      "2025-08-17 15:34:22.878255: I tensorflow/stream_executor/cuda/cuda_blas.cc:1760] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
      "Numba: Attempted to fork from a non-main thread, the TBB library may be in an invalid state in the child process.\n",
      "Numba: Attempted to fork from a non-main thread, the TBB library may be in an invalid state in the child process.\n",
      "Numba: Attempted to fork from a non-main thread, the TBB library may be in an invalid state in the child process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "327/327 [==============================] - 91s 247ms/step - loss: 0.2058 - binary_accuracy: 0.9409 - auc: 0.7443 - pr: 0.1537 - val_loss: 0.1866 - val_binary_accuracy: 0.9435 - val_auc: 0.7863 - val_pr: 0.2148\n",
      "Epoch 2/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1875 - binary_accuracy: 0.9434 - auc: 0.7810 - pr: 0.2166 - val_loss: 0.1847 - val_binary_accuracy: 0.9436 - val_auc: 0.7932 - val_pr: 0.2300\n",
      "Epoch 3/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1852 - binary_accuracy: 0.9434 - auc: 0.7884 - pr: 0.2288 - val_loss: 0.1841 - val_binary_accuracy: 0.9435 - val_auc: 0.7944 - val_pr: 0.2306\n",
      "Epoch 4/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1843 - binary_accuracy: 0.9435 - auc: 0.7911 - pr: 0.2329 - val_loss: 0.1837 - val_binary_accuracy: 0.9435 - val_auc: 0.7948 - val_pr: 0.2304\n",
      "Epoch 5/1000\n",
      "327/327 [==============================] - 79s 233ms/step - loss: 0.1838 - binary_accuracy: 0.9435 - auc: 0.7930 - pr: 0.2358 - val_loss: 0.1881 - val_binary_accuracy: 0.9435 - val_auc: 0.7964 - val_pr: 0.2294\n",
      "Epoch 6/1000\n",
      "327/327 [==============================] - 79s 233ms/step - loss: 0.1832 - binary_accuracy: 0.9435 - auc: 0.7951 - pr: 0.2384 - val_loss: 0.1836 - val_binary_accuracy: 0.9435 - val_auc: 0.7958 - val_pr: 0.2332\n",
      "Epoch 7/1000\n",
      "327/327 [==============================] - 78s 232ms/step - loss: 0.1829 - binary_accuracy: 0.9435 - auc: 0.7961 - pr: 0.2404 - val_loss: 0.1831 - val_binary_accuracy: 0.9436 - val_auc: 0.7978 - val_pr: 0.2374\n",
      "Epoch 8/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1827 - binary_accuracy: 0.9435 - auc: 0.7967 - pr: 0.2413 - val_loss: 0.1840 - val_binary_accuracy: 0.9435 - val_auc: 0.7973 - val_pr: 0.2378\n",
      "Epoch 9/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1821 - binary_accuracy: 0.9436 - auc: 0.7984 - pr: 0.2448 - val_loss: 0.1822 - val_binary_accuracy: 0.9437 - val_auc: 0.7998 - val_pr: 0.2418\n",
      "Epoch 10/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1819 - binary_accuracy: 0.9436 - auc: 0.7991 - pr: 0.2461 - val_loss: 0.1838 - val_binary_accuracy: 0.9437 - val_auc: 0.7991 - val_pr: 0.2397\n",
      "Epoch 11/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1817 - binary_accuracy: 0.9436 - auc: 0.7996 - pr: 0.2468 - val_loss: 0.1951 - val_binary_accuracy: 0.9428 - val_auc: 0.7936 - val_pr: 0.2252\n",
      "Epoch 12/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1814 - binary_accuracy: 0.9436 - auc: 0.8008 - pr: 0.2484 - val_loss: 0.1828 - val_binary_accuracy: 0.9436 - val_auc: 0.7981 - val_pr: 0.2359\n",
      "Epoch 13/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1812 - binary_accuracy: 0.9436 - auc: 0.8015 - pr: 0.2497 - val_loss: 0.1825 - val_binary_accuracy: 0.9436 - val_auc: 0.7992 - val_pr: 0.2395\n",
      "Epoch 14/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1808 - binary_accuracy: 0.9437 - auc: 0.8030 - pr: 0.2516 - val_loss: 0.1905 - val_binary_accuracy: 0.9433 - val_auc: 0.7982 - val_pr: 0.2368\n",
      "Epoch 15/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1804 - binary_accuracy: 0.9437 - auc: 0.8044 - pr: 0.2533 - val_loss: 0.1817 - val_binary_accuracy: 0.9437 - val_auc: 0.8013 - val_pr: 0.2430\n",
      "Epoch 16/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1802 - binary_accuracy: 0.9437 - auc: 0.8052 - pr: 0.2547 - val_loss: 0.1829 - val_binary_accuracy: 0.9437 - val_auc: 0.8019 - val_pr: 0.2429\n",
      "Epoch 17/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1798 - binary_accuracy: 0.9437 - auc: 0.8067 - pr: 0.2558 - val_loss: 0.1814 - val_binary_accuracy: 0.9438 - val_auc: 0.8035 - val_pr: 0.2470\n",
      "Epoch 18/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1793 - binary_accuracy: 0.9437 - auc: 0.8088 - pr: 0.2584 - val_loss: 0.1841 - val_binary_accuracy: 0.9435 - val_auc: 0.7964 - val_pr: 0.2353\n",
      "Epoch 19/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1793 - binary_accuracy: 0.9437 - auc: 0.8089 - pr: 0.2572 - val_loss: 0.1873 - val_binary_accuracy: 0.9435 - val_auc: 0.7913 - val_pr: 0.2336\n",
      "Epoch 20/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1789 - binary_accuracy: 0.9437 - auc: 0.8105 - pr: 0.2589 - val_loss: 0.1824 - val_binary_accuracy: 0.9437 - val_auc: 0.8005 - val_pr: 0.2433\n",
      "Epoch 21/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1784 - binary_accuracy: 0.9438 - auc: 0.8122 - pr: 0.2614 - val_loss: 0.1830 - val_binary_accuracy: 0.9437 - val_auc: 0.7981 - val_pr: 0.2362\n",
      "Epoch 22/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1780 - binary_accuracy: 0.9438 - auc: 0.8138 - pr: 0.2631 - val_loss: 0.1811 - val_binary_accuracy: 0.9437 - val_auc: 0.8050 - val_pr: 0.2456\n",
      "Epoch 23/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1776 - binary_accuracy: 0.9438 - auc: 0.8159 - pr: 0.2641 - val_loss: 0.1813 - val_binary_accuracy: 0.9436 - val_auc: 0.8059 - val_pr: 0.2413\n",
      "Epoch 24/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1769 - binary_accuracy: 0.9438 - auc: 0.8188 - pr: 0.2658 - val_loss: 0.1815 - val_binary_accuracy: 0.9437 - val_auc: 0.8092 - val_pr: 0.2485\n",
      "Epoch 25/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1764 - binary_accuracy: 0.9438 - auc: 0.8209 - pr: 0.2681 - val_loss: 0.1803 - val_binary_accuracy: 0.9437 - val_auc: 0.8102 - val_pr: 0.2473\n",
      "Epoch 26/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1762 - binary_accuracy: 0.9438 - auc: 0.8219 - pr: 0.2678 - val_loss: 0.1802 - val_binary_accuracy: 0.9436 - val_auc: 0.8103 - val_pr: 0.2478\n",
      "Epoch 27/1000\n",
      "327/327 [==============================] - 80s 233ms/step - loss: 0.1756 - binary_accuracy: 0.9438 - auc: 0.8240 - pr: 0.2698 - val_loss: 0.1839 - val_binary_accuracy: 0.9436 - val_auc: 0.8016 - val_pr: 0.2402\n",
      "Epoch 28/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1753 - binary_accuracy: 0.9438 - auc: 0.8251 - pr: 0.2715 - val_loss: 0.1795 - val_binary_accuracy: 0.9438 - val_auc: 0.8114 - val_pr: 0.2476\n",
      "Epoch 29/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1750 - binary_accuracy: 0.9438 - auc: 0.8260 - pr: 0.2725 - val_loss: 0.1803 - val_binary_accuracy: 0.9437 - val_auc: 0.8098 - val_pr: 0.2468\n",
      "Epoch 30/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1748 - binary_accuracy: 0.9439 - auc: 0.8269 - pr: 0.2736 - val_loss: 0.1796 - val_binary_accuracy: 0.9438 - val_auc: 0.8110 - val_pr: 0.2500\n",
      "Epoch 31/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1745 - binary_accuracy: 0.9439 - auc: 0.8282 - pr: 0.2747 - val_loss: 0.1798 - val_binary_accuracy: 0.9438 - val_auc: 0.8108 - val_pr: 0.2472\n",
      "Epoch 32/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1741 - binary_accuracy: 0.9439 - auc: 0.8294 - pr: 0.2761 - val_loss: 0.1807 - val_binary_accuracy: 0.9437 - val_auc: 0.8096 - val_pr: 0.2472\n",
      "Epoch 33/1000\n",
      "327/327 [==============================] - 73s 221ms/step - loss: 0.1739 - binary_accuracy: 0.9439 - auc: 0.8301 - pr: 0.2766 - val_loss: 0.1794 - val_binary_accuracy: 0.9437 - val_auc: 0.8127 - val_pr: 0.2518\n",
      "Epoch 34/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1735 - binary_accuracy: 0.9439 - auc: 0.8313 - pr: 0.2789 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8131 - val_pr: 0.2526\n",
      "Epoch 35/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1735 - binary_accuracy: 0.9439 - auc: 0.8318 - pr: 0.2780 - val_loss: 0.1791 - val_binary_accuracy: 0.9438 - val_auc: 0.8128 - val_pr: 0.2515\n",
      "Epoch 36/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1731 - binary_accuracy: 0.9439 - auc: 0.8332 - pr: 0.2801 - val_loss: 0.1797 - val_binary_accuracy: 0.9437 - val_auc: 0.8121 - val_pr: 0.2495\n",
      "Epoch 37/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1729 - binary_accuracy: 0.9439 - auc: 0.8337 - pr: 0.2806 - val_loss: 0.1795 - val_binary_accuracy: 0.9437 - val_auc: 0.8133 - val_pr: 0.2503\n",
      "Epoch 38/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1726 - binary_accuracy: 0.9439 - auc: 0.8348 - pr: 0.2813 - val_loss: 0.1795 - val_binary_accuracy: 0.9436 - val_auc: 0.8127 - val_pr: 0.2487\n",
      "Epoch 39/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1724 - binary_accuracy: 0.9439 - auc: 0.8355 - pr: 0.2817 - val_loss: 0.1794 - val_binary_accuracy: 0.9436 - val_auc: 0.8132 - val_pr: 0.2512\n",
      "Epoch 40/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1722 - binary_accuracy: 0.9439 - auc: 0.8361 - pr: 0.2831 - val_loss: 0.1790 - val_binary_accuracy: 0.9437 - val_auc: 0.8135 - val_pr: 0.2493\n",
      "Epoch 41/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1722 - binary_accuracy: 0.9439 - auc: 0.8362 - pr: 0.2833 - val_loss: 0.1791 - val_binary_accuracy: 0.9437 - val_auc: 0.8131 - val_pr: 0.2493\n",
      "Epoch 42/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1720 - binary_accuracy: 0.9440 - auc: 0.8371 - pr: 0.2835 - val_loss: 0.1790 - val_binary_accuracy: 0.9437 - val_auc: 0.8146 - val_pr: 0.2527\n",
      "Epoch 43/1000\n",
      "327/327 [==============================] - 72s 214ms/step - loss: 0.1717 - binary_accuracy: 0.9440 - auc: 0.8381 - pr: 0.2850 - val_loss: 0.1797 - val_binary_accuracy: 0.9437 - val_auc: 0.8127 - val_pr: 0.2485\n",
      "Epoch 44/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1717 - binary_accuracy: 0.9440 - auc: 0.8381 - pr: 0.2847 - val_loss: 0.1794 - val_binary_accuracy: 0.9437 - val_auc: 0.8132 - val_pr: 0.2498\n",
      "Epoch 45/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1714 - binary_accuracy: 0.9440 - auc: 0.8390 - pr: 0.2856 - val_loss: 0.1813 - val_binary_accuracy: 0.9436 - val_auc: 0.8112 - val_pr: 0.2455\n",
      "Epoch 46/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1713 - binary_accuracy: 0.9440 - auc: 0.8395 - pr: 0.2860 - val_loss: 0.1790 - val_binary_accuracy: 0.9437 - val_auc: 0.8144 - val_pr: 0.2517\n",
      "Epoch 47/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1710 - binary_accuracy: 0.9440 - auc: 0.8403 - pr: 0.2869 - val_loss: 0.1783 - val_binary_accuracy: 0.9437 - val_auc: 0.8157 - val_pr: 0.2544\n",
      "Epoch 48/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1709 - binary_accuracy: 0.9440 - auc: 0.8409 - pr: 0.2875 - val_loss: 0.1786 - val_binary_accuracy: 0.9437 - val_auc: 0.8150 - val_pr: 0.2528\n",
      "Epoch 49/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1709 - binary_accuracy: 0.9440 - auc: 0.8410 - pr: 0.2878 - val_loss: 0.1792 - val_binary_accuracy: 0.9436 - val_auc: 0.8128 - val_pr: 0.2485\n",
      "Epoch 50/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1708 - binary_accuracy: 0.9440 - auc: 0.8413 - pr: 0.2871 - val_loss: 0.1785 - val_binary_accuracy: 0.9437 - val_auc: 0.8151 - val_pr: 0.2514\n",
      "Epoch 51/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1706 - binary_accuracy: 0.9440 - auc: 0.8417 - pr: 0.2882 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8147 - val_pr: 0.2537\n",
      "Epoch 52/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1705 - binary_accuracy: 0.9440 - auc: 0.8421 - pr: 0.2885 - val_loss: 0.1810 - val_binary_accuracy: 0.9436 - val_auc: 0.8136 - val_pr: 0.2484\n",
      "Epoch 53/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1703 - binary_accuracy: 0.9440 - auc: 0.8430 - pr: 0.2895 - val_loss: 0.1800 - val_binary_accuracy: 0.9437 - val_auc: 0.8132 - val_pr: 0.2499\n",
      "Epoch 54/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1702 - binary_accuracy: 0.9440 - auc: 0.8431 - pr: 0.2894 - val_loss: 0.1804 - val_binary_accuracy: 0.9436 - val_auc: 0.8131 - val_pr: 0.2468\n",
      "Epoch 55/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1701 - binary_accuracy: 0.9440 - auc: 0.8435 - pr: 0.2902 - val_loss: 0.1789 - val_binary_accuracy: 0.9436 - val_auc: 0.8155 - val_pr: 0.2506\n",
      "Epoch 56/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1699 - binary_accuracy: 0.9440 - auc: 0.8442 - pr: 0.2912 - val_loss: 0.1796 - val_binary_accuracy: 0.9436 - val_auc: 0.8131 - val_pr: 0.2496\n",
      "Epoch 57/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1699 - binary_accuracy: 0.9440 - auc: 0.8444 - pr: 0.2908 - val_loss: 0.1786 - val_binary_accuracy: 0.9436 - val_auc: 0.8161 - val_pr: 0.2508\n",
      "Epoch 58/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1697 - binary_accuracy: 0.9440 - auc: 0.8448 - pr: 0.2917 - val_loss: 0.1798 - val_binary_accuracy: 0.9435 - val_auc: 0.8150 - val_pr: 0.2498\n",
      "Epoch 59/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1695 - binary_accuracy: 0.9440 - auc: 0.8457 - pr: 0.2924 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8164 - val_pr: 0.2533\n",
      "Epoch 60/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1694 - binary_accuracy: 0.9440 - auc: 0.8459 - pr: 0.2922 - val_loss: 0.1784 - val_binary_accuracy: 0.9435 - val_auc: 0.8160 - val_pr: 0.2533\n",
      "Epoch 61/1000\n",
      "327/327 [==============================] - 69s 204ms/step - loss: 0.1694 - binary_accuracy: 0.9440 - auc: 0.8460 - pr: 0.2927 - val_loss: 0.1783 - val_binary_accuracy: 0.9437 - val_auc: 0.8161 - val_pr: 0.2542\n",
      "Epoch 62/1000\n",
      "327/327 [==============================] - 72s 210ms/step - loss: 0.1693 - binary_accuracy: 0.9440 - auc: 0.8464 - pr: 0.2927 - val_loss: 0.1782 - val_binary_accuracy: 0.9437 - val_auc: 0.8166 - val_pr: 0.2548\n",
      "Epoch 63/1000\n",
      "327/327 [==============================] - 227s 690ms/step - loss: 0.1693 - binary_accuracy: 0.9440 - auc: 0.8462 - pr: 0.2926 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2535\n",
      "Epoch 64/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1692 - binary_accuracy: 0.9440 - auc: 0.8466 - pr: 0.2932 - val_loss: 0.1791 - val_binary_accuracy: 0.9436 - val_auc: 0.8145 - val_pr: 0.2517\n",
      "Epoch 65/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1690 - binary_accuracy: 0.9441 - auc: 0.8473 - pr: 0.2940 - val_loss: 0.1790 - val_binary_accuracy: 0.9436 - val_auc: 0.8155 - val_pr: 0.2534\n",
      "Epoch 66/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1689 - binary_accuracy: 0.9441 - auc: 0.8475 - pr: 0.2939 - val_loss: 0.1787 - val_binary_accuracy: 0.9437 - val_auc: 0.8145 - val_pr: 0.2515\n",
      "Epoch 67/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1689 - binary_accuracy: 0.9440 - auc: 0.8477 - pr: 0.2943 - val_loss: 0.1800 - val_binary_accuracy: 0.9435 - val_auc: 0.8136 - val_pr: 0.2487\n",
      "Epoch 68/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1688 - binary_accuracy: 0.9441 - auc: 0.8480 - pr: 0.2947 - val_loss: 0.1791 - val_binary_accuracy: 0.9437 - val_auc: 0.8156 - val_pr: 0.2531\n",
      "Epoch 69/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1686 - binary_accuracy: 0.9440 - auc: 0.8485 - pr: 0.2949 - val_loss: 0.1782 - val_binary_accuracy: 0.9437 - val_auc: 0.8166 - val_pr: 0.2539\n",
      "Epoch 70/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1685 - binary_accuracy: 0.9441 - auc: 0.8488 - pr: 0.2955 - val_loss: 0.1779 - val_binary_accuracy: 0.9437 - val_auc: 0.8180 - val_pr: 0.2561\n",
      "Epoch 71/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1685 - binary_accuracy: 0.9441 - auc: 0.8490 - pr: 0.2958 - val_loss: 0.1785 - val_binary_accuracy: 0.9437 - val_auc: 0.8159 - val_pr: 0.2533\n",
      "Epoch 72/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1684 - binary_accuracy: 0.9441 - auc: 0.8492 - pr: 0.2961 - val_loss: 0.1788 - val_binary_accuracy: 0.9437 - val_auc: 0.8152 - val_pr: 0.2534\n",
      "Epoch 73/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1683 - binary_accuracy: 0.9441 - auc: 0.8494 - pr: 0.2963 - val_loss: 0.1788 - val_binary_accuracy: 0.9437 - val_auc: 0.8157 - val_pr: 0.2532\n",
      "Epoch 74/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1682 - binary_accuracy: 0.9441 - auc: 0.8498 - pr: 0.2966 - val_loss: 0.1794 - val_binary_accuracy: 0.9437 - val_auc: 0.8157 - val_pr: 0.2537\n",
      "Epoch 75/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1682 - binary_accuracy: 0.9441 - auc: 0.8497 - pr: 0.2968 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8169 - val_pr: 0.2558\n",
      "Epoch 76/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1681 - binary_accuracy: 0.9441 - auc: 0.8502 - pr: 0.2971 - val_loss: 0.1784 - val_binary_accuracy: 0.9435 - val_auc: 0.8173 - val_pr: 0.2546\n",
      "Epoch 77/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1681 - binary_accuracy: 0.9441 - auc: 0.8502 - pr: 0.2972 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8160 - val_pr: 0.2526\n",
      "Epoch 78/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1679 - binary_accuracy: 0.9441 - auc: 0.8507 - pr: 0.2978 - val_loss: 0.1782 - val_binary_accuracy: 0.9437 - val_auc: 0.8170 - val_pr: 0.2552\n",
      "Epoch 79/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1680 - binary_accuracy: 0.9441 - auc: 0.8506 - pr: 0.2976 - val_loss: 0.1787 - val_binary_accuracy: 0.9437 - val_auc: 0.8168 - val_pr: 0.2547\n",
      "Epoch 80/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1680 - binary_accuracy: 0.9441 - auc: 0.8505 - pr: 0.2970 - val_loss: 0.1786 - val_binary_accuracy: 0.9435 - val_auc: 0.8173 - val_pr: 0.2544\n",
      "Epoch 81/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1678 - binary_accuracy: 0.9441 - auc: 0.8510 - pr: 0.2980 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2556\n",
      "Epoch 82/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1678 - binary_accuracy: 0.9441 - auc: 0.8511 - pr: 0.2979 - val_loss: 0.1791 - val_binary_accuracy: 0.9437 - val_auc: 0.8160 - val_pr: 0.2553\n",
      "Epoch 83/1000\n",
      "327/327 [==============================] - 74s 224ms/step - loss: 0.1677 - binary_accuracy: 0.9441 - auc: 0.8513 - pr: 0.2982 - val_loss: 0.1783 - val_binary_accuracy: 0.9437 - val_auc: 0.8171 - val_pr: 0.2541\n",
      "Epoch 84/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1676 - binary_accuracy: 0.9441 - auc: 0.8516 - pr: 0.2990 - val_loss: 0.1789 - val_binary_accuracy: 0.9436 - val_auc: 0.8154 - val_pr: 0.2527\n",
      "Epoch 85/1000\n",
      "327/327 [==============================] - 71s 213ms/step - loss: 0.1676 - binary_accuracy: 0.9441 - auc: 0.8518 - pr: 0.2993 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2538\n",
      "Epoch 86/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1676 - binary_accuracy: 0.9441 - auc: 0.8519 - pr: 0.2989 - val_loss: 0.1790 - val_binary_accuracy: 0.9437 - val_auc: 0.8157 - val_pr: 0.2530\n",
      "Epoch 87/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1675 - binary_accuracy: 0.9441 - auc: 0.8520 - pr: 0.2994 - val_loss: 0.1787 - val_binary_accuracy: 0.9437 - val_auc: 0.8165 - val_pr: 0.2544\n",
      "Epoch 88/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1674 - binary_accuracy: 0.9441 - auc: 0.8524 - pr: 0.2997 - val_loss: 0.1787 - val_binary_accuracy: 0.9436 - val_auc: 0.8164 - val_pr: 0.2544\n",
      "Epoch 89/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1673 - binary_accuracy: 0.9441 - auc: 0.8526 - pr: 0.3001 - val_loss: 0.1778 - val_binary_accuracy: 0.9437 - val_auc: 0.8177 - val_pr: 0.2572\n",
      "Epoch 90/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1673 - binary_accuracy: 0.9441 - auc: 0.8527 - pr: 0.3002 - val_loss: 0.1781 - val_binary_accuracy: 0.9437 - val_auc: 0.8174 - val_pr: 0.2548\n",
      "Epoch 91/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1672 - binary_accuracy: 0.9441 - auc: 0.8530 - pr: 0.3005 - val_loss: 0.1778 - val_binary_accuracy: 0.9437 - val_auc: 0.8174 - val_pr: 0.2558\n",
      "Epoch 92/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1672 - binary_accuracy: 0.9441 - auc: 0.8529 - pr: 0.3000 - val_loss: 0.1786 - val_binary_accuracy: 0.9437 - val_auc: 0.8159 - val_pr: 0.2544\n",
      "Epoch 93/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1672 - binary_accuracy: 0.9441 - auc: 0.8529 - pr: 0.3001 - val_loss: 0.1782 - val_binary_accuracy: 0.9436 - val_auc: 0.8172 - val_pr: 0.2561\n",
      "Epoch 94/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1671 - binary_accuracy: 0.9441 - auc: 0.8532 - pr: 0.3006 - val_loss: 0.1794 - val_binary_accuracy: 0.9437 - val_auc: 0.8154 - val_pr: 0.2527\n",
      "Epoch 95/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1671 - binary_accuracy: 0.9441 - auc: 0.8533 - pr: 0.3007 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8174 - val_pr: 0.2557\n",
      "Epoch 96/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1670 - binary_accuracy: 0.9441 - auc: 0.8534 - pr: 0.3007 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8175 - val_pr: 0.2563\n",
      "Epoch 97/1000\n",
      "327/327 [==============================] - 74s 224ms/step - loss: 0.1670 - binary_accuracy: 0.9441 - auc: 0.8536 - pr: 0.3012 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2557\n",
      "Epoch 98/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1669 - binary_accuracy: 0.9441 - auc: 0.8537 - pr: 0.3013 - val_loss: 0.1777 - val_binary_accuracy: 0.9437 - val_auc: 0.8178 - val_pr: 0.2557\n",
      "Epoch 99/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1669 - binary_accuracy: 0.9441 - auc: 0.8537 - pr: 0.3013 - val_loss: 0.1782 - val_binary_accuracy: 0.9437 - val_auc: 0.8168 - val_pr: 0.2553\n",
      "Epoch 100/1000\n",
      "327/327 [==============================] - 72s 214ms/step - loss: 0.1669 - binary_accuracy: 0.9441 - auc: 0.8537 - pr: 0.3010 - val_loss: 0.1786 - val_binary_accuracy: 0.9437 - val_auc: 0.8162 - val_pr: 0.2538\n",
      "Epoch 101/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1669 - binary_accuracy: 0.9441 - auc: 0.8539 - pr: 0.3011 - val_loss: 0.1787 - val_binary_accuracy: 0.9437 - val_auc: 0.8159 - val_pr: 0.2544\n",
      "Epoch 102/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1668 - binary_accuracy: 0.9441 - auc: 0.8541 - pr: 0.3014 - val_loss: 0.1794 - val_binary_accuracy: 0.9437 - val_auc: 0.8156 - val_pr: 0.2514\n",
      "Epoch 103/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1668 - binary_accuracy: 0.9441 - auc: 0.8542 - pr: 0.3018 - val_loss: 0.1788 - val_binary_accuracy: 0.9437 - val_auc: 0.8172 - val_pr: 0.2551\n",
      "Epoch 104/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1668 - binary_accuracy: 0.9441 - auc: 0.8541 - pr: 0.3014 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8178 - val_pr: 0.2570\n",
      "Epoch 105/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1667 - binary_accuracy: 0.9441 - auc: 0.8545 - pr: 0.3021 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8180 - val_pr: 0.2572\n",
      "Epoch 106/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1667 - binary_accuracy: 0.9441 - auc: 0.8544 - pr: 0.3016 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8173 - val_pr: 0.2549\n",
      "Epoch 107/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1666 - binary_accuracy: 0.9441 - auc: 0.8547 - pr: 0.3021 - val_loss: 0.1793 - val_binary_accuracy: 0.9437 - val_auc: 0.8141 - val_pr: 0.2516\n",
      "Epoch 108/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1666 - binary_accuracy: 0.9441 - auc: 0.8546 - pr: 0.3023 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 109/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1666 - binary_accuracy: 0.9441 - auc: 0.8547 - pr: 0.3024 - val_loss: 0.1781 - val_binary_accuracy: 0.9436 - val_auc: 0.8182 - val_pr: 0.2578\n",
      "Epoch 110/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1666 - binary_accuracy: 0.9442 - auc: 0.8548 - pr: 0.3025 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8173 - val_pr: 0.2558\n",
      "Epoch 111/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1665 - binary_accuracy: 0.9441 - auc: 0.8550 - pr: 0.3025 - val_loss: 0.1787 - val_binary_accuracy: 0.9437 - val_auc: 0.8157 - val_pr: 0.2536\n",
      "Epoch 112/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1666 - binary_accuracy: 0.9442 - auc: 0.8548 - pr: 0.3027 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2575\n",
      "Epoch 113/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1665 - binary_accuracy: 0.9441 - auc: 0.8550 - pr: 0.3027 - val_loss: 0.1781 - val_binary_accuracy: 0.9437 - val_auc: 0.8169 - val_pr: 0.2568\n",
      "Epoch 114/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1665 - binary_accuracy: 0.9441 - auc: 0.8551 - pr: 0.3023 - val_loss: 0.1783 - val_binary_accuracy: 0.9437 - val_auc: 0.8174 - val_pr: 0.2556\n",
      "Epoch 115/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1664 - binary_accuracy: 0.9442 - auc: 0.8552 - pr: 0.3030 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2561\n",
      "Epoch 116/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1664 - binary_accuracy: 0.9442 - auc: 0.8553 - pr: 0.3031 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2553\n",
      "Epoch 117/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1664 - binary_accuracy: 0.9442 - auc: 0.8553 - pr: 0.3029 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2556\n",
      "Epoch 118/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1664 - binary_accuracy: 0.9441 - auc: 0.8553 - pr: 0.3027 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2569\n",
      "Epoch 119/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1663 - binary_accuracy: 0.9442 - auc: 0.8556 - pr: 0.3032 - val_loss: 0.1779 - val_binary_accuracy: 0.9437 - val_auc: 0.8181 - val_pr: 0.2559\n",
      "Epoch 120/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1664 - binary_accuracy: 0.9442 - auc: 0.8554 - pr: 0.3033 - val_loss: 0.1788 - val_binary_accuracy: 0.9437 - val_auc: 0.8166 - val_pr: 0.2546\n",
      "Epoch 121/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1663 - binary_accuracy: 0.9442 - auc: 0.8558 - pr: 0.3035 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2564\n",
      "Epoch 122/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1662 - binary_accuracy: 0.9442 - auc: 0.8559 - pr: 0.3039 - val_loss: 0.1798 - val_binary_accuracy: 0.9437 - val_auc: 0.8148 - val_pr: 0.2526\n",
      "Epoch 123/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1662 - binary_accuracy: 0.9442 - auc: 0.8560 - pr: 0.3038 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2562\n",
      "Epoch 124/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1662 - binary_accuracy: 0.9442 - auc: 0.8560 - pr: 0.3036 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2565\n",
      "Epoch 125/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1662 - binary_accuracy: 0.9442 - auc: 0.8561 - pr: 0.3038 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2549\n",
      "Epoch 126/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8562 - pr: 0.3040 - val_loss: 0.1779 - val_binary_accuracy: 0.9437 - val_auc: 0.8177 - val_pr: 0.2567\n",
      "Epoch 127/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8562 - pr: 0.3040 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2557\n",
      "Epoch 128/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8562 - pr: 0.3044 - val_loss: 0.1785 - val_binary_accuracy: 0.9437 - val_auc: 0.8169 - val_pr: 0.2560\n",
      "Epoch 129/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1662 - binary_accuracy: 0.9441 - auc: 0.8561 - pr: 0.3035 - val_loss: 0.1791 - val_binary_accuracy: 0.9437 - val_auc: 0.8165 - val_pr: 0.2551\n",
      "Epoch 130/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8563 - pr: 0.3040 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2571\n",
      "Epoch 131/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8563 - pr: 0.3039 - val_loss: 0.1779 - val_binary_accuracy: 0.9437 - val_auc: 0.8179 - val_pr: 0.2573\n",
      "Epoch 132/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8562 - pr: 0.3039 - val_loss: 0.1789 - val_binary_accuracy: 0.9437 - val_auc: 0.8167 - val_pr: 0.2559\n",
      "Epoch 133/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8563 - pr: 0.3041 - val_loss: 0.1794 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2550\n",
      "Epoch 134/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1660 - binary_accuracy: 0.9442 - auc: 0.8566 - pr: 0.3044 - val_loss: 0.1793 - val_binary_accuracy: 0.9437 - val_auc: 0.8157 - val_pr: 0.2541\n",
      "Epoch 135/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1661 - binary_accuracy: 0.9442 - auc: 0.8564 - pr: 0.3039 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 136/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1660 - binary_accuracy: 0.9442 - auc: 0.8568 - pr: 0.3043 - val_loss: 0.1779 - val_binary_accuracy: 0.9437 - val_auc: 0.8183 - val_pr: 0.2564\n",
      "Epoch 137/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8569 - pr: 0.3049 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2564\n",
      "Epoch 138/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8568 - pr: 0.3047 - val_loss: 0.1792 - val_binary_accuracy: 0.9437 - val_auc: 0.8166 - val_pr: 0.2554\n",
      "Epoch 139/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8568 - pr: 0.3048 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2567\n",
      "Epoch 140/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8568 - pr: 0.3047 - val_loss: 0.1792 - val_binary_accuracy: 0.9437 - val_auc: 0.8153 - val_pr: 0.2544\n",
      "Epoch 141/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1658 - binary_accuracy: 0.9442 - auc: 0.8571 - pr: 0.3049 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2574\n",
      "Epoch 142/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8569 - pr: 0.3046 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2576\n",
      "Epoch 143/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8570 - pr: 0.3049 - val_loss: 0.1781 - val_binary_accuracy: 0.9437 - val_auc: 0.8179 - val_pr: 0.2573\n",
      "Epoch 144/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8570 - pr: 0.3049 - val_loss: 0.1789 - val_binary_accuracy: 0.9437 - val_auc: 0.8164 - val_pr: 0.2547\n",
      "Epoch 145/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8570 - pr: 0.3045 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8184 - val_pr: 0.2579\n",
      "Epoch 146/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1658 - binary_accuracy: 0.9442 - auc: 0.8572 - pr: 0.3053 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2555\n",
      "Epoch 147/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1658 - binary_accuracy: 0.9442 - auc: 0.8573 - pr: 0.3052 - val_loss: 0.1786 - val_binary_accuracy: 0.9437 - val_auc: 0.8171 - val_pr: 0.2560\n",
      "Epoch 148/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1659 - binary_accuracy: 0.9442 - auc: 0.8570 - pr: 0.3046 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8168 - val_pr: 0.2557\n",
      "Epoch 149/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1658 - binary_accuracy: 0.9442 - auc: 0.8573 - pr: 0.3052 - val_loss: 0.1783 - val_binary_accuracy: 0.9437 - val_auc: 0.8178 - val_pr: 0.2569\n",
      "Epoch 150/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1657 - binary_accuracy: 0.9442 - auc: 0.8574 - pr: 0.3055 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8184 - val_pr: 0.2579\n",
      "Epoch 151/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1657 - binary_accuracy: 0.9442 - auc: 0.8574 - pr: 0.3053 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8173 - val_pr: 0.2555\n",
      "Epoch 152/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1657 - binary_accuracy: 0.9442 - auc: 0.8575 - pr: 0.3053 - val_loss: 0.1781 - val_binary_accuracy: 0.9437 - val_auc: 0.8182 - val_pr: 0.2574\n",
      "Epoch 153/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1657 - binary_accuracy: 0.9442 - auc: 0.8576 - pr: 0.3053 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2569\n",
      "Epoch 154/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8577 - pr: 0.3057 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2566\n",
      "Epoch 155/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8577 - pr: 0.3056 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8179 - val_pr: 0.2575\n",
      "Epoch 156/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1657 - binary_accuracy: 0.9442 - auc: 0.8576 - pr: 0.3051 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2567\n",
      "Epoch 157/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1657 - binary_accuracy: 0.9442 - auc: 0.8575 - pr: 0.3054 - val_loss: 0.1786 - val_binary_accuracy: 0.9437 - val_auc: 0.8167 - val_pr: 0.2546\n",
      "Epoch 158/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1657 - binary_accuracy: 0.9442 - auc: 0.8576 - pr: 0.3054 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8185 - val_pr: 0.2574\n",
      "Epoch 159/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8578 - pr: 0.3058 - val_loss: 0.1788 - val_binary_accuracy: 0.9437 - val_auc: 0.8174 - val_pr: 0.2552\n",
      "Epoch 160/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8578 - pr: 0.3058 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2571\n",
      "Epoch 161/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8577 - pr: 0.3055 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2570\n",
      "Epoch 162/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8579 - pr: 0.3058 - val_loss: 0.1781 - val_binary_accuracy: 0.9437 - val_auc: 0.8174 - val_pr: 0.2561\n",
      "Epoch 163/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8581 - pr: 0.3060 - val_loss: 0.1779 - val_binary_accuracy: 0.9437 - val_auc: 0.8183 - val_pr: 0.2576\n",
      "Epoch 164/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8579 - pr: 0.3059 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8169 - val_pr: 0.2557\n",
      "Epoch 165/1000\n",
      "327/327 [==============================] - 72s 208ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8580 - pr: 0.3059 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2561\n",
      "Epoch 166/1000\n",
      "327/327 [==============================] - 70s 205ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8580 - pr: 0.3058 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2559\n",
      "Epoch 167/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8578 - pr: 0.3059 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2567\n",
      "Epoch 168/1000\n",
      "327/327 [==============================] - 170s 514ms/step - loss: 0.1656 - binary_accuracy: 0.9442 - auc: 0.8580 - pr: 0.3057 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8169 - val_pr: 0.2553\n",
      "Epoch 169/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8581 - pr: 0.3061 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2577\n",
      "Epoch 170/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8580 - pr: 0.3059 - val_loss: 0.1793 - val_binary_accuracy: 0.9437 - val_auc: 0.8158 - val_pr: 0.2537\n",
      "Epoch 171/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8581 - pr: 0.3062 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2569\n",
      "Epoch 172/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8583 - pr: 0.3061 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8186 - val_pr: 0.2585\n",
      "Epoch 173/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8581 - pr: 0.3060 - val_loss: 0.1782 - val_binary_accuracy: 0.9437 - val_auc: 0.8174 - val_pr: 0.2561\n",
      "Epoch 174/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8582 - pr: 0.3061 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2569\n",
      "Epoch 175/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8584 - pr: 0.3065 - val_loss: 0.1782 - val_binary_accuracy: 0.9437 - val_auc: 0.8177 - val_pr: 0.2564\n",
      "Epoch 176/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8582 - pr: 0.3061 - val_loss: 0.1798 - val_binary_accuracy: 0.9437 - val_auc: 0.8151 - val_pr: 0.2538\n",
      "Epoch 177/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8582 - pr: 0.3059 - val_loss: 0.1786 - val_binary_accuracy: 0.9437 - val_auc: 0.8169 - val_pr: 0.2552\n",
      "Epoch 178/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1655 - binary_accuracy: 0.9442 - auc: 0.8582 - pr: 0.3060 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2581\n",
      "Epoch 179/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8583 - pr: 0.3063 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2571\n",
      "Epoch 180/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8584 - pr: 0.3062 - val_loss: 0.1777 - val_binary_accuracy: 0.9437 - val_auc: 0.8187 - val_pr: 0.2583\n",
      "Epoch 181/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8585 - pr: 0.3065 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2581\n",
      "Epoch 182/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8585 - pr: 0.3067 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2559\n",
      "Epoch 183/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8586 - pr: 0.3064 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2568\n",
      "Epoch 184/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8585 - pr: 0.3063 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2570\n",
      "Epoch 185/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8586 - pr: 0.3069 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2575\n",
      "Epoch 186/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8586 - pr: 0.3065 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8178 - val_pr: 0.2575\n",
      "Epoch 187/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8585 - pr: 0.3064 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2565\n",
      "Epoch 188/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8587 - pr: 0.3064 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2570\n",
      "Epoch 189/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1654 - binary_accuracy: 0.9442 - auc: 0.8585 - pr: 0.3065 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2572\n",
      "Epoch 190/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8586 - pr: 0.3068 - val_loss: 0.1782 - val_binary_accuracy: 0.9437 - val_auc: 0.8178 - val_pr: 0.2572\n",
      "Epoch 191/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8589 - pr: 0.3069 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2569\n",
      "Epoch 192/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8587 - pr: 0.3067 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2571\n",
      "Epoch 193/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8587 - pr: 0.3067 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2559\n",
      "Epoch 194/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8587 - pr: 0.3067 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8185 - val_pr: 0.2585\n",
      "Epoch 195/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8587 - pr: 0.3067 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2573\n",
      "Epoch 196/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8589 - pr: 0.3071 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2574\n",
      "Epoch 197/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8587 - pr: 0.3069 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2574\n",
      "Epoch 198/1000\n",
      "327/327 [==============================] - 73s 221ms/step - loss: 0.1653 - binary_accuracy: 0.9442 - auc: 0.8587 - pr: 0.3067 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2579\n",
      "Epoch 199/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8589 - pr: 0.3068 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2590\n",
      "Epoch 200/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3073 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2570\n",
      "Epoch 201/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3071 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2567\n",
      "Epoch 202/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3071 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2587\n",
      "Epoch 203/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8589 - pr: 0.3068 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2574\n",
      "Epoch 204/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8589 - pr: 0.3071 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2576\n",
      "Epoch 205/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3069 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2578\n",
      "Epoch 206/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8589 - pr: 0.3071 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 207/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3071 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2571\n",
      "Epoch 208/1000\n",
      "327/327 [==============================] - 63s 182ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3071 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2563\n",
      "Epoch 209/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8589 - pr: 0.3070 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 210/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3073 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2576\n",
      "Epoch 211/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8591 - pr: 0.3069 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2577\n",
      "Epoch 212/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8590 - pr: 0.3071 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2566\n",
      "Epoch 213/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8592 - pr: 0.3073 - val_loss: 0.1785 - val_binary_accuracy: 0.9437 - val_auc: 0.8176 - val_pr: 0.2577\n",
      "Epoch 214/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8591 - pr: 0.3075 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2567\n",
      "Epoch 215/1000\n",
      "327/327 [==============================] - 75s 227ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8591 - pr: 0.3070 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2559\n",
      "Epoch 216/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8591 - pr: 0.3073 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2578\n",
      "Epoch 217/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1652 - binary_accuracy: 0.9442 - auc: 0.8591 - pr: 0.3071 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2579\n",
      "Epoch 218/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8592 - pr: 0.3073 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2575\n",
      "Epoch 219/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8592 - pr: 0.3074 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2584\n",
      "Epoch 220/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8593 - pr: 0.3076 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2572\n",
      "Epoch 221/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8593 - pr: 0.3076 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8184 - val_pr: 0.2586\n",
      "Epoch 222/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8593 - pr: 0.3076 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2566\n",
      "Epoch 223/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8593 - pr: 0.3077 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2573\n",
      "Epoch 224/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8591 - pr: 0.3071 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2582\n",
      "Epoch 225/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3078 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8181 - val_pr: 0.2580\n",
      "Epoch 226/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3078 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2577\n",
      "Epoch 227/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3078 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8185 - val_pr: 0.2584\n",
      "Epoch 228/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8592 - pr: 0.3076 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2580\n",
      "Epoch 229/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8593 - pr: 0.3072 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2570\n",
      "Epoch 230/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3077 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2568\n",
      "Epoch 231/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3077 - val_loss: 0.1775 - val_binary_accuracy: 0.9438 - val_auc: 0.8185 - val_pr: 0.2587\n",
      "Epoch 232/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3076 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2580\n",
      "Epoch 233/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3077 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8184 - val_pr: 0.2587\n",
      "Epoch 234/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8593 - pr: 0.3075 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2583\n",
      "Epoch 235/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3076 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2583\n",
      "Epoch 236/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3078 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2570\n",
      "Epoch 237/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3079 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2574\n",
      "Epoch 238/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3076 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 239/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3080 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2585\n",
      "Epoch 240/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1651 - binary_accuracy: 0.9442 - auc: 0.8594 - pr: 0.3077 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2567\n",
      "Epoch 241/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3077 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2587\n",
      "Epoch 242/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3076 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2581\n",
      "Epoch 243/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3078 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2581\n",
      "Epoch 244/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3075 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2574\n",
      "Epoch 245/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3078 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2583\n",
      "Epoch 246/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8596 - pr: 0.3081 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2577\n",
      "Epoch 247/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8596 - pr: 0.3080 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 248/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8596 - pr: 0.3079 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2582\n",
      "Epoch 249/1000\n",
      "327/327 [==============================] - 72s 214ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8595 - pr: 0.3078 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2579\n",
      "Epoch 250/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8596 - pr: 0.3079 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2591\n",
      "Epoch 251/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3079 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2587\n",
      "Epoch 252/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3081 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2582\n",
      "Epoch 253/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8596 - pr: 0.3079 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2583\n",
      "Epoch 254/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3078 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2573\n",
      "Epoch 255/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1650 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3079 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2564\n",
      "Epoch 256/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3080 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 257/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3083 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 258/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3082 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2579\n",
      "Epoch 259/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3083 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2574\n",
      "Epoch 260/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3080 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2582\n",
      "Epoch 261/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3082 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2575\n",
      "Epoch 262/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3081 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2574\n",
      "Epoch 263/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3084 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2577\n",
      "Epoch 264/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3083 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2569\n",
      "Epoch 265/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3082 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2587\n",
      "Epoch 266/1000\n",
      "327/327 [==============================] - 77s 231ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3082 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2588\n",
      "Epoch 267/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3081 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2574\n",
      "Epoch 268/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3082 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2576\n",
      "Epoch 269/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3083 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 270/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8597 - pr: 0.3080 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2576\n",
      "Epoch 271/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3082 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2579\n",
      "Epoch 272/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3082 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2584\n",
      "Epoch 273/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3082 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2585\n",
      "Epoch 274/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3082 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2575\n",
      "Epoch 275/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3080 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2577\n",
      "Epoch 276/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8598 - pr: 0.3082 - val_loss: 0.1780 - val_binary_accuracy: 0.9437 - val_auc: 0.8182 - val_pr: 0.2586\n",
      "Epoch 277/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3083 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2575\n",
      "Epoch 278/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3083 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2580\n",
      "Epoch 279/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3084 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2588\n",
      "Epoch 280/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3083 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2579\n",
      "Epoch 281/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3085 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2575\n",
      "Epoch 282/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3082 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2577\n",
      "Epoch 283/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3080 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2590\n",
      "Epoch 284/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3085 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2584\n",
      "Epoch 285/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3086 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2583\n",
      "Epoch 286/1000\n",
      "327/327 [==============================] - 65s 192ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3082 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2575\n",
      "Epoch 287/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3083 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2578\n",
      "Epoch 288/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3084 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2577\n",
      "Epoch 289/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3084 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 290/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3084 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2576\n",
      "Epoch 291/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3085 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 292/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3088 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2572\n",
      "Epoch 293/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3085 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2577\n",
      "Epoch 294/1000\n",
      "327/327 [==============================] - 77s 231ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3082 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2587\n",
      "Epoch 295/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3085 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2586\n",
      "Epoch 296/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3084 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2574\n",
      "Epoch 297/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3083 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2575\n",
      "Epoch 298/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3086 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2590\n",
      "Epoch 299/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3086 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2592\n",
      "Epoch 300/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3088 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2579\n",
      "Epoch 301/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3084 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2581\n",
      "Epoch 302/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3085 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 303/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3084 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2584\n",
      "Epoch 304/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3085 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2586\n",
      "Epoch 305/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1649 - binary_accuracy: 0.9442 - auc: 0.8599 - pr: 0.3084 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2590\n",
      "Epoch 306/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3086 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 307/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3086 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2584\n",
      "Epoch 308/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3086 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2573\n",
      "Epoch 309/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3086 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2581\n",
      "Epoch 310/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3088 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 311/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3088 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2587\n",
      "Epoch 312/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3088 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2579\n",
      "Epoch 313/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3089 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2583\n",
      "Epoch 314/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3084 - val_loss: 0.1785 - val_binary_accuracy: 0.9437 - val_auc: 0.8175 - val_pr: 0.2573\n",
      "Epoch 315/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3087 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2579\n",
      "Epoch 316/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3089 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2576\n",
      "Epoch 317/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3090 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2588\n",
      "Epoch 318/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8601 - pr: 0.3086 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2582\n",
      "Epoch 319/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8600 - pr: 0.3087 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2582\n",
      "Epoch 320/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3087 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2584\n",
      "Epoch 321/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3088 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2582\n",
      "Epoch 322/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3085 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2587\n",
      "Epoch 323/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3088 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2585\n",
      "Epoch 324/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3087 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 325/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3089 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2584\n",
      "Epoch 326/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3088 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2587\n",
      "Epoch 327/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3089 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2586\n",
      "Epoch 328/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3089 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2585\n",
      "Epoch 329/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3088 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2587\n",
      "Epoch 330/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3089 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2581\n",
      "Epoch 331/1000\n",
      "327/327 [==============================] - 78s 231ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3088 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2586\n",
      "Epoch 332/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3087 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2572\n",
      "Epoch 333/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3088 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2584\n",
      "Epoch 334/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1648 - binary_accuracy: 0.9442 - auc: 0.8602 - pr: 0.3086 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2584\n",
      "Epoch 335/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3087 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2591\n",
      "Epoch 336/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3090 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2592\n",
      "Epoch 337/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3090 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2583\n",
      "Epoch 338/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3087 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2582\n",
      "Epoch 339/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3091 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2580\n",
      "Epoch 340/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3091 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 341/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3087 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2576\n",
      "Epoch 342/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3088 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2581\n",
      "Epoch 343/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3089 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2582\n",
      "Epoch 344/1000\n",
      "327/327 [==============================] - 73s 221ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3091 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2572\n",
      "Epoch 345/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3088 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2583\n",
      "Epoch 346/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3090 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2589\n",
      "Epoch 347/1000\n",
      "327/327 [==============================] - 76s 230ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3090 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2589\n",
      "Epoch 348/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3085 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 349/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3089 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2571\n",
      "Epoch 350/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8603 - pr: 0.3088 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2572\n",
      "Epoch 351/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3092 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 352/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3092 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2586\n",
      "Epoch 353/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3089 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2583\n",
      "Epoch 354/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3090 - val_loss: 0.1791 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 355/1000\n",
      "327/327 [==============================] - 72s 211ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3089 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2582\n",
      "Epoch 356/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3090 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2578\n",
      "Epoch 357/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3089 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2577\n",
      "Epoch 358/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3090 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2569\n",
      "Epoch 359/1000\n",
      "327/327 [==============================] - 73s 221ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3090 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2581\n",
      "Epoch 360/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2581\n",
      "Epoch 361/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2572\n",
      "Epoch 362/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3090 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2584\n",
      "Epoch 363/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8604 - pr: 0.3090 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2579\n",
      "Epoch 364/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3093 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2586\n",
      "Epoch 365/1000\n",
      "327/327 [==============================] - 76s 220ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2582\n",
      "Epoch 366/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2579\n",
      "Epoch 367/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2577\n",
      "Epoch 368/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3092 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2575\n",
      "Epoch 369/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2578\n",
      "Epoch 370/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3093 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2575\n",
      "Epoch 371/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3091 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2581\n",
      "Epoch 372/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3092 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2579\n",
      "Epoch 373/1000\n",
      "327/327 [==============================] - 77s 232ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3093 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2575\n",
      "Epoch 374/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3094 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2571\n",
      "Epoch 375/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3093 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2575\n",
      "Epoch 376/1000\n",
      "327/327 [==============================] - 71s 211ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2582\n",
      "Epoch 377/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2581\n",
      "Epoch 378/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2575\n",
      "Epoch 379/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3091 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2588\n",
      "Epoch 380/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3092 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2579\n",
      "Epoch 381/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3089 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 382/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3091 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2587\n",
      "Epoch 383/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2582\n",
      "Epoch 384/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2581\n",
      "Epoch 385/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2579\n",
      "Epoch 386/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1647 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3091 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2578\n",
      "Epoch 387/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3093 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2576\n",
      "Epoch 388/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2578\n",
      "Epoch 389/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3092 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2585\n",
      "Epoch 390/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3091 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8181 - val_pr: 0.2587\n",
      "Epoch 391/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8605 - pr: 0.3090 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2570\n",
      "Epoch 392/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2559\n",
      "Epoch 393/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 394/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2575\n",
      "Epoch 395/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2583\n",
      "Epoch 396/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2586\n",
      "Epoch 397/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2582\n",
      "Epoch 398/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3093 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2576\n",
      "Epoch 399/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 400/1000\n",
      "327/327 [==============================] - 73s 212ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2581\n",
      "Epoch 401/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2577\n",
      "Epoch 402/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3092 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2584\n",
      "Epoch 403/1000\n",
      "327/327 [==============================] - 72s 211ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3092 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2582\n",
      "Epoch 404/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 405/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3095 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2575\n",
      "Epoch 406/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3090 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2576\n",
      "Epoch 407/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2585\n",
      "Epoch 408/1000\n",
      "327/327 [==============================] - 71s 214ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3093 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2569\n",
      "Epoch 409/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3091 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2580\n",
      "Epoch 410/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3096 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2579\n",
      "Epoch 411/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2581\n",
      "Epoch 412/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3094 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2577\n",
      "Epoch 413/1000\n",
      "327/327 [==============================] - 76s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2577\n",
      "Epoch 414/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 415/1000\n",
      "327/327 [==============================] - 71s 213ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 416/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8606 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2573\n",
      "Epoch 417/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 418/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2570\n",
      "Epoch 419/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2577\n",
      "Epoch 420/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2574\n",
      "Epoch 421/1000\n",
      "327/327 [==============================] - 73s 221ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3094 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2576\n",
      "Epoch 422/1000\n",
      "327/327 [==============================] - 76s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3097 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2582\n",
      "Epoch 423/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3093 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 424/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3093 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2581\n",
      "Epoch 425/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2581\n",
      "Epoch 426/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3096 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2583\n",
      "Epoch 427/1000\n",
      "327/327 [==============================] - 72s 214ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3095 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2577\n",
      "Epoch 428/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2575\n",
      "Epoch 429/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3093 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2575\n",
      "Epoch 430/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2580\n",
      "Epoch 431/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3097 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2584\n",
      "Epoch 432/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2579\n",
      "Epoch 433/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 434/1000\n",
      "327/327 [==============================] - 74s 213ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3094 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2582\n",
      "Epoch 435/1000\n",
      "327/327 [==============================] - 71s 215ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2583\n",
      "Epoch 436/1000\n",
      "327/327 [==============================] - 70s 209ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3096 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2582\n",
      "Epoch 437/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3093 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2581\n",
      "Epoch 438/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2587\n",
      "Epoch 439/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3094 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2586\n",
      "Epoch 440/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3097 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2585\n",
      "Epoch 441/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2581\n",
      "Epoch 442/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2587\n",
      "Epoch 443/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2587\n",
      "Epoch 444/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2577\n",
      "Epoch 445/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1776 - val_binary_accuracy: 0.9438 - val_auc: 0.8183 - val_pr: 0.2592\n",
      "Epoch 446/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2585\n",
      "Epoch 447/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3094 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2577\n",
      "Epoch 448/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3096 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2579\n",
      "Epoch 449/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 450/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2582\n",
      "Epoch 451/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3095 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2576\n",
      "Epoch 452/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2586\n",
      "Epoch 453/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 454/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3097 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2583\n",
      "Epoch 455/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2584\n",
      "Epoch 456/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3096 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2581\n",
      "Epoch 457/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2583\n",
      "Epoch 458/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 459/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2579\n",
      "Epoch 460/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3093 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2583\n",
      "Epoch 461/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3097 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2585\n",
      "Epoch 462/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3095 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2585\n",
      "Epoch 463/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 464/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2581\n",
      "Epoch 465/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2582\n",
      "Epoch 466/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2591\n",
      "Epoch 467/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2585\n",
      "Epoch 468/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2579\n",
      "Epoch 469/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1646 - binary_accuracy: 0.9442 - auc: 0.8607 - pr: 0.3095 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 470/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2583\n",
      "Epoch 471/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2585\n",
      "Epoch 472/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2578\n",
      "Epoch 473/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3096 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2580\n",
      "Epoch 474/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3097 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2577\n",
      "Epoch 475/1000\n",
      "327/327 [==============================] - 75s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2575\n",
      "Epoch 476/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2574\n",
      "Epoch 477/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3095 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 478/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 479/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2582\n",
      "Epoch 480/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3097 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2578\n",
      "Epoch 481/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2580\n",
      "Epoch 482/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3096 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2582\n",
      "Epoch 483/1000\n",
      "327/327 [==============================] - 75s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3096 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2584\n",
      "Epoch 484/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2584\n",
      "Epoch 485/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3095 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 486/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3095 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2577\n",
      "Epoch 487/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3096 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 488/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2581\n",
      "Epoch 489/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1645 - binary_accuracy: 0.9443 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2588\n",
      "Epoch 490/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8182 - val_pr: 0.2588\n",
      "Epoch 491/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2582\n",
      "Epoch 492/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3095 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2582\n",
      "Epoch 493/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2577\n",
      "Epoch 494/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3096 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 495/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2574\n",
      "Epoch 496/1000\n",
      "327/327 [==============================] - 72s 211ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2582\n",
      "Epoch 497/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3096 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2579\n",
      "Epoch 498/1000\n",
      "327/327 [==============================] - 70s 210ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8608 - pr: 0.3095 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2583\n",
      "Epoch 499/1000\n",
      "327/327 [==============================] - 72s 210ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2586\n",
      "Epoch 500/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2576\n",
      "Epoch 501/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3096 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2575\n",
      "Epoch 502/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2578\n",
      "Epoch 503/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2579\n",
      "Epoch 504/1000\n",
      "327/327 [==============================] - 72s 209ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3099 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2577\n",
      "Epoch 505/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3097 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2577\n",
      "Epoch 506/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3100 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2576\n",
      "Epoch 507/1000\n",
      "327/327 [==============================] - 73s 211ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2579\n",
      "Epoch 508/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3097 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2581\n",
      "Epoch 509/1000\n",
      "327/327 [==============================] - 72s 209ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2577\n",
      "Epoch 510/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1777 - val_binary_accuracy: 0.9438 - val_auc: 0.8180 - val_pr: 0.2587\n",
      "Epoch 511/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 512/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 513/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2577\n",
      "Epoch 514/1000\n",
      "327/327 [==============================] - 71s 208ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3097 - val_loss: 0.1778 - val_binary_accuracy: 0.9438 - val_auc: 0.8179 - val_pr: 0.2582\n",
      "Epoch 515/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2584\n",
      "Epoch 516/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 517/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 518/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2582\n",
      "Epoch 519/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 520/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 521/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2580\n",
      "Epoch 522/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2576\n",
      "Epoch 523/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2581\n",
      "Epoch 524/1000\n",
      "327/327 [==============================] - 71s 210ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2577\n",
      "Epoch 525/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2578\n",
      "Epoch 526/1000\n",
      "327/327 [==============================] - 74s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2573\n",
      "Epoch 527/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3101 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2578\n",
      "Epoch 528/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2580\n",
      "Epoch 529/1000\n",
      "327/327 [==============================] - 69s 207ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3100 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2580\n",
      "Epoch 530/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2575\n",
      "Epoch 531/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2574\n",
      "Epoch 532/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2573\n",
      "Epoch 533/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3100 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2571\n",
      "Epoch 534/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8178 - val_pr: 0.2582\n",
      "Epoch 535/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8609 - pr: 0.3098 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2571\n",
      "Epoch 536/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2578\n",
      "Epoch 537/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2572\n",
      "Epoch 538/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3097 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2582\n",
      "Epoch 539/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 540/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 541/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3098 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2578\n",
      "Epoch 542/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2577\n",
      "Epoch 543/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3097 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2568\n",
      "Epoch 544/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3098 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2577\n",
      "Epoch 545/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 546/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2574\n",
      "Epoch 547/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2579\n",
      "Epoch 548/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2577\n",
      "Epoch 549/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2576\n",
      "Epoch 550/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 551/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3098 - val_loss: 0.1779 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2580\n",
      "Epoch 552/1000\n",
      "327/327 [==============================] - 72s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2574\n",
      "Epoch 553/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1645 - binary_accuracy: 0.9442 - auc: 0.8610 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 554/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3102 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2572\n",
      "Epoch 555/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2572\n",
      "Epoch 556/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8177 - val_pr: 0.2578\n",
      "Epoch 557/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2569\n",
      "Epoch 558/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2574\n",
      "Epoch 559/1000\n",
      "327/327 [==============================] - 73s 212ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 560/1000\n",
      "327/327 [==============================] - 71s 212ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2569\n",
      "Epoch 561/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2574\n",
      "Epoch 562/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 563/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2579\n",
      "Epoch 564/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2569\n",
      "Epoch 565/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2576\n",
      "Epoch 566/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2569\n",
      "Epoch 567/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 568/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2571\n",
      "Epoch 569/1000\n",
      "327/327 [==============================] - 72s 211ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3102 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2577\n",
      "Epoch 570/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2572\n",
      "Epoch 571/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3102 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2574\n",
      "Epoch 572/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3099 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2572\n",
      "Epoch 573/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2572\n",
      "Epoch 574/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2577\n",
      "Epoch 575/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2577\n",
      "Epoch 576/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2574\n",
      "Epoch 577/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3100 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2575\n",
      "Epoch 578/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2576\n",
      "Epoch 579/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 580/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3102 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2574\n",
      "Epoch 581/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2575\n",
      "Epoch 582/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 583/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 584/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 585/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2569\n",
      "Epoch 586/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2568\n",
      "Epoch 587/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2579\n",
      "Epoch 588/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2575\n",
      "Epoch 589/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2575\n",
      "Epoch 590/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2579\n",
      "Epoch 591/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3100 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2576\n",
      "Epoch 592/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3100 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2571\n",
      "Epoch 593/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 594/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2567\n",
      "Epoch 595/1000\n",
      "327/327 [==============================] - 75s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2572\n",
      "Epoch 596/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8612 - pr: 0.3104 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 597/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 598/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2575\n",
      "Epoch 599/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2567\n",
      "Epoch 600/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3100 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 601/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3101 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2570\n",
      "Epoch 602/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2575\n",
      "Epoch 603/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 604/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3104 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 605/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2575\n",
      "Epoch 606/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2572\n",
      "Epoch 607/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3104 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2571\n",
      "Epoch 608/1000\n",
      "327/327 [==============================] - 75s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8611 - pr: 0.3102 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2583\n",
      "Epoch 609/1000\n",
      "327/327 [==============================] - 72s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3103 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 610/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3100 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 611/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2572\n",
      "Epoch 612/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2572\n",
      "Epoch 613/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 614/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3104 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 615/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 616/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3103 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 617/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3101 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2571\n",
      "Epoch 618/1000\n",
      "327/327 [==============================] - 71s 208ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3103 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 619/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2579\n",
      "Epoch 620/1000\n",
      "327/327 [==============================] - 73s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 621/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 622/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3102 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2576\n",
      "Epoch 623/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8176 - val_pr: 0.2580\n",
      "Epoch 624/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 625/1000\n",
      "327/327 [==============================] - 72s 210ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2580\n",
      "Epoch 626/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2578\n",
      "Epoch 627/1000\n",
      "327/327 [==============================] - 75s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2572\n",
      "Epoch 628/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2575\n",
      "Epoch 629/1000\n",
      "327/327 [==============================] - 76s 220ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3103 - val_loss: 0.1780 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2581\n",
      "Epoch 630/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3101 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2571\n",
      "Epoch 631/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2578\n",
      "Epoch 632/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3101 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 633/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3104 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2573\n",
      "Epoch 634/1000\n",
      "327/327 [==============================] - 70s 210ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2569\n",
      "Epoch 635/1000\n",
      "327/327 [==============================] - 71s 208ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2573\n",
      "Epoch 636/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3101 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2576\n",
      "Epoch 637/1000\n",
      "327/327 [==============================] - 73s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3103 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2576\n",
      "Epoch 638/1000\n",
      "327/327 [==============================] - 74s 215ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2576\n",
      "Epoch 639/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2579\n",
      "Epoch 640/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2575\n",
      "Epoch 641/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8612 - pr: 0.3103 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2577\n",
      "Epoch 642/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 643/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3106 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2577\n",
      "Epoch 644/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2577\n",
      "Epoch 645/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8175 - val_pr: 0.2582\n",
      "Epoch 646/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2577\n",
      "Epoch 647/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2578\n",
      "Epoch 648/1000\n",
      "327/327 [==============================] - 72s 210ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2577\n",
      "Epoch 649/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2576\n",
      "Epoch 650/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2577\n",
      "Epoch 651/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2578\n",
      "Epoch 652/1000\n",
      "327/327 [==============================] - 71s 215ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3103 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2578\n",
      "Epoch 653/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2574\n",
      "Epoch 654/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2577\n",
      "Epoch 655/1000\n",
      "327/327 [==============================] - 71s 209ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2574\n",
      "Epoch 656/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1644 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2574\n",
      "Epoch 657/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2575\n",
      "Epoch 658/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2578\n",
      "Epoch 659/1000\n",
      "327/327 [==============================] - 72s 214ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3102 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2578\n",
      "Epoch 660/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2575\n",
      "Epoch 661/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1644 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2579\n",
      "Epoch 662/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8174 - val_pr: 0.2577\n",
      "Epoch 663/1000\n",
      "327/327 [==============================] - 78s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2574\n",
      "Epoch 664/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2577\n",
      "Epoch 665/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2576\n",
      "Epoch 666/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2573\n",
      "Epoch 667/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2571\n",
      "Epoch 668/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2574\n",
      "Epoch 669/1000\n",
      "327/327 [==============================] - 71s 210ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 670/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2576\n",
      "Epoch 671/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 672/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 673/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2579\n",
      "Epoch 674/1000\n",
      "327/327 [==============================] - 76s 219ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3103 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 675/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 676/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 677/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 678/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2569\n",
      "Epoch 679/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3104 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2574\n",
      "Epoch 680/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2570\n",
      "Epoch 681/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2575\n",
      "Epoch 682/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2576\n",
      "Epoch 683/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2576\n",
      "Epoch 684/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2574\n",
      "Epoch 685/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2578\n",
      "Epoch 686/1000\n",
      "327/327 [==============================] - 80s 241ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2570\n",
      "Epoch 687/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2573\n",
      "Epoch 688/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2577\n",
      "Epoch 689/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2573\n",
      "Epoch 690/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3107 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2581\n",
      "Epoch 691/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3103 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 692/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2574\n",
      "Epoch 693/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2574\n",
      "Epoch 694/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 695/1000\n",
      "327/327 [==============================] - 77s 231ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2578\n",
      "Epoch 696/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2573\n",
      "Epoch 697/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2575\n",
      "Epoch 698/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2576\n",
      "Epoch 699/1000\n",
      "327/327 [==============================] - 78s 233ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2574\n",
      "Epoch 700/1000\n",
      "327/327 [==============================] - 78s 231ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2572\n",
      "Epoch 701/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3107 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2569\n",
      "Epoch 702/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8613 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2575\n",
      "Epoch 703/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2575\n",
      "Epoch 704/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3107 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2576\n",
      "Epoch 705/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3107 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2579\n",
      "Epoch 706/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2572\n",
      "Epoch 707/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2577\n",
      "Epoch 708/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 709/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2575\n",
      "Epoch 710/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2573\n",
      "Epoch 711/1000\n",
      "327/327 [==============================] - 75s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3104 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 712/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2575\n",
      "Epoch 713/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2571\n",
      "Epoch 714/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 715/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2572\n",
      "Epoch 716/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3104 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2575\n",
      "Epoch 717/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2571\n",
      "Epoch 718/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8171 - val_pr: 0.2574\n",
      "Epoch 719/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3107 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2570\n",
      "Epoch 720/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2576\n",
      "Epoch 721/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3105 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2572\n",
      "Epoch 722/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3105 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2570\n",
      "Epoch 723/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8173 - val_pr: 0.2578\n",
      "Epoch 724/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2573\n",
      "Epoch 725/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 726/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2572\n",
      "Epoch 727/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2573\n",
      "Epoch 728/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2568\n",
      "Epoch 729/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1782 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2577\n",
      "Epoch 730/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2573\n",
      "Epoch 731/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 732/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2573\n",
      "Epoch 733/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2574\n",
      "Epoch 734/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2575\n",
      "Epoch 735/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2570\n",
      "Epoch 736/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2574\n",
      "Epoch 737/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2576\n",
      "Epoch 738/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2573\n",
      "Epoch 739/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 740/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2576\n",
      "Epoch 741/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2577\n",
      "Epoch 742/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8614 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2570\n",
      "Epoch 743/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 744/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 745/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8170 - val_pr: 0.2573\n",
      "Epoch 746/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2570\n",
      "Epoch 747/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 748/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2566\n",
      "Epoch 749/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 750/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8614 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2572\n",
      "Epoch 751/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 752/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2567\n",
      "Epoch 753/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2567\n",
      "Epoch 754/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2573\n",
      "Epoch 755/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 756/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 757/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 758/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2570\n",
      "Epoch 759/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2571\n",
      "Epoch 760/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2572\n",
      "Epoch 761/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2572\n",
      "Epoch 762/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 763/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 764/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 765/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1781 - val_binary_accuracy: 0.9438 - val_auc: 0.8172 - val_pr: 0.2579\n",
      "Epoch 766/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 767/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 768/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2572\n",
      "Epoch 769/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3106 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 770/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 771/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2573\n",
      "Epoch 772/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 773/1000\n",
      "327/327 [==============================] - 74s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2569\n",
      "Epoch 774/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 775/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 776/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2567\n",
      "Epoch 777/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2573\n",
      "Epoch 778/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3110 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2572\n",
      "Epoch 779/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2567\n",
      "Epoch 780/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2570\n",
      "Epoch 781/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 782/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 783/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3105 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 784/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 785/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3107 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2571\n",
      "Epoch 786/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2570\n",
      "Epoch 787/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2568\n",
      "Epoch 788/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 789/1000\n",
      "327/327 [==============================] - 77s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 790/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 791/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2572\n",
      "Epoch 792/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 793/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 794/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 795/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 796/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 797/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 798/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3106 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 799/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2568\n",
      "Epoch 800/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2570\n",
      "Epoch 801/1000\n",
      "327/327 [==============================] - 73s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 802/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 803/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2571\n",
      "Epoch 804/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8615 - pr: 0.3106 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 805/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 806/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2563\n",
      "Epoch 807/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2569\n",
      "Epoch 808/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 809/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8168 - val_pr: 0.2569\n",
      "Epoch 810/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2563\n",
      "Epoch 811/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2569\n",
      "Epoch 812/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2570\n",
      "Epoch 813/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 814/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 815/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 816/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 817/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8615 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 818/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2570\n",
      "Epoch 819/1000\n",
      "327/327 [==============================] - 78s 231ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 820/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2564\n",
      "Epoch 821/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2570\n",
      "Epoch 822/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2571\n",
      "Epoch 823/1000\n",
      "327/327 [==============================] - 79s 230ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2562\n",
      "Epoch 824/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2566\n",
      "Epoch 825/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3111 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 826/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2572\n",
      "Epoch 827/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3106 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2563\n",
      "Epoch 828/1000\n",
      "327/327 [==============================] - 77s 231ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8169 - val_pr: 0.2571\n",
      "Epoch 829/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 830/1000\n",
      "327/327 [==============================] - 79s 230ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2570\n",
      "Epoch 831/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 832/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 833/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 834/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1783 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2572\n",
      "Epoch 835/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 836/1000\n",
      "327/327 [==============================] - 77s 232ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2569\n",
      "Epoch 837/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 838/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2563\n",
      "Epoch 839/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 840/1000\n",
      "327/327 [==============================] - 78s 231ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 841/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3107 - val_loss: 0.1786 - val_binary_accuracy: 0.9437 - val_auc: 0.8166 - val_pr: 0.2568\n",
      "Epoch 842/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 843/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 844/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 845/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3107 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 846/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2567\n",
      "Epoch 847/1000\n",
      "327/327 [==============================] - 78s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 848/1000\n",
      "327/327 [==============================] - 76s 228ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2570\n",
      "Epoch 849/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1643 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2563\n",
      "Epoch 850/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2565\n",
      "Epoch 851/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3106 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 852/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2569\n",
      "Epoch 853/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 854/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2568\n",
      "Epoch 855/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2566\n",
      "Epoch 856/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2562\n",
      "Epoch 857/1000\n",
      "327/327 [==============================] - 65s 191ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 858/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 859/1000\n",
      "327/327 [==============================] - 77s 232ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 860/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 861/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2563\n",
      "Epoch 862/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 863/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 864/1000\n",
      "327/327 [==============================] - 77s 231ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2569\n",
      "Epoch 865/1000\n",
      "327/327 [==============================] - 78s 230ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2572\n",
      "Epoch 866/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9437 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 867/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2562\n",
      "Epoch 868/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2569\n",
      "Epoch 869/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 870/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1643 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3106 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2569\n",
      "Epoch 871/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 872/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 873/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 874/1000\n",
      "327/327 [==============================] - 79s 236ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2565\n",
      "Epoch 875/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2572\n",
      "Epoch 876/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2562\n",
      "Epoch 877/1000\n",
      "327/327 [==============================] - 60s 180ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2567\n",
      "Epoch 878/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 879/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 880/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 881/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2562\n",
      "Epoch 882/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 883/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2561\n",
      "Epoch 884/1000\n",
      "327/327 [==============================] - 78s 231ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2568\n",
      "Epoch 885/1000\n",
      "327/327 [==============================] - 78s 231ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1784 - val_binary_accuracy: 0.9437 - val_auc: 0.8166 - val_pr: 0.2567\n",
      "Epoch 886/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2564\n",
      "Epoch 887/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2567\n",
      "Epoch 888/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2564\n",
      "Epoch 889/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2563\n",
      "Epoch 890/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3108 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2563\n",
      "Epoch 891/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 892/1000\n",
      "327/327 [==============================] - 77s 228ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2567\n",
      "Epoch 893/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2561\n",
      "Epoch 894/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2570\n",
      "Epoch 895/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 896/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 897/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2565\n",
      "Epoch 898/1000\n",
      "327/327 [==============================] - 75s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 899/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 900/1000\n",
      "327/327 [==============================] - 77s 230ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9437 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 901/1000\n",
      "327/327 [==============================] - 74s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2562\n",
      "Epoch 902/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 903/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 904/1000\n",
      "327/327 [==============================] - 76s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1792 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2564\n",
      "Epoch 905/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2566\n",
      "Epoch 906/1000\n",
      "327/327 [==============================] - 77s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 907/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 908/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2572\n",
      "Epoch 909/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 910/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 911/1000\n",
      "327/327 [==============================] - 78s 228ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 912/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2572\n",
      "Epoch 913/1000\n",
      "327/327 [==============================] - 75s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 914/1000\n",
      "327/327 [==============================] - 76s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 915/1000\n",
      "327/327 [==============================] - 76s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 916/1000\n",
      "327/327 [==============================] - 75s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3108 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 917/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 918/1000\n",
      "327/327 [==============================] - 77s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 919/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 920/1000\n",
      "327/327 [==============================] - 77s 232ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2562\n",
      "Epoch 921/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 922/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2567\n",
      "Epoch 923/1000\n",
      "327/327 [==============================] - 72s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2571\n",
      "Epoch 924/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 925/1000\n",
      "327/327 [==============================] - 71s 207ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2571\n",
      "Epoch 926/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8166 - val_pr: 0.2573\n",
      "Epoch 927/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 928/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2563\n",
      "Epoch 929/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 930/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2571\n",
      "Epoch 931/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 932/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8616 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2568\n",
      "Epoch 933/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3112 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 934/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 935/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2570\n",
      "Epoch 936/1000\n",
      "327/327 [==============================] - 75s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2564\n",
      "Epoch 937/1000\n",
      "327/327 [==============================] - 77s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1791 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2562\n",
      "Epoch 938/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2568\n",
      "Epoch 939/1000\n",
      "327/327 [==============================] - 66s 198ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2568\n",
      "Epoch 940/1000\n",
      "327/327 [==============================] - 71s 212ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2568\n",
      "Epoch 941/1000\n",
      "327/327 [==============================] - 78s 232ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3108 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 942/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2568\n",
      "Epoch 943/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2569\n",
      "Epoch 944/1000\n",
      "327/327 [==============================] - 81s 239ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2569\n",
      "Epoch 945/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2565\n",
      "Epoch 946/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3113 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2566\n",
      "Epoch 947/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8616 - pr: 0.3108 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2569\n",
      "Epoch 948/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 949/1000\n",
      "327/327 [==============================] - 76s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3112 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2565\n",
      "Epoch 950/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8165 - val_pr: 0.2572\n",
      "Epoch 951/1000\n",
      "327/327 [==============================] - 77s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 952/1000\n",
      "327/327 [==============================] - 76s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2566\n",
      "Epoch 953/1000\n",
      "327/327 [==============================] - 73s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2568\n",
      "Epoch 954/1000\n",
      "327/327 [==============================] - 74s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 955/1000\n",
      "327/327 [==============================] - 75s 226ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2565\n",
      "Epoch 956/1000\n",
      "327/327 [==============================] - 74s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2567\n",
      "Epoch 957/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3112 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2566\n",
      "Epoch 958/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2561\n",
      "Epoch 959/1000\n",
      "327/327 [==============================] - 76s 227ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 960/1000\n",
      "327/327 [==============================] - 72s 209ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3111 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 961/1000\n",
      "327/327 [==============================] - 72s 213ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2565\n",
      "Epoch 962/1000\n",
      "327/327 [==============================] - 76s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 963/1000\n",
      "327/327 [==============================] - 73s 214ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2566\n",
      "Epoch 964/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2564\n",
      "Epoch 965/1000\n",
      "327/327 [==============================] - 75s 220ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 966/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1791 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2563\n",
      "Epoch 967/1000\n",
      "327/327 [==============================] - 74s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 968/1000\n",
      "327/327 [==============================] - 78s 229ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1784 - val_binary_accuracy: 0.9438 - val_auc: 0.8167 - val_pr: 0.2571\n",
      "Epoch 969/1000\n",
      "327/327 [==============================] - 77s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2566\n",
      "Epoch 970/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2570\n",
      "Epoch 971/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3112 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 972/1000\n",
      "327/327 [==============================] - 73s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3112 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2568\n",
      "Epoch 973/1000\n",
      "327/327 [==============================] - 73s 217ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2564\n",
      "Epoch 974/1000\n",
      "327/327 [==============================] - 77s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8159 - val_pr: 0.2562\n",
      "Epoch 975/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2563\n",
      "Epoch 976/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2563\n",
      "Epoch 977/1000\n",
      "327/327 [==============================] - 75s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 978/1000\n",
      "327/327 [==============================] - 70s 210ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2561\n",
      "Epoch 979/1000\n",
      "327/327 [==============================] - 72s 215ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2563\n",
      "Epoch 980/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2565\n",
      "Epoch 981/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2564\n",
      "Epoch 982/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1790 - val_binary_accuracy: 0.9438 - val_auc: 0.8158 - val_pr: 0.2559\n",
      "Epoch 983/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 984/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2563\n",
      "Epoch 985/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2565\n",
      "Epoch 986/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 987/1000\n",
      "327/327 [==============================] - 73s 213ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 988/1000\n",
      "327/327 [==============================] - 74s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9442 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2563\n",
      "Epoch 989/1000\n",
      "327/327 [==============================] - 75s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1785 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2570\n",
      "Epoch 990/1000\n",
      "327/327 [==============================] - 75s 223ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8162 - val_pr: 0.2564\n",
      "Epoch 991/1000\n",
      "327/327 [==============================] - 74s 221ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2566\n",
      "Epoch 992/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8160 - val_pr: 0.2563\n",
      "Epoch 993/1000\n",
      "327/327 [==============================] - 74s 219ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3111 - val_loss: 0.1786 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2567\n",
      "Epoch 994/1000\n",
      "327/327 [==============================] - 76s 222ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3112 - val_loss: 0.1789 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 995/1000\n",
      "327/327 [==============================] - 76s 224ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3112 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 996/1000\n",
      "327/327 [==============================] - 74s 218ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3109 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8163 - val_pr: 0.2568\n",
      "Epoch 997/1000\n",
      "327/327 [==============================] - 72s 216ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2566\n",
      "Epoch 998/1000\n",
      "327/327 [==============================] - 75s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8617 - pr: 0.3110 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2564\n",
      "Epoch 999/1000\n",
      "327/327 [==============================] - 72s 212ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3111 - val_loss: 0.1787 - val_binary_accuracy: 0.9438 - val_auc: 0.8164 - val_pr: 0.2567\n",
      "Epoch 1000/1000\n",
      "327/327 [==============================] - 76s 225ms/step - loss: 0.1642 - binary_accuracy: 0.9443 - auc: 0.8618 - pr: 0.3111 - val_loss: 0.1788 - val_binary_accuracy: 0.9438 - val_auc: 0.8161 - val_pr: 0.2563\n",
      "Saved model to data/2_cross_samples/train_out/epoch_model/epoch1000_model.h5 at epoch 1000\n",
      "8. Save training results...\n",
      "=== Training completed! ===\n",
      "Best model: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/2_cross_samples/train_out/E1000best_model.h5\n",
      "Training history: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/2_cross_samples/train_out/history.pickle\n"
     ]
    }
   ],
   "source": [
    "history = xc.tr.train_XChrom(\n",
    "    input_folder='./data/2_cross_samples/train_data/',\n",
    "    cell_embedding_ad='./data/2_cross_samples/train_rna_harmony.h5ad',\n",
    "    cellembed_raw='X_pca_harmony',\n",
    "    out_path='./data/2_cross_samples/train_out/',\n",
    "    epochs = 1000,\n",
    "    verbose = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84b8fd9-6473-467f-92d4-e4ba2375339e",
   "metadata": {},
   "source": [
    "Plotting training metrics to monitor overfitting and evaluate the final model performance. Ideally, after a certain number of epochs, the validation curves plateau (i.e., remain stable).\n",
    "\n",
    "The metrics computed during training are saved in `./data/2_cross_samples/train_out/history.pickle`. Load them with:     \n",
    "\n",
    "```python\n",
    "import pickle\n",
    "with open('./data/2_cross_samples/train_out/history.pickle', 'rb') as f:\n",
    "    history = pickle.load(f)\n",
    "```\n",
    "\n",
    "You can then use this `history` object to plot training/validation metrics (e.g., loss, auROC/auPRC, NS/LS) over epochs.                   \n",
    "\n",
    "The function `plot_train_history()` will automatically detect whether NS and LS values have been computed during training. If these metrics are available in the history data, they will be automatically included in the training metric plots along with other evaluation metrics such as loss, auROC, and auPRC.                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "661dcb99-c9a3-46ba-8b88-39d5a207c3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xc.pl.plot_train_history(\n",
    "    history = history['history'],\n",
    "    savefig = True,\n",
    "    out_file = './data/2_cross_samples/train_out/train_history_plot.pdf'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c24a178-6b5f-473e-92b7-97b36cc00a96",
   "metadata": {},
   "source": [
    "### 6. Evaluate model performance in cross-sample scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ef3e11-5284-462b-808e-f33468463063",
   "metadata": {},
   "source": [
    "The following metrics will be computed on the s2d1 test set data:\n",
    "\n",
    "- **Binary classification metrics**:  \n",
    "  - (overall, per cell, per peak) auROC                          \n",
    "  - (overall, per cell, per peak) auPRC                      \n",
    "  \n",
    "- **Cell state fidelity**:  \n",
    "  - Neighbor Score (NS) (k=10, 50, 100)                     \n",
    "  - Label Score (LS) (k=10, 50, 100)                                 \n",
    "\n",
    "This combination of binary classification metrics and cell state fidelity provides a comprehensive evaluation of the model's performance on unseen data from a different sample. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "08807d98-f569-46d6-9f97-41a69b466c0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict done! prediction shape is: (56439, 4220)\n",
      "Overall auROC: 0.8363, auPRC: 0.2284\n",
      "Per-cell auROC: 0.7982, auPRC: 0.1614\n",
      "Valid cells: 4220\n",
      "Per-peak auROC: 0.7544, auPRC: 0.1154\n",
      "Valid peaks: 56439\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 1- calculate cross-samples auROC & auPRC\n",
    "metrics1 = xc.tl.crosssamples_aucprc(\n",
    "    cell_embedding_ad='./data/2_cross_samples/test_rna_harmony.h5ad',\n",
    "    input_folder='./data/2_cross_samples/test_data',\n",
    "    model_path='./data/2_cross_samples/train_out/E1000best_model.h5',\n",
    "    output_path='./data/2_cross_samples/eval_out',\n",
    "    cellembed_raw='X_pca_harmony',\n",
    "    save_pred=True,\n",
    "    scatter_plot=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07657f13-4821-4145-be5f-d7450fd0fec6",
   "metadata": {},
   "source": [
    "- If `save_pred=True`, the prediction results will be saved as:   \n",
    "  - `/2_cross_samples/eval_out/crosssamples_pred.npy`  \n",
    "- If `scatter_plot=True`, the following relationships will be visualized and saved as PDF files in `/2_cross_samples/eval_out/`:  \n",
    "  - (i) auROC per cell vs. log-transformed peak counts per cell; \n",
    "  - (ii) auPRC per cell vs. log-transformed peak counts per cell; \n",
    "  - (iii) auROC per peak vs. log-transformed cell counts per peak; \n",
    "  - (iv) auPRC per peak vs. log-transformed cell counts per peak. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "03c11226-f0af-4356-8738-9ac8a9048b06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict done! prediction shape is: (56439, 4220)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/miaoyuanyuan/miniconda3/envs/py3.8_tf2.6.0/lib/python3.8/site-packages/scanpy/preprocessing/_simple.py:842: UserWarning: Received a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbor score(100)=0.3999,label score(100)=0.8042\n",
      "neighbor score(50)=0.3094,label score(50)=0.8397\n",
      "neighbor score(10)=0.1630,label score(10)=0.8799\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 2- calculate cross-samples ns & ls\n",
    "metrics2 = xc.tl.crosssamples_nsls(\n",
    "    cell_embedding_ad='./data/2_cross_samples/test_rna_harmony.h5ad',\n",
    "    input_folder='./data/2_cross_samples/test_data',\n",
    "    model_path='./data/2_cross_samples/train_out/E1000best_model.h5',\n",
    "    output_path='./data/2_cross_samples/eval_out',\n",
    "    cellembed_raw='X_pca_harmony',\n",
    "    celltype = 'cell_type',\n",
    "    save_pred=True,\n",
    "    plot_umap=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d10e09d-c2ee-4087-a99b-2f6fa51708b9",
   "metadata": {},
   "source": [
    "- If `save_pred=True`, the prediction results will be saved as:   \n",
    "  - `/2_cross_samples/eval_out/crosssamples_impute.h5ad`  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.8_tf2.6.0",
   "language": "python",
   "name": "py3.8_tf2.6.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}