{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1. Within-sample analysis**\n",
    "\n",
    "This tutorial demonstrates the procedure for training and evaluating XChrom model on a single sample. The workflow includes splitting the sample into training and test sets, preprocessing the data to generate model inputs, training the model, and performing prediction and evaluation.\n",
    "\n",
    "- **Data Preprocessing**                                        \n",
    "We used a publicly available 10x Genomics Multiome dataset from fresh embryonic E18 mouse brain (paired scRNA-seq and scATAC-seq) as an example. The scATAC-seq data was partitioned by cells and peaks to generate train_val set, cross-cell test set, cross-region test set, and cross-both test set for model training and evaluation. For the scRNA-seq data, we performed PCA to generate initial cell embeddings, which served as cell identity information for XChrom.\n",
    "\n",
    "- **Model Training**                                                                \n",
    "XChrom takes two inputs: ① DNA sequence (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. \n",
    "\n",
    "- **Prediction and Evaluation**                                                          \n",
    "To comprehensively assess the predictive performance of XChrom, we perfomed 3 test tasks: \n",
    "    - \"cross-region\" test, which evaluates the model's ability to predict new genomic regions not seen during training; \n",
    "    - \"cross-cell\" test, which assesses the model's generalization to new cells not included in the training set;\n",
    "    - \"cross-both\" test, which tests prediction of unseen genomic regions in unseen cells and represents the most challenging task. \n",
    "\n",
    "    For each test, we used two binary classification metrics—auROC and auPRC—as primary evaluation metrics, which were summarized at the overall, per-cell and per-peak levels. To evaluate the cell state fidelity after prediction, we adopted two metrics: NS, which quantifies cross-modality neighborhood concordance by comparing scATAC-seq and scRNA-seq data, and LS, which measures 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. \n",
    "\n",
    "For datasets lacking prior cell annotations, we used cluster indices identified via Leiden clustering of scRNA data as proxy cell type labels for LS calculation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Download data (mouse brain)\n",
    "- The raw data are available at https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets/2.0.0/e18_mouse_brain_fresh_5k. \n",
    "- Download e18_mouse_brain_fresh_5k_filtered_feature_bc_matrix.h5, save in `./data/1_within_sample/e18_mouse_brain_fresh_5k_filtered_feature_bc_matrix.h5`\n",
    "- Download the mouse genome file from:\n",
    "https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/mm10.fa.gz\n",
    "and then decompress it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Read and filter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xchrom as xc\n",
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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:1840: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully converted data/1_within_sample/e18_mouse_brain_fresh_5k_filtered_feature_bc_matrix.h5 to data/1_within_sample/m_brain_paired.h5ad\n",
      "Data shape: 4878 cells × 204478 features\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/xchrom/readfile.py:327: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n",
      "  ad_atac.var['chr'] = chromosome_start_end['chr']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully extracted ATAC data from data/1_within_sample/m_brain_paired.h5ad to data/1_within_sample/m_brain_paired_atac.h5ad\n",
      "Data shape: 4878 cells × 172193 peaks\n",
      "Successfully extracted RNA data from data/1_within_sample/m_brain_paired.h5ad to data/1_within_sample/m_brain_paired_rna.h5ad\n",
      "Data shape: 4878 cells × 32285 genes\n"
     ]
    }
   ],
   "source": [
    "input_file = './data/1_within_sample/e18_mouse_brain_fresh_5k_filtered_feature_bc_matrix.h5'\n",
    "output_file = './data/1_within_sample/m_brain_paired.h5ad'\n",
    "ad_rna, ad_atac = xc.read_10x_h5_to_h5ad(\n",
    "    h5_file=input_file,\n",
    "    paired=True,\n",
    "    output_file=output_file\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This process generates three H5AD files (saved to `./data/1_within_sample/`):\n",
    "- `m_brain_paired.h5ad` — converted raw paired data\n",
    "- `m_brain_paired_rna.h5ad` — extracted scRNA data\n",
    "- `m_brain_paired_atac.h5ad` — extracted scATAC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNA data after filtering: View of AnnData object with n_obs × n_vars = 4878 × 10721\n",
      "    obs: 'n_genes'\n",
      "    var: 'gene_ids', 'feature_types', 'genome', 'n_cells'\n",
      "ATAC data after filtering: View of AnnData object with n_obs × n_vars = 4878 × 40313\n",
      "    obs: 'n_genes'\n",
      "    var: 'gene_ids', 'feature_types', 'genome', 'chr', 'start', 'end', 'n_cells'\n"
     ]
    }
   ],
   "source": [
    "### Filter data\n",
    "ad_rna, ad_atac = xc.pp.filter_multiome_data(\n",
    "    ad_rna = ad_rna,\n",
    "    ad_atac = ad_atac,\n",
    "    species = 'mouse'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the scATAC data (corresponding to the aforementioned `m_brain_paired_atac.h5ad`) was filtered to remove peaks accessible in fewer than 5% of cells and sequences not belonging to mouse chromosomes chr1-19, X, or Y. Similarly, the scRNA data (corresponding to the aforementioned `m_brain_paired_rna.h5ad`) was filtered to remove genes expressed in fewer than 5% of cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Process scRNA-seq data to generate raw cell embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/miaoyuanyuan/miniconda3/envs/py3.8_tf2.6.0/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:169: UserWarning: Received a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dimension of raw cell embeddings:  (4878, 32)\n"
     ]
    }
   ],
   "source": [
    "# ad_rna = sc.read_h5ad('./data/1_within_sample/m_brain_paired_rna.h5ad')\n",
    "ad_rna = xc.tl.calc_pca(ad_rna, n_comps=32)\n",
    "sc.pp.neighbors(ad_rna)\n",
    "print('The dimension of raw cell embeddings: ',ad_rna.obsm[\"X_pca\"].shape)\n",
    "sc.tl.umap(ad_rna)\n",
    "sc.tl.leiden(ad_rna, key_added='pc32_leiden', resolution=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we performed PCA on the scRNA data to generate raw cell embeddings. Each cell is represented by a 32-dimensional vector. The resulting raw cell embeddings are stored in `ad_rna.obsm['X_pca']`, and the Leiden clusters (calculated based on these 32 principal components) are saved in `ad_rna.obs['pc32_leiden']`, serving as cell types if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = str(output_file).split('.h5ad')[0]\n",
    "ad_rna.write(f'{prefix}_rna.h5ad')\n",
    "ad_atac.write(f'{prefix}_atac.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Training/test data prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/xchrom/pp/generate_input_data.py:92: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  ad.obs['b_zscore'] = b\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train/test data is saved in:  data/1_within_sample/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": [
    "process_folder = './data/1_within_sample/train_data/'\n",
    "input_fasta = '/picb/bigdata/project/miaoyuanyuan/mm10.fa'\n",
    "# ad_atac = sc.read_h5ad('./data/1_within_sample/m_brain_paired_atac.h5ad')\n",
    "dict = xc.pp.process_train_test_single(\n",
    "    ad_atac=ad_atac, ## can be a str, Path, or anndata.AnnData object\n",
    "    input_fasta=input_fasta,\n",
    "    output_path=process_folder\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We preprocessed the filtered scATAC data by partitioning it into training and test sets using a 90% ratio for both cells and peaks. This process generated the following files:\n",
    "- `ad_trainval.h5ad`: composed of training cells and training peaks               \n",
    "- `ad_crosspeak.h5ad`: composed of training cells and testing peaks             \n",
    "- `ad_crosscell.h5ad`: composed of testing cells and training peaks                \n",
    "- `ad_test.h5ad`: composed of testing cells and testing peaks           \n",
    "\n",
    "Corresponding count matrices were also generated:\n",
    "- `m_trainval.npz`                          \n",
    "- `m_crosspeak.npz`                          \n",
    "- `m_crosscell.npz`                       \n",
    "- `m_test.npz`                                   \n",
    "\n",
    "Additionally, base sequence files were created for the training and testing peaks:\n",
    "- `trainval_seqs.h5` (for training peaks)\n",
    "- `test_seqs.h5` (for testing peaks)\n",
    "\n",
    "All files are saved in `./data/1_within_sample/`\n",
    "\n",
    "Note: The parameter `input_fasta` should be replaced with the path to the genome file corresponding to the species of your dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Train XChrom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the preprocessed data obtained above as sequence inputs for the model. The `input_folder` should contain all the files generated during preprocessing. The `'X_pca'` embeddings derived from PCA dimensionality reduction applied to the scRNA data are used as raw cell embeddings and serve as cell identity inputs to the model.                  \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": 7,
   "metadata": {},
   "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/1_within_sample/train_data\n",
      "Cell embedding file: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/1_within_sample/m_brain_paired_rna.h5ad\n",
      "Raw cell embedding key: X_pca\n",
      "Output path: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/1_within_sample/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/1_within_sample/m_brain_paired_rna.h5ad.obsm['X_pca']\n",
      "Initial cell embedding saved to: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/1_within_sample/m_brain_paired_rna.h5ad.obsm['zscore32_perpc']\n",
      "Initial cell embedding shape: (4878, 32)\n",
      "2. Load training data...\n",
      "3. Prepare train/val data split...\n",
      "Training peak number: 32653, Validation peak number: 3629\n",
      "4. Create TensorFlow dataset...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-17 13:11:09.740605: 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 13:11:14.774049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20361 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:31:00.0, compute capability: 8.6\n",
      "2025-08-17 13:11:14.775854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 22350 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:4b:00.0, compute capability: 8.6\n",
      "2025-08-17 13:11:14.776281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 22350 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:98:00.0, compute capability: 8.6\n",
      "2025-08-17 13:11:14.776697: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 22350 MB memory:  -> device: 3, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:b1:00.0, 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, 4391, 32)]   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 1, 32)        128         dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 4391, 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, 4391, 32)     64          lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gelu_9 (GELU)                   (None, 1, 32)        0           dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 4391, 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, 4391)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "final_cellembed (Dense)         (None, 4391, 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, 4391, 1)      0           sequencing_depth[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.linalg.matmul (TFOpLambda)   (None, 4391, 1)      0           final_cellembed[0][0]            \n",
      "                                                                 tf.expand_dims[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 4391, 1)      2           tf.expand_dims_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.compat.v1.squeeze_2 (TFOpLam (None, 4391)         0           tf.linalg.matmul[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.compat.v1.squeeze_1 (TFOpLam (None, 4391)         0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add (TFOpLambd (None, 4391)         0           tf.compat.v1.squeeze_2[0][0]     \n",
      "                                                                 tf.compat.v1.squeeze_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid (TFOpLambda)    (None, 4391)         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/1_within_sample/train_out/E1000best_model.h5\n",
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-17 13:11:18.711820: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2025-08-17 13:11:26.270177: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8800\n",
      "2025-08-17 13:11:27.146315: 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": [
      "256/256 [==============================] - 65s 169ms/step - loss: 0.3817 - binary_accuracy: 0.8492 - auc: 0.7332 - pr: 0.3105 - val_loss: 0.3553 - val_binary_accuracy: 0.8571 - val_auc: 0.7699 - val_pr: 0.3846\n",
      "Epoch 2/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3564 - binary_accuracy: 0.8566 - auc: 0.7667 - pr: 0.3777 - val_loss: 0.3522 - val_binary_accuracy: 0.8580 - val_auc: 0.7750 - val_pr: 0.3963\n",
      "Epoch 3/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3530 - binary_accuracy: 0.8581 - auc: 0.7724 - pr: 0.3923 - val_loss: 0.3517 - val_binary_accuracy: 0.8584 - val_auc: 0.7771 - val_pr: 0.3986\n",
      "Epoch 4/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3516 - binary_accuracy: 0.8587 - auc: 0.7747 - pr: 0.3982 - val_loss: 0.3565 - val_binary_accuracy: 0.8572 - val_auc: 0.7757 - val_pr: 0.3958\n",
      "Epoch 5/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3508 - binary_accuracy: 0.8592 - auc: 0.7759 - pr: 0.4018 - val_loss: 0.3530 - val_binary_accuracy: 0.8587 - val_auc: 0.7789 - val_pr: 0.4052\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f88541194c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "Epoch 6/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3499 - binary_accuracy: 0.8596 - auc: 0.7774 - pr: 0.4057 - val_loss: 0.3678 - val_binary_accuracy: 0.8515 - val_auc: 0.7720 - val_pr: 0.3935\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f88350bf550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "Epoch 7/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3488 - binary_accuracy: 0.8602 - auc: 0.7790 - pr: 0.4101 - val_loss: 0.3691 - val_binary_accuracy: 0.8538 - val_auc: 0.7705 - val_pr: 0.3900\n",
      "Epoch 8/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3486 - binary_accuracy: 0.8602 - auc: 0.7795 - pr: 0.4105 - val_loss: 0.3507 - val_binary_accuracy: 0.8596 - val_auc: 0.7814 - val_pr: 0.4147\n",
      "Epoch 9/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3475 - binary_accuracy: 0.8607 - auc: 0.7814 - pr: 0.4147 - val_loss: 0.3479 - val_binary_accuracy: 0.8600 - val_auc: 0.7814 - val_pr: 0.4128\n",
      "Epoch 10/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3469 - binary_accuracy: 0.8609 - auc: 0.7824 - pr: 0.4164 - val_loss: 0.3781 - val_binary_accuracy: 0.8471 - val_auc: 0.7614 - val_pr: 0.3770\n",
      "Epoch 11/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3467 - binary_accuracy: 0.8609 - auc: 0.7828 - pr: 0.4170 - val_loss: 0.3514 - val_binary_accuracy: 0.8566 - val_auc: 0.7812 - val_pr: 0.4135\n",
      "Epoch 12/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3460 - binary_accuracy: 0.8611 - auc: 0.7840 - pr: 0.4192 - val_loss: 0.3967 - val_binary_accuracy: 0.8310 - val_auc: 0.7665 - val_pr: 0.3885\n",
      "Epoch 13/1000\n",
      "256/256 [==============================] - 38s 146ms/step - loss: 0.3451 - binary_accuracy: 0.8615 - auc: 0.7854 - pr: 0.4224 - val_loss: 0.4080 - val_binary_accuracy: 0.8180 - val_auc: 0.7660 - val_pr: 0.3872\n",
      "Epoch 14/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3445 - binary_accuracy: 0.8617 - auc: 0.7865 - pr: 0.4244 - val_loss: 0.3495 - val_binary_accuracy: 0.8601 - val_auc: 0.7831 - val_pr: 0.4155\n",
      "Epoch 15/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3443 - binary_accuracy: 0.8616 - auc: 0.7871 - pr: 0.4245 - val_loss: 0.3512 - val_binary_accuracy: 0.8590 - val_auc: 0.7761 - val_pr: 0.4010\n",
      "Epoch 16/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3440 - binary_accuracy: 0.8618 - auc: 0.7876 - pr: 0.4255 - val_loss: 0.3488 - val_binary_accuracy: 0.8599 - val_auc: 0.7796 - val_pr: 0.4117\n",
      "Epoch 17/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3431 - binary_accuracy: 0.8622 - auc: 0.7889 - pr: 0.4290 - val_loss: 0.3467 - val_binary_accuracy: 0.8616 - val_auc: 0.7847 - val_pr: 0.4222\n",
      "Epoch 18/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3428 - binary_accuracy: 0.8623 - auc: 0.7893 - pr: 0.4299 - val_loss: 0.3769 - val_binary_accuracy: 0.8401 - val_auc: 0.7785 - val_pr: 0.4107\n",
      "Epoch 19/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3422 - binary_accuracy: 0.8624 - auc: 0.7905 - pr: 0.4316 - val_loss: 0.3467 - val_binary_accuracy: 0.8614 - val_auc: 0.7825 - val_pr: 0.4190\n",
      "Epoch 20/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3425 - binary_accuracy: 0.8623 - auc: 0.7901 - pr: 0.4306 - val_loss: 0.4061 - val_binary_accuracy: 0.8239 - val_auc: 0.7708 - val_pr: 0.3940\n",
      "Epoch 21/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3415 - binary_accuracy: 0.8627 - auc: 0.7917 - pr: 0.4336 - val_loss: 0.3476 - val_binary_accuracy: 0.8607 - val_auc: 0.7856 - val_pr: 0.4227\n",
      "Epoch 22/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3410 - binary_accuracy: 0.8628 - auc: 0.7926 - pr: 0.4351 - val_loss: 0.3479 - val_binary_accuracy: 0.8601 - val_auc: 0.7869 - val_pr: 0.4244\n",
      "Epoch 23/1000\n",
      "256/256 [==============================] - 42s 156ms/step - loss: 0.3405 - binary_accuracy: 0.8629 - auc: 0.7935 - pr: 0.4367 - val_loss: 0.3825 - val_binary_accuracy: 0.8333 - val_auc: 0.7744 - val_pr: 0.4022\n",
      "Epoch 24/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3401 - binary_accuracy: 0.8630 - auc: 0.7943 - pr: 0.4379 - val_loss: 0.3444 - val_binary_accuracy: 0.8611 - val_auc: 0.7884 - val_pr: 0.4253\n",
      "Epoch 25/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3392 - binary_accuracy: 0.8635 - auc: 0.7955 - pr: 0.4414 - val_loss: 0.3445 - val_binary_accuracy: 0.8615 - val_auc: 0.7875 - val_pr: 0.4261\n",
      "Epoch 26/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3390 - binary_accuracy: 0.8634 - auc: 0.7961 - pr: 0.4411 - val_loss: 0.3598 - val_binary_accuracy: 0.8504 - val_auc: 0.7846 - val_pr: 0.4216\n",
      "Epoch 27/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3385 - binary_accuracy: 0.8634 - auc: 0.7970 - pr: 0.4422 - val_loss: 0.3471 - val_binary_accuracy: 0.8593 - val_auc: 0.7852 - val_pr: 0.4204\n",
      "Epoch 28/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3385 - binary_accuracy: 0.8634 - auc: 0.7971 - pr: 0.4418 - val_loss: 0.3477 - val_binary_accuracy: 0.8606 - val_auc: 0.7842 - val_pr: 0.4169\n",
      "Epoch 29/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3377 - binary_accuracy: 0.8637 - auc: 0.7985 - pr: 0.4450 - val_loss: 0.3437 - val_binary_accuracy: 0.8617 - val_auc: 0.7892 - val_pr: 0.4281\n",
      "Epoch 30/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3375 - binary_accuracy: 0.8637 - auc: 0.7988 - pr: 0.4451 - val_loss: 0.3438 - val_binary_accuracy: 0.8609 - val_auc: 0.7892 - val_pr: 0.4254\n",
      "Epoch 31/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3374 - binary_accuracy: 0.8637 - auc: 0.7991 - pr: 0.4454 - val_loss: 0.3470 - val_binary_accuracy: 0.8592 - val_auc: 0.7897 - val_pr: 0.4286\n",
      "Epoch 32/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3370 - binary_accuracy: 0.8638 - auc: 0.7998 - pr: 0.4462 - val_loss: 0.3496 - val_binary_accuracy: 0.8578 - val_auc: 0.7894 - val_pr: 0.4281\n",
      "Epoch 33/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3366 - binary_accuracy: 0.8639 - auc: 0.8005 - pr: 0.4474 - val_loss: 0.3431 - val_binary_accuracy: 0.8613 - val_auc: 0.7901 - val_pr: 0.4272\n",
      "Epoch 34/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3364 - binary_accuracy: 0.8640 - auc: 0.8007 - pr: 0.4482 - val_loss: 0.3563 - val_binary_accuracy: 0.8525 - val_auc: 0.7888 - val_pr: 0.4277\n",
      "Epoch 35/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3358 - binary_accuracy: 0.8642 - auc: 0.8018 - pr: 0.4500 - val_loss: 0.3461 - val_binary_accuracy: 0.8596 - val_auc: 0.7894 - val_pr: 0.4268\n",
      "Epoch 36/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3355 - binary_accuracy: 0.8641 - auc: 0.8024 - pr: 0.4502 - val_loss: 0.3474 - val_binary_accuracy: 0.8606 - val_auc: 0.7857 - val_pr: 0.4220\n",
      "Epoch 37/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3351 - binary_accuracy: 0.8643 - auc: 0.8031 - pr: 0.4517 - val_loss: 0.3467 - val_binary_accuracy: 0.8596 - val_auc: 0.7886 - val_pr: 0.4287\n",
      "Epoch 38/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3347 - binary_accuracy: 0.8644 - auc: 0.8038 - pr: 0.4530 - val_loss: 0.3425 - val_binary_accuracy: 0.8617 - val_auc: 0.7908 - val_pr: 0.4286\n",
      "Epoch 39/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3343 - binary_accuracy: 0.8645 - auc: 0.8045 - pr: 0.4538 - val_loss: 0.3426 - val_binary_accuracy: 0.8615 - val_auc: 0.7913 - val_pr: 0.4302\n",
      "Epoch 40/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3341 - binary_accuracy: 0.8646 - auc: 0.8048 - pr: 0.4548 - val_loss: 0.3453 - val_binary_accuracy: 0.8602 - val_auc: 0.7892 - val_pr: 0.4287\n",
      "Epoch 41/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3342 - binary_accuracy: 0.8645 - auc: 0.8047 - pr: 0.4539 - val_loss: 0.3436 - val_binary_accuracy: 0.8601 - val_auc: 0.7916 - val_pr: 0.4287\n",
      "Epoch 42/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3334 - binary_accuracy: 0.8647 - auc: 0.8059 - pr: 0.4563 - val_loss: 0.3443 - val_binary_accuracy: 0.8606 - val_auc: 0.7910 - val_pr: 0.4313\n",
      "Epoch 43/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3333 - binary_accuracy: 0.8647 - auc: 0.8061 - pr: 0.4562 - val_loss: 0.3432 - val_binary_accuracy: 0.8605 - val_auc: 0.7922 - val_pr: 0.4324\n",
      "Epoch 44/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3334 - binary_accuracy: 0.8646 - auc: 0.8061 - pr: 0.4559 - val_loss: 0.3436 - val_binary_accuracy: 0.8601 - val_auc: 0.7924 - val_pr: 0.4321\n",
      "Epoch 45/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3332 - binary_accuracy: 0.8647 - auc: 0.8064 - pr: 0.4564 - val_loss: 0.3445 - val_binary_accuracy: 0.8604 - val_auc: 0.7923 - val_pr: 0.4331\n",
      "Epoch 46/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3328 - binary_accuracy: 0.8647 - auc: 0.8071 - pr: 0.4575 - val_loss: 0.3453 - val_binary_accuracy: 0.8595 - val_auc: 0.7896 - val_pr: 0.4284\n",
      "Epoch 47/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3329 - binary_accuracy: 0.8647 - auc: 0.8071 - pr: 0.4571 - val_loss: 0.3417 - val_binary_accuracy: 0.8623 - val_auc: 0.7929 - val_pr: 0.4324\n",
      "Epoch 48/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3324 - binary_accuracy: 0.8649 - auc: 0.8077 - pr: 0.4589 - val_loss: 0.3448 - val_binary_accuracy: 0.8610 - val_auc: 0.7902 - val_pr: 0.4288\n",
      "Epoch 49/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3321 - binary_accuracy: 0.8650 - auc: 0.8083 - pr: 0.4597 - val_loss: 0.3432 - val_binary_accuracy: 0.8609 - val_auc: 0.7914 - val_pr: 0.4306\n",
      "Epoch 50/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3319 - binary_accuracy: 0.8651 - auc: 0.8086 - pr: 0.4601 - val_loss: 0.3431 - val_binary_accuracy: 0.8617 - val_auc: 0.7897 - val_pr: 0.4276\n",
      "Epoch 51/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3319 - binary_accuracy: 0.8650 - auc: 0.8087 - pr: 0.4596 - val_loss: 0.3417 - val_binary_accuracy: 0.8623 - val_auc: 0.7922 - val_pr: 0.4315\n",
      "Epoch 52/1000\n",
      "256/256 [==============================] - 44s 168ms/step - loss: 0.3320 - binary_accuracy: 0.8649 - auc: 0.8085 - pr: 0.4595 - val_loss: 0.3441 - val_binary_accuracy: 0.8613 - val_auc: 0.7900 - val_pr: 0.4270\n",
      "Epoch 53/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3314 - binary_accuracy: 0.8651 - auc: 0.8095 - pr: 0.4609 - val_loss: 0.3411 - val_binary_accuracy: 0.8623 - val_auc: 0.7935 - val_pr: 0.4326\n",
      "Epoch 54/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3314 - binary_accuracy: 0.8651 - auc: 0.8095 - pr: 0.4611 - val_loss: 0.3437 - val_binary_accuracy: 0.8599 - val_auc: 0.7925 - val_pr: 0.4319\n",
      "Epoch 55/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3311 - binary_accuracy: 0.8652 - auc: 0.8100 - pr: 0.4623 - val_loss: 0.3425 - val_binary_accuracy: 0.8620 - val_auc: 0.7922 - val_pr: 0.4320\n",
      "Epoch 56/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3311 - binary_accuracy: 0.8652 - auc: 0.8100 - pr: 0.4620 - val_loss: 0.3431 - val_binary_accuracy: 0.8608 - val_auc: 0.7916 - val_pr: 0.4320\n",
      "Epoch 57/1000\n",
      "256/256 [==============================] - 40s 156ms/step - loss: 0.3314 - binary_accuracy: 0.8651 - auc: 0.8096 - pr: 0.4611 - val_loss: 0.3409 - val_binary_accuracy: 0.8625 - val_auc: 0.7937 - val_pr: 0.4348\n",
      "Epoch 58/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3311 - binary_accuracy: 0.8651 - auc: 0.8102 - pr: 0.4616 - val_loss: 0.3433 - val_binary_accuracy: 0.8605 - val_auc: 0.7919 - val_pr: 0.4311\n",
      "Epoch 59/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3310 - binary_accuracy: 0.8652 - auc: 0.8104 - pr: 0.4620 - val_loss: 0.3410 - val_binary_accuracy: 0.8626 - val_auc: 0.7927 - val_pr: 0.4335\n",
      "Epoch 60/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3305 - binary_accuracy: 0.8653 - auc: 0.8110 - pr: 0.4634 - val_loss: 0.3427 - val_binary_accuracy: 0.8617 - val_auc: 0.7902 - val_pr: 0.4278\n",
      "Epoch 61/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3305 - binary_accuracy: 0.8653 - auc: 0.8110 - pr: 0.4633 - val_loss: 0.3413 - val_binary_accuracy: 0.8620 - val_auc: 0.7934 - val_pr: 0.4339\n",
      "Epoch 62/1000\n",
      "256/256 [==============================] - 38s 147ms/step - loss: 0.3305 - binary_accuracy: 0.8653 - auc: 0.8110 - pr: 0.4632 - val_loss: 0.3414 - val_binary_accuracy: 0.8622 - val_auc: 0.7930 - val_pr: 0.4342\n",
      "Epoch 63/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3302 - binary_accuracy: 0.8654 - auc: 0.8116 - pr: 0.4642 - val_loss: 0.3413 - val_binary_accuracy: 0.8626 - val_auc: 0.7937 - val_pr: 0.4345\n",
      "Epoch 64/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3302 - binary_accuracy: 0.8653 - auc: 0.8117 - pr: 0.4639 - val_loss: 0.3414 - val_binary_accuracy: 0.8618 - val_auc: 0.7934 - val_pr: 0.4347\n",
      "Epoch 65/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3302 - binary_accuracy: 0.8653 - auc: 0.8116 - pr: 0.4641 - val_loss: 0.3405 - val_binary_accuracy: 0.8628 - val_auc: 0.7937 - val_pr: 0.4351\n",
      "Epoch 66/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3300 - binary_accuracy: 0.8654 - auc: 0.8120 - pr: 0.4646 - val_loss: 0.3415 - val_binary_accuracy: 0.8623 - val_auc: 0.7925 - val_pr: 0.4329\n",
      "Epoch 67/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3302 - binary_accuracy: 0.8653 - auc: 0.8117 - pr: 0.4639 - val_loss: 0.3406 - val_binary_accuracy: 0.8623 - val_auc: 0.7943 - val_pr: 0.4350\n",
      "Epoch 68/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3297 - binary_accuracy: 0.8654 - auc: 0.8125 - pr: 0.4652 - val_loss: 0.3403 - val_binary_accuracy: 0.8628 - val_auc: 0.7941 - val_pr: 0.4356\n",
      "Epoch 69/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3296 - binary_accuracy: 0.8655 - auc: 0.8125 - pr: 0.4658 - val_loss: 0.3428 - val_binary_accuracy: 0.8605 - val_auc: 0.7938 - val_pr: 0.4349\n",
      "Epoch 70/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3298 - binary_accuracy: 0.8655 - auc: 0.8121 - pr: 0.4653 - val_loss: 0.3407 - val_binary_accuracy: 0.8626 - val_auc: 0.7937 - val_pr: 0.4346\n",
      "Epoch 71/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3301 - binary_accuracy: 0.8653 - auc: 0.8119 - pr: 0.4642 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7940 - val_pr: 0.4366\n",
      "Epoch 72/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3299 - binary_accuracy: 0.8654 - auc: 0.8122 - pr: 0.4650 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7932 - val_pr: 0.4351\n",
      "Epoch 73/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3295 - binary_accuracy: 0.8655 - auc: 0.8128 - pr: 0.4655 - val_loss: 0.3422 - val_binary_accuracy: 0.8625 - val_auc: 0.7925 - val_pr: 0.4323\n",
      "Epoch 74/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3293 - binary_accuracy: 0.8656 - auc: 0.8131 - pr: 0.4666 - val_loss: 0.3407 - val_binary_accuracy: 0.8626 - val_auc: 0.7943 - val_pr: 0.4366\n",
      "Epoch 75/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3291 - binary_accuracy: 0.8656 - auc: 0.8135 - pr: 0.4670 - val_loss: 0.3409 - val_binary_accuracy: 0.8621 - val_auc: 0.7942 - val_pr: 0.4355\n",
      "Epoch 76/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3290 - binary_accuracy: 0.8657 - auc: 0.8137 - pr: 0.4673 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7947 - val_pr: 0.4377\n",
      "Epoch 77/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3291 - binary_accuracy: 0.8656 - auc: 0.8135 - pr: 0.4667 - val_loss: 0.3403 - val_binary_accuracy: 0.8627 - val_auc: 0.7944 - val_pr: 0.4352\n",
      "Epoch 78/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3291 - binary_accuracy: 0.8656 - auc: 0.8135 - pr: 0.4668 - val_loss: 0.3407 - val_binary_accuracy: 0.8627 - val_auc: 0.7941 - val_pr: 0.4352\n",
      "Epoch 79/1000\n",
      "256/256 [==============================] - 44s 164ms/step - loss: 0.3287 - binary_accuracy: 0.8657 - auc: 0.8141 - pr: 0.4677 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7941 - val_pr: 0.4365\n",
      "Epoch 80/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3287 - binary_accuracy: 0.8657 - auc: 0.8142 - pr: 0.4678 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7942 - val_pr: 0.4368\n",
      "Epoch 81/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3285 - binary_accuracy: 0.8658 - auc: 0.8144 - pr: 0.4687 - val_loss: 0.3409 - val_binary_accuracy: 0.8623 - val_auc: 0.7941 - val_pr: 0.4368\n",
      "Epoch 82/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3287 - binary_accuracy: 0.8657 - auc: 0.8142 - pr: 0.4678 - val_loss: 0.3419 - val_binary_accuracy: 0.8619 - val_auc: 0.7933 - val_pr: 0.4341\n",
      "Epoch 83/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3286 - binary_accuracy: 0.8657 - auc: 0.8144 - pr: 0.4680 - val_loss: 0.3407 - val_binary_accuracy: 0.8627 - val_auc: 0.7935 - val_pr: 0.4347\n",
      "Epoch 84/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3285 - binary_accuracy: 0.8657 - auc: 0.8144 - pr: 0.4680 - val_loss: 0.3404 - val_binary_accuracy: 0.8627 - val_auc: 0.7943 - val_pr: 0.4363\n",
      "Epoch 85/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3283 - binary_accuracy: 0.8658 - auc: 0.8148 - pr: 0.4686 - val_loss: 0.3416 - val_binary_accuracy: 0.8612 - val_auc: 0.7939 - val_pr: 0.4360\n",
      "Epoch 86/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3284 - binary_accuracy: 0.8658 - auc: 0.8147 - pr: 0.4688 - val_loss: 0.3409 - val_binary_accuracy: 0.8625 - val_auc: 0.7932 - val_pr: 0.4338\n",
      "Epoch 87/1000\n",
      "256/256 [==============================] - 39s 150ms/step - loss: 0.3283 - binary_accuracy: 0.8658 - auc: 0.8147 - pr: 0.4687 - val_loss: 0.3405 - val_binary_accuracy: 0.8626 - val_auc: 0.7944 - val_pr: 0.4359\n",
      "Epoch 88/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3282 - binary_accuracy: 0.8658 - auc: 0.8150 - pr: 0.4689 - val_loss: 0.3403 - val_binary_accuracy: 0.8627 - val_auc: 0.7943 - val_pr: 0.4359\n",
      "Epoch 89/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3281 - binary_accuracy: 0.8658 - auc: 0.8152 - pr: 0.4694 - val_loss: 0.3421 - val_binary_accuracy: 0.8610 - val_auc: 0.7936 - val_pr: 0.4350\n",
      "Epoch 90/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3281 - binary_accuracy: 0.8658 - auc: 0.8151 - pr: 0.4693 - val_loss: 0.3405 - val_binary_accuracy: 0.8627 - val_auc: 0.7942 - val_pr: 0.4370\n",
      "Epoch 91/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3278 - binary_accuracy: 0.8659 - auc: 0.8155 - pr: 0.4702 - val_loss: 0.3406 - val_binary_accuracy: 0.8627 - val_auc: 0.7942 - val_pr: 0.4353\n",
      "Epoch 92/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3278 - binary_accuracy: 0.8659 - auc: 0.8156 - pr: 0.4703 - val_loss: 0.3402 - val_binary_accuracy: 0.8628 - val_auc: 0.7946 - val_pr: 0.4368\n",
      "Epoch 93/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3276 - binary_accuracy: 0.8660 - auc: 0.8159 - pr: 0.4706 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7941 - val_pr: 0.4352\n",
      "Epoch 94/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3277 - binary_accuracy: 0.8660 - auc: 0.8158 - pr: 0.4704 - val_loss: 0.3409 - val_binary_accuracy: 0.8621 - val_auc: 0.7941 - val_pr: 0.4363\n",
      "Epoch 95/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3278 - binary_accuracy: 0.8659 - auc: 0.8156 - pr: 0.4701 - val_loss: 0.3416 - val_binary_accuracy: 0.8616 - val_auc: 0.7943 - val_pr: 0.4367\n",
      "Epoch 96/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3275 - binary_accuracy: 0.8660 - auc: 0.8160 - pr: 0.4709 - val_loss: 0.3403 - val_binary_accuracy: 0.8624 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 97/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3277 - binary_accuracy: 0.8659 - auc: 0.8158 - pr: 0.4702 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7947 - val_pr: 0.4376\n",
      "Epoch 98/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3286 - binary_accuracy: 0.8657 - auc: 0.8144 - pr: 0.4681 - val_loss: 0.3402 - val_binary_accuracy: 0.8628 - val_auc: 0.7942 - val_pr: 0.4359\n",
      "Epoch 99/1000\n",
      "256/256 [==============================] - 43s 167ms/step - loss: 0.3280 - binary_accuracy: 0.8658 - auc: 0.8154 - pr: 0.4695 - val_loss: 0.3411 - val_binary_accuracy: 0.8626 - val_auc: 0.7940 - val_pr: 0.4357\n",
      "Epoch 100/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3276 - binary_accuracy: 0.8660 - auc: 0.8158 - pr: 0.4706 - val_loss: 0.3409 - val_binary_accuracy: 0.8620 - val_auc: 0.7943 - val_pr: 0.4365\n",
      "Epoch 101/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3275 - binary_accuracy: 0.8660 - auc: 0.8161 - pr: 0.4709 - val_loss: 0.3407 - val_binary_accuracy: 0.8626 - val_auc: 0.7948 - val_pr: 0.4372\n",
      "Epoch 102/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3272 - binary_accuracy: 0.8661 - auc: 0.8166 - pr: 0.4718 - val_loss: 0.3403 - val_binary_accuracy: 0.8626 - val_auc: 0.7945 - val_pr: 0.4367\n",
      "Epoch 103/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3273 - binary_accuracy: 0.8661 - auc: 0.8164 - pr: 0.4716 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7949 - val_pr: 0.4377\n",
      "Epoch 104/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3272 - binary_accuracy: 0.8661 - auc: 0.8165 - pr: 0.4718 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7949 - val_pr: 0.4377\n",
      "Epoch 105/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3271 - binary_accuracy: 0.8661 - auc: 0.8168 - pr: 0.4720 - val_loss: 0.3403 - val_binary_accuracy: 0.8625 - val_auc: 0.7949 - val_pr: 0.4371\n",
      "Epoch 106/1000\n",
      "256/256 [==============================] - 39s 146ms/step - loss: 0.3270 - binary_accuracy: 0.8662 - auc: 0.8169 - pr: 0.4724 - val_loss: 0.3404 - val_binary_accuracy: 0.8623 - val_auc: 0.7950 - val_pr: 0.4372\n",
      "Epoch 107/1000\n",
      "256/256 [==============================] - 41s 153ms/step - loss: 0.3270 - binary_accuracy: 0.8661 - auc: 0.8169 - pr: 0.4721 - val_loss: 0.3402 - val_binary_accuracy: 0.8625 - val_auc: 0.7952 - val_pr: 0.4371\n",
      "Epoch 108/1000\n",
      "256/256 [==============================] - 37s 140ms/step - loss: 0.3270 - binary_accuracy: 0.8661 - auc: 0.8169 - pr: 0.4723 - val_loss: 0.3399 - val_binary_accuracy: 0.8626 - val_auc: 0.7956 - val_pr: 0.4378\n",
      "Epoch 109/1000\n",
      "256/256 [==============================] - 40s 152ms/step - loss: 0.3268 - binary_accuracy: 0.8662 - auc: 0.8172 - pr: 0.4731 - val_loss: 0.3395 - val_binary_accuracy: 0.8629 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 110/1000\n",
      "256/256 [==============================] - 37s 140ms/step - loss: 0.3268 - binary_accuracy: 0.8662 - auc: 0.8171 - pr: 0.4727 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7949 - val_pr: 0.4367\n",
      "Epoch 111/1000\n",
      "256/256 [==============================] - 36s 136ms/step - loss: 0.3269 - binary_accuracy: 0.8662 - auc: 0.8170 - pr: 0.4726 - val_loss: 0.3399 - val_binary_accuracy: 0.8628 - val_auc: 0.7946 - val_pr: 0.4368\n",
      "Epoch 112/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3268 - binary_accuracy: 0.8662 - auc: 0.8173 - pr: 0.4728 - val_loss: 0.3395 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 113/1000\n",
      "256/256 [==============================] - 38s 147ms/step - loss: 0.3267 - binary_accuracy: 0.8663 - auc: 0.8174 - pr: 0.4733 - val_loss: 0.3406 - val_binary_accuracy: 0.8619 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 114/1000\n",
      "256/256 [==============================] - 36s 137ms/step - loss: 0.3266 - binary_accuracy: 0.8662 - auc: 0.8175 - pr: 0.4733 - val_loss: 0.3402 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4371\n",
      "Epoch 115/1000\n",
      "256/256 [==============================] - 35s 135ms/step - loss: 0.3266 - binary_accuracy: 0.8662 - auc: 0.8175 - pr: 0.4731 - val_loss: 0.3403 - val_binary_accuracy: 0.8623 - val_auc: 0.7950 - val_pr: 0.4351\n",
      "Epoch 116/1000\n",
      "256/256 [==============================] - 39s 150ms/step - loss: 0.3267 - binary_accuracy: 0.8661 - auc: 0.8174 - pr: 0.4726 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 117/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3268 - binary_accuracy: 0.8662 - auc: 0.8172 - pr: 0.4727 - val_loss: 0.3405 - val_binary_accuracy: 0.8620 - val_auc: 0.7950 - val_pr: 0.4352\n",
      "Epoch 118/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3267 - binary_accuracy: 0.8662 - auc: 0.8173 - pr: 0.4727 - val_loss: 0.3406 - val_binary_accuracy: 0.8628 - val_auc: 0.7946 - val_pr: 0.4368\n",
      "Epoch 119/1000\n",
      "256/256 [==============================] - 34s 132ms/step - loss: 0.3269 - binary_accuracy: 0.8662 - auc: 0.8170 - pr: 0.4726 - val_loss: 0.3400 - val_binary_accuracy: 0.8627 - val_auc: 0.7951 - val_pr: 0.4365\n",
      "Epoch 120/1000\n",
      "256/256 [==============================] - 38s 146ms/step - loss: 0.3264 - binary_accuracy: 0.8663 - auc: 0.8179 - pr: 0.4739 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4362\n",
      "Epoch 121/1000\n",
      "256/256 [==============================] - 35s 133ms/step - loss: 0.3263 - binary_accuracy: 0.8663 - auc: 0.8180 - pr: 0.4742 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4383\n",
      "Epoch 122/1000\n",
      "256/256 [==============================] - 36s 139ms/step - loss: 0.3263 - binary_accuracy: 0.8663 - auc: 0.8179 - pr: 0.4739 - val_loss: 0.3400 - val_binary_accuracy: 0.8628 - val_auc: 0.7954 - val_pr: 0.4370\n",
      "Epoch 123/1000\n",
      "256/256 [==============================] - 39s 149ms/step - loss: 0.3264 - binary_accuracy: 0.8662 - auc: 0.8179 - pr: 0.4736 - val_loss: 0.3397 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4372\n",
      "Epoch 124/1000\n",
      "256/256 [==============================] - 37s 141ms/step - loss: 0.3263 - binary_accuracy: 0.8663 - auc: 0.8181 - pr: 0.4740 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4373\n",
      "Epoch 125/1000\n",
      "256/256 [==============================] - 34s 132ms/step - loss: 0.3262 - binary_accuracy: 0.8663 - auc: 0.8182 - pr: 0.4741 - val_loss: 0.3409 - val_binary_accuracy: 0.8625 - val_auc: 0.7933 - val_pr: 0.4337\n",
      "Epoch 126/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3263 - binary_accuracy: 0.8663 - auc: 0.8180 - pr: 0.4738 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4369\n",
      "Epoch 127/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3262 - binary_accuracy: 0.8663 - auc: 0.8182 - pr: 0.4742 - val_loss: 0.3396 - val_binary_accuracy: 0.8628 - val_auc: 0.7957 - val_pr: 0.4383\n",
      "Epoch 128/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3262 - binary_accuracy: 0.8663 - auc: 0.8182 - pr: 0.4743 - val_loss: 0.3396 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4370\n",
      "Epoch 129/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3264 - binary_accuracy: 0.8662 - auc: 0.8179 - pr: 0.4734 - val_loss: 0.3397 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4375\n",
      "Epoch 130/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3261 - binary_accuracy: 0.8664 - auc: 0.8184 - pr: 0.4747 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 131/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3261 - binary_accuracy: 0.8664 - auc: 0.8184 - pr: 0.4746 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 132/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3260 - binary_accuracy: 0.8664 - auc: 0.8185 - pr: 0.4750 - val_loss: 0.3398 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4371\n",
      "Epoch 133/1000\n",
      "256/256 [==============================] - 37s 138ms/step - loss: 0.3261 - binary_accuracy: 0.8663 - auc: 0.8183 - pr: 0.4744 - val_loss: 0.3407 - val_binary_accuracy: 0.8625 - val_auc: 0.7945 - val_pr: 0.4348\n",
      "Epoch 134/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3274 - binary_accuracy: 0.8659 - auc: 0.8163 - pr: 0.4707 - val_loss: 0.3402 - val_binary_accuracy: 0.8625 - val_auc: 0.7947 - val_pr: 0.4374\n",
      "Epoch 135/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3264 - binary_accuracy: 0.8663 - auc: 0.8179 - pr: 0.4737 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7942 - val_pr: 0.4360\n",
      "Epoch 136/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3261 - binary_accuracy: 0.8664 - auc: 0.8183 - pr: 0.4746 - val_loss: 0.3400 - val_binary_accuracy: 0.8628 - val_auc: 0.7951 - val_pr: 0.4372\n",
      "Epoch 137/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3259 - binary_accuracy: 0.8664 - auc: 0.8186 - pr: 0.4750 - val_loss: 0.3406 - val_binary_accuracy: 0.8620 - val_auc: 0.7950 - val_pr: 0.4362\n",
      "Epoch 138/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3258 - binary_accuracy: 0.8664 - auc: 0.8188 - pr: 0.4750 - val_loss: 0.3397 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4379\n",
      "Epoch 139/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3258 - binary_accuracy: 0.8664 - auc: 0.8189 - pr: 0.4754 - val_loss: 0.3397 - val_binary_accuracy: 0.8628 - val_auc: 0.7955 - val_pr: 0.4380\n",
      "Epoch 140/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3258 - binary_accuracy: 0.8664 - auc: 0.8188 - pr: 0.4751 - val_loss: 0.3396 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4379\n",
      "Epoch 141/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3259 - binary_accuracy: 0.8664 - auc: 0.8187 - pr: 0.4753 - val_loss: 0.3392 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4392\n",
      "Epoch 142/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3256 - binary_accuracy: 0.8665 - auc: 0.8191 - pr: 0.4759 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 143/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3256 - binary_accuracy: 0.8665 - auc: 0.8192 - pr: 0.4759 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4380\n",
      "Epoch 144/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3257 - binary_accuracy: 0.8664 - auc: 0.8190 - pr: 0.4756 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4374\n",
      "Epoch 145/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3256 - binary_accuracy: 0.8665 - auc: 0.8191 - pr: 0.4759 - val_loss: 0.3392 - val_binary_accuracy: 0.8630 - val_auc: 0.7960 - val_pr: 0.4393\n",
      "Epoch 146/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3257 - binary_accuracy: 0.8664 - auc: 0.8191 - pr: 0.4755 - val_loss: 0.3397 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4373\n",
      "Epoch 147/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3259 - binary_accuracy: 0.8664 - auc: 0.8188 - pr: 0.4750 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4377\n",
      "Epoch 148/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3255 - binary_accuracy: 0.8665 - auc: 0.8192 - pr: 0.4761 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4387\n",
      "Epoch 149/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3257 - binary_accuracy: 0.8664 - auc: 0.8190 - pr: 0.4756 - val_loss: 0.3392 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4393\n",
      "Epoch 150/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3255 - binary_accuracy: 0.8665 - auc: 0.8194 - pr: 0.4762 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4376\n",
      "Epoch 151/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3256 - binary_accuracy: 0.8664 - auc: 0.8192 - pr: 0.4757 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 152/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3255 - binary_accuracy: 0.8665 - auc: 0.8192 - pr: 0.4759 - val_loss: 0.3397 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4382\n",
      "Epoch 153/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3255 - binary_accuracy: 0.8665 - auc: 0.8193 - pr: 0.4761 - val_loss: 0.3396 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4376\n",
      "Epoch 154/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8197 - pr: 0.4769 - val_loss: 0.3396 - val_binary_accuracy: 0.8628 - val_auc: 0.7958 - val_pr: 0.4383\n",
      "Epoch 155/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3254 - binary_accuracy: 0.8665 - auc: 0.8195 - pr: 0.4763 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4385\n",
      "Epoch 156/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3253 - binary_accuracy: 0.8665 - auc: 0.8197 - pr: 0.4766 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7949 - val_pr: 0.4372\n",
      "Epoch 157/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3255 - binary_accuracy: 0.8665 - auc: 0.8193 - pr: 0.4759 - val_loss: 0.3399 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4367\n",
      "Epoch 158/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3253 - binary_accuracy: 0.8665 - auc: 0.8196 - pr: 0.4765 - val_loss: 0.3404 - val_binary_accuracy: 0.8622 - val_auc: 0.7951 - val_pr: 0.4370\n",
      "Epoch 159/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3257 - binary_accuracy: 0.8665 - auc: 0.8190 - pr: 0.4758 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4379\n",
      "Epoch 160/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3254 - binary_accuracy: 0.8665 - auc: 0.8195 - pr: 0.4763 - val_loss: 0.3404 - val_binary_accuracy: 0.8626 - val_auc: 0.7948 - val_pr: 0.4363\n",
      "Epoch 161/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3253 - binary_accuracy: 0.8666 - auc: 0.8197 - pr: 0.4767 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4377\n",
      "Epoch 162/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8198 - pr: 0.4769 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4374\n",
      "Epoch 163/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8198 - pr: 0.4768 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 164/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8198 - pr: 0.4769 - val_loss: 0.3395 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 165/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3253 - binary_accuracy: 0.8665 - auc: 0.8196 - pr: 0.4765 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 166/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3251 - binary_accuracy: 0.8666 - auc: 0.8199 - pr: 0.4770 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 167/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3253 - binary_accuracy: 0.8665 - auc: 0.8196 - pr: 0.4763 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4366\n",
      "Epoch 168/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8199 - pr: 0.4769 - val_loss: 0.3407 - val_binary_accuracy: 0.8621 - val_auc: 0.7949 - val_pr: 0.4372\n",
      "Epoch 169/1000\n",
      "256/256 [==============================] - 40s 152ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8198 - pr: 0.4769 - val_loss: 0.3403 - val_binary_accuracy: 0.8623 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 170/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3250 - binary_accuracy: 0.8666 - auc: 0.8201 - pr: 0.4774 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4373\n",
      "Epoch 171/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3250 - binary_accuracy: 0.8666 - auc: 0.8201 - pr: 0.4774 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 172/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3250 - binary_accuracy: 0.8667 - auc: 0.8201 - pr: 0.4775 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 173/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8198 - pr: 0.4771 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7948 - val_pr: 0.4379\n",
      "Epoch 174/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3251 - binary_accuracy: 0.8666 - auc: 0.8200 - pr: 0.4769 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4386\n",
      "Epoch 175/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3253 - binary_accuracy: 0.8665 - auc: 0.8197 - pr: 0.4765 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 176/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3251 - binary_accuracy: 0.8666 - auc: 0.8200 - pr: 0.4770 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4390\n",
      "Epoch 177/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3250 - binary_accuracy: 0.8666 - auc: 0.8201 - pr: 0.4773 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4382\n",
      "Epoch 178/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3250 - binary_accuracy: 0.8666 - auc: 0.8200 - pr: 0.4773 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4383\n",
      "Epoch 179/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3249 - binary_accuracy: 0.8666 - auc: 0.8202 - pr: 0.4776 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 180/1000\n",
      "256/256 [==============================] - 41s 153ms/step - loss: 0.3249 - binary_accuracy: 0.8666 - auc: 0.8203 - pr: 0.4775 - val_loss: 0.3402 - val_binary_accuracy: 0.8622 - val_auc: 0.7959 - val_pr: 0.4384\n",
      "Epoch 181/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3251 - binary_accuracy: 0.8666 - auc: 0.8199 - pr: 0.4770 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4383\n",
      "Epoch 182/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3249 - binary_accuracy: 0.8666 - auc: 0.8202 - pr: 0.4775 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4370\n",
      "Epoch 183/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3252 - binary_accuracy: 0.8666 - auc: 0.8199 - pr: 0.4769 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4384\n",
      "Epoch 184/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3248 - binary_accuracy: 0.8667 - auc: 0.8204 - pr: 0.4781 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 185/1000\n",
      "256/256 [==============================] - 40s 151ms/step - loss: 0.3248 - binary_accuracy: 0.8667 - auc: 0.8204 - pr: 0.4779 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 186/1000\n",
      "256/256 [==============================] - 40s 149ms/step - loss: 0.3249 - binary_accuracy: 0.8666 - auc: 0.8203 - pr: 0.4776 - val_loss: 0.3401 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4369\n",
      "Epoch 187/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8205 - pr: 0.4779 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4377\n",
      "Epoch 188/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8205 - pr: 0.4779 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 189/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8205 - pr: 0.4783 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 190/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3250 - binary_accuracy: 0.8666 - auc: 0.8201 - pr: 0.4773 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4390\n",
      "Epoch 191/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3248 - binary_accuracy: 0.8667 - auc: 0.8205 - pr: 0.4780 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 192/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3248 - binary_accuracy: 0.8667 - auc: 0.8205 - pr: 0.4779 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4377\n",
      "Epoch 193/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8205 - pr: 0.4781 - val_loss: 0.3397 - val_binary_accuracy: 0.8627 - val_auc: 0.7957 - val_pr: 0.4382\n",
      "Epoch 194/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8206 - pr: 0.4784 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 195/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8205 - pr: 0.4781 - val_loss: 0.3396 - val_binary_accuracy: 0.8629 - val_auc: 0.7958 - val_pr: 0.4378\n",
      "Epoch 196/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3246 - binary_accuracy: 0.8667 - auc: 0.8207 - pr: 0.4784 - val_loss: 0.3394 - val_binary_accuracy: 0.8629 - val_auc: 0.7957 - val_pr: 0.4381\n",
      "Epoch 197/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3246 - binary_accuracy: 0.8668 - auc: 0.8208 - pr: 0.4786 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4375\n",
      "Epoch 198/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3246 - binary_accuracy: 0.8667 - auc: 0.8208 - pr: 0.4785 - val_loss: 0.3392 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4390\n",
      "Epoch 199/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3245 - binary_accuracy: 0.8667 - auc: 0.8208 - pr: 0.4785 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4383\n",
      "Epoch 200/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8206 - pr: 0.4784 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 201/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8206 - pr: 0.4778 - val_loss: 0.3395 - val_binary_accuracy: 0.8629 - val_auc: 0.7958 - val_pr: 0.4379\n",
      "Epoch 202/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3245 - binary_accuracy: 0.8668 - auc: 0.8208 - pr: 0.4786 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4385\n",
      "Epoch 203/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3245 - binary_accuracy: 0.8668 - auc: 0.8208 - pr: 0.4786 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4379\n",
      "Epoch 204/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3248 - binary_accuracy: 0.8667 - auc: 0.8204 - pr: 0.4779 - val_loss: 0.3393 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 205/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3247 - binary_accuracy: 0.8667 - auc: 0.8207 - pr: 0.4781 - val_loss: 0.3393 - val_binary_accuracy: 0.8630 - val_auc: 0.7960 - val_pr: 0.4383\n",
      "Epoch 206/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3246 - binary_accuracy: 0.8667 - auc: 0.8207 - pr: 0.4782 - val_loss: 0.3399 - val_binary_accuracy: 0.8627 - val_auc: 0.7957 - val_pr: 0.4377\n",
      "Epoch 207/1000\n",
      "256/256 [==============================] - 38s 147ms/step - loss: 0.3246 - binary_accuracy: 0.8667 - auc: 0.8208 - pr: 0.4785 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4388\n",
      "Epoch 208/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3245 - binary_accuracy: 0.8667 - auc: 0.8208 - pr: 0.4786 - val_loss: 0.3393 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4382\n",
      "Epoch 209/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3245 - binary_accuracy: 0.8668 - auc: 0.8209 - pr: 0.4786 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4391\n",
      "Epoch 210/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3245 - binary_accuracy: 0.8668 - auc: 0.8209 - pr: 0.4788 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4380\n",
      "Epoch 211/1000\n",
      "256/256 [==============================] - 39s 147ms/step - loss: 0.3246 - binary_accuracy: 0.8667 - auc: 0.8207 - pr: 0.4784 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4380\n",
      "Epoch 212/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3245 - binary_accuracy: 0.8667 - auc: 0.8208 - pr: 0.4786 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 213/1000\n",
      "256/256 [==============================] - 37s 142ms/step - loss: 0.3245 - binary_accuracy: 0.8668 - auc: 0.8209 - pr: 0.4787 - val_loss: 0.3394 - val_binary_accuracy: 0.8629 - val_auc: 0.7960 - val_pr: 0.4384\n",
      "Epoch 214/1000\n",
      "256/256 [==============================] - 38s 147ms/step - loss: 0.3246 - binary_accuracy: 0.8667 - auc: 0.8208 - pr: 0.4785 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7962 - val_pr: 0.4395\n",
      "Epoch 215/1000\n",
      "256/256 [==============================] - 39s 149ms/step - loss: 0.3246 - binary_accuracy: 0.8667 - auc: 0.8208 - pr: 0.4783 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4384\n",
      "Epoch 216/1000\n",
      "256/256 [==============================] - 194s 753ms/step - loss: 0.3245 - binary_accuracy: 0.8667 - auc: 0.8209 - pr: 0.4786 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4379\n",
      "Epoch 217/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3245 - binary_accuracy: 0.8667 - auc: 0.8209 - pr: 0.4785 - val_loss: 0.3393 - val_binary_accuracy: 0.8629 - val_auc: 0.7961 - val_pr: 0.4388\n",
      "Epoch 218/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3245 - binary_accuracy: 0.8668 - auc: 0.8208 - pr: 0.4786 - val_loss: 0.3391 - val_binary_accuracy: 0.8630 - val_auc: 0.7963 - val_pr: 0.4393\n",
      "Epoch 219/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3244 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4788 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 220/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4793 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 221/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3244 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4789 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 222/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3244 - binary_accuracy: 0.8668 - auc: 0.8210 - pr: 0.4789 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4386\n",
      "Epoch 223/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8212 - pr: 0.4792 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7961 - val_pr: 0.4389\n",
      "Epoch 224/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8212 - pr: 0.4792 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 225/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8212 - pr: 0.4790 - val_loss: 0.3392 - val_binary_accuracy: 0.8631 - val_auc: 0.7962 - val_pr: 0.4390\n",
      "Epoch 226/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4790 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4390\n",
      "Epoch 227/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3244 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4790 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4388\n",
      "Epoch 228/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3245 - binary_accuracy: 0.8667 - auc: 0.8209 - pr: 0.4787 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4384\n",
      "Epoch 229/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3244 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4789 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4384\n",
      "Epoch 230/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4793 - val_loss: 0.3398 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 231/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3244 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4788 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4388\n",
      "Epoch 232/1000\n",
      "256/256 [==============================] - 44s 166ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8212 - pr: 0.4793 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4381\n",
      "Epoch 233/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3244 - binary_accuracy: 0.8667 - auc: 0.8211 - pr: 0.4789 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7961 - val_pr: 0.4390\n",
      "Epoch 234/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8213 - pr: 0.4794 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4393\n",
      "Epoch 235/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8212 - pr: 0.4789 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4384\n",
      "Epoch 236/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8213 - pr: 0.4795 - val_loss: 0.3391 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4397\n",
      "Epoch 237/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8211 - pr: 0.4791 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4392\n",
      "Epoch 238/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8213 - pr: 0.4795 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4388\n",
      "Epoch 239/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3241 - binary_accuracy: 0.8669 - auc: 0.8214 - pr: 0.4796 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7963 - val_pr: 0.4401\n",
      "Epoch 240/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8212 - pr: 0.4794 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7963 - val_pr: 0.4395\n",
      "Epoch 241/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8214 - pr: 0.4794 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 242/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3241 - binary_accuracy: 0.8668 - auc: 0.8215 - pr: 0.4794 - val_loss: 0.3390 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4395\n",
      "Epoch 243/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3241 - binary_accuracy: 0.8669 - auc: 0.8214 - pr: 0.4796 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7963 - val_pr: 0.4392\n",
      "Epoch 244/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8214 - pr: 0.4794 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4379\n",
      "Epoch 245/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8214 - pr: 0.4793 - val_loss: 0.3393 - val_binary_accuracy: 0.8629 - val_auc: 0.7961 - val_pr: 0.4388\n",
      "Epoch 246/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3241 - binary_accuracy: 0.8668 - auc: 0.8214 - pr: 0.4796 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4391\n",
      "Epoch 247/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3241 - binary_accuracy: 0.8669 - auc: 0.8215 - pr: 0.4797 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4381\n",
      "Epoch 248/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8213 - pr: 0.4792 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 249/1000\n",
      "256/256 [==============================] - 44s 167ms/step - loss: 0.3242 - binary_accuracy: 0.8668 - auc: 0.8214 - pr: 0.4795 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 250/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4800 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 251/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3241 - binary_accuracy: 0.8669 - auc: 0.8215 - pr: 0.4798 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 252/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3243 - binary_accuracy: 0.8668 - auc: 0.8213 - pr: 0.4792 - val_loss: 0.3391 - val_binary_accuracy: 0.8632 - val_auc: 0.7960 - val_pr: 0.4394\n",
      "Epoch 253/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8216 - pr: 0.4799 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 254/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8216 - pr: 0.4799 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 255/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4800 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4386\n",
      "Epoch 256/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4801 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7957 - val_pr: 0.4382\n",
      "Epoch 257/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3241 - binary_accuracy: 0.8668 - auc: 0.8214 - pr: 0.4796 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 258/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4799 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4391\n",
      "Epoch 259/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4798 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4383\n",
      "Epoch 260/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3241 - binary_accuracy: 0.8669 - auc: 0.8215 - pr: 0.4797 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 261/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8216 - pr: 0.4799 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 262/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4800 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4387\n",
      "Epoch 263/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4802 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4391\n",
      "Epoch 264/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4800 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 265/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8216 - pr: 0.4799 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4392\n",
      "Epoch 266/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8216 - pr: 0.4799 - val_loss: 0.3409 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4381\n",
      "Epoch 267/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4799 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4386\n",
      "Epoch 268/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4800 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7960 - val_pr: 0.4384\n",
      "Epoch 269/1000\n",
      "256/256 [==============================] - 41s 154ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4802 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7962 - val_pr: 0.4390\n",
      "Epoch 270/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4800 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4384\n",
      "Epoch 271/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8219 - pr: 0.4803 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4381\n",
      "Epoch 272/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4799 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4388\n",
      "Epoch 273/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4798 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7960 - val_pr: 0.4387\n",
      "Epoch 274/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4799 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4388\n",
      "Epoch 275/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3238 - binary_accuracy: 0.8670 - auc: 0.8220 - pr: 0.4806 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4386\n",
      "Epoch 276/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4802 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 277/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8219 - pr: 0.4803 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4385\n",
      "Epoch 278/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8219 - pr: 0.4802 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 279/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8219 - pr: 0.4803 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4391\n",
      "Epoch 280/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8217 - pr: 0.4801 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4391\n",
      "Epoch 281/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4802 - val_loss: 0.3391 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4392\n",
      "Epoch 282/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8219 - pr: 0.4802 - val_loss: 0.3392 - val_binary_accuracy: 0.8630 - val_auc: 0.7961 - val_pr: 0.4392\n",
      "Epoch 283/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8220 - pr: 0.4805 - val_loss: 0.3391 - val_binary_accuracy: 0.8631 - val_auc: 0.7962 - val_pr: 0.4392\n",
      "Epoch 284/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3240 - binary_accuracy: 0.8668 - auc: 0.8217 - pr: 0.4798 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4385\n",
      "Epoch 285/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3240 - binary_accuracy: 0.8669 - auc: 0.8216 - pr: 0.4797 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4388\n",
      "Epoch 286/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4802 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4393\n",
      "Epoch 287/1000\n",
      "256/256 [==============================] - 40s 152ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4800 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4393\n",
      "Epoch 288/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4801 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4388\n",
      "Epoch 289/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8220 - pr: 0.4806 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 290/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3237 - binary_accuracy: 0.8669 - auc: 0.8221 - pr: 0.4805 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4397\n",
      "Epoch 291/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4801 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 292/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3238 - binary_accuracy: 0.8670 - auc: 0.8220 - pr: 0.4805 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 293/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8220 - pr: 0.4805 - val_loss: 0.3394 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4385\n",
      "Epoch 294/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3237 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4808 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7957 - val_pr: 0.4383\n",
      "Epoch 295/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3237 - binary_accuracy: 0.8669 - auc: 0.8222 - pr: 0.4806 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 296/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8219 - pr: 0.4803 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4387\n",
      "Epoch 297/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3237 - binary_accuracy: 0.8669 - auc: 0.8222 - pr: 0.4806 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4379\n",
      "Epoch 298/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8220 - pr: 0.4803 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4386\n",
      "Epoch 299/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8220 - pr: 0.4804 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 300/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3237 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4806 - val_loss: 0.3396 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4380\n",
      "Epoch 301/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4808 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4385\n",
      "Epoch 302/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3238 - binary_accuracy: 0.8669 - auc: 0.8220 - pr: 0.4803 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4376\n",
      "Epoch 303/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3239 - binary_accuracy: 0.8669 - auc: 0.8218 - pr: 0.4800 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4379\n",
      "Epoch 304/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4809 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4393\n",
      "Epoch 305/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4809 - val_loss: 0.3392 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4391\n",
      "Epoch 306/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3237 - binary_accuracy: 0.8669 - auc: 0.8222 - pr: 0.4807 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4390\n",
      "Epoch 307/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4807 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4383\n",
      "Epoch 308/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4808 - val_loss: 0.3397 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4382\n",
      "Epoch 309/1000\n",
      "256/256 [==============================] - 42s 156ms/step - loss: 0.3236 - binary_accuracy: 0.8669 - auc: 0.8223 - pr: 0.4808 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4381\n",
      "Epoch 310/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4807 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 311/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4809 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4385\n",
      "Epoch 312/1000\n",
      "256/256 [==============================] - 41s 154ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4809 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4385\n",
      "Epoch 313/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4810 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4393\n",
      "Epoch 314/1000\n",
      "256/256 [==============================] - 42s 156ms/step - loss: 0.3237 - binary_accuracy: 0.8669 - auc: 0.8222 - pr: 0.4807 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7960 - val_pr: 0.4392\n",
      "Epoch 315/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4809 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4387\n",
      "Epoch 316/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4809 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4391\n",
      "Epoch 317/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4808 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 318/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4811 - val_loss: 0.3403 - val_binary_accuracy: 0.8628 - val_auc: 0.7955 - val_pr: 0.4380\n",
      "Epoch 319/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4811 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4388\n",
      "Epoch 320/1000\n",
      "256/256 [==============================] - 40s 149ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4812 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4388\n",
      "Epoch 321/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4810 - val_loss: 0.3391 - val_binary_accuracy: 0.8632 - val_auc: 0.7962 - val_pr: 0.4394\n",
      "Epoch 322/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4811 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7960 - val_pr: 0.4386\n",
      "Epoch 323/1000\n",
      "256/256 [==============================] - 42s 156ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4810 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 324/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4811 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4386\n",
      "Epoch 325/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3237 - binary_accuracy: 0.8670 - auc: 0.8221 - pr: 0.4807 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 326/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3236 - binary_accuracy: 0.8669 - auc: 0.8223 - pr: 0.4808 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4386\n",
      "Epoch 327/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3237 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4808 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4384\n",
      "Epoch 328/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8223 - pr: 0.4809 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 329/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3236 - binary_accuracy: 0.8669 - auc: 0.8223 - pr: 0.4807 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 330/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4811 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7960 - val_pr: 0.4394\n",
      "Epoch 331/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3236 - binary_accuracy: 0.8669 - auc: 0.8223 - pr: 0.4807 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 332/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8225 - pr: 0.4813 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 333/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3235 - binary_accuracy: 0.8669 - auc: 0.8224 - pr: 0.4809 - val_loss: 0.3395 - val_binary_accuracy: 0.8633 - val_auc: 0.7958 - val_pr: 0.4396\n",
      "Epoch 334/1000\n",
      "256/256 [==============================] - 40s 151ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4810 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4394\n",
      "Epoch 335/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4811 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4390\n",
      "Epoch 336/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8225 - pr: 0.4812 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4390\n",
      "Epoch 337/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4815 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 338/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4811 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4384\n",
      "Epoch 339/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8225 - pr: 0.4812 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4394\n",
      "Epoch 340/1000\n",
      "256/256 [==============================] - 39s 151ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4815 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7960 - val_pr: 0.4393\n",
      "Epoch 341/1000\n",
      "256/256 [==============================] - 40s 152ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4815 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 342/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3393 - val_binary_accuracy: 0.8631 - val_auc: 0.7960 - val_pr: 0.4390\n",
      "Epoch 343/1000\n",
      "256/256 [==============================] - 86s 284ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 344/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4809 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4392\n",
      "Epoch 345/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4813 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4394\n",
      "Epoch 346/1000\n",
      "256/256 [==============================] - 39s 150ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4808 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 347/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3237 - binary_accuracy: 0.8670 - auc: 0.8222 - pr: 0.4809 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 348/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 349/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4392\n",
      "Epoch 350/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8225 - pr: 0.4811 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 351/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3394 - val_binary_accuracy: 0.8633 - val_auc: 0.7960 - val_pr: 0.4398\n",
      "Epoch 352/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4812 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4392\n",
      "Epoch 353/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 354/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4814 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4378\n",
      "Epoch 355/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3236 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4809 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 356/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4815 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4386\n",
      "Epoch 357/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4813 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4387\n",
      "Epoch 358/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4815 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4393\n",
      "Epoch 359/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 360/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8225 - pr: 0.4810 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7961 - val_pr: 0.4393\n",
      "Epoch 361/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4817 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4395\n",
      "Epoch 362/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4814 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 363/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8224 - pr: 0.4810 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 364/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4817 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4383\n",
      "Epoch 365/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4815 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4385\n",
      "Epoch 366/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8227 - pr: 0.4817 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4388\n",
      "Epoch 367/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4817 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7961 - val_pr: 0.4395\n",
      "Epoch 368/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4815 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 369/1000\n",
      "256/256 [==============================] - 39s 147ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4818 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 370/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8228 - pr: 0.4817 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7960 - val_pr: 0.4392\n",
      "Epoch 371/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8228 - pr: 0.4816 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 372/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3235 - binary_accuracy: 0.8670 - auc: 0.8225 - pr: 0.4812 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4391\n",
      "Epoch 373/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4815 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 374/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8227 - pr: 0.4816 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 375/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4815 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 376/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4818 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 377/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4817 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4391\n",
      "Epoch 378/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4818 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 379/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3392 - val_binary_accuracy: 0.8632 - val_auc: 0.7960 - val_pr: 0.4392\n",
      "Epoch 380/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4818 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4388\n",
      "Epoch 381/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4817 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 382/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3232 - binary_accuracy: 0.8670 - auc: 0.8229 - pr: 0.4817 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 383/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8228 - pr: 0.4816 - val_loss: 0.3414 - val_binary_accuracy: 0.8627 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 384/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3234 - binary_accuracy: 0.8670 - auc: 0.8226 - pr: 0.4814 - val_loss: 0.3399 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 385/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4817 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4383\n",
      "Epoch 386/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 387/1000\n",
      "256/256 [==============================] - 40s 156ms/step - loss: 0.3232 - binary_accuracy: 0.8670 - auc: 0.8228 - pr: 0.4817 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4384\n",
      "Epoch 388/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4820 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 389/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8228 - pr: 0.4816 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4392\n",
      "Epoch 390/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4818 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 391/1000\n",
      "256/256 [==============================] - 34s 129ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4818 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 392/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3233 - binary_accuracy: 0.8671 - auc: 0.8228 - pr: 0.4818 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4382\n",
      "Epoch 393/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 394/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 395/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4820 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4388\n",
      "Epoch 396/1000\n",
      "256/256 [==============================] - 41s 154ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 397/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4819 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4392\n",
      "Epoch 398/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4385\n",
      "Epoch 399/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4817 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 400/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4385\n",
      "Epoch 401/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3232 - binary_accuracy: 0.8670 - auc: 0.8229 - pr: 0.4818 - val_loss: 0.3392 - val_binary_accuracy: 0.8632 - val_auc: 0.7960 - val_pr: 0.4394\n",
      "Epoch 402/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 403/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 404/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3232 - binary_accuracy: 0.8670 - auc: 0.8230 - pr: 0.4817 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4384\n",
      "Epoch 405/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4391\n",
      "Epoch 406/1000\n",
      "256/256 [==============================] - 40s 150ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4822 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 407/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4820 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4381\n",
      "Epoch 408/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 409/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 410/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4823 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 411/1000\n",
      "256/256 [==============================] - 40s 151ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4377\n",
      "Epoch 412/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 413/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4385\n",
      "Epoch 414/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4819 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4380\n",
      "Epoch 415/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4822 - val_loss: 0.3405 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4386\n",
      "Epoch 416/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4379\n",
      "Epoch 417/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7957 - val_pr: 0.4382\n",
      "Epoch 418/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4382\n",
      "Epoch 419/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3400 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 420/1000\n",
      "256/256 [==============================] - 44s 166ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4387\n",
      "Epoch 421/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 422/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 423/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4823 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4381\n",
      "Epoch 424/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4823 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4380\n",
      "Epoch 425/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4820 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 426/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4819 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4383\n",
      "Epoch 427/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 428/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 429/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3399 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 430/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4823 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4390\n",
      "Epoch 431/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4823 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 432/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 433/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 434/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4820 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 435/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4821 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 436/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4393\n",
      "Epoch 437/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4379\n",
      "Epoch 438/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4821 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4379\n",
      "Epoch 439/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7959 - val_pr: 0.4387\n",
      "Epoch 440/1000\n",
      "256/256 [==============================] - 41s 154ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4823 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 441/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4820 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 442/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3230 - binary_accuracy: 0.8672 - auc: 0.8232 - pr: 0.4825 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 443/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4823 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4383\n",
      "Epoch 444/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4389\n",
      "Epoch 445/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4823 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 446/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4819 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 447/1000\n",
      "256/256 [==============================] - 40s 156ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4822 - val_loss: 0.3396 - val_binary_accuracy: 0.8630 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 448/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 449/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4823 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 450/1000\n",
      "256/256 [==============================] - 39s 150ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4825 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4392\n",
      "Epoch 451/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4826 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4390\n",
      "Epoch 452/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 453/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3230 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 454/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 455/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7957 - val_pr: 0.4386\n",
      "Epoch 456/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4391\n",
      "Epoch 457/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3232 - binary_accuracy: 0.8671 - auc: 0.8229 - pr: 0.4818 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 458/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8230 - pr: 0.4821 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 459/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4823 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 460/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3231 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4822 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4382\n",
      "Epoch 461/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3230 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4827 - val_loss: 0.3400 - val_binary_accuracy: 0.8628 - val_auc: 0.7954 - val_pr: 0.4379\n",
      "Epoch 462/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4822 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 463/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 464/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3230 - binary_accuracy: 0.8672 - auc: 0.8232 - pr: 0.4826 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 465/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 466/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3230 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 467/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 468/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 469/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 470/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 471/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4827 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 472/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4385\n",
      "Epoch 473/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 474/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4823 - val_loss: 0.3404 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 475/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4824 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 476/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3411 - val_binary_accuracy: 0.8624 - val_auc: 0.7951 - val_pr: 0.4375\n",
      "Epoch 477/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 478/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8232 - pr: 0.4823 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 479/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4824 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 480/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4827 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 481/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 482/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 483/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 484/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4382\n",
      "Epoch 485/1000\n",
      "256/256 [==============================] - 38s 147ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 486/1000\n",
      "256/256 [==============================] - 37s 141ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4386\n",
      "Epoch 487/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4828 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 488/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4382\n",
      "Epoch 489/1000\n",
      "256/256 [==============================] - 39s 152ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 490/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4826 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 491/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3395 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 492/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 493/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4827 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 494/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4828 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 495/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4825 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4382\n",
      "Epoch 496/1000\n",
      "256/256 [==============================] - 43s 167ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 497/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8234 - pr: 0.4826 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4385\n",
      "Epoch 498/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4389\n",
      "Epoch 499/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4829 - val_loss: 0.3405 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 500/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4392\n",
      "Epoch 501/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4829 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 502/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 503/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4828 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 504/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 505/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4827 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 506/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4828 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7953 - val_pr: 0.4377\n",
      "Epoch 507/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 508/1000\n",
      "256/256 [==============================] - 44s 168ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8234 - pr: 0.4826 - val_loss: 0.3405 - val_binary_accuracy: 0.8628 - val_auc: 0.7951 - val_pr: 0.4375\n",
      "Epoch 509/1000\n",
      "256/256 [==============================] - 40s 150ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4389\n",
      "Epoch 510/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 511/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 512/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 513/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4828 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 514/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 515/1000\n",
      "256/256 [==============================] - 44s 164ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4829 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4382\n",
      "Epoch 516/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3228 - binary_accuracy: 0.8671 - auc: 0.8235 - pr: 0.4828 - val_loss: 0.3401 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4372\n",
      "Epoch 517/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4828 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 518/1000\n",
      "256/256 [==============================] - 44s 163ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4828 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 519/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4378\n",
      "Epoch 520/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 521/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3228 - binary_accuracy: 0.8671 - auc: 0.8235 - pr: 0.4827 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 522/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 523/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4385\n",
      "Epoch 524/1000\n",
      "256/256 [==============================] - 44s 168ms/step - loss: 0.3228 - binary_accuracy: 0.8671 - auc: 0.8235 - pr: 0.4828 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 525/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 526/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 527/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4378\n",
      "Epoch 528/1000\n",
      "256/256 [==============================] - 44s 166ms/step - loss: 0.3230 - binary_accuracy: 0.8671 - auc: 0.8231 - pr: 0.4823 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 529/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3233 - binary_accuracy: 0.8670 - auc: 0.8227 - pr: 0.4815 - val_loss: 0.3403 - val_binary_accuracy: 0.8627 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 530/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3230 - binary_accuracy: 0.8672 - auc: 0.8233 - pr: 0.4826 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 531/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8233 - pr: 0.4827 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4382\n",
      "Epoch 532/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4827 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 533/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3229 - binary_accuracy: 0.8672 - auc: 0.8234 - pr: 0.4828 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 534/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 535/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3229 - binary_accuracy: 0.8671 - auc: 0.8235 - pr: 0.4828 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 536/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 537/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4388\n",
      "Epoch 538/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4828 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 539/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 540/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4831 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 541/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4393\n",
      "Epoch 542/1000\n",
      "256/256 [==============================] - 44s 167ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4387\n",
      "Epoch 543/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 544/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 545/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4831 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4388\n",
      "Epoch 546/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 547/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4831 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 548/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 549/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 550/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 551/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 552/1000\n",
      "256/256 [==============================] - 44s 167ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4831 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 553/1000\n",
      "256/256 [==============================] - 44s 167ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3394 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 554/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 555/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4833 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7959 - val_pr: 0.4391\n",
      "Epoch 556/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 557/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 558/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7956 - val_pr: 0.4385\n",
      "Epoch 559/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4393\n",
      "Epoch 560/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7957 - val_pr: 0.4384\n",
      "Epoch 561/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 562/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 563/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 564/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4391\n",
      "Epoch 565/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4828 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 566/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 567/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4388\n",
      "Epoch 568/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4388\n",
      "Epoch 569/1000\n",
      "256/256 [==============================] - 37s 143ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 570/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4829 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4390\n",
      "Epoch 571/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4830 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 572/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 573/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8235 - pr: 0.4830 - val_loss: 0.3393 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4391\n",
      "Epoch 574/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 575/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 576/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 577/1000\n",
      "256/256 [==============================] - 43s 160ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 578/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 579/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 580/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 581/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4392\n",
      "Epoch 582/1000\n",
      "256/256 [==============================] - 38s 146ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 583/1000\n",
      "256/256 [==============================] - 37s 143ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 584/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7954 - val_pr: 0.4386\n",
      "Epoch 585/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4381\n",
      "Epoch 586/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 587/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 588/1000\n",
      "256/256 [==============================] - 39s 148ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4831 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 589/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 590/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4831 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 591/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4392\n",
      "Epoch 592/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 593/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4831 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4391\n",
      "Epoch 594/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4831 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4389\n",
      "Epoch 595/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 596/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4833 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 597/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4833 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 598/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 599/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 600/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 601/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 602/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 603/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 604/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4390\n",
      "Epoch 605/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3228 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4830 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 606/1000\n",
      "256/256 [==============================] - 39s 151ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 607/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 608/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 609/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4831 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 610/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 611/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 612/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4831 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 613/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 614/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 615/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4833 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 616/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 617/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3397 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 618/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 619/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4377\n",
      "Epoch 620/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4833 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 621/1000\n",
      "256/256 [==============================] - 39s 151ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3394 - val_binary_accuracy: 0.8632 - val_auc: 0.7959 - val_pr: 0.4390\n",
      "Epoch 622/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8236 - pr: 0.4832 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4384\n",
      "Epoch 623/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 624/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 625/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 626/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 627/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4833 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 628/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4833 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 629/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 630/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4833 - val_loss: 0.3407 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 631/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 632/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 633/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 634/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3399 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4377\n",
      "Epoch 635/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 636/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7958 - val_pr: 0.4390\n",
      "Epoch 637/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 638/1000\n",
      "256/256 [==============================] - 38s 145ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 639/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 640/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4373\n",
      "Epoch 641/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4833 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 642/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 643/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 644/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 645/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8673 - auc: 0.8237 - pr: 0.4835 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 646/1000\n",
      "256/256 [==============================] - 44s 166ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 647/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 648/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 649/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 650/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 651/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 652/1000\n",
      "256/256 [==============================] - 38s 145ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 653/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4832 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4388\n",
      "Epoch 654/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4832 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4387\n",
      "Epoch 655/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 656/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3395 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4382\n",
      "Epoch 657/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4833 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4378\n",
      "Epoch 658/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 659/1000\n",
      "256/256 [==============================] - 39s 152ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 660/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4835 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 661/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3227 - binary_accuracy: 0.8672 - auc: 0.8237 - pr: 0.4834 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 662/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 663/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 664/1000\n",
      "256/256 [==============================] - 43s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7956 - val_pr: 0.4390\n",
      "Epoch 665/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4834 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 666/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 667/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 668/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 669/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 670/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4836 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 671/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4378\n",
      "Epoch 672/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 673/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 674/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4834 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 675/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4377\n",
      "Epoch 676/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3226 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 677/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7958 - val_pr: 0.4391\n",
      "Epoch 678/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 679/1000\n",
      "256/256 [==============================] - 40s 154ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4835 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 680/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3226 - binary_accuracy: 0.8673 - auc: 0.8238 - pr: 0.4837 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 681/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 682/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 683/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 684/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4834 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 685/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 686/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3401 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4375\n",
      "Epoch 687/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4836 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 688/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4838 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4377\n",
      "Epoch 689/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7957 - val_pr: 0.4387\n",
      "Epoch 690/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 691/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 692/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4835 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 693/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3403 - val_binary_accuracy: 0.8628 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 694/1000\n",
      "256/256 [==============================] - 44s 168ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 695/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4836 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4388\n",
      "Epoch 696/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4833 - val_loss: 0.3403 - val_binary_accuracy: 0.8627 - val_auc: 0.7952 - val_pr: 0.4376\n",
      "Epoch 697/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 698/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4835 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 699/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 700/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4378\n",
      "Epoch 701/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 702/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 703/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 704/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4380\n",
      "Epoch 705/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 706/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 707/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3396 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 708/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 709/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 710/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 711/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4838 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 712/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4835 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 713/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4385\n",
      "Epoch 714/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 715/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 716/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 717/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4838 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 718/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 719/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3405 - val_binary_accuracy: 0.8628 - val_auc: 0.7947 - val_pr: 0.4370\n",
      "Epoch 720/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 721/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 722/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 723/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4836 - val_loss: 0.3405 - val_binary_accuracy: 0.8627 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 724/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 725/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 726/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 727/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4836 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4383\n",
      "Epoch 728/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3403 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4374\n",
      "Epoch 729/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4836 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4390\n",
      "Epoch 730/1000\n",
      "256/256 [==============================] - 40s 151ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 731/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 732/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4838 - val_loss: 0.3408 - val_binary_accuracy: 0.8628 - val_auc: 0.7949 - val_pr: 0.4371\n",
      "Epoch 733/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 734/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7957 - val_pr: 0.4389\n",
      "Epoch 735/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 736/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3226 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 737/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 738/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 739/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 740/1000\n",
      "256/256 [==============================] - 40s 156ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4836 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 741/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 742/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 743/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 744/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3403 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4376\n",
      "Epoch 745/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 746/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 747/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3406 - val_binary_accuracy: 0.8628 - val_auc: 0.7951 - val_pr: 0.4374\n",
      "Epoch 748/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4836 - val_loss: 0.3399 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 749/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4836 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 750/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 751/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 752/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 753/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 754/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 755/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 756/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 757/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 758/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 759/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 760/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 761/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 762/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4837 - val_loss: 0.3411 - val_binary_accuracy: 0.8627 - val_auc: 0.7950 - val_pr: 0.4372\n",
      "Epoch 763/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3398 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 764/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7948 - val_pr: 0.4371\n",
      "Epoch 765/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 766/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7954 - val_pr: 0.4387\n",
      "Epoch 767/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3402 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 768/1000\n",
      "256/256 [==============================] - 44s 168ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 769/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 770/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7956 - val_pr: 0.4386\n",
      "Epoch 771/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 772/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4376\n",
      "Epoch 773/1000\n",
      "256/256 [==============================] - 39s 151ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3405 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4374\n",
      "Epoch 774/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3409 - val_binary_accuracy: 0.8628 - val_auc: 0.7949 - val_pr: 0.4372\n",
      "Epoch 775/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7953 - val_pr: 0.4377\n",
      "Epoch 776/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 777/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 778/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4837 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 779/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 780/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4840 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4376\n",
      "Epoch 781/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8672 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 782/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4381\n",
      "Epoch 783/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 784/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4841 - val_loss: 0.3405 - val_binary_accuracy: 0.8628 - val_auc: 0.7951 - val_pr: 0.4374\n",
      "Epoch 785/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3405 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 786/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 787/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4373\n",
      "Epoch 788/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4375\n",
      "Epoch 789/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 790/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 791/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3225 - binary_accuracy: 0.8672 - auc: 0.8240 - pr: 0.4836 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 792/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 793/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4375\n",
      "Epoch 794/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 795/1000\n",
      "256/256 [==============================] - 42s 164ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4373\n",
      "Epoch 796/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4378\n",
      "Epoch 797/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 798/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 799/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 800/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 801/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4377\n",
      "Epoch 802/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4386\n",
      "Epoch 803/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 804/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 805/1000\n",
      "256/256 [==============================] - 44s 165ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 806/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3415 - val_binary_accuracy: 0.8626 - val_auc: 0.7944 - val_pr: 0.4362\n",
      "Epoch 807/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 808/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 809/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 810/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 811/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4374\n",
      "Epoch 812/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 813/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7948 - val_pr: 0.4371\n",
      "Epoch 814/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4373\n",
      "Epoch 815/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3410 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 816/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 817/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4380\n",
      "Epoch 818/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3404 - val_binary_accuracy: 0.8628 - val_auc: 0.7952 - val_pr: 0.4376\n",
      "Epoch 819/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4841 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 820/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 821/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4384\n",
      "Epoch 822/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3226 - binary_accuracy: 0.8672 - auc: 0.8238 - pr: 0.4834 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7949 - val_pr: 0.4374\n",
      "Epoch 823/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4838 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 824/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 825/1000\n",
      "256/256 [==============================] - 41s 155ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 826/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4837 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 827/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4374\n",
      "Epoch 828/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3406 - val_binary_accuracy: 0.8628 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 829/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 830/1000\n",
      "256/256 [==============================] - 39s 146ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 831/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8240 - pr: 0.4839 - val_loss: 0.3410 - val_binary_accuracy: 0.8628 - val_auc: 0.7947 - val_pr: 0.4371\n",
      "Epoch 832/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3225 - binary_accuracy: 0.8673 - auc: 0.8239 - pr: 0.4839 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7954 - val_pr: 0.4382\n",
      "Epoch 833/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3410 - val_binary_accuracy: 0.8628 - val_auc: 0.7949 - val_pr: 0.4373\n",
      "Epoch 834/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 835/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 836/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 837/1000\n",
      "256/256 [==============================] - 38s 147ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 838/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 839/1000\n",
      "256/256 [==============================] - 40s 155ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 840/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 841/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 842/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4382\n",
      "Epoch 843/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 844/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 845/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7955 - val_pr: 0.4383\n",
      "Epoch 846/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 847/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 848/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3411 - val_binary_accuracy: 0.8627 - val_auc: 0.7947 - val_pr: 0.4370\n",
      "Epoch 849/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 850/1000\n",
      "256/256 [==============================] - 38s 145ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 851/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3410 - val_binary_accuracy: 0.8628 - val_auc: 0.7949 - val_pr: 0.4373\n",
      "Epoch 852/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 853/1000\n",
      "256/256 [==============================] - 40s 150ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 854/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 855/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 856/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3416 - val_binary_accuracy: 0.8626 - val_auc: 0.7947 - val_pr: 0.4370\n",
      "Epoch 857/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4838 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 858/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 859/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7955 - val_pr: 0.4385\n",
      "Epoch 860/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 861/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 862/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3408 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 863/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 864/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 865/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 866/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 867/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4382\n",
      "Epoch 868/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4385\n",
      "Epoch 869/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 870/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3399 - val_binary_accuracy: 0.8632 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 871/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 872/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4839 - val_loss: 0.3403 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 873/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 874/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 875/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4841 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 876/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3407 - val_binary_accuracy: 0.8628 - val_auc: 0.7948 - val_pr: 0.4370\n",
      "Epoch 877/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 878/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3409 - val_binary_accuracy: 0.8628 - val_auc: 0.7949 - val_pr: 0.4374\n",
      "Epoch 879/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 880/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 881/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 882/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4386\n",
      "Epoch 883/1000\n",
      "256/256 [==============================] - 43s 165ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4380\n",
      "Epoch 884/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 885/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3407 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 886/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 887/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3403 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 888/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3396 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4388\n",
      "Epoch 889/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 890/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7953 - val_pr: 0.4385\n",
      "Epoch 891/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4839 - val_loss: 0.3405 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4379\n",
      "Epoch 892/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 893/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3408 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4377\n",
      "Epoch 894/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4380\n",
      "Epoch 895/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3397 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 896/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 897/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3406 - val_binary_accuracy: 0.8629 - val_auc: 0.7949 - val_pr: 0.4374\n",
      "Epoch 898/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 899/1000\n",
      "256/256 [==============================] - 39s 151ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 900/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3403 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4382\n",
      "Epoch 901/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 902/1000\n",
      "256/256 [==============================] - 41s 154ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 903/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4382\n",
      "Epoch 904/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 905/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3409 - val_binary_accuracy: 0.8629 - val_auc: 0.7947 - val_pr: 0.4372\n",
      "Epoch 906/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4379\n",
      "Epoch 907/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4386\n",
      "Epoch 908/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8241 - pr: 0.4840 - val_loss: 0.3408 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 909/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3401 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 910/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4383\n",
      "Epoch 911/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3409 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 912/1000\n",
      "256/256 [==============================] - 42s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 913/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3406 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 914/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3407 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4374\n",
      "Epoch 915/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3408 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4374\n",
      "Epoch 916/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 917/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 918/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4376\n",
      "Epoch 919/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 920/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 921/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4384\n",
      "Epoch 922/1000\n",
      "256/256 [==============================] - 41s 157ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 923/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3404 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 924/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 925/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3224 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3410 - val_binary_accuracy: 0.8629 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 926/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 927/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4378\n",
      "Epoch 928/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4379\n",
      "Epoch 929/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3406 - val_binary_accuracy: 0.8628 - val_auc: 0.7950 - val_pr: 0.4375\n",
      "Epoch 930/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3403 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 931/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 932/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3405 - val_binary_accuracy: 0.8629 - val_auc: 0.7949 - val_pr: 0.4374\n",
      "Epoch 933/1000\n",
      "256/256 [==============================] - 43s 164ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3404 - val_binary_accuracy: 0.8629 - val_auc: 0.7951 - val_pr: 0.4376\n",
      "Epoch 934/1000\n",
      "256/256 [==============================] - 43s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4844 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 935/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 936/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 937/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3403 - val_binary_accuracy: 0.8631 - val_auc: 0.7950 - val_pr: 0.4378\n",
      "Epoch 938/1000\n",
      "256/256 [==============================] - 41s 160ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 939/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3395 - val_binary_accuracy: 0.8632 - val_auc: 0.7955 - val_pr: 0.4387\n",
      "Epoch 940/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4840 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 941/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3405 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4381\n",
      "Epoch 942/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3403 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4379\n",
      "Epoch 943/1000\n",
      "256/256 [==============================] - 40s 153ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3402 - val_binary_accuracy: 0.8630 - val_auc: 0.7953 - val_pr: 0.4379\n",
      "Epoch 944/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3403 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4381\n",
      "Epoch 945/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4382\n",
      "Epoch 946/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3398 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 947/1000\n",
      "256/256 [==============================] - 41s 156ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4841 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4382\n",
      "Epoch 948/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4841 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 949/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4841 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4382\n",
      "Epoch 950/1000\n",
      "256/256 [==============================] - 43s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4841 - val_loss: 0.3401 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 951/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 952/1000\n",
      "256/256 [==============================] - 41s 159ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 953/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3404 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4380\n",
      "Epoch 954/1000\n",
      "256/256 [==============================] - 42s 157ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3398 - val_binary_accuracy: 0.8632 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 955/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4380\n",
      "Epoch 956/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7953 - val_pr: 0.4383\n",
      "Epoch 957/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4384\n",
      "Epoch 958/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3399 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4385\n",
      "Epoch 959/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3407 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 960/1000\n",
      "256/256 [==============================] - 41s 158ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 961/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4843 - val_loss: 0.3397 - val_binary_accuracy: 0.8631 - val_auc: 0.7954 - val_pr: 0.4388\n",
      "Epoch 962/1000\n",
      "256/256 [==============================] - 42s 162ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4842 - val_loss: 0.3405 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 963/1000\n",
      "256/256 [==============================] - 42s 160ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 964/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3405 - val_binary_accuracy: 0.8630 - val_auc: 0.7951 - val_pr: 0.4377\n",
      "Epoch 965/1000\n",
      "256/256 [==============================] - 43s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8242 - pr: 0.4841 - val_loss: 0.3409 - val_binary_accuracy: 0.8629 - val_auc: 0.7949 - val_pr: 0.4373\n",
      "Epoch 966/1000\n",
      "256/256 [==============================] - 43s 166ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4843 - val_loss: 0.3404 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 967/1000\n",
      "256/256 [==============================] - 42s 161ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3400 - val_binary_accuracy: 0.8631 - val_auc: 0.7952 - val_pr: 0.4379\n",
      "Epoch 968/1000\n",
      "256/256 [==============================] - 42s 159ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3403 - val_binary_accuracy: 0.8630 - val_auc: 0.7950 - val_pr: 0.4376\n",
      "Epoch 969/1000\n",
      "256/256 [==============================] - 42s 163ms/step - loss: 0.3223 - binary_accuracy: 0.8673 - auc: 0.8243 - pr: 0.4842 - val_loss: 0.3402 - val_binary_accuracy: 0.8631 - val_auc: 0.7951 - val_pr: 0.4378\n",
      "Epoch 00969: early stopping\n",
      "8. Save training results...\n",
      "=== Training completed! ===\n",
      "Best model: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/1_within_sample/train_out/E1000best_model.h5\n",
      "Training history: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/1_within_sample/train_out/history.pickle\n"
     ]
    }
   ],
   "source": [
    "history = xc.tr.train_XChrom(\n",
    "    input_folder='./data/1_within_sample/train_data/',\n",
    "    cell_embedding_ad='./data/1_within_sample/m_brain_paired_rna.h5ad',\n",
    "    cellembed_raw='X_pca',\n",
    "    out_path='./data/1_within_sample/train_out/',\n",
    "    trackscore = True,\n",
    "    celltype = 'pc32_leiden',\n",
    "    epochs = 1000,\n",
    "    save_freq = 1000,\n",
    "    verbose = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "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/1_within_sample/train_out/history.pickle`. Load them with:                \n",
    "```python\n",
    "import pickle\n",
    "with open('./data/1_within_sample/train_out/history.pickle', 'rb') as f:\n",
    "    history = pickle.load(f)\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JJ5plaWlpgrOzs+Dj46N3G/Xv5sGDBwIA4c8//9SsW7NmjQBAuHfvnmbZRx99pBXLnj17BADCsmXLtPb79ddfCwCEWbNmZRmvQqEQUlNThRo1aggTJ07ULD979myWv4PM75fr168LAISxY8dqtTt9+rQAQPjyyy81y9Tv19OnT2u19fT0FDp37pxlnGpubm5Ct27dsly/d+9eAYCwaNEireWhoaECAGHVqlWCIAjC5s2bBQDCxYsXs9zXxx9/LJQpUybHmIiKq9J+zejVq5fQo0cPrWXq51+yZIlmWWxsrGBmZiYMGjSoUF6LNm3aCG3atNE8Xrlypc65XRAEYcSIETle6xQKhfD69WvByspK6xz/xx9/6LyOapmvEbk9LwpC7n+PWVFfC9LS0oTU1FTh+vXrgp+fnwBAWLFihdbzdO3aVUhLS9Nc/0aMGCGYmpoKf/31V47Pk5lSqRTS0tKEuXPnCmXLltW6huf2mPr37y9YWFgI0dHRmmUKhULw8PDQuQ5nZe7cuQIAYe/evYIgpP9P4O/vr9VO/T78448/9O5n3LhxWu+r0aNHCwCE//77L8cYiEoq9f+8Z8+eFVJSUoRq1aoJTZo00fw9t2nTRqhTp46m/alTpwQAwnfffae1n8jISMHCwkKYMmWKZlnm8+KuXbsEAMLKlSu1tl2wYIHO/87q83zmc+jYsWMFc3NzrfMNgCzPI9WrV9cs++CDDwQzMzMhIiJCa59+fn6CpaWl8PLlS0EQ0s8VrVu3zva1y0pxODeqX7+nT5/mOu6cPtNVqFBBSEpK0iyPj48XHBwchI4dO2qWde7cWahUqZIQFxente+PP/5YMDc3F54/f661z5w+d6pUKmHUqFGCVCoVAAgSiUSoXbu2MHHiRJ3XwN3dXXB3d9eKMbMWLVoI5cuXF169eqV13HXr1hUqVaqk+V2p/y4GDx6stX1ERIRgYmIijB8/Xmv5q1evBGdnZ6Ffv37ZHg8VDCvnKEv29vZ6uzncvXsXAwcOhLOzM2QyGUxNTdGmTRsAYjfLnDRs2FCri4S5uTlq1qyZY9cUQJytKHOFQf369bW2PXLkCGxsbHS+pRowYECO+1f78ccf0bhxY5ibm8PExASmpqY4cOCA3uPr1q2b1iDg9evXBwBNTDdu3MDjx48xcOBArS5bbm5u8Pb2zlU8VatWRbt27bB+/XpNBdaePXsQHR2tqZoDgJiYGIwePRqurq6auN3c3ADk7neT0aFDhwAAgwYN0lqurgDJSKFQ4H//+x88PT0hl8thYmICuVyOW7du5fl5Mz//kCFDtJY3a9YMtWvX1ikzd3Z2RrNmzbSWZX5v5Jf6G9bMsbz//vuwsrLSxNKwYUPI5XKMHDkSa9eu1en+oI7/5cuXGDBgAP78889CmWWKqDgoDdeMhIQEhIWFabq0qrVp0wbu7u5aXVvXr1+PlJQUrXN0QV+LjA4dOgQbGxudmbr1naNfv36NL774AtWrV4eJiQlMTExgbW2NhISEfJ+jc3teVCvI7xEQJ8QwNTWFXC5H7dq1cfLkScydO1dToam2e/dumJqaaq5/P//8M77//vtcj8t68OBBdOzYEXZ2dprf0VdffYXY2FjExMTk+ZgOHTqEDh06wMnJSbNMJpPluppfEARNV9ZOnToBEP8naNu2LbZs2ZJtlQURaZPL5Zg/fz7OnTuHTZs26W3z119/QSKR4MMPP4RCodDcnJ2d0aBBg2xnBVfPbtyvXz+t5dl9Bsl8Dq9fvz6Sk5N1zjdZnUdu376tGe7l4MGD6NChg84EAkOGDEFiYqLOBE+Zr2XZKW7nxtzKy2e63r17w9zcXPNYXXF29OhRKJVKJCcn48CBA3jvvfdgaWmp9f7o2rUrkpOT9Q5tlB2JRIIff/wRd+/eRVBQEIYOHYq0tDQsXboUderU0bynbt68iTt37iAgIEArxowSEhJw+vRp9O3bF9bW1prlMpkM/v7+ePjwoaZHk1rm90BYWBgUCgUGDx6sdXzm5uZo06ZNtu9/Kjgm5yhLLi4uOstev36NVq1a4fTp05g/fz4OHz6Ms2fPYuvWrQDEbjY5KVu2rM4yMzOzXG1raWmpc0IyMzNDcnKy5nFsbKzWiV5N3zJ9lixZgjFjxqB58+bYsmUL/v77b5w9exZdunTRG2Pm4zEzMwOQ/lrExsYCEJNHmelblpWAgADExsZix44dAMTuUtbW1pp/AFQqFXx9fbF161ZMmTIFBw4cwJkzZzQXidy8vhnFxsbCxMRE5/j0xTxp0iTMnDkTvXr1ws6dO3H69GmcPXsWDRo0yPPzZnx+QP/7sEKFCpr1agV5X+UmFhMTE53uUxKJBM7OzppY3N3dsX//fpQvXx7jxo2Du7s73N3dsWzZMs02/v7+CA4OxoMHD9CnTx+UL18ezZs3R3h4eIHjJDKm0nDN2LVrl6arbEYSiQTDhg3D5cuXce7cOQDiOVr9xQpQOK9FRlnFre8cPXDgQPzwww8YPnw4wsLCcObMGZw9exaOjo4FOkfn5ryoVtBzdL9+/XD27FmcO3cON27cQGxsLGbOnKnT7p133sHZs2fx999/49dff0WVKlXw8ccf4/jx4zk+x5kzZ+Dr6wtAHKT7xIkTOHv2LKZPnw5A93eUm2OKjY0t0PX/4MGDuHfvHt5//33Ex8fj5cuXePnyJfr164fExERs2LBB01Y9kLtSqdS7L4VCoTXYu/rD871793IVC9Hb4IMPPkDjxo0xffp0vUO+PHnyBIIgwMnJSZPoV9/+/vvvbL9UVZ8XHRwctJZn9xkkp88RatmdR9Tn29jY2Cz/b87YTk1fW32K47kxN/L6mS6reFJTU/H69WvExsZCoVDg+++/13lvdO3aFQDy/aW7m5sbxowZg9WrV+PWrVsIDQ1FcnIyJk+eDACaYTOymzjrxYsXEAShQO8BdRfZpk2b6hxjaGgoiwoMjNOxUJb0zdRy8OBBPH78GIcPH9Z82w9AZ/BnYypbtizOnDmjs1zfINn6/Pbbb2jbti1WrlyptfzVq1f5jier589tTID4bY69vT2Cg4PRpk0b/PXXXxg8eLDmm5ErV67g0qVLCAkJwUcffaTZLrvBXXOKW6FQIDY2Vusiqy/m3377DYMHD9aMV6T27NkzlClTJt/PD4jjWGW+ED1+/BjlypXL137zG4tCocDTp0+1PogKgoDo6GjNRBcA0KpVK7Rq1QpKpRLnzp3D999/jwkTJsDJyQkffPABAHFCjKFDhyIhIQFHjx7FrFmz0L17d9y8eVNT6UhU0pSGa8aWLVvQvn17vbNXDhkyBF999RWCg4NhamqKCxcuYN68eZrXpbBfi9zGHRcXh7/++guzZs3C1KlTNctTUlI04/Hk9/lze14sDI6OjmjSpEmO7ezs7DTtmjdvjubNm6NBgwYYO3YsLl68qDO4dkYbN26Eqakp/vrrL62k7vbt2/Mdd9myZQt0/V+9ejUA8UPmkiVL9K4fNWoUgPQEwKNHj/Tu69GjR1pJgs6dO+PLL7/E9u3bsx0Ti+htIpFIsHDhQnTq1AmrVq3SWV+uXDlIJBIcO3ZMkyjLSN8yNfV58fnz51oJurz8v5+V7M4j6v+Zy5Yti6ioKJ12jx8/BgCd/51zOytncTw35kZeP9NlFY9cLoe1tTVMTU01VWjjxo3Tu4+MM64WRL9+/bBgwQJcuXIFADTX2cyT4mVkb28PqVRaoPeAer16XEMqWqycozxR/wFnvjD99NNPxghHrzZt2uDVq1fYs2eP1nJ9MxrpI5FIdI7v33//1SkFz61atWrBxcUFGzZs0JrR6MGDBzh58mSu96MeVHzfvn1YuHAh0tLStLpLFfbvRl3tsX79eq3lv//+u05bfa/Zrl27dD4gZPVtoD7q7nGZJ3Q4e/Ysrl+/jg4dOuS4j8Kifq7MsWzZsgUJCQl6Y5HJZGjevLlm9qZ//vlHp42VlRX8/Pwwffp0pKam4urVqwaInsh43qZrRnJyMnbv3p1lN6AKFSqgS5cu2LBhA1asWAGpVKr1RYkhztGvXr3SVFOrZT5HSyQSCIKg87y//PKLToVVXs7R+TkvGkONGjUwZcoUXL58We9g2BlJJBKYmJhoDVWRlJSEX3/9Nd/P365dOxw4cEBrwG6lUpljLIBYBbFt2zb4+Pjg0KFDOjf1rMLqD281atSAm5sb/vjjD50ZFJ8+fYpDhw6hY8eOmmWNGzeGn58fVq9eneUA+efOnUNERER+Dp2o2OrYsSM6deqEuXPn4vXr11rrunfvDkEQ8OjRIzRp0kTnVq9evSz3q/7iJfPfd24/g2Qnq/OIu7u75kvsDh06aL4IymjdunWwtLREixYt8vXcxe3cmFt5/Uy3detWrcr6V69eYefOnWjVqhVkMhksLS3Rrl07XLhwAfXr19f7/tBXNZgdfYk0QKy2j4yM1FS81axZE+7u7ggODtaZcEnNysoKzZs3x9atW7Wu4yqVCr/99hsqVaqU4+RInTt3homJCe7cuaP3+HLzJRnlHyvnKE+8vb1hb2+P0aNHY9asWTA1NcX69et1ZhE1po8++ghLly7Fhx9+iPnz56N69erYs2cPwsLCAOhOSZ1Z9+7dMW/ePMyaNQtt2rTBjRs3MHfuXFStWhUKhSLP8UilUsybNw/Dhw/He++9hxEjRuDly5eYPXt2nku3AwICsGLFCixZsgQeHh5aY9Z5eHjA3d0dU6dOhSAIcHBwwM6dO/PdXdLX1xetW7fGlClTkJCQgCZNmuDEiRN6L8Tdu3dHSEgIPDw8UL9+fZw/fx6LFy/WqXhzd3eHhYUF1q9fj9q1a8Pa2hoVKlTQXHgyqlWrFkaOHInvv/9eM1vS/fv3MXPmTLi6uuqdyaogoqOjtWa8UqtSpQo6deqEzp0744svvkB8fDx8fHzw77//YtasWWjUqBH8/f0BiONaHDx4EN26dUPlypWRnJysmTpe/YFoxIgRsLCwgI+PD1xcXBAdHY0FCxbAzs6u0CtNiIztbbpm7N27F4mJidnObBkQEIBdu3bhl19+QefOnbXG/Sns12Lw4MFYunQpBg8ejK+//ho1atTA7t27NXGr2draonXr1li8eDHKlSuHKlWq4MiRI1i9erVOZXPdunUBAKtWrYKNjQ3Mzc1RtWpVvR82cnteLA4+//xz/Pjjj5gzZw769eun9QEzo27dumHJkiUYOHAgRo4cidjYWHz77bfZVsrkZMaMGdixYwfat2+Pr776CpaWllixYkWuZuhev349kpOT8cknn6Bt27Y668uWLYv169dj9erVWLp0KQDg22+/Rb9+/dChQweMGDECzs7OuHXrFr755hvI5XKdrsDr1q1Dly5d4Ofnh2HDhsHPzw/29vaIiorCzp07sWHDBpw/f15r/Ciit8HChQvh5eWFmJgY1KlTR7Pcx8cHI0eOxNChQ3Hu3Dm0bt0aVlZWiIqKwvHjx1GvXj2MGTNG7z67dOkCHx8ffPbZZ4iPj4eXlxdOnTqFdevWAcj5M0h2ypUrh/bt22PmzJmwsrJCUFAQ/vvvP63E36xZs/DXX3+hXbt2+Oqrr+Dg4ID169dj165dWLRoEezs7PL13MXt3JjRzp07YWNjo7O8b9++ef5MJ5PJ0KlTJ0yaNAkqlQoLFy5EfHy81iysy5YtwzvvvINWrVphzJgxqFKlCl69eoXbt29j586dOc4EnNnXX3+NEydOoH///mjYsCEsLCxw7949/PDDD4iNjcXixYs1bVesWIEePXqgRYsWmDhxIipXroyIiAiEhYVpiikWLFiATp06oV27dvj8888hl8sRFBSEK1euYMOGDTlWS1apUgVz587F9OnTcffuXXTp0gX29vZ48uQJzpw5Aysrq3zNSku5ZKyZKKj4yGrmvYwzF2V08uRJoWXLloKlpaXg6OgoDB8+XPjnn390ZqTJauY9fbNiZp6FLquZ9zLHmdXzRERECL179xasra0FGxsboU+fPsLu3bv1zmyXWUpKivD5558LFStWFMzNzYXGjRsL27dv15mRST0Lz+LFi3X2AT2zmf7yyy9CjRo1BLlcLtSsWVMIDg7W2WduNGrUSO/sToIgCNeuXRM6deok2NjYCPb29sL7778vRERE6MSTm9laBUEQXr58KQwbNkwoU6aMYGlpKXTq1En477//dPb34sULISAgQChfvrxgaWkpvPPOO8KxY8d0fq+CIAgbNmwQPDw8BFNTU6396Ps9KpVKYeHChULNmjUFU1NToVy5csKHH34oREZGarXL6v2a29fXzc1NAKD39tFHHwmCIM409cUXXwhubm6Cqamp4OLiIowZM0Z48eKFZj+nTp0S3nvvPcHNzU0wMzMTypYtK7Rp00bYsWOHps3atWuFdu3aCU5OToJcLhcqVKgg9OvXT/j3339zjJOoOCit14wPP/xQ53yWWWpqquDk5KR3ltiCvhb6zqcPHz4U+vTpoxX3yZMndfanbmdvby/Y2NgIXbp0Ea5cuSK4ublpznFqgYGBQtWqVQWZTKa1H33n09ycFwUh97/HrAAQxo0bl2O77GbeVs/Ku3bt2mz3ERwcLNSqVUswMzMTqlWrJixYsEBYvXq1zjUzL8d04sQJoUWLFoKZmZng7OwsTJ48WVi1alWOMxI2bNhQKF++vJCSkpJlmxYtWgjlypXTarN//37B19dXKFOmjGBiYiK4uLgIH374oXDr1i29+0hKShKWL18utGzZUrC1tRVMTEyEChUqCL179xZ27dqV5XMTlQQZZ2vNbODAgQIAvdev4OBgoXnz5oKVlZVgYWEhuLu7C4MHDxbOnTunaaPvvPj8+XNh6NChWv87//333wIArdmxs5ptVN//6OpzYFBQkODu7i6YmpoKHh4ewvr163Xivnz5stCjRw/Bzs5OkMvlQoMGDXRmDM1pZmd9itO5URDSX7+sboKQ9890CxcuFObMmSNUqlRJkMvlQqNGjYSwsDCd5753754wbNgwoWLFioKpqang6OgoeHt7C/Pnz9fZZ06ztf7999/CuHHjhAYNGggODg6CTCYTHB0dhS5dugi7d+/WaX/q1CnBz89PsLOzE8zMzAR3d3dh4sSJWm2OHTsmtG/fXvPebdGihbBz506tNtn9XQiCIGzfvl1o166dYGtrK5iZmQlubm5C3759hf3792d7PFQwEkHIVPdO9Jb63//+hxkzZiAiIiLbwTSJiIgyXjPKly+P8uXLY968eRg/fryxQyMiohLk999/x6BBg3DixAmtXi9ERBmxWyu9lX744QcAYlfPtLQ0HDx4EMuXL8eHH37IxBwREWnJzTWjOE1iQURExdOGDRvw6NEj1KtXD1KpFH///TcWL16M1q1bMzFHRNlico7eSpaWlli6dCnu37+PlJQUVK5cGV988QVmzJhh7NCIiKiY4TWDiIgKg42NDTZu3Ij58+cjISEBLi4uGDJkCObPn2/s0IiomGO3ViIiIiIiIiIiIiPJ/5QxREREREREREREVCBMzhERERERERERERkJk3NERERERERERERGwgkh9FCpVHj8+DFsbGwgkUiMHQ4RUYknCAJevXqFChUqQCrl90IArzVERIWJ1xldvM4QERUuQ15rmJzT4/Hjx3B1dTV2GEREb53IyEhUqlTJ2GEUC7zWEBEVPl5n0vE6Q0RkGIa41jA5p4eNjQ0A8QW3tbU1cjRERCVffHw8XF1dNedX4rWGiKgw8Tqji9cZIqLCZchrDZNzeqjLvm1tbXkhIyIqROxWk47XGiKiwsfrTDpeZ4iIDMMQ1xoOyEBERERERERERGQkTM4REREREREREREZCZNzRERERERERERERsIx54hKGUEQoFAooFQqjR0KvUVkMhlMTEw41g8REREREVEeMTlHVIqkpqYiKioKiYmJxg6F3kKWlpZwcXGBXC43dihEREREREQlBpNzRKWESqXCvXv3IJPJUKFCBcjlclY5UaEQBAGpqal4+vQp7t27hxo1akAq5agJREREREREucHkHFEpkZqaCpVKBVdXV1haWho7HHrLWFhYwNTUFA8ePEBqairMzc2NHRIREREREVGJwNIGolKGFU1kKHxvERERERER5R0/SRERERERERERERkJk3NERERERERERERGwuQcEZU6bdu2xYQJE3Ld/v79+5BIJLh48aLBYiIiIiIiIqLSick5Iiq2JBJJtrchQ4bka79bt27FvHnzct3e1dUVUVFRqFu3br6eL7eYBCQiIiIiIip9OFsrERVbUVFRmvuhoaH46quvcOPGDc0yCwsLrfZpaWkwNTXNcb8ODg55ikMmk8HZ2TlP2xARERERERHlBivniEopQRCQmKowyk0QhFzF6OzsrLnZ2dlBIpFoHicnJ6NMmTLYtGkT2rZtC3Nzc/z222+IjY3FgAEDUKlSJVhaWqJevXrYsGGD1n4zd2utUqUK/ve//2HYsGGwsbFB5cqVsWrVKs36zBVthw8fhkQiwYEDB9CkSRNYWlrC29tbK3EIAPPnz0f58uVhY2OD4cOHY+rUqWjYsGG+fl8AkJKSgk8++QTly5eHubk53nnnHZw9e1az/sWLFxg0aBAcHR1hYWGBGjVqYM2aNQCA1NRUfPzxx3BxcYG5uTmqVKmCBQsW5DsWIiIiIiIiKhysnCMqpZLSlPD8Kswoz31tbmdYygvn9PPFF1/gu+++w5o1a2BmZobk5GR4eXnhiy++gK2tLXbt2gV/f39Uq1YNzZs3z3I/3333HebNm4cvv/wSmzdvxpgxY9C6dWt4eHhkuc306dPx3XffwdHREaNHj8awYcNw4sQJAMD69evx9ddfIygoCD4+Pti4cSO+++47VK1aNd/HOmXKFGzZsgVr166Fm5sbFi1ahM6dO+P27dtwcHDAzJkzce3aNezZswflypXD7du3kZSUBABYvnw5duzYgU2bNqFy5cqIjIxEZGRkvmMhIiKidEFBQVi8eDGioqJQp04dBAYGolWrVnrbDhkyBGvXrtVZ7unpiatXrwIAQkJCMHToUJ02SUlJMDc3L9zgiYjI6JicI6ISbcKECejdu7fWss8//1xzf/z48di7dy/++OOPbJNzXbt2xdixYwGICb+lS5fi8OHD2Sbnvv76a7Rp0wYAMHXqVHTr1g3JyckwNzfH999/j4CAAM0/1l999RX27duH169f5+s4ExISsHLlSoSEhMDPzw8A8PPPPyM8PByrV6/G5MmTERERgUaNGqFJkyYAxIpAtYiICNSoUQPvvPMOJBIJ3Nzc8hUHERERaQsNDcWECRM0X8j99NNP8PPzw7Vr11C5cmWd9suWLcM333yjeaxQKNCgQQO8//77Wu1sbW11qvKZmCMiejsxOWdoggA8/gdw9ADkVsaOhkjDwlSGa3M7G+25C4s6EaWmVCrxzTffIDQ0FI8ePUJKSgpSUlJgZZX931/9+vU199XdZ2NiYnK9jYuLCwAgJiYGlStXxo0bNzTJPrVmzZrh4MGDuTquzO7cuYO0tDT4+PholpmamqJZs2a4fv06AGDMmDHo06cP/vnnH/j6+qJXr17w9vYGIH5L36lTJ9SqVQtdunRB9+7d4evrm69YiIhKK5VKgEQiXicAQKkSh2mQSoBUpQopChUEATAzkUIlCEhTCrCUyyCTSJCsUEKpEpCUpoRUIoGJVAKpVPwpk0qQolAhJU0l7h+AVCKBUhCgUApIU6oglUpgbWYChVIFlQCoBOHNTYxLEAClIMBKLoNUKkFSqhISifivqFIlIC4pDSpBgIlUChOZ+JwSABIJoFAJUL7Zh0wqgVQigQABKpX4PADSn0sQIGR4XpUAzeM0lQoWpjLITaRITlUiRaGCmakUKhWQplRBoRI0x2siFV8TmVQKcxMpUpUqpClVUKmAjp5ORvoN58+SJUsQEBCA4cOHAwACAwMRFhaGlStX6h1Cws7ODnZ2dprH27dvx4sXL3Qq5dT/jxARURYUKYAyDTCz1r8+NRF4eh2o0Fi84BVjTM4Z2qWNwPbRgHN9YPQxY0dDpCGRSAqta6kxZU66fffdd1i6dCkCAwNRr149WFlZYcKECUhNTc12P5knkpBIJFCpVLneRv1BLeM2kkwXgNyOtaePelt9+1Qv8/Pzw4MHD7Br1y7s378fHTp0wLhx4/Dtt9+icePGuHfvHvbs2YP9+/ejX79+6NixIzZv3pzvmIiocKj/vpUqAYlpSpibyKBQqRATnwI7C1MIAFIUSphIpXiekAqlStBOzLxJlihVgEKpQopSBdWbZEuyQkyWpCiUiE9SwMpMBltzU0TFJSNFocSLhFRUcrDE84RUCAKQqlDBRCaBuakMSakKpCoFKFViUkWpFDRJHJlUAqVKTDylKFRQqFRvkkhiLAqVAAGAm4Mlnr5KQapShVfJCsikYrJITPhIkKJQvnkN3hx/qgJyEynMTWVIU6qQlKqEiUwcIlkmlbxJ3ghQCtqJI1OZVJP8kUoA1Zv9SSRiAilNKd5PVahgIZchMVUJc1Mp0hSCZrtUhQrmplJIJBIkpioAABJINMky9XJzUxkEIT3ZBECTBKOCM5VJcOvrrsYOI9dSU1Nx/vx5TJ06VWu5r68vTp48mat9rF69Gh07dtSpan/9+jXc3NygVCrRsGFDzJs3D40aNcpyP+ovJNXi4+PzcCRE9FZSqYDoS2I+QqqnQCI+CrB2AqS5mI5AkQI8OAk4VAPs9fTCUaQCTy4DLg31P5cgAIIK2D0ZiHsIfLAekGX4DBZ7B9g4EGgSALi3A8rVAP7bDZwPAXoFAVblgIjTwO/9gOSXwMjDwLbRwKto4P0QIOxLIOYaMHATkPAMqNEJOLYEOL1S3P+AUODOAcC2IvDOhJyPt4iV/E/mxd2F38Sf0f8aNw6iUuLYsWPo2bMnPvzwQwBisuzWrVuoXbt2kcZRq1YtnDlzBv7+/ppl586dy/f+qlevDrlcjuPHj2PgwIEAxNlpz507pzW5haOjI4YMGYIhQ4agVatWmDx5Mr799lsAYveY/v37o3///ujbty+6dOmC58+f53n2WqKSLkWhRJpSQHRcsqbiKSlVCVOZFMlpSrxMTMPrFAWkUgmUKhXkMhliXiXj8sM4SCQSVHawRHR8Mu7EvEaFMuaQvkmQx7xKgUQC2JqbQm4ixbPXKXiekAqJBIiOS0FCigJJaUrYmJvAwlSGmFcpOURKRS0pTZljm8RU3TZ5SczlNZGnrjRLe1Mxp96HVCKBTCLR3JdKACFDfOamUijeJCXlMilMTaSwNjOBUiVkqJQTk6gmUimkEmg9j/TNftP3r/1c+tZJJBIkpSqgUAmaCroUhQomUgnkJtI3X3yJz69QqmBuKhOTyGlKyE2kkJtIYSqTan3xVNw9e/YMSqUSTk7a1X5OTk6Ijo7OcfuoqCjs2bMHv//+u9ZyDw8PhISEoF69eoiPj8eyZcvg4+ODS5cuoUaNGnr3tWDBAsyZMyf/B0NEb5/9s4CTywG/RUDzUdrrLqwH/hwLNBwEvPtD9gm6f34FdnysvaxyS8C5HuAzAbAuDxz6GjgRCFRtDXSaBzy5Ajw4BdhWACAA/6wDXj9J335eOSAgXEzk/dw+ffmeybrPH1gfeO9HYFP6Zyusapt+/9de6fd/76f/GDb0T7/fcBBg7Zj18RoBk3MGx69RiYpS9erVsWXLFpw8eRL29vZYsmQJoqOjizw5N378eIwYMQJNmjSBt7c3QkND8e+//6JatWo5bpt5fBlAHCR6zJgxmDx5MhwcHFC5cmUsWrQIiYmJCAgIACCOa+fl5YU6deogJSUFf/31l+a4ly5dChcXFzRs2BBSqRR//PEHnJ2dUaZMmUI9bqLClpymxOOXSUhIUSJVqUJUXJJYiaVQISFVCSu5DE9fpcBSLsOzhFQ8fZWCxFQFLN5UN8Unp+Hxy2QkvkkYKJRCrhIwhvQqWYFXyYoc28llYlc/QEzQKFQC7C1NYSqTahIkEonkTTdE8b6pTEyCyKRiiZq6YszMRAoLuQwKpYD4ZAXK25jhVXIabse8hmcFWzhYyWFhagK5iRQKpQpJaUpYyU3e7Evcr0wq1SSKktKUMJFKkPqm26KpTFynetMt8tHLJPx54RE8XGzRpqYjzEyksDE3Fav8AE1SSS6Tit0qlQLSVCooVQJs37STScUKb6VKBUAChUoFU5kUpjIJJBC7hErf5G8USgEmMglMpFIkK5Qwe5PkSU5TwsJUBhOZ2M3UVCoVY5dJ3lTKyd509ZSgjIUcKQolVAJgKU//xl8QAAHpXT4TUhWwNDWBiUx8chOZmBUzM5XB3FT8YJOmFCv4JBDbqwQB1mYmkEokMDMR26gEQKESu3GmKlUwlaVXtKu7iap/r+I+xS6zpjJJtomrJ/HJsDU3hYVcrD6UvnmPkGFlV9menZCQEJQpUwa9evXSWt6iRQu0aNFC89jHxweNGzfG999/j+XLl+vd17Rp0zBp0iTN4/j4eLi6uubhKIio2Di/Frh/XEx+vbgHeH8iVnyZyPO2n5NvzhdHFwNuPsDdQ8DLCCDlFXBpg7ju4nrxNvwAUMZNrFC7d0SsqCtfG0iOA86s0t13xCnxlnndvaPAqja5i291p9y1S0vQTswV1O1woOHAwttfIWByztCE7LvFEVHhmjlzJu7du4fOnTvD0tISI0eORK9evRAXF1ekcQwaNAh3797F559/juTkZPTr1w9DhgzBmTNnctz2gw8+0Fl27949fPPNN1CpVPD398erV6/QpEkThIWFwd7eHgAgl8sxbdo03L9/HxYWFmjVqhU2btwIALC2tsbChQtx69YtyGQyNG3aFLt374Y0NyXsRIUkPjkNlqYy3H2WgDsxr7HnSjTik9PgbGuO8w9e4HlCKqzMTBDxPBEWprIiS6I5WMkhCGK3RolEAhOZBJamMljIZXiZmAazNxU/Za3lKGdt9qarpThGWNtajpoklbOdBSQAXiWnaRKHZqYyWJuZwMpMBntLObZfeITq5a3RwLUMouKS4WhjBplEgvjkNHg428LG3AQpaSrIZBJYyWVIThO7jNqYm2q6k5YUc96tA1NZ6TzHmGX4D9tCrn+cVZkEkL3p9mMB7TYSiQSyTL/q3L6WTrbpEwaU1te/KJUrVw4ymUynSi4mJkanmi4zQRAQHBwMf39/yOXZf+CWSqVo2rQpbt26lWUbMzMzmJmZ5T54Iio8t/cD13cCnRcAckvd9dFXxMRYk2HiWPTxj8WkVsuPxWQYICbQ7h8HWn0O7PxEXHZ5k/jzXDBQq6tYAbd9DKBSiDfHWsCjf8TunNXaic8BAGa24nq1hKfAj+ljV+v1S4eCvQbGJjUFVGnifRNzQJEs3je1BNrPELu9AkDXb8XXspiRCAUZBOktFR8fDzs7O8TFxcHW1rZgO1vdGYj8W7w/u2iTA0QZJScn4969e6hatSpn+jKSTp06wdnZGb/++quxQzGI7N5jhXpefUu8ja+JIAh4HJeMZ69ScCvmNW49eQVHGzPcefoam88/RJqyYP9yWMllqO1iCxOZBPefJUIqAepVstNUI5nIpKhkbwFHGzOkKsSueXYWpjCRigm3KmWtYCGXITlNCZUKcC9vBZUAWJvxu0qiks6Y59TmzZvDy8sLQUFBmmWenp7o2bOn3gkh1A4fPox27drh8uXLqFu3brbPIQgCmjVrhnr16iE4ODhXcb2N1xmiYifxOaBSAt9WFx+3ngK0n67dRpECLKkNJMbqbu/SQKxYS3wOfFfT8PFm5tociDxddM/n9g7QeT5QoRFw9zBw8gexii2jpiMAv4XAtzXEqj2fCcDDs2I1HyB2wa3fD7i6Dbj4O1CnF1C3L2D+5jz3Klqs+lOmAfGPgLQkwMkTSE0AJDLANP+fhQ15XuV/owbH3CdRaZSYmIgff/wRnTt3hkwmw4YNG7B//36Eh4fnvDFRCREVl4Tjt57hWlQ87j5NwJGbT/O1nzoVbOFW1hJxSWnw9XTGqTux8K5eFlZyE8Qnp6GPVyUolAJszU00kwMQERUXkyZNgr+/P5o0aYKWLVti1apViIiIwOjRowGI3U0fPXqEdevWaW23evVqNG/eXG9ibs6cOWjRogVq1KiB+Ph4LF++HBcvXsSKFSuK5JiIirXk+PRETEEIArBluFjJ1mOZOKDnzX1iEqiRv1iVFnkGODAXePkAGPQHYFcJWNNVnMhg8A7gu1qAMsPEc0cXiYmg10+B6h2Asu5iF1V9iTkAiLokjr1mSA0Hid1W1dx8gAcnAK8h4nEvrAIkvch5Py3GAQ36AxYOwL+hYpVfyiug3TTgVJA42UKvlendRc/8LI4n12QY8PSmOJGELEMKqlpb8aamSBF7HppaiI8/Pie+tjZZzFrd4APxlpm6vYkccKiavlxupdu2GGFyztBYmEhUKkkkEuzevRvz589HSkoKatWqhS1btqBjx47GDo0oX5LTlNh//YlmUoS1J+9n2+1UPWOm2tyedTTdNqUSCWo6WUMikSA5TanpNqr2kXcVAx4JEVHh6t+/P2JjYzF37lxERUWhbt262L17t2b21aioKERERGhtExcXhy1btmDZsmV69/ny5UuMHDkS0dHRsLOzQ6NGjXD06FE0a9bM4MdDVGylJQHHA4Ej3wCD/9RO7OREEMREmFMdMbEmCEBINzFJBQBXtgLV2wPX/hQfn/pBdx9BLbQfL6io/7n+GJL7uPLLspzYHfbpf1m3aTMVqPOemGRUJ+c+vQTYV9Fu12kusGM8UKMzcCssfXnrKYCbtzjxQ8QpoEqr9ORa68/Fm1r1jmJC0ipDorHZiPT7jrmoCjTJ1C3fsnRNWsdurXoUaqniLx3FEkyA3VrJqNitlQyN3VrzpiS8JgkpCoRfe4JLD18i9Gyk3lkq3R2tUNHeEu1qOSIqLhkOVnKMal0NEokEN5+8gu/So6hkb4HjX7TX8wxERIWjJJxTixpfEyoRbh8A/pooVnC5t9Nel/hcTNgoU4EfmgEJMenrytYAxp8TE3Y3dovjrVk6AOGzgOd3gffXirOPCoI4fts/bypXbSoA1doAHWeLVW9FqdePgEUZYMObaq/+vwGhH+q2K1NZnLRBnxZjxfHSqrwjVvqlJohdPyPPAMe+BfoE60+EPb0hVrlVaqK7ThCAlHjA3A54cR+4GSZWudXI5WQNpQi7tZZkzH0SEVEJE37tCT7/4xLiktKybOPqYIHNo721Bp/PrKaTDfZOaIWyVhygnIiISjFFqjg4fW66Yr56AiQ9F2fJfFtd2wFc2gj0WgH81ltcFvoh8OUj8X5qgtiV9PSPWe8j9hZw/4Q4EcPxJbrr/+cCtJkiVsupq+EA4NVjcZZS9UylRcVvEdBwgHg/Y9FOl4XA3i/E+zI58H4IUKGx2G30XLA4cUTnr8WuqU//002uya3EW51e4i0rjtkkIiUSMTEHiFV1zUfl7dioUDA5Z2gZZ2vdNgawdwPaTjVePERERHrcefoaS/bdxP7rT5CiSL92lbOWo6K9JQa3cEO3+i7YcCYCf/0bhZWDGqN8Nok5NQ9nVmsQEVEptnuyOCsnICZockp8qCcFGHEQOPg1UO99oF5fsSvm05vA4f8BrSeL3TMLQhDEpEx+pbwSB/R3bQ5Yl8/btpv8xZ9H3NKXpb4GrmwB4qPEcduSc9HrLCSbGTcVyWKCr6Aca4uf3//8GEh9lbttylYHXFsAF38D7FyBD7dknRxrMRpoNhJQJGmPifbOBPGWkb6qN3prMDlncBkq5y79Lv5kco6IiIqJu09fY9aOqzh265nW8hbVHDCgWWX0bKg9nspQn6oY6lMVRERElAvqxBwA7JkCmNkARxYCvX8RZ6zMOEC+KkNhx89vhoO4cwA4sUwc32tLgLjswSng8xvZP+/ze+KMlXJL3XV/TRJnyBx1FLCwz/2xxN4RY/CZAGweml6I4jtfXNd9adYJv6vbgSdXxG3VHpzUbrN5WO5jMYRP/wUOzgcubxIfjz4hdjE1txUnUXh6HYi5Dpz8HvD6CHjnM7Hdlc3i+kfnAStHwK2luLxXLidwkUqL/WQFZHhMzhkau7USEVExdP7BC3y26SLuxybqrPuiiwfGtHU3QlREREQl3I09YvKt9RQg/pHu+u1jxJ+rOwLlagFjTooJOpVKTP7o8/R6emIOAF5Hp99XKYFb+4BKzQCrsuKYYcsaiOvc2wP+29Lb7p8N2LgA51aLjy/+DjQfIz6+ul0sIqnaCoi+IiYSk+OBJ5cBj+5it8oj34jdRP/4SDu+fTPEn1EXxckD/g4C/LenjyH34GT6NkcXp28XdVH/8RYF8zJA8sv0xz2Wi73cui4Gnt8BavcAnDPMpGztKN6qttatfqzfT/xpl8UEEUS5wOScwTE5R0RExYdKJeC78BtYceiOZlk5azm+fq8efD2dtGZNJSIiojxSD/a/cUDObZ/dECcvWOkNqLIe51X/treBsu7Aup7A/WP629w5COz9Eri5R+wee3xppn3cAlY0BWJvi4/Xdhe7zGZMoAHAf3+Jt5w8viDeAODXXsCMp4CJHFjjl6dD08uyHNB0uJggzErG8dsyq+kHtJokVr41HCh2Ez6xHAifCTjXFyvhAHHChhEHCx4vUR4xOWdoGcecIyIiMiJBEDBt62WEnovULOtazxmL+jaAtRn/JSAiIsqz5HhxRlGTfE5+tKJp/rb7wQuQmQHKlOzb/f2ma+WRhbrrzq/RXZY5MVcQ8x0Lb19T3nypWLExUN5TvB+YobLN51Og2QhxDDeVCvhvJ+BUF/i+sbjetVn6Ta3F2DfjwzUvvDiJ8klq7ADeeiycIzK6tm3bYsKECZrHVapUQWBgYLbbSCQSbN++vcDPXVj7ISqoV8lpGL/hgiYx16amI45NaYegQV5MzBEREekTfVmcMVOVqeAiNRF49A+Q9BL4xhWYXx6IPAu8jCja+HJKzJUkneYBHecAww8CPYOAFuPS11VolH6/ZmegjKt4679eXNZhFtBpLiCViY+lUsCzp1hZ6L8daDZKTMRlJjMBPLqK3YGJjIz/jRscs3NE+dWjRw8kJSVh//79OutOnToFb29vnD9/Ho0bN87Tfs+ePQsrq8IddHX27NnYvn07Ll68qLU8KioK9vZ5GGg3H0JCQjBhwgS8fPnSoM9DJZcgCGi7+DBiE1IBAF3qOGPlh43ZhZWIiEqn2DvAXxOBdyamj4umz4/viD8tywGe74r3H18AVrUV79frl952dUfAtpSMOVa7B9AkADj6LWDpAFzfob2+TOXsE5XvrRLHbnt2Q+yWCwA+n6Svr+Ql/mw3Dbj8hzjmnd44ugNT7mU/qYV7u+x/x0TFBJNzhsYJIYjyLSAgAL1798aDBw/g5uamtS44OBgNGzbMc2IOABwdC7HEPgfOzs5F9lxEWQk6fEeTmBvb1h2TO9diYo6IiEqvzUPFiQ3uHQFmx6UvV6YBF34FTv8EOHqkLz++FKjQEIj4G9g6In25elZPNX0TQBRUww+Bi7+J9907AOVrA6d+yH4b+6qAXaWsx6IDgJ4rgD/fVKe5tweeXAMSnwEqRdbb1OsHeHQD6vR6s92bpNf1neIspy8jgbQE4J1JwF8T0rczswPGnBATbe7txdcSAGxdgO6BYtdSfcxsgCY5zOBq6ZD9eqISgt1aDY7JOSqmBAFITTDOLZdJ6+7du6N8+fIICQnRWp6YmIjQ0FAEBAQgNjYWAwYMQKVKlWBpaYl69ephw4YN2e43c7fWW7duoXXr1jA3N4enpyfCw8N1tvniiy9Qs2ZNWFpaolq1apg5cybS0sSBe0NCQjBnzhxcunQJEokEEolEE3Pmbq2XL19G+/btYWFhgbJly2LkyJF4/fq1Zv2QIUPQq1cvfPvtt3BxcUHZsmUxbtw4zXPlR0REBHr27Alra2vY2tqiX79+ePLkiWb9pUuX0K5dO9jY2MDW1hZeXl44d+4cAODBgwfo0aMH7O3tYWVlhTp16mD37t35jqU4CwoKQtWqVWFubg4vLy8cO5bNP7QA1q9fjwYNGsDS0hIuLi4YOnQoYmNjiyja3Lv15BWW7b8FAHi3QQVM6eLBxBwREZVuT66m37+5L/3+mZ/Firqn/wHXtqcvf/wPEFhPOzFnCG4+QPuZwMxYoNdKoPMCoNcK8adTPeDd7wErPV8yq7t3AuL2Iw+L3Tm/fAx8fA54fy3Q/zftCrOGg4Cxp4Ehu8QZXT+/IU7g8OklYMBGoP0M3efx+TQ9MZdR7R7AuNPAhH+BAaGA1xDgo7/EmEceBqZFiN1QW01KT8ypNRkqzhBLVMqxcs7QWDlHxVVaIvC/CsZ57i8fA/Kcu5WamJhg8ODBCAkJwVdffaVJKPzxxx9ITU3FoEGDkJiYCC8vL3zxxRewtbXFrl274O/vj2rVqqF585wHd1WpVOjduzfKlSuHv//+G/Hx8Vrj06nZ2NggJCQEFSpUwOXLlzFixAjY2NhgypQp6N+/P65cuYK9e/dquuDa2dnp7CMxMRFdunRBixYtcPbsWcTExGD48OH4+OOPtRKQhw4dgouLCw4dOoTbt2+jf//+aNiwIUaMyPs/hIIgoFevXrCyssKRI0egUCgwduxY9O/fH4cPHwYADBo0CI0aNcLKlSshk8lw8eJFmJqaAgDGjRuH1NRUHD16FFZWVrh27Rqsra3zHEdxFxoaigkTJiAoKAg+Pj746aef4Ofnh2vXrqFy5co67Y8fP47Bgwdj6dKl6NGjBx49eoTRo0dj+PDh2LZtmxGOQD/1BBCpShXa1XLEsg8aGjskIiIi48tYHfb7+8DgP4FqbYEHJwz3nH6LgT2Txfs9lgEVmwA/+mi3+XALYGoh3m84MH15y7HiDRArya79CTjXAyRSMbnl0gDo8g1wZQvQNECccRQQx1QrV0O8AWLV2k+txQkVJBKgfIbqQEAcq82+inir1BT4+0egVhegbA0g4SngVCf7Y7QqJ7YHxITbmON5eIGISjcm5wyOyTmighg2bBgWL16Mw4cPo107sXQ+ODgYvXv3hr29Pezt7fH5559r2o8fPx579+7FH3/8kavk3P79+3H9+nXcv38flSpVAgD873//g5+f9pTvM2akf3tYpUoVfPbZZwgNDcWUKVNgYWEBa2trmJiYZNuNdf369UhKSsK6des0Y9798MMP6NGjBxYuXAgnJycAgL29PX744QfIZDJ4eHigW7duOHDgQL6Sc/v378e///6Le/fuwdXVFQDw66+/ok6dOjh79iyaNm2KiIgITJ48GR4e4j9oNWrU0GwfERGBPn36oF69egCAatWq5TmGkmDJkiUICAjA8OHDAQCBgYEICwvDypUrsWDBAp32f//9N6pUqYJPPhHHR6latSpGjRqFRYsWFWncOdl24RHOPXgBMxMpvn6vHivmiIio9El6AYR0FxNTLcfprzzb+SmQlgS8fqK7riCkpoCNi9gVtPlIwKEaULaa+BMQK9S2jQKS33StNTHPeZ/mtsDIQ7rLW4wRb9mRW4mVdLlhVQ747IaY4CMig+NfmqEJqpzbEBmDqaVYwWas584lDw8PeHt7Izg4GO3atcOdO3dw7Ngx7NsndkFQKpX45ptvEBoaikePHiElJQUpKSm5nvDh+vXrqFy5siYxBwAtW7bUabd582YEBgbi9u3beP36NRQKBWxtbXN9HOrnatCggVZsPj4+UKlUuHHjhiY5V6dOHchkMk0bFxcXXL58OU/PlfE5XV1dNYk5APD09ESZMmVw/fp1NG3aFJMmTcLw4cPx66+/omPHjnj//ffh7u4OAPjkk08wZswY7Nu3Dx07dkSfPn1Qv379fMVSXKWmpuL8+fOYOnWq1nJfX1+cPHlS7zbe3t6YPn06du/eDT8/P8TExGDz5s3o1q1bls+jfm+qxcfHF84BZCE6Lhlf77oOAPigqSsqlLEw6PMREREVC3EPgW2jgRqdxG6Ydw8DT66It5PLxVk8M3txP/f7b/MFcGSh9rJpj4DgzuJzqPX+GajVFTDL0OOgRkft7Wr5AROvAgsqAdZOYjWboeXlOZiYIyoyHHPO0NitlYoriUT89swYtzz+4xEQEIAtW7YgPj4ea9asgZubGzp06AAA+O6777B06VJMmTIFBw8exMWLF9G5c2ekpqbmat+Cnr/RzNVFf//9Nz744AP4+fnhr7/+woULFzB9+vRcP0fG58qqcinjcnWX0ozrVKr8Jfqzes6My2fPno2rV6+iW7duOHjwIDw9PTVdM4cPH467d+/C398fly9fRpMmTfD999/nK5bi6tmzZ1AqlZrkqJqTkxOio6P1buPt7Y3169ejf//+kMvlcHZ2RpkyZbJ9bRYsWAA7OzvNLWPCtLAJgoAha84gNiEV1cpZYXo3T4M9FxERUbGy81NxIoTwr4BNHwH/Zpq04dqfBdt/68naj+1cxQSccz3t5ZVbaCfmsmJmA0yNAD79t2BxEVGJxuScwTE5R1RQ/fr1g0wmw++//461a9di6NChmsTSsWPH0LNnT3z44Ydo0KABqlWrhlu3buV6356enoiIiMDjx+lVhKdOndJqc+LECbi5uWH69Olo0qQJatSogQcPHmi1kcvlUCqVOT7XxYsXkZCQoLVvqVSKmjVr5jrmvFAfX2RkpGbZtWvXEBcXh9q1a2uW1axZExMnTsS+ffvQu3dvrFmzRrPO1dUVo0ePxtatW/HZZ5/h559/NkisxpY5iZldMvXatWv45JNP8NVXX+H8+fPYu3cv7t27h9GjR2e5/2nTpiEuLk5zy/g7KWy//v0A/0W/gkQCrBrsBblJEV7us/pSKjUBSI4HXscA6mRzXpLOSS8BRaq4Tfxj8XlePQFi/gNePwVu7xdni3t6A7gZBjw8B6S8Ah79A9w7BpwLBk4sAy7+LsaS+FzswvTovDi7XPQV4PFF4N5RIOa62ObZLeDuEeBlhDh4eMKzNzEoAaVC3PeraCDlNfDwvBjP3SPAs9vi+oRnQEKsGFPMdeDpTSDqX0CRIsasSAXiHonLlQpxprznd8VjS44Tj1lN/VopUtNfj7Rk8ZaRIkXcl0qZ/rtQx6x+rFKJbfL7BaJ6+7xKzlQtmpYMpCaKsRUWQdCNjV+UEr2dnt4Uz/tqyfHi3/vze+nLrm0HbhTiRFYmFoDMFPjivjjBQrNRwMdnxXXen6S36x4IlNEdszZL5naAaS66tBLRW4t1qobGfwiJCsza2hr9+/fHl19+ibi4OAwZMkSzrnr16tiyZQtOnjwJe3t7LFmyBNHR0VqJp+x07NgRtWrVwuDBg/Hdd98hPj4e06dP12pTvXp1REREYOPGjWjatCl27dqlM+h/lSpVcO/ePVy8eBGVKlWCjY0NzMzMtNoMGjQIs2bNwkcffYTZs2fj6dOnGD9+PPz9/XWqtvJKqVTi4sWLWsvkcjk6duyI+vXrY9CgQQgMDNRMCNGmTRs0adIESUlJmDx5Mvr27YuqVavi4cOHOHv2LPr06QMAmDBhAvz8/FCzZk28ePECBw8ezPVrW1KUK1cOMplMp0ouJiYmy9/LggUL4OPjg8mTxW/P69evDysrK7Rq1Qrz58+Hi4uLzjZmZmY67wlDUKoE/HJM/GDiV9cZ1cvb5H0nggAo0wATufZyRSpwdRtQtTWQ9Bx4fAE4OB94FaXdTm4jVshaOwE1O4tdjDLOepeRRCZWFqjH2ylTWUyG5UQiA4R8JnW25zAmT3FlagWkJehZIQGkMsDMVvy9ZGReBkh+qb1Magqo3swAbWYHWJcXh+GwsAcSn6V377JyFAcFN7cD4qPE35G5LRBzTXv/giC+VxKevllmJ46b9PoJYO0s7uP1E+DFPXFYA7mVOAZTdKYqERMLMS4zW0BuDdi7iYlYUwsx6SiRiPfNy4jvz5hrgEwO2DiJ+5PJxWWvosUKFmWqmLxVJImvEQSgekdx2/jH4mMLezGJa2ImLpfIxPgfHAfsKgPV2oj7PbdafN1MzMRY6vQCEmPF43wVLc7k2HCQmOC1cRJfr6fX04/NuR5Qvg5g6yImfW+Fi8dXqZn4Pr66XVznUE3cv+2bCZvSksSqHIlETGJKJGJiWaUQf6/J8WLXPdsK4uv/5Bpwax9QuztgXxU4OE/cT/sZ4nHePSL+rl9Fi3/HD06I7cpVFweWVynFdooU4OYewLk+UOUd8W9SIhHfE0+uAhYOYryP/hGPx62lOGA8BCD2tpg0qdgYcKgqvk6vY8Rxv2xcxCqh5Jfi71EiFbscqhRAnffE45CaimNzEWXnXLA4q6pand7A1a3i+8qQOs0Vf1rYizOU1u6Rvs7JE5h0HbAsK54riIjygMk5g2NyjqgwBAQEYPXq1fD19dWaPXPmzJm4d+8eOnfuDEtLS4wcORK9evVCXFxcrvYrlUqxbds2BAQEoFmzZqhSpQqWL1+OLl26aNr07NkTEydOxMcff4yUlBR069YNM2fOxOzZszVt+vTpg61bt6Jdu3Z4+fIl1qxZo5VEBABLS0uEhYXh008/RdOmTWFpaYk+ffpgyZIlBXptAOD169do1KiR1jI3Nzfcv38f27dvx/jx49G6dWtIpVJ06dJF0/1SJpMhNjYWgwcPxpMnT1CuXDn07t0bc+bMASAm/caNG4eHDx/C1tYWXbp0wdKlSwscb3Eil8vh5eWF8PBwvPfee5rl4eHh6NlTz7g0EGfeNTHRvoSqxwnU11W6KO2//gQRzxNha26Cb99vkHVDlUpMxCjejIP3d5CY2JBbA//9JSZKKnqJCTi9CaFspL4Sf6bEA6dyqGQVlOmJOSB3iTn1doamThap8lElZghZ/h4EMcbMiTlANzEHpCfmACAlTrzpk/A0PeGmlnmoRPX+UzIuiwPwZp+vo8WbWlqieMu8X+BNEg3icSQ9B+Jy+V5IiAGiM43LGXk6U6M3f5e39+dun4D4/Bd+TX+sSgNS37x2l//QbX9xvfgz5qruuujLujE+uyne1J7fFW959fCM7rILv2k/Pjhft82VzeLPV1FAhP7xNRH9r24SVZ/b+3Vf29z+/tRO/SD+lMmBZiOKZuwtKrkyJuYAMTEH6D/n5dbII8CqNtm3Uc+kmhV1Yp2IKI8kgrE/RRRD8fHxsLOzQ1xcXJ4HfNexrKH4TXFGs3OXNCAqTMnJybh37x6qVq0Kc3OWzVPhy+49VqjnVQMIDQ2Fv78/fvzxR7Rs2RKrVq3Czz//jKtXr8LNzQ3Tpk3Do0ePsG7dOgBASEgIRowYgeXLl6Nz586IiorChAkTIJVKcfp05qSAfoZ6Tfr9dApn7j3H2LbumNLFQ3tl4nPg6LdAWXdg/2wxeVYQ5nbaiTUdb6qVAMDNR0z83donVtQ41QEiz4j7eBmhXX1nXgao4StWIL24D7x4IMZcoRFQtjpgV+lNZZ/5m0q7B2JFVnKcWKkjCGL1VfS/YvWCo4dYWaZ6kwiMvS1WI6nSxCodQSl2sTQxEz94mdmKiUsrR0BqIm6nShO7qb6OEZOPr6LFSqG0RPH56/YFXj1+U/Vl9ab7rkLcZ2qCGKtKIc5+9+Sq+DzPbomzB8rk4v8Kjh5iMlSZBlg6iFVTp1aIlYVl3MSYbZzedAd9LT62KCMmeGwrvKlKswbiH4nPJZOLv+OUV+JrXMZNfO2sy4vJMUWqGF9cJBBxGnCsJcYefVk8/iqtxOqzF/fF34ncCrCtKD63Mg2Iuigef3yUOKh5zHWxss7KUazourkHaOQvPp9MLr42yS/F96FEKlbbWTuJv4/4KLG6LOGpmKCycRHfGxb24thRMlPxd2/jIr62L+4B948DLg3EdcCbrrsqcT9mNuL76+J6oKYfILcUuy871RHXJTwVXw+pTOwibFdJXP7yAXDnkHgMZtZiJV3qK+BSqPj8phbiPizsxWOxdhaT2s9uiccT90iMRW4N2DiLScaEp2IFmkwubm9iDlzfIb6+FRuL3aJPrxT3WamZWGVoZgOUry2+l5Oei+8hGxfxdyUzE/8eTi4Xn8vCQXzfm1qKr+u9I4BjbXEf6sRdTT/xvfP8LhB7R3yPSE3FY5TJxeo7iUx8P1k4iBWyURfFdQ0GiNVvyXHi7/vReaBcLcClPnBlq1gBaGEvVsZZO4kxKFLE5dFXxAo7RYp436Or+Doq09LfC89uinGVfxOzjQvQ7buckyCZFPfrjDG8ta9JzH9AUPPC3+/ku8DiN7Ooqt/TgFhFW7s78OAkMHSPeD4holLJkOdVJuf0KNQXPLC++I9eRkzOkREwOUeGVpKTcwAQFBSERYsWISoqCnXr1sXSpUvRunVrAMCQIUNw//59HD58WNP++++/x48//oh79+6hTJkyaN++PRYuXIiKFSvm6vkM8ZpcfhiHHj8ch4lUguNftIez3ZvfQ1oysLaH/gqbrDhUEz+MJ8amL3PvANTtLXYJrNpaHOwaeJPkkYvVeIokIKil+AF72F7xg7xKCVQ2wAcpotIu9o7YbVgqy7Hp264kXGeK2lv3mqQli4nclS3zt33Lj8Vr2M/t9a//8rHYNf3MT0CXb4Alb4bxqPc+0OeX/D0nEb1VDHleZbdWg2Puk4ioJBg7dizGjh2rd11ISIjOsvHjx2P8+PEGjir3BEFAjx+OAwC61nMRE3OpiWJCbscnul8UZVauFtCgv5iUu3MI6LpYrKSRSMTqAatyWW+rHptOKhWrqz65AEAiblupSeEcIBHpKutu7AiIisaxJcCh/2l3y8+rzl+LP0ccEqtXXZuL1dzngsXlJhZAFR/xBgBNh4vrWn1esNiJiHKByTlDY26OiIiKwMk76RVuHT2dgEsbgW2jcr+DgaFi1zhAHJg9o+wSc/qwioeIiPLq1RNgzxSgvCfg/bH4ZY8giF/0HJhTeM9TsbF4A8Ru5GrSTDObd/0W6DhH7P5NRGRgTM4ZHLNzRERkeMv2ixMvNJH8h+7H52nPFJmV0SfE8bISn6cn5oiIiIxh1yRxQqJr28VJXZoMBUI/BKrmMEmDPjNjgXllCxaPRMLEHBEVGSbnDE1QGTsCIi0cZpIMhe8t47nyKA5n7j+HDEpsNpsL6JkIEwDw2Q0AEnGAfvVMiM51iypMIiIi/QRBTMyp3Tkg3gBxkhN9+q0DNg3WXT7xKiDL9DHX+xP9+yjvmfdYiYgMgMk5Q+OHVSomTE3F2ewSExNhYZG3GdCIciMxMRFA+nuNik7wCXFW8HmVzgLPMq7JMFtquZri7JFERETFTUJW3yplo7KeiSEsHMTZlwGg2SjgyhbgvZ8A93b69+HaDOizmuM3EpHRMTlncEzOUfEgk8lQpkwZxMTEAAAsLS0hUVfOEBWAIAhITExETEwMypQpA5mM440VpYQUBfZeiUaAbDcGPvst01oBqNUNuLEL8JlgjPCIiIhydmRR3rcxtQQ+uwl8VzN9Wb916fe7LhJnXc08llxm9frm/bmJiAoZk3OGxso5KkacncWqGXWCjqgwlSlTRvMeo6Kz50o0aqX9h5lmmRNzAKSmQO9VwJOrYnUAERFRcXT257xvY2opjgn3ZRSw81OgRiegaivtNjkl5oiIigkm5wyNY85RMSKRSODi4oLy5csjLa0AU9ETZWJqasqKOSNITVNgZ1gYtpnN0l1pUwEYtEn84FK5edEHR0RElBuKlNy1c6gGdF4AbOgPNByUnniTWwJ98pHcIyIqRpicMzhWzlHxI5PJmEghegtEb5qEtalr9a9sNQlwrle0AREREeXWjk+AyDNAY//ctbcqD9TqAsyOM2xcRERGYPQ636CgIFStWhXm5ubw8vLCsWPHcrXdiRMnYGJigoYNG+qs27JlCzw9PWFmZgZPT09s27atkKPOA3ZrJSIiQxAEVL6VRWLuzXoiIqJi65+1wNPrQNiXObd1bQH0/MHwMRERGYlRk3OhoaGYMGECpk+fjgsXLqBVq1bw8/NDREREttvFxcVh8ODB6NChg866U6dOoX///vD398elS5fg7++Pfv364fTp04Y6jBzwwxERERU+5b4ZObTg9YeIiIpY4nPgVBDw6kn27fL6BVJAGFCuRv7jIiIq5oyanFuyZAkCAgIwfPhw1K5dG4GBgXB1dcXKlSuz3W7UqFEYOHAgWrbUnT47MDAQnTp1wrRp0+Dh4YFp06ahQ4cOCAwMNNBR5ICVC0REVNgUqZCdyqGCgNcfIiIqatvHAmHTgPUZZkBVqYC4h9rtUhN0t/3spv59jj5eePERERVTRkvOpaam4vz58/D19dVa7uvri5MnT2a53Zo1a3Dnzh3MmqVn8GuIlXOZ99m5c+ds95mSkoL4+HitW+HhhyMiIipk/2TTnVWD1x8iIipiN/eIP6P/TV+2dTiwtA5wfWf6sqc3tLezrQjYOGkvG30CmHyH46cSUalgtOTcs2fPoFQq4eSkfRJ2cnJCdHS03m1u3bqFqVOnYv369TAx0T+XRXR0dJ72CQALFiyAnZ2d5ubq6prHo8kGKxeIiKiwxd42dgRERES5c2WL+PPYkvRlfwzRbqNSiD+nRojjy3X5BnCuC1iVK5IQiYiMzegTQkgkEq3HgiDoLAMApVKJgQMHYs6cOahZs2ah7FNt2rRpiIuL09wiIyPzcAQ5YHKOiIgKmfJVTM6NODYPEREVJ4//AVITgbRkIC7zGONvPquZ24njy7UYU+ThEREZk/7ysyJQrlw5yGQynYq2mJgYnco3AHj16hXOnTuHCxcu4OOPPwYAqFQqCIIAExMT7Nu3D+3bt4ezs3Ou96lmZmYGMzOzQjgqfZicIyKiwpUU9xTW2TWwcQHcdSdNIiIiMqqNA4FGH+pZwc9MRFS6Ga1yTi6Xw8vLC+Hh4VrLw8PD4e3trdPe1tYWly9fxsWLFzW30aNHo1atWrh48SKaN28OAGjZsqXOPvft26d3n0WClXNERFTIVAmx2Tdo/BGQTcU4ERGRUdw9BCiSdZcLqqKPhYioGDFa5RwATJo0Cf7+/mjSpAlatmyJVatWISIiAqNHjwYgdjd99OgR1q1bB6lUirp162ptX758eZibm2st//TTT9G6dWssXLgQPXv2xJ9//on9+/fj+HFjzfLD5BwRERUuWfLz7Bs4181+PRERkUFIoPX5R6Un6aZM1V1mV8lgERERlQRGTc71798fsbGxmDt3LqKiolC3bl3s3r0bbm5uAICoqChERGQejyB73t7e2LhxI2bMmIGZM2fC3d0doaGhmsq6IsfKOSIiKmTy1JcAgIdm1VEpJcPkEIN3AC8fAB7djRMYERGVbhKJ9ueftXquR/qqv3v/bLiYiIhKAIkgMHuUWXx8POzs7BAXFwdbW9uC7exrFyAtUXvZ7LiC7ZOIqIQp1PPqWyLfr4kgYO22nQg7dx3vNKiNsdf909dNewiY2RR+sERExRyvM7qM8prMcQAEpXi/RmfgVphum+ajgdM/ai/j5yMiKgEMeV41+mytbz2On0BERIVJIsFNSVWcVNWFmWWmaSFkcuPEREREBGiPd6ovMQcAt/drP67Q2HDxEBGVEEbt1loq6CtMFAQO1E1ERPmWqhC/+DE1zZSMk5oaIRoiIiK1XHzGiX0zHEOrz4HEWKDVZ4YNiYioBGByzuDYa5iIiAqXUv3FT8Zk3MBNgJQF8UREVEK4eQPVOxg7CiKiYoH/xRsah/QjIqJCplKJ1xapTJa+kN2CiIjI2CR5+HhZtrrh4iAiKmGYnDM0jjlHRESFTKkunOMQCUREVJzk5bpUprLh4iAiKmGYnDO4LMacIyIiyid15ZxCbpe+0KKMcYIhIqLSISEWeHQ++zZ5qZzjF0xERBocc87QmIgjIqJCpnyTnJOYmAJTI8SFMk4GQUREBrSsAZD6Chi6RxwvTh/7qkDM1aKNi4joLcDKOYNjco6IiAqXekIImUQCmNuJNyIiIkNKfSX+vLVPd13ic+D+8awnJur/m+HiIiJ6C7ByziiYsCMiovxTd2uV8Ss2IiIqcnq6o/76HhB1Mf1xuVrAsxvifakpUMZNu323JQaLjoioJOK/9URERCWMunKOE0IQEZFRpSYA++doJ+YAoM8v6fclUt2hF7yGGDoyIqIShZVzREREJYxSUznH5BwRERWxjF8MHVkEnAjUbWNVLkN7KSDN8LFTagpIZQYLj4ioJGLlHBERUQmjEpicIyKiYiD6X/3LLcum35dIodUVdsJlg4ZERFQSMTlnDJzBlYiICkBdOcdurURExUNQUBCqVq0Kc3NzeHl54dixY1m2HTJkCCQSic6tTp06Wu22bNkCT09PmJmZwdPTE9u2bTP0YeSDnuuQhT1gYpahiRQQVOmPzW0NHxYRUQnD5BwREVEJo3rzGYeVc0RExhcaGooJEyZg+vTpuHDhAlq1agU/Pz9ERETobb9s2TJERUVpbpGRkXBwcMD777+vaXPq1Cn0798f/v7+uHTpEvz9/dGvXz+cPn26qA4ra8e+S7+v70uiil7aj81ttZNzMrlh4iIiKsGYnCMiIiphOCEEEVHxsWTJEgQEBGD48OGoXbs2AgMD4erqipUrV+ptb2dnB2dnZ83t3LlzePHiBYYOHappExgYiE6dOmHatGnw8PDAtGnT0KFDBwQGBhbRUWWir+dP5Bng9n7d5Y0Hiz/7/QrYVwH6/wrYOKWvl3LYcyKizJicMwp2ayUiovzjhBBERMVDamoqzp8/D19fX63lvr6+OHnyZK72sXr1anTs2BFubm6aZadOndLZZ+fOnbPdZ0pKCuLj47VuhUal1H6sTANWd9Je5t4eGLAR8OwpPvZ8F/j0klhJZ2EPjDoKjDurv9qOiKiUY3KOiIiohEmfEMLIgRARlXLPnj2DUqmEk5OT1nInJydER0fnuH1UVBT27NmD4cOHay2Pjo7O8z4XLFgAOzs7zc3V1TUPR5KDJ1e0H//WW7eNQzWgll/W+3BpADjWLLyYiIjeIvy3noiIqIThhBBERMWLJNP5WBAEnWX6hISEoEyZMujVq1eB9zlt2jTExcVpbpGRkbkLPje2BGg/vndUt43UtPCej4iolGGHfyIiohKG3VqJiIqHcuXKQSaT6VS0xcTE6FS+ZSYIAoKDg+Hv7w+5XHuSBGdn5zzv08zMDGZmZlmuL5DXMTm3yTjpAxER5Qkr54xB34CqREREuaRJzrFyjojIqORyOby8vBAeHq61PDw8HN7e3tlue+TIEdy+fRsBAQE661q2bKmzz3379uW4T4PJzQyrqjTDx0FE9JZi5RwREVEJo5mtlZVzRERGN2nSJPj7+6NJkyZo2bIlVq1ahYiICIwePRqA2N300aNHWLdundZ2q1evRvPmzVG3bl2dfX766ado3bo1Fi5ciJ49e+LPP//E/v37cfz48SI5Jh0m5jm3USkMHwcR0VuKyTkiIqISRsVurURExUb//v0RGxuLuXPnIioqCnXr1sXu3bs1s69GRUUhIiJCa5u4uDhs2bIFy5Yt07tPb29vbNy4ETNmzMDMmTPh7u6O0NBQNG/e3ODHo5drU+Dqw+zbKJmcIyLKLybnjILdWomIKP80lXPs1kpEVCyMHTsWY8eO1bsuJCREZ5mdnR0SExOz3Wffvn3Rt2/fwgiv4Ewtc27Dbq1ERPnGMeeIiIhKGNWbMbdZOUdEREUi5VX6/eaj9bdht1Yionxjco6IiKiE4YQQRERUZBSpwPUd4v1u3wGuWXStVbJyjogov5icIyIiKmHSJ4QwciBERPT2i72Vfl+lAlJf62/Hyjkionzjv/XGIHDMOSIiyj9OCEFEREVGmmGY8uSXgHN9/e2YnCMiyjcm54iIiEoYdeUcu7USEZHBKVLS7ye9BCo0BKydddtVbllUERERvXU4WysREVEJox5zTsrKOSIiMrSMyTnPnuJP93bApQ3i/WYjAcdaQCP/oo+NiOgtweScUbBbKxER5d9P/l5IVajgYmdu7FCIiOhtp0gWf0qkQOU3k0Fk7OraZSEHQSUiKiAm54iIiEoYb/dyxg6BiIhKC3XlnHO99GVyq/T7TMwRERUYz6RERERERESk37Xt4s+M3VtbfQbYVwHaTTdGREREbx1WzhEREREREZE2QQAengMu/Co+fvpf+jrr8sCnl4wTFxHRW4iVc8YgcMw5IiIiIiIqxq5uBVZ3NHYURESlApNzREREREREpO3KVmNHQERUajA5R0RERERERNpkcu3H3ZYYJw4iolKAyTmjYLdWIiIiIiIqxkzM0+/X7Qs0DTBeLEREbzkm54iIiIiIiEibSYbKOVML48VBRFQKMDlHRERERERE2mRm6fdNLY0XBxFRKcDkHBEREREREWkzMdN/n4iICh2Tc8YgcMw5IiIiIiIqxjKOOccxs4mIDIrJOSIiIiIiItKWcbbWlNfGi4OIqBRgco6IiIiIiIi0qRTp95OeGy8OIqJSgMk5o2BZOBERERERFWMZk3OW5YwXBxFRKcDkHBEREREREWnLmJxr96Xx4iAiKgWYnCMiIiIiIiJt6uSc9yeAFSvniIgMick5IiIiIiIi0qZSij+lJsaNg4ioFGByzhgEjjlHRERERETFmCpN/MnkHBGRwTE5R0RERERERNrU3VqZnCMiMjgm54iIiIiIiEibOjknY3KOiMjQmJwzCnZrJSIiIiKiYoxjzhERFRkm54iIiAAEBQWhatWqMDc3h5eXF44dO5Zl2yFDhkAikejc6tSpU4QRExERGRC7tRIRFRkm54iIqNQLDQ3FhAkTMH36dFy4cAGtWrWCn58fIiIi9LZftmwZoqKiNLfIyEg4ODjg/fffL+LIiYiIDETJCSGIiIoKk3PGwNlaiYiKlSVLliAgIADDhw9H7dq1ERgYCFdXV6xcuVJvezs7Ozg7O2tu586dw4sXLzB06NAijpyIiMhA1JVzEn5kJCIyNJ5piYioVEtNTcX58+fh6+urtdzX1xcnT57M1T5Wr16Njh07ws3NLcs2KSkpiI+P17oREREVW4JK/CkzNW4cRESlAJNzRERUqj179gxKpRJOTk5ay52cnBAdHZ3j9lFRUdizZw+GDx+ebbsFCxbAzs5Oc3N1dS1Q3ERERAbFMeeIiIoMk3NEREQAJBKJ1mNBEHSW6RMSEoIyZcqgV69e2babNm0a4uLiNLfIyMiChEtERGRYmm6tMuPGQURUCvBrEKPgmHNERMVFuXLlIJPJdKrkYmJidKrpMhMEAcHBwfD394dcLs+2rZmZGczMzAocLxERUZFQKcWfrJwjIjI4Vs4REVGpJpfL4eXlhfDwcK3l4eHh8Pb2znbbI0eO4Pbt2wgICDBkiEREREVPk5xj5RwRkaHxaxAiIir1Jk2aBH9/fzRp0gQtW7bEqlWrEBERgdGjRwMQu6Q+evQI69at09pu9erVaN68OerWrWuMsImIiAxHM+Yck3NERIbG5JwxCOzWSkRUnPTv3x+xsbGYO3cuoqKiULduXezevVsz+2pUVBQiIiK0tomLi8OWLVuwbNkyY4RMRERkWAK7tRIRFRWeaYmIiACMHTsWY8eO1bsuJCREZ5mdnR0SExMNHBUREZGRcLZWIqIiwzHniIiIiIiISBu7tRIRFRkm54iIiIiIiEibSiX+lDA5R0RkaEzOERERERERkTZ2ayUiKjJMzhEREREREZE2JueIiIoMk3NERERERESkTTNbK7u1EhEZGpNzxiAIxo6AiIiIiIgoayom54iIigqTc0RERERERKSN3VqJiIoMk3NERERERESkTZ2c42ytREQGx+QcERERERERadN0a2XlHBGRoTE5ZxQcc46IiIiIiIoxJueIiIoMk3NERERERESkTTPmHLu1EhEZGpNzREREREREpE3gbK1EREWFyTljENitlYiIiIiIijHO1kpEVGSYnCMiIiIiIiqAoKAgVK1aFebm5vDy8sKxY8eybZ+SkoLp06fDzc0NZmZmcHd3R3BwsGZ9SEgIJBKJzi05OdnQh5KOyTkioiLDMy0REREREVE+hYaGYsKECQgKCoKPjw9++ukn+Pn54dq1a6hcubLebfr164cnT55g9erVqF69OmJiYqBQKLTa2Nra4saNG1rLzM3NDXYcWlSq9PsSdmslIjI0JueMISEGsHY0dhRERERERFRAS5YsQUBAAIYPHw4ACAwMRFhYGFauXIkFCxbotN+7dy+OHDmCu3fvwsHBAQBQpUoVnXYSiQTOzs65jiMlJQUpKSmax/Hx8Xk8kgxUGRKFHHOOiMjg2K3VGFZ6GzsCIiIiIiIqoNTUVJw/fx6+vr5ay319fXHy5Em92+zYsQNNmjTBokWLULFiRdSsWROff/45kpKStNq9fv0abm5uqFSpErp3744LFy5kG8uCBQtgZ2enubm6uub/wLSSc6znICIyNCbniIiIiIiI8uHZs2dQKpVwcnLSWu7k5ITo6Gi929y9exfHjx/HlStXsG3bNgQGBmLz5s0YN26cpo2HhwdCQkKwY8cObNiwAebm5vDx8cGtW7eyjGXatGmIi4vT3CIjI/N/YOqZWgFWzhERFQF+DUJERERERFQAEolE67EgCDrL1FQqFSQSCdavXw87OzsAYtfYvn37YsWKFbCwsECLFi3QokULzTY+Pj5o3Lgxvv/+eyxfvlzvfs3MzGBmZlY4B8TKOSKiIsXKuUL2IiEVu/6NwqH/YowdChERERERGVC5cuUgk8l0quRiYmJ0qunUXFxcULFiRU1iDgBq164NQRDw8OFDvdtIpVI0bdo028q5QqXKUDnHCSGIiAyOyblCdvfZa4z7/R/M3nnV2KEQEREREZEByeVyeHl5ITw8XGt5eHg4vL31jzPt4+ODx48f4/Xr15plN2/ehFQqRaVKlfRuIwgCLl68CBcXl8ILPjvq5JxECkj5kZGIyNB4pjUQQTB2BEREREREZGiTJk3CL7/8guDgYFy/fh0TJ05EREQERo8eDUAcC27w4MGa9gMHDkTZsmUxdOhQXLt2DUePHsXkyZMxbNgwWFhYAADmzJmDsLAw3L17FxcvXkRAQAAuXryo2afBqbu1smqOiKhIcACBQieOLSGA2TkiIiIiordd//79ERsbi7lz5yIqKgp169bF7t274ebmBgCIiopCRESEpr21tTXCw8Mxfvx4NGnSBGXLlkW/fv0wf/58TZuXL19i5MiRiI6Ohp2dHRo1aoSjR4+iWbNmRXNQ6uQcx5sjIioSPNsWsizGfSUiIiIiorfU2LFjMXbsWL3rQkJCdJZ5eHjodIXNaOnSpVi6dGlhhZd36tlamZwjIioS7NZqIOzWSkREREREJZJ6zDmON0dEVCSMfrYNCgpC1apVYW5uDi8vLxw7dizLtsePH4ePjw/Kli0LCwsLeHh46P1GKTAwELVq1YKFhQVcXV0xceJEJCcnG/IwNNSFc0zOERERERFRicRurURERcqoZ9vQ0FBMmDABQUFB8PHxwU8//QQ/Pz9cu3YNlStX1mlvZWWFjz/+GPXr14eVlRWOHz+OUaNGwcrKCiNHjgQArF+/HlOnTkVwcDC8vb1x8+ZNDBkyBACKpDRcwn6tRERERERUkqnYrZWIqCgZ9Wy7ZMkSBAQEYPjw4QDEirewsDCsXLkSCxYs0GnfqFEjNGrUSPO4SpUq2Lp1K44dO6ZJzp06dQo+Pj4YOHCgps2AAQNw5syZIjgiIiIiIiKiEo6ztRIRFSmjdWtNTU3F+fPn4evrq7Xc19cXJ0+ezNU+Lly4gJMnT6JNmzaaZe+88w7Onz+vScbdvXsXu3fvRrdu3bLcT0pKCuLj47Vu+ZXerZX9WomIiIiIqARi5RwRUZEy2tn22bNnUCqVcHJy0lru5OSE6OjobLetVKkSnj59CoVCgdmzZ2sq7wDggw8+wNOnT/HOO+9AEAQoFAqMGTMGU6dOzXJ/CxYswJw5cwp2QG+wVysREREREZVomtlaWTlHRFQUjD4hROYx2gRByHHctmPHjuHcuXP48ccfERgYiA0bNmjWHT58GF9//TWCgoLwzz//YOvWrfjrr78wb968LPc3bdo0xMXFaW6RkZEFOygArJsjIiIiIqISiRNCEBEVKaOdbcuVKweZTKZTJRcTE6NTTZdZ1apVAQD16tXDkydPMHv2bAwYMAAAMHPmTPj7+2uq6erVq4eEhASMHDkS06dPh1TPdOBmZmYwMzMrjMOC5E3HVvZqJSIiIiKiEkmTnGPlHBFRUTBa5ZxcLoeXlxfCw8O1loeHh8Pb2zvX+xEEASkpKZrHiYmJOgk4mUwGQRCKZBw4dmslIiIiIqISjWPOEREVKaOebSdNmgR/f380adIELVu2xKpVqxAREYHRo0cDELubPnr0COvWrQMArFixApUrV4aHhwcA4Pjx4/j2228xfvx4zT579OiBJUuWoFGjRmjevDlu376NmTNn4t1334VMVnTf/Ajs2EpERERERCWRimPOEREVJaMm5/r374/Y2FjMnTsXUVFRqFu3Lnbv3g03NzcAQFRUFCIiIjTtVSoVpk2bhnv37sHExATu7u745ptvMGrUKE2bGTNmQCKRYMaMGXj06BEcHR3Ro0cPfP3110V6bOzWSkREREREJZK6W6uEyTkioqJg9DrlsWPHYuzYsXrXhYSEaD0eP368VpWcPiYmJpg1axZmzZpVWCHmCbu1EhERERFRicYx54iIipTRZ2t9W7FwjoiIiIiISiRVmvhTJjduHEREpQSTc4VMa7bWlxHZNyYiIiIiIipulOrKOaN3tCIiKhWYnCtkWt1a9882VhhERERERET5o6mcMzVuHEREpQSTcwYjpI/VQEREREREVFIo3yTnpEzOEREVBSbnCpm6co6ztRIRERERUYmkTBV/snKOiKhIMDlXyNRjzhEREREREZVI6h5ATM4RERUJJucMhIVzRERERERUIrFbKxFRkWJyrpCld2tleo6IiIiIiEogdmslIipSTM4VMnZqJSIiIiKiEo3dWomIihSTcwbCujkiIiIiIiqR2K2ViKhIMTlXyDhbKxERERERlWiqN8k5Vs4RERUJJucKnSSL+0RERERERCWAksk5IqKixOScgYgTQrB8joiIiIiIShh2ayUiKlJMzhUyTbdW44ZBRERERESUP+zWSkRUpJicK2SSbB4REREREREVe+zWSkRUpJicMxSWzhERERERUUnEbq1EREXKxNgBvG0kb/q1MjdHREREREQlUr33Aee6gJu3sSMhIioVmJwrZOzISkREREREJVpNX/FGRERFgt1aDUScrZWIiIiIiIiIiChrTM4VMs7WSkREREREREREucXkXCGTsGMrERERERERERHlEpNzBsJerURERERERERElBMm5wpZerdWZueIiIiIiIiIiCh7TM4REREREREREREZCZNzBsJurURERERERERElBMm5woZZ2slIiIiIiIiIqLcYnKukEkkuZytlaV1RERERERERESlHpNzhiIgvYyOiIiIiIiIiIhIDybnCpk6HZfjbK2snCMiIiIiIiIiKvWYnCtkLJYjIiIiIiIiIqLcYnLOQHIujGPlHBERERERERFRacfkXCGTvOnYytQbERERERERERHlhMm5Qpbrbq0cc46IiIiIiIiIqNRjcs5ABCbfiIiIiIiIiIgoB0zOFbL02VqJiIiIiIiIiIiyx+RcYcv1bK1M3xERFSdBQUGoWrUqzM3N4eXlhWPHjmXbPiUlBdOnT4ebmxvMzMzg7u6O4ODgIoqWiIiIiIjeFibGDuBtxV6tREQlR2hoKCZMmICgoCD4+Pjgp59+gp+fH65du4bKlSvr3aZfv3548uQJVq9ejerVqyMmJgYKhaKIIyciIiIiopKOyblCJslt6Ryzd0RExcaSJUsQEBCA4cOHAwACAwMRFhaGlStXYsGCBTrt9+7diyNHjuDu3btwcHAAAFSpUqUoQyYiIiIiorcEu7UWMu3ZWnPdx5WIiIwkNTUV58+fh6+vr9ZyX19fnDx5Uu82O3bsQJMmTbBo0SJUrFgRNWvWxOeff46kpKQsnyclJQXx8fFaNyIiIiIiIibnDEjIdlw5Vs4RERUHz549g1KphJOTk9ZyJycnREdH693m7t27OH78OK5cuYJt27YhMDAQmzdvxrhx47J8ngULFsDOzk5zc3V1LdTjICIi4zHEuKVbtmyBp6cnzMzM4OnpiW3bthnyEIiIyIiYnCtkWrVyzL8REZUYEu3SZwiCoLNMTaVSQSKRYP369WjWrBm6du2KJUuWICQkJMvquWnTpiEuLk5zi4yMLPRjICKioqcet3T69Om4cOECWrVqBT8/P0RERGS5Tb9+/XDgwAGsXr0aN27cwIYNG+Dh4aFZf+rUKfTv3x/+/v64dOkS/P390a9fP5w+fbooDomIiIoYx5wrZFl9kCMiouKpXLlykMlkOlVyMTExOtV0ai4uLqhYsSLs7Ow0y2rXrg1BEPDw4UPUqFFDZxszMzOYmZkVbvBERGR0hhi3NDAwEJ06dcK0adMAiF/wHDlyBIGBgdiwYYNhD4iIiIocK+cMKNvCOU4IQURULMjlcnh5eSE8PFxreXh4OLy9vfVu4+Pjg8ePH+P169eaZTdv3oRUKkWlSpUMGi8RERUfhhq39NSpUzr77Ny5c5b7BDi2KRFRScbkXCFj3RwRkXH9888/6N69e562mTRpEn755RcEBwfj+vXrmDhxIiIiIjB69GgAYsXC4MGDNe0HDhyIsmXLYujQobh27RqOHj2KyZMnY9iwYbCwsCjU4yEiouLLUOOWRkdH52mfAMc2JSIqyZicK2S579XKyjkiovwKDw/H5MmT8eWXX+Lu3bsAgP/++w+9evVC06ZNoVAo8rS//v37IzAwEHPnzkXDhg1x9OhR7N69G25ubgCAqKgorbGDrK2tER4ejpcvX6JJkyYYNGgQevTogeXLlxfeQRIRUYlhiHFL87JPgGObEhGVZBxzzkBs8RrSa5xRiYiosK1duxZDhw6Fg4MDnj9/jl9++QVLlizB2LFj0adPH1y6dAl169bN837Hjh2LsWPH6l0XEhKis8zDw0OnKywREZUuhhq31NnZOU/7BDi2KRFRScbKuUImedOx9VOTHBJzHHOOiChfli5div/973949uwZNm7ciGfPnmHp0qW4cOEC1qxZk6/EHBERUX4YatzSli1b6uxz3759We6TiIhKNibnCtubSnMnyQvjxkFE9Ja6c+cO+vfvDwDo27cvZDIZlixZAnd3dyNHRkREpZEhxi399NNPsW/fPixcuBD//fcfFi5ciP3792PChAnGOEQiIjIwdms1EEsk59CClXNERPmRkJAAKysrAIBUKoW5uTkHvSYiIqPp378/YmNjMXfuXERFRaFu3bq5Grd0/PjxaNKkCcqWLYt+/fph/vz5mjbe3t7YuHEjZsyYgZkzZ8Ld3R2hoaFo3rx5kR8fEREZHpNzhUw9RquVJKfkHBER5VdYWJhmrB6VSoUDBw7gypUrWm3effddY4RGREQlSGpqKu7duwd3d3eYmOT/o5Ehxi3t27cv+vbtm++YiIio5GByrpCp50+yyrFyjoiI8uujjz7Sejxq1CitxxKJBEqlsihDIiKiEiQxMRHjx4/H2rVrAYhjvlWrVg2ffPIJKlSogKlTpxo5QiIiKk045pyB5NitlRNCEBHli0qlyvHGxBwREWVn2rRpuHTpEg4fPgxzc3PN8o4dOyI0NNSIkRERUWnEyrlCJnnTr9VCkmrkSIiI3m4pKSlQKBSa8eeIiIhya/v27QgNDUWLFi00/78DgKenJ+7cuWPEyIiIqDRi5Vwhk2h+5lQZx8o5IqL8ePbsGbp16wZra2vY2trC29sbd+/eNXZYRERUgjx9+hTly5fXWZ6QkKCVrCMiIioK+UrORUZG4uHDh5rHZ86cwYQJE7Bq1apCC4yIiEifadOm4fz585gzZw4WL16MZ8+e6Yw5R0RElJ2mTZti165dmsfqhNzPP/+Mli1bGissIiIqpfLVrXXgwIEYOXIk/P39ER0djU6dOqFOnTr47bffEB0dja+++qqw4ywx1F+05Vg5xzHniIjyJSwsDMHBwejatSsAoGvXrqhbty7S0tJgampq5OiIiKgkWLBgAbp06YJr165BoVBg2bJluHr1Kk6dOoUjR44YOzwiIipl8lU5d+XKFTRr1gwAsGnTJtStWxcnT57E77//rneq8NJE8qZjK4vhiYgM4/Hjx2jUqJHmsYeHB+RyOR4/fmzEqIiIqCTx9vbGyZMnkZiYCHd3d+zbtw9OTk44deoUvLy8jB0eERGVMvmqnEtLS4OZmRkAYP/+/Xj33XcBiB+QoqKiCi+6txor54iI8kMQBJiYaF++TExMoFKpjBQRERGVJGlpaRg5ciRmzpyJtWvXGjscIiKi/CXn6tSpgx9//BHdunVDeHg45s2bB0CsZihbtmyhBljScPxYIiLDEgQBHTp00ErQJSYmokePHpDL5Zpl//zzjzHCIyKiYs7U1BTbtm3DzJkzjR0KERERgHwm5xYuXIj33nsPixcvxkcffYQGDRoAAHbs2KHp7kpERGQIX331lc5Mej179jRSNEREVBK999572L59OyZNmmTsUIiIiPKXnGvbti2ePXuG+Ph42Nvba5aPHDkSlpaWhRbcW40TQhAR5cvnn38Oa2trY4dBREQlWPXq1TFv3jycPHkSXl5esLKy0lr/ySefGCkyIiIqjfKVnEtKSoIgCJrE3IMHD7Bt2zbUrl0bnTt3LtQASxp2ayUiMqxy5cqhXbt2ePfdd9GzZ09UqFDB2CEREVEJ88svv6BMmTI4f/48zp8/r7VOIpEwOUdEREUqX8m5nj17onfv3hg9ejRevnyJ5s2bw9TUFM+ePcOSJUswZsyYwo6zxJDkep5WVs4REeXHjRs3sGPHDmzZsgUTJkxA/fr1NYm6+vXrGzs8IiIqAe7du2fsEIiIiDSk+dnon3/+QatWrQAAmzdvhpOTEx48eIB169Zh+fLlhRpgSSVh8o2IyCDc3Nwwfvx47N+/HzExMZg0aRKuXr2K1q1bo2rVqvj0009x8OBBKJVKY4dKREQlgCAIEDjkDBERGVG+knOJiYmwsbEBAOzbtw+9e/eGVCpFixYt8ODBg0INsKTJdbdW/gNARFRgdnZ2GDBgADZu3Ihnz57hp59+gkqlwtChQ+Ho6Ij169cbO0QiIiqm1q1bh3r16sHCwgIWFhaoX78+fv31V2OHRUREpVC+knPVq1fH9u3bERkZibCwMPj6+gIAYmJiYGtrW6gBllxMvhERFSUTExP4+vri+++/x4MHD3DgwAHUrFnT2GEREVExpB6Kp2vXrti0aRNCQ0PRpUsXjB49GkuXLjV2eEREVMrkKzn31Vdf4fPPP0eVKlXQrFkztGzZEoBYRdeoUaNCDbCk4XwQRESGdevWLQwYMADx8fE66+Li4jBw4EDcvXsXjRo1QtOmTY0QIRERFXfff/89Vq5ciYULF2rGLV20aBGCgoI4TA8RERW5fCXn+vbti4iICJw7dw5hYWGa5R06dCj13zRJOF0rEZFBLV68GK6urnorte3s7ODq6orFixcbITIiIiopoqKi4O3trbPc29sbUVFRRoiIiIhKs3wl5wDA2dkZjRo1wuPHj/Ho0SMAQLNmzeDh4VFowREREWV29OhRvP/++1mu79evHw4ePFiEERERUUlTvXp1bNq0SWd5aGgoatSoYYSIiIioNDPJz0YqlQrz58/Hd999h9evXwMAbGxs8Nlnn2H69OmQSvOd8yvxcl03xwkhiIjy5cGDByhfvnyW68uVK4fIyMgijIiIiEqaOXPmoH///jh69Ch8fHwgkUhw/PhxHDhwQG/SjoiIyJDylZybPn06Vq9ejW+++QY+Pj4QBAEnTpzA7NmzkZycjK+//rqw4ywx2KuViMiw7OzscOfOHbi5ueldf/v2bU5ORERE2erTpw9Onz6NpUuXYvv27RAEAZ6enjhz5kypH0ObiIiKXr6Sc2vXrsUvv/yCd999V7OsQYMGqFixIsaOHVuqk3NqOefoWDlHRJQfrVu3xvfff4/27dvrXb98+XK0atWqiKMiIqKSxsvLC7/99puxwyAiIsrfmHPPnz/XO7ach4cHnj9/XuCgSjJOCEFEZFjTpk3Dnj170LdvX5w5cwZxcXGIi4vD6dOn0adPH4SFhWHatGnGDpOIiIqx3bt3a01spxYWFoY9e/YYISIiIirN8pWca9CgAX744Qed5T/88APq169f4KBKBY45R0SUL40aNcLmzZtx9OhRtGzZEg4ODnBwcIC3tzeOHTuGTZs2oXHjxsYOk4iIirGpU6dCqVTqLBcEAVOnTjVCREREVJrlq1vrokWL0K1bN+zfvx8tW7aERCLByZMnERkZid27dxd2jERERFq6d++OBw8eYO/evbh9+zYEQUDNmjXh6+sLS0tLY4dHRETF3K1bt+Dp6amz3MPDA7dv3zZCREREVJrlKznXpk0b3Lx5EytWrMB///0HQRDQu3dvjBw5ErNnzy71Y/2wZysRkeFZWFjgvffeM3YYRERUAtnZ2eHu3buoUqWK1vLbt2/DysrKOEEREVGpla/kHABUqFBBZ+KHS5cuYe3atQgODi5wYCWZBIAkxwkf2K2ViCg/5s6dq3e5nZ0datWqBV9fX0il+Rq1gYiISol3330XEyZMwLZt2+Du7g5ATMx99tlnWpPeERERFYV8J+eIiIiMYdu2bXqXv3z5Eo8ePUKdOnUQFhaG8uXLF3FkRERUUixevBhdunSBh4cHKlWqBACIjIxE69at8e233xo5OiIiKm2YnDMAiUSSc+UcJ4QgIsqXCxcuZLkuKioKAwcOxJdffolffvmlCKMiIqKSxM7ODidPnkR4eDguXboECwsLNGjQoNQPz0NERMbBfj8GwCHniIiMw8XFBfPnz8fBgweNHQoRERVDp0+fxp49ewCIX6j7+vqifPny+Pbbb9GnTx+MHDkSKSkpRo6SiIhKmzxVzvXu3Tvb9S9fvixILKUMK+eIiAyhYsWKiImJMXYYRERUDM2ePRtt27aFn58fAODy5csYMWIEPvroI9SuXRuLFy9GhQoVMHv2bOMGSkREpUqeknN2dnY5rh88eHCBAnobcLZWIiLjuXTpks7se0RERABw8eJFzJs3T/N448aNaNasGX7++WcAgKurK2bNmsXkHBERFak8JefWrFljqDjeKpLcdGzlmHNERPkSHx+vd3lcXBzOnj2Lzz77DMOHDy/iqIiIqCR48eIFnJycNI+PHDmCLl26aB43bdoUkZGRxgiNiIhKMU4IQUREJUqZMmUgyaJEWSKRYNSoUZgyZUoRR0VERCWBk5MT7t27B1dXV6SmpuKff/7BnDlzNOtfvXoFU1NTI0ZIRESlEZNzhsBurUREBnPo0CG9y21tbVGjRg1YW1vj4sWLaNiwYdEGRkRExV6XLl0wdepULFy4ENu3b4elpaXWDK3//vsv3N3djRghERGVRkzOGUDucnPs1kpElB9t2rTRuzwuLg7r1q3D6tWrcfHiRSiVyiKOjIiIirv58+ejd+/eaNOmDaytrbF27VrI5XLN+uDgYPj6+hoxQiIiKo2YnDMQFs8RERWNgwcPIjg4GFu3boWbmxv69OmDX375xdhhERFRMeTo6Ihjx44hLi4O1tbWkMlkWuv/+OMPWFtbGyk6IiIqraTGDiAoKAhVq1aFubk5vLy8cOzYsSzbHj9+HD4+PihbtiwsLCzg4eGBpUuX6rR7+fIlxo0bBxcXF5ibm6N27drYvXu3IQ9Di0QCSHKqjOOEEERE/2fvvuObqt4Hjn8ymqR70gGUDkYZZZaNIMgSFzh+IgguUFwo8vWrIi6Q4UIRFRQFcSIqiIuvUJay9xLKHi2lpS2lu02a8fvj0pTQDW1T4Hn7ysvce88990lakubJc865bKdPn2bKlClERkYybNgwfH19KSwsZPHixUyZMoX27ds7O0QhhBB1mLe3d4nEHICfn59DJZ0QQghRG5xaObdo0SLGjRvH7Nmz6dGjB5999hmDBg3iwIEDNGrUqER7d3d3nn76adq0aYO7uzvr169nzJgxuLu789hjjwFgMpno378/gYGB/PzzzzRs2JCEhAQ8PT1r7XFVarVWIYQQl+WWW25h/fr13HbbbXz00UfcfPPNaDQaPv30U2eHJoQQQgghhBBV5tTk3Pvvv8+oUaMYPXo0ADNnzmT58uXMmTOH6dOnl2jfvn17h2qI8PBwlixZwrp16+zJufnz55Oens7GjRvtKy2FhYXVwqOpKqmcE0KIy7FixQqeeeYZnnjiCZo2berscIQQQgghhBDiijhtWKvJZGLHjh0lJlwdMGAAGzdurFQfu3btYuPGjQ6Tg//2229069aNp556iqCgIKKjo5k2bVq5E4MbjUaysrIcbldCJYVzQghRY9atW0d2djYdO3akS5cufPzxx6Smpjo7LCGEEEIIIYS4LE5LzqWlpWGxWAgKCnLYHxQURHJycrnnNmzYEL1eT8eOHXnqqafslXcAx48f5+eff8ZisbBs2TJeeeUVZsyYwdSpU8vsb/r06Xh7e9tvoaGhV/TYKpWbWz7xiq4hhBDXq27duvH555+TlJTEmDFj+OGHH2jQoAFWq5XY2Fiys7OdHaIQQgghhBBCVJrTF4RQXVJmZrPZSuy71Lp169i+fTuffvopM2fOZOHChfZjVquVwMBA5s6dS0xMDPfddx8TJ05kzpw5ZfY3YcIEMjMz7beEhIQre1CVcWBpzV9DCCGuYW5ubjzyyCOsX7+effv28Z///Ie33nqLwMBA7rjjDmeHJ4QQQgghhBCV4rTkXEBAABqNpkSVXEpKSolquktFRETQunVrHn30UZ577jneeOMN+7GQkBCaNWvmsPpSixYtSE5OxmQyldqfXq/Hy8vL4XYlKkouCiGEqF5RUVG88847nD592uELGyGEEEIIIYSo65yWnNPpdMTExBAbG+uwPzY2lu7du1e6H5vNhtFotG/36NGDo0ePYrVa7fsOHz5MSEhIrS2LrgJUsuCDEELUOo1Gw5AhQ/jtt9+cHYoQQgghhBBCVIpTh7WOHz+eL774gvnz5xMXF8dzzz1HfHw8jz/+OKAMN33ggQfs7T/55BN+//13jhw5wpEjR/jyyy957733GDFihL3NE088wblz53j22Wc5fPgwf/75J9OmTeOpp56q9ccnhBBCCCGEEEIIIUR5tM68+NChQzl37hyTJ08mKSmJ6Oholi1bRlhYGABJSUnEx8fb21utViZMmMCJEyfQarU0btyYt956izFjxtjbhIaGsmLFCp577jnatGlDgwYNePbZZ3nxxRdr74GpQArnhBBCCCGEEEIIIURFVDabTdJIl8jKysLb25vMzMzLmn+uzRvLWWd7CG9VXvkN38i8zAiFEOLqcqWvq9cieU6EEKL6yGtqSfKcCCFE9arJ11Wnr9Z6rZIlIYQQQgghhBBCCCFERSQ5VwNktVYhhBBCCCGEEEIIURmSnKsBkpsTQgghhBBCCCGEEJUhyTkhhBBCCCGEEEIIIZxEknM1QCmck3U2hBBCCCGEuB7Mnj2biIgIDAYDMTExrFu3rsy2a9euRaVSlbgdPHjQ3mbBggWltikoKKiNhyOEEKKWaZ0dwLVI5pwTQgghhBDi+rBo0SLGjRvH7Nmz6dGjB5999hmDBg3iwIEDNGrUqMzzDh065LDaX7169RyOe3l5cejQIYd9BoOheoMXQghRJ0hyTgghhBBCCCEu0/vvv8+oUaMYPXo0ADNnzmT58uXMmTOH6dOnl3leYGAgPj4+ZR5XqVQEBwdXOg6j0YjRaLRvZ2VlVfpcIYQQziXDWmuA1M0JIYQQQghx7TOZTOzYsYMBAwY47B8wYAAbN24s99z27dsTEhJC3759WbNmTYnjOTk5hIWF0bBhQ2677TZ27dpVbn/Tp0/H29vbfgsNDa36AxJCCOEUkpyrATKqVQghhBBCiGtfWloaFouFoKAgh/1BQUEkJyeXek5ISAhz585l8eLFLFmyhKioKPr27cs///xjb9O8eXMWLFjAb7/9xsKFCzEYDPTo0YMjR46UGcuECRPIzMy03xISEqrnQQohhKhxMqy1BnjacvBS5Vfc0GaD7CTwql/zQQkhhBBCCCFqxKVzTttstjLnoY6KiiIqKsq+3a1bNxISEnjvvffo1asXAF27dqVr1672Nj169KBDhw589NFHzJo1q9R+9Xo9er3+Sh+KEEIIJ5DKuRow1Pa/yjX83wvwfgvY9W3NBiSEEEIIIYSodgEBAWg0mhJVcikpKSWq6crTtWvXcqvi1Go1nTp1KreNEEKIq5ck52qADkvlGm6dq/x/5Rs1FosQQgghhBCiZuh0OmJiYoiNjXXYHxsbS/fu3Svdz65duwgJCSnzuM1mY/fu3eW2EUIIcfWSYa01QKVWgbUKJ9hsNRaLEEIIIYQQouaMHz+ekSNH0rFjR7p168bcuXOJj4/n8ccfB5S54BITE/n6668BZTXX8PBwWrVqhclk4ttvv2Xx4sUsXrzY3uekSZPo2rUrTZs2JSsri1mzZrF7924++eQTpzxGIYQQNUuSczVArZKCRCGEEEIIIa4HQ4cO5dy5c0yePJmkpCSio6NZtmwZYWFhACQlJREfH29vbzKZeP7550lMTMTV1ZVWrVrx559/csstt9jbZGRk8Nhjj5GcnIy3tzft27fnn3/+oXPnzrX++IQQQtQ8lc0mZVuXysrKwtvbm8zMTLy8vKp2cl46h9/rRzPrscqf4xYAL1ShvRBCXGWu6HX1GiXPiRBCVB95TS1JnhMhhKheNfm6KiVe1S31UNUScwBIflQIIYQQQgghhBDieiTJueqm0VX9HCleFEIIIYQQQgghhLguSXKuumlkGj8hhBBCCCGEEEIIUTmSnKtul1M5J4QQQgghhBBCCCGuS5Kcq25ql8s4SYa1CiGEs82ePZuIiAgMBgMxMTGsW7euzLZr165FpVKVuB08eLAWIxZCCCGEEEJcCyQ5V900l5OcE0II4UyLFi1i3LhxTJw4kV27dtGzZ08GDRpEfHx8uecdOnSIpKQk+61p06a1FLEQQgghhBDiWiHJueomyTkhhLjqvP/++4waNYrRo0fTokULZs6cSWhoKHPmzCn3vMDAQIKDg+03jUZTSxELIYQQQgghrhWSnKtulzWsVQghhLOYTCZ27NjBgAEDHPYPGDCAjRs3lntu+/btCQkJoW/fvqxZs6bctkajkaysLIebEEIIIYQQQkhyrrpdTuWcTeacE0IIZ0lLS8NisRAUFOSwPygoiOTk5FLPCQkJYe7cuSxevJglS5YQFRVF3759+eeff8q8zvTp0/H29rbfQkNDq/VxCCGEEEIIIRzlGs3YLuRcMvJM5BrNTo6odFpnB3DNuaxhrZKcE0IIZ1OpVA7bNputxL4iUVFRREVF2be7detGQkIC7733Hr169Sr1nAkTJjB+/Hj7dlZWliTohBBCCCGEKENajpHtJ88zoGUQarWKw2ez8XZ1wU2nweCi4c+9SRxPzWHUDZGggrn/HGNIuwa8t+IQXSL8yTGaeT/2MAD/6d+MT/8+RniAO3+MvaHMv/OdRZJz1U2jc3YEQgghqiAgIACNRlOiSi4lJaVENV15unbtyrffflvmcb1ej16vv+w4hRBCCCGEqKvyTGY2Hz9Hr6b10GpKDtI8mpKDn7uO3QnnOZScQ0SAOzdHB5OcWUDX6asAuLlVMGezC3jl1ha8/tt+/k1UpoF5667WBHkZeHjBNgA0ahUWa3GR06zVR+33P1lzDIDl+886XH/GhSTd/jNZpGYbCfQyVOOjv3KSnKtuanlKhRDiaqLT6YiJiSE2NpY777zTvj82NpbBgwdXup9du3YREhJSEyEKIYQQQghRraxWG5uPn6NDmC8Gl/IXNUvJKmDxzkQ0avhuSzyfjoghsp476bkmABLS8/llVyILt8bTuJ47Ewa14O/DqWw8lkZ0A2+i63szdVlchTH9tV/5svzuOZsc9r+0ZJ/D9sWJuctxIClLknPXvMspjZRRrUII4VTjx49n5MiRdOzYkW7dujF37lzi4+N5/PHHAWVIamJiIl9//TUAM2fOJDw8nFatWmEymfj2229ZvHgxixcvdubDEEIIIYQQwu5EWi6/7k5keJdGBHoasNls5Bda+CD2MN9viSfXZAGgVX0vfn2qB1qNGovVxmNfb8fXXcfUO6PZfyaLu2Y7LpI26MN1ZV7zWGouo7/e7rD96+4z1fq49Fo1RrPVvq1WQccwP7aeTMfgoqaRnxueBhcOn83mkR4RPHJDBB+uPML8DSe4s30DAjzq3mgWSc4JIYS47g0dOpRz584xefJkkpKSiI6OZtmyZYSFhQGQlJREfHy8vb3JZOL5558nMTERV1dXWrVqxZ9//sktt9zirIcghBBCCCGuA3P/OUagpwGtRsWu+AzuaFufiUv38XSfJrQL9UWtgkAvA+dzTfR5by0A326Ox0WjIimzoNQ+95/JosnE/9E82JODydn2/T/vOF0jj6FDIx/83PWsPZSCuZQquFE3RFBoseLqouHRXpHsScjgi3UnOJdr5Jcne6BRq9hx6jzdIv1RqytXIPXa7S157faW1f1Qqo3KZpOlQi+VlZWFt7c3mZmZeHl5Vb2DN7yr1l7vDRPiK24nhBBXqSt+Xb0GyXMihBDVR15TS5LnRIirz/4zmbjptIT7u1FQaEWvVXM+z8TUZXHc1iaEEG/XcqvWioy5MZLP/j5eCxGXNGtYewpMFpqHeBJd35vIl5c5HL+jbX1mDWvvsC8tx8jUP+NIzzXxxh2tiAhwr82QK60mX1elcq5OkPyoEEIIIYQQQghxtTJbrKUuhACw4Wgaryz9l2AvA4/1iqRjuC+7EzL4aftpXhrUnBBvA/fN3cyWE+kO5/m4uZCRVwjAkp2JlY6lvMRcgIeO9+9tR7fG/mjVKiImLCuzrVatcqhsOzTlZuatP8E7fx2y72sX6sM9MQ1ZvPM0r97Wkg6NfB362P1af+asPcZtbeqzNzGD29rULyUmPR8MbVfpx3ctksq5UtR+5ZwXTEio+nWEEOIqId/elyTPiRBCVB95TS1JnhMhase2k+n836eb0KpVxI6/sUTVV2Z+IW0nrSi3j55NA1h3JK3aY7ujbX2Gd2nEfXM3A7B8XC+igj0d2qRkFfDTjtPc2jqEez7dSFqOCV83F7a83A+dVs2/iZm8s/wQLwyMIrqBkuuw2WwUFFpZeyiFG5oG4GlwqfbY6yKpnBNCCCGEEEIIIYSoRm/+cQAPvZbn+jez7ztwJov0XBM3NA0gJasAVOCpd+Gv/Un0bhaIRqOioNBCoKeBX3cn8uwPuwEwW230eW8tjeu5cyw1l1dubcHAVsH0fGdNhXFcTmIu3N+Nk+fyAPjp8W54GVwI83fj5LlcVh44y8M9InDXKymfV25tQVqOiWZBHiX6CfQy8FSfJgD8Na4XZouNYO/ilUyjG3jz9SOdHc5RqVS46jQMah1S5bhF6SQ5VwPOBffEP7niceB2UrwohBBCCCGEEELUmrNZBcxbfwKAx3pFcjargNlrj9kXQfh+dBeGf7HF4ZyeTQNIzTZyMFlZBXT+hhMl+j2WmgvAlD/jmPJnXJnXf+XWFqUe/+KBjg6rnQJEBrhzPE1J+A3v0ojvNsczuH199p3O5FhqDh3DfFGplIURmgd70TzYsaprdM/Iip4OgDq5iun1QpJzNeDETbOZsGA+c3UfODsUIYQQQghRi2w2m/0DUmnHzDYzLuqSw38sVgtWmxUbNjQqDSqVCovNQmK2MseQn6sfrlpXNCoNBeYCDFoDBeYC8s352LCh0+hwUbug1+gxWUxoVBpyC3Ox2CwYLUayTFno1DoyjBn4u/rj7uKOChU5hTmYLCbMViUunUaHTqPDbDVzNu8sVpsVtUqNRqXBYrPY22QaMzFoDcRnxRPgGoBapSavMA9XF1d89D6oUJFpzORcwTm8dd6YbWYKLYUYtAb7+UUx27BxMvMkfgY/UMHN4TfX6M9ICHH9KXptjkvK4lyOUhW3cGvxooxT/oxz2AZKJObAscKttMRcef7+b2/++/Netp5Ip3mwJ6N7RmJw0TB//QmOpykJPYOLmn4tg/hsZAzjF+0mzN+dV25rQfNgL3YnnKdPVCAqlYpHeynJtr4tDPRtEVSlOETdJMm5GuDt7csKa6cqnCGVc0IIIYRwjqLky8VsNhsmq5LgsWEj35yv3L+wPzk3mUC3QDIKMkjOS8bX4ItercegNXAm5wwGrYEgtyBMFhNJuUkUWArIK8zDzcXN3mdCdgIalQZvvTfnC84Tlx5Ht/rdKLQUolKpiM+KR6vWYrFZ8HDxQKPSUGgtJMOYgdFiJN+cT1JOEq5aV1QqFf4Gf3ILcym0FuKp88TDxQOLzUJuYS555jysNismiwmTxUS+OR+z1UywezAmq4kcUw4Zxgz0Gj16jZ7cwlwKLAXoNDo0Kg1Zxiw8dcocPVq1ljxzHgXmApJykwBw07qhUWswWUxo1Vp89D64qF04V3AOs9VMkFsQeYV5pBWkYbPZCHIPQq/RY7aaKbQWkl+YT3ZhdqV+XmqVGqvNWr2/BHWIXqNnYNjAMhOcQojr19+HUzmbWcC9nULt+2w2G6fP59PAx5XEjHxmrTrCQz3CaVW/eB7492MPM2vVEYe+pt4ZzcyVxfsuTcxVh8h67tzepj4BHjpGdA1DpVLx0bD2zFl7jAe6hQEwomsYI7qGkZ5rYsofBxh64bENbBXMvjcGolYXvxbe1FyScNcySc7VAF93nbNDEEIIIUQdYbVZsdlsWG1WXDTFFVNFa3KdKzhHljGLhOwEjBYjLf1bkpCdwPmC89T3qE9CdgLrE9fTOqA1ZqsZs82M0WIk7lwcOo0OV60rXjovzuWfAxVkGjNRqVTo1DpMVhN+ej/Wnl5Ltikbb703qgv/adVaUvNT7fF46jwxW82oUNkTcgaNgUJrIRabpVaeqyVHltTKdYrsTdtb+ca5ZR/KM+fZ7xstRnILHRufzDrpsJ2cm1z5616iJhJzOrUOdxd3jBajUkVnM6NRafDR++Cl98Jqs2K1WbFYLaQXpOPu4o6X3ovcwlxS8lIwaAy4ubjhpfMipzAHs9WMWqXGU+eJi9oFq82K0WLEVeuKxWah0FKIl94Lm81GbmEuOo0OvUaPxWbB3+CPyWpCr5GhVUJcDwoKLWw7mU6ncD8MLppS2xw4k8WUPw+w8dg5AF5YvJfJg1vxQLdwnv9pL4t3nnZYUOGnHad5c0g0ahWsOZjCyriUEn1O/OXfK47d182FFiFe+Lrr+OvfZCwXrWraMsSLZc/2LHFOkJeBN+5oVWK/n7uO9y9ZrfTixJy49klyrgb4uF4fK5UIIYQQ14qi6jGTxUSmMZOU/BSifKM4k3OGEPcQdqbs5HzBeU7nnMZL58Xh84cxWoz4GnzBBsl5ySTnJmOymDBoDbhqXVmfuB4AL50XWaasK45x2YllV9xHpjGzzGPZppKVWwWWgkr1W8+1nr2q7VKuWmWYY7B7MAXmAmwoiUqtWoufwY8cUw7HMo8B4Kv3xYaNYPdggtyCKLQWokZNviWfAnMBvnpffAxKVdrW5K1467xxc3HDoDEQ7BGMGjWF1kKsNisqlQqtSoubixtuWjdMVhM6tY7swmyScpLIMGbga/Al3CscrVprr7Y7X3BeGdZpM6NGjdlqxoYNdxd3e1VgiHsIHjoPTmefpp5rPQA0ag2eOk/0Gj0ZxgwlOeXiicmqDBl107oR4BpAvjmfs3ln0aq19psKFXqNHm+9t5Kkslqw2JRhrt56b6w2K6eyTuGp88Rqs+Lu4o7JoiSw9Fo9Bo0Bs82MyWJi05lN1Peoj5/BDz+DHyqVyp501Wv0FFoLsWFT7lsKARySxqVVUgohRGXlmyyoVJSZaLvU9GVxfLXpFGoVjLohgjyThTcHR6NWq9hwNI1zuSaeWbirxHmv/bqfHKOZxTuV+eEuXVDh1aWXn3xr4OPKsM6hJGYUkJpdwNibmmKyWPm/TzfZ28z4v7bcHdPQvm212lCrVSRl5rNgw0lGXqiME6KyJDlXA7QaNV6GKjy1siCEEEIIccWsNitHzitDVEI9Q4nPjufw+cPsPLuTILcg3FzcOJ553F6dpVVrMVvNhHqGkpKXQoBrAIk5idUe1+Uk5nRqHU18m5CQlWAf7tgluAt+Bj/SjemcyjpFbmEuXjov+1BQnUaHm9YNlUpFYk4i3npv+1DM9Px0Mk2ZeOm88NX70jawLccyjvHP6X8IcA1gYPhAWvq3xGw1c67gHLmmXLRqLcHuwahVajxcPPDWK0OECiwF6DV6NCqNfS4yjVr5EGa1WVGhYl/aPsauHssDLR9gVOtR1fRMXjvCvcOrfE6UX1S5x11ULrioXegX1q/ksYvmuNNpikd4XJyUKyKJOSFEVVisNjQXKrwKLVZueHs1Oq2aDS/e5FD5lZCexx97k3DXa0jOLOD3vWcI9DSw49R5AKw2+HydModbl0h/sgsKK6xue+evQ9X6WA5PGYTVZiszsXhs2i0UWqxkFRRS75KFE4oea4i3KxNuaVGtcYnrgyTnaoifu67c4Q9CCCGEqFhSThK5hbnsTt2N2WpmVfwq3F3c2XRmExHeEfgZ/NictJl6rvUwW82k5JcculIWs9UMQEJ2AsBlJea6hXQjwDUANxc3bDYbUX5RJOYkkmHMYG3CWrRqLY+2fhRvvTf1Perz1pa36BjckXaB7TiWcQx3F3f0Gj3uLu4MCBuA0WLkdM5pmvk2A2q2iql/WH8eb/t4if1NaVrueW4ubmUeK4q1Tb02/D307ysLUAghRJ217WQ605bFcTApmw/va8eAVsHsS8zkXK4JgIz8QuUzMbD/TCa3zlpfoo+E9PxS+y6tUq4yVKry616m3hnNmoOprIw7S69m9TiUnMXZLCMA0+5sjU5b/vutRq1Co9ZUuipQiKqQ5FwN8ZXknBBCCFGm8wXnScxJpJV/Kw6dP8SWpC0cOHeATGMmnjpPMowZbE7aXG4f+8/tt98/k3umwmvGBMUQdy7Ovnpl30Z9CfUMpYFHA0LcQ1Cr1NiwUWAusM/vdjjjMKezTzMgbABeei/cXdwByDHlKPO5Gfyq9LgX3rbQfr9vo74ljrup3eyJOZAqJiGEEDWroNDCzJVH6N8yiJgwX0AZonk8LZfIAHfUahUp2QWM/X4X93YMpVtjf9z1Wochno99s6NEv5uPn8PXTYcNG8M/L7nqaU0Y368Zo3tGsnx/MmsOpdAxzJfDZ3NYtD2BhY92JSbMl/u7OA43LSi0SLJN1AmSnKshvm5VWRRChrUKIYS4tiVkJ/Bd3HccPn+YbcnbLrufBh4N8NR5cjD9IAC3Rt6qTEJvysFL78XgxoOJ8I7gdPZpTFYT/gZ/3F3c8dB5XNb1Qr1CS91/uf0JIYQQteXI2Wzq+7iSazTj76G3Dz+92Nx/jvPp38f49O9jnHzrVgAmLNnHou0JPNu3KY/f2JgH5m3lYHI2W06kV/raT363s9oeB8CWl/sS6Knnf/8mE13fm17vrinRpr6PK646DUPaN2BI+wb2/a/c1gK9tvQEnCTmRF0hybkaUrXknBBCCHFtMFlMuKhdOJ19mnn/zqPAUkB6fjqbkjZVfDLKPHB+ej8KrYXotXqSc5OZ1WcWGrWGcK9wGnk1qlQ/TXybXMnDEEIIIa5q20+mc89F1W13dWjA+/e2A+Dvw6nsTcjg6ZuasDshw97GbLGyMi6FRduV6R4+XHWED1cdqZH4nuvXjDyTmc/+OU67UB+m3hlNrtHC/jOZfLT6KPMf6sTquLPMWn0UgEBPPSqViltahwAw5sZIPvv7OEPa1Se6gTenz+czuF39Uq9VVmJOiLpEknM1xFtWbBVCCHEdyDZlk2nM5MC5A2QYM3hz85uVPve2yNv44/gfTOwykbua3sWprFM09W1qX6GzaJEBIYQQ4nqUYzTjrtOgUpWseCuyaFs8U/6I48uHO9GmoQ86rZr9ZzIdEnMAS3YmEhPmi6fBxT6n24ZjaWw+XlwN125yLDlGc408lilDonll6b+M6NqIN25vhVajTNvwQPdwPPRa++fnzhF+PNwjAoBQX1dyTRa6RfqXeA4mDGrBc/2aSeWbuGZIcq6GeLnKaq1CCCGuXRkFGXy8+2MWHVpUqfavdHmF2xrfRlJOEmqVmkifSACm95xub9PUV1mI4OKVJYUQQojrUVxSFrfMWsewzo2YdmdrQEnWHUrOom1DHwotNkxmKy8u3gdQIhlXmktXP704MVfU/+UK8tLbF1e4sVk92jfyYebK4qq7+7s04u4ODTG4qB0SbQ18XMvs099Dz6u3tSzzuCTmxLVEknM1xMsgHyyEEEJcW5Jzkzl8/jBJOUlM2TKlzHbPdngWF7ULX+3/itT8VABub3w7bi5uMtxUCCGEuMBms5WoCFu84zSHz2ZzOiMfmw2+3xLPLdEhLNl1miU7lVXF7+rQgN92nyHIy1AjcQV66knJNpZ5/OfHu1Hfx5X560/QNtSHJTtP89rtrViy8zRz1h7j+QFRtG7ojberC4t3nuaJG5ugUqlw1UkyTYiySHKuhlRtWKtUzgkhhKi7rDYrj654lK3JW0sc6xzcmWHNh9HCvwXJuck08GhAsHswAD56H17Z8AoAbi5utRqzEEIIURfZbDbOZhn57897yDGaWfx4d9QXFmqw2Wz856c9Jc4ZMc9xtdOiJF1iRn61xPRgtzD6NA9k9tpjjLohgoGtgnnt13/5etMpAE6+dSs2m43l+5Ox2qBjuLJS+SsXqtpub6vM9fZcv2aMvakpOq0yZPXhHhH2IapCiPJJcq6G+LhJ5ZwQQoirl9lq5st/vyS9IJ3FRxaTb3b8AKBWqdk4bCPuLu72fQ08Gji0uTniZlbGr6RrSNdaiVkIIYSo62avPca7yw/Zt5/8bieHU7KJ8Hdn0/Fz1X69t+5qTYiPK4eSs5i27GCpbfq3DOaGpgH0jgq073u4RwRfbzpFq/peAKhUKm6ODin3Wmq1Cl0pK8IKISomybka0qGRr7NDEEIIIS7LsYxjjFszjpNZJx323xp5K38e/xOAWyJucUjMlUav0fPRTR/VVJhCCCFEnbYr/jxv/3WQdqG+PNO3CbvjMxwScwB/7U8G4HhqbrVc09Og5YWBUbz5Zxwms5XeUYEEexu4sVk97urQkDMZ+ayKS6Fn0wDmrT/BobPZdAwv+dk1IsCdTRNuwtdNVy1xCSHKJ8m5GuLrXoUXMVkQQgghRB1wJucMZ3LO8FjsYxRaC+37OwR24OUuLxPlF0WUbxR/HP+DcR3GOS9QIYQQoo4rKLRw5+yNgLLwwqd/H6uV697Rtj4ju4Vza5v6ZOUXEuxdPC9dgIeeAA89bRr6AMrw1NLmvSsS4l32Yg1CiOolyTkhhBBCsCd1DyOWjXDY1yW4C2/1eosA1wD7voejH+bh6IdrOzwhhBDiqmC12jiTmc/Hq49We9/DOjeinqeeWauOlNmmkZ8yx6ufuw6/ShSMlJWYE0LULknOCSGEENex5NxkRq8YzamsUw77v7r5K9oHtpc/2oUQQogqmLYsji/Wn7js85sHe3IwOdu+HRPmy45T5wEwuKh5/MZIdsWfp3dUIIOigzGarfyy8zSzVh+lZ9MAHuwefqUPQQjhBJKcq0HrDH3oWbCGs8G9CUpeW05LGdYqhBCidhVaC9l5diePrngU2yXvQ4+2fpQOQR2cFJkQQghRd6VmG9lwNI1bWofYVyW92JUk5lY814tmQZ6sOZjCwwu2AaACXrm1BYu2JfDEjY1x02n5ZlQXh/PGD4ji2X7N0MhiDEJctSQ5V4O+CprAhMODmd5QXX5yzmKqtZiEEEIIgHe3vcvCgwvt25Hekfyn439oW68tXjovJ0YmhBBC1F2PfbOdXfEZnEjLZUTXMB7/dgeDooNp09CHez/bVKk+FjzcCbPFxuivtzvsLxqS2qd5IO/c04YPYg8zaXArWtX3ZnTPyHL7lMScEFc3Sc7VIC83PadtgWSa0itufO4Y+Deu+aCEEEJc18xWc4nE3MPRDzM+ZrwToxJCCCGuDrviMwD4ZvMpPrww91vRsNPK+Hh4e3pHBQIQN/lmtp9KZ+S8rQDoL6rEu7djKPd2DK2mqIUQdZ0k52pQ0co4qXmVGLaanSzJOSGEEDVu/r/z+f7g9/btJ9s9yWOtH3NiREIIIcTVIc9ktt9Pz63a6Kdn+zblXK6Rm1sF2/e56jR0DPMj2MtAI383medViOuYJOdqUIiPsvR0cmWSc6acGo5GCCHE9a7AXMBHuz6yb3/W/zO61+/uxIiEEEKIuutMRj5HUnKICvLEy1XL0M82X1Y/O1/tX+bKqa46Df+80EeGpQpxnZPkXA1qUs8DgH3JBRU3zk2t4WiEEEJczxKyE7jvj/vs25uHb8bdxd2JEQkhhBB1W/e3Vleq3ZO9GzN77bEyj/u4upR7fmkLSwghri/yKlCDOoX7olWr2JPnX3Fjo1TOCSGEqDkP/u9BskxZAHjqPCUxJ4QQQlziXI6RkfO28PueM1UatvpE77KnJ1r7fG/UUhUnhKiAVM7VIK1Gjb+HjrNZNuKG76BFAx94t4wXbqu59P1CCCHEFVp2fBmp+UqFdrhXOEsGL3FyREIIIUTd89Hqo6w7ksa6I2lVOs9Dr+X5Ac3YdvI8r9/ekqFzN5OabeSN21sSHiBfhgkhKibJuRoW4KHnbJaRZKsXLdwDym4oyTkhhBA1wGaz8camN+zbP9/xMy7q8ofXCCGEENcDm81GWo6JXKOZ8AB3cowVfyYL8TZQz1PP3tOZ9m2VSsXTNzW1t1nzfG/2ns6ga0QlRlAJIQSSnKtxAR56AFJzjOU3lOScEEKIGnAi8wT55nwA3un1DnqN3skRCSGEEHXDc4t2s3T3GQBubFaPvw9XPA/4d6O78PxPe+zbseNvLNHGQ6+le+NyCjOEEOISkpyrYUXJubQKk3OWWohGCCHE9WbFqRUAdK/fnUERg5wcjRBCCFF3FCXmgAoTc1PvjOb+LmEA3NG2PjvjM2ga6IGHXj5SCyGunLyS1LAAT2XJ7LTsCiYUtRYq/zcbQStVDUIIIa5cgbmArw98DcANDW5wcjRCCCHE1Wt450b2+yO7hdPI3432ob5OjEgIcS2R1VprWL3KVs798y7MvxneagTnyl6GWwghhKiszUmbyTZl4+Hiwb1R9zo7HCGEEKJWpWYbsdlspR77c29SlfpSqYpXXNWoVdzUPAhfd90VxSeEEEUkOVfDKj2sFSB+E5gLYNMnNRyVEEKI68GahDUA3N74dplrTgghxHXlh63xdJq6ko9WH3XYX5Ss++/Pe0o7TQghnEKSczWsSsm5InG/11A0QgghrhcZBRmsjl8NQJ/QPk6ORgghrm2zZ88mIiICg8FATEwM69atK7Pt2rVrUalUJW4HDx50aLd48WJatmyJXq+nZcuW/PLLLzX9MK4pLy3ZB8D7sYcJf+lPlu5K5OtNJ+k8bRUHk7PoEuHn5AiFEKKYJOdqmL/HhTnnciqYc+5iuSk1FI0QQojrxaOxj5JhzCDQLZCOwR2dHY4QQlyzFi1axLhx45g4cSK7du2iZ8+eDBo0iPj4+HLPO3ToEElJSfZb06ZN7cc2bdrE0KFDGTlyJHv27GHkyJHce++9bNmypaYfzjVDp3H8qDtu0W5e+3U/qdlGbp65js3H08s8t3O4H7HP9arpEIUQwk6SczWsqHLufJ4Jk9la+ROtVWgrhBBCXKTAXMDBdKUC49Wur+KidnFyREIIce16//33GTVqFKNHj6ZFixbMnDmT0NBQ5syZU+55gYGBBAcH228ajcZ+bObMmfTv358JEybQvHlzJkyYQN++fZk5c2YNP5prQ1ZBISZL+Z+n8gstDttv3N7Sfj/baKZpkCe/PNmdyAB3vnhAvuQSQtQsSc7VMH93HX7uOmw22Hs6A9oOq9yJ5vwajUsIIcS1a/Kmyfb7Nza80YmRCCHEtc1kMrFjxw4GDBjgsH/AgAFs3Lix3HPbt29PSEgIffv2Zc2aNQ7HNm3aVKLPgQMHltun0WgkKyvL4Xa9uvOTDVU+56EeEYzrp1QvFiXq2jfyZfXzvenXMqha4xNCiEtJcq6GqdUqukX6A7Dh6DkYMgfu+77iE99rBvnnazg6IYQQ15qE7AR+P67MXeqqdXVYXe56ZbPZMCUklLlinxBCXK60tDQsFgtBQY7Jm6CgIJKTk0s9JyQkhLlz57J48WKWLFlCVFQUffv25Z9//rG3SU5OrlKfANOnT8fb29t+Cw0NvYJHdnU7lppbqXbP9WvGE70b88nwDgCM69eMA5MH0uXC5zchhKgtWmcHcD3oFO7Ln/uS2JeYCSoV6D0rPsmUA3+/CzdPq/kAhRBCXDMOpx+2358/cH6NX89qNKLS6SpMAubt3EXBvr3YzBbAhueAAejK+OBoycykMDkZfWQkKhdlSG7BocNoPNzR+PiQv28frh06oAKyV6/Bmp9PdmwsWn9/DG1aow8Px1pQgGv7DqTMeI+MhT/Y+/Z9YCSGFi0piDuAe/fu2AoLwWajYO9eXDvEoHZzQxsUiNrdnZxVqyhMPos5JYXMX37BNSaGRl/Ox5qdjTUvj+zYlbjf0ANbYSHnv/8efWRjzCkpqN3d8HvkEVLefpus//2F16BBoFZjzc5C4x9A7qaNmI4eI2TqFMypaeTt3IFn//7krluPNScbj969sZktWM6n43Pvvaj0erJXrsS1TVssGRkYjx7BdOw4ru3a4dKwIfrICLT16mGzWinYf+DCD8aC8egx9FFR5O/ZjaF5c1QuLljOn8d49BiWrCwMLZrjEhyMa7t22MxmsFopOHwEQ6uWYDaDzYZKp8yda7NasRUWotbryf93P7kbNqCLjMCjZ08sWVm4BAZiPn8elUqFxscHm82GrbAQlYsL1uxszCkpZC37H1633YYuPEz5ndi+nYL9B7BmZ6Px9sIQHQ02G6ZT8bh16QxWK/n79uE1YAA2k4lz879EFx6O16CbUWm1FBw4gEuDBmi8ve0/X/P581jS0tBfmLvLZjJhPp8BgEtQoL2dzWbDkp6Oxs+PwsREJVaNBpVOh0twMMZjxzDFx+PZp4+9PTYb1txcVGo1anf34r6sVrgwof/FCg4fRuPlhSUrC33TphX+G7FZLKBW16mEus1mQ6VSYcnJUZ4fg0H5vVBX/fv9or5K22/NzUXj4VEdITvNpY+trMcLEBUVRVRUlH27W7duJCQk8N5779GrV/E8Z1XpE2DChAmMHz/evp2VlXXdJeis1qp9CdO3RSDRDbwd9rnp5COyEKL2yStPLQjyMgDKvHMAqCr5B83mTyQ5J4QQokqS85SqiptCbyI6IPqy+rBkZWFOTUXfuHGJY4VJSWh8fTGnpWE8coTE/zyPLS8PTUAALiEhuISE4HHjjaS88w6WzEx87huK9223cWrESId+Ut59DwDfkSOxZGaQ9Vs1rVT+00/lHj7/9Tel3lfMK/fc/B07ONSmrePOd0pvmza7eK6pjDJiSpr4iv1+7j/FKzvmbtxkv3/u8y/KjKesfuuytNmzq3zOmUu2z06bhuV8ydEFLg0aUJiYWG5frjEx5O/YUbUAXFygsNBhl/sNN1Dw77+oPT0pTEgAQO3hgUqnw5Je+iTzrjExeN7Uh/SvvlaSuN7eeN0yCHPyWUwJ8ZiOHgPA0KoV7t27kf7Nt9gKCnC/sReurVphycrGo+cNFMTFkTrzQ+Wa3t7UGzsWjxt6kP7d9xTs3YvxyBH8x4xB4+ND8uuvAyjXSUklb/t2tIGBoFajrVePggMH0Hh7YzObsV4YAql2c8Oal2ePW1uvHubUVIfHovH1xa1jR7JjY5Vtb2/ce/Yk648/ip+2Bg1w73kDhUlJ5P6tVITpW7bAfDYFy7lzJZ4fz/79cGnQEH1UFD53Dqncz6YOCAgIQKPRlKhoS0lJKVH5Vp6uXbvy7bff2reDg4Or3Kder0ev11f6mtcam83GnXM2sicho9Ln+Lrrai4gIYSoApVNxniUkJWVhbe3N5mZmXh5eV1xf5uPn+O+uZuJrOfO6v/0BosZ/ngWdn1b4bncPQ9a33PFMQghhDNV9+vqtaCmnpNpW6ax8OBCRrYcyQudXqjSuTabjbwtW4l/6CEAvAcPxnfYfeibN+fMf18gd8MGhw/t1zptUBDms2edHYYQ1xW1mxtNVq9C4+NTpfOc+T7TpUsXYmJimH1R8rlly5YMHjyY6dOnV6qPe+65h/T0dFavXg3A0KFDyc7OZtmyZfY2gwYNwsfHh4ULF1aqz+vtvfd8ron2b8aWeqxliBf3xDSkcaAHPq4uDL4wJ13c5Jtx1WlKPUcIIS5Vk6+rUjlXC3zdlG9kMvIufOuq0cLgTyqXnFs8CgKaQkjbitsKIYS47m1J2gJA+8D2VTqvMCmJhMefwHjokH1f5q+/kvnrr9UaX2XoGjfG/5FHSHrtNbBYKmzvPXgwand3NL6++I95TBl+mJND7qbNWLOzcO/ZE13DhmQtX0HGTz+hb9YMj169lArA5CRcGjQgZ916rNnZuDRogD4qCmtONm4xMcqwXRcX1Ho9hUlJZP76G5m//Ybp+HG877oLQ/PmmBIS8H/4IfK2b0fj549rdCtM8fGgUuHaujXGY8ewZGbi2q4dqFRYs7IwnzvHufnz8X9kFLpGoeRu3oKhZQtsJhPWvDxsBQWovbw4/cSTaPz8cO/RA21AAB49b8Can48lKxtzyllMp+LJ3bSRgv0HcG3XDr8HHiB/7x4K4+OVqsT0dLJjV2I6cRyv227HJSQYt06dQKPBePgwtoICzi/8gYK4OAytWuESHEze1q3kbd9OwJNPovbwoPD0aXSRkdiMBdjMFrQB/qjd3Dj71tv25OXFiUy3rl0ByNu8Ga9bBuE9eDCagAClIisri4JDh7Dm5eHerTsaD3eMx09QmJyE9y23YMnOVoaz/vsvand3dA0akPPPP2gDAtC3aIGuYUOy164l7eNPMJ06hVuHDuRu3IjK1RW1uzsqjQa/kSNQubpy9s0pABiio1G5uJC/axee/fuj8fHGFJ+Aa5s2GI8exaVBAzx690btaiB/3z5S3noblU6HxtsbjY83xiNHS/zOGdq2oWDPXrzvvJOcdeuwpKVd1u+6x003kXMhGQOARlPh77zGzw/U6su+Zql9+vhgycjA0KYNnv37kTrjffsxfYsWGOPilA21GqzKCpi6yEhMx49X6TregwejcnNF7eaGOfksBQcPYjp2zKGN1+23X3XzQ44fP56RI0fSsWNHunXrxty5c4mPj+fxxx8HlOGmiYmJfP3114CyEmt4eDitWrXCZDLx7bffsnjxYhYvXmzv89lnn6VXr168/fbbDB48mF9//ZWVK1eyfv16pzzGq4HRXPbqrP8dGEWf5srQ9kKLlebBnni5ukhiTghRZ0jlXCmqOxuamm2k09SVqFTKtzMGlwtvAm94l39ikXu/hpaDrzgOIYRwluvt2/vKqInnJDk3mf4/90etUvPP0H/w1pf/PpMy432Mx45hiG5F2qyPqny94EmTsObkkPLuuxW29br1Vuq/87Yyb5lBme4hZ8MGEkaNRtekMSGTJ5P1x5/4jhiBPjICgMKUFIxHjmA5dw7P/v1Ru7pWOUZRO2w2G+azZ9EGBdWpOdOK2KxWMJvtc+jV1DVy1qzBtV07tP7KZPIFhw6h8fXFJdBxvruLnyOlYnWLkoBs0gRrfj7m1FR0jRrZj1uzslB7eYHNhs1sRq3Tkf/vfhLGjCFw/Hhc27ZB4+ODNiDA3r/NYqFg/350jRo5VKGZTp7EkpmJPiqK3E2blHh9fR0ei+l0ItbcXAxRzcp9vIWJifa5Iy3Z2Wg8HedVrmiOtItZc3NRubld9u+Ps99nZs+ezTvvvENSUhLR0dF88MEH9vnjHnroIU6ePMnatWsBeOedd5g7dy6JiYm4urrSqlUrJkyYwC233OLQ588//8wrr7zC8ePHady4MVOnTuWuu+6qdEzOfk5q2/HUHG6a8XeJ/be2DuHj4e0dfresVhsqVcl5/YQQojw1+boqyblSVPcTbrPZ6DxtFanZRn5+vBsdw/2UA4eXwz/vwult5XfQfSx0fgx+HgXdnoRWd15xTEIIUZuutw8IlVETz8kvR37htY2v0SagDd/d+l2J46b4eAr278fz5pux5eVxKKZjla8RMnUqalcD7r1uROOhTIpfmJzM0d7KxPn133mbMy+8CEC9Z58h9cNZBIx9mnpPPXUFj0wIIcon7zMlXW/PycKt8UxYss++veGlm0hIz6OrrLwqhKgmMqz1KqdSqWgR4kVqdirrj6YVJ+eaDYTs5OLknEYPFmPJDjZ+BGlH4PRW+GmrJOeEEEKUan2iMtypQ1CHEsfy9+7l5L1DAVC/MQlrZma5ffk/+igu9UNI/ehj1AYDDefMKbOKxiU4mBYHlWFvVqMRl08+wSUoGP/HH8fr1ltxuc5WCxRCCFE71h9JIzWngF92neGfw8ULl3i7utDAx5UGPlJxLYS4OkhyrpYUFSjOXHmEcf0u+nDjcdGKSwYvyE2lVHmlr/wlhBBCAKTlp7Hi1AoA6nvUdzhms1pJ+/Qz+3ZpiTnXjjF43TyInLVryV2/Hu87h6CPjMR32LAqxaHW62n855+g0aBSqexD84QQQojqtDshgxHztpR67OPhVZt3VQghnE2Sc7UkNbu4Is5ktqLTqpWNJn1h4DRo0g++ur3sDi6eD2Hxo3D358Xbh1dAXhq0G17NUQshhLhabDqzyX4/zDPM4VjKO+86Tjpfinpjn8G9S2d8h96LJSMDbb16lx2LSit/XgghhKgZFquNMd9sZ+2hMooagJ5NL/89TAghnEHt7ACuF2/f3cZ+PzO/sPiAxgW6PQX1oqDc6f8uSs7t+xGsFlg9FQ79Bd//Hyx9As4dK/t0IYQQ17QVJ5WqOZ1aR7f63Sg4cIDD3XuQMmMG6QsWVHi+2s0NAJWLyxUl5oQQQoiatO1kOivjUjBbZep0IcS1Q77ariVtQ33wMmjJKjCTmV9IPU99Ka3KeYO5dCWhfxfDP+847ss5C/6NrzhWIUQdYLVAViL4yJBAUTlHMo4A8PmAz1GpVJy4624Azn3+RaXOV+lcaiw2IYQQoroYzdYyj7Vu4O1QFCGEEFcLqZyrRT5uOgAy802lNwhsUfbJ8Zsct8/+W7LNjw8oN1mAVwjnO7ke/ngOjNmXd/7SJ2Bma4j7vXrjEtckq83K2byzAARr/Eie/GaJNrqwMJpt30bwm5Pt+9x79MB3+DA8+vVF37RprcUrhBBCVFVBoYXP/znOg/O3ltnmt6d70LL+tb8yrRDi2iOVc7XI111HfHoeqdllJOfu/AxWT4Hd31XcWUFWyX25qXDgV8g/D25+VxasqNihv+CPcXDnpxDZ29nRiLpmwa3K/13cYODUqp+/d5Hy/3UzoEU581EKAcSlx2G2mlGr1BQ8+QLGvftKtHEJDUXj4YHv//0fHt27k/nHn/jcczdaf38nRCyEEEJUzQcrD/PZ38fLbaO6dLSREEJcJaRyrhaF+ipLeSek55XewKs+DJlduc7Kq8Y5taGKkYnLsnAoZCfB14Nr97qnd8D/XoSCkqstihqQkwpJey7//LQjZR8z5UFhgeO+A79C2tHibU1pQ+CFcPTRzo8AuDWvaamJOQCb2Wy/79KgAQFjHpPEnBBCiKvGusNpDtsqFSx8tCttGno7KSIhhKg+kpyrRWH+ymTb8WUl54pE3FhxZyfXlX1s0QjY80PpxwoyYdWbcPZAxdeoS05ugJQ4Z0dRN3xxE2z5FFZNrritKF9lhoDPjIbPesHZ/cp27Ovwx/jKDx+3WZV/c3NucEyo/vMuTAuBqUFgvTB3ypGVytD0j2OK22lKmQfs1Cb47v9KLgJzcUybZsNPD8OJfyD1cPH+Y2tgRnOl8rOyjDmQsFWGzNdRNpuNval7iUqwMXLm/rLbFRaWeUwIIYSo6/ILLQ7b2yb2o1tjf9qH+jgnICGEqEaSnKtFjfyU5NypipJz93wJTQeW3ybnbPnHfxlTfD/tCOSlQ/K/sGYarHsP5nRTPrT//W5xYqA8aUdhxwIozK+4bXXLiIcFt8DsrrV/7bqkIFNJyBQpL8GauBOWT1SSKs5WWkLHaoG1b8HxtbUejt3xtfBOBPy7xHG/2ahUyxXFbb5Q2XZyA5zeDhtmwvZ5kJlQuesYs5V/c2f3wb6flH2H/qcMYS9iulAJW1rS/eQ6mNUBMi5cr7AAvrwZjqyAH4YricL4zUrScEZzSNqrxL58AuxfAl/dDp90gg/bQdYZ+GaIUvG5cGjF1ZcZ8fD3O/D9vTCvP+z7uXKPWdSqs3lnyS7M5s1vLeW20zdtUksRCSGEENXrUHI2J9Jy7dvDuzQiwEMZXXBfZ2XxrBuaBDglNiGEqA5OT87Nnj2biIgIDAYDMTExrFtXdkXY+vXr6dGjB/7+/ri6utK8eXM++OCDMtv/8MMPqFQqhgwZUgORV10jP3cA4s/llt/Q3R/u/xHuXwxjd17+BU+uhzO74OOOShLi0x5KxVWRr26HNVNgsq+SdLiUxQy555T78wfA78/Cpo8vP57Lde6iIX7lJRIt5tqr7KlKkrIgS0mq7arEXILl+fsdJSFTpLQ5NfLS4as74PM+ys/qvWaQk3Jl170SWz9XfvfO7HLc/+9iWDu94iHBJ/5REkrlsVqUBHRpP/vCfDi6UklMf3MXLLgNvr1HScx9P1SZn/Hnh5Xkc0EWvOENUwLhvSbw53g48FtxX2oNfNG3eDv7LOz8Bt7wgZ8fUZ77xB2Qekj5t1UkYXPx/bVvweQAWHifY5xFw9QtZcxHmX4M1lyYt277vOL9qQeV7fkDlaRhTjJ81hP+ea9kH+dPKD+Pi73VSKmgyz+v/PuO36LsN5uU6rwfhivXLRoqv3FW6fEJpzqTU/6/kfAfF+E7fDiBzz1XSxEJIYQQ1Wvcot0O2watxn6/RYgX21/px1ePdK7lqIQQovo4dUGIRYsWMW7cOGbPnk2PHj347LPPGDRoEAcOHKBRo0Yl2ru7u/P000/Tpk0b3N3dWb9+PWPGjMHd3Z3HHnvMoe2pU6d4/vnn6dmzZ209nAo1ujCs9fT5fCxWGxp1BROWNu2n/D/qVjj0Z9UvWDQhfWUsHAbP7ASdhzJ81C9S+WB+bBU0vw3yLiTpDi+HXv9VEhkrXoGELXD3PAiOrnp8l6MwF/SepezPh086Q0AU9Hoe/noJbv8QQtoqx3NSYPWbEPMwNOhQ8XWykiD7DDSIgexkOLoKou+GrERIO1xcxVSepL2QcgDOn1SSakdWQLvhjkk1swm0uor72vVtKYlRFcT9Ae71oFEX5Tq/Pu1YfVWYqyShHlujbOekwrx+0Kg7hHWHNveCVq9URHmGQHiP8uOwWpVEjsGr9OGWl1r2/IX/vwCjVihVaS4GJYFVZM8ipU9s0OFB2PcjtLhDWZG4KMn1yArlMRax2ZTEnU8j2Dwbts5V4s9Ogptegeh7wC9CqSrb833JuI7GOm5fPIy0yPb5yq3In+Mdj8/rV3z/38XKrSK5qaXvXzQCzp+C/PSyz92zEHo8C8tfrvg6a6aUvn/9+yX3LRxafH/HAnj+iJKE2/hRybbJeyEzEbwbVByDqDWJOYnlHndt0wbXNm1qKRohhBCi+h1PdRwN4qpzrDEpqqITQoirlVOTc++//z6jRo1i9OjRAMycOZPly5czZ84cpk+fXqJ9+/btad++vX07PDycJUuWsG7dOofknMVi4f7772fSpEmsW7eOjIyMGn8slRHsZUCnUWOyWDmTkU/ohWGuFfq/L5VqnpqUmwJvh4PBW0nENemvJOYADv5R3O70NqVK6fuhSjUPKBV5r2eUXslVnr/fAZVaSaaVx3rRUC1TGcm5k+uVIXgZ8XDib6UC6dt74L8XJuNf9l84sBR2fg1vlDKUz2yCuN8gvCd4BsGc7kqiZMw/sOQxpUJp0yeQUvZ8Tko/Rtg2D1reoVQwAbhf9LPb/T20uhMyTinzhS26v/hYYEvlsQ2ZA/6NLzx2K+z4smRiCODUeuUGSvLqt6eVxOGlzuyEuN+VhKLNqiTxzp9UklZph5XHvHiU0vbGF6HPy0ryS6VSEjQbP4aH/lCqLrd9UdzvU1uVfV71oefzSvsjscqQ6sGfKL9LRU5vhUk+ysqlo1dC+kUrbf1yUWI9N1VZnfSfGdDituL98wcoiVaNHpoOAI96SqXXxbKTlP+vnqLcOj1aemKuLrq0srAstTG0+4fhyr/zssy/Gcbtrfq/d1FjTmSecHYIQgghRI0ymh1Hz7i6aMpoKYQQVyenJedMJhM7duzgpZdectg/YMAANm7cWKk+du3axcaNG5kyxbFKZPLkydSrV49Ro0aVO0y2iNFoxGg02rezsrIqdf2q0qhVNPB15URaLqfPVyE5p9XDc/th749K4qyogqrb09D9GaVSautcpYrtSljNxRVyl1YWXezjjiX37fkBGnZUqplcfZRhhL5h0Oxm8AwuTvYUyUoqHqbX+dHiRI7VoiTg8tJg/QdKBVRR4giUOdRKyc1hu+gNu2hoYG6KMmzv0nm1THlKYiegKbQdBj6hynO6ahL4hoN3aHEF067vlMQclJ+YO3cMfrhfmYfMlKPM91Uk96Jhpb8+qdxKk3JhDrmvh8Azu5RhlOveK36eyjN/QPnHF40off/GWY5DFf9+GzQ6pcowvGdxFV5pP/NPLho6sOMreHo7fHePsn3psM0ihXlK4rMs62Yo/8+MVyriLla0YurprWWff7Ftn1fcprYMmALJ+2DvImdHUrHyEnMAzQaU/PcsnMZms7EuseL3OSGEEOJqlJCexz2flvxsaJDknBDiGuO05FxaWhoWi4WgoCCH/UFBQSQnJ5d7bsOGDUlNTcVsNvPGG2/YK+8ANmzYwLx589i9e3elY5k+fTqTJk2qUvyXy99dx4m0XM7nlTG3VFm8G0LP8UqCoig5128SaLTQ+h7ldnJ91YayVqelj5e+X+2iDJ08vBza3qd8qL/hOfjioiGBOalKci4nBd5r6nj+6R2OybW/XoSYh5RqNKsZAluA3kup5CtNaRPeTwspvr9mKgQ0K57zq6iqrMjWz8p4wJf4qBJDZSsrMx7e9K++/qpq9ZvK/8tbEfhSmQnKqqPXO/d6jkNXh8xREsBFiSzPECXZ+39fKQn12FeL2/Z9XUkyFz3/1aHtcKWSNCtRqTwM7aJc93L4NIKgaBj0LqidPl2puODUiT3U++cAXZNLT5aGvFWyCl0IIYSoy2w2GyqViow8Ez3fWVNqG0nOCSGuNU4d1gqguqT6oujFuDzr1q0jJyeHzZs389JLL9GkSROGDRtGdnY2I0aM4PPPPycgoPKr9UyYMIHx44uHDWZlZREaGlq1B1JJPm7K/GJVTs4VCW4DXZ5Qhj1qLvnxhd8A/zmkVJ/9864yHNLZrIWw+8JCCEVJxc2fOLb55k4lgbeulEnsL61WO7pSuV0sKBq4goUgShsKKq4OXZ5QhtVenOQqy5h/lEUO/nqx5LGWg+Her5X7GQlKslHtAifWQlDr4nnZWtwBnUbD13co24PeVZJWPz0I9ZrDmL+VhSHeiVCOB7Z0rDDrf9GXAD2egcjeSuK6x7PFcw/q3JV5H295D44sh+1fwp2fKontrZ9ByyFKsv6zXmWvGPtSgjLHYYvblcrbiw2cBr8+BfXbK/NJrp1e/G/0hudg23wwlpLUHrVSGfIt6pTzz09g7IHSF8oJevllfOrIgkhCCCFEZeyMP89jX29nwqAWrDp4tsx2kpwTQlxrnJacCwgIQKPRlKiSS0lJKVFNd6mICOWDb+vWrTl79ixvvPEGw4YN49ixY5w8eZLbby9eKdF6YXVPrVbLoUOHaNy4cYn+9Ho9en3tTCLq565Mop+RV3h5HahUMOitso97Biv/v32mUqETv6n42O2zoHEfWPWmMuk+QO8JylDUotUYLxUQBWkXJu/XGsBccHlxlyczvvTEXGWd/bf6YqkJAc2URTQO/1W5RQNK036EMtea1lWpWvr27uJjjboV/5w9giCn7D9kSujyOPR9TVlNtqzfgfK4+cODfyiJImuhstBESBvHVYFHr4aglvB2BJgvrHLr1UBJXN39OayaDA07KUmo01uh8xhl/r+iOeRGLFZWE147HQw+MGKJMozXv2nxv4XW/6fM17ftc2UBlfx02PKZUjkZfRdE3QJufkr1mG+4Ml9iQaYyjBeg1V3F8fqEKjeA0E7K//97TJmzsPX/KVWeT25RFk0pSqiN+xf0Hsr9i+fa829S/vMX0ka5XazrE8X3W9yu3Ir0fa34/nMXfu8z4mHpk9D1SWUItU+YsmBH63tKv6bGBe66qHru9lnKXIjYlNeDfm8o1aMu7srcfunHlQVgJDFXJxkOnHR2CEIIIUS1yMgzcddsZQjrf37aU25bf49KLKgmhBBXEacl53Q6HTExMcTGxnLnnXfa98fGxjJ48OBK92Oz2ezzxTVv3px9+/Y5HH/llVfIzs7mww8/rLFquKrwdVfeSM5k5Nf8xe79WlkAIay7ksApquC5+3PoMgYStirzvem9Sk/MdH9GSQac2a1Ul7W/H1IOwuwLq2Z6NYRn9ygrcu74UkmqVHYYaE16Zrcy99m5o0pVU166Mg/cxdoOU1Y6NWWXPL9hZ0jcoSzcENETCguUlVs3zYZWQ2DfT8VtB72rrD66Zjq0+T9lGG7RIg2gJOVuekW536SfUtVYvz10eAAyTyurlP77s7KK7O/PKD+LwZ8oc7MZvGHlG0q1ZI9nHGMcf1BZkbbzo0rF5JldSqVX0aq5sa+DpVCpyFr3HtTvoKw8evN0Jan0+zOQnwEDpyvJvoeXKX2c3Q9t7oPY15TfnXvmKcOHXdyUxNaBX+GPccpKuM0GKfPiuQco52v1xavjth+hLCTR52Vl7kGAl88oi0X4NIK+F1W63faB8v92w4v33fKOskiHSqUkk5r0U4ZFo1L6e3ob6C6afNDrwlDlG54r3teojMUTom4uvh/RCxJ3KpVz5XEPUCrmigQ2dzzuUa/4vlqj/HxsluKEXU3yaaQs2HG5NFp44bhSmVdUZecbXnzcL/KKwhNCCCGEqIwpf8ZVql3f5oH0alqv4oZCCHEVUdlstisYD3hlFi1axMiRI/n000/p1q0bc+fO5fPPP2f//v2EhYUxYcIEEhMT+fprZbjZJ598QqNGjWjeXPlgvH79esaNG8fYsWNLLApR5KGHHiIjI4OlS5dWOq6srCy8vb3JzMzEy8vrih/nxVbsT+axb3bQyM+Nf17oU619X7b888rQ0rP7laqbkDZw89uOCYdLZSeDWqskLUCZL869njI8LjsJAlvBD8OUYz3GKZU8qybDgKnKRP+FecqiB3+/W3KYa9gNJRNcHUfBrHZK5V7TAUrC67NeyvE2Q5VKn7DucNNrSrIpaa+ySEWnUUqCJ/O0Mhx2w4cQ2QceWKokI/b8oCQijiwHVEplYUSv0h9zYT64uCorsh74ValQ6/pUyfm3jq+FQ38pQxgvHVJ4Lcg/D66+zo5CXGVq8nX1alUdz0lc8xZlHgt6+WX8Hhh5ueEJIcRVRd5nSrranpNBH64jLqnshfnu7tCQKUOicdXJkFYhhHPU5OuqU+ecGzp0KOfOnWPy5MkkJSURHR3NsmXLCAtTKm2SkpKIj4+3t7darUyYMIETJ06g1Wpp3Lgxb731FmPGjHHWQ6iy9o2UpEZ8eh4msxWdtg5MrO7qC4+trdo5RcNni3gEKv9vf9GqoK9nKEk8j0Clmqjb00qyqv39YDEr1715mlKdt3aaUvl14wtKpdaZXcpccqkHlUnsVSp45axSxaZSK8MJn90DOo/iBOHFLh0u6BepDJ0MbgONb1L2qVTQ7kICMaxbxY/ZxVX5v1avzJFXlsjeyu1aJYk5IeoEk+Uy5y4VQggh6iCdpux5x+fc34Gbo4MrnJtcCCGuVk5fEOLJJ5/kySefLPXYggULHLbHjh3L2LFjq9T/pX04W4CHDjedhjyThSU7T3Nf50bODqnmqFTFww2huIrs0uSOiwH6T75oh14Zqgklhya6GIrvXzz0rjJcXMueh0sIIa4yqfmpFbRwWmG8EEIIUWUumrKLFgwuGknMCSGuaXWgbOv6olKpMFuVD0wHk0uZ70wIIYSohNS8ipJzQgghxNVDW07lnNFc+srkQghxrZDknBP8d0AUAOfzZEiSEEKIy5NxYK+zQxBCCCGqjbWMgm+9Vs0NTUuZxkYIIa4hTh/Wej3ycXMB4HxeoZMjEUIIcbUKeny6s0MQQgghqsXstUfZeiK91GN7Xh+AwUUWgRBCXNukcs4J/Nx1AJzPlco5IYSoK2bPnk1ERAQGg4GYmBjWrVtXqfM2bNiAVqulXbt2NRugEEIIcY16569DZR6TxJwQ4nogyTknaOjrBsCh5GxSsgqcHI0QQohFixYxbtw4Jk6cyK5du+jZsyeDBg1yWDG8NJmZmTzwwAP07du3liKtPJXBUHEjIYQQwsksZY1nFUKI64gMa3WCqGBPYsJ82XHqPIt3JvJE78bODkkIIa5r77//PqNGjWL06NEAzJw5k+XLlzNnzhymTy97+OiYMWMYPnw4Go2GpUuX1lK0leM9eLCzQxDXOYvFQmGhTOEhqo+LiwsajVRRXWvKm4f7lye712IkQgjhPJKcc5L+LYPYceo8B5KynB2KEEJc10wmEzt27OCll15y2D9gwAA2btxY5nlffvklx44d49tvv2XKlCkVXsdoNGI0Gu3bWVk19/of8NRTqPX6GutfiPLYbDaSk5PJyMhwdijiGuTj40NwcDAqVdkre4qrS3opU/20buDNkie746KRgV5CiOuDJOecpFmQBwCHk7OdHIkQQlzf0tLSsFgsBAUFOewPCgoiOTm51HOOHDnCSy+9xLp169BqK/dWOn36dCZNmnTF8QLk7dxVfgObDBESzlOUmAsMDMTNzU2SKKJa2Gw28vLySElJASAkJMTJEYkr9fk/x9mXmMnQTqH2fW1DfdiTkMHbd7eRxJwQ4roiyTknaRroCcDxtBwKLVZ58xFCCCe7NIFgs9lKTSpYLBaGDx/OpEmTaNasWaX7nzBhAuPHj7dvZ2VlERoaWs4ZZTv/46LLOk+ImmaxWOyJOX9/f2eHI64xrq6uAKSkpBAYGChDXK9yU5fFAbAq7iwA3SL9+f7RLmQVmPF2dXFmaEIIUeskOeckDXxc8dRryTaa2Xs6k5gwX2eHJIQQ16WAgAA0Gk2JKrmUlJQS1XQA2dnZbN++nV27dvH0008DYLVasdlsaLVaVqxYwU033VTiPL1ej76ahpoWFhoraCGVc8I5iuaYc3Nzc3Ik4lpV9LtVWFgoybmrWEJ6nv1+rskCwI1R9VCpVJKYE0Jcl6Rcy0nUahW9ouoBsPn4OSdHI4QQ1y+dTkdMTAyxsbEO+2NjY+neveRE1F5eXuzbt4/du3fbb48//jhRUVHs3r2bLl261HjMhVaZZF/UbTKUVdQU+d26Noyct6XEvnB/dydEIoQQdYNUzjlRxIU3oKTMfCdHIoQQ17fx48czcuRIOnbsSLdu3Zg7dy7x8fE8/vjjgDIkNTExka+//hq1Wk10dLTD+YGBgRgMhhL7a0phfi7lfTzVN4uqlTiEEEKIy3HyXF6Jfb5uUjEnhLh+SXLOiYK8DQAkZ1Y0PEkIIURNGjp0KOfOnWPy5MkkJSURHR3NsmXLCAsLAyApKYn4+HgnR1nMnJ9HaR9hGi34EtOpeDwHDqj1mIQQjnr37k27du2YOXNmpdqfPHmSiIgIdu3aRbt27Wo0NiHqIg+DfDQVQly/ZFirEzX0USa1PXUu18mRCCGEePLJJzl58iRGo5EdO3bQq1cv+7EFCxawdu3aMs9944032L17d80HeYHVWPqXOoboaHyH3ivDvoSoApVKVe7toYceuqx+lyxZwptvvlnp9qGhofYvB2rLgAED0Gg0bN68ucSx3r17M27cuBL7ly5dWuI1xmQy8c4779C2bVvc3NwICAigR48efPnll/Z5CMX1y2azkZ5rqrBdkJehFqIRQoi6Sb6ecKLWDb0BOJKSQ1ZBIV4GKeUWQghRCWUk51Rq+c5NiKpKSkqy31+0aBGvvfYahw4dsu8rWiG0SGFhIS4uFf/N5ufnV6U4NBoNwcHBVTrnSsTHx7Np0yaefvpp5s2bR9euXS+rH5PJxMCBA9mzZw9vvvkmPXr0wMvLi82bN/Pee+/Rvn17qQS8zk1c+i/fb4nny4c70ScqEIBwfzeHoa0/julGgEf1LJokhBBXI/kr3okCPPQEX/iG6GhKjpOjEUIIcbWwmZQKhAK3S75j08p3bkJUVXBwsP3m7e2NSqWybxcUFODj48OPP/5I7969MRgMfPvtt5w7d45hw4bRsGFD3NzcaN26NQsXLnTo99LKs/DwcKZNm8YjjzyCp6cnjRo1Yu7cufbjJ0+eRKVS2atw165di0qlYtWqVXTs2BE3Nze6d+/ukDgEmDJlCoGBgXh6ejJ69GheeumlSiXDvvzyS2677TaeeOIJFi1aRG7u5Y3kmDlzJv/88w+rVq3iqaeeol27dkRGRjJ8+HC2bNlC06ZNL6tfce34fosyLcQHsYcB2H4yvcScc50jqpbMFkKIa40k55wssp6yKMTxVBnaKoQQonJURmWY2MY7HT/0SuWcqGtsNht5JrNTbjabrdoex4svvsgzzzxDXFwcAwcOpKCggJiYGP744w/+/fdfHnvsMUaOHMmWLSVXoLzYjBkz6NixI7t27eLJJ5/kiSee4ODBg+WeM3HiRGbMmMH27dvRarU88sgj9mPfffcdU6dO5e2332bHjh00atSIOXPmVPh4bDYbX375JSNGjKB58+Y0a9aMH3/8sXJPxiW+++47+vXrR/v27Uscc3Fxwd1dVuAUCotV+Tf58IJtTo5ECCHqHvmK3ckiAtzZeOwcx1Olck4IIUTlqIxK5ZzVoLPv09arBxqNs0ISolT5hRZavrbcKdc+MHkgbrrq+VN33Lhx3HXXXQ77nn/+efv9sWPH8tdff/HTTz/RpUuXMvu55ZZbePLJJwEl4ffBBx+wdu1amjdvXuY5U6dO5cYbbwTgpZde4tZbb6WgoACDwcBHH33EqFGjePjhhwF47bXXWLFiBTk55f9duXLlSvLy8hg4cCAAI0aMYN68efZ+quLIkSP07t27yueJq88vu06z8kAKM+5ti8Gl6u83Rcm57AJzdYcmhBBXPfmK3cki63kAUjknhBCi8lSFygcbm2vx/DxhCxfKQhBC1JCOHTs6bFssFqZOnUqbNm3w9/fHw8ODFStWVLiqc5s2bez3i4bPpqSkVPqckJAQAPs5hw4donPnzg7tL90uzbx58xg6dCjaC0Phhw0bxpYtW0oMma0Mm80mrz3XiecW7eHPfUl8u/nUZZ1vrcZqViGEuNZI5ZyTNQ1UknOHU7KdHIkQQoirRdGwVpWhODmncpG3dFH3uLpoODB5oNOuXV0uHZo5Y8YMPvjgA2bOnEnr1q1xd3dn3LhxmEzlr0h56UISKpUKq9Va6XOKkmAXn3NpYqyi4bzp6eksXbqUwsJChyGwFouF+fPn8/bbbwPg5eVFZmZmifMzMjLw8vKybzdr1oy4uLhyrymuLefzyv49t1ht5BdaOHAmi+OpOXRvHGA/ZpXcnBBClEn+kney5iGegFI5t/n4ObpG+js5IiGEEHXd7tfu4tf9P9E10Kd4p1SuiDpIpVJV29DSumTdunUMHjyYESNGAEqy7MiRI7Ro0aJW44iKimLr1q2MHDnSvm/79u3lnvPdd9/RsGFDli5d6rB/1apVTJ8+nalTp6LVamnevDn/+9//Spy/bds2oqKi7NvDhw/n5ZdfZteuXSXmnTObzRiNRpl37hrzyZpj2Gzwws0lh2M/t2g3f+5Lsg9hbeBTvNqxVbJzQghRJhnW6mT1Lloy/OEvZXJUIYQQFTsf5suhUBVqt+IPPSqZb06IWtOkSRNiY2PZuHEjcXFxjBkzhuTk5FqPY+zYscybN4+vvvqKI0eOMGXKFPbu3VvuMNN58+Zxzz33EB0d7XB75JFHyMjI4M8//wTgySef5NixYzz11FPs2bOHw4cP88knnzBv3jz++9//2vsbN24cPXr0oG/fvnzyySfs2bOH48eP8+OPP9KlSxeOHDlS48+DqH2z1x6j0GIl32Rx2P/bnjP2xBxAYka+/f7xtFyMZgs6rXwEFUKIS8kro5OpVCr6RNUDlEmThRBCiIqYLMqQIp26eEEIqZwTova8+uqrdOjQgYEDB9K7d2+Cg4MZMmRIrcdx//33M2HCBJ5//nk6dOjAiRMneOihhzAYDKW237FjB3v27OHuu+8ucczT05MBAwYwb948AMLDw1m3bh3Hjh1jwIABdOrUiQULFrBgwQL+7//+z36eXq8nNjaWF154gc8++4yuXbvSqVMnZs2axTPPPEN0dHTNPHjhdAM++IfoN5aTZzJzLDWHBRtOlGhz6VvTswt307tZvVqKUAghrh4qW3WuM3+NyMrKwtvbm8zMTIc5NWrKvtOZ3P7xegI99Wyd2K/GryeEELWttl9XrwZX8pxM2zKNhQcX8lTYCG58fAEATTduQOvnVwORClF5BQUFnDhxgoiIiDITRKJm9e/fn+DgYL755htnh1Ijyvodk/eZkqr7OQl/6c9S9382MoYx3+yodD83NqvH34dTHfadfOvWK4pNCCFqQ02+11x7k4BchQI8lcqHlGwji3ec5u6Yhk6OSAghRF1WaFUWhHBRu1TQUghxLcvLy+PTTz9l4MCBaDQaFi5cyMqVK4mNjXV2aOI6UpXEHFAiMSeEEEKGtdYJgZ7F3/q9H3vYiZEIIYS4GlisyjQIqovmnFO7upbVXAhxjVKpVCxbtoyePXsSExPD77//zuLFi+nXT0ZiiOplkcUchBCiRknlXB2gUav4Y+wN3PbRehIz8knPNeHnrqv4RCGEENclG8qHJJteR9g3X2Oz2SQ5J8R1yNXVlZUrVzo7DHEdMJmtzg5BCCGuaVI5V0dEN/Am2EupoJvyxwEnRyOEEKIus9qUD0kqlQq3Tp1w79zZyREJIYS4lpkskpwTQoiaJMm5OiTISw/A6fP5FbQUQgghQC1v40IIIWpBTVbO+bjJ/KlCCCF/1dchU+9sDcDR1BwnRyKEEKIuu7hyTgghhKhpNVE5N/XOaKKCPPnqYan+FkIISc7VIY3reaBSQXquibQco7PDEUIIUUcVzTmnQpJzQghRF8yePZuIiAgMBgMxMTGsW7euUudt2LABrVZLu3btHPYvWLAAlUpV4lZQUFAD0Vcs32Sp1v52vNKP+7uEsfy5XrQN9anWvoUQ4mokybk6xFWnoYGPMqF3xykrOScJOiGEEKWQyjkhhKg7Fi1axLhx45g4cSK7du2iZ8+eDBo0iPj4+HLPy8zM5IEHHqBv376lHvfy8iIpKcnhZjAYauIhVCgp88qm3Xm0Z4T9/veju+Dvob/SkIQQ4poiybk6pmfTAPv97afOOzESIYQQdZZSOIdaJW/jQgjhbO+//z6jRo1i9OjRtGjRgpkzZxIaGsqcOXPKPW/MmDEMHz6cbt26lXpcpVIRHBzscHOWxCrOib3kye4O2/d2DLXfbx7iVS0xCSHEtUT+qq9j/jMgyn4/OdM5ZetCCCHqNiuyap4QdU3v3r0ZN26cfTs8PJyZM2eWe45KpWLp0qVXfO3q6kdUnclkYseOHQwYMMBh/4ABA9i4cWOZ53355ZccO3aM119/vcw2OTk5hIWF0bBhQ2677TZ27dpVbixGo5GsrCyH2+VKysznYHKWfSTPuVxTlc7v0Mi3zGN6rXwEFUKIS8krYx0T4KHn4R7hALz+237eX3HIuQEJIYSoc2w2pXROKueEuHK33347/fr1K/XYpk2bUKlU7Ny5s8r9btu2jccee+xKw3PwxhtvlJibDCApKYlBgwZV67XKkp+fj6+vL35+fuTnl6ymKitROG7cOHr37u2wLzk5mbFjxxIZGYleryc0NJTbb7+dVatW1VD01S8tLQ2LxUJQUJDD/qCgIJKTk0s958iRI7z00kt89913aLXaUts0b96cBQsW8Ntvv7Fw4UIMBgM9evTgyJEjZcYyffp0vL297bfQ0NAy21bk7f8d5OaZ6/hlVyJw5au12i66L8k5IYQoSV4Z66BbWofY789afRSr1VZOayGEENcbWRBCiOozatQoVq9ezalTp0ocmz9/Pu3ataNDhw5V7rdevXq4ublVR4gVCg4ORq+vnTm8Fi9eTHR0NC1btmTJkiWX3c/JkyeJiYlh9erVvPPOO+zbt4+//vqLPn368NRTT1VjxLXj0jlAbTZbqfOCWiwWhg8fzqRJk2jWrFmZ/XXt2pURI0bQtm1bevbsyY8//kizZs346KOPyjxnwoQJZGZm2m8JCQmX/XjUaiV264Uvg65ktdZATz0RAe409HWlVX0vtBr5CCqEEJeSV8Y6qGOYLwaX4h/N5hPnnBiNEEKIukYq58RVw2YDU65zbrbKfbl52223ERgYyIIFCxz25+XlsWjRIkaNGsW5c+cYNmwYDRs2xM3NjdatW7Nw4cJy+710WOuRI0fo1asXBoOBli1bEhsbW+KcF198kWbNmuHm5kZkZCSvvvoqhYWFgLJ656RJk9izZ4995c6imC+tVtu3bx833XQTrq6u+Pv789hjj5GTk2M//tBDDzFkyBDee+89QkJC8Pf356mnnrJfqzzz5s1jxIgRjBgxgnnz5lXYvixPPvkkKpWKrVu3cs8999CsWTNatWrF+PHj2bx582X3W9sCAgLQaDQlquRSUlJKVNMBZGdns337dp5++mm0Wi1arZbJkyezZ88etFotq1evLvU6arWaTp06lVs5p9fr8fLycrhdLvWFxGJRTi67QPndGHNjZKX7+GxkDBEB7nzxYEdcNGrWPt+b35++4bJjEkKIa1npddTCqVQqFZMHR/PCz3sBWHMwhe6NAyo4SwghxPWiaLVWIeq8wjyYVt851375DOjcK2ym1Wp54IEHWLBgAa+99pq92umnn37CZDJx//33k5eXR0xMDC+++CJeXl78+eefjBw5ksjISLp06VLhNaxWK3fddRcBAQFs3ryZrKwsh/npinh6erJgwQLq16/Pvn37ePTRR/H09OSFF15g6NCh/Pvvv/z111+sXLkSAG9v7xJ95OXlcfPNN9O1a1e2bdtGSkoKo0eP5umnn3ZIQK5Zs4aQkBDWrFnD0aNHGTp0KO3atePRRx8t83EcO3aMTZs2sWTJEmw2G+PGjeP48eNERlY+YQOQnp7OX3/9xdSpU3F3L/kz8vHxqVJ/zqTT6YiJiSE2NpY777zTvj82NpbBgweXaO/l5cW+ffsc9s2ePZvVq1fz888/ExERUeIcUL6U2b17N61bt67eB1AGjaq4cq6g0MK3m5WVZ/UaNSvH9+K+uVtIuzAf3cu3NMdmg32JmfyxN4n63sqKsgNbBTOwVfEiFlIxJ4QQZZPkXB11b8dQTGYrryz9l8/XnWB4lzAiAir+A1MIIcS1r2hYq1TOCVE9HnnkEd59913Wrl1Lnz59AGVI61133YWvry++vr48//zz9vZjx47lr7/+4qeffqpUcm7lypXExcVx8uRJGjZsCMC0adNKzBP3yiuv2O+Hh4fzn//8h0WLFvHCCy/g6uqKh4cHWq223FU7v/vuO/Lz8/n666/tia+PP/6Y22+/nbfffttezeXr68vHH3+MRqOhefPm3Hrrraxatarc5Nz8+fMZNGgQvr7KZP8333wz8+fPZ8qUKRU+Bxc7evQoNpuN5s2bV+m8umr8+PGMHDmSjh070q1bN+bOnUt8fDyPP/44oAw3TUxM5Ouvv0atVhMdHe1wfmBgIAaDwWH/pEmT6Nq1K02bNiUrK4tZs2axe/duPvnkk1p5TEXDWi1WG/vPZNr3azVqmgR6MmVIKx7/dic6jZrHejUGICPPRIsQLwa3c1JCXgghrmKSnKvDOoYXr3L0xbrjTL2zdr4pE0IIUbcVDWuVOedEnefiplSwOevaldS8eXO6d+/O/Pnz6dOnD8eOHWPdunWsWLECUOYJe+utt1i0aBGJiYkYjUaMRmOpVV+liYuLo1GjRvbEHEC3bt1KtPv555+ZOXMmR48eJScnB7PZXOWhiXFxcbRt29Yhth49emC1Wjl06JA9OdeqVSs0Go29TUhISImKrotZLBa++uorPvzwQ/u+ESNG8NxzzzFp0iSHvipifw0rZU62q9HQoUM5d+4ckydPJikpiejoaJYtW0ZYWBigLNgRHx9fpT4zMjJ47LHHSE5Oxtvbm/bt2/PPP//QuXPnmngIJRQVuVltNkzm4iHi5gtzYQ9sFcwXD3SkZf3i308fNx1P9WlSK/EJIcS1Rr5yr8OaB3vxSA+ltP27LfGEv/Qn3205JQtECCHEdU4q58RVQ6VShpY641bFxM+oUaNYvHgxWVlZfPnll4SFhdG3b18AZsyYwQcffMALL7zA6tWr2b17NwMHDsRkMlWqb1sp899dmpjavHkz9913H4MGDeKPP/5g165dTJw4sdLXuPhaZSW9Lt7v4uJS4pjVWvaQ+eXLl5OYmMjQoUPtc6Xdd999nD592p7EBGVobmZmZonzMzIy7MNwmzZtikqlIi4urkqPrS578sknOXnyJEajkR07dtCrVy/7sQULFrB27doyz33jjTfYvXu3w74PPviAU6dOYTQaSUlJYfny5aUmdGtK0ZxzVqsNo9li32+58DuiUqno1zKI+j6utRaTEEJcy+Sv+jpudE/HeScm/vIvX2866ZxghBBC1Aky55wQ1e/ee+9Fo9Hw/fff89VXX/Hwww/bk1nr1q1j8ODB9tUzIyMjy52Y/1ItW7YkPj6eM2eKqwg3bdrk0GbDhg2EhYUxceJEOnbsSNOmTUusIKvT6bBYLJSnZcuW7N69m9zcXIe+1Wp1uauDVmTevHncd9997N692+F2//33OywM0bx5c7Zt2+Zwrs1mY8eOHURFRQHg5+fHwIED+eSTTxziLJKRkXHZcYrqYV8QwmYjM794oRCzRYoEhBCiJkhyro6r7+PKmud7O+z7fW+Sc4IRQghRJ0jlnBDVz8PDg6FDh/Lyyy9z5swZHnroIfuxJk2aEBsby8aNG4mLi2PMmDElVucsT79+/YiKiuKBBx5gz549rFu3jokTJzq0adKkCfHx8fzwww8cO3aMWbNm8csvvzi0CQ8P58SJE+zevZu0tDSMRmOJa91///0YDAYefPBB/v33X9asWcPYsWMZOXJkqauHVkZqaiq///47Dz74INHR0Q63Bx98kN9++43U1FQAnn/+eebNm8fHH3/M4cOH2bNnD08//TTHjh3jqaeesvc5e/ZsLBYLnTt3ZvHixRw5coS4uDhmzZpVqxVionQadfFqrVkXJ+dkBI8QQtQI+av+KhAR4E5MWPH8c/HpeaUOjxBCCHF9uNbmaxKirhg1ahTnz5+nX79+NGrUyL7/1VdfpUOHDgwcOJDevXsTHBzMkCFDKt2vWq3ml19+wWg00rlzZ0aPHs3UqVMd2gwePJjnnnuOp59+mnbt2rFx40ZeffVVhzZ33303N998M3369KFevXosXLiwxLXc3NxYvnw56enpdOrUiXvuuYe+ffvy8ccfV+3JuEjR4hJFw3wv1qdPHzw9Pfnmm28ApQJxwYIFfPXVV3Tq1IkBAwbY5/ArmoMNICIigp07d9KnTx/+85//EB0dTf/+/Vm1ahVz5sy57FhF9ShKztkuqZyzSHJOCCFqhMomWZ4SsrKy8Pb2JjMzs8qT8NaUhPQ8Jv1+gJVxZwFY90IfQv0qP9GxEEI4U118XXW2K3lOxsSOYeOZjUy7YRq3N769hiIUouoKCgo4ceIEERERGAwGZ4cjrkFl/Y7J+0xJV/KcTP9fHJ/9fZzRN0SgUsHn604AcH+XRrJInRDiulWT7zVSOXeVCPVz44sHO9KhkQ8APd9Zw45T550blBBCCKcomnNOKueEEELUBE0Zc8490C3cSREJIcS1TZJzV5l+LYvnCpm3/rgTIxFCCOEs9jnn5G1cCCFEDSga1mq1Fifn/jswiqhgT2eGJYQQ1yytswMQVTO8cyPe+esQAMv2JbN8fzIDWwXbj+ebLKjVoNdqnBWiEEKIGiZzzgkhhKhJRau1Wm3Yk3MypY5wNovFQmFhYcUNhbhMLi4uaDTOyaVIcu4q4+OmY/dr/Wk3ORaAMd/sYNqdrRnepREms5Ve765Br1Wz7oU+8qFNCCGuUUWVc/I6L4QQoiaoHYa1mgHwdnVxZkjiOmaz2UhOTiYjI8PZoYjrgI+PD8HBwbX+d7Yk565CPm46nuvXjA9WHgbg5V/24e3qQvMQT1KzjQCczyvEz13nzDCFEELUEPucc0hyTgghRPXTXJg1wWq1kXWhck6Sc8JZihJzgYGBuLm5yZeTokbYbDby8vJISUkBICQkpFavL8m5q9RTfRoT5u/GuEW7le3vdzJrWHv78aTMfEnOCSHENapoWKtaJXPOCSGEqH7qC3POWS6ac06Sc8IZLBaLPTHn7+/v7HDENc7V1RWAlJQUAgMDa3WIq/xVf5XSatQMad+A/ZMG2vc9s3CX/f6ZjAJnhCWEEKIW2Ie1SuWcEEKIGlC0WmuhxUqOUYa1CucpmmPOzU3mPBS1o+h3rbbnN5Tk3FXOXa/l9dtblth/MCnLCdEIIYSoDbIghBBCiJpUNOdcWo4JAJUKvAwy6Eo4j/zNI2qLs37XJDl3DXiwWzjRDbwc9q06mFLhecmZBUz/XxwJ6Xk1FZoQQogaYEXmnBNCCFFzioa1nkrPBSDEy4BWIx8dhRCipsgr7DVArVYx6772hPu74a5TxkTvTshgwYYT5Z43duFOPvv7OA/M31obYQohhKguSuGczDknRB3Wu3dvxo0bV+n2J0+eRKVSsXv37hqLSYjK0lz47ichPR+Ahn4ypFAIZwoPD2fmzJmVbl+Z95QFCxbg4+NzxbGJ6iF/1V8jIut5sPa/fdg/+Wb7vjd+P0DsgbMUFFpKPWfbyfMAnEjLrZUYhRBCVA9ZrVWI6qNSqcq9PfTQQ5fV75IlS3jzzTcr3T40NJSkpCSio6Mv63qVVfSBrejm6+tLr169+Pvvv+1tHnroIftxrVZLo0aNeOKJJzh//nyNxibqDo3a8f3FXxaaE6LKil5L33rrLYf9S5curfLQyW3btvHYY49VZ3i17vjx4wwbNoz69etjMBho2LAhgwcP5vDhw84OrU6Q5Nw16MuHOtnvP/r1dpq/+hcfrTpC+Et/8tnfx5wYmRBCiOpgXxBC5l8R4oolJSXZbzNnzsTLy8th34cffujQvrITRPv5+eHp6VnpODQaDcHBwWi1tTOv18qVK0lKSuLvv//Gy8uLW265hRMnikdd3HzzzSQlJXHy5Em++OILfv/9d5588slaiU0436XvL64utbdioRDXEoPBwNtvv33FX27Uq1fvqlkUo7T3SZPJRP/+/cnKymLJkiUcOnSIRYsWER0dTWZmZq3GUldJcu4a1Kd5IOte6EOQl96+b0asko2e/r+DzgpLCCFENZHKOXG1sNls5BXmOeVWtHBKRYKDg+03b29vVCqVfbugoAAfHx9+/PFHevfujcFg4Ntvv+XcuXMMGzaMhg0b4ubmRuvWrVm4cKFDv5cOaw0PD2fatGk88sgjeHp60qhRI+bOnWs/fukQpLVr16JSqVi1ahUdO3bEzc2N7t27c+jQIYfrTJkyhcDAQDw9PRk9ejQvvfQS7dq1q/Bx+/v7ExwcTJs2bfjss8/Iy8tjxYoV9uN6vZ7g4GAaNmzIgAEDGDp0qMNxcW27tHLOoJPknKg7bDYbeSZzrd8q+75ysX79+hEcHMz06dPLbbdx40Z69eqFq6sroaGhPPPMM+TmFo9wu3RY68GDB7nhhhswGAy0bNmSlStXolKpWLp0qUO/x48fp0+fPri5udG2bVs2bdpU4tpLly6lWbNmGAwG+vfvT0JCgsPxOXPm0LhxY3Q6HVFRUXzzzTcOx1UqFZ9++imDBw/G3d2dKVOmlLjGgQMHOH78OLNnz6Zr166EhYXRo0cPpk6dSqdOxcVFp0+f5r777sPPzw93d3c6duzIli1brjiW33//nZiYGAwGA5GRkUyaNAmz2VzGT8M5ZMmda1Sonxs/junG6K+2cyQlx+HYgg0ncJU3WCGEuOrJnHOirss359Pl+y5OufaW4Vtwc6meKoMXX3yRGTNm8OWXX6LX6ykoKCAmJoYXX3wRLy8v/vzzT0aOHElkZCRdupT9eGfMmMGbb77Jyy+/zM8//8wTTzxBr169aN68eZnnTJw4kRkzZlCvXj0ef/xxHnnkETZs2ADAd999x9SpU5k9ezY9evTghx9+YMaMGURERFTp8RVVY5RVYXD8+HH++usvXFxcqtSvuHppLqmcM2jls4OoO/ILLbR8bXmtX/fA5IG46aqWQtFoNEybNo3hw4fzzDPP0LBhwxJt9u3bx8CBA3nzzTeZN28eqampPP300zz99NN8+eWXJdpbrVaGDBlCo0aN2LJlC9nZ2fznP/8p9foTJ07kvffeo2nTpkycOJFhw4Zx9OhRe5V2Xl4eU6dO5auvvkKn0/Hkk09y33332d9nfvnlF5599llmzpxJv379+OOPP3j44Ydp2LAhffr0sV/n9ddfZ/r06XzwwQdoNCVfL+rVq4darebnn39m3LhxpbbJycnhxhtvpEGDBvz2228EBwezc+dOrFbrFcWyfPlyRowYwaxZs+jZsyfHjh2zDxF+/fXXy/zZ1TZJzl3DwvzdiR1/I6viznL4bA5v/6VUzb3x+4ESbW02mwyPEkKIq4RUzglRu8aNG8ddd93lsO/555+33x87dix//fUXP/30U7nJuVtuucU+NPTFF1/kgw8+YO3ateUm56ZOncqNN94IwEsvvcStt95KQUEBBoOBjz76iFGjRvHwww8D8Nprr7FixQpycnLK7O9Subm5TJgwAY1GY78OwB9//IGHhwcWi4WCggIA3n///Ur3K65u6ksq51x18mWQEJfrzjvvpF27drz++uvMmzevxPF3332X4cOH26utmzZtyqxZs7jxxhuZM2cOBoPBof2KFSs4duwYa9euJTg4GFDeK/r371+i7+eff55bb70VgEmTJtGqVSuOHj1qf98pLCzk448/tr93ffXVV7Ro0YKtW7fSuXNn3nvvPR566CH7e9f48ePZvHkz7733nkNCbPjw4TzyyCNlPgcNGjRg1qxZvPDCC0yaNImOHTvSp08f7r//fiIjIwH4/vvvSU1NZdu2bfj5+QHQpEkTex+XG8vIkSN56aWXePDBBwGIjIzkzTff5IUXXpDknKhdfVsE0bdFEPHpeSzcGl9qm38Ts2jd0LuWIxNCCHE5ZM45cbVw1bqyZfiWihvW0LWrS8eOHR22LRYLb731FosWLSIxMRGj0YjRaMTd3b3cftq0aWO/XzR8NiUlpdLnhISEAJCSkkKjRo04dOhQiXngOnfuzOrVqyt8TN27d0etVpOXl0dISAgLFiygdevW9uN9+vRhzpw55OXl8cUXX3D48GHGjh1bYb/i2nBJbg4XjSTnRN3h6qLhwOSBTrnu5Xr77be56aabSq1w27FjB0ePHuW7776z77PZbFitVk6cOEGLFi0c2h86dIjQ0FB7Yg6U1/7SlPUeUpSc02q1Du9xzZs3x8fHh7i4ODp37kxcXFyJhSh69OhRYj7WS98nS/PUU0/xwAMPsGbNGrZs2cJPP/3EtGnT+O233+jfvz+7d++mffv29sTcpS43lh07drBt2zamTp1q31f0xVNeXl6dmctPknPXkWl3RvOfAc34etMpZq064nDs9o/X069FEMlZ+dzToSEP9ajacAghhBC1x145J8k5UcepVKpqG1rqTJcm3WbMmMEHH3zAzJkzad26Ne7u7owbNw6TyVRuP5cOC1WpVPbhOpU5p+jf/MXnXPo6UNk5kRYtWkTLli3x8fHB39+/xHF3d3d7xcKsWbPo06cPkyZNqtIKtOLqdemcc2cy8p0UiRAlqVSqKg8vdbZevXoxcOBAXn755RKrgFutVsaMGcMzzzxT4rxGjRqV2FeVUW8VvYdcvL+sfaW9z1y6r6Ivp4p4enpyxx13cMcddzBlyhQGDhzIlClT6N+/P66uFX+pdjmxWK1WJk2aVKICHihRlehM8hXIdUSlUhHgoWd8/2YcmnIz0+5szYRBxcMoVsad5d/ELN74/QCvLv2XhVvjCX/pT/7cm+TEqIUQQpRFLW/jQjjFunXrGDx4MCNGjKBt27ZERkZy5MiRik+sZlFRUWzdutVh3/bt2yt1bmhoKI0bNy41MVea119/nffee48zZ85UOU5x9VFf8mE3PffqWfFQiLrqrbfe4vffi6byBQAAMyhJREFUf2fjxo0O+zt06MD+/ftp0qRJiZtOpyvRT/PmzYmPj+fs2bP2fdu2bbusmMxms8P7xqFDh8jIyLBX1rVo0YL169c7nLNx48YS1XyXQ6VS0bx5c/vCF23atGH37t2kp6eX2v5yY+nQoQOHDh0q9flVq+vO39JXV7pZVBu9VsPwLkoWvkmgB6O+cvxD7pvNp+z3n/p+J266TjTyd6NxPQ8KCi3oNOoSc1EIIYSoHVI5J4RzNWnShMWLF7Nx40Z8fX15//33SU5OrpYPK1UxduxYHn30UTp27Ej37t1ZtGgRe/futc/fU5169+5Nq1atmDZtGh9//HG19y/qlksr5x7oFuakSIS4drRu3Zr777+fjz76yGH/iy++SNeuXXnqqad49NFHcXd3Jy4ujtjY2BJtAfr370/jxo158MEHeeedd8jOzmbixIlA1f82dHFxYezYscyaNQsXFxeefvppunbtah8m+9///pd7772XDh060LdvX37//XeWLFnCypUrq3Sd3bt38/rrrzNy5EhatmyJTqfj77//Zv78+bz44osADBs2jGnTpjFkyBCmT59OSEgIu3bton79+nTr1u2yY3nttde47bbbCA0N5f/+7/9Qq9Xs3buXffv2lbqyrLPUnTShcJq+LYJY+lQPBrerj4e+9Hztwwu20XfG39zy4Tqav/oXU5fFsePUeWIPnC21vRBCiJpjn3NOFoQQwileffVVOnTowMCBA+nduzfBwcEMGTKk1uO4//77mTBhAs8//zwdOnTgxIkTPPTQQzU2TGf8+PF8/vnnJCQk1Ej/ou649Dv4Xs3qOScQIa4xb775ZonpB9q0acPff//NkSNH6NmzJ+3bt+fVV1+1zxF3KY1Gw9KlS8nJyaFTp06MHj2aV155Baj6ME03NzdefPFFhg8fTrdu3XB1deWHH36wHx8yZAgffvgh7777Lq1ateKzzz7jyy+/pHfv3lW6TsOGDQkPD2fSpEl06dKFDh068OGHHzJp0iR7YlGn07FixQoCAwO55ZZbaN26NW+99ZZ9ZdfLjWXgwIH88ccfxMbG0qlTJ7p27cr7779PWFjd+tJBZavsxBTXkaysLLy9vcnMzMTLy8vZ4dS6Mxn5dH+r4omEi7w5uBWpOSYe7BaGv4e+BiMTQlytrvfX1dJcyXNy+y+3czLrJAtuXkBMUEwNRShE1RUUFHDixAkiIiLq1Dwu15P+/fsTHBzMN9984+xQakRZv2PyPlPSlTwnKw+cZfTXxSNrTr51a3WHJ0SlyPtK5WzYsIEbbriBo0eP0rhxY2eHc1Ur73euJt9rZFirKKG+jys7XumHVq3GZLHy0eojfL3pVJntX/11PwCzVh3hr3E9UaHi83XHaR7syeie1T+sQgghrndFlXNqlRTAC3E9y8vL49NPP2XgwIFoNBoWLlzIypUriY2NdXZo4iqn1UhlthB12S+//IKHhwdNmzbl6NGjPPvss/To0UMSc1cxSc6JUl1cATd5cDSTB0dz4EwWW06co2ukPy/8vJd9iZklzrt55jqH7RkrDmM0Wwj3d6dXs3r8d2AU7notJrMVF41K5ksSQojLYJ9zToa1CnFdU6lULFu2jClTpmA0GomKimLx4sX069fP2aGJq5xOI1/+CFGXZWdn88ILL5CQkEBAQAD9+vVjxowZzg5LXAFJzolKa1nfi5b1ldLN38fegNliZcHGk/y6+0ypiTqA/EILAMfTcjmelsuCjSfpHOHH1hPKCiwtQ7zo3tifx26MJNBTypSFEKIyimakkC84hLi+ubq6VnlSbiEqQ3tRcu7xG6USR4i65oEHHuCBBx5wdhiiGklyTlw2rUbN6J6R9qGreSYz32+JJ/bAWbxcXQj2Mjis+lqkKDEHcCApiwNJWXyx/gQtQryIS8qyH5swqDlNgzzoEuGPexkLVQghxPVIFoQQQghRk1wuGtbaKdzXiZEIIcT1QTIeotq46bQOyTqAN4dE2+8v2HCCN34/QH1vAyaLjbQco8P5FyfmAKb/72Cp1+nQyIch7Rtw4EwWW0+kk2sy89bdbQjzcyOyngcAsQfO0rieu31bCCGuJUWVczLnnBBCiJrgclHlnFaGuAohRI2T5JyoNQ/1iOChHhEO+w4mZxG7/yzLDyTTuJ4HG46mkZZjKrefnfEZ7IzPcNj38JfbSrQL9jKw8aWbUKkgK9+Mt5sLAMdTc3DRqAn1cytxjtFsQadRy1AxIUSdZkXmnBNCCFFzLk7OuajlvUYIIWqaJOeEUzUP9qJ5sBdj+za174tLyqLQYmX90TQiAzxYdySVczkmVh9MwWSxVrrv5KwCIl9eZt8O9XPF29WFfxMdK/QGRQfTJyqQXQnnWbg1gebBnswZEcP89Se4J6Yh8el5BHkZ6Bzhh8msXF+nlW8QhRDOI3POCSGEqEkXr9YqlXNCCFHzJDkn6pwWIcqiE20a+gBwc3Sww/Etx8/RwNcVd52WDcfSaBfqw7RlcSzbl1xuvwnp+SSQX2L///5N5n//Fp97MDmbPu+tBXCYM69JoAdHU3Jw02l4qk8T+rYIZPaaY8SE+TKgVRBmi63Uary6KDEjn1VxZxnWuZHDN6NCiKuDPTknlXNCCCFqgM5hWKu81wghRE2T5Jy46nSJ9Lffv61NfQBm3x/j0CbPZCYjr5CT53LRqtXsPZ3BuiNpWG02Aj0NeLu6MH/DiSpd92hKzoW+Lby7/BDvLj8EwG97zvD6b/sBiAry5MS5XGw2G4UW5cNzq/pedGjkS36hBQ+9Fg+9lrQcI0FeBv76N5l+LQMJ9jLg46bj3eWHGNyuPuP6NeOdvw6yZFciM/6vLd0a+1drEu2u2Rs4m2UkK7+Qp29qWvEJQog6pWhBCJlzTgghRE24OCGnky9yhRCixklyTlyT3HRa3HRa6vu4AtA5ws9hoQqA125vCUBajpH49DwSz+cTEeBOocWK1Qa74s9zPs/E0l1nSMxQKu7UKnB10ZBrspR63UNns0vs238mi/1nskppXfo5H60+ykerj9q3H5i/FYAQbwM2m/Kh/GyWka6RfrRu4M1PO07Tqr4XvZrW40xGPgeTs/F2dcHfQ0+gp55Gfm6EB7hjtlhRq1V46LWczVIW4/h5x2nuaNuAQC89BhdNmTHabDYZPidEHWK1VX6IvxCidvTu3Zt27doxc+ZMAMLDwxk3bhzjxo0r8xyVSsUvv/zCkCFDruja1dWPEEVcpHJOCKdbsGAB48aNIyMj44r6kfeIq4Mk58R1L8BDT4CHng6NHJeJjwlTtv87sHmJc/JMZs7lmAj1c+NEWi7puUYCPPT8m5hFclYBAR461h5K5ZddiTQJ9KBZkAfpuSaSMguw2mzotRp7JV5lJWUWOGxvPp7O5uPpAGw4eo4NR89VqT+Ak+fy6PXuGvt2RIA7+SYLeSYzehcNwV4G9iVmOpyj06hpWd+LpoEeuOu1FFqs5JksuGhUNPR1Q61S/qA7kJSFWqWiZYgXgV56XF00WG02EjMKCPNz4/T5PLQaNU0DPfB11xHkaSA1x8ipc7moVSqyjWaigjzRa9UEexsckocWqw2j2YKbzvElzGK1oVbJPFzi2ieVc0JUn9tvv538/HxWrlxZ4timTZvo3r07O3bsoEOHDlXqd9u2bbi7u1dXmAC88cYbLF26lN27dzvsT0pKwtfXt/STqsmCBQt4+OGH7dvBwcH07NmTt99+m4gIZcGv8PBwTp1SpgQxGAyEhYUxatQonn/+eXlvvsq4qOX9RYgr9dBDD5GRkcHSpUudHUq5UlJSePXVV/nf//7H2bNn8fX1pW3btrzxxht069bN2eFdNyQ5J8RlcNNpcfNT/vlEBLgTEaD88R3mX/xH+OB2DfhgaLtK9VdosXIoOZvmwZ4kZRbwz5FUvAwutKrvxdJdifh76NFr1ZxIy8XPXce2k+fJLiikSaAHCefzycovRK9Vsyshw75oRWUYXNQUFBa3P5GWW3ywwExqtrHEOSaLld0JGexOyKjUNX7ZlVjpeMrTMsSLPJOZ/EILeUYLeYUWNGqV/fFGBLhzIi2XAA8dzYI82X7yPCaLlaaBHiRm5BMT5kuQlwE3nYbkzAKMZisGFzXncwuJCvYk1M+VUF839iVmknA+nwY+rjTwMaDXajh8VqlG7NWsHkazlcSMPM5kFNC3RSAGrQaj2cq6I6kEextoEuiB2WIj70KSs01DH/zcdQD8b18SU5fFMfamJnQK92NfYiZNAj2o56nHXafFTaehoNCKi0aFVqOm0GJFo1KhvmiVNKvV5rAtrk8y55wQ1WfUqFHcddddnDp1irCwMIdj8+fPp127dlVOzAHUq1evukKsUHBwcMWNqoGXlxeHDh3CZrNx8OBBxowZwx133MHu3bvRaJQv0SZPnsyjjz5KQUEBK1eu5IknnsDLy4sxY8bUSoyierho5f1FiOvF3XffTWFhIV999RWRkZGcPXuWVatWkZ6eXmPXNJlM6HS6Guv/aiTJOSHqABeNmugG3gCE+rlxf5fiDwfjB0SVaD/mxsr1m5JVgFajxk2nQatWkVWgJLfMFiuN/NzIM1mw2GwcS8nhbFYBvm464tPzKDBbOZycjV6rRqNWsTP+PGcyCigotDCiaxhmq5XVB1Pxd9cR6KknwFOPWqUiI89EYkY+Z7MKOHxWqQwM9NTjolGTnmsiv9CCwUWNi0ZNdoG5Ss/RgaSSQ4MtVpv9flFiMS3HRFpOcRXhkQsViuuOpJXZ99aTlXvjmRF72GG7aN7Biug0aiw2mz3eFxfvq/AcL4OWrAvPUQMfVzwNWjLyCknOKkCnVePr5oJOqyanwExDXzfyCy3otWryCy00qff/7d19XM13/wfw16lTp1sprTuUiIU0KnK75mZk7HK3S1qS37AZtcy2ZEQYGZMul8nFtYWx1czN7A4xrJRFtIXmbhEmua2I7s7n90fry1ER16lT57yej8d50Pf7Od/zfX91zkufPp/P1wyGcj3s+yMPd0vKYWKoj+ZNjVFwvxRmCjmaGBvAysQQRob6MFfIIURFR21LKxPk5t/HuWt3cLekHM2bGqOkTIn8e6UwMtCHp5MlvFpZomsrq1rVTXWHI+eosRBCQNyrejOm+iAzNq7VaK2hQ4fCxsYG69atw9y5c6XtRUVFSEhIwKJFi3Djxg0EBwcjKSkJN2/eRJs2bfDhhx/C39+/xuM+Oq31zJkzmDBhAtLS0tC6dWv861//qvKcGTNmYNu2bbh06RLs7OwQEBCAOXPmwMDAAOvWrcO8efMqavu7rri4OIwfP77KlKXMzEyEhoYiNTUVJiYmGDVqFKKjo2FmZgbgwWiO3r17Y9myZSgpKcGYMWMQExMDAwODmq+pTCZ1BNrb22Pu3LkYO3Yszp49i+efr/j/irm5udRm4sSJiI2Nxe7du9k518jIOXKOGjIhgNKi+n9dAxNAjaOAo6OjERcXhz///BNWVlZ49dVXsWTJEumzutL27dsRFhaGnJwc9OnTB59//jlatmwp7f/uu+8QGRmJEydOwMHBAUFBQZg1axbk8id399y+fRvJycnYv38/fHwqfsh0cnJCt27dqrQLCwvDt99+i/z8fLi4uGDx4sUYOnQoAGDLli2YM2cOzp49C3t7e4SEhOC9996Tnt+qVStMnDgRZ8+elfJq/fr1SElJQXh4OA4fPgxra2uMGDECUVFRah953hiwc45Ii9k0MVL5unIEVyVTRcVHQJeHpvQ+fMONx6luuu+zKC1XQgigTKlESZkSCrk+CotLYWygj3ul5TiVW4i7xeW4W1wGQ7keBACFXA/WZgr8mn0DRy/cQlMTQxjK9aQptXful8HMSI64g+fxurcjLty4i6YmhjhztRCmCjmsTAxReL8MVwruwdbcCG2eM8P1O8VIO38TZeUVnWgyGeDhaImSciUu37qH0r/X7DNXyFEuBC7cqP4/BAq5HoofGb1YUv7064MVPNR5WbnmoXS8MqW0biAA3CpSnXr857W7Kl8XlZRLnZRXUXU0ZG3tybqKXi7NsGli92c+BqmHtOYcBzZQAyfu3cMpD88nN6wDzx9Nh8zkyXdRl8vlGDduHNatW4c5c+ZIHV+bN29GSUkJAgICUFRUBE9PT8yYMQNNmjTBDz/8gMDAQLRu3Rre3t5PfA2lUomRI0fC2toahw4dQkFBQbVr0Zmbm2PdunVwcHBAZmYmJk2aBHNzc4SFhcHPzw/Hjx/Hzp07pSm4FhYWVY5RVFQEX19fdO/eHYcPH0ZeXh4mTpyI4OBgrFu3Tmq3b98+2NvbY9++fTh79iz8/PzQuXNnTJo06Yn1VDI2rljbt7S0tMo+IQQOHDiArKwstG3Lm081NgYPrTNnJK95XWIijSgtAhY51P/rfvgXYKi+TiM9PT2sWLECrVq1QnZ2NqZMmYKwsDCsWrVKalNUVISFCxdi/fr1MDQ0xJQpUzBmzBgcPHgQALBr1y6MHTsWK1asQJ8+fXDu3Dm8+eabAKDyC6eamJmZwczMDNu3b0f37t2hUCiqtFEqlRg8eDAKCwuxceNGtGnTBidPnpRGTKenp2P06NGIjIyEn58fUlJSMGXKFDRr1gzjx4+XjrN06VJERERg9uzZACp+kTRo0CAsWLAAn332Ga5du4bg4GAEBwcjLi7uma9rY8XOOSLSqMoFhw2hB5O/+w6NDSs+6M2NDGBjblTTU6V1AWsy99WO6jnJGpSUKXHu2h04WBjDRFExOrGkXAkZZBVTUvVkuHm3BACgJ5PhbkkZHK1MYKCvhws37sK2iREK75fh8u17OJ1bCMgAU0M53Jo3wa2iUiiFwMWbRVDI9VFcVjEyrpmZAkqlwN2SMpy5egdNjA3QxMgAyWevwdHKFHoyoEwpYGSgj5wbd/H75Xy0ec4MTYwMcOHGXbSxMUNLS2Ocv1GEm3dLoP/3TUKuFRYDsor/gJeUK3G9sBhlSiU8HC1x/K98mBrKoRRA11Z1u6YR1Y77c+64W3oXxvrGmj4VIq3wxhtvYOnSpdi/fz/69u0LoGJK68iRI2FpaQlLS0u8//77UvuQkBDs3LkTmzdvrlXn3J49e5CVlYXz58+jRYsWAIBFixZh8ODBKu0qf2ABKkYZvPfee0hISEBYWBiMjY1hZmYGuVz+2GmsmzZtwr1797BhwwZp5MHKlSvx6quv4uOPP4atrS0AwNLSEitXroS+vj5cXV0xZMgQ7N27t9adc5cuXcLSpUvRokULtGvXTto+Y8YMzJ49GyUlJSgtLYWRkRHeeeedWh2TGg6ZTIbwwa64VVSCVta6N4KFqD48/EsaZ2dnLFiwAG+//bZK51xpaSlWrlwpZc369evRvn17pKWloVu3bli4cCHCw8MRFBQEAGjdujUWLFiAsLCwWnXOyeVyrFu3DpMmTcLq1avh4eEBHx8fjBkzBu7u7gAqMiwtLQ1ZWVnS533r1g9uthgdHY3+/fsjIiICANCuXTucPHkSS5cuVemc69evn0qWjhs3Dq+//rp0Hdq2bYsVK1bAx8cHsbGxMDKq+edAbcTOOSKiZ2Qo10N7+yYq2xR//3bZUF7R6Vh5x+BHVa5PaGSgj+fMFejcsukj+yv+fPRGJQ/r52or/X2Iu/1TnTs1brEDYjV9CkS1IjM2xvNH0zX22rXl6uqKnj174vPPP0ffvn1x7tw5JCUlYffu3QCA8vJyLF68GAkJCbh8+TKKi4tRXFxc62k3WVlZcHR0lDrmAFS7yPY333yDmJgYnD17Fnfu3EFZWRmaNGlSpd2TXuuFF15QObdevXpBqVTi1KlTUudcx44dpVEPQMU01czMxy+7kJ+fDzMzMwghUFRUBA8PD2zdulVl3aAPPvgA48ePx7Vr1zBr1iz069cPPXv2fKoaqGGY7NNG06dAVD0Dk4pRbJp4XTXat28fFi1ahJMnT6KgoABlZWW4f/8+7t69K32Gy+VyeHl5Sc9xdXVF06ZNkZWVhW7duiE9PR2HDx/GwoULpTbl5eW4f/8+ioqKYFKLEeSjRo3CkCFDkJSUhNTUVOzcuRNLlizBf//7X4wfPx4ZGRlVfhHzsKysLAwbNkxlW69evRATE4Py8nIpax6uA6gYcXf27Fls2rRJ2iaEgFKpRHZ2Ntq3b//Ec9cm7JwjIiIiojohk8lqNbW0IZgwYQKCg4Px6aefIi4uDk5OTujfvz8AYNmyZVi+fDliYmLQqVMnmJqaYtq0aSgpKanVsStv4vKwR9fDO3ToEMaMGYN58+Zh0KBBsLCwQHx8PJYtW/ZUdQghalxr7+Htj64tJ5PJoFQ+fhkGc3NzHD16FHp6erC1ta22c9La2houLi5wcXHBli1b4OLigu7du2PAgAFPVQcRUY1kMrVOL9WECxcu4JVXXsHkyZOxYMECWFlZITk5GRMmTKiyVEB1n+mV25RKJebNm4eRI0dWafM0I8+MjIzw8ssv4+WXX8acOXMwceJEzJ07F+PHj5eWMKhJdblTXe49mhlKpRJvvfVWtaOrHR0da33u2oKdc0RERESk80aPHo3Q0FB8+eWXWL9+PSZNmiT9sJGUlIRhw4Zh7NixACp+oDhz5kytf6vfoUMH5OTk4K+//oKDQ8U6SampqSptDh48CCcnJ8yaNUvaduHCBZU2hoaGKC8vf+JrrV+/XmXkxcGDB6Gnp1fjqIfa0tPTg4uLS63bW1paIiQkBO+//z6OHTtWqxt0EBHpgiNHjqCsrAzLli2D3t83YPn666+rtCsrK8ORI0ekGzScOnUKt2/fhqtrxfrfHh4eOHXq1FN9NtdGhw4dsH37dgCAu7s7Ll26hNOnT1ebIx06dEBycrLKtpSUFLRr105lhPajPDw8cOLECbWfe2PF2/AQEREBWLVqFZydnWFkZARPT08kJSXV2DY5ORm9evVCs2bNYGxsDFdXVyxfvrwez5aI1M3MzAx+fn748MMP8ddff6msk+Pi4oLExESkpKQgKysLb731FnJzc2t97AEDBuD555/HuHHj8NtvvyEpKUmlE67yNXJychAfH49z585hxYoV2LZtm0qbykXDMzIycP36dRQXV73JT0BAAIyMjBAUFITjx49j3759CAkJQWBgoDSltT5NnToVp06dwpYtW+r9tYmINC0/Px8ZGRkqj5ycHLRp0wZlZWX497//jT///BNffPEFVq9eXeX5BgYGCAkJwa+//oqjR4/i//7v/9C9e3eps27OnDnYsGGDdLfWrKwsJCQkqKxh+jg3btxAv379sHHjRvz+++/Izs7G5s2bsWTJEmmqqo+PD1588UWMGjUKiYmJyM7Oxk8//YSdO3cCAN577z3s3bsXCxYswOnTp7F+/XqsXLlSZX256syYMQOpqamYOnUqMjIycObMGezYsQMhISFPc4m1BjvniIhI5yUkJGDatGmYNWsWjh07hj59+mDw4MHIycmptr2pqSmCg4Pxyy+/ICsrC7Nnz8bs2bOxZs2aej5zIlKnCRMm4NatWxgwYIDKlJqIiAh4eHhg0KBBeOmll2BnZ4fhw4fX+rh6enrYtm0biouL0a1bN0ycOFFlfSAAGDZsGN59910EBwejc+fOSElJkRbXrjRq1Cj4+vqib9++eO655/DVV19VeS0TExPs2rULN2/eRNeuXfHaa6+hf//+WLly5dNdDDV57rnnEBgYiMjIyCdOmyUi0jb79+9Hly5dVB5z5sxB586dER0djY8//hhubm7YtGkToqKiqjzfxMQEM2bMwOuvv44ePXrA2NgY8fHx0v5Bgwbh+++/R2JiIrp27Yru3bsjOjoaTk5OtTo/MzMzeHt7Y/ny5XjxxRfh5uaGiIgITJo0SSU3tmzZgq5du8Lf3x8dOnRAWFiYNJLbw8MDX3/9NeLj4+Hm5oY5c+Zg/vz5Kr/kqo67uzsOHDiAM2fOoE+fPujSpQsiIiJgb6+ba2nLRHWTgXVcQUEBLCwskJ+f/9SL8BIRUVUN/XPV29sbHh4eiI19cKOF9u3bY/jw4dX+R6k6I0eOhKmpKb744otatW/o14ToWdy/fx/Z2dnSKFQidavpe4yfqVXxmpA2YK5QfXvc91xdfq5qfOScuqcRrV27Fn369JFuez9gwACkpaXVdRlERNRIlZSUID09HQMHDlTZPnDgQKSkpNTqGMeOHUNKSgp8fHxqbFNcXIyCggKVBxERERERkUY75+piGtH+/fvh7++Pffv2ITU1FY6Ojhg4cCAuX75cX2UREVEjcv36dZSXl1dZi8nW1vaJa0q1aNECCoUCXl5emDp1KiZOnFhj26ioKFhYWEiPli1bquX8iYiIiIiocdNo51x0dDQmTJiAiRMnon379oiJiUHLli1VphU9rEuXLvD390fHjh3RqlUrjB07FoMGDVIZbbdp0yZMmTIFnTt3hqurK9auXQulUom9e/fWV1lERNQIVXcL+CfdWTApKQlHjhzB6tWrERMTU+36T5VmzpyJ/Px86XHx4kW1nDcRERERETVuck29cOU0ovDwcJXtzzKN6KOPPqqxTVFREUpLS2FlZVVjm+LiYpW7XXGqERGR7rC2toa+vn6VUXJ5eXlPvLOhs7MzAKBTp064evUqIiMj4e/vX21bhUIBhUKhnpMmIiIiIiKtobGRc/U1jSg8PBzNmzfHgAEDamzDqUZERLrL0NAQnp6eSExMVNmemJiInj171vo4QgiVX/QQ6TLeb4zqCr+3iHQT3/tUXzT1vaaxkXOVnnUa0Z07d3Do0CGEh4fDxcWl2pEKS5YswVdffYX9+/c/9s4uM2fOxPTp06WvCwoK2EFHRKRDpk+fjsDAQHh5eaFHjx5Ys2YNcnJyMHnyZAAVOXH58mVs2LABAPDpp5/C0dERrq6uACpuWPTJJ58gJCREYzUQNQQGBgYAKmYuGBsba/hsSBsVFRUBePC9RkTajblC9U1TOaOxzrm6nkb0ySefYNGiRdizZw/c3d0fezxONSIi0m1+fn64ceMG5s+fjytXrsDNzQ0//vgjnJycAABXrlxRuVmRUqnEzJkzkZ2dDblcjjZt2mDx4sV46623NFUCUYOgr6+Ppk2bIi8vDwBgYmLyxF+6EtWGEAJFRUXIy8tD06ZNoa+vr+lTUrFq1SosXboUV65cQceOHRETE4M+ffo88XkHDx6Ej48P3NzckJGRobJvy5YtiIiIwLlz59CmTRssXLgQI0aMqKMKiBom5grVF03njMY65x6eRvRwyCQmJmLYsGG1Pk5104iWLl2Kjz76CLt27YKXl5fazpmIiLTXlClTMGXKlGr3rVu3TuXrkJAQjpIjqoGdnR0ASD9IEalT06ZNpe+xhiIhIQHTpk3DqlWr0KtXL/znP//B4MGDcfLkSTg6Otb4vPz8fIwbNw79+/fH1atXVfalpqbCz88PCxYswIgRI7Bt2zaMHj0aycnJ8Pb2ruuSiBoU5grVJ03ljExocPJ2QkICAgMDsXr1amka0dq1a3HixAk4OTnVahrRtGnTEBISIt0UYsmSJYiIiMCXX36JXr16Sa9lZmYGMzOzWp1XQUEBLCwskJ+fjyZNmqi5aiIi3cPP1ap4TUjblZeXo7S0VNOnQVrEwMCgxpEMmvxM9fb2hoeHB2JjY6Vt7du3x/DhwxEVFVXj88aMGYO2bdtCX18f27dvVxk55+fnh4KCAvz000/SNl9fX1haWj72zuAPY86QtmGuUF17XM4Adfu5qtE15+piGtGqVatQUlKC1157TeW15s6di8jIyHqpi4iIiEjX6evrN7iph0TqVlJSgvT0dISHh6tsHzhwIFJSUmp8XlxcHM6dO4eNGzdKgwwelpqainfffVdl26BBgxATE1PjMYuLi1VmFBUUFNSyCqLGgblC2kzjN4RQ9zSi8+fPq+nMiIiIiIiIanb9+nWUl5dXWTPb1ta2ytralc6cOYPw8HAkJSVBLq/+x7Hc3NynOiYAREVFYd68eU9ZARERNQR6mj4BIiIiIiKixuzRBeqFENUuWl9eXo7XX38d8+bNQ7t27dRyzEozZ85Efn6+9Lh48eJTVEBERJqk8ZFzREREREREjZG1tTX09fWrjGjLy8urMvINAAoLC3HkyBEcO3YMwcHBACqW7hFCQC6XY/fu3ejXrx/s7OxqfcxKCoUCCoVCDVUREVF9Y+dcNSrvkcF1GoiI1KPy81SD9yBqcJg1RETqo6mcMTQ0hKenJxITEzFixAhpe2JiIoYNG1alfZMmTZCZmamybdWqVfj555/xzTffwNnZGQDQo0cPJCYmqqw7t3v3bvTs2bPW58acISJSr7rMGnbOVaOwsBAA0LJlSw2fCRGRdiksLISFhYWmT6NBYNYQEamfJnJm+vTpCAwMhJeXF3r06IE1a9YgJycHkydPBlAx3fTy5cvYsGED9PT04ObmpvJ8GxsbGBkZqWwPDQ3Fiy++iI8//hjDhg3Dt99+iz179iA5ObnW58WcISKqG3WRNeycq4aDgwMuXrwIc3Pzx67rUJOCggK0bNkSFy9e1MnblrN+1q/L9QO8BtXVL4RAYWEhHBwcNHx2Dcf/kjX8HtPt+gFeA9bP+htSzvj5+eHGjRuYP38+rly5Ajc3N/z4449wcnICAFy5cgU5OTlPdcyePXsiPj4es2fPRkREBNq0aYOEhAR4e3vX+hjMmf+Nrl8D1s/6dbl+oP6zRiY4x0jtCgoKYGFhgfz8fJ38Rmb9rF+X6wd4DXS9/vqg69dY1+sHeA1YP+vX5frrA68xrwHrZ/26XD9Q/9eAd2slIiIiIiIiIiLSEHbOERERERERERERaQg75+qAQqHA3LlzdfZW5qyf9ety/QCvga7XXx90/Rrrev0ArwHrZ/26XH994DXmNWD9rF+X6wfq/xpwzTkiIiIiIiIiIiIN4cg5IiIiIiIiIiIiDWHnHBERERERERERkYawc46IiIiIiIiIiEhD2DlHRERERERERESkIeycU7NVq1bB2dkZRkZG8PT0RFJSkqZPSS2ioqLQtWtXmJubw8bGBsOHD8epU6dU2gghEBkZCQcHBxgbG+Oll17CiRMnVNoUFxcjJCQE1tbWMDU1xT/+8Q9cunSpPktRi6ioKMhkMkybNk3apu31X758GWPHjkWzZs1gYmKCzp07Iz09XdqvzfWXlZVh9uzZcHZ2hrGxMVq3bo358+dDqVRKbbSt/l9++QWvvvoqHBwcIJPJsH37dpX96qr31q1bCAwMhIWFBSwsLBAYGIjbt2/XcXWNnzZmDXNGFXNGt3IG0L2sYc40bMyZxv8eexJdzBlAt7OGObNdZX+DyxlBahMfHy8MDAzE2rVrxcmTJ0VoaKgwNTUVFy5c0PSp/c8GDRok4uLixPHjx0VGRoYYMmSIcHR0FHfu3JHaLF68WJibm4stW7aIzMxM4efnJ+zt7UVBQYHUZvLkyaJ58+YiMTFRHD16VPTt21e88MILoqysTBNlPZO0tDTRqlUr4e7uLkJDQ6Xt2lz/zZs3hZOTkxg/frz49ddfRXZ2ttizZ484e/as1Eab6//oo49Es2bNxPfffy+ys7PF5s2bhZmZmYiJiZHaaFv9P/74o5g1a5bYsmWLACC2bdumsl9d9fr6+go3NzeRkpIiUlJShJubmxg6dGh9ldkoaWvWMGceYM7oXs4IoXtZw5xpuJgz2vEeexxdzBkhmDXMmW0q+xtazrBzTo26desmJk+erLLN1dVVhIeHa+iM6k5eXp4AIA4cOCCEEEKpVAo7OzuxePFiqc39+/eFhYWFWL16tRBCiNu3bwsDAwMRHx8vtbl8+bLQ09MTO3furN8CnlFhYaFo27atSExMFD4+PlKYaXv9M2bMEL17965xv7bXP2TIEPHGG2+obBs5cqQYO3asEEL76380zNRV78mTJwUAcejQIalNamqqACD++OOPOq6q8dKVrGHOMGcepu31C6HbWcOcaViYM9r3HnuYruaMEMwa5sw26euGmDOc1qomJSUlSE9Px8CBA1W2Dxw4ECkpKRo6q7qTn58PALCysgIAZGdnIzc3V6V+hUIBHx8fqf709HSUlpaqtHFwcICbm1ujuUZTp07FkCFDMGDAAJXt2l7/jh074OXlhX/+85+wsbFBly5dsHbtWmm/ttffu3dv7N27F6dPnwYA/Pbbb0hOTsYrr7wCQPvrf5S66k1NTYWFhQW8vb2lNt27d4eFhUWjuyb1RZeyhjnDnNGlnAGYNQ9jzmgOc0b732O6mjMAs4Y580BDzBn5/1IQPXD9+nWUl5fD1tZWZbutrS1yc3M1dFZ1QwiB6dOno3fv3nBzcwMAqcbq6r9w4YLUxtDQEJaWllXaNIZrFB8fj6NHj+Lw4cNV9ml7/X/++SdiY2Mxffp0fPjhh0hLS8M777wDhUKBcePGaX39M2bMQH5+PlxdXaGvr4/y8nIsXLgQ/v7+ALT/3/9R6qo3NzcXNjY2VY5vY2PT6K5JfdGVrGHOMGd0LWcAZs3DmDOaw5zR7veYLucMwKxhzjzQEHOGnXNqJpPJVL4WQlTZ1tgFBwfj999/R3JycpV9z1J/Y7hGFy9eRGhoKHbv3g0jI6Ma22lr/UqlEl5eXli0aBEAoEuXLjhx4gRiY2Mxbtw4qZ221p+QkICNGzfiyy+/RMeOHZGRkYFp06bBwcEBQUFBUjttrb8m6qi3uvaN+ZrUF23PGuYMc0bXcgZg1lSHOaM5zJkHtOU9pus5AzBrmDNVNaSc4bRWNbG2toa+vn6V3tG8vLwqvbGNWUhICHbs2IF9+/ahRYsW0nY7OzsAeGz9dnZ2KCkpwa1bt2ps01Clp6cjLy8Pnp6ekMvlkMvlOHDgAFasWAG5XC6dv7bWb29vjw4dOqhsa9++PXJycgBo/7//Bx98gPDwcIwZMwadOnVCYGAg3n33XURFRQHQ/vofpa567ezscPXq1SrHv3btWqO7JvVFF7KGOcOcqaRLOQMwax7GnNEc5oz2vsd0PWcAZg1z5oGGmDPsnFMTQ0NDeHp6IjExUWV7YmIievbsqaGzUh8hBIKDg7F161b8/PPPcHZ2Vtnv7OwMOzs7lfpLSkpw4MABqX5PT08YGBiotLly5QqOHz/e4K9R//79kZmZiYyMDOnh5eWFgIAAZGRkoHXr1lpdf69evarcav706dNwcnICoP3//kVFRdDTU/241NfXl247ru31P0pd9fbo0QP5+flIS0uT2vz666/Iz89vdNekvmhz1jBnmDO6nDMAs+ZhzBnNYc5o73tM13MGYNYwZx5okDnzVLePoMeqvO34Z599Jk6ePCmmTZsmTE1Nxfnz5zV9av+zt99+W1hYWIj9+/eLK1euSI+ioiKpzeLFi4WFhYXYunWryMzMFP7+/tXeirhFixZiz5494ujRo6Jfv34N8rbLtfHw3Y2E0O7609LShFwuFwsXLhRnzpwRmzZtEiYmJmLjxo1SG22uPygoSDRv3ly67fjWrVuFtbW1CAsLk9poW/2FhYXi2LFj4tixYwKAiI6OFseOHRMXLlwQQqivXl9fX+Hu7i5SU1NFamqq6NSp0zPdelyXaGvWMGeqYs7oTs4IoXtZw5xpuJgz2vEeqw1dyhkhmDXMmYadM+ycU7NPP/1UODk5CUNDQ+Hh4SHdmruxA1DtIy4uTmqjVCrF3LlzhZ2dnVAoFOLFF18UmZmZKse5d++eCA4OFlZWVsLY2FgMHTpU5OTk1HM16vFomGl7/d99951wc3MTCoVCuLq6ijVr1qjs1+b6CwoKRGhoqHB0dBRGRkaidevWYtasWaK4uFhqo23179u3r9r3fFBQkBBCffXeuHFDBAQECHNzc2Fubi4CAgLErVu36qnKxksbs4Y5UxVzRndyRgjdyxrmTMPGnGn877Ha0LWcEUK3s4Y507BzRiaEEE831o6IiIiIiIiIiIjUgWvOERERERERERERaQg754iIiIiIiIiIiDSEnXNEREREREREREQaws45IiIiIiIiIiIiDWHnHBERERERERERkYawc46IiIiIiIiIiEhD2DlHRERERERERESkIeycIyIiIiIiIiIi0hB2zhHpIJlMhu3bt2v6NIiISIsxa4iIqC4xZ0ibsHOOqJ6NHz8eMpmsysPX11fTp0ZERFqCWUNERHWJOUOkXnJNnwCRLvL19UVcXJzKNoVCoaGzISIibcSsISKiusScIVIfjpwj0gCFQgE7OzuVh6WlJYCK4dmxsbEYPHgwjI2N4ezsjM2bN6s8PzMzE/369YOxsTGaNWuGN998E3fu3FFp8/nnn6Njx45QKBSwt7dHcHCwyv7r169jxIgRMDExQdu2bbFjx466LZqIiOoVs4aIiOoSc4ZIfdg5R9QARUREYNSoUfjtt98wduxY+Pv7IysrCwBQVFQEX19fWFpa4vDhw9i8eTP27NmjElSxsbGYOnUq3nzzTWRmZmLHjh1wcXFReY158+Zh9OjR+P333/HKK68gICAAN2/erNc6iYhIc5g1RERUl5gzRE9BEFG9CgoKEvr6+sLU1FTlMX/+fCGEEADE5MmTVZ7j7e0t3n77bSGEEGvWrBGWlpbizp070v4ffvhB6OnpidzcXCGEEA4ODmLWrFk1ngMAMXv2bOnrO3fuCJlMJn766Se11UlERJrDrCEiorrEnCFSL645R6QBffv2RWxsrMo2Kysr6e89evRQ2dejRw9kZGQAALKysvDCCy/A1NRU2t+rVy8olUqcOnUKMpkMf/31F/r37//Yc3B3d5f+bmpqCnNzc+Tl5T1rSURE1MAwa4iIqC4xZ4jUh51zRBpgampaZUj2k8hkMgCAEEL6e3VtjI2Na3U8AwODKs9VKpVPdU5ERNRwMWuIiKguMWeI1IdrzhE1QIcOHarytaurKwCgQ4cOyMjIwN27d6X9Bw8ehJ6eHtq1awdzc3O0atUKe/furddzJiKixoVZQ0REdYk5Q1R7HDlHpAHFxcXIzc1V2SaXy2FtbQ0A2Lx5M7y8vNC7d29s2rQJaWlp+OyzzwAAAQEBmDt3LoKCghAZGYlr164hJCQEgYGBsLW1BQBERkZi8uTJsLGxweDBg1FYWIiDBw8iJCSkfgslIiKNYdYQEVFdYs4QqQ8754g0YOfOnbC3t1fZ9vzzz+OPP/4AUHHXofj4eEyZMgV2dnbYtGkTOnToAAAwMTHBrl27EBoaiq5du8LExASjRo1CdHS0dKygoCDcv38fy5cvx/vvvw9ra2u89tpr9VcgERFpHLOGiIjqEnOGSH1kQgih6ZMgogdkMhm2bduG4cOHa/pUiIhISzFriIioLjFniJ4O15wjIiIiIiIiIiLSEHbOERERERERERERaQintRIREREREREREWkIR84RERERERERERFpCDvniIiIiIiIiIiINISdc0RERERERERERBrCzjkiIiIiIiIiIiINYeccERERERERERGRhrBzjoiIiIiIiIiISEPYOUdERERERERERKQh7JwjIiIiIiIiIiLSkP8HdtD1nk2FFMkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xc.pl.plot_train_history(\n",
    "    history = history['history'],\n",
    "    savefig = True,\n",
    "    out_file = './data/1_within_sample/train_out/train_history_plot.pdf'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Evaluate the Model Performance  \n",
    "- Evaluate performance on cross-cell, cross-region, and cross-both predictions.  \n",
    "- If `save_pred=True`, the prediction results will be saved as:  \n",
    "  - `1_within_sample/eval_out/crosscell_pred.npy`  \n",
    "  - `1_within_sample/eval_out/crosspeak_pred.npy`  \n",
    "  - `1_within_sample/eval_out/crossboth_pred.npy`  \n",
    "  - `1_within_sample/eval_out/crosscell_impute.h5ad`  \n",
    "  - `1_within_sample/eval_out/denoise_impute.h5ad`  \n",
    "- If `scatter_plot=True`, the following relationships will be visualized and saved as PDF files in `/1_within_sample/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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-18 09:36:13.652973: 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",
      "2025-08-18 09:36:18.223823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18781 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:31:00.0, compute capability: 8.6\n",
      "2025-08-18 09:36:18.225618: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 21186 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:4b:00.0, compute capability: 8.6\n",
      "2025-08-18 09:36:18.226034: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 21186 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:98:00.0, compute capability: 8.6\n",
      "2025-08-18 09:36:18.226421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 321 MB memory:  -> device: 3, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:b1:00.0, compute capability: 8.6\n",
      "2025-08-18 09:36:19.451998: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2025-08-18 09:36:26.268729: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8800\n",
      "2025-08-18 09:36:26.891604: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predction shape: (36282, 487)\n",
      "Overall auROC: 0.8220, auPRC: 0.4839\n",
      "Per-cell auROC: 0.7710, auPRC: 0.3707\n",
      "Valid cells: 487\n",
      "Per-peak auROC: 0.7522, auPRC: 0.3402\n",
      "Valid peaks: 36282\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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+wMCBA7v025qm4eqrr8Y777wDALjsssvwyCOPdO0CBEEQWiHytyUif2NLVlYWnn32WQDAli1bcNddd7VYf/jwYdx0000AgL/85S/d9ih25brVNQPRXbfI3/gjHlAh4QgXIHfffXfUoR3hOQIA8Ne//rU5lOjUU0/Ftddei2nTpmHgwIFwOBzNQmvWrFn4+OOPoWlajK6gezQ0NKCwsPCIjzNhwgRYLJYu7WO325GTk4OKigoUFxd3uG1VVVWzMFB5lrFA9f4Eogu/jZajjjqq+fPBgwc73X7lypW49NJL4ff7kZWVheXLl3eayxSJa6+9FkuWLAEAnHPOOViyZEmXPaiCIAi9icjfIyNR5W9rJk2ahHHjxqGgoAD//e9/ceeddzave/LJJ+F2u5GZmQmXy4WXXnqpzf6fffZZi8/K23nGGWdgwIABABh+rejsuouKipo/R3PdIn/jjyigQsLhcrmaP/v9/m6FxWiahieffBIAcPLJJ+Ojjz5qd/Cpqqrq3onGmA0bNuD0008/4uMUFhZi5MiRXd5v0qRJ+Pjjj7F7924EAgGYzZGHj6+//rrFPrGgvLwc77//PgBg2rRpMQ2F6srEZv369bjgggvg9XqRlpaGd999F8ccc0yXf/O3v/0tHn30UQCcYL366qtdnpQIgiD0NiJ/j4xElL/tkZubi4KCAuzfv7/FchUOW11djSuvvLLT4zz22GN47LHHAAArVqxoVkDHjx8Pk8mEYDDY4roi0ZXrFvnbNxB1X0g4Jk+e3Jynt3z58m4do7KysjknZd68ee0Kv/r6euzcubN7J9rPOPnkkwHQEvzFF1+0u92qVauaP8+cOTMmv/3iiy8iEAgAiK33E2BOk6KjUKEtW7bg7LPPRn19Pex2O/73v/9FrDTZGXfccQf+8Y9/AGBhi7feeiumubKCIAg9hcjf+BBP+dseKmKop0J9rVYrpk+fDgBYt25dh3mg6rptNhtOOOGEdrcT+dt3EAVUSDhSUlIwe/ZsAAyHXL9+fZePoZQZoG1uSjhPPfVUi4p88eS0006Dxt69R/TqjvUVAC666KLmz4sXL464TSgUag6VzczMjInFGNDDb81mMy6//PKYHBMAampq8PLLLwPg31V7gmvXrl2YM2cOqqqqYLFY8Oqrr+K0007r8u898MADzYWLpkyZgvfee69N8QZBEIS+isjf5JO/kdiwYUOz53PKlCkt1t12222d3ofwa1i8eHHz8tZyVV13bW0tXnvttYjnUlxcjA8//BAAMHv27HZlqsjfvoUooEJC8oc//KG55Pr8+fOxZ8+edrcNBoN48cUXW+QQ5ObmNlf5e+mllyJa1jZs2IA//vGPsT3xBGb69Ok45ZRTAHBiEKmf2j333IOvvvoKAPCLX/wiYljLM888A4PBAIPBgNtuu63T392+fTu+/PJLAMzVyM3Njep833vvvQ4LV9TV1WHevHlwu90AgB/+8IctSrkrDhw4gDPPPBNlZWUwmUx48cUXce6550Z1DuEsXrwYv/zlLwEwtOiDDz5oUZ1REAQhERD52/v0lvxdv349Nm7c2OG5HDx4EAsWLGj+/oMf/KArl9IlrrnmGjidTgDA7373u2Z5rQgGg/i///u/5txkVfyoNSJ/+x6SAyokJDNnzsSf/vQn3H777SgsLMSxxx6LH/7wh5gzZw4GDRqEpqYm7Nu3D+vWrcN///tflJSUYOvWrc1J7UajEVdccQUefvhhbNq0Caeccgp++ctfYuzYsaipqcE777zT3N9q8ODB2LVrV5yvuG/wwAMPYObMmfB4PJgzZw5+//vf4/TTT4fH48FLL72Exx9/HAAH+F/96lcx+U1VbQ9AC6HXGX/7299wxRVX4OKLL8bJJ5+MMWPGIC0tDdXV1Vi3bh0effTR5sIFEyZMiCiM3W43zjzzzObtfvWrX2HixInYtm1bu7+blZXVptfbG2+8gR/96EfQNA0ZGRl44IEHcPjwYRw+fLjd44waNapN4Q5BEIR4I/I3PvSG/N2xYwcWLlyIk046CRdccAGOPfbYZqPvwYMHsWLFCixevLi5b+aZZ56JhQsXxuYCI5CdnY2///3v+OlPf4r9+/djxowZ+MMf/oApU6agpKQE999/P1asWAEA+P73vx/R6yvyt4+iCUKMuPXWWzUAWmd/VgsWLNAAaCNGjOhwu1NPPVUDoJ166qntbnPfffdpNput+Xfbe1mtVq2goKDFvtXV1dqxxx7b7j7Z2dnaqlWrOjyPFStWNG+/YsWKDq9HbXfrrbd2uF1f580339QyMjLavW/jx49vc6/DWbx4cdT3IhgMakOGDNEAaFlZWZrX6436PNVz6+w1a9Ysrbi4OOIxwp9vtK8FCxa0OY76m+/Kq7O/J0EQBIXI3xUdXo/IX9KZ/A1f39nr6quv1hoaGrp1HeG/s3jx4k63/9Of/qQZDIZ2z+Xcc8/VPB5PxH1F/vZNxAMqJDQ33HADLrvsMvz73//GBx98gN27d6O6uho2mw1DhgzBlClTcNZZZ+GSSy5BTk5Oi32dTifWrFmDe++9F0uXLkVBQQHMZjOGDRuG8847D7/4xS9alAEXyAUXXIAtW7bggQcewNtvv43i4mJYrVaMHTsWl112Ga677rpOe8xFS35+fnOhg+9973sRQ2Tb45///Cfy8/Oxbt067Ny5ExUVFaiurkZKSgoGDx6MGTNm4Pvf/z7mzJnTHE4mCIIgRIfI396np+Xv9773PQwePBgfffQR1q5di4MHD6K8vBw+nw8ZGRkYN24cZs6ciR/84AfdqgDfXW6//XZ85zvfwcMPP4yPP/4YZWVlyMzMxNSpU7Fw4UJ8//vf77VzEWKDQdPi3FxJEARBEARBEARBSAqkCJEgCIIgCIIgCILQK4gCKgiCIAiCIAiCIPQKooAKgiAIgiAIgiAIvYIooIIgCIIgCIIgCEKvIAqoIAiCIAiCIAiC0CuIAioIgiAIgiAIgiD0CtIHNIxQKISSkhKkp6dLX0BBEIQERNM01NXVYfDgwTAaxcaaSIgMFgRBSFy6In9FAQ2jpKQEw4YNi/dpCIIgCEdIUVGRNLJPMEQGC4IgJD7RyF9RQMNIT08HwBuXkZER57MRBEEQukptbS2GDRvWPJ4LiYPIYEEQhMSlK/JXFNAwVMhPRkaGCD9BEIQERkI4Ew+RwYIgCIlPNPJXEmQEQRAEQRAEQRCEXkEUUEEQBEEQBEEQBKFXEAVUEARBEARBEARB6BVEARUEQRAEQRAEQRB6BVFABUEQBEEQBEEQhF5BFFBBEARBEARBEAShV5A2LDHC7/cjGAzG+zSEBMFkMsFiscT7NARBEBIekb/JgchNQeg/iAJ6hNTW1qKiogJNTU3xPhUhwbDZbMjJyZF+d4IgCN1A5G/yIXJTEPoHooAeAbW1tTh48CDS0tKQk5MDi8Uizc+FTtE0DX6/HzU1NTh48CAAiDAVBEHoAiJ/kwuRm4LQvxAF9AioqKhAWloahg4dKoJP6BIOhwPp6ekoLi5GRUWFCFJBEIQuIPI3+RC5KQj9BylC1E38fj+amprgdDpF+AndwmAwwOl0oqmpCX6/P96nIwiCkBCI/E1eRG4KQv9AFNBuogoeSEK8cCSovx8poCEIghAdIn+TG5GbgpD4iAJ6hIj1VTgS5O9HEAShe8j4mZzIcxeExEcUUEEQBEEQBEEQBKFXEAVUEARBEARBEARB6BVEARUEQRAEQRAEQRB6BVFAhYRi3759MBgMLV4mkwnZ2dk49dRT8cwzz0DTtA6P4ff78eijj+L000/HgAEDYLPZMGTIEFxyySV46623ojqPzz77DD/60Y8wfvx4pKenw263Y+TIkZg3bx5ef/11hEKhWFyuIAiCIPQJ2pO/OTk5mDNnDl599dU2+1x99dVt9nE4HBg3bhx+8pOfoLCwMA5XIghCvJE+oEJCkpqaiksvvRQAFcqCggKsXr0aq1evxsqVK/HMM89E3G///v0499xzsWPHDjgcDpx88slwuVzYv38/3njjDbz22mu45JJL8MILL8But7fZ3+/349prr8UTTzwBABg/fjzOPPNMWK1WFBYW4tVXX8Urr7yCM844A/n5+T12/YIgCIIQD1rL36+++goffPABPvjgA/z2t7/F3/72tzb7zJw5E2PHjgXAHq6fffYZHn/8cbz00kv4+OOPccwxx/TqNQiCEF9EARUSkpycnDZK5htvvIHvfve7ePbZZ3HNNdfg5JNPbrG+pqYGp512Gvbt24fvfe97ePTRR5GVldW8/uuvv8b8+fPx6quvIhQK4bXXXmvzuwsXLsSSJUswfvx4LF68GCeddFKL9SUlJfjzn/+M5cuXx+5ihbjgdgOVlUB2NuByxftsBEEQ+gaR5O8zzzyDhQsX4h//+Acuv/zyNgrlNddcg6uvvrr5e01NDebOnYtVq1bhxhtvxIcfftgLZy4IQl+Z3EgIrtBvuOiii3D22WcDAN5///0263/7299i3759mDNnDl588cUWyicATJw4ER9++CEGDhyI119/HS+//HKL9a+++iqWLFmCvLw8rF69uo3yCQCDBw/GY489hueffz6GVyb0Jh4P8OyzwM03A7feyvdnn+XyruJ2AwUFfBcEQeivXH311Tj99NOhaRrefPPNTrd3Op34+9//DgBYtWoVvF5vT5+iICQ3nU1uennCIgqoEFPefvttLFq0CJMmTUJGRgZSU1MxdepU3HnnnWhqamqx7W233QaDwdBuuOzIkSO73O9r8uTJAIDy8vIWy91uN5599lkAwP333w+jMfKffk5ODv70pz8BAO65554W6+6+++7m887Ly+vwPGbOnNml8xb6DkuXAsuWASYTMHw435ct4/JoiaUSKwiCEA3xlr/HHXccAKCoqCiq7ZW8DgQCqKqq6tJvCYLQRdqb3CxZEpcJiyigQkz54Q9/iFdeeQVOpxNnn302TjnlFBQVFeEPf/gDzj33XASDwR79/bq6OgDAgAEDWixfsWIFvF4vjj32WEyaNKnDY8yfPx8GgwEbNmyA+xtLUEVFBdavXw+DwYD58+f3zMkLccftBtasAfLy+LLb9c9r1kRvGIyFEisIgtAV+or8tdlsXdreYDDAJXkOgtBzdDS5eeEFTk56ecIiCmgCkQjhfI899hhKS0vx6aefYunSpXjvvfewf/9+nH/++fjoo4+wZMmSHvttv9+Pjz76CACaQ3EVmzZtAgAcf/zxnR4nKysLo0ePbrHfpk2boGkaRo8ejczMzJidc7LSW3/L0fxO+DaVlUB9PeB0ttzG6eTyysrofjMWSqwgCH2IBBDA8ZS/Xq8XH3zwAQBEXVDovffeAwDMnj0bVqu1x85NEJKG1uOU+r5nDz8Hg0Bjo7691QoUFwMZGb0+YZEiRAmAx0NDxJo1nASnpQEzZwLz5gEOR7zPriUXXXRRm2Xp6em477778NZbb2HZsmW46qqrYvqbqgruLbfcgt27d+P//u//2oTAKk9ma89oe+Tm5mLPnj2oqKhosX9ubm4Mzzz56K2/5Wh+J9I2U6dy/K2p4buipobrs7M7/22lxA4f3nK50wkcOMD1kYz9faQugCAI4SSQAI6X/P36669x8803Y9++fXC5XLjssss63KeiogLvv/8+brrpJuTk5OCBBx6I6TkJQtLRepyy2+nJDAb5/eBBYO9eICUFyMwEhg0DJk8GVLqamhs3NvJYVitw+HD7E5YYIApoAqDC+fLyOKmtqeF3AFiwIL7nFomCggK888472L17NxoaGhAKhZp7cxYUFMTkN/bv3x8xP+X2229vzuEMR/1+Zz1CW2+vfiPa/QTSnjLVE3/LkX4rmt+JtE1+Po9RVsZtnE4uLysD5s6NbhzOzuYcNVolNoHmt4KQfCSYAI6n/M3Ly8Orr74KZ+sQErCC/MKFC1ssGzFiBD7++GMMGzYsJuclCEmJ2w08+SSwejXHqOHDOaHYuRMYP54TmZISIBAAGhoAmw3YsYNjWUoKMHQoFc/9+4GiIsDn47Z5eT06CREFtI/TOpwP0Ce1a9YA55/fd7wlmqbhpptuwn333deuwqZyPo6U8D5k9fX12LBhAw4cOIA77rgDM2bMwHe+850W2+fk5ABoW5yoPQ4fPgwAzXkpan+1XIhMR8pUY2Ns/5bb+63Zszv/HfU50jZNTTzG5s30WKalUfmcNy+683K5eB5qjtqZEptg81tBSB4SSADHS/6aTCZkZmZi2rRp+O53v4uUlJSI+6g+oKFQCMXFxVi9ejX279+PBQsW4IMPPoDJZIrJuQlC0qAmQfn5HI+sVsBopKfzq6+oSO7YAaSmArm5QFYWrfUWC+D3A4cOAT//Ofd77DGgqooTFrOZk6q6Oh67hyYiooD2cbobzhcPXn75Zdx7770YOnQo7r//fnz7299Gbm4uLBYLfD4fbDZblzyJoVCo3XWt+5AFg0H84he/wMMPP4wFCxagoKAA6enpzeunTp0KAPjiiy86/d3KykoUFha22O/YY48FAOzduxfV1dWSB9oOHSlTJ50U27/l9n6rrKzz3wE63mbOHGD+/O6HxCpldc2ajpXYBJrfCkLykUACOJ7yNxpa9wHdtm0bTj/9dKxYsQL33nsvfv3rX3fpeIKQ9KhJkM2mv774gsplKMSJR0MDFUuV2+n3AyeeSEXV7eZkx+FgJdzGRno+rVbmI+Xm9uhERIoQ9XHCw/nC6UpOWm/x+uuvAwAeffRRXHLJJRg8eDAsFgsAKm6tUUUH6uvr26wLBoMoLS2N+rdNJhPuv/9+TJ48GWVlZbjvvvtarD/jjDNgs9mwefNm7Nixo8NjvfTSS9A0DSeccEKz5zMnJwfTp0+Hpml46aWXoj6v/kRnNTg6K74DRP5bLitjmkJXfr+938rIANau5fE6+p+J5v/K5QLGjeveuOtw0Gh4113A7bfzfcGCttEssSh6JAhCD5FAAjie8rc7HH300fjXv/4FALjrrrtQ0/oeC4LQPgUFwDf/88jJ4eRC0+gVbWoCDAYqm3Y7FdOqKk4qrFaOWyYTJzfZ2dxn8GAqo6edxhCwY4/l+h6ciIgC2sdR4XxlZXx5vfrnmTP7jPEVAJr7eEXK51gaoZzzoEGDAAC7du1qs+6jjz6C3+/v0u+bzWbccccdAIAHHnighWB1uVzNxRd++ctftmvdraiowF/+8hcAwI033thi3U033QSA/dM6C+Vdu3Ztl869LxNtT8vOlCmg5d9yXR1TFlasYOrB3XdHPm6k3//Xv1i4LRikMlpbC2zaxNeXXwKFhcD69cy7j/Q/05X/qyMpftmZEptA81tBSD4SSADHW/52h/nz5+PYY49FVVUVHn744R7/PUFIeDwe5ntecQXw4YfARx8B//sflc6CAk4e6uqA6moqnVYrJ2GNjUBpKScVdXUtxzA1EfH5+F2F0ffwREQU0ARg3jyG7wWDjPoJBruWk9ZbjB8/HgDw+OOPtwj1+fjjj3H33Xe32f7UU08FALzwwgvYt29f8/K9e/fi5z//ebfO4aKLLsJxxx2HyspKPProoy3W/f3vf8fw4cOxfPlyXH755W0aX+/cuRNnnnkmSktLceGFF+L73/9+i/WXXXYZ5s+fj7KyMsyaNQvr1q1r8/ulpaW47rrrcOWVV3br/Psi0fa0jEaZmjePxrXKSmDlSiqKo0ZxHGx9XKX4Pf20/vuDBnHZU08Bn3zCbV9/Hfjvf4HPPuMYnJUFTJjAYxQU6P8zs2cD06bpimRn/1fRKt5HQgLNbwUhOUkQAdwX5G9XMRgMuO222wAA999/PxrD20MIgtCWpUuBRx+lMmmz0et5+DDw9ddUMtX/fijEsaqiQp+UBQJsx/LVV8C55+pjWJwmIpIDmgCocL7zz+/bbRquv/56PPPMM3jkkUewcuVKHHPMMTh48CA++eQT/OpXv8I///nPFtuPHj0aV111FZ577jkce+yxmDVrFhoaGvDpp5/ivPPOg9frxf79+7t8Hrfddhvmzp2Le++9Fz//+c9h/yapLisrCytXrsS5556Ll19+GW+++SZOOeUUuFwu7N+/H59++ilCoRAuuugivPjiixGP/dxzzyElJQVPP/00TjrpJEycOBFHHXUULBYL9u3bh88//xzBYBBnnXVW129gH6QrOYqdFd9JSeHYuXkzx8mqKmDIEOaGWiyAStldtYoe082b+fvbt1PxnDSJn0tLObb6/VRKa2v5HeD4euKJwJgxevrDNdcAW7fyeGvWtCyM1Pr/CqBnNTsbeOut9vNZY/m/GG2+qCAIcSBBBHBfkb9dZe7cuZg2bRo2btyIJ554Ar/4xS96/DcFISFxu1kUqLGR41BNDSdLrSP6DAZdEfV6OVkaNgz49rc5YWpq4qQrPCcoDhMR8YAmEEeSk9YbjB8/Hhs2bMAFF1yAiooKvPnmm6ivr8e///3viBZYAHjiiSfwu9/9DhkZGXj//fexf/9+/P73v8d//vOfbp/HhRdeiBNOOAGlpaV4+umnW6wbNWoUNm/ejIcffhjTp0/HF198gf/+978oLCzEhRdeiGXLluH111+Ho53S0xaLBU899RTWrl2LRYsWIRAI4L333sMbb7yB0tJSXHLJJVi2bBnef//9bp9/X6KrOYodOQvCPamDBvG9vJxKZfhxd+zQt3O5eIyDBxlaW1REo18wSEU4N5e59GqMbWzkNps2UeENBIBPP+WY3Z4H1+ViFfK33tK9nb/8JbB4MdeF55i6XFz+y1/Gzisabb6oIAhxpI8L4L4if7uD8oL+85//hM/n69XfFoSEobKSlnuDgRVsPR5+7oxgkG1Y3nyTk6OiIk5kiov1beIwETFo0uCwmdraWjidTtTU1CAjI6PDbb1eLwoLCzFq1KhmD5sgdJW+/nfkdlPJMpl0DyigFw66667I87HWvTnVcXw+3dO5di3HT4eD4bEpKcwFXbcO+Na3gJEjqVDm5+vjbCjE7QoLmdowZgzH0JISLrdYgIEDeW6DBlGxBFiFvKPzf/ZZ3dvpdPI8PvkEOOYYelQVGzYAW7YAJ58MjBjR0sMrLVP6Bl0Zx4W+RbTPrq+Pm0LPIs9fSErcblq/V6/mpMdkoqezqanttuFeUKOR2xqNnHClp3Pdz38O/OY3MT3Frshf8YAKgtAGlX8JdC81INxZ4HZTqfziCxrfVq6k8qlpVEhra5kvX1ZGT2dmJpVIgEplXh4V0Pp67qMiTpRhrqFBDwsGqGwaDGyFNXIkvaCtPbhWKw2Ie/ZErqg7bBgjUAoLqQQDfN+7l8uHDWtb5bc7RYoEQRAEQUhiWlc6bK/yYUoKQ8AqKxny5fVyEhWJcN+iwcDeniYTrfTK8r9tW1wnLpIDKghCM6qv8Zo1VPTS0oDp04FzzqH3ryupAeHH2rgR2LmTbVJGjODYWV/PsTAQoMcxL4/H/eILehZNJr2ibV0dt1NGvYED+b2sjMa/jAw9raG2lmN0RgZw1lnMGa2pocLo9zPkd+dOfn7oIWDyZCrAo0fr556Swu9btjBaZcQIvtfVsT1WeK/1PtgSUBAEQRCEvkzrCZfdzolPMEjlUhWsmD2b2y5fzrylkSOpPGpaS0UzEip0TNPoAQU4WRo8mJOoOE5cRAEVBKEZlacZXnjnnXeoGN51V+RiPe2NXepYGRm6F7K2ll7O3Fy9eNuYMfRIAhxvTzqJqQorVzLKRNM4hg4YwJDaoUOpaG7ZwrFY0/S0CL+fY+yIEcDEicBRR7UsjHTgAHNMDQYWNUpNZTRLQwOvI9yTOngwl1ss3M9iAcaO5fJwpGWKIAiCIAhR43azncrq1ZxsDR/O3nFff80y/jNmcJv77gMeeYSTpoICTl4aGznRidRAPTz01mLhq6mJkyOLhcpodjbDuByOuE5cRAEVhASmda7lkR6rdcXbUIjewfx8FoEcOrSth1RVlA3PVQ8/ltnMcXLoUIa8lpdTYVTGuyFDgPHj9Sqz55zDMNyiIo6xKSk8ttHIMdTjYeuWhgYqthUVfE9P16vi7thBz6bLpXtq8/Pp+XQ4+HuTJ3M8BliV/MABflbVe91uYOHClsUvVWVcs7ltlV/xfgqCIAiC0ILwiZpqB/DOO8xNsts5EbHbOaHIzOQkxudjONiePVQqKys52QgEOBmy2zkJao3JxInZoEEM2aqpoYVf0/j7ykLf0ACceWZcJy6igApCAhIpVDaSItgVVMXb4cP1UNWiIv6Wz0djncvFcTNSa5IFC2igUy3l1LFCIT3n0u9neOygQfweCjHSxG7XvY8ff8zxd8AA9vS02zlmFhfznLZuZYGgceP0sFuASq7JxONbrRxf3W6e84IF9HhWVTG0NitLv26nk791wgnA7t1tw4wdDn2MlpYpgiAIgiB0SqSJmt/PpuVVVVQQbTZ6PpXSmZHBycsXX3BCYjLxBXCyZDRSCW3dekURDHLS8p3v8PvWrQwlKyvjvgMG0IJ/6qlxn7iIAioICUikUNlwRbA7ZGdzfKypoXK4cydDVG02rv/wQ46dkya17Qn60Udcv2EDx1wVUuv16jmfpaVcZjLxN0IhKoJlZYwoSUmhMrh3L8dfh4OGP4uF4+fhw3qxN03j+RkM/C2HQ/emWiwc1+vqWqY3jBlDxbd1zr4yOv7wh/zekUc5QVoCCoIgCIIQT1pP1PbvB15/XW9iHgxSmfT7ab3PztbDw3bs4ATKYOC2TU165cZIeZ8mk57r6fXyuC4XS/bv389J1q9+xUlXH5m4iAIqCAlGpFBZpQiuWUPlqDtji8tFL+rSpRwLzWaOYU1NTElwOGiomzq15X5OJ3PjlSLnclHZrKig4uh0csxU463RyLHQZuNY6PNRabVYaBgsLuZ47PNxLM7J0YsNAVQ4U1K4TyCg5+1rmh7uq2n6ONv6+pSi3l4IbTT3zuXqE+O3IAiCIAh9DbebeT82G/OD7HZWY2xq0i3nPp+uUNbUcPJSVUVF0u/XC2CYTDxeKMTvqrCQQvUCVVUdvV5a7FNTedyaGk5ypk+Pz71oB1FAjxBpoyocCd35+wkPlQ0nFtVYTzoJePllekBV5e6BA5mvrgqolZdzTFWVvAsLWVjIYmG+qMrJtNn4XlvLsdJkYh79gAFcXl3NsTYtjcdeu5ZezYkTedzt2+nFPHiQHlGjkdeobpnDwfUGA9dbrXq7luxsFo5rfR8khFYQ+g8if5MTee5Cn8bjAZ56ihMNmw3YtYsW8YMH9W1CISqlKsfJ76dyqopdhP+NBwK6MgrQKq9asSiUFd5qpWdC0/r8JEcU0G5i+iYm2+/3w9HdpDsh6fF/M4Cov6doCA+VDa/aeiTVWIuLgSVLWH22sJDjnFI4i4uBN95geKvLxYI9mzbpRriSEt2IFwzqY6JabzJRcVT5mbxerktNpUJaXEyld+JE4Nvf5jEOHaKSqjyfZjPPSSmZZjNfw4dznFXrBg8Gzj2XCmhrJIRWEBIfkb/JTXfkpiD0GkuXAqtWURlU1vpdu/SiQZrGZeEVazWNVvzWIbaapk+qTCZOmPLyOIE6fJjLzWb9fehQht3efDOX9eFJjiig3cRiscBms6Gmpgbp6ekwKMuEIESJpmmoqamBzWaDRVXSiYJoQ0mjQeXIL15M76PHwxx4i0VXcI1GHvvwYYbYqoJBBgMVRDWOGo0cKwMBHltFjpjNDLlNS6Mnde9eKn8Av8+dy4JCixfz3WKh99Pj4fr6ev62x8PjpafT8+nxMBT36KOZvzlhAgsYFRZy/z//uf3CTBJCKwiJi8jf5KW7clMQegWVIzV8OCdEa9fqJftbE65oGgwMnY3UWkVhtwOXXMLKuF4vJ0EqFC0nh0UusrNZYGjcuNhfW4wRBfQIyMnJwcGDB1FcXAyn0wmLxSKCUOgUTdPg9/tRU1OD+vp6DBkypMvHiFUo6dKlfJWVMUKkqYnKnYoOUd5HFf7q9VIJHDGC4+uGDVQum5q4ruV16l5QlT8fCvGVkcGemoMGAe++S0VWpSs0NDAH1WbTvZ5er54X6vXqlW5HjwZuuYVj7bPPsuBbXh7XlZfz2oDuF2YSBKFvIvI3uYiF3BSEHic8R+rQISqIHSmVChVCFgmV92mxAF9+ycmNwUBl02zmBCsrS2983gfDbSMhCugRkJGRAQCoqKjAwfDYbkGIApvNhiFDhjT/HXWFroSSttcrVBnqMjI4hqWkMCfT66VSqYoGmUxU6DIy2JYFoKJXVqZXs21dWVahlE+/n8fatInKssXCsXn3bo7VmzaxMriqFF5RofdQzsrSixT5fFw/aBDH99TUltficvG4RUXcNhCgZ3X2bEam9ASx7MUqCEJ0iPxNTo5EbgpCTOhI6Kscqf37ORFJTeWEqr3qtdGSksKJz8GDPF4wSC9BdjZ/Y+BA4Ne/1j2fCTAxEQX0CMnIyEBGRgb8fj+C0Vg5BAHMXYlF+FBHoaSd9QpVhroBA6hgBoP0ctbWtgyjVekK+/dzucGgt0iprm7r+QwnGNTDc4NBvSAbwGUmE4/V2EijXnU1vZtq+2CQ46jZrOeQqlDe4cO5Pjtbv5aaGob4pqbyWhoaGK2yZAnw298e8e3u0v0VBKFnEfmbXMRKbgpCt4hG6Kscqccf5+QoJUUvzd9dLBaGdo0frzdadzr5+14vJ09q0pZAExNRQGOExWKRgVHoU3TWK1QZ6nw+KnQ7d+r9O1VBNRVGqwqzqTSFQIDL2vN8hhMp9QHQw3HVsUpKaMTLzOSruJjKZzDIMby6mmkOmZnM8/T7gTPP5LFUJIpSPtPS9OtIT2dOqNtN2eB2UykF2Bu0u8bBnujFKghC1xH5KwhCjxOt0J83j5ORLVto0T/Sqs2aRm/nuHGczDgcnOj4fHquUnV1186xDyAKqCD0Q6LtFTp1qj5WjR4NfP01x7qcHI5xpaVUAFXLKYtF/676JR8pKvUhGGTESk0NMG0alWFVaTw9nS1clDe2spIhwV98QWVy5kwaB1es4HWqVlgNDbyuQIAK7muv0RuqQomHDQOuvBK44oquGQd7qherIAiCIAh9jGiEPsCJxjvvcDKVns68oiPNTQ8GOZnZsoWKps/X8riqHUBVVUJNTEQBFYQEItqw/s56hZaUAG+9RQWutpbLMjOBKVP01ilGI5VAk4nbAFzncDCv02rVq4rHCk1jEaSNG4HvfIdhv8Gg3tOzsRFYvZrvkybRY6oMfKecQo+mikaxWlkZNzeX4/Tq1cDTT3OMdjr5e/v3A48+SsW6K8bBnuzFKgiCIAhCHyJc6Dc20hIO8PPBg8ADDzBsa9UqFqIwm+mlNJtb9uuMFtViSOUfKcu/ycTftlo5GVMWfNXjLoEmJqKACkIC0NWw/s56ha5eDeTn00h22mk0phUWcixLTaWRLT2dY111te7pDAT4+0B0hd26y759wI4dVIKNRv5mairbsZSWUtEcOZLbhkI8xy+/BC67jMbHjAzmtipD4ezZwGefUVaoewPw+hoaeC+6YhzsiV6sgiAIgiD0QbKzKew/+4wTjvJyCnyVY7RhAydNJSX6hKmuju8WS/eUUIuF1vPcXIallZdzmWoDMHgw19fUcDKTlZVQExNRQAUhAehOWP/YsTTGAS17hc6eDWzerEdp+P30Ch48CHz6qe719Hio+LUXZqvCcmNNKESP6wcf6H2c33uP4bLDhtHrOW0az3v7dobTqnZYLhfzQr/6isZIs5nXO2sWj2EwtByX7XYqpVVVXTMOxrIXqyAIgiAIfRiXixOjnTv5vaZGL7VvMHBCokLFWtMd5TMYZFiaplGxtNt5fLudCqlqwp6ezvYryqqeQBMTUUAFoY/TlXzDcE9pdTW9e199xf2cTo5B06bpfZIBKnE7d3Jbn0/vvWm1tl9AKBpUBd3uEgrpufdGIxXFGTPYuqWxkbmfO3fSA1xfz7H5P/9hu5UxY/j7gQCVbYDjtKYxN1SN1V6vPr531TgYq16sgiAIgiD0YVRFxDFjOGlSiqcKj41FQYxwlGfTYOAkLi2NEzkV0jtsGHDiiZy41NXpLQESaGIiCqgg9HG6km8Y7ikdPZrLDxxgLuT553PsBPQojYYG5sr7fOy9qWlU7pTC190x9UiVT0DvIVpZyeiTQADYto1K6DvvsMic3c71lZX8rLyiO3YAI0YA557L68jP571ISeG2Kny4uppjtsox7Qpd6cUqCIIgCEKCUlnJSZOq0BgMcoIC9Ew+kqZx0mOz8fhVVZzAjBlDy/vEiTyXSB7OBJmYGON9Av2ZlSs5EY/0cjjoqTnnHOCRR/S8ur7IunXAD37AnDu7nb1wzz4beOml2By/vp45if/8J400o0bp90nl+UVLbS3w978zCiE7m/+7w4czN/C992Jzvr1NeL5hOK3D+gsK9PzHvDy9R7Hyiv7lL8D//R+f29SpDLd9+WV6ElVbE0BXHrsTNQLouffdofX/CaCH5NbWcgyeNYsvn48KdH09r1XT9PYxZjPrACjvb14ex/Arr+TfQ10dXyNGAD/72ZEZB10uVkfvo2O8ICQvfV0I97RwVSxfDnz/+7RKpqTwt4YNAy66iMIhGmthfT1w//3A6aczBNBqpbCZMgW47jpa/gShP+F2c2JVXMzJ0urV/F8qL+eEpKmp+xOlaAkE9LzPqipONM47j2PGgQOc2ETycCbAxMSgabH2GycutbW1cDqdqKmpQUZGxhEfb+VKjtXRMGIEPVdTpx7xz8aUP/8ZuP329vP9LriA8is8r66rnH4671UkRozQ++52xrp1wCWXUPFoj6uvBp58Ui8wlig8+6zu2Wwd1j9vHp/BO+9QqczK4tg0eTLnBNu3c6zMyKAyGgoBQ4bofTZbj59ms15YrTtkZPD+VlV1fV+zWQ/7Vf1HVbG3YJCVcR95hOt/+Usa+Hbt4vUZjdy3tpb3oKmJ87nzzuO6Awf4t5ydHZs+oELfJNbjuNB7xPzZ9WUh3BvC1eejgrt0acfbnXYa8MYbennw1mzZAlx4IcuGt4fZDPztb8CvftXdsxWEvoGy2ufnMzSsoIDFhTye2IfatkZ5AIxGfs7IoAI6cCCrMD74ICe60bZE6GW6MoaLB7SX+NnPgK1b9deKFcBjj7F3IcBx/Zxz9KJZfYEnnwRuvZXyccwY4KmngPXrKaeUTP/f/4Brrjmy3wn/f87KAs46S8/Ri5adO3n/Dh3i/+2Pf0xD1RdfAP/9L0MsAeCZZ6i4JBrz5lHZDAZ1o9fs2cznfPppzptSUpiz7vPxfnz5JQv0NDRQKTt0SK8i+/nnVGA1jWNbOIEAj9/d1lUGQ/eV1/Cc03Al2O/ncz3+eI61LhevX23T1MS/U1WNXLVhCYUoM8K9xS4XMH06X31o3BYEoSfpS0K4t4TrDTfoyueAAQwz+ugj4OOPackbMYLrVq4ELr888jFqauiVVcrnrFlMtv/sM+Dtt/kbFgsH3Ztu6lzZFYS+jNsN3HsvXxs28P9SFZ7oDX+d+g1VACMtjZ8bGqiMHnMM1yeAh7NTNKGZmpoaDYBWU1MTk+OtWKFp/MvRtFtvjbxNU5OmnXGGvt0//xmTnz5iqqo0LTOT5zR8uKYdPtxyfSCgaRdcoJ/3qlXd/61//1vTlizRtIICfdmIETzuiBHRHeO88/RzWby47fpQSNOuvprrDQZN+/zz7p9vPKmo0LQtWzTtoYc07Uc/0rSLL9a0CRM07fTTNe33v9e0uXM1beJETRs3TtNGj9a0IUM0zW7XNItF02w2TbNaef3qXpnNfKnvsXilpPC3YnlMh4PXtGuXfi8aG3kfjjuO651OTRs7ln+vaWmaNniwpk2dqmkLFmjad7+rac88E59nJvQusR7Hhd4j5s+uLwrh3hKuZWWaZjTyGFlZmlZU1HabmhpNGzlS/60vvmi7zT//qa+/7LLIv7Vsmb7NlCndO19BiCeNjZwkzJunadnZnMhkZcV2IhPNy2zmRM1k4nnk5nJyk5enaddcE++71CldGcPFAxpnrFbgttv07x98ELdTacETT7BAC8CcypyclutNJhpQVSjr3Xd3/7d+/GMaX8eO7d7+FRUMPwWY+3n11W23MRiA++5jbqKmMVIoEXG5gI0bGRliMtE45vHQOL19O435Dgc9nQcPMkLD79fDWQOBlka8nijediSVc1tjMPB/JDub1/XaazRQqgiZzZv11ixGIx0KTidTu8xmGhCt1j5bBE4QhHjT20K4t4Trp5/q4b0LF3JQbE1GRsuQoLVr226zZo3++ZZbIv/WhRcCxx3Hz1u39q1QLkFoD7ebHs716xkNcd99/HuvqWFYVXfyiLqDzcYy/ZMnAyecwOqJkydzglZfz8nPBRdwvOhHSBXcPsC0afrnoqL4nUc4b7zB94wM4OKLI28zdCh7Lr7/PmV2fX3XQ2djweef60rUOee0v11mJvCtb1F5e+cdRlSkpPTKKXab1mH+qtBQaipDagsLGV6racCmTXwGpaWcd1gsHL/UKxJHkuvZHj7fkR9DpT9omn4tDQ3A449T0bbZGEI8ZAgwaRKjy9av5/Jhw3ivpk5ltNjgwYkdpSIIQg/Tm0K4t4Rr+EA8enT726nS6IBe1bO7x/nyy7b7CEJfwu1mPueHHzJ3qaiIf/cVFZwMqYlTbzJ+PC33dXUci+x2Kp0TJ7Jq4lVXsdhXP0MU0D5AeEEccztPJLyWwoIFzGXsKXw+TuYB4NvfpoG4PU49lTKyqYnh8tHWe4gllZX6Z9Unsz3U+sZGKq6zZvXceR0J4f08VZVXk4nX+vnnHKv8fnr+cnJYlO3wYRrsNI3Km83GPpeJSCjE6zWbKQvKy3kPbDYaJzdvZrVk9TyHDAFOOolK6nXXSXEhQRC6QG8J4d4Uriq3FWDPqvZQFdla7xO+7O239eO0NxFWx+ljRVGEJEdZ8R0Oeh/y81lUS+U0p6RQ+VRGk1iGcEWD3a6Havn9/H9XkQb9/H9JQnD7ADt26J+72nakJygo0P8HJ07seNvw9V991XPn1BHhLT9atyppTfj68Pve11D9PE0mGsD27eMcoLSUz0Ypm/v28XtKChXPujqOYcEgr9Xr7X4xoXgTDOoVelNT9d6kNTU0UlZV8XtjI2WM1cp7kZXVr8dsQRBiTW8J4d4UrsccQyUXoLJcUtJ2m7o6tlYBeN1z5rTd5pprdAX9r3+N/Ftvv617P3/yk66fqyDEGo+H7QNuvpkFv77/fb6vXs3/p+pqvkpK4uuxT03lhMbr5Wc1PiR6gaEoEA9oH+Cf/9Q/X3pp/M5DER6BFCltJJxhwyLv15tMmqR/XrWq/SrwPh8L9ykOHOjZ8+oubjc9n6p/pVK6MjOZ1+n16hnr9fV8qRxPTeP68HYmiY7JpOevZmZS8bRaOXfauJHffT6uz8ujoVMQBCFqeksI97ZwffppvYLttGnAb37Dd7MZ2LYN+Mc/mMfhcgFLljDEpDVHHQU88ABw/fXshVheTiVz9Gh6jj78EHjoIW575pnA73/fvXMVhFiirPhOJxW8nTupcKpy+X2F1FR6PRsaGNLmcunN3fs5ooDGCbebRpg77wTefZfLvv1tYP78+J4X0LJ+QGdpJ+Hex3j08QYYITRpEu/n229TeZs5s+1299xDeanoq3USKit5L4cP53ePhwqWw0EPaHY2z711y5LwdnKJqnwajbryrGmcDylPaG4uvZv19czXLytjD1CXi/s0NPC+5OczQk4QBKFd4iGEe1u4TpzInI1HHmExo9bWWYuFy66/Xhc4kbj2WhZH+cc/WAluxYqW68eMAX73O1YAbC+EWRB6C7eb3ojqahpfqqs5sepLiqdCWc8HDuTEZubMfu/5VEgIbi9x++30UqlXTg5wyimUe2YzcOWVwHvvte3JqDjtNN3r1ZP5n0DLvMGOUlSAlgZTj6dnzicaVGRQKMQCYg8/TGXN72fU0y9/CfzhDy2vJ57n2xHZ2ZybqHBhh4Pnrb77fH1zHI0FoRCvLxTi/4XFwvSI1FTeE6+X156Swv8htczn41xr2jQaINzueF+JIAh9ir4ghOMhXN95h57LSEqs389G2a+80nE59Lo64Lnn6O2MxN697A26YUP3z1MQYkVlJcPqi4v5v66KSfQFVLhaaiqt6scdx2iHAQOA73wnqcr1iwLaBxg/npExGRk9+zu7djHqJtJLVYUHOOFXdBYaH140L56hj9/9Lg3ZBgOrwl53HTBoEGX8+PFMc7Hbgbvu0vdJT4/b6XaIy0UjWFkZX0Yjo0jq63k9hw+39Hb2V/x+XrPHo1cjr6jgOH322axSPmSI3n6lrIzpHDU1LQtTCYIgdEhvCeHeFq433cRwkB07gIsu0qvaeTzMX1i4kB6im24Cvve9yIKltJSe4UceoafmrrtYcMjno6Xv9dc5GH/0EQsl/fe/3TtXQYgl1dVUPNPSWoa+xZPw8v5+Pw1g551Hi3ogwOqKS5f2Xe9IjBEFtJf42c/YHmvrVubqv/020ygsFsqG005jiHpPMmcOC9hFeqnK8EBLxayzyJ+GBv1zPFqwhHPzzSxUeM45LeW8xUIF9csvWxb5y8rq2fNxu+l97Y43bt489q4MBpmrOnIk20ANHJi4lW27gwotrqri3PDnPwdeeAH44Q95X3fv5tzM5eK4vmMHFdEkSaEQBCFa+oIQ7k3h+tZbzDsBGBr7+ussFZ6aSgF53HHMEVUVN195BXj00bbHue469r4yGHjM3/2O+Z8WCwfaiy4C1q1jHkxTE3+rrKzr5ysI0RJpctV6WWYmlbqKipb/S/EkFOLLYKD1fPp05gxlZ7PokMnEvNWlS+N9pr2CBOv3EgMGAEcfrX8/9liGil5wAXs4V1YCl1/OCu3hFeHjQXhthOLijrcNr40QXjMhXsyaxZfPxz6ZwSD/z1U006uv6tsedVTPnEPrFippafRozpsXvSHb4aDh+vzzW/YBffFFGq1VjmRHUVP9AVWAyG5nP88f/pD3QcmY1hV+E7XiryAIPUxfEMK9KVyfekr/fMcd7W/3+98D991HYfXkk8z3VFRVUXEFWGCovVYwaWnMcbnySk72X3oJ+MUvun7OgtARkSZX06dzIrRhg75s6lR6G4qK6OEPBPRQqXihJjMOBz2eP/0pLeiq2iSge07WrOHkr5/ngooHNM6cdx7/DgFGxPRkfue+fbrS0vp19dX6duPH6/L36687Pmb4+vBqtPHGagVGjKChNjyV5pNP9M8zZvTMb7duoRKNUas9g1648gkAY8cyHDfeRorewmajUhkKUYao0NrKSs4nJ03i329NDd8nTeJyCcEVBCEqelMI96ZwVa1b8vJohW0Pu50htJHOaedOfdI+bVrHv3f88frnzq5NELqK2w08+CAnUmpy5fMxv+qhh1pOuPLzGX5bWakXjoh33pLVyglNRgaVy3POocLsdLbcTuVbJcEkRjygfYBbb2W7ooYG1kn4wQ86r0/Qk1itNCqtW8eXz9f++axaxXebjUXy+jKHD+s1FL71rZ7x2LZuoQJ0bNSKZNA78UQqXatX0wCdlQXMnq17UFW/4v6OSpUAKFO8XhpRsrP5yszkvZw8mffR4WCtjGBQQnAFQegCvSWEe1O4qmq00ZREVwKldQXb8O+dHSdcKEklXCFWqEnSO+/wf8Zupyfx4EF6Nw8c4PchQxhWnpfHv9WPPuKErC/kLBkMnNyNGgVcdRWwaBH766lqk+E5YzU1XJ4EkxjxgPYBBgzQezcXFVEOxpuLLuJ7bS2rrkeiuFhX6GbP7rtFfRS33abLyPAoo1iiWqhEa9SK5C195BFGTG3YwFoPGzbQyPeTnwCXXZY86TWaxvmZ0ch7t2kTHRWXXMKosBNP5L2oq2NaU10dvydRFXNBEGJBbwrh3hKuo0bxXbWbaY/KSlYiDN9HMXKkntfw8ccd/55SmCMdRxC6ywsvAP/6F9sJVVfz73n1aobKB4PMRTYY6K3fvp2TvLVrqZz6fC2LecWLjAz+T8yfT+VTFa4Irzbp9eqfk2QSIwpoH+HXv9aNIH/7W9uK0StX6tXjw8Nle4prrtGVqN/9rm0hnWAQ+L//08/zppsiH+fqq/XzXrmyp86WY05pafvrH3uMih3AwmNXXNEz59G6hYoiklGrtbfUbtf7W7rdNHxnZ1O5Ki5mccEDB1oay/oDZnNbg7nNpi9TToLBgzmO79/PWhkGA+dmlZUMXw4GWbgpiaqYC4IQK3pLCPeWcL3gAv3zDTdErrobCjFXU607//yW63NyGC4EcMLfnmK+f7/eC81gYFizIBwpbjewZAlDwdLT9SIaHg+t0ikp+rYOB1BYyB61hYX8O+wLrVeysznhnDaNRpzwXKzW1SaTbBIjcRJ9hIEDWWDl4YfZUuvFFxkFFC+ys4G//50ep/37mS/5hz+wYm5JCT1yqhf197/ffm2CaNi9u2VuJqAXCKyvb5uSc/bZvF/h1NQwpHbuXL4mTODyggLg+eeB99/n9xEj+L2nitUoo9ayZfzudPLcysp4XuFGLeUtDe//XVnJyAyzmUqXpjGapKGBY67JxHXh+fSqrVRfGGu7Q+tiSrm5fHm9QHk5r3/UKP5N+v181dQAixez/3kgwHsydWrXCj0JgiA001tCuLeE69VXs7jQ118Dy5czjPfnP+dAaTKx8u+jjzKsEaAV9MYb2x7nzjtZgCgYZAW8/HwOtEOHMuxk1Sqes1KkFy3SBbAgHAl79jAiwenkS3kJAU4Eioo4MQqFqHQGg/qkyWTqvNVRT2D8xq8XCtGgNXw4vQhKWQ7PxWqv2mSSkPAKaHl5Of7xj3/g3XffRWFhIUKhEIYMGYLZs2fjN7/5DcaOHRvvU4ya3/4WeOIJ/s/ceSeNJsY4+qh/8hPKw7/8hePAokVttzn3XFZyPxI++YRyLRJud9t1K1a0VUABjjmvvtqy0m043/425xQjRhzZ+XaGMl6tWUOjVlpaZKNWuLdUGd5VwR2zmUa/+nrK+PC2UH5/y3z68DzJRFRCW59zVhafkd0OfPYZI9VMJnq4a2p0hfzwYaZ9jBvH5fn5vJ8LFsTnOgQh2ehP8hdA7wnh3hCuVivw3nsUPps3s/3Mj38cedtRoxgOnJPTdt1pp9Hz+ZOfcOB9/nm+IjF/vh5qJAixJjeXE4bGRk4E6uq4rLGRk0U1GTIY4lcow27XK96OGcOxxOOhAup0clJYWdlS0XS5kkrxVCR0CO7OnTsxZcoU3HPPPSgoKMDw4cMxbtw4lJSU4IknnsDUqVOxKjwvoY8zbJg+ef766/YVqd7k9tupIF5+Oc/PamW6zFlnUZl7++2+ERKamckK8t//Po2vTifDOIcPBy6+WC/0M3Jkz5+LMmrddRfv31138Xtrz1ykFIBAQC8ydPiw7vFTtFfMLRhMTOWzNSYT5zgbNzLiq7aWKRwHDtAbGm7gDAY5jjc0MDonL4/PuDt9VwVB6Br9Tf4C6F0h3BvCdcQIFhF47jm2mhk6lILRaqUVd84cKoxbt7ItTXtccQXvxy230JKbnU0raWoqBe6CBSz68p//xLeCotC/GDOG/xvV1bTGaxondykpnCzk5fFvMVz5BOLXn85oBE4+md6GMWOoeFqt+uQviQoMRYNB0xK3k+CZZ56J/Px8zJw5Ey+99BKGftNjy+12Y9GiRXjzzTcxatQo7NmzB4YoYi5ra2vhdDpRU1ODjIyMnj59QYhYBbe+niHDHg8VrUBA94z2Z1Q6kwoxttlYX6Cxkcq50djSy5uezmW5uZRHAwfy/t1xB72iQnIi43jvEGv5C8izEwShFU8+yVDxxka9NL7BwAlSaiqLD0VT6bm3SE2lEur1Art20UAzY0bLXKx+HKbVlTE8YRXQxsZGpKenIxQKYcuWLZgyZUqL9VVVVXC5XNA0DTt27MCkKPpoifAT4kV4z8+SEhZKPHyY63w+jrd9oZp4T6GUz3BF225npdvSUubyAlRILRbeE5OJhsVRo7if202D/6uvJmU0i/ANMo73PD0hfwF5doIgtEJZ6fPz9b50xx0HvPIK85hbV3yMN0YjreNTpgATJ9Ji7vXSOj5zZr8vVNGVMTxhc0B9Ph9C38xUR48e3WZ9VlYWsrOz4Xa7EehL1hFBiEB4CkBlJZWsgQMZWlxVRWXU66WipbyAiWk66hiTia9QiO9KKU1P57JhwziO79jB0FzlMQ0Ge66wlCAILRH5KwhCr9C6UA9AhXTrVr1aZV/CZmO5/sGDWU0bSMoCQ9GQsDmgmZmZGDZsGABg7dq1bdbv3LkTbrcbmZmZGCfxeEIv4HbTU3ekOYhVVfT++XyMOsnM5HimFCwVhdIfUAqmpulF49Qyq5W1Mw4e1HM+lbJpNOrbNDZy/0mTmELVuteqIAixReSvIAg9gtsNfPAB+86p0CeAeTZr17J10S239E3l02TiZERNTFSxoXHjRPmMQMIqoABwxx13AAAWLVqEV199FW63GzU1NXj//fdx0UUXwWAw4B//+AfsfaFKjtBv8XhYJPDmm4Fbb+X7s8+2rFzblePcfz+VrvJyVhYvKKDCNXIkPYGte2YmKkqBVFitvE6jkdcYCHAsVwXlrFbWIigp0XNCU1JoWJw1iwWnMjMlv18QegORv4IgxAyPhwWxZs4ELrmEbYRmzWIbhMpKVoRevBh4882+aYFXDc2NRp5fVpZMRjohoaeyV111FdLS0vCXv/wFl156aYt1xxxzDN555x2cffbZ7e7f1NSEpqam5u+1tbU9dq5C/2XpUvb9zMujElRTo/cB7UquuTpOVZUeimowMKLD56MBbcgQ4Msve+Y6ehplHFQ5nnl5LGhXUcFKtrm5VLCzs9mZoKGBY3lqKr2eGRm60dNi4bEOH+b+5eU0Ml5yiRgaBaE3OFL5C4gMFgThG5YuBf7xDwp11cqkoYHtgXbs4PLi4r5VcEihcoHUpC0tDZg9WyYjnZDQHlBN07B371643W6YTCaMHTsWRx11FKxWK7Zt24bHH38clR3E4911111wOp3NLxVSJAjR4nazgm1eHl92u/65Ky1B1HEyMth6ZMAAtmQLhej1q6tjAbWdO+PTWzkWhLeQsVrZF33sWGDaNOCyy6hAZmVRztTWctvsbOA73wGmT+f4Xl3N46Sn69VvNY39qDMz2/ZaFQShZzhS+QuIDBYEAZwAvfMOBXxKCl8WC/M/GxvZk62wMH69PVtjMHCyl53NyUhaGicomkYvwXXXyWQkChJaAf3pT3+KX//61xg2bBh2796NgoICbN++HUVFRTj33HPx+uuv4/TTT0ewnQaJN998M2pqappfRUVFvXwFQqJTWUmvnNPZcrnTyeXR5iOq4xiNel/Qigoqm0Yji+8EAlzWV8bgI8HnY6uZdesYeTNjBr3HxcUMr9U0KpplZUwHAdhWS4XYDh3K6rdjxlBxHTCARYkaG+N6WYKQNByp/AVEBguCAE6AystpdQ4EGAZWVcUJT1/0eKoQYFXNVtOA0aOBG24APvwQ+NGP+nWl21iRsAro5s2b8cQTT8BiseCll17CyJEjm9cNGDAAS5YsQU5ODrZs2YKlS5dGPIbNZkNGRkaLlyB0hexsGr9aVwIvK9P7VXblOLt2URH1+fTKrqEQx2K/v39VevX76dktKmKUza5dwPHHs5VKWhq9pFYr78eXXwL79rGwXDCo92e3WHhPMjIop6QAkSD0PLGQv4DIYEFIegoKWG3Q4aAQb2igUA8PmeqLKO/AuHGcwGVlAePH0zouREXCKqBr1qyBpmkYP358xLCdjIwMTJ8+HQDw+eef9/bpCUmCy8WceeW1rKsDVq8GVqwA9u8H7r67ZUGi9irlulzA1KlUxgAqXR4Pxzifj70wPZ6+mXt/JPj9vE8ffwxs2AB8/jmwfTsVy6wsplQEAoxuycoCLrxQVzQDAd6nhga9wrnk/AtCzyPyVxCEbqEmQbt3A9/7HnDGGcD11wNffKFb3r3evpVrpMruA3ovPJOJk7JgkPlSI0d2Le9KSNwiRHV1dZ1uo30zW/d6vT19OkISo0L916zhq6yM4aHTpjEkdNkyjqVWqz4+mc0s8DZ3Lqu6AsAxx3Cc83giR510xaOaaKiw4rIyGj1ra1l8yWRiOsipp1ImzZ1LhfWDD7hPaiowaBDTMGbOlJx/QegNRP4KghAVbjctxg4HkJ+vT4I++IAWe1X23mym8A+vVNiXcDhaemaVdbyuDpgyhY3bDxzQW68InZKwCqjqLbZr1y4UFRW1scLW1tZiw4YNAIDx48f3+vkJyYPqk3zSScAf/whMnEhjGEDFCACWLOF2Xi/DaevqmCrw5z9TMTWbWVSnspLjb3/q9dkVwo2eqt+nz8f2XyedxBDcf/2LFdlXr+b4r7zQkvMvCL2DyF9BEDrE42Fl2zVrGKpUUsIiQ6NHM+RJGbFCIT3Uq6+SksIJmsvFcLSyMr1H3IQJwOTJnLylpUkYVhdIWAV0zpw5yMnJQUVFBebPn48lS5Y056GUl5fjhz/8ISoqKmC329uUiBeEnsJkoiEsHKuVobW5ucwVTU2lgllbSwXKbqdyWlXFZQLHdk3jeG4w8P4pZRMArr0WmD+fY74Kv42EMr6qbVp/FwSh64j8FQShQ8L702VmMi+pvh7Yu1dXPhOB1FSef20tJ3guFydt1dVUPo85hpOKAwcYqiVETcIqoGlpaXjuuedw8cUXY+3atRg7dixGjx4Ni8WC3bt3w+fzwWw247HHHsOQIUPifbpCEhBekCi897oq7lZfz7HMZuMyVWTIYGDOY3V13E69z6BpejVzk4n3xWzmPWtspAKpFEeXq30lsrXxVbUVU+klaWm611SK1QlC1xD5KwhCu7TuT/fJJxTEBoPezDtRGDCAXgS7neFtVivbHKgJRWEhPaIAi1js3i2TiyhJWAUUAM455xxs3rwZ99xzDz766CMcOHAAmqZh0KBBmDVrFm644QZMmzYt3qcpJAkqFHTZMn53OqmM1tbS+3n4MBWp6moqQQDHsFCI2/XlCJTeQo3pZjPbaWVmUpk0maiURpteEW58HT6cbcS+/poGyxkzeL/Vc1qwoEcvSRD6JSJ/BUGIyJ49wKFDDLetrQW++kov2JNIZGYy76ehgS1hpk0DrryyZUjVk08yH2j4cH3SJ5OLqEhoBRRgfsm///3veJ+GkKS0DukML0h04AA9bd/9LvDRR8COHUwfCAb1ojvBYN9scxUPlMfT6+V7IMCCQ34/76PbHdmgGCnMNtz42thImaCiaEIhLge43fnnSziuIHQHkb+CkMS0Fr4q9Cg/H9i2jZMes5nKW6Ipn6mpwFFH8fxNJk5CCgvb5u/s2UPlU00qVPibTC46JeEVUEE4ErqbE9g6xDM8pHPBAo476rhvvUUDWno6vaDhvTyTsdBQawwGFhsymTjmKyW0pEQf9y0Wpo3k5+tGxfaewejRuvFVbefzsVdoQwO/p6TQWClF6wRBEAShFR1NjiIJ36lTWchizRqgqUnvl5aI/eMMBlqsVchwTQ37ezY0tJwwVFZy/fDhLfeXyUVUiAIqJCUdKZDRhO0vXcpXRgZTBHy+llEXKj9ReeOyslicyGLRPXLi+eT4riqwGwy8J0OHctz2evWq5y4Xo1/CjYqtw2zdblbItdvpad69myG3o0YxbaO2ls9WPd+aGilaJwiCIAjNRDM5Che+gwYBGzcC771HoW00MswoO5tCOdGUT4ATD1XVVuVLHThAy/by5ZykOBztF/6QyUVUiAIqJCWtlZeuhO0XFwOPP04lx2qlN23YMOZ5to66UAaytDQqV8OHM1+9qYnGtNZjsxq7kwm/Xw9JNpt571T/T02jcq+KNR06xIgXoGWYLUDvsmo3Nno0t9u8mcd2OrlvXh7vcVkZX3PnioFSEARBEAB0PjlqneOyaRMnQ2Yzw5QCAU5iDh+O62V0C2UR9/t5HSYTJwx2O78PHswwrLQ03dMQqfCHTC6iQhRQIeloPX4CnYfth/dSvvFGjrlWK4sKeTxUMpWiEx51kZ2tp0CEQnyvreW+gYDe01h5/5JN+VTVblV6SCDAe5uZSW9xRgbXFxVx3AeAhx4Cjj5abykG0KtcVMT9AgFg7FiG8+7aBezcCUyfDowZw99Rublz50rvUEEQBEEAEN3kSFnVc3OBgweZF5maymXKW5ioaJo+IQM4WVN98oYN0/t9hk8UIxX+kMlFVIgCKiQdXQnbbx2Nsn8/C7pZLFQ+jUa9qvjevcCJJ+pRFx4P8z8LC5mPr9IiNI37m0x65dtEy8+PJa2vvaGBynxtLZVGt5tGxawsYMoUyrpVq7idasml8jzNZir36emUnyNG8LnccAOVUOkDKgiCIAgRiGZy5HCwQMOXX1J4Hz5Mwet2x+ecY43qj+dw0JI9bhwt2ykpXN96ouhwtC38IZOLqBAFVEg6uhK2Hx6NkpsLfPYZFZ3UVCqTVqseeeLz0TOnxh61bzBIZdNs1r2cPh+V0MxMGg0bG3v1FvRpVC9Qn4/hzg0NHPOnTaMB0mLhdl99RTkA6B7lhgb2hVaywudjisqYMfzeUe9QQRAEQUhaopkcvfUWJzxNTZwI+f1cr/KJDIbEzPsMx2hk8Yhhw/R8IEV7+Z0yuegyxnifgCD0NipsX+UBer3655kz9TGkdTRKKKSH3QYC9Mhpmt5WZdgw4PLL9X1XraJxcNcu3ctnsegKFMAQU0FH5XqaTLrSPmoUW9kce6x+75xOPpNZs3QjbF4en0lubvvPVBAEQRCECHQ2OQI4KZo2jZbeYJCKaLjCmejKJ0CFc8ECYPbszieKQrcRD6iQlEQTtt86GsXh4LiUlsZ1AL1rtbX0tF11FYujqX1V30+V1+nz6TmPJhPHM+XBE4iSXcoLmpUFTJzYNje2poZK6DXX8LuKDMrPl1QMQRAEQegWHU2Oios5KRo0iLlFqrF5f/B6hnPiicBPf6p/l0lFjyAKqJCURBO23zoaJSWFRdD27eOYW15OY5jVClxwAbBoUcv9q6u5TvWwtFjoOfX79Rz3ZCs6FC1NTQxPbmjgfS8r4/L2isypd0nFEARBEIRu0tHkSFVV/M9/WFFRFbUwGvuPAmqzAfffr7eckUlFjyEKqJDUdBS2H15h2+ulAul2U/nMzKRyGgzqbbJa9w/NzGRoqMFApVPT9HeTqaevLLHx+Tjeqyq3s2ezpUo0RkhJxRAEQRCELtC6Ql97gnTbNno+TSbd89kfLOlGI1/Dh9PTEI5MKnoEUUAFoQMuuAD45BPg448ZeaKiT848k+sdDno3169nnqIaoxwOej8bG1mhNRiksRDgmJ2SwtDd/mI0jDXKoOp2M4f2lluA+fPFCCkIgiAIMaO4GFiyBNi+nZMUZVGfN0+3qqt2AK+/zup/BgOVzv6keALAwIEstR/eCkHoMUQBFYQO+N//qAR961scg9evpzJZVMSiOADHrtbtW/LzGT5qt+v5nn4/15lMXCfKZ2RUBfRAgPetvJzLxQgpCIIgCDFAKZWLFwO7d7N32ejRrGy7bBm3WbCA70uWAA8+CBw6lPi9PhUmE691xAh+Vwp1amrbCrdCjyBVcAWhHVQVXKeTY/OAAXoKRFGR3jqldVVutd+0acCkSRyvU1IYwqtpeisWlQcqtMRgYA6o308ZWV1Nw6vHE+8zEwRBEIR+wNKlfJWVsXR8aiqbZquS8mvWcDJTXAz87W/0kFZUxPusY4vyCmRk6MU6Jk8WS3cvIR5QQWiHkhJgyxYqPqoFi6YxP9Hvp2JUV9e2IE549Vy7ncXiPB49B1SFl4oHlLQuoKda2xiNvH8uF1va5OXpBllBEARBELqBspJnZNCinprKd4DW9REjqHiuXAm8+y4nMf1t0mK18qVyoQIBNgy/4op4n1nSIAqoILTD6tVULh0Oeje9XiqWypPpdlM5al0QJ7x6bno6x7jycr0Ni+oJmmyYzbqMC4X0gkytZZrRyHtmNvM+Tp7MvNs1a1iMToyTgiAIgtBNlJV8wAAKW6+XwtZuB6qqOPkpLgY+/ZTb9odcz3BSU6l8O50MxR03jmFX8+bpvfSEHkcUUEGIgNvNqqsjR7Lgm9fLsbmxka+f/Qy4+OLIBXFaV8+12aiMJqviCVBpt1r1isA+X9ttrFbKAk3j58xM1gOYPJn3rnWerSAIgiAIXURZyX0+YNgwYOdOvehCSQlDtoxGWt9VmGp/IT0dOP54Xn9RERVui0X6e8YBUUAFIQLKQDhtGrBnD8epmhoqRhkZwLnn0mjWHuHVc2trk1v5BPRWYaoisEKF3xoMlAsnn0yZaLcDY8cydxbg8wjPsxUEQRAEoRuEW8mzsjixKSxkdcRgkAqZ08lt+9PkJTeXuVGlpcCECcBxx9GjcMcdHU/ohB5BFFBBiIAyEDY2strt+PFUnurqOFYPHqy3zQrfR3nnwqvnVlcDb73Vv8bx7qBCkCOF3BoMvNdTp7IQ37JlvNdGIxX/1nm2giAIgiB0E+Xte+opWthVg3KVJ+Tx9J9Kiaq0fkYGv9vt9PqOHs37IMpnXBAFVBAiEG4gBGgMDATozTznHCqUq1YBO3ZQwczMBI46Cjj1VGD2bOYr5uXRq1dV1b9y97uDqvqrlPDwQkwGgy4fzjwTOOEEbrNmDcNuzWYq8rNnx+/8BUEQBKHf4HCwqMLjjzPUKDWVeZ+qOEN9fbzP8MhJT+e1uFy83iFDOCFTZfZPPVXCbuOIKKCC0A5qXFKKUFoavXB+PxXTqip6QM1mvu/bR2W0tJTvDQ387Hb3vxz+rhLes9pgoKHVZNKXWyzAxIlU4h0OVrudPVvvj71pE1uVte6PLQiCIAhCN9i4kd7PrCyGIDU19R9ruVKwt2/npM3hYE4VwGu2WIAf/lAmE3FEFFBBaAelCJ1/PhVMlX94882M5Ni/n+9paTQW1tSwevn27cDBg8zld7lohGvdaiTZCQRa1jYwGBgNo3I+ASA/H/jsM3qSnU7e39b9sQVBEARB6AIeD3uAvv46Jze1tSxA1B9QYcPBIHuaZmcz3HbgQD2np6kJOPtsyemJM8Z4n4Ag9HVcLqYIuFx6cSJNY46iycRt7HbmOIZCzFcMT59oaOg/qRSxIFIeqMNBxX3pUn5XbcqcTnqYQyFdEX3nHaCgoPfPWxAEQRASFrebwvPppylsNY0Ctq6OSll/KFRhtXKyZrMBX33F9+nTqYAeOMBrlIq3fQLxgApCF3A46NksKaFxrbISyMmhAlpXB6xbR0OiwcC80KYmhuOqFi5CWwwGRsN4PPR6nn8+7++WLVwWClFGqhYutbXAH//ISsQSjisIgiAIHaA8nvn5FK5bt9IbqNb1l/Ass5m5rLm5eh7Utm1UQgcOZAGPq6+WXp99BFFABaEL5Ocz97OxkeNcfT3DbY1GGt5CIbbVOnyYitKgQXo1XSEyJhPlhNOp59WuXk1PssPBCJriYhpv09PZO9vhiByOqyoTR+rPKgiCIAhJgxKIb70FvPACJyIVFVyuqv+pioD9gUCAL7dbn3RlZVExLSlhyPGwYZw0yGQh7ogCKghRUlwMLF6sp0qoMFuDgZ5Om41jeUWFnhNaVsbQ3P4Q2dJTaJqeE5qVxWWbNwMjR7KIU30977XZTJkyeDDXlZUxTPf885k7unQpv9fXU+mXgkWCIAhC0hHu8SwrYxU/FZpVW9syD6a/TU5UuJSmMTxNKZfK0r18OScJmzfLZCHOiAIqCFHy4ovAnj0c03JzOXbV1fH7rl30gBoM9OJVVVGpEjonGKRsyMjQW63U17Ng3Z49TFnxeqngW60s9ATQY3rggG7gXbaMeaLDh0vBIkEQBCFJWbIEePRRWmzr64Hy8nifUe9gMrGaYVkZJxSDBunrVB7U9u0MURszRiYLcUaKEAlCFLjdTCVIS+MYZzYzxzMrix7PYJAKZ10dlSVRPrtGSgpwww1UQKuqdG/nsccCZ5zBlI2sLL23KkC5kZbGz6rval4e5Yz6vGYNn50gCIIg9HvcbobbVlYyT6iyMt5n1DukpLCJ+J13At/+Ni3U4aX21cSsoYH9QGWyEHfEAyoIUVBZybFr9Ghg714uUxEddXVUiurrqTj5fPE910TCYGAKxqmn8h7/6ld6CkddHb2gLhfzPnfupNwwGmngLCtjMTuA93748JbHDveQSoqHIAiC0O/Zs4d9LjMzqXQlQwEKs5l5OT/8IXDWWcz33L2bwl+FGFdXUyFPS6PCGY5MFuKCKKCCEAXZ2Ry3UlOpAG3dSg9cMMi0AZeLxrZgsP+lVPQkRiMwcSL7YX/2Ge+lzcZ3v5/htw0NwKhRwNixvLcHDvBZqErqjY38XlNDg6ZCeUhV/1ZBEARB6FdEKqajaVze30NvHQ69iNLkycCVV3L5vHn0BCxZwuIdAHN35s7VJ28yWYg7ooAKQhS4XMxTX7aM3jajkcs0jeNaZSUr4GZlUUmqqYn3GScGwSAjXywWvTJ6aSllR1oaPcs/+xlw1FG835FkrcOhPxuAxkxVAGruXDFoCoIgCP0MVWiodeW92bNZLKGkpP+HY6Wm0pvp8+k5ng4HXz/6EXDxxfQIA8z5dLmAZ5+VyUIfQRRQQYiSefM4zj/4oF40Z9gwGt7Ky/VwXKORr1Ao3mecOPj9vIeaxlQOu53RQ4WFwNq1wCmncDuXK7KMUD2l16xp6yEVBEEQhH7F0qWRK+8VFVF4+nz9p71Ke1RXcyI2cSJzd1qH0EaaMMhkoc8gCqggRInDAcyZA3z0Ece0zEwqS34/x/zqao6BPh8VVFFAu0ZDA9/9fqZ0ALzH27fT89meBxTgs1mwgC1ZpLWXIAiC0G9xu1tW3gM48Th4EFixgsK0vyufACdcRiOLRFgs0YXQymShzyAKqCB0EVVoKCWF37dvBz7/nL1AR45kFdeKCkZ3GI1UTJNBFsQKv1+vIlxfz7ZlJSVstdJZn8/2PKSCIAiC0C/Yswc4dIhVEf1+fRJSVpZcJfgtFoYib9kCXH9914S/TBbijiigghAF4ekW+/dznN+9m/mfW7cy4iUzk8pQRQWVU49HChJ1F01jhVy3G1i3Drj7biryQ4ZI6y5BEAQhyXC7aYldvZoV+3bs4CTEZmMIVlVV8li6DQa+UlNZKCI9XW8iLiQMooAKSU97YZ3hhKdbTJ8OfPABsGkTe4N6PBz3GxuZs1hXx/Bb1YLKYOi1S+k3GI2soxAIUIlfsYJVcFW0kSpgt2YNI2nEkCkIgiAkLO1NRJT1Oz+fnr7Dh1l8YuRIYNculopPNkt3SgpL43/rW/x8+DDvk5BQiAIqJC3tFZFrHdbZOt1i0yYuHzyYnrimJl1RCgbb5v4ni1EyligDZ0oKjZuhEA28jY166LO07hIEQRASms4mIkuWAI8+ylyUgwdbFtuprU0u5dNoBHJzgaOPBmbMYAhuWZm0UElQjPE+AUGIF8qraTIxrNNk4velS1tuV1lJueB0UgEqKmLkR1YWvZwul56K0NQkCueRYjDolYSzs1nkLj2ddRXCjZzSuksQBEFIaDqaiLjdwAsv6JMQv1+vcHj4MMOtkgmbjQr37t30BJSV0Qo9dmy8z0zoBuIBFZKSSEXk2gvrzM6molNTw/G/ro7Kp6raOmgQx8WSEj3sVugaZjNlqqbp+Z8GA2XtqFE09Krw5tRUad0lCIIgJDidTUQGD6aC5fezCEIoRA+owZB8kw2DgXk5JhPDzL76ivfEYmEBpt27I4ewCX0W8YAKSUm4VzMcp5PLKyv1ZS4XcOKJwPr1zP8vLwd27qQCZLPRK+dw0HiZni45n11FyZXUVGDgQMpfs5nLhw5lxE16OnDmmdzuwAEq/9/6ltQdEARBEBKUziYiO3dym6oqLjeZ6AFMNuUT4GTLaGS1x7Q0TgYAegAGDWo/hE3os4gHVEhKwr2ayuAItB/WqZRKk4nKZlUVK98aDIyEAXgcr1dCcLuKKuAE0MCbnc22XvX1VP4NBuDii2nYdLuZErN9OyNwxOgpCIIgJCTtTUTKyoCNG+kFra2VSQVAr6fZzElAYyMV8epqvux2FmbKzZXKhAmEKKBCUuJyUXFRrTyczvbDOt1uej+nT6cn7vPPqQDV1bVUOFVIrtB9lFI/cCBwww3AnDktiwLm57MCvdNJmeP3SzsWQRAEIQFpbyKyYgUVq6wsev683rieZlwxGDjJCoVYZCM8/NjjoWLqcLAtzeDBvKdSmTAhEAVUSFrmzeP7mjUM60xLo/KplitUlMzw4RwDa2oY8aG8dhYLjXGhUO+ef39E5X7a7XwWQ4fq69xuYNUqyuX9+yl3rFbK7FWrxOgpCIIgJBitJyLBIBXOlBROPAKB+J5fvFDFlgBdCfX7ucxk4sTLYtELMVVVAaWlzKVdvpyTBwmL6tOIAiokLQ4HvWbnn99xH9DwKBmzmYqP0agb40ym5JURscJopHwxmyl3s7NZ1Mnj0Z9LZSWNnJWVrIzrdFJOFxfTGCBGT0EQBCGhCJ+I7NnDEJ/ly/V+bskYfms2U/A3NXFCoHKeTCaG2Xq9VEZVCJpap2ncLz+fkzYJi+rTiAIqJD0uV8eKS3iUjNNJZUmlZSjFKRllRKxQbVeU/GhsZCj0Qw9RsVdt0aZNo/fTbOYygO+NjVwuCIIgCAmHxwO89Ra9oPv306unaVRO7fbkyu9RkwGfj+HH2dm8H3Y7ldFhw9gLz2Si4hkI0BNqMOiTNY9HckETAFFABSEKwqNkVBEih4PKj9+vR4gIXSe87UogwFdjI6viqpSYZcsYXZOZqYdEq6JPgYD0AhUEQRASlKVL+crIoJAzGBhqqvJMkomcHN6DoiJeezBIxVOFSIVCtDyXl/NzRgb3CQSAyZP5XZXLl7CoPo0ooILQCre7bUhueJTM9u2M8Fi+HNi8mcY25QkVuodSPE0mKp7HHde2Ldr27cDo0XwWtbVUTK1WYMgQ9goVJVQQBEHok0SaWADMIVm8mGE/ZjMt2uZvpuZ+f3K1XFFW6KlTWfWxsZFhuJrG0vhOJ3NzVN+2YFCfIEyezBfQfjsDoU8hCqggfIPHQyPkmjX0sKnQT9XiIzxKpr6eSk9ODrBunYSAHgkGA++1UkAzMqhUhqM8occeS6/niBGUQT4fldFTTxVDpyAIgtDH6GxisWQJ+4nl5lKpqq7Wla5kwGLRm3+npFCpnDQJuP12XWlfvpxW/7w8WqHLyyn4zz2X4cmrVrEyZDDI7SO1MxD6HKKACsI3LF3KUM+8PFa8VaGfAL2f4VEyAwZQ+fF6KUuWL9cNccmUrhFLBgxgFfWKCsqPESP0dcqgefnlfD7hsjxS5WJBEARBiDsdTSxUSFVKCj2dmqY3Fk8WHA5ev9PJd48HKCyk8qkKdAwdSmG/Zg3vT3o6cPbZuuBXk4KO2hkIfQ5RQAUBHOvWrOE41jr0c80aFsAJj5KxWrldWhqXqQrhUg23a5hMTPc44wxg5EiGMq9cCRw8yPvfuj/r0KHRVS4WBEEQhLgSaWIRCtHLmZ9Pb96uXVS6Dh3iOtXfLVlISaFiHgzSej96NCdS4fmbnbUskElBQiIKqCCgZa/PcJxOGtWee06PkrHbWRCnoIDbNDXRG2oy8bsUJOoa2dm8zwCVzaOOAo4/nvm1BQVU+GfPbmnQ7KxysSAIgiDElfCJhd9Pb2dRERVOn4/hPkVFVMLsdn5PNvx+htOmpAATJnCS1dTESo/KC6roSPDLpCDhMMb7BAShLxDe6zMc1fvzwAFGfZhMHBMrKmisq6ujHDEYaMALhUT57ArBIGsK5OcDb7wBrF8PfPvbwKJFrENgNtMYunkzI5k8nnifsSAIgiBEQfjEYvt2YOdOThbMZipdK1dy3cGDyal8Asxj8np5r7KygI0bOeG6/37g5puBZ58Vwd9PEQVUEKC3jyor48vr1T8ffTQVz9GjdVlRX09lUyFKZ/cwGHivS0pYV8DtpkF06VIqpdnZwLhxvP/LlnG5IAiCIPR51MTiwAEqn3Y7Jw4HDlAB9XppyU5mBctup8fz669Z0RGg0B8+XAR/P0dCcAXhG8J7fYbnss+ezfDb1FTKij172j+GhN92DZOJnmWjERg4kPd3yRJgzJj283HDe0u3V9leEARBEOLOvHm0ZO/cqed/er3Suw3gBGDgQL4PG8Z7dMwxehn89gS/0C8QBVQQoCsy558fOZd95kwa4iyWtjIjXOlMdnnSVZQX2WBgYadQCNi3j8r/cce13Fbl41ZWMl2ko8r2giAIghBX1MTinHOA11+nl6+ujrknAjGZKPhTU5nPZLG0XB8u+EUB7VeIAiokNZ216FIo7+jzz7dUOMXjeWQYjZTHJhNDm30+5nwePkzZHd4PNLy3dGctcxTiIRUEQRB6BSVwHA7mkKiJRXExsGULJxwyYWiJz0frc0MD3/3+luvDBb/QrxAFVEhqWisypaUMAa2vB669Vt9OVQEfPZppCqpSusiSI0O1rQkEqIhaLAzJDYVYi8BsbtuKBdAr26enU26lp+vLzz9fPKSCIAhCL9Hakl1SQoE2bRoL6/zvf8nXXiUaQiEWf7DZqLinplJRN5spxMvLmSs7b55YkPshooAKSUt4i67sbL1Cem0t8OCD3GbRopYKy969lCdeL5VPUUBjh6rDcOyxlDU7d1ImKQOo6i1dXMw0moYGGgyUAXXgQG5XWQm89VbnHlLxjgqCIAhHTLglOzcX+PJLFtYpLWWfz/r6eJ9h30TTqJjn5rLwQ0YGS96vXKlProYOpZD3eMR63M8QBVRIWsJbdKkK6ampVEbcbuCVV7j+mmv0ZWvWAGedBXz4IY2cooB2H4OBIbgqHUaFMxcV8d5WVQGDBwMnnQRcfjnlEECFsbwc2L+fz8XppEFgxw5gxAhu07r3d3gtg9mzW0ZHiXdUEARB6Bbhluy8PH4H6NXbuLFtb7dkx2plyJLFQgHscgGnnw4cOkRl3e3m8okTgRkzOCl4913uF55fIyQ80oZFSFpUi67SUio9qan87vMxDaGkBHj8ceCXv2QrqpISet7Ky4GcHMoXoWsYDPpnk4kyyGhkxI26n3v3Uv5kZfH16adUGDs6Vvj3qiru73S2XO90cvmSJTRWm0xS6V0QBEE4ApQl2+nkxGHPHhYxKC5m3khTU7zPMP5YrXw3GoFRo2hNTkmhIpqSQuvx1q28V5pGoVxdzUmXUuzXrNGVe6FfIAqokLSoFl0lJQy7NZspR1RYZ2oqlSK/nwrK6tUslLN5M6NBRAHtOkajriiGQrqsAZgH2tTEV3k5tx08uK3sqawEBgwAJk3i/jU1fJ80icsBvfd3ODU1fMbbt+syzW4X+SYIgiB0E2XJrqmhcNm1i2E9jY18V4UOkoVwy7DBQCuzw0FBb7VymckETJjA++Z2U/msqqLC3tBAwZyeTs9AY6NuPa6sjN91CTFHQnCFpGbePI5rDz7IcTAlheOly0UFyOFge6qqKuDFF4GCAsqZ8nIqUFIFN3pUq5XUVNZnUMpma8xmIDOTcmj7dkbihFdhz87mepcLmDxZTw1R1e3HjNHb5gAtixh961vApk2RvaNS6V0QBEHoEsqS/Z//sIBOba1eJCIZSU2loK+r4yTJaKRC6XQyx/Poo2kp9vno+VTFH9LSOCHw+SjQlVLv8VCJl0q4/Q7xgApJjcPBarc//zkjQ8aO5fipaVSAhg2jUlpSwsiaQIDGO4OBY6v6LHSOplGWWK3t51oaDJRNo0frBtDS0payR8n7sjLKOKXQlpVxuctFw8LcuVRIDxzg+9y5zCVtzzsq8k0QBEHoMvPmUcGqrtY9n8mqgHq9nBzl5LC4UFYWJ0pnnAH84hcU7IcP6xOso4/mpMDr5bsKZS4v53719S2Fu9BvEA+oIIDVbtPSmGuo0hAmTKCHrbGReYmqT3JqKg161dV6BdbGRobkJmPETVcpLaV8AihfWlcT9vt5b+12eqVLSoArrmgpe1Rf1jVrqGCGV8kF9LY555/fttJte97RuXNFvgmCIAjt0F7pdDUBMBqTV/EEeA+U13PCBN4r5eEsLAT+8hfgu9/l8qoq4P77WYghM5NVIFVxogMHeE+zs/k9XLgL/QZRQAUBLRWWp54CVq0CBg2iQllURCPcmDFUTj0eLk9Lo5HO5+MxgkEqTVJxvWOU8gno3mMlszWNhtDiYiqnmZmRZU9HCmY4Llfb5Z0pr4IgCILQTOs+n61Lp1dWAhUVUnDI4eD9qa9n/szw4Xqz7j17mMf0m9/obQVUONJxx9GSX1RE63NWFrBwIXDuuSwEIZbhfokooIIQhsvFcFxVlKaggIrl8OGMFqmp4fjo9XL81DSOm8Egcxel13TX0LSWebQGA+WN8oJOn84Q6faIpGB2RrTKqyAIgiC06fNZXq6XTV+wgELk4MGW1tVkQ1UYNBg4IQoG6RE1m2lNTksDtm3jxEkJ7vBwpIkTaXEuKaFFuCPBL/QLRAEVhFY4HHpxotWrucxiYUsv4zdZ0yrSxGRiyG0opPezFKKn9T1TFXHT01mp3WLR5VWs6Y7yKgiCICQRqs+ny8VelUVFDHsKBIDFi9lY2uFg+FMyF4QIhThpUuXtPR5a5E0mekBHj+Y9C6/0Fykc6YorJBwpSRAFVBBa4XYDTz5J5XP4cCpB27YB+/bp6Q3mb/5zVL6n0Zjcxs9YkZvL0Odp03hPVWVaQLyVgiAIQi+j+nzW1OjFIFJSWO3266+Bv/0NOOEEKqfJboU2m1lFsKyMSvqhQ8CQIcwHzc2lgh5e6U/CkZIaUUAF4RtUmkd+Pg1yViuVz717gf37W4aJ+nwtZY0on91DRewYDJRFAwawNgFAeW+3A8uXs/dqpNQbQRAEQegxsrOpWO3dq+c4Vlay9LrPR2v1U08xLycZUTk0BoOuPBoMtCA3NlJo5+bSst9epT8JR0pKRAEVhG9QaR42m/7asoWpHcGgHlkiVW5jh1LqVX/QYJDyfc8ernO5aBDIy6M3uqZGTxlZsCB+5y0IgiAkAS4XW4WsWEHhVFXFYkNqIpDshYdMJr0pelkZJ06ZmbTeV1Wxuq3BwKJCElorhCF9QAUBeppHXh6LDTkcej6i8nYmc3X1nsTh0FuG7d/PGgRffkmFtLaW8iw9nd7QvDy9QJTbHe8zFwRBEPo9l19OC6jbTYVTKVwCPZ1paboF2WCgct7QwD5nM2bw3p1/voQtCS0QBVQQoKd5OJ1M7xg2jOOnhNb2LBYLq94GAnoRwbQ0KvsffMDX+vXA++8DmzaxOq7TqUdBCYIgCEKPMnQocOaZ/Gy30zotCijvQUoKvcRKMTeZqJSqSriBAF979rCtgFiOhW+QEFwhaQnvKZ2drbekstuByZM5dq5bF++z7L+YTLzvoRAjdcxmyjGzGSgt1VvaeL2UceXl3HbIED6r8FoGgiAIgtAjFBTQ8mm3U9Hy++N9Rn0DTWMubChEJdNk0vNB8/IouPfsoWB/6CFuI4UchG8QBVRIOtrrKT19OvDOO9xGedmMRo6hHo+E4MaaYFCvUeBwMARX02gYCJfvPh/lmt8PfP4595s3T2oWCIIgCD1IZSXw298CK1dyQlBb27Z5dbJhNPLa1fUHgxTORiNzaRwO5sykpTGMrLycAjw1lRMrKeQgfIOE4ApJhyo2ZDIxNcFk4ndNY5G2YJAGz0OHWHvg1FM5ngqxw2Bg+G0oBFRXU8EvL2cYrmqxFo7Xy+dSV8eK96qWgdvNZ6Vebre+TCJ9BEEQhG5z003AK68wJKeiomU1wmQlNZWFGVQIsqp4a7fTWj9qFBVSt5sKqM3GkDKzmfdOCjkI3yAeUCGpCC82lJfHZXY73zdsAO66i7nyGzcCjz0GjBtHWbN3L42fQmywWvVopsZGyiW/n/K9qallyxujkQqp38/9AKC4GHj+eRYrOnSIxumMDB7TaGQ7l8xMifQRBEEQukFBAUOivF4KHykIQYzGliFKZrO+rKiI98lspjA3mSiUd+4EvvqKAnzYMGDMGAruykoJZUpiRAEVkgpVbGj4cH1ZYyPHSpUTOm4cMG0ax0W3G/jiC1ZmNZn03p9Go8ijI6GpSb+XqnheKKS/AF35VAUHQyEaWF97DXj8cRoEVNG9rCw+u1CIimhqKp+fRPoIgiAIbQgvAqGEffj3zZtZnCDZPZ6tqa9v2QTdbKbQraykElpfz1Bcs5khS42NtABnZ1OZ37mTwnvsWCnkkOSIAiokFeHFhkwmYPt2Gu1qa/l9+XIWvHO56D279156Py0WvbCb0ai3vhK6j5JhqtqwUjIV4akmZrNetKikhM8rJYXbB4P8bjAw2icjgxFTkyfzOGvW0KsthlZBEIQkp3URCFXVNhikgqSKQoRbSUUB1QlXPgEqnTU1XG61UkjPnMn7+uGHeo6o18tljY2cVF1yiQjlJEdyQIWkQimWZWWscLtjB+WRwQAMGgTk51M2AcDs2RwvDQbKJ5uNy1W4qHBkKOUyENAV0IwM3fOperE6HJwT5ORwP+X1tFq5ndXKY/h8PJ7RyM8ej7RsEQRBEMJoXQRi3z7g7beBwkJ+1zTgX/9iDg7QVuESdJQl3uPh5/R0CmeV16SE+siRvK81NRTYAwcCs2bF9dSF+CMeUCEpCI+umTePSsmDD3JMdDiYljB5MovgvPMOcNJJ3G/oUHroACqg27ZRuRFiTzDI52G383NmJj+rooMjRwL79/O7asMWXgchPITXauVzramRli2CIAgC9CIQTifDahoaKCQyMxlGEwpxElBWRoGflcXiQ0JklMUX4LvFwtAkVXRBCe/jjuN3j4dhuVYrG4ALSY0ooEK/pr2WK8ccA4wYQUNcXh49aOvXMy++rg744x9poHM6qbzs3s2IHKHn0DRG5wweTCXfYGAuZ00Nlc/x46mAhkJcrmodqBY5RiNftbXApEl8jmVlrGx8JJE+rVODBEEQhASkpATYsoWTAZ+PAqO+nuFPdXVst7J7N4VLebl4P9tD9ftUIUxGI5X1QICTJqORgjslhdvX1XF5IEABfaRCWegXiAIq9GtUtE1eHqNr3G5G15hMzBPcu5dRI2436w0YjYwYsVoZjutycexUIZ3i/exZVD5nXh4wdSpw7bXA1q16PQilnKpc0bo6XfkcPJgea6NRL2w0d67esqWrtGe8kKq6giAIfZDOrIUffkgFMxDQ24J4vRQkKgQnENDzQoTIKOuv0aiXrTebWd126FDgwAEKzJ/+lJbk9ev1ZUcilIV+hSigQr8lUsuV0lJ60QIBKpmlpRw7rVZG3AQCHE/r6mgUVcVurFYuE3oWTWNfUJuNHmj1UvMKhwN4911gyRK2YsnMpGf0ggso1+x2KqoAZeGRGFlbGy+kf7YgCEIfJBprodutD+AAFSaAQt7r5XebjQUepOhQx2ga75Nqs5KWpofZzpgBzJnT0gjw3e9KGJHQBlFAhYQj2pDI1i1XGhvpTfP7qWgaDHr7j0CAny0Wft60CRgyhLIpLY2v6mrKqECgN64yuampAVas4H2fN4/PWT3rH/0IuPhiYM8efh8zhkaCp58GPviAc5G8PBaR6q63sqN+sVJVVxAEoQ8RjbVwzx6WvB8yhFbmmhq9v6dSqKS6YFtsNoYdWa26l1j1+TQagdxchhyZTHxt3gzMn99SQIYLcEH4BlFAhYShqyGR4S1X7HYqpKp3pOqRbLNRFqkqrFlZHGsrK9n/c/x47uN2U/FUqQ+SGtKz+P3A55+zMKHfD1xzTcv14fLM4wH+7/+A//2Pz04ZZbdu5fcf/ajrvx+pXyzANJYDB6R/tiAIQp8gGmshwIHb76dQcLmYa3PggHg7O8Pn072cDoeeq6QU96oqCsSSEiqmmZnMbRIBKXSCtGEREobW1dNNJn5XbVNaE95ypaxMN3A2NXE8dTj0KqoAlUpVaMho5Hg6YwZwwglURtS+onz2LGYzXw4H5doLL3CO0R5PP03ls6mJnlCbjUppcTFDdTvatz3CjRfhSFVdQRCEPoSyFjqdLZc7nRywn3wS+PWvgb/9jRVt9+xhiNNXXwGHD8fllBMK1YcuEKC3Uyn5oRCFrlJQ09O57NAhthgQhE5IWAV03759MBgMUb1WrVoV79MVjpDWRk413tlsLBbUnpIxbx5zA4NBvee00cj91LgaCvHdYuFY2thI4+jgwayW63ZLPYLexGjkc8rOpjG1uFgPt3W7gYIC/Xm73Qy79fkYBWS385WSwme2f7++b1dobbzwevXPM2eKcVcQRAYLfYKOrIXFxSwasGsXQ5mcTnrtPB5OCESwd4yaJAWDenGmtDRgwABOxAwGClslcE0mhpGtW0fB3B3rr5A0JGwIrt1ux8yZM9tdf+jQIezduxd2ux3HHnts752Y0CNUVnIsc7koVwoLmc7h8dAI99RTwM9/3jYU1+FgCsj55/MYL78M3H8/FZZgkJ42i0UPxx00iK/cXBryHnqIYymgj8NCzxIMMt3EaKS88/mo+D37bNvw62nT+DdgbGVKU+1ZjuR5qUJ9a9ZIAT9BaI3IYCFutC4EMXOmnvPpdHL9+vVspWK3s4JgejrzP91uCbuNFoNBz4sdOpTW+bo6YPRoYPp04LnnODHyevmenc3tDx0C7rwTGDdOSscL7ZKwCujAgQPxySeftLv+yiuvxN69e3HhhRfC2To0Q0goPB5g+XJg+3YqFCqNY+BAKo2aBqxaRYNce9VJVc7gr35Fr9j773NsTU/nMSsrgVGjGHLr81F2ATT0eb2S99mbBINMK/niCz4jh4PRU14vFc7wGhP19XzuKSmUjUajrnwGAsCwYSxS1B1aGy+kgJ8g6IgMFnqd9gpBXHAB1ytrYUkJBUlKCoV8TQ09oHV1UmioK6jiGKEQ753DQYGrmqjn5HA7p5OWfLebL7OZyqfKkwKkdLzQhoRVQDuivr4eb7zxBgDgBz/4QXxPRjhili5lmO2gQZQt1dV67rvdDkyYwHXRVCd1ONgH9OmngdWr9b7JyrtZXMx3ux046ijg4EF6WMX72buoaB+A4dB799LYcPgwjdiqxsTmzTQabN3KZ9fUpCufLhewcOGRK41SwE8QuobIYKFH6Kza7fnnM+fioYeAkSMpGMIFeOsw3WTFYtFDjVQfz0iYTHrp/4oK3vMhQ5jv8tlnnHjt2sXtHA72tQsGgbFjmcOkkNLxQgT6pQL62muvoaGhAbm5uTj77LPjfTrCERCe+zlpEse5qiqOl3V1XDZ5Mse8aKuTOhzAtdeyUrjybKk2HqtX07BaWsplFRVMaTh8mJ5RoXdRVYcbGykzi4pYmTglRa9IO2sW5eiSJfRuB4P0fF56KZXTggIeSzyYgtA7iAwWYk60vbEqK6kwDRpE4VFUROHt80nOp2LqVFrya2uplIdCFLAALbjh1RkNBj3HxWqlNdhi4f1NS6NSun49J02hEJXPM8/U95fS8UI79EsF9IUXXgAAzJ8/H2Zzv7zEpCG8HYbFwhDMcGVwzBgur6yMXJ00Us/Q8GXjxnHZs8/Sy5qXx8iSAwdYJA+gwa++nkqQ0Luo4ntmM9DQQMOrx0MFVFWkHTy4ZW/QpiZg7Vrgk0+olNbXs5jRUUcBp57aNh0l2r6ygiBEh8hgIeZE6o3V2EiLoxrEAVqog0Hg448ZFqPahYjyqZOSwpyjjAzgzTcpXB0OCtiyMm6jaVQ+U1N53wFuEwrRqjtgAIXz7bdz3ebNwCuv6BZ9hZSOF9ohZpJh9erVAIATTzwRjiiTjb1eL9Z/k2w3a9asmJzHoUOHkJ+fD6Dz0J+mpiY0qb4bAGpra2NyDkLsaN3LU42bW7YwJNNo1KuTzp3bsjdk61SRE0/keLp+fcv0kdmzqXzabEwXSUlhWO+XX1KW+Xx8F3ofo5Fy0OGgwdbna/+Zu1x8dtdfzwJ8Hg/zRtPSKCcLC3kMgNFaXe0rKwh9lb4ifwGRwUIPET4ZMJlYFKKoiF48oxH4858p4Hft4mDvdutVWlNTKQQkj4bU1fHeAbwvKi82N5fWXq9Xr86oen5aLNyvtpbr8/MZauRwsEDRuHFUZMOLQdXUtBXUgvANMVNATzvtNBiNRmzZsgVHHXVUVPscPHiweb+ASvg6QpYsWYJQKIQJEybgxBNP7HDbu+66C7cr643QJ4lU4C43l2Gx6en0hkaqThopVeSxx7hu+nR92auvAu+8A3z+ORXQXbs4po4aRRlWVsYxV4gPoRCN3Kr/qtHISB+nM3JF2qefBj78kJFCHo+e4mK1Um6OGKFHa731VsfpRIKQKPQV+QuIDBZ6iPDJwNdfs0CD2Uwl02CgQuR06kUcQiEKDFUmXZRPnU2b9CqMWVkUkJWV9BgrA1Zqqt7nMyWFluCKCr3Sn9/PyVF+vi4wpXS80AViGhujdbO0dXf3i4QK/Ymm8MHNN9+MG2+8sfl7bW0thg0bFrNzEWJD+JhWUMCxb9EijmseT9vQyUipIkqRATjmqn6RX3/Nsdhu13uD7typFztKS+P4K1XbY080hZ2CQb0V2eDBfJ4TJ9LL2dqg6nYzh9ds5jOurqYsDQT4d9LYSDlbX89Q3WjSiQQhUegL8hcQGSz0IPPmcQB/8EE9NCYvj20/NI0W6aFDOZiXlHCZyURFStAJz+usq6MSqoTl3//OkK/PPmOomdtNC+3+/bz3fj8nXcceS29Afj6LcYwZQ6EppeOFKIlrckbom5h8k8kUk+Nt3boVmzdvhsFgwJVXXtnp9jabDTabLSa/LfQcDocud1Tl2s2bqRxGCpeMlCoSnlevcggbGznO2mzc9uBBGv0MBuZ/+v1iNO1JwmWgzcb73doRk5bG+YXNxnlEaSl7uaamAuecQ6VUyTdVeyI1lc8tPH+0vp6/VVfH5799O+WqygFWSL0EIVmItfwFRAYLPYzDAcyZA3z0EQfozEwO6EVFXOf3s/en260LExHiLVHFhiwW3iNN4+RHCeEvv2Q48/z5VOJXr+aruJjK/aBBLDQEALt3M1SsqorLVQ6LlI4XoiCuCui+ffsAIGY9wp5//nkAzGcZMWJETI4p9A1UK5a8PCoJpaV6gZlrr225beu8UYCySRn6lcLq8eiFbaZM4T6FhTSmer29d23JSriyqQwERqPedkzTqGCOHMnnYrVyvnHgAFvpvPYacMwxusxTxtbKSv592O0Mu/V4OAdpamJuqNFIA4bXy3nKzJm6TJZ6CUKyEGv5C4gMFnqI8EpxaqA3mfRiN1YrlSCPh1ZGCVlqH5XvqYoMKaGrabTWbtrECZcKq50zhwU07rmH97mujoU0ysooMNPTmbekaZLDInSJbiugBw4ciLj80KFDSEtL63DfpqYm7NmzB7fccgsMBgMmT57c3dNoJhQK4T//+Q8A6TvW3wgPqc3Obll74MEHuc2iRbpiGSlvtK5Ol1V1dbo3LBDQi7YFg0xxkFZh8UHNGTRN/1xUxOeUm0vFcN8+KpKZmVQg/f6WMm/mTCqfAP9uGhv5cjg4PzEaaag1GnnsTZv4efp0qZcgJA59Tf4CIoOFHqC9SnHTp7N4A6CHMxUVtQ2hEdpH3SurVf8+fjy9m++/z3YrhYVcnpbGirmff67nJnk8+rFKShiSC0gOixA13VZAR40a1WaZpmmYM2dOl4911VVXdfc0mlmxYgWKi4tht9tx6aWXHvHxhL5DeEjt9u3M0UxN5fjmdlMBSUtraXSLlAv/05/S0KeiSbKy2K6qvJxtO776SgoOxZtww7XBQNnndlNGhkKUfVlZVEiVt9Ju12Ve+HPPzmZqysSJwMaN/LvJyOA+6vilpZy3ZGXx70nqJQiJQF+Tv4DIYKEHiFRNcNky5l/MncuBfs0aCm67nYqotFvpGsGgnoc0YwYnTBs2ACtW0NI7ejQnXMXFVDodDlqBNY0969LT9QbdksMidIFuK6DtFS7oSkEDu92O66+/HosWLeruaTSjQn8uvPDCmIYUCfFHhdQqZSE1ld/r66lQDBnS1ujmcLTNhU9JoTwLZ/p0HueRRyi77HaOq+HGPaH3UZFBVqteW8Js5nMfOJDeT6uVz9lobCnzWj/3ykoqoGazHpIN8O8hM5PRQz/9KXvMiswUEoG+Jn8BkcFCjIlUTVAN4Bs2AHfdBZx0EvDHP3K96q8mdA2Xi4LSZKJXub6elt/hw6mY7t3LHJXMTC4/4wwK5fXrudxup2HA49G9pZLDIkRBtxXQxYsXt/i+cOFCGAwG/OUvf8GQIUPa3c9gMMBut2PQoEE47rjjOg0XigaPx4PXXnsNgIT+9EdUSO2SJQy7dbk4RjY0sF9nXl77RrfwAjUvvaTnkQ4ezDHz3XeBb32LRr5AgAqt282cfCE+KOXTaOTzGD6c3ssRI/T6EsEgn31KCovzta4z0boGQlYWZabqCwrws6bx70GUTyGR6EvyFxAZLPQAkaoJArqXbc8evVy916s3eRaix26n8FRFF1Szbbud91kJzcJCekcBepsnTGCvup07W5aXr6mRHBYharqtgC5olWS8cOFCAMBFF10UdR+yWPHGG2+grq4Oubm5OPvss3v1t4XeIbz6uttNxWTCBGDyZMqpSEa34mLgxReBL77guLp3L71dkybphjuAeYBVVXzV1NBTpnovC/FB0xjZEwqxKBTAEOnGRhpaU1OpeBYW0hAxeDBw9916QaLwysguFzB7NsO3Kyt1ZbW6mn8zs2eLvBQSi74kfwGRwcIREF5gKHwgjlRNUG1fXAz8/Od8r6mh9VgV1xGiw2ikIM3O5j0N7/lpMFDABgL6y+GgoK2tZbGEMWN47/ft44TMYpEcFqFLxKwK7ooVKwBEzk3paVToz/z582E2x7Wwr9BDOBx6tdtlyxh2m5dHudW6cIyqW/DUU8C2bVRirFYur6riOPntb3NbqxXYsYNKjMFAGab6WocjCmnvYTDofVprahgFZLNRRnq9evV4VXNi+HDg+OP5t6BCrFsX4Zs3j7J1yRLOWQB6VK+8UuSlkPjEU/4CIoOFbhCpwNDUqcCsWXp/rdbVBGtqWAinqIjCwGjUS5wL0WMyUcAqS+3QobT6Fhfzfvp8vL92O++xplHRPOMMFhvato3PYtw44NJLWz4zQYgSgxbrLtQJTG1tLZxOJ2pqapCRkRHv0xEi0F5RvHCv17PPcpsvv6RiabHQc6YMpDYb97FaOY6WlHAbJcs0TVdEs7O5r88nMq6nMBopD5VnMhSi3AuF+KwAveKtIiOD61NS+NydTn4PBGiYeOEFytTWuN2M3AL0vtlC/0LG8cRFnl0S8eyzeoGhlBQK7L17meSv+mtdcAHwv//pAt9s5ueiIh7DYKDgkF6f7aNarAC64hkMMi9F9aEbM0ZvtH3gAAWuzUahGgpR4Obk8DhTpnCfyZOBK66ILGiFpKUrY7iYKoWEIlJxoXAlQtUtsNs5hjocVEpCId2r2dTEGgZ+Pz+r6B3lebPZKA+zs4FTTwW+/prbC7EjXCaqtis2m67kqz6sfn9k77Mqeujx0ECQlsYaCQ0NVDCXLAF++9u2vyv9sQVBEOJM6wJDmzYx1yIlhYO/z9eyv5YS+Js3Ay+/LOG2XSHcxxQMUkimpfFeq7zNoiL2J/N4eG81jRMju53Kp9/PnJWMDL0H62ef0VggPT+FbtIjCujmzZvx8ccfY+/evairq0OwE+uUwWDAU0891ROnIvRT2lMkVN2C1FR+D4Uoz8xmfjaZqOTU17dUStW7UnhUsRq7nd5RiROILep+Ggx8Nuq7UjRVmK2mRTZuaxrnKD4fc0WdTh7HZOL37ds5xxFlU0g2RP4KfZ7wAkOqh2dqqp53kZ5O63F4eXuXC7j+eg76QvuEW3cjfVchR01NXKcKCO3fz3eTictTUvg5JYW5S8GgXjpeNVWXnp/CERBTBXTnzp1YtGgRPv3006j30TRNBKAQM1TdAr+fxjpVdMZo5HqjUY888ft1Yx+gR/MEAhybKyuB995jCxAxuPYMKr1E1TkA9HYpjY0dR1YFg3xmqal8V5WRVUXj8KrI7dW5EIT+gshfIWEILzBkNlOpdDo77q9VUABs3RrvM+/7RLKWR1JCvV4q+nV1FKJWq15sweHQBWxlpR5GlpurK5/S81M4QmKmgB48eBCzZs1CRUVFcy+ytLQ0ZGVlwahm/4LQC4wZA6xezSIzjY0cZ1U0SUoKMHYs6xio8dVkaulp8/vpgTvqKMq8mpr4Xk9/xmDQFU/1XXk3VeGojlCG3MOHOZ+ZMIEyUuXvejzA00/z7yEQ0OtazJ7NdaKQCv0Bkb9CQhFeYMjppEJUWcnBX/XXKiujcrpvHz1wRUXcRugarRXSUIjCLxRqmaPk9+utVzIz9dY2Ki8mPZ35n4qamu73/BSLsIAYKqB//etfcfjwYRgMBlxzzTW46aabMH78+FgdXhA6JLw4UXU1PWGhEDByJCNLvF5gwABg/Hga+zRNVzzDQ3EBLrNYOLY2NMTrivo/yvtpNPL+h+d5BgKUdx1V1zcYKD/r6vh5+HAqn243qyKnpDBi68MPOY9JTaWiumEDsHgxi/ZFKmIlCImGyF8h4VDlx9es4eBbW0uBPWYMG3F//jkF+5o13C41VS8OIBwZytquwoxycpiLO2ECBerOnRSOqoKjw0HvqCpOVFPTtv1ANERTRVJIGmKmgL733nswGAy46qqr8Pjjj8fqsIIQFUuX6gX1Ro/mmHjgAIsInXMO8N//sn6BSm8YNIjKpeorGa78WCw0zG3bJpVvexpVfEgpoupZqAgh5aVundKi8j1VSLXHw+is2lrgqqsoz55+GvjgA72glNcL7N5NhdbrZcX/1rUuBCEREfkrJBzhFQVLShim8tlnwK5d9I5VVXG7rCy+FxdLMYZYEF5y3mSi19PhoDDcsQOYNo1CsrCQyuiYMexXpmnAxx/zOWRlda/nZ/hEbfhwKrIigJOWmCmgJSUlAICrrroqVocUhKhoXVAP0PtWf/01DaeHDnGsNZs5vtpsjOipqKAhr7paP15ODteXlvb6pSQdyhOdns7nsmsXn4tqe6N6Zft8/OzzUQFVUYUeD/cH+GzLyzmHAYCPPuJ2druu6Pp8LQseqb8XqaUgJDIif4WExeWilXDjRn73+Siwm5poKVatHGprKbCFI0MJQJuN93fAAHojx4+n5/nwYQrNE07QW624XHqT7e7S0URNBHBSErPkkKxvrFSZmZmxOqQgRIUqqOd0tlzudNKgt2wZlZShQ6mQrF7NsTcrCzj+eOCkk6h0DhwITJxIxeTgwbatP4TYotqvNDTQ2P3113xOY8ZQ7pnNuidU9cVWBYuammikNZv5WYVKOxxc/tprwBdf0LCwbx/byx06xG2Uh1VF/Did/PvZs4c5v253PO6GIHQfkb9CQqM8YyYT8w99Pg7KdXX6NuFWYiF6LBZOeBwOvdCQ0UhB2thIAWo2UwiXldGqazYDM2Ywh2XoUP35qP6fJhN7nT39dPTn0dFErb5e8nuTkJgpoCeccAIAYNeuXbE6pCBERXhBvXBKSymz8vKofHzyCcNq9+6lsjNjBpWTzZs5/gUCNAaqvpPKsyb0HCq8VsnCUIiG7l27qEiqCsZqu3CjQDDIeYrfr/d9tViAYcMoU91uyltV6Kiykr8RCLBAlSrm53YzAuyhh4BbbwVuvpk90jsrgCQIfQWRv0LC4nYDq1ZRWG/ZwpfKTays5GCuqq0KXUNNYlS7FZNJDwUzGHjvDx7k5927uc3EiZxU5edT8Qz3XGZnc/K0ZQtDdB98EHj44eiEZXsTtSMpZiQkNDFTQK+//npomib5J0LUuN2x8TipgnplZXx5vXwvKaExtbKSOfUGA41tdjsVz40badD71reojNps3M7n0w2GI0boUSJCbFE5nCaTLhtVb+ymJl12dtQCR6Wy2O18brm5/F5fz2M5HIzgMpmovAaD9HwPGaL/nWzcqFeiHz6c2y5bduQRR4LQW4j8FRKWykpagffu5QDtcnHQDgZ1a6QKXxG6hqZRCKam8n5mZjLUy2KhQFThuKqS3/jxFJAqTHbNGoYGKc/l9u36ZMrl4jGjFZbtTdTKyrhcwm+TjpgpoGeddRZ+85vfYMWKFfjZz34GvzROFNrB46GH6eabY+dxmjePOfHBII2lwSC/jx5NuZaaSiObKl7jcFDxyM2lx2zsWGDSJC73eDguK0Vl8GAqpArVn1k4MqxW3avZ2MjaBps26bUngkF9fbjn0+Hgs3E69UrGqo3csGHcv6GB8nPSJBpWMzIYZj1hAvDjH1N+HjjA7VT+aV4eFdlw2SvhuEIiIPJXSEg8HuCFF6hkut1UNA8e1PMzVOiKVAOMHlUePjWVAvH44ylAVa7K4cN68QWDgYqg18vcl0GDKIwBPTQW4OSprIwFGtRkKhCgYB08OHph2d5EravFjIR+QcyKED333HM46qijcNJJJ+Hxxx/H//73P1x66aWYOHEiUlSsWwdI8YTkoScKoTkczGGfNInfx4yhQa2+njmfdjvHS6+XY/DgwVRM9+1j9VTVd3LQIL0liNdLg199vd6aJSeHCmhTEz2skifafZSc64hIRQ9VgSKLhcqjpunhup99xudcVUXjwXHHAV9+yWfp93PfrCzg6qs596mqAu6/v63xVXpsC4mEyF8h4fB4mGP47rscmJWArqjQE/WtVlqNAwFuI3RMVhbvWXihhA0bOOlRglI1P7da6REFKEyLi5m3ZLVScc3NpaI5Zgw9lEuW0COtJlYNDbToDhwYvbAMr3wsfUCTHoOmxaautdFohCG8mWJXTsJgQCC8G32cqK2thdPpRE1NDTJU5TUhprjd9HiaTHohNIDGtWAQuOuuro9HHbWWcrtZQVz1tFZja3o6+0NaLMz7tFg49lZU0NvpdNIQG254VV5PVbm8urrj8FChZ0hN5XMJBGhYcDr5/FJT9eq4jY2UxZmZfI5qDjN0KJfNnUs52BN/j0J8ScZxvD/IXyA5n13S8vDDwN13U+CWlOhKUjhGIwf3YJADuNAxyiIbCtFaXldHK6tSOtU9NBgoMPPyKBwrKvhd5WFWV/Pz9ddTUHo8LDj04IN8FhkZnEhNnkxFUoSl8A1dGcNj5gEFgBjpskI/RhVCGz685fIj8Th15lFduJDbZGRQ2fT5mNZgtVKBDAS4T2WlnifY1KT3oFQeUfWuthfvZ3xQRfssFn4fPJg5vOnplJMOB2WuSitSbV7C5WV41feZM/W/F6ez+z22BSGeiPwVEga3m6FJynLYniU3FOI6EbbR4fHojcxzcynI1CRGKaEqt0XTGCZUVUXBN2oUP/t8FKLp6cDs2TyuwwFcey0/L1tGoTtwIIWpCEuhm8RMAS0sLIzVoYR+THghtPDiPt0thBZNaymVXrBmDb2cJSV6uyuAxj5lGLRYaIgNBPRlal6nxm1AooHiiXo2NpueR6pyOV0uzldKSvis/X7K1rw8Kp8WS1tjR/jfx4ED/DuUtBQhkRD5KyQUlZV67mFZmeR4xopAgBMVt5uWftX4unUJeYD3/tAhXUhOm8blHg89z4cPty3MsWgRBaQISyEGxEwBHTFiRKwOJfRjYu1xitajqtIOnnySVcQDARr1NI1pDcGgbhwMBCIbZMXB0LdQKS4FBfRoZ2cDU6Zw3Vdf0RCRkUG5W1hIZfXYY9saOyQtRUh0RP4KCUV2NgfbqqrOqw+K9zN6VMiyprVtd9Iag4F5n0YjvZ8qVzwlhROySB4BEZZCDIlpCK4gREMsPU5d9ahu3EiFNTub42xRkd7KQxkJRdHs26ieoACfV3o6cz7dbhYhMpn4t3DUUdxGVY3ft495oTU1EjEkCIIQV1SIihA7bDYqlF5v59uaTFRWVXhuWVn0HgGXq3MB6naLkip0iCigQq8TSyNatB5Vjwd46ingk0+4vraW3jGDgeO1UjzNZpGJfZ1wA4HRyGfudDJiqL6ey44+miG3in37aGxvbGxr7FBFrPLzuU1WFlNf5s3j36ogCIIQIzwe4F//ogJqNIrFN1bY7VQq09I6V0Czsjj5GTSIk6Fp0zgxikVYbUdVIUWgCmGIAirEjWiMaNEQjUf1hRdYRbyuTq/43tjIMVgV2bPb+flI+pEKsUO1KVO5twYDn5EqHAVweUVFS0OupvE5A1x+7LGsfOvxAHfcAYwbx3XKQPv228Dzz+t/D5rG9jt+P3DNNV07Z3VMRU8bf8XILAhCQrF0KbBqFQdb1dNM6B5Dh1LJq67WlU6Ph/dWoQolKMu6yaQ3RPf7+Qx+/GM9LDqSMAkXNEDHQqcn+uz1JiJUe42YKaCjR4/u9r4GgwF79uyJ1akISUZnHlW3W1c+s7PpKQtXYkwmvlwuhnNWVcXnOoS2WCwtlc3WxgHV8qy2ltFHyoiwbx+wdi2r4yqP99y5VD7DDbRuN7fz+ykrU1Iox91uGi2++93oZJA65qpVwI4dnA9kZjIM+NRTY2/8FSOzEI7IXyEhUFUDR47kIF1WJh7Q7qDKwBsMrMAXTus8okgVE/1+7l9XB5x3nm6VbS3swgVNdTVQXs7lAwZQwLUWOtFUheyrSp0I1V4nZgrovn37ot7WYDC0KBnf3f5lghBOex7VPXuY6+l0ckyxWin3PB69GFFODt8PHuz98xYiowpCGQyRQ6PVsNHUxM9NTazof+KJlMlFRXzWeXktPeLhBtq0NG5rMNADmpHBZcEg6zPs2ROdvFTHLC/n35bVSmPIvn2U20Bsjb+JbmQWYovIXyEhqKykkuJyAaNHi8DtLhkZbIVSVEQhqUKZ1XvrPJXwQk4qVCgtDZg1C/j73yP/htvNvKVVqyhkGhqA/fv1HqIuV1uh0xN99noLEaq9TswU0AVRPKCGhgbs3LkTW7duhcFgwHHHHYcpqmylIPQCJhPH7dxcKguHD3Osqavj2Cjht32LYJDKnNOpRxJVVdHbmZpKGen36y3Oxo4FJk1iCG0g0Nb429pAe/CgHurrdtMDaja33/M8UnSO200ZXV7OCsuhEM/PZuO5jhjRdeNvR1FAiWxkFnoGkb9Cn0UNZg4HsHw5sGWLHiaqKgAK0aME3bRpzB9pbKTQAih0mpr0iYw5bIqvlFCDgR7MK68Err4a2L2by8eMoeAIL4qwZo0ueA8d0gVLaaleZEEJHYACz2yOXZ+93kKEalyImQK6ePHiqLfdtm0bFi1ahO3bt+Pmm2/GJZdcEqvTEIQ2jBkDDBtG452qkNrUxLHSZNJbdVRV6d40iQrqG4RCVAbLy/VQaRVhNGIEn6VKfzGZaEj48EN9vnPMMVRClSHzpJNaGmjT07lfTY3eksdsptF4yBAaK4COo3MqKxl2W1bGc0tJ4XttLc/dYuE+0Rh/o4kCSmQjs9AziPwV+hytB7OiIgrhmhoqTeGNtYXosdkoZDwevXWK18uJi9er31ODgcpjY2PL/QMBCqvHH2eeiQotGjaMSqmmAe++q1tRbTaWkm9qYug0wGfo8VDo7N1LT+nu3XzOJSUUxNOmURAdSZ+93kKEalwwxuNHjz76aHz00UcYNGgQrrrqKnz99dfxOA0hSXC5OK5mZzOKpLKSyoFa53brKRVSlK/voYzkoRCfUyhEWXjoEN9DIb6cTr4XFFCWTJjAYn/KqLlmDY9jNnMe1NhI2amMxOo4fj//BhwOGoEBPTrHZKKMMpn4felSrq+upqy32XgMq5XbeL2UxdEafzv7HaBl66Fw+rqRWegbiPwVeoXwwSw3l/kMqvWK5NR1HVUxUQlAq5V5mMoi2zr0VtNaFnhSobcAhV9pKZUrq5WK7O7dwP33s2BGXh4VUoeDzy89ncKsvp7vVivX1dTQOrxqlS60VD5pQQGPHwweWVXd3kCEalyIiwIKAGlpabjxxhvh8Xhw9913x+s0hCThiiuARYuolAwbxuIwI0fSg5aaSiVEGRGFvomm0ZtpNPI51dZSjubm0huqPKCquGJ4XRbVnue11ygTV63i/GjDBsoXi4XyND2d33Ny2MplzRrK0fx8Kpfp6boinJHB9VVVnAdoGrfxemnoCAZ5Tm43MHWqnv7UHq2jgOz2lsqz2le1Hior48vr1T/PnCmGWqFzRP4KPUrrwczt1gdnQM91EKLHYqEAysqiMLnmGuCPf+R3iyXyPuEhzqqUvNmstwLw+1nsYP9+CtR9+4Bt23QhmpdHARcMcr9Dh6hwDhzI53ngAI89fLgutIYMAaZMoVBcuBD49a8ZetTaE9uXEKEaF+I6Apx44okAgHzlZhCEHkBFAm3eTIUgMxM4/njgs8+YjpKRwXHG79eVG/GC9k1UUSJVST4zk9/LyylT1TpVYV6hooBWrOB3k4ntW7xeGiCyshhuq4zEjY26gffxxzmXsliAdeu4f2oqv6tw7qOO0nuNBgI8rsXCv628POCLL3iMjgrrdSUKKJrWQ4LQESJ/hR5DDWaDBgGbNjE53uvVw24bGqThdlfx++l5HDqUwqymRg/HHT6cE53CQl0Qts6vVSG54WXlAe6nChgB3P+zz2ipP3SIz+3QIVpe09J4HocPU7DNmkXh5nTq57h9O4VhZSXwhz/w/NqrmtuXEKHa68RVAVWV+MrKyuJ5GkI/Z+lSvjIyOHb7fMCnn3J8UfmFtbVUGJqa9NYf4YXjhL6FKsJXWsrn6XTy+TY1US77/TTQpqZSVhcW6gaGsjLKztxcRh2FFxFMTeW8yWrl/oWF/A2rlXOmujpuZ7HwvbqaRoxTT+XnESO4rraW8nfgQB5PVWD2+9svrBceBdRZ/YbOWg8JQmeI/BV6DDWYffkllRe7ncqSUjxF+ewegQAFjcOhh/VYrVQis7Ko+CnF0mTiu1JElZBT35WSajDo69Q+O3dSyXS59GUpKcBxx3ESdfAgrfjz5zO0Wgmt7du5r/J0l5bSUtxe1dy+hAjVXiduIbgA8P777wMAnMp6IggxprgYWLyYuX7btgGffKIXczObgZNPpkysr9eNs34/lc/wcFwJze1ZVOGfaFDG2lGj9PQXk4nLlGfUaKSyuHIlvZbV1fxb+OorPYxXeScBvShVeTm3bWykIXTfPi6zWChjzWbKWVWwauBAGotnz+arsZHRZhkZNJ4CupK6bh3fq6sZAtw6HLc7UUAuF1NuRE4KXUXkr9BjuFzMO9i7l98bGiSs6EhQYVkNDRQ+AwcCTz7JyY3JxMI/xcWcxIQrk0p5BPTcURWKq3JGVaW/UIjb5+ToVlmvl0ItL485S5WVNCy4XBR8gC609u3jy2DQrb0DBlCRKy1l+HDrfJK+iAjVXiNuHtCXXnoJd911FwwGA04++eR4nYbQz1myhF6u3Fwa4bxeGuj8fhoQf/lLjq/LlrU0DKpUCRUp4vPpY7cQe0IhRugEArqXsSNGjqRsU8pmIMDnqORmKERFLzUVmDiRRuL//Ify22ikEcLp5N+Fy8XnreosaBqPNXgwj6Eq7SoFNxCgwdlm4/JPPgFuvFH3qJvNnHvNmgU8/zxldkYGf8/r5TyhsTFyYT2JAhJ6A5G/Qo+jBsDiYuY7tNfbSuiY8Jwgv5+CUhUHGj6cgmjFCt2KbjLppdh9Pn7XNCqCXq9emCgUapsjqsrMm83AjBkUnJ9/TkEVDLKS8Ucf6QWOnnqKuagA8M47VI5VPovyfAMtq+ZKVVnhG2KmgC5atKjTbUKhEKqqqrBx40aUlJRA0zSYzWb87ne/i9VpCAIAGtj27AE2btRbbZjNnNADNMwecwzw1ltUSBWtx/qMDLa7KiykDJU+oT2DCne+8EJ+XruWxlQgck5uRgbll9HIV2oqn1daGuWk38+0o/Hjqay++SZlcXixwIoKKoLTpjHd5brreOx77uEx09KA9ev5t+N0UjHOyaHS6fNReVXFkFavZh7o9OmUtfn5nA9UV7f8u0tL429WV0e+DxIFJHQHkb9CXInUuHjwYOYk7N7dtkKrED3qvqWk0JpaWUnlLy+PwsRkYjisx0OhWVpKIaiUUU2jEBo3jp7KoiIuU6G7CpUjWl1NpdbhoJU3JYWKa0UFhVpOji6UV63ieSxYwEJDf/wjt8/NpRD0enns8Kq5UlVW+IaYKaDPPPMMDFHGKarck4yMDDz55JM44YQTYnUaQpIT3nrs0CH2Z0xLo/IA0CDn83EcLihgLmhZmW4IbC0jlSJbXS3KZ09TV8dnZ7XyFR4lpIYWJUtLSxmC63QyRPbQIW6jjLujRtHgXlXFdJUDB2iI8Hj4CoWoZKpIodmzqTxu3cqCgGqb2lpuk5vLc3K7KYOzsvR+26qKfU0N91FhvRs38m+vtpb72O2Ux4FA5/LX5RLFU4gekb9CXGivcfEFF9C6+/XXfTvcMlGw2ajgjR3LRtcVFczp2LmTAkb1/jr+eL0hteonpsK59u/n51CIwig8FBfgeyDAZ5iVxRxQh4Phvlu3UphmZ/NYDQ1sKTBoEJ/9+edTwT33XIaT2e3cb8cO/sakSRTwfb0fqNCrxEwBHT58+P+z9+XxUddn/s937iszOQgJdwBBBBVEBYUKKor1qlUrttZW19pra2372227bg/bdXdt13ZrV9u6tdZlW7sWq9Z6H6igqKCgiKAcgZAEcjFJZpK5r98fbx8/35nMJDPJ5Jjkeb9e88rMd74XzOf73M/7GVABGgwGKisro9mzZ9Pq1avp2muvpUmTJhXrFgSCj0aP1dRgDMfevZDVVVWQrz4f9KHBgF76xsaB+RCOHoU85dYKCeQOD4xGxYkQDkP3cdkzsxNbLOjH7OlBIHfhQuzLvynvU1EBvRwIQI/GYnBAuWTXaFStKqeeqkpcN2+GjrTboWvNZji7HR1YA+XlqChzOHBfdXVYH2VluBbPBmfHuK4O9+b3K2d12jQ4yP05odkSCgJBLoj+FYwK9Ap35kwIucceQ/artRVCUFAYuKyHSRFmzoTiqqtTZcyNjVCAiQQUUyIB5VNfDyUVDEIxGo2Kst3vx3l4xAorXC7F1TREWo8/HudcvhzZa5cLziQr5VQK+yxahOP0JbX6HhKXCxlwItxDrnmgouwmLIrmgDZwvZxAMErQjx6rrAQhG7Oi9vQgeFhRgf1OPBEyuqdnYLbbVEqynyMBzm66XIr7gLfzKx6H7oxG8ft2dakAr9GIQHEqhcCryQSd19uLfXw+VZXEzqfNhmC93Y51sXMn9HxrK+7B4YA+7+0l+tzniK69lui223DsjBm4v85O6Ha7XfUM+3yKh2PjRuhhiwX37feDNTebrs2VUBirzPWCsQHRv4IRR+asTyIo3MZGRH5Npty9BoLcYOeTZ28mk1AMr72GaCdnPImgyKJRvLfZVKkNz/AMBvGZCYZYyQaD2IeDVhYLjKYLL4SSTSSIvvAFfNfZib+33YbzV1Wp2aNMSsTR1Gw9JHyOTAdTlN2Eh0wCFowb6OcoMht4RQUyU52dcC5MJjgpHR2QwTyTWTD64BE4Pp+ak83gEulEAr+d04k+UJ4LmkrhmGBQZao5gPzqq4rZOBPRKNE//zN4FJYuxfpZuhSB5KYmlbWcOpXoU59CldGaNQjy9/So8S8tLQgS85gXrjRatw56Va9j+yMVypVQIBqbzPUCgWCCQq9wef7j22+rAcuCwSFT8R06pPone3vTS7CYDIhIRdtNJiimadNwXHe3GkxtMsHx7OyEAuMRLE4n0Ukn4dqZZbJVVYp1b/NmnMfhwPayMqIrrugbTc3sIckWbRVlN+EhDqigJJBPlQaPHuM+e57pyI6m3a4yXwcOpI+4Eow+NE2xwWfO0M4EEw3xXG0ex0KkHE2DAb97KJT9dzYaoUv37yf64x9BTMXB4SVLkOHs7sb5y8rghBL1ZaqdPRvZda5G0juZdjuCwSecgGPmzs29frMlFJhEkNtspEJJIBAMGwoth0wkoHC7uuCABgIqaycYGEzZ3h9SKcVem8+ssngchk80CgUydSq2cZnOaachwtrYiAg9ZzPd7txlshs24HeePRu/dSCA8513Xvq++a4fUXYCGmYHNJVK0cGDB6nzwxR+ZWUlzZ49mwz5DvwTTHgUUqXBcxQfeACy0e1GCwqXbbrdOB+P2eBefcHYADuQAzmfRNDFViscUP1os8zz8HbOivL3/D4eR8D+7behk6dMwRqx2RTZIBHRWWeh+okI6ywbUy0TVhHB0SQiWr8+/wojfUJBD2GuFwwGon8FeaMQRavf9/BhsLwlEoriW7Kf+SMfA8RsVkQV+ShHjrK73SDCKCtD5jIYxPXefhuKc8oUZEn/8z+hxHI5juwsTpuGc3V1qX4YTVO9MoWU04qyE9AwOaDPPvss3X333fTyyy9TkBfnh3A4HHTOOefQTTfdRGvXrh2OywvGEQqt0li3DnLtrrvgWCQScFR4RqOecEicz9IF6z+7XbXLsFPJc1yTSfUbx+OqLFfviPI+4TB0ck0NdHVvL/S+2Qxn9MUXifbtw+ge1qv6KqNQCKSPev2bSiEAMnVqfmuXM/g+nwoGEwlzvaAwiP4VFIxCFK1+35Ur0ePALKnifBYfhbAesoJLJvE7lpWBNe/oURg/iYQatJ1K4btHHyX6+tdzO3ydnSgFCgRAjhCNQlnW1qLMrLMTyq8QQ02UnYCIihoKjUaj9JnPfIYuuugieuqppygQCFAqlUp7BQIBevLJJ+nCCy+ka665hqLSgCfIgcwqDZtNvd+yJTu7u91O9LWvQZ7Ono2RVZwlY93IRHD8XlB64Mol/ZgxsxnvTaa+Opt/78ygA/M8WK3Qq16vyq7OmQP9ykRV4TD2eewx2GB6sE3Gs8GjUbDl9/Tkv3Y5g889pOGwer9ypQSEBf1D9K9gUChE0WbuW1aG+VV2O4QeU5ELigd9T+hAYING04guvhjkBnPmqNErRiN+J47Es6LKVGheL3pTvF44g+3tRO+/j/N6PPj7/vuK5bhQQ02UnYCKnAG95ppr6NFHH/1owPX5559Py5cvp9raWkqlUtTW1kbbtm2j559/nmKxGP35z3+meDxOGzIXv0BAQ6vSuOEGBNLuv18FZnlUhs+XXoIpKD0wg3zmeBybTZVZE6W32OhHnhGpjCiPRLNYECg2mRSrbjyuSAQjEawhuz29TSVbOwuPfOnsVISDRAOv3cz+0oFIiwQChuhfwaBQiKLNRjxUX9+XmVVQfLBzmctoYRKFVAoR1KefJnr8cZTcspIMhVSPLs8mq6pSCs3h6FtKu3hxOskCgz93dQ3OUBNlN+FRNAf0ySefpEceeYQ0TaNzzjmHfv/739MsngGUgcbGRrrhhhvoxRdfpIcffpieeuopuuiii4p1K4JxgqFUaTAb+Jo1RL/+NdEzz6AkNxhUbRRMTJNPW4VgbMBgADkQz8K+9lqiz3+e6C9/IfrDH1RZbne3KsPl35n1NveEcqmuxYL15HCothYGl3Azuy6X/Or1ajb7zW5H9lQ/G5Ro4LWbjcVegsGCgSD6VzBo5KtovV44GyYTvmtpAdW83983sicoHjQNioFnjLlcyFz29ipHUk94YLeDWMjhQFmQvjdFHyQwmTBjbPJkUMv3V0obDmPodmurooZfuBD3QjQ4Q02U3YRH0RzQ//mf/yEiosWLF9MzzzxDZq6Hy4KZM2fS008/TcuXL6edO3fS/fffLwpQ0AdcpcGtBB4PZFomS3g2eL3IZm3eDMdz9mzVZ28y5R7LkQv5kNUJhh92O5xEkwnMso2NaD/icWdtbXA4ucxa09Cq0tOj5oG6XIozgxlyDQZwLDQ3K/bcZBL7BwLQ4bNmQae3tfXVq4kEdHNdHT47HLABurtxXaez/7WbSR6YyWKfbR+BgCH6VzBoDKRoHQ7Fpub1Qkj6fCgJ4ZITgwFOid4ZEgwdmka0YIFixDMYoOycTii244+HgiKCorFYQPE/ezYUVjAIhRGJ4Hu7Xc0DTSSwXzSqHEku5eFyMR7MfegQHM5Fi/B72+1QqokEGPcy109bG8ipBjLUiLIrO8GEQNEc0DfeeIM0TaN/+Id/6Ff5McxmM/3jP/4jXXvttfTGG28U6zYE4wyFVmnoCfrefRdysK4Osx337FGspoVmPcX5HH3wzO22NujHcJho61ZwKMTjan43B4KNRhV04Pnckyfj2NZWHGM2Q4+zwxiLQbeWlcHGIsI1Z8yAw6uf8am3yw4fxvbDh7HWgkGc4/zzcS+51m4+5JMyr1swEET/CoaE/hTthg1EDz8Mh8PrhfDs7oZStNvxt7wcStVohJASFAcuFxRdbS3+z30+KKm5c4m+9CXM4Hz8caUcEgk4kKecguMdDigvvx/fGQxq/uecOYi6skIjUmRDR48iK8rsfLEY0VtvEZ15JpzFzGgqr59Nm4hefhnnKS8n2r4dSlKUlSALiuaAdnR0EBHRwoUL8z5mwYIFRER07NixYt2GYJyh0CoNJoPxeFSgrrUVbSqc9UwmIVML5d8wmaRndDShaQj8plLQtdzLmzl2xWCA3i0rAxN9YyO2u93Q45zx9PsR1L3jDuWo2u1EGzeqYP9xx+FcJhMqzjLtMj0Z5NtvY5ZoTw/sgwsuQC9yMJh77eZDPinzugUDQfSvYEjIpWi9XgjE9nbF0BaNohw0HFb9DTYbBGVzs2rQFwweBgOUjcUCxWU0El16KbKKySSU1rx52Ff/uxHhO46AEiFr6ffDqZwzR5ECVVXh92SFFgzid2aFyZntYFBlTvfvh4OaGU3l9dPbizWwYAGUrSgrQT8omgPqdDqpu7ubvNkYr3KA55M5uEFKIMiBfKo09GQwXEJZWQk9eegQnEejEdsHQ/4ozufoIplU7UbZWG6Zfd5kgl6dPBm6NxyGzrRY1IzPRALvV6xQepzXVy47LPNzJvHQmWdiXR09Ct27cyecRx7Zkol8ZnHze5nXLegPon8FRUHmbKnf/Y7olVfQ+xmL4cXCNpFQZZ8dHZgraTAg+svlmVI6lB8yh1gTQYnx/3UohF4iIqLLL1dKi6H/3bKVU5eXE115JdGqVehL2roVv2k2xONwOs1m/J6xGAIOp5yC3/OmmxBhzdYjsnMnvhNlJcgDRRvDcvzxxxMR0Z///Oe8j3nwwQfTjhUIhgIutfR4EJCzWOB8cJsK9/IJSg/Z9HO274mgIzkbWlODdVBejnaZVEq1L82dS3TNNThGzzpPBD05b57Sl5mf9WuNsXs3AshGI2Z/Go3ZR7YQ4VqPPw5nVX8OInzu7cU1sl0ncx+BQPSvoOjYsAHOisGgRnfE4+qlpxqPxeAgmUwQkm63OJ/5gp1M/f+nxUJUXQ3jpaMD//9mc+7+I70CW7cO+yUSyGYmEvh8ww1EO3Ygo+10IjrrdCol1dmJqC2PbeHfuLoa+1ks2FZRkd2RHKyyylS+ggmDomVAP/GJT9Abb7xB999/P61cuZKuv/76fvf/n//5H7r//vtJ0zT65Cc/WazbEEwA5CJjYTK/1lZUn9TUIPMZDKrsF2dBiaRKqJTAujlXdZfe1mEm2ylT8J7ZbT0eounTEZw/dozovPOwP/dx9vbCfjrxRDimdnvu0tlM4shgkKipCcfb7XB4ObGkD/52dhJ997tIKgQCuJf6elRX5WLKlXndgoEg+ldQVOzfT/TUU3BIIhHlyGSWnyQSigQnGsXnw4cHV2I0UaH//zSZYKxUVMDxYxr1z36W6OaboUBefRXEFvPmZScIWLwYmc4VK9Kv09zcfznNihVQXE4nsqOJBJRmJIJ7ZLKiXEqn0LEFQm4w4aGlUsXJCQUCAZo/fz61trYSEdHHP/5xuuGGG2j58uVU8+Fib2tro61bt9J9991Hzz77LKVSKZo2bRrt3bt3TJQB+f1+8ng85PP5yO12j/btCDIwkLwKhSCjX3hBOQLRKLbX1iLb1Nw82v8KwUihqgp9mFYr7KmjR9W8z+pqOKjt7XACTz4Z77mH0+XCmpk8GTo5m15cv171ZiYSRC+9BMd34UKiJUuwTzgM2+3HP4a98MUvEj35JM7J/amdnSAjvOyydG4HbpnRXyeToFLaavpiIsrx8aB/iSbmbzemwEr2qaeI3ngDQioSgQAdCEIVP3TwYGqrFf+XBgOc0f/+b6I//xmRSya3OOsslMW+8AKUg8OBDGdDAz4vXKgituGwCg6sXKn6Q4nSldRrr0HZdHfDWGLii2nTcB8DKZ1ClJUotnGJQmR4UXtAn3jiCTrvvPOoq6uLnnnmGXrmmWdy7p9KpaiiooKeeOKJMaP8BGMbA5GxbNgAJ2L2bBj1gQAqg5Yvh/x87TVkvqLR9HFYPB7LZIK8t1oV14KgdNHdTfT++wgWn3gi2Gm3boV+5uqmo0exRnbuhF53OpHNPHJEsd1XVWGd9fYSrV2rMqJ64kivV5XeLlqk7kEf/N2/H/YDj14jQhkwEQiOduyAjl+zJr3KSuZ1CwaC6F9BUcBK1u2GMGpvV72H/UE/Y1JQGNgI4flf0SiUBM8Pi8WIbr0V5Vzl5USTJinj55VXiM45B0bRO+8oEqjeXiic+nr0npx8Mkp0jh4FW96qVer6bW2qrEjPZhsMQolWVsKoWr16YKWTr7LKhwBB+kXHPYrmgBIRnXLKKbRr1y76xje+QX/9618pkaPG0Wg00uWXX06/+MUvaNq0acW8BcE4xUDyasUK/J06Fd8Hg5Dd770Hefvmm2oMR3u7GsfC4LEdFgvkO1egSJlu6SKRgGNpNmOESiyG391ohGN69Ch+Z4cDgeEZMxS5o9kMG6y1Ffq7q4vorruIXnxRjc1bty6dsOi559Be09mZfWbtjh1wcqdMUfdoNCI4Ul+Pe4vH08mL7HaZ1y3ID6J/BUNCppLlkpB8iuSEXGHwMBigCFwuNcImElFjbSoqQDBQXQ0n/+hRldVsaMC+fj8cUD4+HofSKiuD49rZqfo6330XSmfyZEXdXlsL9lxWbHpWXaL8lU6+yor7RWfOTN/u8aiyIFFy4x5FdUCJiKZOnUoPPfQQtba20ksvvUTvvffeR2x7lZWVdOKJJ9LZZ59NU/RWmEAwAAaSVw0Nfb/nskueEUmEAOOkSdCtsRi22e14z9UvDgcche7ukfiXCYYTySSCwrt3w8Hs6FBkVFYrAg8cbIjF8LvHYnBAORu6axcyovE41k9PD9EDD2C9fe1rioBw+nTYELmCv3V1WGt+f3qLzJEjCHpPnYrZ4bmY62Vet2AgiP4VDBqZSnbWLETNuMdTMDzguXAciYzF8H/e0YE5YGVliKRHIorQwmbDccEgZm1Onozfj6sZEgkommgUx7lcyJ4aDMiE7t0LA6mtDdlNHl6tVzxDUTYDKatC+0UF4xJFd0AZtbW19JnPfGa4Ti+YYBhIXtXV4a/XC7l96BDkLLdRpFKqysXlIjr9dKIDB3Cu1avhNDQ0wBnp7JTM53iBpkFfNzUhkNzdDZ1sMMAxjcVUSXZ9PRzESAQ6n+d2t7bCLotEQAoZiyEwffgwrnHDDfllKufNQ9vOk0/is9uN9ebzweZjMlKpRBIMFaJ/BQUjU8nabBB0+uwmM7YKigeDARFPVkbxON63tKD8mZVRd7eaDUqklFt7u2LQY6KoSZOghHw+HO/x4HuzGdnOqVNxjgULYDwRqb7QkVA8XEaUOS5GXzIkGPco2hgWgWA4wfKqrQ2vcFi9X7kSxv3KlQjYvvsu5HgqpYKGRqPSnV4vvp82DbK3txfOAM+HFOez9MFtNXY7fnO/H1nQWEzZT+Ew1ge3LnEG1GDA/q2t0Pft7fisaVg3/Le7O/uYlcyRLXr89KdEF1+Ma7a04Jq1tUTnn5++n4xZEQgEI47jjkP5Rlsb0b59EIipFIQiR3IFxQNHxeNxKAS98aFXNEYjfgvu0+jpgWFTV4dy6VgMBkwsBkdy0iQ4m4kEHE9Ng0IJBJDxZEOntjb9fkZS8eQaFyPkBhMGRc2ANjY2EhFRTU0NWa3WfvcNh8PU3t5OREQzM+sqBYIs6K+/3evF+ComkCOCzDWZ8Nlkglzu6sL2Y8dwbCoFPXvsmCrB5fFX8fjo/VsFQ0MqBedzxgy0zrS24sU6OhxW+xFBV3PAgh3ReBxZzp4erB22FSwWRXBlNqPvM9+AcWUl0b33ovqpoQH6/ne/U+XgDKlEEhQK0b+CQSEUIvr971HeEQ5DuL37LhwiFmqBgLDyDQd4rA0TFGQ6+Bw5d7vVAOtgUCk4nss5d64iH2ppQclPIIBjQyHFvHf88fjLJBijWQIr5AYTHkVzQF999VVatWoVlZWVUUNDw4AKMBQK0YknnkjBYJBee+01WrZsWbFuRTBOkU1eORxqNEtLC5yMOXMQyN2zBz32BgOCa1araoPo7CT63/+FvnU60/VAMimB3vGAUAiBCoeD6KqrsBbeeQcZzczfN5mEM5lMqsBFRQVePT3YnkjgXOGwSgzs3Yu/991H9PWv5z++bN48vIhAiCSVSIKhQPSvYFDg2WXPPw8B6HBA6BgMcHpWrwYj6nvvjfadjl+wwcF9QjyoPJXC+1gMfUVc1mM0omfD6YSyCIdhDHm96Nvl/SsqYAx1d0N5TZoEBjxWLkRjQ/EIucGERdFKcP/85z8TEdEnP/lJqqioGHD/iooKuvLKKymZTNKDDz5YrNsQjBN4vcgSeb19v9OXODJrvNEIWWuxoLfz8GF89nggj5lQzu2G8xkOQ+7bbAgG9vQgmMhs8uKAlj64gqmjA0Ffvz93ZRG3zrB+Z/uLCMdyKxRXPvHasdux5jZt6luKmy+kEkkwVIj+FQwKv/890bPPIuJWWQkntLkZgq6rC5m09nYZsTKc4NIaIigZ/r82GFQvkaYhM2kwKAfTYMB4laVLoTTWrEHW8+BBlOwsXYpB2IsWIfp+4AC+Z+UiikcwyihaBvT1118nTdPo/Mxmpn6wdu1aWr9+Pb366qvFug1BiYPnYG/ZgmoSl0sxg2dml7KNZjnuOMzPfu01EMMlEpDn8+djZvPLL8PhLC+HbLdYcJ14XBzP8QJNg751uaCrLRasCQ4y5JqXHongO33pNrPTvvcenFAu0TWbcf54HFVNU6YMnrtBKpEEQ4XoX0HBaG5GGVB3N4Qdj/Do7VXltk89pZwgotzCUzB4ZJJOJJN9/49tNhgtTFZEBINm+nRE0xsbMaT69NMROJgzBwpq925kNTmTWleXbkyJ4hGMIoqWAW1qaiIiouOZyjEPHHfccUREdOTIkWLdhqDEoc9ozpyJv9mIXogUa7zHk77daFQynOdp22wo0Q2HERycMgWOht+PbeJ4ji8YjWquayymGI+Nxv5Jpngd2GzIdHLAWdNQ8cQs9xysOP54BJiZu6G+Pnvmvr+MPqM/8iKBoD+I/hUUjD/9CRlOsxmCMRwGGQI3yBuNihCBSJUHCUYWFgsMlpoa/CZ6wgKi9L7NuXOxbzQK53PvXqW8XC6MbCmENW8gZFNs/Sm7fBShYMKgaBlQn89HRBhynS94X68sRgFlz2j2N5IikzU+GMTcz8pKHL9smerhCwSIPvc5EL7s3IkKllBIBX2JkPViPSwoXbC9xJntri6sLZtNtdpwkEIfeDCZVNVTTw/Wh8OBtVNWhvdccmuzYdv8+bAH2tux9u6+G9fkzP2llxI9/nh+GX2BYLAQ/SsoCF4vyjo8Hgg6vz+dBIf/6iN2EqUdGVitioyAS3iqq6FoPB4oGoMBCoRHAej7NleuhJN58KAyoMLhoZfq6JGtVG3ZMqyRN9/sq+yI8i9tE0wYFC0DOmnSJCIiOnjwYN7H8L759KwIxj9yZTRzMYNnjmbp7oYejcfBND5tGpwGjwfb6uqILroIsvhDAsiPRrNw6wX3igpKG0wqyOWykybhN9bbUnp7ijOm+hLcsjKsm0gE26qqoDeJEOwIBrHm2tow/qenB4HmmTNx3QceIPrGN/LP6OshgWJBIRD9KygInZ0QbnPmwOGx21W/SioFp4eVoSjEkYfRCGexrAwRUZ49x/2itbUwirL1ba5bB/IoPbNuvqU6RPkpn2ylar/5DdE992RXdoWUtgkmDIqWAV2yZAm1tLTQn//8Z/rUpz6V1zFMfnDiiScW6zYEJYzMjCajP2Zw/WgWr1fJ7UWLsh+/Zg3Rb3+LrFggkF5RxEQ0RAOXagrGJgwGlclOJhGA4Oolnw+B22hUcT4YjbC9jEasj3gc71etIjp0CNlyIthrTif0fm0t2JaJsOacTtgJ8+bhHLt3o7Kts5No2zaik04iOuEEOML9ZfSJCuuBFggYon8FBYGVrdMJgff22+o7jsIRQSAJRg6swBwO9HxaLMooaWmBErjqKqLvfAfbsvVt2u1EX/gCMtyxGGaRce9IrlKdQrKU2UrVkklEZDUNytBmU8pu40b8zbe0TTBhULQM6GWXXUapVIoeeeQReuihhwbcf8OGDfTII4+Qpmn0yU9+sli3IShhZGY0w2H1fvFiGPSZQTkmcLn9dqKf/ASjMCoqFNMtH79yJc4fCqmRLHY7XgbdU8C9o1JtVJpIpdAGs3QpMuALF4JEiMeslJXhN/d4kBW12fCdxYLPnBRoasLoNE2D7k4k1CxRLuG95hqsuZtuwjWqqlTbTSKhZo42N2M7o79Z38MdKJbM6viE6F9BwZg7F85IZ6diWTWb8Z4HHQtGBuzwc29nIoGIqd1OdOONRL/+NdGPfwzSqHvvVXO8cjluVVVEy5fDaT1wABH3bKU6g8lSZitVC4WyBy08Hly7q6v/0jZRTBMSRcuAXn/99XT77bdTQ0MDXXPNNfTGG2/QN7/5TZoxY0bafk1NTfSLX/yC7rrrLtI0jWbMmEE33nhjsW5DUOLQZzQbG2HEV1Whd37LltxBOR4lNX069uHjXa6+FSpeL2S8nqwoE8K1UHowGPCb1tXB0fR6YUP5fCiV7elRJbkGA3S91YqA84wZ0Lnd3XA8WQ+GwyoTHo3CXps6FbbbF7+I9eb1Yp21tWH8TziM8/CxoRC2z58PZzZXRr/QHuhCIJnV8Q3Rv4K8oBcEXi/Rvn2IkLHCk9Kf0YGe6Inp1k0mCOqHHya6/35kRBcuxODo/gR3KIT+jz//meiDD6CIbDZEVh0OogUL+iqYQrKU2UrV7HY1LkZ/Xz4fMgL8PrO0zWYjeu45lBqJYppwKJoDajab6ZFHHqFVq1ZRb28v3XnnnXTnnXfSzJkzacqUKaRpGh09epQaGxuJiCiVSpHL5aJHH310wKHZgvELrzedATxzJMVzz4EJ3u3GWJVoVM1Ovu66vufrb6SF1wvnwmpVREMctBOULgwGBFMTCfz+4TDY6A8cwO8djcK+4jErTidaYoxGtMpcey3O89xzaI1pa8O5EglFNMjJgWAQny+6SOnHqipk6P/0JwQ94nHYDpxU6O2F48pOcK5Z3xxYnjlTbQsGcR/8nAzWAeXgdk0Nzu/z9f8cCUoLon8FeYEFgduNcg6fT/V6Wq2I0Enmc+RhNuNFhAin1apKcjo6UKLT0QFH7eBB/HZXXKEMHFYQRESPPgpl5POhDIiZ+Lq74cQ6HNjfblckGc3N6tp6eDxQanrlw6VqrEA8Hig2LvPt6YGi9fmUsiNK35+/q6qC8yuKaUKiaA4oEfpQ3njjDbr22mvpnXfeISKiw4cPpyk9xqmnnkp/+MMfaMGCBcW8BUGJYKCMDJfLPvQQ5JTJBBk8YwYI4QbKCHFGNPNaXi/kI/MtCEof7FzabPitDx3CGrFY4MDFYvi9DQbo9UQCJbZnnYUKJc5E7tyJud7bt0PXsx3Gay8ex2e/H3NmDxxQxH9bt+KcXV1wPC0W6PpZs6C/maF56tTcs771gWWjUfWS+v34/NxzyLgWGhgezsyqYOxA9K+gX3i9RJs2YdTKm29CsaZSEGyxGN7rZ34KRg4OB4ICoZByzJxORETZWIlEoBAMBpALPPggCAasVny3bx9+464u/J6sUDi40N0N5djZic96g6q/LGW2cp3MUjWXi+irX1UsuLnKz/T7r1kDZSuKacKiqA4oEdHChQtpx44d9Nxzz9GTTz5Jb7/9Nh07doyIwNS3dOlSuvTSS2nNmjXFvrSghJBPRuaBB2DkV1dDFvf2Eu3ahUqS6uq+GaHMbGq2a82bB74FZjnn+cyC0kYkgpfLhfLbN9+EE+lwwIH0ehW/g9mMdbJ3L9bVjh0IyvKc2FAI5+juVnPXk0msFZcLdoLbjUDGXXfhnB6Pmjsaj2NthUJwOl0uOJ7f+AZ6U7PpVF67ixcjILx3L4LSnEmdMgXbXa78A8N8zq6uvplVouzBbUFpQ/SvIA16pdjZSbRnD8pCmDBGH4VlJ1Qw/LBa+85YjcWUoxkKQQFw1LO3N50hMR6H8A6FoKicTnWeUAj7+v1QgrW1cOx6e9Hby2VAySTRu+/C+bz5ZhyfLUuZrVynv1KzK67Ibohl7t/ZCUeT+2U4I2uxIMtbXy+KaZyj6A4oY+3atbR27drhOr2ghJFPRoYIGaCyMsjUY8cgDyMROBeLFqlMUH/Z1GAw/VrBIGQvBw3F+Rwf4LYlnw9BCj1MJjWChZnrIxEEIiwWlOC63XA+vV7lcHIZLhGcOJMJejMaRbY0GoXTarPhvceD9eXz4XMshnPOmIHWnWzOZ+batdmwft97D/dst+P4RYuUvh4oMJx5TpMJjrDTiYosRn/s0oLShujfCY5sSnHuXChSJonRCzgiKQkaSbCzyejuhqHDr64uREr1JASMeBxOpN+P39Ziwf4szK1W1Svi86EUh8mmmPSgq0sZQmVlyEayUumPQCMT+lKz/rZl+44jtG+/jXviaGsggH/z3XfjvqQfdNxi2BzQoWL//v10wQUXkKZpVF9fP9q3IygisvW6EaVnZIgUI+nbb0Oe2myQyYEAHIXf/hbG+Jtv5m4jWLEC16quxjHc+1lWpoiIiIR3YTyD9TWRckJTKUVa1NsLXc76nr/LRCIB+83lwnoigg3R04P3s2Ypx9VgwH7RqCI0yqaTN2xApt9iQZbUaETg2+2Gw8otO0T5ZyyzVRf09CDTy9na/oLbAoHo3xKEPtv5xBN9hcDGjRBIPBtSCBBGD8yCp88482ezGX+zOZ/645NJpdzYmWXHVdMU+UFDA7YTQXFVVmI9VFdDaTFJQSgEgyoXgUa2rOZQsHEjFFM4DEXn9eLlcKC3xemUftBxjjHrgEajUWpoaCBNhOS4Q77zPl0uyFaLRcnSZBJBu85OojvvJPqf/0Ew8fjjs89aXLo0PchmMCDYGArhPct6cUDHN7jkmkjpfHYeOeisJ6bKTAawbcDn8PngtEYi2LerC8fzOYjwPplE8HbrVqJf/YrohhtUMPfAAYwP4tEuZjNsxeOPh6MZiynnk685UMYyV3XB0qVguQ8E1HkGCm4LJi5E/5YQspU8NDai30QvBMJh9AkSqUiZYHQQj0Pg8+/ASoiV00ClWXrnNRyGMRMMwjhKpeAkxmKK+Y57T9iAisVQZtPQACX2rW+hPKa8PJ2MY7jo01lRLV2KMuFDh1Q21+kEZTyPbZF+0HGLMeuACsYvspGoZRKmcT/cY49Bbs6aBTl57BjkbTyObJHZDFm2Zw/en3KKInZrbASDbk+PIqmJx3GtUAhy2O2G3Ga2U8H4BY9dyfyt9bo8l73N/Z2hEJw4ngVqMqly21RKObpsV9TUILPp9ar1vnYtnMh//meMZ3E4oMsjETiJiQTsgCNHsGYtFswP9/uh9/vTw7mqC6qqcN833YSWn2IGsgUCwSgis+Th8GHlaFZUqCiW262cBu4tFIweNA1CmMtnDQY1O0yf1cwGdlr5POxcBgJQTMcdB0PJ6YSCqqmBcjh2TPWhcDkQEXpJystxPxs2ICp6443ZM+m5spKFZEn1imraNGRjmXwhGlXrU4gKxjXEARWMCrKRqF14IWTvLbeofrjaWgTp/H7oUbNZkbyYTNCvbW0I9L35Jgz12bMhz0wm9JEuXQp5umsXzsOZrGQSMk908cSA0QjdNtA+7EiyU8rsuQ4HnMR4HOuGHVB2YDlozXaByYT1G49jfQeDIC168UV8t2kTzsttOaYPpfHhw0TnnaeCNE1N2D5jBq4RCuUOPg9UXTB3ruhxgWDcILPkgQcfB4Ooue/qUtGoAwfwnSi8sYFoFNFxnr3KfZtEAzugeiVjNsPRjERUCc7kyTj3yScjOq9poFBPJGAkWSw41m6HsWQwoFQslUI28oMPsH7a2vpm0onSs5KDyZJmKqqKCjifrNz4OCEqGNcQB1QwKshGopYt2NbdTXTGGXAgXS4E+Hp7VfCQ+/nYaWCCGLeb6IILQKRWUaEcBh6x1dyMc8nYs4mFgUgeDQbYZ1x9xMEKg0H1DCeTqlKIg8lGo2LZ5QxrMgmdbrfDRvD7sU9VFbb7/apPlEiViEej6H2urMQ1li1TM3CfegrnytUSM1B1gTifAsE4QmbJw+7dcCDcbjifgQAis4mEovDu6MC+BoMSeILRQTgMwc9lNYxEAoJePy+OPxuNKrNJhMxlWRn2i8Ug9K+/nujhh5Fd7OkBrToRlIrXi3Uwdy7WD2fIDx5EhrS8XJEB1df3Za/LzEoOZsh0NkXldiMTW1uLddnWJoprnEMcUMGogknR+mPGjUQgg7ZuVczlHg8cy4YGGPicEWUW8/Z2OKLvvov92XkwmxXhCxG+F/K/iYF8W57YyWTo1w9v50woEw4lk1hblZXQ9xwY6epSmfZUCrrd6USW3mqFDTF5MvYPhxWb7iWXEP3xj9Dn/DwwBmqJyVZdIP2eAsE4hD6TlEyiXMLphFLk0o32dryvq0Nprr4JXpTf6EJfOjPQdu4bdbnwG/PQ6WPH8BsbjVgPCxci8/nss1gXixbheB4qbTZjLSxdSrR5MzKvx47BoeRh2i4XSm7q6xHQWLRIOar6rORQhkxnKqrZs1E6nEiI4pogEAdUMCYwEDPu2rVEn/400X33gTW0pwfZUSZ6cbuJliyBAV9fD9nd0aFGamkagoRtbXBATzoJzisznhoMkHsyBm3igvs8NQ260GjEWgqHoQvZETWZVPktB6S5bYdHp+gZlvXZ1GgU+nzJEvQ1sz04dSr0em8vdPaUKYOf3dnfiDaBQDCOoM8kMbue1QqhtWQJyiu55j8SgUAQJTe6YMXAhEDx+MBZaJNJkVuYTESTJqm5njabIjfo7ER5zbx56RnGBQsQsT9yBI5iIIA1UluLyD7PB2XGx0gE2cg5cxClb2qCwsosp9m/v/iKajgYdwVjEuKACsYE8mHGraoi+vrXIUcfeAC9clxZdOqpCOr99a+QX/G4anWw2RDkY2c1FkO1SVOTKq/MZEQXTDwkEtC97HhWV2PNxGJqNBCRynzG46pXlFnvOzthH1RV4Xt2RI1GZD8rKrDu5s9HADoSUeNd7HaiSy8l+ulPcc58mKL7Q3/j2AQCwTgBZ4g2blQjVo4/HlmrWAxKkIVM5vxJwchDX4qjp03vDxUVavanwQDjJhJR/ZLJJBxUpxOO4/792UthPvtZKJnHH8d2sxkvj0dRutfW4lxNTcimzp2LfbJlJfMdadAfMhWVKK4JA3FABWMC+fau2e1EX/wi0RVXwEl48kmit95Cf/2uXZDLySRkIQfy4nHoYCaYMRpVRopIKpEEALPgc5l3Z6d6nznChTPnHLjgQAez3/L6O/lkBJ+5konHuzU1YW3ecgtm1TY0IIAyb566n5Ho5ZRgs0BQ4tBnku67D+xmU6YowhkiCLLubhm9Uopg9rxUCoaLw6FYcI1GlHZVVKge0SNH4FxeemnuUhjevmMHzjF9OvqHDx+G42gyQTk0NRFddBHR5ZfjuExFIaQDgiFAHFDBmEEhvWscJDvpJPTAb9wII59HbXBZJDsUPIZF0xQDOZHM4xYocNuN0Yg14fenf5e5Trhk22TC2nI4VAZV01CVVFeHdep2w0nduxfXMJuJPv5xRRSodzwZw9nLOVzj3QQCwSiBS4RqauCEvvgiHIhYDMJKL9AEow/93M/+kEwqI4ZLtioqEMlMpSDAJ0/G9oYGlNP+5S9Er72mhHo2R7CqCmU4VVVQXMuXQxE0NaF/KRjE2nnrLTAo87kyIaQDgkFCHFDBmMFgeteCQWSQXC4Y95EItvX09JXtPK+5qgoBv/p6NZZFIGDkShJkrhMmH5oyBZVPp52GMttIBMHj5cuJ3ngDDqrHg/1iMaLVq4m+8AW1tjkLyeB1P5y9nIMhLhQIBGMcLDRaW0EwYzSipDISgVIUjA700UuXCxHLY8fyP577PRwORDMXLgSVf309FFYgAKfR60XU88QTlVDv7VXDpzMVSGYGk3tFt23DfZ58sspq5lIQQjogGCTEARWMOeTTAsAZnE2bMObK6wUpXCSCYF6uktpkEpVIvb1wIASCwULTFPuywwEH0+FA9VFVFdE118DB0weG161TWcbMNdzdDQd24UI4qbxfsVtihkJcKBAIxjBCIaLf/57od79TJAihEJwWZk0TjDz00ctIJD/jQ9/3wbO9eAbdrl34bDAgsn74MIybujqiT3wCAt1oxDxPHj7NzmZmmUtmBtNkAgHCQPM/MyG9m4ICIQ6ooCTBGZzubgT+2BngHjwi1aPH2xmJBDJRQkAkGAoSCTULtK5OjS5rbCRatUoFhnP1ePIa7upS5EWdneCPaG7Geb/2teLf90CM0/0RFwoEgjEMFirRKOr8TSZk2jg6Kxg9sJHCfR5MvZ8LXGLDhgrvGwqpc9hsYLSLxfCbX3CBGpeyezf6QVMpCHSjEeujtZXoxhvTiTX0GcyuLqI77+yrBERBCIoMcUAFJQfO4DidRNu3KwZxJoNhoj92OjOdS/12cTwFgwWvsbPPRnbz4EHF+bF9O0rCjUbYDTzKhQPQwSDWsNuN4LXbDTuguZno/ffx+a67cK4bbihuX2YxiAsFAsEYAyvGqVMRCQsE4LyEQhA4gtGFnkiA+zf7g945zYygp1Iq2t7WBqMnGCR6+mmU0MyejV5OkwnKw+kEC97Bg8iK7t5NtGZNejZUP5RdFIRgBDBmHdDJkyfTrbfeOtq3IRgihoNlkzM4hw/j/A6HYh2NRhGoK6S9QiAYLBIJrLef/EQRUM6ciW1bt8KZrKtDP2g0qtpoVqxQ5D+8Zjs6VE+yw4GWrYcewj65+jIH83wJcaFgIIj+LRHs36/KK4hUacPs2YiG+XxwQAVjC5qm5rXmA3ZWOYtKBKcyEkEZmMGAaGcwiLmd3d1QJppGNGMGnM+9exUTYyymFEBm76YoCMEIQUulJAfE8Pv95PF4yOfzkdvtHu3bKWkMJ8tmczPR1VejDYLnLDocCPb19qrZigy97JbVLsgHhawVux2O4mOPYS3W1ED3P/KIsg1mzIBNWF2Nc3/720R33AHnc9eu9EB2LIa1nEph3vjUqUR//COIsxhDfb7GMwuuyPHShfx2eaKzk+i73yV65RU8zHY70bJl+M7phDOxdSvRyy9L3+dYgb6ncyi9P5qG8mru7zV9mEcymbAGWlrg2JpMUDyLF4OQSn/9NWtQart/PwIW8Xi6EiAavwpCMKwoRIYXnAFtbGwc9I31h5mZDUmCksZwsmxu3IjzJRKQwdGoYpjnfk+bDTKTmW8jEcVGzxCHVDAUsD4PhYj+3/8jOvVU1Vf57ruwEW02rLFEQo1g8XiwDweZ3W4EqAMB2AEc5K6uhi1ZX0/0pz8Rfec76tpDfb6EuLA0IfpXQERwPp98EqxlU6ZAAb7wAvoBuUQyHh/VWxRkQN/HOZTB4+y8hkJqFmg8jnMbDGC8PXgQI1Z27ULPZyikMq7HH4+I/e7dUC5TphDNmtVXiYiCEAwzCnZAZ8+eXfSb0DSN4iIsxw2KybKZWWLI5z7lFATmmFk+GoX8dTggj81myGKXC7LZZkufw80cALGYupbViu9lKQq4dzMXTwQHoRMJ2BLNzUQnnAAdnkyinNZiUXNCPR4EQfbvR4sOkQo0b9oE+7GzE/vb7XA+q6thN7hcRO+9h7WvfwaK8XwJcWFpQfSvgPbvR+azvBzzH4mUAGhpIbrwQjgXBw8qISUYGzCZ8jcwXC4VOedop8kEQ4WHSbPjabPhc0cHlNCUKURf/CKi9Rs34jypFJzPRYtQqnvwIFFZGbKkNlt2JSIKQjCMKHgQRSqVGpbXUJBIJOjee++l1atX06RJk8hms9GsWbPok5/8JD3GER3BiIF7NDnTw/B4sF0/8zAXQiGi9euJbrmF6NZb8Xf9eqKjR3GOadOIliyBYzprFsobLRbVcx+NKueV2yGcTmSLnM6+zicR5LjZrD47nUP9nxCUEkwm6GCTCaPQysthA1gsfVnz9UFnItWKc+SImv3ucCDgbLPBKW1vx/cNDSi/3bABTugddxA9+CDRV76CZ2TqVJTehkLIis6Zg2vxc1OM50tQmhD9K6CGBjVaRQ+3GwJn0SIoRKK+QkIwekgkVFSToZ8Pqgcz4DqdUCA1NQg21NamRz75vJwN7e2FkbRyJXo2rruO6Be/IPryl6FIpkzB/k1N2Hf2bMWaSyRKRDCiKDgDev/99w/HfQwaXV1ddNFFF9Ebb7xBmqbR/Pnzqa6ujo4ePUqPPfYYmUwmuuyyy0b7NicUisGymavEkNsRfD7oWSKUL3Z2KpnMc7dbWtTIrcmTcR6TCa9Dh/pekwOTnD01m/HKdFQF4xeJBPS4y4W15vdjXfCaYD4PXit6Toh4HOs1ElGtObNnw1ltasKa9XhQGXXsGNH996tRK1VVRJ/7HLKhXV3Y12LB8U4nzsXPjbDYTlyI/hVQXR2irH6/EgCxGISKxYLs52uvQWiVlUE5ihIbXXDZLBGMFP3nbGB2ukgEwl/T4Cgmk1AWTHZhNKqSr2AQ+152GXo8t22DUiAiOuMMKJKdOzFKxWyGQeRwIFLPOHYM3+mVyHAwSQoENAgH9LqhNvAVEclkkj7xiU/QG2+8QVdccQX98pe/pOk6po7m5mY6ePDgKN7hxMRQSdT6KzHcuRM99Rs3wuCPRNDi4PMpApdoVBntmobrL1gAw76lBTKWA4yZ5HLsdIZC0O8Gg/SKThSwU8nzOI1G/O78N5sNxzwSRiPWbSCA9fnBB3A6rVZkU30+ZFQnTcJMcO5h3rsXx1RUEL35JtZaOKyyr4cOwdaYO5foiSeQMRWSwokL0b8CmjeP6Kyz0AOaSkEJdnbi79SpiGwR4TMzmwlGFxYLMtQ+H36X/npAKyrwG3u9EP5Wa7pSqqhQ/R1OJ95PnowSnOnTYQBdcw0o2LlHyeVCj8gVVxB97GNwTg8dQt8wzxolgsGzaBGUzaWXEj3+uBARCYYNY3YMSz747W9/S6+++iqdc8459NBDD5Eho05u+vTpaQpRMHLg/rYtWxBwc7lgHPP2/sAlhpm8GDwHedUqnO/++0H2EotBRrMDEY1CNsfjMOSNRsjaigqco6lJlU6aTGqeI/f1x+M4H78XTCwkEnAKHQ7lYA5kw5nNqIxrbUUF1OLF6OE8eBBOZjIJG6G5GevTaoXO9/mIfvlLZO1PPhmOZixGdOAAzsvbp05N54cYyvMlEBQDon9HCV4v0Ze+BEfm+edVucScORAq+/ZB0VVVqQyYYHQRjSLynU8kOx7HvtxLFAxCwHP5bkcHzmM24y/PFF28GMfddx8CD729ytAJBhEV/eMfiXbswPfJJBSR34/rWCxqtMsDDyDAEQjAECs2k6RAQCXugP7yl78kIqLbbrutj/ITjC6GwrI5UInh1Kl4PfUUnEq7HTKZnQQmckmlFDlRdTW2HTzYd0RLpk7gTKpgYoKDF+FweqtNNujnivPfXbtQ+bZkCYLJH3xA9PrrcBSZCyISUdfyerHe2tuh75NJfO9yEa1eraoATKZ0fgghKRSMJkT/jjAyZyfxmI0TT4QgaWmB88nluDIMe+ygENbbnh7sb7dDocRiKN9iw4VLsniep9kMJVFZid+fI+qapgwo7j9tagKJVUWFirK6XGpEQCKBEm6jEcbT9OmqJJcVUaFMdwJBDpSsA7p//3764IMPqLKyklasWEGPPfYYPfTQQ9TS0kLV1dV03nnn0ec+9zmyWq2jfasTGoMhUcunxHD/fgT1rFa8UinI2lBIOZV2O4x7lwvfNTWhSkXf1ykEgYJciMUGDlhn+76nh2j7dujwJUuwxqJRvJJJ1WdqNCLoHAzCiXQ48IrHYRv09GCds97nCoDOTvVMCUmhYDQg+ncUkEmMcPiwIjqIRlF+ISU74wOJBIwVnsulBysdvfLZtQsRTKdTObBEivAokcA64Uip3Y79/H58ZzKp6Ggsplj2gkGU8BBBmWVTQgLBIFGwA/q///u/w3Ef9PnPf76g/bdv305ERAsWLKDPfe5z9MADD6R9/+c//5l+/vOf0zPPPEOzZs3Keo5IJEIRTkUQBqgKxgYGKjGsrEQQj2csci9+MqkqUpxOvJJJVZFSVQUn1euVvk5Bbgym71d/TCSCsu8ZM+BE8hiWY8fSiYuYdZmZ9G02vO/txbptaYEN4HAMjmRI+CPGF8aT/iUSHZw3shEjzJgBR+LwYdTqB4Oje4+C/MAERP0pmGRSjWzhcluOXjKMRvz+RqOiXZ8xA84oO57siLKhxP2kPM6FiY6iURwTjys6+GgUxtTkyYjez58Pp1WvhETBCIaAgh3Q66+/nrRc1NGDhKZpBSvAlpYWIiJ688036bXXXqMbb7yRvv/971NtbS29+uqr9KUvfYk++OADuvLKK2nbtm1ZS4Ruv/12+vGPf1yUf4OguBiohLeqCkRvu3fje7NZzWB0u9EOU1UFh7W3l+hPf0JvXlUVSiLF+RT0h8GsDyYjIoI90NNDtHkzRq94PIoUK5VSVVLMEcHHs43BtkdvLwLhPT2FkQxlVusJf8T4wHjSv0Sig/NGNmIEhwO9KE1NqrlcMPbAjIeMgcpxNQ2lMfpsdmZPkMGAPo/Zs7F/Tw8CE8uWgYygq0v1hxIpxWS3Y4ZdKAQnMxzG/TDxBQ9NTyahtEIhta6amuCsXnYZ1t769aJgBEPCoBo3xsIcskAgQEREsViMzjrrLLr33ntp1qxZZLVaac2aNfTII4+Qpmm0fft2evLJJ7Oe45ZbbiGfz/fRq6mpaTD/HYJhRFUVCOGyGd3r1hF99auYA2oywQm12SBf58xB/9yaNUQ33ABSOKsVLRKRCOQ7y2SBoFjQNBV89vvRm1xWhpfHoxxQFnnMusxkWJy9r6pSZIdeL5zSQkiGuFrPaITNajTi84YNw/PvFowcxov+JRIdnDf0xAh6TJ4MgSHZz7GLZBJCPl+Dg/frr5yanVS7XfVzVFQQffazRNdeCyPIaoUyMhpRCubxwFhauZLo+OPB0GixYD+7XRlRFgvo2qdMwbqLx+GEms1KCYmCERQBBWdAD2UboDgKsOnYab7xjW/0+X7x4sV0zjnn0IsvvkjPPPMMXXrppX32sVqt0qNSwrDbiW68kejjHye6916MaGGyucOHITcPHIC8veEGZJB+9jNVtdLbO9r/AkEpgxNRevudbYbaWmTi7XaUkLe0QH+zA8r9n3a7IiSy2+GomkzoAZ06Fet27drCKpz6G2Mk/BGljfGkf4lEB+eNTGIEh4Po7bdRzsN9glLWM7ag/000LT3zyX0YTBZksSDLyaUxzHirr3bQRy+5LDcYRIlMZSXGAzzzDNYI92vMnIlIvMuFcpzGRoxemTMH5FXV1UTnnYfzP/88oqY8py4YxH52O87xhS9gHYqCERQJBTug/fVzjCQqKio+er9gwYKs+5xwwgn04osvUkNDwwjdlWAwKLSNIHP/jRvR9jBlCuRrRwdkdXk5vmedvWwZZGksBjnLjoAQEQkKBWc5uW2GwfaC2w397fUi8Gw0Qq/zWuNAM/cqOxywBXhm/MyZKphdaEXTQGOMhD+idCH6dwKD+0k2b8bQYK8XQsPrlfLbsQarFcI+Hlc9n3pw1tJkgsDXNFUCy/2azEhHpEplNQ0Kg8/R3Y01cM01OO6eeyDgy8uxj8+HWZ5VVURLl2L7oUOYXzd3LtHf/Z0qq6mpIdq0iWjPHuXU1tXB+dSX1oqCERQJJcuCe/zxx3/0PlcElbcnxMMYkyi0Ty3b/osXg3G0pgbZo9ZWJftaWzEGgwjH3Hgj5O+RIyoYKUtDYDBA14dC+a8HXj/JpCLASqXgVEYiyMYzOZbdjmvoW4EcDvw1mbCP2Ux0993q/HPnDl6HDzTGqBASI4EgG0T/jjBY+e3cifKIzk6UWc6eDSIEwdgCZzOJVFZTDyYZcrlUNtPtxu/c1YXjIxHs53Dgdw6HoUROOQXO5/Tp6CkyGjHbs75eMS26XLhOKoWeUIsFJbrTpsEoamrCNS+5RBlbetINRrasgCgYQZFQssO7TjnllI/KgA4ePJh1H94+bdq0EbsvQf4otI0g1/579qT3yzObaDSKbR4P5HJFBVobdKSLAgElk2r+5mCQSKggNtsIPKaNiYQyndtQCHo/mVRM+BUVyNIvWza0ADJX67W14RUOq/crV0pwWjB0iP4dYeiV39Sp+NvejuirlN6OPeSjTKJRCH9NQ7lMKgUyoVgsvcTG74fDSIQyr0gEWdADB/B53jwcc/Agjtc7hRz1TCahdIjg0M6aBeWkdzaJFOkGv7IpC1EwgiJhWBxQv99Pv//97+mLX/wiXXrppbRmzRo6fPhw2j5Hjx6lPXv25FReA8HpdNJFF11ERETr16/v831rays9++yzRER07rnnDuoaguFDZhuBzabeb9mC7/vbnwOIlZWoFmlrg0FvsUAehsN4T4R+UNOHuX6RjQI9rFaVvRzM2BUGM9jabND5ZjPOzd8RqfFqfD12Vh0OnKurCyNbMtf+YLBuHfgiEglURRVKYiQoXYj+HWfIVH4stAIBOCbSQ1t60CuPaBSfTSYIardbEQS5XFAmvb1QFlOnglkxkUg3nmbMQAlYMJhObpFMqvIbIpR/NTfDYBpKtlIUjKAIKHoJ7q9+9Sv63ve+Rz0fzhdIpVKkadpHrHmMTZs20Wc/+1my2WzU3NxMlYN4EH74wx/SY489Rg8++CCtXbuWrrvuOiIi6u7upuuvv55CoRDNmTOHrrrqqqH/wwRFRaFtBLz/lClE77wDvcujq2IxHGOzgfxlzx4Y+B4P+u17eoiOO47o0UdxDm61kMowQSSighOF8nikUlh/fLzBAJshEECAOpVSs725nSeVSh/DFg6r+eA33YSWm/LyoTPaDzTGSDA+Ifp3HEKv/LZvRxlua2vf0RyC0gErmkQC7085BcZLS4uKYiaTcDg7O0EgwGQCvb14TZ6szudwIGN57JgaNk2E7KnbDSXz8MNwUJNJKKpLL1W9IIVCFIygCChqBvRHP/oR3XzzzeT3+8lisdCpp56ac9+rr76apkyZQpFIhB5++OFBXW/x4sV09913UyqVouuvv55mzZpFp59+Ok2bNo2effZZmjRpEj388MNk4VSYYMxA30bAZC3BIAJz2RxD3v/tt4n27oV89Xigg+NxtDYkEthn1izI8GPHcOzixQgQPvUUtjEDaZHH6QlKFMzzUOh6YA4JXq+ahvVosSBobberzCpXVVVVwR5IJGAbEKlzHDmCqrpotP9SdK83/0xpf2OMBOMLon/HKfTKb/v2dDYzQWlD36vh8UBJhEL4zFnuri4olLo6fL95MxRF5kieqVPBbDtrFsrCvF4ELc4+G9/39OBaZjOuu3Ur0e9/P7T71yuYQhSTQEBFzIC+/fbbdNtttxER0bXXXkt33XUXeTyenAOoDQYDXXXVVfRf//Vf9Pzzz9MXv/jFQV33K1/5Ci1atIjuuOMOev311+ndd9+lqVOn0sUXX0y33HKL9J+MUVRVEZ1+OkjbuP3B74fxPWcO0R13pGeBqqrgSD73HIJ2PEM5lcL+NhvRt7+tzn/bbTD6a2sV6duhQ7iW6G5BNhTSA8os+nyczQa9zjo+EEjPpgYCatxaKIQ1bDRiDQcCeO/3I7nR1QWbc9OmdEb7Qkm7BBMHon/HMVj5Pf00hAVnzQSji2KNvgmFwGpcX4/z+XyIStbUgNI/EkHUcs8eRDfdbiidxkYc7/HgGK+X6POfx/7PP4/zlpfjvHY7mO1MJuzb1YVS3DvvxDluuGHwSkQUk2CQKFoG9K677qJUKkVnnnkm/e///i95PJ4BjznzzDOJiGjXrl1DuvZZZ51Ff/vb36ijo4MikQgdOnSI7r77blF+Yxz6jJPfr4z3ysrshESrVsGhtFggQ1MpzFNeulS1Pcybh7/xOAKBhw4hY3rkCOSkpsFZkOynYLAwmxGITiQU0y2367hcsA2y2SVMiuVy4S+vWe4JjUZVafiRI7A39BwRMvtbkAuif8c5Vq2CI5pKqVp+weiiWL8Bj2vp7oZxwgy2Ph8ilHY7GG89HhguR45g++rVfXswUynMpZsyBWW9BgMcUL8f5+3thfNpMkGRDVRukw9EMQkGiaJlQDdt2kSaptFNN92U9zF1dXVERHTkyJFi3YagBOD1QiZu3gzGT5OJ6MUX0dJgMiHod/LJ2Fc/13jqVGyPRtFvb7cjk9TWhuO6unDuykp83rcPDqjRiMwn9+Lrs1WC8Q396JNigJnzEwnlaLJNWFUF/fvuuyqrmUoh86lpqmd5yRKiV17B/hUVmCXP4PFvySTsEUYmD0kwiHvxeGT2t0D077hAtoHYvC0UQglmRwfeD4Y1TTB2wZHx8nKi889HmXVjI5htHQ41NNrlgvD3+4kuvxxrhddMZyfR978PpVBTg/POmIGMaXs7FIrPh3OxAnO50L80WCWSqZiIFAuvKCbBACiaA9rS0kJE6fPBBgLPCYvIXIwJAX2lRksLMjwLFsBoNxjSA388PkVPSMTs3489pmYrHjmCEVhlZagmsdlg+Dc0wAENBiG/w2Gcm9lHBRMDxXQ+ieBodnX13W40Yk1Pm6YIB00mRXDI7LecvDCbsU80iu08R95sxpqNx9MJCvsj4bLbiY4eFT0/kSH6t4SRrYTx9NMhOF57TZVCdHdD+HDTumD8wGCAIkgm8fv6/TB+uJeztRXMdk4n9ps0CcdVVcHA2bABJBdvvAEntqsL8z715ERczptMKgr2QADOqcvVl/kxHxTKJikQ6FA0B9RisVAkEqFYAcxsrDTLy8uLdRuCMQyu1KipQd/mgQOKsdZggKw1mRCgs9vTCYk4ELxmDT5v2QL5dvQoPnMf/LZtyCjV1YH5dtcuHCvBYsFwIpGAjtfbhvye1zAz4MbjsBGmToWdwSy6/BwEg0TV1UQLFyonlLP6mzbhOi4XMqs9PTjH5s1EJ500Yv9cwRiD6N8SxoYNeLndKAOKRkGOEI8rNr1QSJVViAM6/pBKqd7eAwfwisUg9JmkKBCAAzlpEgwoVg5sWDHJQDiMvqNoFErGZCKaPx/n3rlTrauaGjiKe/ZA4ezcifNxH1M+0LNJ6ueP+nxDG/MimBAomgM6ffp0ev/992n37t20bNmyvI557rnniIjouOOOK9ZtCMYoslVqHH88sjnvvAMZ290NA7yuDo5kQwP0MfNjTJ6sRlT88IdwPu++G0FBLkvs6sK59u2DTOV+O4FguJFMpvdssp3IGXeDATbC++9jzQaDKCmvqAB5YHc3jmlvR7DabsdaDoUwQuj111FSzo6q2YwXT2fweiXYPFEh+rdE0dxMdP/9qo/EYoGDoSct4D4CntvEDHyC8YNkEpHESAS/vd0O48VmQ4YxmYRDmUohG3r4MNETTyAiv2kTDB+/H/tyb1Fjo1o7M2YQXXstzt/eDmOKe0KPHcP6+9a3oJjOOovopz/Nz3nUl6URKUKktjb0pIpCEvSDopEQnXvuuZRKpej+++/Pa/+DBw/SfffdR5qm0fnnn1+s2xCMUXClhp4bY9EiJa+MRsiqsjLo5PfeI5o9GzLw8GHIUg4AP/YY+uwrKhR7ORH0dUeHGnXlcKRfTyAYTaRSsCtOPpnoQ/4XamqCLZBMqoC3w4FkyN69RN/9LgLcv/kN1jUTLyYSsEfMZrzPJCwSTCyI/i1R/OlPIESw2RTJzNtvIxrFIzr0ozkSCVXjLxj74P6LQhCNIsAQjyO6OGkSfvdIBOfyeNDr8dhjRA88AOF/5AiuNWOGIrgIhVS5bjhM9OCDCHCccgq2+3wwtqJRKJ6qKqytJ5+E4skX69bB2cwkRFq3rrB/t2DCoWiS7KabbiKTyURbtmyhH/3oR/3u+9Zbb9HatWupt7eXrFYrffnLXy7WbQjGKPSVGkRq5mcoBFl6wQVEn/400ZVXYt/KSoy06uiAXKysROCvrAzZzi1bcB79OYkgZ5NJ1XPnckHmCgQjCYsFCYvycjiUJhOqocrLUT01axYcUYcDLM52OwIuCxagAmrePDihTz+NUW09PfhsNOK8TKRlNKrydcHEhejfEoTXi0iry6WGClutiiDGYIBTwe8jEcVmJhj7cLsVW2I+TihnwMvK1AzQ7m5FGmS1YgzAlCkoE6upQVlMS4sKTCQSyuk1maBsZs+GExqNIlM6eTIyp4sXq0g995dymdkrr6AsJx/Y7UTXXUd0++1EP/4x/l53XfoIFpkRKsiCopXgzp8/n37wgx/QrbfeSrfddhs9/fTTdOWVV370/TPPPEOPP/44Pffcc/Tyyy8TEZGmafSTn/yEpkyZUqzbEIxRcKXGI48gs+P1QrYeO4ZezcmTYVCHQpCzRPg+GlVZzExyIqL06o9IBK9wGNmkvXtVpkggGElw3yfPqmVeid5eohdewBo/dgxr/M038d3UqWrtHz0KuyIYRBAmlYIdwiRGJhPsjXAY65vL2gUTE6J/SxCdnXig58wB2ymRKm9IJNScJj2Tmt0OJScY+4jHYdTE4ypr3d8Q8ngckUa7XfWD7t+ffkx9PaKURDCC3n0XxlQ8DoXBPRupFJzQ7m44ljYblInFgmypzQYjictuPB7FjOd241wNDYX1gzJTpB4yI1TQD4rmgBIR/eAHP6BYLEb//u//Tm+++Sa99dZbpH04cPHb3/72R/ulUinSNI1++MMf0s0331zMWxCMYaxbR/Tqq5CZJhMCfdEoHMvduzGewm5XhEHl5ZCX3O7C5ET6/nau8tiyBT30zCyeSKjeT2bMFb0tGClw0sJkUus3GsWaDgTQy0kE26CiAnZCS0u6A8oVV+xo8sxRItgbmoa1PW0aOCaE72FiQ/RviYHLgpxOGP9NTXASmDqbHYJYTEpvSxHBIAQ0RwtTKSiGgQyRUEjNjMt0WONx9CS98IKK0LNS4HVCpLKgnEF1uXD9RYuIVqyAscQ9TXY7CDMYfj+2fTimaUjQM0/OnAnjjTMG11039PMLShpFl2j/8i//Qm+88QZdccUVZLfbKZVKpb3MZjNdeOGF9Morr9Ctt95a7MsLxgiyVVxwD9vq1UQf/zjRRRcRLV8OObt3L6pDenpQEeJwQNbW1ioG3NpafN/WhiAaEVoYLrmE6NvfRib1pJNUKw1XlaRSmOPM+lwgGG5omqqW01dHGY2wL5JJvCorkfn0eLDejx4FRwRn7SsqYD/wTFEOzjBr/7x5qB5Yvbpv8Hn/fqLnn8+/kkpQ+hD9W0LgsiCvF2WVH/sY0dy5ymGx2xGxqqxEtLaiAtkpU1HzBoLhRCgEUp+LL8ZvN316fpk/zmCazaqHQ9PwNxQCS240inNVVSGjaTCoKD33DmsaSmja23GetWuJvvY1lMn+4hfofWLmu3BYzQs966zCsp/ZkMk8ycy73EMl5bgTHsMiyU477TT6y1/+QvF4nPbs2UPt7e2USCSoqqqKFi1aRHZJvY9b9FdxoR8ZxYzdixapctmDB6GHv/IVyM1t23D8rFnY1+mEEX/hhTjmllvUNWbORFYpEFDXYRlMpKpUBILhBou3eBzrlOd+couX34+1abGo8vK5cxVzPgfIKyuR3Wxqwv6BALZzVtRqBefEpZem8z10doJD4pVX8Dza7YURGwpKG6J/Swj84G7aBPKh1laVyerpQdTWakUPH7OLykyx0sI77xB985swYDZtwraBmIzZcGEWW4a+P3jqVCgFt1vN9EylsF7YEeWh0nPnEn3pS2q9ORxg0bVaEdxoa0NPyKRJcJZ/+tOh/7tlRqhgAAxrKM1kMtHJJ588nJcQjDH0V3FxySV9R0aZzdivqoroppsgJ1kmXX45ZBQbzfz+iSf6XuMvf0E2lLNFZrMy/pNJVdWSSChHoKwMwcH+2jIEgnxgtWLNRaNYv9XVWFs2G4IfXFU3aRLWZ3s71iGz30YiSHBwO97Ro3hWTCaV8Cgrw/o/4wzVC/oP/4DjuNqLCM7nk08ieTJlChzeJ5/Ed/fei79cVVBZKTbAeIXo3xIAE7i0tiICVVmJB5OVEpc+dHQoB0NQWujqIvr1r0HQ89WvEv3oR3D49u2Dg6Ynm+LeTKtVERHp2ZB5X6cTxtK+fdh/yhRcJ5lESYzVCpa7zk4opjvuUBlNr5fod7/D8OiZM4muukqNGvjEJ4huvFENTx+KcpAZoYIBILUcgqIh26xPljtbtsAB7W9kVOb4usye9qqq7NfgtgqTCfLcakWAr7tb6XF920U8ruY6CwTFQDSqymI7OqD33W41vi0YxPdMeskj/g4exPd8fG8v1rHfrxzYVArBcqcTs3PLysAjUVMDO0JfabB0KTKf5eWwQ4jUM/jKK0S7dhHt2CGcEALBmEAoBJrr+++HEPD7FdsYs+kJSheapoyRp56CEXTxxUQ/+5kiwdArj1hMjU5hRaA/F8/yqq7Gd2636h2ORLBu4aJCcwAA+8BJREFUGhpwzd5eGEDnn4/SXy5P27gRCsBigXNaWQnF4nKBdn33bpxnqMpBZoQKBkDRHNBwOEwbNmwgIqILL7yQqvVNzVnQ0dFBTz/9NBERXXPNNWSSvoaSRz4VF3rSoMZGyLhCRkZluwYb8w4HPnPAkOW1QDDcSKVgR1RUYD2aTLA5urtVRp4I+/D3nDENh7FWee6416tKbLkaz+NBAJvL0GtqkEWdOjW90mD3bpw/k9iUiQ3/8Ae0DwknxPiC6N8SBZcMJRJ4SHmeUjIpymu8gMtV2tthwKxaBUEcDOJ7ZkNmevOPfxy9mvfd17cE12gEOdCCBUSnn46seWMjnFGXCwojFFLBi7lz4fB9KBvoscegWPi1dy+2L1mCspv6eiiPWbOKoxyGavAJxjWKpnWeeuopuv7662natGl0zTXXDLh/RUUFfe9736OjR49SZWUlXXLJJcW6FcEoIZ+KC644uuSSgUsAs5UJZruG3Q75nUxiP7MZ7zkAqJffAkGxwYEOHrdisSBA3dqqyCw5G8rO5pQpauxQZ6ca2xaNKuKsmhr0SHd3w7H9139V17zjDjifmZUGhw/j+l1dilnfbFaJlcOH+69QkKB0aUL0bwmCy3mmTsV7nvUYicB5yHQ+BKUHpjC32VCSwmWnJ5+M39jlwufubjiKFRUo1yUieu45lMFwTyc3/3Nf5xVXYL9AANtdLhBnaBrWUCqFYerxOLKeRBD8ZWUo3eVS3qYmkAkcPIhzzJiB+y2GcijE4BNMOBSNBfehhx4iIqKrr746r2iqyWSiz3zmM5RKpT6K3ApKG1xx0daGVzis3q9c2becdt687LIoFCJavx4kQ7feir/r12N75jV6eojeeguyze+H4d3SomYzSxBZMNzgjDs7mHV10OHMdMtBkHgcpbGpFLL1sRi2m83Q07EY9uFRLF4vMprNzUTbt2OG7vTp+K63VxEYMfhzRQWO+eADMODu24fn4pRTYMNkO663F8+QoDQh+rcEweU8tbUQGJEIHv5AQAkPQWlD06AAyspA+899RStXIooej8MJdLnwuugiKIff/Q79EvG46v+127FmjhxBme0dd0AppFLIWNpsqreIDaEXX0Tm8dgxFZV0OLDeAgE1r+7QIRhTc+aoUjKi4imH/gw+wYRF0RzQXbt2kaZptGrVqryPOeuss4iIaOfOncW6DcEoY906VFgkEpB7iUThFRdclWQ0okzQaMRntpP019iyBbLzxBPRQ+p2Q176/TDs2ZgXCIoNLvNmnhBNg/4+/3yMA+LgNjufkyapKQpMGsTZzkhEMeVGIniFQmoNWyzgjNiwIb0KQA9urykvx3PDGVa/Hy0+//ZvuY8TTojShujfEoT+QV60CA8pCwvB+EAyCSH/9a+nG0H9GUobNkDYcykND4rmSKLHAwfWaMR+7e1YQ3Y7jB9mVrTboQT27MH3FRVK+PN6C4dV2c1xxyEbr4coB8EwomgluM3NzURENGPGjLyPmf5hOP/IkSPFug3BKGOoFRe5SIa6u1FFwpUg111HdMIJIJSbOhXjKux2BNmeegrH1dSguqSrSwiHBEMH9xkzq7LVijVmMiHY7HKhLYfHqU2ejGNsNnzmaqxJk+AU8ki49nY4pFVV2L+9Hdeorsa5AwGi2bNxvUcfxRzxbNwOjY34PHcu9vF68dzE44pBVzghxidE/5YQuLeECEb/Cy/gQfV48OBzLb6gdMGC3+kk+sd/BLPsh88oEUEYX3IJjBgiRf/v9cLQcbth1Bw4ACUxeTKUjNsNJaPv8X7/fQj/6mrVB5JMKiIj7gNZvlyV4no86AOJxTBE+gtfUOMFTCZRDoIRQdEc0PiHUbuInm50AEQ/FLJBbsYWjBtkMtjmCz3JUCyGEsSmJhj8kQj68m+8kejxx4n+9jeirVtVSWN1Na7J5HENDZChBSxJgSAnNA2OIpd1RyIINrtcsBVcLqJnn0Xwmbcdd5yqpOvuht0xfz6cyUQCa33BApwnElEsuNEo3re3I3D93nuokIrFiK69lujzn0e11rZtitth9WqUo3OJLT+D4XDxSMAEYxOif0sAzEK6aROyUp2deKCDQcViKqy34wfJJMqr43Gib38bv3l3N5xImw0RycmTUbKyciUGOt93H9Grr6rSFV4PBgMUz9y5yF4yPB6c47TToAx6e7FfIqHGsixYgGuuWgWBrxf+69YplltRDoIRRtEc0JqaGmpoaKD33nuPzjjjjLyO2bVrFxHRgIx9gokDfVVSSwtI2pxOGOypFHT3/v0IFLa3Q7YbDJDX3d3IFrW3w+jmWZ82G3Q999xHoypIKBDkAw4iRyKq9JYZb41GojVroLOZS2TuXDifbW347vvfV+fiqgA9yRYR0S9/iYqqKVOI3n1XOaSHD+N7oxFrurUVkxtuvpno9tvTz3HgQPFIwASlA9G/JQDuLenqUpFW7hWJRGQg9XiBpiGKaDTCEHntNcU2ZzJBoDPrsdMJAfzYY3A8jx7FMTxDTtNQiptIKDIBplQngnAvL0cGM5Uiev11ZD4rKmAMRaNYW+XlKBU76aTcwl+Ug2CEUbQe0BUrVlAqlaJ7edJ5Hvjv//5v0jQtb4UpGP/g/vzGRjifbEiHw2hZmDwZ8wytVsh0j0cF/Hp7VUCZWey5R4/79EIhxVgqEOQLTVPElOyAsjPa2wuOh+ZmZOEnTYId0dMD/b95s3IQd+7EHE6vt+81Ghqw/nkMHPNJBAJ4H4lgvVdXYx1zNZWe22HuXDw7QyEBE5QeRP+OcXBvidutKKkjERU9JVIKS1DaYAND01TGs61NjVmJRvHe7UY0kUnDXn4Z24hUZN1oxHuTCQ5iYyMUhV64L14MpbJpEzKuRDCIystxXEMD9mFhP5DwH6py8HpVlkAg6AdFy4Bec8019MADD9Bbb71F3/jGN+jOO+8kLYcwTaVS9M1vfpO2b99OmqblRRsvmDhYtw5y+YMPIMPtdjifixahVDEUgjPQ2gojnx1OIgQEuWolFsP3enAgUSAoBNmy5b296v2rr8ImsFhUz/L+/Sr4cdppWKeBgJr5OWeOKt31+WA/MMM+kWrnIVJ/Dx/Gmq6oUIkUhwPJlS1bVBXA+++r6i6pohr/EP07xsEZT5sNisvnU3MgJRo6/hCPqwji3/6mBjoT4fc2myHA43Ew1UYiKPnauhUCPpFQQp/JByoqoCxCITiiNhsikn/6E4wlnw8KyGaDMiorw2cuvx1ucIn5li1Y6y4XIp9c4isQZKBoDuiFF15I5557Lr344ot0991302uvvUY333wzrVq1iqZ8OBW9paWFNm/eTHfddddHym/VqlV02WWXFes2BOMAdjv6PHfvhtyeMUMxg8di+J7JheJxJW8TCWmhERQfbMf3Zydy+xa34HClFK/Jw4cR0K6shN3Z3o59y8uRPd2+XdmjmqbmkmcimURgORYjOuMMnI8r+2pq4NRWVcE+Oe00VGZJlnP8Q/TvGAf3luzZA8XF5RQSDR2/4DmuwaAqmyFSkcX2dmyrrlY9ni0tKtppMKi/8TjOs2IF0Q9+gO3PPYdejMOHsZ44WxoOQ/nMnw/jyWzuy247HNAropkz4RAz49111w3/9QUlh6I5oEREGzZsoLPPPpvee+892rFjB11//fU5902lUnTSSSfRww8/XMxbEAwB+p600TZaq6rQO7dhA/raysogh30+zHB+9VXsx2O2uOJFX3YrbPaCYiCfBEVPD8rCec0Zjah+0ge+eSZoKqXag/bsSR+7xvNEB7qfYBAZzvp6sD5brYox12SCTXPgwOD+vfkiU16MJfkxESH6dwyjqgr18X/7Gx5gdk4E4xOZ1QdsmPDvnkyiFJdLsUMhvI9GoSQy1wfvv3w5ymP37yd6/nmUvBAhuGGxIKrJ+zY2QjGsW1e4QM4lzPvbnjm+gPuntmxR4wsKRbbriaIZNyiqA1pZWUlbt26lf/7nf6Z77703J7ue0+mkL3/5y3TbbbeRXVLzo46xWDkRCsGYrq9HP10yifuaN0+Rv8TjqjdPL6u5bz/TAWUHVSAoNrjiipFIqIwmI5XqO8+7qQl62mhUNks+SZFUCkzQDz+MQLrZrOwbux262eHAs1NsHZ0pL/j+Ewk41WNBfkxEiP4do+AH5s03FdkQZ7cE4xPc75lpmCSTyhBJJiEw9+2DAGfiimRSrQ8muDAYMItr2TKi9esRdXzzTUQ+UykIYZ4RFgzCKAqHUXpbSP9FLmPw0kuhcHIZifrxBXp4PIqCvRBFlO0+Tj8d/3fM9iuKpuRRVAeUiMhut9MvfvELuvXWW+mll16it99+m44dO0ZERJMmTaKlS5fSOeecQx6eFSAYdYzFyokNG8BIHo0SzZqlKlZ27SI68UT0zjU2qkomkwlyOBpVo9T0GVEJOAvGIjQNdgI7n/muUaMRtizPEefSc6sVf48dQxBm82YQHxYTmfJi61YQhi1YAPtoLMiPiQrRv2MQ/MDwbMemJuVoSJnO+ARHwVkwaxp+/2RSOY36iHg0CqGuh9OpGHCdTry2bQP7nMeDSHxvL5QA93PYbDhvWRmypTfeWJhzlssYfPVVZB5zGYn68QW5KNgLQbb7uOcefLds2dgxVAVDQtEdUEZ5eTldfvnldPnllw/XJQRFwHBVTgz1njZuRCCP5VosBgIiTYP8rq0Fl0MwCLltNitDnuc1cosNlzxKu41grMHhgCNZ6KxahwP2RlubYoTWB9yDQbT/bN5MtHYtKgeKgUx5EQyC1LO8HPZAMqnkyGjJD4Ho3zGDzAemuxvbfD4oLMH4RCqVLtRtNhggoZCKkLMTqs+KMri0y2yGo8mft26FI+tywQnr6MA6CgSUwWOxQOhedFH+wtfrRbnZxo19jcFQCKMHzjyzfyNx5UrlEHo8WONtbWDBK0QJZDNKObNLpGbrjbahKhgyhs0BFZQGil05Uax76uqCzGUZE4sp57KtTfXcc18dV61wTyiX53Jm6cOZ6wLBmEIwqOyOfEvEnU44fBxUcThggzBXBZ+rqwtBmu9/H7ZIMSqVMuVFKIRny+2GDRQK4X5GU34IBGMGmQ/MokVQXps3Sz/IeAcLY6sVDmM4rDKi/F1maZbdrhxPdiZ5hmcshgHRRIrin8e1xGIQwG43SlE+//n8Sm/1pa4tLSAlWLAAkX+eN2qxYD/9/FGivkKer7dlC7a7XIOjYM9mlIZCyphjJZPtHgQlBXFAJziKXTlRrHuqqIB+5p4ys1l9ZrlcV4fsSzSKY5xOlB5y4LG6WhGzfFiFJhCMKXAQnEvGOXBClG6XGAwI/MZiWOdlZXgOjEbFZcFERBaLGvdSUQGbpliVSpnywm7H9fx+vGcHdzTlh0AwZpD5wJjNePE4DSLV+ycYf6itxe/udOJ3rq9XGdBIJJ3x1mxWZACTJ8PJWrECQrynB86n16sUAZfysrM4eTLRhRcS3Xxz/s5YJoX6gQNwQs1moiVLsE80qhxjPTKFvN0OBXPJJUMjCcpmlNrt6hnRR1FF0ZQ0RrUTPpFIUGNjIzU2No7mbUxocOUEzzTub3j9cCDbzGJmwHU4IMd8PuVoRqMqoBgIKELBUAj7sKPK8thmw/ECwVgEZ+89HkUklGmL8hg4di5jMaxrzp5ycJqrAnp7sb/BgCBNXR3siy1bCpsNnuvZ1MsLgwFB9+5u/BsMhpGVH4LBQ/TvCCDzgenqQsO0xQIFJWRE4xuRCARjKETU3Axn0mbrG3Rgo4X7iUIhCO2qKjiaBw6obZEI1pHVin0DAaKFC4lOPRVZzEywIOcXC/TMUteKCgxc1zSs0a4urFm/n+iss1RJ7UBGYlUVej7yEf75KJlwGP8HDgdePT0jb6gKhgVDyoAGg0H6xS9+QX/5y1+ovr6eDAYDLViwgK6++mr66le/SjZ9Si0LPvjgAzrppJPIYDBQXJrxRw3FqpwoBAMx765bB2fzD38gev99GLjcz+n1Eu3YodjGUynIJINB9eDH40QNDTIbVDD2kUxCx/P4IF7nRIqXwmjEM8PB77Y2OKDc95xKQTezgzppEgLaixbh+EIqlfJ5NomUvJg9m+i443DfIyU/BKJ/Swb6B4aNbbMZD5cop/GNnh5lvMRiCDoEg3BG9TCZsCZsNjiWp58OQXrwIJzK5mb0exqNMHSiUZVRt1rB1Jgp5FmQb9qErGZ3N8p5Fy4kWr2aaOnSvqWuXCK+dy+uPWUKhLmeBbcYQr5QJeNyEX3lK4oFVxTNuICWSg2u9qOpqYnOP/982r9/PxFhrhgRkfZh/diMGTNo/fr1tHr16pzn2L17N5100kmkaRolxgBDjN/vJ4/HQz6fj9xu92jfzohjJMcrrV+vKj8y+9X1ZYK/+hXRv/4rgnE2m2L95NJFPZjtlhnQq6ogc3NMIxAIxhQMBjU/1GRStga/DwRQ0WW3w+Y4dAjO3+LFsHO8XvBEfPAB9qmrU+dua4MNdPvtAz/b+T6bY3UO6ESQ4+NR/xKN89/O6yX6r/8iuv9+RJykNGf8w2JRTmU4DAHOZSLBoCIgqqlB9rG6Gk7lL36B4++7j+iBB2AABQLYFovhVVWFSKPRiJKxnp50Ic+CvLsbDqzJhMj8tGnIdq5ZQ7RzJ45nsh8i3FsgQHTTTZhdm88c0EIxWCVTzHsQDAsKkeGDqv9IJBJ05ZVX0r59+yiVSpHZbKbTTjuNTjrpJDIajZRKpaixsZHWrFlDd9xxx6D+EYKRRyGVE0NBZuWHzabe68sEvV5wNQSDSoZbLOo8XH6b2UPHzilXaggEpQCuykoksNbtdtXz3NODNiKDAXwU8bhi4bdaYbvMnYtg+apVsG8PH8Yc0MOH+1Yq6SufMt/rn01uV3K7+5bwZsqLkZIfEx2if0sYhw7hwWVnQjC+wSUp4TDR9OlwnI4cUf0UqRSEa3k5SraOHYNjSIR+0e3bEXGfPBnOqaZB4BsMcCw7O+HU9vSkC3kW5Ozcud04h9sN5eB2g1G3thblvfqS27Y23MOyZbiPzBLZoSJfA5Aou1IRRTNuMKgS3Iceeojeeust0jSNrrrqKvr1r39NlR82AXd0dNB//Md/0C9/+UuKx+P0T//0Tx9tEwiI8mfePXqU6J13sC/36mf2yHHLRCoFOc+B/HgcrRLC7SAoJSQSysbgGbehkCI7PO442Azvvqt6ol98EfqYHdBly/DcvPIKjrXb0cJz6aXplU/d3ZitSwTbpLwc5+juxgiXd97ByEIeUWez4ZkUvT+6EP1bggiFiH73Ozx40ajM/5wo6O1V7/ftUzT94TAc0NpabGtqwpooL0fJ7KZNKLnduVNtr67Gebq7oQz4eJcL59SXo7KR5XJhvfHcX5sNjubhw3i98w6uk0io0S6f/SyUxfr1qkTWZlP9IcwMqS+ZLQRjcfSCYFQwKAf0wQcfJCKi008/nf7v//7vo7IfIqLq6mq644476FOf+hRdddVV1NzcTD//+c/J7/fTPTxIVjChkS/z7ubNCMblcj6J0BPHDOehkGI1t1rTR0cJBKUAHj1UVoa1fPLJCLBs2aJIho4cgf7mfc1mBLB9Pjii27YhiHzGGbBRolF8fvxxXIMrnwIB2CCahuxqVRWeuUAANklrK7Z7PLAJurvx/Uknjep/0YSH6N8SxIYNeHgsFrzynbkkKE2YPjSt9YEG/r2Zua28HMK1pwcGC8+2fOYZjEI56SQI9pYWvOrqFKtuTw/RaaehP4mobzmqfoC6xaKcRp4ZWl+vKNcrKrB98mQIfIsFyoIVxcyZUCoffIBSm+XLoWwGS60+FkcvCEYFgyrB3b59O2maRjfffHOa8tNj+fLl9Oabb9LSpUsplUrRvffeS5/97GfHTK+JYPiRjeCMaGDmXSLIu82bFakQUXZ20EgEsjgUUr1yTOaSrU9UIBjL4HnjoRD+ulywOU46Cet7zx5wQ3CG1G5XdsaePbBpdu6E3VBXh3JdZsHduFHNGS8rQ1uQ0wlbqLlZ2UHBIOwTdnDDYTxLc+bg3MWsxhIUDtG/JQYuOZw5E/133d2jfUeC4UY8rsqxGPpeIR5s3tCAv2Yz1gezvXq9EMYLF8Ih7OiAgeTz4VVejuHO8+bhRZRubLGR5fPByfX7Ue7S2alKaoxGOJ8VFbhmKIQsq15RcB8GX9Pvx+dcJbO5jD49Rnv0gmDMYFAZ0GMfDlU84YQT+t2vpqaGXn75ZfrEJz5BL7/8Mj344IMUCATooYceInPmUFvBuMFABGdE2UnOLrwQcvmWWxDw4+oTo7FvxZLZrEZN8FiKadMgpzs7YUQLwaCg1MDruaUF6/6ll2A/TJ2K8tgdO/B8MbdFLEb03nvYlkoR/elPsFsuvTT9vB6PIl6srsZ5GhvVHNFIBO1pHMBxOmEr+Xy4lr7HVCqkRheif0sMnZ0wyA8cUJEeyX6Of2T+xjxzi8tnk0kIXqMRvRWBAISywYA18+abarhyby/KdB0OCOPrroMR1Z+xxUbWpk0wiLq6YEgFgzhnMAjhz2XBiYRyRo1GKB0iXCMaRaQyEMBnhyO9ZNbhGNjo02M0Ri8IxhwG5YAaP5wNEM6D4cXlctHTTz9NV111FT3xxBP0+OOP0yWXXEJ//etfB3NpQQlAP9t45szs1RrZZhY/8UT6TOS9e1UJrn68SjIJOXn22UQ33ojzeTww1u++G7LdaBQHVFBa0DSVsWdbhUkSDxzAdrsd2cuyMjia+/YhgMzMuFYrnMRnnyW6+mp1bp8PzwwRnM/mZvWMMBmjxQLbJxLBNevqMIvcbod90dYmFVJjAaJ/SwyVlUS7dysHQmZ/TlzoWeaWLEEP5uLFEOJNTap0y2DA/DmDAZnH8nII5N5eoquuUoaPnk02m7GlN7IeeYTo6afRyM806oEAoo02G6578CAIAObOVSWydjuUg9+vHGKi9JLZfIw+PbIZgBLVnHAYlCScPn06EdFHFPADwWq10qOPPkpXX301pVIpeuGFF+iCCy4gv98/mMsLhhn5VFH0d2x/BGeZs5CrqiB76uvTywOTScgxTcP7WEwZ4yYTZPiqVUTnn49XRQUM68mTsU8oVNz/E4FguJFKqQA5j2DxelU5blMTsvzz5sFe4ReXmrMOt9kQVN67N726aflyjG2pr0cFgdsN55PHFrHTW16Oz4cPw97hqQFSITU2IPp3lMEKMlOZ5dqvvh4GOVO2S2R0fGAogYRYDI7X6aej2X7XLghdhwPZRocjvRfjuOPQ83nCCaoPIpex5XYTPfUU1h5RupF1wgko6+UoIxGURCQCZ5JZHZcvV0LfYECEv7sb585UCEQDs9r2148ljLYTFoPKgC5ZsoT27dtHL774In3+85/P6xij0Uh/+tOfyOVy0X333Udbtmyhq/UhesGoI5/S2YGQi+DM4cB5v/99yDiXC2ydqRQqTVpaUErI8o1ZbLmNgitEjEY4mdOnwwHle370UaIXXoDsFv0uKFUkEumVW9EoAuHM2M/PVyQC/c+tRPE4nqHDh3GOeJzo1VcVAWJVFRj9W1vVaCKzGTYPs+0yyVE4jOOtVgS92TaRCqmxAdG/owRWNps2oeG6uxvRmoULiVavVooyU5F2d+NBrKxEjwiz6gnGPnKRRfGAZiaiKBTd3XAIm5rw3u/H+ex2rBO7HduNRqJZs4gWLcJx+rJXonRjKxZDpr2hAeW23/8++kTXrUs3zCor8b6jQ5EJWK2I4k+dCmG/ahX+colsXR2yoolE35LZ5ubcrLYHD2KW6YEDgzcqBeMWg3JAzz77bNqwYQM98sgj9Ktf/YqcTmdex2maRvfeey+VlZXRnXfeSUeOHBnM5QXDhEKrKLIhF8HZjh0wfk84Aef3+Yh+8xt8t2wZSm63b4ecqq6Gg/n++2r8RHk53jMxy5IlkJV8z7t3w3AW3S4oFXArkNGITCRn96PRvvvyfNueHgTDg0H1bDA7fjCI49k2YmKuU09V1QXz5qnKq+nT8YyyU5pKoRqLr0eEZ/OKK6RCaixB9O8ogZVNVxcMepMJfxsaFLHQddf1VaSpVHo0VVC6KCuD0OzuRgRvyhQ4YPnMfDOb8WKHNRKBwRIOKwZFpxPR+mnTVERw6VIcR9SXKVZvbO3ejbIXZrZ1OJQBd8klat+aGpyzowOKwGolWrECx/T0YJ1OnQrmu8wSWa+3b8lsf6y27e0I2MycOXijUjBuMagagosvvpg0TaNAIEC/YS+iAPznf/4n/fCHP6SUNOKPGRQyG7g/ZCM4a2jAa84cBPN4zEQwCCOZ+9nMZsjb3l7IwUAAxrTBgO88Hux39CiCcUS4N6sVNoHVKtwOgtIC2x1czZVt/XIZusGAZ6a7G3+tVhxLpLgkkknYD1VVqBTYsQNs0vwsV1SAw8Jkgm3APBORiLJxwmHYLscdhyC9OJ9jC6J/RwGsIN1uZKvcbjxgbrdiGuUek0xFevzxeNB6e6U8p9Sgf0ZcLjV0nJkRQ6F0pysXDAaVbbRY8J5LVRwOFYnkqOGRI7heWRmMoWxMsXpji40sztjW1cHYYgOOKN0wMxhwfDCIdVxVhetk9llklshmK5nNxWrb2IjvZ84cmlEpGLcYVAZ0xowZ9P3vf5+OHj1K3kEuoh/96EdUVVVFjzzyyKCOn6jIFoAqBoo5GziT4CyRgMw55RT1b2hshNFstap+TW5B6O7GPomEqtLg+cdGI4xssxmkQ/v3Yx+W6zL3U1AKYDshlcK6Nxhg12abT889njwmzu9Xdk9lJZ6ND4lRyWCAkzlnDrY3NcHRnD0b3weDCNoHAsiEms34bu9exYhbXo7g9/z5wno7FiH6dxRQX4+HobJS1bUT4SH0+fAg9fYSvfsu9pszRzHfBgJ46A4fzl7eIBjb0DQIaacThgaXYSUS+BuN4vfXBxcygztMLmQ0QsByb2c8rhgWLRZs9/lwzOWXYx09/zxmcE6eTLRmjdrm8eAzEXo+u7og/OvqspfssmG2cSOytrW1uctqGXqDkyi38ZmN1XbVKpS18bPCGIxRKRiX0FISBv0Ifr+fPB4P+Xw+crvdo307aShGf2Z/8Hox/sRohLPIaGuDfLr99sJlBcsuIqI77oC83blTOZ+cafnc5/Bv2LgR/06jEZ937lQEKTyOqr1dtSwwS6jZrEoIpQRXUCpgZ9FgQDVUNujHPLITOnkybI1EAs9RMqkcV6uV6OST8Vzxs7J0KRI1HR1wSKNR7F9Zied6yhSi226DXKmqUtVbQ3n2RxNjWY4L+seY++1Y8W7cCOXLUZrKSjxUvb1QPAsX4uGqqcEQa69XfScYXzCZVAP9jBmICHZ3Y61kK7Nmwc0U40R9m/15PyKc2+UCzb/Xi3UVDOIYJsiIRnHOBQtgQJ1xBtG//isE96xZ6px6Ic6jUjZuVM7qmjV4hULpjqXe4OzuhjIhgvIpL89tfGY6rMU2KgVjHoXIcOEDLxFwW4nRiCyl0YjPGzYU5/zDMRuYqzXmzcM5XnoJmZZUSsnjQADkQXqitVgMf+12Jc/b21GVEo1iXzai9e014nwKSgnJJOyAXM4nkcp+csaUbRAiVdHHXBhEap5nezueobPPBg/Fjh1IziST2J8ZdA8dwvPJgXTuIRXWW4GAlOJ1OmHsx+N48I4cwUPm90Nx7dmDEkYuSZBZn+MXXHrLzG3cgJ+rx5eNFO4TZcMl236plMqMvvYa0RtvqDlbfj/WHGfcIxEQZdxzDwT8RRfhu1wGnH4tL1qEv489Boc0s6xWb3DyfNLDh/G+P+NTX6I7HEalYFxBHNASQLH6MwfCunWowOCKjESieMyXS5eqPjOjUZG7TZqkGHDr6mAIswyurUXrjMUC2ZVIqDFqZjOyPdz3JhCUIvJZu2azKrd1OlV5uh52O5xHIgSge3qIzjuP6Kc/xTPFfdZMXLR4MZ5Jlh/D+ewLBCWJTMW7ZAnKCyZNUkqqshKKqqwMD5TJpEooBeMfqRQCEvpG/kyYTErQDyTwmUEulUJfBbPS6dcUD212OvHe60UJ7po1uYV45lrmSKTb3deI1O9bVgaWOs6OtrZiW77GpygWQT8YVA+oYGRRzP7M/jCcs4FbWyEzp05Vo6bMZlSBHDlC9KlPEV16KQiG/u7vVPVSpoMZj+NlNKrgoz6YaDSqsRQCQanDZAIrdDyOAE08DmZ77nXmgLrFgueJnweuEHv8cdjFU6fC6Uwm8Zw7HAjq6OWHzAUXCHTIVLxmM5zQWbPwEN58Mx7Kri6iO+/EA9PYKEQEEwU+X36ZbjZGuA+U58FyEMNgUOfgKCEbQPG4okhnQyiVwppjIqRIhOj114keeABrMpsQ51EpU6YQvfOO6sUwGhHdPHpU7atf94FAes+zz4fsb77G53AalYKSR9Ec0HPPPbfgYzRNI5vNRh6Ph+bNm0dnnHEGXXDBBWQYyoDfcYj+WK71jNzFAldPFBN1dWpE2uTJarvfj0AeV2Q8+CACfzyH+dgxNeuQHUueCaqX24xkUjGDCgSlDrsdur65GUEcItgj+qBMIqEqwojgeCYSKK/t7lY949FoeitONvkxHM++YPgh+ncYkEvxRqMw5JcuVaMpeL/2dinJmUgo5LdmZ1PTlPHCzxobONmi6lyaqwf3gRqNiD7abKA7r62Fw5cpxHkt8zw8pxOKpbMTSmLzZjDP6ff1+ZDt5BI0Iry32ws3PkWxCLKgaA7oyy+/TJqmUSqVIk3PnEH0Ed17Pttramro5z//OX3mM58p1q2VPLiUnscneTyq1P+yy0rjuZ43j+iss4iefFKRxoXDCLB9/OP4fts2fO/xQI/39qrxE4mECiAyWPYbDH2zpALBeICmKWKu1tZ0RtxgMN024aBMVRWypn4/kjVbt4Lp9rXXIDP0rLuXXYZj9+/PTnQ4XKzbguJC9O8wIF/Fy/vdfz9mMUrv58QFZzazbU8m8Zf7J7gXKR5XTikz6XIUPVdfKW9nA2nmTET2N27EsHWeU7djB6KX06dj2/PPw4G02WBgBYNYv1u3En360+m9m7zua2vVUPY5c0Ba4PMNj/EpCmdCoWgO6KpVq0jTNGppaaF9+/YRERTbnDlzqLq6moiIOjo66ODBgx8pyfnz51NNTQ35/X7at28fhUIham1tpWuvvZaamproO9/5TrFur+SRjeW61Erpv/tdoldeQfUSO4lOJwzhs8/G9qNHc8vcXLJd73yK7heMJ/j9eGXCaFRltPrnxWaD02kyoUqroQHcEWyH8MzzsjL0iAYCICrMJDrkkUg8/qjYrNuC4kL07zAhH8Xb2YnI6auv5lZegomBbAaIvi+Io4Tcg1lWprbrR/QkEunsc3rotyWTEOJ79mBUSzSKAElPDwyq7m6cn0cJ8GD1Q4fgRKZS2KepCXO3/uEfIOD1695mUyNiDh5EJPSss9AzVSwM95gHwZhEUcewPP/88/TpT3+aNE2jW2+9la699lqqqKhI26erq4v+8Ic/0L/8y79QKpWiBx54gD7+8Y9TPB6nRx99lP7hH/6BmpubyWg00s6dO2nhwoXFur0BMeYo4LOglANEX/wi9DSTxnFQ0GhEG4OmqUqPbMjMdA4EDiqKTSAYT9A0xXzLvdCcGbXb0e/pcqF8nUe0MHEjj7Qzm/G+qopo2TLY1u+/ryZKBINgrF6wAN/rEz/XXTfa/wP9oxTk+HCg1PUv0Rj+7fpTvF/8ItH69elzIAUTEzYb1gGTXDBZUFsbhLDTCUcwGIQgvvpqomuvJfrjH4n+8AcwMjLJhX6+Fjf2l5dDGHMZGRGc2HBYObWzZiHyyIy1ZrM6j82GjKbPB6OLh6hHo8iQfutb6QLe6yW67z6iTZtQVmOxYF8unymWMli/HhnXmpq+lQZjXeEI0jAqY1jq6+vpU5/6FGmaRq+//jp9/etf76P8iIgqKiro5ptvptdff500TaN169bRvn37yGQy0VVXXUWbN2+m8vJySiaT9Otf/7pYtzduoGe5LiXs34/sZ1kZnMKyMuhynt/NZG8ZVWL9YqB9eWxFPvsKBKUADn7r2fpNJrw8Hnzf2ooqKR45x7ZMWRlsnlQK77u7ESg3mdKJDpubYWuzrZNMDg/rtqB4EP07zMilePfvJ3ruOen7EACJBIQrEwjZbHDWEgkQDE2Zgghhba3KRBLhr9WK7dxvzH2i7HwyyxwPP08mcYzNpogx3G4IcJ5Xp2mKRp3LfY8cUaNdeJ9Jk6AwNm7sK+APHECJb10d7r2urrjKYKTGPAjGHIrmgP7sZz+jnp4e+qd/+ieaN2/egPvPmzePvvOd71Bvby/97Gc/+2h7XV0dffnLX6ZUKkUvvfRSsW5PMMzweqGL+ZUpMxoakIHh2Z48MkKf1eQWiVwkQpyr5+8Lzd33d26BYDTB1VaF7MsBFo8HmcqaGtgasZjiipgzR9kz7Kgyx0UyiVd3t+onNRrxnIZCsGWiUbwnwnV6e4nq67M/44LRg+jfUUJDg2IsFYxf5GM4sDPndCLSV1uLdREKQfDW1iKLSATB6/ejzOTVV1ECm0hgnylTILTZMDIaEfgoL1f9ohYLejCY2ZEIzqjHk14Sww4s3xuXhRGpeaSTJuG6moas444dSrgzIy6z4DJYGXR2Dvz/wsZhLoVRjGsIShJF6wF97rnnSNM0Ouuss/I+ZvXq1URE9MILL6RtP/fcc+mnP/0pHTlypFi3JxgmcOn+pk1oQ+juhpxcuJBo9WpVwq9nweVAnMWiMpRcess99ZnguZ+9vYMrqdUTtQgEow2eYcvr3mxWWc3+wOuY9+OgTTKJbGU4rEpxnU6iGTPQC9rVlT4NQG+HtLQgY8rPnt2OZ9jvx3tuwfF60aN99904l7TpjB2I/h0l1NXhQejqEid0PIEb4BksaPv7jdnZCwYhQE8/HQbRu++iHDaZRETv8GEI03gcAnXPHpXdjMVAGFRdDaF89Cgii2vXwolrbsbLYICDVlEB5/bwYSXUOQrJAp0VBGdSKyvx72OKdbMZwr67G8fdc48iIlqzZvAjGPLt6xzpMQ+CMYOiZUCPHj066GNbeb7Ah5j84ZyOCNeQCcYsNmxA6f6hQwhUGQz429CA7Rs2YD9mwe3pwT6hkBopxXOXOcio7/PUs5YPZryalN4KxiKiUcUzwZ8NhsFl6I8dQ//msWM4vrISz1lLC2ybqio8Yz09eIbMZmUj8VxRhwPPXSQCGyQWgz3i8eC+ODDe0wPHduZMXEv/jAtGD6J/Rwnz5sE5EEUzPsC/Y2akOtsolEykUqqXKBxGz1E0inE9FgtY3t57D44lZzKZ5jwUggBua1OzZONxZCfLy5EpbWyEMHY61WiU9nb85ab+QAA9oBaLcjpjMdUjWllJdP75OGcwiJfPp645YwbWNAv3jRvhNLa14RUOq/c8Oy8X2Dg0GvtXGOzsDuYagpJG0RxQz4fp81deeSXvYzZv3px2LCMQCBARUZUsvDENLt13uxFAc7vBoOl2Q6Z5POkl/D/9KdHFF6sxENwWMXMm5J7brfrqiSDLXS4EBNlALhRSdisYq9A0tAuZTCrgzgHsfEYx6ktquZpq8mTYH1VVqj2othbbXC7YJSYTnrmvfhV/rVbcR3k5jnM6ca41a5DgaWyEXVNWBltK2nTGHkT/jiK+8x0oKUFpg0tD9LBa8zcgjEaVsYtEIDD9fqLjjydasQJGDDuaJhMEvd2unEWXC0ZQVxeifzNnEn3720Sf+xwyoakU9l++nOjMMyGsk0k4mDNnYvzKrFlwIBcvhnA2mfC90YhZXN/5Dgyxr3wFx/T04FpmM9GSJThvpnBfswZkQIkElEEiMfAIhkL7OtetK/wagpJH0UpwV65cSY8++ij95Cc/ocsvv5zm8hyiHDhw4AD95Cc/IU3TaMWKFWnf7d69m4gwk0wwdsGl+zzknu0Ymw0OKBMMdXZCVlZWEt17LypJGhqwz/r16FOrqIBM6u7GuY4cIbrxRgTAiIgefpjoRz+C/A0E8iMc5IoZTYMe0ZOySCmuYKThcuEvkw+ecAJsj/p6fNfbqwi6wmE8D6EQniNu89EH4fUj5QwGnMtgwPPBa7+ykuib38TzRQTbhgiEh52dRDt3Ep1yCuwYu12NbgmFiL7+dbVfVxfRnXf2DUZ7PLAX+BkXjA5E/44yTj4ZD5zXi4dSsselB5NJNcbrYbEoxjfejwmCOPLHfQ+TJkFgd3XhmPZ2ZDwXLIBQ3bRJMeP6/aq0lwjnPOEECP8bb0QfEwvgV15RfaAOB/afMwflKzffjEghl6rW1+NvRQXKf/fvJzruOKJzzlFC+oorsP/+/VA+L70Ex1WfAWDhfvQoHGiWE/mMYGDjcObMvv+XBw/iHvXnsNvBdnvJJYptmgjlxqU48kGQF4rmgH7zm9+kv/71r9TV1UVnnHEG/ehHP6Jrr722T3S1u7ub/vjHP9KPf/xj6urqIoPBQP/v//2/tH2eeOKJrIpRMLbApfvRqKoIcbnwl2V2thL+efPw8npBIMjjr3gGclsbZN+llyq5c+65RLffDr2er/Oo7/vUHyPOp2A00Nur3sdimP3NYAeSSQr1jmauYIt+eyqF8+/fr0p5bTa0Ek2dmjtBw8+v20307LMgPGTb+bOfBSv/3/2dtOmMdYj+HUVUVsLYt9lgSIfD4oCWIiKRvqUn2X7HbIzHTDbU2orfPxRSAv/Pf0ZU0W5XTiuPUtEL+vZ2FSH8t39DUKOqCtlMj0cNf2ZEoyAsWroU++l7LjMHOx88iMjkpZcSPfgg0X/9FxzLZBLOsMcDJTBrljp/f03/AyFTYcRiRLt3o5Q4GsU516zp2w9aVYV/o8wEnRAoWgnuWWed9dFssc7OTrr55ptp0qRJNH/+fPrYxz5GH/vYx2j+/PlUXV1N3/jGN8j7YQr+tttuo5Wc5iLQyT/55JOUSqXowgsvLNbtCYYBXLrP5bd+P2Se369GOfVXwl9I6f+yZZC1PC9UIBhPSCax/jmgPlgwu200imclHEYbTzbon7+//Q19pGyDWSxwhP/jP2ALSJvO2Ibo31FEVRWM6bIyPEDifJYuBtPnoz82EEgfZq5pEOpdXSh3ra5WwjlT0OsZ5Y4cQTTQaIQANxoHFr76nstAAMRE3D/B/Zff/S6EelMTsp1cFtzcjCyo/vxDafrPVBjvvINsbDiMbLDTmftc+faOCkoeRcuAEhF973vfozlz5tC3vvUtam9vp0QiQQcOHKD6D0sCUroHbvLkyXTnnXfSpz/96bRzzJ07l+IyU6tkwMGwTZsgx7q7Efyqq1MsuJkzvPkzEdoSTjqJ6IMPIAMrKlD6v2YNsjl8zLZtyOR4vXBsJYspEGQHc2EwX8VTT6GyKZuTuGYNAtPPPadak5xOZZd0diKAv2KFeta3bIFd43JJm85YgujfEYLXq8ocudR56VKiCy5ApkmU08QEs89ySS5HytmxjMfRY/m3v2XPovJ+5eVYQ0eOQCDX1MBhXbMGPRPZhK++57KsDJlYFvitrUSLFqloZFcXBLzTie9NJhAQhUJgrmtuxvdlZShV41J8Ln3ZsiW7Qsk09PjeNm6EgWe1oh920SJV6pt5rsze0XyuKyhZFNUBJSL6zGc+Q1deeSU9+uijtHHjRnrvvfeo68OygoqKClq0aBGtWbOGLr/8crJarcW+vGCEkVm6z6is7FtJwbOQIxHIo6YmJYcNBsgbhwNjsTZtgrw0mSBvDx9GIE2ynwLBwIjH8XrrLTxDX/0q2PW5VFY/Pum112B/6G0mrhQLhYi2byf6/veJLroINoW+TUdsgbEF0b/DiFCI6IEHiP74RyivZBJGNZc/7tuHbJNg4sFgQKlra6syavTZVC7RbWlRZEcWC74LBNRA9GgUL7sdRlN3N9G0aTCC1q4l+vSnswtffc9lIJBOysHjBsxmfMfsjwyOVkajOIfdrspn3O70f2e2pv/+xq1cdx36Wru6FNlHf+fK1TsqZAPjEkV3QImILBYLXX311XT11VcPx+kFYxDcv6nH+vWonKipgTzZuhUtAB4P5FEgoNjIjUaU70ajijhu+XKiv/4VpCjl5aqyRMatCQT5gZ+tzZtRfXXvvdjOVU7d3bA1mJgrHoczGg7jWdQ02AwOB/Yngk0hNsDYhejfYcKGDUS/+Q2M4PJy/G1qQqYqHk8vvRRMLPAgZqLc80JTKYxh4flbHg+MHT7eYFDz5mIxOInl5emN9tkMLaL0nsuyMkXKQYT37NA6napH2fSh+c9r12AAiVJdHRzpnTuJ3n6baNUqdZ1sTf+sTNjQ8/nSlcXcueifYrKP/s4lZAMTCkXrARUI9MispEgmIWvLypDJjEYhCznj4nBgW3u7Yi8/dgwv7t0XCCYCipnlj8VgU1RWgkhx/371bHKfdlUV9iFShF3BIP46HGjZmTVLRq4IJjC8XpQSBoOKKCUahZHMcx8FExuBgJoxlwu9vWrOFs/mZIFvMsHYSSSQVaypgXOYT6O9vueypwezt7gktrYW23w+lPFWVGAd82Bovx/XmT4dwt5mgxNaV4eS8sOHc/ed5jNupRACASEbmFAYlgyoHvF4PK0EyGQa9ksKxgD0lRTBICpPgkHI11gMslZf7pdMQgYmEpDPwSAynzyygoNnPD+UKHegUSAYT+AxdDxXvNBjrVY8bx0dCGrPnInn0eGADVJRgeqoRALPbSKB67jd0PmLFuFcUgVVehD9WyR0dsII5pmKsRgeFB7dIZi40M97s1jghGWWYjM1eSyGoed2Oxy7aFRlIq1WfM8Oak1NYfMw9U36LpditHU61XkuvRRzt5gFlw2s6mqU+BKpftCFC/E3GMzd9J9vyWwhBAJCNjBhMCza6P3336df//rX9MILL9D+/fs/Ij/QNI3mzZtH559/Pn3lK1+hhQsXDsflBWMAlZWQw1u3IsAWDMIA5vnLROlOZ1eXCgbW10NWHzuG45jEjSjdAOexFeKQCsYTMtdwIoFnSdMQEGaixFxj6/RIJmHnMLvt7bfj+auvVyONvF7YH9On429FBY752MdQCs+QKqjSgOjfIiMUAkvXwYMo0enuxkNiMMBhEGKCiQkevKxp6cI0VwaU52aFQkTz56PUa+dOJcAdDuwTi6lZoYsX5z9+JNcszcye0b//e6KrrwbLrc8H5/F3v8N9vfMOysqjURhXNTUgALDZsjf951sym+3eckUxC9lXUNLQUqnimuy33HIL/exnP6NkMkm5Tq1pGhkMBvr2t79N//7v/17Myw8Jfr+fPB4P+Xw+cmc2XwsKxhe/SPTkk2hjcLtVQKyyEjK3uzu9LYD78IngcPIsUdbx+uwn70+kjtE0GNeZrQYCwXiAfr0zj0Uq1T/pptWqMpomE2wCTcMzwgEg7hN1OBD0rqhQbNU1NapUt60NgejrrhuZf+9gMZHleCnrX6Ix+tsxmUFXF9GePYiKGgx4oHjmo0Q+Jx6MRpXVJMJ6SKX6F8omEwyiVEod7/UqBjgWxk4nhHF5+cgI3fXrkRXl/mYiCP2KCqKbb+7/+nqyj1JTFoKioxAZXtQM6Ne//nX69a9//ZHiO+GEE2j58uVUW1tLqVSK2traaNu2bbRnzx5KJBL005/+lAKBAP3yl78s5m0IxgC8XsjgBQsgjwIByCe3G9vtdmRGzWbVk88VKIx4HEFCn09lR7mkMJFQpbwWi8oKSTWUYLyCeSo48E7U/8hBTcNzYjTCLuAJAeyMVlbC3mD+iVQKo47WrkWl1uOPSxVUKUH07zBA3+N2wglQSO++iwcnGsVDdPQo9hUndOKADQ4iCFizGcYNsyhmg9mMXodAQBEWTZumooGBAM47bZoyfGbNGpnxI2vWEN1/P4Ip8TiUzMknoyRmoOtLyaxgkCiaA7plyxb61a9+RZqm0cKFC+m3v/0trVixIuu+r7/+On3lK1+hXbt20d13301XX311zn0FpQk2bJctg2wNheB0GgyYO7hyJdGLL8IQfu89BNpSKVR/cMaGSdm40snjwf7nngsZz2WC3H7Bc0j/9je0JWRmSAWCkYamYc1yYEXfCjSYcxkMsHPicej59naVjOGxaZEI7Ga3G9eqqcH39fXpY4/KyvDq7CRasgTP3Te+gWeWSFVB6Uce5lMJJhh5iP4dJuh73MxmzPtcsADK5/BhNXrDZkNmVJTN2IXRqIyJfKHvF3K5EP2Ox+FIckSP1wiXYOn7rLmf0+HA9w6HKmFJJrF+uA80kcC+ZjOuwf2gvb0QwpnlqJlzN4eCUAjRx8WLcV92O+41HB648V9KZgWDRNEc0P/+7/8mIqLZs2fTli1byMMziLLgzDPPpM2bN9Opp55Khw4donvuuUcU4DiDvjWA53vGYpg52NICWbtrF+R3JILgH89DTiRUtufYMTV6hQ3wbdsgk48cQY+pHvo+UM6Ysp4QCEYaqVS6vTOULD3P5uzsxPvu7vTvuVSdqwOSSdgByaQae6Sfk75/P44zmVBZOHUqXoxQiOiJJ7KPdxNHdGxB9O8wIbPHLRbDEOs33oByYsXS2zu69ykYGEzxXQj0wpuzlkQQwhYLjBufT7HK8nUYPCs2mcT+ySScytZWCNj2dlyDS1BSKZyLCPvt3Inj775bRR1PPx0CnA2hYghmXufRqIpk8r8538b/XCNiBIIcKNoYlldeeYU0TaN/+qd/6lf5MTweD333u9+lVCpFr7zySrFuQzBGkI1N+7XXMAd06lTIz1gMMptHX3m9kLFcbptK4XuuUGEW0P37EZTLVn6odzR5rqE4n4LxhFxsuDw7nG0cHk136BCeQa4s4HPwvE/9s7hxozofj3czGpEAMhrxecOGkfl3CvKH6N9hQqYie+cdKDJ2PoWAaOIiGkX5NRso2RxcgwGC2efDWgkGEQ1kJ5ONHWZT5PMQYduuXTB2nE4lhO+5B/NoiymYZfyJYBRQNAe0tbWViIhOOeWUvI9ZunQpERG1tbUV6zYEYwjr1qEVIJGA09jSguqlxYsRAJw5Ey0GLIP5ZberahdmurdYsI1797mUcCBIRZRgPIDJFvMBtxHV1iqCRiI8Qw4HbBlmlTabsc8JJ6C6kEe35TPeTTB2IPp3GMGKLBBAqUAkonpEuERTMPYxHMECJrCw25VQ5WsZjRCcPHPOasV+gYCaJasfC6C/R7NZZTPNZvRK2Gz4GwzixduKJZj1BltjY2EjYASCQaBoJbg2m42i0SgFMucf9YPeD8tWrFZrsW5DMMLI1oag38atATt2IHA3bx5RczNGspSVKUbcsjLI4yNH0F5hteIcLS3oA9U09MR/8AH0/0AVT0Yjxm3V1UFWv/WWOKOC0oGeEZoIxITM9M8z79nG4YwoB2fYzikrw+dAAEGf/ftVJdWhQ/g7dSqOnzsXjim3+xCpGeLBIJ5NIpkFOlYh+ncYwT1uJ5wAkgKeDZZZXy8Y2xiuUqhkEjOsolEYN0YjSC3icfzVNJSX/PCHeP+DH0CQHj2qekKTSRzvduM+q6sRMWxqguD/cJYvNTRg7RkM2FZMwRwMEq1YgReR9HIKhh1Fc0Bnz55NO3fupL/97W+0atWqvI55/PHHiYhozpw5xboNwQghFELFh74/bNkyyM433+zbmrB0KQzcv/4VMpWZ63l2s8MB+Ws2Q64ymVwohP1NpvSZzQMhkYCsbmgYxv8EgWCYkBksYadQD+4J1SORgH1SX49nie2TgwfxPPX2wibSZ0CNRtjY3O5jt6Oia/dunM/tRjBn0SKZBTpWIfp3BDB3LtGUKSjDlb4OASMaVRE9jgb6/RDOgQCEqNmM6PoFF2A7O5bhMLKY+l7iVEpFGblv4umnYQyFQqpv4umniU45BZH5oQjmbMYcG24CwTCiaCW4F110EaVSKbr77rtpo76RKAc2btxId911F2maRhdddFGxbkMwQsjWH/ab3yDLma01oaoKAbr6eshrrjThIDLPBU0mYWz7/ap0kB3VQADO6bFj0nojEPSHZBJ2Cj9TTOIYi6H8nQmLfD6U6vb0qHafjRvx4l7tUAiVh6+9Ji1BYxWif0cAVVWIouYasyGYuOD+TSLlQOoJKIxGokcfJfrtb9W8OZMJ+/T0qIHMzITLGVEW3seOqcHpTJBx7Bii/UMVzNLsLxglFM0B/eY3v0lut5tisRhdeOGF9LWvfY22b99OSV0oP5lM0vbt2+nv//7v6cILL6RYLEZut5u++c1vFus2BCOAbP1h3JoQCmVvTdi2DUE/ZilnRluWpTyDkEnj9LNBeXwLO50Ggxqxkg/EWRVMJPCIOqsVz5rHg+rBqipVlmuxIKEzaxYcU273WbNGPdtnnolZ6HY7ns+WFnwvgfGxB9G/IwCvFw+BQJAJk6mvYcJGzqRJELY9PUQvvwzBumABBDSXonA/sdWqHFCTCYYUUTrznNsNQU6EqHxz8+AFszT7C0YRRSvBnTRpEm3YsIE+8YlPUDQapXvuuYfuueceslgsVFlZSZqmkdfrpWg0SkREqVSKLBYLPfTQQ1Ql4fSSQn099PCUKZBPdjscT3b0QqG+rQl79iAoaLdDrvr9+MtjWBwOyOS33iI67jiUDPb2IuDM41mqqnAOHtXCsjufMSvs6OrHtAgEYxFcFsukigP1Ljsc6YzQVivWeUUFns9IBM/O3Ln4PhLB83vzzXg+ifBdVRX6RPVjD+fPRzsSj0pau1ZGsIxFiP4dZni9IDJoahIFMp5QVQWHTtMgFEMhbGcBTJTdaDAa0ynFLRY18JmNk2QSAra6Gp/DYVVCe955anB5by/Rq69CcFdWwsDp6FCCPRyGMOYmf33fqMsF0ozTT4cjWmjfpn7OrR7S7C8YARTNASUiWrt2Lb3xxhv0pS99id566y0iIopEItSSJWp4+umn029/+1tavHhxMW9BMIzgVoFnn8X8zWgU8q+6Gs4oz+7UG6jcmrBwIeRmIKAM7FQK50gkIIdffBGfvV5ldAeDih2Xmcu5coX1w0D2gP57sR0EYx1ckp5K5Ueclcn+z88J89EcOUL0t7+BkGv2bDimPT1Ef/iDGi3HLT88Ds7rhQ3U1IRnMh5HUFycz7EL0b/DAFZ6mzah9/ODD0SJjCcw5Xcm9AI12++dOW6FhW4meJQJO7EGA9HzzxO9/TZ6N084AZnGSARR+WPHYCDpx7Iw6zI3/Xd0KAc0HMa6/NnPYBgVOhM0c84tQ5r9BSOAojqgRERLliyhbdu20ZtvvkkvvPACvffee9T5IYNGZWUlnXjiiXTeeefR6aefXuxLC4YZ3CrQ1QU5mkjAkDUa4UCmUgiW8WxPnw+y97LLQFB09tlEDz2kSNy4395gSG+X0BvdLHeJVEWL2Qy5K3aAYLwi33np+jEr/SEYRIDc71fPqdOJQLfPh+eaCGSfK1cS/dd/4Tn3eGDX9PaqOaHXXTf4f5dgeCH6t8jQK72jR6WfQzA46HtBk0k4vVu3ouSEnT2mOee5dCzY9VTnRIp5mUvAOjsxL/Syy/oK84HA8z/5GFYIbLhJ9lMwjCi6A8o4/fTTRcmNI3CrgNtNdPgw+jWDQQTsenrQ5lBbS3T55UTvv4/qDZcrfYzUT38K+fr00zCEeYwEl+KazSqQyH1sethskI/BILI4nZ2FOaFcKiwQTDRoGpzIsjLYNQsXIqNJpALfW7ZgZNKaNUT334/nLB5Hddnixah04H3ELhnbEP1bBOiVXn09HgZ+gASCwYAb7rmvKBSCMA6FYAD5fGpEAJeJcT9TOIxyFCI1e5TJMTo6ENlnoV6IoGYDbcuW7IabQDBMGDYHVDC+wK0CLhdkoMcDvezxIDh8xhnQz5dcQvT5z/edDUqEz7//PYJ+zzxD9MgjRJMnIxDIWdNAQDmlkYhyTokwCsJmQ39oTQ2+r6jA9TlLQ4TzlJdDjlut2F5VhTLht9+GQc29p0SSSRWUJpijQj8TN1e7EjPgHnccnr/M6ix9yw8RAkyLFyMIZLfDBgqHpS1IMIGgV3p6kgMhEpg4MJtVxtFggMDlnoTBwmqFMdLVhXPPmgWSjBkzME+0txffJZNwSKdPh0HT1QXhrWm4jxkz4HiyM9vdDcFcaP8mz7m95JLshptAMEwo2AFtbGwcjvugmZlN0IKiwetNlyuZn/MBtwpEo3DgwmHI0UAA8stohEzMp2Vg3jzsx/NCnU785V5PznzyZyIl+3t6FMu52ayI4+JxVRZMpFjQmZTOaiU6/niinTvTWyoEglKGy5XugGZb0zzqyGxG0MhuV6z+bAv19KS3/PCzXlMD28br7buPYOQh+neEwX0mdnv2YbyC8Q8OOCSTan7VYMGltMEg/nIW02KBILdYsOY44mc04no9Pap8i49zOrEmQyE4oVYrGv69XiWoCzH2qqrE8RSMKAp2QGfPnl30m9A0jeJDiSgJsiJzvrDNpsjdwuHC+tX1rQJOJ9G+fThHIoGqpHffJfrCF4ieeCL7PGP9+UMh7MdzQRMJGLvRqHIMeQ4zV5wYjeB/4GBkby/kNo/Q0oPPx9ciQtb02DF1vEBQ6mA+ioGQTKrnf/9+HPfUU2rUkcMBgqKbblL2x8qVRA8/jGeuqwuBpngc5I1cESYYeYj+HQHoFefhw+iHs1qhlJjZSzAxkGkwDNWA8PkgdL1eOI1VVWpEQEsLzh+Pw2Di7OsHH/QlBWASDB7UXFFB9NxziComEkTTphH94z/i+0KNPYFghFDwHNBUKjUsL0HxkTlfuKGB6MkniQ4dGty84XXr0BpApBy8sjJkVYiQ0cxnnjHf17x5IIJzuxWTZ+Z8T31WNBKBTOYS2ng8P5ZQhswPF4w35EtWxPseOoQgOZHisOjtBUGRXgyvW4fs56FDsLmdTjDotrfLfPLRhOjfEYBeca5ciYXf0ZGb6VQgKATsOJaVwZg5cgTRcSLVH0qkHNBsQj4QQFS9rAzRw0gETq3ZDEKOzk6iRx+F0TcYY08gGAEUnAG9//77h+M+BEVG5nzhYBDBt/JyOGLJZOH96nY79tu4EQG2SZPU9vZ2otdfx4zlXOQmXP6rv69p0zDGijMyBgNkJ/d+ahpkcDAII3jmTAQKEwmVLeXxW2JHCcYD9M9ofzAYFIs0H2M0qmeB56IbDHh2+DkiwrNoNism/3ic6IUXiK64At/x+KNzzkHwnPtA29qEiGg0Ifp3mJGpoIiITjsNxr7PV1jERzD2wdT6zEKr7+MhwnuuDuDouF4wGwwQuvoeIJMJ2/RkVZqm5seZzejTNBrhQLJhZrXiO4cDAvjIEWUE8ZzRVEpd48QTib7/fczTeuMN7MODnffuxV+eaTcYciKBYJhRsAN6nXDwlwQy5wuHQshaut0InoVCkHOF9qt3dkKGzpqVPjbKYlFEbnpkkpvs2AE2e2bR5XI+ls3xOOQyG8eBQPpMRG6FYB2RLWsqEJQyuEd5IPA+PE+XCI4iB2eMRtVSZDYrln8mTiTCcRxkb29HSfyOHbBfGhoQXOeKLa8X5+voyF9eDKbfXJAbon+HGaw4q6uVA+D3i/M5XsGC0GZTZa0sPPMtr2KBqu8N5aHo+s+Z12VBnUhAOBuNWHcmk+ojYgc0kwCLS8aamyGsEwk4sUxIRITzhUL9G3uFEoTk+l4EvWAQKGkW3Ouvv57Wr1/f7z6hUIhsek9pgiBzvrDdDuPR78d7NioLnTeca25xNIpzZrZI8H7PPUe0fTt6RQ8cQLBv6lQQvEWjyKxwSW0ukqCeHrwyIXaBYDwh31FBegeUZ+pymTnbLByYDwbVc5VIwFbIrBrYto3o6qtRDcZEj3a7smucThVMH6iNKLP/XFqQxifGnQ6222HUv/QSFnEwKMQB4xnsGLa15b9v5rZYDH/5+1z91BxJj8chqC0WNNizYI5EsM/06SpqGA6rMpVMvPUW+kNDIdyDz0c0f77KAiQSuY29QglCcgn0Sy8levxxEfSCQaGkHVDGvHnzaPLkyVm/M0zQ9Fi2+cIeD8pXa2pglLa1FT5vONfcYr+f6KyzEAhra0ufZ1xVhbLd7m7s53Dgb3s77oerUgbT1ykQCFTJLRMLWa3qeTIYYFPw2JZIRGVM9QgGwbnicCjHNBDAsU6nukZPD57n/pJx3EZXU4MqjELnowtKC+NGB2/ciCwRZzzF+RQMhHwNFhbORBDMiYSaFedwQEB3dEBAu1zKUMqFaFQNd+7pgTO7dy8ELl+nqiq7sbd+fbqA3roVxy5YQLRsWV+BnUugv/oqjD4R9IJBYFw4oP/8z/9M119//WjfxphD5nzhujqiuXMh94YybzjX3GJ9MIy3r1mDzKfHA+PW7YasamqCbI3FEHg75RTVZiNzvgWCwsAtRmx3OBx4rjjzaTajumvuXDyPmZUEXNnFGVKrFXZVKKQC94EAbJNp0/pvJcrWRpetH1wwfjAudLDXCwfUZgOraGvraN+RYKxDPxOWy04yy7dMJuWkulwQsJw5NZsxRN1mQ+Y9HofQnjwZ78vKVIYz87pmsyr/nT4dvU1+P9bxCSdA2GtaX2MvG0GI349SF5+vb8/oihXZBXooRPTKKwMTfwgEOTAuHFBBduSaLzzUcv3+5hZnbu/shCziuYLcez99umIdt9vx+dgxvOeRD1arqm4RCASAwQC7JBBQY5AMBszzLCtDIHz6dKJbboE98atfIThdUwObYd8+OKCcKeVWIbaZEgnFNM0cHZMnw96ZOxfZ0P76xjP7zxmF9psLBCOKzk48PMzqlU9ZpmB8Qe9QcuZe02DMhMP4azIhOOHxYLj43r1qIDr3kZrNyuBZtAilskYjnDVeW2++if0WLIBAPOEElIm1tBBdeSXR//0fHDqTCXTkPPycyTL0pESVlbjW0aNE3/420TXX5Db2mpsLIwhpaMgu0PMh/hBBL+gH4oBOAGTOFx7qvGG9TJs3b+DruVxwJC0W1V7Af3t7IUtTKewTCg1tzrNAMN5hMCidz04iJ22Yy2L6dKKlS/Gccul9KAQbw+VSwfpM24HPz1lP/myzwVm12wfuG8/VJ15ov7lAMKKorMRDxLXm3BMnmDjQZy6ZKMhoVI4lGytGI4TbtGlwJsNhxaLLc+PMZkQEJ03Ce5MJUUJGNIr99b2SPT04dyqF7Xw9m02tRb4Gl4+ZTDh/by+E/cc/rgywbMZeoQQhdXWFE3+IoBfkgXHhgP7lL3+hv/71r+T3+2ny5Mm0cuVK+vznP08epqQWFAWDIRbR94x6PAi+BYOQXWYzAn6hENEjj2CbXt9LKa5A0BfxuBobRwS7wWDAM+n3Q++fdRaeqf/5H5Tcsm1ks8Em4tFFzHuhrzLgHlGjUTmpwSCYr3t6Bu4bz9UnXmi/uaB0MC50cFUVekZ270bkxunM3iwtmBjQU+1zz0Jvr/o+FoMxxOQWyaQSmkwiZLdjREpXF9bTK6+gBMXvV87tX/8KJ6+hQTEvv/IKBH04jH0MhvT5WuyAcjlvZyeE7MUXZ88K6JFNQLvdiFLW1vbtGZ03r3DiDxH0gjygpUp4CnV/DHwVFRX0pz/9iT7+8Y/nfT6/308ej4d8Ph+53e5i3ea4gb5vPVPW9Ndvzo7rpk1Ee/ZAXsdiimXz6FE1bkUgEOQPtnc0DZVdCxcSXXstvvvNb2DrMKtuMqlKamfMgFPZ0ZH+3GVWoM2YgSC/2Ywy3PLy/EgOR5MFV+T4yGHc6eBQiOiBB4j++EeQFjQ1SRZUkB1GIxw3zn4SpQ9nJoLAnDoVfw8dQukuO54Wi4q68wzQUEhlFdm5NJtVFpbBPRIsrD0e9D799Kf5ZR6FBVcwTChEhpe0A3rbbbeRyWSiiy++mGbPnk2aptHrr79OP/jBD2jr1q1ktVrp1VdfpdNOOy3r8ZFIhCK6NJvf76cZM2aI4ZIFXi96yoxG1W9OBAc0kSC6/faBA15cutvVRfTzn0NG79mjsqLJpKpKYaPYbFZyetIkBN3CYSXfhaRQMFFhseA5qKuDfbBwIdEvfwn741vfgk1w5Aj2sVhU65DFgmf4178muvVWzP202WAjJRIIBkUiqEa8/36U8hINrm98NMbDjboTM4EwbnWw10v08stEN9+slJxg/IIjc4X8zpqGrOaUKVgvRIjWhcMoUamshGN57rlwxh5+GAENhwNlKOxUer2qRNdqhTDm89ntcDLPOw+C+f33Iehra9U4lUOHcB+//nXhQlbmgAqKjEL0b0mX4P7gBz/os+3888+n1atX01lnnUXbtm2j7373u7Rx48asx99+++304x//uKj3VArPYb73qN+vP2KR/fthxC5divPt349qEn311dy5qh1h2zY4nIkEAoKxmJL/RH3nf/I4rEhEOZxmsyKSywV9NkcgGG9g/gmDQbHednUR7dwJRmkur7VYVECHs6V+P3gxOPDOLUZGo+oR5WP07USFYqj95oKxjbGog4cEdjyPHlVljqJExj9SqcJLsFIpGCY8q5P7Qs1mGDbMfhsOo+yL532y8RKPqxLvZDKdfpxhMuE4qxURwvfeQ0Z16lR873AMzArXn8FXKEFIru9F0AsGgZLOgPaH5557ji644AIyGAx07Ngxqqio6LNPMaOvpTB4Pd97zLbf4sXoJbNaVQY0FiN6/XXo6kWLlBw8cgTyLhSCTK6sJJo9G9exWIiefproySfTezw1DfvmmuEsEAiyo7wc1QGTJsEZPXIENgeXuXNgh3s9NQ3P+9lnowKhqUkRGzHpYjiMsS1PPTVwS9FYg2RAxwZGWgcPCaEQ0T33EP3HfyB7xWWUUmIjyBdWK9GJJ2LAeXu7Gm5eUQHj6MgRZDHZMdUzvRmN2Mdi6ZsB1TSiz3wGfahvvAEm3Vmz1HVzlaGVglEqGHeYMBnQ/nDmmWcSEVEymaSDBw/Sqaee2mcfq9VKVqu1KNcrhcHr+d5jtv02boRsY2Z6jweZzA8+ABP5vHk45tAhyM9oFLo7FoPsO3wYur2qCr3umdwOTDAnEAgKA8/TjcVgMzORZ0sL7BKef64PNVZUwBbq7FStP7GYIh+yWuGglprzKRg7GGkdPCRs2ED0b/+GEgKTSc0mEgjyRTSKiB6zvTGlP8+8IlJ0/5ngrGcoBEOIiYzCYaLjjsO2Qkl/SsEoFUxoGEb7BoYLZt18gfgwezaZc31tNvV+yxYVzBpN5HuP/e2XSIAkMJFAme3Ro3A+V6yAMdvRAcOVK024GiUUwvbubgQGvV4E+ux26HmBQDA4cOaSy9h5HNzMmSANMmRIeKMRzqfHA+ezthaZzrIyfM9jXS66CHwWAsFgMZI6eEjweokefRQGPjOXyiwwQT4wmVRfA/d0Wq3oBS0rQ9aRKco9nr5OIg9jrq5W2VI2nsrLwQJ33HEwui67DEL5ssvwubFRbV+3Lv28pWCUCiY8xm0GdPfu3R+9nz59+rBeqxQGr+d7jwPtt3Yt0ac/jZ7Pe+5BhoTHqcRikKVcests4ZEIAnjcDpFIKOfTaETgkFsiKisRMDSZ8DlbsFB6OwUTEbzumaSLe6wnT1ZknTymjcnCfD7sN2cO+rKrq/Hstbdjv8pKPL8XXIDntKcHz92Pfyxj3ARDw0jq4CGhsxPlAtyfRySkQwJFTGG3I1LX0gLBW1GBMm2zWQnlWAyC+NgxouXL0aO5dSt6NONxGEjcYO/zodyWiYcSCUVkdNttIBny+cAuxwQc+v7N664D421/RB6lYJQKJjzGrQP685//nIiIFixYQNOmTRvWa43U4PWhEBzle4/Z9gsGierrlU6uqlKEQ21t2J8NYx5bRaScSg4QcmUKO51MjqKf983bOKOTDeJ8CiYimKQxmVTtaZqGygP+vrcXf+NxVUHIZblHj8IO6u6G3WOzQZ5wkGjKFDzL/Czu359d1pQC0Zpg9DGSOnjI4BKASESRxAgmNrgchEeeuN1qG0cBmaKfjR5msq2sxHfcA2H60NQOhXAs93zyscEgPq9cqfoecglaZnmsq1PbeV89chl8JhNKzb1eEeCCUUXJOqDPP/88vfjii/SlL32JZs+e/dF2n89HP/jBD+j//u//iIjohz/84bDfy3APXi9GL3m+96jfLx5HZuXdd1XQ7otfJPrsZ4muvBKy+MUXcX+JBP5Go5B3qRRkaioFedfaCrlcVYUsi8+XfcZ3a+vQ/q8EgvEKfVImmcTzpQePLeQADf/t7cV2PbjSMBjE89nWhmB9dTWCS3fc0VfWEAmnhUBhLOngQUE/oPrgQUVCIMRDAkY8Dseus1M5lMzkxsQVnDn3eiEU334bBk5VFUrFwmEI2WgU+5tMEKBMYW6z4fPFF8P5zGXwnXce0Y9+RPTKK2pe6JlnEp1+OtFbb6kh6zwQ2mhE1JEIBp/Xi/spKyO6804R4IJRR8k6oIFAgH7yk5/QT37yE5o2bRpNnTqVYrEY7dmzh6LRKGmaRj/84Q/pM5/5zIjcDxtoW7agwsHlyl6aPxgUq5c833vkz/ffD+czFoMsLS/HcffcAwIiNlq7uyGDmcBNXzrLFSxOJypU3G5cVz+7WSAQFAf5Jm6YtMhoxDOcTCIwxHN4zzijr6whEk4LgcJY08EFgxVrV5cqHZDMpyAXkkkYMD09KhrIJVyahn7N884j2r0bbIwMLrO1WPDebsd6i0bx3uUiWr1aNd3nMvjuv59o3z4YYlOmoGf50UeJXnoJPRadnTh/ZyeuX1amyDsaG5UzOm8eDDoR4IJRRsk6oKeeeip973vfo9dff50OHDhA7733HqVSKZo2bRqdddZZ9Pd///e0fPnyEbsfuz2/0vxCkdlLTqQqKrZswfXyvU6+92i3Y5+nnkLpXlkZZB4RZKjfj1Fpp50G43X27PRKlNpaOK4nn6zGVVVUoO/sjTdA5OZyEb3wAsoHOVuaC5oGuWqxIPCXTCLgaDarWYdSlisQAEziGYv1tae5HD6VwstoBKO/yYRnsLkZhGJlZZAzLGt4jGMx5JBgfGCs6eCCwIrV7UZ/STyuFIpAQNSXbCKZVEIxFILxc9xxWDeTJhEtWID9Vq+GEbR1K9GqVap/yeNRw8znzsX5Pvc5GEr6sttsBl93N9E77yCKP3kytnOGs7MTEX6O7vf2wkibNQv/hm9/G0GWu+/GfiLABWMEJeuAzpgxg/71X/91tG+jD4o9j3c4esnzucfOThikVitkGoNlITuC0SjuhR1E7ik1GEAExw4okXIgy8pwXi4DHKjiiY1lu10RFnHQkVsyxAEVCPpHtp5qriJzu1V1WTKp7CsiPN/NzXivf575O+G0mJgYqzo4L7BidblUs7RkPwX9IZWCkLRaVRO90QjDpK4ufd+yMuzL/cTl5YoxNxKBw9rbm+58EuU2+JgYg4UykSphiccRNeQ5uzYbDDGLBeciwnfxuGKuY4gAF4wixu0YlvECPSmQHsUmOGJ4vehxJ1KM4OGw+j4chnPpcOCvxaK+D4fxmctuM3s8e3ogL/1+fG+3Q67mQzjIWVLeN5EQ51MgyIVsz4X+c6YzGoup8Ukc7GG0talS3WxySM9pIRCUBFix9vQodi+BoD8w+VA4rIIV7IQePpxexsXltVyGwo5gb6/qd8gmOHMZfDxvS38Ns1n1kTqd6YaYwQBGXpMJ5xyMIcnGoAh2wTChZDOgEwXDTXDEyNb3brVChnq9Sj/7fHBMly7Fdreb6MgRyMV4nGj6dBiy+nnJDgd63xsaIBe3bgXZEMvlfDgfEol02cs9bAKBIB2JhOJTyQQ7mFyKm0wiAB6L4TijEc/4W28RnXgi0XvvgZ+ltha21oEDigFbOC0EJQuHAw/Ca6+pHlCJZAr0yBbB6+yEoORMJs+Wi0RQGjt3LspEurogNHfsgHHFs+l4VtbzzyML+rOfITvKgjOXwReJEC1Zgh7Q9nYYXn4/9qmsRFltczPWcXc37s/rRYnwE0/g3PkaksVgvRQI8oA4oCWA4SQ4YmTrez96lGjxYvSzcwnezJmKBffxx0EgGApB5lVWohJl9WqiSy/F91u24NXaij55NmrffVfJYq5skQoogWDoyMeO1jQ8e2VlsGOiUQTLZ8+GLXLoEIL6ySS2LV2K/XbsQFA8EBBOC0EJY8MGGPIOB5wFLmUUCPoDz8DiEpJgEBH57m4IyHffhXCcNw9GzezZcAQbGpD1dDjUsPRwGPtWVaULzlwGn54Ft6UFzuDllysW3GAQQjsaTXeG+dz5GpLFYr0UCAaAlkpJ2I/h9/vJ4/GQz+cjt9s92rfTB8M1f8/rJbrlFjW8ntHWBhnKPexEkGn6a2eOn8o2sur734fcnTUL24JBor/+VbU0mM2QmQcOKDbdSAQyXmwCgaAvMktsDYZ0YiG7XT1DDJ6JXl6uSmpXrIA9YzDALjIaidaswdiWrVuJli1T3BpEkAmBALgz/vCHdE4L/j6RILr99tFrKRrrclyQGyPy27HCi8XgMEQiWLiRiGRCJxry6eGZNQsGkNuNSNzevWq2HBHR8cenkwuZzRCmLBy9Xsyri0QUQxxnElMpCFxm1tULznzmgOrJi+rriX7+cwjzGTNUv2imUO7PkBzIGBxNwS4oCRQiwyUDWkIoNsERYyCiIyIYooO9p0xZFgpBDnNffUUFgoeapgzjWGxgvSD9n4KJCl73XErL7Un6gA3PRk+l1Li5eBzb+btIBAEf5qbw+RRRWCIBm0sPrtwiEk4LQYlCT0AUjcJZ0DQ4DqJQJhby+b05sme1QiCazRCaRFg3sRiEK5MLtbQgyq5nbDMY8F1nJwwfJgxigZtNcOYyrubNSycu4n07OyHYZ85UDLdEfc/dn9E2HKyXAkEOiAMqSOtP18utQomOsmVDKyvhbB4+DPkbCkFO8/xmTYO8M5nwmWc8G439l+SywS3cEYKJDLaf2EYiUrY0c1JwoIYzok6nIh3q6MB+7JDyM8ccGn6/6gVnEiKXC8F3lhnMmksE3guzWcmM4araEAgGDVZ4sRgcCW5+jsUkqjnRMNDvrWlYI9zzyQREekbEeByfjUYI1FAo3aCy23GOri4YLUajEs5ManTsGI57913sV1GRXnZGlF2Q6gUsr+vWVkQQM4W23pDLJZiLZQwKBHlAHFDBkImOuGd90yaiPXvQDlFeTjR/PozX+nps5xFrrOs529nbqwhRkkkYvTzOKhfsdsh/cUAFEx2Z9lM8jmcwczv3eZaX43ufD0Ft5tRgwsQXXsDzVV2NMlyTCU4rzwS+8koE4JctI/rNb1CS6/fDBjMY0Of9yCM457ZtwmMhGGPQKzz9jKFAQJzPiYaBfm+bDY5gOAxD54knlOHBx773Hv4ajSjPdTrhcFqtROecA6EaDKKvoawM37W2qkb8xx6DMNY0CF8uaeEZeFYrejpraxVhkZ5kgwXs6adD+G/alF1oV1UNTDA0UqyXAgGJAyr4EEMhOuKe9e5uBNVMJvx9800VOONyWyJFOGQypc8edDjSyeWsVvzlbCmDywlTKWRb+nNUBYKJiFx2lcEAe5t7RTVNPYNcvqtpiuxx9my8DwTwfJ9/vpIJfA2/X41Y4sq0e+7B32XLhMdCMAbBi3jTJjgHfr+w4An6gjOULhcEHvdqEqnsKRswPPfT7Ybx0tpK9PrrcBqDQQhCiwXCtKtLlXHxaBYuY2GDh6nJw2FkLMvKFGHRq69im54o6J57FGtcLqGdD8HQSLBeCgQkDqjgQ9jtkD+XXFJYyZzXCznl8aDM1u2GvOruhqFbXo7qEqMR50wkIBdZnqdSYMc1GvH+3HOxf0cH9uH5zR0dyOAw2QoRrvnuu2AmF7IiwXgE923mu771FWVcXUiEbSYT0ZQpGJtEhOfRYMCzyvs5nSAm2rIF9s9pp2F7KKQqFYJBvN58E3PU43Gcy+NB8IjbmoxG2Ew2m6rm2rIFMkYC6YJRhV7h1deDXfTll1UtuWBig6NpZWWIjJeVIQtoMqlgBUfCNQ1GjabBYAmH0f/JzmtlJdEJJ4DMKBiEgbV5s5rhyZF5djqJlKETCqmRQc3NRIsWYdsrrxCdeaYi1+CyYKL+hfaWLTiGj8smmAdrDAoEBUIcUEEaCiU6yuRzYFISLrM1mfCXieGYXMhsVr2g+lYFIsjp3l7IZ54/OH06vguHiXbvxvuyMqmaEoxv8PzxfB1Q/bPA77mfmgM++jmg/LJY8PzG4+nsuaEQ5IHDAeeUeSiI1HOfTKpAkabBPksk8JntJyLhsRCMQTB5SygkGVCBgsGAv9EoBB8TThiN6rtkEsYJBy14PEsiAUPH7VbROHb4HI6+LIz6a+ob+Tm7ygqA54laLPhrNqtjQ6H8hXa+BEPDxXopEHwIw2jfgKC0kcnnwL31XI0Sjys5yZUlHPRjshSe5WyxwBn1+dC6UFGhnFKG/rtYTOkGgWA8Qj9yLt/9GVxiy84nB/V5H34WmciRCYrKy5UdpO/X1PNQ5Hruw2EcwxUNuY4XCMYMKivRY2eSePyEQT5ClUs4DAYIURZqREqgsgBkh5G3m82IxDkcWFt6Q8ZuV8JXb8AwAyOfn9kY2Um12/Fihji988rn7E/o6gmG9BDBLBgliOku6BdeL8ZOeb3ZtxOhZ93nQyDN78d8754efO7thfy1WLBPTw9kM1eLTJmCY7xeZDvb24nefx/vZ88GedFrr2Hb3r34W1OD79rbITO570wgGG9g1uh8ndDMJA63FHFAKBCATWQ04j0nfmIx2EHV1SiBt1rxec8eoqNHMXauvp5o8WKct7MT5IxHjuB8/Nz7/Ypnw2rF8x4Oo3qtrQ2yQoLqgjEBvRK76CI1GkMwvpGPME2lIKhWrYIgbGtT7LXcE8H9REYjMqHJJAwesxlCtLub6OyzsbYaG2HAdHVBKHJZb02NisIzMyORckZtNhhLqRQMqQMHIIyXLVPkQOGwOqfDkVvoMsEQb89XMOuNwEyD0OsF09y2bX2NRIFgAGiplBQwMmSAuUIusrRs5GvLlkE+vv46COGamiCbnU4YoR4P5KbPl864WVam+vWrqhDYa2qCTOee/lhMGdWcMWXj1miEbBYSIoFgYDDnBU8F6OlJH4dksahSeW5fisdVpdmcOap3m3s943GVIOAkgMmEZ95mw3c1Nfg8Uiy4IsdLFyPy22UqN5sNi/2558SILnWYTMUhhDAa0QtUVUV06BCEJfdoZprMmoaIeSKB/YggTJcsIfrd74heeonogQcU2/L06RCEFgui67t3g0AjGFSCNHM8jP6z3Q4a8vnzsZ1Jkk4/fWDq8YFYcPXQ79vdjQgjERxhVgT19YpUYMYMos9+lujaa4XqfAKjEBkuDqgOYrgorF+vyNL0TNxVVYp8LZOh+5JLiO67D0ziVVWQU9Eo0ZNPIvBXUwNZ2dWlyIdOOglZk6oqou3bQRwXj2O/aLTvfWkagnzsnHKbRC7oK1oEgokIgwFOJXNemM3pc3SNRpWtdDpV1tVqxYuzpJMmqcoyHtkSj+M5dzjU6LqpU2GP8ZiXVauIbrxx5DKfIsdLFyPy22Uqt61b8WL6dUHpgKPZRiMEDzPMshNa6O/JUW7OPFqtcPbicThbuUiq2ClcuFARFEUi6QaTxaLKRNatA9GPfnh6V5cqJXvpJaKnn8bxfj/Rjh24n6oqlKlwL9INNxCtXZtOFJTP8OV89tE/J42NKIfRNBAqhUK4J5MJjieRuqebbxaq8wmMQmS4ND0I+oCZbTPJ0sJhkK+dcUZ2ErUVK5DpnDtXfe/1Qn4yC25TE+RrLAZn85xzIOO3bFHBP6sVeiQbUik1got72/qDOJ6CiQ7OYHKfps2GZ9JuV/3ZqZQiW2S2aZsNz6Hbjee1o4Oorg77x+OwNSIR2CIzZhC9/TaIGWfNUtchgt0mEIwJZCq3YBBOgb7URjD2wRlBjjCbzcqRY6IgbmzP1wjg8pBEQg0tZxILs7kvYZC+wT4chgM2Y4ZiXTt8uK/BVFGBiL2edTbTAfR6kTk94QSUiT37LK7lcuHeuIciGETg5NOfLpw8aKB99M9JWRmMNd6/uTk9y+xw4P+H72njRqE6F+QF6QEV9AEz2zKjLcNsViRsenCvZ0ND3+O6u1X/fjis2DGZEbe7G+cLBtPZyPvTGfoZ0AKBYGDwmBUi9Xzx+DpuP+JnkgP4fBwnBRIJ2B1s88VicDKjURyTScxIpGQD24YCwagiU7mFQrmjnYKxD72xEI0qQacnDBrMOblchJ3LcDh7gEJfYsXROMZABlMuoahfo8x8S6TKWFjwEiFrOhzCNfMeolE1T4ufGWa5Y8NtuO9JMO4gGdBxhnwqK/5/e28eH2V19v9/Zs9MkpmsJBAImwiiAoKgFRUqtioWbUtFW3zqVqvS2tbW1trH56utdrF28am1q2tFn4rWteKKa1EB2RQRRfY9kGWSzEzWOb8/rt81557JTDL7klzv12temdzLuc99z7mvc66zfK6BMIqlsU0ByM44nTRVj6f9l5fT/yUlNDoSeV5ZmZ7qx8rjfr9eW8br0Fwubd8HcjCNgnOCIAxMT4+ess7tKrNZO5ocMsXoeLKgIy99sljoOH7vbDYdQ51tQ3c3vd+BgLYVIrAo5A0VFVSId+3SUwdNJgkkXWgY1Wh7e8lB5MXoxilSiabZ26udWO5t6+zUarXGNCOv4XCQ0WMDePgwGU1eF+r3AwcO0DQwHiUF+jbauIx+8gldl3v1ONRKRwel1dlJ6bBxjdb4S6ZB2NhITqTVSo05VpXjKTQ8daalRfdOAnp/ebkYfCEuxAEdJCSytnwgWCzt6afpf17r2dhIyw9efllP03O5aL3XkiW0BCLyvJ4e+tvQAGzZ0neZzXPPkbN6/PGU7717o6/9NDLQfkEQwmHVaUAP+Ph81LawWum95JjlPNLJHUk+H22rrtbvnsWiZzzwcqlRo4ANG6gdwp1ELhdwzTUyG0vIAwIB4N//pvVsW7fq3tDW1lznTEgF7iFLRzrsRBn55JPw+FVAX+eWDeeqVeTwHTqke+H+8x/gvfdIGbe1VU8Jmz+f1ixYreFCQt3dJCS0Zw8dx1NbeUpxSwvl1eEgifInn6T/16zpqwxp3DZQgzCyEbl/PznP06dTIy9yDSiPQnDlwmtA580Tgy/EhTigg4Rly/R68fp6sgXsCCazHnzRIvq7ciXV1yUllPauXeHxO9vayDZzh2S08+bMIUeTba8RnrmyZw/ZRemIFoTs0N1N77HTSU4mx+212/XUW56aW1dH7Y7mZtq3bx918LtcZBeqqqiDyuvVnfsiACbkFVxJTphABfnjj/VaP0Hoj64uMoxjxmiZf8Zspl52v58UbS0WKldut56yumOHPsdqJeO4fTsZ0pNP1mFV/vIXrcRcWUmGubVVG2Redwpo5/TPf6b/Z83Sjb9o2wZqEEY2IouLSWho61baxov7i4v1/9u2kaMK0DmLF+tGoCAMgDigg4BYokFA+Fr3RHA6yU594Qt6Ov+tt5IdHj1arxfr6iJb+9ZbwJe/TNeJdt6wYbRv504dugEgm+50kn1sbtYzWID+G642mx65McZqFoTBiMVCI5AHDw58nMMRPuI50PHHHAOccAK1x1lluquL3qm6OurUvu02Or6igt7rm26iY6urdYf6s8/SufPm0f88BXfNGm0bBCEnRIqqAFSJiAMqxAMLV0ydSgZz7VoygBUVZCB9PmrclJXpqbhlZTRa2dSknUeevtrdrddzHjpETmVpKRnMlhZydMvKyJB/8kl4AGezmf729lIvYHe3jmvH8Uj9froebxuoQRitEVlXR+n6fMC3v03qkkD4lN7GRq0yN368GHkhIUSEaBAQSzQoHQIglZXUYQyQg8ixkW02soclJbQtct15tPN4qQDbT4CcTB55MU6tjRUrmrfz+jUZXREGO/yu8LT3/mAVaf4+ELxcqaqK0i4r0++12UztIL7+hAm6fWGx0L7KSjqelycxvF1EiIS8IJqwi3EUSxD6g0ceDx6kslNZCRx1FPWs22x6fSir4paU0HkWCxluVuxlI2k0lj6fLpOchtHQs+Iun+dykZE3mfT6CECLFRmNsVEUqT9j3F8jkiXPWTnXWBFUVtIo66xZ4nwKCSMO6CDAKBpkxOtNnwAId/RFLpPo6KBtsdadG88zmXRjlkOusFq61UqjobwtFkaHM1JIRRAGI1zO3e6BnUqrVbdXjFEKosHK/sOG0Tto1Jno6KD/u7r62pBo9sbpDI+TzqTTBglC0hgLrdNJH6tMABuyxNM7F4nZTGshhw3r2xBip5HXNPC+3l7dsAHCxZOY4mJdJrlBw9NsOWgzX99qJYewp4fSKC7WjSo2vMkY42w0IgUhAmm65xGNjTTdnpcAxAuLBh06RJ+ODv199uz4OqYirx35f2UlTa1zuaizzOvVwkQ87Y5nZLz8MvD443R+ZSVw0klaDI7Vbtl+ApTfigpgyhS9rT8BO16bZhwREkdUGIxwh4zbDYwdS+2N/uAOHW6T9PdOWK2kL+F203Ikj4eWGzU0aBHEfftoZlVTE+livPwyLQsaPpzWeLO9aWigNINB+p6MDRKEjMGV5O7dtIaPlUaFoUmiU6eUIgP5uc8BM2bQCGRjI30aGkhgqLhYT3dtbqbtfj85rexI8polHhW1WmnKq9lMDaTSUpqG0tpKDaymJjLoPG2MVeFYUKOoiBpVvN6ho0M3tIzbBjLG0RqRu3bR9NqpU8WACxnBpJRMYmRaW1vh8Xjg9Xrhdruzdt10KNgmm0bkeUVFenkBC7NxOgCwdCnw8MOkVgsAI0cCF19Ma7z+7/+Au+6iNenBIJ179NF0zKefUr3f00Pp8tIbh4MasyeeSI3gTZvIZg8UhoX/ut2UhstFNr+lJb7nJQiFwrBhwBlnUBtm0ybSuehPCZpnhHFMz2g4HNTW6ezUwl9uN72LBw/qbRaLXpft8+nt3Ck+bBilYYxNbrWSIu5xx5EAWTJK3KmQKzsupE7GfrtAgCqvhx4iOXaObSiS6gKgp4zE2udykXHkEdCDB6nB0tamHUQejWSjy4Zw0iT6/513qBHEZY5DCAwfTp+yMq2Cu3Qp8MEHOrwMN3I4/ElkA+qkk2gfN9pSUcF94w1SvG1poTxNnpwbQy4UJInYcOkCzAPSoWAbKRoUb9inyGuvWkViJJMmaWE2Y16uvJKczch15w8+CNxxB62JZye2pYVs7vjxwBe/SDZ7506yZeeeS53RHg+tsb/vPsr3hAlkQxsbKZ0JE8jG791L6bH6Lq9zmzUL+N//BV56CfjlL+nYgZb2WK30sdm02EqiIcMEIdPwmsxjj6X2xqmnklP3yivARx/pTqBIgkF6/9vadNgjl4veJ6sVOPNMeufef5/sRkkJtT1YBOz446mD/fBh2s4dSizEaDbrUC68bMlmo7YUQO+x308DBckocAtC2lm2DHj+eS3WAtDL4XDoyiCaTLuQfVhoByAnym4nQxPvWEmkM8lrf3h9JW/jAOWAXrvQ26un05pMtH3CBHI6P/2UpoRwr7dStHg+GKSGBK+hLCoidTa3m4ztFVfo6WG/+hWwYgU5jUcdReft3k098HwcALz7Lhnpqioy5oEAGdbqanJ029t1DC2fjxzGyy8HPv/58Mbfl78cf4OQG5EcD2/SJDLqqYZUEIQYiAOaY9KtYMvrxJO5NodEKysjmxMM6jwZ8xJ5jcZGYPlychBdLrKPvb16KUNDA9nIMWPIxh04QE7prFl07kMP0bUrK6kx7PFQGq2tZJuPP546G4uL6cOzV3jqYHMz8OabdDzXMwONoBYV6dBeIoQo5CvBIL0jo0dT5xBA74kxdGFkeLpgkMq0UvS+cZuqtpb+NjXRIJDTqTUn7Hbat28fdXh3d1P7pr1dp8ttModDO6ReL20bO1anxWHrVq0CLrpIZm8JOYYrOo+HphVyweZ1GyUl4RWWkFtYLAeg38bni9/5tFi0YBD/lrw20yjIw9NZ/X69Jshu142vnh7ds9bTQ40YNmTc8+d2kzGtraUy5nRqNVuWEP/00/D8eb00WskNK8Z43NattN5h2DD6AJRmby8dV11N12ShI5Y9j2ZwE2kQAnQfGzdSAy1dIRUEIQayai7HZFLBNtFr86wkt5v+sr0eKC9NTWSfjSOTRtvf3a2nxkam1dQUrq7LGNXEOzp0PEKAOv16e8neBwLU+cfX728mDcN1U09PcloEgpAtgkEq4zYbvSfNzbrDHYgea1MpvZ91K9gJBcjJ7O7uO5uKHUufT6+v5nckmnYGv0O9vXrNKaDf40hlbEHICVzR8ZQXrpSMazlkGkz+wAIRrKKWiFoxnxfPNuPvzf8b5fVZ/Y1Vanl9JyvWulxUbpSiPDocuiOjq4vKW2RjJ57G3s6dlH7k9EWnk67X3d23sQSkx+DmskEqDDnEAc0xuRQfi7y200mdgK2tOj5nPHnhtWDG0UQeleFOxLKy6Gn1p65rNuupg8aQbT091OANBCiPkyfr6w+k/Ml544a5rIAW8hmzWbc7ysvpYzbT+wno8mss8yaT3s9OonFWW11d3wEBgN4vXsrEI6L8jkSLHsDvEK8ZZ/g9jqWMLQhZhSs6lnrmSsnYq8ILp4XcY7XqkCVKhfduDYQx1MlA24y/N//PvdN8TjCoVWo7OujDirV+vxYXslh0UPJgkMpZd3ffxk48jT2eKmac5gLonkhe68mk0+CKGq6QRcTi5ph0KNgaSURJN/LarLbZ0kIdXp2dtB509+7+81JZCcyfr+MmG9eeBYO03WzW15k6VatqrltHa9xcLsozq+s2NdGsk7lz6Rrl5ZRuQwPt7+qiDj9ep8rX4um5/cH1hcmk6wxByEe6usgmfPwxdbDX1ND7yLOvAF32jdoXLheV70BAd+Y0NlLb5dxzaYaVz0fbAgF6hzo7gXHj6DoeD73LFot2QCOn91osugOJ31t+d53OcGXsZNS9BSEtcEXn9dKLwfPGedSLKywhP2ht1b3JbW2JVdCRxpC3dXWFO6FK6UYKoBWR2WByp0QwSAaOhYcaGkhVsbKSRgTLy/UUYb+fnM6mJjp3xw5a68lUVlLjZ9s2GuWM1dibMAE47TRqiB04oP+2tZFseVlZ38YSG1wgNWOb7gapIPSDqOAaEBVcGm1Uimzkvn10zKhRpHS7eHH/Amr33ReugssKmixKVFdHDd/OTlqu0NioG7HDh+ulFgDZ+MWLybG9/nrgySf7thG4M9Dvj+8ZRRLPdF1BKCTMZt1J7fVSe4gxmWifx0NtmuZm7Vw6HKSDcf/9ZAveeIPUdnftovYVz/xiXQ63mzQqLrqI0l62LFwZe/FiYOFC4NlnU7NrySAquIVLxn67pibghhuoYDc2UmHu7U1M3EYYvBjXMnAPtdNJDmZnZ7hY0ahRJCC0fz81klh1zRhbjtcUVVbSiOaiRdRYeeed+BRm9+0jI7phA5VVmw2YNg245x7g9ddJJddocC+4gIz46tWpG9t0NEiFIUsiNlwcUAO5brg0NiamYGvkwQe1mq3HQ43PQ4eA88+PT7jMeO1//5vsj9tNHX9dXfGn1dhIo5pPP02jNkcdRbNRGhpIuZMFSvbsIZsKaJt25pnAt79N343quj/4gW4sS2kVhNjYbPT+d3frjhl+Z1jFFqB30uXSnTp1dcBPfqLf78ZGauu88goJMZaWUqi7Q4eAz3yG2iL8jvLx0ZSxU7FJyZJrOy4kT8Z+Oy6MbjcV/o0bSQbaOJVRKFwS6U02Hss9ajzt1ukkg9fRQdvLy/XC+2OOIWdv3z4SAeK1kjt2kKPY2Ullq7NTT/eurtbpV1bSlC2Phwzhvn1kDL/1rb555PJqt2unuKtLG89Ig/vvf6ff2KbSIBWGLBKGpUBJVLCMSYeSLl+b06qv7yvUFk9alZU0S+Sxx8j55DQcDrLRPHXW6eyraLt6Ndn7CRP0fT35JM3IsVr1CAxPBxSEwUQqI/LsWPb2UvvH79dtK15S5fPpdhZADqjDod/BFSvC3+9t28Lf4REjqE3T2hrufALRlbHTqe4tCEkTTe69vT18eoBQ2LDhZGcvsoEQqaTG//NCdhan4vhx9fV07N695HRareSMnXACbX/nHeDkk8nBXLdOL4Znw8uKiZ2d1LjZu5ecydJSMoSjR9PfjRupfA5kPAEyvpHhCGIdnw5jm2yDVBDiRNaADgLSKVyWjrSipREIaLHBrq7YirY7d4anc+BAeH0hqrXCYCUdZZsFGY1tLV5OFXkciwwB9N0oopiqHRAxRSFviCb3nkhoD6GwiCYmNVDjgcsCrxdl1TZWcCsq0qEBWMGN45Ny48Zm08aXDStPy2Wn2Kj8FssYJmo8xdgKBYo4oIOAdAqXpSOtaGk4nXpdP89SAfoq2o4ZE57O8OHhzqe0GYTBSjrKtlFgi/83jnoaj+P2FUDfjSKKqdoBEVMU8oZocu/FxdKbOViJNj1qoMYDlwWWGOe4VdxD3tGhQwNwDKuuLq2IqxRtZ+PLhpWnn7D0vnENZSxjmKjxFGMrFCjigA4C0ilclo60ItNobqY1nw4HrbuvqdHKm36/Dmh/2ml6+i2n86UvkQ1lex4ZvksQCp1osTwTxRi2zmKhd4YjAnR2hgs+Mn4/DQSZTDQzjFVrgdTtQLLni2KukHaiyb3X1OhYRULhw2tz2HGM7FwwGlfjlBB2HHmE0mymtQZHjgCHD9N60KYmvRZyzx4StDjtNFqL0NZGQhksaMXy411dtI1VEsvKyMi2tfU1hkC40UvUeGZSuVYMspBBZA3oIGHRIvq7cqUO03D++Xp7ttNatIjsulGsbcQIrYLLNrK3lz51dSTyxiOhAH3v7iandONGUcoXBifpHNXnae4uF3W+t7dT5w23ycxmPRjQ2UnHTZoE/Nd/9X2/U7UDiZwvwotCRoksjLW1NL1m+/bc5ktID9z7BlDPGwcwZuPHRpbjSjHG9aI9PeRUslPKTqXNRv9/8gmVl1Gj9FrQZcuoPPHaU0CPdgLUs15XB1x9NY26r1mjjeE551C6N97Y1+glanzT2QAExCALWUFUcA0MBvXEdAqXpZrWgw9GV9OdN49GXp57jjoFR4ygeiFStM2oovnppyRixGroxiU8waDeLgiDEZMJOP540sPYupXWRrM6P6CjSZSX09Ifh4PaUxUVJCbk99M7WFFB7YnGRmDmTOCnP+0rKBRJqnYgnvPTqZg7GOz4UCXjvx0XxltvBZ55hkakZEpN4WK1kkNUUkK/o8cDLFhAAkFTp5KK4aOPUiOkvp7C8OzfTyOS3d0kOATomG6BAKVZVkbGtKuL/m9ro/VBJ52kGzKVlZQWN3C8XuDdd8lYT51KRphjzC5apNVrjeEGBjJ6iRrfdDUAcyVhLhQ8ooI7hEmncFkqafWnprtqFf2dPDm20i5/r6khJ7WpiYTjOjrIGXW56DurnRvX9ieD2SztECF/UYo6XcaNA7ZsoTZNaSl9eBZBdze1d2praeZYezv939VFbasRI+hvaSl1+OzYQW2seBWyk2Wg80UxV8galZVUmbzzTrhal1A4GCtrjtepFBm+ri6aIvvZz9L+Tz+l0Cc1NWRo2ttpJFIpajRYLHQcj5LabNRz191NRtTnoylclZW0zeEgoxkIAG+9RTGpRo+mNBwOPfW3vp4aKUB09dp4jV6ixjcdDUAxyEKWkDWgQkboT5ituZk+/Ym2Gc8PBKheKSrSKp+8jMM4uyYVpB0i5DttbdRh390dLrRonPnFS6AAel98PtpmsYRHnXC7+6pO5woRcRSyys6dfYPkCoUJT71lIwdoOe9Iw8LGk4WEeNoUNyZ6e/XU3WBQiwoZRYe4p5t7vXkKCqDVcE2mgdVu89no5XPehEGFOKBCRuhPmK28nD79ibYZz3c6yd53dGiVz8g1bakigohCvlNaSjPDbLZwoUVebhQM6ugBAL0vxcW0jfUwmNbWvqrTuUJEHIWsMmaMHp0Sw1/YsMw3GzlAy3lHGhY2nuw4Gkc/uWHR06MbFhxWxRh2hdc/sgKusVePR2KVGljtNp+NXj7nTRhUiAMqZIT+hNnmzaNPf6JtxvN5WmFjI3VcV1eT/efOz1TiiXNdIx3hQj5jsQATJ1L7qKyMRjA7OvQSIx71rKzUs9BaW2lpUnk57WtupnMaGmgwIFJ1OldkUsRREPowYQIwd254b41QOHBlzZ0HHCKFe9VYzjvSsBQXU+OBHdDSUi0gYbXquFQ2G02nbW7WDY62NhoBNJsprdZWMqC8NpKNsctFn2hqt0ZDls9GL5/zJgwqZA2okDHiEWYz7ps3D5g+nURWmptpvdu8ebRmVCm9hq2sjNbBNTVR45un57JonRGOb+j3kx011l3FxXoaYyBAdY+IGQn5hsMBHH00lX2fj0ITmUzAvfcCBw9SmS4pAY46it6Z7dvJwXS5qF12ySUUPeDtt4F9+yidM88Ebr89/DrR9CuM24D0CZxFkm4RR0GISWMj8M1vUmFesaLvSI+QfpxOqlxZlTZVXC4dn41HL+vryWhMnw6sXk2/69ixJEi0di2t5ZwyhdY17tlD+eF8cUdERQWp1gaD1CiorKSeP44NajROCxYAzz5LZWjvXmpoXH015Wf16oENWT4bvXzOmzBoEBVcA6KemBn6E2ZrbCQhuTffpDpi0yZdN7hc5FjylN2qKhKhmzWL7Pubb9JoDk8z7O4mW3n4MNUno0fTevniYuDVV4HNm+laHPbLbKbGPau1A6mNphqXjwAyqioQJSU0Enn4sI57Gw9uN7WVTjiBOlr27yfnsbpad8AEApReVRUN6px0ErWx7roLeO89Ks/FxaSVMWoUbeNQd6yqD/RV3J85U7ejWlroPQPoPsrKMqfInw4RR7HjhUtGfzsOLfHGG8D771NYjfZ2MdSZxrhGIBKOCRVtu8dDRtBqJaPAPcjGdTcOBzmen/88hdVZv54M2ZEj2rEsKiIj6PGQM+lyAR98oHvjamp0b3RzM41uFheTPPisWcDixSQ/HmmcuDytWEHnlZdTj/miRWTo4zVk6QxdkG7yOW9CXpKIDRcH1IA0XHIDK363tJCT6PdT/WK1Up3jdFLDur5eq583NmqF8NWrqUHudlMdZ7WSQ1lXR3UJQPvWrdPhW4xxqAFRwRUyi8lEbaXOzvjbuzYblcuxY2lkc/NmHeezqUnrbtjttG3UKOCYY2jfJ5/oqbqtrTrywDnn9FXVB/oq7q9eTdtnzaJOHb72Mcfo9zBfFfnFjhcuGf3tjBXN2rW6chDyE5uNjKXFQsazt5caB+yEFhVRZW+3k8NYWUn79+zRIkK8RpR7AS0WimFltZLBBGj0srub1vkYGxAjR5LRjGXoJFSJIPQhERsuCyCEnMKK3x4Pfe/pobrC6aQOxqIiqkP27qUlG243qZ97PGT3g0G9Nv7IEercHDaMjmtqoqUYbW3UAO/s1AIuQPjyH+mGETKJcZQ9XjjO56FDJNxZWUnl+/BhanPx2mV+Xw4fpunoGzbo9hbPIACo3W210jZW2F+xgj78f1ERvWd+P32sVprmW1lJneAHD9L+mhp6bxsb0/ygBCETGCuaQ4dSj9slZB6eqsSGUKm+olFOJx3T1ES/8aFD5KyazXpKksVChpHXb3Jlz+s1uUe6oSG8AeH1UnmJZugiQ5UYjaoYRkGIC3FAhZzCit8sNAdQvWEyaW0AgPaxEJ1R/ZxDtLhcVE+xU1lUpKcnBoN6phUL3xlHQfl/QcgkiYQM4nLMU8t9vvAwRFyOOV2Hg45rb6e/LPIJ6GgCwaCOuw7EDolkjCbQ0qLXWBcV6UgEosgvFBTGisbnk+kuhQJP0TXGl2JYvTYYpE9nJxk7o+PJf3n9KY+K8lQSXnPDaoZsWNnY2WzRDZ2EKhGElBEHVMgprPjNobYAqie43uBRI6dTh+Iyqp9ziBa/n+oKrqN46i4LHZaUaKcW0HUQI2r8QqZJJGQQl2OOAlBcHB6GyLiO1GzWo/slJfSXwxwCOpqA2UwzyphYIZGM0QTKynQIJBabdDpFkV8oMIwVTXGxqN8WCjzqGU2xmKeAmM1a0MFm08JEvb36r8VCjimPpCpF37knOxjUhhLQxq67O7qhk1AlgpAyYoWFnMKK314vfbdaqQMxEKCGLusOjBxJU2kj1c/NZt3pWFVFDW8OQVFRQdMFS0tJdIVHiXgUytihKg6okEl4TXMi2GxUXmtqKHRhZBgiHgDg96W6mtpM06bRtoYGen+amym9sjJqmw0UEskYTaCnR4dAamqi721tosgvFBjGiqamJv3qWUL64UXwbAijCRYFAnQMi+TU1Oi4VEZH1G6nbTU1urLndQbcGz1sWHgDgtd1RjN0EqpEEFJGwrAIADIvdtZfOAdW4nzjDZryt3ev1g2oqKAO664uOuf448kBXbuWRIUaGqhxPn481RkbN9K1rFZqKJ92GjWkN20iJ3T3bqqfAKpzeI0pj7ryvmThDlVOTxA8Hq2C29sbn9Iyq93W1OhYnaNH62VRnZ267dTTQ+/J1KkkMnTmmcAtt9BaaRZ6nDeP3p3t23UnvVFVf9s2ev8cDnqfjNEESkro3eHZbb29osgvFCDjxtGL0txML2RXV2KqYEJy9Kfwx6OYkb+BxUK9XSNG0G+0ezcZOpdLp8ejmJMmkdx9Tw+wZg1NseZeN4uFOhuKi+nco4+msCzbtpFYUXc39W6PG0dp7tlDvXcVFWQI58wJN3TGhgz33G3aFN2o5gpRrhUKBHFAhzisJG4MwZDOEAvG9PsL57BoEV3f56M6hzs7OUzL7t1UV6xYAdx9N9U7w4dTvXH88XT8H/4A7Nql67r9+8lRtdm0sB3Xc9zAnzED+MY3qKH93nvUJtm9m5zZtjbtkNrtFGfxgguAJ5+kWKWsqGvEuA610OFnJvTP6NFUFo4cCd9eXa0Vm/fv1wKLHHquo6PvVNqyMmo38Mi/10sdMp/7HIVXueceHaYIoHLvdJJmhjGU3R/+QJ9XXyXhoA8/JGd03DgaIf3a16jdtW8fRRnYsIHKvsVC93LzzaQiffbZwCOPkGPq81H+p07NTAgWQcgIXMjXraNem0QWYwupwxUyjzwanz3vM5vJQXQ49HRbq1WvySwpIYM6bhx9DwR0g6W8nAzcJ59QLx9A24qLSUK8tJQMZmcn9TjPnk1y3v/8JxnHPXvob20tpT9rFjB/PjVE2IHrryFTVERGkcO15IpMN+YEIc2IAzrEWbZMK4nX11OD9+mnaV86lMSN6ft85CCaTFo1na8FaDXOsWPJIVy3juogl4tsa3c3NczZody3j0aXli6ldFtaone0GrUGAL2Grq1NxzmsqCBHgh1dFl9hQSSA6rc//Ynywg5CNAaLvoU4n/Gxa5f+zmI/SlFbqLWV2ieAHmzp6Yk+Qs4iQT4fpVNVRY7lgQPAAw9Qm6K5Obx8KUXlcfduap8Z36dVq6gsBwI6lB5Pa6+pofd78WLg3Xdpe1kZHbt2LW1//XV6J999l44fMYLsw4oV1LaRSANCQcCFPBiMPwivkH76c/pZKTAQIEMDkFN4+DA1FioqyGhu305GdeJEis22ahUZLI+HfttAQE+f7e0Fduwgo8XHe73An/9MvYK8LsLnI+N84AA5rqtWkTN6/PE6fwM1ZPicXBrFTDfmBCHNDJKxGiEZMq0kbky/tDR2OIfIUBDBoL42N7BNpnD9AJeLjmtooGO93vjbFlzvdHVRh+uGDfSX88hrRXmKLoe84JAYXV3SjhGiY7HoadiA7sRgLQ3jdG8j3MkRDNI5drtWd+aQQi0t+jzjmmWlyKlsaKBj+X3iJUxut44s0NpKf1euBF5+mcq+00nH2u301+mk7S+/LJEGhAJn9WoqzHZ7uAy6kJ8EgzRK3dmp1Qa7u8kxdLlomklpKRkyn4/+lpbq2FRK6dAsdjttLymh44JBOratjYxpcbEWpSotJcPc2Ng39Eq8DZlcGkUJCyMUIOKADmEyrSRuTJ/DpUQL5xAZCoJDrvBoEo9esqq6MRyYz5e4Q2hUZmc19mBQ59HoILDInlHVXWZwCbGIFt6HowQA0XU0GOMMNbNZrxc1m6lM9ieapRS9C3a7fp9sNv3OAfq9s9vpvVy7Nlx9muF239q1EmlAKHA2b9aLlwfL1JTBTmcnGTxjjzOHSGGD1dWlpyk5ndpA9vZSD3Jvr55u4nLpBocxNhuHd7Fa9VQnjvFmNHDxNmRyaRQlLIxQgIgDOoTJtJK4MX0OlxItnENkKAgOucKhI3hEiR1GYyO+uJjSSqRj26jMzjoGZrPOo3HaLYcZM6q6JxJOQxhaRAvvw1ECgOix1Bnj6GYwqEdTee2ocW1xtBFUFuvi96m7W79zgH7vurrovZwxIzz+LsNtsBkzJNKAUOBMnqxDcwyWxfmDHYeDDJ4xZieHSGGDZbfrGFG8xsBsJqPZ2Ul/eaqT368bHMbYbBzexbgmgnvfjAYu3oZMLo2ihIURChCxyEOYTCuJG9Nva4sdziEyFITZrK/NIivcCcr1kd9Pxw0bRsd6PPE7odwxardTXTVtGv3lPHJMRZ7ayCEvOCRGog6vMHSIVLm123U0AO7MN3ZwMOxQms3aSeT4thxSqKwsuo4HTxUfNoyO5ffJ66X3orVVRxbgabizZ5Ow0bRp1H7zeumaXi/9P20a7ZdIA0JBM2sWFeaurvBA0EJ+YhQj4p4wm42mdPj9tDC+rY0MWXEx/W1r07GpeBTTbKb/q6tpBJBV2traaMpsWRlNGbHZ6G9bGxnmysq+oVfibcjk0ihKWBihABERoiEOK4avXElCJokoicej9m1Mv6SEhH4AqjuihXPgfIwZQ7b9k0/0sg9jR6fdTiq4paXAZz5DddaDD5LmQGQbw2LRYcB45o3dTvmeORO46SYSPFqxgpzP0lLK6759VHcFg3TN+nrgd78D/vUv4N//pnuPNasr2lTL/qZf5iMcfq2QieawpTv9iROpnBw4oMseRwcwm0lkkWeCMTYbtRGM5cdspvZSdbVWyG1ro3I3axbw6acUWsVY7sxmepc4wsSsWcD06TQCCgAvvkjH+3zkjFZWAp/9rH7nHn5Yq+B6vZSvk0+m7UB0+zBvHl2jsTH1do1EDBAyDhfy9etFiCjbcKXHPcc8vSPSIFut5BSWlJCxtFjI8FVWUiXEvWejRulevU2baP+ZZ5LB/OQTStvppOu4XGSc3W76zdmAXXMNOacPP0zb2MkdNSp66BVAy/S/+SYdO1BDJpuwEZ03j/5PpjEnCDnApFQhNYkzS2trKzweD7xeL9xud66zk1USaQgmo/bdXxzQyOP27yc7zzE9u7r0VNy2Nqp/7HYSojtyhOqqUaPI1o4aBTz1FPD++9RpqhTVa/X1utHf0UH7ioqovikpoeM+/JDCg7Fa+6hRdK87d+o8FBeTY9HdTeExWC8hmmKszUajUl4vPadksFj0VOFCdQZNJnKqiopo2U5PD90LT7Hm5TrG47mdkso1U7VsZjOVDbudyl1XV3iaVivtKy4m9f0jR6i88nphY/i54cOpPGzeTI5qtN+SRzKrqqjszZkDnHgi8Otf07vAIYGsVhKK/MxngK98hcrs6tVUTtvbqbwffbQOndLSQnkzmSgfw4b1fV9Xr6a8TZ5MTmwkke9lqir/mYwYMJTteKGTsd8uEABuuw14/HEy/p2deoE1h+iQplB88LoCt5uMDE9J4rUCbNyKi2nK0OmnkxjQ66/Ty87TRCwWvTbX49GVdFER9QJ/7nNUqTc3A8uXk5HatUvHRzOZtNMaDFJ+XC4tHtHZST1xPCUkEKBGh8tFxmfFChohdDrJmEaGXmGMxoqDjJ9+OuWN08xF71ksI2q8V+nVE7JMIjZcHFAD0nCJjwcf1GrfrLR56BDZ43SofUemv3o1sGVLuPL6+vVUD4waRefwWlIemWlpIQeRY1laLHTMpElU32zeTPXXMcdQ3bl+vXaI+JzOTnI6AHI0TCY9HZfDjx0+rNfYRSNVR4gFkFJ1yHKN3U5tFZ7+zDoQxhFKfk75dJ+siMxOpbGjgZcQWf//eSRcfnipGa+1HDaMjtu1Kz7BrIoKKvfl5eRAfvqpflb8nBwO4NhjdXlvbqYRey67ZjMNGkQr7/X1yb2v6XzvM2lDxI4XLhn77R58kALj8vSBw4d1j1I+GZxCwKjGZ7NpBVnuibVayUCVlekKrL2dKlqnU4c9Aej84mK9TrOqinrBjEYKIGPBlbrPp+W9eUS7pITOCwTCGwvRDEuixifTDZ5kydd8CUOaRGy4rAEVEiKboVs4JIvXS3UZK68bBd1cLrK9FRXUMfr661SPGcNPuFzU3igtpfT37tUq6nv30vo4XuPJna8Oh26fmEyUpjG8RkcH5WWgtkuq3TuDwfkE6Fl2dGhFVu4s52nR/D3fusM6O2OrzxqVkbk9ZZxxxqF+zGY96hnP7+jzUZltbCTnkx1dnkrOYpANDVTeHQ49Q43L+5Ej1CaLLO/JRg1I53svEQOErNLYSKNdfj9VFtxjyApfQmIYDbVS9Fx5JJSNHyvEFhdThc0CPg6H7hEGyHksKtJy9MXF0WO0sYPF8ddYpZB7Bz0eMnSNjbqxEAz2NSyJGp98NVb5mi9BSABxQIWEyGboFkCrnrvdWnmdQ7RwnQWQ/Q0GqfGuVHj4CaN6Oyuxs4p6IEDncEetUa3UqEoaGV6D4zVm2mHKN4csFYxtFmN4G+O+fCZaWzVa2BXj4EBvr55yzPsHgqck82i7UZEZ0M+ts1PPfDOWdw7b4nL1Le/JRg1I53svEQOErNLURFMEjKqnQvJEGrHINRTGOFI85YXl5iOnf7ChM1ay0WK0cUwpTpcdWTaQFos2dtxYYHlvo2FJ1Pjkq7HK13wJQgKIAyokRDZDtwBa9by1VSuvc+cn12kAdWrzGk0eseSObqN6Oyux81pQXqfH9ZgxXqNxemhkeA0WQsp0OJbBFO7F6EgZw9sY9+Uz0aI4RAu7wkukjMucuJzGc5/cuWK3R3fS+bk5HORkclnk8s4jyjzjzVjek40akM73XiIGCFmlooLmsxvjPgrJE2nE2GAZ93McKZ4Kwr3FkfLxbOiMlWy0GG0cU4rT7ezUioT8u7Kx48YCLyY3GpZEjU++Gqt8zZcgJIA4oEJCZDN0C4dk8Xho5JOV14221e8nm9vURLN25s6lzk9j+Am/n0Rw2too/ZEjtSjSyJE0bZFFjjo6tMYBO5jcKWsUjikqorwMFFouVceKr1/oIezsdnpm7CTxKB2vmeTv+eaIOhyx428aY8M6HHo//2Yc6icY1CJE8fyOvCSqshI46ijdvurt1WtIWeBq7lwqqxxehct7VRV1hEeW92SjBqTzvZeIAUJWqawkYRaXiyoLnirAIkRCYhgNNavNsogTGz/u7fL59KL2QEA7jjwSarHQsdyb7PNFj9HGMaU4/hpPg7LZ6JpeLxm6ykrdWDCb+xqWRI1PvhqrfM2XICSAdAUKCZNK6Jb+2LqV1Gu5vbBxow7JMn481Vlbt9L++fMpvMWBA3RufT0p7S9cCDz7LPDGG1RXtbRQ/TdxItWHvb1Ux40eTfubm6meOuEEqsNY2dZiAcaOpXvbsoXqO6Wo7XLMMbR/zx76n2f/RE7RdLnIgdi3L7klGTyay05PLLXdfKeigjoAWlvpwxoVPKOK79Fq1ffI01iTmZpr1MhIhaIiKidOJ63F9Pv1Pu7YB+geeIClu1uLLLlc9CktpbZRXR3w8cfURoilaGyzUf5LS4HTTqPPL35B7wXHE3U4qLwuWgSceiqJdK1dS2WwsZHKzdFHU3utt5ccTw7Lwm2/ZN7X/t77xkZS4wXoXR2o/ZMpGyIIUZk+nZRLV66kBdJOp+796u7W892FgWEVtOpq7QwCerqSz0fGqqODjN5559Hz/ec/qYeMnU2HQ0+JrqkhRUEOwL1rlw6H0tgIbN9OsdLcbr3G1GolI8fplJRQOnV1ZKw3bSJjePLJVJm//DIdP306rTP98EM9Ytif8TGGYGloIOOWD8ZKjKhQ4IgKrgFRT0yMdMXwa2oCfvAD4IUXqOPTbCY19MsvB845h767XMB991Ed0NND15s6FZgyheqfyEYv543hPHJd9vvfU33U1KTFZDweqtfa28kR4jrNbqfzeF1dVRW1VXg0z+Gg45qbqX7iWV6lpdQZecopJBazciXdn/GNY9V5dspY9MZu1zOWenqoPmXl2Gi4XFTXWiyU10gHh2dDWSx0nbo6Ov7AAXIKOf1kMU5XNpvpnioq6HfZsYMc+/5iprIz11+oGZtNT8G2WOhZc7zM/uB77urq68CbzdHzVV6up63GayHNZnI058+ne//0U+oA2bqVylRPD32Ki6k9NHkyMGEC8PbbwLvvUlnk/JlMOjydxaLbypWVwBe/SCOqH32k1fdZzfn99+lveTm1txsbgSeeoGcF0KjpRRcBV1+dfLgT43vvcgFLl1JIvT17aP+oUcDFF1OH0EDXyEQcULHjhUvaf7umJuCGGyiALgeSZtVWDvRciL168VBVRQ5WsvG7Kiqokj3qKGDGDHJynnqKXlpjPE/uFauupmtt2xYu9FRaSo5jT48WByot1TGsamro5Z8xgwISv/uuFm2orSUDf/AgnTdsGLBgATlaPJLNU6I4TtTatWQIGxt1Re3367wWF1OP9VFHAdOmAV/7GhnuaMQKwXL55anHi0oXEkxZyCMkDEuSSMMlN1x5JfDYY3rUqKeH7P6wYcBPf0qK4ulUHOfrtbdr5yMyvmNFBXXkskCR3U558/vJkbFYqG40mUhhlx1FntVVVKSnTg4bRnXV1q16ymakE8qaCiwK6HDo2JPAwEq4PJI4kDPGTiqr1/MMqHS0wYzOHI/+pdL+iYSXBw0fTul6vfk3aGGxkJM8ejTF03z7bfrdg0F6PhzWZdQochorK6kjZP9+/RtETvMFqExUV+vfrrKS0ud3YfVqOi5yG7cVy8poPy+r+s530hcy6Q9/oM4X1sPgWQfpukaiiB0vXNL+2115JfDcc/QCuN3US9LQoHt1ImWuhXBGj6ZKGNBhbMrKyBltaaHKa8QIqkQOHw6fhsuVEY9YGtcrWK1ahOGkk8ghfP55SnPECPqtdu+mNIuKyFkE+jdg3EjgmFTt7WQA+bpcQXFv8+TJlFZ/jQgJdSIICSFhWISCYetWGh0EqFPU6aS/xcVUFy1fTsekS3Gcr2dcgsKhLRh2FowhUHjWEWMykZPMdWhnJ33YmQwGKZ89PTTja9++8LAdRnp6tMgf19Xt7Xp/pNJqNNihHIhgUI8GdndT/o3CS6nAyq/s1AYC6XU+eYqrUtQOiNSzSCStTNHbS3lradGDLDzbD6DRSqeT2lUARRhoatLto8jfOXJZlctFabe10XvCYYP8fvoYt7W10bEej/5UVNDvsmJFekImcXQLXubl8ZBz7Pen5xqCkDRbt9LIZ1mZDsbLvTzxBOQVqPJ68kmqiHlhOiv68aL2tjZtmLlXkwNqs/HiNaLcC8fS3CYTTY/p6SFjBZBTaLFox7G7m67VnwHjsCS8GN7lCv99OT3Oc08PGV63O3YjQkKdCEJGEQdUyCk7d9IoI6uFMuzEHT5Mx6RLcZyvZwxZFkm0EBhctxodBF7vx+Hk2MGMdCR6esJHMqNdj9M2dh7Hyl8qcLqRn3TC928cVU31PoydAbweN1/hmLKHD+u2k3Efj3ZzLNmBRp+NokYcN5WfA0B/WUDSuI2nbBsddZ611tycnpBJHN2C0+VrmEzpuYYgJM3OnTosB6ANNvcG5WPg4Xyjp4fWaTQ06Bed1z2wYePezMggyfw3cvpOpBpxIEDGko1Vd3f4b8W9b0BsA8ZhSex2PUWJF80bVfw4j2xA7fbYjQgJdSIIGUUcUCGnjBlDo50cL5Hp7KQ6o7qajkmX4jhfz+jsRRItBAavnzTWsTYb1aE87ZanwBodV4COsdvpe6zrcdqcL+P/6YTTjfykE75/Y4dCqvdhbEc4nfmnlmuEFZWrq+mvcRSYZ/1ZrVpJeaCoEFwmOOoAf3gJktOpOxKM27gzxDgQwO248vL0hExi8SVOl6+hVHquIQhJM2aMDssBaIPNzlA+ym7nG1YrrXkYNky/6Ky8x4bNbtfiQYxRJTdSxj0yHqvTqQWN2LE1/lbGHq5YBozDknR1abVBq7WvA8x5ZAPa1RW7ESGhTgQho4gDKuSUCRMolARAM3kCAfrr89HMqfnz6Zh0KY7z9XhUyBjaguGlIsa602Qix4Hhxn5REeXZ4dDr+9h57uigOrCqikR/jGE7jLATy+HQOjqofmPicRLN5vimpLIDxG2GoiLdNkjVSWRBSV7H6nSGj/6lArcjWHWfFV6TTStT8PKisjL6XevqwhWS29vp/+pq+n/ePGrHsGpv5O9s7JQoKqJZcGVleoothw3iNbfGbaWldCyvl+VwRU4nXTcdIZM4ukVTk75GYyNtS8c1BCFpJkwgGemWlnB1OKDvugshOlVVwJe+RBWxy6UV7pxO+suCQmyYeaprMKjlzHndJzuCnZ06OLFSJABkteqF6s3NeiovK+Z2d/dvwDgsSWsrjXizlD3D6XGeWeihtTV2I0JCnQhCRpEwLELOuf12qquMKrj19aQzwIri6VQc5+stX66F8rj+83i0A2wy0XKP4mKqpzo76bplZbSPO3Dr66kuCgRIYZfXVZaWAmeeCXzmM8CqVbR93z59HosVsaM2YgRdu62N8sP1Z0+PFmuMNmuMhW/Ky6mePXSob8gQ7lTmtsCECaRQu307hZlpadHOMTs98TpqFotuK/BIXXU1PZe6OhI13L27fxVczltXV/TrWix0f0cdRfkvLQXee4/aKsaOgWhp2+2Un5YW7QxyWwnou06Vj/d66TnGq1HCoXu+9S0qJ6tX03Pu7KRr87Tumhpg5kxqQy1YQCFV7ryTIg/wTDZ+jjwSz+3l+noq8zYbsGaNfheuvprOWb1ab7vmGrrmsmW0zArQ4YrSpdS/aBFd4+GH9TVGjyYVXIkGIOSc22+nv2+9RVNJS0rI8HV1kVorT70ZbEJEFguNXDY26nn5iWAy0Qjy9dfrF7m7mySv9+4lJ497THt6wp/r5s1U2XGczpISOp6nvQaD5MxWV9PIakkJ/Q4/+hEZtXfe0b/VUUfpSg3o34Dxtjfe0OsSHA69QN6ogjt6NBlrDvUSCwl1IggZQ1RwDYh6YmrECn0SuT/W9uZmEil0uylUF9B3mUVzM/2NFXYlmhJ5rH2rV9NHKaqr6+tp+4cfUn1XUwMceyw5Ppw3pUiZHiCl92h53bmTHNYpU8gBaWykEGYff6yVdQFg3Dhy0DwefQ1m926qK/fvJ8dkzBjK4/79tFyGO4JHj6Y6kZXuvV5qV73/PuXh6KMpDAxrM7S10TXHjKF8btlC6bGegtNJbYNhw6iePnSIwok4HMCkSZSnQ4coH14vXf+UU0jvY88eUnf1+Si9WbPIwdq2DXjtNeA//6GBiBNPpFAira3Uzmhv18/a66VntXu3nlra0kLP8Ywz6Bpvv03bioro9+Q2TFtbeDtDKUrX7abvHg/9Bjt2UNtnwgTat28fhYwLBEiZdtYs/XtwWSgp0aPSbDF9Pt0u4mgCp55K6UaW69Wr6T4nTKDzOXwKlzmvV/8+/FtyzNBp0+i5RpZ7jr3JZWXKFCrjxrLOx3CIFC6TkaSq5B8tDmiuogOIHS9c0vrbGQtgUxMZvDFjaN/GjfTStLVRwS0ups+GDWSk3G7axy+4y0UvLK/9Ky2liuCYY+iF3rCBesR6euh6HLJjwgRyypqbyYFzucjwd3SQYW1ro+NPPpn2vfgiXWPSJDJCBw/qOKV+v44RVlsLXHghKciuWkVO1/btlMYZZ9DI75YtFKuJFWrb2+naJ5xA97RlC6XvcFDvJ6uH+f008skGkisofo4bN1JabGhLS6NXdmwwP/iAnu1pp9HzN1binB7H56ysJGO5eTOp1M6a1de4AP0blsiGCEDX9Hr1vQCJGaZoaRrJVQgUCcEi5BkShiVJpOGSHBwq6403qN5oaaERucmTqYNxwQLg2WepE5Eb3rNnD7yd02tupnrdaqW6g9PlTkgO02VMo799kdflkCkff0xtAu48dbnIQZg0iero3l463hhTsaxMXy8yLFggQB3GDzxADiF3RLMQYFUVOXAXXED/r15Nz+7gQXKK9u7V4oIWC+XHatWjmzYbbWNdCJ+vb1xMk4muM3Ikned20/V37qR6K5pKLQsYOp3UAT15so5RumULOcRerw4bY7xWpDXhEd6WlvB9RUXAWWdRHvbto221tfSX42eycBPfayylX7cbuO46csTeflsLLA4bRuccOKBD2hhD1/G0VV5qxOXr6KP1721c8sT/x1PGFi2ifBjj2xrDrLA+RmRZWLwY+OxngW98g9q0LGI0bRqNMtbV6bJ1//0UGWH/fj3ifvbZwG9/S8+8v3eSy6sxzF1k/pMNc5eJNBNB7HjhkpbfLlYBPPNM4JZbaDS0vZ16fPhF5Jexqyv2NIxYxAomnGksFnIm7Xbdk8dTc7ze2GsUWPHPOCXVbicD4vHQ+Q0N2gAXFZGBGj6c/t+7lxza3l46r66OpitddJGuWBsaaCTzyBH9bJxO6nn84x/p+8MPUwXJvWN1deRgmkx9DW2mjFW8RDZyuFFisZDzF2lYM02ujawgxEAc0CSRhktycKislhaqm6xWss11ddSg59GQyFBaA23n9Lit4PFQR+rIkdSYPv98un6sMF2x9kVed9UqYP16qk9ZB4HXb/LUVq8XmDiRHJbNm6mOPOYYclBjhQXjGImffKKXuxhDo5WX0/WNcR1376b8NDeHr0M1ws4YiwayQ9WfmmpREc04amrSzt1Ab77FQs97xAjdedzURJ9IReBkYecYoFHUQED/Bomkb7FQHWwMIdfQQHVxeTm1lzo79dRbnmJssdAxPT10XkkJbfN6qeNh1iz6PT7+WP8fTxk7/3wa8eX4tiYTdRAYQ94YBa64LJSX03E7d1K+uN0VCNAgyeuv0zkPPgjcfLO+R+6YsFioQ+PUU8ND4vE7aXx30h1fl8l16Dyx44VLWn67WAWwqYmMcVmZnvbB8bVY+rwQYUGclhYyAokGdeZ1CjwVmcV7eGE6h1NhJT2j7LbdTscMGwZ87nO6Yn3uOe1YGqXkbTYyAqeeGh5XFNA9rtOn9zW0mTJW8RIZY9Tno+tzpcGNnWwZuVwbWUGIgcQBFbIGh8piG+h2U13EUz7tdupwdrvDQ2m53bTd4wnf7vHQdrud0nO5tOAPLx1hZ2jFCvpEC9MVa1/kdYNBqgM5xiTXsbz2rrOT7HppKR23dy85ixUVNFJZWho9LBjHSGxpoXS5Dgd03e33U9uH4zparTokGjsqxvMYo5It6z0MJMjDyqudnfF38HOs8IMH6bkfOqTDuaVLPJJjW7LOBDvWiTq3vJTLGEKOBzR4yrMx+gKv/+QQddwGtdupbVpaSuXM56NyzGI+vIazvzJWU0Pri1esoGtwSLpoUQqMZYE1PrZto/x4PJQfj4fK/4YNNEre2Ejpt7RQB4Exfi5A112+XL+DxneS352VK9MbX5eR0HlCTolVAB0OeoFKS/VUWlZu45AfhUpHh65MkhmJZUPJTmd3t5bQ5tFdNpTck2q368DYTidVjitW6BHUgwd1+rzIn5VvX3iBYotyXFGPR1f0gBZmMBqOTBireIkWYzQY1GtEXC5taLNh5MTICoMEcUCFlOBQWTYb1WGsll5UpOs0DrdlxG7XsayNcIxr7pDm0TCeEsl1pc1GnZHNzdHDdMXaF3ldHl0yhlxRSosl9vZq0T8+tqhI318gED0sGK/9M06hjVSp57TZ6WKRHG4LxRIDMo6iMQM5bLzuMJH2Cbc9+Hmzwm0650xwyDdu13Bek4Hjb/LHYtH5N6bJysa8jcUx+d749+7q0qPFbrf+vYH+y5jHQyOTHN/W2IaLBpcFbuNxrFAjLDq5eTOVrYaGvsfxrDqeJs4h8SLfSZuNjklnfF1GQucJOSVWAWTjwOqr3CPEhqCQRYj43pK5D6PEemTPYqShNFZGxmfHU3J8PvrO03P5OD6Pj29roynDxvAqvBbEYtEVLaANRyaMVbxEizFqbJRYLDr8SzaMnBhZYZAgDqiQEhwqi0cPOUxXRwf9bzLpxryRri7dqDbCbQTj7B6LhRrm7FDY7XRcebmeHmvE6429L/K6PM3RGALDOJ2VhfwCAX1sR4e+P6czelgwjpHI6q7RRsA4be5oLivT0yn5mGghWKKFTRloRNJkojyaE3jjg0GtwMprESNjoaYKq/MWFYU//2Tg+Jv84Q4LLocMRwPgbdwxz/fGv7fdTr+J3a5H83l5TX9lzOvVIk48Qt1fyEEuCyzayCPvRrjTZPJkKlvDhvU9jtfklpTQfm4TRb6T3d10TDrj6zISOk/IKbEKIBsH7kHkHkE2BIkYxnyD7y2Z+4h0KiMrlciK0eis8j4OhF1cTN+rq8OnmPB5fHxpKa0nNQYQ5h7h3l5d0QLacGTCWMVLtBijxkYJr4XtL6ZoJvIjRlYocArY6gr5AIfK4ql9ra00+sIzUrq6SPyutTU8lFZrK23npQu83eul7V1dlJ7fT3UWj1L6/Xq677x59IkWpivWvsjrms1kr9nxYEeZBWscDprZwmKFI0dq4bnaWi2UGBkWjGMklpVRuhwSDdB1vstFdRjHdeQ1elarbkcYz2N4tIwdl3higDoclJbD0dchi0VPD7XVamvpufNMtmjrUpOFY1uyg8vOeKJOKOtqGEPIcWdFcTEdY4w/b+ygZ4ElDgNTXU2/K685drtpJNTjoXMHKmOHDlHYvHnz6Bq8NjNah4GxLPCMtPHjKT9eL+XH66XyP20aLY2qrKT0y8po0MEYPxeg686fr99B4zvJ787s2emNr8tI6Dwhp8QqgJ2d9AK1tWnRFl67YLPpXr9CpKhIVybJONJsKNlR5N47nprDBtnh0D2FXV269ysQoMpx3jwyLlarVpQD9LQSnmpy9tkUW5TXHHC8KzaKlZXhhjZTxipeosUYNZv1WlhW9+svpmgm8iNGVihwCtjqCvmCMfyW30+N9YoKHWbLqDprDKU10HZOj0dRWaV0zJi+4bv6C9M10HXHjqU0jSq4rJAaqYLr85FaKaBHuGKFBeMYiUYVXHYYHQ6qs+rrw1VwS0polMuoggtoFVybjfLAnd4ul54eHI8KLoc/27EjcRXcri5S9QfSq4K7fz9t4/A2qargHjhADv348VoFt7RUj6gbVXCLi7WzyuVr4kTd0c3lg8PRxVvGFi2icmaMb8ud/KyCy862sSxEquB6vXTeySeTaKSxbPl8WgW3s1Or4N5+ux5A4JB4/E5GvjuZCHMnofOEnBKrABpVcB0O2m5UwWUjV6gquOwgJqOCazbr9Zgcd5PXwLAK7ogR9FyMKrgmkw7abVTBnTGDjFI0FVw2UMa4ogCp+rEKbjTDkUvDEi3GKI+kGxs72TJyYmSFQYCo4BoQ9cTUSDUOaKztkSQS6zOR67IATKxwYcbjgfjDbxljMSqlYz8CfeM6GtPfto3W/LW308jX+PE6f0B4HgEd6qy1lTQbAAoJx+HWjM8PoHBte/dSXcqDAnV1uqOXQ+ZF3j9fi+NPcucvQN/37qURt/HjyZniEHwff0wO7DHHAF/9avgzNz6LrVt1mD5eu1pXR+KI69YBb76p2wCTJwPnnaef4datOtwfh6Xj9Dm2Z+TvG0m033uguJb97du6VceM5TB4A5UFoG84vGhw2D1j3Nlo+Yq8t2jppDucnMQBFRIlY3FAjQXQaCSA8IDOTU06tAVAx/n91FPFggYtLeRATZ1KcTgBeqH379eBnjs6yGljh+rAAXLqTCY6b8oUuu6GDbSNg0J//DFd+4wzyED95z9aRW3nTr128rjjgIULoxs5gIzM5s10TZdLK5SVluqgwmwIudJgA8LPhzHGAQWiB+2OVbFu2wa8+ipVLmedFd1AJRpAOJexLxNplGQzPxIHVMgTJAxLkhRiwyVf7U+y+UrmvEw/g3gcz2gNfSDcoY0VQztWvHSuq/tzImLV35HtkYoK7byNH09Ojderr8NtDm6P7N1Lo6bcRmPYgePzIp0ko/M4alR8Mb85jbo6ravAzyoy/njk80zEEYx8Xsa89Zd2sh0P8eYp3mMSJVa5iexIAKI/S6CvQ18IFKIdF4i0/XYDFXKjIQB0T9/EidrosRMHaIO5b59W+VKKHMdjjw2/NhsR/k43Rh+fT8frVIpeqilT6Bh22NgYcm/V5s00hWLSpHCDHHlffM9GI81pRt4/YzT87GQmalRjEa1yipYmkLgTmktynbdcX18Q+iERGy5TcAuUfI1DnGy+kjkv08/AmH5LC7U5ABJ4KSujaxmnC2/eTMe53TTD6cgRagtwmI+KClpHWVtL58+cSe0QDq3x4Yd6CU1xMXDKKTSTae1anXZZGbWFPvMZmsG0bFl4HO8xY2h67d69WoW1ulqLCBq7mzweqr9Y4bapic4xCia5XHpJTlubDufi8ehpv7zmksONtLZq8SiPh6b9Hndc3zjd+/bRlNP16yktngZbVUVpsb4FQNuDQXo+nZ00+2nSJODrX6c0gP7LQiBAs70efphGbtvbKX91dfTbHDlC989pT5xIM8p4anSs3z/V8pmJMhwZM53LzdFH6/WuW7ZQuWHRIp7+e/HFOqrCDTfQbEUW4DrtNJo9JxoXQt5iLPybNvUt5BdcQIZl2TIyikeOaOMD6Ln5LBXNU3SZaP31PB2TF5cbF5nz+sf+JLAdDnK8jhzRU2dZNba3N3zBenExvYAsl11SQj2FvHB81SqqTHiNBk+hNZupR5Bf8kCAXvA336Tr9vbqtYXz5umpnMlW5mxsuXIaNYqu++Uv62m6LS00ostBms1mmubLPV0dHfnTsAFy3+jK9fUFIc3ICKiBQuo5z9c4xMnmK5nzMv0MjOnv3k3TR5UiB7C+nq7FnbUtLeT0Wa30v89HS2h4LaXfT3W8x0MjhfX15NgA9P/bb1NntMVCDo7DQTO6yspoecm+fZQWCxV5vbqdwZ3te/bo2WIuF+WJ1fFjveVOp3aQe3rC14RyJ73FokX+OPybz6eFg6qqqA3D5/JaS26LVVbSM4uM0z13LvDuuzq0Dju+Rj0Q1rI4eFCLDbpclFebjZzb73yHjumvLDz4IK2XbG6mc1tbdf6CQS1oyGmzeFFlJf0+sX7/VMtnJsowp2ksk7zmlKdXt7ToDgruMCkvp2d5ySXAlVdSLPmyMj3FuqUFOPdc4O9/Ty5f2aKQ7LgQTsq/nbHwb96sRQQ8Ht2LBdDL4PWSQUhXEyhS7TWZdFnwZ6C4TWyIWamvp0fHo7TZyKDx+k7uBWTp7u98h6b2PvecFtEB6PiRI8nAnX8+bUu2Mmdjy5UTL0KfPp0qLq5UV60ix6qqivbv2UP3Mn06Gd58adgAuW905fr6ghAHidhwc5byJKSRfI1DnGy+kjkv08/AmH5pKTlAPOPl4EHa5nZrPQuvl/4vL9dxKLnjnIUD2dlhp8Dv122HffvI4SktpW0lJXRuczONvLnd5Ji63drh5dFWYxzvnh76dHRQejZb/+0gXkvZ2dm3s58FFXt7tYPG7SN2NtnRNGpw8DF8fmcnjagZ43SvXk3Ln1hVl8UXjWmz0CDnkWHxIIBGZZcvpxjoscrC1q20n8UKu7t1Gh0dWgHXKEwUDFKe29rot4r2+6daPjNRhjlNbp9wuXG5qJPA5aLy1N1N9+By0Xe3m57zihX027z1Fjmfw4ZRvnjU96239PpiQcgrjIW/sZEMWkmJDg/icpFBbWoiB66/nrlkiAw2nAzxOK68n3sPOSRKY6MWV2J5bauVjJzNRgYsEACefBJ4/XUyAMEgpePx0N8jR+jZrFjRv1HtrzJnY1tRQenyVJu2Nrqux0PXZvEhl0sHKDamEwzmR8OG85PLRleury8IGUAc0AIkX+MQJ5uvZM7L9DMwph8IUP1YVESfri4dJ5Jnb/F+dp6MMb05pih3bLOOBTs9hw/TeQ4HtRd6e6nNwCHGfD4dr7uoiM43ihcC4bO8envpf05rIHhgwBgOju+B4ZFQpXSaHPqF20AMp8Vxx1m7wxine/Nmfc/G2W/GGKfsjBqdeW5rWa06vurhw+Gd7Ywxhnlzs06fowFEG2jgtPk6vb1akTfy90+1fGaiDHOaHFKIyw0LXvJ0a75PLiP83HnJWSBATqkRdlKN+iSCkDcYC38gQNuMhZyNVzDYV8I7XaTLoY3HCeWA1WxAOaQMGzV+yblCYWNw4AA5iE6nNoaAngajFBmC/oxqf5U5G1u+HkDfe3vpuvz7cM8iS8x3dND/FoveH881s0GuG125vr4gZIBB54DedNNNMJlMMJlMuO2223KdnYyQr3GIk81XMudl+hkY03c6yXnq6KCP3U7burror9ms93MnrrEjm+t3dsqcThpNYuesulrPmurpofqX62uLRY/UAfTX6dTLe7jtwaOHAJ3DsUcHig8K6OmZkY6ncdSRnT1uHwC6/cajmIxxFhk72fy8+LeZPFnfs9HpNDqjStExxlFcdiK5ncRrXMvLY5eFMWNoP6dvsejzI2OOctp8HYuFfqtov3+q5TMTZZjT7O7WeQZ029QYb5VHyzkMDUDPafJkuj9WNmZaW2l7pDCVIDA5rX+NhZ/XxBkLORsvjjMVj3FMlEQDGCebDhtIY9Bo7mUyTkExVihsDIYPp1FHjgvGPaS8wN9kIkPQn1HtrzJnY8vXA3SPKk+5cDrpo5SeXlNUpDsJeH8818wGuW505fr6gpABBpUD+tFHH+GOO+7IdTYyTr7GIU42X8mcl+lnYEy/rY3WIjY10UyX2lra1tpKwiydndQR2dpKnb+RTlNHh3Z+zGZaZsNLdlwuqvPr6miks62NtnEUgPJymv7Y2kpTJ1tbKW9lZfRpbQ2P480d/kVFep1mf20ZbgPw6GvkaCBAbQPupObOdXa02Ukzxj83dsADWoDJGKd71iyKC8+jp0atDk6bZ4dxHhm/X0cmKC0F5s8n3YxYZWHCBNpvXCLFaRQVUf44D7zdbKY8l5bSbxXt90+1fGaiDHOaLMTJ5cbvp2VWfj+VJ5uN7oEHJNi5nDePfpvTTtPCSx0d9LelhbYXihqukF1yXv8aC39lJRm09nZ6ofmlZ6XXri7q2UuXwwj07bFLBuPUmYGuEwjo3j9ebN/ZqVV2eYSUp+Y0NdFL/qUv0QL8tja9zsHr1Wsxu7rIEPRnVPurzNnYNjVRuixYUFpK1/V66dojR9I5fj/1lhkFCFj5Lh8aNpyfXDa6cn19QcgAg0YFVymFq666CjabDaeeeipeffXVXGcpo+RrHOJk85XMeZl+Bsb0i4t1zMaSEnLGzj8/XAXX76dG+pgx1PbhqaGdnbqTsqaG0urtBa6+WqvgTphAx3m9ejnMl7+sVXB52m5FBaVvVMHlpTSTJ2sV3H37qG1isVCkgB07oqvgVlVRXgdSwbVYyEnhEcuRI7UKrtdLbZyKCq2CC1CbwuOhPEWL0/3ww1oFlzvhY6ngjhkTWwXXmGassrBoET1XVsG12xNTwY31+6daPjNRho0x07lMVlSEq+B+/DFdj2fgsQoun3v77fT3rbdoxp7TSQJEvF0QjORN/Wss/O3tWgWXC3mkCm5nZ36q4BrXC/Sngmu1kuGPVMEtKiKHj1Vw29rCX/IFCyjNN98kZ6a3lyqnWbPCVXCB5CpzNrZcOY0e3VcFt6SE8t7QQMe3tdH/Rx1F5+RTwwbIfaMr19cXhDQzaFRw77nnHlx55ZW4/fbbsXnzZjz44IO49dZbcdNNN8WdRiGqJ+ZrSCiJA0p/JQ5o3zxHInFAJQ5oOilEO17opKP+BSQOqMQBjbiXfGvYALnPW66vLwj9kIgNHxQO6OHDhzFp0iTU1tZiw4YNuPLKK4eMAzrUiLS9hdJAjlVnRNueSv2SLsc/0nkBorfjYj3//u63v/ZQpEMbK49A//lN9dkZOwL4/3yq76UNEhux49klXfUvkKHfLp6XJZoha2yk0bpt22i9ADuKbAgje9dWr6ZPbS3w2c/2b+ijOYvRevQGyn8iFUsiz4opFAMjBlEQ8oJEbPigmIJ73XXXoampCU888QRsRilvYdAQGYPZaqVZKAcOaGGe006jKYL5tB4/Vuxonrpr3D5zpp6Sm2ic6WRjVEeeV1REU1M7O4FPPqE6vbtbz1LjeO5f/CKNdL7zDu3j53/LLcArr0S/3yeeoPjkxtjkHBe9qYm+b9ighXKmTaNZXBUVOo+8LhGgtYwlJeH5bWmhtbGTJ+spv4k8u8ZG4MMPqU3I8TM9Hhrk4GU4uYz7LbHIhXwjb+vfeF6WpibghhtorjkbslNOIeN0553hSlx2e7g0tNVKx40dS+sI9uzRi+6rqijdr3893NCzgfX59HRZDsbb0aHXQNhswPHHAwsX6tFZY/6B+CuWRCqCN96g6yVjSHOBGERBKFgK3gFdsWIFHn74YVx88cWYM2dOrrMjZIhly3QM5vp6+r5jBzkn9fXUTnjuOTr273/PbV6NRObb66X///MfHY+bt//lL3TOrFnhxwIDx5mOdZ2Bzo08b9UqWh/o8VA7qL2d8snLnHp6yPG/7Tb6PmIEiSry89+6Va91jbzfdeuovVdWRtfevZvu2W4H7r8fePddHZIuEKD/Fy8GLrtM59Hno/OUouVQhw9Tft1uvSSKRy9bWhJ/dp9+Su1IXjrV2Un35nCQwxvv75Epkv2dBSET5HX9G8/LcsMNZLjKyrQhe+IJMnyRYVq6umidJgd2tljIGH3yCaXNRrK3l3rJbruNerOMhp4NLKAV0ZSiqb3c81ZVpQ3gRx/RutXI/APxVyyJVAQcJzUZQ5oLxCAKQsFS0Cq4HR0duPrqq+HxePCb3/wm4fM7OzvR2toa9hHyj8gYzD4fOR6sjmqxkHNQVkYd2Vu35jrHRKzY0R4P5dPt1ttLS0kwxu+n74nEmU42RnXkecEgtYlKSuj52u3hehgspuhw0HFK0Sy0oiJ6/qWlNIJpt/e939dfp2VMlZU6NjnHRX/0UXJO2flk8SKnk7Y/+SSlU1oKHDyoZ1nt3UttpNJSahe6XJQPt1svw0rk2Vmt1A50uci59fm0UvG+fbQ/l3G/JRa5kE+kWv8CGayD43lZtm4lQ1xWRoajqIgMWnd3uOGLVKVlwSKXi0YteZ2nxUJGguNkeb3ASy9pQ88GlhViHQ4yUka1NVbBLSmhv62ttM+Y/xUr6BN5b2433Y/Hk1xF4HbT9dzuxA1pLhCDKAgFTUE7oLfddhs+/fRT/PznP0dNTU3C5//yl7+Ex+MJfUaNGpWBXAqpEhmDuaVFhxLr7dV1t9tNDs3OnbnKaTixYkdzHG67XW/jmNwmk46/DcQXZzrZGNWR5wUC1MnPodo4Xju3vzgWKIs6GttNAP0e0QQfOQJCb2/f2OSA7vyPnDHF6R04oEdFu7rovKIiHavc6QyPeVpUpJWEE3l2XK44sgG3QTk+e0tLbuN+SyxyIZ9Itf4FMlgHx/Oy7NxJBsQ4rZYNHxMtLArvN6rdRgZRttnouPZ2bejZgFmtepqt8RyLhb739OiYXUpRb6Ax/83N9Im8N7udrhE5DTreisBu1wYWSMyQ5gIxiIJQ0BSsA8oxx6ZPn45rrrkmqTRuvPFGeL3e0GcPL04T8orIGMxlZdqJs1h0fcuxDCOVW3NFrNjR7Gx1deltPJqrVLgjFk+c6WRjVEee53RSG4RjQ3K8dm4f8WizzaZFHI1tHW77RLbXurvJqbVY+sYmB0jwkX9PI5ze8OGUR85fR4de9+t00nHcpuN0OaxcIs+OyxXHJuX2IMdnLyvLbdxviUUu5AvpqH+BDNbB8bwsY8aQATGOurLhY9jQGeH9waDu9eJj2Ph1d9NxJSXa0LMB494y7r3jc7jHy2qlc4NB+r+6Ojz/5eX0iby3ri7daxfrnvt7Vl1d2sACiRnSXCAGURAKmoJ1QJcsWYKenh78+c9/htmc3G04HA643e6wj5B/RMZgLi6mOplHDXnJTUsLCeHkixpurNjRXi/ls7VVb29r09M929oSizOdbIzqyPPMZhoMaG+n59vVpZ2wYJDSZQfN7aZn39xM2xsaKN/TptF5kfc7dy5NlW1s1LHJOS76hRcC06freOhdXTou+vTpFDf90CFKv7aWzmtspDAxFRW0vaqKHOeGBnquHE4mkWfX00POsN9P02+Li/W06Lo62p/LuN8Si1zIF9JR/wIZrIPjeVkmTCBDzMpmHR1k0Gy2cMMXywH1+2mUkEfgenvJSAQC5LR5PMDnP68NPRtYv1/H8vR6w3vy2OHloM1uN+0z5n/ePPpE3ltrK92P15tcRcDTb1tbEzekuUAMoiAUNAUbhqWsrAzt7e2oqqrqs8/r9aKjowMlJSUoLi7GqFGjsGbNmgHTFPn+/EVUcJO7Tj6q4HJs8pEjU1PBrakhJ1FUcEX00YjY8cyTifoXSPNvl24VXIeDetEALUI0ahTFAl23LjEVXL+fRIZEBTc1xCAKQl4xJOKAlpWVwRs59SIGo0ePxs44FgZKwyX/kTigyV0n0fMkDqjEAS1UxI5nnkzUv4DEAZU4oEkiBlEQ8oIh4YD2x6WXXppUIGxpuGQXqTPifwbpfFaxnHiPh9pUsa6RSB7S/dvmwilPlFyX53zrjMnV8xA7nluSrX+BHP52A/XADeTgJeMkRjsGiO3gJnPtVMjkC9xfb6IgCAVLIja84OOACoWHzJqJ/xmk81nFmsa8bx/V/729eprpvHn6GonkId2/bSrpZauc5bo8R5tFmMvp6Ll+HoIQNwOtQYiciho5xZWP7+2lKbTxTJM1vgjGqa+bNtE03p4e2me10hTf445L/NqpvGiZfIGNaRvXU3AcNTEUgjBkEAdUyDoSOzr+Z5DOZxWZ1tNPAzt26LjqJhPpN6xZQ+0OvkYieUj3b5tKetkqZ7kuzzfcADz3HLXfhg+nZWvPPUf7/v73zF8/klw/D0GIm8jCuno1sGULTQfp7SUnkOfjt7QA//kPjd7x8atWAR9/DEyaRFNmjYUdGPhF4Ou3tFBvIK8BBejau3bRQvdo1+a8TpwInHRS+l60TL7AxrR9PrpnpegeKyvFUAjCEKJgVXCFwkRiR8f/DNL5rCLT8vkovJzDQaqzHBPd6dTbV66kaZ3x5iHdv20q6WWrnOW6PG/dSiOfZWU0iFBUpAcT3nqL9meTXD8PQYibyMIaDJKzVVpKRtDlopfJ7abtDge9VG63Pr61VcdnCgZ1WitW0Ke/F4Gv7/HQ954ecsQYl4vSbGrS1/Z4wvNaVkZ5MF47lRctky+wMe3SUuDgQT3t9uBB2iaGQhCGDIPSAX3ggQeglEp4/YmQeSR2dPzPIJ3PKjKtlhYSbHQ4dPg5gP7nOOjt7dTxH28e0v3bppJetspZrsvzzp00qy1yqYXbTdvj1H5JG7l+HkLuKZj6N7KwBgLhsTQ5xmdREW0PBukYuz38eLeb/nIgY4+H1nE2N/f/IvD1jUGQjUGU+TtLkHNg5IGuncqLlskX2Jg257+oSD/fQEAMhSAMIQalAyrkLxI7Ov5nkM5nFZlWWZmO6ckh7wD6n+Ogl5SQoE28eUj3b5tKetkqZ7kuz2PGUHvZGC0CoP+dTtqfTXL9PAQhbiILq9NJziU7ejwVtqODtpvNdExXV/jxra30l9cter0kIFRe3v+LwNfv7tbnGjUh+bvTqa/d3T3wtVN50TL5AhvT5vx3dOjn63SKoRCEIYQ4oEJWkdjR8T+DdD6ryLSKi4HqanI47XYdEz0Q0NtnzyY11XjzkO7fNpX0slXOcl2eJ0wgwSHW8+jooL8tLbQ922q4uX4eghA3kYXVbKYRuLY2MoJ+P71Mra20vbOTXqrWVn28200vm8dD/3Na8+bRp78Xga/v9dJ3q5XWRjB+P6VZUaGv7fWG57WlhfJgvHYqL1omX2Bj2m1tFK6mqYmm29bW0jYxFIIwZBiUYViSReT7s4OoZOaXCu7+/dQGEBXc5Mh1eRYV3HDEjhcuWf/tYqngdnWRuFA2VXA//JAM8lBUwa2pIYd6qDUEBGGQMeTjgCaLNFyyS67jJuYDEgc0uTxn69x8vE4sJA4oIXa8cJE4oJA4oIIgFCzigCaJNFwEQRAKG7HjhYv8doIgCIVLIjZc1oAKgiAIgiAIgiAIWcGa6wwIg5dcT0kcLCTzHAt1htNQLzOJ3n8upnALQl6Tq/n6qb5k0c4fKM2BpgpnKq+CIAgpIg6okHZyLUIyWEjmOcbSeBg2jLQ08vV3GOplJtH7z4WIlSDkNblSLEv1JYt2/qxZFIZlzZroaRrFizZv7iuWlKrhEARByDAyBVdIO8uWAU8/TQJ99fX09+mnabsQP8k8R+M5Ph+waxcJK/p8+f07DPUyk+j9x3v8UH+uwhAilcKeq3Njnf/nPwN/+UvsNPmcnTtpJNNspr87dqTHcAiCIGQYcUCFtNLYSJ2rNTX0KSrS31eupP3CwCTzHI3nlJYCBw/S7KqKCvpeWpqfv8NQLzOJ3n+8xw/15yoMIVIp7Lk6N9b5paUUAzQQoO+RaW7dSn89HooL6nbTFBe3m2KUut2pGQ5BEIQsIA6okFaammhmj8cTvt3joe3G5SpCbJJ5jsZzAgEKZVdURJ+uLtqWj7/DUC8zid5/vMcP9ecqDCFSKey5OjfW+YEAYDLp75Fp7txJf202beQBbejt9tQMhyAIQhYQB1RIKxUVtKzE6w3f7vXSdhbFEfonmedoPMfppHZIRwd97Hbalo+/w1AvM4nef7zHD/XnKgwhUinsuTo31vlOJ63/5O+RaY4ZQ3+7u7WRB7Sh7+pKzXAIgiBkAXFAhbRSWUmaBocO0aejQ3+fPVsE9+IlmedoPKetDait1WKHtbW0LR9/h6FeZhK9/3iPH+rPVRhCpFLYc3VurPPb2gCXi5zPtra+aU6YQH+9Xhq9bG0ltTmeftvamprhEARByAKigiuknUWL6O/KlSSAU1ICnH++3i7ERzLP0XhOSQkwejT9X1wM9Pbm7+8w1MtMovcf7/FD/bkKQ4hUCnuuzo11/jXXaBXcaGny3zfeoPWiLS00gjl2rFbBzUReBUEQ0oRJKZ7rIbS2tsLj8cDr9cLtduc6OwWPhBpLDxIHdOggcUBTR+x44ZKW307igGY+r4IgCFFIxIaLA2pAGi6CIAiFjdjxwkV+O0EQhMIlERsuU3AFQQAwODvFY93TYLxXQRDylMgpKdu20d/x48UACYIwJBEHVBCGOIEAxSFfuZLU+EtKSJNi0aJwEcZCItY9LVgAPPvs4LpXQRDyFKMhammhgMwNDUBnJ2A2A6NGAYsXAxdfLAZIEIQhhajgCsIQZ9ky4OmnAYsFqK+nv08/TdsLlVj3dMMNg+9eBUHIU4yGyOcDNm8G9uwBgkFSrN29G/jLX8QACYIw5BAHVBCGMI2N1DlfU0OfoiL9feVK2l9oxLonjwd46y1q9w2WexUEIU8xGqLSUmDvXtruclGsTpeLpuQGAsCKFWKABEEYUogDKghDmKYmmorq8YRv93hou1FgsVCIdU82G7X17Pbw7YV8r4Ig5ClGQxQI0MdkAhwOionV3U29YADQ3CwGSBCEIYU4oIIwhKmooHWQXm/4dq+XtrNmRiER6566u2mZVVdX+PZCvldBEPIUoyFyOumjFK3/tFioR6yjg44tLxcDJAjCkEIcUEEYwlRWkgjPoUP06ejQ32fPLkyBxlj35PUCp50GtLYOnnsVBCFPMRqitjZg5Eja7vfTNAy/n0Y9nU5g3jwxQIIgDClEBVcQhjiLFtHflStJE6OkBDj/fL29EIl1T0YV3MFyr4Ig5ClGQ1RSAkyeTCq4XV3klNbXkwquGCBBEIYYJqWUynUm8gUJgi0MZQZjbEyJAzr0EDteuAza307igAqCMARIxIbLCKggCACoHTTY2kKx7mkw3qsgCHlKpMER4yMIwhBH1oAKgiAIgiAIgiAIWUEcUEEQBEEQBEEQBCEryBRcQRASJnJJUyGtp5T1n4IwxMj0S19oRqXQ8isIwqBDHFBBEOImEACWLSNRx5YWEnQEgGHDgLIyijqwaBFFFsg3jHlvbydRynzOryAIKZLpl77QjEqh5VcQhEGLTMEVBCFuli0Dnn6a4qj7fBTKZNcu+m6x0L5ly3Kdy+gY815fn//5FQQhRTL90heaUSm0/AqCMGgRB1QQhLhobKSO85oaoLQUOHhQz+A6eJC21dTQMY2Nuc5tOMa819QARUX6ez7mVxCEFMn0S19oRqXQ8isIwqBGHFBBEOKiqYlmbXk8NJOrq4vaMEVF9D0QoH3t7XRsPmHMu5F8za8gCCmS6Ze+0IxKoeVXEIRBjTiggiDERUUFLRnyemm5kN0OdHTQx26nbV4vHcPiRPmCMe9G8jW/giCkSKZf+kIzKoWWX0EQBjXigAqCEBeVlaRXcegQ0NYG1NZSp3ljI31va6N9s2fnn7CiMe+HDpHTzN/zMb+CIKRIpl/6QjMqhZZfQRAGNaKCKwhC3CxaRH9XrgSKi0nHAqAO9N5e4Pzz9TH5hjHvu3dTnvM5v4IgpEimX/pCMyqFll9BEAYtJqWUynUm8oXW1lZ4PB54vV643e5cZ0cQ8haJAyrkK2LHC5eM/XYSBzScQsuvIAgFQSI2XEZABUFImMrK8HZLIbVhIvMuCMIgJ9MvfaEZlULLryAIgw5ZAyoIgiAIgiAIgiBkBXFABUEQBEEQBEEQhKwgDqggCIIgCIIgCIKQFcQBFQRBEARBEARBELKCOKCCIAiCIAiCIAhCVhAHVBAEQRAEQRAEQcgK4oAKgiAIgiAIgiAIWUEcUEEQBEEQBEEQBCEriAMqCIIgCIIgCIIgZAVxQAVBEARBEARBEISsIA6oIAiCIAiCIAiCkBXEARUEQRAEQRAEQRCygjiggiAIgiAIgiAIQlYQB1QQBEEQBEEQBEHICuKACoIgCIIgCIIgCFlBHFBBEARBEARBEAQhK4gDKgiCIAiCIAiCIGQFcUAFQRAEQRAEQRCErCAOqCAIgiAIgiAIgpAVrLnOQD6hlAIAtLa25jgngiAIQjKw/WZ7LhQOUgcLgiAULonUv+KAGmhrawMAjBo1Ksc5EQRBEFKhra0NHo8n19kQEkDqYEEQhMInnvrXpKSbOEQwGMT+/ftRWloKk8mE1tZWjBo1Cnv27IHb7c519vIaeVbxI88qfuRZxY88K0Iphba2NowYMQJms6wyKSSCwSA+/vhjTJ48eciX44GQ9z0+5DnFhzyn+JDn1D+J1L8yAmrAbDZj5MiRfba73W4paHEizyp+5FnFjzyr+JFnBRn5LFDMZjPq6uoASDmOF3lO8SHPKT7kOcWHPKfYxFv/SvewIAiCIAiCIAiCkBXEARUEQRAEQRAEQRCygjig/eBwOHDzzTfD4XDkOit5jzyr+JFnFT/yrOJHnpUwGJByHB/ynOJDnlN8yHOKD3lO6UNEiARBEARBEARBEISsICOggiAIgiAIgiAIQlYQB1QQBEEQBEEQBEHICuKACoIgCIIgCIIgCFlBHFBBEARBEARBEAQhKwwpB3T58uU488wzUVFRgeLiYkyfPh133XUXgsFgQunccsstMJlM/X62bNmSobvIDul6VkaWLVuGs88+GzU1NXA4HKirq8PZZ5+N++67L405zz7pelYDlSn+PPjggxm6k8yTznLV1taGn/3sZzjhhBNQUlICu92O+vp6LF68GOvWrctA7rNLOp+V1+vF//t//w/HHXccXC4XysrKcPrpp+P//u//MpBzQdCkuy555513cP7556O6uhpOpxOTJ0/Grbfeio6OjjTnPLuk6zmtX78e/+///T/MmTMHVVVVsNlsGDZsGM455xw8+eSTGcp99shE24S55557QvXsN77xjTTkNndIGy4+pE2SQ9QQ4Ze//KUCoACocePGqSlTpiiz2awAqPPOO0/19vbGndbNN9+sAKhRo0ap2bNnR/3s2rUrg3eTWdL5rJRSqqOjQ5133nlhac6cOVONGjVKmc1mNWPGjAzdSeZJ57OKVZZmz56tJk+eHLrOli1bMnhHmSOdz+rQoUPq6KOPVgCU2WxW48ePV1OnTlUlJSUKgLJYLOqRRx7J4N1klnQ+q71796oJEyaEnsvUqVPV5MmTlclkUgDU1VdfncE7EYYy6a5Lli5dqiwWiwKg6urq1AknnKBsNpsCoGbOnKl8Pl+G7iSzpOs5ffrpp6F0AKixY8eqGTNmqPLy8tC2Sy65JOHnni+kuzwZaWhoUBUVFaH0r7jiijTmPLtIGy4+pE2SW4aEA/r2228rk8mkzGZzWAHYsGGDqqmpUQDUHXfcEXd67IDefPPNGchtbkn3s1JKqa9+9asKgDr99NP7OE8NDQ3qxRdfTEves00mnlUs/vu//1sBULNmzUpLetkm3c/qiiuuUADUxIkT1UcffRTa3t7err75zW8qAMrtdiuv15vW+8gG6X5Wn/3sZxUAdeyxx6odO3aEpTdixAgFQP3jH/9I5y0IQtrL8Y4dO5TD4VAA1K9//WsVDAaVUkrt3LlTTZw4UQFQ3/rWt9J+H5kmnc9p69atavjw4er2229X+/fvD23v7e1Vd911V6jT6a677kr7fWSaTNe3ixcvVmazWZ177rkF7YBKGy4+pE2Se4aEAzp//nwFQH3zm9/ss+/hhx9WAFRlZaXq6uqKK73B7ICm+1k9//zzCoCaNGmS8vv96c5uTkn3s4pFMBhUY8aMKdiGg1Lpf1a1tbUKgHrmmWf67Ovu7lZVVVUKgFq+fHnKec826XxWGzZsCPXwvvPOO332//Of/wz1/gpCOkn3O79kyRIFQH3+85/vs2/lypUKgLLZbOrgwYMp5z2bpPM5BQKBfkeBr776agVATZkyJaU854JM1rcvv/yyAqCuueaaUPuuUB1QacPFh7RJcs+gd0C9Xq+y2+0KgFq1alWf/V1dXcrtdisAcffiDFYHNBPP6qyzzlIA1NKlS9Od3ZySiWcVizfeeCPUuDp8+HBKaeWCTDwrj8ejAKhNmzZF3T9jxoyYlUE+k+5ndffddysAauTIkVH3d3Z2hqYcvffeeynnXxCUSn85DgaDavjw4QqAevTRR6MeM2nSJAVA/fWvf005/9kim/WIUko98cQTCoAqKipKOa1sksnnFAgE1FFHHaWGDRummpubC9oBlTZcfEibJD8Y9CJE69evR1dXF4qKijB9+vQ++202G2bOnAkAWLVqVUJpv/baa7jgggtwxhln4Ctf+Qp+/etf4+DBg2nJdy5I97MKBAJYsWIFTCYTzj33XLz++uu44oorMG/ePCxcuBB33nkn2tra0n4f2SCT5SqSpUuXAgDOPvtsVFVVpZRWLsjEs5oyZQoA4O233+6zr6mpCVu2bIHVasW0adOSz3gOSPezam5uBgDU1dVF3W+320Nl6t13300224IQRrrL8e7du3HgwAEAwOzZs6Mew9tTtbfZJJv1CICQUJPT6Uw5rWySyed022234dNPP8Udd9yBsrKydGQ3Z0gbLj6kTZIfDHoHdOvWrQCA+vp6WK3WqMeMGzcu7Nh4efPNN/H444/jtddew7/+9S/ccMMNGDduHB544IGU8pwr0v2sNm7ciJ6eHowYMQK33347PvvZz+K+++7Dq6++iieeeALXXXcdJk2ahA0bNqTtHrJFJsuVkc7OTjz22GMAgP/6r/9KOp1ckolndcstt8Bms+GHP/wh7r//fhw6dAg+nw8rV67EF77wBfh8Pvz4xz/GqFGj0nMTWSLdz8rj8QAA9u3bF3V/V1cXjhw5AgD4+OOPE86vIEQj3eWYj3E4HBgxYkTK6eUL2apHmGXLlgGI7cTnK5l6Th999BHuuOMOnHbaafj617+eekZzjLTh4kPaJPnBoHdAeQSgvLw85jG8j48diOHDh+MnP/kJ1qxZg8bGRvj9fqxcuRLnnHMOAoEALr/8cjz77LOpZz7LpPtZcY91Q0MDfvWrX2HBggXYsmULOjs7sXr1akyfPh379+/H+eefj/b29jTcQfbIRLmKxrPPPouWlhZ4PB4sWLAg6XRySSae1RlnnIGXX34ZU6ZMweWXX47a2lqUlJTg1FNPxYEDB7B06VLceuutqWc+y6T7WXEv7t69e7F69eo++5966qmQ3Hwq5VQQjKS7HPMxZWVlMJlMKaeXL2SrHgGAl156CU899RQA4Ic//GFKaWWbTDwnpRSuuuoqBINB/OlPf0o9k3mAtOHiQ9ok+cGgd0B5yondbo95jMPhAEDTDeLhqquuws9//nOceOKJqKiogNPpxCmnnILnnnsOX/rSl6CUwnXXXQelVOo3kEXS/ax8Ph8AoLu7G+PGjcO//vUvTJw4EXa7HTNnzsRzzz0Hl8uF3bt34/7770/DHWSPTJSraPD02wsuuABFRUVJp5NLMvWsduzYgYaGBphMJowePRrHH388nE4ndu7ciXvuuQc7d+5MKd+5IN3P6qSTTsKMGTMAAJdeeik++eST0L5Vq1bhuuuuC/2fSjkVBCPpLsfZsrfZJlv3tXv3bixevBgAsGTJEpx++ulJp5ULMvGc7r33Xrz11lv43ve+h+OOOy71TOYB0oaLD2mT5AeD3gHlRntXV1fMYzo7OwGkvi7CZDLhV7/6FQBg27ZteP/991NKL9uk+1kZHaYlS5bAZrOF7a+trcVFF10EAHjhhRcSzm8uyUa5amxsxPLlywGgoKcHZeJZ/fKXv8Rll10Gk8mEDRs2YOfOnXj//ffR0NCAK664Aq+//jpmz54Nr9eb+g1kkUw8q4cffhi1tbX46KOPcMwxx2DixIkYO3YsTj75ZPj9/tDIeklJSYq5FwQiU3VJNurxbJKN+2pqasI555yDI0eOYO7cufjd736XVDq5JN3P6fDhw7jhhhswcuRI3HzzzenJZB4gbbj4kDZJfjDoHdB4htHjGY6Pl6OPPhoVFRUAgE8//TTl9LJJup+V8ZhJkyZFPeaYY44BgILrGcpGuXr00UfR3d2NMWPG4NRTT00qjXwg3c+qoaEBP/vZzwAADzzwQGjxP0BO1F/+8hdMnjwZ+/fvL7ipVZkoVxMnTsT69evx3e9+F2PGjMHOnTvh8/mwePFirFu3Dm63GwA1JgQhHWSqLmlpaYk5syid9Xi2yHQ90t7ejvnz52Pz5s2YMWMGnnnmmdDITiGR7uf0ox/9CE1NTfj9738/qDrepA0XH9ImyQ8GvQM6YcIEADQFpaenJ+ox27dvDzs2VbiXKNb18pV0P6uJEyeGvseq9Hh7b29vQnnNNdkoVzz99uKLL4657qkQSPezeu+999DR0YGSkhLMmjWrz36r1Yq5c+eGji0kMlWuamtrceedd2Lbtm3o7OxEQ0MDli5dirFjx4aeEU/VFYRUSXc55mM6Ozuxf//+lNPLFzJZj3R2duL888/HqlWrMHnyZLzwwgsoLS1NLcM5It3Paf369QCAb3/726itrQ37/OY3vwEAPPLII6FthYK04eJD2iT5waB3QE844QTYbDZ0dHRg3bp1ffZ3d3djzZo1AGi9VKocOXIEDQ0NAICRI0emnF42SfezGjlyZEjxi1/mSHh7rDAR+Uqmy9W2bdvwzjvvACAHtJBJ97OKR/adR0l4rUehkG179eGHH+Ljjz9GUVERzjzzzJTTEwQg/eW4vr4+5AisXLky6jG8PR3vRbbI1Pve09ODRYsW4dVXX8W4cePw8ssvF2QILyZTz+nQoUN9PrzuMRAIhLYVCtKGiw9pk+QHg94BdbvdoYbVvffe22f/Y489htbWVlRWVoZ6KFLhd7/7HZRS8Hg8IQXKQiETz+qCCy4AAPzjH//os6+jowOPPvooAFIQKyQyXa4eeughAMCsWbPCeiELkXQ/K+6RbG9vj6rs2tPTgzfeeAMATYkvJLJpr5RSuPHGGwEAixcvLqipi0J+k+5ybDKZ8KUvfSlmem+//Ta2bNkCm82G8847L7XMZ5FMvO9KKVx66aV45plnMGLECLzyyisxQ9cUCul+Ths2bIBSKuqH14ReccUVoW2FgrTh4kPaJHmCGgL85z//USaTSZnNZvXII4+Etm/YsEHV1NQoAOr2228PO+f3v/+9Gj16tLrwwgvDtm/atEldc801atOmTWHbA4GA+vnPf67MZrMCoH7xi19k7oYySDqflVJKHThwQJWUlCgA6rbbblO9vb1KKaX8fr+65JJLFABVXl6uGhoaMntjGSDdz8rIUUcdpQCou+66KyN5zzbpfFbBYFBNnjxZAVCTJk1SGzduDO1rbW1VV1xxhQKgAKj33nsvszeWAdJdrt566y31yiuvqGAwGNp25MiR0PtXU1OjDh8+nLkbEoYk6S7H27dvV3a7XQFQv/71r0PleefOnWrixIkKgLrmmmsye1MZIN3P6dprr1UAVFVVldq8eXPG858tMlnfGrn55psVAHXFFVekLe/ZRNpw8SFtktwzJBxQpZS67bbbQgVg3LhxasqUKSFn8dxzz1U9PT1hx7MRmjNnTtj29evXh9Kprq5WM2bMUDNmzFAulyu0/Yorrghr7BUa6XpWzDPPPBNqONTU1KiZM2cqj8ejACiXy6VefPHFLNxVZkj3s1JKqbffflsBUDabbVA5Bul8VmvXrlXl5eUKgDKZTGrMmDFqypQpyul0hq5x2223ZenO0k86n9Xvf/97BUCVlpaqKVOmqOOPP15ZrVYFQNXV1akPPvggS3clDDXSbR8ffPDB0Pl1dXXqhBNOUDabTQFQM2bMUO3t7Vm4q/STrufEdQcANWrUKDV79uyYn0IkE/VtJIXugColbbh4kTZJbhkyDqhSSj377LPqjDPOUB6PR7lcLjV16lR155139ilkSsUuaM3NzerWW29V55xzjho7dqwqKSlRdrtdjRw5Un3lK19RL7zwQpbuJrOk41kZef/999VFF12kamtrlc1mUyNGjFBf//rX1UcffZTBu8gO6X5W11xzjQKgFixYkMFc54Z0Pqt9+/ap73//+2ry5MnK6XSGytXChQvVq6++muE7yTzpelbr169XX//619WECRNUcXGxKikpUVOmTFG33HKL8nq9WbgTYSiTbvu4cuVK9YUvfEFVVFQoh8OhJk6cqG655RYVCAQyeBeZJx3P6bXXXgs1dgf6FCrpLk+xzilkB1QpacPFi7RJcodJqQKa4C4IgiAIgiAIgiAULINehEgQBEEQBEEQBEHID8QBFQRBEARBEARBELKCOKCCIAiCIAiCIAhCVhAHVBAEQRAEQRAEQcgK4oAKgiAIgiAIgiAIWUEcUEEQBEEQBEEQBCEriAMqCIIgCIIgCIIgZAVxQAVBEARBEARBEISsIA6oIAiCIAiCIAiCkBXEARUEQRAEQRAEQRCygjiggjDIufTSS2EymTBmzJhcZ0UQBEEQhiRSFwuCRhxQQRAGFWPGjIHJZOrzsVqtqKysxKxZs/CDH/wAW7ZsSTjtbdu24Wc/+xlmz56Nuro6OBwOVFVVYcqUKfjOd76Dt956K6k8b9myBT/72c8wZ84c1NfXw+l0oqSkBKNHj8aCBQvwu9/9DgcPHkwqbUEQBEHINgPVxSeffDJ+/OMfY/v27THTiHa+yWSC3W5HdXU1Tj31VPzP//wP9uzZk1DeXn/9dVx33XWYMWMGhg8fDrvdjrKyMkyaNAmLFy/G0qVL4ff7U30EQn8oQRAGNZdccokCoEaPHp3rrGSF0aNHKwADfqxWq7rjjjviSrOjo0N973vfUzabbcB058+fr/bs2RNXus3NzerSSy9VFoslrvxeffXVqrGxMZXHIwiCIOQAqYujfxwOh/rLX/4SNY14zgegiouL1cMPPzxgnj744AN1+umnx5VmeXm5+s1vfqN6e3vT/WgEpZQ1jb6sIAhC3jBixAi8+OKLof87Ozuxbds2PPHEE3j00UfR09ODH/7whxg7diwWLlwYM522tjacf/75eO211wAAFRUVuPTSSzFv3jzU1NTA6/Xivffew/33348tW7Zg+fLl+MxnPoOXXnoJxxxzTMx0d+7ciXPOOSc0EltdXY2vfe1rmDNnDoYPHw6TyYT9+/fj9ddfx7/+9S/s27cPf/nLX3DWWWfhi1/8YnoekiAIgiBkkMi6uKenB3v27ME///lPPPLII+js7MQ111yD+vp6nHPOOVHTOPHEE3H//feH/vf5fNi2bRseeOABvPzyy/D5fLjkkkswfvx4nHTSSVHTeOmll3DBBRegtbUVAHDsscdi0aJFmDVrFqqrq+Hz+bBr1y688MILeOaZZ9Dc3Izrr78eV1xxBcrKytL3QAQi1x6wIAiZZaj2uvZ3v/fcc0+ol/O4447rN70LLrggdOy8efNUQ0ND1OO6urrU9ddfHzp2/PjxqrW1Neqxfr9fHXfccaFjL7vsspjHKqVUZ2enuvPOO1VxcbF68skn+82vIAiCkH9IXdyX3/72t6F6cMaMGX328745c+bETOOmm24KHfeFL3wh6jGbN29WxcXFCoCyWCzqD3/4Q78jmw0NDWrJkiUKgGpubo55nJA8sgZUEIQhxxVXXIHx48cDADZt2hRzfeWyZcvw2GOPAQBOOOEEPPvss6iuro56rM1mwx133IGrrroKAK0X/fGPfxz12J/85CfYtGkTABKmuO+++1BaWhozv3a7Hd/97nexatUqjBo1Kr6bFARBEIQ85nvf+x7q6+sBAGvXrkVDQ0PCafz3f/83iouLAQCvvvoqgsFg2H6lFBYvXgyfzwcAuPfee3HttdfCbI7tAlVXV+Puu+/G448/DpvNlnCehIERB1RIiltuuSW0GBwAWlpacPPNN+PYY49FSUkJKioqMHfuXDz88MNxpdfT04N7770X8+fPx4gRI0LiLqeffjruvPNOdHR0xDx37ty5MJlMmDt3LgBg69at+Pa3v40JEybA5XLBZDJh586dcd/bAw88ELq3nTt3orOzE7/5zW8wffp0eDweuN1unHTSSbj77rvR29s7YHpKKTz++ONYuHAhRo0ahaKiIpSXl2PWrFm49dZb0dLS0u/57777Lm666SbMnTsXtbW1sNvtcLvdmDx5Mq655hps3rw57nuLxYoVK1BaWgqTyYSjjz4au3btSuj8YDCIV199Fddffz1mz56Nqqoq2Gw2lJWVYdq0abj++uuxe/fuftNgwYJLL7203+PSpSR4wgknhL7HEjD41a9+Ffr+t7/9DU6nc8B077jjDtTW1gIA7rvvvj4V6pEjR/C3v/0NAFBbW4s//OEPcef52GOPxYwZM+I+XhCEwY3UxVIXGym0uthsNmPmzJmh/wfKWzSKiopCy138fj8aGxvD9i9fvhzr168HAJx77rm45JJL4k574cKFIedWSDM5HoEVCpSbb745NOVh+/btavz48TEXcn/lK19R3d3dMdP69NNP1eTJk/tdDD5hwgT1ySefRD1/zpw5oSkaTz31VGiahfGzY8eOuO/t/vvvD523bt06NWPGjJj5OvXUU/udOtnQ0KBmz57d773V1NSod999d8C8xPpYLBZ19913x8zDQNN+nnjiCeVwOBQANW3aNHXo0KG4nxVjLA+xPi6XSz3xxBMx0+DpOpdcckm/1xrofuKZ9qOUUhdeeGHY7xzJxo0bQ/tPOeWUftOK5MYbbwyd+7//+79h++66667QvptuuimhdAVBEIxIXSx1sZFCr4sjnz9v728KrlJKnXTSSaFjm5qawvYtXLgwtO+VV17pNx0he8gIqJAyF154IXbs2IGrr74ar7zyCtasWYN7770XRx99NADg8ccfx/e///2o5x44cACzZ8/G5s2bUVpaih/84Ad4/vnnsW7dOrz22mu48cYb4XK5sHXrVpx99tnwer0x87F7925cfPHFcLlc+NWvfoWVK1fi3XffxV133YWSkpKk7u2qq67C2rVrceGFF2L58uV477338Mgjj4R67P7zn/9g8eLFUc/1+XyYM2cOVq5cCbvdjquuugpPP/001q1bh7feegs///nPUVlZiUOHDuGcc86J2tPZ09OD8vJyXHLJJbjvvvvw1ltvYd26dfj3v/+Nn/3sZ6iqqkJvby++/e1v49VXX034/u6//35ccMEF6OzsxGmnnYbXX38dw4YNSzidnp4eDB8+HEuWLMFDDz2ElStXYu3atXjqqafwox/9CCUlJfD7/fja176Gjz76KOH0M4GxtzpaD+6bb74Z+r5gwYKE0j7vvPNC3yNDs7zxxhuh71/4whcSSlcQBCEWUhdLXVyIdfH7778f+j5ixIiEz+/t7cXHH38MAHC73SgvLw/bz3VwcXFxaHReyANy7QELhUlkL9sjjzzS55jW1lY1depUBUCZzWb1/vvv9znmC1/4ggKgRo0apbZt2xb1WuvWrQv1pEYbMeJeVwBqxIgRateuXSndW2RP5y9+8Ys+x3R3d6uzzjordMy///3vPsd8+9vfVgCUx+NRa9asiXqtnTt3quHDhysA6uKLL+6zf+/evcrn88XMa0tLi5oyZUqoBzgasXop77jjjlD+58+fr/x+f8zrDMSOHTtUV1dXzP179uxRdXV1Me9Tqez2ur744ouhez/jjDOiHvONb3wjdMyLL77Yb54i8fv9ymw2h0YMjEyYMCH0TnR0dCSUriAIghGpi6UuNlJodfETTzwRuvdx48b12c/7+hsB/etf/xo67vLLLw/bt2/fvqRnMgmZRRxQISmMlV4s1TGllFq1alXouCVLloTt++CDD0L7nn766X6v96Mf/ShUqUVirPT+8Y9/JHdDBoyV3pQpU2Iqpe3ZsycUF3L+/Plh+w4fPqyKiooU0HcKZiR/+tOfFABls9n6reBi8dRTT4Xye+TIkT77o1USximiX/3qV/utsNLFnXfeqQAot9utgsFgn/2ZrvQ6OjrU5s2b1c9+9jPldDpDU5FiTbn64he/GHpGGzdujOcWwygrK1MAVEVFRdj28vJyBVCMMUEQhFSQuljq4kTJdV3c09OjduzYoX7xi1+EfhsAUWOBxnJAfT6f2rhxo/rud7+rrFarAqCqq6v7dJ4Yl9J86Utf6vd+hOwicUCFlLnsssti7ps1axaOPfZYfPjhh3jllVfC9j399NMAAJfLhXPPPbffa5x++un49a9/jf3792PPnj1RlUDtdjsuuOCCJO4gNpdccklMpbSRI0fi85//PJ577jm8/vrr6O3thcViAQC8+OKLIbGGRYsW9XuN008/HQDQ3d2NtWvX4rTTTot5rM/nw+HDh+Hz+aCUAoAwhbaNGzfijDPOiHl+MBjEkiVL8Ne//hUAsGTJEvzxj38MCViki9bWVjQ2NsLv94fy6XK5Qvt27NiBcePGpfWakezatavf+5o6dSruuuuumDHD2traQt+TESEoLi5GS0tLKOZYZLoibCAIQjqRuljq4kgKoS4GgGuuuSakIB+NN954o980TjvtNPz5z3/ucy+p1uNC5hAHVEgZo4JZNGbNmoUPP/wQW7duRVdXF+x2OwDgvffeA0CqZVZr/EXx4MGDUSu9CRMmoKioKIGcD0w89/bcc8/B7/dj+/btmDBhAgB9bwAwfPjwuK8XLRzIkSNH8Lvf/Q7/+te/sHXr1lAlEo0jR47E3NfT04OvfvWrWLZsGQCSLr/tttvizttA7Nq1C7/5zW/w7LPPDqjcd+TIkYxXev3B64D6a2AYw6K0t7cnfA0+x+1290m3ubk5JAkvCIKQDqQulroYKJy6uKSkBKeddhquvfZanHPOOUmn4/F48L3vfQ/HHntsn33Gelzq3PxCHFAhZQZaKF9TUwMAUEqhubk59H8y8Z4AqiSjEbnwPB3Ee28A0NTUFPqerntbu3YtzjrrrD6y4rEIBAIx9+3bty9U4c2fPz+tFd7zzz+Pr3zlKzF/m0j6y2e6GDFiBF588cXQ/42NjVi/fj3uvPNO7Nq1C0uWLEF7ezt++MMfRj2/srIy9P3gwYOYOnVq3NcOBAKhnldjOgBQVVWF5uZmeL1edHZ2wuFwJHJbgiAIUZG6mJC6OL/rYqvVCrfbjdra2n5jcRo58cQTcf/99wOg8tvQ0IB33nkH//u//4sjR47gggsuwCOPPIILL7ww7LyqqqrQ90OHDqXhboR0IQ6okDIDTa2I1UvIcbvGjh2LZ555Ju7rjR07Nup2nnKTTlK9N7vdjrVr18Z9vZEjR4a+d3V1YdGiRWhsbITNZsO1116L888/H0cffTTKy8tDjsv27dsxfvz4fvMDUAV91FFHYeXKlVi+fDl++9vf4gc/+EHceYtFY2Mjvva1r8Hv96OkpATXX389zjrrLIwfPx4ejyfUy/7qq69i3rx5A+YzXdhsNhx33HFh2+bMmYNLLrkEp5xyCrZs2YKf/OQnmDt3btTedaPDuX79epx11llxX3vDhg2hYNiRjuvUqVOxdetWBINBbNiwIeYUYEEQhESQurgvUhfnZ12cKMXFxX3SmDdvHi6++GKcdNJJaGhowDe/+U185jOfQX19feiYESNGoLq6GocPH8bGjRvDpmcLuUUcUCFlDh06FHUaDsM9kCaTKaxnlEeGDh06hEmTJiU09SdbHDp0KCRhHw1j72pFRUXoO99bV1cXKisrE5r6w7z66qvYvn07AODuu+/GlVdeGfW45ubmuNIrKirC888/j7POOgvvvPMOrr/+elgsFnzve99LOG9GHnvssVAA7yeeeAKf+9znkson94Sy4xaLVKfRlJeX48EHH8TJJ5+Mnp4efP/73+8TKgXQ64EA4JlnnsGPf/zjuK9hbMRFTvOdM2cOHn/8cQDAc889Jw6oIAhpQepiQuriwqiL08GYMWPwxz/+EYsWLUJrayv++7//Gw899FDYMaeffjr+9a9/wefz4Y033uh3ba6QPSQOqJAya9asiWv/hAkTQj1wAHDCCScAoKkuK1euzFwGUyDee3O5XGHrKPjeAOCll15K6toffvhh6PtFF10U8zjjGpeBKC0txQsvvBByeq677jr88Y9/TCp/DOezoqIiZoUXTz55rcZAlSPH+0qFWbNmYeHChQAoftwLL7zQ55ipU6eGRi/feeeduJ9zW1sbHnjgAQCAw+Ho89tddNFFcDqdAIB77rknLypxQRAKH6mLpS4GCqsuTgcXXHBBaBbTI488EhbjGwgX57rzzjuzmTWhH8QBFVLmwQcfjLnvvffew6ZNmwAAZ555Zti+888/P/T917/+dWYylyIPPfRQzCkq+/btC1Voc+fODZvWcc4554QU8X7/+9+jp6cn4Wsbz4m1niMYDOJvf/tbQum63W68+OKLIYN97bXX4s9//nPC+YvMZ2dnZ8weU7/fj3/84x/9psPTudatWxfzmW/atAkffPBB0nk18j//8z+haV2x1uDccMMNoe/f/OY341ov88Mf/jAkYHHZZZf1WbtUVVUV6kE/cOBAQr3eH374YULTyARBGDpIXSx1MVB4dXE6+J//+R8A9Dv8/Oc/D9s3f/58TJs2DQDw7LPPYunSpXGn+8QTT0gncYYQB1RImWeeeSa0oN5Ie3s7vvnNbwKgKR2REtszZ87E5z//eQDA8uXLcfPNN/d7nZ07d+L//u//0pTr+NiwYQPuuOOOPtt7enpw5ZVXoqurCwBJiBupq6sL9bpt3LgRV111Vb8VX0NDA+65556wbaziB8RuWNx4441Yt25dfDdjwOPx4KWXXsKMGTMAAN/61rfw97//PeF0jPn0+XyhqaVGent78Y1vfAP79+/vN505c+YAAPbv3x/1d25ra8Pll1+eVB6jMWXKFJx33nkAgJUrV+K1117rc8xXv/pVfPnLXwZA60DPO+88HD58OGp63d3d+NGPfhSS1R87dixuv/32qMf+8pe/xOTJkwHQKOiVV17Zr9Jud3c37rrrLpx88snYs2dP/DcpCMKQQepiqYuBwquL08GCBQtCTuajjz6KTz/9NLTPZDJh6dKlofAzl112Gf70pz/1O8X4yJEjuPbaa7Fw4UJ0d3dnNO9DlqxGHRUGDcbg1yeeeKKyWCxqyZIl6tVXX1Xvvfeeuu+++9TEiRNDx1x77bVR09m3b58aPnx46LiTTjpJ/fWvf1Vvv/22WrdunXr55ZfVb3/7W/W5z31OWSwWtXDhwj5pcPDryEDFyWIMfn3iiSeGAkQ///zzau3ateqf//ynmjVrVuiYBQsWRE2nra1NHXfccaHjJk+erO6880711ltvqfXr16vXXntN/fGPf1Rf/OIXld1uVzNmzAg7v729XQ0bNkwBUFarVS1ZskS98MIL6r333lP//Oc/1bx58xQANXv27NA17r///j756C9YdFNTkzrhhBMUAGUymdS9996b8PPas2ePcjgcCoByOp3qxhtvVCtWrFBr1qxRDzzwgJoxY0affL722mt90mloaFBut1sBUEVFReqnP/2pevfdd9WqVavU3XffrcaPH6+KiopC+U00+HU01qxZE8rTGWecEfWYlpYWdfrpp4eOq6ioUNdff71avny5Wrt2rXrttdfUHXfcoSZNmhQ6ZsSIEeqDDz7o99rbtm1TRx99dOicYcOGqeuuu0499dRTatWqVerdd99VTz75pLruuutUfX196Lgnn3xywPsSBGFoIHWx1MVMIdfFseB8xlOmHnvssdDxl19+eZ/9y5cvVyUlJaFjjjvuOHXrrbeqF154Qa1du1a9+eabaunSperrX/966P4BqObm5qTzL8RGHFAhKYyV3vbt29XYsWND/0d+Fi5cqLq7u2OmtXPnTjVz5syY5xs/l112WZ/zM1nprVu3LmRko31mz56tWltbY6bV2Niozj777Lju7bOf/Wyf81944QVVVFQU85y5c+eqTZs2JV3pKaXUkSNH1NSpUxUAZTab1QMPPJDwM7vvvvuU2WyOmc8LL7xQvfLKK/1WekoptWzZMmWxWKKmUVRUpJYtWzbg/SRa6Rl/n7fffjvqMYFAQF177bXKarUO+Dt+/vOfV7t27Yrr2o2Njeq//uu/+n12/LHZbOo73/mOamlpiSttQRAGP1IXS11spJDr4mjwNeMpU8FgUE2ePDlUX0arhzdu3BjmgPf3qaysVH/4wx9Ub29v0vkXYiMOqJAUxkpPKeq9+8lPfqKOOeYY5XK5lMfjUaeffrpaunRpXOkFg0H15JNPqosuukiNHTtWuVwuZbPZVHV1tTrllFPUD37wA/XGG2+oYDDY59xMVno7duxQgUBA/fKXv1TTpk1TpaWlqqSkRM2cOVPdddddqqenJ640V6xYoS677DI1YcIEVVJSoqxWq6qoqFAzZ85U3/rWt9Ty5ctjprVp0yZ18cUXqxEjRoSeyZw5c9Tf/vY31dvbq3bs2JFSpaeUUocPH1bHH398qOJ76KGH4rovIytXrlRf/OIXVXV1tbLZbGr48OHq7LPPVo8++qhSSqnXXnttwEpPKaVWrVqlvvSlL6nq6mplt9vVqFGj1CWXXKI2bdoU1/0kWumtXLkylK/58+f3e+zWrVvVzTffrE4++WRVW1urbDabqqioUMcdd5z61re+pV5//fW4rhnJ5s2b1c0336xOPfVUVVdXpxwOh3K5XKq+vl4tWLBA/f73v1eHDh1KKm1BEAYvUhdLXRxJodbF0UjEAVVKqYcffjh0zpIlS2Iet2LFCvWd73xHTZs2TQ0bNkxZrVbldrvVxIkT1eLFi9UjjzyiAoFA0vkWBsakVBaCAAmDjltuuQU//elPASArcaSyyQMPPBBaM7Jjxw6MGTMmtxkSBEEQhChIXSwIQiEiIkSCIAiCIAiCIAhCVhAHVBAEQRAEQRAEQcgK4oAKgiAIgiAIgiAIWUEcUEEQBEEQBEEQBCEriAMqCIIgCIIgCIIgZAVRwRUEQRAEQRAEQRCygoyACoIgCIIgCIIgCFlBHFBBEARBEARBEAQhK4gDKgiCIAiCIAiCIGQFcUAFQRAEQRAEQRCErCAOqCAIgiAIgiAIgpAVxAEVBEEQBEEQBEEQsoI4oIIgCIIgCIIgCEJWEAdUEARBEARBEARByAr/H1OuCB9BUCoFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 1- calculate cross-cell auROC & auPRC\n",
    "metrics1 = xc.tl.crosscell_aucprc(\n",
    "    cell_embedding_ad='./data/1_within_sample/m_brain_paired_rna.h5ad',\n",
    "    input_folder='./data/1_within_sample/train_data',\n",
    "    model_path='./data/1_within_sample/train_out/E1000best_model.h5',\n",
    "    output_path='./data/1_within_sample/eval_out',\n",
    "    cellembed_raw='X_pca',\n",
    "    save_pred=True,\n",
    "    scatter_plot=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predction shape: (4031, 4391)\n",
      "Overall auROC: 0.7944, auPRC: 0.4380\n",
      "Per-cell auROC: 0.7330, auPRC: 0.3374\n",
      "Valid cells: 4391\n",
      "Per-peak auROC: 0.7323, auPRC: 0.2919\n",
      "Valid peaks: 4031\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": [
    "#### 2- calculate cross-peak auROC & auPRC\n",
    "metrics2 = xc.tl.crosspeak_aucprc(\n",
    "    cell_embedding_ad='./data/1_within_sample/m_brain_paired_rna.h5ad',\n",
    "    input_folder='./data/1_within_sample/train_data',\n",
    "    model_path='./data/1_within_sample/train_out/E1000best_model.h5',\n",
    "    output_path='./data/1_within_sample/eval_out',\n",
    "    cellembed_raw='X_pca',\n",
    "    save_pred=True,\n",
    "    scatter_plot=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall auROC: 0.7964, auPRC: 0.4440\n",
      "Per-cell auROC: 0.7324, auPRC: 0.3373\n",
      "Valid cells: 487\n",
      "Per-peak auROC: 0.7365, auPRC: 0.3236\n",
      "Valid peaks: 4031\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": [
    "#### 3- calculate crossboth auROC & auPRC\n",
    "metrics3 = xc.tl.crossboth_aucprc(\n",
    "    cell_embedding_ad='./data/1_within_sample/m_brain_paired_rna.h5ad',\n",
    "    input_folder='./data/1_within_sample/train_data',\n",
    "    model_path='./data/1_within_sample/train_out/E1000best_model.h5',\n",
    "    output_path='./data/1_within_sample/eval_out',\n",
    "    cellembed_raw='X_pca',\n",
    "    save_pred=True,\n",
    "    scatter_plot=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting adata.X to dense array. For large datasets, consider pre-computing and saving as sparse matrix.\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)=0.5119,label score(100)=0.7537\n",
      "neighbor score(50)=0.4533,label score(50)=0.8055\n",
      "neighbor score(10)=0.2926,label score(10)=0.8669\n"
     ]
    },
    {
     "data": {
      "image/png": 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UMktCSWKKQG+xH4CRGYTPlvTTrIicT4Ov+WQv9ZzhhuWN3Pqu86bVGWo4CXWGbr75ZsbGxvjCF77AwMAAy5cv5+6776atrW3W5jyEV2fIw+NUYxShlIJQPXg1VjzOMbZmCwyWDZaF/DRpCmP6MIIgsHH8EXpLneQtBdMRicrlyjEXxC7h/Njsu05OV07GPdSyHZ45OM5wtkRd2HWNzZZF6HTAswx5eJxKuh6F/Q+AbYIWhVXvgUgTWDqUM+Cv8QSSx1nNkqCfZk0lobq3o6TmBstelriOB0Z+w0Q+Q0yuzl46ZCHyGrrOHpIosL4jcaqXcdLwLEMeHqeKwig8+Z9weImzSAs0Xwh7/hessiuGVtziCiQPj3OQzvwB7hu5Y9p4nVqP6dgsCC1lVWTtSUm/Pl3w7qEnHs8y5OFxMtHzIPtAlDB7DyAfWes10wvZfnAm668Ux2Hrj2DetRCdA8Hak79mD49TSFugnagcJ21OVI0P625M0dMTjyAAq6IXnoLVeZwtePZ3D4+TRSkFqS44+BDOvgexMgLOkTkbWnRKCB1+3M7bYfO3ID9yslbr4XFaIAgCG+rfSpt/PkEpTFiOV7blTIUhPcju3K5TuEKPswFPDHl4zBa2BZk+V8yAaxGSFDBLCMVh5KiB03wlSKq73Z+A5rVHP5+Rh67HZ3vVHh6nHVElzg31b+ZdrR9kVeT8ynjK9JEyfRRMk+7CAU5B1IfHWYLnJvM4LvS+HNlHerCyOoHz6gmtbXjV53RsB2uihBRRERTpBKzyNKM44Qohqwy+mCuGEgsgkICxfUiWDrFWmP/3oOfcfWwDEKDzD25Q9ZHoGfd/S3ddaL44yF4Qqce5w/zQUrZlniVtTlCjlAg7BnVaA4PlPmq1BvxS4FQv0eMMxBNDHlUYo0XsrI7aEqoIFHO8xPA3nwfDferSD2bAcQhd+MrrTei9WcZ++iLWeAnBLxN7QwfBNXUn4hJOGo7j4Bg2oirhWDbGUAEprCKFVTc2KD8MjgPhyeDndM+kMIqDGnIFjb/GtfhIKkzsd48LN8JFH4ehrXCguhorcgAGngct7B4PEHr1wtTD40xBEzXe0vRu9uV2UbJLzAssxHQMTNv0hJDHK8YTQx5V2Dkdu2hily2kSTGUe6qvIoQOkd809KrE0Phte7DG3XRZp2gycfsefAtiSCHXZeRYDnbJRAzIp1eWiGO7Fh8tgjmqY46VUJpCCKKAlSrj6JYrhiTFtdhoYdciBK54sU1XCEVkUAJgltygaVGeTKGfvFbFB7WLITuAMzbZvDLWjqCEYHwf1C6FYNK1Jnl4nGOoosbSyOpTvQyPswhPDHkAYOUNjL4c5e4MiCDWaJR2jaPOi4I63YV1pD5xDMu1jMQ0pJCKldOx8wZyMgC2ja3bSEEFK13GTJcxhwtHLMChuGuc4AX1CIKAMZRH780iiAJywkf+6UHMtI5/WYLQxU0IooBjOZQPphBkEW1udYPH2cJJ9WDtfwFqGhGiy0EUEGQBMaCg1AcQA4q7o6RCTUf1wbG5rpiSFHd7cQJkFfxxUIKuSMoPg1kE2Q9GGcKNCPXLoWa+K5byI5AfcrPKvMwyDw8PjxOCJ4Y8ADD6c4z//EXsghunkv19N9qCOGJQwb8sSf7JAZziVAyLNj9WdbyVM7AyOjgghVTMkSJ20cQqmpgjBaSgitYexRguYOsWUo2vYhkCQAArVaK0ZwLf/LgrKiYFz+gPduKULAD0g2nsokn0ujZwHBzTmZ599UoopUDSQPEf+32aELHGHJwSBOb5kRN+N1BazyDHQyAeI/ZJlIDJ7UbBFTVHiqZyxrUe2QYEE1C7BCwD0t3u8TUL3ErVR8YJ2SaM7XXdcslFx16Hh4eHh0cVXjaZBwCFF0YqQggAG8q7J3AcByXpJ/5HCwhc1IBvRZLYHy0geISLTIpqiH4ZFPcjpdQHkBI+yp1p9J4cjuMgKCJKYxC1IUj8rQsQg64VRVBEIte1IYZVnLKFo5uUXhxH78pQPpiuCKFD5DcNuMfJIr75MbR5r9IqZBQgO+DW93kJ5OZG7Pr1yO2L3YFMP/Rvdv+luqCcdcXRS6H4XYtQIFk9Hp0D0dYp11q4EaIt7v5KwDXJzRQwbZRgeCcM74BS+qXn9/Dw8DgWtgUHH4Ntt7v/H8/32qvk0Ucf5cYbb6SpqQlBEPj1r38963MewrMMeQBUW2kOo7RznMCyJIFlSeS4DytVRq71I8fcG7KVLoMsIgZk7KIJRRMn4UfwySiahJn0I4dUfAviCLLoxgSFQE74afzkhRiDeeQaH2JAobhjFMeyGb9tD6Vd40ddqyBNaXhBPgF6XvaBL+q6pmbC0l0XlSiDA1prGCnucy1SegbMsrvdKLjxP76YK2IOx7FdkaKGXDeZIM4c+CxrwBFiRxBdF9ux0EIQn+taiHxeRVoPD49Xwc674N6/cx/2DhFpghv+DZa+YdamzefzrFq1ive973289a1vnbV5ZsITQx4A+BbFKR+YblEQQ671xhwvgeO4QqjGtVrYZQu9L4cgCfgW1aA0hcB2LUCl/SnsnA6i4IqdmeKOZBG1JYxdMtH7cwhBBSerH1MIAYQuPcEdqwVxKuPrSCwdJg6AqEBNB8ZAHrtoIigSUkR1RUpkDmADIuQHXQvOkZTSkBt0xVC09cSu/xBN5x91k+M4p1cguoeHx+nJzrvgtvfAtOr4A+74TT+cNUG0YcMGNmzYMCvnfik8MeQBQOjSFvSBPMXnpyocC0GZ0DrXwmEMF8B20DpiFcuMoIhIMa0idA5ZiwAESUBQJQRVRJzMEDsa5kTZtTgl/UiHApCPXN+VLdg5A60jdnJT8AXJFUKThRHlhB8rpyMGJ/90DrmzwA2INssznARQJwOkT0H2V2ZkmJHug9S2zSOS9IKuPTw8joJtuRahI4UQTI4JcO/fw+LXnXVxiZ4Y8gBc8ZJ4+2LM18wl/UAXAhC+dg5y3L3ZC6qIXTAR5CnrgiAKqE2hGc+nzY1WrBF22aK0dwJBlVBbwwhitYVCTvgwx4pkH+0FB+SGAObgVLaZOi+KNieCrVuIquSm3PtO0kdXlKoCnKWI6lqEyllID0+6ziy3waoz6VM3i+CEq7vNS+rsWYReAtty1+WcBJ+/h4fHGUzXk9WusWk4blX9rieh/bKTtqyTgSeGPKqQ4z4SNy2avsF23HR2w66K2TkmloNVMLANy3WDiQJSTEOOVsfEWOMlxn++G8zJrDAB/CuT2AUTdU4Y39IEdkZHSfgQNBlBOw2eSMyi60LLDU0VUAwkXUvS2F73C6VxdbUgOkXEGhoJ1SSQ1WNb6Dw8PM5xckMndr8zCE8MeRwX6pyIW235MIuMldXR+3IoDQHkmG/aMcZAHiurIzcE8c2P4VgOUnC6Gyz/zOCUEALXGutA7Z+tmBoy7RMTLP1qGNsD6V5oPN+tHJ3pdd1ih+oElVKTgdSTnektvdqNdgrxhJCHh8dLEqo/sfudQXhi6BzHNizX2qNJGL1ZxKCCkpwKADZHi256fW0AVImiZfOrvYOkBnIsSYRYL2s4uitkHNtxCyg7oPdkKHdnkEIaUlBBiGmU96fQe7OvqEDiSRNCRtEVMDMFG6e63KKHmW4ItwCCu5/kAz3rpufLPmhYA9m+00oMeXh4eLwkbRe7WWOZAWaOGxLc7W0Xn+yVzTqeGDqHsQsG+S1DOBaozWHK+924Hvlyv+sSsx03cBrXfSbIIu/cup/+vixzchb3jGW4fkEdH62twTFtygfSCLKI0hTEHCvhlG2UuX5ETcIYL1I+mEaKa6htEQRBwMrpCLJIYG0DuWcGwJz64wuue+WtPl4RmT7Y8Uu3EKK/Bpa8ZXoF6fpVMHEQCuOw9x63tk+8zW28asdAi7iB0rbtWoj0vDvm4eHhcSYgSm76/G3vofJkW2HyAfGGf5214OlcLse+ffsqrw8ePMjzzz9PTU0Nc+bMmZU5DyE4jjOT/JtVMpkM0WiUdDpNJOLdLE4F5kSJzIPdlA+mkWv9xN4037UM+RR8h1WXtjJlHAfkqMZz6Tyvfc6tctySt2nP21iaxC/euAbZdijvT1HuyWDlDcz+PPpoATnuJ3b9XJSGAOWuDEpdAKU+iF22KO93W2n4FsbR+3JkH+/DTpfxLakhfFnLyXszHAee/H9QHJsaUwJw6d+7lp/R3a4bTM/B0A4Yfr76+LrlsPxmN5gaJusP5dxjzrKMCw8Pj1PPrN9DZ6wz1OwKoVmsM/Twww9z1VVXTRt/73vfy/e///1Zmxc8y9A5iV0yGf7GC9hZt+u5NVaitG0MtTWEoFV/JKTIVLCzMZRnftZiX1hiICDityGrQda0CPbnGbtzL05KrzrezOcY/cF26v/6AvzLkpVMMkERkSIqwmRavtocouaPFqD3ZCtjs4FjO5QPphEEAbUJBElx434OF0LgFlDMDbiiKNvvurtsG9Kd0086vm9KCIFrFfIsQh4eHmcqS9/gps93PekGS4fqXdfYLD/cXXnllZwC+wzgiaFzCseyMUeLFHeOV4TQIXIb+4kl5iEYNhCc4ViHZabEKlui17QJWrA3LHJpLMTEaJ7Cf2+Fo2VuW5B/sp/Y66fcToIooLaEq3YTpJPQcNUBTBvHMSEzOpU6rwRcAXQIUQZ/AqwyRFrBF4fOR3CLKx55zlPzx+vh4eExa4jSWZc+fyw8MXQOYY6XKO2ewBjMT9smCAJaexTHttH7c8hxn9tr7NB2ScDfFuFT9Qto3zVAKlWkqTbM9aJGdNMwpZcoYSOFT49sJgETrT0CRg7BjLhd4wURFr0Bdt7utrMQRJh/g9s2I93tusr8UbdOUDntNlM9HHWGitMeHh4eHmcMnhg6i3FsB7tgIGgSVkbH6M9hl020JXHKB9PYmSnrUOjiJkS/jDFaxEqVwXFdV4cjBRWagwofXtrM1t4UWo2P5pxDWSkecx1KW5jQpScxBuholDOQ6UMQZVf0qEG3TUY5A/UroWa+mwUWanBrB2UHwTLdAGlJg+bz3WNSB6rPm1xyaq7Hw8PDw+OE4Imhsxi9N+v2G5Og8MwQ5ogrWsSATPxtCykfSGNldPzLk/iXJSi+OI4UUZBr/UjRGTqjHyKqkbED+CURX0MQdU6YwrZR7InprSikGo26969CkE6HvliCK4K0iJv2roaq22eoQUgsdH82y5AbxMgMsDudpsF0SNY0Q/0KN26o92k3ULqmA+Zde0quxsPDw8PjxOCJobMYc7yEPpQH264IIQC7YJJ7or9S1NBMlxn6/55zW2CIEL6ilehr5h71vGrJYm1XESWsQVRACijUfXAl4z/djd5V7UJyytbpIYTG98GuO6E4DuFmWPY2N4X+UK+ww+sKDW2DvXdDKYUZaGAktJxyaoRkJOEGUi96gyuALMPtdu/h4eHhcUZz6nsFeMwacsKPUhvAKU8P+jVHpoKF0/97YKoXmA3ZP/RM62Dv2I5bVBGw0mWE8TLORAlnsnK0HPVR87aFIFcLH/+y5Im8pJePY7tWnq0/cYUQuK6wbT9zfxbE6pYZxQl3WykFgL8wyIXlgywKqG7doEMoAU8IeXh4eJwleJahs4TC1hGyj/WBYeNbnsC/uAYhKKMk/NgFgzITVftrC+KVn/Wu7LTzlbvSaPPcm70zWUMIAbR5Mbca9QoBKaRWOtYDyEk/yfcsI31fJ1a6jH9pgujr583OBR8L24LcoGvFKYy6afNmqXqf/JArjtSwGyh9iPF9HFl5NZjrhXiL61bz8PDw8Djr8MTQWUD5QJrxn71YuYcbg3nsoknkihao9aO0hhAc3GrTJQv/0gSx102JFLUlRDFdHe+jNAZxrCOask6eX1BE1Ibp6fcAvoVxfAvjM26bVSwdyjnwx9wU+XJmKlVei7rWH+cwC5kSxMkOIIgjbpyQILgp8uIMfxKBpFc3yMPDw+MsxhNDZwGFrSPT2siY/XmkiFYpmhh93Twir2tHcIRpMTyRG+aiD+SwxqcEUeb33dh5E/+yBKJPRuuIAVSKJp525Ibcqs+CAL4YTqAe21QQwwEEQYSO62Df/YAreDKtl7NxLMv8aJx5Y3ugMAbxua41KT4PJiYzxiQVFr72VF6Zh4eHh8cs44mhswBxhk7wUqw6G0wQBQRmFjKCLBK+Zg6pO/ZWagoavTmyT/TjX56oHH9a44+71h81BIKAmdUwx0so9TJywg9zr3R7i2X7AYFCsQCqD73vWUhvmzyJAItudFtrlLNQmoB4Byj+U3hhHh4eHh6zjRdAfYZj6xb+FUnEyFRRQ0GT8K+pI/tYL/nNg9gF45jnkCIa1kR5WnFlsz8HooBjzVB1+XRDDbm9cybjf0S/jKCKCLLhNmE1y65gSiwEUaQhGOJyrcSiihACcKD7cdclFml2e455QsjDw+McxLJtnh7Yw+8ObOLpgT1Y9uzeB770pS+xdu1awuEwdXV1vOlNb2L37t2zOufheJahMxC9L4eVKWPbNmZ/HqU+SPIDK8g/1g+iQGh9I2M/2IE56gYNp/wHid04D7UphDJDrI8gCVXVpg8hJXzoB91UeW1+7PS3DpXSk6nzjUhRn1srKTcIxQyOqeOotYihEETnQmEM38iL089xZHVpDw8Pj3OM+zu38MWnb2OwkKqMNQRifOqim7h+7ppZmfORRx7hwx/+MGvXrsU0Tf7hH/6B66+/np07dxIMzhyjeiLxxNAZSGn/BLlHerHzJgCCX6Lm3UvR5seQwirl/amKEAJwiib5pweQrmmrEkNWVsexbOSYD/+SBHpPluLzI+45FZHojfNAEqbFI51W2KYbL6SG3Jghs+T+k33udn8CLAPzwD7M/DDKkpXIYRvKacq+OKKooNiHWc5ql52a6/Dw8PA4Dbi/cwsf/8O3p33tDxVSfPwP3+arV71/VgTRvffeW/X6e9/7HnV1dTz77LNcfvnlJ3y+I/HE0BlGfusI5X2pihACcIoW2Yd7kIIKUlTDKU9vFOYYNmpbdUZUuTODXTTwr6xFrvGRePti9CtaMUeLaPOiSDPEIp12GAW3JpBtuA1VZb+bFebYbgyRpIAWQVBkkCQERQJFI61EuF/wk+h4F1cP/96tL5RYBItef6qvyMPDw+OUYNk2X3z6thmffx1AAL74zC+5Zs4qJHF2o2zSabfWXU1NzazOcwhPDJ1hmCMF7II5bVw/kIJDw/J0d5Z/WRJRk7BLpmsR0m0cw3I7uJdM8LkfBbUxiNo4+ybJE0Kqy60qnR+CSItbVdosucURLR30LPhqwB9HXrAKWfZXYoq6pBr6yKD4YrD2L07tdXh4eHicBmwe2lflGjsSBxjMT7B5aB8XNS6ctXU4jsNf/dVfcemll7J8+fJZm+dwPDF0hmAM5ck9NYBdNvGvTGL05qp3OFwfmY6bTeaAXTLxzY8RvKyZ1F37yT09AJaDXB/Av6oWqTXMhCJS+zLWYpfdyUTtFH58bBNe+BEYk1WhM73wwo/h/D8DBLdekGWAWQShZlqdoBa/QlKVafGp08/t4eHhcQ4yUky/9E4vY79Xykc+8hG2bt3K448/PqvzHI4nhs4AygdSjHxnO1iu8bK0N0Xk+jbyGwewyiZyVMMcPqJzvAC1f7EKQRQQfTLFnWPknuyvbDaHCuzqTvFnSprRXovlIT/fXNbG/ICv6jRdxTKyINA8KRocy6E8GVTtWxA/dX3Hsv1TQugQhRHs1DBCzTwERXRbZkgzix1FEKlTFXT7dA6I8vDw8Dh51PqPr8XQ8e73SvjoRz/KXXfdxaOPPkpLS8uszXMknhg6A8g+0lsRQgBOzgBRoO5ja9C7s+h9WbIP9lQdo7ZHGf32NszRInJdACkyXRSMDeYYbQsAsD1X5C93dfO786dMnyXLZnfeDcRu1BREQcAWHEy/hCaKp7YwwwwaxrFB781BKoW2MI5wjLT4sCxxcSyEerpnyHl4eHicJC6on09DIMZQITVj3JAA1AfjXFA//4TP7TgOH/3oR7nzzjt5+OGHaW9vP+FzHAuvztAZgD1TQHTZQhAFHAG0hXGib5jn1hrSJHxLExRfHMMcda1F5nBhWuNVgN3h6l//5kwB/bBaEj5JZGHQx5KQH1EQ+Gn/GKue3MnCAwf5k4kRxo3p65p1LMO1ConS9G0CCOE6BL+MIAhumryen77fJCFZQp3lIEAPDw+PMwVJFPnURTcBTCvRe+j1py5826wET3/4wx/mxz/+MT/96U8Jh8MMDg4yODhIsVh86YNPAN6d4AwgeF599YAIgVW1CH4Za6JEaesoUtJP+OpWghc2uFljhSOEyhHuIF2A78yrthYtCGjTxMFcv0arT+XFfJG/2t3DqGHiAL8fy/CZfX0n6hKPn3LarScEkFxStUmoX462sAltTsQNoM70QaZnhpN4eHh4eMzE9XPX8NWr3k99IFY1Xh+Mz1paPcCtt95KOp3myiuvpLGxsfLvF7/4xazMdySem+wMIHhhAwiQ2zSIqIiEr56DXBdg4jf7KGwcdHd6op/AugYiV7bilC0yD3SCeZgAEqhyLakOvFP08w3KWEBUlvjXhUf3zz4+kZs29uiE2+0+b1logoh8ElxOthNG8DkIqs9tm9H3jBs8HZ0DLRdN7SgqbsXpmRqvenh4nDMYfX1M3H47Tlkn+sY34ls0e1lQZwvXz13DNXNWsXloHyPFNLX+KBfUz5/VdHrHObXxm96d4gwhuLaB4NoGAKy8QX7T4JQQmqTw7DCBlXX45kWJvX4eqbsOuBYhSSB8ZQu5x/srNYjU9gifvGoh77MtDhbLrIkECUhH/6AvOCKw+tBYyjB5Jp2nRpG5IDq7KflWVkfvySFpFmqk1+1G33bZzDsLAoQaZnU9p4JiPkd2eJhYYyOqz2sV4uFxOI5tg2kiqK7VW+/t5eBb/wh7smbNxI9+xJwffJ/AeeedymWeEUiiOKvp86cbnhg6E3EcrJw+fdywMYbzqC0hQuuasDI6+Y0DIAkIskTD/1lLafc4UkittNdoQqLpONLLL4+HeGt9nDuGJgCoUSQ+3FJLdjCHT3YI+Gbf4yqoIoIqIQZFN2ZIPnfS4nt2bSc7OoJeKFDIpOk4/0Lq5534IEYPjzOZ0tatmKkUgQsuQAqFSN32y4oQAnAMg/Ef/BCluRkrlULr6CD3+OOk7/w1YiBAzXvejW/JkmPM4HG24omhMxAppBK+uIns471QnAp4FuMqok/CGMxjTZTJPjQVL5O5rxMpqhI8rx7HtF9WnzHTdshYFl9f2sZfzKljoGywMuTngQMjDA0WuK4tQW2jn125IkXLZlUkgCSceJeZqMn45scmX8VP+PlPJaVcjlI+R7Su3g3+BiYG+rBME0GSOPDsJkRZJFbfSKS2jmBNAtuyEKUZAsk9PM5Bii+8QP+n/gH94EG0xYupee97sHLZafvZpSJ6Zxd2Pk9pxw4GPvUPlW2Ze+9l3p2/Qp079ySu3ON0wAugPkMR/QqJP1qEnPSD5DZVDV/ahJIIIMd8FHeOTTumtHMMK12mtGcCY7hw3HNtzRb4xcA4z6RyLAv5uTYRwb9tDGfHGIyXKKsijuMwWDYYNUxKs9zd+Gwin5rA0MuMdB9krLeLzPAQo92d6MUiY329DB7Yx76nn0SQRBrmLWTZFdeQbJnD3o1PsGfj4xQys1v8zMPjROLYNnpXF1Z2ukg5HGNoCHN09LjPa+s6PX/xYfQDB8BxKO/axejXvk74hhtArn7m9y9fgVQTxzF0xn/4o+r1FYukfv3r457X4+zBswydwahtEYIXNeIIDoIoIMgiiAJSREXQplsMpJgPRMFNQX8ZliFFFCjbNmOGwbPpPMKmIep+38cVwIQCT4yVWPi2xVwYDaI7DsFXa60wy4Az1Wz1DMYol8hPTBBO1iId9qU81tdDuZCnmEnjC0WINzRRzGYoZNMU0ikGD+xDVlWa5i9kYnAAcIg3tdC7c4f7uxYFBK8sQAVT1xFEAUk+A/rpncMUt28nd//9+C+8kPARzTdtXccaGUGMxSjt2AkChK68smIpPRal7TuwxqofAI3eXrS2NuZ89zuM//CH2Lk8gUsuRlJV7HwBR9ex8zOU3pAkjIEBzPFx5Pp65ETiuNbgcWbjiaEzDGOkgFO2UJpCCKqEnPSBIqI0BDF6stglt1WGb2Gc4tYRnJIbMC1FNUKXNSOFVZTGkFuDSBHBcrCyOmpTyK3aPAMrwgFqVQXHcdiWK5LfOUIdboKa6oCT0bG7MgRXh3jVIdSODamD7s81C2auJ3QG0ffiTtIjQ8xZvop4QxMAhl4mNdiPZVoEojFC8RpCNQl3/z27kGSFQDSKgECwJkG8qYVCOsWejY8jiCJzV51Py5Ll2JZVJbDORSzT5MHvfIPtD/8egMYFi3nj3/wDgcjsVcj1eGU4joPe2YneP0Du//0n/X/5CbSlS2j4x3/Et3gx+v79GAODyC3NFDZvwujvBwfCV1/1kudWmptBksCaKikiRiJIsRjB+nq0efMobt2GVSyCZSH6ffhXrSJw4YWke3vd5s6AGArhW7ac9N33IMgSUjCIf/VqtPlefN7Zzrn9TXoGYk2UcUwbudZC1GS0jhiCJCBIImJHrGLx8S2pIf7OxZR3jSPHNYLrmhBVV1jYBYNyVwZjpIAgi4iqiF3jQ1KOHpDcoLlP3KooMpGdcoOFTLhgzMIWRAzbQXm16fWCCGp4quv8MShYNhOGWamOfTpSzucwy2VU35SVS1E1atvmIckywdhU7JNeLiFKMjVNLcQaGihmMvTu3E4oXkMwXkMokSQQiSJrGnueepz6jgXE6s++jLmXw3P33MW2h+6vvO7fvZMf/u1H+LP/+g6yWv15LhfyOLaDLxQ62cs857F1nfyTTyL6A5S2bcPoceMZi5ufpfvP/px5v70LHAcpHmfwn/6J0qbNAGTvvY/khz9M9M1vwujtxbdsGVI4PO38Sn0dyQ9+kNGvf90dEEXq/vZvEDUNAEHTEFQVrakJyachNzUhqiqxm96G2jaH0r59CA74zzsPQZZQmpuQwmEcy0IMniGNqz1eFZ4YOsOQa/3ovVnsoiuGDgkcoOpnJ2cgx3wIC+JIERW9J4vRl8O/Iokc9yHIIrZuogR8iH4FKTyzEBrRDXbnSyyUZKIpg3h9EOWiJjK/767s09fkY6JW5emBMZo0lQtjQcLyq7DoRJoZLhtMFMosCGhHFTq7ckXGDJO+kk6TTz0tm642LVqKqZcJxmoY7emia9vztK1cQ7JlTtV++dQEE/29hGJxapqaAZAU1xqXT6dIzmlj3poLkGSFg1s2M9LTiSNw1oghK5fDGh1FaW1FOMzN6jgOo92dCKJIsrVt2nH7Nz89bSyfmmD/s8+waP2lAIz392KZJtnRERzHYe6qNZ477SRT3rOX0vbtSIlERQgdwhodJfWb3yAFApjDIxUhdIjx732P0OWXYReK2Pn8jGIIoPajHyGy4QZKu17Ev2YNaktzZZsUDhO69JJpx8iNjWgdHURe+1rK+/ZRPnAA/7JlWLkc+v4DaAsXoHd1o/f0EFi71nOXncV4YugMwLEcii+OgWm77jFJxC4YENNm3N8uW+h9ORDcHmWiJlHcOoJdMjEzOooqodT6J9PUFeSjnAdgXDcpbhmm/IdBRgwbQZOIv20h8bctpPTiOHKNj2XrGvhDuchYya1OXbT8xxRDpu2wKZNHEaAj4COuTP8Y7imUKFg2NbLEd/tG+W6fG0z5J81J/k97A4Ig0ORTMBybId0kZVo0nQQLkWWapIYGKOfz2JZJ44JFx7yxhhPJys/ZsVGK2QyZ4aFpYkhWNWRFq7JajPV08+B3byU3NkogGuO693+UxvkLKRcLxBqaaVu++oRf36lC37cPc3wCQVVRmpoq47ZlkhkdASDR3DotTipSW0ffizumnc8yDQrpFOnhIQb27aaQSdM4fxGheA2idPxfe8bQMOM/+AFGXx+hK68k9uY3uefPZhFDIe/meBSsXB5BoGJVEQN+1PZ2pLo6BL8f5/AWC4KAFI4gRyIYwyPTzmXrOmpHB2pbG1Isdsx5tfnzj8ulZWWzFDZtonywE6dQQKqrwymWsLM5jP4BkCWMwUHU9rk4ho5j2Ti2Te7hhxE0jeAll3i/+7MMTwydATimjTGYxylbqO1R1DlhRL+CmS5jDORRm4JIkSlBIygiUkzDGMhh9GbROmL4liVRW3WkmIogiahtEURVqsQJObaDXTLdMXnqhuM3HRb+YRDBcF1jTtkidedeGj95EcHzp9qE3GCqdBd1alWJOm1mcZAzLYKSyGDZ4IVMgc5imRpF5h2NCVr91VadxUE/adPkgbE0X+4aqox/uWuIORR4x9w2GjWVRk2lq1hGEoQTLoRMw0BWFGzbwtQNyoUclq6TGuwnNTiIaer4QhGSrVPCxjJNRFGcMbh5zvKVBOM15MfH6d21nZYlywHQS0WGDuxDEAUK6TShRBJZVrj7a/9BbswVgYV0inu+9v/Y8OFPoBcLNC9ahv8oT8inGlvXMXp7URobEf0vXRjSti3GbANBgmAyWbVNkhUaFyxCEGYOGL/ilvdxcMtmSoelUPvDETrOv4ix3i6K2QzBeAItEKShYwGBSJTx/l5Uv59wTXLa+arWVSrR+Y53YPb3A5C97z7KvT1ErroKY2gIrb0dbd6843lLzikc06SweROCIBC89FIESUKbN49yVxf5ZzYRfctbSP3kJ5X9o295C+Grr0KOx5FbW0jddhvW8HBle+S1G5BOoGsz9/gT9H3sY9gFN6M2fN11JD74AcxkEgcH0e/DzuWQa5OImoY6Zw6lPXso7dxJcfsOBEUmeOGFoJ5+lmiPV44nhk5zHMtGkATkpB+7YCIoIoIoYOV0HN0C28HKGa7bq2SBJCBHNZSGIHbeAMDWLZyCiRTXKjFFUlCpKn9e3DVG5oEu7KKFb0mc2GvaEf0yNRmTlFGdKm/nTczRAnrSz/+OpinbNq9NxlgWnvnGtytX5GO7utmWK9LiU/jInDqaNHf+kuPglwRsx6FkO5Uq2ElVJqnK/OuBwWnne7BzJ+/o+RmseAfUdNDmP7pl62W9145TVeNnvL+X2rZ20kODjPZ0uXE7NUlEWaF50VImhvoxy6XK8aau07NjK4rmo2Xp8mnnl2SFRHMr+fExTF2vzGeUShjlItnxMcZ7exjYt5vGBYvIjAxXHa8XC0wM9CPJMsVcBss0T6sAanN8HGNggNTwENbQEIlVq/EvWYLjOK7YE4QZC0VahkHZsTFVmdToCPGGxirhE4hE0YsFRrs7idY3Mrh/L/n0BO2rzicYr+FD3/4xj/z4u/Tv3kVNcwvr3/oOtECAREsbhUxqsnaTiCAIlHI5UoP9SIo6oxhybJvhroMomob5zGYGMxOEZAmfaVFQZEZ+9UvmJ2rwSzLismWz+n6eqdiGgej3I2gaxvAwUjzO1kwe6eFHiQ0OEL/lFmre+Q4Kzz2Hb8lS/MuXYeXyGAMDOIZJ3cc/Rv6ppyju3IVv4UIaPv3pV7UeY2iI0s5diMEg/hXLGfynL1SEEED2gQco79qFfuAgY9/9LnaxSGTDBiKvfx1ybS3m6CiCJCPICmp7O45hYOXz02LSzjYcy6Kw+VnMkRHk2loCF5xf5cI+0dx6663ceuutdHZ2ArBs2TI+85nPsGHDhlmb83BOn29Sj2k4jkN5fxrHcVDqAji6jTVRdq1Epo3WHkWdF0XvzGCOFUFwU66liIogCmgdMQCMvhxWVgcR5JgbyGuXLcqdaaSggtIUIvXb/dgpt6p1YeMg1kSZ2vctJ9gYJK1JlTYeAGJApmskx00Huugvu4Lri/sH+M15C1gY9E27hg/s6GRPoQxAb8ngiwcG+e2a+byuLoYkCGzNFvjdcIp6TeHCaIiEOvWxbPdP/8Jpd7Jg5GHXHXDx37xkoPXRsG0LyzBQNB8HtmymkEpR1z4Po1wmOzZCdnSE2rnzECUZfzgCjsATv/gRY73dNM5fyLq3vJ3EkXEsR7FgHEKSZVqXr0IQBEy9jKyoBGNxmhctw7Isnu77BTseeZBHf/qD6QcLAtsf+T3BWA35dApTL+PYDo3zF52SoOBiNgOCgD/kWqiMvj6M4RHSxRy2ZRL0aaimweC+vYz39xKIRIk3NU9rI6JoPpoWLWVg724mBnrRAoGqwHKA1NAg2bEREAR6X9zBwS2bmejrZc2G11NIZ7jiXX8yrQClFgigBQLVY8EgNc2tR21lYuhlcuOj0NNHetMzpHwKimGimhZ5RaYsgFBTQ2jN+Sj1dRgDA+A4Va49ANuyeOGBu+nZuY1k61zOe+0b8AXP/sBtx3Eobt6MY1loDQ2Ud72Ik0wwEooTSiRpnNNCcO0FiH4/WkdH5bjyi7uwMll8y5aizW0j+sY3MvY/38Eul7COESd0PBi9vRSfexbHATEaxejqnrZP5003V70e+/a3kZNJfAsXojQ2ItfVUdi0CTuTQYpGsLNZiJ9dhV8PJ3P//Qx98UuYg1MPo3JDA/Wf+iSR66+flTlbWlr413/9V+ZPujl/8IMf8MY3vpEtW7aw7CQ8eHhi6DRGENzaQY5lI6gSUkyjtGMMfTCHGFCxxksotQGkiAqWg+BzXVyHrBsVK1BMA8G1BlVwHLAdHNtxA7JT1e09yrsnKO4aQ20JU/OOxUzcsQc7ayBFVUKXt/Dlcr4ihAAmTIuvdQ/x/y2pFgePTGQrQugQGdNizLBYNLnObdkCO3IlZFFAPSIb7Y11Me4aTtFZctc3z8nyfmePu7E4AUYB1Fd2kxnav4/R3m5ESeTAc5sIRGOAw+CBffTu3Ep6ZIR9mzdy1Xvfz6KLL+dn//g3DOzbDcC+TRtJDw/xnv/7X5XzyarK3JVr3L5oh1FIp3Ach2AsjmPbyIpCPjXB4P49hBO11M2dhy8UIjdpMRrt7px5wY7DeG8P47099Gx/gU133UHbyjWcd8ONBBMJendsp2XJMurmzq7rxjQM7rv1K+x+8jEQYMmlV3L9Bz6G1tGBEwzSWhOna9vzjI+PIIVDlPJZArE4pWyGfZs20nH+hWiB6gwdfyhMw7z5DHcdpH/fbpoXLqlKj481NCLJMtG6BiK1tSiahlEu0rtzO4VMGkmWiNZNBZM7to1t29MtZ45DJFmHqesUc9mKkDuE6vPT0LEQo6BDewfp557FN56jpMiUVQl52TLU5ctR6utwTJPSrhcBkGtrEZSpv6/f/8/XK1lue59+kqdu/ymL1l/Gde//6DSBdjYhCAJSJIJjmkg1NcgTE0i1tazYvQdn2RJqLr0EQZZxDANzeBhB81HYvAlzbBytowMxEECcFPbBSy6muHMXpZ07ETUN+TDx4dj2MR86rFQKvacXtb0dbdEizLExN/g6Eiawdi2FTZte8loy999HzXve7V6XJKE0NyP4/ChNjci1ta/ynTp9ydx/P30f/8tKuYFDmEND7vhXvzIrgujGG2+sev0v//Iv3HrrrWzcuNETQ+c6jmUj+GXSP9uFOVxE0CQC59ehNIWQwipSjQ/HtFGbZhYDVqaMOVZCbQlNe7ISfTK+BXEQBdeqdCSS4Fqicgb+xTVYr5lL7pFeHMH9wjOCChxRRPZgusjGsSwrYoFK4cW4LBORRTLmlKtNFqAjMOXaWhLwsT9fZkmgOvC6YNn0lQ0+O78JydZhxy+50u5HZfJcgQQoryztVS8VsWyLoQP7KOWziJJCOJEk3tTMk7f/lOxk0G7/7l3c8cXPsOEjf10RQocY6TrIaE9XJcvJKJdIDQ4QratH9bs3PNuyGNi3B8dxCIQjlAo5WhYvQ5RlBFFEmryBlgsFBg/spW/X9uO+Bse26XphC40LFhGeSFLIpJgY7J91MbTl7t/w4hOPTC4Cdj76EA3zF7Jo/WUMTIwQEhwaOhZgWxZaIIikqAQiMQ48+zSCINBxwUXopeI060wgGkNSFHp3bUdWVNpXTTXTVDQfoiyTHRtl7oo1RBJJ4g3N9OzchqmXUTQfvS/uIJKoJVJbR//e3ZTzOVqXrkBSFOxJEdq/50VKhTyWaSBJMs2LljDW34dRKlI7Zy7BWNwVrevWEVqzGmnZYsb/9x7k8XEizU3ErrqyImYEWUZbMB8cB0FRsMtlsCxMUWDHIw9Oe992P/UYWiDIde//yKz9bk4H/KtWVX72LV+O3tdH0DLd6s533IGTyRJYdxHW+ARiKEhx506wbbT5HRQ2bUZtbUFbsAD/ihWIgQDm6Fgl9szRdfLPbMIcHsa/csW0YGlb10nf+WvyGzeiNDcRe8tb0ea1E776auxcDnN8guhb3oJdLlPauhVEEY5SMV9OJKpeq62tqK2tVWPG4CAIAkp9PWcDjmUx9MUvTRNC7kYHBIGhL36J8DXXzKrLzLIsfvnLX5LP51m/fv2szXM4nhg6TbFLJuXODOl7D2IOu2LFKVvknxqg5p2LESMKxkgRK1V2Rc0RZB7sJvNgF9iuWyvx7qVo7VNP2o7j4FgOoiyi1Abwr0xS3DpV/j58eQtaexQxKFPaM0Hq9r2Vbam79vO6t8zl50fMeZ6gMJEtkQ35KmJoVSTAvy9s5WMvdlO2HUTg79sbqT8syLpfNyg6NgdLZc4/rGyjXxRIqjJFy2Z9rBbRuQRevAvMIvhisOymaVaY40EvFenZsY3H9u1nXHdY3TKXluYWWpYsw3GcihA6RCmXpXfndgRRwrGn3IWCIOI7zLKQGR0hM+rG+dS2tQMgShKx+gYG9u4mOzJMKJHEcRz8oTDz1qytHKv4NMyyjvYyXSmOY2OZJh1r15OfGMcfCpIdH33J4OBXiqnr7N28cdp4364dLLnkSgRBRJLdWkkAo92dGKUiJUmktm0ewWiM4c4D9O/ZRay+iUAkQuOCxciKglEuEa2tJ9k6l3C8pur8erHA8MH9dL7wHLH6Rs573RtRVI3mRUsZ6TrID//PR8mnJkAQWHnNDcxfuw5BEBjr66Zr2wvEG5uYd96FAIiCgD9egyhKlPN5RrsPMt7Xi2NbtK++AABBUSjncyiJJDVv+yNyE2PEfH7mrjyvIoZGe7pwbJvknLkAFJ7ZhGOZqOetOer71/3YwxTWXY5v4UJEn68qTu1sxBgYQN9/AGt8AjmRwO7qcusJTVZ1dnCv3XEcrFyuIiwPoXV0VLnTrHQac2wMc2wUx5ouYno/8hHyjz5Wee2YFg1//3cIsoyZyZB77DGkQICWr/0XgijS88EPUdo+/QFE8PvQliyl1NmJb+5cygcPImpalTvU0XVKO3cBICeTsyoOThaFzc9Wucam4TiYg4MUNj9L8KILT/j827ZtY/369ZRKJUKhEHfeeSdLly494fPMhCeGTlcEAcdxMAeP6CHmQGlfCnFAQY6oyM1TN2Mrb2COFBAkgczvu2BS3NsFk4k79xF7YweCAFbRJH3XAax0GaUlROzGDmrevpji8lGM/jzavCi+hVMCq7B1errrml0ZvnJ9K1/rHiZjWlwXC/O3yQSFqDotm+yN9XEujYfZki2wOOibVg9IAOKyxKpQtaVAEARwIG/Z9JcNWhpWQ+0yKKfBX/OKY4UkWUb1+eiXVNKJBpymRvzhEKrPjyhJ+MMRNx7mMPyxGM1LltK7Y1tlbOV1GwgddtOO1taDA5FktQk9VJNElPYRqaunddmKGeNVRFGiccFCVho3kB4epJBOue+BJOFYFpKsYJnGtONkVSUUr8E2DUrZNF3bthCrb0RWVDfO6VUy3t/LU7f/jNTQAPPWrOW8176BWH0jA3terNqvrr0DXyhE+5oLqm7uwVgNPS/uxHEc5l9wEQ4O2x68n2I2jShKyIqMZRjgOPTs2IYoyyy86OJp8T+qP0CiZQ7j/X0ok+9famiQQnqCTb+9wxVCAI7D1t/fQ6y+gZXXbuDpX/+SvRsfRwuFMHAo2gJzGhsr1jPLMhFEcbK0gVppn2JbNoP7dxOqSVDX3kHx4AGc4f1IC5dCIIBj26SHhwCHmuYWJFlBikZwdB01GGLZFddUFYM8RFJUMAcHsZJJBjIpjHKJ1mUrztq6R3JtLXY2izpvHtgWgfXrcCwLORSC1lZKL76INn8+dqGAIIgELlqLIMuUu7qx8zk3Xucw95hYU4Nv2VLkyy9HilVXGS/t3l0lhABSP/sZdR/7KOXubnKPPIoYDuFbsRylrg6A2r/8SwY++UnMEfc7zrdiOcHLr8AcGyX7yCOYw0NE3/Y2rIFBEIUqMSSoKmr7XARRPCuEEFB5H07Ufi+XRYsW8fzzz5NKpbjjjjt473vfyyOPPHJSBJEnhk5TRE3Cv6gGpSmI0V/dP0dtDGCOl0EWUVpcS4KtW+g9WRzTxkqXK0LoEOZwAUe3sA2biV/uwZnMEDN6c0zcvoe6j64hsLIWVk73hc9UkFFQRd7emODmhhq6SzoBSaTPsGiadHP1l3R25UssC/lp0BQSqsy1CffmXLRshnWDJk1FEQXa/Bp1mkpHcHovshafiiAIJBSZkmWzu2AQlcKMZIo0aQrNr6DQ4p6SSbGlg0YlwkQ6S8kqoBcLmIaOTw1x2Tv/mPu//V8VU/GKq68nVltPsmUOoVgCRdOYv3Yd7WsuqDqvrKokWqrN6JmRYbq3v4AaCJBsbTtq4C5AtK6BaF0Di9ZfysHnn0XVfLQuX0lmdIRgvIbOLZt58clH6dmxjVIuS6gmwZob3oAoSQx3HSAzPITmDxCKxafF5LwS9FKRX3zu7yvCbHDfHgqZFFf98QfIjAxX6vvMWb6SNa95PbZtUcrlECUZQQAtEGSstxvBsd1Wc5qPicE+QjUJ/NEIgXCMWEMTWiCAbVmIsoQWCEwTQqmhQWzLJNnaRqJljms9EEVy46OUC3lGZoixKuVzbLn3t2z6zS/dgSG472tfJnDptZiWVRFDxUwax7KJ1NZS197BaG83T/z8h3RtfR6A5iVLeePffIZQKIxsU6mPM9x5ANMwSLS0IooSlmmgN9QRqkkgSBJXvuFt1Ok2m55/msxk2r/mD9Dy+jeQj4TxKwqWoWPbFo49g0viLEH0+fAddiMzBgcp7dqFb9EilKYmtAUL3FgcUcTWdaRQiOLWrZR270EQwDGMKjFkHDiA0dOLFAggxGNu09dMFt+SxdW1iyZxdB3bMND37ccpFfFfuBbf4sWV7aFLL2H+g78nv3Ejgt9PcO1aClu3kr3vfqR4HHN0DLO7B3XOHKSammnn19rbT/A7dmo53lio2YqZUlW1EkB9wQUXsGnTJr761a/yrW99a1bmOxxPDJ3GCKJA/M0LGPnedpyCCQL4V9Uixf3INQE4zDBiDhWwMmXskoVcF3Absh72Jau2RfAtiFPcNVYRQpVjR4oz+4gnCa1vJL95EDvrWiYEVSS41g1WFQSBJk3l10PjDKRLXFYbYU0iTMl2sByH4gym7P2FEnsLJe4sTFByHBQRVoZmDiqt05SKpam/pDNUNhh0dDdQU+AViaHnMgVezBUBGSccpSbRQnNAqWT7rLj6epoWLaFnxzaSrXNoWbIcvVxCCwQRRIFEy5zj7n2VGhpEkFx3WmiGL9OZUDQfCy+aqpZ7qKfZwnWX0jB/IanBQcKJBNH6BoxSibHebvLpFMFYnIb5iwjXJI526pdF5/PPVoTQIXY88hDX/MmHePvn/80N9BaESszUaHcnw537SQ8PUzt3HvPWXEA4mURSFOra57Fv09P07tpGpK6eUKwG1e+fDFp3g8wtw8C27KqSAY7jMNbbDTiEk7UoqoZt2+iFAvXz5qMXi7QuXTGtEvXSy67iji9+pmrM0ctohQx+wc0kFEWJsZ4eFE2jYcEitECQ/j27KkIIoG/XTp6/97ese8vNlHftwtbdQH6jXEIvFhjr7qTQ2UWhvxehuZnEnDaSrW3Y6TQdy1ax9M1/xEguQ25inKaFixjcvYvCYD+ZAwfR8gWiS5ZAOg1ncUCuMTxM/rHHkOsbUOa0YpdKZB/6A8FLLkFrn1uJZxQUBb2nB6kmgTavHVs3MPr70WMx1Db3Myb4AwiKjDDZ3sbo78culrDb5uBbuRJtwXzKe/dV5vatWUPqF7ehdswjsmED2oIF09YnqCrqwkXkNz6FFI+jzetAeP3riIgSjmGAoaPN76i470q791Deu5fgFZcjBYM45fJx1dM6EwhccD5yQwPm0NDM9wRBQK6vJ3DB+SdlPY7jUC6XX3rHE4Anhk5z1NYwdR9eTXnPBOq8GAIOgia7GWQwlTkWkEEWsVJ5zHSJ0LpG8luGcYomSkOQ2Js6MMdLSPHp1hepxoegVj+NW5M1iqSgghTRaPjE+YzfsZfS7nEcwya/aQh1jlu40bRsAoZDOGfSIpuQgHkBjTpVJjRDJepmn8rXu4e5czhVGXt9bZTrkscWGA2aguk41MgSedsh+gpbfog4yILAmnCAlRE/cwNT70nRshFwqx0nmqesPKrmY87ylS97rrq57ZQKOfITE5RyuWkp4y+XYjaLUS4iKQqiKKEFgjQtXEIpl6OUz6EFAujFQiWA++WSHh5iYqCPuvaOGa1Y6mFf+odiZQ7hC4UpZnMIgoAouYUnD1m7tj54Lw9991tYpoEWDHLtn32E9tVTX6iCKFLIZMiNj6EX8giCQO3ceQRjcernzce2TBTVDbofOriPQjpF4/xFBGNxrn7fBylk0gzseREtEOSSt7+bRMucGb/LW0JBikP9FNJpguEI8aYmArEY8fpGAEa7Oqcd07fbDfA1htx4MLWlhaaFS4jU1jHe20Nx+w7sbBo5MdVwV+voQE4mkRIJmoWWyrlsYS8WEubQENZEitzBA1hdncRuvhnxNKoZdaLIP/UUPR/8EM7kDS146aXE//RPKO/eQ+HZZ9Ha51b2Le3ZQ2Hzs/hXrSRw/vmuFWlnEcc0K/uoLc1VbTZ8K1bgFItIEdfq3Pqd7zD2zW9S3LET/+pVBC66iPL2HYiqim/hwqOuM3vffWT/8AeKzz5L3Sc+gW8yTkk4rJaQlcsjSCLF57dgDLjVqU1RxBgYxL9yBXJyduL0TiaCJFH/qU+6WWOCUC2IJu819Z/65Ky4BT/1qU+xYcMGWltbyWaz/PznP+fhhx/m3nvvPeFzzcTZ99d3FqIk/Cjrq29MVqaMmdHBtLFKJnbGQGkIYI6XsMeKSHMiJN61GKU2iBRRMUaLmMMFxICMf0WS4jY3WFpQxclYoqk4D8dy0LvdmBnfwjiCJKL35yjtGKvsU3xhBCmqEbm6FTozXO5XEOvjaIe51I4UQmXbRrcdNFHk3tF01bb/HUnTVSzT5tdwHIdnM3nm+jWS6lQshSgIzJkssHi0MGPTchhOl5FFAfwCvWWDRQEfsijw6HgWvyRSo8gsCMCViQgRZWqNhu3wRCqHCFxZEz4hFa0lVaWUzZAdG6WmqflVi6HaOXMJTzZsPYRtWaQG+1EDQfpe3IltWbStWP2KisLppSKWaWDqZeasXE3D/IUM7ttT2X7Rm9521GNDNQmWXn4VhXSaeFNT5TOVT03w4He+iW25N7VyPs+Tt/2IResvddubDPYTTtTScf6FHNyyCaNcRlJk9FKRIPGquCxw3W96oVi5PllVef3H/w5ZUciMDNO17Xl2PfYHFl9yBZt/e0fluGAsjijJ6KUSpf4+nOEX0FpbiRyWkTRTJl7d3HkIsoxv2TLADfAVgEiyjkiyjkLzHHI93dSsWIk8aT0QFKVyc8yOjzLW001tWzuBtrmYwRDy1VdTevFFSk8/jd7bS/G559yqxmcZw//55YoQAsg//jih669HbmwkuH4d5sgIY9/7PkZPN+r8+YjhCLauU9zhumADF69H8k1/gDuEFApBKIRdLJK5+x7M0VFiN99MXWsr4mRsV+C885COUs7AsSzsbJbAunUYw0MoTc1YmQylHTsAgeAlF7suvHKZwqZnQBDRFi9GaWtDnT8ffd8+BEmsCvo+04lcfz189SvT6wzV189qnaGhoSHe/e53MzAwQDQaZeXKldx7771cd911szLfkXhi6AzFHC1hZXU3RihTwhgpgQT+jhhGwofaFEKO+yrNW+WoBqaNFNOIv3k+4atbsdI6WlsE0S9jpkrYRQulPoAgCQiqRGGsiFK2kAMi5b2paWso75vAuaIZx7bRJAW15diF0Tal8xQsm4UBjeIRcRIOMKq7N8t3vLCfA0UdRYC/bGvgr9uPrxlprmTy2I4JSpNuQM0vIjUqJBS3mvWOXBEBaNYUDhZ1nknnuPYwa5QkQEgSkU9gaw9F1eg470KK2SyxxqaXPuAlECVpmotOLxWZGBxALxWxTYtESzPiK7SaJVvmEEnWVmKObvrMF9n56EOkhgZpX30BrUuXkx4eIhCJosxwk/KHI1WB25ZpsPXB+ypC6BATA/2UC3my42MM7d+DWS4TqavHFwojqxoNHQtQ/QEK6RSlfL6qKnVNU0slW81xHPY+/aRbqgC4/1v/heO4v//GBYu47JY/pnvbVkI1CZZcejmlXJ5YQxPRUJjSsPtAUC7kGe/vwzINll5+DZ0vPMeB5zZVznHhG10BqNTXzfieBWprCRzDzaUXXYFplEoEm5oqQbjKhRfia2sj/9wWcBz0wUGEsyhNG1w31pHkHnqI2FvfgqBpdL7zFozuySKID/ye2DveTvxtf0Th2WcB8C1aNON5xw2T7qLOgqCGlsmw/4YN2Gn3AWvkq18l8aEPkfzTP0EMBJCP6Gdm5fKImupmDO7di9E/gG/RQhr+9m/JPfEE5X37EWQZBLCLRaRgEEGWkaJRyt09WJk0/iVLKG3bjpVOE7zgfKRodPLcOZxS6Yy3EkWuv57wNdec1ArU3/nOd2bt3MeDJ4bOUOT6AGJYQfDLWGNFkLLIMR9qWwS1JVzpOXYIQRERVInS7nHEoILaGkZtDGGXTWzLprhtFCtvELyoASXuJ6cK7HFMgiM5VtSHQZmeuSXGNIyBAoIsIje+dMBuXJERsWjyqayLBtmYngoMj0giQ2WD/+wc5EBxMi7DgX/vHOSaRITVkaknuxHdICCKBI+44b/Ym68IIYBy0aa1LKJGBCQELq8JU7RsNEEgbdnEjmgQKwoC62InvkpwMF5DMH588UKvhGImjW27PdFCtXFq53aQGx8jMzpCJFlLJDnzTXwmBFGsCr5WNB+rrntt5XVmdITRnk784Si1c9uxDGNaZeVCOkUhk0ZWNW7/53+YzLqqJhivwRcMcXDLszx3z28xdZ35F1zE4kuvIBCOomg+BEFgtKcLo1zCFwrhD0cY75vsKzbZAFcvFtz2NKbBs7/7dUUIAQzs3c36t76DC9/wR5WxQ7FCAPJll1EulejbtZ2JgX7ijU2Iosib/+6zbHvofkr5HMuvuPZVV/euaWoheJSgdqW+Hv/iRZT3H6D80B9QGhqQ4/Eq98yZTPjqq0n98pdVY2YqhZyspfj881NCaJLM3ffQ+NnPEjjvPLea+1Fch30lnWHdcF3lH/qLihACwLbJ/PYuaj/w/mnHWZkMhc3PusUXL7jALfLo9yEGg1iZjCtk4jG0pUspPP00hc2b0ebPRwqH0RYtIv/0Mxg9Pajz5lHevRvHsRGUdZP92J6ltGc3aksrgbUXvKqq2acDgiTNSvr86Yonhs5AHNvB6M+BA1qNH2mOgpzwYxVMJn61FyujE1ieJHjhlEUl80gPmXs63RcChK9sJbSu0e1ur4rknhnEGimSe7yPyFVzUC9tQrdN4qLD8Deer9Q6OoTgl1HqAthFA1GTwQZe4qFh2WGp899eNpfP7+/nnpEUBdshY9n8yY5OZroFPJvJV8RQyjDZkikQkEQujbtfNpbjsLdQoq+gTzvW0G2eHs9RI0lcVhepuG4umAXRczKwLWvGtPNgrIZ4YxOKqjHSeZCRnk6KmTT17R2Ea5LHrNb7cghEogRjNYQTSfp378LUy7QuXVEVozTe10tmdJidj/1hRiGkBYIsu+Ia9FKBP/zg2xQz7o1s01295MbHWX7VtYx2d1LT2kYp78ZZ+UNhyoU8qaF+JEmuiCEtEGTO8tUomsYTP/vhtLkKk+c2DYPs2AjhmmTFWirIMrKqovoDtCxdTryhqWLtSrS00rtrB/17dzHvvAuPWQvIsW2siQmkWGzGJ2dBEI7ZikOKRHAMHUGRkRsbzhohBFD/939HcccOyjt3VsbKzz/vBkrPIDIPvc+HYoCOxvyAj4gs0ayp7Nu3b9p2K5ud8X0UNA0x4Efw+Sls3Ur+iScIXnIJYihE5p57sHUDbeFCRE1DikaxMllKO3ag9/SgNDahNNQjRcIIjoNdLuFfuQoxEMDWdexCHsHncxu8niUB1ecSnhg6ExGYDGZzYLIitCAKjH7rBez8ZFzGngmsgkHkylbsgkHm/s6p4x3IPdFP4MIGtybRI71YI5Nix3TIPNBF7bwoV7YnSN1zkNwRQsh/fh3+ZQkc3UZpCAEOhS1DKE0htJdwlXUVy6RMi6VBP381t547hiaqtpszHLMmPHWjDUoSSUUmqkg4jsOjEzm2ZgvEFQnFD+oRZZma4hrpQomQJGHaDor08txfPSWdb/UM018yuKE2yk0Ns2fheSmKuSzP3X0X+zc/jWNbLL38albfcCNqIOC2AZncxygXCUZjk20nyuTTqWlxN68UWVVp6HAzcgqZNAee28QTv/gJxUyKRRdfxsU3v5twbS16uUR2bHTa8XOWr6JlyXLyqXEe/M43K0LoELse/wOFTIrzNtxIKZulnM+TGhzAH4pQ09xCoqUN9Qj3XKzeFf2LLr6cFx64uzKu+v2V8gfpoUFSQ/1Yul4V+C2rKq1LV0xbZ+N81z0jilLlBu1YFuV9+5Fi0SpXltHTQ3n/AZSWZnwLF2IaBrnxMcKJxHHVD5ITCdTmZszRMeTo8WUpnimIwSCCMv02U9q+nfq/+WvUuXPRJxtzAsTf9a7jOq+ml2mbFBxqezvlbduqt8+bR7mz0235ISsEJgthipqGnEyid/dgGzpOWcfOZjFGR8k9+SQgEH396xBEkcCaNdi6TmnnTvTePvSebsJXXYUYjZL6xW1IkQhKs+vyLGzZQnHHTqRwCHXu3KNatDxOX7zf2BmIIAhoHdHKzwDFHWMVIXSI3BN9hC5swEyVwao+h6Nb2FkdwSdjDFTXMQIo7hwFScAYLkzbhungX5zAKZmgilgjJayJMggCanPomE/RPSWdgmVTsGxeyEw/t43bnPVgUUcRBD48p45FQR+7ckXm+jX8ksh50SAThsnNz+/n0VQOcKsM/L+FLcyT/XSNFJElkYVNAdpq/DRFNCzbQZFe2jqSKZi82JsjX7aoiSi8r7eHft3NrLt7NM1Q2eCjbSc+pqO7WCYgSSTVmf8kLdPgwLOb2HjHzypjj/7kewx3HWTJpVcwZ/lqUoP9jPV2k2htI1bfSHZshPG+Hrcm0Sy46URJ4snbfoxtuR+uZ//3NxSzWRauu4S69g46zruQ8b6eqmOalyxj029/hVGaoQXMJF1btxBvaGLFtTcgiAJ6scB4fy81zS0V4TMTV77nz1B8PvZvfppIbR2X3PSuSnxVOJHA1MuEk8eXwi6IIkl/CCuVwjEMBEXBymQw+vrIdh7AWbSQ5Jw2RFFyb/iqgjQZm5Ia7Cc9PIhl6G5dpONAW7DAjTNJJCj39pL+xW1YqQkir3sdwXXrjuscpytqSyulF7ZWjWntcxFUlbaf/oTxH/0Io7uH0JVXED2iP9VM6D09lPfuQ+uYh9rWRv3f/g09f/pnbho8bkPR+Lvfg53J4BSLOKLuFrAdHiH3+OMgCtjpNJEbbiBw/vmIqooDSOGI+7uUZRzLAttGVFUCq1fjmCbD//4fpH5xG9r8+Shtbch1tSgNDZgjIxSf20J571609naMwSFETaN8sBO5rraqTpLH6Ysnhs5QpgmOo1g8rLyBUh9ADCnYuakKxlLCh6CKWONl5LoARq76CV0Mqtg5A7U5RHnXeNU2bW4EQRQw8wZmp1ujSAjKaHMjbvNXYapJ7JGsCgfImhab0nnGDZOkIjNqTIm4y+Mhfri0nc2jeeaEVOZEfOzMFXk+U6CnpHNdwnV1/WZooiKEwBVRX+0ZZuO6paxqd03sJcvGsB0UWeTQ83nGtPhx/xi9JZ3XJKNcUTNlyTJMm4d2jOFMCsdU3uRSx89tTL1v3+8bPW4xVLRsxg2TRk05ZkD2RKnEi/kyiiBwVWJm90BmZJgDzz0zbbx/107mr13H/37139i3yW2TIUoyr/3oX7v1kUqlV5RVdjzs3/x0RQgdomv78yy+5ApUX4BL3v5uJgb7OPDcJkRJZuW1N1BIp48phCrnfu4ZmpcuRwsESbS0MWfFqpc8hnyeC1et5bK3vB3xiOwh1R+gft78oxw4RXH7DrBMfCtWYPT1YxcKWC055HgcKRZDW7iA8Z4uzLERwokk/nAEvasLxzAQJ2OCwokkpq4TqklSPnAQxzTQFiw45kOC6PdjSSKdTz3ByGc/R3zQrfCb+uXtNH/ly0RuuOGlr/80JfnhvyC/cSPWmJuNqi5cSPSNbwRArqmh7uMff1nnExTFtYhPZnAF1qyh8V+/RHH7doLr1xO69FLsfB4xGMTRdbf/GFDcupX8Y48h+DTC112H6PdjjoxQ6u7Bt2QxyQ99EEGWESSJ/MaNOLpOYN06sG36/vITWKOupdPs7ydw4YXUfeIv3TISkQi+5ctQOzpQ57ahzmmlsGMHhcefwL96lVtmob7+rKlSfbbiiaGzBP+yJJlYF1ZqKo3Vt7gGc7KzffjqVrIP92JndJTmEPG3LkCpDSD6FCLXzGFiZDd2VgcBAufX41tag4CbWm8M5iltd7/IlDkhfCsmMyUmv9z1oQKYNo7lUNqbQlRFtHmxGdcZliUkQeDiWIiILHFRLMgP+8fYkStycSzEZzqacYAOv0ZAdr/E2vwqm9N5dNsmZ9mEZQlZENAsh7l5mwGfSEYV6C9NiZaSZfPYhJtKfyi2SLdt3vDcXl7MlwD4bt8oX1rYwvua3ev5tx19LLOqhcM6yc9txlRrjpeTZbYrX2RUN3FgWguSzOgI4/29yIpCMZ+nrqmVxDH6iYVqEhgzFB9Tg0H8oUhFCAHYlsnDP/hvbv78v6H6fG67iyM4VPl4Wlf3o1DMZtACwap4pZmKO/qDYULxmkr/ruve/1EG9+3BNHRi9Y1suuuO6cdEIhQz1e1P4g1NqJMFFrVAANXnf8lO5UZ/P+bIKFI0ijrn+Cwyh+PYNtbYKI7jgGniW74cu5CvPNkLgoDa0kJdLIpeKFT60gmaD0EuIkwG9GuBIA0dC3Bsm1xXJzigtrUhaNrRpp48v0hp0ybkiVTVeP8/fprU7XcQf+c7CF999cu+rlONNm8eHffdR+Z3v8McHSF0+eXTxOrh2Pk85sgISkvLjO4mOZkkeMUViJOfBUFRiFx/PZHXvKayf6WQo8+H3tVFaccOzFIZpbUF37Ll+JevcLPD9u9H7+520+UPs+BUrEOOQ+7RxypC6BCFTZsobNqE6PdXhG5p5w6cfA45FqO8ezflvj6sfA5zYoLAqlWVwpEepyeeGDpLEDWJuo+sJvdEP1ZOx78siRRUKhYjtTlM9DVzUZpDqA1BHNvBHC0iBmS05hA1Ny/E1i3UhhBiQKZ8II2giCh1QULrm/AvS2KlSziGg50uQ0hFSfqR4xpy0oeV0pEiKnZGh8PcUWXb5tm0G9OzJORnRDfYkinQpCkVK8g1iSiO4zBQNvBLAn5JQpHEymmCksSG2ijFSSEE8Nq6GN/c2kNdycHBJqNKnBcJULZtNFFEEgR8ooj/sJvng2OZihA6xNe7h3hfc5JduSIPpXIs045oEHpEX5M/bzn+lNlmTQUHamaImdCLBSxDBxxEQWBhMEDgGNW0+3fv4uCWTdPGF1y4DnuGnmW5iXFUf5DWpSsqvbws06jEsPS9uBOzXKZ12cqXtBxlx0YZ7txPKJ6osq4sWHcpdb+7k+HOAwBIisKKq6/Hd1gWTSASZd55aykXCqg+H0suu3Kq4/0k593wBvY8/QQjXQcBqGlu4dKb30N9x3yK2QyiLJMeHmK0p5O6uR2V4Okj0To6kKJRlIbjK8VwJIIoErjgArd5qqoiqSpSaHoGmD8Uxn9Yg17/8mVHPZ9/1SqwLMp79uDoOv7Vq2e0EDiOg6woNMWTDJerf59OLkf+8cfJP/44rf/934Quu/QVXd+pRAoFib/9Zqxc7iWDi8sHDmCOjIIso7a0VG2zcnkKmzch19TgX7kSY3AQu1BAbW93W28UCtOCr43+fqxsFikUIvK+9yFXVYIXUOfMmfY78a9ejTEyMpl6P708gKBpZO5/AG3BAqRoFClegyBKmOkM+r59+FetQj9wADESRVAUpKN8Zj1OHzwxdBYhhVSir5k74zalPuCmwNe4wad23nDFkE9CmxfDN3/qqcixHaSohuibTEGOadhFA9+aOiiYyAn3y0zvz7mutLkRlLogjuWAIlSl9Zdth5xl4UyKClkQGCwb6LbDkpAfadLSMqKbbM8VickSF8ZCyEe4/WrV6kDUGkXm/66dzze2d9PpuDePjek8F2/cxW2rOkioMpdNusAcxwHLmVbbCKAw2S6kr2ywyy5z0NZpF6fEwbzGAB+z6ugvG2yojfK62tgxfgPV1GsK9drMAbSJ5lZC8QSypmEbBmoggF4qsevRhwglknSc76a0ZsdHcWybvZuemn6O1jZWXfdaFE1D8fmr3E/J1jYG9u6ioWMhqj9AZnSYka6D1DS3Em9ocptLCgJ6qchI90HiDc3TUshty2Jw/17SI0NkR0cJJ6rjbWRF4Z3/8p88c9ft5MfHWHH19dTPm97uAKhYiuatWctrP/o3PHfPXYiSxJrXvJ7Fl1zBure+nYG9u7FMg+ZFSysWoENFKkuT/b2OdMsdjujzTbt5vlzE4Kvv6WaZJoV0imA8Xrnxll7cjWOZOJY17cZb2LqV0vYdBNevQ5kzZT1wgCPtkOk77zzjxJCVN9x2QdkBnPw4/jVrEI4hiA5ZhGbqfyUI1SEC5b373P5lySSlnTuxC0X8552HUy5VOsn7VqxA7eioNGc9HN/iRVjpNHJd9Vx6Vxd6VzeCLKF1dBC44AIKmzdXtoevvw517lwCa1ajtbcjqCra/A7MgQHEUAhB0whcdBFYFuFrrz1h2Zwes4cnhs4RRJ+M2jx1sxODCnLSjxicullbGR1zooTSEKzsa+V0yp0Z91u5YKE2Tp3DMW3M8SKiX0JtjeBYNk7RwjYdzHQZHIdIzMe6WAhtMoYorsi0+lQMx6Fk2ZVaQVFFwniZDSsvTobZu6Ce3+/prYz1lQ0+t7+fdzclWBUOUK8pGH05rKzONc3BaTFKb5vMDlsfDRKVJb5cHuMiyU9SkJGDIl+bW8/lxI5rPcZkfM7xfPEJoogvFGLf5o307NxONFlHw4KFZCfGMHTXHebYNsMH9wMQSUy/MSSaWyti4U1/+2ke+t43GevtJjlnLovWXVrVrFcQxKr/WxYvw3Ecxvt6KaRTKJpvmhga3LeHgy88iyQr1DS3IogieqmIIIqIouh2apdlVl27gUJqAjUQoGfHVpJt7VWWkyNZcumVLLn0ymnjjQtmLrAHrtssXJOctfinE8l4fy+ZkSFq9BbijW7riMDaC8CyEGdYv51KYY4Mo3d2MvL1rx/75Nrpf/1HYucN7JKJNZEHu4yj61jlspvmPoMokuPxowYdi8EgwUsvrcQB+ZYsdgsjRiJI8RqsbBfl/fsobtmC2jqHyGuuRwqFpqXxO5blZu8lalAaG6evIZHASqdR29vduk8+jfzjj4MoEb76KtS2NhzLqlqnqGmoc+dWXgfOOw9R0zwhdIbgiaFzFEEUUOqq/fZWpux+ceUNRM0VKXbeQFDFKquSY9qUD6TcJ1efjJU1cCwbUZVQGoNM/GY/5RfHERSR0BUtRK+t9pWfHw1Stu2qoomOA4ookDIt103xErE5um2zM1fi+Rky0gbLBgFJJHjIzyYKIAikHZu/bW/gsYksw7rJVTVhPjYZDB2UJX68sp2/2d3L86UyV8VV/mnh8VsZcuNjDB3cRyRZR23bsTtZW6bBwN7daMEQiuYjOzZCKZelprmFZZdfMxULIboNXvMTEwRrEgiiiGO7lixZ83HRm2+qnLNp0RL++P99A9uyOPDcM+TTKZoWLalUgw4nkgTj8UrBQXCfsGMNjciqOmMTWS0YpHbuPCK19SiKSufW5/AFAvgjMURZrqTzByJRApEoI92d6KUi5Xz+mGLolXImCCFwrVl6sYj/sErh4jFaSgQuuQS5pQU5HkffM9X6ZLJ4RpV1KHvvfYy2zyP5/j8/4esuWUVKdpGIHEMUTtwNXE76EX0yWscKMA0cBwrPPIPo9xFcv/64zmGOj2ONjaG2t2OlUhgDA5X+b+A+OEiJGoS+PsyxMXCcqgKcdqGAoKqVmCK9qwu9swt1TivaYe1YDsWlSbGYW/hxksCqVfgWL0b0+8k/8QSlYuklm5V6WWSvji996Ut86lOf4uMf/zhf+cpXZn0+Twx5VFAagkhhFfGw/mJybQAxoCAGFRzDxsobOCUTvTeHoIj4FsQQAwqCJOJYDtlHeym/6GafOYZN9vfdmCMF4m9dWCl2F5ElQKK7WKa7pLMyHCAiSywO+pAF4SWFEEDatHguk2dEnx4vc30yUgmaHijr+Gp9xBqCOGWdRk3h0x1NtPmnB7NeEA3x8IWLX8lbV2mcejw3bMswKBfylR5ivnCE/MQYDfMXkR4eIDU4SO+LO6ltm0shlWbP04+z95mnKkIIIBSvoW7uPGzLIjs+xmj3QaJ1DSRb2wgnaquE0yEOF0KVdcsy0bqZs+MSLXMIxmro3vECEwP9ZEZHSLS0Ek5qKDMEAyeaWwlGY1Ui4FzkkDg8XkRJwjfP7YnmW7aM0mE1cwRBQG5sxJyMW3GKRUb+8z8JrFlNYO3aE7rusbFOdMlCiaoE5RNXlFQQhUpjaTTVLVWgqtj5AuZEitL2bcj19VWNVB3HwRwZQYpGETUN/eBBrHQGMRLFGBrCGh1FitegBgLYxWIlmFlO1CAlkgTXrcNOp3FM092+eTNSNFoROFI8jjQ+jhAMVVyXxW3bsSbGK5WpD8ccH0fv7ESKRClu3YoYDOFfsfyY7r4zHcdyGNkGpTEHX0KgdgUIL7NO2ytl06ZNfPvb32blypffHPuV4okhjwqCLCJFq29ygiggTYqjcncGx7BR28Jo82OImoRS78ZYOJZNccdYpQHs4RRfGMVBIPmOaqExYZiM6AZZwyQiS5UmrMfDb4Ym+Md9U4GNCu5T9DXJSMXakzMttmWLyIJAu19jUDdYGwkSP0otn1eDPxypFPh7KVR/gJYlyyvBzPVz58HceeRTE4x0HSSfShOMRSkX8mihIJnREWyzuoZUarCf0d5uCqkU+dQ4kqJWMr3q5s6jpqllxt5hR8Oxbbf9wRFCVNY09GIRvVgkGIvTtGBxpejikYiSRCAaO+45PabT8O//xn3/5xP0GkVqSgbL1l2G8sTGafvlNz59QsSQY1kYPT04koRvdxdOehSrZgRz9epZs2wUENz4KdvC6OvFMUycUnVigzk0RGnnLuRkAv/KlWgdHZjjE5jDQ5jDQ2hz56I0HhYo7zggSW7AOq7lp7z/AHaxiBgOY+VyVe4wxzBwgPLOHZgDNW5RRsvEse1pDxEAVjqDXSgi19URvPhipESi4uJzLIvy3r3I9fVnjTWo91GH579mUxw5NOLgr4XVHxFpuXx2BVEul+OWW27hv//7v/nnf/7nWZ3rcDwx5HHcSBEVu2whKDKCKOBYDmaqhBzzAQJ2yUQMVtczOkRp2yjO22wEecr8rokijgPllxcqRMmy+beDg1VjBvCr1R2cFwmiTbqZApJIs08lIIqMGSZZ08I8LJDGsGxk8fgsUSeamfpUyaqKKCsUMimidXU0LVxMamiQOctXMrB3t/uFf+j4YAg9n0eSZfyRKC2LlyKrrpgURPHlCSFdJ//0M5TKRez5HSRb2yrCSlYUOi64iL6d28mNj+E7AQHGHkdn0xMPs18wQVUYUBWGdj3H66PTa09ph1lRXimOrjPy7W9T3PI8QiyGk0pRePJJso6DoGk0/ce/EznBHcM7i2V2dPXSlisyRxbwLVyI09EBikJp9x4EVUFrb3djgGLRShC1FIshxWIUX3gBQZKQGxoqgeii30/wkksqcUQAYiSCGAqS+vnPmbjtl2CaBC66iOavfBkpFiPzv/+LOTaONq8dpbGR8r59OIJAYO1a7FzejfWZrGNk6zpyYwNyXS1SLIaOW2tIqa1FDAYpbt9O9oHfo85pJX7TTdOu+Uyj91GHpz47XRAWR+Cpz9qs//zsCqIPf/jDvO51r+Paa6/1xJDH6ckhK5CVN7AyOsZoESXpR1QlxIBC4Lw6pBof4z97EeeIatioohu7M4lu2/hFkYSqEJZfXnxC1rLIWtP/WCVBwHdYWr8oCJV+aE22Qs60SUxahQpli3TBJKCJRAMv3TLhlTKa0bEdh2REfckaRVogSP3cDib6ezF1t8/a/k0bEQSRC17/Zp67+zfYloWsaVx80y0kWtsqWVqvHofs6Ch2PEKoJlHl5vGHwtTObXddoSdoNo+Z2fvMk1WvHcsmu3A+of6BStFC/9q1hK+9BnBv1CNf+SrZ3/8epb6e2o99tGIxKts2iiDM+LmzMhm6brmF8t7Jvl6SBLZdEdxOuczQP/8L4auvPqHFAksHO7F7+tCSSQLz2tw4Htzu8EZfH1YmjTU2hrZoUVXMziF8K1bgmOa0QPTD6xEZfX2Udu/ByueY+OlUxfbC008z8p9fpvGfvoC2YCFSfJTIhhsQNY3cY49jZTJkH3gAo6ub8LXXVApDFp/bgp3N4L/oIqzRUcyhIexSGbtcBknC6O3FLpdRJ12dZzKO5VqEjsXzX7NpvkScFZfZz3/+c5577jk2bZpeRmS28cSQx8tGCioozSGkuOa2R/O5HyNRlvDPj1P/4dWMfGc71tiU6Tu3vsG1JjkOX9jfz/f7RrEcuKmhhotjLx2ofKBQxnIcmnwKO3JFlgZ97DysZlBSkVkTObowEG2IiCKO42BYDqLgajP5KJWyTxS27WA7zJwnPQOJllZWveZ1+AMhREmmYcEismOjaH4/TQsXo/oDhBJJyrncUat8A5QLBSRZxjINhg/uJ1rfcNTu9YKquqb/fB69VKwEXR9OtK6BQCyOoh6/K9Pj5RNO1JIaHKi8dkQB//XXMue97yP74ENI0Qjha66tCJThf/8PJn70IwCM7m66//z9dNx3H4WaGp5O50mqMudFplvzUr/85ZQQApihZIE5NITe1YV2xE3+eBIcABzTnFY0cbS7h2ipRMNF5yFPti8B17rjW77MrTGUTiOn01Vd37uKZcZ0k+VhP+pLxOU5k9dSeHp6xfbC5s3o3d0EL15fleWlLV1C97vehX6wE4DsAw+AKBG98fWYExMUn9+CbRgIsozo8yElE5T37MW3fBlKXR2xN7wB/4rlL/menO6MbOMw19jMFEfc/epWn9i5e3p6+PjHP87999+P72VYtk8UnhjyeEXIUQ2iM98YpZiP5HuXUT6YRh8vMdEapGmxW6349qEJbu2Z+mv78cAY8wMaH5wz840a3K70+wqu8IkrEuO6yUfm1HHXSIqnUnkWBjQ+2d7I/kKZeQENTRQpWjYbUznqNIUlQR9jWdd1p6kCJd0h4pepjx39xu44DrrpoMqvzo2WnAwcFV+G6ApFp+IO5ixbianrDB7YSyASo6apmaED+yhm0xQzGVTf9ABOvVSkd9d2FE0j1tCIXipSzGSOKoYABEnCH4ngP0a3cE8IzT4Xv+2d3LF3N+ZkeYXGBYtYcPHlOBMT+JcvQ1u4EKV+6veYueeequOdUoncQw/hXHQRKH4Ubebfp9E3vZDg4eiygr8mjt7ZhVJfjxgMYk5MYAwMYI6MIEej+FevnnacY1nkn3mmEgMkJWuR/D7M0VF8y1eQXLOacqFAcIaGtEpdHfr+/TjFUiVL7BB9JYOcZZE1bRKqK2LsQgFjcAi1tQUkyXWhiSK+lSuR6+sxR0dI33571XmkZJLyvv1YqRR2sYRv6RKkcJjCk09WhNAhxr79bSKvf507V75AYccOfC0t+Navxxwfxy4UXPfbCQ5kP5WUxo7P9uvud2IfJJ999lmGh4c5//ypLD3Lsnj00Uf52te+RrlcRprFliaeGPI44RxK2z+Uun940vajE9lp+z86kT2mGJIEgdXhAHnL5u/29HL3SBpZEHhXU4Jdl7YjCkKld1lAEslbNjc9v79ST+jGZIx/nduEKAiokoAhWgiCQypvENAk1MPcdLbjkCmYlA0b24GwXyLke+V/Ji9HBB0NWVWpb+9gYqCfcqFAomUOWjCELxh0g6wn44/0YoGhg/sJxmuQVRVF8xFJ1iGr2owxSh6nHy1LlvOnX/02+599hkAsxrw1a92WKXV1MxYNlBOJaa0ibL2M2tnJJdEYwdbqatx2oYCgaYSuvpqJn/50xjX0JevoamxhRUMtrfV1lYwp/cABjJFRnFIJ6SjuWb2rm+Kzz7o9wQIBpL4+sG2UxiacYoHF9S/RKFcQkaKRqvgfgFURf5WbG6B88CDGwCB6by8YOqV9+1CbmzEnJhj75rcwx8dR589H3+dawKT6euLvfhdKU6NbrTqfx85m3Uyy51+YthQrn8McHkYKBpAb6nFKZXwrVqLOnYtcX+9Wuz7MunU24EscKuhwPPudWK655hq2HZZJCfC+972PxYsX83d/93ezKoTAE0MeJ5mFgenmz0XBlzaJ1mkK//fgAP874jaUNRyH7/WNcn4kwB811NAR0AhKIk2aynu2HagqrPjb0RRva4xzfdJ9Gg36ZFIFg95cmXpHpTY0ZXY3TIeibmPbNqosHVen+5NBdmyU7JhrUaubO4/s2AgHt2wmEI3ROH8hkdo6suOjlPI5bMvCKJcZOrCXciHHnBWrcWyH9PAQwVjMzWITBBzHrqTb9+3ehV4sMGf5ykqWm8epIVSTYNV1G45r39qPfZTej/8lHJZtOPx//534u99F8v3vr9rXSqUoPLcFuSaOOr+DxIc+SPrXv8EplxGTSYzJGkfypJvJv3QJ/mVTrUbUefOQ4nHkxsYZi0e6OKgLFmBnc0iRMGY2R+7BB/EtmE/oyite8noCF64F257mXgtKEkFJwsrlMAcHUebMwS6VKL7wAqlf/xp7xP3bkOe0Yvb0ViUbxN75DsIbNmCNjSEqCtqCBeA4WJkMUjyOnS/gW7US4Y47qrLaYm96k1vV2jCQamqQo1F8i93CoMUXXqg0cj36e3HmUbsC/LXHdpX5a939TjThcJjly6tdjcFgkEQiMW18NvDEkMdJ5X3NSe4dTfPsZLHERUEfH2qtxTGrM81m4ulUfvpYOs/raqLYhk1HQEMUBPYc0X8MYG+hzPWTP+u2zYPpDBMlk4v8IrVMfZlpikg0IKNIAsrLDOw+FiXDoj9VpDnuR5Nf/hNOpNYtFxCebOaq+vxowSCyqiJrGvnUBEP795EaHkJSZLbc81vyE269p0TLHK79sw9TymXp2pYmWltPIZvGKJVYetlVyKo6WTV5mETrnBmrXXucHljpNOX9+yuVkcPXXEP7Xb+h6x3vxE6nJ3eymPjhjwhdfgWhi6eKGgqKgqCqIMukf/1rBFWl+atfwTd/PsbAAJ3vvAU7naZ+Yowmn0r7W99cNfexKkMfQps3D6WpCRyH0u7d9P/9J3EKBfIPPUT+yado++EPsAsFzOFh1I4O7EwGMRxG1KYyIY+0Ch2O3tmFOTzsXosgUNq9uyKEAMzunmnHFDdtouHTnyZz992U9+5DTCQJLFlcaZUihYJEN2zAzufJ3PVbHMMgfMNr8F+wltLzzyPGY2iLF6PW1eGYJubwMOXubqyJCfxr1sBZJIYESWD1R8QZs8kOsfojsxM8farxxJDHSSUkS/zuvAVszhQwHYeLokHKu8bI9OYIrKqtZKzNxLKQnydSuWljuaKFaTv4VAdNEbi8JszPBsYr+2gIrPX50U0bSRQYmCgjGSAqIo0zNEcNaCfeHHtwNE/fRBHbgfl1L7+gnawo1DRNBZrXz5tf1TS1XChMVn/OMbhvT0UIAYz1drPz8T+QbGnFttyO9ZnhISzDwNT1ihvOFwwhK2fPF/vZiDk2hpVKY42OVoSJUls7JYQOYdvoXZ1wmBgSg0FCl16CXSySe+QRyl3dyK2tiLKMb+lSOv73d2Tuvc/tAr/hhmkNT4+XQ9W2x/7nf3AKUxXiS9u2kf7d79Dmz8eaSLktMYZHkGviM8YfzYQ6tw1RU5EbG1GamnCKxZc8RkokEQQB36JFbvXq/fspaypiMIicTCL6fAiKQvT1ryd81VUoDQ1YuRzZBx9C7+pCGOhHP3AQ1l7gZqpt344Yi7lFIk9gpe7ThZbLBdZ/XjyizhAnrc7Q4Tz88MMnbS5PDHmcdARBYG10SvQ4DpNum2Mf97G2ejamcmzNuV+AV9eEeXtDDeL/z955h0l2Vdf+d26qHDrnND09eUYTNMpCEhIKCJFMEmCE8QMbbDDJAbANtjG2sY15TvgJhESOwmQQCIQklONocp6ezrlyuuG8P053dVeHCdIMCvT6vvmm69YN51bVvXedvddeW4LteFiGukg/2t3MRMnh5xMpqkydP29tpM20yBddpRHSNM6NhKiPWVhapV4oW3Dxm9oZjQoBtFQFcD1Jc/zsVEn4gkHWX34VY8eOMtF/fMH7yZEh6ju6kNIhWlNHbVsHruuU+5HVd66o8BdaxnMTVkcHWjCIUVNTXqZHIngrV6IdmlMdZllEr7124Q7Sw5QOHSR3x9fIHxone999NP3lR/CvW4dRW0v1m990xsZq9w8sWJb66U9p/uQnMWpqkFIiB4fQ54ml3UwGe2BwmvhUivb1cBi9p0dpknSd8MUXkZ9Xhq03NuIOT/uQmSa173wnAL6VK9HicZzBQaTjUDxwEC+Vwr9uHaCiUvbQkDJT1HWKe/fiJpP4163Fs228YhHNNEFoWF1dBNavXzC+FwpaXyRouVh71hyonw0IKU/2CDrzSKVSxGIxkskk0ac5+1jGCwtuwUE/RaHyU+kcPk07qdYo53r4NIGQkCu5BCwdXRM4rkTTWOC/ki24pPIOPlOjOvz81c3s+tWd3PGZT1csu/ymd9CxaTPZqQnq2ruWnaJfYMjt3cvgBz6IfeQIek0NjX/1l5VkKDcJ33wLHLsXz4N0n5+xnVHsjIEWCrHy7rvRw0tHZYtHj1Lq7SWwaVM5vXQyjP7f/8vEZ/5nwXLfxo103HoruUcfAU8SuvCCioathT17VJVYVye+LtXnzysWKezciV5Tg1FfT+qHP0KvrSF86aUM//VHSf7whyq68/IbiL/xjeQfexx7cJD4616Lr70de3gYe3AI/5rVaMEgXi5H8fARzNYWjKoq0r/4BYN//hd4mQzC7yf+5jch02n0mhqqb7yR0nTPM7O9HWM6oiSexfTY8jP0zGM5MrSM5wROlQgBbIqcmtFgUNdUs0ZBRUWYscTsxm9pOK6G33p+R0fWX3YlE/3HefJnPwJPsuHFV7Pl2uvRNJ3a1vZne3jLOAPwCgWckRHM5maEaRJcu5aVP/4R9sgIRnV12T25jF/+HRy7F1CSnFhHgeSxAHbGwMtmKezaSeiCC5Y8nrRt8CTSdpZcZz7q3vlOnNExkrffXrG8uHMnqZ/+BD0Ww+roQMzzlLE6OhA+H2ZLC9K2KfX3IywLN5UGIVSZ/+goXqGA0HVq3vF2at/9bvR4jNxDD1M6eIjo1S+pKM93RkdxEwncZBItGEQLBvH1rMQZHsazLIb++qN4GZWCl4UCU1/4ItW/91bCF12kXLBNk+J05M06//xnlQgt4+xgmQwt4wWHXNFlNF9iRNo0O70EdI2aqrWIk+T3dU0QCz1/I0IzEEJw2ZvfxqU33oSUUpVmL+MFhdJ0Wbn0vHL0BMBsWLzpLscX9jcL1tpkhwJgGFhdJ3ZP9vX0YLW3l/VAi8IpgeeApSYrwrKoe8+7F5AhgFL/AIHoNBmaF6HVQiF83d1qvd5eSkePYTTUE9iyGS0YRFgWketfih4O405OUjrWixYOYbU041uzmux995N78knCL3pRudLLt3o1ItCHm8uhpVJ4JZuJm/8f+Sd34Nu0sezuXYZtY7a1Y3V3I0sl0nf8jFJ/P36/v8KscRkvHCzfJZfxgkM67zCUs0lqDmHpEniOlMf/prGs/3nhwmxuRnre0uRnPho3weieikWFKRN0nfr3vQ+tvo6xkk2taSxqMiqEWBDBWYDe+8AtQdeLwFRpL7OhgfCLX0zml78sr+bfuBH/qlWYLc0n1dwYDQ04ExM4IyPo4XBZNO6fdsWWrovV2VH2+zHr6qYrvsZgziRA8/nw0imcySkKO3cyeeutFPcrK4HCU08hgsEKsbcWiRB/2fUgJZl77ilH3MKXX76go/0yXhhYJkPLeMEhHjJZ49MoaJJ6cyOaEOWokOdJEJV6Ibck0QxO2N5iGct4LkGPxQgs4uK8JK78Kxh6Esb2AQJv3e8Qv+qtNPSs4j4zwAce2MNA0aY74OM/13WcsLXNkvBFwc4iHUnx8D6M+nqM6mpa/vVfmLztNnJPPol/7Trib3gDMp/Daq1swyM9j9yjjyJ0neC0C7Hm92O1tZFPJHETCejoqNhG6HpluxAh8HW0g2GgzYvgWF1d6JEI+V27y0SofOxSCaOxEWd4GL2ulqa/+zu0QABnago0neCWzQS3bz9xZGwZz2ssC6iX8YJAf6HEQKHEhkiA0BIREc+TjKVKCAH1061EnLxk7EkwI1C7YZkMLeMFDClhaAcE4lDVCagigy337ybpzPYm6wxYPHD+2qfdhsYeHKSwb/+Cknkvnyf38MNokSjBrVtwk0lyTz6JHo0R2LQRPI/sAw+A0AhdfFFFOspNJNBCoYVaqKeB/M6dHHvtvO7yuk7P/fchs1mMurqK47iZLJrft8AI8tnE8jP0zOO58+0uYxnPAKMlm6TjkrTdCjI0WrTx6xpRQwehuulUVJEJEDpoy1fCMp7HkFJWOIovCiGgeXPFoj2ZfAURAjiWLzFQtGldxIPrVGA0NOArlRaUzJfHMH35Ffbvp7BzF2ZbK/41q5G2jdnWhtHcvECXc7K2F24iQeqOn4GA6DXXoM+Lmkkpsfv60CIR/Bs24F+/nsLu3eX3I1ddhRACo7l5wb5PVGW3jBcOliNDy3jeQ7qS8WMe+aBLW6NZntGmHJcHExl8msZl1aoD9kzHbc+WlNLgmzbUfSbNWJexjGcbA/v2UMrnaF2/8bQa6k7aDlvu201xzmOgxjR44qJ1FR5cABQzeLYEXXVu52lcM9J1QdMQQmCPjGIPDWK1tWHU1JB98CG8XI7Als0ndbqei1L/AMfe8IZyjza9rpaub3xDOWFPw5maIv/EkwjLVDorwyD5ne9Q2Lef4NatWN0rEJaF1d6BMzKMf+NG9PDpm6P+prD8DD3zWJ4PL+N5j2IK7BENf1BDNM3eoEO6RoPPJDInUjRDehJHoDAB8ZUQrF8mQst4YcDL5JBVFkIISn19FI8cwde9Equ1ZdH1q3SND5Pj4/ixEQQ0jX9v9DBH9iDr1yJ0HUo5OHQnMjmE89RdmGP3IIVEbHsrXP1xOFE0ah7EnGvRbKjHbJhtPmu1t+Emk+iRyGmd8+QXv1DRrNYdG2fyi18i9spX4IyNEdy6VZXxt7XiFYuU+vrRY1HqP/ABQE2Qinv3ghB42QxevqCcrZ/DZGgZZx7LZGgZzzmMFG2O5IusDfmJmyf/ifriEO0AKwKpnINEIsZ0ilOCDWsD6L6FZMcfB7eotlnGMp7vaF69lmJfP6Unn4SOdnzd3bhTUxR27cadmESPX71oukdoGm+pDnFD7y84ktzHptIgkcI5FLJVePXjhC65HOw8Xj5D6bGfoff9AhGYjiI9+N8Qb4cL3nlGzsFsbq6I5syHm0pRPHAAq6NDef9Mwxlb2FXUGRtDFgpIx0G6Lpqm4evpQboupWCIh51xvnbnZ/CQ3Lj6RVy8Zq0qtHA9vI7c025Fsoynj4997GP8zd/8TcWyhoYGhmfcxM8ylsnQaUBKiZMDI7icVjmbGCnZ9OWLNFrGKZEhIQThFvX9TCSU/kFP6jg5cAqgL5I1CDYIgqdYlbyMZTzXIYTAMDUcO1OuePKvWYN0XUaLkp39Gc7ptIj6lTA43S8pTkHVaghY/QQe+xhlGnL8HsS694FbgNQQhGpxa7ZhT3wFL2tgBuzZAx/8+RkjQyeDOzWFMzkJkjIZctNpglu3kv7JTyvWjVx7Df6NG5G2XVEBJnSdXSGXP/jJrXjTqcFf9e3kfVtfzjl1XVzQrD4zL5tFC/12a4U8z2Vg724yiSnC8Spa1q4/sSbtDGD9+vXceeed5df6b9AeZJkMnSKKCY9D35OM74LCOETaYO1bBIFqVYmkm7+dXjZnAzoCELinqWYTQpTbaBhrBG4BrOgyaV3GCw+FfftwRkYIbNtW1rYYdh/hTgNiSvgsLIsvpaJ88o79uN4QQUvnP27cwpVrGyhMgp0BJw/6zm8u2L9PH4AGDw7dCb4oRtMm/OdsR3/0vsoVa1SzYCklpWPH0ILBSu8jz4Xe+/EciRtdjVFf/7QnkmZbG4VDh7DHx3AmJ9GjUVI//jFuIkHNH/wBqTvuQNo2VTe+gehLXqI+A13HTSQo7NmD1dmJ2dzM7QfvLxOhGdw3uJctDd24mQz5J55E8/sIXXTR0xrnCwEHH7qfX952M5nJ2fRjuLqWF7/1HfScf/Y+F8MwaGxsPGv7P+Gxn5WjPg+Q6pX4YuCLqwt3Yo+k/1eQOjb9/jEYeVTS/SoINQpWXC+XfWpOA1nH5d96R3gwkWFdOMAHOhtp8CkiU+8zSDoW1dbp/zx9c0ipvuyYv4wXKKRtIz0PnDntMcLT+hufIkfDycI0EVIP/lzJ5S+/u4vLV9dTvQacnDKMLhSrMNwAupYva6JFsAbqVkPiONh5BGBe+z5KvQ8hxnZiGmmId8DFfwKAl82SuedeZCFP9VvfOmumKD2w8xQP9uEEHPxSYi7xsHOmphj/r/8mv/MpAhs2ErzwAsyGBgIbN6oxaRrCsigdPIS7Zo1qq1Fdje5JotdcQ/zVr1L9xpqbKvbrZbN4hSJuOo0J+Be5MbSGa9ne0EO2kEWvqkKP/vbmzw8+dD/f/9QnFizPTI7z/U99gpe//8NnjRAdPHiQ5uZmfD4f559/Pp/4xCdYseLE7uhnCstkaB6yw5Jff9gjdVSVXK96jWDtTZA8BtmhynWdPGQHIdKmIkbBekWM3JIk1ate+2LLBGkx/PHe4/xkPAnAo6kcDyez3LV9NRKlGWrymVTPS5Gp8mHQTkY6C0mYOgbV3eUHw4mQy4/jugXCoeaTtuxYxjKeC/CvX69SQHMdnKtXqH/TODSaKROhGQwlCxx+qETHWosD3/TY/w0ImH/ExhUJ4qFdREMHkL4qxKbXwcRhqOlWuiDdxC1Jxs/9IWJ0J43rRhDdV4ChiIUWCmFUVyGdqOpfNjMu3YSuF2EEx2B8oqLk3Rkbw02nsTo78XJ5+t7xBxR27gSgsOMpsg8/TONHPow9PIybSODr6cHX2Qm2gxYMkn/iCdzRUcy2NrRgAD0eQ/h8CxrJmi0taKEQ2rQw+41rL+O7hx4k5xQB8KHxpvbtDGWnOHBoJ00HjtN53iVn5Ht6vsHzXH55280nXOeuL9xM9/bzz3jK7Pzzz+eLX/wiq1atYmRkhI9//ONcdNFF7N69m5qamjN6rMWwTIbmYcdnFBECkC7s/4akZqPAFxUYQYlbrFy//QpB9VqYOijIaxBqVFVK2WGJZy+TocWQsB1+Ok2EZrAvW+CJVI5VIT+DRRtTOHQFK8U+U1mHku1REzExjROQlkQfpAbBCEDdKkARKVhc65XNDeFJF7+/BtMILHh/Gct4rkFoGuIkrSw2tcUIWjq50qyPUE91hLC0GPxZP/u+1gDo5Ipd7Dj8Udb3/De+tRswL/5dRLgRMiNg5xShQXlxBWo1ROM5iO7K60gIQfTaaxVBm9+uwvRjtrZhtrZVLC4eOoSXL6BXVZG+884yEZpB6eBBjIYGSsd6sQcHKfUeJ7j9XMKXX4YzNo4UGtL1kMUislgETcNNJhUhmtdIVY/HOZQYwvFcVle1cPvLP8R3Dt5PaWSE64c8WnccpHRBFHNwBKO3n6zxBAPVAWr8EWK+3x7t0MDe3RWpscWQnhhnYO9u2tZvOqPHvu6668p/b9y4kQsvvJDu7m6+8IUv8P73v/+MHmsxLJOheZjcu3BZpg86rlYX/2OfkjA92Yr3gBkFOyeoXgPmdBDCjEhKSTDnXEPp4xLNUpGj+XDyErf020OcTCGwNEFx3qw1aGiEDZ1NkSC+RaI/YsEfS6CmW/VGis3efCczNrYjqYtZ6PP2HY104Hn2MhFaxgsKUb/Jp6/exsfv2s3xbJY1jRH+7XXnENMlR+4ao6nmKVLZlVhmilR2JUeH38yKLbuAIvijqsfYnGtCaIKq1UsfT5jmaTlEu10dJMZHCMaimC0tyrdorpZHCEY/+c/4N2zAt3YtIPEyGYSmUdi9Gy0YoPrGN+AVS+jhEKX+AeyBQWSxuMCnSEpJf3ocT0q6og10xRr4wLmvQto2uccfx8vnqQ5EufCqV5KNPkyqsYZjyVEShSxbG7pP+Zye78gkps7oes8EoVCIjRs3cvDgwbN+LHgaZGjHjh384Ac/oLq6mte97nXUznEZTaVSvPe97+Xzn//8GR3kbxI166D/7spl1WsF/mrBipcJYiskg/dJgg1QvVYyeB9EOyVtl89GKjRdEKgDY7qIwclL0v3qWg9Np8vdosQpKAI0sUeVeddtlpjBFz4hChk6v9dSy//0zZbEXl0TZU1I3XgbfYvfUKvCZtk08YQwA4oQzcGJrEV91nIZ7TJeWMgMSO7/a4/kkWre67+UFW/22PwmdV1JKanbFkEcuIPz17wd08hiO2EOef8X1lynus8vkV5O3fEzcg89iG/VKmKvetVJG62eCEe0ApNRHTOXpOmii4je8DJS3//B7ApSkrnrLjJ33UXo8sto+cd/RIvFKO7bh5fLYa3sRpgm+jQBMxsbkHapouz+sZFDfOKhb3E4McS5DSv54PZX4zNm7y/CNAls28ZEPsXRwgRddhhsm0gyS0tXI0Hz6Z/f8xHh+KmZXZ7qes8ExWKRvXv3cumll571Y8FpOlD/7Gc/44YbbqCnp4d0Ok0ul+Ob3/wmV1xxBQAjIyM0Nzfjuu4J9/Ncds9MHfP49Yck2WEQBqx+nWDj2zXsnEQzJamjAs2EWJcgMyiZ2COJdUJ8ZWXaRnqVgurskIoMBWrUsrEdEjsLtRsgPwGZfklsJYSbXriaFU/KcisMKSU/Hk/ywLSA+jUNVQsdb88gpOchhahsxbGMZbxAcc+fuow8Wrnsmls1op3Tv387j/sPa9C9RPl9aUURH9wH1uJpodF//RQTn/1s+XXoooto//wtT3uMY7kkI8N9tCVtwj2rEJbJ1Je+TGHfXlI/+OGC9bvv/DlmUxOZe+4BIHzxxYjpdJgslZRhY21tebKUsQtc8c0Pky7ly/u4sGk1t1773vJrZ2KC/FNPcSxiMFYfY6W/iuoDxzFqaxhojNGbGmNVVTOtkUVaizyLOFvPUM9z+ewf/f4JU2WRmlr+z3/ecsY1Qx/84Ae54YYbaG9vZ3R0lI9//OPcfffd7Ny5k455DXrPBk4rMvSxj32MD37wg/z93/89Ukr+5V/+hZe//OV861vf4tprrz1bY/yNItIhuOSfBPkJSXyFwBcTlFJKIG0EVQWGECoaFG4W+GJq+XzMrywLNVW+9leB0ED3Q6QVskOC1FEI1UuEXrmunZV4rkQ3BUbgzDzM8xOSYgKinSqS1ZtXYqiOwNmZCfXmi+zPFtgQDtDsVw6519fFub4uflaOV4HsOGLgMUR1N9SuPPvHW8YynmVM7F64bHy3nCVDieMVRAhAlFIweRQaNyzY1isWmfzSlyqWZe+/n8KePfjXrXtaY6wLxojYw9hTYzhjo2jBIFZXF2Zb66JkyE0ksFpbVfNXzysTIYDC/gM4Y2P416wuGzc+NnyogggBPDC0n/TQAMFgmINumuRQPz2uQ0e4gerqBppCVWj1Kr0eyExiajoB47enLFXTdF781ncsWk02gytuesdZ8Rvq7+/nxhtvZHx8nLq6Oi644AIefPDB3wgRgtMkQ7t37+ZL0xeEEII//dM/pbW1lde85jV87Wtf47zzzjsrg3ymKExKShkIN0s8e5ZQSE+lr5y8xBcVhJqEEgK2Q7RdUExIhh6SBOsluk8Ro1iXIjEzsw8z9PTISaRdMLd4M9oh1X7nESEnLxl9UpI6BvFuaDhXohmL6450S5kMevasJQBAesCjMKnOaUaXlOlT2kh/Fehxyf5sAYBmn4U5h8h5riR1FIINYEVOfK5zIz8L31P/nzhmWIlJ28H2ZLnk/mnDtVWJr1t6ZvtZxjKeJ4j3wPhTlcuqVgnwPNA0qOqCUB1kZ1PV0l+FqF6ijNlxkLa9YLGXzy+y8qnD6upCC4UwGxpI2wV2R6GjaRXhq64kc+cvyusZzc1k7rkXq71j0b5lRl0tslREi85WqzWHqqjKFEkGTbzpqHOD8CH37iNvWgx1xOglQ25FNRubWmkJzEZYPOkxnk9RG4hSE3huZS/ONnrOv4iXv//DC3yGIjW1XHHT2fMZ+vrXv35W9nuqOC0y5PP5SCQSFctuvPFGNE3jDW94A//6r/96Jsf2tDH2lCR1TFK/VRBpFSSPKk1OflyZqlavkfirBXYG0n2S5GHVo8pfI9Et9TB38pLcqES6ygCwYdvpk55iUmKGWJS8zEe4ZXYd6UoKk+CrBs1UQuxAnRJoL1b5XUxKJnarthR2VpGh6jUe/moNtyiZ2gup40qn1HiuIO965FscYnmd7LBAmxCsbVN6nXwf3PdvLhN7VYuL/DgUp0CzYMPboO4cDTMMkdbKcxoolNidybM2HKBtkW7XXUEfzX4T35xUmCclrqSCfM3FY8ksErjEiBDUn0EKLdoEgfisiGsZy3iBY+ufaPz6wx65EWURsuaNgqrwU3BoBNovUALpV98M//tOyAxDsA5x/h9A/8PQddmCJqxaKET02mtJ/ehH5WVWdzeBLVue0Tg1y8JqbQUgVUiSr69hspSh87WvRQtHcCcncDNZCo8/zvi//zuTt95K63/8B6ELzq/Yj9nYuMC/qCNl81a9ndsnDnO8LowmBK9YfxlWuBktEABSCAE5JKlijto5pMd2XSYKaXQhWEtlFdxvA3rOv4ju7ef/xh2on02clmbo6quv5uqrr+aDH/zggve+9rWvcdNNN+G67rOqGXr4H11675h93XU9tF8p8NcAEnIjgup1YGckmUGJZgoMC8ywqCAkI4+pMvrYSkmwTpy2a2p+TDJ1EPw1UL365NtKqUiZZqliqXQ/hJqUNulksHOKDAVqFVlK9aooU6xLnVO63yM7DLEOQaBO8Hgqy+igS3vBTyxjoBmCxumg3k9v8sj0L3EgAVvfLwg3LSSHM2mwVSE/nUuk2g5kCxwvlDg3GiRuGjyQyJB1PS6OhwksQnYO5woUPcnakH+5/ckylnGakK4kcUhNpHIRl93HdrDCnqCl6zw1OQDVhLXvIZUaG96pZl9diwtWvUKBiZs/S/bhh/D19FD7h++saLT6tMfpeRQPHkILh0hWhTGf2Il3vA89HMGor+f4TTdVrG91d9P9o4VptPlwEwnS+w5wJG5wTC9yblU7sSN9+Gvr8a1YQX96nGQxS5U/TGOoioNTg4TMAK0R5WmTKubQNI2w+dybRD2XdbfPV5xWZOid73wn90yL1+bjxhtvBODmm09s2HQ2kTwqK4gQwNEfQaRTEncFVasE0Q6B50gGfi0Z3wG1myTNF2oVaSWAYB2UMhCoPn0iBEpHZATAd4q/U68EuVE1IatZD8Uk+Cu9w8iNSLJD0yX9c9JzZlDQuL3y2P13S0p5D6og0qoTaZ19v0FapA7Z+MI6wpJoJiAF6X6WJkIAUhG16jXgliTZfX0Eq/IYravoCPhosEz8QuIdeQhPWhjdlbPGoufhSYnteeSKLkKCxtKV8t3B595NaBnLeL5A6LOl8KN5l3ysmwmvh2Z/ZPaaywyr3HqyH7ouXxARmgvN76fuPe+mjnef0XF66TT2wADCZ1HXcjH2yh7sSBS9sZH0976/YP3S4cNI10WcpG9VKRjh8XgHQcvg1StqON5/mF1H99JeyNG2YgWtkVqGslMcGj7Erbt+zr6pAQBetmI7/3TpW4n6lBh032Q/qVKOzXUrsPRlN5oXKk7rm33Vq17Fq171qiXfv/HGG8uk6NlAbnTx5dlBiDQLikmQeCT2KwOxUDM4WZjYA1U9kkDd7I0g0v7MIhFmSFA/zQWklEhPCZVzo6qkPtJWaQCo+wTVayRCV/20ajcu3GcxoXQ+dq7Sw2g+gnWC6tXwWCrHvimXi8wQsTluzv5+g7oJTdnxNwFFgWsr8qVZipgtCg0azhWYIUGqV5LZP4asyRFraAMriF/XwCkysRfsAtQ1eJjh2YjP+nCAlUE/nu2RzDussXxEg8YJK7wStsM3hyeZtF1eXh9nXXjZC2gZyzhdtPkttDEN2adT2PcdAgPfUNGh7W+HWKv6dxarOU8EPRbDt2oVWkiRD1ko4I5P4IyNYdTVgmFUtB0JnnvuSYkQqNMxdQ2/Ob1uNEpp/Wq0pq7yOj7d5EdHHikTIYAfHnmEy1o3cEO3CpcnillydpGS5yyToRcwTvub/da3vsV3v/tdbNvmqquu4h3veMfZGNfTQt0mlSaSXuXyhq1qhmSFJf33QHYYGrYJ6jYJnIKkODlrmHim0HePx9ReSVWPINQMxSlBzUYlhE4ek6SPQ+N5lDVKAP7qExOwWLcSMs+PYhWTan+R9lnjxpp10JjUmJAexrQmx7MlwlBtQoJ1AiumolCaDoZfgB/Wv1Ww8+bZzOkMOdJ8sPldomwaGWwAb00noboiWNPldIUkTPVitq3Ay4XRrMpxakIQ0AUO4LMlAUs/IRFKOS7XPnaAY3nFzv7z+Ahf2LiCK2tOIdwmpRqPP3bC2e4ylvHbACEEtWGD4vCXCez849k39v4Q3vUABCpFyaX+AbRgYEFri7MBN52mdPQIZnMzejyuqsQEaIEgVlsbzf/wCUb+6ZO44+PItatp+sTfn9J+fYbOpT3T3e0zWeqHJmno2oQvNHuz31TXyWQxs2Db3ePHy2Roa/0KSq7znEyXLePM4bTI0M0338wf/uEf0tPTg9/v5/bbb+fo0aP8wz/8w9ka32nBCAg2vl3w1P+bfZhHVyiRdDEhkQLcLMRXqpSZEjYLaD+z43jiP10O3T7zStKwHXpepR7I8ZVQSqkokTyd0iqUENsXX7i8OAWltEqt+aaLKYQuOKd6tua/mJBM7FFEKL5S0PVSscALCWDNjRpNF0gm90mqVisheXEKGrZpFWX9hl8Q3zivX0ziOKQGiDcHoLaOpWDoGtWRxWehvfkiCcdlfSjAd0amykQIwJHwH70jp0aGJg7DxEGoXbXAgHEZvz1wnALDY4/huiUa67f9Vhts+qsF/snbKhfaWdj5LXjRrA7UTacpHjiAME3Cl579Hl3StpG2g5cvUNizR0WEautwxsbA84jdcAPW1Vfx8OEnseJVWM2nf8O2B/op9fXhM01YUTnz3VK3gl3jvRXLqvyz61i6iaU/w4rWZTzncVpk6D/+4z/4yEc+wt/93d8BcNttt/Hud7/7OUOGAFa/QaN6ncfg/ZJoF+y5FXZOy5g0C7pfAated/bCwU5ecnhemnvkEdj0Tg8zqEEQYiuVuNEIqHGkepUBY9VqpZ+ZX15/MoTbVNpsvsYIlI4qPwaRduXcLOZEl+cToRnEusQpCbcXoLpbCaXiT59d9hVKZB2XFssk7Sxki67tnbCEvwwrpNrWL2Egt4wXBjLZQSSSSKgFz3MoFBP4fXE0zaBYSvHAo39PrqDKx/cf/hbnbfkz4tHOU96/becAMM1FzMSeh5C6tVCjN6+LuxYKYbW1ooV+M9eOUV1N6ILzEX4/xX371LK6WqRdwh4ZQQuFCLS1sb1nK8bTrGbyCgWcsTECGxfqD/54y/UcTg5x/+A+DKHxotYNnNuw0I+s1NdH+ud3oldXEb32WjT/cqTohYTTIkNHjhzh937v98qvf/d3f5d3vOMdDA8P0zivrPHZRN0mjeo1kl+8S5WWzsArQbpP+Q5lBiHWdeo+QYlDqn9Y9dpKrU9+QvUhi3YoEuMWQS6042BiN4w/KQk1STx32s+oU6LpQpX8FyF5WPkeGQFJzVpRkQ7LT0gy/RBbsdDvZ6b9x2Jwi+A5KmpWvVaSG1OpQcO/9HlLT+Lkn4aHkhVE1nRjp8EML4w6nQo2hQMMJIo4OY+X1cX412PDFKZNihqEzquq4mTyLtHgSX660Sb1bxkvWHieSzavhIKhQAPZ3Ai5whieVyIcaubg0e+ViRCA4xY4cPh/aazfRjjURHW8B9vJowkNXffN27fDzn1fYHDkAZDQUL+NzevejqY9vzUjdserMY8/gJhpsBiqg3PeULGOl06DplW0tTjbGPLyPHR4B911jWxafSlC1xGGQf6pnTjj41htbc+oNYbm82E2Ni7aPiTmC/H5a/6EkWwCv2ESNgMVk62S67DjJ98h/KGPw7TX0uStt9H59a9Nl+gv44WA07qy8/k84fBs+FDXdXw+H7lc7owP7HRRmJRkR5QhYXwl7P2qR/LwwvWcrGp/UUqptNKJhMigvHnu+0uvbG0fWwEv+metrO+ZMS/0xVVkxhcXNGxX0aAZBJuUjik7okTaVauVtmnG5yjSpkwdi0mBnVWkKz8Oum+2gWsxoSLaxZRcQIZSjktY19CEQEpJzvUIGWoGVbVKeQ0afsHUAbVfMwjhlqXPOXlEidHni8pPBicv6b9HEanajYognorH0lxEDJ16v4mU0Bmw+OY53fz78VGGCzbX10R5eV0cn1kZ2ZsoOUQM7ay281jGs4dcfhxNM/D74thOnkx2gGCgDp8VIx7tAgmaZqBrJqYRxLQiuK5NPq8M40y7hOHa5H1BSnaGfHEc8IhFOphM7EcTBnU1la7LxwfuYmD4vvLr4dFHuCfdy0Xn/iXWmRYY/gYh1l5PPi/xF3egxRvg3LdBuLI8vnT0KM7kFJrfrxqonmX88Mgj/Pk9t+FOiz1fv/pS/uaiN2LU1hLYfA5a+Jl/3v41a/CtXIkwln7kNYTiAGTtAjvHjtEVa+D+wX3808O384Hbfk2rCSFPYLqS4v79pH7yU+KvXrqgaBnPL5z2NOdzn/tcBSFyHIfbbrutomHre97znjMzulNEflwyuVeSGVJVWsE6ycDdi6/bfLEg1gn+uPIAOhmO/lhW9PhJHoG9X5FsfDv0/kySPCKpWVcZxbngrzR2fMZjfKcqQ1/1WlW27lsrab5IlCMmqWPK66iUgqbzFekJNUiKSYG/CsZ3QWFKEmmVRDuVmWLqKOiW8j6CWX+fjoCP1SE/+3MFjudLrA8HqJmuIPP7FUmItKmy+2DDic/ZCKhqO/00J2JuCRCKQCYOKwLXsO309iGEoCYyG7Y/Lx7m1miIuyZTAMTDFkXPo+S4RAydsZLNE6kcNabBtthySuyFBsctks72IxCKDNlpSnYGXbPwWTF8lhLJlewM6dwgSEn/4K/xWVFamy9lbHIn0UwC3XNwdIPWpouIx1YQ8FUjhI5phNDnpYkAxiZ2LViWy49y4PB3WNPzeow5F4eUEtvJYhpBxGKuqM8hmM3NmM0nLnqxOjvRQiGM+mfuIXQyeNLjk4/cXiZCAN/Yfy+/u+4KVsabzqiA+0REaC4OTQ1x38Be7unfzRf2/FKNTUoONkWJ5UqsGkoD4E5OnLGxLePZx2mRofb2dj47p1EfQGNjY7lFB6iH2W+aDJkhMCPgy0miXRKhaQTqVeXWXPjisPMWyeHvSza9Q6P1sqWjFoUpSfIITOxd6EmZOCy554Oy3P/n8PcUoel5jdqfFRF0XqvRcomKzPirlfmxFRUVqaNwGyR7lUP+jK+Q7tPwxSVjO6CUUcLvVC8UppSbdmES5GyVKUFdwxSC8LRpYUDT0IVAQ3BfIoMALq+OoAnVhmSu39Bc2BnVnDbcqtywQ82ynE4MNbHAa0m6SpAtdEmgFgK1qtVHy8UqejX2BGiWZGkXoVOHqQm6po0cNQEPJLNIKbmsWjlTh3WdavP5nb5YxuIwdB+hQAO6pgSsAX8tQhgLhNC65sPUA7j9DyN2fwMvM0ZT9Tls3voG+o79BFnKsKLnFXS0vrjit1wd71lwTNe1MZcIGY9P7mFiai/xaFeZiOULY6SzgwT9tUTC6gKbSh7i6PE7cNwirU2X0Nzw3GxVtBj0eBw9Hi+/tgcHEYHAom0wnilKrsNoLrlgeX96nJXxM5/mdjNZnKFBzI4ONGshCQZoj9ZRHQizd6K/TNLu2NzCJXtHiWenCzoMg/CVV57x8f22Y2BggD//8z/nJz/5Cfl8nlWrVnHLLbewbdtpzqqfBk7rCXLs2LGzNIxnBiMgEJpkar9K76y5Eba+V3D3ByS5IbWOrxqKk+rv7CA8+Lce135Rq3Cdzo1JzKDSyjg5pbeJd8Pxn1ceL9gAx39WuWzvVyUdV6v2G0IXRDumCU6VEioH6iWaAdlhpTGKdasUUqRVkhlQGhtQabL0oMQpgj8uiPfA8IMqShNuEQRqVUptBnWWyRU1s5UOrX6LCdthyrYJ6ho6047W06m0pQwks8Pqs9N9EGyQJI5IRh6BcIvECgvmF+F4LiQOqqozXzXUb5Y0XaBjhgRT+zV8cYhO66jtrBKIB+tPjxhJKXFciWlo9IRmxYp1loHrSQwhsDSNi6pUpHJnOse3h6cI6hpvbq6hZZG2IMt4/iEcmn0oCqERWKRSQNdNqo0Y8nsfJI6L4diI/n00S0nz73x2wfongq6bdLa9hPGp/ZRKUxXv+f3V2HYW150VBhpGEM+zmUgcoFBMYpkRHnr8k3jTs5bxyV14nk1r08WnNY4KFJKqx17oN9s93U2lKOzbj/BZhC9+BuNfAn7D4qLmNdw/uK+8LGIFOLdxIUk9Eyj1HsMZGUWYJlZn56LrOJ5LR6SBiBXi+0ceBuDXa+tYMZxi42Aaa8UKGj70IXxdXYtu/0KB9CTFo0m8dAktYuHrij0tHeipYmpqiosvvpgrrriCn/zkJ9TX13P48GHic4j52cQZnU5PTEzwpS99ife+971ncrenhEiLcpI3glBISAI1gpd+SXD0pxIpYc+tlREe6cHww5KVrxI4eYlTlCQOCvSApGGL8gYyw1C/FcaegqEHAQ9aLoOm8wXHf1a5Pzuj0lrBBkWgfDFRLnN38pKxJ1WHevUago2qDN7OCDRD6YHsjGTgPkWoYitUWssrCbquVx3rgQoitBgKnsd4ycEUgiumS9CP5YscyBZYEfSxcglX53CLKioxApKBX6sKNysKoUaxqAeTkwd/nSSch9wgZAaVbstfLYitUO7dMwRq6oBaX/fJsg+SnVHNc4MNC6NOM0jlHHIlj3jQQAhI5hyiAYNzIgsre349leYNOw7jTH8ttw2M84vtq2k+w4TItnMMjz2GEBqNdVsxjGUB5XMGh3+JcApUFEHv//HC9VxH5XStpSvE/L44G1a/iSPHfkoifQiAYKCBnq5X4Lh58oUxggFFTCwzjGmESaZ7cewcxVKqTIRmsHv/lwkFmqmKP80HaN/DqhKi67ITjhuAzJg6v9jJ9T7JvE3/VI7uuvCsOeEcaOEwUkooFJCOc8qpptPBP176Vj52/1e5f3AvK+KNfPi8154xTx8pJe7kJHo0qgjQdETIaFo66pR3iiBgS10X74pv5H8PP8Rw3M/3rlzHda+5iOaejQTWrz8j43uuIr9rnMQPDuMmZ61N9JhF/IZuAhvODiH/p3/6J9ra2rj11lvLyzqXIKxnA8/4ly2l5Gc/+xm33HIL3/ve94hGo88KGfLXaKx7iyQ3JkkeluAJ6jbDiutV+qj3py6FycptQk2CdJ9H8qgiA2ZUku2HiT1KB2QGJWNPaXRcLel4iYqQBJsgMwBWHEqJ2X21XKJ6hGX6INxMhSePZqiyfiOgCI6dmzVHjK+EUlqi+yE7IFRkKgjhZqW5mdwn0U0lSD6V525I1zk3FsKaQzCCmoYhBL6iRi4j8ddKCuMCKzI7TiMgiLQrvyInJ/HFBbXroWrVQg1EMeVx6LuKLPa8QpCfFGSHFBkFJSK3orNkMdSkBOtIj7GnIDOgCKAZEhh+FvVOAjB0gSZA0wS269FXKJEvFTmvNlxu+OpKiSMl/69vrEyEAKYcl68NTfKBrjNX5ZjLj/HAY5+gWFJh/QNHvstF534Evy+O69kIRLnaKJ8fRwgdv//MpxZOBZ7nIIT2nNewnFHEFmmoudiygUchn4COi1TD0kVgmmEC/hq2bnoX0nOw3TzRcBue55LO9mMZlWm0SLiZkp0CNIp2asH+XK/Ikb4fsS3+xwveOyXEWlXbjFNpNjz4uJrtBapOSpz6JnMMJwsELYOu2oWpQaFp6AE/XqGIl8+jRyJPb/wnQH0wxn9f9c4zvl8AZ3iYwt59GHV1BDZuQA+H0XsWRp2Kjk1fZhxLM3js0FM8MnKAtFviwvECn669iHtXN3Fpz1a696rIkjetq3ohIr9rnIkv712w3E2WmPjyXmrevPasEKLvf//7XHPNNbz2ta/l7rvvpqWlhXe96128/e1vP+PHWgxPmwwdO3aMz3/+89x2220MDAzwpje9iR/96EdcccUVZ3J8pwXdgtyQID0w7aszx69n49s17v0LD7egXjeeD/XbJP13Q3ZARWLCLQInrSZgoP53S+CLCuq3aDz1Px77vylBTtvp9KjIdbwb2i5TzWCLCUEpXUlcNFNUCInnyhHMkCA3ooTZsRWSus1KPJ08Ihl9UlKYUkaJZkgQapLsvs0jdQxaLhFljVJ+HDKDHsMPCwK10HmNXlEWX+8zebHPZHynJJGGYBpyI2BFWND2w4oIOq+TFKdmDR6lK8mNTWuaLNXbbXKP0juNPSFZ9xZB0/mzx5OeZPQJ9XfdZg8hBHrA494PwdQ+mGlytu7Nksbzln5YZ6VDNGzgMzR8pkbelOQ8j7Tj4bPUdo8ks6Qdl8winkR5z1uwbCn8eirNZ/vHKHmSV9bHafJZrAj6aJ0TWTp6/I4yEQIoFCfo7f8FPV2vYGJqLwKN2ur1lEopBkYeQBMGzY0XkUwfJeivI3KiEr5FIKUHnH5vPMfJM5k4gGmGqYr9FhlOdl4Cq6+H/dOd1XULXvI3C9czg1DKqlnKEtA1k1iko/x65nLWNL1i+Qx8Voz62s0UilPEI5309v8CqIwe+33PgBjXrz31dWtWqhuXefLZ04q6EEFLpyW+9Lr+Tecgi4WzQoRApaW+e+hBnhw9wvradn6n5+Iz1vZCi0TRI2GMmqWF2BP5FCPZBMO5BJ5t85Pvf4GsU+SJrip+bmi8s62DtT2XkdQ0zNYWvFwO8QItqZeeJPGDRcqw5yDxgyP419Wc8ZTZkSNH+MxnPsP73/9+PvzhD/Pwww/znve8B5/Px1ve8pYzeqzFcFq/uGKxyHe+8x0+97nPcf/993PdddfxqU99ihtvvJG/+Iu/YN26dWdrnKcEoYMVg7pqQdW8TvF15wiu/5rG8CMSf42K3kgHgrWCYL2KDAkhaDhXdXwHFS2p36K0PolDkv3fmL25OXlwcnDZv2lkBlSz06oeQTElT9pWA6bDtwV1DM1SHSOMgBIhT+zxuOfPJE5WrVu1Chr+0uOu96pSfoDxnZL8OLRfDQ98VJIdhJmb75EfSK76f1q51YeUM2JyNbZggyJ6/irVcLWUUpV1QihX6ql9AsRstV12GFK90Ptzj9HHIDsCuWH1Xn4ERp/0eOlXtHK0i9mhUEpC4rBKNU7NygJAwqHvwdolfuMD6QxD+39JxB9g9YaXALA1HuLxZI7dmTznxoKEdB1LExhC8IamGh5IZsvbW0LwmsZTe/g8mcrx+h2HcafHfNdkmj/pqKfOMpDSQ0pJoThJOjuwYNtCcZJCMYFtZwHBZGIfoWAzfl8NmmZSLCZIpI6Qy4/huAUM3Y/n2RhGgNAJyvpc12YysQ/DCFAVW2gAN4N8YUJFoCrCa8pZ/bcqKgTqInrDV+Do3TDVCyuvWjxV1LTprBze0H2EgyoS2VR/LkOjj1S839a0eDf4M47TcFwPWgYr6k5cuq6HQxA+9SiI9DycoSH06upT8uH5q/u+wv8eegCAbx+8n/sH9vEfV/4Beyb6yNh5ttR3Yz5Ns0U9HCK4ffuS7yeLWXaMHcPUdFojNTw5cph+nwSfhTc9Cfnu5G4uMV9OxNBf8Dqh4tFkRWpsMbjJIsWjSfzd8TN6bM/zOPfcc/nEJz4BwJYtW9i9ezef+cxnnntkqKWlhXXr1vHmN7+Zb3/721RNVxc8m81Z50JogpoT8DFfXNDxEkF2WFWK+auhbvPsA7ww5eEWJZqhMS0HKJsTTu5fGGXIDIMZktgZFf1JHFGRobpNJ09ppfuYNlGURForq7z2f32WCIHS3Bz54SwRmsHhH0gyw0wToVmkjkH/3R4dL1E3ELcA2SH1+TRdoB6U1dPdrKf2S/ITKjIWalTR9ZKK9itCI9Tn1PdLj92fX/xcnCwM3ifpeqn6rISmSCSARGBGlIB6PkoppZWa0VbNIF9ycfISQ49QNUcAEtJ1/LrGlO3geBJ02BqdvUn7NMHXhyYJGRp/2FbPmlDll+B5DtncMD5fHMsM4zh5CsUE3xiyy0RoBk+l8vxJs5+xyYN4nkuhMEko2MhkYn/Ferrm5/jAXYRDrYyOP04idZhwsJk1Pa8jGm4jlx/HZ8aIhNop2WmKxRRCE+hOdlEylMuPYztZgv561eB3fqO9OXDdEqnpH4XPOqccQTIMP3U1G/CkSzJ1DJ8vjt8XJ5cfJZcfIxbpesE4Ki+AELDi8md7FGxY/RY8z2Fk/ElMM8SqFa8iGjmzfX/snCR1VKWhT2UCdqbhec6iJpTjvYc5/vhDtLR307j9whPuYyKf4nuHH6xY9vPjT9KXHiNZzFJwbWzXOWUy5ExNUdizB19n55IeSV6xSP7JHRjVVZid7RScEo2xelZVtbBvsp8jjZURMF1K1h7cq/YXWlpr9EKAlz4xETrd9U4HTU1NCwIqa9eu5fbbb19iizOL0yJDruuqlg5CoJ9C1+DnKnxxFRUJzrHRKCYlj/yzZPQR8ByPaBdc+g8awQaB505HiwQVke+adZA8rNJUtRslbkGAsqQ4IZy8xMl7CKGhWyrqlJ9gmkQpT6L5yI8tXCY0GHls8WNkh2f/NgKC+EqJ0CSltMoeFKYgWKeiP8rYUa2rGYK6zSqNNRMGNQKC8YW2KxUww5U345kUpUCJ28MtShc1k6YEiHYpkbWUsiIV5CLJaILuVRcRCVT2BNoSCVKUHqHkcTXw2lXlRqyvbKjilQ1LR4MKxQTZ/BiuW8SKhcnmRiiUEphy4cw4rHukM8fJ5ycwjQCmGaS2ehOGHmBw5EGE0CjZGY4P3gWo0m7XK04fZ4oHH/tHtm9+H9JzCQRqCIfqQQh03Yfj5NA0k2xuBNMMVZj45fJjuF6RgL+G2up15ehOyc5g6P6Kh4+uW4QCDWiaviCVJoSGXUpSKCXwpFM2LHQ9m1x+DN1WJevztyuV0hhGAE0zKBSnyOXHiYRbMc+yULxYTJLNjxAJtS5J1KT0GJ/cQ74wTl31BgKBsyPkPBMwzRDbNr37rGq3SklVsaqZi7fiOVW4nkQ/zZRHJjdMNjdMqBTBLGpYHR2IaR3fpF9nLKATr46dZC/gSNVeZz5s12VrfTe2dPEVHYp9A1itraqJ6wkgczlksYSbyVYK6VE90OyBAUQggJfN4mqCsXwMv2GVx3Bl+zk0haoYys5WEd7Yug03lUb4JjBPILx+IUCLnFrByamudzq4+OKL2b+/crJ54MABOjoWpqXPBk6LDA0NDXH77bdzyy238Cd/8idcd911vPnNbz5tTcOzDcMviHXLsjYIIDMoGX5g9nXqKDz2aY9L/0FHCJVWW/V6OPZjFdGoWgXnf1gjP6YiKjPVWNJVUaL5KCaVzibcCpN7Jel+QftVEn+1RnZYIj1Vrg7QdJ4gdXSe3qAGfFWqKesMel4tGPj1Qj8lYUKwQUXAyl3m6wUTeyA3KpFSYvg0kEpEPv+ZMlf8PQP9BM/CSLuKcM14Ckl3up1HeEbTJCkmYOWroO8uRR5jnbD1fTDymMROQ/NFEt2nbqZHiyWGcAlrJpF5rtKGJjDQYfyACmPFWk/af6xUSpNMH8MUNeQP1SIjIeIxCAbqEZrB62s9vj4qSE2Hh4LC4+2tdViaRiTUit8XZzI3zmN5k+qqJjZWr6G3/y7GJnaUjzFDhGbguHkmp/bT3nIFtpPBsiLlB6Kh+ygWk2RyQ5h6gFh0Bdn8MH5fFbFoB45TwDLDlOwMUnoIBFOpw5hGaIEvTvgEM1WfFSMSai2TrWi4jaC/lmT6mGpb4ZaIhFvL4yoUp5iY2o8Qgsa6bRRLKWwny+j4kxwf+BXFYoLG+nNZteJVJ2xLkUofp1hKUhXrwTgVwS9QtFPYTo6SnVmUDEkpefSpf2ds4ikAhNDZuvGPaKjdfEr7n4tCcYpMdohIuPWsN249m+07gg0gjIWR1VPF3qEUf3H7U+zoT7KuKco/vHoj57TFy++P5ZJMFNKsqmpGm0fmxPS1bh8+irR19GgUo0bl1YuGoLiqi2DDyYsXGoJxLmvdwL3Hd1KXKpAIWmxoW8WKuNrWh0l+/26c0VGEYWC1nzi6Zra0oEUioOsUDx9Wr6d7iNlDQxSPHMVoqCe4/VyEz0ejJig4JZpC1eTsInmnxNev/zO+tOcuhnKTvKR9M9d0bsWdmECPvvCb/Pq6Yugx64SpMj3mw9f1NH90J8D73vc+LrroIj7xiU/wute9jocffpibb76Zm2+++YwfazGc1pXq9/t505vexJve9CYOHz7Mrbfeynve8x4cx+Hv//7veetb38qLX/zi53zUSHqSYz+VSKDzJerhnziwcL2J3eA5KsWjmYK6TYKVr/CY2C3w1whCjYJgvSIyM20nhAZDD0iO3eFhBAQ9vyOIdauqsFQfmHFVOm8GRbk/YvUapeGZ0fis/z1BflLS90uVblv3FjUDbL5I+QBN7Iaa9bD+bYLjv5g3qxJw7gfUdk7BY+DXgnCLxMlpaJYkN6rsB3xRia+qUvQ8dwxzUUpJWi8VDNwnYZpAan7ouFJFdwI1gqH7JNkuSe0mlf6baefhqwY0iHWr1NzG31cCbOkJdB/03inJDUPyGOXUXZ1lknO9CiPFhO1Q9CQNPjXf8xo3k8/nwfNTSJeIBAwsY/aG7bgeJTuDzwzgejaedJHSxXELZArj2LYf0wyiaTq1hb18fSX8vNCAKAxzZdRhQ0jgs2a1F4FAHXoxi/RySDyKc1npEojHujHNYPnhXrIz2HaWYKAO0wzjM2NYVoRiaYp8YQLPc4hHuzANtX4ieRiJpCq6EkMPnHIbiGxuhJKdJhbpLJd/g4oWmWaISKiVyeRBcoUJDMNPMKBCpIbux3ZyaEKnWEoSCbUgpcvjO/8bOV0qfuT4T5BI1q583ZLHn/28T13AHg42YZmRJcnJxNTeMhECkNJl/+HbK8iQlJJ0pg+ERjS8hLsoyh7B9Uo4TuF53cVeaILg02wf5nmSP/zyY/ROqFZKe4ZS/MGXHuPXf34FhvQgmeSuwR0MljJoQmNVVXPF9qFgA8FAHe7aSdxkEn2OIWNNIErBtYn4Tp6K9aTHH22+ntVFg3xiHw3xLl535f+pWMdqb0OYJkbDia3zM6U8B6YGaYvUEukfwO4fAM/D19ODm0ighUKYjQ2Yzc1lMbgPWF2tfisPDu0nZxfZ1tDNB7e/CtfzEEJpKY3a524U8kxCaIL4Dd2LVpPNIH7DirPiN7R9+3b+93//lw996EP87d/+LV1dXXz605/mTW960xk/1mJ42tOW7u5uPv7xj/O3f/u33HHHHdxyyy287GUvIxKJMD4+fibHeEYhPUkxqXp+efZsOXh85bwcGKrsPXEICpPKTVloAs9WJeXGtBu/0AQIyfDDklSv8jR66jMz+5H0/Upy5f8I8AS+uEdxQmmXVp4ryp5BQhMc+LbLwW+rsvv2KwTb3q+x9f0e0hZ4tmD0SU85RDepyjIzCNlBSXZo/glCMaWE2OnjkDioPIAirRDpUIRDaIKqNeXsEnZGMrbLI3lY0HyRJNY1Syr2fdVj920Sz1bRqaYLIdQg6LxWEKhVOxjb4ZE4DLlxRXKMgPpcPU8y8rDAiqlzmiGM+pwqv+YLJJP7Kyv/Gn0mjb7KIPcTqRy2lFykhwkbOjmzhrTr4mZLuFmBzLjUNqlx53IJHnj0h+S8fizRx8qO62lruozJ3Tp28SCRroJKOxkBdN1HONjM2rDBVn816axHLj/GfOfskK5zeVUEXURw3VoyDReQyhxf8ndWU7WOmqo1FcvSmQEcN49h+DH0AMn0MXTdR1P9uXieO08EDYFALdJzMc0QNVWrlzzWfBSKUzhuAcctYGkLCZTPF6M63kM+P45lKjKQSB3FtrM01m0lnenHtvME/DUkkkfKRGgGA8MP0tN5w5IeS/FoF57nouvzExWVSGX6cOwc8Vh3uffYUkjPF8wBhXleGVK65IuTCASRUPOSqalwqAmfL4ZpvDBLo08FvZO5MhGawXCqwP6RNOuDEtJpNphxNJ+P2sDiVWTO2DhCCHzdlYLtlnANLeFT6HUEZO0i4/kU29efz7ldl2A2NWHMI1F6NFoRlXEmJnCTSazOznJqDmCykCFRzBIwLGpaW0GqSJFXKPD1e27nK+mDOJbJ691L+f341eXtvOlUXWOwirFcgqlCFkszuad/FzFfkAuaK6/jFzoCG2qpefPaRXyGfMRvWHHWfIYAXvayl/Gyl73srO3/RHjGMVxN07juuuu47rrrGBsbq2jN8VyEakKqBL7BBvWAlq4k0g6rXic48G0JHgTqYcsfa+h+Ze9RtUrgFmByn3JXrl4rSB+XlLKSfV+RDN63+PHcAhz7sWTt74JbFCQPq4iI7hO4RYmdg8IE7JpjknvspxKQdL9CozgFtZugZp2gMCkppVV6zRcXeLaqYvPsOQcUKgUVrAM01cqjag2kjnj03zNNaM6HxAFBbkzSe4fH0EMq3F69WlK3aZYEpI5Jdn52liAWJhQ5PPcDlQ8ZM6LsBQJ1qlFuuEUQblEVd0zPrJZq2GpFNcygJD8K/ipllrkYOgI+sq5LcLrtiN/ScTyJpesM7ZQM92poF3hUr9bYf/ibFJx+OkaO09X7C/IP383IBX+MyxsQXgBJikxuEE0zCPirCc0Rj0VCLYSDTYs+SA1tpjrPo7Z6LbZ9HYMjD4LQaKzdQnXVatKZAUKBehrrzwVgynaYsB26AqrSqGRnsMwI+1MJRvJFWswcIBZNd0VCLZCdgOzYgmaaJ0Is2qV0USeIJM309fI8h0d2/BtjEzunl8dZ2fVyPOkQCtYvGjkxdB/p7AAlO8OhYz/AtrO0NF7Iys4byvoYXddUn5mRnaqUvXaht4ttZ3DcIp5nnzCdpNpjhBFCR8pZC4WGui0V62maQVW0G8SJK+mE0J7XzVZPBiklw9kpolaQkLV4mrI+4iNk6WRLs5+nZWiqxN6n47hFVtS2sS68eOhJ2jaFXaofUfhFlz4tM8bikSOIgQF6VnUQjlUT8C/+ndiey3B2kvpAHJ9hUjx0GC+bRY/Fyqk5gNZIDT7dpMofRtMN/KtXAfDrvt383cR0g8kS/POj/0tU8/Ha9ZcB8NjIYfJOkfMbV5O1CxyYGuDh4f0cTY7QfRZagjwfENhQi39dzW/UgfrZxhlNaNfV1fH+97//TO7yjEO3VCorWCewpjUtyWPKd6fn1cq7Jz8GVatBm45WhKcjxEJIlWKaTpem+yTDjy9NhGaQG1NprkC1oPG82R/T5H7lXD2xe2E6YfgR6HiJx+EfwBP/BXZKGSKC6gW27vcU8Wh7saT3jtntOq+Dxu3qQZDu14h3w+5bPMZnMwzs/yoIUxJugvR0cEPaMLGrUpM0uW+hsDE5z4LCyUtyI4pQFiYFYzskjdtnhdeN26WqTDsB/DVKdG6FwXMVGZ2vu1oRrOwa6ySheNTA3wXxBolMgnTUNlOpA6w/9Es6B9VgI6Rwf/XXyD96JXkjitBDSptiLB7GP5nYNZcfo1BK0NZ8GWtWvhbPc7DtLJYVpbGusofOgWyBpOMS0jWafDF8vhglz+O4rZML9NCljzKVPEht9bwySNeB778bnvq6+nDWvozSDZ9GzEm72U4e6TlYVuXM3dB9FY1ET4Sh0UfKRAigWEowNv4U61e/CV23aKjbSizaRTJ1dHoNjY6WK3Fdm8d3/mc5FXbw6PfQhEF35/WzO3fykBpUYcJFyFA8uhJPOid18dZ1k1i0kw2rb6J34Bfk8+PU125m3ao3Llh3/mexFJQgPYD2NEu2n8sYzSfZPXiIwLE+trWuxb+I5UnIZ/Chl67lo9/fjetJNAF/ds1q4kGVu0/pSexCDmH5FiXEqp1FByDIPfIIuUcexbdmNZGrrqqI1gC4ySResYg5r/Grl8sjbYcmI4ThD+NmMth9fVjt7RWGhn2pMY6lRsnZJVZXt+DrWYmbSFSk5gA0oZU7zwPk7CK9qVF+dOxR5uNnxx4vkyFNCJDgSpeAYWFpOrrQqA/GOe8stQV5PkBo4oyXzz+XcVpkaMWKFae03pEjR57WYH4TiLQrp2UnL8mNSgJ1ygRRn3aItqJL5+GNgKB+s/rbsyVDD0qO/PAUDqopkpHuVULjGVFzoEYRs8U0sJE22PsVGH184Xv5cTj0v7Dy1ZKu6wXdr4Den6lqrapVUEx6+GIaminJDMgKIjQDac8SobkY2wktL1J/V69dmDqsnuf9pplq4m9FBamjqsJlpi0HUJH+AuW2nTisBOe+qLIBCDZA7Qa13shjSrtUv0WW9UultEQXRfR8b1kwbWdU7zg7AzVrNWKtRTRTksqM4fdV0zRWmVbR3SLZ/T8lfMFNpyz49zyHycQBhKMTStZj1AcxYj5CwUZ03VfukaVpBr4lVKzdQT/jtk2dOZsysjSNDeEAhJrxF5YgA098EXZ8lXQwQtHyEzvwE6Ye7kZseDV1NRsRQiORPIQnXeLRFRi6f9Hu6ydDZr4vA5AvjhOYNpnSNIMLtv4FI6OPUSglaKjdQijYwIEj312gCRoafaSSDFkhaN4KRuW4pPToH7qPqeRBouE22povO+nYI6EWIqEW2povOe1znI9CcYpkuhefFSMefXZ9Y6T0yOZG8FnRJZvDngy251aUnsetEDVWiIjwI217ye3efEEHV6ypZ0dfgg3NMdprFMlOl/J88+hujqdGubpLcHn7OYtu71uxgrH/+E/G/+u/ysuiN9xAyz9/svzaKxbJP7UTadto54WUZ9E0/OvWIld0oQWnyf3gIPbQMMKyKlJv9aE4WadIU0iRH6O6utzN3vU89GnyNTTUS38hxermbqK+IMPZKYayU/gWSdfWxWooOjZZp8APDj/MsdQoO8d7WVvTxqqqZrqRlFyH4VyCKn+EiPXCNFlcxixOu1FrR0cHb3zjG6mvP/Ww/XMJnqMiKonDqipMaKr/VmiJwodSSlKYVFVgc1M9Tl5y7GeLbzMfdlJ1dbczlEXTbkn5E4WalAA5tkJ5H4HSJK27SXD3+5au0RdAy0XK6NBzlYh77ElJ6ojACkPjdiViThw8tTHOID4n/R/tEGz6Q8HuWyVuEcJtsO19lbM+zRDUnaNG5IupdJfnqfHMD6nmxhQ5c3KCUlJV3uUnVOBjRi4y8/nMBGfsrGR8JxjFMeqbj6hy+saNhFuVwaYVAaTEGLkXPI9CvI6WxgtwrCA+u7IiomhVY7kSc27Kzi6AbsIiEQIpJYXeSTJ3DZEfbVRC8Gs6iVzWRtg4tfB5rWVQay28zMo90wJLaIGOPwRAybCYitTgIfCP7ENsq0IIDc9zMIwgUkqSYzvAF6GuZtOSUS3Pc8nmhrGsSpFyPLpwglNbVRlJ0DWT5sYLyq8npvYykdg3f7PFxciRhaLX3Qe+zPGBX5Vfj07s5LzNS0eU0xmVkovHVpS71z8T6LofXfNhPU3ycSZRLClLgZKdWVApCFBybUAs6cg8lkuyc7yXjmhdOaXjM0y2dK7Ha+5ZMn2VLuXJlPK0xKsr3Kdtz+XNP/5X9k8pg9HvHHmSP9h0Le/b9goARnIJSoePUKf7oa2ViVtuqdhv6gc/oPZd78QZG2P4ox+jdPQovlWrqP2jd6EFKwmF0DREcDY6a7W3I0xzgT9Q2PSzsXZhefVQZpK9k/2sqmqmSQRIPPwQRVniMV1i6SYNgTiJxASvrdvAPQO7GcwojVncF+Il7Zv5Vd9OPvnodxjITABKQH3jmhdxbsNKor6gMn0sFUiVcstk6LcAp0WGvv71r3PrrbfyqU99iuuuu463ve1tvPSlL0XTTpIHeY4gPy6ZOgCRVkmwQbnxn6yYJN2nUlxGsNKXaHIfeItUH1rRadPCOYi0iQWEq5hURMBzoHqd4NJPKqdmOwON5wnyYyc2K2q6UImk7ayklJKUkoJIm8TwQ/VqQW5EIpDUb4Ohh1XEZjH4a5QWCKBhu4pIOXlZLq9f/XqNFddPE8K2pZuqwkyES5I4ILAblZHjXIw+LslPSJovlETaNWXb5IE/7gGKjNRurNy/binCY9bWQqwd4qq0VmiisqTYCoPnUB1fhSckxrX/gvzOOxDT0QuvdTvB9ddgTleceVJydGCKQ4NZXDQ6m2KsbQ1VnJ8mDMRPNUKp6S/eg+QdxwhurkePnVoa6mmj6Rx46utEMwmS4Tgly0d93UaItHPk+B0cPPo9XLdAk9VMl68FUb0CUbv0dVgoTJLJDuFzsmXS4nkOJTtLQ91Wxib2IGWJxrpz6el6ZXm7YimlhObTJGRg+AF27FnYBV7TLLo7bzjpadlOjr7BeyuWjU/uIpXuR9N0DMNfbltRsjMkU8co2VkMw4fn2WeEDJlGgNr5Ic5nCZYZIeivxVrkRuR6Hg8NHQAh2F7bgqbpWGa4wrpgBoKF16V2Ak+ePRN9ZO0COCkMN00s2oVlhvn5sSfKRGgGX99/L+/b9gpsz2X3WC/p47u5LNDMbmeS6kJhwb6dsTEG3v0e3KS66RQPHGD8P/+T6DXXLDmeocwkBdems7OT/sw4w5MJ1te0EzSXvs48JAW3hCclWsBHS+sK4qbGgBUgbRfIuUXYs48p4eO7V/4Jd04dxvYcru7YwlQxw3cOPlAmQjP4Vd9OPnqhMhHuqWqmIRij2n922pAs47mF0yJDr3vd63jd617HwMAAt912G+973/t4xzvewVve8hZ+//d/n55FGuA9JyGUZmhuOiw/4eEUVIPUuQ/ESJuK1PjnFUfoAUWQnDkFGYF62Ppe2PlZ5VMEEGqGNTcuvFEFalRkxBdTBCNQIwhMH6OYkOVxzstSIXSo2wzBBkm63yN9XFWWRVrBF9ewokq/o0wSNZoulMRWwKOflCQOzTm+NUCoNs2LblvL1H61D+mpaFJ+ggpHbDO8eOf6+Rjfocwcdb9E9y88Z18c3KLECOizeqzYOPQ9iu1bgRfvwRef3k5KmDiMZgao3diC6g51gk7R7SpyUf5Bb3wt1K+D/T+BWBva+lfiny4BLJRc9vZlOTZmA+qBsX8gi8/U6G6cnal6eQeZdBFzRU8e2KO5s0+Gzv09OPxLtCO/IFDMI2pXIy/8Y5LJw+w79I3yauPZo8TsEit6FlZglOwMmewgAX8d6dwAtpOlukqJSj3PYWJqP5nsAFWxVaxf/buYerCiAqxYTDKVOkyhMEUk3Ex1fA1Hj9+x4Di11RvZsPp3K8r4F0MmO0SxlF605P6pfbfQWLeNWKSzTIZct1gWcoeCDZhGEMcp4EnneS2A9jyXdKZPmVsKHcMILhpVk9LFtafQNT+J1BE0oVFbvYFMdoick0fzN3E4OU57pLbsyzMfB6cGODxxiK0NPdRHZsvjWyM1JApZfJrEdlxctwQmpEr5Rcarvi9T0zE0g2xzPen6FfgNF3vbOZiPzfptme3toGllIjSD4sFDHD92AFFTRVtkoQ7hYGIQx/OoC8aYyKdJl/Jk7QJB00fGLmBpOta8dJfreehCx5UewjCIbjuXKFDnudOpQ43h5hZSyRSTmsurey5kMD3BYyOHOK9xFaurFjpUO3P6GZqaTk3g+Wu9sIzTw9MSULe0tPCRj3yEj3zkI9x999187GMf45//+Z8ZHx8vt+h4LiJQK/BVSfJjMPqEpGqVKlO3s5Jjdyju0XltpZurFRVYUciNSDKDkngPWGFB9SrB1vfCjs9IilMQbIQL/lKjZr2g6XzJ2FMqclS/lUUrqYS2eGrOcyQD93mkjkPTRTA0R5zdfrWK4ow+pv5pPsnq10v8NUoHZUVndTqxFSpV5Ytq+KJw1c2SR/9ZcuwnLueueg+djV9DCAmf30ztG78JkQbckhKIn6Bl1tKYOIyZTSOjG6nboi16zvVrkjj6Q1heM2QaYfAJCNYAkonHk3jeDupbdmOMPqA6bvui4I/BtrdCKQPB07TZbViv/gH943l29Y1RLHk0VflI550Fqw9O5KmNmgQsHcvQ0EMmZmMIe3i2l4iwdKy2sztTzLouITMAb/42+vgh4oVJtLrVCF+MiZFKtb5t+uj1gSVrOLR/lPXNMeoiiqiV7Azp7CCFwpQqJQ+FsEw1dtXqQ+mNNM0glTpGVawHiYdtZwn4q9ENP6YewDFyeNIDJJ63UIPi98VPaK5YLKVIJA9TLKUwzRBVsW6mkocq1kmle8lmh7lo+1+VlwX8NRhGAEP3l9N/U8lDeNKhJr76pMLr08HM5zG/qs128mSyAwQDdfis0zeaU419RQXZcb0ihVICUUoip2c7fl98QYrTdXKsjYTRNT+WGUQIDcfNU/A8jqQTGJ7Se2WdSsPPucgW0+QKUyQygxVkaKb8XUoPx60vO4xf0baBTz0WJFWaneW9ZtXF5b87Y/U40sWqqmJbMI77X//D6Kc/Te6RRxE93fS/8eWIqiAYBjiz15ioquKQl8GdzHE0OUqVL8TGuk6cqSm8ZJLVtS0UPYew6WdtdRs5p0iVP0zGLvDI8EEChsUFTZUpZZ9h4tfVv7kwNB1jOu19/nlXcu/gHo5kxmiM1vDoyCGOJEeo8od5cfsm6oMxRnOzxO31q5+5Jm0Zz0887WqyQqHAt7/9bT7/+c/z0EMP8drXvpbgnPzvcxWaLiglJYUpSfKoaqmhGdMEQAJCkhlQKZ+5mpfMoOToTySlb0LDFkH3KwUdL9GIdyuPncbzVXd7mO7Ntfn0xuWWJL0/kySPSdycKnXveTV0XCkYeVRiRgSRVslj/zq7jVeEoQdg/VsFblERqYndEs8WRNolblE1XhWainZt/zPBhgu/R+AXX53dydCTcNtLoXET+uY3Eu55ydP7YKeOUVVfgo4VYCw+m9JNiR4BhFTaH+mBLwKNmwimDuE8+WP0nX8PYnp25ovClX+txpibgMaNSkB9mkjnHR45NJu7HJgsYi1SRCQR5IoengdWWD2Yqt+wmsmv78ceziqfjVetRPOfPVfh3nyR/dkC3UEf3UE/1K5k7iM/HF44m40GG8knJ3Bci4I9WyodCtSTSveiaSbRSHtF9Zyum9RUrUUIQTo7QLGYoHfglxzu/RGuWyQa6WDrhj+ipnot3rRVuxAabc2XsffQ18v7EcKkJr72hJ49nueAEASDDfjMCJ1tVxOLdHF84C68OR5GrlckkTxEONiI4+TxpFNBQHK5UbL5EXxmDE2z5h3DJVcYI+CrJl+cZHjkEQwzSEvDhafUhy2VOU6hOEVVbGVF1KlUSlOyM+iaVR7LTONe0widkAR6nkNiOkRcP0fPZRpBYpFOdN1HqZRasl2HZUUIB5uxzFBZXC2lJB5pJeSY1ITi1PtDRALxJcewsb6H1mCIiH9xIieEVtFqpSFUxeeufjdf2fsrhrNTXNa2kZvWvbj8fnu0jtZITdmRWo/HafrYxwClX3LGe5HRKure/W7G/u//Bc9DmCZNH/4wsfpOsnaR/sw4GVul14oHDuJls9RENmHU1DHce4jHRw7j1sQ5r3E1Vf4wIdNH1Jr9Du8+vpNv7vg5Pl+At2y+hqbw0pOkmD/E5rouSp6D37DoqWphLJ+kM9JAyPTzpes+wBf3/JKBzARXtm/iNT0XL7mvZbywIaQ8WSetSjz00EPccsstfOMb36C7u5u3ve1tvOlNbzqtiFAqlSIWi5FMJok+CxbnniMZeVSSG1NtI6pWCWo3Ap5gYrcyPqxeM9v80MlL7vh9j9wcg8Ot74XuV+jl/U3uVem0WNep+zCU0tNmhtWCX73fZeyJ2fdWvFxFqXxRddNJ9cLUAcmeL1R+Xb4auOYWwVM3S3p/poTHLS+CzquVWWPNOmZTTwCfuxL6F5aalvH6L8Pak2s/FiA/BU4RIiex4HdtxT6FgFIOzID623Ph5stheF7p21V/AxteDSO7INIEjZsWFTufCIeGsuzszZx0vdqIQUPcor0ugH+e6NnL2Qi/cdZ9NoaLNk+lsjhAo2WyJRqsSNtK6fHErv9heEx9h35fFRfUX0nAhWztZsK1lU7BtpPHc0tLVruph30O28nwwGN/X/FeXc0mtp/z3gXb9A3ey9DowxhGkPqac6ipWkcm249pBhcVZAM4TgFd91EoTJDK9hP01/H4rv8imxuuWO+ctW/H54uTTB0lFGqkJr4G0wySz48zPrUXz7OpqVq7wJdpdPwpplKH0ITFwaPfBRShDvrruHj7R09KiCYSvdj2FDXxnoqqLik9CsUElhkmVxjD0AMIIUimezENpTHzPJfqeM+ihCad6UcIjXCoecF7p4JCcYqSnUGgYei+in5sU8lDlOzMAgL3TCFLJbIPPYwWDBDctu3kG8xByXXKYu9Sfz/F/fsJnHNOhYNz3imhCw1T03HHx3ETCawVK0AIjv/0hzyRHCC3cRXbmleX3aFncFffTt5553+XX5uawbdv+PMF6y2F7x9+mCOJYS5qXkNdMMbR5Ajra9tpCMZP6zyfbTzbz9AXIk5rirt+/XpGR0d54xvfyL333sumTZvO1rjOOKYOeAgD4itUCifeA6k+ydR+Ndsyg4L8hNLiWFHVaX3Ghbj/HllBhAAOfRe6VYEFblF5AHk2cBqVupN7p9tw+L0KIgRw/E5o3C5IHpbEVwrqtyrR954vVK7XdD48dbPk2I/Vawn03QmN2yDUpNydNUtQs0ZgkIDBJ088qEc+9/TIUOAUyfDckPac2R6avniH28En4JL3Kr+a9DCEGyB6eg+WkG8heTI0cOZJVwq2x0TaIeizaa2tvDS04DMX7p4KGn0mtTUxfjWVZtJxmZWWKwihsXXju5hKHsZx8tRUrUFLHIfUIOHoQsJjGgHlGbEEEumjJFNHKRQTC96bWqIUsa35UtqaLy2XhUvpIKWL5y5dxj0TQQkEajHMIIbup6vtWnbtv628TijYQEPdVhKpw+WqN33aL0loBj4rSjBQV0GEfrprmG88chyNIi9bE6Ba/JoZIgSQK4wxMHwfHa1XIoSmqu9SR5CoSjohBIWSS0k24vM3ous6rmuXtVNCaAT81dh2jlx+DNvOEQm1YJlRfFaMbG4IKT3VQ24RMhQ5QVuQU0E2N0yxlMGTNpYRqiBDhhHAdUtoZ0BUPhdSSpAe0nFPvvI8zK16s1pbsVoXnn/AsJjIp3hqvJeuaAOd01rT/vQ43w9lkL4w1zSvZmV84XX+zf2V4nvbc/jOwQf40PmvPaXxra5qxvEcVsQaSNsLxd/L+O3FaZGhvXv3EgqF+OIXv3hCp+nJyckl33s2UJjyGPi1eiYE6z3cosBfDS0XCwy/JNouyiZ/gRplwDXyqCDcLIl2iEWrxtw5y8yQoHaj5HRtXkJNyt26sEibK6cIxZTECEBhUhDrUr3MVr8Ren+umpvWb1H/Hv7Hhdsfv0v1E7MzgkC98r8zRIqK7rSL4Sx01z5lnPtW+NEHKpft+S4MPAY1KyEzAqHTb8bUWOWjPmYxOm0trwtoqfHTN17Am+ZfIb9GQ9xHvuiSzjs4rocmBCXHw79YTu0swtAEF8ZCIEBfonqvKjbHA6F6hfr3NBAKNFIqZTDNhbPLaGRhOXOhmCCdHSTor8UwfKQz/Thukfq6zVhGEMctomvmCY0rZ9J10XALq1b8DvnCBMFALW3NL8Jxcwih0VC3taL03e+L4/fFVfrMc9A0gzt2D/OHX36svM7dB+Ejl1o0zNO2pzIDjE3uojq2Cl23KNlKA6YIjI6uCQxNYJkak4n9eJ5LTdXaCjG5aQaJhJpJJI9SKE2VNUuGbiKEUdYalewMufwY4WDTKTepPREi4TYCTg4pWeDFNOO9dKah+XyELrwQzmKVcMGx2TfRR6qYozNWz77Jft74o38hN61/+urIDr59w4dojVRWrhiLRIXNRdzLvVIJYRgITWMsl2T3xHH8uknBtbmibRNV/jC1xGiL1C66z2X85tHZ2Ulvb++C5e9617v4rzleVmcLp0WGbr311rM1jrMKI6AExb64qvLKDHoE6gRVKwUtl6jnvy8m0C2XvV9WKalALdRuUBVaLS8SPPVZRUBm0PXSyoeUFalseirlrIP1Uoi0qfc9W0WkciOz70XbVcPT1ssh1KDad4QaJV3XClouBjsjMEIqsoW7eKZT90s8F6KdYjrl1wEt2xS5mIHQlHZHvVBVWM8Wtv8fuO/fITHvgtj7Q7jqoxCanhXnJlWqr6Zb/TsJhBBctCbOWKpEoeRRHTHJFV3a6vzkCi6WoVEft3BcmEiVsD1JOu/iuC6pnEs0ZFAXnRYlOx7JrEM0aOAzZx8WnicpOh5+UztlU8cTIWTo5F2PA9kCrX6r3IZkBlLKM3OcYH25HUk+P8rRPlUtZpkxerpege3ky5oS17OZmNpLNjeCFteorlqFppsIaVMqJhCo1I1lRspkzXWLaJqJ7eTKqRzVsDaH7RQwjQDt3a/Bmk5jJdO9qq+aky+ToUIxQb4wjt9fQyrdi6H7qalaw9ceOjr3VHA8eHx0Lde1zf5+hNBVjzgpkUiE0KiuWg1Slt2nTUOjLqaIhl30AaVFP9tgoB5d9+N5NoYRwLZzTKWOYBpBquOqSq9QmKRYSiIBQzMJBhtOaAdQcm1GcykaQvEK48QZWGZ40RTYVCHD/sl+umKNFa7LZwrCPPORUCklo/kkMStIVabI+r4E+gpFdm7b/YsyEQJIFLN8fd89fHD7qyr28bvrruCu40/hTN+zQqa/QuAtXZf8k09S6uvDt7KHwIb1FNwSeac43XhVYHsunvR4auwYuqYv6mG0DDVZmEwcoFhM4PPFqY6vOqk7/zPBI488guvORiN37drFS17yEl772t/MM+m0yNBNN910tsZxVmH4BW2Xq5vb8V+6JHvVw0u3BE5e9SuLdbk89ikqCElxSjVord0gePG/a+z7mvLJaX2RYMUNSz+Ixp5UAZj6rXLJnlwz8GyJMODyf9PY/QWPVC9EO5W3TqBWNUXd9zXJ+E6PqpWCtb8LpbRQJKdDEA3tZ6zhKKMT27Gd2VSVFYaqHsgMaBWZKV7/Ffj5X0Hfw9C8BTa8Bp78smr/0H25Snflp0497XWm0bBhIRmanxZzisqXwF5YBrwUJBD2G9RGNDRNEJ4RQc8JiOgaNFb7SOccciUP6UkcD1JZB9v2yNseu49ncD3QBGzqjNDVMO3am1fbRAL67L6fIXoLRY7nS7hSsjY8m+o6mC1wLF9kazREzSKGjieC57mU7DQ+K7rgxra6+zXUVK1ldOIp/L5acvlxHCdPQ91mHLcIUhIKNuKzYsRiKh8cCbXgOAUCgTpFMIRRbgWSzY2QyQ0hEEgk0VArgUAt6Uwfw2NPcLz/HiQlDh37AVs2vJPq+CoioRb8VrzCd2dGN2OaYQzdX44sFQt9QKVwOxaqZWXHDYxO7MA0Q3R3vJSaqrVI6ZWjNydqVVIVW3nCz29uVZimGeiahTFHmB4KNmE7OdKpXkwrjK5bBANLG9QeTY4ykJkg7xYxhE6NP0L0FLq9Z+w8OadEspQ9K2RoBrJUAl1H6M88ejKWT7F7/DhV/jAdkxlarAjRaS1bepGS/rkVbTM4r3EVX7v+T/nfQw9g6SY3rNhOR3Q2WuxOTVEaHiY10E/NdG+yZDEHrmRDbRuxQBi/YVFyHRLFLJoQZ2xi8ULC8Ohj7Dn4VQpz+jP5fVWs63kjjfWnpyM7VdTVVUb9//Ef/5Hu7m4uu+yys3K8+TitO6mmLT7rjUajrF69mj/7sz/j1a9+9Rkb3JmGZyv340CNpLpHEKyH7PB0JdedlUQIYPgxqN0iSR+HxvM0zvvQKV4wGvObni+KYlIysUeRnlgXdFyloRlqPMmjUHuO5OF/kAwrM2Im90iGH4EX/wfEu6e73qdLrHiZx+TXbOw5GbC+XyqSd8knJKGmOQ+9aBP8zudmXzsltSxQpYTMheS0rfOzhMv+FI7dC8Xp6q/69XDOGyrXiTaBP7q4FmbyiDqHho0wR7+QLbhkCi6OTycaXPpnrwlBOGCAcAhaJtURSabgMJGx2d+fw50OonkSdvamaanxYxkaPlPD8SQ+48zNnNr8FlJCe6AyPeJIVZC9J5MjauhsjARVf6VTQDY3RK4wTjjYRGiOh4LjFpmc2o9hBAn4qxkcfoBsfhjPc2ms20JT/XnouoUznR82dB+Z7BDZ/AihQH2ZYNTVbAAU6crkhnGdIsFAHY5bLJfCW2aU3v5fMqPtKZaS7NjzOS6/8B8XbW0SCbXis2KYZgzXKTKR2I+mmVzc8CiP9F2CI9WDOmCUeEmPQ2f7S1jVXRlRmEv8ckWXkuMRDRhoz0AQr+vWgp5yum4ihI5hBvGZUfy+E9tBNITiFF0bIeFoaoSpQoatDSqqNpSZ5O8e/AaPjRxiTXUrHz7/dayuVmmx1nAtYTNQUWV1puHl82Qfegg9FCK4fTv39O/iu7vuIZQt8JbN19DTrWwrCk4Jv3FyjUDUClDlD+NJj1uzh3DMFBf6uqgDXt59Hr84PutXJBC8vPu8Rfezsa6TjXWdDGQm2D85gKUP0VOlJkx6dTWprhYOt8fI1kdYCwQwqNp9EG0wi+9S9WC1dINzG1aiiTMTyX0hYXj0MR7ftTAtVShO8fiu/2Lrhj86a4RoBqVSiS9/+cu8//3v/419P6dFhr7zne8sOrBEIsHDDz/Mm9/8Zr7whS/8xsJapwspIVAn0C1BfKU6Dysq8Gm99GZMYJ7BjoT8KATrpCJSRSiMgy+uytYD9SoFM9PiYwZ156ht51Ye5SckbgHCLbPLhKY4k9DU+tJTRVU16wVClyQPUSZCM8gOweD9ks7rpvfTsIHG1/dw1fV+nvp/kt47ZlNmqWNw+Aew6R3TJz/93Ukpke60/5FhQeu5i39g+SQUEqqKa+ZmV8qpcrtQ7eLbLALV4+sgum6edOZN8xZ410Pw639Tva0u/3NVcTYf1pyIgOeqajN/TJGhYgaqOmejW8cfwl/IYNdfgN86efhf1wSxoEnR9pjM2OiaIGhpuF5lOtL1VEQo5FO+RKerLXJcRbQCll6RcptBSNcrIkIzWBPyU2XofGlogrCuszLkJ3SKM3fLimI7uSX7YAkh0PUA6exsb7fhsccAQWfbVSTTKmo3mTiEXUrjYvC5+wb41cGjdNYE+ZOrVtFVGyKRPEwidZhwsJn4PCvykp1irsgZIF8Yn45YLRSB67rJoSGdscw4neEhbDtNoZRgbYPDn11wFw8OdmBqLhe3HOWcrg+ctLIqW3BxPEnAkvjOQnVgLNJOMFCHf6bHDJDLj1KyM0TD7RVeRnFfiHhdCNtzcaWkPjh7/n9y1808Na4+74eGD/AHP/8v7nzt32FoOkIIqpbo8n7GoGkI3UCYJj8+8ijvv3u29cZP7z/ODxo/SsGxOZIcZlVVS4W+ZzyfYiKfYkW8qZz+8xsWW+pXMJSZxGf6KIZCDOcSjOaSXNO5lU++6K18Y/+9GELnLetezLnzmqSWXBtTM8rPoIBhYWp62aX6wNQArufRvKKHwFQ/cV+IvvQYY/kEK+NNBKxKDVd4ucXGAkjpsefgV0+4zp6DX6OhbstZTZl997vfJZFI8Na3vvWsHWM+TosMvfKVr1zyvZtuuol169bxL//yL89ZMqRbgoZtkgo+53kE3T2sugSOP3wZqWOzIfT2K1VFV7BeCawzh1VbisRhFZTQfao8Pt0HVT2SQN20z5AQCyJDiUPTjtNVqnINlM6o8XxZJk31W9XYhK5aTRSTIAyJnKd5lggKUygHbU0DLYC/CqSzUDuUPi4hn4C+hyDWBg3rmDoAxUmo6RjCyu6C2tXgC8+aGjolFV3Z90P1/8orlccPKL1RKQNNm1WvjJC6AWaHJJo1LUCfBylnzPoWOg8vikgjbHqtYomaCZNHIVxfSYDmopBU1Wb5KXBdRdbmik2dAiYu1SFdlZEtAc+TOJ7EMlTV0VTGJpVziAUNmqv8BH1ZcsXZc9A1sAyNqaxDwNKIhxYnWkXbI5G1iQQMgnMq2wq2S76k9uczNRzXw3HlSUmVEIKQobM66KfGMiqI0F0TKf7h6BDDRZuX1sX56+7mCr2Rz4ou6nZs6L7pqI5gbGJhZ990th9d99NYtw0pPUp2GjTBv96Z4vYnEwA82Zfg/sMT3PNnV+D31+C3qogEF1YERcJtCGEg5/ywA/5aLDOC7eTwPHsBKZoxJ6yq2ohBG6FAI2u6X0OxdAuvWa3G21h37ik1Xo2HDGxXLkpAzwR03VeugptBvjCptFBuAUtbSGJMTS9HfQCGs1NlIlRelpti53gvW+qfnlj+dKH5fIQuuRghBF/58b9UvJd2inz/8MNcv+JchBBlwlNybZ4aO8ZgZpKwFSDuDy8oWy+4Nr84voO9k31ErQDpUp7f3/gSXt59Pi/vPn/RsSSKWZ4YPUJ9IMb6WtWSZyynTCtjVhBPegykJ0AIuuONbG/o4fHRwzw0tJ+Q6ad9y2Zqpv2I8k6JwClEsn4bMZk4UJEaWwyF4iSTiQNKi3eWcMstt3DdddfR3Pz0LCmeDs6oe9zVV1/NX/7lX57JXZ5xLNDwaBo0nYMuPS7/dICD35YkeyWN5wpWvEwgNBX5GXlM4rmScLNAM8HOKl+h0ox9zUkmmLEuVYI/P7OTHVLpsvhKyl3aAWLdEGkXrHwFHLx9luRUb4D6LYLAvOi7W5Is9hxo2Dbt4SO9cgmcEIDnIqYOg1aA4R3K+6fpHFW6fvQeNVgpIdkP2Qn1r5BQpKTgg70/gOQAbP99HH8byaOKuwTitiJfodpyJGquwd8pQdOgbTpEnjgOY/uUaLp1idBssFqRNSsMU0fB9KnzmUHHxYqJGotoReZEzB4/PkWqYHPBihqCliIuPlMQDRjousY5nVGeOJqiUPLQBXTV+/FbGkXbO+GD1XE9PKkiQQDZggOIMjHym+r/qYyD40lqNIGuCSZSJUxDoyq8kGRFDJ2XN1TquvoKJW7aeZTStEXBbQPj2J7Hv65pX3JsczEz01vME0fXLEbHn6S54QJCwXpc18b1Svxw190V642mi9xzYIyr1zfS0XrFgv240+X3q7tfw4Ejt+N5NpYVZdPatyGERiJ5GE+61MTXVFRjbWyJ4XgSU9eY0Qm1NF1ELNrF+ORuQsFGaqtP0K5lDkxDwzx7vpmLIhbpVERoiaiV63kcS40Q94WoCUSJWUH8ukVhTtmqJgSNZ8gPxysUEJaFOEnFmBCCfZP9ZBbR9AA0mWGaWzdQch2Kjk3RtUmV8kStIF3xBuoWaWfxoXu/yN5JFXlMlfJ8+vHv84qV51M7ve7u8eNMFTOc27CynH7TpruvGdPj/faB+/j3x39Axi7w8u7zedfml5JzijQFq/lZ75N8+N4vUpqunLU0gxWxBpqjtRxJDHMsNUpHtJ6OaN1yJdk8FBex2Hgm6z0d9Pb2cuedd/Kd73znrB1jMZzRW0I+n8fvf+blpL9xTAt0fcCG318ksuGpii/NFEQ7BXOZT6QVNMvDLZ34QR+sX/z9/LgiVs68YIYQqpT+nD9SzUv77/bQLWi/TjWMFfMq1TIDSnjdeR303QVI6Lpe0P1ygdSqSRtXqOoWoMq4B/nAOxGZAQjVw8XvBdMPreepc9MtRRxykypFtfNb8M03QzGtUmavvhmyo3DkLigm0be8hUhtO2ZyB/zXn8LkYdVQ9Yq/VJ5FVmXPq9NCqB4iExA9iV/LjDN1YMvC93SDRX/qThGO/Vq5YLedhz7t1D3T9HK+tshnapzTEcZ2JUXboypski96hP36CaM5Ib+hHsC6wPMkqbyqmAhYVoXYOuDTKDkSQxN4UumSUjkHIZRgO1dUlXDWEtGtOydSZSI0gx+NJfjXNe3kXI+fjSdxpeTa2hghY+nxNtZto65mUzlC5PNV0dp8KZpmlfuG6bqJrpuEfCZFp9J74kQC8mT6KLaTo7XxQtqaLiVfVPqlmdRRwF+L65UWlJELITAXqc4Mh5oWGDCeKbie+p4D1tK6EtvxkLDkdzIDw/CfsNR+qpihNzXGhJWmJhAlYPp437aX8w8Pf7u8zu+uveKEbsunCjeZJPf444iqOMXV3VT7IyecqIynJ7mkYQ37pwbLyyKmn2uDbWTu+zVmVxePmoosXdS8lq0N3QR0C0s3GMhMEDL95ZReyXV4cuxIxf5tz+GJ0SO8pGMzAH2ZMTLFAk79bBQ275YwNJ26YIxHhw/yl/d9ufze1/ffQ8Tys7luBbfuvpOfH3+yYv8lz+G/d/yEy9o28qMjj/LFPb+k6Npc2rKOT7/4HYtW8f22wjcntXsm1ns6uPXWW6mvr+f6668/a8dYDGeUDH32s59ly5ZFHkbPc+iWMj0US1wzqaNiuumqxAydngahapXyAKpwiZ4DIQStl0HrZTqTeyWFKShMsqCvWbABpCvY8m7BtmmrnpnSfjsryQxbaLpDsM6D2/8PIjuqVsqOwt3/CK/4TxU5Gt2jzI/az1fppqljsOt2NUiA9BB8+/dVJVcpDcM7EE9+hciWm2Dv99T+QEV0fvg+RVI6n4HFvelXOqKzgRmR1vTse0t7FZ4nlxTVhvw6Q5MFNA26G0O4nmQq61ByRAUZSuUcDg/ncFxJe52fhriv/LAUAmLTJEvTxHRPLPW37UiKtosndQxdIxrUOTxUJJG1qY2YaLpGoeRiaGLRMTb7FhLOBp/JaNHmZY8f5HhBnWeTz+QHW3to9S+eKtA0g+3nvJdE6ii2naW6ajWa0JFzytFn8K7Lu/n4j/aWX29uDXHhipr5uyxjpkpM1/3ouolptlW8f7aIzdNBMueQKzhIoDpsEvIblByVypyJ6k2kbSRQH7PQn4H+KO4L0pce41hqlMl8mguaVrOlvpuvvvSD7JvsZ21N2ymnxzzpcSw1StwXWrzjum4gDIOjXo6xsWOsjDfRPl2R5aZSaOFwOWIkSyXWHBljpV5LbNsruLd/DzX+KOc3r2LHVB/1E8Os6+rCp5scSgyxe7yXTfUqRJ0oZjkwNYjfMLmoeS2ghMud0XqOpUbLwxEIumINjGQTxKwgk/kMlmZQdG38uomh6aRLeQpOiV/37+WegV0LTumBwX34dHMBEZrB0eQIDwzu4792/Ki87Bd9T/E73/8E33zZn5+SAPy3AdXxVfh9VSdMlfl91WUriTMNz/O49dZbuemmmzCM32z49rSO9v73v3/R5clkkkcffZTDhw9z7733LrrO8x1zU1jzEesCt6C62OPa6gFrnlqEzAiIExkEVyDaCVZ0Wis0D2ZQEJ/WJheTkuwgRDslRkBghgTx0OMY7hgc9GYJywyKKfjph1TKaMXlUNWhHKHDjUqU7MwLkc/f3i3B419ggTulnYWx/c+MDJ1NmAF1vnMe8DMko+i4DCeKVAVNIgEl2jQ0Ua4aUzNpScl2CVommbxDtujiMwT37kngTIut+ycKnL8qRnP17O9hbqRhIm2TK7o0xi3yRZdEzsHQlO+N7SoNkyYEoYCBoYtp/yMHf1BnTyZPs8+keZrUXFUT5aJ4mPsTKndrCcHfrGzhrw4N0FcoEUYji8dQ0eZ/+kb5eM+Jo23z9TeLBQ/+z6UrWNMY5Vf7R6gJJHjpugBSugix+K0lHGxUXY2fg5jK2JQcj5qIhaELApZG0Ra4HtjTKc5E1sb1wNAFlqERsDQ8qSJ3zwR/88A3+NaBXwPwtX33cFnrBl7RfT6rqlt449rL6E+PM5SZPKXI0FQhw7HkKCHTz/lNETzpoQkN6XnkHn4ENEHokktoyKfIJUcIGBYjuQSxiQz2wYOYrS34V00/7DSNQCCEsCxes6qHK9vOIWz62TPVT8b0MRqPsbG1mc2aTs4pkpxTDh+1ArRGahZUvP3V1lfz7rs/R046aAjeufmleNJj13ivEocjKLolHhzcT8CwuKJtIytiDVhC56HhA7jeQmfsiBXkO4ceKL82HI91/QlyPoNDTVEuaVnHr/p34S859AylmYj4GKwOcmBqkFt2/Zw/2vybjUI8VyGExrqeNy5aTTaDdT03njXx9J133snx48d529vedlb2fyKcFhl64oknFl0ejUa59tprede73kVHx2+fgVWwXlDKSEpJMIYfBLeIvuqSUyZEJ0IpI8kOqXQcoKJTJ7rx5ibJH8pRKDRjRQUzfT2DdQKyGoSblBC5lK3cLjWg/t/xVei6HNCgaSM0bwXNqiQ6mjnde2QOvNL0wOakaYSmPgPPO6tuts8IS8wID41mODCUoSUeZGNblIClo2lC+QoJpd8o2upci7aHFJApOIxMVyrNxdGRfJkMeZ5kLFmk6Ehqoxb5kir5L9iScEAnU3CYyWD5DI3aqEXA0ogFDVwPCiUPw4CxfImJko1b8qjRlQGkLgRf3NjFFwfHKXqS1zZW01co8b3RBA1Cp14YjEqHEekyUFi8dYbteBi6OK1y1kt6armkp5ZiSdkhzO/8XrBdBhN5WqoC+E6Qnnu24U1H6ZRYWxCwdAKWTmn6MwGVArQdr5yyiy0hmj8djOWS3H7wvopld/fv4tLmdXRE6yk6NgemU1SNoaqTfjdV/jAd0TpivhBjuSS7Jo7TFW2gI1SNtEvTRquqcq0+GGP3+HFGcgm6tTA1loUentU1CcPA2n4uBaeEpWkcSgwRsnxcGm7l8PEDHI9ajOZSdMbq2d7YU27gCqAJjVVVCx2yz4u18Z3Ga9hl5Nl6wYtpDtcwWUhzPDVGspQl6guwtrqNu/t3ETAsJgsqdZgo5RjITFByHeJWkMQ08Qqbft61+To+ev/XysfQPYnpSny2S3eskQ+e+yru6nsKn+3hs11ChVnx/kNDB5bJ0Bw01m9j64Y/WsRnqJp1PTee1bL6q6++mtNsl3rGcFpk6K677jpb43jeY2KX8jFioh5d5DEcDbvgEayX5EY1qlcvnQpbgFIWzCAIQW4Y8mNg+FV1WSmlAhmBpbpSjOwi4stjaUP4/XVAp1revEWlhTQdXv7f8P0/VmmuxYhNqh8sP4zuhVXXqcqup76hnCStkNIB7fj6/CMDcnZ/QoOL3gNdlz13idAcqOoxdYOsChs0xQKk8w7NcX+FHmRueipgKQNHx5Mcm0yTLXpU+RaKtD0pyyJriYoy5Eqq7UdtxMJvqmq0VN4hU3CZyGSpj9q01xo0VfnRNZUuNXSojVpMZWzMEnQIC8OVpPMOPlORuozr0eH3MVKyeTSZ5SfjSQByUlIUktz0jeaa2oXC1mzBJTVtFTBXL2U7eXK5EYLBhooO5/OxWJUawNHxLANTeTwJK+vPcjn4M0B12MSTLEh3zf3+gz4d5lQEZgsqShHyn5zkZe0CJddZUBKfc4p4izwAVlQ1Kj2LprMy3lQuqV9sv7vGelld3ULcH0YTGt1xlW4cySWQUuK4Dug6ofPPByHKaTDX80iWcpQch3hLI+HWham4b+2/j0PJQV7TczFHUyNYusnq4wlCiSmieg3Beov9kwM0hOLEfX4lzvb5EELgpFIc3PEwQxGTLWu2UuWPcFTkGWivYXtLDw1hlVKt9ke4qHkteyf7aAxV0RKuIWBY7Jo4zlQhS00gSmOoipXxZqSUnNe0mrxTpC4Q4+rOLUSsAL+34Ur+6r6vAFC0dPp6WvnrS96IZlr/n733jpPrLq//37dN72V7X0mr3mXL3cY2GAwYh04goQUI5AckgRACDiRAIOSbhIQQSAKEhBZKMM10g23cJMtWsfqqbK8zO73e9vvjMzu7s7uyMLgA2fN6+WXtnTu3Tblnnuc85+DRnNy65jI+8+iPOa5IVLSF12tgBcL2fx0tTbtoju94Sh2on248xTMVv73wtgipjRlYiyRL6CWL1EkxLeaOC1+iFZEZg/yMcF5WHZAeFZ45kT6ID+DrAMUltq+6BNdQ3DaVjIgQWYboWhRtHE9uGpJzEO0Ry8XMvvj35hdA+04xeTX+CNz5/sZtBNoBBaJ9wg266zII9YNZhF2vA18U1j0HvvH6xpA2gFd9U7THmjYIEfVvCGxE1MY8Il4He/svrH0BQVAMw2J4rkK2ZCMrEl0dbiZT1frkGEDQozGX14kHNBRZwu9W8ToVPC5R0XHXbq6nxgtMp8X1nEpVmUiWuHQghqo03jwdqvA88lVtKjZ1/cpUqYpk2PS6HQyVyvwkWyBrCIKXwyJXq+5t9bt5WWuUvGFyKFek0+Wg2+1EVSRkiXoVZB7lyhzlahpZ1tB8j//G0RF2Y9k2baGFSumvo+uvJElcJEGnAUIML66v2ylf1Pjy4Mw5qqbB3taBujcOQHegie3xvgZh8ZpgK+2+hfdfV+BCv37g9jMP8PDUWa7t3MItaxpH05s9IYIxDePhRyh5p/Ds3CmO3bbYPzVIvlrCtC1G8rMEkx72tg6I97VlIksSsiRTMEpkKyVKRpXru7eRr5TwNfvJjJ1FDoXJ62XG80nKZhWv6aZ8/ASO7i6c/f2MTY3w3clHySVl5GiUazs3iwkuvw9tyQ8Hj+ZkV/MabNsmUcoScLhRJJmR3Cy9wWaaPEGe278H2MN0IU1BL9MbbK6/j1687krafVF+cP4R/A43r950PWGXj4qp461V6b/zgtt45ff/nhNzYwCsDbXxxm03Pebr9n8VkiQ/qePzv25YJUNPEBZPmRklm+woyE4bbxtEN4HjAsJqa24EK5dGDbSDv1lMcklyfQxcdUn1Fpk7Lv5LHBUVosgGG1d4yXYDreK/9IhIeT9zJ3ReKnyE5qGXYOaYGD/f81pR9ZmtiWBdQdh4ixix79orpq2y44JISRJkhiHUBptuEeP3t79xIdss0g//+zrhDn3Vn/5GkSFZkogF5pPKV36t8mUD3bAJelVkSaJUtTg2WqjrSdBhLFHmms0Rzk0VqegWQY+KyyHjVEUVqaxbFMomFcPC6VhcYTDqRGgeibyLqiGx1CfS61LwuhRyJQPTskVVyTA5mCygWXBZk5/rIn58qkJUUzmQKZKrWWe7ZZl/XN/FeLnK/XM5LAM8sky324lTE2G1S+FxNyFLKi6X0KuYpo5hli5YCVoKv0tjU9uCb5CuF0hlzuB2RX/lVPenE7IsbBekWtv0Ymj2hCjoZZwrTFb+6w1v4uMHv8uR2SHWhdu4rG09yVKO7sCFozzm0e6NMeZJsjbcRqqcR1NUfIta9A5FxbBt4cFVg22Dbpk4FY24O0i2Uqy3CMuGzv7J0/gdbnY29/Oy9UKz1B1oaiBxGSvLTCFNJW/R5AnSHWhCyhZAWsg2C3f1sL64jbwm1zPA+oIt9AaaL/g5e2DiJPdPnKTVG2J3yzpCLi/KkuryYHqCqmkQcwca4ksub9tQF2vPY/H4vNfh4vZb3sPJuTHKRpVt8d5fO1K+iqcHq2ToCYZt2cwegdyQjq9NI9QnX5AIAaSyW9CTaSLdcRwgCJH/WY+5D1dY8I/HFF4/9GkReooNziC87IvQe5V4THEKsqM6Bfl5491w8g5hmrT+uUJYbBlCDN51ufh3bkaYGurlBW+erS8R7tUPfFKYOk4dFtvPT8HtbxLkqHPP47l8TysUSUwziDyV5ShWTEwLvKaNQ5VI5fUFIlTDeLLCzv4g23sD2LZNsWKiKnLdh8ipyTg0mbJhcu/pBM0hJ9s6Q6zcJpdQlAvrzvzuhY+vR5aJexw4bXA5FDa6vPR73TgkiS1+D/8zOYcFvKQlTL/HxdliGZ8lE1IV+rTlBKjhushaQ3TH/Hh8ONCP45eIbrFtCxsby14uhP1Nwy/SHpvHfGTESoi4/LzvspfX/06UMoxkE/zV/V/moelBOv0x3rbz+ayPLCePl7evR5UVCnqZgzPn0GSFqzoWPJfqKfQ1c07dMtFkhb2tAwAcSwwTdHpIlrPsP76fTc19opBcIwke1YksyeSqpQYytDnajYREulLAqWj4HW6IufFde239uUGXj+dtf8ayY74QASnoZY4mhhnPJ4m5/UgSy0wbAdZHOijoZbHPXwIrXcdV/N/GKhl6giHJEi5XAk9wP86mdlyxbY+5vhrwYRg+5MU63pmTQrfTcYmosiyBr31BGL0ipo/Dff+08HclA997J7zlQfG3LIs2Wf0gnLB5SaZcZgymHhW+QvEBoR+aOSmIUs8VC8LjSB/c9GH496VherZwsL4YGbJtuPuj8NB/iOrT3jfBlX/82M95kjAxeIpKMU/nhi1oLpcIr02cBk8U/M2EvVrdoRqWt5MWL7MsG8u28S7x3JEliahfQ5bAsGxms1UM08LnVokHHMxmF6pDLWEn7l8w4kOVJXZHGh26nbIYxXeW4Y/a4vVjMUyLbqeDaEwBE3zOef2IYGQXGxGfd4d+LKL2WHA4/MTCm5YJrVexAFVW+cShO7hvQlRsz6Qn2Td1mrgrQLKc58bu7bzn0hfjc7ixbRvTNqkYEHMHcKmiKjOWS+JWNUqGjiRBuy/K6dQ4Y7kk2+I9RGsmhzF3kKppkCtkUE8MYs+USHd4+NHQIR6dHeLG7u2M55OkyrmGUFinqrEp2sVkIUWbb8EAVJIkTiRHSZSyNHmCjOWSOFWNS1rWocoyk4UUYaevgVjNQ5Fk1kXa2RzrpifYTNTWKD70EGprG46OhS+9mDtQN2lcxSqeCKx+Gz0JCK+Vwa1A8OKXN9grEVzqHK0XRFXGXHni56KYb3k1LDvZ4LZ8UTj9QiwtyULHFGgTRKjr8uUTWIoKkV6hdVqM4C/w6+vQF+Guv1n4+yfvF+21zS/8xY7zCUTdcHH+EpXmhGaqNAf+ZmGcuGj9pqCDsFclVViYTLEVi+lsmbMzeXTL5vL+KKos1cwTJQIeMaYf8TvY0S0qQkXd5Mx4hjXtLoJelUS2StCjsrXnV/+yt2yhh5ovYJmWzZnJIrIk0dPsJlWqkisZhL0ahYpZPy/DFH5LKxEjr6e5oVL0y+CXNuH8P4Kgw8PD02calhX1CsP6LCB0QgAfvur3cKkOLm0dQJEkHLXrmq+WOJ0ab5jvbPaEmCmmOZ+ZZnNsoYXd4Y/S4Y9iWiYV3cvnTt3NPw8tRLL8ePgQf3PVq/AtyQgs6GVmi1k6/NFlTs4VU6egVxhMTTKeTzIQbmcin8S0LIZzs0RcPrav4JvkUh1c27ml/rc+PUM1l0dyJqFjVei8iicPq2ToyYAnAmtubJyism0oJMAVguyoaF15LyDQbd0u1NgXyuK6GLouXz4l1nPlciJUSIh+m28FXYIrKMjMA/8qHKU3/Q5c/Y4L7/Oad8G5e0QVCoQgfGna/Eo49f2Vlz0NZKh17QDMC3v1MnhiEFu3EPi6BJIkceXGCKOJEvmyiS1ZzOTLouW1iFPZNhRrGWR+98JEUGtNUDycLDCWLJEvGuztj9Add+NQpRUrT48XnlqI7KJ4MuG0DeRLBiXdxjBF20qRhUN2qWKSKugUKibxgHPFOJClsCwT2zaWZXKtohHZSpHjc6N0++MX9AxK5CucnMzhd3gpl9IX3NadI4eA3wNYlrXl1Vx0BeJ4VCeWbYENf/XA/9S9jL548i4+feP/R19owfNJkRWUtWv5t/2fbNjWQ9ODpCtFOv2NIu5z6SnOZqYYzc1yWdt6TsyNoZsG2+K9bIn1sD5iMFVIsyHawUOTgwxlZxiItBN3B8hVSxyYGmRbUx+arDBdSGPaFm2+COcyU4xmE2xr6iXU3ISkqQ3j/qtYxZOBVTL0ZGHpOHl6RFROylnRDvI3Q+tWod9ZYkqGrDw+ImTbYipMlsUIfaAVbv0U/PAvID8N8Q3w3H9qfI5lwtgBwIb+6xeqPfPVo+wk/Pu1IgQV4OxPRbVoQ1SHBQAAwhZJREFU0wtWPoaWLfDWg3Dqe2K9gedc0MOnAaEVfKlWWvYEwLJsTk7l8LtUOiOeZY+LgF0Jpo+J16ttJ0T7xYPZCWEgaRmichVbB5IgLL3NC9vSTT+aIhN0q5ydLVCsmAQ9GhHfcnF2sWqgKTIdYQ+FsonXoWEjEfCIX9l2bSQ/WxLJ9poqIUvSRaMflmIxqVJkiTWtHjJFnULFwuOUCXocODUFr9NmfK5MclZkosmSTKlqohTFlJlDky+473T2LLpRJBIaQFPdTJSr+FUF/yJfoYplISOh1Zy3/y8KV3N6iaJeIV0pNJAh0xJkWZFl8mWDsm7y8jU38M+Pfv1Cm6JpkZamYujIsowmK5QNEV2xJrTg5n3f+Ik6EQKYyM/x0QPf4FM3vLlhm6ZtYSwyNdwzmOD5D40ifedtpF/xSkIvehGpcp5zmSmiLj+GZVLUKyRLWYayM3gUDcMycaoaiuygJ9jE8eQoE4UUDkWhN9hMizfMAxMnyVZLVE0dRZI4lhwBREVsNJugbOropqi4qpFfPYJkFau4GFbJ0FMFV7AW/aCDbIG/DUYeXHBB/lVgGVBMANKCweGWF8GmW4UwWnEIgrQYsiJaW7YlpsJmjgsBdblmZHTgPxeI0Dz2ferCZAhEpWvnqx7fsV/2FjjxbciI4EbCvXDpGx/XJmzbRh/Pgw1ah++CN9lcxWAiXcKpySuSoTpkTbQHF2taUkPCiqCchdSw6D04feAOCeJqGmCbaKoTJg6RmZlmWtkIQNATXBbkmivr7D8/R8CtsacnwoY2f93ZuL5OySRd0LFtoT86PpzBoShcuTb2uK7PUuTLJkeG8qQLBhGfyo6+IE4NChUT07JRag7YsiSRKlRJ5qvICGG2Qy6j61ME/S0NyfKK4sS0dGRJJVE1OJov4VMULg+LX/QVy+K7M2k0WaLT5SSlG+wN+fAoj4/Y/aajzRvBrTq4Z+Q0nzz0Q1p8AV6+/hom8nNIEuxtHaA76iHo1niG+xlc1d3PK7//D1RWaJmfSU/ysu/+HR++6lWMZBNoisL2eB/7p07j0Zxc0rIQmXCyNkq+GCeTy5fl9BK74n3snz3L+rEMf/6No7VxggyT770Nyeli7qpdZCpFQk4vN3RvJ10uUDKqOCSFsNuPs6ZZKhlVTMukxRtiZ3MfA5EOWryiyrqzqR/dMuoj7+vCbWQqRb53/gAVU2dzrIe4J7js+FaxiicLq2ToqYI7BOtvhsSgEOS6Q1BKCpL0q0LRhBeQJC1UpPIzUExC/3WC8MxXnyp5GNsvyFhTzUNi5qTIHKvkxbYMC0YeWL6f4tyvfqxLEWyHt+yHwR8KArL2Wb+4c7dehpH7wRXGzItqkmYBF9AcB90aG9oCeC8mSo6vg9jaxrZi82ahm6qUIDcuCOTUOdH27L5MTNNV86IdWckRc5r0BRw0xVau8GmKjFNV8DrER3DeVLFQNqnoJkGvhiyDpojcM7dTxqUpuFSFim7hUBtdoi3bJpnV0VSJ0GO4Itu2zQOn0nWzwNmszoOn01yzKUy+bOLSFLrjIgrkwVNpkjkhvm0JOehr8ZAulrAMHbczj0MLkC+bqIpE0N9dPw4zX8VjSXQsOg7LhpFyFVWSaNI0TNsWhn+WRfCpjpB/imBZNqmCjqbKBNwLr/O9Yyf48/v+s77et87s47a9L+WByVP848PfosUT5g+3P4ewt5Mt8R72tg5w99jyPC6AQ7Pn+MCDX+UNW56JU3WgSDKarOJWF9qVumWyu3nNsufuaVlLqpynaho0e0PYtk2ymOPZ/ZcwEOui576vLpurHPral9Au28qmaBcxdwBFlgk5veSqJaLuLJ2+BaL+4MQpTsyNsru5v0EHBEJ87VykwOvwx/BoOYazM6iKSk/w4pYCq1jFE4nfzm+hX1fIygIBAeHj80Rh6dRZ4rRImXdHREtuHpkx0e5R3aIlZtuiQqRoYnJMVoWRomWxDJ2XPHHHuxgOj6hiXQgzJ4S+qWO3qKTNw9LBqCLpRZw1sbF0EZ1Ne8gtLAIyM8sF3rlpcSxOvyCQs4K4FgyVufFRop3deFp6oGWDSLw3qiKUrpwRE3lGWbRAOy9FtXR6H6PV6dJWrvBMZ8rkyyZel4rPpVIomUymqsT8Di7vj5ErGczl9bpLdKagM1fQOTlaoKyL16w55GDvutCKYa7ZolEnQouXlasmPpci9ESSxMhMkWROVCNsm/oxhP1himUHkuJHN4XpoG5YRHwaFd3G51KwLFjrcNLsXrghuxWZm+MhJKDf48SwbYZKVYZKFQa8Lrpr6xqWzUxVp2wJ88jf5FaaadlUDVvYNSx6237xZKOTf6Za5Gun72Pf1GkAHmWYB6dO8aMX/jVhl4/b9r6UmZ+mOTE3hiar6JbR8PyHpgb57LPeWv/7inbhs2PbNnm9zMPTZ4i5A7xj963866HvUTQq7G5ey7sueSFHE8NkqyX6Qy34NBdHEucpGVVu6t1Fsuk+4FjDvh7Ij/Ox73yYjZFO/u3Gt9SrN36Hm90taxvWDbu8OBUVw17hu2QFRFx+ru3cgkvVGmI9VvF/A4Zh8P73v58vfvGLTE1N0drayqtf/Wre+973Ij8FKQarZOjpgF4SWp5Ah5jEejLQtFG0czxCpG3pNslj4Cql8fubBfE5f4+46fdevaCNMQ1RJereC4M/WtiepMB173lyjvWxUMnXjkMS57SYDDn94tgVB3LtOtqGRXUij+LVUKMreJD86L3w4KfA0im1XsrQ9Z9iXV8vSiUN4w8LIjjwHEGYUuehmKCsdlEtlyjn83gCtUqe6oRgG0weFpW1rkuX7Kiml7JMziVLjKVKeBwKm9uDuLQLV6bGMgXKVZtu3S3yxhQRAlqziMGhyqiyqAwZpkUiW2V4tlwnQgDT6Sr3n0yzsz9Qd6cGMVIvyxKSRIOvkSwBkoR/kQ1AMr+8LTObrdIZd1M1vFQMG69LQlMkDAPSBQNNFXEjwjpgOYlZ511kBChJ+BQZTZLwKDKGZXIuM8NgVeNc2WTA6yLuUPEoMgXDYrxSJeZQiS91oPw1hqbKRHzasom8lTyljtY0M/PIVUvcOXKYF627gg5/jNtveQ/j+SST+RSv/P7fN6y7WBs0nJ0hWcrRE2ziaGIYlyLehxISr9/yTH53w7UcTQzxk+HD/PuRH3BF20Z0y2A8n6Q30ESLN0zVNBhKT/OPzVn+zqMRKor3QkmT+dYlnQAcnxvlE4fu4P2Xv+KC57+9qY+N0c5lxMbMZMC2UUKh+vj//Jj/SuP2q3h6YNo2D6bzzFQNmhwqe0M+lCfxx8nf/u3f8qlPfYr/+q//YtOmTRw4cIDXvOY1BINB3va2tz1p+53HKhl6OjB7ShCO+apMahiwIdzz+Lajl4U7dLBzuVi5WhDmh7MqtGzBMgQHQ96Av61JVEVS5xq/mYfuEw7SuUmRjRbugWJaEKUb/nK57uhXxeJR/9wUTB4RMR6SLI6heZN4PLJGkKCVpu+WiM+tsoGV18GwBRlKDYuqUssWOH833P/x+rruyX0E7vsb8o6/x+vRUCVZjNFPPwqtOyC6Ftxhwu4QLp9fEKHEGcrJYeT2XTg8MXEdfRdIYZ84hPno/zIy4+akaxs9G3aSKxs4VfmCFY+1zX5yZaNOTGIBJ1H/QoXEqQk9D4jKg9+t1v2BFmM2W+Xuo3NcvzVCxbBxajKpvC7ywVo8DE4upIu3R13kSiZuh1K/cQc8KhNzlUWvlYXXKeHUxA2+aprsPz9Hs99F2OvA6ZAft7i7zeWgzSXOZTyfZCg7w6ThoskTo9/txKfIHMgWOV+s4JQlsob2G0WGgGVaMYBXbLiGIz8fqv/tUR2EnV4KerlhvcWGgpZtkShlcasOXr3pej537E4AvKqTN2xdMGmdLWbIVksUqmVM28ahqOxqXlN3cB7OzvAHP/oXyjUN0ldO3csnnvEmIm4/Hf44fTVi9b77vkgi4OLtr93DdY9OoVoW92xsZia0cEzHk0LnVzaqnE1P0eoLE3E1mnA6llgo2KZJ8eBB8dm/ZBenU+PLTCJX8fTjjtk07x0cZ7Ky8KOo1anxwbXt3BwPPSn7fOCBB7jlllu4+WYRmtvT08OXv/xlDhw48KTsbylWydDTgWDnwki7aQjxMohMMEUTeh9JufDo/TySZ4Tw2LaExsWoiEqKNypEvaoTnOIXl+qC+BYL2RGAeeFrb80oUVZEK+prr4bCjFimFyEzAS/5T+FK/USjkhe6JG8c2rYLplYtwOiDYqTdKIvqTKANBp598bBX24bsOIo7gqPDjzTvDJwZE9Wb8QPiei1Bc/owet7AKFuoPVfC1BHwNon9xYTOQga8ISH8rBYzPDBaQSnPcvW2tYJkLYJuWiTzVZr8TuQ7/xrl7J1ssgP02Q7QXk2m6e0cGUuzuT1Ic2C5Nqo16KZ1iYzsQsRJkSWaQk5aw9UGcjOPsm4xPFvG51apGiIo1rJsNnX5aIu4SBd0wrXKhW3bLC5g9De7mZwrkymaYJv4tFFCrgAjs4KclXSTXNlAU6v0xBvbgYZpM5ev4tRkPA6Fsm6hyhKJXBWvUyHiXz5lGHcH6QmUieFm1hBkUZIkXLJEzKHQ6nDQ6vr1J0KWbTFbyhJ1+Zd578zjBWv24tfcfPfcQzgUlZv7djNXzvPun/83ds0VaCDcznWdWwE4nRpHNw3mynkUSeJde17I7264hjOpCZq9kbqztW6ZDEQ6yFWLtHojNHlCaIrSUJn58sl76kQIhB/Q94ce5kNXNg4+eB3ivZnxOvjm3pVjdXY0i2pyopRjupjGsq1lZGgpKraJFYuiG1WGslMEHO4LWgys4unBHbNpXn90iKU/saYqOq8/OsSnN/c8KYToyiuv5FOf+hSnT59m3bp1HD58mHvvvZePfexjT/i+VsIqGXqyYNtiemxxGyw/I7Q88Q0LDtC56ZrDcUtNvFwR7RokWPvM5STAtoUJYmpoQdsy74MzeViIpl1BcUPvX2SDP7oPrVIT+LLoJlzJCcI0c3KBCM3DqoIz9MRcj6UoJUXVZuaEID+hbhh7GEb3C2K37ibRyoPHJEJWxUQfz6OoSdTSKSgkUGJrhXs3YLdsRRrdL4wsncvF6mrnTmj1IjkUcGroHXtFQKVtk52dwenx4lrkcSK3bsVZmMTpWdn3ZHA6z0S6xFpnivYzP0WVIC5lKaNx38F7SDa9lEgoiD55HOZKQof1KxoQru/wkS3qTGeWt7Y0VcLrVHBqckOVIuLXiPgvvN9M0WR9h49C2cChQiob4+FhL9hpFCnDnnXt7OgK4VvisG1Uq5i2hGnBTLbCbK5CwOng7GSR+QJWR9TFnrULr8VMRWeqqjMQbCWlm6TzJby1KbMt/seY+nsMVHQLw7TwOFdOeq8YOkWjsixBHmqEppgl7PLheJxt7JFsgnOZKTr8UdaEWikbVcqmTsjpbSAl13dv4/ruRnf63mAzPxk+TKs3zC1rLhWZYpbJeF4MLmyL9+KQFe4cOYJhmVzVvrFOWizbYv/kaebKOdyqg6JRbWifzWOp3uhCy16+/mr+9/T9ZKqCZMtIbPe3cjg3iYnNFW0beMu25wDQ6g1j2dZFHaFNy2L/1GkISbR728jmZmn3RRsCaVfx9MK0bd47OL6MCIEYoJWA2wbHuSkWfMJbZu9617vIZDKsX78eRVEwTZMPfehDvPzlL7/4k58ArJKhJwtTjwpvms5LhAkjiGmsSk5UPLxRQWwmDgL2QoVBcQgiI8krk4D0sGhn5aZEiOrcWfHczS8S5GviALgigAU9V0PP5eJ5tlULVF30Np86IrZjW0I47W8V7al5qG5oeRLK1/kZmDgkjr04J6pYsXVQmBUtLVmBSnahhVbOiGsZ6asH2M7DLhtYZQPJ7RNVpnJWbLOQwJTDVEd11I5b0eQp0fY79zPs8QPCmTfcg3T9X6L6NbAMKobJDw4PkypUefb6OPnRITSnm67NWxcuiaZx+YZFv5SrNbdwdwiAuN9JsWpQyGS5y9rBRnmINmkOFRO/naEprrG2J4Zr9KSYTDMqvzIZUhWJyzdEODWe5/hoob7c5ZBpDTspVa0VW2kXgyJL9DV7qBo2B8/VSIOdxjanOHy2yLN272ggGtVyiXsf/jmWQ+HRVAf/evdZNFnmT67dSNS78LqNJcv0tbiJ1ipEI+Uqc7pBTFNpczloca58PaqGCLn1u1VURRhEVg0LSTZJVYo4bQ8ep4pTk8kU9ZpVgYxTW/6lfSw5QrpSYEdT3zJCNJZLciY9Sas3zIZo5+O6ZmGXl0DJTdTl50RyjCOJ83hUFxuiXczgwynL7A54qJg6riWt7W3xXrbFhR39UGaG05UJNkW72BbvAcCnuXj1Dz7GIzNnAfBrbj5309vZFOtCQkKVFTRZQUJCkWQm8nMcmj1Hly/Gplg3kiTxwrWX860z+zBromYJuKX/Uh4aOkrE4aG/TbhCd/rj3H7Le/jEoTsYzs5wedt6XuLqoTI6ht3bSccaYRsxkp1lrpxjY7RzWUtsKWRJwqe5az5GNjF3gJxeIlspNoStruLpw4PpfENrbClsYKKi82A6zxXhx59L+Fj4yle+whe+8AW+9KUvsWnTJg4dOsTb3/522tra+P3f//0ndF8rYZUMPZlYypxjawUx8sQWHo+vE62yxcLgUgpsU0wrLdUCuYIQ6hSeP1M1y/z0sIjbuPTNwsRRc4tKE4umODr3im0u/sJyBWHe4dYyhDHjt/5QVJccPnjO/7ug+/KvBEkWbbL4RnEO+UTNH0mBna+EmVNw518L88P4OmEKGe4VRChSs/A3qiCrKEEnDkVCdqmgNgmd0YHPwY/eg5yfRWu5FuvaDwtBeH4WLnsz0uxpcIeRNr8QfHHh91TOIHVchjRxGKliU1x3Df5oHJfPj1XUkVwq0kqZXSMPiNev92pweIj7nUS9Dh4Z7scIdGHnhwFQJYtLusIw8gXItMP659XamE+cs+5Auw+fS2h93A6Z/hYPIFGqWuiG3SCmvhDMgo6kSsQCGjbiBpbNVRYotOTFlnyUjEAtpNbm1HiBZE4n5JaZNcsksxL/9LNTYCvopol/hfbWcCLHXM5FwKOy3uskaagrkqDF5ozFiklZt9AUE1mBXMnAsiRGi2Mkyzna3a10BiI4NRm/S6VUtUjlqyiKhMcFY/kEHf4oXs1FyOnDsq1lzs0AEZePkNNLxOXj0Mw5FElmS42QXAxBp7c+VZWqFPBpbjyqA7/Dw0jZ5qHxw7zz2HeYKqbZ3byWj179atpWaBPNlNLkq2UKepmwU3hnfevMvjoRAuEJ9IlDd/CvN/whkiRxaavwFbJsC1mSOZoYZjA1SUmvEPcEmSykWBtq4zPPeiufOvR9LGzetPUm1ofbefCu71KUVPpiHUgOcU0mC3N4VCeXNK/jlRuuw+9wY3V0N7hBTxfT5Kol8nqZyEXI0L6p03zy0PeYLqbZHOvm6vaNjOfnGM3O8oI1e3+jJwd/WzBTXV4l/FXWezx45zvfyZ//+Z/zspeJ5IItW7YwPDzMhz/84VUy9BuN1q1i+mlxmV1WFqIv9LKofkT6RGUhMy6S5BVVEBBLbzT9KyREW0zWRHVpngjNI3lGLPfGRZRFKSWqPMmzMHceWjbXCNIiRPrEf/Mj9rIMf3JCCLwjvWJ/TwYKs4IUhroh3CXISGYM2ncJgnDgP2DunFg3W7MCeOkXhaZKLwth+MxJQWQCHSi5CXBuAFxCe/TTvwK9hASoI9/BfkiCvs+LdmTzFui+UngZzXs8mRXY9ykc3/7/uMnTQmrT7xOPBZHVKPpskcpQFjXmRmta4derpEB+QhDNGkZTRdIlg7bn30b7sb8T5xfugbGHhCYK4Of/CH/w0yf80rZHXbRHG7VIQY+KtshywDYtbMNCdjZ+/JOpMvpYnpBTwbUuXI8TEboiMC1AcmArnXhdCqencyTTJnO1EfxUHgLB3RjVAtin69s9PpVme/vCDd+yLX527gx93h4AuuMutvcFKJRNnItcrpO5KoZpEws4UGSJiUyRctXC7/bx4Lk5ZCQ2t4Vo9gXIlqtotkbBzBK0w7idCqoiMTan8+hQCkPNEgqK42x3N+O2Q2wMx3Cpywmiz+FmZ3M/umVyYm4MCQnTMlGWaIAKFYMzM3k6Ix4i3uWkak2otaFVZdop/vjhL6HXHJ4PTA/y4u98hIFIB8/t28PvrL0M27aZKqToD7aSruS5Z+wYYZeXy9o2MJydWbaP0VwCgEdnhyiZVXY09aPVjnNduJ2xfBKP6uT43Ch3jx5jc6yLW/ov5dWbrweg1RfmdGaS9c29hFUXliJzPj3JfePH+fD+BffrHw4/wjee/xfoThWHaaBIMmWzyqZoFwW9fFGt0Ghuljf8+F+o1lylh7IzxN1BBiLthJ0+bGyRC7iKpxVNjl+MEvyi6z0eFIvFZSP0iqIIa4qnAKtk6MnEhfQGMyeEvscVFJEPpTmhAYr0i0pIx67G9XPTMPGIqCi5Q2LdhghGxN/ZcdjwXEG4jn9LbHc+o0xfLrBdeKq0UMVSneCNiapM8+YntHJRR7BDjLAHO8T0WCEhCE+4B6YOLRCheWTHBYHKTgjBeHpMHKfUIv4uzNZ0R51w7me1sblFp3fqe1gVA2viJFJ5lJPaBvxhP101zlB64DO4zwpi4shP05x5H1zyQlBDyC4VSZOR3SrnEwWms2W2dYQW0uRdAUHKSmlw+ilWDc7O5inrJq2dndD3TzB8rxjn1xdaWGRG4OH/hGv+7Im/vjUcn8iSLlXZ1R1GWzTlVR3NYRUNnL1BZLeKbljcdzJNqjZOHzJsrjQsNFWmWDEplE229QQ4dD6LZQsdkikbTKRMCvnGfZZLCnu6W5Cl03WN0NcPj+B3avTH/Ni2TaqaIqgt6EuGZ8tE/RqaKlM15Hr7bJ6jz2M6W8GybSTA41BxawqxgIOIHcNlBxjOJBhPJZnOl+n0R2kKuGgOOjgwYpIraqxr8dEViFO7H180s1iTFXa3rOX+8eN8+tEf8ey+3XQtyueazQlNlCJLK5IhEGGmpmURcHp4dPZcnQjNI1nOcf/ECe6fOEHV1Lmuaysn5sYIOb2YtsVEYQ5FkrBtiyvbN/Kvh7/X8Pyra1NYOb1E1TQwLZPpQgpNVtFkBYesYtkWTZ4wWu1GY9oWu5rXkKkUGM/PUdQr+DZsJOwJkq4UGMnO1qfV5nE2M8W3z+4n6vYTcLjRFJVkKceOpr4LOkVnS3kqtkncE+RHQwfrRGge3z23n7fs+Cucyqqv0K8L9oZ8tDo1pir6irohCTFVtjf0xN8Xnve85/GhD32Irq4uNm3axMGDB/mHf/gHXvva1z7h+1oJq2ToqYZpCOGwUREaH80FSrPQuvjiKz/H6QOHX+hTfM1CBL3mRjizyAeo5wph6BjsEBWo5k1CO9O1V0xmLTVlnEehFuOxeHItNyUqS6XUE0+GinNCM9S0aYEstu8U+wm0CiK3NGRWVoXw3DZh+gQkB+GKt4nzrRagEBdVI4DgCpMvwQ708Tz2dI4SBSZLg8xVJLqi3eiGiXPwjsb1Kzk4dxe5/ps5PpujM+6hze9gbi5PvmxQrBoLZKhpIyVHlMNJF21mEZdDxrKgLewm7q/pZNbcAA98Yvlx5aZ+tWt5EWRKOqWqiW7aLC4CyS4V27CQVMEGzk4V60QIIF2xODtVZH2Hj6phYVg28aCDm3bGmMtXOTaZxbRtemM+juULy740I14nH7xlCx/+wQlyZYPmgJOAayGbLepcLpgtVi1iTqVBkB31a7WCpYRl22xoCaAo4NAU9vYtbEOWJAJulUDJTbnsZzJpkcpkuG69E49TpTfuI5WvEtWCuFUHj6aHqRo6TUHRcjUtmxOTWXxOlZ4ljuE+zUXVNCmbBvlKCRYVQDrCbhRZIu53Yts2+6ZOM1vMcGX7RsIuH5Zls29iEEmGy1oH6vljF8LXB+/nd9ZeRtwdIO4J4lBUgg4vXYE4DkVjZ3M/f7n3ZXzi0B3o2Rwv09r5g05h3Lq7eQ2GJXLFTqcmmMgnafNF6fBFafKGCDm9zBQ2UDUNlJquaDA9iW1b7Gjqr+umgg4Pa0KtdU3RYkwWknT6o/gdHmRJIqeULigwn5gY5s47/gd3Rwfb9l6HtALZSZZyHJk5z+U1k8hVPP1QJIkPrm3n9UeHVvq5DcAH1rY/KX5DH//4x7ntttt485vfzMzMDG1tbbzxjW/kL//yL5/wfa2EVTL0VENRhZNydlK0gCaPCALTsefCU1MOr2i7Dd8nSMHaG+AVXxGZXiP3C6LUf51oa/lbYfa0iLnovUo8/0KBqaW0aN0gwdobBYkCQaT8raIKNfyAICjzpoy/KhKnhWg8Oya0QJIkWnKh7prpYQUu+QN48F8XnjPwbFFFi/QJomJVhaZp/to4vBimRblk4GnfhTzwHBEYC4JI3fjXKGEXlmMzIcnPpukRPN400I2mKujeZuT8eMNhlt1NZKfOk0tZzDlbaXPpbFYnKbV3EfItEnFrbnLOFvKVDMlChR3RMLu6ZbxLWlBsfIEIu12Mx8p5ewKwqztM1bTwLTkWrcWLxsJNP1Nc3v+fXxbwqLgddj3+ozXsRlYknKpMyOMgX7AYmlmoxBkY3HV6lmsH4vzOrnbSRZ2WoIts0eCRoTSGZaPYMunCwj5dmkxbxEnArSJJEpNzZUpVi86Yq17RKlZMbFuiWrVJGDoBt4rXtdC20lSZqMdHW9DPrK+CLEl1z6Qt7UFmsxWwZXIlg3SlgGmZGJaJIssUqwZTmTIOVV5GhgBu6tlJqpIn6m5sBamKyLgzLYs3/Phf+Pm4sMhwKw4+86y30uvtwtBVfE6FP7vnc/xo+OBjvl7HEyN84MGv8J5LX1IXVy9uP82Vc1zTuZlXbLiG8sgw+plzaOkstLThUDQcitBYdQfi+DQXumXS6ovU/YrcqvCrsiwLTVGIuHyUjKpIta9BkiS6AnGe3bOL/zq+8H71qk52NK1BlRW6A3FcqqM+0p8q53EqGh7NybfO7OPjB79DZnaKgfEMibmjrHPmuaRlYNn5WtgUjMqy5at4enFzPMSnN/es6DP0gSfRZ8jv9/Oxj33sKRulX4pVMvR0wBsTxj+VrBArV/Oi7RNou/BzXAExcTUf8SDL4mbad43QpCgO0daaOyfE1IpDiI5XYvCVnNjvzAnRh9CcIqU9IiZZxLG0iqpROS228USRoUg/TB8HvSpE2/Oiy6kjwhQx2AHXvhvCfaK91LZdEAmjIs4/VhOcq41izVwmS9kAKZvFu+NVwpZAkqDvOgh3izd62AXGWlqdLkEWa9Ce+Vdw+xtq03aQaL8eb3wTbeP34VAVQi3rYOZRnPkJnE4nDeUBoCngYme3XCcd4RVaJpkNr6A0O0PT4FeQNRdc9v8J0fWTCId64ZT5xYgHHI0Gi0AsIM5BlqSGaaxiVRhCemqage29fuIBB3P5KiGvRrJUZiRZRJElXJpCS1DBtGze880j/Oj4NE0+F1Gfg7dePUBFF6LujpirwZW7UDapmmJSbJ4MuTQZ3bCRJChXLUZTRfIVna0dIeHKrUhE/RqJbJWg01E/fgBNlon6nKQLBoossad5DaZt1QNF/S6NrR1BnBdwBlcVpd4KyldLnMtM0xWIE3KKz+JdI4c5fPowTk2m4lAomVX+dv/X+fAVrwMbhnNTy4hQb7CZXLVEopStL7Ow+drp+/Bpbt51yQvry23bZiyf4NHZYVyqg6DDg7ejE8XlRg03DjhIkkR/qJX+Fcbqd7WsxbLEeeeqJRyyykw1w0+GDyFJEntbB+gKCE3jtZ1byetlTqcm6PTHeOPWmyjoZdKVAgW9XCdreb3MwZlzuFSRVv+un39O7MytcbA3gilLDA8f4vm9lxJ0eOqj+iCqbpe3rV96mKv4NcDN8RA3xYJPqQP1041VMvR0wekTra3ctBAEe1bw2pgvq89XjBYTknIGfvgeEVUR7IJnflBoiZJnxDSZqcMXXiQqLVf9qagcAYw+JCpM807P8XWCPM2eFGRs8ei6NyYqVo4nsFXmi8Pm3xHEY/H0ieaG+AAEOoUGKNQBrW8WFbH5Sbt5EckSIkS1iG/iHrTECK7WAVEhm3gETnwXHv4v0VKbr8KozrqZYh1bXwyt2+DsnSApRNp2IHt9EFtLXHWRKOmcSgVZ54J4aGHUOlPUuevUDLplce1A02MSj+G5AjNdv8faPW+mO3rhzLKnAz3NblIFndHZMkjQFXfTFNI4M5OnK+Kpn5dhWuw7Jzxvrl4Xr+eYdcRcdMSEAKvDcrKhNdBAbn50bIrvHBGWDaPpIqPpIv9zaJh/ePE2LFtUfQ4Mz5Ep6ezoDNEVd1E1bLwu4a5drJhoimiV+ZwqIa/G+bM5ErkqfkeR1pCYSgMRCCstqu2XqibpgoHPpdAann9vi2OzLBvTttEUmaYVDDBXwkwpQ6KUxalodTI0Oj1GqGBQVSWmImLbk4UUEa8bf1ni9HR22Xby1RLfeP5f8M67P1vPJKtfr+GDvHXn8+qTbtlqkcHUJLplstYfw6M5kSQZrWkhzHQwNcGdI4eJe4Lc3Lsbl+pgrpxjMp9iTagVp6oJYXWt+juUmWa2dh55vUyuWuZceqpOhtZH2mnxPpPuQLyu56kYOgWjUSztkIWPU9QV4LNHf9xwHmbNK8oGHpg+ye9uuJbPn7iLXLWI3+HmA5f/bj21fhW/flAk6Qkfn/91xioZeqowul+YGzYt+SXkb24MUi0koZgQYuLh+8S0Uu/Vyys833k7HPuG+Hd+Gr70EvjD+4WQuJyBr75KVF5A+BK95nsiXb2SEwJjV2ghT0sRPjuLiZBZtSnNgqcpiryCT8vjRnZStN00d92TpwHBTqEnGvyBqGiFuoQgerHlwOg+MZLfc2Vjsr2iobl8aC4VnG7Rirvn7xYe/9qrIfCjxw6aja+D6Bo48xPkYlJcj5gYkS4kC5Rwk/ZGiatOsG1SI8eYKkmkix5M26ZUNS+8baA/7sPnVGlbFGfwKyM7CafuEER64OZl7dB8xcCy7bpe50I4OJLi//vqQRL5KkGPxvuetxFH0mZwOodpWQy0CL2Z0OZoSBKs5DJQrpqkasRj8S5PTC4nA4PTOb780DCpos7v7e0GbE5P55AkuG6gCV8t5T1fMihWLWSEUUS5KibONrYGGdVK2BZ1DyVZlmgKOho+KolcBcMEv3t51efwWJpUscqensiK4/8r4b4TJb54YAKPkuE1V9hcsS7IlWt38h/7v01x0efkuq6thJxeLmtbT5MnyL8e/h7WIjX4Fe0bafIEedG6K5aRofF8kmu+8ud85Krf5xld2/A73HTVWl8t3uVWF3cOH+atP/v3us7niyfu4n9ufidjuSSJUpag00uHv/HHVk+wGbfmxLBMcpUSvUGNS1oXWllhl2+Z/5JT1erVNIB0pcDHD36X03PjXNKyruYftDJ+PHSYN297Nrc//91kq0V6As2rOWSr+LXCKhl6KpCdFBUcxbmcDC1F4rRoTWmeWnbXCpp+2xZ6ocWoZETkxKYXwLf+aIEIAWDBw58TZKjnCqEJ8i38qqynt6dHaxlgMYpHHqWcMLE2bCMQnRXVIccKo+UXQ3oUvvp7olLj8MMz3gt73yR8glLnRbuwkBCTYsUkYAtrge7LVjjvJaaR81A0WPMM0TJEgrs+svSJcOz2RjKUOCMmutp3ixZk4oyoqsUHhD5JdTKVKaPIEl0RDyG3A7+ig2VxbmKaE/v3c2Qiz6en+on4HPicGp0RD9myzomJLOOpEt87Ookqy/z+5T1c1h+lL+6jVDWRsFCVX3F6ZnQ/9n+/AGl+Qq1pA2bHZYynCsQ2XYdr58t46PwcNjZXrolfsGpl2zZ/8tXDTGRELtZsrsI7vnaY/3nDXjIlnbmC0AzkKwYuVWZX98V9p2yEeLtQMWgLudnTu9xHJ1Go8KHvnQTgi/uG+eobL+eZm5pr02MLr7HHqWADXqeMbop2GUDArdIV8yDL4F3knyQvYmmpQpVT0zncDpnemi1CMl/hXKLA2iYfsiQhIa0YKmtZNnN5HU2RCHoFAbj94Bi3fbMWnUOWg8OHedetTbxo6wDvfe6b+MeHv8VsKcMzu3fwzj2/AwgfnmQ5x22XvpRPHvk+iVKW67u28e5LXgTA2lAba0NtDKYnGvafrZZ4z71f4O6XbsShaCu6Sc/jE4fuaBA8H0+OcufIEa7u2EReL/HpR39IplLk5r7d3NC9HRC5Z36Hm5HsLKZt4pA9KI9josu0LH7v+//I6ZTQ2j00PcjV7ZuWtcLmMVNK8/8evh0kiU3RzlUitIpfO6ySoacC3rhow7giYvrJ8RhtklAX5F2iZRVoA6TlVSFJEgLnzGjj8np8xQrah/llqlNMZZ3+oSBMriB0XSaMG8ceEpWl/mfg0iaQHDZOVzOMHxZVrZ4rHv+5f+8dgggBVHPwg3cJ0qI6hb6plBbnYZnQfQVsunX5BJtlwdRhcEdEzMZKEyy2vXCO3hWm8rwxsU5uSngcVbJChzR7SrQSnX7AFmTQE6GsmxwdzyBJ8Iz1TQStFIw+DIE2JostfH86xL5JJyYwm6vyrm8cYWNbgKBb44FzST72k8H6rn98YprPvWYP//3AMHeemMbnVHn5JV287qpemvy/ZJvgro8sECGAmRNkpkY5ba8jdfYhtpWnifW8BtO2KVQMzicq9Ma8y0hRIl9lONl48yrrFpmizsa2IC0BF6lClYeHU0R8Ds7M5PnsveepGhYvu6STN1y90Lp1ORSaVRlZlvjZyRlGkkVu2NjEVWvjvPUZa/i3e85SMWy6o56GfWZKBv/x83P8vxdvYyZX5t7BJF1RD+ua/SDB2USeoFtlTdNCyV6SpHprbDFmcmWcqkLQreFzqcT9TnJlg4eG5tjSHiRZqJIp6iTyVda3+jkxkSVb1pcJ3i3bRjfthmrOtw41EhYbOHi2wu/vcvHs3l08u3eJJQaQLufJV8tc2bGRl2+4BsMyKRtVhrIzyJKMqij88a7nczY1xd8/8s2G56YqeSbyKXqCTYzlkng154rxIalKftmyuXKOgl7hj3/26To5+f7Qw3z4yt/j1rULPzS6AnHmyjnmynmS5Rwt3hAS0kUNEB+cPFUnQvPYN3WaH73wr/jB0COMZBN88eRdDY/n9TKHZ88Rcfl+YRPLVaziqcKqucNTAUWFDc8T4+vn7xEGgyB0PdPHauPtNSROi7aXURE39wtNmF3/l0LzM4+B5yy0vXa/VlSh5iFrsPt1C3+bVcAWFSi9KATcx24XZCjUBbKMtm4vvr2XokaiHPvhZr79tt18+1aDY58zOfhxi++8yOQnbzSZ3HeRmIfhB5YvG3lAjMKHuqHvWtGe6tgjqkFGaeH6zF+j9IiormXGViZC5Qw88t9w8ItQnKOy963YiwlnsAt2/n4ttPWQuOat28TE2WduhM/dLPRV7mjdmNKlKfTEPPTHhfsvsiqud+I02/RDHMj4mSVU34Vtw92nZ+kIu3l0LN1weKZl81ffOc6Pj09j2ZAtG/zbPef44dEpSlWTYxMZMsXlFvhl3eTh4TmGk4VljzXEptTgl4o0Syk6pFl46DNs6QiyvTPEyFyR0bki09nysudEvQ7al7TuHKrMlo4Ql/RG6Ip6cGoyLk3h2HiGP/v6EU5O5TiXKPA33zvJVx4aAeDBswn+9vsn+Py+IX58fJrxVAnDtuqVpT955gCP3PZMDrz3Bq5eu5ysTmbERJosSbU2nLgZl3STVKHKTPbiU0f5isGR0QyHRtMAaIrMts4QmiKRKeqMpYocn8hQqOhkizp3npjm4Eia8VRp2bZURcbGIlEoY5ii6hJwLX/vDcTiK7pYz6M/1MrWeA8dPuE6r8oK08U0M8UMk4U51oRauapjM6/adB3BJZXXsNNHkzdEulLgdGqcRxPD9cdKRpVUWZCgm3v3NDzPrTq4oWs7d5x7aFmV5vMn7iJfXTjffLVExdRp9gYJODzcO36cw7PnL3g+87BWGL0H0TrrDjTxpm3PWtGI8fqubWx8nBEnq1jFU4HVytCTjdy0IDjNm2pGgbKY9AIhFE6P1JyjaxEdxYTQDc2PxV8IW18ibuhn7hTC6jU3LjzWug1e/xPRGrMt2PX70LZj4fFwTy2ZXakJmZ3ib08Uwt1inZquZ+j7FsdvX5hyO/5fMN/GKCfhvvda3PRfMr62C/ySbNkipsIWI75emE56orWx/TWiVaVoYrltierZubvExFtsndD0zEdxgJigswxR1arkhfdQMQ1mlSPqLqyr/4udc99H84ZETIk3JtZzhwXhGd0vCNQ8CjNw51/BaxY8h5oCLmayFcq6icsTgXXPgtGH8JVS9MU8TOWqDafVE/UiSdKK+pOZFYjISLLIvvNJSrqJbUNwiXldoWKQKuhYNnXRdalqkshXaO+/AXnmeMP6mmSxRardyORYfXlf3IvXqdISFFWoYs1K3+NQkWWJj75oK3/0pUdIFXXcmsL7n7+xwUTQ41C5cm2M7xxurIwA3PHoFLu7w7z+vw+QrwjNSLimO7q0LUJbyE25alKqWlgIbdUzNzbz+QeHG7bTHfHyo2NT9MW9XDfQVG93BVwaO7vD9fbYY8GtKcR8jmVVjW2dIUpVk5NTOYYSRVwOBd2ySReqhLwO1resLBIdSRXJlw3ifhdxv5NrB5r44bFpKoYgAk1+J6+4tPsxj0mVlWUBpp3+OKZtM1tMo8kqvcFmNFnhI1e/mvfe+3mS5Rwhp5dXbbyOXKVI3BPAr7kYziU4nRpnXbidw7PnKeoVdjev4W27no9DVfnB+UcIOT28edtziHsCK1Z3SkaFL5+8h93Na9nR3MdMMUNBF4G1siSJfGn7Ij9wgL1t6+kJNDG0yBX7ZQNXMZ6fo6RXGExN8oYtz+T/Hbgdo0ac3rztOfU23SpW8euGVTL0ZKM0JyovpZQgLfPj6yAMFGPrGifJvE1Cn2PboioCQvBcmhMVjsWVonCvMCx0R5ZXkFq3wnP/4cLHtVT/s+v3avlgte1kxkFxMPnAcr3HYtgGTN5vs/ZFi754xx+B77+rVoHZKs4zPy2I4N43CzI0uk9UqDwR4TBdTEKgQ1SLSmn4z+dA4lTtWH3wym8sxGfYtrimpiG8idxh2PISMSmHREvifpTECSQPYl/zURlOnzChhAUfosWYPtrw59mZPOdmCzw8PMfungj9cZ/wiLIt/sKb55Wf2Ue6VtG5el2cZ20SQvjrNzTz7cMTdQdmr0Nhc3uQ+88mG7avqTJl3cTnVOmLN7ZOLcsmU9Lpinroiiy8Vqenc8L1eM1LaEufh5F9gujlZwSRnsfeP8S0bA6NpnGqMpvbxbVbaSLsijUxHnj39QxO5+mOeZYLri0DJBnnCrqjJr+TT997vk6EAFJFnVNTeV6wQ2jRUnmdsm5xajqDYdns6Arxkd/Zwr/fc46ybvKMDU3s6gqTLuqki3qD7se0bDRFqo/ygwhsLesG6aJBa8iFVtNfKbJESTeZSJdpDbnqLUiXpuDSFAZa/IQ9GgGXyl2nZ2kKuLhufRO+C4in17f4mc2XGC6MkrN87OwK84FbBzg4NUqHL87zt7YjqWXg8bU6HYpKzO1nMDXBt8/uZ7aUZXtTL6/bfCM/e8nfMFmYI+DwMFfOM5KdwbQt+sOt5I1KfbIr7g4wh/D30WSFt+54Htd3buNsZpKJQooTyTGe27eHfzv8fVKVhcris3t2CdF0zaU96g6gygot3jAOReWK9g3IksT9Eyf4yP7/ZTg7w1XtG3n/5a9oIHUyEp+64c187fR9HJg6w4ZoB8/ru4RjyRHi7iBlU6c/1MoHr3wVZ9KT3Ny3mw2R1YrQKn59sUqGnmzMkx1PrDYqb4ubruoSYt2l/j2dlwpd0dgBMe4eWyPS4ytZUcEJLBJS6kVBskxdVE4eA7ZtYxRB865QwSnOCaIyP+VVLQrfH0nG23IjK4qWF8EdX7RNowJfeqmotIBoibXvghd+Rixr3iIIUPvummGiR1SPVKeoBE0dhrmhBSIEgvjs+9RCG1CSoOcqmDqGdeoOCHYgt2yFYBvMnKQzNwhSDvwbhGh6sVh8Ht1XsizSpOfKhlV6Y15Kukm2pGPNMxtJAkmQm3vf9QzuHZzF79JwqjIT6TJdUQ87u8K87spezicK9Md9vHRPJ5Zt87uf3sd0rd2zsyvE9s4Q3VEv3VFPwyg6QKpY5dxsAY9DEdqZGtrDbiQJwvEN0PIxQQRlBXJTZO/+BJW5UYwNt3LCv5fW6RzpYrVuPgiCMKw0EebSFLZ0LFSmZnMVBqdzrI27iOtjVG2NbV0hoo9OkiyIiljQrfGma/r4yPdPLru8ycJCW8vvVnFqFgMtflJFnUS+QkvQxe1vuYKgW8OybApVAxuWZYUdn8gynS2ztTNIk9+FZdk8cC7JSLJAk9+JblmCpNYwkSkzkS7VQmQbEfZoRLwODNOiJ+bFqSr4XRpHZoeomDo7mvpQF+ntQh4HkqIzOl3EtC0ubW3BVN3E4mE6/SFGihNkUgUua1u/opYHYLaYYTKfIlXJIUsy3YEmugJxIi4/XzxxVz149f6JExxLjPBvN76F7tp4+3h+jvsmTtIViHNL/6Vc3b4JRZbJVIocnjnHI7PnSVcK3NizHYAWb4h7xo4xW8rQH2oh5g7w1ee9i88e/QnZSpHn91/KNZ2byVQKFPUqb/iRMIoMOb28befzedn6q1BlhblSljf/5FOUTfE6/2TkMIZt8akb3rzwusyNMlvM8JrNN3DLmkupGDqmbVExdSJuH22+KC5FI1stsT3eR09whc/gKlbxa4RVMvRkYz6cVS/D2Z+JkXC9KKob8eWurGgu0SbSSyKSQnGKClBxdrkXkTskyJPmXr6dJciNQH4cgr023tZFd0G9LKo0kiJcqCVJbC/cA4qDdS+RGPu5TbGWHOEMQzW7UGyJbYHWy2zqZu3jDy8QoXmMPyyqQ/MVMmiMHgl2iCqPrAiCVFk+ik12SYtGdWL5mnjIXItWDrE92IVsGSA7aqLwS0D1iNbYSmLQpvVw89/DnX8ttFNdl8Nz/l/DKiGPg8v7Y1QNa8VpLJ9T5abNrSTyFQ4OpxicyZMtB9ncHuQt163B41AbnnfPn13Hp39+Do9D5Yo1MSbTJVRFQjctxtMluiOe+pRZ2OOgJ+Yl4G78iMZ8TmLzDtjOhVYY/hbObftTErkKrSEXVrqMJMGe3ghqjfVkSjqD0zl6Yt6FbVwAmZJOsWoymang0WD/RAm/y8tX37iXL+4bwbbhLc9YQ8zn5DlbWvnJicbX/DlbFki7qkioioLH6cbv1rhvUFSwqrV2kyyv3FpMF6sMJfMYps1QooBbU/A5VVyqTGvITczroHmJP1DE4+DsTJ6RRIHWgKteZXrwXJKKbrKjK4TXqXF5/8K1y1aLGJaJaVkNZAhECv2Opr66yWBXoAmX6qDJE+JcZorpYvqCI+WnkmO8574vcDQpWoJRl5/3XPoSUZmplhoS6AHuHjvKmdQkpm0Scvk4lhjG63ChWwYPTp5ib+sAE7k53vCTf+FMWmjGbj99Px+//k08o2sriqzgczixCdBZy1Dr9Md532Uvb9iPKit85KGvcc/4MUAIsN//wJewbZvru7fxzcEH6kRoHveMHcW2bSRJomrqVIwqDlnFpWisCwsD00ylgCzL9Aab8dWsLwLOX2ICdRWreBqwSoaeKqRHxOh7tF9MfRUTQjjtjS1f1+GBvqthzfVCuCvLouqxGKWUEBXHBkBZ2Tl3MVSX2JSy9B6oOISGRnEskAZJEqaMgDs/y023Pcrk8GYkX5yWS6GSgokHbNxRCWfEZuYRifg2G9UtCWIjyXU3Z0B4GoW7BSnULvDlqKhCTN13LbRsg+PfbHx8w/MW/l3JQ3oYO9iN6g7gKk0L3VXTenFN42uhc49oIRUTolqlOkkVqkznyvTFfIKk7Hkd7HiVIGmeC7cDL+biHPM52dQe4PBohtlcBdu2CXnEzbOsm/Wqj1NVuHptE2XDJOp1MJIsMpurkCsbJHIVHLV4BxAEoTng5MRkDtOyaQ0uJ7y2bZOrGPW21sbWALmwTtTnpC/mw6XJDbqRuUKVdFFnMlNCAqI+p6gqKsuJSF/MiypLDE7nKLlijFby6MUiV6yJ1Q0m5wnV7+zsIF3U+dL+EVRZ4rVX9HLtwMqVgDMzeWQJemLehey2CyCRr+LWVHBAtmQwkiwS97lYE/cT9TuEQ7XSSHRDHg1VkZjOVqiaFq4auTFMm8NjGQ4Mp9jRFSLmcwrLBI+DPc1rGxypl2Jx1UeTFdp94kdJX7CFgMNTNw40LQsbG1mSMCyTb559sE6EQISyfmPwfm5dcxkfPfCNFff1wQe/wnP6duFUVCYLKdaEW3EpGnatgnlgerBOhABMbP5m39do80UYCLdzbccWVEWlyRMkVc5TNqq0+hrf24dnz/PAxCmW4q8e/DIf2vcVnt9/6bLH2n3R+nvpVGqCbLXEunAbZzNTaLLKxmgnQaeXLc5fL0PRVfxmIZfLcdttt3H77bczMzPDjh07+Kd/+if27Nlz8Sf/ilglQ08FMmNw+MvCy6bzUkE+TF2wkwvB4YXkWUgMCvHzYmNGgOH7RcZX95XQf+1FD8HTLOFpXuEBWW4UVy9GOQvlDIpSoWPXHNQcbz3NsOYF4osxeRxYnC4e6hK6oAf+pbZAghveL9pg6mPf/OpkLNoPA8+Fc7VspK0vFducR3oE0iMolRzbPXMgZZDdQVER6rxEVNecfnFeYwfElFzPlZxPFpjLV/G7tIUJKtUB6mPron4RtIU8eJ0a+bJOxbBwqjIHhlKMpYp0R73s7A6jmxY9MU+9CrKrO4zbodQIk0xToPH6ZEo62ZJOMq+sSIbOJQqcny2wttlHd1SMzUdrBKUeJLsI3REPbk1hdK7AXadm2BtM0W6OizbmklaiLEu0BF1MpEuEfU7WIMTbkgRXronVXyrTsvmf/SN8/eExZAlefkkXL9lzYW1Id8SDQ5Hpij52xWA8VWQmW8bvUtncHmAqW6El4CJXNAGbRL7CoZE0HRE362umkJmSzonJLG5VZWtnEJemcHo6x0S6xLaOIFXT5Oh4lnzZIFPU0U2LXd2RBhJUMqp88pEfs3/yLJe2reHNO27EqWrMZMuUdJPuqBfTsvnOyeOoms71vRtxqw5s22b/1GnOpCZo98W4pHUtBX35BNxEYY5jcyNMF9MrnveDU6c4kRrjs898K36Hl4jLS6KUJ+ryc3xulBNzY8ueM5ZP8JF9X+ffn/kWOgMLFdejiWF0y8TvcONzLLx/mj0huvwxjiZHlm3LsC2+d/4AN/ft5o5zBwCQJZm373h+fZ0md5BspciR2SGS5RzdgTi23XHRcfxV/GbBtm0SWb3+gy4W0J701/j1r389R48e5fOf/zxtbW184Qtf4IYbbuD48eO0t7dffAO/AlbJ0FMBWRNTZXpJVH2aNtQrL48Jy0AwjVoZPjctWliugMj4yoyv7KnzRKA4J9pn7ojw/3H6BYGbOCSqKDWtU2S9KALJ6qIPybM+BJtfKLRR3Vc8/lyzu/4WTn134e+j34DL/khMk5lVscNAO4R7UBSH0M14ay3Exe03dwjcQXEOwJomH7PuCs0XqUjUMXdenPdK1buVYMOJyRw+V4m+mJdzs3kencigWzYdETejcyVShSq7usOEvY56hplLU+qVpMVoD7lxqDIh98JjxarB6ek8HWE3XoeKqkgrEp+VUCiOI+tzVI0o09kKSaVCu1tiIY9aoFQ1GZkr0hlxc/kace4xn4OxVAlZkhoEzl94cJj3fftY/e/3ffsYAbfKrTXx9FIsPu8LYa5Q5eBomqFEQSTVS3JdF+TSFCSEpkqSqGclTaRLvOAT9zGTEwTk3+45y1ffeBmJQoWj41kxDbauiavXxsmUdb5/ZIrjE1k8DpU1Tb66CPttP/33evvo0NwJzqRHec3aF3NsIkt7yM1QssA7vnqY2eo0qmrx+7skbrt5GyC8j5AkbGxM2+IZnVv46unGScqt8V70x3BqBtFuumv0UeFe7Q6RrZbRLZPTqXFavGE0WVm2jQenTvHfx3/G67c8s76sN9hMQa8sMzjsD7Xy11e8ktf84GMrGiSWTZ3XbLqBHfE+Tqcm2BbrpWCU+e7Z/exuWUu2WqQ30MSp9ARrXD62xntWidBvGcbnyhwZylGuLlT4XQ6ZrT1+2iNPToRKqVTif//3f/nWt77F1VeL3Mb3v//9fPOb3+STn/wkH/zgB5+U/c5jlQw9FfA3w+7XCFHw4vHw9KgYHW/dWve3aYA7Av6SmDArZ4R5oazWAkvXCs2RUa45VT/BX0aqU7S0XEHRZpIkQYaSgzCji6kvRUWSJZYZ16aGhS5q062CTFhWLe7jsW+CmDo8+nU4+vXG5ZUM3P9xuPSN4jgyo0LT5AqIabrHOodFouiAS1uYlCqnRSacv00YSi5FKSUE7KoT+p/x2Mddg8shE3BrRLwaAbfGQGuA/iYfsizV9i1+ZTmXjIlbljD3W+pKLUkSTX4Xk+kS7/v2MY5PZtnUFuDGDc2ossTm9mB9XH7xtg6OplFkie2doSWPGZi2ic8hs7EtQFNLB/jUZd5NYynhS2Rj16suE+kyM9kKfpdG76Jk933nGifkAO44MnlBMvRYsG2b0bkSLodMT9RLR9iNT9NIFQxisoSmynUxuN+lcdWaGI5aC/Jz9w/ViRBAuqTziZ+d4Z03DZAqVIUnpywhIyEBbqfCyckctj2H36XSEfYwmputE6F5/GTkCM+I3UjI46Q37uVP/ucQs/kqEMKoWnzm52Pcur2Hze1BLm1Zx+7mfmwkTiZHsYA3bHkWXzx5N0W9wuWt67mqfRPJ0gqauCWYK+eYyM/RE2wi7PJxMjmKbhlsinbyqRvewjvv+Sxz5UazxYemBhvIUId/ZRJfNXV8mosvP/sdfPPcPm4/8wCzi44p6vKzLtxO3B1kT8s6ZEli/+RpLMVmNJcgVy0RdwfZFO0k5g7UJ9xW8duB8bky+09nli0vVy32n85wyTqeFEJkGAamaeJyNW7b7XZz7733XuBZTxxWydBThc49IoF9sT5DL4mqj7HEg8YyhZg4OShI0LymZ+heOPwlUbXxNcPOV4sKU7RfkA9FEy2PJwIOr3CKLmdES051ibBXZ0CQo/QIRPtWfm4xKTQ8ldxCaGoxKTyBXCsQj3l8/TVw4jsrP6a5xTF5IqI6FPzFbra2aWEkSiQkm+PJAgMtfjrCHkG8bFsIt1eCMygI10pE6QJwqgqXLIqeqJOR4hwM38Xa6BrWrulm37kk955J0B5ys7M7TCJXoWJaXNYXXTZVdj5R4NX/ub/u2DyWKjGcLPI/b9jboEeah2nbZEpVJEnCsuyGKk7A30XRjDFbKBBwq8uI1DzificHhlOEvQvv1Z6Yl3xZp1DRMS27Tkp648s1ItGLVH4uhExJ5/R0DrdD4YpaRSpfMtBNG1WR6vutGCb3n0mQKelcv6GZYtVkIrW8wlEyDDHppymEPeJcChWDR4bT+B0ql/SE+dz95/ngHceRJIlL1y2vGEqyxfbuAO3+MC5NYXAmN/8I84Gvh2smm80BV10H5XW4yFZLvGHrs/ijHTdTNQ00WeV0apzNsW4SpSzfH3oYwzLpDjTx4OSChmdXUz/XdW6hN9hMxOVnMp/C7/CQrRQwLZMr2jfwus3P5O+W6I66A3HuGn2UdeF22nwXbv0OZ2cZzSWwbIvtTX1sjHbyL4fu4Ex6ku5AEx+84pU4FJVmbwjdMigaFZ7fv4dEOU+7L8JILkGqnONoYoTNsW6alvhjreI3F7Ztc2Qo95jrPDqUoy3sfMKrgX6/n8suu4wPfOADbNiwgebmZr785S+zb98+1q5d+4TuayWskqGnEkuFqrG1ogVz7BvCm6djN6x9JkwdFQQnvl7cSL1Nwjn54f8UY/cgfHv2/xs8+6OCOJUzYiLsia4SaV4xoVVKC3LTc6UIkJ09IYjOvBZoXg9kmaJqFOpaaC/JCiCJasv8xBiIY547LwhdaviCRGiKKH90fye9mRned2s7vtZtFz1s27ZFgGe2ipEsU7RMbLk2wVTOCDIU7ruwjkmWf7FW5kqYOSEcojsvBYeXcjHHt/ed5ccTYwwp3QzOLPi+bGoL8Obr+vE61BVftqFEYVlcxsmpHHs+9BMAXrSrkw/csglVkakYJlXD4tLeqBidX5KmWqxaSLJCW8hNzH9hwiLLEk1+J4u993xOFQuYylRoDVaxERNvL93TyVceGiWRF9NHHofC66++AEm+CAIuja5oo8/RfGDrXKHKwZEUzQEXiiTaZNOZCg+eTSJJEgOtAb776FTD9p6/rZ10sQq2iPxoDwsxvN+lMpws8tUDIxwYTtfWtrnneIn42nZy8kLMxI3d2+mPLFRYdvVE+PlgomE//3znINPZCqos8aZr+nnHswboC7bQF1yo9qqygm6abI51o1smf7L7BfzRjudSMioEnV7uGz/BAxMnWRNu5brOrRycOctYfo52f4xNsS76Q608MHmSoqljWCYvWncF94wdrYe8dvpjfPnkPfz38Z8hSxJ/uusFvG5RlWgelm0xnJ1hMDXBc3p3k9fLdAebuKl3FyPZWcJOL/5FE2DnM9P8aPgg3z37ECWzysZoJ/983Rto8oQwrDk8F6v2ruI3Coms3tAaWwmlqkUiqxMPPvGv/ec//3le+9rX0t7ejqIo7Ny5k1e84hU88sgjT/i+lmKVDD2dkCT47h8vTE4d+iKsfy7seKXQxXgiC1NOpr5AhOZRTgsCFe0X5ENSnvh2maKCvwmsqmjrVXK1EFlTZHqNHxCmiN2XQ2pIGChW8sIFe35SKdInnjfxCHii6PGrqN77eTwPvwtJLwrLgEvftGzXNvBJ43ncZ24mKCXZf/gIH3a6+NCtWy562I+OZ0jkK+zuDOGOuuj1O2hTZaGvOXe3qKR1XnpxUfcizOeVNQdctIfcjKVKTGfLWNj0RL0LY96VvKieGRVwePnLu9J8/9EwOVxA42t4bCKLz6Fw1do4siyRK+ucTxSwbWgKONncHkSRpXoy+zzmfXS+vH+E3piH11/Zx8GRNPmywaV9EfzO5ZNRj4ykqOgWe/uj+JZkcemmhWXbOFWFgEvjsv4ojiVtuw0tAR4ZSfGyf3+QwZk8cZ+D117Ry203b2QoWcDv0nj25hZaQ8vF3kv3dW62QNzvbHC6lmWpwVNpMewaMxOTcBJrm/ysaw4QcKnMFXVe0NJO2OPgc/cPYds2r9rbzXO3tmFZNm5NqeuUNEXm0r4oJ6dyHBpd3gpIntnKS2/YSJ4EW2I9vGrjtQ2Pf+CWzbzx8w9zajqHQ5GJeh1M1tzFDcvmX352hmdtamnwbAI4MjvETDGDjIwsw/amPoYyM6QrBbY39XJF+wauaBfke66UY6aYpi+4YE/gUjW2x4Vh61w5z9HEMO+77OVULYOyUeUPf/LJuo7Ism0+9si3uWXN3mXu16ZtM1VIY9oWhm2yKdYFQFGvcCY9iSYrXNWxqb6+z+Hma6fvq/99PDnK++7/Ep951lvrfkir+O1BWX9sPdvjXe/xor+/n7vvvptCoUA2m6W1tZWXvvSl9Pb2XvzJvyJWydDTifTo8hHyk3fAs/5mIRZjHs0bRatqcUvNE11oFzlXvon80qgWRXUj1CU0Sq6gEIKnh4VguWuvuNkrTuF+PHUUchOi6pIeE1NwzZug7xqs6bOUB8fQwi605laKQ0kC+9+BZNU0HsUkPPDJZYcwasX5qPFyAhRoJcmUHeGnJxf52WTGhSdRbOCCGW6SIqM1i0pU/RYdrrlcOwMk8hVcNf+aiyFbFg7JkiThUGVOT+c4l8gjSxLJfJWdXWHRemrbga2XkGrZTN88NEGVC09PzeSq9SrOeLrE+dkC2bJOc9DFjRuaedXebj53/9AFn/+9I1Nc3h+rGximilU0RV7WQmsNusiVRfr8PDIlHa9D4cFzSQzL5or+GA5Vrjs+p4vVul9S2Ovgoz84xeCM0KrM5qv8w49P8fnX7+WGjc3Lwk4vhNlchdG5IvmK0UCGHgtRn5Or18UxLZvhZJGOsJvJTImxdIkdnWIq75V7u3nl3sbPjSyL0NF5A8z59t7NW1r5xJ2DzBQa/XT8Tid/fe1zKVZMDgzPcWqqyPbOhWO0gT9/9nq8DoWZfIV3fu3wsmN9dDzTQIYm83McT46RqeRxKg6CTg8SEmGXD90ycS8h5EPZGTKVEnOpGcoZE0dHB7LXWx/xny0ukLj1kQ7OpqaWhbXqlskPhx7hJQNXoS3yTtJkhWf17GQin6TNt+Bb5lI14p7gspy1c5nGahvAw9Nnli1bxW8Hln5n/Krr/bLwer14vV5SqRQ//OEP+ehHP/qk7g9WydDTC2t5OCfYjQGslbzQyzj9cPlb4f5/FoTI4Ydn3CZ0R4+juvELI3F6IQx0vvIE4tjmdT+qUzhkTx8TxxHshKZN4D4hqkS19lhxxqYw40YLbCVSSePTDyNZFcq2ho6KXypBeW7ZIQy6t0EVsnjJ2oLQdC6KpiBxShAyfwu4wxSrRv0mvqU9iGktFyUDQgsUhlxZZ9+5JH6XytXrGn/lnpnJk8xX2NYZqn/wm/wuemIGTX4nbodCa8jF2mYfhapJrqTjd6k8MpJiOlNGkaErYjPQ4ifk1hrEvYsRcqncsKGJYtWgolv0RL04FJnxVIlEvsLZ2Tzve95G9vSEue9MknxF59uHG0NaW0MuHKrMumY/09kyj45lmM2JqbXFmE99T+YrBNwa6aLO4dE0cb8Tl6agG1aDK3UyX+HgSJqwV2NXd4RUocqp6UY9gW5BqlBdkQidns5h2TYDzf4GfUGT30kp7sXrUDg5laUr4mmI27gQNEVGU2CgliWWLuoUKyYVw6xP1BUqxrJjOTubJ182CHm0ekRHzO/kr2/dwpu+8HDDum+/cR2npnKEPRqz+QrhJVN+Zd3EtGy6Y17WtwXY2R3hvjONbbOd3aGGv/N6mRZviI2RDmwJ1gRb8TvdOBSV0ewsE7kEs6Uss6UscXeAQ7PnyVWKSENDnCdMnyzjrGkmbh98gI898i3S5QLrox186MrfI6eXaPGEmFo0ru9VncTcAQzLbCBDAHFPgLinsWIkSzID4XbGcglmixmmimk6/bEVIzTWr8Zq/NYiFtBwOeTHbJW5HTKxwMqeXL8qfvjDH2LbNgMDA5w5c4Z3vvOdDAwM8JrXvOZJ2d9irJKhpxORPui9Bs7fvbBszQ1ibHzunGiVJQaFB0z7LnjGe+DyP4LZ00JAPXNMxGb0Xv3EH1uoS7TcHD7hjxTuqbXMlpgVBdqFTsjXtKAF8rcIIlT7Enb3NmNWZdztMuRnUFwuUFw8XO2njIO98nG84RaR23buLrENZ4BrX/rHXHOXxt2nxc3G51R5102LXLtbtor2myvEcLLA4HSeNU0+emIiMFVVJGzbpmJYlHWTT/zsDEPJIjdsaOLFuzpJl6pMpMsNIuB5UjJXqDKbr5DMV2gPCwKWyFc4N5NnKlMm7ncym6uIOI1FN99y1aSsWximxYPpJKZt8/Yb1vEXtz9aXyfoVvE6VTrDHp67tZVziSKlqklZN9nbH6Uv7hMxEWNpsmUdw7LpjflwqgpNAScjc6V6Mnt3xMNzNrfSXfPtCbo1Ir5GZ2bLsjk1ncOybAZncuQrBts7w/TEvLgdCgF344TYPDwOFZ9LJexxUKwaTGZKtAScTC1KkJclaAstF2Kbls3onNA69cd9DCcLDeSyP+7j9HSOsbkSmZLOprZgvTqXrxhYtl3XDs1ky3icKmXdpFgx6x5F2zpDlHSzvt5Issjp6Rx9cS99iyI6Bpr9pIpVYt6FHw0nJrO0BF388O1X8e/3nEM3bV66p4Oo18mZ2TxzBY2Y17lMd7W+xU93dIG8/c2tm/mDz+/jTHYUB27+5Npd9Qm8efSHWmjyBNFklbJZxe90M1vM8Cd3fYaHpgdRJQXdkJBKXpzuApd0rWNvy3ra17VhFky0TkE+fjpyhHffuxAufHh2iN/73j/w1ef9Oe++5MX83YFvMJZP0umLcdvel7GzpX9ZpeexMJGfYyg7g2lZKLKMKslsjHby+s3P5LNHf4yFTdwd5La9L/2Ft7mK3yxIksTWHv+K02Tz2NLjf9KsFDKZDO9+97sZGxsjEonwwhe+kA996ENo2pNDvhZDsm3bvvhqTyyy2SzBYJBMJkMg8ItP6/xWopyF+/5J6Gnad8MVb1vIJiulFjx9lkZ3mIYgQ+4IhJ74X2rH8iWKpsWOzHHUUkK0vOarQ0th20Lo7Q6v2K6yLBvDsoWTc2Ycph+Fofs4uf9HZKqwM1hEe+Gn4MR3BflrWieE5H3XYtsii2o2V+GadfEV/XhAaElOTGZZ3+KnJeDGRuRwnZzKMpIo8tEfneLMzEIr4Xcv7eKPb1jLkfEM/XFfPRX+3sEEU5kSu3sjHBlL43dqXLNO6HlKVZNvHhrHsm3aQ24M0657Bs1DNy1MyyZb0nl0PE1L0M2mtiA/PTnNbd88yni6jAS84tIu3vOcDRyfzNIccFHWTXIVA5eq8K3D47g1hcv6ouTKBh0RN90RL5OZEh1hD5oicXA0jW3bIrPWtLikJ8JcsUpFt+iMeMhXDFKFKu0hN2XD5P4zSfIVA0WSyJR1rlvftGA8+Qvg8FiKczMF8lWDv/3+KfIVAxm4dUc773nuxhXbXcl8hcOjaYIekQeWKxvs7gnjVBVyFZ2AS+PkVJaJdJmAS+PKtTEsy+Znp2Y4O5tnR1eYtU0+Dgyl8DgUDMsWpplula0dIeJ+J4O16bOOsIfpbJljExkGWgIXPLej4xn+95ExpjNlrloX49YdHbg0hWMTGcZrk3qzuQov2NGKjURr0H3RVl66UuDbJw/hc2o8f92uZZEe83hw4iRFo8qelrX8xc//mx+PHALAzDWjT2xn+8x5FKoMX1LkthuezdUdmwg5ffUbzxt//AnuHju6bLt/susFvHrT9Uzk58hUC2yJdiMv+RxWTZ28XibiWmin56slDNsiVHONrhg6Q9lpmjwh8nqJJneobkg5VUgxWUixOda9rNK0iqceT/Y9dCWfIbdDZsuT6DP0dGO1MvR0wxWA629rXKa6hCePrIgqi+oShouLqzKKKkTKTxISVYOKZVEJ96I63OBbwQdpHqnzMHtKVI9WmMA6OiFiKnZ3RwgG26E0B+uexfqr3ymeG11DStfwqX60ypzQR7lFi0eSpIYcqQZkJ0WrrHkzLYEoiixxairHA2fn8DoktnRG0BSZY5PZBiIE8LWHx/jDa/vping5N1sg5HYQ9Gg4FIlkscpEqkRn2INuWhw5+CDrDn4IT/Iol8Sfydj2t7GtsxWnqoj2THEORvcDoHVeguaJUNFNxuZKSMhsaoOHzs8xnhZ6Lxv44r4R9vREeMGOBVfVh4bmuOVf7kWviaU/5x7inc8aqFdS+uI+TMsmka+yvSO0rGrx0Pk5MiWd525t4/R0jnRRx6HKNAdcbG4P4lRlYdKoKWRKOvcOJljT5LvgiH3DpS4a5MoGN25s5tYdHRwZS9MecuNxqBckC0G3cKzNlnUu7Y0wmS6z//yciMlQFXZ0hdjcFgQk/C7xVSTLElGvg/MJodFyawpxv5OgW8OhShzXLXRD6KJkCb51aAKHKvHW69fRHHAtyypruD5Dc7ziPx6si8/vGZzl8v4Y3VEvPodKsWqgSOB1KkS8rmXXpWpYjMwVaQu5Gtp6IaeXdU1RcpUS4/kk+WqZoNNLh19ocnLVErZt0+QJkSxlcakO7p8U4ba2JaNPbwM0TEkG20FqtJuBYDdhV6MO0Kut3A73ak5kSVoxDNWwTPJ6maHMNHPlPFvjPXVR9SMzZ5kqpGnxhOgLtSBLEmtCbSiyvCx4tsUbpsUbXrb9Vfx2oj3ioi3sfModqJ9OrJKhXzfMnhITWa3bRIyHJwozx8WkmLZXtK5+WbG0XhZGgsGO5a7Kti0CVW0L2nezJ+hFN6p4NT9cwLytDmewZtAYWvFhRZZqzsW1BS21abBKHhKDzE2P84hjF3F/F9v6t4oYEqOyUPGyLDE55w4vTMtZpqic6SUoZ5jUvQyeO48+cZiA4sJUHFjRHXg8YSorTD5IQNClkSkVBeEZS9MacrO1M4SqysR9TjojHjL5Eo6vvQ53cZwRq4lSYR9XVt+CsvOuhY2lhsR/qkscD5AtG8zmq8yHpz94frkm6sDwXAMZ+vTPz9WJEAjjwIdr4+TZksgcOzubZyRZpL/Jt6y1pcgSliV0M50RDy5tQfPSEnRRqBiMpop0R7zkK4aoRpX1ZTf9wekcyUKV7Yv0Uh0RD26HmMpyacqFCeoiqIrM3j5BCH58Ypp3fPUwVdNCQsR2XLk2hqrIy8whN7cL8XHM58SpKWyrPW7ZNlgyubJBX0y0yvriXgKulb/GpjJlvE6lHn/y8TsHG9Ls8xWT/3lolHfdtB5ZlnCpChvbAmzvCq8oED07m+f4RJay7q8f4zwGwh1MF9O4FI2zxSlyeokOfxTTsrh98AEG0xPcNfIos+UsO5v66fRFOZkaxzacqOGzyO4UJ2JBjOQaMJ3olUbBvW3bvHrj9fxw6CDmoty/va0DtHrDTBfT9cy0xTg1N875zHQ9Q82ruUhXCng1F82eEMlynvOZKR6YOEmrL8LVHZtXE+ZXAYgfok/G+PyvK1bJ0K8LTANGHoCf/wNMHYLoWnjmB0VryqiIKtHIA2LdNTfU9TiPC/lpIYq2zToZGi5VUCSJDocqyEW1AOWf4Al2ipaW6mjQJKUGbax8jsgWL5JaOwZvVBg0XgCb2oJsaLGXVTLQS5AeweWI4vEr4sZdLQrxNhJVXzuWbeNKnRZkIz5Qi+TQhc5K1sR4vDtMsGoSVsu0trgJu8AqziK7LX42kSHo0egMuxhNLUzivXJvN363xgZngJaAi4MjacZSRdY0+djZGRKarZyXYHYCisJ3ZsRuooyDzsmTVEYHSVg+1nY0o8XWCYLqjtRjQTojHl6yu4ORuSI/PTlNxLO85y2qIgsoVpeTNp9TZWNbgKjPSbasc3Iqi2HYyNJysfDunggz2QoxvxNFkpjJlkkVKjTXcs0ODM8xkiwyV6hi2zZrm320B93LtjM4nSdVrOJUZCxstnaEqBgmn73vPOOpEjdubOaN1/SjKXLdPRtYUazudijopsVfffsYVVPcxG3gqwdGedsNa1ckHclClUS+StW06VgkmJcliVjAQSzgqO/rd3Y2mm/qpsV0toymyBwcTeF1qFy9Ln7B6zs/Ihxwq4ykihR1J7uWvk+BT919ln++c5BiVSTf/9srd9G0qAoVcHgZT5pkDJsOX4xMwWIyl6FslSjoZW4ffACjRmIemTnLhkgHAYebjF1CjYr0etkzh+zO4E5czdrmhcqMZVvsnxR+Qrff8h4+feSHTBfT3NSzC7/DxWwpy9YVwnYBykaVk3OjhJw+9rat49DMWYazCTZFOxmIdJCtljiQn0OSJfxON1H3EzyVuopV/IZglQz9uqCShbv+Rrg9AxT3wed/B95+BOLrROWmnAYkludfIETVlSy0bl8Wr1BHsENUfjQPWBZlG07lRWWlraUZuftyMSY/exLKOVGFWbKv8mgCOzmEFXGhdF/c72cey4gQiBZh56V4PBEuj0cFOZIkYYbo8vPguSSmZXN5kw+n5hYVqPQoo0OnMctFeppD4IlgWjbT2TLdazYTnMzAt98KmRFwh+m/8u/p2/QsXrqni//4+TmOjWe4dqCJV1/eUz+uqM/J9q5QPZ+KSlYQMkWDpo3YkoxkW2yRz1HCSdAhcXz0NNlMintTe9i5ro9gTEz7nJrKMZUts7MrRHvIzdmEEA5f1h/jxGSOiYwgZJf1RUkXq+z98J0UKgZxv4PuSGOlR5UlXrqnk7aa/qWsm3g0lVBQ48xMnnOJAtctSoeP+ZxEPA4ePJ9ksjai3xR08YpLuvjTrx3mW4cmADGN9YdXixbhqZkck+kyWzuCNAVcJHJlzifzxHwuMiUh3h6ZK/KSTz1Atizcug+PZciUdN5y3RoODM0xk6vQ5HexrtlHW8i9jBSlizrJJSPshmUzlCjU21rFqsGJyRwdYTd+p0pb2EV7cLkdgUNd4b2/CMPJIkOJAlGfxmSmTNClYds2kiTx6st7ODCcqq+rKRIv2tXBXKGKqogR/H/56Rk+/P2T3LSphb94zgbcDoVDo2k+8v2T9ecdHEnzgTtO8PGX70CvTWvlywZDc1mGclMYShqrEiDg0+mLBQg7fXUiNI8Tc2P8+41/xBt+/C8Ny2XPHH9xS9cykjj//P5gCx+9RkzWzJVzHJo5T5svusxPaB59oRaS5RxFo4IiKaTKBbLVAkGnl+lCmn848E0OJ87T7AmxLd6L3/GL68hWsYrfJqySoacapi70Jb4m4UA9j+RZGH2ocd1qDs7+FLa8SJCEx4rayI6LkXu9CMoFBHWyImI9Jh6BYCeuls0M6EmUM99BPluF5s0iYqP7itpkmLTMxDG+XcUaL6EEmhZMFX9ZqE7ouUL8e+IQ/OR9cP5esc0dr8S3/h1UFA0l2A6RTrBMMj++ncPTVZzbriTesh4vMJMrc262QKpYZdf33imIEEApRd/P/hB2nASPj3fdtB4Q00p3D87S7HexsU1cq5hvkR7DGRCVOYeHsruZqXWvoefUZwhKRYIUMa75AN0dHexP5RhJG7TnygRrlZ+SblLVRVyEz6Wxp1u09goVnQ0tfqqmTW/My09OTPPBO07Ud5krG5ybFf45XRGhxXnxrs56vAOI0f69/SpOReboRGaBvNVg26JKUzUsvE6VtrCbNU0+vnlook6EQBC2+88luGVHG0NJMWn28Z8OkixUuLQnwu6eCF1hN9u7wuQrOv91/3CdCM3jfx8Z503X9GPZNhXdYjZXFq7fNsvad3G/k/64l7OzC4aTQbfG1o4QIIjQQ0Nz5EsGqgwnSwamZdEZ9vBgzfoAR4aKqbMt3ouySBxcqlV75kfrmwJOzszkGEoUafI7afK76jqH7V0hXnN5D4dG07SF3Lz6ih764z7uHUyQKlX5ux+cqlevPv/gMAAfeMFmDgwtb3HuP58kVc5zaPY8bd4wYZcf0zFLR1RCkaNk8gpbWzuQVIPpQnrZ85s8QWaLaSREsOtiXLmmsU0lSzJ7WwdIVwqcy0zRE2xGkxUiLj87m/vxPIa1Rtjl45k9O+p/R9x+LpPXE3b5eMfdn+Xg7DkAJgsp/vSuz7D5Rd0rtttWsYrfdqySoacSs6fhC78jgkZBuE3vfh00rRfVHHcYCjONz/E1L9/OSujYI8jQY2V/gfAskjVwiF/d3blBSJ0SPj+zJ2DDLRBdI0jVyD4ItIpJshrUSBiqETHtBiIypGnDBU0Pf2Ec/d+FsXrDgIf+g53usLATqOHUVI50tYP+YJF0qIUHzqfY3B4k5nPSEXETV0pYiTOUbCcFyUWMLLJREeRz4Kb6dkzT5tRUjs/cex7LtHnGhiZec0Vv3ZAPSYLYGsq6yYPnkuQ3vgPHlhcQz53kIOsoRzexqzmMkW/GXdBpW6S52dIepBT3cnA0jWnZuB0qiizhc6o0BxZ+dX/2vvMrXoaxVIl//d2dbG4L8rNTMxwZzXD1Oke9IjI/fr6jKyzG7k0LVZHJVwweOj9H3O/k8v4YkkSdLH33yPIJpFNTOf7lp2f43qOTnJzK1W/HY6kJnKrCS/Z0YVo2xyayK7rN+pyirdkRFsSnLeTGsGggb4vxr7+7k7f+zyFOTeXoi3n50K1b6gRmLFWiqluEvBoDLQFOTGbRTTEply/rmJaFZeXIVQyK2Vl64wHagi4S2Sqpgo7freFU5Xog7kBLgLMzeda31nLoahhKFFnb5OWW7W1s6QjVNFY2TQEnDw0n6kRoHj84NsUHXrCZDa3LP1MbWwOUjCrnM9O4VQ1FVvC7VfpDLcTdwXpSfL5aIlnKcnPvbu44Lz4zLkXjT3a9AJ/m4pKWtfVIDYCbenbWyYhpWYznk0RcPnwON6O5BKlyHq/mqmePzU+C/aJo9oTq/146mWbYFvdPnODF665kFav4v4ZVMvRU4ifvWyBCACe/Kyowe/8Q2nYI5+nb3yBaWSC0QT2/4BeT0yf+uxg8EVh7w8Lf7hBsfJ4QMxfnRFts8rAY+a/mQF9hgkQvCSfqwixkJ0Ro67pn/cKEqKyb6IZF1bSIzldkpo4sW686dYpMrkLM58CyYSxTwVq/lW39USZLBmdrxoiyjPB2sf0c9F7BkaybMHm2yufoUZIiIHcRzicL/N0PTtbFzQ+cS7KuycfVA03kKwZDiQJdUQ+KJGHZNk0BF229V5Mp7WXk+DRkyzxwNklnxMP2Tie+RVlaigS+/BCXNwewvfE6wTItm2S+QtTnrAvKLwRH7cbeHfVi2fay1pBuWpyYzDKRLtESdNMb9ZIt6yTyFcZSJTrCblJFHZ9TIZGvrjhmbtk2f//j08uWA/zk5Ax/I4tzlyWJgGf518SmtiDfOzrJRLpEyO1gR5f7gkQIYKAlwA/ffnVDuOx0towkQWfYgyxBR9iDS1PY0SXec/OuHxXdZk9nP9PZEkOzVY6PZ6jqJm5Vw6HK+JxyvQ27//wcDw/PcUV/rIEIgWiLDSWLIMn4XBprmnzIssTWjhD3DJ1ddsytNZJ7xZoYv3tpF1/aP4JtQ0fYzXtu3ghU6fLHyeVlYoqXHU1+UuU8nz/+U9p9UW7u24PP4eaK9o20eSPsbl6DZds8p283YZePiqHTFYjzyPRZTs6NsTnWzXP69tT3P1NMcyY9SdjlY0dTH33BZpIOz+MORjUti9Fcgpjbj29RG6zTF+P43GjDup3++OPa9ipW8duCVTL0VGL62PJlilO4NgNsfbGYIjvzYzGmvu6mJz5rbB65aajmhbfR2EO1CbO4CIRNnBYkKb5eRHEsRftO0XJLjUBuXOSThbqgZdPydZcgXaxyYChVdz6+dVMIf3ZQBMIuwYngFcyOptnUHqA16GZnV1gIqr1Oer1OWgMu7jubYDJT5qp1Gk5VQbv2Twn+8JMEq3lCmgk3fRTT0UzqqI07Bt4Wia88NMqioSJs4GuPjHP1QBNTmRJTGSHAHWjxc3l/rF5hGU+VCbkdODUZw7Jp8ruWj5WXUpSmBxkvu+jYeg3zGvPziQJDYxP02OOsWbeRt1y7hncvMmKcxxVronSGPdi2Talqrsgvz80WGJkTRo39cYUHzyeRJVBkqOomY6kiUxlBEi0L+hM/42XyCb5mXYOJwpXqCY7Obb7gazRPahRJIuLWOHA+tWyd0ZTYv0tVGGjxPyYRWox5IlQ1LB4dyyBJcN1AU90dezFsG5yagm2DR3PSH3Pid1Y4OJzi7GyBy/qjmEWL8WyJtS6VL+4b5n3fOlarcp3iJbs7+NsXbq23ydpCbja0BlBkiZhPvG6FisH+oTnWxoNs7sxydFQYRaqKxJ8+U3h7JUtZXveMOG+8ppdEXmdrexBVkbFsC2ybw8Nlzs4UaPbHcChlDMvCtG3G8gkGUxP0B1uJe4IMRDroC7UQcnoZTE0wnk+yvamPl62/mqOJYcZyCe4dO0pfqJU1oVaibj+t3nCd/ASdXoKPsxIEMJie4OD0WdZF2tnVvKa+/HVbnsm7f/5fVC3RAr2pZyd7WwcutJlVrOK3Gqtk6KlEz1VwaLhx2TwZ6dorCEZ8nfjvycb0UTCrQkxdzoi2WOtWoeORNSinhLv0SlNrsiIqWc2bRLts6N6auBuhfarkxPj80ueaBsrMMaSCio2DsmGiVuagmIBNL6By6iBOhLYlkbmEUv5WAm5VOAzbNmGP1kAOXQ6F7qgX07LroaKbL3kGm3dcLgwpI33gDlNN2lSz4qneFlEhWAq1tqgz4kGV5fq4uVOV6zfTzoj4Vd1bc26eR8Uw+fD3TvLdIxNEPA5esjFKTzwEqWL9Jh/xOpiTy0QkIVjf1d3C87e18tBQCsuy6In5CHk07j+bZPP7f8h1A03cuqMNr1NlQ0ugQYDeHHBSqBr0x31oisR9ZxMijNStIfkkeiMesmWduM8pSNvw57jB8SjvtL9CBY02aY6r5f8mvcLHX5Xhz2raqplchQMjqRXbZKencvzg6CR/9qz19DUtVCQrhsmxiWzdmuBCcKgyPTGvqJKtJK5HJNMLTZAwsQx7hbP21s4Qlm3jdaocHktTrJj4nRp/+/2TDeqb/31knFt3tBP3O1nT5Kcz4iHmc9ZfO9u2efBckiNjaa5ZF+ebb+rjb39wktPTeV6yu4NdvX6KeoXB1ARFo8r6sKvB30mWZNr8UdSuMvvOJzkwnOKy/ih/sFWkxY/lkpxJTZIs5ekLNaPJKoFaZaZqGlQMHcMSmqJUuUDFMnDKGvNXw6FobIj+6oaqmXKB6WJ6mcj6pp6ddAdinE5N0BtsYUdT36+8r1Ws4jcVq2ToqcSNfw3JQRjdB7IqKj+BFlGhsYxfblz+l4XTL8bsXWEhmvbVErI9QouA6yIjtpIkiFO0X4zqz5stpoeFFUCkb7l+qZLFnznN9XKJLZe+CGQVt88BTgeZ2SgHDrYTDz5IKreVmfQ1xCZPcu2NISRnBIbuRc9VsZu240g9AA99BkYeYE20X7QXJTH+X83Z5EadBPt2orrEbcUdlZDW22h2AnQfr7i0i68eGKWsi3akIsMLd4kRbaeq0FMTAOfKOgeGUjQFnGxqC+J3aWxsWy4Y/4cfn64HqSbyVf5mNs9HX9jGbK5CS9CNzymMCS/ZuhlK7XzlWIF33/5z5i2FtnWGuG4gzkd+cKq+zZ+enMGtKTxnawszuUqDF1DI42Bnl6hslHWTnqiXoFujL+bDsCwKukUiV+XRsSwdYTceyU0UiEoLuWJ/tLHCnz208PHvCLv5vb3dPGtzC91Rb61FZePSZC5fE2U8XaqbRgJUTZs7T86Sr5h85Y2X1ZfnyganJnOMOIQY/LFM2tY0Xbitm8xXuP9MgkS+giLLTKRLFHWTJr+zPl0HQruTrQmuC0tG5+c1T70xLz1RL6oiN5BY07IZSRYxTJuYz4mqyLz9hrWMzBXpibrZN3UKWZJYH+lgIpPn2GiJs44ie3piuGoxF7myzlS2TIksiUKRS6wQLk081htsZnfLWh4dy3EwO0d/i5uqaeBSHRiWSbKc5b6xE3gdLmLuALtb1qDJygUdrB8LumViWOaK8Rtrwq0MpiewbJupQopmTwhJklBkmc2xHjbHeh73/laxit82/Iqq11U8Ljj9wkgRBPk5+V04e5eoGD0ZYauPhewE5KbEZFnyrEic/2XgbxE+RPMkqn2XqBqtJOT2RIRGyuGjScoInxZZhmAHpZSLVHY3Z8b/gJn0M9DULJoxijEptBy2YZA7bzJ3NI1990fh7J2imjX1KPaXXg6lFOfvsPjuiy1++hbx/8l9C3UCl3MOJXEAJg6yviXAt//oSl59eQ8v3tXBv71yN1euXaSVsCwY2Yc1+giWbWMs6qmZls1EuoS+SGz742PTDadp2cJ1u1AxmV0c0CrL4I3y8Z+eYZG3IodH09x/Lrnsch0aTeNUFKazZe4+NcvpJSGpAGOpIpZl0xfzEfRoRH1Ogh6NTElHkmA8XWSm/4UAZGwPZVuDYBcvee6z+fqbLuON1/TxgRds5kd/fDVvuKaf7qiXsm7y0PkUh0bTlHWLzW0hvv6my3ntFT3L9r/v/ByZ4kLgcMznxKHKOBS5TjYfCyPJIvedSZAtL2zj6HiGz957nuOTGeJ+F9dtaEKWJU5MZPn5YIKhxMJUWsjjoCvqoSvqZV3zEtfkmvN2U8BVH/cvVAzOzebRa8LzK9fGuKQvUo9U8To1cmWDHx2fBdNJ2OmjyRNiTaSFslnlXHaCI4khQDhkn08UmMlWkBWDaMjkcPIcRV285pIksTnaS6e3lXZPGzvivXUSVbUM7ho9yo9HDpGtFmn1RXCrDspGlYqxUoDzY+OR6bM8OHmqvu/FUGQFv8NDplrgeHKU8fzy99oqVvHrgHvuuYfnPe95tLW1IUn/f3v/HSfXXd/7489zpve+vTf1Lktyb9jYGIODjTHG2BgCJJgEwi9gbrhcfPmRShJqSODGICChBYwxBgzGuMtNsmU1q6x2V7ur7Tu9n5lzvn98dmd3dleyjS03fZ6Phx6r08/MlvOad3m9Fe68886q7YZhcNttt9HQ0IDD4eCCCy5g//4lyk/+CGRk6JXCMOCp/xQt8PPZ+xO4/O9f+fuxOESKzBkUdszO53cUPinZqIhw+VvAfpICz7ZzIDMlRNQ8ImvB6jdTiIsfSa0UQG1exdSUF/ewgafjXFS9jE1X4OHdVccqWprC3sd5+quXos/Y2WhpeOqLOm/9sYpqUoQIcwSYVgJEx1N0Rtzc9rYT1DgZZcgn8CkK53QGsZjnfk2OTWfom8zQFHSwvM7L0cn0ksaBqxt8LK/3ULfEeIipzOIHVnfEw0OHq6efr2zwsq0zxP0Hx3ng0CSbWgN0hF1VPj55TUc3QNPnhIfNbOKCZTUkchpBt4Wp1HXEOrvYtfNxHG4fZ7/5XWDz0F2rsabJh81cff9HJ9OMJXOUdIPNbUIo1PsdnNMd4duPDlTt67SasFurP1Od2xNGKxtVUZgTEc8VyeRLJHMaU6kC6UKJR45MkSuVqXHb0DGwm014fRZSeY2ReJ7eiTSNAUeVtUBZN/j392zkM3fuY9exGKsafPzvK1aIyfbFEuCrvLaJZAGzqtISctJd66G71sNwLMv3HjtGSdcxKQpl3eDdHS0VY0VFLeH0xSnkCkQcPgqlMk/1R9ENg55aDzaLmydH+siaChxPT5Eq5ok4vTS5w2xoFo7W898Ph8lKoztELG2w0tdFrdNPVitwz8DTFEoal7RuoDcxQru3lvqZzrHKe1bIkCpmaXKHURSFWD7Nd/ffx76pY6wJt/LJM66ucpHWDR2P1UFI9WAxmaWXkOSFYejC7LaYBKtX1LEu5XH3MpLJZFi3bh0333wzV1999aLt//RP/8S//uu/sn37dnp6evjCF77AJZdcwqFDh/B4XpphqBRDrxSlAiSHl17/SpCLwS8/Bgd/BZ4GOO+T0H4O+NtExKKYEa7L/tY/Ll03+iyGlmN0OoE/N4izYaWYQr8Qh1/8W4DJpnDuP6k8++86yQGo2aCw/PoWsuMzaRbVhK9LJfoc6M5mTNnqLphUpqsihGYpTEMhCo4IYLFDyzaO9kdJprKkFIM6r50G+xJ28ybLjP+Rgs1anRYLe2zEopPUzKyPZYuc3RWifypDbqa2ZkW9h3XNfqxmlS/+9hAD08Ic8V1nNKMoCm9ZU88dT8+JYo/NzC0XdTGazPHrvWMA1Hhs3HrZMtw2MwGXlY6wi64a9yJDw5X1XjojbhxWE7o+5/LdHHQyW20yGs+z17IO97kb8Dst4PZWCtlVVSHostIeFqk2gEa/g4lkAa2sU+u1V4qjtbKOWVUozQtrBV3WRWKqxjMnAFN5jXhWq6TMcsUy+0YS1HntNAedpPIamq5T0oXdweHxFAPTWVY1eNjUGmQ6XSSV1+iIuNnSHmI0IcadzAohraxjGAbTSQ2/w8aPPnRm1b0MRbPY5nXjtczUhNV45yKxB8eSXP2NHZU0m89h5uvv3lDlMG0YBiZFpStQT7uvFl03CLisWE0qrSEXzw7FMZeC+MwGXquLoZSIvkwnzDw22M/Keg+5UpmAy8KW+g5iafjNEwpjkw5+9eBOPnJBFx+9uJN8qVgpvs6XNJLFHPVUc3B6iGypiNviIGB38/978NvsGBGeVfcN7eFgdJjfXvP5SrotaPdwdsMKrCbzG3q2lORlZGIfHLobCvOm19t8sOytUHPi5ouXyuWXX87ll1++5DbDMPjyl7/MZz7zGd7xjncA8N3vfpfa2lp+8IMf8OEPf/glXVuKoVcKix02vg92/1AMKp2lZdtLNy98IdzzN3DgF+L/iUG4++Pw0afm2uEnDwr3acUEgdYXf/5QJ9HpSY5GNWrzGXpqi89/zAIC3QoX/Gv1g9XbaqDOVDcbhuj2TzZ+DH/vrSjGTI3I+vfg29KF2aFTys0dazQW2Z3I0Gl3U6NmYOIAyzWVUWcT/diYSueos1lEm3sxK+qgLLOfmhVhg+BvAYuD4/Ecg9NZVoVVNnEQYjYIXoRZVWgOOvmrN3WjI4SE1aQyMJ3hb7/3nGjlBn67f5wjEyk++9ZV/P/fvhq3zcx9z03QEnTyycuWEXRZ+cZ7NnHPvlGOTma47ozmiu3AtvYQ3TWeamPIGVRVwWEVU9cnkgU2twUqs7hmyRV1yrrBhhZ/RbjYLSbcdjOpmYiM22aqiKG8puO2m0lUoiriuopBlRAC4RGUyGr4nBa0ss73HjvGk/3TrKj38v5z2jk4miKZ07BZVNw2MxPJAomshmnmfSvr4l7CLhvtM3PGajw22iNu1jX5SeSERcBzown2jyTpjLjZ0BIgldf49M/2cs/+MTx2Mxcvq+XPzu8k5KkWtwuLuP1OK35n9T7/+XB/Vb1RIlfi0aNRzu0R0RVdN3BbHWwI91QKtFVVYWPLnO1Eg9+BokBPrQe7xYRJVXFb7BwcS1EolxiMJ5nOZvE4zGyqa+Or9x5jbMoKugUNg6/cd0QMwe0+kz2TA8QLGVo8YVwWG0/PdIK5LUKctfvqiBcyeK1OYvl0RQjNcjwTZc/kABtrOyvrZqfPSyTPy8Q+2PPfi9cXEmL92vecUkF0Ivr7+xkbG+PSSy+trLPZbJx//vns2LFDiqHXFZEeuOiz8OQ3xcO36QzYfPMpDT0ahsHhbAHr4B6q4jRGWbhbh2b+YPpbhRBy/5FDGv0teNxNBOwpfPY2CM7zJypmhIXAwjEh5ZIwenSGwNuw5GlnhRCAalKIrDNg+bUoE61CrNStgeYtWIAtf6Oy6581CgkTzlpovKXMVLFMNFOkRp2E2ADecgFvrRmruw6TIuZdUS6JjjhFgc6LGEkW6T18gJXWCcKGATXLiWWKZAolJtIKB+Ne6oI+WoD2kJtoukihpLO60Udz0MlYIs/jfdMVITTLD58Y4rNvXYXLZubzb1/N598O0UyRZE6rjIy4bPXCOICY+XWyaewg0kS6YaAvUaaztUOMLJkfwbFbTGzrCFEsiVleswXaea3MvuMJwOCMthBex9z3bPkS5oMhlxWXTZz30z/by8+eFtHP3+4f56HDk3zjPZt4tHeS6//f4/RNZsRrXFXHP7xjDdliiWK5jMNsxmk10R52sazWg3J0GptJRVGE/+YfDkfZNThNpqThtpnRdYN/vOcgv9o7CohxHz97Zpgr1tXRXbd0UfbOgSj7RpJcsaaOiKf6vYxnF9foJHJCzGeLJZ7ojxJ0WknmNSaSeWo8dta1+EWX4wwRj408SZ6ZPMLqcCsuix0UM621Pj4QWcOO44f41p6HmNDG+PXwA+yPRlDtKnouALoVxZriN0cO8Ff1W6hx+hhMTTGQnGQsG6PG4aMm76+IoVqXn1qXHwCH2YrDZCVXrv7wsXDqvETygjB0ERE6GYfuhsjKU54yW8jYmIia19ZWGxHX1tZy7NixpQ55UUgx9ErjqYPWc0SkqPNiMfpiYj9Y3Uunlf4YtBzERVQjr1o5litAeANtsX1UBcmD81ppXeHFk+xfJFazumiaN7kYDD7OwZyPhHcZG1oCcyaCuagoKM8nTiiGFmJ2KODwgf/SRdsaz1GoDz5ALmbBuWkLhtXFVMZM0GkFukSNVDENnjranfOiLIoqCr4VFRSVTKFE0R4hZzczYarBltVYVueh3pqnPLyLAc2L2dZIC5Aultg9nCBT1Oic6Y6q89mrRMQcBsm8VvUQPTiaJFssUyrrDMdzdEbc1HrtmBe0nKfyGuPJPC1B15LzuVY3+Cjpiw0aQaSUFpY1FUplcsUyfqe1KnpiM6u0hpxYTCoum4npTJGQy4qiKLSEnNx8dhvfmVc39KfntmM2qSTzGnfurq6He3owziO9k9z60z1zvk6Gwa/2jrK2ycf7zm4jVywzlSpi9InOroDTwkSqSEvQSTJXYsfBGLoBbQEPmWKJJo8TVVV4+Eh1fRWIYu4Ll9UsmQoamM4wlSowmsgvEkN/sqGR3z83VwSvKPD29Y2zt4thGJR0g5DLxlgiT7ZYIp6p/j4WyxrRfIZcqciR2Iio61G9OOwBeuwKn370O2QLKrrmY9/EKM5IhsLQctDFz4lizlMbUMiXNTr99bR5a9k53ouqKHQH6mlwL2F+CtjNVv5s/eV8adcvKuve3rmVdt8LdK6XSOYTG6hOjS1FISH2m//8eAVZ+Ps9+0HypSLF0CtNz5uh80LIxcX4jWJKCAKT9eUTQ9F+0W1l9+NY+y7WeByYt74bZeCnQigBrLgSOi8SnWTRfmGkONsRNnVERF0aNz//eI/nw2QFk5VoIkU200uhbj3W2c45V0S05Nv9L+6ciePCFqB21by0lkBtWInLPwk2J4qizKtfUSHQsvT5VFWkK2fojLip8dqxmBrZ0TtNsTzFinovzU47ZpeJDT4X7gYvQ9Esb/3aIyRyIrLw/ccG+dGHtrGxJcDW9hAdYRd98zqfLl5Ry3S6WPUQ7apxk8hp2Cwm0oUSn7lzL4/2inlcf3lRN+8/R/xM9E1mmEwVsJpMtIQW+/eoqoL1BH49IGprMoVSJUW0dzhBPKuxqTVALCumxNf7bCTzJXpqPVhMKgfHkgxHc/TUeirX/NyVq7hybQN7huPYzCZqvDbxxwhY6uq/3jNaZXA5y+N903z4/E7aw27GEnmOTWU5PJZkIl2gp9bLlevqGY0WKOsGw4ksCgpNfidjcY3VrdARdnFsQeRNRWEknqfBb1/0x/GyVfUcj+eo89rIFks4rWZyxTJ7jydY0+jji9es5QdPDIKi86fndLCtY2Y+l1qip8FCg1v4C3XXuollipWU5aHoMMOpKTRDx222s6m2k6xWIFHMErRYUFV4brKPXKmIUfaAbsLQzeQs03T5GjhSSGG36nz03K1cuay5Ev3JaHmypTyN7uDzukJ/eO1lbK7t4qmxI/QEGrmg+ZVPYUjeIBSTL+9+LyN1daLpZmxsjPr6uQj6xMTEomjRH4MUQ680qglUx9xD3GKHurVzy+kJ+qNjTLhaWOf3Yj/yGzHJvm4NrL7mxBPp40MQ6xfn8jeD2S46qMpF6lOjUNcDH34Y9t8hRE7XxeK4YgZ0TRRyp8bFfRRSYrmUB2bEkGG8MDfsxHFxbdfMw8Tqgq6L2ajfT7EwjYcMszUoYkJ924t/D5MjwqgxF4PxA8IgcjbaVsoLywBrb/Ug3BeBqir4HBZ03aDeb6d/MsPRiTTWBi+N3ZcQykzC8OP8fJ+5IoRAuCrf/kg/G68P8PRgjI+9qZuDYymGYznO7QpzTnd4UbqrxmuvFOr+x4NHKx1l8azG5+8+wNomH2ub/NT67DisJmp9i+uGZguniyWd0USOOp8dm9lEIquhqsL7aCotanXWNvuo8dgJuKyUdNHx1TspUnWpvIZhQMRto8ZrJ+C0EstoOG0q+0cSBF1W4QTeGmBja4CReK4y7d1jt3Dlunp+/sycRcOmFj/jqaUbBJbViZ+rnloPPoeYLF/WPXRGPDQEHARdNqLJMrohomlaWcekgtcpOkY++ebl7BlOMJ0R6aGzO0Oc3xNCKxnE0iWCnuoaGbfdTFvYySNHpjCpChcsqyFV0EjmNMwmhXdubmZLj439U0N4rQkMo4FsscxXH91JiSJ/esYmQg4PVrNaVVhdKJcwEOlWp8VWcYkOO308PnIQpZyk2y9Sz4otjWIugKlIozvE9j85k9/07qYhYOfi1q6qup6SUcYwoLRU3hPYN3WMkl5mbaQNVVHZVNtV5S4tkfxRWF/gh98Xut/LSHt7O3V1ddx7771s2CCGDxeLRR588EH+8R//8SWfX4qh1wK+xrn/R/uZjKdI4CG962vYd/zz3LaDv4J3fV+IAIsL5hus5WJC2OTjQrjkkyLtU8qLepj+BwEVGjdUC5Da1SIipZdh8DEhZMLLRJeZayZSNLxTnHdWcCzFVK+YVZaLimhQ9yVVm+1N67DnE3PRp5dC7Srxej31Ilyrl4Wgwy68nMw28fVFkimUePDwJB67mbM7RcpwWa0Hm1nl0aPTBFwWcsUyHcYUaiHJ8YnF54imi+RjI4SThzF7urnikp5FHWBLMZUu8NjRxf4vDx+ZolDSKZTKnNUZXtS5dWgsxfF4lg3NAaYzRQZmutqCLit7hhKkCyXcNjMWk4rTZsJlFb/ynRE3nRGR1lvT6CNbLGM2KcQzWiXqUeu1U+u1M5UuMBrPk8yVqPfNReIaFsw8+8JVq/E7LByZzHBmR4ibzmrj8i8/tOg1+e1mPnyeCLFbzSrNQSfJnEZz0FlVl9QcttM7mqEj6GY0lSOaKWK3CUHeXevmX65dx9GJNGsbfVgtOqO5CRzFEH7n4qnrU7kkB6PDGKqHkEtco8ZjZ0OLgtsu3hO/zUW8kKaoa0zkEthxQcmODhyf1tifnWRNk69K0K4MNWFVTYSdviqHZwXhUB3NpegJNHLTqov57v77wFzEajLzqc3X8NxIErPhoN7txWKq/r4G7R7OaliOdYkPP4ZhEM2nhAeWrlfc1yWSl0ygTXSNnSxVZvP9cR9iXwDpdJre3t7Kcn9/P7t37yYYDNLS0sLHP/5x/u7v/o7u7m66u7v5u7/7O5xOJ9dff/1LvrYUQ681apaz1jFJ2u4n/MTXqrc9dxcMPSWiIo4gtGydd9xKMT5j5Gk4er+ojbF7RZTnd/9bLAPs/7kolp4tnFZVIRzKJVE8XczC2LOitmlWDJWLQnAYZU5IbEBEkzy1oiB6IZOHxL24a8F64jENz0s+KVyugx0istS8VUSKBh6FUBeEu8Q9vEh6J1Jc963HmUqLSMPGFj8ff1MPZcOgJeik0efg6GSaoNOGv7GVcEOIx6f2A7mq87SHnRztH2CVOwe1hrC3niFbKHFkIk1ryFnV0ZQplPjd/rGq9NksnTVuXDYzikJl6Ot8yrqY7m4gxnRkiyWCLitOqxmrWcWjmAm5rHTXenBYTByZSDOVLvCNB3o5MJJkc1uQ//u2VRVhs9Q9hFxWltV58DpO3JGUK5aZTBX5mytWVvn/1PnsDMWq36P3ntVaMTkE4d30wycGaQ45WVbnqaS4bBaVC9eEODaZYzrpIKVptIacTKULOMwmyrrB8nov3bVeDo5GMUpWQl4zflf1fUbzKQZjMSZTWbJZnX+9Z4CJVIG3rKnjry7tRNOL2HDgs7m4uGUdx5ITTGbitPvt/Pk5aynrBomcRjybWRQcTRXzjGRixAuZKjFkVk2sDrWwa0IMYf1fW67h6u4z6U+Mc0ZdN0G7h/6pDD1Kz5JpT6Bi0LgQRVHYVNuFYRhLiiWJ5I9GUUX7/FLdZLMse+spK57euXMnF154YWX5E5/4BAA33XQT27dv51Of+hS5XI6PfOQjxGIxtm7dyu9+97uX7DEEUgy99rD7sNt92DPTUF4ixRDtBW+TqDeaj8ksUlN6SURoQl2iKPnJ/zcnhADQ4bF/g5VvA6BsiIep2WQW7tGFFIztA9e8rjKTFVBEt9mJaNo8J4aWoGjA4FQGqy9PS61T1EklhkX6z/rCh08WpgawpIZRTTbRnaeaxGgTjJOLtefhy78/UhFCMFv8O8XZXWGaAg5qvXbyxRLxXImgx4mOk2PRnYvO0xRw0tDRAiSFn9M87tk3xm8PjFLvs/Ph87twWs0cGEliMykUSjpvW1/HSCLHxExq6cJlES5fXYfFpFIq6zxwaAKH1cTZXaKGpKwbmFVQFdGV1lXjxu8QEaGeWg91PjuD01ncdjMum5lYpshjR6f4l98dJlUQwznvPTBO/1SGuz56Nk7r0n8OFEU56ZwxEGaGQ7EsY8k8m1sDlWjYRy7s4oPf3VlpyW8OOrhhW1sltXfvgXE+8l+70Ga2/2bfKB8+r5O1bXZ8Njtuq4OeBhdGvZNMvszxRJaDgznaIy7O7AxhUhUsqkpnxI/F4sfvrE4jjmViHJgeYmC8hKG5+fu7j1KayTx977FjjGuDXLe1mZBaz3C0yNomPwG7m6HUFDazlU5fPUcm0jgsJi5cXoNJVUjkNEbiOeHNZHPS5qvBu4TA99qctHlrRGcZ0BNopCcwFwVuD8/93BfLGj869DC7J/rp8NVx3fJzCTu89MZHmcwmWBdpx2mZe22uE0VoJZKXSs1q0T7/KvgMXXDBBTOjgJZGURRuu+02brvttpf92lIMvVZxhaD7zXDkt3PrvE0iItR+7omP87WIqEk+AcefgfT44n1Mc584d8TSaIbBuQEPFlURUaLWauM6SnnRcjkz3ZrMlCi6jiwTQiY5IoqhF5opFlKiS05RSIU3MpCJ4skYtICoT8rFRCH5CxRD8WyRZ2Je6qhhxXwvJG+9SL/NH2lS1oRIeoFdBguLcUGkOs7tCqOqCjazSE/UW83sm6mf2dQaYNexuYnuZlXhHRubCPjsQPV7Ec8W+cp9hzgWFfO9/uvxQVRFpThjZHjlugY+d+VK3rSyjr3HEzT6naxvnjtHtlhm91ACi1lhU2sQu0WM6Tg4lmI0mcdmNtEUcDAcz5LXymS1EvVeB5lCiYjHjq4b+J0WClq5IoRm6Z1I0z+VYVXDSZzDn4fmgJO+yQzTqQJDsRxNAQeHx1OsavDyq788l1/vHSXktnLZqjr2jyRx282c0Rbk7379XEUIAeweSvDI0XHyFoN6r4NtDWJorKIouB1mQmUb0YyG32GpEm+RmVqqaKaI3aJWtrmtDtxWOytqHDywP10RQrM805/lhi029h1PM5kUg2Gj2TwNYSfNDWGyWpmhaBaTSalEcIaiWcYSeewWYQfQ4at2U59FURQ6/EtvW8inHtrOPQNPV5Z/07+Tr1z0IVLFHLlSkUJZqxJDEskppWa1aJ9/hR2oX02kGHotc/V/woP/CMceFRGULR8WBc6xYyc2Rpx8TtQS1W4VqalgF+KxPk9tb/mgSHulxjDpdnTVumQnUIXmrVDWSOkWnLqBKTUqUnWZIGSnhWGjpx4a1s8dEx+C8X0QaCfh6aR3Ik19wDn3abh2FeQbRdos2ifa3j0nf3CYTapIsXl7KsJH1w2OTqbx2C3UzT7Ls1EYelKIpPp1Jz3nrmMxvnTvYYZji8XQhctrKu3tU6kCv3x2BJ/DgtdhIVss80/XrOUD259iYDqLx27mc1euEmmhaJbeyTRrGn2V+pvvPDpQEULAzENZn/m/wd3PjrCh2c9QPMu5XRFWN3hJ5LSKEaLXYeHSlbUUSmX2DsdxWM20BJ3U+xysqPPQGHQyGM3yyJFJfvjEILFcibVNPr5ynSg0fODwBPU+B29Z28A/3HOoarq702qiI/zifGl03WAqUyDotGI2qficFs7pDjMwnSHotBDNFBmN58kWy5zRFmRZnYeybpArlqpSfku97x0hD50hE35btUhO5jVcVjNndi6Rhp3Z/vSxGE6ribO6RM2X22JnS10P48k8+z2L/YTqHXVsrO0gYMqgNMJYIs9gNIfV8KAbBmUKdNa4yBbLlHUDk6rQHnZht5hoXFAz9ccyno3z24FnqtYdTYwxlomxtX4ZhVIRtxyhIXmlUdRXrX3+1UCKodcydi+8+W+r1/X+XkQ9HP6lZ4C5Ztpw288H1QJ7fyra6LPTYv8NN8DyK4QAmTzEmd4GqFsr6jRKBZEKW1iHYLIwkS2zZyhKnc/O6toe0Q7vbRCt+tnpxT5BZruIzFjsJLIaqXyJqXQRrWywttGHarGDpQ6iA8KV298MG2+sOoVhGByP5/A5LHjsFtw2M2sbfUykxKgIy4y/zbHpLDaLOjfZXVFIFUoMjaWIuPP4ndaqOpZZJlJ53nv7E2TnuQ+bFAi5rXz0ou659mogUyyRLpbwF0ZYZc7jadmK2+3mNx87j4NjSZbXeSuzp/JamYJWZjiWJei0oqoKqfziB/F8NN1g/2gCBYWw28Jzoyl6J1IEXVa2doSwW0ysbfYzFM3yRN8Uz42msJhVLCaVbR0huuu8OCwq33q4vzJYds9wgr/60TN87fqNGIZIqzUHndx8djvffrS/cu1PXNLzguaIzad/KkPfVJqWoItldR60ss5YIk9BK7NrMMbG5gA9tZ6q+p0n+qcplHTO7Ahit4ifsYuX13DPvEG3dovK2zc0LXLbzhZLPHJkkt1DCZqDTi5bVVcZEzKLw2Ii4LJWBOR8jk6kaQo42NDi55nBOABWk8KbVtSy93ii8r3uqfOwttmHy2bm6YnDFMslvEY98YyOx2ahJeTEZTPTVeOmWNJ5dihO2GN7UcJokS+KAQaLUwMtnggW1YRFCiGJ5JQjxdDrjfAyUQNkXVAwlk+KlFTLNpHWsvtEa3nLNiFYzni/SIFlpqD/YVFs7QyhOEIi8mRxiloji3PJNJzdYsJqVnHZzCIq428WxdZDT4jC64XO1e5IpaOsVivz3FiCiWQem1mlUCzhMJVFZ5rJKiJCvuZF15yOJTh46CjuUD3behpI5TXu2T+Gw2zC77RQ73Pgc1joiLjIFEvktbIYmuoIMBY5h6HpPM8+N06j38k5M+mu+dz33ESVEALwWHR+9e4IkY62uZXZKM2xZ7iyrRm/ZnB4rIg6GmdTlwuH1cSGlur6ra4aN8l8ialUkcFolpDbSkfEhaqAfoJ0uN9p4YPndGAyqbSHXRybzpDMlzAMODSWZG2TH0VRaPQ72NASYDxVJFcooSoKpZKOVtZ5bjRVEUKz7B5OsG8kwVkd4YoR5P+5ciUXLY9weCLFtvYwKxuWbpONZYrce2CcqUyBWKZIwGXl2s3N+BwWDo6nmEjmWd8sXvt0ushIXHR8hdxWDFhUGCxqnwy0koHNLATB379jLSX9WR4+MkWt184XrlqFWVXYORClM+KuFFpnCiU+/8sDjCVFPdXf//oAt125ivN6aioi2GJS2dQq7qeg6cTSGm6HGDuyot5LMq/xkw9tY/tjAwxO53jr2npMqlJVzG4xqRVTxojDR7ZUoMHuwqQUCLmrC5rjuSKTqQKFko7ZUuDeY7txWqyE7F6CdjdrIm0sZDwT50B0iC5fPaqq4LU6qXX52VLXzZNjRyr7nd2wghbvyf2FJBLJy4cUQ683/PNEQyEFpaIotB59Vgic+rVztTPBDhGhsThFlAhmWvDTohW9eYuoLSqk5uajmW1z53XNRUa8dgvn9Sz446xr4rjijLFgNgqjuyHYWZXGKxsGZlW0UG9uC+CI7hM1Q7WrxZy01rNF/dEMsUyRw+MpepQh6vRxgiYb0MB0uojHbsZhMVfMFBVFwayqjCcKKCgVB+z2Gi9Oh5W+iQzmmdEOCwk4F3fr+Oxm/HVt1SuLGdRykTZ3gVLwDDLaBIpuPaH1kig4dqAbIq2iGwbL67x87spV/OjJY/RPZ8lr1cUrf3lRF121cwK3Jejk7esbeLxvmqFYjmhGY22Tj5DbRmeNhxs8dhRFdKjtO55k7/EE3bUeFEU4K8wSdlkIu224bCZ2HotR0HRWNnjIazor631LCqGJZJ7BaJY//d7ORaMqvrfjGHfechYuq4lldZ5KdKbGY6Orxk3AZcFhMS/phL22yUcyW+SJ/pkIY6OPgMvKf9ywiSMTaaxmlUePTHFgJEVXjZupdKEihn6663hFCAFkizrf2TFAS8g1FxGch26IWEteK/P0YAyP3VwRre/d1sZ0pki9175IIM8n4vTisTqxqCZqvYsLpCNuGysbvAxmjnP5HV8nOzN0ud4V4P9su65q34yWJ13Mo+llDMNgKpckVkjjtoo03jcv+Sjf3ncv+6cGWRNu5cZVF53wviQSycuPFEOvR8olGHgYxvaK6E/taiFkbAvqPlQRJWHgYSGIOs4XbfW6PlNLhIggtZ4lRNOsiOq9T7TTt5934uLm8sxDsv28uYLsQlrUKZU1IYZKRZg8iNMVYWtYw+yJYLdbIWkR+WgtIxywtb0UHGGKFh+/2TvGZ3+xj0JJJ2zV+NdL/KxuFnnrlqATm0Ul5LJVtZmHPVb2jej0TqQpaGVWNfpmajqcNPrnHmJjiTzxXJHuGg8mVeHiFTWsb/azeygu3i4F/vz8TiyxPiiGhbhUTUKA2jxg82JWVc5cJlKCJ3uQ1njseGwWdhydwmxSOb8nwua2IGd3hbj2m48tEkO/f26C958zl5/fP5JkPJmnNeziyFiKRLbIUDSL12Ehli0ScdtmhKCC02bC77DQHnbxFxd18/U/HEE3RNros1eu4ow2YZFQ0HQ0Xa94Di3VRp8tltgznOBbD/UtObNrLJnn7j2jvO+sNjHXbQZVVWgLn7gQPpop8kT/NFZVJauJAu5ktkQ0XSTgstAWcnHTd57kyX4xxHhFnYfvf2BL5fiJVH7ROc2Kwop6L08PxvjOowPktTLv2tzMm1bW4rCasJhUiqUyWrnMaKLE8mIZh9W0qN5nMpugpJepd895YI1n4uyfHiTi8FYiPJpeRtf1ijmioig0+B18fufvKkIIYDQTYzhVPS7kwPQQqWKONeEWHGYrBgZ1rgDBmRliDrOVW9ZfccL3TyKRnFqkGHpdYogCaLtftMB7G6oiRlGtRLJUptVuRTFZweYV9UdlDUb3QHYSrA4xkR0W1x55G0W0x3yS9t2R3aKIuumMOd8gu1fMN5vtOMhOQfI4RI+iqnaOxWvp6FiPpW61KKBWFEhPQC7Gvr4RjlPg/8wIIYCpooWP3V/k8TNt2BAP3HqfY+Z17BN+Rr5GnFYzfoeFfSMJVAXq/Y4qQ8DD4yniWY28VqJYMoi4bYTcNiwmlR99aBu/3jvK8XiO87rDrLONwfB+KOWgeZuItEFVp5x94aCvE2A1qwRcVqZSBe7ZN8YZbQGCLhvKEuXqE6kCTw1EUYBNrQEURbw9NW4bx8wZnj4Wo8Zr4/B4itF4nmV1HpqDTpxWM2d1zs2U+8QlPVy7uYn+qQwbWgK4bXO/4ls7guiGGNg6/5jJmVb+iMeG3Wyiwe+YmVa/NMdjOUyq8qLmAZkUhb7JDCrQEHAwFi1yZGhO4GTLxYoQAnhuLMXn7z7An1/QxYp6L5evrmf7joGqqNc7z2hmNJHjum8+TrEsfmbuPTDOt967iUtX1TGayOGwmqgPmDk0mmb/SILNbUEGplLESwmWhWuwmyzsmx7EMAyCdg+TuSTxQpomTxi31U7A7sEwDCayCQ5Fh9ExOLN+eZVb9GR2sUHdWCbG7ok+mjxhwg4vDe4g0VwKp9lOoaxR0stsqOl4WWYqSSSSl44UQ69HTBYRkVHU6mLn2AAc+g0HbG1kG7fiMZkIWa3QdrbY3vcAJEZEfY/tJHbqNcuf/x4cftCygAIjz2C46xgu+fDVbME7a4DlroNIAUpFHh+dYmfCxkZzlDe1h+fyS61nQewYzryf1FCK/ILe51hWYyyRpzU0L+qQiwuRVUhW3LvXNftpDoohnnULRl5MpQtkC2WW13tQpw4THHlWpAgdfuwWE+/Y2DS3c8kpCsnTEyLilYsvtgx4gZhmfHT+/YGjgBBHX71uA1esred7j1VPWd7Y7CddKKEg6opWNfhYXufFmEm1+Z1WbBYzYbeNTKGMzykextFMkf/ZOUQ8p/HWtfWsavDRFHDSFFic1lmqiLxUFkXAAOcvi2Axqaxs8PKOjU3sGzmwaH+zquCxm+mfytAReeEdaD6nhYtX1DCdLqLrBtOx6u+zXbXgtJjIanM1XH1TGeIZjWhKzFD70rXr+fofjpDMa/zJhibeu62Vz921vyKEZvnhk4M0B2389JlheiJeAuEE06UiGz1hknmNH+3bxd7J41y1cgXvXLWRTl8dJb2MzWzheHqajJan0R1iS10PADvHjvDI8edwmq2sibShqtXv46WtG9g/PVhZVhWFjbVdTGTF+xp2eGl0h2h0i7TzGXXdmBRVCiGJ5DWEFEOvVxa60x75Pfzo3VAu0mUNEWs6G//1t89tL5fEg90ZFOky08wn20IKxveL9Fl8UEREWs8W/kQ2z9IdayC8hQ78Ag7/DurXMJkscEhvx+vwsKV2Jt0w6249+RStjhADZj+N5TjkLXPnzUzB1CFWuGpo37SeL/7uUFV6psFnX/xgd4WhZiV5s5cnDk/inakHcdmW/nHe2BKgUNJFl1EOSJXn0nyL3ldbxSIguf9eBg73YW7ZzMqWmqX3Z2422EIGpjL8x4NHK8vFks7nf7mfX//luTx8eJL+GW+jiNsmhosC2zpClRSg+Co6ni7oiczUPimVcRDxbJErv/YIx+PC4flbD/XxzfduojXoJOy2EXBZmUwVGI5l6an1LHp/4tki48kCjQH7zGR78ZCPZopcsbaeRE7jx08Nki2WcdvMdEbcrGv2sarBWxlhsZDpdIFMoUxz0LHoYb98Zh5ZKlfi99Hq0SOqouBzWMnODhIGLllRS73PQTxbxGFVuWpDI6savBybzrK60YeiiHqshVhMKv3JUfJGEr/bR6ngoMHjoDXkxjAMmn0BxrIJvBZxP/MLlZscdeguDYfZSlYr4LTY8Fgd2M1Wat0BlgUbsajVkcGNtR28tf0Mdk/24bU5+bO1l3Ne0yp+3b+TaC5FWdcxzRNQ0jBRInntIcXQG4X7/1bU+QB1xWnq+u6Cw7+BlW+HiefEFHqzTXRumeYVU2emxXBVFBHpKWtCoIztFfVC7ectvtazP4aff2hu2dNA4P33UT8WJaxroPuFEIKZWiQ7nV4/nS4HDO2BkeNCkMHMgFoDpo9gd0f46nUbuPVnexhN5GkOOvjStesXj6FQFAi0YhTLlPUpigud9BZgt5jmUlt1aykFuiiZHcw+ksq6wfFYjpDbWhEMg9NZ9kc9TKfzNGZ0Vixsh55hYCpD70SaNU0+Im4bzw7HMasqa5p89E9nWPisHknksZhV7v7Lc/jhk0PsHopz955RvnyfmMfz7i3N/O1VaxhL5EER878URcFiXpya+/kzxytCaPZ1/Nv9vdxyQReJnMZmV5DxZJ7pdJGou1glhvqnMuw6FsVrs7Cy0VsRnLpu8MxgDMOAD57XzpXr6hmYErVKE8k8RybSNIeclQL2hRwYTRLLFDkWzbCpNbCkq7XbbsJtN5HOz0WBFMVgS0eAX+/NU9YNrlzXwAfP6+Dxo9PoBjSFxPVmi+FjmSID0xnetbmZO58ZIT1jJGlWFW4+u42Qv0TAbWZtuJbH++IAaGWdQklnWaCZ81u7F81We3YoxmN9U9hdGayuBA3uIGc1LCfi9LGpppPD8RH64uME6+YK3QslDZNi4u1dW/m/Z12PSVWxm63ohk7DTCRIlREgieQ1jxRDbxSSxxevS42LiFDsmEj9+Brn6oRS42KOmdUjokEju6HtXAh1CH8gT31lqGpB19k/0kckN05zy1rYsWBmWmoEy6G7WKXlAB0iEWF4qOXh+E5R2xPpEffiqZ9LO5U1sb1UFJGiXIzzelp45NaLmE4XCLttJy1SFqMpwpjVxemfE6Kq7B4vkshl2NoRwm0zM5bMc3g8RShjrXQc2a0q9poONnXZaQwsjnLMUp5RO2JopkEsW0RBQdcNNrUGcFlNZOa1729qnYtg3XhmG1+9796q8/3wySHWNvmYShVZUe8l7LYt2ZkFot18IVpJpy3srPj0tIddTKYLLLSxGYpmsZpVan32qrSiqiq0hpyUdIOhaJbjsTxNAQddNW52D8WFo7X7xJGNw2Np/um3BymUdDx2M19+13ouXiFGtOSKZawmhalMkUjAhJKAbF7HblV4amiKoMvKf31gK8vrvZVJ9mGPDcOYS/G1h120hZwcGk+Rzpeo99m5+y/O4YdPDpLTyly9sQmrWWVwvMz6llZcNhsbWwIYwIOHJvn4j3eTLpSwmVX+79tWcd2Wlsq9R7NFcpqGmSJWwGN1UCyX2T3Rz+HoMD67m8jM/DHDMEgWszw90UfY7uHsxhXsGj9KWstzRl03boudM+q6T/g+SSSSxTz00EN88YtfZNeuXYyOjvLzn/+cq666qrL9jjvu4Jvf/Ca7du1ienqaZ555hvXr178s15Zi6I1C23mw73/mlk1WWHaZEEnlonCwDs1zE7U6RYeZKyJa5Ceeg+Qw1K0U2+e5SSdLZaYyCbRCjuZCWvgYLcQwICxqLISwic90jOVBTc3ck7napdowID0JzgA0bq7UMZlUhRrtOAwMQ+0ayM2YOlpd6LpB33gMv6lIuKZu0RT3ExIfmnm9wsAwUyxjnhFaIZeVOp+9ahp5jcdOsEukIk82db4z4qYp4KjcxxltQVRFER4ydgv/fsMmPnfXfvqnMqxr9PGBs9srfkg5rUwyv1jQJHIlmgIOOmvcJxRChVKZi5bX8LU/9FYKzgGu2dREvc9REVxaWadcNhiMZWme5/uzrtlPQStT410sbLpqRORjOl1gPJmnPFO3tLElwOqZTr2lSOS0ihACSOVLfPqOvez4dIRYtsieoQRZrcThsRS5os4Z7QHWdXr5k2/sIJoRUc3vP3aM7Tdv4ZzuMIqisLbJX3WNiWSefSMJOiIu1jX7CbmEqeX/esuKyj5HxlOoqijaBgi4rGhlnU/fsbcSQSqUdD53134uW12H32klUcjg8WR5x8YmQm4LVpMZq8mCbujUOH14rHYiDj8t3ggj6SgHo8O0eiOYFAWLyYyqqDjMVorlEpaTzfCTSF4n6IbOWH6YbDmD0+Sizt6EeorHcWQyGdatW8fNN9/M1VdfveT2s88+m3e+85188IMffFmvLcXQG4UrvyRa63vvFYXLF31GRIFGdou0mGoStUHOkEiV2TxzqaqaZWLq/Wz6rJgRxdiBNrC6iFgtrKlpwpsyRMdY18UwPWcQh90Ha66eM14c3ikMHBs3CwNH02I/H6aOwE9uhIkD4vhL/xY2vhfDMDg8nsY1NUqTJQtThyAfBy3LtGc5mUKJaO9TaOU4YedFwtwRODKRIpHVWNngrUrNGIaBUkiJ0SAmC3S9qeJFVLl9i2nRurJusOOoqGtZyrBxPvMFmWdBu/p5PRHu/+sL0GYKleMzbtx2iwmfw8KqBi/7R5KV/SMeGzee2YrdbDrpNXcNxMhpZb79vs1859EB4lmNK9bW47aZ+eETg1yyqpbWkAu/08qKBi/uBekqn8MCJ5hC/8xgjJ8/c5xUvkR3jZueWmjwOQi4rNjVEz/oj06mq4QZiE618WQet82MSVVwW81EPHYCTgs9NW5+/NRQRQiBGE3ytT8c4Zzu8MLTA0LE6DqUdRY5UIPwFequ9dAZcVe9f5OpAlPp6sHHhZLO0ck0m1qDjKSjJEspwiYXbqsQ5X2JMZKFLKvCrVV1QiVdRPpsJgvnNc0NrVwdPsGIHInkdUZf5jA7oveRKc8N+XaZ3JwVvJgOV88pu+7ll1/O5ZdffsLt733vewEYGBh42a8txdAbBZsHrvzy4vW1q4Uo0stCeGSjQgwZhpjfBWJafWpERGasbjhwlzBmVFSoEZ+461P9osaob0IUV9evE2kuZwhWX1NdkGz3C0FlcZzYp+gXHxX3A8L48Zcfg44LKLgaGIpmUZV2ahrsmKwOTLGjHKeGR5+boNZro72uHp9mqbT0T6TyPHRoEk03CLtttIXFj3WprPNY3zQWFbb5W17QQNjZ+VMKVCJH80nkNEplnZDbJqJpxQw0bBBi8yRYTCqrGnykChoRj43JVAGXzcRfXdLNtx7qp3cizfI6D//7ipUnnB4/H6/DwmS6gN1s4yvXbcBlM5MtlvjVntFKbczUTKrxxYyKuO+5cT74vZ0Vp2yv3cy3btyMd0Y4jSVyPHxkiuV1XtY0VQvIZbUeXDYTmcJcWrDR76DB50BVFS5cLsSyrhsUyzr//fgx7ntu8SDheFYjN+MJtJDmoJOgy4pziW2D01kOj6fornVXdR9qZR2/w0zEbRMpwxk8NjPL67xMpgoELEGcflulzgdgIhMnWyqSLxWrRmK0eCPUOH3YFzYxSCRvAPoyh7l38heL1mfKae6d/AWX8PZTKoheLaQYeqNjMovaH8MQLs92v1hv6KI13TDEKI/UmBA7rrAQMUZZRIZmCXbAxEH4yfsgP9sJpEDnxRDrg/RYpc2dcJf4V0jD2D4ItlcLEcOAocer79Mow/CT2FdfzdomH2XD4NGRFHZzjjM713D08CTxnEZ3rRuLr5upYgdei5OJZJ7pdIFltW7MJjG5vXJKhLhRUDFqVj5vK/NsMfTaJh81XjtndobIFEoUSnrlwfz0sRhl3eCc7jD25IhIQZbySwotwzAwDFGH0zuRZjyZZ32zn3i2yLNDcZw2E29aUcfFy2sr95Ytlth3XMzfWmrGFogUmctmpsHnIJHTiGc1HBYTTquZt65twGxS+PFTgxgGvHNz8wv2RQL4fw/3VY0MSebF/WzrCDGZKvCubz7OsajogrtwWYT/eO+mSmTMZTPzpWvX89f/8yzJfImA08I/Xr2GbKGM3apW0o2qqjA0mWU6XcBjN2NWFUrzLnrNpsZFQmgilcekKARdVqwmhV3HYjisJlY1CEE2kcwzksgBxiILgSf7oxRLOn95cSdf+8NRJlIFIh4b//CONZhUhWeH4qgqXLS8tuq4tZF28uUiniVmg0khJHkjohs6O6L3nXSfHdE/0ObsOuUps1caKYZOFxSlegKxahIt9CAiLA0bhAiy+6Bpi0hPJUcg1Cn2cdfA7h/ME0IAhphY33q2mJmWmRIiy+YRA1/jg6KLzdCFI/VsO72iiFqg8b3V91gnDA5rvHaKJR1VSVc6ydrDLkJuK8vrvDx8ZJJSqUxNYYhDMRtFk4sz2oIV751ZLCaVs7vCjCVz7DwWY0W9t2JCWCzpjCfz1PnslMoGQ7Es2oxfTdkQxcMHx5IVT5/zuyOoqkJjwEGxpGM1qdC8VYihE0ScHu+LUizrbG71MzCdBkMhr5XxOiyE3FYURQiwlqCzYrs0lsgzlsijKOBzLG1rMDid5dh0lnqfneagg4NjKY5FM5zVGcZhNaGVZ1JJhoHpBTYyZYslDo+nq6I6c9vEuq//4UhFCAHcf2iSn+4a5j1b59JDl66qY7vbxm/3jdFd56Y95CaVL6OVDQJu8cczni3ys13DPHB4EqfVxAfOaWPXsTiT6QIX9IRpC7m46t8eZTCa5aLlNdx62TL2DCUAA4fFTE4roxsGDm1OMB0aT1HQdDa3BatmjQHYzCqGAddtaeU9W9sYT+WJuG2YTSqGYdDgdyxZm+W02HBaFqfiJJI3KmP54arU2FJkyinG8sM0OFpOut/rDSmGTmes8/x7PHVz/zdbRbQjMzknhkCYNi7E4oS2s2DwcfjVXwkB5AjClg/BmR8Vois5Kgq5GzaIKJSvEd76JfjBO0U0ChXOukWMFkkMQ+I41rrVnNsVroiE5uDcva5s8JKLjuLJDLDc4iMTXlcZQjpLMq+xZyhBY8BBOl8ikdVI5LSKGBqYzjA4naVQKmMYQmA0BR2c2xPGZjZxeCyJrhs4rCYCM5PnAXrmzQ9bNP5kAbOTyH+1Z5TB6SzrW/zYLGJMRFvIxY+eGsRpNXP56rpKIXNTwImiKNR5bUyk8vgclkVF4vV+B4WSTkvIic1sQlHSVe3bFpPKZWvqUBAt+Ymsxjce7GXPUIJNrQH+/ILOqjb73+4f469/8iypQgnrAvVkNausmhly+nhflIU8N5qsWjYMgzWNPjoiLhRFwW0zk8qVqiI9j/ZO8R8P9VWWnx6M0xZycuubl5HVynz0h89Uao9+umuYYqnMRy7swqTARKpISTfw2E0sq50zDl1W5yGdLy0ZTdvcFqxarvfNRXoURTnhoFqJ5HQjW868rPu9npBiSLIYV1hEh+ZHPPofhr77F++77DJo2Qb/tk0IIYBcFB7+F6hbDR0XirRYZlJEjhJDQmg1nwGfOAiHfi06zmZTbKkxcXwujupbOuJS47GDsxmm8wQdEQqaQqGkV6WD8sUyuWKJPcNxVtZ7WdssfIBmqfXYyRbLhFw27BYTBiI68lR/lGV1HkYTeewWE+cvO7HZYqmsM5bMk8hposBYzQhx528DVWVbe4hETuPX0SwGBrGsxv7jCbZ2hNANg1qvHZvZVBlGimFgPfYg7ZOHmIpsY0+hnrDHxvpmf9V13TZzVcH3uV1hiqUyO45OEXbb6Kn1VPkA/en3nuKpgRgAj/VNs38kwXduFnO/CqUyn/7ZHlIzXVbFsoHNLKa/u21mtnYEaQo6ZwqT3RwaT1Xdy9b2UNXys8MJYpkiWzuCldonv6taoNz17Mii93JgOssjvVM0BhyLirDv2T/OV9+9EYCOiMGhsSQjcTFnLlwZFGunxrPotBKJ5EXgND1/XeWL2e/1hBRDkqVxVT/kOPqHxfvYfSK1Nd0Lk89Vb9M1+PENYPOJzrbI8jmPo9mvFjt0XwrRPvDNjMSoXS0ExfxI1VKYzFCzgqGpDEcnUqTypapP+DWp/XTkotw13YBiQFvExVg8z5om4Vzsc1oI5CzsOhZjdaOPnloPj/RO8tRAjOl0AbfdguN5am0eOjzF4/1T1HsdrG/xE9APiDosiws8taiqQt9UmlqvnbM6QyTzJUJuIXxCbhtvXduAxSRmfGllnT3/9b9wHf0Vy9UhQih0nvX3WLe87+TvA6IGp1A2yBbKxNVqZ+3eiXRFCM1y/6FJxhIiRXg8liO2YCBroaTzN29ZwepGX5W79ufeuopiqcy9z01gVhVuPLONt66trzpWTIs3FplNzudE7+vTg3Gu2tC0aH3DvKn0JlWhI+LGYTXT4JdOzhLJy0mdvQmXyX3SVJnL5KHOvvj39OUgnU7T29tbWe7v72f37t0Eg0FaWlqIRqMMDg4yMiI+UB06dEjcd10ddXXP88x4HqQYkrwwQl2L19VvEKkirQDeJuFTtJBCAh79MlzxJeE9ZOjwi1sg2g+dF4hhqKp5zqPIYqcv7qGUS9Nd415c9Dw7HdwsIgIRj41ETsNpNfGdR/vJFstcubaB/j29/PejvTxYWolusvEnGxqp89krU8sdVlPFtHB2pMOmlgCGbuDQs6xpr8VsMdM3mWYqXWRt02J/HQMDr91CR8RFV40btC6M9ASKc05ItofduG0FmoLORYW98+tUisf3Eet9khw+YAgFg7Zn/4VDa97NUa1M5/PMAfM5LGztCC5KqdmWqIVRFTDPpMOag85Kd9ssfqeFrho3ybzGRDJPS9CF1awSdFv5+rs38uCRKVAMzuoIV31/9h1PkMgW2dxaPRolr5UZTeRp8ItI2PvPaefuPaNVRdMAa5t8bGkPcsnKWu49ILrMzKrCrZdVz8qzW0y0h994n0wlklcbVVE5K3jxkt1ks5wVvOiUFU/v3LmTCy+8sLL8iU98AoCbbrqJ7du3c9ddd3HzzTdXtl933XUAfO5zn+O22257SddWDONkn+FODclkEp/PRyKRwOuV+frXBaUC/Pc10P+QWPbUw3vvnJlUXwNH74Ofvl+05C/Fn++AQ7+Bh/5ZOF7P0nYeXPxZ0d5/7FGMvXfwh1wnRtelnLO2C5tZZTxZwGcu4bDbRFpNL8OKt1WG1E6lC7zta48wkhBmkHaLioUyJa1Abmboht2s8vm3ryToFsXZ02khUM7qDFULiOmjMHUYAu1Qs5ynBqIkshobWwMEXdWFuYVSmVyxXCnYTRdEmi3isS3yLXpejtxL4r9uwoqGQxG+OwYK97/jWbDauXBZTZXwODadIZkrsaLec0JTyLxW5pnBOP/x4FEePDxZWX/Npib++Z3rKss7eqf4q5/sZjwpWvG/eM1aLlxew7NDcSZTBXpqPdT57Ow4OoVW0vE5LYwnC7htZs7viVQiR08PxohlipzRHsRrt3BwLEkso+GxmxlL5GkLOyuGjofHk/zlD3dzcEyk3VY1ePnO+86gxmvHMAweOjLFYDTL+d0RWkKLh85KJKczp/oZurTPkIezghe9IdvqQUaGJC8Usw1u+qUolM4noP18keaapefN8InnoPc+uOODIk02S9MWQIWhJ6qFEMDwk2KC/J7/gTv+FAVYZ3jRB/8L+8rfMpa1s69/hGDqEBtbg8IMUi+JuiOTiJb8ZOdQRQgB5DWdPAowd3/5ko7FZOKMtiB9k2m8djMBlxWTojAUzRLxiNoh7D5RFD4zMmRNo49csTxX1zMPm9lUJaTKuiHGcpR10PW5+WwvhNaz8TltkJtXmNh1CVaHE6fVvChCNhTNkdfKtISc+ByLr7NnOM77tz/FVFoIqzM7QrSFnWxoCXD1xuoQ91ldYR699SJG4nnq/XYyhRIPHJog5LLSEnJS57OjKGLGViyrYSCiaZOpAnuOJyo1Teua/GhlHYtJ5c//axe/2TcGQMhl4X1ntWM1q3RGRLSvp9bLPR8/j9FEjlS+VFWYrigK5/dEkEgkrw4drh7anF2vuAP1q4kUQ5IXR8u2E2+ze2H1nwAG3PPpOXPGq/4dPLVCQB35XfUxDjELjMfm5p2FlSRkkrDvDvzN56KWMiTyZR4YKrOy7nxq7LoQLDMkFtS8AFhMClp5Lujpc5jpiLjxOSyV+WO9E2kePDyJbhgk8w7hWeMKQ8f5FEplnu2PEnBa6K59YZW5PoeFc7rDWIafgKNZaDunWjAuwWz7/tomH5Ybfgb3/h+yE/0cqX0LgQs/Rj6ho+mLX9/aZiHSTuRF9PlfHqgIIRCF05+8bBkbZ177QswmtRKBKZZ0SmVRKzRfpJzTFUYr60ymCwScVp7onyaeLZLOF3miP0bILYq9f7VntCKEAKYzGg/1TrKmyYduUNXuX+9zUP8ig2gSieTUoyrqG659/mRIMSR5+Vn9DpHGKiQrw14B2PYROHg3DD42t+78T4mvpSKLyMewJ3oJZQvsc67Cl+4lmyxA54Wi6HryMM8pXdT77JhUhfK8GpT3n9POvfvH6ZvK0Oh38IWrVrNuXleWYRgcm86QK5ap9dmq2q1BRJeSOQ3dMGid50m0sO5nISJSZFA1GbVcEpEyS/U1UnmN+w6MYzIp+J0WYlob3e/6OcmcxuRYCsWwsbbZjs20uODYa7fgtS8thIBK+mk+h8ZSJxRD86nx2lnXDB5b9flVVcGmmioT7re2h1AVhYePTLJ7KMYZbaJWamHHGcB0usiZnaGKb5REIpG8lpBiSHJqmHW+no+qijqjfT8VBdTdl0LLVrFt0/vgnlvn9rX5YN31UEhR57bgVHzUxgfxzhorloqAgVYq0hwM8dXr1vO9x46RKZa4dnMzN57ZxqcuXcadzxwnkdcqXVyzKIrChpYAJV2vakOfpaCVURXojLjom0ozNJ2lfyrDmkbfkimzKlrOFIXiMzVNDD0BhZSIFM3zJjKrKl21HpwzRonxrMYDhyZ4+lgMm0XlfWe1L3lvJ6NQKjOeKLClPcgfDk7Me72wtT14kiPnGEvkeHY4gcdmxu+0YjEpS0bHXDYzA9NpHu2dwmkzs6nFD4iU3FfvO1K17zld4Rc+VFcikUheYaQYkryyWOyw4YbF67f9mUiz7btDuF2f9RfgrQfqqQVqAeovmts/sgwCrawy2dHKwmPoirUNVafUdAOX3UxGK9MaXNx9tLAgej4TqQL90xmcVjPL6z1MJAvsHorxtT8cQVUUrtrQyLu3nCCErKocjxc4Mh5ldaOPsMUB5QKoZvYOJ0jmNTa3BXBYTbx5lWgHzRRK/M9TQ9z+6EDlNL/eO8ZvPnYubpuZ4/EcqqLQ8DxzxvqnMgxHc3zg7DaSuSI7j8XxOy186s3L6ViiI00r60ykCtR6hCPzdLrA7sE446kC4UYvI/EcqgpdS3X2AfGMRoPfQWfEjWcmZXdmZ4hPXbaMb9x/lEyxxMXLa/nrNy876X1LJBLJq4nsJpO8oUnkNMyqUtXq/UKYShV48PAkDqsJv8OCSVW48dtPVhkCXre5mX+4Zm1lOa+VK+33R8ZTHJvOsqzOU+We/ciRSeJZjQuX11TuqXcixR1PD/PTncNMpKvThf949RouWVnHb/aOEs0Weevahkpb+URKFI3Pjx4lshqHx1NMpvJ4HRZWNfhw2cxLjpsAODyeYnA6S1tY2ANkiyWeGYxT67XTVeNmKl3ArCqLRlzMUizpJPMaYffisRWFUpliScdzknSeRCJ58chn6MuPjAxJ3tCcqMD4+Qh7bJzdFWY6U2A0nufhI5OLnJHvPzzBVLrA3uEEmaKGisrKBi8lXSfgtFDnC1aEQLZYQlWUGd8fg8eOTuOymUkXNG75wdMUS0t/JhlL5OmbTGO3qJR1o1JzkyuWZuZ1wfnLrJVaJp/TwrpmP//9xDEGY1nWtwROKIQAajw20oUSkRknZ6fVzNld4bn3YQmRMx+rWT3hPgu77SQSieS1ihRDEskJqPPZqfWK4ur5A0pnafA7yBbKjCXyTKTzuK1mmoMO4cFjN7OtQxQU57Uyj/dNY1JVGv12cppOTiuT00rcdteBEwohj81MZ8RFPKthN5tYVe/FZlbZO5ygfyqN12GhKeBY0sxxQ7OfaHaJovQF+J1WNrbICewSieT0RoohieQkKIpC0GXlHRsa+c+H+xid8TMyqQp/cVEXLSEnVnOEsUSeRr+DoNuGw5qpqkeymFTcNgs2s0pXjYeuGg/Fkk6mqHE8nlt0zUa/nXqfgxX1XgolnUa/heOJMjaLCbtZ5dB4kuOxHG9b33hCJ+Z1zX7KunFCQ0aJRCKRzCFrhiSSF8hUusCPnxoikdO4cm0Da5peukHOO77xKE8PxivLDouJe//qPI5NZ9lzPE7AaeXi5TU8MxQnp5VpC7kwqQp2i1pxc5ZIJKcX8hn68iM/NkokL5Cw28YtF3bxN29Z8bIIIYAvvWs9G5rFuZoCDv7tPRtoCjrZ0hHkstX1vG19AxGvnXO7I7htZlL5EqsbfVIISSSSU4uuw8hx6D0ivur68x/zEnnooYe48soraWhoQFEU7rzzzso2TdO49dZbWbNmDS6Xi4aGBm688cbK0NaXikyTSSSvIq0hFz+/5RyyxRIOi6nSvm4xqVUpMIfVxNaOEGZpWiiRSE41fX2w4xHIzBsP5HLBWedAR8cpu2wmk2HdunXcfPPNXH311VXbstksTz/9NJ/97GdZt24dsViMj3/847ztbW9j586dL/naMk0mkUgkEsnriFP6DO3rg3t/e+Ltl7z5lAqiWRRF4ec//zlXXXXVCfd56qmn2LJlC8eOHaOl5aWNDpFpMolEIpFIJCIVtuORk++z49FXJGX2QkgkEiiKgt/vf8nnkmJIIpFIJBIJjI1Wp8aWIpMW+73K5PN5Pv3pT3P99de/LNExKYYkEolEIpFAdrGf2kva7xShaRrXXXcduq7zjW9842U5pyyglkgkEolEAk7n8+/zYvY7BWiaxrXXXkt/fz9/+MMfXraaKRkZkkgkEskbH02DUgle+Z6h1w919aJr7GS43GK/V4FZIXTkyBF+//vfEwqFXrZzy8iQRCKRSN645HIwMQ6JOJR1cDigtU18lVSjqqJ9/mTdZGedLfY7BaTTaXp7eyvL/f397N69m2AwSENDA9dccw1PP/00d999N+VymbGxMQCCwSBW60sbKyRb6yUSiUTyxiSVgoEBURRsUsRD3GaHhkYIBqFchukpsU5V4JFHYHwMIhEhCgKBV/sVLMkpf4Yu6TPkFkLoFLbVP/DAA1x44YWL1t90003cdttttLe3L3nc/fffzwUXXPCSri3FkEQikUjeeGgaHBuA0RGwWGH5ckilwWaDcFgIo3wejvZCMgGDgxCPzx3v9cJ114Py2jM6fUWeobouusayWVEjVFd/yiJCrwVkmkwikUgkbzxKJUgkoLcXikU4+BysWg3tHXMPdYtFtIrH4tVCCCCZFFGjcOSVvvPXBqoqIminCW9cmSeRSCSS04+pSTFLy2yGfXuFEAIoFMTy/EiKpoHdCQqLI0CKAs7nKSaWvGGQkSGJRCKRvDEoFsVg0UJRFEvn84u3J5MiTQaiuFoxoLkFHE44dHBu33XrX9UWcskrixRDEolEInn9MzoC9/xmLhI0MS7SYJo2t4/VKlrHczmRQpsYh+i02K++AYIhKGnQ2AS1ta/O65C8KkgxJJFIJJLXP08+OSeEAKJR6OoWRdSaJgTP1jNhfByyGdFB5nKJjqlUCtx5aGgQXWYm06v2MiSvDlIMSSQSieT1Tz63eJ3VCu+9CeIx8PnB0KH3qIj+WK3Cb6ixSXRNmVRIp0WLfSj8St+95FVGFlBLJBKJ5PVP2xIeNN09IiIUqREt4umMEEHptKgp6u+DYgFaWsFiExEkVUaFTkdkZEgikUgkr382bRbRoSNHRAps0yaoqxPbYjEYHga3G6wWkQqbrRlKJETLfT4v0mavUaNFyalFiiGJRCKRvP4xm+H8C8W/haSSMyIoILrGMhmwTEMsCm6PGM3h9wkRJTktkWJIIpFIJG9sIjWgl8VICRCpMrcbwiHweIWnkF9GhE5npBiSSCQSyRubpYayWixSAEkqyAJqiUQikUgkpzVSDEkkEolEIjmtkWJIIpFIJBLJaY0UQxKJRCKRSE5rpBiSSCQSiURyWiPFkEQikUgkktMaKYYkEolEIpGc1kgxJJFIJBKJ5LRGiiGJRCKRSCSnNVIMSSQSiUQiOa15VcZxGIYBQDKZfDUuL5FIJBLJ65bZZ+fss1Ty0nlVxFAqlQKgubn51bi8RCKRSCSve1KpFD6f79W+jTcEivEqSEtd1xkZGcHj8aAoyit9eYlEIpFIXrcYhkEqlaKhoQFVldUuLwevihiSSCQSiUQiea0gJaVEIpFIJJLTGimGJBKJRCKRnNZIMSSRSCQSieS0RoohiUQikUgkpzVSDEkkEolEIjmtkWJIInmdcsEFF/Dxj3980fo777yzYlmxfft2FEVhxYoVi/b7yU9+gqIotLW1LdqWy+UIBAIEg0Fyudyi7W1tbSiKgqIoOJ1OVq9ezTe/+c3K9tHRUa6//nqWLVuGqqpL3qdEIpG8VpBiSCJ5g+NyuZiYmOCxxx6rWv/tb3+blpaWJY/52c9+xurVq1m5ciV33HHHkvt8/vOfZ3R0lD179nDVVVfxZ3/2Z/z4xz8GoFAoEIlE+MxnPsO6dete3hckkUgkLzNSDEkkb3DMZjPXX3893/72tyvrhoeHeeCBB7j++uuXPOb222/nhhtu4IYbbuD2229fch+Px0NdXR1dXV184QtfoLu7mzvvvBMQkaOvfOUr3HjjjdIhVyKRvOaRYkgiOQ34wAc+wI9//GOy2Swg0meXXXYZtbW1i/Y9evQojz32GNdeey3XXnstO3bsoK+v73mvYbfb0TTtZb93iUQiOdVIMSSRnAasX7+ezs5OfvrTn2IYBtu3b+f973//kvt++9vf5vLLL6/UDF122WVVUaWFlEoltm/fzt69e7n44otP1UuQSCSSU4YUQxLJacL73/9+vvOd7/Dggw+STqd5y1vesmifcrnMd7/7XW644YbKuhtuuIHvfve7lMvlqn1vvfVW3G43DoeDW265hU9+8pN8+MMfPuWvQyKRSF5uXpWp9RKJ5KXj9XpJJBKL1sfjcbxe76L173nPe/jUpz7Fbbfdxo033ojZvPjX/7e//S3Hjx/nXe96V9X6crnM7373Oy6//PLKuk9+8pO8733vw+l0Ul9fL4cuSySS1y0yMiSRvE5Zvnw5O3fuXLT+qaeeYtmyZYvWB4NB3va2t/Hggw+eMEV2++23c91117F79+6qf+95z3sWFVKHw2G6urpoaGiQQkgikbyukZEhieR1ykc+8hG+/vWvc8stt/ChD30Ih8PBvffey+233873v//9JY/Zvn073/jGNwiFQou2TU5O8stf/pK77rqL1atXV2276aabuOKKK5icnCQSibyg+9u9ezcA6XSayclJdu/ejdVqZeXKlS/uhUokEskpRoohieR1SltbGw8//DCf+cxnuPTSS8nn8/T09LB9+3be+c53LnmMw+HA4XAsue173/seLpdrySLoCy+8EI/Hw/e//30+8YlPvKD727BhQ+X/u3bt4gc/+AGtra0MDAy8oOMlEonklUIxDMN4tW9CIpFIJBKJ5NVC1gxJJBKJRCI5rZFiSCKRSCQSyWmNFEMSiUQikUhOa6QYkkgkEolEclojxZBEIpFIJJLTGimGJBKJRCKRnNZIMSSRSCQSieS0RoohiUQikUgkpzVSDEkkEolEIjmtkWJIIpFIJBLJaY0UQxKJRCKRSE5r/j+4iySzIdzzEgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 4- calculate crosscell ns & ls\n",
    "metrics4 = xc.tl.crosscell_nsls(\n",
    "    cell_embedding_ad='./data/1_within_sample/m_brain_paired_rna.h5ad',\n",
    "    input_folder='./data/1_within_sample/train_data',\n",
    "    model_path='./data/1_within_sample/train_out/E1000best_model.h5',\n",
    "    output_path='./data/1_within_sample/eval_out',\n",
    "    cellembed_raw='X_pca',\n",
    "    celltype='pc32_leiden',\n",
    "    save_pred=True,\n",
    "    plot_umap=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NS and LS metrics are computed based on the predicted test data. If `plot_umap=True`, UMAP visualization of test cell predictions by XChrom will be displayed. In this plot: larger points denote test cells, while background points denote training cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting adata.X to dense array. For large datasets, consider pre-computing and saving as sparse matrix.\n",
      "Denoise done! denoise shape is: (40313, 4878)\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)=0.5119,label score(100)=0.7515\n",
      "neighbor score(50)=0.4508,label score(50)=0.7994\n",
      "neighbor score(10)=0.2938,label score(10)=0.8547\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 5- calculate denoise ns & ls\n",
    "metrics5 = xc.tl.denoise_nsls(\n",
    "    cell_embedding_ad='./data/1_within_sample/m_brain_paired_rna.h5ad',\n",
    "    input_folder='./data/1_within_sample/train_data',\n",
    "    model_path='./data/1_within_sample/train_out/E1000best_model.h5',\n",
    "    output_path='./data/1_within_sample/eval_out',\n",
    "    cellembed_raw='X_pca',\n",
    "    celltype='pc32_leiden',\n",
    "    save_pred=True,\n",
    "    plot_umap=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NS and LS metrics are computed based on the denoised scATAC data. This visualization is generated after denoising the entire dataset, `plot_umap=True` will display UMAP results."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "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": 4
}