{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6e1238fc-aa4c-4fd9-8d42-b48330e51009",
   "metadata": {},
   "source": [
    "## **3. Cross-species analysis**\n",
    "\n",
    "In this tutorial, we demonstrate how to perform prediction and evaluation on cross-species scenario. We train XChrom using sample from one species and evaluate the model on samples from other species, showcasing the model's performance across different biological systems (effectively covering two dimensions: cross-sample and cross-species prediction).\n",
    "\n",
    "We use a publicly available single-cell multiome dataset collected from the motor cortex of human, mouse, macaque, and marmoset. Each species contains multiple samples. In addition to providing raw sequencing data, the authors also offer pre-processed and cell-annotated scRNA data for each sample. We extracted paired data and assigned the annotated cell types to obtain the raw dataset for our study. We then concatenated scRNA data from different samples within each species, and performed cross-species integration based on 1-to-1 orthologous genes using Harmony. The resulting batch-corrected cell embeddings are stored in `RNA.obsm['X_pca_harmony']`. The processed scRNA and scATAC paired data (after filtering and batch correction) have been uploaded to https://doi.org/10.5281/zenodo.16959682. For detailed procedures, please refer to https://github.com/Miaoyuanyuan777/XChrom_analysis.\n",
    "\n",
    "- **Data Preprocessing**                           \n",
    "As an example, we extract two samples from human and mouse respectively, labeled `m1d1` and `mop3c2`. In this tutorial, we use mouse (`mop3c2`) as the training set and human (`m1d1`) as the test set. For the training scATAC data, we split it by cells and peaks to generate training/validation data for model training, following the same procedure as in the `1_Within-sample analysis` tutorial to create model inputs. For the test scATAC data, we use the entire test set for evaluation. For the scRNA data, we directly use the batch-corrected cell embeddings (`RNA.obsm['X_pca_harmony']`) as inputs for model training and prediction.\n",
    "\n",
    "- **Model Training**                                   \n",
    "XChrom takes two inputs: ① DNA sequences (one-hot encoded, 1 × 1344bp) and ② cell embeddings (cell number × 32). The model is trained to predict chromatin accessibility probabilities for each genomic region across all cells. Importantly, the cell embeddings used here are derived from batch-corrected scRNA-seq data from both samples.\n",
    "\n",
    "- **Prediction and Evaluation**                                                                \n",
    "After training, we evaluate the preprocessed test sample from two perspectives:                                          \n",
    "    - **Binary classification metrics:** auROC and auPRC, as primary evaluation metrics, which were summarized at the overall, per-cell and per-peak levels.\n",
    "    - **Cell state fidelity:** Using the neighbor score (NS) to quantify cross-modality neighborhood concordance by comparing scATAC-seq and scRNA-seq data, and the label score (LS) to evaluate the consistency of cell-type labels within local neighborhoods. Both metrics depend on a parameter k, the number of nearest neighbors in the cell-cell graph.                           \n",
    "Together, these metrics demonstrate the model's ability to generalize across species.                                 \n",
    "\n",
    "- **Interpretability analysis**                                                \n",
    "We perform in **silico saturation mutagenesis (ISM)** on sequences of interest, predicting the change in accessibility for every cell when mutating each position to its three alternative nucleotides. For each mutation, we compare the mutant prediction with the reference to obtain the per-cell change in accessibility, producing a per-base ISM importance score that quantifies each position’s influence on the model’s predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7b1655-8d08-4439-adc5-0fd442b5d990",
   "metadata": {},
   "source": [
    "### 1. Download Data (Human: m1d1, Mouse: mop3c2)\n",
    "\n",
    "- The original data is available at [GEO accession GSE229169](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE229169). We have processed this data by performing batch correction using orthologous genes to generate raw cell embeddings for each sample as model inputs. The data were filtered (filtering threshold: 0.05) and are available for direct download at Zenodo: https://doi.org/10.5281/zenodo.16959682. In this tutorial, we use only the human sample (m1d1) and the mouse sample (mop3c2) as examples, training XChrom on the mouse data and evaluating on the human data.\n",
    "- Download the human sample files:\n",
    "  - `m1d1_atac.h5ad` and save to `'./data/3_cross_species/m1d1_atac.h5ad'`\n",
    "  - `m1d1_rna_harmony.h5ad` and save to `'./data/3_cross_species/m1d1_rna_harmony.h5ad'`\n",
    "- Download the mouse sample files:\n",
    "  - `mop3c2_atac.h5ad` and save to `'./data/3_cross_species/mop3c2_atac.h5ad'`\n",
    "  - `mop3c2_rna_harmony.h5ad` and save to `'./data/3_cross_species/mop3c2_rna_harmony.h5ad'`\n",
    "- Download the human genome file from:\n",
    "  [https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz](https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz)\n",
    "  and decompress it.\n",
    "- Download the mouse genome file from:\n",
    "  [https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/mm10.fa.gz](https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/mm10.fa.gz)\n",
    "  and decompress it.\n",
    "- The raw cell embeddings are stored in `RNA.obsm['X_pca_harmony']`, and cell type labels are available in `RNA.obs['cell_type']` for computing LS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1819b1d6-69b8-476b-bd4f-3cb381aec0eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import xchrom as xc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ea446d-06df-40d9-bf46-f2311bedc37b",
   "metadata": {},
   "source": [
    "### 2. Read and filter data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808cbce3-95be-40be-a24e-b7d731cc61f1",
   "metadata": {},
   "source": [
    "The data hosted at Zenodo: https://doi.org/10.5281/zenodo.16959682 have already undergone comprehensive filtering and preprocessing, so you can skip the filtering step when using these files.                \n",
    "\n",
    "Ensure the `species` parameter matches your input data (human or mouse). XChrom uses this to remove sequences that don’t belong to the specified species—e.g., keep chr1–22, X, Y for human and chr1–19, X, Y for mouse . Also make sure chromosome naming schemes are consistent (e.g., chr1 vs 1).              \n",
    "After this step, the scATAC-seq AnnData will include a per-cell peak count in `ATAC.obs['n_genes']`, which XChrom automatically uses when generating training inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae43dd09-3dea-43fc-a578-d53c2de43281",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNA data after filtering: View of AnnData object with n_obs × n_vars = 6668 × 1193\n",
      "    obs: 'cell_type', 'n_genes', 'sampleID', 'species', 'batch'\n",
      "    var: 'feature_types', 'ortho_h_id', 'ortho_h_names', 'n_cells'\n",
      "    uns: 'batch_colors', 'cell_type_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sampleID_colors', 'species_colors', 'umap'\n",
      "    obsm: 'X_pca', 'X_pca_harmony', 'X_umap', 'zscore32_perpc'\n",
      "    varm: 'PCs'\n",
      "    obsp: 'connectivities', 'distances'\n",
      "ATAC data after filtering: View of AnnData object with n_obs × n_vars = 6668 × 107451\n",
      "    obs: 'cell_type', 'n_genes'\n",
      "    var: 'ids', 'names', 'feature_types', 'chr', 'start', 'end', 'n_cells'\n"
     ]
    }
   ],
   "source": [
    "mop3c2_rna = sc.read_h5ad('./data/3_cross_species/mop3c2_rna_harmony.h5ad')\n",
    "mop3c2_atac = sc.read_h5ad('./data/3_cross_species/mop3c2_atac.h5ad')\n",
    "_, mop3c2_atac = xc.pp.filter_multiome_data(\n",
    "    ad_rna = mop3c2_rna,\n",
    "    ad_atac = mop3c2_atac,\n",
    "    species = 'mouse',\n",
    "    filter_ratio = 0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9161c08f-6a79-444f-aadc-c2fa4076950c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNA data after filtering: View of AnnData object with n_obs × n_vars = 4489 × 1193\n",
      "    obs: 'cell_type', 'n_genes', 'sampleID', 'species', 'batch'\n",
      "    var: 'feature_types', 'ortho_h_id', 'ortho_h_names', 'n_cells'\n",
      "    uns: 'batch_colors', 'cell_type_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sampleID_colors', 'species_colors', 'umap'\n",
      "    obsm: 'X_pca', 'X_pca_harmony', 'X_umap'\n",
      "    varm: 'PCs'\n",
      "    obsp: 'connectivities', 'distances'\n",
      "ATAC data after filtering: View of AnnData object with n_obs × n_vars = 4489 × 37363\n",
      "    obs: 'cell_type', 'n_genes'\n",
      "    var: 'ids', 'names', 'feature_types', 'chr', 'start', 'end', 'n_cells'\n"
     ]
    }
   ],
   "source": [
    "### Filter data\n",
    "m1d1_rna = sc.read_h5ad('./data/3_cross_species/m1d1_rna_harmony.h5ad')\n",
    "m1d1_atac = sc.read_h5ad('./data/3_cross_species/m1d1_atac.h5ad')\n",
    "_, m1d1_atac = xc.pp.filter_multiome_data(\n",
    "    ad_rna = m1d1_rna,\n",
    "    ad_atac = m1d1_atac,\n",
    "    species = 'human',\n",
    "    filter_ratio = 0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "54afac38-ffe0-4a0f-84db-adab52704377",
   "metadata": {},
   "outputs": [],
   "source": [
    "m1d1_atac.write('./data/3_cross_species/m1d1_atac.h5ad')\n",
    "mop3c2_atac.write('./data/3_cross_species/mop3c2_atac.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b27be4-50a3-49b9-9c8d-058329f214b7",
   "metadata": {},
   "source": [
    "### 3. Training/test data prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa06fc52-256f-44bb-a8ba-c2de2f19945c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train/test data is saved in:  data/3_cross_species/train_data\n",
      "successful writing bed file.\n",
      "successful writing train/test split file.\n",
      "successful writing train/test anndata file.\n",
      "successful writing sparse m.\n",
      "Successfully saving all sequence h5 file...\n",
      "Successfully saving trainval sequence h5 file...\n",
      "Successfully saving test sequence h5 file...\n"
     ]
    }
   ],
   "source": [
    "## Generate data for model training\n",
    "train_folder = './data/3_cross_species/train_data/'\n",
    "input_fasta = '/picb/bigdata/project/miaoyuanyuan/mm10.fa'\n",
    "train_atac = sc.read_h5ad('./data/3_cross_species/mop3c2_atac.h5ad')\n",
    "dict = xc.pp.process_train_test_single(\n",
    "    ad_atac=train_atac, ## can be a str, Path, or anndata.AnnData object\n",
    "    input_fasta=input_fasta,\n",
    "    output_path=train_folder\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87abec58-dd8c-43ae-84ec-9979d408719b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train/test data is saved in:  data/3_cross_species/test_data\n",
      "successful writing bed file.\n",
      "successful writing sparse m.\n",
      "start saving all sequence h5 file...\n"
     ]
    }
   ],
   "source": [
    "## Generate data for model evaluation\n",
    "test_folder = './data/3_cross_species/test_data/'\n",
    "test_atac = sc.read_h5ad('./data/3_cross_species/m1d1_atac.h5ad')\n",
    "input_fasta = '/picb/bigdata/project/miaoyuanyuan/hg38.fa'\n",
    "dict = xc.pp.process_test_dual(\n",
    "    ad_atac=test_atac, ## can be a str, Path, or anndata.AnnData object\n",
    "    input_fasta=input_fasta,\n",
    "    output_path=test_folder\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bad988-2d3d-4827-bded-9c8c5d06fd28",
   "metadata": {},
   "source": [
    "We performed preprocessing on the filtered scATAC data of human (m1d1) and mouse (mop3c2) samples separately:\n",
    "\n",
    "**For the mop3c2 mouse training sample:**\n",
    "Following the same procedure as in the `1. Within-sample analysis` tutorial, we partitioned the data using 90% of cells and 90% of peaks to generate training/validation sets. This produced:\n",
    "- `ad_trainval.h5ad`: containing training cells and training peaks\n",
    "- `m_trainval.npz`: corresponding count matrix\n",
    "- `trainval_seqs.h5`: base sequence file for training peaks\n",
    "\n",
    "All files were saved in `'./data/3_cross_species/train_data/'`.\n",
    "\n",
    "**For the m1d1 human test sample:**\n",
    "No partitioning was applied. The filtered scATAC data was directly converted into the required model input files:\n",
    "- `ad.h5ad`\n",
    "- `m.npz`\n",
    "- `all_seqs.h5`\n",
    "\n",
    "These files were saved in `'./data/3_cross_species/test_data/'`.\n",
    "\n",
    "Note: The `input_fasta` parameter should be replaced with the path to the genome file corresponding to the species used in your specific dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67b5316-6dab-4ae0-b19e-c151af2f8f3a",
   "metadata": {},
   "source": [
    "### 4. Train XChrom"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b32619c-d9b1-4507-8c90-596708237055",
   "metadata": {},
   "source": [
    "- We use the preprocessed data from the mouse (mop3c2) training set obtained above as sequence inputs for the model. The `input_folder` should contain all files generated during preprocessing. The batch-corrected `X_pca_harmony` embeddings derived from scRNA data are used as raw cell embeddings and serve as cell identity inputs to the model. If using dimensionality reduction results from other methods, they must be stored in `cell_embedding_ad.obsm` under the key `cellembed_raw` for extraction.                                 \n",
    "\n",
    "- If you want to compute NS(k=100) and LS(k=100) for monitoring the XChrom training process, you need to set `trackscore = True` and specify the `celltype` in either the RNA or ATAC H5AD file. When `print_scores = True` is specified, the computed NS and LS values will be printed during each training epoch.                            \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,
   "id": "5059ac43-c5c4-4d2e-b563-8566c5b89ce8",
   "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/3_cross_species/train_data\n",
      "Cell embedding file: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/3_cross_species/mop3c2_rna_harmony.h5ad\n",
      "Raw cell embedding key: X_pca_harmony\n",
      "Output path: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/3_cross_species/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/3_cross_species/mop3c2_rna_harmony.h5ad.obsm['X_pca_harmony']\n",
      "Initial cell embedding saved to: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/3_cross_species/mop3c2_rna_harmony.h5ad.obsm['zscore32_perpc']\n",
      "Initial cell embedding shape: (6668, 32)\n",
      "2. Load training data...\n",
      "3. Prepare train/val data split...\n",
      "Training peak number: 87035, Validation peak number: 9671\n",
      "4. Create TensorFlow dataset...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-18 10:30:14.683558: 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 10:30:16.403718: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17996 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:31:00.0, compute capability: 8.6\n",
      "2025-08-18 10:30:16.404799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 19873 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:4b:00.0, compute capability: 8.6\n",
      "2025-08-18 10:30:16.405349: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 20401 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:98:00.0, compute capability: 8.6\n",
      "2025-08-18 10:30:16.405857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 70 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, 6002, 32)]   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 1, 32)        128         dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 6002, 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, 6002, 32)     64          lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gelu_9 (GELU)                   (None, 1, 32)        0           dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 6002, 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, 6002)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "final_cellembed (Dense)         (None, 6002, 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, 6002, 1)      0           sequencing_depth[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.linalg.matmul (TFOpLambda)   (None, 6002, 1)      0           final_cellembed[0][0]            \n",
      "                                                                 tf.expand_dims[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 6002, 1)      2           tf.expand_dims_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.compat.v1.squeeze_2 (TFOpLam (None, 6002)         0           tf.linalg.matmul[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.compat.v1.squeeze_1 (TFOpLam (None, 6002)         0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add (TFOpLambd (None, 6002)         0           tf.compat.v1.squeeze_2[0][0]     \n",
      "                                                                 tf.compat.v1.squeeze_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid (TFOpLambda)    (None, 6002)         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/3_cross_species/train_out/E1000best_model.h5\n",
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-18 10:30:23.501891: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2025-08-18 10:30:27.890841: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8800\n",
      "2025-08-18 10:30:27.969441: 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": [
      "680/680 [==============================] - 168s 232ms/step - loss: 0.3234 - binary_accuracy: 0.8749 - auc: 0.7746 - pr: 0.3225 - val_loss: 0.3126 - val_binary_accuracy: 0.8760 - val_auc: 0.7962 - val_pr: 0.3724\n",
      "Neighbors Score: 0.2596, Labels Score: 0.6347 \tUsing time in epoch 1: 40.7640s\n",
      "Epoch 2/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.3116 - binary_accuracy: 0.8781 - auc: 0.7914 - pr: 0.3629 - val_loss: 0.3117 - val_binary_accuracy: 0.8765 - val_auc: 0.7977 - val_pr: 0.3809\n",
      "Neighbors Score: 0.2772, Labels Score: 0.6639 \tUsing time in epoch 2: 35.7925s\n",
      "Epoch 3/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.3099 - binary_accuracy: 0.8787 - auc: 0.7944 - pr: 0.3697 - val_loss: 0.3105 - val_binary_accuracy: 0.8780 - val_auc: 0.7988 - val_pr: 0.3842\n",
      "Neighbors Score: 0.2904, Labels Score: 0.6946 \tUsing time in epoch 3: 32.6157s\n",
      "Epoch 4/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.3089 - binary_accuracy: 0.8790 - auc: 0.7964 - pr: 0.3736 - val_loss: 0.3104 - val_binary_accuracy: 0.8774 - val_auc: 0.7998 - val_pr: 0.3861\n",
      "Neighbors Score: 0.2887, Labels Score: 0.6991 \tUsing time in epoch 4: 35.3956s\n",
      "Epoch 5/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.3082 - binary_accuracy: 0.8792 - auc: 0.7978 - pr: 0.3765 - val_loss: 0.3121 - val_binary_accuracy: 0.8762 - val_auc: 0.7993 - val_pr: 0.3840\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ef98c4669d0> 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",
      "Neighbors Score: 0.2901, Labels Score: 0.7303 \tUsing time in epoch 5: 33.4384s\n",
      "Epoch 6/1000\n",
      "680/680 [==============================] - 166s 241ms/step - loss: 0.3075 - binary_accuracy: 0.8794 - auc: 0.7992 - pr: 0.3793 - val_loss: 0.3115 - val_binary_accuracy: 0.8785 - val_auc: 0.7999 - val_pr: 0.3890\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ef98c3c4a60> 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",
      "Neighbors Score: 0.2894, Labels Score: 0.7053 \tUsing time in epoch 6: 36.7949s\n",
      "Epoch 7/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.3070 - binary_accuracy: 0.8796 - auc: 0.8002 - pr: 0.3812 - val_loss: 0.3117 - val_binary_accuracy: 0.8787 - val_auc: 0.7996 - val_pr: 0.3889\n",
      "Neighbors Score: 0.3055, Labels Score: 0.7336 \tUsing time in epoch 7: 33.9053s\n",
      "Epoch 8/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.3064 - binary_accuracy: 0.8798 - auc: 0.8012 - pr: 0.3834 - val_loss: 0.3107 - val_binary_accuracy: 0.8785 - val_auc: 0.8023 - val_pr: 0.3919\n",
      "Neighbors Score: 0.3216, Labels Score: 0.7909 \tUsing time in epoch 8: 31.1259s\n",
      "Epoch 9/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.3059 - binary_accuracy: 0.8799 - auc: 0.8022 - pr: 0.3855 - val_loss: 0.3114 - val_binary_accuracy: 0.8780 - val_auc: 0.8039 - val_pr: 0.3946\n",
      "Neighbors Score: 0.2996, Labels Score: 0.7235 \tUsing time in epoch 9: 39.2971s\n",
      "Epoch 10/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.3052 - binary_accuracy: 0.8801 - auc: 0.8036 - pr: 0.3878 - val_loss: 0.3081 - val_binary_accuracy: 0.8787 - val_auc: 0.8037 - val_pr: 0.3943\n",
      "Neighbors Score: 0.3167, Labels Score: 0.8101 \tUsing time in epoch 10: 27.1583s\n",
      "Epoch 11/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.3048 - binary_accuracy: 0.8801 - auc: 0.8044 - pr: 0.3894 - val_loss: 0.3094 - val_binary_accuracy: 0.8787 - val_auc: 0.8032 - val_pr: 0.3927\n",
      "Neighbors Score: 0.3052, Labels Score: 0.7592 \tUsing time in epoch 11: 38.6105s\n",
      "Epoch 12/1000\n",
      "680/680 [==============================] - 166s 241ms/step - loss: 0.3042 - binary_accuracy: 0.8803 - auc: 0.8056 - pr: 0.3915 - val_loss: 0.3074 - val_binary_accuracy: 0.8784 - val_auc: 0.8065 - val_pr: 0.3980\n",
      "Neighbors Score: 0.3021, Labels Score: 0.7722 \tUsing time in epoch 12: 33.8718s\n",
      "Epoch 13/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.3036 - binary_accuracy: 0.8804 - auc: 0.8066 - pr: 0.3935 - val_loss: 0.3074 - val_binary_accuracy: 0.8786 - val_auc: 0.8048 - val_pr: 0.3936\n",
      "Neighbors Score: 0.3187, Labels Score: 0.7981 \tUsing time in epoch 13: 30.7452s\n",
      "Epoch 14/1000\n",
      "680/680 [==============================] - 152s 218ms/step - loss: 0.3031 - binary_accuracy: 0.8806 - auc: 0.8076 - pr: 0.3953 - val_loss: 0.3063 - val_binary_accuracy: 0.8789 - val_auc: 0.8066 - val_pr: 0.3987\n",
      "Neighbors Score: 0.3070, Labels Score: 0.7923 \tUsing time in epoch 14: 31.8505s\n",
      "Epoch 15/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.3027 - binary_accuracy: 0.8807 - auc: 0.8084 - pr: 0.3974 - val_loss: 0.3062 - val_binary_accuracy: 0.8791 - val_auc: 0.8070 - val_pr: 0.3985\n",
      "Neighbors Score: 0.3101, Labels Score: 0.8103 \tUsing time in epoch 15: 31.7760s\n",
      "Epoch 16/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.3023 - binary_accuracy: 0.8808 - auc: 0.8090 - pr: 0.3988 - val_loss: 0.3063 - val_binary_accuracy: 0.8793 - val_auc: 0.8061 - val_pr: 0.3990\n",
      "Neighbors Score: 0.3075, Labels Score: 0.8233 \tUsing time in epoch 16: 27.7196s\n",
      "Epoch 17/1000\n",
      "680/680 [==============================] - 155s 223ms/step - loss: 0.3018 - binary_accuracy: 0.8809 - auc: 0.8101 - pr: 0.4008 - val_loss: 0.3067 - val_binary_accuracy: 0.8791 - val_auc: 0.8074 - val_pr: 0.4001\n",
      "Neighbors Score: 0.2781, Labels Score: 0.7023 \tUsing time in epoch 17: 64.1304s\n",
      "Epoch 18/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.3013 - binary_accuracy: 0.8810 - auc: 0.8109 - pr: 0.4024 - val_loss: 0.3065 - val_binary_accuracy: 0.8784 - val_auc: 0.8065 - val_pr: 0.3972\n",
      "Neighbors Score: 0.2924, Labels Score: 0.7998 \tUsing time in epoch 18: 56.8161s\n",
      "Epoch 19/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.3010 - binary_accuracy: 0.8812 - auc: 0.8114 - pr: 0.4041 - val_loss: 0.3078 - val_binary_accuracy: 0.8769 - val_auc: 0.8062 - val_pr: 0.3947\n",
      "Neighbors Score: 0.2739, Labels Score: 0.7017 \tUsing time in epoch 19: 64.6538s\n",
      "Epoch 20/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.3006 - binary_accuracy: 0.8813 - auc: 0.8121 - pr: 0.4056 - val_loss: 0.3073 - val_binary_accuracy: 0.8769 - val_auc: 0.8066 - val_pr: 0.3956\n",
      "Neighbors Score: 0.2874, Labels Score: 0.7850 \tUsing time in epoch 20: 58.5339s\n",
      "Epoch 21/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.3003 - binary_accuracy: 0.8813 - auc: 0.8127 - pr: 0.4066 - val_loss: 0.3061 - val_binary_accuracy: 0.8787 - val_auc: 0.8070 - val_pr: 0.3981\n",
      "Neighbors Score: 0.2876, Labels Score: 0.7339 \tUsing time in epoch 21: 56.8782s\n",
      "Epoch 22/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.3000 - binary_accuracy: 0.8814 - auc: 0.8133 - pr: 0.4079 - val_loss: 0.3058 - val_binary_accuracy: 0.8788 - val_auc: 0.8075 - val_pr: 0.3982\n",
      "Neighbors Score: 0.2890, Labels Score: 0.8015 \tUsing time in epoch 22: 58.3669s\n",
      "Epoch 23/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2995 - binary_accuracy: 0.8816 - auc: 0.8140 - pr: 0.4098 - val_loss: 0.3061 - val_binary_accuracy: 0.8787 - val_auc: 0.8075 - val_pr: 0.3980\n",
      "Neighbors Score: 0.2786, Labels Score: 0.7091 \tUsing time in epoch 23: 61.0071s\n",
      "Epoch 24/1000\n",
      "680/680 [==============================] - 167s 244ms/step - loss: 0.2993 - binary_accuracy: 0.8816 - auc: 0.8144 - pr: 0.4107 - val_loss: 0.3054 - val_binary_accuracy: 0.8792 - val_auc: 0.8080 - val_pr: 0.4000\n",
      "Neighbors Score: 0.2593, Labels Score: 0.6600 \tUsing time in epoch 24: 86.2076s\n",
      "Epoch 25/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2989 - binary_accuracy: 0.8817 - auc: 0.8153 - pr: 0.4119 - val_loss: 0.3073 - val_binary_accuracy: 0.8771 - val_auc: 0.8064 - val_pr: 0.3953\n",
      "Neighbors Score: 0.2836, Labels Score: 0.7286 \tUsing time in epoch 25: 56.3402s\n",
      "Epoch 26/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2985 - binary_accuracy: 0.8817 - auc: 0.8159 - pr: 0.4134 - val_loss: 0.3054 - val_binary_accuracy: 0.8792 - val_auc: 0.8086 - val_pr: 0.4012\n",
      "Neighbors Score: 0.2868, Labels Score: 0.7377 \tUsing time in epoch 26: 55.5648s\n",
      "Epoch 27/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2982 - binary_accuracy: 0.8819 - auc: 0.8166 - pr: 0.4148 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8094 - val_pr: 0.4036\n",
      "Neighbors Score: 0.2861, Labels Score: 0.7357 \tUsing time in epoch 27: 60.0166s\n",
      "Epoch 28/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2980 - binary_accuracy: 0.8819 - auc: 0.8169 - pr: 0.4154 - val_loss: 0.3051 - val_binary_accuracy: 0.8793 - val_auc: 0.8090 - val_pr: 0.4013\n",
      "Neighbors Score: 0.2834, Labels Score: 0.7222 \tUsing time in epoch 28: 61.8453s\n",
      "Epoch 29/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2976 - binary_accuracy: 0.8820 - auc: 0.8177 - pr: 0.4170 - val_loss: 0.3065 - val_binary_accuracy: 0.8790 - val_auc: 0.8088 - val_pr: 0.3986\n",
      "Neighbors Score: 0.2873, Labels Score: 0.7373 \tUsing time in epoch 29: 59.9457s\n",
      "Epoch 30/1000\n",
      "680/680 [==============================] - 156s 228ms/step - loss: 0.2972 - binary_accuracy: 0.8820 - auc: 0.8184 - pr: 0.4179 - val_loss: 0.3051 - val_binary_accuracy: 0.8787 - val_auc: 0.8094 - val_pr: 0.4008\n",
      "Neighbors Score: 0.2931, Labels Score: 0.7556 \tUsing time in epoch 30: 49.8141s\n",
      "Epoch 31/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2970 - binary_accuracy: 0.8821 - auc: 0.8187 - pr: 0.4190 - val_loss: 0.3056 - val_binary_accuracy: 0.8791 - val_auc: 0.8080 - val_pr: 0.3994\n",
      "Neighbors Score: 0.2895, Labels Score: 0.7816 \tUsing time in epoch 31: 69.3337s\n",
      "Epoch 32/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2968 - binary_accuracy: 0.8822 - auc: 0.8193 - pr: 0.4200 - val_loss: 0.3046 - val_binary_accuracy: 0.8793 - val_auc: 0.8099 - val_pr: 0.4031\n",
      "Neighbors Score: 0.3013, Labels Score: 0.8010 \tUsing time in epoch 32: 54.1505s\n",
      "Epoch 33/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2964 - binary_accuracy: 0.8823 - auc: 0.8199 - pr: 0.4213 - val_loss: 0.3049 - val_binary_accuracy: 0.8789 - val_auc: 0.8097 - val_pr: 0.4006\n",
      "Neighbors Score: 0.2859, Labels Score: 0.7322 \tUsing time in epoch 33: 58.4731s\n",
      "Epoch 34/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2962 - binary_accuracy: 0.8823 - auc: 0.8203 - pr: 0.4220 - val_loss: 0.3051 - val_binary_accuracy: 0.8789 - val_auc: 0.8109 - val_pr: 0.4037\n",
      "Neighbors Score: 0.3138, Labels Score: 0.8509 \tUsing time in epoch 34: 49.2989s\n",
      "Epoch 35/1000\n",
      "680/680 [==============================] - 168s 243ms/step - loss: 0.2959 - binary_accuracy: 0.8824 - auc: 0.8207 - pr: 0.4229 - val_loss: 0.3054 - val_binary_accuracy: 0.8790 - val_auc: 0.8091 - val_pr: 0.4011\n",
      "Neighbors Score: 0.2900, Labels Score: 0.7512 \tUsing time in epoch 35: 59.0502s\n",
      "Epoch 36/1000\n",
      "680/680 [==============================] - 158s 231ms/step - loss: 0.2955 - binary_accuracy: 0.8825 - auc: 0.8215 - pr: 0.4243 - val_loss: 0.3046 - val_binary_accuracy: 0.8794 - val_auc: 0.8109 - val_pr: 0.4048\n",
      "Neighbors Score: 0.3075, Labels Score: 0.8577 \tUsing time in epoch 36: 49.3392s\n",
      "Epoch 37/1000\n",
      "680/680 [==============================] - 167s 240ms/step - loss: 0.2955 - binary_accuracy: 0.8824 - auc: 0.8216 - pr: 0.4244 - val_loss: 0.3054 - val_binary_accuracy: 0.8791 - val_auc: 0.8094 - val_pr: 0.4008\n",
      "Neighbors Score: 0.2801, Labels Score: 0.7215 \tUsing time in epoch 37: 60.7498s\n",
      "Epoch 38/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2951 - binary_accuracy: 0.8825 - auc: 0.8222 - pr: 0.4258 - val_loss: 0.3061 - val_binary_accuracy: 0.8792 - val_auc: 0.8104 - val_pr: 0.4030\n",
      "Neighbors Score: 0.2930, Labels Score: 0.7657 \tUsing time in epoch 38: 45.9519s\n",
      "Epoch 39/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2948 - binary_accuracy: 0.8826 - auc: 0.8228 - pr: 0.4268 - val_loss: 0.3045 - val_binary_accuracy: 0.8792 - val_auc: 0.8107 - val_pr: 0.4031\n",
      "Neighbors Score: 0.3157, Labels Score: 0.8579 \tUsing time in epoch 39: 54.7439s\n",
      "Epoch 40/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2945 - binary_accuracy: 0.8826 - auc: 0.8233 - pr: 0.4277 - val_loss: 0.3050 - val_binary_accuracy: 0.8793 - val_auc: 0.8101 - val_pr: 0.4034\n",
      "Neighbors Score: 0.3027, Labels Score: 0.7590 \tUsing time in epoch 40: 58.8478s\n",
      "Epoch 41/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2943 - binary_accuracy: 0.8827 - auc: 0.8237 - pr: 0.4282 - val_loss: 0.3049 - val_binary_accuracy: 0.8790 - val_auc: 0.8104 - val_pr: 0.4035\n",
      "Neighbors Score: 0.3099, Labels Score: 0.7830 \tUsing time in epoch 41: 58.7770s\n",
      "Epoch 42/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2941 - binary_accuracy: 0.8827 - auc: 0.8242 - pr: 0.4289 - val_loss: 0.3045 - val_binary_accuracy: 0.8794 - val_auc: 0.8109 - val_pr: 0.4044\n",
      "Neighbors Score: 0.3294, Labels Score: 0.8789 \tUsing time in epoch 42: 49.3580s\n",
      "Epoch 43/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2939 - binary_accuracy: 0.8828 - auc: 0.8245 - pr: 0.4297 - val_loss: 0.3054 - val_binary_accuracy: 0.8791 - val_auc: 0.8110 - val_pr: 0.4038\n",
      "Neighbors Score: 0.3260, Labels Score: 0.8071 \tUsing time in epoch 43: 49.0609s\n",
      "Epoch 44/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2936 - binary_accuracy: 0.8828 - auc: 0.8250 - pr: 0.4306 - val_loss: 0.3052 - val_binary_accuracy: 0.8790 - val_auc: 0.8089 - val_pr: 0.3996\n",
      "Neighbors Score: 0.3186, Labels Score: 0.8047 \tUsing time in epoch 44: 48.9006s\n",
      "Epoch 45/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2935 - binary_accuracy: 0.8828 - auc: 0.8253 - pr: 0.4309 - val_loss: 0.3046 - val_binary_accuracy: 0.8788 - val_auc: 0.8105 - val_pr: 0.4034\n",
      "Neighbors Score: 0.3363, Labels Score: 0.8834 \tUsing time in epoch 45: 48.3915s\n",
      "Epoch 46/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2932 - binary_accuracy: 0.8828 - auc: 0.8258 - pr: 0.4316 - val_loss: 0.3042 - val_binary_accuracy: 0.8793 - val_auc: 0.8109 - val_pr: 0.4038\n",
      "Neighbors Score: 0.3340, Labels Score: 0.8449 \tUsing time in epoch 46: 36.1434s\n",
      "Epoch 47/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2929 - binary_accuracy: 0.8829 - auc: 0.8263 - pr: 0.4328 - val_loss: 0.3046 - val_binary_accuracy: 0.8792 - val_auc: 0.8111 - val_pr: 0.4035\n",
      "Neighbors Score: 0.3489, Labels Score: 0.8699 \tUsing time in epoch 47: 28.7461s\n",
      "Epoch 48/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2928 - binary_accuracy: 0.8829 - auc: 0.8266 - pr: 0.4329 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4055\n",
      "Neighbors Score: 0.3401, Labels Score: 0.8674 \tUsing time in epoch 48: 30.6263s\n",
      "Epoch 49/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2927 - binary_accuracy: 0.8829 - auc: 0.8268 - pr: 0.4334 - val_loss: 0.3038 - val_binary_accuracy: 0.8794 - val_auc: 0.8117 - val_pr: 0.4061\n",
      "Neighbors Score: 0.3379, Labels Score: 0.8587 \tUsing time in epoch 49: 32.3220s\n",
      "Epoch 50/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2923 - binary_accuracy: 0.8830 - auc: 0.8274 - pr: 0.4344 - val_loss: 0.3038 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4056\n",
      "Neighbors Score: 0.3376, Labels Score: 0.8654 \tUsing time in epoch 50: 30.3013s\n",
      "Epoch 51/1000\n",
      "680/680 [==============================] - 168s 242ms/step - loss: 0.2922 - binary_accuracy: 0.8830 - auc: 0.8277 - pr: 0.4349 - val_loss: 0.3043 - val_binary_accuracy: 0.8791 - val_auc: 0.8113 - val_pr: 0.4041\n",
      "Neighbors Score: 0.3509, Labels Score: 0.8757 \tUsing time in epoch 51: 26.9339s\n",
      "Epoch 52/1000\n",
      "680/680 [==============================] - 168s 244ms/step - loss: 0.2919 - binary_accuracy: 0.8831 - auc: 0.8281 - pr: 0.4358 - val_loss: 0.3041 - val_binary_accuracy: 0.8792 - val_auc: 0.8114 - val_pr: 0.4043\n",
      "Neighbors Score: 0.3566, Labels Score: 0.8883 \tUsing time in epoch 52: 27.3166s\n",
      "Epoch 53/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2918 - binary_accuracy: 0.8831 - auc: 0.8283 - pr: 0.4358 - val_loss: 0.3040 - val_binary_accuracy: 0.8793 - val_auc: 0.8114 - val_pr: 0.4041\n",
      "Neighbors Score: 0.3566, Labels Score: 0.8812 \tUsing time in epoch 53: 26.4427s\n",
      "Epoch 54/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2917 - binary_accuracy: 0.8831 - auc: 0.8285 - pr: 0.4361 - val_loss: 0.3055 - val_binary_accuracy: 0.8794 - val_auc: 0.8114 - val_pr: 0.4046\n",
      "Neighbors Score: 0.3575, Labels Score: 0.8936 \tUsing time in epoch 54: 28.3369s\n",
      "Epoch 55/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2915 - binary_accuracy: 0.8831 - auc: 0.8288 - pr: 0.4367 - val_loss: 0.3069 - val_binary_accuracy: 0.8789 - val_auc: 0.8117 - val_pr: 0.4048\n",
      "Neighbors Score: 0.3582, Labels Score: 0.8925 \tUsing time in epoch 55: 27.9466s\n",
      "Epoch 56/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2914 - binary_accuracy: 0.8832 - auc: 0.8292 - pr: 0.4371 - val_loss: 0.3039 - val_binary_accuracy: 0.8794 - val_auc: 0.8115 - val_pr: 0.4055\n",
      "Neighbors Score: 0.3568, Labels Score: 0.8906 \tUsing time in epoch 56: 25.8270s\n",
      "Epoch 57/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2913 - binary_accuracy: 0.8832 - auc: 0.8294 - pr: 0.4374 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4056\n",
      "Neighbors Score: 0.3429, Labels Score: 0.8860 \tUsing time in epoch 57: 25.9176s\n",
      "Epoch 58/1000\n",
      "680/680 [==============================] - 169s 244ms/step - loss: 0.2911 - binary_accuracy: 0.8832 - auc: 0.8296 - pr: 0.4378 - val_loss: 0.3037 - val_binary_accuracy: 0.8793 - val_auc: 0.8119 - val_pr: 0.4056\n",
      "Neighbors Score: 0.3518, Labels Score: 0.8866 \tUsing time in epoch 58: 25.0842s\n",
      "Epoch 59/1000\n",
      "680/680 [==============================] - 166s 238ms/step - loss: 0.2910 - binary_accuracy: 0.8832 - auc: 0.8299 - pr: 0.4382 - val_loss: 0.3043 - val_binary_accuracy: 0.8792 - val_auc: 0.8117 - val_pr: 0.4047\n",
      "Neighbors Score: 0.3543, Labels Score: 0.8891 \tUsing time in epoch 59: 25.7932s\n",
      "Epoch 60/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2907 - binary_accuracy: 0.8833 - auc: 0.8303 - pr: 0.4390 - val_loss: 0.3048 - val_binary_accuracy: 0.8794 - val_auc: 0.8111 - val_pr: 0.4049\n",
      "Neighbors Score: 0.3616, Labels Score: 0.8879 \tUsing time in epoch 60: 26.4854s\n",
      "Epoch 61/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2906 - binary_accuracy: 0.8833 - auc: 0.8305 - pr: 0.4393 - val_loss: 0.3045 - val_binary_accuracy: 0.8790 - val_auc: 0.8114 - val_pr: 0.4029\n",
      "Neighbors Score: 0.3568, Labels Score: 0.8894 \tUsing time in epoch 61: 26.7751s\n",
      "Epoch 62/1000\n",
      "680/680 [==============================] - 166s 241ms/step - loss: 0.2904 - binary_accuracy: 0.8833 - auc: 0.8308 - pr: 0.4397 - val_loss: 0.3044 - val_binary_accuracy: 0.8793 - val_auc: 0.8120 - val_pr: 0.4058\n",
      "Neighbors Score: 0.3560, Labels Score: 0.8857 \tUsing time in epoch 62: 25.9551s\n",
      "Epoch 63/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2903 - binary_accuracy: 0.8833 - auc: 0.8310 - pr: 0.4400 - val_loss: 0.3048 - val_binary_accuracy: 0.8792 - val_auc: 0.8113 - val_pr: 0.4042\n",
      "Neighbors Score: 0.3636, Labels Score: 0.8852 \tUsing time in epoch 63: 27.9633s\n",
      "Epoch 64/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2903 - binary_accuracy: 0.8833 - auc: 0.8311 - pr: 0.4401 - val_loss: 0.3048 - val_binary_accuracy: 0.8794 - val_auc: 0.8114 - val_pr: 0.4050\n",
      "Neighbors Score: 0.3542, Labels Score: 0.8934 \tUsing time in epoch 64: 25.5709s\n",
      "Epoch 65/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2901 - binary_accuracy: 0.8834 - auc: 0.8315 - pr: 0.4409 - val_loss: 0.3040 - val_binary_accuracy: 0.8792 - val_auc: 0.8118 - val_pr: 0.4049\n",
      "Neighbors Score: 0.3554, Labels Score: 0.8868 \tUsing time in epoch 65: 25.4701s\n",
      "Epoch 66/1000\n",
      "680/680 [==============================] - 166s 239ms/step - loss: 0.2901 - binary_accuracy: 0.8833 - auc: 0.8315 - pr: 0.4406 - val_loss: 0.3055 - val_binary_accuracy: 0.8794 - val_auc: 0.8117 - val_pr: 0.4060\n",
      "Neighbors Score: 0.3568, Labels Score: 0.8869 \tUsing time in epoch 66: 26.3894s\n",
      "Epoch 67/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2900 - binary_accuracy: 0.8834 - auc: 0.8317 - pr: 0.4407 - val_loss: 0.3044 - val_binary_accuracy: 0.8792 - val_auc: 0.8115 - val_pr: 0.4047\n",
      "Neighbors Score: 0.3478, Labels Score: 0.8877 \tUsing time in epoch 67: 27.0294s\n",
      "Epoch 68/1000\n",
      "680/680 [==============================] - 170s 246ms/step - loss: 0.2899 - binary_accuracy: 0.8834 - auc: 0.8319 - pr: 0.4411 - val_loss: 0.3042 - val_binary_accuracy: 0.8793 - val_auc: 0.8116 - val_pr: 0.4051\n",
      "Neighbors Score: 0.3524, Labels Score: 0.8849 \tUsing time in epoch 68: 27.1872s\n",
      "Epoch 69/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2896 - binary_accuracy: 0.8834 - auc: 0.8322 - pr: 0.4419 - val_loss: 0.3042 - val_binary_accuracy: 0.8794 - val_auc: 0.8115 - val_pr: 0.4058\n",
      "Neighbors Score: 0.3490, Labels Score: 0.8819 \tUsing time in epoch 69: 28.1986s\n",
      "Epoch 70/1000\n",
      "680/680 [==============================] - 170s 247ms/step - loss: 0.2896 - binary_accuracy: 0.8834 - auc: 0.8323 - pr: 0.4418 - val_loss: 0.3039 - val_binary_accuracy: 0.8792 - val_auc: 0.8115 - val_pr: 0.4047\n",
      "Neighbors Score: 0.3550, Labels Score: 0.8830 \tUsing time in epoch 70: 26.5124s\n",
      "Epoch 71/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2895 - binary_accuracy: 0.8835 - auc: 0.8325 - pr: 0.4423 - val_loss: 0.3046 - val_binary_accuracy: 0.8792 - val_auc: 0.8114 - val_pr: 0.4046\n",
      "Neighbors Score: 0.3642, Labels Score: 0.8806 \tUsing time in epoch 71: 26.2156s\n",
      "Epoch 72/1000\n",
      "680/680 [==============================] - 168s 242ms/step - loss: 0.2894 - binary_accuracy: 0.8835 - auc: 0.8327 - pr: 0.4425 - val_loss: 0.3047 - val_binary_accuracy: 0.8791 - val_auc: 0.8114 - val_pr: 0.4036\n",
      "Neighbors Score: 0.3593, Labels Score: 0.8859 \tUsing time in epoch 72: 24.8393s\n",
      "Epoch 73/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2893 - binary_accuracy: 0.8835 - auc: 0.8329 - pr: 0.4429 - val_loss: 0.3049 - val_binary_accuracy: 0.8791 - val_auc: 0.8115 - val_pr: 0.4045\n",
      "Neighbors Score: 0.3657, Labels Score: 0.8837 \tUsing time in epoch 73: 25.4754s\n",
      "Epoch 74/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2892 - binary_accuracy: 0.8835 - auc: 0.8330 - pr: 0.4431 - val_loss: 0.3041 - val_binary_accuracy: 0.8793 - val_auc: 0.8118 - val_pr: 0.4058\n",
      "Neighbors Score: 0.3639, Labels Score: 0.8872 \tUsing time in epoch 74: 27.4441s\n",
      "Epoch 75/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2891 - binary_accuracy: 0.8835 - auc: 0.8332 - pr: 0.4433 - val_loss: 0.3049 - val_binary_accuracy: 0.8788 - val_auc: 0.8109 - val_pr: 0.4021\n",
      "Neighbors Score: 0.3704, Labels Score: 0.8858 \tUsing time in epoch 75: 26.2960s\n",
      "Epoch 76/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2891 - binary_accuracy: 0.8835 - auc: 0.8332 - pr: 0.4430 - val_loss: 0.3038 - val_binary_accuracy: 0.8793 - val_auc: 0.8118 - val_pr: 0.4052\n",
      "Neighbors Score: 0.3607, Labels Score: 0.8816 \tUsing time in epoch 76: 24.9156s\n",
      "Epoch 77/1000\n",
      "680/680 [==============================] - 166s 241ms/step - loss: 0.2889 - binary_accuracy: 0.8835 - auc: 0.8335 - pr: 0.4437 - val_loss: 0.3038 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3616, Labels Score: 0.8820 \tUsing time in epoch 77: 25.9712s\n",
      "Epoch 78/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2889 - binary_accuracy: 0.8835 - auc: 0.8336 - pr: 0.4438 - val_loss: 0.3039 - val_binary_accuracy: 0.8794 - val_auc: 0.8117 - val_pr: 0.4063\n",
      "Neighbors Score: 0.3675, Labels Score: 0.8838 \tUsing time in epoch 78: 25.7065s\n",
      "Epoch 79/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2888 - binary_accuracy: 0.8836 - auc: 0.8337 - pr: 0.4442 - val_loss: 0.3048 - val_binary_accuracy: 0.8791 - val_auc: 0.8116 - val_pr: 0.4047\n",
      "Neighbors Score: 0.3710, Labels Score: 0.8860 \tUsing time in epoch 79: 25.9834s\n",
      "Epoch 80/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2888 - binary_accuracy: 0.8835 - auc: 0.8337 - pr: 0.4440 - val_loss: 0.3050 - val_binary_accuracy: 0.8794 - val_auc: 0.8118 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3680, Labels Score: 0.8838 \tUsing time in epoch 80: 25.8499s\n",
      "Epoch 81/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2887 - binary_accuracy: 0.8835 - auc: 0.8339 - pr: 0.4442 - val_loss: 0.3045 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3729, Labels Score: 0.8832 \tUsing time in epoch 81: 27.7426s\n",
      "Epoch 82/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2886 - binary_accuracy: 0.8836 - auc: 0.8341 - pr: 0.4446 - val_loss: 0.3046 - val_binary_accuracy: 0.8792 - val_auc: 0.8115 - val_pr: 0.4054\n",
      "Neighbors Score: 0.3688, Labels Score: 0.8825 \tUsing time in epoch 82: 25.6480s\n",
      "Epoch 83/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2887 - binary_accuracy: 0.8835 - auc: 0.8340 - pr: 0.4442 - val_loss: 0.3058 - val_binary_accuracy: 0.8793 - val_auc: 0.8108 - val_pr: 0.4048\n",
      "Neighbors Score: 0.3677, Labels Score: 0.8841 \tUsing time in epoch 83: 26.2671s\n",
      "Epoch 84/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.2885 - binary_accuracy: 0.8836 - auc: 0.8343 - pr: 0.4447 - val_loss: 0.3037 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3654, Labels Score: 0.8820 \tUsing time in epoch 84: 25.0156s\n",
      "Epoch 85/1000\n",
      "680/680 [==============================] - 160s 234ms/step - loss: 0.2885 - binary_accuracy: 0.8836 - auc: 0.8343 - pr: 0.4447 - val_loss: 0.3044 - val_binary_accuracy: 0.8793 - val_auc: 0.8119 - val_pr: 0.4062\n",
      "Neighbors Score: 0.3678, Labels Score: 0.8858 \tUsing time in epoch 85: 25.5975s\n",
      "Epoch 86/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2883 - binary_accuracy: 0.8836 - auc: 0.8346 - pr: 0.4454 - val_loss: 0.3038 - val_binary_accuracy: 0.8794 - val_auc: 0.8118 - val_pr: 0.4062\n",
      "Neighbors Score: 0.3627, Labels Score: 0.8839 \tUsing time in epoch 86: 25.4751s\n",
      "Epoch 87/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2882 - binary_accuracy: 0.8836 - auc: 0.8347 - pr: 0.4455 - val_loss: 0.3037 - val_binary_accuracy: 0.8793 - val_auc: 0.8121 - val_pr: 0.4065\n",
      "Neighbors Score: 0.3738, Labels Score: 0.8822 \tUsing time in epoch 87: 25.8740s\n",
      "Epoch 88/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2882 - binary_accuracy: 0.8836 - auc: 0.8347 - pr: 0.4455 - val_loss: 0.3047 - val_binary_accuracy: 0.8793 - val_auc: 0.8116 - val_pr: 0.4057\n",
      "Neighbors Score: 0.3718, Labels Score: 0.8842 \tUsing time in epoch 88: 27.0818s\n",
      "Epoch 89/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2881 - binary_accuracy: 0.8837 - auc: 0.8349 - pr: 0.4459 - val_loss: 0.3054 - val_binary_accuracy: 0.8793 - val_auc: 0.8116 - val_pr: 0.4057\n",
      "Neighbors Score: 0.3726, Labels Score: 0.8849 \tUsing time in epoch 89: 25.8252s\n",
      "Epoch 90/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2881 - binary_accuracy: 0.8836 - auc: 0.8349 - pr: 0.4458 - val_loss: 0.3046 - val_binary_accuracy: 0.8792 - val_auc: 0.8117 - val_pr: 0.4054\n",
      "Neighbors Score: 0.3703, Labels Score: 0.8866 \tUsing time in epoch 90: 25.6432s\n",
      "Epoch 91/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2881 - binary_accuracy: 0.8837 - auc: 0.8350 - pr: 0.4457 - val_loss: 0.3048 - val_binary_accuracy: 0.8792 - val_auc: 0.8114 - val_pr: 0.4051\n",
      "Neighbors Score: 0.3712, Labels Score: 0.8837 \tUsing time in epoch 91: 25.8475s\n",
      "Epoch 92/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2879 - binary_accuracy: 0.8837 - auc: 0.8352 - pr: 0.4463 - val_loss: 0.3040 - val_binary_accuracy: 0.8794 - val_auc: 0.8119 - val_pr: 0.4064\n",
      "Neighbors Score: 0.3749, Labels Score: 0.8859 \tUsing time in epoch 92: 26.7891s\n",
      "Epoch 93/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2879 - binary_accuracy: 0.8837 - auc: 0.8353 - pr: 0.4465 - val_loss: 0.3049 - val_binary_accuracy: 0.8793 - val_auc: 0.8114 - val_pr: 0.4051\n",
      "Neighbors Score: 0.3807, Labels Score: 0.8878 \tUsing time in epoch 93: 25.5804s\n",
      "Epoch 94/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2879 - binary_accuracy: 0.8836 - auc: 0.8353 - pr: 0.4461 - val_loss: 0.3046 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4060\n",
      "Neighbors Score: 0.3706, Labels Score: 0.8827 \tUsing time in epoch 94: 25.9757s\n",
      "Epoch 95/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2878 - binary_accuracy: 0.8837 - auc: 0.8355 - pr: 0.4466 - val_loss: 0.3045 - val_binary_accuracy: 0.8792 - val_auc: 0.8118 - val_pr: 0.4058\n",
      "Neighbors Score: 0.3759, Labels Score: 0.8855 \tUsing time in epoch 95: 25.1642s\n",
      "Epoch 96/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2877 - binary_accuracy: 0.8837 - auc: 0.8355 - pr: 0.4468 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8117 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3804, Labels Score: 0.8867 \tUsing time in epoch 96: 27.3567s\n",
      "Epoch 97/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2877 - binary_accuracy: 0.8837 - auc: 0.8356 - pr: 0.4466 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3762, Labels Score: 0.8858 \tUsing time in epoch 97: 27.2061s\n",
      "Epoch 98/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2877 - binary_accuracy: 0.8837 - auc: 0.8356 - pr: 0.4467 - val_loss: 0.3042 - val_binary_accuracy: 0.8793 - val_auc: 0.8119 - val_pr: 0.4065\n",
      "Neighbors Score: 0.3726, Labels Score: 0.8855 \tUsing time in epoch 98: 25.2735s\n",
      "Epoch 99/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2876 - binary_accuracy: 0.8837 - auc: 0.8358 - pr: 0.4471 - val_loss: 0.3036 - val_binary_accuracy: 0.8794 - val_auc: 0.8122 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3735, Labels Score: 0.8849 \tUsing time in epoch 99: 26.6835s\n",
      "Epoch 100/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2875 - binary_accuracy: 0.8838 - auc: 0.8359 - pr: 0.4475 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8122 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3788, Labels Score: 0.8864 \tUsing time in epoch 100: 26.1599s\n",
      "Epoch 101/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2875 - binary_accuracy: 0.8837 - auc: 0.8359 - pr: 0.4474 - val_loss: 0.3043 - val_binary_accuracy: 0.8793 - val_auc: 0.8117 - val_pr: 0.4054\n",
      "Neighbors Score: 0.3787, Labels Score: 0.8884 \tUsing time in epoch 101: 25.6366s\n",
      "Epoch 102/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2875 - binary_accuracy: 0.8837 - auc: 0.8360 - pr: 0.4472 - val_loss: 0.3046 - val_binary_accuracy: 0.8792 - val_auc: 0.8118 - val_pr: 0.4063\n",
      "Neighbors Score: 0.3754, Labels Score: 0.8877 \tUsing time in epoch 102: 27.6916s\n",
      "Epoch 103/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2875 - binary_accuracy: 0.8837 - auc: 0.8360 - pr: 0.4473 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3796, Labels Score: 0.8871 \tUsing time in epoch 103: 28.2777s\n",
      "Epoch 104/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2874 - binary_accuracy: 0.8837 - auc: 0.8361 - pr: 0.4475 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3805, Labels Score: 0.8843 \tUsing time in epoch 104: 25.6754s\n",
      "Epoch 105/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2874 - binary_accuracy: 0.8837 - auc: 0.8362 - pr: 0.4477 - val_loss: 0.3046 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4059\n",
      "Neighbors Score: 0.3797, Labels Score: 0.8864 \tUsing time in epoch 105: 31.0918s\n",
      "Epoch 106/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.2873 - binary_accuracy: 0.8838 - auc: 0.8363 - pr: 0.4478 - val_loss: 0.3039 - val_binary_accuracy: 0.8793 - val_auc: 0.8120 - val_pr: 0.4060\n",
      "Neighbors Score: 0.3782, Labels Score: 0.8853 \tUsing time in epoch 106: 29.1876s\n",
      "Epoch 107/1000\n",
      "680/680 [==============================] - 157s 226ms/step - loss: 0.2874 - binary_accuracy: 0.8837 - auc: 0.8362 - pr: 0.4475 - val_loss: 0.3040 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3851, Labels Score: 0.8866 \tUsing time in epoch 107: 25.7348s\n",
      "Epoch 108/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2873 - binary_accuracy: 0.8838 - auc: 0.8363 - pr: 0.4478 - val_loss: 0.3035 - val_binary_accuracy: 0.8794 - val_auc: 0.8124 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3792, Labels Score: 0.8853 \tUsing time in epoch 108: 26.4708s\n",
      "Epoch 109/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2872 - binary_accuracy: 0.8838 - auc: 0.8365 - pr: 0.4481 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8122 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3770, Labels Score: 0.8885 \tUsing time in epoch 109: 25.7877s\n",
      "Epoch 110/1000\n",
      "680/680 [==============================] - 158s 228ms/step - loss: 0.2872 - binary_accuracy: 0.8838 - auc: 0.8365 - pr: 0.4480 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3858, Labels Score: 0.8888 \tUsing time in epoch 110: 26.5648s\n",
      "Epoch 111/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2872 - binary_accuracy: 0.8838 - auc: 0.8365 - pr: 0.4481 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3846, Labels Score: 0.8887 \tUsing time in epoch 111: 27.4412s\n",
      "Epoch 112/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2872 - binary_accuracy: 0.8838 - auc: 0.8366 - pr: 0.4481 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3894, Labels Score: 0.8894 \tUsing time in epoch 112: 27.6545s\n",
      "Epoch 113/1000\n",
      "680/680 [==============================] - 155s 226ms/step - loss: 0.2871 - binary_accuracy: 0.8838 - auc: 0.8366 - pr: 0.4483 - val_loss: 0.3040 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4071\n",
      "Neighbors Score: 0.3835, Labels Score: 0.8880 \tUsing time in epoch 113: 29.2374s\n",
      "Epoch 114/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2870 - binary_accuracy: 0.8838 - auc: 0.8367 - pr: 0.4485 - val_loss: 0.3044 - val_binary_accuracy: 0.8793 - val_auc: 0.8119 - val_pr: 0.4067\n",
      "Neighbors Score: 0.3852, Labels Score: 0.8868 \tUsing time in epoch 114: 27.3317s\n",
      "Epoch 115/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2870 - binary_accuracy: 0.8838 - auc: 0.8368 - pr: 0.4485 - val_loss: 0.3045 - val_binary_accuracy: 0.8792 - val_auc: 0.8114 - val_pr: 0.4058\n",
      "Neighbors Score: 0.3855, Labels Score: 0.8914 \tUsing time in epoch 115: 26.4893s\n",
      "Epoch 116/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2870 - binary_accuracy: 0.8838 - auc: 0.8368 - pr: 0.4487 - val_loss: 0.3045 - val_binary_accuracy: 0.8793 - val_auc: 0.8115 - val_pr: 0.4060\n",
      "Neighbors Score: 0.3834, Labels Score: 0.8875 \tUsing time in epoch 116: 28.0825s\n",
      "Epoch 117/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2869 - binary_accuracy: 0.8838 - auc: 0.8370 - pr: 0.4490 - val_loss: 0.3043 - val_binary_accuracy: 0.8794 - val_auc: 0.8118 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3826, Labels Score: 0.8864 \tUsing time in epoch 117: 29.5220s\n",
      "Epoch 118/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2869 - binary_accuracy: 0.8838 - auc: 0.8370 - pr: 0.4488 - val_loss: 0.3036 - val_binary_accuracy: 0.8795 - val_auc: 0.8123 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3833, Labels Score: 0.8877 \tUsing time in epoch 118: 25.8667s\n",
      "Epoch 119/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2869 - binary_accuracy: 0.8838 - auc: 0.8370 - pr: 0.4490 - val_loss: 0.3038 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3835, Labels Score: 0.8849 \tUsing time in epoch 119: 27.2296s\n",
      "Epoch 120/1000\n",
      "680/680 [==============================] - 156s 228ms/step - loss: 0.2869 - binary_accuracy: 0.8838 - auc: 0.8371 - pr: 0.4489 - val_loss: 0.3040 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4071\n",
      "Neighbors Score: 0.3847, Labels Score: 0.8882 \tUsing time in epoch 120: 25.9605s\n",
      "Epoch 121/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2868 - binary_accuracy: 0.8838 - auc: 0.8371 - pr: 0.4490 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3878, Labels Score: 0.8884 \tUsing time in epoch 121: 26.7785s\n",
      "Epoch 122/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.2868 - binary_accuracy: 0.8838 - auc: 0.8372 - pr: 0.4492 - val_loss: 0.3040 - val_binary_accuracy: 0.8794 - val_auc: 0.8119 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3860, Labels Score: 0.8870 \tUsing time in epoch 122: 26.3848s\n",
      "Epoch 123/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2868 - binary_accuracy: 0.8838 - auc: 0.8372 - pr: 0.4491 - val_loss: 0.3045 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4065\n",
      "Neighbors Score: 0.3884, Labels Score: 0.8891 \tUsing time in epoch 123: 26.4708s\n",
      "Epoch 124/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2867 - binary_accuracy: 0.8838 - auc: 0.8372 - pr: 0.4493 - val_loss: 0.3043 - val_binary_accuracy: 0.8793 - val_auc: 0.8118 - val_pr: 0.4062\n",
      "Neighbors Score: 0.3888, Labels Score: 0.8882 \tUsing time in epoch 124: 26.8536s\n",
      "Epoch 125/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2867 - binary_accuracy: 0.8839 - auc: 0.8374 - pr: 0.4495 - val_loss: 0.3042 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3905, Labels Score: 0.8900 \tUsing time in epoch 125: 25.8470s\n",
      "Epoch 126/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2867 - binary_accuracy: 0.8839 - auc: 0.8374 - pr: 0.4493 - val_loss: 0.3036 - val_binary_accuracy: 0.8795 - val_auc: 0.8124 - val_pr: 0.4081\n",
      "Neighbors Score: 0.3929, Labels Score: 0.8881 \tUsing time in epoch 126: 27.3123s\n",
      "Epoch 127/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2866 - binary_accuracy: 0.8838 - auc: 0.8374 - pr: 0.4495 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8120 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3883, Labels Score: 0.8901 \tUsing time in epoch 127: 26.5573s\n",
      "Epoch 128/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2866 - binary_accuracy: 0.8839 - auc: 0.8375 - pr: 0.4496 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8119 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3847, Labels Score: 0.8877 \tUsing time in epoch 128: 26.2946s\n",
      "Epoch 129/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2866 - binary_accuracy: 0.8839 - auc: 0.8375 - pr: 0.4497 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8118 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3880, Labels Score: 0.8883 \tUsing time in epoch 129: 26.0769s\n",
      "Epoch 130/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2865 - binary_accuracy: 0.8839 - auc: 0.8376 - pr: 0.4498 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3881, Labels Score: 0.8905 \tUsing time in epoch 130: 26.7934s\n",
      "Epoch 131/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2866 - binary_accuracy: 0.8839 - auc: 0.8374 - pr: 0.4496 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8121 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3901, Labels Score: 0.8903 \tUsing time in epoch 131: 26.7222s\n",
      "Epoch 132/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2865 - binary_accuracy: 0.8839 - auc: 0.8377 - pr: 0.4500 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3916, Labels Score: 0.8897 \tUsing time in epoch 132: 28.6470s\n",
      "Epoch 133/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2865 - binary_accuracy: 0.8839 - auc: 0.8376 - pr: 0.4498 - val_loss: 0.3042 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3883, Labels Score: 0.8869 \tUsing time in epoch 133: 26.8708s\n",
      "Epoch 134/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2865 - binary_accuracy: 0.8839 - auc: 0.8376 - pr: 0.4498 - val_loss: 0.3037 - val_binary_accuracy: 0.8796 - val_auc: 0.8122 - val_pr: 0.4083\n",
      "Neighbors Score: 0.3888, Labels Score: 0.8864 \tUsing time in epoch 134: 26.5704s\n",
      "Epoch 135/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2865 - binary_accuracy: 0.8838 - auc: 0.8376 - pr: 0.4496 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8122 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3926, Labels Score: 0.8889 \tUsing time in epoch 135: 27.8381s\n",
      "Epoch 136/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2865 - binary_accuracy: 0.8839 - auc: 0.8377 - pr: 0.4500 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8123 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3861, Labels Score: 0.8874 \tUsing time in epoch 136: 26.4816s\n",
      "Epoch 137/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2864 - binary_accuracy: 0.8839 - auc: 0.8378 - pr: 0.4501 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3876, Labels Score: 0.8890 \tUsing time in epoch 137: 27.3792s\n",
      "Epoch 138/1000\n",
      "680/680 [==============================] - 158s 228ms/step - loss: 0.2864 - binary_accuracy: 0.8839 - auc: 0.8377 - pr: 0.4499 - val_loss: 0.3040 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3905, Labels Score: 0.8885 \tUsing time in epoch 138: 26.0688s\n",
      "Epoch 139/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2863 - binary_accuracy: 0.8839 - auc: 0.8379 - pr: 0.4504 - val_loss: 0.3039 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3873, Labels Score: 0.8876 \tUsing time in epoch 139: 26.4731s\n",
      "Epoch 140/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2863 - binary_accuracy: 0.8839 - auc: 0.8379 - pr: 0.4503 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3928, Labels Score: 0.8888 \tUsing time in epoch 140: 28.9034s\n",
      "Epoch 141/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2863 - binary_accuracy: 0.8839 - auc: 0.8380 - pr: 0.4504 - val_loss: 0.3045 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4065\n",
      "Neighbors Score: 0.3898, Labels Score: 0.8906 \tUsing time in epoch 141: 27.5755s\n",
      "Epoch 142/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2864 - binary_accuracy: 0.8839 - auc: 0.8379 - pr: 0.4501 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3940, Labels Score: 0.8913 \tUsing time in epoch 142: 27.3248s\n",
      "Epoch 143/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2863 - binary_accuracy: 0.8839 - auc: 0.8380 - pr: 0.4505 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3884, Labels Score: 0.8891 \tUsing time in epoch 143: 27.5334s\n",
      "Epoch 144/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2864 - binary_accuracy: 0.8839 - auc: 0.8379 - pr: 0.4501 - val_loss: 0.3043 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3918, Labels Score: 0.8917 \tUsing time in epoch 144: 27.9204s\n",
      "Epoch 145/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2863 - binary_accuracy: 0.8839 - auc: 0.8380 - pr: 0.4503 - val_loss: 0.3043 - val_binary_accuracy: 0.8794 - val_auc: 0.8119 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3872, Labels Score: 0.8886 \tUsing time in epoch 145: 25.6989s\n",
      "Epoch 146/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2862 - binary_accuracy: 0.8839 - auc: 0.8381 - pr: 0.4505 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8122 - val_pr: 0.4079\n",
      "Neighbors Score: 0.3916, Labels Score: 0.8896 \tUsing time in epoch 146: 27.3616s\n",
      "Epoch 147/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2862 - binary_accuracy: 0.8839 - auc: 0.8381 - pr: 0.4506 - val_loss: 0.3044 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3938, Labels Score: 0.8892 \tUsing time in epoch 147: 28.1304s\n",
      "Epoch 148/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2862 - binary_accuracy: 0.8839 - auc: 0.8381 - pr: 0.4506 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3917, Labels Score: 0.8898 \tUsing time in epoch 148: 28.7152s\n",
      "Epoch 149/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2861 - binary_accuracy: 0.8840 - auc: 0.8382 - pr: 0.4509 - val_loss: 0.3039 - val_binary_accuracy: 0.8794 - val_auc: 0.8122 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3918, Labels Score: 0.8894 \tUsing time in epoch 149: 28.2257s\n",
      "Epoch 150/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2862 - binary_accuracy: 0.8839 - auc: 0.8382 - pr: 0.4508 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3953, Labels Score: 0.8912 \tUsing time in epoch 150: 28.4136s\n",
      "Epoch 151/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2862 - binary_accuracy: 0.8839 - auc: 0.8382 - pr: 0.4506 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3931, Labels Score: 0.8880 \tUsing time in epoch 151: 26.2629s\n",
      "Epoch 152/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2862 - binary_accuracy: 0.8839 - auc: 0.8382 - pr: 0.4507 - val_loss: 0.3042 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3950, Labels Score: 0.8892 \tUsing time in epoch 152: 26.1941s\n",
      "Epoch 153/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2861 - binary_accuracy: 0.8839 - auc: 0.8382 - pr: 0.4509 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8119 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3966, Labels Score: 0.8901 \tUsing time in epoch 153: 26.7933s\n",
      "Epoch 154/1000\n",
      "680/680 [==============================] - 166s 239ms/step - loss: 0.2861 - binary_accuracy: 0.8839 - auc: 0.8383 - pr: 0.4508 - val_loss: 0.3035 - val_binary_accuracy: 0.8796 - val_auc: 0.8122 - val_pr: 0.4081\n",
      "Neighbors Score: 0.3942, Labels Score: 0.8879 \tUsing time in epoch 154: 26.4795s\n",
      "Epoch 155/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2861 - binary_accuracy: 0.8839 - auc: 0.8383 - pr: 0.4509 - val_loss: 0.3035 - val_binary_accuracy: 0.8795 - val_auc: 0.8123 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3947, Labels Score: 0.8897 \tUsing time in epoch 155: 28.0350s\n",
      "Epoch 156/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2861 - binary_accuracy: 0.8839 - auc: 0.8384 - pr: 0.4511 - val_loss: 0.3045 - val_binary_accuracy: 0.8793 - val_auc: 0.8119 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3965, Labels Score: 0.8917 \tUsing time in epoch 156: 27.4691s\n",
      "Epoch 157/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2861 - binary_accuracy: 0.8839 - auc: 0.8384 - pr: 0.4510 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8118 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3955, Labels Score: 0.8907 \tUsing time in epoch 157: 25.7028s\n",
      "Epoch 158/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2861 - binary_accuracy: 0.8839 - auc: 0.8384 - pr: 0.4509 - val_loss: 0.3044 - val_binary_accuracy: 0.8794 - val_auc: 0.8118 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3965, Labels Score: 0.8917 \tUsing time in epoch 158: 25.8026s\n",
      "Epoch 159/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2860 - binary_accuracy: 0.8839 - auc: 0.8385 - pr: 0.4511 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3950, Labels Score: 0.8883 \tUsing time in epoch 159: 25.4838s\n",
      "Epoch 160/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2861 - binary_accuracy: 0.8839 - auc: 0.8384 - pr: 0.4510 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3937, Labels Score: 0.8893 \tUsing time in epoch 160: 25.9053s\n",
      "Epoch 161/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2860 - binary_accuracy: 0.8840 - auc: 0.8384 - pr: 0.4512 - val_loss: 0.3037 - val_binary_accuracy: 0.8796 - val_auc: 0.8122 - val_pr: 0.4083\n",
      "Neighbors Score: 0.3964, Labels Score: 0.8910 \tUsing time in epoch 161: 26.6878s\n",
      "Epoch 162/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8386 - pr: 0.4513 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3957, Labels Score: 0.8909 \tUsing time in epoch 162: 26.9841s\n",
      "Epoch 163/1000\n",
      "680/680 [==============================] - 157s 229ms/step - loss: 0.2860 - binary_accuracy: 0.8839 - auc: 0.8385 - pr: 0.4512 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3969, Labels Score: 0.8908 \tUsing time in epoch 163: 28.6346s\n",
      "Epoch 164/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8386 - pr: 0.4515 - val_loss: 0.3037 - val_binary_accuracy: 0.8795 - val_auc: 0.8122 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3961, Labels Score: 0.8894 \tUsing time in epoch 164: 27.0333s\n",
      "Epoch 165/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8386 - pr: 0.4513 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3978, Labels Score: 0.8895 \tUsing time in epoch 165: 26.8961s\n",
      "Epoch 166/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8387 - pr: 0.4515 - val_loss: 0.3040 - val_binary_accuracy: 0.8794 - val_auc: 0.8121 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3971, Labels Score: 0.8923 \tUsing time in epoch 166: 26.6179s\n",
      "Epoch 167/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8386 - pr: 0.4514 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4071\n",
      "Neighbors Score: 0.3967, Labels Score: 0.8925 \tUsing time in epoch 167: 25.3180s\n",
      "Epoch 168/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8387 - pr: 0.4514 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4079\n",
      "Neighbors Score: 0.3967, Labels Score: 0.8898 \tUsing time in epoch 168: 27.5014s\n",
      "Epoch 169/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8386 - pr: 0.4514 - val_loss: 0.3033 - val_binary_accuracy: 0.8796 - val_auc: 0.8124 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3961, Labels Score: 0.8884 \tUsing time in epoch 169: 26.1871s\n",
      "Epoch 170/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8387 - pr: 0.4515 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3996, Labels Score: 0.8916 \tUsing time in epoch 170: 26.0022s\n",
      "Epoch 171/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2859 - binary_accuracy: 0.8840 - auc: 0.8387 - pr: 0.4513 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4071\n",
      "Neighbors Score: 0.3968, Labels Score: 0.8898 \tUsing time in epoch 171: 25.4336s\n",
      "Epoch 172/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8387 - pr: 0.4517 - val_loss: 0.3043 - val_binary_accuracy: 0.8794 - val_auc: 0.8120 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8908 \tUsing time in epoch 172: 26.3683s\n",
      "Epoch 173/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8387 - pr: 0.4515 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3986, Labels Score: 0.8912 \tUsing time in epoch 173: 26.0361s\n",
      "Epoch 174/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8388 - pr: 0.4516 - val_loss: 0.3038 - val_binary_accuracy: 0.8796 - val_auc: 0.8121 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3970, Labels Score: 0.8901 \tUsing time in epoch 174: 28.1757s\n",
      "Epoch 175/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8388 - pr: 0.4518 - val_loss: 0.3038 - val_binary_accuracy: 0.8796 - val_auc: 0.8121 - val_pr: 0.4081\n",
      "Neighbors Score: 0.3987, Labels Score: 0.8912 \tUsing time in epoch 175: 27.3912s\n",
      "Epoch 176/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8388 - pr: 0.4515 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8119 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4006, Labels Score: 0.8934 \tUsing time in epoch 176: 26.5088s\n",
      "Epoch 177/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8388 - pr: 0.4517 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4079\n",
      "Neighbors Score: 0.3971, Labels Score: 0.8905 \tUsing time in epoch 177: 25.4201s\n",
      "Epoch 178/1000\n",
      "680/680 [==============================] - 162s 232ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8388 - pr: 0.4516 - val_loss: 0.3036 - val_binary_accuracy: 0.8795 - val_auc: 0.8122 - val_pr: 0.4079\n",
      "Neighbors Score: 0.3979, Labels Score: 0.8908 \tUsing time in epoch 178: 26.0672s\n",
      "Epoch 179/1000\n",
      "680/680 [==============================] - 162s 232ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8388 - pr: 0.4518 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3972, Labels Score: 0.8918 \tUsing time in epoch 179: 25.5250s\n",
      "Epoch 180/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2858 - binary_accuracy: 0.8840 - auc: 0.8388 - pr: 0.4516 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3997, Labels Score: 0.8934 \tUsing time in epoch 180: 27.2278s\n",
      "Epoch 181/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2857 - binary_accuracy: 0.8840 - auc: 0.8390 - pr: 0.4521 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3981, Labels Score: 0.8924 \tUsing time in epoch 181: 26.4099s\n",
      "Epoch 182/1000\n",
      "680/680 [==============================] - 160s 234ms/step - loss: 0.2857 - binary_accuracy: 0.8840 - auc: 0.8390 - pr: 0.4521 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3982, Labels Score: 0.8918 \tUsing time in epoch 182: 26.4286s\n",
      "Epoch 183/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2857 - binary_accuracy: 0.8840 - auc: 0.8390 - pr: 0.4521 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3998, Labels Score: 0.8915 \tUsing time in epoch 183: 26.0691s\n",
      "Epoch 184/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2857 - binary_accuracy: 0.8840 - auc: 0.8390 - pr: 0.4520 - val_loss: 0.3037 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3982, Labels Score: 0.8900 \tUsing time in epoch 184: 27.7081s\n",
      "Epoch 185/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2857 - binary_accuracy: 0.8840 - auc: 0.8390 - pr: 0.4521 - val_loss: 0.3037 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4080\n",
      "Neighbors Score: 0.4007, Labels Score: 0.8912 \tUsing time in epoch 185: 26.4259s\n",
      "Epoch 186/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2857 - binary_accuracy: 0.8840 - auc: 0.8390 - pr: 0.4520 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3971, Labels Score: 0.8904 \tUsing time in epoch 186: 25.6792s\n",
      "Epoch 187/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2857 - binary_accuracy: 0.8840 - auc: 0.8389 - pr: 0.4520 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3974, Labels Score: 0.8906 \tUsing time in epoch 187: 26.5700s\n",
      "Epoch 188/1000\n",
      "680/680 [==============================] - 166s 239ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8391 - pr: 0.4521 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8119 - val_pr: 0.4073\n",
      "Neighbors Score: 0.4000, Labels Score: 0.8921 \tUsing time in epoch 188: 26.5440s\n",
      "Epoch 189/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8391 - pr: 0.4521 - val_loss: 0.3036 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4080\n",
      "Neighbors Score: 0.4001, Labels Score: 0.8914 \tUsing time in epoch 189: 26.4452s\n",
      "Epoch 190/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8391 - pr: 0.4522 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3987, Labels Score: 0.8918 \tUsing time in epoch 190: 26.6142s\n",
      "Epoch 191/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8391 - pr: 0.4522 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3964, Labels Score: 0.8902 \tUsing time in epoch 191: 26.7307s\n",
      "Epoch 192/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8392 - pr: 0.4522 - val_loss: 0.3036 - val_binary_accuracy: 0.8796 - val_auc: 0.8122 - val_pr: 0.4082\n",
      "Neighbors Score: 0.4003, Labels Score: 0.8924 \tUsing time in epoch 192: 25.7273s\n",
      "Epoch 193/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8391 - pr: 0.4522 - val_loss: 0.3045 - val_binary_accuracy: 0.8794 - val_auc: 0.8117 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4015, Labels Score: 0.8927 \tUsing time in epoch 193: 26.5311s\n",
      "Epoch 194/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8392 - pr: 0.4523 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8120 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3996, Labels Score: 0.8907 \tUsing time in epoch 194: 26.4875s\n",
      "Epoch 195/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8392 - pr: 0.4522 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8119 - val_pr: 0.4077\n",
      "Neighbors Score: 0.4005, Labels Score: 0.8910 \tUsing time in epoch 195: 25.6931s\n",
      "Epoch 196/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8392 - pr: 0.4522 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8905 \tUsing time in epoch 196: 26.3459s\n",
      "Epoch 197/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8392 - pr: 0.4524 - val_loss: 0.3035 - val_binary_accuracy: 0.8796 - val_auc: 0.8121 - val_pr: 0.4083\n",
      "Neighbors Score: 0.3973, Labels Score: 0.8904 \tUsing time in epoch 197: 26.2142s\n",
      "Epoch 198/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4525 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3971, Labels Score: 0.8899 \tUsing time in epoch 198: 26.1011s\n",
      "Epoch 199/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8392 - pr: 0.4522 - val_loss: 0.3038 - val_binary_accuracy: 0.8796 - val_auc: 0.8120 - val_pr: 0.4078\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8918 \tUsing time in epoch 199: 26.5209s\n",
      "Epoch 200/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4524 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4079\n",
      "Neighbors Score: 0.4018, Labels Score: 0.8922 \tUsing time in epoch 200: 29.9910s\n",
      "Epoch 201/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2856 - binary_accuracy: 0.8840 - auc: 0.8392 - pr: 0.4522 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3989, Labels Score: 0.8910 \tUsing time in epoch 201: 25.8258s\n",
      "Epoch 202/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4524 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8906 \tUsing time in epoch 202: 25.6193s\n",
      "Epoch 203/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4525 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3973, Labels Score: 0.8912 \tUsing time in epoch 203: 26.6250s\n",
      "Epoch 204/1000\n",
      "680/680 [==============================] - 160s 234ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4523 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3973, Labels Score: 0.8931 \tUsing time in epoch 204: 26.9688s\n",
      "Epoch 205/1000\n",
      "680/680 [==============================] - 163s 233ms/step - loss: 0.2855 - binary_accuracy: 0.8841 - auc: 0.8393 - pr: 0.4526 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3961, Labels Score: 0.8920 \tUsing time in epoch 205: 25.9903s\n",
      "Epoch 206/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4525 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8119 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3976, Labels Score: 0.8918 \tUsing time in epoch 206: 26.9363s\n",
      "Epoch 207/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4526 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3973, Labels Score: 0.8916 \tUsing time in epoch 207: 26.4102s\n",
      "Epoch 208/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8393 - pr: 0.4525 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8119 - val_pr: 0.4080\n",
      "Neighbors Score: 0.4004, Labels Score: 0.8919 \tUsing time in epoch 208: 25.4325s\n",
      "Epoch 209/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8394 - pr: 0.4527 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4077\n",
      "Neighbors Score: 0.4005, Labels Score: 0.8923 \tUsing time in epoch 209: 25.2572s\n",
      "Epoch 210/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8394 - pr: 0.4527 - val_loss: 0.3037 - val_binary_accuracy: 0.8795 - val_auc: 0.8119 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3990, Labels Score: 0.8914 \tUsing time in epoch 210: 26.1295s\n",
      "Epoch 211/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8394 - pr: 0.4527 - val_loss: 0.3037 - val_binary_accuracy: 0.8796 - val_auc: 0.8121 - val_pr: 0.4082\n",
      "Neighbors Score: 0.3986, Labels Score: 0.8921 \tUsing time in epoch 211: 25.6146s\n",
      "Epoch 212/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2855 - binary_accuracy: 0.8840 - auc: 0.8394 - pr: 0.4526 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4079\n",
      "Neighbors Score: 0.3976, Labels Score: 0.8919 \tUsing time in epoch 212: 25.5433s\n",
      "Epoch 213/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4528 - val_loss: 0.3037 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4077\n",
      "Neighbors Score: 0.3979, Labels Score: 0.8911 \tUsing time in epoch 213: 25.3355s\n",
      "Epoch 214/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4529 - val_loss: 0.3037 - val_binary_accuracy: 0.8795 - val_auc: 0.8121 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3990, Labels Score: 0.8919 \tUsing time in epoch 214: 26.2748s\n",
      "Epoch 215/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4528 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8120 - val_pr: 0.4079\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8924 \tUsing time in epoch 215: 26.0523s\n",
      "Epoch 216/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4528 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8120 - val_pr: 0.4081\n",
      "Neighbors Score: 0.3976, Labels Score: 0.8921 \tUsing time in epoch 216: 26.1962s\n",
      "Epoch 217/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4529 - val_loss: 0.3037 - val_binary_accuracy: 0.8796 - val_auc: 0.8119 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3989, Labels Score: 0.8924 \tUsing time in epoch 217: 26.4010s\n",
      "Epoch 218/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2854 - binary_accuracy: 0.8840 - auc: 0.8395 - pr: 0.4527 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3976, Labels Score: 0.8917 \tUsing time in epoch 218: 25.5677s\n",
      "Epoch 219/1000\n",
      "680/680 [==============================] - 158s 228ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4529 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3986, Labels Score: 0.8939 \tUsing time in epoch 219: 25.4083s\n",
      "Epoch 220/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2854 - binary_accuracy: 0.8840 - auc: 0.8396 - pr: 0.4528 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3987, Labels Score: 0.8926 \tUsing time in epoch 220: 25.4305s\n",
      "Epoch 221/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4528 - val_loss: 0.3037 - val_binary_accuracy: 0.8796 - val_auc: 0.8119 - val_pr: 0.4081\n",
      "Neighbors Score: 0.3987, Labels Score: 0.8926 \tUsing time in epoch 221: 26.7052s\n",
      "Epoch 222/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4528 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4076\n",
      "Neighbors Score: 0.4011, Labels Score: 0.8924 \tUsing time in epoch 222: 26.6325s\n",
      "Epoch 223/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4529 - val_loss: 0.3044 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3976, Labels Score: 0.8913 \tUsing time in epoch 223: 26.3406s\n",
      "Epoch 224/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8396 - pr: 0.4531 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3981, Labels Score: 0.8918 \tUsing time in epoch 224: 26.3440s\n",
      "Epoch 225/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8396 - pr: 0.4530 - val_loss: 0.3042 - val_binary_accuracy: 0.8794 - val_auc: 0.8115 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3978, Labels Score: 0.8922 \tUsing time in epoch 225: 26.4339s\n",
      "Epoch 226/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8396 - pr: 0.4530 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3973, Labels Score: 0.8908 \tUsing time in epoch 226: 26.1894s\n",
      "Epoch 227/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8396 - pr: 0.4530 - val_loss: 0.3036 - val_binary_accuracy: 0.8796 - val_auc: 0.8118 - val_pr: 0.4078\n",
      "Neighbors Score: 0.3977, Labels Score: 0.8917 \tUsing time in epoch 227: 26.6399s\n",
      "Epoch 228/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4531 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3990, Labels Score: 0.8910 \tUsing time in epoch 228: 25.9400s\n",
      "Epoch 229/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8396 - pr: 0.4531 - val_loss: 0.3038 - val_binary_accuracy: 0.8796 - val_auc: 0.8118 - val_pr: 0.4081\n",
      "Neighbors Score: 0.3998, Labels Score: 0.8921 \tUsing time in epoch 229: 28.7426s\n",
      "Epoch 230/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4531 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3998, Labels Score: 0.8934 \tUsing time in epoch 230: 26.4627s\n",
      "Epoch 231/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8396 - pr: 0.4531 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3980, Labels Score: 0.8922 \tUsing time in epoch 231: 26.3153s\n",
      "Epoch 232/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2854 - binary_accuracy: 0.8841 - auc: 0.8395 - pr: 0.4528 - val_loss: 0.3038 - val_binary_accuracy: 0.8796 - val_auc: 0.8118 - val_pr: 0.4080\n",
      "Neighbors Score: 0.4004, Labels Score: 0.8920 \tUsing time in epoch 232: 26.5829s\n",
      "Epoch 233/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8396 - pr: 0.4529 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8119 - val_pr: 0.4080\n",
      "Neighbors Score: 0.3989, Labels Score: 0.8921 \tUsing time in epoch 233: 26.4672s\n",
      "Epoch 234/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4532 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3972, Labels Score: 0.8917 \tUsing time in epoch 234: 26.1049s\n",
      "Epoch 235/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4531 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8118 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3969, Labels Score: 0.8906 \tUsing time in epoch 235: 25.6987s\n",
      "Epoch 236/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4531 - val_loss: 0.3037 - val_binary_accuracy: 0.8796 - val_auc: 0.8119 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3967, Labels Score: 0.8906 \tUsing time in epoch 236: 26.8325s\n",
      "Epoch 237/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4531 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8921 \tUsing time in epoch 237: 28.6292s\n",
      "Epoch 238/1000\n",
      "680/680 [==============================] - 162s 237ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4532 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8118 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3990, Labels Score: 0.8922 \tUsing time in epoch 238: 25.3784s\n",
      "Epoch 239/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4534 - val_loss: 0.3042 - val_binary_accuracy: 0.8794 - val_auc: 0.8116 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3979, Labels Score: 0.8932 \tUsing time in epoch 239: 26.0145s\n",
      "Epoch 240/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4533 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4074\n",
      "Neighbors Score: 0.4000, Labels Score: 0.8918 \tUsing time in epoch 240: 26.2505s\n",
      "Epoch 241/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2853 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4530 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4071\n",
      "Neighbors Score: 0.3992, Labels Score: 0.8917 \tUsing time in epoch 241: 26.4473s\n",
      "Epoch 242/1000\n",
      "680/680 [==============================] - 158s 231ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8397 - pr: 0.4532 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8115 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3971, Labels Score: 0.8922 \tUsing time in epoch 242: 25.9373s\n",
      "Epoch 243/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4533 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3977, Labels Score: 0.8933 \tUsing time in epoch 243: 25.8746s\n",
      "Epoch 244/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4533 - val_loss: 0.3038 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3990, Labels Score: 0.8923 \tUsing time in epoch 244: 26.1865s\n",
      "Epoch 245/1000\n",
      "680/680 [==============================] - 164s 235ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4532 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8116 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3983, Labels Score: 0.8911 \tUsing time in epoch 245: 26.0588s\n",
      "Epoch 246/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4534 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4071\n",
      "Neighbors Score: 0.3994, Labels Score: 0.8920 \tUsing time in epoch 246: 28.1342s\n",
      "Epoch 247/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4532 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4008, Labels Score: 0.8940 \tUsing time in epoch 247: 25.9360s\n",
      "Epoch 248/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8398 - pr: 0.4534 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8117 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8921 \tUsing time in epoch 248: 27.6657s\n",
      "Epoch 249/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4535 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3990, Labels Score: 0.8919 \tUsing time in epoch 249: 26.1518s\n",
      "Epoch 250/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4534 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8117 - val_pr: 0.4076\n",
      "Neighbors Score: 0.3959, Labels Score: 0.8914 \tUsing time in epoch 250: 26.6414s\n",
      "Epoch 251/1000\n",
      "680/680 [==============================] - 168s 242ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4535 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8116 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3995, Labels Score: 0.8922 \tUsing time in epoch 251: 27.6650s\n",
      "Epoch 252/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4534 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3986, Labels Score: 0.8920 \tUsing time in epoch 252: 26.3316s\n",
      "Epoch 253/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4534 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3983, Labels Score: 0.8923 \tUsing time in epoch 253: 27.1345s\n",
      "Epoch 254/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4535 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8117 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3994, Labels Score: 0.8924 \tUsing time in epoch 254: 26.5604s\n",
      "Epoch 255/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2852 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4534 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8926 \tUsing time in epoch 255: 26.3406s\n",
      "Epoch 256/1000\n",
      "680/680 [==============================] - 158s 228ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4536 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4067\n",
      "Neighbors Score: 0.3983, Labels Score: 0.8926 \tUsing time in epoch 256: 26.1567s\n",
      "Epoch 257/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4535 - val_loss: 0.3043 - val_binary_accuracy: 0.8793 - val_auc: 0.8112 - val_pr: 0.4060\n",
      "Neighbors Score: 0.3974, Labels Score: 0.8920 \tUsing time in epoch 257: 25.5996s\n",
      "Epoch 258/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4534 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3993, Labels Score: 0.8928 \tUsing time in epoch 258: 26.1340s\n",
      "Epoch 259/1000\n",
      "680/680 [==============================] - 157s 226ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8399 - pr: 0.4536 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3991, Labels Score: 0.8925 \tUsing time in epoch 259: 27.6440s\n",
      "Epoch 260/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4536 - val_loss: 0.3044 - val_binary_accuracy: 0.8794 - val_auc: 0.8112 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4005, Labels Score: 0.8932 \tUsing time in epoch 260: 25.6918s\n",
      "Epoch 261/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4538 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3993, Labels Score: 0.8923 \tUsing time in epoch 261: 27.0893s\n",
      "Epoch 262/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4536 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3993, Labels Score: 0.8931 \tUsing time in epoch 262: 25.5836s\n",
      "Epoch 263/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4538 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3992, Labels Score: 0.8923 \tUsing time in epoch 263: 26.2886s\n",
      "Epoch 264/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4536 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3998, Labels Score: 0.8932 \tUsing time in epoch 264: 26.0599s\n",
      "Epoch 265/1000\n",
      "680/680 [==============================] - 155s 226ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4538 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8923 \tUsing time in epoch 265: 25.6511s\n",
      "Epoch 266/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4537 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3990, Labels Score: 0.8921 \tUsing time in epoch 266: 26.0658s\n",
      "Epoch 267/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2851 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4537 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4073\n",
      "Neighbors Score: 0.4001, Labels Score: 0.8930 \tUsing time in epoch 267: 26.0226s\n",
      "Epoch 268/1000\n",
      "680/680 [==============================] - 162s 232ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4539 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3993, Labels Score: 0.8930 \tUsing time in epoch 268: 26.1294s\n",
      "Epoch 269/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4537 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4071\n",
      "Neighbors Score: 0.3993, Labels Score: 0.8928 \tUsing time in epoch 269: 26.6127s\n",
      "Epoch 270/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4539 - val_loss: 0.3042 - val_binary_accuracy: 0.8794 - val_auc: 0.8115 - val_pr: 0.4067\n",
      "Neighbors Score: 0.3984, Labels Score: 0.8923 \tUsing time in epoch 270: 26.3897s\n",
      "Epoch 271/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4538 - val_loss: 0.3041 - val_binary_accuracy: 0.8794 - val_auc: 0.8115 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3991, Labels Score: 0.8930 \tUsing time in epoch 271: 26.6222s\n",
      "Epoch 272/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4539 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3980, Labels Score: 0.8922 \tUsing time in epoch 272: 26.4532s\n",
      "Epoch 273/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4538 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3983, Labels Score: 0.8928 \tUsing time in epoch 273: 26.1069s\n",
      "Epoch 274/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4539 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3997, Labels Score: 0.8925 \tUsing time in epoch 274: 26.1206s\n",
      "Epoch 275/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4538 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8116 - val_pr: 0.4074\n",
      "Neighbors Score: 0.3983, Labels Score: 0.8921 \tUsing time in epoch 275: 26.1607s\n",
      "Epoch 276/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8400 - pr: 0.4538 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8114 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3994, Labels Score: 0.8928 \tUsing time in epoch 276: 28.0108s\n",
      "Epoch 277/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4539 - val_loss: 0.3043 - val_binary_accuracy: 0.8794 - val_auc: 0.8112 - val_pr: 0.4065\n",
      "Neighbors Score: 0.3991, Labels Score: 0.8931 \tUsing time in epoch 277: 26.3624s\n",
      "Epoch 278/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4540 - val_loss: 0.3039 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3996, Labels Score: 0.8933 \tUsing time in epoch 278: 25.5283s\n",
      "Epoch 279/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4539 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8925 \tUsing time in epoch 279: 25.9752s\n",
      "Epoch 280/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4539 - val_loss: 0.3040 - val_binary_accuracy: 0.8795 - val_auc: 0.8115 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3996, Labels Score: 0.8927 \tUsing time in epoch 280: 26.3769s\n",
      "Epoch 281/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4540 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4070\n",
      "Neighbors Score: 0.3988, Labels Score: 0.8927 \tUsing time in epoch 281: 25.2263s\n",
      "Epoch 282/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4538 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8932 \tUsing time in epoch 282: 26.1189s\n",
      "Epoch 283/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4540 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3999, Labels Score: 0.8932 \tUsing time in epoch 283: 25.4343s\n",
      "Epoch 284/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4541 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3991, Labels Score: 0.8926 \tUsing time in epoch 284: 28.1892s\n",
      "Epoch 285/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4540 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3999, Labels Score: 0.8924 \tUsing time in epoch 285: 26.2722s\n",
      "Epoch 286/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4539 - val_loss: 0.3039 - val_binary_accuracy: 0.8796 - val_auc: 0.8114 - val_pr: 0.4072\n",
      "Neighbors Score: 0.3996, Labels Score: 0.8929 \tUsing time in epoch 286: 25.7945s\n",
      "Epoch 287/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4539 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8115 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3987, Labels Score: 0.8927 \tUsing time in epoch 287: 26.3021s\n",
      "Epoch 288/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4540 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4073\n",
      "Neighbors Score: 0.3998, Labels Score: 0.8929 \tUsing time in epoch 288: 26.1884s\n",
      "Epoch 289/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4540 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4065\n",
      "Neighbors Score: 0.3995, Labels Score: 0.8929 \tUsing time in epoch 289: 26.9336s\n",
      "Epoch 290/1000\n",
      "680/680 [==============================] - 166s 239ms/step - loss: 0.2850 - binary_accuracy: 0.8841 - auc: 0.8401 - pr: 0.4540 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4007, Labels Score: 0.8932 \tUsing time in epoch 290: 29.3165s\n",
      "Epoch 291/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4540 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8114 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4005, Labels Score: 0.8928 \tUsing time in epoch 291: 25.9669s\n",
      "Epoch 292/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4540 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3998, Labels Score: 0.8933 \tUsing time in epoch 292: 26.4676s\n",
      "Epoch 293/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4542 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8114 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4011, Labels Score: 0.8939 \tUsing time in epoch 293: 26.4253s\n",
      "Epoch 294/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8930 \tUsing time in epoch 294: 26.5586s\n",
      "Epoch 295/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4540 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8111 - val_pr: 0.4063\n",
      "Neighbors Score: 0.3992, Labels Score: 0.8935 \tUsing time in epoch 295: 26.6753s\n",
      "Epoch 296/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8402 - pr: 0.4541 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8115 - val_pr: 0.4075\n",
      "Neighbors Score: 0.3999, Labels Score: 0.8929 \tUsing time in epoch 296: 26.3977s\n",
      "Epoch 297/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4541 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8114 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8933 \tUsing time in epoch 297: 26.4423s\n",
      "Epoch 298/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4543 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8112 - val_pr: 0.4068\n",
      "Neighbors Score: 0.3997, Labels Score: 0.8934 \tUsing time in epoch 298: 25.7832s\n",
      "Epoch 299/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8112 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3993, Labels Score: 0.8926 \tUsing time in epoch 299: 27.1060s\n",
      "Epoch 300/1000\n",
      "680/680 [==============================] - 157s 229ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8402 - pr: 0.4541 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4013, Labels Score: 0.8936 \tUsing time in epoch 300: 26.3033s\n",
      "Epoch 301/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4543 - val_loss: 0.3040 - val_binary_accuracy: 0.8796 - val_auc: 0.8115 - val_pr: 0.4074\n",
      "Neighbors Score: 0.4000, Labels Score: 0.8927 \tUsing time in epoch 301: 25.9557s\n",
      "Epoch 302/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4069\n",
      "Neighbors Score: 0.3991, Labels Score: 0.8933 \tUsing time in epoch 302: 25.8283s\n",
      "Epoch 303/1000\n",
      "680/680 [==============================] - 168s 243ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8113 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4007, Labels Score: 0.8933 \tUsing time in epoch 303: 26.3401s\n",
      "Epoch 304/1000\n",
      "680/680 [==============================] - 157s 229ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4544 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4008, Labels Score: 0.8940 \tUsing time in epoch 304: 26.5406s\n",
      "Epoch 305/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4543 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4066\n",
      "Neighbors Score: 0.3998, Labels Score: 0.8936 \tUsing time in epoch 305: 25.7941s\n",
      "Epoch 306/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4541 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8111 - val_pr: 0.4067\n",
      "Neighbors Score: 0.3999, Labels Score: 0.8937 \tUsing time in epoch 306: 26.4846s\n",
      "Epoch 307/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4016, Labels Score: 0.8936 \tUsing time in epoch 307: 25.3711s\n",
      "Epoch 308/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8114 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4008, Labels Score: 0.8932 \tUsing time in epoch 308: 26.0078s\n",
      "Epoch 309/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8111 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8932 \tUsing time in epoch 309: 26.1601s\n",
      "Epoch 310/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4543 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4016, Labels Score: 0.8940 \tUsing time in epoch 310: 27.6337s\n",
      "Epoch 311/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2849 - binary_accuracy: 0.8842 - auc: 0.8403 - pr: 0.4543 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4003, Labels Score: 0.8934 \tUsing time in epoch 311: 26.9113s\n",
      "Epoch 312/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4543 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4004, Labels Score: 0.8934 \tUsing time in epoch 312: 26.5262s\n",
      "Epoch 313/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2848 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4543 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4007, Labels Score: 0.8936 \tUsing time in epoch 313: 26.4944s\n",
      "Epoch 314/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4011, Labels Score: 0.8941 \tUsing time in epoch 314: 26.9519s\n",
      "Epoch 315/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4544 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8114 - val_pr: 0.4074\n",
      "Neighbors Score: 0.4012, Labels Score: 0.8938 \tUsing time in epoch 315: 25.7663s\n",
      "Epoch 316/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2849 - binary_accuracy: 0.8841 - auc: 0.8403 - pr: 0.4542 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8940 \tUsing time in epoch 316: 26.0887s\n",
      "Epoch 317/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4543 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4014, Labels Score: 0.8939 \tUsing time in epoch 317: 25.6482s\n",
      "Epoch 318/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4544 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4011, Labels Score: 0.8935 \tUsing time in epoch 318: 25.9984s\n",
      "Epoch 319/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4545 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4016, Labels Score: 0.8940 \tUsing time in epoch 319: 26.7171s\n",
      "Epoch 320/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4543 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4018, Labels Score: 0.8937 \tUsing time in epoch 320: 26.9295s\n",
      "Epoch 321/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4544 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4016, Labels Score: 0.8938 \tUsing time in epoch 321: 26.8719s\n",
      "Epoch 322/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4543 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4073\n",
      "Neighbors Score: 0.4011, Labels Score: 0.8940 \tUsing time in epoch 322: 26.1811s\n",
      "Epoch 323/1000\n",
      "680/680 [==============================] - 159s 228ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4545 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4007, Labels Score: 0.8936 \tUsing time in epoch 323: 26.2263s\n",
      "Epoch 324/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4544 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4014, Labels Score: 0.8942 \tUsing time in epoch 324: 26.6262s\n",
      "Epoch 325/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4544 - val_loss: 0.3041 - val_binary_accuracy: 0.8795 - val_auc: 0.8111 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4004, Labels Score: 0.8937 \tUsing time in epoch 325: 26.1858s\n",
      "Epoch 326/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4544 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8113 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4010, Labels Score: 0.8936 \tUsing time in epoch 326: 26.3029s\n",
      "Epoch 327/1000\n",
      "680/680 [==============================] - 156s 227ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4545 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4002, Labels Score: 0.8939 \tUsing time in epoch 327: 25.8112s\n",
      "Epoch 328/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4545 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8112 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4016, Labels Score: 0.8943 \tUsing time in epoch 328: 26.0231s\n",
      "Epoch 329/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4544 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4008, Labels Score: 0.8931 \tUsing time in epoch 329: 26.0475s\n",
      "Epoch 330/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4545 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4004, Labels Score: 0.8937 \tUsing time in epoch 330: 26.8060s\n",
      "Epoch 331/1000\n",
      "680/680 [==============================] - 155s 226ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3041 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4015, Labels Score: 0.8941 \tUsing time in epoch 331: 26.3166s\n",
      "Epoch 332/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4545 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4011, Labels Score: 0.8938 \tUsing time in epoch 332: 26.0371s\n",
      "Epoch 333/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4545 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4024, Labels Score: 0.8944 \tUsing time in epoch 333: 26.4487s\n",
      "Epoch 334/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4545 - val_loss: 0.3042 - val_binary_accuracy: 0.8795 - val_auc: 0.8112 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4014, Labels Score: 0.8942 \tUsing time in epoch 334: 26.4609s\n",
      "Epoch 335/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8404 - pr: 0.4545 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4018, Labels Score: 0.8938 \tUsing time in epoch 335: 26.3117s\n",
      "Epoch 336/1000\n",
      "680/680 [==============================] - 159s 228ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4545 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4015, Labels Score: 0.8938 \tUsing time in epoch 336: 27.1727s\n",
      "Epoch 337/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4022, Labels Score: 0.8945 \tUsing time in epoch 337: 25.8320s\n",
      "Epoch 338/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4023, Labels Score: 0.8940 \tUsing time in epoch 338: 26.9206s\n",
      "Epoch 339/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4013, Labels Score: 0.8942 \tUsing time in epoch 339: 25.7523s\n",
      "Epoch 340/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4025, Labels Score: 0.8942 \tUsing time in epoch 340: 27.0810s\n",
      "Epoch 341/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4023, Labels Score: 0.8944 \tUsing time in epoch 341: 26.3963s\n",
      "Epoch 342/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4547 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4018, Labels Score: 0.8940 \tUsing time in epoch 342: 28.3228s\n",
      "Epoch 343/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4013, Labels Score: 0.8938 \tUsing time in epoch 343: 26.7720s\n",
      "Epoch 344/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8111 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4019, Labels Score: 0.8949 \tUsing time in epoch 344: 25.6290s\n",
      "Epoch 345/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4018, Labels Score: 0.8948 \tUsing time in epoch 345: 26.0054s\n",
      "Epoch 346/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4025, Labels Score: 0.8943 \tUsing time in epoch 346: 26.1565s\n",
      "Epoch 347/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4021, Labels Score: 0.8943 \tUsing time in epoch 347: 26.2583s\n",
      "Epoch 348/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4022, Labels Score: 0.8941 \tUsing time in epoch 348: 26.4781s\n",
      "Epoch 349/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4547 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4025, Labels Score: 0.8942 \tUsing time in epoch 349: 26.2991s\n",
      "Epoch 350/1000\n",
      "680/680 [==============================] - 166s 243ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4006, Labels Score: 0.8939 \tUsing time in epoch 350: 27.7703s\n",
      "Epoch 351/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8945 \tUsing time in epoch 351: 27.1451s\n",
      "Epoch 352/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4019, Labels Score: 0.8935 \tUsing time in epoch 352: 26.1835s\n",
      "Epoch 353/1000\n",
      "680/680 [==============================] - 157s 226ms/step - loss: 0.2848 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4546 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4015, Labels Score: 0.8941 \tUsing time in epoch 353: 27.9595s\n",
      "Epoch 354/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4546 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4021, Labels Score: 0.8937 \tUsing time in epoch 354: 26.3600s\n",
      "Epoch 355/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8943 \tUsing time in epoch 355: 28.3205s\n",
      "Epoch 356/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4018, Labels Score: 0.8947 \tUsing time in epoch 356: 27.9057s\n",
      "Epoch 357/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4549 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4014, Labels Score: 0.8942 \tUsing time in epoch 357: 25.4334s\n",
      "Epoch 358/1000\n",
      "680/680 [==============================] - 161s 230ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4024, Labels Score: 0.8944 \tUsing time in epoch 358: 26.4876s\n",
      "Epoch 359/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4015, Labels Score: 0.8941 \tUsing time in epoch 359: 26.2537s\n",
      "Epoch 360/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4547 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4014, Labels Score: 0.8935 \tUsing time in epoch 360: 26.2018s\n",
      "Epoch 361/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8405 - pr: 0.4547 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8940 \tUsing time in epoch 361: 25.7822s\n",
      "Epoch 362/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4072\n",
      "Neighbors Score: 0.4016, Labels Score: 0.8932 \tUsing time in epoch 362: 26.3426s\n",
      "Epoch 363/1000\n",
      "680/680 [==============================] - 157s 227ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4022, Labels Score: 0.8939 \tUsing time in epoch 363: 26.7600s\n",
      "Epoch 364/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4546 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8111 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4022, Labels Score: 0.8937 \tUsing time in epoch 364: 27.3886s\n",
      "Epoch 365/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8112 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8947 \tUsing time in epoch 365: 27.2448s\n",
      "Epoch 366/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4021, Labels Score: 0.8939 \tUsing time in epoch 366: 25.7467s\n",
      "Epoch 367/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4021, Labels Score: 0.8939 \tUsing time in epoch 367: 26.5232s\n",
      "Epoch 368/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4023, Labels Score: 0.8945 \tUsing time in epoch 368: 27.1910s\n",
      "Epoch 369/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4021, Labels Score: 0.8942 \tUsing time in epoch 369: 26.6351s\n",
      "Epoch 370/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4024, Labels Score: 0.8946 \tUsing time in epoch 370: 26.6780s\n",
      "Epoch 371/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4020, Labels Score: 0.8947 \tUsing time in epoch 371: 25.9955s\n",
      "Epoch 372/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4025, Labels Score: 0.8939 \tUsing time in epoch 372: 26.1710s\n",
      "Epoch 373/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4022, Labels Score: 0.8938 \tUsing time in epoch 373: 29.0019s\n",
      "Epoch 374/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4549 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8945 \tUsing time in epoch 374: 27.1538s\n",
      "Epoch 375/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4024, Labels Score: 0.8940 \tUsing time in epoch 375: 26.1450s\n",
      "Epoch 376/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4549 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8950 \tUsing time in epoch 376: 26.3168s\n",
      "Epoch 377/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4549 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4020, Labels Score: 0.8938 \tUsing time in epoch 377: 26.3400s\n",
      "Epoch 378/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4024, Labels Score: 0.8941 \tUsing time in epoch 378: 26.7839s\n",
      "Epoch 379/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8940 \tUsing time in epoch 379: 26.3153s\n",
      "Epoch 380/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4025, Labels Score: 0.8939 \tUsing time in epoch 380: 26.6066s\n",
      "Epoch 381/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8941 \tUsing time in epoch 381: 25.8157s\n",
      "Epoch 382/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4547 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8940 \tUsing time in epoch 382: 26.0942s\n",
      "Epoch 383/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8949 \tUsing time in epoch 383: 26.2433s\n",
      "Epoch 384/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8944 \tUsing time in epoch 384: 25.9181s\n",
      "Epoch 385/1000\n",
      "680/680 [==============================] - 156s 227ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8944 \tUsing time in epoch 385: 25.7665s\n",
      "Epoch 386/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2847 - binary_accuracy: 0.8842 - auc: 0.8406 - pr: 0.4548 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4024, Labels Score: 0.8944 \tUsing time in epoch 386: 25.6672s\n",
      "Epoch 387/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8943 \tUsing time in epoch 387: 25.5049s\n",
      "Epoch 388/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8948 \tUsing time in epoch 388: 26.2275s\n",
      "Epoch 389/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8111 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4020, Labels Score: 0.8943 \tUsing time in epoch 389: 25.5347s\n",
      "Epoch 390/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3042 - val_binary_accuracy: 0.8796 - val_auc: 0.8111 - val_pr: 0.4070\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8940 \tUsing time in epoch 390: 26.1125s\n",
      "Epoch 391/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8941 \tUsing time in epoch 391: 25.7795s\n",
      "Epoch 392/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8944 \tUsing time in epoch 392: 26.2190s\n",
      "Epoch 393/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8938 \tUsing time in epoch 393: 28.3245s\n",
      "Epoch 394/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8938 \tUsing time in epoch 394: 25.5769s\n",
      "Epoch 395/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8947 \tUsing time in epoch 395: 26.6785s\n",
      "Epoch 396/1000\n",
      "680/680 [==============================] - 167s 242ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4548 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4029, Labels Score: 0.8944 \tUsing time in epoch 396: 27.0949s\n",
      "Epoch 397/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8941 \tUsing time in epoch 397: 26.8967s\n",
      "Epoch 398/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8942 \tUsing time in epoch 398: 26.3756s\n",
      "Epoch 399/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8939 \tUsing time in epoch 399: 26.2394s\n",
      "Epoch 400/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4550 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8943 \tUsing time in epoch 400: 27.3135s\n",
      "Epoch 401/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4029, Labels Score: 0.8940 \tUsing time in epoch 401: 26.6033s\n",
      "Epoch 402/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8944 \tUsing time in epoch 402: 27.2872s\n",
      "Epoch 403/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8941 \tUsing time in epoch 403: 26.3789s\n",
      "Epoch 404/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4021, Labels Score: 0.8938 \tUsing time in epoch 404: 26.7400s\n",
      "Epoch 405/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8940 \tUsing time in epoch 405: 26.5276s\n",
      "Epoch 406/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8940 \tUsing time in epoch 406: 25.2860s\n",
      "Epoch 407/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4551 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8941 \tUsing time in epoch 407: 26.0997s\n",
      "Epoch 408/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4550 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8939 \tUsing time in epoch 408: 26.5976s\n",
      "Epoch 409/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4551 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4024, Labels Score: 0.8939 \tUsing time in epoch 409: 25.6186s\n",
      "Epoch 410/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8939 \tUsing time in epoch 410: 25.7084s\n",
      "Epoch 411/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4551 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8943 \tUsing time in epoch 411: 26.1009s\n",
      "Epoch 412/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4550 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8943 \tUsing time in epoch 412: 26.2242s\n",
      "Epoch 413/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4550 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8945 \tUsing time in epoch 413: 25.2165s\n",
      "Epoch 414/1000\n",
      "680/680 [==============================] - 167s 242ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4029, Labels Score: 0.8940 \tUsing time in epoch 414: 26.7376s\n",
      "Epoch 415/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4550 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8941 \tUsing time in epoch 415: 26.7829s\n",
      "Epoch 416/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4550 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8944 \tUsing time in epoch 416: 26.6685s\n",
      "Epoch 417/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8945 \tUsing time in epoch 417: 25.8558s\n",
      "Epoch 418/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8942 \tUsing time in epoch 418: 28.0287s\n",
      "Epoch 419/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4025, Labels Score: 0.8942 \tUsing time in epoch 419: 26.6578s\n",
      "Epoch 420/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8407 - pr: 0.4549 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4029, Labels Score: 0.8941 \tUsing time in epoch 420: 26.3780s\n",
      "Epoch 421/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8941 \tUsing time in epoch 421: 26.4655s\n",
      "Epoch 422/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8940 \tUsing time in epoch 422: 25.4604s\n",
      "Epoch 423/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8938 \tUsing time in epoch 423: 26.2722s\n",
      "Epoch 424/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4550 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 424: 26.5808s\n",
      "Epoch 425/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8942 \tUsing time in epoch 425: 25.7038s\n",
      "Epoch 426/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8939 \tUsing time in epoch 426: 26.5646s\n",
      "Epoch 427/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8939 \tUsing time in epoch 427: 26.5342s\n",
      "Epoch 428/1000\n",
      "680/680 [==============================] - 167s 243ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8110 - val_pr: 0.4071\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8942 \tUsing time in epoch 428: 26.1330s\n",
      "Epoch 429/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4026, Labels Score: 0.8939 \tUsing time in epoch 429: 26.4052s\n",
      "Epoch 430/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8941 \tUsing time in epoch 430: 26.6625s\n",
      "Epoch 431/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8945 \tUsing time in epoch 431: 26.6522s\n",
      "Epoch 432/1000\n",
      "680/680 [==============================] - 157s 226ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8945 \tUsing time in epoch 432: 26.4615s\n",
      "Epoch 433/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 433: 28.1209s\n",
      "Epoch 434/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4029, Labels Score: 0.8939 \tUsing time in epoch 434: 26.4649s\n",
      "Epoch 435/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8941 \tUsing time in epoch 435: 25.4627s\n",
      "Epoch 436/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4553 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8110 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8941 \tUsing time in epoch 436: 28.0365s\n",
      "Epoch 437/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8938 \tUsing time in epoch 437: 26.1593s\n",
      "Epoch 438/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3043 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8942 \tUsing time in epoch 438: 27.8824s\n",
      "Epoch 439/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8943 \tUsing time in epoch 439: 26.5968s\n",
      "Epoch 440/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8942 \tUsing time in epoch 440: 25.7309s\n",
      "Epoch 441/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8939 \tUsing time in epoch 441: 26.8016s\n",
      "Epoch 442/1000\n",
      "680/680 [==============================] - 169s 244ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 442: 26.9079s\n",
      "Epoch 443/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8943 \tUsing time in epoch 443: 27.2990s\n",
      "Epoch 444/1000\n",
      "680/680 [==============================] - 167s 243ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8945 \tUsing time in epoch 444: 29.7383s\n",
      "Epoch 445/1000\n",
      "680/680 [==============================] - 168s 243ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8940 \tUsing time in epoch 445: 28.7030s\n",
      "Epoch 446/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8942 \tUsing time in epoch 446: 25.9940s\n",
      "Epoch 447/1000\n",
      "680/680 [==============================] - 171s 247ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8948 \tUsing time in epoch 447: 27.4216s\n",
      "Epoch 448/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8944 \tUsing time in epoch 448: 27.2266s\n",
      "Epoch 449/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8938 \tUsing time in epoch 449: 27.6862s\n",
      "Epoch 450/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8940 \tUsing time in epoch 450: 27.3277s\n",
      "Epoch 451/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2846 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4551 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8943 \tUsing time in epoch 451: 27.2799s\n",
      "Epoch 452/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8945 \tUsing time in epoch 452: 26.2154s\n",
      "Epoch 453/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4029, Labels Score: 0.8942 \tUsing time in epoch 453: 26.0842s\n",
      "Epoch 454/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4069\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8942 \tUsing time in epoch 454: 26.1970s\n",
      "Epoch 455/1000\n",
      "680/680 [==============================] - 167s 244ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8941 \tUsing time in epoch 455: 27.4023s\n",
      "Epoch 456/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8942 \tUsing time in epoch 456: 26.6443s\n",
      "Epoch 457/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8944 \tUsing time in epoch 457: 28.9587s\n",
      "Epoch 458/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8943 \tUsing time in epoch 458: 26.2281s\n",
      "Epoch 459/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8943 \tUsing time in epoch 459: 26.6087s\n",
      "Epoch 460/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 460: 26.9272s\n",
      "Epoch 461/1000\n",
      "680/680 [==============================] - 168s 243ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 461: 26.6022s\n",
      "Epoch 462/1000\n",
      "680/680 [==============================] - 169s 245ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8943 \tUsing time in epoch 462: 28.2549s\n",
      "Epoch 463/1000\n",
      "680/680 [==============================] - 156s 227ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8944 \tUsing time in epoch 463: 25.9796s\n",
      "Epoch 464/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3043 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8942 \tUsing time in epoch 464: 28.5159s\n",
      "Epoch 465/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8943 \tUsing time in epoch 465: 26.7988s\n",
      "Epoch 466/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8945 \tUsing time in epoch 466: 25.9974s\n",
      "Epoch 467/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8408 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8942 \tUsing time in epoch 467: 25.6901s\n",
      "Epoch 468/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8946 \tUsing time in epoch 468: 26.1917s\n",
      "Epoch 469/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8946 \tUsing time in epoch 469: 26.3190s\n",
      "Epoch 470/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8941 \tUsing time in epoch 470: 26.0626s\n",
      "Epoch 471/1000\n",
      "680/680 [==============================] - 157s 229ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4028, Labels Score: 0.8943 \tUsing time in epoch 471: 26.7134s\n",
      "Epoch 472/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4027, Labels Score: 0.8941 \tUsing time in epoch 472: 27.3463s\n",
      "Epoch 473/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4029, Labels Score: 0.8940 \tUsing time in epoch 473: 26.0008s\n",
      "Epoch 474/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8943 \tUsing time in epoch 474: 27.5634s\n",
      "Epoch 475/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8940 \tUsing time in epoch 475: 26.1508s\n",
      "Epoch 476/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8942 \tUsing time in epoch 476: 26.5051s\n",
      "Epoch 477/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8942 \tUsing time in epoch 477: 26.1069s\n",
      "Epoch 478/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8943 \tUsing time in epoch 478: 25.8547s\n",
      "Epoch 479/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8944 \tUsing time in epoch 479: 28.2326s\n",
      "Epoch 480/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8941 \tUsing time in epoch 480: 26.3475s\n",
      "Epoch 481/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8944 \tUsing time in epoch 481: 25.5991s\n",
      "Epoch 482/1000\n",
      "680/680 [==============================] - 160s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8945 \tUsing time in epoch 482: 28.9547s\n",
      "Epoch 483/1000\n",
      "680/680 [==============================] - 159s 228ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8943 \tUsing time in epoch 483: 26.4688s\n",
      "Epoch 484/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8941 \tUsing time in epoch 484: 26.1514s\n",
      "Epoch 485/1000\n",
      "680/680 [==============================] - 159s 228ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4030, Labels Score: 0.8944 \tUsing time in epoch 485: 26.2000s\n",
      "Epoch 486/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8945 \tUsing time in epoch 486: 27.2865s\n",
      "Epoch 487/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8946 \tUsing time in epoch 487: 26.6579s\n",
      "Epoch 488/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8944 \tUsing time in epoch 488: 26.2124s\n",
      "Epoch 489/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3046 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8945 \tUsing time in epoch 489: 26.3402s\n",
      "Epoch 490/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8944 \tUsing time in epoch 490: 26.2997s\n",
      "Epoch 491/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8944 \tUsing time in epoch 491: 27.1043s\n",
      "Epoch 492/1000\n",
      "680/680 [==============================] - 167s 240ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3044 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8945 \tUsing time in epoch 492: 26.5355s\n",
      "Epoch 493/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8945 \tUsing time in epoch 493: 27.0946s\n",
      "Epoch 494/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8944 \tUsing time in epoch 494: 26.1262s\n",
      "Epoch 495/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8942 \tUsing time in epoch 495: 25.7980s\n",
      "Epoch 496/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8109 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 496: 26.6710s\n",
      "Epoch 497/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8944 \tUsing time in epoch 497: 25.9491s\n",
      "Epoch 498/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8944 \tUsing time in epoch 498: 26.5785s\n",
      "Epoch 499/1000\n",
      "680/680 [==============================] - 167s 243ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8944 \tUsing time in epoch 499: 28.3711s\n",
      "Epoch 500/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8947 \tUsing time in epoch 500: 27.2558s\n",
      "Epoch 501/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2845 - binary_accuracy: 0.8843 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8945 \tUsing time in epoch 501: 27.5375s\n",
      "Epoch 502/1000\n",
      "680/680 [==============================] - 170s 245ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8944 \tUsing time in epoch 502: 26.8621s\n",
      "Epoch 503/1000\n",
      "680/680 [==============================] - 168s 244ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 503: 26.7400s\n",
      "Epoch 504/1000\n",
      "680/680 [==============================] - 169s 243ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8946 \tUsing time in epoch 504: 26.9084s\n",
      "Epoch 505/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8942 \tUsing time in epoch 505: 27.5773s\n",
      "Epoch 506/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8945 \tUsing time in epoch 506: 26.4318s\n",
      "Epoch 507/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8943 \tUsing time in epoch 507: 27.3273s\n",
      "Epoch 508/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8942 \tUsing time in epoch 508: 26.8104s\n",
      "Epoch 509/1000\n",
      "680/680 [==============================] - 169s 244ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8945 \tUsing time in epoch 509: 26.7727s\n",
      "Epoch 510/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8944 \tUsing time in epoch 510: 27.3177s\n",
      "Epoch 511/1000\n",
      "680/680 [==============================] - 164s 240ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8945 \tUsing time in epoch 511: 26.7822s\n",
      "Epoch 512/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8945 \tUsing time in epoch 512: 27.7790s\n",
      "Epoch 513/1000\n",
      "680/680 [==============================] - 168s 242ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8943 \tUsing time in epoch 513: 26.0559s\n",
      "Epoch 514/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8943 \tUsing time in epoch 514: 25.8331s\n",
      "Epoch 515/1000\n",
      "680/680 [==============================] - 167s 240ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8945 \tUsing time in epoch 515: 26.9898s\n",
      "Epoch 516/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8945 \tUsing time in epoch 516: 26.2207s\n",
      "Epoch 517/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8843 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8946 \tUsing time in epoch 517: 25.6909s\n",
      "Epoch 518/1000\n",
      "680/680 [==============================] - 158s 228ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8943 \tUsing time in epoch 518: 26.6467s\n",
      "Epoch 519/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3048 - val_binary_accuracy: 0.8796 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8943 \tUsing time in epoch 519: 27.1685s\n",
      "Epoch 520/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4555 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8945 \tUsing time in epoch 520: 25.8468s\n",
      "Epoch 521/1000\n",
      "680/680 [==============================] - 157s 228ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8947 \tUsing time in epoch 521: 26.4076s\n",
      "Epoch 522/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4552 - val_loss: 0.3044 - val_binary_accuracy: 0.8795 - val_auc: 0.8109 - val_pr: 0.4068\n",
      "Neighbors Score: 0.4032, Labels Score: 0.8943 \tUsing time in epoch 522: 26.0874s\n",
      "Epoch 523/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8944 \tUsing time in epoch 523: 28.9510s\n",
      "Epoch 524/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8943 \tUsing time in epoch 524: 28.0659s\n",
      "Epoch 525/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8843 - auc: 0.8409 - pr: 0.4555 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8942 \tUsing time in epoch 525: 26.3612s\n",
      "Epoch 526/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8942 \tUsing time in epoch 526: 26.6961s\n",
      "Epoch 527/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8944 \tUsing time in epoch 527: 26.3650s\n",
      "Epoch 528/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8944 \tUsing time in epoch 528: 25.2531s\n",
      "Epoch 529/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8944 \tUsing time in epoch 529: 27.0097s\n",
      "Epoch 530/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8943 \tUsing time in epoch 530: 26.0932s\n",
      "Epoch 531/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8943 \tUsing time in epoch 531: 26.2342s\n",
      "Epoch 532/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4031, Labels Score: 0.8942 \tUsing time in epoch 532: 26.2764s\n",
      "Epoch 533/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8943 \tUsing time in epoch 533: 26.4139s\n",
      "Epoch 534/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 534: 27.9451s\n",
      "Epoch 535/1000\n",
      "680/680 [==============================] - 168s 242ms/step - loss: 0.2845 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8943 \tUsing time in epoch 535: 26.4775s\n",
      "Epoch 536/1000\n",
      "680/680 [==============================] - 166s 241ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8946 \tUsing time in epoch 536: 27.0117s\n",
      "Epoch 537/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8843 - auc: 0.8409 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8942 \tUsing time in epoch 537: 26.5226s\n",
      "Epoch 538/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8941 \tUsing time in epoch 538: 26.7800s\n",
      "Epoch 539/1000\n",
      "680/680 [==============================] - 159s 228ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8945 \tUsing time in epoch 539: 25.6775s\n",
      "Epoch 540/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8946 \tUsing time in epoch 540: 26.4259s\n",
      "Epoch 541/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8943 \tUsing time in epoch 541: 26.9488s\n",
      "Epoch 542/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 542: 26.6148s\n",
      "Epoch 543/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4038, Labels Score: 0.8942 \tUsing time in epoch 543: 26.5515s\n",
      "Epoch 544/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8945 \tUsing time in epoch 544: 25.8333s\n",
      "Epoch 545/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8944 \tUsing time in epoch 545: 25.9740s\n",
      "Epoch 546/1000\n",
      "680/680 [==============================] - 162s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8941 \tUsing time in epoch 546: 26.8651s\n",
      "Epoch 547/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4037, Labels Score: 0.8945 \tUsing time in epoch 547: 26.3738s\n",
      "Epoch 548/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8944 \tUsing time in epoch 548: 26.1662s\n",
      "Epoch 549/1000\n",
      "680/680 [==============================] - 168s 245ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8942 \tUsing time in epoch 549: 26.7643s\n",
      "Epoch 550/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 550: 26.1975s\n",
      "Epoch 551/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8945 \tUsing time in epoch 551: 26.2603s\n",
      "Epoch 552/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4035, Labels Score: 0.8942 \tUsing time in epoch 552: 26.4170s\n",
      "Epoch 553/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4555 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4033, Labels Score: 0.8943 \tUsing time in epoch 553: 26.0806s\n",
      "Epoch 554/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 554: 26.9141s\n",
      "Epoch 555/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4036, Labels Score: 0.8942 \tUsing time in epoch 555: 25.7895s\n",
      "Epoch 556/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4034, Labels Score: 0.8943 \tUsing time in epoch 556: 26.8671s\n",
      "Epoch 557/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8942 \tUsing time in epoch 557: 27.4356s\n",
      "Epoch 558/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3045 - val_binary_accuracy: 0.8796 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8940 \tUsing time in epoch 558: 26.8245s\n",
      "Epoch 559/1000\n",
      "680/680 [==============================] - 158s 228ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8943 \tUsing time in epoch 559: 25.8415s\n",
      "Epoch 560/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8943 \tUsing time in epoch 560: 26.3968s\n",
      "Epoch 561/1000\n",
      "680/680 [==============================] - 157s 227ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8941 \tUsing time in epoch 561: 25.8946s\n",
      "Epoch 562/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8943 \tUsing time in epoch 562: 25.8206s\n",
      "Epoch 563/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8941 \tUsing time in epoch 563: 25.9643s\n",
      "Epoch 564/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8796 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8940 \tUsing time in epoch 564: 27.2743s\n",
      "Epoch 565/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8941 \tUsing time in epoch 565: 25.2865s\n",
      "Epoch 566/1000\n",
      "680/680 [==============================] - 167s 243ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8944 \tUsing time in epoch 566: 25.6251s\n",
      "Epoch 567/1000\n",
      "680/680 [==============================] - 169s 243ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8941 \tUsing time in epoch 567: 26.1671s\n",
      "Epoch 568/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8943 \tUsing time in epoch 568: 26.5614s\n",
      "Epoch 569/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8942 \tUsing time in epoch 569: 26.9641s\n",
      "Epoch 570/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8941 \tUsing time in epoch 570: 26.0525s\n",
      "Epoch 571/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8942 \tUsing time in epoch 571: 26.4535s\n",
      "Epoch 572/1000\n",
      "680/680 [==============================] - 166s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8942 \tUsing time in epoch 572: 26.9593s\n",
      "Epoch 573/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8943 \tUsing time in epoch 573: 26.4717s\n",
      "Epoch 574/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8943 \tUsing time in epoch 574: 25.7396s\n",
      "Epoch 575/1000\n",
      "680/680 [==============================] - 157s 229ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4050, Labels Score: 0.8944 \tUsing time in epoch 575: 27.2546s\n",
      "Epoch 576/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8943 \tUsing time in epoch 576: 25.4038s\n",
      "Epoch 577/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4049, Labels Score: 0.8941 \tUsing time in epoch 577: 26.6842s\n",
      "Epoch 578/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8409 - pr: 0.4553 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8942 \tUsing time in epoch 578: 25.6121s\n",
      "Epoch 579/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2845 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8941 \tUsing time in epoch 579: 26.8963s\n",
      "Epoch 580/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8943 \tUsing time in epoch 580: 26.4413s\n",
      "Epoch 581/1000\n",
      "680/680 [==============================] - 169s 244ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8943 \tUsing time in epoch 581: 27.6905s\n",
      "Epoch 582/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8942 \tUsing time in epoch 582: 26.7142s\n",
      "Epoch 583/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8944 \tUsing time in epoch 583: 26.4858s\n",
      "Epoch 584/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8942 \tUsing time in epoch 584: 26.9917s\n",
      "Epoch 585/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8941 \tUsing time in epoch 585: 26.3702s\n",
      "Epoch 586/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8941 \tUsing time in epoch 586: 25.6340s\n",
      "Epoch 587/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8942 \tUsing time in epoch 587: 26.0560s\n",
      "Epoch 588/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8942 \tUsing time in epoch 588: 25.9205s\n",
      "Epoch 589/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8942 \tUsing time in epoch 589: 26.0714s\n",
      "Epoch 590/1000\n",
      "680/680 [==============================] - 159s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8944 \tUsing time in epoch 590: 27.0912s\n",
      "Epoch 591/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8944 \tUsing time in epoch 591: 25.6912s\n",
      "Epoch 592/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8944 \tUsing time in epoch 592: 25.7180s\n",
      "Epoch 593/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8945 \tUsing time in epoch 593: 26.4358s\n",
      "Epoch 594/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4049, Labels Score: 0.8943 \tUsing time in epoch 594: 28.5816s\n",
      "Epoch 595/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8943 \tUsing time in epoch 595: 26.9840s\n",
      "Epoch 596/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8943 \tUsing time in epoch 596: 26.2111s\n",
      "Epoch 597/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8942 \tUsing time in epoch 597: 26.4327s\n",
      "Epoch 598/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8941 \tUsing time in epoch 598: 26.6405s\n",
      "Epoch 599/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8941 \tUsing time in epoch 599: 25.8165s\n",
      "Epoch 600/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8943 \tUsing time in epoch 600: 26.9153s\n",
      "Epoch 601/1000\n",
      "680/680 [==============================] - 164s 240ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8943 \tUsing time in epoch 601: 26.8945s\n",
      "Epoch 602/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8945 \tUsing time in epoch 602: 26.0138s\n",
      "Epoch 603/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8945 \tUsing time in epoch 603: 25.7147s\n",
      "Epoch 604/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 604: 27.1178s\n",
      "Epoch 605/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 605: 27.4167s\n",
      "Epoch 606/1000\n",
      "680/680 [==============================] - 167s 243ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8945 \tUsing time in epoch 606: 26.7280s\n",
      "Epoch 607/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8943 \tUsing time in epoch 607: 26.7105s\n",
      "Epoch 608/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8943 \tUsing time in epoch 608: 27.0266s\n",
      "Epoch 609/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4047, Labels Score: 0.8943 \tUsing time in epoch 609: 25.7279s\n",
      "Epoch 610/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8943 \tUsing time in epoch 610: 26.2634s\n",
      "Epoch 611/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8942 \tUsing time in epoch 611: 26.7480s\n",
      "Epoch 612/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8944 \tUsing time in epoch 612: 25.5611s\n",
      "Epoch 613/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 613: 26.5327s\n",
      "Epoch 614/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8943 \tUsing time in epoch 614: 26.8506s\n",
      "Epoch 615/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8946 \tUsing time in epoch 615: 26.0953s\n",
      "Epoch 616/1000\n",
      "680/680 [==============================] - 167s 242ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8944 \tUsing time in epoch 616: 27.9056s\n",
      "Epoch 617/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8794 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8943 \tUsing time in epoch 617: 26.4294s\n",
      "Epoch 618/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 618: 26.3818s\n",
      "Epoch 619/1000\n",
      "680/680 [==============================] - 157s 226ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8945 \tUsing time in epoch 619: 26.4011s\n",
      "Epoch 620/1000\n",
      "680/680 [==============================] - 159s 228ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4061\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 620: 26.5704s\n",
      "Epoch 621/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8943 \tUsing time in epoch 621: 25.6792s\n",
      "Epoch 622/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8945 \tUsing time in epoch 622: 25.2321s\n",
      "Epoch 623/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 623: 26.6760s\n",
      "Epoch 624/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2845 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4048, Labels Score: 0.8945 \tUsing time in epoch 624: 25.3311s\n",
      "Epoch 625/1000\n",
      "680/680 [==============================] - 157s 226ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8943 \tUsing time in epoch 625: 26.5838s\n",
      "Epoch 626/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 626: 27.1080s\n",
      "Epoch 627/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 627: 26.2460s\n",
      "Epoch 628/1000\n",
      "680/680 [==============================] - 162s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8942 \tUsing time in epoch 628: 26.0676s\n",
      "Epoch 629/1000\n",
      "680/680 [==============================] - 160s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8945 \tUsing time in epoch 629: 26.1244s\n",
      "Epoch 630/1000\n",
      "680/680 [==============================] - 164s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 630: 27.1519s\n",
      "Epoch 631/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 631: 27.3224s\n",
      "Epoch 632/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 632: 26.3197s\n",
      "Epoch 633/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 633: 26.6563s\n",
      "Epoch 634/1000\n",
      "680/680 [==============================] - 156s 227ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 634: 26.6180s\n",
      "Epoch 635/1000\n",
      "680/680 [==============================] - 155s 223ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 635: 26.5962s\n",
      "Epoch 636/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8945 \tUsing time in epoch 636: 25.8121s\n",
      "Epoch 637/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 637: 26.3353s\n",
      "Epoch 638/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8945 \tUsing time in epoch 638: 26.1734s\n",
      "Epoch 639/1000\n",
      "680/680 [==============================] - 165s 241ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 639: 26.1706s\n",
      "Epoch 640/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8943 \tUsing time in epoch 640: 27.2800s\n",
      "Epoch 641/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8946 \tUsing time in epoch 641: 26.0623s\n",
      "Epoch 642/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8943 \tUsing time in epoch 642: 26.2574s\n",
      "Epoch 643/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8945 \tUsing time in epoch 643: 25.8883s\n",
      "Epoch 644/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 644: 26.3368s\n",
      "Epoch 645/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8945 \tUsing time in epoch 645: 26.4456s\n",
      "Epoch 646/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8943 \tUsing time in epoch 646: 27.3798s\n",
      "Epoch 647/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4046, Labels Score: 0.8944 \tUsing time in epoch 647: 26.2791s\n",
      "Epoch 648/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8108 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 648: 30.7334s\n",
      "Epoch 649/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 649: 25.5560s\n",
      "Epoch 650/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 650: 26.6001s\n",
      "Epoch 651/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 651: 26.2162s\n",
      "Epoch 652/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8944 \tUsing time in epoch 652: 26.2840s\n",
      "Epoch 653/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 653: 26.5172s\n",
      "Epoch 654/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8945 \tUsing time in epoch 654: 25.9808s\n",
      "Epoch 655/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 655: 25.8025s\n",
      "Epoch 656/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8945 \tUsing time in epoch 656: 26.7239s\n",
      "Epoch 657/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 657: 25.4024s\n",
      "Epoch 658/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 658: 25.9539s\n",
      "Epoch 659/1000\n",
      "680/680 [==============================] - 157s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8945 \tUsing time in epoch 659: 25.9262s\n",
      "Epoch 660/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8794 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 660: 26.5214s\n",
      "Epoch 661/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8946 \tUsing time in epoch 661: 26.2990s\n",
      "Epoch 662/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 662: 27.1350s\n",
      "Epoch 663/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8943 \tUsing time in epoch 663: 25.8870s\n",
      "Epoch 664/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8104 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 664: 26.4353s\n",
      "Epoch 665/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8944 \tUsing time in epoch 665: 26.7292s\n",
      "Epoch 666/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8946 \tUsing time in epoch 666: 26.6350s\n",
      "Epoch 667/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 667: 26.3662s\n",
      "Epoch 668/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4045, Labels Score: 0.8946 \tUsing time in epoch 668: 26.7622s\n",
      "Epoch 669/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 669: 26.1632s\n",
      "Epoch 670/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 670: 26.6049s\n",
      "Epoch 671/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 671: 25.8494s\n",
      "Epoch 672/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 672: 26.3856s\n",
      "Epoch 673/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 673: 26.2552s\n",
      "Epoch 674/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 674: 26.4420s\n",
      "Epoch 675/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 675: 25.3186s\n",
      "Epoch 676/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 676: 25.8004s\n",
      "Epoch 677/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8946 \tUsing time in epoch 677: 26.4425s\n",
      "Epoch 678/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 678: 28.9157s\n",
      "Epoch 679/1000\n",
      "680/680 [==============================] - 158s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 679: 27.4351s\n",
      "Epoch 680/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8794 - val_auc: 0.8107 - val_pr: 0.4067\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 680: 26.6241s\n",
      "Epoch 681/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8943 \tUsing time in epoch 681: 28.7903s\n",
      "Epoch 682/1000\n",
      "680/680 [==============================] - 172s 251ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 682: 28.1748s\n",
      "Epoch 683/1000\n",
      "680/680 [==============================] - 169s 246ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 683: 27.8467s\n",
      "Epoch 684/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8943 \tUsing time in epoch 684: 26.1840s\n",
      "Epoch 685/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 685: 25.9770s\n",
      "Epoch 686/1000\n",
      "680/680 [==============================] - 166s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 686: 27.1455s\n",
      "Epoch 687/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 687: 25.3691s\n",
      "Epoch 688/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8945 \tUsing time in epoch 688: 26.6876s\n",
      "Epoch 689/1000\n",
      "680/680 [==============================] - 158s 227ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 689: 29.3897s\n",
      "Epoch 690/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 690: 25.7582s\n",
      "Epoch 691/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8944 \tUsing time in epoch 691: 26.9017s\n",
      "Epoch 692/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 692: 25.3755s\n",
      "Epoch 693/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 693: 26.5362s\n",
      "Epoch 694/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 694: 26.3595s\n",
      "Epoch 695/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 695: 25.6506s\n",
      "Epoch 696/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4555 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 696: 25.9611s\n",
      "Epoch 697/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 697: 26.6862s\n",
      "Epoch 698/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 698: 26.7241s\n",
      "Epoch 699/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 699: 25.5120s\n",
      "Epoch 700/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 700: 26.4685s\n",
      "Epoch 701/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 701: 26.4609s\n",
      "Epoch 702/1000\n",
      "680/680 [==============================] - 162s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 702: 26.3969s\n",
      "Epoch 703/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4061\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 703: 24.9918s\n",
      "Epoch 704/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 704: 25.9144s\n",
      "Epoch 705/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8410 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8943 \tUsing time in epoch 705: 25.2874s\n",
      "Epoch 706/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8946 \tUsing time in epoch 706: 25.9577s\n",
      "Epoch 707/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 707: 27.7957s\n",
      "Epoch 708/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 708: 26.0305s\n",
      "Epoch 709/1000\n",
      "680/680 [==============================] - 163s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8945 \tUsing time in epoch 709: 25.8008s\n",
      "Epoch 710/1000\n",
      "680/680 [==============================] - 161s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 710: 26.5283s\n",
      "Epoch 711/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4061\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 711: 26.2247s\n",
      "Epoch 712/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 712: 26.2635s\n",
      "Epoch 713/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 713: 25.6986s\n",
      "Epoch 714/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 714: 25.5556s\n",
      "Epoch 715/1000\n",
      "680/680 [==============================] - 165s 240ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 715: 29.3275s\n",
      "Epoch 716/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 716: 26.4826s\n",
      "Epoch 717/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 717: 26.0123s\n",
      "Epoch 718/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 718: 26.0632s\n",
      "Epoch 719/1000\n",
      "680/680 [==============================] - 162s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 719: 25.9942s\n",
      "Epoch 720/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 720: 27.7034s\n",
      "Epoch 721/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 721: 26.4635s\n",
      "Epoch 722/1000\n",
      "680/680 [==============================] - 166s 241ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 722: 25.7165s\n",
      "Epoch 723/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 723: 25.6817s\n",
      "Epoch 724/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8946 \tUsing time in epoch 724: 26.6409s\n",
      "Epoch 725/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 725: 26.3338s\n",
      "Epoch 726/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4044, Labels Score: 0.8945 \tUsing time in epoch 726: 26.6551s\n",
      "Epoch 727/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 727: 26.7102s\n",
      "Epoch 728/1000\n",
      "680/680 [==============================] - 160s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 728: 26.5161s\n",
      "Epoch 729/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 729: 26.4245s\n",
      "Epoch 730/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 730: 25.9027s\n",
      "Epoch 731/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 731: 26.6797s\n",
      "Epoch 732/1000\n",
      "680/680 [==============================] - 166s 242ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 732: 26.7222s\n",
      "Epoch 733/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 733: 25.7440s\n",
      "Epoch 734/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 734: 27.1168s\n",
      "Epoch 735/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 735: 26.3092s\n",
      "Epoch 736/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 736: 25.9396s\n",
      "Epoch 737/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3045 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4066\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 737: 25.4787s\n",
      "Epoch 738/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 738: 25.9447s\n",
      "Epoch 739/1000\n",
      "680/680 [==============================] - 165s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 739: 25.9817s\n",
      "Epoch 740/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 740: 28.3003s\n",
      "Epoch 741/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 741: 26.8035s\n",
      "Epoch 742/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 742: 26.0236s\n",
      "Epoch 743/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 743: 27.0472s\n",
      "Epoch 744/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 744: 26.0117s\n",
      "Epoch 745/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8410 - pr: 0.4554 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 745: 25.8830s\n",
      "Epoch 746/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 746: 26.5531s\n",
      "Epoch 747/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 747: 25.8887s\n",
      "Epoch 748/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 748: 26.5491s\n",
      "Epoch 749/1000\n",
      "680/680 [==============================] - 163s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 749: 26.7386s\n",
      "Epoch 750/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 750: 25.9141s\n",
      "Epoch 751/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 751: 25.8881s\n",
      "Epoch 752/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 752: 26.6084s\n",
      "Epoch 753/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 753: 26.3025s\n",
      "Epoch 754/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 754: 25.9009s\n",
      "Epoch 755/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8944 \tUsing time in epoch 755: 26.0222s\n",
      "Epoch 756/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 756: 25.6020s\n",
      "Epoch 757/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 757: 25.3653s\n",
      "Epoch 758/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 758: 28.1364s\n",
      "Epoch 759/1000\n",
      "680/680 [==============================] - 160s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 759: 25.9294s\n",
      "Epoch 760/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 760: 26.2088s\n",
      "Epoch 761/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3050 - val_binary_accuracy: 0.8794 - val_auc: 0.8105 - val_pr: 0.4061\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 761: 26.3555s\n",
      "Epoch 762/1000\n",
      "680/680 [==============================] - 159s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3046 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4039, Labels Score: 0.8944 \tUsing time in epoch 762: 26.6099s\n",
      "Epoch 763/1000\n",
      "680/680 [==============================] - 159s 230ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 763: 26.1762s\n",
      "Epoch 764/1000\n",
      "680/680 [==============================] - 155s 226ms/step - loss: 0.2843 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8946 \tUsing time in epoch 764: 27.5179s\n",
      "Epoch 765/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 765: 26.6615s\n",
      "Epoch 766/1000\n",
      "680/680 [==============================] - 162s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8943 \tUsing time in epoch 766: 25.6849s\n",
      "Epoch 767/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 767: 25.7013s\n",
      "Epoch 768/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 768: 25.3271s\n",
      "Epoch 769/1000\n",
      "680/680 [==============================] - 161s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 769: 25.1153s\n",
      "Epoch 770/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4065\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 770: 27.8052s\n",
      "Epoch 771/1000\n",
      "680/680 [==============================] - 167s 243ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 771: 25.6624s\n",
      "Epoch 772/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 772: 26.7946s\n",
      "Epoch 773/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8945 \tUsing time in epoch 773: 26.8704s\n",
      "Epoch 774/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 774: 26.6447s\n",
      "Epoch 775/1000\n",
      "680/680 [==============================] - 164s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8945 \tUsing time in epoch 775: 25.6960s\n",
      "Epoch 776/1000\n",
      "680/680 [==============================] - 166s 241ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 776: 30.0840s\n",
      "Epoch 777/1000\n",
      "680/680 [==============================] - 168s 245ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 777: 26.1256s\n",
      "Epoch 778/1000\n",
      "680/680 [==============================] - 172s 249ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 778: 27.7628s\n",
      "Epoch 779/1000\n",
      "680/680 [==============================] - 162s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 779: 25.6946s\n",
      "Epoch 780/1000\n",
      "680/680 [==============================] - 165s 238ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 780: 25.6997s\n",
      "Epoch 781/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 781: 26.9482s\n",
      "Epoch 782/1000\n",
      "680/680 [==============================] - 164s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 782: 28.1996s\n",
      "Epoch 783/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 783: 25.8336s\n",
      "Epoch 784/1000\n",
      "680/680 [==============================] - 158s 229ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 784: 26.3321s\n",
      "Epoch 785/1000\n",
      "680/680 [==============================] - 159s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 785: 25.5135s\n",
      "Epoch 786/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 786: 26.8543s\n",
      "Epoch 787/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 787: 25.6389s\n",
      "Epoch 788/1000\n",
      "680/680 [==============================] - 164s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 788: 26.5119s\n",
      "Epoch 789/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8944 \tUsing time in epoch 789: 26.2064s\n",
      "Epoch 790/1000\n",
      "680/680 [==============================] - 161s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 790: 27.2500s\n",
      "Epoch 791/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8946 \tUsing time in epoch 791: 25.6078s\n",
      "Epoch 792/1000\n",
      "680/680 [==============================] - 163s 234ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 792: 26.1731s\n",
      "Epoch 793/1000\n",
      "680/680 [==============================] - 159s 228ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 793: 27.9380s\n",
      "Epoch 794/1000\n",
      "680/680 [==============================] - 163s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8945 \tUsing time in epoch 794: 26.0274s\n",
      "Epoch 795/1000\n",
      "680/680 [==============================] - 170s 247ms/step - loss: 0.2844 - binary_accuracy: 0.8842 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 795: 27.5840s\n",
      "Epoch 796/1000\n",
      "680/680 [==============================] - 160s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 796: 25.9114s\n",
      "Epoch 797/1000\n",
      "680/680 [==============================] - 166s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4040, Labels Score: 0.8944 \tUsing time in epoch 797: 25.3906s\n",
      "Epoch 798/1000\n",
      "680/680 [==============================] - 167s 241ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 798: 26.0545s\n",
      "Epoch 799/1000\n",
      "680/680 [==============================] - 167s 242ms/step - loss: 0.2843 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8944 \tUsing time in epoch 799: 25.6660s\n",
      "Epoch 800/1000\n",
      "680/680 [==============================] - 162s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 800: 27.6218s\n",
      "Epoch 801/1000\n",
      "680/680 [==============================] - 162s 233ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8946 \tUsing time in epoch 801: 25.8527s\n",
      "Epoch 802/1000\n",
      "680/680 [==============================] - 162s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8946 \tUsing time in epoch 802: 26.1854s\n",
      "Epoch 803/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8946 \tUsing time in epoch 803: 26.3495s\n",
      "Epoch 804/1000\n",
      "680/680 [==============================] - 160s 231ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8946 \tUsing time in epoch 804: 26.0265s\n",
      "Epoch 805/1000\n",
      "680/680 [==============================] - 161s 232ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4062\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 805: 26.7937s\n",
      "Epoch 806/1000\n",
      "680/680 [==============================] - 169s 245ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4556 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 806: 26.5007s\n",
      "Epoch 807/1000\n",
      "680/680 [==============================] - 166s 240ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 807: 27.1087s\n",
      "Epoch 808/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8946 \tUsing time in epoch 808: 28.5146s\n",
      "Epoch 809/1000\n",
      "680/680 [==============================] - 163s 236ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3048 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 809: 27.9205s\n",
      "Epoch 810/1000\n",
      "680/680 [==============================] - 170s 245ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3049 - val_binary_accuracy: 0.8795 - val_auc: 0.8105 - val_pr: 0.4063\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8944 \tUsing time in epoch 810: 27.6159s\n",
      "Epoch 811/1000\n",
      "680/680 [==============================] - 161s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4041, Labels Score: 0.8945 \tUsing time in epoch 811: 27.0448s\n",
      "Epoch 812/1000\n",
      "680/680 [==============================] - 165s 237ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4558 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8107 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8945 \tUsing time in epoch 812: 27.9927s\n",
      "Epoch 813/1000\n",
      "680/680 [==============================] - 164s 239ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8795 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4043, Labels Score: 0.8946 \tUsing time in epoch 813: 29.4603s\n",
      "Epoch 814/1000\n",
      "680/680 [==============================] - 162s 235ms/step - loss: 0.2844 - binary_accuracy: 0.8843 - auc: 0.8411 - pr: 0.4557 - val_loss: 0.3047 - val_binary_accuracy: 0.8794 - val_auc: 0.8106 - val_pr: 0.4064\n",
      "Neighbors Score: 0.4042, Labels Score: 0.8946 \tUsing time in epoch 814: 26.4777s\n",
      "Epoch 00814: early stopping\n",
      "8. Save training results...\n",
      "=== Training completed! ===\n",
      "Best model: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/3_cross_species/train_out/E1000best_model.h5\n",
      "Training history: /picb/bigdata/project/miaoyuanyuan/train/XChrom_test/XChrom/source/Tutorials/data/3_cross_species/train_out/history.pickle\n"
     ]
    }
   ],
   "source": [
    "history = xc.tr.train_XChrom(\n",
    "    input_folder='./data/3_cross_species/train_data/',\n",
    "    cell_embedding_ad='./data/3_cross_species/mop3c2_rna_harmony.h5ad',\n",
    "    cellembed_raw='X_pca_harmony',\n",
    "    out_path='./data/3_cross_species/train_out/',\n",
    "    epochs = 1000,\n",
    "    trackscore = True,\n",
    "    print_scores = True,\n",
    "    celltype = 'cell_type',\n",
    "    verbose = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd9ac7ef-aec7-4381-98d6-03401172d307",
   "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/3_cross_species/train_out/history.pickle`. Load them with:                        \n",
    "\n",
    "```python\n",
    "import pickle\n",
    "with open('./data/3_cross_species/train_out/history.pickle', 'rb') as f:\n",
    "    history = pickle.load(f)\n",
    "```\n",
    "\n",
    "You can then use this `history` object to plot training/validation metrics (e.g., loss, auROC/auPRC, NS/LS) over epochs.              \n",
    "\n",
    "The function `plot_train_history()` will automatically detect whether NS and LS values have been computed during training. If these metrics are available in the history data, they will be automatically included in the training metric plots along with other evaluation metrics such as loss, auROC, and auPRC. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53d28837-3872-4da4-8cfa-12038a3e6186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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/3_cross_species/train_out/train_history_plot.pdf'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d6ae5b-72c5-44e2-9374-36b358195713",
   "metadata": {},
   "source": [
    "### 5. Evaluate model performance in cross-species scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1b1896-84bf-4747-b393-4aded8bf7b0a",
   "metadata": {},
   "source": [
    "The following metrics will be evaluated on the **human(m1d1) test set data**:\n",
    "\n",
    "- **Binary classification metrics**:  \n",
    "  - (overall, per cell, per peak) auROC                               \n",
    "  - (overall, per cell, per peak) auPRC                                 \n",
    "  \n",
    "- **Cell state fidelity**:  \n",
    "  - Neighbor Score (NS) (k=10, 50, 100)                        \n",
    "  - Label Score (LS) (k=10, 50, 100)                        \n",
    "\n",
    "This combination of binary classification metrics and cell state fidelity provides a comprehensive evaluation of the model's predictive performance on data from different species.                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "219eb1f1-1fd4-4927-94a9-b9788b4cd816",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-08-20 14:08:32.946716: 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",
      "2025-08-20 14:08:34.542231: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 19982 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:31:00.0, compute capability: 8.6\n",
      "2025-08-20 14:08:34.542839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 21971 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:4b:00.0, compute capability: 8.6\n",
      "2025-08-20 14:08:34.543350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 21971 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:98:00.0, compute capability: 8.6\n",
      "2025-08-20 14:08:34.543748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 572 MB memory:  -> device: 3, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:b1:00.0, compute capability: 8.6\n",
      "2025-08-20 14:08:35.615747: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2025-08-20 14:08:37.202628: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8800\n",
      "2025-08-20 14:08:38.020093: 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": [
      "Predict done! prediction shape is: (37363, 4489)\n",
      "Overall auROC: 0.7519, auPRC: 0.3469\n",
      "Per-cell auROC: 0.6918, auPRC: 0.2696\n",
      "Valid cells: 4489\n",
      "Per-peak auROC: 0.6965, auPRC: 0.2516\n",
      "Valid peaks: 37363\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 1- calculate cross-species auROC & auPRC\n",
    "metrics1 = xc.tl.crosssamples_aucprc(\n",
    "    cell_embedding_ad='./data/3_cross_species/m1d1_rna_harmony.h5ad',\n",
    "    input_folder='./data/3_cross_species/test_data',\n",
    "    model_path='./data/3_cross_species/train_out/E1000best_model.h5',\n",
    "    output_path='./data/3_cross_species/eval_out',\n",
    "    cellembed_raw='X_pca_harmony',\n",
    "    save_pred=True,\n",
    "    scatter_plot=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4cbae84-725b-4421-b4cb-f9721da19ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict done! prediction shape is: (37363, 4489)\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)=0.2347,label score(100)=0.8496\n",
      "neighbor score(50)=0.1708,label score(50)=0.8776\n",
      "neighbor score(10)=0.0772,label score(10)=0.9098\n"
     ]
    },
    {
     "data": {
      "image/png": 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68B4LQm8rOPPB8PVSNt5q72HZgMmjcQ0UINNk4NScNA4dMNqdbTaSbTby200NPNbYAcDGQJjbivNYW9dLpz9GfrqZzHwzTzV3MdxmZnqa42v165s6NTedZ5u72BaK8NPibHLHXUJ81SqidfXkXHcd7hOOT7ZNqHa2LB6NcQ1UnqohGyXcJ52E75130cJhOvKuJ+AvpdN2PjlpH1I5bQXdTzyh76z15/6bysuTwTtAoruH5t9cn3wcWr6cjEsvIbBwEdaJEzGXl+3190EQBEEQhL1PBO/7o7pPyej10tb3MJMh0k5mXo/ZYMYMJBIxliz7A8O7O8hih8A8oxKivbD4QYj4wJ4NB175tQL4Gzc3suMSUblmI19MHz1k+63BME83dSYfr/UG+Wh1V/JxfUeYy1sbaIzGAIhGVMYmTKQ5jBRnWVF2mKD6dUTr6og1NQES1nFjkW02ehs1Vj2qophUcrruQt28lLcuvBDLSSdh6bszUPr000Meb/GdKi2L+47tg3GXS9gPOojhH31IcMkStF9eT3YiTJf1R4TH3E7mjycTXr2aWH09Wb+4GjUcRrHbcZ14IraJE2n7859Rfb5Br9P78cf43nmHREcHss1G2euvYSos/MbvhyAIgiAI3y0RvO9vvHWw6U3KACceEkA2+uRLTQM1akJSJOQBE1q7vVvo9m5ho2TAoslYLVkYSg8HZ66eT9++Tg/cAQJtEGjVt39FWSYDzRE90PYoMr6ESqbJQCCRwD5EisfTTZ1YfDIJW4KEDCdZXTAgTV5Fo7kvcAdY2OjDpTiobQ+zotrP6GIHlfn2QcfdUe+CT4k1NOA64XgUhz56H2lupWPuu3gf+DNSVE+LMY8cSenzz/H53QodqwFk/MERFHj/TfNNN1F51JHg1nPuNVUluHI1sfY2jC4ntkmTkIxGgq39rxto6f/akJZGaPkKpIT+Wp7Qm2zYcjvdjemUPjP0hUDamWdgnTCeuosuJuHz9efJA7GGhv73KRgksnGjCN4FQRAEYT8gSkXub7T+se1MzFgtmYRt6WgadK6cTPP8o2hZfCTx7v4Fi+y2HAyKBZ8UZ5ExSPzAy6BwSn+A7ioEU19KijUd7DlDv3aoG7a+Cy0rhnz6tmH5ybSdnoSKil5N5o22niHbV/ls3PRQFff9fgy3P1zJAQMq4kiAw6xwXUkuZllipN3CQXJqvvqGhp2XYtyu+d8vU3/ppbTceiv1P/kJakKlc+kaVry0iNqsSXiv/Quq0wNAZP16Yg0NqP3XC6h9F0aoKonu/vPYcv411J11Js1XXUXdhRdRc9bZaIkEY38sY3KBLQdGnqO/G9FejXVPqfRo00DWfyVjSg6FLVfR+K8FhFauJFJXT++nn6IGAin9t4wYQeWihVQtWUzaBRekPGcs1r9/pmHD0DSNrcfNpuacc4k1N3/p+yIIgiAIwveTGHnf33hK6fDkY+yppVkKsy3aikWTmREvJtyRDYAagtCGLpwzCwCwWtI5aPKNdHatIyN9FBZzWuoxzS6YehX0NoOrKDVlpnEJ9NRA3iRY/5IewAMgs95RwQWrq/HHE9zSWULsUQs3W6p4+Kxq2rKiyUNkbVxPTK7AmJN6UXBQh5slUT3PO63DTMvWIFI2GBSIJyAYUTlYs/KLQ8cDsLExwMbqLhJ9de9tPo0FNySIh2DCz2Q8w1PTaEK9UVo+Wsz2Ka7h1WtYPb+GgFeGokoANE8msWFjMa+Yj7mqCmNhIcN+EMW/th1zbBu5/vv7D9h3eC0WI7703ZTXCq9ZQ/3lPyHh93HEVZdjP3gmskm/27Dk9yrNnwHMZNQFLxKd9yyW6pcwJ+rR5n5IzVz0oF5VMVdWUvqfF5EUhe6nnybe3k7a+edjzM4m94brQVXpfuYZLKNGUfT3R1H9fgx5eWw74QfE6uqgupr6n/6MnOt/g33KlKF/hgRBEARB+N4Swft+qDujhM2+5cnHYUllRaZCaY6ZWGsEFAlzaWopRKe9AKe9YOcHNTv1fwN1bob1r+hft6wGrX80n2AHD3S6qAvrQXrPU0YywgqWsMKxC7J4/vhqfjL3Pxy0dgW5jfVUu92UvfwSxoL+PmQfIGHJ1Ah3ACUxyNDTQgyyTLyvDrxlwCJMVQV2tBMOxj98HNata5EiKl7T0RhRWNV+Boc8Mw4toVH3voo/2k3I0Il6wKGYln6EHPQTHHkmYW9/5RgAIiE8wwvJvGoOtvHjkU0mtr7Zg1LpwrC1B9Wn/wq5f/QjDDk5NFxzDeHVa4jKWZjU9gHvn5nAggUANPz0pyhuN0WPzcE6ejT+AZU6O9YbGHFUJl364qzJOxWo+vlGNm2i9tzziHd0EG9q0t/qz5dS+vxzAOTe+FtybrgeqW8En75VXGWbrf+U1q2j7uJLyPvdHfTOX0BwyWJcRx9D7s03IQiCIAjC95sI3vdD5cWziIW7ifVU0xZuJiFBaelsssaOJby5G2O2DWPOl+eCf6nYgBQOLQ4lh0DdArBlQsFkCgekrXS6ImR06SPiR3zxKo74Bk7+8K3k8wmvl9DKlVR7Mvjd1iZcBoU7KgqY9bjC2x92E0uPIfXF6QdWuGjsjGAyyFQWpJ6HZcQI1OWfI/VNjU0LvaZ374uX2TQ9naYR/6K9roTMk/2YCxOQV0rPb/5O+5xc4j3p5PTWoDjiSNEwljefQAoF8GoqFs8irGPGEA3HsRzeitWUINE7kvZ//JTClt8iKQpbbnmRxH/1lVWNQETKwaS16gF4JJLSz4TXS8+LL2IdPZoxF0ksuTOGHOnGufR6ens0ZLs9NUVGkpLVZsKrVqUcK1xXh+/td7CMGU2soZGOv/0NY3ERuf/3f8lylGowmLIP8TjN19+QfNj97LPYDz0US1UlxtzcnXzDBUEQBEH4rongfT+kdG5hVFsdxCJotkq0yZcj9+Ws28buwUWXssdAznroqYXCqVB2OAw/Vl/1Ffh1mYPnm7toi8VZeMBHzFjTgqJ6yQg8Q2P7uJRDGbKysE6azFXr61jh1wNNuyLzh8pCVEcrGg4kTJRkmcl06f8Aqle30F7vxZ1tp/KAArLveYQVZzxKWudjAGhISGhIQKKrC9+mBBhl1FD/iH2sJZNYTH9f2p4tITf9SVybnyJePorgOdcCUL9yAd13P0zvwSchmfQ7AIojQaZVv/MQyjmW2qdaSU4JlWTM2oDZqUOwjBwFQNHhMpF7TiFWry+wFd1iQElLg4HB+4AykYNEIjT+4hfIDgdIEqrfD0uWYCouIfOyHxNYuFBPmdkVWabxqqvQIhGyf3UtGZdeuuv2giAIgiB8J0Twvr/proZVTyUfSsEOpN5WSHegRhN0PbuBaKMf58GFOA/5htVHZAOMPSt124AqNiZZ5tgsN081dbJueC52zyM46utQiouZcOMNmO/9I1p7O54zz8Bz0kkoHg/xuu7k/jFN482//JEti+ZjdKUz8qKbqMrKJ9baijEnh4A3TPM2vXRkZ6OP7kI3gU0OGo0/J+xMx6D24Ix+hCW2NXlMrS8RpeeDHBIBA2jg+yyTtMhr+A0zsHStw7P1b8iECVZN6+9L+Tha/R5Mkf6KLkpXK+amNRgKCmh/4iV6rdcRME5A0XpREj0YjV6IDZjdOpCi4D7xxP7Hif6UI1NpCe4fnkT7vffqb6nZjLbDyP12huxs4m16UVC1txfM/fMROv/xDywjR5LweYfcdyDJYEi+Rve/nxPBuyAIgiB8T4ngfX/T25L62GgHh54GEVzeRniDHux6/1uNbVIOin2IGvDfQNSnsW2uhjUTSo6WuauikGluOxZFZtKrLxPauIX/OLO4u7WT8l/8H0+MLSfD1P9j+OcRRdy8uRGXQeHqNAv/WTQfgJivC0/tIpp+80/i7e14TjuN9N/ehCRLaKo+Km2yGDBWSCgWA53ShRgSrWQEngRANXtosN6EM7oQe/AhTKOPpvqjKSSkDBS1h+ye+ylQb9RfS86kOu2fqFuHkzGmCcmg4VtXgn9RNs4pHbimd5JZs5j4P/+CFIsQb2zESTNloQ2YE9XJc1HcmXhOOxUN6Hr4kf43qS8ffdPUqeTf+ydcRx9NxsUX0XLb7UgGA7m//S326dNxHXccoNF+3/345s7t339ACs32wF1/QSUlPUf1+2n9/e8pf+N1OkeOJLJ+/U6/b1q0fwKxdcKEL/9GC4IgCILwnRDB+34mpgyHoAOjrZeYVoBx2gVg0vPCFUd/oC6ZFSTjnq8U+un/qX010CEWUBl+kswpuekEWzU2vqqx/pkxKHGVvFNCLK3080RjB5dJOdS+p5E+QmLcwTZePaACgEQ8TlpePt3NTUiyjKO+iXi7Pgm058UXMf/iOqIlWWiROCPyrDg8VvDA+DNqaH1gDo7wJ8joQakc6SE3dg8mte/i5uOPGB53EjRPxhpdhXF0CdraTiSHizauJGSaAPXQMqccyayS6NHz9SPbPBRdLOO2F9EY6w+UJdSUwB0g8/LLST/vXGovuDBlu2QyoYXDaIkETdffgPPII+n9+BNQVbRolFhbG77//hdzRQXm4cPJv/sP2A85GO9rrxNcuDAlhUZOT9fz2WMxrAdOJvTZ4pTXMmRmIhmNGPPzU4N3oxHH4YcT3baNaE0NxPWRf885Z5N7/fUIgiAIgvD9JIL3fVxwdQfdL25E08DgMqEBic6DkZQEGkYKj+6vEGMdnYnnpGFEG3qxT8lNlirck3y1g7/21Wp8cKVKvG/OpBmZoz/NYm2lnxzFwEc/V4n6ADRm3iWRN02/qFAMBs687Y/MXzCfBdY03opKHCc9iaRpyOXDWVofJhBOABIdqkwu4N3cy8qnMylKNGLUuhgoGbgDxGJknDgDy6JFmGccSpulEKPZTXTsVKx14+ld3YwjsoBgbBwmB5h7P6XDdj5lrmcwvtBEYvQoMn95DR33/yVZCQYAWcZUWkr+3XdjHTsGgER3fyqQkp2FISubyNq1AGiBAKrPR3DJkmSblj/chy9UgU3bzLBnHkJxu2j7w90pxwGwTp5MoqsLpbycaG0t0W3bUkblDTk5xHt6aPn97+n98MP+LjqdqH4/ve/2l7NUsrLIuPACMi655Mu+xYIgCIIgfIdE8L6P635lM1pUDx7jndvLHEpoCQPGwsEVZRzT8vdqfyrPgDV9ZQ7VmB5E3rW+mY/O9HPmmwXYQwbsQQV3icQfukvJutmJ39e//9I/acx6UsNo03PTzS4317rL6IjFqWpwYDztVUYrn+GYfWjKHE5NA+8bb9B0/W+pUo3Ueu6j0X07Ew6bS/STF0m0pk4eNeTkYCouIbR8BVp3J46PXwLAtOITcp56Gc8l5yAH29CQoUNCIkFh+RLiny6g+1NQMSNL0UETSbN//SsyLroIgIhXo+YdDcMJd2F4+gpkux3H1ffT9ClYNp8J0RD2Iw5H8XjwnHIKXf/6F5LZQi2/IeyqJN/7OxpvvBXPCccOCtwlu53Q2rUQCsG2bcntSlYWmZf9mFhzM12PPQ6trUS3bEk+L6eloe5wLADZZBKBuyAIgiDsA0Twvg+JtQSQTArxzhC9Cxox5tmRLApaMD6orWRWyLp4zLfWN03T2PicRuP8/m3VcyHwIz8rtkTwSEbuvmwLmgxZCSP3tK+n+qmNtAWPwsrk5D7hTuhaDzmT9McRVaMzFmfMRieXP18KQFdeCUU/jjPcaWRNXS8Wo0xVgZ2GXzwOiTgycTKCzxI/8W+U3HoldRctIbhD8K6kpdHx178CEKuv18/BYEIzWzB21SIH9Vzy7SUnAWIrFyTrrqtYQFOR6Z+QqmIi+MUXZFx0Ed3PPc+nj44lRBVQxdSbPyLvIIk3z1CJB0BO+4Bpv2gn/4fDAci54Xo8Z5xBd4OT1bekUdZxPvbYMmKbILQ8e9CkVcvIkYRWrBj0fUi0t2MsKtppdRpDVhZSZiaRzZsHvBkKmT/72ZDtBUEQBEH4fhHB+/ecFlOJeUN0Pb2BeEtf3olBgrhGeGM3zuNKCS9vI94dQTLKqOE4xDVcx5Qg2/bsZNSd8VZrLL1HpWuH+ZABe5xbVjVz3tvFGOMyK0atA8DeuJa1bz0JJggal1LQ8U8UzQOAyQXuMn3/WELFCNw8LJ9VnwxITWlRGJ5jQlYkDh2dntxsGTEimdddesFIcn6ppwXJrv7UIdntxnbgZNLPP5+6iy9J5nprU2fSedyPsa5dQsPPr0LqSz+RbbZkjfSB67MGzRNwRT7uO6hMnetefNajSVv2CgXBIC233044e2lyp55tkDUB4n3VH1XZRdTiBk2j56WX0GJxPD86mYwcI1Z7F1JHf1WbaE0NktWaEryHVq9O9n1Hvv++jX3aVCSHQ8+j317jXZLIu/P3mEvLCC5ZTKylBcuYsVhHjUQyiD8FgiAIgrAvEJ/Y32P++Q1436qGHQdR4/0bFLNCzi8mJR9rcRUtriJbvr1v7cKbVXobUrd9MqODeeM7aXdH+dMlW7n8uRJyvCZa3VEs0QFlD6UYXa4AH08N8oNsmdnH52NJl2jqCvP5Zi8acOJwN+deYmbeapVwJ1SeKiMrEjtSr/ktwUNPIi/aRfYJxyS3Z//yWuLt7WjRGHl33E6isxOAwr/cT88rrxBcvIT64pHYrA6sbz+LtD2HXVZQFROgB7/ba8b3WGYhDfimJOyF+KxHA9BtPplPb9fIcrrJ6v07bc6fYbaHKZtlw5opMeoCic0va2SMguIjJNr+fC9dc/Sa9OG1a8i74w7GlN9DZ000+XrR6v6JsIacHKyTJuF/q3+BK9ntxjp+PIFPPgHA98Yb+N58ExL9FwD6CWgoFguKw47ziCO+/BsrCIIgCML3jgjev8d879cNDtz7KFlWzMUu7JNTV8OUDDKSYc9XkdmVaO/gbZ9O76Tdqld66fbEeO6ERn755DCWlXmZfmAZxhFd1K9ZhyN0LK5QAVn+Ro47Nwd7rh6Ub20J0lcBki0tQQ4dbWH2szLxIJg9gwP31p4In28LgKeEVmMZGd09tN9xB/H2diSDgurvJfOKn+B96WW6//1vAAx5edjOvQivpYpQNIoNSGTmIfv1nHBNTSD5e5Kv0Vv1c2ivxezbjGqyITudJIJRGpWr0L9Rer9aFkuU/eZJXIv/yYiyZ8i89FwUg/7c6Atlios+IN7WhhQ/kfCq1cnjh9bpdw0yTjyK0IdzGeqbn3HZZXhOO5Wty5cTb24GwFxWStEjD1N36aUEFy7SJ9DKg38GnEcfham8fOhvoiAIgiAI+wQRvH+PGXNsROv8Qz7nmV2GdWTGt9yjoblLoX1F6rZb6kr5w/hqqsN6TrjNLWHrNjCzOwOWa6ill5HfZUq2P3RRAU+m+2g7McBxmW7S7EY6fDFa1DhvhHpZXRvjyuLsIQN3/4cf0vV/N1Nqc9H4szuI5BbS9s9/0fv22yntmn97Y0o4HGtuptYyAg62446GqJ33FtaRo8mPBrB1tBJQJ+OKLQD0koyTX7gY2WwmUl2N4vGgeDy8ekIMyddEcedV1KX/GSQjkgKeqcNw/OgPg/ra/cILtNx8i97vDz5EDYWSzxk8HgC6nvjXTlNi2h94gNY77sB18kn0fvAhWiSC47DD9FKaM2bqwTtgnTiBeFs7sfp6JKuFwgcewHHwwUMeUxAEQRCEfYcI3r/HMi4YTeDzFmSLgmQ1EtnaQ3hdJ5bhHixV6V9+gG9Jb1P/19ZMaDu+l9+X1ZKIaIz22bCEFc78PK+/kSahVZvAnQCvDEj4He9yz8Qc1EaZZ5o6mD9lBJNsLn64cSvN/jhv+f0Ymxu4bOoEJCm1xGXrXX+Ark4sXZ1kzH0Gx//dhuIbXFEFSYIBixFpJjM49Io8BpMVt+Ec1BmNNJ7+U/CB/Gcf9pqlKIT1eukm/WLDXFaWPEbxkQbCT/0DV/QjSrsuw2uZjWH6aTgKBl9kAITXrkt+HVq5EsXtTj6WbTYAEv6hL9gAVK++WqrvlVeT29r/+hDuk04i/eKLMGRmEO/qxnPaach2G5H161EyMzFmZ+/0mIIgCIIg7Du+3fwK4StR7EZchxXhmJaPfXwW6T+qIP//ppF+5ggkeejg8Lsw7Ad9fZHAPRzeGdNOQFUJaxpBL1z4SCmWL8zIRkimgkgahkt8HP4vifyD4iiGp1D7Uj2iGqx4L06uxUz3gGB76/rFbF12H52tdSmvb8jKSn5dkm1jbImT8Jq1yW2W8eNxzpqF49BDU/aLjZ6W/FrTQOraSiy3SN/ggvSKT1DQy29GN20itGzZoHOf9EuZohMyAXBEP8cTeo3iI3f+vfGceipyX8Ae90fxNVuRrDbMVVU4jjmaaEMD2b+5DslqHbSvbcaMoQ8aj6MGQ0iShPuHPyTjogtRHHYkScIyapQI3AVBEARhPyJG3oVvrOosiXVPa6gRaPkMppak83GVngif225OtlMTMOlaibolKsroKBUHOclIl5l0q4J9Si1HfP4pn405kAnrcojMtbDw3QB3n/Q5v4uPIq++gZP//gTxIhuNvyoi0G2hqCoLSZJwHn1UMrD2f/gh0VCUoDMHydaEHPST6OlGi0XxnPwjJJOJWEsLobVrkeIDVkiNxylccwPVvhdIuGQkbxTP4jkp5xmprsE2aRJaLIYaCKD0pbkU3vRTOrIU/Bu6ST/9MnIO678mDq1ZS+sf7kJxucm7/TasY8dQ9uqrNN79MCuWXUZcycWsVjOx6imaf30dktGIqbQEbUA6zXbOI4/EdfxsfK+/TnD7SqqyTMZPLsdcXjaovSAIgiAI+x8RvAvfWKBZQ+0vd87sbDeuigLa16lULk7H4AGjFYadJNG+CowJmfS4hQVngsGi4rzFS+T6u7jq7Rc5ZeEsVG8hAF1bDZwh1XDIy6/RM1+vuKJVhzFcehu9tnvZEItgzMzEeWx/ZZm4KrH842oSp12N7O3Aed8vidXqI/WtG/9AxYL59Dz/AqEvvsC4fikZ/lp8jR2YFr2DWVE5YpyDjQuNWD/8G9IOqTf2g6YRbWig9tzziLe0kH7B+eTccAOyyUT2VVeRDYQ2bKDz6Q9wHXkkfl8u7T+9BrVVryPfnpmJkp5G5yOPgtGCyTmLuJJLRC6j46OtWAAtFiOyeQs7Mo8dS+sf/qBXkEkkkIxG3KefRvpZZ2EePnzPfTMFQRAEQfheE2kzwje24q8a29cyko1QcYLEpYVZ/ObwbKZdpSArEGiGuvc16t7TaFoIax4DLQGxAHjXyERKq5j3418TCBclj5sxXB+9Tz+snEiGi7jRQDyhIWkaUsAP0SixpiYS3T3YDzkYY34+X1z6CxIRvUSi6s4kkVuSPJ6kKEiKQvfzz+uP43Ecyz9g1OFVFMw+lJJnnsaR62TSjyyU//Q46Kt9npDsdLvPorslF98bbxBvaQGg619Pog5I6+ld9Bk1J51M2+9+z+rjf8VHl3lJtPbX0Iy1tND5z77R/FiY4u6rAbDnaWRML97p+ytnZBCtr9fz9fvKP2qxGNbRY0TgLgiCIAj/Y0TwLnxjEW//12oMIj0SakJj3i9VFt6kEdbLqtMzcEB5wJpL5jX6RM31RIkr/fVgcmZkwLhz4LCLuPDuf3D0X55kS34JO4pqmRQ9/DDuZ57ltmEVtEj6MUJaFEOTPmJvyM+n4P77aVkYJtrS1b+zLGMdM4bMy36cMhHVMnIklZ8twnXSSdSXPkuj7UY++RX00J93bigqRDb1V8zp6bsoALAGlyGpgZR+Og45JDkpFcCg+SnpvIxJx7xK+unHJy8WdqR2dqZMtAW9zKV95k5y4AVBEARB2G+J4F34xsZeIiVXErVmg9kD7StVOtektrPlQPZksGaBo2+AXVJg7PEGSpdlMqs1m8fPqGPdMD91B/upOEWC7DGYCybx8gFVXF+eR8bf/obnzDNTjrtxXgXrjjiT1iMOJ6/dy2vrnTzdbMY6o5y8669DsliINzURWLiQbf9Yg6z157r73niDuh9fNuR5KQ4H+Xfdib39dUY1T6C84xwCvTnIdr1CTaKjk2hD/8i68/DDkl8nTBkkDLm0uK6DtEJcs2djGVGF6vOlvIYzupDuR+/F++prOy0PCfSvktqn7JWXxURUQRAEQfgfJHLehd0S69WofU8jEYWoD7IPkMiZpEfsOZNljv67RuMCjfqPNF45TkVTBx/DngtqFELt+uOSY/VFiz67XaVrPRiwc/mZWTRe5GOE2UJYVbGjl4Ust5n5Ram+IJX22xuIbNlMaOkXJCQr7vA7yF79SuG6vypEDAUANL8EJnkb9rBeMabnpZcwHHs50Q15mBLNyX6Fli1DSySQlNQSlP56jc7PO8jqnYNqNmHMasfpfZZwQB9R10IhIlu2YCrUc/TdP/whks1GaNUq0s85lwKvjBo+HYfjYLxz36Lj4UeGfG+17h69Yo4k9ZW9kZBMJgy5ucSamiAWS91BlpM14QVBEARB+N8igndht8y/IXUkfePzGkf/Q8ZdpgfwnuESn/9RxV+3kwMAHatBsfQ/blwIZo9Gb2P/tsxXl1Ix/0Vaz72api47Zek2FBMppTElk4nCBx9k88xDURIh3JEPk+ubqvIwJDWIUW0hqpXQEZmEjaeR0LBNnsywmzLYVPYywf/cg7X+ZSRAMhoJr16NdcIEAHpeeRXflhBffHgaiZCdSnsugbOPQfW4iBhMZE6dSnjxYkyVo6B4cso5Oo86ivDqNTT+6lpcRx1N15w5tLW3J5+XnU6MBQU4jzoK39y5RKurMeTkkHHJxbhmHUu8owP7wQfTcuut+P77No6ZM+ldsCAlgLdNmbK73zZBEARBEPYzIngXvlSwVaNzbeo2LQGBFnAPrFD4JUlYWkIvF7ld3A+bnteQjWCwgyHSQG7dHzFX16OZLPia/sQrT6tYMuCwP8s4i/QAfuMLKvVva+QntO3ZOkiAddIkRkztIfLouZgSTfSaplGT/ijyJU/jLqyht6uXlvvvoeKc0+moBW99X7+iUWov/TGVny6g5z//ofWO3+G1HIvRXoE93kDbzBuwejYDEI1HSbvzDro+MfDFP9LhxwYmX6tSNls/+d4PPqDz738HIPTFMn0kPeVN0Ch/9RUA0i+8gPC6dVgqK1E8HgwZ+oq5oVWr8L70sn68jz7Cc8YZKfn0wcWLSfh8KC7Xrt9wQRAEQRD2OyLnXfhSn96sJtdW2s6SDrmpg85MvVHC1BdPKmYYexnMuEOi4GD6FmgCNZy6T3NmGIxxiq+oI/PCDhipT0jNcRupeV4GDcIdsPrGBWjxOB1rNFY9rOHfEqXB83sSUv8E0FhzM9Zlf8SU0Jd8dUQ/o2RqI0WXjqKhxk/swT/if+oJas67gOxf/wrrgBFsrbeX0MqVRDZu0vcNL6C883yyqz7Aelg2yHq9erPdgSMzi9rPskEzgAqb5zSwacZMGn/5S8Lbtu30fTQWFpL3uzuSjxWHA/uUKcl68dsNXM0VRSHtnLNRMjP7G2gakc2bd/o6giAIgiDsv8TIu7BL8bCGd4h4NNxFcpLqdq4imR+8pBFoAlsuKCYJTdOofT+1Dvx2XkeM6oIgI9PjROIhcFjpPfUaMvMs5NxwHVtuVOnZol9fxtZ+hve1drThJyNpUQxqO17r8QRMUykx3oO16S3iTU3Em5r0qi3xOLLLRcXExUSjxxPc7KY2dwmSFqak62dIRiPFjzzMxgOnJMsvdv5zDsHPPwdAJqCn4RjNSLIJzTMZWe1l/PGTMZgtZI1X6VilX9GYat8g0duJ763/wnvv6ycny9gPPZTARx8lz7f8zTeQLQPyhnbCmJ9P0d//jv+DD3DMnIGlspK0s8+i44EH9QaShGXMmC89jiAIgiAI+x8RvAu75N0K2hBFUBQrtC3XyBqvB+nbyQYJ54CS5YEmaPh48P7LR/awYoSPi14pJlzhI9KQhyk/hLk4SN6fHkAxS8y4NcD7Z9YTMVTQ5ryGmhXrmXCKxMiLzAQfepQW7SfElBx8ysFYtLnJawnrgQcSa2wkXldHy623kR2N4Q38CE0yo0lmfGNvQ3E4aLzuN8nAHfSR++0rm0roNxusn85FMpoJzz6HknEjMfYF32MulnFat+Kf9zHKpwMmom7PTVdVwqtXgywjGY1kXX31bgXu29mnTcU+bWrycdaVV4IkEVq2nMwrr0Q2m3extyAIgiAIX6ampoaysjKWL1/OhL55b/sCkTYj7JKzRC/tCCANuNRLhGD+dRr/PVfFW60NvTN6eo2lL+NDUmD4KRB3JFgxxYs5U98vvNlJaLML78c5JFYX0r4SNr+kojhsRIx9ixBJEo2bRwFQ6HkDU6yasLGKhJJOe+wEes2HJF8ztGgR8br+mbPR6m1kjeivx+4ZrZd69L31Vuq5Hn1UyuPtFwPmtWsYMbWIcCBKV4sfNRql/YEHCd15MsrH90A8jn3GDOyHHZbcV3a5SHR0gKqiuFxkXHzRTt+j3ZV+7rkU/PlebBMnfONjCYIgfB8tXLgQRVGYNWvWoOdeeuklpk6ditvtxul0Mnr0aK699tqUNtFolD/+8Y+MHz8em81GZmYmM2bM4PHHHye2Y+UuYb9w4YUXctJJJ+30+UWLFnHEEUdgt9vxeDwcdthhhPoG6vZVYuRd2CWTQ+KoR2Q61kDLEpXquanPh9ph3i9VfvCijGzoH4FvWaLRvlKj8BCJkefAllchYxRM+KnMxJ8pnEU5AOu1BGv+2b9fWrmR+b/R60xW/1fDYJWI95U4T6vU/2+7736IgL7Sk379KWmpixjpGyUM2dmkn3suyvv/Ju5rQtZC2N/6CG78BOu4cYSWLQPAffJJZF91Febhw/G+9jr+mhhy7UIA7IfPZMPiejQNWmq6Ka75BP/f/pbyUs5Zx9L9zLPJx4aMDKJ9Nd2Nxamrp6qhEE2/uZ7Ipk1k/ORyPDv5o6OGQrTd+2fi7e1YJ4yn7c/3AVBwzx9xDfHBJgiCsKckVI0l1V20+cNkOy1MKUtHkaUv3/Ebeuyxx/j5z3/OP//5T+rq6iju+/v5/vvvc+aZZ3LnnXdy4oknIkkS69at44MPPkjuG41GOfbYY1m5ciV33HEHM2bMwOVy8dlnn/GnP/2JiRMn7lOjq/skTYXuGr2mtMkFaaUgfXfjxIsWLWLWrFnccMMNPPjgg5hMJlauXIks79tj1yJ4F76UJV2i8BDImyoT6VHp3gShTpKrpEZ7IBEFue+nqWujxvwbVFBhw7+15GRXfy3kHqhRdHj/B8DIcxQUk8rW1zQyRkkYrP2j+N6tqf2Y9Cv9f1NREfGWzyns+S3BkVeRd2w2tvdihL5Ibe858wzybrkFAPm998gM/Et/QnYAUPT3R/G98QbGwiIcB88EwD17Nu7Zs/X+Ll6DRIhIURXa532LMWng/XxF/y0rWcZ+8Excxx9PvK2NyIYNgF5JRovFibe3k37+eSn96nnxRfzvvgtA8//dhOu444ZMg+l49FG6n34agN6PP06m5DTffAv2GTNQnM5B+wiCIHxTb69p5rY31tHs7a8wkOe2cMsPRjFrTN5ee91AIMALL7zA559/TktLC0888QQ333wzAG+++SYzZ87k17/+dbJ9ZWVlyojr/fffzyeffMLSpUuZOHFicnt5eTmnnXYa0egQgzzCntO2Bja+mbrsutkNVSdA9nczT+uaa67hqquu4vrrr09uq6ioGNRuw4YNXHnllSxbtoxhw4bx0EMPcdiAu+nfN/v2pYfwrVLMEjN+p3DCCwrH/FPCVabnvo/9sYTR1h+QNy9Sk4H9jlVqhpq4WnmazHFPK0z5rUzJsVKyFrzJndpu7eOw6u8qmbfdT+aVV1L16wM44ulCbO9eSugLPXKXJhxD5s9+Ru4dt5N7443JfdPOPQ/zjOOgtJLAWb9g0xeNyHY7aWedlQzcB9JUFVkK0vHOh9TdeDNyVyvEopiWf4K09BOQZeS0NPLv/ROJnh42TZmKGgxS+uILlL32KmlnnEH6ueeQfc0vkiUgt5Md/UG3bLUOWhwq+V4NWFVVG1ByUvX5qDnttJRtgiAIe8Lba5q54ullKYE7QIs3zBVPL+PtNc072fObe/7556mqqqKqqopzzz2Xxx9/PPl3Ljc3l7Vr17JmzZqd7v/MM89w1FFHpQTu2xmNRux9q2MLe0HbGlj1TGrgDvrjVc/oz3/bXWprY/HixWRnZzN9+nRycnI49NBDWbBgwaC2v/71r7n22mtZvnw506dP58QTT6Szs/Nb7/PuEiPvwtfiLpM59jFIRDVWPaLyzsUJnEWgxqF54dD72HKhZ6tGYURDMUusf1ql+r8a2RMkJv1SQlIkAk0SibD+xzrqRb+8VPXFnba+CqDRsdLN2Mt+hnsESIpEeM0aokohGgZ8m/OJHHIF40/rq7s+fwHeV1+ls/cAtnp/R84VNQCEG7xkFrhIzx169LrpV79O5sRbAGP1enrPuw77c38B9IWiKhfMp+fFFwmvXAVA15zHMA8bhnsXuXcA7pN+SKylmcimzaSffx6SYehfw8zLL6fnPy+hBYMQifSvwApEa2pJ9PRgSEvb5WsJgiDsroSqcdsb63YccwFILoR32xvrOHpU7l5JoZkzZw7nnnsuALNmzaK3t5cPPviAo446ip///OfMnz+fsWPHUlJSwrRp0zjmmGM455xzMPfdudy8efP3erR0v6Wp+oj7rmx8E7JGfaspNNv6Sjffeuut/OlPf2LChAk8+eSTHHnkkaxZsyZlBP5nP/sZp5xyCgAPP/wwb7/9NnPmzOG666771vr7VYjgXfhGFtyg0qanjeOr3kkjCRwF0NsAm14AxaRROgvWzNE/IqqbNPIOkiiYCUbbgN0UOPxBiXhAouYdlbq+Koyd62HeL1TSRsARD8rEjv4Dm744EiQFS2Q16lo/4Cbe1UXDT3+KFo1iZC7Wgny0eA6SQX9dozn1x1+LxWj985/xv/Vf4q2tqafg68KweRVS1Vgku5W8889CUhSMRUUp7Zp/eyPhdevJ/b8b2RlJlvXqMV9CSUtLVr/RO9j/kWoeOxbF7R5iL0EQhK9nSXXXoBH3gTSg2RtmSXUXBw3L2Gm7r2Pjxo0sWbKEl1/WF6gzGAycccYZPPbYYxx11FHY7Xbmzp3L1q1b+eijj/jss8+49tpr+ctf/sKiRYuw2WxomoYk7f28fGEH3TWDR9x3FPHq7dLLv40eAaCqegrA5ZdfzkUX6UUjJk6cyAcffMBjjz3GXXfdlWx70EEHJb82GAxMnjyZ9evXf2t9/apE8C58bS1LtGTgvksSxAfEoFGfvoiTZOgvQ2nS09Bp/FRL7jPibImMkfpVetgrUfd+33N9/3VvgGAr9BiP6S8NIxvIXXMmid7/oAZDaANyHKVAgM7XCyg5w0/BOAfONGtKN7v//Rxt8xZj3CFwx2LB+sNTSGRNpmvasSCBuyoXF+CYMYOCv/yFtj/9iVi9vmSr/7Ml5O7G2/JlJFnGMno04R1uE2dcfTWZF16AtI9PuBEE4fulzb/zwP3rtPsq5syZQzwep6CgILlN0zSMRiPd3d2k9d1lHDZsGMOGDePSSy/lxhtvpLKykueff56LLrqIysrK73XAtd+K+vZsuz0kL0+fnzFq1KiU7SNHjqRuQEW6nfk+XwiKT3/hawu07GbOtQqRHn2RUlsuRAMavQ0w/XaZosMlDrhGImuC/ktS+25/gB7q6D9EvHfwYd3DwJYNOZP6f8EckUWorXU03/0XJEUm86qrIK2QbuuJ+CxHEamzM3JmPpmFg0etvZqd0ImXom2/rbc9OA6Hic59k06tIHmRULfSy6snJHj/igSmKUeTfe21oChokoRv1MG013/JKMSX6PjnHBquuYbMn/0UpbAg5TnL8OHIVutO9hQEQfh6sp27txbF7rbbXfF4nCeffJJ7772XFStWJP+tXLmSkpISnnnmmSH3Ky0txWazEQgEADj77LN5//33Wb58+ZCvsb2dsIdtX1p9T7XbQ0pLS8nPz2fjxo0p2zdt2kRJSUnKts8++yz5dTwe54svvmDEiBHfSj+/DjHyLnxt6VV6+pqmfnlbLaH/C7bo/5o+VTn+OZn8g1KvH3MmS9S9p4EEOZP6txccIrHpJY3eeig4FMqPl8kcA7JRovI0CVdOD42/uRmb/z0A/C8+hf/FpzCWFJPzt3+z4aY0CED2JDDupEiLYdI0Elt7CJz3K4ybVpBdloH33//W++/rJFprwFyot+3+woQmBeneYGPr6xqjLziWjd1OEuEoano2vq4grlgX4XXrsE8/CEN6+i7fHzUYpP2hh1D9vaixGL5XXtHP4/0PqPh4HvVXXEl49WpsU6fgPOzQL3/DBUEQvqIpZenkuS20eMND5r1LQK5bLxu5J7355pt0d3dzySWX4N4hHfDUU09lzpw5dHR0EAwGmT17NiUlJfT09PDAAw8Qi8U4+uijAfjFL37B3LlzOfLII7njjjuYOXMmTqeTpUuXcvfddzNnzhxRKnJvSCvVq8rsKnXG7Nbb7SVer5cVK1akbEtPT+fXv/41t9xyC+PHj2fChAn861//YsOGDfznP/9JafvQQw9RUVHByJEjue++++ju7ubiiy/ea/39pkTwLnxt3urdC9yHkghDLKD/Pg805TcSRYdLWDyQPrJ/RN2SJjHrcZlYEEzOwbeycg/xYLvzZBqveC9le6y2jvB/7mfmmZNhzAlkjFGQJIneRo31T2tYMmDU+RKKSSJneBYdrUFCo6eS+6MTyMkzEVm3lsi69aRddCntLQV43w5QNLuDjNNfRbkgQtMrB2HLnoqmaliL8+muDxPfZsPq9lJ94dlooRDGoiLK33h9lyustt1/P91PPgWANLBdLEaix0vZ88999TdZEAThK1BkiVt+MIornl6WXGV6u+1/dW/5wag9Pll1zpw5HHXUUYMCd4BTTjmFO++8k3PPPZc1a9Zw/vnn09raSlpaGhMnTuTdd9+lqqoKALPZzHvvvcd9993Ho48+yq9+9StsNhsjR47kqquuYsyY76Zc4X5PkvVykKuGvkMC6M/vxcmq8+bNG1Rl6IILLuCJJ54gHA5zzTXX0NXVxfjx43nvvfcYNmxYSts//OEP3H333Sxfvpxhw4bx2muvkZmZudf6+01J2ndQb87n8+F2u/F6vbhc3+5tFGHPCbZpzD1j96J3aya4y/U5lz1boPwEiTEXf71f5FhAw1enYcsBa3r/MTZfey3tH32AJxhJftAMrNCSdsEF5N6g13p995IEXn0iOqMvlBh1Qf9xvmzSU1vb+/h8eoUZ4haGVV3Bus/q8LYF0BLQ8UohWqdCec2xGFW91FT5f9/CXFa202M2/urX+N7UZ+sr6ekkuroAsB54IKVPPbnb740gCPu/vf0Z+l3VeRf2cd/DOu/7KzHyLnxttmwJW66eBgN6zfdE38RUSQbZpI+wI8EBv5TJP+ibj9aEuzTe/bFKRI9tGXFWgrGXKbRu28Lcug0kKgrJ9gaYXNPXqQHXpt3PPktt+Ce82a2ScGiMx42MRGSHOTRfNknFYsmhb/FUrM4cmrd14W3TcyklBSylAXwN2URzZqJ4P8QxZiTGvDxaH3yIeFMTmZddmgzk28NRVnT6yagchRU9eDeWlFL40F+RFAXruHHf8B0TBEH4amaNyePoUbnfyQqrwj4se4xeDvJ7tMLq/koE78I3csSDMmsfV5FNEjmTNT67TV+IacylEsNPkmhcoOHIl8gYvWf+6Let0JKBO8DGF2DMjzW2LfucRN8HS5vbTkICZYd7SiEqeVgO8vbJbQAcVRrkkpp8Rpz11frmco1FUezE47342rKoX9dfnUaLhTHltJH/sx4S5gvxJs5Hy7BS//BjBB/9KwCBpV9Q+d7bAHzY1EUwoaJs3sz2KaihdesxjByLySJ+PQVB+G4osrTHy0EK/wMk+VstB/m/SkQHwjdizZSY/Ov+FUKPf14jEQF7rh4Qlxy9Z0dq0kdIyGYNNaI/dpfpI+UFpeXIqoYqS2T4gygGI8RSl3NVEp1UF/VXG+iYEuLwa4Ze3TTZ5pFHCC79As+ppxBeu47eTxfgPuEHZFys14ztqOlfbVDr+QIt7sNkBcIFJAyVAPg6Q0TjjmRwHu9btS3W2oYaCoPJRNeMQ8h55y3kaITIAYfi7QiQNURFHEEQBEEQ/reJ4F3Yoyxpe/e2qiNfn7i67U0NgxWGnSihxuPwzyc4eGM9QbOB9N4QaKDk5JAYULM979IT+fGYTK73B9FkjYvKdz0ZxT9vHu336yuqBhYtgkQC1Won9Og/cRx6COZhw8gflk5Pay/hYBTivv5c+0grOCqTx9JGTyZWNhKlqx3zJT+l5c476X7yKSampVFzz18wTZlC8Na/k2jrhMJSHJ49W4pNEARBEIT9gwjehX2OPU9i7I/1MDlSXc3mI89A9fuxA/Zo/2h7e+hwFEsH7rC+NKuptJRzp6RxXNSJikaWybjL1xm4wBOqSqyogt4f3wJmK80dKqXDwOow48qwEQnF0Mx5EOkbiTelllLLG5GD8R+PgwYZ+Q42jtVz2bXubg5Y+inZMw8kevwBeNsDODwWrA7zN3yXBEEQBEHYH4ngXdin9Tz3PKrf37/BoKCZ0wjGSmk3XUBG/FkwGLDPmIHrhOMByDDt3o+986ijSDv/PEJfLMNz2qk0ynlg1pNf2rs1SvvayQZ9Mo7kHIE9dxjpuWbqtiRSjmWzm3Bl2pEVva1l9GjCq1cDYB07FgCT2SBSZQRBEARB2CURvAv7NNOwARNjJInYFQfRYziD1ucOp6DnJtJC+mJHgfnz2XbCCbiOP57sq68GIN7VBaqKYSe1XCVZJve3v00+jtZ241uhj6w70/tXOC0ZlQ1AIpageGQ2FruJxur1qGr/jNl1n9XjTLcyZmYpkiRRPOef+P77NqaSEuzTpn7j90FVNWrXthL0RygYnoEn2/GNjykIgiAIwvePCN6FfVra6aeT6Oqm/YEHqDt1DDXHVyJHPqektw3rf7ZAX+lKVJVYXT2dDz+C87DDiDU10fjr60DTyLvjDjw/OvlLXyunJA2z1Ug0Eiczv7+2ssGoMGx8au1js81IqDeass3fFaKrxY/VYcbmcpF2xunf+Py3a63ppnlbV/J1ps6uQtpLZd2atnbS3dJLRr6T3D280qIgCIIgCLsmim8K+zxDTg6oKk0njgRANRsIln+O45ZhYNzhR1ySkKxWul94AeJxSCTo/gqrl3qyHWQXeZAVGW9HgPWL66hd18r2tc56O9tp37qJ4RNzySpyk1OallLiduOSBlZ8uJWOxl0sI/01pK61pg25tPmXaanuYs2CGho3d+y0jb8rSM2aVrwdAbataiHgC9PR6GXtwlrqN7YDEI8lkncd1IRK89ZOmrd1pdyJ0DSN72B9OEEQBEHY54mRd2Gf5zjsUEzl5bg2tNMx0w6Aq96LNLsIw7/OxfT3alqnF7Fqmgmraia/LAvbxIkEF30GgG3CxF0dfkiaqrFhcT2JuEp3Sy+93SFC/jDR7mq04DbsGZmM/+HpSJKEJEFLdXfK/q213WiqRnq+C0X55tfQuaVpBH0Rgv4IhRWZyF9x1D3oi7Btlb6wla8ziDPdhivDNqhda21PyuNIMMrmLxrRNPC2B2ja0kkiriIbJMYdXMbmZU0E+lZp7GzyMmZmGd72ABuW1ANQdWChSPERBGG3SZLEK6+8wkknnfRdd0UQvjNi5F3Y5xnS0ih/7VWOGXMJY19vYfJzDYw45EhMpkxyhv+Qkkf+zobD0kmYZHotMb7YPJf0yy+n4MEHKLj/PrJ/cx1aNIrvnXcJrVw55GskYjG8LR3EonEANEgZSfZ2BIlGVLCVgOIg0NlBoq/OvMlqZK2cQO0bD9fQ8LYH2bysiS/e3ZxyHIC2e+9lw/gJ1J5/AWogwO6QFZnhE/MZd0gZ6XlOAMKBKOFA9Ev23G7HUXCNmrWtLH5rAxsW16MmVIBkIJ7sa5134CK2JOJ6OzWusfHzhpT2vs4Qod4o9ZvaScRVEnGVhk07H+UXBOE7pCagej6s/o/+v5r48n32gJaWFn7+859TXl6O2WymqKiIH/zgB3zwwQdf6TgvvfQShx12GG63G4fDwbhx47j99tvp6ur68p2Fr03VVJpCdWzpXU9TqA5VU/f6a1544YVfejHX0NCAyWRixIgRQz6vD7RJfPbZZynbI5EIGRkZSJLEvHnzBrWXJAmn08nkyZN5+eWXk88/8cQTKW22/wuHUz9Dvy4x8i7sFySjEeeEA5g+4QCi0W40LY7ZnJV83ml0E4oEAYg/+xoNNe9Q/NSTSJI+Ql1/zS/p/eADkCQK7r8f17HHJPeN9PpZ9vILqLEIkns8Zlc2+cPSKR6RRe26ttSOaBpoCcwOJ32HpssbYrTavxiU1F8Nnng0QcAbwmg2oCZUum+7Cd+bbwIQXLIE33//i+fUU7/y+9Fa283Wvsm15ePzyC1N22V7m8tC2Zgc2hu9xGMqW1Y0E+7L2e9q8dPV4iezwE1GnjMlIN/VxUE4FBu0TZL08pq+Dv17YbGbvvK5CYKwl617Hd7+Dfia+re58mHW3TDqxL32sjU1NcyYMQOPx8Mf//hHxo0bRywW45133uGnP/0pGzZs2K3j3Hjjjdx9991cc8013HnnneTn57N582YeeeQRnnrqKa7uK1og7FnbAptY2PUBgURvcptdcTA9/UjK7ZW72HPve+KJJzj99NP55JNP+PTTT5kxY8agNkVFRTz++ONMmzYtue2VV17B4XAMedH3+OOPM2vWLHp6erjnnns47bTTWLBgAQcddBAALpeLjRs3puxjseyZNVzEyLuwX/H7N1C98Z8seegWlj78e2JBPUg8NuskqmqtDPvbIgpfXktw6VISPT3J/YKLF+tfaBrBJUtSjtlZW40aC4M5C4xpREIxqte0YrIOce2r6QFrpNdP4+oV+qZQfKf9lSQI9ERY9t4WVny4jUbncFRrfxpJ1LXrhaR2pr2+P6e+ZnUL7buRY583LANPloNwbzQZuG9nsug18Qursigfl4vBqGA0KxRWZiVLZQ7kzLCiJfqH5CUZhk3Iw2I3UTYmh9LROZSMzqZsXO7XOj9BEPaSda/DC+enBu4AvmZ9+7rX99pLX3nllUiSxJIlSzj11FOprKxk9OjR/PKXv0wZEe3o6ODkk0/GZrNRUVHB66/392nJkiXceeed3Hvvvdxzzz1Mnz6d0tJSjj76aF566SUuuOCCvdb//2XbApt4r/21lMAdIJDo5b3219gW2PQd9UyfY/X4449z3nnncfbZZzNnzpwh211wwQU899xzhEKh5LbHHntspz8zHo+H3NxcRowYwSOPPILFYkn5WZQkidzc3JR/e4oI3oX9Sm/vRhpfWE/7+7XUvbGQlY88AoDN4GBa7tEUvl+LpGpYJ09C8XiS+7n7qs1IViuu2celHNOZnQNIKbeNZVnCnWUns9CFYpT7K7vIJj11xpSB2ne3sKQqCxQJFdBksDr7R5ttTjP1G/tH7xMV4+m97FbCB/+A3rN/iTZiPJqqp7AsfWcTm75oHJRmMxR3lj35tapqbF7aSPWaliHbhvwRVny0laXvbiIa7r/QMJgUckvTqJxckJL/nluWzpTZVRw4q4rMAleyVGb/82lUHViUMlF3xJQiHB4rdevb8HUGyR+eQcHwzD2S7y8Iwh6iJvQR9yGnvPdte/v6vZJC09XVxdtvv81Pf/pT7Hb7oOc9A/5e33bbbZx++umsWrWK2bNnc8455yRHRp955hkcDgdXXnnlkK8z8DjCnqFqKgu7dp3WtLDrw28lhWYoH330EcFgkKOOOorzzjuPF154Af/A9WH6TJo0ibKyMl566SUA6uvr+eSTTzjvvPO+9DWMRiMGg4FYrP+Oc29vLyUlJRQWFnLCCSewfPnyPXZO4pNT2K/YbGXEvJHk43BHZ/JrS1UVw958g+LHH6N4zpxkygxA7m9/S/ncNxn+/nvYJk1KOaYzK4exx59CTmUF2UVO0vOcVE0pwmQ2UjmpkKmzR2B39a+IKllykVzjaGtzsW5RLUazgQOPqSAr34nDaUmOYgOEgzFikf4PQs3fQyKrgNAJF6IdeAgZeS5WzttG05ZOouE4HQ1eWrZ18mWKqrLwZKd+ALbX9QzZtn5TB0FfhGgojq9LD6zT85yMOqiY8vF5ZBbseuGorEI3Vqd+/g6PhdLROQS9YehLD8ou9uBIs7JmQQ0NmzpYt6iO3p7QLo4oCMJ3onbh4BH3FBr4GvV2e9iWLVvQNG2nOckDXXjhhZx11lkMHz6cO++8k0AgwJK+O6abN2+mvLwco3HXK2gLe05LuGHQiPuOAgk/LeGGb6lHqebMmcOZZ56JoiiMHj2a4cOH8/zzzw/Z9qKLLuKxxx4D9LSY2bNnk5WVNWTb7SKRCL/73e/w+XwceeSRAIwYMYInnniC119/nX//+99YLBZmzJjB5s2b98g5iZx3Yb+haRoRSxbDL72YTX/+F7JsZNQF56e0MebnY8zPH3J/87Bh+hfRXtBUMPfXcnfl5uDKzdnpa4+YWszq+dVEgjG2B63xmEpPWwBfZzXZJR46m/QrfcUgk1ngIuALE+iNISX3AHKLkRWJKbNHIMsSm5c1EvRHUl6rflMHOaXpKAPSVTRNS7kYAaicXMiGeRvxBfvOITM1mI9G4lSvbqG3uz+QNluMlI5OPc94NEHT1k5kRSZ/WHpyldjtZEUmu8hNJBSjsCoLWZFp2tqF1neHIOANEwvHk5NZAcKBGA6PlUgoRlezH4fHgjN9cHWb7SKhGA0b21GMCkVVWSnnLgjCHtLbumfbfQXbS8fu+HdsKOPGjUt+bbfbcTqdtLW1JY+zO8cQ9pxgYvcKK+xuuz2pp6eHl19+mQULFiS3nXvuuTz22GNceumlg9qfe+65XH/99Wzbto0nnniCBx54YKfHPuuss1AUhVAohNvt5k9/+hPHHaffuZ82bVpK7vyMGTM44IADePDBB3d5zN0lgndhv/Fx59ts7F2D4lI45q834oz5MZm+4oTItrWw+t968D7iRCic9uX7ACaLAbPV2Be8p1ITGj1t/X+0FINM8ahsPvy8lUi2E0MsTlq3D7nvw8tkMSDLEvFYIiV3fbtETGXtpzUggcNjJeiP4OsMYjIbGHVQMTaXPiGm894/oTzxBK6ykaT9+nryJo9MOU7dujY6G33J10zPc1FYOTjHfvPyRrpb9FGVeDRO6ZjUvL36DW00btbvBkRCMUZOLcbuNtPTpu9jd1tIJFSsTjMhfwRXho20HAdqQmX1/BqioRiSBGMPLsORZmUoW5Y14u2b5KqpGmVjRa68IOxxjp0PUHytdl9BRUUFkiSxfv36L60csuOouiRJqH15ipWVlSxYsIBYLCZG378lNmVwmtM3abcnPfvss4TDYaZO7V/JXNM0VFVl3bp1jBo1KqV9RkYGJ5xwApdccgnhcJjjjjtuyBQbgPvuu4+jjjoKl8tFdnb2kG22k2WZAw88cI+NvIvhK2G/sTWgz+pOkGBt+1t0dn5Cc/MrBALbdv8gjZ+DlgA0aFhC5zqNZfep1L775bl65ePycGXacKRbBwyl6xSDTF55Op4cO1VTiujsDhMx6xcWcaOBiHlAKk0gRleLH1mWMJgUdmQwKfT2hOntDtNS3a1XbtEgGo7TtK1/Rry3b+KMUr0e+aM3BtV+H7hIkslqpHxcLibL4Ov5gRck4SEuTsKBAc/3TXQtHpnNsAn5lI/LpWhEFms/rSPUdwehdEwOikEmFkkQ7atIo2mpZSg1TSMciCZH62PR/tSiaHhwHwRB2ANKputVZXb8A5YkgatAb7eHpaenc+yxx/LQQw8RGKJEbs+AAgO7cvbZZ9Pb28vf/va3IZ/f3eMIuy/XUohd2fV6HXbFSa6l8FvqUb85c+Zw7bXXsmLFiuS/lStXcvjhhyfTY3Z08cUXM2/ePM4//3wUZfBn8Ha5ubkMHz78SwN30D/TVqxYQV5e3pe23R1i5F3YbxRZS6kObkbSIE1V2f4BFIl0YLOWomka8i5+EQHwlEKnfhEQMw/jk+tU4gHY+jpYMjRyJkkkQj6Utc9BoA3Kj4QivSyUzWVmzIxSwoEoy97fknLYQE8Yk9lAT2uAsL+JsrEFKFqQhCSBpmGIp04A62jwkp7rZMSUQtYsqE1uLx6VTcu2ndcptg4ovWifPh3vex+QyCvBMm3wh23JqGwSMb3eeunY/pG0eCxBR6MPq92EO8tOyahsNi5tRE2oRIIxouF4SpBfUJGBvytIIq5SPDKbRCxBV4sfm8uMM81KwBdO1okH/WLA4bFitCik5znpavZjsRtJy3Um22xa2kBnk34B4/BYcKRZCPr04L+3O0w8mki5sNm8rJHOZj92p5kxB5fuE7fNo+EYBqOCrMiE/BF8XSE82XbMVv1CLhKKEQlGcaTZvvKiW4LwtciKXg7yhfPR/34OnLja9zM46w96u73gb3/7G9OnT2fKlCncfvvtjBs3jng8znvvvcfDDz/M+vXrv/QYU6dO5brrruPaa6+lsbGRk08+mfz8fLZs2cIjjzzCzJkzRanIPUyWZKanH8l77a/ttM309COQpb03Xuz1elmxYkXKNp/Px7Jly3jmmWcGzaU466yzuPHGG7nrrrsG3aGZNWsW7e3tuFwuvq7bbruNadOmUVFRgc/n44EHHmDFihU89NBDX/uYA4ngXdhvHJExmy/8f8WsaTj6PmgMBhexRpk3f3IWiUiEqrPOxF1aSu6BBw4dyJcdpt8SVuPEpFHEBwwA9bbE+eSOW6lbs5LhpS5+cGQx8qY3oeBAkPt/lSx2EwUVGclUku26W/U0knAwwuJ7ehl3Vgbh7DAdG9uQdwjee9p6WfLWRsrH5VI8MpvGzR3YXGZyS9No3MnCRha7kfzhGQCoCRXHb26m6ZDziWkK20xGxu8Q8JosRioPLKR+YxvbVraQVeQmu9jD8g+2JCfRjphSRHqeE0WRUOP66HjTlo6U1BlN1cgu8ZCW7cCZbmPVx9vo7dFH0UdNL8adaSeryE17vbcvt91Kd2svGz+vR1U1CiozSMQ1vB0BMvKchAKx5PwAVdXwdYUIDihdqZfqbCGr0I3dbSEUiCbTi/zdIeo2tFMyUh8JadzcQfO2LkxWI1UHFiYD4+9CPJYgEVcxW41sWd5EW10PBpNM0chsate0oSZUjGaFiUcMJxKKsXp+NWpCw5NlZ9T0kuT5+LtDpOU4kudcPj4Pm9O8q5cWhN036kQ4/cmd1Hn/w16t815WVsayZcv4/e9/z7XXXktzczNZWVlMmjSJhx9+eLePc/fddzNp0iQeeughHnnkEVRVZdiwYZx66qmiVOReUm6v5Gh+OESddyfT04/Y63Xe582bx8SJqauln3DCCYwaNWrISdAnnXQSV1xxBW+88QY/+tGPUp6TJInMzK9Xpnm7np4eLrvsMlpaWnC73UycOJFPPvmEKVOmfKPjJvuoDbx3/i3x+Xy43W68Xu83urIRhGCwjnC4BaezEqPRg9+/ge7uzzGbM8nIOARFsbL4zj/Q9OmnKfsVHnIIU67/zZcef81jKlte1bAVeLFOeY0lb7+UfO6sE4eRX5SDNvM3SH0jUcHuLsJ+L56CYtYtqsfXqedpSzIYjEoyKG7/TxG5I+xMvVFm8dwNKZM5U0gw7pAyHB4rmqbRWtvDtlXNyQGx9Dwn3a1+tlfgSstxUDG5gLULagethjrqoGI82am3NuvWt6WscmowKcQHpKgYLQYSsYS+9lTfBFRXpo0xM0oBfZGm5R9uRVM1ZFli3OHlrPhga3L/gooMSkblEPRHWPnR1uRqrEazklJlJ/n6Rpl4TEVWJNQBdeJ31t5oNjBsQi4bFvdXMcgfnkHp6Bw2L2tMmTNg91jIzHfh6wqSU5JG+oCR/r0h6I8Q9IVpre3BYFLobvGjJjTyh6fTtGXnd08qJhUQ6o3SsLE9uW3q7Cp62gNs/HxwtQZ3lp0RU4robvUT8EVAg7yyNEzf4YWKsHd9K5+hakKvKtPbqg9olEzfayPuwv5D1VRawg0EEwFsip1cS+FeHXH/XyVG3oV9Unf3Uny+NcRiehDk9S6npORinM4ROJ39V9malsCUM3hUsm03662OuVgm7cDl1CxZSMjfiyTJaJqK0WTEWjKJzrRKNj35DwwWK8UHTGHrgnlomoo7v5DKw4+naas++l5QkUkilmD50z20L7IQbbCR0Xexn1noorWmZ+gOaHqAPeqgEtYtrE1O2gQ9UB8+MZ+1C2sJ9I10d7f20t3SOyhw19Q4atQPpAbvvs7U3NKBgTtALDx4galEXGX94jqioTiZha5kUK+qGiF/hLQcR/IuQ0ejj6IR2bTWdDNwmGCoQBz0Cj1ASuC+q/axSBw0KRn06/1Q0VRt0GTfQE84+T71tAWYfEwFRrP+J9DfFaS5uguby0LB8Ax8nUEaNnZgceiLSu1YYQfA2xFAkiQsNqNejccgU1iRiazI1G9op35A8D3QUN/rgf3f/EUjoC/gpWngyrChGJWdXuDJisS6RbX4u/qrBvW09TL+sPIh2wvCbpEVKDv4u+6FsI+RJZl8a/F33Y39ngjehX1OJNJBZ+cnKdsSiQCJRAhZTh1tbGt9H+chUGCsQgo4aXx7BVo8TvGRR+z268XCesBntTsYc9AMDC43ajDCuk1hFONq1ESCaKCXprWr0PqGwL1NDRhNckrZRaNJYerFWdSXa5icEnnT9NSe8nH6yqP1G9oHBa2gp7eoCTUlcAd94qfBqPTVVO/X3uhNjlRrmookyUiygaYtraQXpk6WcWXY8XUOXXPdZDMSHWKCqsGoJKvPhHojyLKUXDiqrbYnJXiPBGNsWdY45CjwjqPrX5cjzYrRbCAe01Nr1ITGxs8bkGQpeWGxI03ViMcSGM0GVFVj3Wd1JGIq4MNoVqhd00Y8lsDbEUCWJcrG5lK7tpW2ei/ONCtBXzg5eXfgeXQ2+phwxDDa6nt22l+724K/K5i8mJFkqDywiOatncn3beC5VUzSS5tmFrrxdgTwdQSIDFi1N29YBus+rU3ZLxzoTzOqW99GV4uf9DwnxSO+fGKVIAiC8P0mgnfheyWcCOGNdaNICm5jGkZ5cKlHSRp869bpHI3ROPj2cVftaowZMhkzCtF8Zsae8RhRnx93Welu96lgzAR621sJ9/qpOmIWTZtb8Ac9gIYa2AjoK5e6cnIJ+3pQ43EyyoYjyYNHa2WjRMnRqZMPJUmiYHgmWUUeIsEYQX+YYE+YRELDaFaSo7mONGuyJrsny47FbkLTNHZMfOtp6cWT7SC72M3mpbVofUWlJKODbSubkRSJoqosDEaFgopMetp76e1OvQAwGJWUwN3hsVAxqQCQaK/vwduuj9jvGHxHwnHScpwYzR36qDj66LvBrGBzmZOTTkGvSGOyGGit7T/erthdZj0tZACjWcFkMTB8Yj7Va1oxmhUMJoW22p4vPd6qj6sZOa0ISZb6Anfd1hXNKRNeW2q6ych30rhFv4vS1ZJaNmzgexDqjRINx3GmWYcsG2owyow8qJiOBi+169rQNI1h4/NxpVtp3praVtOgtztE05YuMgpcmK1GKg4owNseYO3C/mBdkqSUCyYAs93Isve3YHWakhdaQV+EtBwnzp2U4xQEQRD2DSJ4F/aqiBqhMVRDuikLjzGd2uBWVC1Bqa1iUEUQb6ybV5qfJqLqgaRddnJy/rnYDQ5UTU3mzZlMaWRnH4PfvxGbrRS3e9ygEfftuj5pIusH+aBqBFYFsB6QgTUj4yudg9FqZczsk5KPazeG+vouYXCVkjuyCKPFSu6I0RRNmEwkEMCRuesV2YZiMhswmQ16cDXEXcfKyYX0dgWRFRlV04hFE/o+VmOy5OJ2qqqSWeBG04rZtrIZg1FG0wy01HQD+gi5O8tOxQEFjDuknHWLalNq0cdj/Wkq6XkOHB4D7/39b8hKjKknnbnTc0jEEmxZ0cSIKYWsW1SXTPWIRxJISEw6pgJZlkgkVIK+CIpBxuow7TJ4lyT9DkHhiEzWfVqLpoEsSzjTrRT3TUx1ptsYd0gZmqaxct7ulQZNxFUaN3cm5yUkaaRUytNU/QJpVyP5A7XVdtPZrAf4ZruRyIBSmiarEUWR8bYHkilK21Y107zNlEx7kWUp5aKspbqL5m1dSLLEmBklGMwKmYUuOpv8KIrE1uVNhANRjBYDY6aXsHlZY3LC8MAReNAnvFYdWLhPVOMRBEEQhiaCd2GPiiTCbA1soDPWRp65mCU9n+CPezGjUGYtY0NIL6E4zjWZg9IPByCuxnmn7WUawqm3/p1xP03t79NuMLM6sIZMUw4n5JyOWbHgco3B5Rrzpf3JG300K3/7CAajmWn/d9MeOceMwkyatuq59lpnPkvuzyZjDGT9QcJks2Oy2Qn7fWiqitXt+VqvoWka1ata+tIdXIBGS3W3PoE0nkCNa5itRsYeUjoocDdZDcl0HYfHiiTLREIJEvH+UetEXKWr2U9rbTf5wzLILHCnBO+gB6tmqxGrw8zHTz1GyyZ90q+vvZWxx/4sJcca9CA7EowRCcaoTaiDcrRjkTghfwRPtoNtn9cnK8oUVWUm87t3JMlwwJEVmG36xdmYg8vo7QqSluvEYh98VyYWSaSM7u9IMcgp/fJ3h4bMJTeZDcSicdSERlaRG3emnRFTi9i2sjk5oi7JEiaLAU3TiA5IY+lq8SeD/IGBO0BuWRqbv2iko29xLNDnGQx8L9UdLhC2vy+aqrHm01o0VcNgVPTUH1VLpgvFwnF6OgJEI6nzFOxuS3IORFezn+7WXqwOE2arcchcfkEQBOH7TQTv/0PUcJjIxo2YystRnHu+0sayns/4vGd+8vE6/0oAzBocmNBYF9iSXBasJdxIT7STt9tewRvvIbWeMBg1GKnKSIFtmNHAAB3RVrYGNzDKOWG3+pOIRunetBF3URmVp55C1tixe+AsoWR0Ds50G+EuWPhzGwAdq6BxgUbJ0RLtWzez6eP3QNMonTKdgrGp5asSsQTVa1qJhmMUj8zG4RmcxuDvCiZHyVuq+yuTDJxAGgnpNdfdWXa87Xpu9qgZJbjSbck2nc3+5AhvPJZAkklWpgE9PSYcjJJIpAawaXlOKg8oQDHIbFnelBL4R0Ohvrzt1OA9JfgeWJe8r1y02WbE4bHS1exPBu4Avq7QkIE7gNVhTgbuAM406y7TPowmBaPFMOREWyRSAnWT1ZBa0cai4Ml20F7nJTLggijgDbF47gac6VYsdlMyeNdUDWPfglkDBbxDXzxkFbmxuyxsW9mS3Dawuo9skJGl/km7Q9l+UTDwzsjA86tZ3aLfmVD099/hsVIyOps182uS73Hd+jaCvghWh4mxB5cl74I40610t/SSiCfILvaIwF4QBOF7SgTv/yPUSISas88msm49htxcyv7zIoZvWMd0oLgaY3nPZ0M+59EkgvSF55qeo1tmr+Tl5qeJadEh9ylSQe7LXbAOyGHwGNJ3u0/Vb/2X2nffA+CLP99H/kEHISnKN04ZkCSJjHwXje1ryTtrPlrcQMcHJ2DP00e727duSkaybZs3DgreGzZ30FbXA0A40MgBRw0f9BoGY2pe/1CTO+1uCzanmZHTivF1BLDYTcSjCerWt+HOtOPOsuPavtprcvQ29XVaarrYsrwJSdZLQGqqRt6wDDLz++cPuLPsFI09hkhvF4l4mMMu+AlN1d27fI8SsQQjphTh7QjgyXZgMMpYnWZ9smtb6qTMnaXNKAaZ8vF5+lLWCQ3F8OXBpCRLWKzGQcG7waSgJtSU9zAaiuPKsCXTZtKynUMGxUGf/jPa0xagqCoLX2cwGUTvGLjvSv6wDAwmJSX9pmxMDnUb2tE0jcpJhTg8FrauaCbgC+NMt+68CpEEmfmulBF8u6t/hN1oMjDpmIrkc1VTiuhs8iUnRoOen9/e0EPd+nYScRWj2ZCcp9Ddqs+bcGXYsLstu32OgiAIwt4ngvf9jBaOocUSyE4L0bo6Yg0N2A48kOjWrUTW6avTxVtaCC79AtesY7/26yS0BHXBbSzzLsIb60aWZOIMvWy92ZzHimgTal/MfETGbJCknQbuMgpNcoKchIYZUFyjcURqsCp23MbdD96lAYswSbJEc/MrhCL1OBxV5OTM/sZBfO3y+UhKAklJUHDSh2SOORs1kUBT+yNkT/7g5aAHjjJvX2YhEoxRt74N2SBTMjIbm8tCdrEnGeTvGLhXTi4gPdeZHB31ZDuIhuOs+XQbakKlcXMHnhwHiZhK+dhc6ja0DyoDCSQnqmoq+DqClIzKpnlrJ9WrmikemY3VaWbbqmbMdg+jj/qJvo/XiqyEk33yZNv19JMBI8Yhf4R4LEHzNj1fu3xCHs6+OwLpuQ7aavXSkZkFLgors+hu7R00wTMRV+ls9LFuYR1qQqV4ZBaFlV8+lyA934m/b2KvJEtMPb4KWZZZv7guOXlzu4E54dvf60EGXPz4u4MUVmYmA+Cvom5DGyOnFmN1mghuH52XJCYdXZHSTp8YrMsflkk0FKOnvTdl0a9RB5XgybJjc3XQ3uDFk22no6G/NOb2lKJELMHm5U2EeqMUjcjCk2WnpbqbWCSOrOgpRNvvRsQGpNt0t/XS3dqLJEuMP6xcLAIlCILwPSKC9/1IdE0jar2eYqFa49RddSFaNIr94IMpvP8+jEVFxOrrkd1urOO+WQrJW63/oSlc179hJ2kPR2QeT6YxhxeaH0tu8yZ6qLSPxixbiKhhzLKFSe7pbAtuwqJYmOyZyfvtb/BZrIsCSwmhWCu9CT+9CT+f98znsMzjhnytRCJEJNKO2ZyNolgoO24WgeYmfLV1FJ8wnVBkAwC9vRvxeCZhseQOeZzdJSElT1vVAoS8PbRv3URPo/6+WN0eSqfOGLRfYUUmkWCMaDhGySh9tH7L8sb+UpCaxrAJ+RjNQy+IYnOZiYbj1KxpJacsDbtLHxmNhmOofekvmkZ/lRF/hKoDC6ld20rAG95pigpAe4M3mTO+dWUzJoshJSgHPc3FnWVHlsHfFSYSjGGyGAjF+gNhVYWtK/pXZ9y2ohktoZFXnk5ajpOJRw4n6I/Q3dpL09ZOxh5cyqpPqlNyx0EPqLefU92GdqwOMwFvmKwiN1bH0AFlZoGb2rVt+vugamxb2cLwiflUTS6ko8lH89bOZGpLdIj0Gtkgow5Ir6k4IJ+GTZ2E/BF62gJoGoyeUULT1k687YHdLne5/X0ND1gttmZ1C+4sux5MSxIWhwlN1fB1BWmp7sbhtlBQmYnRbKBpS1fyYm97oF1YmUlmgQvZINO8tT+9SlVVata24OsI0dujX8hs+aKRkTOKGTOzBH9XCGe6FUmSaNrSRTyWwOowEQ7G9LsCA/Lsg76ICN4FQRC+R0Twvh+I+4Ksbeoho81HMhHGG0eL6kFCYP58JJOJ0heeJ7R8OZZRozDmfv3ANaJGUgP3nZjknk6FYxThRGputIKC0+jmtPwL6Yi2kWXO4bOuj/HHekg3DSeaiNAT00cZG8I1GKX+nGeZoQPaRCJEff3TxON+DAY3RUXnEk/0YptkwHPEODLzR9PUtAlQkSQDBoNjyON8FRWHHMmmj99DU1USsSgbP3oXR1Z/HW01Hh9ydN9gUqg6MHVEfvvop2HDMmK1Ktros8grz6CrxU/I3x/sZRe7cWXY2LK8GYCOJi/55RkYLQayitxkFuipFBabMVmHXJLAnWln3KHlfPHuppQa4ZKs50X7u0LIsoQr094/4VNjUDC9naLIRMIx4rFEsl76jna8SKhd30peuX7nxGI3sW1VCz19KTS9PaEhXyslF18jucJoa21PsnIN6HncLdV6RZa0nNTvbVtdD8UjszGYFLwdgUE56Rn5LqLhWDKH35VuRU1o+DqDuDNteDuCGIz9KTv+riAbl9TvMjd9u4HpTtnFHlbPr06ZkBqLJtj8RWMybWjHibtdzX4sdhMZBS4MJiUZtLfX96BpGl0tfrqa/BiMCplFbjr6Fqbyd4UGzUlQVY2182uRFYmJRw3HbNF/ryYcOYxwbxSHx4IGqHGVzcsa6WkLYLEbad7WSUejl/LxeZiG+D4LwvfVvHnzOPzww+nu7sbj8XzX3RGEPUb8Jd7Hxes6WdfQxbIsO6UeC5nbaz1bZCSLBS0cxnH44UhGI4a0NJxH7P7iRDuzoW8i6peZ5Jmud0WxUmodTk1oCxbZSpm9EgC7wYlJtvBy05P0xPVRw3X+FTiU1HrtMS1GgbkEm8HOlLShV/wLh1vpWllNzBshbXKCaLSdJXf/gc4l1Sh2I2N/GyW/6keEQnXY7cO+VvCeiMdZ/95cfC1NZFeMYPjMw+lpbqB1w1q9D71+nNm52NIziYdD5IyeQvO2LjILXMng1tcZZNPSBjQNqg4swJVhB6BsXB419/8dw/OPoAFNDWso+OMfmXjEcMLBKIGeMN6OACaLkVi4P/0lHlWp60vhiMcSVE4upGKSniNes6aFcCBG0Yj+uQ02t4VIqD91ZOKRw7HYTIQDUWSDjKJIKLKkXzT0jRBLkr4QUHqek9q1rUiSRMnoHGrWtrA9U92ZZqHnS0ahpQFzF+KxBEFff774jqP72xnNQ08+jUXiNG/t1NNXpNS0orY6b0oQLMkSGhorPtxCeIfqL0hgdRrpbOrPHe/tCWO2GhkxpZCNSxvR+u6IWBwmwr1R1IS226PtA9u11vYMqgwEJN9nGLriTiKusmV5U0pai7c9MKi0p8mkMHpmCWsX1A4+yA59WvNJDaqmYXeZGX5AAa6M/knOiiIzYmoRm5Y20tXsT75nRpPCsAn5JOIqaxfWEPBGcHqsjJxejCImt+53EqrK0tYttIe8ZFndTM4ZjjLE2hV7yoUXXsi//vWvQduPPfZY3n777b32usIepqrQ0gzBINhskJsHe/HnBvp/di6//HIeeeSRlOeuvPJKHn74YS644AKeeOIJAFpaWrjrrruYO3cuDQ0NuN1uKioqOPfcczn//POx2fr/Hi5fvpw777yTTz75BK/XS3FxMYceeii//vWvqays3KvntSsieN8HadE4WiSO7LSQaPYS6qssUeM0E1QkjlYMmEcXMGzsXGJNTVgnTNijr7+0e+FutfPHvbiMHgCOyT6J7lgndsWBWdHTPCKJMFuDG5OB+3ZF1jI6o21sDeppLi7FyfiEhklWMA2xQBNA68erqXlUv6jwruiEQxfTuaQagEQgRteyasonnovN9vWXbe6uq8bb1Dfyu3Ed+WPGUzThQMI+L/72VhKRMC3rV2N2OBl+xCms/6weaKGluosJRwxDkiTqNrQlUzXqNrQzZoYevDvTrKT31rM9hAwtW07AG0YxyPoo9crmZLCWV56OYpQHBby169po2NRB6ehsckrSySrysPHzetZ/VofBZECWpZRAUZJJ3hmw2E3EYwmWf7CVaDiOrEgYTDLxmErJ6Bzyh+m18cceXJbcP7vY05+a0xtl4pHDCXjDbFhcP+T7pw2ITDcsrk++D0aTwrCJ+bRWd9HrDWMw6CPkoFfXkRU9nSQ4YMTck+2gdl3bTr9XmqZfdGQUuMgpSSPcGx0UuOeWpdFe30PDxs6UVWLj0QTxaIL6je0pdd3LxuSw+YumoSu97IYda8RnFbmxuy00b+sasr0+aVmjs8VHT2vqhN6hgnxvR4CmrV2DKgoNZXs1nZ5wnIaN7ZSPyyMajrH5i0aikQTZRW66mlMXo6LvZ6Vxc3tyroSvK0h7XQ+5Zbs/F0X4/nu3Zjl3Ln6BlmBPcluuzcNvp57OMaUTd77jNzRr1iwef/zxlG1ms0jZ2mds2wYLF0BgwN8rux2mz4Ty8r360kVFRTz33HPcd999WK16RbJwOMy///1viov7P/e3bdvGjBkz8Hg83HnnnYwdO5Z4PM6mTZt47LHHyM/P58QTTwTgzTff5JRTTuHYY4/lmWeeYdiwYbS1tfHiiy9y00038fzzz+/Vc9oVEbzvIzRNI76xBbWzF237iKVJQc50UtnqpdVmpNeoMNzjwFSij7Qa8/Mx5ufv0X5E1QgJhk6lGMgq27Eq9uRjSZJIN/WPAPfGfbzY9ARRNcL2GYEyMqOVDOLdSymzl2NVrLgMaZi6lhBJNBIJN2IwuMjImJ48jqZphNra6d7Uvzxl74Z21mx4DBQgAcgShZO+/uTc7czO/jsCitGI0WLDaLEw5rgf8vm/nyAa0wOiSK8fX0f/SG6ob7RWMeh105PHs6YuLOX+0cn4338fLRKBI07QFxuS9Bx5X0f/QkKxSDxlFDtJ00ewt65oIS3HScOmdmIRPdBMxAeP+GoqrFlQk5ww6esMJgNqNaGRluOkYlJBMjVlRwMne4Z7oygGmbRsR0rVkoHUhEZHoxfFoKQsjGSyGlkzvxqTxUjJ6Gw29aXGDNxvYOButBjoaU+deJo0sLKOBl0tvQR9keS8gO1yy9NxuC209FXNUVWNwqpMfJ3B/vda0uu4x6IJMvKdeLIdVE0pZOuK5kGLH+1M2dgc/F0hjBYDniw7Gz/X77pkFboAiY5G75ArsUJ/Ocie1gBmqzGlfGXydLffYZD6S1TuNHCX9JV5ve2BlOA/6IvQ0eDF1xVMzrto2tqZsqvDYyGzwKXXsN/hZ2/HykjCvu3dmuVc/dHfB01jag32cPVHf+cvh1+21wJ4s9lM7k5SOiVJ4h//+Adz587lnXfeoaCggHvvvTcZaAG89dZb/OIXv6C+vp5p06ZxwQUXDDrOSy+9xM0338yWLVvIy8vj5z//Oddee+1eOZ//Kdu2wXvvDN4eCOjbjz52rwbwBxxwANu2bePll1/mnHPOAeDll1+mqKiI8gGve+WVV2IwGFi6dCl2e3+MMnbsWE455ZTkIFMwGOSiiy5i9uzZvPLKK8l2ZWVlTJ06lZ6enr12LrtDBO/7CLWxh0R1R+rGaAK1uQfXgaWcIMnI6fahd96DwokQ2hCzUx2Kk9GuiaQbs/DFeyixDce4k1VPAWqCW/oCdwCNCtsoCiPdyJE2qqOdrArrqShOg5sp0RjbU90H5pBrmsaSu+6iccGnGGw2JEVBSwwYFe37cvR555M7Yco3Om8AZ1YOI48+Hl9LExllwzFa+gPCrIoqGlcuA8BotZFdkkl7Q4BoOE5OaVqyzGH5uLxk3fKC4amlOh0zZlDx8TzUcJhVq/0QjIEGDZv6v+8Go0xBZSY2t4W6dW16YC0NrkYTjyWw2ExA6oitxWEk3NsfBEaCMbpb/XQ2+3HsUBLQZDPQsKkdxaCQV54+KIjPyHPRtKWLWCROZqE7GcSNmVlCw+YO/J0hIsFoSqDYWtOdrDqzXag3gprQCAeiNGzs2OWE2vxh6dg9VjZ/0ZjcNjCvPC3bgTvLTtOWTqLhOGpcr2G+PY9fkiXScx2UjsomGo5jMCrEYwnsbguFlVkk4gm9dGIsQUeTL3khkFXoQZIkZEUmHOwL3CWQJWnQokrbSTLklWeQ1/e50Vbfox/DINHZ7B8yVchoVpIXXAOl5znpbutNmexKX+BudZowWQ1424KD9hv43tjdFqoOLKJ6dQvtDd7knQBfZxBfZxCLo3/Rqx3z+a1OM2s/1dNxTJbUYN1k3fnvubBvSagqdy5+Ycj6A9sXHr5zyYscWTx+r6bQ7Mxtt93GH//4R+655x4efPBBzjnnHGpra0lPT6e+vp4f/ehH/OQnP+GKK65g6dKlg4LyL774gtNPP51bb72VM844g4ULF3LllVeSkZHBhRde+K2fz35DVfUR911Z+CmUlu7VFJqLLrqIxx9/PBm8P/bYY1x88cXMmzcPgM7OTt59913uvPPOlMB9oO0xxjvvvENHRwfXXXfdkO2+6zkUInjfByQ6ekl07mSkUQO1J4xx2JeX0PumuqIdzG19Ycjnjs46EQ3INuftVgnGLFPq6EqBtRhrpIcIEBjw0eGPe9n08AryjynDZM3AUz45+Vy4u5vGBfqqn/Fg/2gpmobBaiVrwgQyRo2k8pRTvtqJ7kJ6cSnpxaXJx5qm0bpxLb6W5uS2WDiExWbkgKOGE4+pmCz9v2aKQaZ4RDZDUVUNf8yAxZmOJ1ujtWZwLfWCikzsLgt2l4XckjRkRQ8oW2u72bayGU3T67XbnBZKx+RgMCs0b+0iEVeRJPT89gHBu91j6UvvgTagbFwuHQ1eDCYFX3v/5M54NEHJqNR+W+wmDjhqOLFoPOUugtVhpmKiXu6wcUsntWtbk8850mx6jfzN/UH6wGB94GqgoKe9dPbVMpcVibxhGZgsBrpb9JVCMwpcFAzPoGZNK0hQNjaXhk0dQ478g5664ky3ISt6OtKEI4YR8kdwpFmRZQnZZGDY+DzCwWhKDfVkoNsR6K+spIE6oPNGk0JsQDlOTdUrABmMCqFAlC3L+qrv7NC17XXfHWkWRh1UQt36NvxdoeT7IMn6glIpgXvf6wOE/NGUSc0DDZ+Yz5blTWgaZBd5WPb+5iEvDiC1VKSmaljsRsKBmF71aMCvdDScur+/O5iSMy/su5a2bklJldmRBrQEulnauoWpeXs+3/fNN9/E4Uidj/Sb3/yGm27SV8e+8MILOeusswC48847efDBB1myZAmzZs3i4Ycfpry8nPvuuw9JkqiqqmL16tXcfffdyWP9+c9/5sgjj0wer7KyknXr1nHPPfeI4P2baGlOTZUZSqBXb5dfsOt238B5553HDTfcQE1NDZIk8emnn/Lcc88lg/ctW7agaRpVVVUp+2VmZhIO639vf/rTn3L33XezefNmAEaMGLHX+vtNiOD9e6YlFEECcqx6nl+ixUtseV9lF1Pftyua+umvdQeAvR+8r/OvIJgY/AuaYyrg9ZbnSJCgwj6KI7KO3+Vx4mqcYKKXsc5J1Aa3YlGstESaGJE2BalnOeWSTIfWhTfuJWt1jPCmHrZtWo6tuIBRhw9IO3G5sOfnE2jqL0loMJsZdcEF5Ew6AGfh4Brre1rbpvVs/fTjlG1GqxVJ1peYMn2FiXwbFtfT06bX1h49vRiz1UDd+v564hn5rmS1FtCr1myXU5JGTklayvFkRSY910nDRn3kXtNSJ0fa3Wayiz1U9/Sv+JmIq7gybCk1xYH+0eYdKAaZkD9BzepWzHYjJSOzk7XnW2t76G7xk1eejs1tRjEoZOTpCyENDNglGXLL0lHjKvkVGbjSrTRXd+PwWCgZnYPRqNDrDVNYkZG8SKicnPq9HTlNz2n0dgRoq+1Jbrc5zQT9qdVlBpaYNJoVGjb52fb/7L13mCVlmff/qXBy7nM65zg5z8CQc0bAXSWqsJhdxV1z+O0ri7puML7L4isrwVVUVJLISlDJMAMDk2PnnPvkfKrq90d1V58z3T0BZmCQ87muuaa7qk6dp053V93P/Xzv771jmNJaL55SB5lUFhAMSYqn1IGvQg8odDnSREGnVuOzMIkFwTvA1j93oOQ03IHC4NYdsJMIp8hl1enGWCU0LCtHEASaVlYycGDCCN41lXl95WfGJ5vEeZ1v6paUEaj2UFLpBk1jx3PdBYG7bNIbRs0E7UpWRZIFfbx5zatyGYXxvvCc8898fiUV7nn35aNp+sqK2Wo6omZbRd4expPz/5zf6HFHyznnnMOPf/zjgm0lJbP3vJUrVxpfOxwOXC4XY2N67cvevXvZuHFjQfLolFNOKTjX3r17ufLKKwu2nXbaafzwhz9EURQkqSgBe0Mk5q76vanj3iCBQIDLLruMn/3sZ2iaxmWXXUZgnmaUBycYX3nlFVRV5YYbbiCdnpYfHmoJ+ASgGLyfQGybjLJ1Si8SWx9ws8LnJNeTJ5XJ5MB00INPAKnaW7BJS+dQxiKILiui99hlxHwm/7zbbZINZVqj0hnfz1kl5yOKMsICxaVPjD3IQKoXAYFlrjXsir7OWGaY/mQXrc5loGm81/seZEGmv/+PbJVfRlNUIqc46E10Um9vBkCUZc767n/Q9eijdPz+UXLxOIuuuYaWK6+Y933fLJqq0vHiM0THRqhcupLKJcuZ6uspOKa0dTHVy1fPvkbT9KJOAUoqXAueW1U1wzZRUzVC43HqFpeRy6qExmKU1niobj36jrh2lwWn16o7qNhN1C0upXP7MKIo0riiErNVpjvv+JIKJ+2vzU6GEMBslak5xHvve2W2+NRskaluDZBKZAyf98hkgiUb6wwLx4MLIV0ldkKjMZIxPdu96uwmyqYnIgMHJhiZXoEYaJ/UA9FDoOYKg2dRmr1JC6JA69pqHO7Z4D00FmOkWy8Y7ds7BnofM2SzZEwwMqmscbO3uy2sPb+FWCjJ/lcHCuRKc5xsACU3k7HP1/jLLN1YR8e2YaOx0uRghMbls6tReoOuoHHOmfcxWWV85U4EQSCVyGB3WvCUOejeMUIuo2CySlTU+wjUejFbZDRNo2PrIJNDkTla+IpGLyaLib69Y7OTEUGgoslLPK9zbP4zzFWiF4Llsgo1baWUlDuRpuVSobEYqqoZ4zM+g6zCnk19uh2pJLD0lDrDZanIiUWpzXNMjztaHA4HLS1zO07PYDIVSrQEQUBVZ3pbHD7Y0jRtTuB2ogdp7wjsRxhnHOlxb4Kbb76ZT3/60wD813/9V8G+lpYWBEFg3759BdtnNPEzha6A4SSzb9++OZPAE4Fi8P4WM5JM8/JYGLMo0uyyMpLMUO2w0uq2M5CYfWAOxFMss5jRgrMPfbHUhTo1m/mW6v3IjQGwzt7QNFUjs7kTLZ7RA6+TmxB9x+ZB2eJYwotTfy7QvMuCzFh6NnO7UvTQ3f1fSJKD6ur3YzYXulBomsbgtEe8hsZEZlZSkVDibAtvBmAiM8bJvjORGiIs+ebpaIpK2GNmKNVHaczF4Asv4G1upnTVKpZ+8IMsuvZalFQKs2s2QI7mIlhFKz2xHLuDMfwWE6eWe5HeQGdVVVEY2rODsQN6dNf10rM4/AGm+roLjms9/RyEPE1f354xBjv0LHZNW4C6JfNLZkRRwFfhJDgSQ5QEfGV6oNuwrByWlR/1eI3zSiI1bQH69o3jcFspqXITqPGgqhq5jILZKrPs1HqmRqP4ypzkMkqBbKVlTRVltd6Cc2qaRteOEcITcawOM9m8laAZX/b8gtTpVwEwNRKlc9twwR6zRSI87aSj5FSiUwmjQ2h+keZChZ35DHfPSo0EQcBf5SY2HYg6PBZ6do+QSeYorfXQurZ6wSxwfjfaZDTDYPuEMXkyWWR85S6sdvOcrP6CCHqm22KVqVlcSiqRRclzrcmkcmz9cwcta6qZHI6g5FSWnFLH7hd7C/zvBQFKazyG/jwyHmdyKGJMnqprA1RN11Koikrf3jEmBiLMx8AB/ffS6jSjTK/IKFmVka7Zz9BiM6FpqiGViU4lWbShBn9V4SRqYP+4YVlqd1uRzSJOr43RnmDBKoWqaHRsHWLt+YVdZYucGKwvb6HC7mU0EZpX9y4A5Q4f68sXDrDfLpYuXcrDDz9csG3Tpk1zjnnhhUJt9ksvvURbW1sx6/5mqKjUXWUOJZ1xOPXjjjMXX3wxmekeNxddVGhU4ff7ueCCC7j99tv5zGc+s6DuHeDCCy8kEAjw7//+7wUFqzOEQqG3VfdeDN7fIoYSaV4dDxPO5GbqKBlL6b9g3bEUXrNMk8vGeEoPUBpdNgRJBFGAab2tWOZCLHGgDIVAElGGQijDIciqYJUxr29AMEl64A66JjecPGbB++vhl+cUq+a0HDltVo9fktEDBUWJE4nsIhA4s+B4QRBodS7jQGwXJsHEWs8pvB5+mXA2iFm0Ep62jRxI9TAw3MNquQq/Q5+cpAVoNTXy7D9+ieT4OAgCJ3/9axz4zW9JjI+x4sMfpm7ax/7ZiSfYF9uBVbSjpM9DwE0wk6PcZqbNc3SfRy6dYsejD5IM5wWGkkQulSo4ThAluje/QMNJpyFOPwgiU7NBbP7X87F4Qy2RqQQWu2m62PTNo2ka7a8PoUwXbtqcZrzlTg5sGSAVz1JS6WLRhho8pfpncrA8I2DJoD32KBlNJrvhFJzlXiaHIoYef0aHLUoC3jKnYSmZPwEAXW4CzHFpsbnMTA7NZuJNFhl3YPbnU9XsJzIRJ5PO0bh8/klMeCJO+2uDCKJQEBD7q91UtwZ0p6aMgmyWDBnSeH+YhuUVuP0OGldUEB6Pk0pk8gpbwe13GM2T8oP3GayO2eDd7rYQqNYnClaHmaGOQtkR2qwF5YEtA0iyVDBBAF3StO+VPkPaEgsmCgN3UZhejcnT1WsUSHVGe4K660+Fkx3PdpHLLGA9k+fKo+TUBQtlNU3DX+0p6N66/9UBTr58seHtnkpk6M8rqp7x789fbchHLHrCn7BIosjXTr6azz59Z/6vCDBb9vC1k95/3IpV0+k0IyMjBdtkWZ5X+nAwn/jEJ/je977H5z73OT7+8Y/z2muvGb7eM3z+859nw4YNfPOb3+Saa67h5Zdf5vbbb+eOO+44lpfx7kMUdTvI+dxmZjj1tOPu9w4gSRJ79+41vj6YO+64g9NOO43169dz6623snLlSkRR5NVXX2Xfvn2sW7cO0FeBfvrTn/L+97+fK664gltuuYWWlhYmJib4zW9+Q19fH7/+9a+P+/UsRDF4P45MpTKMJDPUO608NTjJoayXM4rKUq+TKptFt3Qzm0grKokV1TjGYwiaRm63LkOQW8tRhoKQ7zedzKL0TmJaVo1Y4UEdCYPVhFR+eD3qkTKvPWEeIhKyuQQlowcuFsv8Ovyz/Rez0r0Ou6TbSdbade/w0fQQfx7/A/FcFHX60wqarLTaTyWpJlnvXoMUU/XAHUDT6H7sMYIHDgCw7b/uoO7cc8mpWfbFdgCQUhPIYh+CuhzgDWXdQ0ODBYG7u7KK6uVr8NXWU9rcxnhXO2gamqowvGcnNo+XyqW6NtNf5TE6XQaqFpbNgB6ceQLHV07Qf2DCyJCCLmFJJ7NIkkgqnsFb5mCwYxJVUSmpdCE+8xcIBrEAk8EswQ0bZzuw5mGyyLSsqSIRSWN3WzBbC5e3hzonMVtlAjUeJgbCxCNpqlv9CIJQMGFYdVZjgXuJzWlmzXmHzvL17Zn1zrc6zeSyum1lZaOP3j1jDLbrgWVFXr2A1WE2uqZWNpUgm0RjhQSgcUUl2XTOCN7tB1lNgr4qMdgxgSiJVLX4CxoVSbLIUMckDo+FyGRhp1NNZU7gPkN+IJ46aKVhySm1eANOEtEUJotELqtSVu9htDtkHJNOZuneOcJIj3nhwB1Amy2UrW4uwVvupHPrMNFg4VgzqRwTgxHMVtn4jAG2P93FkpNrsbks05KcQ0sPJJOIxSYjiCItq4+tfW2RY8uFDWv40Tkfm+PzXu7w8bWT3n9cfd4ff/xxKisLs7OLFi2aI3OYj7q6Oh544AH+8R//kTvuuIOTTjqJf/mXf+Hmm282jlm7di2/+c1v+D//5//wzW9+k8rKSm677bZiseqxoKlJt4Oc4/Pu1AP34+zzno/bvXDc09zcbDRe+upXv8rAwAAWi4WlS5fyhS98gU996lPGsVdeeSUvvfQS3/nOd7j++uuJRCLU1tZy7rnn8q1vfeutuJQFEbS3QfAViUTweDyEw+FDfsjvZIYTaR4f1IMBSYCFGjI6ZJFah5WNpZ5ZLV7/K6T7NvOkcyMT9mpKrSbOH47BtOOM4LEhyCLqZOESlVjpQXTbEGt8CIoKZlnP3h8j0mqalyb/zIH47jn7JGSuqrwen+wmGt2LyeTB4Wg+6veY2r+fPz73Y2IXVIMGZ5dewiLn8oJjXvvhD+l98imclVVULV/HgaceBcBZU8OFd/4EgN8N/YzJjF7IdLLnSiaSfvwWE6tLXEfkhpNPMhJi20P3o+ZymOx21v7N9ch5jUM0VWXT//w3qqIHN+VtS2k+/WwEQaD99UHGp1vWl1S6WHxS7VF/Jm+W8Hjc8Hw/WOZhtplYurGWXS/2kssouP12WtdVk03lcHitCL+5H0L6xGXYXk23ezaQdngtpBP6NTcur6Bv3xjpRBarw0TTqkr2vNQ3ZyzlDT6aV80+nMPjcXa/NGNBKLPuwtaj/vnsf3XA6JBaXu+lbrpoVpJFtj/TZawCeMuc1LQFSERSlFS5UXO6rCSTzhVkiSWTSNu6avZt7kfTdI/zpafWH9LPXNM0EtE0ZotsdNOdoXfvKEMdU8Y1VrX4ScUzDHdO4fRa8ZQ6CgqELXYTgiBQVucxVgoEUeCkSxYhySKb/3efYTMpygJq7ghv4QelUmsXBZBMEr17xpAk8ZDNp/xVroIVEtCbTFU0+OjaPkx8ekJntpnwljlIRtK6Y9FkArNNbxCWy6o0Lq+gpPLQk9gih+eteIa+1R1Wi/yV8DZ0WH03Usy8HyfaI7PBgKLBCq+DveEEiqYhKmlW7bwDW2oC33lfobQszzZxaBvccwkWNcd51gAPXPE44ziIlzlxTAfvUrkbwW0rCN4Fnx11OIw6HEaciGI+6djPci2ihXNKL2Wd91S2hF6kM74fdVoEJIsyAYsua/B6177h9xh49llsD7cjvz6ChETDD28imRzCZpvN1q37h39gxUc+gvJKHySziIkcKSlD243XGce8p/wauhMH8Jr8VFirwTffux0ZU709qLkcktlM21nnFwTuAIIoUr/hFLo3vQBojB7YgyAKNJ92dkEmdaFs6/HGU+rAU+pgfCBs+KM7vFbK67z4KlwER2PG2AytugB7N/VhrttAhbqNVEZjwN1QcN5EJKNnXAXIZXKGJj0VzyIg0LKmitB4jGQ0YwTQodEo4Qm3scIwY9GoqhqZVI7JwQiBmqMrhmteXYnVoQe81a2BAh27v8ptvHeg2o3bbzdsDXe+0m2siuSjKipTIzGjUDMZyxy2EVHH60OMD4SRZJFlp9Xj9M4WPtUvKad+STmapqFpGH75M+4ymXSOke6gYee55ORa7G4rmqphd1uJTiZweKxMDIaxu60F/vD5gftC0hfQ33PxybWYrSYG2iew2PTC4i1PtqOpGjn10L+bNpcFhGhB8G+xm9i/ZcCQ9jh9NppWVqDkVPZu6kNVNERJwOawMDWiB/6d24eLwfs7BEkUj4sdZJG/ckTxuNpBFtEpBu9HwYynsygIxLI5nhmeIqFonBRw0+CyzfZkB5pdNjqjemAgCbDIY2d9qR6U7PrtF1m++04AYpF2+PsX0BQVLZ5GGNmLoOoPQ3tqAltqAsHuxlMXQCxxggaixzbH910wy8ZzVT3SIro3iEk005fsMgL3KlVimX0RqppDFN/cr5R/2TI6Hn4EU2+UsnNb6O//OQA+30n4/aeTCgbJME5WncAsSJhEO01rz0JqKcNUOauJtkhWFrtWLvQ2AKhqjkxmApPJhyQt3IK7f9urACiZDFO9PXir5mbPk6Ep8iObqf5emoH6JWVkklk0VaP+TRSeHgtKazyYLBLpRI5AtdsIct0ldqOZj8NjxWyR2f1ir6FPF5edirfMwUq3lW1PdxkFiIZUQoPefWPG+5itMnaPnlEuq/My1DlpBNDpZI7dL/ZS1eKnYZke0OY3OcrNY8F4OGSTRP3S+T/bmraA7n4iCthdhT/jfKeYmQmEKOsWn+G8Dq6+cidjvSGmRqM4fTaqmv0FDas0TWN82jVGyalMDUcLgneAWDDJnk19KFmFplWVlNf7EAQBTdPo3zeOySLh9tupavFjd1sN337ZLOtdZ18fnO2Sk4en1IGSUxFFAavDzFhfqGB/RaPeIGy0J8hA+wSLT6qlbd3sg9XmNBsTmBkZzXyfb1WTH1+5i8hEnFQiiygJRCcLNfmxYJKdz/fg9FqNz1ZVNGLh2QmSKc/etG/fGMGRGP4qNzVtATRVIzKVQJQEXL6ib3yRIkWKLEQxeJ8mrajsDcWxSCJlVhNbJqNIApxS6sFhkumJJnluNIgoCLS67OwJz2a9t3btoeGZD0N0GC75N1h3E9UOK5fVBGiPJOiKJPhd7zjr/S5WOKCtY7bRkSU1haaoZDZ1okVSCLZVmMvXIYy+RnbxFWxoXUWF3YosCuDWAwJN1cjumG0hLzWVIlV6yAQTkMkht8zvaHKs6El0kFZnCxLLNA01sotxTaO8/KJDvPLwlK9daxhY2xpnA6BYrIOB3+6m8/e/R3abab5lHbbaEioHz0Ky2ZHr57exXAhNyzE4eD/p9Ciy7KSm5gZkea7efKK7EzU3G6DYfYXuOaloBEGUsPsK3z+XTpFNJXF4bKw+5+jlQ8cLb6lzzja728Lqc5pJRNN4/HY9iMtT0410BxnpDuKvdNG6rno6e68ZNogAalalPDmMWUkTOPVUZFmkb98YqbguozmY4GiMhmXl5DIKJZUuYsEkbr+dstojy7pnUjlGeqaw2MyU1XkOKbVxeObq1QGaVlbQuX2YZCyDqmrIZhFN0x1XlJxKaa2Hsjovk8MROqZtL6eGo2iqRu2i2XoOQRBwB+y69EZg3oZFw91TxurGwP4Jw5O/oPg3njUkRX17x/Vi1OmsfH5jqHxks4SqqCSiqXkcfmY66OoTkchEgrHeEFUts7+ri0+qZbh7ivB4vGAVQhCgZlGA8HgCVVEJjkVxemxUtwZIxjNs/VPHvJ+ppmpzOs7KJolAtYdcRqG6VX/vyGTC6D8QD6fwlDro3DpkyLrK6700F7XxRYoUKTIvxeB9mqeHpxhOzm1EM5QY44q6UnYEYygaKJrG3nCh1jxq9pKNjGBSUvDUN2DdTQCU2cwciCSMhoq7Q3FWTO7CnA4ZrzVt/ARqNKUH7okuTNv/CeQsXPMLTEveQ20sTW7XIFmLjNxarmvYcwqkZovZBIuM6LZhOXcxqBqCJKKlsqBpCLZj41qST37gbtXAM+OHnZlc4BVHzp777jOMpSM7x/GsLAMBTEopnY/qzTtykQyh10awXGJHPrUWk+noNZ+ZTJB0WrepzOViJJMDuFyL0DSNqd5uJLMJb1UtnS89gzbtI+ytqadi8TLjHEO7ttO9+QUEQaTtnAtwlpYRG9cz0Goux54nH2PVFe97U5/HW4XVYTbsGQFa11XTu2uUWDhlZGMnh6MsOqmWky9bjKqo7N8yQGQygbfUial9L01RPaBTno4zsu48IzgzWaQ5BY++cieqorLrhR5je2mt54idSPZu6pttYqRpVDQcvS7KVWKneVUlO5/vASCXUbHYTIYsZWo4isNlJtIzBsxOQGbkQaGxGB1bh5BkkdI6L6KoW1N6y+ZOkGx5WX+b24KqaqTjGZS87L8gCgjTGX2by2w0T5ppHJVJ5ubo1ifzusDORyKaLniN2VZ4yzdZZARBKAjcLXYTa85tZrh7ypgQzHj/17QF5rXHdHgsRjfe6lY/fXvHjE6+MwXOdUvKjAZZglg42cokswXnHR+MFIP3IkWKFFmAYvA+TTAzv4e0okFHJInXLDOZ1o85eGFZkW08c/r38Ea6WDv+PBJAKgK7HyLgXUU7eiY8YDWDYwlYPJAOg90Pq69HMFvAIiMfuBsxpRfv8ey/wZL3kN3WhxbVgxR1/xY0zYooTCE2nYo6EAK7GcwymqLqgb0koAyHyG7vBw3kpVVHnZU+HI32VraFNpPWUtSYKpDUKRRNYacawpmZxGd+4+/X88fHja+Dr4yw/pb/w8Tu7Wz61x8XdIux1bpxu1e+ocAdwGTyYjJ5yGbDiKIFq1WvO+h66VlG9ukFuY0bz8BktRmWkJ7KwmBidNrzXdNUxjr2s+SCy9hy//+gKdNWf+OjxKcmcJQcfXOltxt3iR2L3VTgPiLnNQgTJZElJ9cZ30fH9sJ0PaMWCpPJ014rOZX1F7USnkiw/xV9xWisL4jFVhjQd2wdZt0FugwklcgSnogRC6XwBhxUNBaueOR3ik3G3rhMzO62YnOaScYymK0ybRtq6N09SmQygZJTib+2k7JsjB53C2gaZqtsZI97do8a4+/bo0/awuNxgiMxyht9hlc/QHWLH7NFJpvJEajxsPO57gI7TZvLTN2SMkwWmbG+EMlYBqvDRGVTCRWNJdS0Bcimcwy2TzDaE5r3WmSzhCSLBX74M18LAjSuqCBQra9shCfiTAyEcfvtBEcKC1FzWWX6uuauZgx1Ts5ZSZHNIvFwGkkW8ZY7GDwwYQTuMyQiafZt7sdTqltzunw2GldUEByN4q9y4yl1FHSK9cyzelGkSJEiRXSKwTt6Nl05hNXZjEuJ36I/tF6diMzxvx2oOYcBzsHs/AirAO57P/Rvorn6ZkxtHyfltNBWU8oYTuI3PUvdyEtIDaeDI4AAWE5tQZtqgBlHQpe+fK7lezqbGwBQqUAY7kJsaEPtnSC3vR/FZ8d8chOCIKAMzC6zKwPBYx68e0w+rq35CLFchBJzKb/s/wkJJYamxXFEXuWswMX6OJUcw5tfQbZaKVuzhkwkgtnpRMjzXtVUlT2/uI9wVxctV16Bqsxer82UJnbfPzO2c9S4HmsgwPrPfY7SVSsQBJFUaojx8WeQJAtlZRchy4VZz6l9+wh1dVN16ilY8xoqiKKJmprrSSYHsFjKjUlAeHjQOCYyMsSS8y9lcMdWLE4nVctXFX4OlVUkgvpqg6eiCrPNTqCxlfGOWVuzTCKBozDufMeQbwkpmyRWnNWw4LHpxkXIw4NYlBQHPIuJ94UQJQHZLNOwrAzZJBd4vOcyKt07RwvOkU3nmBqN0bl1qKCxz9RQFKvDXJDRrl9aRvfOESRZRMmqZNM5vXnU9hEURXc1sbsXrmOYQZJFVp7ZSDySwu6yMtg+URBUa5JMVXiQkvQkgihgueE6sFhQcmrBxMM4XtMbUQXHYqy/sNVwntHdY7yAHjgf7IOfyyiUVLhQsgodW/UsdxZdPhMPp/WiXLuZ6pYAwdE4mWR2Rl0GgNNnpWV1FTaXhf594wzk+a6DnmGfmQAlY2nd4UeD0d4Q5fVeo5EV6LKh0Z4g1W0B4sEEU6OxAg17IpLBbJNQshrugF3vIIw+SZscLJwIHEx4PM7uF3rYcMkiKptK8Fe5CI3HiUzEqWz2k03nsLksc1ZSEtE0qqLOqSUoUqRIkXcjxeAd3WM9e1DsXmEzUe+04TLJlFvNpBSFZT49eFA0jdcmZx9S+S/Nmhz6E3VwC5poQam5mepEFhJZ+r1TPKsqgIXa8os5P08nLVhNCFd8B0rKIBOHMz6HpqiI5W7U/inIpQEBRH0CoeU8aF0jMF0gqgUTkFPBJCFmdyH2PIzqWY/QcOXx+MiwSjaskv4gdZt8xFX9Ae6WvQBMTDzL7jvvY/IFPRh21dYS7e/H09RI29VXE2pvp2rjKQQ72tk/3ehgfMcOPI0NBPcfQBBUzly6E8fkq1RUwnPhFUxGPdSdcw5lq2eD6LGxpwy5ztTUS/gCZ2MSdfnH5O49PPvlL4Oq0vHgg5z/k/9nNE8CkCQbTmdhp8eytiX0vvoyoAfv/du2kEundK27BqqqIIr6ORo3noGztAJNVShvW6JvO+X0guBdO7gn/TuI6rYAHVuHpp1KarA5Fg6GfYtqOBA5l+hUAkEUDMmHu8RmZHsPtlA8GKfXSjyULAjcZzjYqaeyqUS3W+yaYqwvRCaVw2SW8lxNhlhxRuMRXadkknD7HSRj6QKvd7ffTuXS9dDjIDESo10tRXq2jyUn1xKZTBzSPUhTtYKC2HxsLguyubBJUzat8OrjB2hcUVh4q+RUxvpClFS6KKlwYXWYWXdBC5qq8cof96NNv0c6kWX3S73ULyundlEp2YxCdCqB2SqjKhq+Cqfhwd+xbbjgpuUpdVDe4KP9tUFjRUOQBF7/U8eC19iwvBKzRaZn96g+gVqg0HimEDqfbEZhciiCt9TBjue6C4peZZNo+Pp37xwhOBrD5jIbE4TaxaUF9QZFihQp8m6kGLwDNlmi1W2jPTIrEciqsNTrZCKV4bc9o2RUjeU+BxsCHqLz+CGLgEkU6Y4mCFhMNJz8CXj5vyAzDmb9YTMqCzCdfBxOzNXXY7bDuV+fHcNrvahj05pWyQzp2XORiyEN3otS//cgyAjhLbB3L5l4A6anbkbQMmhjD8I5pwLHVzt6QdkV7Iq8jiTIxHMxXpp8ipLQDmKdIeOYaH8/AOGubl79138DoP3BhwqkMEo6zZpbbmHHT+5EzIZwWF4C9CV/jz2Off1VLPvQB5nat4/9v/0d/a9s4RVPGweqWvjQBWnq23p4qO9HVFnruLT8bwl2dOies0BsaIhsLIbFc+iCyJqVaxGAnldfJptKMt6xH4Bgf6+ub5dllp5/Kd7qWpLhEN2bnyeXShEc6MNst5OYKtT9p8JheOut3Y8JpTUe/JW6L/7BGuWDkSSRJRt1Gc2el3oJTTc3MllnbzG+8rla8BkqGn3ULysnOpko8DwH8FU6KamaK4/Kz3ynk9lD+pQfCZJJKgg265aU4SqxQ8k6ev/SiRJNoySzDByYwJ/XcEuSBV27PqMrt8pUNJVgsesT7chkgo6tgwiiSNv6ahxuKyvPamRg/0SBO0wuoxAai88J7EF3aYlHUgx3TmFzmqlq8WNzWowM/oxFZPeOEcpqvTSvqiQRTdO1fZjIZILIZILe3WN4yhxE8wpbRVHA6bOz/5V+clmFQLWbkkoXobH4nDFIJhGTWZ/o+CtdbP1zB6m4Lo/xlDrwV7kY7QkVrCosNIGJBpNYHeaCwB0gl1VJxTOoEY3hLt0bP3/FZnIoUgzeixQp8q6nGLxP4zEVfhSl0xKZzmiSzLSkZncwzr5gfN5OqSZRIK2qpFV4djRI3YXfQjzl7zFrdpTRFKLLSpPXTsfgJDlNo81zeE2nGj7IPcJcCqHNYG8GQQLZiRT8PYrjNMiGUP5yP1rNR0GU0VQVQctBdAStYuVRN745GmySnQ2+03li9CF6knrRYrVown9SDcOP6MGvIEmGFtzgoP5gss2Gt7GRM//1O2RiMcZ/sItSZQexlJWhqQCrTjuVzd/5DkMv6ZlxGTh1ajenTu0mdsDBzn9aAX4rQ6k+hlMDVJ16Cu0PPkhyfJzas88+bOA+g9WzcPGjlssxsGMr4eFBxjsPGHr4ye657hs2rw9XZRN7N/chiiKNKyowW+f/k9M0jZHuIKlEhsrGkoLi0beTN9LKvmVtNYPtE0iySHXbrN7fZJZpXFlBz64RTBaZtnU1iJIAmu4RDnojpZVnNZIIp5HMIi6ffcHPrHaR3nBJyamoikpyuuDR6bW+oWJHs0WmbX0NPbtGEcXCCYvVbjLOL8kigWoP2bRCPJyivMFHOpllcjCCZBLRFA27c3aVomf3qBHk9u0dY8nJdVjtZppXV5KMZwqCabvbSv2SMsITcRRFmy4IduAqsbPliQPGhEUySSw5uZbh7iCpeMZoUiWbJT34VTV2PNs1J3gOjxUW2zu8Vl7/U7sx8ZgcimB1mAs+c4vNhNVpxlfmpLTOg8k8LQXKa7xidZjo3jl6yE6rM1aUkiwSmYwzNRw16g0EUe886/bbcXhtc2RFM9hc8/9dhCfihMfjlFS4jN+lIkWKFPlrpRi8T9MXLyx6W+ZzoGkaZVYze9AfeBowV+Wqk8l7aKka7AzGWFVShQiI0zFjOfD+xjLSiobHfPiPXm4MkNs3AqKgnxTAvRpEPTBQam6eHpWIZiknZ/aDo430uidAiSJP/hrBuZ7sU7tBEjGva0D0Hr9CsKSa0C0enx+htDdARcnJDKMH75qiUCDSnYcZS8ZUMMjTn/0sa0r6wau/xFZZg9nlMgL3g3Fm4sT3hsmebkUWZNyyF3uplwv/+07SkQj2wOGLRpPJIRKJbuxl9TRuPJ3o+CjhoUGyycJJlChLDGx/7bDny8QidL+0hZiqSyEEAYTkAaJjI1QuXUnlktnOsaO9Ibp3jgAQHImx9vyWec/5TsBslWlcUTHvvsrGEiobD10E4PTajkjbbHdbWXNeC4qisvkPs1KlkkrXHE/3IyU6lTQyvQe2DLDuAl1WVdXqNywXJ4ci1C8rx1fu1HX9Jgmr3UQurdC1YxiAicEwVS1+BEEoKPTNlw4JgkB5ndcI3kVRoLLJRyKSxuW3Y7WbDe23pmlk0rN3n0wqi9lmon5pGWN9ISN4z6VzvP6nDsw2ecGs98x7ufx2wuOFwbymwcCBCUxmidrFAaaGo2QzCpFJPTgeaJ9g3QWtSLLehbZv3xhmi0xJpXveQlpXiQ1REjBZZcrqvJgtMv37JozxSrLI6nObiQYT9OwaJZvJkUlmcflsNK+uZHIwYqziAMbvjpJTjT4FiUiKPS/1oml6Qe3a81swW+fakxYpUqTIXwvFnrXTpJXCfPoDveM80DvGruChC7BmkIRCb4bYAkv4VkkqCNw1VSM3EEQZDhV4awPIjaVYzluCVJOXCRbzghJZAjGv86N7jf6/IIDsRl37JXKdk7plTjIOv74Ovr8UNv34iK5pIVRNoz+eYjw1u5ytqSprtdX4npvC+ev9DL78IvtffsJoWqUfNHt99qoqBIsFTZbJLF6G6vZQtkYf/+SePcixfiq8evWuy5bCGd/F81/+yoJjEkwmzlh/DSd5z+CKiutwm7wASGbzEQXu0bEeBvp+RTC4mcHB3xForWPR2RdicRZ2g/TVNVDWsrhgW82qdfOeU8kpKOlZK79UNMrYgb0kQ0G6Xn6OTN6kIJPMzvt1kcMjSSJl9V5AD479VUfXobWQ2d/R/D/HbKrQPSceSbLj2W46tw2z49kutv6l0wjcZ1472D7JwIEJlJyqu8Y0lZCMpnn50b107xohNBZDlPOaPQFd20fY8Ww3W//UQXB09t4jCEKBX302raDkVCYGw8akAjCsJzPJnL6qsQDljSUFGf+DyWYU+vdNEA+nySRzzJRu5DIKezf3cWDLACM9Qapb/DSvrsJskXD65vrpa5pG06oqwmMJ9rzYx47nuo3AHXRNvN1loX/vOEpWJRnNGHUH5fU+lp5aT8uaKrxlDhqWleP02tj1Qg+bH9vHrhd76D8wztRI1PhZqYo2R4pT5I2hKQrxza8Q/sNjxDe/Mnfl9Bhz0003cdVVV827b2pqis985jMsWrQIu91OXV0dt9xyC+FweM6xyWQSu93Ovn37eOGFFzjttNPw+/3YbDYWL17MD37wg0OOo6enB0EQ2LZtG7feeqsuGzzEv56enmNw9X89aJpGeCLO+ECY8ER8TlxzvOjv7+fDH/4wVVVVmM1m6uvr+exnP8vk5KwM8+yzzzZ+bhaLherqat7znvfw4IMPznvOp59+mksvvRS/34/dbmfp0qV8/vOfZ3BwcN7j30qKmfdpGl1Wtk0Vdi2NZhUOF7pX2MzEsgqxnH5jEwCXSWK5b66+N6dqbJ+KklJUVpU4cZpkcrsHdXcYQKqOIVZ5kQL6a9VEhuz2PrR0rjD7Dvr3ORWcFkSHBS2VRcvrZAh6J1Y1p6LF0kgTjyOOPAOA9vhXydnPwbSyMAg9Ul4cDdEx3T32zHIvjQ4LL/5//8T49u3IeUvpqVgIQRDRNP2zsXi9pEMhBFFEy2bR0mkEwLR/D4KmkYvrGTbfokXkLAGSGTM2cwZFFQgl5jZQmsFeVsapt/0z7ro6Gt7QFcG+3/4c30Uz76GSy0Uxm300bTyd3X/8A9mkCqjEJ8bxn38p1SvWMNXXQ0lDIxb3/HaVkqDS7NhLf85JZnAKy4E+oiV6gCOKolH4CrrmOzgaIxXPvO2dWN+JtKyuonZRqW6X+AakPjNUtwZIJ7JkUjnqlsw2O/OVOXB4rcRDKUoqXKBhaOznK9bM185nkjkaz6hgtDfIyLSOe7hziuHOKYS8oWqqxtSwHthqGkwORfGV500e8/78M6kse17uNfzZZ94vv0vqQpn3kkonSi5X0EzJ7rYgm0RS8ey8Ljr5RCZmg/7R3iANS8vp2V3oHDTjLR8Lpti3qc8oYFZzhWPylDrZ8WxXQb2C5SCZVFmd13DqCY5GDe/5yETCGMvMdevdfd/YqkuRWSJPPsnov3yH3MiIsU2uqKD8a1/FfeGFb/l4hoaGGBoa4rvf/S5Lly6lt7eXT3ziEwwNDfG73/2u4NinnnqK2tpaFi9ezNatW/n0pz/NypUrcTgcvPDCC3z84x/H4XDwsY997LDv+4UvfIFPfOITxvcbNmzgYx/7GB/96EeNbaWlxRqMGSaHInTvHCm4h8ysxPrnqVs6VnR1dXHKKafQ1tbGr371KxobG9m9ezdf/OIX+eMf/8imTZsoKdFX7T760Y9y2223kc1mGRwc5KGHHuLaa6/lpptu4s477zTO+ZOf/IRPfepT3HjjjTzwwAM0NDTQ19fH//zP//C9732P73//+8fteo6EYvA+zRq/m4yi0hVNkNX0ZPWRMJrM4D5IL39FXSkmsTCAiGVzvDwWZiChy3OCmSyX15aiTM0uCSuDQZTBIGpjANPiSjKvdMFMFvbgLNrMgzeWRlpahei0kH6pU2/eZDMht5UjV/nQqnwonkkEWyP0TL9WcqD0x5DqEoeV0WiZHNkdA2ipLPKiCqRSF0OJWYnRUCJNYHKU8e3bp8elgiAgSBKumlpCvd3GselQSD+nqpIcHze2C9Mz84lduxjsegS7r5LGK/6GF/6QwS8PEIw5iSYdCLKMlpsbWKi5HFbf0TfpySfZH0fcHse9opTMsIKtWW8hb5LLGX3oA2TCJmTPFIs+sRVBEMimUyTDQQa3BzFZZyUe+mRFD+YUTUTUoOKl/8fYw7tQNaj+8f8lqWSpWLQM2TIbZJitJlad3fSmruHdjsX25qUSskmibX3NnO2SSWLlmY0oORXZJKEoKk6vlVgohd1lIZPOFRR4qoqm699VqF9WxnD31Bw/ddB13na3hUwqR1mdl1goaQSk3rLCCWvt4lLaXxtEEAWqmv3sfrHX2FfRVIK7xEZ4PEEslCxounQwU8OFSQqTWTJsQUVJwFNqJzyewDzjw3+oe6HG3MAd8JU5jRWBfE/+g5kYKMycWh1mqlsXXimzOswFExRjGNPfZ1I5UvEsiUiKzm3DyGaJqhY/JRWuY/L78W4g8uSTDH72H+ZIHHOjo/r2H/3wLQ/gly9fzgMPPGB839zczLe//W0+8IEPkMvlkOXZZ/AjjzzCFVdcAcCaNWtYM72iC9DQ0MCDDz7I888/f0TBu9PpxOmcTcRJkoTL5aKiYn5Z4LuZyaEI+18dmLM9k8qx/9UBFm2oOW4B/N///d9jNpt58sknsdn053FdXR1r1qyhubmZr3/96/z4x7riwG63Gz+/2tpaNm7cyOLFi7n55pu5+uqrOf/88xkYGOCWW27hlltuKVipaWho4MwzzyQ0Hcu8nRRlM3mcXObluuYqzq88cmNuUYBG5+xysQZkpx8ke0Nx/jgwwd5QnEf7J4zAHSA1LdMRTBIHo45H0dLZ2cAdQJYQvHYQQPDZYSa7KImIDguCxYTljFbMp7VgObMNucpnnF9uLkM6+wOo53wLJXAJmUXfBRSEB98P36mDZ/9jwevLdY3r44mmyO7U/zCb3XrALwINLhvWQADRlPdg1DS0XI5gVweaomDyuGm4+GJMLtec82fa6tFk/TOw13tJKB30vPgIe37+C6LBHD3jFYSTTkpXreSSX/2S9Ge/ROQ7P0T8u9nMR2pqiv5nn13wGo6EVR//OJEXMgz99yh1zTcgCPqYJnZCJqxfWy5cQnnDeQCEh+bepAA81YXWMr3pJvr3ZUAD0eWids0Glpx/Kb7a+jc13iJvPbp+Xf+9kCSR5Wc0svb8Flae3cSac5tZckotojx7S61bXMbGyxcjCALdO0YIjcURBL3j7IykRZQEWtdWc9Ili7C7LUbg7vBYDIvNGUoqXFQ0lSCbJCaHIgSq9QehZBJx++zsf3WQ4a4p4qGUEahKJhFvmdNwvpmP7EGTDkEQsLvNeAKOQwfuhyBfypOv+T8cqqrNcTbSNI3gaIxYKInNaaFmUQDJJBrnzW8aNdPNt3f3GEpOJZ3I0r1jhG1/6TzkJKKIjqYojP7Ld+avTZreNvov3znuEpojIRwO43a7CwJ3VVX5wx/+wJVXzm+RvHXrVl566SXOOuust2qY7wo0TTNqthaie9fIcZHQTE1N8cQTT/CpT33KCNxnqKio4IYbbuD+++8/5HvfeOON+Hw+Qz7z29/+lkwmw5e+9KV5j/fm9Yx5uyhm3ueh1GbGJokklfm9i0FfFZYEgUUeO40uG3vDcdKqhtskMRhPsWMqRmRaSjOSLHxomATwm03sDMZYXOZCOUjuIlV5wSQj2M1o05aS8pJK5EqvcYwaTaFOxhD9ToTp4ixBlhDccwv99Ox5P5p0MeIF70dIZTGNP4zQ94J+wNPfgg0fBvvcSYuQJ0GY+Xp9wE2zy4ZJFHGaJDoffQo1ewiddk5h0ac+xeBJp6P+5Q+IL2wydmXX2hA/eB5rk8tQfdv1JlOJwux6yYoVrL3lFgYUgVSTXkAYXH0S/oanyE3rDV3V1Qu+vaIpDKX6cMkevKb5J2aexkbO+cHcZTBXUxKL10I6BM4a8NTrn0FZ62L6t76KIIo0nHwaieAUssVC5dIVHHjmKab6dN1kaGocLr8Qz7nnUnneBUgnwB99kWODKAqGK5BokfGVuQpKPNLTk+/84FjTZm0dQc/0W536OeJ5jZKSsbl/T/FwisHp5ksj3RmcPivLTqvH7tb98Q25jKqRzeQorfXQsqZK/5vKqYz0TDHcNXVITbhsFglNO9IkInODXZffTjycwuYwk03nUBQVJbvwfdLttxGZnL2/WR1mVFUtGIPJKqFkVDRNo3ZRAE3TCtyxOrcNG5aareuqGe6YNN6zsqmEhuXlxEIpRrunsDjMup7VbjI+f9ClTdGpBDbnieHidKKS2PJagVRmDppGbmSExJbXcJx80ls3sIOYnJzkm9/8Jh//+McLtm/atAlVVTn11FMLttfU1DA+Pk4ul+PWW2/lIx/5yFs53L96IpOJw8rtMskckcmEnhQ4hrS3t6NpGkuWLJl3/5IlSwgGg4znrfYfjCiKtLW1GfUL7e3tuN1uKisrj+lYjyXF4H0eTKLIpTUBeuMpsorK9mDhMrMsCOQ0jZymsScUpzOaJD394IxkFV4Ym1tEI4JhMZnVoCeeoieeos9i4uzFlVhUDSHgQBBFRJeeyTdvbEYZiyC6bYiewqBcdFmN4w6Hnj2f7oKYyWE9dwkH9p/E4Ok/oKHvcRrHXwHT/PIZqakULaegpXLILXkaYMtstis+PDzfSw3MHg/bJqNM+Es5+f0lTFrqmXimD9EqU/ZchHXrziewbhmRSAVTUy/j3VBN5x4nYlcnyqlnctrHbsIkitjzVi6ETJqGM85APmUj3pYWytasITowSHx4iNJVq5DMsw/px0cfZCDVg4jE5RVXU2mdK4uYj84Xn2Vk3y5K3xOguvlyKtbZkW16UFG39iQCTa1IJhMWh76sOrR7B1t/90scgVLWXf0BXrv/58a5rOedg3Xp0iN63yLvXGSTZASW8nQWvrTGQ3A0SiyYQjZLhuUk6EvKmWQWm9NCWb2X8YEwuYxCaa0HVdXIJLOIooDZZtLdVaa15KDryccHwrSsduAOOPCUOgz3GFXRGO8PU97gw11iRxCguiVAeb2P4EiU0d4QiUgab4WDif7ZAtJc5tBNxWaKXCWTRMOKcvZvnl2BmrF7nN1AgXzHbJPJZnJzgn2n28rijXUkYxn2buqja/sw9cvKqWrWm9jlZ/FDYzFks0wumzHOKQgCsWCSsX79vhsLJWnbUMPrT7Ubun9B4JgHDX+N5A4R4LyR444HkUiEyy67jKVLl/KNb3yjYN8jjzzC5ZdfjniQbPX5558nFouxadMmvvKVr9DS0sJ11133Vg77r5rDBe5He9yxZCbjfji77PykwcEJhBORYvC+AG6zzAqzE0XTiGQVemNJI/gW8x6gIrMSmEOx0BFj6SxPmCT+pnlukaJgkZFr588Ua6pK/9PPoCo56s47r6Bz6Bzys+eyyHgqw4tiLdTX0lt3IX5HELdp/omAIImYlhzaM7vq9DPoePiRBfcLkkT4+/+OfPFlmGrSVF7ZQuWVLQw91M7E031s+ua3sHjc1F9wIW3v0+Uw6c/GmEhnWeSxG/UDVXYLSyYG6dx3ANOrm+jo7ebiu+/GXl7G1L59PPelL6PmcgSWL+fMf9cbQamaykCqR/8ahYFkzxEF70o2y8i+XdPnmED07sPsLHSVsXtndfa5dJruTc/rY4/HcJVXUrN6PYM7XsfpL6W0edFh37PIO5/FJ9XSv38ci91EVYsefEqyyJKT9QZWmWSWjm1DRnYbIB3PYrWbGe6cQpJFNFVltCdIaDRKejpDXdVcQsPyCtrW19C5bcgIgGPBJCPdU5Q3+Fh2aj2xcIodz3aBpgesZotM755RBtsnsTrMLD+9ntJaL6W1XkB/SE0NRRcsbl2oe2pkIs6+zcmCQFxTKZhcoIGW9/yzuywF1z1DcCzO5sf2YTLLRra8b++YEbyX1rgZ6pxCEMBf5aa6NcBg+wRWu5nKJv2YWGh2khAPpTBbCq0yNWDn8924Suxk0znK6r2UTX8GRWaRj7D48kiPO9ZEo1EuvvhinE4nDz30ECZToRzs97//Pd/5znfmvK6xUe+2vGLFCkZHR7n11luLwfsxZKFeHG/0uKOhpaUFQRDYs2fPvG5F+/btw+fzETiE65yiKLS3t7NhwwYA2traCIfDDA8Pn7DZ96Lm/TBIgsDZlT6ub67gzHIv51X6CjzdFcAuv7mPMTzttJDIKbw2EWFf+PD2Srt/9j9s+d73eP2HP2L7j//fIY+Vm0qRGvyIFR5Ma+oNTT6AJohkA2/MdWYGi2vhzpkAsf5+Uju347z7TiaVpQiChey4xtSmIQAykQjR/gF23X03seks/jKfk7MqfJRbTMRHRlAyGTKxGBM/+j623/4SuacLmJXyjG3bZvjET+zaZTRPEgWRBrsutZEFE/X25iO6JlGWseUF5w7/oR9WgiQhmWaz/b2vvIRkMnHq332SlVe8r6A4tchfLw6PlcUn1dK4vGLeBldmm4mmlYUPAw2NyaEIY30h0oksyrQjSzpPWjLUOUU8nCJQ5WbF6Q14y5xIskgikqZrxwgjPbpjldNjZcnJtVQ0+liysQ6TRTY61qbiGSM7PYMgCLSsqy7Qjes79OC/eVWlYcN5MPMF9XM08preTGvZafWHzHyrilYgc7HlNbmqbg3QuLKClWc1UVKhe/i3rq2mdnEp4rQ+vqLBZ/i+l9X7CI5GMdnyAgVNz/pNDkX0jrevDxUE/EV07OvXIVdUFFr85iMIyBUV2NfPb497PIlEIlx44YWYzWZ+//vfY7UWJpza29vp6enhwsMU02qaRjqdPuQxRY4Ot3/hZnozmG0ybv+x7zPj9/u54IILuOOOO0gmC/+mR0ZGuO+++7jmmmsOmUn/2c9+RjAY5G//9m8BeN/73ofZbObf//3f5z3+RChYLWbejxCTKNLstqNpGnZZJJH34DrYI34+XJJI9BDH3dc5TE7VjAy9qsFSb+HDLpTOciCSoMRiItQx29Ez/+v5ODh7XqlpLPE6GIynaHTZ8FvenAuDq7aWRddcTe+f/oyvrY3hl+dvpKRNTjD0uf8ks3IFEzt3YXY6sdR4iA4MTI9TKpC7aIrCS7f+M6OvvYbJ5aL27LPJRGaX+Fv/5r3YpmfTFSedxIHf/o5cMknFySch593YLyi9grH0ME7ZjVOeLZrtnYzz4Z9tYTSc4rarlvHeNXpGXlNVhvbswFNVQ2nLItxlFXgqF9bUA0iyzNKLLqf9uT+TiugB0tDObdSsXHs0H2WRdwFWh5nGFRWM9YXwlDrw5jmz5CNIAlpe9jgaTKLkFNKJLG3rq9nyRLuxLx2f1af7yl2GxaSmaQXZ83SiUEvfuW2I0d4QFrvJsJuUZJGVZzVhtkiEJxOM9YbmjE2SRVRFPaJ61kQso3egHZrrtmN1mIzus6B32HWV2Civ1yfOmVSO7c90kU3nsDpMrDqrCWmeIn9XiZ31F7WRTmTY9WIvA/sPX1A5ORQ9omZg7yYESaL8a1/VXWUObqo3HfyUf+2rCIda6X0ThMNhtm3bVrCtpKQEn8/HhRdeSCKR4Be/+AWRSITI9LOgtLQUSZJ45JFHOP/887HbZwPE//qv/6Kuro7Fi/UE1QsvvMB3v/tdPvOZzxyX8b9bEQSBxhUV87rNzNC4vOK4SVFuv/12Tj31VC666CK+9a1vFVhFVldX8+1vf9s4NpFIMDIyQi6XY3BwkAcffJAf/OAHfPKTn+Scc84BdBeaH/zgB3z6058mEonwoQ99iIaGBgYGBvif//kfnE4n3/ve947LtRwpxeD9KBEEgQqrma7YbHHZzPO1xm5mIDG/o0FGVQtWlPORhcIOrQB9sWRB8K5qGo8PThpFtGv+9momdu9GU1Varpq/sv5Q17Cx1AOlb6aZTSHLbryRZTfeiJrN8viHP0JqYmLBYyd27AQgE42SiUYRRJHA8uU0XfEebH6/cVxseJjR1/ROptlolK5HHzW84h0VFTRdfjnpSASL2423qYkL/vtOkmNj+FpbC95PFEQqrHOD77te6KZjTA+avv3YXiN4H9jxOn2vbQZAjVew9Ly1cAQrZ+7ySqpXrKHzxWcA/UEYmxjHGSj6ABcppLKphMqmEmKhJCPdQTxl9gILRNkksurcZrq2DROZTKCqGl3bZ2tLHB4rZXUeRrr1jHsikqZz+zB1i0vndHE1WSQjeM+mcySiabp3DKMx69meTmSpavFjtsp4y5xGYefBwf4MNpeFRDSFljt8+D45GJl3u6vERjycKtgWGovRsLzcyOLFQknDIz4VzzI+EKaisYRsJsfel/tIRNPUtJVitsqM9YVIxtIFlp2HwlVSDNznw33hhfCjH871eS8vP+4+788880yBtSPoTiA33XQTmzfr9+SWlsLu093d3TQ0NPDII49w4403FuxTVZWvfvWrdHd3I8syzc3N/Ou//uucQtcibx5/lZtFG2rm+rzbZBqXH1+f99bWVrZs2cKtt97KNddcw+TkJBUVFVx11VV84xvfMDzeAf77v/+b//7v/8ZsNuP3+1m3bh33338/733vewvO+alPfYq2tja++93v8t73vpdkMklDQwOXX345n/vc547btRwpgvZWtb/KIxKJ4PF4DKundxrRbI4XRkOMpzJH7AefX7Caj9ckkcjEyAiFDxIBuLGlkpFkhtcmI9gkib747IPupICbRVYJTdMw2Y/9UtSbITE2xoHfPcDYtm3EBhaeieez5pZbaLz4ooJtuVSKJz/6MVJ5HdJKV69m2Y0fIhUM8eq//RtKNsuqT3yc5ssvL3htLJ3DYZYOOdP/ybOdfOeP+wBYVePhkU+fDkDHC08zun8PAErcQWzzB7n0vsJMk5LL0f/6K2SSCWpXr8fm8Rr7+l5/hf6trwIgmS1suPZDBZKaIkUAEpEU25/tnuNZDlBa66F1rT7hHO6cpHvXXC91u9ti+LPPMPMAzWfgwDh9e8dhWgYzsH/CkKiIomA0a1p6Sh3eskIJXCqeYd8r/SQiKURlFIvDh80XoH5pGZlkjoEDE6RTWVJHYcMoSgKlNR4mBiPzSm8EEQJVHlrWVhENJtn1fE/B/pIKJy6/nd7dY0f8nsZrK104vVbsbqvebOsdylvxDNUURXefGR9HLi3Fvn7dccu4v1kmJiaorKykv7+/6MH+NqNpmuE+Y7bqUpkTvfjznUgx8/4GcJlkLqkJ8NJokP0RXWMloQfnC8XyLpNkaNsFTSOQzFJtkmn0OHg4m5h+ZWGr9GdGgowlMyQUFchSbjMzmcris8i0uO3Ib6KT5PHEXlbG6k99EoCJnbsYeP55uv7wh4WPLy9HVXL8+TO3kA4GKV+3jjWf+TSy1YqnsaEgeJ/YuZOpffvpeuwxlGndYufDjxQE7195YAe/frWfljIn939sI37n/Hrzj5zRhEkSGY2kuOmUOiIjQ1jdHqqWr2Zs3yBKNklk60aUZIxs2oLJMivFGdj+GoM7twIQn5pgzXuvNfaZHbMBkJJJo2SzxeC9yBzikfS8gbsoCfhHO8jc/xKmJW04a9vmKBhkszTHUQP0zqsHU9NWSkmlG1ES6Ng6VKAt95Q6sLstjPWF2PNyH7WLS6ldpK8UpZNZdj7fQyYehNAWFCARhMYVV2JzWrA5LXhKHYz0TNG1fa69oCBB/eIyeg4KslVFY3QeKc4MmgrjA2Fcfvu8fuNTIzGcvtmEhWQSD2lXWfDa4ShNKyuPS+HcXxuCJL2tdpBHw9TUFN///veLgfsJgCAIRWent4DiHexNsMbvJp5TmcpkCzTw8xGwmLDJEslUlvUjUco1AdO6SgSzjBaKzVsg1BNLYc8L0CttZi6tWbhi+kQksGI5gRXLKVmymG3/dQe5xGxrdU9zMyd/7asoqRR//vRnjAd171NPEVi5grI1axh97fWC82mKwo6f/KRgW37zp7FIil+/2g9Ax1iM/905zAdPaZh3bJIo8HenNZAMB+l4/nG6xkaQzGZWvud9rL/2ep76h32oaQvujY8zvKeJujUbjNcqmdmMZy6dJpfJsOt/HyYVCeEsLXyATPX1ULF42VF8akXeDfjKnNhcFt06clpTJ0oCJWKCkj595YeXJ3BV17DyrCYmh8JMjcVJhFLkMgqxbBJ/latAS76Qg4rdpU9gk7GDM/UuRvtChvd8/75xatoCCIJAeDyuS1aEwmyrbC6ciLp89nm7npaUu6lqCSDKEn17x45YzjJD145hmlZWGFr8fAbbJ7DYTXjLnFQ0eGnfOkQifGRFiAc3gSryzqetrY22tra3exhFirxlFIP3N4FNlrig2s/eUJxN43qRojD97+BQvtRm4cwZDXtLoYBaEgVDfmOa9pDXAJskclaFjx3BGE5ZYrnv0K4uJzJ155xD+dq17PnFfUQHBqg8aQPN73kPoiwT6uyck2GTLVb9sxSEAucdUZYNV5kZgvv3EzzQjq+tFY/dRIXbykgkhSBAW/mhl8bbn/0T450HjO+VTIbQYD9Vy3yUnfcKmYRubWeyFgbfNavWkQhOkUkkaNx4Ot2bnic+qXsfh4f6C47tevk5vFU1WN3HrsagyDsf2Syx+uwmspkcuYxCZDKBO+Cg+0/bCo6LhVM4m/wkY2kGDsyuQqFBIpw2ij5dPhuB2kP/jlU1BejdMyvB6dha2KPB6jAZS9yuEpte7IodU2AlTkeMkroGnIGygteM9YfmBO6iKFC/VD/OV+akd/f0e+bb7M4TlFscJtIzBawaDByYoGVtFclIhmQ8rS/HJ3MoORUlp2K2yDg8Npad2sCrf9w/7zU7vVZi0w2w7G4LJvOJKf0oUqRIkSOlGLwfAxZ57ESzOUKZHCt8TtxmmQPhOA5ZQkUPwuudCxdHnVXh4/mREJIocEm1n4yqMZ7KUOe04jLJVNj/OmwGLR4Pa/7+U3O2e5ubWXz9dfQ++RSiLFN/4QVUn34aAOu/8Hl6Hn8Cd2MD3tZW7GVlPP+Vr4JaOD3KTgfZFlnit584hT/99n8p699DZY8Nms6ddzxqLlcQuAMgCIazzNKL3sPQ7u3Y3N45mXOz3cHyS68yvh/fvwe5fxDF50VzOnCUlhMf1wMWTVVJx+PF4L3IHARRwGw1YbaasLt1WZZ7SSM9yRC+1CST1lJcJgdOChsezZCMZxAEWHNeE1aH5bDa0somH2N9QZKxzLzZ8rZ1s3p5m9PCqnOaSITTuAOLkOdxeQHmaAV95U7qlpYZ3WfjkdSstj3v2OpWP4lohtBobNYNZ3oSEg3q15pJ5mh/bQhN1bDYTaw+p5ntT3cZ0p8Zvb46j5PXstPqcXqthCfiRCYSSCaJisb5+2YUKVKkyDuJYvB+DBAFgZMOcm5Z4z/yIqJ6p436lsLgvsz27tJIL/3AB1j6gQ/M2V579tnUnn12wbaz/uPf6Xzk9ySnpkhNTlKxYT2lq1YZ+72JIJ5Hf0ZaVdmy9RXc9fV4m+f6u4uyjLO0jNj4rCbXZLXhKNEdbxwlflrPOJfYCy8y/sMf4brwAmzL5spfNEVB/u97cW/bjhIoI/GF75ESLGAB0qMgCLjLi1rMIkdG7eIyujIr2dMbwl1io75Sv5eU1noY6wuh5NSCQlN9AmA+oqIwURJZcWajHsyaRQ68OmBIZkoqXTi8VjKpHCPdU1gdJsrqfFjth74X1SwKMNobNLLoZXVeHO7Z+pBUfP5iVk3V7eO2DBVOoKPBJJIsGH73MxOMdCJLIpJm6al17H6pj0wyy2D7BC6fDbtnboLDZJHp3T1meOA3raosZt2LFCnyV0ExeC/yjsO/ZAn+JUsW3J9LJGYz85pGNh5HVXUpkjStdx3vbGdqOIi9ej2uqjDBzp3kchmaTjmz4FypPXvo/9jHQFUJ/uIXND75OD07txIdG6FiyQqqlq9iz/8+TOj805HbGpBjbnKCHVQQnG1o6VHQNPb95QmaTzsLs+3EcgYqcmLSsKycQLUbq8NiNB9yem2su7CVnl2jjPWF9AMFvaurpml0bh9GyanULSmdN+Du2ztGaDxOaY2HyiY9A732/FbSyQyiKGKx65KZHc92GVZviWiGhmVzuz/nYzLLrD63mbG+EHaXxbCE690zxtRwpKBAVhABDSx2s+HlPh9KTsPutmBzmolOJcmkclgdZuxuC7JJIpeZlc6FJ+I4vVbMVlkft4BxfeMDs02pQmNRKhoWfs8iRYoUeadw1MH79u3befTRRykpKeHqq68uaDkbiUT4h3/4B+6+++5jOsgiRY4GX1srbe/7WwZeeIHKk06ix1XN+bc9SSqr8P1rVnOKN82B554H30kIQhJRtLDmPddisc9tVhU7sN+YCKjxOBNbX2e8V2+O0/PKi9i9JYTGRkAQyNXXoqZnV1wEQTFUAlO9XUTHhll91TWY7cVK/CILo6kau1/sJRpMIpskVp7VaEhQZJOE02tlrE8/1uYwM9w9RXp31rCNzKSyLD+toeCcobEYAwf03guxYBJvmQObU58Y2F16ljwZS9O5bbjAo3m0N8hYbwh3wE7b+hqjo+nBWO1m6hbrGvdUIsPA/onZCUYeNqeFVWc3oSoaoiToHV5XVzHaG5z2aJ+Vv5Q3+KhsLCGXUYhHUjg8VkO6U1bnZaQ7iCiJBKrdjPQEjXEL6JOfLU8cKLCijE4lUVV1XpeeIkWKFHkncVR3sSeffJKTTjqJX//61/zbv/0bS5Ys4emnnzb2J5NJfvaznx3zQRYpcrQsv/lmLr77blZ94hPc8ae9hFM50orGd3+/lVQkApIVQdB//VVVIz2PxR5ArKyEbF01miCQXr4Ec+tsgxBRkjE78gJxTUO1RNDinWjJQcTk3oJzZZNJomNz/bqLFMknncoamu9cViE8Hi/YX97go3l1FbWLSsmkcwRHYgV+7/PZJh4sqZlPYtOza5TIZKJgm5JVyWUVpoajTA3P7ZB6MKqqsev5nnkDd9ClL3s397H5sX28+vgBUokMZXVeGpaVI5v0XJIgQnmDF2+Zg46tQwy0T+D02Qo0900rK1l9bjPrLmjBVWIvdJARBBLRtCEHmiGbVtj9Yh9vQ2uTIkWKFDmmHFXwfuutt/KFL3yBXbt20dPTw5e+9CWuuOIKHn/88eM1viJF3jSV8mxgU0acQEsbDo8MmUlAw11i4sBfHuK13/6CkX27Cx7u9rIKote8l+DnPwU3XE2gvonGjafjq62nYsly/fzLViGIIlavD1dZBST7INGOxVKorzXZ7bjKDi1BKFLEbDXh8OjZcEkWcfsLpVaCIFBe76VmUaCg4FQyiTg8VppWzW0H7Cl1ULekDHfATvPqSiOTn4+YZ0trd1koqSx0txLlw2vqc1mlIHN/8BxByamERvXJSC6j0LltCICObcOGNr6szkfzqiraXxtirC/EUMckA/vGC84x1DlJLJhENkvEwyljVQH0lYuxBXzko1OJo2ooVaRIkSInIkclm9m9ezc///nPAf0B8sUvfpGamhre97738atf/YqTTnpnNHQo8u7i8xcuwhJ8mpQK71vmYnT8IZzLxqjxVuD3L2XnYw+RDocA6HzxGSIjQ7SdfQEAZS2LECWJVCRMWZuus/c3tND3+qsE+3sZ2rUNk81G82lnIZnMeKvrCA/1owl22rcOg80FyW4ASpvbipKZIodFFAWWn95AZCqB3WXBYpsr54LZZijB0RigB62rzm5a8Lw1bQFq2hbuE9G4ogJRFNDQqG4NsP3proL9kwMRSg5jvWq2yJQ3+BjtCWJzmhFEYU4X2Hzi4TSv/6mddHI24J+R5uT7wmezs18feG2A4Ih+zeGJBG6/bY5rjqqo2JxmkrFMgT2l2SpjtplQVY2RninQoKLBVzBxKVKkSJETnaO6Y1ksFkKhUMG26667jrvuuotrr72Whx566FiOrUiRo0bTNCZ7Opno7jAy6P7qGr5482V86Zozad5YTzo9AqiEQltQlAQma6HTz1Rfd8H3gcYWalatM4pNk6GpgiZN2WSSjuefZv9fnqB70/P4G5oZG86iCVbIRYzjQoOF/u9FiiyEJIv4ypwLBu4z+Ktnayyc3oXtaI8Es1WmdV01betqsNrNcwJa5RCN6FRVo2fXCHs391Fa6+HkyxZTv6y8MAs/j14+l1FIxbNoqoYki5isMhODYUZ6gjStrMDqMOMqsVHbVmq8Jn8yMN4fYrhrCpNVX+Wy2E3YXWZGe0MkYxmaVlSw7NR6WtdV0bSqkhVnNiLJItuf6aRn5yg9u0bZt7n4dzkfmqIxtk2j788qY9s0NOX4yo1uuukmrrrqqsOPS9O45JJLEASBhx9+eM7+ZDKJ3W5n3759PPPMMwiCMOffvn37Fjx/T08PgiCwbds2br311nlfn/+vp6fnjV/0XyGaqhIeHmS88wDh4UE09ci6H79Z+vv7+fCHP0xVVRVms5n6+no++9nPMpnXof3ss882fm4Wi4W2tjb+5V/+BUWZTQ5omsadd97JySefjNPpxOv1sn79en74wx+SSCTme+u3haPKvK9evZqnn36adevWFWy/5pprUFWVG2+88ZgOrkiRo6Xvtc0MbH8NgKrlq2g8+XQA7N4S7N4S8v/2JMmGgAmz3YFssZFL6zpjTdU48MxTNJ92NpJpbvDkKqvAURIgPjUxZ19kRJcBGJZ0lgBk9ZuHv37hrGiRIm+EslovskkinchSukCDpqmRqLF/Qa/2g5BkkSUbaxk4ME48nMZqN1G/rGzB40e6pxjqnAL0wtANF7dxYMtggf/6wdnxgxFEgex0sN+1fZiN71nC8tMb2Lupj21Pd1K3pIzKphJq2gJ0bpttLpWIpBFE/W+udV01+1+ZDcaHe4J6B1ugeXUllumsezI6K505WOdfBAae09h2u0rSUCtp2Eph9adFas58ezvU/vCHPzykLepTTz1FbW0tixcvZmRkBID9+/fjds9OdEtLSxd6eQFf+MIX+MQnPmF8v2HDBj72sY/x0Y9+9KjP9W5gsqeTrk3Pk4nP1umYHQ6aNp6Bv2GuXfOxoquri1NOOYW2tjZ+9atf0djYyO7du/niF7/IH//4RzZt2kRJie5A9dGPfpTbbruNVCrFH/7wB2655RYkSeLLX/4yAB/84Ad58MEH+f/+v/+P22+/ndLSUrZv384Pf/hDGhoajmiC+VZwVMH7Jz/5SZ577rl591133XUA3HnnnW9+VEWKvEGiYyN5X88tDrXb66mouJxUahS3eykTXR2M7N1ZcIyq6M2bJnu6WHrxe/BUVBXsl0wmVl7xPvq3v8bA1lcL9qWiEcY722lc0YRsltDUEvxlKxBFTdfDFylyjCmpWFjKMt4fov11fUI5ORRh+ekNR3TOTCpLcDRGKpYll1FICxzSpSW/U6qSVdjzcu+crsmHI18mA3BgywCySde0A3TvHKGi0Ud5vY/B9glS8dkic02FbEahf+84Vc1+eveMIcki2bxC9L5940SDSSby7CMBbG4L6WSWRCSFq8R+xBOcv1YGntN4+Rtzs6XJcXj5Gyqn/PPbF8Bv376d73//+7z66qtUVs6t7QB45JFHuOKKKwq2lZWV4fV6j/r9nE4nTuds7YckSbhcLioqivfyg5ns6WTfn+fWP2bicfb9+XEWn3fxcQvg//7v/x6z2cyTTz6JzaavQNbV1bFmzRqam5v5+te/zo9//GMA7Ha78fP79Kc/zSOPPMLDDz/Ml7/8ZX7zm99w33338fDDD3PllVca529oaOCKK64gEonMffO3iaOSzbz3ve/lBz/4wYL7r7vuugL3mSJF3mrKFy/TXWQEYU5X1BmczjYCgTMwm/2HXNJTlRy7//gI2XSqYLuSzSIIAv76RsOxJp8Dzz4FWg6nPUqk6znGDuzC4S9mZ4q89cRCqbyv53ZoXYi9m/oZ6pg0PNqzaYXxgTCDHRPsf6Wfru3DBc2Xyuu9uge7WULTIDyeQFU1bM433mxuajjKxGDhw3LgwARKTl1QIhQNJqhoLGHDxW2sv6gNUZ4NxLOpHGO9oYKJhiDqGfmtf+5g76Z+dj7fM2+31ncLmqJn3A/FttvV4y6hmY9EIsF1113H7bffvmDwrKoqf/jDHwoCL4A1a9ZQWVnJeeedV4xRjgOaqtK16flDHtO16YXjIqGZmpriiSee4FOf+pQRuM9QUVHBDTfcwP3337+gy5TNZiOb1e9z9913H4sWLZrz+wPTNUaeE6dL+lH7vP/2t7/l4YcfJpvNcv755/Oxj33seIyrSJE3RGlTK97KGjS0I2qIVNa6hKneHqYGelAlkDWp4AajqSp7n3yMZCRMLpXE7HCSiceQLVaWX3olzaefTffmF1AyeQ4WmkZoaID2Z/+EpqrEJ8dxlpZTOe1OU6TIW0VZvZfxgTC5jEJ168LFqgeTiBROWAVRoG/vWMG20b4QK85owOm10btnbN7CVLffTs3iUtpfG9SLRgVd3nKwjeNCHBxI9+8bJx5KUd7oY3I4iqZquPx2otPSF1XRyGUULHaT7nyTnN8CdgaH28qeF/uMgD4ZTZNOZLG55nZsfTcwvpM8qcz8JMf148pWvyVDMvjHf/xHTj311HkDqxk2bdqEqqqceuqpAFRWVnLnnXeybt060uk0P//5zznvvPN45plnOPPMMxc8T5GjIzI6XCCVmY9MPEZkdBhPZfUxfe/29nY0TWPJAo0blyxZQjAYZHy88BdbVVWefPJJnnjiCf7hH/7BONeiRYuO6fiOF0cVvN9555184hOfoLW1FavVygMPPEB3dzff+c53jtf4ihQ5aky2Iy/cEyUJzSHTvjFFxqFRMmrh4qZr2f3HR1CyGUx2R4EUJxPXXS5y6RS9r72CAIWBOyCIEqGBwiI4UXp3L8UXOXb07B5lvD+Mt8xBy5qqQ+p/HW4r6y9sRVG02TqMBYhOJXSduqoiyqLhF2/3WEjG5gbmmqoRmUjg9NoKsvCCIKBpGoIoUNlcgt1lxe60EJlK4Ctzkopn2PNy3xFdqygJBZlygHgkha/MydrzWshlFcw2mV0v9JKMpnF4rXRsHaSkyk1ZzeGzZJlUjlyek43DbcEyj43mu4XU5JFl1PXj3jrpzO9//3v+8pe/sHXr1kMe98gjj3D55ZcbEq9FixYVBGOnnHIK/f39fPe73y0G78eQTOLQgfvRHncsmcm4z9wn77jjDn7605+SmX5uf/CDH+Qb3/iGceyh7qcnEkclm/nP//xPvv71r7N//362b9/OXXfdxe233368xlakyFtCtBIyDv0PfKo8jeizc/IHPsyG626itKllwdclpiYQTbPzX9mie3NrqsLo/t1oqorN46N65VrKWt4Zs/kiJzbxSIqhjkmy6Rzj/WH2vzrASE/wkI2HRElcMHDXNM0oJO3fN046mSWbVlByKqKkP8QS4TTaPIlyQRTwlul64Jq2gOEm4ym1s+a8Zk66ZBGJSJpXH9/P7pd6GemaIjwRx+mzYbIc2WTWPI/bTnVrgNBYjOBoFKvTjMks07K6EovDRDyUIjyRoHvHCKlElrJ675zXS6bZx55szv9aYtkZDQt2kX03YPUf2bUf6XHHir/85S90dnbi9XqRZRlZ1u+7f/u3f8vZZ59tHPf73//+kJl5gI0bN9Le3n48h/uu40gtkI+HVXJLSwuCILBnz5559+/btw+fz0cgoK883nDDDWzbto3Ozk6SySR33XUXdru+St/W1sbevXvnPc+JxlEF711dXfzd3/2d8f0HP/hB0um0UdFdpMg7kfrqWTmLQ3JilawIoojZ7qB2zUl4a+ryjp59aOUyaSa7OwH9pqSqcyOcbCpJNpkglyk2hiny5pFNUkHjo6nhKF3bhxdsSnQo4uEUW55o5+U/7GXfK/0I+U2YDpoL6J7tGAE96PIXu1uXl5gssjEJCI3FScUz5LIKXduHyaYVchmFZCxD57ZhIpOJObIZQcBoTAX6+6w6u4llp9YXHOcJ2Mmksux5uY+uHSNsf1r/++veOUI6XiiR2f5MF+N9IUoqXVQ1l+h/usJsB1pBgERk9u/S5jQjy+/uFbLSFWA7THmOrVQ/7q3kK1/5Cjt27GDbtm3GP4Af/OAH3HPPPYAueejp6eHCCy885Lm2bt26YLFrkTeGu7yysNv4PJgdTtzlx/5z9/v9XHDBBdxxxx0kk4V1PSMjI9x3331cc801Rkbd4/HQ0tJCbW0t0kEr4tdffz0HDhzgkUcemfM+mqYRDofnbH+7OCrZTDKZnFN5bbFYTijvyyLvbGY8Yk02O44S/1vynpXWGi7xvpfRA7upKGlEEvKy6WYzyy56D8GBPhLBKdKxKMN7dgAgiKKhj88k4lQuXWnsmyGXTjHWvg8EgdYzzn1LrqfIXy8Wm4nFJ9cxORRhvD9kGLqkEnoQms0odG0fIptWqF9ahqtk4bqP4a4psmndmnFqOHpIFYS/2k3d4jJGeoJ0bddtGj2B2Ye1ZBIRhFmDmfGBCBMDc/3TRVEgOBads13TdHtK4zrtZuxuC4looVxHEGFiYLaINRXPomkaolyYQVdyCpqqn3dqOMrqc5tx++2M9oZmm1odNEGZmTwMtk/Qt28cm9PM0lPqMVuPujTsHYsgCaz+tDiv28wMqz8tIkjHJ/MeDoeNwHyGkpIS6urq5i1Srauro7GxEdAlM+eff76RRQUMe79ly5aRyWT4xS9+wQMPPMADDzxwXMb/bkUQRZo2njGv28wMTRtPRziEY9Wb4fbbb+fUU0/loosu4lvf+laBVWR1dTXf/va3j+g8V199NQ899BDXXXcd//RP/8QFF1xAaWkpO3fu5Ac/+AGf+cxn3plWkQA//elPCwL4XC7HvffeayxJANxyyy3HZnRF3nW0P/dnxjsPgCCw+NyL8Te8Nd7ooee2kRkfpY8BzKpMedtSAPq3vkposJ9AcxvVK1YTGh40AvRcKsVMxOIur6LplDOoXLaC8Y4D9B9kIalkD104V6TIkeIrd+Ird2Jzmundq1si+qt0u8iB/eNMDunBcfvrQ6w9f2HZ1xwnmIOCWVXRsDrM1C0uNZpBVTT4sNhkJoeiuEpshkbUajez6KRaJocieEsdBT7skknA5rBgsshUNPqM7qj5iJJA7eJSuneMkMspuHw2Xv79Xg6Wn2aSObzlTka6dE95QRDo2T1K8+pK+veOI8ki9UvL2fl8t95ddZqxviBDHVMLfhaVzSXULy1HVTV69+iFuYlImtHeILWL3l1OUTVnCpzyz+JBPu+8JT7vzzzzDGvWrCnYduONN3Lvvfce9rWPPPLInF4zmUyGL3zhCwwODmKz2Vi2bBmPPfYYl1566bEcdhHA39DM4vMunsfn3UnTxtOPq897a2srW7Zs4dZbb+Waa65hcnKSiooKrrrqKr7xjW8YHu+HQxAEfvnLX3LnnXdy9913861vfQtZlmltbeVDH/oQF1100XG7hqNF0A4lljyIhoaGw4r5BUGgq6vrkMdEIhE8Hg/hcLigcUKRIpt/8VNyaT3bVr54GS2nnf2WvO/LP/sJak7PQlYtW0XjxtMJDQ2w+4+zy2dr/vZ6xnbsYLB915zXC6LIyve8D2dAf9B3b36BoV3bjf3+hmbq1m7A7ntrVhOK/HWTiKTY98qAUSjqKXNgtZmITCaMoNXmNLPmvIWDd03TGOqYpHfv2JzAfQazVWb9RW3G95l0jn2b+4kF9eXpmrYAdUsKmzd1bR9mpDc455yrzm7C4bEy2huic5vuPS+bJcPfvWlVJRUNPgBeemR+/SrAijMaCE8m6Nsz637TsrYKVdGITiXwljuxWGQGDkyQSecIVLvp27uwhUrr2ipKa73GROS1J9sNi8zWddWUHkHh61vNW/EM1RSN8Z16carVL1C6guOWcX+zTExMUFlZSX9/f9GD/W1GU1XdfSYRx2x34C6vPG4Z93czR5V5L7YBLnK8CTS2MLJvN4IovqUdSWvXbKD31Zcx2e1Ym6v45cCdJHNxKstEPGP6n0l4aIADjz6CJoC1ugLZOSsb0FSV6MSoEbyXtixmrH0/uUwaNI3Jnk4iY8NsuPamd0w1e5ETlz0v95GZ7kYKelFpeEzPdpksEg6PdU5QfTCCIFDdGsDtt7Pn5T6U3KxUQhAFRFGgbmkZQ52TJKNpKhp97H91sMBZZnwgjNtvx1vmRNM0Ol4fZHxg/kYmsVASh8dKeb2XTCrLWF+IbF5jpqnhiBG8C6KwYEfW0Fh8TsFrz85RwzVmvF/XpfrKnZTWehk8cFAnZAFjYmG2mfBVuDiwZYCJwQjugJ0lp9Qx3hfC5rKckIH7W4UgCdN2kCf+/Wpqaorvf//7xcD9BEAQxWNuB1lkLsdUzDc5OcnPf/5zwzOzSJGjpfm0sylrW4LJasPqOv6rMuHhQTpefAZRkmk+41wCjS08H/4T0ZweAEwss1IrVOIIlNH18nM4FjejJJJMPf0S5e+5sKBINTE1CegSmb1PPUbuoOZO2UQSTVMRhHd3UVyRN4emaYZWHfRiUYfHSmhMl6NIssjSU+oXevkcXCV2nD4b4fHZpW4BvXlRNqPSs0vvVDw1Ep1TaJpOZNm7qY/qtgCJSFrXzs+DKAn4yl3kMjnGByKM9YVIJwqlZPmdYmsXlxZk1o3ziALeMgeyWUYUBdTpAD/f7nGG4GjM0LfPIJslVEVFVXQry8UnVZOMZYxmUJGJBBODEeqXlS/0cRU5AWlra6Otre3wBxYp8lfCm17L0DSNJ554gquvvpqqqqojLgwo8u5DVTXufqGb7/zvXobDC3d7dJWWvyWBO0DXy8+RCodITE3Q+fxfeP2BezGnk7jGJKwRAZ+9jBWX/03BeCS7jcpzz2LDDTdjztuuTTeUGdi+xfCDL0QjdQK1Vy7yzkQQBJpWVmKySLj9dlaf20TjinLDqjEVzxKeODo/ZX9V4d+bqmoc2DJIz65ZJ7FcRiEwrX0X85xpNA0G9k/MCdybV1Wy5vxm2jbUsOHiNmKhJK/88YDuDJMXuJdUulh5ViMVjbO61Orm+eVlpXUeOrYPs3dzL5Jp7iR4nobHBhabieZVlYZvvKZqKDltTkHqcNckyru4y2qRIkVOfN5w5r2np4e7776be++9l8HBQW644QYee+wxzjnnnGM5viJ/Rdz9Yjffekz3UH2xc4I/fOaMt3lEIFsLGzpl4xmEvwxSn9Et8JrOWcnIvt3kMmmsbi+pSAiAnE1mqq+b1tPPoevl59AUhUwqwesP/KogcM93pAEY3b8bX009rrJyJNO7txlMkTdHeYOP8mmJCQAaBTKTRDhV4AZzOCoafLhL7Kiqyo7nug1ZSf4565aUUd0aoH5ZlpGeKQYP6CtN+TIUAIfXSlWz35Cc2Bz639JQ5+S8752KZbAe1BhJEAXK672MHmSBOdpT+P3BaAvE3Ha3heWn6x7urhIb0akkbr8dl8+GKIlYnWZS07UCSlYlHkzSs2eMeCiJp9RBbVspyXiGVCJDRb1vXv/5IkWKFHmrOKrMezqd5le/+hXnnXceS5YsYdeuXXz/+99HFEW+8pWvcP7558/xzSxSZIaB4Gy2fTC4cOb9raTtrPMP8nEHLTObVRzbuYvOF5+h//VXsPsKK9YTU5N4q2qoWbWOVDRCsK+HZGgKJZtBlGUc/gBLL3oP9es3YvPqgdbQru3sfvz3bHvkt7oevkiRY4DJIlNW5wXA6jAZ7jBHg91twem1UTftrpKfkbY6zFROZ8MtNhO5TF6UfJA0PRlJ4y2bO3Gwu6xztgEkomnDIadge+zIeyOYDmPn6PbbkU0S0WCSsnovay9sYdlp9YiS/ghcurGuwNlmcjhKLJhE03SN/c4XeujYOsTA/gn2bDqy7rBFihQpcrw4qsx7dXU1S5cu5QMf+AC/+93v8Pn0gOS66647LoMr8s4lnMzy9Yd2MhxO8aWLFnFyk58Pn97Iix0TjIRTfP2ypW/3EAGwOJwsveAyNv38p6i5maV8/SkuShJC3mQ0PlnoWDG0Zwf+hqZ5Wz47/aWsuPxvAPBW1ZDLZBgMBY39qXCI6NgovoMmDkWKvFFa1lRRv6wMWZYMCc185LJ606SDs90z1Cwqpao1gCgKRKcSxCNpSipc0x7tMWJTSQJVbiaHIoZTTD6qqhnSlHwalpdjsZsYPDCha9TzMvZ2lz6WVCJDcCRGNp0jOnnk/UOyecW7M4iSYIwjOBajb/8YA/v04lVvuZOlG2f/9g7+LGbcZuYjdRSTiiJFihQ5HhxV8K4oCoIgIAhCMcNeZF56JuL8xxP76RqPsXdEz6b9w/3bePmr51FbYuepz531No+wkHQsimy10nTKmXQ8/2dAl7pYHE5S0Qi5VArJZEZVclQtX03PKy+hTRepaorCjsceon7tSbgrqogHJ7HYHWSSSayeWes5AEEqXOQyWW04/AGKFDmWmMyHvqXHwyl2vdiDklWpaCyhaeX87hzidPDvKrEbjZ6CYzH2vtxn7K9o8s3rnV7TFsAyj6xEFAWqW/xU1HtJxjIIot5AKTyRYP+WQQLVLsb6wvNOCPJxeC2oikYyOjeINln0Qtb6ZWWM9oaMItx0PGsE7gDhsRi9e8aoaQsgySKhsRgWu4nUdJfWsjovnoCdnl2jRjMnUdIdcOqWHtrFp0iRIkWON0cVvA8PD/PAAw9w11138dnPfpZLLrmED3zgA0XruyIAvNQ5wdce2knPRGHGTDpEFvDtpP35vzB2YC8mm52qFauN7ZqqkorqhaXJcJBll1yJu7wSUZKQLRban/3T7ElUld4tm2g67SwW11/MK7+6BzSNsQN78VbVUtrcqp8zVxiQ1J90Kmbbwt0vixQ5HkwMhFGyuuRltGdqweB9hkQkzcRQGJfPVlCQqqoadrcVV4kNJafqnVA1Xb5S3XroXgaSScLp02tN4uE0kUk9qF6oiZIoC3q31Gn9fTy0sNysdV0V3lInyVgGf7WqF+7mLQLMONRomt5JNZvOUVbnZc/0pESSRRafXGvUCyg51fCIr2wqoW5JGYKgB/HZrILZ8u7pvlqkSJETh6O681itVm644QZuuOEGOjs7ueeee7jlllvI5XJ8+9vf5qabbuLcc88tZuXfRbzeG2Rz9yTxtMLtT3cU7GsMOKgrsfO5C048Cy8lm2XsgF48m00m0JQcpS2LiI6NEGhuZXjXDpRsBslsZqKrg86XnsVbVUvdupPnFKECdL34LANbtxT0XC+QLhw0we15bRvjo1YaV1QYbdmLFDkckckE7a8PIggCbeurcXpth39RHnoWfTLv64VRsgq7XuwxMuGNK2YDfUGA4a4p4iHdDtXmMpOMZsimcvTsHqN5VSUD7RNMDkUoKXdRu3j+LqWy+cieFQt5vh/Mnpf7cHqsxKbHFah2MTGoTzokk8iqs5vY/+qAMe5MKqtPPGauOadidZiJTCXo3z9ueOcDjPaGGOsLIYoC2ayCmtOwuyysOqepmMAqUqTIW8obThs0NzfzrW99i9tuu40nnniCu+66i8svvxyXy8XExMThT1DkHc9FP3iW/dM+yvmPLpMksLHJz7+/byWVnqMLLt4qJJMJu89PIjgdyJRVULt6g7G/rHkRgzu3Mrp/D6P7dwMwEg7hqaikoCmxIBgB+4z2XRAlqlesNtpBJ0JBBne8XvD+OcVGZDJBx9YhVp391jWjKvLOpnf3qGGz2L9vnCUbD18zkc3kyGVUbE4zJZUulp/eQCqemWMPOed109r4GURJZNmp9YTG9b/5wfZZ95hcnv97PJIiHk4ZPu3xUApPqQOr00wyksbhtSJP2zyWVLgorfMw0R9moV7fgiBQtyTAcNcUIBR43Lv9djRNIzo1XQCvYQTuABODUeqXl2Eyy4z2BOnYOkRVUwm9e8cRBKhdVEo0mDA6vZbVe0kns+x6vmfOOOaT8ySiaUJjMSaGIqRiGarbApSUu+YcV6RIkSLHkjft8y6KIpdccgm/+93vGBgY4Gtf+9qxGFeRE5wv/W6HEbiDvjJtmm6d/ZlzW/n5h08+YQP3GVZcdhUtp5/Disv/Fm9VbcE+m8dLJj63ENVsd9By+jnYfSW61eM8EYejxE/9+o1M9nTx2oMPsf3JTYXnLmsBhx7YH6qwsEiRAqJRTNlZl6bDOaxMDUfp3z/OlicOsPXPHfTs1pstuf12yuq8SPKhb/9Wu9mwo3R6rfirXHhKHdS0lTLUOStxEUQKPdc1bU5TzsGOCbb+uYPdL/Xyyv/uL/CPnxqKLhi4g27d6PY72HDxItZf1Erz6kqcXitOn5XyRp/hGLMQvbvH6Nk5QnQqSWQiQeeOEWwO3fM9HknTs2uMXEbB5jTTuKycru3Dcz8L58K2rh2vDzHeFyY6lWTf5n4y6bnFs+9EVFWhf/cO9r74LP27dxQ0pDse3HTTTVx11VUL7j/77LONmruZf9dee+1hz3nwawRB4OKLL+aZZ56Zd1/+v3vvvffYXuS7AE3TUCZjKEMhlMlYYbLrOJH/czaZTDQ1NfGFL3yBF198EUEQeOGFF+Z93UUXXcQVV1xhfD8wMIDZbGbx4sXzHi8IAg8//PC8+w7+fbLZbCxbtow777zzTV/ffBxTwV5paSmf+9znjuUpi5xA/OTZTm7/SweLK13EDnpANQUc/PwjJ5FTNOr9R+4v/XYiW6yUL1rY9cbu9xMc6DW+L21djLuiCmdZOeGhARLBuRpdR6AMh7+UXY8/SnhwrqWcyWZn+QXn0rN7DCWn0LC02MmxyBGQTsPDD9KUzJApWUHW5TN02fFwilgwia/cafiP9+8fp39foTvSaE8QX5kTd8B+xDKP5lWVlNa46dg6zM7numlbX0M2nTtIxiKQis8WjwqCgMNtparVz9B0dj44Uti0bKhziuq2ACazjMkio+T015utMpl5nGM6tg3hr3JTVuelf/8EmWk3mPYtg1S3+tFUjWgwOb+8RoNcdlbmpuZUwhMJIlP9BcenE1mGu4MkIrMyGlEW8Pgd+KtcdGydDeqdPiuqouHwWBjvz2u8punnH+ybIBpMUtHgw1vmPMQnfGLSvvkl/nLvncSmZlfRnSUBzr3pY7SefOrbNq6PfvSj3Hbbbcb3NtvhE0QXX3wx99xzT8E2i8WCw+FgeHj2Z/rZz36WSCRScKzH4zkGo373oIyEye4dhlSeW5PVhGlJJVLF8f0sZ37O2WyW559/no985CPE43FWrVrFPffcw+mnn15wfH9/P3/605948MEHjW333nsvV199Nc899xwvvvgip5122lGPY//+/bjdbpLJJI8++iif/OQnaW5u5rzzznvT15jPUQXvTU1Htrzf1dX1hgZT5O1hMpZmS0+QkUiKljInJklkXb3PKDTd0jPFn/eO8f+e7UQDXu0J8jdrqtk7XcC2rMrFY7ec+TZewfGhesUahndtR1X0jFNZyyIAxjsOMN55YN7XKNmMIbOZj2wygZpN0rau+tgPuMhfL9EoJBJELAFiJjekFDpeH8SipNi9YwpN1TBZZSobS3B4LESn5tosKjmV3S/14q9ys2hDzRG/de+eMSM4790zRvPqylkbRrFQj26xm2hcrmvjy2q9RvAOIEgC2rR1o8kiIU1nzN1+O6l4BskkLpi1TsUyDB6YYLwvNCe4H+sP4/RYsVhlUon5LR5nGjPlc3Cgb3NbGOsLGd/LJokNl7QhCAKD7YVS0Lb1NfTvG2e8P1yw3WyViYdT9E5LhkJjMdZf1GbIhN4JtG9+id9//1/mbI9NTfD77/8LV3zua29bAG+326moOHSR9cFYLJYFX5O/3WazkU6nj/r8RXSUkTDZrfP0QEhl9e1r6o5rAJ//c77++ut5+umnefjhh/na177G1772Nf7v//2/OByzicV7772X0tJSLrvsMkBfMbjnnnu44447qKmp4a677npDwXtZWRlerxeAW265hR/96Ee8/vrrb2/w3tPTQ319Pddffz1lZUW7rHcaqqoZFnAAI+EU+0YifPIXr5PMFi6JXraykv+6fi3b+0O8/ycvz1nWPm9JOV+6eDGprELDUXRyfCdhslhZdvEVjHUewF1WibdKD3hEKe/PRhRpPeNcJrraCfb3kgqHDn1Omw2z/a/z8ypyHPH5oKoabWo2ONVUDetTj2H3LiNucpFN5ejbqweN3jKnUY5R1eInEUkRmi6+nBqZ2xDpYFRVo3fPKKl4piBLb7JIWGwmVp3VRHgygc1pZs/LfWiqhiTrBaGapj8I7S4LreuqmRqO4qtw4i11MtI9hZJTKW/Q5S6KohoBs5JdoD1qHvNl5bOpHMFUbJ6jZzk4cAdd7jPTkVW2SEYRqySLeMscuPwOJociZDOKEYwDlNZ6UBVtTuAOUN0aQMnlZfkV7YiLbU8EVFXhL/ceepn/6Z/dSfOGkxHFt35Cct999/GLX/yC8vJyLrnkEr7xjW/gchVrDN5uNE3TM+6HILt3GLHc/ZYVd9tsNrLZLDfccANf/OIX+e1vf8tNN90E6OO99957ufHGG5Fl/Xn+9NNPk0gkOP/886mpqeHkk0/mRz/60Rv+/dI0jSeeeIL+/n5OPvnkY3VZBkcVvP/617/mnnvu4fvf/z6XXHIJN998M5deeimi+Kal838VpLIKY5E0NT5bQZB8JK97sWOChoCD5tJju8SayipkFJUP3fUK2/pDuCwynzqnGY/NzNcf3qm3VZ/ndX/cOcxN97zCVCxTELhfuqKCM1tLuWxl5TEd54mKu6IKd0WV8X1schyrx0Ptmg2EBvuJT44XWkdOY3W5cZZVEBrsI5dK4fCXEmhqpbxtMWLRjanI0SJJcNnlBGIxoh1hol1DVCYGMStp/FqEOIUPmBnf8mWn1mF1WAiPxwlPJNBUDX+lfmzf3jGCozH8VS5q2grdYIa7phie1rXLZonKphIEQW/gBGBzWbC5LEwORYzgVMmp7H+ln/BEAqvDzJJTahnpniIZy+D22xEEvZg1m8pRUukClwVRFPJrvgsQBKhoLGFiKDJvE6bqVj+hsTjxcGrui+fB7rYUSGIaV1aQjOqTk6GO2RUCBHAHHHTv0HX5Yl5tgLfMQevaanIZBckkGhOOmkW6X3xkKoHLZ6O01kMsmKSiqQTTO8hOcnDv7gKpzHxEJycY3Lub2mUr36JR6dxwww00NjZSUVHBrl27+OpXv8r27dt56qmnDvm6P/zhDzidhc/VL3/5y/zTP/3T8Rzuuwp1Kl4olZmPVBZ1Ko7kP/4ysldeeYVf/vKXnHfeeZSUlHDVVVdxzz33GMH7M888Q1dXFzfffLPxmrvuuotrr70WSZJYtmwZLS0t3H///XzkIx85qveuqdGTfOl0GlVVue222zjzzGOvTDiqu8rVV1/N1VdfzeDgIPfeey//+I//yMc+9jE+9KEP8eEPf5jW1tZjPsB3CmORFH/z45cYCCY5d3EZP/3QejZ3T/HZX29FFgXu+MA6LLKIx2Zi/2iU7vE45y8p5ffbR/h/z3YQSyuYJIH3rqnmwqUVnJ+nhc4pKvG0gsc+t/EJQFZRuW9zLz/6UzsWk8Q/XbYEp0Xm4794jVRWpa7ERt905imazvFvj++ntcw5/wMTPZhXNXhmf6Fm1mYS+c/r1p6wvu1vlsjIEAgC7vL5JyZDu7bTvVkvfGk46VSsbg/RsZGCY0RJpnrlaurW6jNtTdPIpVOYrCd28W6RdwCiiOB207TWDUPbYHIUZJmqM1fhd3ro2jai+5pPk05k6d45Sv2ycjylDtac20wmlcNVojsdDRzQg7R4OIW3zFlgO6kqs9ljTdVoWF4+b8bs4ELO8HSPh1Q8w87newwXmu5dI8RCKUP7vv+Vfk66dLHumb7A5WoaTA5FaFpRQfvrg0a3VJvTjLfMia/Mic1loWfniKFpN9tkHG4rpfVeOl8fKsiCJ2OF/vC5tELj8goyySzDXZNGFl7JqgXBvJp3Drdft9eUzRLLT2tgcjiCu8SOzWnhtT+1gwaTgxHql5bRtLKSzu1DjPWGqFkUwF95aHefE4FYXhfoY3HcseSjH/2o8fXy5ctpbW1l/fr1vP7666xdu3bB151zzjn8+Mc/LthWUlJy3Mb5ruRIi7SPYzH3zCQtl8uRzWa58sor+c///E8APvzhD3PhhRfS0dFBS0sLd999N6eddhqLFulS2FAoxIMPPlhQ2PqBD3yAu++++6iD9+effx6Xy0U6neaVV17h05/+NCUlJXzyk588dhfLGyxYra6u5utf/zpf//rXefbZZ7n11lv5j//4DyYmJvD5fMd0gO8U/rJvjIFg0vj6qw/u4LevDTCzYvrpX77OQDCJKGBsu+0PhefIKhq/2TLAb7YM8O33Lue6DXWMRFJc/ZOXGQgm+cDGOhQVOsdjfOrsZlZUe7j+vzezfzR/GTzL3/9ya37ncSNwz8dhmZv9XV7t5kfXruHHT3fwu9cHje2XLK8glVX4/IWL/uoC91Q0QiI4SXR8jIFtWwCoX38KNavmPgwme2drOSZ7uvA3NDF+0DHLL70SV1m+H7ZQDNyLHHsuuRRGR8HtRnQ6ifQECwL3GYKjMRLRNOsuaMXqMGN16MH2wSuDB39f1VRCIpImFc9Qu7h0waVuh9uKt8xhSHLyybePNFvkguA5l1WNDsQu36we3ROwk0pkyaSyaKouk+nbO1aQaPBXuZFMIrte1IvJ65aUYXdZMNtkYwKSyygFgTvMSmRm6N8/TnVrALPNxIozGunYOmRk5rMHBRmSLFLd4i9YoXB4rEaPhuBotGAJc6B9gv7948aEo/21QUouc53wfvBO75E9v4/0uOPJ2rVrMZlMtLe3HzJ4dzgctLS0vIUjexdypKtLx3EVamaSZjKZqKqqwmSaTXaef/751NfXc++99/KlL32JBx98kNtvv93Y/8tf/pJUKlUgb9E0DVVV2bNnD0uXLmxscTCNjY2G5n3ZsmVs3ryZb3/72ydG8A6QSqX43e9+x913383mzZt5//vfj93+7usY2Tke4+sP7WQskkYEZp4P928ZKDhuZHpp90jlj19/aBfP7h9nebXHmBT8YtNsMcjuwTCfu6DtoMB9lsO9zbZ59JrhZJbmUiev9sxmVQQBvnrJEur87+yf7WRPFyN7d+EsK6du7UkIgkDXy88xvGcnAEKelGWiq53I6BAmqw1VUXCWllG9fDUldY16dh4w2e0EmtswWe2Md3eQSyQobWkrCNyLFDluSBJUzcq50smFl6wz8+xz+mw0rqwgOBLFX+XG7i5sFCaZpDlFraqiMtobQhAFyuu8hs2pzWkxgnejkHWaqhY/ak6lorGE8EScWHA2kRCeiJOMZohOb6tq8WN1mAmNxbC7LUaW3myVqWwqoXfPGFaHmYrGEg7k3V+Do1Fq2gIAZDMKXTuGiU4lkEwiak41Av+D5TmaCsGxGCXlLpxeGzVtAQ5sGZy+JjPxcGHzpv4DE5TV+zBPW3SmEnpTKqvDjNvvwGSRyE5PWObo90/smN2geskynCWBQ0pnXP4A1UuWvYWjmp/du3eTzWaprHx3SDhPZMQSB1hNh5bOWE36cceJQ03SBEHg7/7u7/jpT39KTU0Noihy9dVXG/vvuusuPv/5zxuymhluueUW7r77br773e++4XFJkkQyOTeB+mY56uB98+bN3HXXXdx///00Nzdz880388ADD7wrM+6P7xrhH+7fSirvRu2xyYSThVmbBr+dnsm57g8HYzdLJPIagTy5Z5Qn94wa35slkcz0craiaUelq9dfL5BR5ob1PptMOJVDFkWGw0lKnWZ6p90qNA0OjEbfUcF7/1SCrz20E0XVuO3K5dS7ZfY//QSaqhIa6sdksxPs7yE0MDsZ0pS8JjNTE8TzHl4TXe1YXW6qV6xGtlroeP5ppnq6iAwPYvf5KWtZdEjLySJFjjcVjT6CozFS8QxVzSUMdkwaQbR9gQ6+lY0lVDYeuXyga8eIUVyaimdoWKZL++qmu6fmMgrViwJMDISJh1MEqjyU1s66S9jdFkZ7Z60Y973Sj5qbvR+N9oZQ8grnK5p8CAh6dtwqU5E3Vn+li8j0PTW/2dTAgXEmB/OsG4H6ZeXk0jn81W52v9hbkJHft6kfp9dKeUMJLp+NiiYfI93BgsB9Bk3VmBqJUtHgo/21QcYH9ASIJIusOKOBdRe2kZxu2pRf4ArQurbqhM+6A4iixLk3fWxet5kZzrnxY8etWDUcDrNt27aCbSUlJWSzWe677z4uvfRSAoEAe/bs4fOf/zxr1qw5rCNIOp1mZKRQ3ijLMoFA4FgP/12LIAiYllTO7zYzjWlJ5dv6N/B3f/d33HbbbXzta1/j2muvNZxntm3bxuuvv8599903x9/9uuuu4+tf/zrf+c53jEx+d3f3nN/R/EnD2NgYqVTKkM38/Oc/533ve98xv56jCt6XLVvG2NgY119/Pc8//zwrV761BStvF5qq3+yFvMLcnzzbyXf+uG/OsdFUDq/dRCiRRRJA0TiiwB2gxGHGbVEZic59cNhMUoEjTCqr8s+P7imQxxyOgwN3UYDPX7iIe17sRtWgeyLORT94jkgqZ0wkllS62NjsP8J3ODH41mN7eH7a2u3/PLKLn31gVUGjiKGdW0nHClcsbG4vi867kGQ4zP6/PDHnnLm0/jNRc4qRvsul00RGhoiMDDG8dzerr3r/8bqkIkUOycRgBIvNRE2bXjgpSkHD4tTmNDPUMUl5g++wjZkORSIyWxganoiTSeUwW2Ukk0TNogCD7ZNMDkaoaQ0YjZNC4zEmBiJ4Ag5Kaz24/XYjeM8P3AFMZrEweK8vwe62zDuWymY/7mmXK0fe5GS+hjBmq0x5vZedz3Wj5NQ5qwOxUIrYtiGO5GYaHI3gr3IbgTvoWfkd0x74JRUu7G4LuazKUOcEmjqj0X/nOKK0nnwqV3zua3N83l3+AOfceHx93p955hnWrFlTsO3GG2/km9/8Jn/+85/50Y9+RCwWo7a2lssuu4xvfOMbSIcxAHj88cfnZOcXLVrEvn1zn99F3jhShQfW1L1tPu+Ho66ujvPPP58nn3xyTqHq0qVL523MdNVVV/HJT36SRx99lL/5m78BmLeX0dNPP218PaOjl2WZ2tpaPv7xj3Prrf8/e+cdJ1dd7v/3OdP77Gzvm23pPQSS0Am9XhQRUEEQ8YJ65V5RwQIWUNEr96pY7o96vSCoSFGkSwKBJKT3ZFu2952d3uec3x9nd3ZmZzYFsqGd9+u1r+yc+Z6a2TnPeb6f5/PcdYzPBgT5KNpfiaKIxWJBq9Ue8gnK7c5uXpOOz+fD4XDg9Xqx2z/YRTye3i72v/oCsizzjnMJj+/2srDSSUKS+Of+yYpnhVPqC7h2ZTVf+N8th92+VhRIHEZLY9FriCQkkjnGFVoNDI1pSXPde0rsBvp92Q8DAKc0FPDAtcs4/Wdr6Mvh2PD4F05k+QwX2sN0MPygccvjW3l+p2JbdVpjIY9ev5yBpn3079uNraiYkc52YmnBu8FqY8knr0HUaJCSSbb8+f+IBQMgCOjNFuzFpTScehaiRkM0GGTX35/KCv4BZp99Ia6qmuN1mioqgOIss3e9kvESBMWfPD42g2e06okEFI/2gnI7jcuO3N99MiO9Ppq29KTcZbQ6kfmn1mKy6tm3oZPRsY7LpXUupRA0kmDLK82p8fNOqcHiMLJ7XfuELaNORJbAaNExZ2U13QeG8AwGKax0UDmzMOdxRMNx9m3oREpKNCytwJZnQpJk/CMhettGMhtCCbD07AZCvgj7NnRlLD/SrMfkYL9haTndB4YIB2JZYw1mHXNXVWM069n+emvqQaVqVmHKqedYcDzuoZKUVNxnPKNYnXmUz577vthDqny4kGVZcZ+JJsCgRXRZPhSzTh82jirzPrlL2UeBzpEQX31iG/5InB9dNp8Vk7LM3Tu2kozH8SVE/mv9ADICL+8dOKSE8c2WYfb2+bAbtfgmWZxpRCEjCE8P3CffT5wmLdecVM2F88v4xSsHeHVf5lSsXiNSX2RNBe+57kVTBe5aUeDSRWUs+9Gr+HPYsJl1GmaX2j90gTvA9y6agwAkkjLfvnA2AMWNsyluVH53Vc2gdf0baDQaimfPo7h+wr5R1GhY+slrCI6OYLI70RoyM38Gi4Uln7iaWDjEnpf/TiTNdaHlrTXYW0poOHU1Gu2Hxx5O5cNNMt0ZRiYVuEOm1DqUY0bvaMgvpCGwMAAAqTlJREFUs7PQbmD7a62AUnTauW+QmSdUZGjuxzX2iXgyw+N8qMtLwB2mcWkFbTv7CHrCaHQaqucUU1CuBKC1R2BBu29DZyoo3re+g5nLK2nb0ZcVTOuNWhqWlGMw6RAEMqwdj3i6EqhfVEbrzr7UusM9Xkrr80nEkrh7fQQ8E4mPaCjO3rc7WLK6gUTa/8NUzac+yIii5rjbQap8+BEE4bjYQX7cOarM+7Hig5J5/9PmLn7xSlOqmLSx2MrLt56WMaZt/Zv07d1JWBL417ZK4nJmMJvuHjOZijxTqtj0cGPHOXGGi40HJ2YuRAF+8alFnDevhOsf2cTbrYqF2eIqJ/dfvYSnt/Xwny8fyNhurv2YdRpCaVPSt58/C4dJx7f+uiu1zGbU4o8kqMwz8eurl7Cw0nnog/2Ys/nJ/82ZgZ9x0imUqTc9leOELMu07ezHNxJCZ9DgG56Q6c1eUUnTph4kSaZhaTkFZe/t+1aSZLa83JzhxqIzaEgmZYSx36vnFI8VtirNkdKDWABXqQ17vpn23Uo9jyAKnHjhrCOq4ZGSEltfbcnZrCkdjU5k1gmVOAonCuS6m4bo3Jd7tvRQmO0GHAUW+trcWcvnnVxD14GhlCc+KOez4uLZjPT6aNvZh0arYdaJlZhtuSVA74YPyj1URUXl/eGo0oOiKOac/rDb7cycOZNvfOMbKV3QB5Uud4jPPfQOg/4IwWjmTcVp0meNr1m+EpPDwdr9g0it2RXDS6vzUu4sOlEgnhY1n9JQQG2BlWAsQZ5Fx9Nbe9ne5clY/9SGAt5qGWZ8VrZo0he8JMPjGzs5paGAM2cVMb/CQZHNyJUnVBKKJvjla82pQL0iz0Q8KTEwKdtu0Wv4680r+dIftnJwJIjLrOOC+aX0+zKlMr+6ajGNxTZK7MajLob9OKK3WHMG74HhwRyjVVSmB0EQqFuoZKxHBwL4hhUJjT3fTF6RjeUXzESWM+0gRwcCeAYDuEpsGQHu4RBFgcpZhbTt6BvbNymHFYCFp9fStKUnw1VmMmF/FGfRRGZOFIWsmcygN0IkGMNZbEUzNvvnHQ6yb0NXhgd9LjQ6pYDUbFO08LIkM9zryylzSe+yOhU6oxaNVsRkMxBOm70I+aJISZkZ80qw55tp2dZLMi6hM2jY8koTziIriViSeDRJ6/Ze5p8y49A7UlFRUTlCjip4/+tf/5ozePd4PLzzzjt85jOf4dFHH+WKKz64hXu3/XkHB3P4IVfkmfivTy8CFMvElsEAs0ttmPVaSucsoMY4gmbLRpJp2kerQcsTX1zB263D/GF9Oy+nOQycPbuIb503i1+/3sJft/ZgN2n5wim1tA4GiCaSnFxfwDUnVWM1aHmjeaIoKCHJWZnzJdV5XPX/NtA0piktcRj5xcsH+NyKaqJpzgnfvWgO65qH+MOGzIrvhZVOnt7Wy9O3rKTLHabSZcJp1lPpMvPo9ctZe2CIixeWsrjq4+cY9F6Yfdb5HHj9ZUIeN/HwRLYzEgikPKxVVI4necVWFpw6g0goRl6xUiQpCEoX03FC/ij7NnaCDP3toyw+sw6jRU8kGKN5aw/JuETtolLsrgmHqUQ8yVC3F51eQ3G1EwEIBaIMtI8ip30nDnV7c7dLTaOisYCCCgfRUIyQP4qz0MLW11oQBIHGZUr30r0blOMzWvUpL/h4LHHYwB0Um8Yda9pwldhAEAj7oinJkNluQJYVtxxZknMG7jqjFpNFh81lRhAF+lrdeHP42FscBiLBGHqjlrA/ht6oIxyPEhtzGxto96TG+t1hkgnpPRUMq6ioqIxzVMH7ZZddNuV71157LXPmzOHnP//5Bzp4bxkK5FzePRqm1xPGqNNw8a/W0eMJ01hs5U83reA/X27iDxuUhiBldgO9Y5ntYDTBUCBCryfMuuZMX9yvntXId57Zzd/GCidHgjF+/c9m/GPTzf88MMSeXh+fnFRAJslyRuBeYNXz/M5eutIyWeMyn9+ubeOMmYVs6Rjl5IYCGous3PSHzCJZjSDwdusIb7eO4IvEuedf5rO318flv3mbaELiF59ayPcuVm0O3w06k4l5F1zK5nXr4cDW1HJ/fw/7X3uBWWedrwbwKscda54Ja97UjcFikXhK8y1LMrGo4lXeuX8w1Szp4M5+Fp5eC4B/NMzude0p/Xp5Q4TqOcV4hgIZchFQilgLKh3KNs167AUWBtrdxGNJdHoN1jwTMsoDRfVYF+ldbx4kGlJ08p37hrA6janjGy+2Dfuj5JVk62hFjYDJqie/zE7XgeHUMcoSjPRmz4rFYwkKK5wZEpi8Ygv+0UhK3hOPJIhHEvhGwhTXODOsJQsq7BhNOoL+KKP9AXava6eszkVv66FNGqxOoxq4q6ioHDOO6bfJOeecQ1NT07Hc5DFnUZqOWxSUn/HfnWYdWztG6fEoN7CmgQCf/p8NqcAdYDAQS03xXrqojE/+dj3ffGoXoTSvdwEotOpTgfs4yUlWjQP+KEU2I3lmXWq9l/YMoNMoe8gz6xgOxOgaDVM8ZplmM2Y+b2k1IjvvOpffXLMUu0mX0QG1zGGkoXjihjc49tDxi1cO0DYcpMcT5p4cdpcqR8dLIyYGYpn/L+6OgwTdvinWUFGZPmRJJuiLkIgnc77vKLCQX25H1AgUVjqwjQX6Wu2Ek4hGN3FrGOz0ZBSejjdVmix2MVj0mO1G2ncNEAsn8LtDuEqs5JfZsdgMxKNJRvsDtGztxZOWRNGnfafpjVpcpbacga7BqKN+cRn2AmVGwGw3UDWriKA3Sue+IWz5ZgTNoR+W45EkvS0jqfMprXUxa3kVJ5zbiMmaLZt09wUoqnYCSgBeM7eEqjnFqYJZIKNgdRxlxmDiddAXzWljqaKiovJuOKaWGOFwGKMxd0OQDwq/uHIRX3h0M93uELdfMBtREHh+Vy8NRTaK7EbsRh35Fj0jwRgFVj37+zOzN+nuMFpRyChIBdBpBH73maWY9FoMWoFompfx4qo8Xt0/mOE20zIUYO03zuA7T+/muR1K906DVsOfblrOYxs6+MtWpePfsmoX9/zLfAw6kc8+sJFNHYrOfkXthDtOvtXAQ9ct479eaabAZuBnn1xA00CA//jzdgxaDf9xTiMARWndFIuPYRHVx5W5VYV8bX0Fs4xhvlM5iIAMGjNdLR5m57+/3rYqHy9kWWbvhk68Q0F0Bi3zT63BaM4MSgVBYGYOy8iqOUUgCCQTSapmFaWWW51GBtLGldS4UvvKL7MTi8TRG7UYzPqUXaTyPjRt7iYSzO66OJ5pl5ISNfNKELUiIW8Uk1WPxWFk8ep6ooEou9ZNJE6seSaKqpwUVTlTsrT9GyfsH31D2dKWw9HX5sYzFGD+KTPQGbVZunhHoZn6RWWKs0yfn+3/bKHxhAoKKhz0NA2P2fnIWBzK96jRqgdZceYZ79YKY7p+dRZORUXlGHFM3Wa+8pWv0Nrayj/+8Y9DjvsgVMof6Pfz0xf3I8syu3q8DAdiFNkMfOakKs6eXcw3/7qLnWmNOHJRYjcwu9TO6wcyHQy+f8kcQMBu1PKTF/enCkhNOg1Lq500DwQYSCt82nnXObzVPMzNj29FluH8eSXcd+UiHnnrIG+3jtBQbOOrZzbgGMvQS5LMa/sHseg1rKw/dJe47V0efvyPfRRYDdz9L/NwmvUEowl+s6aFaFziljPqybNkZ5xUjo41Bwbp9USY7R8lNDIC+nwKKvKzWsyrqEwn0XCcLS83p17XLijJ6Ew6FX53CI1WxGzPnXxx9/nwDAXRm3WYzHpi0QQHdypdKy0OI8ExKZ/epE1pvgG0ek2W24zOoCEeTaIzKO8JgoBWr0k5yMyYX0JprXLMI30++trcWB1GqucWZwXAAx2jtG7PnOE8HOP6/nRmLa/EbDfQsW8QjUbEVWpDlmRcJTbi8SSbX0ybURaUYuB0Vx8Ai91AcDwjL0B+iZWRvgBancjMSc4375UPwj1URUXl/eOoMu+5OkuB0tJ48+bNtLa28uabbx6TA5tu/v1P29nTmylrGPRH+cUrzTz+TldKV34o+n1Rrj7RydwyG79/4yDxpIxOI/Cj5/cRT8rYjFruvGgO3/zrLpKSTDieZF3LSMY2im16NMD580t55uZVDPginDGriG8+tZO/jmXdZ5faU4E7KFmcs8f0ooc9zye30zZWoFtkN3DnxXOxGLTcdm52N7HJrDkwyOv7BzlzdjGnNWY2GIklJPTHUsMZDsP2baDRwOIloNMdfp0PEKfPVDKVkWAJB3f1I4gCNfOO7P9IReVYoTNoU64ooihgSys6HUeWZdx9fhKJJBa7keFuH71jFrQNS8oprMyeLRro8GRk1dMlJsE016r0wN1o0VE1p5iDO/sQNSK2PBMGi46eJmVf4y41sixnWD9GQrFUZj0ciIGsSHLGA3dZlhnu8YEsK5nuI6BiZgGB0TDe4VBW4A4QjcRwldqYuayCSDDGcI8PW54JQRRw9/nRaMUJ7btMVuAOZGbtZRjpC2Cw6IgG4/QddGMvMKvZdxUVlWPCUQXv27Zty7ncbrdz3nnncfPNN1NdXX1MDuxYE4knue3PO3ht/yB2oxaDdupOcbkCdwHF+vHN5uGM/h6/X9tGcCyzpNeIXL6knCc2KVO5/kiCh99u51dXLebmx7ZmbbPIZmDAH2XuXS/z9XMa+fKZDan3mtNulE0D2YVXR0r6vUJzFDeOtqEAX3h0MwlJ5rGNnbzy76cx7PVz9992cNAdxxsT+OqZ9fz7OTPf9bFlsHYNdLQDIEejPFkIHb4hrp59KpW2Y9eZcLoxWvTMPqnq/T4MlY8poigw/+QaPEMBzHZjTm/xrv1DdDcN51gb3P1+CsrtDHZ5EUUoqHAgS3JG4A5KPakgCop2fIq522g4QfNYR9byBifVc4pp3tqTc6y9wEzQG0Gr09B/cJShLi8VDfl0jjl4+UZCCCjH09M8TE+z8gCgMxy646dGJ2J1mtBoRKpmFbHzjYM5x/U0j1A6I59kQmLXm+0pH/ua+cW07xrIuc5knMVW3H2Z39XRMcmQu89PyB/FMsXMhoqKisrRcFSp09dffz3nz7PPPstPf/rTD2zgDnDhL9/kbzv7CMWS9PuiFNoMuCbJRUxjRVpO08QzjUYQ+OTScu66ZA4b2twZ96l55fZU4A4QS0qEYpnNQ/b0+rjvlSbuvHgOS6qczCqxUe408cmlFQymSWf+8+WmjIKmm0+vQ68Vseg13HhK7bs+7//+9GJOaSjg8sXlfOWshsOvMMagP5rS9yckmf5RP1948A229yfwxgT0cphfvd5CNJG7KO6oCU3oVXsHu7hr/R95eM+r3PDSr47N9lVUPiZo9RoKyh1TNgXyTOG4BYrGu21XP63be2ne2kvnvkFEjYijIFPyYbYZkCUZIccdRBgrmlesGJXvkJ7mEaKheNZ27IUWaheWMuuESuafUoOUlJAlmUQsSfuezH4JbTv72bn2YKpgFjK7yeYiGZfwDgXp2DvIcK83oxA3nXEPfMWXfeI7PDRFh+rJaHUaYpE4JbV5iDmKZjU6EYPxwzWbKEsykVYPoe2DRFozi5ang+uuu+6Qjnann376mO3pxM+nP/3prHHhcBiz2cz+/YoZQzQa5dvf/jbV1dUYDAbq6up46KGHptxPe3s7giBQVFSE35/5MLZo0SLuuuuunMdkMBhobGzknnvuIZk8RvfFDyGyLBEKdeH37ycU6kI+XCOFY8Dg4CA33XQTVVVVGAwGSkpKOPfcc1m/fj0ANTU1CILAhg0bMtb72te+xumnn56xzOfz8e1vf5tZs2ZhNBopKSlh9erV/PWvf82I0VpaWrj++utT+ywvL+ess87iscceI5GY/o7KH4se7i2DflonFTPNKrWxsNLBg+vaU8vCY44xnrSp36Qs85ctPRzoDxAb8xgWBfjsSdWcMauI6x7elBpr0Wu4fEkFz+/sI91YpmUowNXLq9jU7uaF3f2c3ljI9SfX8Net3SlbSBl4q2WEkxsUDfv580s5a3YxoqA4yrxb5pU7+MMNJx71estrXFy+uJyX9w5wztxiGm0R5QM5dig22YslL5//fLmJXk+Yr5zZwMwS27s+Tk5aAa//EzQa1hQbYEzR1BccVT3TVVQOw8Fd/Qx2eXAWWmhcWpEKoNPxj4YJeiNYHGYCo7llgfmldgY7JvpEjDupzF5RhW84iEYrojNq2fpqC5DZ4Mhg0lJWn8/BKTLV2/7ZwoLTazOaHUUCURyFZnasaSMazi5snUwkGKOg0o5vJAQyOAsteNI82E02PXaXmYEOT9a6owMBkvHsQEIQBBqWKrUpBrOOoiong50exc1mZiGJWBLPYDDDY35ctz9OIp4kMJpUrleOGNfqNKLVH3qW4INEePcwnr+1kvROSIE0Dj3Oi+swzTt0ndV0cuONN/KDH/wg9dpkyrZEfeWVV6isrGTWLEUa+qlPfYqBgQEefPBB6uvrGRwcPKLgyu/38/Of/5zvf//7R3RMkUiEv//973z1q19Fo9HwzW9+8yjP7sNPINDM0NDrJJMTCQKNxkph4RlYrUeePDxaPvGJTxCPx3n00Uepra1lYGCA1157Dbd7wsLVaDTyzW9+k7Vr1065HY/Hw8knn4zX6+VHP/oRJ5xwAlqtlrVr1/KNb3yDM888E6fTyTvvvMPq1auZO3cu999/P7NmzSIQCLB3715+97vfMW/ePBYuXDht5wsfk+D9H7v6M16fUOPkX0+v55Sf/nPKdfKtekbSNIzpFo0XLSjj+5fOY2e3J2Odb5w7k9NnFvHSrafx9NZunnink5FQnC+eWsvuXl/qOF4/MMR1q2Zw35WL+LcntufcB3BsNeVHiSgK/OLKRanXyUScK+3recE7G5sQYPVJK0gY8vmfN9oAeGXvABV5Ju7+l/mclOaAc8SUlcM1nwXg7JCXp9wHaPcO8h/LLlMDdxWVQxD0RVK+5SO9fkYrArhKMx+k/e4Qu9a1TylxAcgvs6EzaCmty6dlaw8IQqpwVBSVotJIKI7FYcRonij6LKnJw2jVYzDpGJlUR4RAap9SUqanaTijS2ksnGC425cVuKckOSiBb7od42C7Z0y3o7w3HryLokD13GL2b+giJ1N8j8w6qTJllwlQv7iMGfNLEDUC/QdHEUUBV4kVd78fKSnjKLTgncrZZorrK4gfHo/38O5hRv5vX9bypDfGyP/tI/8zs9+3AN5sNlNSUnLIMc8++yyXXHIJAC+++CJr166lra0Nl0v5LNfU1BzRvr7yla/wi1/8gltuuYWioqIpx6Uf05e//GWeffZZnnnmmY9d8B4INNPf/7es5clkgP7+v1FScvG0BPAej4d169axZs0aTjvtNACqq6tZvnx5xribbrqJ3/72t/zjH//gggsuyLmtO+64g/b2dpqamigrK0stb2xs5KqrrsJoNCLLMtdddx2NjY289dZbiGl/24sXL+aaa645LrawH+ng/Wcv7Wdjm5vlk9wW2oZCbO8cZapZwMWVTrZ1eQDl3jO3zM59Vy5ie5cHbzjOZYvKAVhQ4eSKpRX8eUs3APe+dICrTqymvsjKbefN4t/PmUkolsBm1I01gBKJxCV0GoGKPBOnNRai04g8v7OP0xoLWZjmQf9BQ6PV8a3bfsrnDu6koLQOq6OQH/9j4gs+mpBoH/Rwz1Nv8dxtyhfn5v5m2n2DnFuzBJt+6qYxyvpxDFplWrnI7OCvl9wxfSejovIRQqfTIIoC0tgXmn5SEiDoiyiZ6Cm+74xWPfWLSrHnK5KWwgoHecVWBECjU7LFowN+9o0FxY4CC3NXVjPQMYoMuIqtCIKQpSd3ldoIjIYzilHt+WaGuiZcvLR6DXnFVrqblAZLggAarciMBaXoxhxokkmZgGfCUSa1PRlG+vzY8kxIkkzlrEL8I9mFpKAUz4ZzSGDyii3YnCbi0QSyPHHtNFoRd5+fg5MSP85CC3WLStn+eluqgFXUCjgLrXgGA0jJ3Bc5v/Q9zEoeR2RJxvO31kOO8fytDeOc/JyzO9PNY489xv/93/9RXFzM+eefz5133onNNnFtJUni73//O0899RQAzz33HMuWLePee+/lD3/4AxaLhUsuuYQf/vCHObP26Vx11VW88sor/OAHP+DXv/71ER+jyWRidHT03Z3ghxRZlhgaev2QY4aH12Cx1CHk0tq9B6xWK1arlWeeeYaTTjoJgyG3XLCmpoYvfelL3H777Zx33nkZQTcon50nnniCa665JiNwT98PKLWf+/bt449//GPWNsY5HgnHj0zwHk9KPPFOJwlJ5sL5pXzq9+tpH/si39IxSkORleZBZSpnJBjjvleaqHAa6R7L6IgCqWB+ODDxJS8Df/ziSdiMOs6dm/3EP6/ckQreY0kJKe2JSyMK2MZ0jmVOE4/feBKv7RvglIZC6gqVD8IF80u5YH7psb0Y04TeYKZm1kmp1zedVsf+fj87ujwkgsN8Mvr/sHV5ef7RjVjPvIovvfobZGSeOPAmf7n4W1nbi0fDCDo9t7z2O9Z272Zl2Wx+t/pm9JqPzMdSRWXa0Zt0zD6piuFeH85CS0Z3Vd9IiD1vtSPLSuI5V0LIkW/Gnm9BSkokEhJ6gxatLlPike6u4h0JojdpETUinfsG6WkapqA8265QFAUsDiOJRBBZgpLaPIqr8xjp9eMZ+y6uX1SK1WliwWkzCHoiOIus6I1a+trceIeDaHUig11eBDFTojNO2D8xO6ozaHGV2OhtHckam8trHmB0IMjml5tSQXfdolKKq/MApRvrZIK+KAc296QC94IKOwFPBHe//5CzGpP94z+oRA96M6QyuUh6o0QPejHWOY/PQY1xzTXXMGPGDEpKSti9eze33347O3bs4JVXXkmN2bBhA5IksXLlSgDa2tpYt24dRqORp59+muHhYW6++Wbcbvchde+gBGA/+clPuPjii7n11lupq6s75HhJknj55Zd56aWX+NrXvvaez/fDRDjckyGVyUUi4Scc7sFsrjym+9ZqtTzyyCPceOON/O53v2PJkiWcdtppfPrTn2bBggUZY7/zne/w8MMP89hjj/HZz342473h4WFGR0dTcqupGG9EOnPmhFnH4OAgtbUTtYn33nsvN99883s9tUPykYiS/JE41z+yiU3tytPuk5u6UoE7KN+p44H7OC1DQYw6kaf+dQWxhMQ/dvWnOqme2ljI6/sH6fVGuGp5VSoAz8WVJ1SyvcvDvj4fN59Rj1E3ta5xSVUeS6ry3sOZfrBwWfQ8ev1ygtEEv3zgl+gPKBm1/VteoCvhQxaVu9me4U4SUhKtqCGZiCMIAs8+8O+07VlHsnEpay3Kx/Dt3n1s6m9mVfns9+2cVFQ+jDgKLTl9xH0joVTALsuw8IxaYpE4UlJmsNODVquhak4R4UCU3es6iEcTlNa6mDE/M1GRX2anv32UZEKisMKBIAiMprlgRcNxHAVmvGNBvs6gUewcx9AZNAx1eLA5zcw+qRK/O4zOoMFo0SPLMha7Ea1Ow+iAn3gsmXKZySBNgpOLaChGQbmDJasbCHkjBH0ROvcNTb3CGOnZ8v720VTwXljhYHQggM8dQkpISEmZeDSRUdDqHQpmaN+nwur8cLjMSP4je8g40nHHkhtvvDH1+7x582hoaGDZsmVs3bqVJUuWAIpk5qKLLkplRCVJQhAEHnvsMRwOxQL1F7/4BZ/85Ce5//77D5t9P/fcczn55JP57ne/y+OPP55zzG9+8xseeOABYjHlmnz2s5/lzjvvfM/n+2EimTyyBmlHOu5o+cQnPsGFF17Im2++yfr163nxxRe59957eeCBB7juuutS4woLC/n617/O9773Pa688sqMbYxLXY40a54+Lj8/n+3btwNKEfP4Z2E6+dAH75Ikc95/vUFPmibySK0VJUnxUDfrtZw4I58Ta10ICFwwv4R4UsYfiZNvPXQHUqNOw31p2vCPIzqtzIKlNexvUlJ7UVGD4cBW7POW4UvG+PSsU3j9zz9h9/pnkaQEFfVL6W7ZAkD44C4MC5YRlZLoNVqq7O9fMZSKykcNV4mN3pZhEnEJV6kNi92YsivML53Ilve1uVNBaV+bm5q5xRmyCIvTiD3fzOhAgJEeH1aHkfwyO3634vxSUO6gtNaFLMvIMgRGQ+xO6446HuA2belmvnkG9nwz3qEgO9ceRJZl6heV0b5ngFgkMZU0Ha1Og06vyZnFdhSYyRsrmDeYdBhMOpzFViUr3nfkVrvp2ndRI1K/uCx1k974/P6MsYIooNVpDhu8ixrhmDZomk5E25H55h/puOlkyZIl6HQ6mpubU8H7c889x49//OPUmNLSUsrLy1OBO8Ds2bORZZnu7m4aGg6vwf7JT37CihUruO2223K+f8011/Dtb38bg8FAWVkZGs2HpzD5WKHRHNnn+0jHvRuMRiNnn302Z599Nt/73vf4whe+wJ133pkRvIPSr+g3v/kNv/nNbzKWFxYWkpeXx7592fUe6Yx/Zvbv38+iRYsA0Gg01NfXA8pMwPHgQx+8+yOJjMAdyNKyzyuzk5BkRAG+eGot61vd7O/3ceOptZj1yiUQRYGLFkzonPRa4bCBu4rCl179LW/37qO4YQ554RDtjjwc0TAPzb8YV8MShrr2c9+rf6DAbKI04E8F7gDWeIxvxPR0LlhBX3CUF1o386nqxTjzDl2UpKKicnjMdgOLVzcQi8SntI4EsDpNGetM1jNHw/GU17skyRzcPcCycxpSQen4A4HSWClKV9NwbpmODJ37Bpm7spqeluGU/KS7aTilZZdl5RiULPdEYGzPNzNreSWd+wfpPjDhUz95pmB0MEBPyzB6o47R/iMP3LUGkcJKByFfhEgoTiQYo33PAIIA9nwLziLF2cZg1jFjXjFmhxEpKdO2vRffaBhk0Bo0WOxGYpEEWr1iD1la60Kn/3Dcag0zHGgc+kNKZzQOA4YZ2Y28jjd79uwhHo9TWqrITpubm2lvb+ecc85JjVm1ahV//vOfCQQCKc1yU1MToihSUXFk3a+XL1/O5Zdfzre+lS39BHA4HKnA7eOKyVSORmM9pHRGq7VhMpUft2OaM2cOzzzzTNZyq9XKd7/7Xe666y4uvvji1HJRFLnyyiv5wx/+wJ133pmlew8GgxgMBhYvXsysWbP4+c9/zqc+9akpde/TzYfjG+UQOMw65pTa2JuWXanMM9E1GsZp0nHtyhpuOGUG9jTpy2WL1Zb1x4q4lOTtXuVJdcBiZcBixRUKckpXO+88/xtO+8TX+er2ZxioqAZZ5qLmfZQGM2+o/bvWsMZuoCPk4eWObbz1t19xReVCLrruxzn2qKKicjTo9ErG+lC4SmzMWVFFOBCjoCI7MNMZtGh1Iol0q0WBnE2HWrb1EkjzYrcXmEjEpJRnut6ozXJjiEUTWPNMBEbDGC165q6qJhyIsfvN9tQYo1VHT8sw3QeGETWKt7bRqqewyolvJIjVaaJlRy/DXZMcbyZhtOjR6ESCk5I+iajErjfas8bLMil3mdknVuIcK9AdZ/ZJVfR3KD7oxdVO5bjHLIhDuii1Cz8cNU2gzCY4L67L6TYzjvPi2mkrVvV6vSn5wTgul4t4PM5jjz3GBRdcQEFBAXv37uU//uM/WLx4MatWrQIUyczq1asxmye6Cl999dX88Ic/5POf/zzf//73GR4e5rbbbuP6668/rGQmnbvvvpu5c+cet6zqhw1BECksPCOn28w4BQWnH/NiVYCRkRGuuOIKrr/+ehYsWIDNZmPz5s3ce++9XHrppTnX+eIXv8h9993HH//4R048ccJK+5577mHNmjWceOKJ3H333SxbtgydTsebb77Jj3/8YzZt2oTT6eThhx/m7LPPZtWqVdx+++3Mnj2beDzOG2+8wdDQ0HGZfflIfBL//pVTeG5HD5vbR6krsnLV8kqG/DEKrAZMHyJv3Q8jOlFDidlJf8iDIMsIssQJfd3kRcKMRtp56ndfYWDRmGWTIOA2mSkN+rE5iwl4h5QGDoLASGziZh/S6Tmw9SXOuPzrWOzvwnZSRUXlqHEWWXFO4Yin0YgsOK2W1h19JGJJyury0R9h0yHfSJjl58+kp2kYBChvKKB1e1+GP3siliSuTTBnRRX2fDOiRkR0iJhs+lRRam+LO9VMSdGpywS9EXava0dKSFmWkpPR6kQ0Wg11i0qx55vZ/VY7/pHwlONzEQnGUoF7d9PwWHGsnJpBMNsNJNIaRyXjEolYMqsA+IOMaV4B+Z+ZncPn3YDz4tpptYlcs2YNixcvzlh27bXX8sMf/pDXXnuN//7v/yYQCFBZWcmFF17InXfemQqUnn32Wa699tqMda1WK6+88gpf+cpXWLZsGfn5+XzqU5/iRz/60VEdV2NjI9dffz3/8z//895O8COM1dpAScnFWT7vWq2NgoLTp83n3Wq1cuKJJ3LffffR2tpKPB6nsrKSG2+8kTvuyO1ap9Pp+OEPf8jVV1+dsTwvL48NGzbwk5/8hB/96Ed0dHSQl5fH/Pnz+dnPfpaSX5100kls2bKFe+65h1tuuYX+/n4sFgsLFy7kvvvu4/rrr5+Wc01HkI+HIeUkfD4fDocDr9eL3Z7tUqDy4eL0J2+nP+RJvT679QA1PuW1x2BgR3EZTflFVNkKuSO/EX0kyPLVn6dt9xu07Hqd2rmn0uzM4571T6D1DnN22wHKHcVc9+2n0Gg+XF0JVVQ+jMiyTE/zMGF/jNI6V4aMJuyP0tfmxmjVU1rrOmxBV9AXYc9bHakgVqMVOfHCCQeHgfZRWnf05VxX1AgsPE1p5ASKBGbf+ommUZObIx0KrV6kek6xks236ukY69haWOGgYWk5kVCMnWvaSMQljBY9OoMmpeEfp7DSTiQQwz/W1Epv0rJ0dQOJeJJNLzZl7bOisYDyhgL2bejE7w5RXOOidsGxlwAej3uoLMlED3qR/DFEmx7DDMf7Yg95JAwPD1NaWkpXV9dhfeBVphdZlsbcZ4JoNBZMpvJpybh/3PlIZN5V3l8+PetU/mvrc6nXa2rqWNLXg4jM1uIyFg728d+Fc1h9wS2MhuL85IX9/OXpvXzrvJO5YPFqAGYBF9ctp699F7uaNrLFoOXvB7dyaf3Rd4dVUVGZwDscJOSLkl9uR2/QEhgN07FvEL1Ry4z5JWh1GgY6PClnFs9QgGXnNqaC9D3rO4mNNVHSaESKaw7tmGWyGiiscDDU5SERl0gmJDr3DVI1u4iQP5rhQgNkdFyVkjIBTwSDRU8insRZaKG4Jo/Rfj82l4mR3kzJnbPYgm84lOEYY7LqseebmbGgFFEUSMSTGa4z7gE/w70+epqHsTqNWPPM5Jfbadnam9EcShAhMBrJKJCNhRMkExKDnZ6s89bqNOSX29FoReadXHPIa/RhQBCF424H+W5xu9384he/UAP3DwCCIB5zO0iVbNTgXeU986WF51NtL+IH65/AGw2iN1jZWFGden9beQ2PXvhlBEHgzuf28PxOJevmjyT4f59bBsBQTxPPPfh1YtEwL1dUss9ogLaN2PQmzqxakHO/Kioqh8YzGGDvWOa6/6CbRWfW0bSlJ9UdVWfQUjO3OEPqkYhLqQ6msiwTi0x4pEcjuf3Sxxnp9dG2q594JNMjfbjHh81lZt/Gzgy7x/KGfKpmFbHn7Q58IyEMZh0mu4Ht/2whEoxPZMnr89n5ZmYTqOq5xZTX5xMJxtj6aktqeTgQY8HptYz2+xns8jDan1lEl19qo3lLTypI9wyFcPf5CfkzmzjJUrY/u7PQglavPOyMY3EamXlCBTqdJtXUSuX40tjYSGNj4/t9GCoqxw11LkPlmHD+jKUsLa5DQiaYiCCmTa3rdQauffG/2DnUTijNIzmU1gRl/Yv/D89wNyH/CLPbm9Alk2gkid6AO+f+upq3sHfT88RjU2tcVVQ+7gS8E38f4UCM/jY3kpRWdDqmmiyZkYezyIrBrKNuUWlKHiFJMgaTIl0TNQLFVc4p9xWPJWja3J0VuAPklVgVt5qxwF0QBZasrqd6jmJJOXdlNYvOrGPRGXX43aFUU6Whbi+xSJwDm7pJTJLL9DQNs/ut9pzHEgvHadrcnRW4F1bYmbEgu4A0HDwyX+aSsW7dNteErKiw3IHRrFcDdxUVleOGmnlXOWZs6m9O/S7JMt9Y9gn+cXATu0c6eae/idveeJj7L7wNX2QnkizzvYvmAhBNxLHlFafWLTDa+NzOzcgaLctXXYfP3cvb//g9epOVky+8mba963j+kdsB2PXs45TGK8lrbGDeddchfAw9dlVUpqKg3E5/mztlw3hw9wAABrMOW56JisZCQJF8zFlRlbV+wB0mGlICaSkpEwnGMZhze3zLcrY1pN6oxV5gxmwzEg1NZLYLyu0YLRPbEUQhZWWZjGcG6Qc2daeOP51EPIlvOMSuNw5icRoIeqIgKNse7PRkHItGK5JMSAx1+8grsWFxGAiMTjzYCAJo9CKJWHYbV5NVj8Gsw1FowVWqeMnXLSrDWWhFoxNxjfnLq6ioqBwv1OBd5T3hiQZ5qultyq0uzqhcwDOtGwC4pG45189fzVDYy+4RZdpeFATqCi387tq55BmtaEUNd6z7X/7avJ45rgpuOe+L6OJRmra/ihcgmaBpw3Ns8w7R3bo1tU95vPe5DN2+fQyHWil5aieOmhlUnXXmMT0/WZYZ6PCQiCYonuE6rOWeisp0E48lCAdiWBxGNBpl8jQajtOxZwBBEKieV4zeoHy1G816Fp5Rx6YXDmRsIxFL0risgoGOUUb7A7jKbBRVOlPvS0mJaCiOzqTN6GyqM059y5AlGYvDSDgQzdCgD3f7GO6e0LlrtCIz5hfn2gRBb3ZXVL87TMmMPPoPjk5xPZLE40kWn1lH267+1L70Ji2iKKI3avGlddwOeCIUVeURGJ0ompWScsYxgyKHyS+1U1afj3coQOe+IfzuMPWLy9DqNBRWvv9e5yoqKh9P1OBd5V3jj4W57Nm76Q8qN9XvnnQl589YitNoYWHhDAD+deH5uCN+hsI+vrbkEm5+7be83rWLemcp9556HX9tXg/AXnc33pPP5PKGFXhHevCO9AAQ9A4RDnpS+4yGfCw8+Qp2vf0MyYSSyYuYY8R1CeKxMC8+dhf+0T5KV13Of3dvQ6/Rcs/Jn6ParnjgRUJ+DCbFp3mgax+e4W5q557ChsFW7t74JwpMDv7ztOsptjgB6GkZSbVq9w6HmLtqQsuvonK8iYbj7FzbRjyaxOI0Mv+UGYiiMGa9qEhEZFmmcdlELwudXkPlrAK69k80NhI1IgFPmNbtSgDr7vdjdZow2wwk4kl2vdlO2B/FZNNnaNTD/mjOZk8BT5j9G7uyMuSxaHbGPJmQ2LWug9p5JVmdR6Ph3Jp6WZIVP/WDbgKeMMmEhCBCMj7e0lx5sPCnBemxcAKtXpPS94+j0YqU1OTRdWAoS+JTXO0kHkuSX2qjoMKRKtpt3tJDIi4R9EbYG+mkZm4R9vwPR9dUFRWVjx5q8K7yruj0DfGJ536MPz5hrfbDDU+ypKiOh879amqZ3WDmp6deB0Crp4/Xu3YB0OLp456Nf8amM+KPKxr5OqfiFHDeZ37AyMBBRvpa6W7dSt18pbmD3mRl5QVfwpFfzqe++j/86Zc3kkzE0Mo68hctoC/Zzp6NiutNS8duds1RCl1/vvlp7jvlOv73p59mdLCDgrIGVl34rzz3wNeRZYnK+mX8rqSQ3qCbdt8gv9/5It9b8WkAImkFa+FAZkGbisrxxjccTFklBj0RIsEYZpshQ8cejyZJxJOKV7ooICWlDE91gKIqR8qbfBwpqbz2u0Mp95ewP4ZGJ5KMS4gaAXOOpkxAzsAdwFlgwTscVJo8GTSEvGPb9UVp3trDsnMziwydRVbyiq14BgPoTToMJi2+kTADHR5GBwIkkxLJsUZRFquR4Jim3+YyE/CEkSa1104vxB1n/O945rIK9q7vQErKGC16yhvyKa7O7aQjakQY229gNMyetztZclY9BrNqZauionL8UYN3lXfF/dufzwjcx9k62MqLB7dyWcNJWe8VW/IoNNkZCivT2psHWtCLWr4w72xWls9OZet1eiM63USQIAgC197+p4xtldXM5zNf/z82vvwg+7e+xM7Rf2J4c0J7ml6JPRzyccEDX+LcwQ7ldW8zezc9n5LfdLVuwVJ5WWq8VWeEvl5Yv55Sg51RQxWJuETV7Ck62KioHCdsLnNKv63Riux+8yCuUjslM/LwDStZZ+9wkHf+cQCtTmTuyTUkoskM//LCSgeVs4oQRYGy+nxGBwLkl9lS3u5muzG1D61ew9wV1QS8YWwuMyZrbr17+oOAwayjanYReoMWR6EFWZJTBbAd+wboaRoBlALYyYiiwOyTJrT3Pc3D+MYaKU1+OAj6JjTrkUCMvW93ciQUlClyF3u+mZMump31vpSUaN7aQ8AToawun9JaFxWNBfS1ulOFrbKkuPCowbuKisr7gRq8q7wrnEZr6neRMWeKsfn1X277GxfVnYBWzNSHW3VGfnnGTdz11v9xwKtM18ekBGdXLWLrUBudvmGuaFyFRhQ57V9u5aXHv49Wa2DVhTdn7f+Au5vvbHiM2fvWMa48jYb9GEw2CssbmXXmZ3C7D6LXaPlb6zvIWg1RjQZDUsnEzVpyHh37NxKLBJh7wkXsME5MgYcSUVjzOvh8WBjkhHkm5JWrDtucRkVlujFa9Cw6s47+dncqCB7s9KA3ZQeRibjEYIeHsvp8RI2IlJQQNSJVs4tSnUpr5hZTM7eYoC9CyBfBbDdiMOlYeHotvpEQjgILBrMOizN3xn2chiXldOwbxGjW0bC0PKOjaHpjn4qGQqWbaiRJxczDd+osrHQw2OkhHIhRVp+PzqChp2kYvVFLyJ/mv54j62+06DE7DLjTvOHLG/JTRadTMdTtTfnJH9zVj5SU6BiTzhnMOmLhOPnldqx5pkNtRkVFRWXaUIN3lXfFvy2+GK0g4o2G2OfuYs/IRNarN+jGE/Yx2rodm6OIkuq5hOJRHt7zKo/vW8tIRLkxapNJZo8M8vj6P/HsqJIV7w+6+drSS6moW8IN3312yv3/dNNfCR3cjSOcKQfQGcxc+dX/B0Bd/ETWdu3ize699CfjPNc4lzN6e7j0lKtoXHQWFfVLCPnd5JfU8ptnJtplD4d9oE3rWqjRqIG7ygcHmazizXgklrP7qMWhBOPzT63BOxTEUWhB1Ig0b+0hGZeonlPE6GCA9jEXmhkLSiid4cJo0We4wRwOV6ntsEFxJBhj7/oOouEEVqeRrv1DlDcWMNjhIRKKUTmzEEdBpo5cb9Sx+Kx6pKTS7GnTS00gQyJ+eGvHqtlF5JfZ2PRiU0o+Yy+wpB5ipiL9wUPUiPjSZi0EQWDFJXMOu28VFRWV6UQN3lXeFWadgdtOuBxJllj6f7dmvKdNJnnwN/9KrO8gr85oIO4oIConCUqZ2bE5wwOcGQjzUmLi5tjiyd02fTIOvZkmowkJRSIjihocBZWc+Ymvp8Zc+8J97BnpRERgddVClhbXc+3cMxHHWjWbrXmYrYrGNT+i3NxFGU6vnA8LG2HTO2A2wdJlR3t5VFSOGYOdHtr3DGA065h1YiV+dyil+x5noMMLgM4gImo0iBqRsloXRWO+7Ba7EcuYXr11Rx9DXcr4WDRB+mPpSK+P0jEv82NN30F3yr99XMYzOjDhw97k7+aE82YC0LFvkIH2UaxOIzOXV6LRiAx0jmYUzxosOqLB3AWujcvKKSi3EwnFMnTv+zd2gSwzY34JyYSE3qjDZNMjJWVsLhN9Y7aaFTMLiATjFFc7ScSSeAb8yDKp6/lxQJYl3J4molEPBoMTl7NxWtvcX3fddXg8Hp555pkpx6xfv55vf/vbbNy4EZ1Ox6JFi3jhhRcwmaaeBenv7+fuu+/m+eefp6enh6KiIhYtWsTXvvY1NBoNZ5xxxiGP6+GHH+a66657l2f18UOSZQbCMcLJJCaNhmKTPqPvy3QwODjId7/7XV544QUGBgbIy8tj4cKF3HXXXaxYsYJt27bx3e9+l3feeQefz0dJSQknnngi999/P7/+9a/5/ve/f8jtHzx4kJqammk9h6NFDd5V3hPuSIBwIjMLdmpHG0m/h10l5QxYbZDMLvSssrj40spTKatZQGCgieY9r2HS6fn8vNU59xNLJhgJ+yi2OBEFke+uuJL/NpjxFjfi9I3S6SrkdV8vzx74J991FfKvr/w2pcmXkFlQOGPKbUcPHuSdYB9oRCQBDnQ1Qf1JcO557/HqqKi8d9p2KtKNQCxJX6ubkloXWp2odEKdRDwqARI6g4aiamf2+7EkIf+EVlyWZFylNvyjyt9KXrE1a513S2A0TDQSJ6/YhigKmI4wk+93h+lpUpxxPINB+trcVDQUoDNkSoMKSu1Ew3GGe30ZQT2QkgXp9JqUfh9IdVVt29mftV97gTlVN6D401vQ6jQ4CiwsObsBKSlhsmY77XwU6R/cwt7mx4lEJ2Z4jIY85jRcTUnR0vflmNavX895553H7bffzq9+9Sv0ej07duxAFKd+oGhvb2fVqlU4nU7uvfdeFixYQDwe56WXXuKWW25h586d9PVNJIz+7d/+DZ/Px8MPP5xa5nColqBHSnsgzMYhL6G0GhizVuTEQgc11umTmX3iE58gHo/z6KOPUltby8DAAK+99hput5vBwUFWr17NxRdfzEsvvYTT6eTgwYM899xzhEIhvv71r/OlL30pta0TTjiBL37xi9x4442pZYWFhdN27O8WNXhXeU+4jFYWFNawc6gdgLxQiCq/hxFnPvpE2hS+LCt+bkCZxUVPyMNXD74FbW+RkJMsKKzh8fO/jjZHkyVPNMjVz/+cNm8/J5fP4ferb8FltPHdk65k9Z+/Q78QgjHZzYHRHr637rGMYlqrzsRZVQumPId4Ty+LDo6ypT4fUZI5QcztOKGicrwJeiNotALS2J+S3qRDhixXFUEUUoEpKC4raX9ygFKIufvNg4THHJTMdgN1i0qxOk2KnESSGWgfZfvrrVQ0FlJQbudQyLJMPJqgc/8QoihQNasI7VgfhJE+Hwfe6QbAVWJj1omVFNfkEfBEGOz0ZG1LEBSXnE0vNlE86aHDNxKCBmU7RouOSDCOqBUpqs7DaNYx0utLxe6iRkBKyux/p5v6xWXIspzlqjMVQc/Ed0YskmC424t3KMgJ5zWmusx+HOgf3MLW3fdnLY9ER9m6+36WzLvlfQngb731Vr761a/yrW99K7WsoaHhkOvcfPPNCILAO++8g8UyIcmaO3cu119/PXq9npKSktRyk8lENBrNWKZyZLQHwrzel92LIZSQeL1vlDNKmZYA3uPxsG7dOtasWcNpp50GQHV1NcuXLwfgmWeewefz8cADD6DVKiHvjBkzOPPMiZ4wVutE0kKj0WCz2T7wn4HpmwNT+VggCiKPnncr953yeUoTSYJ6PXsq6/jyl/8f8vyVEwPTooihsJekLJGQkiRkJSrZOdTOCY/9O198+ddEE5lT4W/17KXNq2TK1qX97o0G6Q95so7poG8w4/XvVv8rdc5SBoIePvHcj1nx+G0817ox9b7lxOX8YCSPb/9lF78/oOOMky94T9dEReVY4HeHUp7uWp2GqjlFuEpt7Hi9NaOhkKgVUplmUALYmnnF9LaM0N8+ijzWajQWSaQCd4DSWlfKYcaebyboCTPS6yPki9KytSfjYWAyI30+Nj5/gC2vtDDY4aH/4Ch7N3TSsq2XgCeMd2iiFsU7rPwuSzIjvenNmiaOebwbajyaoLtpwo9+/DqAkk2fu6qGsjoXJouevlalYLduURkGs27MFjOtOVSPF3e/P2NbFsfUhbdmmxGTVZ9RYJtMJFPX7+OALEvsbX78kGP2Nv9xolHecWJwcJCNGzdSVFTEypUrKS4u5rTTTmPdunVTruN2u3nxxRe55ZZbMgL3cZxO5zQe8ccLSZbZOOQ95Jh3hrxI0/C3ZLVasVqtPPPMM0Sj2bP8JSUlJBIJnn766Y/U37IavKu8Z9Y/81/85Ynv06fVENNq2eRykdDq2OLuSo0xCFp+uupz/Pmib2LXm1PL05Vw4WSMN3r28HLHtozt73f3pH4vNNkptYzp1E12Ti6fKB4TxrYmp82hW3QGlpUo2ZlH977GnpFORqMBfrjhSYhG4blnEB55iLoTl/OZXzzAKff+CiFH9l9F5XjjHw2ngtpEPElxlZOQL5Kldy+pyUsViwoCzDyhEs9gkM59g7Tt6GPfhk5GB/wYzDrsBcrfns6gxVmUKZERtRO3A1EjZP5xTqJz3xBSUsoI8AOjYQY7Pex9u5P8UlvKCnK8E2nQF83IghstRyZBGdfqdx8YYsvLzfS2ugl6I/S3jzLU7aWoysmS1fWTlTNEQ5lJAI1OTPnC5yKvxMbis+o54bxGbC4TgqDMaKTr8j/quD1NGVKZXESibtyepuN0RAptbW0A3HXXXdx44428+OKLLFmyhLPOOovm5uac67S0tCDLMrNmzTqeh/qxZCAcy5DK5CKYkBgIH77Q/GjRarU88sgjPProozidTlatWsUdd9zBzp07ATjppJO44447uPrqqykoKOD888/nZz/7GQMDA8f8WI4navCu8p7wDHWx7Y0nMEcnbopOg4Vis5Nyaz6gBNU/Pe06Lm1cwfzCGv5wwb8zK6+cefnV/OXi23ngnK9g0kzoYceDc4DRSIAHdr2cev3ZOWdi1U9MvT1wzlf480Xf5H/P+xo19mwf9uUlShOYJ/a/yf/tXZNaPsOYp9hB9vWBJEEshvDmG+/9gqioHCNcJTZ0BuVB0lVqQ2fQYnYYM/zRNTqRvGIb9YvLmHdyNYvOrCOv2JqRYfcMBtm3oQt3n585K6qZtbwSs01P246+jADXWWDBWWjBXmBm1olVh3RYSteva3QietOEAjMRTyLJMktWN7DwjFpqF5QCSvdUrW7ilhMJRTFappajuEqsVM8pYtbySgC6m4ezxowfoyAI1M4vydh+OBBjtH8i8LY6jVN6y7tKbZTWKoW6Wp0Go0WPLEMyLtG+O1sf/1ElGvUc03HHivEmZDfddBOf//znWbx4Mffddx8zZ87koYceyrnOeJZVdQqbfsLJ7GZo72Xc0fKJT3yC3t5ennvuOc4991zWrFnDkiVLeOSRRwC4++676e/v53e/+x1z5szhd7/7HbNmzWLXrl3TcjzHA1XzrvKeMFqc6I1WaryjnNrZRv6KS7h22aVYdEYev/DrvNK+nTn5lSwprkutU+so4ZnLvpOxnYfP+zeeb9vM0uI6krLM6j9/B7POyD0nfxazzkAwrjwcpAfof2/bxN6RTv6lXnkoSM+8WbQGPjvnDG5ZfBGSLHH3xj8hJxOpbOIKPzB8MPNkckytqqi8X4gagfrFZeiMOix2JUvt7vNnSEOScYm2nf0sPrMOe77y+Y3HEuiNWiLBzCxX0BvB3ednuMeXCmwO7u7H6jAyOhQk6I0gjWXPBjpGseebyYUsy5htevweLfFIgmRcoqKhAM9QUJHIyLBvfRdzV1WnrB97Wkbo2DOQOi8pKZOMy0iJ3G4xykClidR48GVxGFMuNSabHlexDUeRhX0bOomG4lTNKWLpuY1s+seBrJoAAO9QaMrzaVxajiAKtO3sY3QggN44cWvUGz8+eneDwXlMxx0rSkuVB8A5czJtOmfPnk1nZ+7mXA0NDQiCwL59+7jsssum+xA/1piOcLb6SMe9G4xGI2effTZnn3023/ve9/jCF77AnXfemXIKys/P54orruCKK67gxz/+MYsXL+bnP/85jz766LQd03SiBu8q7wmj2canvvw79m97mUtnLKJu3qn84w/f5R9bX6Jm9kqu+sJ/otEe/ua3qKiWRUW1APzLs3fTHVD0rA/sepnfr76ZPzWtY15+NefULAZgTdcuvr5Wybj83941rCybRSSRpncT4GtLL029nGF0Eg766EIJaA4mg8BYkxizGYpL4ITl7/VyqKgcNbIs07azH99IiOJqJ2V1+YT8UXa9cZBkQsKeb2buqmogdxZRp8+8IbbvHlCKPFEKXGPhOHqjFr1Jl6UnHy84ncxQl5fKmYU5vd7dfX66m0cylg12eph3cg2bXpyQUwS9kVTw7h+ZCJwz9fpilgxonEQ0yYa/7UNv0jF3ZTWzT6qit3UEURQory9AEAU69w2mZC1Nm7uxucyKbE4gy4FmKmRZqQeIBGMp//xoKE5xTZ6yr4b8I9vQRwCXsxGjIe+Q0hmjwYXL2XgcjwpqamooKyvjwIEDGcubmpo4//zzc67jcrk499xzuf/++/nqV7+apXv3eDyq7v0YUWzSY9aKh5TOWLQixaYj7x3xXpkzZ86UtqN6vZ66ujqCwWDO9z8MqMG7ynumuGoOxVVKRqS/Yw/7t7wAwMG96+g4sJHKhmXs2/wPTBYnDQvPPNSmAEXLnv77spKGlG59nFbvxFR2TEqwpns3VdYJO6fTK+ZnjP/94itZu/YZfqDpQY/AhUmn8oYowsWXgvolrvI+4e7zM9CuBEvtuwdwldrwDAZS+nDfSIh4LIneoKW4yknYHyXoi6DRieh0WipnZtqYpQfHBqOWRWfUImrEDDcVAGeRhfwyeyqbnY6oEVLOMekEvZEMq8lxbC4zolbEUWDGOxzCYNaRX6b8HXuHgoT82YVkwJSBO4B/NIQsK4F030E3RZVO+lrdJBMSvpEwc1ZUZTRbkpJyRqHsVGj1GoqqHfS3jSIlZbR6DaJGyJqpKJ2Rh9l+6M6yHzUEQWROw9U53WbGmdNw1bT5vXu9XrZv356xzOVyUVVVxW233cadd97JwoULWbRoEY8++ij79+/nL3/5y5Tb+81vfsPKlStZvnw5P/jBD1iwYAGJRIJXXnmF3/72t+zbt29azuPjhigInFjoyOk2M87yQse0+L2PjIxwxRVXcP3117NgwQJsNhubN2/m3nvv5dJLL+Xvf/87TzzxBJ/+9KdpbGxElmX+9re/8Y9//CPDEvTDhhq8qxxTLI5CtHojiVhEaZwUF3nhD9+hecc/ATjjE7ex5LSrDrmNn5xyLb/d8QJmrYF/XahkVQLxCG2efuqdpZh1Bpz67Cl9byzIf59+I0lZSmXoxymtbuDTlhrO9TvQ6vTYxt0SJAnsh7bEU1GZTjRphaKCAKIojnVCVeQl1jxTKrsuiAIz5k9YmElJifY9g0TDMSoaC7HlmahozCfkjyAgMGNBSapjqM1lpmFpOd0HhggHY0hJmfwyO2F/FN9IiMIqJ4IIYX+MwgpHRqdRgJ7mYTr2Zjo5ARRW2KldUMKetzrwj4YRRKhfXEYyniQmwN4NnYd0rpnyuug0JMY6xopjhaPjDzSewQDJeJKyOhfhYJShzkM7XWRsVytishgQNSJSMkkilmT/O12pBlLj5Hp4+ThQUrSUJfNuyeHz7mJOw1XTahO5Zs0aFi/O/O6+9tpreeSRR/ja175GJBLh1ltvxe12s3DhQl555RXq6uqm2JpiCbh161buvvtu/uM//oO+vj4KCwtZunQpv/3tb6ftPD6O1FhNnFFKls+7RSuyfBp93q1WKyeeeCL33Xcfra2txONxKisrufHGG7njjjvo6+vDbDbzH//xH3R1dWEwGGhoaOCBBx7gs5/97LQc0/FAkN8H7xyfz4fD4cDr9WJXA6ePHL0Hd9L6/P9SFTVSbS1nnWcXG3s2ADDnhAs5/7M/PKrteaNBrvjbT+n0D1HnKOHJi7/JY3vXcN/WZ7PGXj9vNd844RO5NxSNwtCQMk/+8ouQSEBNDZybe9pVReV40ds6gm8kRFGlM+UcEwnFCAdi2F3mjAB/nGRCYu/6jlTmXG/UsuzcRnaubSPgiYAAc1cqunMpKSFqRCLBGFtfbUlto2pOEUWVzgyN91TsWNOWcmtJ95WfeUIF9nxzhmRGFAUkSUZv0hILJ3Ju73CU1rkIjIZT51dWn09fmxtZkrHnm5l3cg2gFMhufbUlo5NqLsYfho6EoioH9YvL39VxHw+Oxz30eHdYVflo8H50WP04ombeVY45ZTMWUJa/GHyKp/OcysVs7N2IXm9m/srLp1xv36Z/sOaZX2DPK+WSL/wncaOJ+7Y8S2/ATadf0eW2evvZP9LFkuLanNswaQ9hP2cwQEWF8vunPg2BABQXv7uTVFE5ApJJCUHI9GHPRVldPmV1mdpqo1mP0Ty1RrS7aShD8iJJSkOigGdM1iIrkpve1hFG+wPYC8w0Li1Ho5vQmXfuHaR7/xCzTqzMso6cTPoDRHom3TcSwlVqSwXs48cCZATu6e9rtCKWPAO+obHjz6FRH+n2oknLfg+0jyJqBIpq8qieM1G4rtVpmHdyDe4+P7IsM9TlycqiAzkDd6vTiMluyMrcF1Y4D3ElPh4Igkh+nmqzqHJ0iIJAqfnj0Yn4/UQN3lWmh9lzYOMG0GhwnXwON196BVqtAZ1h6qmz1/7yU6JhPyG/my1rHuMvNjOvdm4HQCdoiMtJis1O6vPKcBos/OvC8/lz01vUOUqx6Y2UWlzcOP+cIzs+m035UVGZJgY7PbRu70XUiMxeUYXdNSH1ikcT+EZCWPNM77p7ZyJNLy6IAo1Ly0EAV5kNd68/ZXnYNVaQ6hsOEfRGmLeqhuFuLwMdoyTiEpIkM9TtPWTwLstyqlkSgM6gIT4madEbtAiCQEGlg8EOj3I8wlhT5bQMvb3AjGdQ0aQnExJhr6IxtzgMSEk5w94SIBZNYrcZCPtjqXVAua7p0iEAs82A2aYEDIWVDnauPUgilsTmMhH0RZESEqJGwGjWERrbnlanoX5pOX2tIxnnAx9fyYyKisqHAzV4V5keFi2GujrQasFk5kjUblZHIdGw0hHR5ihk+8CEB6vdYOYHK69hUdEMnAbFNeDfllzCvy25ZDqOXkXlPdPdNKx4hSck+trcqeB9uMdL67Y+kkkJrU5k4Rl17yqAr2gsIOyPEo8mmLGgBO9QkL3rOzGYdcxZUY3VaRyTyyhyEUEUMFr0mKwGLA4j0Uic4W5ldmzcZjIXsUgcvzuMzjghgUkPdDv2DSILULewlPxSG1qdBo1Og380ROu2vtQ430h68K8lHlW2FfRG0U0h24kG4xjMOgxmHb7hCQcdWZaJBGPoDNqUNj8WidOxd5CQL5qS0PjdYRacNgPfSAiz3UBvywiIAsVVTqKhBNtfa83Yn6vERn65/ZCdWFVUVFTeb9TgXWX6sB2dFvOyL/4XW17/P2x5JSw+7SoK/9bBcFgJLmocRZxVvXA6jlJFZVow2w0pBxOzzUBP8zCjg4FUEApK9nzbay3ojTpKZuSRV2TFZDuyKWeDSZfSfUtJib1vK37X0VCcgCeEs8gCKJKS0YGAYkm5ow9bvpmqWUU0LC7HVWpHp9ekLB0nE48m2LGmjXg0iZCjwdE4Iz0+KhoKcBZZ6W8fJRlLUljlzBiTLluRJlnKxSMJbHkmbPlmJCnJUKePZFIiGo6nzim/zIbeqKO0zkXz1h6Gu31odCKzT6zE5jLTuqMvoynTOLFIgrK6fJq39KQy/wMdHkK+bAcc73AQBMV+83AyIhUVFZX3CzV4V/nA4Cyo4KwrvpV6/d2TPs033ngYnUbLt0+88n08MhWVo6dhSTkDHaNodRoioRgde7P91EEJaiPBGO27B+jUDLLg1FrM9kMH8JKkyFhEjUAyLmHNM2G06NMeFowMdXkZ6fNRUG6nuMrJ5leaQQbvcAir04SrxEZB2aEfsIO+SCrLLiflDL18OnnFSqDb3TSckukM9/qn3G4ymb0NrUGTKkjNhdGsp3JWIX53ODVjkIxL7F7XgTXPNHVdgSCw/fXWjGBd1IgZ1yt1XAkJd5+f0YEAS1bXv2tJk4qKisp0ogbvKh9YlhTX8eoVP3q/D0NF5V2h0YqpItSda9tyjhE0AnJ6Rjop4x8N5QzeZVnGNxxCb9JycNcAnsGJLLPFYWTOiiqGe3yYLHq6m4ZShavuPj8184ozC0KP0GPM6jRl6NYLKhyU1+UT8ISRZBl3j5fRwRCD3V5cpbYM3Xo8lkhp3w+FIufREfZHpw7cLTqKqvPYta6doCeStd3AaJjaBSXEIgniMSWLr9VpcBRaiUfiGYG7s8jCjPkliBqR/vZRAqMhAp5IxkOJLMkkYkk1eFdRUflAogbvKioqKtNILJIgmEOiAaDRiCSSE/pxvVE7pVyjaXMPI72+nO8FvYo1ZEVjASN9vgnHmbT36xaVMtDhwZ5vJq9kaknIwd39jPT4cBZZqVtUis6gSWndBzs8+EaChH2TiktDcdp29qFJa5pUUGEnv9iGzx3CMxjEPxpGqxMz9PKgBMrjRamTqZxVmGpCFfRFCI6dlyyjFKN6I0hJGVEjYC+w0L5nACkp4xkMUr+kjIJyB4G05lQ6g5aZyyvxj4To2DuI3qSlcVklB3f2Mdwzdm0FKK11qbp3FRWVDyxq8K7y/pJIwEsvQm8PzJoNp5z6fh+RisoxY6jLS2/rcM6MsqPQgizJqULOvBIrjUvK0aQ1R4qF44z0+7E6jLj7J2Qok/3TrXkmDEYlSzz+bzpSUmak10fVrMJDarkDnjB9rW5AcXUpKLdTv6iMfRs7kaWxQNuXO9BOxCUCoxMPDX0tboa7fNQtKqXrgHIN4tEkZXV59LZO3YlxHK1OQ2GlI/XaaNaj1WtSxaiB0TDzT52BfySEvdCC3qhNWVECtG7vo2VrL7ULSpizsgqfO4yz0IJGI9K8tZd4NEHQCx17BzLcZWxOEzPmZbrZqKioqHyQUIN3lfeXjnbo7lJ+37sH5s2HvLz39ZBUVI6EaDhOb8sIOoOWsvr8LM31cI+X5q09GctErUD9orKU40skGKN9zwAajUjNvOKMwF1KSux6s10p2hTAUWDBOxREEAXqFpYhCKDRapAkRfMujO3fmmdi1vJKvMNBLE4j8WiCjj1KZ1TfSIjl589E1ORutqPVaTIkKVq9BqvThNGsz7JyTEcQBSoaCmjZ1puxPB5NcHBXf8bDy5EE7qA0X/IOhTBWK173yUSSgnI7/QdHx/YpYrYbsTonvKzqF5XR0zKSIcHp3D/I/JNnMHCwh+79QxRVOUnvTTjY4cFsN1BU7URKSFTNLkJFRUXlg4wavKu8v6R7ret0YFSnqlU+HOzd0Ek4JYeRqWgszHg/kiPYrZ5TTEF5WjbZomfW8sqc24/Hkim3FWRwlVipnFlI0Bsh5ItQVO1Ep8/9Fe4qtaU6tfYddKeWy/Kh5e5Gi56ZyysZ6fWRV2TF6jQhSzKNJ1Sw9+0OpKRMaa2LnpZh5DGJuMmqZ8FptSRiSVylNnzDQZJJORU86wxaoqHspkmTMdv0hPyxlMZeoxWx5yv2mpFQjJ1rDpKIJ9HoRHQGLXnFViLBKAc29ZCIJahdUEpRlROjVc/uN9tT29UbdAx1e1NyncFOT9a+Q74oM+aV4Cic2jJTRUVF5YOC2utY5f2lqBjOPR8WLISLLgbTkTjCq6i8vwQ84bTAHbxDwYxsLkBRtTOjKykoPuKgBKPNW3to29lHIp6pAY9HE4R8EfRGLfljbjB6k5ZkUsY3EuTgrn469g6y+cUmhrozO4PmorjKSVGVE4vTSMOSsgxdei5cJTYalpSTX26neWsP6/+2j5ZtvSw+q54TL5xF1ewiTNaJgtpwIEYoEGHnG224+/wk4hKyJGO06pm5vIJZyysQD2EzKYgC5Q35qeZJsiRT3lDAwjNqiYZi+Nwh/COh1HVKxiUigRh9rW72bewa87pP0r5nAACrw5jSqxstOmavrMaal/t7Zfy4tDrNEVt0flxIyjJvjfp5emCUt0b9JA9Xefweue6667jssssOOWb9+vWceeaZWCwWnE4np59+OuFweMrx6dsUBOGQP9ddd92xO5mPMXIyydDOnXStWcPQzp3IyeThV3oPXHzxxaxevTrne+vXr0cQBLZu3YogCGzfvh2A9vb2jP/7vLw8Tj31VNauXTutx3osUTPvKtNHPA67doJGo8hhNJO6FsoydHXB4ABEozDNf+QqKu+VZFLC3etL+YWP4x0O0dM8gm8kiG8kRHFNHjPmldCwtJz9G7tS49p29FFa52L/xq4M3/PaBaUA+EfD7HmrAykpUVztZOYJFQS8Efasa6dz7yCkxcCyDAd39VNYMZHJz4WoEalfXJZ6HQnFGOnxIUkyeoMWrV5DNBynoMKB3qDoxiOhGM1bugl6lAeUoCdC244+dEYtpbUu7PnmDAcX72AwqxAVGfJL7STjyYxzTad6ThGFlU5EjaB0fI0p6fz8Mht9be6U/r5yVmGG3n2caHAiox+PJti/sQutXqMU8AKuUjsGoxZDiY25K6sJ+SOMDgbxDAQwmHU0Li0nHIhhzzejn6JR1MeR54c8fKe5h77oxPUtNej4UUM5FxY635djWr9+Peeddx633347v/rVr9Dr9ezYsQNRPLIcZF/fRMOwJ598ku9973scOHAgtcykJo7eMz1vvcXO3/8P4eHh1DJTQQELbvoi5atWTcs+b7jhBi6//HI6Ojqorq7OeO+hhx5i0aJFuFyunOu++uqrzJ07l8HBQe644w4uuOACdu/ezYwZM6blWI8l6reVyvSxdg20tii/B4OwctIf7xtrYf++idetLfCZz4FBzYCpvP/EIglGen2Y7QYcBRZ8w0H2rO+c0s7QMxhIFZ/2tbopneHCUWDBaNWnJDSjAwH87nBGMBuLTBSejvT6kMY80Ac7PdQtKkNKSCTHmxrJIIpCqjDzaAPORDzJzjVtJHJ4tQ+0j1Izv4SWrT3ZgTik3FgGOz1YHEZ0Bg3xaBKjWUdRpRN3nz/D5aZorNg0mnZ+k1F86pOIGi16o45ETHkgcPf5Mx6QAp4wi86so2PPAENduWcbpKSMu9+PkPaA09sygj3fjKvEhqPQgqPQQskMF7FwAp1Bg6gRsY11vlVReH7Iwxd2t2fJq/qjcb6wu50H5tW8LwH8rbfeyle/+lW+9a2JXiANDQ1HvH5JyUQRssPhQBCEjGUq742et95i4933ZC0PDw+z8e57OPHbd0xLAH/RRRdRVFTEI488wp133plaHgqFePLJJ7nnnuxjGic/P5+SkhJKSkr4/e9/T0VFBS+//DI33XTTMT/OY40qm1GZPrze3L+P092V+TqRULL1KirvM7Iss3tdOwd39bPnrQ7c/X72Tgrc88tsKctFrT7TGUWjFdHqNGi0IrOWV2Rse7JMpqB8ou7DUTARSI4Hlf7RdKtDDUvPbaCs3oXJqsdiN2Zsb7jHy441bWx68QBtO/qypDwt23pzBu6gyF/2re/MGbgr3VoVknEJ33AIGUWSozfr8I2GmHfKjAx5TGxsO9FQpvZ/fIyoEehtc7Ptn61se60l4yEmHktmzCgUlCuzAlpd5uyd0ZLtrDNZ3eHuy2wWJQgCOqOWvjY37XsGMvb7cScpy3ynuSdnXcT4su8290y7hGYyg4ODbNy4kaKiIlauXElxcTGnnXYa69atO67HoZIbOZlk5+//55Bjdv7+f6ZFQqPVavnc5z7HI488kvF99+c//5lYLMY111xzRNsxm5Xv2/iHJAZRg3eV6WPZCRNFqIuXZL9fn5Y10evhhOVgVVuSq7z/JBNSRvfNwGg4w4YQIOSPMdofQKMVmXdyNcXVecw8oYLSWhdzVlan7AfNNiONyyrQGSbJxgCTzUBeyUSX03QXmFgkPtaYaSIDLUkyGq2GkDdKOBBjqNtLx5jW2zcSpGlzD0Gv0hW1v30U71CmvGdcTpKOMFVnUgABBEGxtRQmDUvGJQY6PfiGQzRt6iHoCWMvmAjy7fmKDMFRaEWXZsVotCmWj1JSTsle4tFkShIjakUqGgqoaFR070tW16cCeaNVj1YnImoECisdR+TFHo1k34y7m4bo2DtIb8sITVu6D7uNjwsbPIEMqcxkZKA3GmeDJzDlmOmgrU1pcnbXXXdx44038uKLL7JkyRLOOussmpubj+uxqGQzvGdPhlQmF+HhYYb37JmW/V9//fW0t7ezZs2a1LKHHnqIyy+/nLwjcK8LBoPcfvvtaDQaTjvttGk5xmONKptRObb098Oaf4Iowllnw+dvIOuuP86cuRCJgNMJCxdlvifLEAopgf9krbyKyjSj1WkoqnYy2OFBb9JRXJNHMinR1+ZGoxWpmVtM63ZFQ5tMKJloo8WAqBEors7L6pDqKrXRtDk7SAz7o+xc24bZZiTsjxBKc6iJBOMk4xKuEhujA0qwlIxLNG3qysjGj2feo+HsDPLkTHVxTZ6inR+jsMpJXqGFpi09k1fFbNcT8sWQgZ7mkayM9mT5UDScYNYJFYz0+TGYtNjzlUDeMxjImB0IeXI3rBpHSkh0Nw1R3lCAxT4RnPe0DKcsLwHK6vIJB6OM9PpzbSaFosdPoDNM3O7S3W+OxAnn48Jg7MhmIY503LFCkpTZoptuuonPf/7zACxevJjXXnuNhx56iB//+MfH9XhUMom43YcfdBTjjpZZs2axcuVKHnroIc444wxaW1t58803efnllw+53sqVKxFFkVAoRGlpKY888gjz58+flmM81qjBu8qxZcPbExKZZ/6qSGF0Orj4Eigc80+OxcDrgVdfAd9YV0OzGRoald9lWXmvrRXsDrjsMjCpulSVY0vIF2WgcxSrw0hhpTPr/fpFZVTPLkKj0yCKAjPmlaSa98iSTE/zCJFgTLE0LLCwf2MnnsEgggBzVlRn2A6KooDZbkgVeZps+lRX0bA/lrPDqKgRaN8zQNXsIoQ0r3R3/0TW02zXU1DuIB5NkF9qY6jYim84iMGko7yhIMtlpaKhgKEuL2G/chxSQqKgwkEkFKevdYR4LImzyMLM5ZX0NA8T8inZtENm5wGL04irxIqoESmscJCMJ2ne0kM0HCcciGUF/ulY80wEveGU9STAQIcHv1vRuYMyY5D+0AGKz76rxI7ZMUzIO/FA4Ciy4E3Ty+uN2gz/fIDyhgL87jCJWJKaucWHPLePE0VTWI++23HHitJSpaB7zpw5Gctnz55NZ2fncT0WlWyMUxSEvttx74YbbriBL3/5y9x///08/PDDVFdXc9ZZZx1ynSeffJI5c+bgdDrJz8+ftmObDtTgXeXoaWuFvXsVfXptHSxYMJFdT6/YT4xlZ+JxePqvcNXVoNXCX5+CwKRpV48HJEnJ2AcCyj4AfF5ob4fZmV/aKirvBUmS2fN2e0rfrdFqUr7o6aRnawOjYeLxJM5CC30H3SlZjc6oZfe6gymnFFkGz1AwI3iPhuMZ7iy2PBORwwS1UlJmsNOD3qRl5gkV9LW50erEjEyzzqDjwKbuMelODXNOqjrsudfOL6Fley8ajUjlLMWbvqJRkanIkpwK1CtnFmIw6ZASEnqTjuYtPUiyjECmrtziNLLwtFoAYtEE+9Z35pTnKGMNhH2xDAmSRitSUuOiry0zKxeLJJCSEv3to/S2ZGf+7fkmRFGgZm4Je9/uSC0fD9xFjYAt34xWKxL0hDMKU01WPUtW1x/2Wn3cOMlppdSgoz8az6l7F1BcZ05yHl95Y01NDWVlZRnuMABNTU2cf/75x/VYVLIpmDsXU0HBIaUzpoICCubOnbZj+NSnPsW//du/8fjjj/Poo49y4403Ikw16z9GZWUldXV103ZM04kavKscOaGQ4g6z6Z2JZYMDoNXA3HnK61NPB+sWcI9Ab1q3RVmG7m4lKJ8cuIsiHNgPW7cofu8lJUqQPx78BzN1uyoq74V4LEHIF80ozIyEpu4eCjDUNdEttajamWFZOLkZk6gRcJVMCm4mRUJD3T5mLa9iuNeLfzSMgJIxlhIyokZIdREFCLjDVM0qSnnEd+4bxDMUpKDcTvtuRe+eTEi4+/1HpAF3FFpYevZEvUkskiAciGK2GTiwuRv/mNVlYYWDgQ4POoMGvVGHLMiIgpAll8krttLXOoIkySSTUlbgrtGKKbecYA7JTNATZsa84ozgXRBgxvxi2vcMZFyL8e3VLixJWQQ6Cszkl9kZ6fNhsRtT+5eSciqQ9wwGWXZuI9FwfKwoN0HtQqWpk8oEGkHgRw3lfGF3u/KQlvbeeBj0w4ZyNIcJit4tXq835cU9jsvloqqqittuu40777yThQsXsmjRIh599FH279/PX/7yl2k5FpUjR9BoWHDTF3O6zYyz4KYvIkyjBNZqtXLllVdyxx134PV6P/K+/WrwrnJoJAlefRk6OiZeT2Y07eZqMsGqkxXf9sf+MOEe43LB9m0TMpnJ+xgP0HfuUH7S2b1LKX5VUXkPREIxPINBOvYOkIxLGMw6YpE4VocJe76ZWDiO3pTtXgKKbnuc0YEAJrN+4s20KKd2USmuIitag5a+ViVbXDwjD4NZR2ntRHZZlmTa9/an5DK1C0spqVEKqwa7PJAWsGrTij1D/ihSUqJkRh5FlU5Gen343WGlqLTg6LuDRoIxdq5VrCP1Rm3KeaX/4ChD3V6Sk5xp5LETFUQBR4GZ0rp8PIMBug8oGTerM/PhQdQIWJymjKJbg1mXoTNPxKUszb0sKz/e4VBqmSAK5JfZKKpy0rS5h5atvdQuLE0VCsuyjJSU2behE99ICJvLhN+t1AYkExJSUqKvdSTVtbZj76AavOfgwkInD8yryenz/sNp9nlfs2YNixcvzlh27bXX8sgjj/C1r32NSCTCrbfeitvtZuHChbzyyisf2szpR43yVas48dt3HHef93RuuOEGHnzwQc455xyqqg4/C/lhRpAne4kdB3w+Hw6HA6/Xi91uP/wKKu8fXV3wj78feszcuXDyqdnLwyHo6wOrDQoK4JGH3p0VZEEBnLgC2g9CZRVMasSgonI4wv4oO9YeTHmoj7P0nAaGu7107B1EEKDxhAryS7O/k9z9fg6804Usk6FdtziMVM8pYrjHh6PAnNLOt+3sp/+gEqgXVTmpX1yGJMnsevMgQU8Ee4FZcbAZ83sf9xzPL7XR25opH2lcVs5gp4dEPEkkGE9l/WeeUIGzyIpnKIDJosdsP3zWfTID7aO07ug7/MBJaHQiJ14wC4C9GzrxDOR2H3GV2HD3ZxaUWpxGtHoNkUCUaCiR8oufTN3CUgY6RwmMKpl0USOw5Kx62vcMpDzn9SYdy85RZhFi0QRd+4cQgIpZhWg0Ik2buwl4IpTVuyivL+DApq6U7MjmMjH/lA9+M5ZcHI97aFKW2eAJMBhLUKTXcpLTOm0Zd5WPDnIyyfCePUTcbowuFwVz505rxv3jipp5Vzk0Vqsia8mVcR/HntbhMRZTZDFOp1JkWl6hyGFaW5QAfMtmJaifzOR9iKIStBeXQFWV8gAhy7B3D1z6L1CsFpmpHBmJeJK+dndW4G62G9AbtAx2egDl4zXU5U0F77FwHK1eCapdJTYWr64nGVccZ9L1684iK86iTJlMODDxfmisODTkjRAca2IUGA0jaoRU8K4Er8mswH3m8gqGu71ZHV1BcUnRaMWcDxtHQjIhYS8wk6WPSMNkM2BzmbA6jXQ3DZOIJtHoRGoXlo4ddwJnoQXvUDBLTlNck0fQm926fvwaaPUalp3bQMeeQYa6M/tACKKiV5eBwKjycCElZba/3prhU2+2Tbj6tG3vSz0oJBMSDUvLmZ1WA5BMSBme7+MyJJXcaASBVXnqNVI5OgSNhsIFC97vw/jIowbvKlPj9cIrLykdT8vKFRvI4KQMW2ERzBuzVgoE4OmnFG18RSVccCG8tQ6am5T36+vhk5+EP/zvxPpOJ6xYBS88n7ldSVL07848ePbpiQo5WVZcbMrL4fwLVRtJlUPSsXeQnubMIiqLw0h5Yz7OQiuCKGAvsBAe062PS0+atnQz3O3DYNIx75QaDCYdxjGpTNXsIgKeMCFflHAgit8dyurSWd6QT2A0jCzLVDQWAJm6eikpZ3RZzYXBrKP7wHCGhlxv0pKIJjHbDRS+S8mHLMvsf6eL0f4A9nwzZXX59LaMAIrzS2DMhlIQYN7JNYgC7H6rg1g4gdGqZ+7KajwDAfa83U5gNEIyIaE1aEhMyp4HPOFUoJ4LKSmhM2ipmV9MKBAl5I2mmqzIkszOtW3MP3UG9gIzvjH5THrgXlBuTz1EKO9N7D8eTyLLMn1tbnwjIQorHNhcJuWkxvahO8rutCoqKiofFNRvL5Xc7NsL696cyIZ3dkDNjIlA3GZTGi81zlSy5OEwvLFGCdxB6Z4aDE68hrHfBcW7PTJ2U191ChQWgtkCoUnZRb8fhoZyS216euCZp+Gyf1EDeJUsRnp9dDcNZxdP6kTmnFSJzjihba9dUEJekRWNVsRRaCEWSTDcrcgyouE47j4/pbUTFmd6ozZVgCklZfoPjmYF785CK8svmAmQcjzIK7GlAtGiKiexSDwjo27LN6HVaRgds4KMhuJEmfjs21xmqmYX0t00jJSQiIZi6PQT7k7hQAxZkg4pn0nEk+zb0JnSgvtGQpTV5+MssiBLMvZ8Cwd39xMNxamaVYhOr2Go25u6jpFAjH3rOwhNsrZMRJOU1OTR3zGayuIfKnAH5UHJO6R4sOcaKyVlhru9aMTsXoKCoNg9pvvY18wrpmVbL4Ig4Cy0sOHv+1L2kynpjjy+b3NGB1cVFRWVDxNq8K6STSwGb76R6QeXSMDMWdDZCciKxt1ggJFhKCpWilrT3WUcDsXf/cQT4TW/ctO02+EPjyrrzZuv6NcrxlrHf+KTyvp9vYqjTX6+sr++XqZkeAi6OpWHChWVMRLxJE2bu3PaMCbjEltebWH2SVWpLLsgCBk2kVq9BqNFR2Ss+6fFmR0MW+zGVNHl5IZM40y2KdNoROatqkGWZQRBoHX7pM+2BLNPrGKo20Pzlsz3NFqRxmXldOwdTHVNbdvRx4Ixi8Z0N5z8cju180sybC7HGWgfTQXu40RDcdrHurQWVzsRBIGqWYXYXGbCgSgBT+b4WA59usmqx1Foob890xlGFAV0Ji2OfAve4WBGoeroQIDRgQAaXeZ10pt0xMJxEBS/93RnH71JS3F1Hs5CS5azjtVponJmIX1tbrqbhjN84yfLgrzDIRKxZM5rpKKiovJBR/3mUskmV9RzwnLY/A5ExzJk72xUAneAk1YoWfJxnE7FVeYPj8JZq+HTVyvLH/gf5d9oVMnWp1eDm82KrKa+Hk5JK36trVOC8/aDuY/VphY8q2QiJaXD+qcPdIxO6c4iigLzTp6Bu8+HxWHMyqoDNCxViki1eg0F5Uf3GRwP6o3WzKDfPxpmoH0UR6EFrV5DIpZEq9eQTCQRNSLRUAwxrVmSqJnISPe2jaR+H+nxEfSEWXh6Hcgy7gE/1jwTJosBbY7mOkPd3pRefdyWcbjby/xTZ7DrzYNZjjOFFQ6SySSj/QEchRaKKp1Y80zs39iVtW1JkolHEqm6AlD07Jn6+AnRvbPIwswTKhkd8OPu96dmQECZ8Vh8Zj0abWYmXpZl+ttHCfujDLSPZv3fixoBUStmyHo0OjHj+qmoqKh8mFCDd5VsAoHMAN7hUApO020e07XvLS2waDFs3KBk1SV5Qm7z8ktK0er5F0B+geILD8rv6YyOwosvQCQMp52uBO2gbKemRpHp7NqZuc6ChUqGXkUlDe9QjoJoyCgQtTpNOceMozdqKZkxdTdAjVbMkNIcDlmW6WkeJhyIUVafj8VupKzOhSgKdOwdSB2XbyTEcK8vlW2WJRlZUgpDd6/roLwhn8JKB1JSonqOUrTd3+7Okp1EgnHCgSh73upISXzmn1xDUZUD30iQoS5v2thsj/tkQiLoCWcF7nqjlsJKR2q77j4/VbMVtx3fyMR1T7/WWdciLXDXGTTkldgIjIax5pmYMa8EjVakoNxBy7ZMFxxXmY2uA0MYLfrUDAEo2fmDO/uz9uMqs6HRiAx1eZGSyZTVpKgRKa7Oy3oIUFFRUfmwoAbvKtnYbIrLzHgzJW+aE4QoKsF0nktxjgFFvrJuWCkuHXVnbY6ebhgcVApYmw4o258sddm+TemmCrBh/UTwvnHDhO+7Tjehf5+3AFasPCanq/LRQm/K/bVmzTNRWOFAq9e8a4eWoyEWSTDS60tZS3buGwLAOxxk2TmNKbnOYKcnpSkP+iIZTjaiRiCZmNhmT8sIGo1IWX0+RotSQJuenR7H5jKRiCVSgTtAf8coDflm6heVkYhLjI7pwMcfFDRaEYvTmCoO7TowjMVpIOiJYrTqaVhajtlqIOiNZGj+923ozMpiS0k55ec+ZWGuoDwMDHZ4UsecHlDbC8wpC8rKWYUMd3tThcWCAMXVii9+uoc8KDInu8tMzbxiBtpHUw8qsiRTu6A0QyevoqKi8mFEDd5VstHr4bLLFaeZgYHM9yQJ5s6HsjIluD+wX1kuy5mBu9WqFKhKkhJ0i6LyM38KCylbmiWZza5o7Ne+Du3tE8udTphRC3l5qs5dZUrs+Wbyy2yKn3eaDWIsnFAC5uNgESjLMnveak8Fm+m6+XgkgSzLtO8ZoG+SNWR64G4w62hYWkZP0wij4z7qspIV79o/RElNHjqDlrxiayrrrdVrmHlCBTaXmWQ8kbHtcV2/ICqa9tFJ/utanQZdWkOoSDCGq9RG0BMlEojhHw4iCgJmuwF7vjm1z7A/d3fadH07KFKVjEy+DEHvxPkOdnqYMa+ESDCOVicy64QK3H1+9GYddpeZ7qYJ16Dhbh+eoSDF1U5cJbaU7ztA/cJSLGMzK4WVTjxDQYLeCGX1+WrgrqKi8pFADd5VcmOxKF1N//F8tgY+cQSNluoblOx5Tzf09yn2jmYzXHKZIsOZzOIlSpAfiShymKYDihwnnWgUHE41cFfJiSzLtGzrZajLi6PQwgnnNZJMSrTvHsA7HCQSjNGyrZeBTg9FVQ50ei2uEhsBT5iRXh8mm5GBdjfRcJyauSVHrWVPR0pIqcAdyCy6NOoQBIH+thyzVGMIAsxdWY3Rosd+kgV3n5/RAT8DY1lqrU6DOJalLm8owOYyIcuZXVb70/Xik2YjzHYDecXWiYcCoGpOEUNdnoxjSH+Y6Nw3hLRnEJvLxJyV1exaezDlYX8kTJbgTEaWoH33gFL0KkDN3GJKavJSWf2aucV07B1AoxXxjmXbR/v9LDy9NmM7fk8kFbxrtCKzllce8TGqqKiofBhQRX8qU1NRCZ/9nFJweuJJ4HLB3HmK9r25CebPB1MO7fCJJynBfzSijO/oUJaHQtDWmj0+GlVsJnu6lYJVk0nJ/o8zbhXn88E/X1Wy8ioqkwj5oimJhHcoiG8khNGsZ9byStIbSftHQrRu62P/xi72buhk97p2eppHaNnag98dJhZO0PYuuo6mo9FpKK52AmMOKWme7EVjy615U+vua+YVp2QxsiQTjyWw5pmoW1RKyYw85qysQpMmVbHnW7IKcNM7m8bCCQ68082et5W/RUEQqJxVmDHeN6JksjU6EUGE2oWlFFVNPGhLY1p1vzvMaL+fmcsryC+3IRzBXUTUCJnjhOwHCo1OZKRv7IFDVgL5ves7U/93rlIbGq2Y0Y1VSirOPekZdaNZh4qKispHGTV4Vzk0JrOSKV+0GObMVTqcvrUO/vmaYicZntRBsbxCKW59ax08/3fo6lLkLuPs368E6els2QxNTcrY115VltXVKw43tbUTTaBASQeqLbpVcqAzajO01wd39ac6d1bNLMq5jmcgkFOTrTO8d3lF3aIyTjivkSWrG6iYWcjC02uZf0oNlTOVoHn2SVXULSrFUZjtepOXJu05uLuf1u19tG7vI+SLUrug9LAFt/FYIqVdT8c7FJzywWSg3UPz1l5mn1iJxWGibWc/8WiSRWfWMWdlVca1bdrcQ9gfY+aySioaChDEzL9Js02Ps8hK7cISaheUsPiseuatqsGeb8JVamXJmfUsPbuBWSdWpHTuZbWurPPyjYRS5+EbCWUE7nqTlhnzSzBa9MxdVU1pnYuGpeVZ3W5V3h2yLDPkjdE1HGbIG8t4AJ4OrrvuOi677LJDjlm/fj1nnnkmFosFp9PJ6aefTnjyPWiKbQqCcMif6667bsrt9Pf385WvfIXa2loMBgOVlZVcfPHFvPbaa1lj77nnHjQaDT/5yU+y3nvkkUcy9llcXMzFF1/Mnj17Msb99re/ZcGCBdjtdux2OytWrOCFF1445LX5oCBJSbqaN7Nvy4t0NW9GkrKtZY8lF198MatXr8753vr16xEEga1btyIIAtu3b885bvz/Zfbs2Vnv/elPf0IQBGpqajKWx2Ix7r33XhYuXIjZbKagoIBVq1bx8MMPE8/Vm+YYo8pmVI6cDeszJTTphaygeLaffiY89oeJZSPDip+7x6O89nmVoL6gQPGKLyrK3Ob474KgdHVtblIy7i4XuMdkBm630thJRSUNvUHLvFXVHNzTj38kTCySoHVbLwXldsrq8zGYdbTu6MuQsKRjyzfhKFCaNJXXH5mLkXc4SMu2XsWHfWlFlud7uo/4ZF9yQRDwDoeIx5IpPbjeqKVuUWmqmyuQ0Wgq6Dt04yOAtp19KcvHXPS3j1LWkI/ZZsDiMGZsX0rKNG3uIRZRZrf62twUVNhxFlpZdEYt2/7ZkvJP720dYaBjNCW9SWteSjyWJBGPoNGKNCwtJxaJs3tde+r9RKyXmcsr0Bt1LDuvke6mYboOKJr2gnI77j5/KtO/5+0OyurzEQRFBpNMSJhsBhacNiM1+2BxGJnhKDnstVE5MnrcEXa2+4nEJqRORr3Ighob5a6pm4BNJ+vXr+e8887j9ttv51e/+hV6vZ4dO3Yg5mjilYu+vomH1ieffJLvfe97HDhwILXMlGsWGWhvb2fVqlU4nU7uvfdeFixYQDwe56WXXuKWW25h//79GeMffvhhvvGNb/DQQw/xrW99K2t7drudAwcOKA5UPT184xvf4MILL6SpqQn92IxzRUUFP/nJT6ivrwfg0Ucf5dJLL2Xbtm3MnTv3iM73/aB5x2v886mfEfAMppZZnUWc+YnbaFh41rTs84YbbuDyyy+no6OD6urqjPceeughFi1ahMt1eGcwi8XC4OAg69evZ8WKFRnbqEq3tUYJ3M8991x27NjBD3/4Q1atWoXdbmfDhg38/Oc/Z/HixSxatOiYnN9UqMG7ypFjt08E0KBo2CNpwYQrX3k9f4HiEGM2Q0MDtE6Sysiy0jn1hefhjDNh6TKlu2ooDGl/NLz84kRmfzzbnkgoeng1eFcZwzMYwN3vJ6/ERl6RlYIyB/4R5XOj0WlSloL5ZXZ0Bi173mrPLOMQYOnZ9RhM+hxbPzTtuwdShZmd+wePSl/d1zrCcPfEA3BxtZPiGXlEg3GSCWkiI12XT5OnBwEoLHew+612knGJ2gUlWR700VA8I3DXGTUUVTrxDAYyikO1Oo1SACpAYaUDz1CAeER5qBkP3MfZ+3YnZfX5OArMFFXlMTDWiCndGhIyn8HHM+QjvT5cJTbc/b6M98PBGNtebSURT+IsthIJTBxbLJZg4Rm17HxjwmO+t3UkVXjsKrXhHw1zYFM3M5eWo1GLUI8pPe4I7zR5s5ZHYhLvNHlZ3sj7EsDfeuutfPWrX80IiBsaGo54/ZKSiYc7h8OBIAgZy6bi5ptvRhAE3nnnHSyWiVmyuXPncv3112eMXbt2LeFwmB/84Af87//+L2+88Qannnpqxpj0/ZaWlnLrrbdyySWXcODAAebPV2aZL7744ox17r77bn7729+yYcOGD2zw3rzjNZ578BtM7ogW8Azx3IPf4JIb7p2WAP6iiy6iqKiIRx55hDvvvDO1PBQK8eSTT3LPPfcc0Xa0Wi1XX301Dz30UCp47+7uZs2aNdx666388Y9/TI39r//6L9544w02b97M4sWLU8tra2u54ooriMVyF/EfS1TZjMqRc/6FiovMOCWlme/v3KEUps6dB5+7Dq7+jGIfuXCRIrmxWhUf+HEiEXjpRcXb/exz4dLLlG6t46RPh2rTnjOL08aofOyQJDmVlR3XRfcfHGX/hk7CgSglM/KonFVIQYWD2SdlBtP2fDOLzqzDmmdEoxMw2/VotCLbXmvD3efPtbtDkp5ZH+9UmohPPU0syzKeoSAHNnUxOhjIeG+gw8PONQc5sKl7LEutnGN+mZ3l58/khPNn4nMrMpKgN0JrDvmLVq9Bm+YYE48kMVoMNC6rSDnJlNXnE/CE6W4aJuiJMNTlTQXuuRh3t9m9rgNr3qGDtlz695A/gt6UqUN3FlpS18kzEMjoRhsYVewyJzvTjOPu8xOPJPAMBLI6uqq8N2RZZmf7of8OdrX7p11CM5nBwUE2btxIUVERK1eupLi4mNNOO41169ZN637dbjcvvvgit9xyS0bgPo4zXRIKPPjgg1x11VXodDquuuoqHnzwwUNu3+Px8PjjjwOg0+Wu1UgmkzzxxBMEg8GMjPAHCUlK8s+nfkZWK2NILXv9qZ9Pi4RGq9Xyuc99jkceeSTjc/nnP/+ZWCzGNddcc8TbuuGGG3jyyScJhZTExCOPPMJ5551H8aSY47HHHmP16tUZgfs4Op0u52flWKMG7ypHjk4H+rHgu6AQujqzxyQSSmOmx/4A699Wlmm1StfU8y+As88Be5rbjCQpGfdcpGfXV6xUmjddeLHiZKPyscTd72fj8/vY+Pd9tO/pVzKyY8iykjUWBIHKmYU0Li3PqQ03WQ3ULSoDWSDki5GMS0hJic79g1lj3f1+Rnp9qZtCLJqgt3WE0QElwKlfUkbJjDzMdsX/vLdlhNbtuTXl/tEwG5/fz963Oxjp9eN3h9FoctdvBL2RjIcAjVZM/aQvS0eWZAKjYeXc0ogEo5isBhavrmfxWXXUzC3OCJZz4Si0ZEmAANpyNENKR2fQIk46p+Fu34QXvQDVcwoprXWldPIWhzHjXKSElLq+h+P4hpAffYZ98QypTC7CMYlh3/RretNpa2sD4K677uLGG2/kxRdfZMmSJZx11lk0NzdP235bWlqQZZlZs2YddqzP5+Opp57iM5/5DACf+cxn+Mtf/oIvvbkh4PV6sVqtWCwW8vLyeOKJJ7jkkkuy9rFr1y6sVisGg4EvfelLPP3008yZM+fYndwxpKd1W4ZUJhsZv2eAntZt07L/66+/nvb2dtasWZNa9tBDD3H55ZeTl5d3xNtZtGgRdXV1/OUvf0GWZR555JGs2RWA5ubmI/pMTCeqbEblyOnsAPdYsDQ8NLFcEMBihYBf8WsfGfNj3rNbcaTp74fNmyaaPqVTV6/o2V97FYJBWH4ijE9lXnSJUvxqtyuFsOP09sLr/1QC/7NWK57zKh95Rgf8NG/pmdBct2RaLdpcJiw5Ak5QZBed+wYxmPXMXVlNx+6BjAZGACZrpmym68AQXfuVz3lprYsZ80vY81YH4TF7RKNFj9VppG5hKQc296RsFWOR3IFNT/NwVnFsMimj1YskxgOmMV96Z5EVnT7767lqdhGyDMl4kqrZmUW4BzZ34+7zIwjgLLLgGVTsFC0OI/3tbjr2DJJMSGh1ihd89Zwi3P1+gt4IUlJGqxNJjGW7jRbFpWd0wE9v6wiBUUUeJ6cdv81lQqvXKJp3GUw2AxaHIatplFavmdDVy9Dd7KZkRj4LT69ltF/xcRcEaNrUk1pnsDNbtjGOICq2klq95qi63KocnsghZo3ezbhjhTTWsfumm27i85//PACLFy/mtdde46GHHuLHP/7xtOx3/KH9cA+7AI8//ji1tbUsXLgQUALB2tpannjiCb74xS+mxtlsNrZu3UoikWDt2rX87Gc/43e/+13W9mbOnMn27dvxeDw89dRTXHvttaxdu/YDGcAHfMOHH3QU446WWbNmsXLlSh566CHOOOMMWltbefPNN3n55ZePelvXX389Dz/8MFVVVQQCAS644AJ+/etfZ4yRZfmIPhPTiRq8qxw5eXkTVWkaDSTHvsDLypSMeDgMXg8896yyXKeDdeugu2vqbRYXw47t0DKWPXntFbjms8rvej3MTvuiSiQU3/m+3oll72xQGkqpfKSJRRPsf6cbWcoMfscLGEGxMNz8Sgu1C0ooqnSmxsiyTPtupdlY2B9l/zudqWAUlC6fRose30iIpi3dNCwuRxAF/Gmabp87hCTJqcAdlCZGkWAMk81A1axCwoEosixTVpdP285+tHqRioaClEuLYUoLQwGTTa80O5IVDXr9otwPpEFvBL87hDZNyz/OeNMlWSbDaaZtZ39GkW4inqRpSzcnnDeT8oYC4rEEkWAcvVFL1/4hZEmiek4RokagoNxBJBTPuF6gWD/ml9spq81P/Z8IosD2f07Ut5jtBgrK7eSX2TP068l4ki2vNFNa60o9HOWX21lw2gwGOjwpTX3q6ogCsiSj1WuoX1SGyWYg5IvgKLRk2GWqvHeMR1g/cKTjjhWlpYpEc3LgOnv2bDo7c8wAHyMaGhoQBIF9+/Yd1gnnoYceYs+ePWjTJJ6SJPHggw9mBO+iKKYKUWfNmkV/fz9XXnklb7zxRsb29Hp9atyyZcvYtGkT//3f/83vf//7Y3R2xw6rveCYjns33HDDDXz5y1/m/vvv5+GHH6a6upqzzjp6jf0111zDN77xDe666y4+97nPZfx/jtPY2Mi+ffuOxWG/a9RvPpUjp6AQLr5U8XG/7HJFyz5rNixbrmTVzWYoLYNzzlMaLRUXHzpwB9j0DnS0T7wOh+HhBxVnm8n09mQG7qBk/FU+8khJKStwB7Ky51JComVrb0bzoMnrRcOZBZkz5hWnbAiHu32888IBWrb1YBjLCAOUVOchigJlOVxoErEk1jwTS89uYNk5jfS1uek/6Kb7wDBdB5TgNB5NUFztxF5gzrl+JDhR4JRuhziZth2KXaRvJETXgSHFsaJ5mH0bu7A4JiRCUto553LXkZJyqtBWp9diyzMRDccZ7vYy1O1jx5o21j+3j11vHqSwwpGhox9fv32X0vxKEIWUBCY/rbFVWX0+FY2FmKwGKidZdSZiyQzJ00iPD3efn8qZBSmbTkEUKKh0sPz8may4ZDbLz5+Jq9SGyaonv8yudkudBgrsOoz6Q4cFJr1Igf34eunX1NRQVlaW4Q4D0NTUlOUwcixxuVyce+653H///QSDwaz3PWMuart27WLz5s2sWbOG7du3p37eeOMNNm3axO7du6fcx6233sqOHTt4+umnD3kssiwTjR55U7TjSXndYqzOIpSpw1wI2JzFlNdla8SPFZ/61KfQaDQ8/vjjPProo3z+859/V9lxl8vFJZdcwtq1a3NKZgCuvvpqXn31VbZty5YBJRKJnJ+VY40avKscHaWliue7Xg8HD8KB/fDs0/DHxyay5zNmwLx5iqwlHUGA1WcrMphx4nEYHcu0GY1KNj8WU7LxB/bDC/9QAnxZVtYbtwUTRaUw9tTTpv2UVd5/jGY9VbOL0OiO7CvLk1YMKmpEXKW2sd8FKhsnsj9avQaTw5jROiCZkBjs9DLQ4UHUCsw8oZziGkU3WTO3mOUXzKRi5sQ2hrq9GYVS6W4tsUgCvzvEllda2P7PNsQpbiZy2p+KZzDArnXtJJOZfz/JhJShg3f3+di9rp2OvYOM9vsJeMKUNRza4nI8CE8mJA5synywTrdnHA/s/e4wowP+Ke0145OWV4752S8+sy5j9iPXaU/Omnc3DeMZDDJjfgl2l5nKxgIal5Sj0Yrv+xT1xwVBEFhQYzvkmPk1tmn7//B6vRnB7/bt2+ns7EQQBG677TZ++ctf8pe//IWWlha++93vsn//fm644YZpOZZxfvOb35BMJlm+fDlPPfUUzc3N7Nu3j1/+8pepAtIHH3yQ5cuXc+qppzJv3rzUz8knn8yKFSsOWbhqt9v5whe+wJ133pn6Hrnjjjt48803aW9vZ9euXXz7299mzZo1R1V8eTwRRQ1nfuK2sVeTPxvK6zM+8XVEcfoeuK1WK1deeSV33HEHvb29OX37Dxw4kPX5yuUM88gjjzA8PDylrv1rX/saq1at4qyzzuL+++9nx44dtLW18ac//en/t3enQXLd5b3Hv6f79L53T8++akaa0WJLsrwSgTcgBuoS7uViuMaBXKhKUqEqIQmEF7xJpZJK5b5JJZVKihcyIiTUJRCKwrkJQRC8gFfJkrVY+2yafenpfe8+98V/epvukWyjxSM9n6opa7pPd59WyTO/8+/n/zw88MADN3QfRoWUzYhrMww1WMlqU33ZQdWzx2ONx1w4rzaTLszDvz2rgrjVqsJ45Zif/xeMjqljN05KrW87qWnwwvPqAmB6Sg2KWlioXRAcuBfuOVA7fn4OZmdhYFDaSN6mene00bUtyLGfXKjWZtezu61kk+rf2uRptSq88wHVn3fs/j6yqTwWqxmzxYzTayMdzxHq9nLutSts1jyjVDC4+MYcvrCbfKbA1NkldIsZu6u28ljMlyiXDMy6+iU1dFcHl0/MY9ZNBDo9rMzGKa8H8ehK44qMr92FrptYnWvcoJlcy5BcyzRMTb1yfrlhVb5YKJOING72ji2lCHa5WVtM4Q+7SMWz5DNFPAEH2w/0MHtptVqWkt+wwu8Pu5i/vKr+LtZr72H9YqTu+4q2Hi+hzuagt7GfPUB7n4/Igtqka7GZCXZ6sNp1ps4uNTxvIV/kyrllyiWDeCSNy28n0HH1MCmur56gnft30NTn3WE1cdcN7vP+3HPPNXXw+PznP8/hw4f58pe/TDab5Q//8A+JRCLs3buXI0eOMDw8fMPOB2BoaIg33niDv/iLv+CP//iPmZ+fJxwOc+DAAf7hH/6BfD7PP/3TP/G1r32t5eM/+clP8pd/+Zf81V/91aav8Qd/8Af87d/+Ld/73vd48sknWVxc5Dd/8zeZn5/H5/Nx99138+Mf/5gPfehDN+pt/sq2732cj3/x/zT1eff423n0k1+5YX3e633xi1/k0KFDfPjDH27qzQ7wmc98pum2iYmJptscDsemff8BbDYbR44c4a//+q/5xje+wVe+8hWcTic7d+7k93//99mzZ8+v9kbeBs242T2fULuyfT4fsVgMb/0qrHhvevEFNVkV4JFHVY37K6/A+Ib+7Q8+pEppXnsVjr9Ru71+ekvF4BBMNv9P06D+ce//gHrOyqbX7TtUj/gzp2FpSa36G4aqs//0/4Kb0KpJ3BrJaIaFiTVWZmMNG0C7tgWYH2+sl97/+DAOt9rEGltOkVjLEOr2VG8rlw1eefbatYv3PD7CmZemyGXUirTDY0W3mEnHc/TuaKNne2MtZyKS5sxLU5RLBp6goxqy7S4L2ZR6DrNu4v6PjlKqlPokc+Qzherm0X2PjWC16xTyRXSLmfGTC0314K10j4QY3K1am5WKZfLZAnaXFU3TyKbznH99hny2QM9IGzanBV+bq1qCko7niCzE0S1mps8tb7riDrDvsWGcntYbhFuZH48wcaqxW42vzYU35GB1PoHLZ6dvtI03flr7uTKyv5v2fv/bfo07xc34HWoYhuo+Uyhht5hp81rkExBxTeVySXWfia/g9rbRM7z/hq6436lk5V1cW31LyDdPqBaQuZwK16Ojqs7d6VITVgH6+tRxlVXyjcHdZFKtH40yrKyoLjMVlcCuaXDf/WrAUyikVuvjcfW8mlZ7jVdfaXzuQkE9XyW853KqVaVZfnjcLtx+ByP7HXQOBjj14kT1n9fG4A6wdCVGPl3A2+astnCcH1/lng9ux6ybMJk0wn0+lq/EMOsaBhrlYmPnl/Z+HxfemK0Gd4BMIo/Lb8PhtjYNSgLVYrJyYZFYy7Dn4CD5bIF0PKeGI0G1peLKbBxf2MWOe3uILieZH4/gb/dgNmu8/uMLFHJFTGaN3b82SC6dJ5cp4PTaWZ2tdXWpTGgFGmrBzbqpeqECqvzIH3Yxe3G1uonX6tA58MHtaCaNlbkYM+ev3RFCt5rIZ4u1TzrOLGCx6ey4txebo3U9dDySbrottpKif1c7FrsFk0nD5rTSv6udhYkInoCTtl5fi2cSN4OmaYR973xwmbizmUxm+rbfe6tP47YnK+/i2jaupNd77HG1Ch5ZhaNH1abVBx6EdFrVql++1PyYBx5UK/Tf+qYK1/XuuQeWVyCTVm0k99V9hFoowA++D+ubhFoa2qZ6yWuausioTHr9tYPQ169W5sVtIx3PsjqfqHYtqecLO4ktq8BoMmsNq/T3fHAEu6sWTNKJHLlUnrOvNm+wrpTYbMbusnLPB0cabostp3jr5SkMA/wdbnY9qD7CLRXLjJ+cJ5PM42tzUsiVWJqOAtDW6yW6lKqudvvCLmLLtQvbSrvKVDzL1JlFEpEMpWIZTVP95qOLKWxOC+2DAS68PkM2maN/VweeoINiroS3zYmmabzy7NmGDa2gJrGO3dfHqV9MVstYNv6d2Zw6oJFLF9Ct5up51h9nsZkx62YG93QQXC+pia4kOffKlaY2mRW+Niex9e44PdtDDOySIWzXIr9Dhbizycq7uLb7H1A158/9vPF2s1kFYoAjRyC6vvI5P6cC/Z67GsO7w6EeU6mbr9/Q2t0DY2Nqauvxf1ar7ysrqkzmwYfUav3pk62Du6apspodo7UV9lJJBXdQFxJHfqImvN73AIyM1Da+ii3N6bXj8NhIRbNEFmp143a3FZfPUQ3vRtmotmMM9/kagjtw1fKP+s41rWwcSgQqeO99dJhcOo835GJhIoJRNugYDLDtrk6O//wysxdXG/Z2ReYSDaG6PriDWu0GuHis1lPeF3YxsKudctkg2KVq7GcvrJBcU2U643VTWL0hB2bdrDrDbAjvhWyJS8fnGurP3X4H6USOYr5EuM9H17YgJ59XpW715TT151zIlSjkSpx79QrD+7qYvbja0EkHwGLXcXltRJdSeNucDZteU7H3ZjcNIYR4L5HwLt6e7TvghRegfryx1ao6xADk6jabRiKqnOXhR1W5y/lz6vbM+ua6F19QdemPfxCOHVMXBh94WK2Kx2KNZTZnTtfq7cON7eaqdoyCzQ7//m/qmAceVCE+EKh1sgF1IfDzn8G5s/Dx3/jV/j7Ee4amaYw90MfqfJyLx+Yol8uUCmXmLq2q0hhdo3+snY6BAKViuWkyKagwOj8ewem1kUnmG9tLGpXXqf3T7B4OUioZFLJF+ne23iDt9NhwemxMnF5g/rIaKJWIZunZHiJfaVdZ9zL1IdjmtFQ7vlR4Qi7S8VzDuTnWN+leODZbfdzGYVMV8dVNJhmvs9h0Msla0I6v97kPdLrZfk8PxUIJq10nny1iMmmUDaPal15DIxXL1oYxoS4cWn2uW8gVSa4ZDN7VQfe2ECszMS6+MYtm0ugalqFLQghxLRLexdtjMsHBg6oDTEUmA8vLqrtLqK25p3s209gWssK2vso5MKi+QK3CLy6q1fGH3qdKXioqCWBpUZ1HuQz+AHS0q9cdHoF//ra6fW5O1chv3wH/7eNw5owq+alf5Z+fq9XVi9tGqMtL4KMeVmaiXDquVpxLxTKGoeEOqM4BrYI7wOSZxWr5ymZsztpm0+WZGPc9Mfq2zmt1rlabHpmPE1tunjRssZmrnWSsdp09Bwe5cHSGxFoGi9VMuM/PuVevUC6WMZk1PEEHFptO32iY6bqSoVy60BD662vh65lMGppZo1QoY7HrdA4G6BwKMHFygWwqTz5XrF5grC0kKeZL6FYzdz88xMpcnPhyCpNuItTlwRNyYbGaKRfLvPn8ePXvaNOCTEMNipo6vUjnYJC2Xh+BTg9oze0jhRBCNJPwLt6+nbsgGIIf/qB2W6Vmva+vMbzb7LBrtwrI0aiavOpyq9X1Ay02s/z0J6pvfCWcV/670YF7VWlNZ2et9OXkm43HVkJDOg0XLzSnCKtVgvttaub8cnVDaEW5ZBCZT+Dybt7irr5/emWi50aVUAqqPCS2msLutGJzWIgsJMilC4T7fOgWM9lUnpW5OG6fvWFFvVwyKJeaO7jUt4DMZ4ssTq1x1/uHAFiYiDB3ebW6kbZcMgj3+elc7z3f1uNlcWqtqZUj0DK4AwS63Lj9DqbOLFHIFlmcitI3GmbHvWrT+YWjM6xUNsRqVMt7rHYLa/NJYustL1dm1DF2t5VSodR6wFRdm0mTrlEuVurjdUzrw502u6gSQgjRTMK7eGc6OtRm08uXoKcXwm2qv/rcHHi8kIirYPzIIypQ2+1qU+vVlEoquEMthJfLagW9f0AF8GQSLFbYNgx+f+2xhULjNNauLlXTfvECPP+ceu6N9u57129fvHeVy0ZTcK/wtZhsWm9gVzv5bBGjbNA3GiYVz7bcBFvvzC+mVKnHtgBzl1RZzPTZJawOnUKu1LLN4sYLA01bz7UbgremaeSzBRYn17jSovuLJ1DrQexrc3HXwUFO/2Kyep1q1k1N02frrc4myNVdjGw81+0HeiiXDdYWEhgGXHxjlrH7+9A0jWKx+X1lk82DTnSrmUCHm7ZeL5ePz1PIlxja00m5ZJBJ5ugckhIZIYR4NyS8i3fuwYdUiP5/z6p69o2r5IYBPz2igvO+/aoGvSKbhUJeBf0Ks1m1m5yfa3ydVApWV+A3/rtavQ/41ep9PZNJreZXBkGN7aoNeGoV3KFWey9uKyaThsNtbajbBlWGsraYRLfoWOw6uXQel9euNm6uS8dzdG8LEurxomkawS4PseVUte57M0bZYG2xtrG0VCyTSTQH2QrdYqquTusWU9OwKatdx9/hpr3fz8nnJxqmtVZoJjDKZeKraSLzcXxhN4EON3a3jcz65lqb03LVDjkAyVgWb5uTbCpPoN3FpRNzeAIO2np8mHUTLq+NyLzaBLy2kGR+IkJ0Idm6a0zd6ron5KR7WxBfuNY//t5f92AYhvQJF0KI60DCu3h33jpTC8ytylsqwfnNE6pbjaapFfof/7uarHrgXrj3vtrxO3c2h/dsFqan1Sr6/ntan4fZDB/9mKptDwZheFhtmLVYGie4VnYb2u0wtvNdv23x3rb7fQMsXYmiW8yszifIZwpkknlmL66q0hI0ivkSvrCLXQ/1o2kap38xWQ3pzosr7H14G2dfnSa+msbhtqLbzGBoDO5uZ2Fybb0nfG1l2x92kc8WWpaobAzoGyekNp57P942F5qmkYxmGoJ7/fMYZTj5wmT1vrnLEdwBB6W6FfGNwd0ddOAJOlgcX6uW8Xj8DjoHAlw4NsvilJqWvDQVZe7yKtv3dzNzsW7FX4PJU4tN78/uthLu8+Frc5Fcy1AslOjeFkK3Ns9VkOAuhBDXh4R38e60tcH59T+HQmrVTQNWVxuP03X4+X+pPusXztcC9dmzjeE9GGo9iRVqG1w309GpvgwDfvwfMD2lHtPWptpNgrrv7r3qQkIGNt22rA4LvTtU95fOoSDzExEmTqqpnsW6Ue+x5RTRpSTR5WTD6no6nmPizALRJbWanknmGdvTh9WmM3FqAbNuYvfBAc6/NgOof7Lz4xEsNh2rR29add//wRHWFpJcOqHaMGoamC21HumVf/I920P4wm4mTi+wMhPD1+bC5beTimaxu6z07wwzcWqhdU05VFtD1qvvxZ6MZLBYzNXgrmkwdHdntUtNvUwiT2QhiVF/bbHJ5tPOwQDdwyEAvC2GVQkhhLj+JLyLd2fPXWr4UTqjpqxWhh8d+QmMr4831zRVk37xgjq2q0sFeFCdaM6fU11hXntVhWyvV7WKrLd9h9oo+3ZkMyq4g9pIuzH0Z7MS3O8w4V4fy9NRUrEsbb0+VmfjlMsGJt3E2VeaBzIBLEw0Tmo1imUun5snFVVtEI2yUQ3FlWvNQq5IYUOVSnu/H4tVp73frza1Libw+B1kMwWunF3CMCDQ5cVqM5PLFFiZi1VbSq7Mxhm9vw+Hy0JkMcmFo80h22TWVBhvEay9YRe7H+rntf84X/1EoH5CrMNr49SLky035tpdVtp6fSxOrTVcLFhsZkqFcvUCoHdHWzW4p+M55i6vYnNa6N3e1lCSJIQQ4vqS8C7evW3DzbfpV/knNbYTZmbUZtdyWdWll8uqtGYzU5Pw0i/Vyj2oyatWW3MITybh2R813havtejDaoX77r/auxG3Id1i5u6Ht2EYBrl0AZfPBmhMnm4uAanakGcT0UxD+8V8tojVoZPPFDf9sKhnRxsur71a5+0Lu7A6dE6+MNFQXhOpayMZXUw21I7bHDpOr52JUwstT3OziaUAQ3d1cvGNWUwmjUr8TsdzWB06nUNBMskc6Q0DkYLdHjr6/XiCTnSLGU/QSWQ+Ud1kWwnyvTvaCPV4G7r3nH11uvp3ZNZN1VAvhBDi+pPwLq6vsTGYGFcr7jt3qamrTletZr2zqzZ1tVyG8fGrP18+D6dPqWFPbx6HS5fUZtff+AS4XKoM57mfq37z8Vjr5/jYf4PubpmqegfLpQu8+dw4pWIZq0PH6bU1b+isC871Kp1kKjLJPNv2duLy2rHYLazOx5k6s9jw2Nn1rjc920MM7OoAYHU2vmnrRlA18IFONxarjr/dhduvOsoEu7zEVpo3ztqcFgq5YssQP35ijkREldKYTFp1tTyfKTL91hIOb/Mgp+hikrH7+gC18bayWXXj6rzdbcXltZNLF5i9tILVrlPI1erzNyvtEUIIcX1IeBfXV1c3PP05FaqdG2pgDUMNYXI6VQ92UMeNjcH585tPdTGb1WTXS+uhPxGH//sd+MjH4LVX1HCnej6fqqFfXFAXEL291/c9ii0nGctWN5jmM0V23NPD2lKS2Yur13hka4Zh4Fmv8e4ZDpFYTVfDbr25yxHa+/043DZWZje5uKw/z7Usdz88xNJ0lOWZGHanhVCPl1w6z9zl2kVEuM9H/852FqfWmGnRSrK+J71JN2GisR2k0eIaYmMf/PrQ3zkUYG0hQS5TZOLkAjaHhcnTi9WJqqFuL4m1DHaXhW6ZkiqEEDeUhHdx/Vmt6mujF1+As2/Vvtc02LVL1bWXyqo2fqPhEVVuE2pTwT+5Pp2yWFSTU9eidQdrcOCAOt7tbn4uccfyhZzYnBZy6QJuvx13wIG3zYXVYaluaN1sUyZQnf5ZuQBIrGboGqJaFmO2tP5UxygbnHv1CvsfHyH/NlakC7kip38x2VCmgwZOj41Ap5tCrkTvjjaCnR7G35xnYbKxPt9q1zFbzNWWkQCBTjf+sJuF8VUSaypsewIONNQGX2/QgQF0D4col431rjHFhuFS/nY3sWW1ibdULDdtnrVYzdz74e3XfH9CCCF+dRLexc0zO9P4/eioqps/f07Vsm/kD8Cjj9Xq2w++X22IrbShdDhUz/iK3bsbO9gIsc5i09n36DDZVB6Hx1ad7NkxECC2lCKy0Lxq3sCgYehRIV/ijZ9eJJsq4PTa2HZ3F9HFVEP5SEVxk2FJLp+NzqEgV84vk8/UHtcQ3NdfOx3PYZQNRvZ34wk6iS4lm4J75X1u7Mi4PB1jeVqt+vvCTtr7/Vw8ptqyZpJ5Bna1Y7HpXDo+R2w5VX2fZouJUqGMbjXj9tuxOizVHvrpeA7dYsKsm7C7rHRvlxp3IYS4WSS8i5tnxygcfb32/blzajJr/cbSetE1OHZUbXK122B+vhbcrVZVDlO/Wr/rbXalEXcks27C5WsuDRl7oI9TL07UasR1E3anhXy20NBeEtSHRXaXFatDr65Ep+M55iciFAvNwR3UavqxIxebppimYjlmzq+w5+AgF4/NkohkMJk1zLqpZd14Jpnn1IuT9I2FNx0eVSljsTktFAulphr7dCKPzdH4qVg+V+Tym7VuOhW6xczI/m7cfgdWu4WOAX/1PYOq0ddMGrt/baA6jEkIIcSNJzv4xM1z4F54/wcaa+E3BvdwuPH742/A8hJcudI8dKmvDzwe9X1nJ/j8N+S0xe1veF83vjZVWhPscLPzgX72HBxSQ5qsZkxmDU0Dh8dGJpln5Upj/XoxX2pZRw6A0biaXr8ynssUmLsUYXhfF/52F+WS6uri9G4+22D5ShSn5+qzD0xmrWUbyFCXt6mNYyaeawruoC46Ykup6gVBsMvbdPGD0bpVpRBCiBtHVt7FzbVrN/QPwPe+qzrJVHqx59ZrdJeXG483mWoTXM1m9Wddh8c+CA4nfOrTkEioTarSw128A+l4jpkLy1jtOn1jYfK5Irl0gVy6QDad5+4PbGPfY6rUJhnNsjS1Vu36YhjgcFspFks43aps5uwr02RTqqzE5beTjmVb7sF2eG2UCuVqoF+YiLA0tYav3VU9xths8zbg9tkZ2N2B1WEhn87j9NmZvbRKNlkrIds4LApUSU3/znDTOTWV36x33SmXDBYm11iYXGNkfxfekIu+0TDnXqv1x/d3uDet9xdCCHFjSHgXN5/bDf/jf6oymO5ueP7nqnymwmxWm049HrXqfvly7XGfearxuSwWCEp3C/HOnX/9SrWGu1QsNwTeSg36hWOzrM62LutSLSO76BwMALD/8WFW5uJkEjkyyXzL1WyAdCyHN+RsWI0vl1X3GotNxygbmMymTQK4mZF7esAwWJmJkYplsdjMbNvbVZ36uplCrsjZV6YZvbe3Ws8O6lMBh9tKPlvEYlMXwPXdagAuHZ8HVFivt7aQ5OUfncXf7mbs/l5MZgnyQghxo0l4F7eGz6e+AO5/EH7872oCauW+g+9Xf85kVCeabAYefOjWnKu4LdVvQDVobI3YO9pGsVDaNLhXVFbaQa1Uz55fIZ3IXeURysaadZNZw2I1M3NeleMM7GrHrJsazlEzQbDTQyaVR6NW317IlSiXDA58aIQTz4031LnbHDq5us2wiUiG80dn2HGgh/Ovz1R7xGdS+aZNua2kopmWt0eXkqwtJgl1e6/53oUQQvxqJLyLW6+jAz7/v1XXmXhcldZUOBzw60/cunMTt63h/d1Mnl7EZtfp39lOR7+fyHwCX9iFv92NYRhNw5xMuomOAT+LE2vYnBb8YRfx1TTekJN0PNsyuFts5msOLiqXDJamo9UwPXGqeQKsUYbFqSiRhQSDd3U23JdL57k4tUa4x0cqniWfLdI3Gqa938/8eKRhSms6niPQ4WHPwUHOvXqFfLa4ad26L+wktly70NBM2qbDrGxOy1XfoxBCiOtDwrt47xgdu9VnIO4ggXY3gcdUGUghV2T20iqFXJFgl9oErWkaew4OsraQwOqwoJk0pt9aYvlKDN1qxum189bL04AaYtS/sx3dYqZYqAV1zaTRPRJi6swSoIK8J+hsHuikQWrjxNd1dpcVf7ubhYnI+rmWmsLzlXPLGAbEV9JsP9BDuNdHqVRm/vIqqXhj+Y7NaSEVz6oWlRtaW1ZW+zWTRqjby8pMbWOuZqKhpSWoTwxCPV7aur3VibBCCCFuLAnvQog71pXzy0TmE2gmjeSaKgm5dHyOXQ8NoFvN6BYz4T4/oOrf68tdVudqJTWR+QRm3dQQ3EENaSrmS2y7u5NsukDXtiDRpWRjeNfUhNLNSnSCXW56d4SrK/uBdheZZI72AT/pWJa2Hh9Tb9VW6i8em2Vxcg2LTW84x4qhuzo5/9pMQ8kPgGbWqmUzRtmgVCyhW83VFpeapmHUXTVoJo1dD/XjDbkQQghx80h4F0LckRKRNFfOLTfdnknmOXbkImjQORhg6K5ONUVV33wzZiFXZPbiasv7Zi+usu3uTpweG1a7jsXe+GN39/v6WZqOtXxsW6+Xwd2qRGbPwUHGT86zMKG6wzg8NvY/NgyAxa4zc365ugE3vppuaglZeb7lKzHy2ULTfUapcTl/bUFNM/a3u+geDnH+6AysH+MO2NlxoBe7q8UkZSGEEDeUtAYQQtyRmsLtxp+GBixMrDE/rspVfOHGFebKsCab09LUfjHU7Wl4nfGTC1w6PsfZV6Ybu8JoMH12GbNuwuawYDKbGNjdjrbe9TQRSZNKZMllCkyfXaoGd4BsMldtKRnu9bFtb1fj6ZeN6kVH31iY7ff0YJQba+vrVcqFNnJ4bPjb3QzsbAdNlf4M7+2W4C6EELeIrLwLIe5Ibr8Du8tSa4u4SaOVwnpduLYh628sO6kw6RrFfIm+sTC5dIFULFvtDBNdSjUebKgOMIlIhh339tDW42PpShRjvfomly7y5n+Nt3yd/p3taOsnlc8WW36KMLing87BAJpJI76arvapb8XmtGCx6RRyRSw2Hd1iwua00LO9DYDOoSDt/X40k1Z9XSGEEDefhHchxB1r6K5O1TKx3HpSqMNjxem1c/xnl1qWoQCgQbDTTSqewygb5DNFYitpzBYzY/f3sTof58LR2ZYTT+uVimX1Vbh6ZxpQq/lmXSOdyDF5epF0Itu0mbT+2OmzS8xcXLnqNNRirsS+R7eRjudw+e3oluahZ9LHXQghbj0J70KIO1agw8P9Hx0jEUlz5pdT6kYNeoZDJNYymMwak6cXq6vvrQzsbK+uTr/10lQ1RJvWw36oy8t9T6iSm8WpKEvTUTxBBzaHTiqaJZPM4w448IScHP/ZJdW68RqMssH4yUUcniiZFu0pNU0FbYfbSmItw8yFlas+n1k30T0SwmLT8YXl14IQQryXyU9pIcQdzWTS8LW52PvINqLLSdp6vMxdijQNUgJo6/GRzxYa7mvr9VX/vG1fF5OnFzGZNAb3dFIulbl0Yo5UNEv3SIie9a9W5i6vNgX3yibZzYYnlYqtV+mN9YFLb708jd3V3H+9Z0eIhfG16vNuP9CDy2dv+VxCCCHeWyS8CyEE4PLZqwHW2LgDdd22fZ0sTKw1hPf68hKbw0LfaBiLTcdq11mYXGNlRrVrvPzmPKFuL7rFvD4YycDqqAVrT8DRNABpeF83wU43bz4/Tj5bxOW3E68bmtSqDEbTaNhAW6np10xatXQnm8yz5+AgS9NRXD47wc7Wm1WFEEK890h4F0KIDfrGwhRyJTLJXHXCqi/sQtfNtPf7WZmJkUnk6B0NN7SQvPTGHMvrg40CHe6GDjXaeqo+89IUsWW1cXV4XxcdAwEAPEEnd79/iJmLK0TmE1jtOu6AHZPZxP7HRgAoFkqc+eUUqViWYJenedgTKrj7Qk5iGz45sDst1VaSnqATl8/O0IZJrUIIId77NGOzJaYbKB6P4/P5iMVieL3em/3yQgjxtiWjGXLpAoEOd8OGTcMwGrquGIbByz862/BYq0Nv2Eha6eZS4fLb2fvwtobHvPLsWbWBFvC1OfGGXHRuC2KxmjEMA6OsRiVpmsaZX06SiGQaHm+xmdn36DBv/OwSpUKt3Gb0vl4MA8wWE4F297v/CxG3nPwOFeLOJivvQghxFW6/A7ff0XT7xnaJmqbhCToawvTGDjAbN76mYllW5+KEumsBzO6ykl7fhBpbUe0dE2tphvd1c+aXU2RTeXpGQgzs7sDhsTWFd1/YRXw13RDctx/obngNIYQQW5eEdyGEuE52v2+AK+eXSaxmiEc276leZcDlE3MYhkFkPkGpWGbo7k5iyyliyykSayqYZxJ5LhydqfaWn720Su9omOXpaNNTdvT7sTmtmMwa5ZKBxaYT6JCadiGEuF1IeBdCiOvEZDYxsKsDgOmzSyxOrVHIXb1ve7FQ5sLR2er3iWgGXTcT6HSRyxQoFkqYzFrDCrvNacFk1vC2uar18zUadpeVux/eRiKSwd/uatmzXQghxNYkEzeEEOIG6N/Zzn1PjBLobF1fvtnQp2KuRDaVZ/7yGrsPDvDAx8aaWkUO7ulA0zR2PtDHyD09ODw2ANwBB+6gKvFxemx0DPixOZpbRQohhNi6ZOVdCCFuoLH7+khEM9gcFuYurzJ/OQKoQUuegINsukCox0M+XSQZzTT0ei8VymiaRvdIiMnTi9XbrTb1o9tkNtHe5yPc6yWfLWK16021+EIIIW4vEt6FEOIG0kwa3qATgL7RMKlolmQ0g1GGxFoGh9vK4K4OTGYTibUMp16YAEC3mnGur6h3D4cwmTRW5+IEOj141p+v+hqaJivsQghxh5DwLoQQN4luMVeHI106PgdAJpknlyngcNvwBBzc9YEhUtEMwU5PQ2vKzqEgnUPBW3XqQggh3iMkvAshxE3mC7uqPd89QQd2p7V6nyfgUNNWhRBCiBYkvAshxE1mc1jY//gw2VQep9e+6eZVIYQQYiMJ70IIcQvoFnPL4U9CCCHE1UirSCGEEEIIIbYICe9CCCGEEEJsERLehRBCCCGE2CIkvAshhBBCCLFFSHgXQgghhBBii5DwLoQQQgghxBYh4V0IIYQQQogtQsK7EEIIIYQQW4SEdyGEEEIIIbaIWzJh1TAMAOLx+K14eSGEEGLLqvzurPwuFULcWW5JeE8kEgD09fXdipcXQgghtrxEIoHP57vVpyGEuMk04xZcupfLZebm5vB4PGiadrNfXgghhNiyDMMgkUjQ3d2NySTVr0LcaW5JeBdCCCGEEEK8c3LJLoQQQgghxBYh4V0IIYQQQogtQsK7EEIIIYQQW4SEdyGEEEIIIbYICe9CCCGEEEJsERLehdiiHnnkEb785S833f7DH/6w2oL18OHDaJrGzp07m477l3/5FzRNY3BwsOm+TCZDIBAgGAySyWSa7h8cHETTNDRNw+l0smfPHr7xjW9U75+fn+epp55idHQUk8nU8jyFEEII8c5JeBfiNudyuVhaWuLll19uuP2ZZ56hv7+/5WP+9V//lT179rBr1y5+8IMftDzmz/7sz5ifn+fkyZN84hOf4Hd/93f57ne/C0AulyMcDvP1r3+dvXv3Xt83JIQQQtzBJLwLcZvTdZ2nnnqKZ555pnrbzMwMzz33HE899VTLxxw6dIinn36ap59+mkOHDrU8xuPx0NnZycjICH/+53/O9u3b+eEPfwiolfm/+Zu/4XOf+5xMgBRCCCGuIwnvQtwBvvjFL/Ld736XdDoNqHKaJ554go6OjqZjL1++zMsvv8yTTz7Jk08+yUsvvcT4+Pg1X8Nut1MoFK77uQshhBCiRsK7EHeAffv2MTw8zPe//30Mw+Dw4cN84QtfaHnsM888w0c+8pFqzfsTTzzRsGq/UbFY5PDhw5w6dYrHH3/8Rr0FIYQQQiDhXYg7xhe+8AW++c1v8vzzz5NMJvnoRz/adEypVOJb3/oWTz/9dPW2p59+mm9961uUSqWGY7/2ta/hdrtxOBx86Utf4qtf/Sq/8zu/c8PfhxBCCHEn02/1CQgh3h2v10ssFmu6PRqN4vV6m27/7Gc/y5/8yZ/wp3/6p3zuc59D15v/9//P//xPZmdn+fSnP91we6lU4ic/+Qkf+chHqrd99atf5bd+67dwOp10dXVVO9wIIYQQ4saRlXchtqixsTGOHj3adPvrr7/O6Oho0+3BYJCPf/zjPP/885uWzBw6dIjPfOYznDhxouHrs5/9bNPG1ba2NkZGRuju7pbgLoQQQtwksvIuxBb1e7/3e/zd3/0dX/rSl/jt3/5tHA4HR44c4dChQ3z7299u+ZjDhw/z93//94RCoab7lpeXefbZZ/nRj37Enj17Gu77/Oc/z8c+9jGWl5cJh8Nv6/xOnDgBQDKZZHl5mRMnTmC1Wtm1a9c7e6NCCCGEqJLwLsQWNTg4yIsvvsjXv/51PvzhD5PNZtmxYweHDx/mU5/6VMvHOBwOHA5Hy/v+8R//EZfL1XLT6aOPPorH4+Hb3/42f/RHf/S2zm///v3VPx87dozvfOc7DAwMMDk5+bYeL4QQQohmmmEYxq0+CSGEEEIIIcS1Sc27EEIIIYQQW4SEdyGEEEIIIbYICe9CCCGEEEJsERLehRBCCCGE2CIkvAshhBBCCLFFSHgXQgghhBBii5DwLoQQQgghxBYh4V0IIYQQQogtQsK7EEIIIYQQW4SEdyGEEEIIIbYICe9CCCGEEEJsEf8fdBQo9Y18/+UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 2- calculate cross-species ns & ls\n",
    "metrics2 = xc.tl.crosssamples_nsls(\n",
    "    cell_embedding_ad='./data/3_cross_species/m1d1_rna_harmony.h5ad',\n",
    "    input_folder='./data/3_cross_species/test_data',\n",
    "    model_path='./data/3_cross_species/train_out/E1000best_model.h5',\n",
    "    output_path='./data/3_cross_species/eval_out',\n",
    "    cellembed_raw='X_pca_harmony',\n",
    "    celltype = 'cell_type',\n",
    "    save_pred=True,\n",
    "    plot_umap=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5980bc08-9181-4260-91a9-b3016c6680bd",
   "metadata": {},
   "source": [
    "### 6. Calculate ISM\n",
    "- We computed ISM on a human ATAC-seq peak. The corresponding BED file is provided in our Zenodo repository (DOI: 10.5281/zenodo.16959682) under 3_cross_species/m1d1_peaks.bed (chr14\t73168099\t73168956)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f11d2831-3e04-46f3-9882-eff260c002b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting sequences from BED file...\n",
      "Converting to one-hot encoding...\n",
      "Calculating ISM for 1 peaks...\n",
      "Processing peak 1/1\n",
      "All ISM matrices shape (n_peaks, n_cells, seq_len, 4): (1, 4489, 1344, 4)\n",
      "ISM calculation completed. Results saved to ./data/3_cross_species/ISM_results/\n",
      "ISM matrix shape: (4489, 1344, 4)\n"
     ]
    }
   ],
   "source": [
    "m1d1_rna = sc.read_h5ad('./data/3_cross_species/m1d1_rna_harmony.h5ad')\n",
    "model = xc.tr.XChrom_model(n_cells=m1d1_rna.shape[0],show_summary=False)\n",
    "model.load_weights('./data/3_cross_species/train_out/E1000best_model.h5')\n",
    "\n",
    "ism_results = xc.tl.calc_ism_from_bed(\n",
    "    cell_embedding_ad = m1d1_rna,\n",
    "    cellembed_raw = 'X_pca_harmony',\n",
    "    peak_bed='./data/3_cross_species/m1d1_peaks.bed',\n",
    "    fasta_file='/picb/bigdata/project/miaoyuanyuan/hg38.fa',\n",
    "    XChrom_model = model,\n",
    "    output_path='./data/3_cross_species/ISM_results/'\n",
    ")\n",
    "print(f\"ISM matrix shape: {ism_results[0].shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057cb39c",
   "metadata": {},
   "source": [
    "We normalized the ISM scores for the four nucleotides at each position such that they summed to zero. We then took the normalized ISM score at the reference nucleotide as the importance score for that position to plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92e4bed1-b182-4995-b3b8-b07ef01ab0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "peak0_ism = np.load('./data/3_cross_species/ISM_results/peak0_ism.npy')\n",
    "tmp,seqs_coords = xc.tl.ism_norm(\n",
    "    peak_ism = peak0_ism,\n",
    "    peak_bed = './data/3_cross_species/m1d1_peaks.bed',\n",
    "    fasta_file='/picb/bigdata/project/miaoyuanyuan/hg38.fa',\n",
    "    seq_len=1344)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a4581ae-06a6-4002-9f9d-22abcc6757dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z0uyYcHFxITQ0lGXLlhmWffLJJ4SFhXHp0iXDMdqmTRuCg4N57LHHmD9/PkOGDGHPnj3MmjWLJUuWkJSUZDj2MjIySEtL4+7duzg7OxumJgPrPmtLPD096d69O7NnzyYxMTHXi0yJiYk5XsjIj0uXLtG+fXvs7OzYsGEDpUuXNlsfGRnJ9u3befPNNy0+X39xp2nTpvTu3ZsGDRowevRoDh06dM91y4uTk1O2/zdCCPGwkqBbCCEeAqVLl8bW1parV68W6nbvtSXu2LFjHDt2jEWLFmVbV6VKFerXr09YWJhV27px4wYrV66kZ8+elCtXzuo66BNMnT59Ot9Bt74HgCVKKbPW3LCwMGrVqpVt7HaTJk0Abeqv3KZiKlWqFMHBwZw+fbpA+7e23OTJk3nttdfM9ptVSkoKv/zyC40aNSIkJCTPfeiZvtf5ld/3ukKFCtkuCpm+16BN46aUsngB4MqVK3h5efHFF18wZsyYQnuvIe/vjbe3d55BfF4uXbpkGBu9efNmixfcli5diqurK506dcpze3Z2djRs2JA//vjjnuplrdu3bxMYGHhf9iWEEMVNgm4hhHgIODs7ExoayqJFi/joo4/w9vYu7ioBsGnTpmzL5s2bx08//cSyZcuoUKGC1dv6+eefSU1NZdiwYQWqgzXZt7Nq1qwZFStWZO3ataSnpxtaQMPDwzl06JDZPMR+fn4cPXqUuLg43NzcDMv1Sbjy6oUQFRXFkSNHaNWqlWFZ165dcXFxYdWqVYwdO9aw/MCBA0RGRtK8efNct3nhwgW2b99Ox44dDcsCAwPzDHZWrFhBVFQUU6ZMybVcVvfyXvv6+tKiRQu2b99OTEyMITO9fuiE6Wv18/Njw4YNXLt2zewYyvped+nSxeIxOGDAACpXrsy0adMMdc3PZ23JnTt3WLlyJSEhIXm2YgcHB7NgwQKio6OzZWC3xuXLl2nXrh3p6els3ryZgIAAi+UWL15M9+7drcp8n5SUxK5duwr02eVXWloaV65coVu3bkW+LyGEeBBI0C2EEA+JGTNm0Lp1a5o1a8aECROoWrUq169fZ8WKFXz77bf56sadl0uXLrF3714Aw7Q/f/75J6AFdfoux5bGhuq7Mrdq1crs4sCWLVvo0KEDkyZNsjj+9ocffqBSpUo5jpd97733uH79Om3btqVChQrcvXuX1atX89133/H000/TqFEjQ9mEhARD5mR91+stW7YQFRWFq6srXbt2BbTprr744gv69etHr169GDFiBPHx8XzwwQc4ODjw1ltvGbY5ZswYevfuTadOnRg7dize3t7s2rWLadOmUatWLcM2o6Oj6dSpEwMHDqRatWo4Oztz+vRp/u///o/k5GTee+89wzY9PT2ZMmUKr7/+Os8//zzPPPMMkZGRvPvuu/j7+/Pqq68aynbs2JG2bdtSr1493N3dOXLkCNOnT0en0/HBBx9YfM9y8sMPP+Ds7JxjoPntt9+ybds2OnfuTKVKlYiPj2fbtm189dVXtGzZkl69epmV1x8b58+fB2Dfvn2GCxNPPfWUodxnn31G+/btefzxx/nvf/+LTqfj888/Jyoqyuw1jBw5kvnz59OpUycmTJhgGNP94Ycf4uPjw7PPPguYDzUw5eTkRJkyZcyOz/x81gMHDsTf35/GjRvj7e3NmTNn+Pzzz7l+/Trz5s3L8/3Vt1Dv3r2bzp07Z1t/5MgRw3tmqkmTJjg7O9O+fXsiIiL44YcfuHHjBjdu3DCUqVixIhUrVuTWrVts2bKF33//Pdt2WrZsSc+ePalZsyYeHh5cvHiRb775hnPnzrF06dI863+vDh8+TEJCQqFmbxdCiAdasaVwE0IIUeiOHz+unn76aVWmTBnl4OCg/P391fPPP2/I6KzPXr53716z523atClbBm9LGZ719NuxdBsyZEiudcwpe7m+Du+9916252zfvl0BatKkSTlud8WKFapjx47Kx8dH2dnZKTc3N9W0aVP15ZdfZsvcrM+wbOkWEBCQbdvLli1TTZo0UU5OTsrDw0P17NlTHTt2LFu5jRs3qs6dO6vy5csrZ2dnVb16dTV+/HgVFRVlKJOUlKSGDx+uatasqdzc3JSdnZ2qWLGieu655yxuUymlvvvuO1WnTh3l4OCgypQpo5599ll15coVszJjxoxRtWrVUqVKlVJ2dnbKz89PPffcc3lmn8/q8uXLysbGRg0ePDjHMtu3b1fdu3dXfn5+ysHBQbm4uKj69eurDz74QMXHx2crn9N7bek0ZNu2bSo0NFS5uLgoFxcX9dhjj6nt27dnK3fgwAHVp08fVbFiReXo6KiCgoLU8OHD1eXLl/N8jbkd29Z81tOmTVMhISHKw8ND2draqrJly6o+ffqoPXv25LlvpZRKT09XgYGB2WYRyO24JDPru/57ktNN//35/vvvlYuLi8XPY/z48ap+/frKw8ND2dnZqfLly6s+ffpYfJ8tudfs5e+++67y9vbOlmleCCEeVjqlMgcgCSGEEEKI++Lzzz/no48+4tq1a3lm9i+Ibt264ezszOLFiwt92/ciPT2dqlWrMnDgQD766KPiro4QQtwXEnQLIYQQQtxnSUlJ1KxZk5EjR/L6668Xd3Xum59++onXX3+dM2fO4OnpWdzVEUKI+yJ/k2gKIYQQQoh75uTkxC+//GJVkrOHSUZGBvPnz5eAWwjxSJGWbiGEEEIIIYQQoohIS7cQQgghhBBCCFFEJOgWQgghhBBCCCGKiATdQgghhBBCCCFEEZGgG0hISODAgQMkJCQUd1WEEEIIIYQQQjxEJOgGTp48SaNGjTh58mRxV0UIIYQQQgghxENEgm4hhBBCCCGEEKKISNAthBBCCCGEEEIUEQm6hRBCCCGEEEKIIiJBtxBCCCGEEEIIUUQk6BZCCCGEEEIIIYqIBN1CCCGEEEIIIUQRkaBbCCGEEEIIIYQoIhJ0CyGEEEIIIYQQRUSCbiGEEEIIIYQQoohI0C2EEEIIIYQQQhQRCbqFEEIIIYQQQogiIkG3EEIIIYQQQghRRCToFkIIIYQQQgghiogE3UIIIYQQQgghRBGRoFsIIYQQQgghhCgiEnQLIYQQQgghhBBFRIJuIYQQQgghhBCiiEjQLYQQQgghhBBCFBEJuoUQQgghhBBCiCIiQbcQQgghhBBCCFFEJOgWQgghhBBCCCGKiATdQgghhBBCCCFEEbEr7goIIYQQQojiF50UTbpKx87GDndH9+KujhBCPDSkpVsIIYQQQtDyx5aUmV6GDj93KO6qCCHEQ0WCbiGEEEIIQXRStNlfIYQQhUOCbiGEEEIIwd2ku2Z/hRBCFI4HMuieNWsWlStXxsnJiUaNGrFt27Ycyy5ZsoROnTpRtmxZ3N3dadGiBWvWrLmPtRVCCCGEKNlS01OJT40HtKBbKVXMNRJCiIfHAxd0L1y4kDFjxjBx4kQOHjxImzZt6Nq1K5cvX7ZYfuvWrXTq1Il//vmH/fv30759e3r06MHBgwfvc82FEEIIIUqm6GRjl/LUjFQS0xKLZkc6Xe43IYR4COnUA3Yps1mzZjRs2JBvvvnGsKxmzZr07t2badOmWbWN2rVr079/fyZNmmRV+QMHDtCoUSP2799Pw4YNC1RvIYQQQoiS6uzts1T7qprh8bVx1/Ar5Vf4O8orsH6wTkuFEKJQPFAt3SkpKezfv5/OnTubLe/cuTM7duywahsZGRnExsZSunTpoqiiEEIIIcRDJ+s4bhnXLYQQheeBmqc7KiqK9PR0fHx8zJb7+PgQGRlp1TY+//xz4uPj6devX45lkpOTSU5ONjyOi4srWIWFEEIIIR4CWTOWSwZzIYQoPA9US7eeLkvXI6VUtmWWLFiwgMmTJ7Nw4ULKlSuXY7lp06bh4eFhuIWGht5znYUQQgghSipp6RZCiKLzQAXd3t7e2NraZmvVvnHjRrbW76wWLlzIsGHD+OOPP+jYsWOuZd966y2io6MNty1bttxz3YUQQgghSioJuoUQoug8UEG3g4MDjRo1Yt26dWbL161bR8uWLXN83oIFC3j++ef57bffeOKJJ/Lcj6OjI+7u7oabm5vbPdddCCGEEKJQFEOGbwm6hRCi6DxQY7oBxo0bx6BBg2jcuDEtWrRgzpw5XL58mVdeeQXQWqmvXbvGzz//DGgB9+DBg/m///s/mjdvbmgld3Z2xsPDo9hehxBCCCFESaEPsnXoUCgJuoUQohA9UC3dAP3792fmzJlMmTKFkJAQtm7dyj///ENAQAAAERERZnN2f/vtt6SlpTFy5Eh8fX0Nt9GjRxfXSxBCCCGEKFH0QbZ+mjAJuoUQovA8cC3dAK+++iqvvvqqxXXz5s0ze7x58+air5AQQgghxEMsOlnLVl7JoxLXYq8ZHhc603m49V3lZW5uIcRD7oFr6RZCCCGEEPeXvmW7knsls8dCCCHunQTdQgghhBCPOH2Q7e/hb/ZYCCHEvZOgWwghhBDiESct3UIIUXQk6BZCCCGEeMQZgm4PCbqFEKKwFSjojo+PL+x6CCGEEEII0BKL6W+5LStE+sRp+u7lRZZITQghHkEFCrp9fHwYOnQo//77b2HXRwghhBBC3EfpGenEJMcA0r1cCCGKQoGC7gULFhAdHU2HDh2oXr06H3/8MeHh4YVdNyGEEEIIUcT0AbejrSPeLt4AJKUlkZSWVJzVEkKIh0aBgu4ePXqwePFiwsPDGTFiBAsWLCAgIIDu3buzZMkS0tLSCrueQgghhBCiCOhbtT2cPLC1scXNwQ2A6CTpYi6EEIXhnhKplSlThrFjx3Lo0CFmzJjB+vXreeqpp/Dz82PSpEkkJCQUVj2FEEIIIUQR0Afd7o7uAHg4epgtF0IIcW/s7uXJkZGR/Pzzz8ydO5fLly/z1FNPMWzYMMLDw/n444/ZtWsXa9euLay6CiGEEEKIQmZo6c4Mtj2cPLgWe02CbiGEKCQFCrqXLFnC3LlzWbNmDbVq1WLkyJE899xzeHp6GsqEhITQoEGDwqqnEEIIYU6ny7tMEWV6FuJhos9U7uGkBd36Fm/JYC6EEIWjQEH3Cy+8wIABA9i+fTtNmjSxWCYoKIiJEyfeU+WEEEIIIUTRytbSLd3LhRBF7cpSuL4RnHyg8iBwDSjuGhWpAgXdERERuLi45FrG2dmZ9957r0CVEkIIIYQQ90e2Md1OEnQLIYqIUnD4HTg+1bjs+FRoPg/8+xVbtYpagRKpbd68mTVr1mRbvmbNGlatWnXPlRJCCCGE9Vavhvr1oUwZeOwx2LatuGtUQqkMiNoN4asg8Xpx1+a+ydrS7e7gbrZcCCEKzYnp5gE3QHoi7HmxeOpznxQo6J4wYQLp6enZliulmDBhwj1XSgghhBB5UwomToSuXeHwYbh9GzZtgnbtYOnS4q5dCZMSDdt6w7rmsKUbLPeD/aMhPaW4a5aryLhIeizoQY8FPfjl0C/mK3W6vG+YTxlm+leCbiFEoUqNhePTLK97yHOwFCjoPnPmDLVq1cq2PDg4mLNnz95zpYQQQog8KWW8WVr2kP+AAyxZAlOnZl+ekQF79tz/+pRY6cmwvhVc+8u4TGXA6S+12wPsxM0TrDy9kpWnV7LmXPZeiNaQMd1CiPvi2gpINUnQ6B4MblWLrz73UYGCbg8PD86fP59t+dmzZ3F1db3nSgkhhBAid/pWblEITn8N0ccsr0tPur91yaczt89YvJ8f+izl+jHdkr1ciJIjORkWLYIvv4RVqyA1tbhrlItrK433q70K3Y5B99PQbC7Y2Bdfve6DAiVS69mzJ2PGjGHp0qVUqVIF0ALu8ePH07Nnz0KtoBBCCCGy27EDTp0yPh45EsaMgSNHYNy4YqtWyaMUnJ1tfOxUHsqFwq09EH+h+OplpTO3zli8nx/6Fu3/7f0fy04t43L0ZbPlQohikJEO576DG1vA1gnKdwb/p8HGGL79+y+89BKcOGF8Wo0asHChlufjgaO/uOniDw1ngi6z/TfoebB1Lq5a3RcFCro//fRTunTpQnBwMBUrVgTg6tWrtGnThs8++6xQKyiEEEKI7EyTpQ0cCF99pQ3RrVoVmjfXup4LK8SchLjMoXHuNeGxDeDsCxlpEPZm8dbNCqat23eS7nAr4RZlXMrkaxv64PrQ9UMcun4o23IhxH2WcBW294eoHcZlF+bByc+g4zawc2HXLi1/R9Y0W6dOwdy5MHPmfayvNVQGxGX+v/Lrmr1lO6D//a/TfVSgoNvDw4MdO3awbt06Dh06hLOzM/Xq1aNt27aFXT8hhBBCWHD4sPH+mDGGnFgA+PrCiBH3vUolU/RR4/16H2kBN2itSQ0+h9hTlp/3gNAH3Z5OntxNusuZ22eMQbdpXgPTAyRLvoOcgmsJuoUoBkrBzsHmAbfenQOQFoeydeGll8wDbp0u51QmV2OukqEycLF3wdvFu2jqnZf4y8bhOt6tiqcOxahAQTeATqejc+fOdO7cuTDrI4QQQggr6INuNzdo3Dj7epsCZW15BOm7O+psoXxH83U6nZbo5wGVoTI4d/scAO0D27P05FLO3DpD84rN87Wdwg66J22axG9HfgNg8/ObqehesUDbEeKRdH0T3NhkfFz+cXDwhIjVhiRkmzZpQ4kAHB21nk5Dh8LFi5B1Iqm4lDj8v/BHoWjj34atL2y9Ly8jm9jTxvsedYqnDsWowEH3hg0b2LBhAzdu3CAjI8Ns3Y8//njPFRPioWba4mDJI5B1WQhRcMnJcPKkdr9Gjbz/pYhc6INur4ZgX6p465JPV6KvkJyejK+bL3XL1dWC7nwmU8tQGcQkx1hcF51UsERq686v49wd7WLAvvB9EnQLkR9X/jDebzYPgoZo91PuwK4XANiyxVhk0iR4MXOK6ypV4I8/YPdu4/pDkYdQaOeVByMPkqEysNEVw1XZlNvG+w6e93//xaxA7/j7779P586d2bBhA1FRUdy5c8fsJoQQQoiic+eOsVthtWrFW5cSL/6i9tfZr1irURD6ADvIK4ggryCzZdaKS4kjQ2VYXBefGk9qev5SIadlpHEo0jgufH/4/nw9/5GilDY//AM+F7y4z27v0/6WbmIMuAEcvKD1n+DgZRZUv/yy+dN1Oi2vh96BiAOG+3EpcZy9XUzTO6cnGu/ba9MScnUZrKxhvN0oplb4+6BALd2zZ89m3rx5DBo0qLDrI8SjwdI4O2ndFkJYKSHBeN/Lq/jq8VBIy3wzHUzeyKMfQlKkdt/ZD2q/ff/rZYXTt7TumlVKVzEE3fpl1sqrC3l0cnS+xoCeuHmCxLREdOhQKPZHPIRBt1Kg0go+xVFyFJz6PzjzDaTc0jI4ezWEqiOgytDCrasoWdKT4W7m2KGKvbKvt7FDKdizR3sYFARl8sibeCBSC7qd7JxISkviQMQBqpepXoiVtlKGycUlOxftb2qMebfzNOOP25XoK/x5/E8AHqv8GPXLP4jp2K1XoJbulJQUWrZsWdh1EUIIIYQVTOdhtbUtvno8FDKStb+mXcsvLYAz/9Nul343Lx97Bk5+AUcmw5lZcOdwsV001U8RFuQZRJXSVQzLVD7qk1fQnd9x3fvCtVa6TlU6GR7npz4PtNv7Yecg+LMULHSAxaVha2+4ud36bSTdhLXN4diHWsANWlbn2/sg7PUiqbYoQWJPQ0bmP3jPehaLxMRovZ0A6lgxNFrf0t2rRi+zx/edjaPxflp8nsWXnFjCuLXjGLd2HN/s+6YIK3Z/FKile/jw4fz222+8++67hV0fIYQQQuTB2WQ60+Tk4qvHQ0HfWmnSwmJRcpSWUThiVfZ1weOhgcmUqekpWubhlFtaN0rPeuBUrvDqnEnflbxK6SqUdyuPk50TsSmx3Ii/gY+bj1XbKOygW9+y3TmoM4ciD3E9/jpXY65SyaNSvrbzwLmyWJvCSZmki065A9eWg50blLUyG/OhtyBOG+9O4CAIGqptM3ItXF5Y+PUWJUuqSR4Fx8weJnHntZ4RmeLtBwLNgLxbuRNTEzl24xi2OlueqfMMC48tLL7eJ7ZOxvupMeCYe+U3XtxovH9hYy4lS4YCBd1JSUnMmTOH9evXU69ePeztzbvXzJgxo1AqJ0RJFp8ST3K6djbs5eSFTjIdCSEKiaur8f61a8VXj4eCbeYVjNS7OZdRCrb1hZuZk6OX7wTeLSHpBoT/rbVegtY19PC7cP57LSAzFTQMmn1fqFU3HdNto7OhsmdlTkSd4MztM/kOusu7leeVRq8Yln+992uiEqIKHHSHlA+hfvn6rD23lv0R+4sm6E5L0HolXF4EsZmZBV0rg89jUH0UOHgU0n7iYfcwLTh2raxdYCkXqn3+V5dCYoR128lIhStad1n8B0CLn43ryneAGuMKp76i5Eo3uYpql9n7JjECTn9pWKwqNkAfdOflyI0jpKt0apetTdMKTQGtpVspdf/PSx1Nhqmk3NX+VugBXQ7B+laQFmdYnZ6RzpaLW9Chw9vFmzO3z3A15mqJTspYoKD78OHDhISEAHD06FGzdRJYCKHp9ls3tl7SEkLsfXEvjf0szOkjhChZEsIhfCVE7dROxB28wLMuVOgJrv73rRqlS4OnJ9y9C6ce7GmkH3zOfnDnoNaSrddpB5z/EQ5mBkG39xoD7gafQ7BJcJSRZpzre89wuPirdt+3C3i30MYx3tgKMccLtdppGWmcv3MegAt3LhCXEoeLvTZO8sytM7T2b23VdvQZyquVrsZ77d4zLP/7zN9EJUTlK4O5aRK1+uXrE+ITogXd4fvpHdzb6u1YJfk2bGxvHP/q5AOOZeHWHrixGXwfhzJNC2dfEWuMLZAtfjG2ajuWAY+akJGe83NNRe00bqf6yOzrna27UPKoiE2ONeQoCPAMKL75pe8nW5NuTKmWv3uuzsbxRXfv5r45fVfy+uXrU96tPN4u3kQlRHHx7kUqe1W+19rmTymTceR3D0PpBtpvqIOXNmWjiYORB4lOjqaeTz1ql63NgqML2HRhE4Pql9x8YgUKujdt2pR3ISEeYZejLxsCboD5h+dL0C1ESXdyJoS9oSVQsnUB1wBIi4Vzc+D2AWh+/6bL1OmgXj3YuhUuXIC4OG2+blEA7rW01urb+7SWSBt7rYXUzqQ7weXM1klHb6j2mvnzbezAKwTiLhgD7jqToe575uXirxRqtS/dvURaRhoAzy19zmxdfjKY61uyPZzMW4X1j/PT0q1PolahVAW8XbwNiY+KpDvr0SnaibutM7ReDH5dteXpKdrn6Vi28PYVsVr761rZcjdyGysTK8SaZI3Wz/9+94gW1Ov5P639bxH83+7/491N2lDW50OeZ26vucVco/vAsbTxvv5CoGsA1JsKZ76CxAjc3VIoVQpiY+HYsdw3pw+665Wrh06no75PfTZc2MCBiAOFG3Rb0+iaka59X9MT4dZO88zsWei7k7fxb2MIujde3Fiig+57mqTt7NmzrFmzhsRELQX8Q5MoQzxQzt4+y9yDc5l7cK4haUxRSc9IZ9KmSUxYP4HPdnyW9xNy8NuR3wAYWHcgOnQsOLrAcHIkhCiBbh+Ag2O1gDv4DXjyFjxxHHpdgZ6XIPC5vLdRyOpnJnLNyIClS7Ovv3Dh/tanxPKorf1NizdO1ZNVbGZ3AvdgsHXQ7p+ZDXtf1W77R2tdnAF0dlDrv9m34WrevTouJY6rMVe5GnOVlAJMGZVbYF2goNsxS9DtmP+gWx9cVyldhQt3LuDl5GVYXqjniOkpcPEX7X6114wBN2ifT6U+4FaIAUXSde2vaW+Wva/AikDt9peV8/aZZW/OvEp2a492MU9/Mw3MH2EZKoPvD2jDMVztXVl4dGGB540vUdyqGlu7b+/V/rpUhNpvgZMvADY20KSJturMGYjO5W3RfydtdDbsvLITL2cvs+WFRinzm6XlOhsolfldCf/HmDDOAn3Q3TagLW0C2hiWleRYs0BB961bt+jQoQPVq1enW7duRERoY1mGDx/O+PHjC7WCwjrnbp/jw60f8uHWD1l7bm1xV6dQvfr3qwxdMZShK4byyt+v5P2Ee7Dg6AI+2PoBn2z/hDfWvcG2S9sKtJ35R+YDMKrpKFr7t+Z6/PWHIgmEEI+ss7O1v25BEPKxlhAmI127OVfQxnfeZ/VMEttOngxXrxoff/cdzJlz36tUMnnUMt4/Pk3rLp4jk9aciNVw9hvtdm4OJIZry539jAmDDk+CP72Mt4Rww9MH/DmASl9UotIXlfhgywf5rnZuF6Hzc4E6p6Db3dHdbL019HNyb720laAvg+j2WzcAbsTf4FpsISYfSIqElNva/TKZ0UfyLdja03g7X4itovqsyyZjTkm6CfGXtFvCJeu2Y5o4Sn+8uAVBwDNaQCIM1p1bx6XoS7QPbM8zdZ4hMS3RcG5lNZ0u79uDRt9zBuDq8hxnRmiWOaRbKZg3z3ydUnD4MKSkp3Dk+hEA3lz/Ji1/bGmYgqvYMph7ZKZbT7gC+17VMvenxZsF4CnpKfx7+V9Aa+muVbYWXk5eXI6+zIW7JfdqcoG+4WPHjsXe3p7Lly/j4uJiWN6/f39Wr159z5WaNWsWlStXxsnJiUaNGrFtW+6Bz5YtW2jUqBFOTk4EBQUxe/bse65DSaKU4uWVL/Pupnf5ZPsn9P+zP9fjrhd3tQrFlotbWHd+HS0qtqBtQFs2XthoMXiNTY5lx5Ud7Liyg0t3rfzxyyItI433t7wPwMzHZwLw3ub3cnmGZYevH+bojaP4e/jTtEJTnqr1FED+fyysURJ/UIQoBkopTt86zelbp4mItTLpkamEzK7BrkHGk+NtvWGhnXZb7FlYVbVa9+5glzlI7Px57SRszBjo0AFeeum+V6fk8goBh8xg6NpfsKWbNpTgnEnSM33rTMwpY1Be7wMIMekRpZ/nO/mmdiIJWrdQrxAtSVvqXUA7gd5ycQt/n/mbZhWaUc61HDN2zcj3calvza7pXZOnaj3FU7Weols1Lcg9e/us1S1COXYvv4eWbovrwguxZc00C7J+6qGMVLhzCCLXa59jdCGOoffK7FYScwpSY7X7LX6B0H/yt52ybYz3wzOz4Pu0h5a/gY2T5ec8ouYc0K4aDqk/hCEhWjfkOfvn5K+l01LLa27LHxSlMy8kRR+FYx8Z/5+YZM5vY3IovfMOLFmi3Y+KgldegR9/hGM3jpGaQ2uyPpnafWc69/i572G5PyyrCOnG2SP2XttLfKr2vW75Y0uqfFmFuBTtgldJbsAqUNC9du1aPvnkEypWNM8gV61aNS5dKljAo7dw4ULGjBnDxIkTOXjwIG3atKFr165cvnzZYvkLFy7QrVs32rRpw8GDB3n77bcZNWoUixcvvqd6lCS/H/2dDRc20KtGL2Z0nsHdpLu8se6N4q7WPVNKMXHjRADeafsOE9to9ydunGj2jyI9I53+f/an9Y+t6bmgJ02/b8qV6PyPnZt/eD5nb5+ld3BvRjUbRYPyDdh0cRNbLm7J13Z+PayN6dOh4/nlzxt6Hiw5sYSE1DympMmvvLryPKg/KELcZ1O2TKHG1zVo+l1Tqn1VzZDsKavTt05z4uYJQ4IqA/34y5gTWtdW0KaJqmmhG/F9Ur48PPWU8XF4OPzf/8HGkntOUjxs7M2HB0Su04YS6Lt2AlR6UvubfAPO/6Dd96wLXg2MZSp01/6mJxozVFcZBs1NMlSj/ba9uf5NAKZ2mMp/W/2XhNQEJm+enK9q64Pu15q+xqKnF7Ho6UUs7rcYHTriU+OJiLMuiL+bfBew0L1cP6Y7c31e0jLSCIsMA7QuoR2DOtIxqCMBHtr45ELtzupYFjwzA+GrmdGGc3nodQl8u+b8vIKq2Ef7mxYHJz7RflvtXMzH/VvDuTyUbqTdPz4Nok8Ubj0fEpFxkaw4tQIAWxtbohKiKOVQikPXDxnmgdebuWsm3tO9KfdpOVr80IKY5JjiqHLhqjzYeP/Iu/B3TfinDtw1/m517gzVM/OSxcXBk09qCTZ9fIy9nPSt2X6l/Ogd3Ntws7Ox42bCzcLtfWItvyeMFzkBEq9lmzli00Vj7rCLdy9y8e5Fw8UD03UlTYGC7vj4eLMWbr2oqCgcHR0tPMN6M2bMYNiwYQwfPpyaNWsyc+ZMKlWqxDffWJ4Uffbs2fj7+zNz5kxq1qzJ8OHDGTp0KJ99VvDxuCVJdFI049ZqWVRbVWpFaefSeDp58svhX/IdLD5o1pxbw/Yr27HV2TL/yHx+OvQTdjZ27Lq6i3/OGK8uv73hbVadXcXENhNZPmA5txJu0Xth73wFuKnpqUzZOgXQssD2+r2X4SrbpM2TrL4amKEyWHB0AaD9aPxx7A82XNiAjc6GuJQ4w4/Ig+pqzFU+3PohU7ZM4eN/P+ZWwq3irpIQ9+yPY38wectkulXrxt4X92JnY0ePBT3MegRlqAzGrh5Lja9rMH7teKp8WYUpW6YYv/vVXtWyqyZegz3DIOGq1qXctOWqGEyfDqVKFWsVHg613gJ7d8vrnP2gTDMtEznA3hHa9GHHpsKZ/xnLeTU0ltk1BI5MhvDVEGHeGvrn8T/Zc20Pbg5urDqzynCB54eDP3DipvVBmL4LeRWvKoZlTnZOVHCvYLY+L/pxsjm1dFs7jvZk1EkS0xJxc3Bj05BNrBu0jnWD1vFmK+0CQ6EG3TodVP+Pdv/aX7BziNbCffuAsdt2YXKvaQzmj30Ea5vB/lHaZ5xftbXEYCReg1V1YG1zLaBKL+SL8iXYvLB5hjw4g5YOos/CPsSmaD0M5uw3jpv549gfjF0zlhaVWvBDzx/YfXU3T/7xZIFyJDxQSjcC/37Gx7GnIdokY5rOBltbmDXLvEPjnTtajg89fdA9sM5AlvZfarjV9K5ptv6+snOF2u/ksK4UlKpqaM3+T9P/ML/vfOb3nc/HHT4GSva47gIF3W3btuXnn41XbnU6HRkZGXz66ae0b9++wJVJSUlh//79dO7c2Wx5586d2bFjh8Xn7Ny5M1v5xx9/nH379pGamvMA/YfFu5veJTIuktb+rTkedZyVZ1bSpWoXAEb8PaLE/uNRSvHORu1L+WqTVwnxCSHEJ4SRTbQpNt7Z9A4ZKoP5h+czfcd0yrqU5UDkAT7Z/gk1vGtwIOIAw1YMs/qL+fOhnzl/5zyN/RoztMFQOgZ1ZGSTkVTxqsLWS1utvrK29dJWrsZcJcAjgMSJiYabvrt6gbuYF3FXcaUU3+3/jtqzavPbkd/w9/Bn1t5Z1J5Vm6UnLGRoEqKE2Be+jyHLhlDKoRQp6Sm8uf5NqpauypWYK/RZ2IektCQSUxPpt6gfM3fPpLFfY9oHtqemd03e2/wew1cMJzU9Vete3Gqh1oX44q+wvBL84QxbM1s37QtpPuB8qlQJ9u41H98NWpKd0aOLpUolk7MPdNgCLlmmffPrBq1+14YUtF4C5doBSpub+fBEYytrmeba/+jWi7WLMelJcPR92NJVS7gF4FyBFGx5a8NbAAwNGYqzvTOlnUvTv3Z/0lW6YV1eUtNTuXj3IqAlLTMV5BUEWJ9MLcdEavnMXq7vPl7fpz42JuOT6/vUN6wv1JPloKFQ5z2wcYCLP8OmTrCmEdzaBei05FOZ0tMhMVFrEYyJ0bI+JyRAmrX5TXU6rQu4X+b3/fZeOP0V3Mg8NyiXj3Pfir2g8f+0/xkqA27tNgZUniHgViXXpz/sTBOoLXxqIRdGX+DC6AvsGrYL0HLvxCbHsuXiFgYtHYSrvSs2OhsWHltItTLVWH9+PcNXDC+xgZlBkzkQMDDLQh1UfRmCXgC0oUQrVkCWjsf4+kK/fsYLXfpZBPQMswoU5pCP/KgxGup9aL7MuQK0X0uSc0V2XNFivtHNRjOw7kAG1h3IuBbjcLF3ITIukpNRJ4uh0veuQFOGffrpp7Rr1459+/aRkpLCm2++ybFjx7h9+zbbt28vcGWioqJIT0/Hx8d8nkIfHx8iIyMtPicyMtJi+bS0NKKiovD19c32nOTkZJKTjZPPx8Vp4wTmHZxHqYBSVCtjzEK559oeNl3YRA3vGqSmp3L+znk6VelEQ9+GhjIX7lzgj2N/4OXsxUuNzAfShYXBjh1gawuPPQbVqmlXg5efXE55t/KGcSoACakJfL3na3To6Bnck2UnluHu6M6IJiPMtjl732yik6LpHdwbNwc33mz5Jm+3edvsKnWzCs2IiI3gVNQp9obv5Wb8TZ6s9SRVS1c1lPnj2B9cuHOBx6s+zt5re7mdeJunaz9t+MEG7SrYunPrCPIK4unaT5vV48AB2LkTPDzgiSfAywt2X93N5oubCfYOplewcdxGeGw4vxz6BTcHN1r7t2b12dUEeAYwoM4AQ5n0jHRm7JxBhsqgZ42edKjcge7VuzMpdJLhB1wphZeTFwmpCVy4c4Ezt8/wZss3GddiHD5uPoYyn+34jKiEKM7cPkNkXCQ7r+ykZtma+Lgaj5WDkQdJTU+lf53+JKcn82bLN3m69tNmU3vVKluLdefWaS1iMafgTpg2bsyjtta1LClSS6aSFAH2HiSll+PNlm/SyK+R2Zz1T9d+mqsxV7G3tSdDZZidkNwvcXGwbx8cP66dbLi4QFAQNG8O+25v4Ozts8zqNosGvlp3ydXPrebfy/+y6+ouKrpXpEmFJsaNpdyBK0u16ZJK1dDGpNlm7+USHw8LFmjHSN++2vdgf/h+1p9fb/GYmrFzBqnpqQxtMJSyribTvcRfgmsrtAQcPjmf3Bw5Art3a/sqXTrHYrnLSIPLf2hZZiv0NJ++Q+/2Ae3ELj1J6+ZYupF5MqaMNO2k7M5BSEvQWtDcqkKZpig7V2KSY7ibdJfk9GRc7F1ITU8lLSMNF3sXPJ08cXXI7LKYGgc3/9VaRFLuai2tjmXArTKxLtWYdXAeOp2OcS3GYWdj/He+5MQSztw6w2OVH2PLpS2kZ6QzvOFwyrgYu3RtubiFXVd3Ub98fcOFOr1Dh2D7dggMhC5dtEypi44t4vyd83QM6kgjv0aGstdirvHr4V9xsXehfvn67Lyyk3o+9ehazdjFMzopmtn7ZmOjs2Fci3HY5jG9zt2ku8zZPwc7Gzv+0/Q/2NvaA9p3++s9X5OQmsDzIc8bvvM5UUqx48oORjUdxbP1nqWejzEy/SnsJ47fPG7431fFqwrL+i+jY1BHQLvYt/L0Sg5EHGDzxc10qtJJ62Ls112bA/jWHuPn71UfvFsatp2WBrt2waVLcOuW9rhMGa0rYNnq51l8YhGlnUvzYqMXDc9JSkviq91fATCy6UjDfMuAdgzcPgAOpaF8p2xz+daooe1v/nwtmVr9+tCjh3G8d75d+1vrRluxt+F7ndPnHxkXyU9hP+Fo58iY5mPMNnP1KqxZo8UrPXuCt6UpdqOPwfVN2hhG72YFrLBmwwY4cUI7Ea2pNeTw6fZPyVAZvNjoRUo7G7/LO67sYNulbdQqW4seNXpoC71C4PG9cHG+1upYti2UbW284OlcHjps0n4HwldBerwxiZ7+++/sCx02a6/r2t/aPLuOZaB0Y/BuyblbZ3iy5pN4u3jzRivjMLCktCQqe1YmQ2UQnRSdrdU5q/jUeMa30BLX6rtv673S6BWaV2iebXlOBtQZQIfKHbKdmDfybcSbLd/Er5QfK0+v5NiNY7T2b00rf+OUWVeir/Dbkd8o5ViKIK8gw++fqfrl6/NmyzcNr9PZ3pl8ub5JmxrM3kN7r/VZyXU6qDsZaozREk7FndWGCrhV0YJgFz+2b4eDB7XfvEaNtGPQXvt3Qmqq9jtYzcrE4zh4QuhfmZ/tX9pYcucK2m+Se438vaZqr0LAs3BpASRe1b7b3q20ecXze4E9+RZc36jlndDZgUsF7fvk6p/3cwsi9ixc36C9ficf7X9f1kzxKdFwbbk25ZWTj/Y9Mp0GLf4S3NqrrU+L0+rt4AkulbjpVJknaz6Jva09T9V6ynC+FOgZyGedPuNG/A0u3LnAzqs7GdNsDC83ftlw3qqU4svdXxIeG86xm8eoU65Oni/n18O/ci3mGt2rd6d2udqG5cduHGPl6ZVUcK/Ac/W04SfR0do5fWSkNj+2Utq5RtWqYBuwk62XtlKzbE161uhp2E5iaiJf7v4SnU7H0JChhtlsetTogb2NdjDGpsSy6swqvJy9GFJ/iPab5+ABLedD4CDtfMPBS+tt4V7drP7du0NoqPb/Pzxc+53p0wdcXaFLShdCA0JpG9DW7DlD6g/Bz82PGt4mx61SELUD7hzQzmGcfLQkhaXMvyBJSdr/9QsXtN+Y2rWhdWvj98oqOh3Unqj9X4zapSUTrNQX7Fy5FXON0c1G42DrYBaP2Nva80nHT7gSfYWktKR87CwfUu5qF1TTErTzOu/mhbt9VUARERFq0qRJ6oknnlBdu3ZVEydOVOHh4QXdnFJKqWvXrilA7dixw2z5hx9+qGrUqGHxOdWqVVNTp041W/bvv/8qQEVERFh8znvvvafQspmY315Cvfb3a2ZlH//lccVk1Lf7vlXjVo9TTEaNWDnCrMznOz5XTEb1WtDLsCwqSqmhQ5XS6YyDa21slHrpJaXikhJV1S+rKiajXv7rZTV61Wg1etVo1fXXrorJqJdWvKRik2OV84fOismoS3cvGbZ7+e5lpZusU84fOqvY5Ng831OllAqLCFOlPymtXD9yVU8ufFK9uvJV1WB2A8Vk1FN/PKVS01PVkwufVExGffLvJ2bPfWnFS4rJqDfXvmlYlpKi1Jtvaq9H/9q8vJRaulSpqPgo5fOpj3L4wEE9v+x5w02/v692f6XO3jqrmIxy/chVxafEG7a77tw6xWRUtS+rWfW68mP75e2q78K+6rGfHlOTNk5SdWfVVVM2T1E3429av5GkW0rd3KFU+Bqlbh1QKvqkUjGnlYo+pd1PiCz0eheGmBjtWLSz0z6rOnWU6tFDqS5dlPL3V2rgwHxu8OwPSv1ZRqnfMN6W+ip14VezYhERSlWsaDxGWrdWKj5eqWUnlikmo2r9r5ZZ+ZvxNxWTUUxG3U28a1wRtUephU7GfW3to1RacrZqrVqllLOztq8aNZS6c8dC3U1Hu1uSnqbUujbGff3ppdT1zSaV3KnUXzXMX7v+trqRVubiAqUWe2vLFpdVak1T43N+d9COI2scGK+VXx6o1NGpSl38XalLfyi1xEep31AZZ+eqstPLKiajdl3ZZfbUwJmBismojec3qjqz6igmo5aeWGpWpvfvvRWTUZ/v+Nxs+W+/GY8VUCo0VKm7d5UasnSIYjJq4oaJZuWXnliqmIyqM6uO+uvUXxY/29+P/K6YjGr6XdPsrzOHz+Plv15WTEY1mdNEdfm1i+ryaxfV8oeWismoAX8OsO49LAZvv62Up6dStrZKde+u1DvvKDV1qlJjxijVpo1SEZFphtfRc0FP1X9Rf9V/UX/V6odWismoUf+MMm4sJVqpnUPMj7PfHZQKe0up9NT8Vy6v418ppY5+aNzXmuZKpcQopZR66o+nFJNRH239yKz4pgubFJNRFT6vYLZ882alypQx7s7dXanly7Ps68oypRbYGve35xWlMjJyrncunn3WWMzWVqlvvtGWl/6ktMXvSE6/58Kyb/Z+o5iM6vxLZ7PlU7dOLbrvZNwlpTY+nuV/rU6pfwcolZSP3+78sOY7kt9tFdV2UuOV2vGcUgtstPdmoUvm74NOe3zkA8vbya1OuZWJv6bUho6Wf//WtTWWOzxZqT/csn9um7spFXtBqbUttWX/9lfq6kqlbmxXKny18Xc+fG2B3qaCenXlq4rJqPFrxpstH79mvGIyauTfI9Xt20p166b9b6lQQamJE5WaN0/7vZw5U6lhw5Raf269YjKq8szKZttZeWqlYjKq5tc1lVJKnb99XnWb303Zvm+r3lj7hhqydIjSTdap55c9n79z0sJ07R+lllW0/NlufFwppVRyslLjx2v/y7MmD/LzK55qG1jzXcvr+I/cpNSySuavfW0LpWLO5m87uSjodXDKly/P+++/XyiBv563tze2trbZWrVv3LiRrTXbtB6WytvZ2VGmTBmLz3nrrbcYN26c4XFYWBihoaEAfL33a/631zhGS6FwtnNmQJ0BbDi/gRm7ZrDz6k7uJN4xlNl+RWvdb+3fGoCUFOjWDfbsMd9vRoaW3GD6dCdmdZtF518788exP/iy65dcjr7M/+3+P8q5luPjjh/j5uBGt2rdWHxiMW3ntqWUozZoLzY5FoWiW7VuuDm45fhemqpfvj7rBq2jw88dOHXrFF2rdmX2/tn0qtGL3/r+hp2NHaEBoSw+sZg159bQLrCd4blbLmnjwkMDQw3LBgwwZknUu3NHy5Z740YZvnniG/r+0Zdtl7Yxt9dc/jnzD/Mi5xEaEMrIJiPR6XS0qNiCnVd3UnFGRUOrV2KqNt/7s3Wftep15UfLSi1pWcnYEvV++wIcu46lwbFFIdbq/njtNfj5Z3B0hH/+gU6dzNdHReVjY1eXaeNZs0qMgH2vQaDxs3vxRfMpjP79V+vuOuET7crz2dtnSctIM7TQ6rsLVXSvaGzlSU/Sxkamm1zVvLpU667Z/EfDIqVg3Dit+yDAqVPw1luQQyqInB2fCjdNZktIuQNbnoDHD2hX4Tc/DqkxWitC3fehVHVtvOb+UdrV/+gTsPM5LcNo/WnafM42thB7DlZW1VrPTbKP5uj6Zjj5uXa/9Z/aFdfb++HQxMwWgTLo7Jx5rPJjLDy2kNn7ZxvGhcalxHHx7kWc7ZxpWaklTf2acvTGUfaF76N3cG/DLvTdyppWaGpY9ssv8Pzz5mPCtmzRvtvBr2itFFdizJMUXo7WklwGeATQMagjrvauHL95nL4L+xpaKI7eOApArxq9sNbMLjPZdXUX+8L3sfq51ZRyKEXovFCqlq7KnO4P5lxY334LU6dq93/8EV54wVIpW37u/TMh34aw4fwGVj+3mgt3LjB42WCCvYP5uOPHxqK7hmjfOVMZKVoCJvdg82Q7hSE5ShunrHdrFxwYC82+N8y3nLWrsf6xfu5XgFWroFcvrSVRLyYGnn4aIiIye6Ek3YQ9L5l/H87O1noO1JuSr2p/8YXWyqOXng4jR0JwMJRzLcftxNvcTLhp9pwbCTcAbb3IW5/gPoz8ZySbLmzisZ8eMyw/dlPrFv1kzScLd4cZqbClO0QfybJCweXftdblqo94iv6Dr2tDXRy9odUi8GmnLf/3aS2RX8qdnFvNTZdnTcRqicrQsvrfPaSNyQ0apuUwuL0HzszSWkgBjk+Ho5O1+3altBwHSZFaT4Xwf8B3lbFs/alaC+ftAxD+N9TIPC93C8q2+6LUqUonZu2bxV+n/6JGGWPL71+n/9LWB3XijTe08yeATZss945IywjF08mTC3cv0P/P/jjYOgAYpuzSt35X9qrM3wP/NuRzCPAI4KVGL5mdo95XMSe1mTgyUsC3i9YC7RoI5+dpSdzitXOLoUON/2erVtViAScn2L/f+N6UWNHHtfO7jCxDcqN2wrY+0O1woezG6qD78OHD1KlTBxsbGw4fzn3n9bIOLrOSg4MDjRo1Yt26dfTp08ewfN26dfTqZflkrUWLFvz1119my9auXUvjxo2xz6Gvg6Ojo1nCNzc3LXitWbYmJzhB9+rdqe9Tn00XN7H9ynb61uyLu6O7oUtVWGQYpadn73KqD7pnzzYPuKtW1boWHjhgPAnpVKUTz9R5hgVHF5CSnsLByIMAfN75c8PJS//a/Vl8YjHXYq8RZKf9E9JnGuxfu7/F15aThr4NWfPcGqZvn84/Z//h1cav8vnjnxu6beoD7Y0XNtLse/MufrY6W9r4a8mC/v47e8Ctp/9f3admH/rX7s/CYws5dvMYvx39DRd7F37o+YOhy/WgeoPYeXUnOp2O9oHtSctIY+lJbeywvhuPuHfp6fCrlkydFi2yB9yQQ5dPS1QGHNR3h9RB5SFQvrM2V+1F8+y8ixfDypXZN/HDDzD908o42zmTmJbI1ktbDV0gt1/WLl7VLmvs3sX5H7Vs0VldmKuNaSqnHZfLl2vdSrPu6913wc/PyteXclcLurNKi9cyGXu31gJuew9ot0rrMpqWoHUvNNT3By2IcCoPtSZoy45P115HfnjW00484s5rgUidyVrg3X41pCVq3RtLN6TjrVgWHlvIvLB5zAubZ7aJ1v6tcbRzpFnFZvwY9iNf7v6SP479YVh/JeYKdjZ2NPLVuoLGxWnTTZkG3Hpnz8LjnplBd5aZAfSPAzwCcLJz4vGqj7PkxBLD99mUadBvkMNJnpOdE388/QeN5jRi5D8jDeP2/njqD8NFyAfNjRvG+1nH2JmqUroKn3f+nJdXvsx3B77jyPUj2NnY8UufX4xdb29sMw+4PesDSjt5hTzmki6gk19kT+Z0/geoPirvoDtzfVqadnHNUkqVlBStayIAJ6ZrmcCzOj4NKg/K1qUxJzdvahfYssrIgNWrwSfYh5NRJ7kRb76vm/FaEG465EjkzMfNh9b+rS3mOHGxdzFMVVZozs8zBtyugVDzTW1c/aG3jfNzP8pS44y/K7Xe0gLu1BhtXniTDNeFNoNJ5AbjdlsuhApPaPfdgrSgG7QLJfrf0LKtoe0KrUt0YiQsyxzq6dcVdP/TutWvb6MlonT20z7b5Fta0FPq/o5pbx/YHludLadvnealleYXcuxs7GhfuT3xj2kXUpWChQvh9de1gFMvIgJ8fe3oUrULvx/93ey3Vs+0yzlgNgSrWJ3+WnvfXQOgzVJtOr7E62aZ+U+fNgbcbdpoQ3lMQ6yzZ+9znQvbobfMA257D21oEGizURQSq4PukJAQIiMjKVeuHCEhIeh0OotJCnQ6HenpVrTk5GDcuHEMGjSIxo0b06JFC+bMmcPly5d55RUtEclbb73FtWvXDIncXnnlFb7++mvGjRvHiy++yM6dO/nhhx9YsGBBvvfds0ZPTpw/gbujOx889gFNv9NagF4I0ZoryrmWo1rpahaTkzjbORvGec+aZVz+zjswebI2lvXKFRhokhPhi8e/YNXZVYxdM5aY5Bg6VO5gFnB2r94dNwc3ElIT+PcFbZL48p+Xx83Bje7Vu+f79TWt0JQ/+/1pcV2dcnUo41yGW4nZs1U38G1gOMn9v/8zec3O8N//goMDfP+91pKh93W3r9l4YSMj/tbGo898fKZZspf+dfozevVoElMTmdtrLtsub2PpyaW0qNgiW1IYUXC2ttrYxmPHtNbf6GhtfHWB3D2ijZkDLfOkvjXKwSNb0P3991ikFGSk21CzbE0ORBygw88dspUxC7ov/JJzfeIvAlrQ/emn2VenpsJvv2k/jla5vNC8Rd1U0g1jC3jpxlrADfDvUxCxyljORruyrbVoK601wbcLlDLmUrAq4ZZjaa11/coibazejme0f/w2DtqYRddAaPq9YfwxQN1ydbHR2XD+znliU2LpUFl7b5tV0C6ixabEEns71mw3dcvVNQR5c+fC7VzOZQM9AwFjy7aevuVbv753jd4sObGElpVa8kPPH9gXvo9BSwdRvUx1apWtRX5UL1Odn3v/zB/HtROYN1q+Ycg58CB64w2tR8fatVorwGuvQUgIuLlpAfn+/dqFIC8veKnRS/x95m9+P/o7AJPaTjLLJ8GFn4z3G8+Capm5Pf6uqbVMFDalzOelNnXiE7yc6wIQnWyeyVqf2Vp/sfiff+BMXvm7VAZc+i2HdWla9mkrg+7vvweTFC3Z6Mf964NsPX3Ld155AYTRkzWfZOulrQysO5Cfe//Mjwd/5KWVL9GlahfzHASFQZ+gzrEcdDlgnAP99P8k6AYtIMjIPPD1F35Vuva76F5Lu3nWLbz9Xc28iOpa2RhwX/jVPDP/jW3GQKX+J8bPLKtqr2o3pbSL2qkxWr4SBw/z+dfvEw8nD5pWaMrOqzupXbY2lTwqcSX6CsduHqNphaa4O7ozcCAEBMAff8DSpTBvnpYPwMFBu9Do5aW1gPeq0Yvfj/5OPZ96zOo2i7O3z/L88ucp51qO5hULeXxwYYnaqf31bm18/7d008Z2Z/rF5FTs/fezj9+uWpWSK+kGhGe2Ejn7askwvVvAqf+DA2MKdVdWB90XLlygbNmyhvtFpX///ty6dYspU6YQERFBnTp1+OeffwgI0FpZIiIizObsrly5Mv/88w9jx47lf//7H35+fnz55Zc8+WT+uzp1qdKFmZdmsvzUcg5FHmJv+F4CPAJ4rLKxK1Vr/9acuX2GXjV60Tu4Nzuv7GTOgTk0qdAEB1sHbtzQghvQssd+8IFx+5UqackHHDLPy33cfFj41EL2XNOaxQfWNc9S6GzvTI/qPVhwdIFhqqkMlUGP6j3yn4gkDzqdjrYBbVl6cimfdvqUQfUG8eHWD/l679e0C2gHaIlINmzQytvaanPBNs/8H/Laa1q3VD1vF28OvXKIO0l30KEzT9YAlHYuTddqXVlxagV/nf7LMJe1tHIXvnnztOEOERHaMTlsGFSurAWlx49r3c4nTbJiQze1Cz/obIwBgKViN2Hdutw3Vbts7RynqjAkMkm8npmFNndRUVpCP0tMLwTl6Voe07npE5WZXg2t/h8t2RpoCad8OsDJGdpJ4f7/QL2PwKuedku8DtFHIbPLWZ4cPKDKcO2Wg0DPQIK8gjh/5zxze82lkV8jqnxZhdiUWENAXqdcHVztXYlPjada6Wo42zsTHhtOVEKUWdfyP7Ncj2vbVrtIcyizcUPfI+HC3QsEzDQmxNG3IAZktoR3r94dOxs7DkYcJMAjgPmHtcvj+elabqpPzT70qdkn74IPACcn7X/8qVNaK+ulS9oJmj6RWo0a2oma3vIBy3Pe2K3M7lLerbJ834poFoPoo5ZbngGij+FVVkvEk1dLt+n1bk9P7biqUUO7GD1tWuaK2/sKbVqnhQtzX69vyZaW7nvXt2Zfxqwew+qzqwH4+8zfQBF0LVcZxkAgcGDOwdujzNlXG94Ue1r77Qror71PbZcVzf6SMud8N02Gdm25cT56gKjMRMo2jsap805/DWe/tbxNnQ7s3bRbMesU1ImdV3fSMagjM7vMZMzqMRy7eYxOQcbuga1aabfcdK3aFXsbe05FnaKBbwP2hu8FoHu17sWSQNcq+iS46fHGZSHTteEJAPalOG0SdNctxGs5D4Rbe7X/OQANZhiPXYeCZuPNmdVBtz7ozXq/KLz66qu8+uqrFtfNmzcv27LQ0FAOHLj3uebcndzpHdybhccWMmTZEEDL8Geahbq1f2vmhs3FRmfD8yHPc/zmcW15Ja1ruenMZk89lX0fWac371ylM52rdM5eMFP/2v1ZcHQBS04uQZd5spXfruXWCg0IZenJpRy/eRwfNx8O39C6MerHcx8+bOx6+uSTxoAbtHlif/rJfHu+pXzxLZU9e7zeoHqDWHFqBfOPzGf75e3Y29gX2Wt7lDVurHX9WbRIy3C8fLl2AcXVVQu+n3nGyg3d2q39da+l/eADXP7TvKUX2LxZ69aeG7PW7JzWRf1rvqJ0Y637mmnXObQxx/fcg05lwE2TmRdKN4JKT8PZb7Qsq6C1WF/7S8tKHntO6wLnZ6F7WLvV2hjvM//TuoY7+Wj1Tr4J5R+H8tlb9+9Fh8odOH/nPFsubcHHzYfzd85T2rm0oUXY1saWhr4N2XZ5G9M7Tad3cG96/96b5aeWG1vBY7Vs5XrjxsHnmUPKP/5YO3YqeVRCh44MlZGttRuMQbmXsxdt/Nuw6eImNl3cxD9ntZYQi13LH1I1ami3AktLgBjtt4WKvQujSnm7vinX1Z5OnkDOQbd+/S6T62QLF2qZxEEb666/4MwNk7wJoGXkTbhqYfxu7m7c0H6X9MqWhZdf1v4nbMvchX7M9rk75wy/12Bs6ZYx3dar6F6RphWasvvabjZc2MC68+twsHUoUM+7XMWcNLaY+mT25km4CpcWQkp+kpBYKb/jnot6O9bQ2UDIp/BvX63XSOpd8OuhBd4JlyFirZZXpEzjPDdlFX0PraTrxmWNvtLGZWsVMgbXTmWNrzkxXLug94DrXKUzU7ZOYfPFzQCGv7mdn1vi4eRB24C2bLiwgU0XNhkuUGXtWv5AqdBbu8gVuR7iLoJboPl5SnqK8X832lA0q4clFjdrvpNHMltHbRygQo+Cb8eK77bVQfeKFXm0Apno2fMBPrjyMLTBUBYeW8ih64fQoeOFBuaZcPTjtndf2232V7983z5j2YYNuWddqnbBw9GDDee1JmYPR49s0/sUFv247u1XtpOansrea3ux0dkYxnMfPGgs293Cb6xbPi9W9qjeAw9HD1ae1rp19KzR02xKI1F43N21Fu5hFnKgWS0ps6XIyaRl6Nz3ELnGrNi/WWLlIUOgQQPtpFs/5lXfml29THVmPzGbxLREnvhN67Jm6IIcZXL2XrY1PLZZu78iQJtCK9OWLffwmvRiTxtP8px8oO1KbXogOxctgAYIHAzHP9amZdnQFoJf15JZpUbDjS3a+KcGn2lj67odhoRwrUUvLRbs3LRueR45X2woqI5BHfnuwHda0J3Zatc+sL3ZVfVmFZqx7fI29ofvp3dwb8Pcnc0qakH36dPGCyWVK5t3158wQftdcbB1wLeUL+Gxllso9S3doAXYmy5u4seDP3Iw4iDl3co/uF3rHkTRx4wJxrwyu9PHnNS6uyVZnj7znulb1nOg7z6eNejWdzf3cvIiIUFr3QftokPnLOer776beZydN/luV34ems+F9GRY5KZ1L7fStm3G8xw7O61VvW1bbdmAzNko9d+J5aeWs/xU9p4F0r08f56s+SS7r+3m9bWvk5CawBPVnsDd0b1wd5JsMhRA/3sTexbCrB0r9Iio2BM67dQu7kau16axQ2k5RUo3Nv+tvld+3bQhLzEntaSepRtpv5GU19Yrpf3OgZaQUa/aa1DJpAXK2dokK/dXs4rNcHd05/D1w5y9fZbD1w/j7uhu1hvMWj1r9GTDhQ0sPrGYLZe24GznrE05+aCq+iKcnaU1MKxpBFVe1KbdS47Shrd5t6BVqymG/EAbN2pJ1UoEay5ybcvsqVO6kXEce/RxiDfp1V1IF8usDrp79+5tVbl7HdNd3DoGdeTlRi+TlJaEv4e/YZyiXvUy1SnrUpbw2HAu3r3I/vD92OhsaFFJ645gOiaySiEMTXa0c6RXcC9+PqSNme1fpz+OdtnnQy4MdX3q4uXkxelbp1l9djWJaYk09G1oyCQdFmYsWyt/QzMtcrRz5OlaT/P9QW0c4aB6g+59o6LoqMzMSCbJNSwxTSLYvLmW0MzWFjp21OZJBWNr9s34m7Sv3J5DkVrrtb+HvzFJVtx544aafqdlAQewNR9aYXqhq8D0rdkA1UdnnkxkYe8GHbbCgVEQsQYOGmdAQGcDdbJkxHfxA5eivwD5WOXH0KFj26VtlHPRWu3047n19MH1/oj93Iy/ydWYq7g7uhPsHQyYj8Ht31+bl9vUeG06YAI8AiwG3U52TmbddHsH92b06tEsPrEY0C6wPbBd6x5E+gtAoGUmBoi/op1cF5XoY7mutiZ7+YkTxnMTS0kbbW21m+FkxtYZGmUmCrF11K7u5OPc5tw54/3+/bWAG7TNzJkD332Xe1DtZOdU+AHjQ+7JWk/y5vo3OXJD65VQ6F3LAdJNhvDk8XtTKAqr9bmwtpMfZZpoNzB2kS2K/7UVemo93BIjYFtfLa9LmWZaYBa5Vru47JfZGpOeBLf2aa3sLn7a7QFnZ2NHu8B2rDi1gg+2foBC0T6wvWF2lfzoWaMno1eP5udDP5Ou0ulevXvh5zwoTA5e0GkXHJmkJe888YlxnXMFqPoizzxjnCFm4kSti3mTzMMuPl77fzt2bLHU/t7ph044mvR62jVEazQpZFYfTRmWUto+hGx0NszunvuJTSv/Viw7uYzv9n9HfGo8dcvVNXStM2RmRRsrWxhGNxttOKEdUGdA4WzUAhudDW0D2rL81HKm75gOYBjPDRBucq4dHFw4+/ym+zfM7DIT4MH+pySMJz+mrRDNfzJmO9ZpQbFJygU+/jjzJBuoXdv4TznQMxBXe1fuJN3hetx1w3RhZt3O9a3Z7sHaLQdZs5YXSILJ3Ga+uXQncwvUMrKmRGvdzNPitHE/nvW0KcWKgbeLN/XL1ycsMozfj2lJuUwTrIExmdr+iP2GVu7Gfo0NgbBp5lF94GLKLvOXItAzkJ1Xd1KhVAXKu5UnJjmGM7fP4O/hbzYMx9/DnwblGxhmZSjoeO5HVrpJZjD7+5CpXWVA7Cnj4wo9od6HsOdFw7ASfUv3jfgbOH1oTHaUmqFdjPNy8uK4SVfv6tVz2Z/++1auHdgXPOi9ZHKtLGsaFw8Prav58eicg24Zz51/QV5BNPZrzMGIg9jb2tMruAi+23Ym5wLJN4Ga4N0cupv8o3KSYQHZFOWFTVsnrQfYtl5a9/W9WaZrC3gWfB7TWrvT4uDQf7XydpkXyVPjtDHgJtOKPmg6BXXShjxm5iExHc+dH4GegdTzqcfh69o/xBLx++dcHprOgSazIe6ClrjVsSw4a/8jPdCmYB06FCIjoWlTrTeTszOcPKklsyyxQXda5jlsEYzhzqrA83Q/ylpXas2yk8uYc0CbK1bftRzMx2zHx2d9ZsE09G1oyIxe1EIDQll+ajn/Xtb6CJvOz52QeVza22vjgQuDnY0ddg5yGJYIbpnpKeMuGDNzO5uftKalaf+QQcvm2bq1+SbGjtXGdep0OmqWrcm+8H2cjDppOejWn5h75jwFYUQE3L1rfOziov0orFhhHvznKSFzGiydjTZmPS8OHlC+Y97l7pMOlTsQFhlGXEocldwrUa2MeebnSh6V8HXzJSIugmUnlwHGQBzMW7qDcpkiVT9u+5k6z/Bp509ZeHQhAxYPMCw39b9u/zN8rlkvAog8mPbm0Cez8ayjXeTS884jo09+xF82TotiY68lkylVRev6rQ+6nYzJrJJNLwpk8nL24oBJb7wce3plpBqHqtxjduWLF433LSU4KlUKyqXlHJzJeO6C2fvi3qLdgWnm+rjzUK6tFvTd56mkRBalG0KP89p82xGrtazjjmWgdFMt94StI9QYC8c+0Lolr/DX8pikxWhDsErVeOCDboD0zKE999IlfGKbiYZx4Q/0eO6sdDY5fs+GDNGylP/vf1puIH3SaG9v80TKJU9mg4Hp0CYHL2MvM8fCC8YLHO3Ex8ezZcsWLl++TEqK+WTio0aNuueKPcj083VHJWjjVlpVMv7a+5jEICdOQDXrZj15YOjHdYOx5VsvLfN41LdcikdM2TZw6gttTOmNzeDT3nx9RjqRkbaGZHtNmmQ/VsqZnOPWLlubfeH7OBF1gpO3MoNufebyjHTj2NV8tHKPGAGffQajRmnjyK2mD/BdA41X5kuQDpU78PlOLfNZhyDLidqaVmjK8lPL+e3Ib4bHeqbddP39c96Pfty2fpow/V9LQXeLSi0Mw25EPul/7AGSMnuWOPtC5cFFsz/TrOUV+1o86fJ08kSHDpVD/28vJy+zC80VKlgslpm1PHMb1lzgyoU+6HZ3N//fYiq37uUynvsB5VTOmJn70u8Q9Lz5eqUAVbQtu8IyG3uo2Eu7WVLnPW14zNnZWtfzS/ON6/QJWB9QNbxr0NC3IRGxEfiW8qV6mdy66+SuX+1+9KvdrxBr92DQZ3BPSdFy9Njbm8c9JZK+F2eKyZTJ7dcWza4K8qSDBw/SrVs3EhISiI+Pp3Tp0kRFReHi4kK5cuUe+qC7kW8jnO2cSUzTWgZMW7pNM3rv2wclLadc/fL1CfIKIiY5htplaxu6zYOxFT85WctinnXcp3jIlW2NdkVQwcHx0GYZuPprJ0CXF8KdMK4mfWwoXqdO7pvTt2pbbOlOijQmknKvmeM2TFuzHRy0MUegXewanvNMW9npW7rdSthVskwdgzpyZIQ2xrK8m4Xx6Ggt28tPLSc2JdbwWE8/tVrZslp3sZzog2t99vIr0ZlBt2f2oFvcA49aWsteepKWxd+vaJJnGpgmPirXzmIRWxtbSjmWIibZ8jx8Xs5eZsOrcjyOTIdyeOT83baGvnt5jgE+2rAlNwc34lLisq2T7uUPsHJttaA7ci2cnwtBmUltE67BvpFa8j2ZSuzBY2Or5Wmo9ZZ2wSQpUvucvFtmnkM82Pa/tL+4q1AiODhAxYrFXYtC4hqgTXdn+ttURAoUdI8dO5YePXrwzTff4Onpya5du7C3t+e5555j9OjRhV3HB469rT2vNnmVU7dO4e7obnbC2by51rqXng7z58M772CWav/KFe2KfGGN9y5sNjobzo06Z3Gde+bQO6W0YCcw8P7VSzwAnMqCf3+4/DvcOQh/B4NnfW18V2I4VHmJq6ZDo/O4qK1v1T4RdYLTt06jQ2fMXG46h68+A2vSDW1aC5O5JG+ZXJjs1An8TPK1vPYa/GIyt2Su9N1dTceXbukBd+59KsL7wd7Wnjrlcr/KoU+mBlDJvZLZdH76YKlUHsOH89PSLe6BjR14hmjz1F9epCXpszHpNhJ/CZx8rZ/zPS/JJl+kXIZzeDp55hh0ezp5WpdHyvS7rW/RjzkJN/81JoKyQnS0cQhXbkE3aN3I41Li+LLLl9QuV5tZe2ex+MRi6V7+IKs6As79ACjYPRROfKa1SN05mK8M96KYOJeH4DHFXQsh8la6kTbt3t0jWqLAIuyRUaC2yrCwMMaPH4+trS22trYkJydTqVIlpk+fzttvv13YdXwgfdb5M/565i/m951vtrxUKWOG5vPntYyqUVFaoPrPP1q3DNPWgJLENGN5oSSvEiVPw5ngktn/OD1RCwr0J9E6HdeMM3nl2eVI36r97+V/SUhNIMAzAFeHzG4+qbHGgraZXSxu74dtvbV/iplMg+5mxpgS0MYe1a9v3csiI/NLaZrAJ+akeYBQwjXxa0KFUhXwK+WXbe5R/QghhzxiOP1sDhGxEaRnpBuC7qyzPIhC4N1S+xt7GvYM0xIRZaTD5T9hXQvISMn9+flhGnS75jy+wHRct6V1pq3bOf7OpZm0OOu/29c3a0nblPUznyQmGu/ndYFP36Jd2asyj1V+zJC0U1q6H2ClG0Jtk/PJmONa8kpDwJ3DvLlCiEeDTme8WVqW09zaWXnpc2YpuPir+bq0BAhfXSjVhQIG3fb29oZMtT4+PlzO7OPp4eFhuP8oe+MN4/1ly6B8ea1r9hNPaC3dJZXpGNndu4uvHqIYOftAtyNQ879gl9ks6lwBaoyBeh9x0ySxeV5Bt7+HP24ObiSkahn6zJKomQYUpoFwFqZBd0hI9vVdrO2Vm2HddGglWSnHUlwdd5Vr467xfc/vzdbpx97nNduji70L3i7epKt0wmPDpXt5Uar6knHM6oWfYEkZWOQK2582u/BUKFIyu5frbLUW9BzoM5hn5WjriLO9s1ki0YicqmjldzsvpqlkXPLYjH7s9s147R/UzYSbZsvFA6rehxD6D3hkJtzT2UHZttD2r2KbLeK+ySugEEIUjtKNjReAD78DJz7XEjheXQZrmsDNbYW2qwIF3Q0aNGBf5uS47du3Z9KkScyfP58xY8ZQt+69ZSN9GDz1FAwwmdkrPb3ktm6bMg1q5s3TxnabWrr0ftZGFBt7dwj5GJ6Ogf4p0PsqNPwCHMuYHed5Bd06nUl3cnIJum2tC7qrVs2+Pq+TceP+UvPc18NMP9wl63faEn1X8rO3z3Ij/gZ2NnZUKJVH/16Rf+41oMZ44+OMFMiw4gMqCH1Lt7OfeTf2LHJq6dYH46ZJ+M5ZHqVk/K7BPX3fTI9Ve/vcy+pbtG/Ea8NI9MG3tHSXAH5dodth6Jek3TpugQrdi7tWQoiHhb2bMUlpRgqEvQ5/VYFtfbQeNoWoQEH31KlT8c3sz/XBBx9QpkwZRowYwc2bN/n2228LtYIl1a+/amn1S5tkmvf2htmztflDSyJ/f+PYuUuXtHlRo6O1Cwqffgqvvlq89RPFwMb8bDfV5HzamoDXNNA2ZC4H8zF7tk7kxDTorlw57/3lKR/dWx8m+m7B16+T57hcfav2zqs7USgqlKqAbS6BmrgHIZ9A41nmuQYcy0GTb7UThcKiT6TmUinXYvqgO9g7mF41ehlm7tAvNx2CZDoNnRn9d0xno41dL6D8NPbpx27rW7j1f2VMdwli65jrBSEhxCNGqbxv1qr3AThZTkJbmD1LCvSLV7t2bVTmiylbtiyzZs1i6dKl1KpVixBLfTwfQba2WhD63HPa+GdbW218aV5X5B90w4bBlCna/b//1i4kKKW15nt75/5c8fAz7fJpzbHeNqAtYZFhADQobzJ+QWfyryk9524i+qC7VClwyjk2z5tN5mDmNJM5jyo9aZy2zC2XyasfAn5+cOCA1np4/bo2JCYngR6BAOy4skN7LOO5i45OB9VGaAkMo49qwx88699TsGqRvqXbtMtuehKoVLNi+hbt3jV6M63jNP469Rc9f+9pWG4adG/aZHlXGcpOu9qvMiA9pcDJ4EzzD2SZtTQbfYu2IeiOl+7l4gGXn4BBCHFvHL3h8f2w90VtHnrQLgz7D4Aa4wptNwX65e7Vqxd9+/bllVde4e7duzRv3hx7e3uioqKYMWMGI0aMKLQKlnTu7tkTPJVko0drrfU3MpM9p0kSUWHCdEywNVPKPR/yPM+HPJ99hY3JGXW6NuYbrxBoYZLkwrulIej29MxnRXPan2nQHfKx5bIPIdOu+Zcu5R5061u6d13dZfZYFCHH0toUSkUlLTNxoY3JtBqrG0KMecZMfYv23aS7Zn/1y0uV0qaRuXoVDh2CXbvMp9GcNQsGNnPAU78gPUELuiv2Bg+Tni65ZFDXM50BxHR+cEtMx3THpcSRmJaInY0dZZzL5LkfIYQQjwAXPwj9G+4ehZTb2tSdjoXbmlig7uUHDhygTZs2APz555/4+Phw6dIlfv75Z7788stCraB4sJQuDX/8YfmkvEmT+18f8WCxM7mMd08XZEyDbn0g7OwLgc8ab26VCy/o1id00gf4j5hqJtOTHz6ce1n9mO5bibfMHosSTD/O2jTotsDTyROA6ORos7/65QCNGxvLP/kkHDumtUR/9RWMGgVKZ9IFxvDdLg/l2hhvDnmPwTIdvhKexyQDpt3L9a3c3i7ehoSwQgghBACedbSL3IUccEMBg+6EhARKZU7ounbtWvr27YuNjQ3Nmzfn0qVLhVpB8eAJDdVOzAcOhIAAqFtXG9O9fHlx10wUN9Mun/cUdNubTBidZjkQjo83JlO65zwJzpkTfJtOnfQIMQ26//47+/qjR433s7ZsS9D9ENDnULDNPejWdyPPqaUbzJOIhodDnTrg6qoF3OnpoOxMvqz3cJGrVCmtJxlgNlWhJYbu5fE3jZnLJYmaEEKI+6hAQXfVqlVZtmwZV65cYc2aNXTurM35euPGDdzd3fN4tngYlC0L8+fDxYtaAP766yV/vLq4d6ZBd2xszuXypA+CAZKuWywSZzLd7z23dOsTSEUfu8cNlUxZg+4jR4yPr16FN980Ps4aZEv38oeAIXt/7okRcuxebjKVWK9eWhdzU6YX4NIdTDLdJ+bRRJ2HgMxDL6+WbkP3cpOWbhnPLYQQ4n4qUNA9adIkXn/9dQIDA2nWrBktWrQAtFbvBqaTOQshHin6LNgAkZH3siFfYzK1HKZsMM2UnmvQndPcpqbL9UF3yq3CnwO5BAgIgMwJKUhL0+Y337ABliyBdu205Gp6Xs5euDsaL65KIrWHgL6lO4/u5VlbuqOTtO7lpi3dTk5azydLPbd1OrBxM4nI7/EiV2Cg9jcuLvfA29PJE0dbRxJSE7hw9wIgLd1CCCHurwIF3U899RSXL19m3759rF692rC8Q4cOfPHFF4VWOSFEyWI61v+65QZq6+hstHGeANGWg27T1jO3e509yXSqpDth2dfHXbjHHTzYdDro2dP4ODwcOnbUxuRamm9Z39qtQ0cl99ynmRIlgMrQ/upyn5JJH1zrx3LfTb6rLTdp6Qati/kff5iPu3ZwgO++gzIVyhsvqN1j0B1g0sli69bs602nFCzrWhaA4ze1/ycyXZgQQoj7qUBBN0D58uVp0KABNiYpips2bUpwcHChVEwIUfJUMOk5ek9BN4Bz5sbuWsjsFX+ZtBRj1G13rzMomQbdlxeZr7uxDa4svscdPPheeMH6sj1r9KRzlc48XftpHO1ybx0VJYA+CM7Ife4ta1q69Z56Cnbvhg8+0G5Hj2pTTmoX1DK7Vdzen30nt/ZaXW19SzfAoixf25s3Yc4c42N9y7Y+6JaWbiGEEPdTgYNuIYTIynQsZ17JjfLkkrmxuLNw94j5urA3SUvNMDy0zb2BLm9uVYz3L/4E4au0+zGnYEd/Y0vgQ6xZM3j8ccvrsnYV/vCxD1nz3BoWPrWw6Csmip5+3u/0JOMyJx8tt4KzHzhprcL6LOUJqQmkpqcagm/T7OWm6tSBd97RbqZ5Awzf7Vt7IPascXlaAhx83epqm25z6VL46y/tfkoKDBoE0dHG9fox3IagW8Z0CyGEuI8k6BZCFBrToDss7B435lrZeH/Pi8ZkT6e/gssLzbqX33tLtx+4Bmr3VQb8+xSsbwurGzxSY7wXLNCCb1O2tvD228VTH3GfGFq6k43LOmyC3te0W/u1ADjYOuBir/UZj06OtphIzSpu+onhFex4BtKTtYB757MQc9LqzbRuDfrOdkrBc8/BSy9BgwawZo15WdNpw0wfCyGEEPeDBN1CiEJTtqwxg/mBA5CYaL7+5Em4e9fKjXm3MN6/tRvWtYIN7WD/qPxVSqm8bwDl2hufk54AN7dBeqLlbT6kvLxg/Xro1w98fKBpU1i9Gvr2Le6aiSKls9DSnQPTDOb6sd2Wupfnqmwr4/3b++CvIFhWAa4uy9dmSpc2nxc8JkYbN37cQhqIrN3JpXu5EEKI+0mCbiFEodHpwC9ztq/ERFi50nz9Rx9pc/VapWxr88e398KNLYaHpl3Krd5mbvz7FcJGSj43N1i4UMs+v3u3llBNPORsM6+UmbZ058B0XLdhTHd+W7rLhZo/TgyH1Lv520amZ56xrly2oFu6lwshhLiPJOgWQhSqyia9wsePN46rXLYMfv01HxtyKgdlmue42rRLeaEE3b6djV3Ms8ojq7MQJZp9ZtCcj5buyLhIktOTsbOxw80hn9MHuAeDR5381tKiF16wbvYC0yBbh066lwshhLivJOgWQhSqli2N969cgQ4d4Nln4emnC7CxoOdzXGVnpwz3CyXo1tlAsx+1v6ZcKkLQkELYgRAPKEdv7W9iZJ5F9a3al+5e0h7nt2u5XtBQy8uzfv/y4OEBX32Vfbm9vZZBXc80yPZy9sLO5l4TQQghhBDWk6BbCFGoWmfpFb5/P/z2m/m82lar/Dx41M2+3Lcrdo7GqaoSEgqwbUt82kPI52DrpD0u3QQ6bDEGJUI8jPTHd8LlPIvqg+yLdy9qj/PbtVyv2kjwapB9ebD12cv1nn8exo41PnZxgfnzzcd7m3Yvl/HcQggh7jcJuoUQhapNG3B1LaSN2TpCi1/B1tm4rHRjaPGLWfdyq5OzWSN4DPS6Bj0uwON7wC2oEDcuxANIH3SnRkNqTK5F9dODXYq+ZPY432wdoMV8bUoy0JK51ZkENccXaHMzZsDhw/DHH3D2bPaeNabdy2U8txBCiPtN+lcJIQqVqyv06ZPP8du58aoHT5yCSwvA1R8qPQU2dri4GIsUatAN4FhauwnxKDDtyRF/GTxzHm+tb+nWB90F7l4O4FETup+FW7ugVHVwqVDwbQF162o3S7xdvLHR2ZChMmQ8txBCiPtOgm4hRKEbOVLr3qmU+XJnZ3ByKsAGXStBrTfNFnl4aMnU0tKKIOgW4lHiWMZ4P/5C9qA7/hK4BgDG7uT33L1cz85ZG9ZRxGx0NpwbdY4MlYG7o3uR708IIYQwJd3LhRCFrnlzGDMm+/LJkwux6znaPL0gQbcQ98S0pTtqp/m6lDtwZYnhob5l+0b8DbPHJUGgZyBBXkF4u0iOBiGEEPeXBN1CiCIxdSp06aLd1+m0IPzNN3N9Sr6VyWygk6BbiHtgGnRf+8u8i8rpryA90fAwa8t2kQXdOp3xltsyIYQQogR4oILuO3fuMGjQIDw8PPDw8GDQoEHczeVsOjU1lf/+97/UrVsXV1dX/Pz8GDx4MOHh4fev0kIIi5ycYNUqLbnR+fPwxReFvw990B0TU8Ds6EIIcDYZSx19FC5mJmSIOQUnppsVzRpk33P3ciGEEOIR8EAF3QMHDiQsLIzVq1ezevVqwsLCGDRoUI7lExISOHDgAO+++y4HDhxgyZIlnD59mp49e97HWgshclO3LgQGFs229UF3RgZcznu2IyEePTm1DpsudywNTiYZvcPegIOvw8b2kBZv9rT71tIthBBCPEQemERqJ06cYPXq1ezatYtmzZoB8N1339GiRQtOnTpFjRo1sj3Hw8ODdevWmS376quvaNq0KZcvX8bf3/++1F0IUTxKmyQYP3cOgrLM7pWRATYP1KVFIR5QHrUh6bp2P+k6nPzcYrGsU4QVeMowIYQQ4hHywJyO7ty5Ew8PD0PADdC8eXM8PDzYsWOH1duJjo5Gp9Ph6emZY5nk5GRiYmIMt7i4uHupuhCimJQxSbp89Gj29f/+e//qIkSJ5pHDXFtZSPdyIYQQIv8emKA7MjKScuWyz51Zrlw5IiMjrdpGUlISEyZMYODAgbi75zwlyLRp0wzjxj08PAgNDS1wvYUQxcc06N6zx3zdrVuwZcv9rY8QJVbZ1lYVc7Z3xtHW0fBYupcLIYQQeSvyoHvy5MnodLpcb/v27QNAZyEjqVLK4vKsUlNTGTBgABkZGcyaNSvXsm+99RbR0dGG2xY5MxeiRDINuteuhXiT4adz50Jq6v2vkxAlks9joLPulMC0dVtauoUQQoi8FfmY7tdee40BAwbkWiYwMJDDhw9z/fr1bOtu3ryJj4+PhWcZpaam0q9fPy5cuMDGjRtzbeUGcHR0xNHReKXezc0t1/JCiAeTn5/x/u3bMGcOjB2rZTOfOROGDi22qglRsjiWhjLNIcrCcC4bB7OHXk5eRMZFGu4LIYQQIndFHnR7e3vj7e2dZ7kWLVoQHR3Nnj17aNq0KQC7d+8mOjqali1b5vg8fcB95swZNm3aRBnTpi8hxEOtZk3zx59+CuXLw3ffwbVrxVMnIUqsmv+Fbb3Ml9k6QcBAs0X61m0bnQ3ujrlf5BZCCCHEAzSmu2bNmnTp0oUXX3yRXbt2sWvXLl588UW6d+9ulrk8ODiYpUuXApCWlsZTTz3Fvn37mD9/Punp6URGRhIZGUlKSkpxvRQhxH0SGKjNB64XEQEDB8KmTcVWJSFKroo9wauh+bI674GLn9kifeu2p5OnVcO/CkSp3G9CCCFECfLATBkGMH/+fEaNGkXnzp0B6NmzJ19//bVZmVOnThEdHQ3A1atXWbFiBQAhISFm5TZt2kS7du2KvM5CiOJjYwM1asChQ8VdEyEeEs1+hG19IOEKVP+P1vqdxTdPfMPdpLvY29oXQwWFEEKIkueBCrpLly7Nr7/+mmsZZXKFOzAw0OyxEOLRExIiQbcQOcrvb6RXfeh+GjJSwM7FYpFKHpWo5FGpEConhBBCPBoemO7lQghREG3bFncNhHjI2NjlGHALIYQQIv8k6BZClGgyikQIIYQQQjzIJOgWQpRoQUFQtarldXYP1AAaIYQQQgjxKJKgWwhR4o0ZY3l5nz73tRpCCCGEEEJkI0G3EKLEGzoUypUzX9alC9StWzz1EUIIIYQQQk+CbiFEiefsDBMnGh+XKgVffll89RFCCCGEEEJPRjwKIR4Ko0aBpyds3gyvvQbVqhV3jYQQQgghhJCgWwjxEBk8WLsJIYQQQgjxoJDu5UIIIYQQQgghRBGRoFsIIYQQQgghhCgiEnQLIYQQQgghhBBFRIJuIYQQQgghhBCiiEjQLYQQQgghhBBCFBEJuoUQQgghhBBCiCIiQbcFycnJTJ48meTk5OKuihCFSo5t8bCSY1s8rOTYFg8rObbFo0SnlFLFXYnilpCQwMmTJwkODsbFxYWYmBg8PDyIjo7G3d29uKsnRKGRY1s8rOTYFg8rObbFw0qObfEosSvuCjwIXFxcaNiwYXFXQwghhBBCCCHEQ0a6lwshhBBCCCGEEEVEgm4hhBBCCCGEEKKISNBtgaOjI++99x6Ojo7FXRUhCpUc2+JhJce2eFjJsS0eVnJsi0eJJFITQgghhBBCCCGKiLR0CyGEEEIIIYQQRUSCbiGEEEIIIYQQoohI0C2EEEIIIYQQQhQRCbqFEEIIIYQQQogiIkG3BbNmzaJy5co4OTnRqFEjtm3bVtxVEsJq06ZNo0mTJpQqVYpy5crRu3dvTp06ZVZGKcXkyZPx8/PD2dmZdu3acezYsWKqsRAFM23aNHQ6HWPGjDEsk2NblFTXrl3jueeeo0yZMri4uBASEsL+/fsN6+XYFiVRWloa77zzDpUrV8bZ2ZmgoCCmTJlCRkaGoYwc2+JRIEF3FgsXLmTMmDFMnDiRgwcP0qZNG7p27crly5eLu2pCWGXLli2MHDmSXbt2sW7dOtLS0ujcuTPx8fGGMtOnT2fGjBl8/fXX7N27l/Lly9OpUydiY2OLseZCWG/v3r3MmTOHevXqmS2XY1uURHfu3KFVq1bY29uzatUqjh8/zueff46np6ehjBzboiT65JNPmD17Nl9//TUnTpxg+vTpfPrpp3z11VeGMnJsi0eCEmaaNm2qXnnlFbNlwcHBasKECcVUIyHuzY0bNxSgtmzZopRSKiMjQ5UvX159/PHHhjJJSUnKw8NDzZ49u7iqKYTVYmNjVbVq1dS6detUaGioGj16tFJKjm1Rcv33v/9VrVu3znG9HNuipHriiSfU0KFDzZb17dtXPffcc0opObbFo0Nauk2kpKSwf/9+OnfubLa8c+fO7Nixo5hqJcS9iY6OBqB06dIAXLhwgcjISLPj3NHRkdDQUDnORYkwcuRInnjiCTp27Gi2XI5tUVKtWLGCxo0b8/TTT1OuXDkaNGjAd999Z1gvx7YoqVq3bs2GDRs4ffo0AIcOHeLff/+lW7dugBzb4tFhV9wVeJBERUWRnp6Oj4+P2XIfHx8iIyOLqVZCFJxSinHjxtG6dWvq1KkDYDiWLR3nly5duu91FCI/fv/9dw4cOMDevXuzrZNjW5RU58+f55tvvmHcuHG8/fbb7Nmzh1GjRuHo6MjgwYPl2BYl1n//+1+io6MJDg7G1taW9PR0PvroI5555hlA/m+LR4cE3RbodDqzx0qpbMuEKAlee+01Dh8+zL///pttnRznoqS5cuUKo0ePZu3atTg5OeVYTo5tUdJkZGTQuHFjpk6dCkCDBg04duwY33zzDYMHDzaUk2NblDQLFy7k119/5bfffqN27dqEhYUxZswY/Pz8GDJkiKGcHNviYSfdy014e3tja2ubrVX7xo0b2a7ACfGg+89//sOKFSvYtGkTFStWNCwvX748gBznosTZv38/N27coFGjRtjZ2WFnZ8eWLVv48ssvsbOzMxy/cmyLksbX15datWqZLatZs6Yhiav83xYl1RtvvMGECRMYMGAAdevWZdCgQYwdO5Zp06YBcmyLR4cE3SYcHBxo1KgR69atM1u+bt06WrZsWUy1EiJ/lFK89tprLFmyhI0bN1K5cmWz9ZUrV6Z8+fJmx3lKSgpbtmyR41w80Dp06MCRI0cICwsz3Bo3bsyzzz5LWFgYQUFBcmyLEqlVq1bZpnY8ffo0AQEBgPzfFiVXQkICNjbm4Yatra1hyjA5tsWjQrqXZzFu3DgGDRpE48aNadGiBXPmzOHy5cu88sorxV01IawycuRIfvvtN5YvX06pUqUMV489PDxwdnY2zGs8depUqlWrRrVq1Zg6dSouLi4MHDiwmGsvRM5KlSplyE2g5+rqSpkyZQzL5dgWJdHYsWNp2bIlU6dOpV+/fuzZs4c5c+YwZ84cAPm/LUqsHj168NFHH+Hv70/t2rU5ePAgM2bMYOjQoYAc2+IRUoyZ0x9Y//vf/1RAQIBycHBQDRs2NEy1JERJAFi8zZ0711AmIyNDvffee6p8+fLK0dFRtW3bVh05cqT4Ki1EAZlOGaaUHNui5Prrr79UnTp1lKOjowoODlZz5swxWy/HtiiJYmJi1OjRo5W/v79ycnJSQUFBauLEiSo5OdlQRo5t8SjQKaVUcQb9QgghhBBCCCHEw0rGdAshhBBCCCGEEEVEgm4hhBBCCCGEEKKISNAthBBCCCGEEEIUEQm6hRBCCCGEEEKIIiJBtxBCCCGEEEIIUUQk6BZCCCGEEEIIIYqIBN1CCCGEEEIIIUQRkaBbCCGEEEIIIYQoIhJ0CyGEEEIIIYQQRUSCbiGEEEIIIYQQoohI0C2EEEIIIYQQQhQRCbqFEEIIIYQQQogiIkG3EEIIIYQQQghRRCToFkIIIYQQQgghiogE3UIIIYQQQgghRBGRoFsIIYQQQgghhCgiEnQLIYQQQgghhBBFRIJuIYQQQgghhBCiiEjQLYQQj5B58+ah0+nYt2/fPW1n5cqVDB48mLp162Jvb49Op7PqeevXr0en06HT6YiKisqz/MWLFw3lLd26dOliKHvlyhX69OlDUFAQrq6ueHh40KBBA77++mvS0tLMtnvs2DFeffVVWrRogaurKzqdjs2bN+dYj6ioKEaPHk1gYCCOjo74+PjQtWtXbt++bVbu4MGD9O7dGz8/P1xcXAgODmbKlCkkJCSYlfvyyy9p3rw53t7eODo64u/vz4ABAzh27JjF/V+6dImhQ4fi5+eHo6MjFSpUoE+fPmZl9J+tpVtkZGSe7zWQ63sdHBxsKBcfH8+AAQOoUaMGpUqVwtXVldq1a/Phhx8SHx9vts2rV68yZswYQkND8fT0RKfTMW/evBzrEB8fz6RJk6hevTqOjo6UKVOG9u3bc+bMGbNyZ8+eZdCgQfj7++Ps7EyVKlUYN24ct27dyvU1Pvfcc+h0Orp3725xvTWf9ebNm3N8n3bt2pXr/k0NHTrU7BjWH++fffaZ1dsYN25crq/H9Ds0efLkHOuhL2OqXbt2Zq/N2dmZ+vXrM3PmTDIyMgzlJk+ebPG9cHJyMtvenTt38PT0ZNmyZVa/PiGEeBjYFXcFhBBClDxLly5l165dNGjQAEdHR/bv35/nc+Li4njxxRfx8/MjPDzcqv34+vqyc+fObMuXLVvGJ598YhZ4xsfH4+7uzrvvvou/vz8pKSn8888//Oc//yEsLIzvv//eUHbfvn0sW7aMBg0a0KFDB/76668c6xAeHk6bNm2ws7Pj3XffpVq1akRFRbFp0yZSUlIM5Y4fP07Lli2pUaMGM2fOxNvbm61btzJlyhT279/P8uXLDWVv3bpF165dqV+/Pl5eXpw/f56PP/6YZs2asX//fmrUqGEoe/ToUdq1a0dQUBCfffYZFStWJCIigjVr1lis79y5c80CZIAyZcrk8i4bWXqvd+/ezZgxY8ze69TUVJRSjBs3jsqVK2NjY2N4rZs3b2b9+vWGsmfPnmX+/PmEhITQrVs3FixYkOP+4+LiaN++PeHh4UyYMIF69eoRHR3Njh07zC5c3Lx5k+bNm+Pu7s4HH3yAv78/Bw8e5L333mPTpk3s378fG5vs7Qp///03y5Ytw93d3eL+rf2s9aZOnUr79u3NltWpUyfH12fq4MGD/PTTT+zevduq8pakpqby66+/ArB69WquXbtGhQoVLJYtVaoU8+bNY9KkSWbvTVxcHIsWLcLd3Z2YmJhszwsKCmL+/PkA3Lhxg9mzZzN27FgiIiL45JNPzMquXr0aDw8Pw+Osn4GXlxdjx47ljTfeoFu3bjg4OBTshQshREmjhBBCPDLmzp2rALV3794CPT8hIUEppVR6erph2ciRI5U1PycjR45UDRo0UO+8844C1M2bNwtUB6WUateunXJxcVHR0dF5lu3Xr5+ys7NTSUlJhmWm9V+0aJEC1KZNmyw+v1evXqpChQrq9u3bue5n4sSJClBnz541W/7SSy8pIM/nHz9+XAHq3XffNSzLyMhQISEhKiQkxKz+ltzrZ5uT559/Xul0OnXmzJk8y7755psKUOfOnTMsM32v9+7dqwA1d+5ci88fPXq0cnV1NXu+Jd99950C1Pr1682WT506VQHqwIED2Z5z9+5dVaFCBTVjxgwVEBCgnnjiiWxlrP2sN23apAC1aNGiXMvlpl+/fqp58+Zmyy5cuKAA9emnn1q1Df2x+8QTTyhAffTRR9nK6Lc5fPhwBai1a9earf/++++Vs7Ozeu6557J9j0NDQ1Xt2rXNlqWkpKigoCDl4uKiUlJSlFJKvffee1Z/pyMjI5WdnZ2aP3++Va9RCCEeBtK9XAghHiInT57kmWeewcfHx9BtefDgwSQnJ5uVi42NZcSIEXh7e1OmTBn69u2brfU5MDCQ7t27s2TJEho0aICTkxPvv/8+kL0FKy/btm1jzpw5fP/999ja2t7Tazx37hxbtmyhX79+ObZYmipbtiw2NjZm+7W2/hcvXmTFihW8+OKLeHl55VrW3t4ewKylD8DT0xMbG5s8W/XKli0LgJ2dsRPa1q1bCQsLY8yYMTg6OlpV58IUGxvLokWLCA0NpWrVqnmWt/QarH2vExIS+P7773n66acJCgrKtWxu7zWQrVszwPjx4/H19WXUqFEWt5mfz/peXb9+naVLlzJo0KB72s4PP/yAg4MDc+fOpVKlSsydOxellMWyNWrUoGXLlvz4449my3/88Uf69u2b7b3Mib29PY0aNSIhIYGbN2/mu84+Pj506tSJ2bNn5/u5QghRUknQLYQQD4lDhw7RpEkTdu3axZQpU1i1ahXTpk0jOTk5W9fY4cOHY29vz2+//cb06dPZvHkzzz33XLZtHjhwgDfeeINRo0axevVqnnzyyXzXKzExkWHDhjFmzBgaNmyYYzn9ONmcxp3q/fjjjyilGD58uMX1SinS0tK4c+cOCxcuZN68eYwfP94sELTWtm3bUErh5+fHM888g5ubG05OTrRr1y5bV+whQ4bg6enJiBEjOH/+PLGxsaxcuZJvv/2WkSNH4urqmm376enpJCcnc/LkSYYPH065cuV44YUXDOu3bt0KaF2Du3XrhpOTE25ubnTv3p2TJ09arHP37t2xtbWldOnS9O3bl6NHj+b7dev9/vvvxMfH5/lex8TEsHr1aj7//HOeeeYZ/P39872v/fv3Ex8fT7Vq1RgxYgReXl44ODjQuHFj/v77b7OyvXv3xt/fn/Hjx3Ps2DHi4uLYunUrH3/8MT169KBmzZpm5devX8/PP/+c60Wf/HzWeiNHjsTOzg53d3cef/xx/v33X6te69q1a0lNTc3WNT0/rl69ytq1a+nVqxdly5ZlyJAhnD171nDMWDJs2DCWLVvGnTt3ADh16hQ7duxg2LBh+dr3uXPnsLOzy3Zxom7dutja2uLj48PgwYO5fPmyxee3a9eO7du3c/fu3XztVwghSqzibGYXQghReB577DHl6empbty4kWMZfRfkV1991Wz59OnTFaAiIiIMywICApStra06depUrvvNq3v5+PHjVVBQkKFrek5dUTdv3qxsbW3V+++/n+O20tLSVIUKFVRwcHCOZaZNm6YABSidTqcmTpyYa/1z616u35a7u7vq1auXWr16tVq8eLGqV6+ecnJyUocOHTIrf+LECRUcHGzYP6BGjRqlMjIyLO7b0dHRUK569erq+PHjZutffvllw/6HDRum1q9fr3755RcVEBCgvL29VXh4uKHsqlWr1MSJE9Vff/2ltmzZor7++mtVsWJF5erqqsLCwnJ9D3LSrFkz5enpqRITEy2uX7BggdlrfeGFF1RqamqO28ute7l+W+7u7qpVq1ZqxYoVauXKlap9+/ZKp9Op1atXm5UPDw9XLVq0MNv/008/na0bfmxsrAoMDFRvvfWWYZml7uX5+awPHDigRo8erZYuXaq2bt2qfvzxR1WzZk1la2ubrZ6WjBgxQjk7O2c7LvLTvXzKlCkKMOzv/PnzSqfTqUGDBuW4zdjYWOXm5qa+/vprpZRSb7zxhqpcubLKyMiw+D3Wdy9PTU1VqampKjw8XE2YMMHwXuv9/PPP6qOPPlL//POP2rhxo/r4449V6dKllY+Pj7p69Wq2uq9bt04BatWqVXm+TiGEeBhI0C2EEA+B+Ph4ZWtrq1566aVcy+mD7qyBwerVqxWgdu3aZVgWEBCgGjRokOe+cwu6d+/erWxtbdW6desMy/Iz/jOrlStX5hmUREREqL1796o1a9ao//73v8rBwUG99tprOZbPLej+6KOPFKBq1aql0tLSDMvDw8OVi4uLevbZZw3LLly4oKpWrapatWql/vzzT7VlyxY1ffp05e7uroYOHWpx3/v371c7d+5Uv/76q2rUqJHy8fFRR48eNax/8cUXFaAef/xxs+cdPHhQAXleULhw4YJyc3NTPXv2NCxLT083BFGpqalmr8vU0aNHFaBGjhyZ4/Zv376t9u7dqzZu3Kg++ugj5e7urnr27Gk2jttUbkH3/PnzFaC8vb1VTEyMYXl8fLzy8/NTrVq1MttvkyZNVO3atdX8+fPV1q1b1axZs5Svr6/q3LmzWeA/cuRIVa1aNbMLB5aC7vx81pbcuXNHVaxYUdWrVy/XckppY8f9/f2zLbc26M7IyFCVK1dWlSpVMnuv27dvny3XQdZtvvDCC6phw4YqNTVV+fj4qClTpiilLH+PQ0NDzS5qAMre3l49++yz6u7du7nWcffu3crGxkaNGjUq27pDhw4pQH3//fe5bkMIIR4Wkr1cCCEeAnfu3CE9PZ2KFStaVT5rNmv9eOHExESz5b6+vvdUr6FDh9K3b18aN25s6EqalJQEQExMDI6OjpQqVcrq7f3www/Y29szePDgHMuUL1+e8uXLA9C5c2e8vLyYMGECQ4cOpUGDBvmqv/596tixo1m3ZF9fX+rXr8+BAwcMyyZMmEBMTAxhYWGGruRt27bF29uboUOHMnjwYEJDQ822r+9u37x5c3r27EnVqlV5++23DZnO9ft//PHHzZ4XEhKCr6+v2f4tCQwMpHXr1mbTWA0dOpSffvrJ8Dg0NNTidGk//PADQI5dy0HLRt24cWMA2rdvT5UqVRgwYADLly/PNqVZXvSvtWXLlmbHhIuLC6GhoWbTTH3yySeEhYVx6dIlwzHapk0bgoODeeyxx5g/fz5Dhgxhz549zJo1iyVLlpCUlGQ49jIyMkhLS+Pu3bs4OzsbpiYD6z5rSzw9PenevTuzZ88mMTERZ2fnHMsmJiZaHHdurY0bN3LhwgXGjRtnlnG8X79+bNq0iQULFvDyyy9bfO6wYcNo3bo1H330ETdv3uT555/PdV9VqlTh999/N0wBVrlyZVxcXPKsY9OmTalevbrFKdT0rz3r/xshhHhYSdAthBAPgdKlS2Nra8vVq1cLdbvWzr+dk2PHjnHs2DEWLVqUbV2VKlWoX78+YWFhVm3rxo0brFy5kp49e1KuXDmr69C0aVMATp8+ne+gu169ejmuU0qZJQkLCwujVq1a2cZuN2nSBNCm/soadJsqVaoUwcHBnD59ukD7t7bc5MmTee2118z2m1VKSgq//PILjRo1IiQkJM996Jm+1/mV3/e6QoUK2S4Kmb7XoE3jppSyeAHgypUreHl58cUXXzBmzJhCe68h7++Nt7d3nkF8bvQXRGbMmMGMGTMsrs8p6G7VqhU1atRgypQpdOrUiUqVKuW6LycnJ8OFlfzK6X3Tz3nu7e1doO0KIURJI0G3EEI8BJydnQkNDWXRokV89NFHD8zJ7KZNm7ItmzdvHj/99BPLli3LcU5hS37++WdSU1PznfRJXwdrsm9n1axZMypWrMjatWtJT083tICGh4dz6NAhBg4caCjr5+fH0aNHiYuLw83NzbBcn4Qrr14IUVFRHDlyhFatWhmWde3aFRcXF1atWsXYsWMNyw8cOEBkZCTNmzfPdZsXLlxg+/btdOzY0bAsMDCQwMDAXJ+3YsUKoqKimDJlSq7lsrqX99rX15cWLVqwfft2YmJiDJnpExIS2LJli9lr9fPzY8OGDdnmpc76Xnfp0sXiMThgwAAqV67MtGnTDHXNz2dtyZ07d1i5ciUhISF5tmIHBwezYMECoqOjrc4abrqfpUuX0qpVKz788MNs67///nvmz5/P0aNHc5wz/J133uHPP/9k5MiR+dp3fuzatYszZ85YzBZ//vx5AGrVqlVk+xdCiAeJBN1CCPGQmDFjBq1bt6ZZs2ZMmPD/7N13eBTFGwfw76X3hBQSAgmE3glFpHcCSEdFRZqAFQuiIuiPIiqgCGLBgiCIAqKCgIL03iH03gklARLSe3Lz+2Oyd7e5S3IJSS7l+3mee7K3O7c7l2zu9t2ZeWciatasiXv37mHdunX48ccf89WNOy83b97EkSNHAMhMxgDw119/AZBBndIy1qlTJ6PXKl2Z27Ztq7o5sGvXLnTt2hVTpkzBlClTjF63aNEiBAQEGHW1VkydOhX37t1Dhw4dULlyZcTExGDjxo346aef8PTTT6N58+a6sklJSdiwYQMA6Lq/7tq1C5GRkXB2dkavXr0AyOmuvvzySwwePBj9+/fHq6++isTERHz88cews7PDpEmTdPscN24cBgwYgO7du+Ptt9+Gt7c3Dh48iJkzZ6J+/fq6fcbGxqJ79+4YMmQIatWqBUdHR1y6dAlfffUVUlNTMXXqVN0+PTw8MH36dLz77rsYOXIknnvuOURERGDy5MkIDAzEa6+9pivbrVs3dOjQAY0bN4abmxtOnz6Nzz//HBqNBh9//LHJ31lOFi1aBEdHxxwDzR9//BF79uxBSEgIAgICkJiYiD179uCbb75BmzZt0L9/f1V55dxQgq2jR4/qbkw89dRTunJffPEFOnfujB49euD999+HRqPBnDlzEBkZqXoPY8eOxbJly9C9e3dMnDgRAQEBOHPmDD755BP4+vri+eefB6AeamDIwcEBXl5eqvMzP3/rIUOGIDAwEC1atIC3tzcuX76MOXPm4N69e1iyZEmev99OnTpBCIFDhw4hJCTEaPvp06d1vzNDjz32GP755x+kpKTgzTffNPn/5eXlhWXLlmHRokX48ssvTR5/6NChJmcrKKgmTZpg6NChqFevHhwcHHD48GHMnj0bfn5+mDBhglH5gwcPwsvLC40aNSq0OhARlWgWG01ORESF7ty5c+Lpp58WXl5ews7OTgQGBoqRI0fqMjoridSOHDmiet2OHTuMkomZSjalUPZj6jFixIhc65hTIjWlDlOnTjV6zb59+wQAMWXKlBz3u27dOtGtWzfh6+srbGxshIuLi2jZsqX4+uuvjTJqK8mlTD2qVq1qtO81a9aIxx57TDg4OAh3d3fRr18/cfbsWaNy27dvFyEhIcLPz084OjqK2rVri3feeUdERkbqyqSkpIgxY8aIevXqCRcXF2FjYyOqVKkihg4danKfQgjx008/iYYNGwo7Ozvh5eUlnn/+eXHr1i1VmXHjxon69esLV1dXYWNjI/z9/cXQoUPzzD6fXVhYmLCyshLDhw/Pscy+fftEnz59hL+/v7CzsxNOTk6iSZMm4uOPPxaJiYlG5XP6XZu6DNmzZ4/o2LGjcHJyEk5OTqJLly5i3759RuWOHTsmBg4cKKpUqSLs7e1F9erVxZgxY0RYWFie7zG3c9ucv/XMmTNFcHCwcHd3F9bW1sLHx0cMHDhQHD58OM9jCyGT2VWrVs1oFoHczktkJaALDg4WFStWFKmpqTnuv1WrVsLb21ukpqaanZwtt+zleXn22WdFzZo1hbOzs7C1tRVVq1YVr7zyiiq7vkKr1YqqVauKN954I8/9EhGVFRohsgYgEREREVGxmDNnDj799FPcuXMn16RrZc22bdsQEhKCs2fPom7dupauDhFRsWDQTURERFTMUlJSUK9ePYwdOxbvvvuupatTbDp37oyaNWvip59+snRViIiKTd6pOImIiIioUDk4OODXX3/VTddXHkRHR6Njx4749NNPLV0VIqJixZZuIiIiIiIioiLClm4iIiIiIiKiIsKgm4iIiIiIiKiIMOiGnK/12LFjSEpKsnRViIiIiIiIqAxh0A3gwoULaN68OS5cuGDpqhARERFZRP359aH5SIMWC1oU3UE0mtwfRERlEINuIiIiIkJieqLqJxERFQ4G3URERESEpPQk1U8iIiocDLqJiIiICIlpiaqfRERUOBh0ExEREZVzWqFFckYyAHYvJyIqbAy6iYiIiMq55PRk3XJKRgq0QmvB2hARlS0MuomIiIjKueyt2xzXTURUeBh0ExEREZVz2YNsBt1ERIWHQTcRERFRSWKBuayzJ09jMjUiKlIZycDl74C4C5auSbGwsXQFiIiIiMiysncvZzI1IioyQgvsfRII/w+wdgC6bAe8W1u6VkWKLd1ERERE5Ry7lxNRsbm/SwbcAJCZAhx5FdBmWrZORYxBNxEREVE5V2zdy4XQP3JbR0Rl1/Wl6ucxJ4G7/1imLsWEQTcRERFROceWbqLS6/hxYOZM4NYtS9fEDBlJwK2/jNdfW1LsVSlOHNNNREREVM5xTDdR6bRvHxASAiQlAT/+CBw9Cnh7W7pWuQjfCGQkGK+/v6P461KM2NJNREREVM4xezlR6TR1qgy4AeDmTWDaNItWJ29Rh02vL+PDSxh0ExEREZVz7F5OVPqcPw9s26Ze99NPQFSUZepjlujjlq6BRTDoJiIiIirn2L2cqPSZP994XVoasGxZ8dfFLEIA0ccsXQuLYNBNREREVM4pLdsudi6q50RUMgkBrFxpetu1a8VbF7Ol3AdSIy1dC4tg0E1ERERUziljuL0cvVTPiahkunIFiCxt8WviDUvXwGIYdBMRERGVc0p3ci8nL9VzIiqZDhywdA0KgEE3EREREZVXSndybydv1XMiKpnKRNDd6COgzjhL1KTYcZ5uIiIiopLEcOocjcZ4XRFQWraVoJst3UQl27lzlq5BARgG3Z7NgYZT5Gdb/BXg/i6LVas4sKWbiIiIqJzTtXQ7sqWbqDS4dMnSNSiAlHv65cBn5U+NBqj3rmXqU4wYdBMRERGVc0riNF1LNxOpEZVY8fFARIT+uZUV8M47QGCg5epklvQ4/bJ/b/2yTwfArU7x16cYMegmIiIiKufYvZyo9LhyRf387beBL74Adu0CnJwsUyezpMfKn9YOgGtt/XqNBqjc3zJ1KiYMuomIiIjKOaU7uZK9nN3LiUqusDD18xdekD+rVQOGDy/26phPael2qwdYWau3GbZ8l0EMuomIiIjKOXYvJyo9Hj7UL9erBzRooH/+8svFXx+zKS3dTlWMt1UILtaqFLcSGXR/9913CAoKgoODA5o3b449e/bkWHb16tXo3r07fHx84ObmhtatW2PTpk3FWFsiIiLLSk4G3nsPaNcOWLLE0rWh0kg3T7cj5+kmKumio/XLjz2m3takCVC1avHWx2xKS7d9ReNtykwNZVSJC7pXrlyJcePG4cMPP8Tx48fRvn179OrVC2HZ+1Fk2b17N7p3744NGzYgNDQUnTt3Rt++fXH8+PFirjkREZFljBkjx/Pt2ye7Gf7yi6VrRKWJEILzdBOVIoZBd3CweptGAzzxRLFWxzzadCAzWS47mAi6y7gSF3TPnTsXo0ePxpgxY1CvXj3MmzcPAQEB+P77702WnzdvHiZMmIDHHnsMtWrVwowZM1CrVi38888/xVxzIiIqVhpN3o9y4MgRYPly9bo33gCioixTHyp9kjOSdcuejp4AZNCtFVpLVYmIcmEYdNeqZby9dm3jdRZnmLncVEt3GVeigu60tDSEhoYiJCREtT4kJAT79+83ax9arRbx8fHw9PQsiioSERGVKO+/b7wuPh5YuLD460Klk9Kq7WTrBAcbB2ggb1ilZKQUaH+z981G7W9qo/Y3tRGTElNY1Sx70uOAm78DsectXRMqZQyDblNdyUvkPWdlPDdQLlu6bSxdAUORkZHIzMyEr6+var2vry8iDCejy8WcOXOQmJiIwYMH51gmNTUVqampuucJCQkFqzAREZEF3bkD7NhheltMTLFWhUoxJWmak60TNBoNnGydkJieiMS0RDjZ5n/+ocsPL+Pyw8sAgKikKHg4eBRmdcuG5AhgS2sg8QagsQKafQXUft3StaJSwjDoDgiwXD3yxbCluxwG3SWqpVuhyXZ7RghhtM6UFStWYNq0aVi5ciUqVsz5jzlz5ky4u7vrHh07dnzkOhMRERW39estXQMqC5SkaUqA7WjrqFpvaOeNnfjlxC9YeWZljvt7mPzQ5DIZOD1VBtwAILRA6JvA/V0WrRKVHkr2co0GcHe3bF3MZtjSbethsWpYSokKur29vWFtbW3Uqn3//n2j1u/sVq5cidGjR+OPP/5At27dci07adIkxMbG6h67dvFDjoiISp+NGy1dAyoLDLuXG/40lUxt1t5ZGLl2JEauHZnj/hh05yH2HHAt+/gPARx/xyLVodJHael2cyuhXclNMWzptra3XD0spEQF3XZ2dmjevDm2bNmiWr9lyxa0adMmx9etWLECI0eOxPLly9G7d94Tq9vb28PNzU33cHFxeeS6ExERFbejRy1dAyoLDLuXG/40NVd3VLLM0JeSkZJjhnMG3Xm4/ots3c4u4Vrx14VKJcOgu9TINMgRYWVruXpYSIkKugFg/PjxWLhwIX7++WecP38eb7/9NsLCwvDKK68AkK3Uw4cP15VfsWIFhg8fjjlz5qBVq1aIiIhAREQEYmNjczoEERFRqRcVBdy6pX9ubw+MHFmKxvdRiZGflu6opCiTy6oyyVEmlynLXY4LoYITQh90l5qu5QAgMvXLVnaWq4eFlLig+5lnnsG8efMwffp0BAcHY/fu3diwYQOqZqXmCw8PV83Z/eOPPyIjIwNjx45FpUqVdI+33nrLUm+BiIioyB0/rn7+3nvA4sXAwYNAHiOyiFSUsduONnIst66l28SYbnMCarZ05yLpLhB71tK1oFIsJQXIyJDLxd3SLYT8nmnRAujXDzhzJj8vNgi6NeWvpbtEZS9XvPbaa3jttddMbluyZInq+c6dO4u+QkREVPIIoV9WBrUZrivjbt7UL/v4ABMmyGV/f+Cjj4AbNyxSLSqFzO1enp6ZjrhU/bhMUy3d2budM+jOJvp43mWIcqEE3EDxB91LlwKjRsnl0FBg715g/36gbl0zXmyqpTv6FHD3H/36wGcB1xqFVt+SpMS1dBMREVHe7t/XL/fpA7i66p+PGAF4ehZ/nah0yt69XGnxzt69PHvLdmRSpNG+opOjVc8ZdGcTc0L9vP4koPYbFqkKlU6WCrpv3gTeyHaqRkcDL7wAeeM7r4cq6M5q6Y45AZz6n/4Rf7m43k6xK5Et3URERJS7e/f0yx06qLc5OADPPVe89aHSK/uUYTl1L8/esm2qe3n2IJtBdzYxp/XLVQYATWbIZXtv4OI8S9SISplMg9jVthh7aX/2GRAfb7z+0CEzd1DOx3Qz6CYiIiqFDFu6GzUy3l6lSvHVhUo3cxOpZQ+yTXUvV4JsO2s7pGWmMejOLjlcv9xkpn653kTg7n/FXx8q2dITgDv/AA6+gG8nQGOlaum2tjZvN18e+BKpmamo510P/ev2z7lg6kMgLQpwqQFo9B2iU1OB338v2FvQMdXSXY4w6CYiIiqFDFu669SxXD2o9DN3THd+WrqDPIJwMeoig+7sUrPulrlUB9wMBsJa2wF1x1umTlQyJd4CNrcEUiLkc5/2QPs1yMzUjx0yJ+jO0Gbgnc3vQECge/XuOQfdl38Ajo8HMpMB9wZAm98Bj4YAgJ079RnTC6wc5VwxpUBjuhMTjbNZEhERUfFRWrqdnQEXF8vWhSwnJSMFq8+vxurzq3Eh8oJ6oznjLGF+93JlDLcy5tvUmG4lyK5eobrqOWVJyfrH9Qg23lZlYLFWhUowbSZwYKg+4AaAB3uAfYORkaZvMTYn6I5MioSADHjvJd7LodBBIPR1GXADMsP+9s5A0m0AwO7d6uLW1oBdfnuIawwqq03P54tLvwIF3b6+vhg1ahT27t1b2PUhIiIiMygt3V5elq0HWda16Gt48o8n8eQfT+Kn0J8KtI/8di+v4VlD9dyQYUu38lyU8xYuHW06kJZ1E8KluvF2K3ZApSy3/gQe7DZef28bkKZvclYm7shNREKEyWUdIYDDY9TdvwEgNRK4sgAAsGePfrWTE3D2LBAVBQzMz30iw6BbMOg2y4oVKxAbG4uuXbuidu3amDVrFu7evVvYdSMiIiITMjOByKxGRgbd5Vt4vH6McHhCeC4lc5bfRGo1PWuqnqvKZAXiFZ0rwsnWCZkiUzXNWLmWatAzwCXIcvWgku/azzlusjG4N5OZmWMxHcNA+0HiA2RoM9QFIvfnMne8FgBw7px+zWefySFNLi5yCrEgc09ltnTnX9++fbFq1SrcvXsXr776KlasWIGqVauiT58+WL16NTIyMvLeCRERUWkkBHB7HXBhruySZwGxsfrhcQy6y7e78XdNLueH0qKtdBvPa8qwmhVqqp4bUlq6PRw8UMGhgmpduZdikP3Q2URLNxEApMfJFu0cGHYpz2/QLSDwIPGBukDYH7m+PiVFtmoDgL09MHKkfpuLCzB5MuQXkuFDd0CDdaqgOy3vipcxjzRPt5eXF95++22cPHkSc+fOxdatW/HUU0/B398fU6ZMQVJSUt47ISIiKi2EAE68B+zpDxx/B9jSGjg4EsjeclDE0gyuVxh0l2+GrdsFbunOSpj27ZFvMeD3Afjp2E+q9Qpd0J1LS7cSYFdwrIAKjgy6VQyDbsdKlqsHlWwP9gJCq39esRMQ+Kwum7iNjT6oNaedM3uXcqMu5vd36pet7ACfdoC1k27VnTv6za1aGecQef75vOsAoNy3dD/S4JGIiAgsXboUixcvRlhYGJ566imMHj0ad+/exaxZs3Dw4EFs3ry5sOpKRERkWWc/Bi7MUa+7/gvgXA1oNK3YqmF4ocUkauVbYbR0K93Ij4Ufw7HwY0brFUriNCVJWmxqLNIz02FrrZ/+Rxd0O1RgS3d2aQa/BxunnMtR+WbYg8qnHdBpk8xuf3cYsLuvqqU73YzYNdegOy1WPXd8q1+Aqs8CCdeA7V0AALdv6zc3b268f7PnCrdi0J1vq1evxuLFi7Fp0ybUr18fY8eOxdChQ+Hh4aErExwcjKZNmxZWPYmIcqXVAps2AWFhQMuWAD9+qNCl3AfOzTS9LTGsWKtieKFl9gUPFa/4K3KcpGttwL1ekR3GsHU7IS0B8anxcLV3lSsMu3kaZlzKltgsezfynNYrLds+zj5wt3dHbGosHiY/hK+Lr64MW7pzYdil1srBcvUoQ7RCq+su7WznDBe7MnAXMvGGfrn5tzLgBgD/J4D6k1RjuuPMSJegZCx3tnVGYnqiOoN54nUgK7M5qgyUATcgE/21WwXcWacKus0ev22K4Tmv/C/4tANa/wYcflGfOb2MKlDQ/cILL+DZZ5/Fvn378Nhjj5ksU716dXz44YePVDkiInPExwPDhgFr1+rXjRoF/PADAxIqRNeXApkplq4FAHXQbcOExyXPtV+Aw6P12YCrDAAeXwzYeRT6obK3bocnhOuDbjNl70ae03qle7mnoyc8HT0RmxqLqOQo00G3QwV4OHio1pUpQqvr7mv+awy6qGSNm0dyOBB/Sb/eozFgV+HR61dOXI++jprfyOEO4x4fhy97fmnhGhWCpFvyp1OgPB8M1Z8Em2T9DTRzgm6lZbtBxQY4fOewuqVbORYABDytfqFnc8DWA7c36Ff5+ZnzBnJg66ZfVoJul+rycXQsg25TwsPD4eSUe7cYR0dHTJ06tUCVIioL5h+ej41XNwIA/njqDzjaOlq4RmVTSgrQvj1w8qR6/c8/A/XrA++8Y5l6URl0d71+2dYNqDIIeHgkl6yvRccweY7VI2VnIZWMZJlUKDUS8G4tH+bMyWPoyo/AkVfU626vAaAB2q8urJrqKNnLK7tWxp34OwiPD0dtr9r52oc5Ld1aodUFz0rQfT3mutG4blVLd1nsXn5/N3DoBdka6d4IaDgFCBhk3msNu9QqrX53N8jpmhSdtwJ+XQutumXdjZgb+uXYGzmWK1Wy5saGR2Pjzx8bZzi4yJutGRn5C7ob+jQ0EXQbNGN7mugi6FpDNabb19e4iNkMg+702EfYUelUoKB7586dsLa2Ro8ePVTrN23aBK1Wi169ehVK5YhKsw1XNmDDZXl78GbsTdT1rqvfmNdFHOc0Nds33xgH3IrY8veZTkVFmy6T2wCAlT3Q4R+gYgeZQO3IK+qkN8Ugv1PGkBmS7soEeQ+P6tdV7ie7Ptqa2XKcFgucmGh6W4qJ+XEfkRBC1728iV8TGXQXIJla9rHbptbHpsRCK7SwsbKBs60zPB09AagzmKdnpiM+LR5AGR3TffN34OBwffAccxLY+yTQYj5Q67W8X2/Y0m3N7uWFQRV0GyyXWkIAyVmBsHtDk0U0GqBCBeDBA/Ouc3RBd8WGqucA9C3dVvaAS02Tr09I0C8XWkt36oOcy5VRBbo/PnHiRGSa+JYXQmDixBy+bIjKmWvR10wuU+HRaoGvv7Z0LahcSAzTXzAHDZMBNwBY2QCP/QB4P16s1TEcNsFZOgtBRhKwta064AaAO+uAS9+av5+w34H0mEKtWm7iUuOQlJ4EJ1sn3TRe+U2mJoQwq3u5kkTN09ETGo1GF3Qr6wEgOiUaAGBnbQcHGwf9mO6UMhB0p9yX405NJYB6sN+8fahuqOezBwWZVOaC7tRI/TAmt7o5FquQNQIhr5bulIwUxKTEQAMN6vnI3BImg263OvL7zATD2TIqPMrIB1t3g4ox6DbL5cuXUb9+faP1devWxZUrVx65UkSlnVZocT36uu45g+6isWePOqvm2LEykdrq1UAlzsZChSnB4H+40hPqbVY2QI0Xi7U6hkF3Wvmb7rTwXZ6vTl5kSJjoSiC0slU7e6+kWwbdx71aAiFHgHZ/AS41Cq2qhpRWbV9nX/i5yCYopbu5uVIzUyFgundVUnoSRNZ7NBzPDUDXim3YvVxZruBQARqNxmSZQicEcGk+sLUDsK0TcHaGnOe4sF3+HshIyLtcbqwM/nG1qY+2LwKg7lIekxKDmJQYi9WlUCQb/P/mMrbfU/4bIi5ONkDk5H6inKbO28kblVzkhZEqkVpSVt9xe+8c92H4HfNIeXJULd33cy5XRhUo6HZ3d8e1a8ZBxJUrV+Ds7PzIlSIq7cLjw5Gaqf9CZdBdNA4azKrRr5/sah4QAAwcCOzYAbjmL5cQUc4S9TfR4GlizpT8JlR6RIYXPhxG8YiEAK4u0j+38wT8ewMOJgYvCiFbvv9yB1Z5AGsqAWemA5lZV6VKS7ljJaDzFsCrBRDwJNB9f45dNx+F0qrt5+KnC7rvJuSvpTunVm4AEBBIyZCtbkrgrATdprqXG47nBlD0idQyEuWQgNDXgQd7gPu7gFMfAv81AZILuTt/2Er9sm8XoNNmoOmc/CU90xiOCykZSRlLsqikKKy9sBZrL6xFWKzpGSKU1m2rrM/gmzE3i6t6RUNrcF7Y5BxTKS3OQuTe2q20avu6+KKic0XVOgD65GXWOefqKrSg29pezgMOsKXbXP369cO4ceNw9epV3borV67gnXfeQb9+/QqtckSl1fWY67k+hxD6R07rNJrcH4Rz5/TLb72l/rXUqQO8ZsYQOyKzKC3ddp6AU4Bl6wLA3l6/HFWEjYjlQsJVIP6iXHYKBEIOAx3/BfpeA6o+ry8nBLB/CBD6hr7FM+UecHoqcOI9IDVKPw9ztWHqVh2HikDzwh8Lo7Rq+7n46TKI57elO6fx3Nm3Z2/p1gXdSSaC7qwW7iKfMuz0NODOP8brE28ASYU4jV9aDBB3Xi5XaAp03ABU6g7UHQ902wfYe5m3H8OWbgbdedp2fRsGrByAASsHYNmpZSbLKEF3o4qNVM9LLcNp5ZSgO/YcsPMJ/ePOP6pu3rduIUdKgF3RuSK8nWRrdkxKjO5mmu54hgH+2U+Bwy/Lx6nJqmSdj5xySOlizjHd5pk9ezacnZ1Rt25dBAUFISgoCPXq1YOXlxe++OKLwq4jUamjtGw39m2sek6F63zWNZC9vcxgnh073lB+/XD0B8zcMxO/nPhFvUG5gHfwLRE3vdzcALusBgMG3Y/IMPt8o+mAa1ZXcBsnoNUv+kzSt1bJMdumpMWop33yaWdcpginC1O1dOdzTHdOmcuzbzcc0234MzJZP6Y7e0t3kSZSS30IXPpG/9za0XTvhMIQd1G/XPNV2WKncK8HNP7YvP0YtnQrLZrWDoB9xUevYxl07oH+zvq5yHNG29My03An7g6sNFZoVaUVgDIQdGeaCLrTooHw//SPxDBV0H3TROO+EhwbBt221ra6/9t7CVldzE0F3bdWA1cXyEfYSt13DaCerrJAlJuRyfn7nCoLCpS93N3dHfv378eWLVtw8uRJODo6onHjxujQoUNh14+oVFKC7I5VO+LUvVO4Fn0NQghoSsDFelkhhL6lOyiI83FT7lIzUtFzWU8IIdCpWidM6zTNqEx6Zjre2vgW0jLT4OPkg+FNhuv/Z5UueOZmsS5iGg1QsaLMacCg+xEpLZgA4J9t9hUrazltGABcnKtf71obqDZUBmNKt+M4g6Dbq3gS65kc053P7OW5dS833K7rXu6Qd0t3REIEvj38LaKTo3XrC/078PZq/bho77ZAu1WAo6+cZeDA8MI7DqDvCQEAFTsabzfs1ZAbw67oymdKtefl4w9nIDP3GyDljSrofmAcdN+Ouw0BgcqulVG9QnUAcraY0s28ZHuGQffly8bbL14E6tY1CLqd5I2dis4V8TD5ISISIlDVo6p+5g2NdY7HMgy6Ux61g4byvxJ3Xl7IlaPr4gIF3QCg0WgQEhKCkJCQwqwPUYFceXgFr294HQAwtPFQDG081KL1MWzp9nDwQExKDCKTIuHj7GOxOmVmAitXAt9/D4SHA/7+wJNPAi+/DDiU5JlLMlOAqz8B0SfkmKOK7YHKA/Agyg6JWdeKNYomRxGVIecenMPOGzsByBY7U0H3xaiLSMtqZXiQ9AD3Eu/pAhkoORpsXPQv2N0fuPuvfv1TxTu42jDoLmfXLoUrNivodqsru4GbkvoQiDwglz2bA1126G/A1HlLBt5KIjaNNWBfPJ/1hi3dPk7ymHGpcUhMS4SznXldfcxt6Va6lx8NP4rJ2yfrEliZGtN9LPwYjoUf061P16YjMT0RLnYG/z+P6mHW/jXWQNuVMuAGZC+DbntMZxkvqLgL8qedJ+Baq+D7MTy/Uu4D7g0erV5lnGGgff7BeWiFVjd2G9C3age6ByLALUC1rtSyMohwc7kJYxh0nzih3iYEsH59tqDbWR90X4i8oB/XrRwvI+djGQbdkZGPOm1YVvfytGjZ2u1U+RF2VroUOOjetm0btm3bhvv370ObLW3ezz///MgVI8qPfWH7sOnqJgCAi51LiQm6q7pXRVX3qohJicG16Gv5C7oNB84oV9MFHEzz4AEQEqL+YL56VWb/PnMG+OmnAu226N3fDRwYph6bd/lbwKUmEuufh/IRVqWKZapHpceJiBO65QuRF5CcngxHW0dVmVP3Thk91wXdygW8YbIZodW3EhTzPN0A4JsVY6Slyf/xiuyhWjAJWbOu5JZhPOakfrneBHWPB6/HAI/Gcmw3IFtylM/s+Kv6KXkA2Wpu2DX5ESmt2n4ufrC1toW3kzcikyIRnhCOmp7mJW7L75juvWF7sTdsr267qZZuUx4mPyzcoFv5m3g9bnzhXtgX8srf0N7n0e5uGd6MySlbPgEAMrQZuBR1CVYaK/i5+OFu/F2ExYahmkc1XRklwA5wC0Cge6BqXallGHTnEggbBt1Hjqi3HT+uH+etZCpXgm5fZ1/VelhnHS+XAF/JlA4AERFAQ9PTh5vHsFdI+CagxqhH2FnpUqAx3R999BFCQkKwbds2REZGIjo6WvUgKm6h4aEmly1FCbqreVST3XdguXHdQgBDh6oDbnd3wCbrlltSSe3NlnwP2DPAdDKchCtITtIHOY6OxkWIDBkG3ZkiE2cfnDUqowTdjjaOqucA9AmQSlDyI8Mg+/x54+2PnPCmvEiPlz8N55CNPiFbtiMPANEn5XNAtqr6mejhZ22vPzcMLyqv/ABs76x/pEYav/YRKC3dShI15YI6P+O6le7jwX7BiJsYp3vU866n2p7TtF8Pkx9Cm3XTKbf5uAt1XLfQAjGn5bJvl8Lbb06UruB2BufIjeXAyQ/l49QU8/bjYBB0J1zPuRzh6sOrSNemI8gjCA0ryigvexdzw5buMhN0G96Uy8i6IWbjIm8uGdz0NQyEz5+XDSiKH3/ULxtmLwdgnMFc19JtcPPNqYrqBpFhw8Y9g9nGCsSwt8etv+TPzNRyMYVegYLuH374AUuWLMGhQ4ewZs0a/P3336oHUXE7eveobvlGzI2inRM0D8npybrWhwD3AFRzrwbARAbzYrJvH7B5s1z28gL++QeIiZGPb74BnHKeJeKRpaXJu623b+c+j6RJF7+U3Y8AoMoAoOdJoPcFIHg2YOthsiOASXllgWef3HLheMRxAEAtT9k19Hj4caMySpDdu3Zv1XMA+guhR52ntxApLd2AbNnILnvrB+VASWhlGHTvfx7Y0kY+DgzTB3jOQbkkRFM+5Ipn+jghhCp7ueHP/GQwV7qPu9q5wtVe/1BapbMnUssuU2QiNkUOrcjtu7dQv5fTooGMrJsljsXQPVW5oWJjcEPl1irg3Az5OD/LvP1YOwA2Wb0k2NKdKyXAruNdB3W86qjWKQyD7kqulWClsUJ0SrTufCyV3/8OBn23lfnmKzQBQg7KITBZAgPVL1u8WP68fh349Vf9eiW4XhC6AC/98xL23dqnWq9LPqj8PwFAh7XyWiuLYdAdnr+UEcacqxlUbhNwbYmc/cHEDe3YlFjsuL4DO67vyHeCyJKoQN8MaWlpaNOmTWHXhahAMrQZOBFxAhpo0LqKTHhjydZu5UugkkslONg4WLyle+NG/fLXXwN9+shlZ2fg9deBOXMK/5hXrsh9+/rKL4aAAMDbW07hlWruzcxwOVwAHo2Btn8BFRoDbnWAeu8CPY/B0Un/8fXIiT2oTNMKra6l+7mGzwFQt3wrlCB7cP3BAICT9wy6FFtndacwvDDxbg14W+670LCle8cO9bZ794B//y3e+pRahtM05iQ9Rv60N2heerAXCPtDPm6tAqyykmMYniO5mLV3FtxnucN9ljs2XN6Q72rHp8Xrun73Wd4HwT8E48hdeaclXy3dWftwslXfgVWeZ+9eboqyLa/u5YXGsNutkpwsNRLY3lX/uPKj6dcWhJJkynA6p4JSWvpijXvbWIQlAtPUSODMJ8A/NYDfbYG/PIBtnYFr+lkjzkfK7jt1vPRB9/kH6i49hkG3jZUNKrvKGzClOpmaQ0V9lvsEExnSstTMNnpk3jxg3DigQwcgOatjhhBCF1yvv7wePx37Sffdpwu6HbMi6vgrOR7LMOi+cMHM95ETp6r6ZaEFDr2gnoXAwJ/n/kSXpV3QZWkXzNlfBBerxaxAQfeYMWOwfPnywq4LkUnLTi1DlblVUGVuFSw9udRo+/kH55GckYzaXrXRNqAtAHXLd3HTjefOCraruls26N66Vf60tQUGDTLe7mZm0lUj2kwgPcGoD+vRo0Dz5sD8+bI1HQCsrIDoaJnELcGchsK0GP14veqjZAZhQy5BcHLRp6S4W/pvgFIRuhFzA/Fp8ajkUgmdqnUCAJy4d0JVJiopCnfi78DJ1gk9a/YEID9blMRquguTFIO5RRt8ADScVrSVz4Vh0P3ffzJPg2LWrEKY2qW8UG6opOWSCE+Xvd5Dv+78F8C+Z+TjwFDZignou6sDsqt5DtNB/XflP8SlxiEuNQ6br27Od7UNW7PPPjiLk/dOIi5VtozlJ4O50pKdU9CdlJ4EIYRZrdjFFnQLg5PbJqvemanAve36h0EQceUKMH060Lo1ULs20LKlvDGcPQFVjpRzRGl5fBRKt93o43Ju9/Im8RawsTlwejKQcA0QGUB6LHB/J3BsnK6YrqXbqw7qeGe1dEeabulWkqgFuGdLpiaE/mEop/UlgcZKn5Mg1jhju8LVFahUSf9cqwW++kr2LFQkpCXkmChRF3Q7ZX23pT5Qf78ZMAy6Dx0y3p6vLufOVfMuk2Xb9W0ml0urAiVSS0lJwYIFC7B161Y0btwYttnm6pk7d24OryTKv1XnV+FO/B3d8vAm6qlAlFbtYL9gBPsFq9ZZghJcB7oHIlObqfsSKJKg24w70Cfs5ZdKcHAhZCkXWuD6Uvl4sEs+t3YEPFsA9d6H8O+N4cOBuDhZtWnTgDfflIH9iRPA55+beZyYk9BNm+HZwmQRHx85ljs5WR1sGNc5h37oJfHLloqE0pW8kW8jNPJtBAA4GXESmdpMWGfd0FFauev71IervSuqeVTDjZgbuBh5Ub7GRU5Hg9T7QHIE4Pgo6VsLR506+uX0dGDCBOCvv+RwkvnzgXfesVzdShVlftp0g6A7YCBwz12fsVwZ05/buEPlQlmbKgNvW1eg4WQgaCSwTt0XNCEtAQduHYCVxgpaocWWa1vyXe3cWrMLMqY7x5butEQkpSchNTPn917sLd1WBl9meQTC33wjWwCzD3E6ckQGKGvWmtGSe+ilrGMZnCM1X5IBxMUvzauzQvfZIYBzs4Cms/XPLaG4vyOPvy1ztWhsgIZT5XRpQgvc3wFcXaQrpgTdtb1qo4ZnDd06Zeq59Mx03bXhz8d/hus5VzxIlEFjqR/X7VgFSLyZZ2+IWrVy7+6tS5aW2zanAP3K2LOAQyejsr6+gLW1nAXnzBk5L3hVg9h53jxg5sxcq6pn2L08F0IIbL++HYAc+nLy3klEJkXC28nbzAOVPAVq6T516hSCg4NhZWWFM2fO4Pjx47rHCbNvGxLlLTUjFVuubYGNlQ3sre2x9dpWpGSo+xIrrdqGQXdJaOn+4+wfsPnYBo8vlPO13oq7pW81KyapsNN1536kKR4A+QV8aJTsCnR/h+wi5P8EUKEpEHUIuLMOx47pEzo9/zwwZQrg4SFbups1A37/XZ38I0eGLU7KeKN7O4F11XQP67urdUHH1avyy4DIFKU7XaOKjeDt5A0/Fz8kpifiarT+bo0SdDfwaaD6qRvX7RKk32G0iQHUFtCokbwQUqxeLf/Pe/ZkK3e+uGb10zQcY9v4ExksK5QERkqeCUAG60rADsi5uxXR+umyTNl9czfStenoGtQVvs6+OPfgXL7HLObWmp2flm5zupfn1rUckC3dGdoMxKbKz+6n6z+N4U2GY3iT4ajvUx9AIQfdtgZZ0NNi5E9rRyDwWdUY7/375Y1frVZ2x/3nHyAxUbbMLVsGNDB3xi5lmrCkW/qu7ZV6yHwj+eVWX7986Ss5POHMdH1virIsPR64849crjseaPg/+dnqWgOoMUZO9QY5JOhCpOzHXMe7Dqq4VYGjjSPiUuN05/btuNu6BH7fHvkWM/fOxOWHsjv2zZhS3L0c0Lc+x54Fku7kWKx+/Rw3ATBozc5hmxBCfywAuGe6NdnaWk4zq/joI/3y+fOyhd1sTpXNmtf+7IOzuJ94H3W86qB7je4AgB3Xd+TxqpKtQEH3jh07cnxs3769sOtI5diesD1ISEtA24C2aF+1PZLSk7Drxi5VGcOW7jredWBvbY+w2DDdHc/idi3GdIu2VmgRFmsiE/ejMNVFymCdTYa+ZeKRL8KjDgHXs8ZbNZwC9L0CdFwPdN8HDAgHgobhn3/0xYcPN70bs4aHqQplvbfMZHnnV3lkJOq+cJKTTXd5SiveexxUQildyRtVlK3cSiZcw3HdeQbdzgZBd0T+WyWLgqOjnIfV0P37lqlLqaYkJ4o9qw6qDSl//6Q7+unh2iwD2q/TlzGcv1lpIc/BlqvyHOpcrbNuyMPWa1vzVe3Cauk2p3u5kkTN3d4d99+9r3v0rd0XgEyyFpMSo3vt4v6L8cuAX/DLgF/wdP2nARR20O0GOGb1rY3PGvdq7wm0XSGzPGf54Qf5U6MBVq6UOU2cnOTQjCFDgE8/hXldkJVzRGQAUYcfre4VgvXL2nQ5PEGZbq6wlbREYvd26MfFB40w3m4lO+DejLmJ5IxkWGms8O+lf7Ho2CK42ctATWkBz601W5lDvtRyUnrGCODU/ww2qM/R1q1z301uQXdSehIS0hIMjgU524IuyFcfq3lz/fLixcAXXwB//imno81XXh2NFeDRJM9i267JGwAdqnZA+8D2cl1+u5iXsPP/kVJsXrlyBZs2bUJy1oh9we6aFrPh8gbU+qYWan1TC+9ved/S1Sk0/16SmYBCaoQgpHqIah2gT6IGAFXcqiA2JVY39sdSXcyvR+ecpTy3bUXB2lo/l+PlnPNxmEcJuJ0CgQZT5Aenwt4T8GmnGteTPbNmviiJcQAgJWunjpWAwGdULUuGd3m//169i9u39RdcVL4pnxGH7hzC7H2zEZ8ar1oPAKfuy+D611O/osdvPbD24loABsnUnKvqkyldXajPZm2pLqFZ8rroIjO41ctaEEBEDhd1SqCUFgXEnDJdxtFfP/Y37I9c527fel0G2J2qdULHqh3lunwG3cqYbkcbR/g4+cDHyQeejp6qbebIqXu5MnVeYlqibsy2t5M3fJx9dA8lW3pUcpQuqLa1slXtq4KD/DzPbTqxAnGXN9FwL+ff2/r18mft2rK3VYG5GYzlCP/vEXYEddBd3qQYBIHK2N6wv4B/aukfD/bqAmut0OLlf1/GS/++pOsObVbQXdq7l3u11C9fXwIcfR04/p5+6sIseeW0VoLu2l61MandJN3DNmu4TERChGzpVnoUpkbKqVovfw9cUA8Vbt9eve/33gMGD1aPITdbbv8DWXXbfkM24nao2gEdqnaQ667ns2G3hI3pL1DQHRUVha5du6J27dp44oknEJ41oGDMmDF4h4PIip0QAh/t+ghXHl7BvYR7mHdoXplIrS+EUAfdNWTQvf7yet0NnnMPzum6mzf6vhG8Z3vrWqZC7xZ/0C2EyHXstiWSqSkflFevAhE53/TMm9Ka4F5fn9gsdBywuZV8bO+umi9bSaJWIB7B+gBHaTGqEAy0/R1w0GcOeewx/Ut++w345BPZun3wINC1KxBZuFPiWkZyBBB5CLi7QT6iDgOphXzxWoZFJkXidpy8Kvj+6PeYsHUCDt2R3SKUacQytZk4c19Ocnr6/mlsvroZF6MuAjBo6ba2B7xayeWMeGBnD+DY20Dom8X4boz17m3Rw5cN7gZ3705PAZKVu4cGF2QVDFpmwv4wvR+NlZxtAZBDEI6+DmgzgAR1VuDw+HCcuX8GTrZOaOHfQtXSnZ/GC6Wb7ZSOU3D/vfu4/959XBgru+TGpsbmmEApu6SMvFu6le7lSlCvUJ5HJemD7gqOFaAxaEHycPAAUMgt3YD+dx17Fnho+vs+Lmu4t4+Pyc3mcw7SJ8q7PF9mrgcKls3cpaa6S29RKmFBh25OaEAmYgXkmPyEK/pHRrLR1GCGykXQ7ZMtwr08H7jwBbLf5K1RI/dzWwm62we2x4yuM3QP5WZZREKEbO316aB/0cOjwNHXgNjTqn116VLgd2OsQnPT621cAO/WyNBmYOeNnQBk0N3Etwlc7Vxx+eFl3Iq9VYgVKV4FCrrffvtt2NraIiwsDE4Gk/w+88wz2Gg4P1EBfffddwgKCoKDgwOaN2+OPXv25Fp+165daN68ORwcHFC9enX8UM6at7Zd34bDdw6jXWA7TGo3CWmZaWUitf6lqEu4Gn0VdtZ2uB13GzdibsDBxgHXY67rppLIbez20fD8j+vWCi02XN6AdRfX5f+OGoAHSQ904+NWD16Ndc+uw7pn16FfnX4ALBN0d+umX86eyEyrBfL499IzlWxImyrvXEcdAh4eRatW+k1m79cUWxc5VhwAri2WXfBM6NJF/YUzeTJgby9b/y5deoTjlwS31wEbmwFrKwMn3gVu/w3cXQ+cmwlsag5klIPxf4XA1NRg2bddeXjFKFeEIjwhXD9Uxb+XfkNyOHBxHhB/sXAqWkDdugF2dnmXo1y41tEnyos7D2x+DNj3nLpbp0stfQ+cC18AVxbIeWWzT+lTub9++cr3wB+OwHb11arSRdLGygZP//k0Jm6bCECea7kFG9kpN9eVC2gA8HLygnXWDUtzW7vzTKSWrm/p9nLyUpVRWrENW7qVdboyjlkt3YUddPu01S/v6g3c+Rd4eEx1k0PpcXXlinEitXyxsgEqPSGXMxKBHd2BbZ2AfYPzvy+NBqjUK+ftSnBfFhl2K47OulFS7XngyYeqXmzKNZ4pyjalC/mH7T9E/KR4xE+Kx+235Q3Wh8kPdZn8SyVH3xyTyBrSaIBnnzW9rUYN4F6CvIHo6+yr2lbRWc6ooEum5v9Ensdq0gSoVy/PYuap3EefnFK1vj9g44TQu6G6v9/AlQPRcmFLpGddB5bmLOYFCro3b96Mzz77DFWqqO/U1apVCzdvPlrygpUrV2LcuHH48MMPcfz4cbRv3x69evVCWJjpsbDXr1/HE088gfbt2+P48eP44IMP8Oabb2LVqlWPVI/S5NM9nwIAxrcaj5dbvAwnWyf8EPqDbgxWabX+suwXlpaZhoErB2LAygG6C+P1l+Q2pTW7mkc1XReUFv4tVNvyY8aeGei9vDfe+O8NdF3aFb+d+i1fr1eCal9nXwysNxB96/RF3zp90S6gndyew3jvotS/v/6i/MsvZYKzjRuBpUvlfI5m36NSLhIeHgXisxJQPfY90OI7XZE+fQCXrPw2n38OXM/Wm37LFv2UYUIIzN43G0NXD8XQ1UNVwwYAAP5ZE4rHnZcXOOFbgLsbgTT9hZuNDfDKK2bWvzSJuwjse1q2lrX8WSaXaTYPqP0W0OQzoNOmwpkvthxQAut63vXwZss38WbLN/FGyzdgpbFCREIEIhIidK3ZNSrUwJc9vtQ9KrnIXhWn72fd8Q8aoe+BUUK4uAAjR5reVmgXSGWdRqMeX5p0Cwj7XXa1VFhZ68to04EjL8uA+ki2D6Bqz+vn2AXkGOBslEzlcalxWHtxLdZdXGe0zRxKS7dh0G2lsYKvi69qe17MSaSmXE/k1NIdmRSpC8yVIFuhC8xzmXKsQCo9oZ+OLeUesLuvvCFp0P1/6FD5MyJCjkPN7mF+7gMYniOZKcD9XTnnAMhL1SGm13s0AbzL8JiRCsH6buVnP5G/R2v7rBta+t4Rys2nH3r/gDvj7+DO+DvYNHSTapvSml3TsyZc7FzgYucCf1d/uNq5AigDydRMjXlXGAzBe+0148329vJaLyJRtnQrQbZC+YzQjfkOeEqdFNJQ1jSJGg0wapTpIvkeGm3vBVTqaby+xhgA6m7kx8KP4Vj4Md31f0EaxEqKAgXdiYmJqhZuRWRkJOzt7R+pQnPnzsXo0aMxZswY1KtXD/PmzUNAQAC+zz5gM8sPP/yAwMBAzJs3D/Xq1cOYMWMwatQofPHFF49Uj9Ji/639ui4Yk3dMRudfOgOQ3cG+OpifdIIljxJ0d67WGUMbD8XQxkPRJaiLapvSmj2z60zsGrkLu0buwo4RO6CBBrfibuF+oj6rUIY2AzdjbuJmzE3Vet3xLq3HlB1T0KFqB5x65RTqeNXBi/+8iGPhWVlohQDiLgC31wCXf5AtsNeXAtd/Ba4tAaKO6oLuah7VVPtWnluipTswUGYRVyxfDvTqBYwYAezbl48dVX0WsPeWF5w7ugJXf5Zdv6OO6Io4OQEffCCXHzyQSZ6eegp48UU5ni4kBLps6tN2TsOErRPg7+qPu/F38eQfT2LjFYOeMnXekmMkAXlxszME2NVLFXQDclxRUBBMesSPoyKXoc1ASkYKUjJSdFlYAcgLOSWoVrJmx1+W85puagasryPPQ8qTEnQPaTQEX/X6Cl/1+gpf9/oaNT1lxuqTESd147Y7Vu2Ica3G6R4tK7fUlQEgu4TWet30gWq+XKTvIzcffaS/2aWoW1cmiiIz1RmnH9eYnVtWVvJ6E9XzdBtSprByDgRqvZrDfupC2Hroxm6/2/pdzAmZgzkhc/B8o+cB5G9ct6mWbsPn5g4zMyeRmq57uUMO3cvNbOku1Nw/1nYyy3xObF3x0kuAd9YMQ6+8Ir/3li6VU+oNGgSMGZOP41Xum3NA7GZuGvQsvp0Av+7ZVmqAZl+q86WUNRoNUPdduRx5APinBnDkNeDgC0CmPA+FELrA+vEqj8Pf1R/+rv5oXUX+7iOTIvEg8YEu6A50DzTYvUb3vNR3MQ8aKYciZOfTTua3yVK3LtA926n04otyphglqFaCbIUShOuCblsXoP4k42NprOQN/yxvvAE0bmxcrECNH1WfVz+v2FH+X0Dfmv1Ss5ewsO9CLOy7EB91+ki3rbTmECvQf3aHDh2wdOlS3XONRgOtVovZs2ejc+fOBa5MWloaQkNDERISolofEhKC/fv3m3zNgQMHjMr36NEDR48eRXo5mDNFaeV+rcVreK/Nexjfajy+6P4FNNDgm8PfIDYlNo89lEyxKbHYfXM3AODXgb/qHssHLQcA7A3bi/sJ93UXw039mupe62LnglpeMots6N1QCCGw7uI6NPmhCZ7840ksPLYQtb+pjRfWvqDLJn456jKeX/08nGydIITAs6uehaejJ7RCi4ErB8q7/CfeB9bXA46/C1TpD1R/Qc43mJkkx5PdXKYLqqt6GExgCMsG3YAMhGfOlPNlG7KxAdpm9dCLTo7GN4e+wbC/h2FB6AIMWTUEPx//Wdf1EHYeQMf/AJcaMnv44dHAljbAmWlye1ZwOHEiMGuWnCosLQ1YtQpYuBA4flwmdbO1BWbumYnpu6ejgkMFxKXGwc/FD0IIDFw5UH8X085DZkf3MPEJ79cD8JU3YFxd5Rjutm3VRXr3Bt5+28xf0I3lwIERstXq9logYjtw8Svg3OfA2ZnAnfVm7sg8CWkJmLV3FirPrYxxG8dh4MqBqD+/PlacXoFMbSbg3QpoMFm2mO19EjgxSbZ6+/fRJ2oisyjjtpXM5Qrl+fGI4/rM5RXVF866DOb3DRJnNZ1j3EpV81WgyazCrHa++PnJfAYKV1dgxQr5/01msnWTPUqyX+QGPA20ykoi6egLdN4iPwMNudaS6xVNvwSqj1aX8XwM6LwF52Nu4m78XTjZOuHTrp9ifOvxGN96PD5s/yEAYOeNnWZNLRmfGi8zDyPnoLvQupenJeZ7TLchJQhPzUxFcmEPi6kxRv5ParKd7LXGAvUnoXJlYPt2mXk5I0MG3CNGAK+/Dvz9N+CcQ+OeSRqN/E4yHP8KABU7AZ035b/uTWapu5IHzwZ8C34NXWrUGitnQLF2AJLvymEY15fIxIPOQbgLB8SnyWSXtTz1MwK42rvC31XeiD99/7QuV4dh0G34vNQH3bYuQOtf1ed2hWZA+7/1eXWyGN54DQoCpk2Ty0pQnb2lu6JTtqAbkDcVfdrpn1vZAa2XAT76bG329sCvv8oGFkXbtsZDF81SuZ9+uIGtG9DsawBASkYK9t2SLULvtX0Po5uNxuhmozGx3UQ42TrhbvxdXc6V0qZAX8mzZ89Gp06dcPToUaSlpWHChAk4e/YsHj58iH35ajpTi4yMRGZmJnx91XdkfH19EZFDBqiIiAiT5TMyMhAZGYlKlSoZvSY1NRWpSnMbgISs/q5Lji+BY4Aj6vno++Ttv7Ufe27uQYOKDZCYlogbMTcQUiMETSvpg7wbMTew8sxKeDh44OUW6taO8+eBXbvkxU+PHkBAAHD2/ln8e+lfVHarjKGNh+rrlZGKrw7J1ulB9QZh1blVcLZzxust1S0r8w/PR0JaAgbVG4SGPg3R0KchpnScAmc7/bdHpsjErdhbuBp9Fftv7UdiWiKGNRmm+8ACZBbws/fPol1gO5x7cA4Pkx/iqfpPoYan/oLiWPgxbLm6BdUrVMfTDZ5W1WPfPuDYMXmB16MHUKkSsC9sH/aG7UWDig3Qp3YfXdl7Cfew5MQSONg4oFO1Tth4ZSOqelTFsw31g1EytZmYe2AutEKLkJohGN9qPNwd3FHZTT/npq+LL2Z2nYno5Ghcj7mOtx5/CxqNRtdqpZjYdiIuRF6As60z1l9ej31h+/C/9v9DgHsAAJmYbd+tfVh0bBHGNBuD/bf24+XmL6N37d66LIkAsO7iOuwL24f9t/ajX5MZMtiOuyCDNHtP/YehUxXAsRIaOrlhQpsJaBuojgBredXChDYTAADpmemwtTYxlqUIaTQyGH7pJTlXdni4nHOxb1+gShXZXeuPs3/A09ETI5qMgJOtE6p5VMOpe6fw1aGvMKTREHnjwKsF0OeSTCJzfzegTQEc/GSCKc9mumO9/76cG3XtWuDCBbmuUSPZ0v0wMwwxKTH4pPMneL/d+7DJmiJkTsgczDs4D3vD9qJtQFvY29jLrmg9jgG3/gRiz8kuVb5d1EmNIKd/2bZNzrt665ZMsNazp5wf3CzebQBoZGZiZE0j4d4ISL4tu5qmZxsblnhLZrDNSAKcA+RFl33WWMeMJJlhNP6SfJ02FdDYAnbugEMlxHo8hoUnliAxLRHLBi3Tvf+41DjsC9uHDG0Gnmv0HGwaT5fzmEZsBZLCgPgrsmtzwymAa20kuDXG/L2fQaPR4O1Wb6vOqVXnVuHKwyvoEtQFO27sgBACL7d4WZfQCJA3rvaF7UPDig3Ru7Y6G9epU8COHTLjb8+e8tex5sIaXIy8iE7VOuHxKvopeW7G3MTvZ36Hu4M7+tTugxWnV8DDwQO9aunHLN6KvYXdN3eb/Bwx5U7cHfx26jeTn38Ljy1EVFKU0WeVKUII9K/TH31q9VHVGQBGNR2FGhVqoEaFGrCztkM973q6ZI2KAXUHIEOboe65YmUNtFoC+HWTF4x+3dWZZg2cPCk/JzUaoGNHmW3/fuJ9LDmxBE62Tni5+cu6v1tkUiR+Pv4znGyd8FKzF2GHTNnbQWOTNY2OBhCZWd2VNXLeaIMLr7fekv9jBw/KjLI1TTSQmCUtGrj1t/zf9moNeMrvOuWc6lq9q24IDyBbU389+SscbR0xvM6bOHJEduV1cQEqV5ZDW5Suh5mZso62hh9/4VuAuHOAU4Cc9zinLo55SE0F1qyRx65TR7b8WFsDX+z/ApnaTIxuNhreTt668sr5r/qucq0FhBwAbiyT43Z9O8vPNsO+k14tgJ7HZC+n1Cj5WVSpl+wiq7CyBlr+JM+RuIsyP4V/b8DKGskxoZjQZgIC3ANgZ60fjF/Xuy4+bP8h0jPTEZUUhUquxtcuhjK0GbrvFMP3BQDDGw9HQ5+GqOtd19RLjQxrPAyRSZG6m0yKJn5NMKHNBFRyrQQPBw9Uca2CrtW7qsoo3232NvZo4d8CE9pMMPpf83Ly0tU1U5tpVp1UHuyXw5rsKsj/N0eDmwwajfyc9OkAhG8EbJzk79og23ijRsDRo8C5c3JoVVSUvCncsiXQrp3x4XJlVwHovFnO5pFyT05P5tetYK3Tns2AHkfl/5tvZ/UY9fy6txOIOSnHyXo0lt9pRdViHnMGeLBbdg239wE8m8tkhBlJMtFn0i3A1l32jNNYy+/A6BPyb1P1Ofk/3ugjoPabQNhK+Tlq6yE/R73bIj3uFia0mQBnO2fVdS0AfNDuA4TFhsFaY413W8sW8wC3AFWZkcEj0ahiI13jS16WnFiCewn3MKDuAN3sNwBwIfIC1l5YC18XX4wMHql6zfHjcg54R0f5HenvL3tEmbq2jUqKwsJjC2FvY4/u1bvj30v/ws/FDyOC1d3Hvzr4FVIyUjAieIT+Rpp3K+CJM8DN3+XnU+DTJsdCt24N7N0LbNoku5V7ZV2OjGgyAumZ6ajjVUdVXvl+Vt1otrKWNw+v/yKvXaoMkvOnZ9O4scyZs2SJfN9Dh2b7TDeXjaMcKndtIRD4rO5YUUlReLPlm7CztkONCvrj21nbYVbXWbgddxvJ6UWY0+bW30B6jLwpoFzXFRZRQOHh4WLKlCmid+/eolevXuLDDz8Ud+/eLejuhBBC3LlzRwAQ+/fvV63/5JNPRJ06dUy+platWmLGjBmqdXv37hUARHh4uMnXTJ06VUCmAFQ/XoJou6it+OHID7pHra9rCUyD2HZtmxi9drTANIiJWyaq9vdT6E8C0yA6LemkW5eeLsRHHwlha6tPEWlnJ8SMGULEJMeICrMqCOuPrMX3R74XS44vEUuOLxFvbHhDYBpE72W9RUZmhgiaFyQwDWL+4fni34v/in8v/iu+O/ydwDSIoHlBIiMzw6zf69QdUwWmQXT5pYuYtmOa7uE6w1U4feokwmLCxOA/BwtMg5ixW/27HLN2jMA0iElbJ+nW3bolxKBBhqkvhbC3F2LmTCEuRV4SNtNthMcsD7H4+GLdQ9n/mxveFJejLgtMg3CZ4SKS0pJ0+916davANIgaX9Uw630RFbv0JCH2DxdihY0Qy6F//G4rxOGXhbj1txB/OMvHzT+ESLwlRPhWIU7+T182+X6hVSfwy0CBaRA7ru/QrdNqtcLvCz+BaRAHbx0ULjNcBKZBhN4NVb32zQ1vCkyDeGPDG6r1n36q/t/u2lWI+HghZuyeITANYvCfg1XlP9/7ucA0iCdXPimEEGLPzT2i3rf1hOsMV/HlgS9F11+6CpvpNuK9ze+JxLRE4zehHMhAUlqSrt6XIi/p1t9LuCesPrISttNtxcOkhwX5lRWLiAghhg8XQqNR/y6feUaI1FQhlp1aJuw/theOnziKiVsmijFrxwjNNI3w+8JPHLlzpOgqZlgZU+7vE2JNoPrc3tFLiOR7uu+/97e8r3rJmvNrBKZB1J9fX4SFCbF0qRCffy7EH38IceiQEMePC3HypBAnTggRGipEWlrWC9PihNg3RH2sVT7y/yaneudg1y4h6tRRv70GDYQ4d06I6l9VF5gGsenKJtVrRq0ZZfTdRiVMWpwQB0dn+6y1E+L4BCEy0y1du+KT2/kfFSrEhmD172g55P/x3Y2FW4+YM0L819T4WMshxPpGQsScE+LUlKzvwrXy8+TW30Jc/kGIS98JcXWJEBmphVunQtB7WW+BaRBfHvhStf7LA18KTIPos7yPbl18vBBjx6o/2+3t5WdeYlqiqPl1TWH1kZV49q9nxZBVQ8SQVUNEiwUtdNfX1x5eE5gG4fCJg+r690rUFYFpEE6fOonk9OTieutlRx7fEWY7MFL9fRR5OOdjFeB4Be585ufnh48++qhQAn+Ft7c3rK2tjVq179+/b9SabVgPU+VtbGzg5WX6DsWkSZMwfvx43fMTJ06gY8eOqOxWGftu7UNKRgoC3ANw/sF5XH54GV2DuqJLUBddF8RzkersoucfyEyKDX0a6ta99JJx0o60NNnN97XX3PFO63fwvx3/w/tb30fzSs2Roc3AnjCZ7nl65+mwtrLGhLYT8Or6V/HGf2/oMg8qmQYntJ0A62zdS3Iyoe0E/Hz8Z2y/vh2VXCqhlmctLDu9DPFp8ZjacSoC3APQpVoX/HH2D6y7tE7V0q/MJaqMpY6LAzp3lplADaWmAnPmABMn1sLwxsPx84mfMXHrRDSo2ADxqfE4cvcInGyd8EH7D+Dr4ouWlVvi8J3DCPoqSNfSo3SVG9KIAxGphDr0grwzDwCOlWULVtwFmS335u/yLr6dp2wBSQyTPSBsXORYJfeGcjx8Id457Vu7L+YfmY/3tryna6VKzkhGREIEfJ3l/1lF54pISEvQZ+DO8iBJPjfsdrZ8OfDhh+pjbNsGvPoqMP7znvhg+wfYem0rZu6Zqdv++9nfAQA9a8qkKO0C2+HEKyfw8/Gf8TD5ITpX64zZ3WeregflxdHWEX1q98HvZ35HyG8huhb6hLQEaIUWPWr0MOrCWlKkpMicCcePG29buRKYN09+xgW6B8rhFDe24278XTSs2BD/DvnXqKtksYm/LLNsa1PV68P/A/YNRpCHHDR4M1adnEgZohPkEYSAAGDYMDOPt/85mY3fUOoDYN8zMpO4Zw5TymSzd6+cxSAzWwPq2bPAL78AdRrVwbXoa7gcdVnVk+FKtPwSy94CRCXIodGyl5MhbRpw/nOZcb5GDlmdyouU+3LqQiXhn1s9OSzr4THZOyp8s+w9UhgyEoHt3eRsJS41ZEu1Wz3gwR7g2DiZ8NS9nlxfytT2qo31l9fj6sOrqvXK89qeMqeDEDIx7fZsebxSU2VPwvfec8IvA35B+8XtsfPGTqx5Zg1239yN5aeXo1WVVrrr9rredXEh8gJe+vcl3bX9pSg53UqXoC5wsCnD2etLsvBNcpiDIvWBnBWh12k5tKgQmB10nzp1Cg0bNoSVlRVOnTqVa9nGpkbZm8HOzg7NmzfHli1bMHDgQN36LVu2oH///iZf07p1a/zzzz+qdZs3b0aLFi1gm0N/B3t7e1XCN5esgRCvtHgFky9MRmW3ylg9eDUafS/H/M3oOgMA0LCiDKpP3TuFg7cP6l5/5O4R1fa9e01nyTT0Vqu3MO/QPMSlxuHXgb9i2/Vt2BO2BwPrDkSzSrKb7gvBL+Dj3R8jIiECu1/YDQ00qP1tbfi7+uOF4BdyP4ABJ1snfNbtMwxZPQTxafEY1mQYPt3zKQLcAvB+2/cBQNdl7ODtgxi4cqDq9fbW9mgbILs9ffqpOuCuUkV24TNMWj+542T8eupXCAj8+9y/mLl3Jo7cPYKxj43VJXN4vtHzOHznMFIyUtDErwkytZm6xAlKQhmiEiXukj7grvEi0GK+7OZ1d6NM8AbI4Lp/mOx+Hn9JBt8ZSbJ7sK27DCYKsctfvzr9MP/IfBy9e9Ro+rw+tftAo9GgonNFXIu+pguyFUo2YiXoTkkBDO5Fqpw7BwT7BcPPxQ8RCRH4YPsHRmWUoBuQ3cBeafFoaeWfafAMfj/zO8Jiw3RdUpVszIMbFGCanmLy9dfqgLtbN9kFb/t24PZt/fp2ge1wfux5XVLHqu5VjbpSFquTHxoH3Iq0GARVlDkbsmcENgy6zXZvh3HArSOAjASzdiME8PLLxgG3obredfHflf90F7WKy1GXddupBIo8pA643RsAsNLPHWwiK3y5c+lrGXBrrIC2fwABT8r1e58Gbv1VuMe6uUIG3FZ2snu9MsVeRmLhHscCanvJoFq5Eae4Gn1VtX3ZMuOAO7s2AW3wTut3MHv/bPx76V8sP7McjjaO+GXAL7qGst61euNC5AWTs+P0rtXbaB0Vk2MmLoBSH8gpW1v/WiiHMDvoDg4ORkREBCpWrIjg4GBoNBqT2eM0Gg0yc/sGzMP48eMxbNgwtGjRAq1bt8aCBQsQFhaGV7JS402aNAl37tzRJXJ75ZVX8O2332L8+PF48cUXceDAASxatAgrVqzI97F71uyJlQ9XYt3FdZiyYwrOPjiLAXUH6DLYKkH1jZgbaL3IOIOlsv0rg6Th3t7yIszfH/jtN5lQCpDJvt5r8x7e3/o+Zu+fjc1XN0MDjS47HwDY29hjfKvxeHfLu5h3cB400EArtBjfarwc75oPzzV6DvOPzMe6i+twJ+4O0rXp+KzbZ3C0lUmZanrWRKB7IMJiw+Dt5A1HG0fEp8UjJiUGrQNaw9HWERkZ6umlPvkEmDRJjpvduVOftKqaRzWMbjoaP4T+gC8PfolvD38LFzsXTGg7QffaZxo8g/GbxiNTZGLts2ux/9Z+bLu+Dc0qNVONqSEqMe6slT+tnWQCHFNzTCqcA+SjiHWq1gmudq6IT4vHbwN/g7+rP15Z/wouRV3SzQ2vBNV5tXQvWwbcu5fzsTQaDUJqhGDpyaVoWbkl2ga0xZWHV/DPpX/QwKcBqrhVyfnFBdCrZi+42rkiIS0Bh188DGdbZ3jP9oadtR0G1B1QqMcqTAsW6JeXLJFJmwDZGpL9poa3k7fReFyLSI0E7qyRy9YOMqFNpRDg3CzgivzQV4LqGzE3VMl3Lj+UwWtQhXwE3VcX6ZerDpGJsFIfABubynHrZtqyRd4QAmSCny+/BF54QY43fO89uV4JqhefWIyNV/WzIyg3cBh0l1DXf9EvN/0SqDtOLm9oCMSetUiVSpw7WQ1OlfvpA+6iEpE1N7JHI33AfWBE4Qf3FqAkazt4+yCe+uMp3foDtw/I7Vljw7/8Uv+aVq2ARYsAd3f5OW+YyPLjzh9jw+UN+GSPXDmvxzxd4A4AT9R6AnMOzIGnoyc+6fwJMrQZGLdpHLRCiydq5T1fNhWB6JMyt4gpkQdNry8As4Pu69evw8fHR7dcVJ555hlERUVh+vTpCA8PR8OGDbFhwwZUrSqzQYeHh6vm7A4KCsKGDRvw9ttvY/78+fD398fXX3+NJ5/M/weQlcYKn3T+BANWDsAnez7RPVf4ufjB28k7x/mvG1ZsCCGArVkzftjYyGC0QVaego4d5eTySk6W11u+jrkH5uLrQ19DQGBwg8Fo5KvOsPtKi1cwY+8MLDmxBBqNBp6OngVuQZrXcx6e+esZPEx+iD61++C5Rs+ptncJ6oIlJ5ZgUrtJGN96PMauH4vvjn6HrkGyFfzkSdm9HJDdJw27oXbqBKw3aLj4sMOHWHxiMf63/X8QEPiw/Yeqi0tfF190rd4Vm69uxsYrG7H56mYAbOWmEixSfgHDo5FMogcAh1+S3W8txM7aDj1q9sBf5/5CSkYKmlZqiqsPr8LRxhHdqncDoM9Smv1zSwnClaB71Sr1vqtUARITgWiDaWh71OiBpSeXwsvRC3N7zMWUHVPwz6V/0KNGIXVhNGBvY49+dfph2ellWHthLTwcPJCWmYY+tfuoEsKVJHfvAlezeih2764PuAEZFM6fD2i1pl9rUVFH5NAHQGZQrvmiXFYyy0I/A0N4QjgqzTFO8pV9msRcRWbNRuIRDLT+TX4pOvrJFrt8BN07d+qXp02TwyAAmehnwwaZxE5kdR+PT4tHfFS86vW+zr5wd3A3v95UfKIOyZ9eLfUBN4ACTrpT9qTH6ecj98saNpH6ENj/rGqe8kKj9CzQGNxs9u0kE4QCMmFaKaUExDEpMVh1fpXJ7bGx+h5Mfn7A5s0yiTAgr4UNO/ja29jj2MvHdLMQuNip53NsH9gebvZuiE6OxsB6A3Et+hq0QouGFRtabnhReXf772I5jNlBtxL0Zl8uCq+99hpeMzXbO4AlS5YYrevYsSOOHTtWKMfuX7c/lg1ahrTMNHg7eRtNIdOwYkPdvNiGAtwC4O7gjitXgJgYua5vX33ArXjdIBGvk60TFvZbqJsa68VmLxrt19nOGW+3eltODSaA99u+X+AuiC38W+Dqm1dz3N6lmgy694TtwfjW43VjzJXx3AcO6MsOHWr8en99YnRUcauChf0W4tyDc9BAg3dav2NUfkjDIdh8dTP+OPsHdtzYASuNlSrjI1GJomQwt/fRr0u6LR8W1K92P/x17i9svLoR7g7uyBSZ6Fq9q266H11Ldy7dy1NS1N3m+veXQXh6ugxklBFFITVCYKWxwu6bu5Gema4bEmLYtbwwPdPgGSw7vQx/X/hbN4Z7cP2S27V89279cq9epsuYnVG/OMVkzUOusZJ5CUzwc/GDg40DUjJSTG43u3t5WiyQeEMuBw5WZwbPp8OH5U9ra+N5Yq2tgQ4dgHsJObdks5W7hMpMBWLPyOXKAyxalRIrLUa/7Jh1EywzBYjYYrL4I/NqJVu1Y0/LseQOFeW0qTB/qGNJVcWtCpxsnXTz1RtytnVGZdfK2LZNDmcB5M1UJeBW9O2rfm5nbaeancCQrbUtulXvhtXnV2Prta248lB2a3+iJlu5LeZBwWfeyg+zg+5169aZvdN+/foVqDIlRW6JvBr6yKB7fKvxmNB2Av489yfe+O8NXdfyowbDKjt0yGEnBvrU7qOaWsuU/3X4H/7X4X9m1f1RKOO694btRVRSFM7cPwNXO1dd93rlAgcAmpqRE8lwOjRTBtUbhFfXv4qVZ1dCK7ToEtRFNaUZUYlimzXJeYpBH+yWC4HMrDFtFrrT/0StJ2CtscbWa1vhbCtvyPWtrb8CUILuRccXYenJpbr1qZmpuu1XLsvuzwDg6SkTqllby8fChXI6KkB2h25eqTmO3D2Crde24sgdmSDRcJq9wtSjZg+427tjx40dcLBxgL21PfrXNZ3foyQ4a9Drtbl5ecBKhuisoNsjOMdEfxqNBtU8quFC5AWT283uXh5zEnLCEMiplgpICOCITKeCmjXlFFCm+Lr4wsPBAzEpMajqXhUONg54kPQAD5MfMolaSRV3QSZMA3RTUSL+CnDxKzm9FAHWjvrltKzuSPaecg5nRfY55x9F9ZHAmalyDPeeAUDTuTI5aEY8cH8XkBwO1H278I5XjJRpZ5VkyYZqetaERqPRfdYAwOOPGxXLt961emP1+dXYfHWzbux49qk7qZgILfDQIMAJHAw0mAyc+p9+WGEhMTvoHjBggFnlHnVMd0mnBNe342/D18UX4fHhqvWnTxuUbWj08hLN39Ufdbzq4GLURSwIXQABgfZV2+vmEX6Q1VCm0cj5ex+Vq70r+tbpiz/O/gGAXcuphPNuK7sgxZ6RY2DtvQEny98k8nLyQpuANtgTtgfLTy+HBhqTQbdWaHWBtsLBxgFu9m6q5IjPPAM4OemfW1urx6v1rNkTR+4ewbRd05CuTUf3Gt3znWPCXMr47V9O/oK0zDT0r9MfbvZuRXKswqAMvwFkF8RSIyHrBHCupl+350ng4RFVsSCPIJNBt4eDh/ld/hMMhqc5ZeUBuLZYJrFRuribITJS//tu1Cj3snW86uDQnUP4rvd3eKLWExi5ZiR+OfkLW7pLqvQY/bJd1k2gpNvA5W8tUh2LMNUDxHCdVgs4+subEA+PyVZnawegyoCiqY+9F9B+jZx1IPIAsCVbXqOqpXvWmVqetXDq3im83/Z9PNPgGfxx9g/M2jdLN5778mV92bw+b8zRq2YvaKDBf1f+Q0xKDDwcPNAmoM2j75jyL+mWviejY2XgsQVy2ETAk4UedJvd0U2r1Zr1KMsBN6APrpVpws5Hnletj43Vlw3KR16ZkkIZv/3lwS9VzwEgKavnjb29vBAvDF/3/BqHxxzG4TGH8VxD090aiUqEKlktrJnJwPF3ZVc+Rdwl4Eb+kzcWFiVpWro2HS38W6CSq37MreGUYNn5OMmu8oZBd+fOxuUMWxGVruSH78g7wz1rFKBruUajf+S2DjKvxdDGQzG08VC80fKN/B+rGBnmFn2EXtPFT8kWruQqAOQUQEm3VMWUcdv96/THrwN/xbut31WtN0umQbZju6zjZaaqAy0zJBgkOPfxybkcAF1yTiVjuZL8jUk7SyjDm4O2rjmXK880GsA/q2X0+hI5Y4ahhOtAUiH3CvDrBvS/DbRfC9R5G6gxBqg3AWi9HGjxXeEeq5gp47oztZloWqkpMrRyDLsyXVi8QTqIwrihWsm1EoL9ghGZFIkMbQa6V++ua+CiYmbYeyZohD5PQRHgXziflOD6UtQlZGozjYLuJIMhIY6ORi8v8boEdcF3R7/Tjf9UxnMDMtgG5HzjhcXXxVc3jRhRieZaU97Nv7lcZta9+5+cSzglAog+IS9AqlnmxtGT9Z7E8QiZ5aVPLfVwldyCbmWbYdBdq1bux3q88uOo4FAB0SmyS2NRjedWtKrSCq2qtCrSYxQWN4NG+PDwvH+XJYbSldc693whyrhtL0cvDG08FKkZqar15h1LmepJI6cfKiDD+/t5jZOv6yVbtJVgm9OFlXDWBvMUK12n3eoALX/Sr/dpX7x1Km4mZgcyUuNF4NoiedPsv8ZA9VFyrPXDUNlC13V34ffGsrYHqvSTjzJECbqVrt7ZpwvLMJihzqaQIqfetXrrvrc5VVgB5NUbBDDv/yg5XL9csdMjVSkvBT51EhMTsWvXLoSFhSEtWxT25ptvPnLFSip3B3dUcauC23G3cSnqEq4+vAorjRXq+9QHANgZXEOkm99TrsToHNQZVhoraIUW3k7eaOKrz15bQeYxglYr5+Uu4nx6RCXP44sAG2cZdKfe12cu11gBroUw5qKAgioEYdmgZSa3mRN0XzXIrxiYR/JUaytrfNf7O9yIuQEHGwdd97t8MeeLsBQyTJx57Jh5eT1KBKusICfdoKtW7TflNF4A4CCbdpRx2zdj5VzdBZqjWzcWVcjWbbsKQKWe6rGo7nmPzXI2uD9g2K3fFF1L98PLiEuNw4OkB7C3ts9fCz0VH8O8AqlZsy44VpI3NknP6zGg+TdA6Fvyf+niXPX2UpxRvLjlFXQbDrlKTAQcHPDIJnecjHGtxgEAZ1GwJMOg261or+MKFHQfP34cTzzxBJKSkpCYmAhPT09ERkbCyckJFStWLNNBNyBbtW/H3cY/l/5BujYdtTxrwcFG/gcaXgjcv1/6AlNPR0/89fRfSExPhK+zLzQGd42aNgX+kMOvceJE6XtvRI/M2gFouQBo9DFwd70cB+RYCajYQZ9BtoTxdvLW3UjLTgm6I7Oua11cZCK1vHCWAdMMg+z//gPGjTMuI0QJ7HquJAlMe6hfV8t4akoluFaCbSX4ztcc3XYV9MupD+Vzl2rykQ9eXoCtrby5ff587mWVFu3LUZd1rdy1vGrBSlMSU8kTXOsC1k5AZpLMKlw592Sz5Vqt1+Q83Ze/k1P/AYB7fSDwacCrhWXrVoooc3Vfi74GIQSuRV+T67NuKnsZ3Ae6dAlo3dpoF/lmZ20HLyfTiSvJDIV1817pXm7tADgV7ZRtBfrGefvtt9G3b188fPgQjo6OOHjwIG7evInmzZvjiy++KOw6ljgNfeRd+NXnV8vnFfV35WvU0JczzGRbmgysNxBDGw9F9xrdVevbGOR4MJXMPjXVeB1RmeToC9QYJeePrfpMiQ24Adky7ekoI+meNXviuYbP6bI2K0F3StbwdC9+/z+SypWB6tXl8ubNwDKDzgfp6TIIv3fP5Estyy2rm3Wc6czkCqVl+FbcLQghdMF3vlqMPQyyEEWH5qOSanZ2+rlxz55Vd//MrqZnTVhrrBEWG4ZzD84BYNfyEs3KGqiQNUXKrb9kdmFDSXfV477LO6cqQJMZQJct8tH8K8CnnaVrVar4OPuggkMFJKQl4NS9U0hIS4Cnoye8nbwBAM2a6csaJkymMkBp6XapWeR3xAsUdJ84cQLvvPMOrK2tYW1tjdTUVAQEBODzzz/HBx98UNh1LHEa+cqLBiWRUKOK+osIw2liCmnq8BKjRQvZsgAAS5cCmzbpt927BzzLxi+iEkkJrie2nYjlTy5H64DWqvXKDTP7oklCXq68+KJ+eehQoGdPYPRoOa3VV19Zrl65qhAsf8ZfUmcXz8bLyQuudq5IyUjB/cT7Bete7lpLDtEAgDv/Gm/PMJ4rNyct5WyWSE4G1qwx3h4RIX/aWdshqEIQMkUmNl2VX1ycLqyE885qSky4Ahx+SZ4XQshcGlvbyFZwokKktGornxFK6zeg/6wBgH/+MX5tcnKRVo2KUkpW0G3vrV93fzcQXfhBXIGCbltbW123Y19fX4SFyS9ed3d33XJZprRsi6y5Rg1bups00SdZWLlSjv0wdO5c6f3ndHKS0wkBslWhVy9g4EDg6adlwqC9ey1bPyIyTdeNPClS9VM3nVhWQ1KJ6/ZcCr31lvweUGzaBPz8M1Civxo9DCp8eb56W+x54OrPuqdKV/IbMTdwK05mTM5XS7fGSn+8sD/0XWIBOS3U3qfM3pVhF89335Xfr4A8n+fMAb7+Wr9dadlWLqjZ0l3C1XhRniuATBb2d0VgVQVg1xNA4k3L1o3KJGX89uarm1XPAaBuXX2izPXrgbUGM0nFx8vrYCqllNk7rA2yXx9/B7g4r9APVaCgu2nTpjh69CgAoHPnzpgyZQqWLVuGcePGoVFhTGBXwtX3qa8aC2YYdDs4AMHBcvn+feD55/VTDezaBXTvXrjZv4vb9OmAe1a+ByFk68Jff6mnUyCikkUJrpVZCR4kPlCtL4qZCcorR0dg40b52Z/9JsaTT+oTUpYoXo8BNllTM12YAxx+WbZCn54KbG4pM/RnUVq1D985jLTMNFR0rghnu9yznhupnJX5WJsKbG0PHHkFODgSWF9fZl4206BB+hwEN2/K3lidO8t8I+++q7+ZBOhbtpUbTmzpLuHcagN1xuufZySqE/0RFTKlZXtv2F7Vc0DOkKAE1kLIBqdhw4DXXwfq1JE5PKiUUmbvsHHKvVwhKFDQPWPGDFSqJMcwfvzxx/Dy8sKrr76KBw8e4McffyzUCpZEDjYOqFFBDt62t7Y3yt5r2L1w7VogIACoUgXo1Am4W8jTJha3oCDZtaZmTeNtXboYryMiy/N1ltPy5dTSrQTdvHlWOPz8gN9+A44ela2t33wDnDolb1CWyC78Ns5yflLF1QXA7r7Amen6VoAsSqv2nrA9quf5UmOMfo5ubSpw5Uc5I0BG/k5AZ2fg44/1z5OTgZ07gdu3jctmb9lmS3cpEPwZ0PRL/XAEALB1B4K/AGw9LFYtKpuUlu3UrHwBhi3dADBpkn4qYCHkZ/z8+XJ6SCrFtFlTTRm2dBeRAmUvb9CgAURW1jgfHx989913+Pvvv1G/fn0EK828ZdwPfX5AeHw4XOxcjCa0HzFCjt1TurrFxspHWdG+PXDmDPDdd8Dx4zLjcf/+QI8elq4ZEZmia+nOauHOHnQrvVciI2Xg4lj03z3lQrNm6gQ8JVqjj4Dbq9TTpygMph5SWrqVoDtf47kV9l5yzuUDQ4HMbOOtnKrka1evvSbHbs+apZ6m085O3f3csGW7kksluNq75r/eVLw0VlnJKp8Fok8ANo6AZ0v5k6iQZQ+ysz+vUQP46SfZsJZ9mKh/IU+HTsVIF3QXfUt3gYLu/v37Y9CgQXjllVcQExODVq1awdbWFpGRkZg7dy5effXVwq5nidMlKOdmXXt7YNUqYPBg4yyHzZqp5/srreztgbfftnQtiMgcujHdyZFIz0xHbGqsan1QkD4nQ1iY7C5H5Yy9J9AjFDg4AojYItdprIGgkUDt13XFlDHdEQmyy3mBgm4ACBgEuIXKruXRxwB7HyBwMFB/Ur53NX267Po5d65sdapbVwbjtQ2umQ1bttnKXco4+gGOPS1dCyrjsgfZ2XuxAnLY0OOPA2++Kb8zHR2BAQPkZxCVUspwYZHLFBiFpEBB97Fjx/Dll18CAP766y/4+vri+PHjWLVqFaZMmVIugu681K0ruxbOmgVs2QJYWwP9+gFjx+ozgBMRFQfDlm6lldvd3h121nYA1MNFbtxg0F1uOVYCOm2SQXBatEx45uCjKpI9yM7XHN3ZudcDuu0q+OsNNGoELF6c83YfZx8s6LMAmSLT6OKaiMjFzgV+Ln6ISIhAJZdKcLFzMVmuZk1gw4ZirhwVHausoMxw5gz3BgCykrI4F97c3QUKupOSkuDqKrtmbd68GYMGDYKVlRVatWqFmzeZVVJhZwdMmSIfRESWYpi9XEmmpqwD1EH3vn3GQ0W0WplIhsoBjQbwbJ7j5uxjuAs0pttCXmz+Yt6FiKjcWtx/MaKSouDl5GXpqlBxsZKND6ppCFstKZpDFeRFNWvWxJo1a3Dr1i1s2rQJISEhAID79+/DTcmpT0REJYJh9vLs47kBOVZN8eefxq9fsKBIq0eliKu9K7wc9RekBe5eTkRUwvSs2RPPN34ePWtyOEO5oQTdhi3dRXWogrxoypQpePfdd1GtWjU8/vjjaJ2VrWTz5s1o2rRpoVaQiIgejaqlO9G4pdtw7OuFC0DW6CEAwIoVwKJFxVJNKiWULuVWGitU9ahq4doQEREVkDKTRmbRB90F6l7+1FNPoV27dggPD0eTJk1067t27YqBAwcWWuWIiOjRudm7wcHGASkZKbgRcwOAOuiuUEEmeTx2TD5/5x05+0JMjEwKyXuppVj2ycJNrc+ajcRcrz/2Os49OAdnO2ddXgAiIqJSxzEr9XxSWJEfqkBBNwD4+fnBz89Pta5ly5aPXCEiIip8Pk4+uBV3C+cjzwNQB92AzMCqBN1CAAsXFnMFqdQYETwi70JEREQlnWMl+TPpDpCeANiaTqBXGJgah4ioHFCC7HMPzqmeK0aPlskfiYiIiMoFpaUbAoi/ZLqMRqN/mLM+Bwy6iYjKASXIvhB5QfVc4e8P5DTbo0vR3fglIiIisgxd0A0g5rTx9pgzhXYoBt1EROWAEmTHp8Wrnhv64gsgazIKHR8f4Lvvirx6RERERMXLMOi+s069LfokEPZHoR2KQTcRUTmQPcg2FXTb2ACrVwMzZwLduwOvvw6EhgINGhRXLYmIiIiKiVOAfvn230DEVrmcFgscGAqIzEI7VIETqRERUelhTtANAM7OwMSJ8kFlQD4zkxMREZUbdh6ASw0g4SoAAex/Hqj6HHD3X7mucr9COxRbuomIygHDINtaYw0vRy8L1oaIiIioBPBurV9OvQ9c+iorCC9cDLqJiMoBw6Db28kbGjOzbRIRERGVWb5dc98uRN4PMzDoJiIqBwyD7py6lhMRERGVK1UGAlZFP2cqg24ionKAQTcRERFRNnbuQNUhprc5+hXaYRh0ExGVAwy6iYiIiExo+gVg76Ne59USqPlKoR2C2cuJiMoBO2s7nHzlJLRCC28nb0tXh4iIiKhksPcC2qwADo0Eku8C/n2BVosBK9tCOwSDbiKicqKxb2NLV4GIiIio5PHrCvS9AWjTABvHQt99iepeHh0djWHDhsHd3R3u7u4YNmwYYmJiciyfnp6O999/H40aNYKzszP8/f0xfPhw3L17t/gqTURERERERKWblXWRBNxACQu6hwwZghMnTmDjxo3YuHEjTpw4gWHDhuVYPikpCceOHcPkyZNx7NgxrF69GpcuXUK/foU3kTkRERERERFRQWmEMHNysSJ2/vx51K9fHwcPHsTjjz8OADh48CBat26NCxcuoE6dOmbt58iRI2jZsiVu3ryJwMBAs15z7NgxNG/eHKGhoWjWrFmB3wMREREVgrzmkS8Zly5ERERmKTEt3QcOHIC7u7su4AaAVq1awd3dHfv37zd7P7GxsdBoNPDw8MixTGpqKuLi4nSPhISER6k6ERERERERkUklJuiOiIhAxYrG09hUrFgRERERZu0jJSUFEydOxJAhQ+Dm5pZjuZkzZ+rGjbu7u6Njx44FrjcRERERERFRToo86J42bRo0Gk2uj6NHjwIANCa6kwkhTK7PLj09Hc8++yy0Wi2+++67XMtOmjQJsbGxuseuXbsK9uaIiIiIiIiIclHkU4a9/vrrePbZZ3MtU61aNZw6dQr37t0z2vbgwQP4+vrm+vr09HQMHjwY169fx/bt23Nt5QYAe3t72Nvb6567uLjkWp6IiIiIiIioIIo86Pb29oa3t3ee5Vq3bo3Y2FgcPnwYLVu2BAAcOnQIsbGxaNOmTY6vUwLuy5cvY8eOHfDy8iq0uhMRERERERE9ihIzprtevXro2bMnXnzxRRw8eBAHDx7Eiy++iD59+qgyl9etWxd///03ACAjIwNPPfUUjh49imXLliEzMxMRERGIiIhAWlqapd4KEREREREREYASFHQDwLJly9CoUSOEhIQgJCQEjRs3xq+//qoqc/HiRcTGxgIAbt++jXXr1uH27dsIDg5GpUqVdI/8ZDwnIiIiIiIiKgpF3r08Pzw9PfHbb7/lWsZwWvFq1aqhhEwzTkRERERERGSkRAXdREREROANdSIiKkNKVPdyIiIiIiIiorKEQTcRERERERFREWHQTURERERERFREGHQTERERERERFREG3URERERERERFhEE3ERERERERURFh0E1ERERERERURBh0ExERERERERURBt1ERERERERERYRBNxEREREREVERYdBNREREREREVEQYdBMREREREREVEQbdREREREREREWEQbcJqampmDZtGlJTUy1dFaJCxXObyiqe21RW8dymsornNpUnGiGEsHQlLC0pKQkXLlxA3bp14eTkhLi4OLi7uyM2NhZubm6Wrh5RoeG5TWUVz20qq3huU1nFc5vKExtLV6AkcHJyQrNmzSxdDSIiIiIiIipj2L2ciIiIiIiIqIgw6CYiIiIiIiIqIgy6TbC3t8fUqVNhb29v6aoQFSqe21RW8dymsornNpVVPLepPGEiNSIiIiIiIqIiwpZuIiIiIiIioiLCoJuIiIiIiIioiDDoJiIiIiIiIioiDLqJiIiIiIiIigiDbhO+++47BAUFwcHBAc2bN8eePXssXSUis82cOROPPfYYXF1dUbFiRQwYMAAXL15UlRFCYNq0afD394ejoyM6deqEs2fPWqjGRAUzc+ZMaDQajBs3TreO5zaVVnfu3MHQoUPh5eUFJycnBAcHIzQ05GZ6sQAAghZJREFUVLed5zaVRhkZGfjf//6HoKAgODo6onr16pg+fTq0Wq2uDM9tKg8YdGezcuVKjBs3Dh9++CGOHz+O9u3bo1evXggLC7N01YjMsmvXLowdOxYHDx7Eli1bkJGRgZCQECQmJurKfP7555g7dy6+/fZbHDlyBH5+fujevTvi4+MtWHMi8x05cgQLFixA48aNVet5blNpFB0djbZt28LW1hb//fcfzp07hzlz5sDDw0NXhuc2lUafffYZfvjhB3z77bc4f/48Pv/8c8yePRvffPONrgzPbSoXBKm0bNlSvPLKK6p1devWFRMnTrRQjYgezf379wUAsWvXLiGEEFqtVvj5+YlZs2bpyqSkpAh3d3fxww8/WKqaRGaLj48XtWrVElu2bBEdO3YUb731lhCC5zaVXu+//75o165djtt5blNp1bt3bzFq1CjVukGDBomhQ4cKIXhuU/nBlm4DaWlpCA0NRUhIiGp9SEgI9u/fb6FaET2a2NhYAICnpycA4Pr164iIiFCd5/b29ujYsSPPcyoVxo4di969e6Nbt26q9Ty3qbRat24dWrRogaeffhoVK1ZE06ZN8dNPP+m289ym0qpdu3bYtm0bLl26BAA4efIk9u7diyeeeAIAz20qP2wsXYGSJDIyEpmZmfD19VWt9/X1RUREhIVqRVRwQgiMHz8e7dq1Q8OGDQFAdy6bOs9v3rxZ7HUkyo/ff/8dx44dw5EjR4y28dym0uratWv4/vvvMX78eHzwwQc4fPgw3nzzTdjb22P48OE8t6nUev/99xEbG4u6devC2toamZmZ+PTTT/Hcc88B4Oc2lR8Muk3QaDSq50IIo3VEpcHrr7+OU6dOYe/evUbbeJ5TaXPr1i289dZb2Lx5MxwcHHIsx3ObShutVosWLVpgxowZAICmTZvi7Nmz+P777zF8+HBdOZ7bVNqsXLkSv/32G5YvX44GDRrgxIkTGDduHPz9/TFixAhdOZ7bVNaxe7kBb29vWFtbG7Vq379/3+gOHFFJ98Ybb2DdunXYsWMHqlSpolvv5+cHADzPqdQJDQ3F/fv30bx5c9jY2MDGxga7du3C119/DRsbG935y3ObSptKlSqhfv36qnX16tXTJXHl5zaVVu+99x4mTpyIZ599Fo0aNcKwYcPw9ttvY+bMmQB4blP5waDbgJ2dHZo3b44tW7ao1m/ZsgVt2rSxUK2I8kcIgddffx2rV6/G9u3bERQUpNoeFBQEPz8/1XmelpaGXbt28TynEq1r1644ffo0Tpw4oXu0aNECzz//PE6cOIHq1avz3KZSqW3btkZTO166dAlVq1YFwM9tKr2SkpJgZaUON6ytrXVThvHcpvKC3cuzGT9+PIYNG4YWLVqgdevWWLBgAcLCwvDKK69YumpEZhk7diyWL1+OtWvXwtXVVXf32N3dHY6Ojrp5jWfMmIFatWqhVq1amDFjBpycnDBkyBAL154oZ66urrrcBApnZ2d4eXnp1vPcptLo7bffRps2bTBjxgwMHjwYhw8fxoIFC7BgwQIA4Oc2lVp9+/bFp59+isDAQDRo0ADHjx/H3LlzMWrUKAA8t6kcsWDm9BJr/vz5omrVqsLOzk40a9ZMN9USUWkAwORj8eLFujJarVZMnTpV+Pn5CXt7e9GhQwdx+vRpy1WaqIAMpwwTguc2lV7//POPaNiwobC3txd169YVCxYsUG3nuU2lUVxcnHjrrbdEYGCgcHBwENWrVxcffvihSE1N1ZXhuU3lgUYIISwZ9BMRERERERGVVRzTTURERERERFREGHQTERERERERFREG3URERERERERFhEE3ERERERERURFh0E1ERERERERURBh0ExERERERERURBt1ERERERERERYRBNxEREREREVERYdBNREREREREVEQYdBMREREREREVEQbdREREREREREWEQTcRERERERFREWHQTURERERERFREGHQTERERERERFREG3URERERERERFhEE3ERERERERURFh0E1ERERERERURBh0ExGRypIlS6DRaHD06NFH2s+///6L4cOHo1GjRrC1tYVGozHrdVu3boVGo4FGo0FkZGSe5W/cuKErb+rRs2dPXdlbt25h4MCBqF69OpydneHu7o6mTZvi22+/RUZGhmq/Z8+exWuvvYbWrVvD2dkZGo0GO3fuzLEekZGReOutt1CtWjXY29vD19cXvXr1wsOHD1Xljh8/jgEDBsDf3x9OTk6oW7cupk+fjqSkJFW5r7/+Gq1atYK3tzfs7e0RGBiIZ599FmfPnjV5/Js3b2LUqFHw9/eHvb09KleujIEDB6rKKH9bU4+IiIg8f9cAcv1d161bV1cuMTERzz77LOrUqQNXV1c4OzujQYMG+OSTT5CYmKja5+3btzFu3Dh07NgRHh4e0Gg0WLJkSY51SExMxJQpU1C7dm3Y29vDy8sLnTt3xuXLl1Xlrly5gmHDhiEwMBCOjo6oUaMGxo8fj6ioqFzf49ChQ6HRaNCnTx+T2835W+/cuTPH39PBgwdzPb6hUaNGqc5hw/N92rRpOb5GKZOdVqvFb7/9hh49eqBixYqwtbWFh4cHWrVqhS+++MLk/1xqaiq+/fZbtGvXDhUqVICdnR0qV66MwYMHY9euXbpy27Ztg4uLC+7cuWP2+yMiKg9sLF0BIiIqm/7++28cPHgQTZs2hb29PUJDQ/N8TUJCAl588UX4+/vj7t27Zh2nUqVKOHDggNH6NWvW4LPPPlMFnomJiXBzc8PkyZMRGBiItLQ0bNiwAW+88QZOnDiBhQsX6soePXoUa9asQdOmTdG1a1f8888/Odbh7t27aN++PWxsbDB58mTUqlULkZGR2LFjB9LS0nTlzp07hzZt2qBOnTqYN28evL29sXv3bkyfPh2hoaFYu3atrmxUVBR69eqFJk2aoEKFCrh27RpmzZqFxx9/HKGhoahTp46u7JkzZ9CpUydUr14dX3zxBapUqYLw8HBs2rTJZH0XL16sCpABwMvLK5ffsp6p3/WhQ4cwbtw41e86PT0dQgiMHz8eQUFBsLKy0r3XnTt3YuvWrbqyV65cwbJlyxAcHIwnnngCK1asyPH4CQkJ6Ny5M+7evYuJEyeicePGiI2Nxf79+1U3Lh48eIBWrVrBzc0NH3/8MQIDA3H8+HFMnToVO3bsQGhoKKysjNse1q9fjzVr1sDNzc3k8c39WytmzJiBzp07q9Y1bNgwx/dn6Pjx4/jll19w6NAho22urq5YsmQJpkyZonofCQkJ+PPPP+Hm5oa4uDjVa5KTk9G/f39s3boVzzzzDL7++mv4+/sjLi4O+/fvx+zZs7F27Vrs2bNH95rIyEj07NkTp06dwqhRo/Dee+/B09MTd+7cwdq1a9G1a1eEhoaiSZMm6Nq1K1q2bIkPPvgAv/zyi1nvkYioXBBEREQGFi9eLACII0eOFOj1SUlJQgghMjMzdevGjh0rzPnKGTt2rGjatKn43//+JwCIBw8eFKgOQgjRqVMn4eTkJGJjY/MsO3jwYGFjYyNSUlJ06wzr/+effwoAYseOHSZf379/f1G5cmXx8OHDXI/z4YcfCgDiypUrqvUvvfSSAJDn68+dOycAiMmTJ+vWabVaERwcLIKDg1X1N+VR/7Y5GTlypNBoNOLy5ct5lp0wYYIAIK5evapbZ/i7PnLkiAAgFi9ebPL1b731lnB2dla93pSffvpJABBbt25VrZ8xY4YAII4dO2b0mpiYGFG5cmUxd+5cUbVqVdG7d2+jMub+rXfs2CEAiD///DPXcrkZPHiwaNWqlWrd9evXBQAxZswYAUBs3rxZtX3hwoXC0dFRDB061Oh/TjnPli9fbvJ4iYmJYsGCBap1vXr1EjY2NmLbtm0mX3P48GFx8+ZN3fO//vpLWFtbi7CwMLPfJxFRWcfu5URE5cyFCxfw3HPPwdfXV9dtefjw4UhNTVWVi4+Px6uvvgpvb294eXlh0KBBRq3P1apVQ58+fbB69Wo0bdoUDg4O+OijjwDAZCtibvbs2YMFCxZg4cKFsLa2fqT3ePXqVezatQuDBw/OscXSkI+PD6ysrFTHNbf+N27cwLp16/Diiy+iQoUKuZa1tbUFALi7u6vWe3h4wMrKCnZ2dnnWEwBsbPQd1Xbv3o0TJ05g3LhxsLe3N6vOhSk+Ph5//vknOnbsiJo1a+ZZ3tR7MPd3nZSUhIULF+Lpp59G9erVcy2b2+8aABwcHIxe884776BSpUp48803Te4zP3/rR3Xv3j38/fffGDZsmMntderUQZs2bfDzzz+r1v/8888YNGiQ0fsODw/Hzz//jN69e+O5554zuU8nJye8+OKLuuehoaH477//MHr0aHTp0sXkax577DEEBgbqnvft2xcuLi746aefzHqfRETlAYNuIqJy5OTJk3jsscdw8OBBTJ8+Hf/99x9mzpyJ1NRUo66xY8aMga2tLZYvX47PP/8cO3fuxNChQ432eezYMbz33nt48803sXHjRjz55JP5rldycjJGjx6NcePGoVmzZjmWU8bJ5jSWVfHzzz9DCIExY8aY3C6EQEZGBqKjo7Fy5UosWbIE77zzjioQNNeePXsghIC/vz+ee+45uLi4wMHBAZ06dTLqij1ixAh4eHjg1VdfxbVr1xAfH49///0XP/74I8aOHQtnZ2ej/WdmZiI1NRUXLlzAmDFjULFiRbzwwgu67bt37wYguxs/8cQTcHBwgIuLC/r06YMLFy6YrHOfPn1gbW0NT09PDBo0CGfOnMn3+1b8/vvvSExMzPN3HRcXh40bN2LOnDl47rnnVIGauUJDQ5GYmIhatWrh1Vdf1Y0vbtGiBdavX68qO2DAAAQGBuKdd97B2bNnkZCQgN27d2PWrFno27cv6tWrpyq/detWLF26NNebPvn5WyvGjh0LGxsbuLm5oUePHti7d69Z73Xz5s1IT0836ppuaPTo0VizZg2io6MBABcvXsT+/fsxevRoo7I7duxARkYG+vXrZ9bxlToA8ndpLjs7O7Rp08bo70FEVJ4x6CYiKkfGjx8PGxsbHD58GC+//DK6dOmC559/HitXroSrq6uqbM+ePfH1118jJCQEo0ePxqRJk7Bjxw6jhFv379/Hf//9hxdeeAGdOnXCY489lu96TZ48GZmZmbpW8pxoNBpYW1vn2jKamZmJX375BXXr1kXbtm1Nlvnss89ga2sLT09PPPfccxg3bhxmzJiR73oD0CWNevfdd5GcnIxVq1Zh+fLliI6ORpcuXXDq1Cld2WrVquHAgQM4c+YMatSoATc3N/Tt2xcjRozAV199ZXL/zs7OcHBwQL169XD+/Hns3LkTAQEBRsd/4YUX4O/vj/Xr1+OHH37AmTNn0L59e4SHh+vK+vn54cMPP8TChQuxY8cOfPzxxzhy5AhatWqFkydPFuj9L1q0CB4eHjnebFm5ciVsbW3h7u6OXr16oVevXli6dGmBjqW8188++wynT5/G0qVL8ffff+t+j4Zj2N3d3XHw4EGkp6ejYcOGcHV1RceOHfH444/jzz//VO1XySXw7rvvokmTJnke35y/tbu7O9566y38+OOP2LFjB7766ivcunULnTp1ynGsvaEDBw7A0dHRaOy9ocGDB8PGxgbLly8HIP8WQUFB6NSpk1HZW7duAQCqVq1qtC0jI0P1UISFhQEAgoKC8qyvoWbNmuHEiRNGCfOIiMorJlIjIionkpKSsGvXLowePVrXxTc32VvEGjduDEBmyfbz81Otr127doHrdfjwYcybNw8bN26Eo6NjrmU7duxolGU8u40bN+LOnTuYPXt2jmVGjhyJbt264eHDh9i+fTtmz56N2NhYfPPNN/muv1arBQBUqVIFq1at0rWStm7dGjVr1sTnn3+O3377DYDsnty3b1/4+vrir7/+go+PDw4dOoRPPvkECQkJWLRokdH+9+/fj7S0NFy9ehVffvklOnfujG3btqFBgwaq47du3VqVCK5hw4Zo2rQp5s+fj08++QSAvJFimAm7Q4cO6N27Nxo1aoQpU6boErlptVrdfgH9zY7szp49i0OHDmHs2LEmu2sDQI8ePXDkyBHEx8fjwIED+OyzzxAVFYW///4730MQlDrZ2dnhv//+090o6ty5M2rVqoWPP/4YPXr0AABER0ejf//+SEpKwrJlyxAQEIAzZ87g448/Rr9+/bB+/Xpdz4aJEyfC1tYWU6ZMMev45vytmzZtiqZNm+pe2759ewwcOBCNGjXChAkTdPXMyd27d+Hj45Nr1n8XFxc8/fTT+Pnnn/Hyyy9j6dKlGDt2rNkzBQDAiRMnVPUEZBI6b29vs/eRXcWKFaHVahEREYEaNWoUeD9ERGUFg24ionIiOjoamZmZqFKlilnls2ezVsYLJycnq9ZXqlTpkeo1atQoDBo0CC1atEBMTAwAICUlBQAQFxcHe3t7o1b43CxatAi2trYYPnx4jmX8/Px0Nw5CQkJQoUIFTJw4EaNGjTIKQPKi/J66deumCkwrVaqEJk2a4NixY7p1EydORFxcHE6cOKHrSt6hQwd4e3tj1KhRGD58ODp27Kjav9LdvlWrVujXrx9q1qyJDz74QBcgK8fPHsQFBwejUqVKquObUq1aNbRr1041jdWoUaNU2ac7duxocro05SZBTl3LAaBChQpo0aIFABkc16hRA88++yzWrl1rNKVZXpT32qZNG9U54eTkhI4dO2LNmjW6dZ999hlOnDiBmzdv6s7R9u3bo27duujSpQuWLVuGESNG4PDhw/juu++wevVqpKSk6M49rVaLjIwMxMTEwNHRUTc1GWDe39oUDw8P9OnTBz/88AOSk5NzvcmUnJyc440MQ6NHj0a7du3w6aef4sGDBxg5cqTJckp3/ps3b6rW16lTB0eOHAEALFiwQDUWW3nN9evXVdny86LUO/tnBRFRecXu5URE5YSnpyesra1x+/btQt1vflrVTDl79iz+/PNPVKhQQff47LPPAAA1atRA+/btzd7X/fv38e+//6Jfv36oWLGi2a9r2bIlAODSpUv5qzz0PQBMEUKoWnNPnDiB+vXrG43dVrrk5zW22tXVFXXr1lXVMz/HN7fctGnTcOTIEd3jxx9/NHpNWloafv31VzRv3hzBwcF5HkNRnL/rypUrG90Uyv67PnfuHIQQGDhwoOocvHXrFjZt2oQKFSrg+++/z/fxcysH5P1/4+3tbTTHuylt27ZFnTp1MH36dHTv3l019MBQp06dYGNjg3Xr1qnWOzo6okWLFmjRogX8/f1V25QbOYY3M8yh1PtRWsuJiMoSBt1EROWEo6MjOnbsiD///BORkZGWro7Ojh07jB4jRowAIC/2DbtM52Xp0qVIT083mUgqrzoAMCv7dnaPP/44qlSpgs2bNyMzM1O3/u7duzh58iRatWqlW+fv769L6mVIScKVVy+EyMhInD59WlXPXr16wcnJCf/995+q7LFjxxAREaE6vinXr1/Hvn37VOWqVaumC8RatGhhspVz3bp1iIyMLNbfdaVKldC6dWvs27dPNQe1MnQi++/69u3bunHYiuy/6549e5o8B319fdGqVSvs2LEDTz31FID8/a1NiY6Oxr///ovg4OA8W7Hr1q2LqKgoxMbG5vl7+d///oe+ffvinXfeybFMpUqVMGrUKKxfvx6///57nvsEZC+LXr16YdGiRdi+fbvJMkePHtWN/VZcu3YNXl5e8PX1Nes4RERlHbuXExGVI3PnzkW7du3w+OOPY+LEiahZsybu3buHdevW4ccff8xXN+683Lx5U9dt9erVqwCAv/76C4A+qANgMumT0pW5bdu2qtayXbt2oWvXrpgyZYrJ8beLFi1CQEBAjuNlp06dinv37qFDhw6oXLkyYmJisHHjRvz00094+umn0bx5c13ZpKQkbNiwAQB0Xa937dqFyMhIODs7o1evXgDkdFdffvklBg8ejP79++PVV19FYmIiPv74Y9jZ2WHSpEm6fY4bNw4DBgxA9+7d8fbbb8Pb2xsHDx7EzJkzUb9+fd0+Y2Nj0b17dwwZMgS1atWCo6MjLl26hK+++gqpqamYOnWqbp8eHh6YPn063n33XYwcORLPPfccIiIiMHnyZAQGBuK1117Tle3WrRs6dOiAxo0bw83NDadPn8bnn38OjUaDjz/+2OTvLCeLFi2Co6MjhgwZYnL7jz/+iD179iAkJAQBAQFITEzEnj178M0336BNmzbo37+/qrxybly7dg2ADOZcXFwAQBf0AsAXX3yBzp07o0ePHnj//feh0WgwZ84cREZGqt7D2LFjsWzZMnTv3h0TJ07Ujen+5JNP4Ovri+effx6AeqiBIQcHB3h5eanOz/z8rYcMGYLAwEC0aNEC3t7euHz5MubMmYN79+5hyZIlef5+O3XqBCEEDh06hJCQkFzLDh061OTMAtnNmzcP169fx/PPP49169ahf//+8Pf3R1JSEi5cuIDff/8dDg4OuunWAHkjq2fPnujVqxdGjRqFXr16oUKFCggPD8c///yDFStWIDQ0VJWN/uDBg+jYseMj94IhIiozLDI7OBERWcy5c+fE008/Lby8vISdnZ0IDAwUI0eOFCkpKUIIIRYvXiwAiCNHjqhet2PHDgFA7NixQ7euatWqonfv3iaPo+zH1GPEiBG51nHq1KkCgHjw4IHJOkydOtXoNfv27RMAxJQpU3Lc77p160S3bt2Er6+vsLGxES4uLqJly5bi66+/Funp6aqy169fz7H+VatWNdr3mjVrxGOPPSYcHByEu7u76Nevnzh79qxRue3bt4uQkBDh5+cnHB0dRe3atcU777wjIiMjdWVSUlLEmDFjRL169YSLi4uwsbERVapUEUOHDjW5TyGE+Omnn0TDhg2FnZ2d8PLyEs8//7y4deuWqsy4ceNE/fr1haurq7CxsRH+/v5i6NCh4uLFizn+zkwJCwsTVlZWYvjw4TmW2bdvn+jTp4/w9/cXdnZ2wsnJSTRp0kR8/PHHIjEx0ah8Tr9rU5cqe/bsER07dhROTk7CyclJdOnSRezbt8+o3LFjx8TAgQNFlSpVhL29vahevboYM2aMCAsLy/M95nZum/O3njlzpggODhbu7u7C2tpa+Pj4iIEDB4rDhw/neWwhhMjMzBTVqlUTr732mmq9cl7Onj0719ePHTvW5O8uMzNTLF26VHTv3l14e3sLGxsb4e7uLlq2bCkmT54sbt++bfSa5ORk8fXXX4vWrVsLNzc33bkzaNAgsX79elXZK1euCABi1apVZr1PIqLyQCNE1uAiIiIiIiox5syZg08//RR37tzJM7N/STF58mQsXboUV69eLdC890REZRHHdBMRERGVQGPHjoW7uzvmz59v6aqYJSYmBvPnz8eMGTMYcBMRGWDQTURERFQCOTg44Ndff9VN11fSXb9+HZMmTcpxnD8RUXnF7uVERERERERERYQt3URERERERERFhEE3ERERERERURFh0E1ERERERERURBh0A0hKSsKxY8eQlJRk6aoQERERERFRGcKgG8CFCxfQvHlzXLhwwdJVISIiIiIiojKEQTcRERERERFREWHQTURERERERFREGHQTERERERERFREG3URERERERERFhEE3ERERERERURFh0E1ERERERERURBh0ExERERERERURBt1ERERERERERYRBNxEREREREVERYdBNREREREREVEQYdBMREREREREVEQbdREREREREREWEQTcRERERERFREWHQTURERERERFREGHQTERERERERFREG3URERERERERFhEE3ERERERERURFh0E1ERERERERURBh0ExERERERERURBt1ERERERERERYRBNxEREREREVERYdBNREREREREVEQYdBMREREREREVEQbdREREREREREWEQTcRERERERFREWHQTURERERERFRESmTQ/d133yEoKAgODg5o3rw59uzZk2PZ1atXo3v37vDx8YGbmxtat26NTZs2FWNtiYiIiIiIiEwrcUH3ypUrMW7cOHz44Yc4fvw42rdvj169eiEsLMxk+d27d6N79+7YsGEDQkND0blzZ/Tt2xfHjx8v5poTERERERERqWmEEMLSlTD0+OOPo1mzZvj+++916+rVq4cBAwZg5syZZu2jQYMGeOaZZzBlyhSzyh87dgzNmzdHaGgomjVrVqB6ExEREREREWVXolq609LSEBoaipCQENX6kJAQ7N+/36x9aLVaxMfHw9PTsyiqSERERERERGQ2G0tXwFBkZCQyMzPh6+urWu/r64uIiAiz9jFnzhwkJiZi8ODBOZZJTU1Famqq7nlCQkLBKkxERERERESUixLV0q3QaDSq50IIo3WmrFixAtOmTcPKlStRsWLFHMvNnDkT7u7uukfHjh0fuc5ERERERERE2ZWooNvb2xvW1tZGrdr37983av3ObuXKlRg9ejT++OMPdOvWLdeykyZNQmxsrO6xa9euR647ERERERERUXYlKui2s7ND8+bNsWXLFtX6LVu2oE2bNjm+bsWKFRg5ciSWL1+O3r1753kce3t7uLm56R4uLi6PXHciIiIiIiKi7ErUmG4AGD9+PIYNG4YWLVqgdevWWLBgAcLCwvDKK68AkK3Ud+7cwdKlSwHIgHv48OH46quv0KpVK10ruaOjI9zd3S32PoiIiIiIiIhKXND9zDPPICoqCtOnT0d4eDgaNmyIDRs2oGrVqgCA8PBw1ZzdP/74IzIyMjB27FiMHTtWt37EiBFYsmRJcVefiIiIiIiISKfEzdNtCZynm4iIiIiIiIpCiRrTTURERERERFSWMOgmIiIiIiIiKiIMuomIiIiIiIiKCINuIiIiIiIioiJSoKA7MTGxsOtBREREREREVOYUKOj29fXFqFGjsHfv3sKuDxEREREREVGZUaCge8WKFYiNjUXXrl1Ru3ZtzJo1C3fv3i3suhERERERERGVagUKuvv27YtVq1bh7t27ePXVV7FixQpUrVoVffr0werVq5GRkVHY9SQiIiIiIiIqdR4pkZqXlxfefvttnDx5EnPnzsXWrVvx1FNPwd/fH1OmTEFSUlJh1ZOISprUSODhMSCD/+dERERERDmxeZQXR0REYOnSpVi8eDHCwsLw1FNPYfTo0bh79y5mzZqFgwcPYvPmzYVVVyIqKa7/ChwaCQgtYOsBBH8O1HzR0rUiKlFCQ4E//wSsrIChQ4H69S1dIyIiIrKEAgXdq1evxuLFi7Fp0ybUr18fY8eOxdChQ+Hh4aErExwcjKZNmxZWPYmopLi2GDg0GoCQz9NjgCMvARoroMZoS9aMqMT46y8ZaKemyuezZwMLFgAvvGDZehEREVHxK1D38hdeeAH+/v7Yt28fTpw4gddff10VcANA9erV8eGHHxZGHYmopEiLBkLfhC7gNhR1uNirQ1QSrVoFDB6sD7gBICMDGDMGiIqyXL2IiIjIMgrU0h0eHg4nJ6dcyzg6OmLq1KkFqhQRlVDXFgMZCZauBVGJlZYGvPceIEzcl9JqgfT04q8TERERWVaBWrp37tyJTZs2Ga3ftGkT/vvvv0euFBEVLcMWuHy5vUa/bOMKVB0CuNYujCoRlQkbNwLXr1u6FkRERFSSFCjonjhxIjIzM43WCyEwceLER64UUZmn0eT+KCKrVwMBAYCDg/z51Vey9c0sGclA5D65bO0EdN4EtFkGPHEGqM6x3EQA8Mcf+mUHB/n89Glg1CjL1YmIiIgsq0Ddyy9fvoz6JtKw1q1bF1euXHnkShFR4RICGDcO+Ppr/brbt+W6Bw+ATz4xYyeJ12W2cgCoMQbwbi2XrWyBx34Ebq4o5FoTlT4HD+qXf/4ZePppubxwIWBT0PlC0mKB/7d33uFRVOsf/2x6L6RSQ6ihGnrviAKKFcWCei1XrCh2uSo2sGD5qRcVrx2wi4IUQUR6C4TeayAQQnrv5/fHyWxJIwmbhIT38zzz7OyZdnZ3dma+522pe8DJC/w666SFZaAUZGfDeaK/BEEQBEGoZapl6fb19eXo0aOl2g8fPoynp+cFd0oQBPvyyy+2gtuaSrvCZhyxzDcZY7vMwRFa3latvglCQyEnx/J/atpUJ1MzMJng3XfBx6eKO41dCH+0gb8GwNLLYGEbiF1ks0p+PsyYAX5+4OkJYWH6/15pLxZBEARBqE0Kc2HP67D6Wth4N5xeXHYylAZEtcbdx40bx2OPPcb8+fNp3bo1oAX3E088wbhx4+zaQUEQLgyl4JVXbNuaN9dZlLOyqrCjdCsvlka9Si+vQbd4QbioyImHQx9DzlnwbgthE8C9MYcPW4TuFVeAo6PtZlUekz7yOWy+17Yt8xisvhrGp4OTJ2lpMHy4rgluEBMDkyfr3A1PPVXlTycIgiAINUfaQVg3HlJ2WtqOfQnNb4SBP9Vdv2qYalm63377bTw9PYmIiCA8PJzw8HA6dOhAQEAAM2fOtHcfBaHhoZRlqqjNDmzbpmNKQYuAzz7TD+Vnz8Ijj1RhR4al26MFuDayax8Fod5w8hdY1BF2T4PDH0P0FFjYGmJ+ZP9+y2oRERd4nLxUiH6inIWW68STT9oKbmuSki6wD4IgCEK9p0gVce331zJ23lhe/uflOu5MAay72VZwG5wpnaS7IVEtS7evry/r169n+fLl7NixA3d3d7p27crgwYPt3T9BEC6QLVss8/fdp2sFA3h56URqf/9dyR1ln9avrgF27Z8g1BuSomHdBFAFtu2F2XB6KQcPWvzJW7W6wGOdmAv5qXre2Q+6vqpjuo9+DufWAnrw7IsvLJtERGiX9gMH4OefL/D4giAIQoPgRMoJfj/wOwCHEg/x0tA6LOl86jdI2W5579MBVCGkH6yrHtUa1U3rgslkYtSoUYwaNcqe/REEwc5ER1vmS2ZQNplgxIhK7qgwR786eVvaEjZB7jk97+AMja+odj8F4aJn90sWwe3sA8FDIf0QpO0DIC3NsmpY2Pl3V1hUSKHSlUCcHJxwsE6QFrdCv5qcYOQanUANIPwO2P4MAL/9BkYhkQEDdLkyLy/9fvVqWLOmGp9REARBaFDsit9lnj+cdJis/Cw8nOso4+aJuZb5bu9B+8n6YTRuBUQ9WDd9qiWqLbpXrFjBihUriI+Pp6hEtpYvrIfeBUGoU7Zv169OThAZeQE7Kiou7u1sJbp3vQRxxe5Azn5wY/IFHEAQLmLyUiH2Dz3v3Q6GLQPPMO3mHfMDnFtLdrZldUP8VsTra17npX+0xWHpbUu5oo3VoFXKDv0aOtIiuEFnLo98C7D1YnnzTdtjDh4MvXtX5QMKgiAIDZGdZy2u3ArFnvg99GpaRm6e2iApSr8GD4GIxyztoSNgaMN2L69WTPfLL7/MqFGjWLFiBQkJCSQnJ9tMgiBcPOzTRjhatwZn5wvYkVEuzFTtsTpBqDOUUiw6uIg/Dv7B9rjtVd9B8jagON9Cl5e14AY9Qh82AS6bQV6eZfXK/Nc2x24uc578NEsOhcajS29oMoHJZBbdvr7Qv3/p1dzczt8HQRAEoWFjWLr93fxt3tc6uQmQdUrPN7229HKvlrXZm1qnWk/Pn3zyCV999RUTJ060d38Eof5QVAiFWTrO8iLN3K0UpKfr+RYtLnBnDq76tSDjAnckCLXPoaRDXPXdVQD0btqbTfduqtoOjNF5B+fSJfMAnL1xdbW8zc+veHdKKVvRfdpKdKcdsMz7dixz+8xMHbsN0KXLRXsJEgRBEOoYw9J9XcR1fLH9CxvLd62SvN0yHzyobvpQh1RLdOfl5dG/rGF1QbgUiF8F+9+D03/o5A9OXtpNptNUCOxnXm3Gmhn8fVxnKfv+hu8J8Kj9BGQ5OZZ5d/cL3JmjIbrTL3BHglD7rDlhCXDedmYbmXmZeLpUoYZXcrG7t1cbHc9dBtaWZev47rI4kXqCc1nn8HfzJzknmc2xm1FKYTKZtKXbwKW4UkDmCTg539KdotFAewDatq38xxAEQRAuHbLzszmYeBBHkyPXRFxTt6I7J94y7xqkX/PTLbmBANxCwamO4s1rmGqJ7nvvvZd58+bxwgsv2Ls/gnBxc+QL2HyPbVtBBpxeBB7NzaJbKcXHUR9zMu0kAH8f+5vxncbXdm9xsvqHGwmXqo1L8aCBtSDo/SmcXtzgk18I9Z81MVp0O5ocKSgqYOOpjYxoVdksglgyiVtn7z/2LaQV1wlzcMHPz5IR9sSJimOqDSv30JZD2XJ6C6fSThGTGkOYX5jOhm7g7Ktf0w5C9OPm5qygZhiiu8r1v4WyyUuFkz9B/Gr9G7g3gaCB0PRqcBRffUEQ6h/7EvZRpIqICIygW2g3QFu+zYO8tUlhlmXeGFA+NR823mlpH7IEmlxZu/2qJaolunNycpg9ezZ//fUXXbt2xblE8Nq7775rl84JwkVFbiJsfUjP+3SE7u9BYH/IOQMxP+nRumIOJB4wC26AZUeW1YnodnbWU36+HWr2ehXXQMo4AoW52vLtGWaJbRXsT2Gurlt5aj5kxYCDi7a0Nr5Suzhf6j7Flfn8xfWsDdF9U6eb+G73d6yJWVM10W1OJGhl5Y75UXu8ADh50b69RXQfPVrx7gzR3aNxDwpVIafSTrE5drMW3Q4ulhWtH1KscHayJDAtKChzFaEqnJwPGydCQaZt+8EPoMur0Pk/ddMvQRCEC8CwancM6kgzn2Z4u3iTmJ1IXEYcjb0b13JvrO/ZReWu1VCplujeuXMnkcVpkHfv3m2zrNZHTQShtohdaCmbNeB78Oui553bQqfnLYnG0CIbYGzbsSw6tIhlR5dVfVTxfOsWi4nz0aSJtrodOKA3qfZf1Ku1fi3Kh9Td0KhHNXckVIqs0/DPKEjdo9+bnPRAx5mlcOgjGJ8FThcaM3BpEJsWy9HkowS4BzCx60S+2/0dq0+srtpODEtnfvl+4xERlvm9eyvenSG6uzfuTkFRAQsOLGBz7GY9OGftWpdXnJzUyV0PfGXGgCrA08OitM+erdInEUqScRTW36IHVvwug47P6Vj6rFO6pqyj63l3IQhCMUpByk4dipefosuM+rSHoEG21U+EWmHXWZ00rWNgR0wmEx2DOrIpdhO74nfVvuh2tLq35SaVG6rVUKmW6F65cqW9+yEIFz9nlupXv64WwW2NVY1dQ3TfFXkXe87t4XjKcQ4mHqR9YPva6KkNkZFadCclQXw8hIRUc0ferS3z59aJ6K5poh7UgtslAPp+pWugm5y0p8Gxb2zOt0sW64EnYzSpjMEow8rdt1lf+jbrC8DGUxvJK8zDxdGl1Ppl4qyzvpKbaGkLm6Ddv4vrjrZuDY6OOpTjzz+1Bdo6xCMlRb938yhg65mtgEV0g1UyNS+r/1rmCf0aNBCuPgKLOkLaPgL9c/D1hdRU2FVHiWgbDMe+1YLbyUuXrHEvvkj6dYEmo20GVO3JydSTHEo6BOjzwM/Nr0aOIwi1RtJW2HC7JezG5AQU6f9QQB8YtbFOu3cpsjNeW7o7BXcCMIvunWd3Mqr1qNrtjGdzy3zOGZ2tvMVN2nNvQasGnzPogp7aDh8+zJ9//kl2cXFSVUnLmyDUS3KKzUkeVmnA198GvzXT02JdSzevMI9/jv8D6HjNYS2HARYhXmmUskwVtZ0H69rcixaVXp6ZWbqtTKyFwKFZNu70gp3JOg2nF+r5y16HplfprNkmE3i3ga6viPWtChhJ1Po164e/uz8RgRFkF2Sz7cw2m/WUUvy450e+2fENv+//3XYn/pfp1/RDOvYXoOVtEG6JRXNx0cIbtPX5yy8tmxcVwaRJkJEBe8/tJSs/iybeTQjxCqF74+4ARJ2O0gLcLdSSZObs32V+JgcH6NlTzx85AomJZa4mVIYzi/Vr6OUWwZ0VCxnH9ZR9pkYOe/8f9zPimxGM+GYEb697u0aOIVykZJ+B4/Ng2xOw8W7YMgl2vwqnl9Z1z6pPQRb8M1oL7qCBcOUOuClHT6O2QPhddd3DSxLDvfyFlS/Q/dPuLD602Ka9VvHrapk/+49+dXQD18BLwpBQrU+YmJjIiBEjaNeuHWPGjOHMGX1Duvfee3niiSfs2kFBuGgw3EutS2blJkB2bPGk/wcbTm4gMz+TLsFdCPQIZGjLoQAsP7q8ljus6dbNMv/GG5CcbHm/fz/8p7Khiq4BOp4YIP0ArL5ax7If+cxufRWKSdlhsa4FFZfVSNkFm+6xTOfW1V3/6hmGpbuReyN2xO2gtb9WxtYZzQFWHFvBzT/fzJ2/3cm1P1xrK8obFStcVaCTB5aDdWGPBx+ETz6BVavghhvghx90u+FanpydTNePu3LlXJ00Jis/i33n9unBFb9ikX/6D8gvu0xfr17FXVL6ONbk58OKFeV2U7DGcOF3s3IDWjkKFobr6Z8yaqVfILvjd7Pk8BJa+7fG3cmdj6M+JiNPyjFeEux/DxaE6RwC6Qf0eecSAKl7YfuTdd276nN8riUL9YCfwL8rODjqAeOAntB2Uo0cNjErkc+2fsZnWz+rethQA+dsxlniM3XG8MNJh4mOi+ZspjYg1UmtbmcfiwHn1C9VMiA1BKoluh9//HGcnZ2JiYnBw8Pin3/zzTezdOmFj9LNmjWL8PBw3Nzc6NGjB2vWrKlw/VWrVtGjRw/c3Nxo1aoVn5R8+hAEe+BfrF5Tdlliuwf/DgN/tlnNsGhn5mdy74J7mb9fl/lZeXwleYV5tdZdgyFDwPibHjoEffroB/TnntPz8fEVb29D06ss8/GrYN1NOuZRqBZTV0yl/+f96f95f37c86Nlgck67XzxuZZzFk7+Cke/hKNfQPrh2u1sPSUpO8n8cPHg4geJ/DSSRYe0y8fqGMsDmlKKqX9PBeCZAc8A8J+/rUak/LtZRuJ3/UdbvEGXDbTi5pst8wUF8MADMHQo/Pabpd0Q3dkF2eyK38Xu+N2llpnLD+YmwOqrIOOYnvJTzOsOGWLZ5yuvwEcfQVYWREfDFVfAX3+d79sRAIvYzrIkvyTsFl0K0kApPdiy8S74IwIWtoXFXWDJZfp138wqHfLdDTrh7ON9H+e2LreRnJPMl9FfnmermiWnIIf//P0fnlr2FC+ufJH8wvMUmxeqTsouLayL8qHvHBjyB0TO0B5NA76D0fU4VsS4JrqFgnuont/9GvwaZJkyY+x+2CeXP8m///g3jyx5hKvmXcXp9NN2P0Z9pSJhvffc3rr5jwf00a9JW2HrozphbG6i/k80cKolupctW8abb75Js2bNbNrbtm3LiRMnLqhDP/zwA4899hhTp04lOjqaQYMGMXr0aGJiyv6jHjt2jDFjxjBo0CCio6N5/vnnefTRR/nll18uqB9C/eJ4ynG+iP6CL6K/qLmRzmbX6te8RD1SrZS2fjvYlpJZdlSL7qPJR/k8+nN+2/8bABl5GWw8VfvxTH5+cMcdlveHDmkh8MYb568lXIpWd9uza5c0Cw4sYPra6Xi6eHIy7ST3LbyP4ynH9cLAvuBUXAcqdoF+DR0JNyZr93Kh0qyLKd8jYF3MOoqKPQoWHlzI5tjNDG05lOkjptM1pCtLDi+xbO/sDc2u1/MZR2FJJCwfCBtutdnn5ZdDhw4V98kQ1sGewTT3aU5zn+bmeF6z6A6/E3Om1/hVsLCVnqxcnUeNgvbFaSLy8uCRR3T5sO7dQVKvVIGm1+rXuGWQfkTPd/4PhFn9tkc+g1VjdSWBXp/AVQdhzC4YvQOu2Krj+yvJmfQzzN01F28Xb+647A4e6q2rYry38T0Kiy60tmP1eeLPJ3h9zetsjN3Iq6tf5fkVz9dZXypNQRYkRkHsIjjxAxz/Dk79rj2BCrLPv31tk7DR4sXUdKx+PfE9/B6mpwUt668Xk1HhJOesJfdF6Aho/7gePMxNsHt+hH+O/8NX27+iT9M+zL56Nul56UxeOtmux6jPGEnUejftzeyrZpsnLxcv8grzzDklahXr58hDH8HP3jA/uNxKHQ2JaonuzMxMGwu3QUJCAq6uFxZn+O6773LPPfdw77330qFDB95//32aN2/Oxx9/XOb6n3zyCS1atOD999+nQ4cO3Hvvvdx9993MnFm1UWeh/rLv3D4GfDGAGWtnsOTwEoZ9PYyvt39t/wM16mmxfOx8Hpb3h6hHYO908yqJWYlsPb0VEyZeGfoKrw17jdeGvcbAFgOBasR124lXX4WgIDvsyK+LtgCVRYvaL4lWXzmTfoZ7FtyDp7MnH1z5Ae+Oepe03DRu//V2HdPr7G2Jf9s7HXZNg8TNkLzdNrxBOC+Ga/ktnW9h67+3svXfW4m6Lwo/Nz+Sc5LZE7+HIlXECytfACAzL5M7f7PEaU/9e6olX0mXly3lvAqzIGGdxTW5WCA7OsK779omUDNwdoZ8U6bZsr3t39uIeTyGmMdj+PIabeU0J1Pzbg1t7i//gzm64+AAs2ZV40sRbGl5O7j4a0vLiiGw9004+QuctRq5OFvsq+/dTt8HTCbY+R9Y0g1+8dXW7kry0eaPyCvMIz0vneCZwfT7XHs1HEs5ZvaMqm1+3PMjs6JmcUXrK1h550oGhw1m5oaZLDywsE76c14K82DDRPjZB7Y/BUV5WvR5NNXuzGn7LHlYLiZChur+gRbbAI1Hw7DlkBuvS0Ma3k31jZa36f8RCtbfqvMhBPS1yXthT3ILcpn0h3ZZH9t2LEEeQTT2aszPe382xy1f6hhJ1K5ofQX39bjPPHUM6qiX10Vcd8hwnT/DoCjfdjDGoZLJTesh1RLdgwcP5ptvvjG/N5lMFBUV8fbbbzNs2LBqdyYvL4+tW7cyapRtNr1Ro0axfv36MrfZsGFDqfWvuOIKoqKiyM9v+K4KlzrRZ6IZ/NVgTqefpmeTnjT3aY6royt3/X4Xs7bYPo0mZiUSfSaa6DPR7I7fXXW3GpODdiUPGVm8w416lC6h+NwMGcaKYytQKLo37s4LQ15g6uCpTB08lYd6aUtGXcV1BwbCpk3Qt69te8uW8MwzVdxZny+h6dW2ba3uhh4fXUgXLxmKVBF3/X4XCVkJdAruxAOLHuC/W/5L+4D2rDu5jhlrZugVI9/WlraiAtj9MizrA0u7aUungwu42WMUpeFjiO6xbcfSvXF3ujfuTo8mPejTtI95+Y97fmTn2Z10Ce7C+I7j6Rrcldu73E6bRm1YdWIVfx0t9tP27QiDftfuk9Z4NIOBFu+qK6+EJUvAx6oaioeHjuk+kRtNoSokxDOEJt5NzMt7NNbVAHad3UVWfvGIf7d3SwtvB2ddN7rYsjp8OCxeDCUczwgPhwmVN75e2riHwJDF4NlS5+fY8SysvRFiikWRbye4bAZ4t4ekKFh7Axz9GgL6QbNrtEgqqpxQysjL4OMobUT4+tqv+Wn8T/w0/ideGfoKAG+vf7vmktKaTJbJisNJh7l3wb14uXiRlpvG8K+Hk1+Yj6ezJ3f+dicxqfZ3Cb5givIgZbcO7/BqBf6R4N222H37Wdh8Hxz6r/2OlxWra7kf/VIngErapi3siVv0gGjagcrtx7st9PpMx7ZuuR+W9YXoKbB3hv5M9Rlnbxj8h042G7dM50P42Ud76IC+bxkDDnbgrXVvcSDxAD0a9+Bg0kHm7Z7H0JZDMWHiocUPWa6jlzCGqO4U1Mmm3XhvV9FtfX0pbzLW6z8Pmo6z3d7RTd/zQqqvIy92qlUy7O2332bo0KFERUWRl5fH008/zZ49e0hKSmLduuq7xSQkJFBYWEhIiZpGISEhxMXFlblNXFxcmesXFBSQkJBA48ala9Dl5uaSm5trfp+RoS1HX0V/hXeYN20D2pqXbY7dzMpjK2kX0I68wjyOpxxnVOtRdGtsyU51POU4P+z+AT83P+7vafuAdOqULh3j4gLXXQdeXrD19Fb+OvoXrfxb6ZqsxSilmLl+JkWqiCtaX8GfR/7E392ff/f4t80+P9v6GUnZSdzU6Sa8XLw4kXqCpOwk3JzccHdyJzE7ES8XL5r7NKeZTzNmbZlFVn4Wd1x2h01NvkUHF7E7fjcDWwxkV/wuUnNSubnzzbT0a2leZ3vcdv48/CdhfmFM6Gz7BHf8OCxcCM2bw9VXawvP+pPrWXNiDR2DOnJ1e4swi8uI4+vtX+Pu7M6A5gPK/PwAM9fPpLCokDFtx7D40GK8Xb15sNeDNut8EvUJqTmp3NDhBpYeXsrdkXfzQK8HCPMNA+DNkW/y9vq3OZFyghMpJyhUhaw5sYaErARaN2pNmG8YW2K3sPjQYiJDI+nXrB/xmfEsOrQIPzc/2jRqg5uTG1n5WexP2E9BUQHj2o/TD8iugTB8ubY4nl4Mhdn6BhMyHLxb43lwEU/3f5reTXvb9HlE+Aie7v80DiYHCosKcXRwpLYJD4fVq+H77+HwYWjbFq6/XouBT6M+JSUnpdTvv/PsTpYcWkJz3+bc2qXY1dLRVSdJOf6ttiSEjNDu0CUoLNQxpQcO6Gtsp04waBDsSSz/nDJ+//t63Ecj90aWBfGr9XfuGqAHPdxt//PG8RYuhJMndYKpkgMMlSZ5J5xbrR/mPMO1VcvF13adjOMQ96d2bfRoptdxC9bLivJ1jdLUvZCfqs8RBxdtAXBvxmGnxkSGRDK85XCeHvC0uXZ7Sk4Kb6x9g5yCHLLys/Bw9oD+cyHyTe0umX1au5z7REDIMNKVE7PWvonJZGJKvyk4OVgu57/u+5VDiYcYHj6czPxMNp3aRMegjjYib+uZrWTlZ3Fzp5ttrgtFRVrEHTkC/v46NjgkBH7e+zNHko4wstVIejSxlIuLTYtlzs45eLp40jGoI1tit9C9cXcub20Zyc7My+SjzXpQZkq/KTg7VvzgZVxTfd18mdTTNvnOV9u/4mzGWa6NuPa8JfiUUowIH8HgFoMZFm57I3+k9yNcFnIZIZ4hJGYn8nT/p7m+w/X0adbHvE7PJj1ZenipOfEMAE2uhDG7dZmp/BTt/dF4TKma6SNHwpYtsGCBzjQ+fry+Vm465czT/Z8m3D/c/NsDNPNpxtRBU8kvzCc1J1X//k7u2pW52XXaLdU1UD+oWJddAUaPhj17YO5ciIvT7u3XXgtutpEv56cgS//X0vYDRfp4vp3BP5Kf9/1a5u9vXNvdnNyY3NfWrTM5GX7+WffjhhssuSVsV9qh/2/+3XTG42qQna0HFffs0fO+vvo76N0bPox6h4KiAu7pfg+BHoHmbdbGrGVdzDo6B3dmbLux+hp29RGIW6EHVIvywaM5BA/WNYYBxu7TSQ4TNur477T9+j/Z+3MtzG0+13a9XkGmFlg+7SGgN8dTjnNf9/sI8QrhjssscT9XtL6C7IJsCosKSc5Jtr3+lUGRKuLjLTr52p2RdxLqZRkI+jL6S+Iz47m+w/U2zzPlsenUJh7o+QBXtLmC4eHDze3LjixjxdEVbDy1kc2xmzmSdIQRrUbQs0lP8zpnM87y1favcHF0oU+zPmXe/9Nz05m1ZRYOJgce7/e4zbXqvKQfgXNrtWuyo4u+3zbqqS3aV0RBcrT+TU58r72ATCYIuxlcH4agAQAcPKjvQ2fO6PuEk5P+TyoFEREwsDKnXUGmvp4XZBbndjDpPuXEQeYxXW7O56nKfaZWd0LzGyBhAyRv1dUQnNyh9//Av3vZJUkrInm7vt/kxANK/2992oNfpP7O7ElOvHZ/zzmrv28nT30P9LsMPFtAUH+4+qj+D51bA/lp4OwHvh0gaLDlXppxVMf05iYW/25O4OIHHs0obNSblac2s+3MNjoEdjDftxKyEtgdv5vWjVpzeavLKSgq4On+T/NY38ds7mFDwoZwNPkoe+L30Ktpr/N+pDk75xCbFstV7a4yl9UC2BO/hz8O/kFTn6bc3vV2m21WrtSlGl1d9fnTqZMOV1obs7bU+Z9TkMP/bfw/APOzbbBnMP/q9i+bfb634T3yCvO4u9vdBHkG6RM0ba8e1MlN1M8R7o11BnAfy70vIwM2b4Z9+3ROD29vaNdOPwNd3e5qRoaPpF/zfjbHuq3LbQR5BJUS4wCcXqKP1+xacPYq8zvLz4dvv4U2bWDw4OLGkoOFFZTxxDUQBv0Gp37Vz0vuTfW9zT2UM+ln+GbHN7g7u/Non0dtNpu7cy6n0k4xtt1YOgd3LrNvF0R+ms6dU5AJAb0goHfpdawHLas6QKqqyZkzZ9SLL76oxo4dq0aPHq2mTp2qTp8+Xd3dKaWUio2NVYBav369Tftrr72m2rdvX+Y2bdu2VdOnT7dpW7t2rQLUmTNnytzmpZdeUkDp6d+oRxY/YrPulXOuVExDfbzlY3XP7/copqGeWf6MzTqfbf1MMQ019KuhNu3btikVHGyp8dSkiVJRUUr9deQvxTRU2HthNuvvjd+rmIbyfN1TnUg5oZiGcnzZUaXnppvXycjNUE6vOCmmoWLTYsv8fCV5dvmzimmoMXPHqHfWv2OefGf4KpdXXdSx5GNq4BcDFdNQX2z7wmbbqSumKqah7px/p037zz8r5eho+Wx9+yqVmKjU8eTjyu01N+U13Uu9tfYt8zRm7hjFNNSzy59Vfx7+UzEN1er/Wtns83DiYcU0lOurrio1J1X5veGnmIbad26feZ1DiYcU01A+M3xUVl5WpT5/ZUnLSVNzdsxRTy97Wn2/63v1xJ9PqPn75quc/By7HqfGMH6MatBrdi/FNNScHXNs2mesmaGYhrr+h+urtL8PP9Tnu22NM93298GNimmooLeCbLbZE79HMQ3lPd1b5Rfm68bcJKXW3abUPCzT965K7ZiqVGGBedvkZKVGjbI91i23KJWbW6Jj1iuUJOO4UssH2x7L+nhKKZWXptTam5T6zsF2ne8cldpwp1JnVyn1S5BS3zsrtf99pZJ2KBW3Uqm9b1vWzY6v0ndZHkVFRSrk7RDFNNSGkxtsloW9F6aYhvrn2D9KKX3NGf71cNV5Vmf12JLHVOjMUPXYksfUqdRTNtutWKFUly62X5Orq1JvvKHUXb/dpZiGev6v5222+WXvL4ppqC6zuqj3N7yvmIYaO3eszTobT+rfPODNgNIfpIzfY9+5fYppKJdXXVSB1e+slFIBbwYopqE2ntxYpe9LUOWf/0VFSu14QZ/r89Dn8KIuSv3sr98vH6hu/ulmxTTUK/+8YrOpcT9r8V4Lm/Zdu5Ty9bUcLjxcqUOHSvTn6NdKzTNZ/htrb1KqoOSfVpX7n83MVOrBB5VycdGLfX2V6tRJqZYtlTKZlHr2WaXafdhOMQ216OAim23vmH+HYhrqhb9fqOSXV0nOrlJqQSvLZ/olUKkfPfX8j152+/8rpdSKoyuU22tuyu8NP3XLz7eofy/4t+r0306Kaah7fr9HFRUV2e1YN/54o2Ia6vXVr9u0rz2x1nw9N+abvtPU5thzdsxRTEMN+HxA6R2Xdz1OO6jUn70t1+BFnZRa0k2p75x029FvK9XvYcMs954DB/SpblBYqFRqajn9qc69tOQNr6ypOpS37bn1Sv0Rob+PZf2Uipqs1PbnlFrQWrdte6L8/lWVvDSlVl2t730L2ysV/YxS+95RatN9xfdAp8rtJ/us5XddP1Gp00v15zi1UKkf3HT76WVKKaUOJBxQr656Vb219i01a/Ms9fxfz6utp7dWve8G5Xz+Rxc/qpiGmrxksk375CWTS7WvWaNUr16lf9axY5X6bd9vimmozrM62+xn5bGV5mtkUlaScnvNTTm87KCOJx9XOfk5Kic/R60+vloxDdX83eaqsKhQfycL2+rv4/eWSq2+Xqm1E5T60Vu3HfpEJScrdccdlmfxNm2UGjFCqf799bXwsceq8R3tetly7fqjg1JZpXVUXp5SAwdaPvtHH1XjOBWQlJWkmIZiGiohM8FmWfDbwYppqPUxtjqxUuf1+c7/cxuV+j3c9tnuryFKZcRU+7OUpFqWboDQ0FBefvnl6m5eJoGBgTg6OpayasfHx5eyZlv3o6z1nZycCAgIKHOb5557jilTppjfb9++nSHFaWA/2vwRn2791LwsrzAPNyc3JnSeQG6Bto7vS9hns7995/T7LsGW0cmoKBgxwjZR1enTMGYM7DvcFycHJ06knmB/wn7z6Pvfx3Q91j7N+tDCtwVtG7XlUNIh5uycQ2RoJKAtjwVFBbRt1NbGalURzw16jv9F/4/FhxbjYHKgqXdTlh1ZRmpuKlP6TqGlX0siAiJYG7OWg4kHbbY1kixEBEaY26KjdWKuQqt8Lxs36rY//gjj0d6P8tb6t/g46mOuancVp9JOsfjQYoI8gnhu0HMopTBh4mjyUZKzk/F39we05Q2gW+Nu+Lj6MKHTBD7Z+gkd/ls6K9HNnW7G3dm9VPuF4O3qzW1db+M2btPH6HzzebZoOLTwbcGW01s4lXbKpt1438KnRVmblck778CTxVVPnJ31Oe/nB9u26ZHhTn498XX15VzWOWZtmUWQh3aTXnViFQBDWg6xWEI23F66PFNRLux5HXw6QstbyczUI7oHSnj3ffcdXHZZJd3nC7J1iaD0g4AJGl+p3QDjV2krypk/oetrsO5mOLNEb+PXVVtcUnZB0hadxT1osC6Z4uyjrXb+XbUFwqeDdrvLSwZn34p6UmlMJhMjW41k7q65fLDpA3PClIy8DE6knsDT2dM8uj2i1QhGtBph3va9K98rtb9167TFNK+Eh2NuLvz4I1zzbjgAJ1Jtk2Uarqfh/uFmK0HJa+T+hP0ANlaEimjTqA0uji7kFeZxLOUYbRrp5HGJWYkkZusEPUZMmmAHjn4Je17Vrp+D5luSRh76BKIegPx08//U+P4NjPfGctA1w8eNg9RUy3rHjmmPqO3btXWItAMQ9SB6zLuYmB91Ysp+lcvJMXky/O9/2mr52Wfwr39pjyuAhARt2Ty4rzMHEw+y99xexrQdY97WuG/b1VqSm6iTrRVkQIubdMiNWxBknoQFLXS7HV2Ih4cP56fxP3H9D9dzLuscI1uNZPa22UzoPIHZV8+28aS4UHxcdKxEWq5t9s30vHS93NWHAS0GEO4XzrGUY4TMDMGhONu/UQqtpLWwXJSCteP1tTd4GAz4wRJOs7grpO6qdNKlb7/VoR4HD8Lnn0NYmL4vOTjoZ5hmzfQ9yi7UZvmjwlz9HWXHQrvJ0ON9fR+L+RFcGgFHdH/KOweqaq3b9SLELtTeX1dGa8v88bnaWl0VEjZqyy1Ap+e151bKLn2v7fySbvfW3hntAtrxn8GVrWlaDpX4/KMPLeGDzR/wy75f8HT2NLf/uu9XvbyNLhm4bZtOXpldRn6+JUtg7s9DcTQ5sid+D7/s/QVXJ53jav4+nadhWMth+Lv7c13EdXy3+zta/l/LUvu5K/IuHAqzLb9t2wehx4c6zCw3HlL36PO/qIB//xt++gnc3WH+fO2VZlBYqDVHlciK1WEOBmn79DPYsOU239c338DatZbVnnxSH7uNnXK8+rv706ZRGw4nHWbbmW1mr7nYtFjiM+NxcnAyayK7kXEM/h5e+roSvwrWXAtXVvE8L4dKi+6dO3fSuXNnHBwc2Lmz4hiArl27Vri8PFxcXOjRowfLly/nuuuuM7cvX76ca665psxt+vXrx8KFtok+li1bRs+ePXF2LtuF0dXV1Sbhm5eXdp9oF9iOgxxkRPgIOgV1Yu3JtWw8tZFr2l+Dn5uf+ea8I26HTUZcI/GN9c17ypSyM0PHx4NjkSeRoZFEnY4qU1AObK59nYaEDeFQ0iEeWPRAqXWGhA0p87OVhY+rDy8MfoHJSycT5BHEq8NeZc7OOfi7+ZsvaB2CdD8OJtmKbkOEdwi09HPKFO3CUpJNm/Tr84Oe54vtXxCXEcdzA5/juRXPAfDSkJfwcdU37vaB7dmfsJ+OszriXBzjY9y8jVjLu7vdzSdbP8HZwZmpg3QpnxlrZ5BbmMvd3SSLtj1p4atFdbmi27dyojs7G4yxuOBg2LABWrWyLF+2DDw9HBnScggLDizgocUPldrHiPBicRi3wlZwN+oFqhCSi2snF5dqmjmztOA2yMysVLd1RuL04nN/0K8W0XHwQ13SArQLnyG4O/0Hur6q508vhVXFdXxb361dks6uhMOzAaVdwkwO+qbZqLtd3f0ub3U5c3fN5bvd3/Hd7u9slg1pOQSXSh6rqAgmTbII7qZN4f779e9p1JYO99Oiu2R854mUE+blxjXweMpxok5HmQdP1sboO3TnoMoJHCcHJ9o2asuec3vYdmabeWDSqJvdzKcZ3q7eldqXUAkOFycqbXa95dzfdK9OFFeM8RuUEt1ZiTbLAWbM0CK7JPv3a0HepAmw+1XtwleS499Au0d0Xd8KSEiAL77Q81dfDffea7s8MFBPnRM68+u+X1kbs5YBzQdY+lI8EGRX0X30Sy2sHd2gz1damMT+ocVKDXFVu6uYe/1cPtz8IV/v+Jp7u93Lx1d9bBa89sLXTQ8Wpuem27Qb743lt3W5jdfWvIaTgxN9mvUhMy+T5UeX4+Lowk2dbqrcwRI3acENerDTLUjHUG+5HzKqViqxadPS50Yp7CVMa5P8VC3KQN9XAFSBFkohw/UUPBjUO/Y5nlHOyeQApuKRLZ8OehA57BZLOcXz0Wwc9P8OYn6A1ddq0e3iBzjoASmTI3i1tE+foVK/29CCHNyd3DmVdorpa6fbLPNw9mBoy6EAPPSQRXAPHgxTp+pqEUuXwgcf6P9At8bdiDodxY0/3VjqOEboxj3d7uG73d/h4+rDtCHTyCvMMz8n393tbh0yYfy24Xfp7zZlB6wujoF2CyUhrRE//aTfjhljK7hBDz42t41COj97Xi+dxO/sCv181FxX7igs1JVvrMnJgTff1AOf9qJXk14cTjrMQ4sfIsRLG12Na03n4M52N7qx84XyB/Lyq1rmp3wqLbojIyOJi4sjODiYyMhITCZTmck+TCYThdYm0CoyZcoUJk6cSM+ePenXrx+zZ88mJiaGSZN0XN9zzz1HbGysOZHbpEmT+Oijj5gyZQr33XcfGzZs4PPPP+e7776r6DBlcnW7q3nn+DsEewbz9qi3GfCFvkHfFXkXYLk5n0g9wcAvSwcBGcs3b4bzlBZnYPOBRJ2OKntZcabroS2H8r/o/5W5jnERqCwP9HyADzd/yDc7viE1N5XM/EymDZ1mtjIbluw/Dv5Bk3csFnQjltFYvmMH/PNPxcfydfPlxcEv8ujSR7lv4X0sPbyU9gHtbeLd+zTtw/6E/cRllI7VN0R3r6a96Bzcmd3xu7m89eU4OTgxbdU0OgR2oG+z6gbsCmVhiOqTaSdt2qsquhcuhPTiZ7Jnn7UV3KBHiUEL6wUHFuDu5E6Yn47FNx6CzaL7mJW1q+/XEF4c/7iwrfnBq6AA/lsiV46jo60XRqU4WZwEq1Evi+jIPGkpewL6xgM6NrtjcSmdogL9oGONX5eqx+RVE+u46ZGtRuLs4Mzm2M0kZidyeavLK9jSlm3bYHdxqeiOHfVgiZEE7KWX4NNPMcf6H085TkJWgnnbI8m6xFJLv5aEeoUS4B5AYnYivT4rHUtXFYHTMagje87t4eafS3uciJXbzhQW5zixzhobNFA/VAO4BROUoP/Yhsg2MFu6PbUlMitLWxQrJC8FTlVQ1rPw/KNlBQV6sAiKLefl0CVE/xd/P/A7vx/43WaZi6ML7QLanfdYlSan+H7m0sgS458TD9mnoEmxOdWxqoH252d8p/Gl8qPYG2PAPC2vfEs3aGv2a2teA+CXm35hzs45LD+6nNFtRp83Tt2Ctdgtvph7NIP2j1najVq/9uBiE9SVwS0Y2jygB8x2vqC9qwL6QOcX9DmXtFXHWduLrq9pQX/2b1jWS8eku4Vqz7OMo+ASYDnHz0fYBNsSe0WFxYm27DtQVFncnNwYFj6MxYcWc3W7qxnQfADrTq5j4cGFDGs5DFcnV06e1B6dAF27wooVlgoVAwbArcUpb4a3HE7U6ShcHV3xcfVBocz3S0N0Dw8fTiv/VhxNPsrIViM5nHQYhWJkq5H6PpvrrQcz8lPhzNLi2OJecJ2lXKRXjs5dkZoKMTEVOzVUiqICODG37GX73jaL7vnzdU6gksyda3/R/d3u7ziUdKhUWbNeTc4fp18l8lLh5M963tFD51FpMhb2v2NTncgeVPoMP3bsGEHFNYeOHTvG0aNHOXbsWKnp6NGjF9Shm2++mffff59XXnmFyMhIVq9ezeLFiwkL0w/mZ86csanZHR4ezuLFi/nnn3+IjIzk1Vdf5YMPPuCGG26o8rFHtxmNs4Mz8/fP50DCATac3EAT7ybmh9cQrxAbF7qSGA+UJUuEjxwJ992nkxsYGMK6lX8rvr3uWz4eqy0NjiZHs0uoIazdndyJfzKe+CfjdXIdqi66nR2dmT58OoWqkF/3/UqYbxiP9H7EvNwQ1XmFeZzJOGOeilQRzg7OtG7UGtBJcSrDpJ6TaBfQjkWHFlGoCnlz5Js2yVMM0ezr6suQsCEMDhtcahnAvyJ1oom5O+cyb9c8mzbBfhiiek3MGsZ9N848GW7ClRXdxk0JKk5OYwhrPzc/9j20j98n6IfhYM9gizBLLHadCBlpEdwlWLECzp2zvH/rLW2t3bpVJzapNKnFitM/0tL293CdNdwgOVq/+na2PFCvHgerS2Ryr0WaeDcxC9BpQ6ax+LbFuDnph/qqiG7rms4vvWSbddvVFR59VLuPgx6YCXo7yDwtPKiteIYlvCIX8qqK7nKXBYrotitGSZ9T83VGZoBWd0GHJ7TVOWS42ZJtPeACVpZud738r78gJeU8xzv79wWXRQoN1YkgAX7/XScstaagQD+MVnTORQRGVC2p1/kwEsFln4ak4utF67thyCLL5Fp22NvFjiGqy7N0W3ux9WzSkzMZZ1gbs5af9mpzXKVdy0EnL2pU7Omw4xnIjNEis+Vt0GICeIbpJHeXOj3/q5NRBQ2AXS/B0u661vc/V8Cxr0oPCF8ILn4wfAWMOwERT4Kjp7bGFmTqJIhhFxCO5+BYZ4LbYEwbPWDg4+rDMwOfMZ/PRkiK9fXl4YdLl4TsUDw+aQjrriFdiX8qnj9u+QOAto3a0sxHl5gwmUzm59hvd37LnF1zAG0BB/Q1os+XeiBl14uwYhhEP61Lh266B/5ojxvxzJyphfaWLXDTTdogduyYTij53Xc6LKzSJGwo36KbZxloLeFYbCbHzlXuKkqAZ3fRnbRVDx6BrlIRPhFcG2kvDDtT6buNIXpLztcEDz74IA8++GCZy7766qtSbUOGDGHbtm0XfFx/d3/GthvLb/t/41+//wuFYmLXiTaZpjsHd2bl8ZWltm3h28L8J11mVYr5ppv0ye/gAE89pbM3g0V0n804yy2dbzHHs14WehleLtrdvalPU1r7t+ZI8hGzBTIrP4vW/q1p6tO0yp9vfKfxHGt6DKUUPq4+5ngT0FYqNyc3cgpK/3NaN2ptfjBZssTS7uurR73c3HRsnbU7obOjM9H3R5NTkIMJk9mibmBYs33dfPnnrn84kHCAiP9GEOwZbH64B5jYdSLP/vUsP+79EScHJ5wcnGwyvgr2wcj8npSdZBZRNsv9Kveft77wWgu3knQK7kSoVyhnMs5wKPEQq47r8394+HAdh5iXCunFo5vNyg4tAX2zMbjnHv0fA+jeXQvyX3+tVLctDyclLX2eLYs/THtLSRhnq9Ez1yBtgQG7xWpXlctbXc7ec3v55/g/BHsGE5seSxPvJpWOnwZb75WhQ8tep4l3E3OcdVkY/9vOQZ1ZfWJ1metUpU8Vim6xdNuXdo9o98Xj38Ky3tpd1b2pzkGQHA0dnyPIQw8GlxvTXWzptr5HgHY77N4dXn1VV08AbOtfezTX5dYyj8G6CdjEeJ+HTz/Vg2x//KHLtHXooMsgZmZqr6xJk+D16W1xdXQl17DmW2H37LdNr9GVDOJXwT+joO3D4NdZu+Ymb9divN839j1mLWG2dJeI6TbeG8tBu5hHnY7if9v+x7Ijy/B19eWqdldV/mAmky7PufFfEL9Sl5zyaKaFXtYJLfRuSNaVNC5lTCZ9f6zgHml3PFtoUdLAGNN2DCyBv47+hVKKFcdWAJZ4bmuDwoABZe1BMyhsEM4OzkTHRZORl2G+F1pXBQBtPJr2zzS+3fktydm6WsF1EZawWppfp0ME4v/ROWNyzkFBur5eXjYDXBpx773Qvj3Mnq2904yKzQ4O+jr4n6qEw59dUanVVpaWPzVC98bdcTQ5UqSKSH02FW9Xb8LeDyMmNaZSGemrhBHKggla1Gwup0qL7gULFlR6p+PGjTv/Shcpd112F7/t/40Npzbo98Wu5QaG6H6q/1M8P+h5vt/9PQ8sesB881bKEl/q4gIffqj/AKBLNH3+ub5OhniFmBMF7IrfxaZT2qpnxHMbDG05lCPJR2xiyKtq5bbGuhyUNQ4mB9o2asuu+F10DelKkEcQMakxHEo6ZLaCFxToJDgG33xj+ZMvXVo6psTD2cNsmS9J15CueDh7EJMaQ0JWgjmJmiHGDYI8g8wDIaBDAIz4DsF+VGTJdnNyI9gzuFL7aWo1FnT0qL4hlMfw8OHM2zWP1SdWszpG35jMruWpuzE/fBsucsk7Yf9MXaqkmD17LPubZFtZipAQ+Ldttb3y8QjTLnLpVn5Tfb+0XWdD8YNGplUisUomfKpJLm91Of+36f9YeXyl+Xca2WpklfZx/Lh+bdxYx+KXhYPJgRa+LTicVHZMpXFtMa6Fo1qP4oMrP2Bfwj6u++E6Gns1roJ7qaWOqJuTG+9doRO/Tf17KknZSSK67Y2jixaDnV/SVujUPTq+0q+LdmFtPIrAFJ2VJy4jjpnrZ5o3NeLsDUv45s2W3T7+ODz9tJ4fMAD69y9eYH7IQYsrw31yw+2W2NFKEBioLS/Hj+uQrt27LSXD7r8fLr8cHB0ciQiMYMfZHfw0/ieGhA1hyrIpzNk5p9I5BiqNyQRD/9S5IE7Nhz2v6QE9k4MewGtZfweMzZbuvBKWbsO93MUium/pfAtPLnuSb3d+C8DtXW43e+BUGs8wGPE3ZJ/VuQVy4nRZKfcm2gru4lf9DyMIJQj3D6d9QHsOJB7g+93fE5cRR0RghHkwOTnZsm6TCnIYezh70KdZH9bGrGXDyQ2sidGxpiVFd1OfplzR5goWH9J5a+7vcr+NIQzQ5dXOM6gyaJDFmJeXp69/np6lLfHnJfn8hssjR3RJ1trAw9mDjkEd2RW/ix1nd9AxqCMxqTG4O7nbf7A0ebt+9e1cZjlae1Lpn+Xaa6+t1HoXGtNd14xtN5Zr2l9DTkEOYb5hNlm7AZtEQX5ufuakQsbN++RJS6KFIUNKP8COHWuZH9B8AIeTDrPx1EY2xuphtAEtbIfQhrYcyufRn7P25FpMxXFOFyK6KyIiMIJd8bt4oOcDTOo5iaeWPcXMDTOJCNDfQUyMJVa2XTudndagUSN4993KH8vRwZEejXuwJmYNUaej2Hq6bNEN8HT/pwn11DVIq+SidilQVhBPybZKxKsFeQbh7uROdkHptJzNfSrvxnf99ZbR1U8+0dmwS3ZFKT0QNTJ8JPN2zWPViVXm0WCz6M63SnvsWpygKTtWW+Ks2LtXv3p7Q48elMLRsXRbmTS7RltUzi6HlN3aOmVNbgI0Hg3H52hBkrTNkrzG/OGK6sRFbkjLITg7OLP+5Hr83PyAqrmWgyUxYkXeCaBdyMsS3Y3cG5kfyg1r9pn0M7QPbM+Os1pg2dwoz3feKkW7gHY4OTiRU5DDhM4T8HT25JElOiRGRHcN4d1aT2UQ6KE9HLLys3hqeel6xIboNiLMnJxsKwe4u+trgoMDkFUcIhbQu+w6qFWkZUs9lUfn4M7sOLuDuIw4gjyDzMn/aqTOq6MrdHhST0WFOjmPozvY0429Dqisezloo8LIViP584j2yb2g+7Z7iDme9JKiEtdIwb6MbjOaA4kHeGHlC+b3BtbPEud7rhjecjhrY9ay6sQq1p1chwkTw1oOK7Xe9OHTGdVKJ7q5JuLCvRVcXPRULVJ2WebdG0Pzm3RSyzzLaIMdHIqrRK8mvdgVv4ttZ7aZq0dFhkbaNyQItJcVWLwWAf6+XNeatzOV7nmRkbGkgePk4MRvE34rd7lRFsyIdTVejWQth6zi/fvZ1qIvxcAWA/l6x9dsit1ksXS3sLV0G1nK18WsM5f/qErm8qpgZCg/lKg/hJG8wMhsbh2uX9KqDZbRtsrSp2kfs+iOOqPjCPs0Ky26+zXvZ45zF2qO5r7NS5WMg8rHc4N27+zfH9avhwULdNbYhx+2lAx7910dfxkQgLmE1YIDC0jNTSXcL9wSWmBdVsfJs/SBgMJCk9mrpHXrC0wi0uJmHROXnwp/j4D2j+qSZJknIOZ7ndW57SRw8tYuXusnQORbENBPW2BO/apdHrvNPP+x7IyXixf9mvdj9YnV/Lb/N0yYqmzpdis2QpVVCsUaw5p9dburGdd+HDvP7uTDzR/aeNAYQuZg4kGKVJGlXFhQVYLsdYhKm0Zt2J+wnwMJB/Bz86OgqIBQr9BS4SpCzWOdnbwsgjyCSE62xHP36KG9Tazp0wc9OJVVnJk3oHYSYhrnpFEmzLhv14jotsbBERwaRpZ9X1cdPlNeyTAje7nBzzf9bC4VFuIp3mnCxc+YtmN4f9P75uSg1iUGra9l+/dDrwo8nIeHD+eV1a/w5fYvScpOoktwF3P4jTWXhV7GZaF2THZXXfIzbD34evxXu7d7t4WtD5ubDSNHbdGraS++2P4F0XHR5rA2u8dzg6WKhouVJ15+iq5EYWfq99BrHWD9QFlYVFiq1udBK81S0cg7WAT2ggMLSMpOItwvvFTt7ea+zc1ZDkEnXmvuWzMJRAyrvlE2zBBgRru16C7LqlhVjIRpW05vIfpMNA4mB3o3vXCrxyWFHUe7W/i24GDiQd4Y8QZXtbuKz6M/572N71VJdAPMmaOTBx49qsMpystk3MK3hTnEAqys3KAzSBrkJek4skY9dNKYYuJze5FbHKZ5wfUh3UNhwE+wbryuhbmzRDBU8xt1UpM+/4P1t+l48zXX2a7T+r4L7ET1GdVqFKtPrKZQFdI1pCuhXqFV2r5xY/0gcfKkLnVYnsXbSJbW2Ksx93a/l8+2fmbTDtrqHeoVSlxGHMeSj5UtcCp53nYM6qhFd+IBsxVfrNxVxE7lkJwdnfF19SU1N7XM5YEegTZ5Pcp9KM0+bcmhUEtZ/s2iO2EfCVkJJGQl4OXiVW64lVAaw5KdkpPCnJ1zzO3GoJq1pRv0YKCRn0aoBmLJrnUGhw3G09mTzPxMvFy8bBL8Dh0KH32k5//5p2LR3a95P9yd3DmdrkNySrqWX3Rkx2IO5wvoowU3gJPt/9c6nA/ghRcgIkKH8ZVVRvhC6dlEJ1OMPmMR3UabXTFCmmqgskRJqi26MzMzWbVqFTExMeTl2SbWefTRRy+4Yxcr3q7etPBtQUxqDPsS9nEk+QiOJkezlfiE1WDR+UR3RGAEgR6B5mywJa3cBkPChphFd01ZuY3+gLZ0F6ki8zHLEt1Vrv9XBoZVe9mRZeQU5NAxqGOpG7dQe7Tw0eLaweRAp+BOFCnt3VJV0R0eDtHRMG+ejvvfv1+7lHbqBDffrK3eBiPCR1hEdysr0e1mFZeRE29ps4ptyrY6H0ta1KpF48vhmpNwfG5xduVsnbQkdCQ0LY6laHETeLeDAx9A7AIoSNNlU4IG69jXOuLKNlcyK2oWANe2v7bK2w8erBOkKKW9FK68suz1DE+EmDTtHmyE11iLbtAiJy4jjn0J+y6oHnLHwI78yq9mS7fRJtQNQZ5B5YruIM8gVlmJ7hblXTYyreq8+9aO6Lb2UDMGyjsFdTJ7jwnnx7g3ZxdkM3F+6URacu8W6juuTq5suGcDGXkZeLl44eJo8dUeOVKHzBQUwHvvwcSJuoKCwdy5cNttet7F0YUBLQbw19G/gHogunOtSsA0KT/h4f79lvk2bXSlE0dHnbjy/vvL3azadA3pioujC3vP7TV71Ng9iRpYqtFYudIT+ZY2+ECpwYcLOlR1NoqOjmbMmDFkZWWRmZlJo0aNSEhIwMPDg+Dg4AYtukE/PMakxrDgwAIKigpoH9DenAAh06q8qHVSqfIY0HyAuXZoeaJ7dJvR5ozp1jEm9qZ9YHtMmDiafJRjycfILcylsVdj883UekChWbNydlIFmvk0o6l3U2LTtath36ZSe7suMcS1UZu7qjW6rfHx0YnNSiY3K8kLg1/gls63ACVGMH076VHHwhydvbPxqFLbWrtCV1Snt0qWPmdv7UbetoKO+0dC3y8qOGDt06NJD2KnxFZ7+2HD4OXi6mhvvKHrqTtYhacvWqTzURiWQSMm9kSqfi1pMewc1Jm/jv7F3nN7OZBwABOmalmojW32J+7H383fpk2oJHa0mAV6BJYZ0+9gcqCReyMbS3e594jsU5Z5Y3AteQec+VO7ntcAYX5heLt4E5cRx/qT64FacC1vYJxPVIvoFhoCRqhoSXx94cYb4fvv4cwZbUR44AGdtGzJEli3ziK6AV4c/KL5eb2seO6Lihwr0R1Qvqg9fdoy//TTltj2u+6C11+3f7dcHF24LOQytpzewtHko/i4+tA+oILsvNU+ULFbuSGyAUJq5jerluh+/PHHufrqq/n444/x8/Nj48aNODs7c/vttzN58mR79/Gio0twFxYfWsyv+3Q9Ius/qXXJJHf38+/rjsvuMGf4Li8Oc3yn8YzvNL76Ha4kHs4eNPdtTkxqDMuO6Lpn1onkMqzCG+whukFbu43vsax4bqH2MIvu9AsX3ZWlqU/TssvfOTiDX6ROZBHzI3R5xTYRUcouHE0dMC5h9Th340VBv376P33qFKxapWusP/mkHtj45htISNCi27BoGxZus6Xb39bSbSRTW3p4KdkF2YT5huHtWvXYVkNgH0g4YI7jFtFdd5QX1+3v5o+DyYFUKyN4ufeIfKsbiZGvIWGDrsdcg3QK7sTGUxv5Zd8vgIjuquLt6o0JE6qckm5VEt2SJEyoh7z/PmzapMvjJiXZCk2HEjlUB4UNYlBYFRMd1RXWlm6jTGoJiopsM7hbJ4V2dtaW/5qgV5NebDmta8P2aNzD1jvJTomE8e0MZ5bqqhpFhToXRw1RrVS727dv54knnsDR0RFHR0dyc3Np3rw5b731Fs8//7y9+3jRYdysjTJX1mVHCgos61UmZf/1Ha5n3g3zmHfDPFr5t7JrP6uDIbIXHVpk8x4wx886OIBH2ZXAqswNHW5gVOtRjGo9qkZd54XzY4jr2DRtMa0N0V0hQcWeHxlHYN1NkHUacpPg4Efw10Dc3SxK29rDRKg6Li621Qc2bIAbboDbb4dlyyztIV4heDh7kJmfSWJWYoXu5YA5K311BU77wPY4mhw5nHTY7BYsorvuCPLQyYAiAiO4pv01DGiuq20YSYKMewSAf3m57iqRJLEmMO7TUad10k4R3VXDweSAp0v5v5dYuoWGTkiIrtf9xBPawm0QFAQzZtRdvy4YI4QPky7VVwbJyVp4gw4vLVk2bXwN2QWt3clrJIkagH83/ZqXDIkbSi8vo6pPdamW6HZ2djaPNoSEhBATox+8fH19zfMNmZI3a+v31un6S4S61wuM8mCGO3tZorvaJQnK4NYut/Ln7X/y5+1/0j6wBtxGhEpj7V5eWFRIXEacTXut0/YBXZcVdM3b35vCrwGw9RHIT7NJ9mUd+lAKo05ZRZPA+PG6pFNZYqmnled/mK++KR9POW4emCnpXm5kKi9UhTbvq4qbkxvh/uHkFuaSmJ1IoEdgmVlghdrBsHSPazeO3yb8xlP9n7Jptxbdzs7l7MQsuk22CRNrGOM+bVhqRXRXHSODeVWXCUJDITgYZs7ULubbt8O+fRAXp92t6y2GpdstuNxkYomJlvkOHUov79q1BvqFziD/+bjP+Xzc59zT/Z6aOYghugF2TbMV2ad+h/3v2O1Q1XIv79atG1FRUbRr145hw4bx4osvkpCQwLfffkuXLrWTGKUu6RDYAUeTo/mB0vrm7WZ1vlq7Y9cXjPJgWfk6FaGRIA4sXhuiURomzX2bY8JEXEacFt6qkCCPINycaj6jY5l4tYIuL8POqWUu9vc3ERQE587B4dJhpkI1uP9+nezu00/hyBEtwMeNgwEDLOuE+4ezL2Efm2I3kV+UT4hnCO7OtrE01gkn4cIETsegjuY4YrFy1y2GpTsxO9Hm1Wi3rix6vlq2tY31ORjoEVjlDP+CtmYbOVjKWlZp5CFCqOd4e8NlF0G1L7tgiG6P8g0s1qL7gqvFVIFgz2Du7nZ32QvtdR3xidDl0dIPwdkVsKwXNBkLydEQtxw62s+Du1qW7unTp9O4cWMAXn31VQICAnjggQc4d+4cn376qd06d7Hi6uRK24C2gLbEtGlkOQOtrW/10ehvbdku+d5IVpWfX5s9EmoLNyc3gjyDKFSF5hiaOrNyG3R6Hgb+Au6NLW1+XWHIInB0oWOxBouJgfT0uuliQ8PPD555BmbPhjfftBXcYHElXxOzRr8vEc9tYC1yLkh0W2Url8zldYth0TaL7qxEm/ZKeXo5GCspKCyuM+MZBk3GWCaXAHt33eYcrK7nxaVORcJa3MsFoZ6Sqyso2WTpPrMckjab3yYkWBaVW5mivmIyQcSTlvepe2DfW1pw25lqWbo7deqEKh5hCAoKYtasWcyfP5+OHTsSGRlpz/5dtHxz7TecyzqHh7MHjlZB961bW9Y5frz2+3WhWItsLxcvmvlYsuEYoruoSFvxvaQEZ4OjhW8L4jPj2XRqk/l9ndP8emh6tS415OQF7pb6YJ066cRfhYWwcCHceqvtpsePn790n1A1DFfytTFrbd6X5P4e99M1WPucGR401cHaui2W7rrFcO03xHZJS7d1FYHs8sLgHKyUeUGmjutuMlpPNUiIVwg/3vgjhaqwVA4CoXKUJ6xdHF3MFVwEQahnFBQPflq7lu94FpK3md9aW7rLzddRn2l9H5xZAqd+K73M3X5eUdUS3ddccw3XX389kyZNIiUlhb59++Ls7ExCQgLvvvsuDzxQd/Vqa4vyasW1a2eZP3KkljpjR0K9QvFz8yMlJ4V2Ae1sMgV6WyUfPnUKIiLK2IFQr2nh24Ko01FsjN1ofn9R4OAM3q1LNXeyMlhNn64zavoWhxb+8APs3WsphSXYB0OwGPHc5QmYce3HMa79uAs+3jUR17DhHp3cpF1Au/OsLdQk57N0W1fsOH0aevQoYyfWydMKMoDgsg9WAxmua6MKSEOmPNEtVm5BqMeoYvfVcuK5wdbS7edXs92xK5UtGWsywaD5cPC/sPd1yE3UXpXtH4eWt5a9j2pQLdG9bds23nvvPQB+/vlnQkJCiI6O5pdffuHFF1+8JER3ebRta5lftar08qIi/ftebPFu1ux/aD/5RfmlYnmtLYYiuhsmLXy0yN56Wmfmv2hEdzmMsirfvWcPDB0K//43bN0KX30Fl0AxhVqnpDt5TVsNfVx96Nusb40eQ6gcZtFdwtJttDe1qv536hRl42G1Us45nbtBqBcY4trNyQ1XR1cKVSEZeRkiugWhPlNUXHapAtHd4C3dBu0e0lMNUa2Y7qysLLyLzZ7Lli3j+uuvx8HBgb59+3KiwjTCDZ+gIMsJGRWlhYA1779/8SdYC/EKoZlPs1I1WcOtnq3rY7y6cH4MkZ1dnL3xYhfdbdpA//6W99u3w4MPwuefS+3umqKkO3l57uVCw8NwI0/KTgKs3MuL3c5bWenn8kW31TUlZafd+yjUHEaG8il9p5DybArzb55v0y4IQj1EFYtuh/JFt3WN7npl6b7IqJbobtOmDb/99hsnT57kzz//ZFSxuSk+Ph4fHxnx7GyVM+iWWywn688/w7PP1k2f7IH1A1VUVN31Q6g5Sorsi110g7iP1zaN3BvZWLbKS6QmNDx83XxxdnAmvyiftNy0Uu7l1gOz+/aVsxP3ppZSgCK66xXG/z49T2etTMtNs2kXBKEeYrZ0l5+XwToxZoO2dNcw1RLdL774Ik8++SQtW7akT58+9OvXD9BW727dup1n64bPVVdZ5nft0m7YHTroOrj1OfO3teheurR0SN1ff9VufwT7Ux9F98iRMG1a6dAdJydbK7hgPwzrtoPJoV6cI4L9sHYxL5lIrVkzS33uNWtKe5ukp0N6piO4N9ENiRtKH+Dcuhrpt3DhlBTd6bnpNu2CINRDTMVSsKh8gVJQYJmvV7ZVpc4/1SLVEt033ngjMTExREVFsXTpUnP7iBEjzLHelzLjx4OD1TcbHw/799ddf+xF8+ZayAAcOwbff29ZdvYsPPVU3fRLsB/WAsrV0ZUQz5AK1r54eOkl+OknaNxYi+/ISFi2DK68sq571jC5O/Ju7o68m0d7P4qLo8v5NxAaDNbJ1Epauh0dLeVkEhJg8WLbbV99tbi0n2eYbkjaCgmbLCtkn4Wtj+r5i+hBSdCYRXex2DbEt4huQajHOBSPlBbmlLuKteg2BlaFqlOtRGoAoaGhhIbaplHv3bv3BXeoIRAeDhMmwLx5dd0T++LoCN27w+bi0n333gtubnp66iktvIX6TbBnMG5ObuQU5NDMp5lN9vqLnRtu0FN+vtwUaprJfSfXdReEOsKI3z6WfIz8onzcndzxdLFkJO/QwVK54/779T2jaVOYMwdmzoTHHgP8usA5XeedtTfA0CWQnw5bJkGO3EguVsTSLQgNECPcp8hKdI/aCKqoeLkDBd9YFjlVWzkK8tXVEO+8Azt3wu7dtu09eoCnZ9nb1AfGjLGI7qwsuP56y7LAwLK3EeoPJpOJ8R3HE5cRR5fgLnXdnWohglsQag7Dqn0w8aDNe4PLL4c//tDzZ87AZZdBSIgu32cmZDgcmqXns2NhSVfLMtdySogJdY5YugWhAeJQLAWtLd0Otg9S1pbui7n60sVOtdzLhfMTGgqrV0NxuDsAo0frWOj6PEo0XsqcNni+ue4blk1cxjtXvFPXXREE4SIj0L1YdCeVLbrHjrVdPzGxhOAGLbodyk/aI1yc+LrpLOVGAjVDfEv2ckGoxxjX4grcy60jehxEOVYb+epqEH9/WLcODhyAo0d1fFt9twZ37AhXXFHXvRAEQRDqAsO93LB0G+8NWreGESPOsxMXf2h+Y010T6hBSrmXi6VbEOo/rgH6tTC73FWsrdtSjrX6iOiuYUwmaNfOtpRKfee998C3jIHtoUNrvSuCIAhCLXI+93KA558vXU0AtJeXm1EKttNUcCoj1spfKqBcrJR0L5eSYYLQAHAtHjjNOlXuKtYeutau5kLVENEtVJkOHXTmcvPDEzBqFHzxRd31SRAEQah5jPJgSdlJNu+tGT5cZyovycyZ0KhR8RvfDtDrU9sVGvWC/nPs2V3BjoilWxAaIG7FeTSyYy01u0tgLbrF0l196nF0sVCXXHklxMTAkiW6RIxYuQVBEBo+JS3bZVm6AaZOBS8v+OwzcHeHJ57QVT1saHkb+ETA6UXg3R6a32BJ6iNcdBjiuqCogJyCHMleLggNAcPSrQohKwa8WpVaxVp0Z2VZDZ4KVULubkK1CQqCO+6o614IgiAItUVlRTfA5Ml6qpBGPfQkXPQ4mBzwdPYkMz+TtNw0sXQLQkPA1cpbKeNYadFdVIiTkyWoOyUFmjWrna41NMS9XBAEQRCESlEycVpZ7uVCw8XIYJ6em27JXu4m2csFod7iZlWmMWVH6eWxC2xKHScn13yXGioiugVBEARBqBRVsXQLDQ/ruG6xdAtCA8Da0h233HZZ/CpI3kpAgKUpJaVWetUgEdEtCIIgCEKlcHF0sRFZJS3fQsPGOoO5xHQLQgPAPdQyf+ZPSNig5wvzYKuODxLRbR9EdAuCIAiCUGmsrdti6b60MAT22cyzFKpCXBxdcHNyO89WgiBctLgGWlm7FWx5AGIXwpprze7mgVaXeXEvrz4iugVBEARBqDRGHLcJEwHuAedZW2hIGKI7Ni0WAG8X77rsjiAI9sCvq2U+ZQesHgdnlpibrC3d587VYr8aGBeV6E5OTmbixIn4+vri6+vLxIkTSanAjyE/P59nnnmGLl264OnpSZMmTbjjjjs4ffp07XVaEARBEC4hDOu2v7s/jg6O51lbaEiYRXd6rM17QRDqMf6RFS62Ft1HjtRsVxoyF5XovvXWW9m+fTtLly5l6dKlbN++nYkTJ5a7flZWFtu2beOFF15g27Zt/Prrrxw8eJBx48bVYq8FQRAE4dLh9wm/k/ufXOKeiKvrrgi1jK+rzlR+Ol0bNyRzuSA0AIKHVLjY2r388OHSy7Oz7dyfBspFU6d73759LF26lI0bN9KnTx8APvvsM/r168eBAwdo3759qW18fX1Zvtw2096HH35I7969iYmJoUWLFrXSd0EQBEG4VHB0cMQRsXBfihiWbUN0i6VbEBoAISPAwRWKcstcbG3p3r0b8vPB2dnStngx3HBDDfexAXDRWLo3bNiAr6+vWXAD9O3bF19fX9avX1/p/aSmpmIymfDz8yt3ndzcXNLS0sxTRkbGhXRdEARBEAShwSOiWxAaIE4e0GR0Ocu88fAAd3f9Njsbtm+3XeWHH2q0dw2Gi0Z0x8XFERwcXKo9ODiYuLjKubDl5OTw7LPPcuutt+LjU/6NYMaMGea4cV9fX4YMqditQhAEQRAE4VJHRLcgNFA6vwymErLQxR9a3wfYupjPnm2ZP3YMfvut5rvXEKhx0T1t2jRMJlOFU1RUFAAmk6nU9kqpMttLkp+fz4QJEygqKmLWrFkVrvvcc8+RmppqnlatWlW9DycIgiAIgnCJYK7TnVdco9tFRLcgNAj8u0LY7VYNJujzBbg2AiA83LLk66/h5ElQCh57DAoKarWn9ZYaj+l++OGHmTBhQoXrtGzZkp07d3L27NlSy86dO0dISEiF2+fn53PTTTdx7Ngx/v777wqt3ACurq64urqa33t5eVW4viAIgiAIwqVOScu2WLoFoQHRezZ4Noe0A9DmfggdaV7UsSOsXq3n8/Nh+HDw84OoKKiEbVSgFkR3YGAggdY+CeXQr18/UlNT2bx5M7179wZg06ZNpKam0r9//3K3MwT3oUOHWLlyJQEBUjNUEARBEATB3hjZy83vJXu5IDQcHF2h62tlLurUyfZ9WVnMhYq5aGK6O3TowJVXXsl9993Hxo0b2bhxI/fddx9XXXWVTebyiIgI5s+fD0BBQQE33ngjUVFRzJ07l8LCQuLi4oiLiyMvL6+uPoogCIIgCEKDQyzdgnBpUlJ0C1XnohHdAHPnzqVLly6MGjWKUaNG0bVrV7799lubdQ4cOEBqaioAp06dYsGCBZw6dYrIyEgaN25snqqS8VwQBEEQBEGoGBHdgnBp0quXbZkwoepcNHW6ARo1asScOXMqXEcpZZ5v2bKlzXtBEARBEAShZhDRLQiXJl5eWniLTbP6XFSWbkEQBEEQBOHiRES3IFy6jBhR1z2o34joFgRBEARBEM6Lo4MjHs4e5vciugXh0mHiRHB0LN3erl3t96U+IqJbEARBEARBqBTWGcxLZjMXBKHh0rYtjB9fuv3xx2u/L/UREd2CIAiCIAhCpbC2boulWxAuLaZOta3LHRkJ99xTZ92pV4joFgRBEARBECqFiG5BuHTp3Bm++w78/XVitfnzwemiSst98SJfkyAIgiAIglApDKHt7OCMu7N7HfdGEITa5uab9SRUDbF0C4IgCIIgCJXCEN3ert513BNBEIT6g1i6BUEQBEEQhErxy02/UKSKMFkHdgqCIAgVIqJbEARBEARBqBQmkwlHUxl1gwRBEIRyEfdyQRAEQRAEQRAEQaghRHQLgiAIgiAIgiAIQg0holsQBEEQBEEQBEEQaggR3YIgCIIgCIIgCIJQQ4joFgRBEARBEARBEIQaQkR3GeTm5jJt2jRyc3PruiuCYFfk3BYaKnJuCw0VObeFhoqc28KlhEkppeq6E3VNVlYW+/fvJyIiAg8PD9LS0vD19SU1NRUfH5+67p4g2A05t4WGipzbQkNFzm2hoSLntnApIXW6AQ8PD7p3717X3RAEQRAEQRAEQRAaGOJeLgiCIAiCIAiCIAg1hIhuQRAEQRAEQRAEQaghRHSXgaurKy+99BKurq513RVBsCtybgsNFTm3hYaKnNtCQ0XObeFSQhKpCYIgCIIgCIIgCEINIZZuQRAEQRAEQRAEQaghRHQLgiAIgiAIgiAIQg0holsQBEEQBEEQBEEQaggR3YIgCIIgCIIgCIJQQ4joLoNZs2YRHh6Om5sbPXr0YM2aNXXdJUGoNDNmzKBXr154e3sTHBzMtddey4EDB2zWUUoxbdo0mjRpgru7O0OHDmXPnj111GNBqB4zZszAZDLx2GOPmdvk3BbqK7Gxsdx+++0EBATg4eFBZGQkW7duNS+Xc1uojxQUFPCf//yH8PBw3N3dadWqFa+88gpFRUXmdeTcFi4FRHSX4IcffuCxxx5j6tSpREdHM2jQIEaPHk1MTExdd00QKsWqVat46KGH2LhxI8uXL6egoIBRo0aRmZlpXuett97i3Xff5aOPPmLLli2EhoZy+eWXk56eXoc9F4TKs2XLFmbPnk3Xrl1t2uXcFuojycnJDBgwAGdnZ5YsWcLevXt555138PPzM68j57ZQH3nzzTf55JNP+Oijj9i3bx9vvfUWb7/9Nh9++KF5HTm3hUsCJdjQu3dvNWnSJJu2iIgI9eyzz9ZRjwThwoiPj1eAWrVqlVJKqaKiIhUaGqreeOMN8zo5OTnK19dXffLJJ3XVTUGoNOnp6apt27Zq+fLlasiQIWry5MlKKTm3hfrLM888owYOHFjucjm3hfrK2LFj1d13323Tdv3116vbb79dKSXntnDpIJZuK/Ly8ti6dSujRo2yaR81ahTr16+vo14JwoWRmpoKQKNGjQA4duwYcXFxNue5q6srQ4YMkfNcqBc89NBDjB07lpEjR9q0y7kt1FcWLFhAz549GT9+PMHBwXTr1o3PPvvMvFzObaG+MnDgQFasWMHBgwcB2LFjB2vXrmXMmDGAnNvCpYNTXXfgYiIhIYHCwkJCQkJs2kNCQoiLi6ujXglC9VFKMWXKFAYOHEjnzp0BzOdyWef5iRMnar2PglAVvv/+e7Zt28aWLVtKLZNzW6ivHD16lI8//pgpU6bw/PPPs3nzZh599FFcXV2544475NwW6i3PPPMMqampRERE4OjoSGFhIa+//jq33HILINdt4dJBRHcZmEwmm/dKqVJtglAfePjhh9m5cydr164ttUzOc6G+cfLkSSZPnsyyZctwc3Mrdz05t4X6RlFRET179mT69OkAdOvWjT179vDxxx9zxx13mNeTc1uob/zwww/MmTOHefPm0alTJ7Zv385jjz1GkyZNuPPOO83rybktNHTEvdyKwMBAHB0dS1m14+PjS43ACcLFziOPPMKCBQtYuXIlzZo1M7eHhoYCyHku1Du2bt1KfHw8PXr0wMnJCScnJ1atWsUHH3yAk5OT+fyVc1uobzRu3JiOHTvatHXo0MGcxFWu20J95amnnuLZZ59lwoQJdOnShYkTJ/L4448zY8YMQM5t4dJBRLcVLi4u9OjRg+XLl9u0L1++nP79+9dRrwShaiilePjhh/n111/5+++/CQ8Pt1keHh5OaGiozXmel5fHqlWr5DwXLmpGjBjBrl272L59u3nq2bMnt912G9u3b6dVq1Zybgv1kgEDBpQq7Xjw4EHCwsIAuW4L9ZesrCwcHGzlhqOjo7lkmJzbwqWCuJeXYMqUKUycOJGePXvSr18/Zs+eTUxMDJMmTarrrglCpXjooYeYN28ev//+O97e3ubRY19fX9zd3c11jadPn07btm1p27Yt06dPx8PDg1tvvbWOey8I5ePt7W3OTWDg6elJQECAuV3ObaE+8vjjj9O/f3+mT5/OTTfdxObNm5k9ezazZ88GkOu2UG+5+uqref3112nRogWdOnUiOjqad999l7vvvhuQc1u4hKjDzOkXLf/9739VWFiYcnFxUd27dzeXWhKE+gBQ5vTll1+a1ykqKlIvvfSSCg0NVa6urmrw4MFq165ddddpQagm1iXDlJJzW6i/LFy4UHXu3Fm5urqqiIgINXv2bJvlcm4L9ZG0tDQ1efJk1aJFC+Xm5qZatWqlpk6dqnJzc83ryLktXAqYlFKqLkW/IAiCIAiCIAiCIDRUJKZbEARBEARBEARBEGoIEd2CIAiCIAiCIAiCUEOI6BYEQRAEQRAEQRCEGkJEtyAIgiAIgiAIgiDUECK6BUEQBEEQBEEQBKGGENEtCIIgCIIgCIIgCDWEiG5BEARBEARBEARBqCFEdAuCIAiCIAiCIAhCDSGiWxAEQRAEQRAEQRBqCBHdgiAIgiAIgiAIglBDiOgWBEEQBEEQBEEQhBpCRLcgCIIgCIIgCIIg1BD/D5hx0oszHjivAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ad = sc.read_h5ad('./data/3_cross_species/m1d1_atac.h5ad')\n",
    "cts = ['L2/3 IT','LAMP5','MGC']\n",
    "f, axs = plt.subplots(nrows=3, figsize=(10, 6))\n",
    "a = 510\t\n",
    "b = 610\n",
    "for i in range(3):\n",
    "    # aggregate cells\n",
    "    cells = np.where((ad.obs['cell_type']==cts[i]))[0] \n",
    "    # normalize\n",
    "    toplot = pd.DataFrame(tmp[cells,:].mean(axis=0), columns = ['A', 'C', 'G', 'T']) \n",
    "    toplot = toplot.iloc[a:b,:]  \n",
    "    toplot.index = np.arange(toplot.shape[0])  \n",
    "    xc.pl.plot_logo(toplot, -0.3,0.35, axs[i], '%s:%d-%d (%s)'%(seqs_coords[0][0],seqs_coords[0][1]+a, seqs_coords[0][1]+b, cts[i]))\n",
    "f.tight_layout()\n",
    "# f.savefig('./ism_peak0_100bp_celltype3.pdf', bbox_inches='tight', dpi=300,format=\"pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab55c79d-6eab-42c6-bcc9-507ec875a889",
   "metadata": {},
   "source": [
    "### 7. Calculate PWM-ISM dot product"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d4ed98-0b91-45ed-914a-edd40baeffb6",
   "metadata": {},
   "source": [
    "After obtaining the base-level importance scores for each input sequence, we sought to determine whether specific motifs play key roles in model training and prediction for particular cell types. We scanned these sequences (using the FIMO tool) to identify candidate motifs and then calculated whether the PWM scores of these candidate motifs correlated with the ISM values computed for the region. A correlation value was obtained for each cell, and the final result was derived by averaging these values across cells of the same cell type. The specific analysis code can be found via https://github.com/Miaoyuanyuan777/XChrom_analysis.\n",
    "\n",
    "Here, we only show correlation values between the PWM score and the ISM values computed for the candidate MEF2C region (chr14:73168422-73168436) identified at a human sequence (chr14:73168099-73168956) (in `m1d1_peaks.bed`) for three cell types: 'L2/3 IT', 'LAMP5', and 'MGC'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cfae30c1-b184-4fd8-af44-5dcd2846cbd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ism = np.load('./data/3_cross_species/ISM_results/peak0_ism.npy')\n",
    "ism_norm = ism - np.repeat(ism.mean(axis=2)[:,:,np.newaxis],4,axis=2)\n",
    "motif = 'MEF2C'\n",
    "pwm = pd.read_csv(f'./data/3_cross_species/{motif}.csv',index_col = 0)\n",
    "start_site = 566\n",
    "ism_matrix = ism_norm[:,start_site:(start_site+pwm.shape[1]),:]\n",
    "m1d1_rna.obs[motif] = [np.dot(pwm.transpose().values.flatten(),i.flatten()) for i in ism_matrix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5a1262f-b96e-4830-be49-b00fa03efd55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fb74a977d90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cts = ['L2/3 IT','LAMP5','MGC']\n",
    "f, axs = plt.subplots(ncols=1, figsize=(4,3))\n",
    "sns.violinplot(x='cell_type', y = motif, data=m1d1_rna.obs, order=cts)\n",
    "sns.stripplot(x='cell_type', y=motif, data=m1d1_rna.obs, order=cts, alpha=0.6, color='0.3', size=2)\n",
    "axs.set_ylim(-1,2.5)\n",
    "axs.axhline(y=0, linestyle='dashed', color='0.3')\n",
    "# f.savefig(f'./corr_{motif}.pdf', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3966d392-9723-43fa-93cb-a337d8ed11f0",
   "metadata": {},
   "source": [
    "The vertical axis represents the computed MEF2C PWM-ISM correlation (dot product) described above. A higher correlation suggests MEF2C binding motif has stronger predictive importance for chromatin accessibility in that specific cell type.\n",
    "\n",
    "The results indicate that MEF2C binding sites has the strongest regulatory importance in LAMP5 (inhibitory neurons), a weaker regulatory importance in L2/3 IT (excitatory neurons), and the weakest regulatory importance in MGC (non-neuronal)."
   ]
  }
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