From 93eb07536aa972bf5f722ed3ccc782120842ada7 Mon Sep 17 00:00:00 2001
From: Varshini VIjay <99629838+varshinivij@users.noreply.github.com>
Date: Tue, 1 Jul 2025 17:53:40 -0700
Subject: [PATCH 1/6] Created using Colab

---
 docs/notebook/celltypist_tutorial.ipynb | 2834 +++++++++++++++++++++++
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 create mode 100644 docs/notebook/celltypist_tutorial.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "understood-farmer",
+      "metadata": {
+        "tags": [],
+        "id": "understood-farmer"
+      },
+      "source": [
+        "# Using CellTypist for cell type classification\n",
+        "This notebook showcases the cell type classification for scRNA-seq query data by retrieving the most likely cell type labels from either the built-in CellTypist models or the user-trained custom models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "exterior-thousand",
+      "metadata": {
+        "id": "exterior-thousand"
+      },
+      "source": [
+        "Only the main steps and key parameters are introduced in this notebook. Refer to detailed [Usage](https://github.com/Teichlab/celltypist#usage) if you want to learn more."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "assisted-clear",
+      "metadata": {
+        "id": "assisted-clear"
+      },
+      "source": [
+        "## Install CellTypist"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "automotive-traveler",
+      "metadata": {
+        "tags": [],
+        "id": "automotive-traveler",
+        "outputId": "f50ab3a6-2fd9-412f-9e20-950492737afa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting celltypist\n",
+            "  Downloading celltypist-1.7.1-py3-none-any.whl.metadata (45 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/45.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.1/45.1 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.11/dist-packages (from celltypist) (2.0.2)\n",
+            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.11/dist-packages (from celltypist) (2.2.2)\n",
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+            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from celltypist) (2.32.3)\n",
+            "Collecting scanpy>=1.7.0 (from celltypist)\n",
+            "  Downloading scanpy-1.11.3-py3-none-any.whl.metadata (9.0 kB)\n",
+            "Collecting leidenalg>=0.9.0 (from celltypist)\n",
+            "  Downloading leidenalg-0.10.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB)\n",
+            "Collecting igraph<0.12,>=0.10.0 (from leidenalg>=0.9.0->celltypist)\n",
+            "  Downloading igraph-0.11.9-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)\n",
+            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.11/dist-packages (from openpyxl>=3.0.4->celltypist) (2.0.0)\n",
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+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->celltypist) (2025.6.15)\n",
+            "Collecting anndata>=0.8 (from scanpy>=1.7.0->celltypist)\n",
+            "  Downloading anndata-0.11.4-py3-none-any.whl.metadata (9.3 kB)\n",
+            "Requirement already satisfied: h5py>=3.7.0 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (3.14.0)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (1.5.1)\n",
+            "Collecting legacy-api-wrap>=1.4.1 (from scanpy>=1.7.0->celltypist)\n",
+            "  Downloading legacy_api_wrap-1.4.1-py3-none-any.whl.metadata (2.1 kB)\n",
+            "Requirement already satisfied: matplotlib>=3.7.5 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (3.10.0)\n",
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+            "Requirement already satisfied: pynndescent>=0.5.13 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.5.13)\n",
+            "Requirement already satisfied: scipy<1.16.0,>=1.8.1 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (1.15.3)\n",
+            "Requirement already satisfied: seaborn>=0.13.2 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.13.2)\n",
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+            "  Downloading session_info2-0.1.2-py3-none-any.whl.metadata (2.5 kB)\n",
+            "Requirement already satisfied: statsmodels>=0.14.4 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.14.4)\n",
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+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (4.14.0)\n",
+            "Requirement already satisfied: umap-learn>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.5.7)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=0.24.1->celltypist) (3.6.0)\n",
+            "Collecting array-api-compat!=1.5,>1.4 (from anndata>=0.8->scanpy>=1.7.0->celltypist)\n",
+            "  Downloading array_api_compat-1.12.0-py3-none-any.whl.metadata (2.5 kB)\n",
+            "Collecting texttable>=1.6.2 (from igraph<0.12,>=0.10.0->leidenalg>=0.9.0->celltypist)\n",
+            "  Downloading texttable-1.7.0-py2.py3-none-any.whl.metadata (9.8 kB)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.7.5->scanpy>=1.7.0->celltypist) (1.3.2)\n",
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+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.5->celltypist) (1.17.0)\n",
+            "Downloading celltypist-1.7.1-py3-none-any.whl (7.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.3/7.3 MB\u001b[0m \u001b[31m60.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.5/144.5 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading igraph-0.11.9-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.4 MB)\n",
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+            "\u001b[?25hDownloading legacy_api_wrap-1.4.1-py3-none-any.whl (10.0 kB)\n",
+            "Downloading session_info2-0.1.2-py3-none-any.whl (14 kB)\n",
+            "Downloading array_api_compat-1.12.0-py3-none-any.whl (58 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.2/58.2 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading texttable-1.7.0-py2.py3-none-any.whl (10 kB)\n",
+            "Installing collected packages: texttable, session-info2, legacy-api-wrap, igraph, array-api-compat, leidenalg, anndata, scanpy, celltypist\n",
+            "Successfully installed anndata-0.11.4 array-api-compat-1.12.0 celltypist-1.7.1 igraph-0.11.9 legacy-api-wrap-1.4.1 leidenalg-0.10.2 scanpy-1.11.3 session-info2-0.1.2 texttable-1.7.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install celltypist"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "organized-wedding",
+      "metadata": {
+        "id": "organized-wedding"
+      },
+      "outputs": [],
+      "source": [
+        "import scanpy as sc"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "intelligent-standard",
+      "metadata": {
+        "id": "intelligent-standard"
+      },
+      "outputs": [],
+      "source": [
+        "import celltypist\n",
+        "from celltypist import models"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "julian-banana",
+      "metadata": {
+        "id": "julian-banana"
+      },
+      "source": [
+        "## Download a scRNA-seq dataset of 2,000 immune cells"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "naval-seminar",
+      "metadata": {
+        "tags": [],
+        "id": "naval-seminar",
+        "outputId": "42536227-0864-4803-ac74-a041c2f41fe6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "117d53639d7e41f093c3c4f1d340db6a",
+            "c1007a4a47c546768a71cbfc2536fb0a",
+            "4e99394259c44f6782fd5be15fdd5890",
+            "a4e849127a234f768c7c9cc95ab47ea0",
+            "97381406977d4f6fa50d519d891d5702",
+            "1bf40693f81e491a8dea2694e2edc347",
+            "0cbad6912c88424cac819a842e25779d",
+            "3934dc02b4474578a1d1d27ed8a11b49",
+            "e29e63f124ba449fb92080d55cfb6b11",
+            "4ca07a98307e4c3baf0cda44bc99fb38",
+            "910c5dbf9cea438691fc8f27a136afe8"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0.00/34.1M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "117d53639d7e41f093c3c4f1d340db6a"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "adata_2000 = sc.read('celltypist_demo_folder/demo_2000_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_2000_cells.h5ad')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "passive-classics",
+      "metadata": {
+        "id": "passive-classics"
+      },
+      "source": [
+        "This dataset includes 2,000 cells and 18,950 genes collected from different studies, thereby showing the practical applicability of CellTypist."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "sensitive-chancellor",
+      "metadata": {
+        "id": "sensitive-chancellor",
+        "outputId": "f065360e-39cd-49b7-9076-88aed7f6c7fd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(2000, 18950)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "adata_2000.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "destroyed-distribution",
+      "metadata": {
+        "tags": [],
+        "id": "destroyed-distribution"
+      },
+      "source": [
+        "The expression matrix (`adata_2000.X`) is pre-processed (and required) as log1p normalised expression to 10,000 counts per cell (this matrix can be alternatively stashed in `.raw.X`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "sized-grocery",
+      "metadata": {
+        "tags": [],
+        "id": "sized-grocery",
+        "outputId": "1cef427b-6c59-42dd-aa6c-31ee86fd6f7e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "matrix([[10000.   ],\n",
+              "        [10000.002],\n",
+              "        [10000.   ],\n",
+              "        ...,\n",
+              "        [10000.   ],\n",
+              "        [10000.   ],\n",
+              "        [10000.   ]], dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "adata_2000.X.expm1().sum(axis = 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "approximate-excerpt",
+      "metadata": {
+        "id": "approximate-excerpt"
+      },
+      "source": [
+        "Some pre-assigned cell type labels are also in the data, which will be compared to the predicted labels from CellTypist later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "downtown-arbitration",
+      "metadata": {
+        "tags": [],
+        "id": "downtown-arbitration",
+        "outputId": "698971c1-9b2b-4e0e-efbc-cf1c052daf5a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                              cell_type\n",
+              "cell1                      Plasma cells\n",
+              "cell2                      Plasma cells\n",
+              "cell3                      Plasma cells\n",
+              "cell4                      Plasma cells\n",
+              "cell5                      Plasma cells\n",
+              "...                                 ...\n",
+              "cell1996  Neutrophil-myeloid progenitor\n",
+              "cell1997  Neutrophil-myeloid progenitor\n",
+              "cell1998  Neutrophil-myeloid progenitor\n",
+              "cell1999  Neutrophil-myeloid progenitor\n",
+              "cell2000  Neutrophil-myeloid progenitor\n",
+              "\n",
+              "[2000 rows x 1 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-75022be7-5fa7-4cac-b071-9abd779629ff\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>cell_type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>cell1</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell2</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell3</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell4</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell5</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1996</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1997</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1998</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1999</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell2000</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2000 rows × 1 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75022be7-5fa7-4cac-b071-9abd779629ff')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-75022be7-5fa7-4cac-b071-9abd779629ff button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-75022be7-5fa7-4cac-b071-9abd779629ff');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ad9ce39c-df9f-4c89-9e74-9784eb19acf4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ad9ce39c-df9f-4c89-9e74-9784eb19acf4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ad9ce39c-df9f-4c89-9e74-9784eb19acf4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"adata_2000\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"cell_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Mast cells\",\n          \"gamma-delta T cells\",\n          \"Macrophages\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "adata_2000.obs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "economic-penny",
+      "metadata": {
+        "id": "economic-penny"
+      },
+      "source": [
+        "## Assign cell type labels using a CellTypist built-in model\n",
+        "In this section, we show the procedure of transferring cell type labels from built-in models to the query dataset."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "quick-stanford",
+      "metadata": {
+        "id": "quick-stanford"
+      },
+      "source": [
+        "Download the latest CellTypist models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "premier-measurement",
+      "metadata": {
+        "tags": [],
+        "id": "premier-measurement"
+      },
+      "outputs": [],
+      "source": [
+        "# Enabling `force_update = True` will overwrite existing (old) models.\n",
+        "models.download_models(force_update = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "stuck-judges",
+      "metadata": {
+        "id": "stuck-judges"
+      },
+      "source": [
+        "All models are stored in `models.models_path`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "above-length",
+      "metadata": {
+        "id": "above-length",
+        "outputId": "b2c9c214-a5b8-4e61-ca6b-b81e5f416d45",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'/root/.celltypist/data/models'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "models.models_path"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "understanding-slovak",
+      "metadata": {
+        "id": "understanding-slovak"
+      },
+      "source": [
+        "Get an overview of the models and what they represent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "breeding-grace",
+      "metadata": {
+        "tags": [],
+        "id": "breeding-grace",
+        "outputId": "28ca42a8-27a4-4a54-aac8-9688f337259a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                      model  \\\n",
+              "0                        Immune_All_Low.pkl   \n",
+              "1                       Immune_All_High.pkl   \n",
+              "2                    Adult_COVID19_PBMC.pkl   \n",
+              "3   Adult_CynomolgusMacaque_Hippocampus.pkl   \n",
+              "4                       Adult_Human_MTG.pkl   \n",
+              "5           Adult_Human_PancreaticIslet.pkl   \n",
+              "6          Adult_Human_PrefrontalCortex.pkl   \n",
+              "7                      Adult_Human_Skin.pkl   \n",
+              "8                  Adult_Human_Vascular.pkl   \n",
+              "9                       Adult_Mouse_Gut.pkl   \n",
+              "10            Adult_Mouse_OlfactoryBulb.pkl   \n",
+              "11                Adult_Pig_Hippocampus.pkl   \n",
+              "12      Adult_RhesusMacaque_Hippocampus.pkl   \n",
+              "13                 Autopsy_COVID19_Lung.pkl   \n",
+              "14         COVID19_HumanChallenge_Blood.pkl   \n",
+              "15             COVID19_Immune_Landscape.pkl   \n",
+              "16                   Cells_Adult_Breast.pkl   \n",
+              "17                     Cells_Fetal_Lung.pkl   \n",
+              "18                   Cells_Human_Tonsil.pkl   \n",
+              "19               Cells_Intestinal_Tract.pkl   \n",
+              "20                    Cells_Lung_Airway.pkl   \n",
+              "21               Developing_Human_Brain.pkl   \n",
+              "22              Developing_Human_Gonads.pkl   \n",
+              "23         Developing_Human_Hippocampus.pkl   \n",
+              "24              Developing_Human_Organs.pkl   \n",
+              "25              Developing_Human_Thymus.pkl   \n",
+              "26               Developing_Mouse_Brain.pkl   \n",
+              "27         Developing_Mouse_Hippocampus.pkl   \n",
+              "28            Fetal_Human_AdrenalGlands.pkl   \n",
+              "29                 Fetal_Human_Pancreas.pkl   \n",
+              "30                Fetal_Human_Pituitary.pkl   \n",
+              "31                   Fetal_Human_Retina.pkl   \n",
+              "32                     Fetal_Human_Skin.pkl   \n",
+              "33                  Healthy_Adult_Heart.pkl   \n",
+              "34                 Healthy_COVID19_PBMC.pkl   \n",
+              "35                  Healthy_Human_Liver.pkl   \n",
+              "36                  Healthy_Mouse_Liver.pkl   \n",
+              "37          Human_AdultAged_Hippocampus.pkl   \n",
+              "38              Human_Colorectal_Cancer.pkl   \n",
+              "39           Human_Developmental_Retina.pkl   \n",
+              "40              Human_Embryonic_YolkSac.pkl   \n",
+              "41              Human_Endometrium_Atlas.pkl   \n",
+              "42                       Human_IPF_Lung.pkl   \n",
+              "43       Human_Longitudinal_Hippocampus.pkl   \n",
+              "44                     Human_Lung_Atlas.pkl   \n",
+              "45                        Human_PF_Lung.pkl   \n",
+              "46               Human_Placenta_Decidua.pkl   \n",
+              "47                  Lethal_COVID19_Lung.pkl   \n",
+              "48                  Mouse_Dentate_Gyrus.pkl   \n",
+              "49          Mouse_Isocortex_Hippocampus.pkl   \n",
+              "50         Mouse_Postnatal_DentateGyrus.pkl   \n",
+              "51                    Mouse_Whole_Brain.pkl   \n",
+              "52                   Nuclei_Lung_Airway.pkl   \n",
+              "53                      Pan_Fetal_Human.pkl   \n",
+              "\n",
+              "                                          description  \n",
+              "0   immune sub-populations combined from 20 tissue...  \n",
+              "1   immune populations combined from 20 tissues of...  \n",
+              "2   peripheral blood mononuclear cell types from C...  \n",
+              "3   cell types from the hippocampus of adult cynom...  \n",
+              "4   cell types and subtypes (10x-based) from the a...  \n",
+              "5   cell types from pancreatic islets of healthy a...  \n",
+              "6   cell types and subtypes from the adult human d...  \n",
+              "7            cell types from human healthy adult skin  \n",
+              "8   vascular populations combined from multiple ad...  \n",
+              "9   cell types in the adult mouse gut combined fro...  \n",
+              "10   cell types from the olfactory bulb of adult mice  \n",
+              "11          cell types from the adult pig hippocampus  \n",
+              "12  cell types from the hippocampus of adult rhesu...  \n",
+              "13  cell types from the lungs of 16 SARS-CoV-2 inf...  \n",
+              "14  detailed blood cell states from 16 individuals...  \n",
+              "15  immune subtypes from lung and blood of COVID-1...  \n",
+              "16             cell types from the adult human breast  \n",
+              "17    cell types from human embryonic and fetal lungs  \n",
+              "18      tonsillar cell types from humans (3-65 years)  \n",
+              "19  intestinal cells from fetal, pediatric (health...  \n",
+              "20  cell populations from scRNA-seq of five locati...  \n",
+              "21  cell types from the first-trimester developing...  \n",
+              "22  cell types of human gonadal and adjacent extra...  \n",
+              "23   cell types from the developing human hippocampus  \n",
+              "24  cell types of five endoderm-derived organs in ...  \n",
+              "25  cell populations in embryonic, fetal, pediatri...  \n",
+              "26  cell types from the embryonic mouse brain betw...  \n",
+              "27  cell types from the mouse hippocampus at postn...  \n",
+              "28  cell types of human fetal adrenal glands from ...  \n",
+              "29  pancreatic cell types from human embryos at 9-...  \n",
+              "30  cell types of human fetal pituitaries from 7 t...  \n",
+              "31  cell types from human fetal neural retina and ...  \n",
+              "32        cell types from developing human fetal skin  \n",
+              "33  cell types from eight anatomical regions of th...  \n",
+              "34  peripheral blood mononuclear cell types from h...  \n",
+              "35  cell types from scRNA-seq and snRNA-seq of the...  \n",
+              "36  cell types from scRNA-seq and snRNA-seq of the...  \n",
+              "37  cell types from the hippocampus of adult and a...  \n",
+              "38  cell types of colon tissues from patients with...  \n",
+              "39                 cell types from human fetal retina  \n",
+              "40  cell types of the human yolk sac from 4-8 post...  \n",
+              "41  endometrial cell types integrated from seven d...  \n",
+              "42  cell types from idiopathic pulmonary fibrosis,...  \n",
+              "43  cell types from the adult human anterior and p...  \n",
+              "44  integrated Human Lung Cell Atlas (HLCA) combin...  \n",
+              "45  cell types from different forms of pulmonary f...  \n",
+              "46  cell types from first-trimester human placenta...  \n",
+              "47  cell types from the lungs of individuals who d...  \n",
+              "48  cell types from the dentate gyrus in perinatal...  \n",
+              "49  cell types from the adult mouse isocortex (neo...  \n",
+              "50  cell types from the mouse dentate gyrus in pos...  \n",
+              "51        cell types from the whole adult mouse brain  \n",
+              "52  cell populations from snRNA-seq of five locati...  \n",
+              "53  stromal and immune populations from the human ...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-138e7b95-bec9-4b75-bf1e-ebb9992ff44f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>description</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Immune_All_Low.pkl</td>\n",
+              "      <td>immune sub-populations combined from 20 tissue...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Immune_All_High.pkl</td>\n",
+              "      <td>immune populations combined from 20 tissues of...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Adult_COVID19_PBMC.pkl</td>\n",
+              "      <td>peripheral blood mononuclear cell types from C...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Adult_CynomolgusMacaque_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the hippocampus of adult cynom...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Adult_Human_MTG.pkl</td>\n",
+              "      <td>cell types and subtypes (10x-based) from the a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Adult_Human_PancreaticIslet.pkl</td>\n",
+              "      <td>cell types from pancreatic islets of healthy a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Adult_Human_PrefrontalCortex.pkl</td>\n",
+              "      <td>cell types and subtypes from the adult human d...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Adult_Human_Skin.pkl</td>\n",
+              "      <td>cell types from human healthy adult skin</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Adult_Human_Vascular.pkl</td>\n",
+              "      <td>vascular populations combined from multiple ad...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Adult_Mouse_Gut.pkl</td>\n",
+              "      <td>cell types in the adult mouse gut combined fro...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Adult_Mouse_OlfactoryBulb.pkl</td>\n",
+              "      <td>cell types from the olfactory bulb of adult mice</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Adult_Pig_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the adult pig hippocampus</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Adult_RhesusMacaque_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the hippocampus of adult rhesu...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Autopsy_COVID19_Lung.pkl</td>\n",
+              "      <td>cell types from the lungs of 16 SARS-CoV-2 inf...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>COVID19_HumanChallenge_Blood.pkl</td>\n",
+              "      <td>detailed blood cell states from 16 individuals...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>COVID19_Immune_Landscape.pkl</td>\n",
+              "      <td>immune subtypes from lung and blood of COVID-1...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Cells_Adult_Breast.pkl</td>\n",
+              "      <td>cell types from the adult human breast</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>Cells_Fetal_Lung.pkl</td>\n",
+              "      <td>cell types from human embryonic and fetal lungs</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Cells_Human_Tonsil.pkl</td>\n",
+              "      <td>tonsillar cell types from humans (3-65 years)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Cells_Intestinal_Tract.pkl</td>\n",
+              "      <td>intestinal cells from fetal, pediatric (health...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>Cells_Lung_Airway.pkl</td>\n",
+              "      <td>cell populations from scRNA-seq of five locati...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>Developing_Human_Brain.pkl</td>\n",
+              "      <td>cell types from the first-trimester developing...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>Developing_Human_Gonads.pkl</td>\n",
+              "      <td>cell types of human gonadal and adjacent extra...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>Developing_Human_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the developing human hippocampus</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>Developing_Human_Organs.pkl</td>\n",
+              "      <td>cell types of five endoderm-derived organs in ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>Developing_Human_Thymus.pkl</td>\n",
+              "      <td>cell populations in embryonic, fetal, pediatri...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>Developing_Mouse_Brain.pkl</td>\n",
+              "      <td>cell types from the embryonic mouse brain betw...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>Developing_Mouse_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the mouse hippocampus at postn...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>Fetal_Human_AdrenalGlands.pkl</td>\n",
+              "      <td>cell types of human fetal adrenal glands from ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>Fetal_Human_Pancreas.pkl</td>\n",
+              "      <td>pancreatic cell types from human embryos at 9-...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>Fetal_Human_Pituitary.pkl</td>\n",
+              "      <td>cell types of human fetal pituitaries from 7 t...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>Fetal_Human_Retina.pkl</td>\n",
+              "      <td>cell types from human fetal neural retina and ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>Fetal_Human_Skin.pkl</td>\n",
+              "      <td>cell types from developing human fetal skin</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>Healthy_Adult_Heart.pkl</td>\n",
+              "      <td>cell types from eight anatomical regions of th...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>Healthy_COVID19_PBMC.pkl</td>\n",
+              "      <td>peripheral blood mononuclear cell types from h...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>Healthy_Human_Liver.pkl</td>\n",
+              "      <td>cell types from scRNA-seq and snRNA-seq of the...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>Healthy_Mouse_Liver.pkl</td>\n",
+              "      <td>cell types from scRNA-seq and snRNA-seq of the...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>Human_AdultAged_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the hippocampus of adult and a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>Human_Colorectal_Cancer.pkl</td>\n",
+              "      <td>cell types of colon tissues from patients with...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>Human_Developmental_Retina.pkl</td>\n",
+              "      <td>cell types from human fetal retina</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>Human_Embryonic_YolkSac.pkl</td>\n",
+              "      <td>cell types of the human yolk sac from 4-8 post...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>Human_Endometrium_Atlas.pkl</td>\n",
+              "      <td>endometrial cell types integrated from seven d...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>Human_IPF_Lung.pkl</td>\n",
+              "      <td>cell types from idiopathic pulmonary fibrosis,...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>Human_Longitudinal_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the adult human anterior and p...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>Human_Lung_Atlas.pkl</td>\n",
+              "      <td>integrated Human Lung Cell Atlas (HLCA) combin...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>Human_PF_Lung.pkl</td>\n",
+              "      <td>cell types from different forms of pulmonary f...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>Human_Placenta_Decidua.pkl</td>\n",
+              "      <td>cell types from first-trimester human placenta...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>Lethal_COVID19_Lung.pkl</td>\n",
+              "      <td>cell types from the lungs of individuals who d...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>Mouse_Dentate_Gyrus.pkl</td>\n",
+              "      <td>cell types from the dentate gyrus in perinatal...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>Mouse_Isocortex_Hippocampus.pkl</td>\n",
+              "      <td>cell types from the adult mouse isocortex (neo...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>Mouse_Postnatal_DentateGyrus.pkl</td>\n",
+              "      <td>cell types from the mouse dentate gyrus in pos...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>51</th>\n",
+              "      <td>Mouse_Whole_Brain.pkl</td>\n",
+              "      <td>cell types from the whole adult mouse brain</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>52</th>\n",
+              "      <td>Nuclei_Lung_Airway.pkl</td>\n",
+              "      <td>cell populations from snRNA-seq of five locati...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>Pan_Fetal_Human.pkl</td>\n",
+              "      <td>stromal and immune populations from the human ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-138e7b95-bec9-4b75-bf1e-ebb9992ff44f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "</svg>\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --hover-bg-color: #434B5C;\n",
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+              "  .colab-df-quickchart {\n",
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+              "    border-radius: 50%;\n",
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+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "  @keyframes spin {\n",
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+              "      border-top-color: var(--fill-color);\n",
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+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1f17f70b-ee76-4345-b59a-f84a01f2df38 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"models\",\n  \"rows\": 54,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54,\n        \"samples\": [\n          \"Cells_Intestinal_Tract.pkl\",\n          \"Mouse_Isocortex_Hippocampus.pkl\",\n          \"Mouse_Dentate_Gyrus.pkl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54,\n        \"samples\": [\n          \"intestinal cells from fetal, pediatric (healthy and Crohn's disease) and adult human gut\",\n          \"cell types from the adult mouse isocortex (neocortex) and hippocampal formation\",\n          \"cell types from the dentate gyrus in perinatal, juvenile, and adult mice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "models.models_description()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "tribal-broadway",
+      "metadata": {
+        "id": "tribal-broadway"
+      },
+      "source": [
+        "Choose the model you want to employ, for example, the model with all tissues combined containing low-hierarchy (high-resolution) immune cell types/subtypes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "boring-challenge",
+      "metadata": {
+        "id": "boring-challenge"
+      },
+      "outputs": [],
+      "source": [
+        "# Indeed, the `model` argument defaults to `Immune_All_Low.pkl`.\n",
+        "model = models.Model.load(model = 'Immune_All_Low.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "lovely-advisory",
+      "metadata": {
+        "id": "lovely-advisory"
+      },
+      "source": [
+        "Show the model meta information."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "anticipated-picking",
+      "metadata": {
+        "id": "anticipated-picking",
+        "outputId": "ca40c21a-b03e-484a-ac4a-70a2d2845af2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "CellTypist model with 98 cell types and 6639 features\n",
+              "    date: 2022-07-16 00:20:42.927778\n",
+              "    details: immune sub-populations combined from 20 tissues of 18 studies\n",
+              "    source: https://doi.org/10.1126/science.abl5197\n",
+              "    version: v2\n",
+              "    cell types: Age-associated B cells, Alveolar macrophages, ..., pDC precursor\n",
+              "    features: A1BG, A2M, ..., ZYX"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "exterior-driving",
+      "metadata": {
+        "id": "exterior-driving"
+      },
+      "source": [
+        "This model contains 98 cell states."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "hundred-receipt",
+      "metadata": {
+        "tags": [],
+        "id": "hundred-receipt",
+        "outputId": "c57de50b-424e-4b17-838e-d038c2ad1e3b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Age-associated B cells', 'Alveolar macrophages', 'B cells',\n",
+              "       'CD16+ NK cells', 'CD16- NK cells', 'CD8a/a', 'CD8a/b(entry)',\n",
+              "       'CMP', 'CRTAM+ gamma-delta T cells', 'Classical monocytes',\n",
+              "       'Cycling B cells', 'Cycling DCs', 'Cycling NK cells',\n",
+              "       'Cycling T cells', 'Cycling gamma-delta T cells',\n",
+              "       'Cycling monocytes', 'DC', 'DC precursor', 'DC1', 'DC2', 'DC3',\n",
+              "       'Double-negative thymocytes', 'Double-positive thymocytes', 'ELP',\n",
+              "       'ETP', 'Early MK', 'Early erythroid', 'Early lymphoid/T lymphoid',\n",
+              "       'Endothelial cells', 'Epithelial cells', 'Erythrocytes',\n",
+              "       'Erythrophagocytic macrophages', 'Fibroblasts',\n",
+              "       'Follicular B cells', 'Follicular helper T cells', 'GMP',\n",
+              "       'Germinal center B cells', 'Granulocytes', 'HSC/MPP',\n",
+              "       'Hofbauer cells', 'ILC', 'ILC precursor', 'ILC1', 'ILC2', 'ILC3',\n",
+              "       'Intermediate macrophages', 'Intestinal macrophages',\n",
+              "       'Kidney-resident macrophages', 'Kupffer cells',\n",
+              "       'Large pre-B cells', 'Late erythroid', 'MAIT cells', 'MEMP', 'MNP',\n",
+              "       'Macrophages', 'Mast cells', 'Megakaryocyte precursor',\n",
+              "       'Megakaryocyte-erythroid-mast cell progenitor',\n",
+              "       'Megakaryocytes/platelets', 'Memory B cells',\n",
+              "       'Memory CD4+ cytotoxic T cells', 'Mid erythroid', 'Migratory DCs',\n",
+              "       'Mono-mac', 'Monocyte precursor', 'Monocytes', 'Myelocytes',\n",
+              "       'NK cells', 'NKT cells', 'Naive B cells',\n",
+              "       'Neutrophil-myeloid progenitor', 'Neutrophils',\n",
+              "       'Non-classical monocytes', 'Plasma cells', 'Plasmablasts',\n",
+              "       'Pre-pro-B cells', 'Pro-B cells',\n",
+              "       'Proliferative germinal center B cells', 'Promyelocytes',\n",
+              "       'Regulatory T cells', 'Small pre-B cells', 'T(agonist)',\n",
+              "       'Tcm/Naive cytotoxic T cells', 'Tcm/Naive helper T cells',\n",
+              "       'Tem/Effector helper T cells', 'Tem/Effector helper T cells PD1+',\n",
+              "       'Tem/Temra cytotoxic T cells', 'Tem/Trm cytotoxic T cells',\n",
+              "       'Transitional B cells', 'Transitional DC', 'Transitional NK',\n",
+              "       'Treg(diff)', 'Trm cytotoxic T cells', 'Type 1 helper T cells',\n",
+              "       'Type 17 helper T cells', 'gamma-delta T cells', 'pDC',\n",
+              "       'pDC precursor'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "model.cell_types"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "entertaining-sensitivity",
+      "metadata": {
+        "tags": [],
+        "id": "entertaining-sensitivity"
+      },
+      "source": [
+        "Transfer cell type labels from this model to the query dataset using [celltypist.annotate](https://celltypist.readthedocs.io/en/latest/celltypist.annotate.html)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "postal-chicken",
+      "metadata": {
+        "tags": [],
+        "id": "postal-chicken"
+      },
+      "outputs": [],
+      "source": [
+        "# Not run; predict cell identities using this loaded model.\n",
+        "#predictions = celltypist.annotate(adata_2000, model = model, majority_voting = True)\n",
+        "# Alternatively, just specify the model name (recommended as this ensures the model is intact every time it is loaded).\n",
+        "predictions = celltypist.annotate(adata_2000, model = 'Immune_All_Low.pkl', majority_voting = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "personal-marijuana",
+      "metadata": {
+        "id": "personal-marijuana"
+      },
+      "source": [
+        "By default (`majority_voting = False`), CellTypist will infer the identity of each query cell independently. This leads to raw predicted cell type labels, and usually finishes within seconds or minutes depending on the size of the query data. You can also turn on the majority-voting classifier (`majority_voting = True`), which refines cell identities within local subclusters after an over-clustering approach at the cost of increased runtime."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "future-finder",
+      "metadata": {
+        "tags": [],
+        "id": "future-finder"
+      },
+      "source": [
+        "The results include both predicted cell type labels (`predicted_labels`), over-clustering result (`over_clustering`), and predicted labels after majority voting in local subclusters (`majority_voting`). Note in the `predicted_labels`, each query cell gets its inferred label by choosing the most probable cell type among all possible cell types in the given model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "expired-authorization",
+      "metadata": {
+        "tags": [],
+        "id": "expired-authorization",
+        "outputId": "49b00e70-f5f5-4e7c-f54d-6189c87ce860"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predicted_labels</th>\n",
+              "      <th>over_clustering</th>\n",
+              "      <th>majority_voting</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>cell1</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>44</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell2</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>12</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell3</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>36</td>\n",
+              "      <td>gamma-delta T cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell4</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell5</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1996</th>\n",
+              "      <td>HSC/MPP</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1997</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>27</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1998</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>28</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1999</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>27</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell2000</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2000 rows × 3 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                       predicted_labels over_clustering  \\\n",
+              "cell1                      Plasma cells              44   \n",
+              "cell2                      Plasma cells              12   \n",
+              "cell3                      Plasma cells              36   \n",
+              "cell4                      Plasma cells               1   \n",
+              "cell5                      Plasma cells               1   \n",
+              "...                                 ...             ...   \n",
+              "cell1996                        HSC/MPP               9   \n",
+              "cell1997  Neutrophil-myeloid progenitor              27   \n",
+              "cell1998  Neutrophil-myeloid progenitor              28   \n",
+              "cell1999  Neutrophil-myeloid progenitor              27   \n",
+              "cell2000  Neutrophil-myeloid progenitor               9   \n",
+              "\n",
+              "                        majority_voting  \n",
+              "cell1                      Plasma cells  \n",
+              "cell2                      Plasma cells  \n",
+              "cell3               gamma-delta T cells  \n",
+              "cell4                      Plasma cells  \n",
+              "cell5                      Plasma cells  \n",
+              "...                                 ...  \n",
+              "cell1996  Neutrophil-myeloid progenitor  \n",
+              "cell1997  Neutrophil-myeloid progenitor  \n",
+              "cell1998  Neutrophil-myeloid progenitor  \n",
+              "cell1999  Neutrophil-myeloid progenitor  \n",
+              "cell2000  Neutrophil-myeloid progenitor  \n",
+              "\n",
+              "[2000 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "predictions.predicted_labels"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "separated-first",
+      "metadata": {
+        "id": "separated-first"
+      },
+      "source": [
+        "Transform the prediction result into an `AnnData`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "sharing-router",
+      "metadata": {
+        "id": "sharing-router"
+      },
+      "outputs": [],
+      "source": [
+        "# Get an `AnnData` with predicted labels embedded into the cell metadata columns.\n",
+        "adata = predictions.to_adata()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "varying-characteristic",
+      "metadata": {
+        "id": "varying-characteristic"
+      },
+      "source": [
+        "Compared to `adata_2000`, the new `adata` has additional prediction information in `adata.obs` (`predicted_labels`, `over_clustering`, `majority_voting` and `conf_score`). Of note, all these columns can be prefixed with a specific string by setting `prefix` in [to_adata](https://celltypist.readthedocs.io/en/latest/celltypist.classifier.AnnotationResult.html#celltypist.classifier.AnnotationResult.to_adata)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "virgin-export",
+      "metadata": {
+        "tags": [],
+        "id": "virgin-export",
+        "outputId": "5cf13a18-35bc-4b69-e1a8-540b5a5fd873"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>cell_type</th>\n",
+              "      <th>predicted_labels</th>\n",
+              "      <th>over_clustering</th>\n",
+              "      <th>majority_voting</th>\n",
+              "      <th>conf_score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>cell1</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>44</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>0.999762</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell2</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>12</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>0.999926</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell3</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>36</td>\n",
+              "      <td>gamma-delta T cells</td>\n",
+              "      <td>0.955991</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell4</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>0.999883</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell5</th>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Plasma cells</td>\n",
+              "      <td>0.999890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1996</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>HSC/MPP</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>0.152962</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1997</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>27</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>0.810408</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1998</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>28</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>0.961021</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell1999</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>27</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>0.131777</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cell2000</th>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Neutrophil-myeloid progenitor</td>\n",
+              "      <td>0.985607</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2000 rows × 5 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              cell_type               predicted_labels  \\\n",
+              "cell1                      Plasma cells                   Plasma cells   \n",
+              "cell2                      Plasma cells                   Plasma cells   \n",
+              "cell3                      Plasma cells                   Plasma cells   \n",
+              "cell4                      Plasma cells                   Plasma cells   \n",
+              "cell5                      Plasma cells                   Plasma cells   \n",
+              "...                                 ...                            ...   \n",
+              "cell1996  Neutrophil-myeloid progenitor                        HSC/MPP   \n",
+              "cell1997  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
+              "cell1998  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
+              "cell1999  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
+              "cell2000  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
+              "\n",
+              "         over_clustering                majority_voting  conf_score  \n",
+              "cell1                 44                   Plasma cells    0.999762  \n",
+              "cell2                 12                   Plasma cells    0.999926  \n",
+              "cell3                 36            gamma-delta T cells    0.955991  \n",
+              "cell4                  1                   Plasma cells    0.999883  \n",
+              "cell5                  1                   Plasma cells    0.999890  \n",
+              "...                  ...                            ...         ...  \n",
+              "cell1996               9  Neutrophil-myeloid progenitor    0.152962  \n",
+              "cell1997              27  Neutrophil-myeloid progenitor    0.810408  \n",
+              "cell1998              28  Neutrophil-myeloid progenitor    0.961021  \n",
+              "cell1999              27  Neutrophil-myeloid progenitor    0.131777  \n",
+              "cell2000               9  Neutrophil-myeloid progenitor    0.985607  \n",
+              "\n",
+              "[2000 rows x 5 columns]"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "adata.obs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "knowing-steps",
+      "metadata": {
+        "id": "knowing-steps"
+      },
+      "source": [
+        "In addition to this meta information added, the neighborhood graph constructed during over-clustering is also stored in the `adata`\n",
+        "(If a pre-calculated neighborhood graph is already present in the `AnnData`, this graph construction step will be skipped).  \n",
+        "This graph can be used to derive the cell embeddings, such as the UMAP coordinates."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "decimal-sweden",
+      "metadata": {
+        "id": "decimal-sweden"
+      },
+      "outputs": [],
+      "source": [
+        "# If the UMAP or any cell embeddings are already available in the `AnnData`, skip this command.\n",
+        "sc.tl.umap(adata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "effective-terrain",
+      "metadata": {
+        "id": "effective-terrain"
+      },
+      "source": [
+        "Visualise the prediction results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "tough-insider",
+      "metadata": {
+        "tags": [],
+        "id": "tough-insider",
+        "outputId": "222b6b0a-e241-4a08-cdbe-7a88116fe7f4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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IIcodS0IKp//3FeZLsSh3F9ApsF35YUneAmwYD53h3Ovf4zewK8FP9CZp9U703p54392i5CouhBCiXIn69lviJ/8MOh06D4+rPt8cFkZiWBhJixdRZ+NGMvbtIzMsDK/u3XEKCiqBGgshyookssopTdP4fMfnjp5XSaakay7LqlnpPre743cn5cTsvrOp7Vv7uusphBCifIue9i/mS7EAaOmZ11VW/N//ET97jSMRltq/E5Vfe/S66yiEEKJ8sxqN9iQWgM2GLSXlmsvS0tI50boNZI06ifm/76m9YgV6n6t/8C+EuDnJ0MKrFB4ejlIqz4+Pj0+R5ymlaNy4MQDjxo1DKcXcuXPz7bsSs83M1oitTDs8jcWnF2O02od3RP0TxaGhh0jaee3JrGxnPj3D3if3UsevDu4e7rRp04ZNmzZdd7mQ89n16dMHgKFDh6KUYteuXTekfCGEuFWUZawBMEXFEzt3LfFLNpO84wgAP4bvoMn6H1kZc/raLiqbTeOpfQtosv5HqowciIerG21atZJYI4QQZaAs440tI4OU1auJ+30aibNnO7ZPjI2h4fFjrEhJvraLAkcS68lzZ6m/YzsGXx88PTykbSPELUJ6ZF2jFi1aMGrUKACcnZ2vq6yZM2cWGTAyrZk8uexJDscdvq73yk2zaih9watJVRpaCWuald1zdzNixAgOH75x7yuEEKJ4SjvWAMQv3ULElzNyNlznooMWzYahkIVFxtbtTJI5g+/2bGPEs89y+OjR63szIYQQ16S0440lLo6whwbkzH91A1a4tWgahkLKGR8cQpLNyre7pW0jxK1AemRdo8DAQLp160a3bt3o2rUrAIcPH6Zr1654eXlRvXp1PvzwQzSt6HlEBg0axBtvvAFAcnIyzz33HJUqVcLd3Z3HH38cgLqt6zKr7ywsKRYsKRYODT3EmU/PFFje6Q9Oc+S5IxwefphTY0+RdjwNgNSjqRwaeojwb8I5Pf40pz8s/Km6R30PPBt7ggEsFkuBx5w4cYLevXvj4+ODr68vEyfaV03cunUrHTp0wNPTk7p16zJz5swiP4Po6Gi6du2Kp6cnFSpUoF27dsTExBR5nhBC3MpKO9Z0at8R/z53kGA2kmA20mT9jzy1d0GB5T22Zx7tN/1Cm40/88juOexOjABgZ+JFmqz/kecPLmHQnrk8tmdeoXVq7V2Jjr5VcVJ6TKnpBR4jsUYIIUpeacYbzWbjzoaNqLt+HQkWCwkWCw2PHeXJc2cLLG/g2XDanDxByxPHGRAexq50e7zYkZ5Gw+PHGHHhPI+eDWfQ2YLPB2jj7k5Hdw+clJK2jRC3AOmRdY1WrlxJYGAgAHfffTerVq2iX79+REVF8cknn7Bq1Sref/99KleuzLBhw4pd7quvvsrUqVN5/PHH6dy5M/uP76fvP32JNcYWuwzPRp743u2LNc1K3Mo4Lk65SN3Pc5Y+TzucRtADQTj5ORVaxsnRJx2v33vvvXz7LRYLffv25cyZM4wZM4bg4GBcXFyIj4+nT58+BAUF8c4777B27VqGDBlCgwYNrvhkZsaMGaxZs4YxY8YQGhrKrl27sFqtxb5mIYS4FZVWrDl79iyXvp+D8XjhjYDLtfetwoMVG5BkzmTGxQO8f2ItS9s+5ti/LeECL4S2IcTFq9Ay+u78y/H67Zdfy7dfYo0QQpSO0oo3p7Zt42Snu7FdxbDBjh4eDPD2IclqZXpiAu9GXmJ5zVqO/VvT0ngxIJCKToU3bXuH5XQAkLaNEOWfJLKuUbt27fjoo48A8PX15fjx45w5c4bBgwfz8ssv06tXL5YsWcKyZcuu6ma/ePFigoKCmDZtGjqdjlfWvEL4+fBin2/NsGI8ayRmaQzYcrbbTDm/eDX3IrBP4BXLqfZiNTpX6cyyb5Yxbtw4Bg4ciMGQ8+dy/PhxTpw4wYABAxg/PmeZ26VLlxIfH098fDxjxoxxbF+zZg0PPvhgoe9Xp04dANavX4/VamXgwIGEhIQU+7qFEOJWVFqxJnXvCcJf+w6KuRhhutXM0dQYfju3B2uukzKsOU+57/avzjPVWl2xnG8b3oshNIQPti7mk8kTeeK1FyXWCCFEGSiteHNm4SIyY4v/gD7NZuNIRga/pMWROw2UYctp23T29GS4v/8Vy/kutAYeXe5hzJIl0rYR4hYgQwuvUUBAgKP7batWOV/U1Q0Y352bLmteEaWzl6tZNazphWfzk7YkkXoglQqtK1B9ZHVcQ13t55lzGhoGn6Lzl1899RXT3prGPZ3v4fTp05w4caJY9c3ubvzEE0+watUqx0+/fv2ueF6fPn3Ytm0bPXv2ZNOmTXTp0oXVq1cX6z2FEOJWVVqxJjvG6LPKtWoaKRZToccviTrOxvhzdAusyY+N76Ohp/3hiEnLiU+BzkUvnd6xdVteWDKVe7p3k1gjhBBlqLTiDTp720aXVa4FSM6VlLrc4uQkNqSl0d3Li0mVq9DIxd62MeUa4hhkKLpt88Cf03n6t9/o3LmzxBshbgHSI+sGqVevHrVq1WLhwoV8//33jhtV7969r6qcvn37MnXqVJ588kk6d+5M0qEkvNt64xRgHwaYsD6BzEuFL3+uZT0Z1zI1Mi9mknnh6pZK93DyIJ10EncnMmnHJFatWoWzszOVKlXKc1y9evWoW7cuCxYsYNy4cQQHB+Ps7MwDDzyAn58fy5cvp02bNlgsFpYsWcJ7771H9erVC33fuXPnsn//fmrXrk2jRo3YvHkzERERV1V3IYS41V0p1oSHhwP2OU3WrVvnOOfhhx/myy+/zFPO5bEm7PhJPl7/I14G+wS/cyIOE25MLLQe2c2HDKuF0+nxnEyLy3fMf7Fn+DviEABuOgM1PXwZVetOWnpXdByz/86q7Jk8+apjTXYj6++//6ZNmzb88ccf7Ny5k0mTJnHvvfcWWm+JNUIIUTwl0ba5u1MnTri5MszFhcpO9rbNnMREwkyFPzjJDjgZNhunTJmcMF1d20bv6wtGI8uPHMF68KC0bYS4RUgi6wZxcnJi4cKFvPTSS4wZMwZfX18++OADhg4delXlTJgwAScnJxYtWsSsObNwb+FO1eZVCegVQPrJdOJWxeFzh0+h5/t08CF5d7J9gnc9uNdzJ+1wWrHfP8g9iBhiGD58OE5OTtSqVYvx48fnGwNuMBhYtGgRr776KhMmTEApxQcffICfnx9LlizhjTfeYPTo0bi5udGhQwdCQ0OvODmku7s7c+fOJTw8HFdXVx599FEGDBhQ7HoLIcTt4Eqx5myuSW4/+eQTOnbsWGg5uWPNvLlz6eIXyhcNupNmMTHtwn7+uniQfiH1Cj2/T1BdVsecYVdSBHqlo5V3JbYlXijwWMfKhGHb+ODEOha0GYTOzQWSuOZYk32ch4cHo0ePdkzce3nD5HISa4QQonhKom0zd+ZMurp7oFWqxFO+fuw1GvkzMYH+FSoUen5f7wqsSk1hp9GIXilau7mxNb3gxUHyUQpDcDBEREjbRohbjCrOyhNXq3Xr1tquXbtueLm3E6PFSNsZbUv1PX/r8RttK5buewpxO1FK7dY0rXVZ1+NWIvEmR3h4ODVq1KBChQokJyezc+dODh06xFNPPcWXX37JG2+8wauvvsqMGTNITk6mcuXKvPXWW/RJ8iTh3y00Wf8jtd39+L35/dyz9Xfu8KvG9417Y9M0umz9HX9nd+a1fpT1ceH8X9h2zhuTqOrmzRu1OtLBt2pORfQK7m3DU798zq5TR1ncZjBGq5nH986nkqsX617/nCpvP4He3dVxyokTJ3j11VfZsmULSik+/PBDXnzxRbZu3crIkSM5ePAglSpVYvz48QwaNMhxrffddx9Llixh6NChTJs2jZ07d1KtWjUGDRrE9u3b0el0NGjQgCVLljgmMRbidiDx5saSWHP9Mo4fJ6z//aX2fsrDg9AZf+Jav36pvacQt5uyjDUyR9ZNylXvSrB7cKm9X9dqXSWJJYQQt4A777yTxo0b88knn+Tb16BBAz7++GO++uorgoODeeGFF4hyzftAy9vJlbv8qrMl/jxpFhO7kyKIMxvpE1yX8PREXju8HFedgeHVW+Gs0/Pq4eXEZObq+fvAneieuhcV5APYVyZ8ZM8cTJqV52q3I+TZ/nmSWNkrRa1atYpXXnmFjz/+GDc3N8dKUYmJibzzzjuEhoYyZMgQ9u3bd8Xrz14p6pVXXuHrr7+mefPmslKUEEKUMUNgIDp391J7P9+Bj0oSS4hbmAwtvEkppfilxy/8fvh3vJy8qOxZmRXhK9gXsw+rdmO+kCsU9f3qM7j+YO6rdd8NKVMIIUTZUkoxevRohgwZ4lg1KduZM2eYOHEi6bmGZUTV8qP1s/1h/Y8YAr3x6dGWfpmXWLMtjHVx4exPjkKHondQHdbEhmHWbBxIieJASpSjjP3JkXQLtC+FrlrVzfOe3zbuiebqzCfH1/Nz8nFer5R3ZSlZKUoIIW59Bj8/qv76K4lz5mCoWBG9pydJy5eTeeDAjXsTJyfcmjXD78knqNC9+40rVwhx05FE1k2shncNxnfM+VI/qMEgxm4Zy/yT8x3bGgc05lDsoasu28/Fj1l9ZxHiIV/uhRDiVjNw4EDef/99Jk2a5Nh27NgxvvjiC5o3b87YsWNZvHgxU6ZMIdNsIvCx3vA46D3dqTLmSZ5+pg/vVVrKypjTHEqJpm1QdUIq+KHFhgHwVNUWdPCt4ii7pruv47W24QCqds5cVfeNfoE6j/Vh26OPMnv2bE6cOEHDhg2LvIbcK0UNGTLEsT00NPSK52WvFLVq1SqWL1/O559/zqpVq+jWrVuR7ymEEKLkuLdsgXvLFo7f/Yc9RfjjQzDmGrbp0rAhmUeOXHXZTrVrUXPWLHQeRa+YK4Qo/2RoYTnzXvv36FK1C36ufjxc52F6Vu95TeV4u3rj7+pf9IFCCCHKHb1ez6hRo0hOTnZsy04MGY1GoqKirrgMuGeQP/179GJtXBjRpjSGvvYimslMR9+qOCkd/8We4WJGCsdSY/kubBsWLdfS6ct2YnvrF7Rwe4+tf+PCmTSp+Kvg/vTTT/z222907NjRsVLUsWPHOHToEJ999hkXL1684rXPnTuXJUuWULVqVRo1agQgK0UJIcRNqupPP+LWqhX6wAACX30F9+bNr6kc54oVJYklxG1EElnljEFn4Lsu37H+0fW83/F9eoT2INDNPoFtVa+qDKw3sFjlhCWFsfHixpKsqhBCiDI0dOjQPEmjBg0a8NprrxEREcGvv/5Kr169rnj+U2+8jAa4uroy6H/P4tf/LkLdfZjQuBde/r58fmoT0y/sp6qrNxUMLnlPPhMJaRkAvPrKS7z88ssEBwczffr0QleK6tatGxMmTGDMmDGkp6c7VoqqXbs2o0eP5uOPP8bd3b3IHlnZK0U999xzzJ49W1aKEkKIm5jey4vQGX9Sd+NGAp57Du8H7nckpFybNsWrZ/Ee2qdt3ETGiRMlWVUhxE1EVi28BaSYUjibfJaa3jX5af9P/H7492KdN73XdJoHNS/RugkhcsgqUjeexJvSlXH6Ijp3F5wCfTnc7eUij09+sCve3dvQqEHVIo8VQtw4Em9uLIk1pcsSF4c54hKu9epy/oUXSNu4qeiTnJyovXoVTsGlt1iWELc7WbVQXBcvZy8aBzTG3cmdZoHNijxep3SMbjNaklhCCCGuimutyjhXDEAZ9Oi8il59qoKbM3VqVSyFmgkhhLhVGPz9cWvSGOXsjFvTots2ys2Nyl99JUksIW4jMtn7LaZb9W6MajOKJWeWEOgWyPoL6x37vJy8MOgMjGk3hp41rm1uLSGEEAKg1uS3iPhyBhpgPBaOZjTZd+gVysUZzxb1qPrkveic5auGEEKIaxPw4gvYMjJI374dnZcX6Vu35uz08sTg5UXlzz/HvU2bsqukEKLUybfLW9CQhkMY0tC+wtOq8FUsPL2QnqE96VOrTxnXTAghxK3CpVIANb59BQBLcjqXvp+NMhio9PpAdAb5eiGEEOL6KaUIfvMNx+/xf/1F2sZN+D75BJ7t25dhzYQQZUm+ad7iuod2p3to97KuhhBCiFuYoYI7Vd8ZWtbVEEIIcYvzGzwYv8GDy7oaQogyJnNkCSGEEEIIIYQQQohyQRJZQgghhBBCCCGEEKJckESWEEIIIYQQQgghhCgXJJElhBBCCCGEEEIIIcoFSWQJIYQQQgghhBBCiHJBEllCCCGEEEIIIYQQolyQRJYQQgghhBBCCCGEKBckkSWEEEIIIYQQQgghygVJZAkhhBBCCCGEEEKIckESWUIIIYQQQgghhBCiXJBElhBCCCGEEEIIIYQoFySRJYQQQgghhBBCCCHKBUlkCSGEEEIIIYQQQohyQRJZQgghhBBCCCGEEKJckESWEEIIIYQQQgghhCgXJJElhBBCCCGEEEIIIcoFSWQJIYQQQgghhBBCiHJBEllCCCGEEEIIIYQQolyQRJYQQgghhBBCCCGEKBckkSWEEEIIIYQQQgghygVJZAkhhBBCCCGEEEKIckESWUIIIYQQQgghhBCiXJBElhBCCCGEEEIIIYQoFySRJYQQQgghhBBCCCHKBUlkCSGEEEIIIYQQQohyQRJZQgghhBBCCCGEEKJckESWEEIIIYQQQgghhCgXJJElhBBCCCGEEEIIIcoFSWQJUY4cj0zhVHRqWVdDCCHELcyUlk7SuYtYTeayrooQQohblaZBxF5IOFvWNRHlkKGsKyCEKJ435+xnzu4LAPRqHALA3XUDGdi2GgA2m8bWM3F4uhhoVtWnrKophBCiHEu+cIldk//AZrbg6udDhSoVcXJ1pda9nXH2cAcgPS6B9Ng4fKpXxeDqUsY1FkIIUS79fh+c3QzooPGDYDND04FQv7d9vyUTwjaCd2UIalCmVRU3H0lkCVEOHIlIciSxAJYdinT8Nyw2jbd7N+CNOfuZv/ciAF3qB/LNI83xcXcuk/oKIYQon/ZNm43NbAEgIz6RjPhEAGKPn6bDa8+SGhnDnl9nYLNYMbi50njQ/QTUrVWGNRZCCFHu7JuZlcQCsMGhufaXRxZB/++h2WD4oz+c2wooaPkk9PocnFzLqsbiJiNDC4UoBz5ffqzQfZM3nOGXDacdSSyANcdieODHLVhtWmlUTwghxC3AZrFgSil4+HpmUjL7ps3h3Oad2CxWACzGDPZN+Zu4E2dKs5pCCCHKu/WfF7JDg0WvwM7fspJYWdv2/A5/Dy6lyonyQBJZQpQD6ZmWK+7/+N9j6FTebWGxacSlZZZgrYQQQtxKzMYrx4zEsHPEHD6eb3v86fASqpEQQohbktVU+D7NCsvezL89fEPJ1UeUO5LIEuImM3f3BUbN3c/Kw/bhgzabRu0gryLPu7zzlbergQAPmbtECCFEfulxCRz9Zxmnlq/FarI3KDISE1F6/RXP02y2fNsC6tUukToKIYQo52w22DIRFr4I57bZt5mN4F/n6svyk2HsIofMkSXETeBCQjrJRguXkoy8MWc/YE9o/d/AFmw5E8fMneevuswHWlRCd3k3LSGEELctq8lMWnQMbv5+7P3tL4xZ819lJCYR2KgeR+YsRrNar65QpfCtWe3GV1YIIUT5FXvS/t+Tq2DlO/bXh+bDw9Ng91QIW3f1Zbb/3w2rnij/JJElRBn5deMZ/t55HjSNM7Fp2DRoVsXbsd+mwYsz915z+c/eLU/IhRDidmdMTOLQX/9gTjc6fpw9PTClpjmOiT50jMh9h6+pfL86NW5UVYUQQpRnK96BkytB0yAuK5FVuVXOfnMa/DXg2spWemj0wPXXUdwyJJElRBnYcCKGj5Yezbd9/4Ukgr1ciErJxM1Jj9F8lU/GATcnHZMeb0VlH7cbUVUhhBDl2M6JU/MkrQBMqWlUqFaZ5HMXQSnH5O1XK7BRXZoNefhGVFMIIUR5tuV72Dox//aEcHCpAJnJoHcB6zXM3+vmD0OXgGuF666muHVIIkuIMrDpZGyB210MOt7uVY8ZO86TYbZy8GLyVZWrgO1julHBzSnfvgyzlQsJ6VTxdcfV6cpzoAghhLg1XJ7EyuZXuwbOnh5Y0o0knrton8fkKlS9sy31+nQvcF96XAJ6JwMuFYqe31EIIcQt4OSqgrfrneHOkXBsCRgTIKbwldgLZHCDtwpZGTcjGVIiwb8W6KRtc7uRRJYQZaBJ5YKfKGRabLw2+wDZ87Zn984qLg3IsFipgBOZFisRiRl4uxkwmmwM/Hkr5xOM1AnyZO5zHfF2z5/sEkIIcWtRTgY0c/6Vb8+u2+KYuF3v6oI14+qekut0OesFZSQmo3QKnZMT4eu2cHb9VpROR6NH+hHSvNH1XYAQQoibX8XmELY+//aUSzlzZAG4+kBGYvHL1XL1GM5MhdQocPOD5Avwe1/ISIAaneDx+aCXts3tRBJZQpSBzvWD0AEFPf/OvfhgQK5ElqeLgdUjO3Ex0cjOsHj+3nmeyj5u1A7yZOaOc5isGkM7VifIy5WYlEx6TlhPXJo5X/kno1MZMmU7D7eqwpAOoSVxeUIIIW4S/rVrEHv0ZL7tuVcftGZmonQ6x7Y693XDv25NjPGJnN2wFVNKGiEtGnNx5z4yE5Nx9vKk6h1tADjyzzIitu8psPxjC5YRfzqcWj3uxsXLs4SuUAghRJlr8zRs+a7o4ypUyklk+VSH4esg5igcXw5HF0FwY3svrMPzQbNB9w/sx0YdhV+7gDk9f5lhG2D6A9DmGWh0/w26IHGzk0SWEGXAy9WJu+oGsv5EDGAfEpidwKoX7MXxqBSCK7jw5YCmzN19kYuJ6fQKNVDRxx0AFxcXAgMDqdypE0M/+ghnQyi/bAxj0k8/8cMLy4i9dB69WwXcarfF/94XsSRHE7voSzIjT4LVQlr/0Ry4cCdnYtPoUNOfBhUrsDM8nmZVfagVKI0NIYS4VVS7o02BiSxnL0/MRiOaxUqtHp3xCArg0u4DJCoroZ3aAzmxplOnTnz0UE+aN6jD7l9mMGf9aob+8CUXoqPwcnOjQ+36vHpvf6KTE/l40WxORl7EbLXyXv+BdMrIJCUiiirtWuBfrxbJ5yNQOh0BDeqglKysK4QQtwTf6uBXG+JPZW3I1brxqwXxpyGwATzyB2z+DkyphNcYQg0PfyB3vIGPPnibGtYMOLqYHz57j4m7xnAmMhF/N0W/egYm9XHjXJKNQfOM7IqwYrLCnIfXMODsZvvQxeodwT0AIg/Ye2tVqFQmH4koWZLIEqKMTHi0ORNWn8BotjKiUy2ORabg6+5Ex9oBxKRk4u3mhLNBx/uV7CsZhoeHA9CiRQtefvll1q5dyx9//MF/a9bgNOArUvYsJWnLTAy+lfC952k0iwnjia0AaBYzBp8QlMGFjLP7HHWYujmcqZvDHb/rgPf7NmToHbIKlRBC3Ar8ateg4cN9iTtxBv+6NfEMDiQ9Nh7/ujXRGfTYLFac3O2LgwQ1qldorFmzZg2zP/qS31Yu5c8ta6ns68/we3pispjZfOIIACaLhYo+vrganNhz9rSjDikXL3F0/qU89fKuVplmTz6Cs4d76XwQQgghStaQebDhS3Byh/YvwMVd4FsDKrWAtBjwCLDPZdU/a1L4wuLN6pXsfyKDH3aY+GBDMnX8dHzdwxWjWeOfY/ah8pkWjZq+OtydYPWZrOGHmg3WfZq3TnpXeHgK1L+vlD4EUVokkSVEGfH1cGZ8/8aO32sF5fSECvRyKfS8WJsHn54KxlzpEap2iOf81iUEHFhO8o55oDcQPPAjDBWCAKjQ1r5MrZNfZQL6vE7iphl5ElmXswHjFh/BpsGwOyWZJYQQt4JKrZpSqVVTx+8VqlR0vNY7F3yOr6s7NS8mUaVSPeJatGXp3h38tXQxs3dsxEmv54uBTxFUwQeAAW3vAKCKXwCj+zzMH5v+y5PIKkjSuYvs+GEqHUc+h84gk/QKIUS55xsK/X/I+d0vNOe1V3Chp1WyXWDo+TcYWtOGyz01+GVtGD/tcuWLLSac9bD6CXeqedvnZXy9oz1o1fHXM/0BN8aty8hJZBXEmgF/D4ZBf0O9XtdxceJmI4ksUW5omsbFDBOn001Ud3cm1K3wZM+tIDY1k1f+3kt4bBotq/uyY/9xx/Ygi30ek4yQpsASUsIPoFlMOAXVcCSxAJTSFVR0kWbvOi+JLCHEbctqtZKeno6maXh5ed3yQ+DObtjG+S27cPWtQERcHABp0bGYUlIBaB1am6V7d7Bt905MFgs1g0IcSSwA3TXGmoz4RNLj4vEMDrzuaxBCiPLGqmmEp2dyIdNEPQ83Qlxu8cnKY0/C/OH21QsrtYB9G+zb0xPAau+d2yswkl+A9eFmMizQLFjnSGIB6K41Hu+dIYmsW4wkskS5EGuycPf2I8RlJXAMCv5sWpPOfgWv/ncr+G71STafsjcoLiZewpJkzH+QdvmLG9PYikzOuCHlCCFEeXPmzBmio6Mdv/v6+lKvXr0yrFHJSouO5eS//wGQkZhEclJCvmM0zR5jciLNjUvsGRMSJZElhLjtHEtNp9euExizbqyeeh2LW9ahgadb2VasJC0bBRFZi4MkhEFa/mWvtMv+e8OeIyWevUEFiZvFtT1CE6IUWWwabbcediSxACwafHAqogxrVfIsNq3IY4xh9mDgWrUJyuCMOe4cluRYx35NK2hdxKJ5ukiOWwhx+zl79myeJBZAQkICJpOpjGpU8rKTVFeyO8w+WXyzqjVwNhg4FxdNTHKSY7/tGmMNgLuf7zWfK4QQ5VFsppkuO3OSWACpVhtfh0WWXaVKg81S5CErTtmP6VzdgKsBjsTYuJCcE2NsxYhZBXLxurbzxE1LWqviprIvOZ0vwy7hrFM8VzWIdfEp/HohhvQCkjrH0m7tXkOvdK3DnF3n8yW0rKnxpB5cjSH6KKkHVqL38MWrRS9AI2nzTKJmvUuFVn3QLGasJ7by/ePfE2tK55ujqzBF2ecsyTy7H1tmGl7N7i3wvQe3q1bSlyeEEGXGarVy4cIF0tPT8fLywsfHh9OnT2M0FtDzFXsyKzi48Pk9yjPP4ED869Ui7njeOa3iUpNZcXAPBy+cZeXBPfh5eNKnRVs0NKZvXstbs6bSv1V7zBYrm04eYe53P6O5uTJjxjRORtkndj8QdYHUzAx6N2td4Hsb3FzxCAoo8WsUQoiy8l9sEpMvxBDgZODFakH8fCGWBdEJFJT+35yYWur1K1X3fgqT7si3OSLFxu/7TKyPrsC0vcmEeCqea+2Ehsb49Sa6/ZHOS22dybRqzD9qYdMwD1JNGn8fMrPnkv2T/O+MhcQMjWdaFjLxY/sXSvLKRBmQRJa4KZhtGlMvxvB+rl5Wy2KTr3iOm+7WnrMkxNuVqr5uhMWl59luijpN4sofqBgSTMce/Tlboy96D1987nwMnVsFUvYsJf6/X9G5ejKg/l10wYnzxnQuLv8/Rxkp+5YBFJjIqh3kwXOdapXsxQkhRBlJT0/n6NGjmM1mAJKSkrhw4cIVz9Hrb+3JyEOaN86XyDoVdYnvViwkKDiYQYMG0TcwFF9Xd564sysV3NxZtGcHk/5bhqerK93vvJsqrVty9vw5Pp4xxVHGwm2bAApOZClFk8EPlOh1CSFEWUmzWPk2PIqJ53N6+c6PTrziOcHOt3jTPKSxfUVDc962zd5IGyP+NRMU7Mzgzg35qOk5gj11jOvsir+bjh93mXhtRQa+bop+9e1zJMemazy7OKdTw6TdZthtLjiRVfMeaCCrFt5qVHG6lF+t1q1ba7t27brh5Ypb07bEVJ4/cpZLmeYijw0w6FEKXHQ6JjSoxp2+t3Y30UE/b2Prmbh82xtXqsA99YOYuOYUhf0frAPW4oU+ay6Tw1gYQXohR9sFeDqzYdQ9uN/qgbSMKKV2a5pWcNcEcU0k3ojiMpvNhIeHExeX/55aECcnJzRNw9fXl5o1a97SE76nXIpi+3e/5tvu5OFOcNMGxJ04gzEu/9xZ2Wp0upPgxo0cvx+cO5+06JgrvmfLZx/Dr1boNddZXJnEmxtLYo24GouiEnjzxAWSLFdYTS9LRScDmZqGv7OBnxuF3tpzZAF80wiSL3t4pPRQrQNUbALbfrqx7xfcFEasA92t/UCqrJRlrJHWqihTcyPjefHouWIdu6VdfWq6u5ZwjW4uXz7clLu+WMvl+eZDEckcvZRcaBILwAZMIZNncSUTjT/IP8eLs16HyZrTufnzh5pKEksIccuxWCwcPHiwWHNd+fn5Ubdu3VKo1c3Dq2IwnpWCSY2IyrPdnJbOha27izz/0oGD+NYIxdnDg/gzYQUmsfTOzlizPn+f0KqSxBJC3JK+Dovky/Ci57rSA2GdmuKsv82mrO73Pfx5WW9czQpnN9l/rpfBDSxZ0wQoAwz4VZJYtyhpsYpStzg6kSiTmQeDfVkZd+Xhg9naVXC/7ZJYAFV83Vn8wp18suwoHs56wuLSORVtHz9f0FzwCvIkt6ZhYiFmTGj5+mLdXTeAbx5pTqbFxpbTcTSqVIEGFW/dVSCFELcXo9FIYmIiHh4eKKWKPWF79erVS7hmN6d2Lz7NsUUrSLkUhbufL5F7Dxb73IzEJPb+ORODiwvm9MuijVK0ef5JKlStRMzh42iaRlCjW3cVSCHE7cVi05gTFY/JpvFIiB8r4pKKPgkYWjng9ktiAdTuAoNnwabvwKcKHPsXTNlzg13ekrlKLYZAz88g+SJc2AWhd4Bv6A2otLgZSSJLlKqJZ6P46Ix9EtgPT0XgaSjeDTzebMFks+Gsu/1u+I2rePPXs+0B2HYmjhf/2ku6yUK6KX93ZU8XHekmG9ZcMSAxKyC4GhQZlpwdZquGv6d9nPmAVlVK8AqEEKJ0mUwmDh06hNVqv08aDMX/umM0GnFxcSmpqt20lE7R4P6eANgsFixGI3Enz6B0Omzm/CtN6V2csWbmJAc1q9WRxFIGPZpjSI2Gi7cXSimCGtcv8esQQojSNPrEBf68ZB+y/sGpi8Vuq4QbM0uyWje3uj3tPwAH58KS1+yvMwvo4OAeAOnxUND0+E6eYM41Qb7eGVw8IbCe/Ufc0m6/rIAoU1tyrcaRqWnEmYseOw5w0mgizHjrLn9eXO1r+rPr3W40r+Jd4P6UzLxJrNxyJ7HgOpavFUKIm1x6erojiQX2oYXFFR8fXxJVKld0BgPNhz5Ku5efKTCJBeRJYl1Oyz0vjAaWjNu4wSaEuKXlbtuk2TQSijEvFsB/8SmYbAWtXXibaTIA3j4Prr4F70+PpcAkFuRNYgHYih/rRfkniSxRqnoGFJyAKYqHTlHFxekG16Z8stk0tpy5/obWzrB4Dl8sXvdnIYQoTzw8PK6qF1ZuXl639iIiV+PYguU3pJwzqzfekHKEEOJmc2/AtU3LUdPN5bYcaVKguDBIOnv95RyYBSnRRR8nbgnyf48oVU9UDqDXNSSzpjSpgYdBJuqDG9eTyqpBn4mb+G3TmRtSnhBC3CycnJyuacJ2Dw8PAgMDS6BG5ZPVXPRqwsURffAo2/7vV6yF9O4SQojyamztyjTxvPp5fOc3r1kCtSmnrDeo164lA75tBMeW3pjyxE1NElmi1P1fg2pUdSnek3Ifg46v61Xhbj+ZhDybQa/j4Rs0p5WmwYdLjrJw38UbUp4QQtwsKlSoQOXKlYt9vLe3Nw0aNCjBGpU/jQf2B6Xybrz892JKjYhi6zeTbkCthBDi5jKtSQ0q6Ip3bwxyMvBX05qEuN5+czEWKqg+VG2fd5vuGkfi2Ezw92C4tP/66yVuapLIEqUqxmTm4X2nOJ9Z9FNZBWxo24DHKgWUfMXKmS8fbsZTHUPR9AoNcNYrXPT5A2gVXzfqBnkWWd7Kw1FFHiOEEOXJxYsXuXixeEn6oKAgGjRocM3DEW9VHgH+dBg5AptNw5Y174uTh3uBxwY2rgdFDJPJSJDh7EKIW0tYeiZ995wkuaDlxC/jqlNs79CQLv7ygD6fp1dAnXtzfje4UGCqIrgJVCjGA/2Tq29Y1cTNSb6xiVJjstnos/skZzOKN2m7Bky9GMtbNSuWbMXKIYtNY3uAjsxulVCpZtgZizLlnwjxzXvr0b95ZZYfiiQ6JYM2ob4MnLyNpIy8icT7W1QqraoLIUSJu3TpEufPny/28bGxsdSsKcM8ChKx8SBHfliJZtOo3K0xfo3yNyAqVK1Es8cHkB4bT+zx01SoHMK5zTuJPng0z3GelYJLq9pCCFHiYkxmeuw6Toq1eJO2Z9g0Fsck8kiIXwnXrBzKSILIAzm/m1LzH6N00OcbqNwKDsy2D0n0qQF/PZx/eGKDviVbX1HmJJElSs2lTHOxk1jZfJ1kXqyCbExIYU+m/YateTphreKB4UxKnmM+f7Ap/Zvbh9X0bBzi2L79nW5sPhXLkYtJnI1Pp3+LytxVR+aEEULcOpKTC1jC+wqcnGQxkcJsGD0RzWJvpEVuPJYvkeVdvQrNn3wEAPcAP6oF2BtoPqFVSbkURfLFSBLCzuHmXYGa3e4q3coLIUQJOpqaUewkVjYfmfO3YAdmQ8qlwvcrPQxZAFXb2n9vPihn3xvH4exmuLAb0mKh7bMQePXzZIryRRJZotRUdnGmqacbB1KNRR7rpdfxSIgfT1WWBEtB/J0v+1/XlHep32furMGjbasWeK6rk56uDYLp2kCejAshbk2+vr4kJCQU61hPT09q1KhRwjUqv1x8veBsJAAGt7xzuji5u9FsyACc3N0KPNerYjBeFYOp3LpZiddTCCFKWxMvNyq6OHEps+iFMQKcDDxdJYAe17iC+y3Po4ipZO7/EWp2Knifmy/U72P/EbcNmSNLlBqDTvFPi9p8Wacywc4GDIXMieip17GhXX3G1q7EtsRUTqVnlG5Fy4GmXu6MDA7AI8WC14V0fGNzerp1rhfImN4yYbEQ4vYVFBREw4YNqVChAuoKk5NXrlyZxo0bo9PpSEpKQrtBq8LeSvrM/Iig1nXwrhNC7YF3OCZ71zk70fypgTh7epRxDYUQomz4OhlY2bou79WoiK9BT2F9rSq7OLG1fQOeqRLI+vgULl7lCJXbQqMHoOlA+9xYLl7glGuO39ZPQ7OBZVc3cVNSJfGlrXXr1tquXbtueLni1rIpIYUB+07n297Vz5M/mtZiwN5TbE1KA6CzrxczmtVEf42rJd0OIpMyiErOoHFlb/TFXDlFlC6l1G5N01qXdT1uJRJvRHHs27ePjIz8D0Xatm1LfHw8p06dAkCv19OgQQM8PYteJON2ZbNYSbkUhatPBVy85HO6WUm8ubEk1ojimBERx+vH88/P+Hr1IJ6rFsy9u45zxmhCAY9X8ufLegWPnhBZ4s9AZipUbFrWNRGFKMtYIz2yRJm509eLoZX8821fn5DKN+GRjiRWzKDezGpRG2dnZwIDA+nRowfr1q3Lc87q1au58847cXd3x8/Pj169ehEZGVkal3HTCPF2pVlVH0liCSHEZerUqYNen/9Z+dmzZ/OsbNivXz+8vLxwcnIqMN6kpqby4IMP4uLiglIKvV5Po0aNiIyMpHPnziilMBgM+Pr6ctdddzFv3jzHuefOneOOO+5wnDt37tw8ddmwYQNt2rTBxcWFkJAQvvvuu2JdW2hoKEopxo0bB0B4eDhKKfr0sQ+xGDduXJ73+7//+z+UUvTo0QOT6ep7BegMeryrVnIksbKvOzY2lnXr1qGU4sUXX7zqcoUQorwbWNGPLn5e+bZPuRjLR6cjOGO033OjB/Xmq/rV0BcSa0DaNgD41ZQkliiUJLJEmRpbuzJVXfJOsmvR4OvwqLwHOjnjOWo8dw56nM2bN9OtWzfWrFkD2G/09957L/v37+ftt9/mo48+QtM0oqIuK0MIIcRtycPDgypV8q+2FxUVhdGYd95GZ2dnxo8fz7PPPpsn3miaxh133ME///yD1WqlX79+dO/enaSkpDzx5scff+T111/n6NGjDBgwgD/++AOAzMxMatasSadO+ef4iIyMpFevXkRHR/P111/z5ptvotNd3Ve077//ntTUAlZ5yuXPP//k1VdfpX379vzzzz84Oztf1XsIIYQonF4pvq1fDffLHionWGxMi4jLe3BW26bfE0OlbSPEtdA07Yo/QAWgVgHbmxZ2TqtWrTQhimt/cpoW8NdSDdCcmrbUXO64R1Menpprt/u0oOXbNV1wRU25umnBa/ZqFdfs1SZNm6YBWpcuXTRN07Q777xTA7SZM2fmKddqtRb53nMvxWnddx7Thh8K05LMlhK5PiGyAbu0Iu65t/OPxBtR0vbs2aPNnz9fA7TmzZtrnTp10jw9PbWePXtq69ev10JCQjQ3Nzdt69at2pkzZ7QZM2Y44s3q1as1QAO0GTNmOMq0Wq2a1WrV7r77bg3QYmJiNE3TtM2bN2uAVrNmTS0zM1M7evSoduDAAe2tt97SAG3OnDmOMt577z0N0FatWqWlp6df1TVVr15dc3Jy0lxdXbUvv/xSCwsL0wDtvvvu0zRN08aOHasB2tChQzWDwaA1adJEi4+PL7CszMxMbfTo0Vq1atU0V1dX7a677tI0TdMSExO1p556SgsMDNT8/f21Z599VktLS9M0Tctz3WvXrtUA7YUXXnC8d1BQkObi4qLVqlUrz+cmSpbEG4k1ouzMiYgtdtum+tp92vTp029Y22bi2Sit245j2qhj5zST1VYi1ydEtrKMNVd83KeUegQ4BsxTSh1WSrXJtfv3a02eCZGbS66nzuZD+3Fq1AznFm3JWL0U45K8Qy9swCd+9tWl9u/fD8Du3bsB6NmzZ55ji3qaPelcNC8ePceBFCMLoxNpsPEgzx4Kx2S1cSY9E6PVRpzJwvr4FGJMRa9GIoS4dhJvRGlwcclZde/gwYM0adKEVq1asXz5chYsWJDn2KioKOrVqwfY4012rAHo3bu347VOpysw3nTs2BFvb2/OnDnDjh07SExMJC0tjdjYWAAyMjIwmUyYTCaOHDkCwIsvvoi7uzvVq1d3DDNJSEggNjaW2NhYzOaCY5GzszPDhg3jm2++ITMzs8Bjfv/9d/z9/VmxYgW+vr4FHvPZZ5/x2Wef0ahRIyZOnEjLli0BePXVV5k+fTpDhw7lmWee4bfffuP9998vsIxsCQkJjB8/ngYNGjBp0iQef/xxbLarW6ZeiBtNYo0oDRWcclYXL6ptk6FpfOx//W0bTdMYfeICH56O4GCqkWkRcdTYsJ/xpy5izGrbmG0aERkm1senkGKxFlqWEOWBoYj9Y4BWmqZdUkq1BaYrpcZomjYfkIl4xA1Rz8OVxyr68R3g3aQ5L73+Oj/t2EvmpjWY9u3Od7zRav8inL0S1ZVWpMrNZLPx8ZlLHE/NoHkFdyaczds91wosjklkfVwSyTZ7ltcZyMD+x94n0JsfG4biJHNQCVESJN6IEletWjXOnDkDQKtWrXj88ce5cOEC69evZ8+ePfmOz046FTfOZEtMTOTSpUtYrfaGgslkwmDI+5Xr3LlzjvfMHi5SrVo1nnjiCT799FNH3Vq0aMHZs2cBWLt2LZ07dy7wPUeNGsXPP//M1KlTC9yv0+mIjo5m9erVDBkypMBjFi9ejFKKWbNm4eWVM8/LkiVLsFgsfPnll45tK1euvOJn4OnpSUhICCdPnmTLli20bduWBx988IrnCFEKJNaIEtfVvwLd/SswE/Bv2px+L7zEogOHC23bxJkswNW3bZLMFsafjiAy00wddxd+vxibZ79Fg5/Ox/DHhRjSNHBSYNXsHQMMwNNVAxlfu/L1XKoQZaaoCRj0mqZdAtA0bQdwD/COUupl7N3rhbghXg0NAaCJlxsf1q3C4IoFPy0GyNy5BYCmTZvy8/lotNr1AfhrydI8xx1NSedoqn3ukwSzhf8dPsvk8zGsS0jJl8TKLdlm/9O2YU9igf2PfXFMEsMOhWHT5E9fiBIg8UaUOE9PTxo3bgzYezE1b978ig2G7du3A9C4ceM8vZiWL1/ueG0ymUhKSsoemkRSUhLHjh1j06ZNpKamUrlyZdzd3a9Yr5AQewzs3bs3Xbp0ITQ0lIiICDIyMpgxYwarVq1i1apVNGvWrNAyqlevzuDBg/npp58K3P/BBx/g7+/PsGHDWLJkSaHlFPZ5hISEOOqxatUqfvjhhytek5OTE/v37+fdd98F4LnnnmP48OFXPEeIUiCxRpQ4vVJ8Utc+L2MDTzemNK3J3b4ehR6fu23z3skL2LLaNgv+XZbnuH1JqYQb7b1uozLNDN5/hr8uxbMmPoWfL+RNYuWWlvWXbc5KYgFYgMnnY3j35IVruEIhyl5RPbJSlFK1NE07DZD19KIzsABoVLJVE7ejrVu38tz4D9m5zd546NipE+tPHkWzWjH+twync2GkzPkTpdfzwCuv8f6pCFyeGI5x1P946bnnmLlrH9WCgti9ajkxjw3HqVY9nq0cwILoRGLMluuu36q4ZJ45FMbAiv70CPC+7vKEEA4Sb0Sp2rp1K5988oljCEfr1q05fvw4VquVVatWERYWxt9//41er+fRRx+lcePG1K5dm1OnTvH4448zZcoUNE3j4MGDfPnll6Sl2Vfa/eWXX0hMTGT27NkADBs2DID09HRWr17N8ePHAdi1axepqan069eP3r17M3fuXObMmcP58+c5ceIEDRs2JDExkbZt2+Lk5FTAFeQ3evRopk+fXuC+evXqsWzZMu655x4eeeQRVq5cyZ133pnnmL59+7Jr1y4effRRBgwYwIEDB5gwYQJ9+vTh999/Z9GiRTRr1ozdu3ej0+m46667Cq1LSkoKo0aNokOHDrRu3Zq//vqLiIiIYl2HECVIYo0oVVu3buXjkWMx79kJQNM77mR3rraN/twZUubOQOn1tB3xAr9ciMX1ieFkjPofTw8fzu8791AzOJg1y/4lZcgIXGrV45XQYCadi8Zoy8m9XmsW9tcLsVhsGvcH+9Lex/MGXLEQpaOoRNbzXNbNVtO0FKVUT+CREquVuG3VbduO31etxbRvJ/49+jDvnVHUn/4b0WYTyZ+9j/L0xKlRMzwef5qFFetAqhGXlu3w+ewH0qZNYtNP36OcnHCq35gKvv4A/HKx8CcU1+Lf2GT+jU3m4zqVebpK4A0tW4jbmMQbUaqaN2/O+vXr2b17NwMGDGDs2LH8/fffmEwmxo8fj5eXF02aNGHYsGGOnlCTJ09m/PjxbNmyhVWrVqGUolq1avj5+Tnmf/rqq69wd3enZs2ajBo1yjEUMCkpiU8//dTx/v/88w8A/fr1o0GDBrz55ptMnTqVffv20aJFC9544w3Cw8OJioqiadOmxRpq0qBBAx544AHmz59f4P7WrVvzzz//cN9999G3b1/Wr19P06Y5S5uPHj0ao9HIjBkzWLNmDW3btgVgwoQJGAwGZs+ezZQpU6hbty5vvvnmFetiMBgICwtj4cKFGI1GGjRowEcffVTkNQhRwiTWiFLVvlFrNi5Zy5aze3iobW9mjB9DlRlTic3dtmlob9usqVofMkx52jbrf/yeDbnaNjbgu/AobuSMg79HxPF7RBwzmtakq3+FG1iyECVHaUUMk1JK3Q/UBg5qmraiOIW2bt1a27Vr1/XXTtw2wsPDqVGjBtXv7kLG2K8d29e1rUd9DzfGn7rIT+dj8pzT3MuNfSnGy4sqNT0DKvB7k5pl9v6i/FFK7dY0rXVZ1+NmJfFGlIbseNOxY0e+/toeb4KCgqhZsyaZmZns3bu3jGuYX8uWLXF2di7raohyROJN4STWiNKQHWu6Nb6Lqfd97Nhe+aM7UAYdww6G8W9sUp5zmnq6cSC17No2/6sayPsyZ5a4CmUZa4patfBH4DXAH/hQKfVeqdRK3LYCcq3yUdvdhVBX+wpTFV3yDqvQA35OBlp4upVm9fKo7e5aZu8txK1G4o0obblXf/Lx8QFAr9fnWxXKxcUFPz+/0qxaHkqpYg8tFEJcmcQaUdr0Hjn3b5da3iiDPcaEXNa2cVaKKq5O1HEvu4cWzStceT5HIW4mRQ0t7AQ00zTNqpRyBzYCH5Z8tcTtJjQ01DFR7rZE+0SG9wZ446q33+yHVQ7knNHEr1nDBK3AmviUQsvzNehJyFpWVgHDqwSwKi6FM1kTJFZ3ceJsZsHLmBdGkTP+3AA8XSXgqs4XQlyRxBtRKrLjjdlsJiEhATc3N8cKfQaDgfr163Py5EnMZnuMyMzMJDMzs9DydDqdY1ihi4sLvr6+REZGOsqzWK5vfsaQkJCrXjVRCFEoiTWiVORu2xiPxWMzWnBvnNN2eKdmRaJMZpbG2HtlmTSNf2OTCy3P30lPnNnettEreCs0hMkXYhzbfA06EixXN+Awd9vGS6+4V+b/FeVIUYksk6ZpVgBN09KVfJMSpaC9j2e+yQYNOsWwKoGORFZh/Ax6HgrxZXSNimxISCHWbOGBIF88DXrG1tZYHZeMm05HitXK04fCizUx4p2+Hvxf/eqEuDgxKzKeI6lGHgjypaKLDPMQ4gaSeCNKlZOTE0FBQfm2V6hQIV+vrIL4+PhQqVIlnJycSEpKwtPTE09Pe+wKDg4mPT0dDw8Pjh07RkZGRhGl2XteNWzYEHd3d0wmE1FRUbi4uDhWNBRC3BASa0Spc6ufv1evh0HPoyF+jkRWYaq4OPFoRT9eqhbMkphErBo8GOyLk07xbNUg1sQnU9HZiZ3JaYw9VbwFNR6r6MsbNSoS6OTEbxdjiMgw82TlAFyKEfuEuFkUlciqr5Q6kPVaAbWyfleApmla08JPFeLGijIV3YNqUas6jiF/vQJ98uzTKZVnpcGVrevyvyNnOZme90m7DviiblXizGaCXJx4NMQPXdb3nEEV/a/vIoQQhZF4I24aRfWi8vHxoX79+o7f3dzyDnN3c3NzbGvSpAnnzp0jOjqay+cl9fb2xtfXF6vVir+/P66uro7zQ0NDb8CVCCEuI7FG3DSiixgd4qQUK9vUwy9r6pUBIXkTYm56HfdltXdaentQz8OV146e59JlbSZXnWJC/aqEG03U8XB1nAMwomr+BzpClAdFJbIalEothCgGi+3K/adG1Qi5qnmrmni5s7FdAw6kpGO02rABmxNSuD/Il9oeMv+VEKVM4o24aeh0OqxWa4H7XF1dqVOnTrHL0uv11KhRgypVqpCWloarqytxcXE4OzsTGCgr3wpRyiTWiJuGdoX+gAr4tn5VRxKrODr7VWDvHY3YnpiKm15HRIaZw6lGnqjkT6CLzLUobi1X/D9D07SzBW1XSt0BDAZeKIlKCVGQu/y8cNUpMi5LaOmAP5qE0i3A55rKbeqVM7Fhh8uGNAohSofEG3Ez8fX1JTo6Ot/2ChUq0KBBg2uas8rJyckxqXzlyrIqlBBlQWKNuJn0C/ThzeMX8m13VoqVrepQ3+vaJl9vl9WeaeoFPQNl3itxayr2QFilVHOl1BdKqXDgI+BYidVKiCzRmWZ+Ph/N8qzx4w/n6lKb/cdrA6ZejC/9ygkhSoTEG1EW4uLiuHTpEmazmaCgoAKTVcnJyVec+F0IUX5IrBFlwRxrJGXjBTLPJOLtZODOAh6imzSNPyOlbSPElVwxkaWUqquUel8pdRSYCJwHlKZp92ia9n2p1FCUDKsFDs6FI4tAK86U56Uvw2qj396TvH8qgqGHwph6MZbP61bh10ahTGpYHUOuRoZZu7pVOoQQNxeJN6UvPDwcpRRKKVxdXalatSqPPfYYYWFh1122UorGjRsDYIqIJnX3YSyJ9pVmO3fujFKK2NgrL95R0saNG4dSirlz5xIREcE777zD559/zpEjR/Dw8CAhIYEOHTrwww8/5Dkve4XCa/HJJ58wYcIEx+/r1q1DKcWLL754zWWWpsvrL0R5I7Hm1pVuTmfR6UVsidhS1lUplDXZRPQP+0haGkbMLwfJOB7Pn01r8kODanxbv2qeY01FTKkixO2uqEG3x7AvS9tX07RTAEqp10q8VqLkzX8WDs+3v67SFp5ZVbb1KcClTDPhRpPj9y0JqTxVOYA+QT4AmDWNsacu4m3QM662DNMQopyTeFNGWrRowcsvv8zatWv5448/WLNmDfv37y9wRb+rlRF2kaip88GmgV5HxRcG3oAa3xgDBgygfv36tG/fnuTkZP744w+8vb0ZOHAgZrOZli1bMnPmTGrUqIGHhwdGo5Hg4GDc3a9tqAfYE0EBAQG8+uqrN+5CSlF5r78QSKy5Jdk0G0+veJpDcYcA6FOzD5/e9WkZ1yo/c2QamjFrMRENMsOS8a7nx0NZI06iMs38eD6aGq4uvFo9uAxrKsTNr6ihhQ8BkcBapdQvSqmu2OeeE+WV1QyrxsLhf3K2XdgB/30EZ7dCwjlIjoDreOJ8o1Rxdaaxp33VJwX0DKiQZ3/vAG9qurkQZjQx7uh54jecJ21v/lWhhBDlgsSbMlKpUiWGDh3KtGnTePbZZ4mMjGTy5MkAbNq0iXbt2uHp6Unt2rX5+eefgfw9iV588UWUUqxbt85RrindyBOPDKTpl28x9O9JJKWmcmnyHLSs1ZSsqenYMk18+umn1KhRAy8vL+69917OnDlTYD1///13lFI8/fTTtGzZkoCAAL766ivH/l9++YU6derg4eFB27Zt2bRpEwCbN2+madOmuLq6EhgYyKBBgwCYO3cugwYNYtu2bQwbNgyj0UhkZCQdOnRg+PDhHDlyhEGDBjF9+nT27dtH27Zt+fbbb4mPjmfvtt0opXjllVcAinUNnTt3Ji0tjbNnz6KUYujQoY59ly5domvXrnh7e/Pmm28C9kSbs7MzMTExALz88ssopTh27BhDhw5FKcXIkSMJCQmhUaNGbN++nbZt2+Lt7c2HH35Y5OdiMpl4++23qV69Om5ubnTq1Im4uDjc3Ny4//77Afvqjf7+/rRv377Q+hd27QsWLKBOnTq4uLhQqVIlXn/99UL/BoUoRRJrbjGpplTe2/SeI4kFsOTMEpaeWcruqN1Ep0UTnZ5/zsOy4FTZE30FZ/sveoVrfd88+1/09WXDdhO/zYvGac5Jktefx3hMhhgKUZArJrI0TftH07RHgfrAOuA1IFgp9ZNSqkcp1E/caFsnwuYJwGXJno1fwtSe8F1T+KYB/Hw3bP8ZkvJPQFhanHSKf1rU5qeG1VnSso7jaUW2+dEJ7EpO5+5IM1/OiyL933ASZh0nceHpMqqxEOJaSby5OfTq1QuA/fv3ExcXR79+/Th79ixfffUVQUFBjBgxgjVr1hSrrJNhZwhyduO++s1Zd/oo/7dpBWSaybxob1BE/N+fTBj4DGPGjKF1w8aMHj2aAwcO8MgjjwCQkJBAbGwssbGxmM05S4kvX76cESNGEBISwptvvsn+/ftZs2YNw4cPJzAwkG+++YZz587Rr18/4uLi+OKLLzhz5gzfffcd77//PgEBAfnq+tFHH+Hi4oK/vz8zZszg+eefd+zTNI2aNWvi6+vLxnUbcc6wMXf2bAAeGzSYadOmMWbMGNq1a5fvGnJ7//33cXFxISAggJkzZ+Z5j+XLl9OvXz/8/f356quvOHfuHP/73/8wm838+eefaJrGggULaNOmDfXr13ect2/fPgYNGsSRI0fo1KkTAwcOxNfXl/HjxxMXF3fFz+Wzzz7js88+o1GjRkycOJGWLVvi7+/Pww8/zNKlS4mOjmbdunXEx8fzxBNPFFj/K1372LFjMRqN/Pjjj7zxxht4eHgU6+9GiJIksebW89Wur1h0ZlG+7aM3jmbo8qF0m9uNrnO68vSKp5l7fC5JmUllUEs7vYcTQS+1wG9gPYJfaoFLaN6J2FO3RGCJNQKQeTie5GXhxP1+mNQtF8uiukLc1Iq1nqemaWnADGCGUsoPeBgYDawswbqJKwnfDNt+BHMGJF+EoIbQ7ztw8bLPeZV0Htz9wTnXF8dz22HvjCIKzkpwRR6AZW/C6vchsAG0Hga17oEKleEaVmu6Vl4GPQ8E+/JfXDLfhkfSK9Cb+h72Xlq+BgPV0mx8tT8jz6M045E4fO+vXWp1FELcOBJvylZ2j1alFFu3biUhIYHXHn6cBz0q4dO6M1u3bmXxzNn0f8w+RFCzWLEkp+Ypw5bV46qilw9v3tMHk9XCrP3b2Hb2VL73++/YQQDm/ruEuf8uASAyMpLYyChat2/H2bP2BcbWrl3rOGfYsGGMGDECg8HAM888w/r16zl37hwA48ePp3v37pw7d45PPvmEbdu2UadOHZYsWcLKlStp2bIl999/f56J3B9++GG8vb3JzMwkMzOTxx57LG8d//uPwMBAEhIS2J2wG6/qgRgMBnx9fGnZvAVffm3vFTZr1ixmzZrluIb4+Hj8/HIewHTp0gWDwYCHhwcDB9o/v+webP379+eVV17h6NGjTJ48mbNnz9KlSxfq16/P1KlT6dixI+fPn2fUqFF56jZmzBiqV6/OhAkTaNu2LSNHjmTv3r38+eefnDt3jn///bfQz2Xx4sUopZg1axZeXl6OMp9//nmmT5/On3/+ycmTJ3F2dmbgwIH4+fnlq392j7iCrr1OnTqcPHmS//77j5YtWzJ8+PB8//5ClBWJNTefFeErWHpmKRmWDKLTo2kT0obRbUej1+mx2CxEpUcR6BaIs97Zcc6ac2vYcGHDFcvVsto2OyJ3sCNyB1/v/praPrV5pvEzNAhoQJD79Q+jvxp6L2fcmweRvi8a49E43FsGY/B2se9zL7hpnr4vBs+OMo2KELldMZGVdWMvyJysH1EWYk7AtD6Qe4LzmKMQexw6vgxHFsLxpfZE1pOLIbgRpMfDnw+CKbXwcgtiNkLEHli0x/67RyDcPwnqdLtx11OE5TFJDD1kn3z4x3PRbGzXgBAXJzqfSqfR1rR8/cGdK+df/UNcu6SkJPbt20dCQgJeXl40b94cf3//sq6WuMVIvCk54eHh1KhRAwAXFxcCAwPp1KkTzz33XL5jV6xYAUDTpk0d28wR0RiPh2OJTQQg/dBJMo7Yh4/9OHkSq+cvoFmTnOMTlm0EIC49ldCPX2HbS+MLrVt2A+O7/kPw97AnU2yaRtx3M/hh+Ks4t2yAMuhp1qwZ4eHhec8tYBh5QSsNfvHFF3Tq1IktW7bw22+/8cknnwAQEhJCZGQkjzzyCGazmaVLl+Lu7k5QUBAnTpxwnN+oUSNCvHN6cXW+sxPrNm0gITGBv2bNdNRjxowZjnnFbDZbnrm0tmzZwsqVK/PUuXPnzqxfvx7AkfAyGOxfy6xWKwDPPfccr776Ku+++y5OTk7ccccdea7xscceo3379gB4e9uf7J88eRKAtm3b4uLi4vhcfv31V8fQ0McffxxXV9cCP68OHTrQvHlzpkyZQlxcHPfddx/Hjh3LV//c/wYFXfuMGTOYP38+u3bt4ssvv2TChAlcuFB4L29N07DGpmNNzABA7+uKIUB6cYkbS2LNzWntubW8sf6NPNtOJ53mXPI5HqjzAH8c+YODsQep6lWVP3r9QYBbACcTTjJy3UismvWq3ivVnMq+mH28uNY+NL6aVzW+uPsLGvk3umHXU5SUjRdIWmpv26TtiCRkZGswKKxp5gKPd6nlXeB2cW1iYmI4cOAAKSkp+Pn50bJlSzw9pf1Y3hQ1R1YssA/YlfWzO9fPrhKtmSjcpm/yJrGyRR2Cf4bbk1gA6XGw+Tv767SYq09iFSQtBmY8BJEHr7+sYrBpGv93Lsrxe4rVxqrYJFK3XyJx0WmcLotdZjc9/k80LJW63YqMh+NI2xmJzWTFZrMxb948vv32W9auXcu+ffvYuHEjP/30E4mJiWVdVXHrkXhzlXKvOpj94+PjU+jxLVq0YNKkSXTp0oW//vqLBx98EIDt27ejlKJXr1788ssvhISEMHz4cFrXa4i3qzuz9m1jxp7NfLnOHls8nV2ocPSco9wTMZEs3pDTY8oSbx+2YbLaJ7T9fO1ibJpGh9C8PWVbfvsOdQMqAvDKwul8tHoB28+e4vtNK3DVG2icrqN5iqJbt274+ubMI/Lxxx/z888/M2HCBJRS3H333fTu3RuwD2ebPHkyU6ZMwdfXl/bt2/PJJ59w/PhxGjVqRNWqVUlPTwdykl67d+/mn3/+wWazkZiY6BjGOHDQYAACvHztk9UDfr6+rN+80VGXHTt30rdvXwCmTZvG+fPnWb9+PR9++CGurq6O47Zs2cL48eNxdXUlJiaGadOmkZaWVui/VbYnn3wSd3d3Vq9ezX333ef4HLITX61bt2bRIvuQmszMTMaOHcv27dsBeO211xy9y8aOHcu0adNIT0/H3d2d0NBQIiMjsdlsPProo0yZMiXPBO7PP/88hw8fJjIykieffLLA+h85cuSK1/72228THR1Ns2bNCAoKIiYmBovF/jdhM1sxpxixmuy/WxOMZGy/gPlEHLboNGzRaZiPx2E+X3ZDgMQtS2LNVbraWFOYguZSzPbzgZ8dr80JZqL+iSJ5dzJbLm3hzQ1vsvqX1RwaeohDaw4x98RcAC6mXiwyiXXhlwscGnoIY5gRU4yJQ0MPcfbbs3mOOZdyjieWPUGcMc6xLTQ09IYlNi6fU7Lz3XdToVNV4tMTAbAmZJJ5LpmkZeGkbsg/hFAf7Ib3vTVuSF1uN1arlQMHDnDgwAFsNhsZGRn8/vvv/PDDD2zcuJF9+/axZs0aJk+eTGZmZllXV1ylohJZ3wMJwHLgSaCmpmk1sn5qlnjtyqHSuNnjntMbJiLFxrh1GSw4lpPBH7cuAzU+mblHzHBsKaTFgn8dqHef/QC96+UlAjB0gRE1PpldEVbCE22o8cn0+Su94ApG7HO8LMmbfZM77mJZ63rYkhKonWzh33WxtP3pENHrzuU5TwNMQS5Uf7tdgU+YRdGS/ztH3PQjJMw7SeyUQxw6dIiDB/MnLC0WyxWfqgtxjSTeXKMWLVowc+ZMZs6cyZQpUwo97vJJ3WNjYwEc/129ejU6nQ5nZ2cWLlxoX52uU09i0pIZu2Iu0anJAHyzYRnnEuMY0b6Lo2wXgxMAb731FpaG9i/crlnbVp08xN01G+BmcObOiePZeT5nHsOudRoxslMv/N09OR0bxaIje/BycSP041cYuehP7nnlGXx9ffnuu+/yXMtLL73E4cOHqVOnDl5eXnTp0oUJEyawb98+nnvuOcxmM56engQEBKDT6fj666954okn2LBhA87O9mEply5dAuD0aXt9spMsYWH2p+R/z/wLgH+WLOSN998GoGHdBnl6Jbm4uPDnn3/i7OzMf//9x/Dhw5k5cyadOnVyHLNu3TrHBO7x8fGkp6czdOhQx+cOMGXKFOrXr09CQgIA99xzD6+++io+Pj6OiddjYmIc997UVPuDqbVr1zp6z61Zs4YPP/zQEQM7depEVFQUbm5ubN++nW3bttGgQQNWrFjB448/DoBOp2PFihU899xzzJ07F6UU+/bt47HHHnMk4vR6fYH1nzx5coHX3qFDBwYMGMDEiRN59dVXGTp0KPHx8UycOBGDwYDNYiU9MpHMhDSMkYlYM82YTsSBNX8PO2t8Id9BhLh2EmuuUXFjzbXwd8tp21gSLcQsjCF5T3KBx847MQ+z1Uy7iu1o7N8YADe9W5HvYahgoMpzVQjonX+eRJPVxIWU0vlua0nMnzCJ/eUg6Xui8m13ruNDyCutSqNat6RFixYxf/585s+fz6JFi9i2bVu+3t0AKSkpeWKyKB+Kmuz9FaA59q62Q4C9SqkvlFKSFi5CSd7suet1qNUNlI6IFI3x600sOGYp+FhTKhyYAzodPPonvLgLnt9U5FsEuitmPuTGW3c4F3zAydKZQsCSq8EwZdd5gjJd8LDocU4w5TnO75G61BzZFp2zvlTqdSvKOJHgeG0KT0YzF75yZe6eBkLcCBJvrl1gYCDdunWjW7dudO3aFchZ4e/JJ5909FbKTtBomkZkZCQAAQEBjgnevby8mDhxIm5ubowYMQLPikFMPbgNgMEt7iAyJdHxnoP+nEjtgBDH79V9A/BydWPHjh2M+PBdLKlptG7TBgA3gxPbz53i+80raRhcmZGdejuSXADd6jQmLj2VdtVrsXrEGFyd7PvmH9zJufgYMjIyGD16tCPRpGkaDzzwAB999BHJycmOycW3b99Oeno6BoOB9PR0zp8/D9iHSfr4+ODk5ETlypXp2bMngOMhU+XKlR2fBUD9ejmNBqV3Jjk1lbR0e++pTdu3OPa1a9eOhQsXcuTIEd577z0efPBBLBYL3377LR9//LHjuIYNGzp6Rj333HPMnDmT1NRUqlevDkCfPn0YMWIEx48fx9XVFU3TuOuuu5gxYwarVq1i586dAI5kEoDZbObrr78mKCjI8cChUqVKaJqGv78/mqbxxRdfsHjxYt577z2GDBmCxWKhd+/eNGzYkHfffRew9/j68ssvmT17NuvXr0cpxQ8//MC///5LZmYmHh4etGnTJl/9ExMTWbZsWYHX3qFDB+bNm8fQoUP57bffePnll1m6dCnPPvssANZMi30uzyzWzIKH0kDBnc+FuB4Sa65dUbGmRYsWeR48aJrGyJEj8fX15e677873EHThwoU0adIEDw8PVr20Cp8TPgCcHm9/uJC4OZFDQw+RsDHn+2n6yXQ2vLyBoKAglvyzhD96/8Gc3nOovak2x149xpHnj3Bu4jksyfnbRZZkCxcmXSD2X3vCIubfGI69eozDTx/m2GvHGP3+6GJ9Djt27KBTp054eXkRFBTE/PnzAViyZAnNmjXDw8ODZs2asXr16oILsOXc/07HnaPvHyOo/XU3Gn7UnYdm2B/koyDw+WYEPd0EpZMH9Nfq1KlTeV5faWV7aduUP0X1yEKzWwuMAiYBTwGlN0FSOVWSN/vGbTuxsMIT0HUcbX6xf7mett+MGp/M7/tyEjybz1mpPzGVwG4vMOfpOvBxRUzfd+SNB9tQ+ZsUfD5L5uE56cSk5f+mGJOuMWiekc8328v7YnMmlb5OwfnDZKp8k8L431cV63O43pt9kLN9vpBvd6dzKTLOcbNvPKF3zs0eyJClaa+ba92coTvONSrQuHkTQkJCCjx23rx5rF69mnXr1klXXHHDSLy5NitXriQwMJDAwED69++fZ9+KFSscE3MfO3YMk8nEokWLWLx4MQBVq1Z19Pzt3bs399xzj+OLX6dOndAM9ocDSZlG3ux8n6Pcl+64l/bVc4YKppoyePkO+4JfK5cv58IXUwg/egyAu2rWp4qPfSjcyhMH+Wr9UjIs9uRFSqaRYbPtQ0o2hZ1g14Uz+Lja55bqVKMerarUICMjg4yMDJKTc57Oz5o1i3fffZfIyEh2797N6dOnmZ21kuCHH37IRx995Bh+99BDD+Hs7Iy/vz8XL150DMXLToxlJ5Sy55jy9855st+ow8u8NCIngRRU/S7APsn5r7/+yunTp4mIiOC9995jzhz79DqrVuWNj0FBQTRv3hywJ78GDhyYZwW/r7/+mrfeegvA8aT4+eefJzY2lh49enD27Fm8vb3p06eP4xwfHx9GjhzJHXfc4fhinp2QM5lMpKamsnHjRtq3b8/bb7/NpEmT0Ol0LFu2jNGjR5ORkUHTpk2ZMmUKI0eO5P7776dWrVp0796dmTNnMnDgQDRN48knnyQ4ODhf/S9cuMDJkycLvPaaNWui0+nYuXMnhw4dokuXLjRr1sxRd72LIc+iMXoXJwy18i5B75BuxnQ6HlNYAtZkiTXixpBYc22KijXPPPMMSilGjx7tiDXffvstTZs25ZFHHsmz4u3x48d5+OGHMZvNfPvttyibYvuX2+nk3Imgh+zz7bnXc6fKc1XwqJ9zv0w5kIJfFz+SkpIY+tJQ2s9oT5cRXZg5eSZezb3w7+FP6sFUIv6IKPJ6nPycCOwXSMjgEFyrurJ88nI2b958xXPi4+Pp3bs3+/btY+zYsYwePRqdTseJEyd46KGHcHNz491338XFxYUHHnjA0fM3N33WxO4Af+z9h32XjvJWp+G8dfcIKlcItu/QIPNUYpHXIK6sdu3aeV63b9++0BV0Z82axcqVK9myZYtjnkpxc7tiIksp5aGUGqyUWgj8C3gCLTVN+6VUaleOleTN3mKx8PDDD3PcvwcfP2H/Ut2pup6ZD7lxd/Wc+fuXnbLwfGsnkjI0Rv9zGqwZfLo+ha83JtG3roFX2zuz7KSF55dmFHk9VSvoeK+TCxN6utI0WM+45VFsnv7ZFc+5ETd7XVavoCZJNv7Yu7zgmz1gPBJPykYZ7nY9KnSthv8TDfEdUJeApxqTkZFBTExMgccajUY2bdrEunXrWLBgQelWVNySJN5cu3bt2rFq1SpWrVrF119/nWffsGHDeOKJJwD7JNxRUVF5hqw/8MADtGvXDrDPGbVq1SrHF7hmzZo5kmDRropOTVo4zutYow5VfXKGgjzT9h6eaG0fTme2WsFqIyGrF9P8gzs5FWsfMuGiN/Dn4P9xZ416Wb878V73BwBoXqkaDYMqk2qyx6QNYcdZd/qo4z0efPBBxwTq2b2VVq1axYoVKzhw4ABWqxWlFCNHjmTkyJGOp6smk4mDBw86eqFlM5nsD2qyhxMozZ5ccc7Vs9ccvxU96YRWsye7dJZYnF08GPh4zgp89957r6Muq1atKnB1visNec9eDRByJnl/6KGHCAoKcgxRHDZsGHp9Tr2csnqtZf8XcEz6npKS4ngYppTCZst5WBUZGckvv9j/l+revXu+uvzvf/8jLS3Ncc6PP/6In58ff/75JwBvvPEGnp6ejuRZQdferFkzDhw4wKBBgzh+/Dj9+/fnt99+c7yHzqDHPcQHFz9P3EJ80Ls4YY0qfL4wa2Qq1ogUTIejsRnNjBs3DqUUc+fOzXfs5dMTXK309HTGjRvH77//fk3ni5ufxJprV1SseeGFF2jbti0ZGRl5Ys3777/PCy+8kKc9tGrVKsxmM6+//jrDhw/n9ddfx2w2UyemDl272TsAOAc449PeB+fAnNEhAT0DCOgRgFOgE+nR6ZhsJhL3JQKQsC6BmIUx2DJtpB4qel5gS7KF6HnRXJp+idQD9uNXbyukF1WWrVu3EhcXx4gRI3jjjTccDwJWrVqFyWRi+/btjBkzhp07d5KamsrWrVvzlaFl5iRJavhWAWBD2E6iUmMZ1nqAY1/Kpouk748u8jpE4fr3789DDz3EQw89RL9+/UhNTS10fsro6GjHwiy52+Hi5nXFVQuBaOAkMBM4hX0qojZKqTYAmqbNL9nqlV/t2rXjo48+AsgzSS3k3OwXL17MihUr8t3su3btyrZt2xxfHHPf7J999lmUUgwfPpzVa9bQ46VveOePNtRo05OBjTbmeZ+RHZwZ3sqZn3aZORlv/1K65KT9Kfjk3Tld+VeeLmRYYi7RaTbGr88kIVfO6+C6+dwxpPBuuNk3+zfeeIM33shZieSHH35w3OyzJ6XNPj73UuUAWq45M7Jv9pujNtPYu2memz0WG0lLwzAEe+BWt5Anu6JIbg1zGqY//d9PxXoicfFi/okpbzSr1ZqnESduSRJvrlFAQADduhXcmeDye2r2E8fsfcOHD+fAgQMA/Pvvv46JzsHeQyv7Hn3m/Dn23194t/v1Z45y8FLeuQuzFiRk4gNPcuDSeX7etoY6gRWJSE4gNi0FAGeDgUCPCgCcT4zHSa9n8WH7Krn3N25N5Qq+/LAlp4eTm5sb6enpTJs2DU9PT1JSUjAajYwebY9Fmqbx4osv4uvrS0REzhP5evXqcfz4cR555BFiY2NZs2aNI5GVvUJhQkL+nr2d2zZl6arlhJ+zTw4cefEoXbo+zI69Jvp2q09ISAhr1qyhY8eO+Pv7s3r1ahYsWECjRo04dOiQo5zs7wHLli3D3d3dMRyyMM7OzgwYMIAff/wRsA8BzC0jIwNXV1fHPdrf35/HHnuM7777Dk3TuP/++6lduzZbt26lZs2adO3aFZvNxoULFwgNDSU2NpYff/wRk8lEzZo1CQ0N5f7776dPnz5UqlSJiIgI3Nzc+O2335g7d66jN7XRaMRqtVK/fn3q1KnDpk2b6Nq1q2NC+gceeICEhAQWLlxIo0aNaNGiBf/++2+efwsAnZMenZP9nm6+kIyWWPQDNWwamrHo7yvXIy0tjfHjx3P33XczdOjQEn0vUWYk1lyj4sSay5PyuRVnpVknvROvt3mdmcykZVBLosmbyNF72O8bSq8cMUbTNNBD9VerO4bhXWkIGYAt00bkzEicfJ2o9GQlMs5lELMkhv2X9l/xvMJkv9+oUaPyPCRo0KCBYyVZx7G5hhYObfUQDVs0ZuOB7azYs5bvt05nzdN/UMu/GprRQvzfx3GqWgEnPxn2di10Oh1NmjQB7A/zJk2aVKzzCupccaNJ2+b6FTW0cDawF6gH3Af0Afpm/fS5wnm3veybfbdu3WjVKu8kfTfqZp9nm0cAPPQb1O0Fyv4/hZ+bfZ9BlzMcW9Psvy9/zJ1VQ+w/cx9xz1dubmkmjZErM/F0Vswa4MaYO+1PRjIyjFc8rzC5b/a5n+Rm9wrITeeWk2t9ptfjrFi0jI4P9mF15Hb6T3+e03F5G05JS85cU51uF5cvRuDn58fAgQOJi7Ov1JI9cf/06dMdkwkXxWq1Eh4ezunTpx3/tpmZmVy8eJGMjJzGiclkYsOGDaxduxaj0YjRaOTs2bMYjUZHOdni4+PZunUrGzZsYMKECXz44Yd88sknREcX/WQqewjvV199leeaxE1P4s01ioiI4O+//3b85E5GXe6dd94hKsreO6p27drMnTuXFStWAPYhcNnD7kJCQjh16hR//PEHOp2O1NRU5s2b5yhnZ0Y8yc45XyFSMzNYcnQf7k45T859snpPLT6yl9r+wbSpWpPTcVG8u2wO5xNzVofKzX4HsceuDLOJ/04dzrPf09MTJycnTp48yfz581m7di2dOnWiY8eO+Pr6otfrmT59Ohs2bCAwMBAvLy+cnZ1JT08nJCSEpUuXcvToUby8vKhVqxZgf7jUqlUr4rISWVO+n0ztWvbhCPWb1aVm9VDAPrE7wCtPPUK3BhrKqlG3bl3MZjNfffUV7733HikpKQVeV79+/WjVqhXz5s1j8ODBhf775Ja96EutWrXyDM0DSE5OJjMz03HfnDJlCoGBgQA0atQIpRTh4eEYDAYuXbrEv//+S4MGDQB7HEhNTcVoNPLTTz/xySefOHpg6fV6Hn30UcB+7xw0aJDjoZyrqyvp6elkZmZiMBh46623sNlsjBo1ipdeesmRNHNzc2PWrFk8/fTTjrnCxo4di5+fH0FBQSilePvtt9Hr9ej1ejYtWU3zwV2o3Kspfyy1Dw89fOY4LR/vin/XelS8tzH3j3ySizGRoFPY0u1/32vXrqVBgwZUDKnI9KnT8nw+WqYFc3gCrzz/IoGBgbi4uFCzRg0mT56MpmmkpaUxYMAAvL298XD3oFmjJuxZsonWTVsCOOYLGzduHJs3b6Zp06a4uroSGBjIoEGDHO8j8aZcklhzja4m1oB90QqADz74gB9++MERX8DeI9TJyYmvv/6aX375hW+//RYnJ6c8q9TGno6lU0wnWri3QFF4r9YKzSuA1T6nljnOTMrBFBLWJhR6PGS1RRTYLDasaVZS9tvv3bYiJubLfmgxefJkvvrqKyZMmMCCBQvo0aMHzs7OzJ8/n7CwMPbu3cvbb79d4Gek98jpSTvz/AoOe0TQYvBd1AqtiU2zEZOe66GKBklLpW1zJcVt20ycOLHYQwZNJhOnTp1yzLUJ9h67Fy9ezPNvmpaWxpo1a9i0aRNms5mUlBTOnj3reFCW+/0uXbrEli1bWLduHZ9//jkffvghX331VbFWMJZYU7CiemQdwv69MvvuoQExwCZN08JKsmLlXfbNPttDDz10xePvueceJkyYwAcffMCxY8cKvdlrmpbnZp89rGDv3r3MPHQv3XtMhDn7gWMFvk/fuk7svpTJtP0mutU0cCTGRliijR61Cv9TyP4DyLRCglFjyUlLzo4ryH2zDw4OxmAwEBoamudmX7t2bRITE5k9e7Zjjo2CVHy7LfMW/kPsjjBqx66jnscFDms20hu5QIxyrHZkTTEVWobI0aJFC958803mzp3LrFmz8PDw4LfffkPTNMxms2MVr+KwWCyOYRitWrWiS5cu/PrrryQkJFChQgXuv/9+NE1j9+7dHDlyBICDBw86bvguLi74+/sTERHhaIgVNKTRZDIxbdo0nnrqKcekzNlOnz5NfHw8gYGBjoZkdHQ0cXFxeYbViJuaxJtrtHfv3jyN6+yV77KFhoZy3333sXTpUo4fP0716tV5/fXXmTJlCjNnzqRr164sWLCAn376ic6dO7Nw4ULeffddXnnlFWrWrMm8efMcK+c99thjzJ8/n2/m/kXvRwbwyl09+W7jch5reQczGjSnx8+fciLGPoRv7fPv8vW6pSw6vIf1p49S1cefUff04ak2d/P64hnMO7Aj37V4OLvwTrf+fLdxBafjonHJeuCj1+vp0qULKSkpODs7U61aNcLCwrBYLHzyySdcvHiRqVOnMmzYMOLj49m2zT5Jfc+ePXnxxRd59913OX78OD4+PnTq1IkHHniAMWPGAPY5KP/++28++ugj1q9fT40W9WncuDHOzs6MHDkSnU6Hq6srm9afpl6IM488/TiPPP04w59+hg0bNgD24XzVq1dn9erVKKUwm82MGDGCWbNm0bZtWxYsWMCuXbsIDQ0lJiaG//3vf+zdu5e9e/dy7NgxXn/9dTw8PLhw4QI///wzPXv25J9//kHTNEcC7dy5c4waNQp/f3+GDRvGjz/+SEBAQJ4VmIrqhVCU7du3c/SofThncHAwMTExjuHj99xzD0eOHHEMxWzUqBEfffQRnp6eHDx4kIkTJ7J+/XreeecdkpOTqVu3LsePH6dixYp069aNtLQ0R8+u06dP4+bqSlp6Og+NGsa7z4xk/M9fMfr7D3nivkdwNjjxWM8B+Hn7cPbSBb6a/iMf//INP4Z8iTXWvpLh+nXrefmJEXz4zWcMf24E9zS/A2uKfR4ta7wRy8UUantWZtwLb5GZlMasVQt54YUX6BzYhL0nDjNv3jye6juIto1acOj0MUyxKYwf/iZPffAK9UNr8+6ot2l2V1veeecdzpw5w3fffUdmupHjB49iiUtH56xHy4ovNqMZm8lS5PcicVOQWHONioo1l+vbty+vvfYaU6ZMwWq1OmIN2HvJzpkzJ0+smT17NvXq2YedDx48mPnz53PgzQNs3LiR7S7b8/XOyhbQJwBbpo3E7Ykk70nGOcAZv3v84Apf//SuekIeCSF6YTRxq+LwaupFxvmMKybMwN679t9//+X1119n3LhxuLu7M2nSJOrWrcv8+fMd15Mda3x9ffOvkpfVayxkdFv8l8fw8QcfceHiBTyc3RnWdRD3DOmNcV1OL1ZrkswPWByFtW1sNhtms5n4+OLPp5yQkOAYGdW9e3fq1KnD1KlTMRqNhISEOBaNWbFihaP31rFjx4iOjsZkMuHp6Ym7uzvR0dFUrVqVxMTEAh90paamMnXqVJ588km8vLzy7Dty5AhGo5GAgABHsis6OprExERp22QpKpFVUKqvOvCOUmqcpml/F7BfUDY3+8GDB7Nx40bwDaWwRNbbdzmTZtaYecjMgmMWavjqGNHKqcBjs3k6K77o7sIH6zP5vx0metc2cCDKBJWaXfG8G3Kzz6Kc9DhnJjD1h6+5kGzFy1nxQhsnetjGY+y5mMR/z4MGXndWvmKdhF2lSpUYNGgQzZs3Z/78+Y7hQxaLxdEQOnPmDEuWLCE5ORlnZ2dq1qxJ3759cXFxYfvu7WzYuIGM1Ay8vLwI7RRK1W5V+e2V37gQdoE77riDPXv2EBgYyOHDh1m4cCHp6el0796dFi1acOzYMebMmUNiYiJ6vZ6qVavSt2/fAut66tQp1qxZQ0xMDG5ubpw8eZKHHnqIf//9l9mzZxMXF0dwcDC9e/emYsWK7Nu3D4B9+/bx/fffk5ycjNlsZs+ePezatYvvvvuO06dP4+XlRbdu3Zg5c2apfOaiSBJvrlJoaGihiYuhQ4fmGRq1ZMmSPPu/+eYbvvnmmwLP7d+/f765HbPNmDHD8dqamk6do5G81qmXY9vK4W87XjvrDbzdtT9vd81f1td9H+PrvvaV8LJ7ZzWrVA2AZ9rdwzPt7E/yZ+zZjM7VBY972vDXX39x7tw5Tp8+zenTp+nduzdeXl58//331KhRgw0bNhAfH49Op8Pd3R2LxUJERAT33Xcf992XM1H9iRMnaNKkCS1atOCZZ57hn3/+4YEHHuDEiRMcP36ce+65h+PHjzPqf09TOSQYk9nMq+9/woldS6nS7V7WblxP7+49eXb4cDZt3cLevXv5v//7P0JDQ/O8R//+/Wnfvj0rVqxg3rx5DBkyBLA/1Y2IiOCrr74iMDCQLl264OzszFdffcUff/zBiBEjeOKJJ/jrr78AqFbN/rm88sorbN26lTFjxhATE0NaWlq+pP71evTRRx1f9tetW0dQkH3S5cqVK/PJJ584EppgH2b4448/5nnwkb2CYp06dRyLBvj6+jJhwgSioqIciaxPPvmEdavXkpaeTs+OXahXrTYms4kMUyaVezeldYPmXIy5xJEzJxxlT1s2m9W7NlKvur0nnSnVyP/GvEb7xq24FBtF9wd6MfxB+2f8++K/aVGvCeER5/hx7lQyTDkNwcHvPMe44aPQ6XTsPrYfT3d37m7Vkaa1G3LynL3nw4mzpxk19h1mfPMrNQOrsjg9nddfG4nJbKJBjbrsWLiWFvWaYD5t/25njUwlc2cEmskKmoblUgpb9u7gxdGvceLECYk3NxeJNVeprGMNwOP6x5nde7bj9zof13G81hl0hDwaQsij+RcoqvJsFao8W8Xxe+PfGzteB/QMIKBnzj00ZGAIX/Wz93gprD0C0LZtW3t76zKXx5psnTt3zvP55Z6ncvDdD9JjUJ08SXDr2VQ87qhE2uYI0IHnHdK2KY7C2jYmk6lYbZtdu3axceNG0tLS8PDwoF27dnTs2JEhQ4YQFhaWp22zc+dOFi5cSFpaGj169KBFixbs2bOnwLZN7l5d2S5v2xw5coSHHnqIhQsXMnv2bOLj4wtt20yYMMHRtjlw4ADbt2/nu+++49SpU7ddrLliIkvTtPEFbVdK+QGrAbnZX+ZmuNnfece/jOvxBByz9+o69L+cmO2sV3zR3ZUvuucfa/37/W78fn/Oak3a2AqO1yM7uDCyQ84qG1++NhgengqU3s1+WBtPhr182UoTyefw1C/B9c1nwKZhCHBDFM1sNud50p7dUNLpcoYKOTs706ZNG5ydnYmKimLnzp2OyYdXrV6FIcBAxQEVcU92J8opCmsFK4nOiYA9cVunTh0OHDjAX3/9xT333MOaNWtYuXIlLVq0QK/X06xZM9zd3UlMTHRMHN+vX7889YyLi+Pvv//Gzc2N7t27k5GRgcViYebMmUybNo1atWrRsGFD9u/fz8yZM3n55ZcLvF5N01i0aBF///03YWFhfP755+j1esfcOKLsSbwpf/Se7gQNe4CoSbOLPvganUtJZPr6TaT/M92x7ejRo/Ts2RODwYCHh4djQvrsLvc2m80xNPrAgQPEx8fnmSss96S8hc3T2L1TR155xj5JvjEjk3Fffc+Un35E+XqRkZnBU88+TZNmTalUqRJ79+6lb9++eRJZFStW5IsvvuDvv/9mxYoV+eLktGnT8Pb2ZsmSJSQkJDBmzBiee+45atWqRY8ePQgKCuLkyZPUqFHDMYfGunXrqFKlCh9//DEmkynPBOo3Snh4OOHh4dSoUYN27drx8ccf4+fnR8OGDR09w7K9/fbbnDlzhp9++gk/Pz8effRRx3DyNWvWMHnyZEaNGsXx48fp1atXniSYj4+PY+HCCh5eeLq5UzEgmLCIc/Tr1JNpS2YB8O7TI/nmz58wZhqpHBjC8AeGsHGvvbdddsMvISUJgGNnT/H+pC8AsGkab343jpT0NOpVr03L+k0JjzjH1oO72Hv8EPPXLmXH7ytYvnUNG/dt4/tZvzH22Tf49Pf/A6BG5eo83msA1uQMejS5gwnaDwT4+JOcmszBU0d5aNQwjszO/90mu17mMwl88ennnDlt78llMpkk3twkJNaUT6PbjuZg7EGOxh8t+uBr9GyTZ6nlW6vEyi+I8UBMvp6cprBkvO6qgme7iihnPQYfl4JPFnkU1rbJPQ/Vldo2q1evxtvbm86dO2M0Gh1touzeTyXZtsnIyGD69OmOtk2jRo2K1baZP3++o23zzTffYLPZbqtYU1SPrAJpmhavrrQEjyhbSsHA6TDnKThcnDkrFYX2h1d60C4bT9z7S2ibf1WmElexecHblQ6DTIJ4VVauXJnvSTvgmLME7AFh586deXoTRkdHo6HhFOKEOcZM2rE0dKE6fDr4AGDL6sfdtWtXEhISOHDgAHXr1qVt27YcPnyYc+fOYTQasVgsHDp0yDFXT3bZlzt9+jRWq5W77rqLtm3b5ql/9v7cvQEKW2Uxm5+fHydOnODff//l7rvv5oUXXij2ZybKhsSbm5trlRBCXhhE5KRZYL2xXd3DbUYmb1pJ8+bNGTt2LIsXL2bKlCmOZMnlfxbZD0FmzJjhuL/ZbDbHSoeXH3elSXkrVsx5su/m6sLDPXswde4CLD9PJigoiF69exVYh2xXmgvTw8MDb2/vPMdf7Z/49Q4hLI6AgAC6du16xTpomkZycjL//fdfnn2vvvqqI7FntVrZsWMHO3bswNnZ2TF3iDLkNC6MpgwuZa1smZ3EAkhLT8Pd1Y30TCNpxnQ83NxoXLsBq3as52KMfThHQnIizk7OmMwm2jZuwZqdm/D39iUyzh5TMkwZrN+9hYjYSEe9tx3YjbeHFw1q1KVZnUas2LqWbYd2YzKbUEphzMygesUqVAmuyFPjXwHg7KWcp+qRcdEcDc87gfPlalcJZdmW/1i5ciUtW7aUeHOTk1hzc3PSOzGrzywGLx3MobhDRR6vQ+f4Tno5PXqsWPMc+0OXH7iz6p03rL7F5VzFi7TtkQXucwq68hzGIq/C2jZOTk6OpFRhbRuwx+2EhATOnj1L5cqVady4cZ7yb/a2zbJly+jQocNtFWuKmuy9QEqpLsCVx8qJsvfwVOj5Oagr/DN7hsC9nxS+//IkVuNHyyaJBVDjLrRB86D9/6BSS3APgIb9ofVTZVOfcqxdu3asXr2aPXv2cPr0aZo3b+7Ylz3Z76ZNm0hMTOTll1/miy/sT7ktFgsKRYcXOxAyMASdq46IORGc/87+Bd/FZn9q5Orq6kiIZT/Fz/5+qGkaGzduJCoqis6dO/P444+j0+mwWK5+NaoePXowZMgQhgwZwlNPPYWPj88Vj+/evTuPPvoojRs35rfffqN169YkJiZe9fuK0iPx5ubnUjGQym8OQ+/jVfhBCrzubg36wuPR6bhoPluzyPET8Jh93mWj0UhUVBSrV+ddFt3X15eYmBimTZvGkSNHHMOTp02bxvn/Z++sw6sq/wD+Obd2190Dxhi1Mbq7UVIJCYkJkqJ0I6WIgAgo8gORUkJSQCSkuzvHiDHGNlas49b5/XHYHZdt5BDQ+3keHrZz3vOe95zde77n2/fuceDAAb766ivUalNHx/MU5bWwdcTSxQeFwgbd1Qd0LV4NnUHP/v376dKli9FAlV2UePny5SYRxM9LjRo1cHR0ZPHixSxcuJDJk6VgkebNm+ca26BBAyIiIhg3bhyff/75G6+RMW3aNAoVKsTcuXOpUKGCyb7ExETmzJkDSPJg8uTJnD171rSpS7bJQK1gwqKZxvS/+aOnA2Bjac1v29fRtXl7LC3UyGQyJiycwbrdmwFwtJWMgaIoMqSz9F5irZYUv2x582mbj7kfE01kXDQVS5UFQC6To9Xr2Hv6CENnT2D+uqU0rlqPmmWrANCgUi2SUpPpOWUwRy7k1HGTy2Q42jrw8Xvt2PL9b/h6Fn7q/Zk6YCxr5y2nePHiZnnzDmCWNW8/giCwuuVqupXu9tRxRe2K0rVU13z3P27EAviiwhdvxIgFYF3FA8ePSmBV3QO5uyUyawXW1T1Rl3Z69sFmTHiabqNQKGjevLlRtxk8eDDjx48HMOofPXr0ML4f7Nq1K1d63pvQbYKDg59Lt+ncuTOlS5f+z8mapxqyBEG4JAjCxSf+RQDfAgP+mSWaeSUSbsHjHTjkKtP9bqWgSi/JKPQ8lOtQcGt7hC4xi8xbiRg0poIl43oCidvvkHU7CVFnIG7pZe4vtSAmrBOGHrth5C10TX9GnyF9jEWtgfg113nw03kyQ0wL+unTtOgSzcUSs8n2tFeoUCFXughIkQkuLi6Iooi3t7excHI2CSsSKBJXhLIOZbGysEIRp6BxRGPsNHa55noaGo2Ga9eu5auQFStWDLlczqFDhzh58iSHDh0iPDycEiVKAHD58mWSkpKIjIzkwIEDBAQEGLt4WFtbY21tmop66NAhXFxcqFChAoUKFSItLY3k5OQXWrOZ14NZ3rzbiJka9ImPFTJ9sqW0CLblSmFfr0q+c9x9GMeCY3uM/7y9vRkyZAiRkZH88ssvvP/++ybjR44ciUqlIjg4mI0bN9KjRw++/fZbQkND6d+/P7/++iuHDx+mZUvJICaKIjOmTadkyZIMGDAAuVxOv379GDlyJBcuXODDNh8YvaC6dA379p6iZc9+FAvuwgfzpxmjrLp37442LYv3GjfF3ckVa2trJk2aZCxoDtKzTSaT0aFDjsysX78+d+/eJS0tDbVaTcWKFblw4QJbtmxBJpPRr18/jhw5giAI9OzZ09hEI5s5c+ZQo0YN5s+fj4ODQ65Is4Iiu0TCk+UPIKfjIUDDhg0JDw/n/v37jB07FlEUjSkdGzdu5OzZs4DkxZ4wYQIVKlTg4MGDiKKIi4sLarUaa2trFqxcjPCoQ/H06dPZfeGIdL8q1yJ86znikx4ydcBYpg4Yi521LQq5gvTDd/Hz9gXgzIrdNKgsKaFerh6kH76Lv09RACb0Hk6belJR3noVa/DpB13RG/RYqS05sWw7cbuvE7PrKlu+/5UP6zfHQmXBlTshfNVvNJP7jqSIRyEm9h4OQLkSZfjms7GUCCjJ8J+m4OTujGDx6HOuzP06PXP1fG6lRBIYGGiWN28RZlnz7nM72bSLn+wJdbayR2V6leuFSvaEzpMHCkFB3UJ1C3R9ANrYdLJuJyLqTaNn087FkLT9DtroNPRpWh78eI6H624gpuvwGFQJry9rYFvbG0O6ZADRp2mJW36FmAUX0ESZdhPXp2jMTa4e41m6TdWqVY26jaenJxcvXjTZv2PHDrRaLZ6enqjV6ny7ED+LgtJtYmJiOHjwIP7+/sZC8NbW1rmcc4cOHcLb25uyZcv+52TNs1ILn2xDKwLxoig+u0+kmbcDhyKmvwtyKZop8izIFFC5JygsYHgoXFoHm/rnjsLyqgD+TaFEM/CpVCDLerj1FhkX41C4WKK5mwx6EaWHNa4DyiEIkLAhlIzzUihl6uH72DYsRGaI5CjThCWTfu4Bhkw9yTvCQACHD/1JPRqJLlrqZhS39AooBQSlHKWHNZo7SdKnVymATkRmp8K5c2ksfF/M8PJfYtq0aQQHBzN37lw+++wzNm7caIxCyMrM4sbfN9BoNDg4ONC4cWPstfbPmDGHOnXq8ODBA86fP0+lSpXyFDgAzs7OdOzYkX379rFr1y4sLS3x9fXF39+fr776il9//ZWdO3fi4uJCkyZN8PHxMc5Vq1Ythg8fzo8//ohWq6Vr164olUoWLFjAwoULcXZ2ZvLkycYcejNvHLO8eYeR29uAQgHZ3ke9HosSvmTdCge9AbV/YRQuDjg0rIZlSV8S/tyPJkIKvy/k4EzYuLmgkGNTuQxKdydsK5RGUChy1Y5csGCB8eeBAwcycOBAk3WMGjWKUaNGARhrPR1LPk/98z1Y4DQabaqUllimVAAHDhzA2tqaCcPHopDLOXryOL4ehUgJj2XfoQN80K0DVpZWDB82nNj7Ufz6+ypK+pcgsERpkiPjOHzsCD9O+57I+/dZtX4Ner2elT8vI+VuLN/MnsG0OTMBGDdyNBMnTjRGbP38888kJSUxZswY2rZty7179+jduzeTJ09m8ODBBAUFMXHiRCZPnsyBAweoW1dSsgoXLszRo0eN15odKftv4HF5M2DAADZuz+ncnJSazNQls0lJT8PXsxBf9R/9QnOP6vE5l29d47dt6+jZugv2NnnLff9CRfl96kImL/qOcfO/wdHOgboVqlO7cSMW//wLM2fOYMicCbi5udGwbn0ElQJBKRmylD72qKv5IKhkIBNQVfJEdcCBH3/8kQcPHpjlzduFWda847haupr87mDhgI3KhvCUcKwUVrTxb4OT2onDnQ6z4cYGpp+enmuOml41qexemUaFG+Hn4PfKaxJ1BhLWhpAVlozS3ZKsm5LeoS7piHNwIIZ0HQmrrpF1S6rpl3oiGqvKbmjvS8apjItxZFVOJONyHGkno0Ehw/njUjzccANDqiRXY344B3IZMisFCic1mrBHxgqF1L1d4WqFc/fSKF3MKYn58aRuk+1IAsjMzGT//v0mus2L8Kq6TZkyZZg5cyYLFy5k586duLq60qhRIzw9PU10m6FDhzJ//ny0Wi2ffPIJKpWK//3vf8yZM+c/J2uE11FnoXLlyuLp06cLfF4zL0hiOMwpS676V+/PhMLVwcoJ7H1M94Xugg2fgjZDMlzVGQbuZUD+UuXU8iTleCRJm27luc+1b1nSL8aSdizqqXM4dSrJw023EDOlh7vMSmH0XrwIlpXdcHi/KHLrZ3tt/ovo9XqioqKwtbXFSm1BenIyew8f4cKFC//oOrILCZ88eZLk5GRq1aplUlgZpE4e2dEAAD179nzrHuSCIJwRRbHym17HvwmzvHk7iJy3Em10vMk2mbUlXgO7oEtJQ+XugvBYWqGo0/FgyR9k3X+A3NoKx5Z1URfxQm5dcC/g2YYsRVV7rCf5U1tVjvLbXJn6/XQW/jCfwaOG4WjvwM51f+Jb2NTp07R9S46dOsHS+YuoUasmpcoFUsjbhyU/LKR65arsObiPL6dN5uj2/fQdNpBV69dQtLAvXp6ebF+zmTK1K6GQy7l9N4wxg0cwdtgoWnRpy8FDB4mNjcXFxYVq1apx8uRJTp06xdatW5k8eTLr1q2jffv2jBo1ihkzZjBz5kyGDx9eYPfkbUafHIaYGYfMpSJkRGPQ2qC9kviPr0Ph74QA6OPSkdmoUBS2N6lhJooimSfvg07ytstdrFCVLNjukQWBWd4ULGZZ83ZwKvoUPXf2zLV95fsrUcgVuFu542zpbLJvTcgavj/9PQbRQBv/NnQq2Ql/R/8CXVfChhukn3qQ5z7PsVVJWB9K1o2nZ6469wwkfskV4+8yWxWGF424EsCmnjd2DQsjUxWc7vZvQpuZSWz4HRw8vJArlKSnpvDnjp3cvn372QcXIIULF6Z58+YcOXIErVZLw4YNjTW+stm7dy8HDx4EpIZcgwYNylVj803zJmXNS9XIMvNqhIWFIQgCgiAYPbTLli1DEARj16WXZf78+UyaNEn65e8J5FnE3dYTPMvmNmIBFG8Co+/ClzHQ8TfwKm80YmWvOztN40kEQTAWxps0aRKCILB+/XqTMaLeQNK2O3kvXgaoZGgfpD/zOkW9iMI5J7RS1OaEb95LiqLQ9LoErx/1zHnK96qPnaMD6RdyF9Jr2bIlgiA8tSvj28qT9/+bb74x1it5XgwGAytXruSXX35h3vRpzO/dlV8G9iTr/HGcnaWXBGdnZ4YMGUKlSpUICAgw3rOCwN3dnbJly9KxY0f69OmDu7s7rVq14uOPPzYpIrxq1SqmTJmSqztmevqzP0dmzJh5dfRpGbmMWACCQoHc1hoLLzcTI1b2Po8+HSgyeSA+I3tiHeBfoEYsgKxk6RmgO5lEUvMz/NV4CVO/lzzzDw3J1K5Zi8joKILqVCaodmUmzfiajMwMAM5fklIOmjZojF8xP1LuxnL16DmqV5YKs+7av4emDSRvbZZBUjS6dujEkRPHWLJyOeER9+jcrmPOYgwiBq3kbImPj+fcuXOEhIQgk8nw8cmRxSkpKYSHh3PgwAGAt84Y/7rQ3NpAyuoypG6sQ8qKYiT/VozUDcUQbB4VVBdAUcIJpb8TMidL5L4OYFlASppCQLCzQO5rj0V5D5TuNijcbbAIdENZxMEo03SxaWQcv0fmiXtGIxaAqNXnN7MZM/8J/jG9BvjmRN51fR0tHQlwDshlxALoWLIjJz4+wamupxhffbzRiFUQeg2APk1D+um8jViCSgYC6GKf/U4qyARkNkrj74YMqW7ji+g1NeZ/hHfrMkROOk7WXdP0sv+6XgOQlZ7OynFDWf3lCH75vBfzP+3Mks974ZAQhY2lpFf6FvJh0KBBBAUFUa5cORo0aFBg1+Hh4UGVKlXo0qUL3bt3x8PDg3bt2tGpUyejESszM5NFixYxZcoUrlzJMWwaDAZjsxszEmZD1hsmu6NCQTF//nxjsVisHHPt11l5QECrAj1nNqtXr36mwErefRc0+RSoNUDsT+fR3E565rmS94aj8LIG+aMie9qXK3o7pckgZrcYQ9KOO+jTtIiGgo9QfJlCf69K+/btWb16NdWrVwde7oEfHx/P7du3MWiyUESGocuSHp73r18h+Yb0YE1NTUUQBFq1asVHH31E5cqV6d+/PxUqVKBevXq89957uLi8uKdaEAQKFy5MYmIiCQkJ+Ybn/vHHH9y4cQODwUB8fLwxh7xYsWL4+xest82MmXeNf0q5EGR5v0o4f9DwpeYuEKeJwYAuRXpmKQJtsJ8bSJl5DenRWSoSvDPpKEvmLmDUF8MICgjkbkQ4s36ayzezZxrPAWDQ6tFlaIwpZAAG0cDf+/dQNiAI2yKuHDsq1RBsWLcB3p5ejJoynioVKlPKv4TxmMBaFTl6UhpXqlQpKlasSFpaGl9//TWffvqpUW737NmTIkWKcOLECVq3bk27du1e6h7+UxSUcqG5tlSq52lRDDH9UQcvbSqZp4Kln0UgUy8ZmEq7ovK2Q13eE0Vhe+SuVihKOiNzsnypa5A7WiIIAmKKBhR5f5YNGVq0N+JBL4IeKZUHQC6gKPR2ecfNmHmTvFa9BrBR2pjsF/UiQc5BFLItVKDnhefTawAS1t7It/m7qDEQNe0k+ofPrtObtO0OFiUe0910L66TZOs1GESS94RLuk0BZ1+9q3oNQMS1y8SEhwFSZJbhUUfh0GOHEM4exubaabTnjmJrbU27du348MMPqVevHsHBwZQtW5ZmzZrRsGFD7OxevDSNpaUlHh4ePHjwgPT0dGO5lif59ddfuX//vlG3ya6HWaFCBdzd3V/4vP9mzIasN4idnR27du0ir1Dla9eu0aRJE+zs7ChSpAizZ88Gcr/gf/fddwiCwLJlywgODjZabgVBoP7MM4QZvBAmJ1NzcTqN/7DD+zsprHXRokUUL14ca2trqlatyuHDh4EcJadXr15UrFgRFxeXXA/xlJQU2rdvj729PV26dDE+IDt37vzUFAhRayDtpOSxqPG/jyj5fTOm7J1HwOz36f3HOA7cOUnVee2oNO8D9t0+gVavo9JPH/Le0l7GOZosCabyT23RxqVzeNMe2izrR8nvm1H35y5suro7z/NGJj+g18axlJnTnEo/fcik3T+QpZM86BN2zWXIX9MwZOgIm3iQ9hXex97ajpbvtXhqobzg4GAEQWD06NEULlyYokWLGtuPZ9/Djh07EhgYyEcffURWVhZDhgzBy8sLBwcH2rRpw717Uqe/8PBwatWqhYuLC6NGjcLGxsYYcaTRaBg+fDje3t44ODjQoUMHYxvW7DWMGDECHx8fChUqZIxKWr9+PZ07d+b48ePUr1+ftLQ07t69iyAIBAcHI4oiU6dOpUiRItja2tKgQQPjZydbMRk+fDg/zp3L/g1rUWSYFpiUPeoulZWVlastrJubG23atKFBgwZUr16dPn36UK5cOWxtbbG3tzfmostkMlq2bJkrjBagUKFCnDp1ivDwcHbt2sUvv/zCtWvXTD9PosjVq1dNtjVq1IjRo0fTrVu3fAWEGTP/RV6nciGztMC+cXXjPrmbE05dmmNZvEh+h78Sz6NcaFIyjD83dK/OruZL2dr8Z4oXLQZAM3V1rCwsGT9sNEe372frqo0AXA2RnjPlg6QOd3sP7UOfrjFG3RgMBu7eu0dsbCzlAoNMzimTyejSriNZWVl0/aizyb6Zk6dRolhxAFYsWMrO9X8SevYKwwcONhk3YcIE9u7dy61bt9i8ebOxQ9Lz8C4rF3KnAPD+HpXvQhAee3brchq3GFJMFUFBJqAsZI+qhAtKF2vJwFXSBdQKUMmMji4Ama0FCm/bnE6J2ajk6GPTMSRlYojPIOtMJNp7SYhPNJ/RRZvKQBRy1NV9UFfzQW5vWnjXjJn/Kq9dr6lfn0G+g7gcfJlbX9/i7oy73B1xl6XvL30jeg2AIV1L1q3E/PWa+e2o9MOz9RqDaOD4qRM0HdKuQPQagJTb8XxUrSUOVna8X7khSQ/zDxT4L+g1PXv2pFHbDvx18Xq+90EAkmNjSEs0bRrm6+tL27ZtqVGjBnXr1qVPnz4UL14cW1tb7OzsjM4vhUJBu3btchm6BEHAycmJ8+fPEx4ezqZNm1i9enWudMaHDx8SGRlpsq1jx46MGTOGNm3a5Lvu/ypmQ9YbpHbt2pQpUyaXgqHT6WjTpg1Xr15l5MiRVKtWjaFDh/Lnn38+db7+/fsbUxRWr17NhImToOd2AI5F6Kj0Xhe++vpr9u7dS58+fXB1deX7778nPDyc1q1bEx+fkxqyY8cO+vbti4eHByNGjDCpiXT48GEqVapEiRIlWL16tVFY5EdWVhZxcXGEzD9MYkJOfni6NoMsnYaK3oHsuHGI0Tu+o1/VzsSlJ/LtgYUo5Qo6l23JlZhQLj+4wZ2HEVyPvc2HAU1IykwleP1okjWpfF6jGz72HgzeOpUrD0Jznf/zP79i982j9KvWhXq+VVl8Zj0/XTRtqSpm6llxfjMbL+6kpk95Kir9TYrq5sfRo0cZNWoU8fHxdO3alaysnBftnTt30rdvX7p3787UqVOZM2cOTZs2ZdSoUWzdupWPP/4YgMGDB3P06FF69+5NbGyssesVSEUJZ82aRatWrRg8eDDbt2+nf//+Jms4cuQI/fr1IyIiwiT8OpsJEyZgYWGBi4sLq1evpn///ixdupTx48dTtmxZpk6dyqlTp2jTpo1J+/k9e/bwcav3CfA0LaopIqBzkKKsHB0d8fLyeuo9UqlUfPjhhwwbNowhQ4YwYcIEBg8ezKhRo6hcuTIDBgxg0qRJ9O3blyZNmtCrV69c+d/3799nzZo17Nixg+XLlzN9+nR27dpljMACcHBwoGzZsrm6eZgx81/nn1AuEn3d8J06iM57fyd4yzL8G9QCCtZpcufOHQRBoHPnzrz//vsIgsC3336b65p0mRq0STmGrD379/Lr9KWULVeBVet/B2D8+Ak4F/emecc2LF31K+0/6QJAYKnSANyPkl4kPx89lM9HD6VM7Uo4FHUnsFZFZvwwi7q16+QylsfExnD24nnUFhZMnfUtK9blyJkRE8dw45Ykn6qUr8TYryZQqnIQbdp+SFJijnKxdetWGjRogJ+fVHz4ZZSL9u3bIwgCFhYWuZSLqlWrGiNe3yblIvDjdXy54jSCqhCKwnMRLCVDouD4gfHeyF1Mu9DmhdzFCstKXlhW8cGyeiHU1XywqOyFRVl3lL6OWNYsjLpGIZR+jiiLOaEq+UQakgi68CQyz0WhjUgm40QEmeejQGZqAVMVdUCQywosld6MmX8Dr12vmTABl0dd1jNuZtC/ZX9mTZvFkQNH3oheExsbS+h3h42RUwWi12Sm8EWd7i+u11z6HaxNHR+/Hd/Axit/U7NwBSrZluLY8WNPvS74d+s1f//9N2PGjqVu9WpPvQeeJUph6+z61DE2NjZ8/PHHDBs2jKFDh/Lll18yaNAgRo0aRVBQEEOHDmXSpEkEBwfTpEmTXNcIEBISwq+//sqRI0dYsGABM2bM4OrVq6hUOXWbCxUqRJEiRfLNTPmvYzZkvUGyX043bdpkEm0SEhJCaGgokZGRfPnll6xbtw6AXbt2PXW+atWqGQ0AnTp1omHDnLSOChUqMH36dPr06cO2bdsAmDx5Mn379qVXr148fPiQ48ePG8f37NmTvn37MmTIEABjvY7s84wZM8aY8vCsXOvVq1fj6upK6cH16LVhrHG7TJAxsdHnNC9RD4CP6rehZ9X2uNs4cy9RKvb+cblWyAU5ay9tZ3uItIZ2ZZpxNvIKiZnJ3Iy7y/SDP3Mo7DR6Uc+Ru2dNzp2mSedkxEUqegUwsEZXpjUbhkwmY8+lI7nWeSz8PADj6g+gT2B7qld5+oMOYOLEiXz22We0adOG6OhoQkJCTO7hF198wQcffMC2bduQyWQsXLiQMWPGUL16dQ4dOkRqair79u3D29ubadOmMX/+fGPEEmBsf75w4UImT55MWloaf//9t8kaJk2axPjx47GwsMjzb9GwYUMUCgXW1tZ06tSJatWqGT8D33//PV988QVt2rTh1q1b3Lhxw3jcyJEjGdC3L8XdpZcGURDI8PKFstXoPmAgnTp1onfv3lhavng6h4ODQ66HsqenJ7Vq1aJQoULUqFEjz0iEEydOcOfOHTIyMjh69CiNGzcmICCAChUq0Lt3b5N7Z8aMGYl/QrnI5tixY1SqVImvvvqqwJ0mp06dMu7z8fFh9erVJh7KbOUiKjySpMciavUGA1lZmVStWJmrNyRPrLuHFJ5/7NRJRk4aR3ZeSLMGjbkVdpu798L5oEVrihcrzrLVv3HvfgS+RYtSpFBhVqxbTZkSpXPdl6++m8a+wwcYNWg4jeo1YOe+vL3pKzes5syFc2RpNNSsUv2FlYuPP/7YpE7Gk8rFhg0bqFu3Lr17986lXGTfQ61W+1YpFyNHjqJ53SYAyO0aoyzyP5R+K7GsOR9VaRcsynmgcHu2IetJBIUMmYWpwVGQCSg8bVF42CCztUBml4eCoDOgu5sIOgNimhYxU4vCxw6ZkyWqUi7IncxdwcyYeZL/ml7j5uZG8C9Djduf1GvaBjZlQM++L6bXJITz7d6FL6bXCDL23TwOaaaRpI/rNf2qdaaSd+BTrwv+/XpN3759adE6d2STo6c3nb/6jg9GfkmHL6cie4FoaJAish0dHVEqlSbbfX19qVWrFm5ubtStWzdP58fevXuJjo4mPT2d3bt307ZtW0qWLEn16tXp1q3bC63jv4Y59+YN06lTJyZMmGDSUjw7pLVZs2YmIa0eHh5G5T47hSAxMdFkvvy8g3lFzTyPJzGvvGonJycAozdar396odNmzZqxa9cuYpddxl6Rk9uuVligkitRPComb5WuAAPIBTl6Uap55WnnRmP/mmy6uhtvOzfKuBenlKsfEUlSDY3u3bvTsdGHJG2/AyL4OHti37IYLMhe/6Nrzc4nEMg3j/1JdA+fv6BeXvfpWZFKj5Pf30IURRQKBVu3bjX+7Q0G03pgj/898vtb5Df/0z4DXl5elKpVD71OR/StULyDyiPYOeLt7Y2NjU2+xxUEXl5eDB8+nF9//ZWoqJwOlk/eZxcXFz766KPXuhYzZt51spWLbt26Ubx4ceP2bOUC4MsvvzRu37VrF0FBQbnmySZbuYiIiKBTp05Azot/tnIBGOXX5MmTadKkCeHh4XzzzTd5KhcKhYJPP/2UAwcO0Lp1a+N5xowZgyAInD59moiICONx1tbWNG7cmPPnzwNw8eJFPvvsM2JiYgBwcXbm4sHTFPYpRHjEPRb9tpRa1WoC0LBOfTzc3Vl1bw0WFioa12vItl07kMlkbNr2J+cuSca0zh92QCaT0aHnxxgMBm7dusWtW1LH3Znz5rBtr/TyHRRQhpGfD2XwuBE4OTqy5o/1+BXxBcBSbcnYISNZuf53RFHE092DxSt/NV7Hl9MmY2drS3JKCiB5edu3b8+uXbto0aKF8aX40KFDREVF4ejoSHh4OKdPn+bsWUnBSUtL4+eff2bq1Kls27YNQRA4ePAgQUFBRuUiMDCQa9euYWNjQ2pqKsWLFzfeKzBVLrLJS7lo2rQpX3/99XMpFwAzZ0o1x77//nuKFy/OiRMnWLVqVS7lol//fhgytOiiUhFUMgRrF2SKQGS2r9cLLQgCqjJu6OPT0YbkblhgHCeToSzi8FrXYsbMv4H/kl4DoFkZZtz+pF5ja2FN5uWEF9JrOtX/kF7jBpAZkkDKofv42Hsgs3pkHJEJufWaRzxP5/YXqQP8b9VrAKp+2AGl2pKkmGiKlK2IIIBP6UBUlq/XQVGyZEkGDx7M4sWLTcrXPH6NgiBQpEgRSpUq9VrX8m/BHL7whpHL5YwcOdLkA12qVCmKFy/O4cOHjZ2NfvrpJ86ePYu7uztqtZozZ86wdu1ali9fbjKfo6NUJHD+/Pkm3uvHad68OSBZ3RcuXMiSJUtwdHQ01rgAWLJkCT///DNz5sxBEATq1av30tfo6elJ48aNaT2iC2U9SuY7TtTlXbC9W4U2PMxI4vKDUNqXeQ+AyiXK42htz/YtfxFy6wYRfhksDN1ARmVLkh/rimhjYUW1QuU4G3mVn46vYNxOqf1uQ7/quc5Ts3AFAKbun8+CE6s5HXoh15gnmTJlCj/99BNbtmzB09OTkiXzvr4WLVpgMBjo378/06dP5/jx49StWxcbGxsaNGhAREQE48aN47PPPjN5oLdq1QqdTsfy5csJDw9nx44dJorG8+Lo6EhsbCzLly/n6tWrtGjRAoChQ4fy448/smXLFooVK0aJEiVyHRtYrxGNevajVJXqlCxZ8rUbsbKxtLSke/fu+aYK1qhRA09Pz39kLWbMvOt06tSJokWL5qtc7Nq1y/ivT58+b6Vy8fizMSQkBFdXV37/XUoVVKvV9O/fn8GDB9OmTRvi4uNZsPRn6fhH13I7TJINxf1yGkHo9Xp6d+uJVqejVPGSrNm0gbMXz2NpaUnzJu8Z/R4ff9SZ6d9IdUcqlq/AZwMGGO+JXq83KhdJSUn0+LADHT9oD0BGZgYXLl9Eq5MK7n70QXt8C+XUDlv64894e3obf38yCu1xY9PNmzeNhq2oqCjmz58PQJs2bdDpdHTo0CHfjkYhISFYWFjkW0crW7nYsWOH8XPwZAH9161cyCyVqPwcUfrYo3C0eu1GrMfXpnCxRu6eT8SXSo6ysLmguxkzz8N/Sa9p3LgxtTo2evrgPIxHeek1lbzL4GBpx56zBzj/5zGuP7jNorubSA1QYFvfxziXjZW1qV7z9/PrNWcjr+Ya8yT/Bb1GJpNTqUUbGn7Sl2KVquBXscprN2JlY29vT9euXfOtf9mqVauXynT5r2I2ZL0FBAcHm7z8KxQKNm/eTK1atfj666/58ssvSUlJISgoCJVKxbfffotWq2XKlCnUqlXLZK5Bgwbh5ubGZ599lu+DoWHDhvz888/ExMQwdOhQfHx82LJlC87OObUimjdvzoIFC4iOjmbGjBmUK1fula/Tupwbrv3LYd+6WO6Cq0+hrm8VfB29UcjkfFClGRYlHXEwWLH0w2kUsfZk/LRJTP/xO+y8nfC28chlEPuh5Xga+ddg/vFV7Lt9nJ6V2jGwRtdc5/m4fGvaBjblSNhZjt87T5VSFZ65ttq1azN9+nScnJz47bff8s1hHjt2LIMGDWL79u1MmzaNli1bsmLFCgDmzJlDjRo1+N///oe7uztKpRIHBwcAxowZw4gRIzh06BADBw5k+/btLyV8R44ciUqlIjg4mI0bNxIcHMxXX33FhQsXGDNmDJUrV2bz5s25QmLfNJaWlowePZq+ffuahCZ7eXnRtGnTN7gyM2beLf5tykWRIkXYtWuX8Tmg0WhYsWIFc+bMYfPmzQBcu21aW6RNc6neVyFvH5Pt9WrVoUSx4qSlp/Ew8SFarZam9Rsht1JSrVIVHB0c+XvvbrZtlVIXzp4/x5wf5hIWfheAtPQ01Grp2W8wGEiOjGTXXslbLwgCK9atJiNdqtnVpd1HtHm/hfHcEZH3jbWzIHeKS2pqTpFxa2trHjx4gKenJ/fv3zcak6pXr86wYcPQarUULlzYaBQ8e/Ysx45JaYs1atTg/fffNxq6QkND3zrl4k2j8ndGXd0HwdHUeWIR4IpgYU5gMGPmefmv6DUA9u8VxeWTQATL539GPK7XtCndGIW7FW7+nixr9y1FbL2Y+L9pTPvuWywMCgI+qIo2KicNHL340npNxedILTTrNa8fNzc3xo0bR5cuXUy2lylThgoVnq17mslBKOiWnACVK1cW8yoqa+btZ9myZXzyySfMnDnzmZ06XoWENSGkn4t55rjkrFROx11l6MavqepTlp8//Drf9ECvCdVJPRlN8o6wV1+gHNwHV0LpmreFPjg4mOXLl3Pq1CkqV678Sqc6f/48Fy9exNvbm7/++ovZs2czbNiw52r5+1/izp07nD17FkdHRxo0aPBOFtkVBOGMKIqv9oExY4JZ3uRPWFgYRYsWpUWLFmzdupWsrCz8/PyIjIw0PuOvXbvG4MGDOX78OHK5nIoVKzJz5kwqVKjA3LlzmThxIj4+PpQpU4Y1a9awdOlSgoOD2bBhAwMGDCAmJoZevXoxfvx4k3Nls2jRImbMmEFkZCQBAQHMnj2b2rVrG2XNp59+ypkzZwgPD2f06NEMHz4817q/++47RowYwcyZMxkxYgQAgYGBXL58mUmTJjF58mSKFy/OzZs3mT9/Pk5OTnTs2JE2bdpw/tx57t+PQKfXM3rQcL6d+x1fj53E1RvXWLV+DWoLC2JvRDB/yUJGTR6PpVpNRmYmZ/YcpYS/lIZ54swpxk2dyJkL59DpdJQuWYqx48aRmZBC78EDaFinPh3atKX/8C8oUagI0fFxWFpb0bblB9y9F87xM6dISU1Br9ORFBZDVlYWxSoHkJScTLOGTUhJT+XoozpZT15zrVq1OHJEqumoUCjw9vZm8eLFXLt2jc8//xyQ0vfs7e3p06cPc+bM4dChQ2zYsAGVSkWjRo3Yvn07tWvXZuXKldI9OX+ewoULExUVRUBAAOfPn0ej0TB+/HhWr15NfHw8RYsWpW/fvnzxxRe55J2NjQ0uLi6EhYUZ7/+6deto37498+bN48svvyQxMZGvvvqKcePGMXXqVBYtWkR8fDxVqlRh3rx5BAYG5jr2bUHUGR51LtSh9LFHZq169kFvIWZ5U7CYZc27yT+l14iiSOyCi2ju5t/xHB7pNRGXGLp9GlW9n67XCGo53pNqEr82hIyz+ehMMiDvhJbc81kr8BhSCblN7meaWa95M1y5coVr167h4+Nj4uR7l3iTssbsYjLzRnDsUALLQGdEGSTvvIvuQXqe4648CKXH6uEUdy7CmPr9pI15PexVMgS1IpcAsanvg8xGidLFCl18Bkl/3s59cF7o4cGsM9i38sO2lvezx78CaWlpTJw4kfv37+Pu7s7nn3/OlClTXus530WKFi1K0aJF3/QyzJh5Z/D19TVJ2bOwsOD+/fsmY0qXLs3OnTvzPH7QoEEMGjTI+Ht2Gh9Au3btjFFD2eTlGOvduze9e/fOd40lS5Zk0aJFT1338OHDjQauESNGUKFCBUaOHMnvv/9OqVKl0Gg01K5dG1EUSU5ONnb1Awi7G0aP7t359bffaNP2A8YNHQVA32EDAdi5XjK6tWzaggnTviIjMxMPN3ejEQugWqUq7N64jUPHjtC80wdYWVuRkZ5ORMQ9ihfzZ/OKdazctBaATz79lCF9+6HTyRD1BvYc3Me23dL9nT7xa0D6OwSWCuDoyeM0a9CYiuUq4ObuRkC18vnep+x7dfnyZUAqdq9UKilWrBj29vbMnj0bpVLJe++9R7ly5diwYQO9e/dm3rx5VKxYkWPHjjF79myTmiRarZbGjRsDUnfZGTNmMGPGjFznXbZsGcuWLTP+/niU2KRJk0wKvw8cOJCBAweaHD9+/HjGjx+fa94nj31bEBQyVEUd3/QyzJgx8w4hCAIuvcqQeTUewVLBw99DMGTkTuW+8iCUHutHPVOvAZCpJTVdez/nmYsc7BoURmajQu5ogeZuMil77z3XGsU0HVHfnsS5cyksA11e6PpeBLNe8/wEBgYSGPjsSDkzeWM2ZJkxITg4mODg4Nd+HkEmYFlGeojKbVTEzs+7HlWNwhW4N+rgM+ezLOuKIBNQelqTeS0BAJmdCvvGRRAUUkqaQaMn/VwM2ohUBLUcMfNRjY+nFIBP3h2epyHryRf7V6FWrVrcuXPn2QPNmDFjxgznzp2jc+fOxt8fPnzItGnTCA4OZu7cuXz22Wcm9Z2ER2nJCisLkMtAn9t9fffeXbI0UpvxkZ8PzbUfoE6NWvzvux+Y+8t8vhg8CFdnF+rVrCPNbaF8dA41Fu4uyFIyyHqYRsM69SnmW5S7Effo0Lqtca4Bn/Qh9PYthn45iu4dP873nPlRsmRJ1q1bx/jx4xk0aBB+fn6sXbuWkiVLmjTIAFi6dCkff/wxy5YtMxoIY2JizMqFGTNm/vX8U3oNgEwlx6q8GwCGFkV5uD4015jn1WsAbGpIdWCVHtZGh7/Syxa7xjl1FlWeNqSfi0H/MAuZtRJDmjbPuYzoRJL33ctlyDLrNWbeRcyphWbeCh5uvknaiWiQCaiK2qK5lZxngcS8kDupcR9UEZmFHFEvknosEn1iFjbVPVG4mBbME3UGdPEZyB3UZFyN5+GakHxmlVB6WuM+qCIAunipxonC2VyEz8zLYU71KHjM8sbMi6DP0pIR+0i+yATkKgX6TC0Hjx5m6uzp3Lpzm6tHz6FS5Z1OZuFii9LKwjiXLj0LmVKB0iZ3U4qH8QkcOXqEnr16UaNKNVb+b+lT12bpZo9crUQ0GDDoDMgUcgTZu5dCbebtwCxvChazrDHzIog6A/GrrpF5PQEUMixKOJJ1Of/OqE+iKmyLa79yCDIBg0ZP6qH7iFo9NrW9c6UGGjR69A8zUThbknwggpRdd586t2UZZ5y7BgCgfZCGoFagsP9nmmuY+fdhTi0085/HsY0/jm1yOkmlnY+RPBnZhdsVAuhyG7YEGyWuPcsgs5DSJQS5gG3t/FMBBYUM5aPORDKrp3/85U5qBBslUdNOoE/SGLfbNiuCfYPCz31t/xTndv7FiY1rEEURnTYLOxc3Wg8bi6PH87fLNWPGjJm3idTUVMaPH8/69euJi4ujcOHCDB06lH79+lG/fn0OHDhAbGwsLi7PlyYht1Bi45NTAFjUG0iPSaJF5w9xcXbhx2+/p023Dhw+fpQ7567j4pQzVmlvZTRiZc8lt8hdSDa71siCBQvo168fpUuXZsroCU9fl1pJ1sNUDNrHugHKwNrLyRhR9k+Tnp7OjBkz8PX1NYloMKTHkPpXKwypkSCTg16DuvIY1GU/fyPrNGPGjJm3DUEhw6W7acpY8t5wkneH5zjqZUKeTnu5myXOPQKNjgyZSo5do/z1DplKjuyRbvMs14fC0xp9hpbIr49jSM2J3nLqUgqrsq7PcWX/LAdWLOHKwb0IgE6jwc3Xj9bDxmJpa/eml2bmLcBsyDLzVmJd3g3rR+G5APpUDZkhD1G4WqL0sCbjUhxyWxXqEi9fx8KiyNMfgvqHmegTcrcyT9l5F4tCtqj9344aGulJiWgys9i75H8m2+PCw1g5Zgh1uvSgXJPmb2h1ZsyYMfNyiKJIy5YtOXDggHGbp6cnp06dol+/foSESBG1rq6uedbneh4EuQxrT0eT45293Xjw4AEepYqgRI5Bp0dhqUKQP9ugFBkZyfnz5wGoVKmScd6M2CT0GaYpH3qdDrlCeg3TZ+aRDmKA9NhkrFzt30hkVnp6OpMnT6ZevXoEBwcjatMR9RmkbG2JmHDJZGzm0ZHoHpzCqtYsZFZvnzJkxowZM28au4aFsWuYY5DSxWeQFZaM0scGubWSzOsJKNyssCj88kYay/KuJD8lIksXnZZnOZWE1ddROFui8rZ56XMXFKIokp6USPTtUE7/udFkX8S1y/w68nMaftKP4lVrvKEVmnlbeDNuPjNmXhC5jQrrSu5YFLZDppJjXcn9lYxY8KiIovwpysFT9KK4Xy6TdvbBK53/VRFFkW0/fsf/+nRl6dB+eY7JSk9j9y/z+WueuVOIGTNm3i327t3LgQMHKF48p/B6REQEP//8M2lpacTE5HRxunz5MgEBAVhZWeHg4EDz5s2Nhe03bdpE8eLFsbCwwMvLi2HDhgGwcOFCChUqhIWFBYULF2bWrFkATJ0+jW6f9CA1NRW5WskPC3+ieMkSqNVqAgMDSU9PZ/fu3fj7+6NWq3FxcaFTp06kpKQQGRnJhQu5az7KlJLBqu+wgdgWcWX0lPGUqFaWKo1rc+rcGeq3bop3GT+mz51lPGbp6t8oV70iNjY2VK1alcOHDwNSLRNBEOjRowcVKlTA0dGRuXPnApJcmDp1KkWKFMHW1pYGDRpw5coVAJKTk+nXrx9eXl5YWVnRtWtXLl++jCAIDB48GIC4uDgUCgWdOnUydq46cOAAgiAwpp0Lcb94M37RWQJGZ1JkaCbBizTEpUjCctGy1RTycc91P3W63AWPzZgxY+a/jsLZEutK7qjcrSU9p7LHKxmxAOSWz4hRyU+3ESHmp3Nk3Ul6pfO/KjqtlrVTxrCgbze2fDc1zzGpCfFsmTWVE1s2/MOrM/O28dYbshJj0tn43RmWjz3C+T3hb3o5Zv5lZHcEeRkyQx4W4EpenPh7d7l2eD8ABt3TizteP7SfPUsWIBqes0evGTP/MURRZMpv52j1zVp6zf2bhOSsN72k/zxnzpwBoHbt2gD4+flx+/ZtDh48yJo1a0yiqFQqFd27d6dZs2aIosj27dupUqUKV65cYeLEiTx48ACNRoOfnx/Lli3D1dWVIUOG4OjoyI8//kjhwoWZMmUKDg4Oxs6AAD/88APDhw/n7l3Jw21pacnWrVtp0qQJZcqU4YcffqBx48asWbOGFi1aUKVKFeOxVapUYdmyZSQlJdH38/4UrVia9Vv+AOD85Yt82PpDroeG8N5HrWnX6gPUFmq+/v5bOnzShRrv1eeL0UNJS0/nm/GTCQ8Pp06dOvj5+fHTTz8BsGPHDqpXr05KSgqDBw8mKCiIESNGMH78eMqWLUujRo04cOAAZcqUISAggIEDB7Jw4UKioqLo0aMHfn5+dOrUCYCVK1eye/duXF1d0ev1nD17FtmjlEa1Wo1SLvDT3xlUmpjFvN16mpSRUcJdYMtZA6VHZ9HlfxombNDhYAUVisi4d+8eGzduxNvbm3nz5r2uj4gZM+8k58If0uKHQ9SbuY/tl6KefYAZM8+JYKGQUhZfBgNkhr5Z3Sbs/Bkirkoy2KDXP3Xs4ZVLOfHH2n9iWWbeUt56Q9bm2WeJuplEakIWR9bd5OL+52sxasbM8+DUrXTe34LnkAHq4g4FvZwXQv2C+eHnd27l+rFDr2k1Zsy82yzdfpN4p+M0+CCG0o1u0PXHjc8+yMw/giBID+TSpUtTrVo1lixZwpIlS0zqYmVlZfHTTz+xadMmkpOTAXjw4AFt2rShWLFiZGRIzTpu3LjBF198QVJSEnq9noiICH766SeOHDlC8+bNGTVqFPHxOQV5v/76awA6derEDz/8QOfOnWnfvj3u7u5s376dvn37smbNGgBsbGyYOnWqybH16tVj8ODBrPx9NV0/6ox/UT9prJMjhT/+QLqukqWo2fFDipQqCcDBY4dxdnQCIDrmAV5eXvTs2ROAO3fuYGkpNRxp2bIlS5YswcZGSgWRyWTMmTMHgDp16rB582acnKR5KlasyLZt27C2luqoNGrUyKRjYVxcHEePHjX+3qNHD0aOHAlAZmYmk7sXY3JbBWmP7Lu/HjFwOkxEBGQy2HnJgAhEPhSJeSil5MfHxzN58mRq1DCnf5gxk41Wb6DTwmNciUzmbnw6/Vee5fDN2De9LDP/EgS5gGNb/7x3Pkvrl4FFMYeCXtILYfNI9j0vh3//leibN17Tasy87bzVhqyUhExSH2pMtp3edvela2GYMfMkal97vCbUwH1IRZRFbHN25PcRkwvYt/DDpVcZrCt7/CNrzA8bRyfUNrbPHvgYOo05ysSMmbxYuusqRYpK3j+ZDNyKJHM/Nu0Nr+q/TXZqW3ZKHUDPnj1Zu3YtR44cwcMj5xk8depUIiIiAFiyZAlKpRI7Oztu3brFl19+SevWrQHJKLNo0SL8/PzQarXMmjWLqCgpIuLu3buMGTMGOzvJSZCamkpsrKRgjh49mj59+jBs2DBkMhkqlQqNRsOYMWPw8pIaaqhUKpo2bWpcU7NmzShatChbt25Fp9Mx538/cjXkOgDnrl7G8Yj0s4OVFa7R8dggNS2pXa0mZQODjPMcvXDKaMyztbU1Fl5PSEhAo9GQlCSlgly8eBH9Iw/2vn37AKhevToAY8aMMc6RF25ubmzYIKVpFCtWjHHjxtGhQwfj/n0hMqKTRFxtBRQy8HOV/D1rP1Oyur+SykUF0rJgfGsFCukykMlkfPrpp1SrVi3f85ox81/jdFgCWXrTl8w5u0Pf0GrM/BuxruyB5/hquA2qgMzpsW6E+SRlyKyVOHxQDNd+5VC/YUOWh3+JF25wotNonj3IzL+St9qQdSmP6KuMZA3/+2wf14+ZQ3HNFAwytQKluzX2jYvkO0bpbYOqiB3O3QKwreONuvibL/QuGgwUekzZeRaCTIZX8dKkJyW+vkWZMfOOcvJSKglxOSIx7LaSLsPW0nTYXjKyzDV+3gQNGjSgfv363LgheVvDw8O5ffs2er0eHx8fY7QRYOLg2rVrF1ptTrr1nDlzSEuTjJIODg7ExsYil8ulgrLp6VhZWQEYDVp50atXLxYvXszgwYNJSUnB1VUqaL5u3ToiIyON4/IzFnl4eLBr1y6aNGkCwIf9+6C8Fm5ynCg+0jJkAs2b5zTouHTpEkuWLAHAy8vLmPKXjZ+fFOX122+/Get/Zdfp2rdvH8WKFaNEiRK0atXKeB927drFhAkTSExMNF7fxYsXgRwDop2dHTKZDBcXF6zsvdh0Rk9otIjOAAmPbLx7rxlYdign/SNDC07W0j1ISozPuSYzZswAsGD/rVzbToc9JGjiDg6Hxr2BFZn5NyK3UaHytMGubqF8xyh9bLDws8clOBCb6l6vXJ+rINBpNDgXev7O8CpLK6wcHMlMS32NqzLztvLWGrISY9K5djTvl0rRAAdWh2DQm1+QzBQc6uKO2NTzyXOfQ0s/3PqXw7LUi4W8vi5EUWTzrG8IPXH02YOzjzEYWDasPwv7B7/Qce8aoigaoxLMmHkWOp2BSUsuotMJ/DTLjm2bLFmx2JpDe9UUddSz50w0i/7MrXiYef0IgsCff/5pjEC6fPky69ato0ePHixcuNBk7Pjx4/H09ATg9OnTWFpakpycTLFixbC2tjZGdWVmZjJv3jyjwWnixInGSC4/Pz+mT59uTE20sbGhQYMGAFy/fp1+/fqxevVq5HI5M2fOxMrKips3b5oYlhwdc5wcO3fuJC4ujpYtWxIdHc2WLVuMhqTok2dJj0k2js2MSUQul0KZDh8/yq37YcYi94cPH8bHR5JNCkVOXccSJUqgUqmIjo4G4MqVK+zfv5+hQ4eieeShlsvldO/enREjRvDVV18Zr2fx4sVs2bLFWBC/b9++xuvITtlUKpU0btyYpKRENm/fj42FgAi0rSxDb5ACl1cf06PVw+k7Ik7WMHuHjlN3JKNi/xoJpG1ri2j49z6PRYM5Q8DM83PhXiLHbsfnuS8lS8+EzZfNWSdmChTrqh6oSjjk3iGAc+dSuPYpi6rQi2V3vC60mix+nziKuLthz32MJiOdpUP6suiznkTeuP76FveGEUW92TGUB2+lISvxQTqrvzpBZmr+XnCdxsDyMUfRZJo95WYKDof3i6LwsMrZoJTh8EExLIrav7lF5UFqQjy3Th9/qWMNeh2n/9pUsAsqYPT6jJc6Ljw8nBkzZjB16lROnDhRwKsy82/k46+P8tWvUmHR5CQZ+/625MYFJR+UzuBgmAqAoT+dYeEWc+rHm8DGxoaJEycC0Lx5c27dusXixYtp3rw5+/fvJzAwEICgoCDu37/PV199RVZWFjKZjLp167J582bmz5/P8OHDAZg/fz69e/c2zh8XF0daWhqDBg3i6tWrTJs2jdatWxMeHo6LiwsrV67ko48+QqFQYGFhwSeffIKVlRUNGzZkx44dgFSrShRFNm3ahJ+fH126dEGtVjN+/HiuX7/OnDlz+PTTT1m7di0XLlygQoUKdGzfnuDFX7O951TGV22HR9ESDB80zHid69atIyYmhiFDhpCZmcmpU6eMaw4ODkYURaZPn87GjRspUaIElpaWLF++HH9/fyZMmEBcXBwzZ87E1dWVb775hr///htHR0djoXorKysCAgKM0Vw7d+7EkEczkI4dO+LjYoFKAXfjRT6tJ+fnT5SEzrCgXwM5aiUcvWmgWZCMA2MtuPWdmo7VpFfLWiVk6O7tRB9/saA/FgWGVqvN87qfhWgQyboaQ+axe2RejEbUmRUMM0/n2K14PvjpCBp9/oaq23FpvDfnIAazgdRMASHIBNx6BiGzfqy5laUc524BKJwt39zC8iA6NIQHt1/uXUuTkc6FXdsKeEUFh+Rkf7nyLg9itrH/QFkOHCxHbOyuAl7Zu43wOiz/lStXFk+fPv3Sx189Esm+357Pqlq+cSFqtS+ea/vtc7HcuRiHTqNHFEUqvV8Y10JvlzHCzNuJaBDJuCKFd1sGuiC8bPeP14hWk8Wiz3qSkfxybXJdfYvx8dRZyBUv37XxdfDgwU5uhE5Go3mATGaJl1cXSpYY+9zHL1261NhdTCaTMW7cOGOUw9uAIAhnRFGs/KbX8W/iVeWNTbPfycgyVUIL22lxshI5H60ybrOzUvBw20e5jo+Oz2DayivEJWWh1empVMKJkV0Cn1qPyMy7T2hoKKtXr2bixIls2bKFVq1avdQ8eo0Og0aHXK3k4OFDNGjQgM8+++wf7fQ3adIkpk6dStOmTdmwYQNqtdpkf8bJyWSd/fYlZxewbrMHpefbVfA9NTWVrVu3cv36dQRBwNnZme7duxvroz0LfXw6mus5aWDKog4ovN58Ws7jmOVNwfKqsmb6juv8L4+0wryY26k8bcp759q+6kQ4p+8mkKXVI5cJjHm/NJ4Ob5cxwszbiajVk345HrmVAnXJtyO75EmS42JYMrgfeu3L1bwqVrkarYeNRSZ7e977AcLDf+VO2Bx0uiTkclv8/AZTuFDwcx9/+EgtsrKkyGtLdWFq1tz3mlb6crxJWfNWRmR5+Nnn2zVOrjTdcX73PdZ+c4r05JwP/YM7yWz/+RLXj0Vx80wMt87GsnbqGU5vv/M6l23mX4IgE7AKcsUqyLXAjFhhYWEIgoAgCMYuWCAVLs7e/iIoVRaUqFYLgGtRMey8fIOEtPTnPj427Bb/6/0xSTEP8txfv359BEEgLi6O/fv3IwgCAwcOfKE1vigXLvbh8pUBaDTSmgyGDCIiFhMZteG558ju5mVtHUflKus4eqwuDx+eesZRZt4kgiD4CoIgPvo3/rHtS7K3v87zN6rkmWtbeLKS89EqoxgyXF9A4vaOWFX/hl933jYZ2+7LQ8zbeIPf99xlw4EIxi66iGvr9WaP+r+cqVOnMnXqVD755BNatmz50vPIVQqUNmpkioJ58X4ZWTNp0iS0Wi1//fVXLiMWgCqgFwhy/r6s59utWsLjXyT6SCRtc0MyL8zJc++bkDXh4eHMmjWL69clh6koisTFxbFo0aLnn0Se8/ocmRRD9NW7PLhgfsc0kz81/Jzz3WetMlXHBv9+nm6LT5CpzUnL3XIhkrF/XGLj2fv8dSmaLReiqPHtXnZeiX5tazbz70FQyrGu4FagRqyC1m3sXNzwKS1FWb+MbnPr9AkWD+pDVnrejXr+aXkjiiLHT7xP6M3J6HRS4IFen0Jo6FekpDx/GqRCkeMkyci8z9FjjUhPDyvo5b6TvJWGLCdPa1oNLJfnPr1WRGlhuuzY8BT+XnyZDTNOc3TDTY5tCs2z69zZHeG5N5ox8w+zdOlSRFEkLS2NdevWvfQ8WY8KG16LimHX1VAS0l4sHS8rPY0Lu7e/9PkLEo0mgbi4PXnuCwmZyPkLnxIff/CZ8zRv3pwyQVoqVNyOhUU6Gk00Z8914k7YTwW9ZDOvh08ECWugwzNHvwA6Xd5p6Gsm1sbOKu/IRPUTsiZTa6D/rJO8N3wv743Yy/p9dzl+NXdx3ocpWrYdv//qizbz1rJs2TKysrJYsmRJgUXf1a9fH1EUCywaq6BkDbp0EPXsumxg+l96wuNf3EibeWLSy5+/gNm+fXuedYhSUlJYvnw5f//9d77Pi2zkDmpEbyt2XjuKl70bzpb22KXK2fTrWlJSUl7X0s28w9Qt4crwprkzSADSNAYsFDnPERE4FBpHn19P03b+ERbsv8X3f4fkeey326+9juWaMfNCFJS8yUiRake+rG6THPOAkGOHXvr8BUlS0jnS0m7kue/c+WAuXuxHcvLlZ85TJnAOanV20X49GRlhHDveiKiojQW42neTt9KQBeBdwhGFKu+XQ21Wbm/g/ZBEom8nc25XOPdD8k63UtsoC3SNZsy8KH5+fty+fZv9+/ezZs0atFot3t454eOXL18mICAAKysrHBwcaN68ubEY76ZNmyhevDgWFhZ4eXmxcP0mTt25x9GbUirdgv3HGb72r1zn1OkNbLt4na+37mX0hu38tPcYABkaLd8tW4GbmxsuLi706dOH9PRnez4mTZqEu7s7arUaf39/Vq1a9cr3RatNyHefwZBBfPw+zl/4hBs3viIj4x7x8YfQ6XJ7XFSqVBwd1/CkXnn79vdkZOTugmrmreI24AfUBzoCSuA+gCAIZQRBuCoIQrogCImCIGwTBMEbQK/X069fP7y8vLCysqJr166AVEdIEAT69++Pt7c38+bN4969e3zwwQc4Ojri5eXF4MGDEdDxactiGI4PwnCoJ4bbazAc6Yvh3CTSk2JMFijGnSb94EB2zuvA37v20+WrI4gPDmE4/gWGgz0wHB2A4cYSRNGAn6cNFy9eJCgoCE9PT7788ksEQaB+/foAJCUl0bNnzzy/f6/jO2bmv0VByprhI8aw6pjILwek6JBWs7U49s/MdU6NTmTyJi1B4zLx/CKT5rOkeiBJGSIDVxjeClkDGIv558WdO3c4evQos2bNIj4+nlu3bhEbG5vn2FW7N+UyiGnSszhy5EiBrNPMv4+PqhTOV/HK0uU2rh4MjeNseCLf7rhOWHze3xlPe3NqoZk3S0HJG09PT1buPvDKus1XP8x/K+RNekb+UbpabSyxcbs4dboN9yJ+JTX1BgkJRzAYtHmMTSQzM7cOc+36WAyG/3at8LerQM5jyJUy2o+uwu9TThbIfAoLGR8Oq1ggc5kx87KULl0aV1dXlixZwp07d/jggw+4fPky9+/fJzMtFZVKRY8ePXB2diYsLIxp06YxadIkFi1axMSJE8nIyGD+/Pmc2bOT0AvnKONbiBLuLtx4EEfjAH/c7XJ3Htl7/RZ7r9+ilIcrQT7+RCdJ3uLN569y/l4UQ4YORSaTMXPmTOzs7Pjuu+/yXf/Dhw+ZPHky9erVIzg4mLCwsJcqlPskFhbuzzXuXsQy7keuxmDIwtq6BFUqb0Quz3mJy8gIJ89wTEAQ3trHnRmJa0As0BMoCmwCygDegAZYDsQDvsAYYBLQ+969e5w/f56uXbtSv359Y420bA4dOsTkyZMJCgri448/5siRI3z99dfcuHGDuXPnYmdnx8wpU1g4TiAlKwt0KeDVCMI3I976DSFwSM5kiVcRPBsghq1HvLsRvcNYUNgiFGoBghwxKQSi9tC2ZSMCijpQsWJDrl27xtSpU3Mpt4MHD2bFihUMGTLE5Ps3bty41/IdM/Pf4mmyRp8c9tyy5mH0LWKOfketGgINS8vYe83AiOZySnrkVsfn7NQzZ6eexoEyWjeXcfW+9Cweu07H2pMahgzt98ZlDYCTk5Oxe2R+ZGRksHz5cpKTkxEEgY4dO1KqVCmTMQkJCTzIiibIyx9vB3fuJkRyIyaMqn7PJ8/M/Pdws1WzqEclei0/89RxAvm9yZji7aDm527mMmhm3ixPkzeazIznljeHN60n4vZN/F5Vt4mIzvVu9Sbkjb1d3tllTxIa+g2iqAcMODnVoXy5pSaR3knJ+TVMERCEtzYm6R/hrdbsIkMeFsg8ghx6fFMLtbU5IsvMm6dnz5588cUXZGVl0bd+DRIfRAGwoF93Sn/QmVWrVnHxYs5D69KlSwAUL16c0NBQdu/ahSHyHtX9CuNgZYmLrTU3HsTh7+aCv1vuGgxXIx8gAF1rVEStzPnKX4uKQafXM3PmTOO2v//++6lrt7GxwcPDg9DQUI4ePUrVqlVp27btq9wOABQKW6ytS5OW9qwQeQGDQfLyp6XdICp6M4g6nJ3rYWlZCDu7ciiVTrkivJwca6NW566FZOatYwnwA2ABvAfMerTdAugClH1sbBBIkU1ubm4sX74cmSy3QP/mm29o3bo1qampHDp0iJo1azJmzBiysrL49ddf2b59O1OmTMFepSMFAcE/GEGmwPDgECSafh6FIm0RnMoi3t0MmY+iNPQZiOFbQJNoHOfvlERycjLnzp2jVq1ajBo1itDQUP7880/jmK1bt6LT6XJ9/6ZNm/ZavmNm/ns8LmvWf2HFxSNSRFXKqtI8lDVh1aq7T5U1e/bsIcgjlR61ZXg7Cvi5Cey9BnVLyqhdIndNrx2X9AgCLPlUia065yV85yU9Oj1vhawBaNq0KYsXL37muOzILVEUOX/+PKmpqdja2lKyZEkAKlSowJEjR1hx6i8UMjk6gx5BEKhdu3aBrNPMv5Mdl/OuTfo4z2PEcrVWcmhkQ2RvYUMiM/89evbsyReff06WRkPfhjVJjJZ0m0UDexHUscczdZu/d+5EGxNdMLpNHu9WT+N1yRtra38UCgd0usRnjDQ8+gcJCYd48GArOn0Kbq7NUKmc8fRozZ07s436TzbeXh3Nhqw3vYCncengi9UYKV3Lk6x0HbfPSQqGa2FbvEo4EFTP22zEMvPWUEQBok6HvaWaYq6O6DRSowK9RsP40aO4ePMOkydPpnr16rRs2ZLMTCmFY+XKlWzcuJGNyxaz4+RZdspkfNmqUX59EUzJY5AgCHh4ePDbb78Zt1lYWDx1GqVSyYULF9iwYQPnzp2jX79+7N+/nxUrVjzv5eeLwfA8BR1lgKSMCYKckJDxgIggKBEEBQqFPVptoukRMivKlJlDcvJFZDILbGxKvvJazRQMgiD4Ao/HXv8OzAYigMd7DI9DMmLtB/4CvgFyV6XOAy8vLwBjClBe9YzW7ruLVi88UghE5IKIIa+Ovkob6X+ZDBd7JclyAc21eYBA9Q/Ho0i+xOE9mzly5MhTz5eenk56ejr29vasX7/euN3CwuK1fsf+aQRBIDAwkMuXn10DwkzB075pWYagwcsRGpTUI+pzmuJM//kvLl40PFXWnDy8k9m/bGWeXOTKNHWulO28yG+Ih6sDv63KqZvyJmXNw4fPdpKq1Wrj/QC4d++esTi8SqVCJpOZpBXqDJJcatCgAXq9nrCwMLy8vFCpVJgxk026Rseuq882ZD3OsKYl+PvKAy7dT0IAKhVxpEYxZ4Jr+pqNWGbeGlxSExD1ekm3cXZA96jzYGZKMqOHDObiletPlTer5//AvgtX2fuquo1M9lbpNqKYO1Uw95icpg4ymSVXrg4GIDT0a0CGUumQy4ilUrnj7z+KxMTTqFSuWFkVeeW1vou8tBnvaZ2mzpw588rFT7PStTyMev5OBQClanoQd18qgC3IYP3p2dTpUILQu1dfaS1mzLwq+keFY2+fO83lnVv4qEpZ2lcOQvbE90SbJT3YU1NT+eOPP9BqtRgeHTtmzBhiYmJws1Jjo7YgNUuD3mDAUiUZaS9GRHE1MvcLUoCXO6IIK46d5eTte2w+dwWAamXLEB0dzZYtW7h79y4bN25kzZo1T72OlJQURo4ciUwmo3LlyqjVaiIjI1/t5jzCzrbsU/cXKtSPbCMWgIWFB9l+S1HUYjBkoNFEk+3VyEYmU3Lr9mxOnf6QEyebE35vKQaDlgcx27kTNp/Y2N1oNDl19bTah2g0uQt4m8mNIAgDH5MDJR/bHvxo2/AXmU8UxWSk1MK+oig+/ofM/qLUJ6d+VhCAvb09MTEx9OjRg8WLFzNhwoQ857aysqJu3bocP36cb7/9lgEDBmAwGGjevDl/HbtPXLoMg8GAW9QSCFsLmofgEJDnXAq5DL1eRKfPVmRFujX2RJ8u1dTy8/PD3t6eChUqcOzYMWbOnMmwYcOMx2cbspKSknJ9//L7jgmCQJkyZV7kdr4yBoMhz6LYZt5+tBF7ke1owI9dFczuosxX4X1c1qDPQtRnGWVNkJ8DLrYQlwo6vYiDlXTM5rMGdl7S55rrvSA5BhF6/qJlxVEdY9ZKL/DNghRExya+NbLG19f3qfvVajXFi5sW5X48FVGj0ZCZmUlWVtaThyKKIj/++CPLli1j8eLFaDQaoqKiOH78OCdPniQuLg69Xrp3Op2OxMRE4+9mnk5By5sXOG+YIAipBTHXrZg0EjOerdhmI5dBk9LuRDyU9CELhYzvO5ZjWNOSONs8XTl/Fd6EvDHz7qF5pLPcPnuKOycO56vbZBdwN9FtHj33suWNs0pRILpNnapV3irdRq32eep+/2JfmvyuUrkafzYYsjAYMsjKisp1nEJuzaXLAzlztiPHTzQlNvZvdLpUoqI2ERb2P+ITjqDT5Ty2srJi0WrzriH+LlNQEVmfCIIwFbCigDpNpSVl4eBuRXxUCnLZ87WlfnArmeQYqbuBaIB716T0oj++P0NIcR3v9w3Cycv6pdcUfScJhVKOUi3j6IZbJMak4+lnT2Bdb3YtvkxKQhZOXtZUbVmUwoHOBdbJyMy7z8EVS6QfHimF5Qt75TmucWl/UhVqli5dSqsGdVErFcTfv8fJzevRaDRMmzaNhPh4bFRKPqwQiFwmo2Jhby7ei+LYzbuERMcS4GVan6NhqWLo9HrOht/n5tl4Cjk5YOvswm/fzWfClK9Yu3YtS5YsoUSJEowYMeKp16FQKLhz5w6bN28mIyOD0qVLm7TcfRVKl55ObNwuDIZsL7iMx41S9+79jJWVH+npYVhYuOHr+zkhIeNMPBl5odMlERmZEwkQGbmGhIQjxMfvMxnn7tYSG5tAbt2eAYi4ubXCwaEiKqUzMbE7sbYuTlHfAQjC8z2P/iN8hPRHkj36+atXmUwQhEnAROAnQRDmIBV/Bynl8KNHP1fOGS5429vbo1KpWLVqFb///jsdOkgiKDvS6dtvv2XXrl0cOHCAW7duAVJRT41GQ7FixQgsW5HZvduiz4gHmZJojSNE7gaVAyTdwHC4NyisjGsUM+PQabN4GBcFt1aBoABRz+BRU1DZSx6xA8elCCRnZ2dEUWTkyJE4O0uh8Q4ODlSunFPT5Mcff0SpVBIYGIiXlxclS5YkJiaGlStXIZPJ8PYtSrxLc2AfKela1pzaQKeq7XH1L4mzrTMRoRcZP348CoWCKVOmULRoUTZt2oSvry8zZsxgzpw5xMXF4ebmRu/evZk4cSIAJ0+eZPjw4Zw7dw5LS0sWLFhA27ZtEQQBf39/ypcvz19//UVkZCQHDhxg/Pjx3L59m6JFizJ16lTatGnD/v37adCgAW3btiU+Pp4LFy7wySefMGvWLKP802q19O3blzVr1lC1alU2bdqElZUVHTp0YPfu3WRkZODn58fUqVP58MMPAZg1axbTpk2jSJEiBAUFsXz5cpYuXUpwcDDHjh1j6NChXLp0CS8vLyZPnkznzp2JiYmhc+fOnDhxAplMRunSpdm6dSuurjkvhf8FdPHSyzz6LEBF28p5P6+Gva/gSrSCpUuX0rtHe+ysZOgTQ0jZUIusjAqSrElIwNNewYyOIgq5QIcqcjafNbD4oJ49Vw00CzKde3AzORlakXUn9RwKMVDRV/oMzJ49G6tfLrw1ssbOzo5y5cpx4cKFPPdrNBpu3ryJtbU16enp1K5dm9u3bxuLEz+NkJAQNI+irB88eMDJkyfZvXu3yRi1Wk3Hjh1Zt24d6enp2NraUrVqVVxcXLhz5w6ZmZnUqVPnP/fZfQ4KVN68bnQ6HQqFqZqVodXhYqMiLlWTz1Gm6A2w+/oDHqZLxq9MnYH35xyilKct16NS8He35ZfulXG1NTVqGQwGBEF4ph6i0xs4G56Im60FKVlaZu4MITY569Fa9dSctoekDC2VfB0Z1Kg4lYo4Pe/lm/kP8Nec6Sa/56fbNCrtT7qFFUuXLqVptcqSbhMRTsixQ0bdJj4+DluV6pV0G1ffovw670vGjB331sibcmV/5uixBjzpZJeQcev2NKysipGefhtra3+8PDsRevPZj7aMzLukZ9wGQBR1REau49bt2bm6JPoWGYBG+5DIyNWADC+vTtjalkYQFCQkHMLRoRo+Pl1f/ULfEMLLelsfSwnJ7jTVEKlA73ylUmmh1Wq5dOkSH330EWFhUmHRmjVrsmjRIry9vUlOTmbkyJFs2bKFxMRE2rZty4oVK+jU/mPWbFhF7YBWXAo7yntVO1PGuxbrjvxIaORFlAoLKvrVo0313ijlKias7EJqZhLv1fiIQ5e34qDy4JNG43Gydee3fdM5ceNvmlbowqnQ3cgVAhu3rKNOnTr89ttvjB8/nujoaBwdHfnwww+ZN28ecrmcUyfO0L17dxKTE/jog4/5YcEsShUuz8D3Z5GRlcqGY//j8t3jiBgo51uH9jUHoFKq+ev0cg5f3UKGJg0XB3dmzvmWrl0/Lqi/lZl3mMWDehvzxZ+FTKnE0cOL+Hs5RavV1jZ8tuR3ANKTEvlzznQiQ65i0OuRKRQIMhl6zfO9GAFUb9eJWh+9fQ+u1NQQwu8twcLCA98i/Qm/t4SYmO2kpuZEVRb3H4+PT1euXB1KfPx+9PqcyM0ff4hj8+ZkvpvlSfnyUhF4W9sygIyUFCk3Xyaz5OzZBIYPi6JNGzs+/8KFPXtSiYjQ0K6dAzY2+Qeqenp0wMa2JK4uTbG09GbSpElMnjw5z7H79u0zdqjLRhCEM6IoPndlVkEQlgE9gCpAHNIz9y9RFFs+7xyvC0EQvIB7wFqgLpAgimLQo33BwFJgBJABzAPaiqL4hyAILYCtwNBH/98AMoEspC6FAUAIsBrJqCUCMUAUUAHYANRDcpyclMlk9e3t7UlKSsJgMFCxYkX2799PUFCQsfB73bp1jQrmw4cPGTBgANeu32Df3kcKptoNMqVoKqFoJ8TEK/DwEihswL0WRO0HtSvoUo21sKzdA0mTuUHUPsnoZekOybdA1IFbHcIubMfX0wZbW1ujggpSHYm///6biIgILCws+OjjXnTt1oOli+bx+6rfUNm4o9HqISsOR29Xxu38kvQ0GROqDcTC0R1sfMm6d0Jad9GOWCTsJispHoVCgSiK6PV6WrRowdatW1m9ejXr169n586dyOVyY82fH3/8kUmTJpGRkYGLiwvR0dE4Ozsza9YsunTpAoCnpyfx8dK8GRkZFCtWjBEjRvD9999z+/ZtLl26RFRUFA0aNMDW1pZvvvmGdevWcfDgQTZv3kzr1q2NStSIESO4ePEiO3fu5Ndff6Vbt27MmDEDR0dHUlNTWbRoEffu3SM2NpaQkBDKly9PQEAAX3zxBZMmTSI6OpqlS5fSunVrihcvjpubG927d2ffvn3s3buX06dPs2/fPoYOHcrYsWPx9fXl9OnTTJ48GQ8PjwL4tL87ZF1eSMbhwc89XuZeA8ODYybbrBouQVWiMwCZ5+eQeW4GZD1Kx1PZg+b5vbqClQd2Xa4iKN6uzmp6vZ5Dhw6RmJhI1apVMRgMHDlyhJCQEGORXxcXF/r378+NGzfYunUrmZmZxuipbdu2cerUKXr06GGM8JLJZNSsWZPDhw8DUlTLnTt3WL58OVWqVKF58+ZcunSJ+Ph4GjZs+NRiwpaWltSrVw9HR0dKliz5wrLm0fmfW968zbIGCkze/ISUlt4ZsEZKXx8gimKsIAiFgB+RZEvGo/OMEkUxSxCEMMClUqVK1t9++y39+vUjIiICGxsbGjduzKJFi7C1tSU4OJjly5fTr18/tmzZwogRIxg8eDAGg8jZ8Ics33mSef3ex8InEJXaktTwK9jX6Iggk5F45HcUDu64tR2Pwt6dpBPrSTm9BTEjGWcXVzT+DXCoLT2bsyJDeLhvCZqY2wgKFR8NmszqbwY90wlRqIgvA4aPo2+3jny3fCPje3+EQ0BtMlIS0cbcwTqoMY4NP0UQBO5Ob4nCyRt1oSDSrh/CwrMErm3H8VH1YtxdO9XshDADwPedWyM+Z1F0CytrVFZWpMTldIH1KFacj7+ZDcDDqPv8OXsacffCEQ0GFCoVer0e8QUiVt8bMITAeo1e7CL+ARIeHiM6aiM2NqXw8vqYO2FziI3dTcZjXQ3Ll1uGnV1ZLl0aQFLy+cec+nnrNi4uTUlJvkCWRopWk8nUnD37MB/dxh4bm/yd8IUKfYql2gsPjzYolQ7vlG5TEBFZuTpNyeXyjlqt9qldCgYPHszSpUtNOk0d++MmYZeklJ5bUZdoUbkHXg5+LN/7Dbeir9CyyifEJEaw//JG1CorWlb5BACNLpOEhAR69fyU72bNYMPR+fRulvMHCI08T63SLdh6aimTJk1iyoAFnN/ygBY1u+BTwpn9ew+yYMECCjuVpkKRBvQe14nI+Nu0qtqTvduPAqDTSga/9Ufnc+rmbhoGtUMQZOy+sBa1yor3Kn7M9jO/4u9ZjuolmxKf8oCEqAKJRDbzL6BkjTqc+GPtc401aLUmRiwAWxc34u7dxdHTm4MrlxJx9VLOeN3zt14VZDLqd+9N+WbNn/uYfxIbm5IElM7x8BT1HYCl2ocrV3M6x92+M5fUtFBiYrYZtwmCClHMbchTqdyoWGEVBkMmly5/TmLiCQyGjFzj9u5J5cSJdJo1s32qISsqeh1Ew927C6hebSft27enVKlSpKSk0KdPH0qXLm1MbQsIyDst7V9EByTP+DokQTRQEIQAURSfzOVeA8x5NP4PoD1SjuhqYNSjMTFIisjYR79/D3QlJ6VwBxD86OfLSFFZlkA9g8FgUvPm7Nmz/PDDDyYLiI+P5+uvv+bLL79EJpPRe9AkarUbnzMgK57sPlHinTUgf+Td1qXC/Z3Sz5kPEIp1Rby1EoUc0h5cQVnxQ7RR+yXFXp8JLpUh9jiCDIRH3reUlBRSUlKMp1q6dBme3oWl02pFflu6kBWXiiJeloxqmtScEPqH92O5su8yZRoGSeMfPqBqg2KcfNSFuXiQDffOl4CkY3z++edYWlryzTffsGPHDjIzMzl37hwbN27kSW7dukV8fDxqtRorKysmTZrEvn376NatGwDW1tZERUUxduxYbt++ze+//07fvn3p06cPgiDQp08fdu/eTWBgIABt2rRh4MCBlC5dmsaNG7N//35at24NSAaxGTNm8Pvvv7Nz507CwsLQ6/VcvXqV1atXG6NXAMLCwti/fz8AQ4YM4dNPPyU8PJxvvvkGgGPHjpGQkEBCQgJjx441Hrd3715KlCgBwIEDB9Dr9XTq1Ok/Z8QCUPg0kmq5aZ/v/eNJIxYIIMgwZMRiSI0g8/gY092aJJ63p5qi8PtY1pzx1hmxAORyea6X8Y4dOzJjxgxjq/a4uDh2797NmTNnjJ9TpVIppWHmwfvvv0+VKlWws7Nj+/bteablXrp0idDQUMqXL4+Dg0O+68vIyGDHjh0AtGjR4r8ua6Bg5M0YYBiwEIgGhgP/ezRmJVALGA+UAAYByYBJrrqNjQ0DBgzAxsaGS5cuMW/ePIKCghg3bpxxzOOdcgEGrj7LtkvR6JKkZ3tW5HVsG35CRkw4iQeWoy5SFpugRqSc+ZPkU5txatwHha0r9jU7Ya+WU0ITys4dq1D7lkfpXIiY9ZMR9Vrsa3ZGEASUCjlavSRvbt68iW3RsvQaNpEvV+xlwdBOOHsUIqjdF5z5awWDevdg+rEU9GlSxkrSzTM41O1OOpByejPqwmWxKl4NAF3CfWT+1bDwKknmnbOkhxxlvVJNt6DyNG3a1OiE6N69u9EJMXz4cAICAujTpw+TJk0y3pOEhARatmyJm5sb48aNM8qb0qVLGx0SjzshzOm27wYlqtUi5Nih5xqblZ5GVrppt1gbJ2fiI+7h5O3DzgVzib0bZtynewHnvNrGlgY9ehNQt+FzH/NP4uRYAyfHGsbfi/uPRiG35vadOcZt10PGY2tbhoeJxx87Us7jJVWysbEJJKjMj2RmRnDhYj/S00NNDF/ZmOo2+Ruy7t37BYD7kb9TrerWd0reFFSp+yVIQqMWkjICQFZWFqtWraJ3795MnToVg8Fg7FLw559/GjtN9erVi/FjJnBuV7hxwlZVe1GzdAs8nXy5GXWJom6laVahC53qDkYQZFy9d8o4VhBktK8xkMF9RuPj48Pdh5cpHOhEdmXS9g0/pVXNHijkSi6fC+H8rnskPExk5ZZFjJsygl2HpU5S+7Yd4/LxMCLibuLrHkCT8p1pW6OfyYVeCT+OwaBn94W17Dr/O6Jo4HrEaSwUlthZORGbFMHt6Ks4WrvQrp2505QZidqdutN+3NdUbN4aIY/Oas8i9u5tlg//jDWTR5OZmvLsA/KhUEAQFd9vhSyPdN1Zs2bh4uJCpUqVCA4ORhAEli1bBkCHDh1wdHRErVYTEBDAH3/8AUiKpyAI1K1bl/fffx9bW1umT5/OrFmzsLe3p3z58oSFhQEY5xw6dCgeHh4EBgZy4sQJqlatir29PV99JYXSxsbGUqFCBWxsbLCxsaFDh5nExOTkmOv1KURFSfnuoijyv/nxtGkdytAhkcTGmhr1Qq6XpVKlmjg6FqZTx784cyZ33b3lyxM4cULa3vXje3zcRXoODRx4n9at7tCi+R3694vg0sUcA5hGE0da+i3KlClDp06djN5INzc3OnXqRKdOnXBzc8vrT+AuCMJNQRAyBUG4IgiClSAIKkEQvhME4b4gCImCIKwTBOGZrkhBEPoKgnBPEIQsQRDCBUEY9qxjCpiOgAa4DjwKETKm/xkRRTEO2Am0FATBBmgN7BFFMRopkhegMFL0VXZXjnSgzqP/BaAfecdlY2FhgaenaUfKbDmTndbxzTff8Omnn2JhYYGlpSUTl17BpEyJbTGwcJaKKyJKEVbZnWBs/RHKjgH3uoh31oJBYyxiOrGD4yMxI6Is1BjfAOlFRWZTiKAPvpWOF+QQOBScyj+6HwYiLWo9WrwTiHp4eBmjYcA+AKHsGKzLdATg1qlQ4zLtXO1o0q9pzrItYnB81Mznxx9/NBp8sg1F2e2m+/TpY2L4ySYzM5Pr168zduxYdu3aZVQestMgDxw4QFyc5Fx6mtKdTV6Ku5OTlIqS/bfQ6/Xs2rWL5cuXU6dOHf78809atGhhXE82eaXEZM/fvXt3du3aZfzXunVrWrZsyfHjx3nvvfc4fPgwDRs2zJXS9V9A7uCPbYfTWNaZi+AU9BIziKTvCSZ5RQl09/fnO+bZyLCsOgm5g3+uPW+rrKlTpw7+/qbrPXbsGBqNBlEU2blzJ1OnTmXZsmXG6Mbsz2lkZCS9evXC2tqaHj16GNOYH2f//v2Ehkrf57lz5zJnzhwAfvnlF6ZNm8bUqVP5+eefjZGk2YSFhb2wrJk1axZAmX+JrIGCkTfZ3v6+SPLGGmj6aFwd4LgoitPIkTfvPzl/RkYG8+fPp2/fvsybNw/IkTfZZMubatWqEfoghW2Xok32W3iWxLrSB6h8SgMidtU7YFtJMvxnG7v06YkkHvyVO1t+ZOcjg2bXEgKFtPcwZCRTuGYbAt/7GPfa7Tmq86PVj1IUoMzSjvgKwfypLcPKzX+j02rRBjQn2qs2dlU+AIOOzLvnjWuxKl4du0qtsK/ZCYDM8JyucnIbJxwb9MSmTCPj2ixkIrdDQxg4cCBDhw7l2rVrpKam5nJC9O3bl549exrnynZCPClv9u7da6xHl53+/191QryLNP9iOG1GfElg/cZGnftFuHnqBMuG9WfrnOlkpb9YXezHKVGtVr5GrLdV3nTr9juR922M68zMjCA2VvquZ+s2H7S5nYduIxASEkSFCpVwdQ2kW9dDBabbpKXdQKt9+E7pNgVlyPodyWRo0mlq6tSpXLx4kcmTJ7Nz506USqXJi+rjHN98m8dL+9pbSy/Sxhfjp3xBBMDezRLXwraIooggE2j1eXlKVZcehAGViqLN1CMT5Oi00odhw9H5aLQZdG8wmvY1PwNAq9MYaxg97etoZ+XEwBYzjP8+qv0FcrmCMe1/5r2KXZHJYM2RuYyaMOQps5j5r1GkbHka9OjDkFWbcfP1A0FAJn+xektRN65TtEIVLKyfv9abTKGgdJ2G1GjfmVZDx+Q55sKFCwwfPhx3d3f69OnDzp07TfZXqVKFGTNmMG3aNEBSJB//Lh87dowmTZrg7OzMmDFj2L59O8HBwVy4cMH4sp7N+fPn6dy5M1evXqVu3bp06tQJR0dHJk+eTHx8PDKZjLZt2zJ37lxGjx7NhQsXWLggDUGQk5pqIClJT1KSHo1G5OjRdDZsSMLPT0m9etacPy89kO3tKxMZqWLAgF+AGIKDfVGpZEya+ID4eFNjV926Nvj7S52lPhvozMCB0rOnUiVL+vVzpnt3RxIS9Hz3XU44tExmjY31i3U//PXXXwF8kLzJnwG7kdwt2V7iP5E8ye8jeYmfxQzgIdJL93zg+UPzXpFHaRjVARVwBchuD9Mxn0NWArbAdMAJyG4Fk/2oPQk0eWyep1EHSQEBOJCVlUVUVBRdunShTp06ALnkTHbnQpAe8VuPPVHE87EaWAA4lMYokAxZiPFnIGoPyNVg62cctnXzOqOMGtS+FMM6lpYOMUBK2iNLmSBHyIqH5MfqFsgspEvXSsqwqE0Fu0fFpTNjIDOOtGgp5NzwmDPOyt70ey9gwNFC+sxXqFCBX375xbgvMzPTqGRnZGSwdetW4z5/f39sbW0BqFSpEv3792fSpEns2iWJb1tbW6NRKD4+HoApU6awaNEiZs+ejVKppHHjxsb5Nm/ezE8//WR8YWvQoAFPI/uepaenExYWxpEjR4z7sqNkZs+ezcKFC1myZIlxX82aNXFycmLHjh1cv36dy5cv8+2333L//n3Wr1/P1q1bKVSokDFSrKCKtb5ryO2KYBHYB/uPTqKuNevR5+0FA/D1mWgjD6Pwz+8r/STSZ03mWglV2UFYt9qG3CV3A4+3XdbMnz8fd3d3MjMzjU0ZdDodISEhHD9+HDc3NwICAoypwoUKFSIrK4slS5ag0Who1qwZGo3G2LjhcQICAowK+nvvvcf770t2Ej8/P5o1a0b9+vVJTU1ly5YtJseVLPnismb48OEgyYR3WtZAgcsbHfAekrxpghSNlS2HnmmhHTNmDLdv3+Z///ufsYD00+TNV1tzN5uSqaXnuCCTvpMyC+scB6dowKDJ5OHexQhKS2p8+hVjxkjvbT52Csa1kKIhOlT2oYy3PRkaPTqDyPVo6bMmt3ZAEJ5Q7V6yXq9Mnd2lV3pPVcuhf/F0Vq34zeyEMAOATCbHv3I13us/mM+XrcHWxRUE4QUc9tLn4sbxw1Ru+SFypfIZ43NQWVpRpmFT6n78CQ0+6ZvnmLdZ3ly8eIlFi6T3zOfVbWxsAomKUvJpr28RxWiCg4uiUskLTLexUPmgVDo/998A3rxuUyDF3kVRTBYEoSeQIoqiwdLSMns78ERXnEe0atWKpUuX0qNHD+rXr8+2347SsFTuelJqlRX+nmW5HX2Zv8+tJjYpAlE0EFioKgAyhYBBNLD1wgLW1l3E/fv3afF+a5M5rh6NooirXa65dQYtmdp0LoblvERbWtjg4+LPnQdX2X1+DTejTT0tgYWrc+LGTi6HH8PLyY97saHIZALFCpXiwN2VNG5Zn4aOAZwbuv8/+xJt5ukIgkC36VLqU3xEOL9PHPXcUVYyuYLCZcoyYNEqlg7rT2JU/p8xCytrev34C5Y2ts+c92npPE9LA1Kr1QBUq1aNoUOHcubMGe7evcuYMWPw9fXlhx9+ML7wZzN27FiKFCnCnDlzqFq1KkOHDuXcuXOsWLGC8PBw3N3d2bFjB8eOHTM+Q65fj6By5d00adyakyfDABgxwpVbt6WipF27OVKxoiXXrmWxe3cqSUmnuRumQas1cPFiNBcv5nhEr17NwtY2R8gWLarC2VnBzZsaatSwwsNDSUaGgdDQLFavSuTx9P+sLAMWFjJUKmcUihxPyvPw559/Zv/YSxTFK9m/CILwuJc4m6Y8m1Ck+oT1gDNIL+//FB8hvfxPQzJCAfRC8oLnFQayGUgF+iNFWf3xaPsepO6DPkhRWdnhEJnAQaRaKCAJMxlSHn0dpMitdKCrXC6P0Ov1bNiwARubZ/9NDIbniCZxrytdXtReSIuQoqYQsLOEdl0+YvnPszHosohO1GNlZUVmZiYLFy5EdJAUdzHtHoLPI2e+QYsYdwocy0Lsceky7u8AxyCpDheATAluteDBIciKQwxdYlQe3P1NjQHCY26W5qUqcexhFleRFImIiAjjPisrK4YMGcKsWbNYu3YtdevWNe6ztrZmw4YNNG/enLNnzxISEkKHDh04dEhKEUhOTmbr1q34+/tTpUoVzp07hyiKDBo0CD8/P9auXUvJkiWJipLq/jVr1ox169Zx4cIFhgwZQqtWrZ56e5s2bUqnTp3YunUrGzdupFmzZkalsFy5cnz33XdMmzaNBQsW0LhxY1asWIGDgwNOTk5s3bqV4cOHM3r0aCwtLalRowa+vr6kpaWxfv1643OpY8eOtG/f/tl/63856qABqIMGAJC2txfaG6ue+1iFgz+WNaeT6VGDzGfU3bKsNRNVmQHPLC79tsuay5cvs3XrVurUqcPp06cBKXU2OlqSIXXr1sXPz4/79+9z8eJFwsPDiYmJQa/XExISQkhIiPH8ERERZL8Pg+TVtrW1JTo6mpIlS+Lg4IBGoyE6OprDhw+bRDRqtVqUj5S6smWf3tH3SR6TNWGiKC7O/uUdlTVQcPLmT6ASUm2W3Uj1GIuKovi3IAgHgVqCIIwGiiPJm21PTiyKIqIokpyczJ49e5658Ev3X6ZLmIggCNipRDws9Xy/WKqPmpyhpWbNmjg7OzNv/gLUVRMQZHIU9m5YlaiRaxZL3wo8lClIPvkHiCIppzeDTIG6SHljamF66HFSzm4l7boU0WVZxPSz5mFnQakgD5Zvhn71/SnpIb1PPssJodPp8nVCVKlSBZ1Ox9atW/nyyy85f/48Fy5cwN/fn8DAQI4cOWLWn95BVGor+vwkJWWFX77Ixm8nos8nDftJ1NY2FK9ag2KVq/HL55+SlZZ/aryDuyc95/78XA3V3nZ5c+NGLBUrbKZJ07acOS195p+m26SmXuHKZUm3uXQphkuXYoznLwjdxsbG/4Ub1b1p3aaguhYiimKu3pbjx4/n4sWLLF26lL59+2Jvb2/cN2fOHJRKJVu2bGHDhg2U96ud79w9Go5h3ZEf2XV+NSqlBfXLfEjTilLRQ9EAapUlqZEyDl3djK9baVqU6/3M9barOYD1R35iz4W1VCnemBuR5437utUfybK909hzcS01Sr7P5bvHsLKQPCjtaw7ArZA9Jy7u50ToTvx8izF23Gg+/LAe696bxtffTizwjgdm/r04+xSmzfDxrJk0Kt8xvuUqYWFthVyhpFStejh6egPQecpMNs2YwoPbN7FycCSoYTM8ixUnJuw2iQ+iKdf4vecyYj1OXg+w7DSgRo0aMXjwYBYsWMBff/1FZmam8WGfnXaU/eJtb2+P/FG02ZO1DhwcHEzGASZjf/jhB44ePcrAgQNp1aoVvXr1IiUlBTvbMkyZ8gUXLko1tHx9VcaHfTaPZzZl/9ixoz0VK+UoE0UKq4i4bypcn7zs3btTOXkig3r1rWnW1JZlyxK4cUODVitiYQEODlXyvYcvQbaXuCU5yfDP485qCLQDKiK94HcE8n+QFiwfId3i2aIoxgIIgqBCuoaOwM3HB4uimC4Iwh9AN2CTKIrZbymjAC1S8d15SIaqQaIorhcE4QRS8d36QHPgB2Dk48V3RVG8X6JECWMR5s8++8xYqyQsLMxYfDebsLAw5m+6wedzTiN41AOHAMQTgwGQVZ+L4foCeHAIQabAp3xb7kXtBadyyIJGIN7bRlrEJlasWInBozFE/MVdbUmuXNlFSkwI3Xp+xo1rp0Fph+BcCUGQSZ9BK29k5bLrp3yOGHcaMWwdJF0HK2+Eoh0QXCojJl6VxrtUBW0KpN7FqlhTFg37lns66XY6WTgypfGXlFsXQIcOHZDJBHbs2EHXrl3ZunUrBw4coGPHjkaj0HfffYenpyfTpk0jNjaWrl27Go1CTZo04eDBgwwfPpwLFy7w559/UqNGDcLCwrhy5QrDhg0zMQotWbIEK6snItce4e7uzrp163Jtf1wxb9++vcnvq1evNhn7+++/G3+2trZm5cqVpKamMmLECGxsbKhevToANWrUMFGesilSpAjNm7+dtf/eFqwa/EJy3EXEhMt57hfsS6BwCgCZEpl9MdQVJbmkLtMX9JlknvoKDCKKoi1QetZEZueH9vZGZI6lUZXp/0IvwG+zrFGr1SxYsIAff/wRg8GAq6ur0ZCVTV5ptDVr1qRYsWLG311cXEhISHjqdV+8eJHQ0FACAwMpV64c+/btIyoqCr1ej1KpNK63gHgXZQ0UnLyZhhTN2xn4AEneLHy0ryuSvBmNVOz9B6TC8CZMmzaN4OBg5s6dy2effWbsjpsfKZnPF7xmbZHzd5apLCnavA/he35j66olWBarTFbMHfZce8AER0e2bdtG/fY9STqyCkFpgXPTz/KcU+nsg+sHo0k8+BsP9yxC4eCOa5tRKJ19jIYsy6IVSbt+GE3MHd7r1Iup04dwJDSOL6ZDCXdbjo9tzPr1iWRLUbMTwszzULhMWZr0GciOn2bnPUAQKFapGgqVCoVSSYX3WqGylN4vunw9i00zppAUE42DpxeBdRvh4V+CuxfPkZmaQpXW7V/Y2PI2yxtHx2qMG9eW0FDpWfImdRsnpwJ9rP8j8ualDVmiKIaRTwZeYGCg0ZN17do14/bHDTt2dnYsXLiQhQslGbJ76VVCTkTTrcEoujUwVeodbdzo0+wrBBk4uFuRkaIlM1X6Y4kGEYNBpFXVnrSqKuVji0kCGakali1bRjO//iRESsXlvu/1l3HOyv4Nqeyfk0+bXTgeAQzKTN6r8DE2VnZciz8IQOt2zalSzxdLWxVD67ZCJst96QcOHHj2jTNj5glunTnxlL0CLb4YgTqPSBMrO3u6fD0r1/aiFZ67cYSRp3nSnpYG9Dp4PJLz0KFDREREGIVCjRqNUSh/NI4tX96SjRuSWfHbQ+6Fazh2LKeQZOVKliiVcPhwGl7eSlJTDRw4kMaECbnzu20eeTH+/juVsmXVRqGRlSkSdlfDnTumRSf9i4184etq1apV9kvvYkEQFiFFIn1JPl5i4O9nTDkHyVtxFmgL5N33+DUgimK1PLatRer0lM2yJ/Z3B7o/sU0DjHz078n57iEpG3md3zf7Zzs7O27cyEnbe7wW1LJly4y1ELKpVMIJmUzAYBAR1K4I9XKcPbJS/aCUVBfRwd0e+Ud/EP5AqjEgFGqOWKg5eh5J4mKSM2Xlrjt83bsqs37+gzZjD5qcS1YvtyNJcKmM4CJ9R8v5O3DhZiIAZf0cuHABqpbz49PBX3HvQRpdmxbF0VaNI2X+U0ahI0eOGNtllylThl9++cVcM6UAEDMe5GvEAlAWbYVV9bwdcOpyg1CXG5T7mMLP42DN4V2RNZUqVaJUqVLGNJOiRYty4sQJDh48SFxcnEnkVbFixZDL5Vy/fh0nJycyMzO5evUqHTp0yHXObAXp/Pnz+Pr6Gteg1WqJjY0lJibGZHx2E4MX4TFZ4ysIQi/eYVkDb4+8qVy5stiwYUPCw3Pq+T5L3pT1sedseCIACnt3iozKSfF2aTEElxZDcLRS4mitQjlhB6lZkuFLH9gC78AWxrGODXoSbyF1pq1atSolen1PYoap0vr43NlYFa+OVfHqqJUy1Ao5iRlaLFVy6gR4sAKoX96fhj3H4OdqzYcVpFqkFQs78vl/SN6YeT1cP3Iw331yhYIPRozPc5+Tlzc95yzMtb1w4ItFpsK7I28qV66OnV1OAOib0W1k+Ph0zzXHs3jTuk1B1ch6Zep3LUmNtsUoU88bpTpvD5RogIdR6UYjVn6IBpGQ49EsG3UYTaYWa3sVCqUM4ZHxycE9b68yQJNPAijf0o2/Lixi4c7x3Iq6wsSJE5k8YwxVW/kRVN8nTyOWGTMvi5W9Q777yjZ5L08jVkGT7UmLjo42etJA8jBke+AuXbpk9MC9Tr744guqVKnCpk2biI6OpkyZMsZ9dnZlTPK3a9Swol07e27d0rB3XyoVKuR4J3wKqZg4yQO1pYz5P8WzcUMSXp4KbGzkODpWNzlny5Z2uLkp+HX5Q1auTKRxYxsqVrTk4sUMrlzOJCjItOtWYtKZF76u7t27g1RH0BWpDXhTJC/FNGAmUsrcPKQ88uexijsAk4EFQApgLsr3HFQLcGH7jAaM7BxAzTIueY6RywSu3EkyGrGext0H6TQduodPvj1OoK89NpZyrB55163VCmws85ZnpYvYcmbR++yb25iNX9flu88qAlCllDO9W/ozpVc5ShTKnRL/ohw5coT27dsTHByMu7s7mzdvLjCjUP369RFF0Vj0uKD47bffjN0ejx07RsOGb2cnoncNQe1CvhVAlXaoK+VuCFDQvKqsuXLlCj4+Pvz2m1QmacOGDcZ9f/31FwqFwqho792717gvIyODWrVqGY/bs2ePiaw5d+4clpaWJCUl4eHhwdy5c01qvZUoUYLq1asTHR3N5cuX8fPLqZXn7OxMp86dUalUbN26ld27d5Oeno5arTY2O7h8WTIg2tlJ3+kDBw5w8OBBLCykDqmhoaGEhYVRpEgRk+vNTt99XoKDg+nRowdffPEFSCnYvyCl4ZllzRtgSXAVBjUqTqcqPijleX/3HqZruR2bZjRi5Udqlo5fDt+m0le7cLRW4mStwlIpN36j/V3zr5268tNq7BxSl5+6VGTnoLr0ql0UgKIuNgxrWtJoxHpVXqe8MfNu8bRo0nof98x3X0Hyrug2Pt5dEISc+mAvrds4mNr8X0y3MZCZee+Fr+tN6zZCXuHRr0rlypXF7IislyE9WcO9awnYu1kSfSuJuIgUnH1sObr+5rMPBhw9rEhLykKTIUWyufna0WF0ZVIfZpGWlIVrIRtCT8eQ+CCda0ejSEuUQvj8K7vR7NMyT5vajJkCR5uVxQ892pvEjpasWZfyTZrjE/DPfR4XLFhA0aJFjZ602NhYQkND37qXkKysWI6faIpOl5zPCBn5NLgzIpfboNc/X3v6vPArOoSiRQe+8HGCIJwRRfHFQ+bM5Murypttx++TlqnH2U7F73vu4u1iybErcew6Hf3sg4Ee7xVl+Y6cWglH5zelQnFHzt98SGE3axJTNazdF07IvWR+3yN1I7O1lHPz9za42Ktfet1mzLwMyX99gOHeYwVvrbxRV/kSi2JtEVQvlor+sryMrBFFkQYNGnDgwAGaNGlChw4dCAsLIzo6msWLF1O/fn0OHDjAwoULiYmJYc6cOcTHx7N8+XK6d+9OaGgoU6ZMITo6mt27d7Nu3Tpj+lJ0dDTFihXDxcWFESNGkJWVhUql4vPPP2fDhg3GrnTLli2jfPnylC9f3rguO3sX0tNT0GmzjGPu3r2LTCbj888/x9fXlyFDhuDq6sqAAQM4f/48mzdvpkmTJnh6erJy5Urs7e355JNP8qzxp1Kp8uw4mh/Z6dSnTp2iSpUql5C843+JotjyWceaeTqvKmvC49M5fTeB4m627A95QHhCBkWcrfju7xvPPhio7e/M0VvxZJd5bFPei7mdKnA3Po2UTB2lPGxZfTKcqKRMVhy/S/KjtMZBjYozpMmLR/aZMfMqJETdZ+lg0yLs5d9rRZl6jXD3y93N9nXxrug2SUmXOXv2IwxiVj4jBJ6vY/DLU7bsIlxdXtxx+CZ1mwKrkVWQWNmpKFlN+oB5FM2pq6XXGDj5522eZXtLjEk3cTpm54jaOFpg4yh5v7Lnr/y+L7fOxWBhpaRImRer1G/GTEGgtLCg15xF7F22AJWlFU16f4aF1fN3JSwo3pV0HgsLV2rW2E98/AHkcivC7y0hMfE0OSnYz46YlMstX8mQlb8Rzcy7RvPq3saf8x3z7wAAJXJJREFUG1aUPu9Xw5J4f8Q+ImKfHZF1+KJpKpBCLkOllFO1tBTt5eFsyYRgqR7xoPYluXY3mebVvcxGLDNvBLsWm0g/MQl99BEsyg1F5fv+P76Gl5E1e/fu5cCBAwQEBLBjxw5kj7piGQymTou2bdvi4uJCw4YNqVWrFpMnT6Z79+4UL16c3377jUmTJuXqiDZ//nzS09NZvHgxtWrVMinQ3q5dOypUqEB6ejp//PGHsQYKgMrCAg/PIty4njtCV6lUcvToUZMOdo8TGRnJgQMHsLKyolu3bvk2qjhw4ABLliwhIiKCYsWKcerUKRQKBWPHjmX16tWkpaXRpEkT5s+fj6vr0zubC4LQFxgPuAEPgLmiKOauT2DmtVDY2YrCzlJGSJCPpNuIosj9xEx+Pxn+TBX1fPhDHu9VIn+k3BRxznlf7FbDF4C+9Yqx++oDCjtbUcXXqcCuwYyZ58XJ05tOU2ZwbN1qHDw8aRDcB7ninzc7vCu6jb19GWrU2MPDxBPIZRbcvvMTaWnXyTFePduIJZdZoTc8+701P7Iy370mC2+lISs/Kjf3Je5eCrfOxT51nPhEMEbhwPwNVHKljBJV374PtJn/Fg4eHrQdPemNriE75eJdQKm0x8ND6k7q6toYrTaJ+5G/o1DYEhu7j4SEvU89XqOJRSazwvCMB74gKLC3r0xqagg6XSIgIpdb4eHxQcFciJm3kgBfe3Z935DS3XLXHHmSW5E5tQvcHC2oVDJ/paFqaRejgcuMmTeFVbVJb/T8LyNrzpyRjEVNmjQxGrEAk58fp2bNmtjb23P79m1SUlKwtc0/2uzq1auAlPpx7do1ChcuzPLly6lfvz5ZWVnY2dlhZ2eHjY0N9erVo2LFiiQkJBAUVIGdu/blOWelSpU4efKkSbfQx7ly5QoKhYJu3boZCwr7+fkRHx9Pamoqer2eCxcu8Pfff1OtWjXGjBnDxYsX0ev1zJw5k1mzZtG3b188PDz47rvv6N+//zMLjyO1Nb8LTADceUZbczOvH0EQmNY2iPPhD7kW/fTu1akaU+WmSYB7vmPtLZW0q1Qw6YJmzLws3iUDaD/+qze6hndJt1GrPfF8pF+4ub1PZmYkUVEbsbQszJ2weaSn33rq8XpDBjKZBQZDflFdEkqlI5bqQqSlh6HXS455lcoVV9cmBXId/yTvlCELoEH3UjwISyb14dP/SI/zgs0NzJgx846hVNrjW0QKYXawr8r/27vv6KqqtI/j33NLbnpCCkkgQADpSi9Kr8GxgTNYcAALoi/iIOpYKYIjNtQZkNHBGbEOSBF0QFDEkZFuQQREIEqQnkoSQki59573j4RgCikQktzw+6zlWtxz9j5n36xlnpxn77OfrV+XncgCSiSxvB0NyM45OxthswXTts0LhIcPLmifS2bmXry9G+LlpRnOuq5lo0AmjWjF35buLb9xgdM5rvIbich5q0y1qjNbZ5TXJycn/+/JDh06MG3aNMaNG8eoUaM4fPgwCxcu5M477yxsu2nTJiB/g+oDBw6Qm51W6jW7devGtm3b2LJlyzm/h9Pp5KeffiIyMpIWLVpw8803FybmUlNTuf322wF48803adeuXWHflSvzE+xniiUBrFlT3v65QCXLmkv1mfvHzgybu4HMSsSQPPfFfc1IRGqWt3eDwm1MTNNk908PldPDLJHE8vaOJjv7cMEnA4cjgvbt3yAwID+mOJ2nyMr6BV/fpths1bO9QFXyuESWw8fO6Jk9WfvWj8R9k1hue8Ni0LZ3tRZYEZEa5OfXnMDATmRkfF+pfmHhsfh4N+TUqTgiI2+kXr3uRc5bLF4EBl5RlUOVWu7l+7swcnATevxfhR4SGRXb9CKPSOTS1LVr/vYbn3/+OW63u8irhaWtytq8eTMZGRk0a9bsnK/tnRETEwPk7y81dOhQZs+ezdatW8nOzmbo0KF8/vnnADz88MMMHTqU2NjYwtcPBwwYwJIlS0pc08vLi+7du7N1a+lVie+9914+/vhjvvzyS0aMGMGtt95a5HxISEhhhcPiTNPEZrOxcuXKwg2Vi79ieQ6VKmsu1ad5uD/fTR3C7/++kR+Plb0yCyDA20ZsGSuyRKRuCQ8fjHd8Q7Kzj1SqX6PoMTidmeTkJtCo0Z34+7Uoct5m8yMwsPIVIWsLj0tkAVgsBrFjL+fwvg2cTs8ts+0fHuuCX5CjmkYmIjXNMAy6dP43SUmfc+zYR6Sklv7qR9E+XjRudBc+Pg3LbSuXlq6tw3jtwS7c99eyK1X2bR/O3EndqmlUIpeWAQMG0L9/f9atW8c111zDiBEjOHToEEePHuWf//xnYbtly5aRnJzM3/72NwCeeuopIL/k+QcffMC2bduA/KqFaWlp3H333dx+++3MnTuXOXPmEBcXx/bt2+nWrRve3t5ERUURFRUFQL169Wjbtm1h5SuAdu3aERISQlxcXJEqiQBXXnklmzdvLvX7NG/enI0bN9KrVy/+9Kc/ER4ezk033VSkzZmy5mPHjmXcuHHs3LmTv/zlL1x//fV89913vPPOOwwePJjdu3cTHx9PbGxseT/Gv1GJsuZSvRw2K0vH96LT05+R7Tz3aiubxWD1A33wtp+7KpyI1C02mx/du60kKelzjhz9gIyMbRXoE0SDBjd75Eqriip9cwEPMeYvVxHdOvic5wNCvYlocuHly0XEs1gsDiIirqNJk3vO2cZqDcIwbFit9ejYcb6SWHJO9w5rxecvDSizzV/Gdqim0YhcegzDYMWKFUycOJGdO3dy33338f7779O5c+ci7e677z5mzZpFq1atWLZs2ZnS4CQnJzNu3DhWrFgB5FeyGjduHJC/2uu1115j+/btPPHEE/Tt25cFCxZUeGxRUVH07dsXh6PopKmPjw9dunQpHD9A48aNC883bdqUzz77jMDAQEaNGlViE/oxY8Ywa9YskpKSmDBhAmvWrMFqtfLEE0/wyCOPsH79eu6//35Wr15Nv379KjLUYCpR1lyqn4+Xlc1PDKZF/XMX/OneNIToer7VOCoRqQ3s9kAaNPgDUZE3nrONzRYMWHE4ounSZVGdTmIBGGZ5JQDPw4WWqK0Mt8vN/xbuY8+Wo7iLbVs5YHRr2vbShJPIperQ4ffYt296qef69d2JzVa9fwzWZInauqo6483qrUd5aO537DtU9NWPNo0D2fWuqtuLXMqmT59e6vHo6Gjuvvvu6h0MijdVrTpjTUZ2Ho8u2cGa3ccpvhXWsvFX0bmJ9ukUuVTti5vJoUPzSz03aGDZG8JfDDUZazx6RRaAxWphwKjW3DK5O0axb+PtZy+9k4hcEoKDOgOlL7+/kBK1cmn6XY8G/PTe9fTvWL/I8chQnxoakYjUFmf22iquNpZ6l9ot0NvOP0Z34c07Sj4b+jn0bCNyKasXfK5tLC696nYen8g6IyTKnzHP9iSyeRA2Lwste0QQ015lzkUuZQEB7ejWdSn+/m2KHHc4GuBl14ymnJ+1fx3E9DuvIMjPTvMG/rw4vlNND0lEathtt93GVVddVeJ427Zta2A0UhcMaBXBqom9aRURgJ+XlfH9m9Mqsm6/KiQiZQsPj6Vjh7fw8ooqcjwoqPM5etRdHv9qoYhIRaSkfMWx4x9it4XQvPmfsdnOvQfFxaJXPaqe4o2I1CYnT55ky5YtnDhxgq5du9KsWbMaGYfiTdVSrBGR2sTtdpKY+BlJSavx8W1Ks6YPYLFUfx2/mow1Hlm1UESkskJD+xIa2remhyEiInVYQEAAQ4YMqelhiIhIHWax2IiMvJbIyGtreig1ps68WigiIiIiIiIiInWbElkiIiIiIiIiIuIRlMgSERERERERERGPoESWiIiIiIiIiIh4BCWyRERERERERETEI6hqoVzSTNPk+FPTSVu+HMPLjl+3btR/9FEcNVQuW0RE6qb00+nsS4jDZbrwtjmICIwgKiiqpoclIiJ1iDPXxeo3dnL4pxPYHBaatAuj14jL8Aty1PTQRKqUEllySTu1cRNpixcDYOblkbnuf2T//DP1Ro7E4uVF4LXXYgsJqeFRioiIp9uXFIfTdAJw2pnNgdRfyXHlYsFCkE8gQT5BNTxCERHxdD9tOsbBXakA5Ga5iPsmgZMp2TRuF4JvgBcte0Rid1hreJQiF06JLLmkmXl5JY45Dx8hadZLACTMfJbQe++l/oOTqnlkIiJSl7hcrhLHjqUfA+BI+hHshp320VfgZfOq7qGJiEgdcSojp8Sx4/vTOb4/HYB1C/YycEwb2vTUimDxbNojS+oEd44L0+mudL/cAwfKbZMybx65Bw/m3yc7u9L3EBGRusE0TUx35WON23RjYpbZJs/M43DakcL7uM3K30dEROqGnNNO3K7Kx4HTJ0tO0hf33/d+wlXw3OTMLTnJIuIJtCJLPN7J9YdJXxWPYbcQOqot3i3rVbivxcenQu2y9+zh6OQpnP7mG6yhoTR8+SUsfn74XHHF+Q5bREQ8iNvp4nRCOqbLjc3XC0doAIZhVKivgYHFsJSbnDqVe4qEjET2p+wHoEFQA+r5BuNj98Vu1Z9sIiKXgq8+2MfOdYfxCbBzwwMdCYsOqHBfm70C61RMSIjP4Mv395CWkEVQfR8GjmmNw8dOaEP/Cxi5SPXRX0Xi0Uy3SfqqeDDBzHWT/vmBSiWyjGKJLMPbG7OUVVdHJj5Q+G9XSgoH77gTgOCbbiLqL0+f5+hFRMRT5GRkYRbMjjuzcrEHOLE67BXqWzzhZTEsmKa7xBqtzJxMMnMyCz8fTT/K0fSj2Cw22je8AodNm/WKiNRlGcmn2bnuMJC/umrbZweJHduuwv3tXkX3v7LaLbjySk6iLH95W+G/0xNPs/yl7wHo+fvmdIptcj5DF6lWerVQPFrugXjcWScKP2d9t5mcI0c4/txzxPXrz6+jR5N3/HipfTPXbyBtyZIix0yXi4Crh1b4/mlLl57fwEVE5KI4cOAAhmFgGAbPPPNM4fG77rqr8Pj5SDyVWPhv0zRJOJVEelY62w//wLZD35NwMrHUfnkuJwdSfi2yGsttugn0CcKgYmNxup0knUw6r3GLiIjnOPpzWpHPB3YkkZFymjVv7uLtxzaw6rUd5Jx2ltp379bjHNiZXOSYzW7QqE3FC1dt++zXSo9ZpCZoRZZ4tFMbN3F6y3s42gzDzMsiZ9cS9g96tfC8MyGB/cNvxDx9GsPhwOfKHuTu2Ys1OIjs3T+Bs1ggyMsj86v1lRqD88QJbPUqvgpMRESqx1tvvcXkyZPJyspiSbGJi8o65kzCNEwchhcnzAzS0zMh/ez5/cn7OZh6EJfbha+XLwAutwuLYZCVd7rE9dJPp5c4VpYcZ8kNfEVEpG45svdEkc95OW7en7qZM3Mh8TuSWTB9CzmnnNi9rURdFkTK4Ux8gxwc/6VkXMnJcpH4a0aF75+X68KZ58JmV2VDqd20Iks8ml/fPrjTD3J6y6tkf/cmZk7JX9TutDTMnBzcGRmcWvM5eQcPkr1jZ8kk1hl2O4ajgq9vmCaJL710Ad9AREQuhmbNmrF//37WrVvHokWLyMvLo2HDhkD+iqqZM2fSpEkTAgICGDBgAD/++CMA06dPxzAM7r//flq2bEl4eDhLlizB18uXX7OP8uCMR+nXYwCxHYbw6D2PknA0ofCeTrcTE5NTuaeY/eJsOjfuxNNPPM2NvW/kxt438sM3PzDp9kkMunwgL0x+Hoc1P9aM+/3dDG4/iAFt+3PnDXew/evthddc8K8F3DRgBG2j29K2XVuysrKq74coIiLVqkW3iBLHim+vmJWei8vpJjszj/jtyWQkZ5eaxDrDZrdQ0cXIrjyT7WsPVWbIIjVCiSzxaI4mTWi8cAGWoCAq/Bu6HGZ6OmZOxWe+nSkpVXJfERGpOm3atKFHjx7Mnz+f+fPnM3z4cIKDg4H8lVpTpkyhffv2zJw5k2+++YZhw4aRl3e22tPatWuZMGEC6enpPP7441ze4HIWzlvIorcW0b1Pd0bdO4pN/93I9Aen4zZN0lLTCv9z/6ay4Y5vd3DDLdeTcPQ4E0bex+Ud29G4WWM+XvgxO3fuBKBb725MnPwAYx+4m5SkFJ59bCYAq5etYu6zrxJcL5iHZzzMwIEDcblUYUpEpK5q3DaUwXe2weZVNc81AKfSczHLLpxbRM6p8isfitQ0vVooHs+vUydabd1C3tGjHBw/nty9+yrV3/D1xbyAGe76Eyeed18REbl47rrrLiZOnEhOTg6ffvopDz/8MACrVq0C4JVXXqFFixZs3bqVBQsWsG/f2fjx0EMPcc899/D6668TFxeHxbDw7fpvsFgs/P21v7My7QgBazfwwzfbSczM4vddf1fY98OvlhX+e8TtN3HN76/hn3/9J6H1Q7n7wXG4TTd7d+3l2KGjRDeJZu+ufbz3+ntFklQ52dls+GIDAE88/yRXtLuc9tHtL+rPS0REal6rHlG06hFF8uFMVszZTlZGbqX6W20WXM6yq+SWpf3ARufdV6S6aEWW1Bn2Bg0I+7//K/1kwWotS2hoiVOWwIqXtC2NmVu54CIiItXj1ltvxWq1Eh0dzZAhQ0qcL2vj95CQ/M1xbTZbkRVWAFFBUbyRY3JmzjrQYjD73dmF/4WEh2C35lc09A/wx2bPnzf0L4g3Fkv+3iMul5vPPvqUzes20f/q/rw8/xVaX94agNzcojPiDrt3Jb+9iIh4srBof1r3jCyzTWC4T4ljNsf5P+Ib2hpLPIQSWVIrnKkydd111wEQFxdHREQEwcHBbN++vcLXCRw8GGtY0WTVS4mJ9Ni3l7Z797CqaQxHOnZkWHw8V+zdw4D9v+DdsmXJC9krVlId4PSOHRVuC/D2229jGAYvFeytFRMTg7+/f6WuISIi5QsMDGT+/PnMmzcPi+XsnzwffvghkL/qaurUqSxcuBCLxVLu/lPXXnstbreb8ePH027xJ2Tt2E3H7h3x9fOlW+/ufL3hayZPmMyANv05sCcegDkzZ9OnRe9zXvPM6x7Zp7OJj9vPL/t+KTzXe1B+v+cef5Z/v/tvJk2axMmTJyv8/RVvRERqRlU923Qa0gSrvegj+0db5vHIWzdw/7xBHHBtJMuewMwldzPxjSFM/fetBIaWnPgofo1zMV2QciSzwuMDxRqpGUpkSa1z5MgRYmNjOXnyJCtXrqRjx44V7mvY7dgbNS78nOV2M/9EKv4WC89HRtFu69cs+O8XxOXmMDK4Hi+MuIkGzz9PwNVXYw39TWnavNLfDXeW8oK5T7duFR6fiIhUr1tuuYVrrrmmxPGWLVuybds2Zs6ciWEYvP3223Qr5/f5k08+yQMPPMDq1atZ9epr9BzYkydffgqA01mn+fcb/8YvwJ9przxFUEQQAInHEvnD6BEAeFnt+NqLzp4PHT6Ubr268f3X37Pjux107NYRAKfTye9+fw33P3E/J1LTeGHq86xZswarVdPlIiKe5EKebbx8bPgEnJ1gz8k7zdofFuPt5ceYAY/DoSg+2/ARx1Lj6dtuGNMeeY7YsZfTuF0oXj5n44Urr/RXDV3uovsuGlaIaBpYuS8oUgOUyJJaJTU1ldjYWI4cOcLSpUvp3Tt/Nrp///4YhkFycjLJyckYhkH//v2BsxWmHnjgAVo0a0b3JYtZkZFfuWPYwYMAHHM6efz4Mb7NPMlbSUkAvJ92gr9//BGulBTei6hP7P79dI3bx7hDhzhWkNSam5xE2717mHzsGLH7f+GlpMQSY/501Sq6dOmCr68v0dHRbN68GYD58+fTqlUr/Pz86NmzJ9u2bSv3+2/cuJH27dvj7e1NeHg4I0eOvLAfqIjIJSYmJgbTNFm5cmWJc7t27SI+Pn+lVGhoKEFBQdhsNlasWMHo0aMBWLduHZAfd5KTk/nxxx/p168fAM899xyzZ8/m5ptvpl5oPfb+8BM/fvMDAH8cehsACUeP8/RDM2jR9uxq38VvL6J7z+7s2b2HT979hE+Xr8bXz5cVi/5Demo6s9+bw613jeSrNV9RP6o+DRo14N2/vwPAbeP+yNJ1S9n689e88sor9OnTR/FGRMRDXOizzWXNLmPi7Bv4Jm4tAM8uvRuAE5mJvPvl8+w7/ANfbF8MwLpdy3n9jbnYva3sylzFjA9G8/D865j7yWPkep0A4JNv3+H+eYN4f90snlowio+2zCsyXtMFa9eu0bON1HpKZEmtsnnzZnbv3s28efNKnUEvyxdffMGksWOxGAbTjh8n2enkievzl/M28/LipagGNPNy0MvXD4DxoaGMDwllzq238uSTT3Jlv348OnEiP3s7ePDgQYyAs3tnbco6xdiQEPr5FV0meyA3l5GPPcbRo0eZNWsW48ePx+VysW7dOsaOHUtMTAxTpkwhJSWFG264gezs7DK/w4svvsj+/fuZPXs206ZNIywsrFI/AxERqZgLjTd3jR+LYTF47olnOZF8gmkzpgEQc1kMM2Y/TcxlMXTv0wOAO/90J6PuG80b/3qDyZMn0/uq3jz45weJ3xfPtIlTi1z76w1fM+reUfQa2KvI8bh9+xg+fLjijYiIB7nQWHPPHfdjYLDgfy+TkZXKpHsfByAyuDF3DJpMZHATWkd3BeDqzqMY2mkU0ybN4sknn6R3v548OPFhUrIP8o+VT2G1n90Xcs/hbxnS8RYub3xlkXsmph9mxC0jFGuk1lPVQqlVLBYLbrebxYsXM2rUKOyV2KvqrhYtGfje+/wYGsbrCcf5pX17Rsx6ifuXLiXUbueawPxlsk287GzMgh6+vnT39WNSXBwAixYtKrxWQmoqJ7zPvv5xV0gItwTXK3HPTadOkeNyMWXKFCZMmFB4/JFHHgFgzZo1rFmzpvD47t27y/wOLVq0YOXKlaxZs4bOnTsXuaaIiFSdC4k3w8fcyJA/DOHQoYP8a86/SDuQxk3DbuK+u++jXmg9hlyfv7F8o5hovl6/lS5XdaXzlZ2Z9qf8pNXixYsLr5WYkEhGWnrh5z/eM4rht91Y4p5b128lJydH8UZExINcSKwZ1HEEAcc70PuKa/lk67sQmcQ9D49k6qxJBPjUo+tlAwGoH9SQPYe/pWXDTrRs0JH5a58Gij7bJCUnknHybKwZ3OEWere9vsQ9fzr0Lbm5ijVS+ymRJbVKbGwsPj4+LF++nDvuuIP3338fwzAK9wRxOp1kZpa+AWHW5s0QHIy7YH+reqNHYfHJ3+zQ4ud37psWbHv1YlQUIVZb4SGf31Szqm+r3P8qZsFeWi+//DLt2+eXS3e73TRt2pQdZWwO/+KLL9K3b182bdrEm2++yXPPPcfhw4cJDg6u1P1FRKRs5xNvzvxudxfsKZLncgLg61WyalRpnK78ftP/Op16ofmTI27TxOFzdmPesPqVm61WvBERqb0u5Nkm+XAmLVuDMzc/drS5KgrDUlCJ3XbuqrtnKu3ePvBJAnyCgfxY4WU7G2uC/EpWci+LYo3UNkpkSa1itVpZuHAhQ4cOZcGCBYSGhjJnzhxiYmIAeOONN9i7d2+pfd85kYoLWJ6RjrfVSuiTk/m5YNbDHhEBThe43WDkv1Fri46G1BP09/dnTeZJPk7P4HeBARzOy+PrrCzeb9yk3PH29PPDYbPxzDPPAJCRkUGfPn247rrrePnll1m4cCHBwcEcO3aM9957jz179pR5vWeffRaHw0G7du1o1KgR8fHxZGRk6Je9iEgVO594cyr3FAAfvLUIl9vNJ0tX4u3tjT3Ki58TfwbAYpzdtcEg/0HjzLxI70G9WPfpl6xatprB1w3m6KEjfL/le15f/I9yx3tl36twOByKNyIiHuR8Ys2ZpNGXOz7E7XazZe+neNm92fORk1/XbgHAP9hR2P5MjPEP9gLg8iZXsT1+PVv3raFL8wGknDxG3NHtPDhsdrnjbdOoK152xRqp/ZTIklrH4XDwn//8h379+vHqq68SFhbGn//8ZzZu3MicOXO4/fbbS+3Xy8+Pt1JTcJkmT0dEEmqanCgope5KS4egwPxElpk/S+FMScHw82M4kOx0sjg9jacTEoiw2fhdQDnVOgwD3x496DniDyyzWJg6dSqPPPIIISEh9O/fn969e/PWW2/xwgsvMGHCBOrXr8+gQYPK/e4Wi4U5c+aQkJBAaGgoM2bMoHHjxuX2ExGRyqtsvDlTuLZ77+4s/OcC3C43jz/3OCHhIZxITStoc7a6rVmw5NduyX+4uOYP15KSlMrHCz/ipamzCI8KZ9C1g8sco5fVizD/MDr36cTy5cuZMmWK4o2IiAc532eb1tFd+GLHYtymm5F9HiLAJ4TMzPzXA3OynBgWA9NtFsYmZ64bw9/gylZDOXk6lQ27P2HRhr8R7BdO5+b9i1682IIuwwpN2oVyfd8OXDlGsUZqP+O3f3BVla5du5rffvttlV9XpDTTp09nxowZ/LVBA4aWl4AqjWGcfTopi9UKrrMlai1BQbTcvAnDopoJUjGGYXxnmmbXmh5HXaJ4I9Xpqaee4umnn+aZuTMZeM3Ai3IPm8WG0+0scqxleAtC/Sv3Gohc2hRvqpZijVSnM882Y4dMo1OzftV23+BIX/44/cryG4oUqMlYoydwqTN8OnQ4v44VTeYWvG9e+DE9nSOTHjy/e4qIiMcxCt7fMIpPZVeh4kksgH1JcbhNdymtRUSkrgoKr9j+i1Ul7XgWX3+yv1rvKXK+tCJL6gzTNMn67jsOjhpdbfc0fH1pve27arufeDbNkFc9xRupCTl5ORxI/ZXUrNRqu2e7yHYE+gRU2/3EsyneVC3FGqkJLqebX7Yl8vn8sisDVqWAUG/GzOxZbfcTz6YVWSJVwDAM/Lp2xRpafa9f2KrxXiIiUjs47A6CfYKq9Z52q7Y1FRG5lFhtFpp2CC+zQmFVC65fvavARM6XEllS5zR85ZWz5TuKMQICsNavT8DVV+Pdrt0F3cfw8SF67twLuoaIiHimiMAIvO3e5zxvt9jxsfsQGRBxwfeKDIjEx0sPFyIilxq7w0rfW1ue87y3vx2/YC/a9GpAYJh3iU3cK8Mn0M6gO9qe/wVEqpGm96TO8evRnWafrCTx5VfI/OILACwhITR+5x18WlxWpK07N5f9115H3qFDlbqHJSyMFl+sxeJwlN9YRETqpA4N25OQkcivqb8WVihsFBxNVFAUVou1sF2T0CakZqYSl/xzpe/RPKw59QPCq2zMIiLiWdr1bkhYdADrF+0jIT4DgKD63tzwQCcCQ4tOcricbv718Fc4cyq3r2JotB+3TO5euBekSG2nRJbUSY5mzWj097mYLhemy4XFy6vUdhYvL5ouX8aRRx7BeTyB+k88zskVK0lburTEJvCBN9xA0PBhuNPSCYgdgmHT/z4iIpcyi2EhKiiSqKBIXG5XkeRV8XZhAWHkuZ0kZCQQ4BNAmF8oexP34XK7irS1GVaah1+Gy+3E3xGAj9e5V32JiMilISImkBGPdcXldGMYYLGW/mKV1WZh1Iyr+OzNXbidbvr/sQ1bPvqZX3eV3NOx09DGRDULAgNirghTEks8ijZ7FymFMzmZpFfnkv3TbhytWhN27z14RUfX9LDEw2nz3aqneCOeLiEjgeRTKVgMg3D/cML8w2p6SFIHKN5ULcUa8XRpCVl880k8aYlZNLgsiM5Xx+DjX/pEv0hF1WSs0ZISkVLYwsKImjG9pochIiJ1XERgBBGBF76PloiIyLkER/gy5K4L2x9YpDbRZu8iIiIiIiIiIuIRlMgSERERERERERGPoESWiIiIiIiIiIh4BCWyRERERERERETEIyiRJSIiIiIiIiIiHkGJLBERERERERER8QhKZImIiIiIiIiIiEcwTNOs+osaRhLwa5VfWETEszUxTTO8pgdRlyjeiIiUSvGmCinWiIiUqsZizUVJZImIiIiIiIiIiFQ1vVooIiIiIiIiIiIeQYksERERERERERHxCEpkSa1mGEaMYRi7ih2bbhjGnw3DeNswjCzDMAJ+c262YRimYRhhvzl2Y8Gx1sWue9owjO2GYew2DOMfhmFYCs59ahhGmmEYK6vjO4qISM1SrBERkeqgeCNSNZTIEk/3MzAMoOCX9QDgSLE2I4ENwK3Fjv9immZHoD3QFhhecHwWMPriDFdERDyQYo2IiFQHxRuRClAiSzzdQuCWgn/3BzYCzjMnDcPwB3oBYyn5yx4A0zSdwCbgsoLPXwAnL9qIRUTE0yjWiIhIdVC8EakAJbLE08UB4YZh1CN/duKDYueHA5+aprkPSDUMo3PxCxiG4QsMAnZe5LGKiIhnUqwREZHqoHgjUgFKZEltZ1bg+DLyZyR6AOuLtfttAPig4PMZzQ3D2E7+TMcnpmmuvuDRioiIJ1KsERGR6qB4I1IFbDU9AJFypAD1ih0LAeJ/8/kDYBvwjmmabsMwADAMIxQYCFxuGIYJWAHTMIxHC/qdeY9cREQubYo1IiJSHRRvRKqAVmRJrWaaZiZwzDCMQQCGYYQAV5O/weGZNgeBycBrxbqPAN41TbOJaZoxpmk2Ij9I9K6WwYuIiEdQrBERkeqgeCNSNZTIEk8wBphSsFT2v8AM0zR/+W0D0zTnFT9G/lLb5cWOfQjcVtbNDMNYDywBBhmGcdgwjKEXMngREfEIijUiIlIdFG9ELpBhmud6TVdERERERERERKT20IosERERERERERHxCEpkiYiIiIiIiIiIR1AiS0REREREREREPIISWSIiIiIiIiIi4hGUyBIREREREREREY+gRJaIiIiIiIiIiHgEJbJERERERERERMQjKJElIiIiIiIiIiIe4f8Boja4JxoLxYQAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1483.92x288 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sc.pl.umap(adata, color = ['cell_type', 'predicted_labels', 'majority_voting'], legend_loc = 'on data')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "julian-mobile",
+      "metadata": {
+        "id": "julian-mobile"
+      },
+      "source": [
+        "Actually, you may not need to explicitly convert `predictions` output by `celltypist.annotate` into an `AnnData` as above. A more useful way is to use the visualisation function [celltypist.dotplot](https://celltypist.readthedocs.io/en/latest/celltypist.dotplot.html), which quantitatively compares the CellTypist prediction result (e.g. `majority_voting` here) with the cell types pre-defined in the `AnnData` (here `cell_type`). You can also change the value of `use_as_prediction` to `predicted_labels` to compare the raw prediction result with the pre-defined cell types."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "solved-weekly",
+      "metadata": {
+        "id": "solved-weekly",
+        "outputId": "11a738e2-2d0f-4f22-fe8e-02c5239e431c"
+      },
+      "outputs": [
+        {
+          "data": {
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\n",
+            "text/plain": [
+              "<Figure size 374.4x324 with 5 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'majority_voting')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "metallic-saskatchewan",
+      "metadata": {
+        "id": "metallic-saskatchewan"
+      },
+      "source": [
+        "For each pre-defined cell type (each column from the dot plot), this plot shows how it can be 'decomposed' into different cell types predicted by CellTypist (rows)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "tender-maple",
+      "metadata": {
+        "id": "tender-maple"
+      },
+      "source": [
+        "## Assign cell type labels using a custom model\n",
+        "In this section, we show the procedure of generating a custom model and transferring labels from the model to the query data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "protected-counter",
+      "metadata": {
+        "tags": [],
+        "id": "protected-counter"
+      },
+      "source": [
+        "Use previously downloaded dataset of 2,000 immune cells as the training set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "rotary-record",
+      "metadata": {
+        "id": "rotary-record"
+      },
+      "outputs": [],
+      "source": [
+        "adata_2000 = sc.read('celltypist_demo_folder/demo_2000_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_2000_cells.h5ad')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "headed-sierra",
+      "metadata": {
+        "id": "headed-sierra"
+      },
+      "source": [
+        "Download another scRNA-seq dataset of 400 immune cells as a query."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "closing-brain",
+      "metadata": {
+        "id": "closing-brain",
+        "outputId": "501ccf3d-7364-4a26-984f-8e50dfce6bf9",
+        "colab": {
+          "referenced_widgets": [
+            "60bb1bc2c899444ea88095e6713a8686"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "60bb1bc2c899444ea88095e6713a8686",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0.00/7.62M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "adata_400 = sc.read('celltypist_demo_folder/demo_400_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_400_cells.h5ad')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "precise-bronze",
+      "metadata": {
+        "tags": [],
+        "id": "precise-bronze"
+      },
+      "source": [
+        "Derive a custom model by training the data using the [celltypist.train](https://celltypist.readthedocs.io/en/latest/celltypist.train.html) function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "consistent-detective",
+      "metadata": {
+        "tags": [],
+        "id": "consistent-detective",
+        "outputId": "8e385d9b-9a37-4b45-b6b0-dd511a7957b0"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "🍳 Preparing data before training\n",
+            "✂️ 2749 non-expressed genes are filtered out\n",
+            "⚖️ Scaling input data\n",
+            "🏋️ Training data using SGD logistic regression\n",
+            "🔎 Selecting features\n",
+            "🧬 2607 features are selected\n",
+            "🏋️ Starting the second round of training\n",
+            "🏋️ Training data using logistic regression\n",
+            "✅ Model training done!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# The `cell_type` in `adata_2000.obs` will be used as cell type labels for training.\n",
+        "new_model = celltypist.train(adata_2000, labels = 'cell_type', n_jobs = 10, feature_selection = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "remarkable-eating",
+      "metadata": {
+        "id": "remarkable-eating"
+      },
+      "source": [
+        "Refer to the function [celltypist.train](https://celltypist.readthedocs.io/en/latest/celltypist.train.html) for what each parameter means, and to the [usage](https://github.com/Teichlab/celltypist#usage) for details of model training."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "emerging-telephone",
+      "metadata": {
+        "id": "emerging-telephone"
+      },
+      "source": [
+        "This custom model can be manipulated as with other CellTypist built-in models. First, save this model locally."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "informal-ladder",
+      "metadata": {
+        "id": "informal-ladder"
+      },
+      "outputs": [],
+      "source": [
+        "# Save the model.\n",
+        "new_model.write('celltypist_demo_folder/model_from_immune2000.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "sublime-oxygen",
+      "metadata": {
+        "tags": [],
+        "id": "sublime-oxygen"
+      },
+      "source": [
+        "You can load this model by `models.Model.load`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "little-textbook",
+      "metadata": {
+        "id": "little-textbook"
+      },
+      "outputs": [],
+      "source": [
+        "new_model = models.Model.load('celltypist_demo_folder/model_from_immune2000.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "weighted-system",
+      "metadata": {
+        "id": "weighted-system"
+      },
+      "source": [
+        "Next, we use this model to predict the query dataset of 400 immune cells."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "forty-turning",
+      "metadata": {
+        "tags": [],
+        "id": "forty-turning",
+        "outputId": "bbe06e18-182d-48ad-808c-16514c429691"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "🔬 Input data has 400 cells and 18950 genes\n",
+            "🔗 Matching reference genes in the model\n",
+            "🧬 2607 features used for prediction\n",
+            "⚖️ Scaling input data\n",
+            "🖋️ Predicting labels\n",
+            "✅ Prediction done!\n",
+            "👀 Can not detect a neighborhood graph, will construct one before the over-clustering\n",
+            "⛓️ Over-clustering input data with resolution set to 5\n",
+            "🗳️ Majority voting the predictions\n",
+            "✅ Majority voting done!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Not run; predict the identity of each input cell with the new model.\n",
+        "#predictions = celltypist.annotate(adata_400, model = new_model, majority_voting = True)\n",
+        "# Alternatively, just specify the model path (recommended as this ensures the model is intact every time it is loaded).\n",
+        "predictions = celltypist.annotate(adata_400, model = 'celltypist_demo_folder/model_from_immune2000.pkl', majority_voting = True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "liquid-happiness",
+      "metadata": {
+        "id": "liquid-happiness"
+      },
+      "outputs": [],
+      "source": [
+        "adata = predictions.to_adata()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "utility-sussex",
+      "metadata": {
+        "id": "utility-sussex"
+      },
+      "outputs": [],
+      "source": [
+        "sc.tl.umap(adata)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "sought-adult",
+      "metadata": {
+        "tags": [],
+        "id": "sought-adult",
+        "outputId": "fd5f4ea9-ef3a-4c73-8c40-5a3b4da7e07b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1483.92x288 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sc.pl.umap(adata, color = ['cell_type', 'predicted_labels', 'majority_voting'], legend_loc = 'on data')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "liberal-strengthening",
+      "metadata": {
+        "id": "liberal-strengthening",
+        "outputId": "c510f9a9-b8c3-4116-bb78-9172a57ba1ba"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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3itOqj5jZCcBs4OA0/beY/ofuTVXKjyy9Tlv2YdeD9mumiJxzbZ0nveUlT29eW895fwIeAEYD8+OxUUAvSZMIU5c3AN8AnSU9KqlPmr7+BpwZr9e9AGyV5ryRhGnT+4Cf6ontVmBQjOOZeOw84L+SxgNPA51StFsReF7SZGAYMK6e92gRexx2EJ23TjVgTs0wtj5kGMU+vemcS8N3ZHEZa8kdWV55YRK3H/FbiuYubPDcHntuxyVP3EeHDh1aIDJX6HxHlrbJR3quVRuwy2CG338t9FkDSzPVWSFY/aAhXPDI7Z7wnHP18g2nXas3aI9d6fvWAJ68/V7eGDmG797/BCuvoEP3rmy08/bscOxBDNptFy9I65xrkE9vuoy19IbTtVVVVVFeXk6nTp080bm88enNtslHeq7NKS4upnPndLcpOudcen5NzznnXMHwkZ4rOGVlZTx5533MmDyNskWLKe3ciXW368sBJ/2Crl275js851wz8qTnCsp9V93AlBvvo3rmN8ttZv35iDFMuvpO+v3yEE666Dy/VuhcO+VJzxWMG8+/hNcvv4MSo071BiH4ai6vXfIf5n0zm3NvvtoTn3PtkF/TcwVh7COP8fo/7qKkgcXKxYgZt/6XR2+8vWUCc861KE96SVIUkh2S5rw6BVglXVtP5YNUfWRVADaW/3ktlg4aI2mFTPuI/bT6IrK5NPmekZRUNa4CezFi6v2P4bfzONf+eNKrK7nSwsTGNjKzM8xsScNnZiZp0+hkJ8TSQVOAA3P9nu3NB2+/w5fjpmbUZsHUd5n83PPNFJFzLl886TVAUl9JUyS9JOmCes6bIKlrmiKuEyR1jY9H1C7QGovHTpA0WVKveOx1STcAd9cT3orAcptStqcisrny0Rvv0GFpZUZtSgy+evfDZorIOZcvvpClriGx7hzAAcDfgRMJJYDGSrq/gfaJIq7XpBmlpWxjZosl7Q+cAlwArARca2YzUpx/pyQDugIXNeL9E0Vk50l6TNK9tTtMKiI70MysduySTo5906tXr0b+WK1DVWVmCS+hsiK7ds651stHenUlT28uIBSMfT+pYOz6DbRPVcQ1+eLQcksCYx27KyS9CFwIJCqnzkuT8CBMb25HSHhnNeL922QR2VxZac2eGdflM4yuq67UTBE55/LFk17DvpO0aSwY2x/4pIHzUxVxnQesI6kE6Fvr/K0JiXUn4FKWJcXGrLqYB6zciPdvk0Vkc2XwsN3p1Lehv1VqWbcnexx5SPME5JzLG5/ebNgFwG2EPxBGm9lnDdy/dStwl6RDgCXAnsCNwMPAR8DcWud/AKwpaSzwXiNjulPSIkKC/GUj3j9RRLYIKCf14pcVgSfiOdWEQrjtQlFREdscMoxX376x0W02P2BXVlghq4WxzrlWzKssuIzlu8pCNhYuXMjF+xzDohffbvDcTltvyB+fvoeea67ZApG5tsqrLLRNPr3pCsKKK67IuQ/fQrdd+6W9vleN0XngZpzxyH884TnXTvn0pisYq6+xBpc9+zDPPvxfXntoNDMnT8MWLoYuHek1YGv6HbY3+x5/FKWlpfkO1TnXTHx602WsLU5vplJZWcnChQtZYYUV6NChQ77DcW2MT2+2TT7ScwWrpKSElVby2xKcKyR+Tc8551zB8JGec1kyMyY+/Sxvjn6exT8soLikmFU36MWevzyatdddN9/hOedS8KTnXBbGjXycZ668mQVT36UkaZOdT4GXrruH9fcdyq/++WdfBepcK+PTm85laNRtdzPi+HP5aep7yyW8hNIFS/ji/me4bN/jmfXlV3mI0DmXjic95zIw7cUpPH3W5ZQsLmvw3KWvf8x1vzzT6/I514p40suRWgVlN5Y0TdLaGbT/WWxzT/LjZop1qKQr4+O2f+9BC3r+tgco+rHxZRO/f/41r8vnXCviSS/HYqK7DzjazDKZ2xoM3G9mx9d63ND7+X/DFvLN118zY3Sj6woDoS7fS/f/t5kics5lyhey5NbKwKPAr83so1iI9UozO1RSJ2CMmQ2NJX5eBwYAE4Ergb8AxZK6AMclPb6ZsOF1N2AW8AtgJ+BswsbQI0kqNCvpQmBvoAI4lVAxoXb7OiSdSti8ejGhjt9jOfpM2o3Xxk2k+IdFGbf7/LV3miEa51w2POnl1s+BSWZWp3RPCmPM7PeSxgDdgcuBrmZ2g6Svkx5fCfzLzF6QdBZwEKFSQzdgiCVdMJK0FdAP2CGpEOw/0rSv7QhgNzNbkGr02JaLyOZK+ZLGT2smq1icXTvnXO751FhujQM+l3RRfJ62eCzLatq9QSjsms5mwMVxdHg4sEY8Ps3qrpDYBJiSOB6LyKZrX9u5wJWS7gQ2rP1iWy4imysdunRp+KRU7VbIrp1zLvc86eXeGUA/SccB84HEYpZ+tc7bJn7fGphZT38fAOfHSu4DCNOdkLrI7AfAoMSTOGJL1762d8zsJEI9vj/UE0/B2n7PXahcbcWM2/UesHXug3HOZcWTXo6ZWRVwFPBbQkJ7XdKL1K1Evq+kKcAbZvZpPV3+DThT0guSXgC2que93wLekPRyPHeTDNr/R9JE4HrCQhxXy2o9e7Lxvrtk1KaiSOx4nFdgd6618CoLeRCnGvc1s8xXRbQC7aXKQjbemvoqN+7zK/TDwkadv8o+g/jb6AeaOSqXD15loW3ykZ5zGdhq4HYccN2FVHdv+Dpdl4Gbccbt17RAVM65xvKklwfx+lqbHOU52PvYIzj+wetYaZf+VBTX3YasavXubHDSwfzpqftYrWfPPETonEvHb1lwLgs77rU7O+61O9NefIlXR41h8Q/zKS4tYZX1e7HPr45llVVXzXeIzrkUPOk51wT9dxpE/50GNXyic65V8OlN55xzBcNHes61AmbGi2PGMfvTLzAzVl57DYbuuxfFxcX5Ds25dsWTnnN5VF5ezohrbuSNkWNY9Nr7FMeNe6owRm3Zh60PGcZhZ55Kt27d8hypc+2DJz3n8mTB/PlcduQpzHt2KkI1CQ+gGFHxzqe89s5NvD9uMmeP+A9r/OxneYzWufbBr+k5lwfV1dX88xenMf/ZV1CK6uvJFk1+h6uO+S1lZQ0XrnXO1c+TnnN58PyoJ/n2iRcbff6PE9/gyTt9dzjnmsqTXhPEaukmaef4vIOkeZJOa+b3vUjSvs35Hq55vTLiSUoaGOElE2L6o083Y0TOFQZPek03jWWbSe8GfJxNJ14BvXAsWbKEj8dOybjdNxNfZ+aMGc0QkXOFw3/RNt3nQC9JIhRofQxA0n2SJkiaLKlXPLZ3rIAwUdLRkoZKGi3pCeC4eOyV+DUstpkg6WpJUyT9Pel9j5L0jKRJkrpIKpL0XDx/rKRusf358T2vl/RaPNZH0rPx3GvisQMlvRaPndpin14Bmjt3LjY/813oOlRW89WnnzdDRM4VDk96ufEyMBhYDfgmHjvZzIYSKpefEkdylwG7m9kQYEQ8rxtwAKGcz3mxnz2B5AQ3xsx2AH4uab147EMz2wt4kVDxvBo4IL7nk8ARktaIfQ0ilAxaJba9AvhNPLdEUn/gEOCEeKxOzT1JJ0uaJmnanDlzsvuUHACdOnWCkszvv6vG6LzCCs0QkXOFw29ZyI2RwEPAPfF5MXCFpK2BjsC7hIT4ZWKjaTOrDoPDUAFd0mrA52ZWBpRJKpOU+O+Tqsr6G/H7l8BKklYAbo6jyh4xpt7A27GS+keSFsQ2GwO3x/dfEXge+Cuh7t4KwI3A1OQf0MxuAW6BUFooy8/JASuvvDIrbtiLqve/yKid1lqFTftu0UxROVcYfKSXA2b2MTAZeDQe6gH0NLOdgEsBAXOAtWNSSb6Gl6iAPgdYV1JHST2ADmZWGV9LVWU9OfEIGAbMMrPBwG3x2GfAlgo2ALrH8z8EfhFHdf2B0YSEfCrwR5YfZbocKy4upu9Be2TcbvMDdqNr167NEJFzhcOTXo6Y2elm9lV8Oh9YU9JYYNf4ejVwAfCCpPHAkbXaVwGXA5OAZ+O5CY2psj4V2E3SU0Df2Oe3hFHcS8Dvgbnx3D8QKqW/AIwF1gIukjSJMDV6W1Yfgmu0vU48lsqVG5/AKjt3YOfhRzRjRM4VBq+c3so1tcq6pFIzq5C0IXCtme3T1JgKuXJ6Lj1+5/08+duLKFlSUe95lSVFDL3sTI49+3ctFJlrDK+c3jb5Nb3272JJOwCdgd/kOxi3zAEnHENJaQmPX3Al+mJ2nZ1ZDKN6jZXY7fxTOfJ3p+QpStcCfOSRvcbf7Jpo4CM9lykf6eXWTz/9xJO338tbj49jwdffghkrrrEqW+y7K/udeBw9Vlop3yG6FHI40vNfwtnzpOeanyc95zzptRIZJz1fyOKcc65geNJzrp0xMxYuXMjs2bMpLy/Pdzgujbh375yhQ4cydOhQJk6cmFU/d911V81/57vuuouXX345l2Fy55130q9fPx544IGsYrvhhhsA6N+/cYPik046iR9//JFp06YxcOBATjzxRCD8uz7hhBOoqqoCYOrUqUg6O9OYfCGLc+3E3Nmzeeq2+3hj5Bh++GAmVlZBcfcV6LPzQAYedQC7HbQfRUX+d24rM3HChAmH1D5YXV3d6P9Wd911F4ceeigdOnRg+PDhuY6PESNGMG7cOFZqgWvLn332GR06dKBbt27cfffdjBw5kosuuojvv/+esWPHcuihh1JcHHYzGjhwIMBQSVfHW8Iaxf8PcK4deGHUaM7fZhgvX/Avlr7+EV0WV7JClej0w2JmjXyBhw89nQv2PZof5s5tuDOXN5ttthnHH38855xzDmPHjmWXXXZhu+224/LLLwfCZuVHHXUUQ4YMYbfdduPll1/mzTffZK+99uK6667joosuYvTo0QCceeaZ7LjjjgwdOpRPPw2392666aYcc8wxbLPNNtx777113v/KK69k++23Z9CgQUyfPp0HHniAV155hf3335/p06fXnGdm/Pa3v2WnnXZiyJAh/PDDD8ycOZM999yToUOHcuaZZ6b9GS+88EK23357Bg8ezNSpy238xBNPPMGgQYMA6NKlC2VlZVRUVCCJsWPHss8+de64eg/YNqMP2cz8y78y+urXr5+51mPi6GfspO6b2ims2+DX2UMPtJ9++infIbcLhC0Es/7/iLBN4JwhQ4bYkCFDbP78+bbiiivaDz/8YGZW89+purraBgwYYIsXL7Zrr73Wrr76ajMzq6qqMjOzIUOG2MKFC83M7C9/+Ys9+eST9uqrr9oRRxxhZmYTJkyw4cOHm5lZjx49bMGCBbZw4UIbOHDgcj/PN998YzvttJNVVVXZJ598Yrvuumud/hNGjRplp59+es3zqqoqO/TQQ23GjBlmZnbaaafZa6+9Znfeeaddf/31ZmaW+L3Rv39/q6ioWO5nSDj11FNt8uTJZmb2xRdf2HHHHWc33HCDXXPNNTZ16lS74IIL7Pzzz7fKysrEf4NfE/YMbvTn7iM959qwyspKHv7DZRQtWNyo83+c8Dr3XXZNM0flMjBxwoQJTJgwge7du7PBBhvUTCO+8cYb7LbbbgwdOpSZM2cye/ZsPvjgA3baaSeAeqc/P/nkE7bdNgyABgwYwIxYkqpPnz5069aNrl27JhJvjc8++4ytttqKoqIi+vTpw4IFC+r0m5AcRyKWDz/8kF/96lcMHTqUl156ia+++ipl20svvZRTTjmFU045hdmzZ9d5vVOnTgCss8463HPPPRx99NHMnDmT+fPn07dvXzbeeGPGjRuXON1Xb7YFuSg+G8sSbdSEGCZI6ippeHMXvXXN55kHH6X83c8afb4Qb/33WSorKxs+2bW45ER2+eWX869//Yvx48fTq1cvzIxNN92UKVNCLcbq6nAZq7S0tGZxR8IGG2zAa6+9BsArr7zChhtuCEDcZD6l3r178+abb1JdXc3MmTPp0aNH2nOT44AwY7jxxhtz9913M2HCBKZNm8a++6aucz148GBuv/12hgwZwi233LLcaxtvvDGffPLJcseuuuoqzjrrLBYtWkR5eTlFRUUsWlSzQdV6wPtpA03BF7LkT6L47HiyKz47NPbxUW7Dcm3JG4+NoSjDP3bL3/uc5x4dxd5HHtpMUblcOOSQQzjiiCPYcsstWSGWlDrppJMYPnw4I0eOpHPnzjz77LPsv//+HH744Rx++OE1bfv378+aa67JjjvuSElJCXfeeWeD77fGGmtwwAEHsMMOOyCJ66+/Pu25++23H8888ww77rgjHTp04JFHHuGKK67g17/+NWVlZRQVFXHHHXekbHvggQeyZMkSysrKuO225bf53X///bniiitqfpbPPvsMSay77rp0796dAw44AEk88cQTiSZbEDbJbzS/OT0PJPUGrgRKgQMJJXtmAgvN7AZJ9wFrE/4oORr4FvgvoQyQCDXy3gcWEq4rnJDU9waEenglwKtmdo6k4cCvCCWPLjSzFxJ7egKHAl2BB+J7APxoZvuni99vTm89fr/tMBZP+yDjdjtd8XuOOff0ZoiocPjN6c3jxBNP5KqrrqJ79+71nvfKK68wcODAc83sn5n07yO9/EouPjuFkHwgFKBdLGl/4BTgDmCpme0rSWZmku4iJLzRtfr8J3C2mb0Rq6mvChwV36czoYrCCyli2Sb2d3ZS2aMakk4GTgbo1atX035qlzNW3eiV2supzrKdc82t9ugvnQEDBpBpwgO/ppdvI4FrgAmJA5ISBWhfBC4E1jKzT4CJMdFdGs9JZ20zewNqyhn1ATYjTKM+DayRpt1EYIGkuwlliJZjZreYWX8z67/aaqtl9lO6ZrPiGqtm3MYwVlx9lWaIxrnWz5NeHlnd4rMQCsUuV4BWUkfg32Y2nDAq3AGoIExX1valpK2gplDtTOBtYGcLRWO3ThNOqZn91cx+AewRK7C7Vm6zvYZimc6OrbcGw44+rHkCcq6V86SXZ7Z88VmAD6hVgBZYlzDSmwKsA7xOmKI8S9LVtbo8F7g2XrO7zMzmAiNi+/HAVWlC2VbSZEkvE4rNpl5v7FqVA088HvXumVGbvgfuUbMs3LnWYsmSJcyaNYsffvihWafffSGLy5gvZGldHr/9Xp787cWUlDV8G0KnrTfkj0/fQ88112yByNo3X8hSl5lRXV1ds1VYQ6qrqxk/fjxvvfUWX3/9NZIwMzp37swmm2zCrrvuypr1/1vN+D49X8jiXBt3wK+OY+mSJYz949WULFqa8hzD6NJ/E353/w2e8FxOVVZWMmbMGN59911mz55dk7Q22mgjdt5557QL3+bPn8+NN97I4sWLKS0tpVu3bsu9/sknn/DOO++w2267sfvuu+csXh/puYz5SK91evu16bxw10O8O2oszPqBIqBCsPL2W7L1wcPY/6Tj6/xicdlrTyO96upqxo4dy7vvvsuPP/5IaWkpvXv3Zo899qC+hWvz58/nhhtuoLy8POXobunSpey1114MHjx4ueNLlizhyiuvbNQ0ZllZGXvssQdDhw5N9bIXkXXNz5Ne67Zw4ULemfY6SxcvZvWfrcXmW/WtdycOl53WmPTeeustXn/9daqqqlhnnXXYbbfdGpxqrKio4KqrrmLx4sV1zq2oqODoo49ms802qxu0GZdddhkVFRX19l9WVsaRRx5J3759a47df//9fPzxx43+d7l06VIuvvhiOnToUPsl34bMuUK34oorMmjnIeyyz15ssfVWnvDamIqKCmbMmMFPP/2UUbsHH3yQESNG8MUXX/D1118zZcoULr/8csrKyuptd99997FkyZKUybG0tJQHHnigzjZnAC+99FKjYuzYsSPjx4+veV5dXc0HH3yQ0b/LDh06MGbMmEafXx9Pes4510rMnj2bv/71r9xyyy1ccskly5Xzqc+MGTN48803l1uVW1xcTEVFBaNGjUrbrqysjI8++qjezaslJW/wXGP69OmpRl4pzZo1i1mzZgFkVSy3qKiIDz7IfOehVHwhi3Ouju+++Yanb7uPT19+g/KfFtOhS2fW7rcFw351DL3W653v8NqtZ555huLi4pq9NseOHUu/fv0abDdt2jS6dOlS57gkPvvss7TtXn311QYTV0lJScqKCfPmzWswroQuXbrw3nvvsdZaazFv3jxKSjJPPQsXLsy4TSqe9JxzNSoqKrjhrAt574GnKP5+0XKvzR7zMq9cfx8bHbI7v7vh8pS/ZF3T1J5GbOz9avWdl+1ryXK59iPfW+D59KZzDghLzy858iQ+uH5EnYSXUPrjEmbe+TgXH3A8S5YsaeEI279ddtmFiooKzIylS5fWVBFvSN++fdP+91hnnXXStuvXr1+D1/yqq6tZffXV6xzPZCXwkiVL6NOnDxCuOWeT+HL1R5YnPeccALdfdDmz/zseNbAgToj5417jxrP+1EKRFY7evXtz1llnMXjwYE4++eR0y/Tr2GKLLVhvvfUoLy+vOZYYne23335p23Xt2pXevXvX23d5eTnDhg2rc7xv376Nrsu4+uqr1yS9RGLPhJnV1ARsqnqTXlKx0wHx+TBJF2XyBrGPPZoQY6Jg6pUpjt8cv18kKXXFwhyQlHZ9fvxMDkpxfGpzxZMJSVtLOjU+Pjnf8bjWqaysjDdGPNVgwksQ4v1Hn+P7uXObObLCs9JKK7HLLruw3nrrZdTu5JNPZpdddmGVVVahR48ebL755px99tkNlug59thjKSoqSjmFWV5ezkEHHZTyut/OO+/cqGtzZWVly41YS0tL2XjjjRvxEy2zZMmSlIk3G40Z6b1H2M8xW72BOkkvVfmaTJnZKU3tIwcxjDGzx5raTy4+j1TM7E0zuyk+bXTSa654XOv05F0PYJ/MyqhNyZwfefKWe5opIpcpSey6666cdtpp/N///R+HHXZYzYKY+nTt2pWzzz6bzTffvKYqeXl5OWuttRYnnHAC2223Xcp2RUVFnHTSSfVe7ysvL2eHHXZg4MCByx0/4IADGn2dMNFH165dGz65ERqzkOV9oFTSJskHJQ0DLiDs9H+9mT0YS99caWb/k3Q5MAY4FRgkqT9wAKGG3DRgTixjcxPhBsOnzOxvcSTZh1ACp5xQ5BRgC0mPE5LosWb2jqRp6W4OjYVaHwS+JpTWuRA4FtgAOALYBNjEzC6T1AN42Mz2kHQ+oUirgN+a2TtJfa4D3E0o/vqOmf0mFmjtGou/ng/sB0wnRQUESe/F17YALjazUXFj6Onx5zscuA/oBnwX460mbBjdI/63WNHMhtfz+VcA6wE/EQrUDiEUi50CbBzf76bYV6rPfj1gdcIfOjU/u2vfZr3zQaNHecm+e/fjZojGtbTOnTtz2GGZV95Ya621OPPMM3nmmWd4//33WbRoUc3tD+uttx6DBg1i6623rtOuR48enHjiidx+++2YWdp79pYuXUr//v3Zf/+0Na0z1tjVm/8EzgEegZpRwJ+BoUAlMF7Sw2na3gR8aWZnx7ZrAzuY2TxJo4ETCZUFxkq6P7b5ysyOj0nkSOAzQumbYZJ2B04gRc23FFYCdoxx/gPYlpCUjgMuAs4ALgMOAx6WtCWwsZkNkbRGjD156vI84B9mNkbSnZKGJF6I5+8JDAI2JCT82nrFeCoINfRGxeNPm9lZks4hJKD/SPoLofjrYuAjMztf0onAjg18/i+a2Unxs9wy8cZm9pikD2N5Ier57L+I5YWW40Vk27eKpfUvZkinfGnqvT5d4ejevTtHHnkkZsbs2bNZsmQJPXv2pHPnzvW2W3vttTnnnHN4+umn+eCDD1i4cCGdOnWiurqa8vJy+vTpww477MAWW2yR03gblfTMbLKkS4CfxUOrEn6xP5f0fDWW304n3Z+NM8wscYNHTzN7H2qum60fj09P+r4jIem9GY99SUhmdUg6mvCL+VXgRuBdM6uS9DXwPzOrjo9XMrNySf+TtDVhNHkEYRp2UBwNAdTehmB94LX4+BXCqDFxTm/gbQtj9o8kLUgR4qdm9kOMtUxS4vNP9Lk+cGtS/zsQRmzTk87bkfSfP8AbDX1OUbrP/rVUJ5vZLcAtELYhq6df1wZ1WrHhabBUOmbZzrU/kujZM7MyV126dOHQQ8Nk3kcffcS3335L586d2XTTTXM2nVlbJtdtriWMjCDUW3sf2D1RmNTMvgXmEeq9Afw8fq9d7DR5rep3kjZVGNv2Bz6Jx7dJ+p441mBCNbMHzGyomZ2bok2q9vcAfwTmm9l8wqhnYuxjKFD7yukMwmgRYACQPLfzGWGKUpI2AFJdPe4tqYekFYCOZpZY+pT4TFL1/wnLPo/EXarpPv90PycpXkv32ef3JhqXF1vsMYTKDLeArMbYeOj2zRSRKzQbbbQRgwcPZtttt222hAeZJb0nicnLzKqBvwHjYmHSxNTYXcDlkp4gJDsI14X6SXpU0oq1+rwAuA14CRhvZp/F471jEdUdCdezmoWZvQJsBTwQn78NfCwpUXD1nFpNrgDOlTQZWGJmk5L6+hYYS7hm+Xvg+xRv+SVhBPoi8NcUr98K7CtpErA54WcfBWwm6XlCIqyo5/NvyHhJT0jaj/SfvStAg4ftTveBm2fUpmSTXuxz7BHNFFFBUaovSaeke62hr6a0bWPvnfmH3dqqLMTFFNPMbHQLvd8kYFczy+zGkezeK+3CmwbalZpZRbymt4qZXdEM4TWaV1lon566dwSPnXgBJeV1NxeurVKw2zV/4Mj/O7UFImudlLsqC+n6z+r3RVPbtuX3boyC3YYsrtgcCTzaEgmviR6X1BUoI1x7dC7n9jnuSOZ+/Q0vXvTvequwVxaJrc/+RUEnPNd2tbqkZ2YXtdD7zAd2bYn3SnrPrP56MbO9cx2Lc6n84rwzWWO9Xky49UFmT3yd0spll3grBCvv0JcdTzicA355bB6jdC57rS7pOefya88jDmHPIw5h+pSXeef5yZQtWkxpl05ssuO2bL/rzl6fr+Xckqe2bfm9G9Tqrum51s+v6TnX/Nf0XPPwraacc84VDJ/edM41q2+++ooXH36CJfPnU1Jayqob9WG3Qw+kuLjOTn3ONTtPes65ZvHBm28z5pqb+WLMJDrNX1xzvMqMFy7/N5sfshdH/eEMSktL8xhl6yCpH2EDkGrCvrvHAAcD/wcsBX5hZl820MdRwL/MbDVJR2TYdijwJ0JOuBro1Nj2cVvEOwl7JgMMJ2x4kbZ9vGd7HOF+5IFxv+Y6MUvaDLg5xvUnMxtX38/RGH5Nz2XMr+m5hrz6/AQePfFcSmal2qMhMDO67LMDZz9wS4P7NLZGubymF/fu/dHMFkv6O2E7wbOBnQi7NP3CzNJWSYmJ5xHChvEDgMkZtO0U2x4St2cszbD9z4FzzezIuDfy3oQ9iNO2j1swrkTY1/lK4MNU7ylpFGGTkO+AMWbWuKq69fBres65nJrx3gc8etIf6k14EPZqXPzUFP596tktFFnrZWbfmlliOFwBbETYO7jczKaQtHl8GkcDjxJGihtm2HYQsAR4UtJjhKSTSfuvIGylQqgGM6eh9mZWaWZzkg6li3lNM/vYzH4Evpe0agOxNMiTXg4pFMydI2mCpCmSNlCaArj5JukuSVu01vhc2zX2P3dR8nXjistKYtbIsbz7+pvNG1QbIakXsBth1PNj0ktpL4BKKgYOBx6Kh3o0tm3UkzBC3I9wu8BFGbafS0i27xOq2UzIsD2kjzn5/pgFwMqN6KtenvRyb2LcBPpq4A95jsW5FrVo0SI+fvL5jNp0Kq/ixbsfavjEdk5SN+BeQum02YS6mgn17Q13LKEeaGIngXkZtAWYD0w2s3LgBcIG95m035OwF/EmwCGE63KZtK8v5uQN8HsAPzSir3p50ms+/wPWTj4g6co4Cnw1ljRKjLhelDQpjhSHSxol6ak4WjwuPp4oqaOknpLGxfMfjX/lJb/HapJGx/Pvi8eGxfd4KV7srkNSh6R2k+I8f7v38YwZja7g7Bo26fGn6Ph1/dOaqXz24qvNEE3bEf8/vh+4xMw+IlRc2Sz+f7kD8HY9zTcDjpc0hjBNeHIGbSGUYtssPt6GULIsk/YQkhaEBLpqFu3T/bzfStow/kGwspk1bgqhHr56s/nsRLg4m+zP8UJ1X+APClXXNyWsXrJ4MRpgTiwE+1fg52a2j6RrCFXQJwDDzKxS0tXALoTqDgnnA7fHorFFanzB33WApWa2ryRZAWSCaa+/QWXHbnw39RV23H5gvsNpF8oXLspqx5alCxY2QzRtyuGEa2srSvoToYD1NcBEwmrG49M1NLOaGaW4WfOZcSVkg21j++8Vqq9MIoysfkm4rteo9oQkeZykiUBHQpWZdRpqL+lpYGtg43p+3vOBOwi56s8NxNEonvRyb4hCEdofgFMJSS3hLEl7Ev5hVcXKCdcBdygUnb0wnpf4K+drwibTiccrEea0/yNpJWBN4K1a778JoewQsWju6qQvOFvDzD6Jo7y7gK8l/dnMaqYl1A4rp2+y0YZMfe11tumbWUkdl15Jx45ZtevQqSAmFtIysweBB1O8lNG8b2I1qZk9lElbM/s38O+kQzMb2z7+njg6xUv1tk+zp/BDtc55jzCAyBmf3sy9RBHag83su8RBSasA+xL+A54WDqkYeMTMEnP4B8fT6yt+ewzwnJkNAUZTt6bU+4SK64llzPUVnF3WsdQR+LeZDSckxR2SXzezW8ysv5n1X221OjmzTeratSu77TyYVVZZJd+htBubbr8tS7t0yLjdqptv2AzROFeXJ72WM49wr8l44Mh4bEXgeYWitMMIN2s25HngVEmPA2ukeP0y4OQ41XBnBgVn1wUmSppCmJp4vXE/lnPLrL/JxvTcNbOp4koztj5sn2aKyLnl+c3pLmN+c7qrz4QnnmL00WfSoaIxi/aAfhtz6eTRFBW1rb/Bc3lzums5betfmXOu1Ru6/z5sce6vqChqeEFLVe81GP6fK9pcwnNtl/9Lc87l3PA//4HtLj+LsrVXTXlLSHkRlA7ehhMfvYWN+ja04YdzuePTmy5jPr3pGmvx4sU8fcd9fPL8ZJb8sICikhJWWr8XA446iO12Hpzv8JrEpzfbJk96LmOe9JzzpNdW+fSmc865guE3pzvn2oy5c+bw8bvvY2b02WQj1lhzzXyH5NoYT3rOuVbNzJj49LO8dN9jzHhmEsULQgWeyhU6st6eOzDgyP3Z/ZADfAWoaxS/pucy5tf0XEupqqri6t+dy4f/GUlJml9VlRi9jtqTP9x5PR2z3AYtG35Nr23yP42cc63WNaefx8c3PZo24QGUIL5+8Fmu+OXpXjHDNciTnnOuVXpt0hTev/lRiupsL1uXEF8+MIax/32iBSJzbZknvWYU69hdVM/rEyR1lbS1pO0a0V+v2GaCpIXx++gsY5sWv18kad9s+nCuOU285xFKqxo/citGvPKgJz1XP1/I0jpsDXQlFHNMy8y+INTFS9TNGtrcgTmXD2VlZbz/1ARKM2w389nJfPftt/RcI9Ve7M61k6QnqQQYQSgn/z6wopkNl3Ql0B/oApxsZm/GWndvAQOBJ4CewHbASDP7Z6wnV0GoQfc+8A2wK/CWmZ0uaXfgj4Qk9V8zu7xWLD2Ahwk1874GvozHhwO/AoqBC83shaRmpwIrS9oH2AcYA3SIcRxiZj9m8FlsB1wNVAFPmNlVks4H9iSUIfqtmb2Tot0GwL2E+n0fmdnJjX1P53Jtzpw5VHz7PaUUZ9SuZNFSZn7wkSc9l1Z7md48iPCLejeWL6r65zgaOhE4J+n4w4SkdxKhKu8OwLFJr0+I7TYnJLshwCBJnYApZrYLMAA4UFLnWrGcCDxqZsOAbwEkrQocBQwGdgMuqNXmJuA6M9srlgI6IL7/k8ARGX4W1wBHxJivkbQlsHF8fjhwSZp2Q4D74/v+uvaLkk6WNE3StDlz5mQYknOZKSoqwrKowA5QVJxZonSFpb0kvfWB6fHxa0nHz4q16m4A1ko6/raFZV7fEpJaFWFUVfN6/D4r6fG3QHdgG0njgAlAH2B1Sf+I19eOBjZIiiUxXdkH2IxQS+9pUtfBA0DSCsCtkiYREuha6c5No4OZfQ2hcjqhcvugOMIdAXRL0+5hYG1J97D8HwDEvtpdEVnXeq222mp0Wjvzf2dVPVZgg802aYaIXHvRXpLeJ8A28XE/SF2pPOn8mqvjlnqNc32Vy88DTgd2Br4g3Ot4bqyW/gAwIymWxD08MwnJc+dE9fJa71cBNfM4w4BZZjYYuI26ldEbUiZpTaipnP4By6q5D439p1JpZueZ2fHAebGtc3lRWlrKFvvtmnG7DfcZwiqrrNIMEbn2or38YhsFbCbpecK0YwWpK5XnwkjgIeA+4KcUr98GHC7pWWBtADObSxhlTYzVy6+q1eZl4DBJ9wJTgd0kPQX0zSK+3wOPxvc5w8zeBj6WlHjvc9K021/Si5JeBcbEUaJzebPLCUdS0bHxU5WVgkFHH9h8Abl2od3syCKp1MwqJJ0IrGJmV+Q7pvbKd2RxLeWmCy7l9b/fSnEDEx7VGBueeihn//tKlOW1wEz5jixtU7tYvRk9LqkrYfVhpos/nHOt0K8vvYBbisSr/7yD0rKqlOeUF4vNf304Z1x3WYslPNd2tZuRnms5PtJzLe3t16Yz/u6HeffJF1j61WzMjI5rrMym++zMkOMPo/9Og1o8Jh/ptU3taaTnnGun+m7bj77b9qPimgrmzp2LmbHKKqu06AbTrn3wpOecazNKS0tZ02vouSZoL6s3nXPOuQb5SM85V1A+fu99xt05gm/fm0FFWTldenRjk90Gsd8Jx/p0aQHwpOecKwjfz5nDjaf9kc9GT6J0cXnN8R+AL0eO4/l/3sbQM4ZzxO9OyV+Qrtn59KZzrt37Ye5cLt3/F3z98LjlEl6CEDbzG8aeeQV3/O3KPESYFIvUW5JJ2jk+7yBpnqTT8hpYlChL1ojz6pQti2XUTk3uR9JwSdtL6iHp8NxHvDxPes65du+m/7uAJVPfa/C8kqpqXrrkJiY+/WwLRFWvacDB8fFuwMctHUBzbEVoZm+a2U21jt1lZi8TquR40nO5I+ljSeMlPS/pHkk94/EiSX+RNFnSFEm35ztW53Ll0xmfMPPJCY0+v6S8ihfvfqTZ4mmkz4FeCnfbHwQ8lnghjoxelPSSpF3isXMkvSBpeix/hqS7JN0qaZykx1Xrzv34+n9iu7uT+n4oboO4m6SzJb0c36tfbFok6d+SXpX029juuPh75XVJxyW9zeGSnpM0WlInSUNjybfkOBIjwlOBIXHz/gGSbo6vF8cYcrLzgCe9wrLAzHY2s10JtfNui8d/RajmsJOZ7UDYV9S5duHZ2++ndGFZRm0+GzOZzz+Z2UwRNdrLhHJkqxHqetZXpuzfseTZnsD5SX28GEuuLQK2TPEer8d2ZZKGxmPlZrYPYZP8/VlWei2xteNKwHXAIOAESV0I9Uh3BbYnbMif8JWZ7QG8RMN7IN/Ess3xXwE2kdQB2AV4Pk1xgIx50mun4l9UT8WvVyVtmPy6mY0Fusd/sEcBlyf+UZnZ+DyE7FyzmPNh5smr5MelvPva680QTUZGEupjTkg6lq5M2TGSXoxtksuRvRG/f0lIVrVNT/q+fnycKM/Wm1B6rdrMZhJKqwEsMrOPzKySMO26JrC7pInAc8BGDfTfWKOBvYFjyOEf4p702rfuhPJKZ1C3cC2EeoFrxq9Z9XXkRWRdW1VZXtHwSanaldVd8NKSzOxjYDLwaNLhdGXKziaUOzu0djdJj1NND26T9P2T+DhRYeUzYOt4+aMPMD8e7yppQ0nFhET2DfAXwqhwT8Kosr7+00kusQYh0Z0A9DKzDxpo22ie9Nq3N+LoLd1fWWsR/sHOAn5WX0deRNa1VV1W6pFxmyqM7quvmvtgMmRmp5vZV0nP05UpGw+8SPjj9scM3mK72E9nM5tQ672/BR4HphAS0HnxpXnAWYTp17vNbDFhhDke+Hd8PaG3pLHAjjHu+nwDdJb0qKQ+ZvYN0CnGkDO+4XQ7Fefn/0qY+x8A/BrYIrFBrqRdgTPNbN9Yjmlb4NdmZpKGmNnEdH37htOuLRl9/0M8fuy5DZYnSla6RW+uffN5iovT1/Nr6xtOS7oLuNLM/pfvWNKR9ARwopnNzlWfPtJr3xYCTwH/Av5GuIY3XtI4YDhwYjzvDsJob3K8LnBcir6ca5P2OvJQOm7eO6M2Wx8yrN6E55qfpFHAh7lMeOA7srR375nZ2UnPN0x1UqySfnH8cq5dKS4uZti5v2bUyX+ipKyywfO79NuYg047scHz2jozG57vGOpjZgc2R78+0nPOtXv7Hn8Uu//zHKpWqH9vzc79NuK0+69n5VXzfz3PNQ8f6bVT8aL0hDyH4VyrccTvTqHP1lsw4a6H+fCJFyiZuxCASowu22zINgcP48Df/JKVVl45z5G65uRJzzlXMLbdaQe23WkH5nz3HW++/CrlS8vosfoqbD90CEVFPvFVCDzpOecKzmo9e7L7gfvlOwwAirqvY6oqR0VFoCJUVIRUBPG7pPBVBJJAxGPhe5FEcdGyx0VFUBzbFBVBEaKo5lwoim2LSPQRNtwWJD1PHDOwasDAEl/V8Xh4bNXxHKuG6mrMqqE68VridcOqLel5NVZlWM3xpC+j5nE1UG1GtREfQzXhuQFzKX/WzIZl8nl70nPOuXyqXEqnbY6ltNMKFHfoTHHHzpR06ExJ564Ud+hMSWkxHTqWUNKhmJLSIkpKiyntWEJJh/C4c4diVuxUQucOJXQuLaZLh2K6diqhc4fwWqfiIrqUFtOxpIiOJcV0KS2iU0kRHYuL6VAiOhSL0iJRUhS+dyhe9rjIqlBlGaoqR5XlqLIMqspRZQWqKsfKFmNlS7Hy8FW95KfwuGwJVr6UysVLqVxSRtXSciqXllP501Iql4bnFT+VUbG0ksollVQuraR8UTmVS8PjyiWVLF1ayZIqY0lVNUuqjEWV1SytDs8XVVZTZXAzn2d88dXH88455wqGJz3nnHMFw5Oec865guFJzznnXMHwpOecc65geNJzzjlXMDzpOeecKxie9JxzzhUMr6fnMiZpDvB5ls1XBebmIIxc9ZPLvtprP7nsqz31s66ZNbmisqT/AUub2k8zyeW/oebQycy2yKSB78jiMtaU/9ElTctF4c1c9dMaY2pt/bTGmFpbP020tBXEkFIr+XzSkpRxNWuf3nTOOVcwPOk555wrGJ70XEu7pZX1k8u+2ms/ueyrvfbTFK0hhnRac2yQRXy+kMU551zB8JGec865guFJz7l2StLaWbaTpM1zHY8DSf+U9KKk+yV1SDpeIunO+Np1rSy2oZK+lDRB0vN5im1FSa9IWiRpi1qvZfTZedJzbYakXvF7F0nHSlojz/0cEL9vJOk2SUOz6SeXJI2K388Cbpd0X6Z9WLjm8Y8cxdMhfpekHSV1aUJf/4nfj5P0rqR/5iLGliJpG2BNM9sJeA84NOnl/YCv42tdJA1qRbEBPGRmQ81s15aMK8kSYF/g0RSvZfTZedJzLa4Jv6zuid8vBdYBRuS5nzPi9z8AtwNZ/xKWdEr8PkDS85KOyLKrbvH7Fma2J9Any37mSLpY0r6S9pa0d5b9PBe//xU4Dngky34ANorfdzWzzYGdMu1A0q2ShtQ6NlhSSyzY2J5ln8cYYFAjX2sJDb3/IXEk9X8tG1ZgZpVmNifNyxl9dn5zums2kn6T6jCwD3BONl3G7z3M7PdN+EWcq35WkLQiUGVmL0v6Kct+AI4AbgZOJSSHJ4CHsuhHkv4BzIjPq7KM59P4vV/8bsDTWfSTWCn3MzM7QdLkLOMB6BT/TX0Tn5dn0ccmZjZxuQDNJkn6axPiaqwewKz4eAGwcq3XfkzzWkvoQfrYpgEbx8ePS5psZtNbMLaG9CCDz86TnmtOFwFnsyzJJGT9i1jSy8B1kkqA6jz3cyfwOHCupE7AZ1n2A9BV0mbAYjOb1YQEejCwDTBBUkfg/Gw6MbOL4zTw+sAnZvZFlvEskvQwMFGSyP6zhvDHwBDg0vh539CEvmorzmFf6cxj2Ui8B/BDI19rCWnf38wWJR5LegLYCmhNSS+jz86TnmtOo4Fnzey75IOSNk5zfr3MbLikEjOrjL9A98tzPzcBNyUdGp5NP9HfCdOkf42/0F/OpHGKUfUmSY9fzDQYSX8Edib8cusv6XkzuzzTfghJeB0zmympFPhVFrEkj8S/JSQ+gEUpTm/IWEk3AZcTRjY/I3zuz9XbKjemAmcRptf3BKbUem0PYFJ87Y4WiKdRsUnqZmaJkdROwH9aOLaGZPTZ+X16rtWT9AjLpslqDhPWXByeh35eq6ef7RrbT4p++xF+CY8G1jKzrzJo+4t0r5nZ3VnEMtnMdoyPBUw2sx0yaP9P6n5GiXjOzTCWv6R5yczskgz7EiHxHgWsQUh8I4A7rAV+GcbPZSDwBXACcL2ZnRJnHG4jjKzfMLPTmzuWDGI7ETgZqASmmFk2lyZyEd/TwNaEze5vAnbI5rPzpOeajaRTgXfNbFLSsZ2Azczs5gz6WTfda2bW6GoPueqnOUi6CigFtjezbSU9Z2Z7ZNA+7apIM1ucRTyTgV+a2UdxZH57Igk2sv2QdK/VvqaWD5I6A2sCs8ystVY4cM3ApzddczrOzJZbSWVmL0r6O2HRRmPVd89YJskqJ/3EhSIpZTqKSbKNme0iaXx8nuk1pqdYNrJKvoZqwC5ZxHMqcJWknoQpxVMzbJ9upV3GcjmylrQ6YVTQjTi9KWkBcKKZzc5FvK5186TnmlNFmuOZLmbYNs3xTFcU5qqfpzI4t7HKJa0HmKR1gLJMGpvZzjmOZxMzq7nWKWkf4J0M2qebAjPgl5kEYmbp/rtl4x/Av8xsXOKApF0Jt5uknSJ27YdPb7pmI+ke4GkzG5F07ChgbzM7Lss+VwfWNrPXJXU0s4ySQy77ideHDgZ6EkauW5nZ61nGsx5wBWFp+AfAeWb2af2tUvYzFLgQWJ2wivMqMzsji35eMLNdkp6PMrMDM+0nti0CVm3qSEphl5i/Al2BvYBzMl1cI2mcme2W4vhYM9u9KfG5tsFvTnfN6XfAvpI+lDRF0keEX1a/y6YzSb8nrBy7LSacUfnsB7gbWJcwjVtFE3YxMbNPzexwM9vKzI7IJuFFlxJWo34fY+qbSWNJJ8XpxP6SXpX0mqRXgA+zCSbeZP8CME5SsaRsNwIA+DdhmrU0/mzZJKnat88k+O/CAuHTm645LQLeJOwKsibh2tDbZLfUHOAAMxsiabyZWbwPLZ/9rGVmx0vaNz5P9wu1QZJeJdxUuwDoDswlXBf7S4ajx2ozWyIpMYWT0bVBM7sVuFXSgWY2KpO2afyOsMz9BTOriiPsrJnZd9n+bNE68bOurXsTwnJtiP9145rThcCKwFAz60O4v2oF4M9Z9lclqTvhuteKZH+jc676WRinE4sl7QDMz7IfCPca7WVm/YBhhF0wzgRuzLCf2yWNATaQ9CRwayaNFbdDA3aQ9I/krwzjSKgmJCeTVEzTfuc8K+kuYG2FbcMy3iHGzDaKi1+OBt4Flsbv2e7K49oYv6bnmk28obnOBrW1rxdl0F8/wnWvLQkjyD9mcw0th/2sApwHbAa8D/zdzLLaSUPSlOT74BLPJU0ws6FZxNUHmGlm32fYdiszeyvVLQfZ3GogaRjwJ2BDwkbGfzezrG8EV9hhf1PgAzPLZGFN7X6mEvZOfR34OXCtmQ3Mtj/Xdvj0pmtO6f6iympkFff7q7MIIV/9AKsB58YpUhEWoWS7fdRT8XaFtwnX4Z6ON91Oqb/Z8iRdYWZ/AL5XcJmZ/bGx7c3srfh9YkwwK9GEaVszGyPpWWBVYG5TbgCXdKaZXQP8L/5sZ5jZtVl2N9fMpsbHUyVl9MeBa7t8pOeajaTZwITah4HBZtYzi/5qbtiOSeZpM9srj/2MT75VIN3KwAz66wn0Bj6zWlu3NSGmbEfVjxGuvSZ2hTEzy3gfT0n3mNnx8bEIO5+ckGk/sX3tny3lTEIj+xpNuFfvDcIuH4sI05xNudfStQE+0nPNKZf3VwHUFLaMo6tsa7PlvJ+oKbXiuhLqha1FzA+W4RZbUZWkvmb2tqSMVm7W0tXMDmpC+4R1Eg/iZ927CX2VSFrDzL5VqIGY7QIkWL4M1H+b0I9rYzzpuWZjud/aa5ZCPa8XgR1ZVmImX/08LWkkYQpyENmV3kkYSdhz8xBCbb4ts+znFOCfkn4GfAmclGU/n0o6ljASMgAzey+LfhYpFNtNfNZNKb/0e+BRhY2rywgLfbLSGrZCc/nh05uuzVCoPnAyoYLAe8Bt2eybmKt+Yl99Yz8fmNnb2fQR+3khbkM2Md5O8WTyjigtTdKdtQ6ZmWW0k0rsZ1VCeaPEZ325mc3NQYjOZcWTnnOtQFzssT9hX8ivgd3j7Qv5jEnAak3dScW51sSTnnOtgCQlXV8cBkw1s1kNtWvGeI4g7H6yMmE7s/vN7Mh8xeNcrvjN6c5lIS6Zz2r/0DQ6xp1dDiHsLZnVKlBJt9d6fmWW8fyOUEQ2sZ1Zk3ZSaQqFat3O5YQvZHGtnupWBa9hZo3esUShhtqBwEJgHHA24T60683ss0xiiqOyQ4B7M2lXjzGExR6NLhybTNIGhPsE+2tZpfESsl9Bm5OdVOIU6S7EVakAZnZPht10zea9nUvFk55rC5qy4i/ZSOAlwtZolwMXEbYOu4Psas4VS5pA2DKsGpp0j9diM/tTlm0hVFzvT7j3rD8hwZQDf8iyv78BEwk7qTwP/D3Lfh4irCLdh1CSqSeQadLrn2K/zCZXqneFya/puTZFYXPo1Vk2avgig7Y1W3pJesXMBsTH2d7A3eStupJGZUcTSgol3yKQ8S0QkjqZ2VKFcj6DgNcti8rpsS/RxJ1UEp950vfHMr3/r/ZN6c41hY/0XJsh6VzgIGA9wuhhEeG6U2N1kbQpcaou6fEKWYY0iVr19LLoIzH9+DFhOrF/fJ5pYduEMcBQ4GLCHwd/JIyyMiLpjlrPK4AZwK1mNj+DripiAp4n6dfA+pnG4lwu+UjPtRmSpprZwDiluDNwn5kdk0H72vee1chmayyFIrlvAoeZ2fY52IZsXZZtFJ3Vjf2JUZGkO83sBEmTzWzHLPr5F/AW8BrQD9iOMI17uJntmUE/qxP2I10FOAZ4PrG/ZwZ9rGNmX2bSxrl0fKTn2pLEtb0KwqKNjHYtyXbPx3rksp7epYTd/t8EtpY0PctrfIskPQxMjNOT2ZZN2sTMTo+P35Z0tJmdKun4DPuZRxh5rkRYpLMxIZk2mic8l0ue9FxbclncTeUiQpXyq/IbTk7r6Q1NHpFJmpxlPwcD65jZzLhd16+y7GdBTMTTCcl4gULVh0wLAD9HKN+Tt3sOnUvm05vOZUm5rad3FeEa3uuEm8H3JRThJZOFKJLWBn5NqFSfWOyTzfZhxYTrp32AT4DHzawyi35Gm9m+DZ/ZqL66Akew/O0P2WzK7QqYj/Rcqyfppji19hrLavRltWQ9TvndZWa/aEI8d5nZcOBkMzsn235q+Xn8SvYU4efNZGXpA4QKAhcQbjvI5lYMCJ9vF8I9cp2z7ANgREzob7NsVWqmtywk5GpTblfAPOm5Vs/MTo3fm1yqKN5U/qOk9czs0yy72TxWaRguaUGt/ht9s3ytdjtDGGHFHVCyVWlmT0o6O34/Lct+7iPUl5sCDIjPs9mG7NfAC0CnLONIVmpm10s6NH5/Mgd9ugLjSc+1epL+SZoq7FneDD4I2EfSD7HfTEeMBwCDCYtEcnLjvKQ9gMuApZI6AOeb2dgsuvo6Xvd8S9JDZD9K65m01+azccVsNuab2Z+zbFtbRbxP8wtJlxOmOZ3LiF/Tc61eqpvAE/JRFy0xvSnpfDPLdqeS2n2+DAwzswWSegDPmNn2TeiviFAR/INsbk6P+12OJtyysB2wbzaljhQqsM9m+enNrEbDKTblftnMsq2F6AqUj/Rcq5dIbPF6XFNvBk8s9jiF5RdEZLLYIzG9+QtJ82vFmtUv9BhHYtT4Exne/lDP/qQDgWxiOpZQc/Bkwo3zjb4fspZRWbZLZTSwT0zi/5V0N5D1tVlXmDzpubbkbpbdDH6jpH+QXTWCpi72SJ7eXEQT7s9Lch0wXdJnwLqEvUEzcSnwEfAYoRJ8U+4ZFPCQme2VbR9JxhN2z+lCWA06Lot4tiWMNjdISu4lhJWlzmXEk55rS3J1M3iTFnvEOncjFAq/dqPpu6iI8P/iNsBqwBwzy/Sm8p6EPwAOIiw8mQA8mk1NvjiF+G68BzF5M+1s9vC8J+65+VdgDjCCcLN6JsoJf1zMY/kNCjK9Ud45T3quTcnVzeC5WuxxFjnYRSVRpsjM7gW+yyYQM6sAngGekTQYuIaQjM/Ipj/C1mPJldszvXUiIfGHSQ8z+33SBtuZ+Dh+PZJFW+eW40nPtSUnEm4GX0QY0ZyUTSdmdhyApDOIiz2yjCdXu6hAE8sUxcU+BwO9gJcJe2R+km0wObyF4tO4SOe6uKNLNtuiJe5XhOVH99kmYlfAfPWmKxj1LPbIagFKrnZRiX01qUyRpGpCwkxMsdb8j21mh2cSS+xvf+DPwFKgI3CJmWV1X5ykEjOrjNO4K5rZj9n041wu+EjPtRmSziPcIL2YZTuyDMqgi1wVo03I1S4qAGua2QioucZ3RIbt18vw/Ib8BRhiZoskrUAoKJtx0pPUFzhXUs22aGQ5OotT2xcSSiZtA1xlZmdk05crXJ70XFtyILBNtgVNzexuAEm9chFMjgubnkJY5JG4xndS4nkjY8lqEU09/keo70f8nu0U8C2EaegbgNPIbleXhEuB3YGnzawqJlTnMuJJz7UlrwM/I5SoaYorCKOxIqAvYVVh2hvg05E0nlo7xVgWFdijjpI6m9mSePN1toVtc6U/8GW8haJ3fPwame9es9jM3pFUFL9f14SYquPnk/jMi+s927kUPOm5Vi9po+liQp24RCWDjDecjo2OSuq7BHgwy9CGJbohJIkDs+wHwijmZUlfEhL7hU3oq8nMbPMcdTU9rpR9TtJUslydGt0uaQzhfr0ngVtzEqErKL6QxbUZkjYys4+Snme1aXQcSSWsAzxiZk2eKpM00cwyHjEmtRfL7tNrd/9jSloZmNeUny2Wc0rcF/l9zoJzBcNHeq4t+Q/LL4K4Ash4ZSLLL4H/ATgzm2AkPZLUz9qE6des1N4aTVJWdfBai/o2CQcy2iRc0l9S9RU/I6+n5zLiSc+1epIOIyS3LSQ9TJhOLCbLm8qbugBF0lpxp5OzE10SbpTPai/QKFd18FqL0fG70fRt2qbG74cB3wKvEqaTf9bEfl0B8ulN1+pJ6g70AE4lbJ4swjZU32QzVSbpOTPbIz4WYTVgo/eZjNcYj0y++VvSMcBvM7yFIrnPF8xsF0mTzGywpGfNbM9s+mqKeCN54vrpKsACwmc/18wGZNBPH8JGAj8DPgQuM7M5TYztaTPbO+n5MznaH9QVkKJ8B+BcQ8xsQVySfwUwHLgE+CXQPcsuOyT1bYTNkDNxLPCgpK2hZvrtCMJy+mwltkZ7U9IIclN0NWNmtn1M3K8Ce5lZP2BPQomhTNwNjCFMHb8L3JSD8IolnShpG0m/wn9/uSz4SM+1GZJGAw8RfiEPAI4ws32y6OcB4BXgRWBHYFBSwdTG9vEzwl6Qswm7oJyR5agzsUuMCCOsEsLIap6ZXZ9pf7kiaYqZ7ZD0/KVMRrGJkWu651nG1J2wFd0GhL04bzezBfW3cm55nvRcmyFpgpkNTXqe1WrJOKI6GdgEeB+41cyWZtA+sYBlFcKOLONYViA1o4U18faLVCWBzMzuyaSvXJJ0PmHk+jawBTDOzC7LoP1sQqUHCD/TkMTzbLZFq9X3kYnda5zLlCc912ZIup8wqkpU8+5lZo0ubiqpo5mV1bplAchsr0xJ66Z7LdOdUSSVsqwk0Ko0oSRQrknqSbgx/TMzy+j+ulx+Rin6bvKo0RUuT3quzZBUTEgO6wMzgFGZVACQdHUsb5O8k0piD8+8/xJNKgn0Yr72lJR0gJk9nmpz7mw25W4Okq4wsz/kOw7XNnnSc61eTAYpmdmklowl11KUBBrZlJJAOYhnTzN7VtIvar+W2Ls0nyR1JGw4LQAz+yK/Ebm2xpOea/UkJa4lbUK4jjaNcC1tnpkdlEE/yTeTL6ep15myleuSQLkU/9hYH/jYzJpSKzBX8ZxLGOmvB3wJLMrxpt+uAHjSc22GpMfN7ID4WMATZrZfBu2b7TpTtlpjTACSbiDc/J9YKbvYzE7LVzwxpqlmNlCh2O7OwH2ZXNN1DnxHFte2rClpAzObQdh/sWeG7evbRDkvCSafia0BWyatjL1ZUmuYRk7UQ6wANga2zGMsro3ypOfakpOAa+Kqwm8Jtx1kYts0x41QAd0tUylpd8LU63aERJNvl8XbTf4C/AO4Ks/xuDbIpzddQYqFZNcHPvHFEHXFDbDPI17TA/5hZk2tY9hkkjoQduIpIqy6nZ3nkFwb40nPtRmJ/S2Tj2Wz16WkPxKuCU0nbFz8vJldnpMgXbORdC2wA8tu4jcz2z+vQbk2x5OeazMkTQcGm9lPDZ5cfz+TzWzH+FjA5OQttwpZUsHe5Q6TZcHeXKq9I49z2fBreq4teS9XHSUVpN2I9HXfCo6Zpbvu2Ro8L2lf4B2WbfvmU9MuI570XFvSD/hC0sz4PNvRx6nAVUkLYursPlLoJG0O/BXoCuwFnNMKpoDXJGzZltiizYCj8xeOa4t8etMVDEk7mdmL8XE3M/sx3zG1VvFeuCOAEWa2s6TnzWzXPMfke266JvN6VK7Vk3RJ0uPfJD2+OsOuLk56PKqJYbV7cZPpxF/FxfmMJZoh6ShJm0vaTNJm+Q7ItT0+venagh2THh9KqJ4OsHXLh1IwnpV0F7C2pFtoHfcxlgJ7xC8ICfmX+QvHtUWe9Fwh2ULSw4TViMmPLd/7XLY2ZnaZpC2ATYEPzOydVhDTCfmOwbV9nvRcW5AuWdW3rVgqrXllYqsi6VlClfpHW8u1T0lnAccB5Ylj+b6NwrU9vpDFtXqtdVPm9kxSd+AwwnTyPOB+4JlM6hc2Q0wvATuaWXW+YnBtnyc951xakrYC/ghsBXwHPGJm/85TLNcBt7WGqVbXdvn0pnOuDknnAcMIN4JfbWavxuPjgLwkPULSfUJS8jZkGW9D5wqbj/Scc3VIOhB4yswqah3vbmYL8hTTy2a2fT7e27UfPtJzzqXyPvAXSSsRRlWY2W/ylfCiVyQNZPltyBbnMR7XBvlIzzlXh6Q3gT8DXyeOmdn0vAUESBpf65D5Di0uU570nHN1SBppZofkOw7ncs2nN51zqdwXywz9j2VTiXnd/URSX+BcwsbTiSlXH+m5jHjSc86l8lfgdJKmN1uBW4CTgBuA04Aj8xuOa4s86TnnUnnfzF7IdxC1LDazdyQVxe/X5Tsg1/Z40nPOpfIzSe8B78bnrWF/0umSOgHPSZpKuG/PuYz4QhbnXB2ptn5rTVu+SVoZmGf+C8xlyJOecy4tSZeb2Xn5jgNA0h21DlUAM4BbzWx+y0fk2iIvIuucq8+AfAeQZBEwBbgWeBGoBn4gVINwrlE86Tnn6nNLvgNIsomZ3W5mb5vZncAGZnY70Cnfgbm2wxeyOOdqSNq71qEFiWNmlu/q6QskXQpMB34en5cQRoDONYpf03PO1ZD0l3SvmdnFLRlLbZKKgYOA9YFPgFFmVpnPmFzb40nPOedcwfDpTedcDUk3mdmpcQuyxF/Eidp12+UxNOdywkd6zjnnCoaP9JxzdUjaHDgBSK6nl9cNp53LBU96zrlU7gPOAb7MdyDO5ZInPedcKp+b2bh8B+Fcrvk1PedcDUn/JCxg2ZLwR/GbLKund27+InMuNzzpOedqSBqS7jUzm9iSsTjXHDzpOefqkNSBUKQ1cSP4CDMrz29UzjWd773pnEvlUWAtwgbPa8bnzrV5vpDFOZdKRzO7PD5+TtJzeY3GuRzx6U3nXB2S7ibcrjAd2AboDYyAVrHxtHNZ8+lN51wqMwlFWvsCVYTretsC/fMZlHNN5SM951xKknoRF7KY2Rf5jse5XPCk55yrQ9IfgZ0J05v9geeTrvE512Z50nPO1SFpspntGB8LmGxmO+Q5LOeazK/pOedSkrRRfLgRy8oMOdem+S0LzrlUTgWuktQT+Bb4TZ7jcS4nPOk555YTpzOPN7P98h2Lc7nm05vOueVYuNC/mqRu+Y7FuVzzhSzOuTokvUfYhmwG4Xqemdl2+Y3KuabzpOecq0NSNzP7Mel5VzNblM+YnMsFn950zqUyqtbzO/IRhHO55gtZnHM1JO0O7AFsIOkf8XAJ0DN/UTmXO570nHPJPgLKgXWAp+KxCuAveYvIuRzya3rOuTpiBfXlfjmY2aQ8heNczvhIzzmXyrD4XcDPCaM/T3quzfORnnOuQZJGmdmB+Y7DuabykZ5zrg5JmyU9XYdQRNa5Ns+TnnMulXPidwN+AI7LYyzO5YxPbzrnUop7cK5mZrPzHYtzueI3pzvn6pB0BDAeGCepWNKIfMfkXC540nPOpfI7QuX0782sClg9z/E4lxOe9JxzqVQDxYBJKsZ/V7h2wheyOOdS+RswEdgQeB74e37DcS43fCGLcy6luJBlVWCu+S8K10540nPO1ZD0F2ptP5ZgZpe0cDjO5ZwnPedcDUl7Jj01YG3gDKDIzLbIS1DO5ZAnPedcHZI2Jtygvh5wrZk9meeQnMsJX8jinKshaVvgXMLvhn+a2Ut5Dsm5nPKRnnOuhqRq4D3gfeqWFjo8L0E5l0M+0nPOJVsv3wE415x8pOecc65g+C4LzjnnCoYnPeeccwXDk55zzrmC4UnPOedcwfCk55xzrmD8PzbkWyilzsmyAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 374.4x324 with 5 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'majority_voting')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "australian-cherry",
+      "metadata": {
+        "id": "australian-cherry"
+      },
+      "source": [
+        "## Examine expression of cell type-driving genes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "southern-female",
+      "metadata": {
+        "id": "southern-female"
+      },
+      "source": [
+        "Each model can be examined in terms of the driving genes for each cell type. Note these genes are only dependent on the model, say, the training dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "liable-liechtenstein",
+      "metadata": {
+        "tags": [],
+        "id": "liable-liechtenstein"
+      },
+      "outputs": [],
+      "source": [
+        "# Any model can be inspected.\n",
+        "# Here we load the previously saved model trained from 2,000 immune cells.\n",
+        "model = models.Model.load(model = 'celltypist_demo_folder/model_from_immune2000.pkl')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "available-victor",
+      "metadata": {
+        "id": "available-victor",
+        "outputId": "f6eae99e-98b1-4206-bdb6-1af502d7baf3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array(['DC1', 'Endothelial cells', 'Follicular B cells', 'Kupffer cells',\n",
+              "       'Macrophages', 'Mast cells', 'Neutrophil-myeloid progenitor',\n",
+              "       'Plasma cells', 'gamma-delta T cells', 'pDC'], dtype=object)"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.cell_types"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "pharmaceutical-remains",
+      "metadata": {
+        "tags": [],
+        "id": "pharmaceutical-remains"
+      },
+      "source": [
+        "Extract the top three driving genes of `Mast cells` using the [extract_top_markers](https://celltypist.readthedocs.io/en/latest/celltypist.models.Model.html#celltypist.models.Model.extract_top_markers) method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "interracial-clone",
+      "metadata": {
+        "tags": [],
+        "id": "interracial-clone",
+        "outputId": "ab78d984-5763-4996-f8ca-9db695b169c0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array(['TPSB2', 'TPSAB1', 'CPA3'], dtype=object)"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "top_3_genes = model.extract_top_markers(\"Mast cells\", 3)\n",
+        "top_3_genes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "sealed-percentage",
+      "metadata": {
+        "tags": [],
+        "id": "sealed-percentage",
+        "outputId": "67bed261-c02d-499f-b9b2-639900d5d108"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1614.38x288 with 3 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Check expression of the three genes in the training set.\n",
+        "sc.pl.violin(adata_2000, top_3_genes, groupby = 'cell_type', rotation = 90)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "yellow-prompt",
+      "metadata": {
+        "id": "yellow-prompt",
+        "outputId": "53c87253-6e81-44fd-aee6-ee17b92c8166"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1614.38x288 with 3 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Check expression of the three genes in the query set.\n",
+        "# Here we use `majority_voting` from CellTypist as the cell type labels for this dataset.\n",
+        "sc.pl.violin(adata_400, top_3_genes, groupby = 'majority_voting', rotation = 90)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "117d53639d7e41f093c3c4f1d340db6a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_c1007a4a47c546768a71cbfc2536fb0a",
+              "IPY_MODEL_4e99394259c44f6782fd5be15fdd5890",
+              "IPY_MODEL_a4e849127a234f768c7c9cc95ab47ea0"
+            ],
+            "layout": "IPY_MODEL_97381406977d4f6fa50d519d891d5702"
+          }
+        },
+        "c1007a4a47c546768a71cbfc2536fb0a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
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+            "_view_module": "@jupyter-widgets/base",
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+            "bar_color": null,
+            "description_width": ""
+          }
+        },
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+          "model_name": "LayoutModel",
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+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
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+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "910c5dbf9cea438691fc8f27a136afe8": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file

From f3d3bcae663d4ed075b494354d5ca2177892ae6b Mon Sep 17 00:00:00 2001
From: Varshini Vijay <varshinivijay05@gmail.com>
Date: Wed, 16 Jul 2025 13:13:04 -0700
Subject: [PATCH 2/6] Added in file CellTypingMetric.py

---
 benchmarking/auto_metrics/CellTypingMetric.py | 30 +++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 benchmarking/auto_metrics/CellTypingMetric.py

diff --git a/benchmarking/auto_metrics/CellTypingMetric.py b/benchmarking/auto_metrics/CellTypingMetric.py
new file mode 100644
index 0000000..82dcb7f
--- /dev/null
+++ b/benchmarking/auto_metrics/CellTypingMetric.py
@@ -0,0 +1,30 @@
+# Don't import AutomMetric
+# from AutoMetric import AutoMetric
+import scanpy as sc
+import celltypist
+from celltypist import models
+from scib_metrics.benchmark import Benchmarker, BioConservation, BatchCorrection
+import scanpy.external as sce
+
+class CellTypingMetric(AutoMetric):
+    """
+    This is a class that computes cell typing using CellTypist
+    Then, it evaluates using metrics from Benchmarker class from SCIB's Metric Module.
+    """
+    def metric(self, adata):
+        #scib_metrics Benchmarker
+        bm = Benchmarker(
+            adata,
+            batch_key="batch",
+            label_key="majority_voting",
+            bio_conservation_metrics=BioConservation(nmi_ari_cluster_labels_leiden=True),
+            batch_correction_metrics=None,
+            embedding_obsm_keys=["X_pca","X_pca_harmony"], #need to check if it has such a label -> if it doesnt perform pca
+            n_jobs=6,
+        )
+        bm.prepare()
+        bm.benchmark()
+        bm.plot_results_table(min_max_scale=False)
+        bm.get_results()
+
+CellTypingMetric().run(adata)

From e8b73778aaf0eb52b14a2e22e21f155651c3c184 Mon Sep 17 00:00:00 2001
From: Varshini Vijay <varshinivijay05@gmail.com>
Date: Wed, 16 Jul 2025 16:17:15 -0700
Subject: [PATCH 3/6] Added in a function load_bp_json to accept relative paths
 for Blueprint JSON and display available JSON files. Instead of traversing
 the user's directory, it makes reasonable guesses to locate the agents dir.
 Modified two files: io_helper, MultiAgentTester

---
 benchmarking/core/io_helpers.py               | 22 +++++++++++++++++++
 .../prompt_testing/MultiAgentTester.py        |  8 +++----
 2 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/benchmarking/core/io_helpers.py b/benchmarking/core/io_helpers.py
index c03d0a8..5f5b828 100644
--- a/benchmarking/core/io_helpers.py
+++ b/benchmarking/core/io_helpers.py
@@ -107,6 +107,28 @@ def collect_resources(console, sandbox_sources_dir) -> List[Tuple[Path, str]]:
         res.append((path, f"{sandbox_sources_dir}/{path.name}"))
     return res
 
+def load_bp_json(console) -> Path:
+    try:
+        #Attempt to create path from home directory or from current (assumed:benchmarking, Olaf) folder 
+        try_dirs = [
+            Path("~").expanduser() / 'Olaf' / 'benchmarking' / 'agents',
+            Path.cwd() / 'agents',
+            Path.cwd() / 'benchmarking' / 'agents'
+        ]
+        agents_dir = next((p for p in try_dirs if p.is_dir()), None)
+        if agents_dir:
+            agent_choices = [f.name for f in agents_dir.rglob("*.json")]
+            bp = Prompt.ask("Blueprint JSON", choices=agent_choices, default="system_blueprint.json")
+            bp = agents_dir / bp
+        else: 
+            bp = Path(Prompt.ask("Absolute path to Blueprint JSON", default="system_blueprint.json")).expanduser()
+        if not bp.exists():
+            raise FileNotFoundError
+        return bp
+    except FileNotFoundError: 
+        console.print(f"[red]Blueprint {bp} not found[/red]")
+        sys.exit(1)
+
 
 def format_execute_response(resp: dict, output_dir) -> str:
     lines = ["Code execution result:"]
diff --git a/benchmarking/prompt_testing/MultiAgentTester.py b/benchmarking/prompt_testing/MultiAgentTester.py
index 3617330..bd33d2b 100644
--- a/benchmarking/prompt_testing/MultiAgentTester.py
+++ b/benchmarking/prompt_testing/MultiAgentTester.py
@@ -57,7 +57,8 @@
     select_dataset,
     collect_resources,
     get_initial_prompt,
-    format_execute_response
+    format_execute_response,
+    load_bp_json
 )
 from benchmarking.core.sandbox_management import (
     init_docker,
@@ -104,10 +105,7 @@
 # ===========================================================================
 
 def load_agent_system() -> Tuple[AgentSystem, Agent, str]:
-    bp = Path(Prompt.ask("Blueprint JSON", default="system_blueprint.json")).expanduser()
-    if not bp.exists():
-        console.print(f"[red]Blueprint {bp} not found.")
-        sys.exit(1)
+    bp = load_bp_json(console)
     system = AgentSystem.load_from_json(str(bp))
     driver_name = Prompt.ask("Driver agent", choices=list(system.agents.keys()), default=list(system.agents)[0])
     driver = system.get_agent(driver_name)

From a432a7402d448ca7e4821efecc1433f17ecb663c Mon Sep 17 00:00:00 2001
From: Varshini Vijay <varshinivijay05@gmail.com>
Date: Wed, 16 Jul 2025 19:34:17 -0700
Subject: [PATCH 4/6] Added in multi benchmark metrics. Changes made to
 MultiAgentTester

---
 benchmarking/core/io_helpers.py               |  4 +-
 .../prompt_testing/MultiAgentTester.py        | 54 +++++++++++--------
 2 files changed, 33 insertions(+), 25 deletions(-)

diff --git a/benchmarking/core/io_helpers.py b/benchmarking/core/io_helpers.py
index 5f5b828..057701a 100644
--- a/benchmarking/core/io_helpers.py
+++ b/benchmarking/core/io_helpers.py
@@ -124,11 +124,11 @@ def load_bp_json(console) -> Path:
             bp = Path(Prompt.ask("Absolute path to Blueprint JSON", default="system_blueprint.json")).expanduser()
         if not bp.exists():
             raise FileNotFoundError
-        return bp
     except FileNotFoundError: 
         console.print(f"[red]Blueprint {bp} not found[/red]")
         sys.exit(1)
-
+    else:
+        return bp
 
 def format_execute_response(resp: dict, output_dir) -> str:
     lines = ["Code execution result:"]
diff --git a/benchmarking/prompt_testing/MultiAgentTester.py b/benchmarking/prompt_testing/MultiAgentTester.py
index bd33d2b..5c04a49 100644
--- a/benchmarking/prompt_testing/MultiAgentTester.py
+++ b/benchmarking/prompt_testing/MultiAgentTester.py
@@ -137,7 +137,7 @@ def api_alive(url: str, tries: int = 10) -> bool:
 # 3 · Interactive loop
 # ===========================================================================
 
-def run(agent_system: AgentSystem, agent: Agent, roster_instr: str, dataset: Path, metadata: dict, resources: List[Tuple[Path, str]], benchmark_module: Optional[Path] = None):
+def run(agent_system: AgentSystem, agent: Agent, roster_instr: str, dataset: Path, metadata: dict, resources: List[Tuple[Path, str]], benchmark_modules: Optional[list[Path]] = None):
     mgr = _BackendManager()
     console.print(f"Launching sandbox ({backend})…")
 
@@ -214,7 +214,7 @@ def build_system(a: Agent) -> str:
             display(console, "user", feedback)
             
         def input_loop():
-            if benchmark_module:
+            if benchmark_modules:
                 console.print("\n[bold]Next message (blank = continue, 'benchmark' to run benchmarks, 'exit' to quit):[/bold]")
             else:
                 console.print("\n[bold]Next message (blank = continue, 'exit' to quit):[/bold]")
@@ -224,8 +224,9 @@ def input_loop():
                 user_in = "exit"
             if user_in.lower() in {"exit", "quit"}:
                 return "break"
-            if user_in.lower() == "benchmark" and benchmark_module:
-                run_benchmark(mgr, benchmark_module)
+            if user_in.lower() == "benchmark" and benchmark_modules:
+                for benchmark_module in benchmark_modules:
+                    run_benchmark(mgr, benchmark_module)
                 input_loop()  # Recurse to continue the loop after benchmarks
             if user_in:
                 history.append({"role": "user", "content": user_in})
@@ -242,7 +243,7 @@ def input_loop():
 # 4 · Benchmarking
 # ===========================================================================
 
-def get_benchmark_module(console: Console, parent_dir: Path) -> Optional[Path]:
+def get_benchmark_modules(console: Console, parent_dir: Path) -> Optional[list[Path]]:
     """
     Prompts the user to select a benchmark module from the available ones.
     Returns the path to the selected module or None if no selection is made.
@@ -252,31 +253,38 @@ def get_benchmark_module(console: Console, parent_dir: Path) -> Optional[Path]:
         console.print("[red]No benchmarks directory found.[/red]")
         return None
 
-    modules = list(benchmark_dir.glob("*.py"))
+    module_names = list(benchmark_dir.glob("*.py"))
     # remove AutoMetric.py from modules (it is the base class)
-    modules = [m for m in modules if m.name != "AutoMetric.py"]
-    if not modules:
+    module_names = [m for m in module_names if m.name != "AutoMetric.py"]
+    if not module_names:
         console.print("[red]No benchmark modules found.[/red]")
         return None
 
     console.print("\n[bold]Available benchmark modules:[/bold]")
-    for i, mod in enumerate(modules, start=1):
+    for i, mod in enumerate(module_names, start=1):
         console.print(f"{i}. {mod.name}")
-
-    choice = Prompt.ask("Select a benchmark module by number (or press Enter to skip)", default="")
-    if not choice:
+    console.print(f"{len(module_names)+1}. Select All")
+    choices = Prompt.ask("Select benchmark modules by number  (e.g. 1 2 3 or 1,2,3) (or press Enter to skip)", default="")
+    choices = re.split(r'[,\s]+', choices) #User input must be seperated by commas or spaces 
+    
+    if not choices or choices == ['']:
         return None
 
-    try:
-        index = int(choice) - 1
-        if 0 <= index < len(modules):
-            return modules[index]
-        else:
-            console.print("[red]Invalid selection.[/red]")
+    modules = []
+    for choice in choices:
+        try: 
+            index = int(choice) - 1
+            if index == len(module_names): #Handles select all case 
+                return module_names
+            elif 0 <= index < len(module_names): 
+                modules.append(module_names[index])
+            else:
+                console.print("[red]Invalid selection.[/red]")
+                return None
+        except ValueError:
+            console.print("[red]Invalid input. Please enter a number.[/red]")
             return None
-    except ValueError:
-        console.print("[red]Invalid input. Please enter a number.[/red]")
-        return None
+    return modules 
     
 def run_benchmark(mgr, benchmark_module: str):
     """
@@ -346,9 +354,9 @@ def main():
 
     sys, drv, roster = load_agent_system()
     dp, meta = select_dataset(console, DATASETS_DIR)
-    benchmark_module = get_benchmark_module(console, PARENT_DIR)
+    benchmark_modules = get_benchmark_modules(console, PARENT_DIR)
     res = collect_resources(console, SANDBOX_RESOURCES_DIR)
-    run(sys, drv, roster, dp, meta, res, benchmark_module)
+    run(sys, drv, roster, dp, meta, res, benchmark_modules)
 
 
 if __name__ == "__main__":

From 7189cff94ef41e2d2876092de29b0b027e5b9ced Mon Sep 17 00:00:00 2001
From: djriffle <djriffle1@gmail.com>
Date: Wed, 23 Jul 2025 11:17:08 -0400
Subject: [PATCH 5/6] small cleanup

---
 benchmarking/core/io_helpers.py         |   62 +-
 docs/notebook/celltypist_tutorial.ipynb | 2834 -----------------------
 2 files changed, 40 insertions(+), 2856 deletions(-)
 delete mode 100644 docs/notebook/celltypist_tutorial.ipynb

diff --git a/benchmarking/core/io_helpers.py b/benchmarking/core/io_helpers.py
index 057701a..651d96e 100644
--- a/benchmarking/core/io_helpers.py
+++ b/benchmarking/core/io_helpers.py
@@ -13,8 +13,6 @@
 import base64
 from datetime import datetime
 
-
-
 def extract_python_code(txt: str) -> Optional[str]:
     """Return the *first* fenced code block, or None if absent.
 
@@ -108,27 +106,47 @@ def collect_resources(console, sandbox_sources_dir) -> List[Tuple[Path, str]]:
     return res
 
 def load_bp_json(console) -> Path:
-    try:
-        #Attempt to create path from home directory or from current (assumed:benchmarking, Olaf) folder 
-        try_dirs = [
-            Path("~").expanduser() / 'Olaf' / 'benchmarking' / 'agents',
-            Path.cwd() / 'agents',
-            Path.cwd() / 'benchmarking' / 'agents'
-        ]
-        agents_dir = next((p for p in try_dirs if p.is_dir()), None)
-        if agents_dir:
-            agent_choices = [f.name for f in agents_dir.rglob("*.json")]
-            bp = Prompt.ask("Blueprint JSON", choices=agent_choices, default="system_blueprint.json")
-            bp = agents_dir / bp
-        else: 
-            bp = Path(Prompt.ask("Absolute path to Blueprint JSON", default="system_blueprint.json")).expanduser()
-        if not bp.exists():
-            raise FileNotFoundError
-    except FileNotFoundError: 
-        console.print(f"[red]Blueprint {bp} not found[/red]")
+    """
+    Try to find a blueprint JSON file from common locations.
+    If multiple are found, prompt user to choose or enter manual path.
+    """
+    search_paths = [
+        Path.home() / "Olaf" / "benchmarking" / "agents",
+        Path.cwd() / "benchmarking" / "agents",
+        Path.cwd() / "agents"
+    ]
+
+    # Search for JSON files in known paths
+    for path in search_paths:
+        if path.is_dir():
+            json_files = list(path.rglob("*.json"))
+            if json_files:
+                choices = [f.name for f in json_files]
+                choices.append("manual")
+
+                choice = Prompt.ask(
+                    "Select a blueprint JSON file or choose 'manual' to enter path",
+                    choices=choices,
+                    default="system_blueprint.json"
+                )
+                if choice == "manual":
+                    break  # jump to manual path section
+                selected = path / choice
+                if selected.exists():
+                    return selected
+
+    # Manual fallback
+    user_path = Prompt.ask(
+        "Please provide absolute or relative path to blueprint JSON",
+        default="~/system_blueprint.json"
+    )
+    bp = Path(user_path).expanduser()
+
+    if not bp.exists():
+        console.print(f"[red]Blueprint file not found at: {bp}[/red]")
         sys.exit(1)
-    else:
-        return bp
+
+    return bp
 
 def format_execute_response(resp: dict, output_dir) -> str:
     lines = ["Code execution result:"]
diff --git a/docs/notebook/celltypist_tutorial.ipynb b/docs/notebook/celltypist_tutorial.ipynb
deleted file mode 100644
index c801812..0000000
--- a/docs/notebook/celltypist_tutorial.ipynb
+++ /dev/null
@@ -1,2834 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "understood-farmer",
-      "metadata": {
-        "tags": [],
-        "id": "understood-farmer"
-      },
-      "source": [
-        "# Using CellTypist for cell type classification\n",
-        "This notebook showcases the cell type classification for scRNA-seq query data by retrieving the most likely cell type labels from either the built-in CellTypist models or the user-trained custom models."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "exterior-thousand",
-      "metadata": {
-        "id": "exterior-thousand"
-      },
-      "source": [
-        "Only the main steps and key parameters are introduced in this notebook. Refer to detailed [Usage](https://github.com/Teichlab/celltypist#usage) if you want to learn more."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "assisted-clear",
-      "metadata": {
-        "id": "assisted-clear"
-      },
-      "source": [
-        "## Install CellTypist"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "automotive-traveler",
-      "metadata": {
-        "tags": [],
-        "id": "automotive-traveler",
-        "outputId": "f50ab3a6-2fd9-412f-9e20-950492737afa",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Collecting celltypist\n",
-            "  Downloading celltypist-1.7.1-py3-none-any.whl.metadata (45 kB)\n",
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/45.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.1/45.1 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.11/dist-packages (from celltypist) (2.0.2)\n",
-            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.11/dist-packages (from celltypist) (2.2.2)\n",
-            "Requirement already satisfied: scikit-learn>=0.24.1 in /usr/local/lib/python3.11/dist-packages (from celltypist) (1.6.1)\n",
-            "Requirement already satisfied: openpyxl>=3.0.4 in /usr/local/lib/python3.11/dist-packages (from celltypist) (3.1.5)\n",
-            "Requirement already satisfied: click>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from celltypist) (8.2.1)\n",
-            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from celltypist) (2.32.3)\n",
-            "Collecting scanpy>=1.7.0 (from celltypist)\n",
-            "  Downloading scanpy-1.11.3-py3-none-any.whl.metadata (9.0 kB)\n",
-            "Collecting leidenalg>=0.9.0 (from celltypist)\n",
-            "  Downloading leidenalg-0.10.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB)\n",
-            "Collecting igraph<0.12,>=0.10.0 (from leidenalg>=0.9.0->celltypist)\n",
-            "  Downloading igraph-0.11.9-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)\n",
-            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.11/dist-packages (from openpyxl>=3.0.4->celltypist) (2.0.0)\n",
-            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.0.5->celltypist) (2.9.0.post0)\n",
-            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.0.5->celltypist) (2025.2)\n",
-            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.0.5->celltypist) (2025.2)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->celltypist) (3.4.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->celltypist) (3.10)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->celltypist) (2.4.0)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->celltypist) (2025.6.15)\n",
-            "Collecting anndata>=0.8 (from scanpy>=1.7.0->celltypist)\n",
-            "  Downloading anndata-0.11.4-py3-none-any.whl.metadata (9.3 kB)\n",
-            "Requirement already satisfied: h5py>=3.7.0 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (3.14.0)\n",
-            "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (1.5.1)\n",
-            "Collecting legacy-api-wrap>=1.4.1 (from scanpy>=1.7.0->celltypist)\n",
-            "  Downloading legacy_api_wrap-1.4.1-py3-none-any.whl.metadata (2.1 kB)\n",
-            "Requirement already satisfied: matplotlib>=3.7.5 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (3.10.0)\n",
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-            "Requirement already satisfied: numba>=0.57.1 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.60.0)\n",
-            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (24.2)\n",
-            "Requirement already satisfied: patsy!=1.0.0 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (1.0.1)\n",
-            "Requirement already satisfied: pynndescent>=0.5.13 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.5.13)\n",
-            "Requirement already satisfied: scipy<1.16.0,>=1.8.1 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (1.15.3)\n",
-            "Requirement already satisfied: seaborn>=0.13.2 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.13.2)\n",
-            "Collecting session-info2 (from scanpy>=1.7.0->celltypist)\n",
-            "  Downloading session_info2-0.1.2-py3-none-any.whl.metadata (2.5 kB)\n",
-            "Requirement already satisfied: statsmodels>=0.14.4 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.14.4)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (4.67.1)\n",
-            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (4.14.0)\n",
-            "Requirement already satisfied: umap-learn>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from scanpy>=1.7.0->celltypist) (0.5.7)\n",
-            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=0.24.1->celltypist) (3.6.0)\n",
-            "Collecting array-api-compat!=1.5,>1.4 (from anndata>=0.8->scanpy>=1.7.0->celltypist)\n",
-            "  Downloading array_api_compat-1.12.0-py3-none-any.whl.metadata (2.5 kB)\n",
-            "Collecting texttable>=1.6.2 (from igraph<0.12,>=0.10.0->leidenalg>=0.9.0->celltypist)\n",
-            "  Downloading texttable-1.7.0-py2.py3-none-any.whl.metadata (9.8 kB)\n",
-            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.7.5->scanpy>=1.7.0->celltypist) (1.3.2)\n",
-            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.7.5->scanpy>=1.7.0->celltypist) (0.12.1)\n",
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-            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.7.5->scanpy>=1.7.0->celltypist) (3.2.3)\n",
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-            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.5->celltypist) (1.17.0)\n",
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-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.3/7.3 MB\u001b[0m \u001b[31m60.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[?25hDownloading legacy_api_wrap-1.4.1-py3-none-any.whl (10.0 kB)\n",
-            "Downloading session_info2-0.1.2-py3-none-any.whl (14 kB)\n",
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-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.2/58.2 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading texttable-1.7.0-py2.py3-none-any.whl (10 kB)\n",
-            "Installing collected packages: texttable, session-info2, legacy-api-wrap, igraph, array-api-compat, leidenalg, anndata, scanpy, celltypist\n",
-            "Successfully installed anndata-0.11.4 array-api-compat-1.12.0 celltypist-1.7.1 igraph-0.11.9 legacy-api-wrap-1.4.1 leidenalg-0.10.2 scanpy-1.11.3 session-info2-0.1.2 texttable-1.7.0\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install celltypist"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "organized-wedding",
-      "metadata": {
-        "id": "organized-wedding"
-      },
-      "outputs": [],
-      "source": [
-        "import scanpy as sc"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "intelligent-standard",
-      "metadata": {
-        "id": "intelligent-standard"
-      },
-      "outputs": [],
-      "source": [
-        "import celltypist\n",
-        "from celltypist import models"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "julian-banana",
-      "metadata": {
-        "id": "julian-banana"
-      },
-      "source": [
-        "## Download a scRNA-seq dataset of 2,000 immune cells"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "naval-seminar",
-      "metadata": {
-        "tags": [],
-        "id": "naval-seminar",
-        "outputId": "42536227-0864-4803-ac74-a041c2f41fe6",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 49,
-          "referenced_widgets": [
-            "117d53639d7e41f093c3c4f1d340db6a",
-            "c1007a4a47c546768a71cbfc2536fb0a",
-            "4e99394259c44f6782fd5be15fdd5890",
-            "a4e849127a234f768c7c9cc95ab47ea0",
-            "97381406977d4f6fa50d519d891d5702",
-            "1bf40693f81e491a8dea2694e2edc347",
-            "0cbad6912c88424cac819a842e25779d",
-            "3934dc02b4474578a1d1d27ed8a11b49",
-            "e29e63f124ba449fb92080d55cfb6b11",
-            "4ca07a98307e4c3baf0cda44bc99fb38",
-            "910c5dbf9cea438691fc8f27a136afe8"
-          ]
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  0%|          | 0.00/34.1M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "117d53639d7e41f093c3c4f1d340db6a"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "adata_2000 = sc.read('celltypist_demo_folder/demo_2000_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_2000_cells.h5ad')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "passive-classics",
-      "metadata": {
-        "id": "passive-classics"
-      },
-      "source": [
-        "This dataset includes 2,000 cells and 18,950 genes collected from different studies, thereby showing the practical applicability of CellTypist."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "sensitive-chancellor",
-      "metadata": {
-        "id": "sensitive-chancellor",
-        "outputId": "f065360e-39cd-49b7-9076-88aed7f6c7fd",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(2000, 18950)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ],
-      "source": [
-        "adata_2000.shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "destroyed-distribution",
-      "metadata": {
-        "tags": [],
-        "id": "destroyed-distribution"
-      },
-      "source": [
-        "The expression matrix (`adata_2000.X`) is pre-processed (and required) as log1p normalised expression to 10,000 counts per cell (this matrix can be alternatively stashed in `.raw.X`)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "sized-grocery",
-      "metadata": {
-        "tags": [],
-        "id": "sized-grocery",
-        "outputId": "1cef427b-6c59-42dd-aa6c-31ee86fd6f7e",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "matrix([[10000.   ],\n",
-              "        [10000.002],\n",
-              "        [10000.   ],\n",
-              "        ...,\n",
-              "        [10000.   ],\n",
-              "        [10000.   ],\n",
-              "        [10000.   ]], dtype=float32)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        }
-      ],
-      "source": [
-        "adata_2000.X.expm1().sum(axis = 1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "approximate-excerpt",
-      "metadata": {
-        "id": "approximate-excerpt"
-      },
-      "source": [
-        "Some pre-assigned cell type labels are also in the data, which will be compared to the predicted labels from CellTypist later."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "downtown-arbitration",
-      "metadata": {
-        "tags": [],
-        "id": "downtown-arbitration",
-        "outputId": "698971c1-9b2b-4e0e-efbc-cf1c052daf5a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 423
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                              cell_type\n",
-              "cell1                      Plasma cells\n",
-              "cell2                      Plasma cells\n",
-              "cell3                      Plasma cells\n",
-              "cell4                      Plasma cells\n",
-              "cell5                      Plasma cells\n",
-              "...                                 ...\n",
-              "cell1996  Neutrophil-myeloid progenitor\n",
-              "cell1997  Neutrophil-myeloid progenitor\n",
-              "cell1998  Neutrophil-myeloid progenitor\n",
-              "cell1999  Neutrophil-myeloid progenitor\n",
-              "cell2000  Neutrophil-myeloid progenitor\n",
-              "\n",
-              "[2000 rows x 1 columns]"
-            ],
-            "text/html": [
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-              "      <th>cell_type</th>\n",
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-              "      <th>cell1</th>\n",
-              "      <td>Plasma cells</td>\n",
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-              "    <tr>\n",
-              "      <th>cell2</th>\n",
-              "      <td>Plasma cells</td>\n",
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-              "      <th>cell3</th>\n",
-              "      <td>Plasma cells</td>\n",
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-              "    <tr>\n",
-              "      <th>cell4</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell5</th>\n",
-              "      <td>Plasma cells</td>\n",
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-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1996</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
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-              "    <tr>\n",
-              "      <th>cell1997</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1998</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
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-              "      <th>cell1999</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
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-              "      <th>cell2000</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
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-              "<p>2000 rows × 1 columns</p>\n",
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-              "        const element = document.querySelector('#df-75022be7-5fa7-4cac-b071-9abd779629ff');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
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-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
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-              "  .colab-df-quickchart {\n",
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-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
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-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
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-              "    border-color: transparent;\n",
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-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
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-              "\n",
-              "  @keyframes spin {\n",
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-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "      <script>\n",
-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
-              "          } catch (error) {\n",
-              "            console.error('Error during call to suggestCharts:', error);\n",
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-              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "        }\n",
-              "        (() => {\n",
-              "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-ad9ce39c-df9f-4c89-9e74-9784eb19acf4 button');\n",
-              "          quickchartButtonEl.style.display =\n",
-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
-              "      </script>\n",
-              "    </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"adata_2000\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"cell_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Mast cells\",\n          \"gamma-delta T cells\",\n          \"Macrophages\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 7
-        }
-      ],
-      "source": [
-        "adata_2000.obs"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "economic-penny",
-      "metadata": {
-        "id": "economic-penny"
-      },
-      "source": [
-        "## Assign cell type labels using a CellTypist built-in model\n",
-        "In this section, we show the procedure of transferring cell type labels from built-in models to the query dataset."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "quick-stanford",
-      "metadata": {
-        "id": "quick-stanford"
-      },
-      "source": [
-        "Download the latest CellTypist models."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "premier-measurement",
-      "metadata": {
-        "tags": [],
-        "id": "premier-measurement"
-      },
-      "outputs": [],
-      "source": [
-        "# Enabling `force_update = True` will overwrite existing (old) models.\n",
-        "models.download_models(force_update = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "stuck-judges",
-      "metadata": {
-        "id": "stuck-judges"
-      },
-      "source": [
-        "All models are stored in `models.models_path`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "above-length",
-      "metadata": {
-        "id": "above-length",
-        "outputId": "b2c9c214-a5b8-4e61-ca6b-b81e5f416d45",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 35
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "'/root/.celltypist/data/models'"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            }
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ],
-      "source": [
-        "models.models_path"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "understanding-slovak",
-      "metadata": {
-        "id": "understanding-slovak"
-      },
-      "source": [
-        "Get an overview of the models and what they represent."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "breeding-grace",
-      "metadata": {
-        "tags": [],
-        "id": "breeding-grace",
-        "outputId": "28ca42a8-27a4-4a54-aac8-9688f337259a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                      model  \\\n",
-              "0                        Immune_All_Low.pkl   \n",
-              "1                       Immune_All_High.pkl   \n",
-              "2                    Adult_COVID19_PBMC.pkl   \n",
-              "3   Adult_CynomolgusMacaque_Hippocampus.pkl   \n",
-              "4                       Adult_Human_MTG.pkl   \n",
-              "5           Adult_Human_PancreaticIslet.pkl   \n",
-              "6          Adult_Human_PrefrontalCortex.pkl   \n",
-              "7                      Adult_Human_Skin.pkl   \n",
-              "8                  Adult_Human_Vascular.pkl   \n",
-              "9                       Adult_Mouse_Gut.pkl   \n",
-              "10            Adult_Mouse_OlfactoryBulb.pkl   \n",
-              "11                Adult_Pig_Hippocampus.pkl   \n",
-              "12      Adult_RhesusMacaque_Hippocampus.pkl   \n",
-              "13                 Autopsy_COVID19_Lung.pkl   \n",
-              "14         COVID19_HumanChallenge_Blood.pkl   \n",
-              "15             COVID19_Immune_Landscape.pkl   \n",
-              "16                   Cells_Adult_Breast.pkl   \n",
-              "17                     Cells_Fetal_Lung.pkl   \n",
-              "18                   Cells_Human_Tonsil.pkl   \n",
-              "19               Cells_Intestinal_Tract.pkl   \n",
-              "20                    Cells_Lung_Airway.pkl   \n",
-              "21               Developing_Human_Brain.pkl   \n",
-              "22              Developing_Human_Gonads.pkl   \n",
-              "23         Developing_Human_Hippocampus.pkl   \n",
-              "24              Developing_Human_Organs.pkl   \n",
-              "25              Developing_Human_Thymus.pkl   \n",
-              "26               Developing_Mouse_Brain.pkl   \n",
-              "27         Developing_Mouse_Hippocampus.pkl   \n",
-              "28            Fetal_Human_AdrenalGlands.pkl   \n",
-              "29                 Fetal_Human_Pancreas.pkl   \n",
-              "30                Fetal_Human_Pituitary.pkl   \n",
-              "31                   Fetal_Human_Retina.pkl   \n",
-              "32                     Fetal_Human_Skin.pkl   \n",
-              "33                  Healthy_Adult_Heart.pkl   \n",
-              "34                 Healthy_COVID19_PBMC.pkl   \n",
-              "35                  Healthy_Human_Liver.pkl   \n",
-              "36                  Healthy_Mouse_Liver.pkl   \n",
-              "37          Human_AdultAged_Hippocampus.pkl   \n",
-              "38              Human_Colorectal_Cancer.pkl   \n",
-              "39           Human_Developmental_Retina.pkl   \n",
-              "40              Human_Embryonic_YolkSac.pkl   \n",
-              "41              Human_Endometrium_Atlas.pkl   \n",
-              "42                       Human_IPF_Lung.pkl   \n",
-              "43       Human_Longitudinal_Hippocampus.pkl   \n",
-              "44                     Human_Lung_Atlas.pkl   \n",
-              "45                        Human_PF_Lung.pkl   \n",
-              "46               Human_Placenta_Decidua.pkl   \n",
-              "47                  Lethal_COVID19_Lung.pkl   \n",
-              "48                  Mouse_Dentate_Gyrus.pkl   \n",
-              "49          Mouse_Isocortex_Hippocampus.pkl   \n",
-              "50         Mouse_Postnatal_DentateGyrus.pkl   \n",
-              "51                    Mouse_Whole_Brain.pkl   \n",
-              "52                   Nuclei_Lung_Airway.pkl   \n",
-              "53                      Pan_Fetal_Human.pkl   \n",
-              "\n",
-              "                                          description  \n",
-              "0   immune sub-populations combined from 20 tissue...  \n",
-              "1   immune populations combined from 20 tissues of...  \n",
-              "2   peripheral blood mononuclear cell types from C...  \n",
-              "3   cell types from the hippocampus of adult cynom...  \n",
-              "4   cell types and subtypes (10x-based) from the a...  \n",
-              "5   cell types from pancreatic islets of healthy a...  \n",
-              "6   cell types and subtypes from the adult human d...  \n",
-              "7            cell types from human healthy adult skin  \n",
-              "8   vascular populations combined from multiple ad...  \n",
-              "9   cell types in the adult mouse gut combined fro...  \n",
-              "10   cell types from the olfactory bulb of adult mice  \n",
-              "11          cell types from the adult pig hippocampus  \n",
-              "12  cell types from the hippocampus of adult rhesu...  \n",
-              "13  cell types from the lungs of 16 SARS-CoV-2 inf...  \n",
-              "14  detailed blood cell states from 16 individuals...  \n",
-              "15  immune subtypes from lung and blood of COVID-1...  \n",
-              "16             cell types from the adult human breast  \n",
-              "17    cell types from human embryonic and fetal lungs  \n",
-              "18      tonsillar cell types from humans (3-65 years)  \n",
-              "19  intestinal cells from fetal, pediatric (health...  \n",
-              "20  cell populations from scRNA-seq of five locati...  \n",
-              "21  cell types from the first-trimester developing...  \n",
-              "22  cell types of human gonadal and adjacent extra...  \n",
-              "23   cell types from the developing human hippocampus  \n",
-              "24  cell types of five endoderm-derived organs in ...  \n",
-              "25  cell populations in embryonic, fetal, pediatri...  \n",
-              "26  cell types from the embryonic mouse brain betw...  \n",
-              "27  cell types from the mouse hippocampus at postn...  \n",
-              "28  cell types of human fetal adrenal glands from ...  \n",
-              "29  pancreatic cell types from human embryos at 9-...  \n",
-              "30  cell types of human fetal pituitaries from 7 t...  \n",
-              "31  cell types from human fetal neural retina and ...  \n",
-              "32        cell types from developing human fetal skin  \n",
-              "33  cell types from eight anatomical regions of th...  \n",
-              "34  peripheral blood mononuclear cell types from h...  \n",
-              "35  cell types from scRNA-seq and snRNA-seq of the...  \n",
-              "36  cell types from scRNA-seq and snRNA-seq of the...  \n",
-              "37  cell types from the hippocampus of adult and a...  \n",
-              "38  cell types of colon tissues from patients with...  \n",
-              "39                 cell types from human fetal retina  \n",
-              "40  cell types of the human yolk sac from 4-8 post...  \n",
-              "41  endometrial cell types integrated from seven d...  \n",
-              "42  cell types from idiopathic pulmonary fibrosis,...  \n",
-              "43  cell types from the adult human anterior and p...  \n",
-              "44  integrated Human Lung Cell Atlas (HLCA) combin...  \n",
-              "45  cell types from different forms of pulmonary f...  \n",
-              "46  cell types from first-trimester human placenta...  \n",
-              "47  cell types from the lungs of individuals who d...  \n",
-              "48  cell types from the dentate gyrus in perinatal...  \n",
-              "49  cell types from the adult mouse isocortex (neo...  \n",
-              "50  cell types from the mouse dentate gyrus in pos...  \n",
-              "51        cell types from the whole adult mouse brain  \n",
-              "52  cell populations from snRNA-seq of five locati...  \n",
-              "53  stromal and immune populations from the human ...  "
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-              "      <th>9</th>\n",
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-              "      <th>12</th>\n",
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-              "      <td>cell types from the hippocampus of adult rhesu...</td>\n",
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-              "      <th>13</th>\n",
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-              "      <th>14</th>\n",
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-              "      <th>15</th>\n",
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-              "      <th>17</th>\n",
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-              "      <td>cell types from human embryonic and fetal lungs</td>\n",
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-              "    <tr>\n",
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-              "      <th>19</th>\n",
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-              "    <tr>\n",
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-              "      <th>21</th>\n",
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-              "      <th>22</th>\n",
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-              "      <td>Developing_Human_Hippocampus.pkl</td>\n",
-              "      <td>cell types from the developing human hippocampus</td>\n",
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-              "    <tr>\n",
-              "      <th>24</th>\n",
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-              "      <th>27</th>\n",
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-              "      <td>cell types from the mouse hippocampus at postn...</td>\n",
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-              "      <th>28</th>\n",
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-              "      <th>32</th>\n",
-              "      <td>Fetal_Human_Skin.pkl</td>\n",
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-              "      <th>33</th>\n",
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-              "      <th>34</th>\n",
-              "      <td>Healthy_COVID19_PBMC.pkl</td>\n",
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-              "      <th>37</th>\n",
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-              "      <th>38</th>\n",
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-              "      <th>39</th>\n",
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-              "      <th>40</th>\n",
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-              "      <th>41</th>\n",
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-              "      <th>42</th>\n",
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-              "      <th>43</th>\n",
-              "      <td>Human_Longitudinal_Hippocampus.pkl</td>\n",
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-              "      <th>44</th>\n",
-              "      <td>Human_Lung_Atlas.pkl</td>\n",
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-              "      <th>46</th>\n",
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-              "      <th>48</th>\n",
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-              "      <th>49</th>\n",
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-              "      <th>51</th>\n",
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-              "      <td>cell types from the whole adult mouse brain</td>\n",
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-              "    <tr>\n",
-              "      <th>52</th>\n",
-              "      <td>Nuclei_Lung_Airway.pkl</td>\n",
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-              "      <th>53</th>\n",
-              "      <td>Pan_Fetal_Human.pkl</td>\n",
-              "      <td>stromal and immune populations from the human ...</td>\n",
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-              "      spin 1s steps(1) infinite;\n",
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-              "\n",
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-              "      border-color: transparent;\n",
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-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
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-              "      border-bottom-color: var(--fill-color);\n",
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-              "\n",
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-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
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-              "            console.error('Error during call to suggestCharts:', error);\n",
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-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
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-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"models\",\n  \"rows\": 54,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54,\n        \"samples\": [\n          \"Cells_Intestinal_Tract.pkl\",\n          \"Mouse_Isocortex_Hippocampus.pkl\",\n          \"Mouse_Dentate_Gyrus.pkl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54,\n        \"samples\": [\n          \"intestinal cells from fetal, pediatric (healthy and Crohn's disease) and adult human gut\",\n          \"cell types from the adult mouse isocortex (neocortex) and hippocampal formation\",\n          \"cell types from the dentate gyrus in perinatal, juvenile, and adult mice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 10
-        }
-      ],
-      "source": [
-        "models.models_description()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "tribal-broadway",
-      "metadata": {
-        "id": "tribal-broadway"
-      },
-      "source": [
-        "Choose the model you want to employ, for example, the model with all tissues combined containing low-hierarchy (high-resolution) immune cell types/subtypes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "boring-challenge",
-      "metadata": {
-        "id": "boring-challenge"
-      },
-      "outputs": [],
-      "source": [
-        "# Indeed, the `model` argument defaults to `Immune_All_Low.pkl`.\n",
-        "model = models.Model.load(model = 'Immune_All_Low.pkl')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "lovely-advisory",
-      "metadata": {
-        "id": "lovely-advisory"
-      },
-      "source": [
-        "Show the model meta information."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "anticipated-picking",
-      "metadata": {
-        "id": "anticipated-picking",
-        "outputId": "ca40c21a-b03e-484a-ac4a-70a2d2845af2",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "CellTypist model with 98 cell types and 6639 features\n",
-              "    date: 2022-07-16 00:20:42.927778\n",
-              "    details: immune sub-populations combined from 20 tissues of 18 studies\n",
-              "    source: https://doi.org/10.1126/science.abl5197\n",
-              "    version: v2\n",
-              "    cell types: Age-associated B cells, Alveolar macrophages, ..., pDC precursor\n",
-              "    features: A1BG, A2M, ..., ZYX"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ],
-      "source": [
-        "model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "exterior-driving",
-      "metadata": {
-        "id": "exterior-driving"
-      },
-      "source": [
-        "This model contains 98 cell states."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "hundred-receipt",
-      "metadata": {
-        "tags": [],
-        "id": "hundred-receipt",
-        "outputId": "c57de50b-424e-4b17-838e-d038c2ad1e3b",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['Age-associated B cells', 'Alveolar macrophages', 'B cells',\n",
-              "       'CD16+ NK cells', 'CD16- NK cells', 'CD8a/a', 'CD8a/b(entry)',\n",
-              "       'CMP', 'CRTAM+ gamma-delta T cells', 'Classical monocytes',\n",
-              "       'Cycling B cells', 'Cycling DCs', 'Cycling NK cells',\n",
-              "       'Cycling T cells', 'Cycling gamma-delta T cells',\n",
-              "       'Cycling monocytes', 'DC', 'DC precursor', 'DC1', 'DC2', 'DC3',\n",
-              "       'Double-negative thymocytes', 'Double-positive thymocytes', 'ELP',\n",
-              "       'ETP', 'Early MK', 'Early erythroid', 'Early lymphoid/T lymphoid',\n",
-              "       'Endothelial cells', 'Epithelial cells', 'Erythrocytes',\n",
-              "       'Erythrophagocytic macrophages', 'Fibroblasts',\n",
-              "       'Follicular B cells', 'Follicular helper T cells', 'GMP',\n",
-              "       'Germinal center B cells', 'Granulocytes', 'HSC/MPP',\n",
-              "       'Hofbauer cells', 'ILC', 'ILC precursor', 'ILC1', 'ILC2', 'ILC3',\n",
-              "       'Intermediate macrophages', 'Intestinal macrophages',\n",
-              "       'Kidney-resident macrophages', 'Kupffer cells',\n",
-              "       'Large pre-B cells', 'Late erythroid', 'MAIT cells', 'MEMP', 'MNP',\n",
-              "       'Macrophages', 'Mast cells', 'Megakaryocyte precursor',\n",
-              "       'Megakaryocyte-erythroid-mast cell progenitor',\n",
-              "       'Megakaryocytes/platelets', 'Memory B cells',\n",
-              "       'Memory CD4+ cytotoxic T cells', 'Mid erythroid', 'Migratory DCs',\n",
-              "       'Mono-mac', 'Monocyte precursor', 'Monocytes', 'Myelocytes',\n",
-              "       'NK cells', 'NKT cells', 'Naive B cells',\n",
-              "       'Neutrophil-myeloid progenitor', 'Neutrophils',\n",
-              "       'Non-classical monocytes', 'Plasma cells', 'Plasmablasts',\n",
-              "       'Pre-pro-B cells', 'Pro-B cells',\n",
-              "       'Proliferative germinal center B cells', 'Promyelocytes',\n",
-              "       'Regulatory T cells', 'Small pre-B cells', 'T(agonist)',\n",
-              "       'Tcm/Naive cytotoxic T cells', 'Tcm/Naive helper T cells',\n",
-              "       'Tem/Effector helper T cells', 'Tem/Effector helper T cells PD1+',\n",
-              "       'Tem/Temra cytotoxic T cells', 'Tem/Trm cytotoxic T cells',\n",
-              "       'Transitional B cells', 'Transitional DC', 'Transitional NK',\n",
-              "       'Treg(diff)', 'Trm cytotoxic T cells', 'Type 1 helper T cells',\n",
-              "       'Type 17 helper T cells', 'gamma-delta T cells', 'pDC',\n",
-              "       'pDC precursor'], dtype=object)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 13
-        }
-      ],
-      "source": [
-        "model.cell_types"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "entertaining-sensitivity",
-      "metadata": {
-        "tags": [],
-        "id": "entertaining-sensitivity"
-      },
-      "source": [
-        "Transfer cell type labels from this model to the query dataset using [celltypist.annotate](https://celltypist.readthedocs.io/en/latest/celltypist.annotate.html)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "postal-chicken",
-      "metadata": {
-        "tags": [],
-        "id": "postal-chicken"
-      },
-      "outputs": [],
-      "source": [
-        "# Not run; predict cell identities using this loaded model.\n",
-        "#predictions = celltypist.annotate(adata_2000, model = model, majority_voting = True)\n",
-        "# Alternatively, just specify the model name (recommended as this ensures the model is intact every time it is loaded).\n",
-        "predictions = celltypist.annotate(adata_2000, model = 'Immune_All_Low.pkl', majority_voting = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "personal-marijuana",
-      "metadata": {
-        "id": "personal-marijuana"
-      },
-      "source": [
-        "By default (`majority_voting = False`), CellTypist will infer the identity of each query cell independently. This leads to raw predicted cell type labels, and usually finishes within seconds or minutes depending on the size of the query data. You can also turn on the majority-voting classifier (`majority_voting = True`), which refines cell identities within local subclusters after an over-clustering approach at the cost of increased runtime."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "future-finder",
-      "metadata": {
-        "tags": [],
-        "id": "future-finder"
-      },
-      "source": [
-        "The results include both predicted cell type labels (`predicted_labels`), over-clustering result (`over_clustering`), and predicted labels after majority voting in local subclusters (`majority_voting`). Note in the `predicted_labels`, each query cell gets its inferred label by choosing the most probable cell type among all possible cell types in the given model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "expired-authorization",
-      "metadata": {
-        "tags": [],
-        "id": "expired-authorization",
-        "outputId": "49b00e70-f5f5-4e7c-f54d-6189c87ce860"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>predicted_labels</th>\n",
-              "      <th>over_clustering</th>\n",
-              "      <th>majority_voting</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>cell1</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>44</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell2</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>12</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell3</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>36</td>\n",
-              "      <td>gamma-delta T cells</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell4</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell5</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1996</th>\n",
-              "      <td>HSC/MPP</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1997</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>27</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1998</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>28</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1999</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>27</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell2000</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>2000 rows × 3 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                       predicted_labels over_clustering  \\\n",
-              "cell1                      Plasma cells              44   \n",
-              "cell2                      Plasma cells              12   \n",
-              "cell3                      Plasma cells              36   \n",
-              "cell4                      Plasma cells               1   \n",
-              "cell5                      Plasma cells               1   \n",
-              "...                                 ...             ...   \n",
-              "cell1996                        HSC/MPP               9   \n",
-              "cell1997  Neutrophil-myeloid progenitor              27   \n",
-              "cell1998  Neutrophil-myeloid progenitor              28   \n",
-              "cell1999  Neutrophil-myeloid progenitor              27   \n",
-              "cell2000  Neutrophil-myeloid progenitor               9   \n",
-              "\n",
-              "                        majority_voting  \n",
-              "cell1                      Plasma cells  \n",
-              "cell2                      Plasma cells  \n",
-              "cell3               gamma-delta T cells  \n",
-              "cell4                      Plasma cells  \n",
-              "cell5                      Plasma cells  \n",
-              "...                                 ...  \n",
-              "cell1996  Neutrophil-myeloid progenitor  \n",
-              "cell1997  Neutrophil-myeloid progenitor  \n",
-              "cell1998  Neutrophil-myeloid progenitor  \n",
-              "cell1999  Neutrophil-myeloid progenitor  \n",
-              "cell2000  Neutrophil-myeloid progenitor  \n",
-              "\n",
-              "[2000 rows x 3 columns]"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "predictions.predicted_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "separated-first",
-      "metadata": {
-        "id": "separated-first"
-      },
-      "source": [
-        "Transform the prediction result into an `AnnData`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "sharing-router",
-      "metadata": {
-        "id": "sharing-router"
-      },
-      "outputs": [],
-      "source": [
-        "# Get an `AnnData` with predicted labels embedded into the cell metadata columns.\n",
-        "adata = predictions.to_adata()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "varying-characteristic",
-      "metadata": {
-        "id": "varying-characteristic"
-      },
-      "source": [
-        "Compared to `adata_2000`, the new `adata` has additional prediction information in `adata.obs` (`predicted_labels`, `over_clustering`, `majority_voting` and `conf_score`). Of note, all these columns can be prefixed with a specific string by setting `prefix` in [to_adata](https://celltypist.readthedocs.io/en/latest/celltypist.classifier.AnnotationResult.html#celltypist.classifier.AnnotationResult.to_adata)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "virgin-export",
-      "metadata": {
-        "tags": [],
-        "id": "virgin-export",
-        "outputId": "5cf13a18-35bc-4b69-e1a8-540b5a5fd873"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "\n",
-              "    .dataframe tbody tr th {\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>cell_type</th>\n",
-              "      <th>predicted_labels</th>\n",
-              "      <th>over_clustering</th>\n",
-              "      <th>majority_voting</th>\n",
-              "      <th>conf_score</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>cell1</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>44</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>0.999762</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell2</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>12</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>0.999926</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell3</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>36</td>\n",
-              "      <td>gamma-delta T cells</td>\n",
-              "      <td>0.955991</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell4</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>0.999883</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell5</th>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Plasma cells</td>\n",
-              "      <td>0.999890</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1996</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>HSC/MPP</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>0.152962</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1997</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>27</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>0.810408</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1998</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>28</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>0.961021</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell1999</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>27</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>0.131777</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cell2000</th>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Neutrophil-myeloid progenitor</td>\n",
-              "      <td>0.985607</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>2000 rows × 5 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                              cell_type               predicted_labels  \\\n",
-              "cell1                      Plasma cells                   Plasma cells   \n",
-              "cell2                      Plasma cells                   Plasma cells   \n",
-              "cell3                      Plasma cells                   Plasma cells   \n",
-              "cell4                      Plasma cells                   Plasma cells   \n",
-              "cell5                      Plasma cells                   Plasma cells   \n",
-              "...                                 ...                            ...   \n",
-              "cell1996  Neutrophil-myeloid progenitor                        HSC/MPP   \n",
-              "cell1997  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
-              "cell1998  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
-              "cell1999  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
-              "cell2000  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
-              "\n",
-              "         over_clustering                majority_voting  conf_score  \n",
-              "cell1                 44                   Plasma cells    0.999762  \n",
-              "cell2                 12                   Plasma cells    0.999926  \n",
-              "cell3                 36            gamma-delta T cells    0.955991  \n",
-              "cell4                  1                   Plasma cells    0.999883  \n",
-              "cell5                  1                   Plasma cells    0.999890  \n",
-              "...                  ...                            ...         ...  \n",
-              "cell1996               9  Neutrophil-myeloid progenitor    0.152962  \n",
-              "cell1997              27  Neutrophil-myeloid progenitor    0.810408  \n",
-              "cell1998              28  Neutrophil-myeloid progenitor    0.961021  \n",
-              "cell1999              27  Neutrophil-myeloid progenitor    0.131777  \n",
-              "cell2000               9  Neutrophil-myeloid progenitor    0.985607  \n",
-              "\n",
-              "[2000 rows x 5 columns]"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "adata.obs"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "knowing-steps",
-      "metadata": {
-        "id": "knowing-steps"
-      },
-      "source": [
-        "In addition to this meta information added, the neighborhood graph constructed during over-clustering is also stored in the `adata`\n",
-        "(If a pre-calculated neighborhood graph is already present in the `AnnData`, this graph construction step will be skipped).  \n",
-        "This graph can be used to derive the cell embeddings, such as the UMAP coordinates."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "decimal-sweden",
-      "metadata": {
-        "id": "decimal-sweden"
-      },
-      "outputs": [],
-      "source": [
-        "# If the UMAP or any cell embeddings are already available in the `AnnData`, skip this command.\n",
-        "sc.tl.umap(adata)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "effective-terrain",
-      "metadata": {
-        "id": "effective-terrain"
-      },
-      "source": [
-        "Visualise the prediction results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "tough-insider",
-      "metadata": {
-        "tags": [],
-        "id": "tough-insider",
-        "outputId": "222b6b0a-e241-4a08-cdbe-7a88116fe7f4"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1483.92x288 with 3 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "sc.pl.umap(adata, color = ['cell_type', 'predicted_labels', 'majority_voting'], legend_loc = 'on data')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "julian-mobile",
-      "metadata": {
-        "id": "julian-mobile"
-      },
-      "source": [
-        "Actually, you may not need to explicitly convert `predictions` output by `celltypist.annotate` into an `AnnData` as above. A more useful way is to use the visualisation function [celltypist.dotplot](https://celltypist.readthedocs.io/en/latest/celltypist.dotplot.html), which quantitatively compares the CellTypist prediction result (e.g. `majority_voting` here) with the cell types pre-defined in the `AnnData` (here `cell_type`). You can also change the value of `use_as_prediction` to `predicted_labels` to compare the raw prediction result with the pre-defined cell types."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "solved-weekly",
-      "metadata": {
-        "id": "solved-weekly",
-        "outputId": "11a738e2-2d0f-4f22-fe8e-02c5239e431c"
-      },
-      "outputs": [
-        {
-          "data": {
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\n",
-            "text/plain": [
-              "<Figure size 374.4x324 with 5 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'majority_voting')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "metallic-saskatchewan",
-      "metadata": {
-        "id": "metallic-saskatchewan"
-      },
-      "source": [
-        "For each pre-defined cell type (each column from the dot plot), this plot shows how it can be 'decomposed' into different cell types predicted by CellTypist (rows)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "tender-maple",
-      "metadata": {
-        "id": "tender-maple"
-      },
-      "source": [
-        "## Assign cell type labels using a custom model\n",
-        "In this section, we show the procedure of generating a custom model and transferring labels from the model to the query data."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "protected-counter",
-      "metadata": {
-        "tags": [],
-        "id": "protected-counter"
-      },
-      "source": [
-        "Use previously downloaded dataset of 2,000 immune cells as the training set."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "rotary-record",
-      "metadata": {
-        "id": "rotary-record"
-      },
-      "outputs": [],
-      "source": [
-        "adata_2000 = sc.read('celltypist_demo_folder/demo_2000_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_2000_cells.h5ad')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "headed-sierra",
-      "metadata": {
-        "id": "headed-sierra"
-      },
-      "source": [
-        "Download another scRNA-seq dataset of 400 immune cells as a query."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "closing-brain",
-      "metadata": {
-        "id": "closing-brain",
-        "outputId": "501ccf3d-7364-4a26-984f-8e50dfce6bf9",
-        "colab": {
-          "referenced_widgets": [
-            "60bb1bc2c899444ea88095e6713a8686"
-          ]
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "60bb1bc2c899444ea88095e6713a8686",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "  0%|          | 0.00/7.62M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "adata_400 = sc.read('celltypist_demo_folder/demo_400_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_400_cells.h5ad')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "precise-bronze",
-      "metadata": {
-        "tags": [],
-        "id": "precise-bronze"
-      },
-      "source": [
-        "Derive a custom model by training the data using the [celltypist.train](https://celltypist.readthedocs.io/en/latest/celltypist.train.html) function."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "consistent-detective",
-      "metadata": {
-        "tags": [],
-        "id": "consistent-detective",
-        "outputId": "8e385d9b-9a37-4b45-b6b0-dd511a7957b0"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "🍳 Preparing data before training\n",
-            "✂️ 2749 non-expressed genes are filtered out\n",
-            "⚖️ Scaling input data\n",
-            "🏋️ Training data using SGD logistic regression\n",
-            "🔎 Selecting features\n",
-            "🧬 2607 features are selected\n",
-            "🏋️ Starting the second round of training\n",
-            "🏋️ Training data using logistic regression\n",
-            "✅ Model training done!\n"
-          ]
-        }
-      ],
-      "source": [
-        "# The `cell_type` in `adata_2000.obs` will be used as cell type labels for training.\n",
-        "new_model = celltypist.train(adata_2000, labels = 'cell_type', n_jobs = 10, feature_selection = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "remarkable-eating",
-      "metadata": {
-        "id": "remarkable-eating"
-      },
-      "source": [
-        "Refer to the function [celltypist.train](https://celltypist.readthedocs.io/en/latest/celltypist.train.html) for what each parameter means, and to the [usage](https://github.com/Teichlab/celltypist#usage) for details of model training."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "emerging-telephone",
-      "metadata": {
-        "id": "emerging-telephone"
-      },
-      "source": [
-        "This custom model can be manipulated as with other CellTypist built-in models. First, save this model locally."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "informal-ladder",
-      "metadata": {
-        "id": "informal-ladder"
-      },
-      "outputs": [],
-      "source": [
-        "# Save the model.\n",
-        "new_model.write('celltypist_demo_folder/model_from_immune2000.pkl')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "sublime-oxygen",
-      "metadata": {
-        "tags": [],
-        "id": "sublime-oxygen"
-      },
-      "source": [
-        "You can load this model by `models.Model.load`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "little-textbook",
-      "metadata": {
-        "id": "little-textbook"
-      },
-      "outputs": [],
-      "source": [
-        "new_model = models.Model.load('celltypist_demo_folder/model_from_immune2000.pkl')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "weighted-system",
-      "metadata": {
-        "id": "weighted-system"
-      },
-      "source": [
-        "Next, we use this model to predict the query dataset of 400 immune cells."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "forty-turning",
-      "metadata": {
-        "tags": [],
-        "id": "forty-turning",
-        "outputId": "bbe06e18-182d-48ad-808c-16514c429691"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "🔬 Input data has 400 cells and 18950 genes\n",
-            "🔗 Matching reference genes in the model\n",
-            "🧬 2607 features used for prediction\n",
-            "⚖️ Scaling input data\n",
-            "🖋️ Predicting labels\n",
-            "✅ Prediction done!\n",
-            "👀 Can not detect a neighborhood graph, will construct one before the over-clustering\n",
-            "⛓️ Over-clustering input data with resolution set to 5\n",
-            "🗳️ Majority voting the predictions\n",
-            "✅ Majority voting done!\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Not run; predict the identity of each input cell with the new model.\n",
-        "#predictions = celltypist.annotate(adata_400, model = new_model, majority_voting = True)\n",
-        "# Alternatively, just specify the model path (recommended as this ensures the model is intact every time it is loaded).\n",
-        "predictions = celltypist.annotate(adata_400, model = 'celltypist_demo_folder/model_from_immune2000.pkl', majority_voting = True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "liquid-happiness",
-      "metadata": {
-        "id": "liquid-happiness"
-      },
-      "outputs": [],
-      "source": [
-        "adata = predictions.to_adata()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "utility-sussex",
-      "metadata": {
-        "id": "utility-sussex"
-      },
-      "outputs": [],
-      "source": [
-        "sc.tl.umap(adata)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "sought-adult",
-      "metadata": {
-        "tags": [],
-        "id": "sought-adult",
-        "outputId": "fd5f4ea9-ef3a-4c73-8c40-5a3b4da7e07b"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1483.92x288 with 3 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "sc.pl.umap(adata, color = ['cell_type', 'predicted_labels', 'majority_voting'], legend_loc = 'on data')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "liberal-strengthening",
-      "metadata": {
-        "id": "liberal-strengthening",
-        "outputId": "c510f9a9-b8c3-4116-bb78-9172a57ba1ba"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 374.4x324 with 5 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'majority_voting')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "australian-cherry",
-      "metadata": {
-        "id": "australian-cherry"
-      },
-      "source": [
-        "## Examine expression of cell type-driving genes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "southern-female",
-      "metadata": {
-        "id": "southern-female"
-      },
-      "source": [
-        "Each model can be examined in terms of the driving genes for each cell type. Note these genes are only dependent on the model, say, the training dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "liable-liechtenstein",
-      "metadata": {
-        "tags": [],
-        "id": "liable-liechtenstein"
-      },
-      "outputs": [],
-      "source": [
-        "# Any model can be inspected.\n",
-        "# Here we load the previously saved model trained from 2,000 immune cells.\n",
-        "model = models.Model.load(model = 'celltypist_demo_folder/model_from_immune2000.pkl')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "available-victor",
-      "metadata": {
-        "id": "available-victor",
-        "outputId": "f6eae99e-98b1-4206-bdb6-1af502d7baf3"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array(['DC1', 'Endothelial cells', 'Follicular B cells', 'Kupffer cells',\n",
-              "       'Macrophages', 'Mast cells', 'Neutrophil-myeloid progenitor',\n",
-              "       'Plasma cells', 'gamma-delta T cells', 'pDC'], dtype=object)"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "model.cell_types"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "pharmaceutical-remains",
-      "metadata": {
-        "tags": [],
-        "id": "pharmaceutical-remains"
-      },
-      "source": [
-        "Extract the top three driving genes of `Mast cells` using the [extract_top_markers](https://celltypist.readthedocs.io/en/latest/celltypist.models.Model.html#celltypist.models.Model.extract_top_markers) method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "interracial-clone",
-      "metadata": {
-        "tags": [],
-        "id": "interracial-clone",
-        "outputId": "ab78d984-5763-4996-f8ca-9db695b169c0"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array(['TPSB2', 'TPSAB1', 'CPA3'], dtype=object)"
-            ]
-          },
-          "execution_count": 33,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "top_3_genes = model.extract_top_markers(\"Mast cells\", 3)\n",
-        "top_3_genes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "sealed-percentage",
-      "metadata": {
-        "tags": [],
-        "id": "sealed-percentage",
-        "outputId": "67bed261-c02d-499f-b9b2-639900d5d108"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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jMRjEYzz4fT52Hixhe9HBTp873uthQnoyW7du5fnnn++r/4KIiIjjQqEQb77xBjkJfvwdTMmSmphAWlIiHk+0rZ0zZhRTRgzt8Lk8xjA4IY6lS5bQ0NDQp7lFDteXBU0fMBu4x1o7C6gHbjr8Ttba+1vmB5ubm5vbh3FE3CsQCLBixQqy4nwYYzhQXsnBquiK5h6PYeGUCZSVl2OM4b333nM4rYiISGzt3buXl156iaEpCaTG+bq9//QRw7jkpFmMyR/ERXNnsKPoIJOGDenyMXlJ8WQlxPHQQw/R2NjYW9FFRERcZfXq1VTX1JCflHDEbQ++voiM5OhIh2AozPtbdzAmfxAJfn+nz5efFE9zIMDSpUv7MrbIEfqyoLkf2G+tfb/l8t+JFjhF5DDvvfcejY2N5CXFEwyHWb1jN6vbrSQXiViaA0Gy4/28/vrrGg4nIiIDypNPPonHGMakdTzUvNWug6X8fekK3tu8nfzsTJ5c8j4HKioxHg+7S8q6fKwxhrHpydTW1vLKK6/0ZnwRERHXeOutt/B7PeQmxh1x2+dPn0djIABEFwgqqaqhsq6+y+fLiPeT6Pfx5ptv9klekc70WUHTWlsM7DPGTGi56hxgY19tT6Q/e/7550n0+2hqqMfv9fLpk2dxwezpVNTWEQqHSYjzM2fMSAqSEygtLeX999/v/klFREROAM3Nzbz++uvkJcYR180CP6FwmN2lZTQFmslKSeamyy5i5bZdNDQ1caBlcYOuZMb7SY/388rLL/dWfBEREdcIhUIsXrSI3Pi4Dqdv+dfaDSz+aAsb9uxnR3EJF8yZzvDcbACWb9lOxNojHmOMYVCCnxUrVlBf33XxU6Q39fUq59cDjxtjPgRmAr/s4+2J9Dtbt25l1apVDE2K56l336e4spqS6hrKamr5aO8BSqpq2u47KCmeRL+Pv/71rw4mFhERiZ1169bR1NR0xMIFHSmvrWNEbjY1DU3c+dLrvLnuI7YVHWTuuNEUVlT1aHuDEuPYtn07ZWVd9+gUERHpb1avXk1dfX2nbeolJ8+mpqGJrNQU4v1+3lj3UdttSfHxdDyDNQxOiicUCrFs2bI+SC3Sse4nIToO1tq1wNy+3IZIf/fIIw/j93oZlprIuII8slKT2V9eScRGmD9hLE+8u5wvn3UqEJ10eURKAh9++CGrVq1izpw5DqcXERHpW62L4WXGdz5/V6vaxmaKq2q44pSTGZSRhjGGc2dMwQJ+r7dH28uKjwPq2bJlCzk5OceRXERExF2WLFmCz+MhO+HI4eYA24sOMmJQNm+v38SVZyxg1OBcrLUYY5g+clinz5sR5yfB5+Pdd9/l3HPP7av4Iofo6x6aItKFDz/8kKVL32NESgJ+j4dPnzSLOJ+P4TnZLNu8Hb/Py+wxI9laWNz2mKEpiST6fdxzzz1EIhEH04uIiPS9oqIiEvw+fB2sxHq4rNRkahubqG5oxLQMpatramZH0UHW7drbo+0l+aKFz8LCwmMPLSIi4jLWWpYuWUJWvA+vp+O+lg3NAYbnZPOZedHlT15YsYYHX1/EXxd33fPSGEN2vI/3ly8nGAz2enaRjqigKeKQcDjMnX/8I4l+HyNTkw65zef18OUzo70yh+dmkxwXR1V9AwBeYxiblsjWrVt57bXXYp5bREQklurq6vB3cuB1uJzUFHLTUlm7ay+VdfVErMVjDG+v38zmA0U9eo7WbdXV1R1zZhEREbfZtWsXpWVl5CZ2PoXLrNEjGJqTRXJC9D7nzpjMNz5xJkNzsvho74Eunz83MZ6GxkY++uijLu8n0ltU0BRxyMsvv8zWbdsYl5bU4RmyYCjMk+++T0pCPPWBwCGry+UnJZARH8c99/yvDrhERERa1Dc3kxQfz3mzpmKM4eUP1lJaXcOcMSP41NwZTscTERFxzIoVKwA6HW5+uHAkwgsr1gBw+qTxTBle0OX9sxL8GGO0gK3EjAqaIg6oqqri3nvuISshjrzDJmQORyL8+Z2lHKiooKaxkXtefYvGQICS6o8XBzLGMDEzmaqqah588MFYxxcREYmZ5ORkQpEjV1XtyIjcHPIy0nl/y3ZWbNvJlOEFPPrWErYVHSQxrmcHcKGWFVyTk5OPObOIiIjbrF69mpQ4P4m+zueU3ltazr2vvs0fXniNB19fxBcXLgCgoq771cv9Hg/pcT5WrVrVa5lFuqKCpogD/vd//5f6hgYmZaa0zfHVKhgOk52SQlpSEl6Ph5SEeBZt2Ey879A1vNLj/AxLSeTZZ59ly5YtsYwvIiISM1lZWTSFwljbfVGzpLqG2sZGGgNBGpqbGZ03iMsWzCU3PY3axqYeba85FJ2fOjs7+7hyi4iIuEUoFGLd2rVkxnW9LnRifBxzx47kq2ed2jZ39YptO9t6anYnK97P1q1baWhoOO7MIt1RQVMkxtatW8err77KyNREUvxHNijWWuqbmwmGwozOG0RdYzOfmDmN+LgjV3cdl5FMnNfD7373O8LhcCzii4iIxFRBQQHWWhrD3S+ENyQrg1MmjSM9KaFt/q9QJEJ9UzPJ8Z3PGdZeQyjanubn5x97aBERERfZuXMnjU1NZMQfeUzZXm5aKnPHjiI9OZnBmekAzBg5jIWTx/doO5nxfiKRCBs3bjzuzCLdUUFTJIZCoRC/v+MOkvw+xqR1PJTtpQ/WkpmSzPaig8wcNZyrzzsDjzFsKzx4xH39Hg/j05PZsmULL7/8cl/HFxERibkRI0YAUBcMdXvfqvoGctJSOXv6FM6ZNgWAGSOHU1pTyxsf9myRgtbttG5XRESkv2stMGZ2U9Bs1RwKEmw5wRfv97N65x52Fpd0+7j0lufXwkASCypoisTQCy+8wK7duxmfntzhQkAA58+exqZ9hWzeX0h6UiLxfh/jC/K4cE7HixnkJ8WTmRDHfffdR21tbV/GFxERibnRo0cDUBvouqAZCIX4v7eX0hwI8uHufXy0bz/rdu3lH8tXkpIQz8a9hT3aXk0gxODBgzWHpoiInDC2bt1KvM9LgrdnJaDEuDgs0BQIAnDmtEkkJyR0+zi/x0NKXHTYuUhfU0FTJEbq6up46KGHyEqIY1Bi5wsTBEJh0pITGZSe1ja/5sGqapZt2dbh/Y0xTMxIpra2lr/85S99kl1ERMQpycnJDBs6lOrmYJf3i/P5+P6lF5IYH8eSTVsprqyioq6OM6dO4qI5M8jPSu/R9mpDYSZPntwb0UVERFxh69atJPu8R6zf0JFnl6/i7Q83YoAtB4qA6FD0wRlpPdpWis+jgqbEhAqaIjHy1FNPUVtby/iM5E4bkjfWfcSLK1aTGOeHdvepb2qmsouV5dLi/AxJTuDvf/87ZWVlvZ5dRETESdOmT6e6BwsD7S+v4P/eXsrCyRMoq6kjKyWFwRlppCQmMiZvULfbaQyFaQiGmDp1am9FFxERcZS1lr1795LSxermrXYdLKUxEGDj/iJGD86hMRCgpqGRl1eu7fH2kv0+SkpKaG5uPo7UIt1TQVMkBhoaGnj66acYlBhHegeL+7SK9/uwGNISEymrrmm7vq65mWE5Xa+2OiY9mVAoxJNPPtlruUVERNxg5syZBEJharuZRzMtMZFPnzSTvMx0Nh8oIj8rg8feWsJra9aTGNf56IhWlc2Btu2JiIicCMrLy2lqaiLZ331Bs7axkcq6eq678BxmjBrB/AljCQRDxPm6Xh29vWSfF2st+/fvP57YIt1SQVMkBv75z39SX9/AqE4WAmp1+uQJDMnKoLiqhpPHj+Gvi5cBMH3EMGaN7npxgiSfl8GJcbz4wgs0NDT0WnYRERGnzZ49G4Dypq6HnT+7fBXpyUk8vmgZg9LTSPD7aWhuZmdxCdNHDut2O2WNAdJSUxkzZkyv5BYREXFacXExAIk96KGZEBfHl888lV0lpbyxLrqwT3ycj7W79hIIdb84X/vtHDx45KK2Ir1JBU2RGHjppZdIi/eT0c2qcjUNjZw2aRzGWt5av5HctJ7NU9JqeEoiDY2NLFq06HjiiohLGGN2G2PWG2PWGmNWOp1HxCmDBg1i5MiRlDUFurzfV88+jeZAkAvmTGf26JHsK6sgNz2NC+ZM73Yb1loqAiHmzZ+Px6OvyCIicmIoLS0FIMHbfUFz/JA80pIS2XagmNTERP66aBl/fuc9zpk+mefeX92j7bUuPFRS0v2q6CLHo+f9hkXkmBQWFrJjxw4mZKR0eb/qhgb++MK/SEmMpyA7E6/XA1gamwMs2bSV82Z2P59XRryfJL+Pd955hwsuuKCX/gci4rCzrLWaHFcGvNNOO43HH3+cQDhCXCertG7eX8iO4hLSk5M4bdJ4rLWML8jD34ODuKrmIM2hMKecckpvRxcREXFMVVUVAHHe7hcEAti8v4iEuDhqGxs5ZeJYkhPj2V9WyfmzpvXo8a1tdHV19THlFekpnX4W6WOrVq0CILeLlc0B0pOSGJaTRSgcYfaoUXzpzFM5b+ZUQuEwa3ftpbCistttGWPIjvezds0aQj0cEiAiItIfnHHGGVhrKWnsfJGBiUOH8Km5M3n3o60s3rCZB19fxJ9eep11u/ZS0cXiegDFDc34fT4WLFjQ29FFREQcU1MTXZvB38PRB+OGDGbrgSK8Hg97SsvJTUsjEAoRjkR69HiPMfi8nrbtivQVFTRF+tiWLVuI83pJ6sGcJWdMncjEoUN46r0VLN20jd+/8CrvbdnOjFHDCIXDPdpeepyPxqYmDhw4cLzRRcR5FviXMWaVMeZap8OIOGn8+PEUDBlCUUPXq6aW19ZxxWkns6ukjC+feQrf+MSZZKYkk+DvfNqXiLUcbApwyimnkJSU1NvRRUREHNPc3IwxBo/pWQ9Nr8fDmdMmEYlEmDl6OGW1tazesYdXVn3Y4236PB6ampqONbJIj6igKdLHCgsLSfR5MD1oQPaWVXDKpHGMzstl24FiJgzJZ3B6Grlp6VTW9Wyhn2S/r227ItLvnWqtnQ1cAFxnjFl4+B2MMdcaY1YaY1a2zpEkciIyxnD+BRdQ0RSgIdT5Sb7Kunpq6hvx+3y8u3ErJdU1BMNhSqo77ylS1higORTmk+ef3xfRRUREHBMIBPD2sJhZUVdPfVMzY/MHc86MKXiM4V9rNjAmL5e5Y0f2eJseYwgEup73WuR4qaAp0sdqamrw97ABMQaeee8DZo4cjtfrYfTgXPw+Lyu372R0Xm6PnsPviW6rtrb2mDOLiDtYawtb/i0BngVO7uA+91tr51pr5+bm9uxzQqS/uvDCC/F4POyrbezw9pqGRkYMymFM/iAKsjI4ZeI4lm/Zzpb9RcR1MVJiX10jWVlZzJ8/v6+ii4iIOKdnh6MUlldSVvPxcaTP6yUxzo/f52NCQf7RbbKHx8Aix0oFTZG+Zm2P7xoKhcjPzGDSsCF87vR55KanUlodbVDifF2vkC4iJxZjTLIxJrX1d+ATwAZnU4k4Kzc3l9NPP50DDc2EOpjLa9GGzewvqyAtKZEzpk5ke/FBDpRXUlxZ3enCQHWBEGVNAS699FJ8Pq2XKSIiJxav19vjQ9KpI4YyYlBO2+X6pgAHq2pYMHHsUW3TWounh3N2ihyrPn2HGWN2G2PWG2PWGmNW9uW2RNwqOSWFUA9bkA17CzlvxhRC4TCLP9rCxn2FZKYkM33EMKJT6XUvFIneT3OAifR7g4Elxph1wArgZWvtqw5nEnHcF77wBYLhMPvqjpybyxiDtR8XOsPhCN/4xJmcNH50pwdWO2vqiY+L45JLLumzzCIiIk6Jj48nHIlge3BM+pd33mP9nn28v3UHAFmpyXzz/LO7nIe6I2FriY+PP6a8Ij0Vi5L5WdbamdbauTHYlojrDB48mKZIz4qR50yfzMptO3npg3VkJCexcMoEJg0dwo6DJcT3sBFpbJlXbPDgwcecWUScZ63daa2d0fIzxVr7C6czibjB5MmTmTtnDnvqGo/opTl7zAiSExLYXnSQiLUUZGfyt3eXk56U2OHBWF0wRHFDM5+59FIyMjJi9D8QERGJncTERCBaZOxOalICcR4fU4cPZeO+A9zx3D85UF5JeW0dL69c16PtWWsJhSMkJCQcV26R7qgPsEgfGzNmDE3BEM09WKX8mWUr+WDHboLhEHPHjmJ70UEWbdzC5GEFPd5eTTCE1+tl+PDhxxNbRETEtb5x7bU0h8Lsqjl0wbwhWZms272PzfuL2FtaxlNLV5CblkZBdiYJcUcWNLdW1ZOQkMCVV14Zq+giIiIxlZqaCkCwB51s5o0bQ1M4RFMwyMSCIXzlrFNpaG7G5/EQDId6tL2wtUSsJT09/bhyi3SnrwuaFviXMWaVMebaPt6WiCtNnz4dgPKmYLf3zUlLYfqIoVy24CSeW76abYXF+D0eGo9ihbiK5iATJkxQF38RETlhTZo0ibPPPps9dU1tIxNafWLmVE6fPJ7G5iDzxo1h/vjR3PnS69z9yhuH3K+8KUBpYzNf/spX1DtTREROWK1tXCB85NzT7VlrWbppK+t372P1jt3UNDYyOCOdg1U1JCfE85l5c3q0vUBL4VQFTelrfV3QPNVaOxu4ALjOGLPw8DsYY641xqw0xqwsLS3t4zgisTdhwgQyMjI42NDc7X2/fs7pLJw6kZrGRvw+D/vLK1mzcw9DszKpqm/o9vGNoTDVzUFOPfXU3oguIiLiWt/61rfw+nxsrqw74rYtB4rZVnSQxRu3sLXoIOlJiWSnpLbdHrGWzVX15OXl8dnPfjaWsUVERGIqJye6yE9zNwVNYwwTCvI5bfI41uzcA8D2ooP4vN6jWuCnueVEY25u7jEmFumZPi1oWmsLW/4tAZ4FTu7gPvdba+daa+fqDS8nIo/Hw3nnnUdZU6DbRuTt9Zt56YO1pCTEU15bx5i8QTQFg+wuKeP9rTu73daB+iaMMZxzzjm9FV9ERMSVBg8ezNe+9jVKGpspbfz4pGFhRSVj8gdx/uxpzBo1nLOmTuKik2Zy8bxZbffZXdtAXSDIjTfeqBENIiJyQmtdW6GxB1Og1Tc3s3zLTs6aNokXV6whKT6O8QV5eIzp8fYaW455Bw0adGyBRXqozwqaxphkY0xq6+/AJ4ANfbU9ETe75JJLiFjL3tque1l+YuZU5o0fi8/r5bwZU1i5fTeZycnMHTuKk8eN6vKx4Yhlf30T804+mSFDhvRmfBEREVf63Oc+x9ChQ9lSVU+4ZYhbYyBIIBQizufjnBlT8HgMg9LTSE2MLk7QGAqzq6aR0047jQULFjgZX0REpM9lZGQQHx9PQ6j7gubs0SM5ZeJYxg3J48tnnUp6UhINTc0cKK/s8fZap4LJz88/5swiPdGXPTQHA0uMMeuAFcDL1tpX+3B7Iq41fPhwFi5cyL76pi7nLnnojUVs2n+A599fzc6DpYwbMpgrz1hAMBwmLSmxy23sq2ukORTmyi99qbfji4iIuJLf7+d73/se9cEQu1pOGo7JG0R+Zkanj9lSVYfH5+O73/1ujFKKiIg4xxjDsGHDaAh2X9D0+7wMz81m3a69PPHu+2zYs5+lm7fR0Nz99Gmt6oMhcnJyNAJC+lyfFTSttTuttTNafqZYa3/RV9sS6Q+uvvpqQhHLjur6Dm+PRCwLp0zgjKkTSU9KJByx7Csr55VVH/LB9l1U1B45R1irQDjCztpGTjrpJGbMmNFX/wURERHXmTNnDmeeeSa7axtp6qD3ibWW+157G4CKpgAHG5r58pe/TF5eXqyjikgMGWMSjDErjDHrjDEfGWNudTqTiFNGjRpFfTfTn7V30rjRXDJvFrPGjGBIZgbLtmzv8UK19eEIo0ePPtaoIj3W14sCiUiLUaNGcfHFF7OvvomawJErnkdshMq6BvxeL6dNnsDe0nK8Hg/DcrMYlJ5KeW3HhVCArVV1hK3lO9/5Tl/+F0RERFzpW9/6FsbjYXsHJw2NMfz7J8/CWsvW6npyc3L4/Oc/70BKEYmxZuBsa+0MYCZwvjFmvrORRJwxatQoGoMhgpHui5qNgQAPvbEIr8dDUUUVfp+PK884hcS4uG4fG7GWukBIBU2JCRU0RWLo2muvJS0tjY2VdUSsPeQ2n9dLYnwcL65Yw7PLVxKxlhGDcpg9eiTLt+xgWE5Wh89Z0RTgQH0TV1xxBaNGdT3PpoiIyIkoPz+fSy65hMKG5k7nCCtrClDdHOSqq6/WMDiRAcBGtQ5x8rf82C4eInLCGj9+PAA1gVCX96usqycxLo6zp03G6/EwYlAO8yeM4e/vfcCWwuJut1MXDBGxtm17In1JBU2RGEpNTeXGG2+kujnI7g4WCJo8bAjzJ4xh9uiRfOWsU7lo7kwS4vx84xNnkpxw5MFXKGLZWFVPXl4eV111VSz+CyIiIq70xS9+EY/Hw7byagCeXb6KkuoaKuvqeWrpCnbXNpKbm8snP/lJh5OKSKwYY7zGmLVACfC6tfZ9hyOJOGLChAkAVDcfOVKwvSeWvM8H23YyZXgB4UiEppaRhRML8imtqul2O9UtBdPW7Yn0JRU0RWLsrLPOYuHC09lZ00Bd8NAzZH955z0yU5IZkz+oR8+1raqO+kCQH/3oRyQkJPRFXBERkR4JhUK89dZb/OY3v3Fk+7m5uSxYsIBF6zYQjlgunT+HQelp0XZ1SB5b9xdy+eWX4/P5HMknIrFnrQ1ba2cCQ4GTjTFTD7+PMeZaY8xKY8zK0tLSmGcUiYX09HSGDh1KVQdTn7X3+dPmcaCiCoBdB0vZsHc/ANNHDuuwg83hqpuDpKWmMnTo0OPOLNIdFTRFYswYw/e+958kJ6ewoaL2kKHn15x3Bj6vl+1FJazZuYeiyirW7dpLSVUNB8orD3meiqYAe+saueyyy5g5c2aM/xciIiKHWrZsGT/96U+prKzs/s595LOf/SwTJk6kpPHj1Vi3Fx0kLjmFzMxMzj//fMeyiYhzrLVVwDvAER8C1tr7rbVzrbVzc3NzYx1NJGamT59OdSCMtZ3PvJCZkswZUybQFAgyanAuU4YXUFFXT2NzgJ0Huy/4VwXDTJ8xA2NMb0YX6ZAKmiIOyMzM5Hv/+Z/Roec1hw49r21s4u/vreDtDZtJSUhge9FBHl+8jGD44znBQpEIH1XWkZ+fz7XXXhvr+CIiIkeoqqrC4/GQkZHhWIbp06eTnp5+SEFzd0kZNRHDSSedRGZmpmPZRCS2jDG5xpiMlt8TgXOBzY6GEnHQrFmzCITD1Aa7nkfz/a07WLtzD1v2F/HUkhW8uW4jRZXVJHWzKFBjKEx9IKjONhIzGnMj4pCzzjqLt956i3cXL2ZQUjwp/ujumJqYwE2XXdR2v8tOOYn6piaS2w0p31ZdT0MwxK9/9CMSExNjnl1ERMSNvF4v8+fP550338BaizGG06ZMZFFhOfPna3FjkQEmH3jMGOMl2pHnKWvtSw5nEnHM7NmzAShvCpIW5+/0fufPns6STVsZmpPJpGFDqKyrJzc9jYyUpC6fv6IpAMCcOXN6L7RIF9RDU8RBN954I0lJSWyqrDui6/+qHbspqa5pud5Q2DKXSXVzkL21jXzmM59hxowZsQ8tIiLSgdbhZV0NZYuFGTNm0BwKt612XtWyAILaTJGBxVr7obV2lrV2urV2qrX2505nEnFSbm4uI0aMoLyl8NiVjXsLWb1zDw++voj1e/bz/PurueefbxIIdd67s6wpQGZGBqNHj+7N2CKdUkFTxEFZWVl8+7rrqGgKUNTQ1Hb9juISguEwDYEAf128jOqGRg62FDc3VdWRlZmpoeYiIuIqrYVMJ+fN2rFjBxMnTgSgpmWl1ZpgCK/Hw5gxYxzLJSIi4gbz58+nsjlIKBLp8n7XfvJMzpk+hfNmTsUYQ05aCt8470ziOllYL2ItFc0h5i9YoPkzJWZU0BRx2IUXXsjEiRPZVt1IKGL52+LlJPj8eI2hORDkilPnsbe0jHifl8L6Jqqbg3z7uutISUlxOrqIiIir/PSnP2X48OEYY6hrmSOsPhiipLSUAwcOOJxORETEWQsWLCBiLeVNna92/vraDZTV1AIwJm8QCXF+9pSUs3LH7k4fU90cJBAOs2DBgt6OLNIpFTRFHObxeLj++utpCoXYW9vAxSfPoiAnk5PGjWZCQT5/XbyMOWNHMXHoEHbUNjJhwgTOO+88p2OLiIh0yMkh548//jhxcXHkZGfT2DLkvCliOeOMMxgxYoRjuURERNxg+vTpJCUlUdpu8bzDzRs/hqx2nWcS4+L4wsL5nD19UqePKWkM4PV6Oemkk3o1r0hXVNAUcYFp06axYMEClm7ZQfxhEzR/9ezT8Hu9HKhrojEY4t///d/VjV9ERKQLgwYPpjkcHU7XHI4wZswYPB597RURkYHN5/OxYMECypqDnZ6A9Hm9/GP5SiItw9JnjhpOWW0dj7zxLpEOHmOtpbQ5yOxZs0hOTu7T/CLt6ZudiEt85StfwRrDgfqmI26z1rKnrpGJEydq1TgREXE1N5x0y8zMJES0/WwOhXnuuefYu3ev07FEREQcd9ppp9EcCrctmne4xDg/hRVV7C4pa7uutrGRC+fOwNNBG18fClMfCHLa6af3WWaRjqigKeISU6ZM4YwzzmB/fTNbDxTz9vpNbbeVNwWoD4a44oorXHGgKCKxY4zxGmPWGGNecjqLSFfcsso5QHJyMiELoZYsV199NcOHD3c4lYiIiPPmz5+P1+ulpLHj1c6NMXz3ovMYnTcIgFA4wjvrNzNyUE6H9y9piA5fP/XUU/smsEgnVNAUcZFPf/rT1AWCDM7N5oypE9uuL6pvJiU5mYULFzqYTkQc8v+ATd3eS8RhbljlvFViYiLhiCUciWbKzMx0OJGIiIg7JCcnM3vWLMo66aHZ6r3N23htzXp2HSzhqnM7Pw4tbQoyftw4Bg0a1NtRRbqkgqaIiyxcuBCPx0NJQ3Nbd/5Iy5wkpy9cSFxcnMMJRSSWjDFDgU8BDzqdRaSn3FDQ9Pv9RKzFtrssIiIiUaedfjp1gSB1wVCn9zll4jjmTxhDKBJmdScrnDeHI1Q1BzTcXByhgqaIi6SnpzNl8mTK250tq2wKUFtfz4IFCxxMJiIO+QPwAyDicA6RHnN6yPm+ffv45z//ieXjgqYWBBKRWNKcveJ2rceWZYcNO28KBnn0rXfbLn+09wBDs7IYNTi3w+cpa1kt/ZRTTumjpCKd07c7EZeZOWsWhRVVNASiRc39VTUcPHiQmTNnOhtMRGLKGHMRUGKtXdXN/a41xqw0xqwsLS2NUToR98rPz2f27NnQrq7qdJHVbbZt20Y4HNbrItIHnnzySc444wwCgY7nJxRxg7y8PEaOHElp06Hv0wS/n4VTJlJV3wBATmoqSQnxZKZ0vHp5aVOArKwsxo0b1+eZRQ6ngqaIy0ycOJGqqipK66KNCP44Tpo7l4yMDEdziUjMnQpcbIzZDTwBnG2M+cvhd7LW3m+tnWutnZub2/HZc5FYcMNQcwCfz8egQYMwxtCaKBJRJ+f2HnjgAW666SYuu+wyvTYiveyBBx5g2LBhNDQ0OB1FpEvz58+nqjlIKHLoya1wOEy4pW3YsO9AhyubQ/RkYWVziPnz57vmO4AMLH1e0NTqrCJHZ8yYMRQUFOBtmS+zPhxhzNixDqdyn23btqlniZzQrLU3W2uHWmtHAp8H3rLWfsnhWCL9QiQSwRjaCprhcNjRPG7zm9/8hldffZUdO3YQCnU+f5qIHL3Wfcrr9TqcRKRrJ598MhFrqWw+tJfmuCF5ZKemAODzeNhedLDDx1cHQgTCYU466aQ+zyrSkVj00NTqrCJHIS4ujkAgQEMoOhSsIRhi2LBhTsdynSuuuIJvfvObTscQEZHDuOFkUygUIhwK09phRL0Qj5SQkEB8fLx61Yj0Ec3dK243bdo0fD4fFU2dr3Z+8cmzGDckr8PbKlqGq8+ePbtP8ol0p08/ZbU6q8jR27FjB5FIhMZQmN2lFYTCEfLz852O5Tqpqals2qRzJTIwWGvfsdZe5HQOkZ5wQ4GsqKiI91eupLpl+hb10DxSYmIiCQkJrvh7iZxIWvcpFTTF7eLj45k8eTKVgY4LmiXVNV0+vqI5yIgRI8jMzOyLeCLd6utP2T+g1VlFjsq8efOYd/LJVNbV8+IHqwmHw2hePJETizHmp05nEOltbuiZ2aqpqYmRw4eRnpwEqIdmVzQsVqRvqKAp/cGMGTOoCYQIddBOvrp6PVsLizt8nLWWmmBIC9eKo/rsU1ars4ocu+ycHPwJiVy28BR8Ph/Z2dlORxKR3nWN0wFE+orTPf6stezYsYNhBQWOZ+kP9BqJ9A0VNKU/mDZtGtZaqgNHzqfs93pZsXUnb68/clRcbTBEMBxh2rRpsYgp0qG+/JTV6qwixygzM5PmcASMp+2yiPQvxpiaTn5qgSFO5xPpK0731DTGkJeXR/sUKiyISKy0fgbqZIH0B5MnTwaguvnIYedfWDifiQV5ZKYkHXFbawG09fEiTujy250x5pPGmKuNMSMPu/6q7p5Yq7OKHLu0tDSC4TDNLV3/09LSHE4kIsegChhnrU077CcVKHI4m8gJbfr06VgLtqWs6fP5HE4kIgOFCpnSn6SlpTEkP/+IHprNwWiBc8TgXJqDITbs2X/I7dXNQVKSkykoKIhZVpHDdVrQNMb8EvgxMA140xhzfbubv9PXwUQGstTUVAAaQ2GMMSQnJzucSESOwf8BIzq57a+xDCIy0Pj9fiLWEmnppvniiy+yePFiZ0OJiIi40MRJk6gLfbx4XllNLa+t2QBAbloq00YMY+SgnEMeUxcKM2HiRBXwxVFd9dD8NHC2tfYGYA5wgTHmf1puO6p3rVZnFTk6KSkpADQGwyQlJqqhOMznPvc5x4c0inTHWnuLtXZFJ7f9MNZ5RGLFDW1WXFwcoUiEcEtb8alPfYr58+c7nEpERMR9xo8fT0MwRCAcHR2Yk5bKxSfPars9KT6OlMSEtssRa6kNhBg/fnzMs4q011VB02etDQFYa6uIFjjTjDFPA3ExyCYyYCUmJhIKhViyei2JSYlOx3GdO+64wxUHzCLdMcb4TMub1RgzzBhzuTFmVnePE+nP3HDCKSEhAWstoZYumrm5ucTF6euriIjI4caNGwdEF/rpyDsbNlNYUdV2uS4YImKtCpriuK4KmjuMMWe0XrDWhq21VwNbgEl9nkxkAEtMTMTn8zFz+jQS4hO6f8AAM3ToUKcjiHTLGPMNoATY0/L7m8DlwBPGGPXQFOlDH330EaHQx71NEhN1clCkPzJRVxhjPtvy+znGmDuNMd82xmi1L5FeMHbsWABq282jGY5EeOLd9/m/t5fgMYY1O3az+KMth9xvzJgxsQ8r0k5XM6R/tqMrrbW3GGPu6aM8IgJtvUhCEUt8ggqaIv3UDcAYIBXYBIyw1pYZY5KAD4BfO5hNpNe19px3Qw/NSMuiek3h6JxgKmiK9Ft3A4OIjhC8BIgHXgQuBCYA/8+5aCInhszMTDIzMqgNNrVd5/V4uHDOdJ5c8j7JCfHsKC5hzthRQLQnp9/vVycTcVynBU1rbWPrUDlrrTXGDAPmATustWtiF1Fk4GldjTVsLX6/3+E0InKMAtbaSqDSGLPdWlsGYK1tMMYEHM4m0mfcMCXIhRdeyIoVK6hubCIUCmlxPZH+63Rr7TRjjB8oBvKttQFjzF8BHZOK9JJx48ax+cO1h1yXlpTINz5xJgBzxoxsu742GGb0qFFtx6wiTulqlXMNlRNxSPvGQQ1F1IYNG7jmmmucjiFyNBKNMbOMMXOAuJbfZ7dcVtdrkT7UWsDcVVxCTU0NSUlJDicSkWPUuqZDEPjAWhtouRwCwl09UER6bszYsdQFonNjdsVaS30ozNiWeTdFnNRVpeQGNFROJKYikQglJSV4vd6261TQjJo6dSoPPvig0zFEjkYR8PuW34vb/d56WeSE4oah5q1aC5j5gwdT0RxUD02R/qvYGJNira2z1p7feqUxJg/QaAeRXjJ27Fgi1lIfDJMa1/nxZ3M4QnMo3DbvpoiTuppIOWCtrbTW7gUOGSqHGg+RPrF9+3buvPNOPJ7orhkOh6mrq3M4lYgcC2vtWZ39AJ9wOp9Ib3j00UfZsWOH0zGOkJKSAkBTOILH49EcmiL9lLX2AmttR1+Ga4BPxTqPyImqbWGgYLDturKaWuoao/NqPvj6IpqDobaV0LUgkLhBVwVNDZUTibHx48fzy1/+sq2g2djYyK5duxxOJSK9oWV11rONMQ8C+53OI9Ibpk6dSnZ2NuCOuTNbtfbQ3FtURGVFhauyicjxMcaMAf4DeNvpLCInimHDhuH3+Q5Z6fxgVQ1V9Q0ATBs+lP3lFW23q4emuEFXBc3W4XG/a/f7He0ui0gfaR1ynpKSwpw5cxxOIyLHwxgzzxjzR2AP8ALwLjDR2VQivWPu3LlkZGQA7hpy3jrEPDMrm1GjRjmcRkSOlzEm3xhzgzFmBfAR4AW+4HAskROGz+dj9OjR1AQ/npp2yvAChuZkATBvwhjG5A2iNhhi8KBBbSMhBpqHH36YvXv3AlBRUcGqVascTjSwdbXK+ZkxzCEi7bSfQ7P97yLSfxhjfgFcAewF/gb8HFhprX3M0WAiA0BrD02v10taWprDaUTkWLUsTvsFYCjwFHAN8Ly19lZHg4mcgMaNH8/SpUsY5jfkZaa3Xf/nd5Zy0thR7CurIDGvgNkTJjiY0lmTJk0iPT362tx2222sXLmSl156Sd81HNJpQdMYcxKwz1pb3HL5K8BlRHuY/MxaWxGbiCIDjwqaIieEa4EtwD3AS9baJmOMe7qwifSy1mHdbuip6fF4SEhIoKmpieQB2otE5ARxN7AM+KK1diWA2lKRvjFu3DgSk5KJj48/5PovLlyA1+NhTP5g3jpQzvjx4x1K6LwFCxa0/b5y5UoAAgEtMeOUroac30fL4j/GmIXA7cD/AdXA/X0fTWTgar+yuVY5F+m38oBfABcD240xfyY6P7V2ajkhtRYy3TJfZetCQAN1WJzICWII8ATwe2PMFmPMfwN+hzOJnJDGjRtHcnIy1c0B6pua2673tqzvUBcKt91PxA26Kmh62/XC/Bxwv7X2GWvtTwDNACvSh9oXMXfv3s3rr7/uYBoRORbW2rC19p/W2q8QbTefJ9rL5IAx5q/OphM58SW1FDQTErSWpUh/Za0ts9beY61dCJxDtHNNiTFmkzHmlw7HEzmhjBkzBmMMO0vK2FdWQcRamtqtel6jBYE65IaRKQNVlwXNdr1IzgHeanebepeI9KG4uLi234cMGcKkSZMcTCMix8ta22St/bu19t+IFjdfczqTyIkuKSmJiooK3n//faejiMhxMMZ8xhjzn8AUa+3vrLVzgM8AzV0/UkSORmJiIgVDhpCZlc3EofkUV1azeMOWtttrmoOkpaaSm5vrYEr3ccvIlIGoq4Lm34BFxpjngUaiq7JijBlL9MyYiPSR9gXNwYMHM3ToUAfTiMixMMacZIzJa3f5Ky1t6i+AF51LJjIwJCYlkZWVxQUXXOB0FBE5RsaY/wVuBLKB/zbG/ATAWrtFCwOJ9L5x48dTF44AMCQrg0/MmgrAroOlrNy8lbHjxqmAd5iSkhL++c9/Oh1jQOq0oGmt/QXwPeBR4DT7cT9aD3B930cTGbh8Ph9eb3T3PHxSZhHpN45rLmpjTIIxZoUxZp0x5iNjjA7cpF9wy4FO6xyarf+KSL+0EDjbWnszcCbRnpk9YowZZox5u2V4+kfGmP/XRxlFesW6det47733CIfDjmUYO3Ys9c0B7nvt7UOuHzkoh2EjR2m4eQestYRCIadjDEidFjSNMQnAfKLDzb/UOvzcWrvVWrs6RvlEBpTCwkLuuOMOAHy+6HznmvtLpN863rmom4kexM0AZgLnG2Pm901Ukd7jlrmk/P5oO9p+1IOI9DsBa20YwFrbABzNGZMQ8D1r7SSix7XXGWMm90FGkV6xe/durrvuOu655x7HMrTOo/nZM0495PqGUJhwJMKYMWMcSuZeBQUFfPrTn3Y6xoDU1ZDzx4C5wHrgAuCOmCQSGcCys7M566yzABjWMsxcw81F+q3jmovaRtW1XPS3/LijUiTSj6igKdKvTTTGfNjys/6wyx929UBrbVFrRxxrbS2wCSiIQWaRYzJr1ixSU1N59913HcswcuRIAOpDh/YSrQtGeyCOGjUq1pFcz+PpqqwmfamrA6rJ1tppAMaYh4AVsYkkMnDFx8cze/ZsAO67/34aGxtJS0tzOJWIHKPWuajLOMa5qI0xXmAV0R6dd1trtbqJuFbrUHO39NBszaOCpki/NgMYDOw77PoRQGFPn8QYMxKYBagdFddyw5QteXl5xMfHU9eyovn9/3qHq89dSF0wWuAcMWKEk/FcSQVN53T1ygdbf7HWHvWEAJr7S+T4+P1+FTNF+rHemIvaWhu21s4EhgInG2OmHn4fY8y1xpiVxpiVpaWlvZJd5Hi44YCsPa/X63QEETl2/wPUWGv3tP8BGlpu65YxJgV4BrjBWlvTwe1qR8UV3FAY83g8DB82rK2H5rWfOBOvx0N9MMSg3FzNS90Bfc9wTld7zExjTE3LTy0wvfV3Y8wRDUEHNPeXiIgMWL05F7W1tgp4Bzi/g9vut9bOtdbOzc3NPe7cIsfKLT0zD+fzdTvDg4i410hr7RFDy621K4GR3T3YGOMnWsx83Fr7j47uo3ZU5FAjRo6kMXJom94QjjCiZTi6HMoNheiBqqtXfp21Nq3lJ9Va62v3e7fdxjT3l4iIDHDHNRe1MSbXGJPR8nsicC6wuZczipzw3NZjVESOSlerY3bZVcxEd/6HgE3W2t/3aiqRE9jQoUNpDASJtDtR2RiKaG2HTqig6ZyuXvnjLj4aY7zGmLVACfC65v4SEZEBZLK19kvW2vuAy4HTj/Lx+cDbLYsefEC0HX2pt0OKnOh0oCHSr31gjPnG4VcaY64mOsd0V04FvgycbYxZ2/JzYV+EFDmR5OfnY4HGlmHnwUiEQDjMkCFDHM3V2NjIypUriUQivP7663zwwQeO5mmlE6fO6WoMziBjzH90dmNPznJZa8NEh65nAM8aY6Zaaze0v48x5lrgWoDhw4f3KLSIiEg/cMhc1Ef7ZadliN2s3g4lMtDoQEOkX7uB6HHklXxcwJwLxAGXdvVAa+0SQB8AIkcpLy8PgKZwhGT/x4XNwYMHOxmLRx99lL/97W/cfffd5Ofnk5yc7GieVjpx6pyuCppeIIVeaASstVXGmHeIzv214bDb7gfuB5g7d66GpIuIyIliZrs5pw2Q2HLZEJ2ZRat+yQmhsrLykIMKtxUQ3ZZHRHrOWnsQOMUYcxbQujDey9batxyMJXJCay1cthYym0KRQ653ys6dOwGoq6tjwYIFjmZpT98znNNVQbPIWvvzY31iY0wuEGwpZrbO/fXrY30+ERGRfmadtVY9LOWE9/DDD3PyySe3XXbr4kAi0n9Za98G3nY6h8hAkJ2dDUBzOFrIbApHC5taNEvcpqu+scdbZtbcXyIiMpCpqiMDwve+9z1OP/1op4iNnUcffZSKigqnY4iIiPQL8fHxpCQntxU0W/99+22dU+iIemg6p6semucczxNr7i8RERngjnsuahE5djNnzuTdd9/lc5/7HBkZGU7HERER6TcyMzOpLy5ie9FBAvGJpKSkUFxc7HQskUN0WtC01upUtoiIyLHrtbmoRfoTt/RU+OxnP8ull16Kz9fV+XsRERF3cEv7+f7775OWnkbZ/n34Qk3gSyArN4vrr7/e6Wgih9A3PBERkb5xXHNRi/RXbppDU8VMERGRo/Pee+8RFxePLz6OWcMHs+JgJXka6SAupPXlRURE+oY7TrP3wFNPPUUwGASgoaGBsrIyhxOJiIiIDCxuOSF44403kp+fT8vi5oSB1NRURzOJdEQFTRERkb5xXHNRx1JNTQ2RSPRb680338y//du/OZxIREREZGByw9DzlJQUgi2rm4esCpriThqHIyIi0gf601zU11xzTdvva9ascTCJnAjccCAmIiLSX7mhp2ZKSgqhSISItQTDEZKTk52OJHIE9dAUERERkV7jhgMxEREROXZJSUkAhK0lFA63XRZxExU0RUREREREREQE+LigGQhHsO0ui7iJCpoiIiIiIiIiIgJAQkICAM3hyCGXRdxEBU0RERER6TWaQ1NEROTYOdmOLly4kEAg0FbADLQsGpmYmOhYJpHOaFEgEREREek1mkNTRESkf1q8eDHwcY/MYDjapsfHxzuWSaQz6qEpIiIiIjIAVVZWUlpa6nQMERFpxw0nBlsLmMGIhpyLe6mgKSIiIiIyAIXDYYLBoNMxREQEiLQUD92gtaDZOuRcPTTFjVTQFBEREZFeozk0+4+cnByGDBnidAwRkQFt69atgDt6Zrb6uIemhpyLe6mgKSIiIiK9xk0HZCIiIm5WVVXF3Xff7XSMIxw+5DwuLs7JOCIdUkFTRERERI6bemaKiIgcnYyMDP74xz86HeMIrQXNkHpodksncp2jgqaIiIiIiIiIyAC3e/du3n777bYemeqhKW6mgqaIiIgLGWOGGWPeNsZsMsZ8ZIz5f05nEhEREZG+4YaRDpFIhFAoRFxcHIWFhVTX1QMqaHZFPTSd43M6gIiIiHQoBHzPWrvaGJMKrDLGvG6t3eh0sIEuEAgA+nLfGX2xFxER6Z9Gjx7N6NGjsdaSkZGBxx/9rqMh553T9x7nqKApIiLiQtbaIqCo5fdaY8wmoABQQdNBN//oZurr6pk7dy5f+cpXnI7jKq1f6N3Qw0RERESOnTGGzIwMml14EjccDjsd4RAqaDqnzwqaxphhwP8BeUAEuN9a677ZbkVERFzOGDMSmAW873CUAW/pkqUA3HnnnQ4nEREREek7/ri4toKm3+93OM3HmpubnY5wCBU0ndOXPTQ1VE5EROQ4GWNSgGeAG6y1NR3cfi1wLcDw4cNjnE7kSOqhKSIicvQiLQvwuEVcSxHT7/Ph8Ti7/Mp3v/tdamqiX4PdVtB0299tIOmzd6W1tshau7rl91qgdaiciIiI9IAxxk+0mPm4tfYfHd3HWnu/tXautXZubm5ubAOKdEA9FURERI5eKBQC3HNisHWYuc8FvTN//etf0/o9t6mpyeE0h1JB0zkxKbNrqJyIiMjRMdFvsw8Bm6y1v3c6j4iIiIj0ndaCplv4WwqacS4oaCYmJrb9rh6a0qrPC5o9GSpnjFlpjFlZWlra13FERET6i1OBLwNnG2PWtvxc6HQoEREREel9gZb5Kt0y0iG+paDpj3O+oNle6+vkFipoOqdPVznv6VA54H6AuXPnumPPFRERcZi1dgngjjFHIkfBLUPlRERE+hO39TyMj4+P/hsX72iOiooKfvKTnxAfH084HCYYDDqap7GxkZtvvrntsgqazumzHpoaKiciIiIy8OiLvYiIyNFrbGx0OsIh4loKmq1zaTolKyuL73//+4RCIfbs2cOOHTsczZOYmMjXv/51RzNIVF8OOddQOREREZEBQj0zRUREjl4oFOKiiy5qG0rtlva0tZDZWth00siRIwmHQowePZqCAufXmp4xY0bb7275ew1EfbnK+RJrrbHWTrfWzmz5eaWvticiIiIizmmd80tf7EVERHrO5/Px+OOPEwgECIVClJeXOx0JaFfQdLiHZqtwywiQcDjMHXfc4XCaj+l7j3Nissq5iIiIiAwM+mIvIiJydNLT0wkGg4RCISorK52OA4C/ZXXzeBf00AQIh8LRf8NhRowY4XCaj3m9XqcjDFgqaIqIiIhIr3HL6qwiIiL9SSQSobq6mjFjxjgdBfi4oNn6r1MikQjWWiKRcNvlyy+/3NFM7Xk8Kqs5Ra+8iIiIiPSa9evXU1FR4XQMERGRfuOaa66hsrLSVQvrxcXFsW7dOscLdr/73e948cUXCYejr42bXiNQQdNJeuVFREREpNfoi72IuIUx5mFjTIkxZoPTWUS68utf/5rMzEzy8/MxuGPqFr/fz4wZM0hISHA0xw9+8AMuvvhiQqEQAMFg0NE8h9P3HufolRcRERGR49Y61HzKlClkZWU5nEZEBIBHgfOdDiHSnezs7I8LY+6oZ+Lz+QDnh5y3CrasAt9a2HQLzR3uHBU0RUREROS4tfaY0ByaIuIW1trFgObAkH7BbT39Wlc3d0tBs7mloNnU1ORwkkO57e82kOiVFxEREZHj1tpjQj0VREREjl7ratluOTHY2kPTLat4txYym5ubHU4ibqGCpoiIiIgcN7fNaSUi0hPGmGuNMSuNMStLS0udjiMDmNtOCLb2zHRDLmttW0GzoaGB8vJyvvGNbzicSpymgqaIiIiIHLdAy1Awt/Qskf4rHA47HUEGEGvt/dbaudbaubm5uU7HkQGqvLy87Xc3FBDh4x6ablBcXEzERti4cSMV5RVkZ2fz3//9307HAtzz9xqIVNAUERERkWNWVFTE9773Paqrq6murnbdZP3SP9x337088sgjANx888289957DicSEYmNxsZGvvWtb7nuhKAbCpqXXnopDQ0NXHXVVQBMnDix7XtGXl6ek9HEBVTQFBEREZFjlpeXx/XXX09tbS1NTU1tPTVFjsbjj/+1raD5m9/8hlNOOcXhRHIiMMb8DVgGTDDG7DfGXO10JpHDJSYm8tRTT7X1TndLYdMNi93cf//9JCUlcc899wCQkZBGfX29w6nELZx/h4qIiIhIv2WMYeTIkaSkpDB48GAVNOWo1NXVOR1BTmDW2i9Ya/OttX5r7VBr7UNOZxLpTPsRDo888giRSMTBNO4orLZOA9FaxMxOTKexqVFTkwiggqaIiIiI9IJgMEhdXZ2GnMtRufTSS11x0Cwi4rTWxfWsta74XHTT3JCtBc3MhDQgujCQiAqaIiIiInLcAoEAxcXFKmjKUXn99dddddAsIuKEmpqatlW8Aa666ipXDPkGdxQ2WxdNSvElsXz5ctauXetsIBfYuXMnTz75ZNvlgwcPsn37dgcTxZ479hARERER6dfC4TBjx45t62Eicqz+53/+hyVLljgdQ0QkZu644w62bNkSveB858xDONlbtHVo+X//938TCoXISspgxowZ3HvvvQN+ipu0tDSGDx/edvmGG25oWzxpoFBBU0RERESOWzgcxlqryfrluH3lK1/hpJNOcjqGiEjM3HrrrRQUFADQ7LJCnVM9NCsrK7nkkksA+P73v4/P5yM1LpnExERuueUW4uLiHMnlFjk5OSxYsKDt8oEDBxxM4wwVNEVEjlM4HOb222/nr3/9KxdddJHTcUREHBEOh6mpqeGjDR85HUX6uezsbOLj452OISISU62LpAUCzQ4nOZRTPTQzMzN56aWXANqG46f4E4Ho8OrWlc9l4FJBU0TkOJWVlfGb3/yGu+66i+eff97pOCIijohEIqSnpzN12jSno0g/VVlZyQMPPOB0DBERRzzyyCOEw2Gam91V0HTDHJqtw8sT/QkAeDweJkyY4GQkcQEVNEVEjtFbb71FWVkZzc3NTJ06FZ/Ph9frdTqWnCCMMQ8bY0qMMRucziLSE61Dzm0kAsDbb7/tcCLpb9LS0rjsssucjiEi4ojPf/7zeL1eKioquPnmm52O08YNK663FjTjPH4A1q1bR35+vpORxAX6rKCpAzEROdGVl5fT1NSEtZbKykp2797tdCQ5sTwKnO90CJGeuP322/nwww8pLCzEWkskEuGNN95wOpb0M16vl6ysLKdjiIg4orVol5qayg9/+EOH03zM6R6alZWV/OlPf8JjPHhbVn73+/0Dfg5N6dsemo+iAzEROYF95jOfYejQofj9fjIzMxk5cqTTkeQEYq1dDFQ4nUOkJy644AImT55MQUEBFovH4+EXv/iF07GkA2+88QYffPCB0zFEROQwO3bsYO/evWzfvp1XX33V6ThtnO6heeutt3LdddfRWlatqakhIyODMWPGOJpLnNdnBU0diIlIXwmFQm2/R1qGNjrhhz/8IStXriQxMdGxDCLGmGuNMSuNMStLS0udjiMD1IwZM/B6vW29S8S9Bg8eTHZ2ttMxRETkMHl5eRQUFJCcnMyoUaOcjtNWyHSqh2ZNTQ233XYbX/rSl/B6vUSsJWItzc3NLFmyxJFM4i4+pwPIwNXc3ExdXZ2+VMtRq6+vp6qqioaGBurr60lNTXUkx+9//3sAHcCLo6y19wP3A8ydO9f5SY5kwLLWsn37dkaPVo8JN5umRZtERFwpHA7j9XoZMmSIRn4BycnJnH322fh8PlavXo3F0hwOkJGRwfjx452O18Za6/iw/IHK8UWB1LNk4Hruuec484wzePfdd7nooouorq52OpL0E1VVVaSmppKbm+uK943mbxERiX6hnzx5MvpKLyIicvSCwY9HoYXDYQeTuIPX62XevHmsW7eOXbt2AVAXaMDv93PWWWc5nE7cwPGCprX2fmvtXGvt3NzcXKfjSAxlZGSQmZXF22+/zbPPPkt6errTkaSfqKysxOv14vf7qaysdDqOiIjw8XQgOggTERE5eqFQkLq6Ovbt23fIFFtOae116OQcmsuXL2ffvn18+9vfBqCquRbAVVN+OT3H6EDmeEFTBKKrlIn0VHl5eYe/i5xIjDF/A5YBE4wx+40xVzudSaQrwUAw+q8LDsJERET6iz179nDLLbdQX99AUlISgwcPdkVB0w1OPfVUbrnlFpKSkgCoaqoB4B//+Ad//vOfnYzWxsk1HQa6Pito6kBMRPpK++kpSktL+e1vf8v//d//OZgoKhAIsHPnTqdjyAnCWvsFa22+tdZvrR1qrX3I6UwiXWkONAOaV1hERKSngsEgP/7xjznllFOor6/H6/ESFxdHMBh0OpqrtBU0m2tZu3Yt11xzDVdeeaXDqaJU0HROX65yrgMx6ZImzpVjdfDgQXw+H16vl4MHDxIXF8fUqVOdjkVjYyNr1qxxOoaIiCMaGhqIRCI0NTU5HUVERKRf8Pv9/OUvfyEQCLBt21b83ngAV/XQdMNxe0JCAhCdQ3PmzJkkJSXh8bhjwLEKms7RKuci0u8UFRWRmJhIIBBgz549/Pa3v3U6EgDp6elcdtllTscQEXHErl27OXiwmOHDhxOJRFxzoCEiIuJ2Z5xxBunp6fh98QTCTTQ3NzsdqY0b5ohsnaKuORw45LIbBIPBtoKrxJa+aYpIv7N//37i4+OprKzkgw8+YPny5U5HEhEZ8Orr69om6VcvTRERkZ7LzMwkIyODBF90aHVjY6PDidzJ4nxx9XBu6k070KigKY555ZVX9EEtRy0SibT10Bw3bhzhcJjPf/7zTscSERnQvvCFL5CVlcXQ/BEEAgEqKiqcjiQ95IaeNyIiA521lsbGRhLiUgAVNA/XOqdoQsuQfDfNMeqmLAONCprimLFjx7qqq7j0DyUlJQSDQUpLSyksLKSwsJAVK1Y4HUtEZED75je/SSQSIdGfTGlpKatWrXI6kvSQDppFZCD74x//yC9+8Qtuv/12xzJYa9m0aVNLO5oKQH19vRbZa6e+vh6AQGMzNTU1bZedsHfv3kOmPFMPTeeooCmOGT9+PD6fpnGVo7N3714AvF4vEyZMoKCgoO06ERFxxmuvvUZTUxPx/mQKCgoYP36805Gkh2pqapyO4EpvvPEGjz32mNMxRKSPPfPMM7z22mvcdNNNjmUoKyvjrrvuorCwkL0HdgHRdvXyyy+nsrLSsVyt3LAoUHV1NQC5SZkYY9ouO2Hw4MFccMEFbZfVQ9M5KmhKzNXV1VFWVtY2xMkNH5DSP0QiEa6++moACgoK2hac2LNnj5OxREQGrH/84x/87W9/4zvf+Q5JSUkk+JMIhUJcc801TkeTLrQfZq7pATp2yy238MwzzzgdQ0QGgNzcXG655Rby8/OZPm4eEJ2L+h//+AeZmZkOp3PH1CTl5eVUV1dz8ogZpKamOtp2xcfHM3Xq1LbL6qHpHBU0JeaWL1/Oc88953QM6Yc8Hg+f+9zn2LlzJ2+99RaLFi0iLi6OnTt3Oh1NRGRAOu+889i1a1fb0K8Efwo+n4+f/OQnvPPOO86Gc5m9e/dSVlbmdAzg0F6ZpaWlDiZxr7i4OKqqqpyOISIDwMGDB3n++ecxxvDR9lUEmgPMmzfPNaMZ3dABqbi4mLqaOmrqaikvL+fAgQNORwKgqKiIxYsXOx1jwHLHHtIL7r33XnJzc0lJScFay/nnn+90JOnEueeeC0S70YM7zvhI52pra/H5fG0r1zpt165dzJ49m0GDBpGamsrOnTt5/PHHmTNnDmeffbbT8UREBpTU1FQyMzPZunUrAMnx6UB0+JzmyT7Ur371K1auXMmSJUuIj493NEtRUVGHv4uIDASPPfYYw4YNc8Wxww033MC1117b1i7kZuRRUrjLVb3+3HC8vn//fuKMj10l+6ipqWH//v1ORwIgJyeH2bNnOx1jwDphCppf//rX8Xq9bR9KKmi6nxvO9Ej3nn76abKzs6mqqmL1qtX88c4/OpalsbGRnTt3kpeXx9ChQ2lsbKSqqorMzExOPfVUx3KJiMTSokWL+PDDD/nOd77DE088wWmnncawYcMcyzN+/HiWL18OQGbSIODjHvXysW3btpGYmEg4HHY6CosXL2b79u2MHTtWoxxEZMA5//zzXdNZ4z/+4z8YNmwYd999N83NzTTGNbB12xa2bdvmdDRXTRG3ZfNmCkuK+PS0c3iv4kNKSkqcjgSA3+8nISHB6RgD1gkz5Dw+Pt41XbLl2GgyXXe66qqruOSSS3jwwQd57vnnHMvR2NjI1VdfTVlZGa+88goQ7VWyd+9ewuEw69atcyybiEgszZs3jy9+8YuUlpZyzz33HLLSphPOOeccQqEQ9fUNZCQNIjE+hV27djmayc28Xq8j262trWXJkiVA9DvX2LFjSU9P5ze/+Q333HOPI5lERJwwePBg0tLSnI4BwPDhwzHG8G//9m+UlpYydNBopk+bwbhx45yO5opCJkTnqAyFQnxi1hm8s/V9tm3bxsoPVjodi507d1JTU+OKHqwD1QlT0JT+77Of/awmpncxY4yjDX9iYiI5OTnk5ua29cbcuXMnF154IV6vl0WLFjmWTUQkVl588UWeffZZsrOz267bu3evg4mi1q5dS3JcOl6Pj6zEfFauXEUkEnE6liu4oUcmRAuaW7Zs4cknn+Tdd98lPT2djIwMZs2axVe/+lWn44mIDGiDBw8mPj6elMQ0UpPTqKur4z//8z+djuUKu3btIhgKMTV/AnNGTMVjDR9t/MjxYfmjRo0iLS1N33ccpIKmOObwMxnPPfccWVlZDqWRnmhdZe/9999nxYoVMd12RUUFmzZtIicnh7i4OABmz55NTk4OgUCA559/XpP3i8gJb+HChXzyk59ky5YtfPTRR64Y3bBp0ybKy8sZmzedsqpihmVNoLy8jKVLlzodzXFLlizhxz/+MfDx955AIOBIlr1797Jr1y5SUlLYs2cPWVlZZGVlEQ6HWbZsmSOZREQk6uDBg+Tm5pIYl0KCP5mKigquvPJKp2MBzs6h+eyzz3LHHXcAMC1vPI8vf5ZbPvNdBg8ezOrVqx3LBR/3YG1ubnY0x0CmgqaIdGvDhg2HXE5KSor5vDP3338/gUCAkpKStkY1KyuLRYsWsX37dsLhMA888EBMM4mIxFp6ejpZWVmsXr2aF154gbq6OkfzBINB/vCHP9BU30ygMcL+0p0MzZpAWmIWd9/9v47nc9ppp53G7bff7nQMAGbOnMmll17Knj178Pv95OXlkZGRQWJiIk899ZR6mIj0Mu1TcjRWrlyJzxtHakI2mUl5rFu7jsmTJzuayQ0jDE4//XSWL19OXnw2t794N5896VOcVDCN7NRMnnrySVcM99YoU+eooCmOccucHNK9++6775AvZdOmTWPatGkx2/6//vUv7rvvPg4cOEBqaioHDx5su2306NFcddVVjBkzhhdffJE333wzZrlERJzyhS98ge9///tkZmY69mU+HA7z29/+lk2bNjF28BwKckaTnTYYj/EwZ8QnKC4q4tZbb1XPhRat33taRxnE2sGDB/nrX//K008/TUpKCtu3b8cYw8iRI9mwYQNPPfWUI7lETjStn8n67JOe2rdvH6+99hrDsybi9XgZmTOVhsYGnnjiCUdzOT2k21rLo48+ijGGS8afzWPX/A8jsofi9/o5f9RprPjgg7a1FWLpueee4/7772+7vGnTpphnkCgVNEWkW3/605/weJz5uFi0aBG/+tWvGDFiBOeffz6NjY2cfPLJbNmyhRUrVjB8+HDi4+MZPXo0GRkZ3Hbbbbz77ruOZBURiZXHHnusbTE0J04QNjU18bOf/YxXX32VKUNO4ZRJF5CSlMb2A9Ee/bmpw5g1/Fzef/99vv/971NbWxvzjHIoay3bt2/H5/MxbNgwMjMzeeSRR0hOTubAgQPceOONmo9apBe0dgIoLi52OIn0B2VlZfzwhzfhNX6mDImuE5CbOpRhWRN4+OGHHf1cbp3WxonvGdZaHn74YV544QUum3Y+s4dMOeT2c0bMZ0L2KH73u9/F/DU677zz2oq98fHxvPLKK1RXV8c0g0SpoCmOU09Nd/vVr37FD37wAw4cOADE7kydtZa//e1v/PSnPyUlJYXc3FwKCwtZs2YN4XCYffv2sX79ehobGwHweDxMnz6d5ORkbrnlFp50yRAEEZG+sHDhQsaMGePItnfu3Mm137iWRYsWMWPYmUwpiB6AZabmcNbsS9ruN2bQDOaN/hQfrvuQr33t66xfv96RvALvvfce3/zmN6mrqyM9PZ2tW7cydOhQpkyZwtNPP82XvvQl5s6dy09/+lP+/Oc/u2KYoUh/1H6ajXfeece5INIvrF+/nn+/9t85WHSQU8ZcQmJcStttc0eeT1ZyvqOfy63bjPX0CQ0NDfz85z/nscce49SCWVw6/pwj7uMxHr454wpGpA7hpz/9KY899ljMXqPly5fz5JNPMnjwYGbMmEFDQwM33XTTgJ9mxwkqaIpIl374wx/ym9/8hoKCgphts6qqiptuuol77rmH3NxcZs6cSVpaGqtWraKgoICSkhLGjRvHRRddxL333sv69eupra3F5/Mxa9YscnJyuPvuu/nRj36ks2UickIaNWpU20JtsRIKhfjzn//MN675BgeLSlk4/nIm5J3U5WNGZE/mrIlfpLE2wHe+8x3uvvtumpqaYpRYampq+OUvf8lNN90ERIfFlZaWcsYZZwBw8skn87WvfQ2fz8fMmTPJzc3lgQce4LrrrmP37t0OJhfpfxoaGvjJT38KxkMkPoXHH39co4akQ7W1tfzxj3/kO9/5Do11Qc6c8HlyU4cdch+/N44zxl/B0MwJPPDAA3z7299my5YtMc3Z2pEllsXUVatW8bWvfo233nqLS8adzZenXIzHdFy2SvIncuPcL3Ny3jQeeughrr/+evbu3dtn2Wpra7njjju49dZbSU9PZ+LEiaSkpDB58mQ2btzIV7/6VT744IM+274cyed0ABFxt8OHmvt8ffux8d5773H77bdTU1PDuHHjGDp0KMYYhg0bxnnnnUdTUxMJCQnU1dUxYsSItobL6/UC4PV6mTp1Kvv27WPZsmV89atf5aabbmL+/Pl9lrmhoaHPnlskltr3aq6qqiIjI8O5MNJjsRjpsHnzZn59+6/ZsXMHQzPHM3vEuST4k3v02OyUfM6b/BXW7XuHJ598ksWLFvODH/6AOXPm9G3oASwUCvH888/z0EMPUV9fz4gRIxg1ahTz5s074v3Svv2cMmUKOTk5bNu2ja9//etceumlfP3rXyc1NbVP87bv/WOt1egd6VfC4TDvvPMOf7rrLirKy2kevZBQ1kiSNr/Cj3/8Y84991z+/d//ncGDBzsdVRwWDAZ5/vnnefTRR6mpqWVM7gymDzsDv7fjuZV9Xj/zR1/EkIwxrNvxDtdeey2f/OQnufrqq2PyfoplQbOiooK7776b119/ncHJ2fznSV9nbObwbh8X543j69MuZXLOGJ7a8ipf+9rX+NKXvsSVV15JfHx8r2Srra3l+eef5/HHH6e+vp5hw4YxZsyYtuPkQYMGER8fz6ZNm/je977HnDlz+PrXv860adPUnvUxFTTFcRoWLBCdj+3uu+/m+eefJzU1lTlz5hxxADV06NAjHufz+Rg9evQh1xljGD58OJmZmWzatIkf/OAHXHrppXz729/utYatVUVFBb/73e/aLv/tb3/j8ssvx+/39+p2RPpabW0tDz74YNvlH/3ox9x8800MGzasi0cNLJWVlW2/u6HoEovtNzc389BDD/Hkk0+S4E/mlLGXMDRz/FE/j98bz9yRn2R41iRW7f0XN954IxdeeCHXX389yck9K4xK96y1LF++nLvuuot9+/aRlZXF5MmTSUlJ6f7BRN9TeXl5ZGVlsXPnTp555hleffVVrrrqKj7zmc/0yUnNhoYG7rvvvrbL99xzD1dddRUJCQm9vi2R3lRaWsorr7zC8y+8SFlpCTY5h6bJFxNJHQRAw6SL8Beu48233ubNN99kwYIFXHzxxZx88sl93kFA3CUSifDGG2/w4AMPUnywmEFpw5k3+TNkJndflDTGMCJ7MvnpY9hUtIzX//UGb77xJpf+26V86Utf6tOTz62FzL4saEYiEV544QXuu/c+mpqauHD0Qi4YfTpx3p4fSxljmD9kBpOzx/D0ltd49NFHee3V1/jef36Pk08++Ziz7du3j2eeeYaXX36Z5uZmsrOzmTx5cocn+dLT0znppJM4cOAA69ev5zvf+Q7jx4/niiuu4Mwzz3RsQcATXZ9+khpjzgf+CHiBB621t/fl9qT/sNayZ88eAPbv3084HG7rISADT0lJCT/4wQ/YuXMnw4cPZ/To0b2yCFFqaipz585lx44dPPvss3z44Yf89re/JScn57iet6ysjNWrV7NkyRKWLl1COBxiwSlBDhZ7ueeee3jiib9y9tnnsmDBAqZOnUpSUtJx/19kYOrrdrSsrIw1a9awbNky3lm0iFAwSDBvKuHkHD7atJQrr7ySGTNnctaZZzJ79mxGjBjheBHPCWVlZSxdupQ//+XPbdd9//vf59JLL2Xu3Lm9fqLELfbt28dPf/pf7NixndG505k+9AzifMdXZBqUNpzzJn+VjQeW8c9/vsqaNWv5+c9vZcKECb2UeuA6ePAgv//971m2bBnJyclMmzaNnJycY9pn4+LimDhxIgUFBezYsYM777yT5557jptuuompU6ced9ba2lrWrl3L0qVLeeedt2loaGT6jBDhEDzxxBO89NKLnHXW2ZxyyinMmDGjxwVZkcP1djtaUVHB4sWLefOtt/hw3TqstYTTCwiOPZtw1khoPzTW4yM4dA6h3PH4SjazbOVa3nvvPdIzMjjrzDM5++yzmTZtmo6BTnDr16/nj3/4I1u3bSUzeTCnj7+cvLSRR/3ZHOeLZ8awMxk7aDYfFS7l6aef5uWXX+aqq67i0ksv7ZMieWvv+b4qaBYXF3Pbf9/Gh+s/ZEL2KL44+1PkJR/7cVpafApXT7+MUwtm8bfNr/Cf//mfnH/++dxwww09Ph6LRCIsX76cp59+mlWrVuHxeBg0aBDDhg3rdrSC1+tl+PDhFBQUUFRUxIEDB7jtttv405/+xMUXX8yll1563MehcijTV73jjDFeYCtwHrAf+AD4grV2Y2ePmTt3rl25cuVxbXfhwoUALF68+LieR3pXfX09e/fuZefOnWzZsoUPVqzgQGFh2+05WVmcNG8ekydPZvTo0YwYMYK0tDQHE8vhWvetd955p1dXPC8rK+Paa6+lqqqKKVOmkJ2d3WvPffh2Nm7cSGZmJvfff3+PtxMIBNi+fTubNm1i06ZNbNjwIYWF0VUrk5MNY8YEmT4jTEaGxVrYu9fDRxu87N3rIxSyeL0exo4dy9Sp05g0aRKTJk1qG0bfHxhjVllr5zqdYyDq7XY0FAqxe/duNm7cyMaNG1n34Ycc2L8/ui1/PIGsMYRyJxBJju4bJtCAr3QrceXboDE6F21GZiYzpk9n8uTJTJ48mQkTJpxwvahqa2vZtWsX27ZtY8uWLWzctJG9e6LzMZkMQ2hGCFNl8G7yYgMWf5yf8ePHM2niJCZMmMDYsWMZNmxYTM7El5SUcPnll5OVlcVzzz3Xq8+9a9currvuOoLNYU4aeT5DMsb26vMDlNXuZ/mulwhGmvn97+9gxowZvb4NNzn33HMJBAK8/vrrvV4EX7p0KbfeeivBYJBRo0YxdOjQXmurrbWUl5ezbds2mpqa+OpXv8rXv/71Hrdj4XCY3bt3t2tH17N79x6stcTHG0aODDF9RojBg6PHJEVFhg8/9LFnt49AINoTevToUUydOo3JkyczadIkhg8f3qvfRTrSW8cUaked01vtaCgUYunSpbzwwgusXLkyOrosKYNA5ihCOeOwCT08ZolE8FbtxVe+A3/1Pmw4RGZmFhdeeAGf/vSnGTJkyLH+V+U49FX9IBQK8cADD/DEE0+QGJfCtIKFDM+a1GvHANWNZazb9zbF1buZNHES//Wz/+r199Dvf/97nnvuOc4++2x+9rOf9epzr1+/npt++EOCzUE+N+F8FgyZ2avHR8FIiJd3LOLVXUsYPmwYd/z+9wwaNKjT+1trefvtt3nkkUfYs2cPCQkJDBkyhCFDhhzzdzprLRUVFRw4cIDy8nK8Xi/nn38+V199da8d8zY1NbFz507WrVvHPffcA8AXvvAF5s2bx7hx4/p8yphY6Kod7cuC5gLgZ9baT7ZcvhnAWvurzh5zNAVNay2BQIDGxkbq6uqoqamhrKyMW265BYDbbruNnJwc0tLSSElJITExkbi4uH5TROiPmpqa2LNnD3v37uXAgQPRn/37ObB/P5XtFmaJN4Zh1jIVmA5sAdYDuz0eGtrNoZSanEzBsGEUFBQwZMgQhg4dyvDhwxk5cqSGp/WicDjctg9VV1e3/VRUVFBeXs7BgwfZvWsX+1tWOc9Iz2DsuLHk5+eTk5NDVlYWGRkZpKenk56eTmpqKunp6T0ecv3DH/6QFStWMHv27D7/wK2pqWHNmjXMnz+fX/2q448iay0bNmxg2bJlrFmzmi1btrbNH5OcbBg8OER+foSCoRFycy2dfaQEg1BY6KHwgIfiYg8lJV6CwejnbUpKMtOmTWfOnDmcdtpprv4CqwMx5/RGO1pUVMS7777L6tWrWbNmLY2N0flejT+BYHIu4bR8ImlDiCRlHdqrpD1rMc21eGuK8NYU4m8oxTbWAODxepk8eTJz58xh/vz5TJ48uXf+8zFSUVHBxo0b2bx5M1u2bGHb9m1UlFe03e5J8BDODGOzLXaIhTSgdZ+PACVgig2eSg+mymBD0X3c4/VQUFDAhPETmDAh+jNp0qSjKmKFQqFDPptb/62oqKCsrIyioiK2bNpERVUVAFOnTmX48OHk5ua2fS6npaWRnp5OWloaaWlpPS4+NzQ0cOWVX6KhtomzJnyBlISMHuc+Wk3BBt7Z8jdCppnHHnu0X88x1/rdtKGhgbq6Ompra6mtraWiooKSkhIeeughAC6++GJGjRpFdnZ2W7uZmppKUlISSUlJR91ba9myZfzoRz8iOTmZKVOmkJiY2Bf/PUKhEFu3bqW4uJgvf/nLfOMb3+j0vjt37mTJkiWsXr2KjRs30tTUDEBCgmHQoDB5+WGGFkQYnGfp7L8bDkNxsWH/fi/FRdF2tLk5uo8lJSUyefJkZs+ew+mnn86IESOO+f9UX19PfX1929+svLycoqKitr/X9ddf37ZfpaSkkJyc3HZs0ZOiqtpR5/RGO/ryyy9z3/33U1VZiYlPoTl7LKHs0djETDr9EtgT4SDeqn34yrfjq9oH1nLSySdz8003qQdXL7HWtu3XNTU11NTUUFVVRVVVFeXl5RQXF7Nt69a2Y5ypU6YwfMQIBg0adMjxTVpaWtvxTU/bcWstv/rVr3j11VcZnTudGcPO6nSezOP9P+6r2MLqva+TlpHCvffe22XR7mjdcccdPP/885x11lnceuutvfa8xcXFfO1rXyPVJHL97CvJTcrqtec+3Obyndy77inyCvJ54MEHOixO1tfXc9ttt7F06VJSUlIYPnw4gwYN6tUTZw0NDezbt4+ioiKSkpK46aab2orpnbHWUl1dTWFhISUlJZSXl1NaWkppaSnFxcUUFR2gvLyybQq/rCyLMZbKSg+tZZX09FTy8oaQn5/PoEGDyMnJITc3l9zcXPLy8sjOznZ9L3GnCpqXA+dba69pufxlYJ619judPaa1AbHW8qc//Ym///3v3W6nu5XjjDGcdtppPcp82WWX8d3vfveIouedd97J9u3be/Qcf/rTn7q8feTIkXz605/u0XONHTuW7373u0dc35t5hg4dyqWXXnpceQoLC/n85z/f5WO9QDbwTDd/r8unTsWbmUkZEOgmz7333tvhwXNPX59gMMi9997b5X2uv/76bp8HOn9t3Oipp57irrvu6vZ+H330ERUVFV3e5/TTT+/RNmfNmsUdd9xxxFCIQCDAueeeS0pKSrdf3v7yl790Od9qfHw8V1xxRbdZysrKqK+v58033zwiT3NzMxdffDGNjY2HXJ+ZGSEvL0Jyyse1jN//flmX2xkxIo3LLpvSdjliobLCUHzQQ0P9oZ8xn/vc57juuuuOeI7e3NenTJnC2Wef3aPnav9+1oGYc46nHQV47LHH2g7IW0XiU4mkDCISn9p2ILbimfuwweYus8z7/KGfbybYiKeuFG9D+SHXjxw5kocffviIfas338vp6el85Stf6dFzdfbZHAqFuOaaa9i5c+cRt1mPhXSw6RYSYen9S7vcRnJuMjMvnQkWTK2BajB1HR/kfve73+Xyyy8/4vra2lq+9rWvUVpa2u3/qbvvPfn5+Ywd27PelP/zP//T4cI8b775JrfeeitDMsaSkZTb5XPc9+Rvurz9K5+5nsT4rgts9c3V7CnfyLe//e1uv0+4wbJly/jhD3/Y7f3279/Prl27urxPT9vRUaNG8b//+78dntC9+uqr2bZtG0OHDu1yyOFzzz1HbW1tp7d7PB6uvPLKbrO0rn7+6quvHjGEz1rLt771LTZuPLQDXGpahLzBlvT0j08E/vGPy+hqFKMxcOONC9o9N1RVGQ4e9FBbe+g+dvLJJx8yn3WrQCDAzTff3KNVZ7vbt5KSknq8kNUPfvADLrroIkDtqJOOtx198cUX+e1vfwtAOCmbcMbQDk/+7f/oAw6s7/p74eHt6CE5A/X4S7e2XX755ZePOMl/NO3oo48+2uW+Dsd/jHM0ee655562zgEdSUxM5JprrjmuPIFAgO9973usW7eu2+fobl9PSEjgpJNO6lGe9vt6exs3buSb3/wmAJOHLDji9va6a0c/dcZnGZo3qsv7VDWUUFi1g0svvZQbb7yxm9Tdu/baa7nzzju56667uO+++zj11FN59NFHj/t5W91111089dRTzMufTk5iRqf321G0hwf+9Zcun+v2r/642+2tK9nC/rqD/OxnP+vweKg1j8fjYdiwYZ12glu5ciWbNm3qcltf/vKXu7y9oaGBkpISINouZ2UdWcz9y1/+wv3339/l8wC8//77BAJdV016+j1jwYIF/PKXvzyiuNmb3+Hh2D57umpH+3IOzY7eBUdUI4wx1wLXAgwf/vEqVj0pZgKMHj26wwOS9rf31DPPPHPcRam8vDyKi4s7vf2UU045ruc/WikpKdTV1XV6+1lnnXXc2zj8S2ur9h8EEaAU8Pv9BIPBTp+rpN0O3f4N1FEha9WqVcfVG8jv95OWlkZNTc0xP0d/9Oabb/bofqNGjeqyoJmfn9/jba5Zs4aqqqoOi5Y5OTmUl5dTX1/f5XMMHTqUffv2dXr7iBEj2uZl7U5OTk6H76nKyspDipmt7+GqKi9VVYd+uA8ZMoTCdtMmHLmNcaxc2XEv1dZdozXDRx9t6FHurgwaNKitcezIvHnzjnsbEnPH1Y4uW7as/X0A8Abq8FYc2iaMGzWCrVu30pkhQ4YQX7i2k4TR5219L+/evbvTfb2nZs+ezerVqzu9/cwzzzzm525VVlZ2xHeH1tfIWANVRH96YFzBOLybDzuz3e4v1/6zZtGiRR0WNHfv3t2jYiZE5wbu6kC1o8XTOvP22293WKCx1uLxeCiq3kFR9Y4unyM5OfmIz29jzMfviYruDy5bH9NfvPPOOz263+DBg7ssaB7NQg67du3iwIEDjB9/5IJMrb1NDrT0MurM6NGjuzzYHzlyZI/b0dTUVJqamo4oaAYCAfa3TGUBH/9d62q9bD/sbTtq1NguD5JGjBjZ43Z0166dhEKhIwq6lZWVPSpmQvR7TVFRUae39/REAcA//vGPDoscEnPH1Y6uWrWq7XdfYwW+xo6/Fw9LtnS193XZjn4cou39vH//fiZNmtT1/btw5pln8uKLL3Z6+7H2aD5Wp556KosWLer09jPOOOO4t1FRUdGjYiZ0344ezZzOr732Wof7evsi06ai5V0+R2Ji4hGdKVp5vV6qw0XUFh3scabe8P3vf5+EhARGjhxJdnb2cS2s05HWkQQritd3eb/uOt+lpqbyyq6uC9Ttn6ezEYSt9QBrLXv37u30ebqbEs/n8/W4HQWoqqrqsKD50ssvdfm41rZ13LhxfPTRR53eLz09/ZDvV129nsuWLaO2tva4FpkaN24c27Zt6/T23n4fQT8eci4iIn1PPUuco3ZURKT/UzvqHLWjIiL9X1ftaF/Opv0BMM4YM8oYEwd8HnihD7cnIiJyIlE7KiIicuzUjoqInMD6bMi5tTZkjPkO8BrRKRQfttZ23h9WRERE2qgdFREROXZqR0VETmx9OYcm1tpXgFf6chsiIiInKrWjIiIix07tqIjIiasvh5yLiIiIiIiIiIiI9CoVNEVERERERERERKTfUEFTRERERERERERE+g0VNEVERERERERERKTfMNZapzO0McaUAnt64alygLJeeJ7e4KYsoDzdUZ6uuSmPm7LAiZtnhLU2txeeR2JA7WhMKE/XlKdrytM5N2UBtaMD0gnajoK78rgpCyhPd5Sna8rTuT5vR11V0OwtxpiV1tq5TucAd2UB5emO8nTNTXnclAWUR04sbnr/uCkLKE93lKdrytM5N2UB9+WR/sVt7x835XFTFlCe7ihP15Snc7HIoiHnIiIiIiIiIiIi0m+ooCkiIiIiIiIiIiL9xola0Lzf6QDtuCkLKE93lKdrbsrjpiygPHJicdP7x01ZQHm6ozxdU57OuSkLuC+P9C9ue/+4KY+bsoDydEd5uqY8nevzLCfkHJoiIiIiIiIiIiJyYjpRe2iKiIiIiIiIiIjICUgFTREREREREREREek3VNDsZcaYZGOMp+X38caYi40xfqdzARhjMo0x0x3O4NrXx22MMR5jTJqD2/caY250avtdccN7uT235RHpz9zcTrhhX3fz6+M2ake75ob3cys3ZRE5Ebi1rXDDvu7W18aN1I52zQ3v5/bclicWTuiCpjEmxYHNLgYSjDEFwJvA14FHHcgBgDHmHWNMmjEmC1gHPGKM+b1TeXDZ63M4F3xo/7Xl75UMbAS2GGO+70QWa20YuMSJbXfEbe9lN+Uxxow1xpzawfWnG2PGOJFJTgxqR921r7dw1etzOLWjH3NbOwruej+7LIvaUekTDrWj4KK2wk37egvXvDYdUTv6MbWj/SePU+3oCV3QJLoDxpqx1jYA/wb8yVp7KTDZgRyt0q21NS15HrHWzgHOdTCP214fV31oA5Nb/l6fAV4BhgNfdigLwFJjzF0tH0SzW38cyuK297Kb8vwBqO3g+saW20SOldpRd+3r4L7XR+1o19zUjoK73s9uyvIH1I5K33CiHQV3tRVu2tfBXa9NNJDa0a6oHe0fef6AA+2or6+eOFaMMf/R2U2AE2fEjDFmAXAlcHXLdU6+zj5jTD5wBfBjB3O0ctvrAy0f2saYK4l+aP8QWAX81oEs/pYhD58B7rLWBo0x1oEcrU5p+ffn7a6zwNkOZHHbe9lNeUZaaz88/Epr7UpjzEgH8kg/ona0W27a18F9rw+oHe2Km9pRcNf72U1Z1I7KMXNhOwruaivctK+Du16bVmpHO6d2tGtuyeNIO+r0jtsbfkl0Rw91cJsTPVBvAG4GnrXWfmSMGQ287UCOVj8HXgOWWGs/aMmzzcE8N+Cu1wfc9aF9H7CbaHfxxcaYEUCNQ1mw1p7l1LY74Lb3spvyJHRxW2LMUkh/pXa0a27a18F9rw+oHe2Uy9pRcNf72U1Z1I7K8XBbOwruaivctK+Du16bVmpHO6F2tN/kcaQdNdY6WWw/fsaY94DrrbWrOrhtn7V2mAOxpB8xxnyX6FmwdcCniHar/4u19nRHg7UwxvistR19QYrFttOB/wIWtly1CPi5tbbaiTzSMWPM34C3rLUPHHb91cAnrLWfcyaZ9AdqR+V4qR3tcttqR/sBtaNyPNSOyvFSO9rlttWO9gNOtaMnQkFzAlBurS3r4LbB1tqDMcrxItGuzx2y1l4cixytjDF/ous8341hHNe9Pt2J9Yd2F0NVALDWOjVJ/jPABuCxlqu+DMyw1v5bDDO47b3sqjwQ/awDngUCRIenAMwF4oBLrbXFsc4k/Yfa0Y65bV932+vTHbWjUW5oR1tyuOb97KYsrdSOyvFwSzvasj3XtBVu29fd9Nr0hNrRKLWjHXNhHkfa0RNhyPkeIPXwK40xg4BYVu1/F8Nt9cRKpwMcxm2vT5uWne+XwBBr7QXGmMnAAuChGMY44j3sEmOstZe1u3yrMWZtjDO47b3stjy0fFE+xRhzFjC15eqXrbVvORhL+g+1ox1z277uttenjdrRLrmhHQV3vZ/dlAVQOyrHzS3tKLirrXDbvu6m1+YQake7pHa0Y67K41Q7eiL00LwfeNVa+4/Drr8SOM1a+y1nkkl/YYz5J/AI8GNr7QxjjA9YY62d5nA0xxljlgHft9Yuabl8KvA7a+0CZ5NJR4wx04CJLRc3WWs3OJlH+ge1o3K81I52Tu1o/6J2VI6F2lE5XmpHO6d2tH+JdTt6IhQ0N1prJ3dy20fW2ikxyrGejrv8GsBaa6fHIke7PK7qUu+21+eQAMZ8YK09yRizxlo7q+W6tdbamTHMcGdXtzsx/ArAGDMD+D8gveWqSuCrtoMVzPowg9vey67KA21zyzwPDAM+JLpfTQP2ApdYax2byFvcT+1op3lcta+77fU5JIDa0U65oR1tyeGa97ObsrRSOyrHwy3taMv2XNNWuG1fd9Nrc0QAtaOdUjvaMRfmcaQdPRGGnJsubovlqnIXxXBbPeG2LvVue33aqzfGZNPygWCMmU/sh4ccMYm4S9S0nCVMA7DW1hhjRsU4g9vey27LA/DfRIcdnG2tjQAYYzzA7cAvgOsdzCbup3a0Y27b1932+rSndrRzbmhHwV3vZzdlaaV2VI6HW9pRcFdb4bZ93U2vzeHUjnZO7WjH3JbHkXb0ROihuYhoF+QVh11/EnCHtXZhx4/s00wjgHHW2jeMMYmAz1pbG+sc7fIkAsOttVucytCeC1+f2cCfiM71sAHIBS6P9VmfwzIlW2vrndp+uxyrrbWzD7tulbV2jkN53PZedkUeY8xGYPrhE4e3DFdZb62d5Ewy6Q/UjvYojyv29VYufH3Ujnaew1XtaMv2XfN+dksWtaNyPNzYjrZs3zVthVv29VZuem1a8qgd7TyH2tFuuCGPU+1orM8Y9YXvA08ZY35mjPl0y8+twFMtt8WUMeYbwN+B+1quGgo8F+sc7fJ8GlgLvNpyeaYx5gUH87jq9QGw1q4GzgBOAf4dmOJU42GMWdDyYbCp5fIMY8z/OpBjojHmMiDdGPNv7X6+BiTEOk9LJre9l92UJ3B44wHQcl2zA3mkf1E72nUeN+3rrnt9QO1oJzlc14625HLN+9lNWVA7KsfHVe0ouKutcNm+7qrXppXa0Q5zqB3tX3kcaUf7/ZBza+0KY8w84NvA11qu/giYZ60tcSDSdcDJwPst+baZ6Ap3TvlZS553WvKsNcaMdDCP214fjDH/dthV440x1UTPJMT6PfQH4JPACwDW2nXGGCfO6k4gOiwjA/h0u+trgW84kAfc9152U54EY8wsjhzyZIB4B/JIP6J2tFs/wz37Orjv9VE72jE3tqPgrvezm7KoHZVj5sJ2FNzVVvwM9+zr4K7XBlA72gm1o/0rjyPtaL8vaELbEvH/ZYzJbblc6mCcZmttwJjo37Gli62T4/pD1trq1jwu4LbXB+BqYAHwdsvlM4HlRBuSn1tr/xzLMNbafYf9vcKx3H5LhueB540xC6y1y2K9/U647b3spjxFwO9bfm+/PxmgOPZxpL9RO9olN+3r4L7XB9SOdpTBje0ouOv97KYsakfluLisHQV3tRVu2tfBXa9NK7WjR2ZQO9ozbsnjSDva7wuaJvqX+y+iZ1o8LVeFgT9Za3/uQKRFxpgfAYnGmPOInql70YEcrTYYY74IeI0x44DvAu85mMdtrw9ABJjU8kUEY8xg4B5gHrAYiGUDss8YcwpgjTFxRP9em2K4fQCMMT+w1v4G+KIx5guH326dWeXObe9l1+Sx1p4FbfOnfBs4jWhD8i7R97JIp9SOdss1+3oLt70+oHb0CC5tR8Fd72fXZFE7KsfDhe0ouKutcM2+3sJNr00rtaOHUTvav/I41Y6eCHNo3gCcCpxsrc221mYR3fFPNcbc6ECem4BSYD3R+S9eAW5xIEer64EpROct+CvR1dJucDCP214fgJGtjUeLEmC8tbYCCMY4yzeJfhkqAPYDM1sux1pro7WS6Ip3h/84wW3vZbflAXgMmATcSXRi8UnA/zmaSPqDG1A72hW37etue31A7WhH3NiOgrvez27K0krtqByLG3BXOwruaivctq+76bVppXb0SGpH+2eemLajJ8Iq52uA86y1ZYddnwv8y1o7K8Z5koEma2245bIXiLfWNsQyh1u58fUx0UmOhwNPt1x1GdEP7+8DL7WebRBxO2PMOmvtjO6uE2lP7Wj/4sbXR+2onCjUjsqxcFs72rJt17UVbuHG10btqJwoYt2Ongg9NP2HNx7QNm+J34E8bwKJ7S4nAm84kAMAY8zrxpiMdpczjTGvOZUHl70+La4DHiV69mkW0TMI11lr62PdeBhjHuvg7/VwLDMclme8MeZ+Y8y/jDFvtf44lMVV72W35Wmxxhgzv/WCiU5Qv9TBPNI/qB3tggv3dVe9Pi3UjnaexzXtaEse17yf3ZSlHbWjcizc1o6Ci9oKF+7rrnlt2lE72nketaP9KA8xbkf7/RyaQOAYb+srCdbautYL1to6Y0ySAzla5Vhrq9rlqTTOruLmttcHG+2m/PeWH6dN7+DvFfOzuu08DdwLPIgDk0Efxm3vZbflgejwpq8YY/a2XB4ObDLGrCf6Vp/uXDRxMbWjXXPbvu6210ftaNfc1I6Cu97PbsrSSu2oHAu3taPgrrbCbfu6m16b1gxqRzundrR/5YlpO3oiFDRnGGNqOrjeAAmxDgPUG2NmW2tXAxhj5gCNDuRoFTHGDLfW7m3JMwJnV3Fz2+tDyxmE1vkd4gAvUG+tTXMgjscYk2mtrWzJloWz+2nIWuuWyfDd9l52Wx6A8x3evvRPake75rZ93W2vj9rRrrmpHQV3vZ/dlKWV2lE5Fm5rR8FdbYXb9nU3vTa0ZFA72jm1o/0rT0zb0X5f0LTWep3OcJgbgKeNMYUtl/OBzzkXhx8DS4wxi1ouLwSudTDPDbjr9QG4C/g80bM/c4GvAGMdynIH8J4x5u9EP4iuAH7hUBaAF40x3waeJTrRMAAtE1THmtvey27Lg7V2j5Pbl/5J7Wi33Lav34C7Xh9QO9oVN7Wj4K73s5uyAGpH5di4sB0Fd7UVbtvXb8A9r00rtaOdUzvaj/LEuh3t94sCuZExxg9MIHpWbrO1NtYrkx2eJweY35JnWUdzvMQ4j9ten5XW2rnm/7d351Gy1vWdx9/fCyKKgHvUiYAokiCLoExEGbeIxriMoo4SowZjjNvBSDJRR9xwJokaEh0TYjTKoFFcokE0GjVKxCVI2ARFcUVNomJAEFGR5TN/PNXS996up+o23fV7quv9OqfP7XpKTn1P3+p6X5/l91Sdt3QKdFV9Jsm9G82zD/BAup/Px5Jc0GKO0SzfWGFzkuw582EY5Ht5UPNIG8UAOzGo3/UB/nzs6PhZBtVRGNb7eUizSBvNkFoxtN/1If1sRvPY0fGz2NE5m2eW3KGphVdVpwEPoluX47vAd4Dfine0lCRpIjsqSdLq2VFpdTbCXc6lG+pJdL8LzwGuBO4IPKbpRANRVTetqmOq6g2jx3tV1cNbzyVJGhQ7OoYdlSRNwY6OYUfVxzM0NTNVVcAvJvl261k0nap6J3AW8OQk+1bVTehOY79728naqartk1zTeg5Ji8eOzh87ujU7KqkVOzp/7OjKbGnHMzTXSFUd1Pc1gPluWlX3rKrbtJoh3d7zk1u9/jhVdZ+q+mhVfbmqvr701WiWV06zbYbunORVwNUASX5CtzZHM1V126rabemrwQhnNHhNacOzo5PZ0almsaNTaNxSOyqtkyG31I6OZ0d72dGV2VI2wF3OB+S4nudCt6juzFTVI4H/C1wKHAP8JfA9YI+qen6SE2c5zzKnV9XBSf610euv5E3A8+iO/FzbeJbDgOdvse2hK2yblZ+NjoIFoKruzLK7y83S6D19HHAH4GJgd+CLwN1mPcqMX09aFHZ0Ona0nx3tMZCW2lFp/QympXZ0m9jR8ezomFFm/HqD5CXnG1RVfQ54HLArcCqwf5KvV9Vt6e5Utl+juS6gu6PcRXTrgxTdwbL9W8wzmumzSX6l1euPZngm8CxgT+Bry57aGfh0kt9sNNdhdP8A2Qf4CHAfugWq/7nBLJ+j+0fYPyU5sKoeAByR5OkznuPfgD8b93ySsc9Jmh92dJtmsqPj5xpMR0fzNG+pHZUWgx3dppns6Pi57OjKc9hSPENzXVTVvnS/cDsubUvylhmPcV2SL4/m+UaSr4/muLiqWq618NCGr72ZZZddnFpVrwbey7KjPUnOnuE4bwc+BPwx8IJl269IcukM59hMko9W1dnAvehi/9wk/9lonKuTXFJVm6pqU5JTG13+sB1wMzwqJq0bO9rLjq7Mjk5nCC21o9IMDKCldnQCOzqZHR3LluIOzTVXVS8F7k8Xjw/SfWB+Cpj1/xHbVFW3oFsn9brR90tv9mZrpyb5ZlUdCuyV5ITRGio3azTOlpdk3HPZ9zO9JCPJ5cDlVXUM8N0kV1XV/YH9q+otSS6b1SzLLYvsd0Z/7lZVuwLfbLAI8WVVdTPgNOBtVXUx0OIfQ99JcmyD15UWgh3tZ0dXZkenNoSW2lFpnQ2kpXZ0Mjs6gR0dy5biJedrrqrOBw4AzklyQFX9AvA3SR4x4zkuAq5j5T32SbLnLOdZMorrPYG9k9y1qu4AvDvJfVrMMzRVdS7dz2cP4MPAKXQ/q19vNM/pwEHAeXTvpX1H398KeEaSj8xwlp2An47meCLd5StvS3LJrGYYzXFOkgNn+ZrSIrGj/exoPzs6cZ7mLbWj0vobQkvt6HyyoxPnad7R0Ry2FM/QXA8/SXJdVV1TVbvQLRQ78w/rJHvM+jWn9GjgQOBsgCT/UVU7txyoqm4FvBQ4lO5I2KeAY2f9oTRyXZJrqupw4DVJXldV5zSYY8lFwG8n+QJAVe0D/E/gFXSXRMwsIEmuHM2wC/D+Wb3uCn614WtLi8CO9rOj/exoj4G01I5K6695S+3o9Oxor4uwoyuxpTQ81XsDO7Oqbg68ke4uZWcDZzSdaFh+lu604KW7lO3UeB6AdwDfBx4DPHb0/TsbzXJ1VR0BPBn4wGjbjRrNAvBLS/EASHIBcODSGjizVFW/W1Xfozsidybd79eZs56j5Roy0oKwo/3saD872mMILbWj0kzY0vHsaD872mMIHQVbusRLztdRVe0B7JLkvNazDEVV/QGwF3AY3YLDTwXenuR1DWc6K8k9tth2ZpJ7jvtv1nGWfYBnAP+S5KSquhPw+CR/MutZRvO8E7iULrIAjwduDTwJ+FSSg2c4y1eAQxovAi1phuzo1uzoxFnsaP88tlRaMLZ0c3Z04ix2tH8eOzog7tBcI1X1S0m+tGzR2s1ktncoG5yqunGSq0bfHwY8mG7diQ8n+Wjj2f6U7qjKu0abHgvcLclL2001DFV1E+BZdJc/FN3lD8fTrRty0yQ/muEs/wgcnuTHs3pNSbNjR/vZ0fk0pI6O5rGl0gZmS8ezo/PJjqqPOzTXSFW9McnvVNWpKzydJDO7Q9mymTYB5yXZd9avvcIsZyc5qKremuRJredZrqquAHaiW7QauqUYrhx9nyS7zHCWveiOFO4D7Li0vdWi2aOZdgD2prss48IkVzea40DgBOCzwFVL25Mc1WieewGvA34Z2AHYDrhylu8XaSOxoxNnsaPTzWJH+2cZTEvtqLT2htZSOzodOzpxJjs6fp6Fbqk3BVojSX5n9OcDWs+yZLQQ9Oeqarck32o8zg5V9RTg3qMFhjeT5L0NZlp67aaLQG/hBLoFof8ceABwJCvfGXAmqur+wIl0izEXcMeqekqS0xqM89fAx4HzuT72Lf0F8ATg3XR3AnwycJemE0lzzI5OZEenY0f7DamldlRaY0NrqR2djh0dz45OtNAtdYfmGlnpQ3G5hh+Qtwe+UFVncP1RHpI8csZzPAN4InBz4BFbPBe6O5Q1U1WPBO47evjPST7Q979fRzdJ8rGqqiTfBF5WVZ+ki0oLxwEPTnIhQFXdFTgJuEfvf7U+rklydIPXHSvJV6tquyTXAidU1WdazyTNKzs6kR2djh3tN6iW2lFpbQ20pXZ0CnZ0LDs6wSK31B2aa2fLD8XlWn5AvrzR627p9kmeWVXnJHlD62GWq6o/AQ4G3jba9NyqOjTJCxqM89PRpRlfqarnAP8O3LbBHEtutBQPgCRfrqpWd7k7taqeDryfzU/vb3WHtx+PLn84t6peBXyH7lIRSatjR/vZ0enY0X5DaqkdldbeEFtqRyewo73saL+FbqlraGomlq1ZcnaSFRepbqWqzgPunuS60ePtgHOS7N9gloOBL9IdOXwFsCvwqiSnz3qW0Twn0J1K/9bRpicC2yc5ssEs31hhc1qt51JVuwPfo1ur5Hl0f1d/meRrLeaRtLHZ0alnsaP98wympXZU0izZ0alnsaP98wymo2BL3aG5xqrqF4A/Au6Q5KFVtQ9wSJI3NZpnEIvEVtVH6c4IvjvwyS2fb3DJwc+NAnL/paMqVXVLutP8Zx6QoamqGwPP5vq7yp0GHJ/RHQIXWVU9N8lrJ22TtG3s6Ng57OgcsqPj2VFp/QyppXZ0Mjs6nh3tt+gtdYfmGquqD9EtpPuiJAdU1fZ0R1f2azTPmWy9SOxeSf7XjOfYATiI7sjK07Z8PsknZjnPclX1BOCVwKl0H5L3BV6Y5B0znOGUvudbBLYGdFdCgKp6HPCPSa6oqmPo3k+vSHJOo3m2Oro7uoTlwBbzSBuFHR07hx3tn8GOTmFILbWj0voZUkvt6GR2dGV2dKp5FrqlrqG59m6d5F1V9UKAJNdU1bUtB8oAFolN8jPg9Kq6d5LvV9Uu3eZcMetZlht9SF4H3Itu3ZICnp/kuzMe5RDg23QLHH+WhneSW5Jh3ZUQ4MVJ3l1VhwIPAf4UeD3wK7McoqqOAH4DuNMW4d8ZuGSWs0gblB1deQY72s+OTqd5S+2oNBODaqkdHc+OjmdHx7OlHXdorr0rq+pWdIsuL51if3nDeYa2SOzuVfVxul+0qqrLgKcmOavFMKMPyeckeRfQe1Rqnd0OOAxY+mD6B+CkJF9oOBMM566EAEv/CHsY8FdJ3ldVL2swx2fofo9uTXfXvSVXAOc1mEfaaOxoPzu6Mjs6nSG01I5K629ILbWjPezoRHZ0ZbYULzlfc1V1EN0aIfsCnwduAzw2SZM3VXWLxF4M3IjrF4k9PslXG81zHvDsJJ8cPT50NE+z9UGq6sXAT4B3svmHZJM7lY3WCTkCeDVwbJLXtZhjNMv9Vtre4pKMqvoA3V32HgTcg+7v7IwkB8x6Fknrx45OnMeOTp7Hjo5hS6XFMKSW2tGpZrKj42exoxrLHZrrYLRGyd50p2lfmOTqxiMNRlV9Osl9Jm2b8UyDuFPZKBwPo4vHHnRH6N6c5N9nOccKc90O+K90R3j/tcHlD0tz3BT4NeD8JF+pqtsD+yX5yIznuILR0e4tn6J738x0gXNpI7Kj49nR3jns6ORZmrfUjkqzYUtXZkd757Cjk2dp3tHRHLYUd2iui6q6N90HwM8v6U/ylhnPcD4rv8EBaHUEqqr+HLgp3docAR4P/AB4z2ius1vM1VpVnUh3BPVDwDuSfL7xSABU1dOAlwAfp/twvB/dUbo3N5zptsCOS48HtJ6KpDViR8ezoyuzo9s8ly2VNrjWLbWj88WObvNcdnQA3KG5xqrqrcCdgXO5fn2FJDlqxnPs3vd8km/OapblqurUnqeT5IEzG2akqp4NvC3JZaPHtwCOSHL8DGe4jusvL1j+S9n0CEtVXQjcO8klo8e3Aj6TZO8GszySbn2QO9BdtrIb8KUkd5v1LMtmOpTuLo0nVNWtgZ2TrHSEVdKU7Gg/Ozp2Bjs63TyDaqkdldbHEFpqR6dnR8ezo1PNtLAtdYfmGquqLwL7xB/s3Kiqc5PcfYtt5yQ5sNFIg1FVHwMemu6ugIwW9P5gkgc1mOVzwAOBf0pyYFU9gC70T5/1LKN5XgrcE9g7yV2r6g7Au1teriJtBHZ0/tjR8YbU0dHrD6aldlRaP7Z0vtjR8ezoxHkWuqXe5XztfZ7uDmHfaT0IbLW2wg50izFf2fAIy0tW2p7k2FnPssymqqql4FfVdnQ/K3ULHn+2qt5H9z7678AZVXU0QJI/m+EsVye5pKo2VdWmJKdW1Stn+PpbejRwIHA2QJL/qKqdG84jbRR2tH8eOzpfhtRRGFZL7ai0fgbTUjs6FTs6nh3tt9AtdYfmGqmq99P9gu0MXFBVZwBXLT2f5JEt5kqy2Zu5qh5Ft6BuK1cu+35H4OHAFxvNsuTDwLuq6vV0f4fPAP6x7UiD8bXR15L3jf5s8SF5WVXdDDgNeFtVXQxc02COJT9Lkqpa+ofHTg1nkeaeHZ2aHZ0vQ+ooDKuldlRaY0NsqR2dih0dz472W+iWesn5Gqmq+/U9n+QTs5plkqo6Pcm9Ws8BP7+T2ilJHtJwhk3A7wK/SrdGyEeAv0lybe9/qJkafTj/lO7v6InArnRrzVzSaJ4/APYCDgP+GHgq8PYkr2sxjzTv7Ojq2FFtiyG11I5Ka29eWmpHt5rBjs6JIXV0NM9Ct9Qdmmusql6Z5PmTts1wnsOXPdxEt77C/ZIc0mKeLY0WPD4jyV6tZ5G2VVUdBjyYLmgfTvLRxiNJc8+Obhs7qnlmR6X1MaSW2lFpfS1yS92hucaq6uwkB22x7bwk+zea54RlD68BLgLemOTiRvOcz/VrqGwH3AY4NslftJhnNNNedEcz9qG77ACAJHu2mknXW7buTo02Lb1/mt5xT9L6sKMT57Gj2ma2VFosQ2qpHZ1qJjs6cHZ0mFxDc41U1TOBZwF7VtV5y57aGfh0m6kgyZGtXnu5qvrFJP9Gt0bJkmuA7wEPbTPVz50AvBT4c+ABwJFc/0GlxrZcd6e1LRY234oxk1bHjvazo7ohhtRSOyqtnyG21I5OxY4O3JA6CrZ0iWdorpGq2hW4Bd2RlRcse+qKJJe2mQqq6kTguUkuGz2+BXBckqfOeI4LgYckuWiL7UcCxyS58yzn2WKGs5Lco6rOT7LfaNsnk/y3VjO1VlWvo/8D8qgZzrIj3cLYdwHOA96cpOXCywBU1bHAd4G3cv0aKjsneVXTwaQ5ZUcnzmFH58iQOgrDbKkdldbeEFtqRyezo1uzo9NZ9JZuaj3ARpHk8iQXJTkCuDnwiNHXHZsOBvsvxQMgyQ+AAxvM8Tzgo6PT6QGoqhcCRwO9i1fPwE9HCzF/paqeU1WPBm7beKbWzgTOorvk4SDgK6OvuwOzXpz6RLq1ds4Hfh04bsavP85Dkhyf5IokP0zyV8BjWg8lzSs7OpEdnS9D6igMs6V2VFpjA22pHZ3Mjm7Njk5noVvqJedrrKqOAp4OvHe06W+r6g0N7zK1qapuMQoHVXVLGvy9J/lgVV0FfKiqHgU8DTgYuO/SbA39HnBT4CjgFcADgae0HKi1JCcCVNVvAQ9IcvXo8evp7ro3S/ssO1L5JuCMGb/+ONdW1ROBd9AdPTyCNnGVNhQ7ujI7Ol8G1lEYZkvtqLROBtZSOzrZ72FHN2NHp7bQLfWS8zU2WqvkkCRXjh7vBPxLw5sZPBl4IfB3o02PA/5Pkrc2mudQ4GTgM8D/SPLTFnNoOqNLMw5ZukRldInI6Un2nuEMmy1qvtIi5y1U1R7Aa4H7jDZ9Cvi9LS9jkbRt7OjEeezoHBlCR0evO7iW2lFp/QyppXZUN4Qd7bfoLXWH5hob3TXt4KUPxtFaC/+6tDe/0Uz70B3lKeBjSS5oMMPyu4LdGLia7shBs7uCVdUpfc8neeSsZhmq0ZoyLwNOHW26H/CypSNmM5rhWuDKpYfATYAf4x3lpA3Jjo6dwY7OoSF0dDSHLZUWyNBaakfHzmRHJ7Cj6uMOzTVWVUfTnR7+96NNjwL+X5LXzHiOXZL8cHRK/1ZaLQo9JFX1feDbwEnAZ9niTnJJPtFirqGpqtsBvzJ6+Nkk3205zxAN5QidtBHY0flhR6djRyezo9LaGkJL7ehkdnQ6dnQ6i9hSd2iug6o6CDiU7gPptCTnNJjhA0keXlXfYPO7gy0dQdhz1jMNTVVtBxxGt87E/sA/ACcl+ULTwQagqn4pyZdG7+WtJDl71jMNWVWdk6TF4ubShmRH54MdHc+Obhs7Kq291i21o5PZ0fHs6LZbxJa6Q3ONjDvytMQjUMNWVTemC8mrgWMb3nxiEEaLhj+9qk5d4ekkeeDMhxqwqvrfSY5pPYc0z+zofLOjm7Oj28aOSmvDls4vO7o5O7rtFrGl7tBcI8uOPBWwG/CD0fc3B76V5E4znqf3VGOPaHRG4XgYXTz2AE4B3pzk31vOJUmLxo7OJzsqScMxpJba0enYUWn13KG5xqrq9cApST44evxQ4EFJfn/Gc6x0JGOJRzSAqjoR2Bf4EPCOJJ9vPNLgVNWNgGcC9x1t+mfgr5Nc3WyogaiqewGvA34Z2AHYDrjSBaGlG8aOzg87OpkdHc+OSutnCC21o5PZ0cnsaL9Fb6k7NNdYVZ2V5B5bbDszyT1bzaSVVdV1XH+nspXWdVmID4E+VfU3wI2ApbvIPQm4NsnT2k01DFV1JvAE4N3APYEnA3dJ8qKmg0lzzo7ODzs6mR0dz45K68eWzgc7Opkd7bfoLd2+9QAb0H9W1THA39J9KP0mcMmsh6iqw/ueT/LeWc0yVEk2tZ5hDhyc5IBljz9eVZ9rNs3AJPlqVW2X5FrghKr6TOuZpA3Ajs4JOzoVO9rDjkrrpnlL7ehkdnQqdnSCRW6pOzTX3hHAS4G/Hz0+bbRt1h7R81yAhQ+IpnJtVd05ydcAqmpP4NrGMw3Fj6tqB+DcqnoV8B1gp8YzSRuBHdVGYkfHs6PS+hlCS+2o1oId7bfQLfWSc0ljVdWvAicAX6e79GF34MgkfWviLISq2h24mO4SiOcBuwLHJ/lq08EkSYNhR8ezo5KkSexov0VvqTs011hV3RX4A7o7lP38DNhWix5X1a50R+eWFtH9BHBskstbzKP5M7rz3t50AflSkqsajyRpA7Oj2mjsqKRZG1JL7ahuKDuqcdyhucZG6zm8HjiLZadCJzmr0TzvAT7P5ovoHpCkd00TLTbXvJmsqh4OvILuKOH2uHi3tCbsqDYCOzqZHZXWz5Baake1GnZ0OoveUndorrGV7ijXUlWdm+Tuk7ZJy1XVCT1PJ8lTZzbMQFXVV4HDgfPjB6m0ZuyoNgI7OpkdldbPkFpqR7UadnQ6i95Sbwq09t5fVc+iW4D556dCJ7m00Tw/qapDk3wKoKruA/yk0SyaE0mObD3DHPg28PlFDIe0zuyo5p4dnYodldbPkFpqR7XN7OjUFrqlnqG5xqrqGytsTpI9Zz4MUFUHAG+hWxwW4AfAU5Kc12IezZeq+gXgj4A7JHloVe0DHJLkTY1Ha66qDqY7vf8TbP4PxT9rNpS0AdhRbSR2dDw7Kq2fIbXUjuqGsKP9Fr2l7tDcoKpqtyTfWvZ4F4AkP2w3leZNVX2I7q5yL0pyQFVtD5yTZL/GozVXVR8BfgScD1y3tD3Jy5sNJWnN2FGtBTs6nh2VNjY7qrVgR/steks3tR5go6iqP1z2/eO2eO6PZj8RJy97/fck+aHx0CrcOsm7GH04JrmGZQuLL7hbJjk8yUuTvHzpq/VQ0ryyo9qg7Oh4dlRaYwNr6cnLXtuOarXsaL+Fbqk7NNfOE5Z9/8Itnvu1WQ4yUsu+b3KZnjaEK6vqVkAAqupewOVtRxqMf6qqB7ceQtpA7Kg2Ijs6nh2V1t6QWmpHtRbsaL+Fbqk3BVo7Neb7lR7PQsZ8L22L3wdOAe5cVZ8GbgM8tu1Ig/Fs4A+r6irgarrf8yTZpe1Y0tyyo9qI7Oh4dlRae0NqqR3VWrCj/Ra6pe7QXDt9H9gtPsAPqKof0r2hbzL6HhbsDa4bJslZVXU/YG+6986FSa5uPNYgJNm59QzSBmNHteHY0fHsqLQuhtRSO6obzI72W/SWelOgNVJV1wJXMvrABn689BSwY5IbtZpNWq2qOhN4M3BSkh+0nmdoqmp/YA+WHRxK8t5mA0lzzI5qI7Kj/eyotLZsqTYaOzrZIrfUHZqSxqqquwBHAo8HzqS7w9xH4gcHVfVmYH/gC1x/R7kkeWq7qSRJQ2JHx7OjkqRJ7Gi/RW+pOzQlTVRVm4CHA39F90H5ZuC1SS5tOlhDVXVBkn1azyFJGj47ujU7Kkmalh1d2aK31LucS+o1OoX9OODVwHvoFmH+IfDxlnMNwL9U1cLGQ5I0HTs6lh2VJE1kR3stdEu9KZCksarqLOAy4E3AC5JcNXrqs1V1n2aDDcOJdAH5LnAV1y9wvn/bsSRJQ2FHe9lRSVIvOzrRQrfUS84ljVVVeyb5eus5hqiqvgocDZzP9euVkOSbzYaSJA2KHR3PjkqSJrGj/Ra9pZ6hKanPpVV1FFvfNe2oZhMNx7eSnNJ6CEnSoNnR8eyoJGkSO9pvoVvqDk1JfT4InM4WR3wEwJeq6u3A++lO7wcgyXvbjSRJGhg7Op4dlSRNYkf7LXRL3aEpqc+OSY5uPcRA3YQuGg9eti3AQsRDkjQVOzqeHZUkTWJH+y10S11DU9JYVfU84EfAB9j8iM+lzYaSJGlO2FFJklbPjqqPZ2hK6vMz4NXAi+iO9DD6c89mEw1EVe0I/DZwN2DHpe1JntpsKEnS0NjRMeyoJGkKdrTHord0U+sBJA3a0cBdkuyR5E6jL+PReStwO+AhwCeAXwSuaDqRJGlo7Oh4dlSSNIkd7bfQLXWHpqQ+XwB+3HqIgbpLkhcDVyY5EXgYsF/jmSRJw2JHx7OjkqRJ7Gi/hW6pl5xL6nMtcG5Vncrma5Yc1W6kwbh69OdlVbUv8F1gj3bjSJIGyI6OZ0clSZPY0X4L3VJ3aErqc/LoS1t7Q1XdAngxcApwM+AlbUeSJA3MydjRceyoJGmSk7GjfRa6pd7lXNJUquqgJGe3nkOSpHlkRyVJWj07qi25Q1PSVKrq7CQHtZ5jKKrq6BU2Xw6cleTcGY8jSRo4O7o5OypJ2hZ2dGuL3lJvCiRpWtV6gIG5J/AM4L+Mvp4O3B94Y1X9YcO5JEnDZEc3Z0clSdvCjm5toVvqGZqSplJVj0pycus5hqKqPgw8JsmPRo9vBvwd8Gi6I2L7tJxPkjQsdnRzdlSStC3s6NYWvaXeFEjSVqpqpVP5v7W03bVLANgN+Nmyx1cDuyf5SVVdNea/kSQtADs6FTsqSVqRHZ3aQrfUHZqSVnJcz3MBHjirQQbs7cDpVfW+0eNHACdV1U7ABe3GkiQNgB2dzI5Kksaxo9NZ6JZ6ybkkrVJV3QM4lG49l08lObPxSJIkzQ07KknSDbPILXWHpqStVNUDk3y8qg5f6fkk7531TJIkzQs7KknS6tlRTcNLziWt5H7Ax+lOWd9SAAMiSdJ4dlSSpNWzo5rIMzQlSZIkSZIkzQ3P0JQ0VlXdGHgMsAfLPi+SHNtqJkmS5oUdlSRp9eyo+rhDU1Kf9wGXA2cBVzWeRZKkeWNHJUlaPTuqsbzkXNJYVfX5JPu2nkOSpHlkRyVJWj07qj6bWg8gadA+U1X7tR5CkqQ5ZUclSVo9O6qxPENT0laq6ny6u8dtD+wFfJ3uFP8CkmT/huNJkjRodlSSpNWzo5qGOzQlbaWqdu97Psk3ZzWLJEnzxo5KkrR6dlTTcIempF5VdRBwKN0Rsk8nObvxSJIkzQ07KknS6tlRjeMampLGqqqXACcCtwJuDZxQVce0nUqSpPlgRyVJWj07qj6eoSlprKr6InBgkp+OHt8EODvJL7edTJKk4bOjkiStnh1VH8/QlNTnImDHZY9vDHytzSiSJM2di7CjkiSt1kXYUY3hGZqSxqqqk4GDgY/SrVlyGPAp4GKAJEc1G06SpIGzo5IkrZ4dVR93aEoaq6qe0vd8khNnNYskSfPGjkqStHp2VH3coSmpV1XtANx19PDCJFe3nEeSpHliRyVJWj07qnHcoSlprKq6P91d5S4CCrgj8JQkp7WbSpKk+WBHJUlaPTuqPu7QlDRWVZ0F/EaSC0eP7wqclOQebSeTJGn47KgkSatnR9XHu5xL6nOjpXgAJPkycKOG80iSNE/sqCRJq2dHNdb2rQeQNGhnVdWbgLeOHj8ROKvhPJIkzRM7KknS6tlRjeUl55LGqqobA88GDqVbs+Q04PgkVzUdTJKkOWBHJUlaPTuqPu7QlLSiqtoEnJdk39azSJI0b+yoJEmrZ0c1iWtoSlpRkuuAz1XVbq1nkSRp3thRSZJWz45qEtfQlNTn9sAXquoM4MqljUke2W4kSZLmhh2VJGn17KjGcoempD4vbz2AJElzzI5KkrR6dlRjuUNTUp9fT/L85Ruq6pXAJxrNI0nSPLGjkiStnh3VWK6hKanPYStse+jMp5AkaT7ZUUmSVs+OaizP0JS0lap6JvAs4M5Vdd6yp3YGPtNmKkmS5oMdlSRp9eyoplFJWs8gaWCqalfgFsAfAy9Y9tQVSS5tM5UkSfPBjkqStHp2VNNwh6aksapqt5W2J/nWrGeRJGne2FFJklbPjqqPOzQljVVV5wMBCtgRuBNwYZK7NR1MkqQ5YEclSVo9O6o+rqEpaawk+y1/XFUHAb/baBxJkuaKHZUkafXsqPp4l3NJU0tyNnBw6zkkSZpHdlSSpNWzo1rOMzQljVVVRy97uAk4CPh+o3EkSZordlSSpNWzo+rjDk1JfXZe9v01wD8A72k0iyRJ88aOSpK0enZUY3lTIEkTVdVOSa5sPYckSfPIjkqStHp2VCtxDU1JY1XVIVV1AfDF0eMDqur4xmNJkjQX7KgkSatnR9XHHZqS+rwGeAhwCUCSzwH3bTmQJElz5DXYUUmSVus12FGN4Q5NSb2SfHuLTdc2GUSSpDlkRyVJWj07qnG8KZCkPt+uqnsDqaodgKMYne4vSZImsqOSJK2eHdVY3hRI0lhVdWvgtcCDgAI+Ajw3ySVNB5MkaQ7YUUmSVs+Oqo87NCVJkiRJkiTNDS85l7SVqnpJz9NJ8oqZDSNJ0pyxo5IkrZ4d1TQ8Q1PSVqrq91fYvBPw28CtktxsxiNJkjQ37KgkSatnRzUNd2hK6lVVOwPPpYvHu4DjklzcdipJkuaDHZUkafXsqMbxknNJK6qqWwJHA08ETgQOSvKDtlNJkjQf7KgkSatnRzWJOzQlbaWqXg0cDrwB2C/JjxqPJEnS3LCjkiStnh3VNLzkXNJWquo64CrgGmD5h0TRLcK8S5PBJEmaA3ZUkqTVs6Oahjs0JUmSJEmSJM2NTa0HkCRJkiRJkqRpuUNTkiRJkiRJ0txwh6YkSZIkSZKkueEOTUmSJEmSJElzwx2akiRJkiRJkuaGOzQlSZIkSZIkzY3/DxVjdB/kCKRoAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 1614.38x288 with 3 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Check expression of the three genes in the training set.\n",
-        "sc.pl.violin(adata_2000, top_3_genes, groupby = 'cell_type', rotation = 90)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "yellow-prompt",
-      "metadata": {
-        "id": "yellow-prompt",
-        "outputId": "53c87253-6e81-44fd-aee6-ee17b92c8166"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1614.38x288 with 3 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Check expression of the three genes in the query set.\n",
-        "# Here we use `majority_voting` from CellTypist as the cell type labels for this dataset.\n",
-        "sc.pl.violin(adata_400, top_3_genes, groupby = 'majority_voting', rotation = 90)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.8"
-    },
-    "colab": {
-      "provenance": []
-    },
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {
-        "117d53639d7e41f093c3c4f1d340db6a": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "HBoxModel",
-          "model_module_version": "1.5.0",
-          "state": {
-            "_dom_classes": [],
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "HBoxModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HBoxView",
-            "box_style": "",
-            "children": [
-              "IPY_MODEL_c1007a4a47c546768a71cbfc2536fb0a",
-              "IPY_MODEL_4e99394259c44f6782fd5be15fdd5890",
-              "IPY_MODEL_a4e849127a234f768c7c9cc95ab47ea0"
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-            "layout": "IPY_MODEL_97381406977d4f6fa50d519d891d5702"
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-            "_model_module": "@jupyter-widgets/controls",
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From 85929c93f1bb8cc187c1f49944d3f55d81748e24 Mon Sep 17 00:00:00 2001
From: djriffle <djriffle1@gmail.com>
Date: Wed, 23 Jul 2025 12:02:23 -0400
Subject: [PATCH 6/6] Applied load_bp_json to AutoTester

---
 benchmarking/prompt_testing/MultiAgentAutoTester.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/benchmarking/prompt_testing/MultiAgentAutoTester.py b/benchmarking/prompt_testing/MultiAgentAutoTester.py
index 5b92e3b..8cf557d 100644
--- a/benchmarking/prompt_testing/MultiAgentAutoTester.py
+++ b/benchmarking/prompt_testing/MultiAgentAutoTester.py
@@ -60,6 +60,7 @@
     collect_resources,
     get_initial_prompt,
     format_execute_response,
+    load_bp_json
 )
 from benchmarking.core.sandbox_management import (
     init_docker,
@@ -153,7 +154,7 @@ def _save_benchmark_record(*, run_id: str, results: dict, meta: dict, code: str
 # ===========================================================================
 def load_agent_system() -> Tuple[AgentSystem, Agent, str]:
     """Load the agent system from a JSON blueprint."""
-    bp = Path(Prompt.ask("Blueprint JSON", default="system_blueprint.json")).expanduser()
+    bp = load_bp_json(console)
     if not bp.exists():
         console.print(f"[red]Blueprint {bp} not found.")
         sys.exit(1)