site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb

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      "metadata": {
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      "source": [
        "##### Copyright 2024 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPixuBZFck9b"
      },
      "source": [
        "# Visualizing embeddings with t-SNE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M43FZggHDEr5"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/tutorials/clustering_with_embeddings\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMCPLbMYsljk"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This tutorial demonstrates how to visualize and perform clustering with the embeddings from the Gemini API. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and cluster that subset using the KMeans algorithm.\n",
        "\n",
        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python#use_embeddings).\n",
        "\n",
        "## Prerequisites\n",
        "\n",
        "You can run this quickstart in Google Colab.\n",
        "\n",
        "To complete this quickstart on your own development environment, ensure that your envirmonement meets the following requirements:\n",
        "\n",
        "-  Python 3.9+\n",
        "-  An installation of `jupyter` to run the notebook.\n",
        "\n",
        "## Setup\n",
        "\n",
        "First, download and install the Gemini API Python library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VYACbzJqseql"
      },
      "outputs": [],
      "source": [
        "!pip install -U -q google-generativeai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d7bEYfTFmvy9"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import google.generativeai as genai\n",
        "import google.ai.generativelanguage as glm\n",
        "\n",
        "from sklearn.datasets import fetch_20newsgroups\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qEunxaDOHzfi"
      },
      "source": [
        "### Grab an API Key\n",
        "\n",
        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
        "\n",
        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
        "\n",
        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`.\n",
        "\n",
        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
        "\n",
        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
        "* Pass the key to `genai.configure(api_key=...)`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7CItpYF3uOEf"
      },
      "outputs": [],
      "source": [
        "# Used to securely store your API key\n",
        "from google.colab import userdata\n",
        "\n",
        "# Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
        "API_KEY=userdata.get('API_KEY')\n",
        "\n",
        "genai.configure(api_key=API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJTxEH7RAOfq"
      },
      "source": [
        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
        "\n",
        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sLeRMa1bz9Ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "models/embedding-001\n",
            "models/embedding-001\n"
          ]
        }
      ],
      "source": [
        "for m in genai.list_models():\n",
        "  if 'embedContent' in m.supported_generation_methods:\n",
        "    print(m.name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnICLtwna2UU"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. For this tutorial, you will be using the training subset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7j4Y2198bdnm"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['alt.atheism',\n",
              " 'comp.graphics',\n",
              " 'comp.os.ms-windows.misc',\n",
              " 'comp.sys.ibm.pc.hardware',\n",
              " 'comp.sys.mac.hardware',\n",
              " 'comp.windows.x',\n",
              " 'misc.forsale',\n",
              " 'rec.autos',\n",
              " 'rec.motorcycles',\n",
              " 'rec.sport.baseball',\n",
              " 'rec.sport.hockey',\n",
              " 'sci.crypt',\n",
              " 'sci.electronics',\n",
              " 'sci.med',\n",
              " 'sci.space',\n",
              " 'soc.religion.christian',\n",
              " 'talk.politics.guns',\n",
              " 'talk.politics.mideast',\n",
              " 'talk.politics.misc',\n",
              " 'talk.religion.misc']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
        "\n",
        "# View list of class names for dataset\n",
        "newsgroups_train.target_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-XyGQsTcdSR"
      },
      "source": [
        "Here is the first example in the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KDELgM0xbpkt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Lines: 15\n",
            "\n",
            " I was wondering if anyone out there could enlighten me on this car I saw\n",
            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
            "the front bumper was separate from the rest of the body. This is \n",
            "all I know. If anyone can tellme a model name, engine specs, years\n",
            "of production, where this car is made, history, or whatever info you\n",
            "have on this funky looking car, please e-mail.\n",
            "\n",
            "Thanks,\n",
            "- IL\n",
            "   ---- brought to you by your neighborhood Lerxst ----\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "idx = newsgroups_train.data[0].index('Lines')\n",
        "print(newsgroups_train.data[0][idx:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G6ldbA4XfPpP"
      },
      "outputs": [],
      "source": [
        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
        "newsgroups_train.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroups_train.data] # Remove email\n",
        "newsgroups_train.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data] # Remove names\n",
        "newsgroups_train.data = [d.replace(\"From: \", \"\") for d in newsgroups_train.data] # Remove \"From: \"\n",
        "newsgroups_train.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data] # Remove \"\\nSubject: \""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "26qIj6fJccVI"
      },
      "outputs": [
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For the purposes of a\\n>contest, you'll probably not compete if'n you can't afford the ride to get\\n>there.  And although lower priced delivery systems might be doable, without\\n>demand its doubtful that anyone will develop a new system...\\n\\nYou're assuming that the low-cost delivery system has to be a separate\\nproject.  But why?  If you are spending hundreds of millions of dollars\\nin hopes of winning a billion-dollar prize, it is *cheaper* to develop\\nyour own launch system, charging its entire development cost against\\nyour contest entry, than to try to do it with existing launchers.  No\\nother demand is necessary.\\n\\n>> Any plan for doing\\n>> sustained lunar exploration using existing launch systems is wasting\\n>> money in a big way.\\n>\\n>This depends on the how soon the new launch system comes on line.  In other\\n>words, perhaps a great deal of worthwhile technology  could be developed prior to a low cost launch system. \\n>You wouldn't want to use the expensive stuff forever, but I'd hate to see\\n>folks waiting to do anything until a low cost Mac, oops, I mean launch\\n>system comes on line.\\n\\nYou're assuming that it's going to take a decade to build a new launch\\nsystem.  But why?  The Saturn V took less than six years, depending on\\nexactly when you date its start.  Pegasus took about three from project\\nstart to first flight.  Before SDIO chickened out on orbital development,\\nthe target date for an orbital DC-Y flight was 1996.  If you really want\\nspeed, consider that the first prototypes of the Thor missile  shipped to the USAF less\\nthan 18 months after the development go-ahead.\\n\\nOne of the most pernicious myths in this whole business is the belief\\nthat you can't build a launcher without taking ten years and spending\\nbillions of dollars.  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            "text/plain": [
              "                                                    Text  Label  \\\n",
              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
              "...                                                  ...    ...   \n",
              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
              "\n",
              "                     Class Name  \n",
              "0                     rec.autos  \n",
              "1         comp.sys.mac.hardware  \n",
              "2         comp.sys.mac.hardware  \n",
              "3                 comp.graphics  \n",
              "4                     sci.space  \n",
              "...                         ...  \n",
              "11309                   sci.med  \n",
              "11310     comp.sys.mac.hardware  \n",
              "11311  comp.sys.ibm.pc.hardware  \n",
              "11312             comp.graphics  \n",
              "11313           rec.motorcycles  \n",
              "\n",
              "[11141 rows x 3 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Put training points into a dataframe\n",
        "df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])\n",
        "df_train['Label'] = newsgroups_train.target\n",
        "# Match label to target name index\n",
        "df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)\n",
        "# Retain text samples that can be used in the gecko model.\n",
        "df_train = df_train[df_train['Text'].str.len() < 10000]\n",
        "\n",
        "df_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZnW2_Tx2_L1"
      },
      "source": [
        "Next, you will sample some of the data by taking 100 data points in the training dataset, and dropping a few of the categories to run through this tutorial. Choose the science categories to compare."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L5LWfJMY3Ii7"
      },
      "outputs": [
        {
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-34f02063-db4e-4027-88af-03b01812b890');\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",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "      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",
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              "    (() => {\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_4d13695b-527d-40c2-aa96-95745849cc3c\">\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_train');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     index                                               Text  Label  \\\n",
              "0     1650   Re: Off the shelf cheap DES keyseach machine ...     11   \n",
              "1     1651   \"Clipper\" an Infringement on Intergraph's Nam...     11   \n",
              "2     1652   Re: Once tapped, your code is no good any mor...     11   \n",
              "3     1653   new encryption\\nNntp-Posting-Host: rac3.wam.u...     11   \n",
              "4     1654   How can clipper stay classified?\\nArticle-I.D...     11   \n",
              "..     ...                                                ...    ...   \n",
              "595   2245  computer cult\\nNf-ID: #N:cdp:1469100033:000:24...     14   \n",
              "596   2246   Re: Inflatable Mile-Long Space Billboards \\nO...     14   \n",
              "597   2247   Moscow Aviation Institute summer school\\nOrga...     14   \n",
              "598   2248  Eco-Freaks forcing Space Mining.\\nArticle-I.D....     14   \n",
              "599   2249   Re: Comet in Temporary Orbit Around Jupiter?\\...     14   \n",
              "\n",
              "    Class Name  \n",
              "0    sci.crypt  \n",
              "1    sci.crypt  \n",
              "2    sci.crypt  \n",
              "3    sci.crypt  \n",
              "4    sci.crypt  \n",
              "..         ...  \n",
              "595  sci.space  \n",
              "596  sci.space  \n",
              "597  sci.space  \n",
              "598  sci.space  \n",
              "599  sci.space  \n",
              "\n",
              "[600 rows x 4 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Take a sample of each label category from df_train\n",
        "SAMPLE_SIZE = 150\n",
        "df_train = (df_train.groupby('Label', as_index = False)\n",
        "                    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
        "                    .reset_index(drop=True))\n",
        "\n",
        "# Choose categories about science\n",
        "df_train = df_train[df_train['Class Name'].str.contains('sci')]\n",
        "\n",
        "# Reset the index\n",
        "df_train = df_train.reset_index()\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FI1FDqirsz3O"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sci.crypt          150\n",
              "sci.electronics    150\n",
              "sci.med            150\n",
              "sci.space          150\n",
              "Name: Class Name, dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train['Class Name'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NbA2hpDH3nl"
      },
      "source": [
        "## Create the embeddings\n",
        "\n",
        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the Gemini API."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05laQxib2miY"
      },
      "source": [
        "### API changes to Embeddings with model embedding-001\n",
        "\n",
        "For the new embeddings model, embedding-001, there is a new task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
        "\n",
        "These new parameters apply only to the newest embeddings models.The task types are:\n",
        "\n",
        "Task Type | Description\n",
        "---       | ---\n",
        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g1NC0e6McsQx"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d0f5fb2d3f224f18a7fe2809de5f5434",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/600 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "tqdm.pandas()\n",
        "\n",
        "from google.api_core import retry\n",
        "\n",
        "def make_embed_text_fn(model):\n",
        "\n",
        "  @retry.Retry(timeout=300.0)\n",
        "  def embed_fn(text: str) -> list[float]:\n",
        "    # Set the task_type to CLUSTERING.\n",
        "    embedding = genai.embed_content(model=model,\n",
        "                                    content=text,\n",
        "                                    task_type=\"clustering\")\n",
        "    return embedding[\"embedding\"]\n",
        "\n",
        "  return embed_fn\n",
        "\n",
        "def create_embeddings(df):\n",
        "  model = 'models/embedding-001'\n",
        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))\n",
        "  return df\n",
        "\n",
        "df_train = create_embeddings(df_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-1QKCK8DHsI"
      },
      "source": [
        "## Dimensionality reduction\n",
        "\n",
        "The length of the document embedding vector is 768. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XODHZlFcFnn6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_train['Embeddings'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "73aAdKo1UCrL"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(600, 768)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
        "X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JB3bsmi4Iuak"
      },
      "source": [
        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OpyoE-RVSzfe"
      },
      "outputs": [],
      "source": [
        "tsne = TSNE(random_state=0, n_iter=1000)\n",
        "tsne_results = tsne.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BbsWqQlxJHas"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          36.85309982299805,\n          -25.488718032836914,\n          2.9596657752990723\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.7554828524589539,\n          5.936662197113037,\n          -19.277177810668945\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_tsne"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-7122a71e-c2a4-4627-ac78-6571b919d0b5\" 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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.613194</td>\n",
              "      <td>-2.590790</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.533733</td>\n",
              "      <td>8.535353</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32.775826</td>\n",
              "      <td>11.671514</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44.522926</td>\n",
              "      <td>-2.058890</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40.518196</td>\n",
              "      <td>-2.139972</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>20.744043</td>\n",
              "      <td>-7.745994</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>-0.322983</td>\n",
              "      <td>-28.657366</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>-8.563044</td>\n",
              "      <td>-6.283251</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>-14.029724</td>\n",
              "      <td>-29.518869</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>3.009676</td>\n",
              "      <td>-16.334478</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 3 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-7122a71e-c2a4-4627-ac78-6571b919d0b5')\"\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-7122a71e-c2a4-4627-ac78-6571b919d0b5 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-7122a71e-c2a4-4627-ac78-6571b919d0b5');\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-9c7f2560-d959-4fc6-880b-5ea835ddee36\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c7f2560-d959-4fc6-880b-5ea835ddee36')\"\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-9c7f2560-d959-4fc6-880b-5ea835ddee36 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0adacf73-c50a-4ecb-92c1-f82e26c84de6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0adacf73-c50a-4ecb-92c1-f82e26c84de6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         TSNE1      TSNE2 Class Name\n",
              "0    27.613194  -2.590790  sci.crypt\n",
              "1    43.533733   8.535353  sci.crypt\n",
              "2    32.775826  11.671514  sci.crypt\n",
              "3    44.522926  -2.058890  sci.crypt\n",
              "4    40.518196  -2.139972  sci.crypt\n",
              "..         ...        ...        ...\n",
              "595  20.744043  -7.745994  sci.space\n",
              "596  -0.322983 -28.657366  sci.space\n",
              "597  -8.563044  -6.283251  sci.space\n",
              "598 -14.029724 -29.518869  sci.space\n",
              "599   3.009676 -16.334478  sci.space\n",
              "\n",
              "[600 rows x 3 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
        "df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne\n",
        "df_tsne"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z4N7d8MlpVCS"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(-46.191162300109866,\n",
              " 53.521015357971194,\n",
              " -39.96646995544434,\n",
              " 37.282975387573245)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='hls')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news using t-SNE');\n",
        "plt.xlabel('TSNE1');\n",
        "plt.ylabel('TSNE2');\n",
        "plt.axis('equal')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skgpKPdEie70"
      },
      "source": [
        "## Compare results to KMeans\n",
        "\n",
        "[KMeans clustering](https://developers.google.com/machine-learning/glossary#k-means){:.external} is a popular clustering algorithm and used often for unsupervised learning. It iteratively determines the best k center points, and assigns each example to the closest centroid. Input the embeddings directly into the KMeans algorithm to compare the visualization of the embeddings to the performance of a machine learning algorithm."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8da-KTwtxk27"
      },
      "outputs": [],
      "source": [
        "# Apply KMeans\n",
        "kmeans_model = KMeans(n_clusters=4, random_state=1, n_init='auto').fit(X)\n",
        "labels = kmeans_model.fit_predict(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mYMIXXRm0ZC8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          36.85309982299805,\n          -25.488718032836914,\n          2.9596657752990723\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.7554828524589539,\n          5.936662197113037,\n          -19.277177810668945\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_tsne"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-970730de-d54b-4012-a15c-2a0043b2aa36\" 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>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "      <th>Class Name</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.613194</td>\n",
              "      <td>-2.590790</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.533733</td>\n",
              "      <td>8.535353</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32.775826</td>\n",
              "      <td>11.671514</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44.522926</td>\n",
              "      <td>-2.058890</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40.518196</td>\n",
              "      <td>-2.139972</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>20.744043</td>\n",
              "      <td>-7.745994</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>-0.322983</td>\n",
              "      <td>-28.657366</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>-8.563044</td>\n",
              "      <td>-6.283251</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>-14.029724</td>\n",
              "      <td>-29.518869</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>3.009676</td>\n",
              "      <td>-16.334478</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 4 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-970730de-d54b-4012-a15c-2a0043b2aa36')\"\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-970730de-d54b-4012-a15c-2a0043b2aa36 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-970730de-d54b-4012-a15c-2a0043b2aa36');\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-efd34921-c8b8-4fac-81e7-913ca802d239\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-efd34921-c8b8-4fac-81e7-913ca802d239')\"\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-efd34921-c8b8-4fac-81e7-913ca802d239 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e57c4e91-ff0a-4bcc-8d42-60d98e35ef8b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e57c4e91-ff0a-4bcc-8d42-60d98e35ef8b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         TSNE1      TSNE2 Class Name  Cluster\n",
              "0    27.613194  -2.590790  sci.crypt        1\n",
              "1    43.533733   8.535353  sci.crypt        1\n",
              "2    32.775826  11.671514  sci.crypt        1\n",
              "3    44.522926  -2.058890  sci.crypt        1\n",
              "4    40.518196  -2.139972  sci.crypt        1\n",
              "..         ...        ...        ...      ...\n",
              "595  20.744043  -7.745994  sci.space        0\n",
              "596  -0.322983 -28.657366  sci.space        0\n",
              "597  -8.563044  -6.283251  sci.space        0\n",
              "598 -14.029724 -29.518869  sci.space        0\n",
              "599   3.009676 -16.334478  sci.space        0\n",
              "\n",
              "[600 rows x 4 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_tsne['Cluster'] = labels\n",
        "df_tsne"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wwuk36dt1XaS"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(-46.191162300109866,\n",
              " 53.521015357971194,\n",
              " -39.96646995544434,\n",
              " 37.282975387573245)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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3b+eggw5qtn/s2LGN5a2ZY+Vi6NChGX83/GYNtuJbtmxBKcWf/vQn/vSnP2Wto7KyksGDBzea7CxdupQhQ4awdu1avv/979OvXz/uuusuIP0hUFxczKRJkwAYOXIkF154IXfffTcLFy5k9uzZzJs3jy996UudYtoD8K1vfYu33nqLM844g9GjR3PkkUdyyimnNLa3Jfb9fSD9GzX179i2bRszZsxodlxTZbgt/O53v+PHP/4x3/zmN3nwwQez+tE0Ze7cuRQVFfHss8+ybt06pk2bxujRo7PacH/66aesX7++0TZ9X5qGC3311Vf5y1/+wtq1a0kmk437s5m15fp9mvoa/OhHP2LBggUcc8wxTJkyhblz5/KVr3yFkSNH5uxbw8dgXV1dzmM6i0MPPZTPf/7z3H777dxzzz0ceuihHH/88cyfP79VE5n2jI8G2vMs6s77T6MpRLTSr9GQjhAyffp0pk+fzpgxY7j66qtZtGgR3/ve99p0fjgc5txzz6W4uJgrrriCUaNG4fF4WL16Nbfcckuzmfj2oOpt8n/yk59kzG41Zd/oPH0pjGFnkmvGseE3bpDTN7/5zZwx/xuUi8GDBzNixAjee+89hg8fjlKKGTNm0K9fP2644Qa2bdvG0qVLmTlzZsaH0YIFCzj11FN5+eWXefPNN7n++uv529/+xiOPPMKQIUPa3SfbtjP6NW7cOBYtWsRrr73G4sWLeeGFF3jwwQe57LLLuOKKK1qsqzsjqhxyyCH88Y9/5PLLL+eiiy7i/vvvb/HDx+12c8IJJ/DEE0/w2WeftTg2HcfhyCOP5OKLL85aPmbMGCD9Ufad73yHQw45hGuvvZaBAwficrl47LHHePrpp5ud19r9A+nVpNmzZ/Piiy/y5ptvcuedd/KPf/yD2267rdGRdV+Ki4sZNGgQGzduzNmn1sjle9M0kEDDcbfeeivLly/n1VdfZfHixVxzzTXcfffdPPzwwy2uNrRnfDTQnmeRjuij0XQtWunXaPahIWTg7t27gfRLzHEcPv7445xK97vvvksoFOL222/nkEMOadyfbRYy18s51/6GGcLi4mKOOOKItnekHXz66accdthhjX/X1dWxZ88ejj766JznDB8+nPXr1+M4ToZS22DuMmzYMCB3v3IxbNgwPvnkk2b79623K2j4rV0uV5t+69mzZ/Pee+8xYsSIRlOPSZMmEQgEWLx4MWvWrOHyyy9vdl5DRJTvfve7fPDBB5x99tk89NBDXHXVVTmvte+McgPbt29vNovs9/v54he/yBe/+EWSySSXX345f/3rX7n00kv3+4Nw+PDhfPrpp832b9mypd11zZs3jxtuuIEFCxZw6aWXctddd2WY2+zL/Pnzeeyxx5BScvLJJ+c8btSoUUSj0VZl+Pzzz+PxeLjzzjszZrkfe+yxdvelKYMGDeKcc87hnHPOobKyklNPPZW//vWvOZV+gGOPPZaHH36YZcuWMXPmzHZfsyEKWTgczohIlmvFbcaMGcyYMYOrrrqKhQsX8qMf/Yhnn32WM844o9VnUVvHR1fQmfefRlNoaJt+TcGyZMmSrJFtGuzYG8xJjj/+eKSU/PnPf242Y99wfoPS27S+ZDLJgw8+2Kx+n8+X1dzH5/MBNFPspk6dyqhRo7jrrruyLv/vm9m0Izz88MMZ9r0PPfQQlmW1qPQfffTR7Nmzh2effbZxn2VZ3H///fj9/saPn1z9ysXcuXNZuXIly5Yta9wXjUZ55JFHGD58OOPHj29X39pD//79OfTQQ3n44YcbP/qasu9vPXv2bLZt28azzz7baIsspWTmzJncfffdpFKpDLOa2tpaLMvKqKO8vBwpZYZpSTZGjhzJihUrMo579dVX2bFjR8Zx1dXVGX+73W7GjRuHUqrdNtzZOOqoo1i+fHlGErlQKJQRErQ9fOUrX+Gaa65h6dKlXH755S22cc6cOVx55ZX8/Oc/b9EX4KSTTmLZsmUsXry4WVk4HG6UgWEYCCEyZsO3bt3aLPxkW7Ftu9nY7t+/P4MGDWpVvhdffDF+v5+f/exnVFRUNCvfsmUL9957b87zG2bYm/orRKPRZiEva2pqmj33GiYzGtqYa8y2d3x0BZ19/2k0hYSe6dcULNdffz2xWIwTTjiBsWPHkkql+OCDD3juuecYPnw4p512GpC2d//2t7/NHXfcwde//nVOPPFE3G43q1atYtCgQfzwhz9k5syZlJSUsGDBAs477zyEEDz55JNZPyqmTJnCs88+y0033cS0adPw+/3MmzePUaNGEQwG+fe//01RURF+v5/p06czcuRIrr/+er71rW9xyimncNpppzF48GB27drFO++8Q3FxMX/961/367dIpVJ84xvf4KSTTuKTTz7hwQcf5OCDD+a4447Lec5ZZ53Fww8/zIIFC1i9ejXDhw/n+eef54MPPuCaa65ptFP2er2MHz+e5557jjFjxlBaWsqECRNy2uVfcsklPPPMM3zrW9/ivPPOo6SkhCeeeIKtW7dy2223NfMh6GyuvfZavv71rzN//nzOPPNMRo4cSUVFBcuXL2fnzp089dRTjcc2KPSffPIJP/jBDxr3H3LIIfzvf/9rNBtrYMmSJfzqV7/iC1/4AmPGjMG2bZ588kkMw+Dzn/98i+0644wzeP7557n44os56aST2LJlCwsXLmxmTnHRRRcxYMAAZs2aRf/+/dm0aRMPPPAAc+fObZTJ/nDxxRfz1FNPceGFF3Luuec2hkwcOnQooVCo3Ss7QGM23ttvv52f/vSn3HLLLVnlLKXku9/9bqv1XXTRRbzyyit8+9vf5tRTT2XKlCnEYjE2bNjA888/z8svv0y/fv2YO3cud999NxdffDGnnHIKlZWVPPjgg4waNYr169e3ux91dXXMnTuXz3/+80yaNAm/389bb73FqlWrsuZYaMqoUaO45ZZbuOqqq/jiF7/Il7/8ZcrLy0kmkyxbtoxFixY1PpOyceSRRzJs2DD+3//7f2zatAnDMHjssccoKyvLmO1//PHHeeihhzj++OMZNWoUdXV1PPLIIxQXFzd+5Lc0ZtszPrqCrrj/NJpCQSv9moLlJz/5CYsWLeL1119vnOkeNmwYX//61/nOd76TsUR+5ZVXMmLECB544AH+7//+D5/Px8SJExvjw5eVlfHXv/6V3/zmN/zxj38kGAzypS99icMPP5yLLroo47pf//rXWbt2Lf/973+55557GD58OPPmzcPlcnHzzTfzhz/8gV/+8pdYlsVNN93EyJEjmTNnDg8//DB33HEHDzzwANFolIEDBzJ9+nTOOuus/f4tfvGLX7Bw4UJuvfVWUqkUJ598Mj/72c9afIF6vV7uv/9+brnlFh5//HFqa2s54IADuOmmm5opJ9dffz2//vWvuemmm0ilUnzve9/LqfQPGDCAf//73/zud7/jgQceIJFIMHHiRP761782i5veFYwfP57HHnuM22+/nccff5xQKES/fv048MADueyyyzKOHTt2LP3796eysjJjRr9he/r06RlmIxMnTuSoo47i1VdfZdeuXY330T/+8Y+szolN+dznPseCBQu4++67ufHGG5k6dWrjPdeUs846i4ULF3L33XcTjUYZMmQI5513XpuU5bYwdOhQ7rvvvkZfhH79+nHOOefg8/m4/vrrO2w+dPnll1NTU9No23/dddd1uI0+n4/777+fv/3tbyxatIgnnniC4uJixowZw+WXX97oO3D44Ydzww038I9//IMbb7yRESNG8KMf/Yht27Z1SOn3er2cffbZvPnmm7zwwgsopRg1alSjotwaxx13HE899RR33nknL7/8Mg899BBut5uJEyeyYMECzjzzzJznulwubr/9dq677jr+9Kc/MXDgQC644AKCwWBjThBIO/KuWrWKZ599loqKCgKBANOnT+eWW27JMBPLNWbbMz66gq66/zSaQkCobFORGo2mIPjvf//L1VdfzaOPPpozG6pG0xZuuOGGRpt07ZCp6W70/afRtI626ddoNBpNu4jH4xl/V1dX89RTT3HwwQdrhUvT5ej7T6PpGNq8R6PRaDTt4qyzzuLQQw9l3LhxVFRU8Nhjj1FbW9tpJkQaTUvo+0+j6Rha6ddoNBpNu5g7dy7PP/88jzzyCEIIDjzwQG644YaMcLUaTVeh7z+NpmNom36NRqPRaDQajaaPo236NRqNRqPRaDSaPo5W+jUajUaj0Wg0mj6OVvo1Go1Go9FoNJo+jlb6NRqNRqPRaDSaPo6O3pODysoI2sVZo9FoNBqNpvchBPTvH+jpZuQVWunPgVL0KqU/lUqxaNETfOELX8HlcvV0czRdjJZ34aFlXlhoeRcWWt6a3oAO2ZmDioreNdOvlEMkEiEQCCCEtsrq62h5Fx5a5oWFlndhoeXd+QgBAwbomf72oJX+HPQ2pV+j0Wg0Go1Gk0Yr/e1Hf27mCalUiscff4hUKtXTTdF0A1rehYeWeWGh5V1YaHlregN6pj8HvW2mXylFPB7D6/UhhOjp5mi6GC3vwkPLvLDQ8i4stLw7n9Zm+pVSWJaFbdvd2KruxzAMTNNs032lHXnzCNPUzj+FhJZ34dEVMk+/BwR6fqf3ocd4YaHl3X0kk0m2b99OXV20p5vSLRQXFzF06FDcbneLx2mlP0+wLIunn36UU045XXv+FwBa3oVHZ8vc5RIUFQmEjAM2KD/xuCAWc/a/sZr9Ro/xwkLLu/twHIdNmzahlKC0tH+f/9iyrBThcIhNmzZRXl6OlLkt97V5Tw56o3mPZVltXsLR5Dda3oVHZ8rc7ZYUF9eBWAc0UfLVCJLJYdTW9u3l7nxAj/HCQsu788ll3hOPx/n440307z8Yj8fbAy3rfhKJOJWVuxg3bixeb+4+a0fePMKytANQIaHlXXh0lsyLihSINWQo/ABiK253BMPQj/7egB7jhYWWd/dSSB9Xbe2rfvLnCZZlsWjRk1iW1dNN0XQDWt6FR2fJ3OUyEGJX7gPEFny+ttdnmpKSEklZmaKsTBEMSv3R0AnoMV5YaHlregPavCcHvc28R6PRaHLh90s8HgXCQuAGsQmoyHG0gZWaRTjcum2/1yvw+6MgNgLFCDEUgQQ8JJMGtbUOjqMflBqNpvtpzbxnwIAhuN2eHmhZ95NMJqio2KnNe/oKSjmEwzUopZ3wCgEt78KjozIvKTHwej9ByPcRYjmItQhaCmNXTFsmG4UAn98GsQbBEIQoQ6kNOOpDHLUU07WGkhIwjMJZQu9M9BgvLLS884/DDpvF66+/2tPN6FS00p8nWJbN66+/gGVpB7xCQMu78OiIzH0+iWFuBtF0Vj+CEMXkCs4mOIBYrPXZea9XItgC+EH4UGoT0PRroRYhVxAIaKW/I+gxXlhoefc+KisruOWW33DaafP53Ofm8KUvncQPf3gl7733Tqdfa+nS9znssFlEIpFOr7s96JCdeYLL5WL+/DN6uhmabkLLu/DoiMy9XgXsbrbfUZuQYjKO+gSord/rATWeaNTVptlGwwBEHUIMR6mtOY5KIWQEwyjGtrWZT3vQY7yw0PLuXWzfvp1LL72Q4uIA3/ve9xk/fjyWZbFkydvccstvePjh//Z0E7OilMK2bUyzY+q7nunPExzHobKyAsfRS4OFgJZ34dExmeeaNazDURsQTEQ5B6OcWVip6YTDPuLxttXvOIDyIvACuRPcCMItxoXWZEeP8cJCy7t38bvf3QQI7rrrfubNO45Ro0Yzduw4vv71c/nnP+9tdny2mfoNG9Zz2GGz2L59OwA7dmznhz+8khNOmMsxxxzB2WefzltvvcH27du57LJLADjhhLkcdtgsfvWra4H0fXHvvXdx6qmnMHfu4Zx77lm88spLza771ltvcsEFX+dzn5vDihXLO9xvPdOfJ9i2zbvvvsHxx5+sX7AFgJZ34dExmRstlCVwFFRXAwiahe9shVhM4fGORhED3EAyx5F+ne23A+gxXlhoefceampqWLLkLb797cvwZQllFgjk9olqiVtuuZlUyuIvf/knPp+PTz7ZhM/nY/Dgwdx00++4+uof88gjj1NUVITHk3Ywvvfeu1i06Fl++tNrGDlyFMuWfcAvf/kzSkvLmDXr4Ma677jjVi6//CqGDx9OIBDsWMfRSn/e4HK5OOmkr/R0MzTdhJZ34dERmcfjAp9vIIg9zcqUKiaVlLRX2d97viIed+PzJhFyOEp9kuUoiVJlWJaevWwveowXFlrevYetWz9DKcXo0WM6td6dO3dy7LHHMX78BACGDx/RWBYMlgBQVtav8aMimUxy7713cdttf2HatIMaz1mxYjlPPPFYhtJ/ySXfYc6cw/a7jVrpzxMcx2HPnl0MHDhYzxIUAFrehUdHZB6LObg9B2BIleHMq1QQ1ETq6vZPGY9FHWyrmOLiAIg4sKNJqYlSU6itzXW2piX0GC8stLx7D121MnnmmWfz29/exDvvLOHQQw/lmGOOY8KE8pzHb936GfF4nCuu+G7G/lQqRXn5pIx9kyYd2Clt1Ep/nuA4NqtWfcAxx5yoHxgFgJZ34dFRmdeEbPz+A/B4xoCwQBkkk5Jo1OmUXCPJpENVFfh8I/B6hwNxwMBx3NTVKT3L30H0GC8stLx7DyNHjkIIwaefbm7zOVKmo5Q1/WDYN9Hal798KocddjhvvvkG77zzNvfeezdXXPEDzjzza1nrjEbTvlK///2tDBw4MKPM7XZn/J3NDKkjaKU/TzBNF8cff3JPN0PTTWh5Fx77I/No1CH9/miw8e98RTwWc4jFQAhP/ceEVvb3Bz3GCwst795DSUkJc+YczqOPPsKZZ57dTKGORCLN7PpLS8uAdJjPYDBtU79hw/pmdQ8ePITTTjud0047nTvuuI0nn/wvZ575NVwuF5D++GvggAPG4na72bVrR4YpT1eiPzfzBMdx2LZti/b8LxC0vAuPfJG59tntHPJF3prOQcu7d/HjHy/AcRy++c3zeOWVl9myZQuffLKJhx9+iIsvvqDZ8SNHjmTw4CH8859/Y8uWLbz55mIeeuiBjGP+7/9+x5Ilb7F9+zbWrVvL0qXvMWbMAQAMGTIUIQRvvLGY6upqotEoRUVFfP3r5/HHP/6BZ55ZyNatn7Fu3VoeeeTfPPPMwi7pt57pzxMcx2HjxnUMHjxMLw0WAFrehYeWeWGh5V1YaHn3LoYPH8G99/6Le+65k1tv/QOVlRWUlpYxadJkfvKTa5odb5oufvWrG/ntb2/kvPPOYvLkA7n00u9yzTU/aTzGth1uueVmdu/eTVFREYcddgTf//4PARg0aBDf+ta3ueOO27j++l9y0kmn8ItfXMell36XsrIy7rvvbrZt20ogEGDixElccME3u6TfQulYa1mpqIjoGS2NRqPRaDSaXogQMGBA8/Ca8Xicjz/exIABQ3C7PT3Qsu4nmUxQUbGTcePG4vV6cx6nPzfzBMex2bz54wx7ME3fRcu78NAyLyy0vAsLLW9Nb0Ar/XmC46h6e0C9/FAIaHkXHlrmhYWWd2Gh5a3pDWjznhxo8x6NRpMvCCHw+QQuFzgOxGLoUJoajaZPo8179qLNe/oYtm2zceM6bFsvDRYCWt6FR0dl7nYLyspsvN6PMMyluNyrCAarCQaN1k/uxbjdBh6PgcuV3/3IhR7jhYWWt6Y3oJX+PEEpRVVVRZdlktP0LrS8C4+OyFxKQXFxCsRyENWkY+fHQXyEaX5MUXHPPeIblHbDEO08T1JWJigu3k5R8UcUB3ZTViYxzfbV09vRY7yw0PLW9Aa0eU8OtHmPRqPp7RQXS9yeNUBd1nLlzKK6unuVZZ9P4vXaCLELRBLUAGy7mHBYtajwSCnw+yUeTx2KNUDTYw2Umk5NyNA20RqNBtDmPU3R5j19DNu2Wbt2lV4aLBC0vAuPjsjcNBW5FH4ARAjT7L7HvNcr8foqEfIDENuAPSDWYpgfUlqaux3FxQYlpTG83jiKdWQq/AA2QqynqKjvzPbrMV5YaHlregN5o/Q/+OCDzJ8/n1mzZjFr1izOOussXn/99cbyRCLBddddx5w5c5g5cyaXX345FRUVPdjizkYRi0Vp/jLU9E20vAuPDshctKwES2F264qlz6cQYlOWkhhCbMHjaf7K8fslLvdWhFiLIgXkUoqimGZfck7WY7yw0PLW9Dx5Y97zyiuvYBgGo0ePRinFE088wZ133snjjz/OhAkTuPbaa3n99de56aabCAQC/PrXv0YIwb///e8OXU+b92g0mt5OaamBND4GKrOWC2ZTVaW65VlmmpJAsAIhPslxhMS2DqamJlNxLysDIZcCHoSYgFIf5rxGT5graTSa3ok279lLnzPvmTdvHnPnzmXMmDEccMABXHXVVfj9fpYvX04kEuGxxx5jwYIFHH744UydOpUbb7yRZcuWsXz58p5ueqdg2zYrV36glwYLBC3vwqNjMneQYjjQ/MUmxFgUdRhG9zzmhRAIrBaOcJotTBiGRIgQAFJMQCCBXEq9C8fpO5F89BgvLLS8Nb2BvFH6m2LbNs888wzRaJSZM2fy4YcfkkqlOOKIIxqPGTduHMOGDeuw0m/bVuO1GgapbVuN25aVud2QZS+97dRvpxq3U6nMbaWabqcd3PbdBlDKabKtsKz0tuM4+2xb9dt247ZtZ273xj45Tua27tPePjmO0+f61Bfl1Fl9sqxUo6NrW/uklMBRG5FiPEKMQzAQwTCkmAYqinIclFJd2ieXSxIICPx+ByH6kQulSkgmm/Yv3Q+FAEpRqgaldiEYkaOCCdTW9rycOuve089y3Sfdp87pU1/l0Ucf5itfOZmjjz6Mb37zfFavzr0K2lbySulfv349M2fOZNq0aVx77bX8+c9/Zvz48VRUVOByuQgGgxnH9+/fnz179nToWqtWLQdg9eoVrF69AoAVK5ayYcMaAJYuXcKmTRsBeOedxWzZshmAN954hR07tgLw2msvsGfPLgBeeukZqqurAFi06AkikQgATz/9KPF4DMuyePrpR7Esi3g8xtNPPwpAJBJh0aInMAyDESNG88oriwDYs2cXr732AgA7dmzljTdeAWDLls28885iADZt2sjSpUsA2LBhDStWLO1VfQKorq7ipZee0X3ap0+GYSClZN26D/tMn/qinDqzT6+8sogRI0ZjGEab+xSPp5VpR61GqR0AKGI46kMUu6itFdi26pI+vfvuG3i9FoHAHlzu5Rjm+yDiKFVCcwTx2FD27Aln9CmRSFJXayJEfxR7UOwCYSDERCAAuIBSpJhKMlnM9u07elxOnXXvvfLKIqZPn0VVVUWf6VNvGk+9rU/r1n3I9Omz+PDD5X2mTz0tp4Z+dDW2bfPGG4t57LFHeeONxd2yWvPii8/zpz/9gYsvvoR7732QCRMm8P3vX0ZVVdV+1Zs3Nv0AyWSSHTt2EIlEeP755/nPf/7DAw88wNq1a7n66qv58MPMr6DTTz+dOXPm8OMf/7jd19q1qxopzUbhGoZR/4UrMAwDy7IQYu+2lAIpG7YlUkosK4WUaeUtlUo1KnKpVArTNBCiYdsE0l/CTbddLhdKOViWjZSC5cvfZ+rUg/B4fDiOg+PYmKarftvBNE0cx8ZxFKaZbrtSe7dBYRi9p08ul6txNrthW/cp3SeA5cvfY9q0Wbjdnj7Rp74op87sUyIR48MPVzBjxuz6a7atT6WlEiHXIkRtkyeYADWFUMiF48gu6ZPPBz7fFhBNJ1YkQkxCqRiwHUiBKkMxhlC1g+PQrE8eDwQCFor1QKK+Hi9CDAFcoOIoEkTrxhCNpnpcTp1176VSCVavXsm0aTMRQvSJPvWm8dTb+mTbFh9+uJypU2dgGGaf6FNPy8m2bYYObb662Jk2/QsXPsXVVy9g+/btjfuGDRvGTTfdzPz5X9qvulvim988nwMPPJAf/WgBkF6J+fKXT+KMM77G+edf2Oz4ttr0m13W4i7A7XYzevRoAKZOncqqVau47777OOmkk0ilUoTD4YzZ/srKSgYOHNihaxlGOuqFYRgZ+xpouAFb3nY1brtcHdsWQuJySWzbxu8vwjTdAI2Dp/m2Qf3mPm3Ptd1zfWq5H4Xdp7S8ixv70hf61Hxb96lpn0zTjd9fBAhcrr3HtNanmhqHQGAyhpEEEQbcKBWkrpbGZfOu6JPXyz4KP4CDUmuAfqCmY9uQTEI87qCUbLTpb9qPZBJSKR+mayCwtX5vHKU2Nx6j1IEkk06vkFNn3Xum6cbn8yOl0di2fO9TbxpPvbFPPp+/UeHvK31Kb/esnLqKhQuf4hvfuKBZfpEdO3bwjW9cwD333Nslin8qlWL9+rVccMFe5V5KySGHzGHVqpX7VXdemffsi+M4JJNJpk6disvl4u23324s27RpE9u3b2fGjBk918BOxDAMJk+eljHQNH0XLe/Co70yd7sNSkokJSVpTbq21k0kPIBwTYBQtUMqlT28pRCC4mJJWRmU9bMpK0snxWoPUgog2sIRVShlU1PjEIs5rUYPCoctlDMUcDcrUyqAYxfR15Jy6TFeWGh55xe2bXP11QuyJhRs2HfNNVd3ialPKBTCtm369ctcxSgr60dlZfZIbW0lb5T+3//+97z33nts3bqV9evX8/vf/553332X+fPnEwgE+OpXv8rNN9/MkiVL+PDDD7nmmmuYOXNmn1H6LcvinXfeaHSo0fRttLwLj/bIPBg0KC7ejWEuQxrvY7pWUhyI4PGAbedWjoUQlJYK3J61CLkUIZYj5Pt4vZ9QUtJ2ZUQpBaLlhWKl2vd6qalRKOcgUKMAH+AHNR7Hnkw43Jfi86fRY7yw0PLOL95++60Mk559UUqxbds23n77rW5s1f6TN+Y9lZWV/PSnP2X37t0EAgEmTpzInXfeyZFHHgnANddcg5SSK664gmQyyVFHHcW1117bw63uPIQQ9Os3ANFKMh5N30DLu/Boq8z9folpbgaxu8neBEKsw+MZTzJZlnOWv7hYIORqms3SiwoM08TrHUk83rqCrRQox4swDLIm01KlpFKC9iQichxFdbXC7R6C2z0YgHgcLKtvhjjUY7yw0PLOL3bt2tWpx7WH0tJSDMNo5rRbXV1F//7996vuvFH6b7zxxhbLPR4P1157bZ9S9JtiGAYTJkzq6WZougkt78KjrTL3eNQ+Cn8TxGb8/jJqarIXm6ZFbrOcXfVKf5uaS20tBIJTEWIV0PRDwYNSE4hGOzY7n0zaJJMdOjWv0GO8sNDyzi8GDx7cqce1B5fLxcSJk3nvvXeZO/dYIG3O/t5773LGGWftV915Y95T6FiWxZtvvqqXBgsELe/Coy0yF0KAiLVUC1JmnxlPn5vIWpZGIUTbZ9Uty6E24sJxDkapclAjUWoKtjU9barTt0zwOx09xgsLLe/84vDDj2DYsGE5V2aEEAwfPpzDDz8ia/n+cvbZ5/DUU4/zzDML+eSTTfz2tzcSj8c4+eT9cxzOm5n+QkdKwfDho+od6DR9HS3vwqMtMldKgTJzJ60FFJJsZjXpcz0tnCvq7fDbrq2nUopQtcIwSpCyFNt2GiMGaVpGj/HCQss7vzAMg5tuuplvfOMChBAZDr0NHwI33nhTlzlmn3DC5wmFqvnHP/5CZWUlEyZM5P/+7/b9Nu/Jqzj93UlFRUTPVGk0ml5HSYnEMJcDzbNRKlVCMlFOXV12xTsQkLjca4C65oVqKNHoSOLxvmlDr9Fo+hZCwIABgWb7uzpO//Dhw7nxxpu6NE5/e2lrnH5t3pMnWJbFa6+9oJcGCwQt78KjrTKvrVWgpgL7zjB5QJUTjeaeraitVShnCkoF9ykZjG1rhb870WO8sNDyzk/mz/8SK1as4qmnFvKPf/yTp55ayPLlK3uVwt8etHlPniClZMKESY3JMzR9Gy3vwqOtMrdtRThsUFw8CyEjCGLpWPaOj0hEZY0r3YBSilDIxu+fiNvtgLBBmcTjglisdyn8UqaX1Pvqiqse44WFlnf+YhgGRx31uZ5uRqegzXtyoM17NBpNbyed4l7gOKpLk1cJAV6vxF2fOyseh0Si8233hYDiYonpskDFAA+O4yYS2f/+uVySoiKJlAqlBNGo0yV90Gg03UN3mPfkC9q8p49hWSleeukZLKu5Ha+m76HlXXh0ROaOo7AsJ6tCLAQYhtxvx0HTlJSWgc/3MYb5Hoa5FH/RNsrKjE6NOS4ElJYamK41CLEMIdch5AoMcyUlJexXP0pKDIIlNoaxFcQGpNxCcSBFaWnPZUfVY7yw0PLW9Aa0eU+eIKXBtGmzkFKn8C4ECl3eLpfENMFxBIlE7zI76So6S+ZCCAIBgWEkQUQAL8oporZWYVntny0PBBRCLGNvLH4HIbaDqCIYnEZNTeesMPj9EiE2gNjXyTiBkB9SXDyNcLj91/J6DUwzhlIf0hCZSBECtRPDmEQgECAS6f57rNDHeKGh5a3pDWilP0+QUjJ48NCeboammyhUeRuGIBgUIPYgRAjw4vcPIxYz2pQpNp/pDJmnZ8slQn5I0yRcwjAIBqdTU2Ng22nF1zAEPp+o/7iCWIxmmXy9XokQm8lMvtVAHGlEMIwAtr3/snG7FYhQjtI4hmHRkcXpoiJQbKB5KFKFYiNu98x219kZFOoYL1S0vDW9AW3ekyekUimee+4JUim9NFgIFKK8pRQESxRCflCvaIaAnQj5AX5/CI+nbz+uOkPmaSX9Y5pn3bVBrKK4OG0i4/NLgiVx3J7VSOM9TNcKigOVBIOZs5BuNyCqc15PiApMs7NMfFqbbbfokDWRsIBcScksEKlONVNqK4U4xgsZLW9Nb6Bvv0X7EIZhcOihR3VZIghN76IQ5e3zCWADWZU/8RF+f3e3qHvpDJl7PApEZY5SC2kkcLkkXm8YIZquBiQRYhOm+Sl+/97XQjqYQUsLwmanBTxQyqTlV5Kng9dq5STVcsSjrqIQx3gho+Wt6Q1opT9PkFLSv/8AHe6rQChEebtcCiEiOUoViNo+nc2yM2QuRCtmNiqJ3y8QYlOOCnalPxzqicVAqeG5q3OGkkp1jj18LAaoYTku1I9ksmOyd2wXuT9cJNAz0T0KcYwXMlremt6AvvvyhFQqxcKF/9FLgwVCQcpbtDbb2rdt+jtD5o5j0PLMvB8pLbJl821E1DV+XFmWg2P3R6nS5sepUSQSe2f6TVPichkdM8EhHQLUsoaBGsXexGMCGILtjM+ZZbg1amsVgglZywTjqKvrmdjMBTnGCxgtb01vQMfpz0Fvi9OvlEMkEiEQCCCE/lbr6xSivAMBicu9mub26GmUM5vq6l40KPcT0zQwjLQJTTJpd4rMXS5JIFAN4qPmhaqMZHI8pksh5fs56xBMo6bGkxHpJxCQmGYy7WCNiVIDSCQMolEHr1fi86XNigQWSvXDtt2Ew82VdCEE/iKB2+UADkqZRKOQTO491u2W+HwgpY1SBvG4aDVTsJRpp2TDANuGWCwzrr/PJ/H7Uyj1KYgoKB9CjCYed3f4Y2J/KcQxXshoeXc+3RWn37EdtizbRu2eWooHFjNq5nCk0XUyXLZsKQ88cB/r16+loqKC3/zm98yde2yL57Q1Tr+O3pMnCCEJBkt6uhmabqIQ5R2NKoKuCQixkrQdtoFgMEIMAAxsJIahOiVSTE+yN0JRRTpCkfKh1BCiUQMh9k/mqZRDIlGGx1MOYjOQBCRKDcFxRlBba1NUJHF7ShCiJlvrEMIhGHQIhWSj4hyJOAjhwjRH1F/HBhy8XoHfn/mRIcRnGCJIaekkamrsxskTKQUlJSDkOiBtxiWQFBePIpkcRG1tWrFPJh2SyXRpW1Z30v2J1Tt/xzBdfjyeMcTjXqLR9PmxmEM8buLzjcc0wbIEsZiDUj13LxXiGC9ktLzzk3Uvb+T5371CZFdt477A4GI+/+N5TDou+wri/hKLxZkwoZz587/MggU/6tS69edmnpBKpXj88Yf00mCBUIjytm1FXa0LpWaBGowU01EkcdQqHLUMIVdSWhrNcDTNN9IRipz6CEWbgCoQ2xByKUVFdSxd+sZ+y7yuziEcLsFKHYRjz8a2DiZaN4KaUFqpjkYVqAmAe58zBUKU46jPQKyhqCjTTkcpRSplZ9jwp2f4m68qCBFGGjspK3MoLZWYZjp3gJCraFD40zggNuN2V+FytV+ubrfE7amsd0quJe0EHgGxCq83lFGnUopo1CEcdohG7R5x3m1KIY7xQkbLO/9Y9/JGHv3xUxkKP0Bkdy2P/vgp1r28sUuue8QRR/Ltb1/GMcfM6/S68/ftWWCYpskXvvBlTFMvzhQChSrvZFIRqgYYjaPWARXsjb4SR7Eany+OaebnoyvtRJsrQtF65s37XKfI3LLSym0opKipcTISnCmlqKkBwUEIMQHBUIQYgxRTUWoHEAbimGbLJjVp+/09LRyxAyGjSGMZwaCFlClyhs4UW/D52ttL8Pupn+HPVucmioraX2d3UahjvFDR8s4vHNvh+d+9kj34V/2+F373Kk6erTzn55uzQDFNV083QdONFKq8hRAIEQViWcsVmwgE8jOKj8ulSM9IZ8PBNOPdEjPecRSOSqHUZyjiKFWBo1aRzo2wtz0tIQQgki0ckSLtkGuBWNWKDWwSKfe+Xdv6EwiZaqGdNkJYbauohyjUMV6oaHnnD1uWbWs2w5+BgvCuCFuWbeu+RnUCWunPEyzL4umnH8WyevdLTNM5FLK8DUOiMpTPfanLUBDzidbarZSFUp0TArM1lHKRnnmvpvmHiKwvz41lOSjVv4UjSkE11GuDipE7PKaJUoJAQFJWpigtTVJWBsXFsuUPANXa10Hv/Tgs5DFeiGh55xe1e1pQ+DtwXG9BK/15gmmanHLK6XppsEAoZHkrpRDkjj6Q3/EHWo4LL4S/22QejwFqZPZCNYpo9iBKjTiOwrH9QLasaQIpRqDYsbdKEpBLrmo4hiFwudek/R3kKoRcisu9gZKS3MmMHMcEcn2ceLDt3psIqZDHeCGi5Z1fFA8s7tTjegta6c8jLEs7ABUShSpvy3KAMnLN0gqGYNu9dwa3JWxbIsQBWcsEw1DKyQg12ZXE4w7J5BCUmgj46v8FUWoSicTAjDCauQiHFY49FaWGsvd1EkSKKThqOxn5AFQA1DiaffSowcBgEKuBuowiIWqQ8qOcztt1dQrUgTS/VySoydTVZTur91CoY7xQ0fLOH0bNHE5gcHHuxUIBwcEBRs3MnbywN6KV/jzBsiwWLXpSLw0WCIUu73hcIJhM8yduKUIMobY2P8176uoclJJIMZX0h40LKEaICSD8VFSESSa7TzGorbVJpUoQHIhgJDAcVIBUqm2/r1KKUMgmWjcCxz4YIWYhKMNRG4CqJkf6sSw3NTUSKzUdpWai1HSUM5tYbBRKWeyr8DciqjKyBDfFshSRiAvlzAY1BhiAUmNQzsGEw65eHd610Md4oaHlnV9IQ/L5H9dHz9n3NVT/94k/PrZL4vVHo1E2bFjPhg3rAdi+fRsbNqxn584drZzZOjo5Vw56W3IujabQCAYN3G4bRZj0jHEAlJu6Okk83vnKnGk2JIWCVCqd4KkrHo+BgIHLtRMhHBA+IIVSYZQ6gHCNwLa778ETDBqY5ieQEYVHgJpMbV0RyUT7fueiIonHUwPiExpn+dUAlBpLKOTk/D3L+iURYlXOepUzk+rqll+uLpeBlOA4ZIQV1Wg0fZPuSM6VLU5/cHCAE398bJfF6V+69H0uu+ySZvu/+MX5/OIX12U9p63JubTSn4PepvTrbH6FhZZ3GsOQ+P0CKdOzuvtmWu0sAkED0wwhxGdAAqUCCA6grs4kkej863m9Eq8PhEiBkliWQW2tTU1NuNtkbpqSYDBH9l7algHZ4zXwehRCQDKZ/lAyTYHfD1I6KCVIJNKJsFqirAyEXJqjVODYBxMK9aIHciegx3hhoeXd+fTVjLwdoa1Kf+9qtSYnlmXz+usvYFl6BqsQ0PJOY9sOkYhNTY1NXV3X2Lv7/RLT/LQ+fn4McNLZasVyiooSGJ34cHe5JIGAxO2GWBSqqwyqqwWRiEMyaXWJzIUAj0fi90vc7r198fkA8VkL5+3B5cruCCuloKxMUlT0KYb5PtJ4D693I2Vlqj4PgEN1NYRCqlWFHyCZlCiVK1vpYBKJ/PThaAk9xgsLLe/8RRqSMbNHMvWkyYyZPbLXKfztQc/056C3zfRrNJquoeVZZi9Wahrh8P6ZEwkBJSUGUu4CsQNQ6XCXaiThsGpm0mMYEo+Hxhn0VKpj1/d6BT6fjZDbgBhKBUANo7Y2PRtvmO+TPfsMwGBqI2NIJpvbIJeWSqSxkubJtiTKmdXqCkE2SksNpPwYRGX9HgEMxrZGUVOjFSWNRpNJd8305wN6pr+P4TgOlZUVOE7vdUzTdB5a3t2DlAJES3GW40hj/2UQDBpI40MQnwJJIIUQOxFyGcFgehbbcRyqqioJBiXBkjBe3zo83jUUByrSSrZs32y32y3x+SMIuQzYDUQQYjtCLqU4kMS2RfojIAdKlWV1hDUMiZA1ZM+u6yDEp3i97X+1hEI2sdhYlDMb5czEsQ8mWjeyzyr8eowXFlremt6AVvrzBNu2effdN7DtvvkC1GSi5d2dtBLLvdUEUHsRIm0uVFKS/ufxGBiGRBphIFvgewvENjweA9u2KSpyMMwNCLEeiAB1CPEJ0lhJSUn7Htd+PwixMVuHEGI9UioEY3Oc7UY5gaxKv2kKRONsfBZECFcHE4/GYg7V1YrqakkopLrEYbu3oMd4YaHlrekNaPOeHGjzHo2mMCgtk0iZw8xFlRKPTyAabV35NE1BIKgQbAJRTTqr7ZB0KEyxkcwQlk1xk0oeRF2doqQ0hhCrsx+mRlFbO4Rksm1KQ1m/FEKszFmunFnEYhK/P17vzBuvLyhDqfHU1GR3mna7DYoDnwK7ctTsI5mYSm1t31XYNRpNz6PNe/aizXv6GI7jsGvXDr00WCBoeXcf0ToFalKWEhdKjScWa/3rXwjSCr9YVq/wQ9rUZTtCbAPV0qPWQKmGGfSdLVxkFx5P594P8biipsZHKjkNxz4Yx55NPD6eUCi303QqZaPUsNyVqhHE4813p52YDcrKJGVlirIyKC5uv9lSX0GP8cJCy1vTG9BKf57gODarVn2A4+ilwUJAy7v7SCYVdXXF9QmeRoEaiFLlOPYMampoU6x+r1ci+BRo/kJX7EKIoTnPVWoo8Xg6pF9On1og7djadgVZOZ4Wjvdg22mzpnSEJIdQKB1tJxp1WlzlVAoScRPU6CyF/bDssvqsymmkFJSWSgLBOtyeaoRcjpAfIORS3J61lJSqTo2Q1Bn4/Qb9+kv69xf062/g87ViAtYB9BgvLLS8Nb0Bbd6TA23eo9EUHg0JnixLtSubazAoMV0fANlf6IIDUVQBmTP5SgVwnMnUhGykFJSUxBHyw+wXUaOJRAa1OZKP2y0pLg7VmxZltgY1lZoaz34lAvP5JV6PA6ISgY2iP1bKRSSS2b6yMomQ6xBiOEqty1KTC8eeQSjUO2ZA+/UzEWIHiu2ABbgRjMRx+lNdrRU2jaa3oM179qLNe/oYjuOwbdsWvTRYIGh59wyplE0iYbdL4QfqJwjM3OWEsFKjUM5BwCBgAEpNwbYmEa6PTmNZNrW1gCrLUoMPxxncrtCdyaRDLF4KagaofoAfGIRSs6itTSv8brfR6GzcXmLRdCz+SHggkchQQtVGM4Xf45EgtiHEAJTamqOmFELW9IrZ/mDQAPEJii2kFX6AJIqPEXIXxcW5Zdxe9BgvLLS8Nb2Bnn/KatqE4zhs3LhOPzAKBC3v1pFSEAhIysqgrMyhtDQz+VR3kjbPyW3nrpwhhMM2oZBJbWQMdbVjqQn5CIf3mtI4jsPLLy8mlRqHUpOBUiAIajyOPY2amvbfC2nF3CAWG08yMYW6ujFUV6X9B8rKHIoDWygq/phgSbg+Tn777esty6m3829els41sBvwAXU56xCidyj9LrciHd40G591qk+FHuOFhZa3pjegzXtyoM17NJrehZQCKQW2rZASgiUKIdayNxSmAWoMiUQ/6uocfD6Jx6MQwkEpg2g0PfvdVZSUGEhjHUKEMwvUGOLxgW2KANSAYQjcbtGYnKupjfz+4vMJfL4KEJuBIoQYTdoXwQKCJOJGp0XeSZs9LUOIMfUz/Vk8fAGlDiASHtDufrpc6ezGSkEi0TzJWXvp3z+BIod5FSDETCorev7jRKPRdKN5j+PApo8Q4RpUsATGjgfZdc+Be++9i9dee4VPP92Mx+Nh2rSDuOyyKxg9ekzOc9pq3tN5a5WaLsVxbLZs2cyoUWOQsvOdyjS9Cy3vvRiGIBAQCFkHxEAVI6UPxQfsNcEAsEF8jMfjxe0uQohPQFQACoFBcfEIUqnBRCJdY5ddU2MTDE7EMBNAJQIDpQYST0hibVD4m8ocjDZFDeoIXq+qV/iLkWIMjlpH09/R7S6lpLScmtD+/06JhMAwBwE7EWIYSm3KcpQA1b9dCr+UgmBQIOQehKgADLzeYVhWgHB4P9otXK04U3fei16P8cJCyztPWbkM+fgjiJpQ4y5VUopz6pkwfWaXXHLZsqV89atncuCBU7Btm7/85XauvPK7PPTQY/h8vv2qW09Z5AmOo+rtAfXyQyGg5Z3GMAQlJQ7SWIYQaxDiE4T8GEQlmQr/XoRMgtgAYg97NTgbxKe4XDu71AQoHHYIVbupjQwnEhlCdTVtUvihe2RumhJEOl+AFKObKfwAiBBSfpa2x2/YJdLnGkb7zH8SCRvUcNJZiCWCfaMYGaCmUlvbtnpdLgO326CkRCCNFQixGagFakCsxTQ/IhDouHyV4wLcOUqDOHbnKWt6jBcWWt55yMplyHv+Dk0UfgBqQun9K5d1yWX/+Mc/c8opX2Ls2HFMmFDOz39+HTt37mTdujX7Xbee6c8TTNPkyCOP7elmaLqJviRvr3RwJ2OIZBLl8RB3+2ljfimKigSI1WQqpl5QuezDBQIPQoRyFG/F7x9MMtn29rcXpRSpVPtnm7tD5kIIRH1EGkWKnB9OYhc+3wiSyXQsfdNM1X8suFEqSF0tbXYqrqlRBIMzEHIPUniR4iCUSgEmtu2htrZ18yW/X+Lx2AixHYSDoB+I0Sj1MRlhUkUVpmsEQrjbFGp1X8JhRUnpVJRaSeZv40GIidSEO09h60tjXNM6Wt55huMgH38EaB74WJCeTpJP/Adn6kFdauoDUFsbASAYLNnvurTSnyfYts2mTRsZO3YChqGXBvs6fUXeQZXAfvIJEm+9CbYNbjfuEz6PZ+48IjlnVPdimBaQ2GdvEkRJDjMME9Xs+KY4CGHTnnj33cX+ylxKgd8vcLkcwEEpk1gMEom9SrFlpUNrCqpIz77nIl1HSYmBkGsRItJYIoQgEJhCJOJrk+LvOIpQyMblGoDLlba/TyZd9fb3rZ9fVCRxe+qTnNWj2A6qBCEmotTajOOF2InLNabNmYubYlkO4RqDYHAmUIeiDkEAhZ+aUPvCuLZGXxnjmrah5Z1nbPoow6RnXwRAqBo2fQTjy7usGY7j8Mc/3sL06TMYN278ftenzXvyBKUUVVUVHZq90uQffUHeflJY/7oPa/H/0go/QDJJ6pmFqJdfwCPb0rdsM9G1CIrJ/viyEfhbrFH10sfe/sg8HeMf3J41CLkUIZchjaUUFe2huHhvf5UCK+UC5YMWfycXYCDk1gyFv74WEKspLm5fG1Mph2jUIRZz2uxwKwS43akMhX8vNUAM2NeRb/8+6FIph8pKh1DIT21kCNXVPqoq7U51poa+McY1bUfLO78Q4ZpOPa6j/O53N/Pxxx9z/fU3dUp9vfPtp2mGaZrMmXMUpqkXZwqBviBvVzyKvXJ51rLUyy/iTUazlmWgskdecNSnSDGZtHLagAA1DMfxADkiNqj+iF762NsfmaedWleSGRbTAbEZtzuUtuWvJxJxsKyx9b9tUfYK1RggPWue4wDIEmbT5ZL4/TKdoVjs/2qK220g5Pac5UrtQoiB++xrXz6DXNi2Ipm0uizEYl8Y45q2o+WdX6g2mtK09biOcMstN/Pmm4u5446/M2jQ4E6ps3e+/TTNsG2btWtXYds6I2QhkO/yFgJUqDr3AZYFsdaV/kRCANkedmEcJ4Fjz0SpmSg1HeXMJhodiuOI+g+CfRX/AFKOAFHXoXj0XU1HZS6lQMgoOc11xKf495nUD4dtwmETwRRQA5qUmKDGkUiU0mDik5sEDXq9YQjKyiSBwG68vnX4/ZsoLUtSVLR/r5h0/dn9DtLYZLzGVH+slCcvZlPzfYxr2oeWd54xdjyqpDRnMC8FqNKydPjOTkYpxS233Mzrr7/K7bf/jWHDhnda3fqTM29QxGJRWoknp+kz5Le8lQJRlGMWuYE2xE+ORh1MczSm6QGxjbSS5wI1mmSyjNraBqVPAg4ul0Qau3HUDoQ4oH5/EoEHRRKwEOzG5TogHVmmHRQVSdxuBcICZZJMCqLRvcm19p+OyTz9AVPbwhFJZBZTqrQZC/h8B+DxjEEIB8dJ5zNIpRwCAYk0fKRNaLI1twTbdhACgkEQchmNCroAQSUez0gcNaTNEYyat1Gh1EBEfcSh5pSBSgIlKDUc2ypqlhW495LfY1zTXrS88wopcU49E3nP31FkGg02SND5yhld4sT7u9/dzAsvPMdvf/t/FBX5qaysAKCoqLjFGPxtQSfnyoFOzqXR7B/FThzrD79B7W6e4VROPhAuvJRYG+YdpBR4PAZut6pXTAWxWPboMSUlEsNsonxi1P9LAQohylGqjtrI8HY5eqaz1W4E0WT1QpXiOOXU1GTPRttan1wuAyEUtk2Hov00rauktK4+UVk2XNjWjHZn9DUMSUlJDETzZFVKFWFbBxIOO/XmPB9l/jZNj3VmU13d8YdpaalEGqtontjLADWTeFyglCCRcHQ4RI2mgOiW5FzZ4vSXlqUV/i6K03/YYbOy7v/Zz37JKad8KWuZTs7Vx7Btm9WrVzBlykHa878A6Avyjpo+gpdfRfyPt6AqKxv3i1GjcF/wTcLCbHHSqzEpl4ggRB2KAMopoq4ud+ZVIRTNEnbRRKFWMZQzpF1Ktt8vkTKLUitCCLkev7+curq2KdSGkU4qJWV9ZBjhB+XBcVzU1josXfpBu2XuOArlFCMMEyhBiEEIJAobpXaiVD+ibXCf2BfbdohGffj8UxBiE+kZ/7S5lXJGNSY5S69+tGDKJaoxzX4ddoQNhxUlJdNBbEWIXYADqj9KjSES6dxsxd1JXxjjmraj5Z2nTJ+ZDsvZjRl5lyz5oMvq1kq/RqPpEhxHEfaVUPTja5DVVThVlcjBg7EDJYSlp8XZ8YakXIi98dIFIAwXweB0ampE1lldpQRp595UjpqLSaYMlGq70u/xqMaEVvsiRA1ut6IuV9qAJjTtk6rvU/o38GAYEykOOJSVdcwpLBJRlJbOAraj1AZUgxkUI0AN6PBKQjzukEr58funYhjpRe5YHBLx9tS3f7PvjqOorrbxeEbg9aZtW5PJdNv0aqxGo+lypITx5X3CMEub9+RAm/doNJ2LlNkV9WwEgxLTtZLmMfoBikgmDqS2tvkMr8slCQSqQHyc5Tw3qBlUVbVPAS7rZyPE8pzlyplBdXXrM3ct9ymAEAOxrf6EQu2fuS4qMvB4PwV2ZWlgOUqVADYKSaqTfRHScfQ3IET20HXKmY1lSVwucBRE6xTJZH7Ozms0mt5Dt5j35AltNe/R0XvyBNu2+OCDd7DtliJZaPoKfVHe7bG3NoxsSbkaqKtPQNWcVMohleoPaiSZrldFKDW9Qwo1qrUF0bYtmGZPNNZABIEfy4rgOG2XuccjKSsTeH0W2RR+IcYgZBIhlyHkB0j5Ph7PxrSdfCdFMIpGFTCetO/EPqhxGIaDy7UBxPtIuZJAoIp+/bR5Q18c45rcaHlregPavCdvEPh8fnpjJlFNV1Do8m5NOXfINWcRidh4vEPweYcghAVILEtSV6c65OiZSAi83n7ZTXxUKclkQ1L21mjtZa/qfRLaNhfj9kiKimpBrAMmZTkiCBgotc+qhwghxCoCgWnU1Oz/dL9SikhYEAjMArENIUKAC6VGY0iBoz5ochvHUXwMhCgtHUcoVMjhCwt9jBcaWt6ankcr/XmCYRhMnjytp5uh6Sa0vN0tlEmUMmnpwyARd0jE08em6bg5STTq4HaPr3fmbaL410fviUbbqLgqTwvv+3Q7TTOIlG1ra5EfqI/YIzCafXYIMQSlPs1xdhxpxBGic2LaW5aiulrhdg/H7R6O44DbLXDE6uwniEoMczSFrADpMV5YaHlregPavCdPsCyLd955A8vSS4OFQKHLOx4HpYZlL1SjOhSNZn8IhWzi8XEoZzZKzUA5s0kkJrQrXGc8Z6IxEAzHURa2nSIYFJhmy8qwYUgQoca/0zkIMu04BS5ymxMBRDCMzlW6k0mH2lqHaNTBMBwyMwRnolQ1Hk/XzjtJKTDNzjNl6kwKfYwXGlremt5A3sz0/+1vf+OFF15g06ZNeL1eZs6cyY9+9CPGjh3beEwikeDmm2/m2WefJZlMctRRR3HttdcyYMCAFmrOD4QQ9Os3oFNS22t6P4Uu71jMwXSNwGX6QGwhHY3HU5+Uq6RHHEGjUaf+Y8OgPh9ju86PRR1c5mhM0wdiK2lzHzdCjACCCMKYrvWAm2CwnNpaX85+pm+LvRGKlNqCFBNw1LrG/Qqnvq25ViJ8XRzXvuV7V2B0WebcxnCvsg6IgirGcXxEIh0z8eoKCn2MFxpa3preQN5E77nooos4+eSTmTZtGrZt84c//IGNGzfyzDPP4K/PMX/ttdfy+uuvc9NNNxEIBPj1r3+NEIJ///vf7b6ejt6j0fQ8LpfE50tHTHMciEZ7Ji57OplWemHUshReL7hc6QdEKiWIRlWbFVi328DnUxiGQkiBUmFgC5mZbwVKzaI6RyJaIaC01EbI5U32+hFiDGCBSqDoB1TW170vBo49q2OOzW0kGDQwzY9BVGYtFxxCZWXnXz+drEwhxEoyQ7e6Uc50amra51Su0Wh6Jzp6z176XPSeO++8k9NOO40JEyYwadIkbr75ZrZv387q1Wmb0UgkwmOPPcaCBQs4/PDDmTp1KjfeeCPLli1j+fLlPdv4TsCyLN5881W9NFggaHmnSaUcwmGHUCj9/55Q+INBSUlpnKLiTRQVbae0zMbtWY803kca7+P2bKC0TLVqktNAMmlTU+NQFxX1NvfryVT4Ie3Uuw2PJ/sjWimwLBdKBZvsjaLUGpTajKP6URMysK0hoPZd6XSBmk5tbdcqvrW1NkKMBZq/dAUT6h2gO5/iYoEQqwEHwRAEoxEMBiyEXIvf3ztmWvUYLyy0vDW9gbxR+vclEokAUFKSTmbz4YcfkkqlOOKIIxqPGTduHMOGDeuQ0t8QVsu2bWzbbtzXsG1ZmduO03Tbqd9ONW6nUpnbSjXdTs8S7rsNoJRDKpVCSsGwYSMb2+U4DpaVarLdsN9u3LbtzO3e1qeGtjfd1n1K90lKwdChIxqv3xf6lI9yCgYlhrkBIT4EKpGyH0otR4gwDQhRgxDLCQREu/pkGtCSzTvUYpq5+1RdncBxJtWHJ20IgRlAOZOJ1hmkUjaVlXESiQNQzsEo50CUcxBW6iCqqtIrFl0pJ8eBqioLwUEIyhEMRDAcIWaRTJYSDltdcu8Zho2gFCkmo7BQVAAOUkwBfLhcTq+492zbYvjwUYAqmPFUyH1SymH48FEoZfeZPvUWOWnaTl4q/Y7jcOONNzJr1izKy8sBqKiowOVyEQwGM47t378/e/bsafc1Vq1aDsDq1StYvXoFACtWLGXDhjUALF26hE2bNgLwzjuL2bJlMwBvvPEKO3ZsBeC1115gz5507OyXXnqG6vq1+kWLnmj8aHn66UeJx2NYlsXTTz+KZVnE4zGefvpRIP1xs2jRE0hpEAyW8soriwDYs2cXr732AgA7dmzljTdeAWDLls28885iADZt2sjSpUsA2LBhDStWLO1VfQKorq7ipZee0X3ap09SGtTWRli79sM+06d8k5NhSISoapJ0qj9K7SF7JCAbIXayffunbe6TbYPj5F6GVcqHZYucfVq48FFqQja7dwfZtXMkjn0I4ZoxPPnkKyQSqrFPdXUOH364hWeffZfqapOVKz/izTdf7xY5PfvsU2zYsIva2lK2bPEQiQymqlJy333/yuiTUg5+v8LlqqOkROI4SZ5//sk2yQky7z3HsUEU46gPgQqgDsUeHLUKIQbsd58669575ZVFjBkzjsrKioIYT4Xep7VrP2TMmHGsWrW8z/Spp+XU0I+uR2EYYUxXJYYRZn+zjLfGY4/9h3POOZN58z7HvHmf4+KLL+Ctt97slLrzxqa/Kddeey2LFy/mwQcfZMiQIQAsXLiQq6++mg8//DDj2NNPP505c+bw4x//uF3X2LWrGinNxi9WwzDqv3AFhmFgWRZC7N2WUiBlw7ZESollpZDSQEpJKpXCMPZum6aBEA3baX9qy7Iytl0uF0o5WJaNEILFi1/m8MOPxuv11c+k2Zimq37bwTRNHMfGcRSmadbPLuzdTt+4vadPLperfsZr77buU7pPSsHixS9zxBHH4PF4+kSf8k1OJSVuPN51QPolJBhdP2uca3Y+QCxaTm2t3aY+GYaktNRByGVZa1POLEIhQTLZt+XkdkMgkAKxqf63NUANxbaHUlPjtLtP/ftLFB+Q3YHZhWAmu3cnevzeSyYTLFmymCOOOKY+ylDvllNfvPe6s0+WZfH2269z+OFzMU2zT/Spp+Vk2zZDh/ZrNso706bfNKvw+rYg5d5VBcdxEY+NwrKaX7szWLz4dQzDYMSI9ErgM88s5F//uo/77nuIsWPHZT2nrTb9eaf0/+pXv+Lll1/mgQceYOTIkY373377bb7xjW/w3nvvZcz2H3vssVxwwQV84xvfaNd1epsjr+M47NixlaFDRyBlXi7QaNqBlnfPU1Qs8XjW0KDkC0agqAVC2U9Q/YjFxhGLtd3vwOuV+PwRhNjIXiXVAFVONFpMPN79PgzdiWFISkpiID5sVqZUECs1kUikfb9Bv/42sDz3AWoWVVU9b9evx3hhoeXd+XS1I69pVuHzf9x4rQYadMNYdFyXKf77cuKJx/C9732fL33pK1nL+5wjr1KKX/3qV7z44ovce++9GQo/wNSpU3G5XLz99tuN+zZt2sT27duZMWNGN7e285FSMnz4KP2wKBC0vHueZAKUGtj4t2I3QgzJebxiBPF4+2YK4nGH2kgAx56Vjv+vZuDYs4hEirpF4Xe7JYFA+p/b3f33mt8PiI+zlgkRxnSlaG+Ew9Yma3rLPJce44WFlne+ofD60pHP9n0GNfzt9X1GV5v62LbNiy8+TywWY9q06ftdX97cfddddx1PPfUUv//97ykqKmLPnj3s2bOHeDwOQCAQ4Ktf/So333wzS5Ys4cMPP+Saa65h5syZfULpt6wUL730TKPTjaZvo+Xd86RSDsoZxN7oM0kggSBL0jA1imSiY9ltU6l0dKI9ux0e/c/zVFQkSKW69kUihKC01KC4eCcu9wpc7hUUFW+nrMzotERWQgh8PklRUe4PCsNQNI9c1JRwOhFZO1COi72OzfvixnF6R3oaPcYLCy3v/MIwIkiZe9JBCJAyiWFEuuT6H320kWOPPZKjjz6M3/zmBn7zm99zwAFjWz+xFfLGvGfixIlZ9990002cdtppwN7kXM8880xGcq6BAwdmPbcleqN5z549uxg4cLCeKSgAtLx7B1IKSkokQuwAsQsQCMYBfpQKped4VCnxuCAWc3C7JX4/CGEDkkQivb8tz5LulHlpqUQaq4D4PiVuHOcgQtXNVxkaciYIAZYFsVjuRFdFRRK3J4HgMxBJlCoFNYxIJDPPQrod75N9tsyFYCqOIwGF4xjEYrSamM3lEgQCdSDW7lMiQE0hHPZiWT3/cNdjvLDQ8u58utK8x3RV4vdvavW4aHQsVqp/h67REqlUip07d1BXV8srr7zMU089zl/+8s+cin+ftenvLnqb0q/RaHoOt1viqX93JBKCZNJunIG27bQSWlxs4HbvBvEZjfb5qhSlJhAKtT15V2fj8Ui8XhBCYVmCZFJQXFwF4qPsJ6jRRCKDSKX2KtfBoIFhhpEymT7ESQH9qaszSSQy++XzSXy+XfWZlJsiQc0gFJKNHwter8RftAXYtW+rkWIitrO5SXhUA9QYEol+1NW1rPh7vRKfz0aIz0DEgCKUGkFdreyRbM4ajabz6Uql3zDCFBWvb/W4utqJ2Haw1eP2l+9979uMGDGCBQt+lrW8z9n0FzqpVIrnnntCx6UtELS8exfJpEMkkv6XTDbEpnYaFX6Px8TjqQSxmYyoMSKEkKsJBFo3melsmaez9hoUFX2GYS6tTyS2huJAHCGSLZy4p/EDB9IOzS5XHCkTKLULpbYjhIWUDkVFqWbmN16vyqLwAzgoNuLz7f0t4nEHxx6NUv592j4OR63PyIcANoiPcXvCmGbLr6543CEUEtRFDyARn0xd7Siqq1pfJehO9BgvLLS88wvbDuA4rpyTv0qB47ix7eYfHV2BUg7J5P7fO1rpzxMMw+DQQ4/CMHLZqmr6Elre+UNRkaS4OIUim6ILEMUwkq06pHa2zAMBiZCrQexkb26BOmAliACQazZIZrzovB5QbEapT+rPj6PYgaPWIGW9M25jHwRkKOqZCBHB5cp8i9bU2NjWFJSaDmoUSk0EJYBEjjo2Z1wzF0pBIm5TV+eQSPQeZb8BPcYLCy3vfEMQj40CmgcHaPg7HhsJdH4ksDvuuI1ly5ayfft2PvpoI3fccRsffLCUz3/+pP2uu3d4NGlaRUpJ//4DeroZmm5Cyzs/cLslbk8FQvhRqoVZGFGHlGWNKwPZ6EyZCyEwzARCZM8poNRmhBiGUs1tVpUaRn18BEzTIK3oZ1PkLZTaiWmO3q+2KgXhsIMQbkxzCFIKiop2tvAuTSBl/tte5usYT6/sKGw7/2XQneSrvAsZy+pHLApe3xaE2Pt8V8pNPDayy8J1VldXcd11v6CysoLi4mLGjZvAH//4Z+bMOWy/69ZKf56QSqVYtOgJvvCFr+ByuXq6OZouRss7P0g77X6KYhLphdMcSr3y5nR6baAzZW6aEkF1C0fUIShu5j6rVAm2XdqYeMc0BYrcGc0VlUg5qvFv21aggjkVdqWCpFKCbI67SilSKRspBQpfC/NnJkplryOfyLcx7vNLvB4FIkTaKTpILEa7w9QWKvkmb00ay+pHbaQMw4ggZArluOpNerou18f/+3/XdlndWunPE0zTYO7cE+tn3jR9HS3vrkNKgc8ncLkVAoVtS6JR1aGILkJYgANqD4LBKHZkOcqF4/hQqmUTk86UedppuCUHNgPH8aCYno5MpBRKDcG2fYTDe30SHAdatgIVOE7myy8eF/h8o0F8us+xEsF4YrGWf2fHUSgngDAMsmbVVSOIRlusIi/IpzFeXCxxubcjxLa9OwX4/Acg5QCi0d5nPtXbyCd5a/ZFpJ11syX5zjO00p8nCCEJBkt6uhmabkLLu2swDEGwRCHEeiAdX9mUHoLB8USj/g4kxEorvIoKpJiMUgmgqkm5G9RUamtb/6DoLJkLAcXFAikD5FxcUEOorYVUyo3bfQCQzhfgOJn9T6UsUIPr/QKyMYRoNPMisZiDlINwe0oQbAWRBFWCUsOIRHKH+WxKba0iGJxen6m3qdnUYCxrEKlU/r9982WMSylwuaOZCn89QnyCx1tCLGboaHetkC/y1vRttCNvnpBKpXj88Ye053+BoOXdNQSDEiFW0KDwp0mAWI3fn2x3YirLMgAfoHDUWgQBpJiGEOVIMQXUDGpqZJvsnztL5sGgRMgPcdQWhChn32VopQI4znCSSQelFImETSJhZ1XGlYJk0g1qUJYr+VDO0KxOsnV1DqFqN7HYOBLxydTWDqO62mnzaoplKWpqDGxrBsqZgXKmoJzZxGOjMlYiugvDkK1GDGov+TLGvV6RDn2aA8G2HsnmnG/ki7w1fRsdpz8HvS1Ov1KKeDyG1+tDtDcvvSbv0PLufEzToKSkCkX2+PRKlZCIl7fLVEFKQWmpA2IlYDUp8aDUgYRrjDY7PO4rc5fLQCmVkcyqLe0pKY0ixJr6Pf2RYhiKWlAWUIJt+6mpsdv1fEubdyQQbAPhgBqI45RQU9M8/0BjgjKZAiVwHJPa2v1z/BSieQSN7iAd718hRAiFDaqUVMqktnb/zVnyZYwXF0vcng/JnTm5hFi0nFhMm/i0RL7IO5/oyjj9+UZb4/Rr8548wjS1808hoeXdufj9EkUoZ7kQtZjtfCI6jqKmRhIIzETIGoSoRakilCqlNtJ+Rdc0XQQCBi6XhRB7UEhQ/UkkZJs+RlwuichwvK3EUZVAEWAAu3Gc6e1WoGtr09F1PJ5xCKFIJskaiSidibcSITYDDggwpJuSkgMJh13t+oBpSk8p/H5/dWMSM1H/H7e7H8Hg+E5ZcciHMZ5KgdtTQk6lX5ViWdmLNJnkg7z7Fr1o5rbLaVtf9ZpcnmBZFk8//SiWfroWBFrenY9hKESLzq2eDimXtq0IhRzCNSXURkYQrikl1A5TlgYsyyKVqsTt3oSQy0BsQYjNCLkUr3cnRUWtP66VEiCyfbnsDbvZUQU6PVNpE4s5WRV+wxC4PVGE2ERmFKMkiJUEuieHTafh8zvZsxaLKlyuKrze/Xt95ssYTyQclDOC7OqCgVKZ2Zs12ckXefcFXC4XQkAikT3XR18kkUggBK1GhtLmPTnojeY9lmVhmqZeGiwAtLw7n9IyMI0kjlqVtVwwkXC4pFUnUSHSs8CmmY5uE4u1zTm1NaQUlJSEEXJD1nLlzCAUat1hsqzMSX80ZKtDjaE2MrBLlLS0Gcga0h8Y2a59AJHwgA7P9ncnbrdBcfE2yOK8msaLYBrV1R2XfT6NcZdLUBywEWIjUAukzeFgPOEaoWP2t4F8kne+kMu8B2D79u1UV4cIBErxeDx0ZYjNnkWRSCSIREKUlZUybNiwFo/W5j15hGWlMNtrf6DJW7S8OxfHljiyAiEmotRGms5GC4YArSv8Ho/EX2Qh+ARELeDB7RlFMuGnrm7/lFmfD4TMpWSCkNtxu8eQSLTcxkTCxOsdCfs4X6YdeLsu8k060Wguu++0+ZRhDGyTKYgQ4PcbuN0qnVk3IYjH2+eHsD8IAYiWZglTQBSfz09dXfNGSSmQUuI4TosfBfkyxlMpRU3IwO+fjGmq+n2CaLS5T4cmN/ki777A0KFDAQiFQkQirRyc5wgBZWWljX1uCX335QmWZbFo0ZOccsrpOrFHAaDl3fnU1UFJyQAUW5HiQBQpwEHgxVFJ4q3EjzcMSVFRrD6MZANJhFiNxzMa2x64n4mKFJnOwPuSalMm2rTt/xA8noEIsRuEhVIDsS0vkUjXRb6xbDBMPw0zwfuiVKBNM8J+v8TnVyjnIxBVCMDn64fXO5ZwmG6ZVbYsBWoAiIocR5QANbhcvoy9hiEIBARC1iGoQ+FHOcVEsvh35NsYdxy1T+jZ/FP20+YP6dWy7g77mm/yzneEEAwbNozBgwf3+YhJLpcLw2hb/gdt3pOD3mbeo9Fo9h+PR+Avsutn6uOk7fiHYKWCRCItz9QHgxLTtRLIPgOsnEOoru74bL/HIykq/gzIERNfjSccLmuXeYzLZSCEwLKyh+TsTNKRg+KIjI+ixlKUc3Crv4/PJ/AXKZRaQfMPIAPlzNqv37g9lJYaSGMFzeUtkGIqjlqLY88kFEq3J91/hRArycwtYIKaTigku1wGmtyIhEVoaw0rnl6N4TaZ+aUpFA0KoFzatTFfacm8R5MdPdOfJyjlEIlECAQCCKEfUn0dLe/9xzRl2mRGQDIJ8bhDIqFIJg18vglNbPKzR6LZF8NwyKXwAyDqEMLfYXOHeNzC5xuGNHaT6QgL4MZx2qfwQ9tnM71eiekCx4Z4vGN26o6jSMR9eL3jQHzC3j54QE1u0xK7zydA7ST7ioeNEDtxu4eQTHa94h8OO5SVTUfxCVBJemY7gBSjcNQ2UAGa+gkWFYn6UKn7zipaINZQXDyNcHjv71ooY1wIMA0DpRwUok1jrdPbkLB5/GfPse61vY7Zi+96hzlnz2Le945Cubs+S26hyFvTu9F3Xp5gWTavv/4ClpX/mSg1raPl3TaEEPj9kmBQUlwsMYz0I62kxCAQrMLlXoXpWobfv5mysvSHgFKKaNQhHHaorc0eiaZDKMn+mDxYls2iRa+jnBmgyur3ClADUc5BGQpjLtxug5ISSWmppKhItppszO0WlPUDv38zbvcqvL71lJTG2xQpKBvRqENtbT8c+2CUmoFyZmGlplNT03q4TrfbSIc8pSb3QaKK7rKMcBxFXR3AYKQ4ECmmIMQAHPUREEGpcRkZnE3TAaI5aotjGJljuRDGuEg5RD4N8eyvX+CxHy3k41c/gliq3Unw9gfDkGx6e3OGwt/AOw99QNXm6m5xrC0EeWt6P9q8JwfavEej6d14PIKiIhuaONWiRqFUAMQGhAjtc4ZEqZmEqjsWtrLl6DQSxz640dRjfxACfD6J253+Ox4nQ7nMRUmJgWHsBLGd9Ex5AKXGEq1zkUg077BhSIIlyXpzlH3K1UhisSHdmnDJ4zEoLq5CUQlU5ziqtD4RVPcpTn6/xONNIcR2IIlSZSg1gEg4c4WorMxGyOU561HODKqru35GubcgLIe373mP1/72Vsb+fiNLueSBc8Hfvq+3jiZoE0mb+y55hG2rs5vNTT1xIl++8WRs/cLPO7R5T/vRM/15guM4VFZW4Di9P9xdb8RlSrwGuM38uOW1vFvGNCVFRXEQy0CESCu5dSDWIuQ2pPBnOctB8EmH46tHowrURNJJrvZBTayfFe44DTK3bYdo1CEUSv9ri8IfCEiksR7EFvaaxkQQYgVFRUkMo/lMpt8PQmwg6+qE+Ayvt3uVIMtSOMqFEINbOGoEiUT3jolo1CFUbVBXO4ZYtJzayABC1c1XiJRykTssoAAyldy+PsZjldFmCj9A1WchXv/HEow2zK5LISCWIvJpNbtX7cQOxZHtdOR2HId4JLdZXiySSNv5dTF9Xd6a/CA/NCANtm3z7rtvYNt6abA9mBJKrDq8b76Ccc/fcD37OCWxEB7Ru39HLe+W8fsBsTFH6TaE6J+9SFTh6WBWdsdRhMMS5cxCqTFAGUoNQ6mDiUaL9jv2fUdlLgSYrhRC5DCLER/h9zdXsAzToaUQm1DbrWYYtu2gnGLAqg+hui9DSCV9PeIMmw4bahOL2TnlHIsBamSOCoaly5vQkrxNKRAxC2qTyISNK8tHW0cwJQSdGCXh3ZSEdxN0YpidVHdTXC6DFU+vzln+/qPLsaPJFuuQAiKfhbj9tLv58+n3cOeFD3HLiX/hxT+8hkxmHyNCgKnACSewqmKo2iTeYg+Tj5uQ8zrTT5qMcHX9Cox+pmt6A9qRN09wuVycdNJXeroZeYWUgqJIBfHf3oQxZgxy0GBUZQXx63+J56JLcMZPJuX0zoQdWt4tk7aPzj17p4gBbmBfxULul9meZSmqqxUu10AMYxCOA8mkzf6GLzQMgdvtZf7809rtrJv2Y6hq4YgohpGlfaqVe78HnA3DYUVJSRlSGkgxAKVqUIAQA0kmjFYjLPUkiYSD2z0Yl8tdv+KSBFygRpFK9Scez1T2so1xIQREU7zyz7d5/z8rSMVTTDhyLCdccTSlw4M4HrPDjuIeYePZ/BHJu+9C1aXDqoqiYvwXXkRi9HgSqnPlnYrnDpNot8HB3K5N8vfzHiBRu3cMKwXvPbKcgWP6MetrM7CazPqnf7skS/6zgrfuf59oKEbZiBJO+N7nOPLc2Sx9bCWxcDzjGqXDgkycO65bEsbpZ7qmN6Bn+vMEx3HYtWuHXhpsBz4nQWrRs/i//V2M0QfgVFYiAkF8l12OvWIZ/lRLs5w9i5Z3y6hWH10GzSPggFJDiMebH91eUimHeNyuV/g7jpSC0lJJsKSW4sAWgiV76N9fUFJiIKXA5Uo7KBcV7XVS3pe0DtiSfbQgm9mJZQmgOPc5qqjbZ9UdR1Fd7VBXF8SyfDjOMFLJYVRXyV6t8DcQiTjpsKqpg3Ds2VipGUQi/bLmR8g2xlU0xT8u+Bdv3fc+yVgKpWDDG5v423kPULm5GmIdjzfurQ2R+PNtjQo/gKqrJfHnW/HVhTpcbzYsy2HaFybnLD/wuHKkJ/eco5SCz5Zvz1D4m/La39/G2rcsluK5377KS7ctJhpKP9urt9bwyIKn+eT9z7j8vxcy80tTMNwGbp+Lw885mG8/dD6y2N3+DnYA/UzX9Aa00p8nOI7NqlUf4Dh6abCtmKkk7jmHEf3zbSQXPYv94SpS/3uN2J/+D3NCOUTC9NZs6FreLZNKCSCXA5dE4Kd52EcvqO63Cc+FEIKSEpDGCoRYRzo+/2YUSzFdYUrLFIFADW7PGjzetQRLaigpaW6GYNsOqBJy25MPIh5vXpb2USgnu4/CeOpyBaLpBuLxtD9DdbVFJGLlVXx7y0pHhgqFFOGwk9McaN8xbhiCbau2U/FJ81UbK2Hxxr3vUrOjJufHX0t4pCK16LnsnrBKkXr+OTxtSPzWVpRSlI4sZcKRBzQrc/tcfP6Hx6JaMCuSUrJ7U67EaBANxXD2+V0TkQTLc5gUPXXDC0hDctLPTuTHL32HHzx/KfOumgt+V7fdW/qZrukNaKU/TzBNF8cffzKmqTP5tRUpIPHoI+kg7U1Rivi/H0R6PORWlHoWLe+WiUUVSpWT1UJRTSSRMFHOTGAIMAClJmFb0zoluk5n4feLer+EfWczFUptQIoEUnpJh4GsRYgNSGMdwWDzx3ZdnQI1KctVfChndDPTEsj0UUCNBkqBwShnJtFoKcle8nHUV9l3jBtSsuKZNTmP3/jWJ8TDSUQHdFTDTqG2bc1Z7mzbhmF3btZS5TY4/TfzOfW6LzDggH4UDyhi9lcP4sqFF+Pp72/RzM5xHEZMG5azvHRYENnEDt8wBBWbc5u4xcMJojUxHAH4XOBzdXu0Hv1M1/QGtE1/nuA4Djt2bGXo0BFIqb/V2kQqhbN9e/ayZBJVUwP+suzlPYyWd8s4jiJcIwgEZyJEZdqJVflQajDRqKyfzZe43aMQIm2L3xNJgVrC5VK5nW9xUKRARYF+pJNDgRBhDDOGlN6MGcpkUlFbV0yRfzaIKgQJlCrDcXyEw7n7vddHYRCmOQjHESQSNtlMo9qLlAKPRyAlpFJ0S0KtfKLZGBdQ3L8o5/G+gBdQCCnaLR7bdGEMGw5bP8taLocNwzJcnSH2DJTH4MD5BzLxuAkoR2H4XDiA04rC7TiKIRMHEhwcILyreVa3L/zgGMyAu9EW33EU/lJfi3W6vD2rbOtnuqY3oO+8PMFxHDZuXNfn7QFNQ1LsJAjYMXzC3i/zm9ZeLKoXR1EoFHnvD7atCFU7RML9qasdR23tMKqryTDfSSZtEgm71yn8AKLVKVsLRRRBpjIjxE5cruaP7mTCobpaEQn3JxIZTk2Nl5oap02On6lUOluxy6UoLU37Gfh8ssPjLxCQlJTG8Pk34vGuobh4J2VlMmvo0EJl3zGeTNoccsaMnMcffNp0SoeXYHXguZV0BK4vnERWgQqBeeJJJDoxqIEQ6QhEUoFAgMdE1Cv8bUUWubn0X+cy8qC9M/6eYjfzf3YiBxxxQIbzrVJQMjRIUb9soXph9KwReEu8He1Op6Cf6ZregE7OlQOdnKv7CZCEFR+QemERKhLBmDQZ91dOo664rENRdgJOnNTN16NCWRL9SIn3+pupMXPPrGk0kM4UaxgK2xb77bjblGBQYrpWkisKkRTTUGoXIFDs2lugBlBXN7Z+Rr5zcLsFRcUphPiIxuRjqj9KjaWmRrXL7rmoSOL2fIYQ+yZDMlHODEIhpZ+tORCWwwePLOf5/3s9Y/+YWSM45ZoTCAwLoDoYXtIjHDyfrCdxz10QrXfY8PvxfOMiEmPKSXTSHKCwHOp21fLW/e8R2h5mwpEHMP3kAzGCnnbbzwshIGmTjCSwEhbeoBcz4MbOUo9pSEIfV/L3C/5FMrrXVCk4OMC3/3UuRqmvw5GPNL0TnZyr/WilPwe9Tel3HJstWzYzatQYpOw7WR1dhsCbimHg4KxdQ/LR/2REl8Aw8C74f0TKhrT7hWEaAv+nG0jc9qdmDmyur5xG6shjSKje+Vv2VXnnEz6fgd/voNhO2q6+CNRQIhGx3zH5AUxTEAxGQTS34xYMBHwIEcRRa4G9Cr5S06gJtV+ByoUQgrIyO53orBluHPugdvlClJUphPwge6HqTyw2tlsz/fZWco1xYTkkqmOseGYNsZo4E+eOY+AB/TCKXChz/54FLgm+VAxRlzaZUUUBYi4fnXA7AyBsxdpFa3nil89n7PcGPXz34W/gGVjUpYq3aQhSoTibl25lz6ZKRs0YztBJgxDF7h53BtfP9M5HK/3tRyv9OehtSr9lWbzzzmLmzPkcptk3XDGCJHDeXIz1ykuoaBRj4iTcJ3ye5IvPY6/ZG4VBjhuHvPRy6loMS5gdDzbecCWpJx/H2fIpsv8AzFO+hD1idIfq6y76orzzCZ9P4vfHUKwmMwa/QDCNmho3lrX/DwivV+AvSgKbgFrAgxBDAQ9ggYqi2LH3BNWPVGpcp4SvlFLg9wvcboWUyfqY+NuBTIdOpSYSrgm2yUTKNA2CwV0gPs15jGMf0qscqnuK1sa4221imgLLcjp1hakrccIJfnfCX1BZFOwDDhnJ2X86DSeLaVpnYxgCKSW27fS4st+AfqZ3Plrpbz9a6c9Bb1P6+xrFKoH99ztwPv4os8Aw8F12OfF77kaF9zo5em74DWFXrpjiLSMEeLEx7BSONIjLjs36CAFuoZC2hWO66ETrCk0vo39/iWI5+yrAadzY1kHU1HSO4iqlIBCQpPUAqz6knxuQCFEFYjfppGLDsC1/i465bcXlkgQCqfroQfXmPJQgxSgctanJPoCBxGPjiEZbj+6ilf7CRUrBJ69/zEM/eDLnMT9+6TsYPWxbr+k7aKW//WhH3jzBtm02blzXJ1J4CyGQFXuaK/wAtk1y0XO4jj567z7DgP2IdqAUxJRBrfQSpWNxmT3YBCMVGP/5F9zxR8wnHqE0FsLdibGtm5Lv8nYZgqAdoyRaTYlVh1fmj5LndhsIkSC7wg+QxDQ7Ty6Oo6ipsdm9O8nSpR9TWSmprk7HqA+HS4nFyolFx1MTajkST3soDjggVpCp3NfgqLVIMTbjWIELrzeO2926X41l2SjVP/cBagCJ3ImUC4p8H+P7IgRYraxI9NSsu5QC05RI2XOO5H1N3pr8RK8x5QlKKaqqKjjggPE93ZT9xjQlzsrlOcvtDetxH39C49/GwYeQdHk7PZxcW3EJheejtcT/9pfGfc6nm7HeXIzv+z/EGX5ARjr4ziCf5V1ECvn+eySeegLq6kBKjNmHUvLVMwhLb69fQUt/X7bWSEVn53hQSrFr105GjBjTGNLPshysfXOM7Sder0Swmex9NFAqDgwE9gAgRH8UKykqOphksnXhJZMGbs9QhNixT4mJUmOIx/PnA7Aryecxng3bVoyeNSJn+dBJg3AXuVsdWZ2JlAK7NsmODXvYsX43Q8oHMmTiIIyAG7uTn9mt0dfkrclPtNKfJ5imyZw5R/V0MzoFpRQUtRA1x+2G+tkQ0a8/7q+cRsjpuUUpnxVLR7zYF8chcdc/8C34ORHZfMlaSoHXUOA4JIWZEWKuNfJV3i5DIJe+T/Khf+3d6TjY7y5B7dlN0bcvp1Z0T9r7jpJWBkzSmWqzzcqZOMqks79Cu0vmLpcCEd5nrw8pDkBhgYohRD8Eg3CUhVK7AQchqjDNfq3ex3V1DkKMwOUegGArCAtUP5QaRDisI/c0kK9jvCVcAQ+fu/BQFt/9bsZ+wyU57fovIrxmVnv/rkBKQaIiyt/PvZ/Inr0rWkX9/Fz6r3PxDiru1pWHBnmnMyqrbv/o0GhAm/fkDbZts3btqj6xNGhZDsaMWTnLXYcfgV1dhfuiS3D/5GrC7o7Z8ncWoqaGXDYJKhRCRuua7S9VMUr2bMX43yu4ViwlULObIMl0CLo2kK/y9iWjpJ58ImuZ88kmjEioW9vTEVIpB8dxIcSYrOWC8TQNMNVZdJfMHUeQ9hlowIMU43HURpTagOIzlFqPo9YjhRdFg29Nos33b22tQ03IQyw2nnhsEpHIIKqrHa3oNCFfx3hLKFNy1LcO54K/ncGoGcMpG17CzC9N5fsLv0XJqLJuVbJVLMUDlz2aofAD1FVFue87j+K0wUelU9uTtIjuqmHdonVsfOkj7JoEQo8HTTejZ/rzBkUsFqV1s4P8IOEtxn32OZkzwoAYNgzz5PnEXH4sh3TEkB6PvtAWU4+9lKk4ib/9BXvTx3t3ulz4LvoWwbHjqcHTpmvmo7xFMpEZcnUfnK1bkVMH95qIGrkIhxUlJaUIMQWlGkJ2+pFiFImEp4uiqXSPzONxhdszElEfKlSIkTjqY5r7MFg4aj1CjEp/DKjSdiU5cxxFLNa75dyz5OcYbw3lkoyYPpxTr/sCFZurCG0Ps3PDbkbNHIHwmd220hOvibPro4qsZRWbq4jXxPF6u2dCSaRs3vjHO7xxz94VECHg5KuPZ9r8KShTz79quget9OcJhmEya9acrGVuA7zJKCIWBZcby+MjKtztjofsMRSeRBSRSqFcbuIeP10VKS6OATMPxTtpMvY7S1A1NRizDobhI6kRPlRnBY7uDEpK0yZHyWSzIhEsQfn3vjg8hiD1zLOZCj9AKkXsn//Av+BqXGVDSLUyw9OSvHszyuVKO17nmL0UZWV5Yd5hWQ6hkMDv9+NyjUMIcByDcF3XhU/sLpnbtsJKFWG6hiHEdgReFNEcR8cRuFH4cByfzibaieTrGG+VhMWj1zzL+v9lPgOHTh7EN/5+Fni7R+1IJVp2hknFU3RHHCEpBds/3Jmh8EM6wMTTN77EAbNHUTyqJC+ei5r8Ryv9eYJt26xevYIpUw7CMPYm9igiiXz7bRILn2w0QZHjJ1DyzYsJuwNtnlENksRZ9ByJ116FVAq8XtwnnIjnc8cSoWtssOOYxH1luE44BSEEMctOt7cbn35SirSzl+3kvGzU5cN33jdI3Pn3zAIh8Fx4EXWmr3E1wpeKEntjcfaKrBT2xx/hndWPVCs5AnLJu7eTdPsx5xyG9dabzcpEUTEMGpI3WTFtWxGJNG1rJ3vUNrte98k8EnHw+4fj8QxDGS2bOSgFyplKOJwfcssX8nWMt4SUgu1rdzdT+AF2rN3NqufWMuX4cqTHBI/Zpc+CojI/hktiZ5lAkoagqF/3ZGNXSZtX//pWzvI37n2Xk39xIlaePBc1+Y1eU8pTfMKiNBnB9dlmjCFD8J5zHnLwYACcjzYS//3vKLZibasLC+uxh0m9+EJa4QeIx0ktfArnhefwdHG4xVTKJpm0utXkw5AQtGMUf7IO75L/Edi1hYDKbrOccgTJiVPx/r9fYBw8Gzl8OOZhh+O79tfERhyA1bTdtp11RaABVVOTXtfto8Qdgfnl05DjJ2TsF8XFeH7wY+pc/h5qmWZfolGH6mqFY7vJHYlIoJSP6monbz7WND2HcBRL/rU0Z/l7jyxn5bNrue+SR3Bq2+7j1BGMYjef++ZhWcuOPP8QzKLuSc7oWDaRPblNHmt2RnDaEeRBo9kf9Ex/nmAYBtOnp51fAySxn36SxMcfo8JhVLgG0a8f3gsuJPHwQzjbt6MqK2D3TsTQA1p9WbuTUeLvLMlaZr36Mt55x5Mwu2dWpL00mjYlkyi3m4Tb32rSLEMKikO7if/+txDba9YgBw+m5Ps/osb0N5v1j2OQKB2M5+xvIK0UlumizhHNzHFt04UYNAi1e3f2a4+fQEyarQZ+aSrvfCMsvRR967u4a2twtm1DlJbCoMHUmj7sXm7L35P0lMyjUSgqGgliS7MypYY1HSKaTqSz5O1ypVcJUqle4BCswG6hHbblIE3JttU7uf+7/+GCf5yVfoh3AQ5wxPmzCQ4q5uU/v0FdVZSiMh/HXHoEB82fgtNNMftNr4sDDhlF1WehrOUTjjwA6ZI42qlX0w1opT9PsG2LFSuWMmf2oRh7diMnTsQpKUH274/w+kg8+Tjxf/wN73kXEPvLnwFQW7ZgjBiLZbXyMAlHWrowROsg2H1Kv0/YuJOxtLmS10vc7SfhNH9AB0iiXnuVxIsvQDwObjeuY+bhPv5EIi04yxZZURJ/+j37ajPOrl0kHrgH7wWXEMsyNJSCuBIg3TmV9qjpo/iMrxH/863NyuSIkYhhw2nF1BTYK++DDjoYw8ivYaoU6bCcgYHIAwehVH2YRj2Z1SI9JfNEwsHtHozL5a5X/JOAC9QorFR/4vFeoEz2QfZX3iJpU7u7ltUvrkdKwdTPT8LX349y9aCpkCk55IwZbHzzk6zFM06ZwvY1OwHYvmYX8VAMz6Cuc6Z13AbTTp3KgSeUYycdpEviCrg7Pa9KS9jAMZcewfKnV2Pv4w/kDXqYMX9Kt7ZHU9jklzZR0AgCgSDFyTpid92J2rN3JlkUF+P91qUkHvwXKhZDlJSgamoQg9sYJcXXijuTuy3RZjqHEhUn9fC/iX/wflp7NAzMo4/BfdIpGb4FXuHgPP8s1ksv7j05mST1wiKMcA2+076WVXEHENVVqEj2Dx1n9Wq8yRgxd8dSe9u2Q2L0OLzf/R6Jh/+dXnExDMxDDsXzldOocRVBmx7wAp/PT2cngOpuenuUnt5Fz8k8EnEwzTL8/jKkVDiOIBbrJbPHfZaOy1skbZ67+SWWL1zTuO+l29/giPNnM/fSI1BtnD0XAgwhUbaDcMl25RLJhm07jJoxnGEHDmb7ml0ZZaXDgow/YgwunwtYDUBtZbRLlf50mxT4XBi+9N/7q2AbUmBFksTDcUyviSfgQbmMFlfUPf18fPfh8/nvz55j2+r0R8/YOaP4yi9Pwgh69HNS021opT9PMAyDgyaMJ/HPOzIUfgBVW0v83rvxfOU0nD17EMEgKpWCEaPa9DCx/cXIIUNwdu5sft2x47C83WOHXUSK5D134qzd+yLDtrFefRlTKbynnEq8PkmXJxUl/uorWeux31mC95QvZ1XchRCo2laCrKeS7I/vchwTa/wU/D+5BpmMIwyDpLeIKku0UeFPy3vy5Gkdb4Qm7+hpmVuWQ7gxZ5dWQrqajsrbMASfLtuWofA38NZ97zP1xEn0nzSoVbNOkbQJfRbirfveJxqKMfULk5g4dxyy2N1hJVQIqPismhMuP5od63ez4pk12JbNlOPKmXDkATz0gyc49bqTGo8PDGz/CrJhSJxoCinBStgkYylMj4mr2I0yRJfGgRAphxVPr+bFP/2PRF3ad2vM7JGc+Zv5mKXenL+bAwRGlXH+388kFU0hhEj7FLgMrfBruhWt9OcJlmVh11XBhg1Zy1VVFcLnQw4ZgrJtvD/6KbUuX5tMKqKGl+D3riT+h9+hqqoa98vBQ3BffCk1wt0tEXXMeB3xtc1fZADW4tfxnvh54mb9rFBdNGdYSJSCSBj6N1f6lVKIeofnrPh8KK+vvU1v3l7LISw84KlfJWln4BfLsli6dAkHH3wYpqmHaSGgZf7/2TvrMKuuq43/9j5y/Y4xuDvBEhIgCZBA3I24e5M2bdrU0vZrammbemqRxt3d3YgQQQIEAsEdhvFrx/b3xx27c+8dgWGAZN7nyZPh7HOPrbP3fs/aa73rm4XttbeXdHj39tw5WACz7v6Yk/94bM5a0vUQtsf7d37Mu3c0HmfZBysp6BnhiofOR4S31+shSNWmuPfKxxkwoS/7nDAGqQmWf7Sad+/4CKXAq6v1MGhSP3wF/obPSykFmhSAwHFzJ44L22PNJ2uJlcWIVcR5546PSFanEAJGHTqcE355BCJk7JTpStMkS99YxvN/eD1j+6pP13Lb+Q/wrYfOh0B+O1qWzWdz0/bWdD19313J8V3oZHTNLHsIhBBortfiQK5SKeSgwRjfvYZaI9jmpEnPU1T7Cwj/5BewrQxVtgXZoxdeUTFV0r/Dqh1SCoSg1WqcqrIyf6PrQiIBkTrS72tlUmqBuNv+ENreE3DnzclqM084iaQZpMUH3QkQQlBc3G2nqlt0YffCN93mPp9s+EZOJsGyvt5JINtrb+UqEtXJvO3xigTKVaDlP26iPJ5B+OtRtamGN2+axRE/OWS7hkClFD2GlSIErJ6zjtVz1mW0F/aKkqxJMeLgIZxy/THpJF4FJB02LNnCZ4/PR/Np7H/mBIoGFGWEKWlCsOClxSx67UuGTxvCK/94p8l54YvXl1K+rpKLbj8DfB1PbdzaFK/8/e2cbRXrqyhbsY3SMfklib/p/bsLuwe6SP8eAk3T0AsKcPMUiQLQBgyg0hdNLxe2c8nQ8xTV0o/o3hfRo19j8uUOEH5TeAScBGrFKlQ8gRw8GCcYIZZHo16EW4jtFAJ8jbkHjj+IHDQYb+WKrF1lz164gfzLxnEMomenJU7tt9+EVAoRLcA48WS8sXvvtIJk7YGmaQwbNnJXX0YXOhHfVJtrmiAaFQixFkQZIAjrPfG8XlRVfX2lQrfX3nrQYMTBQ9i6YlvG9m4DiynoFWWfE0YjTa3Bo94chqHxyUuL8x5/zjMLOfS7B7XotW7x+sIm0y7ZP2s1Qgg46ddH0XNUD4YcPBh0iVIgkg4PfO9J1sxd37DvvGcXsffxozn62kMbiL9Tm+Llv73FKb87huf/8Bq5sGnJFqo21lAwqOOLALqWS/WW/KGh6xZupOf4XnmFM76p/bsLuxe6SP8eAsdx+HzVSsYcfSz2M09ltesTJ5H0h3c4PlCptLfGpyk0x8HTdFKq/XGSpvDwr/yS5K03Z4ThaPtNInL6WTkLfrmhCLJ3b7wNG7LatLHjsMxG731c+Ci47AqSN/41Qx5TFBdjfud71Oj+Fj98qjExjzgO//RDEI6DpxskzSCugpBrITwXV9NJCmOXkA7HcZg9+z0mT57WFerxDcE31ebRqEDIuWTEwIm1SK2MaMFYqipdhABd342kKdsIKQWhsMQwXATpqs61tel72F57O57iwPP249PH55OsSdFrZHeOumY65esq2bqyHDNg4tSkWozNd6z88Yae4+2Qs0dpkikXT6b/+N688d9ZVG6ops+Ynhz1wxlE+hSk4+4BVDpkZtEbyzIIfz3mPbeIiaftTenotCBFojKJFbcx/Qa12/LryG78YjNFQ0pw83z0bC+koeGP+khWp3K2lw4qbnH+/ab27y7sXuh68/YQSClA96MOnIYZCGA//2w6IdXnw5hxKPKQw6lWO25OU3gE41XYr76Mt2YNsmdPokcdQypaTFK1XQoukIqRvPm/WZOH++nHaEOHok+alqUUEdf8RL9zNal/34i3aWPDdjl0KMY551PV5HVVSlFthgld81NkxTa8zZsQpd2hpJQaPdCmjx/LBUsLQt1thVwLbe1K7GefQW0rQ/TrR/TkU0kWlpKic2XwpBT06dM/bfcu5EQgIPH7FWADGq6rUVur9tjEuFw2N83GkJdU6usX8mKaEsQGcie9JJCyioKCQnQ9hWIzIEF1I5nUiMd372eh65KCAhfFYiCGAoT0E40OI5kMUFPjbncfNwr8XPXERcy6+2NGHzaCB7//VEPIzwf3fUqgwM8VD51PoEe2I8hxXMYcOZJ3bsudFzDqkGFIv75DCrvKkAyYMogL9+mL53lohpZT4caNW3x4/6d5j/PBfZ9y8h+PxQO0JlKkuk/DyVOQpahvwU4ZA4ywybSLJvPaP9/NavNHfPQe3avF8+Yb0zUtLU7R0R8pXehCLgj1dV073UGUldXstjk2hi4IpOIIx0bpOkkz2CEhKbomCK1bQfKffwcvcwDyXXo5iZHjyVHRPPv6DA3fO69iP/1kznZRWIR+7f9RK7OlQqUUBN0kWm11Wna0uBgnECYuzQZ7mMIjYCehpgo0HRUOkzSD2K7abpv5cDFmvYX9bPYqiu/b3yU+ZFSXlvJuhGhUQ9PXIERTxSk/So2mukru1AlUiJ2ffyeloKBAIsRGEHXSh6o7SvWmqsrbYz9smiMckZjmAiBfjHoRgmIUy5tt74uV6k1t7e7r9S8uliA+I1tNQSDF3lRV6dhtGVDzQAgg7nDr2fdRvq4yq73bwGIuvf+cnPHtwnJ55lcv8cXryzK2+0ImVz15MWZJoHPmv6TD/87Kff0Aw6cO5rS/n4grQKQcbj//QfqN641uaHz82Lys/QMFfq5+9lII7pxqu8JyefnPbzL3mYUN2yKlIS667Uwi/QraVXxQOB52rcWKj1bhOh5D9h+AryCAMuTOuPSvJYSAbt22T177m4ouT/8eAsdxmDXrTaZOPQTQsbUADcLDHTTvBew4qbtuzyL8AKl77ybwm99ja63LdwohUFu35m1XVZXkG9Y8T1ErfBApRURLGyeeuv8HsdFmf0Dy6afAsdPni0YJXPEdVPe+2Ns5UfmdJMnnn8nZZt1/L8GfX0d1jo+UnYWm9u5aCs6EaWro+hYQzSVmkwjxOZHI3rSUE749kDIdd65pDumVBT+WJamp6TjS2dTm3br5EHI+0CSUQKxDiK1Eo+OorPx6kP50v26J5GhALMf2dfh8JcRi+m7pnDEMWfexlovUKzy1llBoME8//ep293GlIFmVzEuYy1aVk6xK4s+hg69MjRN/fTRjjxzFu3fOJlmdZOT0oUy7eDJ6ga+9KWHbDS1oMPboUbxz24c52yecPBZ0Ca6HCBic8+9TuP28Bzj1j8dRvq6Srz5c1bBvqDjIpXedhRY2t7vyt6ZJ3JiF53hITaCHMwt5KVPjqGsP5dCrplK+tpJA1E+kexgZav2cTfu3gWTuE5/z8l/fythn/3MmMOPbU9tcZ6ELXWgvutjEbgwh0oOQUgrPkwwbNhIpd54XQMbj+RV0LAtRVQnFrZN+13XxjxmD8/57uc8zeAiObH1Qaz6Za5pAX7WS1OOPZu5XXU3y738l9NvrqdTbX+hFCFBbNuf82EkfvwoRj0G480i/lDvf3nsqAgEFYl2eVhsh40jZthCvtkDTBIWFCsUCVBMSbhglFBcPoby8Y4h/vc19Ph0hy8gg/A1IIWQZhtGtwUtsGJJgEKT0UEqQSkmSSXe3JMNQl5Pvk+i6QikJqg+IZXn2LUWpr3K2KbUBn28wyWT++HQpBaGQQNPTz8qxJfH4zg8BS3P4qhb2qEXTvB3u43YLsfkATgvPRvk0hh46jEEHDER5HnrQxFWq0wg/gOsp9j9nAp89OT8rTr/7kBIGTuzfsGrneYpgzwhXPXUxKz9dy9QLJ3LUD6dTsaGaSLcQhX0K2kS+80HYLqs+XM1Lf3mT8rWVhIoCTL/8QMafMBqvCQlXukQvDNCjqHE1xPNUk7k69/nr+7emSarXVGURfoCPHpjDiGlD6DOx39dmNa8Luxe6SP9uirCy0CrLcZctQ0SjyCFD8fXth612/xhv11UwaAiiqAhVUZHZKATmaWdQI812Kwz5nRT209nhNwA4Nt6nn2BMO6zdiX5KAUYry8Fa55JvKSV9+vTv1HPuKRBC0XLhgzhSBjts0iwoECg+B5qpZoltCExCoX7EYjtO/Ott7vNJhMi/UibEVkyzG7YNwaDE569GiBWAjQACgWL8/iFUVqrdTvnG75cEgi6ClSBioEII0Q/FAGB1xr6C7qTtbOc+mHBoiS8bhiAScUAsBdKE0vSFMczh1FRrO1x9tiV4ngAC5Cf+PpQSO9zHQ0VBNEPi5ggT0n0auqGhS4GTpy+4rgc+DYGGu4veFT3q4zuPX8R7d33M5y9+gaZLJp2xD/vNHA/BTCEFz1MQNBg6YyiaJnBdRXRQcYPanLed96BJweI3lvHkL19q2BarSPDCn95g8/IyDv/hdJSe+bLVn0pYLvFtcVZ9tpZA1E//ffqihw28ZtKc9f1bl4JZd3+c91re/t+HnD2uN3SF+XRhJ6CL9O+GKCCFdfst2MuaFOLSNPTLvoU3aDiu6Lh4RSHS4Tiep/CCIURhYW5vv2kiCgvbfNxaPUj0x9diPfwg7oLPQSlE9+74zj6PZEmP7SJkUrk4WzbnbXfXrMYnvHwUoWUUl6QLaaWyvauyd29cf/srR+4IHMfm7bdfZfr0I9D1nROfuqdCKQEY5CWDKtRqTYi2It0/kqjmhL/+VGzG7+9LLFcESjtRb/PjjjuWlkNe0lKHui7x+2tBfNnsossRIkE0Ooaqqt2H9KdXJGIgmhTgEwkUZQhGotT4uqReifJ6IqQP1Oq8xxOU4LTw7RcOA2I+mdWFaxFiHpHIBJr7IzoSqZRLONwbRXaVcwAhepNKKV5//YUd6uN62OCgS/fnrZs/yGqbct5EPnzgM6Z/ewpiJ8W4dwRcVyHCJjO+N41pl05OV6sNmjhefhUh1/Xy1mbcHji1Fi/9JdvzDvDp4/OZ8a0D0Qpz5KBZLk9f9xJfvNG4UiV1ydn/OJl+k/qhmiTt1vfvww4+kpoWpD9ry2Io1+si/V3YKdij3qpPPvmEK664gqlTpzJixAhefz2zMp5Sin/+859MnTqVcePGceGFF7Jq1apdc7HbCVMD943X8JY1q7zruni33UrYyS0X1l5omiDqJYluXUfoy88pqNmK0nV8F11KLveZ79TTcOfNpdBLtEltwvMUVUYYdd7F+K+/Af9v/4D+w2up7TeUVDtUgDIv2kD26p23WfbqhebkJmetIWEE8F1+RV12XBP4/ZiXXE5c823XcbcXUmqMHTsBWRcGZUpFgRunoGozBbVlRLxkg+rDNw3xOKDyeUh9eF6gwzzc6WecaGEPjzaVvW4D6m2eSgEq/3uO6kMqBYEAIFbm2SmB1JIZhYA0TRIMpv/T9c5/dyIR6rzu2VAsw/NMYrWDqK0ZQGWlTk2NQog+kFM5ywcUY+UJb/H5JEKuI5Pw18NDiE1p5aCdiGRSRzAcyBxTBP1AhYnFvIw+vl3QJGOPGMmxPz2UaI90QmNhryjH/ewwot0jzH5kLnZ8u9wgnQ5XKUTAAL+eJvydiFRNqsWCZ1tXlWdNDbommffswgzCD2nJ0we+9wR2M2nP+v6t+QxGHDQk77mGHDAAzd/lj+3CzsEe9WbF43FGjBjBzJkzueqqq7Lab7vtNu677z5uuOEG+vbtyz//+U8uueQSXnzxRXy+ziVt2wu/lSD1Tm6PA66Lt+QLtL333yF1EikFkdpykv/4a4ZXXw4ahH7lVQR+9dtGyc7SUowpU3HmzcV+713Ea68S/vG1VMv8FW/roRQk0EkYYRrqce3AdacMP8FjjyPx339nN5om2tBheLYDWvu9WrYnEP2HEvjt73Hfn4XasB45bDhy34nEzB2vf9BeSCnp0aMXUJ+8/CHJp58Euy55ubiY8BVXESvuwU6MUtitIKVASoHjKGy7BMOw62L76x9AGOWNpKam42yV7mctrfLopH0nO26EepuniXoEVBGIZu5oVYTjRHBdF00T5Fe9AahG07qnQyCiGlKrSqsBAT5/DzyviOqqzon913WJlDaeyueadxHSIZVqJOKW5eE4Bro+Fk+tAcoBgaAUIfpTXZ3/wtPRetX5L0hUo+u98tU57BDEYi5KFRII7AfUgki/S8rTqa5O51/U9/HthVKKRE2Kha99yRFXH0SwIECsMs4nj81nzbz16WTUnVCd9usGvZXE2UDUn9VPnFqL9+6cnXP/YVMGk6pJYsUshCYIFATQQwY9evTC9RTjjt2Lt2/7kERVZv/VfToHXbL/ri4I34WvMfao0eDggw/m4IMPztmmlOLee+/lyiuv5LDDDgPgz3/+MwceeCCvv/46xx57bGde6vZDeeka9HnglZcjpWhxaVPTJD4niVAK2/BjNQt1CDkJkjf+LSuMx1u5EuvhB9DOvhA5ZCiyuBhVWUnitlsbrkltK0MtWYw2dr9O1xW2LIdQ7z74Tj2N1PPPNVyTKOmG/6yzsb9ahttn4HbzL0tJLF8B5pEnIJSHJ2Q6P2AXJFTZts3rr7/AUUcdj75mBanHHsloV+XlJP96A6FfX0+V3rmhR50NTULITsDqNagtmxH9BqB69CAe6Inf3wMhHEBi74QkzXSxOh+IIPVx4U0h6INldUyejW3bfPjh25x44uEg5iNEf6AHSm0DFIJe2E6A6mq37toEaS94vsEgveIRjUqkthghahqvW1QjZYiCgtFUVu58ihEMQst5GM394WlUVjqEwwamOQQhBgICz9OprXVblLt0XUD5QOQp4qR8dXH3OxfxuEs8DqYZQUpwHIXjpJ93fR8/7LBjMVrLKcoDpaB4QBFbV2zj8Z+/kNU+/ri9MMJmF4msgxBgGDqgcJxG6Vsj7GPAhL6snpMtEBAsDBDtmUsWUlFblh3XN+W8/SgZUMxtFzxIsibt7S/oGeHsG0/mq/IvGbf3RHxRH9957EKe+d0rLHsvvVrXf58+nPzrozCLA9s15UgpEEJ06f13oUXsUaS/Jaxbt46tW7dy4IEHNmyLRCKMHz+euXPntpv0u66DlDpuHbvWNA3XdQCBpmk4joMQjX+nvZD1f0uklDiOjZQaUkps20bTGv/WdQ0h6v9Om8FxHFzdQPbpg7c+u0IhgBwxsm6w8vA8F1036v720HWdiErBwi9w3nwNlUphTtiP4LSDqdQCdd5BHVFZnp1gW3/fc+fiO+U0Es8+g6rMs8+nH2OMnUCyjffU9G/DMFDKw3FcDCN97a7b+Heue/I8F89T6LpOQvNhBEMELr40PYJLiaquwnr5ZYyLLiHmyR22k+d5dX8nO+WeXNdFqca/QaFpGvvtdwBBz8Z++oncL6ll4S2Yj9z3QDyPHX73dv49tb8/oVzCVRUk//anurieNERpKb4f/JiqVAjLctH19OuwM+6pvFxRUjIGxVKgsr4nAn2AnlRVpVBqx8cIz/M4/PDpCPkFkEKpZYCJoBAQeGoDjjMITUvn4NTWuhQU9AaxNtdIgfIiuK5dF+ZTk7WHEDGQ2xAiilKyzXbSNIkQst3jnsIlfy6GjucZ2HYyy07V1U5aEcxv4roejpNo9d2LxVz8gX4Ico9hSvUhHrfrrnfnjOVN/06fM/Pd8zyPSZOmIoTAcezt7k9aQOfSu8/mtgseyPAc9xvfm6N+dAgpd+fck2EYSAlu3EYoAQKUTyCE1qljRFvvyVCCms21fPrYfFIxi31OGEPp8G6IgI4yJKf9+Xj+d879VG9u7CtmwODC205HCxk4buY9CSnov0/mh0K0e5hBk/pz/3cza9RUbarhtgse4AfPXYZU6QJpRnGQmX86HjdhAwLp0xA+iafaN5ZLJXBqUqyatx4r5TBwQl/MAj9Kl7vVWL6z5qcutA9fm6DgrXW68CUlJRnbS0pKKCsra/fxFiyYB8CiRfNZtGg+APPnf8bSpekktM8++4gVK9KxfLNnv8eaNasAmDXrTTZuTA8Cb7/9Klu3phNPX3/9BSoqygF4+eWnqalJDyzPP/84yWQCx3F4/vnHqVEaxhln5bwm0bsPZVq6quHWrZt5++1XAdi4cR2zZr1JWKVw7r4d667b8Vavxtu0CfvF50n+4bcE4xV8/vkchBDY21p4HkqhbAsRyB++I0IhlBBtvifHcUgmEzz//OMA1NTU8PLLTwNQUVHO66+nPVS57glgzZpVzJ6dlv9csHwFG7p1R0WiWB99iPXqK3gpC/ec8/hk5ZodspOuS5Jb1xKyYwRxeOedVzrlnlasWMZnn6WrYy5d+gXz53+GlJKNG9djxWvwNuVOBgRwV66goqIs7z1B29+9nX1P0P7+5JZvJvXPv2cQfgC1dSvWPXcSUM5Ov6f333+bykoPKzUI1ASE2BvYh+qqCOXlDgsXdswY8eabL+H3CzJzCCwUW1BsQYht+P0u0eg2CgrjuG41ntcbVHGzt0IDNZa1a7exYcPKhpCeXBBiEzU1W1q101dfLSEUlkSjDtGCGEVFDpoWZ8uWjS3eU/2757qg1BaEGEIun75gOFVVqRbt5LqKzZs3tend++STD7FSPly3X9aZUENYuXIzX365aLvs1FH96c03X6KkpBvbtm3dof70+YK5hPsVcMmDZ3Lh7adx2p+O51uPnMNJfzoSAvpOuydhe2yYs577vvUYfznsJu666CFWvLccYXudOka05Z7Wr1zBe//7iP+cchcfPTSHuc8u5M5LH+b+bz+OSLq4NUlqq6u58qHzueCW05h22WRO/8sJfO+5S/hy8yI8lT1GfDzvQ4699tCMWP/9Zo7ng/tyVxh2Ui6zH5nHR/d8ilVeixAwe86HrNi8Ci3q44NP32PVqpXtspMVT7L09WX89YhbePhHz/LkL17k70f/jzdvfBdS9m41lu+M/lR/H11oO/bYirwjRozgv//9b0Moz5w5czjrrLN477336N69e8N+V199NUIIbrzxxnYdf/Pmil3i6dd1HT8uvg2rsR9+EG/zprRyz+T9Scw4hIS/AJ8vkPWFDorCLWtJ/f0vOe9HP3gG1nEnYyuNglg5yd/8MveN+3z4f3093hcLse67J/cu1/6CmpLeWNau8yAbho6Bh1AuruYjZdk7ZKdCXSEWzsd+8XlUZRVy0CDMmaeRKO6Ohd7pXnHPU7z00lOcfszxiP/+I+/Kj3nWOcS+xp7+goqNJP94fe53FfD/7o+UyUCn3RMoDMPAtp3tvqd8dkok4hQXOwSCuTXrAaQYjacW1f1LAzUOy9IxDJd0ESsDz/MTiwksyyEQEITCy8kvHRkimRhJPE6Ldiou9qFpy5rlGJgobwzV1ZJUym7x3TNNnUikGiHLkKI3Sm1BEUcQRIjuuG6A8nKnw98904RgUKRXNQClQiQSEI/b222njupPqVSS119/gSOOOAFNkx3Sn4SQGIZGKmXv1HtCKZa9ujRD4rIeR/7gYCacuTf2buJB1jRJ9aoK/nvq3Tl7wBHfP4g1c9ez5J101ee9jx/NcT8/DPw6luW0+O4ZQqNs6Rae+c0rbFm+jZnXH8Mr/3g7q+ZAPUZOH0q0NMzit7/iO49fiGOKHbKTW57ixuNuy3mus/5xEv2nDEAIuVuM5TujP7muS69ezZ0eXWgJX5vwntLSUgC2bduWQfq3bdvGyJEj2308TdMbluybbqtH0wqK+f9ujNNsGrPZ2t8pdJz+wwhc8xNM20JpGindT1V1DWEznZBc33nq/zZNDffD9/Pej/PJbPxHH4ctNZxQGDlyJN6SJVn7GUcdQ8IM4hszHrnXGLwvFma2H3EUbnG6Um6++wj5DPx2AhGrAd3A1YPENT+iTmo0PTE1XnvT+8j9t0a9oFC9PZQCCwmkqzXuiJ0iukA9+wT2rMZiYt6ypSRv+D2Bq6/B7T+Mxkzk3Pfd/J4MXSPoJJCxGnAciERJGkFSUs97T03/ltJj+vQjscwA4ZNmkvrvv8iCaSLHjqde6KIj3r2W7ml77NTy3y3bRghQrWlh2hZGONqp95QuxLN995T9d+M1+v1+TLOlOHNBppfcBbEQTdubigqBlJE6vXIFqLrJVaJUL4TITfqV6ollpROR891TMORD09dAVqiMhZALCIf3xnVbfvccx8Oyo/jMFB5fICiuC1vycD2D6moPIUSHv3uWBZalkDKd91Ifw91ZY3nTv5vfk9/v5+CDj8A0DYTouP7kumqn35OK27xwwxvkwuv/eY+9TxiDHjY77J5avo+W78nQNT5+ZF7OawX4+NF5zLj8wAbSP++5RaxftIlL7jkb6ddbfPcU0H1MLy6552zshINuanz25Od5SX+3gcVsXbGNmq21rJ6zjsEHD2mIwW+vnYIBH68/nLuSMcBbt3zARRP7p1NbdvFY3tZ72t7+1IW242tD+vv27UtpaSkffvgho0aNAqC2tpb58+dz1lm5w2V2Z7iuR63wQR3JR0EkUtDCLwS0IP0mZGPniGFScPHlOE89jvPx7HTWm9+PcfSxcMBULBcs4SN8wSWY5VtxPv0YYfrQJk7GCkdJYOCXHqaVQLgermmS0AO4rpdWmpnzKaknH29ItJU9exL51repjXTb7mqJOxNmMkayCeFvitQD9xL40c+olW2vxqtLCJVtIHXzv1FVdWRL1zFPPBl98hRiTT4g8kEISTRakPbgDxiMefqZWE89kaHe47viKmJmqKMUI3c7KAWitHv+Hfx+VKD1CtF7CtLeLYmmdQexJbudXihVBhgISgEdRRypWaRj4rP7lut6eG4EqYURork2eBDllbQaFxvwKyBffYz6nAGzVRWgWK2HbZYSDJbWJV8LHEcjFtv5FXJ3x+qm9X18T0SiKkkqllv6yLU9qrfUUlzQLf3vXZ1YqiBZk18cw4rbaM3Ue7au2EbVhmoKhxRnvde6JnFiFgjQgkZdgTMd3acjpeDw7x/Mbec/kHUezZCMnD6U9+/9BF/IxEk5eLUWyvPQ/AZKl+2SGlaeonJ9foWq2rLadO5BTsnbLnxTsUeR/lgsxpo1axr+vW7dOhYvXkxBQQG9e/fm/PPP5+abb2bAgAENkp3du3dvCAHak2Hb6fi84447NafSg207BKZOw3k/N3nVD5xKyvA3CH1U4cM/8ywCx5+ISKXANHF9fuLSR70GZK0wEd36oB93at05XIQQFLgx7MceJ/XZJ+B5yB49CZ99LlbfgegrVpB68P6Mc3ubNpH88w2Er/sNNUYIn2ejeQ6e0Ehq5i6dkKUUeKtX5W1XZWVoqSQE2k76Q1aM5N//TIYeoONgPfEYvh490IaMbnUibGrvuGFgTppKYJ99obYGNA0vGKbWCO76CXUnw/YF0SZOxv0kWxrPPO4EkmYwv3jNHgbbtnnwwSc4//yz054wsZH0F52GoC+I4nSRO1GKUusBC0EEIXUMQ+ZVs6mu9ohG90JqFQixCVCgeuB5JVRVteH9EenwwfxIIYSvTYTFsry6blHvhPh6v78tobUxfXeGbKXOg25oLHl5CXbcZsgBA/AXBVG7yCvreB77nDiGBS9nr2wDjDhoMKvnZqv2bPpyC8XDujWMsVIKvJjFJ08t4NMnP0cIwcTTxrP38WMaKgd7nqLbsG6c9OsjeeGGN7CT6STuUHGQk647kvfunE1x30JmXn8MHz74GU/84gU8VzFs2iCO//nh+EvDba8qLAXDDxrM4rdyhwMO3Lcfmk//BvewLuTCHhXTP3v2bM4///ys7SeffDI33HADSin+9a9/8eijj1JdXc2+++7Lr371KwYNGtTuc5WV1XSKfnVboZQimUzg9wcyCu40RUhZqCcewf34o4ztoqQbvh9fS1UTbX0hIOolcZ59Cmf2R4iCQkQ0inHk0diDhpHIU/U36iWx/v4n1JZsT2Twt9eTvPkmvI0bcv7W/8OfgK5jPfUEav16RPdSjBNOxukzgPgu+v6UUhBeuSR3+Ewd/NffQJWZS7ItG7ouCXz0LtajD+U+X+8+yKt/REyYLR6nLfb+piCChXrnTew3XoNkEhEtwDjxJLyx+7Rp1WRPQVOb+/06gYBC0zwQDkqtQ2CS9u43V+uRKLU3VZWyxQ9oXZfUlytJpdIhN21BQYFE0+eST3JTqb2pKO/yJrYXe3QfTzrcdu79bFudrY4U7hbi+J8fzkPXPN2wbZ8TRnPUTw9FtaKHv9OQcLj7sofZ9OXWjM2+sMkFN53GnZc9gpPKfL8vuessuo/t1fgxm3D43zn3Ub62MmO/boOKueyec1CBxjlMA9yYTcW6SlIxCytu8e6ds1m3YCOX3HEmj/z0uSy5TzNgcPVzl6IVtM3BJKXArU7xn5l3ESvPDCeSmuB7z1xKoGd4t+IxHQ0hoFu3ts3NXUhjj/L0T548mS+//DJvuxCCq6++mquvvroTr6rz0Fqp9pgwCc88A/3AKThvvg7JJNqkycgx46jRgxl68yEvRermf2MMH0Hgu1fjbViPMAyEz4dZtgmr54AsL7KUAtaty0n4Ia0dn4/wayNGosq2krrnrsb9V9aS+uffMU87A9+kqaRU53uCPE8h+vYD3QAnW0pQjhiJ7Qu07Ohsur+UeHVqBTnPt3kThnJzi5I3Q2v2/qagBhPzkKPwTz0I4bp4uk7SCGK7X7/ZrN7mqZSL6wqi0Wog7ckTYiyeWpDjVx5CLCMQHEmsNv8zcRwPp2Wp/JxIJAShcD9Ejuq/SoVwHYNvssd+R7Cn9nEZNDjnX6dw69n3ZYT56D6dU35zNK//N3PFee6zixg2dTDDDhu+S1YnRVDnotvP5JNH5vLRw3OxkzZ7HTqcqRdM4slfvphF+EPFQUoHlzQQfl2TzH9lSRbhByhbWc6X7yxnr+NGNXxIuwAhg+JBxbz85zeZ+0w6N27gvv1YPW99Tn1/K2Hz3p2zOfSa6bitMHVNE8Q31vDEL17grL+fxNu3fsBXH64CoOeI7pzyu6MJdP96E/4ubB/2KNL/TUa9bFVrS8G1wkT2HYJ5/iCE8khoRnogakL4hQCtfBty/wPx1q8j8Y+/NR5A0/CdcRahbqVUi0yPg6ZJ3C8WkQ/eli2I0u6ordkfBeaMQ0jce3fO31lPPYF/wn6ktF0Tnx3XAwQuv4LUzf+h6SgpolHM8y6gGoO2sn7P85CDh8BHuROsZK/euKJ1b1dL9haCb9xgbrlgaUEawlO/hoS/uc0DAQGiPpzRj8pRHKwRNRj6znkmluXis7thGKquJkBdPJUqBjWUmpqvSXxVJ6OtY/ruCM9ThPtE+f7zl/HlO8tZPXcdfUb3ZNC+/Xjhz2+y4YvsHJB3/vchgw8cBL7O9/YrBfh1Jl88if1O3yetRhMwUI5LUZ8C1i9qlEWO9ohwyZ1nIsONoadOwmbO07k+uNP49Mn5jJw+JK3536QbKlPjqJ8eyowrp7Du8w0UDyzktb+/m/c4S99dwfQrp4C/ZWrm1trcecnDVG2q4cHvP8XkM/Zh6oWTUJ7CStgUDSzG3cMWj7rQOegi/XsIdF3nuONOzciAzwfPUySpq9aZYwlf0yRq0yaEpmG/12wAcl1SD95PcNhwCGWSfqUUsjizDkJT2J99gu+EE0nekUNCTNOytNYb4DhQWQEl+Um/qYHfSiAcG6UbJMyO8/TaSiAGDSfw2z/gfjIbtWUzctRo5PAR1BghVDtyDhzHQ44dBz5fOoaiGYxTZhLTfa2S1ub21iUE7QSiqgJlWYiSbqTMAEnVFVbxdUFzm0tNANnv0K5ATY2HaZYSCJQipYtSklRKkEy637gP0I5Ce8b03RGeAhE2GXPiaMadPBYBPPqDp1kxe3XO/Wu3xVCtJJYKkV6x76g8LyFAWB5WzMJJOfgiPvSwieulS8VhaJzwm6M48oczqFhXSaAwQKQ0jBY2cN2mjjLRYh6Dpmt8/uJixh67V9ZHjTIkZkmQoYcOBU8RKso/zwWLAghNtOpiipXFqNqU1rOPVyZ469YPMtp/9OoV6EX5a+104ZuLPXO0+YYiXbVxx03meQrZuzep++7Ou489613M42ZiNUkOdBwPOX48PPZwTlezNnIvvJGjMY4/EWHbaCNGgm2Bz4+IhFu+KC3/RBDBQr35Jqm6mG5CIfxHH4tv0gHU0nJsfFthKYnli2LMOKqhlLnrZq6QtBUxI0zox9eSuuW/qPrCcD4f5szTsPsOzJhMWkK9vQ2hCG5aS/Kmfzd+OAmBcdTRhGccQe3XKK79m46mfdx1FJoWBmqBJIJgC2QgjO0I2hyHRn1B6zTBagtxb0zCrT9PF9vfUXTUmL4rkQ5p8dB1yahDhrH4zdyJpYMmDcibWFqfJLttTSU1W2vpMbQbgeLgDuUASClwqpI8/rPnWTE7vWLmj/o46prpjDpiZENisadLjOIAPUoCDf2g+Rgt/ToHnLMva+fnDl8df8wo3r/vU/qM6UXxiNKcSe2uq7CsJFMunMTnLy3OeZyDLtkfzdBwhcibGC8ExKsSOdvqYSedLnLXhZzoEjndQ+A4Di+//ExDSfcdgecpRCSCqqzMu48qK0Oo7OE55Y/g/+nPkX37ZmyXe41BTjmIGmWgTzsYb8tmEv/6B4mb/kPiH39FlZcjunXLfbJQCJVHui6Ag/f0E9gvPNcgAUoshvX4o/D2G/jkjhEPIcAvPcJekpCy8DxVV5Bl++NOHU9RW9QD/Uc/w/+r3+L/xa/w/fp6UhMOINFGgt7U3kErRvLGv2WulCiF/dKLyKWL0bSubvx1QPM+Ho8rUAMb2pUqS6v4ZEGi1DAS8bb1BU0TFBRICotsogVVFBa6RKNyz0sm3cPRkWP67gDH8Rg+bTCR0lBWm9Qlh3/vIJSW/Y5JKYitr+LfJ93J/869n4d+8DQ3Hn87j/zgGURq+0PHvJjNbec90ED4AZLVKZ7+9Sus/HAVejPPfUsfvq7rMeSAgQyYkN3/Bk3sjz/qZ+uKbSybtSLruPVwHIcXX3yaaJ8IM66cktU+5sgRWHGLh69+itTWWjSZuz8qBUV98ku9mgEDf8SX/2a68I3GHqXe05noLPWejl7ObCtC0kXdewfu5/Nztptnnk1i0rSGxCRdQsiO4325BG/tavQBA5H9+2OvXYfWuzdOpIAYJgHhIh66F3dOZily2bMXvjPPInHTfzKlLDUN//d+QKzPoFyRSBTYNST/72e5R2TdwP/b31OlZ08ybYEpFMFYBdZzz+B9tQxRUIhxzLF4g4d12ArCjkLXNQIfvJn+yMkB2aMn2g9+Qq3sGuS/jggEJP5ANUKsAGwEA0CEUN4mEBZKFYDqRU2NaJMaj5SCgkKFEAuApjrrQZQ3hsrKrnCdLmw/pBQ4lUme/8NrLH5zGUpBn9E9Ofm3RxPtX5g73Ttu868T7yBemfZeCyk48Nz9GDVjKI7lUNAzSqAomBVy09p1bJq/gTsvfjhne2HvKFc8fAEE2ucPFzGbpbNWsPC1LxFCMOaIEUhN8tSvXsJOOpz4qyMZe9KYVvuisD1SVUkWv7GUWGWCwRP7s2npVl698W08V+ELm1z97KVoUT/C8XATNkiBHjBwBUjH44XrX2P+C19kHfvIa6az39kTWk0G/jqgS72n/ehaAdpFqI/RpmwrxGOI3r2xfCESeUyilEdNTQ2RSKSheuOOIK40Ck46hcSCz7MJdSiEHL9Pw8ClSUGocgvJv97Q4G13AAJBAj/9GdXhkoaPFjMVJzn3s6zzeZs2Yj3/LKHrfoP12aeoFcsRffqiHTiFhC+Sk/ADUFWV3wXj2BCPQbT9pF9KQaBsA4m/3JAuTgaoqipSt9yEPn0GgWNObLNXfmeg3t7duhXhrc/WkK6HV7YVo0s55WuBXH08kfBwnCjB4N5I6eIhsC2JUkMRQmHb6bCbtobahMMiB+EHiCPEUoLBYcRiu/59ql+9+jrXoejoMX13gOcptAIfJ/3+WI6LWyhPoQdNhE/L6dgSQrB1ZXkD4Qc47Y/HseLj1dxx8UMNQ39h7ygX/u8Mgr0ibXKQSSlY93nuUByAyg3VuLaL1k7SL8MmteUxinoXoJTizZvep3xdZfpepGD4tCF5CX9Te2NIgt1D+KM+Fr/1FR898BlWolE9LlVrMfvhuex/1gSevO5Flr23EqlLxh41kiOvmY4s8HPMzw6jqG8BH9z3KVbcJlQU4NCrpjH6qJHfCMLfhe3D12Ok2cNgCAhtWUfqt9eR+tufSN38H5K//DniiYeIZE3GaTiOyzvvvNpq9cy2QimIR4rwX/NjZI8eDdvl0GEEfvpzao1GIh10EqRu+ndjeE09EnFSN/+HoJPM2JaPpLtffYVbGyN10OE4F1xO6vBjqTKjWKqFsAJ/K5rF5vZ5uINuCuu+uxsIf1M4b7+FmWxJKWXno97eqZSDHDY8736yb19c0fXt/nVAvj5u2x5VVR4VFYLKCojFPOJxl1jMqyP8bYemO+RNDhaVmOauJQuhkKSoGKIF5UQLKikqEvj9X89pqqPH9HyQUiBsD686hYpZaDuREAoh0DQNYWrIiA+twI8y8tePkBIqNzZWlR0+dTDbVlfw6ROfZ0wjlRuqufXs+3Brc8+PzeF5im4D84tOBKJ+tFYKjOWCqxQTTh7PuoUbmf3w3AzCf9Y/TkKP5F8hbm5vJ+nw8SPzWPXZ2gzCX49ls1Yw79mFLHsvLZXrOR7zn/+Cm8+4N13J16cx9bID+MGL3+KHr17BVU9fwriTx+y6Wghd2CPQxRZ2AYJ2jOQ//gp2Zkd3Z3+E1rcfxpQZ2E7mIGkYBscff1rWsYRIe8U8r/2l7C2l4fYeROCan2KmEiA1bNNPlTAzFGtkrBZVvi3nMbzNmzETMQjVkW9/K4oBwQC2XT/JtX69XjCCKC1Fbd2a1SYHDcLxb5/Mp2Ylsdevz3/e5cvQxk1s83JyR6OpveWo0RAIpj+omsE85TRqNHO7Eo67sHshXx/vKAgBKLuVGhEuO+IL2hE52UhUw9BXgWgi+SsgGByCEEUkEl+Pd1xKgUo6aCmP4w45Cd0wOqyotJQiXbTJ9VAqbcnq1RU88+tXWPv5BqQmGHvUKI760Qy0aFpFzI1Z1GytReoaoZIgWshse1XYOggBJF3K11Sw/IOVRLpHGDZlEFrYROWJTYd0cmvP4aUN/9735LE887tXc+6bqE6ycdEm+k7u3+pc53mKfnv3xgwaWPFsQj3t4sloEbPV8V3TJG6tRbIqgWN7hIoD6BGT824+lYq1lSydtYJItzDDpw1Bj7R8r837t2ZIQi0o1oWKgsQqshN2a7bWsvjNZYw5cQyO6yFCBkYovSrtfA2ljLvQsegi/Z0MTZN4i5ZkEf562K+8hH/iZGyZSZ49z6OiopyiomKklAiRLrClVZQ3xKPLIUOI6UHsljznzeC6HrXCB/4mHvPmA36OolX52m1fEG3sWNwF2ZrGcthwHH/7QnFiup/oVd8n+bc/oaobPUKipBu+S6+gSpjbxzJaS1o0zIbD+jSFz0qC8nB1k4Q0d3oORlN71xohIj/9GanbbsGr+1ARoTDGGWdi9erX6fkgXdg5aN7HtweGIQkGQUoHBHiuTjyeXi1Iv88trZxJlNJpb6EtIQThsEDXXRA2KBPblsRiXpu7pqZJdL0qk/A3nGA5fv8+JBJ7tsdf0wTSVcS3xnj6N6+w/MNVaKbGhJPGcthVUyFo5lVsafXYQuDUpli7aBOVG6vpO7YXhX0KSNakuOn0e/DqQk48VzH/hS9YPXcdVz5yAZ+/uJiX//YWrpX+7AgVBTjn3zPpNqK0fW9BwuHeKx7L0LuXmuDcf8+kz759WyTDkR4R+ozpxfqFGzECRkaoT3Ns/qqM/gcMaNOYp4V9XH7/udx1ycMZ5HnvE0az32njWyX8UgiqV5Vz31VPULkhPfcYfp2jfjiDMcfuRdGwUqaM6oFSCsfxcrqwNCHwbBdpatiOm9G/lSY5+NIDWPruipzn32/meF644fWcbV+8tpQxx+7VpiKPXehCU3SR/k6GlAK1ZVPedlVbi/C8LGeb67p8/PEsDjvsWKSURL0U1v/+i718eeNOuk7oO9/F6jeQuNeBpo1E05KaOUJhME1kuFGOM45OwbkXou6+A29xY5KRHDoM85LLqRZGu5T+PE9REy4i9PPrYPMmvM2bkL37QLfuVOuBdmnoN4VtBpAjRuJ9uSS7UQjk4CEopShQSZznXyA16z2wbeSAgUTOPJtESS+snRgd19ze1eESgt/7UTrsyHVQwRCJr2lV2t0Rui4JBNLhCI4DiUT7V9ZaQ3Obtxc+nyAUioFYSl3WDZquE4kMJx4PkUx6WJbA5ysBkWPlTvUl0bISYBaEEBQWCoT8AqirMirAMAso0EdQWdk2H3YgAELkz10RciOm2R/L2vMKgQkBKu6wat56PnvycwyfzqRTxzP60OE8/8fX+OTReaz+bB2X3H1Wq0WZckEKQcWKbdx+4YMZXu2eI7sz87fHoBsaVrM4c6lJ1i/YyAt/zCSVsYoEd1z0INe8cDlGUaBNxF8Tgjdv+SCD8EP6A+P+7z7Bj167EtmSmoxf4/ybTuWFP75OsiZFpDRMzdbanLv23qtHm/udpxQFA4q46ulLqN5YTaI6ScnAYsyID68NoT1OdYpbz7k/I/TGTjo89/vXKOlfRJ+JfZusWmdCuIr41lpm3TWbslUV9B/fm8lnT2DVymVEo/ulK7d7im5Du3HQJZN5947ZGb+fdvEk7KRN9ZbczyFUEkRoEryvb85LF3YOutR78mBnqfdIKQiv+pLUf/6Zu70Naiw+DfQXnsJ5M4cXQNMI/vJXxEJFWKpjSKlPeJjvvob13LNZbeaxxyMGDSIxeGRDSJIQEFQ2ejKeTrQNBnH8IeLC2KFnKoSo0xVvuwexpWNFE5Uk//R7iGWWRDfPvQBr3L6Yysb+942odWub/xj/tb+gpqR3l5f9G4BoVEPXy+uq0VooFUEwiHjcJJncfSbd4mIFYk7uRjWBigqBUlBQoCG1dQixibRXXwfVF9supaamffcTiUgMswnhzzhnMcnkEOLx1o+ZPs58shOM61FMPDaUZHIPJP1Jh7sue4RNSzJXMcYeOZKB+/Xjud+/BsDFd55Jz/HtH1NUzObG4/5HKke8+9gjRxLuFuLDBzLFFY78wcEsen0p6xZszHnMgy7ZnwknjyHYM9Jq5KCK2fztyFtwUrmlR2f+/hhGHj2q1aRs6SmU5fLlW1/x5HUvZbVHu4f59uMXQmD7BBZEC9r3zWEYGlu/2EyiKp2vtuz9lXz21OekYuln3HNEdy6888x01cjm9wGsfG8FD13zTMZ2zdS4/L5zKR7WLeNZCCet5PPV+ytRSjFs6mD8hQFWfrCKh655Ouf1ffuxCykYVPSNV9rqUu9pP/bs9dI9EJ6noG8/RFFRznbj1NNIGNlx8Z7nsXnzRjzPw2fFcWblKeXturiLFhHw2pbw1BbYQkPbdyK+M85ClKSTo0RpKb6zz0X4fFj33IU/1RhvrhTEMKjyF1BV3JsqfyExdozwp4+rGmJVdxRKKWqDhfj/79eYZ5yNNmYs+kHT8V/3G+xxE7CEjti8KZvwp3+M9chDBNyOe8bN0dTeXdh1CIUkur4KxFekE2AVQlSDmE8gGO/QGgk7YnOfT0ILnnJYi1lXKbSqyiUe64Pn7ovn7YfyJuC6pThO61FvPp+ksFBSVORRVCQwTZechB9AlOPzta2zuq4AFc2/gyrGcXIfqz6OfVdACEEkkk4+Liq2KSqCUFg2PEdNE8x7ZmEW4QdY8MoSIt1ChOviupe+u6Ld75MQgq3Ly3ISfoBFr3/JiIOGZG0v6BFh2+qKvMfdtqacD+77FLe2ldBOQLleXsIP6Rj0tpSA8KRA+XVGHDaMI6+ZjtFk1aPP6J5c/sB5yND2Sym3lfBLBZUry3nhhje458rHuO+qx6naXM0FN5/WoI+/dUUZ5PmIcWstHvvZC9nbLZdHf/wsbm1mIr3S0xV7x50ylvEzx2GWBPE0wcDJ/Rl3zKis4xxy5RQK+hR84wl/F7YPXeE9uwC1epDoj64lde9dDeElIhLBOPV07AFDcnpEPM9lwYI5TJ9+BCgvU+u+GVRNDaxfizZgRIdI3kkpcBZ8jvvZp/iOOS5d2KuqCuvdd/DWpgufSNehherquyVcT1GlBdEnT0OfNAUlJa7rojkWwYCBt2hh3t96K5ZjeDZoO0fWs6m9tze+uws7DtP0cseZA0IsJxQaS5NUkx3Cjtg8XdS1BcUpEUfXVINuTyrloesafr+HEEmk5qJpcQKBImIxjVQqe9yIRDQMYyOI9aRXCCQwspUra1tisG6AlL3x1Day4/8MhCgmHPbq8hQk8TiYJumPClFDOh8hTDymsKzOYUONoU2LSVdNBgSYZgFGXWiTG7OZ/fDcvMdY+NqXDJ86mDnPLCTaPYSXsNFMDbeN3n4poTpPKAykQ2y8HHNA+boqegzrxqrPcn8odh/SjdVz1rF52VZ67dOnRcKs+XR6jujOpi9z95Mh+w9s1zykDI19z9qHvY8fTaIqie7T8UV9CL++01dWhYDElhj/Pe3ujDyIRa8tZe3nGznlt0dz97cepdvAEoQmc0aqVqyvyvsRtG1NBcmaFP4cqxXNpT6VqXHsLw5n+uUHsvjNpWimzl6HDsdXp4jUhS5sD7pI/y6A5ynigSjBCy9BJBOg6yifj5gWyBujresGhx12bPr3ykP26Im3OXdugDZgAM62csTAjrte2bsP9hOPkVy1MqtdhMIobc99lRzHQ8PDX1WG/dILeBs3oI0dh4y0sGzo97fuFt0BNLV3F3YNpBR1hDIfkkit41ZidsTmjgM+gjSQz+ZQQRy38X2NRDRMM4ZiJUrFAQ0heiBEilBI4Lr+DM+6aUoMY0tdiFM9PITQWvA4CtJTTCuFigRoWhJPrUWK0XhqNVD/3IuRoi9KJZHa5wBI6aOgYDSKDcCmJscRhMIjkPFIp4RdhcPNCH/DdVQh5HLC4SHUJBwcK78X3LVcfEETIaD3qJ787chbOOjiyex3+j54bSB2rqvoPapH3vZIaQjDlzk2CwElAwoZsv90bj33/qzfGH6dIfsP5K1bPmDfU8a1qsgkgzon/PII/pfjWH3H9aKwb/u90h4gwibBcNqzr2C787faA+EqXvnH2w2EvymqN9dQtqqcXiO7c/jVByMDRtbHjBBtuM523IYyNYJ9oky+aDKQP2G4LTCkwIlZSEND+PU2FfPrwtcPey5T24MRxEabM5fkU0+gYrUgBNr4vQmecTbVeiinV8XzPDZuXEevXn1JGAEiZ5xF4l//yNpP9uuHSqXQBg/usE7teQp69017+GuySZBx1NEkzCB0YlKppkkCdhwtmc48dP1BEkZgu1Y2dE3gX78GKrZhHnBgehXF70dEomlXWo5wC2PGISSNAB2mtdcMTe3d5enflWhl+UoJ2jWLt4AdsXkq5REM9UXkWZWAfqTq4uE1TdYR/kVN2l0UG1AqhpDdCYUCVFU13lcgQDPCn4aiBigEKnOcs0dWaY9cSN9rHKjAU3GE6I2gf/r4qgpPfYEUw5sQRx1EFajmTg+FEEsIBPZt03l3FLrhkfcjS2zD7x9EMmQw7phRzLr7k5y7jZw+lHdu/4gTrzuST5+YT7I6xas3vkuiOslBVxyI2wbHQqhbiMGTBrDi49VZbUf/+BB6jenJ9565hGXvr8QfNhl64CD0sA+U4vQ/Hc+zv3+VZHV6Dai4byHH/9/hvP6vdPhon9G9WvWuu66iZGgJ33rgXJ793atsXLIFM2Aw8Yx9OOiSyenk5E4MRWlP7H5zuCkn53Osx6o5aznqhzPoObpnzrlGKSjuV5hercmRdF7YK4ov2v7aMjtSy0EXkCxP8O69n7J2wQYKekaYev5EigcU4/n2sOX5Luwwukh/J0PXJNqihVj339O4USnceXNRmzYR/sGPqRHZg4LneSxbtoQePXrjuh5W/0EErvoeyUcfRm3ZArqOMXES+uT9sWbNQuw1rkOvO2YGifzop6Ru+jfe5s3pjVKizzgEJh/YooqMlOlBuKNiEA2pCG5eQ+quO7C3bgEh0Pfdj8iJJ5MIFZLKE/ubD0EvhVAeyWeeaaxHIATGQQcTvPYXxP94fYarSw4egjb9MLZXSMTQBH47jrRtlK6TNINZx2pq7y7Sv2vgeQrlhRBaHmKvCrGsjiX9O2LzeEwjFBoN4kvq1XvSSbrDicVkw3WGQqBYnucoVQj6IqVLUz1AIT1yfeEqtRYpRuF5WhNFIAH0wHX6k0i03kk8T4Hy150uhVIrmz1RLePfQvRAqdwJqOn2TZhmn05Q+mk53l2RxBcIMOWCScx7bhG12zLDr/qM7kmvkT046ddHMfvhOSx4uVFJ7IP7PuWAc/drWfWm/jymxpl/P5G3b/2Ajx+dh5NyKOgZ4ZifHMrA/QfgSEGwd5R9z9oHpdIyzXWSC4w4YjhXjO5JxdpKpCao3lrLS395ky3LtzHioCEEigNteruVJikZ2Z0Lbj8Tz3IQmkQPmenKsJ1A+DVN4Nba1GypIVYep7h/Eb5o+8NghBCEi0MNH0HNUdAjQv+J/UjlUe0B0IIGx//8MJ7+9SsZ26UmOOG3h4O/84i2pgkqV1Zwy9n3NYQcrZ0PC1/5kmN+cgh7nzCmi/h/w9Cl3pMHO0u9J+IlsG/4PaoydxKV78c/o6Z73zad269BIF6VJqqpFM6XSxAl3RD7Taaa/AlPmiYxcFFCYntkeXJMDfypOMK2UIZBygyS8tLJckEniZaIgZWCcISUGSCpsgeNdB0BC626Em/DekRxMZR2J6YH2xyvmg8FySqSv/kluC7amLH4Dj8C56tlqPJytJGjYOgIas1gmwtrFdnVxH//O4jnKn41E2PSZOzPP0dVV6ONHoNb3I2Y9G3X+xHCRi6Yh/3MU6jqKjBNjOkzkIcdSTXbV124CzsPhiGJRGIgvmjWYqK8cVRWdtzHbEdA1yWhUJ1OP+B5OrFYZrxwYSFI7bM8RwAhBuK5pVRUNP6moECi6Z+RO1RHAvugPEF6xUAnlYREou2rbunjzyMXkRb0IZ2NUFZ3fSNQajmNHzbNUUKsdgip1M4l/cXFAsSnedulGIPjBKipUThVKT64/1MWvLgY3aez/9kTGH3YCB764TOsmZs7rv67T11MqG9Bm6+nXqvftT10n44WNto0BgrHY/28Dbxww+tsW12BL2xywDn7ceB5++0RhFDTJPFNNdx58UNUbWpciR41Yxgn/+7odt2Drku+eGExT/7fiznbf/DCZfi6h3O2NYVwPCrXVPLmzbMoX1NJ3zE9mX7FFPzdgqidGBaadR1JlzsvfojNy3IUt9RlWpq1JLBbjWHtQZd6T/vR5envZEjHaSD82vARGJMmQyCAt3YN9qz38FavROvVPys0x/Nc1qxZRf/+A5EyPYglXbAChfh7hdA9G9F/EEkjf16AEBBxk/DVVzgfzALDwDfjULwevakV6Y+EMBa89x6pl1+EZBJ0HX3qNHzHHE+150tLiYZ8UF9jK89gEXUTpG7+D/bqVY3nj0aJXPNjaiLdtjvB2KeB89rLacI/bDjGAQcS/9eNDTUE7PdnIaJRwj/+GdX+glaXeaUUeKvX5CT8APZrr6JP2JfEflMAgeu66QFyOwZJUwP5ycdYDz/YuNGysF99BW3zZkLnXEiMdIJXLnt3ofNh2x61tSFCof0QogJEDUoVo7wI1dUdS/g7wuaO41FVBY3Js/lIeksrFAaOk0lMkklBKNSrLom3OXRcR1JVVZ/c2/6+XVuriBaMQ4iFQBMvqyoFGQW1uMm2GBAB8qjPqKJOqaRt2xLDKARRmdUm6I5S5UAfXFchwiYHf3sKUy6YmF6ZDJnUbqjOS/gBjHZKU7pKIUJmw6Te1megdEm/yf257P5zcS0XqUv0sLnDzpnOglOT4rbzH6C2LFNFavFbyyi8Ocoh3z+ozVGYjuMxYsZQxhw5goWvfNmwXQg48VdH4i8Otm3lQ5cUDilh5p+Ox7VcdL+Oo1xWrl7RqWO6VZvMSfgBPMdj/aKNDDt02B5Z/6IL24cu0t/JUFKDYJDAeRfgbthA6oXnUDW1aMOH47/kMpTpyxlD6XmK9evX0LfvAJqu/HueIo4Oss6ULQz0US9J6j+ZuvPu3Dlo+00ifNpZ2LoBb76J/cJzjT9yHJy330JVVhI8+wLitD4RBYSL/eiDqCaEH0BVV5P8+18J/fyXVMtsWdK2QHMsvJXpCobmkUeRuO3WrKJhqroa6547CFx+VavXK6XE25iLyNQdq6YGhOyQ/Ai/lSD1zNM529z58/CfEoNAIZDf3l3oXOi6qItnt1CYCNUfx5bU1HT8JNlZNk+lIBAozaNKJBEUEo+rZr9x8fv7oGkpEGVNWvwoNZra2h0jiK6rqKoUhMPj0DQHhAPKV0eOapBiL0CgVBmKzUgxAk/lIv0GnleEEOnVg/owJcuSxONqu2O9c6GmxqWkZHhdqFRjaJOgFEQ3lLeFVJPvF8dTDRrzjuvhi/roNqiYspXlWcfuN643voiv00LhXdcDv45WJ5O5pxB+IWDb6ooswl+PTx6fx7RL90eG2y71qUyN4687kkO/M41l76/EFzIYesAg9IgPpbXupdcAN27jKdB8WjrhVylct/X+LaVACNEhqnvQum9qT/Xwd2H70UX6OxlJX5Dg5VdgvfoK7heNiXTuF4tILFlM8Kc/z/k7XdeZMmXGdp/X0ATu+x/m1J13P/0YffoMfN1KSb36cs7fu/Pm4j/lNPC3vtxsWgmSc3PL1KnqKkRFOZT0ad8N1MHTdGS3UigvRyWTZMyqTff76iv8qQT4Wib9nuch+g/M2y4Ki3A7SJZTJBPIokLksKF4FZUNcqf1UJs2IgYXoZTaYXt3Yceh64Jo1AGxkKahJIZRQkHBEKqqOpb470ybCyEIhwW67gIJhByA59UiRNMVLoFgL2IxcpKOqiqXYHAQPt9AEBYoDdfVqa3tmOrEnqeorlakVwtMolGJEBuB9ag66U9BD2AUSukIMRalvgLSyfxKFYAahuMIIpFyEKuot5vPH8E0R1BVJTpU9jEe9wgGeyJEL+plTJUqR6mvUGpciypCWsjkwltP57bzH8gISynuV8hZN54EPr2LlbUCKQUV66vytjspFyflYLaD9EOa+Ad6RZhw5t5pE3gKpzZFqtbC8OsYER9es9oQUgq8WovXbnqfOU9/jpNyGXrAQI77xeEEe4Rb7N9SKdyYzeoFG4lXJOi/Tx+CJUFUk+JfhqEhRHo1oq3vsC/ip/uQErYsz67ALTVBv3G9u7z83zB0kf5Ohu2lJS6bEv4GeB7Jhx4g8O2rG8I86uG6LitWLGPw4GFoWvuXBn12Eue9d/K2O2+/iXnKaWDnT05T5dsQbSkKYlktTlaquhpZ2ne7Jt+UkkSPOibt7a9T7skLN79UXj08T0G//ohoFJVDcN084STiRjBvIZa2QpMC6TMxph6Et3EDxtBhyJmnYj37DO6KdFKliEQaPJE7au8u7DjCYQFiAVkJrGIbmhbENHtiWR0ne7ezbJ5LT14pAymGAxpKVYHwgSqguiYd0pQP8bhXFwlXPz7tHNk/v19D19c3CydKJxMLkii+AuVHiD4IfICB45rpMKFoTV0xtaaoQcjPiUbHU1nZcUQ6kVBIGcQ0bYRYn14RUsUoNZ6a6pYLQtm2w4aaTVzxyAVUrK2kbHU53Qd3o7BvATJodFX7bgM8T9FjWLe87YGoHyNgpMUkkg6pmhTKU/giPrSQ2apH3XE8hO0y94nPefPm97HiNkLAXocN54RfHgHBxqKTXszif+feT/nayobff/XhKv59yp1c/cyl6MU+li/P7t9Cwcb5G7n3O49nKP6MmD6Emb8/FiUFqcokn76wiNptMfY6bAQ9hpdCQG91LpZBnVP/cBy3nnsfbrN+ffj3DsIImTupB3dhd0UX6e9kaJrEXpyD8NfBW7USw06BkUn6lVKUl5cxaNDQ7TqvFCot5p0HwrZBb5loiHC4TY4n5fOndezz6OaJHj22e0JTCqzi7hjHHo/oVpr/WqMFeP6WQ4ikFATcFEIpAj/6Kcn/3Yy3ri7G1jAwjjked8x4nB0k/FIKIrXlxP/8h8zcAdMkcPkVpJ5+ElVVhSouaXKfO2bvXDA1MO0kCoFl+FtUXPqmI73MXkteTVaxgUCgZ0s18tqNnWFzgFBIIOQSMuUl7bRkp+qGbQ8hHvc6LKSgI+D3KxAbmm0VCNEDTy2o+3c87VGv+5eUexEMhuo8/LlgIWQtUoY6lFDHYh7xuIZpDkTK9DDb0odTPZRSbNy4nt69+1EyspTSvXrgeemK43s64RdCICwXK2bhWg5m2IcR8WWMpZomceu855ouMcI+MLV2hWApBdGeUXoOL2XT0uzY9UO+PQUzalK9popHfvxswz5FfQo45fpj6LFXjyyPfVPoUjDnmYW8/Le3M8656LWlVG6s5vxbTwdTQ9MEK+euzyD8jc8C3r/vEw753tSc/dupSXHPFY/iNRuPv3x7OWvnrad6cy3P/LZRCeijh+bSc0QpF91+ZloOtQW4rqJ4SAnff+4y3r3jI9bMXU9BrygzvnUAxQNL2lQLogtfL3SR/k6GUiACwfw7aBq5Av50XWfy5KntPp8uIZyqxf1yMfr4vbHffiv3aadOw9ZM5Ki98BY3VyoBUdINLxxt0zmTviC+Y47DevLx7POMHImUkoLqrXjhCHE90O740QQ65t6TCXkp9KkH4cx6N2sf86xz6jz0uY9tSEWwfBPWffeQXLsGUVSE77QzkL174zkuBEMkfUFS7o4rLQTdFKlbb8pOFrYskvfeg+/Ms6B7T2r1QIPjdHvtnQu6LolYtbCtHFV3DVoySXDocKqlvyuCIAfShblaWklyEaJjH1xH2rwpDMOjsdhVM4gydH1ghyS+CiHQNNEgC7ljx3LIjkguRqmyXLvX/WgNuj6K+nCfnLtQg6ZF8LyODWlQipxVjFtCU3t3xDPbXSClwKlM8sTPX2D57LTmfbgkyHE/P5xBBw5E6RLheKyevYZnfvMKNXUVhfvv04fT/3Q8vm5tV14DwK9z4W1n8NR1L/HlO+lVUzNoMONbBzLuhNGkKpLcfNa9WPHGVeyK9VXcecnDfO/pSwj2juQdA+2aFG/e9H7OtvULN1G7pZZQ3wI0IVnw0uKMdl/Y5MgfTKe4byHl6yqpWFHB5LGT0XyNqkq6Lvnina+yCD+k1XV8IR/P/PaJrLZNX25l1t0fc/C3p6RzRZpB1yTK9RB6Oh/NKAly1LWH4SbrKj7Ljg1z68Kegy7S38lwXQ+512jylTnUJk7CMgJZq+au67J06RcMH75Xm5f+hYBwrJLEH38Htk3w+9fgzPksK4xFDBiI6j+QuCeJXnAR1r9uxNvQuKwuCgvxX/0DqvUAtGGgsFww95+CqWlYzz8HiThoGvqk/TEmTiR+/W/AshCFRUR++GOUbkA8BqaJ4wsSF2ar3h4LiSUDRE48GXPgQOwXX0BVlCP79cc89XRSPfvitKBiFIpVkvjTHxqSgFVFBcn/3YIIh/H94ldUacEOK7ylJWLYG5t7LdNQ1VXI0u5UF5ZmRBBtj71zQZeCSPkmkrffircpXcxIhMKYJ56E+8EsIgdMpVr6t/v4X1ekJ+WWpOB8eF7HafSnz9kxNs9Ga2Fu6Xj57YUQEIlIND0FVAE+lCogVquw7e19PjlkgDHqpDvzIUValcggn4a+IrjbkJ2dZ+9dCy9mccvZ91G9ufFDs3ZbnId/+AwX3X4Gfffrx5alZdx/VSaZXTN3PTefeS/ffepiCLY9j0ophQgZzLzheKzaFHbSxh/xoYd9CE3w2aPzMwh/w+88xev/fpcTrz8GlcfbbyVskjX537ktX5UxpH8hSIiUNkp5Gn6dc/95Cq/+613Wzm8c+yOlYS69+2yCvcJpVSchqMyTkzBgn7589cHKvOee/dCctBpUE5Un4XjEy2J8eN+nVG6sZvi0wYw+YgRaxIfteWBqaWqxm/SBLnQ+ukj/LkDSCGCeewHWfXdnbBfdumGeeAqVXq4BSJFIxGkPyfDjYj32cEOYTeK+ewhc9i3sOXNwPp+HMAz0gw9BTNiXmjqPb7UWInT1DzGrKvA2bkR06wYl3ajW2zdZ1mJi7n8w/gn7ISwL6drYb71J4qb/NIQZmcccgzvrXaw332jIJZBDh1Fw8WVUm+E2na9Gmej7HIB/zHgkCldo1Gi5FZDq4cPDev7ZLNUfAFVbizd/LvrEaTsc1tOAFsKqADzbzpEy0H5750LYriX+lxsyEp5VrJbUg/cTuOLbuKtXog0d/bXxMnYUlFJ4rh9N9wPZYWpKDcqn8rojZ+0Qm2ejNQKl7dA5Cws1hFxIuqpuGkIIIpEx1NT4tov4W5bA9BUgRCMhUiQQFKLIVrtJ7xAmlQLT1w8hVuTYQaJUwW70ru8se+9cSE/hxGxc28UIGBn1AKQUrFu0KYPwN8ULf3yDS+4+ixf/9EbO9lh5nBWz1zDssGE57SQECEfhxC1cx0ufvy42X+kCvdCPTtqJ4SqFZitWfrIm6zj1WLdgI27KQeSRRzX8Bpohs+Lh61HYOy0LbVkuE0/bmw/uS9dtmHTa3sx+dG4G4Qeo2VrLHRc9yLcfuxCCBq7rMeSAgbx7x+wc59ZJxfLHD1pxO+PVkZ5i8WtLeeq6lxq2LX1vBW/8dxbffvRCzJJgh6pXNYWuSZyYBZ5C6BLh13ebj+suZKIroGsXIIWOPXYf/L+5HuPoY9D3PwDfFd/G/NG1VBuhnL/RNJ0JEyajaW3/TjOcFO6ihQ3/Vlu2EP/H31CVFZiHH4nv7POwDjyYatEY4qGUolb4qCrsSWz0BGq696NaBrarA1uuoloL4vr8JP79T+z33m0gwNpeo1GxGNYrL2ckD3tfLSP5j78QclpJ0m0Cx/WolX6qZYCYMFu9Vt1J4S39Mm+7u3ABeke5+QEVCoEvT+EtTYPCohyb22/v5tB1iTv3s7wKR9brryLitRhey5VFv6morlYobyyopvbRQQ3DSkU6RMa1KTrC5rlgWQJUcZ7W7iST2x/CFghIhFhJU8KfhgKxiFBo+44dj3ughgNNQyGrEKKAXKsA6TMOJB53cZ1uKNWzWasBahy1eaKcdgV2lr13FoQQELd5/jev8NfDb+JvR97CLWfcw+r3VyPq+oKmyQaSXdSngAPO2Zcp5+1Hz+Hp/Ksty8tQjsf6LzblPc/yD1ehadnUREqBW5XiyWuf5y+H3sTfjriF2897gM2fb0TkGfN1n0Zxv8K85yrqXYBm5F9lMcImE04cm7MtUhqiqF+jsEWwNMTRPz4EgOHTBrPotaU5f1e9pZaqjdUIUZeIPLw7Jf2z54ANX2xi9GHD817b0AMGIpqo+9i1Fk//Olt9L1GV5OlfvQQtVBHeEUjLZd7j8/nPyXdyw8H/4d7LH6Vs8RbEbvNx3YWm6CL9uwhJdKoCRaSOOAH7zAuJDRvTIrl2XZfPP5+Dm8M73S54Hs7cOaQefhBn9aoWvV6e1zHFh6RSqIpMTW1jylTs11/Pub/auhWxbSs7UrhQ09J6xzmPLzVENL/0qCgqwhMdt9yeNIKYp5yas8044ihSRnbCcUfYWxNp6dJ8cNevR0YLcuaQdCH9AVxZ6ZFMDsVz90N5E3CdfaipKSQW27EJTdcl4bAkEhH4fOnn32F9vBliMQ/PG5oudNUAAfTEdQa2q3Juc/h8NNPtbwqvLnG2/R1ZKaisVDj2aJS3N0qNQHnjcRw/qL2BplVRfaDGEI9p6dXKapdksh/K2w/ljUF543Gdvamu1jv8Q21HsLPsvdOQsLnjoof4/MXFDTHolRuruf+7T7BxwUY0TeJ5itLB3Zj5+2M49DtTKV9byeavyph42t6c95+ZlA4qBgGFvfLnh3UfUpK7Vk1tOmxoydtfNcxLZavKueOiB6leU5nzPUvVpBh39F5555IDz5+I3kLFXhc47OqDGHrgoIzt0R4RLr3nHLQmUqBKl+x9yliueflb+KN+VAvOp+ottQ3zkwgaXHrvOYw9ehSi7h5K+hdx2p+Op3RoN/qO75X1e6lLjv3ZYVD3wSKlYO289XnPufyj1WlPfAdD2B4v/OF1nv/D6w21EtYv2sT/zr2fDfM3outdc8vuhj3DxfA1hr2Tvr4BLN2Hts8E3Dmf5WzXxo3vlEnQkxJRWora2qiuIAwDFavN/5v165A9B7RrKV4ICHkWWk1aA18UFkHP3sSNAE1vM6n7CR1zLKlbb855HH36ocQ68LlYHuj7TMRXVIT95ON4mzYhSrphnHAiatRYkmrnDIweAtG3H+Sxv+zWDYqKsJSkrSEGPqkI2HFEbS2YBm4gRK30f22XcpVqKlMp6AiJymiBhqZVpCUecdGNYoLBvh0qJdkclZUugcBA/P4BgItSGqmUIJHY0fHHo+V3x64jN+2/N6Xqdfs1pEzL2SrlIqUgGByFbigEHq6rZdUWSMQ9EnEQwldHEHcfsr8nQghB2cpytq7I1nsHeP6Pr3PpveeAX2f41EE8+X8vsXRWY4jVVx+uosewUs755yn4ioIc8u0pPP6zF7KOI3XJmKNG4jiZ76WmSVbMXZ8zbEgpePHPb3DmP0+BZmo0SimWvLOME687kudveB0nlT6ukIKDLp6Mazm4jkp37XzQBMf9/DASlQnK11ZQ2LuAkgFF6AX+rPlT6RKzJIgZMPCFzLzhOaWDihvGzPqchON/fSRH/+QQPNtFrwtb8jyP8/57Kh8/NJcP7vuUZG2KoVMGcsxPDiXUI4JX9/UjhMBpRW9/Z4T2pKoSzH8hW/gD4NnfvsK3HjofAl00c3dClzV2E2iaTC9furkLb2iaxrhxE9p1zBQaBTNPI7lsabqybBMYRxyFFQjn+WXHIqEHCJ00k9RttzRu9Ly0mzBP6Iks7d5uIhn1klj/uwl7+fLGjYEgoR/8kFhxzwbi77oe3pDh6AdNx3n37cZ9hcA86xxSkcJ2nbctiGOgDRlN4AdDMJSLhyRhBvJ+dG2PvZvDtl2Ck/ZPV1jO4U00jzgSN1rY5uccFjbinTdJvPQSOHU5GP36U3j5FdSEirB3Iy/q7opIRKJrS0FUNmwTYiOILRQW7rPDNm8JiYRHIgHpBV5FR8SSe55Eaj7Il2CrIh0SQ9/0HfU81aQCcMsfYruzMlVH9PHOgqYJVn2WXdixHmUry/EsFz1osGn5tgzCX4/Ny7ay7IOV7NO3gGEHDWH/c/blowcaHRJm0OC8/56KUeDPsqimSZa+s5x8WDNvPcrxski/ETIxAyZL3lnOef+eSbwygWO7FPUuYP6LX6CZelriLs87KiyXl//yFnOfXoDUJcHCAKnaFIGCAFc+cj4ilLvolxY2mXHFgRlSn/UYOLEfgeJgVu/zpECGzYbwi4Z+49PZ/5JJTDxjb6QmQdGE7DcqPw3YJ3/By54jumMEzQ7NHpFSsH5h/jCtivVVWHELs4v071bossYuhiEVQSuOt2ARqqwMOXwE9OpDjRbI+DJ3XYf58z9j/Ph9W4wB9UsP006C5+HqJrFgAcGfX4f36ce48+dBJIJx+JE4JT1ItJrg1zFwXQ932EjM08/EevpJsCzsTz7GmHYw9uuvZu0vIhHo2RulVIMEYGvE1C89nCefwlvebGJIxEn+468Er/sd1VpjGE0tJoHjTsJ/6OF4y74Ew0QOHUbSCJDKEzO8o3Bdj1phNnqVWiDJbbV3a4j7wwSvvobkLf9tlAzVNMxjj0eMGEWN17Z3wNAE8rNPST33bMZ2b+0a4n/7C5Gf/oxyLXc+ShfSEEKgG6kMwt8IFyFWsW2bRmFhzz0mzjseh2h0CIhsb59ShTiOTpeXPTc6qo93BpRKx+jngy9kphM4FXz4QO6VRYDZj8xl7LF7gV9nxlVTmXbhJDYt24ovZFIyoBgtZOR8W5RSFPcvzHvcSGk4p7fecRUTT9+bT06/h7suf5RAgR+pSWLlcXqN6s6h3z0o70epEFCzqYa5T6frQniO1xDCYidreOd/H3LoDw7Omf3leop9Zo4DIXjrlvdJ1VpITTD2mFEc/eNDMirttgWum06Q3bBwE2/e9D7Vm2voP6Evh35nKv5uIZQAs8DPAefuy4f3Zz5/qUtO+d3RCL/eYshRe6BJgZew8bVQ6VgI0Fqp/dOFzsfuPdJ8zWFIRXD9KpL/+kejJ/alFxCl3Sm45sdU6cEmnipBIBAk3zqkEIKoG8d56ilSH38Erovs14/gWeeSKO2NOnAG+uSpKE2j2pM7LYs/H2IYmJOn4t9nX0SsFnQDGQjg1dTgzv6w8T5KSvB/9wc4ukFBzTa8NasR4TCiT1/iepB8QiCmlSDV5DgZSCRg0wZE36EZ953AIBEoRNvnAEB1iFZ5x6Fle7cVtieI9R5I8Je/RVRXgm0jSkpIGkFqcqpE5UbASaQVj3JAVVbgrl6Fb/gYUs7u9AzTsfP1H467uty8YUiEyC4g1ACxjZ699iKZ2PHaEJ0Fx/FIJIIEAqNBrCCtka+B6oXn9aamZg+JV98l6Jg+3hlwXY9BE/uj+7SGEJmmOODc/dDDJiiF11KeWBNHh9IlWqGfvpP6A+n8sXy/tG2X8cfuxev/fi8ncT34sgPyVtiVYZNvP3oBnzw6j7nPLkQzNA65cgpjjhqZt6qt9MCNWySrk1x02xnUbK3lrVs+YNuaxty0OU8vYPoVB2ZIZjaFMjT2O2sfxh+3F1bcRvfpVMS34ZoSjfTqhZew8TyFETBwWpiThevxwd2f8M7/Gue4yhe+YOErS/jW/edSPLwbLpLpV05h6P4DeeuWD6jeWsvA/fpx6HemEijtuIJ00lVsXLCR1258h8O+exC6T8dJZSvUDZ82BCPcVfF3d4NQnc3+9hCUldXs9KXhAjdG8lf/R66yntreE1DnXEhCte1LOeolsf7+J9SWLZkNQuD/2S+pKe65W8ZdB4SDmUqgyssRwSBeOIKUEvu+u3AXfN64o2ni/+73iffsj62yJ8mCZBXJ636e9zzm+RcSHz95N5Lr27NQaNWQ+L9r87abxxwLhxxJjbd7eHY0TRCJCKSsAFEO+FBeLxIJjWRy17wDhqERiawHsT7PHhquM4Gqqj3vHdU0STAImqZQSpBIgGXteffRhfyQQNmSLdx12SPYyUaSN3zqYE7903F4dZVpV3+wOkuDvx6HXTWV/S+elLeGSksQnmLtJ2t58PtPZXw8jD92L479+WF4rXjPdU3gxNJhiXrIzCvJLCyX9+/6mFl3f9xwnpL+RZz8m6N47vevsfmrdOK64df54atX5CX9Ld5LymXl7NXMuutjkrUpRh0yLP3hVJBbbtqtTPKXw3PnoHUf0o1L7jkL5Uv7cDVN4CUcPNdD9+l4dU6PjoBUCrsyybL3V2KnHAbs24/qTdU8+IOnM2xS0DPCtx48Dy3q26k8Sgjo1q2leipdaI4uT/8ugpQCtXpdTsIP4M6fi//0M0no6ZAJx3H47LOP2Hff/dH1TLNJKWDNmmzCD6AU1mMP47/s28Q7KZynPUgonYQZgZ7pjmtqAvOtlzMJP6Sr1/7z7wR/+weq9OwwEs80EZFIVu5CPWS//g2EP09dtN0KLdl7V0DpOoRCEIvlbJc9erboqepMCAHRKAg5j6ZFmoTcSDA4DKUKSKU6/1pt20Wp7nUJvNlQqgeLFi2lZ8+Bu4XN2wPX9WjservHe7C7Y3fp40KAVKBsLy0jqUD69SwHiQd0G9Wda17+FpuWbKG2PE7fMb0Idgvi1anIuK6i3/je9B3bi3ULNmb8vqBnhP1O23u7CD+AkoL+k/rx49euZN2CjSRqkgzYpy/+wgCe0boYguMq8Ot1f+cm/Lom+fzlRbx7+0cZ27etqeDBHzzNaTccxz1XPAbA+GNHIwON4UhCgHAVbqJOljqgo5oQ7np7Txq/P8/+5mW+eH1Zw/Hfu3M2nz4+n6uevAitILNYopSC1QtyF3eEtAyqFbMx6ki/6yowNQRaOvQoz+OuV9Zpq5iHcBXL31vBYz97PoPgH3PtoXzvyYtZ+NqX1JTFGD51EH3G9kKGWpfP7kLnY8+aWb5GEIIW1WtQKp0sqdfvLygu7pZThlLXJe7n8/MeyvtqGT7PAbljpF9KQcCz0FNpDX3H5ychWy6E1V747TipN17L3eg4eF8uQRs3MWtCShpB/KecinXPXdnXPWgQXkERUS+BqChHJRKI7j2wfAESqmO6QGuJ2M2h6xpC5FdvasneuwIJM4TviKOwnsr24IlQGNl/AElp5k2I60ykteO/ImdVVrGMYHDiLiH9AJalY/p6I0TzSdyP8vqQTC7ebWzehZ2L3aGPC1cR21TN27d+wNZVFfQa0Z1Jp+2NlbLpMawU5c8cHz0FImTSZ2I/hEgTzKyeFNA576ZTWfzGMj56aA6O5bDPCWPY95RxyPCOEUFPCETYZMCUgQiRHm87sifbtSnevGlWzrZ4ZYKaLbUU9S3Ailkc8p0pDYRfk4JkWZwX/vg6X77zFQjB6MNHcPSPZ2AWB3FdDyEEpaU9qNlYnUH465GoTvLqje9w3HVH4jZ7JVqqJQAgtba/Q8L2SFYmWPDeCqQUDJs2GF/Uj2rlwylVHueRH2eHeL54wxsYvzqSspXbKOpXxID9B+B4qovw76boIv27CK6rkP0H5m0XxSV4ZuMXv6ZpDBs2Mue+SoEozld8B0QohNrBuFFdQqh6K9a9d5NclS4NLvv2I3L+hcSLe2C3Iz68RXgedRIjOaHKttaR68zttqsw9hqPecFF2E8+nvb4axr6pMkYJ81EVZaT+uc/Mj609IMOJnLcSdSQPxmpNQQ0hT8VQ23YDI6DKO2OFwhQk6fmQkDZmPFq3LlzQHkEx0/AiRYQa3YNLdl7V8ByPIIHHIhRthV71nsNSyWipBuBSy7FCe8+lU5NU4GoyL+DqELTorskhyMW80D0wTRK08RfOChVivIKqa72GDp0RKdfUxd2DXZ1H5fAV+8u59GfPNewbePizcx7biFn/e0kXr3xbQ7//vQG73hTtETolAL8OqNPGM3Iw4aBAj1o4nhtc4hkXKMUdVKtuc7f8f1X2R612/KX2t62toJDrpzC0CmD0aNmwxhiVyX5z8w7SdXWrdwrxcJXlrDy49Vc9dQliJCBpmmMGDGSN//xTt7jL3xlCUf/5JCMkCHPU/Qe3QupiYb6CE3Rf58+6G1U5hGWy1s3zeKjB+ZkbD/48gM48MJJeYm/YWi898jcvMed/fBc9jlhDNVbatIeza7Vvt0WXaR/F8IJR9HGjc/ppTfPOIu4GWxQeHEch9mz32Py5GlZS8G27RLcdyL2M0/ljFvRDzuCpBmA7Uiy1DSJgUvAs0j8+8aMIlveurUk//xHQr/6HZW+/MVW2gNP05E9e+Jtyi0FJocNz0su48LAGDcR/8jRSNtC6TqWGUS34yT/+qesUCrn3Xcwe/fBmDgVezsIYFC6GEu+IH7PnY3H1nV8x59AweQDqNRCGYnDISx49QWSbzQWJbOfexbtgAMJn3watU2If0v23lWoxE/0hJMJHXEUXtlWMH2IaAQnEKbG2z2usW3wSCdP7pqJKVbrERc6pjkIKRWWlS7UtDvavAs7D7va3m7M4slfvpS13XMVL/zpDY74/sFsWryZ3vv23S6vret6UBdy4njtcwgIx8Outdj05RZ8IZNug4rTuvXtvor2QRoakdIQNVtzhzEOmNCPAQf0x7a9BsKvCcEHD3zWSPibIFaRYN6zC5l47gSSKZulSxeitVCwSuQplKiFDE7+3TE88fPM2gb+iI+Z1x+D8Gnpjys9LdKRK2RHSsHGxVuyCD/AO//7kJEHD6V4RGlOkQ/lKsrXVua97potNQQL/ex16LC8oVNd2D3QVS5tFyKGgX7uhRgnnATBdLl52bs3/quvwRo0LKPjSino06d/3uqWSV8Y38WX0bz0oBy1F/LAadjtJPxCCKJekuCiz5B33Yr1yEP4Tz8T3ymnZp7DcbBfewWf1jEEKqEHME4/K/c1de8OPXu3OAHZrqJGC1DlL6BaD2ELDW/J4ry5E/aLz+O3868stARfTQXJ22/NPLbjkHrqSdTaNQRoTHYTQqBt3ojzRnYVYvfDD5CrVmTYtjV77ypUK5NyXwGxgSOI9xlIpb9wtyP8jiOAFuRD1a5flVAKUimXRMJruJbd1eZd2DnY1fau2lidU3UF0pV2AwV+Pn5kXqeTBGG5vPWfWfz18Ju5/6onuOOih/j7Ubeycd4G5E7OG9LDJod996CcbaHiIL336oFtZ44dbtLmyxZqCCx+axluKl1Uzu8Psffxo/Puu+/JY9GC2WG4SgqGzxjK1c9dysTT92bYgYM46ofTufrZSwn2iuDFbbYt2cy7/53FvMfm41YmEc0dWbbHO//7IO+5373jo/zPV8LwaYPz/rbvuN70Gd0TsziYd58u7B7YvWbrbyCqMTEPPgL/AVPTcmeaRq0eyCIlUmoMHDgk73FSSNSIsQSuvwHvy8WoWAxt5CjcaBHV2xG+EnXjpP72Z9TWxuRg59NPMKZMxTzhRKxnnm7Y7i1bim5bpKQv6zhCQNCz0BMxVHUVoqAAJxAiLsycybSu62H1HYjviu9gPfJgemVBCLS9J2CcfmZaa78dA7+UArVxY952FYshZJqUt0fIyqcL7DfeyHst1ptv4LugP3GZHgR9wsN57ZW8x3NefQnf5UMaaie0Zu9djZ1ZSXpHEY8rTHMYiPk09+Yr1QPLkuyO2vG7u8270LHY1fZuy3AnOlmMS9clX775VUbRLgA76XDPFY/yw5evQCv05/n1jsNxPUYeOoxDt9Tw9v8+xK2T+e0+pBvn/mcmWsTMCguUmkawMJDrcACEioJIXaKA/v0HIWyPyWdNYPZDmR73gp4Rpl9xIPkWnZUuCfSMcOS1h6CctG6/43qouE3Fmgpi2+IMnjSA6i013HHRgxz+/YMZPHUwqi7e33NcYuX5Q5di5fF0gm6OlQjH8Rg5Yyiv/fNd4pWZTjIhBUddM51w3+jukNLVhVbQRfp3A1iuwmpSOCpXz3Ech1mz3mTq1EPyLgVbSCwjjDZ+cl2SlbddKjWmBs5rr2UQ/nrY788i8J3vgt8PyWR6Y1ERnqZnRUsIAVE3gXXrf0muXNmwXQ4ZSvSyK6jWAjmvL4mOM2w0gZ/+HzKVBF3HMgNUo7W7voDresghQ7MbNA3fcScghwxBLVlMJBRG9O5D3Ai0KT9Bug7e5vzVCL2yMnAc6r+3hOfmVb4BULE40vMa1t7aYu8u5Ea6YqtBODwBWAWiGjBRqi+OU0Astnt+sHTZ/JuFXW3vwl7RvBrrhb2iJKqS7H/Wvp36eezUWnkTaT1XMf+FL5h84cQOcTrousSJWeAphK4hfBqep1CmxuTzJ7LvzPHEK+IYPh1fQQAZ0HPnAZmSgy7dn5Wf5q5YPO2iSXhC4Nh2g70PuWoq44/di/fv/phETYqxR45kr8OGIdqgeOO4Kh2d6HpIy+XFP7/J/OcWNcylJQOKmPn7Y3nqVy/Rf+8+DWpAesBg+EFDGiRHm2PEwUPQTD1vKJYe9XPlIxfw9HUvsXz2agBKB5dwyvXHEOwZ6SL8ewi6ZpY9BFJKhg0bicwT89cUOxq64LOTWLPey9vuLPgcffgInLpcBPPoY6kRepbrKKhsrNtuwWtC+AG85V9h3X0HgYu/lVdG1HEVNdIPgTqvznau6nqeggEDEdECVHVVw3b/JZfifPwxqWeeatzZNAl++7vE+w5qlfi7mo45eDDusqU527W+fVFm48qHoxn4xu+Nu/yr3PuPHYdl+Kgv71hvb13X8BkSEFiOu9tLje4usCyPigrw+wdjGI354W7zDPDdCO3p413Y87Gr7a2FTU753dEZibyQVoI59qeHsvyT1cy4cmqnqrAoT1G1qTpv+9YV2zpE7UhaLl++sYw3b36fqk3V9Bndi2N+cgiFg4pRUuCJdFGvcJOKs/meg+cpeo/txX4zx/HpE5lS01MvnEjJkBKUUhn2FqZGcb8CRh8+nK0ryvny3a+oKatl8lkTEAGjTc4tTQjev+cT5j27KGP7ttUVPPLjZzn+54ez5O2vGD9zHI7j4XiKA87Zl48fmUsqlhnuGoj6mXDS2BZzL1zXwywJcvqNJ+HELDxXYYZMZNDY5eGSXWg7uopz5UFnFOfaXRH1kqSu+1neOHjjwCmoRAJn3lyM405ATZtBLAd5L0hUkvzVL/Kex/+7P1LVJAFYCPDjYjgpAGzDR1JpO2wHKQXRZBXWnbfhrliBNmoU2pChWM8/l72zpuH/7R+oMsKtHrfIqib+6+vS0qpNIQTBa39BbbfeGQnCBW6c1B9+m11LIBAg8MvfUNmk/oAQEHaTiHVrcD98HwwD/aAZOCWlxMT2qw11oQstQUqBYWg4jtc1kX8DIFxF7aZq3vnfh5StKqfXiB5MOm08CijqX4TydW58j3A8HvzOE6yesy5n+2k3HMfwI0bs0LspHI+3/v0eHz2YndB6wa2n03di3+1S9hK2R3xbjMVvLENqglGHDsdfGMhQxBFCgOVgVaUoW12OpmssfHUJnzw2D6Wg+5ASLr7rbAi07o9VMZt/HH0rViKHNDFw5t9OpHx9FZPP369hZUQKQXJrLc/+7lW++mAVQsCIg4Zy3C8OwywJ7nEym13FudqPLtKfB7sb6Xccm7fffpXp049A13dukS2/VMjH7sed/VHO9sD3r8FNWci+fUkZAZJ5FowKKjaS/OP1+c/zi+uoKugBgCYhHK/Cfuwh3IUL09vGjcc89QxqAgW4eQYjKQX1Esa2m98bI4QgqCz0ZBypSRI3/D5vIS/zrHNJTJzSatESQypCZRtJ3nFbQyiUKCzCf+55OP0HZajxQF2V2GQN1tNP4H72KSiFNn5vzJmnUxPIjIeMeEnsm/+FWr064xj6gVPgxFO7iP/XEJ3Zx5tD0wQFBRIhalFUIQgARdTUdExlXSkFmibxPNX1MVGH9tpb1yXYHnbcBpEO12iu5749aCjO5XhIKfFQoMtdQgClFFQsK+PmM+/NagsVB/nukxdDaMf6hlOZ4K+H35KzraBnhCsfvbBNpDsfdF0DshV0PM8hUVbDM794jTXz0gX6pC6ZOHM8A/fty6M/fQ6l4KwbT2LwQUNa7Sd2eYK/HZn7PgCOumY6w6YNJtyvMGPlQAgBjoubcBCAFjBQesdV7e1MdJH+9qMrvGcPgZQaY8dOQMqd73lJeoLCE04i8fn8LM18OWIkbu++xKS/9Ukh2IKCihAQaMz0j1gxEn/8Xcb53PnzSCxbSuSXv6FSy1YFiKgUYs1a3PfeQUmJ/+AZuD16USuyE4qVUunVCH8BUTeel/ADeFu3tGkJ2fYENaV9Cf3oWrRELSiF8geJ+yNYOWJOXVdRaYTxn3EevlNOBxS27qMKDdVkfDc0AZ98nEX4AZwP3sc3ZRqie/925zd0YfdGZ/bxphACCgsFivko0qtsdVUYiETGUlPj227ir2mSSASEqEWIGhQhlBeltlbhbIeE8NcJbbW3VKBSNlVbY7z8t7dZ9v5KpC4Zf+xeHPH9g9GiO1YgUam6qEJd1sXv15Xl3QXwPEXhwCIuuOU0nv71y1RtSo/T/ffpw2k3HI8W9eUlw0KkQ6Y8L38um5SCTUtyVK6vQ9WmGlK1KXzbQfo1TeLGbeyEg+7XaV4aR1rw0HeepWxVecM2z/GY/chczJDJmCNGsuCVJcx9eiGDp+ZXyqmH7tfxR3wka1I520uHlBApDeMlbDSf3vCBqJQCTSLrQpc86JLV/wahi/TvIZBS0qNHr047X40vQuT/fo39+qu48+Yg/AH0Qw9DjNubanxtmhRsXzBvHQJtv4lYviCousThN97KXZQrHsf54H2M6UdkhMpEVBLnjlvxljbG1LuffYo2fm/CZ5+fk/g37Cd1ZN9+eOtyJ15pI0a22Rvpuh7VwgfBJufLk2RmaIKAFQfbxdN0kmYo52qCaSex334r/znfeQvzrAtI5V7V7UInwTAkoRAImTaE5xrE42RJ+rUVnd3H6xGNaii1FERz8qBQLCISmcC2be0/rpSCaIGHEJ9TXx1ZAELTiETHUV2ldWqBNF2XGIaG63rYtrfLP5rbYm8JbF2yBSHgrssewU6mk249x2PuMwtZ/tFqvv3oBZBD5nFPhdIkfSf148pHLyRVm0IzNIyQCaaWc1yWUuDFbCrWVbJl5Ta6DyqhsG8BMkdSrFLpOPSW0JKOPqTJfUM14voihZbL8o9W8M5tH1K7Lc7QAwZyyLen4CsJpquCiLREalPC3xSzH57DGX8+gQWvLMEXMusKUDZeuwa4CRsQ6CEDx1MYYZPplx/Ay397O+t4Jf2L6D2qJ4///AXKVm5jwD59mHHFFPylod1Qt6wLnYl2kf533nmHV199lYKCAmbOnMmQIY1yY1VVVXz3u9/l3nuzl+W60DqESC+75fPY2LbN66+/wGGHHYth7PwB3vWg0gjjO+5kjCOPQQlBwgi0GvLSFHF0Cs65AIyHcOd8Wlc6WKBN2h/9lNOoVmkPl25buIsW5L+WhfPRp03Hrssb0DQJC77IIPwN+86fhz7tYOTAEXmfZVL3ET79TJJ//0tWmygugX79O3xpO4KFev99Uq+8BLEYoqSE4Mmn4gwbRVxk2lPDw7VbYPTJJKLLy79L4fdLAsFqhPiK+uxrTdeJRIYTi4VIpdo/tXZ2H6+HrnsoKvO0ukACIXztXv4PhQRCfEE94W96TCEWEQqNp7p657/Hfr8kGFQgyhDYQBHgw7Ik8Tg7pHS2I2iLvd2YxecvfkEqZjUQ/qao3lzD0lkr2OuYUe0amzsLhhDYtSlQoPkNlNm2sCHXVRDQGzzuCnJqjEopsCsS3H7Bg1SsbxRqKOpTwCV3noURMpABoyE8VCkoHdwNw6/nfJ4DJvTFDPtyEmNJWl1o+cdrqNxQxcD9+tFtYDFokpf/9AZzmyTUzn12IZ+/vJjvPHoh4X4FAGxdmf/L2YrbiLp6DQecsy9O3fXW39+r/5nFote+RDc1Jp6+Dwecsy9u0GCfmeNJ1qR47+6PG+RF+43vzWl/OI5bz7mXyo3plZLytZXMf3Exl993LiUjSrtC7L7BaHNM/3PPPcdPf/pTpk2bRk1NDQsXLuT666/nhBNOAKCsrIxp06axePHinXrBnYXOiunXBQSdOGzdgqqtRfbpgxMIZyXGep5HRUU5RUXFe5y6R0C4mFYi7ckPBEj5giQ92aTdQdx+M+6XS3L+XhszFu/Cyxt+E1Q26qYb8Vavytxv+Ai0wYMRhUXYEw8g4eZ/Tn4czFVfYT38AKq8PP0xMmYM5lnnUm1GOpT0B3CQTz+K88H7WW3m2eeS3PeAjMWBAmHjPvsk9nvv5r72Cy8hNWESyRyTVhd2PoSAoiIXxLyc7UrtS0Vuh16L2FV9vKSbQqnspMZ6CEZQWRltN1EoKgIhP8vbrrx9qKjYufcZCEgCgW0gVjRrKUCI4aAq8JQNqhDHMamp6Twy1Jq9hRBsnLOOyvVVvHnLB1Rvzh2SOOKgIZz29xO2p+D6ToOmSdzqFG/d8j6fPbUA13IYeuAgjvvZYYR6RRpI7Y5CWC53Xfwwm77MDtnpNbI7+581AX/Uz8ADBjbo1Utg49wN3HPlo3hNvOmh4iBn/f0kkNB9VI8M4i+BrYu3cOelDzeQa4Deo7pz+p9O4MYTbs95fQMm9OWc/85EGZKKpWXccvZ9Ofcz/Drn/OsUarfFGTZlIDJg4Alwq1L866Q7skJ4SgeXcMnd6YRfDbBrUiSqk5gBA8Nn8J+Zd1K7LVuTv9ugYi6971zo5ATtnYWumP72o82e/jvuuINrr72W888/H4AXX3yRX/ziF6RSKU477bSddoFfZ+gSQmUbSP7zH5Bo7KD63hOInn1uOoymDlJKSkq67YrL3GEklEbCCEO9Ik6zeTUlDMJHHp2X9OtHHEUt6eQoAKE8VBNPuCjphv/c83GXL8NdvBgCAcyevdB6981Kpq1HEh1nyCgCP/lFuhaApmP5AnXx9R07e5pWnGQOwg9gPfUEgbHjsZvkLCjXxZi8P86cOahYbcb+sncfRI/ue5zKwtcJfr8EVuZtF2I9Pl9/Uqn2yYPusj6udMAg2yNfBxHeTs9ga7/x2NlF4f1+NwfhB6gCtTmdtCyqQKxGN6IUFo6ksrJzZF1bs7cQEKtIkIrbBAv8eUl/uCSI0CQ4XjrZF3a519+tTnL7hQ9lhLMse38l/555J9976hJ83UMd4lRLViVzEn6AjUu2UNS7gDsve4TvPHYh0UHFKKXwgF5jenD5veey5O2vqNxUTd/RvSgdUsLTv3mZ6s01/ODFbyGaJAy7MYu7L38kg/ADSF1j0Ru5ZZsBVs9Zh5OwkbqPwr4FFPaOUrkhW5L0xOuOpKBHhLnPLuS9O2fTY3gpM751IOsWbMgZs791xTbWzFnHoIMGp2vRRHyEIj6kFKz5cHVOwg9QtrIcqzaF6euqnPtNRZtH3NWrVzNjxoyGfx9zzDHcfPPN/OEPf+Chhx7aKRf3dUfIiqVDTBKZHdSZNwf17luYWmMmkG3bPPfcY9gthX20ET6piDoxCuIVRJ045i7+6Pc8hdd3APrB07Pa9EMPx+3ZJ4PkWoYfbdLkuh0MAhdeRPLuO7Cefw53+Ve4CxeQvPFvqKcfJ6TyP6/6WgBVgUKqzDCJDpAHbQ4hQG3JnzhGIoFolsuQ0n1YS5YQuOJKjClTEZEIorgY86ij8Z16Gsowd+uKuF93aBogcuSfNCCO1Nr/InVkH28P4nEQDMrdqLrhOts3QCilk3+KEbAdlcLbA9PUEDJ/31NsRojujVckqpFyI6bZOassrdnb8xR9Rvdk/guL2O+U8XmPc8C5++GmHKyyGHMensunD8whuakWsYvGCMPQWPXZupzx607K5ZUb30HroG8SO49cZcP56p7Bq/96B9HkQyhVY3HrefezdsEGPMfjs6c/567LHkmT4rhNxdoK6rUcpBRsXLIlZziQ63gtij4IIufTkgAA64VJREFUKRraPZ/knJtPovuQksZ2AUd+/2CChQH+ddIdzH/+CzYv28rnL3zBv068HSEEQ/YfkPPYc59ZiMiqON56TkIHlDnowh6MNnv6Q6EQ27Zto1+/fg3b9t9/f2699Va+9a1vsWlT/uqkXciGpgn4clleLXzrjdcJTj0IS6a/yHVd4+CDj6iTA9t+REnhvvgCqXffTevL+3yYhx2B7+BDqMkzCWuaJGAnkK4DUpI0A1gdPJ/UYhA89iT8M/6fvfMOj6JaG/jvTNmaTui9N6mKdBBEFBQVrNgVxd696vXa9VOvXa+9V2wgIoiAVOkdRIpUpUMI6dumfX8sCSzZDQkpJGF+z8PDZM7s7Dn7zpx55z1vORPjj9UIIZA6dCLkicN/lKuTZlh4evRCnzMLpW07QvPnYWVlFTqnvmA+rgGDEEm1Tlg6MssC4TmGVeUof96gKXD16oP/lReRW7bEOfwiMAy0lSswsrIQF15S5uORJFEQnGZTNIYBWN6Yir9leTGOw/OqrO7xkuL3GzidSchyOyz+BnyAiqAeiDpkZh6fG5nPB964xgiRvyoigZ4MIRkcCfijJx0pM8LKTVFfohEOkTzyQ7txu+vEmpbLlOLI253iIbl+It4aHtqc0YINsyOL+w26sy8JteOY/8kS5nywsGD/lJdn0e2STgy6pz9WBVt1JAvWFWH93jx/G1peCBFX+pc+T5IbIYmoq7NCEqjOsIqzf9MBjJCOcIfnWkM3sEyLLYsKZ0gDDuW+F4CFEILctNyox+39az/nPjgwZv/aD2qF4nVgEHbXkhKd3PDZFQSzAwRyQ8TV8CArMm+c/1GhOd2yYNIL07nkufOi9tMV7zz0QmEd8RmL2q1rISkSZpTVnnrtauOIc9rJek5iiq30d+zYkd9//53OnTtH7D/99NN57733uOWWW8q6b9UaIQRmWlrsA/x+hK4XGMOEkEhISCzVd7rR0b/7BmP50sM7g0G0XyaiBIM4zzmf4FGVaMO+79vQvv8Wbd9ecLlwDByEe8CZ+BQ3hmEel6uJJAk8RgDZ7wNdw/LGEXC48XlSUPqcCRS9RJ2jeIh/6BGkzEx8r7wY8zhj8UKUc0ecUMu4lZiMiI+PmiZUatESzekutD/HEUf8vx7GXL4Ubd5ccLtRzjobs15DcsswR78qWXg0P9b2nZCXi9yoMagqpmWhOz34hOOEZzk5UcQKrg8ETFzuRgiilbMXYNU/rhSXZXGPHy+ZmQYuVxwezykIYQGCYFCQm3v8cSOhkIkaTMXhdCJCByFNIThtFtaePUiNm+AcMAjTnUDIKh/To66bYKWCiDXPJoF19D1pVJgltDjytlSJC548hxXj19C6XzO6X9aFbcu2405w0X5wa5xJbtK3HYxQ+PNZ+sNq2p3ZivrdGlaoO6CQBJ6kwnNaPu4EV5lU1QVQ4p10v6wLi74pHJNy6vAOrJu1CYDUJinIqlzgcOaKdxJfM46cKMq8EFCreWrBvGcYJg071Y/6/ZZpsWnBNoY+NJDJ/50Z0RaX6mXALb3ZvWY3tVvWRPY68HrDLq5OVxzOQ4tMebuy8GcHop4/kB1EkqNb7ntceSp6FLc72aMy/OkhjHvkl4j9Do/KJS8MQ7iUMndhtak6FFvpv+6661i5cmXUtu7du/Puu+8yYcKEMutYdccwLOQmTWK2i9RIX09N05g0aSznnXfxcWf2cIT8BI5U+I9AnzUD18AzCSqHK9EqssCxaSPB994+fGAggDZ5EuY/2/CcOThsMajXgBzZjWVZCAFOYeLQwha2kOIgSKTbjCwJ4nPTCb73DtrePYc658B5wXDUbj3J0489PtO0yJI8JCaLqFkd8rEs84QvZ+apbuLuupfAKy9C4PDkLpKTcV5/I1nCUWgMwWCIcZN+YsSFl+Lq1gtLkshFLtOHtypZeHZuI/DO/yJWnJQOHVF79cac8RuJN4wm2xF3UsUQSJIgPl4gSSEgCMKFrqnk5oYzvFgW+H0yHk87EBuBfMXYAVZr8vKO73vL4h4vDYGAceTlWSbk5ZkYoTicG/8h8N47Bde5sXUr2u9zcN9zP0a9JuWywmQYFoYRh6y4gaNXZQSSqI9pRSaesKx4Ksq7qrjyNh0yp43sip4XBNOi0akNkFwywaCBJGDep4tjfnbOBwsZ2bleuPphBRHUDLpd3ClqxVsIZ6dxJLmi1jIpKaaAAXf0wZviYd5nSwjmhXB6HZx+aWfqt6/Ddw+Gq66fdXe/cG7oQ/OY7HUy/Olz+OLWsYXO2XdUDxSvGhGR4qnhoUXvpmyeXziWp2Gn+jTu1pAWPZuy8OtlZO/PpcmpDanfrg5j7hlP+vYMEuvEM/qrq5g6fzJnnTXsKHkX/YBSogTd9rzqVJIaJEZ99FmSoNXAltw9YRTzv1jKwZ1ZNOvWkK4jOqIkFqO+jk21plpW5P3666/5+OOPSUtLo02bNjz22GN07NixROeoiOw9yXou/tdexopi8XdddQ2iQycyhQsIL9sFAn5cLvdxW0kS0nYQfOmFmO2uR58gK+Gwj2u8GUD777NYGRlRj3ffdQ/+d95GpCTjvPdf5Kpe4oPZhMaPw1ixPFxxtsupOEZcTI4roSBtWqLhI/Dsk0TTjpw330Zeqw7FDhx0SSbS2DEYiwpbugBc/36U7JS6J7zaoCIJvFoe5tYtWHv3IDVtBvUakKu4o07CZSHvY5Go5RJ4/JFD/iqROM4ZirFjO+b+faj3P0yO5CqXPlQ2JEmQmGghpD850jXEsrxgtScz0yi4lhQlnKdfkg7lTjcV8vKOv+hURcj8RJBg+Ak+9SjR3ihEcjLqQ4+W2/UlhCApSUJI24H9hIOHE5FEI0xrBxydqtTqRGamWiGKUVnIW+gm39w+jr9X7IzaXrNZDUZ9cQU4K7YkjwjqLP12JdPfmhexv+lpDRn52oWYrrLtjywEek6QQHaAzN1ZLP9pDWun/YXiVDj/sbNpNbAF1lG+7sIwyfwnk19fmsnudXtJrpfIwDv60uT0hlhqYUVbChos+GIpC79eTjAvRGrTFM7791nU7VAHVBlJEki6ycy357F91S52rN4d8fnm3Rsz4qVzkVyOSHn7NN684GN8mZEvpqpLoVaLVK55+2LS/j7IivFrcHhUTru4E/G144vltqVIAlMzkVQZ3SzeM1WywoHLWXtzkBSJ+FpxyF71RNVrKxI7e0/JKfadN3nyZAYNGoTDEXYt2Lt3L7Vq1SpINeb3+/nqq6+46aabyqenxWTy5Mk8//zzPPXUU3Tq1InPP/+cUaNGMWXKFGrUqHHsE1QgIYcT96ibCP48AWP9OrAsRHw8jqHnYjmdBCU1olJecUq1F8UxfcudkQ9eKRSMqfADmLt2ItWogblvL9qPP5Aw4hL8zz0bkXHGWLEM/1/riX/0STJlD7IsYf61KarCDxAaPxb3/S2KLK51JAFTImnYhQTWrCmU6UbpcipSYiIqJqFyzhJyLHTTIkv2ILfpiGjbGdM85BZVxExaWnkXhaJImKv/jKrwA4TmzsE18koCH32AOLAfUbvRCX9xqgji4kQhhR9AiDxgE253C3y+8MNT103CoST511bpoxPLU+YnCpGVEVXhB7AyMpD8eeAtH6XfsiwyMgyczoZ4PI2QJAvTFJgCEC7CsjOBOCyrOb48B2YxlaOyoLTylpwybQa0jKn0t+rbDNmlYlTwzWs5Fbpd3pUO57Rl9eR1BHNDnDK4NSmNkspc4QcwLAsR5yAu3okryc2AOgn0v6kncamHFNZofZQlklvUYOT/RmCGDIQsIXvUmAYn0ynTe3RPul95KpZuIjll1DgnWnaQAxvT2LcpjdRGKTQ9rSF/zdlS6PNbFv+DFTThqEtd9jq47KXz+ezm77FMC1e8kyH3DyChdjy5GXkc+CeD1KYpDHv6HCzLQtOMiLlYkaVwjAQge9WIVTPdtEAWxb6mhW7y5+T1/PLfGRghgwYd6lKvXW26X9aFuAZJlJMnnk0FUuy77/7772fevHkFivPQoUOZMGFCQWBvXl4er7766glX+j/99FMuvfRSLrroIgCeeuopZs+ezbhx4xg9evQJ7dvR+CwVJbkGcucuOM4cBIaBpWnoe/cidz2dgHX4TV7X9VIv/RvuOKQ6dTCjBF1LrVqhOY7yw1SUQ+lnoj8whDcOK3joYe73oy+cV0jxBiAvD33e7zgGDsGSBObfR6XQczhQT++O3LIl6AZIUonKguc444n7z+MY834vqB6s9uwFioLvicdwjrwCqV0nApWgAHV4Qj72snZZyLsohBBYB6L5pB8iLw9x6AXf3LUTqW6Tk6Kgi6zoQAwfF5GB02nhi54Nr9SUt8xPGMdSOCtAHw0GTYJHxfQ6nQ1xuRoihIWuC3w+q0IV/jKZ0w2LTsPaMfeTReRlRFqKnV4Hva/pVuEKfz6WU8ZZO45+t/ZGCAgGNcr75zUsC+FR8XoOx0oU9ZWmaYEiIQ6tAhxrjss/vyC8KhhIy+ODq78ia+/h2JD4ml4uf/kCxj7yS0TBMIDMg5kkelMj5G1YFnU71uWeSTex4MuldBl2CpOen87ONXsKjvEmu7nxiyvx1ksouJ2EAPw6KyevY8l3qzB1ky4XnMJpF3dCiitcjfhYSJIgbfMBfn5mGrVbpHLevwexa90+dv25h5UT/qT75V1wpngwJVvzr8oU2/x5tBdQZfQKCoVCrF27ll69ehXskySJXr16xYxHiIVxKP2GYRgYh6yhhqEXbOt65LZpHrmdbwnUCrY1LXLbssL+wemGgtH5NKyatTGTkqFxU4y+A0nXw6KxLBNN01AUhaFDRxT87qZpouvaEdv5LgZGwbZhRG7nCAXnHXcjjlrxkOrWw3n9TeSYUsSYAooTuUvX6D+Qw4FISMDKzARAbtwEffXq2L/nH6uQgn5M00JqcDgDlNSgIZ4778bSNALffkvw558wf59FohUo6EtRYzIMHcO0yJDcKD16op7eA7lNW0LTfyPw+acQDBD67BOcWQcxjOOT0+Ft65ClJXL7SDnly+PI7ZLKyTB0FEVhyJDhBcvAZX3tGYaJaNU6pryk+vUx08MvBVKt2hiGUeoxHd4un/upNHISAiTJAqvowFWL/L6X/Zgsy2Lo0BEoinJCr72ylpOVlAyO6MHnIiERw+U+IWPy+TSyskwOHNDIytIwTatCrz3LsjjvvIuRJKlUY8KjcNsP19H+rFYFVV1b9W3G7WOvQ0l0npD76Ug5+XxBcnL8mOaJnyNkWToUqF76+8nwhfjqznERCj9ATloe45+cwoCbe0Xs96Z4SKld41CWtMgxmZJATnYy6J7+LPh6eYTCD+F6DR9fNwY9J1gwJjNP45NR3/LL8zNI25pO+vYMpv9vLu9d/gVmbqjkYwrqTH/zd+JrxjHs0cF8c/8EprwyizVTNzDv86W8eu6H7Fi2o2B1uDLN5TbFp2qVdj0GGRkZGIZRyI2nRo0aHCjKqhmFNWtWAbB27WrWrg0rs6tXL2fjxnUALF++iK1bw5kBFi+ey/btfwMwb95M9uwJL7XOnj2NtLR9AEyf/gsZh8p0TpnyEzmHMrlMnPgD6X6NA7KHz2fN4oBwkeEPMWlSOMAoJyeHKVN+AiA9PY0ZMyYDkJa2j9mzpwGwZ89O5s0LZw7Yvv1vFi+eC8DWrZtYvnwRABs3rmPlymVkuxIJ3non0n0P4Bg1GumBh/CPupksxcuyZQsjxrRp+w7USy5Hql0n8sdRFNzXjyI0dcrhfaqCiPPG/D2Fx8uGLRvC5e5btganExQV1+Uj8b3zNvriReD3YWVlEZo4geCbr0L2/mOOafXqcMXPrLRdBH/8geBPPxKaPAlzX+Rqhjb1V/bu2Hrccpo0aSyBgL/AOqfrOoGAP6qcMjIOMn36L8ctp/wxrVv3B+vW/QGU/bVnmhbpLhciRnEgx5Bz0WbPRsTHQ916pKenl8mYjnU/yTKkp+8mI2MvQugsXDinXOW0cuUiMjP38/PP43jttZeZPXsle/bUBKJZXiV8eYESj6m4cpoxYzLp6WmlHlNFyKm4Y5o+/RfSAhqOK68u/HMKgePa65mycH6VG1NZXHszZkxG17VSj2nNmlUoSS663daBO3+5ngdn3U7Pe7qwK2cPplWxY6qsclr7xwr0zADrJq5l4ceLyNicDgGdqVMnxByTLEuE/H62bFqPEKLQmLLTstizIXodiAPbDpJUNyFi3zkPnIHphD/+WBFzTHkZuaz5dX2h8wHkpvvYt2UfWVkZyLLExnmb2LexcDxg5p5sVvy0BtPQSiQnX04e6dsz6XXVaUx7fU6hGAPLtPjmvp/wHcwpNzmV9NrLH4dN8Sl2IG+bNm2YP39+gULdpUsXfv755wL3ngMHDtC3b1/Wr49+wVYE+/bto1+/fnz77bd06dKlYP+LL77I0qVL+eGHH0pwrgwkSSl4Y5Vl+ZAlQiDLMrquI8ThbUkSSFL+tlRgvZEkGUmS0DQNWT68rSgyQuRvh91OdF2P2FZVFcsy0fVwHyZNGss551yA2+055BNuoCjqoW0TRVEwTQPTtFCUcN8t6/A2WMhyeFsIUBQFXdewrNhjkmWFOMOH2LcXa/MmhNeLVKcOoam/hqvfHsL5wEOg6wRffyXq7+m88x4y6zdDkmRkYRGXuR9j8ULIy0NbuCDqZxy33kFe83aHLARFj8ltBOHtVzF3RvdtlRo3wbrlLnxCKVc5qWpYHoZxePt45GSaFpMmjWXIkOG4XK5yufYMQyeFENq3X2P8sTocU5KSgnPY+Rj//IP+xx8477qH3PhUNE0v9Zjyr71o95NpGuTkpPPBBx8yZsy3AFxyyUXceustJCWlIoRSDnLS+fPPVZx77jCCR/h+tGzZkmnTxtGw0QEiXLGshuTk1ELTKNaYSionv9/HlCkTOO+8iwFO2LVXlmPKl5NHMnFmpKFNnoS1bx+iYUPUIefij0smTzOr5JhKO0cEgwGmTJnA0KEjkGWpWoypMspJRvDPwr/55r4JEakqG3WuxxVvjsByRY4JLCyfzj/Ld7Dypz9RXQo9rz6NGk1T0GWrYEy+Xdm8NeLTqM8bgGveuZgvbx9LjSYpDH3wTGqfUouffx3HkCHDUVU16piMgwFeO/fDmOcc+dqFNO7TBMmE7+/5ic0L/456XK3mNbj+s5HoMsWWk9BNxj00kd5Xncano7+P2YfrP7qMOp3rF6zSnMhrzzAM6tZNidlXm8KUyMl57ty5xMeHI6Uty2LhwoVs3BguwpETJQd5RZOcnIwsy6Snp0fsT09PJzU1ukUzFrKshKvbyXLEvnzyL8Citw9bC4/04SvJthASqhpekBk+fGRBe/7NU3hb5tDmUX0vvG2aFpJU9DgsyyJHciPqNUNu2AKvFUL/fRbG9u3h72vUGMdlIwnUrIdqGSh9+6PPncORKL37YDZsjHSoEI5hCXKT6xB/5lkEnnuGWJiLFqC2ao9pHntMpsOB0qRZTKVfbtoUzeFEMqKNtWzlFFs2xZeTLEfKuzyuPVlWyBYKrqtuwBXyIwwdoSiYeXnIDZsgzjmPHMWNaZhlMqbC24f7m5ubwZlnDmb7oesK4PXX32T8+AlMnjyROnUacaT1vSzklJOTwfnnXxih8ANs2rSJ++57jE8/fZy4+IOADFYDNL12Qb2H4oyppHJyuz0RMj+eMZW3nI732gsgEUypi/PqUci6hqGoZFsSlhWu4FoVx1TS7aPHFE3eVX1MlVFOenawkMIPsH3VbhZ+sYw+t/TCsKzDYwoYfDrqW/ZtPuwd8Oe0v+h8fnuGPHgmlhIekyfJg6xKGFrhOAAhCeq0qsmDM29HqBIOr5NQToDhZ1+MpChYilTwwnPkOGSvA1eCk0B29KJytVukAuFCinKUDEP5SIqMEAJVDZ+/OHJClRl8d3+y9xWty4V8Wvj7K9G1Z1N8SqT0P/zwwxF/P/744xF/n+g0cw6Hg/bt27Nw4UIGDRoEhH3WFi5cyFVXXXVC+1ZaLMskJyeH+Ph4hKj4C92yLHTdIAsZtd9ZuHr2QVgmhqSQIzsxTYugkPGcNxzXgIEYK5eDaSF3PQ09LpHco9wlDNNCF0pMX18A3G6OlcM4n6Ah8AwejL5wfuGMNLKM2qcvFVBks8yoKHlbFviR8atxh3XqxMO1GioiT5ssC3788ccIhT+ff/75hx9+GMvo0aNRlGNknyoBQgjWrVuHL0ZU7oQJE0lLewGvtwmmKeP3Q6isy1AfxYm+x8sby4KAJYHkLItER1WeyixvWRboOSE0n4ZyKEuNUQXjNxVFYt3szTGLUS36ZgU9rzkNDlXqVSSJJeNWRyj8+az6eS09Rp5KcstUJAHIghs/vYKQL0T69kx+/2QRmbuzAegxsitKvBNLFuDXmfP2PBZ9u4JQXogWvZty7kNn4qkTX+g2cCa6GXRbHya9MKPQ9zc5tSHuRBcWYMmCnleeyl+/F84SBNDziq6HMhEVf/62LIv4+okoDpmkegkFYzmaem1qYfg0VI+j2GlAbSoPxZ5pNmzYcMx/J9K1J5/rr7+e77//nvHjx7NlyxaefPJJ/H4/I0aMONFdKxW6bjBnzrQCV58TiWZY5EgusmUPeSIyS4BPqGTFpRIcOJTgoHPJTaqF5nShKIUvtaDqQj0jdglzpf8AQsUcrxBghjTcN46O8FMXNWviHn0LwUkTcYXKKe1KOVCZ5F3e+Hy5fPPNdzHbJ078hezsrJjtx4MQsH9/7IrYpmni9wfJyBBkZZnHVWG3pJxMMrepvPIWmsn6yRt4a/gnvDrkfV4e/B6T/28aIqCf8EKHJUUIQfb+KBnlDhHMC2EdoRhreSGWfLcq5vGLvlmObJhsnrmZN87/iPev+opPR3/Pku9XctGz59Koa30G39OfM27rjSkJLL/Opzd9x5yPFhHMDWFZsGneNv434hN8+3IL/Z6hvCCuBCdn39sfd2I4t6ekSHQe1p6Bt/YmOy0v/KwzLeq2r0PrM5oX6mODDnVpM7BliRT+fCxZkNAwieFPDYkq6x4ju7Lql3V8OPJLlny5FPz6CTf22pSME5/DsIwZOnQoBw8e5M033yQtLY22bdvy0Ucfldi9p7KhqirDhl1yortRbEzTJC6Uh7llM+bmjUj16iOd0hGfw0v+aqhmWHhO74lYvgzr78hKh8qAM9GTaxQ7N7wkSZh/b0OfPRPneech4sNBVFZWFsEJ4zF370YdcQkcu55JpaCqybs0SJKEyxU7T7vL5SrzB4tpWnTt2iVme926dfF642K2lwcnk8xtKqe8FVli/W8b+fHRyQX7TN1k5YS1pG07yNXvXhKubFtF0HWTVn2aMvv96HFjDTrURXLKh7PGWhZ6EZWCXXFO9m3Yz3f/+jli/96NaYy5Zzx3/TQKNcmJblhIkmDvpjT2Rgn2NTSTKS/NYsR/zwuvBuTvDxiMfWQyrfo2Y8RTQ5AdMrIiE8wLht0sBcgWGCKcEnXEs+eyb8N+Fn69HNMw6XZpZxp2qgdu5bhT4YY0g7od63L7D9cz5ZVZ7PxzD4m14+l51algwYSnp2JZMO3131nx05/c+MWV4e+zqRIUW1JLly4t1nHdunU77s6UFVdddVWVd+c5GtM0ycg4SHJyymEfvAoiHJQjsCyrWNYDSRLE5WUSfOl5rCNjPRQVz733k1erIflFS7MlJ3G33onYtQN94XyEyxWOC0hMIY8iXH+OwjRNRJ26mDt3Evj8s8IHuFxYVSj3+YmUd0Xjcnm5/fZbWbgwelXlK68cSVxcQtS20lCzZi3OPnswU6dOK9T20kv/JSkptUJrE5xMMrepnPLWcoNMfWVW1Ladf+whZ18OcQ2TKrZTpcCyLGo0TaFu21rsWR+pfAsBwx4djHAqBe4/itdBx6HtWPBFdH2n+2VdGPtI9Iwx/uwAmxduo82QtoCFokj8OXVDzL5tnLcFM6QXuBYBSIpAccpsnLuVjXO3Urdtbc57+EwWfLWGjXO3gBC0HdCCoQ8PwpHixnTK1OlSjxGd6oZPIIvwM7qUXpmmJIhvkswlr14AmsnB7RlMeHoqe4/KFnTg74NsnLOZdue1Q9dtV5+qQLGV/quvjpJy7RD5Vrh8P1mbsscwDJYsmcegQedW2ANCkgRxhh9p926MrVuQatZEbt6CgNODz4jdB7cRJPTx+5EKP4CuEXj7TTyPP022FC4EZlmQI5xIjVqiNm2NBfh0o9gW/sN9lZBTUxEpNbAOphdqV88aTMDhKU5drErBiZD3icIwTHr37s2ZZw5kxoyZEW0DBpzBGWf0x+n0UtJiM8fC5Yrn/fff49133+Odd94lJyeH5s2b8+KLL9C9e88KL0Z2MsncpnLKW/fr5KbHdoPcvX4fbZukVOi9IUsCIzeEL9OPEAJXogslzoFR3PnApXDdB5cx+/2FLP1hFXpQp1672lzw+NkkNUmKmFcMy6Lv9afzxy9rI34HxanQtFvYp37vxuhpOgG2Ld1B+/Pah1NTW+BNiZ3G2hXv4uiYNcXr4NQRnVj8zQokWTDs34P44vaxBHIOBfZaFutmbGLbsh3cNWEUwntUEa7jcOmJhWWFC5dhWvz87LRCCn8+y8evoc2gVuFjbSo9pbb0+/1+vvjiC7788suC9J02ZY+qqgwZcmGFfZ8QkKDlEnjlxcjKrQ4H7jvuQq7XkBwz+uWjBH0E/vkn+ol9PkTGQeRaDXFrfiQ9BLJMUHYT1I5vwnKjoW7dSnDGNNzX30Dgu28OZ/GRJJR+/RF9B1AWcZguYeAM+cHvA5ebkNON3yr7pc2KlveJxutN5sMPP2Dt2j/55JPPABg58jI6d+5McnKtMlf48/F4knjggYe46aabMAwdp9NFfHxyhSv8cPLJ/GSnMspbccgxM9IAJNVJKLd7MRrCsNi+ZDtjH/kFf1a4RkZcarjiba32tYsVD25ZgEth4D396HdTDyzDQnbKCJcSdSxKopPbx17Pwq+Xs39zGj2uOBXVqaCHDCwTkhskcWDbwajfVadNrSMKTRl0GdaeWe/Oj3psz6tORY6LDLY1gIG39Wb7qp2k1E/iz2l/HVb4j8CfFWDF+DV0v6ZbzGBaWZaQZXGo6Nzxz2dCgOKI/YxTHDJVLtjjJKbYefqPxjRNxo0bx1tvvYUkSdxxxx0MHz680lgsSsuBAzkltjaXJ6Zpkpa2j5o1a1fIb+wWOuKzDzDWri3c6PHgue8BclPqoOmFf6TE3HQCTz9e+HOHcN19HxYQGvMVVtp+UFWUvv1Rzh5KlnCWqJ+qLHD9uZzQpx8DIJKScJwzFKl2bZAkRGot/E4PAbP0v1kCQYyfxqEvXED+xSF36ox6xdVkidg+6cdDRcu7siDLAjDQdQ1VdaIXXSC3WnGyyvxkpTLKWwam/ncmy8YWrq7uSnBy9883IbwV4yYpBOTtzObNCz8u1CbJgnsnj0ZNKbuMXkejShIHt6bz2ejvyMsIF6qq27Y2Pa84lR8fm1zoeNkhc9+vNyMnHH6GCd1kzcS1THpuesSxDTrW5ao3L8K0LNQ4R8QCtBCA3yCQ7eeb+yaw96/oKwsNOtTl6vcvxToqbaUkCczcEFsXb+fvZdup3aombQe0RElwHlfSLFmW2DR9E98/+HPU9qvfvoiGPRpX6MtgPkJAamp8hX9vVea4TJTTpk3j1VdfJSMjg9GjR3P11VfjKCr1ok2pMU2DNWtWcMYZgyvkAeEI+QlEU/gBfD7M/ftxxSehRVHSLbcHvF7Iyyv8WSGQkpPwPXnES4Gmoc+cjvnP38SNvp1cUfxryR3yEfr+cOYXKzOT4LdjCv52PfN8mSj8LmFgjB+HviDSamOsXoUVCuG9fjR5Uau4Hh8VLe/KQtjqJSHEyaXww8kr85OVyihvAzjr7n6kbUvnn+WHa564E1zc+PkVyHFqRWTxBUAyLWa8My9qm2lYLPhiGWfe1x+9nDoUyg7w4TVfE/JrBfv2rN+HHtTpN6o7cz9dUhAL4E5wcfU7F6MmOiN+H0uR6DCsPa36NWfN5HXkZvho278F/uwgr533AXrIoOuFHRh0Z19wh2sDhVcmZNwOL54kd8z+eVM8SIoU8cIgSYLA/lzeG/llREXdX1+ayY2fXkFKq5qYJbRmGoZJ815NaNqtEduWRqZVbtWnGQ061T8hCr/N8VEipX/JkiW8/PLLbNy4kWuuuYabbrqpoFiXTfmiKCqDBp1bcV+oFa1xWbm5CNOImg3H7/DguvTyAuv7kahnDERbvizqOc0tm3FkZyCSahd7lUUEfFh5sVOyWXv3IJokcpwLWgU4Q34CMSoHm+vX4QjkgSvpUHyLVepVogqXt80Jpzgyd7lkXC4LsDAMgc9HgSuSJAmCQR/BYACn04XD4Sn1dW9TflTWe9xyKVzx5gjy0vLY89d+EmrFkdqsBrK34hR+ACNosC+GHzmE4wuMkFEuvuSyLLFp6Y4IhT+fn5+dRt8buvOvabdycFcmDrdKQu14JK8jqkJtKRKOGh563diDPat38819P5GTdtggtvT7VezblMaV/7sInEc8UBWJ/jf1YOvi6K6y/Ub1CGf+OUIoVkDnm/smRCj8AHrQ4PNbfuDuiTeCp+TGKcspc9mrF7B3/X4WfxuON+g+siu1W9XEcladbE42JcjTf9NNN3HDDTfQpk0bfvvtN+677z5b4a9ATNNk167tBf6C5Y3lciHiYqcslGrXwYpRUEYzLPS2HXHedS9S/foAiJQUHFdfh3r2OWiTJsY8r7FlS8msXsoxJjBPGSk+fh8xNXlJQpgmiYEs4reuI37fdhIMP0op3BwrWt42J56iZC4EJCXJeLz/IMnLkORlqI4/SUz04XJJhEI+Vq5cwuWXX0bv3n245pqrWb9+NZoWOAEjsSkOlfketxwynvoJtBzUklod6iI8YYVfCBAhg1C6j5x/MtAzA4hyytoiORVqNY+dartOq5rI5ZQ+VJIEaVsLF+jKZ+4ni0EW1Gxfm4SmKeBRj2lBD2SG3XWOVPjz2b5yFzlH1RMwTYs6bWvTfWTh1MK9r+1GaosahSzsoZwge9bvi/r9/uwAWXuLrrZbJC6FeqfW56KXhjH8hfOo06keltNO1VnVKLbE5s6di6Io/Prrr0yZMiXmcUuWLCmTjtlEYpommzZtoHbtehWyFOxXPXguuoTA558WalM6dQZVwe/wQIwJ349CqHFLXHfdj2qZmIjwOYO5IEmFq+YeQkosmVVed7qRWrTE3LypcKPbAyllVJ/B5Q4/8Y7umxC4Rt2ENvkX9CWLIr7be9c95NWsH+snKpKKlrfNiacomcfHSwhpLXCkwuAHsRZZbs/48eO48867C1r27NnDjBkz+eqrLzjnnPOohHrlSYkkCcyAjp4XAhEuhFXq/IrlyJFKpRBg5WqMuXc821fuKtjf/qzWnP/E4DJXAC0JBt7em3UzNhZqE5Kg97Xdys21xzBMGnauH7M9pWESkiKVaEVX82nkpMVeld69bi9tm0QmEbAcMgPv7Eevq05j/czNCAnaDGiJK8ldyJcfwDjGwybkC0V9jBUX07TsYtpVnGLfpc8//3x59sPmGCiKwhlnDI7apgpw6z5ERgaWriFSaxJQ3QSt41cWNcNCa98Z1+hbCI4bi5V+ANweHH37opzeHS0h5ZgZAQzDIk84D2clM0yCDjdKj57o86P4aqoqonGTEvkH+oSDxOtHEXjpBazMzIhzue68mzzVTVnMUiGHG7ljJ4zVqyL2y+3aY+7eHanwA/h9BF57Ge9Tz5Ilx07bFoui5G1TPYklcyEEshJEiCgxMsC+fWn8618PRW274467WL68J/HxNcq0rzYlRwKy/85g3KOTC6yxjbrU56Jna+KpE1f8FJQniqDBl3eMZdefeyN2r/3tLxSnzLmPDcaUyi6Li2VBfL0ERr56AT8+/ivB3BAA7kQXl710Aa4UT7kpoKZpUa9tbRJqxUWt6DvkwYHIXkeJsnypTqXIzEgJteKjGrwsVcJZK47TruoKhAuOxbpSnPFO3ImugkxHRyIkQUqj5EqVoMSm4jnu7D3VncqXvcdg+/a/adSoCZJ0eEnTIVm4d2wl8P47EDh0o8sy6nnnY/XuX+rgUlWV8QZzEJoGQmA5nPglB0Hz+Cf3RCtA8O03sLYfERSkqLjuugdfvcZoJTy3JAnidB9s/xtz8yZE7bpIbdtFVP8tCxKtAKHPPsZcf7gWhfuOuwh8+knMuALH9TfiO+XUEqeAjCVvm+pLLJkrikR8wn6EiO7bu3gR9O59VszzLlmykCZNWpd5f21KhnbQz+vDPgz7oR+BM87B3T/fhBRfuZNhBPfn8tq5H0ZtE5Lggd9ujchcc7wIEU7VaQTCcWWqW0X3a+Sm5yGEwJPiQfE6MMr5AS1JAi3Dz3f/+rlgZcOd4OKcBwbQZlBLcCigGei+sN+/6lHBIUcYrSRJYAV0LMNCYLFs7Gp+e3Nuoe9yeh3c88voUmdGkiURzrTzUGEX2r43dKfvzT3L9MXsRGNn7yk5pVqPCwaDTJ48GZ/PR+/evWnSpEkZdcvmaEzTYteu7TRo0JgjV/7d/hwC/3udiPV7w0CbMB5no0ZIjVuXKrJe0wwyJQ8cOZeXUpHOllzE3X4P0sEDGJs3IZKTkZq3wKd4SqzwQ/i3yZbcSM3aIbc8BdO0wkp2GZuBsoQL7/WjcQTysLKzEXHxCFUtOpB4/z48aJimhqa6CRWzeEosedtUX2LJPKzbxFamFKXoaVyWq7/frSQJZFk6fO9XMmRg+gcLCin8AMHcECsnrKH7td3QK2HfIaxcRfNFz8cyLYK5QTylVPolSRBK9zHl5Vmsnb4RIQk6DmnL4Hv7k9A4ueBZFk3hl2WBGTSwDBPFrZba9cc0LZQkN1e+fTFabhAtqONKCBcGA/Dvz2PCU1PYsij8Mt64awOGPzUEb914DMtC6CbpW9OZ/N+Z7Fq3l6S6CQy4tRdXvjmcMXePLzAqOr0ORn06EjkueiBwSTBMi+Z9m3HDJ5fz64sz2fvXfpLrJ3HmHX1o0a95tVL4bY6PYlv6n3/+eXRd57HHHgMgFApx6aWXsnnzZlwuF4Zh8Mknn9ClS+Ggk6pIZbP0R0NVJZy//YI2eVLUdqlxE6Tb7iavBCkwKxIhwlkSLMuKKFBSlYgzg+ivvBCuNxAF1623o82agXnwIEr3Hsi9+5GjeKplijNZlsIF1zDRJYWA5KiW4zwRJCdLCGkZ0fy/d2yvS9eufcnIyCjU1qBBA37/fQ5xcckV0MuKR5IE8fECSfYhyMbCjWUlkpdroR1nsb/ywArofHTVV6T/U1hGAE27NWLk/0ZgypVXKQum5fHa0A+itkmy4P5pkZZ+cShmIZQTJJgXwp3gQo13YhQxRDMnxJsXfkQgO7IgVVyql9u/vxYLUDwOLEVEPJ9FyGDn6t3M/WgR/uwAbc9sRffLuyAnOIs1B8mSwMjTAAvZc+xVBCs3xBvnf1SocJbDrXL3xBtxpHj4Z/7ffHXnuEKf7XnlqfS6+jS2Lt5OQp146rSuVaTCL8sCw6cTygkCFo445zFdi2RZYPh1MEwQAiXOUaoCXZUV29JfcoptApo/fz733ntvwd8TJ05k9+7dTJ06lXr16vHII4/w7rvv8sEH0ScFm9JikXdwPzWSk9FVF7puIlkW1u5dMT9hHkhDiZFWszJgWVT5icivuvFefCnBd98q1CZSUhCWhbFhAwDaxJ/R580l/qH/kC27cQoThx72Uw2qToJHPA0Nw2Dr1k00a9YSWS6+AF2ShaIFQAgCqhu9gl6m4ggh1q5D/2Ui2sF0pMaNiR9xCcHUOgRKt6B40lCUzHNzIT6+PYi1HKn467qHnJw8Xn/9VW644UaMIwLkHQ4HX3/9BYmJNSql9bu0CAGJiQIh/QGElS8BCCERH9+B7GwVPUrxwIrpm0BoBlqehmmaONwqjTrVi6n0J9aJRyjS8UdYVgCuRBcNO9djx6rdEfuFgLPu6ocnxYNmmpimFXZryQ3x7YM/s3Vx2I1TUiROv6wLA2/rjRkl644sBAvGLC+k8APkHshj2dg/2LZ8Bw6XyrDHzkJNdmOaFiJkMPn56ayedNjtct/mAyz+dgV3jLseJckV82cVQoAvxPyvV7Bs7CoM3aTTue3of1NPlERnVGOULAsWjV0dtVJuyK8x/4tl9LuxOxOejp7wZOHXy+lz/em0G9YOy7LCwbExOigBmVsO8v1DE0nbmg5AapMULvnvMJKbpcRczDYMCxwy+Q//qv6ctSk7iv003r17Ny1atCj4e/78+Zx99tnUP5SSMT9vv03ZE2eFkLZvwzNzOmgh3D16IZ3SCZ/wIrVoCatWRv2c1KAhulwx1RNPVgzDRGvaAsd1N6CN/R4rN+zqI7dug/O8YfiPqlVgZWRgrF5JYvsOhH4aR3DlCpAklJ69cQ85l2zVi2laWJbFwYMHaNq0RbSvLYQiCeIC2Wi/TERfsxpcLtwDB6GcehqGaaHJDgJCLZe87W50rKm/oM04XHXS3LyZwIvP47ztTvTmbSvs5aMqU5TMNc0kJ8dFXPxpaFo6OTkH8fsUFi1azQ033MaAAWcwceJPTJ06jXXr1nPaaadxzTVXUaNGnWqp8AN4PBJCbCJf4T+MCWItcXFdyMys+OtOUSQC+/P46ckpbF74NwC1mqdy4RNnE/JprJ1eOBtNn+u7Ezs8s5LglLnqzYtYP3MjyfUSMTQTh9eBJ9HF5oV/M+W/M2jVpxn1O9TFMC2+uPUH9mw4vAJq6iaLvl6O6lTod1svjp4SjIDOhllRsrAdYvOiv2ncpQG/f7yInWv3cMe46xFelZy9OREKfz7+rABTX5vDsCcGY4joywuWL8SHV31N+vbDL2OLxqzgz6kbuH3cDVF97M2gwcZ5W2P2c8uCbXS/rHOR7lD7NqVRv1ujY87HoQw/7135ZYRb2IG/D/LBVV9yz6TRqCmxi3fZ2ESj2Eq/JEkRF+iqVau47bbbCv6Oj48nOzu7bHtnQ5wVwhzzOdoRWWOMzZsRNWvhuf9B6HoaTJwAwcJWB8eFF5Et1EptPaoO+FFRO5yGu3U7RMCPUBXMzZvwv/duYX9/WUZt2BDf/z0dzv0PYBjov8/G+PMP4h98hCzJjaIodO/ep1jfLwTE+TPxP/vU4esgN5fQ999irFiG2qsPLF1C4uVXkOtNPq4UokXhCPkJzJwRtS005ivcDz9KjuQq2y+thhxL5ppmkZsT4u67n2T16j/YsmULuYdeMn/5ZTJTp07jjjtu5/PPP8Pliqv21j1VtUBkxmjVEVIQIdQKnf4kSaAd9PPuyC/IO+gr2L9/ywE+un4Mt3x9NVuXbMefHU66ICTBsP+cRXzd+Eo/TQshMAyTDbO38NeczbTq15xOQ9vx8Q3fFFSmXfztSpLqJTDq45Fk7M6Kep6FXy+n19WnIeIi3U6FLHAlxJ4n3AkuQv7wymjugTw2zttKx/Pbs2DK+pif+XPKeoY+NBDchZV3RZFYP3dbhMKfT266jyXfraT3jd0LxQZIikRCrdg1bOJSvchK0auzDo+DY6VqlYXg9y+XRY0DMTSTeZ8uZtADA8o9oNmmelHsEMHmzZsza9YsADZt2sSePXvo3r17Qfvu3btJTS2jnOg2QPgBIu3bXShNJICVth9j3u+E3HG4Hvw3Up26BW0iPh7X6FsQyckoono/+CsLmmGRLbvJ8qYQcCegr1oZNcBX6dIVbdGiwwr/EVgHD2L++QeKImEYBuvXr4lw14iFE4PQTz9GffEzNm8GpxNr1078zz1DXDB20PHxIITA2rUz5oullZmBFGWsNoUpjswNw2DTps2sXr26QOHPR9d1pk6dhq4b1V7hBxDHmtss7VCF7IpD0kz++HV9hMKfj2lYzPpgIXf/fCOXvjiMy1+9gLt+uYG2Q9tglUNV2bLGzNP48Oqv2TB7M5YFva/uxrjHJhco/Plk7s5m8oszOP2SzlHPowd1gnmhQvslp0z/G3vE/P4uw9qz9re/Cv7+a9ZmhAWiiEwHQhIczhl91HhCBqsm/hnzs2t+XY/mK1yR1xSCvqNi9/OMm3vhSHDSqEv0PP8Oj0qNxsdOnWmGdP5ZsTNm+/ZVuzCCOpIkcKgyDodMBV/uNlWQYs80N954I6+88grXXnst1113Hf3796dhw4YF7XPmzKFDhw7l0smTFVUS6L/Pjtmuz5+LHPSTk1gL9d4H8Dz2BO477sI58gq0mTPwP/ko7r/W4OLYiqNN2RGyBEr/AVHb5KbN0NevjflZY9lSFFMHLPx+H8Up3OMwQhgxXLwAjHVrkZs2g2AQbdYMyraIpQWuY1jxj5FdpiohywIvGgmyhlc2kMo0G8axZR4X5+Wyy4bHbL/44uF4vSdHYJtpylBkSmJvhQSSy7JEXl4mWVkH8Afz+Gdl7Dir7St3YknQ4syWNOnbhPXb12OKym+plSTB/k1pHNyZCUBSvQQydmZGtUIDbJi9mabdGkVtk1XpkKU7EsOwaNC5Pp2HtS/U1u3iTmSn5Ua4zCTVS0A3LToObRuz353Pa4/sjj7/CFmgRlkByMfhUaPe35ZlkdwombPu7leore+o7tRqXQtLlbnkhWF4UzwR7ZIiceXrw1Fcx3a7lVSZlIZJMduT6ychC9izcjc//ecXfn3mN7L/zkDEkImNDZTAveess87iww8/ZNasWfTu3Zurr746ot3tdnPFFVeUeQdPaoSFKMoccKjNNC2kgB/fs09HWlyFIPTdtzjvuodgYq0TtnzskkycQV+4joDLheH2ogkZzbCqZXYX07Qw6zVA7tS58CqNw4FwuWOrdV4vlhDIskLXrt1jHXUUAlQ1ZpVjHA6sQ23muj9RB51DSCp9Pm0IX26iVm1wOCAUxXrXtBm6s3r4nXqFTsa+vazetIn5i5dQv359+g0YQEpKTXSp9BmyiiNzn8/iwgvP5+WX32TnzkgrYM2aNbnxxhuwLEFlrvJaVvh8EB/fFERhH3msmoRC5f87BIO5zJkzm8cee4Jt27Zx/fXXMaTuBTGPj68Zh5DEoXlPKsE9fmKRZYltSw/XVVFdKoHcwiuL+VgWSHJ0m+KpIzqhxjuimqIsh8yQh8+k36gerJm6HoGgcdcG7Fi9m8n/jXQhPP2yLmiagbdmHKdf1oUl30UaPuJSvZx5Z9+YJi9LCPpcezobZm2O2t77mm5IbjVqTIylSpx2eRc6ndeOrYv+wTItmvVogiPBGV61sSzcyW6ue/8S/lmxi93r9pJUL5Fm3Rsz77MltNmdRfth7YssyGYK6HdjT9ZM2RC1/Yybe/L9gxPZNG9bwb6lY1fTfWRXBt7RB6tsrTs21YRiK/1vvfUWo0aNomfPnlHb77jjjjLrlE0YzQBnn37oy5dFbVe69ySkunAC2ozfDiv8sozjnKHILVtiHTgAGQdJjIsjV/GWW9nyWCQQwvjpRwILF4SVUiFQTuuGZ+CZGHk+aNCIHDl2doWqSq5wEnfFNShnDMSYNR1LN1B690G0bovidBL65KOon1MGDsJvShiGztq1q2nfvtMxs/cEVBdq3/7ov02Nfs72p+D/4D0ARFw8ZhkX+/Kpbty33E7wrTci6kWIuDic148iW3JAFX+5cwqDtL27OP+Ka/hjzZqC/Q6HgwnjfqBbj96YpaiADWHXnWPJXNdNatWqy++/T+ONN97miy/GYBgGrzz/LFddMAyxdx/s2Yto2JCgw0PAqr4Pfk0zCQQScbnagdgG+AEFrAboei3y8srX4mlZBmPGfM1DD/27YN+YMd8wesJoFn0d/TMDbu4VTglpmMWSd2XBNC1SGh1O+3pwZyZ1W9eKeXyNRskk10+kTqua7N2YVrD/lLPbFKmIQ1jx99RPoM/NvUAzmPLSTJZ+v7qgXUiCEc8MxVXDi0VYAT/zrr50HNqWuZ8sxp8d4JTBbegwpC1yfOy0waZpUbNlKp3Pb8+qnyNXX5v3aEyLPs2KDIK3FAklyU3bc9sB4aQOR35TMCvA25d8Tp1WNUltmsK2pdtZ8dMfuJM8zPlwEa0HtER4Ylv8LQsS6icw/Okh/PzM1IJKvpIiMew/ZxHMDUYo/Pks/mYFpw7vQGKzlGr3XLUpPcXO09+2bVvmzZtHjRonRzn3ypKnP94Kon/yAeZfkW/7IikZ50OPkCV7cKMj3nsTY+sWEALXjaPRVyyPfFnweHDddS95qfXKPJAzFm5hII37Bn3RwkJtSseOSA0boa35A+ftd5MlqmegpyQJHMIEy0ITCoZhEk8Q48vPMdasjjhWOWMgDD2fPNQSKwSJpp/Qay9j7tsbsV89YwDC4yE0+RcAnHfeQ27jVmW+wuKQLNyBXIyli7H27EZq0w6pbTtyHXHVInuMU8/l7gcf5rOvxhRqczgcrFuzioTkOqX6jpLI3OGQUFWD9PQMvIqKd+Mmgl9+dni1RwjUc4dh9RtY6qrclR1ZlvB4QJbDipLPF34hKG+ystLo2LEzfr8/Yv+N14/iou6XMuPF+ZhHpKg57aKODL5/AKYafjmsSko/hHPov3LOewUuPWffewbbV+9i/czCGXcuf/l8lo79gw5ntyalYTLBvCC1W9bEneLBKGEtAqGZBDL9bFuyHYfHQZNTGyDHObCOcr2RJIEwLEzDRHYqxS90FjLI2pnJku9WYWgGp13cidTmNcB5/G6JQsC+1Xv4ZNS3ANRvX4dBd/YlNz2PrD051G6RSv1T6iInuY6ZwUcyLQyfxv4tB8CCWi1SkVSZ90d+wcEdmVE/0+WCUzjvybPRqnlsj52nv+QUW+lv06YN8+fPt5X+CkaVLOICORh//hEOAA2FUDp2QunenVB8MnmmjFMWKBN/QJ89C/mUDsgNGxL6dXLhkzkcuJ56lizZWyF9T9RyCTz6cMwgT/c99+F//VXUC0cQ7DsI7SRK6xhnhZAzDqAvXYxQVOTTu6PFJeETx6egCSGIN/ywbTPGksUIjwelU2eMHdsJTfwZAKX/AMS555NL+RVrU1W5IMtHdVD2IfxgyU7bSZtOp6JphQP7AMZ89TmDzzn/hLirJeamE3j68ahtzrvvI7dhi2rpRnei2bZtA92794radulFl/LiUy/w96JdaAGd5t0bkVAnHrMUiuSJRgIO/JXGZ6O/I+TTkBSJy148n4M7Mlnw1TJyD+TSoGM9zvv3IIQkmPT8dHLScmlyakMG3tobZw1PqYqky7IElE8hRyHCcxcW6IZZJvdL9raDvHPp59RtU4sh9w9gzP0/RdQgqNW8Bjd8MhLhLV6GqfygdMuysHwa71z8adS0oI061+fM2/uQ2jQFSZFR4o5dbKyqYiv9JadEM1BFZ0KwAU8wF9+TjyHVqYvSuTNCUdA3biQ09Vecd9yN1LgVQcPCPWAQ+u9zUHv0JPBNjLXlUAhz0ybk9l0rRiHz5RWdLjQYBCHQ58zC2aM32kmU1jFXOBA16qEOuxjLAk2LXPA2DJ3Vq5fTqdOpyPKxb1PLssiWXMitOqK06YiKiZWTBQcO4Lj4UqRTOqB54svd6nv0OKoDYfnoMRV+gL379h2aH4//4VpSmQM4ZYE+87eY7fovE3GOvgO/XSCtzHE4YsfFfD/uex548F5a9G6Ky+tAuNVCitfxyPtEYgKpbWpy7+SbSdtyAF+Gn7pta9GiT1O6DO8AWAhZQrjCY7ny7YswDQvFpWAc+nxpKM9nlmVBqIwDYONrx+NN8TDw1t5899DEQkXH9m9J56fHpzDiv+dhFWP140j7rOJVaTeoNYu/WRFxzKA7+uJNcfPjY5PJ2puDw63S/fIu9BnVA8tZ+VeTbMqfEs00Z5999jEV/yVLlpSqQzaHUVUZY8ES0HXMnTsI7dxR0KZ06owkBAm56VgOJ5o3Htdd9yJ0DfJiFwWx9u1F6iBixnyWKa5jBHA6nIdmW63ogOUKQpUFHiuE0EJYpomhOPDJrnKzkhb9oBG43R5ipZuLRdjCfqhckSsRpUd/wK7IWFq88fE0a9aMrVujF+Xp2bMnplna37jkMhemHo7biYGZkRHOBiVVfqWyqpGcnELr1q3566+/CrXVr1+f1Jq1cMQdsm5Hnd+O7x4/kZgWCK9K7U71ECLsF68BHMqQY8HhFJ6qjFA5aXPHKXEObvj4cg5uz4iawhXgr983o+UGkRNLZvAyLOh/Yw9W/7K24GWieY/GOOMcTHh6WsFxIb/G3E+XsG/zAS7673lYqq34n+yU6Elw5513Eh9vL6VUFBIW1o5wxgRRIxWlY0eELCM1a465ayf+D94NZ8SRZZRup8PFlyJ0HVGzJlZaWvRzNm9RYW4XmtON1LpNoXgEAKlJU8zd4dR2cpeuhFRX6U1BpcAr6ThystCXL8PKyEBu0gSlRirxKTXIK4eCVsdClmXati19Clxb2S8bEpJSee2l/3LBRZcUauvVqyf16jcstTvg8cjckFWUVm0wNkQvUCQ1a4auOE7ovVVdiYtL4ttvxzBw4CAyMjKO2B/HuHE/kJCQUqTBoKzu8ROBZVmVwv21MmOYFgmNksnYGb1IGYTfBfWgwfGo4kqii7vGj2L2+wtYM2UDPa88jfGPR3HrBTbO3Yr/oB9X7dhFxWxODkqk9J977rknjU9/ZcCwBI7mLXC1PwWEQFu2FHQNUas2cuMmCFXFCgTCFV0XLcQ8mI588x04LrqU4HtvFzqfSKkB9Roct+VaVWUkLExEsdw4/EIl4fobCb3/Nua2w1kGpAYNcV10Mf733wW3B3XIUPLMirN2SZLAaWpIpoGmOhFYqFu34Hv3rYLsM9q8uYiUFNw33IjX6SRL8hzjrGWLrussX76IU0/tgVKN8tyXFFmWyM4+SCDgR1VVEhKS4bgekaUjqFt079GTiRN+5L4HHmLTpk243W5uunEU991/L253Qqm/43hkrukmnh490aZOLlycTZJwnDuMrFJmFbKJjmlaNGzYjKVLF7Fw4SKWLl1Gp04d6devH0lJNY85z9r3ePXHME1qNY+tM7kSnDi8x+dyaVoWUoKTs/41gAF39CGUEyIvwx/z+H0b02haN96O7znJKfZMY/vzVzy6bqB0PZXgl59jrD1cOdD46y+kOnVx3XAj/jdeK9hvbtyIMzsTmjbFde31BMf+UFAVVmrdBue115OtuEucPlGVLDyBXIy5C7B27kRq1hzPqd3Ic3iLtIBbFmTLbry33IXDn4vIOAguF+a+fQS++hK5c1fUIUPJccZXmCUyzgohbduKPnUKZl4ejg4dcJwxkLz3341INwnhCrnBXyfjOPsclAbNK9RqLoQgJSX1pL7vQiEfCxfO58EHH2bbtm243W5uvHEU999/Hx5PUoX3R1I99OozkBkzfiPg9yMrCvHxyZSgxmGRHK/M8xxxeB96hNBnn2Bu/yfc19q1cVxzPb64JCzbyl9umKZFQkJNhg49n/POuxDTtMKpG4sZmFnV73FFkdDzQlimheJSMaWiw7hi4XCEMzAJoQMyoZDA57OOmdmmKuBMctF2YMuoWY4G390fJcFZquBkAxBuFUUzESL27x+X6q0Wv6dN6bCz98SgMmTvkSRB/K6tBF57OWq7Y8hQjH/+wVh3OMew64YbCU35FZGYgOOssxEJiVguFyGHG78oXpaAI1Ek8O7+m8Abr0YWf3I6cf3rYXKTaxd7whICFFngDAXAMtFUF8EKtPB7LQ0m/IC+YP7hPiUl4zz/AgJffBb9Q0Lg+ddD+Oo1IWhXOqwwZFkwbdpkRo68slBbv359+fLLr3C7bVfDfCRJ4DaCKCE/mBaGy41fcVebDEo2lQ8pZPDX7C3M/WQReRl+WvRswqA7+5Y4S098vISq7gexg3zrj2UlgNWarKzqUcBRChrM/WQRi79ZScivEV/Ty+B7z6DVGc3LzM9eMix+enQy66YXLlbnSnByz8SboIi6AFURO3tPySm2pX/DhuhV4WzKD1UCbc6smO3a0iU4zjwrQukXqoqZfgB278K/fn04b//9DxKsm4h1HNYEj+Yn8N7bhau9BoMEP3gX9/0PkyuKV93VskDTLbT8arAVrI8ouVkEjlD4AXCoWP7oQVbAodKSUoXXltJ1ncWL59K9e9+Tcuk/K+sg//rXQ1Hbfv99Lnv37qZp09YV3KvypTQyN02LPOEA5xHpWG2Fv1JTWe9xWZYwcoOEfBqyKqPGObBkEVnsPWQw4cmprP3tcBDz6l/WsXb6X9w+9no8dROKZVVWVQlVPQjin4j9QmSDWEN8fAeysqqu0i/LEoYvhGla9L2xJ50vbo9DcaI4FdR4Z/FrCRQDSxGc/9hgDmw7GM7pfwin18GoT0YiedWqXiPRpgyoPDONTSEEIIpKs2OGFdKC4xMTsUwz0rfXsgiN+Qr33fcXWzmP6ENOVrjaTRSs/fuR/XngKfl5KxpFkTFWryy030pPR6pXP+bnRI1USEis8FSUkiSoX78RklR4JUSSBB4jgOz3ga5heeMIqB5C1UjH8/ny2LlzZ8z2VatW0aJF22plyS5K5jbVj8oob0kz2Dp3W0GOfSGgzRktOP+Js5HinAWKfO7+3AiFPx89aDDx2Wlc/vpwLOXYbm8eDyC2x2gNIMlBhHBUmFuKEALJsjB1E8kpl8rtRmgG25fuYOors9i/JZ3kBokMvL03rfo1x3KUoHhYMbEsEF4HN3w2ksydmexcs4fk+onUa18HOc6JeaJdF2wqBbbSX4kJmRDXpx/66lVR25UuXTDWrwPCCr/rhhsJRsnRb+7ehaqFwikyS4quF91eIbk/S46iSDi1w25EJpEvSAUYBubff6N06Yq+ckWhZuflI/G740Gr2AlTkmSaNGleaL8sQVzOAULvvY22b9+hTjpxDr8YpWs3fNWk+qqqqiiKgn7E9ZeUlMS///0Q7dq1BQQ7d24lObkGbnf8CXfFKwtiydymelLZ5K0KwbblO/nmvp8K9lkWrJ+1mf1b0rnp66vApaAoEn/N2RLzPFsXb8cI6Ii4YxcBFMICYte/gFwkqUa5FOQq1BfNJGN7BnM/XkT2/lxa9WnGqRd1Qk10YpTQRC4L+HPKhoj0mRk7sxj378mccXMvet1wOmY5vOxZlgUuheSWqaS2qYVpht2jbIXfJh9b6a/EmKaF1agJUvPmmFuOmGTdHpzDh6O0boOZk4PjwhEIRcH/wbuYe/cWPpEsh/8dD0nJIMvILVrg6D8AFAUUBXP3LkIL5mN5D6cAk2UJt+ZHsgxMIRFQPWVuzSgO8VYQVv2BPnM6VjCIo0tXlDPOhM5d0H76sdDxwUk/477nPqSWrdCm/IqVnYXUuAnOSy8jVKsegQpW+CG89D9v3kz69BkYsfQfp/kIvPgCHOmSFAwS+vZrnCkpyM3bVQvrd2JiMpdeejFjxnx76O9EvvnmKx5//CmWLl1acFyPHt35+uuvSEioUeUV/1gyt6meVDZ5B7MC/PpydHfS9O0ZHNiSTq0OdQBwuGMbFyRFCjtbFwPLEoSzccUyHrkrxMovdJNl363ktzd+L9i3Y/Vu5n+xlNu+vxZnzbgS9UPP1Zjyyuyobb9/tJDTL+uCFF9+ldEty07XbBOdEz/T2BRJruQkfvTtWH/+gT57BsgKrmuuIzjmK4JjDlv15XbtcY64GP87b0EoFHEO+bRuhFT3cfnQB1Q3rtvvxEpLIzDmK6zcQ9mAGjfBPWo0PqcXNBMPGsqmTYR+/AFr/35EcjLu8y7A6tCJXEo+uQkBDhkkw0CXVbRiTmBxVhD90w8wj4hB0aZNRZ8/H/djT6AMGow+fVrEZ4TbDSk1CNRviqtrN4RlYUgK2bLjhAWRSZJEy5ZtkI5YnZBlCXPt+kiF/wi08eNw39OMXFF+D5OKQpIcPPPMM/zxx5/8+eefPPDAfTzzzHMRCj/AokWLueGGUXz11dc4nd4T1NuyIZrMbaovlUneDofMgfQ80v/JiHnM38u2U7dzPTTNoPUZLZj0/PSox3Ua2g7FoxarKJffD3Fx9WO4+KhYpqcMit4dGy03yG9v/E7dNrXoeeWpxKV4EZJg47yt/PLCDEa8cF44q0Ux8Wf5CeaForaZhkX23myS4lPLqvs2NsXGVvorOZYF2cKJ0qU7zlM6oWAS+PBdzM2bI44z1q0lpCg4Bg0mNHlSwX5RqxbqhReRdZxZcjQh45KkQm5D5j9/43/9FVyPPIHlcCMvWxpxjJWRQejLz1AHn4PrrKEErOKvNLjRcWQcQJs+DTIycLZth6dHL3Id3iJjEyVJIO/egxYl6NzKyyX0y0Tk84cjd+6CPm0K5OUhdeqM3K07OQ4vhm6hSUdURjyBUU+SJFG/fqOIfbIsMLfFXlY39+5BtcyqVOCzSBISajBx4s9s27YVp9PBY489EfW4OXN+JyvrILVqVX2l/2iZ21RfKpO8LctC82u44p0EcoJRj0lpmFxgBHEkOBn64EAmvzgz4pjEOvEMvrc/RjHnoFDIRNfroCgBEPuPaHGCdQo5OeU/B8uyxMbF2zn90s40Pa0h09+aR/r2DCRZ0P6s1vS7oTtGQEcqhrtSPoqj6OedWsRKiY1NeWIr/VWEQCDIlNm/cVHvXoUU/nyMP1bjuuQy8HgwDx5Ebn8K1G9Ituw+vuTJgNsIEhr3Q9Q2KycHc9NfuFu1JjB+XNRjtOnTcJ0xkIBSPIXMKQyUJQsI/PBdwT5j00a036YS//B/yPKkxFxmVRQJfeH8qG0A+rKlSOeeT27txjivuQlhGgRlFc2wwnXNS0h4BVuUy/KzrmvMnj2NM84YjKKEHxCmaSE1jK0kiJo1MauLxk/4kvV6k+jQoSsbN64t8tjs7Bxq1aqgjpUT0WRuU32pTPK2LDBCJt0u6czcTxYXalecMk27NSxwHbQUiU4XdqBF76YsGrOC3AN5tD+rFc17NkGKK9kKaXa2gcfTGKezEYggoGDoCnl5VoX48kM4w03zHk0i4hlMw2LNlA3s3ZjGqE8uL9n54l3Uap4akUUnn7hUL94aFVvs0cYmH1vpryJIkkyHDl1jZtLJxwgE8PcagCQJ/LoZnnxLoZTKloEeJYuK8Mah9u2HUjMVcnMgEIh+AtPEOngQimmFdYV8BMZ+X7jB7yf49Re4b7wdX8zLVoRjDmK1yjL5SnrAEoByXMq+Klm4NT+k7YdgEFG3HkGHm0AZ3k758pakwxYjXTeR2ncAh6OQCxeA46JL0VwenJaJZlAt8ltD+PJNSkqK2S6EKLK9qhBN5jbVl8okb103qdUqFdMw2b/5AH/9fnhF0eFRue79S1ESXREBrZYq4amXwNkPD8QywRJgGOZxzTs+n3no0ZZvTa84f3TDMGnYqT5f3BLluQOkbU3nwN8Z1OpQp9hjE26FK98cwXtXfIE/6/CzUXUpXPveJUjeE+c6anNyYyv9VQRJkqhduy4EsmIfJAS4PWUawGMiIVJqYKUftlhITZriHD6C0NQphKZMxv3gv3FdfS0iPh4rGERbuCCydoCzeMuisiwwN2yO+ZJibtyIK+QHR/RiHJqm4+7TD33u71HblT79CKqu2DFjxcAhTNw7toVrFwSDIEnIzZqhnt4Tpcup5FplY7ErkPdR5Dm8eB94iODbb2JlHboWnE7cd90DhoH46mMU08TZtz9W3QbkSJU/nWpxSEpK4YILhjFhwsRCbcOHX0BiYjKSFM4lXlWrTsaSuU31pLLJW453kNo8hU7ntqP31aexf2s6cSkeGnSoi7OGO2YGm+LGW5UniiQwNRNJldCPQ5kWwN6NaTHbtyz+h7qd62GaxXt4mKaFu04cd024kb+Xbmf7ql3UbVOLln2aocSXPBuQjU1ZYSv9VQRN05g+/ReGnz0EqW07zEOpOo9E7noaIacHSjCfqLJAMUIgJIJCKWR9CDjcuM8bRujzT8M7FAXXRRfje+tNCAZxXHAhVtp+gpN/wUo/gIiLQz1jAGqPngQ+/RgRn4AVl1D8Dh0rBWgRCp1lgZ5UA6V3H/T58yLaRM1ayGcMpLRFdd3BPAJvvQGGgdq7D8rp3TH++gtrz26U2rWIr9uAXOEodSaZfHkPGnQuqnr4RUI3ITelLp5/P46UlwOhEFJyMsFvvsY4IrWrvmI5UqvWxI+6mZzjqM9Q2VBVN2+88QYOh5OxY8dhWRaSJHHxxRfxwgvPsXfvblasWEnt2rVo164dCQk1qGrBDbFkblM9qWzyNgwLNdlD837N0PNCpDZPQXaqSG610mYEExYE033M/moZezfsp1672vS86jScyW5KEsYmZFFkPENyvYQSW+ZN00J4VVqc2ZLWg1sRDGpMmfJzpZG3zcmJsKqqWaycOXAgp1KlADRNk4yMg6SkpJBECO2rzzHW/BFuFAK566mol15BVjEVPEkSxOs+zBXL0JcuRjgcKGcOxmrctJCSGEcIa+ov6DNnoHQ7HaGqaPPnoXTsiNy8JcEo/vxKj55IySlIXbqSm1Kn2MVBE4NZBB57JHqfGzdGuu2ecOXRIvBaIeT9e9BnTodAALl7D0SbduTInlJZgVVVxjlnGtpPP6L27YdUsxbBH8dG9rF5cxw3304WpVO08+WdnJxSZHYPSRLEbdtA8O03o7Y7brgRX/tTK+1Du6ToeoCsrAyys7NJSEjA43Fz22138MsvkwuOiYuLY+LEn2jfvjNw4jOjFJfiytymemDLu3TIQrB71S4+G/091hEKuaRIjPr4cmqdUqfYFnVFEiz4eDEz3ykcEyYpEg9MuwUpvmLmdJviIwSkpkZf+beJjq30x6CyKf1H40bHEfRBwA9uDyGnB38JMuQkmn6CLz6PdTA9Yr/cqTPSFdcUqt7rQsep+RHBAIF338FK24/71tvxf/ZJOO/a0QiB55n/I8eVSElWf93oyNN/RZs2JbJBUXA99B9ykmsXy+IihMApWeHiXEIpE6XX4VBQv/kUfdlS3Hfejf/1V6Mepw47n9AZ5xCqgCA0l2QiffwOxvr1UdulJk0Rt91dbYp2HYkQJi+88ByvvFJYDi6XizVrVpGYWMWje21OCixLJyvrIBkZGbjdbpKSkvF4Sm5dPqnwabxx/kcRPvP5xNf0ctu46xElyJIjBQ2+f/BnNi/4u2Cf7JC59t1LqNOxbgVGGdgUF1vpLzn262YVQdM0Jk78AU0LVy/0o5DlTCArsTZZjvgSKfwOGYyZ0wsp/ADG6lVIafsQRxVXCaCQpcYTiktCxB0KyhUiusIPYFkYaQdKHCfrR8EadDbOu+5FbtkKUasWSu++uJ94Bl9yzWI/BC3LImBAwJTKzMptGCZS67bILVpirCvsXpWPNnMGTi3G71JMjpZ3LIRlQlHHhEKIyvz2Wgqysg7y3nvvR+yTJIlzzx3K//3fM6SlpaGqVcfFp7gyt6ke5Mvb58vi7bf/R8eOnenevScdO3bmggsuZN++nUjlULW1upB30BdV4QfIScvDn1GyOdh0ylzy0vncNWEUFzxxNle+OYIHpt5CnQ5lo/Db97dNZcBW+qsIiiLTv/9gFKX0mR4cWgB90YKY7ca833HEKEQSlB2og4fkd6rI7xEez3GtluThILdRS6ybbke+72H04ZeR6UxAO85aA2WFYZhIbdshkpKwfHmxD/T5ECUJrIhCceWtyQ7kHr1in+f07ihagMRgNh604hbKrBJomkZe3mE5NGjQgEmTJtCsWTPefPMtrr76Ot555y1ycw8WeomtjJTlPW5T+VEUmcGDz2XixJ95+ulnCByRAW3lypWcddbZZGUVNszYhLGOZQA6jinYcsh46iVwygWn0Lh3E0Sco0SxAUVh3982lQFb6a8iCCGRkJCIEGUgMoui03gWUQHRMCzM5i2RT++BuXMHcrNmUY8T8fFYiUnH3UXTtPCjkCc5CZ5gZf9IclUv6vkXIrdpE/MYqXUbNKl07jTFlbeum0indELULOzGIpKTkZs2JfDYIwQe+zfWR++QoPuqhAJcHFwuN02aNAHC7lzvv/8Oo0ffyv/+9xb//PMPW7Zs4T//eYyhQ88jJ6fyK09leo/bVHqEkNC0AM88839R23fv3s369etsa38MvDU8OL3R47vciS48ye7jPvfxph4tCvv+tqkM2FdfFUHTNMaP/6ZMlgZDDleR1mG5Tz9CRTji5+JAXHQZomNnnFdcjUhKjjzA5cJ5173kKcc/6VZWDNMi25mAaNEaqX6DwgdIEo5LLiMgSqf0l0Te2bIb5/0Pog47H5GcjEhKQh00GPf1owh88VnBC565cSOh/72O14xRU6GKkZiYwosvvgDAmWcOZObMWeyMUlNiw4YNzJw5E1ku2XSnKFKJP1MayvIet6n8aJrGvn172b9/f8xj/vjjD1vpj4HsdXDhU+dEbbvo2XORS1BBtyKw72+byoAdyBuDyhbIa1kWgYAfl8tdJpbaJNNP4L//h5WREbFfOqUD8tXXFwrkjYXDoeAO5mLt2I65bSuibj2k5i3IU73HlS+5qiAEJJgB9Gm/husChEJIrVrhuHQkvsSaaFbpZHQ88nbIAofmR7UMQuN+QF+xPOqqjes/j5OVWLtU/assBIM+Zs+ezv79aXz44cds2LAh6nH9+vXl22+/Q1FcxzxnIJDD/v37mDFjBl6vl4EDB5KUVANFKd/Up2V9j9tUbizLIisrje7de5Jx1Dycz7hxY+nf/0w7oDcGQjfJ2Z3NjLfnsX/LAeq0qsnA2/sQXycBU65c95B9f5c9diBvybGV/hhURqVf13UURSmTCaMgZefSxehLFiOcDpQzz8Jq1vK48rpLkkCWJUzTrLDS6ZUBp0w4aNey0GWVgFQ2lRZLI+9EXwaBJx+N2e646RbyWnesNoqELEtkZu5n+PCL+eOPP6IeM2jQmXz11ddIUmHrnywLsrLS0XUdSZJ4//0PeOmllwvahRC88srLXHrpZTgcnnIbR1nf4zaVm/Cj1+SDD97hsceeKNSekpLCkiWLSEhIrfjOVSGEAGFYGCED2SFjyaJSPbvzse/vssdW+kuO7d5TRdB1nUmTxqLrepmczzQtsiQ3gV4DEHfcizX6TvJadTzuQk6maaFpxkml8AMEDciW3GTLHnyoZaZIl0belsNRZJC1VKNGtVH4Iex/m5xci1tuGR3zmNtvvy2qpd7vz+aTTz7g9NN70KpVGwYNGkzDhg147LH/FBxjWRb33Xc/e/fuLlU/ZVkiLy+TtLRdZGTsQ9cDEYHVuh5k9+5tbNmynt27t+H3Z9uuHdUYXdf56afvufrqaxg9+saI3O2NGjVi+vSpJCXVPIE9rBpYFpiSQLgUTKlyKvxQ9s9wG5vjwbb0x6C6W/pPBhRFQpXAtCCkm5VKnseiNPJ2ShbKpHHos2cVahOpqaj/+jc54thuLlWNvLxMLr/8chYvXhKxf+jQc3jnnXfxeBIj9muanyeeeIyPPvqk0LmefvpJFixYyJQpUwv23XLLaJ5//kU0rfgJ/CxLIzs7A9O02Lp1C3fddS+bNm1CkiTOO+9cXn75RVJS6uDzZfHKK6/w9tvvFigF7dq14/vvv6VOnYbV6iXNJsyR97iuB8nJyWD37j3Ex8eTmppKQkLKSWdEqc7Yz/Cyx7b0lxxb6Y9BZVT6bX/A4qHIgngtD3PzRvSlS8HtRj1jIFpKTfKqSJGq0so7gSD6d2Mwli8r2CfVqYvjjrvIcSVWSyVSCMjNzWD58uV88smnKIrCzTePpn37Uwop/AAHDuymffsOUas0JyQk8OGH73HZZVcU7DvvvHP57LMvMIuZTSovL4Nnn32OmTNn8OKLL3DppSMLfVfDhg2ZP38uX375Ff/5T2GXrLp16zJv3lzi41OK9Z02VQd7Tj+5sOVd9thKf8kpOtG6TaVB13WmTJnAeeddjKpWDcUVwr7+6qFV65BBVAWrLJElQUIwB/+br2Hu3VuwX1+0EPXMQXjOPq9KVKctrbyzceK+7Gpcwy+CrCzweDA8ceTIrqgKv8Mh4Tpk/A8GIRisevUnLQu83mTOOOMs+vUbgBACIeSo4xUCtm3bFvN6zM7OLvRgHjbsPCRJxiwipW0+fn8OV111NfPnL+Dxxx/llVdej/pdO3bsYP/+fbz88itRz7Nnzx42bvyL007rVe73jk3FUlXndJvjw5a3TWXA9umvIqiqyvDhI6vMZCEExFtB4rZtQPniQ+QxnxK/ZxtxVrBE53EIk0TdR2L6LhIz9pBg+AteIqIRR4jQb9MiFP58tBnTcWRHz5JR2SgLeftRyHIkkFWzIVneGuQKZyEFWJIEyckS3rjdKOpKFHUlHu9OkpOlKutPHh6jjGVJEeM9Uoe3rLA1vygcjsNBv3Xq1OHMM89ELyKV7ZHs3bub+fPDBfDat2/HkiVLYh6blZUVM3sLwLp1dq726khVm9NtSoctb5vKgG3pryJYlklOTg7x8fFVorhHvBlEe+9/mNu2Fewzli1F7noqcZddWayUoG50lNXLCXz3DeTnNna7cY+6CdGoJaGj3lmFAMmXR2DJopjn1OfPw3HBJYRCxvENrIKoKHknJEgIaTVw+GVMiD0g0klM7ERGRtW2LkuSIDv7IHv37iEt7QBNmzYhObkGTqeXevXqUbNmTdLS0gp9rlu3bvz551okSeLCC8/nueeeIympZrHcomRZsHr16oK/MzIyqVWrFnujvIhC+OUiKSmJzMzMqO1t27aplu5YJztVbU63KR22vG0qA/aVV0XQdYM5c6ah65VbWQVQZAlr9YoIhT8fY8VypD27jmm5FELgSNtL6KsvQNPCRadq1oRgkODb/8Pjz4r6GQzj8AtCNIJBoPJbTStC3ooiIaR0jlT4DxNCiP2oRS2rVHJkWbBz5zb69z+DXr36cMEFF9KxY2dGj74Jny+TxMQaTJgwHq/XG/G5unXr8tlnH3PZZZexYcM63n77XWrUqFNsxds0w+fI5+uvxzBq1PVRj5UkiXr16nP//fdFba9Tpw6tW7exXXuqIVVpTrcpPba8bSoDdiBvDCpbIG9VwmsGMd98GXN39BSHcufOmFffRKCIuc8tDPjkPSSHA/WMgVjpB7BCIaR69TA2bMAMBNDOv5iQHimkJD0XbdwP6EcEsEac974HyK7XzLacAh6PhMv9F5Ad4wgvwWA78nLLzr9fkiD7kIuV1xuPEOW32JiTc5DevftEtbDffPNonnnm/xBCIjPzAPPnz2f9+g107346nTp1IjExtVTXSFZWGl27nkZOTg4AX375OWPGfMOvv04pOEZRFL7++kv69RuApgV58cX/8u6772MY4RujdevWjBv3PXXqNLKvVxsbG5ujsAN5S06VUfrfffdd5syZw/r161FVlWXLCit1u3fv5sknn2Tx4sV4PB4uvPBC7r//fpQicpbHorIp/aZpkpFxkOTklIh8zpURrxnEeOUFrLTo5eXldu0xb7iFgBl7HF4zCL9NRqlTl8C3Y8IW/EOoPXshd+5CsFlrApYc+Tk0HAf343v9VQgEIr+3eQvUm28ji/KtrFoWVIS83W4Jt2czEMufPJ6Avw0+X+mV/vzMOl988QUffvgxwWCQESMu5L777qVGjbrHrdTKsiAQ8KGqDoRQCiziQsAffyznzDPPiji+Ro0aPP74ozRu3AjDMGjYsCG1atUhISEZIQSGYZaJgi0EbNiwhiFDziU3NxdFUXjiicc47bRT2bJlK8nJyZx22mkkJKQgy2EfX10PkpFxgMzMTDweD8nJKcTFJdkKfzWlKs3pNqXHlnfZYyv9JafK+PRrmsY555xD586dGTt2bKF2wzC4+eabSU1N5dtvv2X//v089NBDqKrKffdFXzqvShiGwZIl8xg06NxKP2FoqgtHt9PRJk+K2i736k1QKEBsZdKQFdyndsP30gsc/falLVyA1LIVOBzh6lhHkIeKkloLz/0Pos2cjr72T3C5UPv1Rzm9BxlVQOGHipF3MGjhctdDiOhKv2XVD3tDlQG5uRkMG3YBf/75Z8G+99//kB9+GMfChfNJTq5dovPJssTBg/v44YcfmDRpMrVq1eTuu++iRYuWOJ1xCCHYvn17xGeSkpL4+usveOihRyJ87tu1a8dPP/1IcnLtMnOjsSxo0+YUVq1awZo1a9ixYwddu3alQYOGdO/eB9O0CinzliWxbNlSBg06tyDYz1b4qy9VaU63KT22vG0qA1XG0p/Pjz/+yHPPPVfI0j9nzhxuueUW5s6dS2pquGz5N998w8svv8zChQsjMnEUh8pm6a9qJJk+As8/i5UV6Xsv1auHevf9ZEcpDqXKArfmB9MAtxvz10lo06YWOg5AqlsP6Z4HyIsREOyULFxmCEkLYUkSAU88gSqYhrK8iY+XUdWtIA5ENljJ6HpLsrNL738qy4LffvuVyy+/Imr77bffyhNPPA3IUduPRghIS9tN3779SU9Pj2h76KEHufPOO3E4vGzevI5evfoUtD311BNMmzad+fPnFzpn165dGT9+PG532VuNJEkghMA0q1aBOBsbG5vKjG3pLznV5nVz1apVtGrVqkDhB+jTpw+5ubls3ry5xOczDP3Q/0aBj61h6AXbuh65bZpHbpuHtrWCbU2L3LasI7ctLMsqtA3hiP/8z+7du4vgIdOraZrounbEtn5o2yjYNozI7YocU5bswfnwo6iDzwkH4aamog6/CMfd95MtnAXjM83w+OIJ4Zw7neAzjxN85EHMXydhRcmqko+ZmYEoYkw+zSQLJ2nCTTouAkGzQuR05JhKIyfTNNmzZ2fBecpLTtnZOqFQUyyzI1AbqIVldkDTWpCVpZXJmHRd48svv4opy3HjxpOTk1nsMWlakIceeriQwg/w3/++yMGD6ViWRd26denQoUNBW5cunaMq/AArVqwgIyO92GMqyf1kmhbBYOio8RWWUzAYZO/eXQW/9Ym69oozpuLIKb/vleF+qoxjCgaD7Nu3B13Xq82YqqOcympMmqaxb98eNC1UbcZUWeRkU3yqjdJ/4MCBCIUfKPg7Wkq+Y7FmzSoA1q5dzdq1YVeA1auXs3HjOgCWL1/E1q2bAFi8eC7bt/8NwLx5M9mzZycAs2dPIy1tHwDTp/9CRsZBAKZM+akgwG/SpLEEAn50XWfSpLHouk4g4GfSpLALU05ODlOm/IRpGqxevYyZM389NKZ9zJ49DYA9e3Yyb95MALZv/5vFi+cCsHXrJpYvD6ev3LhxHatXL6+wMfl8Pg6YKvNTaiD96xHEvQ8yU5LJEi6ys7OZMuUnADIyDpKxawvG99+g/fQj5OUBYPz1F3LTpjHlIzVrzr6szAodU3HklD+m6dN/KZWcTNNg+fLFrFtX/mPKyAjy9dcTycpswMH0uowZ8ws5OWaZjWn//r14vZ6YsvR4PASDAdLS9hRrTHl52fzyy+SY5/v11ykIYTFnzizGjx/LOeecDUDgqBiPo0k7FINyou6nmTN/ZfXqZZimcUKvvbIcU2W5nyrjmGbO/JU1a1awf/+eajOm6iinshrTunWrWbNmBX/8UX3GdKLllD8Om+JzQt17Xn75ZT788MMij5k8eTLNmzcv+DuWe89jjz3G7t27+fjjjwv2+f1+OnfuzAcffED//v1L1Ld9+zKQJKXgjVWW5UNvuAJZltF1HSEOb0uSQJLytyUkSULXNSRJRpIkNE1Dlg9vK4qMEPnb4dAKXdcjtlVVxbJMdN1AVVVM08QwDm+bpoGi5G+bKIqCaYYti4oS7rtlHd4GC1mufGNKDmYReOLRQjJw330vgU8+wjo0CRQgBK7/PE5GXCqWZVbKMVVHOR3vmEzTZN26lQwcGBlUm88jjzzM8uUr6NKlC7fccjMOR1yRY8rOPkCrVm1i3rvPPfcsN998B4FAEFVV0DQ/mZkZmKZBp05dC6xKR7N27Rpq1qx/0srJHpM9JntM9piq0pgMw6Bu3ZSYzwKbwpxQpf/gwYNFVqIEaNiwYYQ/fiyl/4033mDmzJlMmDChYN+OHTsYNGgQ48ePp127diXqW2Xz6c9396hbt0G1CgKSJEHc5j8JvvdO4bY6dXFdcSXBSRMxNv4FgKhVC+fV1+Gv3bBQca7qRHWTt9+fwxNPPM5nn30esb9Lly48+ui/ueiiSwE4//xhvP32Ozid3minAUDT/Fx11ZXMmjU7avuKFcto0KBZof26HuThhx/kiy++LNR20UUjePPNN1HV2CsS5U11k7lN0djyPrmw5V322D79JeeEZu9JSUkhJaVs3tI6d+7Me++9R3p6OjVq1ABgwYIFxMXF0aJFizL5jhOJaZps2rSB2rXrVasJw7Is8ERX8My9e/B/+D6uRx7HMgwwDQyHm1zVjWFU76Dc6iZvtzuep556muuvv44PP/yIvLw8zj57MC6Xi+uvv7HguJ9/nsgzzzxF3bqxlX6Hw8Nrr71Cr1598fl8EW3XXXctNWvWivo5RXHyzDNP43Q6+eyzzwssSVdddQVPPvnkCVX4ofrJ3KZobHmfXNjytqkMVJnsPbt37yYrK4sZM2bw8ccfM2bMGAAaNWqE1+vFMAwuvPBCatWqxb/+9S/S0tJ48MEHueSSS44rZWdls/RXZxIMP8GnHwO/v1Cb1LYdXH8z/kPvp0KACwM1FEBYJrrqxC87sVMbVg2EgOzsAzz++JPMn7+Av//+u9Ax3347hkGDhhQpUyEs0tP38uab/2PatOmkpqbywAP3cfrp3XG5irb8mKZGVtZBcnNziYuLIzExGUmKlt3LJCcnA8PQcbk8eDwJ9nVmY2NjU0mwLf0lp8oo/Q8//DDjx48vtP+LL76ge/fuAOzatYsnn3ySJUuW4Ha7GT58eDUqzmWwffvfNGrUBEkqXmrDqoIigTdtF4FXX4IjovFFjRq47n+ILNWLZYVTP8b7swl9+zXG2j/BspAaNsJx1bX4UmqjWeIEjqJsqc7yPnBgN+3anRKz/bffptK5c7di3n8GeXm5qKqKy+UtUMqFEASDefh8uUiSRHx8UrGr/+YXEnvttdf46KNP8Pl8dOzYkddee4V27TogyyVL/1tcSiPzcFpQMIxKNGnZFEl1vsdtCmPLu+yxlf6SU2WU/oqmsin9uq6zePFcunfve1wvMZUdVYBHz8Pc+BfW/v1ILVpA3QbkKu4CRS7R8BF87unCgb2yjPuxp8jyJlcqmZWG6ixvTfNzww3XM3XqtIJ9devWZdiw8zj99NPo168f6ekHUVWFlJRUEhJSSmRhtyyDHTu2cf/9DzBnzu+oqsrIkZfz2GOPkpxc65jn8vmyuOSSS1i6tHDV7+nTp9G5c7cysfgbRoi8vGwsC7zeOIRQWbTo9xLJ3DBCZGcfZPHiJQQCAXr27EFycioOx4l1VbI5NtX5HrcpjC3vssdW+kuOrfTHoLIp/ScLsiwhSQLDMCMUK0WRcC9bQGhM4SBMAPnU0zBHXkvAtH0lKztCQGbmfs4+eyh///03L774AjVq1CA3Nxe/389TTz1D3qHUrXXr1uXbb7+mbduOgIQQYaVe0zRcLnchy7YQsGfPP3Tr1qOgpkU+jRo1YtasmcTHx44jEgL+/HMlAwacGbW9Q4cOTJo0qVRFvCRJcODAbh5//EnGjfsR0zQZMuQc/vvf56lTp1Gx5x1NCzBhwnjuuOPOiIxEt9xyM4888sgx3ZxsbGxsqjK20l9ybA2pimAYBps2bShIk1VdMQwTTTMKWVIVy8Rcszrm58xNG1H0UHl3r8KozvK2LEhKqs2MGb+xbNliVq1azeOPP0lSUhIPPvhwgcIPsGfPHgYPHkJ6+j5CIR8bN67lzjtv47LLLuHtt98kM3M/knTYrUvXQzz99LOFFH6A7du3M2/eXGQ59rQnyzJz5syJ2b5mzRr8/ryY7cUhMzONvn3P4PvvfyhIhzd58q/06tWXvXt3FFvme/bs5Lbbbi+UgvS9995nwYL5yHL1cXerjlTne9ymMLa8bSoDttJfRbAsi4MHD3CyLsyYkgTJRVhoExKxRPW5nKu7vC3LIi4umWAwxFdffc21117NW28VTtsK4Uq1f/zxB1999SW9e/flu+9+YN68+Tz++BN069ad3bv/QYiwguv35zJjxsyY3ztu3I9A7MxPlmVRu3btmO0ul6tUS/OyLBg3blzUgoE5OTm89957CHHszFSyDO+//37M9v/+9yXy8rKPu5/lhbDfQwqo7ve4TSS2vG0qA9VHS6rmKIpC9+59TlpfwJBmopwxMGa7OmQoAcVVgT0qX04GeauqzNdfH87C9ddff0U9TghB7dq1+Pe/HynUlpWVxd1330Mw6Dt0rFxkGuCePbuTk5NJZuZ+cnMzClnDDcNkwIAByHL0QLvrr7+OhITkYo0vGsGgn59/nhSzferU3/D7fTHb89F1nX/+2R6zfe/evei6flx9LGuEAJ8vk927t7Fy5WJ27txCbm46inJyvwGcDPe4zWFsedtUBmylv4pgGAbr1685qZcGg3GJOK68upC5UOnTF7Nlm2qVu/9kkbeuh8e3Y8cOWrVqFfWYVq1asXr16pgWslmzZpObmwVAQkIy99xzd9TjnnvuWWrUSKVPn360atWGnj178eWXnxEIRAaGJyWlMGbMV4VyaXfu3IkHH/wXcPyZNxRFITW1Rsz2lJSUIt2P8lFVB4MHR69wDNCrV0/c7ti1DioKISArK41rr72Ozp1PZfDgIXTt2o2rr76O/ft3n9QuSCfLPW4Txpa3TWXAVvqrDNYhC+DJuzQYQCHUuRuuZ1/Acf0oHFdejeupZ7HOv4hcyieN4omj+stb0wxGjrwMgM8++4Lbb7816nEulwtZLto6lu/Xbhgm558/jKFDz4lov+iiEYRCIUaPvoWdO3cCsG/fPu65515effUVdP1wDIAkOejffyDr1v3J//73Bv/5zyPMmPEbP/30E3Fxx2/lBxBC5e6774zZftddd+ByHVtZ13WTCy64gOTkwv1RFIVHH/0PinLi74lAIJebb76VOXN+j9g/f/58brxxNFlZB05QzyoD1f8etzkSW942Jx47e08M7Ow9lZuwhVBUK+v+yYjfn80dd9zBxImTuP/+e0lOTua5514oqLRbp04dvv12DDVqpNChQ+eo5zjttNMYP/5HnM64I86bw65dO/jll8nEx8dx/vnDOPXU0/FHKQAnyzJr164hJaVOlLZwNildN4vliytJgtzcTLKyMjAMA1VVUVUVtzsOjycBy7IIBHJ59dWXef31NyM+O3Lk5bzwwgu43QnH/J7879q9+x/uuutuZs8OBx+3b9+ed999i1at2iNJJ96NYO/ef+jYsUvM9mXLFtO4cSvbz9nGxqbE2Nl7So6t9Megsin9hmGwdu1q2rfvFNPf2Kb6cDLJ2+fLYt68ubz++pt07dqFa665CgCn00mNGqkkJtbA78/jhRee56233o74rNPp5PffZ9G8edtCGZ+EAEWRsSyLTZvW0b17r5h9mD17Ju3adS7lSEw2b17PDTfcxIYNGwBo0aIF//d/TzNr1hzuvfduatSoi2laBIN5HDiwn59//hlN0xg2bBh16tRl8+bNtGtXfJnnFyHLycnCMEzi4+PxehMrTeXgjRvX0KdP/5jtv/02ha5de5yUL+8n0z1uY8u7PLCV/pJz4k1BNjaVECEEHkvDYQRB10FVCahu/MbJ64NcXng8iQwZcj79+w/AMAy83nisI6orG4aFw+HhwQcf4swzB/Liiy+zb98++vXrxwMP3EetWvWiKrmWFXYhAnA6iw7y9nhKX8zqwIG9DBgwKCJd6ObNm7nyymuYOPEnLrroUiZMGE8wGMQwDJKTU7jnnvswzXA/w+k7S/adlhX+bWrUONz/yqLwAyQlJRXZnpqaWijlqI2NjY1N+WBb+mNQ2Sz95YFDBkcoAAJCqouQHV8EhBX+JCMPa/cuQtN/w9y9G6lWLRxnnwMNG5OJ80R38aRFkgSBQB6apuH1eimu3cLvz2bo0HNZu3ZtobYGDRrw++9zSuWvL4TFs88+yRtv/C9q+8iRlyPLMpdcchGXX34lfr+fNm3a8O67b9OmzSnIsnrc312Z0XUfN998MxMn/lKobdCgM/noow/weEoXJ2FjY3NyYlv6S44dyFtFMAydFSsWYxilS8MnSQK3bJJk+XGuXIz+/FPo//ck6uSfSDJ8EYWOTlbiCWGuW4v/zdcx1q3FyszA2PgX/v+9gbl8KR65/N8Gy0re1Q3TDFu2vd5ESrJQ6fUm8s03X1OzZs2I/QkJCYwfP5aEhNhpPotDMOhnwYJFMdtXrVpNy5Yt2Lp1W0FK0Q0bNjBo0GD27NmBENVT5ori4fXXX+OCC4YV1FIAGDp0CO+99w5eb+l+96pMdZS3TWxsedtUBmz3niqDwO32AMevlHutEMru3WjTphDMy0Vp2Rr3jaMJfDMGfcZvGMuXEv/wo2RJ7rLrdhVEDvjwjf0+altw/Dg8nTrjU+KitpcdpZe3zWFM06J+/cYsWrSAZcuWs3LlSk45pT3du3cnOblmqV1iHA4HTZs2YenSpVHbGzSoT1raAdq1a0t6enrBfsMweOaZZ3jnnfcApVrKPD4+lXfeeY9nn00jKyuLxMREEhOTcLsTTvIAXvseP7mw5W1z4rHde2JQ3dx73GhIUyehz5gesV9443DffAu+d98Gvx91xMUE+wxE06vR4EuAJAkSD+zE98JzMY9xP/QImTXqV2CvbMoSSRLIsoRpmhhG2VznQsDmzevp2bN31PYxY77krbfe5ZprruKWW26LaKtduzYLFy4odTrQY6EoErquoSgK+kl6f9vY2FQfbPeekmO791QRdF1n8eJ5x11l0+HLKaTwA1h5uYRmzkA962wAjKWLUbVgoeNOFizLgmNUTBRK+WdeKK28bWJjmtahwNmyU3wtCxo1asI777yNqh72z5dlmUcffYS9e/fx+OP/4fHHnyz02Xr16qEoarnJXJIE2dkH+Pbbr7jmmqu4//572bbtL0KhY1f+tSk/7Hv85MKWt01lwHbvqSIIIUhJSY3wiy0uiiJhrF4Vs11fvQrv8BEYf6xGyDLWcXxHdcGyAG88IjkZKyOj8AFeL1Z8Yrn3ozTytjkxqKqbESMuYtCgM1m/fj26rtO6dUtU1YFpmvTp0z/CtSeff//7IbzeeEIhvcxlLgSkp++lf/8B7N27t2D/p59+xgsvPMfVV1+Lqp7c7nwnCvseP7mw5W1TGbDde2JQndx7FEXC9ftvaD/9GP0AIXDffidCVTHyfOS17nhS5s3OR1Uk4vZux/fqS+F0nflIEu477yG3YXP0k/DnESJcYTUzM4OcnGySkpJISUlFkuxsRkeT/2DPn141LcDkyRO59dbbIyx9d955O//614O4XGWzRC3LEtnZB8nLy0VVVeLi4rnrrrsYNy76vb927Rpq1rRd1WxsbKoetntPybGV/hhUNqU/vDQ4l+7d+6Icw/0kGom5Bwg8/UTUNqVDR6Q6dcHjRvQfSLZeMYVDhKBS/cZH4pDBG8hBmzcX85+/kerXR+13BnnuBEJm+VtqSivvskYIQWbmfq655loWLgxnqZEkicsvv5RnnnmaxMTSB8OWrn9gGEGysjIwTRO320NCQgp6JXo7M4wQ2dkZrFixgkAgwOmndyMxMQWHI5xjv7Qy17QAy5Yt5p577mPbtm3IsszUqZMZPHhIzFz4L7/8IiNHjsTh8Fbae7G6UtnucZvyxZZ32WMr/SXH9umvIkiSoH79RsedUlOLS0Tp269wg9uN46zBhObPxfznH4IV4PHlFjqJoRzid28jMXMfCWYAuZKlCg0ZkKHGEzzrPMwbbiU05EIynIkVovBD6eVd1uTlZTJ8+EUFCj+AaZqMGfMtzzzzLHl5UVyhKghZFqSl7eLGG0fRtu0ptG17Ctdccy0bN65F1/0nrF9HI8sOkpNrc9ZZQxk2bAQ1azYoUPihdDKXZcEff6zkgguGs23bNiCcGSgjI6PI4lf79+/n4YcfJjf3xMnvZKWy3eM25Ystb5vKgG3pj0Fls/SXBUkEsP5ajzZvLlZuLnKrNqhduxL47lvMnTtQh55H8KzzCqqYlgfxhDB/Goe+YF7BPpGYiPPOe8lLqnlSus1UBf7+eyOnn94japuqqsyaNZ3WrU/BsgSSJLAsq8Lun6ys/fTo0buQv7zb7eb332fRuHFzoHqXvff5sjn33MLFx5544jGmTv2NRYui1xAYP34sV1xxNbfddgsPP/wfLMu2A9nY2FQNbEt/ybFn+CqCruvMnj2tVJH/ObIbKzEZuUlTlNO6YWVm4HvtFcydO0CSkHv2LleFX5UF1oK5EQo/gJWVReCV/+LV7Gwi+ZSFvMsKIQTbt2+P2a5pGmlpB8jNzeTAgV3MmjWNpUsXkJWVBpTvW5wsC8aOHRc1QNbv9/Pmm2/h82WX2feZpkZOzkFycw9iWWUrm9LIXNOCUasNf/DBRzzyyEM4HI5CbSNGXEijRg3p0aM777//IdnZmcfTbZvjpDLd4zbljy1vm8qArfRXESRJomXLNkjS8YvMMCyMOvWxnG5CE39GX7kCTBNcLlx33oPfXb5vzO6QD23alOiNfj/WP3/bS5+HKAt5lxWWZdGwYcOY7Yqi0LRpE1588UXatevAJZdcxtCh59GpUxcWLZqHaZbfQy4Y9DN58q8x2+fPX0B2dumVflkWHDy4lwceuI+2bdvRpk07/vWv+8nI2Fdm12xpZC7LMomJhbNK7dmzh1deeY25c2dx2WWXUrt2bdq1a8eLL77A0KFD6dPnDG688Qa6dOmMZdnLbBVJZbrHbcofW942lQHbvScG1dG9Jx+nMHBpATiQBoqKlZyMX/GglfN4E7Rcgv95KGa7euEIAv3OqlTBlzZh/P4szjlnKOvXry/U9p///JtGjRpy8823FWqTZZk1a1aRmlo+GWKEMLnlltExs9N069aNzz//pNTfHwhk8+yzz7Jly1Zmz56DYYRXxGrVqsWCBXNJSKhZqvMXF0kS+P055ObmABAXl4DLFYcQFq+//grPPvt/hT6jKAobN65n0qRfEEKQk5PDt99+zx9//AGE3aAmTPiRdu06RMQY2NjY2FRmbPeekmO/clYRdF1j+vRf0HWt1OcKWjJZipfsuk3ISq1Ptlz+Cj+AJSuIpNhVR6XGTU5oBpjKRFnKuyzweJIYP34sXbt2LdgnhODCCy9g+PALeemlV6N+zjAMfvhhHKpaXj71EnfddUfM1htuuJb4UtRVkCRBVlYa33zzDWlpB2jfvj0//zye888fBoQDYb/55ltkufTW/mPL3OLvvzcyYsRFtG3bnrZt23P55Zexc+dWLAtuuulGBg8+K+ITTqeT8ePHEgpp3HPPfdx559088sijBQo/hN2gsrOzcbvjSj0Gm+JT2e5xm/LFlrdNZcDOG1VFkCSZDh26IkllpzxV9EqGX/XgvmA4oc8/KdQmUlKgbj1b6T9Eeci7NFiWRY0a9Rg79jvS0tLYu3cvXq8XIQQJCQns2LEj5mc3bFhfbgVpLAuaN2/FI488zHPPvRDRdumll9C/f388noTjqjshSYIdO7YyYMCZZGZmFux/++13+OCD98jOzmb27DmMHz+B6667AafTW6qxHEvmBw7soW/fM/D7D2ckmj9/AX379mf58qUkJ9fmo48+Yt++vSxevJjU1FROPbUr8fEp7N+/u8gsPgcPZgD2vVeRVLZ73KZ8seVtUxmw3XtiUJ3de04kXjSkJQsITZwAwSAAcrNmOEaNJtuZYCv9VQBJEmhaENM0cLu95OXlcMklF7No0eKox3/44fuMGHFZuRZ80zQfBw7sZ9KkXwgEAgwefBZ16tQhKakmlnV8LxyBQA7nn38Bq1atKtTm8Xj48svPuOiiSxkw4Ay+/vobFKU8i5QZPPnkY7zzzntRWx9++CEeeOAh8vV6WZawLKvgfsrNPUjfvv3ZtWtX1M8vX76Ehg1blEvPbWxsbMoD272n5NjuPVUETdP49def0LSqvTSYh0qwR3+cTz6L67GncD39HOKWO8ly2Ar/kZSFvC1LJzs7jQMHduP3ZyHLZXO7m6aFLDtQVTe6buJ2x/H884V9yQFq1KjBgAEDyr3Cs6p6qFu3CXfccTcPPvgw7dt3JjGx1nEr/GG3noyoCj+Az+cjJyeXhIQE7rnnbhwOVyl6H6Yomft8ecyaNSfmZ2fMmEkgcDj7lWGYEfdTYmINXn315aifHTLkbGrWrF2KntscD9VlTrcpHra8bSoDttJfRZBlmdNP74MsV/2lwZAJ2bKHrPhUslyJ5OHgyAUnIQTl5A1SZSiNvMPBmgf417/up23bU2jX7hQuvHAEq1cvJRjMKfO+mqZFq1btGDv2e+rWrVuwv3v305k9ewbJybXK/DtjEQzq+P1aqYLBA4FcFi+eV2SaUggr/ldcMZLOnbuUyQtrUTJ3OBzUqRNbMa9Xrw6qWjgtZz6GYdG7d19++ulHWrVqBUBCQgKPPPIw7777Hi6XbS2raKrTnG5zbGx521QGbPeeGNjuPRWPWzJxhfKwdu8BWULUroPf6SVgnORvACUkLy+Ds846m82bN0fslySJ336bQtu2HcrFFUWWBVlZB8nOzkJVVRISEnG54qlKU0wo5OONN17jpZde4dNPP+bf//4Pe/fuLXScEILVq1eQmJiCu5xT3UJ45WHp0gUMGXJu1PbZs2fSvn0XADTNTzAYwOVyoyjOiHlMliVycjIIhQLIskpiYjJmBVWZtrGxsSlLbPeekmNb+qsImqYxceIP1XZpMI4QysLf+f/27js8iqoL4PBvZnfTs5tKCUWKhN6rFEF6FwEVESwgYEHEhmJDQMCCFAEFkSIgigKC9CoovfcOAtJJ78mW+f6IrObLLhBI2WTP+zw+bubOzt7JYSZn79yS9MF7JE+eSPLE8SR99D6eh/fhq7rfYib3Gm9VVdi3b1+mhB/AZrPx8ccjSEyMyaZaZmS1avj5BRIWVorQ0GJ4evrlq4QfIDY2ii+++BKAr7+eyvDhwxzu9/rrgwkJKZytCf/tYm6zaVSpUpW3334rU9nw4cN48MFwUlMTOXJkH88++wzNm7fgxRcHcO7cSazWNPu+VqsNHx8Tfn4m0tJSOXLkIJcunSUxMUbWyMhlBf2eLjKSeAtXIC39TrhaS7+m2YiPj8ff3x9FKVjf1VRVwXj1PMlffu6w3OfDYUT7514XEVdwr/H28NDxxhuvMXPmbIflqqqyc+c2ypSpkOP97LNCUTTi4qKIiIgAICQkBJMpmNtMOJPtdDqFxYt/pl+/AfZtzz77DB06tGPixMkcPHiQkiVL8v77Q2nSpCleXtk7xeXdxDwtLYmYmCi2bNmCqqo0btwIkykInc7AokU/88orGacvVRSFX375iUceaWOPd3JyHMOGDeP77+fYv5Q9+OCD/PrrIooWLSlja3JJQb6ni8wk3tlPWvqzTqbszCcURcVovPf5xl2ZD2bSVixzWp62bi2+T/Yi0Y0aSO413poGxYo5X4gqODjY5ZaBt1rT2LbtT154oT/R0dEABAUFMXPmdOrXb4xOZ8i1uvz/apnffz+HNWvW8vzzz9K37/M0atSQkJCwHPnC9P8x1+tVQMFisdq3eXj4UKiQD48/3hPAXo+YmJu8+WbmpwCapvHSSwPZsmUzwcFF0TQbU6d+w+zZ32fY78yZM7Rq1YatW7fg7x+U7ecmMivI93SRmcRbuAL5uplPmM1mfv31xwL5aFC1mLFFRTot1yIi0JlTc7FGee9e4202W3niicedlj/33DMULlzYpVr5r1y5SPfuT9gTfoCoqCi6dn2cq1edz/+f3axWjYYNG2ZaU+DatWuMGfMZ3303Ax8f/xz73d2KeXJyHJcunWPSpAlMnDiWixdPk5KS8H91tWWox5Url0lJSXF43Js3b3LixAni46OJjY1k0qQpDve7du0ap0+fyrE1FURGBfmeLjKTeAtXIEl/PqHX62nb9lH0+oL3cMZm8ERX8gGn5WqpUlgNOTkHuuu5n3iHhBRm+vRpmZK3hx9uQteuj+Hp6ZNd1bxvmmZh7NgvHfb9t9lsjB8/AbBmKsspRmMgo0Z9kmm7v78/U6ZMyrZVa1VVwWDQZZhGVa/X06pVWz7//DNq1arDsGEfM3z4SOrUqc+wYR+RkuJ85iW9/vYzglgsFvsXg4SEBKf7nTlzRvr255KCfE8XmUm8hSuQf335iF6fe90cclOiTSWgQ0cs+/aSqRO3wYDh4WbEWBTcbcXQe423TudJx46dOXLkIVasWElERARNmjShZMkSBAWFYjB4Z3NN711KSjJHjhxzWn748BFSUpKzvf+8MwaDF8888yxNmjRmwoSJXL58hebNH+HZZ58hOLjwffd3T5//P4J9+/ayY8dOypcvT9OmTQkKKoSiKJw6dZIpU77J9L5Zs2bz6KOdadz4EYd1KFKkKP7+/sTHZ/5iUKJECaKioklJSaV+/QaYTCZiY2Md1q9ChQq3XblXZK+Cek8Xjkm8RV6Tlv58wmKxsHz5Qpfrj50dNA3S/ALwfvU1lJAQ+3a1aBjeg98kzSd/TfuYHe433jqdJ6GhxXnhhf688cab1K3bgLCw0nh6+mZzTe+Pl5c3lSpVdFpeuXIlvLycL3xls5mJibnBqVNHuHDhFAkJ0ffdUu3h4UOFClX55pupLFq0iDfffJvAwML3PahYURSuX/+bRo2a8PjjPfjyy/H07/8iNWrU4vjxQ1gsKf882XDsyy/HkZqa5LDMaAxi5szvMj3dMRgMfPHFp0ycOIlSpUoREBDMW2+96fAYDzzwAGXLlnWpCQwKsoJ8TxeZSbyFK5DZe5xwvdl7NCwWC3q9vsD2ufXSgXdaIiQmpA/L9/Uj2eBDSu717nAZ+T3eigIGQ3qXE7PZ6vRaUhQ4f/409eo1yPTFTlEU9uzZRYkSZR2+NzU1gRkzZjBq1Gh7P9mwsDAWLlxAuXKV7nk13pySlpZI167d2LVrV6ayoKAgdu3azlNP9WL37t0O31+5cmVWrlzpdJpQqzWFU6dOMHPmbM6dO0elSpXo0qUzX345nosX/2bt2jX4+JhITo7jq68m8tVXk+2/t1q1ajF//lxCQsJk9p5ckt+vcZE1Eu/sJ7P3ZJ209OcjFkvBHgCUYoVonS8xpiLEGAsTrbpnwn9Lfo23r5aGMeoahmULMSxbiDHqKr5amsN9NQ2KF3+ABQt+xGT6d2aLgIAAfvllAWFhJRy+T6dT2bx5Ex9/PDzDwLgrV67QsmUboqNvZu9JZYOoqAiHCX96WRTR0dG0b9/O6fvbt2+Lj4/zbk46nRelSpWmZs0a1KxZgytXrtCpUxeioqJZtmwpfn4BAHh7GxkyZChHjhxi585tHDp0gCVLfiU4WBL+3JZfr3FxbyTeIq9JS78TrtbSbzabWb58IR07dsdgkH6BBV1+jbefloZt0U9Yd+3MsF1Xtx5q96dIUDwcvu/WPP03b95EURRCQkIwGoOdXoNJSbG0aNGSs2fPOiyfMmUSTz3VG4vFdfqnnz9/knr1HnJa/uOP86hcuTLNmrUgKioqQ5nRaGT37p0EBha+4+eYzSnExUUTHR2Nr68vgYFBeHsb3a6LnKvLr9e4uDcS7+wnLf1ZJ0m/E66W9Avh6lRVwe/8SVInT3RY7jnwNRJKlc+W1uT4+EjKli3ntLxv3z58+eV40tJc51FRdPQ16tdvSExMTKay9K5MOyldOpxr1y7xwQcfsGTJb2iaRseOHRgzZjRFihTP1cXKhBDClUnSn3XSvSef0DQbcXGxaJr81XcH+THenpoFy7o1Tsst61bjpdpISIgmMTGalJQ4YmNvEht7E4slhax0c9Xp9JQvX95peb16dbFaXetbe3BwYYYN+9Bh2VNP9SAgIICoqGhCQoowefI3nDx5glOnTjBt2rcULiwJf0GTH69xce8k3sIVSNKfT1gsVjZvXpthdU5RcOXHeCs2K1pKsuNCg4GoR1oyfspk+vTpw/Hjx+jTpy/h4RUID69A//79uHHj8l3PvOPvH8jIkcOdlPnTvHlz++JVmmYhJuYGFy6c5ubNS1gsSah5cOez2RS6dHmU7777lrJl0wcnh4aG8tFHH/DBB0Px8TGxefNazGYrer0nJlMIJlMoer2XPHUsgPLjNS7uncRbuALp3uOEdO8RImsMehXPP9ZhXrI4U1lcl248O3Eym//4k5Url9G9+5OZ5pQPDAxk587tBAQUuqvPS01NZMGCH/nww2EkJ6d/2ShTpgwLFvxI6dLlsNkgJSWOb775hvHjJ9pXrH3kkWZMmfIVJlMQBkPuL1Rms6UQHR1FYmIier2e4OBgvLz8XW62ISGEcGXSvSfrpKU/n7DZbERGRsjCOW7C1eKt16vo9be/XZgtNnQNGqIYjRkLvLz429OTjb9volOnjixa9KvDRaSio6P54Yf5d90K7+npyzPPPM+hQwfYsWMr+/btYcOG9ZQuHY7Nlj44ePbs2YwZ85k94Qf4/fdNdO/eg2PHjjqd9z4nqaoXwcFhlCoVTrFipfH0NKJpisvFXOQsibd7kXgLVyBJfz5htVrZtWsLVqs8GnQHrhLvtLQkrl27yMyZ0/nuu2lcuXKe1NREp/vH633wevcD9I0ag8EAegMebduzYddeAGrUqM7Wrducvn/lylWkpGQlEddhMoVSpkxFihcvg69vgH2gcExMJF988aXDdx07dozo6CgiIq5n4bOyl82mZXia6CoxF7lD4u1eJN7CFejzugLi7hgMBtq165LX1RC5xBXinZqawFdffcUXX4y1bxsy5B1efHEA7733Hl5emR+r2mwaMXpfPB/rgWenxwANs6cPgZeuAhAfH09QUKDTzwwNDXG4VL2ikOXudklJiQ6fKNxy7txfnD9/gX79XsJszvs/xK4Qc5F7JN7uReItXIG09OcTNpuN69evyqNBN5HX8VYUhZMnT2RI+G+ZOnUaBw8euO2g21SbQpzqRZzqTbJZo0WLFiiKwi+/LKJ3715O3/f664PR6f6dyz8tLYmIiCvs3LmF06ePEB8fddeDfb29vfHwcLwuAECxYsWIjIy4q2PlhuyKeXpXLF2WZkMSuS+vr3GRuyTewhVI0p9P2GxWDh/eh82W9y2SIufldbyt1jTGj5/gtPyLL8ZmqT+8yRTEd999y8WLF0lISODpp3tm2mfw4EGUL1/RvohUcnIc7703lEqVqtCxY2caNXqYhg0bcfbsibvq9x8QEESfPs85LCtatCgA7dt3cInZNBQFrNYUEhNjSE1NvOsvNv9lNqdw48YlZsyYxoQJX3Dq1BGSk+NyoLYiO+T1NS5yl8RbuAKZvccJmb1HuLPU1ES6du3K7t27HZZXqlSJlStX4uNjdFjuiMWSSlTUTRYuXEiFCuUpVaoU69dvQKfT0b59O4KCCuHpmT6bjqrClCkTGTYs87ScRqORvXt3YzKF3vEzk5JiGDToNX77bZl92wMPPMDUqVNYsOAXhg8fjrf33Z9DTrBa07h06QIjRoxkz569FCtWjPffH0qdOnXx9PS7q2OYzcksWPATb7zxZobtrVq15LvvpuPtbcqJqgshRJ6R2XuyTpJ+J1wt6bfZbFy9eomiRYuj5sUk4yJX5XW8VRUmThzHyJGfOCwfPHgQ77//EZqW9boZDOldTywWm71F22LJ+Mg7Li6SOnXqOly9FmDBgh9p0aLtXa3um5qaSFTUTY4dO463txdJSclomkbjxo1vmwxrmpnY2Ghu3ryJp6cnQUHBmEzB9vn/75bBoAPS5+n+/3uKTqeyZ8922rXrkOmx/zvvDGHQoMEYDF53/IzLl89Rs2Ydh2VffjmW559/IdPvWOStvL7GRe6SeGc/SfqzTv7l5RM2m43Tp09If0A3kdfxttmgd+9eBAQEZCrz8/PjxRdfvKeEH8BstpKWZsVm07BYbA6TUYvF7DThBzh58uRdd4Hx9PSlaNFStGzZimrVatCwYWPatet024Q/JSWBb7+dRtWq1WnUqAl16tSjadNmnDlz/K77yqemJnHp0lk+/3w0H3/8AUePHsjU3SYuLooBA15yGOfPP/+CuLjoO36Oh4eOWbNmOy2fOPErYmOj7q7SItfk9TUucpfEW7gCSfrzCb1eT7NmrdHrZcIld+AK8Q4KKsyWLZtp27YNyj+ZbosWzdmy5Q+Cg4vk6Gd7eHgSFhbmtLxWrVp31cr/X5qmx88vEB8f421bvXU6lT/+2MSHH35Eamqqffvff/9NixatiI6+ccfPSk1N5KuvJlCrVl3GjPmMiRMn0axZc1544QWSkmLt+8XFxXL+/Hkn9dU4fPjwHb/caBpcvXrNaXlkZKQkGi7IFa5xkXsk3sIV5Iuk/9KlS7z33ns0b96catWq0bJlS7766ivS0tIy7HfixAl69uxJ1apVadq0KdOnT8+jGmc/m83K+fNnZRCQm3CFeNtsGoULl2DmzFmcPHmckyePM2fOXIoVK53jXd9MpmBGjPjYYVnx4sWpVKlSlpP+uxUfH+1wLAFAQkICGzduRKdzfutUFLh48bzDmY/WrVvPihXL7Qud3e44AJ6enk5/14qikJaWRHx8FF26POr0GM2bP4K3d+6vPCxuzxWucZF7JN7CFeSLpP/cuXNomsaIESNYsWIFQ4cO5aeffmL8+PH2fRISEujbty9hYWEsXryYIUOGMHnyZBYsWJCHNc8+NpvG5csXcyzREa7FVeKtaaDXexEQUIjAwMIYDN7kxjAgq9VGmzZt+eKLz/Dz+3cw60MPNWD9+jUYjcE5+NkWzp4967R8//796HTOW99VVWHq1GlOyydOnGTvtuPvH0DNmjUd7ufh4UHFihUd/r5ttjTOnj1Or15PU6pUWXx8vCldunSm/QwGAyNGfHxX4wJE7nKVa1zkDom3cAX5diDvd999x48//siGDRsAmD9/PhMmTGDLli32ubnHjh3L+vXrWb16dZaP72oDeYXI71Q1fZVcszkNT09PTKaQOw6KVRSNmJhIYmOj8fT0IiAgEC8v/xz94pGUFEvLlq04c+aMw/Jvv/2Gbt2eclp3RbHxzDO9WLXK8X2ncOHCbN++DT+/QFRV4cKFMzRr1jzTQmLffz+L1q3bo9NlXKxMVRWOHTtIs2bN7d12ihQpwqxZ3zF37g/88stCzGYzjRo1ZPz4LylZsiyqKl0KhBAFiwzkzbp80dLvSHx8PCbTvwPxDhw4QJ06dTIsxtO4cWP++usvYmNjHR3itqxWyz//t9qXzbZaLfbXFkvG17ce2aW/tv3z2mx/bTZnfK1p/32toWlaptcAmmbDbDZjtVo5deo4qakpQPqgIIvF/J/Xln9eW+2vrdaMr13tnG7V/b+v5Zys9vqfPHnM3oUtv59TUlIskyZNoHbtOlSsWJnGjR9m0aKfSE6Ov+05mc1W/P2DKFmyHMHBRTAYfOx1zO5zslot6HQq/v4BTrsW+fv788gjj5CWZnb6bw8UnnjicYfvB2jXrg0+PulPL9LS0ihRogx79+5m1KiRtGrVkhdfHMC+fXto1aoNOp0h03kkJsbx2muvZ+inf+3aNR57rDsGg4H9+/dw6tQJfvrpJ3vC7+7XkyueU2pqCqdPn8BsNheYcyqIccquc0pLS+P06ROkpaUWmHNylTiJu5cvk/4LFy4wb948evToYd8WERFBSEhIhv1u/RwRkfVVNw8fPgDA0aMHOXr0IAAHD+7l1KljAOzdu4Nz504DsHPnn1y8eB6ALVs2cvXqJQA2bVrLzZvXAVi/fgXR0ekzaKxevcTeqrd8+UJSUpKxWCwsX74Qi8VCSkoyy5cvBNK/3KxevQRN07h+/QobN6a3Ht68eZ1Nm9YCcPXqJbZs2Qik9yXeufNPAM6dO83evTsAOHXqGAcP7nWpcwKIjo5i/foVck7/d06apnHhwlmOHz+U78/pxo1LvP/+ewwfPsL+BfzKlSv07/8i8+f/ANjy9JwURSE29iYHDuxizpwZ/P77WmrVqsGYMaPw8vq3W0ypUqVYvXoFgYGFbvtv79ixIzz88MOUKlWK/+fj48OgQa9y7do1+zldv36NgIBC1KpVkxEjPmbkyFEcOnSApKRUh+eUnJzI/v37Mx07JSWF77+fw88//0JISBFiYuLZuHHNXcepIF9PrnhOGzeuJioqokCdU0GMU3ad0/Hjh4iKiuDw4f0F5pzyOk63zkPcvTzt3jN27Ng7DrZduXIlZcuWtf98/fp1evXqRb169Rg1apR9e58+fShevDgjRoywbztz5gwdOnTIdIy7cf16NKqqt39j1el09lY8nU6HxWJBUf59raoKqnrrtYqqqlgsZlRVh6qqmM1mdLp/X+v1OhTl1uv0R+8WiyXDa4PBgKbZsFisGAyGf1oj/31ts1nR62+9tqHX67HZ0qdC1OvT665p/74GDZ1OzknOKXfP6ebNS1SuXM3hdebn58f+/Xvx9Q3Mk3Oy2WwkJkbTsWNnTp48aa+XyWRiy5bNeHl5cfNmBJ6engQGBuLrG4CiqHeMk8GgJzr6BmPHfsmcOXNJTU2lY8cOfPrpGPz9jaSlpeHh4YGHhzeapmTpnJKSYqhUqYrTVq5Jk76iZ89nMJstbv9vT85JzknOqeCek9VqpWjRIIf3QeFYnib9UVFRREfffh7qEiVK2LvsXL9+nWeeeYbq1avz6aefZljgYsiQISQkJPD111/bt+3YsYNnn32WXbt2ZegKdDdcrU9/eveeY4SHV0Kn0+V1dUQOKyjxNhhUtm79g44dOzvdZ8+enZQsWS4Xa/UvszmZF17ow6pVazKVBQcHs3PnjvscNGwlPj4W0DAYPDh79gzDhn3M0aPHKFOmDMOGfUj16jXx9PS965hrmoW3336TOXPmZipTFIVjx44QHFz0PuosckNBucbF3ZF4Zz/p0591eTq6KygoiKCgu/uWdivhr1y5MmPGjMm0ol2NGjWYMGECZrMZgyF94Nu2bdsoXbp0lhN+16SRnJwEuNA3EZGDsjveFqKjI0hLS8PHx5eAgFCyurJslj7NksqNG1eZNWs2LVo0v+2+Xl7eOVaPO4mLi2H16rUOyyIjI7l48QJVqwbfRwOADn//IFQVVq36jd69nwOgdu3aPP/8syQlJfLXX2eoXLkqVuvdxVxR9Hz00Yds3bot0yxD3333LSaTtHzlD3JPdy8Sb5H38sWUDtevX6d3796EhYXxzjvvEBX17+qSoaGhAHTq1IkpU6bw/vvv069fP06fPs2cOXMYOnRoXlU7W+l0emrVqp/X1RC5JLviraoKsbE3GD/+K2bNmk1iYiJhYWGMGPExbdq0w9PT974/4/9ZrWY2bFjLM888h6Zp1K5diyJFitj7sf9Xw4YPYTIFZvkzdDqV+PhokpOT0ev1GI0BKErWb2cpKSm3nQkoIiICRVHue7agmJgIXn11MACjR3+Cp6cnn376ORcvXiQsLIwPPhhKx46d7zrmJlMo69at4cCBAyxfvoJixYrxxBOPExxcCL3e877qKnKH3NPdi8RbuIJ8MWXn4sWLnSbv/+2He+LECUaMGMHhw4cJDAykV69e9O/f/54+0xW79xw9epDKlavLo0E3kF3xjo+P4KWXXmHt2nWZyiZNmshTT/UmuxdrjYm5QZUq1ewzD1WsWJHPPhtN797PZZhJq2TJkqxdu4rg4LAsJdVmcwr79+/hjTfe4tSpU3h5efHss8/wzjvv4O8fmKXrNi4ugtq16zqd4evAgX2EhZW643FuTUcaFRWJTqcjKCgYozHI/jTl/PlT1KvXgO7du1G2bBk+++yLTMcYMuRtnnzyCcLCSt11zFVVQa9X0bT0mY5E/iH3dPci8c5+0r0n6/JF0p8XJOkXeSk74q3TKRw/fojGjZs6LA8MDGTPnl34+2ffQleKorBly0Yee6xbhu01atTgo4/e5/LlK0RHx1CnTi0qVKiI0RhMVhar0elUtm93PEagdu1aLFq0CB+fu+/Op6owb95sBg9+I1NZhw7tmDp1Gp6efg7e+S+zOYU//9zEK6+8SmRkJAAPPPAA8+bNoXz5yoDKpUtnqVWrLosX/0LPnr1JSUnJdBy9Xs++fbsJDS0u17gbkHu6e5F4Zz9J+rMuX3TvEekj5KtVq5XX1RC5JDvinZqazMGDh5yWR0dHExsbk81JPyQnJ2fafuDAAbp2fZwHHniA9evXEhBQCE3TspTwA8THR/PGG285LNu7dx8XLpynUqXqd/2F3WaDbt264+XlzbBhH3P9+nV8fHzo1+8FXn/99Tsm/Iqi8NdfZ+jRo2eG7RcuXKBVqzbs37+X4OCiBAYGU6pUKaxWq8OEH9JnpYiJiaVIkQfurvIiX5N7unuReAtXkC/n6XdHVquFfft22hfGEAVbdsRbVXWEhoY4LVcUJcM89NnBZtOoUaO603KdTodeb7jnPvLJyUmcOnXKafmWLVvR6bJ2W/P09OPxx3uwbdtWjhw5xMGDB3j//Q/v6omB2ZzEiBEjHZalpKQwb94P6PUqRmMQP/wwN8PigY7o9Tq5xt2E3NPdi8RbuAJJ+vMNBW9vH0DJ64qIXHH/8dbrPQgPD3c6Q1bbtq0JCMi+Vv5bTKYgXn/9tUzbVVXlm28mYzTe++wyer3htl9UihYtck/d8qxWDX//IAoVKo7JFALc/vG7TqeiqgopKSkcPnzE6X67du3CbE7DarURHl6JihUrEh4e7nDfEiVK4O/vj1zj7kLu6e5F4i3yniT9+YROp6NixarSF9BNZEe8bTaN0NBCzJ8/959k8l/lypVjwoQJGAzZ29IPYDB48/rrb7JgwY/UqFGD0NBQ2rdvx44d26hevdZ9TRVqNAbw7LPPOPlcA40aNSItLZnk5HgUJfunJE1JiefChdP8/PMPbNq0DrM5la5duzjdv3LlyvYphDVNISioCD///FOmL2JGo5HFi38hLOwBucbdhNzT3YvEW7gCGcjrhKsN5LVYLOzdu4PatRvYV6YTBVd2xjstLZHo6Eh2797N33//Tf369XnwwXKYTKH3PRXl7eh0ComJcVgsFry8vDEYvLLlmkpMjKZ798fZu3effZvBYGDhwp8JDQ1l2LCPuXr1Go880oyXX36J4OAiZEfrWlJSDP37D8gwE5KXlxc///wjX301mfXrN2TYX6fTceDAPgoXLpFhu6oqxMTcZNeuXezbt4+qVavSsGFDjMYgdu3aLte4m5B7unuReGc/GcibdZL0O+FqSb/VauXcudOUKVNOWgrcQHbHW1HAw0OPokBamiXbp+nMTYoCiYmxnD//F1u3bqVIkaI0btyQP/74k379BmTY19vbm82bN/LggxWwWO79glZVjXHjvmD06E8zlRkMBrZu/YMmTZqRmpoKgMlkYu7c76lX7yFU1eDwmDqdgqqq2Gw2rFZNrnE3I/F2LxLv7CdJf9ZJ0u+EqyX9QoiMFCW9b72mwbVrf1O5clWH+9WtW5eZM7+jaNES9/xlJz4+kpo1axMXF+ewfOrUr2nbti1//fUXBoOBIkWKYDIFo2nSf1cIIXKCJP1ZJ3368wmLxcLWrb9jscjIf3cg8b4zTQOLxQZobNu2zel+u3fv5ty5s8TGRjnd504sFovThB/g4sW/MZmCqVy5JuHhVTAaQ7Kc8EvM3YvE271IvIUrkKQ/n1BVhWLFSqKq0nLoDiTeWaMot/89Wa1Wrly5fM/H9/LypkKFCk7LmzRpfF8DlEFi7m4k3u5F4i1cgST9+YSq6ihVqiyqKn0B3UF+iLdenz6oNi0tAb0+7/6QWa0aDRs+5LS8QYMGHDx4iNjYWO7w3cApP78Axo793GFZuXLlqFChQpYXGvt/9xNznU7FYNBJQpGP5IdrXGQfibdwBZL05xMWi4VNm9bKo0E34crx1ukUoqKu8vnnY3jqqad5+eVX2LlzK4mJUfecVN8vozGIYcM+zLTd19eX9957h2+//Y4yZcrc8zgdq9VGjRq1WbjwZ0qWLAmkz87TrVtXVq1ajp/fva89cMu9xdxGXNxNTp06wu7d27h06RwpKbF3fPIh8p4rX+Mi+0m8hSuQgbxOuNpAXpvNxtWrlyhatDiqKt/VCjpXjbeiKFy+fI6HH34kUx/3IUPe4qWXXsTX9/4T4HuRmprAoUMHmDLlG65du069enXp0qUzH3wwjKpVK/PJJ6MxGLzv6zP0epWYmEiSkhIxGAwYjQHodJ7ZUv+sxlxRbFy9+jd9+vRjz549QPpMQn37Ps/bb7/1z7iCbKmayAGueo2LnCHxzn4ykDfrJOl3wtWSflHw2GxpxMfHomka3t5+eHv73ncXkZxmNifTs+dTbN78h8PyPXt2Urp0OFZr3pyH2ZzEmTOn2bp1K9u372Dnzl288cZgnnqqJ97exjypU06Jjb1BmzbtOXfuXKayd955mzfeeBNF8ciDmgkhRM6TpD/r5OtmPmGxmFm/fgUWizmvqyLuU/riTDd4/fXBVKxYmfDwCjz11JOcP38aTbMCrhvv2Ngopwk/wIYNG+znkBcMBh+qVq3JU0/15Msvv2DHjm306/dSvkj4sxJznU7h3LlzDhN+gK+/nkp0dER2V1FkI1e9xkXOkHgLVyDLwuUTqqqjatVaMgioAIiJieCRR1pw+fK/s8ls2bKVhg0bs2fPLooUKemy8b7Tg8H7ncEmO1gsNnx9A+w/59VTh6zKSsxVVeHkyVNOy+Pj40lKSiYob3paibvgqte4yBkSb+EKpKU/n1BVlcKFi0pfwHxOp1P5/fffMyT8t6SlpTFq1Gis1jSXjbfJFEiDBvWdlrdq1RK4vz9qer2Kh4cenc61zj2nZSXmFouN8uXDnZb7+Pjg6+uTndUT2cxVr3GRMyTewhXIv758wmw2s2rVEsxmeTSYn2malaVLlzot37BhI0lJCS4bby8vP776agLe3pkHxPbv34+QkJB7HpegaRYiI68wffrXDB78KkuXLiQuLgKdzj1moslKzDUNypYtS7FixRyWDxjQn+DgwtldRZGNXPUaFzlD4i1cgXTvySd0Oh316jVGp5NHg/mZTqejSJEiTsuDgoLQ6fQuG2+bTaN06XD27NnJhAkT+eOPLRQqFMrgwa9Rs2bN+5i5R2P//t106tTF/kdx9uzvCQkJYePGdYSFlXL5Qc73K6sxDwgoxOrVK+jW7QlOnUrv6qMoCk8//RSDBr2KzSZtOq7MVa9xkTMk3sIVyOw9TsjsPSKnnD9/inr1GjgsmzbtGx5//CkslrzvG38nqqoRFxeDh4cHPj7+91XnmJjr1K5dj/j4+ExlderU4ddfF+Pp6Xc/1S2QVFUhISGaiIibxMfHU6hQIYKCQtDpvPK6akIIkaNk9p6sk6agfMJsNrNs2S/yaLAACAsrzujRozJtf/TRzrRt2w6LxZYv4m2zKfj5BeLh4XtfCb+iwF9//eUw4QfYs2cPUVGR93z8/1JVBVXV0OlwuQWs7iXmNpuGj08AJUuWo3LlWoSGFpeEP5/ID9e4yD4Sb+EKpKXfCVdr6dc0G/Hx8fj7+6Mo8l0tv0tLSyYmJpJVq1aRmJhIu3btKFo0DC+v9FYLd4q3Tqewbt1KevR42uk+u3Ztp1Sp8vf8GYoCCQkxHDiwn5kzZ+HhYaB///6UL18BHx/TPR83O7lTzIXE291IvLOftPRnnST9Trha0i8KJr0+/eafH7rz5BRFgUuXzlGrVl2HU4KGhoby55+bCQpyPhbiThITo3nqqZ7s2LEzw/bHHuvC+PHjXSbxF0IIcXck6c86+bqZT5jNZn799Ud5NFjAWCw2hwm/O8Vb08BoNPHss70dln/yyXCCg0Pu+fgeHjpWrFiRKeEH+PXXJRw9egRVzfuuPu4UcyHxdjcSb+EKpKXfCVdr6dc0jZSUZLy8vF2uL7LIfu4Wb1WFmzcv89tvy/n666n8/fffVKtWjSFD3qJ+/Xr4+99b0q+qChERV+jW7QmOHj3qcJ/27dsxa9b3KMrtJzNLj4OWY/cFd4u5u5N4uxeJd/aTlv6skyk78xG93pDXVRC5yJ3ibbNBaGgxevZ8ilatWmK1WvHy8iIwMAhPT797TrQTEmLYsGEjqampTvdJTk7GZrPhbCY9szmZ2Nhojh07hp+fH2XLlsVkCiYnHpT+N+aqqmCzWVAUBUXRFfgpS92RO13jQuIt8p5078knLBYLy5cvxGKx5HVVRC5wx3jbbODpaaREiQcpU6YCRYqUxMPj3hN+gMjIm6xYsZJ27do63eeZZ3rj4eF4xpuUlHg+/XQ0lStXpXv3J2jbtj3Vq9dk+/Y/sdmyNza3Ym61WkhMjGbjxjU8+2xv+vZ9np07t5CUFJutnyfyljte4+5M4i1cgXTvccIVu/dYLBb0er08GnQDEu/scfjwXlq1asOKFb/x/PMvcO3atQzl5cuXZ9WqFfj5ZV5UTKdTWbZsMc8918dBmY5Dhw4QGup4Rdx7cSvmaWkJPP74E+zZszdDeceOHZg8ebIMOi4g5Bp3LxLv7Cfde7JOWvrzEYtFBgC5E2fxlr8Xmen1KklJsSQkRGOzpdm3FylSBJvNxksvDWT27Bm8+OIAihUrxgMPPMBbb73B8uVL8fcPdnjM+PhoRo/+1GGZ1WplwYKfMRjuvLqmTqeQkpKAxZJ6xwHDiqKxbNmyTAk/wPLlKzh8+JBLDDoW2UPu6e5F4i3ymiT9+YTFYmH16qXyaNBN/H+8VRXi4yM5efIw27f/QUTEFdLSkvK4lq4hOTmWn3+eT5s2balfvwGDBw/i+vW/ARsmUyDdu3fj/PnzdOjQmWvXrjFkyFu89tqrPPBAKYzGIIfThEJ6Yv/33387/dwTJ07c9guYTqcQF3eTyZMn0KVLF55++im2bdtMSorjRcgsFgunTh1h2rTpTo85ZcrXWK2SOBQEck93LxJv4Qqke48Trta9R7gvRYEzZ47TuXMXbt68ad/eu3cv3nvvXQwGD4zGINzxO3xycjwDB77CsmXLM2z39PRk27YtlCz5IImJMXz++efMmDGT1NRUfH19GTx4EP37D8Db2+j02KmpiTz55BNs27bdYfnUqV/zxBM9na6xEBV1jSZNmmaIGcDTT/dkzJhP8fLyy/SepKRYWrVqzenTpx0e85FHmjF//o/odJ7odCrx8dHEx8eh1+vw8wvAy8vX6ZcYIYQoSKR7T9ZJ0u+EqyX9spqfe/lvvOPiIqlZszYJCQmZ9hsy5C2OHz9B586daN++IwaDdx7UNm/c+jL00EONHJY/8kgz5s79AQ8PbzTNSmxsJMnJyfj4+BAQEIym3f46UlWFI0f206xZ80xlQUFB7Ny5A5PJ8VSiNpuZt956g3nzfnBYvmPHNh58sAJpaSkkJqa3/Pv4+GGzaUyb9jWffDLa4fsmT/6Kzp074+npzeHDB3jppYH2LwhNmjTmm2+mUKRISZnpJx+Qe7p7kXhnP0n6s07+5eUTFouVzZvXYrFY87oqIhfciremaezatcthwg8wffoMunfvRr9+Azhz5lSGAWIGgw4PD12B7QOu0+lYuXKV0/Lff99EUlJ6Qq0oOgICClG06AOYTKF3TPgBbDaNcuUqsGjRLxQtWtS+vXbt2vz++wYCA0OdvjcxMZ6ff/7FafnmzZu5ceMyAwb0Jzy8AuHhFejX7wWuXbvEc889R1hYWKb3VKxYkZCQYJ544knOnz9L69btMjwR+PPPLTz88CNER9+447mJvCf3dPci8RauQObpzycMBgOdOj2e19UQueRWvPV6HSdPnnS6X3R0NN7e6dNNjhgxkjlz5qIoKpGRN1i4cBFXrlymffv2VK9eHX//4Dzp+mEw6LDZrICK1eq4K8y90fDxcf5kw2AwoKr3165hMHjRrFlLtm79k9jYWAwGA0ZjAD4+/ndsTXf2u1ZVlaZNH6ZhwybExMTYt69cuYqtW7exb98eNm3awOTJX7N06W/o9Xoef7wbDRrUp0+ffvTu3YuPPx6OzZb5dxkVFcXKlavo3fs5p92OhGuQe7p7kXgLVyAt/fmEzWYjMjLC4R96UfDcirfFYqFOnTpO9ytRogSRkVEAnDx5CrM5hTVrVlC1anWGDx/B9OkzeOyxbrRu3ZbY2JtOj5MTzOYULl/+iw8/fI/nn3+WhQt/JC4uItuePFgsNjp06OC0/Mknn8DP7/6nt7RaNfz8gihWrDSFChXHy8vvjgm/r68/3bt3c1jWunUrliz5LUPCf0tsbCzTp3+HxWLl3LlzDBo0kAED+rF9+w66dOlGVFQUFStWYOfO3U4/e926df98yco+qqqQlpZEQkI0FkuKzCCVDeSe7l4k3sIVSNKfT1itVnbt2oLVKo8G3cGteJvNFqpUqULx4sUd7vf6668xY8YsACpXrkRSUjJ9+vTLtN+ZM2cYOfITNC13Zo6wWNL47bdfadasOTNmzOS335YxYMBLNGrUhJs3r2Rb0hgcHMrIkcMzbS9evDgfffQhqppxBUxFAYslmaioa1y7dpGEhCh0uuzPYFXVwLBhHxIcnD4dqF6vx2g0oigKPXr0YM2atU7fu3LlKvR6HRs3/s6bb77NkCHvsnnzH/byyMjIDN2N/l/p0qXR6bLvIW5qahJ79mzniSeeoFGjxvTp8zynTx/DYnG+yrG4M7mnuxeJt3AFMpDXCVcbyCvcl6oqRERcoU+fF+wzyRiNRt5883VsNhvDh48EYN++3Wzbtp2BAwc5PI6HhwfHjh3BaHQ8+DQ7xcXd4OTJU1y5chUfH28MBgMTJkxi27ZtdOzYgW+/nY5e73gV3KxKTU3iypW/+fbb6Vy7do3HHuvCI488gskUkqFFXlUVIiOvMXjw66xevQZN0yhevDjjxo2lYcMmeHhk7yBonU4hNjaSCxfOk5CQSExMNOXLl6dQoUK88sqrrFix0uH7mjd/hPnz5zNmzGgmTpyUqbx27Vq89tognnnmOYfvP3BgH2FhpbLlHGw2M3PmzGLIkHczlS1c+DOPPNIKq1VulEKI3CcDebNOkn4nXC3pt9ls3Lx5ndDQwvfdT1m4vv+Pt6KkL/AUGxtNbGwM169fZ/r0GaxatRqj0cikSRNp164DU6d+zUcffez0uCdPHicwsHCO1j0tLYFx48YxYcJX9kfZRqORKVMmMW/eD6xdu44TJ44TGFgoWz9Xr1exWi2oqt7h2IGEhCiaNWvOxYsXM5WtXLmc+vUbZWsCq6oK1679zaOPPsbZs2ft21u3bsW4cWOpXbseqamZW8uXL/+NBg2akJAQTd++L7Bjx05at26Fr68vx4+fYNy4sZQuXZpRo0Yzbdq39vfp9Xq+++5bWrduh17vecf63fpSEh0dDUBgYCAmU3CG30FMzHUqV66G2Zx5bYBChQqxY8c2h6sZizuTe7p7kXhnP0n6s06SfidcLem3WMxs2rSWZs1ao9cb7vwGka/dLt6qCnFxUURGRmK1WgkJCcVkCgIUTp06SqNGTRwes2bNmixdugQvr5y7Sep0KqtW/UavXs9kKjMYDCxdupgOHTpz7NgRgoOdd1HJbqqqsGXL73Tp0tVheZUqVVi+fDk+Ps7n7c+qhIRoHnmkORcuXMhU1rv307Ro0ZznnuubYXvfvs/z4Ycf4eOTPhYhMTGay5cvM3PmTCIiIunevSuNGjXGaAwhNTWRmJhIdu/eg4+PDzVq1MBkCsrUpem/dDqFhIQ4FAWOHj1Cnz4vcPXqVQDCwsKYNWsG1avXRlXTuwcdPLibVq3aOD3enj27KFnywSz/boTc092NxDv7SdKfdZL0O+FqSb8QdyM5OY7nnnuO33/flGG7qqps2rSBSpVq5Ogc7klJsbRp09bpjENvv/0mERGRDB8+PEe/fPw/g0HHiBEfMX78RKf7nD59EpPJ+TScWXXq1BEaN37YYZler+fgwX2kpqaycuVqNC19UHJISGG8vHwBSEtLYtq0qYwc+UmG95YuXZr169fg75/eTevWwOjbxVVRFOLjI1m8+FcWLPiZUaNG0q5dh0z9i3U6Hfv376FIkQcAOHbsgMN1Cm7Zv38vxYqVvsNvQgghsp8k/Vknz5jyCZvNxuXLF2Xkv5u413h7exuZNWsWI0Z8TEhICIqi8PDDTdiy5Q/CwytlKeFX1fQuRXFxkSQkRN/VgFebzeqwZfuW8+cv8Pbbb2Vri/rd0DSNMmXKOC0PDg5Gp9Nl2+epqsLly5edllssFo4dO8akSVPo27cPr7zyGmFhpYiMjLTHPCLiRqaEH+Cvv/7i88+/ANITdptNu2Nc4+MjadOmHW+99Tbly4czY8ZMhwMKrVYrEyZMsh+7WLHi+PllXjkYoGzZsgQEBN72c4Vzck93LxJv4Qok6c8nbDYbp0+fkBuGm7ifePv4mHj55UHs2rWTs2dPM3/+j5QrV/m23T7+n9mcwqFDe3nsscd48MFy1K1bj6lTJ5OSEoder+Dh4ThBNhg8qVq1qtPjPvxwEwoVKnJPTxvSV9W9wfnzp7hx4xKpqQkZFiO7HYvFRuvWrTAYHP8OXn/9NUym4CzXyRmbTaN0aect4N7e6YOGZ82azUcfDSMtLTVDzA0GHYsWLXL6/u+/n0t8fOxd1UWnU1m6dKl9Ia/SpUtx7Nhxp/vv27ePlJRkAEymIObOnZ2pD7KXlxdz5szGaJSk/17JPd29SLyFK5CkP5/Q6/X/9AWU9dTcwf3G22YDP79A/PyC8PDwydKiXDqdyv79e2jevCV79+4F0qeJfP/9D3nxxZdYtWo5H3/8IVevXsg0baO3tx9jxoxyeNyAgADatm2LzZb1205ycjxTpnxF1ao1qFevAVWqVKNbt+5ERl6563n/AwJCWbp0sT3hvuWxx7rQu3fvbJ+FpnDhItSqVdNhWd++ffj554UAzJ79PbGxUZli7mwVZoDU1NS7jmlCQizffz/X/vPVq9coXbqU0/3Llw/H0/PWzEoqDz3UmAMH9jFo0EBatmzBhx++z4EDewkPryQz99wHuae7F4m3cAXSp98JV+vTb7NZuXjxPCVLlkJVs68bgnBNeRnvxMQYWrRoyblz5xyWL1myiB49niYlJYWZM7+jXbuO6HQe9vK0tGQ2b97Ia6+9TkREBABVq1ZlzpxZlChRNsut/Dqdwvffz+CNN97KVFayZEk2bdqYhRlkbMTGRnDs2HEiIyOpVasWwcEheHo67sJyPxQF4uIiGThwIKtWrQHA09OT559/jjp1arNs2XJ69eqJpmk8+OCDhIQUIiIiirCw4uh0Oo4edd6fvm3bNsycOeuupj1NSUmgS5cu7Nu3DwCTycTs2TN47LHuDvfftWsHpUuHZ7r/6XQKFosZg8FDVvvNBnJPdy8S7+wnffqzTlr68wmbTfunP6ALfRMROSYv452YmOA04Qc4ePCQvetKv34DiIuLzlDu4eFNmzYd2LlzO3v27OLQoQMsX77cYcKv0ykkJcVy5cp5Ll/+i8TEaHS6jLelmJgIRo/+1GFdLl68yOnTp++6mw+omEyFaNiwGZ07dyMsrNR9JfzpH2vFbE5GVTOem6aB0RjM9OkzOHhwHz///CM//fQDwcFBaJqNypUr8dxzfXniiaeoVasu3bs/jqJY7e8tXboMLVpkTvq9vLz49NMxGAx3t86Bn5+Rfv3+nSUoNjaW5ctXMm7c2AxPPXx8fJgzZzbFiz/gsMHDatVQFL0k/NlE7unuReItXIEk/fmEXq+nUaNH5NGgm8ireKuqgl6vu+080iaTiaSkJODWKpO7MnWxsdnA3z+YkiUfpEiRknh7+zv4Y2fj+PHDtGzZiho1alGzZm2aNn2EPXu2Y7P9Oy98SkqK/YmBI0eOHLnrLj63aNqdB7/eidmcwvnzp3njjdfo1q0bn3wygoiIy8C/SbGmgYeHDyZTIH//fQlVVQkLCyMpKYnRoz8lMTHRvu+uXbvo1KkLSUnpffW9vY3MnDmDiRMnUKZMGYKCgnjqqR7s2bOTsDDHibkjFouNtm3bUrNmDfu2GTNmsnbtOn79dRFr1qxi48Z1HDp0gDZtOtzVHP/i/sk93b1IvIUrkKQ/n7BarZw+fUKW8HYTeRHvtLQkdu/exuLFv9K+fTuH+xgMBsqWLZNhhp6EhLsfUPtfERHXaNGiFWfOnLFvu3jxIu3bd+TatUv2bZ6envj7O3+EW65cuSyNWcgONpuFtWtXUrdufebNm8+OHTuYMGEiNWvW4eTJo5lmOlJVlePHT/Doo13x8PDg00+/cHjcS5cucezYMfuXGG9vE716PceGDRvYvXsX48dPoFChEkDWft9+foEsWrSQr7+eRJMmjWna9GHatm3D9evX6dGjJ716PYuiKCiKdDvILXJPdy8Sb+EKJOnPJzRNIyoqIteTG5E3cjveiqKxdu0q2rXrwJgxnzFw4MuUKlUqwz46nY6vv57M5MlfZ9jesGFDhyvg3o6qakyf/p3DFWmtViufffa5vbXfZApm0KCBDo8THBxMpUpZm4o0O8TFRfHii69k2p6amkqfPi8QHx9j36aqCvv27eW772YAEBBgsi+I5cj+/QcyPLmwWm34+prw9w9CVT2cvu92VFUhNTWFb7+dQa1atahevTrffDONZ599nujoaK5cucKZM2e4h+9u4h7JPd29SLyFK5DnTPmEXq+nfv3GeV0NkUtyO96xsZG88cbbAMTHx/P88y8wbtwXxMbGsXv3HmrUqE6DBvU5d+4clStXYv/+A9y4cYNnnulNUFBIlj8vNTWFHTt2Oi3fvXsvyclJ+Pqa0DSF/v0H8Pffl1i1ajVPPvkEhQqFkpqaRu/eT6OqKomJ0RiNQbkym4yiwNmzZx1+YQE4ffo0MTHR9lV1U1IS+eKLL+3lKSmpBAQEEBMT4/D94eHh2folJiUlgSVLFlOsWBgHDhzgwIEDDve7ePFvatduIElJLpF7unuReAtXIC39+YTVauX48cPyaNBN5Ha8o6OjMyShV69e5amnejFmzGdUqlSRSpUq8e677/HGG2+zb99+vv56Ehs2rGXkyJF4evpm+fM8PDwpV66s0/LSpUvh6flv33JvbyMjR45gwYL5nDlzhqtXr/Hww4158cWXCQ+vQKNGTZg1azrJyXc3d/39ulNc/ps4WywWIiMj7T//8MN8Xnihj8P3mUwmatWq6TDpVxQbCQnRJCREc5shFxmoKixevJBBgwaTkpJCQECA030rV879JybuTO7p7kXiLVyBJP35hkZychIgf5TdQ+7G29miVeXKPQhAixat2LBhI5cvX2bTps107/4k589fwNv73ma+0TSVQYNedVo+dOi7GAz/zixjtZr59ddfad68FadOnaZZs4dp164jmzZtxmKxcOXKFd56awgvv/wKycnx91Snu687hIeXczog74EHHsiQXPv4+NG6dUv7z2vWrKVKlSr06PFkhrEQRYsWZenSxSiKkmEgs6oqxMbeZNSokTRs2IjGjZvw5ZefEx8fecexFDExEXz88QgA5syZx6uvZu6SBFC9enXCword8dxFdpJ7unuReIu8J/P0O+Fq8/QLkZOSk+No1649x44dy7D9559/pG/f/sTHZ06k/fz8OHBgH0Zj1rv3QPrsN2vXruLFF1+2d5XR6/V89tkYnnzyKTw8fOz7xsTcoHLlqpjNZr744jPmzJnH4cOHHR53164dlCoVfsfPNxh09hb5rE5DabWmMX/+XN588+0M23U6HatWraB27QYZxjlERV2lTp369gW3dDodQ4a8xZNPPs7Bg4fx9fUhISGRTz/9nDNnzvDrrwupV68RiqIjLi6Chx9uxpUrVzJ8VpkyZVi3bi3+/s7XKLhy5Tw1atSy/zxu3Fhu3LjB5Mlf2wdgd+zYgQkTxmM0Bss9TwiRb8g8/VmXb1r6X3zxRZo1a0bVqlVp3Lgxb7/9NtevX8+wz4kTJ+jZsydVq1aladOmTJ8+PY9qm/2sViuHDu2TR4NuIrfj7etrYt687zGZTBm263Q6hwk/pM/ac/PmzXv+TIPBi3btOnH48CFWr17J8uW/cfToYXr27I2Hhw9WaypRUdc4deowUVGRjBs3luLFi1O6dCmnCT/A5s1/oNc7v7XZbBYiI68wbtwX9OnzLDNmTCMm5nqWBrHqdB48+eRTbNy4ntatWxEeHk7Pnk+xd+8uqlatmWlgc3BwUbZu/YOWLVv8UwcbxYsX5/33P+LllwfSo8fTPPdcH06cOIHFYuHxx3sQGxuJqsLcufMyJfwA586dY82a1bc9Vy8vL3S6f2fkeeONtzh69BgzZnzLL7/8xL59u5k27VtJ+POA3NPdi8RbuIJ8M5C3QYMGvPjii4SGhnL9+nU+//xzXnvtNX766ScgPQHp27cvDz30EMOHD+fUqVO89957GI1GnnzyyTyuvRCuzWbTKFmyLHv27GLdunVs2bKVatWqUazY7bt8/DehvBeqqicgIJTatUMB7IlncnIco0ePZvr077DZ0hPo8uXLM336VCwWCzqdzukfT5PJ6DSBVRSNAwf20LFjZywWCwBLlixl+PCRbNy4jjJlKtx1v3YPDx+qVq3N7Nnfk5qaio+PL4ri+JaqaRAWVprZs+eQlJT+JcpqtTJw4CCHA2dTUlI4fPgwderU5ZdfFjqtw7x58+nS5TH0em+H5SZTIN26deXnn3+xb1u2bDnLli0nODiYXbt2oNd7ScIvhBBuIN9279mwYQOvvPIKhw8fxmAwMH/+fCZMmMCWLVvw8Eif1m7s2LGsX7+e1atXZ/n40r1HuCu9XkVVVWw2GzExETRo0JAbN25k2i80NJQdO7bftnvJvVBVjUmTJjB8+MhMZUWLFmXBgvmMGzeBJUuWOnivyrFjRwgKKuLw2PHxkdSqVYfY2MwDfsPDw1m7do191h1HdDqF6OgI4uPj8PT0xGQKxMPD+57uFdeuXaRatRpOy6dPn0bbtu149NEu7Nu3z+E+LVo0Z968+eh0zqfyjI+P5Mkne7Bnz177tqCgINasWUnp0uVl8K4QIl+S7j1Zl2+69/xXTEwMy5Yto2bNmvYBiAcOHKBOnTr2hB+gcePG/PXXXw7/wN+J1Wr55/9We4ui1Wqxv7ZYMr622f772vbPa7P9tdmc8bWm/fe1hqZpmV4DaJoNs9mM1Wph794dpKYmA+ndAywW839eW/55bbW/tlozvna1c7pV9/++lnOy/lN/C3v3bictLTXXzyk5OZW0NAtmswUfHyPz5s3JNGhVr9czb94cjMbAbI9TdHQk48dPxJGrV69y/vwFXn75RR544IEMZYqiMGPGdIzGIKdxunLlitP7walTp4iJiXYap5SUBBYuXED9+g2oWbM2lSpV4dlnnyEq6pr9d5qVOPn7+1OyZEmHdQGoXbs2Pj5GXnnlJaf7vPrqQPR6w23/7RmNwSxcuJDt27cwffo0Vq1azs6dOyhdOhyz2eIW15MrnlNqajL79u3EbE4rMOdUEOOUXeeUlpbKvn07SUtLLTDn5CpxEncvXyX9X3zxBTVq1KB+/fpcvXqVr7/+d5GgiIgIQkIyDii89XNERESWP+vw4QMAHD16kKNHDwJw8OBeTp1KH+i4d+8Ozp07DcDOnX9y8eJ5ALZs2cjVq+mriW7atJabN9PHHaxfv4Lo6CgAVq9eYu8nvXz5QlJSkrFYLCxfvhCLxUJKSjLLl6c/0o+Pj2f16iWAgqIobNy4BoCbN6+zadNaAK5evcSWLRsBuHjxPDt3/gnAuXOn2bt3BwCnTh3j4MG9LnZOEB0dxfr1K+ScMp2TQnx8PMePH8nTc1qx4leqV6/Nvn17eO21V2ne/BFef30wO3ZsoXr12ly69He2xyk6OpK4uDicuXjxIkuXLmPu3Nn8+OM8evfuxXvvvcuhQ/spXDjUnsg6Oqf02TOcM5vNDuOk06n88ccmBgx4McP0m+vXb6BNm/bcuHEpy3EymYL54otPHdbj0Uc7YTIFYLXaqFatCk2bPpxpnw4d2lOzZk1iY+Nu+29P0yA6OpYrV67StesTFC4cxpEjh7HZ3Ol6cr1z2rhxDd7ePkRE3Cww51QQ45Rd53T8+BG8vX04fHh/gTmnvI7TrfMQdy9Pu/eMHTv2joNtV65cSdmy6fN5R0VFERsby5UrV5g8eTL+/v5MmzYNRVHo06cPxYsXZ8SIEfb3njlzhg4dOmQ4xt26fj0aVdXbv7Gm9yG2AAo6nQ6LxYKi/PtaVRVU9dbr9O4RFosZVdWhqipmsxmd7t/Xer0ORbn1Or0V1WKxZHhtMBjQNBsWixWDwYDNZsNq/fe1zWZFr7/12oZer8dms2Kzaej16XXXtH9fg4ZOJ+ck53Rv56QoGpqWXv+0NEuOnVNsbAR169ZzunjVmjWrqFixIppmw2bT8PPzR6/3wGy23fGcoqOvU7lyVXvL1X8FBQWxe/dOfH0DM51TSkoCzZs35/z58w7rtGLFMurXb/xPy9Pdxyk1NZHjx4/w1lvvcOjQIYKDg3njjcE8/fTTeHsbUZT0WKamJnL06BFmzpyFXq/jhRdeoFy5cHx9A9zi356ck5yTnJOc0/+fk9VqpWjR7O1eWtDladIfFRVFdHT0bfcpUaJEhi47t1y7do2mTZvy008/UbNmTYYMGUJCQkKG1v8dO3bw7LPPsmvXrkyzktyJq/Xpt1jSu/fUrt3A6fzgouBw53irKkydOoUPPvgwU1nx4sVZt24NAwe+yoYN6a1SrVq15MsvxxIWVhLbHWbetFrTmDbtGz7+eHimsrlzZ9OuXWeHx4iOvk758hWdHvejjz5g8OC3sViyPjOHqiokJsaRlpZCQkI8xYqVwtFDWFVVUBQNRVGwWjMuACbyH3e+xt2RxDv7SZ/+rMvTf3lBQUEEBd3bt7RbfcDS0tIAqFGjBhMmTMBsNtv7+W/bto3SpUtnOeF3RYqiEBQUcsfFeETB4M7xttmgd+/eRETcZPLkr+2t8lWrVuXHH+fStWt3jh8/bt9/3br1NG78MHv27CQgoPBtj63TedC3b1+qVq3C8OEj+euvv6hatQqffDKC8uUrOf3SoNPpMZlMTscDPPjgg/a+qHfjVlg1LX3mJG9vfzw8fLhxIxKrVcPRpEj/DriVZL8gcOdr3B1JvIUryBez9xw8eJDDhw9Tu3ZtjEYjFy9eZOLEiURGRrJixQo8PDyIj4+nbdu2NGrUiH79+nH69Gnee+89hg4dek9TdrpaS78Q7sZmMxMbG0VUVBTe3t4EBgYxe/Yshg3L3EoP6av4vvnmEHvirterxMREkpSUiMFgwGgMQKfzBNJn4UlIiMNiMePh4Ym3tx+3m8VGVWHixHGMHPlJpjJvb28OHtxPQEChuzqnuLhozp07h6IolClTBn//QFRVWv6EECIrpKU/6/LFQF4vLy/Wrl3Lc889R9u2bXn//fcpX7488+bNs3f98ff3Z8aMGVy6dImuXbvy6aef8vLLLxeYOfotFgtbt/7usC+yKHjyS7x1OgWDQcXD4/7m63dEVQ0EBhambNmKhIWVAhR++WWR0/3XrFlLUlIiAGZzMhs2rKFp02ZUqVKNihUrM3Dgy8TG3vyne0x667q/fxCenr63Tfgh/enDCy/0pVu3xzJsN5lMrFmzkoCA0DueT1paEgsXLqBKlWq0bdueNm3aUaVKNZYvX4LZnJJvYi6yh8TbvUi8hSvIFy39ecHVWvptNisXL56nZMlSqGr2J1jCtbh6vBVFIT4+kp07d7Bw4WIKFSpE377PU6RIMTw8fLJwJCuxselje7y8vAgICMLb299hf3WLJYVevZ5m48bfHR6pbds2zJr1PR4eXmzbtplOnR7NtE+5cuVYu3Y1vr6BWajjv1JTE4mKiuD48eMEBwf/030wGE27/SN7RYETJw7TpElTh+U7d26jVKlyXLjgujEX2cvVr3GRvSTe2U9a+rNOkn4nXC3pF8KVxMdH0rp1W86ePZth++jRn/Dss89jMDheIfa/UlIS+P77WYwe/SnJyenrT9SpU4e5c78nNDQsU+u7qirs2rWF9u07OTzeunVrqFGjHklJsbRp05YTJ0443G/VqhXUrdvwjq37t6Oqyj/zRt/d/lZrGv369WH58pUOy5988gkmTZpMPlokXQgh8pQk/VmXL7r3iPRHg5s2rZVHg27C1eKt06kkJcWSkBBFSkoc3333XaaEH+C99z4gMvLmXRxPYdmypXz44TB7wg+wZ88eWrVqQ1xcJDqdSnJyPAkJ0dhsZmw2jSpVqvHqqwOB9KcNhQsXxmQy8cYbr1O+fEU0TSMlJdlpwg/w559b0Onu79Zns919wg+QlpbCuXPnnZafOXOW5ORkl4q5yFmudo2LnCXxFq5Akv58QlVVypWrgKpKyNyBK8U7JSWe5ct/pVWr1pQvX5EuXbpSuXIlXnnl5Uz7+vr6cuLECTTNjE7nvMtLbGwkI0ZkHhQL6dPxJiTEsXTpQtq3b0/Dho14883XuXHjb7y8fBky5F1OnDjK9u1b+OKLz/jxx3m8+OIAvLzSuxXp9QaMRqPTz37ggZL31cp/L7y9fahbt7bT8vr16+Lt7eMyMRc5z5WucZHzJN7CFUj3Hieke48QYDanMH78WMaOHZepbMKEcSxe/Ct//JG+cmO/fi/Qvn1bFi1azMWLf9O0aVN69epJUFCRTNdSZORVKlas7PAzR4/+hJ07d7F06W8Ztnt6erJ1658UKlSYTz4Zybfffmcv8/X15ZdfFlC7dn10OpVx475g9OjMq90aDAaOHDlEYODtp/bMCVevXqBWrTr2RW3+W6f9+/dSqFDxXK+TEELkV9K9J+vkK2c+YbGYWb9+BRaLOa+rInKBq8Q7Pj6GceMmOCwbOXIUAwb0A6Bbt66ULVuGxx7rzrx58/njjz8ZOfITateux8WLZ1HVjK3+BoMHAQEBmY7p4+ND+fLhmRJ+gNTUVKZPn86KFcszJPwAiYmJdO7chaioG9hsCgMGDKBNm9YZ9vHy8mLZsiWYTMFZ+A1kn8KFi7Fq1UpKlixp31amTBnWrVtNaGhRl4m5yB0Sb/ci8RauQEaN5ROqqqNq1Voy6t9NuEK8FQXOnj1rXwjv/0VGRtoXwnv++Wfp0qVbpn0SEhJ44YX+LF26BC+vf1tkAgKCef311zLNuV+tWjW2b9/htE5VqlRhzJjPHJZZLBaWLv2N/v1fwtvbxPTp07lx4zp79uwhJCSE6tWr/5Pw501bh6rqqV27Pps3/05MTAyKomAyBWA0BmG12lBVJc9jLnKPK1zjIvdIvIUrkKQ/n1BVlcKFi+Z1NUQucYV4a1p6y/vtBAYGUq5cOS5duux0gNr+/fuJj4/NkPTbbPD8889z/vwFZs/+3j5FZ3BwEKGhzue8L1QolEuXLjktP3HixD99Zm14eflTsqQ/pUuXs698m9esVhu+voEZpgy1WtO/VLlCzEXukXi7F4m3cAXSvSefMJvNrFq1BLNZHg26A1eJd4kSJTCZTA7LqlWrRrly4axbt/a2A2fh38T2v7y8jHzyySiOHTvMunVr2LlzO5MmTaJDh/ZOj2Oz2ahevbrT8qZNH870WVZrerKv1+sydTNyJa4Sc5E7JN7uReItXIEM5HXC1Qby2mw2oqOjCAwMktH/bsB14m3jwIE9dOjQKcMfq8DAQH7/fT3Fi5fBZtOIjLxCpUpVHS6qFR4eztq1a/Dxcfzl4f+ZzcnMmPEdH300LMP2okWLsnPnNg4dOkTHjpkX3goNDWX9+jUUK1ba3qqvqgqxsREcPnyYffv2UbFiRerWrUNAQChOei3lGdeJucgNEm/3IvHOfjKQN+sk6XfC1ZJ+IfKKplmJjr7Br78u4fDhIzz8cBNatmz5T+KcfpGYzSlMmTKJ0aPHZHivXq9n3bo1VKtWy97ifjfS0pK5efMqU6dO48KFi3Tu3InWrVvj4+PLnDnf4+/vz4gRn3D9+nUA6tevz8cff8gPP8zn88/Hotd7oaoK1679TcuWrbl27Zr92EajkQ0b1lK6dHmX6PIjhBAi6yTpzzpJ+p1wtaTfbDazevUS2rbtYh88KQouV4z3re4xVqvNYXed1NQEDh06yOjRn3L58mUaNKjHu+++S5EixVHVuxs+pKoKcXGR/PXXX5w/f57y5cMpVqw4AQHBmM02UlLi6dSpM97e3rz4Yn98fX3Q6/UcPHiIb76ZRvHixVmyZAmenr6kpMTz6KNd2L9/f6bPCQsLY8uWP/DzC7pjnXQ6ldTUJHQ6PTqdIce+KLhizEXOkXi7F4l39pOkP+sk6XfC1ZJ+TbMRHx+Pv78/iiKPBgu6/BpvVVVISUnEbE7D29sXVb37P26qqnD16kXatm3P5cuX7dvLly/P8uVLCQgohKLAlCkTM836c8uYMaPo3/9lrFaNa9cuUK1aTaeft3PndkqXLn/b+sTE3GTZsmUsWrSYgIAAXn11IBUqVMLb2x9F0YiNjSQqKgqDwUBAQCD+/oH3/KUgv8Zc3BuJt3uReGc/SfqzTpJ+J1wt6ReioEtKiqF585acO3cuU1mTJo2ZP/9HDAYPrl+/TKtWbblx40aGfYoUKcKWLX9gNIYAcP78KerVa+D089avX0u1anWclsfE3KBZs+ZcuXIlw/a+ffvw4YcfsHr1GoYMeYe4uDgAHnzwQebPn0fp0uFy7xBCiBwmSX/WydfNfMJsNvPrrz/KyH834Y7xvnnzpsOEH+DPP7cQGxtFTEwk3bo9wZw5s3j66Z74+Pjg4+ND7969WLp0MQEB/y68FRgYaJ9ytEmTxrz++mAGDOhP0aJFUVWV4sWL3aY2Fj777PNMCT/AjBkz+fvvi7z55lv2hB/gzJkzNG/ekujoG5neczfcMebuTOLtXiTewhVIS78TrtbSr2kaKSnJeHl5oyiuO+2gyB7uGO+DB3fTqlUbp+VHjhzk99838eqrr+Hp6cnjj3endetWAKxevYaVK1exd+9u/P1vJf42Fi78kZIlS7Jt23Z+/30zRqM/PXo8SVBQEHXq1Mdg8HL4WQkJUVSqVIWUlBSH5S+9NIAzZ86ybt36TGUTJ06gd+/nsFiyNj2QO8bcnUm83YvEO/tJS3/WyeJc+YheL4N/3Im7xTssLMxpmYeHB0ajiatX02fhSU1NZd68H5g374cM+1mt1v/8pNKiRQuaNm2eYfaeNWvW0qfP89SqVdvp52na/x8rI7PZgk7neGXNbdu28eyzfYCszwnqbjF3dxJv9yLxFnlNuvfkExaLheXLFzpd9VQULO4Yb5MpkO7duzksGzToVby8fGjRornT91evXh1vbx8MBh0Ggw5NszBq1JgMCf8tM2fO4urVzF13bvH19aNbt8eclrdr14YdO3Y6LKtZs8Y9DeZ1x5i7M4m3e5F4C1cg3XuccMXuPRaLBb1eL48G3YBrx9tGXFwUkZGReHp6EBAQhJ9fQLZMZZmcHMvYsWOZPn0GKSkp+Pv78+abr9OnT1+8vPxJSorhsce6sn//gQzv8/LyYufObcTGxrFkyRJ8ff3o2bMH1avXIi0tzeFnvfXWm7z//keYzY5b9CMjr9CwYROio6MzbO/cuRPvvz+U+vUbZnqPp6cnBw/uJyioSJbP3bVjLrKbxNu9SLyzn3TvyTrp3pOPWCxm9HoJmbtwxXinpiayePFC3n//QxISEoD0FXd//PEHHnjgwftO/L29TXzwwTAGDXqVpKRkfHx8CQgIRtPS/0j6+gawaNFCxo4dy6xZ35OcnEy1atX48ce5vPbaG6xfv8F+rMqVK2K7zbK7d2pxCw0txs6d25k2bRq//bYck8nE4MGDaNKkCaqq4+OPhzFq1Gj7wLyQkBB+/vkngoIK3fP5u2LMRc6ReLsXibfIa9LS74SrtfSbzWaWL19Ix47dZWEPN+CK8dbpVDZtWke3bo9nKjOZTOzZswuTKTRX6qJpVuLjo7Farfj7G1m48BcGDRqcYZ8BA/pz/fp1lixZ6vAYW7f+SXh45Tte54qikZgYh6rq8PU12hcms9nMxMZGceXKFby8vChUqBBGY/A93zdcMeYi50i83YvEO/tJS3/WSdLvhKsl/ULkteTkONq0acuJEycclk+b9jUdOnTC29vf4Yq9OSUpKZYWLVpy9uzZDNt9fX1ZvPgXevR4OlMXnW7dujJu3Hi8vbP2B0NRFBITY7hx4xqXL1+hRIkShIYWwsfHKPcLIYTIRZL0Z508Z8onZDU/9+KK8bZaLU4TfoCtW7eza9ceevd+mooVq6Eojme3yW42m42YmJhM2xMTE3n99bdYtmwJv/22zN5F5/XXX+Ohhx66p4Q/KuoaXbp05fjx4/btNWvWZOHCBRiNIfeV+LtizEXOkXi7F4m3cAXyLy+fsFisbN68FovF+TSCouBwxXirqo4yZco4LS9btgz79x+gVau23Lx5Ndfq5ePjZ5+v//8dO3aMc+f+4p133mP16tUsWrSIVq3a4e1tcno8RQEPDx0eHnpU9d8Bd0lJcTzxRI8MCT/A/v376dPnBVJSEu/rPFwx5iLnSLzdi8RbuALp3uOEdO8RIiOdTmXZssU891yfTGXe3t788stPdOz4KAADB77CsGHD0bTcaVe4ceNv6tSpn2kxrVKlSrFhw7r/LNh1eykpCVy+/Dfffz+HxMREevZ8igoVKuLrG8CVK+epUaOW0/ceOXKIQoWK39d5CCGEuDvSvSfrpKU/n7DZbERGRtx2NhJRcLhivK1WG82bt+S994ZmmIGicOHCzJ07m1GjPrVv27lzF6mpjlezvRNFUUhNTSQy8io3blwiOTkuQ4u7I0WKFOfPPzfRqlVLFEXBy8uL3r17MXXqFK5evYJ6F3e6lJQEPvtsDA891IipU6cxd+482rXrwNNPP01iYgyxsbG3fX9CQvzdn6QDrhhzkXMk3u5F4i1cgST9+YTVamXXri23XSVUFByuGm8vLz9effU1jh49xK+/LuTXXxfy+eefMnz4J2zfvt2+X3j4g3h4eGb5+KqqcPXqeXr0eJKKFStTpUo12rfvwLFjB7HZMk+xqSgKaWlJREff5NSp03Ts2J4FC+Yza9Z3WK1WOnd+jG7dniAuLuq2n6socOHCOaZM+SZT2bZt21m8eDGhoSG3qbdKQEBgls/3v1w15iJnSLzdi8RbuALp3uOEdO8RwjlFUThx4hAPP9wMR7eQXbt2ULp0eJavoZiYG9Sr1yDTwFy9Xs+ePbsICytl32azmTl//izvvjuULVu2EhoaSp8+z1G5ciWee65vhkW5du3aTqlS5Z1+rk6nMGjQy/zww48Oy8uWLcv69WsZPHgwS5cuy1T+3HPPMmbMp+h0Wf+iI4QQIuuke0/WSUt/PmGz2bh+/ao8GnQTrh5vTdMoXboskyZNxMPDw77dy8uL2bNnUqxYySwn/Hq9yqJFixzOxGOxWBgz5lNstvREXlUVTpw4SsOGjdm0aTMWi4WrV68yatQYZs6czciRwzO832y+/UJcmmYjJsZ59534+PSuOxMnTqR3717odOkzExkMBgYM6MfHH3983wm/q8dcZC+Jt3uReAtXIEl/PmGzWTl8eB82mzwadAf5Id4eHj507/4kR44cYs2aVaxbt4bDhw/Srl1HDAavLB8vLS2VdevWOy3funUbSUnpM+QkJsYxaNBgh39A16/fQLlyD+Lj4wOkDzIuVOj2q+Qqio6ePZ9yWt65cyd8fIz4+ATw+edjOXr0MHv37ubIkUOMGDEKP78AwILFkopef2+31fwQc5F9JN7uReItXIF073FCuvcIkbtUVePtt99k1qzZDsvr1KnD4sWL8fLyIzLyGhUrVnJ6rHffHcLatevZt28fEydOoEePp++4bkBCQhTt2nXg5MmTGbYbjUZ27dpBUFARh+9LTo7nxIljTJ48hbi4OB5/vDvt2rXDZArBZpObiBBC5ATp3pN10tKfT9hsNi5fviiPBt2EO8bbZlN4+eWXnJYPHfoOPj7pN3i9XrV3sXHEZDLx4INlWblyOV27drurhcL8/YNZtWoFQ4a8RXBwML6+vjz9dE927NhKSIjzhH/YsI9o06Ydy5YtZ/PmPxg4cBCPPNKCmJgbd/zM/3LHmLszibd7kXgLVyBJfz5hs9k4ffqE3DDchLvGOyysOFOmTMqU0A8a9Cp169azt5z7+QXQvXs3h8dQVZVHH+3Ml19+Sb16jfD09L2rz9Y0DT+/IIYMGcru3Ts5fPgg48aNJySkGI7CoChw6dJFZs/+PlPZxYsXmTBhInD3j/LdNebuSuLtXiTewhVI9x4npHuPEHnDYkklPj6a3bv3kJKSSoMG9QkICMLDwyfDfrGxN2nZsjUXLlzIsH3WrO9o06Yjer0HOcnDQ8e7777D1KlTHZb7+/tz8OB+/PyCcrQeQgjhjqR7T9bp77yLcAU2m5WLF89TsmQpVPXOXRVE/ubO8dbrPQkMLELbtp0AnPaLDwwsxO+/b2D37t2sXLmS4sWL06NHD4KDC+V4wn+L2ZzmtMxiuf2MQf/PnWPujiTe7kXiLVyBdO/JJ2w27Z/+gPL4wR1IvNN/B7c7f5stvTtOy5ZtmThxCm+99S6FC5dAr8+dufLNZhtPP93Tafnjj3fH19d418eTmLsXibd7kXgLVyDde5yQ7j1CiDtJSorlxRdfZPXqNRm2BwUFsW3bnwQFFc2jmgkhRMEm3XuyTlr68wmr1crp0ydkCW83IfHOH3x8TEydOpVZs2ZQo0YNypYty1tvvfHPjD9hWTqWxNy9SLzdi8RbuALp059PaJpGVFQEpUs/mNdVEblA4p1/+PiY6Ny5Gy1atMJms+Lra0TTlCw/xpeYuxeJt3uReAtXIN17nJDuPUIIIYQQrkm692SddO/JJ6xWK8ePH5ZHg25C4u1+JObuReLtXiTewhVI0p9vaCQnJwHy+ME9SLzdj8TcvUi83YvEW+Q96d7jhHTvEUIIIYRwTdK9J+ukpT+fsFqtHDq0Tx4NugmJt/uRmLsXibd7kXgLVyBJvxBCCCGEEAWcdO9xQrr3CCGEEEK4Junek3UyT78TipLXNcjIarVw+PABqlatgU4nYSvoJN7uR2LuXiTe7kXinf1cLU/LD6SlXwghhBBCiAJO+vQLIYQQQghRwEnSL4QQQgghRAEnSb8QQgghhBAFnCT9QgghhBBCFHCS9AshhBBCCFHASdIvhBBCCCFEASdJvxBCCCGEEAWcJP1CCCGEEEIUcJL0CyGEEEIIUcBJ0i+EEEIIIUQBJ0l/PpKWlsajjz5K+fLlOX78eIayEydO0LNnT6pWrUrTpk2ZPn16HtVS3I9Lly7x3nvv0bx5c6pVq0bLli356quvSEtLy7CfxLtg+eGHH2jevDlVq1bl8ccf59ChQ3ldJZENpk2bRrdu3ahZsyYPPfQQL7/8MufOncuwT2pqKsOHD6d+/frUrFmTV199lYiIiDyqschO3377LeXLl2fUqFH2bRJvkZck6c9HPv/8cwoVKpRpe0JCAn379iUsLIzFixczZMgQJk+ezIIFC/KgluJ+nDt3Dk3TGDFiBCtWrGDo0KH89NNPjB8/3r6PxLtgWblyJWPGjOGVV17h119/pUKFCvTt25fIyMi8rpq4T7t27eLpp5/m559/ZtasWVgsFvr27UtSUpJ9n9GjR/P7778zYcIE5s6dy40bNxg4cGAe1lpkh0OHDvHTTz9Rvnz5DNsl3iJPaSJf2LRpk9a2bVvt9OnTWnh4uHbs2DF72Q8//KDVrVtXS01NtW/74osvtDZt2uRFVUU2mz59uta8eXP7zxLvgqV79+7a8OHD7T9brVatcePG2rRp0/KwViInREZGauHh4dquXbs0TdO0uLg4rXLlytqqVavs+5w5c0YLDw/X9u/fn0e1FPcrISFBa926tbZ161atV69e2ieffKJpmsRb5D1p6c8HIiIi+PDDD/n888/x8vLKVH7gwAHq1KmDh4eHfVvjxo3566+/iI2Nzc2qihwQHx+PyWSy/yzxLjjS0tI4evQoDRs2tG9TVZWGDRuyf//+PKyZyAnx8fEA9uv5yJEjmM3mDPEvW7YsYWFhHDhwIC+qKLLBiBEjaNq0aYa4gsRb5D1J+l2cpmm8++679OjRg6pVqzrcJyIigpCQkAzbbv0sfQXztwsXLjBv3jx69Ohh3ybxLjiio6OxWq0EBwdn2B4cHCyxLGBsNhujR4+mVq1ahIeHA+nXq8FgwGg0Ztg3ODiYmzdv5kU1xX1asWIFx44d480338xUJvEWeU2f1xVwV2PHjr3j4MuVK1eydetWEhMTGTBgQC7VTOSEu4132bJl7T9fv36dF154gbZt2/LEE0/kdBWFEDlo+PDhnD59mvnz5+d1VUQOuXr1KqNGjWLmzJl4enrmdXWEyESS/jzSp08fHnvssdvuU6JECXbs2MGBAwcytfJ369aNTp068dlnnxESEpKpVfDWz//fIizyxt3G+5br16/zzDPPULNmTUaOHJlhP4l3wREYGIhOp8s0aDcyMlJiWYCMGDGCTZs2MW/ePIoUKWLfHhISgtlsJi4uLkPrb2RkJKGhoXlRVXEfjh49SmRkJF27drVvs1qt7N69mx9++IEZM2ZIvEWekqQ/jwQFBREUFHTH/T744AMGDx5s//nGjRv07duX8ePHU716dQBq1KjBhAkTMJvNGAwGALZt20bp0qUz9AUXeedu4w3/JvyVK1dmzJgxqGrGXngS74LDw8ODypUrs337dlq2bAmkdwPZvn07vXr1yuPaifulaRojR45k3bp1zJ07N8MXe4AqVapgMBjYvn07bdq0AdJn8Lpy5Qo1atTIgxqL+9GgQQOWLVuWYdvQoUMpU6YM/fr1o2jRohJvkack6XdxYWFhGX728fEBoGTJkvYWo06dOjFlyhTef/99+vXrx+nTp5kzZw5Dhw7N9fqK+3P9+nV69+5NWFgY77zzDlFRUfayWy1BEu+C5fnnn+edd96hSpUqVKtWje+//57k5OQMrYUifxo+fDjLly/n66+/xtfX195v29/fHy8vL/z9/enWrRuffvopJpMJPz8/PvnkE2rWrClJYD7k5+dnH69xi4+PDwEBAfbtEm+RlyTpLwD8/f2ZMWMGI0aMoGvXrgQGBvLyyy/z5JNP5nXVRBZt3bqVCxcucOHCBR5++OEMZSdPngQk3gVN+/btiYqK4quvvuLmzZtUrFiR7777Trr3FAA//vgjAL17986wfcyYMfYvde+99x6qqjJo0CDS0tJo3Lgxw4YNy/W6itwh8RZ5SdE0TcvrSgghhBBCCCFyjkzZKYQQQgghRAEnSb8QQgghhBAFnCT9QgghhBBCFHCS9AshhBBCCFHASdIvhBBCCCFEASdJvxBCCCGEEAWcJP1CCCGEEEIUcJL0CyGEEEIIUcBJ0i+EEEIIIUQBp8/rCgghhEhXvnz525YPHDiQV199lXXr1jF9+nTOnj2LzWYjLCyMhg0b8v777wOwePFihg4dSuPGjZkxY4b9/XFxcdStW5c5c+ZQv379237muHHj6NChA6mpqQwbNoyjR49y9uxZmjVrxtdff51NZyyEECK3SNIvhBAuYsuWLfbXK1eu5KuvvmL16tX2bT4+Pmzfvp3XX3+dwYMH07x5cxRF4ezZs2zdujXDsfR6Pdu3b2fHjh00aNDgtp87ZswYmjRpkmGb0WgEwGq14unpSe/evVmzZs39nqIQQog8Ikm/EEK4iNDQUPtrf39/FEXJsA1g48aN1KxZkxdeeMG+rXTp0rRs2TLDft7e3rRr144vv/ySX3755bafazQaM33OLT4+PgwfPhyAffv2ERcXl6VzEkII4RqkT78QQuQjoaGhnDlzhlOnTt1x34EDB3Lq1KkMTwuEEEK4J2npF0KIfKRXr17s2bOHTp06UaxYMapXr06jRo3o3LkzHh4eGfYtXLgwzzzzDOPHj8/0JOC/3njjDXQ6XYZtK1asICwsLEfOQQghRO6TpF8IIfIRHx8fvv32Wy5evMjOnTs5cOAAn332GXPmzGHBggV4e3tn2L9fv34sWLCARYsW0a5dO4fHHDp0KA0bNsywrVChQjl2DkIIIXKfdO8RQoh8qGTJkjz++OOMGjWKxYsXc/bsWVauXJlpP6PRSP/+/Zk8eTLJyckOjxUaGsoDDzyQ4T+9XtqEhBCiIJGkXwgh8rnixYvj5eXlNKnv3bs3qqoyZ86cXK6ZEEIIVyFNOUIIkY9MmjSJ5ORkmjZtSlhYGPHx8cydOxeLxZKpi84tnp6evPrqq4wYMcJheVxcHDdv3sywzdfXFx8fHwDOnDmD2WwmJiaGxMREjh8/DkDFihWz8cyEEELkJEn6hRAiH6lbty7z58/nnXfeISIiApPJRMWKFZkxYwZlypRx+r7HHnuMWbNmcebMmUxlQ4cOzbTtzTffpH///gD079+fy5cv28u6dOkCwMmTJ+/zbIQQQuQWRdM0La8rIYQQQgghhMg50qdfCCGEEEKIAk6SfiGEEEIIIQo4SfqFEEIIIYQo4CTpF0IIIYQQooCTpF8IIYQQQogCTpJ+IYQQQgghCjhJ+oUQQgghhCjgJOkXQgghhBCigJOkXwghhBBCiAJOkn4hhBBCCCEKOEn6hRBCCCGEKOD+B16SfmJ38Vu5AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Cluster', palette='magma')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news using KMeans Clustering');\n",
        "plt.xlabel('TSNE1');\n",
        "plt.ylabel('TSNE2');\n",
        "plt.axis('equal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tuAx8ZI3ydcT"
      },
      "outputs": [],
      "source": [
        "def get_majority_cluster_per_group(df_tsne_cluster, class_names):\n",
        "  class_clusters = dict()\n",
        "  for c in class_names:\n",
        "    # Get rows of dataframe that are equal to c\n",
        "    rows = df_tsne_cluster.loc[df_tsne_cluster['Class Name'] == c]\n",
        "    # Get majority value in Cluster column of the rows selected\n",
        "    cluster = rows.Cluster.mode().values[0]\n",
        "    # Populate mapping dictionary\n",
        "    class_clusters[c] = cluster\n",
        "  return class_clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Is_GUvFS0GH_"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'sci.crypt': 1, 'sci.electronics': 3, 'sci.med': 2, 'sci.space': 0}"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classes = df_tsne['Class Name'].unique()\n",
        "class_clusters = get_majority_cluster_per_group(df_tsne, classes)\n",
        "class_clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_bf9nXc6Dgx"
      },
      "source": [
        "Get the majority of clusters per group, and see how many of the actual members of that group are in that cluster."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b2GyHE8ahEff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sci.space          0.966667\n",
              "sci.med            0.960000\n",
              "sci.electronics    0.953333\n",
              "sci.crypt          0.926667\n",
              "Name: Class Name, dtype: float64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert the Cluster column to use the class name\n",
        "class_by_id = {v: k for k, v in class_clusters.items()}\n",
        "df_tsne['Predicted'] = df_tsne['Cluster'].map(class_by_id.__getitem__)\n",
        "\n",
        "# Filter to the correctly matched rows\n",
        "correct = df_tsne[df_tsne['Class Name'] == df_tsne['Predicted']]\n",
        "\n",
        "# Summarise, as a percentage\n",
        "acc = correct['Class Name'].value_counts() / SAMPLE_SIZE\n",
        "acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gF0wwWQK9Yek"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          36.85309982299805,\n          -25.488718032836914,\n          2.9596657752990723\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.7554828524589539,\n          5.936662197113037,\n          -19.277177810668945\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.med\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_tsne"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-6c81b6e9-efbf-4b25-8144-d3446abcf438\" 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>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "      <th>Class Name</th>\n",
              "      <th>Cluster</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.613194</td>\n",
              "      <td>-2.590790</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.533733</td>\n",
              "      <td>8.535353</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32.775826</td>\n",
              "      <td>11.671514</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44.522926</td>\n",
              "      <td>-2.058890</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40.518196</td>\n",
              "      <td>-2.139972</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>1</td>\n",
              "      <td>sci.crypt</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>595</th>\n",
              "      <td>20.744043</td>\n",
              "      <td>-7.745994</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>-0.322983</td>\n",
              "      <td>-28.657366</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>-8.563044</td>\n",
              "      <td>-6.283251</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>-14.029724</td>\n",
              "      <td>-29.518869</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>3.009676</td>\n",
              "      <td>-16.334478</td>\n",
              "      <td>sci.space</td>\n",
              "      <td>0</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 5 columns</p>\n",
              "</div>\n",
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              "  .colab-df-quickchart {\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\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",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\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",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         TSNE1      TSNE2 Class Name  Cluster  Predicted\n",
              "0    27.613194  -2.590790  sci.crypt        1  sci.crypt\n",
              "1    43.533733   8.535353  sci.crypt        1  sci.crypt\n",
              "2    32.775826  11.671514  sci.crypt        1  sci.crypt\n",
              "3    44.522926  -2.058890  sci.crypt        1  sci.crypt\n",
              "4    40.518196  -2.139972  sci.crypt        1  sci.crypt\n",
              "..         ...        ...        ...      ...        ...\n",
              "595  20.744043  -7.745994  sci.space        0  sci.space\n",
              "596  -0.322983 -28.657366  sci.space        0  sci.space\n",
              "597  -8.563044  -6.283251  sci.space        0  sci.space\n",
              "598 -14.029724 -29.518869  sci.space        0  sci.space\n",
              "599   3.009676 -16.334478  sci.space        0  sci.space\n",
              "\n",
              "[600 rows x 5 columns]"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get predicted values by name\n",
        "df_tsne['Predicted'] = ''\n",
        "for idx, rows in df_tsne.iterrows():\n",
        "  cluster = rows['Cluster']\n",
        "  # Get key from mapping based on cluster value\n",
        "  key = list(class_clusters.keys())[list(class_clusters.values()).index(cluster)]\n",
        "  df_tsne.at[idx, 'Predicted'] = key\n",
        "\n",
        "df_tsne"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWBhCLr0OTrQ"
      },
      "source": [
        "To better visualize the performance of the KMeans applied to your data, you can use a [confusion matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html). The confusion matrix allows you to assess the performance of the classification model beyond accuracy. You can see what misclassified points get classified as. You will need the actual values and the predicted values, which you have gathered in the dataframe above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CwqggsKD-ywF"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(df_tsne['Class Name'].to_list(), df_tsne['Predicted'].to_list())\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                              display_labels=classes)\n",
        "disp.plot(xticks_rotation='vertical')\n",
        "plt.title('Confusion Matrix for Actual and Clustered Newsgroups');\n",
        "plt.grid(False)"
      ]
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        "## Next steps\n",
        "\n",
        "You've now created your own visualization of embeddings with clustering! Try using your own textual data to visualize them as embeddings. You can perform dimensionality reduction in order to complete the visualization step. Note that TSNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis){:.external}.\n",
        "\n",
        "There are other clustering algorithms outside of KMeans as well, such as [density-based spatial clustering (DBSCAN)](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html){:.external}.\n",
        "\n",
        "To learn how to use other services in the Gemini API, see the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart).\n",
        "\n",
        "To learn more about how you can use embeddings, see these  other tutorials:\n",
        "\n",
        " * [Anomaly Detection with Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection)\n",
        " * [Document Search with Embeddings](https://ai.google.dev/gemini-api/tutorials/document_search)\n",
        " * [Training a Text Classifier with Embeddings](https://ai.google.dev/gemini-api/tutorials/text_classifier_embeddings)"
      ]
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