Netflix_EDA.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/josewborba/portfolio/blob/main/Netflix_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3e71475f",
      "metadata": {
        "dc": {
          "key": "4"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "3e71475f"
      },
      "source": [
        "## 1. Loading your friend's data into a dictionary\n",
        "<p><img src=\"https://assets.datacamp.com/production/project_1237/img/netflix.jpg\" alt=\"Someone's feet on table facing a television\"></p>\n",
        "<p>Netflix! What started in 1997 as a DVD rental service has since exploded into the largest entertainment/media company by <a href=\"https://www.marketwatch.com/story/netflix-shares-close-up-8-for-yet-another-record-high-2020-07-10\">market capitalization</a>, boasting over 200 million subscribers as of <a href=\"https://www.cbsnews.com/news/netflix-tops-200-million-subscribers-but-faces-growing-challenge-from-disney-plus/\">January 2021</a>.</p>\n",
        "<p>Given the large number of movies and series available on the platform, it is a perfect opportunity to flex our data manipulation skills and dive into the entertainment industry. Our friend has also been brushing up on their Python skills and has taken a first crack at a CSV file containing Netflix data. For their first order of business, they have been performing some analyses, and they believe that the average duration of movies has been declining. </p>\n",
        "<p>As evidence of this, they have provided us with the following information. For the years from 2011 to 2020, the average movie durations are 103, 101, 99, 100, 100, 95, 95, 96, 93, and 90, respectively.</p>\n",
        "<p>If we're going to be working with this data, we know a good place to start would be to probably start working with <code>pandas</code>. But first we'll need to create a DataFrame from scratch. Let's start by creating a Python object covered in <a href=\"https://learn.datacamp.com/courses/intermediate-python\">Intermediate Python</a>: a dictionary!</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8b7c188e",
      "metadata": {
        "dc": {
          "key": "4"
        },
        "tags": [
          "sample_code"
        ],
        "id": "8b7c188e",
        "outputId": "f03686e1-e600-4137-dc42-60e93dd33f8f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'years': [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], 'durations': [103, 101, 99, 100, 100, 95, 95, 96, 93, 90]}\n"
          ]
        }
      ],
      "source": [
        "# Create the years and durations lists\n",
        "years = list(range(2011, 2021))\n",
        "durations = [103, 101, 99, 100, 100, 95, 95, 96, 93, 90]\n",
        "\n",
        "# Create a dictionary with the two lists\n",
        "movie_dict = {'years':years, 'durations':durations}\n",
        "\n",
        "# Print the dictionary\n",
        "print(movie_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "930b68e2",
      "metadata": {
        "dc": {
          "key": "11"
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        "deletable": false,
        "editable": false,
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          "frozen": true
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        "tags": [
          "context"
        ],
        "id": "930b68e2"
      },
      "source": [
        "## 2. Creating a DataFrame from a dictionary\n",
        "<p>To convert our dictionary <code>movie_dict</code> to a <code>pandas</code> DataFrame, we will first need to import the library under its usual alias. We'll also want to inspect our DataFrame to ensure it was created correctly. Let's perform these steps now.</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6e0860a1",
      "metadata": {
        "dc": {
          "key": "11"
        },
        "tags": [
          "sample_code"
        ],
        "id": "6e0860a1",
        "outputId": "08834123-4057-4a0a-a2ec-1177131c4c0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   years  durations\n",
            "0   2011        103\n",
            "1   2012        101\n",
            "2   2013         99\n",
            "3   2014        100\n",
            "4   2015        100\n",
            "5   2016         95\n",
            "6   2017         95\n",
            "7   2018         96\n",
            "8   2019         93\n",
            "9   2020         90\n"
          ]
        }
      ],
      "source": [
        "# Import pandas under its usual alias\n",
        "import pandas as pd\n",
        "\n",
        "# Create a DataFrame from the dictionary\n",
        "durations_df = pd.DataFrame(movie_dict)\n",
        "\n",
        "# Print the DataFrame\n",
        "print(durations_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ae2135d",
      "metadata": {
        "dc": {
          "key": "18"
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        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
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        "tags": [
          "context"
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        "id": "4ae2135d"
      },
      "source": [
        "## 3. A visual inspection of our data\n",
        "<p>Alright, we now have a <code>pandas</code> DataFrame, the most common way to work with tabular data in Python. Now back to the task at hand. We want to follow up on our friend's assertion that movie lengths have been decreasing over time. A great place to start will be a visualization of the data.</p>\n",
        "<p>Given that the data is continuous, a line plot would be a good choice, with the dates represented along the x-axis and the average length in minutes along the y-axis. This will allow us to easily spot any trends in movie durations. There are many ways to visualize data in Python, but <code>matploblib.pyplot</code> is one of the most common packages to do so.</p>\n",
        "<p><em>Note: In order for us to correctly test your plot, you will need to initalize a <code>matplotlib.pyplot</code> Figure object, which we have already provided in the cell below. You can continue to create your plot as you have learned in Intermediate Python.</em></p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50a425c5",
      "metadata": {
        "dc": {
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        "tags": [
          "sample_code"
        ],
        "id": "50a425c5",
        "outputId": "67874775-ce57-485f-df08-be3a3256df43"
      },
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Import matplotlib.pyplot under its usual alias and create a figure\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure()\n",
        "\n",
        "# Draw a line plot of release_years and durations\n",
        "plt.plot(years,durations)\n",
        "\n",
        "# Create a title\n",
        "plt.title('Netflix Movie Durations 2011-2020')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9535c244",
      "metadata": {
        "dc": {
          "key": "25"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "9535c244"
      },
      "source": [
        "## 4. Loading the rest of the data from a CSV\n",
        "<p>Well, it looks like there is something to the idea that movie lengths have decreased over the past ten years! But equipped only with our friend's aggregations, we're limited in the further explorations we can perform. There are a few questions about this trend that we are currently unable to answer, including:</p>\n",
        "<ol>\n",
        "<li>What does this trend look like over a longer period of time?</li>\n",
        "<li>Is this explainable by something like the genre of entertainment?</li>\n",
        "</ol>\n",
        "<p>Upon asking our friend for the original CSV they used to perform their analyses, they gladly oblige and send it. We now have access to the CSV file, available at the path <code>\"datasets/netflix_data.csv\"</code>. Let's create another DataFrame, this time with all of the data. Given the length of our friend's data, printing the whole DataFrame is probably not a good idea, so we will inspect it by printing only the first five rows.</p>"
      ]
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                    "cast": "João Miguel, Bianca Comparato, Michel Gomes, Rodolfo Valente, Vaneza Oliveira, Rafael Lozano, Viviane Porto, Mel Fronckowiak, Sergio Mamberti, Zezé Motta, Celso Frateschi",
                    "country": "Brazil",
                    "date_added": "August 14, 2020",
                    "description": "In a future where the elite inhabit an island paradise far from the crowded slums, you get one chance to join the 3% saved from squalor.",
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                    "duration": 4,
                    "genre": "International TV",
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                    "date_added": "December 23, 2016",
                    "description": "After a devastating earthquake hits Mexico City, trapped survivors from all walks of life wait to be rescued while trying desperately to stay alive.",
                    "director": "Jorge Michel Grau",
                    "duration": 93,
                    "genre": "Dramas",
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                    "release_year": 2016,
                    "show_id": "s2",
                    "title": "7:19",
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                  {
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                    "country": "Singapore",
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                    "description": "When an army recruit is found dead, his fellow soldiers are forced to confront a terrifying secret that's haunting their jungle island training camp.",
                    "director": "Gilbert Chan",
                    "duration": 78,
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                    "description": "In a postapocalyptic world, rag-doll robots hide in fear from dangerous machines out to exterminate them, until a brave newcomer joins the group.",
                    "director": "Shane Acker",
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        "# Read in the CSV as a DataFrame\n",
        "netflix_df = pd.read_csv(\"datasets/netflix_data.csv\")\n",
        "\n",
        "# Print the first five rows of the DataFrame\n",
        "netflix_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
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        "## 5. Filtering for movies!\n",
        "<p>Okay, we have our data! Now we can dive in and start looking at movie lengths. </p>\n",
        "<p>Or can we? Looking at the first five rows of our new DataFrame, we notice a column <code>type</code>. Scanning the column, it's clear there are also TV shows in the dataset! Moreover, the <code>duration</code> column we planned to use seems to represent different values depending on whether the row is a movie or a show (perhaps the number of minutes versus the number of seasons)?</p>\n",
        "<p>Fortunately, a DataFrame allows us to filter data quickly, and we can select rows where <code>type</code> is <code>Movie</code>. While we're at it, we don't need information from all of the columns, so let's create a new DataFrame <code>netflix_movies</code> containing only <code>title</code>, <code>country</code>, <code>genre</code>, <code>release_year</code>, and <code>duration</code>.</p>\n",
        "<p>Let's put our data subsetting skills to work!</p>"
      ]
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              "   title        country             genre  release_year  duration\n",
              "0     3%         Brazil  International TV          2020         4\n",
              "1   7:19         Mexico            Dramas          2016        93\n",
              "2  23:59      Singapore     Horror Movies          2011        78\n",
              "3      9  United States            Action          2009        80\n",
              "4     21  United States            Dramas          2008       123"
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      "source": [
        "# Subset the DataFrame for type \"Movie\"\n",
        "netflix_df_movies_only = netflix_df[netflix_df['type'] == 'Movie']\n",
        "\n",
        "# Select only the columns of interest\n",
        "netflix_movies_col_subset = netflix_df[['title', 'country', 'genre', 'release_year', 'duration']]\n",
        "\n",
        "# Print the first five rows of the new DataFrame\n",
        "netflix_movies_col_subset.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "927b906a",
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      "source": [
        "## 6. Creating a scatter plot\n",
        "<p>Okay, now we're getting somewhere. We've read in the raw data, selected rows of movies, and have limited our DataFrame to our columns of interest. Let's try visualizing the data again to inspect the data over a longer range of time.</p>\n",
        "<p>This time, we are no longer working with aggregates but instead with individual movies. A line plot is no longer a good choice for our data, so let's try a scatter plot instead. We will again plot the year of release on the x-axis and the movie duration on the y-axis.</p>\n",
        "<p><em>Note: Although not taught in Intermediate Python, we have provided you the code <code>fig = plt.figure(figsize=(12,8))</code> to increase the size of the plot (to help you see the results), as well as to assist with testing. For more information on how to create or work with a <code>matplotlib</code> <code>figure</code>, refer to the <a href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html\">documentation</a>.</em></p>"
      ]
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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create a figure and increase the figure size\n",
        "fig = plt.figure(figsize=(12,8))\n",
        "\n",
        "# Create a scatter plot of duration versus year\n",
        "plt.scatter(netflix_movies_col_subset['release_year'], netflix_movies_col_subset['duration'])\n",
        "\n",
        "# Create a title\n",
        "plt.title('Movie Duration by Year of Release')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "25c7be00",
      "metadata": {
        "dc": {
          "key": "46"
        },
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          "frozen": true
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          "context"
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        "id": "25c7be00"
      },
      "source": [
        "## 7. Digging deeper\n",
        "<p>This is already much more informative than the simple plot we created when our friend first gave us some data. We can also see that, while newer movies are overrepresented on the platform, many short movies have been released in the past two decades.</p>\n",
        "<p>Upon further inspection, something else is going on. Some of these films are under an hour long! Let's filter our DataFrame for movies with a <code>duration</code> under 60 minutes and look at the genres. This might give us some insight into what is dragging down the average.</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8f07cb3",
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        "dc": {
          "key": "46"
        },
        "tags": [
          "sample_code"
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        "id": "d8f07cb3",
        "outputId": "d5b2187c-400d-43b2-9afe-926ee96aaa23"
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      "outputs": [
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                  },
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                    "index": 61,
                    "release_year": 2014,
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                    "country": "United States",
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                    "genre": "Crime TV",
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              "                                                title         country  \\\n",
              "0                                                  3%          Brazil   \n",
              "5                                                  46          Turkey   \n",
              "11                                               1983          Poland   \n",
              "12                                               1994          Mexico   \n",
              "16                                             Feb-09             NaN   \n",
              "24                ​SAINT SEIYA: Knights of the Zodiac           Japan   \n",
              "26                                           (Un)Well   United States   \n",
              "29                                           #blackAF   United States   \n",
              "35                                          #Rucker50   United States   \n",
              "38                                แผนร้ายนายเจ้าเล่ห์             NaN   \n",
              "45                                              Şubat          Turkey   \n",
              "51                                 100 Days My Prince     South Korea   \n",
              "53                                         100 Humans   United States   \n",
              "55                100 Things to do Before High School   United States   \n",
              "58                                        100% Hotter  United Kingdom   \n",
              "61                                   12 Years Promise     South Korea   \n",
              "63                                     13 Reasons Why   United States   \n",
              "64                 13 Reasons Why: Beyond the Reasons   United States   \n",
              "67  13TH: A Conversation with Oprah Winfrey & Ava ...             NaN   \n",
              "80                                         20 Minutes          Turkey   \n",
              "\n",
              "               genre  release_year  duration  \n",
              "0   International TV          2020         4  \n",
              "5   International TV          2016         1  \n",
              "11          Crime TV          2018         1  \n",
              "12          Crime TV          2019         1  \n",
              "16  International TV          2018         1  \n",
              "24      Anime Series          2020         2  \n",
              "26        Reality TV          2020         1  \n",
              "29       TV Comedies          2020         1  \n",
              "35     Documentaries          2016        56  \n",
              "38  International TV          2016         1  \n",
              "45          Crime TV          2013         1  \n",
              "51  International TV          2018         1  \n",
              "53        Docuseries          2020         1  \n",
              "55     Uncategorized          2014        44  \n",
              "58        British TV          2017         1  \n",
              "61  International TV          2014         1  \n",
              "63          Crime TV          2020         4  \n",
              "64          Crime TV          2019         3  \n",
              "67     Uncategorized          2017        37  \n",
              "80          Crime TV          2013         1  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Filter for durations shorter than 60 minutes\n",
        "short_movies = netflix_movies_col_subset[netflix_movies_col_subset['duration'] <60 ]\n",
        "\n",
        "# Print the first 20 rows of short_movies\n",
        "short_movies.head(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe2270cc",
      "metadata": {
        "dc": {
          "key": "53"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "fe2270cc"
      },
      "source": [
        "## 8. Marking non-feature films\n",
        "<p>Interesting! It looks as though many of the films that are under 60 minutes fall into genres such as \"Children\", \"Stand-Up\", and \"Documentaries\". This is a logical result, as these types of films are probably often shorter than 90 minute Hollywood blockbuster. </p>\n",
        "<p>We could eliminate these rows from our DataFrame and plot the values again. But another interesting way to explore the effect of these genres on our data would be to plot them, but mark them with a different color.</p>\n",
        "<p>In Python, there are many ways to do this, but one fun way might be to use a loop to generate a list of colors based on the contents of the <code>genre</code> column. Much as we did in Intermediate Python, we can then pass this list to our plotting function in a later step to color all non-typical genres in a different color!</p>\n",
        "<p><em>Note: Although we are using the basic colors of red, blue, green, and black, <code>matplotlib</code> has many named colors you can use when creating plots. For more information, you can refer to the documentation <a href=\"https://matplotlib.org/stable/gallery/color/named_colors.html\">here</a>!</em></p>"
      ]
    },
    {
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          "sample_code"
        ],
        "id": "4a1e58da",
        "outputId": "eb61991e-b79d-44ea-e832-81bf3a7c1891"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['black', 'black', 'black', 'black', 'black', 'black', 'black', 'black', 'black', 'black']\n"
          ]
        }
      ],
      "source": [
        "# Define an empty list\n",
        "colors = []\n",
        "\n",
        "# Iterate over rows of netflix_movies_col_subset\n",
        "for lab, row in netflix_movies_col_subset.iterrows() :\n",
        "    if row['genre'] == 'Children' :\n",
        "        colors.append('red')\n",
        "    elif row['genre'] == 'Documentaries' :\n",
        "        colors.append('blue')\n",
        "    elif row['genre'] == 'Stand-Up' :\n",
        "        colors.append('green')\n",
        "    else:\n",
        "        colors.append('black')\n",
        "        \n",
        "# Inspect the first 10 values in your list        \n",
        "print(colors[0:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "73332599",
      "metadata": {
        "dc": {
          "key": "60"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "73332599"
      },
      "source": [
        "## 9. Plotting with color!\n",
        "<p>Lovely looping! We now have a <code>colors</code> list that we can pass to our scatter plot, which should allow us to visually inspect whether these genres might be responsible for the decline in the average duration of movies.</p>\n",
        "<p>This time, we'll also spruce up our plot with some additional axis labels and a new theme with <code>plt.style.use()</code>. The latter isn't taught in Intermediate Python, but can be a fun way to add some visual flair to a basic <code>matplotlib</code> plot. You can find more information on customizing the style of your plot <a href=\"https://matplotlib.org/stable/tutorials/introductory/customizing.html\">here</a>!</p>"
      ]
    },
    {
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          "sample_code"
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        "id": "ebe14d25",
        "outputId": "b15284a9-a9a8-41c6-eb7d-3b28537e82fe"
      },
      "outputs": [
        {
          "data": {
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dOoRTp07BwcEBt27dQkJCAkaMGCF2GBvbcW/fvg0bG5tSn1+WfSUnJ4tcrBcWFiI1NVVo6A4AcLlc5vGfldYzIa6Iwa5du5Camopdu3Zh1KhRQo9duHBBbIGE8ijt8/r+/Tu+fftW5ckbm6SkJNYJ7cnJyQDAJDHFxo8fj7///htnz56Fq6srTp8+DU1NTQwYMKBcx50wYQIOHTqEI0eOoEuXLszk/58XeS1+r1atWiW2AMLPNm7ciOzsbPj4+KBjx45Cj23evFloiOXPSitu8bMfP8vihW1/JO53r6LY4ived+PGjfHgwYMK77si+/Hz88OzZ88wcuRI7N69W+ixxMRE1oRIQ0MDq1evxurVqxEdHY0HDx7g5MmT8PT0RGxsLHx8fITiqYrvF0JI+dEcGUJIhaipqWHgwIGIj4+HsrIyhg4dWuLziyfriytDXHwn++eLgZYtW6Jhw4YICAhASkpKuYaVAUDr1q0BFE2Or6zi13D//n2Rx4KDg1nX1im+cPz06ZPIYy9evKhQHJGRkQCA/v37izzGFhtQ/t4QVVVV1KtXD0lJSXj37p3I48VzcH7FxRvba4qIiEBSUhLq1asnksg4OzujVq1aOHLkCC5fvozU1FSMHDmy3JOtiyf0+/n54fnz57h+/TpsbGzQvHlzoee1adMGQPnOscjISNSuXVskiQHEf4YVVdLvXnJyMsLCwqCiolKm6mcVVatWLTRu3BgRERHMULtftZ/i35d+/fqJPFaW99rMzAwjR46Et7c3jIyMcPfuXab3qiq/Xwgh5UeJDCGkwhYtWoSTJ0/iwoULYocTFWvbti0sLS3x7NkznD17VuixoKAg+Pj4oE6dOkIlhouNHDkS+fn5OHz4MK5cuQJTU1N06NChTDGOHj0aGhoa8PDwwOPHj0UeFwgEePjwIfLy8krdV3HytGnTJmZICQDk5uZi5cqVrNu0atUKAHDy5EmhydQpKSkVLhBgYmICACLrnAQGBopdI6W456R4PZmycHFxAVBUpezH2L99+8aUeHZ1dS174BW0d+9eobgLCwuxbNkyCAQCkR4poCjJHjp0KN6+fYvly5eDw+GUa1jZjyZOnIj8/HyMHj0afD5fpDcGKErm7Ozs4Ofnh2PHjrGulfPhwweh12BiYoK0tDSR9ZOOHz9e5eV6R48eDaCop6e49wUoOveXLVuG79+/Y8SIEeWqJFcR06ZNQ35+PqZOnSpU+atYRkYGnj59WuX7Eff7Eh0djWXLlolsHx0djejoaJH2zMxMZGVlQU5OjhmWVpXfL4SQ8qOhZYSQCjM0NGStvMSGw+Fgz549GDBgACZPnoxLly7BysoKUVFR8Pb2hry8PPbu3cusN/Gj4onXGzZsQH5+fpnWjilWu3ZtHD9+HKNHj0bPnj3RqVMnNGzYEHJycoiPj8fTp0/x6dMnREdHC1XmYtOuXTu4ublh//79sLW1Rf/+/aGgoAA/Pz+oq6tDT09PZJuWLVuiY8eOuHv3Lrp06YIuXbogLS0NAQEB6Ny5s8iFbFlMmDABnp6eGDt2LJycnKCnp4ewsDDcuHEDAwcOhJeXl8g29vb2eP78OVxcXNCzZ08oKirC2NgYzs7OYo8zbdo03LhxAzdu3ED79u3Rq1cv5Ofnw8fHB58/f4azs3O5h2tVRJs2bdCxY0cMHDgQampquH79Ot6+fYsWLVqIHco1YcIEHDlyBJ8/f0bnzp1Rv379Ch27f//+0NHRwefPn0tcg+bgwYNwcnLCrFmzsG/fPrRu3Rq1a9fG58+f8e7dO4SEhODkyZNM9bUpU6YgMDAQffr0wYABA6CmpoYXL14gODgYTk5OuHz5coXiZdOmTRvMmTMHmzdvhq2tLXO8W7du4dWrV2jcuDFTha46jRo1Cq9evcL+/fthY2ODbt26wcTEBOnp6YiNjcWDBw9gb28vUqWwsvvp3bs36tWrh127duHt27do2rQpPn36BH9/f/Ts2VOkt/TNmzdwcXGBjY0NLC0toa+vDx6PB39/f6SlpWH69OlMCe+q/H4hhJQfJTKEkF+mRYsWuH37Njw8PHD79m0EBgZCXV0dDg4OmDt3Lpo2bcq6na6uLrp37w5/f/8yrR3zs06dOuH+/fvYuXMnAgMD8fjxY8jKyjKrmy9fvrzM8wPWr18Pc3NzHDx4EMeOHYOmpiYcHR2xZMkSsb1EJ0+exL///osrV67gwIEDMDExYRaUvHjxYrleC1BUFcrHxwerVq2Cv78/CgsL0aRJE5w4cQLq6uqsiczcuXPx7ds3XL16Fdu2bUNBQQHs7OxKTGTk5eXh5eWFPXv24Ny5czh48CC4XC4aNWqEhQsXMj021W3dunXw9vbG8ePHERsbCy0tLUydOhXu7u5ih4s1adIEzZs3x4sXL1h7UcpKTk4Ow4cPx44dOzB8+HCxa9Do6+vj1q1bOHDgAC5fvoyLFy8iPz8fOjo6MDc3x/r164XOj+7du+PMmTPYuHEjLl26BC6Xi5YtW8LHxwfR0dFVmsgARWvvNG3aFPv378f58+eRm5sLU1NTzJs3D7NmzRIZnlddNmzYgJ49e+LQoUO4d+8e0tLSoK6uDgMDA0ycOFGkfHVV7EdFRYWZtH/v3j08fPgQZmZmmD9/PqZNmyby+9K8eXPMmTMH9+7dw61bt5CWlgZNTU00aNAAa9asEUneq/L7hRBSPhwejyfaB04IIYRIse/fv6NRo0ZQVFTEmzdvKjVsatCgQbh58yYePHhQ5vLNhBBCqh/NkSGEEPLbOXr0qNCq6xX16tUr3Lx5Ex06dKAkhhBCahgaWkYIIeS3kJ6ejsOHDyMhIQHHjx+HlpYWJk+eXKF9HTp0CAkJCTh16hQ4HA4WLVpUxdESQgipLBpaRggh5LcQExODZs2aQUFBAdbW1li3bh1TNa68rK2tER8fDzMzM8ybN6/M5b4JIYT8OpTIEEIIIYQQQqQOzZEhhBBCCCGESB1KZAghhBBCCCFShxIZQgghhBBCiNShRIaQEkREREg6BCKl6NwhFUHnDakIOm9IRUn7uUOJDCGEEEIIIUTqUCJDCCGEEEIIkTqUyBBCCCGEEEKkDiUyhBBCCCGEEKlDiQwhhBBCCCFE6lAiQwghhBBCCJE6lMgQQgghhBBCpA4lMoQQQgghhBCpQ4kMIYQQQgghROpQIkMIIYQQQgiROpTIEEIIIYQQQqQOJTKEEEIIIYQQqUOJDCGEEEIIIUTqUCJDCCGEEEIIkTqUyBBCCCGEECKFwsLCcOXKFSQnJ0s6FImQlXQAhBBCCCGEkLL7/v07Ro8ejefPn4PH48HQ0BCDBg3CypUrJR3aL0U9MoQQQgghhEgRd3d33Lx5EzweDwAQHx+Po0eP4vbt2xKN61ejRIYQQgghhBAp8vLlS5G2jIwMHD9+/NcHI0GUyBBCCCGEECJFuFz2S3hZ2T9r1gglMoQQQgghhEgROzs7yMjICLVpampi8uTJEopIMv6stI0QQgghhBApt3z5cnz58gUPHjwAj8eDgYEBxo4dixYtWkg6tF+KEhlCCCGEEEKkiKysLA4cOICUlBQkJCTA3NwcioqKkg7rl6NEhhBCCCGEEClUp04d1KlTR9JhSAzNkSGEEEIIIYRIHUpkCCGEEEIIIVKHEhlCCCGEEEKI1KFEhhBCCCGEECJ1KJEhhBBCCCGESB1KZAghhBBCCCFShxIZQgghhBBCiNSRWCJz4MABtG/fHsbGxjA2NkaPHj3g7+/PPC4QCLB27Vo0bNgQenp6cHBwQFhYmNA+eDwe3NzcYGJiAhMTE7i5uYHH4/3iV0IIIYQQQgj51SSWyBgYGODff/9FUFAQbt26hU6dOmHUqFF48+YNAGDbtm3YtWsX1q9fj5s3b0JbWxsDBw5ERkYGs4+JEyciJCQEFy5cwIULFxASEoJJkyZJ6iURQgghhBBCfhGJJTIODg7o0aMH6tWrB3NzcyxZsgS1atXCkydPIBAIsGfPHsyePRtOTk5o3Lgx9uzZg8zMTFy4cAEAEB4ejhs3bmDr1q1o06YN2rRpgy1btsDf3x8RERGSelmEEEIIIYSQX6BGzJEpLCzExYsXkZWVhTZt2iAmJgZfvnxB165dmecoKSmhffv2ePToEQDg8ePHqFWrFtq2bcs8p127dlBRUWGeQwghhBBCCPk9yUry4KGhoejZsydycnKgoqKCkydPwsrKiklEtLW1hZ6vra2NhIQEAEBSUhLq1KkDDofDPM7hcKClpYWkpKQSj0s9NqQ86HwhFUXnDqkIOm9IRdB5QyqqJp87FhYWJT4u0UTGwsICd+/exbdv33D58mVMmTIFvr6+v+S4hJRFREQEnS+kQujcIRVB5w2pCDpvSEVJ+7kj0aFl8vLyqFevHmxsbLBs2TJYW1tj9+7d0NXVBQAkJycLPT85ORk6OjoAAB0dHaSkpEAgEDCPCwQCfP36lXkOIYQQQggh5PdUI+bIFOPz+cjLy4OpqSl0dXVx69Yt5rGcnBw8fPiQmRPTpk0bZGZm4vHjx8xzHj9+jKysLKF5M4QQQgghhJDfj8SGli1fvhw9e/aEoaEhU43s3r17OHfuHDgcDqZMmYLNmzfDwsIC5ubm2LhxI1RUVDBkyBAAgKWlJbp3746//voLW7duBQD89ddf6NWrl1R3kRFCCCGEEEJKJ7FE5suXL3Bzc0NSUhLU1NRgZWWFCxcuoFu3bgCAWbNmITs7G/PnzwePx0PLli3h5eUFVVVVZh8HDx7E33//jcGDBwMA+vTpgw0bNkjk9RBCCCGEEEJ+HQ6PxxOU/jRC/kzSPgmOSA6dO6Qi6LwhFUHnDakoaT93atQcGUIIIYQQQggpC0pkCCGEEEIIIVKHEhlCCCGEEEKI1KFEhhBCCCGEECJ1KJEhhBBCCCGESB1KZAghhBBCCCFShxIZQgghhBBCiNShRIYQQgghhBAidSiRIYQQQgghhEgdSmQIIYQQQgghUocSGUIIIYQQQojUoUSGEEIIIYQQInUokSGEEEIIIYRIHUpkCCGEEEIIIVKHEhlCCCGEEEKI1KFEhhBCCCGEECJ1KJEhhBBCCCGESB1KZAghhBBCCCFShxIZQgghhBBCiNShRIYQQgghhBAidSiRIYQQQgghhEgdSmQIIYQQQgghUocSGUIIIYQQQojUoUSGEEIIIYQQInUokSGEEEIIIYRIHUpkCCGEEEIIIVKHEhlCCCGEEEKI1KFEhhBCCCGEECJ1KJEhhBBCCCGESB1KZAghhBBCCCFShxIZQgghhBBCiNShRIYQQgghhBAidSiRIYQQQgghhEgdSmQIIYQQQgghUocSGUIIIYQQQojUoUSGEEIIIYQQInUokSGEEEIIIYRIHUpkCCGEEEIIIVKHEhlCCCGEEEKI1KFEhhBCCCGEECJ1KJEhhBBCCCGESB1KZAghhBBCCCFShxIZQgghhBBCiNShRIYQQgghhBAidSiRIYQQQgghhEgdSmQIIYQQQgghUocSGUIIIYQQQojUoUSGEEIIIYQQInUokSGEEEIIIYRIHUpkCCGEEEIIIVKHEhlCCCGEEEKI1KFEhhBCCCGEECJ1KJEhhBBCCCGESB1KZAghhBBCCCFSR1bSARBCCCEVwefzsWHDBty6dQsCgQBt27bF0qVLIScnJ+nQCCGE/AKUyBBCCJFK06ZNw/nz51FQUAAAePr0KSIjI+Hp6SnhyAghhPwKNLSMEEKI1ElJScGdO3eYJAYo6qF59OgRoqKiJBgZIYSQX0ViiczmzZthb28PY2Nj1K9fH8OHD8fbt2+FnjNlyhRoaGgI/evevbvQc3JzczF//nzUq1cPBgYGcHZ2Rnx8/K98KYQQQn6xuLg4pKSkiLR//foVEREREoiIEELIryaxRObevXuYMGEC/P394e3tDVlZWQwYMABpaWlCz+vSpQvCw8OZf+fPnxd63N3dHT4+Pjh06BD8/PyQkZGB4cOHo7Cw8Fe+HEIIIb+Qubk59PX1RdoNDAxgY2Pz6wMihBDyy0lsjoyXl5fQz/v27YOJiQmCg4PRp08fpl1BQQG6urqs+0hPT8eJEyewa9cu2NvbM/uxtrbG7du30a1bt+p7AYQQQiSmVq1aGDZsGPbs2YNv374BAJSVleHo6AgdHR0JR0cIIeRXqDGT/TMzM8Hn86GhoSHU/vDhQ5ibm0NdXR12dnZYsmQJtLW1AQAvX75Efn4+unbtyjzfyMgIlpaWePToESUyhBDyG3N3d0fbtm1x5MgRFBYWYsSIEXB0dJR0WIQQQn6RGpPILFy4ENbW1mjTpg3T1r17d/Tr1w+mpqaIjY3FqlWr0L9/f9y+fRsKCgpISkqCjIwM6tSpI7QvbW1tJCUl/eqXQAgh5Bfr2rWr0M0sQgghf44akcgsWrQIwcHBuHbtGmRkZJj2wYMHM/+3srKCjY0NrK2t4e/vj/79+1f4eDQRlJQHnS+koujcIRVB5w2pCDpvSEXV5HPHwsKixMclnsi4u7vDy8sLPj4+MDMzK/G5+vr6MDAwQGRkJABAR0cHhYWFSElJgZaWFvO85ORk2Nrait1PaW8KIcUiIiLofCEVQucOqQg6b0hF0HlDKkrazx2JriOzYMECXLx4Ed7e3mjQoEGpz09JSUFCQgIz+d/GxgZycnK4desW85z4+HiEh4ejbdu21RY3IYQQQgghRLIk1iMzb948nD17FidPnoSGhga+fPkCAFBRUUGtWrWQmZmJdevWoX///tDV1UVsbCxWrFgBbW1tZjKnuro6XFxcsGzZMmhra6N27dpYvHgxrKys0KVLF0m9NEIIIYQQQkg1k1gic/DgQQCAk5OTUPuCBQvg7u4OGRkZvH37FmfOnEF6ejp0dXXRsWNHHDlyBKqqqszz165dCxkZGYwbNw45OTno1KkT9u7dKzTXhhBCCCGEEPJ7kVgiw+PxSnxcSUlJZK0ZNgoKCvDw8ICHh0cVRUYIIYQQQgip6SQ6R4YQQgghhBBCKoISGUIIIYQQQojUoUSGEEIIIYQQInUokSGEEEIIIYRIHUpkCCGEEEIIIVKHEhlCCCGEEEKI1KFEhhBCCCGEECJ1KJEhhBBCfhE+ny/pEAgh5LdBiQwhhBBSzV69egUHBwfY2NigXbt2WLx4MSU1hBBSSbKSDoAQQgj5naWnp2P8+PH4+PEj0xYZGQkul4uVK1dKMDJCCJFu1CNDCCGEVKODBw8KJTEAkJeXhxs3bkgoIkII+T1QIkMIIYRUo7i4ONb27OzsXxwJIYT8XiiRIYQQQqrRiBEjoKamJtJuYmIigWgIIeT3QYkMIYQQUo3atm2L/v37Q11dHQDA4XBgYWGB9evXV3rfUVFR+OuvvzB58mQEBwdXen+EECJNaLI/IYQQUs127tyJsWPH4ty5czA1NcWYMWNQq1atSu3z7NmzWLZsGRITEwEAV69exejRo7F69eqqCJkQQmo8SmQIIYSQX6BVq1Zo1apVleyrsLAQ27ZtY5IYoKg62sWLFzF16lQYGhpWyXEIIaQmo6FlhBBCiJSJi4sTSmKKJSYmUjU0QsgfgxIZQgghRMrUqVOHdWiaiooK6tevX+r2ubm5OHbsGBYuXIj79+9DIBBUR5iEEFKtaGgZIYQQImVUVVVhZ2eH+Ph4FBYWMu1WVlaws7MrcdvExEQMHjwY7969Q2FhIU6ePImuXbvi6NGj4HLp/iYhRHpQIkMIIYRIoR07dkBDQwP37t1DQUEBGjZsiC1btoDD4ZS43bx58xAaGsr8nJmZiYCAAHh7e2PAgAHVHDUhhFQdSmQIIURKCQQCHDx4EH5+fgCAgQMHwsXFpdQLWfJ7kJWVxdq1a8u9XWRkpEhbTk4OLl++TIkMIUSqUCJDCCFSasaMGTh37hzy8vIAAMHBwXjz5g02bNgg4chITaakpMTarqmp+YsjIYSQyqHBsIQQIoUSExMRGBjIJDEAkJ2djWvXriEtLU2CkZGarn///iLJjJGREf766y8JRUQIIRVDPTKEECKFwsLCkJCQINL+5csXREZGomXLlhKIikiDmTNnIisrCz4+PsjIyIC+vj4WLVoEIyMjSYdGCCHlQokMIYRIoYYNG0JPT09kLREdHR3Uq1dPQlERacDhcLBo0SK4u7uDz+dDRkZG0iERQkiF0NAyQgiRQvr6+rC3t4e8vDzTpqSkhJ49e6J27doSjIxICw6HQ0nMby4iIgLXr19HamqqpEMhpFpQjwwhhEipXbt2wdraGteuXQOXy0W/fv0wfvx4SYdFCJGw7OxsjB49Gs+fP0daWhqMjY0xdOhQLF26VNKhEVKlKJEhhBApxeVyMXXqVEydOlXSoRBCapCFCxciMDCQ+TkuLg6HDh1Ct27dSl0wlRBpQkPLCCGEEEJ+Iy9evBBpS09Px5EjRyQQDSHVhxIZQgghhJDfiLhFcWlOFPndUCJDCCGEEPIbsbW1BZcrfImnqamJyZMnSygiQqoHzZEhhBBCCPmNrFy5EomJiXj06BF4PB4MDQ0xZswYNG/eXNKhEVKlKJEhhBBCCPmNyMnJ4ejRo0hKSsLnz59haWkJJSUlSYdFSJWjRIYQQggh5Deko6MDHR0dSYdBSLWhOTKEEEIIIYQQqUOJDCGEEEIIIUTqUCJDCCGEEEIIkTqUyBBCCCGEEEKkDiUyhBBCCCGEEKlDiQwhhBBCCKmRQkJCMHHiRIwcORLHjx8Hn8+XdEikBqHyy4QQQgghpMY5e/YslixZgqSkJADAjRs3cP36dZw4cULCkZGagnpkCCGEEEJIjSIQCLBr1y4miQGAvLw8BAUF4enTpxKMjNQklMgQQgghhJAaJT09HcnJySLt3759Q0BAgAQiIjURJTKEEEIIIaRGUVVVhZqamki7oqIibGxsfn1ApEaiRIYQQgghhNQoMjIycHJygrKyslC7tbU1evfuLaGoSE1Dk/0JIYQQQkiN4+7ujjp16uDSpUvIy8tD48aNsXr1anC5dB+eFKFEhhBCCCHkN3P9+nXs2rULGRkZMDY2xqpVq2BkZCTpsMqFw+Fg0qRJmDRpkqRDkTrPnj3DunXrkJqaCm1tbfzzzz9o0qSJpMOqcpTIEEIIIYT8Rnx9fTF79mx8/foVQNFF7du3bxEQEAANDY1qO254OBcnT8pBV1cAV9c8sExxIb/A69ev4erqivj4eKYtNDQU//33H+rXry/ByKoe9c0RQgghhPxGdu/ezSQxxd6/f4/t27dX2zGXLVNA374q2LFDEf/8o4guXWrh6VO6zJSE9evXCyUxABAXF4e1a9dKKKLqQ2cYIYQQQshvJD09nbX948eP1XK8qCgOTp2SR0pK8WUlB5GRMnB3V6qW45GSpaamsrb/nNz+DiiRIYQQQgj5jWhra4u0cblctGrVqlqOd+6cPJKTRS8p4+M5yMyslkOSEoibC1W3bt1fHEn1o0SGEEIIIeQ3snTpUhgbGwu1NW/eHBMnTqyW4xka8iEjIxBpV1QEFBSq5ZBSJysrC6dOncKpU6eQWc3Z3bJly2BhYSHU1rBhQyxevLhajysJlMgQQgghhFQRgUD0gv5Xa9GiBXx9fTFq1Cj06tULCxYsgLe3N5SUqmeo15Ah+TA35wu1cbkC2NoWQE6uWg4pVQICAtCxY0dMmzYN06ZNQ6dOnXDt2rVqO56hoSH8/PwwceJE9OrVC1OmTIGfnx+0tLSq7ZiSwuHxeJL/jSOkhoqIiBC5q0FIWdC5QyqCzhvp9ebNGyxcuBCxsbFQVlZGt27dsHLlyl+y5klNOG/Cw7mYN08R0dEyUFAQoH37AmzalPPHJzIFBQWws7NDeHi4ULulpSXu3bsHOQm/QTXh3KkMKr9MCCGEEFIJ3759w9ixY/Hhwwem7ePHj+BwOFi1apUEI/t1LC358PH5joICQEYG4HCq/5jR0dHw8PBAamoqbG1tMWnSJCjUsLFsL1++RFRUlEh7VFQUXrx4gTZt2kggqt8HDS0jhBBCCKmEQ4cOCSUxAJCfn4/AwEAJRSQ5srK/Jom5f/8+HBwc4OnpiatXr2L58uUYPHgwCgoKqv/g5aCiogJFRUWRdgUFBaioqEggot8LJTKEEEIIIZUQFxfH2v79+/caMWfmd7RmzRqhtVL4fD4eP36MixcvSjAqUQ0bNoSlpaVIu6WlJRo3biyBiH4vlMgQQgghhFTCyJEjocayjL2JiQk4v6J7ogo9fvwYTk5OsLW1Re/eveHl5SXpkFh9+fJFpC0vL6/G9YJxOBwcO3YMHTp0gJ6eHvT09NChQwccO3ZM6s6NmkhiiczmzZthb28PY2Nj1K9fH8OHD8fbt2+FniMQCLB27Vo0bNgQenp6cHBwQFhYmNBzeDwe3NzcYGJiAhMTE7i5uYHH4/3CV0IIITVTVFQU5syZgylTpuDJkyeSDoeQ31arVq3g5OQEdXV1ps3c3Bzr16+XYFTlFx0djfHjxyMoKAhhYWEIDg7G/Pnz4efnJ+nQRPz4Xv+oUaNGvziS0hkYGMDX1xf379/H/fv34evrC0NDQ0mH9VuQWCJz7949TJgwAf7+/vD29oasrCwGDBiAtLQ05jnbtm3Drl27sH79ety8eRPa2toYOHAgMjIymOdMnDgRISEhuHDhAi5cuICQkBBMmjRJEi+JEEJqjLNnz6JPnz44fPgwTp8+jaFDh2LZsmWSDouQ39aOHTtw8eJFTJw4EStWrMDt27elbuiQh4cHPn36JNSWkpKCffv2SSgi8caMGQMNDQ2htkaNGuF///ufZAIqgzp16qBOnTqSDuO3IrGqZT93Ve7btw8mJiYIDg5Gnz59IBAIsGfPHsyePRtOTk4AgD179sDCwgIXLlzAuHHjEB4ejhs3buDatWtM1YctW7agT58+Ul9OjhBCKqqwsBBbt25FYmIi08bj8XDu3DlMmTIFenp6EoyOkN9Xq1at0KpVK0mHUWFfv35lba/uBRwrwtXVFcrKyjh69CiysrJgZmaG1atXo1atWpIOjfxCNWaOTGZmJvh8PpNdx8TE4MuXL+jatSvzHCUlJbRv3x6PHj0CUDSOs1atWmjbti3znHbt2kFFRYV5DiGE/GliYmKEkphiCQkJNW78OCGk5mjZsiVru4mJyS+OpGyGDBkCX19f3Lp1C0eOHIGBgYGkQyK/WLl7ZL58+YKUlBRwOBzUqVMHOjo6VRLIwoULYW1tzfSsFE/i0tbWFnqetrY2EhISAABJSUmoU6eO0GQpDocDLS0tJCUliT1WRERElcRM/gx0vpCKktS5k5GRwVruU1lZGfLy8nRO13D0+ZCKqIrzpk+fPvDx8UFoaCj4fD4AoG7dupgwYQKdl7+xmvzZlja6qtREJjMzE5cuXYKvry+ePHkiMpFeQ0MDrVu3hoODAwYOHAhVVdVyB7lo0SIEBwfj2rVrkJGRKff25UVDzkhZ0RBFUlGSPnc6duyICxcuMBcjAGBtbY0hQ4ZQpZwaTNLnDZFOVXne3LhxA8eOHcPDhw/RoEEDTJ06VezE+vLi8XiQkZGp0LUiqR7S/p0jNpFJTU3F5s2bcfToUeTk5MDKygr9+vWDmZkZNDQ0IBAIwOPxEBMTg5cvX2LOnDlwd3fH2LFjMWfOnDJPZnJ3d4eXlxd8fHxgZmbGtOvq6gIAkpOTYWxszLQnJyczvUA6OjpISUmBQCBg/jALBAJ8/fq1ynqKCCFEGu3atQu1a9fG/fv3UVBQgEaNGmHLli2UxBBCSqSgoAA3Nze4ublV2T5jYmIwdepUREZGgsvlolGjRti/fz80NTWr7BjkzyQ2kWnatCnMzMzw77//wsnJCVpaWiXu6OvXr7h8+TKOHj2K48ePi10c6kcLFizApUuX4OPjgwYNGgg9ZmpqCl1dXdy6dQstWrQAAOTk5ODhw4dYsWIFAKBNmzbIzMzE48ePmXkyjx8/RlZWltC8GUII+dPIyclJXelXIkogEODw4cPw9fUFh8PBgAED4OLiQglpJYWHh2PdunVISUlB3bp1sWTJklKvc0jF8Pl8uLi4ICQkhGmLj4/HuHHjcPnyZQlGRn4HYhOZw4cPo2fPnmXekZaWFiZMmIAJEyYgICCg1OfPmzcPZ8+excmTJ6GhocHMiVFRUUGtWrXA4XAwZcoUbN68GRYWFjA3N8fGjRuhoqKCIUOGAChaFbV79+7466+/sHXrVgDAX3/9hV69ekl1NxkhhPwoNTUVPj4+0NDQQJ8+fSAvLy/pkMgvMmvWLJw9exa5ubkAgAcPHiAkJAQbN26UcGTS68WLF3BxcWHKDN+5cwePHz+Gn58fateuLeHofj/BwcGsczDCwsIQHx9P66mQShFbtaw8SUxFtj148CAyMjLg5OQES0tL5t+OHTuY58yaNQtTpkzB/PnzYW9vj8TERHh5eQmNrTx48CCaNGmCwYMHY/DgwWjSpEmNrHdOCCEVceDAAXTq1AmzZs3ChAkT0KlTJ5HFg8nv6cuXL7h+/TqTxABFIxP8/f2Rmpoqwcik2+rVq0XWSgkLC8PmzZuF2gQCwa8M67fF4/GQk5Mj0p6Tk1MjyzoT6SKxdWR+LhrAhsPhwN3dHe7u7mKfo6Ghgf3791dhZIQQUjN8/foV27ZtYy66CgoK8O7dO8yePbtMPd9Eur1//56p0vmjxMREfPz4keYXVFBKSgpre3Gvwblz57B37158/foVmpqaGD9+PFxdXX9liL+Vzp07w8zMDFFRUULtJiYmNHqGVFq5Epnw8HB4enoiOjoaPB5P5G4Fh8OBt7d3lQZICCF/Ki8vL5E7x0DRxNmUlBRaIfo3Z2FhAT09PZE1gXR1dVGvXj0JRVX1BAIBfHx8cO7cOcjKysLNzQ3t27evtuOJSwDr1auHBw8eYNGiRczCkLGxsVi2bBn09PQqNVLlT6aiooJ58+Zh2bJlzPuqp6eH1atXg8utMcsZEilV5jPozJkzaN++Pfbv34/IyEjw+XwIBAKhfz+W+SSEEFI5ampqrCXp5eXlaZ7MH0BPTw9du3YV+qwVFRXRvXv33yqJnTdvHiZPngxfX1/8999/GDVqFLZv315tx3N3dxeZl2FpaYl58+Zh586dIqvbp6Wl0ciPSnrx4gWysrKYn7Ozs/H8+XMJRkR+F2XukVm3bh2aNm2KCxcu/FZfoIQQUlMNGDAAmzdvxvv374Xara2taR2GP8TOnTthZWUFf39/cDgcODg44H//+5+kw6oynz9/xpUrV/D9+3emLS0tDSdOnICbmxvrwq6V1apVK5w5cwYbNmxAamoqTE1NsWzZMmhqagpdbP9IXDspXUJCAnx9fZGdnc20paenw9PTE5MnT4aSkpIEoyPSrsyJTGJiImbMmEFJDCGE/CKKiorYu3cv5s2bh0+fPkFeXh7W1tbYu3evpEMjvwiXy8W0adMwbdo0SYdSLR4+fCgydA4ouuaIjo5Gw4YNq+W41tbWOHHihEi7uEW5acRJxT1+/FjsZxwVFYXGjRtLIKqqx+PxMG/ePISFhUFOTg6dO3fG0qVLf8lC7+X17NkzrFixAklJSZCXl8esWbMwaNAgSYdVIWVOZKysrFgnHRJCCKk+LVq0wM2bN5GYmAhFRUVoaGhIOiRCqkzDhg2hoaEhUgCodu3a0NPT++XxiEtYJHkxGhMTgx07diArKwtjx46VunXyGjZsiNq1ayMtLU2ovXbt2jAwMJBQVFVLIBBg2LBhePz4MdP25s0bpKamClXjrQliYmIwduxYofUe582bBwUFBTg4OEgwsoop8xyZ1atX4+TJkwgODq7OeAghhLDQ09OjJIb8dqysrNCqVSuhNjk5OXTp0kUi57usLPv9XXHt1e3ixYvo3bs3Dh48iNOnT2PYsGFYvHixRGKpKEtLS9bPuFOnTr/Nd9q9e/fw5s0bobaCggLcuXOnxg1L9PDwEFm0PjU1VWqXLinzb+amTZugqqqKvn37wtzcHEZGRiJ3KDgcDs6dO1flQRJCCCHk93Ty5EksXboUz549g4yMDLp27Yq///5bIrEMHjwYDx48EJqzo6ioCEdHx18eC5/Px+bNm4VGw6Snp+PChQuYOnWqVC0kefLkSSxbtgxPnjwBl8uFvb09Fi5cKOmwqsz79++FzplimZmZSElJgYqKigSiYvdzMYti0rqmT5kTmXfv3oHD4cDIyAg5OTn48OGDyHM4HE6VBkcIIYSQ35uioiI2bNgg6TAAAM7Oznj9+jV8fX2RlJQEHR0d9OjRo9IFFrKzs7Fs2TK8ePECcnJycHR0xJQpU0q8bvr06RPr3JIvX77A398f48ePr1RM4nz8+BHLly/H58+foaGhgblz51a6HLaCggLWrVtXRRHWPD169ICOjg6SkpKE2vX09Gpcwtm6dWtcu3ZNpN3Y2FgC0VRemROZ169fV2cchBBCCCESxeFwsGbNGvz999+IioqCmZkZateuXal9CgQCODs7IygoiGl78eIFEhISsHLlSrHb1a5dGyoqKiILeCopKaF+/fqVikmcxMREDB06FJGRkUxbaGgoDh8+XK1r+1SHgoICXLp0Cc+fP0ePHj1gb29fbTfcTUxM4OTkhNOnTzM9G7q6upg5c2aNm+w/depUBAQE4OnTpygsLARQNPxv9erVEo6sYjg8Hk9Q+tMI+TNFRETQysOkQsp77ggEAqSmpkJFRaVaSs4S6UDfOb+f4OBgDBkyRGToTv369XH//n3m9/3bt28AitaPKjZ16lScPXuWueAEiu6oBwQECF2UiztvcnNzkZGRgTp16pTpIn7OnDk4fPiwSHvXrl3h5eUFoGjIW0REBHR1dWvsHBcej4dBgwbhzZs3yMvLg4qKCtq3b4/Tp09X63ynoKAgnDhxAsrKypg5cybMzc2r7ViVkZubi2PHjuHhw4eoU6cOlixZAnV1dUmHVSGSmb1GCCGEcfPmTaxYsQIJCQlQVlZG+/btsW3bNolNMCaEVJ2XL1+yzj9IT09HSkoKZGRkMGnSJGbIvoWFBfbt2wddXV1s374d6urquHv3LgoKCmBpaYktW7aUmpQIBAK4u7vj+vXryMzMhK6uLmbPnl1qid34+HjW9uKqcqtWrcLOnTuRl5cHLpeLevXq4d69ezVugd5FixYJLbiZlZWFmzdv4tChQ5g0aVK1Hbdz587o3Llzte2/qigoKMDNzQ1ubm6IiIiQ2iQGKCGRqV27NrhcLhISEiAvL4/atWuX+ovD4XBEukAJIYSI9/XrV8yePRuxsbFMW2xsLJSUlLBx40YJRkYIqQqdO3dmLT+sra0NHR0dODg4CJXtjY+Ph6urK/z9/SErK4u1a9eW+5jr16/HkSNHkJubC6BoXs2iRYvQtGnTEnsJGjVqBH9/f5F2fX19vHjxAps3b2ZKVPP5fLx//x49e/bE7du3yx1jdQoPDxdpKywsxM2bN6s1kSG/nthE5u+//waHw2HuCBb/TAghpOrs2bNHKIkBiv7g3rt3T0IREUKqUqNGjdC1a1f4+PggLy8PQNHN4jFjxiAiIgLv3r0T2ebdu3cIDw+HpaVlhY4ZEBDAJDHFEhMTsXXrVuzcuVPsdn/99RcCAwOF5kWbmZlh2bJlmDBhAus6O2zxS5q44bm1atX6xZGQ6iY2kXF3dy/xZ0IIIZUnrhe7+IKHECL9Dhw4gJMnT8LPzw/y8vKYPHky2rdvj0ePHiE7O1vk+dnZ2cjIyKjw8cR9f5S2pom6ujp8fX2xadMmvHv3Dvr6+liwYAEMDAzExvPj/J2awtnZGSEhIUIx6+jo4K+//pJgVNVHIBD8sZ0NNACbEEIkaNy4cbh8+bLIsJPqqkpECPn1uFwuXF1d4erqKtTevHlz1K1bF+/fvxdqr1u3Lpo1a1bh4zVs2FBkgUYlJSUMHTq01G3V1dWxYsUKkfapU6eyru+jqalZ4Tiri4uLC1JTU3Hu3Dmkp6dDR0cHM2fORJMmTSQdWpXasmULLl68iG/fvkFfXx+LFi2Sijk6ValcVcsEAgHu3LmDmJgY8Hg8CATCm3I4HMycObPKgyREUqiCECkPHo+HTZs24cOHD6hTpw5WrlxZptKtixcvxrlz55CcnAx5eXlYWlri9OnTMDIy+gVRS15hYSE8PT1x7do11KpVC7NmzYKVlZWkw5II+s7583h7e2Pp0qWIjo4GUDSUa82aNejbt2+Z9/HzeZOWloahQ4fizZs3yMnJgaamJnr37o1du3ZV6s69ra0t3r17x1z/KSkpITAwEI0bN67wPquTQCBAQUEB5OTkJB1Kldu7dy9Wrlwp1MtmZGQEPz8/mJiYlHk/0v6dU+ZE5uXLlxg3bhxiYmJEEhhmZxwOUlNTqzRAQiRJ2n/Bya/z9etX9OvXD2FhYUxbw4YN4ePjA21t7VK3j4mJwaVLl2BmZgZHR8c/pmKZQCDA6NGjERAQgPz8fABFQ0DWr1+PgQMHSji6X4++c/5M6enpOH/+PLhcLoYMGSJUgrks2M4bPp+PgIAAhIWFoU+fPmjYsGGVxHr79m3s3LkTVlZWWLx4cY2rWFbdMjMz8eTJE+jo6Ej0hkvPnj2FikQUGzduHLZs2QKgaFHV8PBwNG7cGPr6+qz7kfbvnDInMp07d0Z0dDRWrFiBli1biv0lK08WSEhNJ+2/4OTXmTlzJo4fPy7SPnr06BIn1/7p7t+/D2dnZ5Hx9zY2Nrh169YfN+6bvnNIRdB582scOHAAe/bsQVRUFFRVVWFlZQVPT0+JDK/r2LEj62L1/fv3x9GjRzFp0iTcuXMHX758gb6+Prp164YdO3aIfKdK+7nDLesTw8PDMW/ePIwZMwZNmjSBiYkJ6z9CCPkTRUVFsbYXDxch7AICAlgnEScnJ7NOgiaEVN7Vq1fh4OCA9u3bo1+/frhz546kQ6rx4uLisGnTJkRGRkIgEODbt294+PAhpk+fDqBoaPHEiRNhZ2eHLl26YNmyZdVaCMHMzEykTV5eHn379sX27dvx33//4cuXLwCAhIQEnD9/HkePHq22eCSlzGMX6tevL3ZIGSGE/OnE3ZGriRNha5JmzZpBXl5epMqSmpqa2BKqpHp9//4dhw4dQkhICNq1awcXF5c/bvjQ7+z+/fuYPXs2c5ELAB8+fMCFCxf+2LlpZXHgwAEkJiaKtIeFhYHP52P48OF49OgR0/7mzRukpaVh+/bt1RKPh4cHIiMj8e7dOxQWFkJZWRmdOnXC0KFDMWDAAGaobrHc3Fz4+Phg3Lhx1RKPpJS5R8bd3R0HDhxAXFxcdcZDCCFSaeHChTA0NBRqMzQ0ZK3yQ/6Pk5OTSCUhFRUVDBw4EFxumf9E/dYEAgGioqJ+Se9eamoqevXqhaVLl+L8+fNYsGABHBwcWFemJ6IKCgoQFhYmlCRUhbi4OERERFTJDeWtW7eKxJeQkAAPD49K7/t3Jm7eIofDwb1790SGeRUUFCAoKKjUktcVpaenh8DAQCxduhQDBgzA4cOHcfr0acjIyIgdkvs7fqeWuUfG0dEROTk5aNOmDTp27AgDAwPIyMgIPYfD4dBK1ISQP1KjRo1w7NgxrFu3DikpKVBUVMS///7725X7rGoyMjLw8vKCu7s73r17B0VFRQwaNAj/+9//JB1ajRAeHo6pU6ciMjISHA4H9erVw/79+1GvXr1qOd7SpUuFLsgKCgrw5MkTbN68GUuXLq2WY/4uvLy84OHhgU+fPkFFRQU2NjY4fPgwlJWVK7zP5ORkjBs3DuHh4cjLy4OpqSk2bNiAdu3aVXif4pJSHo9X4X3+Cdzc3HD27FnEx8cLtTdt2hQRERH4/v27yDYZGRlISUmBiopKlcdTUFCAWbNm4f79++DxeAgNDUVkZCSmTp0KBwcHPH78GDk5OczzlZWVy1R+W9qUebL/nTt3MGrUqBLvylDVMvK7kfZJcERy6Nz5Pb158warVq1CcnIytLS04O7uDhsbmyrb/4/njUAgQOfOnRESEiL0nObNm+PmzZvVUgihd+/eCA4OFmnv2rUrvLy8qvx4v4vExER0794dnz59EmofNmwY9u/fX+H99u/fX2T+Sr169fDw4UMoKCgwbeX5vpk2bRo8PT1Z21evXl3hWP8EZ8+exaZNmxAXF4datWrB2toaR48eBY/HQ/fu3ZGUlCT0fCsrK9y5c0fkxn9VWLJkCXbv3i00D0dTUxNeXl5o1qwZXF1dcf36deTm5kJRURH9+vXDvn37/tzJ/gsXLoSamhq8vLwQExODtLQ0kX+UxBBCCPldRUREYMSIEbh27RqePXsGf39/jB49WqjkdlV6+fIlIiIiRNo/fvyId+/eVcsxxd05VldXr5bj/S727dsnksQAwPPnzys8HOzr16948OCBSHtkZCT8/PwqtE+gKLn6eZiUnJwcnJ2dK7zPP8Xw4cNx//59XL16FTdv3oSXlxfU1NRgYmICJycn1KpVi3murq4upk+fXi1JDADcu3dPpJhAamoqdu/ejdDQUDx//hw5OTkQCATIzs5GcHCw2KI00qzMiUxUVBRmzpwJe3v7ctc3J4QQQqTd2rVrReaJfvr0CWvXrq2W4+Xn57NWPSooKBApjlBVpk6dCi0tLaE2PT09zJ07t1qOV1YZGRlIT0+XaAwl+XEIz48qU7Xq69evKCgoYH0sMDCwwvvdt2+fyH7z8/OxY8eOCu/zTyInJwcbGxsYGxsLtXt4eODEiRMYMmQIXFxccOXKFYwYMULoOd++fcO3b9+qJA4+n8/anp+fj3Xr1okMgYuNjcWaNWuq5Ng1SZkTmYYNGyItLa06YyGEEEJqrK9fv7K2p6SkVMvxWrRowToXpm7durC2tq6WY3br1g0eHh5o06YNLCwsYGtri507d0psrldqaiqGDBkCW1tb2NrawtHRsUYWHXJzc4OOjo5Ie6NGjSo8BLCkUS6VuRgWdy3387AoUn729vY4ePAgduzYAXNzc6b9y5cvGDBgAHMeOzk5VbogRLNmzUTaVFVV4erqKvbcEfcdJs3KnMisXLkSx44dEyotRwghhPwpxK2V9vOd2aoiKyuL9evXC10g6+rqYtOmTdVafah58+YwNTWFgYEB6tWrJ9GCFWPGjMGNGzfw6dMnfP78Gffu3YOLi4vEloNIS0vDvHnz4OTkBFdXV4SGhgIoSi5nzJjBnCMqKipo3bp1pXo5bGxsICcnx/rY6NGjK7zfn6srFquuAhIEcHV1xe3btxEfH4/4+HgEBQXBxcWFedzX1xdDhgzBgAEDsGXLFrE9cT9at24d7O3tmWGfBgYGGDNmDOzt7WFkZMS6DdvaM9KuzJP9hw4diqioKERGRsLc3BxGRkasVcvOnTtXLYESIgnSPgmOSA6dO7+fpKQk9OvXD+Hh4UybhYUFLl++DAMDgyo5xs/nzaxZs3DmzBnk5uYCABQVFeHq6ooNGzZUyfHYjj9kyBDExMQwbRYWFvD19YWurm6p28fFxcHf3x/169dHly5dKlWQIC4uDvb29iJ3kZWUlODt7Y3WrVtXeN8VkZGRgb59+wpVdTMwMMChQ4dga2sLoKjy1927d6Gvr4+WLVtWuiBDmzZt8P79e6E2JSUlJCQkCLWV5/smNjYWgwYNwocPH5i2Ro0awcfHR2RY4Z8qOjoa169fR6NGjWBnZ1epzzE8PBw9e/YUGRqppqYGf39/+Pn5YevWrczCwDIyMujduzdrQQY2oaGhiIiIQPv27ZmbHvHx8RgwYIDQHDtLS0tcuXJF5DOW9r9VZS6//O7dO3A4HBgZGSEnJ0foF6BYdVRQIYQQQmoCTU1NGBsbIyoqCnl5eZCXl4eRkRG0tbWr5XhJSUlM1aFiOTk5uHr1KhYuXCi02KpAIKiSv8ErV64USmKAogudNWvWYNu2bSVu6+7uDi8vL3z58gVKSkpo3Lgxzp8/X+FFYTMyMoRee7GcnByJDHXftWuXyFohnz9/xvr16/Hff/8BADQ0NNCvX78qOR7baweKVm8PDQ0t8+KVP58bJiYm8PPzw7p16/Dp0yfUr18ff//9N2rXrl3m2KrqfKtpBAIB5syZA19fXyQnJ0NZWRlNmzbF2bNnK1zw4tu3b8jOzhZpz87ORkpKCs6ePcskMUDRvKoHDx7g9evXZRpCamVlJXIuaGtrw8DAANHR0cjPz4eCggJMTU1FPmNx82ykSZn7pl+/fo2QkJAS/7169ao6YyWEEEIkZvv27bh16xYz0T4vLw9BQUHYtGlTtRwvPDwcnz9/FmlPTEzEx48fkZOTg6lTp6J169Zo0aIFhg4dyrryeHmIG7df2ryU4OBgeHp6MttnZ2fj2bNn+OuvvyocS8OGDVmH89WtWxcdOnSo8H4rqngY2c+qa95BYmIi677T09NZS2T/bNOmTejQoQNsbGzQq1cvoTLOOjo62Lx5M86dO4e1a9eWOYl5+vQp+vbtCxsbG9jZ2WHVqlUSG+ZXHQICAnD27FkkJycDAL5//47g4GAsWLCgwvu0sbFB3bp1Rdrr1q0LQ0ND1jl2aWlpePjwYYWPuWHDBgQFBSE/Px9AUVJ8/fp17Nq1CwCwatUqGBoaQltbGx06dKjUUEVJ+/2W+CSEEEKqQVBQkEgVKj6fj/v371fL8czNzaGnpyfSrquri3r16mHy5Mk4deoUIiIiEBUVhevXr8PZ2blMd1k/ffqE2bNnY+TIkdiyZQtTdUvc0KLShs4dO3aMdQL6j8PwyovL5WLFihVCczdMTEywYMGCSi0yCRQlbPPnz8fIkSOxbt26Mq2+3rBhQ9b2ivY4lUZXV5d13xoaGqUOq9uzZw82bdqEN2/eICYmBo8ePcLUqVNZS0SX1devXzFx4kQ8ePAAMTExCA0Nxa5du+Dh4VHhfdY0Z86cYV3YUlwSWxZycnJYsmSJ0Fw3HR0dLFmyBAYGBqyfsbq6eqWGTrJN8xAIBDh58iSuXbuGzZs3IysrC4WFhcjNzYWvry9mzpxZ4eNJkthEpjIVMaqqtBwhhBBSU4ibeC2uvbL09fXRtWtXyMvLM20KCgro3r07ZGRk8OzZM5FtwsPDERQUVOJ+X758ib59++Lo0aPw8/PDihUr4OTkhNzcXPzzzz8ixQvq16+PRYsWlbhPcevPVPa96datG+7cuYOtW7cyd5mHDx9eqX1++PABffr0wYEDB5ghVo6OjiUu+A0AM2bMQOPGjYXaqrM0taKiIgYNGiT03nK5XLRr1w5NmzYtcduLFy+KXJB/+vQJW7ZsqXA8O3bsQHR0tFBbdnY2rly5Uqbt8/Pzcf/+fTx58kQk2ebz+Xjy5AkePHhQponu1UVJSYm1vbLn8YsXL4SGl2VnZ+PFixdQUFDAoEGDhNafKf6MmzdvXuHj/Vx6uVhUVBQWLFjAerOjeHiktBE7R6ZJkyaYOHEixowZA1NT0zLtLDY2FocPH8bhw4cRGxtbZUESQgghkubi4oLHjx8L3ayrVauWyFoRVWnnzp1o3LgxAgICAAAODg5wc3NDbGwsay9Cdna22IuYYv/++6/Q32iBQICnT5/i2LFjcHNzw/nz57F27Vp8/foVBgYGzJ3jksyYMQNXrlwRmoQuIyODjh07luflsqpVqxbGjh1b6f0UW7p0KSIjI4XaXrx4gR07dsDd3V3sdmpqavD29sby5csRFRUFNTU1zJs3Dy1btqyy2H62aNEi1KtXD2fPnkV+fj7s7Owwb968Urdjm5MBVG4YnLhhi2w9GD+7ffs2FixYgMjISMjIyMDCwgL79+9Ho0aNEBYWBjc3N0RERKCwsBD16tXDhg0b0Llz5wrHWlEzZszAjRs3hEpRy8nJoWvXrhXeZ3p6Os6fPy80DyYjIwMXLlzAzJkz4e7ujvr16+P06dPIz89H+/btMX/+/Eq9DnHrF+Xn54v9vCSZQFaG2ERmx44dWLt2LbZu3YrmzZvD3t4eNjY2MDMzg4aGBgQCAXg8HmJiYvDy5UvcunULL168QIMGDbBz585f+RoIIYT8gfLy8vDPP/8gODgYfD4fzZs3x/r16ys97EgcJycnRERE4Ny5c0hJSYGmpiYGDx6MoUOHVsvxgKK7s9OnT8f06dOF2o2NjWFoaCiyXoShoSF69epV4j7Z5sEUTzB2c3NDw4YNcezYsXLFaWpqitWrV2Pz5s1ISEiAqqoq7OzssGLFinLt52f5+fn4559/8PDhQ/D5fDRr1gwbNmwQ2wNUFuIuyMsyz1dLS+uXX+M4OzvD2dm5XNvUrVtXZDiUnJwcHB0dAQDXrl3D9u3bwePxUKdOHSxYsKDUeUeDBw+Gj4+PyIVwaTe78/LyMH/+fKaCVn5+Pl6/fo2pU6ciMDAQ06ZNEyqiEB4ejvnz5+PevXtCvZG/QqNGjbBs2TLs3r0biYmJUFNTQ5cuXUrtkSxJWFgY65C+uLg4vH37Fm3btsXbt2+RlpaG/Px8REZGIisrCxoaGqXu+9SpUzh27BgyMjKgp6eHlStXwsrKChwOh3XukoyMDNq2bQtfX1+Rx6qraEl1E5vIODk5oV+/frh69So8PT2xY8cO5OXliVSpEAgEUFRURLdu3TB//nz06tXrt6xkQQghpGb53//+B29vb+YP9ps3bxAfHw8vL69qO+a8efMwc+ZMJCUlQVtbGwoKCtV2rJJwuVy4u7tjwYIFzER8LS0tuLq6lnpBoqamxtpe2XVEBg0ahAEDBuDz58/Q0NAQGi5TUZMmTcJ///3HDIUp/owrMwxGVVWVtb2qSmjXBO3atYOfn5/QECI5OTl06tQJ9+/fx4wZM5gJ7UDRcLuLFy+KDJ37UY8ePdC7d28EBAQgMzMTHA4HDRo0KHWOzJ07d0R6wICiEsd37txBVFSUyGMfP37EnTt30L1797K83Co1atQoODs7IyEhAZqampW+MWJiYgI5OTmRKnRycnIwMTHBnDlz4OnpyfSIhIaGIjY2Fv7+/iVeT585cwaLFi0Cj8cDALx9+xaRkZHw9/dHnTp1WHvfdHR0sH//flhZWQlV/lNSUsKFCxcq9TolpcTyy1wuFw4ODnBwcEBubi5evnyJ9+/fMy9eU1MTlpaWJS7aRAgh5PeSnp6O+Ph4mJqaVurOeGV8+fIFjx49Ernr+OLFC7x//x4NGjSotmMXl12WtL59+6J169bYt28fMjIyMHHixDKtB/G///0P79+/F+rNsbCwqJLJvlwut8rem+TkZKa37UcvX75EWFgYGjVqVKH9zpw5E2FhYULDh8zMzMo8nCc3NxcfP36Evr5+uUoWl0QgECA6OhocDqdKFi308fERed++f/+Obdu2ISwsTCiJAYCEhASsWbMGJ0+eBFA0NC0qKgoGBgZMzwCHw8GhQ4dw//59XLx4Eebm5hg7dmyp3wEyMjLgcrkiw504HA5kZWVZL9a5XC5kZcu8QkiVk5GRqbLzuKQhW5mZmQgKChJ5zuvXr3Hnzh1meN3bt28RExMDe3t7KCoqAigqsFGcxBSLjo7G5s2bsWrVKkydOlXoHOByudi2bRuUlZURERGB1atXw9fXF/Xr18fBgwer5MaDJJT5LFFQUEDbtm3Rtm3b6oyHEEJIDSUQCDB//nwEBAQgNTUVOjo6GDJkSKWGXVRUQkKCyAJzQFHZ0ujo6GpNZGoSbW1t/PPPP+XaZvDgwZCTk8OBAweQkZEBY2NjrFq1qkxDWX6lxMREkQs1oGjRyejo6AonMt26dcPu3buxY8cOpKenQ09PD//++y/09fVL3Xb37t04cuQIEhISoKGhATs7O+zevVtkgfDyeP/+PaZMmYLIyEhwOBzUq1cP+/btQ/369Su8T3GFC+Lj48VW4AoJCQEAbNy4EadPn8aXL19Qu3Zt2NvbY+vWreByueBwOOjQoUO5yl937NgRhoaGIoUCjIyM0KFDB9SvX19kiKS5ublESmxXh8+fP7MmMwUFBQgPDxeaO1MsOzsb4eHhaNy4Mdq3b4+UlBTw+XzIy8tjwoQJWLt2Let2xcdbv349MjMzsWbNGmRnZ0NZWRkrV65Ez549AQCysrJYtmwZli1bhoiICKlNYoByJDKEEEL+bHv27MHJkyeZUr2ZmZnYs2cPbGxs0Ldv318ai6WlJQwMDPDx40ehdmNj41++4rs06t+/P/r37y/pMEpkYWEBAwMDkQW4DQ0N0aZNm0rtu3v37uUetvTixQts3LiRuejOzMzExYsXoaurW+G5QAKBABMnTmSSCABITU3FhAkTcOvWrVKH6p87dw4nTpwAj8dDs2bNmITUwMBAJGGRkZFB586d8eDBA9Z98fl83L59m0nwil/jmTNnYGpqirlz5yI5ORmLFy9GZGQkVFRU4ObmBgcHBwBFE9iXLVuG169fQ0FBAUOHDsWYMWPEvgYut6hw7oEDBzBhwgR8+PABBQUFqFu3LrZt2yaxHpn4+Hj8888/iIuLg6qqKqZPn45u3bpVeH9WVlYwNjYWSeRMTEzQpUsX6Orqiqwlo6WlhV69eqFr165CvWd5eXnYt28fRo0aBQMDA7x580ZoOxkZGXTq1AkAMHHiREycOLHCcUsLSmQIIYSUyZUrV5gkplhGRgaOHTv2yxMZJSUljB8/Hps3b2YuAjQ0NODs7Fxlw32IZCkqKrJ+loqKiqhTp06p2+fk5OD06dP4+PEjhgwZAhsbm0rFs2fPHpGeg4KCAty9e7fC+wwJCWHtIXnz5g1CQ0PRpEkTsdvu3bsXa9asYarovX79GqGhoQgICMDq1asRGRnJJPqysrKwtbWFi4sL/vvvP5GhZQDQtGlTHDp0SKSnMy8vDwEBAZg8eTIGDBggFO/r16+RmZmJoUOHYsiQIXj06BHz2MuXL5GUlITWrVuzLuwaFxeHmJgYmJmZ4caNG3j//j3y8/OZyeplkZOTg7NnzyIiIgKDBw8uc8ligUCAgIAABAUFoV27dnB0dASXywWPx8OgQYOE1j968+YNtm3bVuHvOFVVVbi6ujLFFYCi7yoXFxeoq6vDyMgIb9++FdqmePgqWwVCPp+POXPmYPfu3YiMjGQSfVlZWbRt2xZjxoypUJzSihIZQgipAQQCAdLS0qCsrMyMga5pxC20WJYFGKvDtGnT0LlzZ+zevRuFhYWYMGFCpe/UE8lJT0+HjIwMM8wlLS2NtcIYj8dDZGQkU5zg+/fvyMvLExoaFxMTA2dnZ7x//x6FhYU4ceIEHB0dsXPnzgoXJCpeJf1nP57/5f09TkhIYC2VW1BQgMTExBITGU9PT5F1+968eYMLFy5gxIgRuH79OrZu3YrY2FjY29tj5MiRkJOTw5w5cxAaGirUC6Crq4sFCxZg3bp1Yl/jwYMHRS64U1NTcfDgQaipqQn1KgFFvTleXl6wtrZmHVpVWFjIvKccDgeWlpZiXyub2NhYODs7Izw8nPmM+/bti927d5f4Gefm5mLYsGF4/PgxsrOzcfjwYTRr1gxeXl7Yvn27yCKuycnJ2LVrF5PIVOS72tDQUGguuZycHPT19ZGTkyPSqwwU3SBiW2+nWEJCAszNzREQEIDt27cjOjoanTt3xujRo/+4OetiF8QkhBDya9y6dQtdu3ZF+/btYWtri+nTp9fImv52dnYicwEUFBQkOkSpSZMm2L17N/bt20dJjJSKiYmBg4MDbG1tYWtri2HDhoHH4yEuLo618lJKSgrevXuH7OxsjB07Fra2tmjXrh169uzJlFCeO3cuwsLCmCQhPT0dly9fxr179yocp6urK2vFs+Kenrt376Jbt25o37492rVrh6lTp4pNfoqVNFG+pMf4fD7r/KH8/HxmoVRNTU2sWLECR48exZgxY5gL3C5dumDz5s1o27YtLCwsYGtri507d6JZs2YYOnSoSJUuDoeDtm3b4s2bN6wlfXk8Hp4/f866dg2Px0PLli1hYmIi8piRkVGl5gHNnTsXb9++FfqMvb29cfv27RK327p1K+7cucPEm5OTg0ePHmHlypWsSUXx6wCAe/fuMZ+xra0tpkyZgry8vBKPl5ubi/Xr1wv1giUnJ8PDwwOfPn0Sqh5WLCMjA69evRKbkFlbWwMo+oyXL1+Oo0ePYty4cX9cEgNQIkMIIRL19etXzJo1Cy9evEBiYiKioqJw+vRpLFy4UNKhiViwYAF69+4NTU1NAEWlPAcPHozRo0dLODJ2AQEBGDZsGAYMGICNGzeWelFJSpecnIxZs2bByckJEyZMEHvhV1Z8Ph8uLi64f/8+Pn/+jLi4OAQEBGDcuHGoX78+64WznJwcbGxsMG3aNPz333+IiYlBYmIiHj9+jIkTJyI7O1tkPgJQ1ENw6tSpCsdqb28PV1dXpkyzhoYG7O3tsXbtWqSlpWHGjBl4/vw5EhMTER0djTNnzuDvv/8ucZ9WVlbQ0dERadfR0SmxmAGXyxW7sKGtrW2pr8XJyQn+/v548uQJrl69ih49egAABg4cCCcnJ2aFezk5OdjZ2WHp0qXo2LEj67wVbW1tdO3albWst5aWFjQ1NVkvyOXl5Zn28+fPY8iQIRg4cCD27dtXpl5ets84KysLZ8+eBQBERERg/PjxcHJywuzZs5mkODg4mPW8evXqFVq0aMF6LB0dHfB4PEyfPp35jKOionD27NlSq929evWKtfx0VFQUEhMTWRMhGRkZ2NnZQV1dnXWfTk5OAIqqmY0ZMwZOTk6YP38+a3LLJjw8HG3btoWZmRkcHR2ZBXelEQ0tI4QQCdqzZ4/QKutA0ZCLytw5ri5ycnLw9PTEhw8f8ObNG7Rs2RLGxsaSDovV7t27sWHDBuYP+507d/Do0SOcO3dOYmudJSYmws/PD8bGxujWrRsz2RkouuPr7e2N7OxsmJmZ1cg7qykpKXB0dBQaevPkyROcOnWqxCFQJbl//z7ev38v0v727Vu8e/dO6D0qJhAIEBUVxfQ8/Ojjx484f/682CE/urq6FYqz2OrVqzFz5kw8fPgQFhYWsLKyAlC0iPjPF9Z8Ph/3799nfi7+jNXV1dGnTx/Iy8tDU1MTQ4cOxZEjR5jERFlZGcOHDy+xipxAIBB7jrCty1JWPB4PL168YHor8vPzERMTg/DwcAwaNAju7u5CvcVcLhddu3ZFu3bt0L59ewQEBDBJiKamJiZPnozHjx+zDhGMi4tDfHw89u/fj4MHDyIrKwtA0Tnx/Plz7Nu3r8RYi5Otn+no6CAkJASjRo1i1lgKCgrCo0eP4OfnJ3Y7JSUlDBgwAMuXLxdJdBo1aoSDBw+yfsYPHz6EQCAAh8NhPmM1NTX07dsX8vLyrNUVi3358oX1HOfz+YiMjGRNuAAgMjISDx48wMSJE5n5R0FBQQgODsbVq1dLrEIWExODjh07CiVQI0aMwO7duzF8+HCx29VU5e6RycjIwNu3b/HgwQPcv39f5B8hhJCy+7laTbHShitIkrm5OQYMGFBjk5ji8fI/3p3k8/kIDg4Wmoz8K61evRr29vaYM2cORo8eja5duzIXd4cPH0bHjh0xc+ZMLFq0CB07dhSZi1BdxF0osdmwYYPI/IHY2FisXr26wsfn8XgiBSSAouE4SUlJrGWNCwsLkZyczNrDxufz8fXrV/To0UNkVXhTU1NMnz69zLGJe290dXUxYMAAJokBwDoEDij6PRYIBDhy5Ag6deqEmTNnYsKECejUqROzmn2nTp2EhqypqakxladKik3cqvc/f6eU5zP28PDAu3fvhNri4uKwatUqnD59WmT4GJ/Px40bN5ibAz8eKyMjAwYGBkhLSyvxM/b29maSGKDoPbt165bIDZ6f9ejRQ2RBWhMTE0yfPh2rVq1ikphiYWFh8PDwwLRp00SKRWhoaGD8+PGYOXMm6/t16tQpsZ9x8UKXx48fF/qMO3bsiJCQkBJvnOTn54tN1lNSUliH6wHA06dPsWbNGpEiCq9fv8auXbvEHg8AxowZI/L3pbCwEIsXLy5xu5qqzIlMcTnAevXqoUOHDnB0dES/fv2Yf8U/E0IIKbuxY8eyVmaqzNjxPx2PxxM77lwSN9zCwsJw6NAhJCQkAACzwPTMmTORmpqKLVu2MBddhYWFePfuHWbNmlVt8QgEAmzatAkdOnSAjY0NevfuXab3Rdyd/h8XliyvLl26oG7duiLtRkZG6NmzJ+vcirp166Jnz56sibSenh6cnZ2xZMkS/O9//4OlpSWMjIzQpk0b7N27F1paWqXGdOHCBXTr1g3NmjWDvb09PD09S91m7NixzJDLH9WrVw88Hg+bN29mLswLCgrw7t07zJ49G/n5+Vi6dCm+fPnCbJOYmIglS5aUOE+Oy+UyxQ5+VKdOHaZq1ebNm4U+47JUVxM3VDA5ORmPHz9mHfL19etXvHjxAgEBAUJJQH5+PsaMGYPOnTuzLvJpYmICWVlZ1vMnKSkJL1++LDHWxYsXC33GrVu3xp49e6CjoyM26fj48SPs7OywYsUKNG3aFEZGRrC2toa7uzscHR1ZeweBonVdxowZI/YzTk9Px8aNG5nPuLCwEOHh4fjrr79Ekq0f6enpiR1a1rlzZ7FJkJqamkjp5WKBgYFijweAtRIaALFDFWu6MicyM2fOxOXLl+Hm5objx4/D29tb6J+Pjw+8vb2rM1ZCCPnt2NjYYMSIEdDW1gZQNG68SZMm2LJli4Qjk17q6uqsw3Jq1aolkUWdDx06JFK2Fygaw3/p0iWRO8dAUU8HW4ncqrBr1y5s3rwZb968QUxMDIKDgzF58mTWErk/YksqAJSpFLI4qqqqmDNnDpOUFC8IuXr1asjIyODff/8VumA3MTHB33//DWVlZWzevBmWlpZMr42+vj7c3NxgYGAALpeL1atXIzg4GM+fP0dAQECZ5o48fPgQCxcuxLNnzxAbG4sXL15gyZIluH79eonbWVlZYfTo0cx8Fzk5OVhZWWHr1q24fPky62ccFxeHwMBA1gQxOjqaKVwgztatW1GvXj3mYldVVRWjR49Gw4YNsWfPHmzcuFHoM54yZQo+ffpU4j7ZEg6g6DMWt8hsdnY2/v33X9YkJzMzE9+/f8fcuXOFzhN9fX2sXLkSpqamrMmllpYWmjZtWmKsXC4Xq1atYj7j69evw87Ojom3pNc3atQoBAUF4fnz57hz5w4mTZoEAKxJNVBU8rtRo0ZwdXUV+owbN26MLVu2wNfXl7UHKTY2FrVr12adWyQjIwMtLS3WRKewsBB3795lvaHF5XLRp08fsXP+ShrKBkDskEVJDbmtrDLPkbl16xamTp1a4UWfCCHkT5CQkIB3795BXl4eFhYWZdpmzZo1cHNzw6VLl2Bqaor+/ftLbDG434GsrCyGDx+OLVu2CJWnbdWqFXOh8yuJm7ArJycHVVVVyMjIiJTglZOTEzt0qLL+++8/oaE8QNFF9ZYtW+Dh4SF2uwULFiAoKAgRERFMm5GREdzd3SsVj4uLCxwcHHDu3DkoKipi6NChTMWu7t27IygoCOfPn0deXh6GDx/O9GBaWVnhzp07uHTpEpKSkjB06FDo6+sL7ZvD4ZTrfdyxY4fI3fzU1FTs27ePmRAvzooVKzBhwgRcunQJJiYm6NevH+Tk5KCmpib2M1ZXV4eCgoLIXXl5eXmR6mE/e/fuHb5//870gnz//h1hYWEQCAS4ePGiyB32T58+YcuWLdi0aZPYfc6fPx+3bt0S6pkwNDSEu7s7Ll68yLqNrKys2LWbOBwOFBQUEBISIjRMKjs7G69evYK9vT369u2Lw4cPM0O0uFwuOnbsyCQdAoEAr1+/Bo/HQ+vWrUXmuLB9xgsXLsTbt2+Feh8sLCyEJuZ/+fIFYWFhaNCgAQwNDQEULc7ZokULJpbieP766y8AwPLlyzF+/Hh4eXnByMgITk5OkJOTw6tXryArKyvSiyYnJ4f8/HzWEtt8Ph/5+flQVlYW6UGWkZGBuro6pkyZgqVLlwo93qpVKwwaNAhLliwR+T0GUGoZa3Nzc6Hf4WI//+5IizL/pVRSUhJ7N4YQQv50AoEA06dPR2BgIBITE6GlpYUePXpg165drGOgf2ZmZsb8sSSV99dff8HMzAwnTpxAXl4e2rRpgwULFkjkruPkyZNx7tw5obvyHA4H7dq1g5OTEzZu3CgypKVJkyZiE6DKEjeEpLQeIG1tbfz3339YsWIF4uLiUKdOHSxcuFBorkhFFU8MZ6Oqqorx48ezPqagoABnZ+dKH7+YuPemrMNuTE1NMXv2bKE2R0dHmJubi8wvsrKyQrt27WBhYYHnz58LPdagQQM0bNiwxGOtW7dOaBJ9YWEhbt++jcDAQLFzK8QNuSqmpaXFfMaxsbHQ1NTEwoUL0aRJE7GT72VlZbFu3Tr4+PiIXMjr6uri+/fvuHDhgtB7yOPxcPjwYUyaNAl2dnY4cOAA8xiHw2FKWn/58gWjR49GWFgYvn//jnr16mH+/PmlTkpv2bIlzp49i3Xr1iE1NRUmJiZYunQptLS0IBAIMHv2bAQEBCAhIQG6urro1KkT9u3bB0NDQ4wcORLHjx9HYWEhOBwOGjZsKHRumpiYiHzGffr0ga6ursiwrbp16yIuLo513o1AIEBMTAysrKxEtjM3N2eKBejp6WHfvn34/v07mjZtin/++QeysrJie17Ezbv88bhs2KrOSYMyDy0bNmwYfH19qzMWQgiRWvv378f58+eZC4uvX7/i4sWLpVbeIdVn4MCB8PLygq+vL5YuXVriWPUfXbx4EX369EH79u0xcOBAvHjxolJxaGtrY+PGjWjatCm0tLRgYmKCwYMHw8PDAwoKCti3bx9atmwJHR0d6OrqolevXtV63rANHyrrekCGhobYt28f/Pz8cOLEiSpJYmoSceV3i9ftqAh5eXns37+f+YwNDQ3Rs2dP7N+/HxwOB8eOHYOdnR10dXWhp6cHOzs7HDt2rMSkm8/niyRGQNFE+YsXL7IOkZKXl4eDg0Op8cbHxyMxMRE8Hg8pKSnMvJnWrVuzPj8rKwu6urpYu3YtlJSUwOFwICMjA11dXQQFBeHBgwesQys/ffqEDx8+4H//+59QAlRYWIhVq1YhMzMTU6ZMwZMnT5CZmQk+n48PHz5g1apVrPv7WZMmTXDy5En4+flh7969TNnsEydO4OzZs8yctS9fvuDSpUvYtm0bHjx4AC8vL6YHRSAQ4O3bt1i0aFGJxxI3zCsvL6/EIZvFVdt69uwJQ0ND6OjooGXLljhw4ADTy9SjRw9cuHABfn5+WLduHVORTFxBmLCwMABF5ecdHBzQvn179OvXD3fu3AFQNC+NrYhGWUcQ1DRl7pFxcHDAvXv3MGjQIIwePRpGRkasb0TLli2rNEBCCJEGV69eFfnDkpeXh2vXrmHKlCkSioqUl7e3N+bNm8cM5Xj79i0+fvwIPz8/GBkZVXi/vXr1Qs+ePREfHw81NTWhu5/NmzdHYGAgEhIS8Pnz52r/O7phwwZER0fj3bt34PP5UFFRQZcuXTBgwIBqPa40mDdvHh4+fIhnz54hLy8P8vLyaNasGf75559K7bdZs2bMZ6ykpCQ0T8HY2BhXrlxBcnIyOBxOmQoScDgc1uFKQNFE+e3btyMyMpL5jJWVldG5c2cMHjwYQNFwuRkzZuD169do1aoVtm7dCjU1NcTExGD8+PFCvYfv37+HgoKC2Mn3xUPg/ve//2HChAl49eoVNDU1YWpqCkD8nA2BQIBHjx6x9h7l5+dj+/btrEOg4uLicPLkScycORNpaWnYtWsXYmNj0a9fPzg4OJTaA+7t7S1SRa2goACBgYEIDQ1ljZet1PePfHx8WBOW2NjYEhOENm3aQENDA+fOnWOqu5V1iJe4npWCggI8fPgQ06dPFyqkEBERgQsXLmDChAm4du0a7t+/j/z8fHA4HFhZWWHNmjVlOm5NU+ZExtHRkfk/26qpxTW0y5IlE0LI70bc3VNpnUD5p9q5c6fIePXY2FisX78eO3bsqNS+ORxOicmQvr4+MjMzK3WMn3358gU8Hg/m5ubMzUcjIyMEBgbi2LFjCAsLg5OTE+zt7elcRdFFua+vL/777z/cuXMHtra2GDJkSJXNWSvpIrV4vkNZEhmgqHgF2zVX06ZNYWhoiMDAQJw4cQKhoaHo378/unbtCg6Hg6ioKLRv355JIGJjY+Hv74/nz59jw4YNIoUJUlJSsGfPHrET4X/s6eRyuWjevLnQ42ZmZuBwOCIX3nJyciWec2yVB3/c9u3bt3BxcWF6jHx8fNClSxd4enqWmMyU9F1dUMC+EGdKSgbz/8LCQkREREBTU5OZ+C8jI8P6GoGiz5PL5YoUQ+BwOEKLoYqbZwQUJaepqamwsLBgfo9lZGRYK9tpaGjAw8NDpBpcYmIi1q5dy8xH+rHXqbCwUGoXDC7zb2ZpdakJIeRPNmDAAAQHBwvdXVRUVGRWYCbSgW24DlC0boM0yczMZO6Of//+nZmUX7xMgpKSktg5KX86GRkZDB48mOm9qG4fPnzApEmTmOpl9erVw759+0oswc7hcNCxY0dcvnxZqF1PT4+pwKWkpAQ3NzeRbYcNGybSC5KVlYWRI0eKTaIyMjIwbdo0+Pn5iVwgl9aD2LFjR1hYWIjMA2vatCnatWsndru2bdviw4cPIomVjo4ORowYgbFjxwqVi87OzsatW7fg5+cndPP9Z0OHDsW9e/eE3gMFBQX06dMHJ0/eZt3m8+eiYWiXL1/GunXrEB8fD2VlZdjY2ODQoUNwdHRE3bp1RcpXW1hYiJ2vxOFwEB8fz/RcscnKysLEiRPx4sULfP/+HYaGhliwYAEGDBgANTU11kTWxMQEoaGhrPsLCQnB7t27ERwcLJR0hYWFYfHixTh06JDYWGqqMicyI0eOrM44CCFEqrm6uuLNmzfw9/fHly9foKmpCQcHB4wdO1bSoZEqIG4YT001c+ZM+Pv7Mz8Xj/O3tbUt8x3/39mDBw+wefNm8Hg8GBgYYMWKFczcobdv32LlypVITk5GnTp14O7uzkw+//TpE5YuXYrY2Fioqqpi1qxZ6NKlS6nHKy5R/Pz5c8jKysLJyYlJMooTzmKpqamYOHEibt68WWKPxY4dO/Dt2ze8fv0aGRkZMDU1xdy5c6Gnp1diLD8WCPhRZGQkevbsKXTeFDM1NWWqfu3btw+fPn2Curo6WrRowVS6+/LlC5ycnPDp0yfIyMjAwcEBu3fvhqysLLZt2wYXFxekpqaCw+FAT08PBw4cgIGBAWu1L6AoATpz5gxrrFwul7WUdE5ODry9vUtNZAIDA5khZgoKCujWrRumTJmCFSvYb9rz+XwkJSVh8eLFzHG/ffuGa9euYdasWTh48CDWrVuHSZMmgcfjMb2v+/btQ61atVhLUwsEAjRu3FhsnAAwe/ZsXL16lfn527dv+Oeff2BrawtVVVXWRKZFixZi18MpHs7H1nMkbp2omq5CfaVfv35l6mWbmJjQlyIh5I/H4XDg4eGBxYsXIzIyEnw+H61atSrz9q9fv8b58+dhZmaGESNGiJQY/d29f/8enp6e0NfXh4uLC1N+91czNzdnHQ9fns/yV+LxeDh69CgyMjIwduxYGBsbg8/ns85niIuLw6FDh7BgwYIKH08gEOD27du4ceMGWrZsCScnJ9b5sjXZ3bt3MXHiRKFFKENDQ3H16lVkZmbC2dlZaE2Q0NBQnD17FgYGBhg0aJDQRWJoaCi2b9+O3r17iz2eQCCAs7MzgoKCmLaXL18iPj4egwcPxocPH0S2+fDhA0JDQ9GkSROx+1VTU8OlS5eY9W769OlTplLT4p6jqKiIGTNm4MaNG3j27Blz8W1paYlVq1YBKJoH07RpUxw8eBBNmjTB5MmToaCggIKCArRs2VJoaOSpU6cQHx+Py5cvY/v27UhNTWX2+fXrV2zfvh2zZs2CvLy8SCLD5XIREhLCekGelJSE06dPM5PefyZuLZxi0dHRePjwIdNTkpOTg1evXuH169dQVFRDTo7ogpEcDgcHDhxgTZ5evHgBgUCAXbt2CSUWSUlJ2Lt3r9gCEgKBAIGBgWJ7/gQCgUg1O6AomT5w4IDY8tyfP3+GkZER60KjdevWZV3UE4DEvnMrq1yJzMOHD7F48WKRL8gWLVpg1apVJXYREkLIn0BDQwMtWrRgnaQqzuzZs3H58mWkpaWBy+Vi//798PT0LHFoSU3F5/ORmpoKDQ2NMs8rWLJkCU6dOoWUlBRwOBwcOnQIhw4dKnVBvOowZcoUzJkzR2j9meLhHDXNzZs3MWfOHERHRwMATp48ialTp2L69Olie5Aqs3p3QUEBRowYgQcPHiArKwvy8vLYvXs3Ll68WG2loqvDpk2bhJIYoGjFdw8PD6SlpYksbPjp0yesXbsW5ubmIhfWSUlJ2LlzZ4mJzKNHj0SS4+/fv8PPzw+9e/dm7Y0oKCgo85wFExMT5Obmlnm9nJkzZ2L58uVCd+W5XC5WrFjBzBE6fPgwHj16BHNzc0yfPh0aGhoQCASYOnUq/Pz8kJ6eDi8vL5w9exZnz57FsWPHWOd3BQcHIzQ0FI8ePRLqlcjNzUVgYCBcXFwgLy/Pel5mZ2ez9mQARe+fvb09Xr16JfQ6VFVVSy2usnbtWtbPeP369ejatSW8vMJEtlFRqSV2iBifz8fTp09FPuPs7GxcuXKlxJv9pS1Cm5GRwdr+/ft3se9Nfn6+2HNBXl4e8+fPR1BQkFDJ59q1a2PChAklxlJTlbn88sOHDzFgwADExMRg2rRp2LZtG7Zt24Zp06YhJiYGTk5OCA4Ors5YCSHkt/P48WN4eXkxE1v5fD7evXuHefPmSTiy8jt8+DA6duyIDh06wM7ODmvXri11mw8fPjBJDFB0FzIiIgJ///13dYfLasiQIVi1ahVatWoFCwsL2NnZ4eDBgyWOY5cEgUCA5cuXM0kMUDS058CBA+DxeKyrsOvq6mLixIkVPuaBAwdw8+ZNZlJ6Xl4enj59WumKXr/aj0nqj+Li4sSus5KamorIyEjWx3g8XonHe/nyJetFPo/Hg5GREerVqyfyWN26dastkZ81axYmT54MFRUVZlHWBQsWYOjQoQCK5otMmTIFR48exT///MNUWLt9+za8vb2Zql6FhYV4+/Yt5s2bh/v377MeKz8/H48fP2YdApWamgoFBQXWAhjGxsbo1asX63tjaGiI0aNH482bNyJDpPLz84WG6bFh66kojqewkD1xMDUt+t3R1dUVeaxBgwZ49eoVa9LB4/GEJvT/rFGjRmIf43A4YteEMTAwEEnGiuXl5YktdlBYWIi6deti//79sLOzg4WFBZo0aYJVq1ZJbdXCMicyq1evhomJCZ48eYKVK1fCxcUFLi4uWLlyJR4/fgwTExOsXr26OmMlhJDfzsmTJ1kvrGJiYiQQTcU9fPgQq1atQmhoKBITExEeHo6dO3fi6NGjJW53+vRp1j/Wnz59EimR+qu4urrixo0bePLkCa5cuQJbW1uJxFGSuLg4kUX0gKL37erVq9i1axcsLS2ZCxp1dXVMmzYNxsbGFT7m7du3WXt6itetkBbiLiwbN24sNmE1MjKCjY0N65yVki5UAaBz586sFam0tLRgYGAADw8PWFpaQlZWFrKysrC0tMSmTZuYIXu+vr4YNmwYBgwYgO3bt1fJfK3Jkycza4z0798fY8aMKXWbM2fOsK4kHxUVhUGDBrFuIy8vj+7du0NbW1vkMV1dXdStW5e194DL5TI9fjY2NsxQ27p162L+/PnQ1tYWSuKL5eTkiJ1XU0xc9TVDQ0M0b95c7GdsZmaGGTNmMIvDKysro3Xr1ti5cyc6duzIOmRLW1tb7FAuQHwJZaDoppa4XpcTJ04gNzeX9bGnT5+K3a74nLK0tESDBg1gYGAAfX19sWsESYMyJzIvXryAq6sr6wdSu3ZtuLq6lnvRsPv378PZ2RmNGjWChoYGPD09hR6fMmUKNDQ0hP51795d6Dm5ubmYP38+6tWrBwMDAzg7O7N+uRNCSE1UvEjbz8q6eGNNsXv3bpG7rllZWbhw4UKJ2xkYGLBeOCgoKEBOTq5KY6wqr19zceCAHEJCyvwntMqpqalBUVFRpF1BQQF6enq4ceMGkpOTmQua9PR0+Pv7i73AKQtxY/LZ4qjJli1bJjKPwtraGrNnz8bEiRNFzjtZWVmMHz8ekydPFiktbGxsjCVLlpR4vEaNGsHe3l7ogr34uklWVhYdOnTA3bt3cerUKZw5cwb37t1jkueNGzdi6tSpCAgIwO3bt7FixYoyFxARCAS4e/cuDh06JHTR//HjRzg5OeHcuXMICgqCp6cnHB0dxRYBKMaWjABFn/+ECRNEHudwOBgyZAiMjY1Ze2QSExORlpbGOu8kMTERjx49gpGREW7duoWLFy/ixIkTuHPnDvP6xc0jLC2xXLx4MRo2bCjUZm5ujmXLlmH06NGs53Nxtb/p06fjzp07OHbsGP777z8EBARAW1sblpaWrNsZGxujfv36rN/n8vLyJc7n4XK5YhOd3NxcsY9lZ2eLTXKys7PB4/HQr18/HDlyBEFBQbh+/ToGDhwodp2gmq7M38IyMjJiVxEFit7U0hYh+llWVhYaN26MdevWiT0hu3TpgvDwcObf+fPnhR53d3eHj48PDh06BD8/P2RkZGD48OFSV2GGEPJnmjRpksgdQjk5OXTt2lVCEVWMuD+cpY3zHzFihMiCcVwuF3Z2djVuEnlBATBqlBL691fB/PnK6NdPBc7OypDE8gsaGhoiF9VA0Z3Wrl27Yv/+/SIXj8+ePYOfn1+Fjzlr1iyRi1VVVVWMGDGiwvuUhEaNGsHX1xcuLi7o0aMHZs6cCV9fX6ipqWHv3r0i52xBQQH2798PZWVleHt7Y+7cuejRowdGjhwJX19f1s/hZwcPHoSHhwd69uyJfv36MfOZisnLy6Nnz57o3r07k0jl5ubi3LlzQj22BQUFuHfvntjyusXS09PRp08fODs7Y+7cuejZsyfmzJkDgUCAlStXivRmfPjwodRRNTNnzmR6I4opKCigZ8+e4HK5zHo1xT1N27dvx86dO3Hp0iXWa7KsrCyEhYWx9rzm5OQw5y+Hw2FWp1dVVWWeY2dnJ7Kdqqoqpk+fXuLrqF27NkxNTZnkQk5ODgYGBtDV1cWBAwdY58IcP36c+b+GhgacnJzQpk0b5iZMaGgo6zyf2NhYmJiYsPZ4tG7dusTFMkvqrZGXlxdb0U5dXV3ssLPIyEhs2LBBpBc1Pj4ey5cvF3u8mqzMmUfbtm1x8OBB1q686OhoHDx4sNzd7z179sTSpUvh5OQkNglSUFCArq4u8+/H7tn09HScOHECK1asgL29PWxsbLBv3z6EhoayLtpJCCE1Te3atbF//360a9cORkZGaNCgAcaPH4+VK1dKOrRy6d69O+vk/tLG+SsrK+Po0aOws7ODsbExzM3N4eLigk2bNlVXqBW2Y4c8rl2TQ1pa0d+r9HQuAgJksWlT5XrP4uPjMWTIEDRv3hxDhw7FzJkzS7xxWGz//v0YMmQI6tWrBxMTE9jb28PT0xN8Pp91uF5OTg7u3r1b4ThtbGywbt062NjYwMjICFZWVpg/fz5GjRpV4X1Kiq+vL169eoX379/j4cOHzBxftt4B4P9KFteqVQtLlizB+fPnsXv37jLPneJyuRgzZgzOnTuHEydOsF6Esx2Tbc5OWloagoODwefzsXjxYtja2mLAgAHo378/c4H6999/Izg4mBkKlpSUhLNnz+LGjRsihQ6K/bxey890dHSwe/dutGnTBkZGRrC0tISbmxsWL14MoGjiekZGBlRVVaGmpoZXr16hoKBAZLTNj8LDw1nnyJiYmKBz584lxsMWL5/PF3sRX2zv3r24fv06c/MlPz8fd+7cwYYNG7B//37WbUrrrXjw4AHrXKmvX78iMTERJ06cQJ8+fWBmZgZTU9P/v2bNSQBFCev06dPRpk0b5jvg8+fPJSYyX79+LbEQgrheUiUlJXh7e7M+9vDhwxJfY01V5qply5YtQ58+fdC2bVv06dMH5ubmAICIiAhcu3YNCgoKWLp0aZUH+PDhQ5ibm0NdXR12dnZYsmQJc0fo5cuXyM/PF7pzWfzL9ejRI3Tr1q3K4yGEkKrWunVrXLt2Dbm5uZCTkyt373ZNMGHCBAQFBeHevXtIT0+HsrIyWrRoUaa7fI0bN8aVK1eQl5cHWVnZGvv6b9+WRWGh8F1QPp+D+/dlAbD3SJWmsLAQI0aMQEhICNMWExOD3Nxc7Nu3r8RtlZWVcfDgQRQWFoLP5wsNiapdu7bIhZ6CgkKlq4sOHjwYgwYNQl5eXol3hX/G5/Nx+vRp+Pn5oVatWpg1a1apa2hUl1OnTmHNmjVMT0dMTAxTOVBfX591m9KGKwFFvSXHjh3DzZs3oaGhgTlz5lSq8qCuri40NTVFetY0NDTQunVrLFmyBPv372d6kOLj4zFmzBjcunWLtccmKysLp06dEltFy9DQEEDRYqo7d+5ESEgIjI2NMW/ePOa6q0OHDggICBD5rsrNzcWIESOE7vRHREQgPz8flpaWuHHjBusxzc3NWcv+ysjIiB3KWIytNHNWVhYOHz6MVq1aISkpCZs2bUJcXByaNm2K6dOno1atWvDy8mJNAnx9fZmiK+XVqlUrcLlckf3m5eVBV1cXCgoKOH36NFOh7sebPtOmTRMaghsVFQVnZ2cEBgaKPR5b8Yhi6enpsLCwYJ1mUbduXdy7d491O3G96jVdmROZJk2aIDAwECtWrMD169eZ1WSVlZXRq1cv/PPPP6xVUiqje/fu6NevH0xNTREbG4tVq1ahf//+uH37NhQUFJCUlAQZGRnUqVNHaDttbW2xVSkAlKssKiF0vpCK+tPOneXLlyM0NBSPHz9G48aN0aZNm1LLi0qTvDxzABos7VkV/qyDgoJEhnkUz20ICQmp8HpCffv2RXR0tNCwpIYNG6JRo0YSOS8XLFiAu3fvMhfdgYGBmDNnDnr06PHLYzlw4IBIgY2EhAT8+++/GDFiBC5evCg0vExWVhZ9+/Yt8X0TCASYPXs2Hj16xAyjun79OhYvXoz27dtXONZOnTohPj6eGe7E5XJhbW0NRUVF+Pn5iQyDi4iIwMaNG8UOry8oKECfPn3g5+cn9Bw5OTk4ODjg1atXmDx5stA56efnh23btpVYKMLHx0cksSgsLMStW7fQqVMnsdtFRESw9nZERUXh8uXLsLKyYioZZmRkoEmTJsyQMLbCA0BRD8vNmzcxa9YsJpn38/PD5cuXsXfvXtZ1e4CiHh4ZGRmx713x55+dnY3Xr1+jTp06TKIaEhLC2oMiEAjw9u1bsWveZGVlISAgQKT9zZs3QsPZfiYrKyu211ZeXl5s9b3ExESxNx84HE6N/JtV0vA7oJzryDRo0AAnT54En89n3iQtLa1qu3v24yJBVlZWsLGxgbW1Nfz9/dG/f/8K77e0N4WQYhEREXS+kAr5U88dCwsLqS3jWZrJk2Xx5g0fGRn/9zevVi0+Jk6Uq/BnHRgYyDqPKDc3Fzo6OqWu0i7OwoULYWNjg4MHDyI7OxtNmzbFP//8I5FF7x4/foynT58Kvc6vX7/i3LlzmDJlSpl7daqKuOPx+XzcunWLdY7M7du3MWzYMLH79Pf3x4sXL4QugpOSkuDp6VmmimDibN68Ga1bt8bZs2dRUFCA9u3bY/78+WIvuAUCAbKystCnTx98+PBB6LXo6Ohg8eLF2LJli8i2+fn5uH37NtTU1EQS6+KFVEsaIpaXl8caT0FBQYmfb1ZWFuv5LxAI8OnTJ9ja2sLFxQVv377F9+/fUa9ePcyfPx/Dhw9nXX8HKFrb8MiRIyI9kmFhYfD19RW7llJ2djZmzpyJrVu3ijxWq1YtWFhY4NChQ9i1axeioqKgqqqKJk2aMMM52RIZWVlZaGtrsw6fA4p6A9kSMj6fX2LxBSsrK4SEhLD2onTp0oU1OQKKhvI1bNiQtceu+DVKm3IlMsW4XG6Zulmrmr6+PgwMDJha7jo6OigsLERKSopQV2lycnKNLJdJCCHSTsbPD4o7d4LD44GvpYWcRYvA/0MWQ3ZyKsD793k4f14OX79yUKeOAAMH5mPo0IrP9re3t2cdkpKTk8O6ZkV59O7du8SFGn+Va9eusZYYT0pKQlZWlti71dWlfv36Iiumy8jIoHPnzrh27RrrNmzzg3907do11kniiYmJEAgElUrWRowYwVpQwdjYWGQ+iLq6OkaMGIFmzZohNTUVt2/fRkZGBvT09DB16lQ0bNhQ7ByS9+/fM2vE/Ozp06clxjh48GDWAhP6+vqwsbHBiRMnWLcradHc2NhYTJ48GY8fP2baPnz4gFWrVqFHjx5ie2Ru3boltkciJCREbI8Ln8/H8uXL4e3tLbRmkJycHD58+IC4uDisW7cOycnJAIrWI3rw4AGmTZuGVatWgcPhiCQzeXl5MDQ0xLdv39CjRw+mrL6pqSn8/f1LrExWPHST7XE1NTWYm5uzJiTy8vJi58/w+Xy0aNGCdTtxFTRrOrFn0OnTpwEAzs7O4HA4zM+lqc7qJSkpKUhISGC+3G1sbCAnJ4dbt24xCznFx8cjPDwcbdu2rbY4CCHkTyQTFATlWbPA/f9/yGUAcD9+xHcvL/AtLSUb3C8yf34uZs7MRVISBzo6AlS2SvbPK54Xk5OTQ1paWolrUEiLZs2aQUFBQeTusZqaWqnzIICicsE7d+5EQUEB3NzcYG1tXal41q5di/DwcISGhqKwsBBKSkpo164dJkyYgEePHrFuU7wopDhNmzZl7SVRV1evth6nNWvWYPz48fj48SOAovezf//+TBW1rVu3IisrC6mpqTAwMGCqAKqrq7Pur06dOsy+fiZuVftidevWZa0yaGpqylQg/Pm9UVFRQffu3bFq1SrWi3U7OztcvXpVpD0uLg4nT54U2yOTnJwstlqiiopKqSXInzx5gtOnT+PIkSNo3rw5li5dCkVFRezdu5dJYn704MEDBAcHs76G7OxsJCcno2PHjkJFFsLDw9G6dWuEhIRAQUFB5P3lcDiwtbWFrKws62vh8/liP0cTExOxPXaysrJiF9ms6DBWSRObyEydOhUcDgeDBw+GvLy8UJlAcTgcTrkSmczMTCbr5fP5+PTpE0JCQlC7dm3Url0b69atQ//+/aGrq4vY2FisWLEC2tracHR0BFD0y+ji4oJly5ZBW1sbtWvXxuLFi2FlZYUuXbqUOQ5CCCGlU9i+nUliisnEx0PBwwPZBw9KKKpfT0EBMDYWX1GoPLhcLutdV2Vl5Rpb9KC8HB0dYWVlJdQLoqysjP79+5f6Go8ePYo1a9Yw816vXLmCyZMn4++//65wPFpaWrh+/To8PT3x6tUr9OjRA3379gWXy8XChQvx+PFjoYnS+vr6mDdvXon7HDlyJA4fPozXr18zbaqqqnB2dq5wnKVp1qwZbt68iYMHDyI0NBRTp05Fq1athJ6Tl5eHzMxMFBQUMInGvHnz8OrVKyQkJDDPMzIygru7OxYsWMBaDay0u/Xe3t6svTmvX7+Gubk5TE1NhXo5gKK5182aNUPdunVFHtPS0kLPnj3FllKWlZWFhoYG6/ArKysrPHv2jHW74veB7SKfy+WioKAAw4YNw71795CXl4dnz57hzp07OHfuHN69e8e6z+/fv5e4CGdAQADrvO2vX7+KrRQmEAhQUFAAOTk51kSmcePGMDMzE5qTBRQlIzNmzMChQ4dYk09pW/OpLMQmMq9evQIAZgGn4p+r0osXL5hFhoCiuyRr167FiBEjsHnzZrx9+xZnzpxBeno6dHV10bFjRxw5ckSojvjatWshIyODcePGIScnB506dcLevXtr3PoDhBAi7ThiKuVwKljphwADBw7E5s2bRS7kLCwsSu0FkBYyMjLw8vLCwoULER4eDnl5eQwcOBCTJk0qcbu8vDzs3r1b6CIwNTUVJ06cgJubW6XeHwUFBYwfP16k3dLSEsePH8eaNWuQkpICTU1NzJ8/Hy1atCh1f8Wv8ePHj1BSUoKzs3Ol5seUhbq6OubOnSsyJ6+goADTpk3D/fv3kZ6eDn19fUyYMAGTJk1Cq1atcPDgQWzcuBFpaWnQ0tLC4sWLYW5ujkmTJuHhw4dC80i4XG6p85LfvXvHOvk8MzMT8fHxrI+lpaUhPz8fwcHB6N+/P16/fg2BQAAzMzP4+flBQUFB7PAxLS0tdOvWTWTejqamJmbPni22iERYWBh0dHSEkrhi6urqOHXqFG7fvi3Ua/P+/XssXLhQ7JQKGRkZsSWtgaI5YuIKATx+/FjspP2ix9h6lrjQ0NBAYGCgSEKWm5uLq1evih2yJyMjI7SMyY9K63WrqcQmMj8vevTzz1WhY8eOrHW3i3l5eZW6DwUFBXh4eMDDw6MKIyOEEHZ3797F1q1bkZ6eDkNDQ6xcubJavh9rIr6ZGcAy9KawkkN9vn37hqVLl+Lt27dQUFCAs7OzVK5NUhEqKirYsGEDlixZgpiYGMjKyqJx48Y4cOCApEOrUhoaGti7d2+5tgkPD2ctIRsXF4eHDx+iT58+JW7v6+uLAwcO4Pv376hfvz5WrVoltvTwj1q2bImLFy+WK1agqGLqoUOHyr1ddVi2bBkuXLjAXOhmZGTAw8MD7du3h7W1Nezs7FjXsunZsyemTJmCixcvIiEhAXXq1EGnTp2wYMGCEo/n6OiI/fv3i1zT6erq4tWrV6zr83z+/BkRERFo1KgRevfuDS6Xi8LCQrRr1w4qKiqsn32xo0eP4uLFi0hJScHz58+Rnp4OY2NjTJo0Cebm5mLnnfD5fDRp0oQ1kalfvz4uXrzIOvTsyZMn2LJlC+s0C0VFRbRp0wbh4eGsx3R2doanp6dI0iEjIwN7e3ux16+FhYUoKGDrseTD09MLmZmiw9z4fD68vLygo6ODjIwMkcdNTExYy1YDEDs/qqYr82T/Zs2aYe3atejbty/r49euXcOCBQuqpeeGEEJqglu3bmHy5MnM3benT58iNDQU165dK9MFkrTLWbkSMq9fQ+btWwCAgMNBYfPmyJ07t8L7LCwsxJAhQ4Qm9L58+RLJycmYPXt2ZUOWCt27d4e9vT1ev36NlJQUWgPt/9PV/X/snXV4VNfWh9+RuAuEJEggEFxDCyG4FncoxQq0OMUpTlvc3d3dCQ7BoUhxCARCCCGEQNyTke+PYaYZ5pxJCuUC/eZ9Hp57e3b2OfvMGVlrr7V+yw17e3uDXXkHB4e/pYATEzHfuBHJ27dkduqEqlAhQNMrZsyYMbq+INeuXeP+/fscP378o2oB1Go1f/75JwEBAZQqVYpWrVrp9e/5Urh48aKB4fz27VuWLFnC0qVLjc4dN24cAwcOJDg4mPz58+t6yBijTJkyNGjQgAMHDuh29j08PBgxYgSenp6C6VwqlYrcuXMzZMgQtmzZoquhunbtGsHBwawykq5asmRJrKys2LZtG+Hh4bx+/ZqSJUvqUqeKFCnCtWvXDOaVL19etEZELpeLNgVNTEzk5s2bgmMKhYKWLVuKChqUKVMGFxcXg/QyFxcXSpYsKSo+4OvrCwjX+sTF2ZCZKSzaEBERwU8//cSoUaMMxgYMGMCECRME532tjkyOE3DDwsJEQ3ygkdDLriusCRMmTHzNzJs3zyCF4MmTJ19kF/pPgTp3bpKOHiXt11/JaNGCtN9/JzkgALKk+/5TAgIC9JpBgsZo2LFjh9HO1l8qarWamJgY0WJjMWQyGeXKlft/E93LCblz5zao+QAoXbo0pUqVQnr9OrY1a2I5ejSWc+Zg06ABFpMmAbBmzRqD5ob3799n7dq1uv/OzMwkOjo62+JvLSqViq5du9K2bVsWLlxI//79qVOnjmAB+OdGzDjOmsakUCiIjo4W/Ft7e3t8fX0FnRiVSs3Tp7EkJOiLNyxbtozVq1dTu3Zt2rVrx+HDh2nUqBHW1taCggdaqehTp07pCUGoVCquXLlCWFiYaJlAtWrVdP8/b968+Pr66tV/9O/f36AY3t3dnZEjRwqeLye8/z2lJSUlxaj9Gx4eLngfUqlUVFwBNNF/MaysbLC3txcc8/b2Zt68hYJjf/wxSfScXyv/qJLQmPLGkydP9GpXTJgwYeK/htiOlVZS8/8F9vakjxpF6rp1ZPzyC3yk0s2NGzdIS0szOB4fH//V5Wzv3LmTGjVq4OfnR5UqVRg7duxX6Yx9SaxcuZKuXbtSokQJihUrRps2bdiyZQsAVqNGIXv6FK1lIn3zRhOdCQsT7NCuUqm4ffs2arWaP/74A39/f6pUqUL16tWN9kjRsmfPHo4dO6ZL2cnMzOTOnTsfJTzwqShbtqzBMTs7O13NzuzZs6latSr+/v5Uq1aN5cuX5+i8U6eewNOzDt9840+hQlWpXHk4GRkaR2jGjBl06tSJ06dPs2PHDvz8/IiIiODBgweCCmNSqZTbt28bSDaDphbq/b48WRHrTq+lefPmTJ06lYoVK1KkSBH8/f1ZuXIlhQsXFo2gKZVKUclza2trkkRqBJVKpahsN2gcEqFi/6ioKNF+L6BpqKrRhjTEy8tC9D5KlChBTIywkxMVZSMaYftfy6D/WxhNLduyZYtePuCsWbNYv369wd/FxcXx4MGDL0Kv3oQJEyY+FWI/AB8rB/v/mTp16rBq1SqDiL+Li8tXJQcaFBTEmDFjdAbL69evefHiBc7OzgwZMuSTXffgwYNs3LiRzMxM6tatS+/evf9TYjeWlpbMnz/fcCA+HqlA3YX09WvMd+4kd+7cBrvdZmZm1KhRg2XLlrFs2TJdQfvr16+ZMGECJUqU0EkXC3Hw4EHBBoRineI/J9OnT+fFixfcunWL+Ph4PDw8aNmyJdWrV2fnzp3MmzdP55BFRkYydepUfHx8qFWrlug5b958xcyZQ1GpNClNKhUEBT2jQQMbNmzowdSpU/X+PjU1FV9fX65fv46bm5tBNDtPnjxUqVIFNzc3AychT548VKhQQTRaJlT/8T7V3N0pFRmJVVISiVIpBd85KWLF9XK5nAYNGggqiZUpU8ZoLyF3d3fRsQoVKggGAqRSKdWrV2fy5MmC83x8fESV0pTKONGNntu3b6NUGm4OaeZJqFWrlmAZiLH3/peM0YhMamoq0dHRunzCpKQk3X9n/WdpaUn37t2Fv2xMmDBh4j/C+PHjDVJ/ypYty4ABAz7Tir5+qlWrRpUqVfSMb1dXV/r16/c/7/j+McyfP99g1zUtLU2wD8a/xYwZM+jbty/Hjx8nMDCQCRMmCCpx/SextEQtICWrlslQeXgwbNgwA+OyYsWKtGvXjgMHDhh0d3/79i2LFi0yekmxvh05lbSNi4tj06ZNHDhw4B+lHmZkZLB//342bdpkVCApKzY2Nuzfv59Dhw6xdu1aAgMDdQbz5s2bDRyBuLi4bKMyAwcu1jkxf6Pg4cNAUXW21NRUrKysaNCggd7rZG9vT9u2bXF1daVt27Z60QALCwsaNGiAh4dHtvLcarWawMBA1qxZoycO8HTPHhzatKFaeDgV4+Ko9egRGf7+xD97Juroq9VqWrduLfi9U6pUKQoXLiw4TyKRiCqBgcbpEopIKRQKg/dhVry9vUXHgoKCRJt+Pnr0CJVKeEytfknbtm0F77Fq1aqi1/uSMRqR6dGjBz169AA03ui0adNEi/1NmDBh4r9O6dKlOXjwIDNmzCAqKooSJUowdOjQrzYk/yWgbbi8YsUKzpw5g7W1NQMGDHhX6Pr1IJZ2Irb7+7Gkp6ezc+dOPYNUoVBw7tw5Hj58SPHixT/Jdb8YLCxQVqqENDQUSZb0PVWRImS2akWdd3LIs2fPJj4+nkqVKtG/f3/kcrlgVAWyl58dPHgwJ0+eJCIiQndM2w8nO9avX8/s2bMJCwtDLpdTuHBhVq9eTcmSJY3Ou3PnDj179uTJkycoFApmzpzJsGHD6Ny5c7bXBM131vsRY7H3ZHbv1YQEYZl1pTKdmBjhCABonKT58+fj7+/Pnj17dC0ztBLJNWvWZMOGDbrPkL29PTVq1AA0LUCEUk8LFChAdHQ07du35/79+6SmpuLm5ka7du2YOHEiGSNGkO+9tDSfjAxO/fgjUhEZZaVSSf/+/QXTQbds2ULz5s0F56nV6r/FJ97DzMzMqAjWpUuXRMfEojHatYqlrUZERCCRSBEeTqZv376Cc+fPn88vv/wies0vlRzXyNy5c8fkxJgwYeL/PQUKFGDx4sXs3LmT33//XbTg0kTOkcvl9O3blx07drBu3bqvzokBTT8YoZ35T+VQvHr1SnBHNjY2lqtXr6JSqRgzZgx+fn6UK1eO5s2b68nDzpkzh6pVq1K2bFkaNmzIxYsXP8k6PyWp8+eT8cMPKL29UebNS2bVqqSsX6/pWIrmtV+1ahU7d+5k2LBhuucj5DyYm5vTuHFjo9crWLAgs2fPxtfXl7x581KsWDH69esn2rRRS1xcHLNmzSIsTBPNUCgUBAUFMXDgQEBTD9a1a1d8fX3x9fWlW7duJCQkABrnKSgoSLej//z5c2bNmvVRClNCAgoSiURXQD9lyhQ8PT1xcXHB3d1dF23p06czYPh95+RUhEmThIvIpVIphQoVIiYmhv379xMcHMzjx4/ZtWsXKSkpqNVqhg8fruccvnnzhsmTJyOXywXrQCQSCQ0aNGDQoEFcv35d54C+fv2aDRs2cP36dWxFIh02L1+KRsPkcrmoNHFqaqrROpiqVasKRo/kcrnevb2PmBIaQHBwsOiYp6en6JharUYuFzbvzc3lBj2rtOQ02velkWP55awkJiaSkJAgmLso5pWaMGHChImvn+joaGbPnk1oaCglSpRg4MCBHy30olKp2LJlC0eOHMHW1pbBgwdTrFixf2nF/xtatmzJkSNHOHnyJLGxsVhaWlKqVCmmTZsGaIzX9evXc/r0aRwdHRk6dCiF3kkFfwh58uTB2dnZoKjdyckJX19fxowZw4oVK3TF0qGhoXTq1IkzZ86wbt06Zs+eratLev78Ob179+bYsWPZdnD/orCwIG3xYk2xhkIB7xp4Z8eUKVN4/Pgxd+7cITU1FUdHR2rWrEmHDh2ynduwYUMaNmxIeno65ubmOUp/3L9/v6CqVVhYGFFRUfz000+cO3dOd/zp06fExsaydOlSnfOTlefPn3Pw4EE6depEcHAwc+bM4eXLl7Rs2ZLOnTvrmiFGREQwa9YsIiMj+eabb+jTp4+u58n7csgymQw/Pz8CAgKYNWuWzr5LTU1l//79DB06lNmzZ7NmTTsePdoHvAXMsbAozp49sylVKjf58uUzuM9JkyahVqvp0KGDnsT6kydPSEhI4Pfffxe8x2fPnnHs2DEsLS0N0uC06WRCfVvi4+NZs2YNvaRSooBJQBhQARgKxDg6itaz5MqVC4lEIthjxtLS0qihf/78eUGbODMzkypVqoim7dWsWZOTJ08Kjok1tQSMqggDWFjYkZlpKKJgaWlPRoZw9PhrFSb5R47MunXrWLhwIc+ePRP9GyH1CRMmTJgw8fUTHh5Oy5YtdTuFhw8f5tixYxw8ePCDu6yr1Wo6d+7MiRMndKktZ86cYcaMGaKpHF8iEomElStXcu/ePY4ePUqpUqWoX78+UqkUlUpF+/btOXPmjM54DAwMZOHChR/cM8bS0pLmzZvrFa1LpVIqV65MyZIl2b9/v4HiU3BwMBs2bGDPnj0GhtCLFy+YN28eM2bM+KD1fFak0hw7MaBR7zp69CgnT57k9u3b1K1bl3Llyv2jS1q8i/rkBFtbW8E+KmZmZoSHh3P37l2DOXfu3CEyMlIwIiGXy7G3t+fo0aMMHjxYZ3hfunSJI0eOsH37du7du0enTp10iopHjhzh6NGjHDhwgHnz5hmsRaFQMH36dJ4+fSpokO/cuZPp06eTO/cjwh9Z4UZpUpAgdYkhKSkEyC0oQ33y5EkqV67Mg3e9p7Jy8+ZNkpOTBe/R3NwcBwcHXWTqfRwcHERT4WxsbPjD0pKQpCS01up+YAfgkycPf4wezZUrV/Rs2fz58zN69GgkEgmVKlUyOGfdunX5888/BRt7AoLOGGheV7FURu24GMbqqIwJeri6upKenh8QsscLY2MTLOgIGXOcvmRyvOoNGzYwePBgatWqRceOHZk4cSJ9+/bF0tKSzZs34+bmRs+ePT/lWk2YMGHCxGdkwoQJBukOd+/eZcaMGUyZMuWDznnlyhXOnTunZ5S8fv2aefPm0axZs6+q4B80RcGlSpXSO3b06FEuXbqkZzxGREQwY8aMj2p+OX78eLy9vdm5cycKhQJ/f3+GDRuGSqUS7W1y8eJF0VoQIYnY/yoSiYR69erp6jQ+JU2aNMHe3t4geubu7k56erqgsZ6QkEBmZiYFCxY0SE1yc3PTRYayRg8UCgUXLlzg9OnTLF68WE8WXq1Wc+3aNdauXSvoOAFcvXoVa2trwTGFQsGuXbuoeukSa1BSiBfEAH9FwKzff6dMlSqCtSyBgRohAKEasuTkZCwtLSlatKhBmmSRIkWoWrWqqBMQHh4uOlaoUCFWCqRd3geeXLvG2nz52LdvH3/88QevXr0iV65cjB49mqJFi9KxY0fBc545c4YmTZoIKveCeI0coBeJep8bN26IjhkTOsiXL5+o85SQkGDE0bYVdVi+tu9aLTmukVm2bBk1a9Zkz549/PjjjwDUr1+fcePGceXKFeLi4kQ9ZxMmTJgw8fUjthsplOKRU7L25chKVFTUZ+sjs2fPHho2bEiVKlVo2bIlt27d+qjzHT16VPBeIiMjP+q8AB07dmTfvn0cOnSIUaNGYWZmhlqtFjVKzM3N8fLyMjhuYWFB06ZNP3o9/595+fIlHTt2xN/fn7p167Jo0SJdg1Qh+ygoKIiyZcsKPg8vLy/KlCkjGnVIS0szkDMGTXPGI0eOCI6pVCouX74sKmmsUCjImzev4JhcLufRnj0MUiopjMZ4dAXqA33v3WP79u2C89RqNZ6enoLn9fT0pEiRIqxbt44aNWrg7u5O7ty5qVSpEmvXrjVaI7Jz505sbGwEx4zVnWhfz8OHD3Py5Elu3LhBYGAg+/btA+Cvv/4SnJeUlGS0jsxYQ8yXLyMAoZ4vcmQy4V4woN/0832MKeVlZGRgYyMcsbGzyxB1AI1Fh75kchyRCQkJoVu3bsDfXqI27OXo6EiXLl1YtWoVffr0+QTLNGHChAkTnxsxYQOx/jo5oXTp0pibmxsYbHZ2djmWtf0QYmJiWLJkCS9evKBFixZ89913SCQSDh06xPDhw3VtBx48eEBISAiHDx82WmBrjNKlSwumFonJ+f4TwsLCWLRoEUlJSXTv3p2KFSsik8nImzevQVGvRCKhbt26VK9endDQUIKCglCpVFhbW1OzZk1atmwJaOoi1q5dy61bt/Dz86Njx46Y/4PULSHUajXnz59n69atODs7M2DAAPLkyfNR5/ySSE1NpW3btnopVA8ePCAtLY3Q0FDBxo7ajvB9+/Zl5syZOsc2T5489OvXj4SEBMFd94iICAICAnBwcDAwoOVyOWXLluX+/fuC6/Ty8sLBwUFQKMLV1VX0s1ygQAE6RkXhCuwDdgMFgcFAqcxMypYtK7rRUbJkScH3j729PXK5nFy5cjF37lxmz55NWloaAwcOpHDhwkajHKVLlxaNLAk5hlqkUikHDhxgzJgxOocuPT2dadOm4eLigqurq2CNTHbRilq1aomKAZiZlQQOC4woUKkKC343ALRu3Zo1a9YInjO7wEFammEqH4BS+UR0rljz0S+dHEdkbGxsdIVA2nzPrA/b2dnZqDKDCRMmTJj4uhk6dKiB8ZkvXz5Gjhz5weds3ry5gYqUjY0NLVq0yLaHxIdy584d6taty6xZs9i+fTvdu3enc+fOqNVqli9frnNitDx//pyZM2d+8PU6d+5soF5mZ2dH+/btP/icALt376ZBgwasWLGCLVu20KZNGyZMmIBEIqFTp04GO9ZlypShbdu25M2bl1OnTjF58mS6dOnCxo0b2bx5M1KplJiYGBo0aMDYsWPZsWMHw4YNo3HjxtkWF2fHL7/8wg8//MDWrVtZvHgx9erV4+zZsx91zi+JjRs3GsjlpqSksG/fPqO1B3K5nB49enD8+HH69u1Lv379OHHiBN26dRM1niUSCXK53KD/Cmi6urdv356ff/4ZFxcXvbEiRYowcOBAJkyYYPDZkkqlLFiwQNRhtba2pnDx4rQCOgCbgIlAJSDc0pJVq1YJznN2dubp06cGnynQRLCio6PZsGEDDRs2ZPPmzezevZvmzZszffp0wfNpUSgUohEEpVIpWkPi4ODAr7/+ahCVUqvVTJ06VbS5sYODg9Fif2PS1dbW4s/f1tZc9HvOWI1MdlHiuDjhevWXL198tQ6LGDmOyPj4+OjSB+RyOaVLl2b79u18//33KJVKtm/fToECBT7ZQk2YMGHCxOelcuXKLF++nLlz5xIXF4erqytjx441ugOaHXK5nD179jBy5EgePXqEpaUlLVu2pFevXv/ewt9j7NixetGK1NRUTp06xcmTJ0V3gYVSdXKKpaUle/bsYdSoUTx9+hQrKyu+//570SaCOUGpVDJnzhy9DcW4uDi2b99O7969GTJkCA4ODuzcuZO0tDSKFCnCtGnTdIXVVlZWghkUEyZM4M6dO3rXuXbtGrNnz2b8+PEftNZ79+5x6NAhvdf2xYsXTJw4Udcv5Gvn4cOHgilb8fHxjB07lo0bNxoYkLa2trqmh/nz5zeoM8uVKxeFCxc2eO8VKlSIxo0bY2VlhZ2dHTt37iQ2Nlankmdubk6rVq2Qy+WsXLmSxMREPD09mThxIs7OznTq1ImtW7dy+fJl1Go1UqmU+vXrU6dOHRQKBWfPntVL95TL5dSoUYNpr15xFMiamBQMDElJYfGzZ4KRVXd3d54+fSooFx0XF0d4eDjz58/Xu8fY2FjWrl1L9erVjb7eYhLUly9fRi6XCxrslpaWgk4VaJ6VmMPu5OQkGKnRIqY8BmBtLV57litXkqjDcuzYMdF5/6Sh6vtYWloabcT5tZFjR6ZRo0YsW7aMyZMnY2lpqWvI5OXlhUQiITk5mWXLln3KtZowYcKEic9MjRo1/nXj08nJKduu4kKoVCrmzZvHiRMnUKvV+Pv7M2rUqGzVd4RSYFJTU9m9ezeenp4GOfYSiYRvv/32H68vK7lz52b16tUfdY6svHjxQtCwioyM5MSJE3Tp0kWvqXVOEatL+Jg6ob179xoUuoMmRSopKek/0VC2WrVqrF271uB4amoqtra2ODg4GKi6Fi1aNNvzrly5kh9//JHHjx+TkZFBgQIFmDZtGlZWVoAmPcvMzAwzMzOsra313vvNmjUTbNa5evVqbt68qcuyUalUXLhwgX379tGiRQt69uzJ7t27efXqFS4uLlSrVo1ff/2V8uXLI1RdcV+tZsOGDYJRiYcPH1K6dGnc3d0N3q8eHh6oVCpBJdzIyEhevXqFRCIRlAX+6aefmDhxouBrlpKSImrop6enY2ZmJlgnIpPJROtHQkJC6NChg2Cxv1aZUAw3N+EGnAA2NsLiCoDehsK/ib29vaAj86ki4J+aHDsyAwYMYMCAAbr/bty4MQEBARw4cACZTMZ3331H1apVP8kiTZgwYcKEifcZOHAg27dv1xlQ165d00kMG0PMcC5UqBDff/89jx490hn0MpmMihUr/iv1n/fv32fHjh0UKFCADh066IzRD8HZ2RlbW1sD49ja2pqCBQt+8HnFCqg/pvGrNurwPunp6R/1GnxJiNVrmJubc+DAAcG0pJcvXxIbG4uTk5PoeT08PDh+/DjBwcGkpKRQqlQpXdrUsmXLmDp1qi4ycffuXe7du8fx48eNOvMHDhwwEJ9ISkpiy5YttGjRgrFjx/LNN9+we/du/P396dy5MzKZDA8PD8GaHZlczpUrVwSvpVKpsLe3F6x3c3Z2Rq1Wi/YviY+Pp0mTJhw8eFDvuLW1NYMGDeK3334TnJeRkSF6zqSkJHx9fbl06ZLBmI+Pj2i6Y0ZGBhMmTBB0ZBwcHHBzcxOcp50rhrF2JsZEVLJznowhFpH60PN9bnLkyGRkZHDt2jXy5Mmj94Xk5+eHn5/fJ1ucCRMmTJj476BWq4mNjcXW1vaji8djYmIIDAzUMxKUSiWXLl0iNDTUaLpbs2bNePLkid6uZOHChenVqxcODg4cP36chQsX8vTpU/z8/OjWrds/6hsixNChQ9mzZw+xsbFIpVKWL1/O5s2bKVy48Aedz97ensqVKxMeHq5ngJQsWfKjNhX79OnDrVu39IydPHnyMHTo0A8+p42NjeDOukql+mIlX5VKJXFxcTg4OBg4Bdr3sZ2dnS5VT0h6GDTRvNTUVEEjUalU5lgpqkiRIgbHtmzZYpBedffuXXbt2sX3338vei6xGgntejp27MiFCxdITk5m//79bNq0iV27drFkyRJ8fX0NnqOvr69omhdoonxC49oIjUwmE3wdPD09adiwIQEBAXqvX4kSJUSvBcYVxEC8B5CZmZlRp6NTp06Cx+Pi4qhRowa7du0SHBcTQQD0JLLfx1j/mY/53HxMWtqXSI7iSHK5nBYtWnD69OlPvR4TJkyYMPEf5PDhw9SsWZMqVarg5+fH8OHDP2oHMCwsTHBn8e3btzx+/Njo3KFDhzJs2DDKli1L4cKFqV27Nps2bdKpiDk5OTF+/HjWr19P7969P9qJuX79Ort27dKlV6lUKh49esSwYcM+6ryLFy+mR48elCxZkqJFi9KsWTO2b9/+UUZO3bp1mTFjBt988w2FCxemcuXKLFy4ULQIOifcvn1bcIf8S83VX7p0KdWqVcPf35+qVasyb9483dju3bupUaMGfn5+VKlShbFjx+rSGoVe97S0NFq0aCFYQ+zl5fXBin8qlUrw/Z+Zmcn169eNzq1WrZrBWmUyGQ0bNmTNmjWcPHlSVyuSkZHB9evXGTt2LM+fPxdsXmltbY2Pj4/o9Z4/fy7YLD0mJgYLCwsKFSpkMJYnTx5q1KjBiBEjDL4nbty4IRhR0ZKZmSmqeOjm5iYqzxwUFGQ0OibU1BM0jm3u3OLpY8ZEAoyJaDiKREc/lq81hUyMHEVkpFIp+fPnNyqFZ8KECRMmTAjx4sULhg8fzsuXL3XHwsLCsLW1ZcKECR90Tm9vb/LkyWOQmuHu7k7ZsmWNzpVIJAwZMoQhQ4Z80LX/KZs3bxbckQ4NDQU0CkyLFi3i7NmzZGRk0L9/fxo2bJjtec3MzHRSygqFgkqVKv0rks6tW7emdevWH30eLbVr12bFihUGNkSuXLlEU9k+F8ePH2f69Ok64zMyMpI5c+ZQoEABSpYsyejRo3WF6a9fvyYsLAxHR0ciIyMFnTUrKyvs7e0ZM2YM06ZN49m7ovgiRYqwaNGiD16nVCoVlFAGjTqdMby8vJBKpXqRGZlMRsGCBVmxYoVgxObhw4ds27ZNMGIRGhoqWkMmk8koX748uXPnNmi46ubmJtjwEzROztu3bwUdXbVazbRp00TTq8qUKYODgwMnTpwwGGvevDlLliwRXGtqaipeXl6Cr6u1tTVVypblsEDqmS0YVVkz5jh4ubpyW2Qsn0KBWGzJwsLigzcBLCwsPluPrk9Bjt2y3r17s27dOtFuwSZMmDBhwsTjx49ZvXq1XsfqBQsW6DkxoNk1/Zgov52dHa1atcLOzk53zNramoYNGxrNV/8cuLu7Cx7X7hp36dKFiRMnEhgYyMWLF+nVqxcLFizI9rwzZsygb9++nDhxgsDAQCZMmED37t3/1bX/G/j7+1OlShU9SVxXV1d69+79SVPLVCoVgYGBrF27NsftIVavXm2wg56QkMDGjRsN1LVAk/5z9OhR0Wdsa2uLRCKhXbt2nD9/ng0bNrBjxw7OnTsnmC4mREZGBgcOHGDz5s26talUKtG0NG1zRzG2bdtm4KxkZGSwcuVK0TmJiYmi0SNLS0sqVqwoOCaVSilUqJBgXVquXLl4+PCh4CZ5RkYG+/fvF3UC8uXLR8eOHQWvt3PnTmqKvLbtGjQQTa1Tq9UGMulabG1tcX238fA+1kCikb4uWb+j3ifViBTyQyPn/BgJZUdHR8Hjn7Jv16ckx8X+KSkpWFtbU6FCBRo3boyXl5dBkZ5EIuGXX3751xdpwoQJEya+bFQqFT///DOBgYHExMRgb29PxYoVBfP4tRjLAc8J48aNo2LFimzYsAG1Wk3btm1p1arVR53zU9CzZ0+2bt2qFz2Sy+XUqlWLmzdvcunSJT3DJCEhgS1bttCnTx/BVB7QvHY7d+7Uk8lVKBScP3+ehw8fihpknwOJRMKWLVtYuXIlgYGBWFlZMWDAAFHj99/g7du3tGvXjocPH5KamkqePHlo3749v//+u9F5Yu/J9PR00ayU9PR0evTowebNm/VkvbWyxVpsbGxo2rTpP7qPO3fu0LNnT548eYJCoWDGjBkMGzaMDh06iM7JbsNZrA4kPT1dtJ4jIiKCAQMGcODAAb2CfwsLC+rVqyeoSgcagzs8PFzwtXv9+rXRgvaQkBBcXFwMnEeJRMLkyZMJDg5m48aNemNOTk44ODgwZ8UKwXP279oVS0tLwZomuVwuev8JCQlEi0QxzNHIZwc/eSI4bsxZjzKSdhZvpH7KWC1Pdnh5eQkqHhpLq/uSybEjk1UdYvv27YJ/Y3JkTJgw8f+Z7du3s2LFCt6+fYuNjQ0DBw786KaH/2vUajWzZ89m3759JCUl4e7uzoQJE6hcubLReRs3buTgwYO6H9iEhAROnz7NH3/8QYcOHTh8+LCBMZPTHWljNGzYMEdpWFlRq9VMmjSJo0ePkpycTN68eZk8eXK2KWkfiqOjIytXrmTs2LGEh4djZWVFrVq1mDRpEkuXLhXMoY+Ojubt27eiO/2vXr0STIGJiYnh6tWrX5QjAxojsU+fPoLqb9HR0QwYMICgoCBkMhm+vr7MmTMHa2txadrsGDRoEH/99ZfuvyMjI9mwYQMtWrSgfPnyovOqVavGuXPnDFKWvvnmGypUqMDx48cN0nKKFSume8ZjxozRPeMaNWroesNEREQwcOBAnj59ilwup3LlysyaNStb0YvBgwfrNdp8/vw5s2bNolmzZtjb2xs4EBKJhFGjRhk9p5hqV/Xq1Zk9e7bgnLS0NNzc3Fi8eDF//PEHr169wtramvr16zNmzBgaN24ser3AwECDtDKAqKgoo/VnhQsXFnSA1Go1YWFhNGnSxGAsOjqaiRMnEiPiBDyKiSFf4cKCMuMuLi6Cqmzaa6rd3XFKTOR9ly0XULJUKU6JRJjT040V14snRhmrIRNTZcsOiUQimpIopmb2pZNjR+b2bbEsPhMmTJgwcf78ecaMGaP3IzF69Ghd0erXwqJFi5g7d66uCDU0NJSePXty7NgxUaMaICAgQHCX8MaNG0yePJnWrVtz6NAhoqOjMTc3p3jx4syZM+eT3Ycxpk6dyuLFi3W7sqGhoXTr1o3Tp0+Lpl18LBUrVuTo0aO6PhbalJlKlSphZ2enF1kBze7o+53Zs+Lm5oazs7OBIevk5ESFChX+/Rv4RKjVar7//nuuXbumOxYcHExiYiKbN2/+4PMK7fTHxsayevVqFi1aRFRUFLNnzyY8PJyyZcvSr18/bGxs+OWXX7hy5QpXrlwhMTERW1tbfH19GTlyJBYWFgQEBHD8+HFiY2OxtLSkZMmSzJw5E9A4CELPWKlU0qFDBz07Kjg4mNTUVKPpXK9evRI0rJ8/f87BgwfZuHEjrVu31kWRJBIJvr6+NGrUyOhrU6lSJZYuXaqXmmZubm50s0JrOFerVo0TJ06QlpaGufnfXelr167N5cuXDeaZmZlRtWpV5HK5QSqcSqUyGlm4d++eaDH8mDFjRKNny5cvR8zMT1ersRNRmHPKzMRaJFohkUi4mZhIKeAmkARIgPxAFIhGTgGePhVviJmYKB51yZUrl1FVM2OYm+chIyPS4Li1tbuuNu99PibK8znJcY1M/vz5c/TPhAkTJv4/snjxYoOdrujoaBYvXvyZVvRh7Nu3z8B4CAsLY/78+Ubnif2Qy+VyJBIJ8+fP5/Dhw4wbN45Vq1Zx+vTpHCs2ZWZmcvnyZb0mfh/DkSNHDFJLQkJCWLp06UefOzssLCz08v6/+eYbKlWqpJd+YmtrS6tWrYzu1ltZWdG8eXO9qIVUKqVSpUofpTD2v+batWuCalB//fXXR9Xkir0fbW1tefr0KQ0bNmT58uUEBAQwZcoUmjRpQnJyMmZmZuzYsYMdO3YwduxYtm7dyr59+7C0tEQikbB8+XIOHTrE2LFjWbt2LSdOnDBwft9/xidPntSLqoDGMfjzzz+NiihZWFgIGpcSiQQ7OzuqVq3Ks2fP+Omnn6hUqRIBAQFGO8xrWbNmjYFTkZGRweLFi0WL9rPKmSckJHD16lW9NDoxee7cuXOTlJQk+rk1tnHg6ekpOmZMYtnc3ByxZC5zc3NeiKSPRcbEiPY8sre3JyYmhvNonBgANfAceAl6r8X7JCYKp90BpKWJF+x/jLpYZqZw36fUVOccS35/LeQ4ImPChAkTJsQRSwH42tRhxNb7fp76+/Ts2ZOLFy/qpUlZWVnpdRYvWrRojrqZZyUwMJCRI0fy9OlTzMzM8PHxYfXq1R/cfwXEn1V2/Sc+FVu2bGHatGn8+eefZGZm0r17d6N9QLSMHz+eggULsmvXLpRKJVWqVGH48OH/gxX/e7x8+VJw1z05OZmYmJgPlieuWLGigYNkb2/PgAEDGDFiBE+fPtUbu3nzJgsXLmTkyJFIJBKjffJKlixJyZIlc7yWsLAwwehBamoqCQkJog1a7e3tBY1OqVRKsWLFAI3AxaxZswgODs5xqqbYZzw5OZnhw4dz9epVPQdLLpfz448/AjB//nzWrl1LaGgoTk5OlCtXjo0bN7J3717Bc759+5bw8HDB4nSZTGa0LsNYZNHa2hqZTCZ43vbt27Np0yaDKCdAIW9voh4+FDynEo06mxDJyclG+68IXUuLmZl4pEMmEx/7mE0btVq40aZK9YQvtHXTB5Njd69MmTKULVvW6L9y5cp9wqWaMGHCxJeLmORpdlKoXxpCkXVLS0s9h0SIGjVqMGzYMIoWLYqLiwve3t789NNP9OzZ84PXkp6ezogRI3j06BEKhYLU1FRu374tWGfxT8iXL5/BMRsbG9q1awfA3r17adiwIVWqVKFly5bcunXro66XHebm5owfP56AgAAWL16cIydGS+fOndm/fz+HDh1i9OjRRlNcvkRq1qwp+Dw8PT0/ylkVS0lKSkoSTB0CPtlzbty4sWCfkTx58uDu7k5aWhpDhgyhevXqVK9enaFDh5Kenk5ERIRgVE6pVHLhwoVsrztw4EDy5s2Lm5sb3t7eHD58GBBvKFmxYkWqVKlCly5dsLa2RiqVYmFhQe3atenfvz8PHjxg7ty5utSk2NhYAgMDGTZsGFu3bhU8Z3p6OkWKFCFv3rwGY56enqLPAuDUqVOiY6VKlRJV2QoJCRFNk5JKpVhnUc/LirVEIpqulpGRYbTvlbEGvMY2b0qVKiU6VqVKFdGx7BFzutK+2Ca0H0qOHRl/f3+Df5UrV8bT05Pw8HDs7Ow+8kU3YcKEia+XUaNG8e233+oMD7lczrfffptt4e2XxowZMyhWrJjux87a2pratWvTvHnzbOf279+fCxcuEBgYyIULF5g4ceJH/WiePHlSMGUjJCTkg3PHQdPzIevuta2tLQ0bNqRGjRocOnSIoUOHcvnyZR48eEBgYCBdunTRyUenp6ezatUqfv75Z1asWPHRymv/33FycqJ79+64urrqjnl6ejJ8+HA9ueZ/yr179wyOJSQksHTpUtFnZkwm92Nwd3dHLjdMgMmTJw8SiYQff/yRNWvWcOfOHe7cucOaNWt0r4lQ6pWtra1RAxg0Tsz69etJSkoiPT2d6OhounTpQlBQEN99953BeszNzWnYsKGu/iYlJQWVSkV6ejp//fUXx48fZ8GCBYLCFIGBgfj6+gquQy6X4+3tLehgOzg4UK5cOdHnXL16dcGInEQioVmzZqLPMTIyUnQsKCgIpUgUSG1nJxoFye69aCy1TCyyB9CgQQPRMWM1ch+DpcB7Ef6BQ/CFkePUMmO5w3fv3qV169a63SwTJkyY+P+GjY0Nhw8fZteuXVy6dIlChQrRv39/QQPmSyZ//vycPn2atWvX8ujRI5o3b07t2rVz7JCYmZn9a/WSYjniEonko/LHixYtyunTp1m9ejXPnj2jbdu2us7sy5cvN+hCHhYWxsyZM5k4cSItWrTg1q1bKJVK9uzZw5YtW9i3b98nEwn4/8DgwYNp2rQpK1aswMrKit69exsVlsgJYu9XYwbpv1F/JcTZs2cF5YmfPHlCUFAQ169fN1jHtWvXiI6Opm7dumzYsEHPMC9fvrxoLYuWnTt3GhxTKBT07NmTPHnyCNbILFy4EGtra4MUy7dv37JkyRLRDvUxMTGMGzeO1atXG6RtlitXjsePHxt8pkCTVujg4ED+/PkNGtu6uLjQpEkTpk6dalArJZFIaNSokWgvFWORE6VSSd5ChYgQUO7KnT8/hQoV4omAjLKbmxthYWEf5OiIFdcDgupxWsSiXNljD4j3oDHPyEAoufZrjdP8Kw5Y6dKl+fHHHz+4Q7MJEyZM/BeQy+V8//33LFiwgMaNG391TowWa2tr+vXrx4IFC6hTp85nS0WoU6eOYPGtt7e3YDrSP8HOzo5BgwYxf/58qlatqrtHseLr169fM3XqVG7cuKEzoJRKJbdu3WLy5MkftRYTGqndGTNm8Pvvv3+0EwMISiy7ubnRt29fUREFY4X3H0NQUJBgXUpiYqKokR8TE0NERAQjRowgT548yOVypFIpzs7ODBs2LNvPpFgdzPPnz0XrOWJjY0WleRMTE0X7QWVmZmJubs7ly5d1PQa1Uc6jR48SEhIiODcmJobIyEiWLVumtzEhkUj4448/UCqV3L9/32CeSqVi1apVok6FsedoZmZmtCHkL7/8Ivja1q5d22g9jzGp8KtXr4qOnTlzRnRM7Fn8jdjvi3DNlRaxV+fDW2x+Xv61SFLu3LmNNjYyYcKECRMm/gnm5ubMnz+fkiVLYm1tjYODA76+vkYla7Oybt06mjRpQoMGDRg+fLho3URWhJSSJBIJ33zzjWC6EiCouvW/QK1Ws3TpUho3bkyDBg2MStL+f2P27NnUr1+fXLlyYWFhQeHChRk2bBje3t6iYg9CdSz/Bg0aNNBLndPi5uZG9erVBetH8ubNS4kSJfj55595/vw5CoUClUpFTEwMw4cPz/Y5i0UsbWxsKFSokOBYiRIlRHvs5MuXTzQio6VAgQLcunWLV69eER4eztatW5FKpaJpZ2lpaXh7e9OmTRu9KIparWbIkCHcvXtX9Frz5s0THUtISMDGxkZwrFixYoLOEWjSwy5cuCDoIB06dEjUkQMEpae12NsLK4iB5jUTw9LSSnRMJrNE3PVIAPKIjOXiv6VZ9i+plsXExLBx40Y8PDz+jdOZMGHChB7S4GDMtm5F5e5OZseO8BGN8kx8Xfj5+XH+/Hnu3r2LpaUlPj4+OYoQTZ06lUWLFumclz///JMHDx5w6NAho/MnT55MUFCQLr1E68T07dvXIAVIi5jq1D/h+fPnbNy4EaVSyeDBg40aP1rGjh3LmjVrdLvvf/75J0FBQezevfuj1yOGWq3m3LlznDp1ivLly9OsWbMc17I8ffqUzZs3kydPHjp27ChqbL5PTEwM69evJyMjg65du5Inj5iR9jfW1tbs2LGDFy9eEBUVRcmSJXXF4WJNGIUiI/8GXl5egjUiHh4eODo6Ctbm2NnZkZGRYSDbDJrX8fDhw7Rs2ZLU1FS2bt2qE8HQqpkVKlRIsOnjd999x6hRo7h586bOAZdIJJQpU4Zff/2V4OBgpk2bZjCvZMmSRtOgjCH2flSr1axbt04wQpSenq4TJxDC2HvH09NTtIbOzMxMVIExLi6OGTNmCI69efPG6PdG7ty5BZt+AqjV4rVXKpWz6Jijow+RkcI9HC0t5SQnC/fD0cRcvADDPjKQF/hwWfMvkRw7Mk2bNhU8Hh8fT3BwMBkZGSxfvvxfW5gJEyZMAFiMHYv51q1Io6NRSyRYrFhBytq1qLIpdjXx30EqlVK2bNkc/71CoWDv3r0GEZibN28SGBhI7dq1Rec6ODjoFJtUKhXm5uY4OztjZmbG4MGDuX79up7BkitXLgYMGJCjdanVauLj47GystIzpmfPns3y5ct15923bx+LFy82KqCTkpLCkSNHDFKIrl+/zq1bt3KkIpqZmUlSUhKOjo45cg4VCgUdOnTg0qVLJCcnY2FhweLFi9m9ezcODg5G5/72229s2rSJt2/fIpFIWLlyJatWrcr2uR48eJAxY8boGkOuX7+eYcOG0b1792zXC5pIwvtpiGKpZTmtkVGr1cTFxWFra5sjlbhr164JGuvPnj0jNDRUMIXo7du3vHz5UrDeQ6lUkpaWRlBQEF26dOHJkyeoVCoOHjxI27ZtmT59On369GHs2LF60ad8+fIxfPhwcuXKxbFjx1i8eDEPHz6kXLly9OrVC2tra/r37y94D8uWLaNatWrs37/fYOx9pyIuLg5LS0ud4ygWAQOM9grSOg9qtRoLwBLQxkTatGnD1KlTBec9f/5cNHpy584d0eup1WqjTSG1axGibt26ohFbpVL8s5WRIT5mb19E1JFJTs4uDVKsTujT1IF9TnKcWqZSqVCr1Xr/QBMW+/nnn7l8+TJt2rT5ZAs1YcLE/z+kjx7pnBgAiVqNLDgYyxEjPvPKTHzJxMfHCxoyKSkp3Lx50+jcUaNGcefOHZ0BmZ6ezqlTp9i0aRPffPMN8+bNo3LlyhQuXJhvv/2WWbNmUbVq1WzXdP78eerUqUPlypXx8/Ojf//+KBQKIiMjWb16tZ5z9OzZM8aMGWPUsI6KihJM9YmPj9frIC+EWq1m1KhRul4pNWvW5MCBA9neg7aRqdZBTE9P5/r164wfP97ovJCQEDZv3qwz2NVqNcHBwfz6669G5ymVSqZMmaLX3T4iIoKFCxd+VD1LxYoVDY5ZWVnRtm3bbOfu2bOHGjVq4OfnR5UqVRg7dmy2DtBff/0luN6YmBhu374taMxHRUWRmpoqKOvr5eVFkyZNGDFiBI8fP9a9V2NiYtixYwf37t2je/fu/Pbbb1SoUIHChQtTvXp11q1bp6s/srOzY+TIkaxfv57BgwfrajzE6jLS0tIYPHiwYCSkfv36gGajoF69evj5+VG5cmV++ukn0tLS6Nixo+A5tUX7YlSqVAlnKys2A/eBe8AFoBxQr1490XnGUsDUarWoI2tmZiaaWSSVSo3WPIpF+QDy5BGWiQawsxMvyo+NvSI6BuLNcjWEiRx/KnL86yXHEZmAgIBPuQ4TJkyYMMAsixOTFemLF5CRAUY6n5v4/4uTkxOurq4GKSQODg7UqFHD6FyhVJ7MzEyOHTtG165dadSokVHjS4iYmBgGDBigp170/PlzLCws8PHxISIiwmDOy5cviYqKws3NTfCcHh4euLq6GqRDOTs74+/vb3Q906dPZ82aNbo6i8jISH799VdKly5NwYIFRecFBgYKKkVlVyO0bds2QWP9xYsXpKSkiBZKBwUFCTYpffbsGRcvXjQqXWuM33//nTt37nDt2jUyMzOxsrKiRYsWOonxV69eMXnyZMLDw3F3d2f06NHky5ePx48fM2rUKN37KjIykhcvXuDk5CTa2R4QVRiLj4/Hz88PuVxuEAmQyWT4+PiwcOFCevTooct8KViwIGPGjMHGxkZQDSs2NpZNmzYxbdo0qlatyqVLl4iLi6NYsWKiXeuz4uLiIujMSKVSypUrR9++fdm6dauu7UapUqWYO3cuKSkp/Pzzz3qKX9r1tWjRQvR6EokEuVwu2PizfPnyzE9J4Xv+3nXPC2wB1u7ZY/Q+xKInxoryJRIJ3bp14/fffzcY+/bbb41uEAitX4uzs6PomJhTBZCSEgtYAULCDdWBk6JzQSzt7Otq0JwTvlbZaBMmTPw/QO3ujloo5cXCAr5SRTATnx6pVEqPHj1wdv47/1wul1O1alXB3fisiBkkxnZcs2P58uUGRqe2qaG7u7tgepKlpaXROgBzc3PBInFra+tsG0keO3bMoFj81atXRguotecWIrvXxt3dXbD43MLCQmfIJSYmsm3bNvbu3atbm6Ojo+A1bWxsBPuLvI9arebMmTOsXbtWz1nUOkjaTu3aRqvx8fG8evWKpk2bsmnTJs6cOcPWrVtp1qwZoaGhzJ8/38A5TktL48iRI7r/joqKYv369Zw6dUoXKRGrEVGpVDx48ECwY7xCoeDNmzcULVqUs2fPsn37djZu3MiFCxdo3bo1EolEtCGku7s7Fy9epE2bNuzbt48zZ86wbNkymjdvnq3ghVhjR+17bcyYMWzdupUuXbromrg6Ojqyfft2QdniwMBAXQ+m91Gr1bx9+1bQmJfJZIQFB1MJQ0O1COBw8KDoPVhaWoqKHcjlctLShI18hUJB7969DQQP8ubNy6RJk0TlngEeP34sOpY1ovg+kZFCdSwaNOtsj+Er4ASUR0wKQFOxJhYF+u/Vl+bIEkhPT2f79u0EBgby7NkzkpKSsLW1pVChQtSpU4e2bdsa9SpNmDBh4kPI6NQJ81WrkGUpWlVLpSj8/eEj+oiY+O/TvXt3ihUrpmuAWLduXXr06JHtPCGjEj6uoF+sDiA9PZ1GjRpRpEgRg6iGr6+v0Wump6cLRitSUlIICgrSFX0LIVYHkJAgnuYC8Msvv3DhwgW9+7Gzs+P77783Ou/7779n6dKlesaeRCLRRSN2797NpEmTePbsGTKZDG9vbxYtWsS3335LqVKlOH36tN75ihUrJqqupSU6Opp27drx4MEDUlNTyZMnD+3bt+f3339n8uTJBq/dgwcPmDNnDtHR0QYG+bNnz5g4caKok6t1vKZNm8aGDRuIiIjA0tKSYsWKsW3bNtHicoBHjx4JRg5UKhUvXrzAx8cHmUxGzZo19cYlEglFixY1MKAtLS3p1q0bnTp1Moj03b59m+XLlzNkyBDR9YihdSjHjRvHzp07iYyMxMrKiu3bt7Njxw5RIYzslM6SkpIE67OUSiVvIyIQcpHlgH1qKpaWloJOybJly/jxxx9F12Nrayv4OTc3N8fS0pLSpUvz6NEjUlJSkMlkuLq64uPjIxg502Ks1ufpU/F0Ls0zkiBUu6JUKnGnIH3IxwqekwaUxILKtGc6y8mNlOcCtTCFgUd4AELOVQFAXA3uayRbS+D+/ft8++23DBo0iH379vHs2TNSU1N59uwZe/fuZcCAAVSuXNkkvWzChIl/HxsbHv32G7cdHHgpk/HMzIyLRYuSPH36517ZV41SqWTMmDH4+flRvnx5WrRoIahw9LVTpUoVNm7cyI4dO+jZs2eO1LXEdrmz7+kgzo8//ijYg8Lb2xu5XM6mTZuoVq0aefPmJV++fLRr144lS5YAmvSjrl274uvrS8WKFenRoweJiYm8evVKcE0xMTH8+eefRtdTpEgRg2PW1ta0b9/e6Lzy5cszZcoUypUrh6enJyVKlGDo0KF06tTJ6DwrKyvWrVuHv78/efPmxdvbm44dO+pSkiZPnqxriKhUKnn8+DHDhg1DrVazePFaPD1bIJcXRCYrQK5c9Vm6dGu24gQDBw7kxo0bOjGEyMhINmzYwM2bN0WNzsePHxMeHi449urVK1q1aiW4aVusWDEeP37MqlWrdM5DWloat27dYsCAAQwfPlwwQmBlZYWPj4/oPVhaWpKens4vv/zCt99+S4UKFWjfvr3OMbp48aLBnPT0dG7cuCH6ftWmR23evJlatWpRrlw56taty759+wDxbvKpqalcv36dNWvW6KII2mMDBw4UfR5KpdJoJMPLy4tMASlpGVC5Xj1CBeZEAPd8fUU3HYzJNoN4ql/x4sU5ceIEW7Zs0QkUaHtF/frrr6JpnmC8Caexmh1NhEzsvWzBCiaQxHMc0bS5lJFOI1ZQhASi+EZgjhVOlAPE0kurZfvZuXnzJo0bN6ZcuXL4+/szefLkT9Ys9t/AaEQmKSmJDh068PbtW8aNG0f79u31CqEiIiLYtm0bs2bN4vvvv+fChQs5llM0YcKEiexITU2l1R9/8Cg+HnNAoVQiDQ6m+/jxojKZ/wahoaHMmjWL2NhYatWqRdeuXXOkTvS1MHr0aFavXq3bYX727BmdOnXizJkzWFmJ9y74HKjVag4fPsy2bdswMzOjT58+fPON0A/4v0PWdLSsiPXeyAllypThhx9+YMeOHbx58wYzMzN8fHyYO3eu7twHDx4kIyODkJAQvWhKp06dOH/+vO6/nzx5QmxsLJs3b8bZ2dmgY7yTkxMVKlQwup45c+bw/PlzHjx4QHp6Ok5OTnz33Xc5qjlp27Ytbdq0IT09HQsLixw3Sy1RogQBAQGkp6cjl8t1TuXx48cNurqDJh0nJCSE0aNL8/LlXkABqHnzxoxfflFw5Egyxi4ttLkaGxvL6tWrRZ9xwYIFRSWYXV1dKV++vMGuvFQqpXTp0qxZs0bQeXjy5Ane3t7Y2toaRLyKFStGiRIlcHJyMniOzs7O+Pj40LdvX/bs2aMzJENCQmjfvj3Hjx83mAOaz8v48eNF77FYsWIcO3aMsWPH6uaHhoYyYsQIPD09efjwoeC8N2/esGTJEsHUtMuXL1OnTh3BeSD8LLT8+eefKAUiXSrg+rVrzAMmAbuAZKAm8BqItbUVdZCWLl0qWiNja2vLoEGDOHXqlMFY//79WbRokWDkLSAgwKizYizqZm1tTbRArSeAvX0BYmIM3/8airGf22wAtO+4EOAlKroBY5kJ/IImwqJEk07Wgru0AhaInPOpUackJiaG7t27630mQ0JCkMlkjBw5UnTe58RoRGbz5s2Eh4ezfft2Bg8ebKDm4OHhwZAhQ9i6dSvPnz9ny5Ytn3SxJkyY+P/F1q1bdZGCDDQ/bgqFgjNnzhj9UfkYzp49S+PGjdm0aRMBAQH8+uuvtGvX7pNd73+NWq0mMDDQ4Mc6ODj4i/wOHzZsGD///DMHDx5kz549tG/fnsWLF3+y640YMcKgs3zRokWNFnPnhMmTJ3P8+HHGjRvH0qVLOXPmDPnz59eNZ2RkcP36dZ48eaIzNB4/fiy4u3znzh2ioqJo1qyZXg2JVCqlUqVKlC5d2uhanJ2dOXnyJFOmTKFjx45s27ZNZ/zlBG19Rk7/PisWFhZ6kTFbW1vBKIe5uTmJibbcuiVD88m/CVwHFNy/L+XuXeMJJWIKU7a2towYMcLAnvHx8WHYsGGMGTPGoE4kf/78jBkzRhdByopKpeLQoUOCvWC06zh16pRgA8u3b99iY2NDw4YN9TZKzM3NadSoERYWFly9etXA8AwKCuLMmTOiBmlycjKDBg0yiCCUKFGCfv36sXz5cgMnKCoqivnz5wuKXYDGCRRLkRJLDwOQSiRGN4Ge3b4t2NZRDRxYvpz7QEtgM7APGARMxXjdSVpaGo6OjoJjJUuWZLpIRH/evHncunVLcCwxMVH0nNpriiEUjdXi6uqImCluZenMGf52YrQ8AgIBFa/RNL/UvoLpwHOSkQPnEeaY6FoAlixZYrCxkJqaqlcH9qVh9Jvg+PHj1K5dm2rVqhk9SY0aNahVqxZHjx79VxdnwoSJ/9+EhoYKOhApKSlGVWI+hmnTpukVpyqVSi5fvsyhQ4c+yfX+1ygUCsG+DiqVSnBn/HPy8uVLDh06pLdebXPET9XB3tfXF29vb51xbW1tjb+/v+gO9z+hYMGCDB06lDZt2ugZd4cOHcLf359mzZrx888/U6dOHcLDw4mKihKsW0lMTOTNmzdMmDCB6dOnU6NGDfz9/Rk2bBgbN27Mdh2pqal06NCBKVOmsHnzZn7++WfBJoj/C6pVqyYoTqCpSXAnIeE+4AvUQLMfX47ExPNERBh3ZPz9/Q3SuTw8PBgwYACVKlViy5YtNG7cGD8/P9q3b8++fftwcXEhf/787N27l5YtW+Ln50ezZs3YuXMnRYsWFd1VT01NpVevXoIGa7ly5QgLCxN8v6amppKQkMCiRYsYP3481apVo1q1aowfP56FCxcSHx8v+FlNS0sjPDxcNFWyYsWK1K1blw0bNvDdd9/h5+dHx44dOXDgAPb29ga9h7QkJyeL1jtLpVKDfjxaLC0tEWuHbg74GokQZhipoYkSUPPTYqw3jVQqFa3NuXnzpmhjzzt37ogKV0gkEvr27St6PWNOjrGUVpksBQRdOVAoryDWovUcoHHpQrIcVQNXgG2i1wNERQKkIJpamZ1IxOfE6DfBgwcPcqSPD1C9evVsJRhNmDBh4p/Qrl07QeMgb968n0xgREhFJi0t7T+zUWNmZiaoduXo6Jht0fb/mosXLwqmbERGRop27v5Yfv/9dy5duqRLH0pJSWHHjh2cOHHik1wvMTGRcePGERwcjEKhIDU1lb/++kunniSkIpU/f35d1KVz587s37+fgIAARo8enaMUyFGjRnH8+HGdYR4WFsby5ctFC7Y/JTKZjDVr1vDNN9+QO3du3N3dqVmzJuvWrcPHR4lK1Q24hUY2NgO4j1zeh4oVjcvITp06lbZt25I/f36cnZ0pVaoUU6dOxdPTE9A4GJs3b+bIkSMsX75cL0JTsGBB1q5dy5EjR9iwYQNFixYF4LvvvhOM9Hh5eZGSkiIYIUlOTqZx48bkzp3bYMzd3V2n6DZgwAAOHjzIwYMH6d+/PxKJhLx58wr2NfHw8KBx48aCCnzm5ub06dMH0PRh2bZtG0eOHGHx4sW4uroCmsiMEBUrVhSV37aysqJz586C37u+vr50tbHBUWBeKZmMhtWqCW4EeHl5Ma53b8HrAYw2Itss9B2mJWuvw/fJzMx8tzlWGk1HmiJAWUDjbImpGlpYWBASEiI45ubmZlR8QqyWB7S/N2JrTRd1ZDRusZBjrQIuIxNRLTPDWtTwl6BpNCqkFJg1evylYdSRiY2NFfzwCZErVy7BfE0TJkyY+FBKlSpFy5Yt9bqGFyxYkIkTJ36yawp1KNfmwf9XmDx5sl7Nh4ODAy1atKBUqVKfcVWGFCtWTPB5ODo66tJmQkJCGDp0KL/88gv379/X+7tXr14xevRo+vTpw+XLl3N0zStXrhhEAZOSktiwYcMH3oVx9u7dKxgJe/r0Kenp6fTu3VsvRShPnjz079//o+Sgb9y4YXAsNjaWlStXfvA5P4aiRYty4sQJzp49y/nz59m3bx+urq48f/4EmcwwnUmlesK9e5pC98jISMaMGUOfPn30it/NzMyYNm0aLVq0oEqVKowaNYpmzZr9fZLkZMznz8eqZ0/Mtm6FHER4a9WqZeAoSqVSqlWrxvLlywWjAEFBQeTJk0fQAcqdO7fR9DyJRMLo0aP1Uh3t7e3p1KkTbm5uTJ8+Xa83jK2tLe3bt89W0W38+PGUL19eF7GSyWRUrlyZIUOGiNp8BQoUoH79+gZpcAULFmTGjBl416hBZ6mUrNtOhYCZ7u44engIfo5z585Ngp0duYGCwEJgBVAGTdQgLU8e0XsQk3QGccEO0EqFewNv0TjIwcBtIBwoKfo87O3tRZUAlUol9+7dE72msWJ/qbQQICQlbgVUEp2nQTh6AmF4MExwpABjyS0iLmAD1KlTh++++05PMdHHx0c0He9LwGixf3p6eo4LXI3J0pkwYcLEhzJnzhx++OEHNm3ahIeHBz179tQL46vVap49e4aFhYVut/Vj+P777wkJCdFL6SlWrJionOfXiK+vL6dPn2bFihVERETQqVMng53I2NhYoqKiKFSo0GcTOihTpgwVKlQgMDBQd8zMzIyaNWvi4ODAmjVrmDZtGlFRUYCmILdfv34MGTKE48ePM2TIEF2qREBAAO3atWPWrFkftJYPqQfJCVKpVLAwWSKRIJFI6NmzJw0aNGDZsmVIJBL69OkjmuKTU8Tu5VPdY055vzZJKpViYSHh/SwimUwzduLECQYPHqx7xocOHaJt27bMmTOH+/fv07lzZ90u+qlTp6hTpw4bN25E+vo1Nq1aIX34EIlajdm+fZhv2kTynj2aHlVoIikvXrwgb968OqNuwYIFBmlZKpWKvXv3igpQSCQSzp49K7jR+/TpU6MNQUFTO5XVsVYqlbo1lCtXjtOnTzNv5TzuBN9hWPdh+FXyEz1X1jUrlUrdeVUqFQqFApVKJfpZt7a2RqlUcunSJb0Iw4sXLwgKCsK7cWO6m5nRNT2dlUAeoDsQUqAA94OCBEUUXr58ye0XL2gHjAJdeloLYDkQ+OqV6D0Ycw7y5cuXpdbHDo2jEA5kYGlpSWpqLjQpWFmJAvIbrRESS2eNjY0VFYkAjH5/urkVIzq6JLAdbZxFgy/29mNISGgoOlcsJc0MKbEitTBvOEQufkBTdaSPD/mQSCSsXr2aCxcusHPnTry9venRo8dHyc9/arLtIxMaGiq4e/M+X1putQkTJv47VKxYUTDkf/PmTQYPHszz58+Ry+X4+Piwdu3aHEeShejduze2trZs3ryZ1NRUvLy8mDZt2hen5vWxODo6MmLECIPjmZmZ9O3bl8uXL5OQkIC7uzu9evWie/fun2GVsGXLFsaMGcPNmzeRy+XUqlWLkSNHkp6eztKlS3VODGh6h6xbt47u3bszZcoUvXzvhIQE9u3bR58+fYx2OK9atSo3b97UU0Syt7f/ZPffokUL5syZY5C2UqRIEV1aZYECBZg6deq/ds1vvvmGu3fv6jlPzs7O9OrV61+7xr9BoUKFKFSokIENUrhwYfz8/GjQoIHeM05MTGT//v306dOH0aNH672mqampnDp1iuPHj9Ni+3ZkWVLhJRkZyC5dwnzFCjIGDGDixIns3buXN2/e4OrqSqNGjZg0aZJoFCAxMZFevXqxa9cug94t2p4kQnUpiYmJxMbGijoyKpWKqVOn6qVXJicns3nzZvr06YNDLgd6n+3NLYdbJJZOZFjwMEa6jKRp4aZGXlX47bffuHPnju6/1Wo1169fZ+bMmXTp0oWjR48aKIJVq1aNSZMmGUhXKxQK+vbty5IlS+ifns5coAcak3wOsPLGDZY9fSroeMTFxfEqLIzeoFdjkwvoBEwz4hwYc/7+djiqAqHAG8ATyEds7DkMy+e1ZIhKc2dkZLBq1SrBMWOpY6CJPImlpbVqVZsHD34AfIAAIBNNyttcbGz2YKy1k3D3GbDHnATuC4xAKrd4grCK3HU0z0gikejqtb4GsnVkpk6dmqMvULVa/dl3c0yYMPH/h8zMTHr37q0n7fnmzRu6d+/+0YX5nTp1yrY3xn+V8ePHs2fPHp0hk5CQwNSpU6lSpYrRJoufCisrK+bMmWNw/OHDh4KFqWFhYZw6dYpXAru5b9++Zf/+/UYbAo4dO5YXL15w6dIloqOj8fDwoF27dgYNCYUIDw9n/PjxvHjxAnt7ewYNGpStMWBra8v06dMZN26cziEvUaIEK1asyPZ6H4rWOL527RqxsbHkzZuXLl26ZJuSlB1v3rxh3LhxhISEYG1tTZ8+fXIk6SyGRCJhxYoV/PTTTzx9+hSlUknBggWZN28eycnJBk4DaJzZvXv3Cr43UlNT2bt3L60ENl4lajXyixfZ5unJsmXLdMXNiYmJrF27liJFilClShUCAgIMjHxPT088PT0ZPXo08+fPJywsDDs7O0qXLs2CBQuIjo5m9uzZBkaym5sb7u7uKJVKZs6cydmzZwFNCtuwYcMIDw8XVAqLiYnh0KFDXM13lSMhf6tJ3X97n5GBI/Hz8MPV2lX0dRXrQn/r1i1cXV0FZY2vX78umj4VHx/P4sWLiQV+fG9Mmp6Ol5cX7u7uBp9Jd3d3OpQvL1hb4wX0LlaM5SL3ULhwYVGpaI0MdnU0URet05IIxKCphxGLLtiJCiGAcaU0Y3h5eXHlyvsRIA3BwW+Bl8CYd/+0vOHVK23T1jZAL8AauA8MB8QjUmlIyURYDCGDFBAZ0yigfX0YdWQ+pcSlCRMmTHwMp06dEvyRf/z4MZGRkeQxkl9tQpyLFy8aGDJv3rxh0aJFLFq06DOtyhA3NzdBBSYHBwcKFy4s2NPM3NxcUCErK3K5nNWrV/P69WtevHhB0aJFRaV1sxIbG0urVq30jMR79+6xaNEi6tWrZ3RuvXr1qF27Nnfv3uXt27fUrVs32+t9DObm5mzatImIiAgiIiIoXrz4R/eAS05OpkWLFnp1Snfu3GHmzJm0bt062/kRERFs2LABS0tLfvzxR136qLe3N6dPnyYoKIjMzExKlSqFVColIyPD6DMWS4Xx8vICEYNcZW3N1q1bDRSaUlJS2Lt3Lzt37uTAgQNcvXpVp5ro7e2tq9nr1KkTbdu25d69e7i6ulKgQAFAE9Fr0qQJ27Zt071fXV1d6d+/P1KplJ9++ol9+/bpznnt2jWePn3K6NGjRXulvI56zZmUMwbHXya/ZNnNZYz1Hys4DxA9p0wmE/2MX79+3Whjy6JFi4qqgfn4+AhGZOLj43EsWpRkmQz7984dDxSoUQOpVCqoXNm0aVMOHjwoeD1NhCQWw8hLPGCJreQOSepiaISMtTGNItgQBBYWooqYxiT47ezsSExMFBy7c0dYthrg1KktwENgAPDbu3VPAJ6gqd3pg6aTjlYsoQoaZ8wfNcLrTBZ1VLRYoJ/GpuXr7ANp1JH54Ycf/lfrMGHChIl/RFpamuAPq1Kp/Ffq9dLT00lNTcXBweEfRZuVSiXx8fFGf/S/ZMTW/ankjj8Ud3d3fH19OXz4sN7xMmXKULZsWapXr05YWJieUVKsWDEaNWqUo/O7ubkZ7eT9PnPmzDHY6X79+jULFizI1pEBjRFZrlw5Xd+kf4JWjtze3v4fzfPw8BBUxfoQVqxYYSC2EBMTw4IFC/Qcmbi4OCwtLfUKslesWMHcuXN1O/Zr165l+vTpfPfdd4AmMlO8eHG9c5ubm1OzZk2eP3+u94yLFi1Ks2bNCA0NJTg4WE+m19vbm969eyMRMYAlZmai73+lUom5uTn79u1j3bp1BAYG6uS0XVxcdH9nYWGBt7e3QSpq3rx5sbCw0DkylpaW5MuXj1evXnH+/Hm9e1AoFJw7d47+/ftjbm4u2KOkYMGCxD+LFwwu3L5/W7yxO+K1UFKpVPT+VSoVuXLlEoxKSCQSatWqxdq1awXnnjt3TlAuOTo6mntRUUQBzdBXn7oNmFlZiaqPiUWVQPMM0tLEnA4VI9RxLCSdN1RDY9Bb4MRf/EISf6SIf9c7OTmJ1sJ42dhwV8SRiX4j/luQkBAHrHv3T4v2+8IbTe2M87t1pgBOaFLPfgCMCZCYASqkKHEE4gAVcjSOW0ngL4E5LgLHvnyMC7GbMGHCxBdK/fr1BaVCCxQo8FHF0AqFgoEDB1KlShUqV65MnTp1dCkf2bF48WKqVq2q69vwJUUwcoqQOpu9vT3dunX7DKsxzurVq+nUqRPFixenaNGitGnThs2bNUWss2fPplevXpQsWRIfHx8aNmzIjh07RBslfiyhoaGCx40VJX8sCQkJdOzYkcqVK1O5cmUaNWok2rTwU3P69GnB40+ePAHgr7/+ol69evj5+VG5cmV+/vln0tLSdGlJWdOOnj9/zsSJE7NtQjtjxgz69OlDqVKlKFKkCN99953uGf/yyy8UK1YMc3NzZDIZtra29OvXD0dHR9RivULi46lfv75BcbZcLtelFgYEBLBp0yZu3rzJiRMnmDt3rs7YvnjxInXq1NE9j379+pGZmUlUVJSBqll4eDijRo3i6dOngnUZUVFRpKSkCCoJ5suXj6ZNmiKLEehPkgjOT433PHq/v46WzMxMfHx8BMcsLS0ZM2aM4JiHhwcBAQGCYyqVymCzIevYiRMnaK9UsgpNf/r7aLqgNAOOHj0q6sgYk8PXSES7gIE6lxVSqQV+EglrScWfc/jwJ36cYylJ1AGjXe8HDBggOlbMiGqvQ6awpDWAUmks2huCRqygBxrnoxQaJbM/yV7RzI/hKLkN3EHTTrY/CjQernA/JGPpal8yn+Yb3YQJEyY+MdbW1kyYMIE//viDkJAQzMzMKFy4MAsWLPioer0xY8awadMm3c5kZGQkAwYM4PTp07o+DEIcPnyYGTNm6AzX169fM3PmTAoWLEjjxo0/eD3/a2bOnMnLly+5desWiYmJeHh40KZNG6pUqfK5l2aAlZWVqLMok8mYPHny/2wtpUuXFkx1yZVLSFr136Fnz556Bl1ERARdunTh3LlzRpvwfQrEGhSqVCqSk5Pp2bOnzqkBjeMnlUpp2LChYE+g8PBwnjx5ImpYg+YZi0mxT5gwgTt37ugiHUlJScydO5fGjRtTMFcuHqNJ5HmDppPIRMCmRAl69uzJhQsXOHHiBOnp6VhYWFC9enUGDRrE48ePGTVqlK7XVGRkJC9evMDJyYnu3bvTr18/PYf2+fPnWFhYULRoUT1RCi2PHj3C2dlZUPVVJpPh4+PD4sWL6dGjB8HBwWRkZFCwYEFGjx6Ng4MDPg99uCO9o6mOtwBiQX5TTos+LURfMwCFQrgHi7l5ISwshAUN8ufPT/v27Vm7di3Xrl3TfT/a2dlx+PBhOnfuLHq9rBGr90lPTycDTQXI+xiLuhirZYmLjQUuo2mk+gRNPCI34INKdYE3Dg7Ujo9nKpqqk1Q0NTnXLC1BIPql5ebNm6JjF0ScQ4CX6eLzzJUvEb8TNXJ+RcG+LMcigM5YUgrxlbrQlnuMBl39kSea9/tjHnNcVOzg0zSZ/tSYIjImTJj4amnevDnnz59n48aNbNu2jfPnz390L5Rz584ZpFeEhYWxZMkSo/PWrFljsPseHx8vmm7xpWJnZ8fBgwc5cOAAq1at4syZM/zxxx+fe1n/OiqVijNnzrB+/XpBYYB/Sr9+/ShbtqzesXz58jF2rHitQk5JSUlh586d7N69W2fAxcTE6ClPaQkODubkyZMffc1/ilgjQXd3d7Zu3SoYKbp69SouLi6CioBWVlaCvUdyypkzZwxqHV68eMHChQv5s0MH6slkbAVOAkuBmhYWvP7xR16/fk1QUJAulTI9PZ0nT57w8uVL5s+fb9AwNy0tjcOHD7Ny5UqDqJxKpeLChQuiamdKpVLQwdHy5s0bihYtyoEDByhXrhw+Pj6sWrWKNm3aADCk1xByHcgFm4CdIFkpwV/irxNYUKlULFu2jJ49e+oVmycmdsVwH9uc2NheorVS2uNHjx5l+vTpuLq64u/vT0hICAUKFBBtsimRSERrRwDBhsdajIlPiL3fANQKBRr1r+to0rIqozF3LwCQZ/JkkoBqaFydqmhMeEn//kY3AI4dE5Y0BrAxIv3vkstRsHOLBMBMPAIEoOCIwNHn5OIIINYUOjc9iDcQUXAB+vMW8aL+r7OFiikiY8KEia8aa2trmjRp8q+dT6y+RqOE88/nfWm1JTmlfPnyH61i9aXy9u1b2rVrx8OHD0lNTcXd3Z3vv/+eCRMmfPA5bWxsOHToELNnz9YVe48cOVJTXP4RHD16lDFjxhASEoJEIqFQoULMmjULb29vwfdWRkYG0dFiqSOfjkGDBhEQEKCnFmZmZkbr1q158+aNYMpORkYGFSpUoGjRoty6dUtvrHTp0v+oRkno3ELExMQwccsWwt7brLiXns6cVauIjo7WixyBpr3EH3/8IVoEnpFhXLZXrAu9XC4nJSVFNIUuISGBefPm8dtvv+mO1a5dm8qVK3P06FFatGiBl5cX8+bNIyoqinrD69GvXz+kUikvX76kcuXKOidi586dFC1alCtXrhASsgDD3fcM7t2bzqxZAwkICNB7b0mlUlq2bAloCve1ztfbt29xc3PjxIkTNGvWjG3btgneo9jrBhit66pdu7bomLGGsDZyOfG6a4a8+6e7G1SzZ/O+ALsX8HzTZsq6uvJXFrlrLWYY/y53t7XlichYIUcFkWGGboIcsFCnGYnIgHBRPkSjQtzxeIiFSJzCAhUglrL5ddZ1miIyJkyYMJEFobobBweHbBti+vn5GaS0SSQSKleu/G8uz8S/wMCBA/nrr7900Y1Xr16xdu1awQjHP8HOzo7ffvuNXbt2sWzZso92YjIzMxk/fjxPnz5FrVajUql48uQJo0aNwt3dXbAWLF++fDkWNPg3cXd3Z/HixVSsWBFPT098fHzo1asXo0eP5ocffhDs7VSgQAFsbGzYsmULtWvXJl++fHh5edGsWTPWrFmT7TUVCgXDhw+ncuXKVKhQgdatW+uK0QsVKmTw99bW1nTt2lXU6Xj8+LGgbDNo3iMtW7YUjB5pG+YKRRcKFixIq1atBJ2y4sWLU6dOHcG1FixYkLJly/L7778bjF25coUHDx6ASkWl3bvZ9fgxp54/Z8yZM1i+61fSuHFjvUiIWq0mKCiImTNnolYbpvJp/uYhDx48oG56OhfQJGVdA3qrVDx+/JiJEycaRJBUKhWNGjUSTbtydnamaNGigmOgqWcRSwU2JqMvplgG4ORsrEZIgupZKEPQ6CSYvfvfHoAk8hVOSUmCs7JL1HxoJA0u9sEDwaQtJZBLkYYZmgag99DoqB1E01BUu16Yikb+IBhNDLF4Ns4PXEFl4K4ogEBU6HftyYqww/2lY3JkTJgw8Z/l4sWLdOvWja5du3Lq1KkczZk3bx6lSpXSFfy6urrSoUMHKlSoYHTe0KFDqVWrlk721drampo1azJs2LCPuwkTH8z9+/fp1asXnTp1YufOnbqoQNbeQ1ri4uJEG959Lm7cuCHYSC80NJR79+4xdepUvd1+Z2dn+vfvr5Mu/l9To0YNTp48yfXr17ly5QqTJk1CIpFQoEABunXrprf7XrBgQWbPng1oisX37NnDjRs3uHbtGhs2bNCTvL58+TLdu3ena9euHD9+XHd8yJAhrFmzhqCgIEJCQjh16hQdOnQgMzOTwoULGxjI2roTZxFDt2DBgqKy7a6urjRv3pymTZvqHBZLS0t8fX2ZMWMGJUuWpGPHjrqaKG0/oHnz5uHq6sqAAQP0HJ28efMyc+ZMLCwsGDVqlJ6j4+bmxujRozl37pxo8fmQIUOwGD8e82XLkD14gOXLl5idPYtV586QnCzqkK1evRpn50SwAuoC7YFagAXkyZPOheXLWYamJNwbqIimfshy716WLVsmeM709HTR78f09HRq1aolOAbGa8gSjHSDTEpKEkzXAkgwEgECGZtRswM4DDxH4xqcARYBN96T3taSDlhjgz3dAe2zkgEl+YYRmBmpkVG6uAimP8mBaKkFq/m7nN8HaILGmdHc3zRgEFAGKAzUAXajxh1x98qC34DTaLrngKaM/zgwCwCx37J/R8Hwf40ptcyECRP/SWbNmsWiRYt0SkGnT5+mW7du2dZ75M2bl8DAQPbv38/z589p0aKF0U7wWszNzdm9ezcXL17k8uXL5MuXj/bt23/WRsFqtZrbt2+TlpaGr6+vgRrTfwGVSsWtW7dQKpVUqFBBl+O+fft2xo0bp9tBPnHiBMeOHWPVqlWiymVivUc+F1ZWVpibmxuk5pibm2NlZcW1a9f0iuxTUlI0O/VZCAoK4u3bt/j6+gpGEz6E6Oho7t+/T6FChQTTpoSuo03j05KcnMyjR4/0ZJU1alP6zJs3j/nz5xP7ThXq1KlTdOnShd9++40LFy4Y1LM9evSIffv2cf78eQMnIDExkcWLFzNixAgePnyo11CzSJEiDBs2jOTkZP7880+9epf8+fMzZswYXYPOe/fuceTIEUqUKEHDhg11KmCTJk2idevWTJ06laJFizJ+/HjdZ+7Bgwd6zyo1NZUHDx7wzTffcP/+fYPneP/+fapWrWrwemhRZGaSvm8flu91lZcEB2O2bp1oOldSUhJTF01lwNUBmvp3gOJAUZjdbCbq1p0M9uWdgdYxMSw3oviXVZEtK6mpqezYsUN03ps3b5BIJIIO2/uS21mxsbEhISEBtUBanpubm6hMMqg4BzxGU+gPGvP9PhodMLFqHjVQkZ85x2w07s8WNAlpbYnhMdEps9DEBt5fjzWRmWrBZC4VIJHJ8FcZuiQlgfrAMRoBlu+NFgU6A+sBwzQ4+JZ0LlMPBbXQdJ45A1wENGZ/czTxtqAsc/ICXYVv/gvHFJExYcLEf47k5GS2bNmi9+OamJjI3r17c1Q/YGZmRps2bRg6dGiOnBgtEomEqlWrMnz4cHx9fT+rExMSEkKtWrVo1KgRTZs2pWrVqp+lCPxT8uDBA2rUqEHjxo1p0qQJVatW5cqVK6hUKubOnauXBpOens6RI0e4e/euYBqgp6cn/fv3/1/fglHKlCkjmJbj4+ODt7c3q1ev1jPY0tLSOHLkCKGhocTGxtKoUSMaNGhAs2bNqFq1KuvXr//oNY0aNYoaNWrQrFkz6tSpQ7du3YzWQICmqP/MmTPvGhVqiIqKYt68eUblblNTU9m4caPOiQGNIb5//37CwsIElasUCgWhoaGiKmrh4eFUqlSJzZs306hRI/z8/Gjfvj379+/HxcWF/Pnzs2fPHlq2bImfnx/NmjXT1ZdoKVWqFMOHD6dx48Z6Usbdu3enXr16HD9+nIULF+Lt7c3z58958uQJx44d06uxiI6OZvny5URHR7N27Vq9NLDExETWrFljVCWxebNmJAkIBciAVxcvis6TyWSct7zwtxOjJQ8cSz9GbgFnEsBWKhWNukilUg4cOCA4lp6eztWrV0XX8/TpU9EaIXd3d9F5BQoU0OtFlJXmzZuLzgMVe/nbidFiCezm7/aYQkgk3mjM5oLAGKAjYE4qtihUXmgiJu9TgaSkFMHUMgVgo8zERsAUt3p3FcOV8m4NhRBsIgRIyHw3DoHAZLRODEAxYA2a/jSd0EgeNEcjev15pNs/FpMjY8KEif8cjx8/1ttt1fLixQuDouL/Kn369OHWrVukpKSQmZnJo0ePGDlypFHZ0q8JtVpNnz6/cPduN1JTz5GefomHDwfxyy+/8vbtW8GUrOTkZA4cOMD06dNp27Yt+fPnx9nZmVKlSjFlypR/rTnkv4VEImHdunX4+fnpGnT6+/uzbt06wsPDBRWvoqKiOHv2LP369ePSpUvEx8ejUql4+vQp06dPF/xc5JT9+/ezfv16XdrS69evOXDgANOmTTM67+jRo4JpQlFRUSQnJ0NyMpb9+2NbrRo2NWtiMXYsKBQ8efJEUFHu5cuXPH78WPB5ubi40Lp1a8H6IRsbG9q2bQtoxCy2bNnCkSNHWL58ud65IiIiiI6OJj4+npiYGL00rYcPH9KmTRv8/f11fWtA46zt27dPz6lLSEigQYMGnDx5UnADJSoqinPnzgmOvX37lpiYGAoXNjSObWxsaN22LYatKTV94bcaibxmZGRw5eELwbFz95+QVrmyoNGd6OHBwIEDBef5+/sbVf779ttvRceMbfZoe0IJERERIeroCH32/76e2qDQX4snYCWi2gbwWnIKiUBBfBI22Ni0B1ag0QaTo6m+KQSsolDhOkABgTPm57XcgpcY1kiFk5v9WAJCKYLxwEs0UsyGqIlE4woJYYZGmnoVMBKN+zYTTTWUJvHs4sWLFClSBDc3N/LmzUv37t1FzvVlYEotM2HCxH8OT09PnJ2dDYw2FxcXwcLa/xovX74U/DEPCQnh+PHj2exYfh08fvyY+/dHotlN1P6UlSM4uADXr18X7VD+9u1bzM3NmT59OnPnziUkJISOHTvqush/Lq5fv86aNWtQqVSMHz9eZ1gXKFCAI0eO8Pr1ayQSia5oPjk5GXt7e4MUGjs7O4oVK8bcuXMNrhEREcGaNWs+WBJ627ZtBpEOpVLJ+fPnjc4rXbo05ubmBkpi9vb2WFlZYd2qFWZZms7K7t5F+uYNHlOm4OTkZHBNJycnihQpQr58+bhx44bemFQqpUCBAkyfPp2OHTsSHBwMaNIG69evr1PCSk5OZtWqVdy/f5/atWvTpk0b5HI59+7do2fPnnrfHcHBwWzcuBEvLy9++OEHnj17pht7FBSERCJhyZIlgpGFt2/fUqJECaytrQ3uw97eXidOIMT9+/f5448/GDJkiE722c7Ojt69e+Ph4cEAa2uWZGSg7bQTh8Ysta5eHfbvFzxnZmYmsa/sBMsh3oQ5Um71Am6VKkXJuDis0IgYP7G0pMCuXXjlyiV4H35+fqSmpgpuEsnlcpo2bcqsWbME11OsWDHR+zcWkXJxcTGQwtZibENCrVaLVpZIEe+HBPDcohbqVMPZqaSSmfwUjWOQ1Sl9AUzgVWgeYAiaehetw+cODEGpesE4yrCY8XihEWF4gwvr6MEr0tG4pq+B+Wicmo5oKpfikJIqmLImIRQ134jchUbtTcZCOrCY+lhymwyWoiAFzW9HixYtdNHT9PR09uzZQ0ZGBps2bRJ9bT4nnzUic/HiRb7//nuKFy+Oo6OjgfetVquZOnUqxYoVI0+ePDRu3JiHDx/q/U1cXBw9e/Ykf/785M+fn549e4rmapowYeL/B7lz56ZKlSp6PQEkEgnffvutoCrZfw2pVCq40ymRSD5ruptRVCqkwcFIjPTVyEpMjBlKZWX09+OkqNW+vH0r3JsENHn3d+/epU6dOixYsIBDhw7RvXt3unTpYjTN6d9AqVTy+PFjAynv0aNH07p1a7Zs2cK2bduoV6+eQTd0Nzc3PeUvGxsbChQw3OV1cHCgYsWKos/5Y57/h56zWbNmBv2drK2tadq0KWYPHiB7T/FKolQiu3QJV6mUqlWrGtQ0VaxYES8vL+7fv29wrYSEBA4fPoyPjw+nT59mwoQJdOnShS1btrB69WokEgmvX7+mXr16/Pbbb+zYsYNffvmFFi1akJ6ezsyZMw02QF6/fs2cOXOYO2uWnhMDEBsXx9qlS0XvXa1W4+/vT7ly5fSOW1hYULduXVFxAdAY8pnv1cDA3zLAd83N+RYYiyYW0BD4GURTtXg3pj7bARI8sEmHYm/AKgOIy4cq8HvMbW3xfvKEP/v354y3N2dbt8bl8WOcihRh6dKlgob+zp07GTdunOD7IE+ePIINT3W813srKwVF0twA2rdvT5qIGMDVLD1zhLgrcvwpGP0OMDcXdoEySUfTo+bWuyPeaDzFTOAUaemJQDfgEjAYTfH+JeAnVCo1h2lAJf5kMqNZTB9qcoZxTETjdCSjqXKZCmwEWgP9AbloJELTfSmBv0UJtHgAkVgAJ4BVqOhMCtNRcPndX/ft21fwPZdTsZzPwWeNyCQnJ1OiRAk6dOhA7969Dcbnz5/P4sWLWbx4MUWKFGHGjBm0bNmSa9eu6RRNfvrpJ8LDw9m1axcAv/zyC7169WL79u3/03sxYcLEl8WyZcvw8PDg4sWLqFQqvvnmG9Eu4P813N3d8fb2Nkg9KlSokK5Z3peE7PhxLP/4A+mLF2BlhbJMGVJWr4YsylXvY2VVGOGfMFdy567MN998Q2BgoN6Itjt5586d9SJWqampnDx5klOnTlG3bt1/6a702bt3LzNmzCAiIgIbGxsqVKjAypUriYqKYteuXXrNVF++fMm0adNo2LChqJOgUqkEd6RTUlKIioqiZMmSBka3p6cnP/300wffQ6dOnTh37hxJWSRq5XI5NWvWNDpPJpOxe/duRo4cyaNHjzA3N6dFixb06dMH6Z49SAUMUklcHJLXr1myZAkeHh6cO3cOlUpFxYoVmThxIsnJyYKNFtPT03nw4AFNmzbFzs6OwYMHG/zNqFGj9EQRMjIyuHTpEitWrDBoaqslISGBiD//FBxLfPyY7hMnCkoQy2QyZDIZ27dvZ9SoUdy7dw8zMzMaNGjA0KFDjab6Va1aldatW+s958TERHbs2EHPnj2xsLAgCk0NRFaEVPm0mJubo37xlNkrK9FMfRTX9HReW1qyTV2L+WbngfpI5XLKT5oEkybpzd23bx8yDLuNhIWGcuLECUEnICYmhnPnzomuJ/LePfG1ihbsayJdGSIOW9hff4nOA1gIrES/hD7j3TEx4QGA9PS5WNKMNPTTFqXcwolYoikLLEYjApABPADaI5NfBWUwGrWwOVlm3kSl2gU0JYqajNV7ko+BPWg01bJG15OBAOAn5Ah3kikDnOcRvXHhNuYkk4ktZpQkndVEMABNZYz221P2bs4cYPR7TV21ZFcH9zn5rI5M/fr1qV+/PqDxArOiVqtZunQpgwYN0qVBLF26lCJFirBr1y66devGo0ePOHnyJEePHtXlYM6dO5eGDRsSHBxMkSJF/rc3ZMKEiS8GuVz+n+xIn1OWL19O9+7dCQ4OJiMjg4IFCzJt2jSjjeQ+C3FxWI0YgUz7AxofjzQyEqv+/Uk1UpxetKiKfPng/cwcN7dUvv0WfH1X0KVLF4KCgkhNTcXLy4vx48fj4OAgKE2bmprKrl27Pokj8+rVK8aOHavr8B4fH09ERARDhgyhdOnSgrUur1694vXr16I79mFhYTx9alicGxMTw5EjR1i0aBFJSUncuXOH5ORkChQowNChQz+qyWSjRo3o0aMH+/btIyIigly5clG9enWGDx+e7VwnJyeWL19ucFxZtSpKDw9k7xn0and3VIUKIZPJBBuVWlhY4ObmZuDMOTk56RrkHj58mBUrVpCSkoK3tzeTJk3CxcXFwMEDjWN48eJFihYtypkzZwzGvb29cb10SfDektPTRXsQubq6olarsbOzY9GiRQbjAQEBgvMANm3aJFh7EhkZyfHjx8mTJw8vXujXu9ja2tKsWTNRKXE7Ozu6psyhUmIqQ1DxFsiTmcJItvDC2R6YSGhoKBMmTCAiIgJHR0eGDRtGpUqVSH71SrBloqtazeLFiwWvl5aWZlRi2dLHR3Qs1IjK3oYNG0TbOqaKNEPVsgnwRZOU6oEmcesoMA+QGnFkHNJD6M9wZqEkgXDkyClKdQawiEuNu7E+oD8aCTgt7sA28uQbxJMnPwJH+DtK8hpNlMYS+AHY+W6uBRrHZQgaXbOXAitJBA4ilgT3Fxo55xlEZ5EDyCCBaGLQd2Ky4o3mM75UIMJobS0kOvBl8MUW+z9//pzXr1/rdXa1srKiSpUq/PluV+Tq1avY2tpSqVIl3d9UrlwZGxsb3d+YMGHi/y8xMTHMnTuXWbNmCRqL/2Xy58+vkxw+cuQI58+fp3r16p97WQaYb9jwtxOTBdnt2yCQ4qDFygp69gRn57/NGQcHFR07SnB2VpMrVy6OHDnCiRMnOHz4MBcuXNA1ihSTWdbWT2VkZDBy5Ejq1KnD8uXLjabq5ITly5frnJisXL9+HW9vb0H1JRsbG6Ndz1Uqlei63r59i6OjI/v27eP06dO6+2/fvv2H38Q7fv/9d86ePcvhw4c5c+YMy5Yt00vh/Keo3dzIbNUKVZbom8rVlfSffgIjqUUSiYQhQ4boFXxbWVnRoEEDSpYsydatW+nXrx9nzpzh6tWrbN26lRYtWpCamoqNSEG3s7Mzo0aNomzZsnrHS5QooTHsRdZiLZcL9q0BcHR01B0PCwtjypQpLF68WCd+UKxYMdE00DJlygiu1crKioIFCzJmzBi9+5fJZNSsWZNq1aqJSozb2NhQVqWiAyoOoin73gt8j4LvMjN5/fo1rVu3Zv/+/Vy7do0TJ07QtWtXrly5Qm2RzvauYLTpZa1atfTU3bRYWFhQ0c9PtB/Mt35+oue0UqnwFDguIWdtHQejcWZqvPvfXu+OG/us91arOcB24tmFmitkcoFwpvCSBG6EtgWENs9LkJbmDgwDcqFxIeTv/v8wIA0Jr4CqaBTFigBl0URiEjCMf2nvsoToOpOAdhhqmtkDPwKxmKMGzgK/onGhlEAyFkycONGgz5JMJmPSe9G5L4kvttj/9WuNNvb7nnyuXLl0OxRRUVG4uLjofQlIJBJcXV2NGi3a4j8TJnKC6f3y5ZOcnIxMJtMzCE+ePMmCBQt03xcrVqygZ8+e/9NC9y/hvSOTybCxsTGq5PM5cQ8PFzRIFGlpPHn8GLURY7ZBA8if34rNm11RKqFduxjKl0/m/Zfdzs5Obxe+SpUqPH78mLS0NN2x/PnzU69ePc6cOcP333+vG7tx4wYzZszgwIEDgn1O3ketVpOYmIi1tbXOmBRTdNJGigoWLKhX/ymVSildurSg86MlKioKmUwmaHhZWlrqvffs7e2N1yl8AI6OjsTHx4umYv0junbFtkQJcu3bh9rMjMgffiDNxweDB/keJUqUYPHixaxfv57k5GQaNGhAjRo1CA4OZvHixXqyzaApnp8xYwYNGzbkzp07ekpquXPnpk2bNrx584aFCxeydetWHj9+TKFChejYsSOxsbHEuruDQBRMbW9PtWrV8PLy0nufmZubU7lyZYKDg1m7di3bt2/XKZQtXbqUcePGUaFCBezt7Q1eR3t7e7y8vChVqhTh4eF6z7lQoUJ4enpy4cIF3rx5ozuurcF68uSJ0ZTEVenpvK9b9gxYnZaG1ciRBpG+yMhIxo8fz+8ZGexFv9eKhHfNM0WK9qVSKXZ2djhaWBDznmKip709UVFRWSSPtQ6xxngPe/YMOQiqqPnZ2XE/OZkTwH4gEqiOJp4xA9CocwlthFjDu1hGLPBPtrzj+bsKRkscmgSwqe1y8gAA2nFJREFUR4+evFvB+8iIiXEFagNZvz/MgVqY40yGTn/ujd5ML1IIFZQmUCMmvfz3VTUokRKPA47EIUUjdDCJUmziL/5E8yzNgdJAXnIzJiSEypMqc3zVcRSPFWAN7p3cKVCmwGf7Pcsuu+qLdWQ+JaaUMxM5xZSi+GXz7NkzBgwYwLNnz5DJZJQqVYqlS5diY2PD+vXr9QzIqKgotmzZQt++ff8nYXLTeydnSAYNQnnwILL3jH1p8eIULlnS6NyYmBg2buxFUFAQKpWKzZsL4++/BE9PIdfob6ZOnUquXLk4cOAAycnJ5MuXjylTplC8eHHKlSun5+CApufHwoULRTuba9m9ezfz588nKioKOzs7vvvuO/744w+GDRvG6dOn9YxO0Kh5FS1alNmzZ9OxY0cSEhKQSCTkz5+fRYsW4eDgIHqtIkWKUKZMGQPVrvz589OtWzddHelXQ5Ei8MMPAO9VIGQ3rYhe5oYWIZlxlUpFREQEEyZMwM7OjvXr1xMfH0/u3LkZNWoU1apV0/1tmTJlDOb3HjKEc1evkvZe6lKF+vUpU6YMu3btYsSIETx//hxra2saNmzIyJEjefPmDfv27dOTWQ4PD2fp0qUEBgZy9epV6tevr/u+8vDw4MSJE+TKlYv169czevRoLl68iEKhoGjRosybNw8XFxfq1atnULsQHBzMgwcPsLa2FnQy8+bNS5hAaiXAI7Uay6AgwbGnT5+yVCJB+V7alRq4CaifPBGcByBNSSFe4HmEv3lD1KtXgCOwAY05LUXTrLErwSEhmGPoyMgAZ3Nz5gF13/3TkoAmgeupTnPtfQoi475onMOY3MefImd8C2QqZgH1MJRZjiAzU4Jh4T1AHizwIMPAPdLgTQqhmAFN0UgRpKFpAJSJLQEkCc7ScAq4wnC20JlYnHHhLd1YSQiLeUY0WZ9WBnADuEE8P1m95vTV0ygiFZoXRAHh58JZWGohO1vtNHLFz8cX68ho83jfvHmjpwf/5s0bnXJL7ty5iY6ORq1W63Yf1Go1b9++1VN3MWHCxH8PpVJJly5duHv3bw2asLAwevTowdixYwV3oJ89e8aff/5JrVq1/pdLNWEEtYcH6YMGYblkCdLnz1FbWaEsWpRUgXqC9+nataue9O/Lly/p3Lkzp06dMqqkJZFIqFmzJrdu3SIxMZFy5cqRP39+4O9sgPc5ffo0oImuTJ48mfDwcDw9PRk1ahR58+YlKCiI0aNH6+ZHRkYSHh6Oi4sLgwYNok+fPqxdu5YXL15gbW1NyZIlWbhwIRkZGQwdOlQvehASEkL//v3ZuHGj0ftftGgRPXr04MmTJ2RmZurSjT6lE/P27VumTJlCSEiIzgHQKgEmJiYyc+ZM7ty5g4ODA0OGDDFI0/oQ9uzZw9atW1GpVDRu3Jhu3bplq5SWO3dug8iCmZkZNWrUAKBLly506dLlH60jb968WMpkmKMxmK3R1FRoHWdvb292795tMO/AgQOCRf0vX74kMjISd3d3bt++LXhNMzMzZs6caXBcoVDoiS5oUavVzJw5k9KlS3PhwgWD8QYNGjBRJPU+QirFXaTPlEKh4LGjIykCBfj3AC8RtViFQsHmmTMFHYcMQPXyJZrmjPWzjOQH9lCr6h12rV9vUAuiBB7GxVEIjQE+Fc3zKA+MA7oABzATrKGRkyhaW6NxYqQg8hfPMEdGhsG9xANm8udkKiagaZRZCI3LEwR0w8zMnszMF8B5NI0nQZP8VZNExCOlZ5AgYQ5qKgPpaKJMGjUyD/x5LDoTptIWC8aQ9k7DLAJPxjKRFIKRZWmPqU8Sqy+uJm1PmuYyWmLg2sZr0MrIBT8jX6wjU6BAAdzc3AgMDNR1k01LS+Py5cu6At5vv/2WpKQkrl69qquTuXr1KsnJyXp1MyZMmPjvcfbsWR4/Nvwqv3fvHiqVCmtra4NdWVtbW6O9CUx8HjJ79SLz+++Rnz6NOndulFWqQDaG6vPnz/WUp7QEBQVx69YtypcvLzr38OHDDB48WOd0nDlzhosXLxIQECBaW2BlZcXLly9p3rw5T7LsPl++fJl9+/Yxf/58AycoNTWVgIAABg0axJAhQ+jWrRuBgYF4enry7bffIpFI2LNnj2DKhtbJMuaUFC9enPPnz+uUxGrXri1a//FPuX37Nn/99RdVqlTR1T/ExMTQpEkTgrLs2l+5coVt27bh7e1Ny5YtuX79um7szz//ZNGiRToBhZiYGA4fPkzu3LmpU6dOjmprJkyYwMqVK3Wyv+fOnePGjRuixeVahg4dSt++ffXSzH19fWnXrl3OX4T3WDZrFidTU3FHU19QDHBRq/lt714YOVJ0npjAQkpMjO55paTA4cOa917jxgqy1rlnZmZy7NgxkpKSaNiwIQ4ODkilUqRSqWC/JHd3d4YPH063bt300hPLly9Pz549mTZtGgoBh8XK2ppSpUoRKlCzVqhQIXzLlydozRqDMWtbW6Opq2qRz5QEiM10Q6OZ9T7FuR8Uih3wvuskAwqlpXEC/c4sJ4BzQDNARrSgO2JDGPFGQy8WQEE0imNaPAFnoqUhWKky9CIhEjRul0KVwVM2ATvQvDMUaFw8WyQKB6AzmtiV9nU/i8b10o/SZkWJORqnyBNNJAY0TtZtkukLTBGdq2aAzonRkoITMAwpx0UiUiqeHX6m78S8I/l2sui1Pjef1ZFJSkrSvflVKhXh4eHcuXMHJycn8uXLR58+fZgzZw5FihShcOHCzJo1CxsbG9q0aQNoisvq1q3L4MGDmTdvHgCDBw+mQYMGppQOEyb+48TExOj6KWQlPT0dJycnSpUqxdksTfZAY/i938/CxBeCgwOKli1z/OcJCQkGKWCg2fB6vzbifebNm2fgdNy8eZPt27dTp04d9u3bpzcmlUqZNGkSkyZN0nNiQBM9mThxouBaAL0mkE5OTrRqpb+tGR0dLShtmp6eTmpqarbRFZlM9q9GGDMyMujUqRNXr14lLi4OZ2dnatasyapVq5g5c6aeEwOaKOjEiROpVauWgfxwZGQks2fPpm7duixevJhly5bx4sULLCwsKFKkCJs3bxbshaMlMTHRoAlnZmYmhw4d0kXCxChQoIBeDYxEIiEzM1PUUc0JtW/fpjyaPfsOWY7/ZKSWCYDgYOF9fqUSuVrNiRMyRo604tkzTUF8oUIqpk9PpU4dJXfv3qVnz54EBwejUCjw8vJi+PDhdOzYkcKFCxv01pPL5cybNw9PT08OHDjAjBkziI6OpkyZMgwePBgrKytRB1IqlTJixAiuXLmi1+vIysqKH3/80aD5qhZLW1vRvk0ARRs0gEOHDI7LAKWZC/pCyLqrEnbnJRWBMPT9Dh+guEzGHP52YrRcB+4jnAIGmuQs5FLIFHBzZBJQDkJTAj8dTUWMK5o4Tyi+qvoGSWBqNO8HR7yBYDSOStbPgQSVIgpNM8uspAKXsklnS0HfieHd1YrxEvGNGg1i4gslsUD49ZEBkpcitVVJHyd48in5rKplN2/epHr16lSvXp3U1FSmTp1K9erVmTJF42UOHDiQPn36MHz4cGrVqkVkZCR79uzR+2JftWoVpUqVonXr1rRu3ZpSpUoJSjyaMGHiv0W9evUEjaB8+fLh5eXF+vXradSoEQUKFCB//vzUrVuXzZs3f7kNIU38I0qUKEGBfIbVFAULFMDPiNoRYNCQEjQG8oULF1i3bh3Vq1fHwsICqVSKtbU1vXr1omnTpqLF9xEREbRo0UJQfUwbzXjw4AHNmjWjfPny+Pv78/vvv6NWq2nRooVgTU++fPk+S4r01KlTOXHihK6xdExMDAcPHmTVqlWCcs+gqT+7evWqYHQgOjqaiIgI5s6dq5MKTk9P5969e/Tr1w/Q1HUULVoUV1dXcuXKRfny5YmJieHp06eCwj3x8fFcv36dtLQ0/P39yZ07N66urnh7e3PlXTPEJk2a6DmXarWaGzdufFSPOX9bW0Gjyf2dYxAQEECBAgVwdXXFzc2NOnXqoFAouLx7t2B0wAJ4eOEKY8da8fSpDJVKgkol4ckTGWPGWKFUanrjPXz4UOfshoaGMnXqVOLj4wkMDMTHxwczMzOkUim2trYsWLBA9346dOgQDx8+5MmTJ1y+fFmXhivmHDs6OlKmTBmmTZtGuXLl8PT0pESJEgwbNoyuXbvyIEsaZ1asExIYP3684JiNjQ2VKlcWHFMA37rHAw/RJIQVR+OmtAJuUNjxNn2Brmi0vPKjKadfDFwoWFAwKUsBJAs6RhoyAQe5sOlrKYO/E9RmoKm22YSmRiUXZrxzhN4jAQi3+wEEC/MVpKnFNyPURtaqqbcRqpOzQpPYaAwLDF0kFWCOu8BfA7gATvZOwmeTf2Gy/Vn4rBGZatWq6b4shZBIJIwaNYpRo0aJ/o2joyMrVqz4BKszYcLEl4yDgwMDBgxgypQpup1CT09PJk+ejEQiwdHRkS1btpCZmYlarc6R4pQJDREREcyaNYvXr19TpUoVfvrppy+u/4xMJmOKpSVD+LtdXAFgrExmdHcYNDK776fCyOVyKlasCGhqGhQKBQkJCTg6OuqkY8VShHLlykWrVq04cuQIJ0+eJC4uDktLS0qWLMmMGTNISEigc+fOeo5AcHAwarWa3377jd69+zB7dihxcd8BqeTNu4MZMwbl6HWQ/vUXFkuWIElPJ6NdOxRNmmSblgeaWp+ZM2fy+vVrKleurGuweOXKFYM+GtrUJjHxgYyMDMqWLcvu3bsN5jo5ObFu3TpB5/HmzZskJydTs2ZNkpP/Tl159uwZfn5+bNq0SbSnR1JSElWrVtWLkEVHR9O0aVOeP38uGj2YMWNGtjLUCQkJLFiwgIcPH1K4cGEGDx6Mo6MjbrVqwXviCgB5SpTg+atX/Pjjj7qu6AqFghs3btCgQQN6N2iA9P59A2cmA0g0r6yLxGQlNFTKqVOx3L9/32AsPDycAwcO0LlzZ65evUpGRgZ3797F19dX9zfLli1j5syZujqa0NBQevXqxdGjR/H09BRU0tPKj7dp2JAfnj4l8+ZNzAoWJLNrV9TAt3Fx7MYwsuSqUlGiRAnBZpJ2dnZMnTrV4FpaNp0/i6auJOv7Ixi4yLU3JdiBpqpkGRqzPAZYCpwODgbB9pzgIvPkjVLY6VZLJHilKhCqSMqXoSaYPfxdx8K7qz4CWqOSgpnKMJphBzxL2y+4FjBDrc4HRAuMgUYEQKxOJuzdvHQ0lUAxQCOg47s12aGvIafFCjj+7tzl3/1dPHANSCWYfFgRzmDU+L67+hTgDb589913Bo2EAXyM9Pv53HyxNTImTJgwkR0PHjzQq4NJSUnh/v37eupDZmZmn2NpXy03b97kxx9/1IklHDlyhICAAPbt2/dFOYOSly9pGhFBLWAjmp/6roBTVBTJ9++jMqJ41rNnT549e6anIlW6dGk6d+6s+2+5XG7QT2HMmDFcu3ZNr4agQIECjBkzBolEwqpVq7hz5w5HjhyhdOnSfPfdd0ilUubMmWMQzcjIyGDPnj389ttvPHgwjNRUOdod3dTUDjx4kI6vr8ZkUiqV3Lx5E6lUSrly5XSOldnKlVhOm4b03X3IT54ko21b0hYs0F3n0aNHvHnzBl9fX52Dd+fOHTp37qz3jA8fPsz+/ftFU6/MzMz00uSyIpfL6dGjBzt37tRrDJkrVy769u3LwYMHBeelpqayefNmPSdGS1RUlGiTSdCoaL2f5gcap2v8+PGiDlB2Tm5MTAxNmzbVcyCOHTvGgQMHyD1gAOYBAciypHMp8+RBOWIEAwcO1DkxWbl37x72I0YIpg+ppVKsHSwwN1eTkaHvfJqbq5HJ0gXPCeiJmZibmxv0HNq9e7eBGMCLFy+YO3euYE8XeNdDJSkJm6ZNkd28ifaVMj9xguRdu/ipbFm2/PWXXuJULmCgqytjx45FolYb3GdKZCSvX4t/byikTug7MVqisHarxT00fV6+B/KgiY+8BKzkclSZCpQCzoOrtwVvjFTCvy89rUXTWnWbyGgIT52lWL5V6TkyEjSpTVKpEyDDDCXfAMnwzllSI5eDQPboO8R7ZWmEkf8ArvB3Et0hYC2aiIuYI1MWmAVcRSP7XAUIBC4CVbAjFyd4wTf8nZbVCGjJa7p27cqWLVv0xCdcXFwYMGCAkXV+Xr7YhpgmTJgwYYwXL14QEBCg58jExsaydu1aUYPLRPb8/vvvekaSSqXizz//ZNOmTZ9xVYZI3r5FkpSEPdAPTR9sF0CSkIBUpG+LloYNG+Lh4aEz2u3s7GjSpEm2UScvLy92795NixYt8PPzo3nz5uzYsUOvJrNMmTL8+uuvNGrUSGcwCqlHgUYh7dEjKcePm5Ge/ndaSnS0jGXLLFAqNU0zq1WrRuPGjWnUqBHVq1fXGBkKBRarV+ucGABJaipmR48iCQ0lNjaWRo0aUb9+fZo1a0bVqlVZv349AL/99pvBM7527Rrr16+nRYsWBjUUZmZmetGG95HJZFhbW7N//366du2Kn58f9erVY9WqVUb7NqnVav766y/RMaGicy1Z61/e58KFC6JOd7NmzUTnAUyZMsUgChIUFMRvv/0GDg4k799P+g8/oPDzI/O770jZsAFlrVqEhYUJnk+hUBAaGiroyFi7uODpGUORIoaJZ0WKqChUKE3UIctOKCFrXVFW3rx5Y9SRsZg/H/nNm3pNKmVPnmA5bhzSwYM55uZGTzTd4RsCW4AWjRrx8ORJwfQ5M6C4YyWEPlm2SCjh/a3oPWgV5tKB9WiqVrTJneXKlQOZcN2GpY3451itViNWQSfkEmQlM0Fq8DdqNLIAHoqaNEbJTTQuw3k0cs0FSMDRzpioszERZSlwFP1KIAWayEoUmiiLEK/RRGwATgOTQKdUFsRk7uo5MaCpqJlLOJaWluzfv58ff/wRPz8/6taty6pVq2jdurWRdX5eTI6MCRMmvkouXrwoKJUbGRkpalSYyB6h11SpVOrJHP8vSUpKol+/flSrVo1atWoxfvx4lEolquLFUQkUe6u8vFB8K24cgUYU5u7du7q6g8TERJYtWya4w/8+3t7erFu3jiNHjrB+/XqjHc21GIsKnjwpJzra8Kc4KkrCq1cqfvnlFx48eEB6ejppaWncu3eP/v37IwkPRyJQPyKNikJ+9ix9+/bl0qVLxMfHo1KpePr0KZMnTyYiIoLIyEiDeQqFgosXLxIXF2dgPKvVauLi4qhcubJgjZnWkXNycmL+/PkcOXKEnTt36gxRMSU1uVxOMZFGigBNmzYVfO2kUqlRw0oul5MnTx7BMW1kbP/+/TRq1Ah/f39atWrFrVu3AHjy5AnV0Chg3ULTj6Mu6Jwqde7cpC1ZQvKRI6Rs24bq3XutZ8+egteztbWladOmgutxd3fHw8Od9etTqFJFQe7cKtzcVFSpomDt2hQkEnGHxdLSEtRqzBctwqZePUr88ANWnTsjefdsheoHzc3NadSokaC4BICFhQUykSiY9OVL1PnzYztoEEttbTkrlRJgbk4NX1/SJk+mRnKyoEGZAdSVXGYXmhaO2giGE3ACC2p4ZFJQwOl0lckoa0SYJSkpSdRZtbU13izSmFthTFpDmSFWz+KKV+ZO5gIl0cRR7IBv0XTHcTQTd5Gspa5oXpGJaFyfv9C4bfZo4l12DETjhtwEdgPuKNC04hS7kzSEq3kAUqlGpuCz8nr3v46OjsybN48jR46wa9euL75dgcmRMWHCxFdJsWLFDNIpQFM7Y+oj9eEIvabwd/78/5oOHTqwefNm7t69y82bN1myZAn9+/cHc3PSBw1C6eGh+1tV7txk/PwziNyDFqGUpTdv3rBkyZJ/ff0AderUEXQA8ufPT6lSSqytDQ0Se3s1YWG3BAvsQ0JCuBcVhVrgPlV2diiKF+fSpUsGY1FRUaxcuVL0GRcsWJBTp07pdZEHjZOzZ88e+vbti7+/vy6SJZFIKF26NJMnT9ZcW60i4GkAvY/2ZtrlacSlxQFQs2ZNwSiAu7s7tWrVEjTWbW1t8fHxoWPHjnpzJRIJdevWpUKFCqJGfoMGDXB0dDQ4LpFIKF68OIcPH2bIkCFcunSJ+/fvc/r0abp06cKLFy8oJ5GwGY3zUhZNYs56oJJIZERL9+7d8fLy0jsml8uZM2cOHh4e/PDDD3qvu4eHhy4lMX9+NXPn/kWtWj9Rq9bPzJ9/i/z51eTOnRsXFxeDa5mZmVG5cmUsZs7k/9g77/Aoyq8N3zPb0hsJISEhkBB6Cb2qIEWkSEeQpgiKSBMsiCiCjaLYABEBsSBdQLr03juEEkghAUJ6T7bO98ckIZvdBEWF8P3mvq5cmnf2nXl3sgnvM+ec52g/+QT1iRM4RUSg3bgRpz59QK9n1qxZViJbq9XSpk0b+vTpU2L6oMFgwOLriwl5Az4Y+ALZO0tyd4f0dLQ//oiYlYVgsSAYDKgiItAsXUpk+fIlRmQygyvQAgcykeMEN5CrPfwQkSQTGXYi6NlmMwGRkfnJlt7IZf6tkJtmgoebG74l/PxblGK9Loqi3ZJ8kEVWWAlF9EGAXuhU0ll5ihjs/YWsCmgy/PPPbgeLHzAXeAtZ+jRANj7YDGh4FwsfISeHhSHbIGwFdDShpH43OkTklDUdMAr5pzkc8u0KSopXeeT/V5Jg61YVI0c68tlnOkopZS8TKEJGQUHhsSQsLMymV4hGo6FNmzYlbtQU7k+XLl1sNp1OTk6MGjXqoa/lzJkzNo0Ci0YOjC+8QPaffxIxZAhXX3iB7G3bMOQ7YZVGSc51JaXc/FOGDBlCzZo1rcZcXFx48cUXefJJM/XqWef563QSHTua0Okku2sVBAHJyQnT008jFXsqbQkLw9SwYYmpRZcuXeLll1+2qf8JDQ1l7Nixdi3NQXYakyQJs9lcGLEp+N5sNmORLAzcOJDhW4ez4soKZhybQfsV7bmacpXnnntOTgUC5CfNTjg6OvL8889Tv379wshNAaIo0r1798Inwzt27CAsLIzatWvz+++/s2rVKhwcHIqc8x7Ozs68++67DBw40MacoEaNGowYMYIFCxZY1UeBbCM9e/Zs3hMEinvh+QNFLYcMBgPXrl2zMSs6ffo0X3zxBVWrVuWpp57izJkzhZEjg8FgFQmxWCyFKbDz5s2ja9eurFz5IytWLKFz584sWLAAJycnnnvuObRa2WmqYKPcrFkzmjdvjnH5clS5uSQiCwQzwMWLaH77Tf6ZFBGkkiRhMpmQJKnECKFKpSJj/Hja6XSMQK5HeQtorlIRO3gwugULUF27hgm51WMKIGZkoF2+nLr9+9s9p1oUiUlJIYU8JGT3scrIW/Db5HA1MpJ7d9GFgphILrD7zBnKE4a8Id+PHJeIRqA1rwcEMDiveKcU2fes5YkTdgUgyL10SvL70gGRmhoUr6ISgUB05En+dmbJq00Tcu1KFQcgPc8RNbb3XI2GHByBDvlXL0oYUJt+CLiC1c+4NvBSKa5lBrKRozkHgK+QJel85N41PoxFjpQVxYzsBidJMHiwI8OGObNihZaZMx1o186F8PCyKxfK7soUFBQU7sPy5ct56aWXCAsLo3HjxlY9pRQejIMHD9p9Ir9r166HvpaIiAi7tRBpaWkkJCQQFxfHM8OH03LLFlpu20bbESNs+pzYw94GuHz58v+ZWHNwcGDdunX07NmT+vXr07x5cz7++GNef/11BAFWr85m4EA9YWEm6tbN4u239Xz6aR4NGjQgJCTE5nwhISHUrl2bvM8/Rz9hAqYmTTCFhaEfMoTsFSuQKDklydfXl759+/LFF1/QqlUr6tevT+fOnVm5ciWenp4l1qXExcUxb948jhw5YmWzHB4ezpQpU9h0fRN7YvaQa7pXs3Y97TqT901GpVIxduxm3N2jUKluotFEERh4njFjJgOwbNkyXn75ZRo0aEDjxo2ZMGEC3xQxLGjUqFFh09KiaS47duygY8eOuOT3MalcuTKHDx8uNB/49NNPadGiBfXr1+e5555jzZo1ODs72xTBF5CQkIC7xf5Tbs98Qblw4UJat25N+/btefLJJxk1alTh/RBFkZdffpmTJ0+yYcMGAvPtwWNjY1m6dKmVuIyPj2f69OlkZWWxePFiEhPvNUZMTEwsbAL6+aRJhPv5cV6t5pRKRaSXF2vffhtBkkiMi6M78jP85kBD5FLwpN27mTRpklWjVaPRyIEDB1i1ahUdO3a0qQdzcnKib9++zNuwgQMGQ+FGVwIumM1M2roV8fp1fkLeYrfI/29/wJiRwdAWLbDXEaiBszOVoqNxxzomISILxJjoaMyFLUavI8drjgG1OZOZRRoG7lXGgFzJEo6U48sAk4nqyBJARJbIIwCviAhefPFFm7UU9Mmxn1gFFgSqcdtmk28BDOiR2G13HmRxWNKW4FkGqeQSaid6EoqUL3DsuQG6ANXRINGTez/jBsAGoC7XKCl9TCILOVWtSf4KClbSApjEQaAfciVNNrIgnYZcSbNtm4pduzTk5t77ad24oWLy5NJsoh8timuZgoLCY4uTkxNffvnlo17G/ysKen0UxWAwsHXr1n/UFf1BeOKJJ/Dz87OxivXz86Ny5cp07dqV48ePF46npKQwYsQI9u3bV2p0Zc6cOSQnJ3Pu3DnS09MLGzD/0/S5W7du8cEHHxAbG4ubmxvjxo0rdNDz9fXlxx9/tDvP1RXmzZM3JREREYU1J4IgFqbSRUZGIggCISEhLFiwQI7UCAL6SZPQF+sqr0Z257LXpPPZZ58FoGfPnvS004C0pMaeKSkpHDt2zO6xGzdusPH6RvLMtnNjM2JJT4cPP/QhPV0WV2YzXLtWntdfN/DLL7k4OjryxRdf2D23xWLh+eefL+wPU7t2bdavX4+DgwOiKLLuuefQ5OYiGAyY69cnz9u7cO7AgQMZOHCgzTkrVqxo12SgcePGWBISYO9em2PmkBDOnTvHzJkzC6M5GRkZrFq1ivLly8tmACVgz0EM5Hqc3bt32xWPMTExXLhwgbYzZhBSxJjBIyUF85tvkrFvH28bjfxRZE4aMB54LSODm0lJtEWOqLgB0cCb+TbaS5cu5c/ff+fQmTNYkL3y2jRuTL9+/ejfv79dg4HIyEiu9e7N5NWruV3keqsAj+xsZm7bxpfIaWhVkEvSDwK9DAaiPTywl+wbCLRp1ozpa0aB5QRy/xYLchrZUp54No25S7bYzJNIYV6sSDCyL1cBGciGAAu0WhwdHW3soNVqNY6OjujRYk8EGFCTa7yLD7LnV+X8c86moJ1lSY1PJVprA9EYrtoc0QOduM17mBiBLNUk5JSzRZh4lQucspvsZUDNSSaQxJ9FRtOQf8aViUaWcNYRVLnBZhBybMoeYeiRxdAGO0c3bNBaiZgC4uLKbtxDETIKCgoKCoWUVJjt51dSG7X/Dj8/P3r06MGvv/5KZqZcMOvt7c3w4cOJjY21euJcQEREBCdOnKBZs2YlntfZ2Zk1a9Zw8+ZNEhISqF279n1tee9HamoqPXv25Nq1e76vFy5cYN68ebRv3/6Bz1u/fn3279/PxYsXEQSB2rVr37epq8ViKdGB7cKFC3Tu3LnEuTqdzq4dsqOjo926E5AfKAS42hovADhrnPn1Vy3R0bYRovPnVRgMUJqrd/Pmza3u6dGjRwkLC+PKlStyjcjXXyPmiwT1sWOoLl4ke/PmUnvpfPLJJ1y5cqXw8yOKIo0aNWLUqFHkGQyojh5Fdf58Ydd1S61a6KdOZf5779mkpJlMJvbv31/yG8hfsz0kScLBwQEPDw+bvjceHh5U0OlQ2YkwijduoN6yBXt+bzHA1ZgYOmo0vAWFzQ9bIdf8LPLwYMPXXxeKGJDTiv7cv5+j69fj6Sk3RHwC6IIsFNYj/4xnXLxYKGIK3wOwPiuLLrdv0x3rNJ8ngWSDAXNQEBL2q0RyVD5gWQos596m/DBwkYuJb5bQMFJD9UZ12HbW9shdYHNuLkfWrbMRZAaDgblz5xKgCcbVGE54kWPVAUEdiJcpkh+AojYDDZBNkKM13hiN9j3PLprvkoNtm8pMIFIUCbTAKeAC8j2rh2w6nY4Hsvwrng4toqEWB7Dt6RIHxGHCHYuNb5kEaMjBWGKxv+3vdlGCguxHJJ2dS68Re5SUXYmloKCgoPDQ6dOnj43rT+XKlRk/fvwjWc9nn33Gzz//TK9evRgwYADr169nxIgR6PV6u+5LRqOxxKhCcSpVqkTjxo3/sYgB+PLLL6023CA7wH399df/+NyCIBAcHEyVKlXuK2JA3iCXJGSKpjfl5OQQExNjlUo4cuRIm2uIosi0adOYOHGijaD19PRkxIgRjG40mmB364iWs8aZXtV72X3CC5CXZ8RsLx8nn5iYGLtmB3fv3mX/nj1o1q4tFDEFqM6cQbW7pBQgmUqVKvHnn38yfvx4unfvzkcffcTGjRtxcnICDw+yt25FP2UKhu7d0U+aRNa2bUje3iXauptLexNQogAsWEv9+vVtxsPCwqhcoQJ2b5DZjJCTYzeVCcBksfCG2WzTwb0OMMXRkUmffWaT6GQCXhs9monjxrFdp2MT8A5yncxRlYqRAwawd98+u9dLyMoiUxDsemgJQAU77oIF3ElIRbYILhpZsACHObL/ABCA7TP3Kri7R5TYYvJcXh43i0SxinL58mVmlLvDceRUqueQ40DHgc9cU+kkihT3SquAXCo/fvwLJVxRxV6VmQvFRk3IlT0xDhpOIt+LesiCUkAWNgnosF+0r0ZHbqldZspj332uOmnI3mnFP696wLXEYn+A117TExJi/clycpLo3r20lTxalIiMgoKCgkIhI0eOxGw2s3r1ajIzM/H392fatGmP1Amubdu2NhagNWrUICgoiIsXL1qNh4SE0KJFi4e5PEDuRm+P9PSSej38Ne7evcvIkSOJiIhAEASqV6/OggUL8C6SQlUclUpFaGioVa8YkOuAXn75ZSwWC23btiU8PByz2YxOp2PEiBFMnz6dt99+m4SEBJYvX45er8fBwYFRo0bRt29fAObPn8+sWbNISEjAw8ODYcOG0a1bNwB+6voTU/ZPIS4zDhetC71CezG64WhOmK8jCJ5IUkWr9eTmnsfRsWT76pMnT9oVCZIkcWLnTrrYsVMScnNRnzyJuV27Es8LsgArMR3M2Rn9m2/aDA8aNIgdO3bYpInVq1ev1Gu98sorrFhh22hRFEWqV6/OL7/8wsSJEzl79iyCIBAWFsYXX3yB5OyMJSgIsZjNtqVyZYzdulFh3DhuFqvp8QGc69bFv4RaJ7c7d8gpoR9Qul5PrYMHcTCZCp9yOwJNzGbqHTnChJK7OtJcXc6uG1guGjxcXOxGYwRRxJR6BZEkO1v5JAwx2xA4iMQTyL1UTMgOZjEc2b6pxA2sq1qNQ2a23b4wzokZBHkaWQqsBJKRq3KcgafUWZRk3KwFUlLi7B7z9/fFz6EhXSM38T1yUpcRuepnIqDN9aAv8B1yHVOBiBkFqEgGopArhopyAw3raIRcMVQUH8CLXGogElFMzroC3VFxkVZg8xPRAq3y5U+H/Heflv/uqwB/4OEBv/ySw+TJDsTGiri4yCLmjTfKbm82RcgoKCgoKFjx+uuv8/pfcP96lIiiyJw5cxgzZgw3btxAkiSCg4P55JNPSuwv8V9St25dux3sfXx8HvickiQxaNAgTpw4UTgWGxvLkCFD2LLFtnagKPPmzWPAgAFcvnyZ3NxcAgMDefXVV6lUqRJdu3a1coPLzc1l7ty5dOzYkdatWzN27FiysrKIj4+nUqVKDB8+vPC19kRlAXV96rKht23m/datS5AkPfK2Lhg5lSYcvX4gOTn7cXJwQLN4MZpt20AUMQwciKlHD1q3bo1arbaJvImiyNO9emHZt49d8fF8i/ysuS3whosLpr/Q9+LGjRt89tlnJCUlERISwnvvvWfj5Facdu3aMXDgQDZs2EB8fDxubm40aNCAGTNmlDrvzJkzdsddXV2Jj4/Hz8+P77//3u5rcr/9Fqe+fRHj4kCSkLy80L/7LqKHBw7Vq1Pr8mWike9oRaCaIPDUoEFYFiyw2cZKgoC5QQPc//yTjCKNhAso7+yMZudORDviURUeTps2bVi5cqXNMWdnZ3YKPXmeJbgXS2lKUYWgavIUTqKIUNxIQa2m7RNP4P/dYopLBE90jK1VjYNHDmChaOqe/MCgYp4rWYLAhWLpYyLgX64cusxMttqsFDrk5bI/TeRz5CJ3gNvAp0BOqoSbxYO2pKIqFl+KpzJ16jRDxy+8i4EWyElanwOnkt2orw7nFDAMua5GhSwCdUAFnxSuJ8BQO+vRqrPA9DKwAqiBXJQfCXxIEplszD9ngWOZGzAW8MCbQO6QBZxEbo3pD/QGTGQjYEay+QQIiJgx0RD4BfAtciwLkGvtagVlsrX++6g4h+Tqir7NG1iEhnZWXzZQhIyCgoKCwmNJ06ZNOXjwIHv27MFoNNKuXTu5UeB/jBgejur4ccyNG2PJb9r3+uuvs3nzZiuBEBAQwJQpUx74OuHh4YWNGoty6tQpbty4YdfRrABfX1927drF4cOHiY+Pp127doUpTqdOnbJ5vcViYcKECSxfvpzevXtbFaAfPXqUTZs2/aWo3K1bt9i5cyfBwcG0bt0aQRDyI0O/IycqPYPclXw/RqP8nN5x5Eg069cj5KduqY4cwXDxIr5TptCoUSMbk4GQkBAaNGrEt9Wr89GlS6Tmb2Z3AvscHVnepIlc3yJJHDhwgOjoaDp06FCYFnf+/HkGDhxYaGyxd+9ejhw5wpYtW0pNAwOYOXMm48eP58CBA1SvXt1uWlhxypcvj0ajwVgsEuLm5lZiTVoB6q1bEdLSEAo27OnpqLdswfj887z09tu89eabhCQn4wxcBixPPskzzzxDXrlyqK5fR7x1r0DdHBaG4ZVXWBgURLeXXrJKTNICPy9fjuG779AAEcgd6sOQu5tkm83MnDmT1atX27gaPvfcc+ibNGf/r+3oxDY0+VGCBLxZpHuVqYl3MCPZbDj1ggVNejovYuAbZAEAsmVxL/QEaTSIInYzr9o0bkxKUhIV0tMp2t61NuDavj2vL1rEFQpkj0w94E1BoHFuHtYVSfK155lM5PEeT/A+TbmXghmDyHv0Q7emPFsx8BT36jJaAuP1aXg7aVidDe2QE+VcgUbAt8BvqoJ3rgMa5///SUCPzlIBA5fyX90W2cFsG5CDhqfYz3ESyS2Mu2Qgf8474son3GYVclzlSv7sW8BTCFTgCnewjRQGcp4Y3sFaxIDsktYPDAace/RAXeThifrYMXK+/hpzvlFIWUMRMgoKCgoKjy0ajYaOHTs+nIuZzTi++CLqgwcRU1OxeHpiatGC3J9/xsnJidq1axMREUFOTg5qtRpfX99CB7IHITo62mbzC3JPl9jY2FKFDMi1Na1atbIZL74RLSA7O5vp06fbuGhdu3aNzz777L4Oge+//z6rV68mPj6+8H6sWrWqyCsygNWF30kSEB2Nes+eQhEDIGZloVm3Dv0bb7B9+3Y++ugjfv75ZyRJomvXrsyZMweLxcLiK1cKRQzI+91DeXkcOXKE6tWr069fP8LDw8nNzcXPz4/+/fszdepUPv74Yxt3vvDwcObMmcP06dNLfY8gm1D8HQe/Z599ltDQUMLDw63GGzRoUHrPK4MB7fLliJn3kqQEsxn1/v2I4eE0a9YMTy8vLqWmYrFYcHNzo1WrVoiiiKVpU7L/+APdrFkIycmY69ZFP2ECODnRomdP9ri78/KLL5Kck4OfqytLV62iapMmfLNkCWeAvci9S9yR06E66fWo166Vz13s8xMVFUXv3gfoxQxG0Z6O/EkujsxhPJc1KYyPv4KHxbaCxiCYSUlP5gOgGfADcvJYP+TOJ3t0WoxqbEs9gDOVdIQ7BpCSPheBDUhkoaUWl/CgT6CEq6hmj8XENOSktJrItTDR7q6kp9nvlZQOmDhBO3J4F1nEJQHTsXCDXfQ7tpbmqBCLpHP5AuNJ5rZzBZ5IBU/kqEgB/YF1RlfkFqtzgYLf2RvAGHSSQCbXkV3UrC3uXanCPM6TiHX07CgQSzmikIXTVOSEu5+QI0sSahLZhFxfVDTKmMgdNgNt7L5/cEKzfDmqYhFEMSEBh6+/JruMChml2F9BQUFB4f8dJpOJN998k+bNm9OwYUP69Olj11r676D99ls0W7cipsrORWJqKprt29HNmcPWrVtZv359YTG9yWTi1KlTTCpmjfx3KM2EoKRi/r9CSfU1AwYM4O7du3aPFa+3Kc6xY8f4+eefiY+Xn4/n5ORw4sQJ3njjjUInrOKo1WrEM2cQi/RQKUBISEDM/3m9//77REREcP36db766itEUSQ9Pd3GQQwgMzOTAwcOMH78eE6dOkVufgrVnTt3WLp0KWfPniUpKcnueoqbNfxbqNVqli1bxhNPPEFgYCBVqlShT58+LFiwoNR5wp07CHbeo5iaiurIEcaOHUtEREShsMjIyOCHH34gMjJSvu6mTYiXL8suZ0ePorpwrxy97tNPc/zmTW4kJXEwKoqqTZoA8NuVK2QD65CjMjuRHb0+T0ri6NGjdg02kpKSOHp0Dyb68w1hdOU7+vIZR4jAwWEsuwMlbtj5CMS4g/651kRrtXRFtgPejJyCdUulgs7tkez37mT7kd1kZs7GwCAkVgNbMfAFFl5n//5cTmnVBAFLgK3AHGRHsT+ctLiUELXVqdWohV1kAe8hu7YNRZYccJknTck42rFYKI8n5pQkTEBXoBpyZGgEcipYHe8K+Suojhw/UOf//xxcK7finrgpij9ZNCPGTuNLPXATFb6IROavsQswGTlBrD06zGwGPgTOAjeBM8BkjOxFlmf2xFwS6kOHEOz8jIUSfmfKAoqQUVB4SKSlwfTpOgYOdOKjj3T8wxpgBQWFUhg/fjw//vgjV65cITIykp07dzJgwAC7EY6/inrPHpt/5AWzGdWBA6xYscLKEayAC0U2j3+XmjVr2nSnB/Dy8iqM9Jw5c4YRI0YwePBgNm7caLcHSHHWr19vJZIEQaBy5cpMnjy5xI7o97PfXrp0qV1jgytXrtChQwfUatsEkEqVKuHQuDEWO7UpUrlyWCpWtBkvwM3Nza5AcnJyomnTpnYbo6amprJo0aISXe3ul1b2T6hSpQobN27kxIkTnDhxgkWLFskuafns27ePF198kZdeeokDBw4AIPn6Inl6cgoYhPyk/3fA7OaGqUEDu/bjiYmJLF68GM2CBehmz0Z9/jyqqCjUR47g9OqrCLdK6oUiUz4pie+R7ZqrIidCfQJ0ycykWrVqdufk5ubStGlTHB0jkROcqgO1gJfx8nKhQbUnmPu0MzH5wScLEOEJs7r7UKlcMPFvvUWkqCrMILspCFx7/nlq12+CXfdlNTzZ7En0+hDkTfpIoBdyb3pXkpL68K5GwwHumQ2nIjcLXapSUaWm/R4rfpUqYRHsWQQAGDkmPmt3+5+MmcMBHnRCFmIRQDiwGHgViPSqg3w3i1OV+ISKyN1vqnHPoLoi8CZ6KiMnqRVHiwkTHbFVeSqgKgYkyiMntjVArr1pCCxCwp9KzENgI/JdAfkuHaQhH2Nu1AjJTjNdqYSHEWUBRcgoKDwEkpIEOnd2Yc4cBzZv1vDFFw48+6wLycn3t1JVUFAoGYvFwpkzZ6wcrvR6PYcPH7ZxvLp69ardgvy/jKaEx8NqdYm1OfY28H8VPz8/OnXqZHUOrVZLly5d8Pb2ZtGiRfTp04fVq1ezceNGXnnllb9kk13gaDZmzBhat25dGKkQRZHJkycXdqQvIDg4mMmTJ5d6zqKb8qKo1Wo6duxo09fHzc2NYcOGQfXqmFq1wqgWORIAp/zA4uiAqVMnuVNoCahUKvr164drsdc0btyYNm3alHjfXVxc7EYVALv9c/4uOTk5HDp0qMTojoODg83aPv74YwYPHMit9euJW7eOQS+8wGeffQYODnxXuTLPAsuQRcxg4BVXVywNGpT4Ht3d3dHasaYWY2PRFUkPvHNH4MABFYmJ9/4dmoyckFQUL+A1QSAjIwN7qFQq2rdvj3Ph75seMCMAddzcCPEKIaZne5q9ItK/CTzfClqO0uH0bA/cdG58ctKZMIsrY4AJQCPJkQkXy+Ho4Ehog1C5gKfo9XxVfDv5WyTpJHK3mu+RY0jjgGexWDLwrFCBp4C+yCllzwB9gBrVqxNTQmQ2MTkZi6WJ3WPgxxZXZ4pXlyUDCxE5WDWQy8WOSchNQaMTbmC/iw4YzJHISWyngQXADOAEMBo5ua88csJaUarizzF+t1M8ZAbm4oFsERCUP1qQmhYCDKGC+gKST1/ku/I+0A/cniTV9QSGoUMx17E2oLZ4e2MYOdLu+ssCSo2MgsJDYPp0HeHh1k85wsNVfPSRjq+++ms9LxQUFKy5fPkyr7zyCtevX8disRASEsKcOXOoXr263eiI0Wi8b4pUaRgGDUJ19KjVBtHi7Izx+ed5vWZNdu/ebZW2pNFoaHcfG+D78d1331GzZk127tyJIAh07NiR0aNHYzKZWLRokVV6VW5uLlu3buWNN96gcuXKpZ5Xq9Xy0Ucf2YzXrl2blStXFjp6+fn58cEHH1CxlOgIwJgxY9i8eXNhahnIG9zWrVsjiiKrV6/m448/5uzZs+h0OoYOHUqPHj0A2Db9Jd5tcITrUjJqSaCathyLBr1s9xl2Ud544w0CAwNZtmwZer2ehg0b8t577yEIAi1btuTq1atW9Rz+/v6MHj2a5cuX2z3f5cvFt6J/jyVLljB37lyio6Nxc3Ojbt26/PLLL6VGetLS0ji/ZAn7s7Kohrz5vZaZyduLFpHy6qvMv3WLool3OcBGvZ4x0dE0adLEps9OgcucsMFe33YQEhOxWGDUKEf27lUTHy9QsaKFTp1MfP55HvVE+8+3K0pSiWmHWq2WcytWkF2sz44EXD57FsliYZjbMA6tPsSqW8kIooBfgjfDug0jLS2P3buXYiSN+UXeZcrF37l4cQwn1pyg17he7N+9H8ks4eXvxb7f9qFSqYCPkFuAFmAGDqBSfUpflYqpyGllBe5lWuBNHx/6ltD3x2Kx4Or6EZmZnfPvdFG6oHXS80y6HKGqjywP5gGbUNHO4GavlIc0wDX3NvaFjIBacw5zXiRyC9JXihy7io7t6DmDHMMqcD1wAK4D9cjinM0ZAUxkInuYbQE+QDbXqIC88hQuV0zNv20n8r+ADLhZ0QAODmRv2IDDhx+iunoVydkZ/ahRmJ9+2u61ygKKkFFQeAhERtpz2IcbN+yPKygolI4kSbz22mtWqVvh4eGFjlJ+fn5Wm2qQrZB79uz5wNc09eiB4coVNGvXIiQnI3l5YezRA2P//tQHJk+ezA8//EB8fDzu7u60adPmH9XIgGw1PH78eJtIS1xcHAnF+osAJCQksG/fvvsKmdKoVasWv/zyi/31nD6Nw0cfISYkYPHwQP/665g7d6Zy5cp89NFHfPXVV9y5cwcXFxdatmzJxx9/DMgRm08//dTmfHqTnjf3v0OEKAtAAxJnLbd49c+R7Oq/y+b1xenTpw99+vSxGZ8xYwYZGRkcPXqU7Oxs/P39mThxIgEBAYglbNb/SfQsNjaWWbNmFX7m0tLSOHDgAGPGjCnxXgJcPH+eWSkpFPU+CwNmJiezf+9euz/jxKQkdu/ezZdffklOTg6nT58mMzOTSpUq8f777+Pl5YUlKAjVpUtW8yStFlPnznzzjZa1azWFrnG3bqlYtkykYUMzr7i4FG6bi1LB0ZFevXqxcePGwrqjAoKCgli3ZEmxknSZa3l5JMfE8Nabb5EUI/+MJSRuRdxi1KhRjBs3F6Pxts08i+UWK1eeoE6dLvz+9e92751WexNbF2kDLi55ZN0u7ksmk37lCh41PEg9Ugu5hsQHOa4yA4fKZylnTObKFXvpmXmEhAzj4J1VjCOr2LGqBIcGs2un3UtS3qkbmXbSwECLm7kjifRGLtWvgyy3YoAxVOEJrth0w5EffCZhRhZH9lNJ3Ykmnd7AGqtxb34hs4TsWqkgb87Dg7yvvrL/ojKIImQUFB4CHh72/9h4etp3D1JQUCida9eu2W1CeePGDY4dO8bUqVMZN25cYQTGw8ODvn37/qMNPoB+0iT0b7yBcOcOUoUKUCSlbNiwYQwZMoS4uDi8vb1xcbnXXk+SJHbH7Gb11dUEuAYwqsEovBxL71tSGj4+PnY7zYuiSI0aNR74vKUhREfjPHRoYRG+ClBdu0aORoO5Qwf69u1Lr169iI2NxcvLq3RHrnz23txLZFqkzXhUWhTR6dFUdq/8QGvVaDT88MMPZGRkkJqaSmBgYKGAqVGjBkeOHLGZ07Rp0/ue12KxsGHDBrZt20b16tUZMWIErq6uLFy40EY4A1y6dAlJkhAE+6lFmgsX7EaeQoE7t27h5uZmY2rg4uJCrVq1cHR05Nf330d45RX0RiOOvXphbN8egNxZsxCjohCvXEGQJCRHR0xPPIGxTx92dr8nYgrIyxNYt07Dy02aoL52DYl722QBsNSqxTPPPEPHjh3ZsWNHYcSzWrVqzJgxgy/GjbP7/szA0QsXCg0IihIVFYWHhx6Vyguzubg48KJlS7kOTLh7F+3cuQjJyRgHDcLcsiUA7u5u5ObaRl5btGjB1uPHbbb4BuC7uDjKDX6KqONzwFwkhVKsgXeziUQtHwf2JJnDaro8O4tDB3shsZ57ZtFV8dG8SVzuQrvvHxzJybEfyQKJHMsF5BSyzkA55O4zcledKEqaByZuUJKIgRz+YAxtqYqFmsjiSI/IJTYxjn5YuGlnlq7kfqdlGkXIKCg8BN56K4/Tp0Vu374XgfH3N/P22/ZtIBUUFEqnpI2hIAiIokibNm3YtWsXCxYsICkpiRdffJGwsLB/5+I6HVIJgkitVtuIJUmSGL51ONsit5Ftkusw1l5dy5Jnl9CgQoMHWkJJRf2CIJRor/xP0c2aVShiChATE9HNn09Ohw6AnE72d8SiIAjyz1KyM15CXcHfwc3NzUZQTZgwgddee80qDbBSpUr3jZ6ZTCb69evHoUOH0Ovlv92rVq1i9erVpX4eS6NcCQ5yElDB35927drx888/W4nW+vXr07x5c9Q//4zThAkIJhMugDRtGrqlS8k6dw4pIICsXbvQ/vgj4uXLGLt2xfzMMyAICIJ8s53JIoA4YggiD9n8IXPiRJJXr8Y//3oCkKhSoX/5ZVwFgaVLl7J31y7W/fILwTVq8PLo0bi6ulKrY0c4dMjmfWjVLqi12hLvQ1CQJ56eDUhKug1FOts4Otbm2WerodqzB6dx4xBvyltvzcaNGPv2JW/OHDQl1KxZLBbEEkw98rKyuLlpkLWIAbD4E7/zJYxem+85BBTFORuXI2Opy5uc52XcmIuJKgi8zgua74mgJnISW/HfSxc06hRkSScSyE0EJG4SBFgQNXe410PUWrDqHXPtaioACQ0FtUi2eGLkDvVozFkGI1s37KERK0jFTI0EkZt26mt8bTXhY4FS7K+g8BCoX9/C4sW5tGljpF49M23bGlmyJIc6dZSIjILCgxAaGkpwcLDNeNWqVQsLy729vZkyZQpfffXVvydiHoCDcQfZHr29UMQARKVHMeXAgzfLTEpKsruRM5vN/5mNsFiCBauQWZLT0/1pU6kNIR629rPBHsEEuQfZmfHP6dChA99++y1PPPEE9erVo0OHDqxYsQJ/f/9S5y1btoz9e/cWihiQXdkmvfMOr776ql1nt7p165YqZoJ79ybWjpV2nIMDwT17MmvWLCZOnEjjxo0JCwtj8ODBrFy5EkEQcHzvPSsXPQEQY2LQLF0qDzg5YXj9dfLmzsXcqRPkr6NjByNfihM4SxhHaM5Z6vOJ+gP69jUyd8MGnjUY2AycQm7uOMRsZsJWudJEu2IF3aZM4ce9e5ny66+UHzcODAaaNBuKIFS2eR9qp9a0a9fO7u9qSEgIFStWxNP1EnJryVrIHV9aU841htSUFDmN8ea9+IGYmYlm/XrEq1dx1ekYCTRBbnbZDuiJnObXAdsn9W7AMLMZc6L93k7GtCDKtfIAe67nfuAZd559tCWaQcSyjSiWcJ6neEM1Az9NX9zwsZnmQAB+FUKowg4O0ZKTNOIEjTlES4L5Ez8fL2yL+QGc8Qxph33XMjcknkFuYmkHVSBvAGcxAT8Cw4FlnMDMm0CGyb6Jhofh8TQfUiIyCgoPiRYtzKxf/5g+8lBQKGMIgsD333/PK6+8wo0bNzCbzQQHB/P111/nFwKXHdZcXUOWoXjqDMRlxj3wOQMCAqhQoQKpqalW476+vjz9HxXmmsPC0Pz5p824pZjL2d9Bq9LyVbuveHP3m0SmR6IRNVT1rMrCZ+RUHUmS+Oabb9i+fTuSJNGqVSsmTZr0j+pZQG5S+ezfbPC3a+1aTHaiXbHHj1OxYkUmT57MN998Q0xMDK6urtStW5dvvvmm1HOKajU58+aRMHIkXvmiJEWtJm/+fARRRADeeecd3nnnHeuJJhOCHUMLAdD+8APGF18s8ZpvOC1ALSxAl/+435N03hC+wuxYjT5Hj3IeuR9KURpFRiJER+MwbRpiQQpdejriunVIHh7s8ZmHJM1GLi6PBJyBOjg6LiUvT8OcOXMYOnQoKSkpCIKAr68v33//PdeuXSM+JhK5gP0ecQlw6M8/GXDnju09S0lBs24dn2Rl0Q05xbGAW8Chhg3psHYtt83mwsaeAcDzQBdnZybn2E/p1GaVw9fJg4SwWDgJGJEf91cEp2ZOqB26IF2exScdMrhUHhyNmbxyKom2kWoSr3/HMpJ5N/+d6JBl2QIiGJqnYgXv0JSLhdcqTxIreJvhhgnIXWteAi4jh2dCgI/xilSRypfIXnVnkNPZqgBvAtXA+xgkFbM8FwC/6ly4lQCSnVRHwYfOah0Yba3SM4rbwz0mKEJGQaEMIEkSx44dY/v27dStW5fu3buXuc2YgkJZo1q1auzZs4fw8HBMJhN169YtsZD7URLsYfs0GsBFW8IT1b+AWq1m5MiRfPzxxyTmN5R0cXGhW7duNvbJ/xb6ceNQ79qF6vRphPzUNnP16uTZcT/7O7So2IIDgw5wPuE8WpWWmuVqFkYx3njjDZYvX14YBTl+/DhXr14ttYD+v8Lvtm1ROoBLfsX54MGD6devH+fPn8fHx+cvp9jV2b4djSQVJtL5AB5//klur14lTxJFUKuhmAOXBFjsRD+Kov1jAxqzdc6SozET46+/4Gmnpw/IZg26uXPviZh8BElCfewYIePMqNW9MZm6I2+6PYBquLqacXTMYv78+SQnJxemRCYlJTF//nzeHTgQN0myKWl3A4KyspCcnW3WImk0mENCaO/oSPF/JSsC3SIisNSuzc/nz7ME2A/0R65CyX3qKSrsOUw5fSiXqE1BYlJ1ruCpPY9XRm0uRFyEcpKsfjKBOJCOB9D5GTPtB8PxIv7Up/zgvWNaXr55kC6YeTb/3Tsjx5ZMZNPFI5nqVu5q5F8zhgblkzgfFwocRhYyWcg9X0RyxX3In4Y/kcVhAnJfGB0QBW2c5ZBZga+BAAQCjS2wKRAMd4FngdbALvlLWxWLRyT2ym8ciwQGL14U+f13DVWrWujTx4i2DGscRcgoKDxiLBYLQ4YMYf/+/WRkZKDVavn2229Zt27df9qgTUHh/wOCIFC7du1HvYxSGVZvGMsuLeNa6r2ULweVA52DO/+j8w4dOpTGjRszb948cnNzGTRo0D+2ey4VZ2eyN21C+8MPqE6dwhISgn7sWPgX/k6JgkiYb5jVWFpaGrt377ZK5TKbzRw5coSoqCiqVKkiD+r18tdfMBf4J7wdEsK269ettqTuwOAizQJ1Oh1NmpTUi8QW4c4d1AcOIBQRJILJhHr/foS7d5F8feXBvDwwGu/11hFFTHXroj550rqSSKslt0ivGEwmyMoCd/fC1DKhBPthzGbGjx/PunXrrO65SqViwIABdmtgCub17Gnim28sXLyoAWTTBLVaok0bE9HR1zly5IhVXZfBYGDHjh18OGgQTVUq1hXr7VMfaFCpEqY2bRCjo61S6CzVq2Pq0QOXzz+3uxxRryf11Vd5eswYzlks6IENwFM6Hb9MmsRH+4fSlli+YhxnaUAtLjGRORwTfXnukgraSHJQxBG5bCcBcjfHsMpylpMF2Yd5bqDOJc3JyJIwI3MTywE3UCE3ES1AAILKu6LFtmZHgxE3l6I/PetGnUl52dyruQnO/yogA7y08DJys5p0oDKyBroigeMgMHyVfyedgTHAKXC6yCn3t+0KmXgXA5IEY8c6sGmThtRUEZVKYu5cHStXZhMYeP9mu4+CsvfoSkHhf4zly5fz559/FjYbMxgMnDlz5h/btiooKJQNXLWuLOu2jKeDnqaqR1Xq+dRjXJNxTG5RepPJv0Lt2rWZP38+P/74438rYgpwdMQwdiy5P/2E/oMP/hURUxI3b960KsgvICkpiStXrkBuLo7Dh+PSrBkuzZvj/MwziBcv2jnTv0P5V17hNwcH2iD3YW8CfAy81LbtA59TjIxEsGOxLCQkIEZHQ1YWjoMHF75Hpy5dEK/LaVg5mzdj8fFBQt7uSioVOdOng7c3SBK6KVNwadEC1+bNcW7TBvUffwBgat3apnu7pNVi7NSJAwcO2DQMLRCPhuHDsdjp8G6uWRONBlauzOaZZ4yEhpqpU8fMa6/pmTkzj0uXLpGSYmuHnJKSQqxWy881azIc2Xy4JtAPWFelCqYnnyRt2jT+qFKFcI2GCJWKAx4enPzwQ9BoMNesaXNOi4cHhuHDeefIEY7nixiQAyvbTCYWHTiAH644k8N7fMZq+jGNabiRSTmcyHO4If9wC+pk1MgtWVrksbmGiCW2JfxwBOZdgvmXYP0ibjjDT01ex2QTH4JsnEio35PrdrzprlOVK2Jl7Bfsg8lkAezVpZnRVvgD4uvLGuWZ/JvWFNQmBzjXEnJ6I9cdFUS0XIAnIbsjGQ3M2HODTqkKhw6pWLdOFjEAZrNAeLiKiRPtFQ6VDZSIjILCI2bLli12bVSvXLnyCFajoKDwXxDgGEqj8M1IJ1W4uED7sXkIgmL2URrBwcH4+vra2GxXqFCB+vXr4zh6tNzTp+DA7ds4DRtG1v79VrbY/xbmdu1oNHQouzZsQIyPx+LmhjksjJwZMx74nJaaNbH4+6OKs66Xsvj7Y6lWDadXXkGzZUvhuOrWLcShQ8natw+Hd95BTE6+9/7NZhy+/56sAQPQLVyI7ocfEPIjK2J8PMI775BTpw76t95CvHgR9ZEjiCkpWHx8MLVpg/Hll9k5YABmOxGb8PBwLA0aYHj5ZTTLl6O6dQuLiwuWOnUKI0AVK0qsXGlbt9OoUSN8fX1tGmqWL1+eoMqVkb7+mgWjRiHeuAFmM5YqVcj96CPMDg6MfeUVVkVE3JuUlka1yZPZ16oVfPUVYnw84sWLiFlZmCtWxDhgAJawMLvNTU1mMzt37qSaLoxGnLQ5nqSrAaHnsVsq4g3xuWpYvxRSi5gFpFTBoDLSdmp1vvvZGUPTDHaEgsYMw07C4hvBPB11ldF8zXeMJBTZhvo6wYzlK4JVMUA0aH3B5AAWFegyQC/i4XmclNT1wAvAXGQ7taZAEwxqZyZuvcopD4EzfhLpjuCfAZ3CLeyPNnLdaM8NTwBDAB4hAnfrAlfzT+kABIBPc4llyzRkZdnGOKKiym7cQxEyCgqPmKK9Jori8B/8Q6zw15Akif3793Pz5k06duyIb0F6h4LCA2A2Q+/ezhw+rKKgw/exYypmzcqlR4/HtHnDQ8DFxYXevXuzYMECsrJkswQHBwc6duyIv5cXqlOnbAyaxevX0axZg3HQICRJ4uDBg0RFRdGxY0cqVKjwj9eUN3Mm+vHjUR04gKVaNSz/0A1P8vLC1KUL4s8/I+TX2khOTpi6dEESBMRztt3bxWvXUG/fjvrQIYRi5gOqqCi0ixej3rq1UMQUHrtzB+1XX5H3zTfkLluGePUq4rlzmJs0QcpP03O2U5MC9/490k+ZguHVV1Ht3YtUuTLmxo0LU9ZKIiAggA4dOrBmzRry8mSvYVdXV/r06YOzszOWhg3JOnAA1Z49CAYDpnbtwNERvV7P8ePHbc53/fp1Vq1axdChQ8neuhXViRMIMTGY27RB8pGdwxxKSJ9z0WpZXvNDQu4cpjbhhePXCOXH0I9xMf5OFnZ+J41weXsbaxEDgBpinuTMlYV82ycDqTIQATjA5u7gdPQivSvncJCdhHGXjjRAAP7kGibW0sY1CFq+jLr5eZ49FYR7joY/WkWQcbsG1bYIHCUaWFLkejvk/2Q8SR/pKp//LHGqAlzxhrYx4J9pYJH4DSN4u4Sfho7asRJ3nwPSgJtAeaACVIwT8Pa2nz7m4FA208pAETIKCo+c8ePHs3fvXqunVc7Ozv+oA7nCg5OYmEj//v0JDw8nNzcXf39/Bg4cyHvvvfeol6bwmLJ+vZqTJ++JGICEBJFvv9UpQuY+TJkyhbCwMH799VcsFgs9evSQ6zUyMsBOJFuwWBASEkhOTub555/n0qVL5Obm4ufnx4ABA/jggw/+8ZokPz9M/fr94/MUkDdjBqZmzdCuXAmAoX9/TD17Ity6ZSNGAASjESEx0e4xkNPSBDv3BkDIyCj8f0v16liqV7c6PnbsWA4cOFBoIAGyoOzbt2/h95KPD6Yi3/8Vvv32W1q2bMn69etRqVQMGTLE2jVOo8HcsaPVHIPBYDdbwWKxkFCQjicImJs2hWLNTF8wGDiHXDpfQHngLScnEka40/P0bsamfUQwN4glkC9dP2Dya550/Lkuw9LPyMVPhQuBqpEaUvLsGyGoDQ4knNyHlAUsAlKRCzfKQW5bCY3DHdT8joEMNheJBGn5HQ+xH7UDDvDbr1Aj6TxaC9yIgE+aHyfNyw2y8uxek4wItOpyQCqN4qFREQ8Gd50H5JYkPCwMOu7APj8dZo8M2ZMBwOBAu8sCr/xoYMMGDTdv3kuT02gknn667P6dUoSMgsIjpmbNmsycOZOvv/6ahIQEXF1d6dGjB6+++uqjXtr/JGPHjuXUqVOF39++fZvFixfTo0ePMl9UrlA22btXjcFOj4akpMezb8O/TUpKCmPHjuXy5cuoVCqaNGnC559/jqOjnJfftWtXunYtZgjs7o4UGAjFnMQsFSpgfP55xo8fz8mT9zaNd+7c4ccff+S55557pD2F7CIImHr1wlTMpUzy98cSEGDTv8ccGIjpuecwr1tn1WMFwOLlhXHoUMSEBFTF6oUkR0eMvXuXupSwsDA+/fRT5s6dS2JiIu7u7vTp04cXS7FzLuDOxSNkjByIT3wGRrWKuMbVCPtxFyqN3BDzhRde4IUXXrjveQpwdXUlMDCQ28V+xr6+vvTv37/Uua84OJAKrEAOPPgCE4CGaWnkPGlm9DRXFi76iuRkAU9PGPKCnu7djaiFN9jy6RDWtgbJFdCD/xU479GFBXfWM03oQbpknbbVIusGwVI6HMm/GIAFSATpACQk7Mdsp7reRAJRScksjIB6RcqkQtJgylHo2SQIbpbUE6oSV8wV2cN1fkOu9fcH3kPLCtVAIJdBrGM031KOZBIpzxdMYC0dWVdhLOYbl6D2ehAsIAmQEsLahDFMKS8xd24u06c7cOeOgJMTtGtnYurUstu8WxEyCgplgB49etC9e3dyc3NxcHAokxay/yvYayaYkpLCokWL+LKoG5CCwl+keXMzy5dLmEzWwsXLq+ymazwsJEmif//+VilE165dIz09nV9//bXUubmzZxM1YRCfB94kSwO949zo1XYYUsWKdmsMU1NTWbRoEXPnzv3X38d/giCQO2MGTq+/LpsCSBJmf3/0r72G5OVF7pw5iIMGIV67hmA2Y/HxwdC/P5aaNcn7/HPEqChU4eEIej0WT09MHTtiKi4I7dC3b1/69Onzt/49MuRmIfTrQdPbBRteE5W3XODQwCdouOrYA9+COXPm8NJLL3H9+nUsFgu+vr4MGzas0GI8LjOOz499TlJuEk9VeooX67yIRqVBuHuX94B3kTuzOCLHQw35zmlDhxoZMsRIbi44Ot7LjjN168ayP/qw/NeNZJr1uJhAqF2HnEVz6NCoLh/XXwLXhkKOLwhG8L5CpdCREO98T8QUQUyDai2aUWHvJm4VO+YHOHhWpIqdecFp0OqaB+edXSDbjohwacmIrHJIrCcXOb3wBvACalIMT9CTn5jDVHxIBqAqkcxlDBlM46DBQxYxYn5aoiCB7yXia+0G+vPkk2Z27swmJwd0OijrnSAUIaOgUEYQBAEnJ6dHvYz/eUpqtFdSLZOCwv3o18/IkiVaTp2699ny9LQwbJj99J//JU6ePMmlS5dsxk+dOkVSUhLe3vaKlmVWayN4t28eCfktUbbWyWNd1ass5v/P77GleXOy9u9Hs3IlQno6xhdeKLRklkJCyNq3D83vv5Ny5gxuI0cW1rpIXl5k79yJevNmVOHhGLt0wVK37l++7t/99+jC/Ck8EW+94dZaoPKZGxjzctA4lH4uSYJz50SMRoGGDc2Fm+fatWuzb98+1qxZQ0JCAs8//zwBAXIjl2O3jzF863BiM2MB2Bq5lY3XN7Ku5zqEtDRAzvAqemVV+L26GEEAm7doMCDGxaEyGPHM3+ebs7IQ7tzhq3pmUp6aBGkL4MIL4B4DdVayJ1nLDIbAwYv33JLzcbKAgyqZDsBvQMFvvBZoB7jkxWKyCBSfaBRAn6qHxmY4hqzGCqgIQtVMcg7tApN1jVQKOWD8nnFEF4qYAipwl7dZRtcm1++JmMKbAfoaW6yGHpftiCJkFBQUFIrQvHlzrl27ZtXzoGLFiowaNeoRrkrhcUajgd9/z+ajjxy4dEmFo6PEq6/qeeaZEvp5/A8RFxdHdna2zXh2djbJycklChlJkvj61Nck5N7LycmzGNgds5vzCedp0aIFV69exVKkGN7f35/Ro0f/+2/iv8bZGeOwYfaPabUY+/cnvlEjXAv66hSgUmF67jlMzz33ny/RHBmBxo4Jn85gQZ+TXqqQuXpVZMQIJ65fFzGZICTEwuzZubRuLf9+ODo6MnjwYJt5Hx3+qFDEAJglM0dvH+X3a7/zsmQ/2llSXVEB2h9/lM0DinxuVNHROEydyq4qdUE8AV5R8NQnhccTtN541QohVJLr/IvSRA/ZkeGMArZxr32LF/A6cDrzAhf0KgKLGQxESJDoeBPCsuTQzQnAiJwj1w6I3AMuCXajQHicQ0i1tckGEFCj0aRi7y6IYtlNHysNRcgoKCgoFGHWrFlkZWVx/PhxsrOz8ff35+2338bPz+9RL03hMcbdHT7/vITC3f8ASZKYe3ouf0T8Qa4pl2CPYGa3nY2vc9ly4HvqqacIDAwkNjbWarxixYpUrWrbe6OAdH06CTm2/VdS9alsjdzKjBkzyMzM5NixY2RmZuLv78+bb75Z+DS/LJGRk8EzXzxDtCkagCqaKvw58U9cHB+f6FGFF8eRtO4w3jnWAiLZy4FKXn5kG7OZtHcS5xLOIQoirQNa82HrD1EJal57zZHz5+/lL12+LPctOXgwC42dficFxGfF24wZzAZ2RO/gJR8fVDExVsckwNywYanvQ3XkiN2GoeKdOwSU+4To3KHgmG51zJwRxOVlv+LTCe46QbYPCGbwvgmSCxz0cuRPoOhq45F7WXbp1Jyph7NYyk3qIrd3iQFew5V6Q1pBylpIGwv658HoBJk3IGMM0q2b4OFoK2REoJqKC8fq8CQHbd7HSRrhnn2CLBcobvmnkR7PByuKkFFQUFAogk6nY/HixaSnp5OamkqlSpWUmiWFx46Zx2byzclvyDHJfT0uJl0kMi2S3QN2o1XZa5TxaPDy8uLFF1/ku+++K2x+WbFiRSZOnIiqlOR8F60Lblo34rOtN7MOKgfq+tRFq9Xyww8/PBa/x41nNibBPaGwRXm4JZxGMxpxddrVR7uwv0GlJh052aYWDXdfwjNfr8d4qcn64H0ABv4xkL2xewtffyHxAgk5CUyqtshuj5IbN0T271fRrl3Jm2t3nTvkusOxcZASDLVXQ7XNVPeqTs6qibg89RTk5VGQuCW5u5OzeHGp7yOpvAv+dsavW5LoHtaGg0e6Q6314JDv/pZcFe8977G5/gIOh521EgfxAZBmgJrmOlxSbbDpe3lJBW3NAaRoptHZ+CHexOAIxOIFvELeVXfYHwrZk8HoKk+6Gwa3q0KjltAkFLLOQFJ+9EgUIdgC3k2Y6d+WRpbFNEwworWAXgXHfbXMNz6BpPvcRsQAqMyPZ82eImQUFBQU7ODu7o67u/v9X6igUMaQJIkNERsKRUwBl5Mvs+LyCobUGfKIVmafiRMn0rVrV3744QccHBwYNWoU/v72tpP3UItqng1+ltizseSacwvH6/jUoVNwp8Lvy/rv8eHwwyTqEgtFDAAiJGgTOHHtBE2qNXlka/u7NP7tEBE7lpO1YA5GHy9Cp8ylVkAolxIvcebuGavXmiUzh+IOkV4lmXvd5+8hCPK+vDTaOY3mzOJWWJLye7uE98Gl1iFGjKqFRedKxrVrMHYU5otn0XTojOWjT6GE2qkCXgg5zfdeUD3l3liiI3xaN43nj/9KvT/Gcf7MKxD2I6RXwvn4iwwyrWB39zrAZpvz5anAy78iFhEbISMJ4OzpgEnVAYzPku0yiVxNEqR+BNQlS5gK6f1l+zSnJHBMgZQQyKgF1ydCUiWQwoD9wEWQOoPJDY574dm+B09UNTL4LLSOhT1VYHkdA00vv4olT81tO71yvPWKkFFQUFBQUFB4xJglM5mGTLvjl5JsC+vLAtWrV+fzzz//W3M+bP0hXo5ebL6+Gb1ZT81yNZnRZgaiUDYjL/a4GHsRyU6zQclB4sLNC4+VkAEI7TAAOgywGruRdoN0Q7rNa9P0aejK3SEkJICTJ61/ZiEhFp54ovRUpxNLB2JJKpJ7ZnLGeK0dh3bn0qmTiTdPTmPnkxdIbZqBr/MeBp2dx7jG40o9Z4T+Nl1fgM//hIAMyNDBvKawqYaBvmt38CfTGRf7NRGxr+FIDgOYQReXJZjDhnEuxs4JRQipA5TDOrcMecyrmgkPzS6q9B3PrfLpGFUSgWm7EDe/i3v1HdxUD4RePcH/BGhzID0A9n4It0PBEgLJjYHG8vkk4KYBMXgjzg63Mangx0byVwFq50Q0JlF2XS56yyVQPZ46RhEyCgoKCgp/nTNnzjBz5kxSUlKoUKECU6dOJSQk5L7zEhMTef/994mKisLZ2ZlRo0bRvn37h7Di/z3UopoKzhWIy4yzGnfRutAtpNsjWtW/jyAIjGs87r6b07LMsw2fZfKxyZjcrJ+Qq7PVPNvg2RJmPV4092+Ov4s/t7Os+8H4u/gT4hHC99/nMHx4QbG/QJUqZr78Mvd+wRPi4mwFqz5Pxbp1Wi6Xm82vl35Fb5YL2DMMGXx54ksa+jbkicAnSl5rshO/l0unR7F2Nz55IofbGHkyIoEVBmuhdthNi9udSLvnEyWoeS0F/7Zwew8UGomVA/+2UPdUBsHdX+R0yD0VkeqUS5UeU6lzuBJHur0BNbffO6FjKnQaDVtegeT6the0aBENDmSl1oegs7b3J6kBBr+T1iIGQADt41kiw+Pz2EJBQUFB4ZFy5swZBg0axLZt2zh+/Dh//PEHffr04dat4h0SrMnKyqJHjx6sWLGCY8eOsXv3bkaOHMm6dese0sr/95jUfBIVXSoWfq8TdbQJbEOrgFaPcFUKxQn0CaS5Y3PILTKYC62cWuFX7v+HwUh55/L0qtYLV61r4Vg5h3K8VPcldGodISESu3dns21bNlu3ZnHwYDbNmtmxQCuGk5P9EEJQkIU/o/8sFDEFpOnTWHhuYannXJjbEfc8rNyQ1SZYeD4Yt15PsKsK5BXZOce4wdxnfWlcsRkqO0LAUy+ir+CN521gMHKF/zBgCHjcgXRPC7fL276Pm14SHuVzofpe25N63IEnNoDGNuoKIGmjMZ56GczFnBJMWnJPvoIkYWMTjQSGx1QRPKbLVlBQUFB42MyaNctGtERFRfHpp5+WOm/hwoU2vUKSkpL4/vvv//U1Ksi0r9yeLX238GKdF+letTtzO87l564/Iwh2qnwVHimb3trEF42+IDAtkMC0QL5q8hUb3tzwqJf1r/Lxkx+zrNsyelXrxfM1nmdtz7WManjP0l4QoHZtC2FhlvvWxhTQo4fRRswEB5sZNcpgI2IKMJqNpZ5T/cY7tL/jgKZAlFggNFWgwdODGVpvKJNHVmNYD1hdC75rBO2Ha/Ht2Zsnu46lQYa1xbRogWf0gSR5ehN/EFgOJACJwG8QfwBuu+kw23m/ZhWYgzxBW4IlcrkoqPELaNOsx92jMId8R0KL1aAq9l7VBu62XMF1XzUFDggeucgCTJBrgR5HlNQyBQUFBYW/REpKit3x+HhbG9Si2Gt4CHKndYX/jiD3IL5q/9WjXobCX+Dlji/zcseXH/Uy/lOeDHySJwOf/NfON368AUmCDRs0ZGUJVKxo4dNP8/D0lMgx5tid4+XgVeo512UdY3OIBWOBPhLhso/EeL/TLNU4sazHct72eJt3n4zGSeNEjyrP8F6L9xAEgZUj9vDGwue4qkpBI4m0Ugfz8aRdHDx6jGQLEJf/lU8K4KSpRIUsSCzmtB2YDprgGpBUgnOdWYNzjcVkO2jg3BDQu4HrbWg3mXL6FDJc7TfbzfCJQ9KrGBRu4o2jUD4b0nWwphZ82KDUW1NmUYSMgkIZJycnh88//5wzZ87g4uLC+PHjadSo0f0nKij8y/j62u9BEhwcXOq85s2bs379eszF+jP4+Pj8a2tTUPi3uHtX4JNPdNy8KVKxooV339UTEPCYVkL/P0YQYMIEAxMm2G7andT2G3Am5SaVes71EevJk2zPdz31OgAhniGs7bnW7txyAdX5ebqt8DCbzHL+U/HUMxXk3IrECHhmQ2r+kh2M4J8KXucugq8ONMWiMhK4pHvg7pRGdvO50Hyu1bFmZz3YXsF+zyqTczJNrwvM3gkV8vvQBmRC0BFIfEyDtYqQUVAow5hMJnr16sXRo0cLx44fP86cOXPo0qXLI1yZwv8iH374IRcuXCAqKqpwrEaNGkyaNKnUeUOGDGHVqlWcOHGicKyg0aiCwn+FJEkcjDtITHoM7au0p4JzhfvOiY8X6NbNmYiIez1sjhxRs25dNkFBipgpa0iSxKFbh4hKi6J95fb4uch1RY4a+3lSLtrSm4y6a+1bdevUusL/FxITUf/5JxZ/f8xPPXVfn2jJo+TPTYaXias++Q5i+UIiTwPHgsAnNRNSA8H7huwaAHJti8GFQXcjWRNqp45IgATPNDQZ1TC72ka8HdL8mBh+jQrZcMMT9lSG+nehyW3oF1Hq2yizKEJGQaEMs3r1ak6dOmU1dvfuXb7++mtFyCg8dIKDg9m8eTOffvop8fHxVKlShUmTJlGuXLlS5+l0OtavX8+XX37JmTNn8PDw4K233qJGjRoPaeUK/2sk5ybz/IbnuZR0iVxTLn4ufvSv0Z+praeWOu/jj3VWIgYgMlLFRx85sGhRbgmzFB4FKbkphT/jHFMOfs5+9KvRj2lPTKNv9b6cTzhPljGr8PU+Tj73dbh7o8kb7IjewZ3sO4VjjipHuoZ0BUA3ezaapUtR3bqF5OCAuUYNcpYvR/Ir2ZjBo6KH7JDsARSYm1UB0sHk54wUg3WDyvz6lZN+AhUTrnMrNQgcM0E0QY4XuMWxpjqk2A86cbkCqDIy7B4TdKmoMfBSd9gSCgku4J4HTW7Be7tLvTVlFkXIKCg8JFavXs2CBQtISUmhXLlyvPbaa/Tu3bvUOfv378dotC1OLOiAraDwsPH392fu3Ln3f2ExnJ2dmTJlyn+wIgUFW8bvHM/J+JOF39/JusOi84voHtqdMN+wEufZs/QFuH1b8Ub6J6TkpjBu5zguJ19GFEQaVWjEnHZzcFQ/eIX5+F3jORF/L8p7J/sOP138ie6h3Xmp3ksk5yaz9tpa0vXp+Dj5MLrhaOqXt2NZXIQqHlUYbAljW/wd0hzAyQg1TA6Mbzwe8cYNbszfzbjUH4mhMk55OTx7divTRo8lb+3qEs9Z3as6dBRBZYEWRQ6YRCq6VbQ/SQAsam65AmKRBjUOGaiMkKQTKTETTIBs90w4BxwHcpB7jjaHzKrpfNkcjgZCnlZ+eboD7AwGswCPo4+kImQUFB4C+/bt49133y0UIFFRUbzzzjv4+fnRsmXLEuc1adKE1atXYzJZ9xjw9PT8T9eroKCg8Dhz+NZhm7FMQybzz8xnYaeSLXh9fe2nAXl7398OuKwhxMej++ILxDt3MDVvjmHECNDp7j/xX0aSJAb8MYBjd44Vjl1LvUaGIYNl3ZY98HmvJtvWo6Tp01h8fjENKzTkzWZvMqHpBHJNuTipnf6SY9+5c9tYdncrt4pkIcblpTLl0yeZlt6e3qmLuUrNwmNXqI7phAfvS5JctGOHjdc3gqQGitfeiGzZd8reFJAg+XQlqHNLTicrcmqzGgRJjaPRQI6dH6dXDmTftsB2ZBEDkApsBbrDvqrY7SOzL8j+Uso6yiMGBYWHwLx582yiKElJSXzzzTelzhs0aBB169a1GvPy8mLEiBH/+hoVFBQU/r+Qbcy2O15QtF0SkyfnUbmydVV2YKCFyZNLsMEto4gXL+LcqRO6H35As2kTDh98gHP37qB/+O/jbMJZLiXZOheejj9NQnbCA59XJarsjjtrnAv/XxREnDXOf9l2/IvfRnLLzXosywEOZF9k0bU2XMU6HdaAA6sz7zUuNZqNHLt9jAuJF5AkWRTnJJcDyY5AlgSErCqQXtG6r4sEmDWozr4ABmfshV40KgG/TBCK6WuNCVI1wDEBcsDHBeoFQTlnIBs4LNjtdwNgeUyL/RUho6DwEMjNtZ9bnZNj3yKyAK1WS/fu3XF1lRuJqdVqAgIClPoYBQUFhVLQqrR2xyu6lpDKk09QkMSaNTn06GGgRQsT3boZWLUqmxo1Hq+IjMOHH6KKji78XrBYUJ04geannx76Wm5l3rKqVSkgy5BFcl6ynRl/jZYVWyIW28b6OfsxutHoBz5nisV2nQB5osT2G77YUxXxki8WSWB71HZaL2tN1zVdeXbVs7Rb0Y6bGTdxS6mO/e22gI+lCXx1A3Z8BlFPQUxrODESZiTiWlEHejvmBAKQEcgNNw1BKeCeC04G8MkC1xwVeToBDBKtuoP4ClwYCtpXoHUXQG/GpMe2ISbYuqo9JiipZQoKD4HatWtz4MABm/F69eqVOi8yMpL58+eTmSl38DWZTJw/f57x48ezePFi0tLSeOutt7hy5QparZaOHTvy9ttvK03vFBQU/qdp4NuAfbH7rMa0opahdYbed27VqhaWLn14hf16vZ4pU6Zw/PhxBEGgadOmfPzxx2i19sXYX0G8c8dmTDCbUR86hPGVV/7Jcv82rQNaU8m1Ejczb1qNV3StSKhnaKlzJUniyxNfsjlyMwaTgWpe1ZjddjZejl589tRnNN56hoY7LuCgN5Ho7Uzih2Op5FYJANXmzejmz0dMS8Pi60veBx9gCQsr9Xpeaje0phQMxXbHXnngmGC/X5aARGZOKu9se4foFdFwF4yikdNVT/Oq+Cp1ExqDaKcRp8pIZNrXoGoBhyfJX4WY0AT9Dmo7/WAsApqr/TBorxNdZ1WhRsrRAgYVXOvGk/U3cLQuhe/jjjuk1odWej2HMgF75m32/QHKPIqQUVB4CLz33nucPHmSs2fPYjKZ0Gg0hIWF3de29rvvviMhwTb0fv78eSwWC3369OHkyXsFrRcvXiQ1NZUZM2b86+9BQUFB4XFhVttZDNo4iIhU2VPWUeVIp+BOtAtq94hXZstLL73E1q1bC1ORzp07x+3bt/n1118f+JxSfhS/OJagh18I4eHgwYt1X2T+mfmFfVz8nf15o/EbqMXSt6FTD05l4dmF5JnlvigXki4QmR7Jzud34rRkKSNWRiBm5G/272RifvdHsnYORHXkCE7jxiHmp3SrLl1CvH6d7I0bkYKCMJqNLL+8nENxh2js15ghtYegU+vwatOFejt/IdxHFgaCBYJTIc+vPI2CLrD1WlckrFPaArjJ1pt7if4uGopqtXg4azhL9e5tIU0HrsXEcbYDnk6NQbKXIqciROjDbeNF5AKXIogSJudY8D1mG+jRGkCTQ0aQZCPG8rSQV8UMdwB7JmvpdsYeAxQho6DwEHB1dWXr1q2sWLGCI0eO0KpVK/r164dGoyl1XkmRFUEQ+PPPP7l48aLVuF6vZ+fOnRgMhn/0NE9BQUHhcaa6V3V29d/FD+d+ICo9it7Ve9MmsI3V39QMfQZ3s+9Sya2SVZ+Qh8nNmzc5ceJEoYgBOQpx4sQJ4uLiCAgIuO85kpOTuXnzJiEhIYj5PU0Mw4cjXruGmJJS+DpzSAiGcaXbD/9XTGg6ga5Vu7Lw7EJ0ah2vNXiNANfS35vJYmJr5NZCEVPApcRLbLq+iReWL0csZjMsXruGbuFCVAcOICYlkQncBioDups30c2eTfKXM+n5e09O3z2NyWJizdU1LLu0jPW91iNqNJz0h8ZxoLPIRmMnKkLFcm5craqj9rXzXKRB4fWcyeAJfuHGOYMsEIpiAcM1A+X9A+CsP1SPAjE/RdEiwC0/qj7VKN8IwJYGnv05YPrM7jG1oEHvXIJ7abnrUFop1NH8G+JVZCwLOFLKnDKMImQUFB4SGo2GwYMHM3jw4L8857XXXmPDhg3cvXvXarx+/fpcuXKFvDzb7r2ZmZlkZGTg7e39j9esoKCg8LjipnNjYtOJNuMWycLE3RPZHbObNH0a5Z3KM6TOEMY0GvPQ1xgXF0dysm2dSHJyMrdu3SpVyOTk5DB8+HDOnj1LZmYmlSpV4r333qNz584Y+/RBEkV0ixcjZGZi8fcn95NPkO7T8+m/pJpXNT5/+vO//PpsYzYZett8J4PFwKXkSwj5KddFEZDFDBkZjAK2ASnIAYhhwPjERGYfm83xO8cL55glM2cTzjL90HSSc5NBgJOB1ufVqXVciIrEQie68jWxVMOBHGryGz+Lm5ie/hrYyR4TDAKBTjrYuR7SFkOlQ4AEN1vDyZfx730LtUpNMWNSQMDHW4DwGlDuhvWh9Iq4xD5NduB60NkxtTBpqJwGF3zBXCTYozZDSBKcMlaGldHQDnAF8pBFzI3Sm4WWVRQho6BQhqlSpQpvvfUW3333HTdv3sTNzY2wsDC+/PJLYmNj+frrr0lNtQ47+/r63rdBoYKCgsL/KnNOzGHZpWUYLHJKUro+nTkn5tDItxEtA0q2w/8vqF27NoGBgcTExFiNBwYGUqtWrVLnjhs3ji1bthR+f+nSJd555x2aNm2Kt7c3pl69MPXq9Z+s2x7Z2TBtmgPnz6vQaiUGDDAyYICd3f1fxE3rRq4pF84OhnNDwaQDv1MI7afwdKWnsVQ4girCuh29pNNhfOYZvrxwgSXcC0ykA58B9cuV42zCWbvXu5xymZ6hPfnjzEmkPz+DtCqgy4BWs/CvrKV+l5Z8fnkTV6kLBAAmjhGMWsyla6fOfPrJUnIyTyIXoEhADAHlO5JyPQhSq8H2L60vKJhIi0jEYtdHQqJ1az0sGgNqPVQ4I68lLRiOjiGp3K+QVAOci9mMWwSIa4G3cA2pWEW/BHib1WhM/hhTo+G3YpdUF1NvjwmKa5mCQhln+PDhHDx4kM2bN7Nr1y7Wrl2Li4sLNWvWpFOnTjg63mso5uvry9ixY5VifwUFBYUS2BG1o1DEFJCal8r3575/6Gtxd3dnwIABeHh4FI55enoyYMCAQrdKe0iSxOnTp23GY2NjWbRo0X+x1FKxWKBPH2cWLtRx9Kia/fs1vP22A3PmPHiKs96sx7TvTdjyLUS1g9jWcHwc0rJNnE+8QN7UqZgrVSp8vaRWY2rZElOPHmzW6Wyyq1KBeWlpuGrt31dntTM9K72Ew/L9cGGwfL3rnRF/X0Un4zeYzVeAy0BtwB0oB0xAEH4gKCiInMzLyCJGQN5eV+Zm9Dm8KuvBIc32gg6pmJwMJQgZWLHCEep/BxsWwaJjsHQfLDwKGQF4VTknu5slh9ybYBbkSE98GCdCHLAUK70xq+B0sCPlPPztXk9b5L5cuiTy0Uc6li/XYLDjN1CWUCIyCgqPAY6OjjRt2tRmfP78+XTp0oU1a9bg7OzMuHHjqFat2iNYoYKCgsLjgUmyyeORxy32x/9rJk2aRJs2bQoFyCuvvGL3731RJEnCbLbvl5uVZd9C+L9kxw4V585Z75wzM0VWr9YyfrwB8QEemxtMZgzn+oLB3frArcacPRiLZXRjsrdvRzdnDsLdu5jat8f4wgugUmEurD8VkfOn5Ep2g8XC243f5Pjt49zNuZeyXc6xHK83fJ3v57uQe9s6LduSWZ51P3mSkvIM4Iw1IkZjZ/r3v4ssboo+RBSwWBxxdXcGv1Nw41nrqX6nCarqhCDYazMjkJkpQI43DGkPRybI/99kLlQ4y8jKu/noxhdgdACzKBfzmB0gxwVVtR3EOthXH5GOeei6xsMCP5CKFvV4INV3RJJg3DgHNm7UkJoqolJJfPutjpUrswkMtN8s9lGjCBkFhccYQRDo2rUrXbt2fdRLUVBQUHgsaOTbiFPx1h3VHdWO9KnW5xGtCJo3b07z5s3/8utFUSQ0NJToIr1iAMqXL8/w4cP/5dXdn5Mn1eTk2GYCpKUJZGWBm5udSffBkueMmGdnm2pyxjNZFgWSnx95s2fbvKRRo0acONEZ6IssMBLQaL6jT5/mNKzQkC/bfcnXJ78mKTcJLwcvXg17lbZBbVkaYV9xpaaK3LrVArvdKdFw6JBbCcfg2sFggge+SOScepDjIw86JVFl0Cx6tVzNOEfJ5t4JgsSkSXn8Vm8emJpD4/GgMUOaC6ycxwyjDp7ZCBWK1M9qcyF0F7qDvckMkeQ0M7GI+LCIZOgk1AFnoX0w7PMBUzqonKGmJ5ZOBzl0SMW6dRoyM+X7YDYLhIermDjRkVWrSu9796hQhIyCwr9IREQEM2fOJDk5merVqzNp0iSrlAEFBQUFhUfL9CemcyPtBifvnCTdkI6vky/PVHmGHtV6POql/S3mzp3LgAEDuHz5Mrm5uQQGBjJ8+HAqV6780Nfy1FMmvvvOQlaWtRDw9rbg6ipHHJYu1bB5swZRhBdeMNCjR+kRMDc3qOLnytU063Gdk54uHXSU1sGxcuWZiKIWi8UhfyQAUfyC4GAAiUYujahxuAY3b97E39+f5q1lERkYmAhUgGIWyzk5UWRkBJd4vZo1szhxwr6YadvWkd/fXgVZRWpXs/zRbViFaoKK2rXNnDhhfd/c3CTc3c3AeNjrBHt/Q05bOwJ8gpH9UM4HsDYCQmMgx/cqhPcEdR4EHgXnZMjygZgnyNNl4l7rOLQ6D62spwqoWbbsnogpSlRU2a1EUYSMgsK/xKlTpxg6dChxcXEA7Nmzh0OHDrFly5ZSc50VFBQUFB4eDmoH1vZcy8XEi1xMukiriq0IdHv8Cp19fX3ZtWsXhw8f5vTp0wwaNAhPT89HspZWrcy0bGlm1y4Bs1nezHt5WRg+3IAgwGuvObB2rRaDQT52+LCKCxcMvP9+yT7BogijRkpMn24hOVneSKtUEk+1FmnRovQ29MuXO2GxWG9x9Xo35swxMGdODN26dePatWuFxw4fPsy6detITv4WeBVoUmTmTbTaj9Fql6AvYbljxngwZIgFOZWtQMxIQDaiqCPyUvGUNIi87MnJk9mcPm3bRyY9XWD/fh3gCKxFFlcAHYCngO/AZHtOAPLqwaX+cOMZ8L4M/qcgrhmkhEK1TTR46gv23tprM83bwRtvb/vpY7pH407+lyi7Egv47LPP8PDwsPoqmv8vSRKfffYZNWrUoEKFCnTp0oXLly8/whUr/C/z6aefFoqYAi5cuMDcuXMf0YoUFBQUFEqijk8d+tfs/1iKmAIEQaBVq1Z06tTpkYkYeR3w2285TJuWR/v2Rrp2NfLrrzkMHWokNlZg1y5NoYgByMoS+f13Ddl23IOLMnSokV9+yaFrVyPt2xuZNi2P337L4X5+NiVFEC5eVPHJJ59YiRj59VFMnz6d3Nxk4GlgBrAV+AVoh0ZziuaBN21PmM+zz5qoVs0CZAKW/K9EunbVkpYmWL33AgwGgXPnxELhZ43Asl9FYAj3RAzI2/bmgB9c6QkGR+tp6f5wYBIY88eTasL5QbKIATA6EZ8bb/c9ZBozGTPGQKVK1iJRo5Fo1+7B3ef+a8p8RCY0NJRNmzYVfq9S3VOuX3/9NfPmzWPevHmEhoYya9YsevbsyYkTJ5Qn4AoPncTERLvjly5desgrUVBQUHj4nIk/w/sH3+dW5i1ctC50r9qdiU0nKi6K/yOo1TB6tIHRo60LzU+fVpGQYCssEhMFYmJEatUqwbYrn5YtzbRs+ffqM/T6LMC2MMdgSLWxui7gzp07vPbaa2zZugWD/l2rY9Wr96bTqZ84yjvocbA61pwjqISarFyZy9ixjkRFSajV0LChB99+m8upU7YRlwISE0v+3bgSIQC+do44Au3hUEXZkrnGOtBlQlYFODAZUVKj8T2N/mZrrOMVJhz9DhOdEg+3kLPnHAADkAM5gSbKl5f49ttcpk93ID5ewMkJnn7axNSppXXYfLSUeSGjVqvx9bX9QUqSxHfffcf48ePp3r07AN999x2hoaGsWbOGl1566WEvVeF/nJKehgUHl5xXq6CgoPD/gaScJF7a+hLR6dGFYzdSb6ASVbzR5I3/7LpXkq/w9cmvyTJm0SO0B72q9frHwikzE779Vkd4uIpq1cyMHatHKXV8cOrWtVCu3L30sALKlZMICChdxDwoWu1l9PpmNuMq1SEqVKhgZwZ4e3vTsE1D1LXVGMINcqNIEUQ/kTpD6hDsHsDIJfP5jYEk4osKIzW5wgfamYjqn6lcWeLVVw0sX67B0RHGjtXj7Aw1aljw9JRITd0PfJd/tZF4eDxJzQZJgA+yOCmKBcfyMRBpL1powqHKcfLqLIHNf8C+KSBa5OJ+SYXHiCDqZqjYl1gPbjUFowtoMyDwEE8GfM/2OFH2Pyj6vD8XpBg56vLUU2Z27comOxscHEBVsg4rE5R5IRMdHU2NGjXQarU0btyYDz74gMqVKxMTE8Pdu3d5+umnC1/r6OhIy5YtOXbsmCJkFB46EyZM4OrVq8TH3wvbVq9enTfe+O/+EVdQUFAoC3x7+lsrEQOQY8rhj4g/CoWMXq/nzJkzeHh4UKNGjX98zbVX1jJ5/+RCG90dUTvYGrmVRc8+eB+V1FSBbt2cuXixYPemYetWDRs2ZFO+fNm0ny3rBAdbaNnSxJYtmsI0KkdHiWeeMT2Qm9lfoVKlRVy65A9uAnhGQkIdyE2gdu3fmTx5MkePHrVyfAsMDGTy5Ml8dfIrcp7LkUtkrgDlwVLTwtb4rYz5dAs+KzszOnsev/ECQdykI9u59Nx7AIwd68CaNVpyc+X3uG+fmilT8hg61EhAwAekps4D0vKvuI2KFV/Hr8Jg5JBIcSEj4ex8FrTl89PHiopzFRWCIogO3A7vOcLV7nC3HlTfAP5nMUS68E5sMw4+Nwhzai25t0zlPajdbjDlQh22O+y1FjHkX97B+vPtXEIJTlmjTAuZxo0bM3/+fEJDQ0lKSmL27Nl07NiRo0ePcveu/IfLx8fHao6Pjw937tyxd7pCIop1glVQKI2/+nnx9/fnk08+4ccffyQrKws/Pz/GjBlDUlISSUlJ//EqFcoiyt8ahQfhcfzcXLl9xe54anYqERERbN26lcWLFxMXF4ejoyMhISHMnj37ges6JEli9uHZVr1A8sx5bI/czsaTG6nh/mBCafbsQC5etN5dX76sYuJEPR98YD8lqaxQlj83kydDhQp+nDzpikoFbdqk0q9fIv/Vkp9qq+VSrTCoJIFzGqSXQ33NQo/un6LX6/niiy+YP38+ycnJeHh48Oqrr6JWq4lMiJRP4Jf/lU9qVipRsVHkrpxN3LildI3fhUHtyLGeUwl4vQl79sSwaVPNQhEDkJgo8s03ULv2GVJSlnJPxACkk5q6lGsXmiLgha1EViFmacEhAwzFf0cEcrLDEDQiksoCtdbJXwASuFhcWJwxC/NyLbT9AIJ3QVYFTH8sYmHNDERtc+zGwVRl8zMUGhpa6vEyLWQ6dOhg9X3jxo0JCwvjt99+o0mTJiXMuj/3uykKCgVERET8rc9LaGgovXv3/g9XpPC48Hc/OwoK8Ph+bgYLg9mfsJ9cU67VeFXvqnh5efHDDz8QGxsLyA0bz507x+zZs1m9evUDXS8tL400c5rNeKYxk3BTON1Cuz3QeRMSnOyOJyd7Ehr64F3q/2se1udGiInBcdIkxNhYJGdnDC+8gHHo0L80V273UrBl98j/Kh1Jkvjyyy/ZsmULer2eatWqMXv2bLy8vEqdp+ukgxMp9wIZnkmIzbS4NXEjNCSU5ORkTCYTJpMJs9mMVqslNDSUPoY+HEw4iMFiXedTrXw1+f6GAqe+LByvmf/f/fs1pKZqKE5qqgO3bhmsMjUKiI+Px9lZSzB3uEF5q2Pe3KFVWHWOn7MXsrIQ4BlEqqUSRqKtDwngUimIO5H1IFENq363OhyrNeJvqEicJcbG7kudrX0s//aUadey4ri4uFCjRg0iIyML62aKF1gnJiZSvnx5e9MVFBQUFBQU/gM6h3SmfeX2OKnvCYFqntWY8dQMfv7550IRU5QrV66Ql5f3QNdz1jjjprXd5DmoHKjrU/eBzgng6Wk/fczdXUkrIyMDp759+XPrVoZdvMinx46hf/99NIsePJXvfkydOpWZM2dx8uRJLly4wNq1a+nduzdmc+n2y4duHbJp6WLAwK+XfuXy5cu8/tJLDNm3j/UXL/LagQO8PXIkx44d4/maz9Om4pO0vCXSMgaejIIwx6rMeGoGAJLZzKFx4xgbEMDMWrVIPSU3Vq1Z04Kzs22cw91dolatinjbKTTxVqupW9GFKtyCYjEZHUYGORwAvYuddyfinFsNtbmi/Tef1hx3KQVfbnAET+4gsB9f3LmDl5jOk9uno7kFmnwjMtEC2gTo9eerpd7TsspjJWTy8vKIiIjA19eXoKAgfH192bNnj9XxI0eO0KyZbYGXgkJZICcHIiJEcspmg1wFBQWFB0IURH7u8jO/dP2FgbUG8kGrD9g1YBdVvaqWWHwvCMIDF+ZrVBo6BXfCUWVdW1DHpw6dgjs90DkB3n5bT8WK1pvkChUsvPXWgwmu/09ov/uOvteu0Q/4GZgKNM/IIHLJkr80X5IkYtJjuJ11+y+93mQy8fvv29Drre/9uXPhVm629jCW4BZssQgsmD6d03fu8BIQAvQHziYm8svHH4PZjHDuDKfLWzgcBPsrg/5ODNItubXC6MBAnv/pJ37OyuKz27dp364dZz76iBYtzNSvby1ktFqJp582Uc1BopUgWG24BaAlEJCTRxTVKK660vDkbpwJJPuJU/HxIuWjR0F2OesDSaHUSHqLKcnDiaQqzUijAtCaBG7hz3vprzEseRm5S+DXdfDiGfhqK6QthKnxf5Z6T8sqZTq1bMqUKXTq1ImAgIDCGpmcnBwGDBiAIAi89tprzJkzh9DQUKpWrcrnn3+Os7Mzffr0edRLV1CwYdo0HRs2aEhOFvD2luje3cgHH5RdS0MFBQWFv4MgCLSr3I52ldtZjQ8ZMoTFixfbRGVq1qyJ7h902pvWehpejl5subEFvUlPjXI1mNlmJqLw4M9oa9a0sGRJDjNmOJCcLOLlZWHiRD0NG/437lqPE1v27WM7UDR58BowMTqaVfeZG54Yzuido4nJiEGFilCvUBY9uwg/F78S52RlZRMfn2UzbrHo2b//cqFjrT1yLz8NXqdBZbo3mOeKV/QwJh580cqYWQBcgGknT7LqpzfY7ZaMUX3v4GUPI1NWvMg4Sz825eRQdEU3gA++/JKN77/P6ncP8d7QFM6nV0EjmOnod54J77ZBvH6L5UYj7wCHkGMvrYCZRiPRkemk27GJzsaV8NyQ/Ffbin1Ldh7PBPRm4WY1NJkv19JkVMTlyEzGfO9O0DMbKZokKQDOQLlTa6iLChXQL1z+KqAGV8gs8Y6WXcq0kLl9+zbDhw8nOTkZb29vGjduzI4dO6hUqRIA48aNIzc3l7feeou0tDQaNWrE77//rvSQUShzLF+uYeFCLdnZ8j+w6enw/fcCNWta6Nu37DaaUlBQUPineHl58eGHHzJz5kxiYmJwcnKiRo0azJ8//x+dVxAExjcez/jG4/+dhebTrJmFdeuUsHlxVpvN5NoZj5ZKT7szW8wM3z6c8KR7u+aEWwkM2zKMrf22ljhPENwwmysAxSM4nuTm9ij1mk7HpkJQNATtB+cEyAiAi8+TrO5GFYPB7hxfo5EdsXsw2vGfiBbSWPPLL6TbmRdrsWDJzKT8hBH8mFKkWD4GjCPakvPTT6gqVeLLIi5pAOagIHy6NSJjQhoUq5HRkkDD/n44b8khm+LpZRJD/bYy8uOOVKqzjP0/7ScTiUqcp3HXXjRrVhEROE8dPuAjEvGhAvF8wmT8uIYa+2l5j2u3pzItZJbcJ1wpCALvvvsu7777bqmvU1B41KxapSkUMQVkZ4usWKFRhIyCgsL/e3r37k23bt04d+4cnp6eVK1a9VEvSeFvUrFFCzh2zGbcKSCg1HmHbx3mRsoNm/GI1AhuZtykklslu/NEUUAQ3kWSxnFPzDgAnZCkMORGL/ZxdlTB2uXgcgc8oyCxFuR54PG8AY2bKyTaZkOonZ0oL9p/EO5sFKiqViNQvJpFjuZod+7EngVb9okTiCYTxhdeQJg/HzEtDQCLhwfG/v25cEvAwBzgQ6Cgv00WBtbzyU+OuOJNNs4UlxkvdojhUK+ufJtwkALfviOYubBpGH0PVyCO2rzARm5SuXDOGcJYRXsaEm1XtDyuVWCPVY2MgsLjSkl1iSaT/fF/k5ycHHJz7T1HU1BQUHh4aLVamjRpooiYh4jJYiJdn450n6jJX2H02LE2DZ6dHR3p/sILpc7LMeZgkmz/sTOajehNJadXiyI4OPQC9gAvA32AX4BlhISUnur34otGPDwskOUHsS0hzwN/fzNvvqnnhz5VbGISZmB5Rz/ezG1M5VTrY2558EKEI0M7daK4qbcO6CyK3AwPt7uhNubkoM/MRP/22+SsXImhTx8MvXuTs2IF+kmTuHbtDvAD0A74EViJXLXzKmfPbiMeP+zFStqtGsPXB++JmALCgQV9+/IBH1uJGIAoQnifmZzxsd8gJs3WdO2xoExHZBQU/r/Qpo2Jw4fVmEz3/iCp1RLt2v13SiY+Pp7XXnuN69evA1CrVi0WLFjwwH0bFBQUFBQeDyRJ4oODH7AtchtZhix8nX2Z2HQi3ao+mC01yCmCS5cu5f333yc2NhYXFxe6d+/O+PHjS533VKWnqOJehetp163GK3tUpqpnyaLW2RkaNjRz8GA14J4zmq+vmSFDSs9k6N3bSFYW/PSTlvR0gfLlJd5+O4/QUAtvNXEm7QKMPQYOZtCrYGl9WN7Bi4ELr7IsHia3g9tusogZfB5GXTaTtW8iv69bx7jsbKIAJ6Ar8EGbNnzv5oYW2TygKNGCgIMgUAkwN2tGbjEzqpycKGShEg4MszomSeHYT/gSiInTYN8oHK7l5JDoVh8ybI/d9W5KooszJGbbHLvh5US1Es5ZllGEjILCQ2DcOANnz6o4eFBNSopIuXIWWrc28frr9nN1/ymSJDFw4EBO5VtDAsTGxjJkyBA2btz4n1xTQUFBQaFs8Pnxz/nh7A/kmeX0qzvZd3h7z9vULlebYM/g+8wumXr16rFhw4a/NcdB7cC01tP44OAHRKZFohbVhHiEMOfpOfd1rZs3L5fBg0WuXhXR6wWCgiyMHq2nfPn7R5iGDjUydKit4AlyC+L99vB+e+vxXi5+CNmXaRkHe3+yPiY5m5GqVCHozTfZsmQJqthYJEdHzDVrkvPddzRJSWHWZ5/xZk4OIYAB2Qjhw4AAllaoQEl07dqa8ePVQAfgNUADbAMWMGBAf+bNszdLonp1Mz4X7J+zgbs7cdUscNz2mFd9ifibQaRgZCrTuEY1fEhgGlNJ1T+ekuDxXLWCwmOGSgU//5zLtWsip0+LNGpkITTUOjR++PBhli1bRsuWLRk4cOA/ut758+e5csW203Z4eDjR0dFUrlz5H51fQUFBQaHssjVya6GIKeBO9h2+OvkV33T45qGvp0vVLrQNasuOqB1o1VraB7VHo7p/LlNQkMTevVns26ciOVmgQwcT7u7/bC1TWk7hyO0jXEu5VjgW7B7M1FZTMTeYifryZZs5lsBAAAxvvIFx6FBUO3Yg+ftjbt0aBIGavr4kPvMMTTdupIPJRAZwws2N8S+/jFYrN1LNS0vj8uefA1BjwgQcvbzQaNyBuUBvoCBboh3QgebN27JgwSHM5qrAJSAOeAa4TefONehUsx9XV62iqBdgQ2DY1q1smfA+ct1N0RhLOGbzdDbHvsWn1CeiyLG9tKFG5lnWPuhNfYQoQkZB4SFSrZqFatWsBYzFYqFZs2Zcv34dSZJYtmwZ7733HidPnsTb2/uBrpOammq3LiY3N5eMDDvxZgUFBQWF/zcYzPaj/RmGR/f330njRPdqJVsml4QoQtu2pTfA/Dv4OPmwpc8WPj3yKTEZMfi7+DO5xWT8XPzQv/UW6t27UcXHF75e0ukw9Ot373svL0zPP29z3sWLF7N48WK2b9+ORqNh0YgRPP300wBc/Oor/D75hNb5DW5ufv891ydNwr3vm4jiy1gsRSNTWqAL587lAR8ACUAksvF1RaA6p06N4MVvF9Ly9zpgmkYKZqriRETQTpxr1sRiuQm0AaYBAUAU8CEWS23OGBpxA+uo3C0CyTbba75Z9lGEjILCI2bq1KlEFHM7SUtLo3Pnzhw/bic2/Bdo1qwZVapU4cYNa6eYoKAgatWq9cBrVVBQUFAo+4R6hnIx6aLVmKPakd7Vez+iFZUtvJ28mdNujs24VKUKuXPm4DB7NsLdu+DqirFzZwxvvHHfc4qiyIgRIxgxYoTVuCkvD5/PPiO4SJfOYKMR88yZZD83BovFnlOagChKmM3ZyNGYAm4BehIT99ClSx8iTe8D7wOwAyBGYtasPMLDmyN3rXml6Aq5fLktFVxF7PlIq0ULj6MH2OO3YgWF/2esXr3a7nhcXNwDn9PR0ZE333yTwPxwOEDlypV5//33UauV5xcKCgoK/5/5/OnPaVC+AVpRTmvy0HnQrWo3uoZ0fcQrK/uYO3cme/duso4fJ+vwYfRTp8J9anlKI3LtWgL0tu5sgQYDtzetQixhJy63u4kHKgMLgTXAi0AKN27kcPu2ys4sgaVLtWRnTwfaIrfBBNkkugMpKe/QvE95O/OgdtMHb077KFF2NAoKj5iShMX9iiDvx4ABA+jYsSPLli1Do9EwYMAAPDw8/tE5FRQUFBTKPuUcy7Gz/042Xd/EpeRLdA3pSr3y9f7yfCE1FTE8HEuVKkj+/v/hSssoggAu/06qlcbVFXseawbArbwrTk4SWVnF/72XqF3bgixGpgMFvXa6Ar1wcztKfksa2+tpQBAcgV3ATuAgcppZGwQB3nk3k737zURE3BNCfn5m3v3w8ewkI6SlpT2eK1dQeAhEREQQGhr6n17jl19+YezYsTY+/0888YTiMPYY8zA+Owr//1A+NwoPwr/5udF98AGa9esRY2ORypfH1LIluT/8AEo0/4GQ0tK4VbkytYuNXwIqRkXRZ3hFdu2yNj6oWtXMoUNZ+PndxWIpboqczUcfZfDHH96cOKGmqEWzIEgsWZLDq686YDDYRmxcXMzExWURFyfw4YcOxMWJaDSZTJumpmHD0nvzlFWU1DIFhUfM4MGD6dq1a6GziVqtpnr16qxbt+4Rr0xBQUFB4X8J1ZYtaJcsQXXzJoIkId69i+aPP9B99tmjXtpji/rKFVyBc8hl+3fz/98VUF++zKJFuTz9tBE/Pwvlyllo0MDE99/noNWCVuth54zOXLigY/PmHKpWNaNSSYCEVivRu7eBnj1N6HQicjTmaSAMaA8cQq2WRU9AgMSiRbls25bNnDk3HlsRA0pqmYJCmeCXX34hJyeHkydPUr16dXx9fR/1khQUFBQU/sfQLVuGmJVlNSaYzagPHMC2yuPxxWg0smLFCg4ePEiTJk0YPHgwOt39a0QkCQ4cULFihQZ/f4lRowx4eZWe2GQJCCDA15dKd+9S4IVWH7CUL09WYCCenhK//55DUpJATg4EBkoIgtwPzmJJAorXtBhJTNyBVtuJvXuz+fJLLadPqxk0SBYxACrVEWAIcKfIvKtoNJuBoL90jx4XFCGjoFBGcHJy4sknn3zUy1BQUFBQ+F9FKmFT/g9rNssS2dnZ9OzZk9OnT2MymVizZg2//vor69evv28d6ciRjmzerCYrS05oWrtWw6JFOTRqVHJEQwoIwNS0KZpNm6iQf38lQcDUtClSEUMeb2/be+/isomUlADArcjoJWrVOk5s7LP07evMlSsiIHDkiJrffjOxcmUOgYGfkJZ2p9jZ4ggOngYsKfU9Pm4oqWUKCgoKCgoKCgrohwzBUqzIXVKrMT3xxCNa0b/P559/zvHjxzGZ5OiF2Wzm7NmzTJ8+vdR5x46p2Lr1nogBiIpS8f77Dve95tWpS1jhO5ozqsacUTVipc/rXP7gx1LnCIJA69aHgHeRrZRPA6soX34oo0a9zKRJjly5oqKgRiYvT2DfPjXLl2twdU2ze05Hx5T7rvVxQ4nIKCgoKCgoKCgoYO7UCcPLL6NZtw7x1i0kHx9MrVujf/fdR720f40zZ87YHb98+XKp89as0ZCRYfv8Py5OhcVCiTbKkgQvvuLB6fhv7g0mQINXTezenV1qsOvbb7/l+vVnuXZtISaTCTc3NyZMmEzFihWJiRGBfcBMIAOojMk0ix07fAgxmzls53w18g0b7hyNJenlabim30KvcePqtLepPqRRqe+/rKJEZBQUHgMuX77MJ598wm+//YbBYL9js4KCgoKCwj9FP20aWfv3k715M1l79pC7aBGo7PUseTxR5b+XtsDHwLP548WdQ4sTHGxBEGxf4+wslShiAMLDRSIibF8QESFy8aI8npkJCxdqmTVLx61b95TN2rVruX37dmH0KCMjg1WrVmE0GjEYtgEvIPeKeQa4AnTAzS2Fz0SROsWuFwZMM5tJuZKEtltvWt9aQ/2sI7RK3U7g+MFc/uGY1Zo//ljHypUayvqWQ4nIKCiUcSZMmMD69etJSUlBpVIxd+5cVqxYQaVKle4/WUFBQUHhsSA3NxeQGxo/cjw8MDdv/qhX8Z+gkSS2AS2RncOygRPA1HyxUBJDhhhYulTL1av3RJ1OJ9Gxo70uMffQ6wWMRtuwi8EgoNcLHDmi4vXXHYmMlGtdlizRMmqUnrFjDfz000+kFWsYc+HCBdasWYPRuAVYD9QDdMBoYDuOjrNxA9yRN/kmQAN4As4WCxeGfUE74zWrc/pZbnPz0y+Qhq9i/HgH/vhDQ2qqiFot8fXXOlatyiYgoGx2a1EiMgoKZZjjx4+zZs0aUlLkvFaz2Ux4eDgTJkx4xCtTUFBQUPg3SExMpHfv3jRt2pSmTZvSp08fkpKSHvWy/t/SOyqK9sgiBuR4Rhvg1Vu3Sp3n7AzLluXw9NNGQkLM1K1rYvRoPR9+WLqfW/36ZqpUsTUDCA62EBZm5p13tURG3qt1iY8XmfedSGIipKam2swzGo2cOHGC5OSRQBNkEQOyVOnO6dPl+cBi4RCyiAEwAnuBmRoN4s2bdtepzkjl0CEVv/8uixgAk0kgPFzFxIllQFyXgBKRUVAow/zyyy9kZGTYjEdFRT2C1SgoKCg8fNZdW8fyy8uRLBLdQrsxuPZghPzCgn039/H92e/Rm/U8GfgkoxqMQqPS3OeMZYtBgwZx7Ni9tJ7Y2FgGDx7M1q1bH+Gq/v/SLC8Pe4ly9f9CDlXVqhZ+/z3nb11PpYIZM3J5882CqIssYmbMyCUhQeBKZDagtZpz946ODX8Y8fHx4WYx4eHg4ECbNm1Ytaqqnas5kpPTmROeG2yOSMABkwl/VQPastnmeIJQnmXLNGRm2sY4CtZdFim7K1NQUMDHx8fu+F/xu1dQUFB43Jl2cBpjdozhz6g/2RGzg7d2v8WYnWMAWHRuES9ufpEtkVvYFbOL6YemM2jToPvWOpQlrly5wpUrV2zGL1++TERExCNYURnEbEa1cyfqFSugWJqVJEkcPnyYZcuWER8fb39+MarUrGl3vFJo6D9daYk89ZSZtWuTqFv3IHXrHmTt2iTatDFj1qRjFrNsJ4h6bnGK0aNHU778vT4ygiDQpEkTunbtSmA5V9t5QP06gaSm5do9lpVlYF/LtzhLPavxSCqzOPgjuxbQAGV5y6EIGQWFMsyoUaMICrJuXqVWq2nbtu0jWpGCgoLCwyHLkMX6iPVkGe9t9PQWPTujdxKbEcuPF34kVX8v9cYsmTl86zDHbh+zd7oySVpaGtnZ2Tbjubm5pKenP4IVlS2E6Ghc2rTBeeBAnEeOxOXJJ9EsXAjIaVfPPPMMffv25fXXX+fpp59m2rRp9z/puHGYivWLMbu6Io4c+R+8A5mxYy9Rr142Z8+24OzZFtSrl82YMZdQO2ZDueu2E1zuUrHBWXr27Mny5cvp3r077dq1493/a+++42u+/geOv+69mULEDCJGEHtvRZASxIpQRc1atX7Vqhq1SmuH1mylVlHU3jOxY+8VJEYESWTIXvfe3x9p7td1740twvv5ePRROeeez+d87j25+bw/Z40axfr161EqlXRlM9bo9w4V4gFj8i7h1i3jw8euXr3HwFFWdMjtw2z+j124sZzutLDYT73+pRk8OBlHR/1hcObmWpo0yXgeUGaSoWVCfMDy5s3L/PnzmTBhAg8fPiRbtmw0atTohevdCyFEVhcYFUhIXIhB+uO4x5wIPkF4QrhBXkxyDIeCDlHHIWtMVK9atSrFixc36H0pWrQolSpVMlHq02E9ZAiqy5d1P6vu38dyzhxS27bl/374gVOnTunyHj58yNKlS2nTpg1Vq1Y1eUy1iwvJ48fDkiUowsMhVy6SO3cmtV27d3INCQlqVqzIBzg9k+rE33+r+fXXXGhiDYePo4Fato3RamHHjnrcvu1CQgKo1Rratk2kdGkNI6NGU5ntFOMuNsQTiR1PsaHMuVRSUowcE0hICKdSJQ29CmxjRUQPIrEjO7E0sTrG11+3BWDevHh+/tmKR48UZMsGTZqkvnAeUGaSQEaID1z9+vXZv38/cXFxWFpaYmYmv7ZCiI9fEdsi5M2Wl/vR+k+X82XLR7UC1UhMTTQoo0BBNftq76uKb8zS0pKRI0cyceJE3VyIIkWKMGrUKCwsLF5Q+iMXF4cyMNAgWfXwIearVuHv72+QFxUVhbe3N/Pnz8/w0Cm9epHSsyfExUG2bKY3gXkVWi1mO3divnYtWFuTNGQImgoV8PK6A5QzUsCRUaNOQ7iRIW2xRVgw8xb29s7sXhDM9ylTyU042wJb0+Orruz3TUARG0MrduqKFE8v+tCRtHXKjPWiqAictpmF11wJxlGXGhztQN06v9P2xFBcXNQcOBBHbCxYWcGHfsvxgVdPCJHOxsYms6sghBDvjZ2VHU2LNWXV1VUkqtOCFjOFGfUK1cPR1lE34f9ZWrREJUa955q+GU9PTxo3bszKlSvThgx17UquXLkyu1qZT6Uyun+NFtDmyKHbD+Z5z/6tVDx8iPLuXTRly6J9/j1VKCB7duPnjo5GdeUKmsKF0b7kVgdW//d/mP/7L8r/ltE28/Eh8aefsLNzIS2oeH7lr2Ty5rVAodFgODMlBTs7JTEbD7I3pS9FeABAK3aw99YGli9bx/9pDFdCA4iJigJ+BH4G1M/kmAHjmD3dTC+IAYgiN0tu1KftM2mm3poPjQQyQgghhPggzWw8kxJ2JdgZuBONVkNDx4b8UOsHYpJjsFQZn4F8N/ru+63kW5A7d26GDh2a2dX4sFhZoa5cGdVzq3ZpnJxI6dyZev7+3LhxA80zN/QFCxZk0KBBkJqKdb9+mB07hiI0FE3hwqR4eJD0EsOyLadPx3zVKpRBQWjz5EFdsybxS5dmOONdcecO2s2bdUEMgDIsDOXcuQw41oWffgpCqy2vX0YRxNixpVmx4iYREYX08iws7jFqVHlCl7TVBTEAliTTmIPcPupDglKJjVrN80IsLTFL+ILU1PLAECAWsAX+wtKyOJFJhj1ZAPFke+F78yGSyf5CCCHEW6C8fp1sHTuSvX59bFq0wGzjxsyuUpanUCgYWG0g2ztsZ2fHnYysMxKVUkVOy5wUyl7I4PV5rfPS3rl9JtRUvAvx8+czuVQp6pibU1WlonPOnDyYNAmyZ2fKlCl4enri6OhIrly5KFeuHD///DNFixbF8tdfMd+yBWVICAqtFlVQEBZLlqDavTvD86lOnsRiwQJU9+6h0GhQhoVhtmsXVj/9lGE5za5dWBnZKiHh7l3MQh6xZIkKpfIiEAKEolRexNtbiUqlYN+uvNgp9qMkGAWhWHOGKSMCyWlrgUOK4aT97MTRIeUfzlrkNchLBa5XdaNUqZKAJ/AQiAYeAG5UrOiEi+oISgw3/yxF1lwlTwIZIYQQ4g0pQkLI1qUL5vv2obpyBTM/P6yHD8ds8+bMrtpHSaFQMP6z8RSx/d+wn5wWOfF09qRErhKZWDPxNk2YNYtfHzzgZEoKF9Rq1jx9isfMmajVaszNzVm8eDFHjhzhwIEDHDlyhI4dOwJgdvQoiud6K5SxsViuWAFAfHw8gwcPpnLlyvTu3Vu3X5vF4sUon1viWaHVovpvUQGtWk3QsGHEFy5MePnyRJ89C8ChiAhijNQ/SqslDPDwKEhERDHWrYth3bpoIiKK4emZFojnd2vNfm13yrCFwuxnEf3o92tHtGoNtirDo6aixF79kJM5BhCFDTMYTg+WshEPElCxTzWRMWMSKMFV7uJAPFYEUBR77jFhQgKDbVfgym7G8zk7ycHvVOAz9uClGP66H1OmkqFlQgghxBuy9PJC9dxGtcqICCwWL35nqyF96lyKuODzpQ/zz83nScITelToQY2CNTK7WuItSU1NZdeuXSQk6O+Jcu3aNXbs2EGbNm0AsLOzw+655ZRNUigICwujatWqxMamLet97949du3axYkTJyhjYg8ixX+bZSY5OlIuPj6tFyA2lhRXV27+8AOhpUpxHmj4TJlE4KCFBQ2s/zc3plkz/b3h4oOeMDeqFDP5DQ1pWy30wI35msX8NXIx5bIp0ETr9zqo0JBavjzV/C5TnxNcpRygZB1f0IhD9Ls5j1PTGuKPJ0q0KIDi3CcYJ0b8sh/XyBCm0ppygBWQylWa0RxF1tl+SY/0yAghhBBvSPnwofH0GGPPacXbkjdbXsbXH8/cpnMliPnIxMXFEWPk9ycpKYkrV65kWDa1fn20zy0GobGwIKl7dzp37qwLYtIlJCTwxRdfoClWjOe3ktQA2vh4HowdS770IOY/5kCRWbNo1bo1Q4oXZwlwFjgBTAWWVK1qcmNrgOhzgcxlnC6ISZOHM3Tjuu8VMDMzeqOeWrcus5MHcpUKpN/KJ5KNAzThYqwTYy5/heq/IAZAQVoA9JNfO/yASqQFMZDWo1EasujAMglkhBBCiDeWWrMmWiNLuGoKFsyE2giR9dna2urtap/Ozs6Oli1bZlhWa2OTtirZMxT/rVIWEBBgtMzDhw+5evYsvwG3SVtn7DGwFRgXG4vtypVGb5qtNRqszMwYOmYMfZVKagB1gRnZsjF/4UIANKmp/O7pSWtHR1o7OvKbhwea1FTu53MgEQeDY2pwYGn2pmlLQz9HAZj7+nJHa/jepGDJhujaWBuEY2lyEEs2jA/HsjNa4sMngYwQQgjxhpL79UP9XDCjdnIicdKkTKyVEFmXQqHgu+++o0CBAro0KysrXF1dqVKlSoZlzfftQ/Hc8sSKpCQs/vgDSxOrj1lYWDD7wQNGAVWBRkBNwAPYm5BAnL29kWWS05aDVlpYMGDAAL0V1OLj43FzcwNgaM2aTDpwgCMxMRyJiWGyry9DqlenmHk0KuKMHDWemuY3wcgCAlpAU7gwj43OyoGbKTfQmLi9V6Mi2WgOJtM/dBLICCGEEG/Kyoq4rVtJnDyZlDZtSBw4kLg9e9A4O2d2zYTIstq2bcv27dv56quvaNu2LQsXLsTb2/vFBY0sSwxpc13Gjh2L8rneU4VCwcCBA0kolbY5ZSxwHHQLH6fkysXDChWMHjMZmDJlCqmphiuBPX78mKsHD+J7757e9pQpgG9QECl3r+DIEXhuFbFsXKW33WkU8fFGz6m8eZNEVgJRz+XcIRs/c59CBkGXFriHAw6krWX2rAjA+Jk+fDLZXwghhHgbLC1JHjiQ5IEDM7smQnw0SpYsybx5816pjLp6dVSnT/Ps4DKtlRXJnp507diR27dv8+eff5KUlISlpSWdOnXi+++/x8fHh72HDhH7XABRwcUFzp3DcAtWiFAo2L1zp8m6HNywgcdGNq98rNFwOTAQP2bgSiy3aYgGc3Jzg130Z2f1Xgzw2W9QTgGozp+nCDe4hxnQnbR9Yh4CY+ho9ZiDKd2wSt5IAcJQokWDgofYsy+bJ63i1/GQIMKAnEAc8ASoio3RHqcPnfTICCGEEEKIj0bihAmkurqiyZkTAE3+/CR7epLaoQMA48ePJzg4mCdPnhAcHIyXlxcATZo0oWu3btjb2wOQI0cO6tevz/Tp01G6uRnttXhqbU3nrl1N1qVlr144qFQG6Q4qFVXbtGFsofycZjAJVCKJstzHg4sWUVRu0wasrDhJTTxZT3N2MZFxJGBJatOmNLb4HlhB2gC40oArKiKoW74te1tM5xR1iSQXCiCcPBzFhdMeP9OdFZTElkpAMaAcUAczGuPzem92JlNERUVlxQBMiPfi1q1blPqvq/llnT17lhs3btCwYUMcHR3fUc3Eh+512o4Q0m7E65B2Y5zy0iVUV66Q+tlnaIsWfXGB/wQHB3Po0CGcnZ2pXr06CoUCTWoqgSVKUOXpU9LDklCFgqv9+lFt2jQKFChAYmKi3nFKlizJmTNnaOjkxKWICL28irlzcyQwkN9//525M2bQLyaGvMAspZLqrVuzfPlytnX8l+/3tSKU9HlCGlxUx9j0qBwNaiZz9V5BYClwDOgP1KRzx6eMGqugXTsbLANvUJPTHKceSucSbNsWx+eVEghKsseTfxnEPHbRnBmMwlYRzf3IrBcSyNAyId6SuLg4OnfuzPnz54mJicHe3p6WLVvi5eWVtlqKEEIIId4bTaVKaCpVeuVyDg4OdOnSRS9NaWaG4+XL+HTrRs7Ll0m1tMR8zBiqffUVAPfv38fV1ZUbN26gUCjw9PRk4cKFREREEJEtGzwXyERYWxMeHs7QoUMpX7483t7epKSkMLRZM/r06QPAnIiehOrdqis5afYZ23cnoLS1+y+t13//pYlPtqZIkQR27Ijj119L8iDYmQZFNYweHUe+fFocS1sQdAk20JENdNSVs7PTgNHBcx82CWSEeEtGjx7N4cOHdT+HhISwdu1aPv/8c9zd3TOxZkIIIYR4U5a2ttTassVonoWFBUeOHDFI9/f359GjRwbpjx49wt/fn3r16uHq6oqrq6tevlYLYWGGM0ASk5QcPGiGs7Oay5f1h6xZWWlp1y5tWYGCBbXMnZtoUL7T15Yc/789wGTSFpi2A/rQ0L0nadt4Zi0SyAjxlly8eNEgLT4+nnXr1kkgI4QQQnyCnJycsLe35+Fzm+ba29vj5ORkspxCAblyabh/Xz+YMTfXUrOmGje3VAIDlVy9qiI5WYGdnQZX11TatUtbAe3p06f8/vvv3Lx5k/LlyzNo0CBy5MiBWu0PfA0EP3PUAO7etQLavZVrfp8kkBHiLVEZmcwHYG5u/p5rIoQQQogPgb29PS4uLqxfv56UlLTeEnNzc1xcXPT2yDGmR49kJk1SEhkZAIQC1ahUyZyOHVMwM4P9++PYvt2MK1dUuLunUKVK2upoYWFhtGnThuvXrwOwbds2duzYwbZt2xj30yzSghg7oAIQCDzk5PG/kEBGiE+Yq6srly5d0n1RAeTJk4eBshSrEEII8cmaP38+zs7O7N+ftpyyq6sr33777QvLtW8fhrd3D2JiLpOaGo2tbXE6dBiEmVlPAFQqaNs2lbZt9fehGTdunC6ISXf58mUmT56MMjEY+BX4EnAkLUA6jEIz7U0vM1PIqmVCZOBVVoLRaDQMGzaMgwcPEh0djb29Pb169aJ///7vuJbiQySrCInXIe1GvA5pNx+n7t27s3XrVr20/Pnz4+PjQ+HChU2WK168OJGRVYHRQG4gDJiIvX0A+aOGcjlpEGm7yKRLwYZ1BEe1IihIwciR1ty/ryRbNi1duiTTo0eKsdN8EKRHRoi3RKlU8ttvvxETE0NYWBiOjo4yrEwIIYQQBAYGsmDBAgC++eYbSpQokeHrtVotR48eNUgPDQ1l4cKF/PLLLybLPn1ahrQ9ZhyeSS3DkyetqV6gGZeDcz5XwhynbFWJiYEOHWzw9//fUPnr15UkJ0Pfvh9mMCMbYgrxluXIkQMnJycJYoQQQgjB4sWLadGiBd7e3nh7e9OiRQv++OOPF5aLjY01mn7t2jXdv4ODY/D1fUB8/DOBhmIM+kEMQBEUinEonEsaPaZ5gTz8+acF/v76oUF0tJJ//rF4YV0ziwQyQgghhBBCvAPx8fHMnTuXkJAQXVpoaCjz588nPj4+w7KmFhHKly8fyclqqlc/T6VKGjw8ClKsWCS9e58CwMw2j9FyFrkLksthF0qePpeTQpz5sf96Ygz3komO/nD3l5FARgghhBAfnb1799K+fXvc3NwYPHgwkZGRmV2lLOH+/fv07t0bNzc3OnXqxNmzZzO7SlnapUuXuH//vkH6/fv3uXDhgslyCoUCKysro3nNmzfH3f0cAQH1UKuLALlJTi7Fpk1V2LgxgJxlnhgtZ1fhEU+erEDDAtJWK0slbQWz9WTLNhU3txQsLQ2nzhcooHnhdWYWmSMjhBBCvCStVsvBgwc5duwYdevWpUmTJigUH+7Tyk/VunXr+PHHH3XBy8mTJ7l8+TJ79uwxeXMo0noKPDw8CAgI0KVdvHiRFStWUKtWrTc+9sqVK7G2tqZLly7kzPn8PI2PU3R0tMm8p0+f7xn5H61WiyIhwWjelUuXuHKlJmDxXJl8TJx4nXIDFnLodhEIq/RfjgbsL1Kh1QrsTtmRtgjAdKA8cAd4iI1Nfdq1S2XlylSOHDEjNTXte83RUcPYsR/uRpnSIyOEEEK8hKSkJDw8PPjqq6+YOXMm3bp1o23btiQmfrh/5D9V8+bNM+iBuXjxIsuWLcucCmURU6dO1QtiAB4/fsyvv/76RsddunQpjRs35ueff2bUqFG4uLiwd+/eNzrmm9JqtURHR6PRGPY2aLVaYmJiUKvVb3yePHnyGH3YoVAoyJcvX4ZlY018t1zcuxet1vgtfEqKkn6nQuFpA2ASsB6YAFENGHbqKcOHD6dQoUJAFHAMeIidnR09e/ZEpYJ//41n5swEWrdO4euvk9i9O5batT/cHhkJZIQQQoiX4OXlxaFDh4iLiwPSxr4fPnyYadOy5v4LH7Pbt28bTd+zZ897rknW4ufnZzT98uXLr33MmJgYvLy8CA7+307yd+/eZezYsUaDiPdhyZIlNGjQgNq1a1O/fn3mzp2ry9u+fTuNGzemdu3a1K1bl3HjxqHVvv5OJVWqVMHJyckg3cnJiSpVqpguqNVifIYM5ElIoFChUCM50fTqBVO334DkaGAc0BGYBElxTNx0kZIlS/JtxYpUUygoBlQEeuTJQ4cOHQAwM4OePVP4++94Zs1KxMHhw96lRQIZIYQQeq5fv87XX39N+/btGT9+PDExMZldpQ/CiRMnjN7QnD59OhNqIzJi6gb5RZOrP3UJJoYypaamGk1/VkpKCr/99huenp507dpVt3TwoUOHCAoKMnj97du3uXnzJpC28/wXX3xBx44dWbFixRsFDi9y6NAhJk2axJUrV3j06BHXrl1jxowZbN68mcDAQL7//nsuXLjAw4cPuXnzJosWLWL27NlAWq/s9OnT8fT0pFu3bpw5c+aF51OpVFhaWhqkW1hYYGaWNsNjxowZlCpVihIlStCtWzeSk5NRKJWUtrY2KKcE2rZqRaFC0wAf0npWIG2uyzJq1HhCQIqJIWlJsdxev57ie/ZwTavlLnANsA8I4PTXX7/wWj5EMkdGCCGEztGjR+nfv7/u6amPjw9Hjhxh586dn/zcAgsL40uQGrtJEZnL3t6ee/fuGaTXrFlT9++LFy9y5coV6tevT9GiRd9n9V5JYmIis2fPJjIykmHDhlGwYEFdnkaj4ejRo5w7d47u3buTO3fuNzpXhQoVuHv3rkF68eLFMyyn1Wrp0qULBw4c0AWRx48fZ9y4cSaDSo1GQ2JiIpMnT2bRokW6pYYPHz7MyZMnmT9//htdiykLFy40GHYYHR3N8uXLKViwoN7qYgDJycmsXbuWb7/9lg4dOnD06FFdoHXixAmmTp2Kp6cnkNb7tGfPHnLkyIGrqytmZmbcvHmThw8fGtTj4cOH+Pv7M3bsWL1hdtu2baNy5cpcv36d/CVLwnO9YQqg6Zgx9ChcGNgN1ATKAgeAYP7v/4oQj/EhcbFoODFyJIOfSVOTNmOm6+bN1Pzrrxe+fx8a6ZERQgihM23aNL0hIAAXLlzgryz4B+5t69u3L3Z2dnppdnZ29O7dO3MqJExq06aN7ml3OkdHR4YOHUpCQgIeHh60adOGQYMG0bRpU4YMGfJOewFe17Zt2yhatCjTpk3jzz//pGLFigwbNgxIm7vi6upKp06dmDBhAi4uLrqeg9f13XffGQTmSqWSLl26ZFju2LFjHDp0SC9oiYyMZOHChSbngSgUCrRaLRs3btTbLyUpKYn9+/fz4MGDN7gS05KSkkymnz9/3mjegwcP2LFjB6dPn9ZrJ2FhYbpNLtesWUPDhg3p06cPX331FQ0bNuT69etERUUZndT/9OlTHj9+jK+vr0Heo0eP2Lp1K0eMDJFUA5N++eWZXrLTpG1+mfa9/fz3tz4N3z8xvqLZqrcwHygzSCAjhBBC54mRP3IajealhlB87Jo1a8bo0aOpUKECBQsWpHz58owYMQJ3d/fMrtp7s3//fpo3b061atVo0qQJK1asyOwqGTVx4kR69epFyZIlKVSoENWrV2fevHnkz5+fcePG4evrq7u5DA0NZf369WzatClT6hoTE0OfPn2oVasWtWrVom/fvrob+wEDBujdeKemprJixQqCg4MZPHgw58+f1w0HCwoKYu7cudy5c+eF51y7di2urq5Uq1aNZs2asXPnTiBtuNfzPSgajeaFx9yxYwfJyckG6Xfv3qVChQoolYa3m2ZmZiiVSoMeEICQkBDOnTuX4Tm1Wi0zZ86kYcOGVKtWjTZt2nDp0qUMywDUr1/faH2qVauGjY2N0TIqlYqDBw8aXdgjJCSEqKgoJk+erHufUlJSuHbtGoMGDaJcuXImg2S1Wk1KSorRvH/++cfkUL/t27cbTU8/ZkaMh3FZlwwtE0IIofN8j0O6smXLvt+KfKD69etHnz59iIuLw8bGxugN0cfq+vXrDBkyhEePHunS7ty5g62tLe3atcuwrEajYdOmTWzevJmcOXMybNgwSpQo8VLnPXPmDIsWLUKtVtO9e3caN278wjJKpZIZM2aQmppKYmIi2bNn1+UZu0FOSEhgw4YNtG/f/qXq9DZ169aNgwcP6n6+efMm4eHhjB8/3uicHrVazZQpUzh16pRBXkREBH/88QdTp041eb59+/YxevRowsPDAQgMDGTYsGHY29uzceNGozfWL3qQYWoZYY1Gw8WLF40OLzMzM0Oj0ZjMK1++fIbn9PLywsvLS/ceBQYG0qNHDw4cOEDu3LmJiIhg9uzZ3L17l2rVqjFgwACsra0ZOnQox44d4+TJk8TFxWFtbU21atUYM2YMP/zwg9FrNTMzo3LlykbrkZSUxLZt24z2IF29ejXDYN9UDxCY/i4G3umS76mpqfz999/4+PiQL18+vv/+exwcHN7Z+d6UBDJCCPGBS0lJ4cKFC2TPnp0yZcq80z9igwcP5vbt24SFhenSypUrxzfffPPOzvku3bhxg+joaKpUqWJyjsurUiqV5MiR460cKyvx8vLSC2IgbfjQkiVLXhjIfP311+zatUv3RHv//v3MmDGD1q1bZ1hu7ty5zJ49m4iICF25nj17MmnSpJeqs5mZmV4Qk55mjLm5+Usd83Wl/x7b2NhQtmxZFAoFgYGBnDx50uC1fn5+PH36VDf86nnZs2c3+bTe1Ipt6RYtWqQLYtKFhIQwZ84ck98tzwY3cXFxXL58mQIFClCsWDEAKleuzOrVq43W09R+MenHNHbO9OWPM7J161aDQO/OnTssWLCAbt260aFDB27dugWkDdHbvn07W7duxcbGhg0bNnDkyBEOHz5MrVq1aNq0KQqFwuhnARAbG2syWIuPjzc6rwjS5tdcvXrV5DXcuXMnbdKLkQ4bV1dX/vnnH6Plnv1+fmUqMDGFBrVaTceOHTly5Ihu6Nq+ffvw9vamdu3ar3/Od+jTeZQkhBBZ0M6dO6lfvz7u7u64ubnh5uZmcDP5Nrm7u+Pt7c3nn39OnTp16Ny5M1u2bMlyN+6hoaE0a9YMNzc33N3dadCgAVu2bMnsamVpGd3IZeTMmTMcOHBAb1jO48eP8fLyynBeSkJCAsuXL9cFMZA2DGvjxo1vdCPXrFkzg3kguXLlon///q99zBfZvXu33u9x06ZNCQ4OJjAw0GhAkpCQgFqtNhoEWFhYMHr0aJOBV9oeIaY9Ox/lWU+fPjUZdKQPG5s/fz4NGjTA3d0dV1dXOnToQHx8PJ07dyZv3rwG5WrVqmVyjkxKSgrJyclGh0Kp1eoXzpEx1e6CgoIYNWqULohJd/bsWd0yywqFgoYNG/LTTz/RrFkzXTBlbJhbel1NfX/ExcWhUhlfKPlFAVlsbKzRIAbgzFXTvWBvtJJkBiPPNm/ezOHDh/VWqQsKCmLChAmvf753TAIZIYT4QEVHRzN69Gj8/f1JTk4mOjqaU6dOMWDAgHd6XhcXF9avX8/u3bsNJuvu37+f1q1b06BBAzw8PDh79uw7rcvr6t+/P6dOneLp06ekpKTg7+/P+PHjM9xJW2SsTp06Rp+elyxZMsNyO3fuNLq7eUhIiG5PHmNu375tdLWn4ODgN5qz9d133/HVV19RrFgx7OzscHZ2Zvjw4dStW/e1j5mRuLg4vd/jmJgYzpw5Q79+/TIcmqhSqdi/fz+5cuVCqVSiUCjIli0bc+bMwdbWlgoVKhiUsba2pnv37hnWx1SAEBkZabCaVzorKytu3LjBnDlzCAwMRK1WEx4ezv79+xk+fDipqalGg9KkpCTWrVtnsi7BwcFG59ZA2pwVrVbL7Nmzadq0qW4ifXodCxcubLSeHh4eekP1nrV27VoATp48Sbt27WjQoAFt2rTRvd7UcC6lUml0Fbx0ycnJmOonz6gH/c5903OPvBd6m8x7V9avX280sLxy5cp7r8vLkqFlQgjxgdq0aZPRIQu3bt0iKioqwzHU78KxY8cYNGiQ3lPLW7dusWXLlpee7/A+xMTE6PaneNbdu3dZt24dffv2zYRaZX2DBg3iwIEDnD59muTkZJRKJeXLl2fy5MkZlitfvjwWFhYGN6w5cuTA2sg+GekKFSpErly5DJ6858qV643am0KhYNasWcTExBAaGoqjo+NLDzvUaDTs2LGDnTt3UrZsWb7++muTE8TTbd261ehk+YCAAPLkyYNKpTK4eVSpVJQoUQIHBweOHz/OxIkTiYqKYuTIkbpNFKdMmUKfPn10x7axsaFNmzbUqFEDSHva/9dff3Hjxg3c3d1p2bIlSqXSaFAJaUGFqXkgtra2LFiwwGhP2Pnz51m5cqXBcDVAtyy0MSqVisTERJO9cqtXryYmJob58+fregguXbpEYGAg+/bto0aNGgYrpZmZmVGvXj2TvTUPHjzgxo0bdO/eXe977Nq1a6xevZq6dety//59o9cfFRVl9JgAV03c6NtiugcMIDzM8D1Ll5r44r173jZTwdqHvJeY9Mi8ZzExMQQGBppcpUKIV6XVarl7967R1aaEeJtmz55tMPTiwYMH73xne61WS1BQkMlhH1lF+upPpp56ZyQ4OPidDil8Xnx8PAEBAXrDwSwtLdm6dStz5szB3d2d8ePHs2/fPqNDip7Vtm1bg4nb1tbWuLu76w3JCQoK4vHjx7qf8+TJY3SYVO7cuXF2dtb9/OTJE+7evfvKyyeHhYVx4sQJozeaiYmJBAQE6N0Qp6am8sUXX9C3b1/++ecfxo8fj6urq14PR/qeIxcuXNClabVak70VgNFhSWZmZmi1Wnbs2EHTpk1Zs2YNu3fvpnXr1kycOBGA6tWr4+PjQ58+fahTpw6rVq3SLQV8//59Pv/8c8aPH88///xDr1696NSpE2q12mQPgUKh4NtvvzUYdmdmZka3bt1MvY0kJSVx7do1o3lxcXG4ubkZvUYrK6sMh6yGhISwYMECg804z507x44dO/T2rEkXGxvLn3/+afKYKSkpjB071uC75MmTJ/z0008m58hERkZmeN8Wfvy40RFi8cDFC6Y3zH3w6N0sMf26dAG3AsgNZIGtwz6aQMbb25tKlSphb2+Pi4sLx48fz+wq6dFoNAwdOpT69evj6upKgwYNWLx4cWZXS2Rxfn5+NGrUiCZNmlC/fn08PT1l6MxHxMPDQzeZ9lklS5Z8770xYPqpnLEnsW/L1atXcXV1pVGjRjRs2JDWrVu/cH5Ejhw59G500xUtWpSOHTu+q6pmaPv27TRo0IAmTZrQoEEDevToYXQp1+cFBgbi5uZGo0aNcHFxoXnz5kafGL8tWq2Wn376ifr16/P555/ToEEDZsyYocufP38+c+bM4fjx4yxbtozx48e/MIAwMzNj2LBh5MyZE6VSibm5OY6Ojrr9UK5du4arqyuNGzfGxcWFVq1aERoaSkpKitG2FRcXR1RUFNHR0XTs2FH3vjZq1Ei3m3xG1Go1xYoVo1q1agwaNAgnJye9YWVTpkzR+1s9fvx4IG05XF9fX93nptVquXHjBiNHjgSge/fu5M+fn/79+9OoUSPs7e15/PgxNWrUMBo8JCYmEhsba3CjDmlBU1BQEKNHj9YLlGJiYliwYAGPHj0iIiKCunXrsmTJEk6cOMGXX37JL7/8AsAPP/zAjRs3dOVSUlLYt28f69at09sU9Fnu7u7s2LHDYJ+V1NRUdu/eTcWKFY2We/r0qck5IunBu7HhSgkJCRkGwU5OTiaDhylTppicRG+sN/ZZBw4cMJp+6tQpk5P2XySbiR6gVCAm3HgPmO4FH5CEhASoAAwA+vz3//akLRDwgfoohpZt3LiRkSNHMmvWLOrUqYO3tzcdO3bkxIkTODo6Znb1APj1119ZvXq17gsrMjKSadOmUatWLZNduUJkJD4+niFDhuitUPP48WP69+/PmjVrMrFm4m2xtbXl119/ZcKECdy9excrKytKly7NH3/8kSn1cXR0NPrEsly5cu/kfKmpqfTt21fvaW9ISAi9e/dm27ZtGZZdtGgR3bp1w9/fn4SEBIoVK8aYMWMyJQAMCQlh5MiRuhvSyMhIHjx4QPbs2TPcvVyr1dK7d2+9p/uhoaH07t2bffv2vZPV65YsWcJff/2lm4AeGRnJ3LlzqVChAtmyZcPLy0s3xCYyMpLly5dTuHBhhgwZYvKY0dHRevOTNBoNN2/eZPDgwSxZsoQ+ffoYfMa9evVi2rRpRm8sHz9+zPHjx1m1ahX79u3TpUdERDB06FCOHDmS4XCvmjVrGgwTun79OqNHj6ZWrVrMnz9f10sTFRXF4sWLKVmypMn5AydPnuT48eNs3bpVLz0pKYkaNWowYsQIo8FeQkICefLkwczMzGDYnUqlImfOnEaH+iQlJbFmzRqWLVum14OVmJjI7Nmz6dSpE/v37zd67fPmzTPocUkXHh7O7t27jeZt3LiR06eN9yyEh4cbncuUztRD2xftT+V/099k3vXr103mZTQnJ/28b1tG4XOW2rclJ9D0v/8DZCNtfNwHfBEfRY/M/Pnz6dKlCz169KB06dLMmDEDe3t7lixZktlV0/Hx8TF46vLkyRNdN7AQr2rjxo0EBAQYpF+9ejXDMbkia2nZsiXHjh1jx44d7Nmzh71791KwYMFMqcvPP/9M6dKl9dIqV67MDz/88E7Od/DgQYOVhwD8/f1fOMzK3t6evXv3smfPHrZv386xY8do27btO6nniyxatMjoBGtTN4bpLl68aPTp8q1bt/D3N32T9yY2b95ssIpWdHQ0y5YtY/HixQYBQGJiom4zRVNWrlxpdI7IxYsXOXDggNHlgm/evMn9+/dN9vY8fvyYy5cvG6TfuXNHN6HbFFNP3ZcsWcKqVasMvj/j4+NZv369yUny8fHx9OnTx2hebGxshiNEbty4YfIaDx8+bLJc+qaYz1Or1Xz33XcmN0X09/fn4sWLRvP27t1LaGio0Ty1Wp1hb4WpyfVAhg8dvJeantC+eq3hcs7iHavH/4KYdEqgSCbU5SVl+R6Z5ORkLly4YPA0qEmTJibHOmYGU18qLzO0QAhjYmNjjf4BTE1NNTpUQWRd5ubmJoeDvE+FChViz549/PbbbwQEBFCxYkUGDhz4wsnOrysuLs5oW05JSXnp784yZcq87Wq9MlMrc73o9zQpKcno0Jrk5OR39rfD1N+qjOZWvGgncVMPVtRqtcnPODU1FTMzM1QqldH8fPnyGT3vy+w/Yipw0Gg0Jj8TtVpNiRIljD48srOzM7mnC5DhPJDk5GSj76tSqXzt68ionKn3M6PjvYyMymbUc/j8MLa3VR/xmkxtpfQBd3tk+UAmPDwctVptsE55vnz5TD5ZMPaE710rVqyYwVOQbNmy0ahRo0ypj3h5H+rnU716dQoUKKA3tADSnkSHhYW92YZZ4q34UNvOm+rSpYvu3xkNKXlTTk5OFC5cmKCgIL30AgUKkJKSkmXe388//5x///3XYJK/g4OD0WtIT7O1taVw4cIGvRkODg5YWVm9k+svXbo0J06c0Bt+Y25uTrVq1bC2tsbHx8dgGFTJkiUzrEv9+vXJly+fwXeSvb09JUuWxMHBweAztre3p2jRohQrVsygx8bR0REnJyfs7e0NeiXs7e2pU6dOhvWxtbU1OpewWrVqVK5cmcOHD+sFSUqlknLlylGuXDkOHTpkcPNdvXp1KlasqJuf8qz0ifIbNmwwGNJkbW1NxYoVKVy4MIGBgXp5hQoVonXr1kaPCTB+/Hi+/fZbgx4yhUJBv379+P77740GV82aNePUqVNGF84oXrw4qampRnvPLCwsKFq0qMn3tWfPnixbtsxo3ty5c+natavRvNV/r6ZJkyZG8/784096de9lNK9UqVJZ5vff3t7e5EIlZmZmH9SDx9wBuYkoE5E2pOxZoZn396xUqVIZ5iuioqKydMj76NEjypYty44dO/jss8906dOmTePff/99o7Xm36bY2Fg6d+7MhQsXiImJwd7enlatWjFz5sx3uku3eDO3bt164S9RZlq6dClz5szh3r17WFhY4OzszLJly164r4N49z70tpNVbNq0iZ9//pm7d++iUqkoWbIkCxcupGrVqpldtVcyffp0li9fTnBwMNbW1pQtW5Y1a9aQP39+vdc9327279/PyJEjCQwMRKFQ4OTkxKxZs2jYsOE7qWdycjLdu3fn5MmTREZGkidPHho1asTixYtRKBQMHDiQ/fv3ExYWRs6cOalRowarV682Oe8i3cKFC1mwYAFBQUFYWlpSunRpVq5cSZEiRdiyZQsTJ07kzp07umWHFyxYQPXq1Tl58iRDhgwhICAAjUaDk5MTkyZNomXLlty5c4fu3btz8+ZNkpKSKFKkCEOGDHnh8toPHz6kUqVKejeQOXLk0A2d6tOnD4cOHSIiIoJcuXJRr149li9fjkql4ocffmDbtm2EhISQI0cOqlSpwj///EP27NmpWrWqXhCgUCjYvHkzLi4uDB48mDVr1ujOaWlpyZIlS3B3d8fHx4cRI0bofcYzZsygUaNGjBs3jt9//12v/qVKleL06dNcunQJNzc3XcCiUqlwcXFh48aN3L59m9q1a+sFZLlz5yYwMJCnT59SqlQpvYDU2tqae/fuoVQqKVCggMHN9cWLFylatKjROWZeXl707t2bPHnyGPSSdejQAW9vb5ydnQ0eLteqVYu9e/dSp1Edbly4oZdnm9eW+7fv4+TkpLcharqoqCiKFy9udAXAqKgoBg8ezMqVKw3yrl69ir+/P+3btzfIGzZsGOPHjzd6jXny5CEgIMDkHLuMlsP/kPJeJCoqCrv2dlCZtCFmSUAIDMo9iF9+Mh5UZ7YsH8gkJydTsGBB/vrrL9q1a6dLHz58ONeuXXvh2N337ezZs1y/fp1GjRoZ3cxJfFiyws3o06dP2bNnD7lz56Zx48YmV48R71dWaDtZRVxcHLt27SJbtmw0bdrU5I7mH7onT56wf/9+HBwcqF+/vtGHWMbaTUJCAnv37kWpVBrdlf5duHr1KufPn6dOnToGD0bu3LnDsWPHqFSpEpUqVXrpY0ZFRbF7927d6qLPbgYZFxfHnj17sLKyMviMk5OT2bdvH0lJSTRv3pxs2f73uFitVuPr60t4eDhubm6vdAM3ffp0fHx86Nu3L56ennp5t27d4uTJk1SrVs1gMYsHDx5w8OBBypQpQ/Xq1fU+xxMnTjBp0iScnZ2ZOXOm3vdxcHAwc+bMIXfu3AwbNgwrq/+tbZuYmMiePXtQKBQ0a9ZMLy8qKoouXboQFRXFwoUL9RYISk1NZcGCBZw7d44xY8botR21Ws3kyZPx8/Pj22+/pXnz5nrXMX/+fLZt20bXrl0NlldesGAB3t7eNG7cmFmzZunl/fLLL/z+++84ODjg5+en1x5XrFjBiBEjyJkzJ6dPn8bW1laXd+zYMbp164aFhQU7duzQ2wvoxs0bNG/VnJTkFP755x8a1v1foH7hwgUaNWoEpC3lvXz5cl3e3bt3dfvqmJubG/T6pbcHpVJpEBA921ae79l6Ns/Pz4+yZcsazVuwYIFeD/WzeS4uLmzZssVo3vPnzCjPw8MDX1/fVy73fP6r5PX7rh/rzq0je0p2bu67qfc796HJ8oEMgKurKxUqVOC3337TpVWvXp02bdrolk0U4nXIzah4XdJ2xOuQdiNeh7Qb8bqyetvJ8nNkIG234f79+1O9enVq167NkiVLePz4Mb16GR9bKYQQQgghhMjaPopApn379kRERDBjxgxCQkIoW7Ys69ato0iRD3i9OCGEEEIIIcRr+ygCGUibnGdqHXchhBBCCCHEx+UDXhlaCCGEEEIIIYyTQEYIIYQQQgiR5UggI4QQQgghhMhyJJARQgghhBBCZDkSyAghhBBCCCGyHAlkhBBCCCGEEFmOBDJCCCGEEEKILEcCGSGEEEIIIUSWI4GMEEIIIYQQIsuRQEYIIYQQQgiR5UggI4QQQgghhMhyJJARQgghhBBCZDmKqKgobWZXQgghhBBCCCFehfTICCGEEEIIIbIcCWSEEEIIIYQQWY4EMkIIIYQQQogsRwIZIYQQQgghRJYjgYwQQgghhBAiy5FARnzUjh07xpdffknZsmWxs7Nj1apVevmhoaF88803lClThoIFC+Lp6UlAQIDRY2m1Wjp06ICdnR1btmzRy4uKiqJfv34UKVKEIkWK0K9fP6Kiot7VZYl37G21m7Nnz9KuXTscHBwoXLgwzZo1Izw8XJcv7ebj8zbaTkhICP369cPZ2ZmCBQvy2WefsW7dOr3XSNv5uHh5edG4cWMcHR0pUaIEnTp14tq1a3qv0Wq1TJkyhTJlylCgQAHc3d25fv263mtepl1cvXqVli1bUqBAAcqWLcu0adPQamUB26zobbSbe/fuMXjwYCpXrkyBAgWoXLkyEydOJCEhQe84QUFBdOrUiUKFCuHk5MSIESNITk5+L9eZEQlkxEctLi6OcuXKMXXqVKytrfXytFotXbt2JTAwkFWrVnH48GEcHR1p27YtcXFxBseaN28eSqXxX5k+ffpw6dIl1q9fz/r167l06RL9+/d/J9ck3r230W7OnDmDh4cH9evXZ9++fRw8eJDBgwdjZmame420m4/P22g7AwYM4ObNm6xevRo/Pz++/PJL+vfvz7Fjx3SvkbbzcTl69Chff/01e/bsYevWrZiZmdGuXTsiIyN1r/ntt9+YP38+06ZNw8fHh3z58uHh4UFMTIzuNS9qF9HR0Xh4eJA/f358fHyYOnUqc+fOZd68ee/1esXb8Tbaza1bt1Cr1Xh5eXHixAmmT5/OmjVrGDlypO4YarWaTp06ERsby86dO/nrr7/YunUrY8aMee/X/DzZR0Z8MhwcHJg+fTpdu3YF4Pbt29SoUYMjR45QsWJFADQaDc7OzowbN47u3bvryp47d45u3bpx8OBBSpUqxfLly2nbti0A/v7+1K5dm927d1OnTh0A/Pz8aNGiBadPn6ZUqVLv+UrF2/S67aZZs2Y0aNCAsWPHGj2utJuP3+u2HQcHB6ZNm8ZXX32lO1aFChXo378/Q4YMkbbzCYiNjaVIkSKsWrWKFi1aoNVqKVOmDH379mX48OEAJCQkUKpUKSZNmkSvXr1eql389ddfTJgwgZs3b+oC7RkzZrBkyRKuXbuGQqHItGsWb+512o0x3t7e/PLLL9y5cweAffv28cUXX3D58mUKFy4MwNq1axk6dCi3bt3C1tb2/VygEdIjIz5ZSUlJAFhZWenSlEollpaW+Pn56dJiYmLo06cPc+bMIV++fAbHOXXqFNmzZ6d27dq6tDp16mBjY8PJkyff4RWIzPAy7SYsLIxTp05hb29P8+bNKVmyJC1atODQoUO6MtJuPj0v+51Tp04dNm/eTEREBBqNhh07dhAeHo6LiwsgbedTEBsbi0ajwc7ODkgb/hMSEkKTJk10r7G2tqZevXq6z/xl2sWpU6eoW7euXm+hq6srjx494t69e+/hysS79DrtxpiYmBjdMSCt3ZQuXVoXxEBau0lKSuLChQtv+zJeiQQy4pPl7OxM4cKF+fnnn4mMjCQ5OZk5c+YQHBxMSEiI7nXfffcdrq6uNG3a1OhxQkNDyZMnj96TLIVCQd68eQkNDX3n1yHer5dpN3fv3gVgypQpdO3alQ0bNlC3bl3at2/P5cuXAWk3n6KX/c5ZunQpCoUCJycn8ufPT79+/fD29qZSpUqAtJ1PwciRI6lYsSK1atUC0LWP5x+m5cuXT/eZv0y7CA0NNXqM9DyRtb1Ou3ne/fv3mTt3Ll9//bUuzVi7yZMnDyqVKtPbjQQy4pNlbm7OypUruXPnDsWLF6dgwYIcOXKEpk2b6ubCrFmzhitXrjBp0qRMrq34ULxMu9FoNAD06tWLbt26UblyZcaNG0e1atVYunRpZlZfZKKXaTsAkydPJjw8nC1btuDr68uQIUP45ptvdEGw+LiNHj2aEydO8Pfff6NSqTK7OiKLeBvtJjQ0lA4dOtC4cWMGDRr0lmv4bpi9+CVCfLyqVKnC0aNHefr0KSkpKeTNmxdXV1eqVq0KwKFDh7hx4wYODg565Xr16kWtWrXYvXs3+fPnJzw8HK1Wq3sSptVqefLkCfnz53/v1yTevRe1G3t7ewBKly6tV6506dI8ePAAQNrNJ+pFbefOnTv8+eefevNoKlasiJ+fH3/++Sdz586VtvMRGzVqFBs3bmTbtm0UK1ZMl57+nRIWFoajo6MuPSwsTPeZv0y7yJ8/P2FhYXrnTP9Z2k7W9SbtJl1ISAht2rShbNmy/PHHH3o9e/nz5zcYihYeHo5arc70diM9MkIAOXPmJG/evAQEBHD+/HlatmwJwNixYzl27BhHjhzR/QcwadIkFi1aBECtWrWIjY3l1KlTuuOdOnWKuLg4vbHK4uNjqt0ULVqUggULcuvWLb3XBwQE6P6YSLv5tJlqO/Hx8QAGT1RVKpWup0/azsfpxx9/ZMOGDWzduhVnZ2e9vKJFi2Jvb4+vr68uLTExET8/P91n/jLtolatWvj5+ZGYmKh7ja+vLwULFqRo0aLv8vLEO/Km7Qbg8ePHtGrVCmdnZ/766y+91TUhrd34+/sTHBysS/P19cXS0pIqVaq8mwt7SdIjIz5qsbGxBAYGAmnDfR48eMClS5fIlSsXjo6ObN68mdy5c1OkSBGuXr3KyJEjcXd3102MK1SoEIUKFTI4buHChXVPPUqXLs3nn3/OsGHDmDNnDgDDhg3Dzc1NVg/Kot603SgUCoYMGcLUqVOpUKEClSpVYtOmTZw+fZrp06cD0m4+Vm/adpydnXFycuL7779n8uTJ5M6dm+3bt+Pr68vq1asBaTsfo+HDh7N27VpWrlyJnZ2dbm6DjY0N2bNnR6FQ8M033+Dl5UWpUqUoWbIkM2fOxMbGhg4dOgAv1y46dOjAtGnTGDhwIMOHD+f27dvMmTOHESNGyIplWdDbaDePHj2iVatWFChQgClTpujtdZY3b15UKhVNmjShbNmyDBgwgMmTJxMZGalbaTEzVywDWX5ZfOSOHDlC69atDdI7d+7MwoULWbRoEXPnziU0NBR7e3u+/PJLRowYgYWFhclj2tnZ6S2/DGmbkI0YMYJdu3YB0KJFC6ZPn6636ofIOt5Wu5kzZw7e3t5ERERQpkwZxo0bR6NGjXT50m4+Pm+j7QQEBDBhwgROnDhBXFwcxYsXZ9CgQXTp0kX3Gmk7HxdTn9uPP/7IqFGjgLRhYlOnTmXZsmVERUVRvXp1Zs6cSbly5XSvf5l2cfXqVYYPH865c+ews7OjV69e/PjjjxLIZEFvo92sWrXK5HyYixcv6nrqgoKCGD58OIcPH8bKyoqOHTsyadIkLC0t3/6FvQIJZIQQQgghhBBZjsyREUIIIYQQQmQ5EsgIIYQQQgghshwJZIQQQgghhBBZjgQyQgghhBBCiCxHAhkhhBBCCCFEliOBjBBCCCGEECLLkUBGCCHEGzty5Ah2dnYcOXIks6sihBDiEyGBjBBCfGJWrVqFnZ2d7r88efJQtmxZvvnmGx4+fJjZ1RNCCCFeillmV0AIIUTmGDlyJMWLFycpKYnTp0+zevVqTpw4gZ+fH1ZWVpldPSGEECJDEsgIIcQnytXVlZo1awLQvXt38uTJw5w5c9i1axceHh6ZXDsRFxeHjY1NZldDCCE+WDK0TAghBAB169YF4M6dO3rpt2/fpmfPnhQvXhx7e3saNGjAli1bXuqYL1M2MjKSsWPHUq9ePQoXLoyDgwPu7u4cP37c4HibNm2icePGODo6UrhwYWrVqsX06dP1XvP06VNGjx5NhQoVyJcvH5UrV2b69Omo1eoM6zpp0iTy5s1LWFiYQd7o0aOxt7cnKipKl+bj40PLli1xcHDAwcEBT09PLl26pFfuypUrDBw4kCpVqmBvb4+TkxO9e/cmKChI73Xpw/0OHTrEiBEjKFWqFA4ODhnWVwghPnXSIyOEEAKA+/fvA2BnZ6dL8/f3p1mzZtjb2/N///d/2NjYsH37dnr06MEff/xBp06dTB7vZcvevXuXLVu24OHhQbFixXj69Cl///037dq1w8fHhwoVKgBw8OBBevfuTcOGDRk3bhwqlYpbt25x4sQJ3TkTEhJo3bo19+/fp3fv3hQpUoSzZ88ydepUgoKCmDt3rsn6du7cmVmzZrFhwwYGDBigS1er1WzcuJHmzZvr3pt///2Xfv360bhxY8aNG0dycjLLli2jZcuW+Pj44OzsDICvry+3b9/myy+/pGDBgty5c4clS5Zw9uxZ/Pz8yJYtm14dfvzxR+zs7Pj++++Jjo5+iU9NCCE+XYqoqChtZldCCCHE+7Nq1SoGDRrEhg0bqFKlComJiZw5c4bvv/+emJgYzp07p+sN8PDw4NGjR/j6+mJtba07hoeHB/7+/ly9ehWFQsGRI0do3bo127Zto0GDBq9UNikpCXNzc5TK/w0SiIqKombNmjRv3lwXfIwaNYpVq1Zx584dVCqV0WubNWsWM2fO5NChQ7pgAmDmzJlMnjyZ06dPU6pUKZPvzeeff45Go8HHx0eX5uPjQ/v27Vm9ejUtW7YkLi6O8uXL4+7uzvz58/XqXKNGDRo1aoS3tzcA8fHxBsHKyZMncXNz0wvm0j+T6tWrs2fPHszM5DmjEEK8iAwtE0KIT5SnpyclSpSgfPny9OjRAxsbG/755x9dEBMZGcnBgwdp164d8fHxhIeH6/5zdXXl4cOH3L592+ixX6WspaWlLohJTEwkIiICtVpNtWrVuHDhgu6Ytra2xMXF6QUZz9u0aRN16tQhT548euds1KgRAEePHs3wPencuTPnzp3j1q1burS1a9eSO3dumjZtCqT1skRFRdGxY0e9c6jVaurWrau3BPWzQUxsbCwRERGULFmSnDlz6l1buh49ekgQI4QQL0m+LYUQ4hM1bdo0SpcuzdOnT1m9erXBamWBgYFotVqmTp3K1KlTjR4jLCzMaA/Hq5TVaDT89ttvLFu2jHv37um9pmjRorp/9+nThy1bttCxY0cKFiyIi4sLbdq0oUWLFigUCgACAgK4cuUKJUqUMHnOjHh6ejJ69GjWrl3LTz/9RHx8PDt27KBz586Ym5vrzgHQrl07o8d4vmdpwoQJbNmyhcjISL3XGRs6VqxYsQzrJ4QQ4n8kkBFCiE9UtWrVdKuWtWrVCnd3d/r06cPp06exsbFBo9EAMHDgQJo1a2b0GOXKlTOa/iplvby8mDx5Mp07d+ann34id+7cqFQqvLy89BYeyJcvH4cPH+bgwYPs27ePAwcOsGbNGtzc3FizZg0KhQKNRkPDhg357rvvjJ7zRYGCnZ0dbm5u/Pvvv4wZM4YdO3YQGxurNxco/doWLFhAoUKFMjxez549OXnyJIMGDaJSpUrkyJEDhUJB7969dcd51rND8IQQQmRMAhkhhBCoVCrGjx9PixYt+PPPPxk2bJjupt/MzEw3NOtlvUrZzZs3U79+fRYuXKiXPmXKFIPXWlhY0KxZM5o1a4ZWq2XixInMmTOHkydPUqdOHYoXL05sbOwr1/dZnTt3ZuvWrZw4cYJ169ZRsmRJatSoocsvXrw4AHnz5s3wPFFRURw8eJCRI0cycuRIXXpiYqLe6mdCCCFej8yREUIIAaQtv1yrVi0WLlxIYmIi+fLlo0GDBixfvpyHDx8avP7Jkycmj/UqZVUqFVqt/rozJ0+e5NSpU3ppERERej8rFAoqVaoEpC25DGkLCZw7d469e/canDMmJoakpCSTdU7XtGlT8uXLx/z58/H19TVYma1JkybkzJkTLy8vkpOTTV5b+hCz569twYIFRntjhBBCvBrpkRFCCKEzePBgunfvzsqVK+nTpw9eXl64ubnx2Wef0aNHD4oXL05YWBhnzpzB39+f8+fPmzzWy5Zt0aIFU6dOpX///tSrV4+AgACWLVtGmTJliI2N1R1vyJAhRERE0LBhQxwcHHj06BGLFy+mQIEC1KtXD4ChQ4eye/duunTpQufOnalSpQoJCQlcv36dLVu2cOzYMb15N8aYmZnh6enJokWLUCgUfPHFF3r5tra2zJ49m759+9KwYUM8PT3Jnz8/QUFBHDhwgDJlyrBw4UJsbW2pX78+v//+OykpKTg6OuLn58fx48fJnTv3635EQggh/iOBjBBCCJ1WrVrh5OTE77//Ts+ePSlVqhS+vr5MmzaNNWvWEB4eTt68ealQoQJjxozJ8FgvW/a7774jISGBf//9ly1btlC2bFmWLFnChg0b9FYZ++KLL/j7779ZunQpUVFR5M+fn6ZNm/Ljjz+SI0cOIG2Oyfbt25k9ezabNm1i7dq1ZM+enRIlSvDDDz9gb2//Uu9D586dWbRoEXXq1DEa+LRv354CBQrg5eXFvHnzSEpKokCBAtSuXZtevXrpXuft7c3IkSNZunQpqamp1KtXj61bt9K2bduXqocQQgjTZB8ZIYQQ4jnXr1+nbt26/Pbbb/To0SOzqyOEEMIImSMjhBBCPGf58uVYW1ubXGJZCCFE5pOhZUIIIcR/du3axc2bN1myZAk9evQgZ86cmV0lIYQQJsjQMiGEEOI/FStWJCwsjEaNGvHHH39IICOEEB8wCWSEEEIIIYQQWY7MkRFCCCGEEEJkORLICCGEEEIIIbIcCWSEEEIIIYQQWY4EMkIIIYQQQogsRwIZIYQQQgghRJYjgYwQQgghhBAiy/l/sMFu8y99nB4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from importlib import reload\n",
        "plt=reload(plt)\n",
        "\n",
        "# Set the figure style and initalize a new figure\n",
        "plt.style.use('fivethirtyeight')\n",
        "fig = plt.figure(figsize=(12,8))\n",
        "\n",
        "# Create a scatter plot of duration versus release_year\n",
        "plt.scatter(netflix_movies_col_subset['release_year'], netflix_movies_col_subset['duration'], c=colors)\n",
        "\n",
        "# Create a title and axis labels\n",
        "plt.title('Movie duration by year of release')\n",
        "plt.xlabel('Release year')\n",
        "plt.ylabel('Duration (min)')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "decacb46",
      "metadata": {
        "dc": {
          "key": "67"
        },
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "tags": [
          "context"
        ],
        "id": "decacb46"
      },
      "source": [
        "## 10. What next?\n",
        "<p>Well, as we suspected, non-typical genres such as children's movies and documentaries are all clustered around the bottom half of the plot. But we can't know for certain until we perform additional analyses. </p>\n",
        "<p>Congratulations, you've performed an exploratory analysis of some entertainment data, and there are lots of fun ways to develop your skills as a Pythonic data scientist. These include learning how to analyze data further with statistics, creating more advanced visualizations, and perhaps most importantly, learning more advanced ways of working with data in <code>pandas</code>. This latter skill is covered in our fantastic course <a href=\"www.datacamp.com/courses/data-manipulation-with-pandas\">Data Manipulation with pandas</a>.</p>\n",
        "<p>We hope you enjoyed this application of the skills learned in Intermediate Python, and wish you all the best on the rest of your journey!</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce6d14d7",
      "metadata": {
        "dc": {
          "key": "67"
        },
        "tags": [
          "sample_code"
        ],
        "id": "ce6d14d7"
      },
      "outputs": [],
      "source": [
        "# Are we certain that movies are getting shorter?\n",
        "are_movies_getting_shorter = 'With the analysis we did so far, we can`t know for certain. What we can tell is that some genres such as Children, Stand-Up, and Documentaries are decreasing the mean of duration of the Netflix catalogue. But we cannot afirm that the movies are shorter'"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "",
      "name": ""
    },
    "language_info": {
      "name": ""
    },
    "colab": {
      "name": "Netflix_EDA.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}