Amazon_Sales_.ipynb

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        "# **Amazon Sales Report**"
      ],
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        "id": "o9vfD-HUa3iK"
      }
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ],
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      "source": [
        "df = pd.read_csv('/Amazon Sale Report.csv')\n"
      ],
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        "id": "GZR5wsW9SXhS"
      },
      "execution_count": 18,
      "outputs": []
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        "df.shape"
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              "(128976, 21)"
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        "df.head(10)"
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              "   index             Order ID      Date                        Status  \\\n",
              "0      0  405-8078784-5731545  04-30-22                     Cancelled   \n",
              "1      1  171-9198151-1101146  04-30-22  Shipped - Delivered to Buyer   \n",
              "2      2  404-0687676-7273146  04-30-22                       Shipped   \n",
              "3      3  403-9615377-8133951  04-30-22                     Cancelled   \n",
              "4      4  407-1069790-7240320  04-30-22                       Shipped   \n",
              "5      5  404-1490984-4578765  04-30-22                       Shipped   \n",
              "6      6  408-5748499-6859555  04-30-22                       Shipped   \n",
              "7      7  406-7807733-3785945  04-30-22  Shipped - Delivered to Buyer   \n",
              "8      8  407-5443024-5233168  04-30-22                     Cancelled   \n",
              "9      9  402-4393761-0311520  04-30-22                       Shipped   \n",
              "\n",
              "  Fulfilment Sales Channel ship-service-level  Category Size Courier Status  \\\n",
              "0   Merchant     Amazon.in           Standard   T-shirt    S     On the Way   \n",
              "1   Merchant     Amazon.in           Standard     Shirt  3XL        Shipped   \n",
              "2     Amazon     Amazon.in          Expedited     Shirt   XL        Shipped   \n",
              "3   Merchant     Amazon.in           Standard   Blazzer    L     On the Way   \n",
              "4     Amazon     Amazon.in          Expedited  Trousers  3XL        Shipped   \n",
              "5     Amazon     Amazon.in          Expedited   T-shirt   XL        Shipped   \n",
              "6     Amazon     Amazon.in          Expedited   T-shirt    L        Shipped   \n",
              "7   Merchant     Amazon.in           Standard     Shirt    S        Shipped   \n",
              "8     Amazon     Amazon.in          Expedited   T-shirt  3XL      Cancelled   \n",
              "9     Amazon     Amazon.in          Expedited     Shirt  XXL        Shipped   \n",
              "\n",
              "   ...  currency  Amount    ship-city     ship-state ship-postal-code  \\\n",
              "0  ...       INR  647.62       MUMBAI    MAHARASHTRA         400081.0   \n",
              "1  ...       INR  406.00    BENGALURU      KARNATAKA         560085.0   \n",
              "2  ...       INR  329.00  NAVI MUMBAI    MAHARASHTRA         410210.0   \n",
              "3  ...       INR  753.33   PUDUCHERRY     PUDUCHERRY         605008.0   \n",
              "4  ...       INR  574.00      CHENNAI     TAMIL NADU         600073.0   \n",
              "5  ...       INR  824.00    GHAZIABAD  UTTAR PRADESH         201102.0   \n",
              "6  ...       INR  653.00   CHANDIGARH     CHANDIGARH         160036.0   \n",
              "7  ...       INR  399.00    HYDERABAD      TELANGANA         500032.0   \n",
              "8  ...       NaN     NaN    HYDERABAD      TELANGANA         500008.0   \n",
              "9  ...       INR  363.00      Chennai     TAMIL NADU         600041.0   \n",
              "\n",
              "   ship-country    B2B  fulfilled-by New  PendingS  \n",
              "0            IN  False     Easy Ship NaN       NaN  \n",
              "1            IN  False     Easy Ship NaN       NaN  \n",
              "2            IN   True           NaN NaN       NaN  \n",
              "3            IN  False     Easy Ship NaN       NaN  \n",
              "4            IN  False           NaN NaN       NaN  \n",
              "5            IN  False           NaN NaN       NaN  \n",
              "6            IN  False           NaN NaN       NaN  \n",
              "7            IN  False     Easy Ship NaN       NaN  \n",
              "8            IN  False           NaN NaN       NaN  \n",
              "9            IN  False           NaN NaN       NaN  \n",
              "\n",
              "[10 rows x 21 columns]"
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d7a8919e-e00a-47de-a1cf-1535aa47e8d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        },
        "id": "sl5_CcaXUsbl",
        "outputId": "04a1f907-297c-4271-8474-60d8ff266f1f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         index             Order ID      Date     Status Fulfilment  \\\n",
              "128966  128965  408-5154281-4593912  05-31-22  Cancelled     Amazon   \n",
              "128967  128966  406-9812666-2474761  05-31-22    Shipped     Amazon   \n",
              "128968  128967  404-5182288-1653947  05-31-22  Cancelled     Amazon   \n",
              "128969  128968  403-7059995-7618722  05-31-22    Shipped     Amazon   \n",
              "128970  128969  404-3802633-7250760  05-31-22  Cancelled     Amazon   \n",
              "128971  128970  406-6001380-7673107  05-31-22    Shipped     Amazon   \n",
              "128972  128971  402-9551604-7544318  05-31-22    Shipped     Amazon   \n",
              "128973  128972  407-9547469-3152358  05-31-22    Shipped     Amazon   \n",
              "128974  128973  402-6184140-0545956  05-31-22    Shipped     Amazon   \n",
              "128975  128974  408-7436540-8728312  05-31-22    Shipped     Amazon   \n",
              "\n",
              "       Sales Channel ship-service-level  Category Size Courier Status  ...  \\\n",
              "128966     Amazon.in          Expedited  Trousers  3XL      Unshipped  ...   \n",
              "128967     Amazon.in          Expedited   T-shirt   XS        Shipped  ...   \n",
              "128968     Amazon.in          Expedited     Shirt   XS      Cancelled  ...   \n",
              "128969     Amazon.in          Expedited   T-shirt   XL        Shipped  ...   \n",
              "128970     Amazon.in          Expedited   T-shirt    M      Unshipped  ...   \n",
              "128971     Amazon.in          Expedited     Shirt   XL        Shipped  ...   \n",
              "128972     Amazon.in          Expedited   T-shirt    M        Shipped  ...   \n",
              "128973     Amazon.in          Expedited   Blazzer  XXL        Shipped  ...   \n",
              "128974     Amazon.in          Expedited   T-shirt   XS        Shipped  ...   \n",
              "128975     Amazon.in          Expedited   T-shirt    S        Shipped  ...   \n",
              "\n",
              "        currency  Amount              ship-city     ship-state  \\\n",
              "128966       INR   574.0  Prayagraj (ALLAHABAD)  UTTAR PRADESH   \n",
              "128967       INR  1132.0         CHENNAI 600042     TAMIL NADU   \n",
              "128968       NaN     NaN                Kolkata    WEST BENGAL   \n",
              "128969       INR   824.0                  Delhi          DELHI   \n",
              "128970       INR   612.0                 MUMBAI    MAHARASHTRA   \n",
              "128971       INR   517.0              HYDERABAD      TELANGANA   \n",
              "128972       INR   999.0               GURUGRAM        HARYANA   \n",
              "128973       INR   690.0              HYDERABAD      TELANGANA   \n",
              "128974       INR  1199.0                  Halol        Gujarat   \n",
              "128975       INR   696.0                 Raipur   CHHATTISGARH   \n",
              "\n",
              "       ship-postal-code  ship-country    B2B  fulfilled-by New  PendingS  \n",
              "128966         211007.0            IN  False           NaN NaN       NaN  \n",
              "128967         600042.0            IN  False           NaN NaN       NaN  \n",
              "128968         700040.0            IN  False           NaN NaN       NaN  \n",
              "128969         110053.0            IN  False           NaN NaN       NaN  \n",
              "128970         400017.0            IN  False           NaN NaN       NaN  \n",
              "128971         500013.0            IN  False           NaN NaN       NaN  \n",
              "128972         122004.0            IN  False           NaN NaN       NaN  \n",
              "128973         500049.0            IN  False           NaN NaN       NaN  \n",
              "128974         389350.0            IN  False           NaN NaN       NaN  \n",
              "128975         492014.0            IN  False           NaN NaN       NaN  \n",
              "\n",
              "[10 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Date</th>\n",
              "      <th>Status</th>\n",
              "      <th>Fulfilment</th>\n",
              "      <th>Sales Channel</th>\n",
              "      <th>ship-service-level</th>\n",
              "      <th>Category</th>\n",
              "      <th>Size</th>\n",
              "      <th>Courier Status</th>\n",
              "      <th>...</th>\n",
              "      <th>currency</th>\n",
              "      <th>Amount</th>\n",
              "      <th>ship-city</th>\n",
              "      <th>ship-state</th>\n",
              "      <th>ship-postal-code</th>\n",
              "      <th>ship-country</th>\n",
              "      <th>B2B</th>\n",
              "      <th>fulfilled-by</th>\n",
              "      <th>New</th>\n",
              "      <th>PendingS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>128966</th>\n",
              "      <td>128965</td>\n",
              "      <td>408-5154281-4593912</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Cancelled</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>Trousers</td>\n",
              "      <td>3XL</td>\n",
              "      <td>Unshipped</td>\n",
              "      <td>...</td>\n",
              "      <td>INR</td>\n",
              "      <td>574.0</td>\n",
              "      <td>Prayagraj (ALLAHABAD)</td>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>211007.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128967</th>\n",
              "      <td>128966</td>\n",
              "      <td>406-9812666-2474761</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>T-shirt</td>\n",
              "      <td>XS</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>...</td>\n",
              "      <td>INR</td>\n",
              "      <td>1132.0</td>\n",
              "      <td>CHENNAI 600042</td>\n",
              "      <td>TAMIL NADU</td>\n",
              "      <td>600042.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128968</th>\n",
              "      <td>128967</td>\n",
              "      <td>404-5182288-1653947</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Cancelled</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>Shirt</td>\n",
              "      <td>XS</td>\n",
              "      <td>Cancelled</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>700040.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128969</th>\n",
              "      <td>128968</td>\n",
              "      <td>403-7059995-7618722</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>T-shirt</td>\n",
              "      <td>XL</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>...</td>\n",
              "      <td>INR</td>\n",
              "      <td>824.0</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>DELHI</td>\n",
              "      <td>110053.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128970</th>\n",
              "      <td>128969</td>\n",
              "      <td>404-3802633-7250760</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Cancelled</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>T-shirt</td>\n",
              "      <td>M</td>\n",
              "      <td>Unshipped</td>\n",
              "      <td>...</td>\n",
              "      <td>INR</td>\n",
              "      <td>612.0</td>\n",
              "      <td>MUMBAI</td>\n",
              "      <td>MAHARASHTRA</td>\n",
              "      <td>400017.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128971</th>\n",
              "      <td>128970</td>\n",
              "      <td>406-6001380-7673107</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>Shirt</td>\n",
              "      <td>XL</td>\n",
              "      <td>Shipped</td>\n",
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              "      <td>INR</td>\n",
              "      <td>517.0</td>\n",
              "      <td>HYDERABAD</td>\n",
              "      <td>TELANGANA</td>\n",
              "      <td>500013.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128972</th>\n",
              "      <td>128971</td>\n",
              "      <td>402-9551604-7544318</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>T-shirt</td>\n",
              "      <td>M</td>\n",
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              "      <td>INR</td>\n",
              "      <td>999.0</td>\n",
              "      <td>GURUGRAM</td>\n",
              "      <td>HARYANA</td>\n",
              "      <td>122004.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>128973</th>\n",
              "      <td>128972</td>\n",
              "      <td>407-9547469-3152358</td>\n",
              "      <td>05-31-22</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>Blazzer</td>\n",
              "      <td>XXL</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>...</td>\n",
              "      <td>INR</td>\n",
              "      <td>690.0</td>\n",
              "      <td>HYDERABAD</td>\n",
              "      <td>TELANGANA</td>\n",
              "      <td>500049.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>128974</th>\n",
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              "      <td>402-6184140-0545956</td>\n",
              "      <td>05-31-22</td>\n",
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              "      <td>Amazon</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Expedited</td>\n",
              "      <td>T-shirt</td>\n",
              "      <td>XS</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>...</td>\n",
              "      <td>INR</td>\n",
              "      <td>1199.0</td>\n",
              "      <td>Halol</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>389350.0</td>\n",
              "      <td>IN</td>\n",
              "      <td>False</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>128974</td>\n",
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              "      <td>05-31-22</td>\n",
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              "      <td>Expedited</td>\n",
              "      <td>T-shirt</td>\n",
              "      <td>S</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>...</td>\n",
              "      <td>INR</td>\n",
              "      <td>696.0</td>\n",
              "      <td>Raipur</td>\n",
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              "      <td>IN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>10 rows × 21 columns</p>\n",
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              "\n",
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              "    .colab-df-container {\n",
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              "\n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.info()"
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      "metadata": {
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        },
        "id": "2iaNrN12UvBU",
        "outputId": "9c05f5d4-76a7-4584-983f-60b6e9e7d02a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 128976 entries, 0 to 128975\n",
            "Data columns (total 21 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   index               128976 non-null  int64  \n",
            " 1   Order ID            128976 non-null  object \n",
            " 2   Date                128976 non-null  object \n",
            " 3   Status              128976 non-null  object \n",
            " 4   Fulfilment          128976 non-null  object \n",
            " 5   Sales Channel       128976 non-null  object \n",
            " 6   ship-service-level  128976 non-null  object \n",
            " 7   Category            128976 non-null  object \n",
            " 8   Size                128976 non-null  object \n",
            " 9   Courier Status      128976 non-null  object \n",
            " 10  Qty                 128976 non-null  int64  \n",
            " 11  currency            121176 non-null  object \n",
            " 12  Amount              121176 non-null  float64\n",
            " 13  ship-city           128941 non-null  object \n",
            " 14  ship-state          128941 non-null  object \n",
            " 15  ship-postal-code    128941 non-null  float64\n",
            " 16  ship-country        128941 non-null  object \n",
            " 17  B2B                 128976 non-null  bool   \n",
            " 18  fulfilled-by        39263 non-null   object \n",
            " 19  New                 0 non-null       float64\n",
            " 20  PendingS            0 non-null       float64\n",
            "dtypes: bool(1), float64(4), int64(2), object(14)\n",
            "memory usage: 19.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['New','PendingS'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "3H18aboWU2os"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
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        "id": "JJUSUuJjVIAd",
        "outputId": "0d3bc83e-13be-49ee-c0b4-3d3c00934199"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 128976 entries, 0 to 128975\n",
            "Data columns (total 19 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   index               128976 non-null  int64  \n",
            " 1   Order ID            128976 non-null  object \n",
            " 2   Date                128976 non-null  object \n",
            " 3   Status              128976 non-null  object \n",
            " 4   Fulfilment          128976 non-null  object \n",
            " 5   Sales Channel       128976 non-null  object \n",
            " 6   ship-service-level  128976 non-null  object \n",
            " 7   Category            128976 non-null  object \n",
            " 8   Size                128976 non-null  object \n",
            " 9   Courier Status      128976 non-null  object \n",
            " 10  Qty                 128976 non-null  int64  \n",
            " 11  currency            121176 non-null  object \n",
            " 12  Amount              121176 non-null  float64\n",
            " 13  ship-city           128941 non-null  object \n",
            " 14  ship-state          128941 non-null  object \n",
            " 15  ship-postal-code    128941 non-null  float64\n",
            " 16  ship-country        128941 non-null  object \n",
            " 17  B2B                 128976 non-null  bool   \n",
            " 18  fulfilled-by        39263 non-null   object \n",
            "dtypes: bool(1), float64(2), int64(2), object(14)\n",
            "memory usage: 17.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.isnull(df)"
      ],
      "metadata": {
        "colab": {
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          "height": 458
        },
        "id": "9m8HtniJVJAd",
        "outputId": "2aab1984-685e-4bca-91d2-094a3d06328f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
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              "        index  Order ID   Date  Status  Fulfilment  Sales Channel  \\\n",
              "0       False     False  False   False       False          False   \n",
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              "4       False     False  False   False       False          False   \n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128972</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128973</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128974</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128975</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>128976 rows × 19 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de3d8b95-250c-4051-bd9c-7cc7794ebdd9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-de3d8b95-250c-4051-bd9c-7cc7794ebdd9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-de3d8b95-250c-4051-bd9c-7cc7794ebdd9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f31493e6-252e-4ea7-af15-e79f0cfc005c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f31493e6-252e-4ea7-af15-e79f0cfc005c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f31493e6-252e-4ea7-af15-e79f0cfc005c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.isnull(df).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gc8p0Ao9VQsR",
        "outputId": "4202c78a-928e-4d74-9cf9-fe38d370e52b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index                     0\n",
              "Order ID                  0\n",
              "Date                      0\n",
              "Status                    0\n",
              "Fulfilment                0\n",
              "Sales Channel             0\n",
              "ship-service-level        0\n",
              "Category                  0\n",
              "Size                      0\n",
              "Courier Status            0\n",
              "Qty                       0\n",
              "currency               7800\n",
              "Amount                 7800\n",
              "ship-city                35\n",
              "ship-state               35\n",
              "ship-postal-code         35\n",
              "ship-country             35\n",
              "B2B                       0\n",
              "fulfilled-by          89713\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfzLGWQSVa3Z",
        "outputId": "2aa5d8dd-e8a5-4ec7-a256-916a567d46d3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128976, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "p7NuCvK-VeQJ"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-gHKN_fVjfK",
        "outputId": "85101011-5353-4400-d4ee-bd2923131158"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(37514, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFUoCGKjVmhC",
        "outputId": "4c676116-778c-4b95-9049-317750c5cf0e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['index', 'Order ID', 'Date', 'Status', 'Fulfilment', 'Sales Channel',\n",
              "       'ship-service-level', 'Category', 'Size', 'Courier Status', 'Qty',\n",
              "       'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code',\n",
              "       'ship-country', 'B2B', 'fulfilled-by'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ship-postal-code']=df['ship-postal-code'].astype('int')"
      ],
      "metadata": {
        "id": "3ZkIc70_VpsS"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['ship-postal-code'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XCKqzi50WKhD",
        "outputId": "e801f388-ed10-4e5a-bd41-cce48033630d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date']=pd.to_datetime (df['Date'])"
      ],
      "metadata": {
        "id": "KzOV2PjPWyxf"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giNLs09gW814",
        "outputId": "5f83c8f4-ef29-40f1-aa68-2614c985a60e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['index', 'Order ID', 'Date', 'Status', 'Fulfilment', 'Sales Channel',\n",
              "       'ship-service-level', 'Category', 'Size', 'Courier Status', 'Qty',\n",
              "       'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code',\n",
              "       'ship-country', 'B2B', 'fulfilled-by'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={'Qty':'Quantity'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "id": "ly9PTz_vXAEo",
        "outputId": "e2dcd2fe-5be4-43f1-faa7-51aa6b6f8362"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         index             Order ID       Date                        Status  \\\n",
              "0            0  405-8078784-5731545 2022-04-30                     Cancelled   \n",
              "1            1  171-9198151-1101146 2022-04-30  Shipped - Delivered to Buyer   \n",
              "3            3  403-9615377-8133951 2022-04-30                     Cancelled   \n",
              "7            7  406-7807733-3785945 2022-04-30  Shipped - Delivered to Buyer   \n",
              "12          12  405-5513694-8146768 2022-04-30  Shipped - Delivered to Buyer   \n",
              "...        ...                  ...        ...                           ...   \n",
              "128875  128874  405-4724097-1016369 2022-06-01  Shipped - Delivered to Buyer   \n",
              "128876  128875  403-9524128-9243508 2022-06-01                     Cancelled   \n",
              "128888  128887  405-6493630-8542756 2022-05-31  Shipped - Delivered to Buyer   \n",
              "128891  128890  407-0116398-1810752 2022-05-31                     Cancelled   \n",
              "128892  128891  403-0317423-9322704 2022-05-31  Shipped - Delivered to Buyer   \n",
              "\n",
              "       Fulfilment Sales Channel ship-service-level  Category  Size  \\\n",
              "0        Merchant     Amazon.in           Standard   T-shirt     S   \n",
              "1        Merchant     Amazon.in           Standard     Shirt   3XL   \n",
              "3        Merchant     Amazon.in           Standard   Blazzer     L   \n",
              "7        Merchant     Amazon.in           Standard     Shirt     S   \n",
              "12       Merchant     Amazon.in           Standard     Shirt    XS   \n",
              "...           ...           ...                ...       ...   ...   \n",
              "128875   Merchant     Amazon.in           Standard   T-shirt     S   \n",
              "128876   Merchant     Amazon.in           Standard   Blazzer    XL   \n",
              "128888   Merchant     Amazon.in           Standard  Trousers     M   \n",
              "128891   Merchant     Amazon.in           Standard    Wallet  Free   \n",
              "128892   Merchant     Amazon.in           Standard   Blazzer     M   \n",
              "\n",
              "       Courier Status  Quantity currency  Amount       ship-city  \\\n",
              "0          On the Way         0      INR  647.62          MUMBAI   \n",
              "1             Shipped         1      INR  406.00       BENGALURU   \n",
              "3          On the Way         0      INR  753.33      PUDUCHERRY   \n",
              "7             Shipped         1      INR  399.00       HYDERABAD   \n",
              "12            Shipped         1      INR  399.00       Amravati.   \n",
              "...               ...       ...      ...     ...             ...   \n",
              "128875        Shipped         1      INR  854.00           ALLUR   \n",
              "128876     On the Way         0      INR  734.29       Barabanki   \n",
              "128888        Shipped         1      INR  518.00           NOIDA   \n",
              "128891     On the Way         0      INR  398.10         MADURAI   \n",
              "128892        Shipped         1      INR  721.00  UTTAR BAGDOGRA   \n",
              "\n",
              "            ship-state  ship-postal-code ship-country    B2B fulfilled-by  \n",
              "0          MAHARASHTRA            400081           IN  False    Easy Ship  \n",
              "1            KARNATAKA            560085           IN  False    Easy Ship  \n",
              "3           PUDUCHERRY            605008           IN  False    Easy Ship  \n",
              "7            TELANGANA            500032           IN  False    Easy Ship  \n",
              "12         MAHARASHTRA            444606           IN  False    Easy Ship  \n",
              "...                ...               ...          ...    ...          ...  \n",
              "128875  ANDHRA PRADESH            524315           IN  False    Easy Ship  \n",
              "128876   UTTAR PRADESH            225001           IN  False    Easy Ship  \n",
              "128888   UTTAR PRADESH            201301           IN  False    Easy Ship  \n",
              "128891      TAMIL NADU            625007           IN  False    Easy Ship  \n",
              "128892     WEST BENGAL            734014           IN  False    Easy Ship  \n",
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          "metadata": {},
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    {
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          "height": 300
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        "id": "SsOCNIyQXGOG",
        "outputId": "e1a8d93c-323c-40cd-8186-97bbf9c73899"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               index           Qty        Amount  ship-postal-code\n",
              "count   37514.000000  37514.000000  37514.000000      37514.000000\n",
              "mean    60953.809858      0.867383    646.553960     463291.552754\n",
              "std     36844.853039      0.354160    279.952414     194550.425637\n",
              "min         0.000000      0.000000      0.000000     110001.000000\n",
              "25%     27235.250000      1.000000    458.000000     370465.000000\n",
              "50%     63470.500000      1.000000    629.000000     500019.000000\n",
              "75%     91790.750000      1.000000    771.000000     600042.000000\n",
              "max    128891.000000      5.000000   5495.000000     989898.000000"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Qty</th>\n",
              "      <th>Amount</th>\n",
              "      <th>ship-postal-code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>37514.000000</td>\n",
              "      <td>37514.000000</td>\n",
              "      <td>37514.000000</td>\n",
              "      <td>37514.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>60953.809858</td>\n",
              "      <td>0.867383</td>\n",
              "      <td>646.553960</td>\n",
              "      <td>463291.552754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>36844.853039</td>\n",
              "      <td>0.354160</td>\n",
              "      <td>279.952414</td>\n",
              "      <td>194550.425637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>110001.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27235.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>458.000000</td>\n",
              "      <td>370465.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>63470.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>500019.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>91790.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>771.000000</td>\n",
              "      <td>600042.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>128891.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5495.000000</td>\n",
              "      <td>989898.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e330771b-7593-4b94-90bd-d1de79653d25 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e330771b-7593-4b94-90bd-d1de79653d25');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b21b78f1-690e-42d2-b3dd-a52adedc1f47\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b21b78f1-690e-42d2-b3dd-a52adedc1f47')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b21b78f1-690e-42d2-b3dd-a52adedc1f47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "OLBd0lYAXMwn",
        "outputId": "87116257-4388-478c-8885-61dab63d413b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Order ID                        Status Fulfilment  \\\n",
              "count                 37514                         37514      37514   \n",
              "unique                34664                            11          1   \n",
              "top     171-5057375-2831560  Shipped - Delivered to Buyer   Merchant   \n",
              "freq                     12                         28741      37514   \n",
              "\n",
              "       Sales Channel ship-service-level Category   Size Courier Status  \\\n",
              "count          37514              37514    37514  37514          37514   \n",
              "unique             1                  1        8     11              3   \n",
              "top        Amazon.in           Standard  T-shirt      M        Shipped   \n",
              "freq           37514              37514    14062   6806          31859   \n",
              "\n",
              "       currency  ship-city   ship-state ship-country fulfilled-by  \n",
              "count     37514      37514        37514        37514        37514  \n",
              "unique        1       4698           58            1            1  \n",
              "top         INR  BENGALURU  MAHARASHTRA           IN    Easy Ship  \n",
              "freq      37514       2839         6236        37514        37514  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3358a407-7be1-4000-90c7-a840aa687a64\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Status</th>\n",
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              "      <th>Sales Channel</th>\n",
              "      <th>ship-service-level</th>\n",
              "      <th>Category</th>\n",
              "      <th>Size</th>\n",
              "      <th>Courier Status</th>\n",
              "      <th>currency</th>\n",
              "      <th>ship-city</th>\n",
              "      <th>ship-state</th>\n",
              "      <th>ship-country</th>\n",
              "      <th>fulfilled-by</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>34664</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4698</td>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>171-5057375-2831560</td>\n",
              "      <td>Shipped - Delivered to Buyer</td>\n",
              "      <td>Merchant</td>\n",
              "      <td>Amazon.in</td>\n",
              "      <td>Standard</td>\n",
              "      <td>T-shirt</td>\n",
              "      <td>M</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>INR</td>\n",
              "      <td>BENGALURU</td>\n",
              "      <td>MAHARASHTRA</td>\n",
              "      <td>IN</td>\n",
              "      <td>Easy Ship</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>12</td>\n",
              "      <td>28741</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "      <td>14062</td>\n",
              "      <td>6806</td>\n",
              "      <td>31859</td>\n",
              "      <td>37514</td>\n",
              "      <td>2839</td>\n",
              "      <td>6236</td>\n",
              "      <td>37514</td>\n",
              "      <td>37514</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3358a407-7be1-4000-90c7-a840aa687a64')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3358a407-7be1-4000-90c7-a840aa687a64 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3358a407-7be1-4000-90c7-a840aa687a64');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2ea4f894-7d54-4375-92de-f3483bcc7346\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ea4f894-7d54-4375-92de-f3483bcc7346')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2ea4f894-7d54-4375-92de-f3483bcc7346 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['Qty','Amount']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "0cz1pyuxXROw",
        "outputId": "bc3a7050-8e7d-4a45-cfc3-7e63cfa58167"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Qty        Amount\n",
              "count  37514.000000  37514.000000\n",
              "mean       0.867383    646.553960\n",
              "std        0.354160    279.952414\n",
              "min        0.000000      0.000000\n",
              "25%        1.000000    458.000000\n",
              "50%        1.000000    629.000000\n",
              "75%        1.000000    771.000000\n",
              "max        5.000000   5495.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa23c768-f710-45cc-83ef-65868ea72bd1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Qty</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>37514.000000</td>\n",
              "      <td>37514.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.867383</td>\n",
              "      <td>646.553960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.354160</td>\n",
              "      <td>279.952414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>458.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>629.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>771.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5495.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa23c768-f710-45cc-83ef-65868ea72bd1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fa23c768-f710-45cc-83ef-65868ea72bd1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fa23c768-f710-45cc-83ef-65868ea72bd1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8f56f2e3-f1b9-4722-bbdf-7b4484c168fa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f56f2e3-f1b9-4722-bbdf-7b4484c168fa')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8f56f2e3-f1b9-4722-bbdf-7b4484c168fa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "D29xhR51XYl_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wevsSa7DXc_-",
        "outputId": "4655efb9-7b93-4cc9-bea2-d23dee6e5a45"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['index', 'Order ID', 'Date', 'Status', 'Fulfilment', 'Sales Channel',\n",
              "       'ship-service-level', 'Category', 'Size', 'Courier Status', 'Qty',\n",
              "       'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code',\n",
              "       'ship-country', 'B2B', 'fulfilled-by'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=sns.countplot(x='Size' ,data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "QwEGQYjxXjxm",
        "outputId": "2b811759-d004-4188-b78d-d5ad5d288dba"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=sns.countplot(x='Size' ,data=df)\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "-A_NATp1XnEM",
        "outputId": "61bd5950-72f0-4a48-bce9-a8c384306ece"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: From above Graph you can see that most of the people buys M-Size**"
      ],
      "metadata": {
        "id": "adaDOORYawj0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "l8FcwV1MawgZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Group By**"
      ],
      "metadata": {
        "id": "iK5NUsDkX1Cv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['Size'], as_index=False)['Qty'].sum().sort_values(by='Qty',ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "355rkqSEX6Tj",
        "outputId": "582e5b67-97fc-461b-da29-bd4ec18f1eff"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Size   Qty\n",
              "6      M  5905\n",
              "5      L  5795\n",
              "8     XL  5481\n",
              "10   XXL  4465\n",
              "0    3XL  3972\n",
              "7      S  3896\n",
              "9     XS  2191\n",
              "4   Free   467\n",
              "3    6XL   170\n",
              "2    5XL   104\n",
              "1    4XL    93"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ab4c85d-3c9d-40d7-ab02-3224f6acf83c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Size</th>\n",
              "      <th>Qty</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>M</td>\n",
              "      <td>5905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>L</td>\n",
              "      <td>5795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>XL</td>\n",
              "      <td>5481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>XXL</td>\n",
              "      <td>4465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3XL</td>\n",
              "      <td>3972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>S</td>\n",
              "      <td>3896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>XS</td>\n",
              "      <td>2191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Free</td>\n",
              "      <td>467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6XL</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5XL</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4XL</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ab4c85d-3c9d-40d7-ab02-3224f6acf83c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8ab4c85d-3c9d-40d7-ab02-3224f6acf83c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8ab4c85d-3c9d-40d7-ab02-3224f6acf83c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-18d7f961-9aed-43b5-b714-2df497f89dc4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-18d7f961-9aed-43b5-b714-2df497f89dc4')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-18d7f961-9aed-43b5-b714-2df497f89dc4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "S_Qty=df.groupby(['Size'], as_index=False)['Qty'].sum().sort_values(by='Qty',ascending=False)\n",
        "sns.barplot(x='Size',y='Qty', data=S_Qty)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "8H82T8tZX93j",
        "outputId": "28ff672d-82cc-4773-a94d-28549ca3e057"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Size', ylabel='Qty'>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: From above Graph you can see that most of the Qty buys M-Size in the sales**"
      ],
      "metadata": {
        "id": "FiwR-1f6aood"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Courier status**"
      ],
      "metadata": {
        "id": "dltsJDWjYVi_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=df, x='Courier Status',hue= 'Status')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "_ZqzGyLoYJ7T",
        "outputId": "4dc8ee84-9c79-48b1-9e74-baf9cf9b073f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Courier Status', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "ax=sns.countplot(data=df, x='Courier Status',hue= 'Status')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "uviU2vyrYa1q",
        "outputId": "434a6dce-d17f-4194-a0a7-4cb79e78bf7a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Size'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "sTm1dRu1Y_j6",
        "outputId": "e5c35046-66b1-4fa5-f7ab-b529d0e7ed40"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Category'] = df['Category'].astype(str)\n",
        "column_data = df['Category']\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.hist(column_data, bins=30, edgecolor='Black')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "jMRhd8H1ZEKu",
        "outputId": "cdd92e6c-b25b-4bd4-94ef-b458776957b4"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "B2B_Check = df['B2B'].value_counts()\n",
        "#  Plot the pie chart\n",
        "plt.pie(B2B_Check, labels=B2B_Check, autopct='%1.1f%%')\n",
        "#plt.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "bFR92uiJZIC5",
        "outputId": "12b64734-a1ab-4123-9014-58777794a977"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "B2B_Check = df['B2B'].value_counts()\n",
        "#  Plot the pie chart\n",
        "plt.pie(B2B_Check, labels=B2B_Check.index, autopct='%1.1f%%')\n",
        "#plt.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "S30HQTrWZN5h",
        "outputId": "76691b17-7fd0-4601-93ce-06b9a2a3a55d"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note : From above chart we can see that maximum i.e. 99.2% of buyers are retailers and 0.8% are B2B buyers**"
      ],
      "metadata": {
        "id": "N-eOCq5uaK2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Prepare data for pie chart\n",
        "a1 = df['Fulfilment'].value_counts()\n",
        "\n",
        "# Step 4: Plot the pie chart\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.pie(a1, labels=a1.index, autopct='%1.1f%%', radius=0.7, wedgeprops=dict(width=0.6))\n",
        "ax.set(aspect=\"equal\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "tj7lhGDMZVE-",
        "outputId": "97138c02-f7d7-4547-fc0e-ee29dd0e2d48"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: From above chart you can see that most of the Fulfilment are amazon**"
      ],
      "metadata": {
        "id": "3_vVZH2oaDVJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for scatter plot\n",
        "x_data = df['Category']\n",
        "y_data = df['Size']\n",
        "\n",
        "# Plot the scatter plot\n",
        "plt.scatter(x_data, y_data)\n",
        "plt.xlabel('Category ')\n",
        "plt.ylabel('Size')\n",
        "plt.title('Scatter Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vEsVrumLZWaa",
        "outputId": "abd0465c-0efa-47d3-a609-3f13cf0bf0d9"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot count of cities by state\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.countplot(data=df, x='ship-state')\n",
        "plt.xlabel('ship-state')\n",
        "plt.ylabel('count')\n",
        "plt.title('Distribution of State')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "NliJbdJ9Zajw",
        "outputId": "9a7fcfa4-a7ba-470b-933d-3759be574c9c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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AuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCPDK7AK7myozPHM6Tu1fHDCgJAAAAAAC02AMAAAAA4NII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcWKYH9r///rs6duyonDlzysfHR+XLl9cPP/xg3W6M0fDhw5U3b175+PgoLCxMJ06csNnH33//rQ4dOsjf31+BgYHq1q2bbty4YZPmp59+Us2aNZUlSxYVKFBAH3zwwT9yfgAAAAAAZKRMDeyvXr2qGjVqyNPTU19//bWOHDmiCRMmKHv27NY0H3zwgaZOnaqZM2dq3759ypo1qxo0aKDo6Ghrmg4dOujw4cPatGmT1q5dqx07dqhHjx7W7VFRUapfv74KFSqk/fv3a/z48RoxYoRmz579j54vAAAAAADpzWKMMZl18CFDhmjXrl369ttvk9xujFFISIhef/11vfHGG5Kka9euKSgoSOHh4Wrbtq1+/fVXlSlTRt9//70ee+wxSdL69evVqFEj/fbbbwoJCdGMGTP01ltvKSIiQl5eXtZjf/nllzp69Giq5YyKilJAQICuXbumO5+vdvg8c/fq6HAeAAAAAMB/V8I41N/fP8W0mdpiv3r1aj322GN6/vnnlSdPHlWuXFmffPKJdfuZM2cUERGhsLAw67qAgABVq1ZNe/bskSTt2bNHgYGB1qBeksLCwuTm5qZ9+/ZZ0zz11FPWoF6SGjRooGPHjunq1asPlOvOnTuKioqyWQAAAAAAeBhlamB/+vRpzZgxQ8WLF9eGDRvUq1cvvfbaa1qwYIEkKSIiQpIUFBRkky8oKMi6LSIiQnny5LHZ7uHhoRw5ctikSWofCY+R0Lhx4xQQEGBdChQokA5nCwAAAABA+svUwD4uLk6PPvqoxo4dq8qVK6tHjx566aWXNHPmzMwsloYOHapr165ZlwsXLmRqeQAAAAAASE6mBvZ58+ZVmTJlbNaVLl1a58+flyQFBwdLki5dumST5tKlS9ZtwcHBunz5ss32e/fu6e+//7ZJk9Q+Eh4jIW9vb/n7+9ssAAAAAAA8jDI1sK9Ro4aOHTtms+748eMqVKiQJKlIkSIKDg7W5s2brdujoqK0b98+hYaGSpJCQ0MVGRmp/fv3W9Ns2bJFcXFxqlatmjXNjh07dPfuXWuaTZs2qWTJkjYj8AMAAAAA4GoyNbDv37+/9u7dq7Fjx+rkyZNatGiRZs+erd69e0uSLBaL+vXrp9GjR2v16tX6+eef1blzZ4WEhKhZs2aS7rfwP/PMM3rppZf03XffadeuXerTp4/atm2rkJAQSVL79u3l5eWlbt266fDhw1q6dKmmTJmiAQMGZNapAwAAAACQLjwy8+BVq1bVF198oaFDh2rkyJEqUqSIJk+erA4dOljTDBo0SDdv3lSPHj0UGRmpJ598UuvXr1eWLFmsaT7//HP16dNH9erVk5ubm1q2bKmpU6datwcEBGjjxo3q3bu3qlSpoly5cmn48OE2c90DAAAAAOCKMnUee1fBPPYAAAAAgH+Sy8xjDwAAAAAA0obAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhBPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhBPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFZWpgP2LECFksFpulVKlS1u3R0dHq3bu3cubMKT8/P7Vs2VKXLl2y2cf58+fVuHFj+fr6Kk+ePBo4cKDu3btnk2bbtm169NFH5e3trWLFiik8PPyfOD0AAAAAADJcprfYly1bVhcvXrQuO3futG7r37+/1qxZo+XLl2v79u36448/1KJFC+v22NhYNW7cWDExMdq9e7cWLFig8PBwDR8+3JrmzJkzaty4serUqaODBw+qX79+6t69uzZs2PCPnicAAAAAABnBI9ML4OGh4ODgB9Zfu3ZNc+fO1aJFi1S3bl1J0vz581W6dGnt3btX1atX18aNG3XkyBF98803CgoKUqVKlTRq1CgNHjxYI0aMkJeXl2bOnKkiRYpowoQJkqTSpUtr586dmjRpkho0aPCPnisAAAAAAOkt01vsT5w4oZCQEBUtWlQdOnTQ+fPnJUn79+/X3bt3FRYWZk1bqlQpFSxYUHv27JEk7dmzR+XLl1dQUJA1TYMGDRQVFaXDhw9b0yTcR3ya+H0k5c6dO4qKirJZAAAAAAB4GGVqYF+tWjWFh4dr/fr1mjFjhs6cOaOaNWvq+vXrioiIkJeXlwIDA23yBAUFKSIiQpIUERFhE9THb4/fllKaqKgo3b59O8lyjRs3TgEBAdalQIEC6XG6AAAAAACku0ztit+wYUPr3xUqVFC1atVUqFAhLVu2TD4+PplWrqFDh2rAgAHW11FRUQT3AAAAAICHUqZ3xU8oMDBQJUqU0MmTJxUcHKyYmBhFRkbapLl06ZL1mfzg4OAHRsmPf51aGn9//2QrD7y9veXv72+zAAAAAADwMHqoAvsbN27o1KlTyps3r6pUqSJPT09t3rzZuv3YsWM6f/68QkNDJUmhoaH6+eefdfnyZWuaTZs2yd/fX2XKlLGmSbiP+DTx+wAAAAAAwJVlamD/xhtvaPv27Tp79qx2796t5s2by93dXe3atVNAQIC6deumAQMGaOvWrdq/f7+6dOmi0NBQVa9eXZJUv359lSlTRp06ddKhQ4e0YcMGvf322+rdu7e8vb0lSS+//LJOnz6tQYMG6ejRo/r444+1bNky9e/fPzNPHQAAAACAdJGpz9j/9ttvateunf766y/lzp1bTz75pPbu3avcuXNLkiZNmiQ3Nze1bNlSd+7cUYMGDfTxxx9b87u7u2vt2rXq1auXQkNDlTVrVr3wwgsaOXKkNU2RIkW0bt069e/fX1OmTFH+/Pk1Z84cproDAAAAAPwrWIwxJrML8bCLiopSQECArl27pjufr3Y4f+5eHTOgVAAAAACAf6uEcWhq4749VM/YAwAAAAAAxxDYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhBPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACAC/PI7AL811yZOcfhPLlf7p4BJQEAAAAA/BvQYg8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhBPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwh6awP69996TxWJRv379rOuio6PVu3dv5cyZU35+fmrZsqUuXbpkk+/8+fNq3LixfH19lSdPHg0cOFD37t2zSbNt2zY9+uij8vb2VrFixRQeHv4PnBEAAAAAABnvoQjsv//+e82aNUsVKlSwWd+/f3+tWbNGy5cv1/bt2/XHH3+oRYsW1u2xsbFq3LixYmJitHv3bi1YsEDh4eEaPny4Nc2ZM2fUuHFj1alTRwcPHlS/fv3UvXt3bdiw4R87PwAAAAAAMkqmB/Y3btxQhw4d9Mknnyh79uzW9deuXdPcuXM1ceJE1a1bV1WqVNH8+fO1e/du7d27V5K0ceNGHTlyRJ999pkqVaqkhg0batSoUZo+fbpiYmIkSTNnzlSRIkU0YcIElS5dWn369FGrVq00adKkZMt0584dRUVF2SwAAAAAADyMMj2w7927txo3bqywsDCb9fv379fdu3dt1pcqVUoFCxbUnj17JEl79uxR+fLlFRQUZE3ToEEDRUVF6fDhw9Y0iffdoEED6z6SMm7cOAUEBFiXAgUKpPk8AQAAAADICJka2C9ZskQ//vijxo0b98C2iIgIeXl5KTAw0GZ9UFCQIiIirGkSBvXx2+O3pZQmKipKt2/fTrJcQ4cO1bVr16zLhQsXnDo/AAAAAAAymkdmHfjChQvq27evNm3apCxZsmRWMZLk7e0tb2/vzC4GAAAAAACpyrQW+/379+vy5ct69NFH5eHhIQ8PD23fvl1Tp06Vh4eHgoKCFBMTo8jISJt8ly5dUnBwsCQpODj4gVHy41+nlsbf318+Pj4ZdHYAAAAAAPwzMi2wr1evnn7++WcdPHjQujz22GPq0KGD9W9PT09t3rzZmufYsWM6f/68QkNDJUmhoaH6+eefdfnyZWuaTZs2yd/fX2XKlLGmSbiP+DTx+wAAAAAAwJVlWlf8bNmyqVy5cjbrsmbNqpw5c1rXd+vWTQMGDFCOHDnk7++vV199VaGhoapevbokqX79+ipTpow6deqkDz74QBEREXr77bfVu3dva1f6l19+WdOmTdOgQYPUtWtXbdmyRcuWLdO6dev+2RMGAAAAACADZFpgb49JkybJzc1NLVu21J07d9SgQQN9/PHH1u3u7u5au3atevXqpdDQUGXNmlUvvPCCRo4caU1TpEgRrVu3Tv3799eUKVOUP39+zZkzRw0aNMiMUwIAAAAAIF1ZjDEmswvxsIuKilJAQICuXbumO5+vdjh/7l4drX9fmTnH8fwvd3c4DwAAAADAdSWMQ/39/VNMm+nz2AMAAAAAAOcR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhBPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGFOBfZ169ZVZGTkA+ujoqJUt27dtJYJAAAAAADYyanAftu2bYqJiXlgfXR0tL799ts0FwoAAAAAANjHw5HEP/30k/XvI0eOKCIiwvo6NjZW69evV758+dKvdAAAAAAAIEUOBfaVKlWSxWKRxWJJssu9j4+PPvroo3QrHAAAAAAASJlDgf2ZM2dkjFHRokX13XffKXfu3NZtXl5eypMnj9zd3dO9kAAAAAAAIGkOBfaFChWSJMXFxWVIYQAAAAAAgGMcCuwTOnHihLZu3arLly8/EOgPHz48zQUDAAAAAACpcyqw/+STT9SrVy/lypVLwcHBslgs1m0Wi4XAHgAAAACAf4hTgf3o0aM1ZswYDR48OL3LAwAAAAAAHODUPPZXr17V888/n95lAQAAAAAADnIqsH/++ee1cePG9C4LAAAAAABwkFNd8YsVK6Zhw4Zp7969Kl++vDw9PW22v/baa+lSOAAAAAAAkDKnAvvZs2fLz89P27dv1/bt2222WSwWAnsAAAAAAP4hTgX2Z86cSe9yAAAAAAAAJzj1jD0AAAAAAHg4ONVi37Vr1xS3z5s3z6nCAP92W+Y0djhP3e7rMqAkAAAAAP4tnArsr169avP67t27+uWXXxQZGam6deumS8EAAAAAAEDqnArsv/jiiwfWxcXFqVevXnrkkUfSXCgAAAAAAGCfdHvG3s3NTQMGDNCkSZPSa5cAAAAAACAV6Tp43qlTp3Tv3r303CUAAAAAAEiBU13xBwwYYPPaGKOLFy9q3bp1euGFF9KlYAAAAAAAIHVOBfYHDhywee3m5qbcuXNrwoQJqY6YDwAAAAAA0o9Tgf3WrVvTuxwAAAAAAMAJTgX28a5cuaJjx45JkkqWLKncuXOnS6EAAAAAAIB9nArsb968qVdffVULFy5UXFycJMnd3V2dO3fWRx99JF9f33QtJID7vp7byOE8Dbt9lQElAQAAAPCwcGpU/AEDBmj79u1as2aNIiMjFRkZqVWrVmn79u16/fXX07uMAAAAAAAgGU612P/vf//TihUrVLt2beu6Ro0aycfHR61bt9aMGTPSq3xIZxEzRjqcJ7jX8AwoCQAAAAAgPTjVYn/r1i0FBQU9sD5Pnjy6detWmgsFAAAAAADs41RgHxoaqnfeeUfR0dHWdbdv39a7776r0NDQdCscAAAAAABImVNd8SdPnqxnnnlG+fPnV8WKFSVJhw4dkre3tzZu3JiuBQQAAAAAAMlzKrAvX768Tpw4oc8//1xHjx6VJLVr104dOnSQj49PuhYQAAAAAAAkz6nAfty4cQoKCtJLL71ks37evHm6cuWKBg8enC6FAwAAAAAAKXPqGftZs2apVKlSD6wvW7asZs6cafd+ZsyYoQoVKsjf31/+/v4KDQ3V119/bd0eHR2t3r17K2fOnPLz81PLli116dIlm32cP39ejRs3lq+vr/LkyaOBAwfq3r17Nmm2bdumRx99VN7e3ipWrJjCw8MdO2EAAAAAAB5STgX2ERERyps37wPrc+fOrYsXL9q9n/z58+u9997T/v379cMPP6hu3bp67rnndPjwYUlS//79tWbNGi1fvlzbt2/XH3/8oRYtWljzx8bGqnHjxoqJidHu3bu1YMEChYeHa/jw/5+e7cyZM2rcuLHq1KmjgwcPql+/furevbs2bNjgzKkDAAAAAPBQcaorfoECBbRr1y4VKVLEZv2uXbsUEhJi936effZZm9djxozRjBkztHfvXuXPn19z587VokWLVLduXUnS/PnzVbp0ae3du1fVq1fXxo0bdeTIEX3zzTcKCgpSpUqVNGrUKA0ePFgjRoyQl5eXZs6cqSJFimjChAmSpNKlS2vnzp2aNGmSGjRo4MzpAwAAAADw0HCqxf6ll15Sv379NH/+fJ07d07nzp3TvHnz1L9//weeu7dXbGyslixZops3byo0NFT79+/X3bt3FRYWZk1TqlQpFSxYUHv27JEk7dmzR+XLl1dQUJA1TYMGDRQVFWVt9d+zZ4/NPuLTxO8jKXfu3FFUVJTNAgAAAADAw8ipFvuBAwfqr7/+0iuvvKKYmBhJUpYsWTR48GANHTrUoX39/PPPCg0NVXR0tPz8/PTFF1+oTJkyOnjwoLy8vBQYGGiTPigoSBEREZLuPxKQMKiP3x6/LaU0UVFRun37dpKj+I8bN07vvvuuQ+cBAAAAAEBmcCqwt1gsev/99zVs2DD9+uuv8vHxUfHixeXt7e3wvkqWLKmDBw/q2rVrWrFihV544QVt377dmWKlm6FDh2rAgAHW11FRUSpQoEAmlggAAAAAgKQ5FdjH8/PzU9WqVdNUAC8vLxUrVkySVKVKFX3//feaMmWK2rRpo5iYGEVGRtq02l+6dEnBwcGSpODgYH333Xc2+4sfNT9hmsQj6V+6dEn+/v5JttZLkre3t1OVFAAAAAAA/NOcesY+I8XFxenOnTuqUqWKPD09tXnzZuu2Y8eO6fz58woNDZUkhYaG6ueff9bly5etaTZt2iR/f3+VKVPGmibhPuLTxO8DAAAAAABXlqYW+7QaOnSoGjZsqIIFC+r69etatGiRtm3bpg0bNiggIEDdunXTgAEDlCNHDvn7++vVV19VaGioqlevLkmqX7++ypQpo06dOumDDz5QRESE3n77bfXu3dva4v7yyy9r2rRpGjRokLp27aotW7Zo2bJlWrduXWaeOgAAAAAA6SJTA/vLly+rc+fOunjxogICAlShQgVt2LBBTz/9tCRp0qRJcnNzU8uWLXXnzh01aNBAH3/8sTW/u7u71q5dq169eik0NFRZs2bVCy+8oJEjR1rTFClSROvWrVP//v01ZcoU5c+fX3PmzGGqOwAAAADAv0KmBvZz585NcXuWLFk0ffp0TZ8+Pdk0hQoV0ldffZXifmrXrq0DBw44VUYAAAAAAB5mD90z9gAAAAAAwH4E9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhBPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhBPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MI8MrsAAOBKJi5q4FD6Ae03ZFBJAAAAgPtosQcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYZka2I8bN05Vq1ZVtmzZlCdPHjVr1kzHjh2zSRMdHa3evXsrZ86c8vPzU8uWLXXp0iWbNOfPn1fjxo3l6+urPHnyaODAgbp3755Nmm3btunRRx+Vt7e3ihUrpvDw8Iw+PQAAAAAAMlymBvbbt29X7969tXfvXm3atEl3795V/fr1dfPmTWua/v37a82aNVq+fLm2b9+uP/74Qy1atLBuj42NVePGjRUTE6Pdu3drwYIFCg8P1/Dhw61pzpw5o8aNG6tOnTo6ePCg+vXrp+7du2vDhg3/6PkCAAAAAJDePDLz4OvXr7d5HR4erjx58mj//v166qmndO3aNc2dO1eLFi1S3bp1JUnz589X6dKltXfvXlWvXl0bN27UkSNH9M033ygoKEiVKlXSqFGjNHjwYI0YMUJeXl6aOXOmihQpogkTJkiSSpcurZ07d2rSpElq0KDBA+W6c+eO7ty5Y30dFRWVge8CAAAAAADOe6iesb927ZokKUeOHJKk/fv36+7duwoLC7OmKVWqlAoWLKg9e/ZIkvbs2aPy5csrKCjImqZBgwaKiorS4cOHrWkS7iM+Tfw+Ehs3bpwCAgKsS4ECBdLvJAEAAAAASEcPTWAfFxenfv36qUaNGipXrpwkKSIiQl5eXgoMDLRJGxQUpIiICGuahEF9/Pb4bSmliYqK0u3btx8oy9ChQ3Xt2jXrcuHChXQ5RwAAAAAA0lumdsVPqHfv3vrll1+0c+fOzC6KvL295e3tndnFAAAAAAAgVQ9Fi32fPn20du1abd26Vfnz57euDw4OVkxMjCIjI23SX7p0ScHBwdY0iUfJj3+dWhp/f3/5+Pik9+kAAAAAAPCPydQWe2OMXn31VX3xxRfatm2bihQpYrO9SpUq8vT01ObNm9WyZUtJ0rFjx3T+/HmFhoZKkkJDQzVmzBhdvnxZefLkkSRt2rRJ/v7+KlOmjDXNV199ZbPvTZs2WfcBAK5i6PJnHEo/7vn1qScCAACAS8vUwL53795atGiRVq1apWzZslmfiQ8ICJCPj48CAgLUrVs3DRgwQDly5JC/v79effVVhYaGqnr16pKk+vXrq0yZMurUqZM++OADRURE6O2331bv3r2t3elffvllTZs2TYMGDVLXrl21ZcsWLVu2TOvWrcu0cwcAAAAAID1kalf8GTNm6Nq1a6pdu7by5s1rXZYuXWpNM2nSJDVp0kQtW7bUU089peDgYK1cudK63d3dXWvXrpW7u7tCQ0PVsWNHde7cWSNHjrSmKVKkiNatW6dNmzapYsWKmjBhgubMmZPkVHcAAAAAALiSTO+Kn5osWbJo+vTpmj59erJpChUq9EBX+8Rq166tAwcOOFxGAAAAAAAeZg/F4HkAAAAAAMA5BPYAAAAAALgwAnsAAAAAAFwYgT0AAAAAAC6MwB4AAAAAABdGYA8AAAAAgAsjsAcAAAAAwIUR2AMAAAAA4MII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYR6ZXQAArmPZ/GccztO6y/oMKAkAAACAeLTYAwAAAADgwmixh0N+n/6aw3ny9Z6aASUBAAAAAEi02AMAAAAA4NII7AEAAAAAcGEE9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFeWR2AQAA/x0NV/VwOM/Xz83OgJIAAAD8e9BiDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFeWR2AfDfcnZqM4fzFH7ty3QvBwAAAAD8W9BiDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCMjWw37Fjh5599lmFhITIYrHoyy+/tNlujNHw4cOVN29e+fj4KCwsTCdOnLBJ8/fff6tDhw7y9/dXYGCgunXrphs3btik+emnn1SzZk1lyZJFBQoU0AcffJDRpwYAAAAAwD8iUwP7mzdvqmLFipo+fXqS2z/44ANNnTpVM2fO1L59+5Q1a1Y1aNBA0dHR1jQdOnTQ4cOHtWnTJq1du1Y7duxQjx49rNujoqJUv359FSpUSPv379f48eM1YsQIzZ49O8PPDwAAAACAjOaRmQdv2LChGjZsmOQ2Y4wmT56st99+W88995wkaeHChQoKCtKXX36ptm3b6tdff9X69ev1/fff67HHHpMkffTRR2rUqJE+/PBDhYSE6PPPP1dMTIzmzZsnLy8vlS1bVgcPHtTEiRNtKgAAAAAAAHBFD+0z9mfOnFFERITCwsKs6wICAlStWjXt2bNHkrRnzx4FBgZag3pJCgsLk5ubm/bt22dN89RTT8nLy8uapkGDBjp27JiuXr2a5LHv3LmjqKgomwUAAAAAgIfRQxvYR0RESJKCgoJs1gcFBVm3RUREKE+ePDbbPTw8lCNHDps0Se0j4TESGzdunAICAqxLgQIF0n5CAAAAAABkgIc2sM9MQ4cO1bVr16zLhQsXMrtIAAAAAAAk6aEN7IODgyVJly5dsll/6dIl67bg4GBdvnzZZvu9e/f0999/26RJah8Jj5GYt7e3/P39bRYAAAAAAB5GD21gX6RIEQUHB2vz5s3WdVFRUdq3b59CQ0MlSaGhoYqMjNT+/futabZs2aK4uDhVq1bNmmbHjh26e/euNc2mTZtUsmRJZc+e/R86GwAAAAAAMkamBvY3btzQwYMHdfDgQUn3B8w7ePCgzp8/L4vFon79+mn06NFavXq1fv75Z3Xu3FkhISFq1qyZJKl06dJ65pln9NJLL+m7777Trl271KdPH7Vt21YhISGSpPbt28vLy0vdunXT4cOHtXTpUk2ZMkUDBgzIpLMGAAAAACD9ZOp0dz/88IPq1KljfR0fbL/wwgsKDw/XoEGDdPPmTfXo0UORkZF68skntX79emXJksWa5/PPP1efPn1Ur149ubm5qWXLlpo6dap1e0BAgDZu3KjevXurSpUqypUrl4YPH85UdwAAAACAf4VMDexr164tY0yy2y0Wi0aOHKmRI0cmmyZHjhxatGhRisepUKGCvv32W6fLCQAAAADAw+qhfcYeAAAAAACkjsAeAAAAAAAXlqld8QH8t3wa3sDhPJ1e3JABJQEAAAD+PWixBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYTxjD8BlzFno+DP63TvzjD4AAAD+3WixBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCPDK7AADwXzJ6aQOH0r/dZkMGlQQAAAD/FrTYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MJ6xBxywa3YTh/PU6LE2A0oCAAAAAPcR2AMA7NZ49TMO51nXdH0GlAQAAADx6IoPAAAAAIALI7AHAAAAAMCF0RUfwH/K9M8cm0e+d0fmkQcAAMDDjRZ7AAAAAABcGIE9AAAAAAAujK74wH/Il/MaOpynWdevM6AkAAAAANILLfYAAAAAALgwWuzhUo583NThPGVeWZ0BJQEAAACAhwMt9gAAAAAAuDACewAAAAAAXBiBPQAAAAAALozAHgAAAAAAF0ZgDwAAAACACyOwBwAAAADAhRHYAwAAAADgwgjsAQAAAABwYQT2AAAAAAC4MAJ7AAAAAABcmEdmFwAAANin8coZDudZ16JXBpQEAAA8TAjs8Z+yf+azDuep8vKaDCgJAAAAAKQPuuIDAAAAAODCaLEHAAB2afK/BQ7nWdvyhQwoCQAASIgWewAAAAAAXBiBPQAAAAAALoyu+AAAl9Hoy8EO5/mq2fsZUBIAAICHBy32AAAAAAC4MAJ7AAAAAABcGIE9AAAAAAAujMAeAAAAAAAXRmAPAAAAAIALI7AHAAAAAMCFEdgDAAAAAODCCOwBAAAAAHBhHpldADjm8swpDufJ83LfDCgJAMBRjVc6fg1f1+Lfcw1vsuJzh/OsbdUhA0oCAMC/C4E9AAD/EY3/N8fhPOtads+AkgAAgPREV3wAAAAAAFwYLfYA8B/y8spnHEo/s8X6DCoJ4HqarljjcJ7VrZ7NgJK4puf/d9jhPMtbls2AkgDAv89/KrCfPn26xo8fr4iICFWsWFEfffSRHn/88cwuFgDARTT+4gOH86xrPigDSgJnPLtipcN51rRqkQElQWYY/MXvDud5v3m+DCgJAKS//0xX/KVLl2rAgAF655139OOPP6pixYpq0KCBLl++nNlFAwAAAADAaf+ZFvuJEyfqpZdeUpcuXSRJM2fO1Lp16zRv3jwNGTLEJu2dO3d0584d6+tr165JkqKiohRz+7bDx/aOirL+fT3N+aMdzp8ljfl9bfLHOJw/KmH+6Ltpyn/jdubmv5nJ+W+lOf+9TM1/O5PzO7OPxPmjb2Vu/jtpzB+Txvx3HcyfeB/3bqXtGnL31p0UUqaev+Wa9xzO/79n//834u4tx6+htuVPa37Hf0Mervy30pQ/rdJ6/Mwuf1p1WLXX4TyfP1fd+nfHVT86nP+z5x61/n331g2H8yd8/7qsPudw/vlNC1n/vnPrepqODwD/tPhrkDEm1bQWY08qFxcTEyNfX1+tWLFCzZo1s65/4YUXFBkZqVWrVtmkHzFihN59991/uJQAAAAAANi6cOGC8ufPn2Ka/0SL/Z9//qnY2FgFBQXZrA8KCtLRo0cfSD906FANGDDA+jouLk5///23cubMKYvF8kD6qKgoFShQQBcuXJC/v7/D5cvs/A9DGchPfvKTn/zkd8X8D0MZyE9+8pOf/P/O/MYYXb9+XSEhIanu6z8R2DvK29tb3t7eNusCAwNTzefv7+/0TcHDkP9hKAP5yU9+8pOf/K6Y/2EoA/nJT37yk//flz8gIMCuffwnBs/LlSuX3N3ddenSJZv1ly5dUnBwcCaVCgAAAACAtPtPBPZeXl6qUqWKNm/ebF0XFxenzZs3KzQ0NBNLBgAAAABA2vxnuuIPGDBAL7zwgh577DE9/vjjmjx5sm7evGkdJT8tvL299c477zzQfd9V8j8MZSA/+clPfvKT3xXzPwxlID/5yU9+8v838yf0nxgVP960adM0fvx4RUREqFKlSpo6daqqVauW2cUCAAAAAMBp/6nAHgAAAACAf5v/xDP2AAAAAAD8WxHYAwAAAADgwgjsAQAAAABwYQT2eMDff/+d2UUAAJcUGxub2UUAAAD/QQT2sNq4caNat26tfPnyZXZRXNp/+cb+9OnTql+/fobtPyoqyq4F/00//PBDmvJfvHhRffr0cSrv8ePHNWjQIOXPnz9NZchsK1asyOwiAElKeG3nNwBIm7i4OB0/flw7d+7Ujh07bBa4rv/MPPb/tNjYWLm7u2d2MVJ17tw5zZs3TwsWLNDVq1fVsGFDLVy4MNn0q1evtmu/TZs2TVO5Unr/Tpw4oVWrVuns2bOyWCwqUqSImjVrpqJFi6bpmGl1/PhxzZkzR59++qkuXryYZJoBAwbYta+JEyc6lD8gIEAlSpRQixYt0mUeTGddv35dmzdvzrD9BwYGymKxJLvdGCOLxfKfrlxxFV27dtWUKVOULVs2m/U3b97Uq6++qnnz5iWZ78aNG3J3d5ePj4913cGDBzVs2DB99dVXqf7vDx8+rK1bt8rLy0utW7dWYGCg/vzzT40ZM0YzZ8506Dpy69YtLV26VPPmzdOePXv02GOP2f0dzyi//vqr5s6dqw8//DDJ7ffu3dPRo0fl5eWlEiVKWNevWrVKw4cP19GjR9WqVask87Zo0cKuMqxcudLxggOpyJ49uy5evKg8efIk+1uQ2m9A9uzZU/wNiZfRPRdjY2O1a9cuVahQQYGBgRl6LPx7xcXF6eTJk7p8+bLi4uJstj311FPJ5tu7d6/at2+vc+fOKfHkaM7cQ924ceOB4/v7+zu0D0dcunRJb7zxhjZv3qzLly8/cA7/9D1gdHS0YmJibNZl5PmnhMA+ndkT3KUmMjJSn332WbItR61bt9asWbOUPXt2p/YfExOjlStXas6cOdq1a5fCwsL022+/6cCBAypfvnyKeZs1a2bz2mKxpMtFIV5q79+4ceM0fPhwxcXFKU+ePDLG6MqVKxoyZIjGjh2rN954I8X9p3fFhKM39gcOHLB5vXPnTlWpUsUmSEnppiNx/niRkZE6efKkhg0bpi1btqhgwYJ2ld/VbN261fq3MUaNGjXSnDlz7O5lklKlVUKdO3d2qnzGGK1fv15z5851uuUzOjpa06ZNS/WznFHi4uL01VdfqUmTJhl6nAULFui99957ILC/ffu2Fi5c+EBgf+HCBbVu3Vrfffed3N3d1adPH40ePVovv/yyli5dqubNm2v37t0pHnP16tVq1aqV7t27J0n64IMP9Mknn6h169aqUqWKvvjiCz3zzDOpln3v3r2aM2eOli9froIFC+rXX3/V1q1bVbNmzVTzZkRwfPPmTS1ZskRz587V3r17VaZMmSQD+19++UVNmjTRhQsXJEnPPfecZsyYodatW+uXX37RSy+9pHXr1iV7nICAAJvXixYt0rPPPvvA/zA9rFixItkKhni//fabVq9erfPnzz9wU5Vc5WhaZdQ1JP531J6gU5Lu3r2rnj17atiwYSpSpIhDx0pPzgYWztiyZYty5Mghyfa3wBGTJ0+2/m2MUa9evTRy5EjlyZPH6XJdvnxZx44dkySVLFnSrn25u7urfv36+vXXX106sF+xYoWWLVuW5Hfwxx9/fCD9gAEDNGrUKGXNmjXVStCUvsMPy+f/hx9+SPb8U7uG37lzR/fu3VPWrFmdOnZagvOXX35Zjz32mNatW6e8efPafd1J6MyZM+rTp4+2bdum6Oho63p7Glju3r2rUqVKae3atSpdurTDx37xxRd1/vx5DRs2zOnyp9WtW7c0aNAgLVu2TH/99dcD2zOrgYl57NNBUsFdy5YtNXDgQIf2s3nzZs2dO1dffPGFfH19k/ygSNITTzyhs2fP6pNPPlHjxo0dOsarr76qxYsXq3jx4urYsaPatm2rnDlzytPTU4cOHVKZMmUc2l+2bNl06NChNLWW2/v+bd26VWFhYRo2bJj69u1rrdj4+++/NXnyZI0dO1ZbtmxJ8WbCzc326RNnKybScmOfUHq8f/GioqLUoUMHZcuWTYsWLUox7c2bN/X+++9r5cqVNj0fWrVqpTfeeEO+vr5OleHQoUN69NFHU3z/3NzcUr0IWywWa/CVEkffPzc3N/n5+cnDw+OB/3vCYzvaWnPmzBnNmzdP4eHhunLlisLCwrR27dpk01+5ckX79u2Tl5eX6tWrJ3d3d929e1cff/yxxo0bp3v37unPP/9MMm9G9Zo5efKkzTncvXs32bTXr1/X8ePHVbJkSfn5+enHH3/U5MmTdfv2bTVr1kwdOnRINm9UVJSMMcqePbtOnDih3LlzW7fFxsZqzZo1GjJkiP744w+bfG3bttWxY8fUrVs3rVy5Utu3b9ejjz6qatWqaciQIXZ1gX/88cdVo0YNjRo1SnPmzNGAAQNUtmxZzZs3T1WrVk01/4QJEzRv3jxdu3ZN7dq1U8eOHVWxYkWHrp9dunSxeZ1ccDx//vxU97Vr1y7NnTtXy5Yt0+3bt9W/f391795dpUqVSjJ948aNdefOHfXr10+LFy/W4sWLVbJkSXXr1k29e/e2qWC0R1quX/b0HLhz506y+Tdv3qymTZuqaNGiOnr0qMqVK6ezZ8/KGKNHH31UW7ZsSTLf1KlT1aNHD2XJkkVTp05NsYyvvfbaA+vS+xoyd+5cTZo0SSdOnJAkFS9eXP369VP37t1TzRsQEKCDBw86HNh07drVrnTJ9ZqJl9ZWvy1btiT5G5TeFQIpSctn+Pr163rllVe0ZMkS67m6u7urTZs2mj59+gMVYYk99thjev/991WvXj2nyi7d/x7Et1omrlhJ7v+XXpWLU6dO1VtvvaUXX3xRs2fPVpcuXXTq1Cl9//336t27t8aMGfNAnjp16uiLL75QYGCg6tSpk+y+LRZLst/heM5+/u35DfXw8FBwcLDKlSsnLy+vJNMsWbJEnTt3VoMGDbRx40bVr19fx48f16VLl9S8efNkr+FXrlxR586d9c033yguLk5Vq1bVZ599pmLFijl0HpUqVVKJEiX07rvvJhncpvT5y5o1qw4dOuTwMROqUaOGjDHq27evgoKCHjh+rVq1UsyfL18+ffPNN04F9tmyZdO3336rSpUqOZw3sT/++EM7d+5M8juU1G9AvN69e2vr1q0aNWqUOnXqpOnTp+v333/XrFmz9N5776V4H5SQo5VjqTJw2p49e0y3bt2Mv7+/KVeunHF3dzc7duxwaB/nz5837777rilcuLBxc3Mz7du3N19//bWJiYlJNk9cXJz54IMPjI+Pj+nWrZu5fv263cdzd3c3b775pomKirJZ7+HhYQ4fPuxQ2Y0xxs/Pz5w6dcrhfMY4/v61bt3a9OjRI9ntL730kmnbtq1DZXC0/B9++KEpU6aMyZcvn3njjTfMwYMHjTGZ8/4lZd++faZgwYIpprlz546pUqWK8fb2Ns2aNTNDhgwxgwcPNk2bNjVeXl6mevXqKX7+UnLw4EHj5uaWYpovv/wy2WXw4MHGx8fHeHt723U8R9+/MmXKmJw5c5q+ffuaQ4cO2Z0vKdHR0eazzz4zderUMZ6ensbNzc1MnDjRXLt2LcV83377rQkICDAWi8W4ubmZxx9/3Bw+fNgUL17clC5d2syYMcPcunUr2fwWiyXVJbX/Qbxbt26ZBQsWmJo1axo3NzdTq1YtM2PGDBMREZFsnu3bt5ts2bIZi8VicuTIYTZs2GCyZctmSpUqZcqWLWvc3NzM7NmzUyy/m5tbsou7u7sZPXr0A/ny5s1r9uzZY4wx5tKlS8ZisZhJkybZdZ7x/P39zYkTJ4wxxty7d8+4u7ubTZs22Z0//vp57949m/XOfv+NcfwzfOnSJfP++++bkiVLmuDgYNO/f3/z/fff21WG3LlzmwMHDhhjjImMjDQWi8UsXLjQqXI7U/Z4P//8sylUqJD1f968eXMTERFhnnrqKZMjRw4zePBgc+HChRT3UbVqVTN8+HCbcly/ft00bdrUfPzxx8nmK1y4sPnzzz+tfye3FClSJMn86XkNGTZsmMmaNasZMmSIWbVqlVm1apUZMmSI8fPzM8OGDUs1f+fOnc3EiRMdPq7FYjGFCxc2zZs3N82aNUt2SU3FihXN888/b44cOWKuXr1qIiMjbZaU9OzZ03oNqV69uqlWrZrJkSOHcXNzM3369LHrPK5evWo2bNhgPv30U7NgwQKbxV5p+Q1u3bq1KV68uFm/fr25du2auXbtmlm/fr0pWbKkadOmTar5v/76a1OpUiWzZs0a88cff1j3Eb+kZsSIEdbfkOeee87u/9+LL75os3h5eZmWLVs+sD41JUuWNIsWLTLG2L6Pw4YNM7179041f1ql5fNv75I3b95k70vLly9vpk2bZoz5//OPi4szL730kvXalJQuXbqY4OBgM3bsWDNx4kRTsmRJU7t2bYfPw9fX1/p75qg6deqYr7/+2qm88bJmzWqOHj3qdP4xY8aYF154wdy9e9fhvKVLlzY//vij08eON3/+fOPl5WX8/PxMoUKF7PoNiFegQAGzdetWY4wx2bJls/4vFi5caBo2bGjX8adMmWL8/PxMnz59jJeXl+nZs6cJCwszAQEB5s0333TqnAjsnZDW4C4mJsYsW7bM1K9f3/j4+JjmzZub5cuXO3xz+Ouvv5rq1aubwoULmwkTJpgpU6bYLElZtGiRCQsLM1mzZjWtW7c2a9asMffu3ftHA1Nn37/ChQubb7/9NtntO3bsMIULF3aoLI6WP71v7NM7sD916pTx8/NLMc3kyZNNUFBQkhfkX3/91QQFBZmpU6cmmbdSpUqmcuXKyS4lS5a0O6hM6OjRo6ZZs2bG3d3ddO7c2Zw9e9aufM68f3v37jU9evQwAQEBpkqVKubjjz+26yYq3g8//GB69eplAgMDzWOPPWamTJliIiIi7P4M1KpVy7Rr1878/PPP5o033jAWi8WUKFHCLF++3KHzSIvvvvvO9OjRw/j7+5vKlSubDz/80Li7u9tV/po1a5quXbua3377zYwcOdIEBgaaoUOHWrePGjXKVKxYMdn827ZtM1u3bjUWi8WsXLnSbNu2zbrs3r3b/P7770nmc3Nzs6lwcOamwmKxmEuXLllfO/r5GTt2rClevLgpUKCAGTRokPn555+NMf9sYJ8lSxbTsWNHs379ehMbG2tdb08Zkjr/48ePO17oBPmduX41atTI1KtXz6xZs8a0b9/eWCwWU6pUKTN+/PgUK7USH/vkyZPGGGMCAwPNL7/8Yoy5X7lYqFAhh8vkiLReQ+LlypXLGhgltGjRIpMzZ85U848aNcoEBgaali1bmrFjx9p1D2CMMa+88orJnj27qVSpkpkyZYr566+/HC67Mc4HFitXrjReXl5m/vz5Ji4uzro+NjbWzJ0713h5eZlVq1aluI/Vq1dbKxgDAgJMYGCgdcmePbvdZUnLb7Cvr2+S9yQ7duwwvr6+qeZPXBkbv9hbORscHJymirl4zr4HPj4+1t/q3LlzW+/ljh8/bnLkyJHmcqXG2c+/PW7evGkuXrxoevfubSpXrpxkGl9fX3PmzBljjDE5cuQwP/30kzHGmCNHjpjg4OBk950/f36zfv166+vjx48bd3d3Ex0d7VAZHQ3ODx06ZF1WrlxpypQpY+bPn29++OEHm232VljWrl3boYrxxJo1a2ayZctm8ubNa+rXr2+aN29us6Rkw4YNpn79+tb331n58+c3o0ePtvkttVfWrFnNuXPnjDHG5MuXz+zbt88YY8zp06dN1qxZ7dpHRlSOEdg7Ia3BXe7cuU3NmjXNrFmzzN9//+1w/oQ++eQT4+7ubvLnz+9QTdPp06fN8OHDTcGCBU2uXLmMm5ubU4GFMz8Izr5/Pj4+KbbiXLhwwWTJksWhsmT2jX16B/aff/55ikGVMcY89dRT1lrmpEydOtU89dRTSW4bMWKEXYu9fv/9d9O9e3fj6elpmjRpYn0/7eXn52dOnz7tUJ548a3VtWvXNr6+vqZ9+/Z2/bC6u7ubfv36PRBU2vsZyJEjhzXdrVu3jJubm/nyyy+dOgdnlC9f3hQqVMgMHTrUGgwZY3/5AwICzK+//mqMud/7w83NzXpDZ4wxJ06cSLVyyRhjzp4969CPqZubm7l8+bL1dbZs2Rz+38e3UMe3jvr6+prZs2dbX8cvqdm2bZvp3Lmz8fX1NRUqVDDu7u5m586dDpUlnqPXgJIlS5rChQubN9980/p/MMa+/5+bm5s5efKkuXbtmomMjDTZsmUzhw4dcril0Nmyx0uPngNBQUHmyJEjxpj7rTfx/7eDBw/afVOVVs5eQ+IFBAQkWbFy7NgxExAQkGp+Z3ocxIuOjrZW9Pv6+prnn3/erF+/3ibQTo2zrX7PPvusGTJkSLLbBw0aZJo2bZriPooXL2769u1rbt686fDxE0rLb3CBAgWswVxChw4dMvny5Us1f8JKzaSW1OTIkcNauZUWzr4HRYoUsbaaVqlSxcycOdMYcz/osqdy5caNG+btt982oaGh5pFHHjFFihSxWVKTls9/cqKjo82ECRNMUFCQMcaYM2fOJNuDMF++fNb/f/ny5a0B2u7du42/v3+yx3BzczMXL160WZewkiAlaQnO4yuMUurp50iPv5MnT5qwsDATHh7uVOVA4h4iqfUYia+0i1+8vLyMm5ub8fPzs1nvSMVeWr5D5cuXt35P69WrZ15//XVjzP1WeHu+/8ZkTOUYg+c5YdSoUZo/f74+/fRTtWvXTp06dVK5cuXszn/v3j1ZLBZZLBanR86/dOmSunfvrp07d2ru3Ll64YUXHMpfpEgRvfvuuxoxYoQ2btyouXPnqmPHjurXr59atGiR6rOH8eLPwxHOvn/R0dHJPuskSZ6eng88n5Lehg4dqqFDh2r79u2aN2+eqlWrpmLFiskYo6tXr6aa/6effrJ5bYzR0aNHdePGDZv1FSpUsCt/vGvXrmn//v0aO3as3nnnnRTLcOTIEdWuXTvZ7XXq1NHIkSOT3Jbavu117do1jR07Vh999JEqVaqkzZs3OzXwWHR0tF5++eUHBp+xZ+AxHx8fde7cWYULF9Y777yjJUuWaNq0aanOKlCvXj3NnTtXly9fVqdOndSgQQOHvgNXr15Vrly5rGXw9fV16PqR1mfsjx07pjZt2qhOnToOj6kh3X9GPn4AKy8vL/n6+to8H54tWzbdunUr1f0UKlRI0v0xNpJ6tizxd8AYoxIlSljf6xs3bqhy5coPjJuR2rPNia+VPXv2tHltz7PBtWrVUq1atTRt2jQtWrRI8+bNU61atfT444+rVatWGToy/tGjR63P1letWlUlSpRQx44drWVPSfx7mPB15cqVbV6ndP6JP3txcXHavHmzfvnlF5v1qY3v8OeffyokJETS/edAs2bNqurVq6eYJ7Hq1atr586dKl26tBo1aqTXX39dP//8s1auXGn3vmJjYxUeHp7sM8qpPePr7DUkXqdOnTRjxowHBgmbPXu2Xc9nnjlzxq7jJMXb21vt2rVTu3btdO7cOYWHh+uVV17RvXv3dPjwYfn5+aW6j1dffVWvv/66IiIiVL58eXl6etpsT+537Mcff9Tbb7+d7H5btGihli1bpnjs33//Xa+99prD48Ek/m7GxMRozJgxDzyPbM/gi2+//bYGDBigTz/9VMHBwZKkiIgIDRw4UMOGDUs1f2rPIKeme/fuWrRokV3Hygh169bV6tWrVblyZXXp0kX9+/fXihUr9MMPP9j1HH/37t21fft2derUyakB0Jz9/N+5c0cjRozQpk2b5OXlpUGDBqlZs2aaN2+e3n77bbm7u6t///6SpMKFC+vSpUtJ7uepp57Spk2bVL58eT3//PPq27evtmzZok2bNqU6bkLie393d/dkx+xIqFKlSg+MDZVwzIz4bUldx9NyvUjKlStXdOrUKZtxY1I6fmL2jCOTUMKBL9NLt27dtHz5cg0ZMsThvF26dNGhQ4dUq1YtDRkyRM8++6ymTZumu3fv2j14a3BwsP7++28VKlRIBQsW1N69e1WxYkWdOXPGrs9DUhg8Lw3ig7sVK1aoWLFiOnz4sLZv364aNWqkmC86Olr/+9//rCMYN2zYUB07dlSbNm108ODBVG+2lyxZoj59+qhSpUqaN29euo2A/vfff2vhwoWaP3++Dh06lGSaxFPFREZGyt/f3+Gba8nx98/NzU2jR49O9obj+vXrGj58uEMjUfr7++vQoUNOj6p6/fp16439/v37U72xjx84LqmvnT0XxJTy58qVSwMGDNDgwYNT/IH09PTUhQsXrDciiV28eFGFChVyuJIkKipKn3/+uebOnZvifOIffPCB3n//fQUHB2vs2LF67rnn7D5G4oHHkpPaD8bvv/+uBQsWaP78+bp586Y6duyorl27JjvoWGIXLlzQ/PnzNX/+fN2+fVtt2rTRxx9/rJ9++inVgWDc3NxsRnd+4okntGzZsgcGf0vupjitA0D+/vvvCg8Pt5a9Xbt26tChg6pVq2bX9cfd3V0RERHWQe8Sf4cuXbqkkJCQVL+HV65cUZcuXfT1118nuT1x/gULFqS4v3iOVnKml59//llz587VokWLdPny5WTTJQ6O27Vrp8mTJysoKMhmvT2DH964cUOLFy/W/PnztXfvXtWqVUvt27dXs2bNbAYljLd9+3a7ziW5gCPxZy8p9tzQubu76/jx48qdO7eMMSpQoIB27typwoUL26RLabqg06dP68aNG6pQoYJu3ryp119/Xbt371bx4sU1ceJEa8VRSvr06aPw8HA1btw4ycBi0qRJyeZ19hqS8Lfh3r17Cg8PV8GCBa2VEfv27dP58+fVuXNnffTRR6meQ3qIv56Fh4crJiZGR48etSuwT+rzYM/vWJYsWXT69Glr5U5iv//+u4oVK6bbt28ne+wWLVqobdu2at26darlTCilAdvipTRwW+XKlW0+JydOnNCdO3es92Hnz5+Xt7e3ihcvnurAV6nNF57aIIJ9+/bVwoULVaFCBVWoUOGBihV7gwtnBxCMi4tTXFycPDzutxEuWbLE+h3s2bNnig0x0v3pa9etW5fqPXN6Gzx4sGbNmqWwsDDt3r3b+lu0d+9evfnmm3r++eftanT7+++/FR0drZCQEMXFxemDDz6wnv/bb7+d7MxVbm5uCggISPVeOqn76HPnztl9nvZcA9OiTJkyKl26tAYNGpTk4HkZffz0EBsbqyZNmuj27dtJVk46MrvKuXPntH//fhUrVizZ+7fEunfvrgIFCuidd97R9OnTNXDgQNWoUcNaOTZ37lyHzkcisE8XjgZ3CZ06dUrz58/XggUL9Pvvv6tdu3Z68cUXVbdu3WQvLFmzZtV7772nV199Nb1PJVUZcXNt7/tXuHBhu2p0U6qVTM+KicTsubG396Kc3AUxufz+/v52T3+YODBLzN7ALN7WrVs1b948rVy5UgEBAWrevLmmT5+ebHo3Nzf5+PgoLCwsxR/PjJgLe9myZZo/f762b9+uBg0aqEuXLmrcuLHTPWckadOmTZo/f76++OILFShQQK1atVKrVq306KOPJpk+rZU7iaVlVOctW7ZY/3fR0dF644031L17d5tW3aTKX65cOevN3E8//aRSpUpZb+LiW/xSK3+HDh107tw5TZ48WbVr19YXX3yhS5cuafTo0ZowYYLDM36kh7RO9ffHH39o7NixmjZtWrJp0is4TuzIkSOaO3euPvvsM/39998pzmqQkr///tta6ZRREs+MEf+ZT/w6o6cLypUrlxYuXKhGjRrZnSet1xB7AkvJvlHBJeen/Ltz545WrlypefPmaefOnWrSpIm6dOmiZ555xq7PqJT671lyv2Nubm66dOmSw79BCSvFrly5opEjR6pLly5J3pA7OiuIvd59912706bWwy25ipF4qX3+nR1VPj0rF9OiSJEi+uqrr5waFV1KfXaH5GYFKFq0qCZPnqymTZvql19+UYUKFfTiiy9q7ty5/8i0aelxH53W6eIWLFigXLlyWX9nBw0apNmzZ6tMmTJavHixXUF5eoys7+yI8D/++KM8PT2tU3SvWrVK8+fPV5kyZTRixIhUK5XijR49WsOHD1fJkiUfqJyw9xqcUGRkpEPTV6a1ciwpBPbpzN5Wm8Ti4uK0YcMGzZ07V2vWrJGfn1+y092dOHFCxYsXT3ZfK1eu1IgRI5Lstp1cF+uELBZLpnXtcvb9s9c/0ep39+7dB24yHiaJA7PE7AnMErb6RkZG6urVq1q0aJFat26d6g/jiy++aNePpyPdtM6dO6ebN2+qVKlSKd6Uurm5qWDBgurQocMDNzEJpTTFSXKuXr2qzz77TPPmzdNPP/2U7PuX1sqdxBwN7G/evPnAowvXrl3T559/rnnz5unHH39UuXLlkn3sw94b29RuavPmzatVq1bp8ccfl7+/v3744QeVKFFCq1ev1gcffKCdO3fadZz04MhUf4cPH9bWrVvl5eWl1q1bKzAwUH/++afGjBmjmTNnqmjRojp8+PA/VvbE7t69qzVr1tg9pVW8jRs3as6cOVqzZk2KLaXpIa09BxLav3+/fv31V0lS2bJlbR4tSE1ISIi2bduWYkVWYhl5DXGUs1P+xU/RVqBAAXXt2lUdOnSwPh70T3Bzc1OPHj2S7UZ/69YtffLJJw9cQ+2tcHCmUujevXuKjo62q6dCerl27ZrN67t37+rAgQMaNmyYxowZk6Zp8FKSnpWLkZGR+u6775J8lKVz584p5v3ss8+0atUqLViwwKkpdps3b27z+u7du/rll18UGRmpunXrJts44OXlpTNnzihfvnyS7j9S891331mDRHulV3DprLRMF1eyZEnNmDFDdevW1Z49e1SvXj1NnjxZa9eulYeHh10NK88++6xefPHFVB+bSY4z0yXGq1q1qoYMGaKWLVvq9OnTKlOmjFq0aKHvv/9ejRs3trvbfvbs2TVp0iS9+OKLDpf//fffV+HChdWmTRtJUuvWrfW///1PwcHB+uqrr1SxYkWH95keCOwzQGqtNn/88UeyXdCk+88ffvLJJxo6dGiyaWbNmmV9Pqhv376qVq2atmzZotdff13Hjx9X586dNWPGjAfypXTTY7FYdOzYMUVHRzv0oxgdHa2lS5fq5s2bevrpp1OsdLDXwxoc2zP2gMViSbY3xZ9//qmbN2/aBG2HDx/Whx9+qJs3b6pZs2Zq3759qsdIy/y/aQnM4h8h2bFjh/URkoYNG1prbp15ZtsR8+bNU2RkpE1vjh49eli7K5UsWVIbNmxQgQIFksxvT68Pi8Wi06dPp6mcP/74Y7It9vb45Zdf7H7u3tHA/pFHHtGCBQv05JNPJrn94MGDmjdvnt3jbDjL399fP/30kwoXLqxChQpp0aJFqlGjhs6cOaOyZcs+8Jx+4t42ybG3t83t27e1fPlyzZkzR7t27VLNmjXVtm1bNW/ePNmAbfXq1WrVqpXu3bsn6X7LzyeffKLWrVurSpUq6tevn5555hm7jp9a2VKbU3758uVavHixjh8/LkkqUaKE2rdvr1atWtl9nHPnzmnevHlasGCBrl69qoYNG6ply5Z6/vnnk0x//PhxRUZG6vHHH7eu27x5s0aPHm29fr355pt2Hz8tLl++rLZt22rbtm3WFpLIyEjVqVNHS5YsSbY1OKEJEybo9OnTmjZtmt0tdf/UNcQejz/+uBo2bKh3333Xeh3IkyePOnTooGeeeUa9evVKMl985UTibuWJ2dtr6siRI0m2uCXX4lu7dm273u+tW7fadXxHrFmzRn/99ZfNjfyYMWM0atQo3bt3T3Xr1tXSpUvt7gGXEbZv364BAwZo//79mVYGe6xZs0YdOnTQjRs35O/v/0BrZ1LX4sSfuZMnT8oYo8KFCz9wz+fMHN5xcXHq1auXHnnkEQ0aNCjJNIl7LWbLlk0//fSTw49kOhtcfvfdd6pSpUqyvXzu3LmjVatWpfqYydixY3X8+HHNmTMn2Yaa5Pj6+uro0aMqWLCgBg8erIsXL2rhwoU6fPiwateurStXrqS6j9mzZ2v06NHq2rWrU71mSpUqpXfeeUft2rWzuY8ZPny4/v777xR7vgUEBOjHH3/UI488ovfff19btmzRhg0btGvXLrVt21YXLlyw630IDg7Wt99+61TcUqRIEX3++ed64okntGnTJrVu3VpLly619kDYuHFjqvsoXLiwunbtqhdffDHdHqsmsHdSWlptypUrp507dybbXWPp0qXq1KlTss84v/feexo+fLgqVKigo0ePyhijt956Sx999JH69u2rnj17OvyjdPDgQQ0ZMkRbtmxR165dNXPmzCTTDRgwQHfv3rU++xcTE6Nq1arp8OHD8vX11b1797Rp0yaFhoYme6yFCxemWh6LxaJOnTo5nE9KvZY4JRcvXtSYMWOSvaAkvvBfuHBBefPmtbmopnRT165dO4WEhGjChAmS7t+clipVSiEhIXrkkUf09ddfa+7cuQ+ce0Ivv/yyZs+erezZs6tEiRIyxujEiROKjIzUK6+8kqHPZXp4eGjw4MEaMmSIzYBpnp6e6RLYG2O0fv16zZ07VytWrHhge/Xq1dWzZ0/rs/br16/Xs88+q/DwcJUuXVp9+vRRmTJlNGfOnDSVIy1++uknPfbYYw6PUXD9+nUtXrxYc+bM0f79+zOsK/6gQYM0efJk9e3bV2PGjEnXVgV7x1mQ7t8UjR49Wg0aNFDTpk0VGBiocePGaerUqVqxYoVOnTplkz69ett8//33mjNnjpYsWaJHHnlEHTp00ODBg/XTTz+l+vl9/PHHVaNGDY0aNUpz5szRgAEDVLZsWc2bN09Vq1a1q3wpuXPnjqZNm6bx48crIiIiyTRxcXFq166dli9frhIlSlif6f7111914sQJtW7dWosXL042cIqJidHKlSutFRphYWH6+uuvdeDAgVRbrJo3b67y5ctbe37FV8LUrFlTpUqV0rx58zRq1Cj169cvxf0k1xskICBABQsWtCvoa9OmjU6fPq2FCxdaW6yOHDmiF154QcWKFdPixYuTzJe4J0P8eBdly5Z94MY0Ix4HSuyHH35ItitqasfPli2bDh48qEceeUTZs2fXzp07VbZsWR06dEjPPfeczp49m2S+9Oo1dfr0aTVv3lw///yzzeNF8fvO6EcpnFGnTh21atVKvXv3liTt3r1bNWvW1MiRI1W6dGm99dZbatiwoV3P1sbGxmrSpEnJ/v+ceaRPuj9A5mOPPfbAoLpJcebzc+TIkVSvdePHj9fAgQNTTFOiRAk1atRIY8eOtbvFPT0fZUjOsWPHVLt2bV28eDHJ7W5ubmrYsKF1kMs1a9aobt26Dg/C62xw6e7urosXLypPnjyS7ldyHzx40Pobbu/jkM2bN9fmzZvl5+en8uXLO1T+PHnyaMOGDapcubIqV66sAQMGqFOnTjp16pQqVqxo12cvpZ4f9vT48PX11a+//qpChQopT5482rRpkypWrKgTJ06oevXqyfZalu6/Z/v371fx4sX19NNPq0mTJurbt6/Onz+vkiVL2t3rbNy4cbp48aJTDRk+Pj46fvy4ChQooL59+yo6OlqzZs3S8ePHVa1aNbsG1J48ebLCw8P1yy+/qE6dOurWrZuaN29u9wCsSXJqLP3/uFWrVhlPT0/rFBGPPPKI2bJli8mVK5dp0KBBqtO/1K5d21SvXj3JaVqWLl1qPDw8zAcffJBs/hIlSpjw8HBjzP35Ui0Wi2ncuLG5ceOGw+dy+vRp06FDB+Ph4WFat26d6pzGZcuWtZkOat68eSZ79uzm7NmzJi4uzrz44oumUaNGKe4j4XyziZeEU1ikV77EfvnlF/PRRx+ZWbNmmatXrxpjjLly5Yrp16+fyZIliylTpkyq+4jn6DQxhQsXtpnGZvz48eaRRx4xd+/etb6uVq1asvnTY/7ftIifu/mJJ54wM2bMsE7XmJZ5vI25/zl8++23Tf78+Y23t7dp3LhxkukSzhVrjDEvv/yyadmypfX11q1bTeHChZ0ux9WrV81HH33kdH5j7k+3Ze90McYYs337dtO5c2eTNWtWU7x4cTN48GDz3Xff2Z3fmWnf9uzZY0qXLm3Kli1rna4oLbZs2WI6duxofH19Td68ec0rr7ySap5PP/3UzJ8/3xhjzA8//GCddjNLlixmyZIlaS5TUtI61Z+/v7913u579+4Zd3d3h+fxjY6ONkOGDDFVqlQxoaGh5osvvjDG3L+W5s2b1+TPn9+89957yeafOHGiyZEjh1mzZs0D21atWmVy5MhhJk2alGTePn36mJw5c5rq1aubadOmmT///NMYY//558+f3+zevdv6etSoUTbTa86ZMyfV6TaNSX7aJTc3N+Pr65vkdKiJ+fv7J/k92bdvX4pTxaU2xVJK0y2lt8WLF1un+vTy8jJNmjQxJUqUMAEBAXYdP7On/GvSpIl57rnnzJUrV4yfn585cuSI+fbbb83jjz9uduzYke7HSzxPeUpLcnLnzm1zzevfv79p0KCB9fW6detMsWLF7CrPsGHDTN68ec2HH35osmTJYkaNGmW6detmcubMadc86omnBzt48KD5+uuvTa1atUyNGjVSze/s5yd//vzW+beTMn78eOPp6Znq8X19fdN1ut70sm7dOpMrV65kt6fX9z9btmzWe+awsDAzefJkY4wx586dS3HqZYvFYi5dumR9nfg+MiIiwlgsllSPn5byt2/f3jz66KOmW7duxtfX1/pbsGrVKlO2bNlUj50e0jJdYp06dUznzp3NwoULjaenp/V3edu2baZQoUJ2l6FZs2bG39/fFClSxDRp0sQ0b97cZklJ3rx5za5du4wx9+OyZcuWGWOMOXr0qMmWLZvdZTDGmP3795tXX33V5MqVy2TPnt307t3b7N+/36F9xCOwd0LVqlVNv379zPXr182kSZOMxWIx5cqVs/tm/Pr166ZKlSrm6aefNjExMdb1y5YtM15eXine1BljTJYsWcz58+etr728vMwPP/zg0DlcuXLF9OnTx3h5eZm6devaXfZs2bJZv0DGGNO2bVvz0ksvWV8fOHDA5M2b16GyxPvjjz9Mz549jaenp80PbXrmS2ulTGKOBvZZsmSxzllpjDENGzY0AwcOtL4+duxYinNXpsf8v7Vr1zZ16tRJcalbt26y+W/dumXCw8PNU089Zby9vU3Tpk2Nu7u7w3PQR0dHm88++8zUqVPHeHp6Gjc3NzNx4sQU59FOOOenMcZUqFDB5gYqtR/U5HzzzTemXbt2JkuWLE7PHRrPnsD+4sWLZty4caZYsWImT548pk+fPnYHV4nncrVYLCYgIMDheVyjo6PNG2+8YbJkyWKeffZZh37QjDHmt99+M6NHjzaPPPKIyZkzp3FzczNLlixxaB7shG7evGn2799vrly54lR+e3h5eZlOnTqZjRs32pTT3vc+tRsyewwaNMgEBASYli1bmrx58xoPDw/z0ksvmfLly5vFixenGtCWL1/ezJ07N9ntc+bMMeXLl09ym7u7u3nzzTdNVFSUzXp7zz/xb0/dunXN22+/bX198uRJu+ZfP3v2bJLLwYMHzdy5c01ISIgZP358ivvw8/MzBw4ceGD9jz/+6PBNlSPSGlgmVL58eTNt2jRjzP9/luLi4sxLL71khg8fnmr+5557zsyePdsYY8zrr79uihUrZkaPHm0effRRU69ePafOLy4uznz11Vc2FabJyZkzp3W+an9/f3P06FFjjDGbN282lSpVSjbfu+++a9eSWErzlts7h3mWLFlsgtqqVavaNKScPXvW+Pr6pnruxhhTtGhRs3btWmPM/f9f/HzYU6ZMMe3atUs1f3IVXKGhoebXX39NNb+zn5+2bdua4sWLm8uXLz+w7cMPPzSenp5m8eLFqR6/efPmZunSpammyyj9+/e3Wfr162fatGlj/Pz8TO/evTP8+M4Gl/YE9o40Djjj6tWrpnfv3qZp06Y2973Dhw83o0ePztBjx+vWrZsZMWKEMcaYadOmGR8fHxMWFmYCAwNN165dU8x76NAhU65cOePv72/dhzH3K6/t+e7FS0vlSO/evU2hQoVMWFiYyZkzp7l+/box5n6FW+XKle0uQ0IxMTFm8uTJxtvb27i5uZmKFSuauXPnOnRfRWDvhPRotbl8+bIpVaqUadWqlYmLizPLly83np6eZsyYManmtVgsNhdkPz8/u1vsbty4YUaMGGH8/f3No48+ajZs2OBQuQMCAmxa9QsXLmxzk3nmzBmHA6uoqCjz1ltvGT8/P1OtWjWzZcuWDMuX1kqZxBy9sc+TJ485ePCg9XXOnDnNihUrrK+PHz+eYktLvnz5zL59+5LdvnfvXpMvX74Uy9CvX79kl27duhkfHx+7f1SOHz9uhg4dakJCQoy/v79p166d+d///pdinh9++MH06tXLBAYGmscee8xMmTLFRERE2BVclCpVyrr/K1euGHd3d5tKrX379pmgoCC7yn7+/Hnz7rvvmsKFCxs3NzfTvn178/XXX9tUtjkjtcC+SZMm1vdq7dq11kDO3uAqPDzcriU1165dM507dzY+Pj6mY8eOdv+grVixwjRs2NBkzZrVtGrVynz55Zfmzp07DvXaiImJMUWLFrW2NtqjSJEidi0pSVgZERISYl5//XXz448/Gk9PT7sD+4ULF5pVq1aZVatWGV9fXzN79mzr6/gltfOIT/Pzzz8bi8ViunTpYvcPd+LAJLGzZ88mew1etGiRCQsLM1mzZjWtW7c2a9asMffu3bP7fxcSEmK9/sTGxhp/f39rYGOMMUeOHDH+/v52nUdKli9fbsqVK5dimqZNm5qnnnrK/P7779Z1v/32m6lVq5Zp1qxZmsuQnLQGlgn5+vqaM2fOGGNseyMdOXLEBAcHp5r/1KlT1sD6xo0bpmfPnqZ8+fKmRYsWNhWg9rC311RCgYGB1nuPokWLWn+DT548aXx8fJLNV6lSpWSXypUrG19f3wwLbB555BGzfv16Y8z9RhYvLy+zc+dO6/b9+/en2NqbkK+vr/W7GBwcbG1hO3XqlF3fg8QVW+fPnze3b9+2+1yc/fzcvXvXPPPMM6Zy5co2FekTJ040Hh4e5vPPP082b8Lr3Jw5c0zBggXNO++8Y1asWGHXdTBxxXRKS2pq165ts9StW9e0adPGzJo1y9oLMiM5G1w+DIG9s6ZMmWL9jKa1cjM2Ntbm/7R48WLz6quvmqlTp5o7d+44Vb7bt2+n+R7OXjExMWb8+PHmtddes+kFNHHiRPPJJ584vK+lS5eaZ555xri7u5saNWqYefPmmZEjR5qgoCCHKisI7J2QHq02xtwPLAoWLGjq1atnvLy8zKhRo+w+fs+ePa21lF5eXqZr164P1F4mJSgoyPj6+prBgwebgwcPPtAVLH5JTvXq1c2ECROMMfe7tLu5udlUKjjSDSYmJsZMmDDB5MyZ05QoUcIsX748Q/MZkz6VMgk5+r9v2rSp6dq1q4mNjTXLly83Xl5e1u7sxhizdu1aU6pUqWTze3t729zIJvbbb7851WJ99+5dM3nyZJM7d25TrFgxu2rrE4qNjTWrV682zz33nPHy8koxrbu7u+nXr5+1dSeePcHFuHHjTHBwsBk5cqSpXbv2A13GJk2alGJLVUxMjFm2bJmpX7++8fHxMc2bNzfLly93KCi9du1aisu3336b4o+yu7u76d+//wOPvaT1cYZ4d+/eTfEzYowxGzduNPnz5zdVq1Z1KLg2Ju2tvvFCQkIcOrbFYjGFCxc2b775ppk8eXKyi702b95sOnToYHx8fIzFYjEDBw40x44dS7UMqS2p3ZB5enqa3377zfo6S5YsNo+XpCZ79uwpXqN/+uknExgYmOI+Tp8+bYYPH24KFixofQTCnuto+/btTZMmTcz58+fNhAkTjJ+fn80jYCtWrDAVKlSw+1xSKl9qXcnPnz9vKlWqZDw9PU3RokVN0aJFjYeHh6lcubK5cOFCsvnig0d7loyWL18+6/++fPnyZtGiRcYYY3bv3p0uFSSpcabXVEJPPvmk9VGSdu3amWeeecbs3LnTdO7c2anuvAcOHDANGjQwnp6epmfPnnbluXPnjjl69KjdgdyQIUNMqVKlzMKFC03btm1NwYIFbXrJzJo1y65u8Mbc7367d+9eY4wxNWrUMOPGjTPGGLNkyRKTO3duu/aRFmn5/Ny6dcs88cQTpmbNmub27dtm0qRJxt3d3Xz66acp5rPnGpjSddDeiml7KqcfVqkFlxaLxWzdutV6v501a1azbt066+vNmzfbHdgvX77cPP/886ZatWoOX7927NhhOnToYEJDQ62/SQsXLjTffvttsnkKFy5s7bafHpWbaXH16lXzySefmCFDhpi//vrLGHO/Yi7h7+vDbv/+/dZH5HLnzm1ef/31B3rr/Pzzzw7d1xPYOyGtrTYJA+ilS5cab29v07p1a7uD61q1aj1QU5l4qVOnTrJlT3jhTep1SheU+Ge869ata4KCgkyTJk1stg8aNMg8//zzKb5/cXFxJjw83BQsWNCEhISYWbNmpdr9NC35EkprpUziIC5btmzm0KFDD6xPzqFDh0yuXLms4wEk7MZqjDEdO3ZM8YYmcW+NxJyp6f3ss89M0aJFTd68ec306dPTXNOd8P1NSv369U22bNmsLeTxLZX2BIaxsbFm2LBhplKlSuaZZ555IDBs1aqVmTNnTrL5c+fObWrWrGlmzZplU6HiSFAa/x1JbkntO7Rnzx7TvXt3ky1bNvP444+bjz76yFy5ciXdAvvUegz06NHDeHt7m3fffdfh7098/vQYZ2HMmDHmhRdesPvztmzZMvPMM8+YLFmymObNm5s1a9aY2NhYh8ufWGRkpJk+fbqpUqWKsVgsyXZjTy9ubm5O97gyxphGjRqZl19+OdntPXv2NA0bNrRrX3FxcWb9+vXm+eefN97e3iZfvnzm1VdfTTb9mTNnTLFixYzFYjEeHh7m448/ttn+3HPPmX79+tl3IinYvXu3XWNlxMXFmU2bNpmpU6eaqVOn2lVJO2LECLuXpGzfvj3VY/Tp0yfVNMbcD4bjK8pHjhxpcufObbp3724KFSpk1+Mw8e7cuWMuXLhgzp07Z7MkJy29phJav369tQfViRMnTMmSJY3FYjG5cuUymzdvtns/jo71Y8z9R3e6du1q3N3djbu7u/V3vE+fPtYAOym3bt0ynTp1MoGBgaZUqVIPjAVQu3btVB+HjDd48GBrL8slS5YYDw8PU6xYMePl5WUGDx5s1z6++eYb07hxY2vlVOPGje1ubEjr5ycyMtJUrFjRlClTxnh4eJiFCxfaddyHzaVLl8yOHTvMjh07Ur3/SE/nz5+3qUTct2+f6du3r5k1a1aK+ZJ7BMPe+/B4U6ZMMX5+ftbHanv27GnCwsJMQECAefPNN1PMu2LFCuPj42O6d+9uvL29rd+fjz76yO7fj/Rw9epVs2HDBvPpp5+aBQsW2Cwpib+XLlasmPHw8LCW/6233jKdOnVyqAzOVo6Eh4fb9FgbOHCgCQgIMKGhoXb3mHJzczMNGjQwy5YtS7Yy6MaNGw6N+UJg74S0ttok/vImFWhnVDec5J5tTLyk5JtvvjH9+vUz77333gMDAI4YMcJs3bo1xfzlypWz9hq4ePFisi2f6ZUvobRWyiQO6pJ7nZIrV66YL7/80lrTn9DatWtTvMlP3Fsj8dKzZ0+7Pztff/21qVixovH39zcjR460a/DF2NhYm9bFGTNm2HS9mj59ul3BVsJu8EFBQea1114zHh4eDrceOyp79uzmqaeeMrNnz7b5rDhyQ7tt2za7ltTcuHHDzJ0719SoUcPaWjZ58uQHWsIdlVpgX7Zs2WQHZbH3+dr0GGehWbNmJlu2bCZv3rymfv36dj/jH9+dvlixYiYkJMQMHjzYrkDAHgcOHEgxsE0PFovFNGrUyHqeHh4eDp3/rl27jKenp3n++efNvn37zLVr10xkZKTZs2ePadWqlfH09LTpWmyvP//800yaNCnVFve7d++agwcPJtkr5ODBg9bWHGddvnzZ1KtXz3Tp0iXJ7bdu3bIZOHDIkCE218CBAwc61J3ZUQEBAUk+2x+vT58+dj/j/9dff1nfx9jYWDNu3Djz7LPPmgEDBthUPCbn2LFj5sknn3S4cjEtvabsOSd7HytxdqwfY4x57bXXTJUqVcy3335rsmbNar2x//LLL1N8vj8j7d6920yYMMGsXr3arvTTp083Hh4epm3bttbf0Hbt2hlPT0/rs/Mpcfbzk/BeZ+bMmcbb29u0atXKoUeKkhM/ILG9Tp48ad566y3Ttm1ba1D+1Vdf2Qxumpxr166Zjh07Gnd3d+u9s4eHh+nQoYOJjIx0pvgOefLJJ62VIRcvXjT+/v4mNDTU5MqVK8kxIuKlx324McaULFnS2ksjYSPVsGHDUh1joFKlStbgOWHeH3/80e7HGVP6vY/vyZOS1atXm2zZslnHCUo8IHZK6tWrZx2fKmH5d+3a5dDgeWmpHClRooS1AnP37t3G19fXzJo1yzpmkT0cfWTKHgT2mSC9vtQp+f77753O6+ggaI5KXAFi702Js/mS24czlTLpFdQ5y57eGrVr105xH/v27TO1a9c2WbJkMf369XNosLLPP//c1KxZ0/raz8/P5M+f39r9ys/PL8UW86Rs3LjROnBd8eLFzdChQ5MNPPft25diK3N0dHSKg/ncvn3b2vXUx8fHtGjRwqxcudLuZ6ztFd8tzF5Hjx41AwcONMHBwdbB7JyVWmCf1LNrzjxfG8+ZcRaMSdugNfG2bdtmateubdzc3OwKhNIquWcJw8PDbUaLT0l6jMi8cuVKaxf6hNe/xGN2PKyS6w5ftGhR4+XlZcqXL28uXryYZN4ZM2bY9BSLH2Ml/toXHBxsJk6caHdZHO3O+frrr5ugoCCbQWTjvfbaayZr1qwZ+huQ0BNPPGGeeuop89VXX5kDBw6YgwcP2izJSUuvqfSQ1rF+jDGmYMGCZs+ePcYY2xv7EydOpFixklqL7r1791IcxyY95cuXL8lZWKZNm2ZCQkIy7Ljp8UiRMca89957NjOYtGrVylgsFhMSEpLi5y/etm3brAOmeXl5Wf+H48aNs2vwxtatW5vixYub9evXWxt21q9fb0qWLGnatGmTav60CgwMtFaOTZkyxTzxxBPGmPujuv8TXdETDiacO3du63t+/PjxVAcB9vHxsY7PkPD7c+rUKePt7W3X8UNCQpJsiFqxYoVdA1AWL17c9O3bN8kZwlLj7+9vHawyYfnPnj1rd/mNSVvliI+Pj7Vn1KBBg6w9BX755Re7x+nICAT2TujSpUuaW9VSY09wff36dXPr1i2bdQcOHDBNmjRxuMU/KirKzJo1y1StWjXFvO+//77NMXfu3Gmio6Nt9tOrV68Uj+VscJzZQXV6aNiwoU1N8rhx42xquP/8809TunTpDC2DxWIxvr6+pl+/fg4PfBIWFmbzQ574UYYZM2akWrGQnL///ttMnTrVVKpUKdnPoJubm82NWbZs2ZwedCa+pSB//vzGYrGY9u3bm40bNzrVPT3ehg0bzPPPP+/UOAfG3L+p/OKLLzI0sI+X1udrE3NknIW0un37tvn000+tFTRt2rSxuQ4l5+TJkzYtwQUKFLAZrCl37twPtGImlNzzhIGBgcZisZgaNWo4XKnjrJs3b5qVK1ea999/37z//vvmiy++sOsG6fjx42bFihXWG7K1a9eamjVrmscee8yMHj06xdbW0qVL25xfr169bCoGL126lOKgafGS6/o+ceJEs27duhS/g08++aRNi2jia9Cnn35qqlevnmoZjLnfnTN+XBFHunN26dLFFCpUyKbXQt++fY2vr2+qg7gm/H6lNl5Hao+p+Pr62jV6elLSo9fUjRs3zNtvv21CQ0PNI488YvdAlmkd68eY+zfV8f+vhJ+BgwcPpvh8eeLfkHLlytnM9JDab8iqVausXWYTt3AnXjZv3pzieCdZs2ZNsoIotUF0E4qNjTXHjh0z3377rdm+fbvNktEKFy5sne5r48aNJjAw0GzYsMF069bNPP3006nmTzhmU8L/4b59+1IdBNiY+5//pJ4H37Fjh90zG6RF1qxZrcHxs88+a32EI7XZedLjPtqYtE0XV6RIEesjHwnf+wULFth9Dzp8+HBTtGhRm0rYJUuWGF9fX+vUbylJy3SJCaetTFj++PGD7JWWypGEZahUqZK198bJkyft/v7eu3fPjB8/3lStWtUEBQU5PIBkUgjsnZD4hyG92Btcnz9/3lSvXt24ubkZT09P079/f3Pz5k3TqVMn4+XlZdq0aZNkN++kODqHdnoGVilx9uY4rTfVsbGxSc4PHe/u3bsPBBARERFmxIgRZuDAgSkOOmJMxr9/R44cMa+//nqKaQoVKuT0qM758+e31pIa8+BN9ZEjR5y+GCWUXIt9es3/mlBsbKxZt26dadmypfHy8jI5c+Z0KP/Zs2fN8OHDTaFChYy/v79p06aNXT9qzkruJjjhuB0pfYbS6/nalGTUc4579+41L730kgkICDCVK1c2H330kUMt9X379rWZLtLPz8988MEH1sGaGjZsaPegXYmdOnXKhIaG2nVDlhx7HoVIS1f0lStXGg8PD+Pl5WW8vb3NggULTJYsWcwzzzxjGjdubDw8PFJ8vjjx9y+p65ej3z9HBQcHW2+mjTEmV65cNq+PHTtm98BzznbnjI2NNc2bNzelS5c2f/75p+nfv7/x8fEx33zzTarHTPgbkNp4He7u7qZUqVLJVhY89thjqf7m2COpXlP2TKHbtm1bkzdvXjNo0CAzadIkuweyTOtYP8YYU7NmTTN16lRjjO04FX369Elx2tu0/oYkzG9Py7eHh0eyPUjatWtnM9VevPHjx9vV4rxnzx5TpEiRJJ/XTst9RGr3QfESTn/52muvmR49ehhj7n8HUxvA05j7gXH8/y3h/+HMmTN2tboWKFAgyYFHDx06ZFfFQFo9/vjjZvDgwWbHjh0mS5Ys1qBwz549KR4/ve4D0zJd3NixY02ZMmXM3r17TbZs2cy3335rPvvsM5M7d27r98oeffr0MWXLljV//fWX+fzzz42Pj4/dvcbSMl1it27dTLNmzUxMTIz1+3/u3DlTuXJl07dvX7v3k5bKkfbt25tHH33UdOvWzfj6+lofQ1u1apXdg4cOGzbM5M2b13z44YcmS5YsZtSoUaZbt24mZ86cdk+bmpiH4DBjTLrub8eOHZo7d67+97//KSQkRC1atND06dOTTT9w4EBFR0drypQpWrlypaZMmaJvv/1W1apV06lTp5Q/f/4UjxcREaHw8HDNnTtXUVFRat26te7cuaMvv/xSZcqUSTFv4nNP7/di48aNmjNnjtasWaPbt29neL54J0+e1Lx58xQeHq4rV67o7t27SaZ76aWX5OXlpVmzZkmSrl+/rqpVqyo6Olp58+bVpEmTtGrVKjVq1CjJ/Bnx/t28eVNLlizR3LlztXfvXpUpU0YffvhhsunPnj3r9LGuXLli8/r06dPKmTOn9bWnp6du3ryZ4j5++umnVI/j4eH8pclisTiU3s3NTY0aNVKjRo105coVffrpp6nmiYmJ0cqVKzVnzhzt2rVLYWFh+u2333TgwAGVL18+xbwtWrRIdf8Wi0X/+9//ktxWqVIlWSyWJD878etTeg+qVaumV199VXv37lXJkiVTLUtiAwYMSDWNh4eHgoODVa9ePVWsWDHZdCtWrNCyZct0/vx5xcTE2Gz78ccfbV6XLVtWly9fVvv27bV9+/YU95uczZs3a+7cuTbrWrZsqaJFi0qSChcurO7duzu8X0kqWrSo3nvvPXXt2tXhvGfOnLG5/oSFhSWbdsGCBVq3bp2aNGkiSZo2bZrKli0rHx8fSdLRo0eVN29e9e/f/4G8Y8aM0aBBgzR69GiFh4fr5Zdf1rhx49SvXz9J0uzZszVp0iQNHjzYrnIn9xm0x9KlS7V69WrFxMSoXr16evnll+3KFxkZqTt37lhfJ74mxcXF2WxPyffff2+9lieUL18+RUREJJvPzc1NS5YsUePGjVW6dGndvHlTq1evVr169VI95pYtW5QjRw5J0tatW1NMG/+73KtXLx09elSSFBUVZd3+/vvva9CgQRo7dqzKly8vT09Pm/z+/v6plkeSnn76aT399NO6evWqPvvsM82bN0/vv/++YmNjU8z39ddfa926dapRo4Zdx4l35swZh9InZezYsWrYsKGOHDmie/fuacqUKTpy5Ih2796t7du3p2nfKX2G4+Likvw7KTExMVq0aJGGDh2a5PexTJkyGjNmjLZt26bQ0FBJ0t69e7Vr1y69/vrrmjp1qjXta6+99kD+l19+WY899pjWrVunvHnzOvzbl5i990HxsmfPrgsXLqhAgQJav369Ro8eLen+dSG1z44kBQYG6uLFiypSpIjN+gMHDihfvnyp5n/77bc1YMAAffrppwoODpZ0//524MCBGjZsWKr50+r9999X8+bNNX78eL3wwgvW36TVq1fr8ccfTzZfet0Hzp492/oZ7N27t3LmzKndu3eradOm6tmzZ4p5hwwZori4ONWrV0+3bt3SU089JW9vb73xxht69dVX7S7DRx99pA4dOqh69er6/ffftXjxYj333HPJpl+9erX178aNG2vgwIE6cuRIktevpk2bJrufCRMmqFWrVsqTJ49u376tWrVqKSIiQqGhofo/9s47Korke/vPDDljXFCJihnMOSEGRMUcQEQFXTPmnHMWMYuSdUUMGFhdUFFBRMyAAcyYVhFFVLLAff/wnf4xzPRMTwBxv3zOmXOY7q7q6qG7uurWvc9du3Yt5/bb2dnhzJkzaNasGdzc3DBz5kwcP34ct2/fljpW2717N5YsWYI3b97gxIkTzFj4zp07cHZ25nT+v/76CwcOHECfPn2wYsUKODs7o3bt2rCxsUFcXJzY514qcpkD/sfh8Xj07NkzqW50knj//j2tX7+e6tSpQ9WrV6epU6dyXjEzNjZmYstSU1OJx+PRtm3bOLVd0RzaXKzdslqK5V3xVHSlNDs7mwIDA6lTp07E5/OpS5cutHfvXvrw4QNrGSsrK6F4QEEsnMC9ft68eRJd0ZX5+8XExJCbmxvp6OgQn88XmyZDHr58+SI27o/oZ1zj2bNnWcueOXOGTE1NJdYvTihS3KoNW9nSyP+akpJCDx8+5CT8J0hN0rZtW9q1axdjpeX6DJWMpVZXV6fBgwdzjrFWVKND0fhaLhoPnTt3pvr16xOfz6fdu3eLrUdW0Roej0e6urpS8yBLQldXV0jFeMaMGUJib5JywHPh5cuXnF3w5A2FUMQVXVdXl/G4KSwsFBE8fPnypURXemU9f3v27CEej0d169alJk2aEJ/Ppzlz5kgtR0RUp04diStCISEhVLt2bU51yePOWTxcac2aNYx4pKw5nLmSmppKLVq0YL5LE3CVRVVbEhs3bpR6jLm5eakJnnIJR3z27BmNGzeOWrVqRQ0aNCAXFxepqSNL6x3Cxrdv31jTD0vznJPmQaetrS3WlV8W5BkHCZgyZQqZmZlR9+7dqUqVKvT9+3ci+pmPnEu6tdmzZ1PHjh3p/fv3pKenR0+fPqWYmBiytLRkzUpRnKZNm5Kuri6pqalR7dq1qXbt2qSmpka6urpllrqyoKBAxGvs5cuXEr3WyvoelEReXh49fPiQbty4wfz/JCEu5OT48eNkYmJCY8eOlSq+yMXLRZb+6+rVq7R7927auHGjXKmrCwsLhUKegoODycPDg3bs2CFWj0jZaGtrM3H6RkZGjLfq8+fP5U55WrFiLyd169Zl3Uf/f8WMzWLp6OiI6Oho9OnTB15eXujVqxdUVFSwb98+TudOTU1lLJzVq1eHtrY2HBwcOJX9559/MG3aNEyaNAlWVlacypQG8q54KrJSKuDWrVvw8fHBkSNHULt2bbi4uCA2NhZ79uyR6rHw7t07od8tMjISgwcPhoGBAQBg9OjR8Pf3Zy3P4/FErOqyWNk/fvyIgIAA+Pn54evXr3B2dmas/e7u7qhfvz7nukoiWM08efIktLW1MXXqVJFjunXrhrVr14r1SCAirF+/XuqqVfHVGiJC48aNce7cOZiZmXFq56NHj5jVNCJCcnIyMjMzAQCfPn2SWNbPzw8ZGRlCq87jx49nVnHr1auHiIgImJiYsNaxd+9ezJ8/HwsWLICenh6nNhen5P1x/PhxbNq0iVk1lgbX34mNiIgIvHnzBv7+/pg0aRJycnIwfPhwANzuRWmrjMUJDAzEqlWrMHnyZJF9e/bswf79++Hs7IyAgADMmzcPlpaWWLZsGdLT00WOl/RccYXP5+Pff/9lvJq2bdsmtD81NVVk1UAW7t+/L/X/c+fOHfj6+iI4OBh16tSBq6srgoODUatWLdjb20tdZX327JlQX6epqQk+n898b926NaZMmSK2bFZWFnPP8vl8aGlpQVtbm9mvpaUlcbVb0f5LwK5du7B8+XIsX74cAHDo0CFMmDABmzdvllq2d+/eWLZsGfr06QNNTU2hfTk5OVi5ciX69OnDqR39+vXDqlWrcPToUQA/r+X169eYP38+Bg8eLLZMyXvG2NgYiYmJQp5IPB6P80pLUVERnj17ho8fP4qsAHfu3BnVq1fH7du3mW2yPH+SKCgoQHJyMtTV1YXGM6dPn8by5cuRlJSEefPmSaxj9erVWLZsGQIDA4XuI3n5/v07goOD4ePjgzt37khd9a1duzYOHDgg0zl4PB6+f/8OTU1NZqyWmZnJeEIU94jgwtOnT3H58mWx/79ly5ZBT08Pnp6eYssq6rnQpk0bPHv2DHXq1JG5rCLjIAHbtm2Dubk53rx5g02bNkFXVxcA8P79e7F9fknWrVuHKVOmwMTEBIWFhWjYsCEKCgrg4uKCJUuWSC0/YMAATu0sTVRUVFCpUiWhbebm5mVybjbvRx6PB01NTZiamkJDQ0NiHerq6pz/34Dk39zPzw9+fn5MG8Q9v9K8XGSlY8eO6Nixo1xlCwoKsG7dOri7uzNjAicnJzg5ObGWSUxMROPGjcHn86V6n9rY2EhtQ61atfD+/XuYmpqidu3aOH/+PJo3b45bt25J/d+xUTGxl5Pjx48z7nSyoozJdfGBHJ/Ph7q6OqdyMTEx8PX1RYsWLdCgQQO4urpKvInF4ePjw3TgBQUFCAgIQNWqVQH8fDFLw8PDA8HBwbCyssLIkSMREhKCKlWqQE1NDSoqKkovVxwbGxt8+/YNI0aMQGxsLBo1agTgp1sSFzQ1NYVc/ePi4oQGo5qamswkUxxEhDFjxjAPbG5uLiZOnAgdHR0AkOpCamZmhiFDhmD79u3o0aOH0H0gD4IJnr+/P16/fg0nJyecPHmSdXK+ePFiNG/eHG3atMGcOXOYAeHjx4+xZcsWPH78GEFBQVKvoTg8Hg+1atXiPGHt1q2bkOuawCWZixv6/v37hVzUwsPD4e/vj6CgIDRo0ABTp07FihUrRNy1i3Pw4EH4+fnB2NgYffr0gaurK2fDmjLYtGkTPDw8GNfra9euoWXLlsw99f37d8yfPx979uxhrcPExATLli3DsmXLcOHCBfj7+0NVVRX9+/fHkCFDMGTIEDRv3lzhtvbu3VvInbQ4r1+/Rvv27QH8nFAK+g5XV1e0bdsWu3btEjp+9OjRCrenUaNGuHjxIqubZEREBBo3bsxanm3Q//XrV9y5cwezZ8+W2k5FQyEUcUUvOTEXN1GXBBGhW7duTKhMTk4OHB0dmfdPQUEBp3pevHgh9DuNGDECY8eOxfv372FsbCyx7KJFi3D06FHUq1cPU6dOFeqDdu3ahYKCAixatIhTOwTunNWqVePszqkMN3IBcXFxGDFiBF69eiXijss2MO7SpYvC533w4AH69u2LN2/eAAD69++PvXv3YtiwYXjw4AH+/PNP/P3332LLNmvWTOieefbsGf744w+Ym5uLGMVKhtOwIWs4IvDzmbtw4QJSUlLA4/FgaWmJbt26STWMEZGQIYOI0KxZM6HvXJ+JAwcOYNKkSahatSqMjIxEnq1ly5ZJLP/gwQPW/ubUqVNiJ1HFJxMeHh6YPXs2Pnz4INaVmW1ioeg4SICamhrmzJkjsl1c2IE41NXVceDAASxbtgz3799HZmYmmjVrxnlcLDAMliXNmzdHZGQkKlWqJPIslETS/a/oOBr4v7A8ASXvXTU1NQwfPhze3t7Q1NTkFAYoIDQ0VOx2ZU/MZYFtLCEOLoZVVVVVbNq0CaNGjeJcb9OmTfHhwwdUr15dbFhk8XEol3CUgQMHIjIykhkXjBw5Er6+vnj9+jXn50jkuuQqVQE6dOiA6tWry1VW0cm14MUkeIAFnWHJSZ64Va+2bduibdu28PLyQkhICPz8/DBr1iwUFRXhwoULMDExkbgKaWpqKmQhNzIyEolJNjU1ldh+eVc8FV0pBX4O/oYPH46uXbvKZKUU0LRpUxw8eBDr16/H1atXkZqaCjs7O2b/8+fPUaNGDdbyJQf9I0eOFDlGUidjZmaGmJgYmJqawszMTK4V+h8/fuDUqVPw8fHB1atX0atXL2zevBnOzs5YvHixxN+ldu3auHDhAsaMGYPhw4cz9yARoX79+jh//rxcqwdcUXRQ/fTpU7Rs2ZL5fvr0afTv3x8uLi4Afq4guLm5SazD2dkZzs7OePnyJQICAjBlyhRkZ2ejqKgIjx49kuu+koWFCxdizJgxzMTewcEB8fHxzIp/dnY2vL29JU7siyNrfO2qVas41bts2TJUq1YNd+7cEbvfyMgI6enpMDMzg6mpKeLi4tCkSRO8fPlS6dodAtzc3DBjxgw0adJEZFU3LCwMGzZsgJeXF2t5Q0ND1oEcj8fDuHHjpA6Ou3XrBl9fX3z8+BGurq6wt7eXaXJdq1YtPHjwgNUokJiYyKqzIu3dIe13LzmQFhdLybbSXZy8vDzGmAn8n3Gaiz7KH3/8gdjYWEyaNAkLFixg2szj8dCjRw/s2bMHf/zxh9R6AMDAwAAXLlzAtWvXkJCQgMzMTDRv3lyixkF0dDQ6d+4ssV4PDw/s3LlT6vnliZFWxorR/PnzUadOHezatQvBwcEIDg5GUlISxo4di/DwcKZvEYeyVkkV0fo5dOgQpk6dKmJoMzAwwL59+xgPJHEoy+MBANasWYO1a9dy1qQoib29PWJiYkRizE+cOIFRo0aJ1asRN5koruvBZWKhyDjozJkzcHBwgJqamlC8tDjExUhL02iJi4tj/mbzdPiV9O/fnzGiy/ssKGMcDQAnT57E/PnzMXfuXMZYffPmTWzduhXLly9HQUEBFixYgCVLlmDLli2MZynws68/efIkDAwMmDHRnTt3kJGRwdkA8PbtW9Z3TVxcHNq2bSuyfceOHRg/fjw0NTWlTtRLTs5LekulpaUhOzsbhoaGAH4avbW1tVG9enXOHlPdunVDVFQUZy+Lly9folq1aszfirJhwwbm7+HDh8PU1BTXr1+HlZUVHB0d5atULgf+/3FKxsfIS2ZmJvn6+lKHDh2YGEsvLy+pqfQECs7SPlxRZg5tLhw+fJi6d+9OOjo6NGzYMAoLC6OCggKpMb7ylivO27dvac2aNVS7dm2qUaMGzZ49m+7evcs5j7kg76qlpSVpaWmJKI9OmjSJRo0axakt8iKIrdfV1aXmzZuTp6enTGmKqlWrRp06dSJvb2+h2DBZVdHv3r1LISEhFBISwsSpykPJ+LLSpHhqEyIiGxsboXhYaWlqxFFUVETh4eE0dOhQ0tDQoJo1a5KHhwfn8rJef1nE57FlJSD6GdfI9mnWrBlpa2uXiqKvtNh6rulhnJyciMfjUYMGDWjAgAE0YMAAatCgAfH5fBo6dKjEsmwpNu/evcspPlGAIqnGpk2bRg0bNhSrfJ+dnU0NGzakadOmiS2r7HeHvPB4PJowYYKQmr+6ujq5u7sLbZPG58+f6caNG3Tjxg2ZM6IUFhaSr68v9enThxo1akSNGzcmR0dHCgwMlJjyz8DAgO7du8e6f+rUqRLzqBdHnhjpkqrs4hTRpcWoVqtWjbmGjIwM4vF4TKqmskARrZ87d+6QqqoqjR49muLj4yk3N5dycnLozp075OrqSmpqapxyqCuDkmrmsiJPujCuGiuSdFYUGQdxzQrAdv+V1GPR19cnbW1tJhZeR0eH9PX1qWvXrmLLV6pUiUmxqYjeiqIUFBRQVFSUULrisqZVq1YUHh4usj08PJxatWpFREQnT54kS0tLkWPmzZtH48aNE0otWlBQQOPHj+esd1Iy/amAmJgYMjAwEFvG3Nyc0bWRR1dCwF9//UUdOnQQSk+bnJxMnTp1okOHDnFqP9HPFM1GRkY0e/ZsOnz4sIh+ABv5+fnk5ubGZHUoT/CISmlp5D+MhYUFbt++LaQGLguvX7+GiYmJkHX+8ePH8PX1xcGDB5GRkYEePXpItYYqm8LCQoSFhcHPz69Mzi1Y8QwICEB2djbS09MREhKCIUOGlEq5kly6dAl+fn4IDQ1Fbm4u5syZg3HjxknUTwCApKQknD9/HkZGRhg6dKiQp8T+/fvRunVrNG3aVOr5iQifP38Gj8eT617KzMxEcHAw/P39ERcXhy5dumDEiBEYMGAAY1EUR+XKlWFtbY2RI0di+PDhjOuimpoaEhISSn3FuSR6enpITEwUWbWQxNOnT3H69GnGDdPCwgIDBgyQGqfeoEEDrF27FoMGDcKnT59gZGSEGzduoEWLFgB+Wrv79esnURFbEunp6QgKCkJAQADi4+PFHlPy2XJ2doaXl5fIKiObIiyfz2dcwYCfv19CQgJz7ampqahRowYnNzBxJCYmomXLliIq9dKIj4/HggULcOnSJbi7u0vVDCkqKkJRURHj1n3kyBHExsbCysoKEyZMEAkvCgwMZP4mIkyaNAmrVq0S8Zzi4rJ/5MgRHDlyBE+ePAEAWFlZwdnZWeawJGUgCIU4efIkTExMpIZCpKamomnTplBXV2d1Rb937x7nVWt5+fTpE/P8mZuby9SH2draSl2d5vF4uHTpkqLNFAsRwdHREefOnUOTJk1Qv359EBGSkpJw//599OvXD6dOnRJbds6cOTh06BBiYmJEvJOmT58OX19fnD17lpPLvJ2dHebNm4devXpxbvurV69gamoKHo+HV69eSTyWLbxJXB9y9+5dmUMDLS0tcevWLZH/fUZGBpo3b44XL16ILaeqqio2HJHLO8jNzQ2ZmZk4duyY2P1DhgyBvr4+E+vLRklXfgsLC3Tv3p1zJgEAGDt2LFq1asU5o4M4PDw8cPnyZURHRyM8PBzjxo3DwYMHOXm+REdHo3379iJZZAoKChAbGyvVswSQfxykDDw9PXHlyhUEBgYycepfvnyBm5sbOnXqhNmzZ4uUCQwMhJOTEzQ0NITeCeJQRviWJDQ1NZGUlCTT2AX4OYaVtYw4tLS0cO/ePRHPzeTkZDRr1gw5OTlISUlBw4YNkZ2dLXRMtWrVEBMTI+L59fjxY7Rv3x6fP3+Wen53d3ckJibi8uXLjBdtdHQ0HB0dsWLFCplcyamY5xUXateujePHjwuF0QA/vQ6GDBnCeTVdUjirNHd6AwMDxMfHy/y/lGV+JSkzACu/0KjwP0vJHJbFKSgooJMnT0pcNb9x44aQla0kubm5cueG5IK8Kx1syLviqehKqYCMjAzavXs3tWjRgng8HllbW8tchyy8f/+eXF1dycDAgFExNjQ0JDc3N05KtOIQ5K+vXr06qaqqSjw2JyeHUePW0tKiQYMGUWhoKCdr/cqVKzl9JCFY2RV8VFRUqFGjRpxVbNetW0eqqqrE5/PJyMiI/vjjD+Lz+aSmpkabN2+WeO7169eTkZERrVq1imxtbUVyjW7bto26desmsQ5p3L59m/r06cO6X1FF2NJesY+Pj5ep/IsXL8jFxYVUVVVp2LBh9OTJE7nPLQul4ekhLX9zZmYmTZw4kWrUqEFVq1al4cOH08ePH5Vy7vT0dNqxYwc1bdpU6u//4sULsre3F8kuYW9vz+k3yc7OptOnT9PmzZtp8+bNdObMGcrOzubUzgcPHjAK2sU/Xbt2FVo9Kc/4+fmRnp6e2PzwkZGRpKenR4GBgazl3dzcyMzMjN69e8dsmz59Omlra7PmnBdHaGgoNWzYkPz9/en27duUkJAg9JFG8YwOr1+/pqVLl9KcOXMoOjpaYjk+n89k9snIyCA9PT1KSEiQKbMPEbv34ocPH0hNTY213PXr12ncuHGkp6dHrVu3pp07d1JaWhqnFXsrKyuJ6tcXLlwgKysriXUcPHiQDAwMRPpdQ0NDOnLkiMSyxVm3bh1VrVqVRo8eTVu2bJE7M8KIESPIysqKtLW16dSpU5zLsY0lP336JPM74MuXL2U6DiIiqlGjBj148EBk+/3798nY2LjUz68oLVq0oIsXL8pcjsfjkbm5Obm5uVFQUJBQphZZaNq0KY0ePVpIvT0/P59Gjx5NTZs2JaKfq+fm5uYiZQ0NDcXea6dOnSJDQ0NO5y8sLKSBAwdSly5dKDc3ly5dukS6urrk5eXF+Rp8fHyoUaNGpK6uTurq6tSoUSM6cOCA1HJaWlp08+ZNke03btyQmNlFmYwaNYo8PT1lLqfszAAi9RNVrNjLCldRKTbhjJLWcllRUVHB+/fvmfL6+vpCMbaSVuzev3+PXbt2McJAHTt2FLLkqaqq4uTJk6w5REmBlQ4Bw4YNg7e3t4iSKPB/K57+/v5ISEjg9HsoUq4k8fHx8PPzY4394WppY7Oyffv2DU2bNkVmZiZcXFyY3+/Ro0cIDg5GpUqVcPfuXUZURVZ+/PiBsLAwzjFSz58/h7+/PwIDA/Hu3Ts4OztjzJgxsLOzEytIWNI6Whwej4fHjx8jNzdXopVz5cqVnNomThjn8uXL6N69O5YuXYrp06cz91B6ejq8vLywbt06XLp0iXWloqioCCtWrEBYWBiMjIzg6emJBg0aMPuHDh2KXr16YezYsRLbFhERgQsXLkBdXR3jxo2DpaUlkpOTsWDBAoSFhcHe3h7nzp3jdJ2ywufzsWbNGuYeEcTYFRfeWbZsmdwr9gkJCWjevLnU8p8+fcLKlSuxf/9+dOzYERs2bECrVq0klpElPlhXVxcmJiasKvUlPRUUgWv+5lmzZmH//v1wcXGBpqYmgoOD0aFDB5w8eVLhNhTn7t27nN4z6enpePbsGQCgTp06nARdz5w5g3HjxolkkKhatSp8fX0lxvV9+PABjRs3RrVq1TBx4kSh/uvAgQP4/PkzHjx4IPe7razo2bMn7OzsWPUQ1q1bh6ioKERERIjdX1RUhCFDhiA5ORlXr17F2rVrsW/fPoSFhXHKZS9A3GoRlxjp+/fvw9HREW/evIGVlRWOHDmCXr16ISsrC3w+H1lZWTh+/DhrDDCfz5couiXt/IL34IABAxAYGCgUu1tYWIjIyEhcuHABjx8/lnj9WVlZjNbPzZs3UVhYCE9PT7i7u7Pq6Ojq6uLRo0esMcivX79GgwYNxManAz+frTZt2sDFxQUzZ84Uuoe9vLxw5MgR3Lp1i8lJLglJK3U8Hk+sx4K4McSPHz8wc+ZM9OzZU2jsIG21js/nIzU1VcRD78mTJ2jZsqXMCv8CJI2DlClgpqenh7CwMNja2gptv3z5Mvr168dJRE5aVonSJDw8HAsXLsTq1avRokULId0QAKzeH1euXGE+N27cQH5+PiwtLWFnZ4euXbuia9eunDyuBDnr+Xw+o6dx//59FBYW4u+//0bbtm1x8OBBfPjwAXPnzhUqO2vWLAQFBWHRokVMfP6NGzewYcMGuLq6ctY3yM/PR58+fZCdnY3ExESsX79ebEYlcSxbtgyenp7w8PBAu3btAADXr1/Hrl27MHPmTIl6Po6Ojnj37h18fHyYd+WdO3cwfvx41KxZs0y8jtesWYOtW7fCzs4OLVu2FPn/y5WDXglUTOzloPjEhP5/iq+JEyeKDKrYFDvZOmOucHHFNTY2FqteuXTpUnz+/JkR1tLT04O7uzvT9n/++QcdO3bEli1bxJ7b398f06dPx+nTp9G1a1ehfZcuXcKAAQOwa9cuiQJw7du3R0pKCg4cOMA5LVF5gYsKvaQB0erVqxEUFITY2FiR///Hjx/RoUMHuLm5cVZ1VhZFRUWIiIiAr68vwsLCoKenJzV1XHFkccN+/fo1atWqJZei//Dhw2FoaAhvb2+x+8ePH8+kTCotfH198eeff6Jy5cr48uULqlSpwrychg8fjunTpwsZC5SNubk5J3c1eYVdpE3ss7KysGXLFnh6eqJOnTpYv349evbsyanu4n2XYHIh6RUkSQxL0Yl9Tk4Ojh07xqTO7NSpE5ycnDBw4EDWQZWFhQU2bdqEoUOHAvg5kGjbti1ycnJE3GEl8e3bN2bQd+7cOSE1eVVVVbHpJJVBbGwsbG1t0a9fP8yePZu5Tx89eoStW7fi77//RlRUlFjRI+CnEenixYu4du2a2FRzHTt2RM+ePbF+/XqJ7ZAmoCWgtMSzjIyMEB4ezhoyde/ePTg4OEgMyREMaBMSEpCVlYXTp09LFN0Th7yu9A4ODlBVVcWCBQtw8OBB/P3337C3t2cEuTw8PHDnzh0hIbLiREVFcWofWziBoO8W9/yqqanB3NwcW7duZTKWcIFrOKK0hRFpoUjKcuWXF67vPUnjCIHh/vTp0+jVq5dQWqzCwkIkJiaiXr16CA8Pl3gOeTILlDRmSBIwYwvFEDBq1ChcvXoVW7duFZpczp07F506dZLqai9PVgllUvx/KYthrDi5ubmIjY1lJvo3b97Ejx8/UL9+fTx8+FBq+e/fv+Ovv/5iwsrq1auHESNGSBWYLioqwpYtW7B9+3a8f/8ewM/UndOnT8fs2bNZM02JM8h///4dzs7O6NOnDyZNmsRsl5burVq1atixYwecnZ2FtgcHB8PDw0PiGDQtLQ2jR49GeHg4Y/wvKCiAvb09AgICZDIuR0VFYcuWLUhKSgIANGzYkLkHJSGPYa8sqJjYKwFZB5h8Ph/jx4+XmveVbVCjSIxts2bNsGPHDuaGLVk2IiICs2bNYu1QFF3pAH52elu2bMHy5csxYsQIeHl5cVqh5jIY5PF42Lp1K+v+58+fY+3atcxL29TUVCg9naqqKq5evSpXGioutG3bFhMmTGBVXvfz88OBAwdw/fp1sftLrrSIg8fjcU47JY60tDQcPHiQ0+/98uVLLF26FCEhIRg0aBDWrFkjNU6zpMeJLFhYWODgwYOseUuvXr2KUaNGyTSpzc3NRUhICLKystCzZ0+pqv42NjZwdXXF3LlzceLECQwdOhRt27bF0aNHWRVii6Oo14eiSFvFSUxMRJcuXVgHJUZGRvj+/Ts8PDzg7OzMej+Ke6nLEh+cl5eHY8eO4cCBA0hJSRHZL+/EXlz+5vnz5yMxMVGqvoSamhpevXollPlCW1sbycnJnFSMAeDvv//G0qVLce/ePeY6iq8u8ng8uTRDuNC7d2+YmJiwGsYmTJiAN2/esHqbNG/eHAsWLMCwYcPE7j9y5Ag2bdokNc1ZSaOwOEozxl5dXR2vXr1iTa3377//wsLCQmzawOIrlt+/f8fq1athb28vslJfmqs1VatWxaVLl2BjY4PMzEzo6+vj1q1bjFZIcnIy2rZti4yMjFJrA/CzP7516xbjLaQMpGn98Pl8ES+B4mRkZMDNzY21/6pbty727NnDaoS5ePEiJk+ezEyUyiOC8UNgYCCGDRsmlMVAXV0d5ubm+PPPPyX+XxTJLCDg8OHD2LNnD3x9fZkx0+PHj/Hnn39iwoQJTLYZNrKzszFnzhz4+fkxXlKqqqoYO3YsNm/eLLICWpKmTZuibt26WLlypdisEmz3iLKQZiCTJTVlfn4+rl27hn/++Qfe3t7IzMwsdcOEAME9wEVfQpxBvvh3WdK9GRoa4tatWyJjxidPnqB169ac+q8nT54gOTkZAFC/fn2ZtSEOHToENzc3DBo0CB06dADwM4XwyZMnERAQgBEjRkitQ2CAkLcfzMrKQlRUFF6/fi2ibSTPe6RiYq8E5JnYt2vXTmLueUmDGkUm9pUqVcL9+/eZCcigQYOwd+9eZoWKTWhDgDJWOgQkJyfDzc0NHz58gIeHh8iKV8kbuuRgMCYmBi1atBB6qUkbDM6YMQNaWlrMipKenh6WLVvG/JYhISEwNTWVKvwlL5UrV8b169dZDQfJyclo37692FSFwE8LPRvXr1/Hjh07UFRUhNzcXKW0lw153LAFKBKKoq2tjSdPnrBOoN++fQsrKyvWtFmzZs3Cjx8/mFRU+fn5aNOmDR4+fAhtbW0UFBTgwoULjFuYOHR0dPDw4UOYm5uDiKChoYHLly8zLwVpKOr1Afy0tgcEBCA0NFRotWXw4MFwdXWVaPyRZhyS9lIuuUoh7gWvrNWSL1++YOzYsQgNDRUxNO3evRsjR44UGbxJWuUtnr/ZxcWFyd/MVThSRUUFHz58EPK20dfXR0JCAmcBnX79+mHAgAFMiqqS/femTZtw5cqVUgnlqFy5MqKiomBtbS12v8Co8+XLF7H7DQ0Ncfv2bVbj17Nnz9CyZctSn1Aqirj/Y3EkvUO5/J+5rtYEBQVJ3M/m+aYsAc13797hxIkTQqt9gwYNYg3F40JGRgazeisvHz9+hI+Pj1jPNUX7T0Vd+YtTPM2cOEpr1V/AypUrMWfOHKkT4JJwCUe4efOmVBFgZQmYZWVl4fnz50ydXK9HR0cHCQkJpZpiVxoZGRnw9fUVWu0dO3asVKNCfn4+4uLicPnyZcYl38TEBJ07d0bnzp3RpUsXsfeooukGFUWaQb44bB5HAjw8PKCmpibyzp4zZw5ycnKwe/duudooCw0aNMD48eNFhP48PT1x4MAB5v9akoyMDCxevBghISHM+7JSpUpwcnLC2rVrORuV7t27h969eyM7OxtZWVmoXLkyPn36xNnrRRwVE3slIM/EXpEYez6fj0uXLjHu8+3btxdaLfz06RN69Ogh9sWmq6uLq1evssZK37t3D506dRJaxS6OIisd4vDx8cHEiRNhbGwsNLHnMjCSZ8XO2toavr6+jNtXyTqioqIwbtw4PH36VGI9ly5dEppUWVhYYMiQIVJjulRVVfHu3TtWV98PHz6gVq1aMq24P378mIntdnFxwapVqyR2qJUqVeLkyi3OuKCIG7YARUJRFHXDbNy4MdatW8e88Pz9/TF79mzcu3cPpqamcHd3x8ePH3H27FnObVBmrDcXiAh9+/bFP//8I5fOhaJuuFxf7NJe6tHR0RL3l3yWlLHKq6GhgeHDh8PV1RXdu3dnngOuE3s+n4/GjRsL9VWJiYmoX7++kKFW0oq1hYUFwsPDGeNeyfvn/v376NatGz5+/Cj1emVFS0sLycnJrP+bV69eoX79+qyGMWneNqmpqahZs6ZCHkNlAZ/Ph4ODg5ALc3Hy8vIQHh5e6itmJXVmfvz4gezsbKirq0NbW5vVwFuyDy2ZWYTLxH7Pnj2YNWsW8vPzmZW6b9++QV1dHZ6enpg8ebLU9m/cuBHm5ubM6u7QoUNx4sQJGBsbMzo88sBV50MeFH2HFGfgwIFC33/8+IEHDx4gIyMDdnZ2CA0NlVqHIqt1OTk5ICLG+/PVq1c4efIkGjZsKPG9rKxwBG1tbURFRYkY9W/evAlbW1vWBSJlIU9WCWVy+/Zt9OrVC5qamsyY8tatW8jJycH58+dZdVLs7Oxw48YNWFhYoEuXLujUqRO6dOnCOq4uTslwNja4GNePHz+Oo0ePir33pHldyUtxA31BQQECAgJgamrKhH/duHEDr1+/xqhRo5gFGDbevn2LM2fOiG0/1zAuDQ0NPHz4UMQ49OzZMzRu3FjsIll6ejratWuHd+/ewcXFRSik7fDhwzAxMUFsbKxYHbGS2Nraom7duti3bx8MDAyQkJAANTU1jBw5EtOnT+esl1Uc7kGBFSgNrukcJNGtWzehlTJBLFvxFTNx1KtXD7GxsawT+6tXr0p0ZSksLJQYS6qiosJpUJeamopx48YhJiYGvr6+pZ6WREBKSoqQG+24ceOELGvm5uZ4+/atxDomTpyI/fv3o1KlSqhbty6ICLGxsdi9ezcmT54ssTMiIqmdMVdb27///ovly5cjMDAQ9vb2iI+PR+PGjaWW8/LyEmoPW9owcdSuXVvEDVtczJW02KqlS5fKHYri4+PDGrohTWzn9evXQpO38+fPY8iQIcxEZ/r06Zzim4u3QfByKumGJW1Q9vnzZyZN1Js3b3DgwAHk5ubC0dFRYmxXQEAArl69isjISFadi6CgINbVPi7ugWwTCkD6hJ0rJQWTAOG+seSg5PLlywqf88WLFwgICMCkSZOQk5MDZ2dnuLi4cO6Txemm9O/fX6Y2vH//XmhCefnyZZiYmDDfdXV18fXrV5nq5IqVlRUuXbrEGgoUGRkpNZTm+/fvIvH1Ar59+8ap/5IkilScZcuWcTpOVri8byTpxCgLcZ4RT58+xaRJk0TErkoyZswY5j7Kzc3FxIkTmZVOaYb1s2fPYtq0aZgxYwZmz57NTCjev3+PzZs3Y/r06TA3N5faF+7btw9//fUXgJ9pGy9evIjw8HAcPXoUc+fOxfnz5yWW/1VERERIdOXnijjRzKKiIkyaNAm1a9eWWl7aap20d0j//v0xaNAgTJw4ERkZGWjdujXU1dXx6dMneHp6CsU7F+fatWuMzpI4Jk6cyMmw061bN0yYMEFEwGzSpEky603Ig4eHB2bPno0PHz7A2tpaRGhV2jhEUWbOnAlHR0ccOHCAGRcXFBRg3LhxmDFjBqvx+urVqzA2NoadnR1sbW3RpUsXzulCi2tnidPR4sqOHTuwePFijBkzBqdPn4abmxueP3+OW7duYcqUKazlFPUYEISgCRCEDwk8NqpWrYqqVatK1ReIjIxEv379GOHixo0bIyUlBUTEWeAcAExMTBAZGSkysb948aLQe7k4q1atgrq6Op4/fy6ySLdq1Sr07NkTq1atwrZt26SePz4+Ht7e3uDz+VBRUUFeXh4sLS2xadMmjB49Wq6JfcWKvRyUVAUtqUotgK1TlmYxzsjIYOKfxKHIitnmzZuxYcMGXL58WaTTS0hIQLdu3ZjrYWu7oisdR44cwdSpU9G0aVP4+flxjk0tiTwrpQYGBrhw4QJjXS3JzZs30b17d9Y45JMnT8LJyQne3t4YPXo0MyEQuEZPmjQJx44dk5iD3MDAgHUiQUT49u2bxN/v69evWLduHXbu3ImmTZti48aNUkU+JCHL76gMN2xFQlEUFY4rGdNlYWGBpUuXMi6VKSkpaNCgAeuKJdc2SPI4UVTRWhk6F2ycP38ePj4+CAsLY/0N2F7mBgYGqFu3LqdVBwAik9cfP37g3r17WLp0KdauXctZXbygoAC5ubkyZ5L4Vfmba9SogaCgINaB7/nz5zF69GhG0EiZbNu2DWvWrMHBgwdFJm1nz57F6NGjsWjRIlZ9DUXDOAQoI7vGr4KrKrgiMfa3b9/GyJEjmdjRkrAZZkri7+8vdrutrS06duyINWvWiN2/ZMkSxMTE4MqVKxLr19LSwpMnT2BiYoLp06cjNzcX3t7eePLkCdq0acMa0iGN0l6x54Iik6bHjx/D1tZW6jOs6Gpd1apVERUVhUaNGsHHxwc7d+7EvXv3cOLECSxbtozVjVhZ4QjiBMx+/PiBXr16ySxgJg/yZpVQFmx55B89eoSWLVuyeixkZWXh6tWruHLlCi5fvoz4+HjUrVsXXbp0YSb68oprc6V+/fpYvnw5nJ2dhcaAy5YtQ3p6Onbt2iW2nDI9BhShdevWcHBwwMqVK5n2V69eHS4uLujVqxerUaske/fuxYwZM+Du7o727dsD+Gn4CggIwPbt2zFhwgSRMubm5vD29oa9vb3YOsPDwzFx4kSx2kAlqVatGmJjY2FlZYW6deti586dsLe3R3JyMlq0aMEpJKgkFRN7OVA0xi4wMBBOTk4ik+PIyEj4+vri5MmT0NbWxufPn5XS3uL8+PED3bt3R2xsLHr06CEkeCKILY6MjGRNMaXogAL4GRe1YcMGeHh4yH4BxZBnYt++fXv07duXVXV+9erV+OeffxAbGyt2f79+/dCoUSNW1ef58+cjOTmZNRZemsqrALYVpU2bNmHjxo0wMjLCunXrZF4tFIcsv6My3LAVDUVRhHbt2mHo0KGMQKSNjQ2ePXvGPNNRUVEYPXo0pw5ZXhRVtFamzgXw83/q5+eHwMBAfPnyBQ4ODhg8eDCj/F4SaS9zJycnHDhwQKpHBhtRUVGYNWsW7ty5I7Q9LCwMnz9/xpgxY5hta9euxerVq1FQUAA7OzuEhIRwcn8rztevX/HXX3/Bz88Pd+/eRePGjaWm4lMEJycnZGdnsxpI+vbtCx0dHYSEhCj93EVFRRg+fDhOnDiBevXqoUGDBkwYx9OnTzFgwAAcO3aM9X+saBiHNGTJrvGrUGaMPRvx8fHo3Lmz3OnKpCEQ22PTenn8+DFatWol9fw1atTA8ePH0b59e9SrVw9r1qzB0KFDOZdnozQn9mXBuXPnMHr0aKSlpUk8ztDQEDdu3EC9evVgaGiI69evo0GDBrhx4wZGjx7NatgRUFy4c9iwYWjUqBGWL1+ON2/eoF69eqwTS2WGIwA/3c+TkpJgaGgol4CZvMibVUJZ/PHHHzh48KBI2ENERARGjRqF1NRUTvV8//4dMTExTLx9QkICrKys8ODBA5FjlZVuUFtbG0lJSTAzM0P16tVx4cIFNGnSBE+fPkXbtm1LZf6hTPT09BAfH4/atWujUqVKiImJQaNGjZCQkID+/fvLNIY7efIktm7dyhjCGjRogLlz57KOrzU0NPD8+XOJWk916tThpHXVs2dPjBkzBiNGjMCff/6JxMRETJs2DQcPHsSXL19w48YNztchoMIVXw7kTSMloPik7c2bN/D394e/vz9ev34NJycnnDx5UuJqFddBpzg3JDU1NVy4cAGenp44cuQIY5G3srLC6tWrMXPmTNZJPSB5ws6V+Ph4ie6eoaGhWLFihch1lvxOREhOThbRA5DkfuXm5oYZM2agSZMmIqn2wsLCsGHDBiFX9ZLcvXsXS5YsYd0/aNAgDB48mHU/FxdQSS/TBQsWQEtLC3Xq1EFgYCCroYBLbJ88KONFqYxQFDakebvMmzcPTk5OOHv2LB4+fIjevXsLDdTPnTvH6s2hLG7dusUoWjdp0gT79+/H5MmTmcmUh4cHa7ox4KebvKQct3/88YfUlbL8/HyEhoYyqd66d++Ot2/f4t69e6zCagLYVrK+fv2KO3fuYMqUKVizZg3WrVsnsR5J7ReX/9rT01NIKT42NhbLli3DqlWr0KBBAyxevBirV6+WOUWagYEBJk+ejMmTJzP5m9lo1qwZp/tXUnzi/PnzGQPTvHnzmEHw48ePsXHjRly8eJHVsKgofD4fx44dQ0hICIKDg4XUhFesWAEnJyeJ5RUN42CjZHaNhw8fSg0J+FUo+v4vTknjDhHh/fv32LVrF2cxTnkoLCyU+J5XU1PjNKkbNGgQRowYASsrK3z+/BkODg4AfhoXJQmaScu4Im1CXJoUFRXh3LlznFL1lbwOwf9P4P0iDTU1Nabfr169OrNSbmBggDdv3kgtX6dOHZw6dQoDBw5EREQEIwD28eNHqQrnioYjSBIPW7NmjcICilwo7Ym7NIYPH46xY8diy5YtQqu9c+fOFUnhJgkdHR1UrlwZlStXRqVKlaCqqsrqbVHSvVtSukFJE3sjIyOkp6fDzMwMpqamiIuLQ5MmTfDy5Uup4VTXr1/H58+fhZ6RoKAgLF++HFlZWRgwYAB27tzJ6tmrDHR0dJi4emNjYzx//pwRw5UlVTPwUyujpF6GJKpWrYqUlBTWif3Lly9F0p+zsW7dOiaEdO3atRg1ahQmTZoEKysr+cU3qYIyJz8/n44ePUo9e/YkLS0tGjhwIB07doxUVVXp4cOHUsvzeDzi8/nE4/GYv4t/F2wra4qKiujcuXM0ePBgqcfu27ePBg8eTM7OzhQXF0dERJGRkdS0aVPS1tamiRMnipQped0lr5frdTs5ORGPx6MGDRrQgAEDaMCAAdSgQQPi8/k0dOhQiWU1NDTo3bt3rPvfvn1LmpqaUtsgjsePH9O8efPIyMiI9ZjRo0fTmDFjpH5kQVdXl54/f87p2I0bN1J2djbzPSYmhnJzc5nv3759o0mTJkmsg8fjUWpqqkxtlMbFixfJ2dmZNDU1qXLlylKPnTFjBm3YsIGysrKE9q1YsYJ27NghsbyDgwNlZGQw39evX09fvnxhvn/69IkaNGjAWr7k9Zf8/T98+CDxPubz+fTx40fW/dLKT506lapUqUJt27alXbt20adPn4iIOPc/0vjnn3+oXr16Uo9LSEgQ+sTHx9M///xDXbp0oQ4dOogcX61aNbp79y7zfebMmWRvb898P3v2LNWpU0fh9ktixYoVnD7SOHXqFFWtWpXpuwWfKlWq0MmTJ0v1GkqLiIgIGjp0qEz9X1paGk2dOpXU1dXJzs6Obt68WYotVA6xsbEUFhYmtC0wMJDMzc2pWrVq9Oeffwr1iZIQ9x77448/yNnZmf7999/SaD4REbVq1Yo8PT1Z92/dupVatWoltZ78/HzavHkzTZs2TejZ9PT0pAMHDrCWs7W15fQpS54+fUoLFy4kY2NjUlVV5VTG1taWunbtyrTXzs6Ohg8fTt7e3vTjxw+p5Xv06EF//fUXERGNGzeOWrduTYcOHSJ7e3tq3bq11PLHjh0jNTU14vP51KNHD2b7unXrqFevXqzlxI2hxH3Y+Pz5M9WtW5d0dHRo/PjxtG3bNtq2bRv9+eefpKOjQ/Xr16f09HSp7VcWDx8+pH/++YdOnz4t9Clt8vLyaNq0aaSurs704RoaGjRjxgyJfUBhYSHduHGDNm7cSL169SI9PT3i8/lkYmJCo0aNIn9/f0pJSZF6/r/++os6dOhAycnJzLbk5GTq1KkTHTp0SGLZsWPHMu+qXbt2kZaWFnXv3p0MDQ3J3d1dYtlevXrRhg0bmO+JiYmkqqpK48aNo61bt5KRkREtX75cavsVoX///rR//34iIpo9ezbVqVOH1qxZQ82bN6du3bpxrsfNzY0CAgJEtn/9+pXc3NxYy3Tu3Jny8vJE9uXm5lKXLl1Yy5YFFRN7OVD0xV6tWjXq1KkTeXt7C3V+XAfWKSkpzOfly5eko6NDUVFRQtu5dArK4sWLF7RkyRKqVasWaWhoUJ8+fSQev379elJTU6MWLVqQjo4OaWtr09q1a8nIyIjWr1/P+kIoeX1sHy4EBwdT//79qUGDBtSgQQPq168fBQcHSy3H4/EUmlSVJCsri/z8/Khjx46koqJCbdq0oU2bNnEuLw8zZ84U+qirq5O7u7vIdnHw+XyhSamenp5Mk1IiooCAAM4DX0m8fv2aVq5cSebm5sTn82nEiBH0zz//UH5+vsx1ffv2jby9valVq1ZS26/ob1DyHtLV1aUXL17IVL537940cOBAsZ/evXtLLK+iokKLFi2ib9++CW1X1sRe0CdJg81Q165dO0pKShI5XlNTk169esV8b9WqldCzkpKSQtra2hLPKRiIS/rY2dnJcLXyk5WVRaGhobRx40bauHEjhYaGUmZmZqme8927dzR79mz6+vWryL6MjAyaM2cOffjwgXN9KSkptGzZMjIzMyN9fX0aPnw4HT16VGq5zMxMWrFiBenr61Pz5s0pIiJCpuv4ldjb25fKoLawsJAKCwuV2FJ2AgICSEtLi3bv3i00Af3x4wczyPf39y+TtsjL0qVL6dKlS5STkyN3HdnZ2RQYGEidOnUiPp9PXbp0ob1790p9BgoLC2nDhg3Url07atmyJc2bN0/I4M2VW7du0aVLl4iIKDU1lezt7UlPT4+aN29O8fHxnOp4//493b17V+jeuXHjhtg+VFlMnz6dGjduLPZ3ev/+PVlbW9OMGTNK7fwCnj9/TjY2NqyLXWVFVlYWJSYmUmJioshigTgEE/kaNWqQi4sL+fj40LNnz2Q+r6WlpZBBTcDt27fJ3NxcYtnCwkKhZz84OJg8PDxox44dYiesxTEyMqJbt24x3xctWiRkjD969KjExQ1l8Pz5c0pISCCin++TCRMmkLW1NQ0aNEim+Q+PxyNtbW3y8PAQeoYkjcPevHlDf/zxB5mamtLGjRvp9OnTdOrUKVq/fj2ZmJhQ9erV6fXr14pdoAJUuOLLwapVq2Bra8u4ody/fx9jx47FmDFj0KBBA2zevBk1atTAihUrxJYvKCgAj8cDj8eDioqKzOcv6X7E4/FQq1YtTm5JFhYWnIS/BAqVbOTl5eH48ePw9fVFTEwMCgsLsWXLFowdO1aqC5i/vz8OHDiA0aNH4+rVq+jSpQtiY2Px7NkziflL1dTUhBTtxXHkyBFOv4OTk5NUt1M2JCm6c03vEhcXBx8fHxw7dgympqZISkrC5cuXFRLB40pJVdL27dtzjgelEi5aJb9zoV27dkhISBByeY+MjMSaNWsYNy42DYQfP37g1KlT8PHxwdWrV9GrVy9s3rwZzs7OWLx4sdR0ZSWJjo6Gr68vTpw4gRo1amDQoEFSc6cq4zdQRNFaUUXvgwcPws/PD8bGxujTpw9cXV0ZF1pl8OLFC6nPKSDq0szn81GtWjVWxfWaNWsiKSkJpqamyMzMREJCgpBb4ufPn6XG9UvKy/z9+3ccPnyYc6pOceTm5mLXrl2YM2eO1GO1tbVlcv9TBp6envj27ZvYPtrAwADfv3+Hp6cnNm7cyFqHImEcApSVXeNXkJCQICQ6d+TIEbRp04bRyDAxMcHy5ctZ3/8l8fX1xbZt25gUq1ZWVpgxYwbGjRun9LYLGD16NO7fv4+pU6di4cKFqF27NogIL168QGZmJqZNmyakZSGNR48eiU05JW8e7aSkJPj6+mLLli2sx1y/fh2enp4oKChAq1atGOGxDh06QEtLS2L9t27dgo+PD44cOYLatWvDxcUFsbGx2LNnD6d3yNq1a7FixQp0794dWlpa2LFjB9LS0mRynSUiVK9enclkU716dYSHh3MuL8DIyAhGRkZC2xQNJ5MWjnDq1Cl4e3uLDQkzMjLCpk2bMHHiRE6q4Iowffp0WFhYIDIyEhYWFrh58yY+f/6M2bNnS7x3lI22tjbnvg/4KWLdtWtXhbUI3r9/LzYLVWFhocT4/oKCAqxbtw7u7u6MO7ksY+IvX74I/e+joqKExhCtWrXiFEoiL4WFhXj79i3zftDR0VFIj+Xs2bMYN24ckpKScPToUak6PbVq1cL169cxefJkLFy4kBkD8ng89OjRA7t27WJV1C9Jamoq5syZg8jISHz8+FFkPCmPzkiFeJ4cGBsbIywsDC1btgQALF68GFFRUYiJiQEAHDt2DMuXL8ejR4/Els/NzcWJEyfg6+uLuLg4ODg4YOTIkRg+fDji4+NlnpzIIn62fft21n0pKSnw9vZGXl4e6810584d+Pr6Ijg4GHXq1IGrqyuGDx+OWrVqccoDDQgr6QI/hShiY2OZtBdsNG7cGDExMayxW0eOHMGoUaNEBhfFKSgoQGFhoVDsT2pqKvbt24esrCypqcZsbW05xdiypebaunUr/Pz88PXrVzg7O2PkyJFo0qQJ5zzaZcWPHz/ExmBKy+HORXRn4MCBsLa2ZlJevXz5Eo0aNUKnTp1Qv359+Pn5YfXq1ZgxY4ZI2erVq6N+/foYOXIkhg4dynTAsvx+Hz58QEBAAHx9ffHt2zcMGzYM+/bt41xe0d9AGQKUyuDly5cICAhAQEAAsrOzkZ6ejpCQEKE4dlmJj4+Hu7s7unTpwjqoy8nJQWRkJDNoXLhwodBkWlVVFatWrRKZ4C9cuBCnTp3CokWLcO7cOcTGxuLFixeMcXT//v0ICgpi+mGuFBQUYPfu3Vi7di0MDAywevVqiQOctLQ03LhxA+rq6ujWrRtUVFTw48cP7NmzB+vXr0dBQYHEGL+yUFVno3Hjxti3bx86duwodn9sbCz+/PNP1lRDHh4eCA4OhpWVFUaOHAknJydUqVJF5v5LGdk1fhWampp4+vQp8/7q2LEjHBwcsHjxYgA/36PW1tZSU28CP9P5eXp6wsPDA+3atQPwc8K6a9cuzJw5k3NaQHmJi4tDcHAwY1SoW7cunJycJGp8FOfFixcYOHAg7t+/L/R/FLwjZfn/ZWVl4ciRI8y4qGHDhmLFw4pTUFCAGzduIDo6GlFRUYiNjUVeXh5atWrF2g/Y2Njg27dvGDFiBFxcXJi4XFnuYSsrK8yZM4dRzL548SL69OmDnJwcmVT3NTU1FdaTuH37Nmsuclm1dp49ewY/Pz8EBAQgLS0NP378EHucMsXDFKFq1aqMXo2BgQFu3ryJevXq4dKlS5g9e7bIIkZ5hIjw+fNn8Hg8zinvBDg6OuLdu3ci6QbHjx+PmjVrSkxHp6uriwcPHsDc3FzmNpuZmeHgwYPo3Lkz8vPzYWhoiLCwMEYb7P79++jSpYtceitc0dTURFJSEicxU0kIxnMqKioYPHgw3r17hzNnzqBy5cqcBCS/fPnC9J916tThHFsvwMHBAa9fv8bUqVNhbGwsMr+QSyD7V7gJ/O5oaGgIuVl06NCB1qxZw3x/+fIl6erqcqrr2bNntHjxYqpVqxbxeDwaMWIEnT9/ngoKCji3R5YYaXF8/vyZZsyYQRoaGtS5c2e6fv0667EqKio0Y8YMoZgeItnceKW5IrNha2tLbdu2FevqFBISQqqqqlLd2MeMGUPjx49nvn/79o1MTEyoWrVqZGNjQ6qqqnT27FlO10H0M0Y0LS2N8/ECN+iS/19luUFzISQkROL+Hz9+0MCBA8XuUzQ+nIioVq1aFBsby3xfvXo1NWnShPnu4+Mj9L04lSpVos6dO9P+/fuF3Im5/n59+/YlfX19cnZ2pr///pv5P8jy+5eMcZfVlb68UVRUROHh4TR06FDS0NCgmjVrkoeHB+vxhoaGVKlSJZGPIM7Q3t5erKu3gL1791Lfvn2Z77q6utSmTRsmTtXIyEhs/G92dja5urqSoaEh1a9fn6Kjo4X229raCrlIc+HQoUNkaWlJxsbGIm7J4rh69SoZGBgw7p6tW7emhw8fkpWVFTVo0ID27t0r1SXX3Nxc6sfCwkKm6+CKtra2UDhDSV69eiUxnEFZYRzKDKsqa0xNTSkqKoqIfsbYamlp0cWLF5n9iYmJVKlSJU51Va1alQ4fPiyy/fDhw1SlShXlNLgU6du3L/Xv35/S0tJIV1eXHj16RFevXqXWrVuLPJ9sxMTEkJubG+no6BCfz6fZs2fL7Eb++PFj2rdvHw0ZMoRUVVUl/nbq6urk6upK58+fp6KiIma7LPewurq6iKuthoYGvXnzRqZ2N2zYUOJ4SxrBwcGkpqZGffv2JXV1derbty/VrVuXDAwMOGvtyBOOUKNGDbp69Srr/ujoaDI2Npb5emTF0NCQefdaWloyYQ3Pnj0jLS2tUj+/Irx//55cXV3JwMCACR0wNDQkNzc3zuFQHz9+JAcHB+LxeKSurs68gx0cHKTqGPXr109sbDkXJk6cSO3ataPo6GiaNWsWValSRch9/9ChQ9SyZUu56uZKixYthPpdeSkeWvnjxw8aO3YsGRgY0P79+8tkHKerq0v37t1Tap0VE3s5UOaLXUBhYSEjPKeuri7TS53rxLgk2dnZtGbNGjI0NKQmTZpwmtD27NmT9PT0mHhmwYtR1on9hAkTZI7x/v79O7Vo0YJ69OghFEd99OhRUldX5zSot7KyEorn3LVrF9WoUYMRQ5s3b55U0Z4vX77Q5MmTqUqVKkKiV1OmTBESURPHunXryMrKikxMTGjevHl0//59Iirbib2GhgadP39e7D7BpJ5NwI/H49HatWtp+/bttH37dtLU1KSlS5cy39esWSO1M9TU1BQaFNnZ2dGSJUuY78+ePSMDAwOxZXNycujQoUPUtWtX0tLSokGDBlFoaCipqalx+v1UVFRo5syZ9OTJE6Htst6/xWPcVVVVqWfPnpxj3Msznz9/pm3btpGNjQ3rMf7+/hQQECDyCQ0N5fQbduzYkc6cOcN8L2kcOnjwILVt21axC5HCP//8Q02aNCF9fX1atWoV59j2Ll26kLOzM92/f5/mzJlDPB6P6tatS8eOHSvV9iqLKlWqMO8ucURFRUl89xw+fJi6d+9OOjo6NGzYMAoLC6OCgoJS6b8EfWN5Q5mDWgMDA5G+iOjnRJWtD1QGdnZ2dOLECdb9aWlpnIxLVapUYeJc9fX1GYO/QAiXjdTUVNq4cSPVq1ePjIyMaObMmXTr1i2Z7iNvb29ydnamGjVqUJUqVWjAgAHk5eVF8fHxQhP2krx9+5bWrFlDtWvXpho1atDs2bPp7t27nN8hROIFTOUZh505c4Y6duwo971ubW1Nu3btYs7//PlzKioqoj///JOWLVsmsezNmzdp/PjxpK+vT82aNaMtW7aQiooKp9+gvIiHdezYkREbdXZ2pl69elFMTAyNGjWKGjVqVOrnl5evX7+ShYUFVatWjWbMmEH79u2jvXv3koeHB1WtWpWsrKzo+/fvnOt7/PgxIxj4+PFjTmX27t1LRkZGNHv2bDp8+LBMwoNpaWnUqVMn4vF4pKenR6GhoUL77ezsaNGiRZzbLw///PMPNW3alMLCwujff/+lr1+/Cn24Ik7MeevWraSqqlom47gGDRqI1UlQhIqJvRyUtrXq48ePtHXrVtb9TZs2pWbNmjEfFRUVatSokdC2Zs2asZYvKChgHmpzc3MKCgqS+CIsSXHRsj/++IOmTZtGqqqq9OjRI07lu3TpIrci7sePH6l+/fo0ZMgQKioqYlRh165dy+nc2traQi/fgQMHCq1OPnz4kKpVq8ZaXllqsFeuXKFRo0aRtrY22djYkIqKCsXExHC6BkmkpqZK/S28vLxIV1eXyUYgoLCwkAYNGkTVq1enBw8eiC1rZmbGacVREjVq1KAbN24w59TX16e///6b2f/o0SPS19eXeq3yeLtcv36dxo0bR3p6etS6dWvauXMnpaWlyTSg5JKVgEtmiN+VgoIC2rBhA7Vv355atmxJ8+fPl0k4ysjIiF6+fMl8r1q1qtD3x48fc/r/F+fr16+0Z88eatGihcTjbty4Qba2tqSpqUkzZsyQyduGiKhy5crMfZKdnU18Pp9OnTolUx2/kt69e9O4ceNY948dO5YcHByk1vPixQtatmwZmZqaMur+yjBuyCJi+atQ5qB26tSpYo3Ys2fPpsmTJyulveLg8XikoqLCOvnj6nUk74qppqYmjRw5ksLDw4UEq2Q1sFavXp02btwo0ySoOJGRkeTi4kJaWlrE4/Fo7ty5nCZG4gRMSxp42bzeimNoaMissmpqaop4QUlDW1ub6TsrV65MiYmJRPTzHSopu461tTWZmZnRwoULhd71XH//8iIeFh4ezhionjx5QvXq1SMej0dVq1ZVympuabFq1SqqU6eOWCHm1NRUqlOnDucxrbxIyobAte/NyMgQO976/PmzVAE+RSnZ3uLZwWR5d1y5ckWsp96FCxc4ZbhRlIiICOrZs6fQGEhRKmLs5eDTp08YNGgQYmJioKuri8DAQCERpG7duqFt27ZYu3ZtqZx/5cqVnI5bvny5yLajR49iyZIlTA7SSZMmQV1dXe62XLhwAf7+/jh58iRMTEwwZMgQDB48WGq8vCK8efMGHTt2hJWVFa5evYqlS5dKzC1fnCpVquDq1atMHF2NGjWwefNmuLi4APgZM9i4cWNWEbwZM2YgMjISFy9eFBGO+fDhA3r27Ilu3bpxFo0RCHb5+fnhzp07aN26NYYMGSI1zy8bCQkJaN68udS4oOXLl2PXrl2Ijo5Go0aNUFhYiOHDhyM6OhqXLl1iBH1KAxcXF3z79g179uxh9Cg+fPjAiMedOHECq1atQkJCAqf6ioqKEBERAV9fX4SFhUFXVxefP3+WWCYrKwshISHw8/PDzZs3UVhYCE9PT7i7u0NPT09i2W3btjH5gsXx/ft39OrVC9euXePU/rKGy73F4/GwdetWsftWr14tJBwVEREBZ2dnzsJRWlpaiI+PR7169cTuT05ORtOmTTnFZ16+fBl+fn4IDQ2FgYEBBg4cKFH8kM/nQ0tLC+PHj5cYm8cW3y5OXyE+Ph61a9eW2tbiFBUVISAgAKGhoUhJSQGPx4OFhQWGDBkCV1dXTjoe8nD58mX06NEDM2bMwNy5c5k+LDU1FZs2bcL27dtx/vx52NnZcaqPiHD+/Hn4+vrizJkzqFq1KgYNGsRZR0CAOBHLwYMHo1WrVjJfY1nx9etX6OrqigjgpqenQ1dXl9N71cPDA0FBQTAxMWHi2m/cuIHXr19j1KhRQjonnp6eSms7n8+Ht7c35syZAzs7Oxw6dEhIuJaLVgoAdOrUCbNnz8aAAQMwYsQIfPnyBUuWLMH+/ftx584d1hj5+vXrIy8vDyNGjICrqyvq168PQLY491OnTiE6OhpXrlxBUlISmjVrBltbW9ja2qJjx45ShTSL8/XrV/z111/w8/PD3bt30bhxY7FijgKUpZMSGBgocb80odRatWrhn3/+gbW1NWxsbLBw4UI4Ozvj+vXr6NWrF75+/Sq2nIaGBoYPHw5XV1d0796d6W9k+f1fvnyJyZMn4/z582LFw+rUqSO1jtIgPT0dlSpVKrU+VBm0bdsWEyZMYL2P/Pz8cODAAVy/fl1iPYWFhQgICGCE14qKioT2X7p0SWltLm9ERUVJ3N+lS5cyaonslLw/s7KyUFBQAG1tbRFtK3l0Ciom9gog6cWup6cnVnwMUJ4yvTwIBrbOzs4S1etlHUR8+fIFhw4dgp+fHxITExUSPWJTxC3+ok1OTsaoUaPQv39/RrRIgCQl5W7duqF169ZYv349rl69CltbW7x9+xbGxsYAfhoqJk2ahGfPnoktb25uDm9vb9jb24vdHx4ejokTJyIlJYXLpQpx//59+Pr64vDhw/j48aPM5QHuE3vg56AyNDQUly9fxpIlS3D58mVERkaWuhJ1SkoKevTogefPn0NFRQU7duzApEmTmP0DBgyAhYWFXIq6nz59wt69e7F06VLOZR4/fgxfX18cPHgQGRkZ6NGjh0TRGS0tLXh7e4tVns/KyoK9vT0+ffqE5ORkmdtfFnTt2lXoe0xMDFq0aCGkJM3j8VgHBYoKR1lZWWHDhg0YPHiw2P1Hjx7FokWLWJ/Bd+/eISAgAP7+/sjIyMCXL19w+PBhDBs2TGq/am5uzqnvZcsSwefzcenSJUYgp3379jh69KiIiJSkZ4iI4OjoiHPnzqFJkyaoX78+iAhJSUm4f/8++vXrh1OnTklsoyJ4e3tj+vTp+PHjB/T19cHj8fD161eoqalh27ZtQs+iLKSnpyMoKAgBAQGIj4+XeryiIpb/BUo+i2xIeh7lQWCg+vz5M/r37w8NDQ2cPn1aJhFUAIiIiEBWVhYGDRqEZ8+eoW/fvnjy5AmqVKmCkJAQiQaia9euwdfXF8eOHUPdunUxcuRIzJs3D4mJiWjQoIFM1/P161dcvXoVx44dQ3BwMPh8vtzCbfHx8di7dy+8vb3lKl+WjBgxAi1btsSsWbOwevVq7Ny5E/3798eFCxfQvHlzVvG84n1oTk4OnJ2d4eLigjZt2sgs4KyoeJg8uLu7czpOliwFZUnlypVx/fp1icbt9u3bS53UTZ06FQEBAejTp49Y4TW2MVRKSgouXLiAHz9+oEuXLoyA5P8KgudFR0dH6kKHMg2qAqQZ9IrDJQuSCEpb+6+AiH4KUQli5dnw8vJi/cyYMYO0tLQkupLk5OTQ6dOnRQSMiH66pJ4+fZo1T7gibvBc2bhxo8xlMjMzycfHh9q1a0c8Hk9sfFTxXKUlc5ZydSG6cuUKaWlpkaWlJWlpaZG7u7vQ/kmTJtGoUaNYy6urq0sUyHnz5g1paGhIuVp23r17R1OmTJG7fHx8vExuSC4uLqSpqUlVq1ZlYiWl8e3bN7p9+zbj/njnzh1ydXWlIUOG0KFDhzjV8ePHD4qPj6d3796JvYbPnz9zvgYB79+/Jw8PD7lFcwoKCujkyZPk6Ogo8bhjx46RpqamSBza9+/fqUOHDmRlZSX2usorsopvKiocNW3aNGrYsKHY/NPZ2dnUsGFDmjZtmsi+48ePk4ODA+no6NCQIUPo1KlTlJeXV6b6FCX7m5LugFz6ID8/P9LT02PclosTGRlJenp6FBgYWFqXQEQ/44w9PT1p8uTJNGnSJNq2bZvMwl/iuH37NvXp00fqccoQsaxAforHlWZkZJCDgwNVrlyZLly4QESKCYB+/vxZptC+79+/0/79+5l3v62tLe3fv1+sm3JJPn36RCdOnCAPDw+ytrZm9G4GDBggV9tzc3Np69at9Mcff8hVngvF439LxgXLGif8+fNn5l1TWFhI69evJ0dHR5o1axankEAi+cMRfiU8Ho/Mzc1p4MCBNGDAANZPeUVFRUWiQN779+9JRUVFaj1VqlSRSeyZiOjSpUukra3NvLfU1NTo4MGDMtVRXkhPT6fNmzeTu7s7ubu705YtWziNHW1tbRk9rNKcC/0qKib2SuLFixe0ZMkSqlWrFmloaHAa3BRHFmV6Ly8vsrOzY93frVs32rlzp0znl4UfP37Q/fv3RTr/U6dOUZMmTUhdXZ1zXbIo4ipLSfnRo0fk5eVFR44cEYrvI/opyCNJoVIZarAPHjygnTt3kre3N9O5pKWl0YwZM0hTU5MaNmwo9RrY4DKxLy5QOHXqVNLQ0KDu3btLFS8k+imupaenRzwejypXrkwRERGkp6dH9evXp0aNGhGfz6f9+/fL3X6in/+f2bNni92Xnp5OTk5OVKVKFTI2Nqbt27dTYWEhLV26lLS0tKhNmzZ05MgRhc7PhQMHDpC2tjZdvnyZiH4apjp27Eh16tT5rSb1RLJP7BUVjvrw4QMZGRmRqakpbdq0iU6dOkWnTp2ijRs3komJCRkbG4sd9ChLkV0RlNEH9ejRg9avX8+6f+3atdSzZ09lN11phIeH0+zZs2nhwoXMfZOUlET9+/dnFJmloQwRywrkp6RgVFFREc2fP5/U1NTI09Pzl2X2EPT91atXJ1VVVYnHNm7cmFRUVKhq1ao0aNAg2rFjByfjdG5uLi1YsIBatGhB7dq1Y8TX/Pz8yNjYmGrVqiVzdg1ZKK7CXTI+WN44YWWQkZFBu3fvphYtWhCPxyNra+syPb8sTJ48mSpVqkRNmzal7du3y7UQ8CsR9w4tDtfnz9jYWGYjTIcOHah///7077//Unp6Ok2ePLlMMhgom6ioKNLX1ycTExNG08LU1JT09fUlCsSWN9iMet++fZNbp6DCFV8B8vLycPz4cfj6+iImJgaFhYXYsmULxo4dK9HNvTg5OTnw9PTEli1bYGZmhnXr1qF3794Sy7Ru3RpLly6Fo6Oj2P1///03Vq1ahZs3b8p8TWxu8AIePHiAvn374s2bNwB+5ljcu3cvhg0bhgcPHuDPP//E1KlTWfObAsDHjx8REBAglM99xIgRaNeuXbl3w3R3d8fz589x4cIFkRjKvLw82Nvbw9LSktUF7MyZMxgyZAgKCgoAAJaWljhw4ACGDRuGFi1aYMaMGejVqxfr+aW5DaWlpeHw4cMSXSi5un9evnxZZFvnzp1hZWWFVatWwc/PD56enpg0aRLWrVsHAFizZg2OHz/OyRW3OFxzGE+YMAHh4eEYOnQoIiIi8OjRI9jb24PP52PJkiWc8y8rg02bNmHt2rU4ffo0li1bhnfv3iEqKkrivV8e0dPTQ0JCAuOGKw0+nw8HBwdoaGgw28LCwmBnZycUpysph/LLly8xadIkXLhwQSQ+c8+ePWLbMmHCBISEhKBRo0ZwdXXF8OHDUalSJZlzqCvCqlWrMGfOHJnid0tiZGSE8PBwNG3aVOz+e/fuwcHBAR8+fJD7HGxICjEpTr9+/cRu9/X1xZ9//onKlSvjy5cvqFKlCpOHffjw4Zg+fTonN+q4uDj4+voiJCQEDRo0gKurK5ycnGBsbFzu3wH/BVRUVPD+/XtGK0LAkSNHMG7cOHTt2hXnzp0T+x4ZNGgQAgICoK+vj0GDBkk8j66uLho1aoSJEyfCwMCAc/sKCgpw5swZifXv3r0bXbp0kVkPZv78+fD29kb37t0RGxuLtLQ0uLm5IS4uDosWLcLQoUNFwiuVSVRUFDp06ABVVVW54oS/ffvGjC+/ffsmsby2tjZUVVVlbuPVq1eZMJnySl5eHkJDQ+Hn54fY2Fj06dMHY8eORc+ePct1fD3w8x1qYGDA2k4iwrdv36SGwmzduhUvXrzArl27OF+zoaEhYmNjmT42Ozsb+vr6SE1NRZUqVWS7kF+ItbU12rVrh7179zLPa2FhISZPnozY2Fjcv39fYnku4Rw8Hq/UnwE+ny/xf1erVi2MGTMGy5cv5xTqCFTE2MvFnTt34Ovri+DgYNSpU4cZZNaqVYvzoKSwsBAHDhzAypUroampiVWrVmHkyJGcHs5KlSohISEBpqamYve/fv0aTZo0wZcvXzhdD9dJFQD06dMHeXl5mDFjBoKDgxEcHIx69eph7NixmDJlilCcLhtaWloYMmQIRo4ciR49ejA3q6ID9NDQUKxYsUKi6I2iA9u3b9+iZcuW0NDQwJQpU4TiY/fs2YO8vDzcvn0bJiYmYsu3bt0aHTp0wOrVq+Hj44NZs2ahUaNG8PPz4yQUpcikXBkYGhoiLi4O9evXR35+PrS0tHD37l00adIEAPDs2TM0a9YM379/51SfIM7y6NGjyMnJwcyZMzFu3DhGTKkkpqamCAgIgJ2dHVJSUmBpaYkFCxYwhoWyZsGCBdi8eTPMzc1x5coV1v97eUbWib2yhKOAn3HZglh6LvGZOTk5OHr0KPz8/HDjxg3Y29vj7NmziI+PL1XBRwFsEyJZUFdXx6tXrxhdj5L8+++/sLCwQF5entznYIPLwIDH47EOKG1sbODq6oq5c+fixIkTGDp0KNq2bStWZ4ALiohYViA/JUUgixMfH48BAwbgzZs3Yu8DNzc37NixA3p6elL7gry8PFy/fh3W1tYS370PHz4UOpeKigrnuN/8/Hy8fPkStWvX5jSJtbS0hJeXF/r164cHDx7AxsYGY8aMga+vb7mfEALCfZC0SQGPx4OVlRX27NnDeewAyKbVUx549eoVAgICEBQUhIKCAjx8+BC6urq/ulmscI2xlhZfPXDgQFy+fBmVK1dGo0aNRHS9xBnXxT37so4BygNsIryPHz9G06ZNkZOTI7E8n8+HmZkZmjVrBknT4JMnTyqlvWwEBQVh8eLFGDNmDFq3bg0AuHnzJgIDA7FkyRKkpaVhy5YtmDt3LhYtWsStUiV5E/xPoaKiQjNmzGBytgrg6kYYEhJCVlZWVK1aNfLy8pLZ3UJXV5du377Nuv/27dukq6srtR5Z3OAFVKtWjXFVz8jIIB6PR0FBQTK1v169emRubk6LFi0SOh+X32/fvn00ePBgcnZ2ZtK1CXLmamtr08SJEyWWl5Tig2uc/osXL6hXr14isf329vb09OlTiWX19fWZYwoKCkhFRYWJaywvSHKFL+nCWdKNm4sLmSI5jFVUVOjff/9lvmtpaZW5627xdEYDBw4kDQ0Nat26tcj28kpCQoLQR0dHh86ePSuyvbzz5MkTWrhwIdWoUYOJ15aUm1sZiMt5KyvKcsP8FRRPr1VUVERqampKSdNJRJScnExz584lIyMj0tTUlKp1UYH8sKV4EvDp0yel6Tw8fPiQtLW1hbZFR0cLpQTW1dUVeZ9Key9mZ2eTu7s7qaiokIqKCvMemjp1qsRQFzU1NXr79i3zXVNTk0kTV1bY2dlJ7KvS0tLIwsJC7L7i/7srV65I/ERERNCkSZOoXr16MrVPVq2eX40gBbOFhQXVrFlT7vSH5QlJaXsFSEu7Kw7BmL14znptbW3av38/5zz25YH27dszYTTFOXnyJLVp00Zq+fISzmFnZ0chISEi20NCQpiQ66CgIJme4YoVezmwt7fH9evX4ejoCFdXV9jb24PH43FecVZUmb5t27YYOHAg5s+fL3b/+vXrcfr0acTFxYnsU9QNXly6p7t378LKykpiuZLIo4i7YcMGLFu2DDY2NkhOTgYRYfHixdi5cyemT5+OCRMmoFKlSjK1QxHkUYMV9/uVB0spV68NFRUVfPjwAdWqVQMA6OvrIyEhgUkdxkVNWRGPjZLn19PTQ2JiosTUZcpGmSvWvwLBKo+4rl+wXdKqbXmjqKgIZ8+eZdIdlma7+Xw+UlNTmftP3jpKhjIUJy8vD+Hh4eXy9y+L/quwsBB///03/Pz8cPr0aaXVW8GvobCwEA8ePGC8ugDA2dkZ7dq1Y9JK6unp4ezZszAzMwMRYceOHXj16hVOnDjBWu/06dNx7do1eHl5oVevXkhMTISlpSVOnz6NFStW4N69e2LLlYd3CJ/PB5/Px+LFi8WmL+aalYALHz9+RO/evXH79m3OZX6HFfvirvgxMTHo27cv3Nzc0KtXL84uy+WRJ0+ewNfXF0FBQXj//r3S61fUa6u8EBISgnnz5sHDw4MJwYyLi8Pu3buxYcMGoXkEW5aa8hDOoaWlhcTERJE51NOnT9GkSRNkZ2fj5cuXaNSoEWsa7pLIHnxTASIiIvDmzRv4+/tj0qRJyMnJwfDhwwGA083QuXNnqensJNXj7u7OuHD37dtXaF9YWBjWrl3LahQwMzPDkCFDsH37dqFJFVd4PB6+f/8OTU1NZgKQk5MjEuslTWOgQ4cO6NChA3bs2IHg4GD4+/sz8TEjRozAgAEDRAbP/v7+OHDgAEaPHo2rV6+iS5cuiI2NxbNnz4RieyXh7u6O7du3K8XNs1KlSozrjCxEREQw8YZFRUWIjIwUmUSzhQJwzW/PNUWHOFd4Pz8/Vld4IkK3bt0Yl8fs7Gw4OjoyegMC7QBJmJmZISYmBqampjAzM2M9F5fz5+TkCJ1fwN27dznXKSvldcLOlZcvX/7qJsjF0aNHMWzYMJHtfD4fjo6OcHBwYH1uSvL161dcuHBBKId89+7dOWmj1K1bV2o/LylNEZf0NeJSKSqD6OhoTsd17tyZdZ+Pjw/j5lpQUICAgABUrVpV6BjBhI0NLvGNv1O85++GtNh4AZJ0MgTcvn0bR48exevXr5Gfny9SXkVFRWhSLyhTMk1trVq1YGZmBgBwdXVFnz59JJ731KlTCAkJQdu2bYWex0aNGkkcWxERxowZwxjWcnNzMXHiRJExBJdrV4S9e/dizpw5SExMxKFDhziPYdjIzc0V+f319fVRvXp1mSb1vwOTJ0/GkSNHYGJiAnd3dwQHB4v0Qb8T2dnZTEjS9evXmRSGpUHJXPe/K87OzgCAefPmid3HZZFCQ0MDzs7OcHZ2ZsI5Jk+eXKbhHCYmJvD19cWGDRuEtvv6+jKhnZ8/f5Zp0bJixV4JXLhwAf7+/jh58iRMTEwwZMgQDB48GC1atCi1c44cORKHDx9G/fr1mRiT5ORkPHnyBMOGDUNwcLDYcvXr10deXh5GjBgBV1dXZlIli7dB8Zeo4MEp+V0ea9+jR4/g6+uLQ4cOIT09HT9+/BDar6WlhSdPnjA3u4aGBmJjY2X6nZURI6sIilpLucTJSct5rIjXhrjVBXEsX75c4n55cxgr6/wV/H5oamoiLCwMPXr0ENlXWFiIYcOGITY2Vuoqx6FDhzB16lQRY6SBgQH27dvHGGnFwefz4eXlJVUITK7cs2WApP5H0I/zeDxWA525ublUowaPx8OLFy+ktkNafCOPxyv1ydX/KmPGjOG0CCHNiHnkyBGMGjUK9vb2OH/+PHr27IknT54gNTUVAwcOZC1f8l0eGhqKXr16MaKUr169Qt26dSXqTGhra+PBgwewtLQU8hxJSEhA586d8fXrV7HlyoPHlcDz5fPnz+jfvz80NDRw+vRpxvOF64p9VlYW5s+fj6NHj+Lz588i+9nKSzPsZGRkICoqqtyu2vL5fJiamqJZs2YS7+Py3n/ExcXBx8cHx44dg6mpKZKSknD58mV06tSJcx3Hjx9nNayV5gLHr+bVq1ecjxUYDCUhWKwNCAhAfn4+kpOTy2Rif+bMGQwdOhT169dndLZu376N5ORkHD9+HH379sXevXvx9OlTzgt2FRN7JfLlyxccOnQIfn5+SExMVKhTvH37Nlq2bCnxmKNHj+Lw4cN4+vQpiAh169bFiBEjxK5qFUfeSRUAqSquAsSpuQqQpuSan5+P6OhokZdPSTdYeVzoJIkG/a9QWuKF8pCZmcl4bMTFxaFLly6sHhsVKI/iysrnzp0TmsipqKhIXS37FWzfvh1LlizBxYsX0aZNG2Z7UVERhg4dipiYGFy6dEmi6Nbdu3fRpk0buLi4YObMmYz45aNHj+Dl5YUjR47g1q1bIiuMAn73/oNtspOdnY3t27djx44dsLS0ZBVPVRZTpkxBcHAwzMzM4ObmhpEjR3IKZaqgfGFjY4MJEyZgypQpzOTawsICEyZMgLGxMashtnr16jh69ChsbW3F7r9y5QqGDh2KtLQ01nN37twZQ4cOhYeHh9BYwMPDA0+fPkV4eLgyLrFUKN6PCIzrN27cQEhICLp37855Yj9lyhRcvnwZq1evhqurK3bv3o13797B29sbGzZsgIuLi9hy5cG4oQjKMkz9KrZu3Sq0sDJy5Eg0adJE5jHYjh07GOG1/fv3w83NDc+fP8etW7cwZcoUrF27tpSv5PemvIRzvHz5Et7e3njy5AkAoF69epgwYQLMzc3lqq9iYl9KbNq0SayLSHEyMzOhoqIipCQfHx+PpUuXsqaaUSa/alIlTclVQMnr5/P5GD9+PGPV3717N0aOHCmyeibJqsXn8/H06VOp18c1XaG85OXloaCgQGH3O3lQ1GujtEhKSoKPjw+rx4aAuLg4hIWFIT8/H926dZOYHrACUf7++28sXbqUiUHV09NDVlYWs5/H4yEkJARDhgz5VU1kZfny5di1axeio6PRqFEjFBYWYvjw4YiOjsalS5ekKuO7ubkhMzMTx44dE7t/yJAh0NfXZ01XqQyPn/KSZgf4aRTx8/PDypUrwefzsWLFCowePVrioIaI8OzZM+Tn56NevXpypdMCykd84/8qyroHdXR08PDhQ5ibm6NKlSq4cuUKrK2tkZSUBDs7O1bvGUdHR1SrVo31ORszZgw+ffqEv//+m/XcMTExcHBwwMiRIxEQEIAJEybg0aNHiI2NRVRUlERPvpSUFFy4cAH5+fmwtbXlrMCvLEoaCIkICxcuhKenJzZu3IgRI0ZwmtibmpoiKCgItra20NfXx927d1GnTh0cPHgQwcHBOHfuXFlcTgUyoqqqivnz52PVqlVCqRVlHYPVr18fy5cvh7Ozs5DXyrJly5Ceno5du3aV1iX89pQM53BxcfmtwzmKUzGxl5OCggIkJydDXV0ddevWZbafPn0ay5cvR1JSEqsb2Zs3bzBs2DDcvHkTKioqmDp1KtasWYOJEyciJCQEAwcOxMyZM4VWpYrDls7NwMAApqamcg2KBPnrDx48KHFSVVRUhM2bN+PMmTPMxGr58uWc0twJKL7qT0To3bs3fHx8ULNmTaHjSq7629racro2SanepBkVSls4LC0tDaNGjcLFixdRVFSEVq1a4dChQ6hTpw6n8qtWreJ03LJlyyTuV8Rro7SRlMP4+PHjGD58OLS0tKCmpoZv375h48aNmDNnzi9o6e9Jv379MGDAAGZwX1IAbdOmTbhy5Uq5HRR6eHggNDQUly9fxpIlS3D58mVERkayCuQUp27dutizZw+6d+8udv/FixcxefJkxnJeEmWs2A8cOJB1X2FhIS5evIi8vLxSN+yGhoZi0aJFSEtLw8KFC+Hh4cEq6Cfg5cuX6NevHx49egQAqFmzJk6cOMEpVackfrd0Vb87ykr1VKtWLfzzzz+wtraGjY0NFi5cCGdnZ1y/fh29evVi9RC5fPkyunfvjlmzZmHu3LnM8/Tx40ds3LgR27dvx/nz52FnZyfx/M+fP8eGDRuQkJCAzMxMNG/eHPPnz4e1tTVrmcuXL6Nv375MOixVVVX4+flh5MiREs+lTNgMhEeOHMG4cePQtWtXTos7urq6ePToEUxNTVGrVi2EhoaidevWePnyJaytrZGZmVmal1GBnKxfvx7+/v7Izc2Fs7MzXF1d0bhxY5kn9tra2khKSoKZmRmqV6+OCxcuoEmTJnj69Cnatm0rNjyjgp+Up3COq1evwtvbGy9evMCxY8dQs2ZNHDx4EBYWFujYsaPsFcoiy1/BT+7fv09mZmbE5/OJz+fTwIED6cOHD9S5c2eqXLkyzZ8/n968ecNafvjw4dS0aVPauXMnde3alfh8PrVs2ZKmTJkisZwAQToYcWnatLW1adGiRZxSZYjjx48fEtOwrFq1ivh8PvXs2ZP69+9Pmpqa5ObmJte5BJRMmVaa8Hg8Cg0NlZomprRwc3MjIyMjWrduHXl6elK9evXI1tZWpvbXrFmTmjVrRk2bNhX7adasGef6vn//Tvv376d27doRj8cjW1tb2r9/v8R0XIpSPKUK2+fs2bN0584dkVSQzZs3pwkTJjD397p166hSpUql1tb/Iubm5kKpOks+f4mJiVStWrVf0TTOuLi4kKamJlWtWlWm1Hw6Ojr06tUr1v2vXr0SSc1VVpw6dYoaNmxIhoaGEtN1KcqVK1eoTZs2pK2tTQsXLqSMjAzOZQcPHkz169enw4cPU2hoKLVv356aN2+ucJv+i+mqyjPKSvXk7OxMW7duJaKfY4Nq1arRuHHjyMzMjAYMGCCx7O7du0ldXZ34fD4ZGhpSpUqViM/nk7q6Ou3cuVOu9nChQ4cO1L9/f/r3338pPT2dJk+eTMbGxqV2PnFISpt57949ZnwpDWtra2a80q1bNyZN7fbt26lmzZrKa3AFpcKVK1do1KhRpK2tTTY2NqSioiJT+lALCwu6e/cuERG1aNGC9u3bR0REERERFeMiKYwePVpqukC2lIHK5Pjx46SlpUXjxo0jDQ0NZiy2c+dOcnBwkKvOiom9HPTu3Zu6detGYWFhNGLECOLxeFS/fn3avHkzZWdnSy1vbGxM169fJ6KfOb15PB5t27aN8/lTUlLEfuLj48nX15dq1KhBmzdvluvakpKSyMrKinV/nTp1mM6DiOjChQukrq5OhYWFcp2PSHkTe0n51wUoIw+1ItSqVYvCw8OZ70+ePCEVFRXKzc3lVL53796kqalJ/fv3p9OnTyv0u5dE8PtVr16dVFVVlVZvSUoapCR9jI2NKTo6mimro6NDT58+Zb7n5eWRqqrqL/2f/m5oaGgwuciJiG7dukX5+fnM9xcvXpC6uvovaJlkZs6cyXymTp1KGhoa1L17d6HtM2fOlFiHtOf/V+SQj4mJoY4dO5K2tjbNmzeP0tPTS+1cDg4OpKamRhMmTKD379/LXP6PP/6gq1evMt///fdf4vP5lJmZKXNdubm5dPjwYerevTtpamrSkCFD6OzZs0rt0ypgp/jvr62tTUOHDqXw8HAqKiriXMfnz5/p3bt3RERUWFhI69evJ0dHR5o1axb9+++/Usu/fv2aPD09adKkSTRp0iTy9PSk169fy31NXDAwMKCHDx8y37OyskhFRYU+ffpUquctTvFc9OL49OkTBQYGSq3H09OTtm/fTkQ/x2KampqkoaFBfD6fvLy8lNbeCkqXb9++0b59+6h169akoqJC7dq1Ywxmkhg7diytWLGCiIh27dpFWlpa1L17dzI0NCR3d/fSbnYFSqBp06bMs158LnT37l36448/5KqzYmIvB9WqVaN79+4REVFGRgbxeDwKCgriXJ7P59OHDx+Y7zo6OkIraIpy7Ngxaty4sVxl4+PjJQ5s1dXVRV68GhoanDwN2FBkYp+ZmUk+Pj7MinOjRo0kHv+rJ/Z8Pl9kQK2trS000ZLGu3fvaN26dVS3bl0yMjKiefPmKfX++fHjB23cuFHsvq5du9KXL1+Udi42ioqK6P379zRlyhQhDwRx/7+y9Pj4L2BsbEwXLlxg3R8REUFGRkZl2CJu2NraSv107dpVYh2CvprNUyQwMLDMJvYPHz6kvn37kqqqKrm7uyvUh3KFx+ORmpoas0LK9pFUvvi7i+jn++vFixcytWPSpElUqVIlsrGxIS8vL0pLS5PreipQDikpKbRixQqytLQkU1NThTwmcnJyaMuWLXIPSokkG+kF3omSPioqKqx1l4d3iImJCU2ZMoUiIiIkTvAlkZ+fT3Z2dvTkyRNmW0pKCp04cUImL6YKyheJiYk0ffp0Tl5zhYWFQvdPcHAweXh40I4dO0S8HYlIar/P5R1QgXLR0tJixv/F+6Hnz5+ThoaGXHVW5LGXg0+fPqFGjRoAfsa16+jooG3btjLVUVyciM/ni+ThVoQWLVqUWq7qgoICaGpqCm1TU1Njjcnniqy6ALLmXxdgZmamcFsVpbhYiuA7ySB1UaNGDSxcuBALFy5EdHQ0/P390apVK1hbW+PixYuc9Q7YxBuXLVuGs2fPihV/vHLlikhKFWVTVFSEc+fOoW/fvpgzZ47I/7R4Hm1AfC5taXm0/5fp3LkzduzYwRpnvmPHDol5zH8VkrQzZEFaKrrSFm578+YNli1bhkOHDqFv375lqmuhqEo0j8dDZmamUJ/B5/Px/ft3oWwn0sRH9+3bB1NTU1haWiIqKoo120p5T1f1X0GgPUNEnLQd8vLysGLFCly4cAHq6uqYN28eBgwYAH9/fyxZsgQqKiqYOXOmTG3IysrCkSNH4Ovri7i4ODRs2BBbtmwROU5S3P/169exY8cOqbm6IyIihER3i4qKEBkZKZQNol+/fjK1XxYOHTqE06dPY8qUKUhLS4O9vT369euHPn36wNDQkFMdampqInpLZmZmnFJ7VVB+sba2hpeXFzZv3iz1WD6fLzSXcHJygpOTE+vxXl5eymjiL6NSpUqc38/p6eml3BrlYGRkhGfPnoko4MfExDC6R7JSIZ4nByoqKnjy5AmqVasGIoKJiQliYmJE/jFsgxs+nw8DAwPmBs3IyIC+vr6IErG8N+b169cxYsQIuSb3CQkJaN68OevLnc/nw8HBQUhkKSwsDHZ2dkIK75IGZCVF0cSVF1eHIvnXBZSHPPbF//eA+P8/1/99Tk4Ojh07ht27d+P+/fv48OGD1EG1IuKNpZnu69mzZ/Dz80NAQADS0tIYA8zXr1+ZQZiy8mj/L3Pv3j20a9cOjo6OmDdvHiP++fjxY2zcuBFnz55FbGwsmjdv/otb+t9EW1sbPB4PU6dORYcOHViPK82JhbyIEx+l/y84WvxvaZPD3z1d1X8BRVI9zZ8/H97e3ujevTtiY2ORlpYGNzc3xMXFYdGiRRg6dKiIAZsNcUb6cePGSTXSF+fx48dYsGABwsLC4OLiglWrVrFOcLmksSpNAd2SPHz4EGfOnMHp06cRHx+P9u3bo1+/fujXr5/Ugf3MmTOhoaGBDRs2lElbK1AOO3bskHoMj8eDh4eH1OMyMjJw8+ZNfPz4UcSgNWrUKLnbWB4JDAzkfKw0A355Yf369Uya9B49euDcuXN49eoVZs6ciaVLl3K6B0pSMbGXg5KDm+IDm+Lf2V4MXG9OeW7MtLQ0ODs7w9TUlDWVjCSkTeyVkf9U3jqUkX/9V+ehVtb//vr16/Dz88PRo0dRt25duLm5YcSIEZys/U5OTnj8+DHGjh2L0NBQREVFoXnz5mjTpg0WLFiAWrVqsZbl8/m4dOmS1JzTXBTKgf8zTPj4+ODatWvo1KkTnJycMHDgQPzxxx+c6qhAdk6fPo1x48aJGJAqVaoEHx8fDBgw4Nc07H+A8jaxkAW2lfWSlMxoUkH5QtFUT5aWlvDy8kK/fv3w4MED2NjYYMyYMfD19eVksFGGkR4A/v33XyxfvhyBgYGwt7fH+vXrpaa8LM98+PABYWFhOHPmDCIjI2FpaYmNGzeiT58+Yo/38PBAUFAQrKys0KJFC5HFEUmpfyv4dVhYWAh9f/PmDYyNjYVSh3JZoBAYsjIzM6Gvry/07PF4PM4LRLm5uSKemKWd8rmCnxAR1q1bh/Xr1yM7OxsAoKGhgTlz5mD16tVy1VkxsZeDshjcFBYWslq82dIzfP36FW/fvkW9evVw/vx5GBkZiRwjzZWloKAAWVlZ5XJQqYz863w+H6mpqVLz2JdXNm3ahICAAHz69AkuLi5wc3PjPIkWUKNGDYSGhqJt27b4+PEjjIyM4OnpiRkzZkgtW9xlsySC7VwmJbdu3YKPjw+OHDmC2rVrw8XFBfPnz0diYiLnQV0FipGdnY2IiAg8ffoUAGBlZYWePXuKDA7/S0RHR3M6rjyGIlRQgbJQNNWTuro6Xr58yaSo1dLSws2bNyWmmSuOokb6r1+/Yt26ddi5cyeaNm2KjRs3olOnTpzO/buQlZWF8+fPQ09PjzVsqmvXrqzleTweLl26VFrNq0CJlEw5y5W6deuid+/eWLduHbS1tWUqm5WVhfnz5+Po0aNi0+KVxzkAG/8Fw0R+fj6ePXuGzMxMNGzYUKF0rxUx9nLAZcIurxv9kydP4Ovri6CgILx//17sMWyrafr6+qhXrx7s7e1ZjQLbtm0r9RjS0iI5OZlx22vVqhWTfx2QLS62W7duQpZRcdy9e1ehtpYWCxYsgKmpKYYNGwYej4eAgACxx0my1KempjIW4+rVq0NbWxsODg6c23Djxg2FDCM2Njb49u0bRowYgdjYWDRq1AjAz2uroOzQ1taWmFP9v4itrS3rPkEfwuPxUFBQUEYtEqW4xkQFFZQGo0aNUmgcUFhYKKQLpKqqKtNA1MzMDDExMTA1NYWZmZlMbvebNm3Cxo0bYWRkhODgYPTv31+mtv9qvn37xkw6iutSiENa/6ws3ZEKfk/evXuHadOmyTypB4B58+bh8uXL2Lt3L1xdXbF79268e/cO3t7ev0Vox+9umHB3d+d0nDye1xUTeyVz/vx5+Pj4ICwsDDk5OZzKZGdnIyQkBH5+frh+/TpatmyJWbNmsR6/fPlyuds3ZswYucuWBzp06IAOHTpgx44dCA4Ohr+/PwoLCzF58mSMGDECAwYMkDrptLe3V8ga9ivp3LkzeDweHj58qFA9iog3mpqaKhTK8PjxYwwfPhxdu3atWJ3/BfTu3RvBwcGMbsGGDRswceJEJozj8+fP6NSpEx49evQLW1k6fPnyRez27OxsbN++HTt27JBbsEZR2DQmKqhA2bAZhLlCRBgzZgyjtZObm4uJEydK1ckRoIiRfsGCBdDS0kKdOnUQGBjIGt5WXoUXK1WqxOj8GBoair1erp5vFfxvY29vj9u3b8v1zgoLC0NQUBBsbW3h5uaGTp06oU6dOjAzM8Nff/0FFxeXUmix8vjdDRMBAQEwMzNDs2bNZBLP5kKFK74SePXqFfz8/BAYGIgvX77AwcEBgwcPxtChQyWWi4uLg4+PD44dOwZTU1MkJSXh8uXLnF3KiAh37txBSkoKeDweLCwspLrW3bx5Ey1atGBd0c/Ly8Pp06cxbNgwTm0oDyQlJcHX1xcHDx5Eenq6xAHxr46xLw8oIt6ojN/v3bt3CAgIgL+/P3JycuDs7AwXFxe0adMG8fHxFZP9UqakgKS+vj7i4+OZwUFqaipq1KhR7geV06ZNQ+fOneHo6AgNDQ3k5ORg5MiROHHiBOc6ioqK4Ofnh5UrV4LP52PFihUYPXo0pzh4ZVAeNSaeP3+O2rVr/5JzV/B7oAytHQGZmZmMkT4uLg5dunSRaKT/3YUXo6Ki0KFDB6iqqkoN66zQqvjfQBZX/DNnzjB/p6WlYdWqVXBzc4O1tTXU1NSEjpUkwKqrq4tHjx7B1NQUtWrVQmhoKFq3bo2XL1/C2toamZmZ8l9QGWBqasoYJvT19XH37l3UqVMHBw8eRHBwMM6dO/ermyiRKVOmIDg4GGZmZnBzc8PIkSOlaldxRq4keRVQXl4eBQcHU7du3UhTU5P69u1LKioqlJiYKLXsli1bqGHDhlSzZk2aM2cOxcfHExGRqqoqPXz4kNP5L126RBYWFsTn84nH4zG5XWvXrk1RUVGs5fh8vlAOVz09PaH8rR8+fCizPM7KJj8/n06cOCHxmJLX/19DUv5fAQEBAZw+4rC1tVVqHvvIyEhycXEhLS0t4vF4NHfuXHr8+LFCdX7+/FlJrftvUjKPc8kczr9LH+Do6EgWFhZUrVo18vLyotatW5OpqSnn8idOnKB69epR5cqVafPmzZSbm1uKrRXm5s2bNH78eNLX16dmzZrRli1bSEVFhXP/ryz09PSod+/edPz4cSIiiomJ4ZQ/uYIKSoOHDx/SrFmzqHr16qSqqvpL2lBQUEDv3r37Jeeu4H+Dr1+/Cn309PQoISFBZLs4BON9aR9p73Bra2u6cuUKERF169aNGTdu376datasqdwLLgV0dHTo1atXRERUs2ZNunHjBhERvXjxgnR0dH5l0ziTm5tLhw8fpu7du5O2tjYNHTqUwsPDqaioSKF6Kyb2cjB16lSqUqUKtW3blnbt2kWfPn0iIu4TcxUVFVq0aBEVFBQIbeda/unTp6StrU1du3alU6dOUXJyMiUlJdGJEyeoS5cupKOjIzRQLw6XQT2Px5PaBjYKCwspLCxM7vKSKNnpsX0kUfL6i1NUVETnzp2jwYMHl0bzOXPr1i2Zjs/MzCQfHx9q164d8Xg8atSokcJtKHlvSiInJ4cCAgJo9+7d9OTJE7nOl5GRQbt376YWLVoQj8ej2rVry1xHREQEDR06lDQ1NeVqw/8K/5WJvYADBw4Qj8cjPT09ev/+vdTjr1y5Qm3atCFtbW1auHAhZWRklEEr/w9ra2syMzOjhQsX0oMHD5jtshh2lcXx48dpxYoVZG5uTs2aNSMtLS36888/5a4vKSmJrKyslNjCCv4X+fHjh1QjfWkRHx9fqv1fQkIC508F/00EE2/Bh+17aeLp6Unbt28nIqILFy6QpqYmaWhoEJ/PJy8vr1I9tzL43Q0TJUlJSaEVK1aQpaUlmZqa0vfv3+Wuq2JiLweCifm3b9+EMM5DbAAAS+JJREFUtnMdmK1bt46srKzIxMSE5s2bR/fv35ep/JQpU8jOzk7svqKiIrKzs6OpU6eK3V9ag/qnT5/SwoULydjYuNQs7SU7v5IfLp1hSkoKFRYWCm178eIFLVmyhGrVqkUaGhrUp0+fUml/cb5//07Z2dlC2+7du0d9+/bl/PvHxMSQm5sb6ejoEJ/Pp9mzZ1NSUpJC7Xr8+DHNmzePjIyMxO6fOXOm0L2Vl5dHTZs2JTU1NTIwMCAdHR2KjY2V+/w5OTm0ZcsWUldX53R8SkoKLVu2jMzMzEhfX5+GDx9OR48elfv8/wvw+Xz6+PEj811XV5devHjBfC+vE/s1a9bQX3/9JbQtLS2N6tatS7169SIbGxvauXOnxDocHBxITU2NJkyYwMkIUBqoq6uTq6srnT9/XsgyXxYT+0+fPlFaWprIdh8fH+Lz+aSnp6fQamVpT4oq+G8g7V3O5/NJRUXll7SttO9hwbUruuJawe/LlStXOH3YiIyMpAYNGohdyMrIyKCGDRtSdHS0TG1KSUmhEydO/DYGpd/dMFGS169f08qVK8nCwoJq1qxZMbEvawSuEzo6OjRs2DAKCwujgoICmQdmV65coVGjRpG2tjbZ2NiQiooKxcTESC3XqFEjOnPmDOv+M2fOsK7aKnNin52dTYGBgdSpUyfi8/nUpUsX2rt3L3348IFTeVkp3uFdvnyZtLS06K+//uLcGRYnNzeXDh06RF27diU1NTXi8/nk6ekpdcVfUV6/fk1t27YlPp9PampqNHPmTMrKyiJXV1dSV1en4cOHU1xcHGv51NRU2rhxI9WrV4+MjIxo5syZdOvWLYUmBVlZWeTn50cdO3YkFRUVatOmDW3atEnssY0aNaLTp08z3/38/KhSpUqUkpJCRUVFNGbMGOrdu7fE8+Xm5tKCBQuoRYsW1K5dOzp58iRTV40aNcjExIQ2bNjAWl6RMJgKfvYBvXv3poEDB9LAgQNJVVWVevbsyXzv3bt3uRxUWlhYCHmzfPv2jZo3b05Dhw6l/Px8On36NDVu3FhiHTwej9TU1MjQ0JAqVarE+ilN3r59S2vWrKHatWtTjRo1aPbs2XT37l1SU1Mr9Yl97969KTAwUGjb33//TVpaWhQYGEgTJ06ksWPHyl1/xcS+Ai6cOnWK9TN//nzS0tL6ZZ5XpX0Pp6SkcP5UUIE4HB0dydPTk3X/9u3bacCAAWXYol/P72aYIBJ2xdfU1KQhQ4bQ2bNnRRYfZaVCPE8BXr58iYCAAAQEBCA7Oxvp6ekICQnBkCFDZKrn+/fvOHz4MPz8/HDnzh20bt0avXv3xpIlS8Qer6+vj8TERJibm7O2y8bGBt+/fxfZx+fzcenSJUakoX379jh69Chq1aoFAPj06RN69OghUTirvOQglyf35507d+Dr64vg4GDUqVMHrq6uGD58OGrVqsUpf66iODk54fHjxxg7dixCQ0MRFRWF5s2bo02bNliwYAHzf2BD0fy/xZFHvLG4SAkAODs7Q09PD/v37wcAxMfHo3fv3vj3339Z65g/fz68vb3RvXt3xMbGIi0tDW5uboiLi8OiRYswdOhQVnFHDw8PBAcHw8rKCiNHjoSTkxOqVKki1/X/r/K7ik9paWkhOTkZZmZmyMvLg4ODA0xNTeHn5wc+n48XL16gUaNGErORsClol2T06NHKarZELl26BD8/P4SGhiI3Nxdz5szBuHHjULdu3VI5X5UqVRAXFwcrKysAQExMDBwdHeHr64tBgwbhxo0bGDBgAGuqVWkkJCSgefPm5V54sYLyx+PHj7FgwQKEhYXBxcUFq1atgpmZWZm3o+IerqC0KSgoQGFhIZNVAvgpWrtv3z5kZWWhX79+6NixI2t5MzMzhIeHo0GDBmL3Jycno2fPnnj9+rXEdkRGRiIyMhIfP35EUVGR0D550qxVwJ3JkyfjyJEjMDExgbu7O1xcXFC1alWl1F2R7k4BLCwssHLlSqxYsQLnz5+Hr68vRo4ciRkzZmDQoEHYsWOH2HLbtm3DzJkzme96enqYMGECJkyYgPv378PLywvbtm1jndhnZmZKzFupra2N7Oxs1v3dunUTSq8gyJfM4/GYNCts/O45yNu0aQMPDw/ExcWhXr16ZX7+6OhohIaGom3bthg2bBiMjIzg4uKCGTNmcCqvSP5fAVu3boWfnx++fv0KZ2dnREdHo0mTJlBTU0OVKlUkluXz+UL3TlxcHJYuXcp8NzQ0ZE0pJuDYsWMICgpCv3798ODBA9jY2KCgoAAJCQlSJ5x79+7F/PnzsWDBAujp6XG42gpKomiqq1+Fubk59u/fDzc3N0ydOhX169fHnj17mP33799HzZo1JdbBZcJelgN6Ozs72NnZ4evXr/jrr7/g5+eHLVu2oHHjxkhMTFT6+QoKChjDx7179zB8+HAEBwejV69eAH4+v+VdDbmC/xb//vsvli9fjsDAQNjb2yM+Ph6NGzcutfNJe64eP35caudm49GjR3j9+jXy8/OFtktSNa/g9+XPP/+Euro6vL29Afxc3GvVqhVyc3NhbGyMbdu24fTp0+jdu7fY8qmpqSIK+MVRVVVFWlqaxDasXLkSq1atQsuWLWFsbMzJ2F/e+J0NE/v27YOpqSksLS0RFRXFmiFDnrSdFRN7JcDj8WBvbw97e3ukp6cjKChI4mrXokWLUKVKFYwaNUpkn6WlJZKTk1GpUiWJ53z06BE+fPggdt+nT59Yy718+VJivdL43XOQd+vWDb6+vvj48SNcXV1hb29fph1aamoqLCwsAADVq1eHtrY2HBwcOJdXJP+vgPnz52P+/PlYtWoV68o4Gw0aNEBYWBhmzZqFhw8f4vXr1+jatSuz/9WrV1LTdL19+xYtWrQAADRu3BgaGhqYOXMmp/YfPHgQfn5+MDY2Rp8+feDq6irT71cB4O7uLvUYHo8HX1/fMmgNdxYvXozRo0dj06ZNaNGiBe7evYvHjx+jXr16uHXrFmbOnCmzt1Rxnjx5Al9fXwQFBcm9Yi0vBgYGmDx5MiZPnoz4+PhSG5S0bdsWY8eOhb29Pfbs2YMlS5Ywk3oACAkJYV0FAn7m4Jb0nBYUFCi1vRX8d/n69SvWrVuHnTt3omnTpoiMjOSc6lcRmjZtyixilITL4oYyefHiBQYOHIj79+8LtUlw/gqvgf8m165dw65du5jvQUFBKCwsxNOnT2FgYID58+dj8+bNrBP7mjVr4sGDB4znZEkSExNhbGwssQ379u1DQEAAXF1d5b+QX8jvbpgYNWpUqbW5YmKvZCpXrowZM2ZIXIE9ePAgXF1dYWhoKGSRzczMRK9evfDp0ydcvXpV4nlKrroLkPZiCgwMxJw5cySu+EvixYsXCAgIwKRJk4RykP+qh0rW80ZERODNmzfw9/dnrmH48OFy1SUvxXNk8/l8qKury1S+Q4cO6NChA3bs2MHk/y0sLMTkyZMl5v8VsHr1avj7++PgwYNwdnaGq6sr5xWSefPmwcnJCWfPnsXDhw/Ru3dvxlABAOfOnUPr1q0l1lFYWCh0zaqqqtDV1eV0fmdnZzg7OzNhMFOmTEF2djaKiorw6NGj39LYVNYEBATAzMwMzZo1E9uHlFdGjhyJTp06gcfjoWbNmhg3bhwaNGgATU1N5OXloV27dli+fLlMdWZnZyMkJAR+fn64fv06WrZsiVmzZpXSFXCjadOmrN5eirJnzx78+eefuHHjBlatWoUFCxYgLS0NTZs2RXR0NPbv34+QkBDW8l5eXqXSrgr+t9i0aRM2btwIIyMjBAcHo3///mV2bkUXN5TJ9OnTYWFhgcjISFhYWODmzZv4/PkzZs+ejS1btvzq5lVQSrx7944JhwJ+rjwPHjwYBgYGAH56lklaHOzduzeWLl2KXr16QVNTU2hfTk4Oli9fznjispGfn4/27dsrcBW/lt/dMFGanpMVMfZywGXgx+PxsHXrVtb9Pj4+mD59Os6ePQtbW1tkZWWhV69e+PDhA65cuSLRpfTVq1ec2ikuPk1FRQXv379H9erVOdUhibKODx00aJDQ97CwMNjZ2UFHR0douyyuKxcuXIC/vz9OnjwJExMTDBkyBEOGDEHz5s2V0uaS8Pl8GBgYMEaEjIwM6OvrC032ASA9PV1s+VWrVok1zCQlJcHX1xcHDx5Eeno6fvz4IbUtUVFR8PPzw/Hjx1GnTh08fPgQUVFR6NChg8RykZGR+Pvvv2FkZAQPDw+htqxcuRJdunSBra0ta3k+nw8HBwcmvkyR/yMRMWEwZ86cQdWqVSWGwVQATJkyBcHBwTAzM4ObmxtGjhzJaG78biQlJSExMRE1a9aUGJNYEnn0JZRFs2bNpBoReTwe7ty5U+ptuXTpEhYuXMj8hvPmzcP48eMVqrOwsFBmT6AK/rfg8/nQ0tJC9+7dJd4r8rihKoMHDx6UajiAgKpVq+LSpUuwsbGBgYEBbt68iXr16uHSpUuYPXs27t27V+ptqKDsqVKlCq5evcosRNSoUQObN2+Gi4sLgJ8LaI0bN2YNqU1NTUXz5s2hoqKCqVOnMmGlycnJ2L17NwoLC3H37l2J3pPz58+Hrq6uUCjl70SVKlVw8+ZN1K5d+1c3pdxRMbGXg+Kux8BPAaIWLVpAS0uL2cbj8XDp0iWJ9WzatAlr167F6dOnsWzZMrx79w5RUVFSBdQUgc/n48OHD0qZ2AsoHh969+7dUosPLU3Rry9fvuDQoUPw8/NDYmJiqbnAKSreJc0wU1BQgDNnzogYQSQhTrxxyJAhpbZy6ebmxuk4cf/HYcOGwdvbW2yoSvEwmISEBIXb+V8mLy8PoaGh8PPzQ2xsLPr06YOxY8eiZ8+ev51LmyyU1JcYOXIkoy9RVuKLK1euZP4mIqxfvx4TJ04UMa7I6n3wq/mVoQwV/F6URwHP79+/Izg4GD4+Prhz506ZuMFXqlQJd+/ehYWFBWrXrg0fHx907doVz58/h7W1tUStpAp+X7p164bWrVtj/fr1uHr1KmxtbfH27VvGff7ChQuYNGkSnj17xlrHq1evMGnSJERERAiFcNjb22P37t1CnpQCio/pioqKEBgYCBsbG9jY2IjE7Ht6eirjUkuN390wUZpUTOyVgDzq7AIWLFiAzZs3w9zcHFeuXIGJiYnUMmfOnBG73cDAAHXr1pUYW8Pn85GamirRVVsR4uPjsXfvXkYU5Hfk7t27pbZizwVJK17KMMxYWlri1q1bYoXy7t+/D19fXxw+fBgfP34U2b9p0yZ4eHgwRqxr166hZcuWzOr79+/fMX/+fCFRM2XSvn17pKSk4MCBA+jTp0+pnON/jVevXiEgIABBQUEoKCjAw4cPOYdG/G6oqqqK1Zf4lVkVFHl//GrEhTIMHjwYc+fO/dVNq6ACTkRHR8PX1xcnTpxAjRo1MGjQIAwePBitWrUq9XN36tQJs2fPxoABAzBixAh8+fIFS5Yswf79+3Hnzh08ePCg1NtQQdkTFRUFBwcHGBsb4/3793B2dhbStJk8eTKysrI4LQR9+fIFz549AxHByspKoj5XyUVJNrgsTP4K/kuGidKkYmKvBGQdmJVcTT137hyaNGki4n7P5oZW0m27ODweD05OTjhw4IDYOPqSruBssLmCSyIvLw+7d+/Gpk2bWIX9FIHLKrSqqiqMjIzQo0cPODo6iuzn6klgY2Mjc/sUhcuKlzIMM1yMAz9+/BCrulrSY0BfXx/x8fHMvZ+amooaNWqU2moHEWHLli1Yvnw5RowYAS8vr//sJLSsEGhOBAQEID8/H8nJyf/Z33T9+vXw9/dHbm6ukL5ExcReNn5lKEMFFSjKhw8fEBAQAF9fX3z79g3Dhg3Dvn37yrwPiIiIQFZWFgYNGoRnz56hb9++ePLkCapUqYKQkBDY2dmVWVsqKFuSkpJw/vx5GBkZYejQoULj+v3796NNmzZo0qTJL2xh+eN3N0yUFRXieb8AgUCGAGdnZ5nKl0zrIODr16+4c+cOpkyZgjVr1mDdunVij1u5cqVIG7iSl5eHFStW4MKFC1BXV8e8efMwYMAA+Pv7Y/HixVBRURFK5adMuLS5qKgIT58+hY+PD+bMmYNVq1YJ7S+piCswcBS3b/F4vDJTo5VHvKtu3bqlYpgpDlsqlZJ2wLK2C/J4PMydOxeOjo5wc3ODtbU1PDw8oKoq3JVNmzatTNv1u1HcFT8mJgZ9+/bFrl270KtXL4mGw9+dhQsXYuHChYy+RJs2bVCnTh0QkdQ0jRUoliqzggrKA46OjoiOjkafPn3g5eWFXr16QUVFBfv27Svzttjb2zN/16lTB8nJyUhPT5eafaKC3xuB0C9bBpLx48dj8+bNpTqx//r1KwoLC0VCwNLT06Gqqgp9ff1SO7e8XL58+Vc34begYsVeCZS3FZfw8HDMmDEDycnJIvsUdeWeP38+vL290b17d8TGxiItLQ1ubm6Ii4vDokWLMHTo0HIhnPT3339j8uTJeP36tdD24sKDRITGjRvj3LlzIkKD4oQHlYm8K158Ph9eXl5SjRyS8nXz+XwEBgZKrUNcDt2S90/Je7+0V+yL4+Pjg4kTJ8LY2FhoYs/j8fDixYtSP//vyuTJk3HkyBGYmJjA3d0dLi4uqFq16q9u1i+hrPUlxFHe3h+SKI+hDBVUIAuqqqqYNm0aJk2aJKRMXnEPV1BWmJiY4Nq1azA1NRW7f8uWLVi0aBHy8/NLrQ0ODg5wdHTE5MmThbbv27cPZ86cwblz50rt3MrgdzRMlBUVK/ZyUNKdm4iQnJyMzMxMoe2/wp0bAOrXr4+3b9+K3aeoFfjYsWMICgpCv3798ODBA9jY2KCgoAAJCQnlysLcsWNHtGzZUmR7yQk7j8dDrVq1Sn0iL0AZK15OTk4Kix9KmvgDZeu1ICupqakYN24cYmJi4OvrK/VaKhBm3759MDU1haWlJaKiohAVFSX2uF+lSM0GWxiNgYEBTE1N5ep/9PT0MGHCBEyYMIHRl9iwYUOpTuxLZmwoKChAQECAiHGlPHqdKJIqs4IKygOC90aLFi3QoEEDuLq6wsnJ6Ze0JSsrCxs2bEBkZCQ+fvwo4o1ZYaD+b9KxY0d0794d165dEwmr3Lp1KxYuXIiDBw+Wahtu3LghNg7d1tYWixcvLtVzKwMnJyexhomjR4/+FoaJ0qRixV4O+Hy+kDt3cYrnkf9VE6NLly5h4sSJePLkicg+RVfs1dXV8fLlS0YPQEtLCzdv3oS1tbVCbf5VlPVqmaIrXspIV6jIPcDn87FmzRomBnv+/PmYO3cuMyn5/v07li1bVmr3/pEjRzB16lQ0bdoUfn5+rBbvCtgpj4rUXGDrd3k8HjQ1NTFjxgyR50oe2PQllIU4teKSlJbXSVBQEKfjRo0aJXG/vKkyK6igvJCVlcWEwd28eROFhYXw9PSEu7s79PT0yqQNzs7OiIqKgqurK4yNjUX65enTp5dJOyooWwoKCuDo6IjU1FRcuXKFWV3etm0b5s2bh8DAQIwYMaJU26Cjo4O4uDiRsfv9+/fRpk2bcp+RoXLlyrh27ZpIOENycjI6dOiAz58//6KW/XoqJvZyoEge+dImPj4e7u7u6NKlC7Zt26b0+lVUVPDhwwfGyqinp4fExEROg9XySFlP7BUV71KGKr4ixgFzc3NOk8KXL1/K0zSp6OjoYMOGDfDw8CiV+isov7D1uxkZGbhz5w6WLl2KmTNnYs6cOWXcst8HSYrJPB4PWVlZKCgo4GyYKw+hDBVUoCiPHz+Gr68vDh48iIyMDPTo0YM1+5AyMTQ0xNmzZysMYv+D5OTkoHv37lBRUcH58+exb98+zJkzBwEBARg5cmSpn79r165o3Lgxdu7cKbR9ypQpSExMxNWrV0u9DYrwuxsmSpOKiX0p8eDBg1JzUWQTVhEMynr06IGjR4+WSowJn8+Hg4MDk94sLCwMdnZ20NHRETquvLnxsvGrDBO/csVLGcaBX8XTp0+F4iIrqEDA8ePHsXLlSty/f/9XN0Uily5dwtSpUxEXFyfSR3/9+hXt27fHvn37ylRh/v3791i5ciX8/PxgZ2eH8PBwmeuQliqzggrKO4WFhQgLC4Ofn1+ZTOwtLCxw7tw5VhG1Cv7bfP36FV26dMGPHz/w5MkT+Pn5wdXVtUzOfe3aNXTv3h2tWrVCt27dAACRkZG4desWzp8/X+4znPzuhonSpGJir0S+f/+O4OBg+Pj44M6dO6XmjhwQECB2Yq+vr4969eqVqviLm5sbp+PKmxuvgGbNmgn9domJiahfvz7U1dWFjrt7926ZtOdXrHi5ublhx44dcrkbvnz58pd6Z3AdbIkT/qvgv83Lly9hbW0tonVS3ujXrx+6du3Kmj1kx44duHz5Mk6ePFnqbfn+/Ts2btyI7du3o1GjRli/fj3nlEJslHYoQwUV/Fc4dOgQTp8+jcDAQLHpiSv4b1J8HPP+/XtMnz4djo6OIpP60h7HxMfHY/PmzYiPj4eWlhZsbGywcOHC32Lx5Hc3TJQmFRN7JRAdHQ1fX1+cOHECNWrUwKBBgzB48GC0atWqVM5XWFiILVu24MyZM8jPz0e3bt2wfPlyaGlplcr5/kusXLmS03HLly8vlfNbWlri1q1bYoXyymrFSxFXfD6fDzMzM3Tt2pX51KpVqxRayX7+4rDFXJdX4b8KSo/r169jxIgRCoeBpKeniyjtKhMzMzOEh4ezrtIlJyejZ8+eIhk9lMmPHz+wc+dOrFu3DlWqVMHatWsxZMgQqeVKCv+Jg8fjVYTKVFBuGTRokNRjeDweTpw4USrnL7m48OzZMxARzM3NRQxiZbXAUEHZwiWlbMU4Rjq/s2GiNKmY2MvJhw8fEBAQAF9fX3z79g3Dhg3Dvn37yiRdyurVq7FixQp0794dWlpaiIiIgLOzM/z8/Er1vBUoDhc3+NJe8VLEFf/KlSvM58aNG8jPz4elpSXs7OyYif4ff/xRCq0Wz++UKqyC0iMtLQ3Ozs4wNTWVux88f/48fHx8EBYWhpycHCW38P/Q1NTEgwcPUKdOHbH7nz17Bmtr61JpAxEhKCgIy5YtQ0FBAZYvX46xY8dyFhws6a3z5s2binSTFfxWlPQ6PHz4MBwdHUU82ErL65Dr4gJQegsMFVRQnNzcXJHUev/L6eJ+dyom9nLg6OiI6Oho9OnTBy4uLujVqxdUVFTKLA+qlZUV5syZgwkTJgAALv6/9u48Lsrq/R//awZIFATN5aOYkAYo4hLulaWoqICiIS4IiIzZVyyXfGNiqanvyjIlNSk1ZxFTBBHX3JVFcktwRcQVLUXQtyDIJsv9+6MH8wthcFhmhoHX8/Hoj7nPPfe5oGG8r3Ofc51jx+Dq6oq8vDy1RgJrQp3RbkB/1thrW11Y3y4Wi3Hz5s1y26y87FVf7Pn5+Th16pQy0T937hwKCwvRuXNnJCYm1mbIKjGxbzheftJV6tmzZ/j777/RqVMnHDlyBG3atFH7mvfu3YNMJsPmzZuRkZEBZ2dnjB07FuPGjavN0Mt46623sGrVKowZM6bC9sjISAQEBGgkOe7WrRvu3LmDmTNnYs6cOSqn/6p7U8e/P9J3/AxTXVNSUoIDBw5g5MiRGusjNzcXn3/+OcLDwyusIK9PswU4MFEW97GvhoMHD2LWrFnw9/fXyZSP+/fvw8XFRfl66NChEIlEePjwocanRZubm5d5rWq0u65SlRy8TJNT4A4fPlzu9/gyTa+tsrW1Vdmm7naNxsbGGDx4MAYMGABHR0ccPHgQGzZswPXr12s7XCKViXBpbZHhw4er9eT5xYsXiIyMxKZNm5Tr9P7++29cuHBBK9t2uri4YNGiRRgxYgSMjY3LtOXl5eGrr77S2A1d6YDbihUr8MMPP5Rr1/VWrUQNUXx8PJKSkgAA9vb2cHBw0HFEpAu3bt2CTCaDQqHA48ePUVhYqLG+5s2bh6ioKPzyyy/w8fFBcHAwHjx4gA0bNuC7777TWL+1pT4NTNQ2JvbVEBcXB6lUil69esHOzg4+Pj6YOHGi1vovKioqd0NoZGSk0S+BUi9PT4uIiMCKFSv0ZrT738mBIAhYvnw5pk+frtE1tS/z9fWttF0bN9YRERHV/plfvHiBM2fOICoqSjklv3379vjggw+wbt06DBw4sJajJaqdaakzZ85EaGgobGxs4O3tjbCwMLRo0QJGRkZqT0evqYULFyIyMhK2trb49NNP0alTJwD/rK0PDg5GcXExvvzyS430HRUVpZHrElHVpaenY+LEiYiOjkazZs0A/LN9p6OjI7Zv3/7KWXWk//Ly8rBjxw7lQPP777+PxYsX48MPP9Rov/v27UNISAgGDRoEPz8/vP/++7C2toaVlRW2bt0KLy8vjfZfU/o+MKFRAlXb8+fPBalUKrz33nuCkZGRIBaLhdWrVwtZWVka7VckEgkuLi7Chx9+qPzP0NBQGDZsWJlj2mBqaircvn1bK31pgrbjF4lEQlpamtb6q+0YHB0dhSZNmgj29vbCjBkzhNDQUOHhw4e1HKH6mjZtKty5c0dn/ZN+MTAwEL744oty39GGhoZCYmKi1uJISUkRnJ2dBbFYLIhEIkEkEglisVhwdnbW+ef5f//7n9rn6vv3P5EuP8Pjx48XevfuLVy7dk15LDExUejdu7cwceJEncRE2nHu3Dnh448/FszMzAQHBwdh5cqVgoGBgdb+HTIxMRHu3bsnCIIgtGvXTjh79qwgCIJw584dwcTERCsx1ET79u2FqKgoQRD+uQ+8efOmIAiCEBISIjg7O+swMt3jE/saMDExgUQigUQiQXJyMqRSKb777jsEBgbCyclJY/ugVvTE19vbWyN9Ue1SZxlAXXby5Em0bdsWgwcPxqBBgzBw4MAKK/xrSvPmzcv8Dp8/fw4HB4dytSWePn2qtZhIO9SdFVTZ2vQtW7ZAJpOhbdu2cHV1hY+PD5ydnWsrRLVZWVnhwIEDyMjIUFbFtrGxQfPmzbUeSyl1igdmZWWVeS0SifD8+fNyxxvy+kaq216+LyspKcHx48dx9erVMse1sWXqoUOHcOzYsTI7ZHTp0gXBwcEYNmyYxvsn3ejevTuysrIwadIknDp1Cvb29gCAwMBArcXQsWNH3L17F5aWlujcuTPCw8PRt29f7Nu3Tzl7pC57+vSp8p7AzMxMec83YMAA+Pv76zI0nWNiX0s6deqEFStWYPny5di/f79GK9TX1T3i6dWEOlCr0srKqkwV66rIzMzEyZMnER0dje+//x6enp6wtbXFwIEDlYm+JqcPrl69WmPXprotJSUFVlZWmDRpUrWLT3p6esLT0xN3796FQqHAJ598gtzcXJSUlODatWsaL3z6subNm2tsW1R1VFQ8MCQkROX5zZo1KzOwJghCmfXAAtfoUx1XUa2O0kLEpbT1GS4pKalwBxwjIyOUlJRovH/SjeTkZEyYMAGOjo5a/zenlJ+fHy5duoSBAwciMDAQo0aNwrp161BYWIigoCCdxFQV+j4woUmsil8NEolErfPq4/ZzL492e3p6YvXq1eW2ONPGaHdt0HZFXD8/P6xdu1anxQZffupdytzcHLa2tggICICTk5Na18rOzkZcXJxyvf2lS5dgY2NT7ukHUU3t2LEDMpkM0dHRcHZ2hkQigYuLS412AhEEAUeOHIFUKsXevXvRsmVLuLu7q7Vfu76qqHjgwYMH1SoeGBMTo1YfrLNB9GqjR49GZmYmQkNDYWFhAQB48OABvLy80Lx5c+zatUvHEZImPHjwAAqFAnK5HHl5efD09ISXlxf69euHixcv6iTZv3fvHuLj42FtbY3u3btrvf+q+vHHH2FgYIBZs2bh2LFjGDVqFARBUA5MzJ49W9ch6gwT+2oQi8WwsrKCg4ODyiewIpGoXm75ps5NdF1+YvPyDfv8+fMxb948tGzZsszxWbNmaaR/AwMDpKam6nS7u82bN1d4PDMzE/Hx8QgLC0NERARGjRr1ymuVlJTgzz//RFRUFKKiohAXF4f8/HyN//8XBAHx8fFISUmBSCRChw4d1N7xgPRb6U2RQqFAbm4ufHx8MHXq1BrvUPL06VOEhIRAoVDg4sWLtRNsHfNy8cCJEycqiwdqY6tWIvr//fXXX3Bzc0NiYiLat2+vPNa1a1fs3btX47scke6dOHECMpkMkZGRyM/PR0BAAD766KNKdy6i8vRtYEKTmNhXwyeffILQ0FBYWVnBz88P3t7eWq2qTtXXoUOHV54jEok0soc0UDf2sX+VoKAgRERE4NSpU+XaSkpKcP78eURHRyMqKgp//PEHcnJy0K5dOzg6Oir/s7Ky0lh8UVFRmDp1Ku7du6ccWCtN7mUyGT744AON9U11S0xMDJYsWYLY2Fg8efKkxmvU4+Pj8dVXX2H//v21FGHdYmhoiPnz5yMwMLDMrCF1E/uHDx8iKCgIixcvLreO/tmzZ/j6668REBBQbgYXUV0RHx+PgIAA7Nmzp8LP8JgxY7B69Wr06NFDK/EIgoBjx44pt4m1s7PD0KFDtdI31R3Pnj3D1q1bIZPJkJCQgK5du+Ly5csa7fP48eM4fvw40tPTyy39qI8zjhsKJvbVVFBQgMjISMhkMpw6dQqurq6YOnUqhg0bxqeGpJJYLMbNmzdfuQ5dl8Wnbty4gf79+1dYgM7MzAw5OTlo06aNMokfNGgQ3nrrLa3EduvWLfTo0QP9+vXD7Nmz0blzZwiCgGvXrmHt2rU4f/48Ll++rDfbL1L15OfnIyIiAjKZDGfOnIGbmxs2b96MRo0avfK9hw8fxtGjR/Haa6/ho48+QseOHXH9+nUEBgZi3759GD58OA4cOKCFn0L7QkNDIZPJcPr06TLFA42NjdVK7AMCApCVlYWNGzdW2D59+nSYm5vj+++/10T4RDU2adIk2NnZYdGiRRW2f/vtt7h27Rp+++03LUdG9I+TJ09CoVBAKpVqrI+lS5di2bJl6N27N9q2bVsub9GHZSAcmKgYE/tacO/ePSgUCoSEhKCoqAiJiYkwNTXVdVgadeLECURGRpaZCu3h4VHnn5aeOHECn376Kc6cOVPhaP27776L9evX4/3339dI/2KxuNKBn7pQfOrKlStwcnLCo0ePyrVt2LABjo6OOpsm9umnnyIpKQnHjx8v1yYIAoYOHYouXbrgp59+0kF0pGlnz56FVCpFeHg4OnbsCIlEolyPqg6pVIpp06bh9ddfR0ZGBlq0aIGgoCDMnDkTEyZMwOzZs8tUqK6vSosHli5nePr0KcLCwuDh4VHp+7p27Yr169djwIABFbafOnUK06ZNQ2JioibCJqqxt956C7t27VI5XffKlSsYPXq0xmbtAai0QOW/TZ48WWMxUN116dIl9OzZU6P3gW3btsWKFSvg4+OjsT40qT4MTGgKE/ta8Ndff0Eul0OhUODFixe4fv16vU7sp0+fjo0bN6J58+awtbWFIAi4efMmMjMzMWPGjDqdVLm5ucHR0RGfffZZhe1r165FVFSUxr4UxGIxdu7c+cqlG7osPjVnzhxcv34dhw4d0lkMqnTt2hXLly9Xuf5/3759WLBgAYv31UP29vZIT0/HpEmTIJFIqjVVtnv37vDx8cG8efOwc+dOjBs3Dv3790d4eHiDXM9a1eKBJiYmSEpKgqWlZYXt9+/fh52dHXJycjQZNlG1GRsbIykpSeWyvLt376JLly4qt3ysDWKxGKampjA0NKy0ThO3bW2YtJHYt2jRAufOndPabMvapu8DE5rE7e6q6d9T8ePi4jBy5EisW7cOI0aMqFGV5rpu165dkMvlkMlk8PX1VY6SlZSUQKFQwN/fH05OTnW2Kv6lS5cqnSY6bNgwrFy5UqMxvPfeezpdYz937twKjz979gwJCQm4ceMGYmNjtRyVeu7fv19p5e6uXbvi3r17WoyItCUpKQkmJiYICQnBli1bVJ5X2c3w7du3MW7cOACAu7s7DA0N8cMPPzTIpB74J3kYPnw4hg8friweWNl2qo0bN0ZKSorKxD4lJQWNGzfWVLhENdaqVSskJyerTOyvX79erphubbOzs0NaWhq8vb0hkUgafLEv0r6PPvoI27ZtU7kkpa578eIF3n33XV2HUScxsa+GGTNmYPv27Wjfvj0kEglCQ0M1/g9BXSGXyzF37lxMmTKlzHGxWAyJRILk5GRIpdI6m9inpaVVuG9sKUNDQzx+/FiLEWnfhQsXKjxuZmYGJycnREZGqlVkUBeeP3+OJk2aqGxv0qQJcnNztRgRaUtlCae68vLylJ8fkUiERo0aoW3btjW+bn3w+uuvY86cOZgzZ47Kc/r164ctW7aoXHIVEhKCvn37aihCopobOnQovvnmG4wYMaJcmyAI+OabbzRevC4xMRFnz55VFnu1trbG1KlT4eXlpdP6OtRw5OfnY+PGjTh27Bi6d+9e7r64ru9lr+8DE5rExL4a1q9fD0tLS3Ts2BExMTEq9/atj9vdJSQkYOHChSrb3d3dMXbsWC1GVDXt2rXD1atXYW1tXWH75cuXNXqjb2VlBQMDA41dXx1RUVE67b+mrl27VuH6fwB48uSJlqMhbfH19a2V62zatEm5VKqoqAgKhUJr213q2rJly155jkgkUnmzFBAQACcnJ5ibm2PevHnK6vdpaWlYsWIFFAoFjhw5UqsxE9WmhQsXolevXujXrx/+85//oFOnTgD+eVK/atUq3LhxAwqFQuNx9OvXD/369cPq1auxY8cOyOVyBAQEYMyYMZDJZGoVAiX95O7uXml7ZmamxmO4fPky3n77bQAot3RRHwqA6/vAhCZxjX01TJkyRa0Pfm08YaprjI2NcefOHVhYWFTY/uDBA1hbW2t0fVpNzJw5E9HR0fjzzz9hbGxcpi0vLw99+/aFo6OjyjWmpFulxQcr+toqPa7r4oNUd7355puv/O7W5HaXuiYWi2FhYYHWrVtXurY3ISFB5TU2bNiA2bNno7CwEGZmZhCJRHj27BmMjIzw448/wt/fX1PhE9WK8+fPY8qUKbh27Zry+0AQBHTp0gVyuRx9+vTRekyxsbH46quvam3rTqq7/Pz81DqvPuYQtcXR0VFlm0gkwokTJ7QYTd3CxJ6qRCwWIy0tTeV2bWlpabCwsKiziVVaWhp69uwJAwMDfPrpp2VG64ODg1FcXIyEhASN7cPcvHnzChMLc3Nz2NraKp+IUcXUXT9vZWWl4UhI29TdwrC+JuW1wdXVFSdOnMDw4cMhkUgwcuTIatWEefDgAcLDw3Hr1i0IggBbW1t4eHg02FoFpJ8uXryImzdvKj/DpU8wteXBgwfYvHkz5HI5cnJylGvuO3furNU4iKj+YGJPVSIWi/Hxxx+rXOecm5uLX3/9tc4m9sA/yaG/vz8OHz6sfGpVWkQqODhYo+vLFQpFhYl9ZmYm4uPjERYWhoiICJVV3+nVrl69iq5du+o6DKplYrEYVlZWmDRpUqXFJ2fPnq3FqPTPw4cPsXnzZigUCmRlZWHy5MmQSCTKQc6aSEpKglQq1XgBUiJNyMrKwtatWyGVSnH+/HmN9RMeHg65XI6YmBgMHz4cfn5+cHV11fkyPWo4HB0dK5291pCfeOs7JvZUJYMGDVJrGYI+rOPOyMhQPnGysbGpE1PfgoKCEBERgVOnTuk6FL2SnZ2N0NBQbNq0CfHx8XV6YImqZ8eOHZDJZIiOjoazszMkEglcXFyq/MS5dAePyMhIpKSkQCQSoUOHDvDw8ICPj49erC+sLbGxsZDL5di5cye6deuGY8eOVbmqfU5ODrZv3w6pVIozZ86gS5cu3G6S9EpUVBRkMhkiIyNhbm6ODz/8EMHBwRrrTywWw9LSEl5eXpXODqyvtT5I917e8rmwsBAXL17E1atX4evrizVr1ugoMvVwYEI1JvZEdciNGzfQv39/7l+rptjYWEilUuzcuRMWFhbK4o26WCNJ2vHgwQMoFAooFArk5ubCx8cHU6dOhY2NzSvfKwgCRo0ahQMHDqBHjx7o3LkzBEFAUlISrly5Ajc3N+zevVvzP0QdkZeXhx07diA4OBhXrlzBo0eP1K7K/ccff0AqlSI8PBx5eXn47LPP8NFHH3EaMemF0u8RuVyOzMxMZGRkYNu2bRg/frzGB/caeq0PqruWLFmC58+f1/lZV/o+MKFJTOyJ6pArV67AyclJZdV3Ah49egSFQgGpVIqsrCyMHz8e69evx6VLl9ClSxddh0daFBMTgyVLlqhdcEoul2P27NnYs2dPueI7J06cwJgxY7Bu3TpMnjxZk2Hr3OnTpyGTyRAeHg5bW1v4+flh0qRJaNasWaXvS09Ph0KhgEwmw7Nnz+Dp6YlJkybhnXfe4d8f6YWdO3dCKpUiNjYWzs7O8Pb2hrOzM0xMTPgZpgbv1q1b6Nu3r94+XNKXgQlN4nZ3VCXqbJcEAIsXL9ZwJPWTVCrVegEffTJq1CjExsbC1dUVq1evxogRI2BgYID169frOjTSovz8fEREREAmk+Hs2bMYN26cyrof/xYaGoovvviiwoq6gwcPRmBgILZu3VpvE/vSLemePHkCLy8vnDx5Et27d1f7/VZWVvDw8MCaNWvg5ORUrcJ7RLo0YcIEzJ8/H2FhYWjatKlOYpg8eTJGjx6NESNGwMTERCcxEFXk9OnT5XaM0ife3t7o27cvE3side3atUtlm0gkQnJyMvLz85nYqzB37twKjz979gwJCQm4ceMGYmNjtRyV/jh48CBmzZoFf39/taZeU/1y9uxZ5fTvjh07QiKRYOfOnWrXx7h8+TJWrFihst3Z2bleb3UZGBgIS0tL5XRjVft1q9oD2MrKCnFxcbC0tISVlRWn3ZPemTp1KoKDgxEdHQ0fHx9MmDBB6/V1rK2t8e2338Lb2xuDBg2Cm5sb3Nzc0K5dO63GQQ2Xu7t7mdeCICA1NRXnz5/HokWLdBRVzen7wERtYGJPVXLhwoUKj1+8eBGBgYG4evUqpk2bpuWo9Ieq35+ZmRmcnJwQGRmp0ar8+i4uLg5SqRS9evWCnZ0dfHx8MHHiRF2HRVpgb2+P9PR0TJo0CTExMejRo0eVr/H06dNKi1X93//9HzIyMmoSZp32wQcfQCQSITExsVrvv379unJtfZ8+fWBrawtvb28AaFBFB0l/bdiwAatXr0Z4eDhkMhnmzJmD4cOHQxAElJSUaCWGxYsXY/Hixfj777+xd+9e7N69G5999hns7e0xevRouLm5ceYeaZS5uXmZ12KxGJ06dcKyZcswbNgwHUWlvvo6MFEbuMaeauTu3btYtGgRwsLC4O7ujq+//ppPUknjcnJyEBYWBplMhnPnzqG4uBhBQUGQSCQ6m15JmiUWi2FiYgJDQ8NKk8jK1gYaGBjg0aNHaNWqVYXtaWlpsLCw4K4Kanj+/DlCQ0Mhl8tx5swZDBw4EJMmTcKYMWNU/n6J6pqbN29CLpdj8+bNeP78OVxdXeHh4VEucdC07OxsHDx4EHv27MHBgwfRtGlTjBo1Cv7+/rC3t9dqLFS/FRcX448//kC3bt3qxG5Q1eHn51fmtVgsRqtWrTB48GC9GJjQJCb2VC1PnjzB0qVLsXHjRgwYMADfffcdK5GTTiQnJ0MqlWLLli3IzMyEk5MT9u7dq+uwqJZt3rxZrfN8fX1VtonFYjg7O6NRo0YVthcUFODQoUMNNrGv7j70pe/bsmULnj59isLCQg1FSKQZJSUl+P333yGVSnHw4EEUFBToLJbi4mJER0dj79696NatGz766COdxUL1k7GxMZKSkvRyhmh9GJjQJCb2VCU5OTlYuXIlgoKCYG1tjeXLlzf40TGqG4qLi7Fv3z7IZDIm9g1UcXExDAwMVLa/PMqvilwur62Q6rza3Ie+sLAQ+/bt0/rTTqLalJ6ejtatW2u1v+TkZABAp06dtNo3NUy9e/fG999/jyFDhug6lGrR54EJTWNiT1XSpk0bZGdnY+bMmfD09FQ5JbYqlZaJiGrixo0bkEqlCAkJQWpqqq7D0Qvch54aInWL037wwQcajuSf6fczZszA9u3blbOEDAwMMGHCBAQHB5dbB01UWw4dOoQFCxbgv//9L3r16lVudwYzMzMdRaYefR+Y0CQm9lQl/97eSCQS4d8fn9LXIpGowU5lJc2SSCSvPEckEkEqlWohGtKl3NxcZZ2F06dPo3fv3hg7dizmzZtXresJgoBDhw5BKpUiIiKilqOtG2q6D31lsyH+jd//VFdVtkVj6YMKkUiEoqIijccyYcIEXLhwAT/99BPeeecdAP9U9Z49ezbefvttbN++XeMxUMP08r18KX25h9f3gQlNYlV8qpK7d+/qOgRqwBQKBaysrODg4ACOSTZMZ86cwaZNm7Bjxw5YWloiKSkJUVFReP/996t1vbt370Imk0GhUODx48cYOnRoLUdcd9R0H3pBEGBlZQVfX184ODhoKEoizVG160Vubi7WrFmDtWvXomPHjlqJZf/+/Th8+DAGDBigPDZ8+HD8+uuvGDFihFZioIYpKipK1yHUiIuLCwDAzc1NLwcmNImJPVWJlZXVK8+p7vpMolfx9/dHaGgo7t69Cz8/P3h7e+P111/XdVikBatWrSrzpDk2NhY9evSAkZERWrRoUaVrFRQUICIiAlKpFHFxcSguLsbKlSsxderUej3SX9N96M+dOwepVIo1a9agQ4cOkEgk8PLyYgEj0hsvT28vKSmBTCbD0qVLIRaLERwcXGkBztrUokWLCqfbm5ub82+KNGrgwIEq2/ThHl7fByY0SiCqBVlZWcKGDRuEPn36CGKxWNfhUD2Wn58vbNu2TRg6dKjQpEkTYdy4ccKhQ4eEkpISXYdGGmRgYCB88cUXQlFRUZnjhoaGQmJiolrXOH/+vODv7y80a9ZM6N27t7BmzRrh0aNHVbqGvouLixP8/PwEU1NToWfPnkJQUJBgaGgoXLt2Te1r5OXlCVu2bBEGDx4sNGnSRJgwYYJw5MgRDUZNVPt27twpdOrUSXj99deFH374QcjPz9dq/xs2bBCGDh0qpKamKo+lpqYKw4YNE9avX6/VWKhhq0/38FeuXNF1CDrFNfZUI7GxsZBKpdi5cycsLCzg7u6OsWPHcus70op79+5BoVAgJCQERUVFSExMhKmpqa7DIg1Yvnw55HI58vPz4enpCR8fH3Tt2hVGRkZqrQ8HAENDQ8ycORPTp09Hp06dlMerco36orb2ob979y6mTp2KmJgYPH78mDNoqM6LiYnB/PnzceXKFcyePRvz58/XWqE6BweHMlOHb968iYKCAlhaWgIA7t+/j0aNGsHGxgYJCQlaiYkarvpyD5+dnY3Q0FBs2rQJ8fHxnIpPVBWPHj2CQqGAVCpFVlYWxo8fj4KCAuzevbtB3RiT7onFYmXRxob8Rd4QLFiwAAsWLEBMTAxkMhn69esHa2trCIKgct3sy4YMGQKpVIr09HT4+Phg+PDhKnf2qO9MTU0xbdo0TJs2DUlJSdi0aRMWLlyIGTNmqLUP/d9//w2FQgGFQoHc3FzMmzevXi9joPrBxcUFx44dg0Qiwe7du9GmTRut9j9mzBit9kf0svp0D1/RwERwcLCuw9IpPrGnKhk1ahRiY2Ph6uoKLy8vjBgxAgYGBg3yiRfpRkFBASIjIyGTyRAXF4eRI0fCz88PI0aMqHIxMNJfWVlZCA0NhVQqRXx8PPr16wcPDw/MnTu30vf99ddfkMvlkMvlyMvLw4QJE/Dzzz/j8uXLsLOz01L0dVNRURH27t2rch/6Fy9eYNeuXZBKpTh58iScnZ0hkUjg7OysdsV8Il0Si8UwNDSEiYlJpYN6T58+1WJURNpRH+7hKxqYWL9+vd7Er2lM7KlKDA0NMWvWLPj7+8PGxkZ5XJ++FEh/le752759e2XhrpYtW+o6LNKxq1evQiqVYuvWrUhPT1f7fUePHoVcLseuXbvQvn17eHh4wMPDAz179tRgtLp34sQJREZGIiUlBSKRCB06dICHh8cr9+5u0aIFmjZtCl9fX/j4+KB169YVnscn91RXbd68Wa3ztFVAj0ib9P0evj4MTGgaE3uqkjNnzkAqlSIsLAx2dnbw8fHBxIkT0bZtW/5RkcaJxWJYWlqWW6f4ssjISC1GRdqg6inyv4lEIrRr1w5OTk4YNWqU2tfOyMjAb7/9BplMhsuXL9frZR3Tp0/Hxo0b0bx5c9ja2kIQBNy8eROZmZmYMWMGfvrpJ5XvVbX3cSmBWw0Rqa24uBg//vgjwsPDcf/+fbx48aJMO2cNUG3T93t4fR+Y0AYm9lQtOTk5CAsLg0wmw7lz51BcXIygoCBIJBI0bdpU1+FRPTVlyhS11kTL5XItREPa5Ofn98pzSkpKkJ6ejpiYGAQEBGDZsmVV7ichIaHePrHftWsXJk6ciA0bNsDX11f5t1RSUgKFQgF/f3/s2LEDbm5uFb4/JiZGrX4q20qJSJeysrIqPG5iYqL15SSLFy/Gpk2b8J///AcLFy7El19+iZSUFOzevRuLFy/GrFmztBoPNRz6eg+v7wMT2sDEnmosOTkZUqkUW7ZsQWZmJpycnLB3715dh0VEDdT+/fsxY8YM3L9/v1xbaRJb0VR0Hx+fel1Mz83NDfb29li+fHmF7fPnz8f169exZ88eLUdGpB2lBVdfZmBggA4dOiAgIADTpk3TSixvvfUW1q5dC1dXVzRt2hQXL15UHjtz5gy2bdumlTioYdPHe3h9HZjQBib2VGuKi4uxb98+yGSyOv+lQET1V2ZmJiQSSbklGYIgYNSoUThw4AB69OiBzp07QxAEJCUl4cqVK3Bzc8Pu3bt1E7QWvPHGG4iMjETfvn0rbD979izGjh2Lv//+u8J2VU87X8Y19lRXqZp1kpmZifj4eKxduxY//vijWjOEasrExARJSUmwtLRE27Zt8fvvv6Nnz564c+cOHBwc8OzZM43HQFRKX+/h9XFgQpOY2BMRUYMgl8sxe/Zs7NmzB46OjmXaTpw4gTFjxmDdunWYPHmyjiLULGNjY9y5cwcWFhYVtj948ADW1tbIy8ursF3V085SXGNP+k4mk2HdunVa2UO+U6dOCAkJQb9+/TBgwACMHDkSgYGBCAsLw8yZM6tUCJSoodPXgYnaxsSeiIgahGHDhmHw4MEIDAyssP3bb79FTEwMDh8+rOXItEMsFiMtLQ2tWrWqsD0tLQ0WFhYqE3Ousaf67vbt23BwcFB7dkpNBAYGwszMDF988QXCwsLg7e2NN998E/fv38dnn32G7777TuMxEFH9wsSeiIgahDZt2uDQoUN4++23K2y/cOECnJ2d8ejRI+0GpiVisRgff/wxmjRpUmF7bm4ufv31Vz5xpwYrISEBo0ePxl9//aX1vk+fPo3Tp0/DxsamSrt6EBGVYmJPREQNwmuvvYZ79+6hbdu2FbY/fPgQHTp0QEFBgZYj045BgwapVRwwKiqqwuNcY0/1WWFhISZPnozCwkJEREToOhwioioz1HUARERE2lBcXAxDQ9X/7BkYGKCoqEiLEWlXdHR0jd7frFkzrrEnvebu7l7h8WfPniExMREikQgnT57UWP979+6Fs7MzjIyMXrkO2NTUFJ07d1ZZE4OI6GV8Yk9ERA2CWCyGs7MzGjVqVGF7QUEBDh06xMRUhX+vsRcEAS4uLti0aRPatWtX5jyusae6SlW1ezMzM3Tq1AleXl4wNzfXWP9isRiPHj1C69atIRaLX3m+gYEBVqxYgc8++0xjMRFR/cHEnoiIGgR1t7CSy+UajkQ3li1bptZ5ixcvVuu8pk2b4tKlS+jYsWNNwiKiCrx48QLbtm3DggULkJqaqutwiEgPMLEnIiJqABwcHFS2iUQiJCcnIz8/X+0ZC0zsqb7Jz8/HunXrEBAQoOtQAADZ2dn46quvEBQUpOtQiEgPMLEnIiJqwC5evIjAwECcOHECEokE69evV+t9TOxJHz1+/Bhnz57Fa6+9hiFDhsDAwACFhYX4+eefsXz5chQVFeHJkydaieXmzZuIiopCeno6SkpKyrSpO3OGiKgUi+cRERE1QHfv3sWiRYsQFhYGd3d3JCYmwsbGpkrXUKfKPlFdERcXh5EjRyIrKwsikQi9e/eGXC7HmDFjYGhoiCVLlsDX11crsfz666/w9/dHy5Yt0aZNmzJ/SyKRiIk9EVUZn9gTERE1IE+ePMHSpUuxceNGDBgwAN999x369Onzyve9XFF83759GDx4MExMTMocj4yMrNV4iWrLoEGDYGFhgS+++AKbN2/GqlWrYGNjg2+++QYeHh5ajcXKygozZszA/PnztdovEdVfTOyJiIgagJycHKxcuRJBQUGwtrbG8uXLMWzYMLXf39CLD5L+a9GiBU6ePIkuXbogLy8PpqamiIyMxOjRo7Uei5mZGS5evMilLERUa5jYExERNQBt2rRBdnY2Zs6cCU9PT5XT6Lt3767lyIi049/bzQH/1Im4ePEi3nrrLa3HMnXqVPTp0wfTp0/Xet9EVD8xsSciImoA/r1vtkgkwr//+S99LRKJ1K6KT6RvxGIxTpw4gddffx0A8O677yI8PBxvvPFGmfO0Mbi1fPlyBAUFwdXVFd26dYORkVGZ9lmzZmk8BiKqX5jYExERNQD37t1T6zwrKysNR0KkG2KxuNygViltD2516NBBZZtIJMKdO3c0HgMR1S+sik9ERNQAqJOwX716VQuREOnG3bt3dR2CUl2KhYjqByb2REREDVh2djZCQ0OxadMmxMfHcyo+1Vsc3CKi+oyJPRERUQMUGxsLqVSKnTt3wsLCAu7u7ggODtZ1WERap4vBLYlEUmm7TCbTeAxEVL8wsSciImogHj16BIVCAalUiqysLIwfPx4FBQXYvXs3unTpouvwiLRKl4NbGRkZZV4XFhbi6tWryMzMxODBg7USAxHVL0zsiYiIGoBRo0YhNjYWrq6uWL16NUaMGAEDAwOsX79e16ERaU1dGdzatWtXuWMlJSXw9/fXyfZ7RKT/WBWfiIioATA0NMSsWbPg7+8PGxsb5XEjIyNcunSJT+yp3vv34JaXl5dycKsu/Q0kJydj0KBBSE1N1XUoRKRnxK8+hYiIiPRdXFwcsrOz0atXL/Tr1w/r1q3DkydPdB0WkdYcPHgQU6dOxdKlS+Hq6goDAwNdh1TO7du3UVRUpOswiEgPcSo+ERFRA9C/f3/0798fq1evRlhYGGQyGebOnYuSkhIcPXoU7du3R9OmTXUdJpHGxMXFQSqVolevXrCzs4OPjw8mTpyok1jmzp1b5rUgCEhNTcXvv/8OX19fncRERPqNU/GJiIgaqOTkZEilUmzZsgWZmZlwcnLC3r17dR0WkUbl5OQoB7fOnTuH4uJiBAUFQSKRaG1wy9HRESKRCKW34WKxGK1atcLgwYMhkUhgaMhnb0RUNUzsiYiIGrji4mLs27cPMpmMiT01KNoe3CopKcEPP/yAPXv2oLCwEIMHD8aSJUvQuHFjjfVJRA0DE3siIiIiatC0Nbj13//+F0uWLMHQoUPRuHFjHD58GJ6enty3nohqjIk9EREREZEW2NjYICAgAP/v//0/AMCxY8fg6uqKvLw8iMWsaU1E1cfEnoiIiIhICxo1aoRbt26hffv2ymPGxsa4desW3njjDR1GRkT6jkODRERERERaUFRUBGNj4zLHjIyMUFhYqKOIiKi+YMlNIiIiIiItEAQBU6ZMQaNGjZTH8vPzMX36dJiYmCiPRUZG6iI8ItJjTOyJiIiIiLSgoj3qvb29dRAJEdU3XGNPREREREREpMe4xp6IiIiIiIhIjzGxJyIiIiIiItJjTOyJiIiIiIiI9BgTeyIiIiIiIiI9xsSeiIiogZoyZQrGjBlT6TlvvvkmVq9erZV4iIiIqHqY2BMREZFKf/75Jz7++GONXT8lJQUikQgXL16s8ntFIhF2795d6zERERHpG+5jT0RERCq1atVK1yEQERHRK/CJPRERUT0XERGBbt26oXHjxmjRogWGDh2KnJwcZfvKlSvRtm1btGjRAp988gkKCwuVbS9PxReJRPjll1/g7OyMxo0bo2PHjoiIiKi0/4yMDHh5eaFVq1Zo3LgxbGxsIJfLAQAdOnQAADg4OEAkEmHQoEEA/pkp4OTkhJYtW8Lc3BwDBw5EQkJCmbgA4MMPP4RIJFK+BoA9e/agZ8+eMDY2RseOHbF06VIUFRVV51dHRESkF5jYExER1WOpqanw9PSERCJBUlISoqOj4e7uDkEQAABRUVG4ffs2oqKisHnzZigUCigUikqvuWjRIowdOxaXLl2Cl5cXJk6ciKSkpErPv3btGg4ePIikpCT88ssvaNmyJQDg3LlzAIBjx44hNTUVkZGRAIDs7Gz4+voiLi4OZ86cgY2NDVxcXJCdnQ3gn8QfAORyOVJTU5WvT548icmTJ2P27Nm4du0aNmzYAIVCgW+++ab6v0QiIqI6TiSU/stORERE9U5CQgJ69eqFlJQUWFlZlWmbMmUKoqOjcfv2bRgYGAAAxo8fD7FYjO3btwP458n4nDlzMGfOHAD/PLGfPn06fvnlF+V1+vfvj549e+Lnn3+uMAY3Nze0bNkSMpmsXFtKSgo6dOiACxcu4O2331b5c5SUlKBZs2bYtm0bRo4cqYxl165dZQoADh06FEOGDMGCBQuUx3777Td8/vnnePjwoepfFBERkR7jE3siIqJ6rEePHhgyZAi6deuGcePG4ddff0VGRoay3d7eXpnUA0Dbtm2Rnp5e6TXfeeedcq9Ln9g7OzvD1NQUpqamsLe3BwD4+/tj+/btePvtt/H555/j1KlTr4w7LS0N06ZNg42NDczNzWFmZobnz5/j/v37lb7v0qVLWLZsmTIGU1NTTJs2DampqcjNzX1lv0RERPqIxfOIiIjqMQMDAxw9ehSnTp3CkSNH8NNPP+HLL7/E2bNnAQBGRkZlzheJRCgpKal2f5s2bUJeXl6Zazs7O+PevXs4cOAAjh49iiFDhuCTTz7BypUrVV7H19cX//vf/7BmzRpYWVmhUaNGeOedd/DixYtK+3/+/DmWLl0Kd3f3cm3GxsbV/rmIiIjqMib2RERE9ZxIJMJ7772H9957D4sXL4aVlRV27dpV7eudOXMGkydPLvPawcEBANCuXbsK39OqVSv4+vrC19cX77//PubNm4eVK1fitddeAwAUFxeXOf+PP/7Azz//DBcXFwDAX3/9hSdPnpQ5x8jIqNz7evbsieTkZFhbW1f75yMiItI3TOyJiIjqsbNnz+L48eMYNmwYWrdujbNnz+Lx48ews7PD5cuXq3XNHTt2oHfv3hgwYAC2bt2Kc+fOQSqVqjx/8eLF6NWrF+zt7VFQUID9+/fDzs4OANC6dWs0btwYhw4dwhtvvAFjY2OYm5vDxsYGW7ZsQe/evZGVlYV58+ahcePGZa775ptv4vjx43jvvffQqFEjNG/eHIsXL8bIkSNhaWkJDw8PiMViXLp0CVevXsXXX39drZ+XiIioruMaeyIionrMzMwMsbGxcHFxga2tLRYuXIhVq1bB2dm52tdcunQptm/fju7duyMkJAShoaHo0qWLyvNfe+01LFiwAN27d8cHH3wAAwMDZXE+Q0NDrF27Fhs2bICFhQVGjx4NAJBKpcjIyEDPnj3h4+ODWbNmoXXr1mWuu2rVKhw9ehTt27dXzhgYPnw49u/fjyNHjqBPnz7o378/fvzxx3KFA4mIiOoTVsUnIiIitVVUiZ6IiIh0i0/siYiIiIiIiPQYE3siIiIiIiIiPcbieURERKQ2ruAjIiKqe/jEnoiIiIiIiEiPMbEnIiIiIiIi0mNM7ImIiIiIiIj0GBN7IiIiIiIiIj3GxJ6IiIiIiIhIjzGxJyIiIiIiItJjTOyJiIiIiIiI9BgTeyIiIiIiIiI99v8BfpwAAnnB+XoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# top_10_States\n",
        "top_10_state = df['ship-state'].value_counts().head(10)\n",
        "# Plot count of cities by state\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.countplot(data=df[df['ship-state'].isin(top_10_state.index)], x='ship-state')\n",
        "plt.xlabel('ship-state')\n",
        "plt.ylabel('count')\n",
        "plt.title('Distribution of  State')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "qtTZRZbNZd77",
        "outputId": "f60c23a6-12c8-4d71-f2a9-60bfbda9646b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note: From above Graph you can see that most of the buyers are Maharashtra state**"
      ],
      "metadata": {
        "id": "ewfYn1BFZsKO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "**The data analysis reveals that the business has a significant customer base in Maharashtra state, mainly serves retailers, fulfills orders through Amazon, experiences high demand for T-shirts, and sees M-Size as the preferred choice among buyers**."
      ],
      "metadata": {
        "id": "zbhdQU6-ZvXe"
      }
    }
  ]
}