Task_2A.ipynb

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        "<a href=\"https://colab.research.google.com/github/praveen555/IMI-BIGDATA--CHALLENGE/blob/main/Task_2A.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "BidZojbcvF9w"
      },
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('clean_uoft_nodes_v2.csv',parse_dates=['BIRTH_DT','CUST_ADD_DT'])"
      ],
      "metadata": {
        "id": "D5xjX_Knva2e"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "c=['CASH_SUM_IN','CASH_SUM_OUT','WIRES_SUM_IN','WIRES_SUM_OUT','WIRES_CNT_IN','WIRES_CNT_OUT','CASH_CNT_IN','CASH_CNT_OUT']"
      ],
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        "id": "NItFLjBY0FdX"
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "df.describe()"
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              "         RES_CNTRY_CA  CNTRY_OF_INCOME_CA          PEP_FL     CASH_SUM_IN  \\\n",
              "count  1000000.000000       1000000.00000  1000000.000000  1000000.000000   \n",
              "mean         0.985686             0.98855        0.097489    15062.980362   \n",
              "std          0.118782             0.10639        0.296623    20323.375203   \n",
              "min          0.000000             0.00000        0.000000        0.000000   \n",
              "25%          1.000000             1.00000        0.000000     2575.721000   \n",
              "50%          1.000000             1.00000        0.000000     5983.672500   \n",
              "75%          1.000000             1.00000        0.000000    19206.000000   \n",
              "max          1.000000             1.00000        1.000000   188953.000000   \n",
              "\n",
              "          CASH_CNT_IN    CASH_SUM_OUT    CASH_CNT_OUT  WIRES_SUM_IN  \\\n",
              "count  1000000.000000  1000000.000000  1000000.000000  1.000000e+06   \n",
              "mean         8.596567    23353.115677       23.146121  3.411956e+06   \n",
              "std          8.426340    33337.695394       20.867079  5.754767e+06   \n",
              "min          0.000000        0.000000        0.000000  0.000000e+00   \n",
              "25%          3.000000     3419.478250        8.000000  1.209508e+05   \n",
              "50%          6.000000     8001.721500       17.000000  2.981515e+05   \n",
              "75%         11.000000    29862.000000       32.000000  4.688108e+06   \n",
              "max         79.000000   310117.000000      203.000000  4.781359e+07   \n",
              "\n",
              "         WIRES_CNT_IN  WIRES_SUM_OUT   WIRES_CNT_OUT   CUSTOMER_ID  \n",
              "count  1000000.000000   1.000000e+06  1000000.000000  1.000000e+06  \n",
              "mean        85.394548   1.382649e+06      152.431128  5.003547e+05  \n",
              "std         66.773057   2.165856e+06      120.158287  2.888723e+05  \n",
              "min          0.000000   0.000000e+00        0.000000  0.000000e+00  \n",
              "25%         33.000000   1.053308e+05       59.000000  2.501808e+05  \n",
              "50%         71.000000   2.550340e+05      126.000000  5.003765e+05  \n",
              "75%        122.000000   1.926422e+06      218.000000  7.505282e+05  \n",
              "max        605.000000   1.893074e+07     1083.000000  1.000677e+06  "
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              "      <td>8.596567</td>\n",
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              "      <td>23.146121</td>\n",
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              "      <td>85.394548</td>\n",
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              "      <td>152.431128</td>\n",
              "      <td>5.003547e+05</td>\n",
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              "      <th>std</th>\n",
              "      <td>0.118782</td>\n",
              "      <td>0.10639</td>\n",
              "      <td>0.296623</td>\n",
              "      <td>20323.375203</td>\n",
              "      <td>8.426340</td>\n",
              "      <td>33337.695394</td>\n",
              "      <td>20.867079</td>\n",
              "      <td>5.754767e+06</td>\n",
              "      <td>66.773057</td>\n",
              "      <td>2.165856e+06</td>\n",
              "      <td>120.158287</td>\n",
              "      <td>2.888723e+05</td>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2575.721000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3419.478250</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.209508e+05</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>1.053308e+05</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>2.501808e+05</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>6.000000</td>\n",
              "      <td>8001.721500</td>\n",
              "      <td>17.000000</td>\n",
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              "      <td>71.000000</td>\n",
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              "      <td>126.000000</td>\n",
              "      <td>5.003765e+05</td>\n",
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              "      <td>32.000000</td>\n",
              "      <td>4.688108e+06</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>1.926422e+06</td>\n",
              "      <td>218.000000</td>\n",
              "      <td>7.505282e+05</td>\n",
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              "      <th>max</th>\n",
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              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>79.000000</td>\n",
              "      <td>310117.000000</td>\n",
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              "      <td>4.781359e+07</td>\n",
              "      <td>605.000000</td>\n",
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              "      <td>1083.000000</td>\n",
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    {
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      "source": [
        "low=df[df['RISK']=='low']\n",
        "med=df[df['RISK']=='medium']\n",
        "high=df[df['RISK']=='high']"
      ],
      "metadata": {
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      "execution_count": 5,
      "outputs": []
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      "execution_count": 6,
      "outputs": [
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          "data": {
            "text/plain": [
              "         BIRTH_DT CUST_ADD_DT OCPTN_NM  RES_CNTRY_CA  CNTRY_OF_INCOME_CA  \\\n",
              "123292 1977-04-21  1995-02-11     High             1                   1   \n",
              "123293 1995-04-07  2003-12-15     High             1                   1   \n",
              "123294 1956-08-01  1999-12-17     High             1                   1   \n",
              "123295 1959-02-14  2006-08-08     High             1                   1   \n",
              "123296 1981-05-29  2016-05-02     High             1                   1   \n",
              "...           ...         ...      ...           ...                 ...   \n",
              "999994 1970-08-21  2014-09-09      Low             0                   1   \n",
              "999995 1939-01-16  1994-03-19      Low             0                   1   \n",
              "999997 1958-09-09  2010-11-03     High             0                   1   \n",
              "999998 1983-09-15  2013-01-30     High             0                   1   \n",
              "999999 1962-01-23  2017-03-07     High             0                   0   \n",
              "\n",
              "        PEP_FL  CASH_SUM_IN  CASH_CNT_IN  CASH_SUM_OUT  CASH_CNT_OUT  \\\n",
              "123292       0     9622.865            1        4898.0             5   \n",
              "123293       0    19651.804           11       28033.0            14   \n",
              "123294       0    25831.652           11        8488.0             2   \n",
              "123295       0    36870.662           16       17456.0            15   \n",
              "123296       0    67572.085           25       34554.0            22   \n",
              "...        ...          ...          ...           ...           ...   \n",
              "999994       0    21161.905            8       30217.0            24   \n",
              "999995       0    18945.108           10       32210.0            21   \n",
              "999997       0     2351.255            2       15997.0            19   \n",
              "999998       0    27312.613            8        3399.0             5   \n",
              "999999       0     1421.363            1        1276.0             1   \n",
              "\n",
              "        WIRES_SUM_IN  WIRES_CNT_IN  WIRES_SUM_OUT  WIRES_CNT_OUT  \\\n",
              "123292       2576890            41        2370275            208   \n",
              "123293       4661257            96        2761577            224   \n",
              "123294       6683716           172        3287715            277   \n",
              "123295       4693840           125        3032695            269   \n",
              "123296       3618783            38        2062459             62   \n",
              "...              ...           ...            ...            ...   \n",
              "999994       6155329            66        3442076            474   \n",
              "999995       1186467            25        2550098            174   \n",
              "999997       3024271            68         948981            124   \n",
              "999998        943477             9         376429             12   \n",
              "999999        683797             5         617628             12   \n",
              "\n",
              "       COUNTRY_RISK_INCOME COUNTRY_RISK_RESIDENCY  RISK  \\\n",
              "123292                 Low                    Low  high   \n",
              "123293                 Low                    Low  high   \n",
              "123294                 Low                    Low  high   \n",
              "123295                 Low                    Low  high   \n",
              "123296                 Low                    Low  high   \n",
              "...                    ...                    ...   ...   \n",
              "999994                 Low                   High  high   \n",
              "999995                 Low                   High  high   \n",
              "999997                 Low                   High  high   \n",
              "999998                 Low                   High  high   \n",
              "999999                High                   High  high   \n",
              "\n",
              "                                  NAME  GENDER  CUSTOMER_ID  \n",
              "123292       sam Peterson Wong Perkins    Male       132001  \n",
              "123293         Newton, Timothy Paulsen    Male       280713  \n",
              "123294                    Eunice Silva  Female       214538  \n",
              "123295           Gloria Cooley Fuentes  Female       345313  \n",
              "123296             John Wixon Dietrich    Male        52253  \n",
              "...                                ...     ...          ...  \n",
              "999994            bev Snell Hood [tern  Female       377454  \n",
              "999995               Bret* Dove Gainey    Male       420884  \n",
              "999997  Bernadette Schofield Pace Mile  Female       731360  \n",
              "999998         Wasson, Russell Terrell    Male       837988  \n",
              "999999                Daniel Mckeighan    Male       166357  \n",
              "\n",
              "[50000 rows x 20 columns]"
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              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>WIRES_SUM_OUT</th>\n",
              "      <th>WIRES_CNT_OUT</th>\n",
              "      <th>COUNTRY_RISK_INCOME</th>\n",
              "      <th>COUNTRY_RISK_RESIDENCY</th>\n",
              "      <th>RISK</th>\n",
              "      <th>NAME</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>CUSTOMER_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>123292</th>\n",
              "      <td>1977-04-21</td>\n",
              "      <td>1995-02-11</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9622.865</td>\n",
              "      <td>1</td>\n",
              "      <td>4898.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2576890</td>\n",
              "      <td>41</td>\n",
              "      <td>2370275</td>\n",
              "      <td>208</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>high</td>\n",
              "      <td>sam Peterson Wong Perkins</td>\n",
              "      <td>Male</td>\n",
              "      <td>132001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123293</th>\n",
              "      <td>1995-04-07</td>\n",
              "      <td>2003-12-15</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>19651.804</td>\n",
              "      <td>11</td>\n",
              "      <td>28033.0</td>\n",
              "      <td>14</td>\n",
              "      <td>4661257</td>\n",
              "      <td>96</td>\n",
              "      <td>2761577</td>\n",
              "      <td>224</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>high</td>\n",
              "      <td>Newton, Timothy Paulsen</td>\n",
              "      <td>Male</td>\n",
              "      <td>280713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123294</th>\n",
              "      <td>1956-08-01</td>\n",
              "      <td>1999-12-17</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>25831.652</td>\n",
              "      <td>11</td>\n",
              "      <td>8488.0</td>\n",
              "      <td>2</td>\n",
              "      <td>6683716</td>\n",
              "      <td>172</td>\n",
              "      <td>3287715</td>\n",
              "      <td>277</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>high</td>\n",
              "      <td>Eunice Silva</td>\n",
              "      <td>Female</td>\n",
              "      <td>214538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123295</th>\n",
              "      <td>1959-02-14</td>\n",
              "      <td>2006-08-08</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>36870.662</td>\n",
              "      <td>16</td>\n",
              "      <td>17456.0</td>\n",
              "      <td>15</td>\n",
              "      <td>4693840</td>\n",
              "      <td>125</td>\n",
              "      <td>3032695</td>\n",
              "      <td>269</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>high</td>\n",
              "      <td>Gloria Cooley Fuentes</td>\n",
              "      <td>Female</td>\n",
              "      <td>345313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123296</th>\n",
              "      <td>1981-05-29</td>\n",
              "      <td>2016-05-02</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>67572.085</td>\n",
              "      <td>25</td>\n",
              "      <td>34554.0</td>\n",
              "      <td>22</td>\n",
              "      <td>3618783</td>\n",
              "      <td>38</td>\n",
              "      <td>2062459</td>\n",
              "      <td>62</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>high</td>\n",
              "      <td>John Wixon Dietrich</td>\n",
              "      <td>Male</td>\n",
              "      <td>52253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999994</th>\n",
              "      <td>1970-08-21</td>\n",
              "      <td>2014-09-09</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21161.905</td>\n",
              "      <td>8</td>\n",
              "      <td>30217.0</td>\n",
              "      <td>24</td>\n",
              "      <td>6155329</td>\n",
              "      <td>66</td>\n",
              "      <td>3442076</td>\n",
              "      <td>474</td>\n",
              "      <td>Low</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>bev Snell Hood [tern</td>\n",
              "      <td>Female</td>\n",
              "      <td>377454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999995</th>\n",
              "      <td>1939-01-16</td>\n",
              "      <td>1994-03-19</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18945.108</td>\n",
              "      <td>10</td>\n",
              "      <td>32210.0</td>\n",
              "      <td>21</td>\n",
              "      <td>1186467</td>\n",
              "      <td>25</td>\n",
              "      <td>2550098</td>\n",
              "      <td>174</td>\n",
              "      <td>Low</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>Bret* Dove Gainey</td>\n",
              "      <td>Male</td>\n",
              "      <td>420884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999997</th>\n",
              "      <td>1958-09-09</td>\n",
              "      <td>2010-11-03</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2351.255</td>\n",
              "      <td>2</td>\n",
              "      <td>15997.0</td>\n",
              "      <td>19</td>\n",
              "      <td>3024271</td>\n",
              "      <td>68</td>\n",
              "      <td>948981</td>\n",
              "      <td>124</td>\n",
              "      <td>Low</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>Bernadette Schofield Pace Mile</td>\n",
              "      <td>Female</td>\n",
              "      <td>731360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999998</th>\n",
              "      <td>1983-09-15</td>\n",
              "      <td>2013-01-30</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>27312.613</td>\n",
              "      <td>8</td>\n",
              "      <td>3399.0</td>\n",
              "      <td>5</td>\n",
              "      <td>943477</td>\n",
              "      <td>9</td>\n",
              "      <td>376429</td>\n",
              "      <td>12</td>\n",
              "      <td>Low</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>Wasson, Russell Terrell</td>\n",
              "      <td>Male</td>\n",
              "      <td>837988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>1962-01-23</td>\n",
              "      <td>2017-03-07</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1421.363</td>\n",
              "      <td>1</td>\n",
              "      <td>1276.0</td>\n",
              "      <td>1</td>\n",
              "      <td>683797</td>\n",
              "      <td>5</td>\n",
              "      <td>617628</td>\n",
              "      <td>12</td>\n",
              "      <td>High</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>Daniel Mckeighan</td>\n",
              "      <td>Male</td>\n",
              "      <td>166357</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92d43824-19af-4167-8307-02be0a1e0080')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "low.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "mZOCzCWTv6kI",
        "outputId": "b2c64699-10e4-4880-e1bc-fa113bcae83a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        RES_CNTRY_CA  CNTRY_OF_INCOME_CA    PEP_FL    CASH_SUM_IN  \\\n",
              "count  600000.000000       600000.000000  600000.0  600000.000000   \n",
              "mean        0.999460            0.991073       0.0    4130.694551   \n",
              "std         0.023232            0.094058       0.0    3123.741412   \n",
              "min         0.000000            0.000000       0.0       0.004000   \n",
              "25%         1.000000            1.000000       0.0    1654.872750   \n",
              "50%         1.000000            1.000000       0.0    3482.839000   \n",
              "75%         1.000000            1.000000       0.0    5955.928750   \n",
              "max         1.000000            1.000000       0.0   27188.673000   \n",
              "\n",
              "         CASH_CNT_IN   CASH_SUM_OUT   CASH_CNT_OUT  WIRES_SUM_IN  \\\n",
              "count  600000.000000  600000.000000  600000.000000  6.000000e+05   \n",
              "mean        5.131852    5400.354812      15.901390  1.790636e+05   \n",
              "std         3.899262    4078.175445      12.015728  1.351831e+05   \n",
              "min         0.000000       0.003000       0.000000  0.000000e+00   \n",
              "25%         2.000000    2158.233750       6.000000  7.174000e+04   \n",
              "50%         4.000000    4567.097000      13.000000  1.514415e+05   \n",
              "75%         7.000000    7787.699000      23.000000  2.579262e+05   \n",
              "max        34.000000   34723.973000      99.000000  1.079777e+06   \n",
              "\n",
              "        WIRES_CNT_IN  WIRES_SUM_OUT  WIRES_CNT_OUT   CUSTOMER_ID  \n",
              "count  600000.000000  600000.000000  600000.000000  6.000000e+05  \n",
              "mean       74.451992  158901.918253     130.713560  5.003396e+05  \n",
              "std        56.277851  119981.035511      98.742543  2.888613e+05  \n",
              "min         0.000000       0.000000       0.000000  2.000000e+00  \n",
              "25%        30.000000   63503.000000      52.000000  2.503628e+05  \n",
              "50%        63.000000  134388.500000     111.000000  5.001990e+05  \n",
              "75%       107.000000  229095.000000     189.000000  7.505672e+05  \n",
              "max       483.000000  989634.000000     848.000000  1.000676e+06  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RES_CNTRY_CA</th>\n",
              "      <th>CNTRY_OF_INCOME_CA</th>\n",
              "      <th>PEP_FL</th>\n",
              "      <th>CASH_SUM_IN</th>\n",
              "      <th>CASH_CNT_IN</th>\n",
              "      <th>CASH_SUM_OUT</th>\n",
              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>WIRES_SUM_OUT</th>\n",
              "      <th>WIRES_CNT_OUT</th>\n",
              "      <th>CUSTOMER_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.0</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
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              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>6.000000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.999460</td>\n",
              "      <td>0.991073</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4130.694551</td>\n",
              "      <td>5.131852</td>\n",
              "      <td>5400.354812</td>\n",
              "      <td>15.901390</td>\n",
              "      <td>1.790636e+05</td>\n",
              "      <td>74.451992</td>\n",
              "      <td>158901.918253</td>\n",
              "      <td>130.713560</td>\n",
              "      <td>5.003396e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.023232</td>\n",
              "      <td>0.094058</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3123.741412</td>\n",
              "      <td>3.899262</td>\n",
              "      <td>4078.175445</td>\n",
              "      <td>12.015728</td>\n",
              "      <td>1.351831e+05</td>\n",
              "      <td>56.277851</td>\n",
              "      <td>119981.035511</td>\n",
              "      <td>98.742543</td>\n",
              "      <td>2.888613e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.003000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1654.872750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2158.233750</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.174000e+04</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>63503.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.503628e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3482.839000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4567.097000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.514415e+05</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>134388.500000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>5.001990e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5955.928750</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7787.699000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>2.579262e+05</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>229095.000000</td>\n",
              "      <td>189.000000</td>\n",
              "      <td>7.505672e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27188.673000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>34723.973000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>1.079777e+06</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>989634.000000</td>\n",
              "      <td>848.000000</td>\n",
              "      <td>1.000676e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b1946b8-b6e0-490b-bd30-032717e0ce58')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\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",
              "    [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-4b1946b8-b6e0-490b-bd30-032717e0ce58 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-4b1946b8-b6e0-490b-bd30-032717e0ce58');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "med.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "lNr1Pj3SwgTv",
        "outputId": "ea4e1122-5506-4624-8469-81578da406c1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        RES_CNTRY_CA  CNTRY_OF_INCOME_CA         PEP_FL    CASH_SUM_IN  \\\n",
              "count  350000.000000       350000.000000  350000.000000  350000.000000   \n",
              "mean        0.964217            0.985689       0.242069   31230.417460   \n",
              "std         0.185749            0.118771       0.428336   23674.484741   \n",
              "min         0.000000            0.000000       0.000000       0.000000   \n",
              "25%         1.000000            1.000000       0.000000   12385.000000   \n",
              "50%         1.000000            1.000000       0.000000   26379.000000   \n",
              "75%         1.000000            1.000000       0.000000   45053.000000   \n",
              "max         1.000000            1.000000       1.000000  188953.000000   \n",
              "\n",
              "         CASH_CNT_IN   CASH_SUM_OUT   CASH_CNT_OUT  WIRES_SUM_IN  \\\n",
              "count  350000.000000  350000.000000  350000.000000  3.500000e+05   \n",
              "mean       13.698514   53237.552249      35.054031  9.018359e+06   \n",
              "std        10.394396   40308.403808      26.547623  6.673229e+06   \n",
              "min         0.000000       0.000000       0.000000  1.800000e+01   \n",
              "25%         5.000000   21230.750000      14.000000  3.703960e+06   \n",
              "50%        12.000000   44998.500000      30.000000  7.712690e+06   \n",
              "75%        20.000000   76672.500000      51.000000  1.299600e+07   \n",
              "max        78.000000  310117.000000     203.000000  4.781359e+07   \n",
              "\n",
              "        WIRES_CNT_IN  WIRES_SUM_OUT  WIRES_CNT_OUT   CUSTOMER_ID  \n",
              "count  350000.000000   3.500000e+05  350000.000000  3.500000e+05  \n",
              "mean      102.944446   3.467534e+06     183.607466  5.005666e+05  \n",
              "std        77.912488   2.509784e+06     139.254253  2.889613e+05  \n",
              "min         0.000000   1.400000e+01       0.000000  0.000000e+00  \n",
              "25%        41.000000   1.470863e+06      73.000000  2.500098e+05  \n",
              "50%        87.000000   3.005816e+06     155.000000  5.010545e+05  \n",
              "75%       148.000000   4.980715e+06     265.000000  7.504710e+05  \n",
              "max       605.000000   1.893074e+07    1025.000000  1.000677e+06  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a65a702-c3f3-4625-8aab-22ce63c567b0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RES_CNTRY_CA</th>\n",
              "      <th>CNTRY_OF_INCOME_CA</th>\n",
              "      <th>PEP_FL</th>\n",
              "      <th>CASH_SUM_IN</th>\n",
              "      <th>CASH_CNT_IN</th>\n",
              "      <th>CASH_SUM_OUT</th>\n",
              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>WIRES_SUM_OUT</th>\n",
              "      <th>WIRES_CNT_OUT</th>\n",
              "      <th>CUSTOMER_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>350000.000000</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>350000.000000</td>\n",
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              "      <td>3.500000e+05</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>3.500000e+05</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>3.500000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.964217</td>\n",
              "      <td>0.985689</td>\n",
              "      <td>0.242069</td>\n",
              "      <td>31230.417460</td>\n",
              "      <td>13.698514</td>\n",
              "      <td>53237.552249</td>\n",
              "      <td>35.054031</td>\n",
              "      <td>9.018359e+06</td>\n",
              "      <td>102.944446</td>\n",
              "      <td>3.467534e+06</td>\n",
              "      <td>183.607466</td>\n",
              "      <td>5.005666e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.185749</td>\n",
              "      <td>0.118771</td>\n",
              "      <td>0.428336</td>\n",
              "      <td>23674.484741</td>\n",
              "      <td>10.394396</td>\n",
              "      <td>40308.403808</td>\n",
              "      <td>26.547623</td>\n",
              "      <td>6.673229e+06</td>\n",
              "      <td>77.912488</td>\n",
              "      <td>2.509784e+06</td>\n",
              "      <td>139.254253</td>\n",
              "      <td>2.889613e+05</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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.800000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12385.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>21230.750000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.703960e+06</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.470863e+06</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>2.500098e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>26379.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>44998.500000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>7.712690e+06</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>3.005816e+06</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>5.010545e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45053.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>76672.500000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>1.299600e+07</td>\n",
              "      <td>148.000000</td>\n",
              "      <td>4.980715e+06</td>\n",
              "      <td>265.000000</td>\n",
              "      <td>7.504710e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>188953.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>310117.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>4.781359e+07</td>\n",
              "      <td>605.000000</td>\n",
              "      <td>1.893074e+07</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1.000677e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a65a702-c3f3-4625-8aab-22ce63c567b0')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-8a65a702-c3f3-4625-8aab-22ce63c567b0 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-8a65a702-c3f3-4625-8aab-22ce63c567b0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "high.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "UPscYHeWwMRd",
        "outputId": "22454fa6-4eeb-4c8a-dd24-b4ebfbcb3c7d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RES_CNTRY_CA  CNTRY_OF_INCOME_CA        PEP_FL    CASH_SUM_IN  \\\n",
              "count  50000.000000        50000.000000  50000.000000   50000.000000   \n",
              "mean       0.970680            0.978300      0.255300   33078.350406   \n",
              "std        0.168704            0.145704      0.436034   25137.849259   \n",
              "min        0.000000            0.000000      0.000000       1.852000   \n",
              "25%        1.000000            1.000000      0.000000   13162.968500   \n",
              "50%        1.000000            1.000000      0.000000   27844.152500   \n",
              "75%        1.000000            1.000000      1.000000   47736.146500   \n",
              "max        1.000000            1.000000      1.000000  184848.335000   \n",
              "\n",
              "        CASH_CNT_IN   CASH_SUM_OUT  CASH_CNT_OUT  WIRES_SUM_IN  WIRES_CNT_IN  \\\n",
              "count  50000.000000   50000.000000  50000.000000  5.000000e+04  50000.000000   \n",
              "mean      14.459520   29595.190060     26.727520  2.961842e+06     93.855940   \n",
              "std       10.985356   22292.808521     20.186653  2.181722e+06     70.779302   \n",
              "min        0.000000       0.000000      0.000000  2.300000e+02      0.000000   \n",
              "25%        6.000000   11744.750000     11.000000  1.219590e+06     37.000000   \n",
              "50%       12.000000   25073.000000     23.000000  2.537678e+06     80.000000   \n",
              "75%       21.000000   42823.000000     39.000000  4.272575e+06    136.000000   \n",
              "max       79.000000  156872.000000    137.000000  1.511669e+07    522.000000   \n",
              "\n",
              "       WIRES_SUM_OUT  WIRES_CNT_OUT   CUSTOMER_ID  \n",
              "count   5.000000e+04   50000.000000  5.000000e+04  \n",
              "mean    1.473422e+06     194.807580  4.990520e+05  \n",
              "std     1.070835e+06     147.507137  2.883837e+05  \n",
              "min     6.100000e+01       0.000000  2.800000e+01  \n",
              "25%     6.176480e+05      78.000000  2.493625e+05  \n",
              "50%     1.275615e+06     165.000000  4.970770e+05  \n",
              "75%     2.123370e+06     281.000000  7.503632e+05  \n",
              "max     7.421421e+06    1083.000000  1.000672e+06  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8df07d6-8072-44cd-a9e5-8e1195a5bdb0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RES_CNTRY_CA</th>\n",
              "      <th>CNTRY_OF_INCOME_CA</th>\n",
              "      <th>PEP_FL</th>\n",
              "      <th>CASH_SUM_IN</th>\n",
              "      <th>CASH_CNT_IN</th>\n",
              "      <th>CASH_SUM_OUT</th>\n",
              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>WIRES_SUM_OUT</th>\n",
              "      <th>WIRES_CNT_OUT</th>\n",
              "      <th>CUSTOMER_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>5.000000e+04</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>5.000000e+04</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>5.000000e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.970680</td>\n",
              "      <td>0.978300</td>\n",
              "      <td>0.255300</td>\n",
              "      <td>33078.350406</td>\n",
              "      <td>14.459520</td>\n",
              "      <td>29595.190060</td>\n",
              "      <td>26.727520</td>\n",
              "      <td>2.961842e+06</td>\n",
              "      <td>93.855940</td>\n",
              "      <td>1.473422e+06</td>\n",
              "      <td>194.807580</td>\n",
              "      <td>4.990520e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.168704</td>\n",
              "      <td>0.145704</td>\n",
              "      <td>0.436034</td>\n",
              "      <td>25137.849259</td>\n",
              "      <td>10.985356</td>\n",
              "      <td>22292.808521</td>\n",
              "      <td>20.186653</td>\n",
              "      <td>2.181722e+06</td>\n",
              "      <td>70.779302</td>\n",
              "      <td>1.070835e+06</td>\n",
              "      <td>147.507137</td>\n",
              "      <td>2.883837e+05</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>1.852000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.300000e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.100000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.800000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13162.968500</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>11744.750000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.219590e+06</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>6.176480e+05</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>2.493625e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27844.152500</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>25073.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>2.537678e+06</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.275615e+06</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>4.970770e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>47736.146500</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>42823.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>4.272575e+06</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>2.123370e+06</td>\n",
              "      <td>281.000000</td>\n",
              "      <td>7.503632e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>184848.335000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>156872.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>1.511669e+07</td>\n",
              "      <td>522.000000</td>\n",
              "      <td>7.421421e+06</td>\n",
              "      <td>1083.000000</td>\n",
              "      <td>1.000672e+06</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8df07d6-8072-44cd-a9e5-8e1195a5bdb0 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-e8df07d6-8072-44cd-a9e5-8e1195a5bdb0');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(low.shape,med.shape,high.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-RX0BCQ81V_I",
        "outputId": "e7cef6b4-b211-4ccb-d3f2-59179e45a690"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(600000, 20) (350000, 20) (50000, 20)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visuzlize data using Kmeans"
      ],
      "metadata": {
        "id": "yMLqSNH-hT1L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "pca_num_components = 2"
      ],
      "metadata": {
        "id": "ZO5UGMgSinm8"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "map={'low':0,'medium':1,'high':2}\n",
        "low['RISK']=low['RISK'].map(map)\n",
        "low_x=low.drop(['BIRTH_DT','CUST_ADD_DT','NAME','CUSTOMER_ID'],axis=1)\n",
        "low_x_data=low_x.iloc[:,low_x.columns!='RISK']\n",
        "low_y=low_x['RISK']\n",
        "low_x_data=pd.get_dummies(low_x_data)\n",
        "print(low_x_data.shape,low_y.shape)\n",
        "kmeans_low= KMeans(n_clusters=3, init='k-means++',max_iter=50000,random_state=0).fit(low_x_data)\n",
        "reduced_data = PCA(n_components=pca_num_components).fit_transform(low_x_data)\n",
        "results_low= pd.DataFrame(reduced_data,columns=['pca1','pca2'],index=low.index)\n",
        "results_low['label']=kmeans_low.labels_\n",
        "sns.scatterplot(x=\"pca1\", y=\"pca2\", hue=kmeans_low.labels_, data=results_low)\n",
        "plt.title('K-means Clustering with 2 dimensions for Low Class')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "8rZmUB0UhTXD",
        "outputId": "cb9aa062-add4-4d66-f4b1-da1edc36fe20"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-15d2b89ff742>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  low['RISK']=low['RISK'].map(map)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(600000, 22) (600000,)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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JphasuoYn9BhSStzxEYLCzOwCSsMD6Kk0Vrb2CT3W/zLcyXFKQ/1EvodmWiTbOrHrGx/zeDfffDOrVq1ixQrVTf41r3kNl1xyCWvXrn1c57nTPSAhhC6EuF0I8ef483IhxE1CiA1CiF/FvewRQtjx5w3x+t55Y5wdL39ACPHiecuPipdtEEJ8aN7yJY9RRRUAejKF0HWs2vryBOmMDJLb9CBebnpnn96TBqHr2PWN1K5eS+2uu5Ns63zMBjcKAwKnROhVeo0yCPCmpxZtH5aKj+k4VSyGOzlOoW9r2WOPfI9C31bcyfHHPGZ/fz/d3d3lz11dXfT39z/uc93pBgg4A9VnfBZfAs6VUq5C9bA/JV5+CjAZLz833g4hxFpUz/bdUT3Nz4uNmg58B9Wnfi3w2njbhzpGFVVg2AmyK3bFyNTgzzc4MqI4sP0ZHy7STBPdsh8z6yooFcltXs/Mg/cys2Ed3vRkOc8jdA09mVq0zzPZs3yqURrqBxlVLpSRWv40w041QEKILuAY4IfxZwEcCfw23uQnwPHx38fFn4nXPz/e/jjgl1JKV0q5GdgAHBj/2yCl3CSl9IBfAsc9zDGq+B+HDEP8Qo7QKSI0gVlTV7E+ch2iBfmhKuYQBQGFvi2ExQKgPJ781o2ETgkAoemkWjsQ+hyrzshk0VOZnXK+z0TsKFf5eHKYnZ2dbN++vfy5r6+Pzs7OxzzeLHZ2DujrwFmoVsgAjcCUlHL2FbMPmL3KTmA7gJQyEEJMx9t3AjfOG3P+PtsXLD/oYY5Rxf8wpJS4k+MUB7aVl5m19ZiZGvy8ylkY6Sya+egjtlHgExSLhE4R3U6gJVJErkOQn0GzbIxMFiORJPQ8ZBigmdYTnvx/IhF6HjLwEYaJbs3djyjwlwynha6LEXs+RipNzao1hK6D0DT0RBLNeGZT3J9KaKa1pLF5LM/tLA444ADWr1/P5s2b6ezs5Je//CW/+MUvHs9pAjvRAAkhjgVGpJS3CiGO2Fnn8VAQQrwNeBtAT0/PTj6bKp5shK5DcbCvYpk/PUmytQM/P4OeTJFoacMZGURPJFVtkLX4Rx2FAUEhjzc5jjBNrPpG/KlJnNGh8jZmthaEwJ+ZAkBLpEi1tlPo34YMfDQ7QaZ7OUYqvWj8WUKEEI9vUnms8HMz5LdvLhugTM9yzEwNoDwcoRvIsDJMqRmVdUS6nUC3E0/ZOe8MSBkReR4I8ZSGGJNtnRT6tlaG4YRGsu2xv2cbhsG3v/1tXvziFxOGISeffDK777774z7XnfmKdRjwMiHE0UACqAG+AdQJIYzYQ+kCZgOX/UA30CeEMIBaYHze8lnM32ep5eMPcYwKSCnPB84H2H///avFCs9whK6zOHYOaJZNZvlqQschv3l9ebmZrSXd3QtCEDkOURigWza+41DctrG8ndCMCuMD4OemlWGLDZBVU0t+2yaIcyWR65DftomalbuhzSuAjXwPd2IMZ3QYNEGyrROrtgFNX7pQVEpJUCzgz0whoxCrtgEjlUZoKvoeOCVCR3kiRjL5sAYtdB3yWzcio1CNH/jkt2ykZpe16JaNblmkupZR2Dp3/VZ9E3picd7nmYzQc3FGhnAnxhC6RrKtC6tux9/TE4lZttsTyYIDOProozn66KOfiFMsY6cZICnl2cDZALEH9H4p5euFEL8BXoXK2ZwIXBLv8qf487/j9f+QUkohxJ+AXwghvgZ0AKuBmwEBrBZCLEcZmNcAr4v3uXoHx6jiGYyxkXFcx6OlrQnTmpvUpZSEjoOfm0a3E8oQzULTEJpO5PuUBrdXjOfnpgldF39mas7ACEGqvQuh68gwnD3CkuezqABzwefIc+MJZO5cvZkpSsMD8QZQ7NuKZphYC3JVswiKBXKbHiiP7Y6Pkl2+C2a2Br+QJ7f5QYiU0dWTaTLLViz5ti6jCJBEvlc2PnPrQiLPK+9n1dSir1pD6LlohoGeSD2tw4lPBrzJcdyJUUDlFYv9W9Es6ymjmtv1jY/b4DwVeDqw4Bbig8CZQogNqHzNj+LlPwIa4+VnAh8CkFLeC/waWAf8FThNShnG3s3pwBUolt2v420f6hhV/JciCAK2bNzGA+s2kMsVKta5rscVf76aE455C8ce8Xo+/eFz2L5VOb2h51Ia7MObmcCbGMdqaMKIE+KanSDd1UuhbwuR5y55XBkEld6NlJRGhkh19JBs7cDI1BB57iLmlzCM8sQPO9DR0rSKZL0MQ9yJsUWbeTM7pob7M1OLDFtpdIgoCCgNDUAUYWZrSbS0Y2ayBKVS5fVJiZ/Pkdu6kZmND8aGaMG5ClFxnkJoGKk0dl0DZqbmf874REGwJOU5KOR3wtk8vfG0eDKklP8E/hn/vQnFYFu4jQO8egf7fw743BLLLwMuW2L5kseo4r8TM9M5rvn7vxnoGyQMQmzb5vlHHU7vSpW3W3f3A3zgtE+Wt7/kN5eTSNic9YnT8cdGcMaGVcFpKkVpsA+zpo50z3JFt44n6Mh10JNpwtKccdNMa5E3ACosFboOzsigYtFJSaqjB3diFD83jZHKkGhuI3BKJNu6QAiMZIpESwfOyEB5nHRHT2WeRNPQLXtRkv+h8gtyiZAiUYSMIkK3RKKlnaCQxxkZBCFINEGUzpSNRtmDilEc7CPZ1klpaC5XluroRk88s/M5jwZCE2h2YtFLi1almi/C08IAVVHF48ED923gj7++jP/8+3YAupd10tbRTGdPB6ZpsPHBLYv2ufT3V/CuM99MGHsU/swUyfZu0DSMVIbiQF9ZjkdPpjFSaUw7gZ5IEBTy6MmUYnUtoWOmJh+vPG66ezmlkUF0O0Fm2Wr0RAKhCSLPoTDQr8YQGunuXtLLVhKWigihURofRbPtskSOEIJEU6vyeGLDIgwDs2bpsE4UBpjprCIGCA0/N01QzGM3tRB5LqmOHvyZSYJCTu0gJc7oEEY6ixWPWV43O6br4BdyZFfupsgSpoWeSCLE0zGYsnMgNJ1kSzu5fK78PWlWAjNdpZovRNUAVfFfj/X3bSobH4DtW/u59uqbOOiwfWlqaaJneRd19bVMTc6Fqrp6OtBMncg0kW6cUA8DhGkv0oILSwXMbA3O6BCprmUQRYSuQ2l6Es1OkOrooTjUB1GEZtkkGprV59n9nRJBbpogN407PkJ2xa4gIwrbt6rwlaEj4/qZRGNLRUivsG0zNavWlPNARjpDzardCJ0iCFXUaSzBJpNhiDMyVDFWorkNu7kVd2yEoJBT7LwFBmb2epk1aksYFhn4ijqtVyfUHcFMZ6hZvYbQKSGEhp5MVottl0DVAFXxXw/XddF1nTCcC4fdd+968vkif/vL7/jXP/7Ny19zNDKS/PQHv6alrYkPfPx0LCL0hiZK86nXUVgumpyPyPNIdS4jKpXwpifnlrsO7tQ4NSvXIKOQoJhXxmeeZzTLOANAyrK6QqKlDaRUdT+WjT8zDQtyQZHvVRS+aqaJMet9PQRC11nEvHPGhuOQWy6+JhfdThL4lYW12jyDZmaylDStIl+VbOl4Sthc/+0wEkmMRHJnn8bTGlUDVMV/JYIg4M5b7+XXF11CPl/g3We9lSsv+yf33Hk/AM8+4kAu+O4v+OOvL6e+oZbauhqe+/yD+Md1F6GHHlYqjYxCvMmJuD5CoNs2oeuSaGol8j2EELiT40Sei1lTi13XgDM+uuhcNE0Hod56habhWmNEMZPObmxenHyWEj2p8k3zCwaTrR2IBT2HzNp6gmIBd2oCM51GT6Yx09mHTexH4eLcFFJWGMagkCfV3k1YKpQZe0Y6i2aYhJ6LbtkYyRQ1K3fDn5kiCnxVmLtEbVIVz3ycfPLJ/PnPf6alpYV77rnnCRmzaoCq+K/E3XfcxymveQ9R/GZ+7T9u5L1nn8r9965nzZ678sKjj+BNrziNo497Aa1tzWzZtI19d1sG44OEQGlqHD2Zwm5uxbATlEaH4sp8QbF/a/k4ybZOojDEzKg8jJnJoln2XIJZ0zCztRQHt5PtWRlP2LsSui5C0wh8HxlOYGSyBPlczGwzkGG4qFrdGR8l1bmMREs7QgiciVGsmjpCp4Rumrhjo2jWNLR2YNXUPaRWm27ZC6jgMWligWEqDveTXb4LUeAjw4AgnyO36QGEYZDtXYWRyjwij+vRIApDhCaetLxR6Dr4+RkCp4SVqcFIZSqo7FU8Npx00kmcfvrpvOlNb3rCxqwaoCqeVoiiiL5tA5SKDh1dbWRrKvMMrusxOjLGlZddUzY+s7j26hv56GfP5K471hEEAY3NDTS1NPDj71/MZ774Piyvkp4dloogJcXBPjTLxsxkKWzfXLGNMzpEoqUdb2oSq74BPc75hKUCUkqEEJRGh0BKosBH13U0wyx7EdG0SvJrlkW6ewXCsghLxSX15GQYEhZyOGPDIDSyy1fjTU8R+R7+jAr7hU6JwrZN6KvWPKRR0G2bTO8qin1bCV0HPZUm3dFDFPi4k2NxSE2Qau/GSKYIinly84pHZRBQ6N9Gdvkuj4tGHQUBoVNCRiHCMJVCxMQoejJNoqllSaWHx4PQ88ht3UgUh1G98VESrR0kY6P+v4KtN93P3ZdcT3EiR6ohy57HHcayg3Z7XGM+5znPYcuWLU/MCcaoGqAqnjYoFopc8pu/8vUvfp9SyWGf/ffgk1/8ACtW9wLQt22A7557IbfefCcHHbrvov2FEFz6h79x6013csIbjuP1b34lv/rZHwGwLJOlCkIjz1U5kUJuybdkGYZohkFYKhHkc4pxJlDFoEKoxH59IzIMCF0XzTCV5xFFlEaG8GaLEaVEBh7ezCTS97CbWlW+Z15IzK5vwIuVEZAR7uQ4Vl19hfpCPBhBqUjk+0q52k5U5pnKxwtUyK62ntBzkWGAma2ldvVaQs+Li0QTyCAgdBwWIiwVlaTOYzBAUkpVazS4HW9qQi0UGqn2TkLPVV5KborsqjVLkigeK0KnVDY+s3BGBrHrGp7x0j+z2HrT/dzy86sIPSWHVJzIccvPrwJ43EboiUaVO1nF0wbr7n6QL3ziG5RKDoahMzOd4yc/+BWu61EslPjtLy7l0t9fwejIOLusWYmxQF/s+Fe/hIMP24/XnfQKdEPnhcccQU2tCp1ddeUNhEYlC0kYRoX0jnpDrnxL1u0Efm4GPz9D6LoUB/qVbE2mhkRTK+7kGM6oklzJb1lf7hcUuCW8eUWjiaYWikP9+NOTBMUCxYHtpLuWq5yLZWM3twJU1o4IJeIp9MUGQPoe+S3rmVm/DndyLC4QnUPolMhv24Q3NY47OUaQmya/bZNSLLATWNmamEYOpZHBRdptMNsX6dEZn9BzccZGyG18oKyZVyZWyAhnfFT1WSL2+JYgfDwuLFX3JOX/VNvvuy+5vmx8ZhF6AXdfcv1OOqMdo+oBVfG0wbZYneBt73oTvSu7mZyYpqGxjrtuv5cbr7uFv/zhSt76rjeyetcVFPJFzvneZ7jmqutxSi57PmsNP/nBr3jwvo2s2X01u65dzbLlXZzyjtfzoTM+w5WX/4uDD9mHQw/YjbQWxWKiGaUGEMOdGCPd1UNxqF95D4kkdn1TmdUmdJ1gagLdttF0XSlW6wZmfROgan5Kg33oVgJvegKhayrnIoT6f94kKAOf0lAfVkMTkVNCt5MU+7ZU3A8rW0ehfyuJppY5+R1AT6Qq8kfF/m3lPEfoOmW1Aj2RxKprKGu9aWZMLrDnDHHoOrjjoxjpLHZjM25MshCGSbpzWTn8JqOIoFQgdEpouoGeSi+iFS/0+oJiHmGYilo+Ngwo1qAWGyDYgQLE44CeSC4SQ7XqGv+nKNDFicXU+odavjNRNUBVPG3Q1NzACW84jq2bt3P+t34KQDqT4j0fOpX6hnpOeNPxJBM2Z5/x2TLl+t0ffBu9y7s59wvfY5c1K/A8n/vuXU86k+Lzn/g6MpR84GOnEUYRdc2NjOQDVvS2o2tqMpz/xiylJPR80t29BIUCkedWUqqF0tiKAh9vehIjW4uRqVH5FE3DqKlDBj5+MY83OYHd2KIUBiSL6NVqPKF00nQDECrXNDkOSKyGZoJSEc0w8GamSLZ1IsNQGRI7gYxCEoaJEEKF48KA0lB/meItTJN0Rw/5rfNFUXUyy1ZWnkN8bUEhhwwDkq0dSCkxMjUV+RlvZorCtk3lz3oiSaZ3dYUaeOi5ZeNTHj7wYV540Ehny72CNCuhPKQnELqdILt8F5yxYUKniFXXgFXXsChE+UxGqiG7pLFJNWSX2Hrn4n/nW6niaY+1e+zK3vvuzhV/vrq8rJAv8ruLL6W2NsvaPXbhu1+/sKLe55tfOh/LMtj3wL3YvHEbe+y1G6e//xRGR8YZG5ngHe97M7uuXcX9967n/e/8JCe++t18/lPnMdw/hNB1kh09mJka7IYmEo3NOKODgCAo5lXuIp6gNctSxAJfhbASrR0IQZmt5k9PopsWup0EGWHV1hMUckqFuKEpfjOvDBna9Y0Ut29WvYY0QeAUsZuaseqbcMdGcEYHsRuaCJ0SpaF+nNEh/HwOhKDYtw1nZFB5RlIqhYJ53Vul7+NOjldo0MkoJFgg46PZCYxMTTnUVhoZxJscR7fsspK2MzFG5LrYDU3l/UKnVCFLpLA4hAlzXo6eSJJsaUezbZId3WR6Vz6sZyLjot9wgayNlFItd51F4TUjlSLd3Ut25W4kW9r/p7wfgD2POwzdqvQtdMtgz+MOe1zjvva1r+WQQw7hgQceoKurix/96PFLaFY9oCqeNmhqaSCfX9zMbP39m0gkbKYmp8nNLBZ0XP/AZi75zeUAbFq/ld6VPbz/o+/k8+d+hBuvvxXLNLjsj1eVt7/8z1ez936784rjjqA4sI1UexdBSU1kieY2ZBBgJFKqSLOYV+G6TBYZRYqQ4Dr4hTxabR3OqDJkdlNrBX17LsTXj9ANjEyWVFcvQSGPDHyMdAZvcgIZhfgzU1i19diNLRCGFAbnPI3SyBDJlnbQDQgDpBCEnodVW4cfjxWUikvqjIWlIkY6U6kdtyBHouk6ybZO/JkpwlIRq76x3OfIm5kmv2WOAKEn09gNTbhTE0p9ewGNWrctEk0teNOT6IkkoavyO2ZNHWZNnWqU5rmEsXH07HHSnT3oyfSSobjQdSgND+BNTSB0nVR7N1ZtvVJ5GBvGGR8BlMJDoqmloqmdWCCQ+r+EWaLBE82Cu/jii5+I06tA1QBV8bRCc0vDomX7H7w3k1MzdHS00NHVxkDfXIW/Yeg0t1TKzm/ZuI0wCLn2HzeydfN2tCXCL1ddcR2vfMULSC9bCVGIjELc8UlkGKjwUyKpwmyJJMIwccdHy6EjoemkupYhZUSipV0Vlc7rogrKQ7Abm9X6RAo/N6Ua1MUsOaX/Nm9718EfnVJGaJ7ygAx8SsMDJFs7cMZGSLa04+WniVw3TuZL3PFR9OTiUJaZrV0ktWOkswROCc0wYqq4R2HbZiJPseD83DR2UyuaaVZ0hgUl0WPV1ZNsbsOdHCcoFoiCdqzaOjTdQAgNq74RCQT5GcxsrTJosRcWeh65LRvKMkdGKo03NUkwsF3pz9U3lpUDpJQ4Y8NlBp0MQwp9W9Asm9BzyjklUCw33U78V7QfeKqw7KDdnnaMt6VQNUBV7FTMzOTZvH4LCEFNbZbWjhbe8d6T+NF5v8BzPVbvtoK3nv5GfD9gamKKN57yai664Lf0bx+ktq6GN5/6WiYnpxaN+8C6Dex74J40tTSQmylww7/+U7F+/wP2oDiwjUQ82SpiQT2aZeHnpmPCtsTPz5BMZ8rGB8Cqq8ebmlS1OUKQ7l5B5C9mkSk9tkElTtnWqbwUKZGB8gDS3csxa+qQYYieSKpWC5pGunOZKooVAmd8FCOZJCgVFZNuuL9snNzxEay6hjjMJrDqGvGmVBsAPZnGzNao8aMQKSWarlMaGSy3AU939Sq6tFdJwXbHRrBq63fQgkJUECKKfVsQ2grsugaiIKDQv7V8r0KnhF/IU7N8FzTTJPLcOeOTzsb1TTFrsFjAm5ogu3JXdMtWebZZ+vY8BE4Rf3rx9+1NTVYN0H8hqgaoip2GkaExvvrZ8zjgkH3YvnWAv156FWv22JXnHHkwbz39DTQ2NdDc2kj/9iF+dN5FHHP8C/nx9y7m6ONfQGNzA6Wiw09/8CveeebJFeMedNi+eL7Ppb/7G9f98ybe+q43snzVMjZvUCGy5at6eP7zDignyEvDAxWTbaKlnbBUINXZS+S7yGDOuMy2nPZnKdZCEHoOVm1dhUYcKCYZqNxLaWQQq7YOd3wUzbJJtXUBqkGcZpgITVOhtijCmRhVYTMhSHf34udmVF4mkazQZAPwpifJLl+Nn88T+R6JlnZAESycMRWiCgo5jEyWRGMLQT6HVVuPZifwZqZUu4hFkEpJu6au3LF17voXe5NuTK0OXafCUANETonQddBMsyIkZqQziqAxf1vfI3QcpeKg6Wh2YtF4umEik6lFKt1G6n+r4+ozBVUDVMVOw913ruO5zz+Em/59G6ahc8YH387oyDi5XIG1e+zKhvWbyWRS6LrGti393HnbvRz07P34/S//Uh7jgEOfxcpdennfR9/JpvVbWbPnLtTWZEikEvzk/F8B8KPv/JzXnvQKzvzgW9BkRE97A1liGrOMFr3pu+MjJDu6VR2O76NrcxOnnkgSzJ8UowiB6vVi1alCUs20SDS3lQ2Ankpj1zcho4hka4dqqz09Qaqtm2RbF0EhR2lorit8orkNGUXIuLGZbqvJdak8iRAafiGPNzWuKNelYpmMYDc0lwtbg3wOz7BI967AGRrAm55UualkEj2ZqsgTWfWN5TwOSPyZaYRhkmxpVz2SFkCz4r5IS9XgqJNU9yEmbzjzPKjFm6ptNV0n1dalOrbGJAM9mUZPpdHsBO7keJlqLQwTcx61u4r/HlQNUBVPOYYHRykUivh+yJc+9S2aWxo55Dn7c/YZny1vc8aH3s5hh+/Pt8+5gN332hWAf197C1/7/mfYZbcVbNqwlZ7eLnw/4O7b1/Gtr/6IltZGmlsa8FyP9fdv4swPn8q6ux7k0t9fwc8v+C2HHrIXu3dkQc7TYFtiUpdRhBAahS0bSHX24E1Pk2hqxRkfJfRcrLr6ijdwb3IcO24wZze1YlgJ/GKOyCliNTQrtYEwUPUpgJZIomsafn4aPZFC6DqJlnakjAhLJdypCVLtnQTFIkJTLRfMzGoQolKHDsq1OzLwcUYGSbZ24OeVqoMwjIq2EpHvQRhh1tRh1dbjTk1Q2L6F7Ipd8XNTBIU8Vl0DZrYOv5Cj2L81bpTXXvYWrVRaMQVnPTEhsOubyG1ej6bpKu80j41nZGrKdUdC00g0tWCmawgDTxnseWE2LZGsaGynWk+siTX61H3QTUX7rlm1G0FcxGokk+jW/4bKwTMNVQNUxZOKMAxZd9cDDA+PUltbQy5X4N477+f3v/wz73jvm5manOY1bzqeH3z7oor9vvPVH5JMJnjpq16sikv3WE1zSxN/+NVl3Hz9rXR0t/Hva2+hWChx3KtfQlNzA6t3W8ldt9/HjdfdAsAlv4EXvOQ5HHDIs/jPv2/nwh/9nnO//VF0V2mxmTV1KqQktIq3d6uusRwecsdH0RJJpIB0Vw/oBtL3KgyBjItUVaM5DXTVudRIpUFGhMUCke9XGK3Z/Isz3E8YK2drlo3d2IKRTBIFSgLIm5rAm55UVO5UOg635Yg8F82y8aYnKoyMn5sm3dOLphtKuTuW+9GTKfRkqkLrLtneBZqBRGLU1GPVN8UKChtBKJ04d2JUdXbN1pLIZNEtm5qVu8UK3xI9laHYv5WwVCQERWdvaSfyPYx0Jm7JPY+dpmn4uSmc0SGs+sY43FnESGcxa2qJfB+vkI+JIwI9kcCuW0xM0e3E/4y0zjMZVQNUxZOKO2+9lztvu5dsTZovfOwbjI6MU1tXwynvfB1CU95HGEUVtT0AQRAyPjbBVz/zbb51wRd5zpGHMD4yzs8u+C2O47Jp/RzlWUrJgYfuS2dXG+ed++OKca66/F9880df4PVvfiUCCEf6iXQdPZlSJALdJN2zHHdsRLVdqK3DSKTwJlWOJ/J9Eq3tFPu340lJoqmF0HUwszXoqUxZIcGfnlKink4R3TTR7AR2cxuFrRtJtnYsSqg7Y8OY2dqy8QElwxO5Dl5uimRzO8V5YbnSYB+prl50wyLR2Ezo+wT5GYxUBjNO6LsTY1gNzXgT4/j5GSWc2t5FaXgQq6augjwwC396glJMSki2dVIc2F42aMVigXTPSrT4fs2qIsxXxw5KxYrwnTsxBkIj07sSK7u4U2voujijisHmTY6rpnq2avsgA5/iYD9mOkNxbASQCN0g07uy3BW2ip2D7du386Y3vYnh4WGEELztbW/jjDPOeNzjVgtRq3jSEAQBD9y3kZW7LOfb51zA6IhiaE1PzfDtcy5g+coePvrZ97Jq1+VlzbZZ1NXX4rkeQRCy4YHNbNvcx/tP/xSHPffARcdZvetyOrvalqRbA2zdtI0ffPsi8oUirqbYWH6cA7HiOhmrth4jW4M/PYlfyJXrahIt7XjTUyqEZlkEpRJmfSNGthZnqJ9i3xYKWzcidB3NtgmKBQrbt1DcvgXp+9itHWi2XSYklCG0JXXQQrekao7CEDNTU7HOmxgjDFSDOn96ksL2zeVi1NApkeldhTcxpkJgUhI6JYqD/aS6etHmKRaAqs3xpiYJ8jOAMn6F7ZtJNDZXbOfPTCkj4y/FiAOh64v14mS041YLUlIhCisjJSAahRSH+rFq6mKKdcxDDAMK27csqR7+UJBRhF/M40yM4U1PES5ofVHFo4NhGJxzzjmsW7eOG2+8ke985zusW7fucY9bNUBVPCkoForcceu9/PXSv3PXbfcyMVbJEPNcj5uuu5WJiWku+tFvOPWME+nqUQyuzu523vauN5bJBoahMzOTI/ADrrv6Jj7xxQ+w1767s99Be/O+j76TP/3uCu6750EOPnx/eld0VRxnzZ670NTSyPat/XzkvZ/nns1j5KSF3dBE5DoU+rbgjAxSHNyOYSeJggC/kCfZ2kmqu1epHWgamWUrSDS2oCcSeBPjuKPDFXpsMgxUg7nYo5FRSHFgG5oQFPu3Ydc3YsYeQbk+ZolWBKoINIk7NY6MQlLtXWVjKHSd0lA/oeOUWWqzxURBsaD02ooLm99FyDAg8v2KfJeeSC5WMlhKtFPXcKcnKWzfijczVVYkkGGIX8gTFAukOnsqilKtugaiMFjSwOqWXRYjnYUwDHQrQeR7yCWIDJHnqvN/FPBy0+Q23E+xbwv5rRuUEOv/kBH6yx+v5MWHnsDevUfw4kNP4C9/vPJxjdfe3s6++yoF+mw2y5o1a+jv73+YvR4e1RBcFU8ISsUS27cOIIRAN3Ru+89dnPe1C8jPFHjV615KMpWkVJybkDRNQzcMvnvuj3nnmW/mW1/5IZ/6ylkIoXHDv27mW1/5IaWSQ2NzA+1dbYRBQF19Lbf95y4OOXx/stk0BxyyD6VCiZPf8TrGRsZ51ylnc8ZZb+PuO+7j1pvuYM991tLV085nPvw13nv229m+tZ+x0Qn2329XdNNQ4aJZSIkbN4AzszUUB7aVWVZWfSN+LofQBM7oMInmNoIwQLMTZYOjWfaSdSsyilQ90OgQydYOglKxXLhqN7Vi1jbgT6v9zGwtum1T2L4FgAhlWJJtXZSG+mOPSFIaHiAo5NATSVIdXZSGBlS/HSEqiljLiCKiKCLZ1okzOlSmlQtNV+y1eahg2gmhQl9hhNB1Ctu3KPmi9i7CQr5cDCpMk0zPctVoTmhEnkPklHDyOeyFhlZTmndaIqnki1IZEo3NGMkkdkMzYgkpH82yCZyikkN6CHXu2f5Mke9R7F9QRFvIE5SKWKa1g72fOfjLH6/kUx/6Ck5JvSwM9g/zqQ99BYBjjn/h4x5/y5Yt3H777Rx00EGPe6yqAaricWOgb4hzv/A9rvjz1SQSNh/93JlEYcT46CTv/+g7+fkFv+WUd76O756rdNyEELzxLa/m73/9F1JKPM+nVHLYsnE7V195PS8+9nkc8/IXksmmSSYTDPYNcdEFv+XzX/8IX/v8d5HA3vvtwR9/fTlbNm0HoHdlD69+/cv49UV/5HkvPpzVu63k7jvWcenvr8AwDQr5IomEzVWXX8Ohh+6NXbe4biT0XCUgOj5aoabsTY6T6uzBGRmaE7aMIoxkCr2+idLIgArRGWYFIQAqJ/SgVCTTu5L85vVKG07TkEFcuyMERiqtxE8XQIYB6WXLVeuEof4y+SF0SpSGB7Abm3HGhomCQLH15tXXGJkagkIePZnCGRvGqlWFq5HnkmhurcgLmTV1MRGiGaEbSnbIc9FMo5y3kWFA5FQqEUjfxxkdxm5upRCLn86y3wKnVDZAQbyfPz2BnsqQ6lqGZiUQcbjNrm/Cm57Abm7FjY8ndJ1EYwvFvm3oyy20BWFJQIUk87m4xspSHtYSntTCbrDPVHzzyz8oG59ZOCWXb375B4/bAOXzeV75ylfy9a9/nZqaxd/Fo0XVAFXxuHHVX/9VFhA98W2v4etf+j4nvP44DjpsP66+8nrW3f0ghXyRt59xIr7nsXq3lfz4e7/ggXUbgNlmcbB1Sx+pdJKvf/H7NDY3UCyUeMnLjmTZ8m7ee/bbufv2dRz6nAPYfa9dufPWe8vGB+bkd459xYu45d93cO0/byIMQjq723ndSa/gskv+ztjoOC975YtV6Kl2sXSNVVuPlkwRDizWo5NBiJZIIjR9btIugtAVRVuGEan2Lgp9W8p1K1ZdA4HjkGhuU9vrGqHrIYXAqq0vj6MYZcqTsBuacUqVoSuh6URegIyCRTVLMgwRhk66ZwWlkSE0odqIKyq5AMNQ9G8/INHURugUiXxPydckUxXbKq9kisAtkWrpIHQdolIRNB2rth4/P4MMwyXDZEGpgBFr0iUalR5cUMijWTZ+sUDkebgTo6ooNpVBBgG5jQ+SbOvEHR8h0dKuimWbWpVUkKarcGAUURoZULki18XMLDo03vTknMdTUJ9T7d0V2nzAY2bNqQZ6qsW6biceV4fYpwJDAyOPavkjhe/7vPKVr+T1r389r3jFKx7XWLN4et/JKp72KJVKXHX5NQD835uOp7G5Ad8LsGyLE95wHLf95y7uv3c9Wzf3cd7XLgDglNNez4P3baK5pZE3v+O1/OuqG2jvbOVZ++9JS1sThz33QLZt6aejq40H79vAN770fd7+npO4+477uPH6W7nr9nVlozUf99x5P63tzfzfm17OC495ntJqSyT40Ls/jecpz+QH374IDckbX3WE0lcbH1W6aHYCPZUmKBaUXtqCSns0jURjc0V7A1AGQLds3Mlx/JlJ0t3LCZ0Sup0gcEqIKFIFqVJiNTQig5B0ezdSqFCUEELlVkpFpO+XG7gJTY9rh3TQdYL8zFxztwV5Gt1OEoUhVrYGZ2SwXChrNzShCVFWXzDSGaIgwKpVtOawVKRUKsZFpm3lvJZVWw9CotkJhK7jTowhI+VdBa67JMHAyNahJ5IkmtsQmoZZW68IEoN9ysjF55xsacfPzaAZBnZbJ+g6dn0j7rhSFE80tylDmJ8pG+byV2AtDp9Fvk9peHDBQhX2VEoO02iWRaqzp0IZ/JEiKBXJbVmPjHNQZm0d6Y4eJawaBkSeh9D0ih5LjxYyitR3/gT1RmrraGGwf3jJ5Y8VUkpOOeUU1qxZw5lnnvl4Tq8CVQNUxWPG5o3bmBibYPe9dkMgGBsZp6OzjXe+9yS+942fMDkxTWNzA6e972Q2PLiFhsY6tm3uY+0eu3Lu+Z/lrtvu5abrbuN5Lzqc5auWsXH9JqancmSyaXZduxLPC7Ask1e+7qX88id/wDQN3v2Bt/DPq25gj33WcvMNt1ecz7OPOJCOrjY+ffZX6d8+SO/KHt54yqt509v+jx/OqzP65UWXcuxRh9Bo2WpSLebR7YQyAMkUemMzke/F3oZqu00UEYXBkgZARhFmJoskG+dhdJyxYeyG5oq3cG9ijERTK8XB7apJWxwqKysfhCFEEcn2LiLPRbfjMJ1UxiJyHVV4Ojb3JmvV1hOFIWEhrzyophbc8TE12QpRVlgIHdVS3G5qQaLYfeXjNzZTHJzre+SMDWNFIWa2ruL8S8MDyphoGnZDM27c+0dPJLFrayls28wse81ubC57hO7kOInmVsx0htLwAEYqrdQXPBctkSh7giHg52bIrtqVVHu3mvjjfJXd2IKRWNqACLG42bqMQiLfp2b1GjTTrKhF2hECpxRTyqVSXbBsnJHBsvEBlIdY24iWCCn0bSUs5pXWX0cXdl0DQnvkCtyR7+Pnp8uhQ7uxFSO1tDL4o8G7z3prRQ4IIJG0efdZb33MY15//fX87Gc/Y88992SfffYB4POf/zxHH3304zrXqgGq4jHhrtvXcft/7mZiYoo99t6NVbv08uVPfZvjXv0SPviuz5QJB57roWk6t918F5s3bGXXtStJJG2+8pnvsGWjCptcf81NfOJLZzHQN8K1V/+pvNwwdD77tQ/zsfd/ET/2YO6+4z7O/PCpFAoljnrpkfz10n8ghOColx7JLmtW8b53fJx8TnkAWzZu43tfv5CzPvGuinOvq68hlUpBGFIaVzkXf2ZKGSEpccdHsOsbEUYDeiKJn88TTI1j1jWqdgTzDIAwTKSUFbmUZGsnZhyyWgi/kMNIpivYZs7YsFLNthOqkdo8qZ9EcxuR0Ei0tsdv21o5bKZZNkGpqJL5iSQyDDGSaYyuNMIwyG/ZUHHsWcJB5LkExXy5+Ryz6tzz4E2OY6YWx7vcyXEVYgt8kq0dCMtGaFrczXVuDHd8FK0jgZmtJQp8ZBDiTU2owlOnpERaDZOE2YpuJ+bVQ0mCQp5kc1usguCq7rO2vSQBQTNNEq0dFPvmDKXQdIQQmJmsyrUtmNCjMCSK+wjpto1mmMrT2fTAXJ5I08gu3wV/IasQiMIAZ7CPMF4no5Bi31Z0O4mZXiJGuAN4M/NCh8UC3vQUNSvXPG5du9k8zze//AOGBkZo62jh3We99XHlf5797Gc/KW3NqwaoikeF/u2D3H3HfZzzufMYHpzrfvm5cz/M6e8/mbHh8Qq222tPegXfOedHzEyrkNYD6zby6Q99lc+e+2E+99Gvkc1mOOCQZ/GXP/yNfQ/Yq2x8QBWjXvj9X/Lc5x/CVZf/q7y8pjbL0OAohmnwyS+dRWNzPRd+/5ds39JfNj6zGB0Zx3GUsvTsD+jdZ72Vmsb6ig6foFoiWHUNMSNOMeSEbpBsa8dIKQ23yI9iNlsBzbQws7WLwnLO2DBWfSO6nWAheVi3bELXqWSGSYlmmERLiHkGpYIiJ8QTrGZaJJpaiQKXSEp006Q0PoJVV09paAB3bBiEINnaiZ5MlyfJWWiGibBszGwdwtARYbiorw+wwzd5oen4uWnVrmJmWhmxMFgywS+DQIX+MiqkqSWSqu1ErE8325Y82dpJaXiO0ltuYGfZj6iZnFVbj6YbuHG7CyORQKJCkEglrBqWSkShj2ZaMRFCnYOeTJHuXo43NVF5DVGEOzmumgMuEE3V7QTBPLmhWYRO6REboMj3F4mxIiVBKf+ECKsec/wLnxDG25ONqgGq4hHDdT3++JvLaWysrzA+AN8990JOf98pNLc2out6Wdmgo7O1bHxmMTw0yujwKB/42On89heXcsuNt/PcFxxGbf1iVs3QwAh777t7+fMLXvIc/vTbK7jtP3cBMDI0ShiE3HrzXTzvhYdVGBoA0zJp72jlfR99J0MDIzQ1N9BQk1yyRgVY9JaXaG7DHR9T2wtBorEFP6/aVwfFgsrLLAzJhYFqnW1aKlcQ158IXcfIZFUb77hr6qwo6Kze2UKY6ZqKyTnyPbyZyTiENkRQyCniwujIHEFBSkpDfaS7einMM0CaaaHZSUKnCFLiT03i5xSJYqHGXKKphShQOaj5E7NdV09pTDHe9FQakMgoVLkv18UZGyrTwDXTgoQOqM6q6Y5l5Ldvqjgfq65BMdfqGvCmJkETGLHqQeCUVCFsIY9ZW4+VrVFjzlsXloqYtfUY6SzZ2noVypQRURAojbwwLBfuIlQ4dX47h7BUxJscJ1xCZDVyHVLNywhy02W1B7upBRErXURuZRsLGQaEnvvIOrAKljT8S7ZufwajaoCqeMTo3z7I6PDokq54biaPpgls2+ZdZ72Fb3zxfKSUJJPJRUbBMA0amhp418lnl0NrG9dv5dNf/iAf/8L7SGfSbHhgE1s29XHYEQcyOjzG6e8/hT/99q/ssttKfv3zS3jNiS9HCJV3mi1yvfxP/+CU015fke9586mv5ec//i2HHn4gmzds5aIf/YZvfOdjSCkXtRvQTAsZzjG8jFSGoJCbM1Zxk7TZN3ahG3GRqGB++MnIZGPD45PqWqaKKKVEM038Qj6mGCt9tmR7V3zcgMj3FGMtnD8ZLr7XQSFPUMxj1dZhZrII0yR0SuWQmhCCwHVA18n0riJwHGTgKyMTeHiTY4rVFr/FO2OqtgkhlLq3YaiJ3ymR6V4eF7mGaKaNMzFGqqMHZ2Icw7YrlK3txha1bmSQRGMLWiKJJgSFgW1YdY1IIctdUWf7FTmjwyAj9ESSdM+KsixP6Hnkt2woG0U/N03Y2EyqvZvI98lterBMefemJ0m2dZYJEDIIKWzbRFgqKj27uHOqZlqLjAao8Guqowd/cqxiud3QhGEnyC7fhdBzELFskNBUd9b81g3llw8zU6PalDulR2SANMMk2dpRoc0nNB1jibBn+UmIv9unKx5LiK5qgKp4xBjsH+L3v7yMT3/1QxiGThCE7HvAXjzvxYex17P24K7b72Hr5j6aWhr48a+/wdbNfdi2yStecyy/u/jS8jinvPP1PHDv+rLxSSRsTn/fKXzu419nYmwS0zJ59wfeQs/yLj551pcBVbj6qS+fRe/Kbvwg4DcX/QlNExx/wtEcfNj+DA2O8OJjj0DTdb74zY9RKpYYH5vk6r9dz7133c/Vf7ued5/1Vh5Yt5Hd91xNWCqqLpoxCcFIpjGztSp57dUSeS52QzOF/i1L3otEczuaHRuZzh6c0SGlJZetxapvwBkdLr81p7uXUxzsI9neiTs61801LBUJrARmrY3QTfA90t3LcceHCR0HI5NFLFE4qSdTICEMfMJCvtymoJyHEoJURw+R66p+RkJTWmu+hzs5pqjEdqKiaNUZHQIhyPSuJsjnMGvrMesalVZbKq0o2a5DsrUDd3QYu6klJh3MwR0fIdnaoYxu3FBPxkbNHRvGndDJdPcSOiU0yyoX3IIKX7kTI2SXrYo/F5dokzGK3dii8jcL6q1KI4OqOV8c4gxjSvj8KTHyPbQlhE2NbC1aIkmqs4fS0AAgVbGsZeOMjSAMQ9V8zaNxC8sq0+uFEAROCW9qYpHKw0PBrKkl07sab3pCtfOoqSt3hF2IRCLB+Pg4jY2NT0sjJKVkfHycROLRUd3Fk5FYeiZi//33l7fccsvOPo2nHFs3b6dv6wCJZILJiSm2bNpOTW2WbE2GyfFJEskk99x5H5MTUxz6nAM553PnUSyUaGis4y2nvYGxsQkmxibp6e0imUpSyBf493W3sO/+e5YVsI8/4WhuufEO+rZVimWe/v5T+PZXf1T+3NBUz8tPOJrOrjampmYoFUvous4e+6xBSsl73vpRwjBk5epeVqxexpWXXVMx3oGHPouzP346HQ2p8mQtdEO1s45rXSLPxUhlcKfG0S27HOaZj3TPCuUZuQ5GOovQdTXhmZaqc0lnCT0Pf2pciZLWNyo6tdAp9m+pCNkJ3SDd3UtQLKg23g3NBE6RyHEISsq4aJpeZpwJ3SDZ2q46gMaFn+nu3rnJXAhSbV24E6MqrGeYJNs6CQr5ssDq7Dh2Q5MyPDGMdCYuXM0R5OfCpqr9OHhTYyQaFJU3dJ3FOQyUwraUUUzUENiNzcgoQjdUKwpndBgZ+BUsvPmo2WV3dd/zM4tIFLPrQ6e0KH+HENTuuge6ZRMU8sxsvD/2iPSKEKbd0EzouWUNPN1OkF62sjzxR76HBMJSifyW9eX9NMsmu3x12QjJKKI4uB13fC4ULQyDmpVrHhcle0fwfZ++vj4cZ7EH93RBIpGgq6sL06xkHAohbpVS7r/UPlUPqIolEYYht918F7ffcjfJpE3/9mF+ceHvyuuPefkLOeIFh3HO585jaGCEd7znJL7wiW8QxK2pJ8an+OF5P+d9H3kHtmWxfGUP3/rqD8sq1kcdeyTZmgy5mTzNLQ2LjA+onNN8TIxNoukaQtP43jd+ghevr6uv5b1nv72cd3Ich2Rq8ZtkfX0tXV0tqrgyhgwDNdlqGlZNHWFRJf2tTA2R75NsaaewfUuZQZZs765QI5jtMBr6nlJ3JpbP6ehWXkcYKGMnZbkTanFwOyCU8nY6ixBa3DFUtV8Qto1ZU1NuTxB6LullK5BhROSUlEp2FMG4RnrZKiJnXjO5ugZ8p4BV14CUUoX2PLfC+Mxet2ZZcc+gACOVIdHaSVgqlI2PnkhiZmuUcU2kMNM1FAe3Yzcoo7IwXCh0Hc20KoyDMzygQpajQ9iNLWXPZam3eM1OEJSKFPq2qHqgefkzAKuhKQ5vicW5qYYm5dHF4xiZGhACd3KMZHtX2fCFTpFEawdhOgO6gZnOVHgdmmkRBQHFgUopn8hzlYxSbICEppFobkO3k3hT4+Wmg0+G8QEwTZPly5c/KWPvTFQNUBVLYlPcvnpmOo/n+fzqZ3+sWH/ZH6/iuFe9hLec9gackkNHVzvdyzrp2zbAPvvtgeu4TE/lWLa8i7r6WrLZND29XWUDVCiU+OSXzuI/N95OU3MjbR0tiyq17fjHbNsWJ7zxeJqa61m920p+8/M/lY0PwNTkNJs3bqOuvpapyWn6tw9x2pn7say3C9fzMHSdP/z6L5zw2mMobt9EsrVz0fWamSxhqYgwDCLPVfpqYYCZqSHV3VsOVUWBvyg05E0rUsAsgy3Z3k1UKinG1by3/Mhz8aYnMWtqMTM1uJPjlKYn0ZNpEi1tSmeurgF3fATXczEzNZjZGjRdRwid4tC2ivbgQTGPkc5AGJaNgZHO4s9MlT08zbTi7q6L65eIJOmOZQRuibBYICjkysrWimnnE8XFsbphEkahYpwZJqHvqULeseFyb6JEcxtubITnIyjmSbZ3E+TmaOmBU6rMwQnVrI7AV+eSmybV2YOfzxGWCnGjvFpF7tA1ki2KiRh5qu9Q5PtETgk9Y6IZBumuZWWSiBOH54RuEDpOmZ1n1tSh6YvZfrPdaBctX8D00y0bvakFu7Fpx+rfVTwkqgaoigoEQcBtN9/F9JSaLLp72unobqezu42RoTHe+NYTMHSdvfbdna989tusv0+97QohOPf8zzI0MMyVf/knLz72eeiGzvr7N3PRj37Dtq39HPmiZ/PO976Z8879Maap888rryNTk8YPAk573yl88RPfoJAvomkabzj5VTS3NNLQVM+bT30tPzn/l4yNTHD4kQczNqLEOw3TYM3uq5manCE3kyeTTTM1Oc1zn38o//rHDfz1UiUPZFomX/7Wx1jZlED6nqpHaW5TemZSFR0aqQyloX7SPSsq3uD9/AzoqvAyCqPFrQcWINnSTuQ5uBOjSuNt4f0t5sms2JXC1o1l7yEsFSgNbCfVtUyFnWJD4eemVfgqmVKhoQWTopFIEQUBmm6Q6uhWtTRRVEGsiHwPPzelrneeMdQsGykj1XxudrxMFs0wMGvrlQqCbRPkZpBBQHFiG5E3Z/RTHd3KiNTVYyRjSnlMJ18IYZg4Q/3YjXOV+P70JKnOZcpLjK+3NDSAZlkkO3rwJkZxJ8bQTAvdTmDEhaGg6niKg9vR7ASaaeKMj0AUYdbUKK8visoUbiOZwp0cV16KncDM1OLlZki1ti9pfEDVFtmNzTijQ+iJJFZDI0I3kb6POzWhzmWep1M1Po8dVQNURQXuu2c9W7f0c/t/7uLPv/8boAgA7z37VKSMuPD7vwSgobGubHwAXn/yKynkC2zesI1DjziQm264nT323o3zv3VeOSx3xZ+v5vAjD+bl/3cMtbU1HP68g/nmV35I37YB9th7Nz5/7kcYG52grr6G4eFRJsYn+fr5n+En3/9V2ejc/p+7ee2JL6exuZ69nrU7d9xyN70re3jh0Udw0LP3Y3x0grr62or23r7n86VPfZsfnv9xMkBQyBH5HpllK9XbbiSJPEeFcOLJULcTmHFCOSwVCZ0ieiKlegUtDA3FVGI0DWHZ+PFEv1SYSU8kkVG4gOlGXGPkL/JSgkIOs65eqVB3LoM4hBWFoQoHyaisYqBZNlbd4iS4PzNDZtkKNMPELxbQTQPNTsbFowqanVDN9TQNhKDYvxWhG8orEaLC+ACURoZIdXSr7aXEmRhTXVsTyQpyg9B0zEwGb3IMPz+jvKSJUWSk6NsLm+SFpQCtxYxp7xpWvUq6+/kZJdiaTKEZplJSiNUhytdgmBT6txEWFInCrmtEygirvrGci0JK5bE8hGKBECp3pZmWaj8RRhQH5ggXeiJJpnfVI6NbV/GQqBqgKiowOTbJ3rusQit47L52NflSCd8PsG2LkeFRJsanOOiw/ZienqsvOeCQZ7Fmj1259677ueLPV1NXX8MrXnMMnheUjc8srv3HjXz9/M9iJ2w+9O7PlD2te+68n09+8Mt8+qsf4uPv/yKvP/lVTE/luOLSq9nvoL0Z6B/mvnseJJ8rMD46xf4H7cPXv/j98rj/uOJaTnzbazjvaxfw6te/jMOPPJhr/3Fjef3QwAhFNyATk8pkGBCUikrMU9MQQpDq6gUEZrYWzbJUgl5KlafRDFWVbxgqNDQzXS4olWFAUCiQbO9W28Q1NUEcOppt0yB0HbuhqULaZT4WTYqaRqqtUzVUc4qYaVXZXxoeQDMtjPYuCttUSFOdo7akh2Zmsjhjo0RuiURbB0EuR5CbJtnaiTAMpRwtBMIw8fIz+PH5zuavkh3di8aUYUBYKuKMDmFkalTdkOfhjAyQbG4rezVCCMLYeAX5GUKnRLp7udLaW4L/JEwTITSSrR3xCwH4M5MExQKlkUFqVuyKkUqT7lpGfttmZYA0jVRHN8XhQcJYw88SgkL/FqUnp2kkW9qRgG5aqn36w0jm6JZNYBjglnDGK8OKYSzZUzVAjx9VA1RFGbnRSRIDBe743c0goXVVB4Gl851vXsAua1ayaheVBN2+tZ+XvuJFaJpGFEUc9dIjefC+Dfzix4qkMDU5zblf+D6f/uoHFx2jrr6Wm2+4nXQ2VTY+s5icmObu2+/jtSe+gp/98DcV69/zobezecNWHMdF0wUXXfCbin0L+SJhoFo9/Obnf+L0959SYYD23nd36lImBCqOX2ZIjY/M6Y01taKZqnK/NDjXFiEo5JSqQbGA9L2YomspIzU2jAwChGkCEs20sbK1BIU8/sw0RjpLsq0LoetEnmpHnWzrjFtnzzGoEs1tSCGVakA+p9hs7T1EgYdu24TFAt70JIbvlXMnke+X1aejMEToGsK0SLR14sQacJqdQE8k0Swbd9wncj3QdYxkSkno9M/1PUJopNo7Wdi2TdONRTkkq64RdJ1EUyvetCpo1ZNpZKi8GuUVxo305qlHy8BHxgKtZqZGSRZNx80Kywy+EfyZOaWBRHObyvH4Hs7EKOlkCt1OkuldrZrVeQ5+bgarphYfJY0TlIpzYqZRRGmon2RrJ4Xtm7FqG1QriAUhOBmGBMVCrHJhIol7Ji2VD1rYc6mKx4SdFrwUQnQLIa4WQqwTQtwrhDgjXt4ghLhSCLE+/r8+Xi6EEN8UQmwQQtwlhNh33lgnxtuvF0KcOG/5fkKIu+N9vinimMiOjvG/jPzYNBNbRwgcl1S9qkSf2DDAskw9bR0tbNvSx4GHPgtQIa1SyeVTX/4gH/zku9A0jcsv+XvFeM99waGkUin2PWDPiuVveuur0XWd5St7yGQrO4Lquo5h6niev8g4/fn3f+PwIw9B13X2etbah72edCZNtkYV9e2yZiUf/MTp1LW0YDe1ke5ZgTBMFQqaN7m4Y8Pl2p6F8PMz2PWNqsYl8CkND+COj5JsblPyL7aicuu2jUSS6VlBsqMHq74BKSOK/VvLBZNCNxGakstJtHYoj6BYoLh1kyINtHeR7lmBOzaEMzyANz1ForWdZOcyrPomzGyN0mHTNJVXEeBNjVMa7MMZHsBIJEl19ZJs7ybZ1hm3ZNBJNLeiWRZGQqkhBMV8ZShQRvj53CLV6Mj3SLV3YaQySgqouU31FxoewJkYjQVRAzTTxKprqNB7Kw0PEOSmseobASVSGoU+6Z4VahKPIlId3aQ6elT+zPcqjA+AMzaiJJKAyHHwC3lyWzcQzEyR37KeoJDHSCSJXBerrgG7oWlJHT7FZBR40xNLFqS605PkNj9IaaifwnbVKXeWnl8BIR5za4fHitDzCJwS0TOsp9HO9IAC4H1SytuEEFngViHElcBJwN+llF8UQnwI+BDwQeAlwOr430HAd4GDhBANwCeA/VFO/a1CiD9JKSfjbd4K3ARcBhwFXB6PudQx/idRnMpTHJ9h4I6NTPeP07Z2GVYqwf1/uwW3f5IXvvhw9th3dzzP46vnfYqb/30b6XSSRNJmoG+QnMjT2FzPyLCi+mZrMuyx92687x0f5+X/dzSHPOcAfD9gzR67sG3zdv566d+55u/Xc+bZp/K7X/6Fe++6H4B3nvlm/nHFtex/8D6Lz7FY4tiXv4gXvOQ5jI5O8Ka3nMDXPv89QIV52jpaMEyjHPrJ1mT49Fc/iGVb6JpG2pBEgY87MYKgWRVXLiHHE7nOItl/oeskW9opjQwqxpdtk2rvojjUT3Gon2RrB1oiQVgsVlS2G5ka1RLBFGSW74IS2izgTvWTbOlQ9OdEkvy2TeWciTc5jhW3J5hl2ynNtP55FG5AqDbhMgzj7q1h+fwLfVuxauvLCgCprmWq0HPemGa2VjHoFl6/76PpBrPTnJ5Iopk2oVtCT6cxyCIMndIsTXlWHaJdMe3M2gaC/HTFvfVnpkm2d5PurgEBkeviTo+Wc1bu+Bj+jPKCliJuzIYIQbVDyG/biJCSKJnCTGeRUVSRS7LqGlWn2QXUcyV1JOPTrvRgQs+r8HrVvXRVnyTTUl1x49YOduz9PRWQUYQ3M1X2VI1sDen2bpVrewZgpxkgKeUgMBj/nRNC3Ad0AscBR8Sb/QT4J8o4HAf8VKoZ5kYhRJ0Qoj3e9kop5QRAbMSOEkL8E6iRUt4YL/8pcDzKAO3oGP9zKIzPUJiY4cYfXY4zo+pJcsOTtK7poXXtMqzaJO2+zWc/8jVOevtr+NZXfsjb330iF57/Sx68TzGoDjz0WZxy2hv44Omfpq6hlvec/XYuOO8XAPzhV5cByiCc/I7X8Y0vnV8+9qc/fA7nfPdT9G0fpKmpgQ0PbuZ5L3o2hmGUlRZmcczxL2DDg5v41ld+CMDhRx7MmR8+lXy+SEtrEwP9wwjgla85lvauVq6/5mbSmRSHPecAPv6BL/HN8z9NW7NKjvv5GbRUGj2xtCacZlhKyLMU99VpbKE4uH3eJO9SGh5ULQnGhhGmpbqEzivqBJXzsOsakEFAGJMWvOkJEg3NRGGgwlGlaFEL7Vm5GrOmrswSk0Gg2kHMQkY4I0OqvcKCt2IZ+KrwNYY7OYEe08tn4eemMWtqF127VVOHZtvoqQxCgGYlKMzTbzOztRUEjFmETqncQylcwrsISwVIpJAyqpDv8acnSbZ3lQ0QoDTS5hkIPZkidB3VP0k3IAgQySRIVTy7kMjgTY2T6V2lWp3H99ZIZ8q5KD2ZQjMtvJkpFV5NZkAs3TFVGLrqFyUU2cSdnMCbmcRqaFq07ZOBsFSsYGUGuRkK9JHtWVHxHf+34mmRAxJC9ALPQnkqrbFxAhgCWuO/O4Ht83bri5c91PK+JZbzEMf4n8Jk3wiTW0cUyymMWH7Y7miaRv+dGxm+bxt7vvww7EySP170W573wsP4/S//Qmd3G+NjE2XjA3DzDbdz0GH78dXzPonv+fztL/8ktaAQ9IBDnsVll1y16Byuvfommloa+Ouf/sFrT3o5d912H70ruvjsuR/hT7++jLGxSZ77/ENobW/m3C/MkQ6u/ceNTE/l2HPv3fjsN39aXn748w4mkUrQ3NLAT3/wa3RN423vPpH71m1il84GEs0doAkIfKyGZojCch7Hbmwm9Lw4XNWmqM9hEGu1LZjkY8FRUG/WUcywWojQ89Bti9BTXUgTzW1lurfUdCJf1bFUNF+TMq7Yd+bqeSybZFvXgrGdHUxClew73bLKlf8V+zsOiVbVkRQZYTe2IAydyHWIvFjOZkGoKYo15eYbbj2ZxkhnlPBnfF+8BUZIs2wQ4I5W1nrJKERGkdLdK+ZxJ8dJ9yzHGR4gdEplLzIsFvAmJ0i2dwIC3bQRprkoP6PFVfgyikg0tcbXbxOFIe7EKHZTK3ZDE970FKXB7WimiRlJgkIOq66+TBhRt1GgJ5K4E+PlfJ3QDbLLV++Qwv1EI1xQcwYQ5KaJAh+9aoAeP4QQGeB3wHuklDPzqatSSimEeFK1gh7qGEKItwFvA+jp6XkyT+MpRRiEjG8eJPJD3HyJdGMNq567N5tvuBcZRfQevJbcyBTJ2jS3XHQVL3vpC3C0kLvvuI9T33sSP/7uxYvG3PTgFgTguB6r167kyKOew4ffM48K7fs0tzSy/v5KCZVsTYbfXPQnXnPiyzEMEyFgZibP9NQML331Udx314Nc/JM/8NqTXkGxUOmtHPLs/fnBt39Wsezaq2/knfu8mTCMOPnU17Lr7qu55Ld/ZXJiitraDIceug/a2PC8xLsodzH1Z6awm1op9m1BMy3spha8qQmSS4WF4kneqmvAn5nCzNZWeE0wy2qTFIcGSHcuU8WQmlame+uJRLnORRimSuTbifht3S3L74AqYvVnJiu8NrOmDmd8jERzqxL1jJFoUi2x55+rnsoQTU+iJ1OYWeX56HaC4mA/qc5uIs9X5xr3pzFr6xGGtailRFgqqiZ++RwSSaq1A7+QwxkZxMzUIHQDs6aOYB5F2shkkYG/446kUaio0k0tCN2gNNBHorkVNB13fKRSWWFkEKuhiSA/g2aYmDWKsYhUStVhLFUjhMCoqUO6DpppotsJrNq6uGDXL8vzmNk6vOkJZBBgZutIdfUifQ8pwcxmMVMZdDtJoqFRUd+txJOmdrAUlmI1aqb1qBrfPZ2xUw2QEMJEGZ+fSyl/Hy8eFkK0SykH4xDb7CtTPzCfD9oVL+tnLpw2u/yf8fKuJbZ/qGNUQEp5PnA+KC24x3SRT0OMbugnkU0xfP827rnkBp71f0ew7rKbyuvv/9st7P2qwxm4bxupQ1ZxxT+uo6mlkXd94C1840vns//Bz+L+e9dXjLlil+UsX93DZ84+h4nxKfY/eB++et4nuf2We6irr2XXNSsBuPnft5ep2Q2NddTUZli2opvVu66gtb2Zb33lBwz2D2PZFm84+VX0LO/iI595L7qp8/yXPIer5um76XrseSyAYRqs2rWb4aFRPvyez5Ulej7yvi/yyS+8j+ftNe8xkhJvakLVjTQ0UxpSTnPkq5xAsr2L0HOxG1vKORWAZGs76DqyVMTPTePPqMp9b2oCPz+Dbiex6xuUFExTC6XhflWBb1qkO5dRGh5AhiF2fRNaQunF2Y0tqn+N6ywZGgximaDQKWHW1CtDEoVEoUrky7gWCIDYAJnZGnTLUjkUTUMTolyQqll2rBiQV7TiVLpszPzpSZKtHUS+r/Th8jNK7DMOh82qeBe2byp7h+7EKGa2Nm521w4IZODHNUs2aDp2c2uZoQezRlptJ4QgDEogpPpew2BRa/TQdUm0tGNmMoqhFklSnUpxfH5dkzc5TqqjC0ks8ZOfwc/NIFAh1dnQnIxC1cDO1hGaRmlgu1L+tmyMVIqZzesxEkmsugas7CNvNvdEwUgmMWtq5xEzhGLwmYsLfv8bsdMMUMxI+xFwn5Tya/NW/Qk4Efhi/P8l85afLoT4JYqEMB0bkCuAz89jsr0IOFtKOSGEmBFCHIwK7b0J+NbDHOMZj9zIFIHjIzOS9VfdTk17I0Prti3arv+Ojej7dPKhUz5MXX0thz/vIKSUbN3cx4tfeiSHPvdAJsYm2fDgZl7ysiPpXtbOR977+bKXcsuNd/DAug188OPv4t67H+DMd3yc15x4PD/4xde467Z1aJpGR1cb42PjNDTW8fmPncs+++/J2999IsODo4RRyLX/uJGXveoopIzY9OB23vSWE2hoqOPvV1xLz7JO9jtob3bfa7cyiQGgpbWJNbuv5lc/+yMdXe1l4zOLC3/4Gw799oeworkJXhWBevjTU4tDOoZJcWQQ3U6SbO1EykhVvseT1SxkGBAFvlIRsCwi16U4sJ1kW6cqFI3hjAyqvE0UIcMAZ2wobvmt2Hd+YYawWIwn8EqYmRqMTA1WfRNRGODFQpjzGV+aZZFs6ybR2qFqkQo51QY7W4td31jhTUSeW1aDCAo5lReK5X/83IzqDjs5RqZ3FbK+Ab+QR4ahIj6MbFV9eFKZclsHULmlREs7UoL0XWQQ4E5NoNs2dkMzmqaTXrYSb2YKITTMTIYoDHGGBkh1dKv6ppQqXLUbmhfdg1k2nUrKhwjTJNnSjj8/hKm+EfxCHjNTS37LBmTgY6QyGDV1FIf6seqb8CbH8GamlIcrZWXfJc+lOLgdI5XBGR3CnRynZuVuT6n3A8rbSXf1qqLbuPj4mUJAgJ3rAR0GvBG4WwhxR7zswyij8GshxCnAVuCEeN1lwNHABqAIvBkgNjSfAf4Tb/fpWUIC8E7gQiCJIh9cHi/f0TGe0QiDEK/oqPbIeYdVz9sbM2mRH5latG2ysYbhiSnO+e6n2Lh+C909ndx+y90kEja2bbFiZQ/Lejt5y+lv4Pab7+KBdZt4y2lv4Mffu5jcjJoMcjN5IiS3/ecuzvr46UxPzfDva28hk0lzw79u5p477+ftZ5zIP6+8HlDFpBse2MQBBz+LP/7mco55+Qtp72zBc30mxqd479s/xkGH7csXv/ExhgdHMXSd95z9Nn5/8V+47ea72HOfNbziNcciNIFlW9Q31i26rnQ6SaKuHkurQwhBFPgITccdGcJubq0IoYFiwAndICiqHjyzSC1RnCk0XYWGTAu/kFOCoPNaaAshiHwfb2oSM1tbFgj1ZqbQU2nMVJpwVB0/dJ1ywzpQiXMlyeMS5pxy2Kq0QFE68jyQMn6z1zBSGcxMjcrbLFD1BtVbyG5sLnsafn5G0ahdV/XoWbYSPzdd0YZcT6aw6hvxJsdJtnZUGCCEwEhncMdG8HPTqvi2qRWQFQzBZFsnwk5ApARWEy1t8T3SVI2V7xEUcqR7VigCiO9jpLJYdQ3kNz9YzrdJ36c0rHJr/vz8DcSdbUfKAqiz35+m61j1Dei2jRs3ozOWaCMQuS5araJ/zwqZPtUGCNRLkJZdTBh5JmBnsuCuY2G2dA7PX2J7CZy2g7EuAC5YYvktwB5LLB9f6hjPdEwPjHHPJf+maVU7Kb8GzdCY6hujpq0BM2XjF1XCU7cMvHqbvo2b0DSNltYm6hpqePYRB1FXX8PPL/gtMzN5zjjrrXzgnZ8sU5/TmRSnvOP1fPMrPwDAsi18z+O9Z5/Kurse4Nc/uwRN1zj+1Udz1Eufz60338Xw4GhZFRtg25Z+XvKyFxCGIX/67V854OBnEUURDY11vPp1LyUIQj5w2if52OfP5JMf+jIf/cyZHPuKF7HLmpWMjUyQy+VpamkgmUoiBGSy6Yo23W9/1xuIxgdxZiV3EknFaBKqE6dqNqd6whiZrKrdWdA4bFZ2Z34XUau+UU3ayRTCMJVKteugGTpBPoc7OaEaryVT2PWNFUwxzTCRngfz2nR7UxMY6UxM8U6q3EXgxzkade7e9CTp7uVEnosMQ5yxEbVORqqLaCpD6Dvotq3qexbQywGMZIpoQbhPTyRJtLRR2LaZdHcv7lhl99uwVMSqqVMfFsgNJVra8SbGykZJhqFSxG7rROgGVl09QtPLenwV99VOlEka5fswPak8gLjHT+SUFnegDXw0Y/FUZtXUUZgXlgMVcrMbmxGxOoLd2IwQ2pJ1Q8IwlmTGPRxCz1V0dsN4yuuF/tuw00kIVTz5CPyA6f4xckOT7PqifSlN5YmCgKntY2Ra68i21bP7sQfjFRyErtGwrI1/33svF1/4e3IzeU5+5+sQQvCn313Bca86ionxKQ577oFcedk1FV0QC/kiE+OTZGsyFPJFzjz7VDRN4+471vGdc+beD77/zZ/wgY+dxn4H7k2xUMJOWMwTSsay5uLbmi748qe/UzZQDY11vOGUVzE5Mc2Ljj2SXL7Ae9720fL2P/8xfPW8T3HJby5X1O9TX8fo6DiFfJGjX3Yku7SmwZub/EOnhIj71jhD/eipNOllKyCKVH3JUD+alVBeTBjEFGGpJtX2HkJH9QFCSkK3BChmlnQdSiNDICM00yLVrkJxYamojjUWkwZi3bHCtk1qMozbI4DyThAaiWQKGQT4uRkW6td4k+NEUYj0fRLNrfi5aaJIGSB/eopkRxfOxCh2TT2BU6rwqoShegLNn6SNdBa/kEdoGqnOHqLAX3RMiLtzGiZ6KkOyrZPI99CTaYhC3NxSRaARiaYWnPERZBhi1Tao+qrWDvx8DqHrmDW1uGPDCw9EUCzgTY0jw3DJ2iU0jcj3SHf14k6MgVAEjdkWGrNItLQTuQ7F/m1xLVe3Inw4JZyx4XJYbvZ7mS/gKgzzEYW+/PwM+W2b1HeoaaS7livyw9OwidzTAVUD9AxH6AdMD4wx1T9GbXsjfbc9iJVKcM+f/g3AwW85mlsv+jtuwaFzn5VYSZu7fn8t2bVt1NXX8tbT30j3sg7OPuOzaLqG4zwPADthMzI0tuh4lm3x8S+8j2QqSRiG/PQHv17yvG696S6aWhrYa9+1Fd1SX3Lc82luVXH+3hXd3HrTnWXjA6rPUD5XYI99dqOjs5UN67dw2vtO5s5b7+W6fyoixciQemPPzeT55ld+QH1DLYlkgtPf/Qa06QUTHOqt2BkZVIoGqTSRo+jNQTFPumsZoeuot/J5cjRGtgZ3cozIKWLVNVbUAOmJJHZDE4mmFhW6K+RxxpVigDc1gYwipU0mJbqdwJlUQp5EkmRrp2o/HbcrMLM1uFOT6KYB0eK3cYlEIAh9D2HoigRQyMd5pZDQcUi1dBC6DnoipdSua+rUsS1bGYb2LoK4LYSI39rdCVUcaje2lEkIsxCGgdAN1cZ6ZpLQczETaWTgExQLaPbicJ9mmmWGHahaHc0w8KYnseoaCD13kQLCUggKeZKtHfNqfwTJlvayWkKiuRV0XbV+MMyybJGRzhKWCrERV15cbvOD1KxaozrT5nMYKVnR1lyYFnoyhZFMKwLGw3gzoefOGR+AKKKwfRNGYu0zKm/zRKJqgJ7BkFIyuW2UKAywUjah65Nta+DO315b3ibyA8yUzYrD92TTdXfjFV169tuFlpXLOOecj/DvW+7EdT1c1+OAQ/YBJNmaDDddfytvOPnVrH9gLqmtaRr1DXV85MwvcMZZb8UpuUxPzrBide+ic+vsbmPv/fYgkbI588Onkpsp0N7ZimWb5eLSvfbdna2bti/ad3x8kv5tQ3z2I18re2AvednzOfjZ+3PjdbegGzoNTfVMjKk3/cmJaTq7k9x1z3oOXNNNlF8w0UkV/rFjQyKjEM22STS0lLuKzqovg8r12A1NeJMT2E2tOMNzeZhZr8KdGFNGJFNDsq1TeVKxpItu2hRGtiDDkHRXL2a6Bt22cYb6Ve6nqZVEJoszOow7NoyRrUEkEpi1KTQ7ofoKxfkOM1M7x9zzfIKiMj5hzM6z65tUXxzbwhkdmStG1TRSrR0UB/tU87dEShXJNraosNhsi4SB7aS7e8tdSvVkErteeXBBfkaRAGKtNjNdizM6TKqjh0Lf3Bh6Mg1iMW3Ym57EyGRxRoeUURkZItnaXhGCm5W9mQ2FGZksgeuqsJynlArciTFFMkinY8JDAmdsGLOukURre5n+vqjDqpSETgkpVQtuYZqKHRcE6mUjDCGKsOoaMHZEIZ+HKPAX68ZJqWrBqgZoSVQN0DMYudEpvJLDLT+9Ersmxd6vOpxsSz3RPIVq3Tbo2X9X7vrDdeVlG6+9G7Ojjmtvuo2//OUfdPV08IGPncZVf/0X99+7nrec9gYevH8jxUKRD33y3Vzyu79SW1vDES84lIt/8nuOe9VR/OLC33H8CUez4cHNHHfCS7j2H/+mVFKhr2xNhr32Xcs1f7+effbbA8/z+cWFv6NYUJPBoYfvz9mfPoN8vkBndxu33nxXxXU953mH8Omzv1oR/rv8T3/ntPedzE3X34oA3vmeN/PH31zGursfZN8D9+J1J72Cz3z4HE5/z4k89+C1GF5JMcZaOygN9WHXN1WyoFxVh5No7SB0SipXFOuRqclQkGhSBmp+qMduaKY42F+u5PfzM0RhiFnfiJSSVHs3fiFHqnOZolyHIYadQMqI0HUUC8yyKM5j2AW5GXQrgTs2okJdiSSpzmUIIVQvnNnvMpFE6HrcnluqAs54QoyCsLKRXjSr+5bGGR0i1dWLbpoQyw45YyPlWqlC31YSLe1KV07XyW+eo+BL38edGMPM1hC4Lsm2DkLfVa25g0BRq32/TASYD8200GwbI5VRQT4ZKdmeuIW40DT0VJrALZHs6IEoVCFH38PKZgldB396UrWNaGmHMEIzTIxMDdlsLaHv446NluuAhK6jWQnMbI36bnLTim4/1K+Kj5vaKp4Bfbaz6iOEphsxNbzSU32mUKafDFQN0DMUU/1jhEHAvZfeyF6veDajGwa4/rxLadm1m95D11KazNO4soPiRB7fURIlQgha1/RQ19PCzXfdy/e/exEAfdsGuP0/d/GW099AImFz/rd+RmNTPY3NDdxw3S185NNn8OD9mygWSnQv62Tl6l5qarO0tDax57PW8KPv/JxTTns9vudT31hPqVgiDEK2bNzOMce/iG995UcU8nMhmxuuvYWDnr0/hUIxzvm8mt/94lI0XeMVrzmWqYnpirDcLFKpJF8//7PkcgXW3X0/rzv5VbS0NjKwfYi+7YO87V1vYsWuy7ESNnZDg5oYNJ1Ec/vSKgauA0JDmCa6phQCpFTkBCklfm5G9euZ39kTKmRkQMnQJFvb8aYnKY6NYNXV489MlVt4m9larMYWVXPklAhKJZLtXRXabX5+Bj2VJpr2VNFsfgaztp7QUR5askXVJTn9c96YOz5Ksr2LSGqLurgCsa5dgrCkqOhliRyh2kDM6s4JTcdIZSgO92MtMSGHTgm7qVU1rhvqw0hlsBqaCP0SgeuQaGhSmmrzSBsIoYRaI1mmIs16jkExT6pzmcrBxeeQaGkvt8ew6hooDfaXFStkFKmXhcYW9KQin/i56bgmSpbDq8n2HoL8dDmvY9U3KqUHIbDqGnDGK8OzoetgN7Y8YiKBbidIdfcqqnv8PCXbu3cqESEKA4TQyqodTzdUDdAzEIWJHG6uROj5dO+/C8P3b2frjfcBMHj3ZtYeezBmyuaeS25AM3TWHn0gVjrB2mMOou/W9Wy+/l5WrWrjrLPezpe/rORvXNejrr4Wy7I457ufYt3dD1BTmyU3U+AdbzoLN26R/emvfog7b72HS377V1KpJGd94l1MT84wPT3DPvvuzvoHNrF2z13p7xvkrE++i6mJaaYmF8f+M9k0QaBkXWrrsvzfm44niiKuvOwa9j9oH1au7mXj+i0A7H/wPhxw8D6s3m0lA/1DfO6j5+J7PvB7li3v4oXHHMEPv62MaSqd5Hs//Aw9mYlyG+niwLYl1Q5me+Xopk1+YK5TqTc5TqpzGf7MlNIoa2pFa2iKacdL/KQ0TSXSJ8dBaItadfu56fKkOj/hn2rvKtcQ6XaiQsE5KOQVa6ypGYkATeDPLE7+e1Mqx8ISISAjUzNHSJi/QioBTCOdJSjkSLS0UxruJ1HbsOjtHlRBq2YYBJ4LQpQp60amBrumtnx/Z1tgz+ZYZBhSGh3CytQQxeE0PZlGsyz8mUnMbB2JlnaCQk55h5atXgJQzMHAKVbmmjSNyPWI4pCXDIOykSeKiDxnTmpHCITQVBi0ezlCN3DHF+c00R7d5G3V1KGvXkvkeXOe1w72D2L1jSBmFZqZrCK0PAEIPRdvclx1cE0kSTS3YcxjWT5d8PQ0i1U8ZgRewOT2EWQUcv33LsV3XMyUzZqXHMCKZ++Bbilq6fq/3wGo0EzoBez6ov2455IbmO4fI3BcRm7fRJtncuiz9y+PnUjYFIsl1t3zIHbCxrJtzv3C98rGp7WtmfvufpDfXfxnAj9gZjrHR8/8PK7jMjYyTn/fEHvss4YrL/snP/rOz7ngvF9g6DqHHXFQxTUYpsHU5AyapnH91TdxwCHP4uq/XcdPf/BrGpvq2WOf3Xj/x05jze6red1JryRbk+F73/gJ7zzxA9x394N8+NNn8O6z3squa1exdXMf5jyKbrFQ4te/vgJhKfWB4sA2kKpo0aprnDuJmAXljo+oLqiWrdontLSTbO0gdEuYDU3lxmnCsjFrVBHqwrBNsqWjPBHqSyXobTumLVd6YbOtEYRhlNla5XuUShHmcypPNDpEabAfK5td9DzoloWfm0K3bBWm0jRAYNU3YqTT6LZi+FVK9yjvyKytU2E3oanuolGIsCzs5ra526TpiiEYBESRVOSHGGa2RuVzogh3fBTdtFS/nXwOYZgqZNfcpibi/Iwaq7UDPZFEt5PIWe9FN1RLBF3DamjGqqnDSGdINDbP3X9dhyjCGR1UbSAG+xCGiZmtJdnaQbK9q0JvL9nWiZ+fpti3hcK2TXhT44uLf4XASM4ZbhlFBMUC7tQ4fm6GKPAJXQdnfIT8ts24k+MqF5VIYtXUlnsuhZ6n1DJy02VB1NBzyW9eT2moH396ksL2zSrsuYQn/mih2JsDlIYHiFwHL24zESwhEruzUfWAnkHwHY+ZoUkiz2dicJznnvkqIj9gfNMQftEl01zLPiccgWmbRPOUpr2iQ21HA2teciDOTBHd1NFNgweuvJXnHn4g9c11HHDQPhimzrYtfdx1+zped9IrqK2robOnnZGhMXzPZ7+D9ubaq29kr31359nPPZAgDMnN5Mnl8gRByM033Mbll1zFbf+5G4D+7YPce+f9fOEbH8U0Da656gZ6V3Rz+vtOYeuWPvr7hvi/E49n66Y+Xvaqo1i+opsH7tvIFz7+DQxD5/j/O5q6hlp+caFqhBcEIb/62R95x3tO4ofn/Zx3vOckBvqGKn7Uhmmwdo/VarKZJyAa5GfiOqAOhGEiw6BcfGmkJHZdY0V+wKprwEhl5pQF4mZqs11Ty2wqTUPKCLO2TjGtsrWqxfS84k2BWHrikTIOMYUVIT5hmFi19XGuZ7bNd6Rqk+a1Cxe6jp5KYyDIb9lAorlNse/CED8/owpJ2zrR7ARCrzxnNF1pz9kJgsJEhfFLdfWqbqqWhWYYOOMjRKUSZm09ejIdd1pVMjmziHwPZ3wEu6EJI50hCnwMO1EmUACqrYNhqIm6VMKqqVNU6NEh9ESCZHM7hGGFmoPQDdJdvapH0zzyQuR7RG4Jq7aO4kCfonlnawkKOSX+mpupaDPuTY6T7l6O3diMNzWp2i40xN7l7DYzkxS2zdUtWfWNoGllRQpvahyroYl0R3dZqy10SuQ2ry9/J5qdILNspWqkt0BV3BkbVm3NH2fILvRcvKnKLq4yDJW469OsLqlqgJ4hyI9N4+ZL5IcnMZIWjSva2X7z/Wy67l6EgBXP2Qu7Js36q29nj+MOY69XPpuxDYNMbBlCty0kgnsuuaE8EVrpBLsddQCyOY1IWVz/r1uoqclw8HP2h0jyiQ98iWQqycnveB3pdJLp6Rx33b6OFx3zXMbHpvju1y+kvqGW1vZmTn3PSXz2I+fyiv87hivnabkBjAyPMTo8zhvfcgJ77L2G3ffchbvvuI9bbrqT5xx5MP1bBwDBHnuvIQpDzv/Wz0ilkxQLJYb6R7j3zgcW3Yv1D2yiq6eDX/30jxzz8heWl3f1tHPhRV/G8vKUBvtU87LGZtx4AgkKOcJSkWRnD87wcJnRZGRqKiY9UIWi8ycK3bKVTH9dA4HnKkMQKNWDZHsnejKNnkgpEkHc1G2Wui1lhFVTN9cZNIZZ16BCT4aJVVOPmalV9OlkivzmB5UiQW0DQTEftxXXsFs7IAjKoTJ/ZkrVLqFUuysYZoAzMkSquxc9maxoa5Bojc/ZtMr3Z26fQUUKCAPyWzeWddXCYgHdTqDZNlEUQjQn+Jps6Sg38kMIEq0dyCXaOriT46phXrGAN600+oyaurgXT6JCjw/i1uCeq+p/FugCyiCkOD6q6q5Gh9BME820MLO1OCOVrTNAGQu/kFMN9gJfeW6jwxhxE8L5NHKgrARRsWxijERjS5k158akiVlEroM/M42eXIIVJ+VSqchHDSHEopYWavnTL+BVNUDPAIR+QGkqT+D65EamqO1qYnzjIBv/pTwNCWy4+g7WHn0ge7/yORTHZ9ANg+WHrmW3F+/PzOA4G665q+It3Cs4RFHE1v5BPv+xr5eXX/anv/OW015PECjv5htfOp93f+CtXPj9X/LxL7wfTdP44ie/yRkffBuD/cOEYcj46ARnf/oMBvqGym2852NiYort2/rZc+81fPfrF7Jx/RbO/tQZfPYjX6tgzn35W5/gHe99MzNTM9Q31jHYP8yaPXahb9tABSmhq6eDm66/jVLJ4YUveS5bN/fxzjNP5pijD8eYHmH26O7YMHZji5owXVcVfba0qxoQTUcSxHmgpXvFKPFPSxU4eoocoOkGWjKlJjzLJtXRjdB1pOdSHOorT5J6Ikm6Z7k6rh7X1XT1qjdXqQopxaxwaPy9zKpFz05ywrTKSXqhGyrkVFMLpomfz+NNjKAnUnPCm0vMbjIKlWTNWOXE7owMkmhqIQoXs9dm21QgZXlsu7FZCYL2b1XFtQ1NCMPEbm5TSg9RMGc8Zgt5l8i7abpR0fXTn54k1bUMgQBdW1qZIAxJtXVS6NuCmc4qxXDXQbMTWDX16MkUqc5llEaHSLYpgVU9lV7UokLoOnZtgyKamKbS9svWqF5OUbTDZ2CJheV1wSKNOpW/s+oblKc9jx1oNzRXqFXIKIpZlhG6baMZj4xNp8LFlXR2LZF8WlLBqwboGYCxzYMEjkdpMs90/zjJugxTfaPs9qL9QQgG797E9MA4um0wtW2Eu/5wvdLd0gRrjj4IoQm8wmL1Zc/xGJ+Y4F0feAt/+8s/eWDdBkrFEjPTeZKpJKWi2qe/b4hEMsF99zxIU0sjJ771BL59zgV4cW5I13W+et4nSSZsjn3Fi/jTb/9aPsZBh+3H9NQMiYTNe0/9OKViidW7ruCfV11fNj6gikr/9Y8b+McV1zEyPMZLXvZ89nrWWu66Yx2vfN1L0TWNH3/vYnpXdqNpGrmZPK987bHkc3luuv42rrzsn7z6+CNgwWW6k2Nklq0ickug6XFifDiu0Fdhucj30O3EgkZrykgl27vxpyaIwgAzncWdHCvngCLPpdi/lcyKXZQXMc/whk6J0HHiN3Gl6FwaHlATuechDIPQc8vFqwDu2AjJzh4MwwQhyvIxs60jhBAU+7cRuQ7CMEi2dysvTszpoC18MzZr6pfu7hl3IV3qrdmqa8CbmSrL8QjdUHI2s2FCKRUDr7UDPZlSbSUWqpZHkdpPNyragtuNzSo8GZ+nMEzCYlFJ8vQsx6pvwhmu9OJ0O0FxYBuZnhU4cddaI5VWRA3TJCwVcScUIxApcIb6FTvQdcreiVXfpNS9+7aW74+eSKLZCQp9W0h39aLHihRmbX2ZqLGQYGBka8s9lJSSeLZCQxBUV1fdtMguX6206Ioq/2jW1JbHiwIfZ3S47CXrdoL0spUYj8CIiPgFQLeT+IUcup3AzGSXlGLa2agaoP9yjDzYx39+eiXPfsdLGd80RM8BuzAzMsmyg9bg5ooYlomRsqgfmqS2o5l/n/9nlS8AZCS5//Kb2f+NL6D34LXc/cfrK8ae8Et89bPfAeBt734TuZk8A31D2AkLTZuLjfeu6GLtHrsw2D/MQYftx18v/UfZ+ACEYcilv/8bxx7/Qo566fPY98C92HD/JpavWkb/9iG+/42fcPanzigbtLqGWsZHK8NRoDylz5zzIcZGJrjztnv54ie/WV63cvUyzvvJl7jjlnv45U//yP+98Xie/byD+OeVN3DFn/8BgB+ELPwJarHGmkoAR0jfV0oBCMxMDRJwhgdJNDXjTk0QloqK0tvWiaYbqp12PGEF+VzM2sqXG6yBKhBdqksoUoXFhGmCppFobkVPpvGDSYqxRpqeSFZU/gspFTNOqgLJZHsXCKGq+Qv5eW28A4r9W5VSdD5PumclMvBJ96zAHR9WBa/ZWhUWjOnm8w2TZlrIwMcr5El39ap25L6nWF52AsMwYnHORFl8dSFCPyDyc2oiTqagWCn0GrqluO+PFjf+sykND4KAZFuHaiBX34QzOoSZrSUslUBAorkdb2YCoRnKYDkl7IZmCv1b0XQDNL3i7V/1Aqqn2LeNdM9yAEpD/cpL0w3FvktncIYGKu5B6JSUCGkhT+SUSHcuw50ax5sYU2HElnb0dAa7URUAm9larNr6crM6Pz+tDNY8mr5V34gV1yEZyVS54+1CqZ6gVKxQ1whdB2d4QDH2HgErTzNMrNo6rNq6h912Z+LpFxSs4hEjPz4FmmCfVz4HZ6ZEqj7D6IZ+sk113PjDy7jlZ1dx84VXYFoGbXv0Eno+oV8ZRohCpUBsZ5LscdyhpJtqqOtupuPFe/P9C39V3u5nP/g1R730SNVrZ5fl5bqdZcu7mBifYu2eu7LXvrsTBEG5+HE+cjM5fn7h73jniR/kyr/8k7V77IoQcMF3f05XTwcNTfXlbe++fR0HHvqsRWPstc9aPnDap/A8r0K+B2Dj+q08eP8mGpoa+PBnzmCXtSuJwogb/nVzeZtrr78DYVaqGdtNLYS+pybuKMJubsOqrVeF8J6LAIQmKA72K9p2SztWrdIZCz13UZzdmxzHqq3HqqsvN36TYaBYXAsgTINUZ4/KeTgldCtBWMxX5DmUp1Qqh09kGIKMMNJZ9YY7M0WQn1HFlQvDQVIiLLvM9isObMMZGcCqa8DM1ODnc6pF9sgQqc6eMgVYiYK2lgVUA6ekWGdNrWVZIs0wcUeHVSO+TM2SzeY0Q0cYBpHrKDbfPE9LGCaabqixTAthWLHnkiLyXNWLqbVT9WqqqVPU9bEhvMlxJTmUqUUzDKTv4Y2PoFlW+eWhTL2evYdxgS/IOaWC2EtzRgYJCnnCUmnJ7qOR7yt6dxgROEXcMcUwlWFAaXA70nVUzZFh4E6MUejbUh4nyM3gTowig4BES7tqU0FM75//HCyhEzfbWG8+VFHz4t9W5Vf+39W2rOoB/ZdiamAcr1AiCkL8kouM1FtU+x7LufFHlxN6cQV8GHHHb/7FYae+FN/1MRNWufAUlPL1zOA49/31P9g1afZ59XMYK+V5+1s+VOHFlEoOre0tfOQz78VO2rz93Sei6xrTUzP85Pxf0drezAc/8S6+c84FvP7kV/LnP1xZcb6HHH4A5537YwCuv+Zm9j94H8IwRAjBy//vaLZs3M7+B+/NLTfeieO4PHDfRk5//yn88qd/QNd1Xn7C0fzrHzfilBzGxiYJQzXxH/XSI1m+sgcpJa1tzUxNTGMYOqlUkosu+C3dyzoY7FcFhp//5Lep+8aHOfSgPRBRhJ5IxCGgkHTPCpARxYE+3DBQQpmdywjyM9jNbRT7tuBPT+ITM6+WrSDIL37rR1BmmJk1ddiNLYSuYnRFfqx3pmkkGlvwp6dUG+iYfOCbFlbdYkPlF/KYNbWKoTUzA0LDzGQrWHn+zDTJ9i5K83oPgZJHym/dxCzFOyypSTTR1IaZqSX0HMxMltBRagNC11TLiIkx7HplqPLbNilqdG290qerbVAKdIapRFWFiJlvczkNzbKVATQtVaQaRapxnOcidEMZmZFBZXyECovJMFAqD21dFIf6CJ0SURBgaPocYSMIlHFqU63EjVQ6JmDMn8SXFk9V34/Aqmsss8Q001K6fhNKq28hyUGL5Xn0ZGIRCQGUnJBiNcYac7Eenm7ZGGmlpj6/lYfKMT78e/9S4TI9lUbbQSfUoFTEmxwnKBaw6hswa1SYb0eQYajo6ztZJLVqgP4LMTMyRWm6gAwCbrv4aooTaiJs2a2bNUcfxG4v2p/86BR9t67HdzxkJJnYOkzTynb2e8Pzue3iq/EKjqoPOuoAHvz77QC4MwVu+dmVrHz1YWgLHsyung6WLe/ie9+4kFe+5li+/82fAHDECw7l1PechKYJLNukb9sAP7/gd7z/o6dx1eXXEIQBL3np8/nH364rd0IFuOu2ddQ31fKGU16F63r84Fs/44vf+CjPe9HhjI2M09BUz8rVvbzslUexy5oVfPlT3+H4E17CHnvvhqZpvOiYI6hvrOP+e9fz10tViK2ru50PfvLd/OfGO7jiz//P3nuHSVJX+/+vytV5pnt6ctqdXTbBsqQlZxBJZsQcUAzoRcV8Fa/hmnNAxYSIijkiIiA5Z1h2WTbOTs4znbu60u+PT03N9M545d6fePF+Oc/D87Adqquqez7nc855h5uYGJvivZe+jS2PbBfqC67Ld775Uw5YdyltrU34to1bLgqn0XgC3wmk+iWxuDr5OexiAdW2ifeuChFpPoAr/H2Q5br5ht6QCRcxOz9HrHsFlTFhV+07ruDieB61uRnR0lpUGXl2DVlb6jejRqMohkllfBQj3YSsKEt4OyBsCRbPVPR0E57jsP+C7FYrSJqKNTeLPTuFEokSaUhTGtxLpEXI7eD7gVSNHc6XQpScJBFt6xIOsLPTuJUSrmUR7egWLcrAZ6k6OSYIu7kZUUkgKk5cF1kWgA81GqMyMY7RmAFJopafC5OBrOmYzS3geuK+IdCHXs3CcwSQwK1Zwm9IUTGaWoQNRZ2DqKi2JEl8tjU7JTYQ3StFi811qIwNE2lpF61Xxxb3dn6TUCoS6+pFVvWgQq1vI8qaFiYXSVGDWaEl5oeJJEo0jhs8v1jO6e+FGhUCqPPzP0lVibZ01FWR4fdZsyjs3Vnne2RYVaJtnUuSnVurUcvNUJudRolEMTMtqNG/r3P3dMWzCehfLCq5EjP9YzR0NrH75kcpzxRI97bSvL6bWGOCmT2jlGcKFCdzrDnjcMozBfbeuRVJkpjcOcKeO7ZwzJvOZuLJQRJtaR75xa1YhYXJvFtzSKkG7//oxXzvsqsYHhxj7YbV/Nt738iu7Xs4YG0fllUjEjE5+YzjyOcKfPNLwmohmUrwtksu4POfuIyv7LicI47axPmvfgH33/0wD977aPgZqqbyvJecwd7dA0yMT7G+r4fDj9rE+y/+BIcftYnDNh/E+MgE7Z2t/Pyq3/H+j17MWc8/lRv/fCsD/WLX/+oLX0pLSxM/u/K34XGHBkf58x/+yvqDDmByfBrf97n8az/itRe+FN+Hrp52Nq7tpLUpRXVyVCgmI2DWir4gEKqnGnFrcyFh1C7kcColjIYMpdEhzJZ20A0BsW4WpFTfdVBjCYGkyjQH5nM1PNsm0tqOnZ/D9/06BQQ1Fl86O5HlOvVpSdVQY4mwsqlOjQd8o2VmSpJMtKsX37YFDNy1wVtaDUiKKqR8YnHBC/F93EoFs7ld8J9mpzAbMpTHhvEKOaLJHoFum4/APVRvbMJ37ADpZohZ2qLr09NNgXbbAtLLmpog2tkb2mdriRRaPB7K7BiNGbGQR2MCqOBLlEYHQlCEmW2llptFVnWibWk828ZzbLxqBa9WDcz3dBQzip2fQ0s2opimgKorKlo8SXViDK9aEcnciBDr7KUa2InHunoFRNzzcSslFM+lPDKEka5gZJqo5WfDDYekqqjRONb0ZCgJ5FYroaadkW4i0dsnvivfRzEiT1kXTtZ0oh3dmJlmPC9wQtWXbk5AVLX7a+1ZU5NCyXwRVcD3PCoTo9RmBKzeDZQYkqvW/a/JBT2bgP7FolIoEU3FyI/NUpiY49i3ngNI2JUaM/1jjD7eT2lK7P4mdwzReehqDn35Sey6dQut67qxChXGtu1j962PserkTTjVei6GFjXQkhE+/4lvcOmn3k2lVKFYKvOxD3yBmelZ0ukGDli7kovefQGGrvOpj3wlfG8+V+Avf7qZY044gjtuuZe7b3+AF7/8XGKJGCecejTrNhyA53tsOGgNn/v4NxgZEm2Vn135W952yQXs2bWPB+55hAfvfZR3fuDNWJUqX/jmx7jrtvvJtmTC5ANw03W3s+nwJV6DPLltF81tTZxwylHccuNd5ObyfPurVyLLMlf+5HO0drThlkth8gGQVTGLmIfZyoa5pMKY93cBAVGOr1iN0dRMZUyQHPVUGnwoDy0s1Eo0RiTVSGVsGLdSDuVQ3GoFLZZANiOUBnYvfIgkISgcsiDEajputVLP3fG8IMk1UR4q1b1XMSOUAsXnaHs3kq7j+84SrToz20J1clwg5Vo7A+2yYFGVFczmVlAUARAIjr1/+K5wXNUSKXzXpVaaRjGjxHtWYeVmkGUVLdVAcWbH0vcuSkhqNFbHP7JmpjCb2ykP7cNIZ0U1OT9nCxK4MLdTKA/vQ43FkTVdKGZ7Hj4+rlXFmp0SiRGpjsMlBxwsJ1C3KI8PYzRkcEtFIbHULrTbSiMDQRsyg5ER1uCe6xFt68RzXGRVDdt/kbZOagFABQSfTI3GURNJdDOyLHzardWCdqSCYhghcXVxyIqKvJz/0ZJYpo0mLX3UtWth8pkP33VxqpVnE9Cz8V+H7/lM949SK1sUxmfpv2srG194PDP7xui/extdhx6AEY9QmsqRas/QeejqAGAg4fnQe+Ratl//AAD77tvOurM2s/OmR9hw7tE88ef7sSsWeszk8FedRmUmz6UffDuT41N880tXcOCmdbzk5edgmAbrDlzNZV+6gq2PPsF7L307b33n63A9j2K+xG9//id2PLGbr373kxx30pFCzVhV6e7pIBIxuexLP8CxHd52yQVh8pmPH//gV3z6qx9myyPbUFWVa35zPcedvJkVfT1c9b1f8N6P1Jvhjo9N0tXTseQ+HXbUwdzwp1s490VngCRx+0330Nae5T8//z4OWLcSv2bh7OcAKuvGgkbYfxFhv9z3cUtlJF0VqtaB2GNpsJ6s6pZFm3S+AqhOjoVtGl+WqOVmQktrSVXRYklQhPGZ7/vg2AK+vWiwrJgCeODWLCKtnbjVMkiy0IpbxOyvjAuwgawbSIpCtLM30FuThWSM64j/nFo9+s0wkDWhlKFGY7iOvSz/Zd47qZabozY7hWJG0JIpnEoJ3/OQTQ3PdQQcer+2laRpgqPT0LjkuwCwC3NoyRSyYSyrPScpSiih5FYrqPEkrm1TC1qfsmESbesUG4KRejsP0eZUQ5h7tLVT3Nd4Eqdcwvd9fNfByc8Rbe2gOi8IK0mYTS1IuoGEHxjfSZjpJhQzilupn7055eKyArfiuRKF/l1h1WJmWzGbWwWC738QihmpU8AAhD7hfhWTxN8iqP7vzYGeTUD/IjHy2B7u/t61HH3hWVj5Ch2bVlGrVJEkidJ0Xmi8+T6pjibaNvSy7U/3CqKkqnD4q05jz71PhK223qPXoxoaq07cSGk6z6EvOwnfByMZYXRLP7tufgRJklh37uFc/D5BMh0dHuN5LzmTi177Pt71wbfQu6KTocERfvz9X+H7PtnmDBe/70Luu+thZqbm+P2vruOJx3ewZv0q3vmBN3Ppez4TDoIdZ+miMs/52XTYgZRLFV5w/llYVYuGdIpVa1ZQKlaIRMzwdY7tkEjEOOv5p/LnP9yE7/scfNgGjjn+CHY/uZc//uZ6WtqyfO37n2bVijbi2Fjjw4JZH4nWkRDdaqXO78cu5EJl5vlQE0mc4Hk1kUJSZKyJMXwEd0Ve5GS6ODzXwWhcMKzzXUf06JuaqY4NY+dzQprGtrFyMxjp7CIXTpVoW7AIWlXUWAItmQrbcZbjoGeaqE1PYQfW1UpAOHSrFSRFoTI6KCoGWaprj4FYuBYnLTUWR43EQokfSVaIdvVSmRQzksrkWMjfiWSFZbeaSGK2daEaBtbUBI4lqjvf85A84Xzq1mrhYqs3ZgJ17VgA1JifgQVbdt9HSzbiOzW84BrqE6Ak7vOixd0p5omlm7Bzs/iOjWdVcUol1Ghs2QTme34A4hgJj200taBGYsFcT0FLN1HLzS2od/t+4FskLNuNdBNOMY+XavibsOj95zWe6wQowpG6lpmAmSeR/xvWD4tDMQwSK1ZTy88JEEKqES2eWJJYZF0n0txaV3HKuvG/SlB9NgH9C8T0vnFGHt/L6R9+BU7NxXWG2HnzFmRFYdN5J4AvlAv0mEnvUet49De3h3Nnz3F5+Be3cMj5J7Pi2AOp5ktk+jrIj0wRy6aItzTy5A0PUhyf5aAXHoddCXgkvs/MvbvoOqaPD3/q3URjEf7t9R8gFo8xOTnD0SccwQcu/kR4jpMT04Enzxv48CWfYnJimhWrejj9rBMZHhzloktez313Pcz9dz+Mosh1yQTgJa84l+mpGSbHp7n6h79hciIQ71QUPv3VD/OlT36TT3zxg/zyJ39gZGiMY0/azMC+YUaHJ/jM1y5l76597Nqxl6mJaSRZ5oRTj+bYE46gks9hFnXsYCGyJkaJdnTXJRyvZmG2tFMZGcSzhaKB3pARZMWahaxqSLpOebAfxTAxGhZ02ECYtkU7e9ESqTqNNxAOqkokipERA3BZUQM0W45YZy/lsWGhQxeNYba01zHnfcehPDokLAGaWgJzM0skmWgMPdFAdWoMNRYX12OL55xSUSCmNB2QxJC9vQuzuZXqxDjgI2macGFdNKTWGzN1LUTfc6mMDqElG/A8l2hbJ77n4dlCUkeNxgQx0vcpDfaH4Ifa3AxqNI4Si4EkC1i7oghLhki0zopCVlTM1o5Ql0+QUxXK48Ohz091fFQkEkki2tZJdT/VBgisExYleteq4Do2RmOTIMKGX4mMYpqUR4fqEps1NY7Wuzp4iYyeaKAwXp+wxd+FqB6smSkibV2AMMxbDDaAYK64aGGft+/wbBtF19Fa2nEqpRAs4dZq/P9xDVLMCJG/k0gEQTWLYkaoFfKopokaT/3N2dI/I55NQM/wmBmawLVsmvra2HvHViINMTxbKFi7OFiFCpHGODtveYS+Ew8m05RagkK1yxaFsWme+PP9rD/7SFyrxsSTgww/vBtFV1n33CNQTR27YpFobkDRFFzbpTxbYGNrOyWvRsG2eNHLz2H9QQcwOjzB+NjkknPd8sgTWFaN5593Jn/6/Y2c/YLTuOyLPwgrnxeefxau43L1lb/hXf/+Fu645T6GBoZ5ztkns7Kvm19dfQ3Hnrg5TD4gSKw//eGv+dgXBCw8lohy4MFrufkvdzA+NsmBB6/lpr/cQTIV54C1fSiayqbDDuSnP/w11//pFn7808/jV+fC4wmCpSM8gBBSMk6ljFMQJFIkCc+2qM1MCjdUWcH3PWLdfURaOlCiUaz9eCYgNMC0hrRQKAi8aMxsG16tSnliH5JuEuvoxa1VqUyMEgncSIWoaUzs9H1fJA1FIdLcLnbQwaJcHu5HjsSIZFuRNV0QImdnMFvbwXGFxlipGFo2CN+gMtG2LqrT43g1C6eQD4mY88nMtUpEWjuxpseXqhUQqGInUuC5oj1FIKkjSaixOJXxEcFv2Y+fMl/leXZNzLYCYqsgQUtEO3sCrouPNblgfidrBlqg6u27DtXJMYFMlBVhk25Vl1QWekMap5CrUyFXIzGs6UmMjm5hVVHIIWtGoPPm1FlbLFxrFRDcLcUw6jYp87G4qpBkOSSdxrt6qeWF4rWeTKElGupaam61QnVyrO54RiYbKmz8s1QKZE0I2S7HS/vfiGcT0DM4ClNzeJbD1O4RRh7dgxE3iTYmqFUsWtf3MLZtH4//8W6OedPZjGzZS2FshuyqdqHAvGgxMZNRnKoo+af7x5AUmaGHxLDasWy2/P4uDnvlqTx540PIisSa0w9n27X30nPUOsYrBW78y23MzMyx4aA1/Pbn1/LIg4/zqS9/iNe+6XxisSie56GoCo8+tI3xkXGQ4D0feisfee9n64hxv/35tbzt3RewY/tumluaePPFr8F1XW76y+0gS7zo/LPrLL7nY2pihj/86jp6V3Rx1LGHcfvN9wDwite9iMOP3ETNtrni21fz5DZxTclUgo9+7n28720fQ1qk2DDfxy+PDYUtHDUaQzEjWDNT6A1pfM9DSzaEsvm+7xFt7aQyOhhYFKTDRWdxSKpKbVa07CJtnUJhIeSeyHiVEqXBPUS7Vogk4HrojWkU3aA6NY5bKQew41Zi3SuFPXewWGmpRmLdfTiFHOXhfnzPx8w0Q6MwmvNqNbRkQ13LEISrq++7ApUnB/p2rlDWtgs5tHgyaP9ZmK1LIbsg5im+5+DValRGhdlcvGulSIDzXkLLzhAkYa5XLmGkm7ArVWEX4TgomiZAEIqCnkjVJS/PsZHVRXpojkhCWioNsoRXrRBp7cQp5kU1GMy+XMtCjYrfvJYUwAhRuQnIuxZL4HmesEcIiL3ukvnTwnXIikq0vZti/86wUtIb0mEbFsRibuWEYrhimESyLUTmgRv7hVezliQza2YKs6kFDZYQeT27hlurLYAU/slCor7n4do1JESb7umaEz2bgJ6hUZor4FRqTO4c5vHf3xU+Pr59kA1nH4nneoxt24fnuMwOTZLuaUZWZGaHJtl03gls+d2dOJaNEY+w9owjGHzgSTE/KlbwPY9j3nwOj/76NkpTeZrXdIIE2VUdGIkIyY4Mq55zKE5LjHe/9VJmZ0Sb4Nrf3chFl1zAir5uLMvihj/fysigaHmYpsEXvvUxPvsfX+P0s05CVpQ6l9P5aG7NcuknL+GPv/kLf73udgA++rn38YWPX8bE+BTv/MCbl7znlDOO45Yb7qIpm+ayL/2Agw/bwPGnHs2qNSv51H98lRecd2aYfECg8a6/5hbe/aG3UrE9IrqBH+iqVWfqh/pOuRQqFtTmZon3rqI01E+8dxWeU8OzLKpT4+GAVyCW5Hr+jyRhNDbh1CzcYh6nVFxQtg5sGsrjwwLp5Ps4xSKKrqNEotTmZhdagXaN8sgg0Y6eusXKrVlB9eCixpIopil295nsgmXEMkoEgIBMj4kWoWyYwmguEg0XlXAeMD0pYL/ZVkEu9f1QckhoufmBekIeuxxDicTCOYZbrdRJDwEY6YwAS1Qr6A1p9HhccGh8f0FWSNWWWBIIuDV1CUJSVNRoNBDxzFAr5lE0DbeQww702CKtnXiOQ6ynT4A6ZBk1FqeWnxPuqraN73tYc9OYmRaUSJTyyCB+wCcyM83iN1AqoOgmnm3hVMvEulaIylSWqUxP4uTnQjh4ZXwUt1ICSSKxYjXafzHDqWtKBO8X92AeTbewqXHKJYr7dgf3RiLS2oGRafofgxT+u+HWLKoTY+J7DzZtZlPL02It/mwCegaG53kUJ+Yojs+x69ZH659zXOxqjURrmvVnH4msKkTTCR740Q2sPvUQWtf1Up7JserEg0m0NZIfnWHbtfdy+CtP5Ynr7md2QCxYeszk8FefxoM/uYmmvnYe/PFfw8+INMRZ94oTuePuB3jRy84hFovykx/+munJGR645xFe9+aX8eC9j4bJB6BatfjlT/7ABW97JV///PeYm8vR2d3O0MDCwFPTNWanZpmby4fJZ+OhG/jrdbcxMS5279f+/kbe9cG38IdfXUcul+elr3w+bZ0tNKYbyM0K4MCjD25l1QEr+e43rkKR5fC9i2Pn9t0cftQmqpZNtLsNt1ZF1owl8GrZMJB0HS3ZELalYh3duFYFz3WF0Oh+i6RdLBBt6xItG18sIqXhfZjNreiNTfXQat8XPi8N6WDRFi6vlfERfM9DTzXWWULg+wKZtigiTS2UhvrrEme0rVOg6wJUk72MNbiWaoTAm6iWmwPPwSkVcRAcHadcv0GojA0T615JLBbHqwqhzsr4CGZjE5XJUYx0FruYxy2XMNs6UXQdp7yXWm5W+PzEE/i+hywryIYRtiq9Wk0kzKYstZmF9qXv2Mj6Uvivj4QaT4jZkaqK6ivQaZvnw9TmZoi2dYEvFKOFZE8Kr1bDd12s3Cw6GdF6UxTkWBy/YomKc3ocI5XGSGcFkbRSprRIey+cBzpC2keNxlBSjcTau/Gyrfi+j1suokYiwqHVcykND5DsW4usLr+kqmYkJAlHmtuoTk8sgFYkicSKA9DiCTzXoTQysOg351MZGxIGhIj2sKxqKJHoU1bH/u+Gnc8tzM0C8IUSiWI8RRLtfyeeTUDPwJgdmqQ6VyKaTqCoS9s9ZjLKwH3bmdo1QkNnFrMxxgnveBHb/nQPE9sH2XDu0QwO7WD3HVs44tWngw+F8dkw+YAALfTftY11Zx3BtmvurTt+Za7IyPZ9fOlT3wZAN3Quft+FfOETl3HCKUfxs6t+S1vb0lbD2vWr+OYXf8DM1Cx/+u0NvPMDb+bnP/49/bsHaGpO84aLXkl7Ryu//OkfwveccvqxXL2ITLrjid0M7B3iPZe+jWxzhu98/UdsfexJVE3lLRe/VihgP7yNdJOwY5Blme7epXDso447DN/3+dbXfsTHP/ImovG4IF8mUuEibTa3CWsBx0FWNVyrImT3fR+vWl3YPbsOkiRRK+Zxq1WMxrQAIcyjn+bFXR2nzkpgPjy7hqQZeI69YLEw/z3MzYgZh6aF/BhJWVhYZN0Qqtf7QXrtYgE1kawTQtUb0kKNwKoiqwGHaHgAJIlYRw/VAKZspIXys2pG8eI1MdT3A7sBHyqjQ3UzkvLYMJHmtlDjTKhC24G8TncoEirJMrWZaWGJbZjoyQZBGtXFuUiSXK8HJyt4rk2sp4/KxCg4tuBTSQKJ6FkWemNGyNzMX2elhG5GUHSd4t4dSIqC2dKOkW3Bq1RwrIrQhIsnhVW4oiBpOpLngWagaAqyblIa2he0QPU6boxbrYjvpLkVyRdw62q5KIjG0TiSqmFNjmHn55A1DbOlDWtGoBR918X1fZxSAbuQQ4lE0eIpVFO06BIrV1OdHBd8pcWISd+nMjGKGhVq2/urLYjrroTWGyDmR5HWzmXbwf9/wve9JWZ2IGwxno4E9KwY6TMs8uMz1PJlZE2hMDrNimPryZZa1ECPm6w4dgNrzjgcxVAxoibVXIkVxx1E89ou7vzmHwS44MzN5EanUTSF8txSX5Lc8BQNHdlQN25x+IHW2kmnHcOnv/Jh+lb18OKXnY0sy9x2491sPHT9kvd0dLWFAALLqvHlz1zOQQev49Nf/TCnnXkiV33vl0yMT9G3uheAw4/aRCFfWkIorVYtEsk43/zyD9j6mDCcc2yHb3zx+7z6jefRmE4xN5Ojo6sVz/N47KFtvOJ1L8YwhK7YSacfi24Y7NnZz/bHdzKWr4EsocUSGJms0DNLpHDKRWRFxZoax5qZxCkVqYwNYRfyKIkEkZY2ysP9lIf6qUyMYSQbMZqySLIaWkAvHtwLI7ClvXIlEkONRsWiuozgpV3Io8XE4F1vyCCrWijpjyTVzfPC7ycY5tc95vvUZmdQzCiVsaFwIZFkRUjWNDYR6+jBqZSpjI9QHhkQaK7W9vB1Pv7SAb3vIWkatdzMAt+nVEJSdWQjihqNUsvNUhkbDmHd8348kbbugFjbhiQLV1IkCTWexMy2YOfmqIwNYTSkMZrbQVXB88LjKLpRB1lWovGQNAqCSFkdH0XyfVBVjFQjZms7shlF1gyiHT1IkoTvg1su4BSFPYGkqUKAdBkwglMqomgG5ZFBIddj21QnxnBKRayp8XAD49k2ldEhjIYm4d+kKFjTE8Lie3aaysggxf5dYeJWIzFinb1LPk8cqyZUsRUVxVgGzbYfnNyansRbhkP1/zckSUaJxJY8vtxj/4h4NgE9g2JmYJx9dz/BvVf8hdJEjsd+fxeRhhgbX3gcvUevZ9WJG1l7+mHs+OsjTDw5xLZrRMWz547HeeSXt+KULRzLxvd9hh/ZzeO/uxN82H7DgzR2Ny/5vPaNK7HKFVYeV58AFF1lqlzkY597P5uPOZQrv/Mzrrj8ag4+7EDWH3gAAL+5+hre/9GLaUynMAydl7/uRUiyTDKVCI9Ts2pc89vrmZqc4WdX/pbjTzmK73ztR/i+z8ZDN7D5mEP5/rd+wqo1K1h3oIDAyrLMy1/7QiIRkye37Wb/2LNrgA987B00pBt45wfeTGM6xW033c3ddzzAxz73ft7z4Yso5Apc9b1f4Ps+z33eKTiuy333bmF0eIxi/y7hhNrUjBqNCzM6u17GpDY7haxouDULM9NMpLWDSHMrpZEBint2YBfzRFrrqy4lItxO7VKRaHt3WB0phonRmAYkoTG3TB9f1o0AaLBSWHdXSkRaO4h1rSDa2rnszlNvaETRdPTGjIBwN7UIz5dorG7Bnmf+W9OTlEcGqEyNhbprEPCSSkX0TDORtk6x+14OjKAoxDp7iLR1UJ2eQtZUajPjWFOjoCzlQKmxOFosgVPMUxrYg2tVcSslPMcm1tmLkW6iMj4iQBSWUL/GdfCDFhqICtVd1P5UDBPVjO4HdYdIc6tI7tUypaF+ysMDorLJz1Ds30VlYhTfruE5Dm61QnlkgEhLe6g3t3+o0ZiohveLWn5W3OvmttAcEADJJ9LaEaL2FodXq9YpW0uyvOysyMhkkQNriEhbe12laGSalzW2W67a/keEMBNc+J3Kmi6MDp+GeLYF9wyJ/EyOkUf38MR19wOgGCoHnHIIk3tGmesfp7GnhcGHdmEVyqw94/BQQHQ+qvkytlVDj4qdc0NXlp7Na0l1ZmnsbqY0lQvf5zkubQf2ku5txZor07S6Q7T17n+SWFOK3uM28OVvXsnm4w7jcx//RvgZ99/9CJ+/7KOcfvZJFPNFhveN8J5L387uHXsZ6B9C3bSed3/orXzy0q9Qs2rIsszr3/JyWlqyXPy+C5EQfKEfBdYOTYEFw7e+/EPOOOdkTjjlGCJRk3gizo1/vpWeFZ3s21vPMNd1jVtuuJPTzzoRWVF42WtfhOd5rFrdy2VfvoL+3QPohs473v8mBvYOcd9dDxOJmJSKZT52zc189bIPs8pxxEzFWxC53D/cSglZ1RbUpSWZaFsH5ZFBrOkJIm1dxHr6RGsJgd6yZqaJda/AKeSFE6ok41SFmZrZpKM3ZASvSDcWKiFJRk8Krxs1nsDOz2FNjYfnEW3rwpqbJtLWKXbevh+SOT3HEa20WAKnVKA6PC4Y9QGvQ40nMdJNlAb2Mj8GF6CKCSF0GVQRrlUl0pihtG93iMSrLiIr6o0ZUBT8motbKaLF41QWKQz4jkOkVRA08X1kw0RLNuCUi0LVINWIZ9fqbBKMppYlJn/zi6zR3CoABIUcqmGGtteeXcOamRD3bx4UEoniVKtIshyKwPquI4RVWzuxmQHPE8KoXb3UZqdRY3GBKlNk3JpVNzuTNQ0jncWtlsX8J5DCkdXAZyjgigmOkkjsihFFNSNC1WEZ9QPXqmLN2EKcVNNRYzFi3X1UxobwPRezqRUjgEXb5SKlgX6MjOBoCe5UjEqxPulKskDHPR2hRqIk+9YFKhtC4unp4go9m4CeAZEbm8a1XbSYwYHPP5bMylaquSKaoeO6Lo0dWSRZYnrPKFZhKbJsPoxElFRHE0de8Fwmnhzksd/eiaxIrDp5E6nOLLnRKTa/7jlIsoxnu2y//gFyw2KAv/KEg+g5ci1Dj+zmrm/9kXe8+w28/5JPhcfu7G7nzOedQiFf4JWvezGXfen7DA6OcNhRB3PcyUey7bEGJsan+OkPf83r3/xyPN9DU1Ucx+Grn/sOYyMTfOGbH6W9s5WRoTGu++NNdPW0E41FKJcqDA+NccC6PkaHx2lucWnraGHDxjV8/Qvfp1gQPfFL/v0tmBGTyclp/vjrv3DUcYfzh19dx/DgKGc9/zSOPPZQxobHecXrX8xPr/h12A7cvnUnJz/nOHr7unn40SfpSkcW5jauu0TGRMj1z4TwWiEmKRww5xd7a2ZSSN5HYuDaSL6PmW3BrZRRY4KD4tk2RrYZtbGJ4r7dyLqOkWkm0tIWEjoFEm1UDKflDqEQICsB4k0swm6lTKVSQY3HQziuXcjX9er1VCORjp4AGFAi0tGDVykHSLL6RdGrWciLqio9kRLyOYaJZ1VDCLLvecIPqFyitG83kZZ2rNkpIoF8zXw45RKyGRXIwZqFUy5RGRsOkV6qGalj3wNY0xOY2VbcRfMwSVFFV9H1KA3sQW9IB/exHpQR61oh5H18T+iladoSCLr4bu06tKJXrYgk5/vY+RxmUwuKpuNZVUEqlQQPSZgOKiBJ4QZEkIfnwjndvIp2tLMXt2bhVIQ5n55uEoZ1i67Jd2xKo4PojRli7d1Cy69BqBXge3VVmFMs4Hsu1cmFTYhiRoh3raQ8OoRTKqAYEaKd3U+rfptiGE9bglsczyag/+UozhZxLJvC2CyV2SLJtgzFiTlm+sfIj8wwvXfhD/SwV5zC2PYBIg1x+k44iJ03PRI+l13TiVOt8cDv7qJ78xr23rkVANeDJ69/kM2vfQ6ptgw7//oItXKV7s1rSbQ2hgloz21bOOyVpwohUx9Gh8aJRE1kWeY1bzyPxqZGvvnFH2BZNVrbm3nthefzpU9/mztvuZdPf/XD7N01wOZjDmVlX09o1bB6zUpOO/MEhgfFNVx95W/5yKfezb+/65PMTM/x+1/9hU984QP88PKfcfzJR/LVz34nvJ62jhZe/toX8YkvfpAnHt+Boetous6nLv1K+JrbbrqHd7z/Qr70qW9z7e9vpLu3gy9+62OMjUzUkVkBbrnhTt70b69BluS6GYc1Oy0Y/q4j5gPRmOBgBArDajwZ7tJ9xxEJp1QQO0JJxquWqU6MCv+glnas2RmqUxW0RIpIUwsgYQe713kuTbSjBzsgLS4Ou5DHyDShpzNYUxPImr6o9eKH/kOyGVny3lpullgiRXlkAM8Syg6ebaEvI2Y5r0oAhK2V8uBeIm2dVCfHArCEXOc3BFCdnhRQ7EDpoM5q2nNxq1UUw8QujIr2VrBrXtYkbf/HJFmY1gViokokKrTkYvElOn2+5xFpFmg0SZaRdV1Yb++nCC0Qgv6ifyrhv33XEfM1fLRECisgHs8nTRx/AUpPkEj2a9Xii3lZdWKUiiyT7FtDpLkdxTAXNjBmNEy+tdlpzKaWEDK/HGJuOaizpOkiCfWuCi03/hba7l8t/m9cxb9oeK5LrVimPJ1ndnCCdLdAlg1t2UtDZ1OYRObj8T/ew0EvOJYHrrqB7s1rWH/2kUzvGSWaTtCxqY87LvsDLet7mNw5tOSzxrbtIzc8RW5ELMxb/3A3a59zOEY8glUU/e6ZfWNsOPdo9t39BHt37eNF55/NMccfQTQe4TP/sWB/PTYywS9/+gfOOPtkzIhBpVSltb2Zu+94gFgiylvf+Tp8oLu3g0vf/RmOOu5wDjx4LY7tkEwl+Mp3P8n01Cw7tu3mVz+5hrddcgGXvuczdec7OjxOYzrF9y77MVsf3c5r3vhSbr7hjrrXuK7Lvr3DZJszTE5MMzQwCkhMTS4VFlUUGc/3cFwfNZHCnpsRYp2aTm1uGsWIoCUbxCKbbBBtH0kSxEWrip5sxAsWuEhrB7JuYE1Phr16I9NMeWw43G3buVmBBjMj6A3BrMHzAr2u4hKhSBCLT3VqEjOTxYLQ5tnaT8FYjUQFFylAaYVETklCiyWRGzRkU/BmrJkpwZ1Z1P4ys60oZlS0H30f3/fRA8Xn+XtSp78mycJxFUFMtUoiEcl2DSOTFdWTpiPrOlYuR7SjR1QLruDluIF6weJjCmBGHLOlHUkCWTdxq1XsShGjsUnIHymKIAU7Dm6g3ac3NuEv4hKBqP7MppYQSi3upTgfM9tKdWIUNdhY1IdPZXQYkDDSTUL2Z3YKI9m4DD/JX+L9JG55AATxPGo5IWAaybaiRuOUhvqXJs+/41iqxZN1xn5IEpHmVmG6B8Kr6f9Q/N0EJElSEsj6vr97v8c3+r7/2NN2Zv8PRH5iNuD1OLSs7QYfqrkSzQd04C/j42IVy6HVwsB9T6LoKj1HriXV1UxlTrSpKrMFUh1ZZvrH694ba0oxtm1f3WODD+2gdUMP++7dDkBDZzP5sRlUU+Pgvl4e7d/Dk0/sontFfcsFYM/Ofbz54tfyrS9fwa9+KuyxD1jXx7EnbuZbX/khZz7/NJ7YuoNPf/XD3PjnW5FlmfUHreETH/oie3bu44RTj+Zlr3khqcYE99z5IMVlSKv5fIHu3k6ec9ZJrFzdw6MPb13yGlVVcIOF7RWvexEDA8P09nXRt7qX3Tv7w9ed/cLnMDE2yRmnHCE8UpKNSLIcDo2dkvCLibR1Bm0rJZhpQKx7RZ2kP7JMrLMXz7ExEskF7s1+i5NbKWE2NWNNTYiKRRILnVeroTelqc1Nh4uyJCsBMXUGmpqJda8UBEhFxXNs8RmyjJltw61WqU6NCZJrS4cg1yJ26U65KFp/mWwouKqSEPYOioLveSEDX1ZUsfOv1YTNuOuGxNZoe7d4bzSOlkyFC6kaS2Cks4FPUbxOO07WdIymZmqzU2iJVMB1com0dhLrWoFbLeP74lpV08Sp1VAjUexCATs/gRKNYTQ2hfda1jQhadSYQcq24FargSRPPUiilptFTTYS710lKkZJaPCVhwdQYwkh+2NZdZDleSdcET7WzCRqPCEIxYU8SiRSl3Cs2Wkize1UxhY2d/OafvPh2TZerYas66KduF+yUWOJv6mQPR+KGSHRt0YQkSUh9SOpmqj2/pfdS5+O+C8TkCRJLwW+AkxIkqQBr/N9//7g6R8Chz6tZ/d/OOZGJ3FrLhPbB0l1ZXGrNvf+8C94gVL0IS87aYmkTvtBK3FqDuvO3IxVrDB4/5P037WNQ85vDmdD+dEZeo9ez/gT+6iVROsomknS2NMc/ns+tIhB89ou4tkGjEQELWqiGRrp3hZ2XvcA684+nK9/+QpWr1255PzbOlooFcv071kYRu94YjfHnLCZT37534nFoowMjzM7M8dBm9bRvaKT97/941SrYvh+w7W3MjeTJ56IsmfXPs56/mn8+uo/LpybrtHS1kwimeCHl1/NGy56Fee94nk8+uBCEtINnSOO3kRLa5Z0UyO/+fmfAOEv9O5/fyu7dvSzd9c+jjruUFas6KTJBN2pUhkfIdLeXcerANGW8e0a1tSCVw4SCyTR+fA8nFIBI53Ft2uidbTM4iBruhCcnG+X+T6+46IHVUO0awXeIsWD6iLxy9Lg3kCNQCXa1omWahAblIkxkAitAqrTExiZ5rB149XE+Sy2C3dKBZxSAS2exHOFrI5sGBT7d9UtiLKqCjSX51HLz4ZV4GI/osroILGuFcKZdL+Wl2cLBJtdEO3FSEs7lbFhquMjRDt7sKanhB2CbiA1NSNJMnY+h6zreJ4hLL6DDYGRbUExIvh2DbuQo5abJdreTa0wh1etEOteKeY+nouWSouBua6HKg7htQdCr9bkGFqyAT2dFUoLuiHIrYvCrZTxPR9F1/E9n1jXCqrjI0K6xzBBUYj39IXznur0hFBCCEIxTcrjw8Tau8FzMRozeHYNp1IRageSFHyGixzYcizncKoaJpLvUxoewCkVQJaJtnWK6vSfpIYwH77vi1mlLD8tbb+/d8R/Bw7zfX9UkqTNwFWSJH3Q9/3fsqwL0rPxVGJ2aIKxrfuIN6XwPR+7VGXHjQ+FyQfgyRse4sgLnsvWP95NcTJH28YVrD7pYLZecy9Tu4aJNMZFC65/jOFH9+A5DiuOO5C9dzzO1mvuYc1phxJpTFDNl8AHPWKgx8y6JNRz5Doe/MlNeI6LmYqx6oSNPPHn+1ENjQ3nHMX49mE2HLSGe+54kJe84tyw0olEI7zzA2/mdz+/dsm1ybLE9NQMX/70t5maELvmV73hPMxIJEw+83H/PQ/zpotfw8033MnhR27iVRe8hNtuupvWjhbOet6pjA6NsWP7Hs5/9QvY9eQeNh12IB/8+DvY+tiTJBIxDj7sQHJzBSRZ4qPv/xzpTAPHn3wk5VKZkeExfvCtn7B2/WpOOXoDsmZQnRwNx/F+IHOyRLk1CKOxSezYHWdZSX+QkGSJytS44O6YEWHZsMjozmxuCxdU2TCJNLfjlAu4xQKSquIj/sCtYBCvRKKosYQgwLZ1Uh4dxnccrHwOWZbr2mjl0SEiLR3UCnPCamF8BN/3MZtbBTJPVupnNIjZkZObIdLaLqy6g4Vab2wSoqkjM/NfItHWDnxFwcnNLbny2uw0aiIZVkuLw3dFQpCkgKskyQKJN7h3AQxQs6hMjArHzlQj1tQ4ajyFrAqiaqStE2t6Eqs2Ht4XM9NMeWRAuKnm56jNTCFrGmosA66DU8wjB2oW9V+TFD5mF/OicrVtUR3u74sjy1QnRVLS003Y+VnURJJIoALuVsqURgVyTU83CfVt10WSpNA+262UMZtaQJYDJQgfWRcGh75jE2lpp7h3QPw+Wtoxm5qXJBXhXDoikg+A5wlouRFBjid4usL3xfwtVICXFayZSayZKSRNExuhRPIfqkv39xKQ4vv+aHBy90mSdDJwjSRJXfytv9xn47+MwuQcU7tGkFUVWVNJtDQw0z9GcbJ+qFyezmMVyxz4/KMpzxRItDTy+DX3ML1L/IFUZos89ts7OPrCs7nrO9eAD4dsWsXRF57N3NAkvg9b/3g31XyZNc85jMLELJtecgKVXBG7apNobqhLetVciWq+hJkUSDrXcek+uI+3buzi4rdcSs2qcdElFxCNRVjR182TT+xi7YbV3H3HA+E5n3HOyXR0tXHzDXdw6hknkMmm+e43ruJnV/6GT3/10iX3IhqLYAck2F//7BqamtMcd9KRrDtwDV/+9OW87d1vYNUBK2jraKFWs9n2+A42HbaB3Tv2ccC6Pq7741+57+5HOOTwA/n69z9NrWazd1c/F7/vTQwPjHLm807l5NOPRYkJHTI9mULWTazpiVA+Zh66C0KbzPc8MQCvWUK2R5aJZFup7CckqcbiAm3W2oFdyGNNjqE3NgmOx7zvUbkkxDLtGmamuc6wTlKUQF9LJ9LWjawq1HKzwvcnvpCEPMdGUtVgVlEfvu9hpNKUhwfCxypjwrvHD+DQ84ALxYwISRnDFK6hlXIoDaPoeiikCoh5xtwMkZYOiMWFkOYi0qOkKAI+3phZ4jGkxmKBfYKHns6gpxpEEtivPek7Ql1CkiWhG1csIPk6WjyJW7PqCLtupSxmIwEsGVlG0nX0ZKNAEzoevmMLcdJIrK4qMdJN1IIWqWpGsEsFZEUVwqLlUjg/E6KsC3Of2swUse4+7GIeSdXA84RnT6ChJwffqxqYB85D0EEkYTUWJ9LSTmlw7wKp1ozUVY3V8RExS9sPKOI5NrVc/XoAAs6tPY0JyM7PURzYswCljyXC+aNvuRT7d5FYtRYt+lRcWp9a/L0EVJAkqW9+/hNUQicBvwM2/MPO4v+RKE7NUStXRWWSK6GZOlahTDybouPgleEsZj5imRT3X3UDB7/oeGb6x8PkMx++51OYmAUfoukEuZFp9ty+hd6j19N/zxPIqsKGc4+mODFLQ2cWu1oj2Z7BrTnsvOmREJAwH+W5In0nHsxM/1gogNq6oYdvfO2jjOdz1ByHm6+/gy984jJWrOrhbZe8nmNP3Mydt95Hb1832eYmPvHvXwyP19Ka5ZWvfzE/vPxnzM3mOPqEI7j7tvvD59/4tldhRgwMQ8eyapRLFQ45/EC2PbaDiy65gG9/5QpmpufItmR4w1tfyc9/9Dsu/+qVfPk7n+CTH/5yWGHdftM97NszyOFHbaJm2Rx8+EF093YwMjSGZVmM9g+SkhYWl1j3iqBVFUfWdZxiQTiBKiqViVHMphbRygHRbiuXMJvbxBxGktEb01QnRsNF2cy2Ys1NY02Ni7adLAcoKxlFb0DSjSUWDr7riv8gcFNdSCJ2ISd23a5DdWIUs7UDWVPrzONAzFKcytLZmV3IgywEUuc5R7Kui1ag76NEY0ECLeJW3GUrPNeq4lhVwXMxI+gN6XDwr6Uaw7aUmW3FmpkUvJhsG9WJsRA1WB0fxcy2Li9iKUnIRgS3VEQO0GLVsWHUAP223PnIui6G8i0dIEmihYgfPNaOXSxiNmXxHAEYUTRdePAEIAizuV20WT0X13FCwqWsaoJ/FcCnQ0TjzISYOwXXXOjfiawKOwPXslBjMZxKuS5ZKpEokipQhloiRXzFanHusoJTKS1p5y4FRojPV0xziXr206X9BkKAdLHeoBZP1lXc4esqlX9qAnor+7XafN8vSJL0XOCl/7Cz+H8girN5qsVqsPB4JFoauf0bv8etiR3RppeeiF2pMbJlD3rUZMM5R6KZOk19bfTfvZVUe1MdYm0+4pkUiq6Sas8w0z9GbmSax/9wF60HrkA1NMozOaLpBFahzMSTQ7i2w/TeMVafdDDTe+p3r+meFlzbZXTLAppobOs+MSNKabzn7R8LH9+7ax9X//A3HHzYBs598Rk0Zhq4+IIP1h1vfGySSMSkraOF3FyeV7/hPE44+Sgcx6Wrt4NyscwPvv1TXnPhS+nt68bQdR57ZBvHnnQkH3jHJ5Akibe+83U4rsv09Cwf/NjFfO4Tl7F9664w+czH8OAYl/z7Mfzoez/nwpe/i/bOVl7x+hdz1fd+gWEYfPFL7yGriYW2NjeDZ9uYmk5lbFhYGuu6qH4ME0lVkBcpNduFHHapQKyjG9+H8vC+ulaPNT1JpL0b37GxrQo4Th2ZNNLWtYz0P4Fwp76kVQaCD6I1ZlDiCWRFw0g31w3AZcMUs5NaUOFEoujJhgVPIVWlNjmOGk8gyUo4UwJgboZY90oiLZ245cLyagDxJLXZqcDuQVgJRDt6hFnc8ECI0lKicWI9ffi+j1MqLrnOWn4Wo6lVGPItqjbNbGtocQEQ61yB77k41Qp6qmHJ4quYkdA5VjFNint3Lr6RwmOprZPSYL+Y37V0IGk6eqOO1tCIJMmUBvbieyK5Rdu7qcxMide2dWAHStuypov5TmCRAQitN7uGns4iy7LQeTNMao4tNgqOQy0QPVXMaGgnIasqeiIFiRSeY1OZXGpwtxzBU/Cwuijs2Rm2CbVECjX6NxTP/wHhO04dStF3HSRNxbfqNyf/aO25/7KZ5/v+o77v71rmcdv3/Z/8Q8/k/3B4noeVL+NULCa2DzFw33Ye+83tHPT8Y5ADsdFHfnErbRtXsOGcozjiNaez546tFCbm6DlyHZW5EgP3P8kBpx5Stx1oPbAXLWaw6aUn0nJgL9nVQh7GtV2GH97FvnueINWRJd6S5pFf3kZjdzPjTwzgVGuUZwv0HX8QiqagGhrrzz4Sq1gJeUEAiqYiyRIz/eNMztSrSAM8eN9jpBqS3H37A+zdtQ9vGYSPYeq858MX0drewp//cCMD/cM8eO8j+J6H47q88W2vYsPGtbR3tPCVz36HltYsjz+6nXKpwhsueiU//sGv+O7Xr+K7X7+Kj7z3s1zwlpejyPKSof8Z557Md79xFQ/dJ4CZI0NjfPWz3+HFLzuHgf4h7r1/Wzjw9V0PSZIDgqloR1nTk1gzk8iajqRoS6V2dAOnXBIL5n7X6Xvugo2ArNQlH4DK2BBmumnJvVHMCLKmLY9ukgSUWzWENUBtdopIawdmtpVISzuRQEhVjSXC2VFlfITqxKhIkI6N3tQseE216pJztqYnxYwLUYEJMz6xHKixBKoZqUsCnl0Tysjj9XbSbrmIZ1lYk+NI+81UAGRFw8nN4lZKRDu6hcRQz0rs/Fxd5VCdHBUJ1LEDK4WFVpOWSov5h2FSnRxdVpYGz1sgiToO5eF9wfF9/IDYOp98ACoTI+gNjQKtNzYiNPLKJaoTo5QG96JGoqFNB4hNiBaLB/48pqg8PQ88l+rkGKoZwXNdtFgcWVVxLdHCtWancSplZFUj3rUCab4alCSiHT1/0w5biyVIrl5HvKePxMoDiHX1LrtR+EeFrOl1x6/l5jDS2frXGOY/XBPuKU2TJEk6SpKk+yVJKkqSVJMkyZUkaWmT8l8sJEl6riRJT0qStEuSpA88XZ8zNyjUrQvjc0Qa40iqTM+R69h33xP0bF4Tvs6p1lB0jft/dAO5kSk8x2F6zyg9R67FKlbov/cJDnzeMaw7czNHvfFMUm0Zdt+2hb13PUFjZ5ZES5pk2wLDPbu6g8pckcpMntYNvSGUM93bQm54mrmRaQ5/zekc97bn43kemRWtpFe00tCV5aAXHEvfiRtZ99zNdB2zDstaKqK5Zv0qOrrauPb3N2LoOi995fPqnk9nGli7fjWNjSnGxyaoWTZXX/kbdu/cx/V/uoWPvOczXPv7G0GSGOgX8Om/XHMzsizR2pZlbGSCQn5hsbGsGg/c+yir1qzk4vddyEXvej0XvPUVqJrKc846iZpVo6OrNXy9XbNxgl3dzp37RC8f0BLJgEnv16GQfNdFCvgjKDLRjh4ibV1E2jpRY3Gs6clAkqX+z0ZvzOCUClQmxuo0tBYO7OO5DmZzm9Azi8aIda1ANoREv5ZsWPIWNZ7Ezs1iTU/ilEtIQbVWnRyjMj6Ca1UFS18Rdgv7J73yyBBeJQBRLD0jYeFQyImNQKVELT8bmuFFWjqWKBeAaA3tj3wDASpwSsUAIl1//UYmi+/7qLGEUOceH8Etl5dUSp7jhN9PdXIcSZaJdvUS610lqjhFodS/e4EMul/SDsVh9zuv0lA/siqsvSMt7eGC7zsOihlFjUSQZSEiWkdQnplCXdxqkmUh9zMzJWZ1NYva3DTVqQmMpizVyTHMdBbPcXAqZQp7nqS4bzelwb3kd23HLhVRo3GSq9aR6FtLcvUG0QKU//YSrJoR9FSjmBM9je03EJD3WPfKsF0qSRKybpDoW0u0o4dY90oSvav/4eoITxVX9w3gZcAvgcOB1wAH/EPP5J8ckiQpwGXA6cAQcL8kSX/wfX/bP/Jz5kYm2XPn4+y5bcv853LoK05hfPsAret66hw7fc+nPJ2nVq6y5rTDcGoOkVSMwvgsfccfxL77trPrlkdZddJGXNuh9cAVTO0aZvVJ7Tz669uZ7R+n99gNrHnO4ZSn8+RGpnjy+gcBOPB5R+N7LpteeiKjW/tJZFPEmxt46Cc3YVdrNHRlyfa14zkuPZvX8eivbwvPK97ayPrTN3L2C0/nT7+9AYBYPMpF73o9l77nM7z1na+jf+8gSBJvu+QC7r7jATq72+ld2cXHPvh5ss1NvPgV5/Ct664A4OwXns4vrvodDY0pjjr2ML7ztSt5w1tfyZnnnsrlX7uS6665mddceD5bHt3OxkPWMzkxzeiwWGCnp2f51dV/4K5bxSypraOFb135efK5PF29HcQTMc5/dS8/vPxnzEzPoWkqhxy+gTdf9HKMZARZVbGmpwAfu5Aj2t4t2i3VKtGObqzpSYozk2K+0dxGLT8nRD4jMaKdMZxSgWiAPvMC/oysG3iOQyQSE7Of/QiLsqbhWVVqszPCQsF1hXyN7oQqCtHO3sB2AWFJLUlCbl9VQ6XkeV7P/OIrqSq4Lt5yKD3fQzFN7GIRLZGok4cBQtdXRTeoBMiualUknVhnb4jqmg81ngzhwHaxQG1uNmwPSaqKmW1BUlSiHT14ViVI5pogTvoe1vRkOGtaDnqspxoFGTjVGBJ4nUIeO58j0t6FYwn7bhAovEhzu7Bw8D0kWVk2acq6jtnUgu86uFYVOyeg5ZbrIhsCPFAdHyPa2Uuxf+eSc1o8GzMzzfhIS9TM3UoZGjLg+zjlItXJcSItbfWzHd8Tdgs9fUJt2jCXhTR7jh0eX9aXf83TGVpMJEjXtkU7MZD60ZZR0/hHxVO+Qt/3d0mSpPi+7wJXSJL0MPDBv/e+Z3BsBnb5vr8HQJKknwHPB/5hCWi6f4zixFyYfEBAHbf87k4Oe+Wp5Ian8BwXPWqw+pRDqMwVSbZn2Py6M9BjJmOP9xNpiLH7ti3EmxtYefxBOJbNjhsfpu+EjbiWw9Y/3s36szYzs1fAfXPDU5Sn84w/MVB/LntGad3Qy8M/vwWA7POP4eGf3xo+Pzc4yRPX3U/HIX3suOHBuvcWx2apjOeYmZrloksuwHEcuno6uOeOB/j8Zf/Btb//K7//5Z8BkZg2HrKezUcfwtTkDMeddBSJZJxEMsEHPnYxk+MzmIbOzPQcb3zbq/jGF7/P29/9Bv7j/Z9DVVVe+NKzuOLyqxkZGuOwzRuZm5lj1ZoVdHa3852v/YiTTj2G7379qvDcZqfnGBoY5mMf+EL4mGkavOni1/DkE7vZfORGzjvnGChOUykJYqWeaUJLNaAYEaoTI4Ls2NRCZbzeTqA8PICRyQrJ/UwWSdWx83PUZqfRUmmiHW2Uh/cFEvrCJE1SVEEODVxU5+HX1tw0vudi52YFtyeTpbRPcLt916E81I/Z3IaWSFKdmqiTgTGamhdaUpIkYMpTQkutPLKPaNeKOrgxiKQgkHBGiKizi/kAndaE77o4hRxOMS9sEQInVxDoPWGh0I5bs4TgaTRGdXIcp1RA1g3BQ5qbJpJpxvcJDeMgMMID7Klx9HQTWiqNU17gXFlzs8J2fGIUz7bRGxrBB88Wluf2jOA3zSfxytgwsa7e8P3zoqTR1g4kXQ+qCL8uYcxbdc9XhpHWDuFj5PtE2ruQJElsQDp6QBLqEvu39mTdwMy2CGsMH3AdYVmxf8L3XHG/XVdUiXVJSsJoahYutrNTIbgl0tyOlmwIZyquVaU0tC+EXqvxBLGOnjAJeK6Y0Qh4dH3V5PvestDov/X4fxX7t+Ke7niqCagsSZIOPCJJ0ueAUf71rRw6gMVMxCHgyMUvkCTpTcCbALq7u/9bBy/O5smNTOPZSxE9tVKVWrFCY28LhdEZDnn5yey5bQsrTziI4sQc1VyZ9MpWZvaN0509ACQoTsyx48aHAJBVBUVTyI+JHapdtUOOT61UpbEri2t3UJyYpZoXffzs6g6GHlkY5znLeABN7Rph9UmbsIrLeKRYNnff/gB33y5g16qq8IVvfYyB/mG6ejp408WvQVVVhgdH+f0v/8xJpx/LVz5zefj+9s5WXvzyc9h87CH87Mrf8YKXnsWKvm7WHXgAt/717lBwdGx0govfdyHTU7P854e+FL4/nWngc5d9lPxcnnRTY6gvd/QJR/CLH/++7lyrVQvHcTEMHV3yYJGSsJZMUZ0YC9steroJNRLFd90lKDPfc8Nqw5qZxmxqDtBpnqhqggG92ZwO4ci+61CeGCHa1o0kCy8f3/eQdSOER+P7eJWloATR9onVJR8Q8xohW9OBappUczNEO7qpzc0I4mc+R7S9i8rYCL4rFLLNphas2RmMTBPVsWHsOWElQED+9GpWuEuvjI8Qae0IF2BJ06lOjoLnCcWDaAy7kA/hzV7Nojw2JLxtfAR3Zr/5j55sQE81CssAzxP8naF+cW6NGWHVnWlGUTXcYEblVCvIpoldzAdmbsnAIG1WLO6LJGo8x0HSNEoDe0WLra1TqEc4NiDhVsuCXxRLCJ04qyog3lZFiIMOD4SGceWhAcymFlyrGtpKaMkGkCRx3ZMLfKRIW6eYsc3/nlKNOFaVSHNbKMW0WGbJbG6lNjuNrGTrtPVKg3uJ9/ShB8m6lp9b4P0gQCi1Qo6IYWKXCpRHhkKF7khLu5g51WrUCmJDJJtRzEwTakRIDtWCjZISiWKks3/bsv1/OZ5qEnl18Nq3AyWgC3jx03VSz5Twff87vu8f7vv+4dls9u+/YVFY+TLj24Rw6P47llR7BmRQDZ3HfncHRjzCyuMPYs/tj7P1j/ew7dp7uee717L+rCMp54qsPnlT3fsPOOUQzISwTwAWAApA+0ErMJMxJAk6D13NujOPQI3oNHRl6+CMira0DRJrSiLrKr1Hr6t7XFZk5uz6BdN1Pfp3D5JqTPKtL1/Bd772I775pR/Qv2eAF7z0LEr7SeuMDIkKzarU6O7tYMcTu9j62HbOffEZDPQvoLv+/Ie/Ylk1fnP1NXXvn5me44ktT/If7/sc573yeTQ0igGxLEs49tIW1NxMjm1bniQZWdjNKZGYABIs6vXXZqbwPT+A6bah7+e9EwIEJMETcisVKmNDlAb2YE1PEOvpQ1Y1IWIZ7DbNTHP4mvKQMLRTI1FqxULYW1ei0SV6cPNIvCXh+4Frqyp2yXNiNiTrZqjk7bsuekM6qKJSlMeH0SIRvEXkX6dcwikVcAN+0uJwyiUUM4KkaiiGgZltRVK1gGSZqePWhOfkeXhOrW7Hb2Zb8e0apcG9lAb24OTn8KwK1clR4isOwGztwPc97NwsleF9oU2D77roiSR+rYaRaRbW2ZPj1GanMdJNYfsv0tKO2dxGtL1LwKZ9D8+uUQ74K/MCpb7tiOqGQGZJlgXIYGpCSBU1NeNWysJu2ozguQ6R5nbMlnbivasC071C3azKrZTxHBuzpR29MUO0vRstkRJ2GjUrrNjcmkW0vRtZN8LEuTi5zMdiBW87v3SkbudzOFaVwt6d4v77QiC1NNSP69hUpycoDw/glEvUZiYp7NmBU61QnRwPH7emxePLGe89E+KpVkBTQM33/SrwsWB+8vRrdT+9MYxIpPPRGTz2DwnXcohnkxQnc2x+3XN45Je3YRXKpDqaWHncgWgRg+GHd3HCv72A2X0TyKrM5I6FhdhzXPJjM0QSMTzX4+gLzwJZRlFlnEoNxdRRNPH1ebaDahqc+I4Xsv36B8P22+TOYRq6mjn6jWdRyZVYefxGJp4UnzE7MEHHpj6GHxFtIFlVWPfczdhVC83UWXncgQw/uodoOs7K0w7hwx/7Ut31HX/yUdi2zdc++91Qiw3g0Qe3cspzjmd6aqkgaDQaYXDfMIoic+DB6zBMg1tvvIuXvPJc7r3jQQ7bfDC+75NtXYoYg4Cp7bpc+Z2f8cLzz+KGa29l02EHsfGQ9Xz50wvVlqqpNLc2MdA/TLnmEAvmt2okQm0ZZr9n18TuNCChms1tVCdG0RvSYi4DmE3NQvxzEZTYrVZEAnNd4anT0hbK1vj7Deurk6OYze3UZqeETI4kEW3rChdjJ2DQS4qyVLjTMJFUNfQwAsH1UQxDzE7MiEisgWwNiFajEoktqymoxhMChLEoZF1Hi8XxHAc/SHh6Qxo1EgHJX7b15LtOQHAVnCJJ00Rrb9Gx7UJOWJ/XbDyrSmV8OLy2ebi42dIhXFAdG1+WQZYFCRgBDKlOjBIL7Aj8RYKw+y/qbqWMEo0hPJPSomUYQNyt6clgzmRSnRglGjiTOuUikbZOkZwmx4h1raQ6NYHZ1LwsdN4NeD/zBFa7mBd6eYkUkqqhxRPiPno+ib61lEcG8D0XaZnh/eJW13zCWxxaIiWS+/4ag8Emqrof8MR3XZFUpyf2e9zBmZcTeobFU01AfwVOA+abpBHgeuCYp+Ok/klxP7BakqQViMTzMuAV/6iD63GTdG8re+7YQtfmtRx94ZlU5kp4jotj21j5MrtveZR4NsXjf7ibNafXy+qtPeMIdt70MJVZccsVXeXwV53K3Zf/BQDV0Djitafz2G/uYNWJB7P1j3ez+pRNS2Y/c4MTuI7DwAM76T58NQe/+Hgmdw0jKwptB68ks6IVIxlDi+j03/0EqqHRfcQaSjN5mg9eweO7dvPhj32FN7791Vz/p1vYvbOfQzdvRFVVrGotBAcsjngiyszMXN1jmq5RLlf4+ue/x2GbN9LS1szunf0kknEOPmQDT27dxbe+8kMATn3uCbzqDefx3W8szHoa0ymcQLVhdibHytU9HHrEQXzrK1dw9gtP5wMfvZibr7+DhnSK0846kS98/DJqVo3bbn+El5x1JH61jFutBu2k+t2mvAhB5VbKAqXW0ycqi4JLtKMH2TTxysuQPosFIfxZLuI5DtHO3iXJB4LFoVREiydFAvJ9yqODQpk6EsNsbqc01E+0o5toew/VacFD0WIJ9Ia0qIwWLUSKYQgR0YY0nmOL2YymC/02WQZJojS4l2hnTx2gQIlE0VON9SrNsoxiCvUHoVOm4OsGlbEhtJ4+XLtGtLOH6uRYCM2eRxL6nifOz3WQdQNn/0oJkahlXcdzbKIdvWI3L0liLjIzhZZICgCB5wlEYXFpteBaFVFRyDJGJitcRveDlkuB4nh1YhTXstASSWGUF2wKarlZ4UFUKYftVlnTUIwItdlpIcAqSfiuKwROY/Fl+UhezcJzhI+UH5gC+r4v9Os0oaZRnRhFT6aEfUVuFiXdBLm5hValJKOnM+Fx9WQDtdwcbpAw1VgCPZkK3XrVqFAPd2sWdiEfzHaWkZKSJCLNbSHi1S4KHUDpGaqc9lQTkOn7fjih832/KEnSM7Op+BTD931HkqS3A38BFOAHvu8vlVv+H0ZDexN21WbVyYfgVGtUixVm+sfQTIPZwQnGHu8XO03X44BTD8GIL/ABNFPHte0w+QC4NYeB+54kvaKVmb1jOJbNY7+5k82vO4O9dzyOXalH58iKTLItQ7VQpjxdYNWJB1EYm+Wx395BQ0cWz3UZfHAHybYMq0/dxF3fvoZYU5KWtd0M3LedREsjIzsGcRtV2rta2bF9NwduWke5VOHGP9/K1MQMr3vLyzj59GO58boFxJwkSeRzRY4+7nCSyTi/+8WfaW5t4tQzjueHl/8MEPyhiy65gGt/fyMXXXIBpWKZgf5hXvWG8xgfneDRh7bxnLNO4q3veh2PPPA4re3NdPd28p2v/QiAFat6ePDex/jDr/9Cz4pOeld08aVPf5sPfeId5PMlDEMnEjWRJImHH36CF513BtGUIGkqZgS3WgnnH1qqYUl7wikWUBMprOkJJFkRFs7NbctCrNVIJGzpeVYFp1RY8JRZFHoqjZ2fRUs2LFQ4wQJqTU+gRKJEO7qD77oqqqGg6rCmJsSgXpKEz04A5faC6sBIZ1HjKSRFRjZM7NxsWEFIkiKsxQPnV8+qYhfzxHtWhY6XQjS1LLg/tSrW7DRqNCb8Z1wPr1rBKgojNDPbGhJTa7lZ9IY0kqphtnYCHk6xuHTRNsRcR1ZUYYboueF3MH+sUCfOFmrSyykEGOkmnGKe6sQokeZ2Fr9CNgxU06Swd+eC9lshJ7TpEinsQk7cz+A50VmVMJpa8F03/F0oJuipBirjo5iZLFqiATUqwAuSoiIbhphfpRpBEYRlLZHEd2ys2TmqQSVoNrXieT5OtYyRbgrRcb7nCZuJWBI1GsW1LAH0UBQS3Stx7RoSAdFYVRcQkrlZqtMTKIZJrHsFshmpq7hBtIglWa63qmhsQlOUv8k3+t+Op5qASpIkHer7/kMAkiQdDiytT//Fwvf9a4Glipr/oMiubCM/OYdj1cgPT1PNV+pM5CRJwnNddt36KBvOPYr1Zx/JzlseQY8YS5SrAcozBWLZhvDfpakcruNSyYtd5+y+cVrWdaPoKo1dzczsG6dpdTtaROf+H93AwS8+gRXHbGBs2z4qs0VkVeHA5x3NAz++kb4TN+J7PiOP7sZMxeg4ZBW1lM6919/OvXc+yA3X3sJHP/teTjnjONZuWEVza5aOrjYejz2B67rccuNdZLKNvOJ1L+ZPv72B22++hzOfdyr/8dn38t2vX8UX/vObeIt28K4jRBx7V3ZRLlXo6ung11dfQ1dPO+/8wJv48Q9+yTkvfA7tna2s3bCab3zh+1QqVXpXdvGO912ILMt09bQzuG+Er3z2O4H9wj5+9N1f8M4PvJkTTj2GQ484iJ9e8Wu2PvYkB3YsoMiMdFOAXDPxPa9uqAxi91mZGMVbxH2qjI8Q7ewNFzMQcGK9IU05sKdWo3EqE6NUxkeI9fRhTY7hOQ56skGoUNvCTMz3fDEvkljYxQctKMWIgCsWaEmShDJApgm3UiHevZLK2HCItou0daBnWlD0BdUGfKgVcqLSaczi1MRiOe/QqjemkSSZ8nA/kbZuSkNCJFSLJ6lVK+G12blZ4b3jOdhB29KzqjilPNGOHgFb7+pFkmXs3AzWzBSKGSHS0oFdzAeadkkUMyr09Fra8SWJ4tBejMYmos3tFAf2IBsCwj4fTrFAtL0Lp1wOqwXZMCEQA0VR0RsySLpOrHtlUCGI5OBaS0nCTrkoZJQKOYxMViTXWCJcwGu5ORRdIBiFoGwCRdfRUg1Up8aJdfZSGh5YOBdND2WJtIa0SNKS0HWbbz36tk1ldJBENEptWkD6jcYMvudhF/OYmSxqNIpdKlDs3xW2JM1sq7BUX8ylkmTRtg0AIm61QmlgL4mVq9HiKZQeIXSqmBG0eIL83no4eW12isSKA/4p7qb/k3iqCeidwC8lSZpPrW3A+U/LGf0fi2S2gfx0jmhaWCLIiszQQzsCV9ON7Lnjcdyaw947trLheUez/qwjiWeS1Co19t5Vjwhv3dBT91hDZ5Z9d29j5XEHMbF9kNHH+1l/zpFISGy95p7wdWYyytEXnkWtZOHWHDoPWUXTynYq+RKRxhixTBIjHmHvXVup5stU82Xu+d61HPT8Y1nvxDnlkx/k3u3b+fXV17DlkSdQFOHBc9Rxh/HaC8+nubWJs194Oo7j8qVPfpOJ8WmSqQTpTCPbH9+BY9t1yScai+D5Hue86Dl093Zy+deu5Obrhdncjid28x/v+xwf/8IH+OJ/XsbUxAzppkbOffEZxGJRDty0lnvveohCrsDRxx+Bpmu87yNvpzGd4iPv/SwAd912HwcftoEffPtqHntoK7FYhFXvegWmZwVznEmMTLMwKoslxExk3m3UMNGSDUtM4ECgv5AI2lwKsqpQHhkCWcZIZwU7PnDJ9KwqRqYFu5gTEGzHQYlEUcxIOE9SonHKATJKUlVkRJvKmp0OZwxGphk8H9+xqYwNL7izug7loX3E520JZJnazBSSqoWPVUYHBaCguQ09k0XRNKrjo9SswLk0NxNWHko0tkRYVNY0qhP1/CGxWEq4ViW0BNcb0kELaZbSwB6inT3hfXaKOfQg4dfmpom1dQIisSqRKE6pQKS5bYHjBFTGR4l1rxCCqZKE5zhUp8ZRY3GBOgsswsPfU3u30MwLqrzFIckKsmaI1qjnEWltDzYBLn6gIeeUCkTaOkX1MDyIZ9fQUg3C/mFupg7hFyZ6WcaemyGSbUVCWjJTA3Ati2hHd2i97pRLuOUSclsXnmNTGhkUrc+gEqxOjgnB03iy7je3RPnB97ALeaqTY0RaOkSLWFGwyyVwlwJy/GUUKp4p8VQT0Bbg28AZQB74A/APa1f9X49kJoUkQYPTJIRHN/UxNzzJ1mvuwbFsZFVh5XEHcs/3rmXjC4/jnu9fR2ZlG+vOPILdt23Brdn0Hr2eZHtTqB0Xa0rSe/R6Hv317RQncxz2ylOZG5qkqa+Duy6vR5BV82UKY7M8dPXN4WN779zK0W86m7FtA6w6eRPDj+yibUMvsaYUT1x3P061hmMJyZ49f7qfTS85ksu/IVpgruuiKArHn3wUb7/ggyEIIZNNc94rzuXbX72SE089hptvuJ1MU5qXvvoFpJvS3HPHA6xas4KXvOJcaraNoevce+cD3HLDnXXna9dsysVSqPc2MzXLj7//SwA++tn3gudjGAYfuPgTC/c4leBVbziP73ztR9x318Nc+G+v4btf/zEAN994F6nGJC94/sn0rexENzTs3ByuVRXw3GSDgBQHnie+T4guWxySqgor7QCxpMYTxLpW4BTzgh0fvF6NxnDKRZzSGEZTixAo9f0ANrxnYZYjTRBt7QySzhRmppnq+PiCwymiPRfr7EUObJ73D7dmCVRXuolaUIVUp8bDhOpWK2IW1N5FdXJ8od0o1wMdlg3fX8Ivkg0Tp5Cra7MJ5ex2yM3iI5xDS/0LkP/KyCBmcxtGOkttZkq0w1SNSGs7vmMLsddsayjYamSy+I5DdXIcxTAW7mtg6Gbvdx8q4yNE2ztxyuW6zQQIDlV1ahzPqohWZTSGb9t4roM1NYGXSIYW5eXRBRCQnZsDz4dl/Hc8xw7ao15gGqiEyuL1L/SwZqdDBKGWbCDW3YesaTilEkZjRmgOug54PpWx4XDmMx9SAMpY1onV96mMDaFGY8jxBEqg4lFHJwhQl8/UeKoJ6EeIxPPJ4N+vAK4Czns6Tur/YiTSKVAlsD2suTKlR3I4lvixtW9cQf89TxDLJMmPzuDaDhNPDpIfnaZn81qSbWlGtuxlbPt9HHnBmcwNTlDNl3nst3fgex654SkUQyPZmmF698iyUN79Z0SOZTM3MIGsKNx/5fXh41rUYM1ph7L1mnuQZBkjGaX5hHW4qsznv/lRtj32JD+8/GeccsZx3PDnW+sQcNOTM1hWjRNOOZr2zlYu/9qVAJz5/FM57czjOfk5x7Jnxz5GBsdYu6GPQq5IZ1cbyVSC3Fy+7vwikQjRWIRyaZENgCQxMT7Nti07OHDT2rrX53MF5AAyfdxJR4Lv05hOMTsjksXvfnkdv/vldVz+w09zyIF9dbtKp1REjSeQJZnyyCBuzSbW0Ul5ZChQtZaEDbeiYWSaccpF8Uevm2InH43BvOR/PImZbaE0sEdUNYpCdXJMDOoXzToAsZiWCmI2ZZj4PnUQcS2eRI3F8fFRo/HlFzlJeOhImhaIhcpLrRt8HxCzHs+yUOMJlGhMzBhUFVk3kDUNI9OCNb0AKnEqZZEUF6GttERqCU8JRGWgN2YEEKGYX/J8LTeLLithi893bMpD+4RTqiWcTmOdvdj5OeTgOiOt7WIeF0+gmlGB0HPdBZ+l0P5AKI+71Qqypol2H6BoOtXpSTyrgtkkyLZhZSvLRFs6KI8OYmSyy1pd24Ucsa4VolJcFIphCk+keDK00Ii0ddS5w2qJFHapgBaLhwnIzs9hpJsojwgRVr1B2Fn4roueShNt71oGmm8Ib6hFlhvzskbzMW/TIGsa8e4+ioN7hQK4phHv7H1Got/m46kmoAN931+/6N83S5L0D5Ws+X8hEklRWttFi96j1zP6eD+1UhU9amIVKxjxCLXyIoHGfJmdNz9C8xohybLhzM14rsu2a++rO27z2m4iqRhzAxOMbdvHymMPZOfNj4TPa1GjjovUtLqd3qPWo6gqiq7Ssq47RM/ZZQvf94llU9hWjbazD+GvN93Fb66+hmQqwUXvvoCvX/FpxoYn+fH3f7HkGmVFJhI1w+QD8Jc/3kxXTwd//v1feeu7XsfO7Xv4tzd8iNe9+WX8/hfX8pZ3vJbPfuzr4esPOmQdW7ds580Xv5avf/67OMG86FVvOI+b/nI727fu5NDNG4lETCqVhQU7lojx/o9ejKLIbHl0O++99G186JJPh4Pn55x1Iiu6WqjmZsXue57c6DrIiirmIcFry8ODmK3tQpxUknFqFsgCVaRoGq7nomgatclRbF+Yz0myjOd6Ykfe0Y2dz4ldtS9gzK67jCK246ClGpEAz66GHj5asgFJksKB8rwhWGmoP3yv3pAWc6hkgxDbdJzQv8iana5n5PtCAUExI9iBOZrZ3Cag5EFFoSUbiHb2CuJrLCZ8kRxbLMKFvBBO1XQhcJqrrw5lwwyqljHMzFLOnCTLuItgxmFyDfhE+B6VsWncagU1kUJWtQWPogLYi/xpZE2vS4xqLIHvuhiZrKiazIiYqXku0bYOMRvCx51adP89j1p+DjWWCP1+lpyzqiEpCkY6izU7JURbs624VlW0+zwvNBxUYwkiLR2B+oBoMXrLcG88u4adnyPS2lmnbl6dGiPS2oG6H1hg3uxOMSKCROt54jtbZJWwGM6tRmMk+9biOUJO55+pavA/iaeagB6SJOko3/fvAZAk6Ujggb/znmfjb0S6p4XidI5j3nwOhYlZtIiBmYzyxJ/vp2fzWkYe3V33+t6j1jM3PMWjv7mdg154LAecegi7bn0Mz3Fp7G4ms6IVu2yRbE0LDkQ8wqbzTmDooV1EMwkau1twA+WDtgN7iTQmeOCqGwHQYyaHvPRE9JjJ4AM7ADBiJl2HriY/m+eRvTu4+oe/AWByYpqPvf/zfPLLH+KyL36P5593Zohsm48NB63h+5fVC6VLkoTv+wzuG8axXaxqjbe847V09rQzPChcSt/1wbcgSRKGqdPW3sLWLU/iA1++/D95cvtufM/jpr/cwfatO8NzybZkGOgXu/2m5jSe69K5sourvvcL7rvrYf543eVc+ZPPMTg0TkNDghVtjeiVOfSWNqyZKaETJkkoWrQOlQZCBaEyMkS8V1RLnlNDluJEWtpwSgXBK4klkDUDWdVCiwYt2YCZacatVsOFXc9kA+STUJmuTk+GUG0tkQw8f9qx5+YwM1kq46OokWgdmsm3baqz08JfplIOxDEFZ6g8MhAy+PFEAoy0tIfvV6NxnGoVLRRjTVEr5rHzc3U7aTs/JyotwxBCqZKMX6tRKxbR00JwM5JtQU41Ci7KfGssLozarFIBRTfC81rc4jPSIjkAAazcDc9P1nTMlvZQcUCNRJfMozyrityQRjCCCYVL1XgSLZ6gPLwPJRJBb2yiEoBCou1dlEeHBFTdXl5EVY0lAmX0Sshnmo9ISxtOpSyQaO3dSMHs02xqpjo5Ec7AQLQ6PcvCKRfmf/RE27spj9TTImRVQ1LUJZpyANbsNEY6uwQwLSsqcjyBFk9gl4p11u16YwZlP5UDWVX/6Tpy/9N4qmd5GHCXJEnzd7MbeFKSpC2A7/v+xqfl7P4PRzyTwrFdYq5LrVihZV0PtmUzsXOIg198PP33PoHvevSdsBE1YoQyPOXpAiNb9rLmtEORZJn82AyP//4ujr3oXHb89SFyw+KPQjU0Npx7NKXpHI/+6jbaNq7gkPNPQlYVHvzJX8PzqJWqPHnjQxxw6qHgQ7I9TSSdIJpJEVPa+M83X7nk3J/ctoueFZ1MjE/x6je+lL/88SbiyRivf8vLmZvJ0buyk/49YneXyaa5+H0X0tKapfpGi8amFDddfztjI4Ist/noQ3Bshw0b1zAzM8cH/u0T9Kzs4uTTj8Wu2fzo+79k3fpV/Oh79dXWqgN6Ofn0Y7nuD38l1ZiiqTnN1T/6Ha+78Hzuu+thAJLJOAm7SPe6+eG06N2rRhQvKkiRkeY2UBR8Zyns18y2UB4eCBfa2uw0ZnMbsm4K0c1abUmisPNzgjgatHSMTBanUqE2b0QmSWHbSVZVMUdwHaqjw8IjZ2wkZP7vH26pgFOK45QKGJlmfEnCrVYWFKIXhaTpRDp6RGVVs5ANk8r4SGjOFm3vDnfvi8OpVTESycAO3BZouoY0pYHdxHv6KA3tE+2xlnZRPdo1PN8XbTDPF46hgarEPEJNVjWhXZdpojw6jBKcy3zME2iVSDRwav0bnjOyQqSlLZxjxXtXC3XwAIqsxZJUx0YwmpqRNSO0KC8N7BUzqv1CSySxiwXUaIza7DR6Y4ZIIrmgpuALReryyGDwG5Awss14qr4EbVebnSLS1oXemMaza+GmSzHM0CI92t4l2qi+h6QuvUZZVZe35lh8zrE4ydXrcC0rNK7b39J7/5i32p6XCXomJaeneibPfVrP4v/RaGhNUzR0PKdGZaZILJ3EtWxmByZINDfSsr6bPXc8TrQhzoZzjmL7Xx6gMldEVmSeuG7BWTTT10ZpOh8mHyDgCd3BYa84hfVnbSbZ0YRTs3EqSxfaucFJYQldrrL1jwI917Kum57TN9HW0RLK6MxHJGKwZsNqfv6j35FtzvCCl55Fd28Hn/mPr1MpV/iPz7yHvbsHqFVrrFq3ki/+5zfJ5wocePBaNhy0Bsd2eNUFLyGWiLLqgJX86Xc38M0v/YANB6/lQ//5Lr74yW/yg2/9FBDK2Qcdsp5DN2/kofseQ5IkTj/rJHZu38N3v/Fj3vbuC/jO16/CMHQ+9PF38qFLPgWAGTFQdANX1euSi5HOBgiuQEevXEaNSsLOORrDXYRmknVjCRChOjWOmclizc2Gemz7h52fwwwkYxTdqHfB9H2qE6PC2yY3GwhetopkIMsY2RbhibPM70WNxnArZWH8ZlkikbnusrMh33VwCnm0REog3ILko0ZjKJEYVj4niJb7sf31WEKY1wXhVsrUEO05p1wKK7fK2HAwl1JQ4wkx/5BkzKzg1aAoQtpfksKKpDo1Qby3D3sZoqlTLqIlUkJw0zDRUg0h/BsIfJpkyqPz8o0zwtoiFl9QEJDF51szU+H3Jms6keY2AX/OtoYip1qqUVRPyQYBA5ekuraWkckiKRqVuZlFvwEfa3IcpSuCkclS2282pEajopU7OoRTyCEpKrHuXmRFVGu13CxWANhQdGPJ96Y3NoH89wmjimEKsVXXFaRY2RHHWyZ5zYMthHK4cMONd/Y+Y3hBTykB+b6/7++/6tn4n0S8MU6tWsWpOvieR3Z1J/mxGRRNYc/tjzO7bxy7uUrrhh6OesOZuLZD+8aVzO4bZ3LnME2rOki1Z7CrSxOL73uUZwpsu/Y+WtZ1Y1dqS3TlADIr25BkiYntC9qs408M0HJgL696w0t47OFt2AH6riMADTz+qLAPn2+FfeajXwv132am5/j9L6/jI595D5e8+dJwBvP4o9v5xU9/zyUffAuf/fg3yM3lWbthNWe/8HRuv+keHrz3Ufbs7OcDH3sHP73i15xwylEUi2WSyQTv/MCbueu2+8H3ue+uh3no/seCa4QXvexsZqbmuO/uh2lMp6hZNT7y6fdy8UUf54UvOo1DN64mFTfRzAh2pYSyqC8+v6DOa47piZTg6Mzv6pfeVEASisxTk2iphiVJSDYjoWX3/sZ2IJBragC19WqW4CRFo8iqSnVqBq9WRc80Cxb/uLAckA1T8E8mxsLz9j0XSdUxm1tFFRDcZ7OphdrcjEhW5ZKANAfn4pSKgUhlBC3dVudiqsYTy/r9uJWykJrZb4FzykVhYTEjFu5IcxvVqQUUnyQrIbkWRKUzr7Swf2gxkQzwfcqD/ULXrqkFp1xEicTQEkmKA3vqz8uq1mn3eZaFFNhX1H2m74UmcmY6ixKJCl5UzcJzXWRVJdraSS0/h+fU0BMNKJEInm2HygSLw3cclFQjib41wpZCktBTDSIhS9ICoVRWUHQdz3Eo9O8UmxtZgCXMphYBfoBA9FWlOjmKFk+i6H9/buNaVUojAziFfKh+YGSal1Q3TrlUV2265RKViTFinT3/pRfRPyueObXY/8OhmybpLhM8H6tYYedND4cIOYCDXnAc2//yALP7xkm0NLL2jMNxbIeW9T2ku5t58Oqb6TykDzMVo5pb2MG3H7SSqd0jRBsTtKztojAxx+zgBGufewRPXv8gvucRyyTpO3EjswMTYaKYj6kdw7QevZr3fvhtTE/Noigy1apF94oObrvpbtYduJqTTjuW1vbmOvFRz/NYd+Bq9u7sX3LMh+59jMM2Hxyi3rZv3YmiKJx0+jH89brbmZ3J4Xkezz33FDKZRj7+oS+y8ZD1PHj/Pn70nZ9Trdb3znOzudCewTQNPvrZ9/Low9v46mcvZ3x0kofue4xYPEpPbyef/sw7aGvLCtXi+XufahT2w7JCLUBgKZGocOWMJ5dooOmpdIjkcipF9EyTaLMEbSFJUTEa0hQDnspyEFgtUa/75dkWRlMrpaEBjHQGKZEU/BfPI9rRhaxo+L6HU8yjJ1PIukmtmMctl5GCFkyse6UYvNeEE+d8UvE9VxBQ000iKQWPO6UipQEh1ePVauC5QkZnuWG8oi75HiHgLumiCtEbGvFtuy5p+54rLA+6Vgj+i6oKf6J4QsgBBWg62TDRGjNCAUDTgwQxEdhld1EdH17WcE6cgxZWqrKuL5ECirR04FoVnFJRfK/BAu2Ui7hWlWh7V2h1IGkqqmEiaRqe66KY0VAhYf/7IcsKcgBg8DwXPB+7WAikfcw6MIEbuOhGWjtCoIis6wKSL0mAJPybIlEk5e8nBd/3qU5PiOQjHqAyPoISiaEnU3WvdatLgRB2YQ7P6XhKie7pjmcT0DMo0j0tFOfyHHvR8xh+ZDeV2QLdm9cx+vheZvcJl8iVxx/E/VfdEEpAaRGDNacfyrZr72PDOUeRG5kmPzpN64ZeIqkYtXKVWFMS1dTpPGQ1t3/jdzR0ZTniNafh+6CoCoqp49kOmqnTd+JGZEW4P0Ya44zeu4t163oZzzZSKpRJpuLs2L6HF7/8XK6/9hb29Q/R2tZMMpXAqlq86g3n0d3TISqmZVoCbR0tzEzWw3i3PradY048gr9edzsAuq7xg2/9lE9/5cO899K3U7NqrF6zkpe+6vl1s6Du3g5ycwWisQivfsNLkWWJQr7EUccdFgInAErFMtse38H4bJnW1gA15Pti9yzL2KWKQHqVi8iyjBKNURkbDtslTqmEVxNy/n4gIwNiuG9NCsl/M9saLjS+56EGi6w1N0OkrTOE22qJVEACXbgHsqbjVStEW9qoTI6FVZmeakSKRPFlmVL/7kVzB4lY9wqcUhFrdAgt1YAWiYt218TYEmsESZYF8GBmf1KpA0FFUhoTaD180W6zA1g5QKRVEG/3h19HmoW9hOfYOMX8ssl23vbBmp1akNupWRjZltA3SJIVnEKO6vQk+J6AsgcIPWtyTLTfZBkj01wnBjs/T4l2dIPv4VQqC1p7iAF9bW463Bx4di2oBDvF9fseTqWMNTNJtLVDDPT1iOAmlfLY1TJmS7sAeQRzNr0hg2dZoqWFmLX5NStscSFJxLpXYgTXBgGSLdVYJ5sj68YiJJwfitP+vXnO/Pe2HBTerZRgvwSkLPOdKGZMgEQ8D7dm4Xseyv/SbOjZBPQMi3hDEt/16T16PcXxGSZ3DjCxXWA/mtd2MfTwzjr9QbtiUStVUTSVLb+7k0RrI80HdNFx8EoGHtjBzr+KgfzeO7ey6bwTADHzue+HC9yfDeccRbQpxcEvOZ5Hf3MHdgAF79jUR/umPvbtHOBjn/s6xYLQWTvimEN4YssOTjnjeLY88gSf/fjXee2F59O3ZgWfuvQr5HMFPvLp9zA8MMIpZxzPTX8RiUXVVP7tvW/kkx/+ct01Z5sz5OfEonHqc4+nUqnywY+/g339g5RLFYoFmaGBEU4760RWrVnBQ/dvobevi+bmJj787k9z8fsu5IpvX01zaxNnnHMynu/ztndfwKMPbeWOm+8FgkXA0PB8X0jMuA5uzUYxI+iJJMV9C8RJSVYEIGB8mMrokFAvaG7DrpTCRVzWDYxMVgzaJYnK5FjIvYi2dYHni8rCF2RFvTGDrGgokajY+UsSkqxgZLLY+Vy4WC4WMq3lhHacGygsLIQfimX6jk1tehI5qwixzaYFtBmIeQGSFECCl4pXeraNpBlidx7Mk+RAYsizawI+7ThY4wOYbZ0Y6abwVOxCDk2RQ2KonmqsS1wAWoOQ/llcvTjFfOin5FpVzExLHddIPK+jNaTBcajl56hOjqHG4oGR3RiyYWA0ZgCJytiwUO9OZ5D1CEa6CWtmClnTqVnTdeczL7gKgcupD0awEXFLRbxymdrcjEiOrkOtWMDMNIeAC7uYRzYM0Wp0HMyW9gCVFtwU36c0uBdFN/FsUY2q8WRd4pw/DymwfZcCrbb9Idh/KyRZRonE8Oz6e73cBkCNRMMNhZZsQI1EBdS/ZlEr5KmODQM+ihkl1r3iKZ/DPyqeTUDPwEhkUliRKoquEG9pxCpUKc8UUHWV6tzSnrRTc1A0BbsChbFZVhy9Add22HXzo3WvG98+SOv6Hsa2LYz0ku0ZtIhBqi3NyGN7WH3SJsBndOs+ypbFvtlpJqjy0c+9D9dxkGWFP/32BmZnciiKwj13PEi5VOE3P/sTx518FDNTs5imQalQYuOh60mmEhxx9CEoikw0GuHeOx7kmBOO4IZrhRurqipc/P4LGRue4O3veQPZ5gy7d/QTi0dRNZWpyRkaGpKceOoxbHtsBw/c8wiHHbkRw9CZm8vzwU+8kye2PIlpGpx+5ol84wvfD6/tueeewlHHHc49dzzAa954Hl0NBvb0BNGObkFaVFRq+Tn8ZYzoPNcOocRutYKdz6HEYoFkiwK+L9psQSWlxuJ4qiZ036wqTrmIkcki66bQeDOjeNWKELyMJ4hGo7jVKtbMpFg8GzNC/2y/8OyaqKz2D98TCU6Sxe6/XEQO/IoigbqCohtImoZbLmIX8sHCvACI0JINojVlOELloVRETzUEqty9SLKC59iomi7kY2yL4vjIQvWIFHBsgt/hvKLBzKRAoGWE544fqFzPD/nVWDwkcyLLy86d7MIcekNazMuiYsdu52YD6/QeqlNjuPMJrLkVr1qlEpBTzWbh6bOstxIgSbJwSDUilIb60RvTWIFKRKSlHTWWoJabCbUA7WIeLSVajHKwQOsNmVCNYP+kLskydikfgi9kVa3Tu1v4bm2s6UlSB6z/b/F1JFkR8kWlQgh1V6LxZQmnsq4T6+zBrbZgTU8szIMkmWhbR7gncatlquMjxLpW/FNnQ88moGdoGFETVdfIjc3Qd+JGpnYNM7F9kFUnbyI3smhXJ0HzAZ1MbB/ATMXo2byG2YEJVFNb8gc4umUvR17wXJJtaSFU2tdO9oBOtvzuTg4892hyI9NMbB9EkiX6nns4v7vlNn73pQWC6Ovf8nJu+svtvOhl5/CDb/2Uz33s67z/Py7mx1f8Csd2GB+dIBaP8qoLXsLMzBxXfvfnIdwaREIYGRoj1Zjkbe++gFQqSWM6RbIhwfDQGBsPWs/szBxHHncYd916Hz+54tfhezceuoHzXvk8BvcNce+dD/Ki88+iMdNIb18XI0PjnPn8U7nyuz+vu97r/ngTn/zSv/Py17yQg9Z1E49FkBQxBJZUTfzxlb2lFssgduySDAR/4EYg+qjpKGaEyiLZltrcDJHWDmGulptBNiIokVhYwUj4lBcRSGXdINrRTWV0OPxsu5QXMjL7WUVIqoYe15bIz2jxJNbMFJHmVjF70HXBa4knQBJzFbdSxhobIhK0s4RdQwe+54gdve+BT9j2MdNNuLZNvKcPKzeHVy2HjH41EsWaWvgua7PTAvHmecIHyLaxCzmcSlkMw4Pd+DzUe95nCUkSSt61Wigxs9zOXTEF8bISJDwlGgu5TW7NwinkcQpCGNV33bpBe3V8WFhpm1H0hkwdX0dLNYAsIRsmpeH+cP4Xauz5vqjOrPqkb+dmifX0UR0foRa0YBXDxMi2LpEr0hsydVydWj4nLDFmF1djkjAn1LRlIfd/L+YtQ5zA5NC1LAp7d5LsW7ME4SarGo5XDmdu4kI9rJmpUMMPhMeU5zj/1NnQswnoGRyKqpDuzDI3MsWxFz2P0lQe1dDY9NIT2XP7FjTToPOw1Wy/4UFWHH8QXs1h9x1bcCo1ZFWhdUMPY1sXqh0zGWVucJJ9925n9WmHUBifZXZgglqxyti2fSEKzvd8JvM5fvfr6+rO56dX/JpXv/Gl/Oh7v+Ad77uQ/r2DfOfrP6Kzp52zX3A6iVSc1WtWksmmmZmarUs+ANf/6Rbe+PZX8Z2v/YhdT+7l3z/xTgb3jTA+Nolh6Fz5nZ+h6zovffXz+eVP/1j33sce2spLXn4Omw4/iBe/7FyhK7d2JX/9822c/+rns2fXviUurAD9ewdpSifZfGA31tR4qP2G76FG49jFPJHmell7EARHK1QISAn0lqzgWdYS624QvkB6Y0bsSn0fM9uCWw1EP+2amCVVygGAQfAyjKZmgFBwU4kLa4d5kqLR1IykqFQDr5ra3DQ+wjvGmhWAgsoipN3+iVGJRDECu4RIawf4Pm6tGiZMfEmIYM4uLN6R1g7Ko0MYjU1U5oQygdncvix02i4WkFWVSFNLAIooCd+aWBwkuU4JwK2UQwJtrLMXJIn4igME5DxoKYWuq7KM2dRMsX+BkO2WS7hB8pcXzRbtorBZWHJuhTyK56OnGgXcvFZF0QwkXcNzHOyZqZC8uxjd5weItOXCrZSXSOD4riMIryND4HtIsoKeTNVVmp5VRYonMJqaqc3NBpbpzVTGhol19iw79/FcV9ybwPJhuecro8NLnGrtUmFZiLW3DM/NrVbQEgszIyUS+9scrKcpnk1A/wLR0N5EbnRaaMWNzZJsbSTd2yr04H5zB7IqoygKxZlZ+o4/CM00sKsW0UySaGOCiR1DZHpbaVnfza5bH6PnyLVUZou0behl162PkV3TwUTgxqoaGqtPOYQRZ6m6r2XVkBWZRDLOrp39/DSoUHbt2Mu9dzzIRZe8nisuv5qLLrlg2euQJIk1a/v48CcvYcXKLn7502v4yzU34fs+57/mBSSScRrTDZiGQc1a+gczPDjGn357AwAXXfJ6JEni5hvu5I5b7+Pr3/9UYMnQH75e0zUUWSaRELpn87vcefKo7/loiRSyoooZT35ODOxTjVSnJoi29+DbFnZJGMgJa2dpCYpt/phC+NEE36M0KMiPpcG9YUIxWztC8zZJ1XCqlTpbZlk3MVvahTSQpmMXi5QGdhNt7RTIKSOCpGkC8WRGwxaaXSyIttFMfcJ3K+WQYzQvHqqYEeK9q7BLJWRFXpC7CaI6OYYRzMjmKxRrZhIjncHeT+JN0Q1cq0p1ZkoAAVwPu1wUbp3Tk4FUUD3XxUgLoVHZMCgP7A5naNH2bnyvUbSUJAm7tPT3Z5cKRFrasRbxbyRZQYsnA8kkIYFj52ZFJQjhbE9SNcxsC15FIOIkTQfEZ3h2LUQyCvHXFQJZt6j1qaUal7XVljUN34fEitX4+IEgqIGZbamrgqzZaSKtncJkznWRFIV4T1+oYuC5DviCtOtUypRHBnBKRWTDINbRixZcTxh/gybwtwRml2vPqfFkqOItKSrRto5lIfJPZzybgP5FItWWoVQsIo3NMDc0RcemPkrTedIrWsisaGNq1zDFqRz992xjw7lHh4TSWCZJY28LDV3N7L59C0bMZN+929GiBi3ruuk7fiNaRMf3IT8yzfqzjmT79Q/QeNjKJWKgK1f3MDo0zovOP5vLvvj9uvOrVKoUC2KRe+SBLZxwytF0dLUxPLjwR/jiV5zDnt37+OkVv6ato5UXnHcmazb04dgOYyOTnPKc45FliRuuu41jjj+cu25fUHtKJOMC7hrEz6/6Pa96w0sAoZ795LbdXPLvb+VbX7mCxx/dTmt7M696w3lc/8ebOP0/3lwHI57/f6dUQE814vke1tSEWLQ8LxDz9PECmRyjMSN2pK6D7xKIdy6oZcu6IUQ4Czn0ZAPFfbtRYnEhsqmoqDEdSdOQVY3K6GDgC6QQaelATaZwAnVtPdUgYN81i8rkILKmE23rxC6XkBUVpySEOaOtncJzp2ZhNGaQVBU1nqQ6sVDJgPArcquVOuXq+XmWbJrLKij4rguyLJKkJOPjiYXRiNRdsyCgCiVpWdNxioXAHK+JakDerE6MEe3owq1WA3+gBLJu4uNTGR1akMfxfcrD+4i0dIQSPNH27iXnpphRQMatlDHSWZDEIloa2Bsi/9REEiPTghqNCZjz/HU5tpA8yrYK/o3vC1XzoBVltrSj+b6AZ9cWOFNutRySd4UK+sIMVtZ0YX2+XwtRiKZ6mNnWEJqtxeL4vodr14g0twm7dMcWXKOK4Or4rouRbcG1qqFYrmdZFPp3kly1rg4gIKsqRlNzOGeaDzUWX3LfAFQzKqSBRodCyHe0rQPf8/E9F0U3/ldES59NQP9CEYvHMQ5ZxUz/OLmRKVRdQzN1RrbsoWVNN0/8+X7izQ3khhZ2taXpPKXpPMWJOSINcSZ3DLH+rCPJjUyx48aH6TxkFaOP7yGzsp3SVJ7c6DS1UpXZB/fwyY+/h8su/zG7duxl8zGHcPwpR3PdH2/iRS8/GzNiYu1XpaSbGrnw317NxNgUTzz+JJd86K1se+xJdmzfzVHHHoama3zq0q+QyaY56bRj+NRHvhK+d+OhGzj1uSdw0WvfC8BF73o9rR0t3H7TPaxZv4pjTjiCy770g/D11Uo1sCUWkc8VeM/bP8oLXvJcXv+WV+C6LkMDo1z6n++kLamJNkvQq1/8/7XcLBEzKjgrixBcaiyBGokSX7mGWn6O2iLZGt/z0BrSKLohCKG+j13IhbvhaEd3WD34gYKz0dBIaWggRLn5rkt5dFDYORTy6A0ZsciV8tSCWYtn13AqJSJtnfgB611PdFIc3LtgIV6tYDQ1YxfzS+YMsm4scSeFwHY8FgdFY39k3DxCSjGjizyJBNov0t6NZ1VCZ1mnUhL20IqCEokBotIIE7OqhG2u+bZceWgvse6+JdwaWPCtUWOJMMHNK2tLihoSN+dttvXGJqrjI3Wwc6eQR+9sxFvOF8cVNtrW1IQAIjS3LmxMfIFaNBqzWLkZ7HwOoyEt2lmSFLbmou3dgWupQFMuB3P2HBtralzIPkVjeDWLcn6OaEcPSDLFvbswMk2i7WpGqYwuJJHKiLBpr+OfBfwu9mutGSkBBLGmxoVBYWs7avA97B+SIhCXWiKJ73mBsoTyd6V/nu54NgH9i4Wqa2RXdyArMp7n4VoObQeupFYUf9C1YoVIw9IfYaI1TWFshtWnHML26x8IHVendg3Td+LB4Pt0Hbaa4UdE371WqjJ7yxO8/UUvJLmimYLssHXLDl50/tnccfN9vPFtr+KLn/xmePy2jhamJqf57tevomdFJ+e/+gU8fP8WEskYJ59+HD0rOvn0R74KwJnnnsKPf/CruvN77KGtdZYM3/zyFaxc3cPb3n0B6UwD73v7x+tIqC946ZnMTs+i6RqxWBQzYlApVbj6yt9y9ZW/5ZAjDuKF558tdL3MKD4Q6+ylOjVOdXZGIKkmRoXhl1URoICxEXzXQY3GMbItWFMTOOUSSjRKvKcPp1xGUmTs/JxAfDW3UZ0YFXIudg01FhfWC6kGJN8Xc4FgcXfKiTqINRBwhlzMplbUWEwABSSZSFsXtdkpsUjPW0jLCgTD5v0Jmdb0JJG2LpRIFM0ViVTWDaKtHdilYkicDX9DsTjl4QEU0yTW1Ut5dEjovpkRzKYWUGTcqoWebkKNxMQiLUmUFkHVQZA8laggoUqSLEiYphki88ymVsrz/CIQSt3Nbfh2LVT9XhzzjqpOpUhpcC+x7pXCitzz8GxbSAR5HkY6K6ywIzGsRfDt+fBqtUUyPfUVC4gB/v7ABRAKEuJ7TIBj1/kDGZksTrmENTWO3pAWOnuyLH5f+y3isqKG1aK9yJrCd2ycYJNTnRwn0tyGZy1NxHZ+DjWeqNsQScvMiWRNI9LULGDkkvR322eSJAlFiHIpNDc00k3CuVZbOmf6Z8SzCehfMCRJoqmvnbmRKSYGBthz51Y2nXcietSgVrZQTZ1oY4LyrOhZa6ZO9+EHUJyYQ9bUJXbf/Xc9zupTD2XnTQ+z5rRDmQzmQZ7jMvXoXppWtfPE1u0cesRGhgdGiUYN7rj5Hi5+34UMD4zS1dtBR2cr+/qHaetoYd/eIX710z9y6afezb7+IbY8vA0fn42HrmfXjr1EYhHyuaX99EK+/rE9O/dx392PcNRxh/Jv730jd9/xABNjUxx74mbWH3QA+HDRu16HYRpY1RqJZJxCPlhwfJ/RwTFu++vdvOQV59DR2oRpuiSzrXilYiB/34lbLeOUi1TGRkJ7AyUapRJ4toCwiS5Vq+gNjVTHAgKioopqKlC5tktFsfBFY3iuB4EQZxi+v6yxGD7IpklpqH9B0RqCdslgaOVQGRsSAqCLEVOyLBLG/PF9D1+WifeuBlmmPDYkeCDxZLgQiraZio+PmW3Dc2z0xkxYFVqz0zjlIrHulUiyLFSmzciySDW7mEPPNAs+UCKFJMtUpyYwsy3Yhbz4zMX8pfmdvKoKa4mBvUISKJ4MkGAS1ty0SEySJBByqoZTLgkrhHSW6tQE1oxIuE6lJK5tvwQrqxpurYrR1ILnuHiW8Akysy2Uhveh6AZGthWlVAjngkKFQMW1KugNaYr9Q3XHtKYnBQqvInhCemMGa3pSbCAyzaK9GCQJWdOIdfZS6N8VVmdGOou9n7NprZBDSyTZPyRVq1ffSDeJxP434r9DIHWqFQp7doTHd0oFIq3/X3vnHSdZWaX/73tzxe7q6pymZ4YhJwmCYRUVAV0FAxgxLWZd/a2urtnVdQ3rGnYx5xzWnAOoGFARRUHJDJM651Dh5vf3x3u7uqu7BwYEegbu8/nMZ7qrbtV963b3e+455znP00+ms/uA3+OuRBqADmG09rZjOLayWwhDTnnmWez85dXc+pu/cfSjTwMB7kIVzdARmkauowXdNOk+Zojx63Y39kehaWrexQ8Rhs7hjzyJnb9Uwp9bH3QsmmPy2Y98hb+edgLPfsFTeMcz3k8YRhRa8hSKeT7+gc9TrdQwDJ2Xv+YFfOT9n+HWW/YwP7fAZz7yJXbfajkzMQAAajRJREFUOkxHZ5lyRxvHn3QMV//pbzzwIacyNTnDOY99OG7dJZPN0L+ll6OO2cH1ieWCYeicctrxdPd0MjUxg21bHHH0dmzHplgs8LLnvq5RBszlszzvpc/g/e/8KABPeOpjWJhfYnJ8iu994yc88KGn8T/v+hgf+8x/0tdVTmZLtMRvpQwllOlXHJMbGFonly/DAM20k3JTNrlbDtQAZ+LZ4k6NIZc39DXlEm9uRm1gq+6qlZJADGHUFHwANZ3fN4g3P4cwDIRuUB8fUWrcpqnO3dGsvYYQ5PqHGo13u1ROmvo01KCXe1GFLcq4zC61422gih3Va42ewHLpai2EbuBNjoFQxIz65BhWsVWdr7sPd2Jk3WtkrD5vGIbkhg4jWJxvykQyXb14s1NYLW3IOKa2b1ejsa6ZSvfOnRxDyhgZRWr+KgobOmuZhOqtWw7+zBSZ7l6iel0Z4I2PQOKnI30vsfhuQ89kkp7QvJqLcrLr1K6Bpj5iWK00MpRKZYn8lu1Yq9QPjHyB4o6jlBK5YRB53jqbd8Vws9ZZVyx/hqhFqWgbmeyBKV679eR8Jtp+VLLVXFZzedKdHFOeQ5sgzZMGoEMc+bYCQoOwHhD5Ptsfchzj1+3l1l//lflh9QuvWwYPfsl53PC9K5m5dZzytm5OPP8h/PXblxMFEUc88mTGrt0NqI7AyJ93cthDTwAk0zvHyMxXeNJTHsMVf7iaqYlpwlD9Ah95zI6mwc8wjPjip77OOY99BL+45Dc4GYd9e1TGEAQhX//S97jwn84nk8uwbccWbr7+Vt73jo80Xv/Clz+bV77xJczPzGPaJpoQfPOrP+BBDz2NtnIrZz/24WiaRnWpyte//D1sx+YxTzibbC7DZZdczvjoJBc8/Vzu/8D7sXfPCBf/1ycAuObP13HZpb/ln1/1XEqFLN78jJLOcV000yIKfMLqErn+rQTVxf0OMGqWRW5gCAl4s9OEtQpWsYSeyxF7deUrEwS4k+PKoM60kYEKZDIM0AyT3JbtxL5SGBCGSVBdaqIVL0NGkXIvddQga6azBxDEvovd0Y2MJDIOm5lQUuLPz6I7WaJ6VTXXO3twp+YJlhYBoay5J8eUGkQQ7DczU9TwQG3S+SK6k2mS00EIzFyB2uK8ktKp1zDyRTVoazuE1SWczh6q+3Y3rdHIFRCaIPJ8oqSktRr1qXFy/UNKGDTwmzbmZTFT3ckgfQ/NsnEnxzHyBczOHoxMltrYcOPmQTHXFJEgXEsj13Q0J4MQqtfizUw3hEdj31NZSBiApim32iBsBCUt6f2thjs1jlkoNijhQghFGli+EdH0lfdMrp9VaKW2nNkKVc4zcjmkhMqtNzWyJ93JKMbcbZAE/MV5qntWaOtOZ4+ahToAaZ8NpdfvIaQB6F6AXGuBuBAzOzyBmXXY9Zu/Nd2tHXX2qVzxqR83hEqnbxmlNrPEsY97EGHdJ9NW4MizTuGab/6aKIioTi9w8y/+QsdDjuKPC2Pc+KddnPGIB3LhRRdgWSaZbIZ6rb6OhAAwPjpJuaPEs5//ZL7ztR9y9HGH89e/XM+XP/tNXvOWlxP4AU7GBgkfft+nMS2TB59xGq2tRdraW3nra97N3t0jFIp5/ulFT+Opz3oCu2/dx+WXXcHhRx3GR//3s5x82gls3TbIM557AV/7wnepVKo8+rwz6R3oZnR4HNf11pniVSs1bMvCCOpYpXbl85NsVLrtYBZbE8+bw4ijALu9s2nw0iy2JgOL6q5UJNTh+vhw487Wam3DNJTsiTc7nTiJziv15EwWd2aSTHuX2liEBkLN9GxkTmaVyg2759zAkBqmlDRKabqT2c+8R4Bm20Suku8Ja1V0J0McBmS7lHabWWhRWmSJdXV+YCvVkT2NLMwud+Ivzid39IL65BjZnn4yHT1KN1NKRcEOfLL9Q8kQ6jx6vohY5Y+0rIZdn5pAIJWW2+IcdrGEjEM0cwPZlyRDEZq+bsYFVH8n09OvBkWFIDswRFSvotsOtfGRpmsZLMwp5Qo93xyAhIA4wp2eINs3BDImqleT5r+GRJVAI7faUAkwWkpoto3T2YvuOIp5txpClRD3B8PJUNx+BGGiDKFblhr6HdiqRE9tVeKUUdRUugNFNAlr1f0GoMjzmgadIclqCi1oa1hxqtTYnHE5Xb2bJky6+XrcKe4SaLpG+5YeMq057veUMzAz6hc6Vy5S7C83qWQD1OaWMCyT0Wt3EdRcpIw58uxTaBtSPYWOBxzOW979Qb74hW/xx9//hf/+zw9x7dXX840vf58X/8uzaWktknGcdQ3YU08/kWOPP5IoiimVSwwMKeHHo489AoCZqVnGRiaJo5jBrf287NXPY2xkgkwuw1c/9+2Gw+nSYoWL3/0J5ucW+PlPfsNjnnAWmq7mnf72l+u53/2P44Pv+RSTE9PUqnW+/qXvUa/V6evv4YZrb8HYoC5u26YSq6zXmjaqyHMVy0k3CJbm8efnMJKGuNPRndyhCjUIKpWqQRwE+HPTTUOp/vysmgcC1RCOI/RMjrBeoz42jNVSojqyh/r4CLXhPdRH96qSiIRs/1aMbK7hD6RZdqNJH9ZV0Fzd0I7c+oYByGotI3RD0Y3bO7FaS1itbUrbLQwaJa/ayB6lqjwxSmXvLnJ9W3A6expBSc/kiMMQzbLI9igq9bLCgpHNU58ax0+GOaWMMQqtyu11tYtrHKmyYamMZtnUxoYxE5aWjGM0y1LBQGiNJrtmWmh2JtEwa1n3+Yxcnuq+3US+q5hrbh13aoLI9ze0wAaB4WSV02iidJ7t6VeutFFE5NaIw4D84DbiMKQ+OabUDuYVCcSdGidYWqQ+PoI7OaEGNYVYF2vstvYGKWF/0G0Hu7UNp60dM1/EKZWxWkqN4LN8XaINPsdaX6rVkIma97rXbCD/YzgZCtuOwOnoxiy2kh/cpkgMm4Q0A7qXodjVRiwlp1/0KOI4ZmFkCrHBnZkQAs3Q2XL/Iyl2tnL1N35D51GDtOo6Jz7pocSWxuBAH9OTK0N/X/vS93j285/CJz/0Jc49/xwyGYc3veNfef87P8rC/CLP++dnMDjUz9+uuQHbtjnmmCMYGRnjn170NO536nG8+60faASYUlsLr//PV/CqF/87Ukr+4WGnNQ2RaprGU575eNy6x9mPfRhRFPPAfziF+bkFhveOce01N677TD/41iW89i0vR9d1Xv2ml/KLS37TUNhuay9x5FHbEJZNtMrobBmx5yYbopLK95cWleLwKpkVPZNFmKZSqTatdUOckMzRCA2r3EFl76047Z0osceMMnRbXVLyfXUzHni4c9MY2TyZchcyDpXF9/K1MK0NrQiCxQXlbDo9jowldls7Ya3SUEq227vw52aamGB2W0eTxcAy3dednsRu70KGPtHiPMSxshLXMonYpyod6U4GI1/ELpXRTBtvZpKwukS2p39DanUc+MRR2MToknGM3dpG6NbJDW4jqlWV0oTlIAxD3c3LGLujW9lIzM0AAqeji7BawUno2EFlASNfJNPdh2bZ61hv6tqZVPbsxC53YrW1E3tuEztRxhHe9GTDyTTT1Yc/P4ORya1jyS2rTceeS6a7T33eOEbPKqagNzeDNzuF5mQVu8x27rCumjAM7FJ5nVvt/ujV6jNa6wZnQZFKIt9bRxU3MtnGjdJmIw1A90K0dpfJlQq4SzV0U8ddrLP9Icez81fXNI7Z+qBjyJbyLIy4+DWfox59f4avukWpZwto397Hv738ubzh7f/DzTeogT4hBLZtMj+3wOcTW4T+Lb286+I3gZR8/INfaHjzGKbBK177QoSm8f1vXUIm4zSCD4DvB9x47S2NO8alpSrljjZmplTAe9bzn8wvfvobvvQZpbaQL+S4+JNvp7u7g9HhccrtKw3fZfT0dfHpj365Ycn96PPO5IUvfxaVSpWHnflA+no7Glpk4RqjMT2TJZhVTKegorTP1MBje5Id6ckMyQRxGGLn8hiZPEFlDQPLdsh0qX4NUipWWHsXCI1gcY2EfpI9Lg9fBgtzqmzUtWJip2dyxL5LGIRKl20V40vGEeiamlnKF6mPDzcNl2q6jrdmQ/bmppP+V12x9jKZxGHVRUaBImIkqI/uU9P7rSU03aA+oUz2rJYS3uw0Zq5AWF1S165a2XCT1Cyrqe+h5/IQRdSSjLC2qvQHkO3fsrLWqXEyPQMNpp+eUWXH2PcRpoFw602DmLnBbcgwVBmE0Mh09zZUtv35GcVEW6PWrSWMM292Bqe9Q7ENu/s31gdElR9lEOAvzasbO00jSIJibWIUs6WEpuvU9u1SEkidPRi5/AHP2wghsNvaVYY9P6OER3v60LP7DxiaaZIf3Epl765EZVupuddG96BbDrnBrRvK+RwMSEtw91KYtkWhvRUrZ6sgVKlx6jMfyQnnP4TTnnM2nUcMUJtfYvLGYX7/yR+xNDHH8FU3ky0VOPH8h6IZGvt+fS3/+aqXct7jzgLg3CeezYmnHEvfQA8A3b2dPPWZj+fK3/2ZqalZ/nTFivp2GIT83xe/yxFHbeeEk45m7xpaaxCEGObK3MIPv30pz37+kzFMg0xGlfZ237qyufQP9vL9b13C+975UX556W9xXY/u3s6Vz2uZnHf+o7jmqutW3vM7l7Jlaz8XXnAm2wtQ37sTGfiNPkjjta1taE6GXN8WQq+O4WSUJUOiEecmXi/VvbcS1qrEvkd9dJ+S/1/FDrNaSgQVVa6Rvseys6owTDTTUHTjVdCdbDJd34w4jihsPZxM3yC6rSy9g8U51YNI1JrtcidmoYXY8/DnFHV5rbLBhsWgVSUi3bYbQcMuldc36gF/cY7YdfFmpxvadVJKjGyOYGlevbatHWSMvzCL09XTCKyqFNiDNzfb8LsREqp7b1XMtGRQdzW8qYkmUzUZ+LjTEyqzMExlkTE+jEA0i2uCkj/qHSTbt4XCth1odqZhsa7bmYYHj1XuINMzQKanHxBq7mfVPG7kuUTL80BNP6+Mku3JZHA6etScVL6oiB3TykBPt+yGG25YWWRp100bDgPfFnTbIde/hZYjjqV4+NE45c7bJRMY2Tz5LdtxOnvUgO7UBLGv5pA2ykwPFqQZ0L0cbYPdCKGRKxW48nNKRy3f2cKx5z4Ib7HO6DUqu5GRKvFsf+jxXP2NXzeYYPNfn+KCxzycrUds5aYbdnLzDbs47UEnUe5oY352gQ+979P4fsCr3/TSdece3jNCqdzCkcfswLIt+OaKB5Hv+Rx+5PZG1rO4sMSXPvNNLv7E2xneO8YtNzU3ef/hYafx6Y98ufH9pz/yZZ5x0QUMbOljbnYeKSVf++J3ePm/PZ+P/M9nG0OtGcfGCVb6X/7crLorlzGZ3gGE0PDnZ6jtU+ezyx0Ekasa08t/uELbUHerPj5MdnArUbWiaLyVxZUSkKaCj9VSQlgW0lM+NFZrW8PG2Sy2Evsr9X5hGGQ6e/DnZ6nMTSuywKoA501PqpmN7l6CyiIyCNX8STJcuHrex2nvUnYMa7TYjFyeqF7HLncS1mvotq28bxJ/orXQNGVctqyXphrWtsp8nCygpH6MQgsCCKoVMp29KmN0MmrOJAlK7szkioW2YEOqcxxFGKsGKoVhJAoEA4o4kqhUb9TfQCqR1/roXqW1NrRDEUMKLfiL89RG9mAUWtBMs0l9wC53YrS0ESYBVeg6QoLemk8UyhfRMxmEbhDWa2iWQ318uEFVt/KFhIpeXGfPjpQEtQpGdv8ltI0gEvbdHcVyNt20hP2wOg8GpAHoPoDSQCdG1qbnuG1UZxaTjlCMbq1OywVmzsavuet+Yfdefh2zZpUffudSHvyw07j0R79qGiRtLbXQ3dOx7rwPO+vBTIxNMzjUR1dPJ897yYV89hP/RxRGnHv+2fhewPlPfSxhGOK5Hu2dZaI4JopjOrvbb/MzhUHIpz/yZf75Vc/l4nd/ovH4TTfcylOe+Xg++aEvks1l6O0pAyubfFirYGe7sHJ5QDT1WkANHDqdPcgoXOVTIxOWUzOE0CBSdgKrdceWHTydzm7VNN+7C6e9C39xHrutU/mz+B5oAquYWFPHMU65k9rYSIMB5c/NKBOxVZI0uu3gTk0QVBYRQlNDmYnttGY7aLaNTD6nOztFtqs3uQt2MQpFzHyRqK6k+a2WVvRsnqAyrkp/3X3rKNlmoUUNxKKo5N6MKimaxVZFzZ6ZUhYMTqax9uUelNnapoJi0ktz2tqJl987GeJdC7tUxpufbSh4yyjCKrQSuW7TnbxS0W62S9csGxkFZHr68WanVSlO0wlr1ZU1Zdf3drzZKXUzgpJR0iyH2tg+wuoihW1HINvaVebrKsdVRUJRpIDIc6mNDTf8eTayVrin/HVUH6zQJJoqdH1TNN4OFGkAuo+gUG7BtEwM28RdrDI/PEWxu61Retj562s45WkPZ2Fsdt1rDcckm83x5ne+iqv/dC2Pe9KjKbbk+eQHv0i97vKkZ5zH5OQMb3rHv/K///Vx5ucWeMgjHsh5FzyKyy65nJ/+4DK6ujt4ySv/iRe9/FnU6i5TE9P88DuX8suf/RbLtjBNg2qlxmMefxb/+IRHsnvnXp79gqfwf1/4Dp7rE8eS859+Ll/57Lca6+ru7WyS7wFFt9YNncMO38oFTz+XYtYGt5lVJMMAkcsjb4NZFCwtYhZLimiwLIdjGE0lI7vcSWXPTqy2drI9AypgCaHIDLFMso8IYRjEYbDKUlrV+Q3HIXLdxpCoIjw0B/9gcZ5s/xbMXB5h2SpoJc6jEnWslCoIxmGI0AysbJbKjKKG18aGMbL5hkW4PzdLHHiKFed7RHMzahpfxgSVJfIDWwndGiSswOUeijBNZKQGZmXilVTZu4tsZzdRGKiMZO3a52fRLbvRtwoW58lt2d7wD/Jmp8n2DCjzt8SUT7cdspksQgi8xQWChOjhtHc1BRxvVg2ZerOKrWZk81gtrWrYVEqlnxf4xF49YQYq6vGGLDUp0XQ98VTyCZYWsYolzGKLGiL1PWrDe5CRckBdy0iLfQ/NsjD1Enou39SXErqOmVVU6DgM1BBsHKPbzl0eGDRdJ9e/hfrUBMHCHHo2S7arLw1AKQ4OOIUsdj7D7O5xcuUimqlz6jPP4ppv/hpvqc70znE6dvRh5ZwmuZ7Dzj4Zb3gfb3zlOxqPtZZaeOM7XkmtWudH3/0ZZ/3jGdx6y27e+u5/w8nYBH7A3OwCpXIrF150Ad/9+o95xQvfxEtfeREfv/jzHHvCkbSVFZHA9/yG/UIYhuy8aTdf+ey3eOwTz+J1//H/GBud5LKfXs4znvsk8vkcf/3LdfQP9nH6P5zMm1/9XwBKSWGwh7GRCbZuG+SwI7by3//5IU7+5sVkl3XHhECzbeVO6nnKcnr1cCArQ4ax7yEKLY15HH9hjlyfylziMMTI5nCnJ9AMA4FqtrtJn0LzrSYTtEx3HwjRpILgzUxhOBkiz0O3LKUdtsE8kFK0dvGmJ7DLncSmhWaaav4mDJASgsW5pj6D1t3X9B5hrYKOVKKopTIxUF01axImZAvNyagBW8/FLLSg6wLNsjELyu7AnZlqSORIGSKEoDY2rJQjgvUlMaHp69h7weI8TnuXCgZJcLY6uggX5vGmJ5vKhZmuXpZ/Mt78LE5nd8O3SUZRsu4OpJSElcUm7TY1EGsTViuE9RpOe1ejl7d2DkZlTjGR5zUyTaWKsIjo29KwPjALLQmjbL21uaLTx+hOjkzPAJFbU2VIy1Z2375PdXgXYWUJzbSwyx2qN2gYSungDjii3hZ02yHXN4hMZJvuaX+fO4o0AN3HIISgvLWHOIyYuGEfXtXlgS98LLEfUp1dZH54kpOf/gjm9kwQ1D3K23pZcqt8/OLPNb3P/NwCM9NzyFhy2OFbae8os7iwxPT0LLfevBvTMvn0h1XPxjB0XvLKi/j8J7+GaRlsPWwL115zI698w4v59S9+37gr1TSNY044ku9+/cecfNoJfPh9n2mcT1lD1Pjip7/B4Udt4zeX/Z5qpcZzX/x0RofHaS21cMuNt3L2Yx7G9NQMP/7ezwG47Bd/4MInPRLdVhlC7HtKMw2p+hMd3fhLSlzUyOVx2rup7FUT5d7MJGZrmfzgNmQYEceREtEMK2q+JVGJ9manVAO+oxvdchpSOMtwp5QywjKs1jY0w6Q2MYJmmBi2o97bcpoo0qCyrGXPIG9mktyW7UqnLrkLz/ZtWdfkjtw6esLiAxUAw1qF6vDuhuCoWWwhWJjDKpXRdINgflYFm3yROAxUppfJYLW04i8totkZ1c/SNPRMltj3GgKmMoogKfWsnmGx27vw5tbIzwiBPz9L5HnY7Sp4EMbreycAq3pSMgwIalXyQzuIfA9N1wnr9QYRYa1XjzBM3GWvpTgGTcPI5vAX55QQbeKRtJw5yTBoBJ9lKLmdBcxiC7qTxcjmqE+OJUPKKwoOVqmsREl1U1mF16popoWMVZDVDBOB0hRECJz2riRYJvT+XF4NpG6gt3dnIIRAbJK46B1FGoDuo+jY0Y9TzBH6AXuuvAHTtug/aQd7/3AjRsZm+tYx/IrLzl/9la5Td+DW19+dz88uIKXkjDMfyFV//Cu+79PZ1cHQtkHe9vr3No4Lw4hPf+TLnHv+Obh1j1NPP5HDDt/Kz370S97zkbfy0+//giiKOeHkY/jq577Nvj0jXPD0c+nt7+Yn3/85fQM9nP6gk9EEnP2Yh6HrOuMjkxxz/OGMDo8zNzPP1xMH1csu/S0nnnIsjzr3Efz2V1fyl6uu48KnnYM3O9U0I2K1dYCmUxvbR7ZvC7qtNv7q3p1kOroRutGYrYg8j+VZHn9hnth3VTNdaE3zGvWxYWXFsAYyihp9AM00m0pbsecR1qpkewap7rtVzassa4oJQbC40JShycBfKQEJ0bjD1WxHyQrVq/jzs2q+pl5DIvEX5huqAlGtSm14j+p12A6EQWMtkeeqMtyW7WpmKQiU2rUQTfRszbQaLqxWaxu1pOTktHcpQkMco2fzKnNYw8wzCy3KW8gwqE+O4YcBTmdPU8CEpJ+RyWK3dxJWK6qMmM1T2X0Lyxu3WWhRJa5ia3MAEpryyymWcKcnsNo60G0HUWxVP1dDqVWb+SKRW1M9nJ7+dT83UFT3qFYlN7iV+vgwsecS6YbqTyVlyjjwEJqubnKWSRKrynRx4DfKuFZLCXd2itUZVFStKELDXRSADiWkNOz7MApdJUoDnbT0tHPt937PT9/6BYYecDRhzWP65hEWx2YQmsAwdJ76zMc3vdYwDXL5LEITfOkz3+TjF38ex7b50x+uZmJ8at25FheWKLeXWFys8H9f+A4n3f84HvSw03ndy9/GueefQ6VS5b//44Ps2zOiVBYyDo5j87yXPoO2cokfffdnLC5VueoPV3PZpZfz7Bc+lVtv2UNrqYWf/eTXTef6yx//xtmPeRhPffbjyeUyLCzV1w0o+rNTmPlCMry41BiolFGkegKGQX1sH7WRPbgTow2igT87pczXJkaT2r+N1VLC6ehuKE2zZuZDsx31uKZhFlo3ZErFgQcIvJlJ6hOjBLUKQa3aNK9kZPNNispmoYhfWSTXP4SRyaoSW1sHTlev6uGYJpphrZO0kXFE5LkYThZvlX9Q8iRhrYo7Oao+YxCsa9ova7Tp2VwTYcWdnsCdGsdfWiCsVRCGTqZ3ALPYitVSItu3BRlLgqV55QAaK0HRZe28TK+iRtvtXUokdX6GqK4swdEE9fF9rN64g6UFjEyWOAzJ9PRjlcrY5Q4yXT24k+NKxy2Txczlqe3brRQoRvZQG92nxGOnJwiWFtFMS/Vk1jDVlIRRqCwanEzC+iNRT1f0/KhWUTp7QlmUb6RubTjZhvPpcqlVz+TIdPXidPao0uoGLMv7AtIMKAUDJx+Onc8wvXMUK+eQKa3MP3Q//Dgu/vSX6d/Sx/Ne+gx+9fPfUW4v8eAzTuOLn/kGFz7nfD75C6W7Vqu5/Opnv+Npz34ChqE3REsB+gZ6KHeUGoy1K357FSN7x3Bdj5037+aJT3kMZz36DObnFqhVa/znG99PvVbn3PPP5vQH35/7P+B+vP4Vb2+838cv/lzD72cj7Nk1zEfe/1kAnviER3B4x3rZGj3JGlarFOuJcVp9crxRCpNxRJyIbCKEsjGWEm9+hmx3v9p0F+YSoU1NKUFPjSsNONvB6e6jNrqXTEc3mu0oCvXaDUc09xWiWpVs3xACVVIzcgXMfKGpt6QZJsK0lJ9P8n5RvYrd1kFsWkqzTtOa1BxWXmsQ+65q7K9Zy+r4KTSxocyLlJJMV3+DeNF4/8QfJ/Y9NezqeWo9gU9YWVSU8cqSoqAnTLZMZ0/D6weUkCYCwsoime5+NTzb3klcryujudV9JaGh6Rq1kb2JIaBsEEU0y8Lp6lW9pVVsucitE4fhSqaXKGVbLW0YmSxhrYaRyaBn89THR7AHtgIqe/Fmplaul6YpWv2yCKhURm9msYVgcSEZCO1SwSdRCPcX5pSSehiuaOYZJvmW1nXXeH9ofEZNHJjY6EGMQ3v1Ke4SGJZB73FbybblWRqfZ2F4ipaBDoSp83/f/wnX/e0mrvvbTRSKeU4+7QTOO/8cfvvrK3ndW/8fb3rVuwA4/KjtHHP8EVxz1bXcctMu/usDb+b6a2/mB9+6BCEEF/7T+bz9De9v0HB7e7u58W+38KJ/eTb5fJbX/r+38e/vfBX/866PNa3t+9+8hB1HbOfWm3ej6zoPeMgptHeUufJ3V3HY4UNc/ssrOenU47nqyhWVhx1HbmPPrhUm0g037ubw7mNh1UaqZ/P4i/PIhJ22YvymqUHEVVmDZivLbaHpycbbS7CoZGCqI3saG2JYXaIeKeFRq6WkyjO+h98YohwlN7gNu6290UwHtQGt+5nkCnizU5iFFpVZaRqR52G1ljFyRWQcNXTJ1gYQb24aR9dB14mkxOnobpoPMQpFhO2ggWrsr2rea6appHLKSjtNSrnOaZXE2MybnUSGAdmeAYJalditoWeyGLkitZHd6Jat5GnmplUfyq1jJiVDJX3kqMASBtiJ5I7KiMbIdPcpu/QwxMoVlAJFFCkCg0xiXhSBptxKrXIH/szKjYSRK4AEGYYbasQt2yS40xONn5/rjqA5GbJ9g9RG96HZGTLdfY0ZHiOTpbD9iIZRoJ7Jols2xR1HEye9ntroXnQ7o6j8cYyRVcZykedi5PLkiq1Enou7SrlahirL1DO52zWVi3wfb24ab0b9TmV7+tTvxyY7m95ZpAEoRQOtfUqqZs/vr2Pw/kfgZnQu/9jHG88vLVa47JLLOf3BpzC0dZBqtc7iwhKGafDYJ5zF6//l7bz3I2/lG1/+Pq980ZtxMg7PffHTOen+x/O6f3lbw9G0b6CHh531IM581EP4wHs+yVV/UMFjanJm3ZriOMbzPLr7Onnl61/Ej7/3c266bidnPuqhVCo1+gd7+IeHn879Tj2WP/z2z5xy+omcdOrxvPx5r2+8xwfe/zm2bX8DJx7eR+zVMPMtahg1mTmqje4l29Ov2FGxUnpe7dhpt3WoO/QEysSrTwWANSyvyK1jFlubNnyr1I4m9ESSB6IgJNPdl8imJO6ZqzIJtYnnG4wzoRtJH0giAX9hrhEgcwNDG/wkBbqToTq8OynHSbK9gypoaboS8owivEQ8NdPTT1SvoxmGKqsFAe70LHHgY7d3KX2ycifB0rzy5GnvSgz0guR6VMj2DhJqGmGi/p3p7muY5AEN983lYKc7WYx8sUFvFroawq0nmaOMY9ypCXKD29S1X+6hFFsIaxXCymLDhnqZIp7p6iVOiBFxFCM0DW9pEbPYgjfTXBbWbSexfthCbXQ48XuycNraqe6+hdzgNjTLwbDX6Kg5GWWzsPqxpLy27JsUuUpg1ixkqU2MEi0rTExBprt/Q2ZaWK2oG4kNnpNxrEqdmpr7Wv7disKApV03UzzsSIxsft3rDgWkAShFE1p72znxyWew9w83QEbN09yQGMQto1DI8fpXvJ2h7YM8/TlP5JYbd3HJD39J/2APl11yOb/6+e8AqNfqXPzfn+Dl//Z8LnrxhRiGged6FFsKfO8bP+XIY3bwxKc+FhlL6nWXwaF+evq6GBtZYRg9+IzT6OruoLXUwite9CYCX216X/rMN3jsE87mhmtvZufNn6ejs8wpDziRHUdtI45j2jvamJxQLCjf8/npT37DCcc9C6eogo83M4UQgiDxAVKsKLWpC11Xxm57b1XumhtImQSLCzgbuUiuGVgVmo5VbCXyVNlHt23MbFapJtQqCN3Abi0jHIf8lsOQcURQraCbNpHvEochwex0E0U709WL67uJmrPbsH9eht1Wxp2ZINPdt8JSA2QgkVImDC0lolmfGCPb06fsoxfnsVCU4uX386YncTq7EaamrLAtW+nGrfXDmZ7AyOaIg4A4WCB0azhtq4aT47jRz7JaSmimRXXvrY3SmIxCahOjOOVO3Knxxh197HmN4GNk84S1aoOtJqOQ2sjehr368s9OxjGZzp4GwUSGIWZR2WQgNOXYujCHpiuPHqultRHkl4dpZRyvCz63B6HrmHlVKgXwFxdWgk+C+sQo+S3b1r3WyOU3tN0Oa1XqU4pZZ7WUNhx0Dev1NACluPcg397C0Y8+jdALeMXrXsi/vOCNVCuKofQPDz8dx3HYvmOInTfv5i+lIk991hP45Ie+yImnHMdvf3XluvebnJhmxxHb+NMVV7N39zDnnn8ON1x3M9/9xo/J5jL8x3+/hrnZBWZn5nj1m17Kn664mit//xdOPOVYHMfmjf/6Tv7ltS9sBJ9l/Pj7P+fCfzqfm2+8lYnxKX7wrUvo7e+mtdTCv7z2hcxOzxIEIV3dZY7a1k00PU4NJTya6Rmgsvtm7FI7kVdvKrnJKMKdnSLXP4RcLcmzGkIQBSFWSxv+wgqpwGnvVLX/ZA5Dtx38xUWChRmy/UPUx4aJPBezpUS2qx9vfoba6D6yvQNIXW26/swkeiKSqen6uvkgd2YSq6VN3Q1PTzQUpSPPVT2Meo2oViPO5hN/G4GQqvlvtSjbcaRMemBGQxjUyHWq0t8qHySQuJNjOF19iggwMYpV2kC+f02fSQZBU39Oz2QVU8zJEdWrykxvreBnrGprTmfPis6btlJaMnL5dSrR0Mw4awTbpE+iaNCG0m0rFJWQ6LzK7vJDhyHDCH9udt1a7oreipQbSODIGGFaqiyafBZhmGR7BtaV3yLPZWnXTY3P5E5PYOSLTcoYwEE/63Nb2JQAJIR4N/BYwAd2As+RUs4nz70WuAiIgJdJKX+SPH4O8D+ADnxCSvnO5PGtwFeAMvAn4BlSSl8IYQOfA04GZoAnSyl339Y5UjTDsE1Of/ApfPQL/83e3SMUWwrohs7b3/BeznzUGZxz3iNYnFuk3N7G45/0aC679HIGt/avY8ENbulnbnaerTu2cMQxh/Gzn/yam65XNfALL7qA/3rLBxqvMS2T937krezbM8ovfvKbRhbj1tfX8YsthUZgBLjfKcdx/d9uAgn/9KKn8a3/+wF//csNnPbA+7HlRU+h1VabZNQwBbPVIOoGigiRWycOVL3dKXet22Ct1jbC6iJmawmjUFTlNKERJAwwUM10b3YaEn+e2r7djY0uWJhTvY9SuxIE1QSaYRKHvtIrs2zCenVDvTQZRY1NR3cyyonVc4k9l/rSQuM1Ub1OVKthtrQipcRp78KbnSJYWkS3bOz2LjL9W4mrS2qORQi82SmMXG4da1AgkVFM7LmKLrxGssdubaO+JjhouqHcSgstSjA1DMj2DibmZ6JxTa3WMrptq5+Jk1ElRreu2GlOpjE4GofBBrYDG0vdLGdQyr7cpzayVxnOJfbpmmEqGv6+3TgdXU1MP6u1bUOvpTsK3XbWyQVZrSUM28bo6sFqbVMB0rI3NIRTCuXNgTGsLOJ09jQCkGZaB421wp3BZmVAlwCvlVKGQoh3Aa8F/k0IcTTwFOAYoBe4VAhxePKaDwKPBIaBK4UQ35VSXge8C3iflPIrQoiPoALLh5P/56SUhwkhnpIc9+T9nUNKubH+egqOv98x7N01goxjKosupz7gJLK5LG2lEpXFKr+89HIOP3o7pz7gfnR0lrn2mhuoVVXWcPRxR1DuaOUrn/02vf3dHHviUVzxmz8BSr0g8IOmgBX4AV/69DcIw6gRfED1n4a2D7B75wq54AUveyaf/dhXATjq2B089vyzyOfzLC4s8dMfXsbpDzoFULNBO2/ew3sufj2doq42vihs2BJsNIVu5ot4c9PEnqccQXsHGwFBdzJ40xNEnqsUEgaGNhSBFEIxyHTLAk1rvssWArtYoja8h2X2m2bZykq53EF1ZA/ZnkFir74++LW0ESwtoGdzWC0l6pOj2K1t1NZYDRjZHO7MJHHok+1Ta1yel4k8l9roXvJbtiMsCy1RCDQ0PbHM9hplNqtUJnRdrNYS2f4hIj9oWB/IMFDinbrRtEaj0EIU+BDLJpJDVK81yBMNW4gwaGL3OZ09mANDjRmp3MBWwmpF+fV091Ldu7txzYx8Yd3PT8/miHxPBb6FeayWVjTDwp0aV30i3yOsLGFpGjIM8BMLDCljZaWdL6BtYGh4R6HM3w6nPjlG5NawWtoS4zp183B7gUNsoD+4LHq6PM9kZHMHtdTO7WFTApCU8qervv09cH7y9XnAV6SUHrBLCHELcP/kuVuklLcCCCG+ApwnhLgeeDjwtOSYzwL/jgpA5yVfA3wd+IBQt0X7O8fv7urPeW/C2Y95GNf/7UZuuXk33/zKD5qey2QzvOviN/H+d36ME08+hpe96nnMzS6g6xqD2/p59UvfCsCf//hXzrvgHI4/6Wgu+eEvyeUz1GvrM5uxkQme/YKncNyJR3HF5Vdx7TU38Jtf/oHnvPBpzEzN4HkBW4b6kUj+832v46o/XMPo8Di5XI6PX/x5br5xRRj0OS94Kvv2jLJvzwhXXXUDZz34WPJtbcQyXhmYjKJEcWAKpFSinYkStUhMvWoje8kNbMFfmG/aUJGSsFrFLnc0NbrNYivCNLHb2qkO7ybXP9T0Gc1CC978DKup17Gv5IFqo3vJdPbgTo1jd3aT6xtUmmeBr+7OLRvNUs3uxlraOpRs0NwsIJXCgW2rLEWobGOtWsBywHAnxxtSRU57J0GtRq5/S6LvJomiANO08aYmkDLGKrYiEFRH9jZIBpppkt+ynWBxAc000ZZtq1cFJbO1jaCyhAwDnO4+ZdxXtKmutZOeGie/ZTtRXFf3CvUaQa2iGHBxTG5giMj3FEXdc4llrMqQnqtEcuIYPaPsIvzZKfy5GQrbdhC5deoTY43AauTyipTh1qm7ipUodINiqbzud/LOwsjmlJJG4il1R9hqupNBz+SaysNOZw9GNo+5xiriUMXB0AP6J+Crydd9qIC0jOHkMYB9ax4/DVV2m5dShhsc37f8miTTWkiOv61zNEEI8Xzg+QCDg+sn3O9LMC2T4086FsM01Z39qo1ly9Z+brr+Fl76yot42+vf23AhfcZzL+BLn/pG47gnPeM83vnm/+W8C87hr3+5nvHRSY6731F85XPfajrXPz7+kXz9S9/nhmtv4oxHPpgLLjyX7p5O3v0fH+SMMx/IVz73rUbp7cKLzueIow6jf7CXPbv2NQUfgK9+4dtc8PRz+fwnvsbC/CJLAVgLs8pMrVbFSCwM7NZ2pdslQLds6uMjjd6Pkctjt3ci0Ta0RlZ9Fkm2b1CZpSVU7trwHiUC2lom8n0lBpr0NjTDWGeKByTMMVVmM/NFavt2qcHMjm5VehNC6bWtCiaaZRFWl4jceoMYESwtNDyPnHInUW1pnQaaWSgq35hlmrKUuFMTiXRPVSkP7N1Jrn+oSe27Xq+R6W5WDogDJbQpLJs49PEnxsgmMzxx4KsZm2yO6vxuJeoaBERBgMhscPcuJXGoekRxEGDkCypbXC5hJtmTvzAHmo7Z0kZtZHeTUKwwDKzWJJDIGBlG63T/vNlpReiYniAO/Ib9uHYXy9gITbtTitgqqG9ThoFuHSOXx8jmDlnK9Ua42wKQEOJSYAOaEK+XUn4nOeb1QAh88e5ax98DKeXHgI8BnHLKKfs3e78P4bAjtvKO/3kD//mG97G0WGHbYVv4f695Aa980ZsotbXyqje9hGwuy/zcAvWayzV/XjGIK5dL7Lx5Nx94z6c4/6mPpVDMky/keM1bXsanP/xlqtUaT3zqYxndN87frr4egEt/9EuklDzwIafyr294MS9/3usbwqUAX/jk1/mP97xGCVIG66uobt3Dsi3Ou+AcrvvbTTz2rPsTVurIZKYkDgPs1jaq+3Y2vS7T09/ILsJqRVGIZyaUZcLqO/ZkJkazbKSMlcZX0huxSuUkW7GJfL8xQ6R0wwysFrGOHqyZpmpU64o8YBZaiGoVvFUlPqezR1ldJ6Z3mZ5+VZaS8YqcTeJHlO0fwp+dJvI9Mp29DWsFSKwWRvexHoLQc9EzWfJDOxpWBqvhL8xi5gtNDq2R5xJWlpBxhNPejTc7pRh0ugFoVPbeglkoojtZ3JkJ7FIHseet65PomSzB0gJWsQUZKp25+vjwyjFSEiwtJkFoFn9+lkxHN+70ZOMGQYbhmk1fruunySikNjmqlMwTbbvK3l0Uth2OmTs4WGW6Zd+rJXrutgAkpTzztp4XQjwbeAzwCLlyOz0CDKw6rD95jP08PgO0CiGMJAtaffzyew0LIQygJTn+ts6R4nZgWRaPPu9Mdhy+lZtuvJW+gR7iOOZ9H30bV115DYEf0DbYynXX3EhHV5nu3k7GRxWrSjdU7dv3/IbV9qPOewQn3O9o/uV1L+RvV99Ae2cbn/3YV5rO+Yuf/oYLLzqfm67f2RR8lrFvzyimaeBk7ES0dIW19shHPZT+gR5uvmEn5z/pHNrKrfjzMWa+SHXfLvRMdkO74rBaaRIGjXyfuFYlyhXU4GVlUbHcsnnc+VlVGjNNZRsgBCKx714e4DRyeTVPNLxHZVl2BqdDlZT8+Tk0w1CkhZlJFdScLJHvYuYK1Eb3Nq1NlagOI46UInXkuspmfFVW5JQ7icMokdZRmZY3P0O2bxAZxUoJIQrXiZ8CaLaFlbDvhBZs3ORPjOpWQ7edxuetT4yQHzpMXctaVWUbnT0IXW/oykVuDd3JkB0YwpsaV4Oq+SJWaxvVvbuU+OfYsBrqXG1AJwRWS2tT6S5YmG26aVCHqUxB9fNm0Cx7HWXdKpZUP2/VNYg896AJQPd2bBYL7hzg1cBDpZSrpXy/C3xJCPFeFEFgB/AHlP75joTxNoIiETxNSimFEL9A9ZC+AjwL+M6q93oWqrdzPvDz5Pj9nSPFHcCOo7ZTdz1+9uNfsX3HEKP7xjnhfscwNjrBj7/7c+7/wPtRr9d51RtfQr3uous6pmWSy2ebmGuaEHT1dPKnK67mlz/7HY994tnrztXRWWZudoGZqVn6BnoY2beSDeTyWYrFPLOzC/T1l3jF617ET3/wC/buHuHMRz2Uk087gamJaZ7/vPPpySvPF7vcsaJEsDxWvxarG/9iuUUPUXWJ2DSxSmXqE6PIOFZePXGshDKT1+iZHEY219jYlKBmTlkXuC5hrUrk1TGLpYaopjc3mxARegl9D3d0mOya3tHymqVULqXetFIjyA/tQLZ3Jp49yp8nrlfQ7RU2l27ZijgAaqhxcgqnvZP6+Ggju8j09OPPzzWyHs2yyfQOwLTW5PdjlzsIK4uEtSpC05ST7FKzmvQykSH0XJz2TvyFuaZgHyzOE9arZJ0BNQ9VaiesV5Woa+8A/qJaw7Jtw3IQMnL5ja3MPTdhvQWK8WY5ZLr7iNx6or8nyHT1IFF9JbPQQuTW1gfgQ5jWfKhhs3pAHwBs4JLkLuX3UsoXSimvFUL8H3AdqjT3kmV2mhDipcBPUDTsT0kpr03e69+Arwgh3gb8Gfhk8vgngc8nJINZVNDits6R4o7h+PsdzeBQH3t3j1BoyTM/v8DcnPIAiuMYQzf40Hs/TRTHPOnp57L98K38878+l1/9/Hfs3T3CQx5+Og95xAP529XXU+5o49GPO5OOzjaOO/Eo/voXVYITQvCSf72Iv119PSfc72i27Rji85/4GtdecwND2wZ48jMeRxiGfOpDX6S9s42Xvep5nP2Yh6PrGl/9/Hf4wie/Rm9/N5/82BsQkYcMFJW3QVV269ilshpSXAWzUKS2uICeyWKXytQnVNAzi62g6coXKPCxW9sI6zWCVYrHoDTZrGJL03vKWBIsLjQYX0FlAc12sMsd6JqmpFhMZaGtO1my/UMIoa2UqDRNESMMo2G1LXSDTEIjjupVFTB6+onqc/gLs1jlDuz2LqWiHUX4q+jGme5+3KkJ7LJSwFDUZK2p5Bb7HsH8LNmBIWJXlS6NbA5vYQ6rWCJjZ9AMHXdqkshttoUgjnCnJ3E6ugg9N8lATEgSNSObxyy2qp7WGisEpQWnDvTnZ8h09iYB/zb+VDVdkRtkoixQXUIzTWWBrn4CSg5p6DCyiQmgv7SAZpgNGnpQWWwIh6a4+yH2J+aYohmnnHKK/OMf/7jZyzho4boeN12/k+G9o7zlNf9NvVanpVTkwn86nxNOOoZf/PRyfv2L3/G4J/0jg0N9FFvyuK7PR//nszz+Kf9IPpelVG5hanKWgcFe9uweprpUQ9MFhx+1Ha/u8etfXEG5o8TJp52A53r86Q/X8LmPfZVnPv/JfPziz2PZFs987pP4xAe/0LS2Y084kve962VkbBOhaZiFFtzJMYRhqNmbwMcqlYk9Fymlou9WFrGKrRCFCdtLYpfKyFgq5WkZE1aXEnO6vNIxW7M5qmHDCZaZbrnBbU3N/GVkunqpT40reZ9E7FRoOmahqLTzwkDJsUQRke9h5PJohoW/MINdaqc2NtysdyY08lu2NXoycRwriZlVckKgBiCtUhlvahyzpYTd1k6wtNjkdQPJrEmugJ7J4CfyPXEYqusQx8oLyLIV622VVQKaRuwuO5JqCNOCpE+Wae8mqCyo8mG+gGaYTUOmme4+3OmJRtYjdEP1z+I40cFTauWrPg35rYfhTU8RLM03rd0stiZOtArL0jWh51K59aZV9hYaha2HYebXK1pvhDhUtu1CKOO+ZXKOEgrV0kwqgRDiT1LKUzZ67mBgwaW4F8BxbI45/gi8usvLXvVc/uutH2BhbpEPvudTvP39b+CbX/k+nufzgf9Watg7jtzOM557AQ975INYmFvk6GN38JvL/sCpp5/ID759CZddejlbhvo589EP5dtf/SEIje9948eAKsm9+t//GQG8/m2voFBU9Xrf89F1nXJHGzNTSp1ACMGLX/5Mim0lIt/Dn5vByBXI9g7gzU4Th0FivWwmE/Eh/twMmZ5+Ys9Dsx2ltxZLQq+OrumqgV7uxEostsOqkuH31zTrlSlYsiHfxqYmhYBYoulGU5Dw5qbJD27Hry8QufUGwWDZSG550n+d2Gai3qDYZ+pzr+3XgBLBtAotmLk8koS+vIH8jJ7NEXl11S+q14jqNfW+yRxUtqef+vgI2f7EPTQMCOs1wmoFp9xBfWKlxWq3dZAb2Ep1z84GI8+fm8HIFzETRmLyg0vsutVQqpkM+y57Fzk9feQGt+HPzzZuKqQQTcEHSFxvjcZ7Zrp6G0OmYWWxmdUo40RSKH+brLXI94m8OjIIqU8pWrdVasculfHnZ/DmZtBtJxEyzd+rWGt3NdIM6ACRZkAHjuG9o+zZNczIvjFK5VZ+9J1LOenU4/nip7/ByL4xjj7uCP7xcWfy/nd9jI6udp78jPNway4ffv9nMEyD93z4rYRByPjYBIZuMDoysY6Y8J6PvJXrrrmR/sFefn/5H9mydYBPffhL6JrG8/75GTiOzcT4FA/8h1M4qisPgdqkhWEonbc9t67paXQSLM43NiSz2JqYzkGwMK96DquUp3NbtqsGebmT2tgwTmc3Ua2akBN07PZOhGEpO4PAJ4pjRBw3NNeWoVk2VmuZsLaEZqoSlYxC/PlZ1aMIQ5xSeV32AipLEIZBbWRf02cByA1sVZYIQYBAopl2UyAANcSp51swnQwyDKgO71bT+VKu9IBsR2V+UYTmZIg9D922VeCtVZUiRL0GcZyYzdl4s1PEnpcoL0yvywxz/UPrZn8AnK5e/PlZ7LZ2dXPgueiZHE5HF0bCjAtrVdUz03Qqe25RWndSEnuuCmwbXKfc4LZGn0d3stityvCvNja8TtpHsx2K24/c7yBqWKuwtHtn0jtbuZ56JodmWc2MQSEoHnbUIa1UcFcgzYBS3KPoH+ylq7uDT/3leq695gYe8vAHUK+5vOYtL0fXNX783Z/x3rd/hCiKOPvRZ/Cx//0cx55wJA8+4zR+c9kVvOIFb+ThZz+YY44/ki1HDBBFEZqmNawcdhy5HUPXGdo+yNjwOIvzS+TzOd76X//G/PwCXd0d7No1zMzULNl8jkxnJ9L3EbqOZlqJn0/zhu3PzWC1lVd00IRGFHgYTlaVoPJKVl8YhmKYRWHjjly5Y0aYLW3YHd2ElUW18YYBwjSxiqXG/EZYXcJp7yKsV9FtB7PYqvpQbe34c7O4c9PqHJ29yj21srjfvoeMY4hinI6uNerbZTXzgyJNaE4GLasCb218BBmq2ZplRQUy2YaMjj8/i5ErJCrbApkIcwqZKDuEPtI0VcYhpbLxTjyH/IU5NN1Q/ZrEEG6jtW+okSaEUplubVMbe0NOqEoc+OitbU0T/8vSOasJBN78DFapHX9uRUFDdzKNnweaRsthRzWeM/IFWBOA7FL7foNPHIZU9ikV8LUSOWahuF4NQ8qGPl+KjZEGoBR3C0zL5IILz+X6v93E+97xUWrVGhc8/Ty2bOvnseefja7r7Lx5N1sPG6RaqXHF5VfxnBc8la7uDq747VVousbW7YP4nk+tVueVr38RruvRWmqho7PMO978Pw3V7LP+8Qymp2Z579s/3Dj/o859BH0D3bzoma/iKReey3n/+GBahK8k9zt71i9Y05QYZgKrpRUZBOpOPPBBqvkbGYW4M5Pk+rYAqsRTHx9WPZvWEnpoNd1VyyDAm5nE0XsQuoZm2QTVCkYmh2bbDXHSTO9Ao/wkwxABjX6RXe5cRx92OnvQDOXXY2SyZLr7lXhpIktT2X1LQ9lAM22EUKZtVmsbwjCIamq63sjkkhmYFZqzcoZNeluLcysGb6aFVSpTG9mj1tXWnqgsaI0eSxSGVPftIrdlO8Rxk60F0Jib0rO5xhqW30uYFnocN8/rCLHx1P9GtgVhiNPd35hP0jM5dMtSAqqtbTjtnaBp+AtzyGRtmf4h3LF9yDjGKXdiJdnRRojDoPFZ1pbolvX51gamOzOAel9CenVS3G1oK5d40ENP48KLzmd47xjve8dH+OkPLuPTH/4yx554FNlcll0791JqU2yxT3/0ywRhyLNf8BSOPHoH46OTuK5H30APN1x7C7fcdCvZXIZvfvUHTZYNP/3BZRSKzRPiP/ruzzAMg7ZyiVtu3sOvfvdXNNNULDZY6QskcDq6kCjztVz/EKHvUZ8aI/ZcnLYOhKbhTo4RLC6Q6egmqFWaJuZlHKE7GTTbaUjma5aNkS+oXpCmIYMQd3oC3ckS1CrURvYSJRuaPz/b2Gj1TJZg1UyPNzuF3daBVWpHdzLkBrYSVBapje6jPjZMbXwEzbIw80Uiz2sEiGVlAxDKfC+OGn4yQWURf2FWlbXcOlZbe9P1WGanrQ5MceA3XWNvdhojk2tSA19G5NaJfQ+nraPRcxGmqcpko/swMjllR93WTnZgK2ahldhTunz5ocMwcgWs1jYK247YkJVmZPNJj231z7AbI2Et5ge3kenowmopUdh+JLmBrQjdYGnXzVT27KS6bxdLO2/AsGyKO46h5YhjyfT03+bQp6YbikEJDRHRZfgLs+tubPRVNt4pNkaaAaW423HGmQ/ize/4Vz77if8DCS2tLeQLOXbdsodrr7mBF778WXzig1+gs7sD27Z42+vf23jtg884jZ6+bnr6u/jMR79CNpNtUldYxtzMAtlcpmnG6Njjj+C8s04lb+sseDGitQyR19Bai8MQGYWYhRaCypIaQMwXCOvVxB8mRNgZvLlphKYnCgQBtYlRzEILejZPnNT8zWKrIi2Ylhr2DELlAeS5OOVOdEcx55ifgTBouvsHVd5SWmmLCKE1kwakpD4xglXuVKKotUrT62PPbfRG/AVF8TYLLRjZnJJM0rQNhS2F0IhlgD8zSX7b4SAl/vwMmmEecNkoDn3sksqE4sBX8zZJBlOfGMVu78QstOB0dhPWlM9Q7NbxXCWyahVLBAtzTX2xbP8WCtsO32/zftmmIr/lMMJ6FRmGGMnc1UZYZqOFtWpzNpZc1/zQjgNirGmmqfqHu2/BnZ7Aae9SmWkcKiX0WoXclu3q98CyMJIMLMX+kQagFHc7WkstPPFpj+WR/3gGYRgyPjrFq17yZs5/+rkUCjkMU+c9H3kr9ZrLyy56XdNrf3PZFbz4X57Dr37+O97zobcwNjrB0lKVn/7gF03Hdfd1NikgPOyRD+KI/hKmXyOqQx7Q3ApxIlFTnxhtqDhrlo2eyWIWWxQBYHapkbksG4XJUCkChNUliJUgJ4lddex7qhkexwTzapjUm5tZcQxNmvVxFJHp6FEZ0hqFa7PYSlhZUjYMtSrZnv51szFWvkB9ZgqxQQ8lrC6hZ3MYdgaRLyLDsNEn0RJGlr8w23zOfLEhzRPV6wQLc2R7B5W9Q+Bjt5VV7ySBljiPLkPoOkLTG/46mmWT7enHnZnCcJSMT31qjKhawSy2YGTXlNKkRHecJisEgNroPsxcYUOV59CtU58YJViYUz46vYNYpfYDKnVtpOMX+Z6STjpAyrSZL1DccRTRsuaf7+GPTzQYdma+iNaS0q8PFGkASnGPodiiNqC2col3/M8b+fXPf49pmbS1tXDZTy/n5NNOIIrWN62jKOZ+px7HG//1nczPLfCat7ycm2+8lV237EEIwROf9hi2H7aFZz3/yfz1z9fx4DNO5+RTj8H0mwcjg6UF1WtZWJmwb9Tuw4A4ESK1SmUQkOsbarKeBsj2DoCkIQhq5PJK6mV6ErOlpEp7QqxzDPXnZ8kPHaZmfIwMub4tqsyWWHAvS9g4Hd1qLVGYBI15hFDEAm9+FjNxvlw7OGvk8mi6oTKxMMCdW7E3iD2XYGGOTM8gceBBFKLZDqHv4XT2IDQdoSuhVaWuMIXZojI8RzcIa1Xl5FpooTqyMnuT6e5XltoJ0SD2E+uKngFq48PKmK+9k3oQoFsO7tSYEjFdFXw3ZOHGsRI39VVGqduOCu7VCv7sVOOzyzCguncn2vYjD0g6Z6MMyS61ryvH3h5022kERzOXxyoUAXGXWDjc15BesRSbgoEtvfzml1dw7dU38LJXPY/+wV4cx+aIo7dz43UrwqBt5VYsy+TEk0+l2FLAtExM0+DEk4/lrEefgaYJMlmHz33iaywtVXnQQ09laPsA27f2EoztXXde3bKbNkDNdpBhkPRKEnfKvi1Ebh0ZRusCiTc/i2aYDd21sFpRzCk7JPZ91QPaIIgqNSmBZttE1SVwHDXpL0QiE6PIBv7C3ErmI4QSE11cUKKhCT06279lxWKaxFZAaFT37lTDmqt10xKEy5bOAgQ2IImqS/gJi8xsbSM7uFUF0mIxGbYFb2EO3TAJlhbw5mbJDWwlcmuJ19F6V1Ml9SPJdHQR1Kq4k+OqXJnJIqcjVfLasr1B1dZtZ102qDsZvJlJNVclBLmBrcvv3iR+uowD1W4zMkoKqTaqhE3tUrsiP/ydczobaQmmODCkASjFpqDU1so73v96vvO1H/Oj7/2MR513Jpqu8Zp/fzlf+OTX+OMVV3PkMYfxnBc+jVJbC9/75k/5wie/xnNe8FQ+fvHnm3o9Qgje8+G3cvWf/sYVl19FZanKA04/nniN0KZVKq9SpAbdNIl8F3dyFWstDIjdOkLTiP31XkUyDBFrGtVxGGBkctQnRsj0DCBM1jl3WqU2wloVKWOMYguxW0fP5hrZmJHNK7n9fEHdqUsQhmJVhWua/PWxYXJbtjea+5FbX2HeCaGC4BqYuQL12WnslhLe1Dh6Jtt0bYL5WQzbwW5RzqbezFRS4hqgNrJHGes5GUVcSNacSeRsmpAEE3dqokGwiNw6YS2L1dqmBkdNS9lKs0RtYpRsd78yzktUHsxi64qoqJR4s9NYLa2E9ZrquaxxRD1Q+2yh68qBNl8EKRXNfSPTt0MEUsZKby+OlOr6IRgI0wCUYtMwtG2Ql736eTzvn5/B177wHV70rFeTz2W56CUX8uRnPg4pwfd9dt+6jy988msAGJbRFHxAlXHGRyeoLFW55qprueXGW3n6kx5Je1uLYoYlPRqh6cRR3BggzA1txxubXrcuKWP82RkyXb3r7BKs1jZlXLcKQtdB08j0blEZk66T7R0gTFQDjFwBZLyKQaX6M/kthynJGikbSgRhrYo/O41eKGIXWxuDr80n1CCMVNBZazEQhkRB0JQh6bajAhuSyPcxW9oaszKaZavMKLmO7sxEI+uTYUBtZI8aag2CplKhbjtgGDid3U0B3OnoJg6jRvBZRlSvYZc7MfJFNF3HnRxvnKc2PoLVWsLs7kczDJZuvXHd9Q1rNfz5WbLdvdTGRlhWmNBzefTsHWOa6Rs44N6TWFam0EzrTsv1xFGENzNJfXwUkGi2Q35w2yE3c5QGoBSbCiEE2WyGY44/kjAImZ9f5D3/+SEA3nXxm3jko8/gB99eMdBdXFiiq7ujycbbtEyWFisUWwsUinlaWosYxLhT4whNRzNVCWnZtwcUDTtyXRVQVumEIQSaaSHDkDiOyPQM4M1OIaNIWS5kcjjtShw0DgM03SSo1bBaSwhJQ9Ry2WZaCI360gJGLo9VbMOdmSDTPYDd0U2wNL8mwAky3b2AxHQc4kT4c7USNKiZmcCt4bR3Nc0cGdkccRSpcpSuY7e1K22yQKkcICV2uRNhOWrjjmM008SdngQkdls7Zq6A76/0j5b7ZDIMkGGgsi7dwGrrIHZdNNtuBCjNdtQgq7OxRbQMfOpT4xS2Hq48kpbLmzLGn5vBzBcbPbTVgVVGETIIEqmcSaVoLZV0kdVa2tBS/WCEjCP8hTlqo/uUqGuuSK5voJHJ3hFEtWqTEkPsudTGhykMblc3RIcIDt38M8W9Csfd72gu/uQ7OPKYHfQNdPO6t/4/TnvQSRiGTm9fN1rCcvreN37Cs1/wFLp7OwFoaS3y0ldexLe/9iOu/tO1HHH0YYyPTjBXVWUaGSd341Kqu+9ks7Ja2vCmJtBsWw11WjZGLq/0xZYWyW/ZBlFEfXwEw8lgFVtU3ycKcafGcSfHCCtL6I6Nmc0ifY/K3p3URvZQ2b0TGQSKTebWlW13ZQlQm2bsVjHzhUbvZwUSGccY+SJGvohAUt1zK7mBrWR6+rFa25S9gOeim5YaYO3qwy53ku3fgtXWATKmOryb2t5dhNUKwdICceCT7e4HlJuqYVkYTgYjk1MBUKrhT29mCs0wEdqqDSyxJHenJwkqS9THR8j1DSA0pXxQH91Hdd8uIremKO35PGG9plQGVsEstChfolitL9vV22SDoTmOCraarj5vVy96Rn3vtHcpWSSSwd+J0RUx2UMk+ACE9TrVfbsbTMKwukjt9hS+94NoTRkSIFxaIt4oYz6IkWZAKQ4K2LbFQ898ICeddgJRENLatmJlcMLJx/CWd/8b//WWi1larPCt//shb/vv13LlFX+hWqnxmY9+mbnZBU570Mlc+burCMOIK668jvPPfQjh/DRI2RhAjeo1Ml29igWWzNMES4uY+QJxFFIb2aN8adx60iMQDZFRp7OH6siext157HvUx0dxunqoj400nFCXZf8zXb3Um4zO6pgtJUVnDsPEUqF581k+J0IQRyFOZzf+7DRxFCqGm+9jt5ap7LkFUGw4oetoVSvJ5lRGJaUq0WW6+pAyxpufwWxpbejd+fNzaMszKpqGbjvEvp/QubMNK4RMVy/e7LQiTyRBJg5CgoW5JoKGPz+LkS8QeZ5SFnfrmLkiUWLDIEO/QemOPRdhmhS2H5XYRITotk0cR1R330gcqPe127uwB4YwnIyihXuu+nwCnPYujFye0K03qVEfzFhblgQlLBv39KNbdyxr2Sjw6tnsIZX9QBqAUhxkKBTWU2Uty+K888/h5FOPZ2FhkZ7eLvbtHeWH376UfXtUGaKru4MzznwgczNzPObxZ9He3ckfrtnFccfvoKWQVXpgiZJyWK+S7RvEbutoqAb4a0Qkle+OTqZ3gNrwbqVyYJjrei5x4DeJlK7GWoqxZqjSXm1yDCljMp09TWUUtakIZTeAhm7Z1EaHG7p1YWVJZUBryBEyitBy9gYZlQp6wlAmbWahBdHapmwSQh8jl8Nu60DoOpGrelW67ahGf0ubcluNI6yWklJjKLU3LLd1J6OyHCkbc0DLNti6kyFYnCfyPeyWNjVrtOpaGLkCwjAJF+ZWVBsAp6sHNB1QAcibnlAq4k4G3bTI9vQrkVdAIqiPjypvJSFwOntwyh13WSM+CnzCyhJhraoo7qaVlFwN9EzmgIkPq7ERTVuzneaM8wChZ7JNundC18n1DtypdW0mDq3VprhPo39LL/0o5lWtWufsx5yBmcix1Gp1Lv3xryh3lDFtiy9++utc/9ebAPiHM07jre98BfmBrUSeixACd2KMTFffun4DrNBqo3qdyPPIDW5TlOMN6M1oWsJYM9Y9L1ZtBnabcvs0sllVckk27tzANsJapTFIGfkudmc37vR4woZrHjr1ZqbIdPetW0YcRWi2s97d0zCVgV1iZqfbGSSKTKHbKlAEswm1eWkR3clgt3c1BYZlxW5JoqBtWtSXNfI0jUxHN978LELTlc9OZalBVvCkVL2q6QnFPLMdsr0DiuCw1mp8YjwhNayIeiomosqGhaZhJAy/+uRYw9gPKXEnRjGcLFZL6/qf0R1EHEXURocJFmbVoLJpNvk42eUOMt19d3izN5Jh54abqxDk+gbv1PyQbiqGotPWThxH6JazoZXGwY40AKU4JLHr1r184oNfbHpMCMHb3vtarr36hkbwAfj1ZVfw80su57H/+BDiMGgMm8ZhsGINsDwXZFoNJYNgYcVTyJ+bRrcd7LaOJhZctrsfd1KVuupjar4EIch096FnMzidilQQLM6DBOlkmmaQ3KkxYt9XMj3J42a2QFBZ2pjRJASR6yZmdyvUa6dUVm6mSwuNUqBmWmhORg3g9vQRux5RWAeBEihdPn4VIreOgIaumT8/S+x7aIaBv7hAtqeP6vCeFVWBOFYuowNb0Wxb9ZVW0cajmpLKyXT1JhmWogsHlaV1gX+Z2db0cTfY5OMobAS41Qgqi3dNAPLcxs/eam1rzIgtw5uZwmppQ8tvIJJ6G9BMi1z/EFG9npQdnTtFQGi8n66jHcD808GMNAClOCRhbHDXqOsa2WyGv/zp2nXP/flP1/GYRz8YI5NTd97J5icMUxEPZqfRM1k1H7NqAwJ15y2jSNk4IJI5IqmUpOMIp9yBlDF2uV1RpFGblCO60AyDOAywyx1EfrMUjG47zf4xCeLQX9EQ07RVvSWVSWm2gzc9SbZ3EHQlclpPylVOexdC0xC6gW7ZRFGA1VIiDgJCt6ro6EIHKTfc3EE1uN3JMRXYOrobszu64xBUltbN4YAqNy6rNFi5Av4qdl/se6oPt2qz1ExrnXq0KkWt9HGsUrlJ8HP1z0PP5Nb1VIxcHn9xHn9xAd22sQotd2qDX63Dp9a4PvO9s81+zTDRCofevM7dhTQApTgkcdjhQxx5zA5uuPbmxmOPecLZ/Ownv+aBDz2V6/7aPEty0slHoxlmwvpaRfENAyK3jtPeRWXfbjRzEavU1vRaNQOTI6pXCWsVxeYSgmzvIN70BHZHD8HcFGG10vS6oLJI7HvEgRLsDBaV1lq2f0hlFZaFWSwRuTXMgtJv85PZHbutnfrkOJnOXuW+GYaYLepYzTAx8wWlsB1Fihjg1tFa26hPjimtulY16xOuEi3NDWxTQ6UJ68rp7MYoFAmXVjTnlpUh1MWRSXbXoxQb5mawyx0Iw1ynELFaGVzPFZRH0ZSid5vFUoPFBhAnxnzZ3kFqY8NKZ88wyPUNEXp1JQ+UEAuMDQKIEBpORyfB0nwjgOn5AnEQNNl0u9MTFLcdeYdLU7rtoNkZYq/e0PlbW9q8LdXsFAeONAClOCTR0dXOf3/oLfzu11dy7TU3cvz9jqa7t5PBoT7CIOIPl1/VUM0+48wHcMrJRxMHAfGqjVPohprn8X38cA4Z+kShj97R1eRXE9WribunIKxVlKdQeyf+0gJCN/AXZjZkJWmGQeTVyfQMIKOYbN8QweJ809xRbnArQUUJeioZoEGVKcWRKoMlfj9xoAKlZljq7lvTG3fnEkmmuxd/YZ5sTz9xEKKZJrVVpASz0KKGTFdRft3JcXIDWzHsjNJ7y+UQQltjrKb6NsvKBP68sh1YbRpnt3c2lQt10yTT1YdValdad5bdxM4KFuep7tuFEBpWqU0RPEwLq9iKEeaU0Keuq9fth9lmZHIUDztKBQahoVkWS7fc0HSMDAIit3aHA5BmmhS2bMOdmSKoLJLp6sWdHFPlScMgN7CVsF6jNjaMkVEKD4faAOjBgjQApThkMTjUx+DQ+oY8wAc+/U5237IbAQz0dZDLZ4nqdWUhXV3CzBWJAw9hmBjZfFNQqI0Nk+kdUCyoyiJGNg9CIHStyZIh09GtMg4hyPUPKeFQQzXi4yDAKLYiLJv6+DC65SC05qFXzXYIlhYb/QwZRdSGlfLAss20ls1BHGOYNrXxfcReUv7SNLJdvbiT4+S3bKc2uhe7vQt/fhbNzjRlJI1zrVFwANXz8Rfnsdu70AyT2sju5gOEaCiGx4GPjCLcqQmcjm7VYzJNdb417DOhaRtmL5HvN0qgUkYN2nimu58o46PpOuZ+bBXWYrUoaOR7GzqtNtla3AHoTkaRJRKxWjNfVH0v3cBPPJUAxa6cm6a4/cgN1btT3DbSQdQU90q0llo44aRj2d5VxKgtECzMQxwp75hcgfrECN7sNO7kGLWxfdhtHY3XxoFPdc9OjEIRq9xJUF1SczSFVvz5WcLqorLVrlYalgxBrUp+aDt2a1llKqYJUYQ7OYYMQ3THIV7DkjPzRbWuNYg8l9rwbuIoIq5VqY/tozY+jN3WsbLJxTFBZQnNthNadUjk1jELRcLKArHvNWUdUa2yobOoME2ccif18WFqI3sa6tjqSY1MZy+1kb1K9y3JRmQYECwtYBaKKmu5AxnGhmxCoaHZNsHSIvWpCdzZKcJ6feM32A9UVtq15kFtQzO7A4UQSuF6+X8jk0VI2aQ+AYn8kXvH1ptCIc2AUtxrIXSdTHcfwdIC/uKc8vxxMiztvrnpuNhziXxXDV0m2Yhd7iCq13ATr5rYc4mqVWU1LWPcyXEiz1WkAMsmjmO82ekGqSD2XMKlxUQuZwIpRKMUtdy3kFGo6Nv+GhtnIRDJQOryZifDkPrYMJnuvsbsUOx7WOUOwnqNTE8fMlZq2bHn4s5Mku3qw1+aJ3JdNNtRrqNhQFSvJXbdXRi5AtXdt0AcIxP5Irvc0bDS9mYniYMAiWyUvISmK8LGnTBbUxlTs02309GlhnqXBUhR4qy5wa0H3GsRDftxE292Gt3J4rR3bpiFpTh4kAagFPdq6JaFXu7AKasMJ/TcJlaZZlrY5Q51dy80nPYu4jjGyGSVBcIqyDhq0JKFaWJls+i2olVrQsNbw2iTcYQwTDKd3fiL88RBQG5gK7XRfcS+R+gqVYbqvl0r67FsZBRh5ov4i+sZcjIMG8w4s6VE7HlJ9lPD6exp2EQQx9TG9mHkCmS6+4jjkOrenVgtJSWACon0jbbu/d2pcZzOnmaFbcNM5Hv+vl6HZpjkB7ZS2Xuruo6ajpHNKYWJVVBkj+odavYvZ0F2qT1xgb3rlRE0y1IU+FV9MmEYfxed+r6MNACluE9BNy2sUln1XZIsoDY2zPIMimbbOB09+EuLaKa1frDTNJFx2Mh07LZ2gspS4rWjrRscFZqgNpo4ftZreFIqwzspiX2f+uSYGiwVIrGA8HCnJjCLrWi6QRQ0s81IAoZVKiMTRWSnq5dgcQ4hJbqTJaqvMN/C6hJ2qYyZKyC6+9XGWVkk2z9E5LoNvx7NNJucSVfbFGR7BxvOp2G9iu5kMXL5O80EM7I5iocdSez7CN0gCjwlNroGd7Z/c3fK0QghcMqqFOrNz6jh19a2tP9zJ5EGoBT3KQhN9TU001Lun3PTrB6AjD0PhJpaJ5kLEkIQeR5BdRGhaWpQdGkR4ljJ3Pge/sIcdrkdb7qZZBCvmf0JlhawSm1401NKAWFZpcDOqF5UaxsIQbC0QLa7LwmOyfuZJmahiJHN4c3P4s9MquwNCVIq6ZtSG3XfbZT5zGKrsup2MpiZnKJDS0ltfLgRRMNaBc2yscsdeLPTZHsG0HM58o6jSoZIZecdxyBUkKxVFsj13LkpfkjmYRLigkSx9NYOxR6s5TPNtLBLZexSebOXcsgjDUAp7nPQbZtMVy+x77G4hroLIMMIYYim0piRL5Dr20Jlz06Epkp1wjAbFN/Y94g9j0yX0mrTbQc0vaHVtRphrYbQDZzOHjXDE0WIMEA3TLzZKZyObrUOILdlu9Jz03RVFhwbIaqruaHC9iMxMlnCeg13akJZP4yPNgZiRVJWXH13rlsWYb22bgBWmcEVscud6ngpcZeWqI8Pk+3fQlhZVEEXAEGmR6lya8bfP4lv2DZOVy8IjWBxDs20yPYO/F0EghSHBlIWXIr7JJYHHe229nXP6Y6jBDRXIfZcNe1fUJYOmmFit7Ri5gpq80RlN/WJEYTQCKpL1New6wDQNDTdIFiax50cw5uZUppvQllyy4Q5506OESzMIXQdw8mhOxkqu28hqlVAxgSL89RG9yLjGDOXp+Wwo9AyGXJbthPWagRLi2qQM19gI4mbjSGVioIQRJ5LfXzZlVSsCj7qOC/xELqrYGZz5Ae30nLEcRQPOwqrpXTQq1un+PuRZkAp7rMQQmCXO5BS4s1MohmmstROVKshUR1uKRF5LrFbJ9PVh2bZTU6WTnsnZi5P5HkIwyD2PMJaBbutnTgM1HCp6yJ0HaHpuFMrDWw17Z9FM03iKCI3uC1hmmkI08KfncFfmE2ypWa2XFSvNXTaEFqiYTaP1dZOVK9S3bdbWReUO3E6uhrDssI0MIslglUkB912CCuL1Mb2Udh6eNPArozW92eUEOmd67XIOCaq14gCD80w0Z2sojtr2iEpqJniziMNQCnu09Atm2xPP057J0JoimQQRRgFZUBntZSaLBPc6UkK249A01f6E5puoOWLmEk1KqhWCGtL+AvzDcma3OA26pNjOG3tSsomitCzOaWGnAyNarqOEJqyVRACw8k2FJ83zAWEUDNAUURtbJ8SPNU0jGXfHACpJGn0TEaxw1AlRs3QcTp7iOo1ZQmwivLtz8+qXtGyUvgGiY6RL94pEoJMVMCrw7sbj9nlzkRd+tDysknx9yMtwaW4z0MkMzrLgUDoOrmefpyO3oSksAIZhU36ahshcusEiwuN4GPk8nizU8RuvSHf4nT2kOnswcg0T/0L0yRyVQ9pWRcOlIfRMn16GZmuXmUk57kq+ACGk91wff78SrYjNA1/YY6wuoQwTfy5mabhyrBWRbcd8lsOU1JD8zNkuvsa4qVGLk+2d+BOBYzY96iusWHwZibTQc77KNIMKEWKDaA7GWxNw5udWP/k7dCD18ngmPbKfE6SAYCyPLASp9bGeS2b3OBW/NlpNMsiSoJYsLiA1dpGrn8IKWMlQ5PJNdxTG0sLfKWGsHy+BMYqeRvdssn2DlId2YOTza9Te7Zb25Rqg0BlJqaJbmewWtuQcax6YHcyW5FRtOH120hxOsW9H2kASpFiP9AtG6e9u0lhGSGaNvONYDjNxmNhrYrZUmo4sjaO28DLRQiBVWxFtzPEvktlz86G6GdYqzWynrXrtFrL+PMzaLaDUWjBX5WBaaal5pRWwWopoVs2cRRiy85Gyc4pd2Jk89THR5oYfJmegaRM+fcRAzTLWqeEQEIISXHfQxqAUqS4DdilNoQm8GamElWDntulB2tWYjzm1omjCN12EEJDBr6y/hYame7e/QYyIQSG4yBtm+L2I5WLq6ahO9kNm/RKcqgXo1BE+h7VvbfilDvVQKYQmBv44ghNawRAs1DEKXcma7cJa9V19PH6+DBmoQXD+fsGLjXDJD+4jerIHqJaNTFp25IOct5HkQagFCluA5qhxDqt1rLSaNMOrG2qGSZavrkUlxsYwunqVWQHy7rdbEIk2dbtZVyw7E8jWEyUpt1V2ZZuO3AbQVOsmRVay7ZTD8omK4e/B0YmS2HrDuIgUAQOMzVou68iDUApUhwA7gqGltD0u3m6X24oX3NHJW30RGF7da9Gt507JT66P2i6gbYfR9YU9x2kLLgUKe4l2J8lwR0VENVth8LWHeiOep1RKJIb3LbO8ydFir8X6S1IihT3EgghsNs7EYaJPzeNbjs4HV13SqnZzBUobDtcKXrrRjqjk+JuwaZmQEKIVwohpBCiPfleCCH+VwhxixDiGiHESauOfZYQ4ubk37NWPX6yEOKvyWv+VySFdSFEmxDikuT4S4QQpds7R4oUhzp00yLT0UVh+5HkBrcqN9c7Cc0w1HxUGnxS3E3YtAAkhBgAzgJWT6U9CtiR/Hs+8OHk2DbgzcBpwP2BNy8HlOSY56163TnJ468Bfial3AH8LPl+v+dIkeLehGVVhRQpDmZs5m/o+4BX0yz0cR7wOanwe6BVCNEDnA1cIqWclVLOAZcA5yTPFaWUv5dSSuBzwONWvddnk68/u+bxjc6RIkWKFCnuQWxKABJCnAeMSCmvXvNUH7Bahng4eey2Hh/e4HGALinlsurjOLDcnd3fe220zucLIf4ohPjj1NTUgXy0FCnu85BSEkcRUt51atkp7p2420gIQohLge4Nnno98DpU+e0egZRSCiHu8F+DlPJjwMcATjnllPSvKUWK20Hk1nFnpwmXFjCLrVil8kFrLJdi83G3BSAp5ZkbPS6EOA7YClyd8AX6gauEEPcHRoCBVYf3J4+NAGesefyy5PH+DY4HmBBC9Egpx5IS27JV5f7OkSJFir8DcRBQ2XMrkaeERaOpcYLKIoWth99p59QU927c4yU4KeVfpZSdUsohKeUQqgR2kpRyHPgu8MyEqXY6sJCU0X4CnCWEKCXkg7OAnyTPLQohTk/Yb88EvpOc6rvAMlvuWWse3+gcKVKk+DsQeW4j+DQeq9eIVuu+pUixCgfbbckPgUcDtwA14DkAUspZIcR/AFcmx71VSjmbfP1i4DNABvhR8g/gncD/CSEuAvYAT7qtc6RIkeLvg9A2lhZKnU1T7A8ibRQeGE455RT5xz/+cbOXkSLFQYs4Cqnu3U2wNN94zGppI9u/JZ0lug9DCPEnKeUpGz13sGVAKVKkOESh6QbZvkHCSitBrYqZy2HkCmnwSbFfpAEoRYoUdxl0y0Jva8dua9/spaQ4BJCOSqdIkSJFik1BGoBSpEiRIsWmIA1AKVKkSJFiU5AGoBQpUqRIsSlIA1CKFClSpNgUpAEoRYoUKVJsCtJB1AOEEGIKpahwd6IdmL6bz3F34VBeOxza60/Xvnk4lNd/T619i5SyY6Mn0gB0EEEI8cf9TQwf7DiU1w6H9vrTtW8eDuX1HwxrT0twKVKkSJFiU5AGoBQpUqRIsSlIA9DBhY9t9gL+DhzKa4dDe/3p2jcPh/L6N33taQ8oRYoUKVJsCtIMKEWKFClSbArSAJQiRYoUKTYFaQDaRAgh2oQQlwghbk7+L21wzIlCiN8JIa4VQlwjhHjyZqx11XrOEULcKIS4RQjxmg2et4UQX02ev0IIMbQJy9wQB7D2Vwghrkuu88+EEFs2Y537w+2tf9VxTxRCSCHEQUMPPpC1CyGelFz/a4UQX7qn17g/HMDvzaAQ4hdCiD8nvzuP3ox1bgQhxKeEEJNCiL/t53khhPjf5LNdI4Q46R5doJQy/bdJ/4D/Al6TfP0a4F0bHHM4sCP5uhcYA1o3ab06sBPYBljA1cDRa455MfCR5OunAF/d7Ot8B9b+MCCbfP2ig2XtB7r+5LgC8Cvg98Apm73uO3DtdwB/BkrJ952bve47sPaPAS9Kvj4a2L3Z6161tocAJwF/28/zjwZ+BAjgdOCKe3J9aQa0uTgP+Gzy9WeBx609QEp5k5Ty5uTrUWAS2HCq+B7A/YFbpJS3Sil94Cuoz7Aaqz/T14FHCCHEPbjG/eF21y6l/IWUspZ8+3ug/x5e423hQK49wH8A7wLce3Jxt4MDWfvzgA9KKecApJST9/Aa94cDWbsEisnXLcDoPbi+24SU8lfA7G0cch7wOanwe6BVCNFzz6wuLcFtNrqklGPJ1+NA120dLIS4P+oubOfdvbD9oA/Yt+r74eSxDY+RUobAAlC+R1Z32ziQta/GRag7w4MFt7v+pHwyIKX8wT25sAPAgVz7w4HDhRCXCyF+L4Q45x5b3W3jQNb+78CFQohh4IfAP98zS7tLcEf/Lu5SpJbcdzOEEJcC3Rs89frV30gppRBiv5z45K7k88CzpJTxXbvKFKshhLgQOAV46Gav5UAhhNCA9wLP3uSl3FkYqDLcGajM81dCiOOklPObuagDxFOBz0gp3yOEeADweSHEsenf6e0jDUB3M6SUZ+7vOSHEhBCiR0o5lgSYDcsOQogi8APg9UmavFkYAQZWfd+fPLbRMcNCCANVkpi5Z5Z3mziQtSOEOBN1c/BQKaV3D63tQHB76y8AxwKXJRXPbuC7QohzpZR/vMdWuTEO5NoPo/oPAbBLCHETKiBdec8scb84kLVfBJwDIKX8nRDCQQl9HixlxNvCAf1d3F1IS3Cbi+8Cz0q+fhbwnbUHCCEs4FuoOu3X78G1bYQrgR1CiK3Jup6C+gyrsfoznQ/8XCbdzk3G7a5dCHE/4KPAuQdRD2IZt7l+KeWClLJdSjkkpRxC9bAOhuADB/Z7821U9oMQoh1Vkrv1Hlzj/nAga98LPAJACHEU4ABT9+gq7zy+CzwzYcOdDiysagvc/dhslsZ9+R+qN/Iz4GbgUqAtefwU4BPJ1xcCAfCXVf9O3MQ1Pxq4CdWHen3y2FtRmx2oP76vAbcAfwC2bfZ1vgNrvxSYWHWdv7vZa74j619z7GUcJCy4A7z2AlVCvA74K/CUzV7zHVj70cDlKIbcX4CzNnvNq9b+ZRRzNkBlmRcBLwReuOq6fzD5bH+9p39nUimeFClSpEixKUhLcClSpEiRYlOQBqAUKVKkSLEpSANQihQpUqTYFKQBKEWKFClSbArSAJQiRYoUKdbh9oRMNzj+DovJpgEoRYp7MYQQPxZCzAshvr/Za0lxyOEzJAO2twchxA7gtcCDpJTHAP/vQF6XBqAUKe7deDfwjM1eRIpDD3IDIVMhxPbkpuZPQohfCyGOTJ66U2KyaQBKkeIghBBiSAhxgxDii0KI64UQXxdCZIUQpwohfiuEuFoI8QchRCE59tdCiKuSfw9cfh8p5c+ApU38KCnuXfgY8M9SypOBfwU+lDx+p8RkUy24FCkOXhwBXCSlvFwI8Sngpagp9idLKa9MNALrKM2xR0op3aQU8mWUmkaKFHcZhBB54IHA11Y5rNjJ/3dKTDYNQClSHLzYJ6W8PPn6CyiR1DEp5ZUAUspFACFEDviAEOJEIELdjaZIcVdDA+allCdu8NydEpNNS3ApUhy8WKuTtbif4/4FpWF3Airzse7ORaW4byK54dklhLgAGnbeJyRPf5s7ISabBqAUKQ5eDCb+MgBPQylc9wghTgVI+j/LlhdjUvnPPANlI50ixd8FIcSXgd8BRwghhoUQFwFPBy4SQlwNXMuKO+xPgBkhxHXAL4BXSSlv14YlFSNNkeIghBBiCPgx8EfgZJRK9DOAY4CLgQyq/3Mm0AN8A5Ux/Rh4iZQyn7zPr4EjgTzKl+kiKeVP7snPkiLF/pAGoBQpDkIkAej7UspjN3stKVLcXUhLcClSpEiRYlOQZkApUqRIkWJTkGZAKVKkSJFiU5AGoBQpUqRIsSlIA1CKFClSpNgUpAEoRYoUKVJsCtIAlCJFihQpNgX/H2H9cyw1ke6uAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "map={'low':0,'medium':1,'high':2}\n",
        "med['RISK']=med['RISK'].map(map)\n",
        "med_x=med.drop(['BIRTH_DT','CUST_ADD_DT','NAME','CUSTOMER_ID'],axis=1)\n",
        "med_x_data=med_x.iloc[:,med_x.columns!='RISK']\n",
        "med_y=med_x['RISK']\n",
        "med_x_data=pd.get_dummies(med_x_data)\n",
        "print(med_x_data.shape,med_y.shape)\n",
        "kmeans_med= KMeans(n_clusters=3, init='k-means++',max_iter=50000,random_state=0).fit(med_x_data)\n",
        "reduced_data = PCA(n_components=pca_num_components).fit_transform(med_x_data)\n",
        "results_med= pd.DataFrame(reduced_data,columns=['pca1','pca2'],index=med.index)\n",
        "results_med['label']=kmeans_med.labels_\n",
        "sns.scatterplot(x=\"pca1\", y=\"pca2\", hue=kmeans_med.labels_, data=results_med)\n",
        "plt.title('K-means Clustering with 2 dimensions for Medium Class')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "aWizDZp2jr-t",
        "outputId": "79c4432f-2993-4c83-b66f-845d12e98fe6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-fd1fc08b3535>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  med['RISK']=med['RISK'].map(map)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(350000, 22) (350000,)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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jhIhEyXg7GKVI1P1CiIi/taXtBPE67LhqKnHVVuNzHdsjrHbaORZwOp1ERUUds0IEQAhBVFTUX5o1HdUZiZRyqWhZ6WxvJgIfBNKTrBBChAshElBKV/4spawGEEL8jCKQ2qoX0s4RxGOz0pC9E6SSVVpotFgyO6ExGI9yy9pp59jmWBYijfzVNh7tGcmBSKJlVbLCwLJ9LW+FEGK6EGK1EGJ1RUXF39bQ/wLS78dZURoUIgDS68Fj3Vcl3nbaaee/wLEuSA4bKeVbUsoBUsoBMTH7DMxs5yCQUuJ3ty4H0tayI43P5cRdV4Orthqvc3+ludtpp519MX/+fDp37kxWVhZPPPHEETvusS5IimhZ3jI5sGxfy9v5G1Gp1egio1st11rC2tj6yOF1OqjfswNr3h5s+dk07N6O126jPeFoO+0cPD6fj2uuuYYff/yRrVu3MmfOHLZu3XpEjn2sC5J5wEWBehRDgDopZQmwADgxUPYyAqVK34Kj2dD/CvrQCAyxCaBSITQaTMnpaIwHWz76r+Gpr0V6PcHf0u/DWVmONT8HW2EeXoftbz1/OweHlJKighLyc4vweLxHuzn/alw1VdRu20j1xtXUbtuIq6bqsI/5559/kpWVRWZmJjqdjkmTJjF37pGpynC03X/noBjOo4UQhSieWFoAKeUbwA8orr+7Udx/LwmsqxZCPAysChzqoUbDezt/LyqdDmNcIvrIaIQQqLS6v/2cvjZUWT63E6FS46qrxl1XjaVDl3aD/1GktqaOrz79njdffB+P18v5U89g2vTziUuIPdpN+9fhqqnCVpgXtEX6PW7lN6CPiPrLxy0qKiIlpUmRk5yczMqVbVXnPXSOttfWBQdYL1FqXLe1bjYHVy+6nSOMEAK17q+VZfe5XHgdNvweDxqjCY3JhFCp97uPLiwCd23LcYLWbMFZVQ6A9PnwOR3tguQosmblBl544s3g749nf0FyagJTLjnnKLbq34mjtKiFQwsA0o+jtOiwBMnfybGu2mrnKOH3efHabbgb6vEdIWO6z+3CmrcbW342jpICGrJ34K6vPeB+GnMIxoQUReAIFfrIGPweN/ibPrZ/g2vl/2d++7X1yHbeF/OP+UjuYxG/x31Iyw+WpKQkCgqanF0LCwtJSmrT2fWQaU+R0k4r/B4PjvISXIERv9BosKR3RGM6PFuIz2FvpaayFxegNVv2qyJTabQYY+LQhYWDBHdDLY7ipg9CaLSo22cjR5UOndJbLevSoxM63d+v+vz/hkqra1NoHK4aeeDAgezatYucnBySkpL49NNP+eSTTw7rmMG2HZGjtPP/Cq/THhQiANLrxV5SgN/nO6zjSr+/9TKvL+h95fd58dgacNXVKF5Ze22v1ulR6/XowyIxp2aiC4vAGJeIJbMTar3hsNrWzuEx4rhBpKQ1jW5DwyxMmnoGKlV7F3OoGOOTQOx134RKWX4YaDQaXnnlFcaPH0/Xrl0577zz6N69+2EdM3jsI3KUdv5f4Xe3Hg157Takzwvq/dsz9ofaYAQhoJnbri4yGpVGi/QpnljOsmIAhFqNOSUD6fOBEGiMpqCwUGm16MMj0YdH/uW2tHNkyeiQxlufPMeu7Xvwerx07JJJWkbKgXdspxWNdhBHaRF+jxuVVocxPumI2EcmTJjAhAkTDvs4e9MuSP4D+JwO3HW1eJ02dGGRaMwW1PvJ7qluQx2hMVtQqTX43W68ditehx2NyYzGZD7oKbfaYMSS0RF7SRF+twt9RBT6qBiESoXXbgsKEQBjbALW/OygHURoNFgyOx+WQb22po5tW3ZRWV5NcmoCXbp1xGg6tmcyHo+XvJwCqitriE+MIzX9yOi0G9m9M4cVv62mvKyS4aMH0btfdwzGv3ZPkpLjSUqOP6Lt+6+ij4g6Zg3rbdEuSP6f43O7qM/ZifQocRieuloMcYkYYxP2aaBWm8wYYhNwlpcAisuvKSEZCdhLCnHXNXlQ6SKiMSemIA5ipiKEQBsSiiXTDH4fQqMNtsHva4o7UBtNeGzWFsZ06fXiqa/7y4KkocHKS0+9zRefzAsuu/uRGzl3yun7Vb+UlVRQVlJOWEQYqelJ/6hR3+P2MO/L+Tx67wt4PV5MZiPPvv4gw0cPPiLHz9mdx+WTbqC6qhaA9978lGdff5BxE447Isdv579DuwLz/zk+hz0oRBpxlpfu1wNEpdZgjE0gtGM3LB06E9qhKxqjCb/L2UKIALhrKvG5D80zR6VWo9LqEELg97gVzy0pg3phlUbTZvuan8fnduGx2w7ao2zPjtwWQgTg2UdeoyCveB97wLrVm5h8+gwuPPNqzjv5Mr796ifcbaj9/i6yd+fy8F3P4Q0E99ltDu664VFKisuOyPE3bdgeFCKNvPTULGpr647I8dv579A+I/l/TttZROS+Vihr/X6k34/GaGq1fF/b/xX8Xg+24kI89TUYYuIxJSbj93oRSgvxOewttteaLTirK0H6cdfWoNJqURvNaAxGtJbQ/Z6rvr6h1TKn04XN2nZUfGVFNXfd8CgV5UpEscPh5L5bnqBTl0y6dO/4l673UCkrqcC/172tqa6jqrKGhMS4wz6+y9laCNtsdnzev/Y82/nv0i5I/h/icbmRXj86swGN0YhQaxRDeQBDdCyqfQQUeu027OXF+J1OdOGR6COiUeuVbdV6PSqdvkWSRrXe+Jc9pnxOJ566aoxxiTirypHeQBtVKizpHSEuEWdFKUKoMMTG46qvRaM3BFVuAKK+DkNsPCq9fr9BkqnpyRhNRhz2Jvfjjp0zSUxqW6dfWV5FUUFJi2V+v5/iotJ/TJDEJcaiUqlaCJPIqHCio49M6Z1uPTuj0WqCMx6AadMvIOoIHb+d/w7tqq3/R/i8Pkq25LLk+a9Y+MQcdi9ej8flx5LZCX10LBpTCKakNAxRcW3q+r1OB/XZO/DW1+F3u3CWl+AoLw7OOFRaHSHpWegiolBpdegiYzCnZaLSHFpZzkak34dQa/B7vU1CBMDvx1VdiSEmnrBO3QnN6oKnrha1RtvCLbnxGNLvx7+X+m5v0jNTeO29J8nqpFR+Gzy8H4+/dA/hkW0nnIyIDCOyjQ41Nq510sojjdPhZNXydaxZuYFb770GrU65v5bQEB5/8R7ij8BsBKBrj47M+uR5Rh4/hI6dM7nn0Zs45cxxR+TY7fy3aJ+R/D+iJq+M3175RtELAWs/XQxCkDW6NxpjKlLK/RqLfU5HCwM3gLumCkNsAprArENjMGJOTkP6fMGZjs/tCto8DgW13oBKp8PvbS0EfC4nPpcTv8uJCNhM1Hup2hoRQqDSHPhV7j+4N7P/9yIN9TaiosMxmZXjVVVWU1leTVh4KPGJSm6ouIRYHn7mDm6acS8ulxshBDNvu4IOHQ+tBOm+cDpdlBaXo9NpSdzL02nJL8u59ZoHAEhJS+LqGy+hc7cOpGekkpyWeETOD6BSqeg/qBc9+3TB4/FiNrd9f9v5/8Gll17Kd999R2xsLJs3bz6ix26fkRwjuGxOKnYXUbwph4aymr90jIpdRUEhAiBUAq/Lw7Yf/2TZa/PIXb4Ve411n/uLtryXVCoELQWEECqESo2nvpb6Xduo27EFe0kBPtehpVJR6w2YElPbjJjXhliw5Wfjc9ix5WWjC4/EXVeDPrJlTRmhVqM2mverXvP7/eRm57Nx3Vbcbg8paYlBIbJh7RamnnkN5558GZNOm85vi5YHVUkjjhvM/358m9c/eIqPvnmdlLQkFs5fyrbNu1qksK+sqGbZ4pV89/XPbNmwHY97/7Ojgtwi7r35cSaOmco5J13K5x/Pw25T7EHVVbU8//gbTdvmFfHik29ha7AfUSHSHJ1O948JEZ/Ph81mP/CGB2Bv21E7B2batGnMnz//bzl2+4zkGMBZb2f9F0vI/3MHABqDjlHXnUF0h0PrOLSmljaCDiN7kr1sM9byWgCKN2bTYXQv+pw7CnUbI3i1wYTKYMTfLI2JMS4JVRtxJV6HHWvenuBvV2U5AoExIfmQZiZacwgqrRaBUNRoUqKPiMbvceN3OUEIpN+Hz+3CEBOLz+XGmJiC11qPSqtHExK639QtLpeb777+if99OJfjTxyBAPoM7EmP3l2xWW3ccvX9lJVUEBkVznlTTmfj+m0IlYoevbsQHhFGRoc01GoN1116Ozl7lLQsWp2WNz98hgFD+lBVWc0Dtz3F0kXLAWV29NwbD3HCSaNatENKSW52Afm5hdTV1FNbU4eUEmuDjYfvepa0jGQGDeuHx+Ohob61sHe0YRj/t7Fz2x4+/eBr1q3axPhTj+eUs04kJfXQ3vGqyhp+X/Incz//kU5dMjn93JPp+g/ZrP5J8lZuZ9Pc37FXN2CKtNBz4nDSBnc5rGOOGjWK3NzcI9PAvWgXJMcANfllQSEC4HW6Wf/5EkbNPAud6eCz7MZ1TqX7aUNw1FgpWL0TvcUUFCKNZC/dRNbo3oQltg52Uut0WNI64LFZ8bucaEIsaEzmFoLB7/EoKiePG11EFO5mdRJcNZXoo+PaDGiEQJXDhjp8dhtaSxjaECXHllqnB0sYQqvF73bhdTrQGs14bVZFhSZUeOpqUOv0CI0GT12tIlxcLnThkftVa+3ens3H73zB6LHDeOOF9/D7/Wi0Gu5/4lY6de1AWUkFGq2GK66dyktPzcLhUFyMx596PHc8MJOomEg2rNkcFCKgxHe8+txsXn3vSbZv2R0UIqAIjEfvfZ5efbsTE9d0j//8Yy3XXXonzoBAmDBxLCOOH8yyQLLD7Vt3M2hYP2Ljoply6Tm88cJ7wX01Wg2dumTu8xr/DRQXlnL1xbdRXlYJwGvPv8v2rbt47Pl7MJkPLjZISslXn37Hy0+/DcCq5euY9+UCPvrmdTI6pP5tbf+nyVu5ndUfL8TnDrh9Vzew+uOFAIctTP4u2lVbxwDOutYuqLUFFXgcBx6FepwuijfnkPPHFjZ8uZQt362gdEsefc47jrCk1sJCsv+qgmq9AUNkNKaEZHSWMFTqpk7a63LSkLuLhuwd2Apy8DnsGGKa9Psqra5t9RhK3Ie7vlYxqkuJrSAHR0VZ0JDvsVuVrMClRXhqq7GXFqKPikGl1SL9vsDxtWj0Bgyx8Rhi4rGkdUBrDtnv9RQXlnLiqcfz7htzguoQr8fLE/e/iNftwWgyMubEkXz12fdBIQKw4Ltf2bp5J0DQBbg5hfnFOB1O6utauxVXlldjb+YdVl1Zw4N3PBMUIgA/zF1I735NeY7i4hWVnRCCsy84lRvvupLk1EQGDevLWx89+495iv1dZO/OCwqRRhYtWEZh/r7jePamtKSct1/9uMWyhnorO7buPiJtPFbYNPf3oBBpxOf2smnu70epRQemfUZyDGCOCW+1LKFHBnrLgfXWpVvy2PXrBjR6LaVbcgGw1zSw+sOfGX7VaZiiQrFX1Qe3Tx/SFUOYAen377PT3xeeupoWsR0+pwON2YLQaJBeL6aE5DZnB363G0dpUbCmiNpowhCbgN/tUiLYpWzh0guAlEgp8VjrERoNxvhkpN+Pu6oCjSV0v5H5zYmJi2b7tt2tdOo2qx2rzc59j9/M7h05/DJ/aat9qyqU9vbq263VurMnnUpkVATpmSmo1Wp8zRJajh47jNj4Ju+u+nprmx2mO2BL6d2vO736dg0uj4uP4ZIZF3DW+aeg1+v+csqSYwmNtg1VqlqN5iCcJBoRQqBStX7mbS37N2Ovbj042d/yY4H2GckxQERqDH3OHY1aq6QZiUyLo8fEYWh0+//I3A4XW39YSUzHREq35rZYJ6WkOq+MjGHdyBzRg+isRPqcO5IOgzOw7dmOvbSwzeSM+0JKia+N2hI+lwNTcjqWzE543S5sRXm46+vwN3Pn9dgaWhSm8jnsSJ8Pv8eDNWcn7rqaNoWaQGCIjsMYl4SzrAhHcT5CrUZw8LUZOnbJpGPnzKALbSMRkWEkJydw4oTjmHDGWEYcp6QdiYqJpGuPTuj1OlICea169O7Cky/dS0xsFDq9jouuOI+J554cPP5Tr9xPfGIsQghGjRnKNTddirFZ5x8VE0nPvt2IiAxj2oxJXHHdVC69ajL9Bvbi9Q+e4rk3HiKhjXiWsPDQ/xdCBCCrUwY9+3RtsezCy84lJf3gbSTxCbFcef20Fssio8Lp1DXrSDTxmMEUaTmk5ccC7TOSYwCtQU/H4/uQ0CMdr8uDOSoUnbl1B2Krrsfr8uCosWKrrMOSEIWUEpfViTEsBEdtSyOtUAm2fLuC0PhIhlwyBmmtQLqV2YmrshyNwYQ+cv9xEUoKkzrcNVWoDQaMCcl4HXbUWi1ehx1daARqvYGGPduDsSCuqgrMKenoI6KRUuK1tTYee631qAKeVu5aJSjRUVrYtIFKBUjw+7EX5aE2GNEaTHjtVhACbfi+E9pt2bidhT8upaG+gXGnHM/g4f148MnbeOy+F7A22IiMCufhZ+8MCoqOnTO54c4ZDBs1kOw9eRTll3DGuScHo8cNRgMnTxzLwGH98Lg9xMZH43K62bBmM7nZBVRX1XLelIk4nE7WrdrEgu8X06lrh2AOL4vFzANP3MrqFet55dl3aKi3YjIb6darM2NPGvWfSLUeHRPJky/fx59/rGP7ll0MHNqXfoN6od1P8tC2mHjuScQnxjL/u0Vkdcxg3CmjSc/8/5VluOfE4S1sJABqnYaeE4cf1nEvuOACFi9eTGVlJcnJyTz44INcdtllh9tc4OjXbD8JeBFQA29LKZ/Ya/3zwPGBnyYgVkoZHljnAzYF1uVLKU//Rxr9NyFUAktc2xHFHqeLglU7yf59C5HpcexevAEAjV5L99OGsvW7FXSdMIhNX/8edEuN7pCIrVIRGroQA8JjbVVPRHGn3bcgkVLirq/Da63H53TgddoxxSch3W6cdTVoTCGojSZlhuFtqdO1lxShDQnD53Qg2vIQM5rwOuwgBMbYeKT0Y0pKxWu3o9JqUWm0OMqKMMQlKcLLZsXvcaOPjMbv9eK1NYD0t0riuGXjDi45d2bQHvG/j+bx1Cv3k5KeyOz/vUR9bT2JyfEk7+UtZDIZ+fDt/1EYiGZftnglJcVlzLztiqD6JTIqnML8EvbszGPPrhxuv+6h4P7nTjmdbZt3sm3zLkLDQsjenUdaenJwJmQ0GYJCBJryZnX8IYOMrLR9PoNjmeqqWooKSjCbjaSkJ6NtQ33VnOTUxFb3/VAJjwhj/KnHM/7U4w+88b+URoP6kfbamjNnzpFoXpscNUEihFADrwLjgEJglRBinpRya+M2Usobm21/HdC32SEcUso+/1BzjyrVuWWs/vgXup48iB0/rQ4u97o8lGzKpt/kMZTtKKD/hSfg9/nxuTxoTQZWf/gzENCtqps8qVRaHfrIaIRag7uhDiHUuOuqQKjQhUUgVColGaLfj6uqXHHJjY5FaDQ4ykuCQsNra8BWkIMpMQVtSKjiUWWtR3q9CLUGr9OB11qHymBCE2LBa1V0vCqdHrXeiLu2GkNMPM6qCmQgKFGl06OPisXn9SB9PlQaDfbCvKDB3eewo4uIwu9yYi0vIbRD52Aa+4qyKn6Zv7SFURvgo3c+JzTMQkiomdvvn9lmCpBdO7KDQiS43+wvOPuCU0nLSKGh3spXn33Pq8+8g9PpYtCwvsyYeTFvvvQ+AF988i033Hklp5wxjh/mLmTqmVdzwkmjuOzqKWR0SGXntj2t3HrdLjelJRVBQdLQYMNa10B4ZBhG05Gr+Giz2vD5/ISGHTnVyM7te7jtmgfJ3p2HRqvh2psv47ypEwkJObwqmu0opA3ucsx6aLXF0ZyRDAJ2SymzAYQQnwITga372P4C4P5/qG3HFDV5SrZXIcDva2k0Lt9RSJcTB9L3vNFs+OI3KvcUk9Q7k8J1u+kxcRhISUhMOBpzGH5nA9LrwxAdh720MJi4URNiQaXR4q6tQGMyK9HkWh1+pwOfyxk0hpuS04JCRKXVoQuLUOI8QFFTCYE+Kha/24U2JBRrzs5gO7XhUZhT0vG5XMo5AvYZoVIFhQhCKMf0+9DoDYjYBPwed1CINKIIoLigsV6l0aA2mFi2eGWbXlQejweNRs38eYs4ftwI0jNTyOqU0cJuIv2tvdmkXwZzW27esJ1nH3ktuO7PP9YRHRNFVqcMdu/MQUpJQlIsD9z2FHab4rE174v5lBSXce8jN7Ft865Wub40GnXQRXjjuq08+eDLbNu0gyEjBjDl0nPweDx06dYxGG1/qLhcblb8tprXX3gPm9XGtBmTGDN+JBGR4X/peI047E6ef+xNsnfnAYoX3AtPvEnPPl0ZOLTvAfZu5/8jR1OQJAEFzX4XAm0WWhBCpAEZwKJmiw1CiNWAF3hCSvnN39TOvx0pJbUFFdQWVqDWagiJDcfn8aIzGbDERWCMUEaS1sp6wpKiqStqcqPUhRgwhJsp2ZiDwWIidUAnyrbnE98tDX2oCZ1BR21hJbaqOgyhBuI6peGx1aHWG4L1073WBoxxiRjjknBVVShqI5Ra6Mb4JBwliu1CBoSY1hKGWm/AWV0BUqJHiTB3VVeiCw1HHxmDx27FmJCsVDgEXNUVqLVanJVlWDI7oQuPBLW6RTJJY1wizsqyoLASWi2mtsqLCoHaYEIXHonf5cSWX4w6OoFPP/iak08f08qL6oSTRvFOwG20rKSc3TuyqaqoZvhxg4OeX1mdM4iNi27honre1InExEZSXFhKXW09Or0Ot6vJyL/i9zUcP24Eu3fmYAkNwePyBIVII6v+WEduTgHf/O8Hrrj2Ql5/4T08bkWw3f7A9aRlpFCYX8zVF98WFILLFq+kqLCUnn26MuvlD3n29QcPOr9WXk4hSxctZ+PaLZx4ynHcfFXT2OvBO55Bo9Ey8dyTDupY+6KmupYVy1a3Wl6YX9IuSP6j/FuM7ZOAL6SUzYemaVLKIiFEJrBICLFJSrln7x2FENOB6QCpqcdm0FJldglLnv8Sv1e5PEOoiQ6jerFt/ir6njea2C4phKfGUrB6Bz3PGE75zkIqdxUp3l1nDGfV+z9Rk68kM1SpVfQ6ayS7Fq+n0/F9WPHRL0G7yeBLTsTvdiE9blR6A7qIKJzlJUifUjddpVYHhQiA9HrwWhtQm8z47Db8bje6iGjUOh2OZtUMXRWlGGITUOl0CI0Gd10NXluDEsyoUo5pSkjF67QRktERv8eDs7wUtcGI2mQORs77XM4Wthbp8SD9yuynuZeWITImUMOE4LX56mvo2DmDzz6cy013XcmKZWuwNtgYftwgVi1fF4wRUalUvPPaJyQkxXLf47cycGgf1Go1SSkJvPHh03z39c+sX72ZU84cR4/eXbj3lif59adlZGalcuMdM3jn9Y+pLFc80DKz0igqKCG9QyrX334FDltrrzaDQY/T4aKqqoYvPvmWS668ACklGo2Gbj07odVqyMspbDWTytmdx4kTjmPeF/PZumnnQQmSirIqbrrqPnZt20NcQkyb6rGPZn/OuAmjgili/gqWMAvdenVm07qWyoPmLs8AXq+XgrxivB4vSSkJBx142M6/j6PpLlIENHe3SA4sa4tJQAtLkZSyKPB/NrCYlvaT5tu9JaUcIKUcEBMT09YmRxWf18uOBauCQgSUlClelxu1Rs2aTxbhcbgZcdVpDL/qNMxRofQ5ZxQnPXARw68+HUeNNShEQFF95S7fStfxA9nYzPhuirBgidThrizGa7fiqavBUVaMIVrpoNQGIz5P6wBIn9MeyGMl0ISYEWo1Xqej1XYeaz36qFg89bW4KsvwOey4qirwWBvQhITic9hR6wy4qirx2W1ozCGoDAakx4MuPApDbEKbVRY9ddUYYhMwxiehC4/ElJyO0Onwu12o1CrURkUn7/T4OOn0MdTV1vPMI69RV1vPlEvP4fclf7Ly97UkpybwzGsP4nC4uOLaqUw852QevusZvv3yJ7789Dvef+tT1vy5kdPPHs/s/73IyRNP4JmHX2Xhj0vw+Xzs2pHDC0+8ybmTFZ+OEIuZa2++jAkTT6Bn767ccd3DLF20nOGjB7Vo/6SLz+Tzj+dx/e3TKSkq462XPuCdVz9Gq9Wwavna4LH2RqPVBINH69tImdIWu3fmsGubMpZyOlyY2+i4o2Mi24zpOBQsFjO3339dC5vLOZNPp1vPzsHftTV1vPnSB5w9/hLOHn8Jt1/3EAW5+/q82/m3czRnJKuAjkKIDBQBMgmYvPdGQoguQASwvNmyCMAupXQJIaKB4cBT/0irjzB+jw9rZX2r5S6rE61RjzHSgsvqwOvyEJYQhTk6bK/tWifAs1XXY4qy4GtWZyJzeFeEu6FlXLvfj5RgTEjGWV6Crg2XWk1IKCqdAXNKOtLjRRNiwWezsndaQrVWyeLrayZkhFqNNiwMtVav1GV3OvDU1+CREl1kDH5rfdAADyjpVQxGJdAxxILWbIFG4aJWMgA7y4rRRUQhVCpc1ZV4bVb00bF8O+833nzlY6Zccg5CJYiIDMcSGsLZk05j7EmjyMhK4+Yr7wvOTCyhIVxy5QWYzEbuuekx1Bo1EyaOpaqyhuPHDUer1bJqxfqW99rlJik1kZfefoz0Dqk01DVw361PBtfP/3YRN999FUNGDqCmqhZziIk/lq5izcoNnDd1Ik++fB9lpRW4nC42rtnC6BOHI6WkQ8d0zp50Kl9++l3wWOdfOJFfflyKEILMg/Tq0mg1XHXDNEAJdrSEhmAJDQka+dVqNZdeNRndPlLYFBeWkpdTiMlsJLNjGhbLvrMG9OrbjU+/e4v83EJCLCF06JiGuZmhff2azbz54vvB30t++YOszhnMvO2Kf7RccTv/DEdNkEgpvUKIa4EFKO6/s6WUW4QQDwGrpZSNdVEnAZ/K5ulWoSvwphDCjzKreqK5t9e/Ca1RT8+Jw6jOLaV8eyFVOSUYw80k9sokNCEKY5iJP9//CVe9HZ3ZwPCrTiM0IZLa/AocdTZMka0rA6b074jL6sAQasJZrwgaxUjfRlSwRqMkS/R68dqtLfJnqY0m1AYjQqUOGrz9LhcacwiiRhs0kguVGo0lDKFSoY+OxdNQh8YUgjYkFHtRfnBffWQ05uQ0bAW5qLVa3NUVLdriqizHlJKGu7oKlVaHu64GrSUMCQiNDp/LhfR6cJaXYIxPwmttwOdyogqP4s1XPsbaYOPNl97nnkdvYs2fG3n56Vk4HU5uufca5n+7qEUKlIZ6K6XF5VSWV6E36Lny+ov54O3/UVpcztzPf+TxF+7BHGLCtpegjk+IoVe/7lRX1uDxeEnPTGH46EE4nW5+/mExLzzxFi+98xgPzPqMijLlPp565on4fD5+XbCMBd/9GjzW77+tIi4uhujYSK699XKGjhpIWUk5BoOen35YjN3u4IW3HjmopIS7dmRz27UPBqPxo2IimXr5edx67zU4HE5cThf9BvWmW89Obe6/ddNOrp52G9WVSubp084ez013XklUTOQ+z7k/d94Na7a0Wrbgu1+ZNmMSYeH7r2bZzt9DQUEBF110EWVlZQghmD59Otdff/0ROfZRtZFIKX8Afthr2X17/X6gjf3+AHr+rY37B/C43BSs2smGr37D63CT2DuTXmeNRGvS8ed7C/C6PGhNenqePgyP04XP7SN35TZispJx1duoL61GH2qm/5QT2PLtclxWBykDOhPfNY2tP/5Jl5MGkvfndmI6JKI2GNBFxuKqaLJtKEWlPEG7hKe+FrXRREh6R7x2a7ASor0oL7CDwJSYijU/B0NUjBKNLlSoDAZclWV4rQ1owyKU7L1eT6AoVpPKzlVdiUpvRBsaTstxQSMS6ZcYomNxB9rirFQ81vQR0ZgSU/C7nEpdFbUatdmCz9YAKjVIiUaj5rb7r2PPrlwqy6s478KJ1NbUsfDHpW2er7amjhCLmbMmncLrL7wXHLmXFJXx8N3Pct2tl/PE/S8Ftx8zfiRhYRbuvP4RFv/8Ox06pXPl9dN47YV3cTlcXHjZueTuKaCspILxp47BZDai0WhY8dtqHr37+WA0fPD5uz2sX7uZ2a9/wpsfPsPdNz6Ky+WmW8/ODB05gBHHDWbIyAGtovLb4se5C4NCBJT0LhXllZxyxgnExe/f68tmtbFx7RbOmXwa61ZtYtXydXz75QLGn3I8o04YesBzt0Vmx/RWy3r27YrpINyaK8uryNmTj1arJSMrjbDwYzei+9+ERqPh2WefpV+/fjQ0NNC/f3/GjRtHt26tUwAd8rGPQPva+YvU5Jax+qOFwd9F6/dgigolZ3keXpcy2vfYXWya9wcdx/RFo9eidetoKK0iPCWWkq15lG3Np895o0gf2g0hBKXb8lk+6wd6nTWCyj3FpPbvhCnCiDFEjxRqTMnpeBrqUGm0SmzHXlHnPoddUSGVl6CLiGpRkVAXFqF07H4fzorS4HIlrXsDqFSo9QYcpUVKLq026pNInxeN2YL0eVuVANaYzHgb6pBGEyqNtsU5XNUVqHQ6HOUlituyEJhTMvCHWFDrdHy7YBbZ+WV8P/eXYGDgquXrOOm0MSAl404ezZqVG1q0Zfjowfj8PtQqVasYjz07czGHmHj61QcozCsiLCKMrM4ZPHrfC6xfrRQF2rltDw/c/hTTZkzijRff57XnZvP8m4/gsDv46J3PWxxPrVYTGtZaVaTRKKVuN6zdgivgEbZ10w62blKyQY8cMzSo2vK4PWzbvItdO/ZgCbXQvWdnklITlH0272p17Jzd+fsUIjVVtVitdgxGPV/N+Y5Zr36Ex+1h9AlDueKaC5n16kcUFZa0ue/B0H9QL/oO7Mm6VUrMcERkGJdMv+CAQjF7Vy5PPvQKm9dvo6HeyojjB3PvozeTkHRkqkL+W/j+m5956alZlBaXE58Yy8zbruCUMw6vemVCQgIJCcr7YrFY6Nq1K0VFRe2C5N9OdSA+pDkFq3aQ2CuThpKm0aXH7iI0PoKCNbsIjY+koayanQvX0WPiMDZuL6C2sJLtCxR3TEOYmU5j+yKlJHNYd1QqPxpvDbjtqKQev9uHxhyC9HqxF+ahsSj1PLx2JQOxxhwSjC9RabQtStiqdHr8zXJmNdI4o9GGhOKprwXA73IG7R3NUev1uBvq0ZotmJLTcVdX4HXY0VpC0YVHYsvPRh8Vg7OsdSfmtVmb3JalxFFWhMZoxlFWjM0Qzvo1W9i0fhspaUlMOGMcb7zwHj99v5jLr70Qc4iJS6+azHdf/YRao+ayq6ewfOkqli1Zyb2P3sT0mReRvSuXX3/6HZ/Ph0ajRqfTBSsVAtz54PVBIdKI0+kiPimOzI7pZO/KZf2azUw85ySMRkMLVdrp54ynS7eO6PU6plx6Dnq9Dr1BT0RUOCazsc0ZkyU0BINBT21NHTl78qmqqOG3X5cz9/P5+P1+TjhpJHc+eD12u4Mx40fwx5I/W+w/YeIJrY7p9/tZ+ftaHr3neQryirj9gZm8+tzs4PrFC/8gOS2JcyafRqcuHfD7/X8phUticjzPvfEQu3fk4HK5ycxKO2BUe01NHatXbiA8PJSzJp2CTq/j3dc/YdXydZx+zuG5LP+b+P6bn3nwjqdxBrJ/lxSV8eAdTwMctjBpJDc3l3Xr1jF4cJsRF4dMuyA5ipgiWk/ZQ2LDse+VMysyIx4pJY4aK0XrdhOeEkPPM4dTk19OWHJ00KgeEhtOxtBubF+wGo/TjTk6lAFTjgMhEBoNKr0eR3FhoFa6GkNsAs6KUqVKodOhRIgLgVCpMKd2wO/zog0Nx1Nfh1CrA95WlhYuwiAQgVTzjeV3Adz1tZgSknFUlCI9HhBKAkZXVQVeuw1PbTW6iCi0kVGonSY8tTU4fT6M8ck4SotQG4xgb3kfVDodXkdTyn2/y4UqLBKVJZwP35nHZx/OBWDX9mw2rNnMI8/eid3mIMRixma3Y7c5GTthNKefPZ51qzexfu1mZlx/Mc899gZlpRV07dGJG++6kheeeJPp101lzvtfB881edpZ6A06TGZjq1iRyrIqrrz+Yt57aw7xCTHs2Lab6++YzsIfl5KclsDJp56AwWQgITGWtz5+jpuvvo/K8mo0Wg29+3Xn8RfvQSVUDBzah1XL1wePe9PdV+Hz+rj5qvtZtXwdoCQ/vO7Wy9HptGzesJ0lvyznqQdfZsz4kZw75XTmfaFUwJs2YxLDR7X0IAPYsyuXay65Ha/HS0xsFDu2tp7JLFn4B/c9cSu//bqCl56axckTT2D0CcMOeVYQFR1JVPS+bSzNkVLy9Wff88LjbwaXJaUkMOnis9i0ftsxL0hqa+pQq9VYQvdf1uBgeOmpWUEh0ojT4eKlp2YdEUFitVo5++yzeeGFFwgNPTL2qnZBchSQfonb7iQsOYqwxCjqigPGba2atEFdcNTaKNuaj/T7UWnUdD9lMGs+XoS9RunAawsq2FbzJ10nDKKhtBqdyQAC0gd3ZfO85cHRra2ynjVzltLzzGEIN/jLaxABm4X0+XCWlaCPilGCBlUCb7MsvUKtwRiXgC48ErXBGIw9URuMOEq8+FxODDHxisuu34cxPgl3TRX6yJhAHiyJvaQIfVS0kmpepcJeXKBUPQzgrqkK1jDRmELQWCzY8nMARY3WmJ4elFgSlUbbIs5E8SjTU1VRx5dzmjyehBBcctVknnvsdQryilGr1Uy9/Fwio8JITkngx3kLsTbYOOv8U3jxybfwBgTxts078Xq9vPb+k7hdHj6e/SUAsfExhEeE8caL7zPl0nOY9fKHwXMNHTmAHdt2s+L3NUy/diqJyQnM/24RLqebKZeeQ01lDffc/DgV5VUkJMVx+/3XMfniswiLCKO6qpZN67ayesV6+g3qzVmTTuX8C8+gurqW9MwUBIIF3/8aFCKguPjW1daz8MelDB89iDdeeA+Xy82P834hPTOFyZeczfEnjqRX365tziRy9xQEr7euroHo2NaeelmdM/h49ucs/lmpf7Fu9Sa2bdnFXQ/dQF1NPX8uX8eq5Wvp0bsLQ0cOPOz8WQDFhWW8+cL7LZYVFZRgsZhJP4aLVtXW1PHL/KXMfn0ORpOBq2+8hGGjB2Iw/PWszaXF5Ye0/FDweDycffbZTJkyhbPOOuuwj9dIuyD5h2kor2H3ko0UrdtNZHo83U4dgrWiDpVa4HV62PztCnQmPd0mDCI0MRJDqJmagvKgEGnEZXWgDzGS1C8LU4SFIZdNwFpe00pFYi2vxefyUl9cjSUunIgYC35nAwgVhth4VDrFNRfpx9lMbSV9Xty1NegCgsZVrUR8GxNTMMQlIlRq7IW5LQIFTclpiu0irQPeQOqSxiJWxtiEFkIkiBAIlRqhVrdIa+912DDExAcqJAolP4yUQQ8yjcmMPioGn9NOZFQ4nbtmsSVgVxgyoj+//rSMgjzFsSA5NYHyskrGnzoGm9XGiOOGkJdTSFlpRbBTbWTX9mz8/oDh/r7rkNJHdGw0LzzxJqXF5az4bTUzb70Ch9NJdHQkBXlFfDT7Czp17YDPJ1nw/a98+cl3CCHo1bcbTz/yWjAtSklRGQ/d+QznXjiR/NxC3n/rM+Wki+D7bxZy5vkT2LMjh0uumkxtTR22BsUIvjdrVm4gOTWRsIhQamrqgstzswt49405ZHXOoE//7q32AwgNbxoxu11uHHYnnbp2YGcg/iQ0zMJpZ53ITVe28Hlh7v9+ZMolZzPnva+CQvvrz36g36DePPfGQ0RGhbd5vn1hs9rZuX0PZSUVJCbHExYWisfTutZ9SGgI/Qf1PqRjHw6F+cXU1zUQlxDbZk62vfnt15U8eMczwd83TL+HWZ88x+Dh/f9yG+ITYykpaq32/qupchqRUnLZZZfRtWtXbrrppsM61t60C5J/EI/Dxdo5v1K2LR9QkilW7CokfUg3DKEmti9Yhc/jIzIznpC4cIrWZ6MPMZLQMx2hEq3yQWmMOvJ+3IbObCBlQGfCk1sHXOotRmryy9m1aD2GcDPjbj8H4YtQ0rQL8DTU4bXblBxXe+F12NA4zHjqa5XkihWlOMtKMKdk4HPaW9UEcVaUYUpMxVVZiiYkFEdpUdDe4vd4WkWoa0JCUWm1OIoLURuM6KNjMcQmKN5jemMwNUsjQqPFlJyK9EtUOj1eax3OinJA8upLt/HeJwuw2Z0MHt6PO2Y+DMD0mRdRWlTGmj834vV4OfP8CeTnFGIw6Ok7oCfTZ16ERq1m+W+rycsp4Mrrp7Fh9WY++/AbzCFmzrvwdJYsWkFqejLbt+xi0/ptbFq/DYCLrjiPuQFV0ulnn0RleVUwFQvA7h3ZLXJrgZIxNyExlqceeqXl8soadFotixf+gSnExNiTRqNSq+k/uDeLF/7RYtsBQ/sSGhaixIVceQEGo4EvPvmW4sJShBD7jf/o1LkDJ4wfyS8LfgOUhJbPv/UweTmFOO1OvF4fuXvyW+0nhMBhc/DL/N+YMfNiANRqFXm5heTsziMyKhy3y832rbvJ3ZOP0WQkJi6KmNgoklISWr4nThcfz/6CV559J7js9gdmMn3mRbz23LvBZZbQEIaOHNAqYv7vwOPx8sv8pTx05zNYG2wkpyby5Mv3taqh0hyn08mc975stXzxwj8OS5DMvO2KFjYSAINRz8zbrvjLxwT4/fff+fDDD+nZsyd9+vQB4LHHHmPChAmHdVxoFyT/KNbKeip2FaG3mJRAQgmuBgdqrYbtP62h2ylD8Lk9WOIiWPnO/OB+ITFhdBjZk91LNgaXpQ/thq2ynvqSakxRoSAlOSu2kTmiB9nLFIOwSq2i87j+QUP88TNPw1mU26x0rQ5TchrumiqEaK0G0ZhC8DrtQTdghAjsK4MlcpujqJ0kKq1OUZc1mx25qiswxiXid7vx2q1KLIpQ4Xe50EVE4vd4sOVnAwJdRCQqrbZF/IpyfA9IJWmj1hyCs7zJqwtrLZdeMpFLp91NWUkF3Xp2Jjo2ilV/rGPdasVzqKighLWrNnLGeRMYMKg3d97wCDXVyoj+rEmnMvG8k8kLjOpB6fSfffR1brhjBiqV4M8/1lIbmAGkZSSTkBRPeEQY5089g4wOqXww67MW9yMxOQGVStWiOqMuUPFw7+SbAGqNmrBwC527dmDNyvUkpyYSGR3J6BOGsuQXJR6374CeDBnej7tveiyYqkWjUXPjnVfy2nPvctEV57VKRVJRVoXHo9RRiYgK586Hr2f4cYMpKS7DYNDz7utzGH/qGDBBfW0DA4b2JSMrlZzdTQLl3CmnYzQbmTHzIl5+5u2gnajvgJ7B8KRff/69hXNC3wE96dApnZNOG8OgYf2Cy3P35Lcw8AM8++hrfPjVq5jNJuZ9uYDO3bKYPO0ssjpltLpPfwfZu3K4Y+bDwWdVmF/M3Tc9xvtfvLzPJJcatYbomNaqwYOZyeyPRjvIkfbaGjFixD7c7g+fdkHyD+FxuPG63HQ5sT/OejumqFAqdxdTuiWXsORoepw+BLVWiynSwoavlNGiWqshuV9HVGoV9aXV9Jg4DJ/bg1qnpTqnFFedjZ4Th2GtqKNowx5iOyXj8/rocdpQdCEGpF+y4+c1uG1OYjonI93WFh2z3+PGa7ehMhgQGjWG6Lhg3IZKp0cbGtYsYaNieNeFheN1ORRjuBCKl5ffj9duC9Q2EWhCQlursaTEVV2BISEFdYgF6XLic7tRGw3glkFvL5C4a6rQmC0Y45OwFzd1ZhpLqFL33ecNepk1RzgauPDy86iprCUqJoKK8ipefvrtFttUlleTlp7Mq8+9ExQiAF99+h1de3ZiUWCk3pyighJ+X/In50w+jbTMFFQqFSaTEY/Hw5U3TuPpB19hwJDeLUbeZ19wGhvWbmHKpefw4dv/U9onBNOvuyjoLVVWUk5FWRVf/+8HQixmOnRM597HbubBO56hz4Ae7N6Zy8rf1zBs1ECuumEaeoMek9nIyj/WBoWIXq9j1AnDsNscvDj7carKKsnslI6UkrzcQtb+uZGXnppFQ72VSRedwYWXnUtCYhzHjR3Oit9X8+tPyzjhpFGMOH4warWazRu2seK31dz76M1sWLuFhgYrHTtlYrXasDbYWPDdohbOButWb6KyvJry0goev++FFvdt3epNDB01kDuuf5g5894iLkGZMdfW1Lfq0LweL263h6mXn8fZk09Dp9MeUhnew6Uwv7RVOebcPfmUl1buW5BoNVw8/XyWLV6BN5DiyBIawsjjhxx2e045Y9wR89D6J2gXJP8AHoebog272fHzWiVzr4Co9ASSB3RCY9BSsauQ8JRYbJV1SKGkTTGGm+k8bgAFa3dhigzBWllP+dwmFUfXCYNI6JVJ+bZ8DGFmqnJK2DxvOZ3G9sPeUE/hgt30PntksM5zVFoc0t06rsPvcmJMSMNekI1Ko8EQn4RKrcFrtymqKRTDuzYsAq+1AWd5qVJPRGfElJSGu7oSoVIrMR1SqWjoc7tQ6w3owiPxBFKgqA0G9JEx4JcgwOf1KPm29HocNYWt2tUoWEyJqUi/D7XBiN/txu/1KMkgNRo0JiUlh9/nxVVZgc3lY/ZrH5OfW8T06y6iY5fMVpmAQUkuuGn99lbnrK+tJyo6kryclu1JTI4nLSOZ2po66tc18L+PFO8wg0HPzfdczWVXTyY/t4iRY4bw0w+LcTqcxMZF8foL79G9VxeuveUyXC43icnxaDQaPnn3C7YF4j6yOmVw/5O3Yq23snrFekLDLDTUW+neszNvBFKM/LF0FX8sXQXAtbdcRkOdlb4DejJuwmi0Wi0ej4dli//E4/FSVFBCaHgo1gYbxUWlPP/YG8Hr+PDtzzGZTIw6YQg11XV069mZU84YhxCCgrwipk+5maJmNVlefudxfpi7kNmvfQLAtOmT2LMrr9V9q6qsxuFwUV1V22pdiMVMdWUtlRXVQUGSlBLfInULQHRsJInJSrnhgwlaPNJEx7SeRURGhR8wCr/vwJ588OWrrFu9CZ1BR78BvejYJfPvauYxS7sg+QeoK6lESklir0ziuqZgirBQsiWPko17yBjeA7fDxc6Fa6gvrqb3uaPIOq43CIHHocSPeJ0eep81grqSKhrKaontnIxao2HD50uo2lOCSqMmc0QPTBEW9izZwOBLT8bZoFQf7DfpeHYuWkd4ShTa0LBWcR0aswWfw4r0evB5PfhKizDExAfVFUKtxpSSgbu2BneNYnD3OR14rPWY4pKU0rcoSRtD0jrgtTUgpcQvVGgsoah0OtRGxc6iqK4C6elj4rEX5mJMSEYbYsHnaDnDUOsNCI1G+SfV2Apyg8GLxrhEnGXFQeO8SqvDGJ/IxjXbyQ8kBvxg1mdcd9vlnDd1InPe+yp43IFD+xIeHkaf/j1Yv6ZlTAjA2JNHsXHdluAIMyY2CqfDRe9+3enWszMzL78ruK3T6eLdN+Zw72M3Y7c5WLbkT6675XJcrqZOdcvG7WzZqAituIQYbr776qAQAXC73Xi9XlLTk/jzj7UMOoBuPSUtiS49OqLWqFvYWa649kLmf/sLY8aP5M0X3yercyYarZrIqHAcdmcwpmXuFz9SWVnFl598h9Fk5OFn72DsSaOoKKsiLSOZ8rJKTj5tDCnpSVRX1fDjvF+C51jx+xqGjx7E/G8XtWhTh47pxMVHc9y44UFPL1BUbn6/n4unn9/CGJ+SlsSLsx7l/tueoiCviMysNB565g7i4o9eUtWszplMn3kRb730AaDMNh586vYDGrhVKhU9+nSlx35sKY1IKY/5PGN/VfXVLkj+AbwuL9m/baYquwRLXAQdRveiNr8ct81JbKcU1DoNtsp6ep6pRKN3GN4Dl83Jmo8X0phlMWf5VrpNGET60G7UFVZSWVRE1R5l9Oj3+ti9eAM9Jg6jYlchfq8Pn8tLXVEVkWmxHH/TGbjKClFptEourdpqJa4jKlbx2kKPMbExEbNACIHwazAlpinuvVLirq1qeVF+P3u/cp6GOtz1dUivB2NCMo6SIgzRcfidzmD+LlBsHe76GjQhoTjLSjClZCA0Tbm7NGHhaEPDkH6Jz+1U0skHhIjQaPF7PS08vPweN26nk2efaFJjOZ0ujEYjPXt3pevTt2NtsJGQHE9sXDRVVTVcce1UHr33eYoLS1Gr1UybMYlVy9eTl1PA4y/ew85te5Soc6+Xd9+cg9ls5KGn72j1bIsLS2mot3LbtQ9y3LjhRI0YgFqtokPHDJb8spyc3U0j+EFD+7F2lWLn0ut1XHnDNLJ35THvi/mMPWk0J08cS2FBCZbQEFatWM8JJ43kl/lNqrauPTrx+5I/6dmnK5998E2Ldrw/639cfMV5LPjuV3r360F6hxQiIsPxeX2EWMzodDrefXMOicnxGA0GxXhud/D0gy9jCTHz/dyFSCl56pX7+eidz/n2q5+47OopLc6xfcsuTjhpJMOPG8Tvi//EaDIy87Yr6NG7KwajgSuvvxjpl/z26wqSUxM4f+oZfPzul1x3y+WtYlA6dcnknkdvoqqimrTMZLp0bzsH2D+FOcTEpVdewOgThlFdWUNyasIRdTs2GAxUVVURFRV1zAoTKSVVVVV/yXW5XZD8jbhsTuzV9az7bDENpYpOu6Gshs1z/6DjmD44amxIKTFFWRg8bTx1xVVEJMfg8/uVuuzNemqv042j1obH7sJtc1K6paWKwRgeginCQt9Jx2Mtr6W+tJrKPcWUxoQR1/EU/C4n9pICzKmZ6MIikX4/7rpqnDk7URvNSkZdIXDVVKLWG1CpNfh8DoQQaEPDEUJFy3IwikduqwUCzMmKjt6UkIytMA9DdGBUp1KhDbEgfX68Dhv6iGhlBuNxow+PBJUKjdGMq7YSa1422hALurBI3M3sISqdTqnauPepPW4effZ2Lpt8My6Xmx69u6DVavjy0++CqVH0eh0vvv0ouXvy6darE1fdeAlGo4G6mjoW/byMoaMG0H9wL6oqapj9+hz8fn9whCYAoWrdAXTskom1QWlf1+4dmfP+V6xZuQGtTsvUy85l4JA+xMZHoxKC+OR4kJJP3/+ayZeczYfvfB5MkrhhzRbOmnQqOXvyuPyaC9m9I4eMDqkMGNKX9as2kdUlk/CIUKyBsrl7jxzdLjfxCbHEJ8bSpXsWUsLTD73CKWeMRaPV4HK6uOLaC4mIDGfhj0u59d5rePu1jzn3woncMOPeoHdZv0G9gvfLYNS3ut5N67fx2PP3sHtHNjnZ+axduYGEpFgGD+tPTXUdDfVWLr1qMmUlFbzyzDs4HM5Wto762nqef/zNYLZjlUrFs288xAnjR7Y63z+JyWzar5fW4ZCcnExhYSEVFRUH3vgoYjAYSE5OPuT92gXJ30RtUSV/vvcTiT3Tg0KkEa/LgxCCxN4Z7Ph5DQjY8u2K4PrQxChS+nWkKrtlmhAhoHhTDvoQI6GJUVTsVHT55ugwMoZ3Z+2cRXhdHnRmAz0nDsNZZwOVwOdTdjYlpeGuqUaoRDDwEFDUShGRSlXG0HBcVRX4fV50YZGozRb8Xq+StqRZ7iuVVoe/WXCgkrzRiMkUgruuBpXBiNYcgjY0DKFSow0NR20w4qmvRag1mBJScDfUKXYUewMaUwgqrR5bcZ4SCY/ineVzOjDEJQZnND6nA0NUbIv086CUC84yh/DOR0/x/uyvmXDGWHbvzGmRX8vlcvPCE29x891X8cv83/j6sx8IDbMw9fLzGHHcEHxeLw67C5VKhVaraVH7/dwLJ7Jz2x5uuecaXnnmbZxOF0kp8Vx94yXceeOjdOycSW52QfB8HreH2a9/wiPP3cU9Nz0WPM60GRdw8fRJ6PW6oBBpZN4XP3LJlRfw/ONvkJqehM6gw+V0cdLEMVRX1pKfW0RcQgxpGUmtIuyjYyMpK61g8rSzSUiO59G7n+Oyqycz+/VPqKmuIz4xltsfuI5d23NYsWw161ZtZPr1F+G0u1q4KHuaxdWsW7WJc6eczleffo/P5yMjK5WZt11Bfk4h06fcHLQ9/TD3F558+T6+n7uQ6Ngo3n71o+AxjEYDmR3T2LhuK7nZ+UREhqHWaFqkzPf7/Tx817P06NWZuITDi5VoTn1dA7t25FBbXUtKWhIdOqWjbqPmTVtYG6xUV9USGmohPDJsv9s6HU5y9uRjbbCRkpbUpjpMq9WSkfHPeKAdDdoFyd+A1+1h09fLcNud6ENNqLVqfJ6Wo3md2UjBml1EpMYps49m1BdXYTyhqU6XxqAjbWBnojok4nN7yVu9gx6nDqWusAK33UXa4C5s+XZF0CXXbXOyed5yBl1yEnVFFaye8xtZI7uhc7mVNCPW1vVPpM+PWqPGXtxU/dhdU4lQq1BrDYqrcFKaEmio1wVdfHXhkUrdkZBQfB43tsJcTPFJuOvr8LucgdxdoAsLx+/zBWY3AndtUxS83+0J2Gi8QSHSiM/pwO92YUpKw1VThTbEgtpoxhCwk4ASBa/S6rDX1xMfFcpZ553Eli27WwUbglJ5sKK8irmfzycpJYH8nEKefOAlnnrlflQqFe+/9Rl+v5+Zt13Bkl+WU1ZSzvDRg3DYneyuzGHk8UN4+Nk70em0NNRbuWPmw7hcbnr27Rp00W3Otk07WqSjf+/NObzx4dMUFypCOTU9ibEnK0bzbVt2kZiSgBCC/Nwievbtht/nY/XyDXz87hfBYw4c2ofHX7yHx+97kdLiclLTk7jg4rN4+RlFtffi248yasxQXn7mnWBp4NLich6953kuvOxcxfjvdJGSmtjCcw3AoNcjhEBKye9L/qRztyzuefRGOnTMIDU9icjoCF5//t1WDgwfzPqMhMQ4YuOiOHfK6fyx9E+SUxO58LJzKcgr5obpdyvHN+i59d5rWt2n6soarA12YuL81FTVYjKbMJr+enR4fV0DLz/9Np99+A2g2Guef+sRRp8w7ID77ti6m8fue5F1qzaSkZXGvY/dzIDBbQdF1tc18M5rn/Dem3OQUhIdG8nL7zxO915d/nLb/420C5K/AVeDg8j0eIzhIZRuyaPryYPZPK/J4ypjeHfC02LYvmAVGcO647a1lSXXT/dTh1C+s4DUQV3Y+t1K9vy2CWN4CD1OHcrOX9Yy4KJxeJ1uRe20l+uiy+rAWWelKqeEpN6ZrJj9E8OuGE9UWhRIfyujOyqB39s6sthdW4MxPhl7YU4w4669qADpU7ZVG02K3aWhDunxoI+MxllZjkqvdAKOZoJJHxWL19aAz+lAF66o1/w+H1pLGI7SIoxxSegiopTZjtuFu64mGM2u0urQWUJxlJeC9KMJsWBOzVQM/w31uFxebrvjJSrKqzh38mlUlFXRZ0DrSgOjxgzF6XByxnknk5ddyKgxQ6iqrMHaYCMqOoJb778Wr8eL3y8549yTWbdmE/O/+zWYoj2jQyoqtQq324NapaLPwJ706deD2Pgoykoq+H2vxIlh4aE47C1VcTl7CggLtzBh4lgioyP4ePYXuFxuho0aCH7JjJkX8fG7XzL25NF4Pd5gcGUjq5avZ/Dw/pw/9QxMZhNbN+3ghSfeZMTxg+ncNYvykko6d89CtZfusbK8msTk+GBdlKjoCK679XJ69ukaDLL87pufuP+JW3nl2bepqqghKSWezKw06usb2LM7ly0bt9OhUwaJyfFBYQig02kZcfxgHrj9aRKS4ug3qBfVFTVERIQz8/I7ATCajDzw5K2Ul1a2iq/p2acrGq2a5x9/g++/+ZmsjhlcffOl9Onfo9UzPBh2btsTFCIAXq+PB+94mk+/m0Vs3L4DHKurarn12geDQZk5u/O4ZtrtfPb9W6RntraZbNu8k3ff+KTFPX7qoVd59b0nCAlpXfny/yvtguRvQCIpWLOT+mYZfAdfehL1pdVo9ToctVaKN2QTlZlI6dY8Ugd3IW95U10ulUaNIdyMq8FBp3H9+fPdBXjsirBx1FrZ8v0K+l8wBq1Bj6POhsFiCo4iG9Ea9dSXVBORGkf+6p3Ed0+ncGMeoZFa9BFR+FzOoHpIFx6J32EPlq1tjkqrRahV6KNiUWm1IATGuISA4JJKFt7SIowJKXjdNUqNE48bfWR0i7ruAK6qCgyx8ficDty11WhCQpFeb6C+iAaVVoOrpgG/241ab8AUn4wUQilwFWJpcTyvtUGZFUmJ2mDA67Bx3cwp3Hf3Czz98KvMmHkR61Zv4vYHruPVZ2djbbAxaFg/Tj9nPK88O5sdW3cDSuW+kyeOJT+ngPVrNpOQGEtsfAzlpRXs3J7Nrz8ta3lDhJLeY9Cwfjx0xzNcfu2FPPHAS3jcHm699xo2rd8WrL8+aFhf7HZHiw7TaDLisDtRqwQnnDSKm69qSkXyx9JVJCTFMeK4Idx67zW4XW7cLner0T+Ax+3llZfe4dnXH6Qgt4gJZ4yluLCM155XIsMbi2U98/CrwX169evOb7+sCHpiVVfV8tbLH3LHAzP5Zf5SqiprOPGU4/hj6Z+cfNoJnHLGONat2cQ7r3/ChNPHsmHtFnQ6pXLk9bdPpyCviPWrN/P7kj+57JoL6dg5k1kfP0dleRUut4e0jGQiokKDHmwXTDuT5x59HZ1ex/W3T+f9WZ9RXVlDzz5duPexm3njhff4/hulrEJleTUb1m7hk3lv0qFTeqvrPxBtuSJXllfTUGfdryApKSptFdnvsDsoyCtuU5CUFLdOZbJhzWbqautbCBKPx0tDfQMhIWZ0+rYrVP6baRckfwP2qoYmISIgpX8nbBV1SiBblAVdiAG1Vk1K3yxKtuQRkRKDSiUo2ZSDOTqU9KHdWfvJIpx1dvpdcHxQiDTisbvwON1s/nY5YYlRdBzTl15nj2DjV8uQfolaq6brSQPZ+ctaYjql4HW4iM5MwON043SowF+JISYBGRWDECqcFaVIKdGFRymp4oPxJopnlxAqPPW1SL8ffVQMfp8PXWgEPo8LlUoQktYBv8eNNiw8mFRxX4WrWvzy+fDaGtCFRaCPjMZWXACBTtfncuKsLMOUnI4qIgoJrdLSexrq0EfFKqo1bx3pkXoeePh6pk25la8+/Z4nX76Ppx56hXOnnI5Wp2XPzlw8Xi8V5S090ObP+4VnX3+Q5x57g8L8Yjp17cANt08nOS2xhSDRaNR07prFfbc+ybwvF3D/E7dw3y1PBNVHrz43m/OnnkFKWhIqlYqa6lr0eh0ZWWnk7M4jITmOa268lPLyKrw+H+vXbGp1h5b+slxxcNBpGXXCUDRqNb36dWPj2qaBRnRsJHa7g6iYSP5cvo6EpFiyOmXw9WdNNeIqy6tZs3IDfQb0oKKsCqfDyeXXXMht1zyAOcTEuAnHkZQST87ufHbtyGbCGeOwWW3ccf3DwdQcA4f2ZfZrnzBj5sXcdeOjQYE4bcYkFv/8Bz/OW0j/wb14e87zpKQl8duiFXz64deo1WrGn3o8EdHh1Nc1cNNdVyk2peR4ykoVY/Ps1z9hwhljCbGY6dlH8fr6Ye4vNMfhcJK9O+8vCZKUtMRWg6vuvbscMN2KOcSMXq8L1oZppHl9+uYkJsW3WtZvYE/Cw5vsKtm78/h49hcs/WU5fQf25LKrp9C5W9ahXM4xT7sg+RtQqVXEdk7BFGnBGGZm5y9rqS1o8tboN+l4pF+yfNaPuO1OMkf2JLpjEvpQE1qdlrVzFgXzarmsDoSqpepKqFQ4aqxYy2uJ7ZTM1h9WkjqoC/0nj8FWVY8QKnYv3oCz3o4xzITfF0ltYQWpAzuz5rPfGH3tydiL8tCFR4IQ6COildK21jp0kdGo1Gr8Xi8CgdtuRaPVY0pKxdNQhwS0RhM+jxMB2IsLkV4vmpBQdKFhCJ0eXUQ0SD9CrVZSpTTeF72+ZX4uqVyT3+NW0s/vpZ7ze9z4XA4cxQVoQ8OVTMVeJUWL125D+n1KZL5GgyEqFo/NSpdIDWedfzLr12zF4XRxwviRNDRYSYpNICU1kZW/r2XsSaNISIrjvTc/pa5WibLeszOXwnxlxrNz2x4evfd5HnvhHi6/5kLee3MOUdGR3HzPVdjtDq66+RKEVCK0m9sYbFY7s1//hGtuvpSCvGLmfTGfXn27M/26qRTmFVNSXMayJSs59cwTmfv5fLLa6CAzslLJyylk1fJ1JCXHk5qexMjjh5CZlc7y31bRpVtHBgztw6vPvMOM6y/mvTc/RUpJQnJCq2Nt2bCdB566jYU/LiEiKhynw8m1t16O1+OluqqWV58NpCmZCxlZaYw/5bigENHrdWi0Gu59/GbuuuHRFrOq9978lGtuvhSANSs38vDdz3HH/dfx8N3PBrdJSUti66Yd/PzDEkCJy3jk2TuJjI6gurKGutr6YHzPi7MeRafTYg4xBT3gGvmrdpKsThk89cr9PHL3c9TV1tOlWxYPPHHrAdO8p6Ynccu91/DoPc8Hl10w7ax9CrMuPTox4/qLmfXyh/j9fuLiY7jl3msxh5gAqKut5+6bHmPLBiWWaP63i1i3ejMffvXqYSdhPJZoFyR/A1JKJFC2PZ+kPh2I65LSQpBIJGs/bhp97Vy4ls7j+hPfPZ0/31+gJCXUqEnokY5ap6Hzif3ZPn9VcPvupw0hb+U2RQUWaiJ3+VayRvfCUWdj168b8DqVztoYEYIxwkJkRgI+t4ec5VvpOr4f+LyYElKwFmQHO2+VVoc+Kga1wYQ1b3eLPFkyJBSVXo9ab8LvVQzqxvgk7IHIdwCvtV4ZAfp86CKjkV4vpqQ0nBVl+Jx2NKYQdBFRStneQG2Sxuh16fMGbSotEEIRUqYQJQ19UZPKQR8ZjTokCnw+nBUlSv6t0HDUOrjskjMoP/tkXnn2Hdas3IBGq+H6267gkXueC+5vNBmZcd1FvPr8bIaPGtgqOLGooJSSonJi42N448NnKC4q5YNZ/+OcyadjrbMipSQtI4X+g3u38AxTqVQkJMUF9fPHjRvGndc/ElwfHRvJsJGD6NazEwlJcfTq1z2Y4dcSGsJxY4fzdEAdteSX5QwY0oefv1+MlJKLrjifuPgYdu/I4YrrpjLvywXU1Sr3PSm59ch46MiBPP/4G+zargSCXn7thfw49xdOOm0Mn37wdYttc3bnkZKeFKh1oiMlLQmvx0t5WWWrzh3A7W6yp+XuySdnL3VQ524deOWZpqSMXo+Xl5+exQNP3sr1l98dnClMvuRsMrPSSUyOZ+ZtV/DYvS8E9+nRuwudunZodW4Au92B2+UhPKLtyHOtTsv4U4+nZ99uWOutxCXGEraPWUVzVCoVp599El26d6Qgr5jYuCg6d83ap70jNDSEy6+5kHEnj6ahwUZKagKxzQIrC/KKgkKkkbKScnKzC9oFyZFCCHES8CKgBt6WUj6x1/ppwNNAY4/1ipTy7cC6i4F7AssfkVK2LGZwlKgvqeK3l78Jlsrd/esGkvt3pOPYvuxZvJEepw/FXtXaa6pg7S6ishKJSI0jfYjiyy6ECr/XT0NpDT1OG4rP6yMiNQZbdT3OOjudTuiLMcLCwGknYqtuwOfx0vf84/AHEiZqjXpqC8rxejyYI0KJzkggNisen9sB0oEhKgZPQ73iGeVxK/YKnxdzchoIlVJTpKwIr7UeQ3QcUviCubhaJW0UAk9DvWIDsVvxud14rfVoQ8PQWuLRGM1I6ccQm4BKo8FdV6vMJvSKR5i7thp9dCyuyqaaC4aYeGV5RFQre4u7rhZzaDjWQLQ8gKeuBsIiCNOr2FZRHezghwzvHxwZN+KwO7DbHdx415VkZKa2Spuu1+vQ6bVs27SDx+59nojIMO57/BbuvP6RoFuwXq/j0Rfu5rixw3nr5Q/w+Xzcet+15GcXkpqWTHVlbQsXXYNBz6VXTubRe54Lqk5uvOtKTj9rPF6fl6qKal5/4b1gJ5uUksD8b39h0kVn8uJTs3j6oVfo3b874RFhLGmWEVhKSVi4hXMmn8bXn/2Az+ejZ9+udOrWgblf/BjcTqfVUlRQgkqtatOjze3yBHOTHX/iCDxuD4OG9WtlWNdoNWiaudFqddoWev8OHdPRaFt3LUUFpRQXlPLUK/dTU1VLdGwk8QkxJCbH4ff7MRiUDLcN9VbiE2KJjo1scR4An8/H6hUbeO2FdykvKef8qWcw4Yxx+7R7JCbFwSEW5DKaDPTu153e/ZpS8efnFpKzOx+jyUBW5wwio5pSquj1un0KPJ1e18qxANqO0dkf5WWV2Kx2YuOig7OdY4mjJkiEEGrgVWAcUAisEkLMk1Ju3WvTz6SU1+61byRwPzAARfG+JrBvDUeZ+pLqoBBppGjtbobNOJXozETy1+wkNDa81X7GMDNuq4PMET1Y8/EvOBqrJArodeYItny7Ap/HS1z3NNIGd6HXWSPY+v0KHLW2YLT8th/+pM95o1n7ySI6julDeFos4SmxrPlkER67C41eS3hKFLEd43GWFuL3uNGFhqO1hOGsKEWlNyD9Su0Rn8OOSm/AFJ+Co6QA6Q90PIEPQgiB1hKGJsQS9LKSUsn867I1oLOEo4+Mxud0IP1+rPlKvQtTYiog0UdGKYLM1oCrpgpTUqriIRaXqBS0khK/xxM87t7oI6PbTNzoqavFEJsQzNILSkfX2PmrVCqGHzeI5NREdHotb7/yET6vj7sfuYF7bno8uM9FV5zHD1//zIChfYhLiMFoNPDzj0taxJa4XG4Wzf+N3Ox87nv8ZrZu2onb7aaosITJl5zFts070RuaOtgTTz2eTz/4uoX+/fnH3uCmu68iPTOFlcvWBEf/kVHhZHRIYee2PbjdHq6ceREms4nI6HBCLCHs3pFNp65ZdO/VGbVaTW1NHUNG9CcyOgKz2USXHh158PanW9ybxs6stLiME089nvnN0p+ER4RRXto0ax4zfiTZu3JZtGApV990KW+88B6F+cVERIZx7S2Xt4gVuWTGBWg0agxGPVdcO5XdO3LQaVvXZu8/qBfLFq9k2eKVAFx94yXcfNX9zPrkOeISYnn0nueZMfNidm7bwwez/kdoWAg33nUVE04/AZ1eh9PhZPfOXK6cekvQAeG5x97A4/ZyxXVTW53vSLF5wzZmXHhLMDfY0JEDDip9CkBaRgpTLz+3qfYMMPbk0WRmpR3Uub1eL8sWr+ThO5+loryKQcP6cseD1/9jWZEPFvF3pRU+4ImFGAo8IKUcH/h9J4CU8vFm20wDBrQhSC4AjpNSzgj8fhNYLKWcs79zDhgwQK5evfqIXsfelGzK4bdX57ZYpjHo6DCyB3qLCVN0GPVFleSu2BacmajUKoZccQpl2wsIibaw4YuWGWhDEyIJS4ymYM1OMoZ3J6lvR5a/+W2L2BRTVCjx3dLQhxixVtYRkRqL3+Nlx89r8TSra6DWqhl9zUmo3M062rAI/G4XhtgEHMUFLewYQq3GnJqpZOQNUdQIQqUClRpPbRUaoxl7aWFQFSZUakxJaYDEWVmGz2Fvug9mC2qDAVdVoMMSAnNyOj6/D43BhLu6AndtNYbYRCWKXEqcleXowiNw19YEU6gASnEtaDVTUWl1+EIi+Obbpcx65UPsNgdx8TFcNP183njhPa6+8RJ+/HYR2btyGXHcYIaPHkR+biGp6cnU1zcoaerdHhb+uJQtG7ej0Wq46oZpLFqwjIioMJb9urLF+YaPHkR9XQMDh/WjpqqWbj06kdEhherqOkwhRtQqNVs27sDv95OSmsBdNz7WQjCOP/V4evTuyrdfLSAuPoazLziVivIqoqIi2LUzl4jI0Bbqni7dO3LLvdewddMO9uzIbTHjuObmSwmPDGP7pl1kZKURnxjLHTMfCuYNu+bmS5ESdu/IIS4hBgEsX7aaLt2yOOn0Mdxy9YM47A5S0pIYM34k77/1qXKvDXpOO+ck+g/qhV6vY+H8paRnpuB2e+jQMZ3vvvqJnN353PXIDTz90MvkZhfSsUsmJ55yHB/M+p+ShLJXFy67egp33vAIILnmpksV12iHi/iEGDI6pPHIPc+h1WpISUsiMiocKSVOp4sTxo9i6a/L+X3xn5x46nEtyvECRESG8cX8d4mJa53S/XBxOJzcfOV9QeHXyLOvP8i4Cccd1DFqqmrZuG4r27bsokPHNHr363HQNVa2b9nFpFOnt5jRDBjcm5dnP/GPzkyEEGuklAP2tf5oqraSgIJmvwuBtirRny2EGAXsBG6UUhbsY9+ktk4ihJgOTAdITf37S3aGJ8cQkRpLTX6TiiZrVC/yV+1ApVUz8KJxaPUJxHVJpXxXIRq9ltC4CNbM+RVnnY1OY/q2Oqbb5kRr1KHRa4ntkoqtsrZVgKO9qh59iBGVRkVEaiwFq3aQOaJHCyEC4PP4cDa4MDWbWXvqajCnZiJ9vlbFqqTPp7jjGky4KssUrykhMETHg0qFJ1BWN7g9in1IgJIaRUp8TsUDa28XXqTEXlqIOSkdT00V0utVyvcKpea71mzBlJiM9PkxJVlwVZbhtdvQmEPQGIx4GupRG00thJU+LpFXX/6EvPwSnnjpPn6Y+zNde3Ri47ot3P/Erdx/25PB4MD53y6itLiciKhwFs7/jcnTzsLhcPLSU7OCnb3X48Xt8rB9yy6uufnSVoKk/6DevPb8bHr17UZ9XT0arYY5H37N8qWrueqGaTz32BvBY3Xr2Ym7Hr6eV56Zjc1mJyIijLiEGJ599DVAMfIvX7aap16+jx/nLWLk8YN55dnZTL38PEICnUZ9XQMlhaUIRAshAvDWyx8ybfr5wajx5JR4nnvjYV5/4T0iIsPo1qsLX3w8L+iJFh0bSe/+PTjlrBNx2BzExkXRd2AvuvXoxPNPNHXWTqeLzz+aS1JyPN17dWbEcUMoLSqlU58sSovK6Nglk979u6NWqykvUzzidm3PprqqlonnnkzPPl1ZvXI9Gq2GB568FSEEHo+XzRu28cXH32I0GXj+zUeYcMY4CvOKWPTTsmCOssbCWLNe/hAhBKNPGMrehIWHotU1dWXVVTX4vD5i9uPme7DYGmxs3byz1fLC/JI2tm6biKhwRo8dxuixBw6G3Ju8nMJWarHVKzdQVlpx0LOaf4Jj3dj+LTBHSukSQswA3gfGHMoBpJRvAW+BMiM58k1siTEihKHTT6Fkcw4NJdUYw0Mo256PvaYBc1QoeSu3k/3bJhJ7Z9JxTF9q8ssp31UYnJ1oTfpWbovpQ7uht5jod8EYyrfnE5rQeuSl0WuRfj8epxetUY8p0oKzwY5ap8HnbtKHq9QqjBGh0CxITqXVKsWmTCHBkrYtEAKvtb7J9VZKnBUlGBNT8NS21Caa4pJwFOcHDN860GjQmEMw6nSo1BrURnOLTL/68ChshbnB2YanoU6plBgVpySBrKlSgh7DIjDEJiB9XqTPj19KhFrJzaULVUavar2BOf/7iQ9mf8m1t1zGa8+9w/SZF3HzlfcjpSSzQ1pQiDSyfs1mZsy8mF9/WsZLT83itvuubaVKU6tV+Hw+yssqufexm/lo9hdIKZkwcSwrfl+N1+sjPjEWk9nIg3c8zcxbryA01MKnH3zT4lhbN+3knCmnc9EV5+H1+OjYJYNP3v+Ki644j9KSChb+sIS4+Gjcbg+52flkdU7nvCmn88Wcb4P1upNS4uk7sCc6vY6OXTIpzC8JpjjxuD3KbDFAYUEphQUl3HDHdOrq6nE7XRgCNU3sNgeV5dVs27QTa72N+vp6Tjh5FN9/9TOp6Uno9bpW1R0NRj1PPvAy50w5ndiEGGqr63j64VeD9pbQMAuXXT0laGepqqjmo3c+59pbLiMtI4U1K9azeuWGYDbk9A4pzLj+Yl57bjaffvAVN9wxg/ycghaJLivKq1i/ZjPpHVLJ3ZOPVqfl7AtOI3tXbrBg2Q13zCA8Igy73cGvP//OS0++hcPuZNqMSZx+zvg2i08dLOGRYYw7eXSL4EaAzt3/GffdtpwJIqMjjjk7SeuyeP8cRUBKs9/JNBnVAZBSVkkpG4fUbwP9D3bfo4XL6sBRa0VKKFy/m01z/6B8RyFhSdH0OGM4hlATI66ZiFqjYckLX7L1uxWoNRq6nz6U7qcOQUrJsCtPJSozAVOEhY4n9MUSH4nH4cLn9RGaoOTE6jS2qeIcQvHk0hh0RGUmkL1sM0Xr91C0IZs+545CpVEMlkKlou+5I1BLG4bYJndRfVQs7uoqXHU1GGNbupHqA7EmPrdbMcA3x+9Ha2l60TVmC+76WnThkficdly1VUi3G2veHhzFBdgKctCaQ5SiWI2o1C1UVqAELqo0ahCgi4hGGxIKajXW3N3Y8nNxVpYpwZRChUqrVTprlZqS0gqGDe/HXQ/fQIeO6dTXWSkuKGtKvKhq/bobDHq8gdiXhnrFG2vmrVfQZ4ASUZ2RlUZaRjJX33QparWiprrjweu44toLWbLwDxKT4nn+rYeREiKjIhg4tC8hoWZGjx1OeVllq/OVFpcTFRMJSDxuL526dOD7bxZSWVbFC289wmVXTeHuGx9j1/Zstm3aSX1dPaPHDuOK66ZyxXVT0Wg07Ni2h9qaOpKS45k87SzOn3oGAOmZKUGB04hapSJ7dx6FeSU89dArbNuyi8uvvpBefbtx2dVTGDpiAK8//y4bVm8hJMRMVVUNH7/7JedMPq3FcWJio6goq2L3zhyefOAl/H4/i+b/1sJoX1/XQH1dAxHNclNNmDgWp9PFL/N/Q6VSBYUIQO6eAkqLy0hKiaeooBTpl8E4k+Zk78qjZ5+uPPzMHdjtDtasXE9IaAjPvfEQH3z1KsOPUxQZG9Zs4c6ZD1NSVEZtTR0vPPEmC75b3Op4h4JGo2HKpecwdKSi1WkMpuzZ++9J7rg3nbpmcfLEE4K/VSoV9z5601FNud8WR3NGsgroKITIQBECk4DJzTcQQiRIKRvnkKcD2wJ/LwAeE0I0uk6cCNz59zd5/7isDnYuWosxTKka2G/SGIrW71Gm2VmJrHznR8zRYThqrRSsUabLXpeHrd+vpM+5o1n/+RKiOySChJ5nDMNldyJQIaUfY5gZV4Mdc1QopvAQHA0Oep45HK/Tg1qrJuePrZijLJijQqkvVtQLaYO6sP2nNXQ5cQBS+lFp1Fjiw/E7qsESijEhGYQKd1U5QqtBazLjsVkxxiWBEKi0GnwuF36vG43ZjEoTjpT+Js+qQES6ITZBmTmYzIHqhiH4HHYMsQktEj0COCvLMMYl4nA6UOn0QSG3N1KCNiQUr92Gz+lBJSW6yGjUGi1epwOVSoXaYMSWn021T8ubs77kpx+WYjIbmXTRmSz8cQmTLzm7hdF3944cBg7ty6rl64LLzps6kZ++Xxz8XVpczmvPv8udD13PpVdNxmgy8MnsL1jyywpuvHMGX875jjHjR/DsI69x+bUXsnn9Nm6+8n5UahWTpp7BiOMGsWjBMsacOIJTzhjHN/9rChJUqVTo9TqsDTaW/7aanD35wdoeVRXVSqqTWY/i8/kwGg1kdEilW8/OPPPIa5SVVqDVabno8vOor61n+dJVbN6wncUL/2DE8YO5aPp59OrTnTuuV9KpnH7OSaSkJRIaFoLD4eLJB14OtuOlp2fxxEv38vlHc1nzp5LWfs+uXBKS4jhvykQ+ee9LNm/YzgNP3srGdVsJCTETYjHz9msfB56NxGg0tCkoG+qsXH7tVLZv3kVSSjw9+3ZDpVaxad02cnMKWm2/Y+se0jJS6N67CwaTgdEnDOOHQHR7I2eePwGn082qFeuZ94VSgjo3u4DVy9fx8bw30Ac8xn5fsrLV8b/69DtGjRlCSlqbmu8DUl1VQ1iYhefeeJiighJ0eh0paYkHnfzxQPj9ftxuDwZD215cEZFh3PHA9Zx1/inUVNeRlpFMVudjr3DWURMkUkqvEOJaFKGgBmZLKbcIIR4CVksp5wEzhRCnA16gGpgW2LdaCPEwijACeEhKWd3qJP8wDWU1JPbqQMXOQjZ+tQyVRk2vs0YQkRLD0pe/ASC6QyJl2/Jb71taTdeTB1JXXMXmb/9ArdXQ7eRBhCVHU769GJVGg0otWPXhz/Q4fSgGi4lNX//e4hj1xVV0GNkLBIQlRlO5pxhbRR1bf2j6wDqM6kGHfnH4nI7AbELgczkxxicFy+p6A/Ed+pg4pNeLu1kkeGNuLenzKqlO6moISe+I1hKKz+ND6/UEo9uB1moyQGh1irFcpQaJ4qXVbB99ZAxIia0gp8X+jbm11HoDrqpyRH0tuph4vn7zKxZ8r7j32qx23nntY2beegWfffANF884nyEj+rNi2Rp++v5XzjxvAqecMZbamnoiosL5/uufKMhTJrOTLzmb3OwCRhw3mOLCMl588i3cLjdnnD+BJ166l/LSCgYN68fcz3/kqhumUZBfHIzG9vv9fDT7C2677zqMJgOP3fcCl1x5AaeedSI/fb+YuPgYzrvwdL6c8x2nnDmOkyee0KJzB8UWkZudz7hTjqNj50xcDhevv/hecJTucXt457WPefzFe5j3xYLgfst+Xcn5F57B70tXcv7UMwixmPl98UrmfTGfEccPpqaqZWJGAJfTHRQijZQUlREapgTsrVi2mv6DejF4eH8+/3guIRYlXsLn9aHWKLaQEccPZlvAfmAJDWHq5edhsZipramja4+OaHVaqiprmP36J5x06vH4fL5WKWcGD++HRqtBr9fz6D3PM23GBUybMYmP3vkcn8/PaWedSExcNJvXb+OHuS0FjMPhZOumHRTmFRMdF0l0bGsVVnRMJEX5xYcsSGpr6vnp+1+Z9fKHqDVqrr7pUsacOJwQy/4DGg+FbVt28flHc9mycQennXUiY08eRXxia1fliMgwBh+g4NnR5qjaSKSUPwA/7LXsvmZ/38k+ZhpSytnA7L+1gQeJy+akKqcEa3ktBouJrT8oifs6j+vHjp/X0OvMEUE7hb2mgZDY8GAJ3EbMMWFYK+oo3qDERfjcXjbN/YM+546mtrCSyj3F6MwGBkw+gW3zV9FjYmvDnUqtwutykzW6NzX55XidbSRhtDoRKhUaswUk+JGY0zq0KBTViBAqXDUt04n4HHYMUbG462pw1yv2Ea/ditdmRRcRhT4iCm+j8dvvR6g1waJUQCABoxbp8+GqrlC8wpLTcdfX4nc50YZGoFJr8FjrMMTEo9JoAYmzohRXdQX4JV6nHVN8EvbiAqxOLz9+/2urtldWVAfUPwlMn3kxp555IqUlFUpHKeHbL+cTEhrCSaedwLBRA/F6lfiEbj070bVHp6ABHOCLj78lPiGWJQv/CCY3tFhC2LUju9V5163eSGGe4lDw7htz6Nwti8eev4s/lq7mrVc+4oprpvDtlz9x1qRTMIeYgi6lHbtk0rVHJ2LjoujRqwvPP/4GV1w3lew2StvaGux498q/VVdbz7df/kRYeCjnTz2DzYEguPo6awtVUyM6vRYhBN17daZbz87kZuezavn6YNGlpJQErFYbhfnFnH/RWXzz2fe89pzyuUVEhjFj5sXk7snnkisv4Ie5C7nkygt45Zl3gu7LRpOR515/kEfufZ6i/BJS05NxuVyMGT+SRQsUr8RBQ/ty3NhhPHDH08HrXPrLcj78+jXOOv8U/H4/SakJvP3qx7icLkwmYzCPWSPFhaXc85zi6Pna+08TFRMZTLCp0+sYefwQ6puV9D1Ylv+2ikfubgpgveemx3j5ncf/ksG8LfJzi5gx5eagm/q2zTvJzy3i1vuuQduG6/SxzrFubP9XUJNfxtqPF2GvaaDbhEF4nW7M0aGExEQQGh+F3+vDHB2KrbKeil2F9DlnNNW5ZcEI9PCUGLRGfdszlbIaNHot3U4ZTNm2fFw2B53H98dldTDkspNxO11oDXqc9XZ8bg/OejuhSdFEdUjA63BTtq1lR5TSrwNqkykwC/AAQvGQMrUeaQmVCqFSKzYG2eQ54nO78DQ0jXLVRiMqrVaJ+fC4URkMGGIT8NTVYEpKxVGixKyotDoM0XG4qyuVKoceNzpDWMC1OAy3y4lKo8HndqExW7AX5iopVlQqTAnJeGw2pNcNfr+SUj40HJXBQIeO6VSWV6PT6zjp1OOJS4ylW4/OSL+f7Vt28eKTb2EyGxk8vD9dumexce3WYO1xIQQmkzFYE72ooIRBw1p7zi349ldS0ptGtWtXb6Jn76asuY106tqBndv2kNkxjXEnH4df+qmqrKF3v+5Ex0ZSVFCKJSyEj9/9gunXXcTzj7/BVTdOY8/OXFb+vgaX08Wg4f3Q6XVUlleRlJLQoo46gM1u56rrp/HS07MAJXNuWUkFiUnx1NbUBWdYABvXbuG2+65lxe9rgvYMS2gI4RGhPPHivSz4/ld++n4xnbpm8uBTt6PWqJhx/UV4PF6++vR7Hn76dmqq61pkNa6pruOXBYrNY+7nP3L6OSeRvSuvRQS8w+5g4fzfeP29J3n1uXcpLChmyc9/MHbCaEaMHkxlRRUdOqUz+41PWghLKSXzvpjPPY/e1PTOpiXy4hNvccHFZ/JmoBQuQHJqIg31Tef8Zf5iHnr6duw2B+VlFTgdLv730VxefLupHszB4PP5+Pzjea2W//jtL0dMkOzekd0i1gngi0/mMeXSs0nLSNnHXscu7YLkCFBXXIW9Rhkp1RRU0PucUTSU1bDtx5WEJkSiM+npcfowHHU2jOEhuBpsjLj6dGoKy9HqdXidblxWB5bYcGyVLV8unVlPzh9bKN6YTY+Jw5B+yfrPluJ1uQlLjCLr+D5smfcHLquTjBHdkX4dap+ftR//QkzHJHpMHEbhml2ggi7j+hOaEAFagfT7UOn04POhDglFaLXoomLw1NYoKUt0etQGI7qoaARCye9VURpQRzUJFV10DNLjaVHHRBcRhTokFENMPLbCPPThEQiNFpVOj6uqAp/DHqjVHoorUGBLbTJjiI7F71GKftkL85rydPn92IsKCEnPUtK3AD6nE01IKGrp5aprp7Bz2x6mX3cRn300l9wvFygxFA9czzOPKHXN7TYHv/60jF9/WsY1N18a7BhPGD+SF598K9j26qoaklJa561Kz0qluKAUg0HPRdPPB6BP/x4sX7aKynLlGtIzk+neqwsdO2awaeN23nntI7xeH5FR4Tzw1G34fT7eeukD3vr4OZb88gc2u43X3n+KF598K6giWvDdr6xfvZlzJ5/GV59+z3W3Xs6rz83GZrUjhODsC05l2a8rSctI5oSTRhIdE0WIxYzd7qCwoASX00W3np1RHB4V3n/rMx588jZys/MBQZ/+3bHbnJQUl5GSlsiyxSv584917N6Rw+XXTqVT1yxKisp49vUH2bx+WzAOpTk7tu7m5NNPYOXva1j5+xpiYlu72hbmF5OWmcqjz91FQ72VcSePZtWK9RQWFNOzT1eW/roCt6v1rFnfzF7g9XgZOKQviWlK6dvrb5tOXm4hEZFhRMdGBj28ps+8iPWrN3HNtNvR63VMnnY2UTGRPPnK/WQcYslclUpFUkoCq1esb7G8rTQ0f5W9q0YCqNVqVKojY3v5p2kXJEcAf7OYDr/PR1V2CYVrdwEQmhhN/uqdlO8opOtJA9j45W84aq2Yo8PoffZIGspq2PLtctKHdafT2H5U5ZQGYz/CEqPweXz4Ax9y8YY9dD6xP3FdUyhav4fUQV3Y+ctakvpk4XG6yfl9Cx1G9aJwrdLZVuwqojqvjCGXjccSpsHbUI23yqqkhNdb8LtdOCvKkF4PaoMRfVQs6mgtQqdDqBQvqUaEWo0pMQWVRov0+zAlp6PW6vB7PdiKWs563DVVhISGYyvIhUCkfCPGuETc0q8Ii2YxJY6SQkzJ6diLCzAlp7dQhylIZVYTqDsv1BrURiM+h50unTN45Lm7eOjOZygrUewJpcXl3Hn9wzzw1G3s3plDXU093371EwOH9CElNYmb7rqStMxUfv5+cYuO0mgwMGBIH2beegUOh4P53/5KTXUt51xwGk8/9AqXXjWZTz/8hurKGq69+TJuufsaigpL8Hq81NbWc+OMe3jypft4J2CYBiWl+XOPvsbkS8/lxbcfZdnilQwe0Y8f5y7CbDYHhUgjZaUVhFhCcDiczHrlQ6Zcek4gc24lvy1azqb123A6nPQf3JsfvllIzz5dlQBDAb36dSM6NpIHnrqN0qIy3G4Pu3fmkr07j4/e+ZxLrprMEw+8HJy1xMZFc9t91zLnva/YsysXv89HdWUNbpebP/9YR68+XcnPa+0QOXTEACKiwomIDCPk/9h76/CoznX9/7PGJe6uBBIIENyluHux4i3WFgqUUqNGW0qFeqlSd6HF3Yq7E5JAiLtn3NbvjxUmDKHffWTvs/c5v31fFxcws2bNO7ae95H7vn309B/Siz/3eRp79R/Si0P7j9G6bUsCgwMIDA4gsYGRvXvbQf74aRuPrnyQ2pp6BEEaj5bJZQwa3hdDvZGTR8/yw1e/43A4mD7nXvbvOsLW33cREhpEfZ0Bs9nCg8vmkJgUR2F+MSePSkMUVquNLz7+gY++eYOUVklN1v63IAgCE6eNYufmfW4lAy9vPQOG9v1Pn+uvkJQcT0xcFHk5Be7b5j48ncjov1+w+p/EvwPJ3wF+UUEIgkBU+yQi2yVyfH0jUSwgNoTLm46RMqQTlzcdc8unGCtqOfvDPuK6t6Tnw6Nx2hzknLhK6zE9sBpMqL201BZUkLn3rPtccqWCoos38Qr2Ra5UoPHVERgfzvUD51HqNLQY0J7y64UENZN21NHtk6QRWrUCp6mROGitLEOh98JcXMgtaXenxYy1ohS5VocMAYfBszeCIJO0v+x2TMX5aAJDsJQWSkq/d+puIWlxia6mO1lRFNGGR2Etb+rjYK+tRq7VAZKZ1Z2Pl6mkJr25RFIcFuRytCHhWO12Mq/dcAeRW7glQ/7pe9/QsnVz3lj3PAd2H3VPNgGsfHkZ1zOzuXblOlGxEbzw6gqeWb6G3Ox8VGoVi5Y/QLMW8Wz8ZTsT7htFfW292yLX5XJx43oOn773jcfzZl9v2tfIyS5AJggoFAq+Xf8LGq2a7Rv30Kx5fBPeEEgqwKPvHYpWqyGpRYJbxfYWevTpjEajZvLMsXj76NHqdISGh+BwOPjui984cfgMIJH1nnh+sTSA8Pg8amvq3EEkLCKE++ZM4NL5dJq1iGfMxKEkNo9j/67D/PxtY2nnxbVPcP+D9/HVJz/icDhJbpVEm/Yt0Wg1LFw6m6sXMti5ZT/zFs9g4y/bcTldTJ01Dq1Oy5ULGdy8ns/AYX04dvg0B3YdoX2n1nTomkZiUhwqlYrQiGDsNjvPv/oYcYkxpLZNZu+OQyx/8Dn3Gs6cuMCypxayY5PcY1rM6XTStkMrdxBTKBUEBvpTWSFNwXXv3anJZ/EfQeu0lnzz+zquXLyGTC4ntU0yzVr8/WRJwiPDeP/zVzh04ATXM7LpeU9XOnZJQ3aXEfX/Dfh3IPk7wCvEn27zh1N5o5ia/HLkikZrXfktxq0gNNHgstSZ8A7xx262Yqo2oPP3Jvd4OsbKWjpM68+FX//0sPCISEuk7Fo+xvJa1D466oqryGkwxHLWGrm86Rhd5gxBplCg1Ki5vOWYNBUlE+gwpQ/+gUo3Z8NltwMiglIJLhei04nTakEVEITDbPLICNxWtsUFCHIZurAokElBRXTY7/AwkV6rIFc0mcZCEFDovbFWlkrPewcEhQLRZsFaVYUuIhprdSVytRqHxSypBdfW4DCbUAeEIDrsWKvKsVRVog2LoGvXtnx0Fx8JhVzOvMUzqK2p4/MPv6dT1zSmzhrP91/+BsDbaz7muTXLMZusWC0WvvviN/r074ZySG8+//B71r68jrc+eYniwlLOnLhA/6G93ecWRRG7zfMzVatVxMQ1nRCKiong8oVr/P7zNpY8MQ9vHy+eW7OcyKhw7p02ip+/aZTV6dGnCzs27+PalSxsVhv19QYGDe/Lhh+34nK5GDqqPzHxURw+cAKFXE5YZAilxdfY/NsuFiyZ5Q4iIDXht/yxmwVLZvLZB995iBveN3s876z52J2R7dyynxffeAJRhDkLJb93gNdeeJ9Hn17AC68/gUIuIyDQn6rKGn75fhNe3npKi8tIv5zFlYsZ9BvcE5kgw+F0snrlW1itNqbMGsem33a6m/VBIYGIgiTPXl1VQ8aVLArzSzi07zhPrnoEpUrBzi1NByiOHTpF63YpnD/dqNTcsUtbAgL9KCupILRfMEEhgZQUlxERGUp8YoxkmnaHU+R/FC1aNiMqJoKsa9nkZufjcDhITIpDqfr7NMPjEmOI+0+W3f5V8e9A8neATClH7aXlxp8X0fjqSejZmqz950no1RpBLu0wZAqZpBtyW2CQK+UYymtI336KthN6odCoiO+dilegD+d++ZNWI7thKKvBaXcQmdYMp91Bsz5S6corxI/8U02lGyqziwmICyVj9xn3c4kukXM/H6bPg4ORUduwZhXasEicVotUJlKpEUUXTqsFuUKJOtQfQ142gkyGTKV280FEB5iK8tBFxqINj8JcVowuJAJrVbl0LqUSXXgU5vJitMHhmMuKpaAkk6ELj8blcqLw8kGmUOIwGhoDkCCg8vEHQYZCowW5HAQBW20NmrAITEX57iDoNBtR+Qei8PFF5RuApayYcLWTFSsX8uIz77jfi3vvGwUi7N3+JzeycgBpZ9ujT2d69OnMkYMnMdQbUamVHD98ht9+2OJ+bEJSLOOnjODX7zdz9eI1OnVLI7VtMiBgqDeye+sBtDqt26BIEASmzBxH2w6t2LF5P1NnjefHr3/H5XLh7ePFoysf5MThsySnNOPEkbPuHo1Go2bVG08QEiq5MoaGB9M8JZFFcxqHFbOuZTNp+hjmPDgVQRDIupZNSEgQk2eMxWq1YqgzUVRQip+fDzVVTUd90y9nkto2mZvXc+nTvzt/7jtGRFQYWRk3m/Q/tvy+G5vVhkKpoE//7hzce5T6OgMymZzL59NJad2cnOx8rmdmExMXSZ8B3dF76Tlz4jwqlQqVSkFIWAhmk5kHl82mtESSoU9KTmDe4hk0S4rjy09+5LcfpB6OQiFn6ZMLePe1T7FabRzcc5TY+ChU6qYXa72XnqjYSM6fvoy3jxcPL7+f1mkt0em1zFs8gw/Wfu6WhwEYOW4wLVo1Iyw85K49ib8Fk9HE5x9+x6fvSwKVMpmMNe+sZMio/v/Px9ltdtKvZJFzIw8fPx9apiZ5SMv/X8S/A8nfAXVFlZKhlEyGoawGQ0gNqaO7o9SqyD50mTZje1J0MZukvmlk7T/vflyLgR3IPZkBwOVNx+g0YyA1+eXkHr1KaPMozDUGii/nkDyoAxd/P4yhrAaAmE7Nie3akvqyGneT/xaUWjX1ZTVuY6xbcNod2CwONHIki1vRhfk2PxFBrkATFIKtoZ8h1FShj4rDbqx3+4bcDofJiMNkQBMQjKmkQHI5DAlDdDgwFuajCw3HUl6KJjRcCkYNtrjmojxp1FgQJC7JreAqCBgLciQvd9ElKQ43jCSLDkcT9rutuhJ9TAKWsmJkKjVqvwDu6SQj6fvXKSqvRaFS8sMXvxEY5O8OIiAFiFZtk4mJjSQ8MpQrFzLw8vLij589dauys3IZPPweAFJSm1NSXM7Ty16hvs5AXGIML77xBJ9/9D0jxw9m8WNzMRpNHNxzFL23jj3bD5LcKolX3llJYX4xdbX1PPfYqwSFBPDo0wt5aNYT7uexWKx8/uH3RMWEc+rYeQz1Rp5/bYXHGOvwsQN56ek3EUWRrj07MGHqKHZuPUBhfhHd+3RGEAQun0/n/ofukyRu7kCnrmmEhgVjtdrIzytk9L1DuXDmMi7nXUqSojRyu2XDLhY8MpODe4+SkppEcEggn+z/hh++ksyo2nZoxegJQ1m57BWCQgKZ+cAkEKCurp7Vz7zlNvyKig7nvjkTeH7FawA8/Oj9XL3UuAFyOJz88v1m+g/pzbaNe5DL5VSUV9EmrSWF+cV06tYOl9PFvl2HGTisD3/8vI15i2dgMVuwmK3U1NSi02upra7jxJEzHq9ly++7aN0uhUP7jjH9gYm079TmP2Vzez0zxx1EQCplrnpqLantUoiKjvjLxx3Yc4TlDz7vLld27t6eV95ZSfBdeC7/V/DvQPJ3gEwuI337SZr1bcu1nacovpxD8ZUcUkd1p7awgqz952gzvjcyQcAvKhinw4nNZCH/dKZ7Siu2czLp205S28BKL88qJLRlLBFtEijLKnQHEYC8U5n4x4WRPLA9x3JL3c14fbAvvlFBmCrrkCsVOG+Tr1DpNehD/FFrpH6O02pBGxqJw2yUbHSdDsljpEFrS3RIEu4yhRKXQgnWRm0ukPS51AFBiILEMJcpVciVKgzFheB0YC4vQRceLRlbmY2og8Nw1Nc28lVEEUtJIfrYRGzVFdjrJNtcpbcPLocDlbefNApsMSNX3uXHLwg4LWb3H7uhDl1QKDH2YsIjkrhw9Sat27f0KGu079SGlm1a8PmH3+OwO4iMDufx5xdx+sSFJsJ4IDWiP/zmDWqraxEEmHb/vWRdy6ZFy0Ryb+YzcfoYjPUmvl7/Mwsfmcn1zJv06tcVnV5LRXkl505f4sevGg2k6mrrOXvqskeQAMkYqnP3dtTW1BEVE4GPtxcLl8ySLnqiiFwhd5do7r1vNGuee8dtF3z0z1NMv/9efHy9eeuVj3jns9VMnTWeX77fhN1mp22HViQmxVFaUo5KrWLbH3tonpLIoOF9adUmmW0b93i89h59ulBXU0+rNsnu92z2wils27TX7SAJkhxJ737dQJBMsdKvZHI9I5uQsGAP18iC/GKqq2ro3qczRw+exGZvOqVVXFBCn/7dEASBUROGsPHXHUyaNpro2AjWf/AdCqWC6fffy5WLGRw7dJpjhyQF72n330txYSkRkWHIZE0DqCiKVFVUc2j/CQ4fOMlnP7xFp27SaHdOdh5XLmRgs9lIbpVEcqukJiWwqkqpF6ZQKhg0vC+R0eEY600Y601NnusWykorWP3sOx49r5NHz5JxJevfgeTf+Gs4rNLuOqZjc/TBvqSO6o5Kr8ElingF+iBTyDHXGLm+7zxRHZqh9tbhHejNqW92Y6pszCZ8o4LQ+XsT0daJXCmnPKuQ0qu5xM0ezMU/jjR53vqSavJPXaPnQ6MxlFZjNVqwm62c/GIHyUM6025yXy5uOIzNaEHjo6PzjAFgrsEpajymqFR+ASj0XjiMBnDr9ko/AlEUsZQVo4+OR1AoQBSx19ciyOQIgoC5pBCvmAQEH39cTicOuwNtSBgumw1BocBUmOfutQgNF/474bSYsdfVovIPksaDqyo8MiBNSDiuu/Rh1AHBnpmS+2IocO78NX79aQcz50/GWG8kpVUS6Vey6NG3s1tQECTOyKfvfUPHrmn06d+NA7eZRYWGByNXKnj3ttFcjUbNs2uWs3LZK7hcLmQyGUuemM/8RTOoqZYa4ds27mHWvMkcPXSK7NsyoVu4ciGdyOhwj0DSq19Xd7lm8owxLFv4rNtvY8CwPkyYMhKlSklQcABlpRVNPOc3bdjJ6ree5s+9x5DJ5RQVlDBr3iRkMjlZGdl88dEPLH/2ITp2SePon1I5z8fPh68/+5lVbzzB7q0HsNlsdO3Zkd3bDnD5wjVWvfEEEVGhFBWUUFJYRmb6jSavJTP9BstXPsRzj72Kj583Gq2GvJymE17ZWblERYez7KkFuFxNlQ4GDO2NQqnglXdWUlpcSvdeHTlx9Cybf9sFgM1qY/2671i+8kG3n7pMJiMoOACNRnLWjEmIJio2ArvNTs++XTEajBQXlZGXW0hKahIhocEc2HOUTt3acT3zJg9MXkJVpfT9UaqUfPb9W7Tr1NpjXVHREWj1WhY9ej+//bCFbX/sISDQj45d25LYPO6u5TKrxeoexrgd/xVS5P8m/DuQ/DdhrKijvrSGiuwSMnafJapDEn4RQSi0Kpw2Bx3u60d5ZiEyhdztMyJXKegyezCXNx2jrriKoKQIFCol53866N7JxHZJJqRFNEq9hpAWUeQe97Tr1PrquXm4HKfNztVtJ7DUNe6Srm45To8FI4jrmoJ/XChV2SWovZRofH0l2ZHbYKupQhsagcNolHzTb3FEBImMqPDyQXQ6cDXIx+siYhBdDWUxUcRWX4vLYkHh44sMMFeW4XI40UVES1NXDRmOy25r2pQH5CqJM6D09sFltTQpo7nsNhyGekmWxXVL0l4ruSzWeF5QBUGg0qXig7e/ITengOi4SCbPGMvSpxaQfT3PLc54Oy6dT6drzw4kJMWRktqcQ/uPk5AUR1xCNMWFpR6juRaLlR+//oPufTpxeP8JXC4X69d9x8Tpo0lu2YzgkEAGDuuLSq1k1rwp3MzOc4+k3kLb9qkolHKuZ2RjMppp36kN46eOZN2bX/Dw8vslo63bWOt7th1k2Kj+PPr0Qr7+9GciIkNZ8MhMBEGgpLgMmUyOX4APv36/mdS2ydRU1jBszABWPPwCaR1T6d2vGzFxkcgEGXMWTqF3/25kXM1i3Zuf43Q6mTVvspQNyASKi8rclrm1NXWIThd9B/XE6XAybvJwXnvhfY+1xSZEs2vrfrr0aE9RQQlGg5GOXdt6CDOCRNL85P1vCA0LZv4jM1nzzjMcOnCCsycv0DqtJWmd2vDTV7+z/oPvCAj048W1T7KxQVPrdly9lMlzax7jyMETpLRuQW1NHRFRoezcsp/P3v+GR1bMI/1yJpt+24Gvrzf3PzQdk8mExWylML+YlNbNKSku4+ifp9xBBKSexhcffU9q21UejfT4ZjGs++JVXnx6rZs0WVVZw2MPvcBPWz+9qyticGgQA4f18dBvUyjk/1KS7/8I/DuQ/Ddhqq5HJheovFFEZFoiTqudy5uPofXzwmmzk9SvHUHNIjj9TaNOkNPm4PzPf9J2Ym+s9Wa8Qvw4/slWj3Q498Q12k+5h7wT14hom0BVTin1JdJOJ7JdM+rLqiVveJfoEURuoTq/nJqCCmoKK5ApZWi91DhtlibHASCToY+JlxrtGomlrvT2xel0oNR7e5ANTYW5aMOi3AFHdDgQXU4sJYVoI6LRBIUhyKWMRRMk6QYJMjmWyjK0IWGSz3tD9qDyD8Rps6EODJFey110uRzGepS+/ljKihHkCmRKJfb6WvRRcY2ERUBQKMkpruKhuc+6CYKnj58nNj4atVpFZvp1EpPimpy/eUoicYkxaHVaPnvvG0ZOGMwPX25g99YDTLhvVJPj83MKGDC0j/v/tTV1pLZN5typS8xeOBVDnYH3Xl+PSqXk1fefZcDQPuzZLumA3TOwJ+06pnL1SiaLH5tLYHAAhnoj6z/4Dq1Wg9VixcvHi8kzx2KxWFGplGSmZ3MjK4cOndvwwmuP89qq98m6JmUHoyYMwWy0uBvX+3cdJik5geVPP8iLbzxBVWUNb73ykXutrdNSWLhkFqVFpTz86P2EhAWRk53Hpt92knUtG28fLxYsmcXal9ah1aipqzfw+ksfYKg3kpLanOdefYznV7yGKIoMHNaXksJSTh49x9RZ4zhy8CQLFs/kyqUMho8dyM7N+5DL5YyfMgIvHy/mPTwdl0skNDyYXVv2cz0jm8HD7yGtYypL5q10r7GqsoZTR88RnxhDxtVGHhNAcGggq599mxffeAKVRkWr1i04d+oSu7buZ9L0MdzIvOmeNKsoqyI3O5/vv/zNLUOTcfU6ZpMZ/0C/Jp9rYUEJdrvDI5AY641YrTYGDetLZUU1v/+0FYfDidPpJD+38K6BRKNRs+ixuSiVSnZs3kdUTDhPvPAIScn/ekKLf0/8O5D8NyG6JG/0ViO7ovHW4bA7CWkRTV1xFQqNCrlScdfxQ1N1PTV5ZcgVChzeWmwma5NjXE4XcqWCixsO0WHqAKwGE6ZqA2XX8ijMKEDr74XaW4vWz6vRmhdAAI2vnuhOzbHWm4lqHY2pKA91QLBki3u7A6JCCaLE4XBarchUahRePrjsNuRqDbbqpgqvDrMRuVqD02pBofPC3JBF2KoqkKlUKPTemEuKPLIbbVgELqdTymgcdmRKFXaTAUtpYUPpTInLammyPhCQqzWofP2x1VbjQpSk7hVy9NHx2OrrqKk3Y7C5mDl5GfcM7El0XCR2qx2TyYxMJvDpB99QUVZFr35dGTluMJs3SIKH3j5eLHhkJvt2HqIgr5j5S2ZSWV7tLs/4+zfVqOrepzNnTl5w/z+hWSx7tv3J5g07EQSB+MQY7psznq8//Zml857hmdWP0qx5HAA6vY4P3/6SC2evANKo8FMvLuHcqYtuBvyEqSN548UP3EG15z1dSG2bQlFhKbnZBe4gApIf+UfvfOWxvqxr2Zw/e4X4xBgPO1yQsq+C/GLCo8L48K0vqK6qRS6XM2XmWPReOs6fvsy5U5d47JmHcDidrH2pUW8s/XImG3/exvOvPkZRQQknjpzl3OlLtOvYmmbN43G5RJ5Zvoaho/szctwgJkwdyelj51GqFGz4YQvXM28yafoYXnrqTTePJePqdTpcbOuhvwVw/MgZ7ps9nuOHz7hlRGLjo1AqlRjqjZw4eo5Fjz1A5tXr1NbUoVQqOXf6MmdPXfB4vYKAO4jcwvdfbuCT797kTkyeMRadvtHeoKSojNXPvs2B3VJZOTo2koVLZ7tLowFB/k3OcQux8VG88NoKFj32ADqdFr+7aJ39X8O/A8l/A+YaAyq9hitbjlOanodcKafVqO5c/K3xR6HUqekye0iTx3qH+WOsrMcr2BcEGd7h/tQXN9ZWBZkMu9mKV7AvQc26YTWYqcguRuujJ7ZLCgGxociUCgwVdbQd34uzP+7HZrRIisNje+AbEUj+qQzKrxcR3lwaPbRWV6ALj8ZWWy0ZWWn1qHz9MZUUog0Jk7xEfP2wlBUhOhwSOfEujW5BLkdQqdH6B7qFG0HigQgyueRYeJuMCqILl9WCKLqwlBaBTAYuF9rQCGggOdqrK9EEBiNXqbDV1uAwG1HovFH5B2AqzEOmVErGVi4XgloDDiciUkks+1o+R4+e4/HnFvPj17+zfdNedHoti5Y/gJePlztDObTvOB06t+HBZXOIjY/iRlYOm37dwaQZYzl/+hJPLXmZvgO6M23OBH746neOHTnNg0tn8/VnP2M0mOg3uCcjxgzkWIMuV2JSHJOmj+H1lz5g4rTRBAUHYDBIu/cOnduQc7OA8rIK98X+wWVz3EEEJAb2z99tYtKMsfj5+5DQLJaXV76FKIq0Tkuh78AeWCxWKsoqQSZw/kwjf+L/CVFEJgge2le3YLPZ+fW7Te6GuNPp5NvPf+Xh5fdz/vRl8m5KGVfhbY31Wzhz8iIjxg12vx7/AF+Gjx3IcyteczfsN/y4lX6DevLzt5vQ6jSER4RyPfOm+/j8O1jyZ45f4InnF3sEkg5d2qLTa3nlnae5npGDyWiipoEHBKDXa3jv9U/Z8ONWXn33GbZt3EPvft3w8/ejML/RtuBu5D6VSklwSCCvvf8c77z6CWaTmVnzp9BvUE+P406fuOAOIgD5uYXcyMwhMSmObr06kvQ3pNxVahURf0dJlX91/DuQ/DdgrjNRV1rtFlsMaxVH7nFPET+7yYqxopa2E3pzeeMRnHYnOn9vUkd1J+vAecJSYrCZzKSN783FDYepLapE7a2lxcAOGMpqsNSbCIgL4/LGxkaw2ktL+yl9cdpdlGcVUnIlh5ShnXE5nHgF+3J5yzE6xoURkBBOcItoZGoNLpsK0W7DVJSHNiIGpY8fDkMdpuJ8VH4ByNQa5FqdxEhv6CU4zSbUfoGSQOOtspMgQ+XjhyBXYMi9fhvhUEDtH4SlogzZXbwaXHYbcp0ebWgELodDmsiy29GGhiNTKKS12Cyog8JQBYWgcjqxlJdizMtGExQqCUjKFdK0ltmETKHAWlWBTK6gS8eWhIcF8+rLH7nLISajmVdfeI9X33vWYx1nTl7kwrmrvPzmU3z2/re4XC7Onb7MuEnDUamU6PQ6Ulo3Z/VbT1GQX0xUdDhvf/ISTpeLI/tP8OSSlxk2egB6bx1xCTF89+WvDBs9gKuXMtyKu0nJCTzw0DTOnryIKIo89Ogcvv7s5yaOgwB5NwuYPGMMzzy6htfef46qyhpCw4Lp1ruTx2BA3wE96NG3s4eXysVzV+k7oLvHkEBsfBSVFdV07JJGv8E92buj8QKtVqsICQ0iJ7upL8gtImfn7u05vP8Y/Yf0aXJMZHQY9XUG3l2/mmtXrmOxWHC5XOj0Wgz1RuRyOTPmTuRGVi43Mm/SpUcHRFHi0dTXGZDJ787a9vbxYtlTC9m+aS8JzWIYNLwvq595m8z0Gzy68kE+fvdrd6BSq1V06taes6cuMnXWOHewrK+rZ+7D03hqycuYGt5nl8vVRPRy6qxxXDhzmdS2ybzyzjPIBIiOj8Tf38/9PuRm53HhLkH76uVMXnn7aWLiovDy1t/1tfz/Ff8OJP9FuFwuavLLsN7Wn5BsbZuONxorapEpZPR8cBQ1hZUYymo4+dVOEnu1QaaQU3DqOk67g+QhnVDp1SAI7t5JfUk1N494fqmtBjO1hVWkbz9JTOcWhLaMJWvfOWK7pJBz7CpxXVty/pcDxHZKoehSNoayGqLaJxLVOhKZtRZzcT5esc0QFArUQZIkvKPB0MphuIPQJoAuLAqX0yHJwisUGPNzEBQK9NEJOAx1ksFVg2qvIJej8PHFbpCmmCTmvIjSLxB7daX7dgB1UAiCWoPLYUcbHiVlOjI5ggjmshKcFum9vUWGVAUEI9dokMnlbu8UF5KMfUR4LNeuXmf+4pnSZyGXUVNdR0hYMPHNYj3sW0eNH0xtTb374lRbU0dYZAhPvPAIZrOFQ/uPs2PTPvfx4yePoP/QXnz92c8Abla8QqngmdWPUlpczsZfJB6KSq1i5LhBPL5olfvxvn4+vPDqChwOJw8um8OJI2c4c+ICgiAwd9EMaqpqmT1/CiIibdq3ol3HVL7/4jePj+HAniNMnD6Knn27cPiA5C+j0Wq4Z2APouOiuHT+KkktEujRpzP7dx3hq09/YszEYQQFB7Jr635i46MZPPIezp68RGx8FLk3CzzOr1arGTqqP06Xk20b9zJu8ghGjB3Elt+lySmlSsnSJxei99bxzppPaNUmmd9+2Iyfvy/33jeK8KgwIiJDuZGZQ3FhCcuefhBRdJF+KZNR4wcTFBKIw+kkrUOqR2Y1cFgfNvy0lTMnLvDCayuITYihtLicvgN6cM/AHpw/dYmlT86nqKAUrV5D527t2fr7LjZv2IVOr+WND15g9IQhtG7Xkn07D/PQ8vvx8/ch72YhgSGBzJw7kfy8YspLK+jUrR0H9hxBqVLx8btfuUuYbdu3YvXbK4mICmXbH7v56pMfGTKyKemwT/9upKQ2/18rY/KPxL8DyX8RhrIaTLUmvPwb5ddLruSS2Ks16TtOuW8TZAJafy9El0j+mSyyDzf+iLR+Xhz/fLubgV6anke7SX3xCvFD7a2l9GoecqUCh63ptJF0coHcE9foNm84flHBZO49i29kIPUl1YSlxHFl8zHsDVL1mXvOY64y0HZsF2RyAYfZ2MAXUSBTa7CUFIEgoPTyRmW3Y6uraSgliThMBgS5AlttlbtRLjodOEwG7GYTTqMBRBdKHz+U3r44zCb0sc1AdOIwGiQPErmi0aekAdaKMrRKSWXYZbNiq6rEYTJIApJBoRIp8faGukyG02xqGFW+A3YLy1c+yGur3nf7ssfGR9GjTyceeXwu505dpqighGbN47CYLWSmezZyvX28eXzRKh5cOtsjiAD8/vM2evbr2uQpHXYHAgJ+fo12wz37dmHrHQ5/tTV1pF/O4utPf8JqtTFs9AD6De5Fr3skKZQTR86i02uZ+/B0xk8ejtlkuWtZqrysCoVSwYJHZqJUKVFpVDyzfA3+Ab4kNo/nz33HuHE9h3ETh1NdXctnH3zLsNH9WbBkNoFB/ly9lMnvP21hyRPz+fjdr6mplnoksxdMISU1iX07D3H1kkSQvXo5k4HD+tB3YA+qKqvxD/CjrLScAIc/FrMFhVLO+Ckj2b3tACaTmYwrWaxe+RYg9RMGDO3DgunL3cx5tVrFc2uWM3BYHzp0aUtJURmRMeGUFpeze5s0jPDdF7/x4htP8N3nv3Lh7BVkMhnDxwzk0vlriC4nvft3Z8eWfWzeIAU3k9HM1UsZKFXKRu+Q3yCtYyoRkWGsX/cts+ZP5o+ftxEQ6EeLlGZkZ+USFBzgMaZ84ewV9u44SPc+XSjILyYpOZHwyFCGjOrPjk2ScVnrtBTGTxnxXwoiuTcLOHboFDcyc+jasyMdurTB7y79t//N+Hcg+S/CZrIQlBCGtc5Ei4HtuX7gIjajBafTRdsJvcg5lo53qD/hqXGYagwExIZScaOI5EEdsdvsBCaEUZae7yGZAlB0MZvkwR259MdRqnJK0PjoiO2STMbuRvFGmUJOULMIutw/BFNlPXazFZ8wP9pN6osgl3H2+33EdUlxB5FbyD93nRYD26H1kmMpK3aXqzRhUegio7GUlUjCjWoNXjHxmEqKcFksaIJDpUziDtKe02xC7euPqSHLUHr5INNoJW90l9Nz1LiiDG1ouAebHqSAZK+vxV5fg8sqDRw4LWbMRfmoA4Pd2YgglyOTy3E0ZD13QiaXc+zQaXcQAShqUL8tKighrUMqLVomUltdR3BoEKuefMN9XL/BPTl9/CzdenW8a/nF5XIhulz4+vl4CCe2bteSwwdOMHR0f7RaDWazBa1O47GGWzCbLSiUCqxWG9s27mHtR6vYs+0gJ45In6vJaOadVz/huTXLiYqJoGPXNA8Zc7VaRVxCNPdOHcX3X/5GXW09rdu1BCR/kFvHBgYFsHPrfg7tO85Tq5Zw7NApUtNSKMgrIjIqjBdef5ysa9k8/dJSqqtqqK6skbxF5DJ3EOndrxtHD57izZc/ZO2HL3DkwEkO7T+Of6Afz7+63N3rKS0uZ8WzD6NUKT0ysPzcQr778jeeeflRCgqKUatVnDt1icsXMmjVJpkvPv6BqbMn8NHbX2K7TRvNbLJw9uRFLp67SnBIIHW19WzesJOHHp2DIAj4Bfhy4cwV5j48DUEmQ6lU4Bfgy7q3vvB4r8+fvkz3Xp2IS4ghJbU53Xp1pGOXtuzYsp/Y+Cgy05sakl08l45/oB+CIBCXEMPXn/1M996dePqlpYRHhtKmXUv3xd9QbyD3ZgEul0hsfBQ+vt5NzncLJUVlPPLAU24hz5+++YNFj83l/gen/p/KbP4dSP6LMNcaqSuUpFGcDgctBnWQ9K9OZ2IoqyahZyo1heVYDWa8gnwREVF7acncd47Wo7tzY/8F9MF+Tc4ryGRY6k0EJ0USmhKDIBOwm22kDO1E8aUctP56otolcerrXTjtTpLuSePGmUwM5TV0mj4AU1U9IS2iEO7C9JUrFciVMncQkWu0Un9EpcJYkOMOFE6rBVNhHkofP6wWM9aqCtSBwVIT/fbzabSILidKHz9ERAS5HEtpIU1ExUBquNttHo6JCi9vqXkul7uDSOPhTuQarSSjIpcjU6ol9r2xHk1giLTeW++ZXA5KNdeuZHmc44EH72P1M2+7CXxarYbFj8/FP8CXV95ZybUrWXj7eNG6bQp7dvxJfZ2BkLDgJszzhKRYBEFg/uIZHNhzlGtXsujaswPT5kzg8w9/IOdGHo89+xAbf93B5fPpjJ4whA/ebDTvlMvltE5LwcfHi6uXMjiw5yhymYyDe49yJyorqgGBbr06otNpOXb4NHEJ0YybNJzM9GxCw4IYNqo/mRnZREaHI5PJEEWR5FZJgEi/wT35rEHW46N3vmTtR6u4dO4q3t56Mq/doGvPjmRlZPP5h9+7J8M0GjXJrZK4Z1BPBg3vi9FgoqqiGp1ey/kzV1BrVTz2zINcOn8Nk9HCzRu55OcWMmHqSKoqqigubKrkfO7kJYKCAtwGUWMnDSOtUyqnjpxj8WNzEQQB1x0uj2MmDiXnRj4PPTqHwvxiAoMC0Oo16L30iE4XP339B7PmTfLIOp9+adldVQlERGbOm0ReTiGx8dHYbHYmThvN9k17ad0upQnXpXP3dnz18U9cz7yJQiFnwn2juHblOn7+fsTGR7uDSFFBCa+teo99Ow83PK49z61Z/pdWvpnXbjRRg/70va8ZOqofUTF/LbPyvw3/1EAiCMIQ4B0kz/bPRFFcc8f9y4AHkDzby4E5oijmNtznBC41HJonimLTof9/INReWvLPZmGtN9FiQAdUXhoEBHSB3qj1Ws79dMB9rHeoP23v7Y1cqZBKT2U1VOWUktCrNfmnMyVpEgABwlLjEBC4eeQyNpMVQSajxcD2lGUWkjqmO5U3ijj1zW73dfrKluO0HtODS38c4eaRq8R0akFYSgy1RZX4RQVTU9Aord5hyj1ofXXYhUDs9XWofAMwlxSiCQlvkm247HapAa/R4rSYJW6Jj18DYVBA5R+Ay2ZFrtXhsllRBwQjii4cRgNKb19cjqa9IgQBbXikRCpUSaq+osOBwsvbTVy883iHyYTKxxdzcT6IIurAYJDL0IZF4rJZpZ6KXI6lrIQRYwfx/tr10nvu44XBYPJggZvNFk4dO8f4ySOora6jbfuWVFXW8s7rn3L1orQbv5H5No8/t4idW/djNluYMms8cpnA1UsZaLQapswah4+PF5UVVdTW1pOalkJEZChPLnmZ7n060SKlGdVVNTzw0DT27zpMQKAf/Yf24f031pOfW0jPvl0YOW4wRqOJuIQYdxZwC+GRYVSUVfL+G+tp074l02ZPoLCghN3bDuLj582Rgyd597PVaPOL8Pf35cOvX6Mwv4Q/9x1DLpcTEORPRHQY2Vm51FTXUVlezZurP0KhkHP/g9P48K0vGDZmAF7eevbvPEx8s1gmzRiDgMDgEffw1isfueX4u/fpTJce7Tl26DRKlYr4ZjE8vmgVarWKyTPHoVKreO+Nz5k8Y0yTjzq1bTLXMxoz0t9/2kZ4ZBi/fL+JX77fRK97urL2w1X89M3vVFXW0rt/N5xOJ14+et5/Yz2CIDBv0QwKcos5d/oSKa2SGDziHn78+g+PjO/UsbN06tbOYwghLCKE5JZJnDh61qPX1L13J8bcOxRkAnk5hRw9eBJBEBg0vC91tfXu6TKHw8mFM1eYOW8SRQUlXL6QjkwmIzo2giMHT7qDCEjyJ7u3HWTOwqlNv+/gdqW8HXa7465mYf+b8U8LJIIgyIEPgIFAAXBKEIRNoiheve2wc0BHURRNgiAsBF4DJjXcZxZFMe1/cs234HQ4AZG4rilY6kzcPHoFja+eyLaJxHRK5tC7jfpKCOCw2aktrCTvVAYpQzpTlpEnOReev06XOYMpvnQTl9NFQGwoljojGbtOu3klosvFtZ2naTflHowVdWTtv9Bks++w2BAEgZqCMtTeWiqzi2k1oiuBiREYy2sx1xqJ65SIYDdguJmFIFegDY3AWiuN7t5VZrvhwi5TqVH5B2KuKEMXES35hYgi9roaafLLZEIdFIog4N5h2g31aEPCMN+RwSh0XjgtZgSFQspOBAFrbRUOsxFNcJiUKTVA5euPtaoctX+QlH00BBlzSSHa8GjMpcUIcpkUhF0ukMno278LN7Jy2LX1AKHhwRgNTfsMhfkl1NUZ8Pb1Yvvm/bTr2NodREAKNq+tep93169m0287+ez9b+jUtR0iIp++/y2PrJjHz99tdO/CH3p0DoJMhsvl4vD+E+7z+Ph68+IbT3D21EVeX9XICD984ARPvLCYvTsOMXLcIG5ez8VsloiiHTq3wWw2U1pSzqLlD/D1Zz9z4cwVUlKbM2zMAN5a/REul4vC/GI0Gg2vrnqfWfMm88ZLH7ifd9/OQyx7eiFrX1pH34E9qK2Rhie8fbwwmUzMf0TyWu/ULY3hYwZSWVGF1WJDo1Gxc8s+D0+XowdP0rZdS376+nccDiet01IYNWEIm37dwaVzV7l5PZfK8iqyrt1k0PB72LVVkn6PiAqjS88Obh5KQJA/Pfp2pnlKIvMWz+DqxQwO7T9Oi1aJDBzeF51Oh8vppCC/mO8+/xWAwSPu4dD+42h1GgYO7U1FeRXffPYLaZ1S3dmEUqUkrUMqdruDqJhwzp2+TJu0lqS0TuLa1eseOmcg6ZJ1792Z0pJyxkwcStt2LRFFEf9AP1574X33cd4+Xgwc2ocnFku+Nb5+PnTsmsaKZx92WzLfjiuXMjh76iK7tx1EJggMHN6X1mkpyOVyEpvHNymJjp08/O/qtvivgH9mRtIZuC6KYjaAIAg/AqMBdyARRfF2U4LjwLT/0RX+BSz1RnJPZKDWa8jcI9W460urqcwupufCkW4vkvDUOAITIzCU1SAA8T1acWnTETrNGETusauUXM3FVm8hIC6Ukqu55J/OJGVoZ8zVTZvJSpWSipvFeAX5emQZIJWsRFEkpEUMldnFDTa/J4nt3hKvYF+phGE3uuVHRKdDkoKPjkf08sZmNKAOCPLQ4NKERCBTaxCsFiylxehiEhGtZuQKFS6XE1VgiKSfZTZhq6lE6e2LTNlgkSq6sBsNkhtiXQ2CTI7SxxeX04m1osxtWCUolGhDwjGXFuG0WdFHS+x6QRBwmE04DPWIDgdKb1/sdTVuvS9bTRVKbx8cxnpkCgUumw1NYAg6o5XqqlrmLJyCzWYnKLipSF73Xp3Q6rQsf/A5HHYHze7Cdp88cyyPPfS8m2uRdS2b/kN6kdYxlU8/+IaJ00bz5cc/AvDxu1/z7meriYwO8+AwdOvVkZKiMjb/ttNDVgSguLCU44dPc+7URWbMlfZFkdFhHD98hpeeepN5i2ew4aetvPXxi5w+cYHM9Ou8/crH7hKOSq3i7affJCU1idPHPSVYRFHk8vl0Xn7rKdRqNVnXbhAZHc69943ii49+4JvPfiEmLoqlT81ny++7iWuY4CouLPXguNxCWWklPr7eVFXWcOl8Or36dQMgPjGGAw2luV1b99OpWzseXDqboNBAFAoFn6/7jrCIEOY+PB2NVk3uzQKefOQlzCYznbq1Y/nKB1EoFLy+6gNMRjPBIYE8u2a5eww5Nj6aZi3iOX/6Mp998B3hkaFMmTXOY+x2yIh+/PzdJnJu5BEVE0FKahIZ6ZJBmcvpumvJy+F0EJ8YTWFeMR27pVFdUUN4VBit01Lc02SDR9zDL99tYvjYAfTu352c63mkX87k4N6jDB8zgL07/vQ458ChfZgz8RH3833/5QY+/+kd2nVqTWx8FJ98t5bvv/yNq5cyGTFuEENG9PsPe5qUFpdxIysXQRBIbB5LSOi/phz9P7PbEwncPtBe0HDbX+F+4Hatb40gCKcFQTguCMKYv3qQIAjzGo47XV5e/leH/adgrbeg1Ki4ceiSx+0uhxOLwURMp+Z4hfjhFeLP5Y1HyTl2lYu/H6b40k3iu7UCUaT0msQ9qbxZjMNmJzKtGaEpMXiH+aP182rynIJMIDItkYTerZEpGpvN/jEhmGuNhLSIRhfgTX2plGXUFlXiFxlMTW453sE+OI11Tc7pspgxl5eg9vbFabOhDY1AFxGNPiYBp8WEMec6LpsNfXQctupyTEX5mMuLkSmVyBQKTAU52OtrcRjqMRcXIDrs6CJjEOQSL8RhNqENiwRBhrm4AAHRw/VQdNhxORzI9d4ISiVOqwVLWTHm0iJ30HM57MjUarThUah8/FH5BaIOCkEdKP1R6L0la15RRKPV0rpdCp++/y3b/tiDj583S59cQECgH1qdlskzxxIZE86Vi9fcJYesjGw6dGnr8b74B/p5KNgC7N91hE7d2mEymlGrPT3FS0vKWfX6E0y4bxRpHVOZOW8Snbq1IzYhivadPc8NEBDoj8loprqqlo/f/YovP/6BwvwStm2Upr3On77Ew4/ez7q3P0d0udi97aA7GLXt0MrdoBYb5PfvhMPh4MuPf+Slp99Eq9MyesIQ3n/jM/euOC+ngDdf/ggvLx1lJRV06dme1LbJdO3Zscm5QsOD3YKUcCsbl963uQ9NY+6i6fTq15UzJy6w7q0vqKqo5sDuwzz90lJGjB3E5x9+j6HeyCfvfu3m0Zw6do70K9fZ9NtOTEbptvKySn7/eRuDRvQFpPLUqWPnOXzgBKIoUlRQwtuvfExoaBDJrSQPmOYtExk+ZgBzF01n9L1DsFntZFy9TkCAL2ERIaSkNvd4LSGhQURHR/DaC+9TXlZJqzbJhIQHk3n1OlNmjeP5V1eg1Wnx9vFi6pzxRESG8+m73/Dh219yYM9RVj/zNocPnGTijLHucw4fO5D9uw57BC2n08nG3xq1wlJSm/Pcmsf4ZsM6Zs+fQnhkaJP3+W7Ivp7LA1OWsWD6cuZPe5T59y0np0EL7V8N/yua7YIgTAM6ArezpGJFUSwUBCEB2CcIwiVRFJtIlIqi+AnwCUDHjh2bijn9F+ByOAlOiqLgbBb2O6RNjOV1KLVqUoZ25uz3nmOkdcVVxHROxlRTj3d4IHWFUgZw88gVFGol7Sb2JWPnadIm9uH0t3uw3+qRDGiPIJdz489LhKfG0WX2YCx1RuQqJTp/b2wNpMf07Sfdz6Xx0QGQtf88xopYmnePgTt2xkIDw9xSVY7S2xdBqUS0OzCV5DT2TEQX1qpy7PXSxcRls2IuLUKu0TV5X+x1NYgyAX10vMSFqSrHcDMLENCEhOMw3THNJJO5eyyO2mo0IU2bjyq/QORKNaaixh+QRhGOuSjf3bS3VVeiDY1ALcgJDApg8Yq57sksb289A4f3RavT0q1XR9a//x1pHVPd59qz/U8WLJlF737dqCivpHP39pjuMnUlV8hxOV106NKWK5caG7UhoUEkJsWzft23ZF2TGuB//Lyd2po6Vr68lEkzxlCYV0T6lSxJjmTWOFq2ae7mtgSHBDL/kZkYDEbmLJzK3h1/EhsfzbEjpzl19DzGejOLV8zFYrHSJi2FgrxiTCbptg0/bqVDl7Yc3HPU3TgXBIEOndu6iYgFeYVEx0U1qcnn5xbyyIq5vLXmI377cQsBQf6seXslGVevc/VSBoIgMPreoWRdy3ZfJPVeOkREQkKDGDtpOOvXfUtBXjFpHVNZ+uQCKsqrCAkNQhRdbP1jD3/8vA2FUkFJYVmT9/PPvUcZMW6QR49o345DrHn3Gfz9/RAEmviLOJ1OSksrePjRB6iprkWhUPD0stXuINt/SC/69O9OTU0dX378I3MWTiWhWQwnj56jZZsWjJs8nO8+/w2Lxcq+nYdIaZXEM8sb27Jdurdn3Zev4uvnw+svvk9ah1R33+QWfv9pKz9s/pgRowfgEl0kNIvluRWvNnl9VrPndUGhUPynzbW2b9pL7s3GvfaNrBz27zrM7L/ox/wz8c8MJIVA9G3/j2q4zQOCIAwAngb6iKLo/nREUSxs+DtbEIQDQDugqdb1PwBypZzqgjKSB3fk7A+N1Tetnx6Xw8n1AxdIm9yHpH5pyOQyDBV17qa6Qq3EO8QPZS8lF3477PYMCUyMoL6ihpAW0dSXSFNfMrkcQSZQcDaLkvRcQlJiuLb9FOGt4yWF3QBv1D56qvJKCUuNwzvUn+LLNzFV1ZM8qCOG8ho6TuvPiS92kNA9Gblgdfca5FqduyHuslqQh4TjMBiQKRUejXeF3tujdyFBlKbCBAGl3hsREYfBADIZMoVSKp2VFKD09kUfk4AoilgrylD5B6BVRSDeUgNWKKVMxin5r7tsVvRxzXFZTYjOBgkUvR5LWemdT4/ocjXwT2yILifWqgp0UbH06NaaK9dyOHrwFHabnarKGn76+g9AEuErKy2neUqCW45cp9ei12vx8/NBpVJyaN9xuvfuSGJSnIch1tiJwzCbzMxbNJ01z78HQLPm8YyfMoJjf57g7MmL1NcZKCpoLG95eXuzaM6T9Bvck979uwNw+OAJfHy8aZOWwiOPz8Xf35fvv9jAzq37UatV3DttNL36dWXJXEnI8OqlDK5eymD2gimsef49CvKK0Om1pLZJZt6i6Xyz/heeeH4xF85eweFw0qlbGmaTmYHD+rJ72wEqK2roN7jRHvgWfHy9ycrIdpfjqiqqWTz3aT76+nXq6w04HA7kcjm7thwgIMif+GYxTJ05DlGE5159jKXzn3FnRudPX0Ymk/Hk84vJzS0kuVULPn7na0DK2LS3aVjdQmJSHMUN75UgCPTu143W7VJwOV1kXstmwLA+TabnQFLS3b/rMEq10iNTA9i74xDPvrKcHZv3Yqg38v2Xv7Fo+QOER4XTJq0lzyx/hX6De7Jy9TKCQ4J45tFXPM594uhZevTtQt8B3Tl++Axt27dqsm5RFJErFKS1bgFIHJT2ndqwf5en1cOYiUObPPZu5/orG2BRFD3Gv2/h7KlLzF74N0/9P45/ZiA5BSQJghCPFEAmAx6hVhCEdsDHwBBRFMtuu90fMImiaBUEIQjogdSI/x+Bw2xDrdNgrjHQcdoA6oorUem1KDVKLmw4TEBCOE6rg6x953HaHfhGBpJ2b28ubzmGtd7Mlc3H6DJnKD0fHo2hsha5IEOuUZJz5Aq1xVVEd2xOxi7P3ZhXiB/mKgPGyjquH7hAQs9UvEJ8qckvwy8yiIJz1/GLDCZ5aGdUOjXnfz5IQs9Usg9dIqFna45/uZeeC4ai9VJJsvB2G9ZKqdSn9PbBVlWOJjisyYiv6HQgKJQeDoUuqxVVRCxKHz9cVgsOsxFteKQkxFhbg8vhQO0XKEmz2G2YiwtQ+QXiMNZjr60BQKbWoAuPwlJZhiY4DLlGKxEcayuxVVcBIuqAIJx2u0c5DECmUqMJDsVpMaP08mnIfqTszkch4ufv2zBG2whBEDAajUydM54zJy/yxAuPcO70JZJaJFBbU8e5M5fZ17CL//X7zax8eRklxWXk3Syge59OBAcH4nS5KCkuZ+CQ3oRGhBAY6E9JcSkqlYp2HVvz575jdzwnWMwWtt1BUBw5bhDr3tpBRXkVsQnR7GxoUlutNr5d/wsx8VG0bN2cMycuEBwS2OC7oaYgr4hhYwYQERnG2ZMXOXvqEsueXIAI/PHLdhx2h7vhPXv+FAKC/GnXMZWa6lqm338v36z/BZDGkZc8MY/3137usS6L2cKRP0+i1WqQyWVs/GUHy56cT2CQPzk386mrM3Dm+HlS26YQGOTPkJH9UKmVxMRFkZ2Zy28/bSU+IYZmyQno9DrqaiW/ndKiMtp1bM2501IpWKvTSvImBiNKlYqk5ASOHzrN+2+sR6NRM2nGGEwGE48+vZCnl652Z1sdurTl+OEzVFVU039wL34s92ym3/qcx0wczugJwzCZTVw4e5Xe/buyc8teZs6dxPbNe9HqtIRHhFJXW4+Xt564hGgK8oqpqa7FaDRRW1tHsxbx1NTUNZFYGTT8HmJiGzPnS+evsm3jXpY9tYDDB04gCAJ9B/T4f8rGl5dWcPjACTZv2EWrNsmMGj+IpGRPJWFBkKbozpzwFKLsN9hTE+xfBf+0QCKKokMQhIeBnUjjv5+LonhFEIRVwGlRFDcBrwNewC8NkfvWmG8K8LEgCC6kPs+aO6a9/mEwlFeDTMBhtZN/OhOnw0nzfu0w1UoCji6Hk6Q+bTnxRWONtLawksLzN2g/uR9Xtxyj1YhuXNp4FENZNeGp8US2TeDShsMYyqW6vFqnQZDJGseCgegOzbl+sPFLVXDuOpHtk9B42XCYbYS1jOXm0StEtU1ErdeQOrIbuSevUZNfTlT7JMw1RgrO55DQJR65QoGtpsH9TeeFOjBUuhhXV6Dy9r1tzBfJeTAyBnNRHiCg8vVHrvfCXl+HtaIEZDI0gSHY62pR+vqh0OlxOuwIMhnWilKUPn5oQyMlngmCpDRcVyN5j5iM0vRYVQXUVaMOCHZLnwBSlqHWog4IxlSY616v3VCHvUbaqdqR+Cza8EgqC4twKrX4+HjRq19Xjhw8SYfObRg7eTg2q52y0gosJiuDhvXBZnMQHReBzWqjpqrOQynX5XLx4lNrWfTYA1y5mMHBfcd4/NmHefGpN/EL8OW+WeP5+duNlBaXM37KCNav+45HVz7I9cybFBWUoFarGDFuMP4Bfm6dqVvw8tYTGOTP6rdXUlFWyS/f/dHkO5afW8gDD02jd79uFOYXU1ZSTmBwAOOnjkAmyNxrPXvqIgd2H+GZl5d5+KYAbNu0h2VPLmDP9oMc2n+C99avJrVdCrnZBaR1TKWmug6zydNWQBAEVColO7fsZ9qcCdy8nktFebUkM5MUy7q1n1NWWkHr9qmMGj/ErR8W3yyWCVNG8OXqH3E6nSx9cgH3L7yPt9ZIEva//biFaXMmMO3+e6mprqWsRHIRrKmuZdL00dhtNs6eughIni9fffITHbqkYbc7eP2D592bgisX0tn0m6TcPGLMIFq2buFRGpPJZFRWVBEWEcLKZa+Q2DyOPv27k5l+gx59u1BSXMaIsQP5dv2vFOQW8cjj86irrSfj6nVGjE1FrVUTnxjDC0+8wcRpo8nOymXpkws4ffy8xPQf2ocBw/qg1TVmWF5eetIvZ5KZfoO0Dq0QRclobNS9d89IHA4H333xm1uA8vTx82zesJM3P1xFbEIUgUEB7mP79O/O5fPpbN6wC0EQGDtpGN37dL7ref/Z+Kf2SERR3AZsu+O2Z2/794C/eNxRoPXd7vtHw2qyYak1cek218ILvx2i5bAuaHx1dHtgKObapjX2sox8oto1I6ZTMhd/P+y2xy04myWxpiOD3IHkxp8XaTu+FyVXc7HWm4jr1pLS9DycNgcxnZPRB/rgGxHIqS93ur1I1F5a0ib24dRXu3A5XYS2iiUgLpTS9DxEl4uUIR2JSA7GWlGCKiBQ4mE47DjNJgw5WVKPRKHAVJwvXfwjopEplDgtZizlJaiDwkB0YS4rhoYAoQkKxVJRiqW8BG1YJE6jAafdhtLLB6fZhNAgCW+rbuRyqPwDUej0OExGZHKFxA9BKrU5DE0HAmx11Sj8AtGGR2GrrkTp64/DUIcmLFJSSK6vxW40cfnCDV5/aR2F+cX0H9qb0ROG8t761ZhMZvZs/9MtwwEwZuIwCnIL6dStHQlJcdhsNh5cOpufvt3oLqWIoojVYnOr1RYWlOB0Oqksr+Ld1z9lyePzuXIpgxYtm9GiZTPee/0zZs+fQvOWieTcyEOj0XA9M5ulTy6ktLgMmUxAqVbSslVz1r39JZfPp9OuU2sef/4RSopKKC2pRCGX8/vP24iJjUQmE/jqkx/dBkwH9hzl1fee5dnlHlQraqprqalp+r5ptRo0GjWlJeU4nU5u3shjx+Z9PLh0NiqVkt1bpX+/9cpH7h7IlJljObjnKE6HE72XjiVPzKe8tAIfX29KCsvczXqlQs7H7zbK19+8nsv2TXvpM6A7+3Ye4rMPvuWND19g6ZMLyM8tJCDInw6d21KQX8iLT3lKuP/y3WYeXDbb47YOXdpgMVt47/VPqSirIjo2kkcen+tR6inIL2Lo6P6Iokj65UwCAv2Y/sBEfv9pKwOH9cXbx4shI/uxY9M+d4kyOCSQqbPHU15WSTO7ncsXrrknsI7+eYqU1s3pN6gn5aWViC6RwweO8+PXv9OlZwdWPPMQqWkpTdjobTu0IigkgIqyKs6cvIggCLz/+St4/4WoY3FhqTszvIWqimpOHDnTIDL6DHEJMQCER4aycvWjzJo/GRCIjovwGPT4W3A6nVy+cI1D+yTnzF73dKVVmxb/EEb9/4pm+78SZHKB4ss3m9xuqKhBkAvcOHiR1NHdm9zvEx6A0yH5orvuaHwWXrhBtweGIlcpyDuZgbGyjvO/HqT34rHSeKvdzvWDF2kzrifZhy5RnplPRNtED0Mrq8FMaXoeWn9vjBW1lF7JReujxy86mODmUagcVSj0KmTqCBRqDYbcxnaSXKtHoffGaTWj8vVHplThMBoRHbZGXSsfPw95E3tdDbKAIGQqFS6bDZfdDnIFosmITKHAbrOi8vXHXOopR36rMe5yOHCaG3keLrsdha7pj0+u0SK4nLgcDjTB4QgKOaLTDg4H1vpaZEoVJXYlix5Y5m4ob/tjD06nkyEj7qGivNojiABs/GU7L619ko2/bufDt78EJAmSJU/M581XPsJus0s1/CKpN+Mf4IvZ2KjcGxMbSXKrZoiI5N0sYPSEIdTW1uPt48VjDz7vvjCHhgXz7CvL+fidLyktKWfanAk8/egr7mB15sQFCvOLWbh0Ni1bNefo4VM8/dJSTh09R0lRGTPnTebY4dN4e+u5eO4qNzJv3vUiILpc+Pn7ur07QDK9euaxV5n70DTSL2fh6+dDVGwEyxY+R0KzWEaMHUh1VQ3Pv7YCm9VKYFAgBXlFfP/lBpY8OR+L2cK7r33qfi2BwQGsXP0oudn52O/iu375wjXm9urIvp2HsFis5GTnUVVZTbMW8SQ2j6O+1ojQMCTasnUL7hnUA5vNjr+/L4kt4nn8+cVUVVThH+BHdFwkyxc+5x4Fzs8t5PUXP2Duw9N56ek3iUuMwW53kH45kweXzubqpQxqaur46pOfqKmuJSomggcenobL6fLoc5WXVZKVkU18s1jatm/VxMsl/VImlRXVzJw3iffe+Mytd3bi8BluZN5k7YeraNfRc/8anxjL+h/f4czJC9RW19G+cxtSG/zu7wZBEP6yL5J+OZNdWw8yb9F0920ajZpmf0Oy/q9w8dxV7p/0iPt38dkH3/LFT+/StkPT3s9/F/8OJP9JGEqqkauavm3BSVEUX75Jy2FdUKiU9HxoFNV5ZQgIlGUVkNAzlbzTmYQkNZ1wVuk0VNwoxj8mhOKLN5GrlaSO7EZ1Xjk+oX5YDGbaT7mHU1/uwlRdj39MCMbKprtQY2UdWj8vjBXSBaW+vIYO9/XH5XSgDo3CUV+NTJB5iicKMlS+fphLCm67SY4uMsatlSXIFU0scgHs9XUo9F7YbFVuLSx7rQuZWoM6MBhB1lQTCxqajEolMlXj7kp02JEplB7GVoJCiUytbSirgcvHF1EUkKuU7v6Oy2ohO7O4yVTS7q0HGTKiH3U19Xd9fpvV5mGDa7Xa+P3nbYwaPwSb1UqLls1465WPCY8M5YGHpvH2mo/dxy59egFPLX3ZnS0kt0pizMRhpF/K8BgDtVisXLqQTmmJtFadl65J87ikqIzC/GI+27iXZU8v5OWVb1FSJLUDbxlfffHxj/Tp3x29l56Z8yZ5XACDQgIoLa1g6ZPzKS0uo6S4gpi4SPZsP4jZZMZssRAbH0ldbT3bN0oChFcuXqNt+1akX8lky++7GT1hCBXl1bROS2Hthy+g1Wv58M0vPF5LZXkVpcVltO/cxr2+2xEVE0F5g+/L+MnDcTqclBaX88v3m2mZ2pw5C6aQnZVDjz6diW8WywdrP+e+2eNxOpy4XCLvv/EZFosVpUrJ0y8tcQeRWygtLkehlDNs9ACpER/kz5svf0h+biHfrP/FfdHv1K0d3t46lCoFRw6caLLO7KxcomLCm9x+C9euXCciMtR9Pp1ey31zJiCXybiecZPykgpKikrpO6gXMXGRWCxW8m4WsPnXHTgcToKCA0loFovvX/BEwiNDmb1gCh/f9hmGhAZhaZjyOnbolEcg+e/g5282evwuHHYHW//Y9e9A8s+G0+GkrqSagLgw8k5l4LQ58A7xw2q0oPLSIIpw/tc/aTOuJ1e+P4694cvR7J62KNQqnFY7NrOVgLgwqnIap3taDu9C3qkMyrMK6ThzIC67k9Pf7HYTG+N7pKLSaTBV1yPIZAS3iMY7xI/AuDCcdgdZ+87htDsJSowga985BJlAy+FdMNcaOfv9PoKSIojvmoJcFKU+iF+AZJcLyBRKLOWeU1lSc1vw+L+gaPrDkKnVks+IRotco8VUXIAuMgYEGZaKMknEUamUspVbj1GpUei9EORy5GoNgkzubqaby4rximuG6LDjtNmkcWWXE0EuR+nti0LvjSCXYSr0nKX38mo6iuwf6IeI5JAXGh7swdhu1jyemtqmgfjmjTzmL5rB+bOXCQ4NYs6DU6mrqcfpcLg5ECmpzdm55YCH5/e1K1lUlleS0OCEeAt+/j4U5DW+t4q7iU3KZMhlMvJzC8m6ls2g4X35+lNJrt5qtXHpwjXMJjM/f7uRAUN6M33uRLx9vDh+5AwRkWEEhwbyyXvfMGnaaPJzitDo1FRWVLu9UeITY1j50qMsmPGY+znlcjk+vl7kZuczY+4k3l+73s2reejR++nao30THg2A2WTmpaffJLF5nFQ22iyNt6vVKuYums6WDTuZ/8hMEpNiWfHwKsZPHcFTqx5hz7aDHNx7jMiYcDp2bcfS+Stp1SaZivIqIqLCWPfW51gs0m/FbrO7jchuh1arQS6X06ZdClcvZpDUIh6bzUZG+g3mLZqO0WBGLpeR3DKJ7OxcHHYHsfHRTc7TuXt7tv6xG1EU6darI8cOnXbfl5ScQHVVDXK5DEEQEEWR+Ytnsn7dd+7BgeCQQO6dNpqnl77Mu+tXk3sjnyXzVrqnxy6dT2fNu88wbHRjVf726Sy5XM7kGWOJT4xh+8a9hIYHuz9DgH6DezVZ838FoihSfxcF6bq6pmTnvwf+ZiARBMEHCL6ToyEIQhtRFC/+Q1b1LwpjRS26QB9MVXV0nzeCupJKyjILiAwPBJdI8cVsYjonc+PPi+4gAnB9/wWCEiMoTc+jND2PpH5pJPRKxWawgAD5pzMJiAklJCUaRJFLfxxBrlSQ2KctcqUCkLzZ5Uo5yYM7cfPoFUxV0hdb7a2jw7QBKLVqnFY7LYd3QevnxdWtJ9zkxOq8MmoLKugwqTuKBotbpc4Lp9WCw2RAHRyGrboSh7Fx9y66nCi9fZCpNcjkCpDL0YZFSj2SBkkSdWAIOJ0ICgUOkxF9VCz22hpkKjUuqwWrzYouLBJbfS1Okwm53guVjx/GglxU3j6IDgf66DgcJqPb08RYkIM2JEJyUuQW+z0CW30NpsJc1AFB0iDCbWOfCVFBtOvUmnOnGgmi0+ZMQK/TodaoeeyZh9n06w6uXMqgfafW9OzbpclUF0C3nh358Zs/aJGSiMvlIudGPru3HSA8MpQFS2bh4+uNl7fe/aO/HaUl5W413lsoyCtm9sKpbGmw9j166BSjJgzh2pUsIqLCuHLhGn1uM6by9tETHhGCTq91E/XsNjtqteRSeejACSZNH0OrtsmcOXWRfTsPUV5WiZ+/L4nN4zhz8iJXL2Uwbc4E/AN86darE3K5jMMHT6DRqt0aVTq9lurqWoY3EAZv14P6YO169Hotg4f3ZX1DQxikkkxKanPWr/ueooISuvbsyIPL5uByumjTviU3r+fRrmMbAoP8qSivplWbZOITYlm5rHHEVu+l47lXH0MURbr37sjnH/7AnAenul/rLRzcc5RZ8ye71QMEQWD5Mw9RW1WHzktHYEgAoeHBhEeGce6U1JcICg7g289/YdVrj/P2Kx8zZ+FUykrKGTNxGFs27MTpdNGnfzcSmsXw8KP3S59XWgqpbZM5e/IizVokEBjkj7ePF5t+28mchVPZt+swGVevu4MISOWxqspq0jqmsn/XYTKu3mD+4hlcz8xxT8x99/mv9BvcC5vVxvHDZ/jth82ER4QwfupIWqe1JDDIn2GjB9C5WxofvPkFHzRMz/Xp372JU+N/FYIgMHHaKP68Qxh01ISmbq1/l+e7NVr3F4uZCLwNlAFKYJYoiqca7jsrimL7f8iq/kHo2LGjePr06b994F+gKr8Uc7URuUpOyeVctzxKQHwYsZ2TqckvIyw1jmOfSPMDvpFBANQWVtBhaj8qb5aQc+wqgkxG+6n3cOa7vR66WSlDOyFTKChNzyWuawoXfz+CzWhBrlTQZnxP9MF+lF7JIWvfeY91tZt8D3mnrlF5Q9r9thrZlSubjzdZf58lY/EN0iI6HZjLSzz4ItrQSCzlJVJ2IAjoYxIQ5BJ7/VZZS5DL0UfF4jBLP3xrZRmi0+nuedjranDZbZJuVnljxqXQe6MODMZaWebhJaLy9W94+SL2ujoQXSh0XpK2V3GBO1PRRcViKsh1r0ETFOrRexEUSgxKL66lZ1NSXoO/vy/HDp0kvlk8PzRIrs99eDopqc3R6rWUFJZitzswGk2se/NzDPVGOnRuQ897unLq2DmiYyOprKhCoVBQXlbJmRMXaNOuFfMfmQEinD11gfXrGi+yAC+8toKb2Xko5HI2/rIDP38fxkwcxo3MHOISo7memcPN67nMfXg6u7cf5NrlLPoM7I6fny9vvPQBLVs3p0//7uzdeYj+g3tx6Xw6hw+c4NlXlrPqyTeIS4xhwtSRbNmwi+rKGsZMHEZi8zipZ+Fw4OvrzdqXP0ShVDD9/nvp0LkNNdV1XLpwjZCwQPz9/Ui/kkl9rYG4xBjCI0LIyy3k07sExWdWP9rAvK9h20ZJIWDcxOEArHn+XY9jF6+Yy28/bPEYkV329ELi4qN5+Zm3PDJBgMefW8Tbr37CnIVTOHLwFB27tOXnbzd6+K8oVUoee+ZhamtqsdnsNE9OYPOG3e6LYkCgH7MXTGHtyx+6H9O5R3tmPDARgJ2b93Hx3FV69OnC2VMXmfHARIqLSjl26DSnj59Hq9MyZ+FU/Px9+GDt5yQmxZKXU0h5WSUdOrehbYdWJCbFExYZyruvfcKFM57SMf2H9MLH15vff2qcExo5bjA52XlcOp9O7/7defvjF9m8YSfPrWhkJajVKr75/QOSWzUy7o1GE3k3CxBFiImPxMvr7+e8aDKaOXHkDF989AMyuYw5C6bQuUd7NBrNf/pcgiCcEUWxqfRBA/5WRvIU0EEUxWJBEDoD3wiC8KQoir9ze+3j/ydwOUSqc0sITori+v7zAKTd2xtzrRGtvxc5x65iM9uISEskODGCihvSxS6uawoaf2+sF7NpPaYHXiG+FJzJcgcRpVZNQFwo5loT4alxKHUqLm08hs0ojWc67Q7O/3yQ9pPvcWcit8Nps7uDCNBE1PEWLPVmdN4y5DLBHURUfgHI1RpEQBcZg622GrV/INbaGuQKz96I6HRira7CZbPitDTuIkVRlJrut3obd5RwHMZ6lD6+TQypbLXVaEMjkKnUyOQKZGotLpsFe201MrUap9mE0tfPI/sQnU7s9bXuqTNBJseGjMLMXLReetqEh/Dmmk8YN2WkB+Hs/bXrmb1gKl17duCNlz6gprqO4NBAZs6bTGqbFhTkF1NdVcP9D95HeWklDoeDwoISuvXqyLhJw8m+nstDsx4nKTmBJ55bzI2sXA7sPoJCqWDyjLHk3ixApVKx8ZftrHhuEVcvZXLx7FW69enEhTNXOH/6El16dODmjVy2/r4bl8tFVkY2A4b24fnXVpCbnc9H73yFKIpkpt9g6ZMLGDNxqDv7GTV+MGtfWufmVHz87lfMnDeJnOx82nVsTWVFNUuemEdgUAD5uYWsemoty55ayOXzV7h0XipzxcZHMXPeZN5c/SFj7h1K63YtCYsI8eh5yOVy1BoVZ05e4OrFDLr06EB4RAiFBSVoNCoCAv3cZT2VWoV/oJ9HEAH44csNPP/qCiZNH4PNasNisfLDlxvcfY9316/m8P7jREaFkdA8jhXPPsya59/FZDSj0ahZ8MhMvvrkRwrzi4mOjSQmLtJjZ11VWUN+bhHBIYFudeeTR84yZcZYyksr6dClLf0H96asrJK0jq24dvU6ic3i3FpiZpMZp8NBfZ2RmupazpxsLKzczM6nc/f2bPhpC/MXz2ToyP5NAkmvft0ajbQasPWP3cxbNJ2rlzKZNW8SBoOpSeZqtdq4cPaqRyDR63VNpFz+XtDptdwzqCfdendCEAR3ZvuPwN8KJHJRFIsBRFE8KQjCPcAWQRCi+cvL1f9dOK12lDotxqo6Yjon4xXqT2l6LiVX80gd1Z3qvDJqCiroOmcIx9Zvc79DhRdu0GnGIEJbxnJ501FShnRC7SXNosd2TUHrq6csswCtKGKqqsNutmGtv4MY6BIx1Rjwiw6m8LxUZQxpEUVQs0h0AT74x4RQnSddEOpKqgmMD6fyZuMPPDJNIjzZ7XLk6gb/64BgnBYTtgZOhiBXoI+Kw1pVLokn3mWKymWzIlOp3IFEplJLgUhszG5s1ZVow6OwVpbjcthR+QUgCE2njQS5AplKjcNsROHlg6koH9FhRxMciqOqEqWvH3KtHrlK4yEz7zAZQZAhUykprTRw8lwGv/+0jaqKavoN6cUDD027qwDh9k176NarI6HhwVRV1lBRVoVOp+HhOU+6a9z+Ab6sfPlR3nj5A3etPiIqjCdeeIQnVy0BREwmEzKZjPmLZ+JyuTiw+whZGdk8tWoJIHlQ/PDlb3zw1au89PRbbpvfvJxCmjWPZ+T4wW5r3r07/uSeQT344qMfPNa6a+t+Fi6ZxdKnFmIxmcnJzufO6sH2jXtZufpRbmTe5PSJC1xseM2x8VHMWzSdgrxidxABybkwKDiABx6axsmjZxBkMla9/jgvPbWWlm2SiU+MJTk1iV++3UjuzQImThtNZvp1PnrnKxYsmcWXH/3AgiWzqKqsxmA0MWL0QIqLSlEoFR7lsdqaOnKy80AEp8uFl5eeV95ZybMrXiMkLIgnF7/oDkbbN+1l2VMLeXf9K9TX1mM0migpKmPkuEEolAqSWiTwTYPF8e0oKymnbYdWtGjZDJvNjkqppK62Hpvdjo+/Nzez8/nui19QqVTMWSiZSD3w0DQcDgfePl4EBgcgOpuKOnbt2YFdWw9wIyuHLb/vxtfXmykzx/Lbj1uRy+WMnzwcuUzWRB7e5ZLkUr769T1atU1u8LBv+p2X36VP9o+GRvMfHxn+r+JvBZJ6QRASb/VHGjKTvsAfwN+/9f8vDrvZSmBCGJU3iijLyKcyu5jE3m1w2J1YaqXU3DcikLzTGZ5hVoSiizfwiQgidVR37GYr/nFhBBaUI1fIubZTKrf5hgfitDvwiwpGpde4MxIABFBqpR1F6mjpHHXFVVzdegJBJhDTORnvUH/yTmWQfzqDHgtGUJVXRl1xFT5hAVhqjZz5di89Fo5A560DQXD3NtzLdDqwVpUjOp2S14h/U/Vcla8/TrsNmVKJXKtH5R+AIFMguFzoouIkXkdtNebSIrShESi0eoyFknqpXK3BaW18TZqQMBwWE/b6WuS3ApJShUylQRcZg8vhwFyUh6BQoo+Ox1xahMtqQenlizooBEQXOVcvs+a5d90TRj98uQG5XE7zlKYjkxGRoZQUlTJs9EAmThvDkYMn2b1dktkIDglk4vTROOxOqqtqGDF2EF9/+jMul4uighKO/nmS3dsOYrPaePOjVZw6do59OyUmfMvWLXjgoWn4B/qyYPFMqqpqGD1xKBfOXPHwige4nnmTAUMbJUsUSsVdt2R6Lz3IZLz01Fo6d0sjsXnT1+MX4MuOTXtJSIpzBxGQrF2zrmV7yIeMnzISL28dG37ayoHdEgfq6J+nOXLwBM+tWcGrq95lx+Z9tO/Uhn5DenH8yBnWvryOp1Yt4eC+44guEYvFSm1NHZ26tePIgZM89vALhIYFs/TJBXz/xa9uuZV77xuFIAh88Obn7jVERIXx6jsrSb+c5TGoANJY6tMvLkGr16LRaNi38zCTpo9GoVRQU13L4JH9OH0Hw7vPwB7k5xSy7s0vJNkSuZyHls9h64bdjJs8gndebZyye+npN3n+1cdw2O2IonQxf/bRNXTo0oZZ8yfz09d/YDZb6N67E5HR4W4lgtDwYD597xueenEJ980ej8vlYs/2g+77bi/btevUhu59OrvdEn39fFi4dDZPPvKS+xidXtukj/Z/BX8rkCzkjhKWKIr1DYZUE/9hq/oXhNPhRKlXUZ1byoVfD7lvP//LQbreP9TtHwJN/ZnUXloi2iRy8+hlDKU1RLVPQuOto/XoHvz57u/IFHJShnam+EoON49eQe2lJWVIJy5vOobT7kCQCaSO6o7OX1L3rSuqJDAxwi03L7pEco+n03J4F2QKOcHNoxDkcrL2nkPr70XJ5RxJ00sAW70Ju8aOPiYBh+EuZTKLCaVvAA6TAYfJiDooVBq1FUVUfv647BKJURsWhSiTgShiLs7HaTFLvRD/QGQqNQLgtFgkM6uwKKl30tDgB4kfYq2qcJMQTUX56KLiQABTfg7a8GhJlkWQScZWpUUotDpkPn44TAZMxflowyLJyc5rIhe+7Y/dDBm5mqiYCArypPKiUqVkyMj+vPDEGzidTrQ6LS++8Tjvr/0cuVzOnIVT3RwSkHb1M+ZOdDd8C/OLCQkNJP1yFps37GLeoumsfflDps2ZQEV5FV989AN6Lx0z502iR9/O5GYXkJudz91wOxdkzL1DkcllHg12mUzGlJljWffm50TFhFNba6C2ppbQsGD3KLEgCIwcP5jNv+1qIlMPcO70ZabMHAtsQqNRExDkh1wmcweRW8jOyuXs6Ytu+9mzpy5SVVXDsNED2PTrDrb8votnXl5GcWEpb6x7AW9fPYf3n+DHryV5ksryKjLTr7PiuUV88dEPDBjam/adWvPxu994rKuooISsjJsI8rurFRfkFXHlUiYF+UU8smIem37dwc4tUvN6+NiBzH14Gt998ZuUFUwZgZ+fDy998qY7S3M6nXzyztcsfWohm29T3r2FW+Zc3Xp1YMfmfTidTk4ePUdhfgmTZ44luVVz8nMLef+Nz9yPKSuRRqnLSys8Msafv9vEw4/ez9VLGVw6n07n7u2ZMXdiE8vdvgN68P4Xa9iyYReh4SEMHdWPFimeUij/V/D/DCSiKF74i9vtwHf/kBX9i8JqtqBUq8k5lt7kvqKL2fjFhpAytDNZ+88R2zWFoos33DvNxD5tOPvDPpw2KR2+fuACEW0TUKiV7vKUXCmnKlsqRVkNZjL2nKXlsM7oA30QFHJqiyq4svm4exIr/0wWcd1aerggGivq6L14DNd2nKYqtwSX00V9STVxXZOJ7ZiAUiVD7euNy1SHMfeGdOG+Awq9NwovH6wVpYhOJzKVCm1YhKQULAhYSotx2e04TAaUPv4Ycq+7+y0OYz2i04E2LEoqV+m9cdksmCvLkas0qPwD3JmQrbYGtX8gKh9fkMkls6z6WpQ+fuijExBFF4JchjYsQsqQrBZsDUFIUCjRBIfhslkJCg5o8hp8/X2oKKti2VMLqK83UFFWRXhkKG+/8rH74mY2mflz/3HGTRrGiSNn2bl1vzuIgLSrV6lV7rJNy9QWfPmxdDHJzsph0PA+vPDqCq6lX3ePwdbV1vPe65+5Lz5ZmdmseO5hqqtqpRn+33fTrnNr/AJ8GT1hCO06t0EAzpy8wAMPTqOqshqlSkFIWBAOh4OrFzNYuGQWH779JYf2H2fW/MmSGZjFSqvWLVj39peUFUsmTbcuurfQtWcHdF56hozsR2b6DW5ez6VZ8/i7frddd5R4cm7kMWh4X+m72zyOoOAAyksrEBG5dDad337Y4nG8w+HEaDDRqWsae7b/Scdu7ZrwZQCqK2sICPJD76XzcDkcc+8wamsNdOzaltZpKZhNFnZu2Y9CIak579yyn9ZpyTz36mPU1dSzfdNelEpFkwBqsVjxD/AlKKRpJh0eEUJ1dS1bf9/t0dgvzC/mi49+YNKMMbRqLbG+b21M9u86zGvvP8u1K9c9zmWz2vhz3zHiE2No274V505fYua8SdwJvZeO3v260bvBw+X/Mv5DPBJBELoC7yFpXKmQtLEMoij6/gPX9i8Fa62ZjN1n0ProuHNwVKVTk7HzNO2n3EPH+wag9tHSfd4I8s9k4hcdjG9EEIEJ4ZRda9yhFl3MJq5bK8JbJ3D+5wNN2PCWWiOXNx3jnuUTOPTBJpr3a+cOIreQe+IayYM7ugOJb1QQFzccpiqnFENFLc37t6OmoIyk7vGIljoEhwJ7jQWl3gu5VofQYF1rraoAUUSh90Km0iA67GhDIxCUSve01C1ow6KggY8i12ibWPQ6LWZcTgf22mrkGq27/+KyWrAb6/CKTcRptaLy9nUTHgGUPn6o/IMAUZrIcrnQBIUiIngw3gWFAk1wKObSQnC5SEmMICIqlKICiYUuCAIzHphEdVUtq558gydfXEJxUSnXrmRRVlrhsdacG/kktYhnyMh+bove21FfZ8Dbx4t+A3uSl1OAw+Gk78AejJs8HJVKhZePnv27Djd53JULGbTrmEpgcACvv/iB2/98+cqHpCbsu1+z7KmF2Ox2MjOyMdabOHTwBNPvv5drl7Oora4nJi6GpBYJuFzSbsRus/Ppe9+gUMhRKpU8s3oZI8YOJCw8BKfTxZOrHmHtyx9is9po36UNHbq0pTCvmLQOqaSkJgECR/88Se9+3TzEJROSYqm446KvUqsQXS6atYineUoic+9bJqn46rS8tPYJ/AN8m4zs2u0Otw6WzWJl6Oj+7mzuFpKSE9DoNKx59xm2/r6b4qJSOndvT3VFDSFhQbz2wvsIgsCLbzzBmEnDCA+XmvwRkaHExkdz6tg5Th+/QGqbZNp2SEWjUbv5JyCVk8rLKunUNY1jf55yN/d9/Xzo3KMDD85cgUaj5v6H7uPiOU9pvoRmcXz2wbcseWIeudkFKFQK+g7oQffenQmPCMNQZ+CLjyUtsZi4SKbdfy9L561EFEXUatV/WiL+vwqXy/UPkTj57+I/+urfR1Ln/QXJF2QG8I8ZNfgXhbG8hvzTmXSfP4KSq7nuXZxSp0br54W13kxtYSVXt56g3eS+ZOw6Q8qwLlzbcQpjRS0hLaIlb/WNR0AEpVqFpc5I3qlrtJ3QG0EmuElQtxDZrhlWgxmnzeHuj9wOUXS5iU4+EYH4RQeTuecsgkwgvFUcKi8taeN74jTWSuUlmxWX04FMpUbrLTkWOgz1aIIl20+n2YTosGG3WgARV11TUprLZgWh4e+7jVsIEplL6e2LpfwO+XeXC6fFjFyjw5if7XGXva4GpbcP1spyNLf8381GVH4BWKsr3EKSav8gLKXF7gAWqHDw7rtPcfV6IdWVNbRs04ITx86R1EKSrz+y/wR9B/egtqquiVTKwKG9+fCtL4lNiGbgsL58e4cGUpt2LenWqxM52Xl89sG3PPr0QnZs2c/i+58iOjaSxSvmcv/CqVRX13H6+Dk3Uz4sMoScmwV8/dnPVDXwVUxGM6ufeYu3PnmJF9c+wc4tB/Hz92bP1oNo9VoeeGga6z/4zm0jGxIaxAuvr+DUsfMkt0ri2pUsQPK16DOgO77+vmz8dQcnjkgj6KHhwax5ZyVms4WL566yaM6T0vfC15u5i6ZTkFeE0+kiPCqUWfMnc/nCNdp1TCUkLJjTxz0LD/MXzwBg0WNzWTpvpXuHbjaZeeOldcx9eBqrnlzrPj46NhJDvVRmjUuIprysipTWzZl+/7388ct2/Px9mTprHFv+2EVtdT2R0WHEJcbQvXcnZDIBi9nKure+AKQJwCMHJV+dW/I1AD36dEahUJBzI4+cG3mUlpTz8ltP8+JTa6mpriUoJICnVi3l+cdfQ6lSMm/xDCxmCzKZnMQWcZQ1lAQtFivnTl3iwaWz2b39IFqtljkLp/DpB9+Sl1PIm6s/IiRUyghbNkxTmc1mnKKLBUtm4rA7KS+rYPOvO2jboRXnT19m3uIZd2XLFxeWcOr4eTKu3iCtQyrtO7Um8C4Z9H8EBXlF7N3xJ/t2HqZb704MGdmPuISmhMt/Fv6fPBL3QYJwWhTFjoIgXBRFsU3DbedEUWz3D1/h3xH/HR5J3plMjn+6jV6PjMVutFBTUIEAyJRyMnZLP+aWQztTkp5LZNtEbCYL6dtOepQNAhPDUek0FF+6Sero7tSXVeMbHkjG7jMotWoSe7Uma/95zNUGIts1I6xlLCq9GlO1AUEm49qOk5hrGtPy6E4tCE6KxGYw4xcVjMVgpragAqVGSeHFm8R3a0nZtXxiOzfDS2f1GKPVhUdjralC5ecvTVfZ7aj8/JHJFYiIyDU6bFXlHs14QS5H5R+Iw2TEaTKi9PYFmQx7bWOmpA2LxFZXi9rPH1tdbRMhRm1ohMRPKfJkp9+6T6ZSYyrKRx0QhMNsQq7WYK0sk8aUtToEmdytBOzx2LAo6iqr+OybbaSnZ/PAg/exZJ7k6bH8mYfw8tJRW1PPV5/+iM1qZ9L00aR1bM2LT62lpKiMKbPGUV9rYMeWffj4eDHt/ntpndaSzGvXuXkjj9H3DuONVe+TeS3bzXLXaNTMmDeJT979msEj7sHhcJB9PY9R4wcTnxjjfv7bsXjFXIwGE82TE3l88SqP+5Y8Md9TiuXJBeTnFdF/cC93FhERFcbRgyeJS4whOCSQT9/7xu35PmBob9p1aM3rt3m4A9wzqCclRWVERIXSb1Avyssqcblc3MjMobysAp1eR+u0FCwWK3q9jti4SOwOJ8UFJbx123pu4a2PXsRilaRBgkICCQkLYu/2g8QlxhAYHIDZZGb1M+8QHRtJ/yG9aNUmmT3bD3Dq+AWqKqpZ/sxDfLD2c8nfZfEMPnn3a4/zP/ToHDdJ73bcKvPdwtqPVnHu1CW0WjUOhxOrxcqxw2fcAw5yuRxRFFm4dBZ+/r68vPIt92N1ei0PLZuDWqOi/9DevP7CB2zbuAe9l44ZD0xEFEXCIkIIDArg/bWfYTFbGTl+MEcPnuLsqYvIZDJeeWclOp2WtA6p+Pr7eKy1uqqWxxe9wPHDjXYQU2ePY+mTC//TY7iGegMrFq3i8P5GyZfkVkl8+PVrHmrB/0j8d3kkt2ASBEEFnBcE4TWgmH+uTe//ODTeOrR+XjjMNuQqBUGJEdSXVmGuMeIbFUirYV0pupSN0+rAYbWj8dE3qT1X3iim8+whRKYlIjpduAJ80PhK2Yy13syVrSeIbp+ExleHb2QwMoWMwvM3CEoI59zPB2k1oit1xVXUlVQRkhSJV4gfZ37YR0KPVESXiFeIH2Gt46nILCCyTTxXNh/DbrWRPKAVotVznNhSUYrCyxtzaREqH39kajWiw4GlQsoidFGx7qChDgiSNLAcdhRaPYJSjULnheiURCh1EdLOSFCosBvrUHn7YK2uRBBkkmpvVQVOq0VivDsduMwm5Fq9h2gjSKPE5tJiVH4ByNQa5IBcrUEbGoHdaMBWXIAmJNxDVkV6YslkyyFXUlRYxsWzV1i/7juGje7Plt938+bL6xg4rC9DRw9gxbOL0Ok0bPxtJ1v/2MPw0QNISknklWffkcor7z1LSGgQ5aWVpF/JxFBnZNSEoZQWldKqTTJpHVPx8tbz1Sc/Yag3ujPInVv2s+r1xwkJC+a91z/j4eX3N5GQFwQBh8PJ+nXf0ad/dzp3b+eh95WXU+DBjairNbB76wG2/r6bD75cw5EDJ1j70jpAah77B/gy7f4JfPq+JCufcfUG7e+wDQbIu1lAXGI0RoOJi+evuo2+AJ5/9TEunU/n2/W/EJ8Yy8BhfXju8dcZOKwviUmxyOVyj16En78vZ05d5Nv1v9C5WzvumzOeT9/7htqaOvbsOESrNsmEhku+4pUVVUTFRLBn+0EEQcbCJbMozCvm0/e+YckT85DJ5AQE+Xn0JYC/vDjeKvPdjtuzSLVaxTOrH+XFp9ZitdpwOp2MmjCY4NAgNBo1K55bxFcf/4hWp2HUhCGER4ayft33yGVyZs6bRHV1LV26t2f9uu/cn1t8sxgWLX+A3dsOsv6D75i3eAbnTl8iOjaCrj074B/gd9e1ZmfleAQRgB+/+oPxU0aS9J8UYcy9WeARRECS5bl5Pe9/LJD8LfxHA8l0pMDxMLAUydlw/D9qUf9qqCurRpDLaD/5HqwGM+VZBeSekEoQai8tnWcP5sz3+9xiiZU3i2k3qW+T86j0GmoLy8nYdYbQlBiiOkhB4xYcFhs3j15BkAmkDO1Mxq7TtJt8D8aKOlwOJ5f+OIJXiB/6IF9yjqeT0Ks1LQZ2oPhyDnVF0sUnuHkkam89PmEB2C22BrXRpq/Jrf/jckmeI77R4HKh0MfjstlBEBBF8IprhqW81G0cZa0sRx0UisNQh+h0og4MxmG1ovLxxVJZhsrL1yPbcJgM6KJicVmlWralXPIw8YpNxFxShNNsRFAo0IZEYDcbcdksCIoABJkMW3Wl21BL6eMnlbmqyiVBycJcqbwlCGhDwrFWlqG1WVm8eConjp3jwtkr3DdnArEJ0VjMVlJSk5DJBAwGIysffcXt7rf+w++ZOG00z65ZjrePN3U1tbzx8jrOn76Mt48X0++/l5zruTz/+Ovui523jxezF0zhvdc/Q3WbOF9+biE/fvU7oijy3Re/seyphax+9m3sNjuCIDBz3iS3bPnBvUeZv3imRyDx8ta7swuA4JAABgzpjX+gH8cPn+H7LzZ4fIbVVbUevIQxE4fSum0y8xbPQKVScubEBY4dOk3/ob0JDw+hrt7A1YuN/h2R0eGEhodQWVHNgKF9KMwv5vUXP8DlcnF4/3HadmjJg8tm8/G7X2Oz2vDy1vPAw9PcbPiTx84RGRNOZUW1Bymxd/+uhIYHk9Qing0/bqV3v26cPHqWX7/fTL/BvZg4bTR6Lz2fvvcVILD0qQX89sMWyksrGDi0D75+PqSkJpF+Oct9zpi4KGLjo5i7aDq11XVUlFVSU1VDXGKM28c8KiYCu93Bu+tXk345C5VaiSAIrH7mbWxWGxqNWjIsKyrj43e+wuFwMmh4XzRaDZnpN3jnk5f46J2v3EFk9L1D0em1vLTyLXx8vVmwZBbl5ZUkJSew7KmFfxlEAGy3DW7cgsvlwma14XK5uHY5i00bdlJXU8/I8YNJ65iKVnt3xvlf9UT+lXol/9FAUgHYRFG0AC8IgiAH/vEsl38RCDIZDosNmVqBwq50BxGQJqyubj+JT3gAMoUM38gg6ooqKc8qJLxNPMUXGxvKzfu3I2v/BRDAYbOj1mtRKOXE92jFzSONPICE3m0oupCNs4GfEhAX4r7PUFaDoayGoMQI6kqq8A72o66oEu8wf2I7JeN0OvGNCHSTrURRxFRjQa+VwW2kQU1QCHajAZlGhyYwGGN+DrhcCHI52pBwRJcLAQHR6fTQ4AJJGkUTHIalrBhzSSH6uCSpNObth63as6EN4DBKo8SuhqkrbcNjNaHhIEoJhbWmym1WpdDqsVSUergy2utqGspicmx1NWiDwyR7XocDa3UFLpsUGEK8lfQf0pPkVs0pL60AEXr07czlC+lERIZTV1PvDiK38Mcv29F76Th84ATjJg/n/OnLgNRsP33iPIDHjrm+zuCWKTl7Gytao9W4MxSX00lJURkPPDiNiKhQigtL2bPjT1xOF/MWz0AAYhOiUCjkOBxOAgL9SGnVHJV6N3EhgcyeP5kfv/mDPv264eWtR6m8u5qsIAgEhQTw4NI5XDx3hXOnL9Ghc1sO7TtOVGwEcx68j/o6A5HR4ZiNZtI6tmL4mIHU1xvx8/fh7MmLCDKBX77b5HHeyJhwjv15mi49O7D2wxcwGszYrFbeXvOx25cE4OLZqyxYMov6OgNKpYLqqlquXb6OQiHn+RWvs/TJBfzw1QayG8pNGVevM2PuJOQyGbk3C4lLiMZisvDAQ/ehVCmpLKuioqyCFc8vYvsfezl++DSpbVMYPPIenl66GkO9kdBwibvy8sq3eHbNcp5d/ioymcDYycNZ9eQbCIJAz75dGDtpGCsWrXJP41ksVlY/+zaTZ4x1N+K3b9pL9z6dqamp442X1hEeGcb4KSP5c+9RvH283BlPVUU1b73yEavfeppxE4cT3yzmrp/HLcQnxhAeKX3ut9CxSxoul8jmDbs4deycW2V4/rRHef+LNX853RUTH8XAYX08enztOqYSl/j/XsP/JP6jgWQvMAC4ladrgV1AU+ON/4Nw2h1Y603I5HJqiyub3F+dU0q3ucMoOJtF5c0SAuJCiWidgM1sJSghHLlKidNqJ+90JkqtiuTBHSm5kkPm3nNEtU8isl0Sockx2EwWlFo19WU1RLVrRkB8GDKFnLxTmaSO7EbG3rPYTVb8Y0NJGdaZyxuPIpPJ0AV4E9OpBVe2HEfr50Vs12S8gv1oP7UfV7cc5+wvh+kyox86LxBdDlQ+vpIBlc4LuU6HIee6m/wiOp2Yy4pR+wch0+ndvu6CQonK1x9BELDVST0RuUaLyj8Qe12NW82Xu+ySBLkcdXAo2O2SDIrdikwlldIcxnochnrkWh3a8CgcJjNOi7lJ2avhTIguEXttNQ6jAZWPrztTugWXC7r17MTqZ952T/REx0aw8uVlbN+0l9Q2KU3O6uWtx2QyU11Zg+IOlWOdXufhw964FAE/P2/++HmbpOg6c6w7qMhkMmYvmMoXH33PhPtGkXktm+OHThMQ5E9CUqxbKDEpOYHX172Aw27HaDBRXFTKksfncfLYOQ7vP4HLKQ1ThEeGoVDIuHfaKLcyMEiaU63btSS1bQpLFzR6qB/ef4JFjz0glZCenM+a595l9dsrEeQynE6RJbc10JNbJXHf7PHEN4t19xbUahVTZ4+nqKAEQ51Bcun7bScz5k5sQiZs1TaZLz76gamzxvLKs+96KATMmj+ZN176gBlzJ5L9fmNf6+dv/mDNe8+SlJxAn/7d+fi9r3HYJcb5C6+twGa18/XHPzNxxmjq6w3cM6gHS+audAfp0uJy1r35Of0G98LPz4e3P3kRl0vkwtkrBAb7U1FWxZ/7jtGiVTOPkW6Qhh5uZ5x3792JfTv+ZP9t/JpuvTpy35wJ/PztxiYfe15uISPGDWr6fbgDDqeDKbPGceXCNTLSb9CuYyqt01ry9ac/eYxq9+nfne69O/HFRz/QpUeHu/ZP9Hody1c+RLdenTh84ASduqbRu383AgL9/uY6/qfwHw0kGlEU3cVeURQNgiA01e7+vwqnE9ElUlVQikrfNP0Mbh5F4YUb7kzFUFZDdV4Z0e2TAND6eZN14hq1hRV0nD6AM9/udf8oKq4XkjaxD7oAb5x2J2d/aCRTBcSFEd2xORc3HKbcR0d8t5Yo1Erqy2pAEEge1AGNnxcxnZtTllmIb1QQ0e2TuLrtJE67A4VaSbtJfaktqqSqoBplUjhKmRlzWaNgoy4ipgmDUnQ6pYAgCMhVarThUeASsVSUSOOOAUEgE9ziim4IMvTR8Z5ER0GG0ssHURSlJr3FjFyrR5ArsFaWuT3iXXab5P0eHIatpkqy1K2/c2pMRK5SSTySwGBkKjXW6kqP9ds03uzcst9jLDQ/t4iTR8/RomUS4VEhRMVEYLFYaNU6mYK8IsZMHEpIWDBeXnrycgp4/YPnqKmqo6amFrvdwchxg/j43a/JTG8UwA4JC6K4sJRVrz9OUUEJNruDoaP6029wL3x9vSktrWDS9DH4Bfjy6vPvMWveZOISY3huxavuc2Rdy2bjL9vQ6nRs39jApg4LZtL0MZSVVWA0mbDbHfj5+/D2mo8JiwhlwSMzuXD2CuGRoQwafg9Gg4FrV280ybJ2bT3QoLEkIyomwq2Su+XQLo/s6tqVLIqLyujSoz1DR/bD5RJp3S6FVU+8TmmJFKRTUpMYfe9Q6uoMdOjaljMNU14xcZEMGz2AgUN788VHP3r0UqqrajGbLMgVcu6srarUKry99QwdPYD3XvvU/VuorzPwyrPvMGhEXyKiQqmqkIiRNzJvNpGHycsp5LFnH8JqtfHW6o+4kZWDt48XM+dNYtvGPWRn5SITZChVSo9gotNrCQoOZNiYAZw4cpa27Vt5NPBB6j8NHNaXgEC/JpsIvzua6nciL6eQM/8fe+8dJkW5rX3/qnP35JwjzMAMOeecs2QJkiQJSBCzYkIQxAAoiGJARQUTqAhKzjlnGGaYnPP0dO6u748aimmGvd993rPd+z3n2/d1eTlU7urqWs+z1r3u+9RF7qZl4e3licFDT0KDOM6cuEj9xLg6/T6H9h1nzqKpHD98ps5nrI2wiBBGjR/CqPFD/u75/134RwNJtSAILUVRPA8gCEJrwPx/2Od/DZxOkcr8MlQaFUW3s6nXrSlpR67IBe6G/Vpz6P2f3PbxDPLFYXeQsu8iostF0xGdSezVgpK0vDoPTMapmzTo3ZLrv7sX1ErT8zGXS/HbUmni9j4pny4IArHtkyjLKsJ8NZ2sM7dQ67W0eLQHpzf9KTswOqx2Lnx/iKT+bcg6d5uYFlFYC91ZVKLTXTMIqJlVCLgcDqkRURSlvo0aWIsLpQK48YEXvejCYTWjD4uU0liCgMrDCxGknpF7L7CKMgwRMbI5lby73Y7L4cBhqsYztj5Om1VOh2n8AnCYTVLNJSwKU14WgkpDqWBAiwOFIFBUbacgM1W2x62NstJyzp26RM/+XZjz1FRSb6dz5MBJmrRIpl5iHItmviynO5RKJW++9yJff/Y9menSsV5e9hS//LALhVLJ9LkTKC0tp2FyAr9v283Fc1cZPKIvb770HhaLFW8fL15b+YzkrGiyIIoimz7ZwvR5E+tc18kj5xg9cZj874L8IjLSs+k9oCudurbB08sTo7GaKxdvcOXiDQ5rNSQm1ePEkbM0SK5PbHwUiofomImiiEarxtPLwONzJ7B8yWomTh1Ffl5dUyq1WkVFeSVh4SEEBgeQevsuFsv9wHTjago9+3Vh3buf02dgN95870UEQeDCmctcu3yDkqIySkvL6xzXaKwmKCQQ/wBf5iyait3hQHSJ+Af4YrNa8fHxqvNbKCosoV3HVrz50nv88uMfjB4/FA+vumPWoOAAvLy9WPnaB7ILYlWlkXXvfs7TS+ZiqjaBKLLsvRfZ9cteDuw5ht6gZ8nyxez6bR9ptzMYPLwvCQ0fXvj28fVi+tzHWPzEK3KADA4NxMfXh61fb6ddx5Z1Ukt5OQUsmP6imyvj9LkTOXbwNGVlFfgH+j30XE6niymzx/1LNLH+KvyjgWQh8IMgCPe0u8OAuq2c/0XUSK2sQWpw/FQUxRUPrNcCXwGtgBJgrCiK6TXrXgAeB5zAfFEU//zvXs/DYLfaMBaUYSyuJLpNAnnXMvAI8KbdtAGYy6vQ+XhgLKmg0ZD2uBxSKiLt+FUC4sO4+st9xdLLPx+l3fQBDx11KDUqnA6nm4fJPbgcLtR6rbzuXiEeQYHdYiPjpNRpbzWaqcgprmPj67DY0Bh0eIX4PdTi01pZjj408r5DoiCgDw5HUGtwWqpRanTujoo1sFVWSA2J1Jp9KBQolSqcDhsupwulXi+JPwrUaVy0lhSi9vLGXlnufi90evQR0VjLStAFh8pFentVZU26S5BYWw4HiHDwwEUunr9Jr/4SrVVv0NN7QDc+r+WlER0bQa/+XWndvorAID+++fwnDtWoyVZVGqVu8VojeqfTye/bJRmUO7fTUSgU/L59D3MXP87Nayn88sMfhEWE4OfnQ5deHencoz3Pz1+K0+lEEAQenTyck0fPcePqbdp2aMHjcyfy2brNboX5e2jYKEESOKyFtJR0fjWaaNuxBe8ue5231i6RGWBWq40rF6XvXG/QU5BbSL3E2Doj77ETh4EgsGH1Jrr27IDdZufksbP0GdCNnTWzH+nrFmjctCHnz1zh/bc2AFJH9rzFj/POm+vll+i94nFFeSW7dxzAy8eTO7fuEhMbye6dBxk6sj+ff/QtDZLr06NPJ1yiSHLjRFq1a0ZGWhYfr/0Kl8uFwcPA6o+XUl5eiVqtYsa8iTgcTiwWK3/uOIBSoUBQCAwbMwAfX29uXL1N7rl8xj72CFu/3g5IqbdZNTOzG1dvu907URTRaNS8s/QL+bc24tHBvLL8aXz8vHj79Q9lmZkvP9nCq2897danA5LxmclkJigogNUb3yTtTgZarYbiwlJeeWYFdpudiKgwNn73PpFR9/tHbl2/4xZEALZ+vZ1howeQlZ5DRloW8QkxpKXcT/PFJ8TQoWtrGiYn1Hk2/ifhHw0kV4ANQD+gEvgVqCuv+l9ATcF+HdAHyAbOCILwqyiKtVtOHwfKRFGsLwjCo8BKYKwgCMlIDZKNgHBgryAIiaIo1hUd+m/CVFrFrT3naTS4nSS2GB3Ele3HUSgVJPZqgSAoMBaXc+eANN1XqpU0HdHl4Va4RRV4+HujVCtl90OAel2a4HI6CUmOoeD6/YdMqVai9/Gg2aiu5Fy8g8PmIK59EmnHruKw2sm74u4dLwgCCqXCjXas0qqpLqkg6+xtGvZtXsexUOvjhygo5P4OQaPBXlWJrVDqLldodGh8fHmQg6LQaFDqDdxr81dqdWj8ArEUF8iKv6Ldjq2sBI1/YJ17IYoiak/3QKLykoQSRJcT0elEtNkkllet4KvxC8CFiK6GENC9ezusNpeb78OTz0xn9IShbP9hF526tSW5SSJPzVqC1WrjhdcXuHV2a3VaafT6AMwmC3abg61fbcfpcvH4nPEc3HOULbWos0cOnGTR87O4eum2/MIdOqo/B3YfJeWm1HB55eINWrVrxqwFk6kor6Tf4B5yesPH15tJM8fy9BOvup27VbtmGDwMHNp3AovFysYPvmLBczPdpMt7DeiKwaDn6METaDQaXlvxDKeOnaO0tJxWbZtxaN9xOtY08VXU2A3fuJrCzCcngwC7dxwgKCSQCdOkWcqRWvek2mhi5y976dStLYf3n6Bdp5YkNIjn+dfnExsfzZkTF3A4HLTr1IromAiSGieSl1PAc6/OIysjl08++BqXy0VwSCCvvLWYjR9+LafTxk0ezqql60i5Jd2f2PgoBj7Sm21bdzLmsWE0bZHMy08tp7ysEp1Oy/OvL2DtkY04HA7mLp6GzWYnNj6arz/byrzFMwgOCayjWFCblg3w85Yd/KJB5QAAwatJREFUtO/ckpKiMjmI3MOH733OS0sXcmjfCa5evEFykwYMHtEHvUHH68+/i3+Ar1THWfMVZbVmXTlZeRw/dFpSIE6qD1DHHlh6jsy0btecvoO6sfSF9xg4rDdpKRlcuXiDxs0a0ndwd1q2aVpnv/9p+EcDyVdIAWRZzb/HA18Do/8b524L3BFFMQ1AEIQtwDCgdiAZBrxW8/ePwIeCNKweBmwRRdEK3BUE4U7N8U7wT4YoighKBQZ/T/KvlpB2RGL0OF0ubvxxhtaT+iC6IGlAWxwWG6ayKpw2O35RQST2akHq4SuSYGINCm5m0m5af0xlRik1FuSDpcoELlGWl8+7nIZnsC+JvVpirTLhtNmJaFYPtV7DyU93SWqnahUGfy83f5KM0zdpProbF388jMvhRKlWkTSgLTmXU2n1aFfUGiUqrwhcFgsuhx2lVoe9sgK1j5/08na5wGFHoVSiCwiS6hilxVIjoEolzQIAFAo0Pn5Yy0vRh4RLDYh+AW60X1tpMRq/AASVCoVK5SYDD6ANCEZUqyWHRLMJpUaHQqulOitdkmgJjcBSXIghNBJreSkuuxV9SASCQiFL2NsrylBrPdj8+f1eApVKSWF+MS3aNKnx2whi5vjF8vri4lKUSoXsZZ2emsmIsQPr5K6HjOjLsiWr5VG+0+nih29+c9smPTUTo7EatUYj90KEhQfLEvH3cO7UJQYP78vh/SdQKpXMeWoaBoMOtUbN0QMnGTyiL79v20O9xFh69utClx7t2bvrkCw4qVQq0Rt0rP1sOQW5RXh6e1BtNLHk6RU8/9qT5OUW4nK5uHLpJgaDng/f+Qyn00lxURkLX5jNmy9JL8Qpsx5FrVYiCArWfLocs8lCTnYedx+YEQGkpqQzfOwgRowbhN1q59l5rzPjycf4dN1mNyvcxS/NwWq20rFLG/z8fbDZHby+6jlyMvNQKARu3Uile59OGAx66iXEYjJb5CACkJ6WRWF+MVqdhk/WfsWiF2bLgc9isfLusvXMXjCZVUvXceXiDRo1bYjL6SKpUSIfr97EY9NHs+btjbKs+4hHB3HuAaVgkGyUr126xdNL5vLhqk/lGpqXlwd7dx3m6qUb1G8Qz5WL17lw9grL3n+R1Nt3SQUaN2tI9UMGGw6HkwO7j0nX0ziR+omx6A16uWEVoO+gHny8dhNZGbnMXfw4K19bS72EWBo1bcCd23eZEPTXdFFYrVay0nNxiS6iYiL+JrX4n4V/NJA0FkWxtv7xAUEQrv/Nrf8xRAC15VGzgXZ/axtRFB2CIFQAATXLTz6wb8TDTiIIwkxgJkB09H+dLudyOGk5tjv5VzPIOp/itk6t16JQCOReSsVcbkTrZaDx0A5c++0ElkoTOm8DTR7pyMUfD6P11OMZ5IN3qB/nvt2PtUp62LReehJ7t8RWbcFcYcQr2I/wib1w2h0U3c7GPy6USz8dofHQDhiLyuWRVuHNLJqN7kppeoGczhIUAkq1imYju6D11EtF9sx8Wg5vA5ZybIVZOLQ6dAFBWMtKsJVJDDSVp5SrVukNmPKzEWvNWAw19Q6tn2Rxi0KJQqXCWlGGWm/AVlWBooaG+yDsleVS70eJFBDs1ZWIDgdqLx+UOj0uqxm7SepetxsrsedJhlXmvGzslRXoAoOxlpei8vBA7RmOOT9bDiKCQok+JJy8u9luAoCzF05h+/e75DRIizZNmDB1JN98IdWwjuw/ydCR/fl56+/yPpcvXOe1lc/y89bfcdgdDH90EDnZ+W6pIgGhjsowSIFr944DLHxuJmtXbXxo+hAgMMifRk0bcOHMVVQqJTnZ+bLkfZee7Vi5dgkH9x3n8oXrhIYF065zK1QqFXdTMzB4GFiyeEWNtMkNTh+/gEKhYMSjg7Db7dy+cQen01lHsv76lVsU5BXSb1APGjdPYs2Kj1EolXTu0Y5Vb3zII2MG4nK5CAiom7vv0qM9SY0TsVltvPHCOyiVShSCoo6f+vbvd1K/QRwOh5Ndv+2nbYcWvP7c23KgrpcQy9Mvz+WDdz7lzu10YuOjGDy8L+XlFZw8chaHw0nq7XSiYiIoyCsi5WYa4ZEhsiR9VaUR/0B/3njneZwOJ6ePn+ez9d8wa/5ktm3dSXFxKTPnPYbd4UCjUdMwOYGTx87RILk+Vy/d4NSx83h5exAeGcauX/Zx/eotRk8cJnucTJ75KG+//gFms0Wuh6nUKvR6razldfn8NUaOG8x3m+738ej1Oiorqlj/3uf8sHk7H321isSk+mz89j02fvg1qbfT6dStLQqlQu6HMVYZmf/sTA7sPoLJZOGF1xfQqFnDhz4v/x0U5Bfx8Zov+em7HYiiSP8hPVn4/CzCI0P/6ee6h380kJwXBKG9KIonAQRBaAf833vW/gshiuInwCcgSaT8V/cXBIGy7EIyz97GK8SP8qz7U+PYDslc+vEIVmON9WyViYs/HCKxVwtu7DqDpdJE3pV0Os8ZCsCln44SWD9cDiLSPmaMBeWYK6uJbZ/EtR0nqcqX8kU+EYH4x4di8PdC7+uJs1b9QxRFbv5xhqYjOuNyOHHaHGg8dKQcukR5ZiFBiZGENIwmvl0imO9LmLisFmzlZaj0BuxVFWh8/CQZE0GJQqmWg4igUkvpKaRAY6+SUnW2smKcFjP6kHDMBbkYImNwWswo1HUfJYVKjehw4LLbsVWUodAbEF0ubJXl6LQ6TDn3R8Ja/yCUGi1OsxldWASW/Fwc5moMETES3VdZ7e7K6JKcEkOCfOnUrQ3HDp0hJi6SjLRseSQPcOHMFVq2aSqPFG9eS6FjtzY8/fIcbt9IJSwiFLvdzo7tu5k4bRSFeUXkZRWg1bvXM86cuEDfwT34s0bpFyA0XPINNxqrERQC0+dOJCwyhJZtm8pU4IQG8Yx5bBhms5UmLZLIzy3E09uTtW9vZNyUEfj6ehOfEMsLi5bJgev4odPMXTyNqiojXXt24JVnJKbX5x99S9eeHVjy1mLM1WYO7T2Ot48X+/88SnKTBnXuf9MWyRw7eBpjlRG7w0FVVTWTZozF18+b6NgI4uvFkJdbiOhyMWXWo2z5chsWi5XmrRuTmFSP3Ow8LDW1OaVKic1WN3VTWVFFo6YN2P/nEQKDA9j+/S45iHTp2Z5GTRpgt9vRaNXUS4wlIMCf37fvJiDQX/Ix2fQTjZsn8euPElvRP9CXyor7agA+vt4U5BVy/PBpnlg4lYqyClQqFbH1JDWFvJwC1r//BX7+PkRGh9MwOYFzpy5x+0YqbTo0Z9X61yjMK2Lblt9JbtqABkn1CQ4J4NHJw2nRugnGSqMbww+g36DumKotfLz5XT7+4EtatW1GUWEJTz47A7VahUqpJDwyhHfelKx+iwpL+f6bX5k4dSQ2q41nX32SfbsO880XP7p5lmSl53D10g3CIkIZP2UErds3r3M//xk4efQcP357f/b8x2/7adoimYmP/3cSSH8f/2ggaQUcFwTh3i8/GrglCMIVQLynv/VfRA5Sh/w9RNYse9g22YIgqAAfpKL7P7LvPwUWo5ny7CIqcoqJbZ9E4c1M2XtE66WXg8g9OG0S0ym2QzJqvZai21lUFZThcrrQ6LWYHlY7Ka0kNDkGp80uBxGQvN5NpVWo9RrKc4rxjw52M7yyVJlQqlVknblFyd182kzuQ3mWxMopup1NZKsEvEO8sRa6NxQ6TEYMEdEo9QaJdmuqRhcSzj0VRqXegMbbV2oKdDpReXih9vTCXJiPLjAYtbcvglqNxi9Asr6trETwFlBotXJxHEDrH4gpP0fS6PL1w5SbBYKAISIGRKnOIQgCtooyrGXF6IJCcZqqcVY78YyOx1IsfRalwcNtlnT/XlvQBYUzY95jeHh54nK5uHXjTp3t8nLyCQ4JIONuNp5eHgQFBfD+io/p0KUVCQ3rUVZaLuXj5y9FFEXe/eh1TNVmN/+P1JR0Zs2fRFh4MOdPX6Zx8yQaJNXDXG1mxNhB1E+M48iBkzhdLibPGMOIsYOw2WzodDrWrtqIzWZnzqKp9B7QDaOxmkkzxpByK41uvTpy6/qdOv0Ou3ccZMHzM6msMDJl1qP8ueMAeTkFHN5/glvX79Cucyuqq80yJfnMiYuMfewRfvjmV1wuF6Hhwcx/dgYpN1M5f+YqW7/azqIXZvHpuvvyH/UbxLFk2WIy0rPZu+sw46eORK1Wc/tmKh++8xlzFk0lscY/w2a14e3jVUcypd+QnojAhbNXGDdlBCePSuPLyTPHcvNaChvWfIkgCIx5bBgGvY5PPpB0tXKy8rl+5RYvvbmIlJt3qSivJCQsiBZtmvL911L/hpe3J0+9OJuwiGAaN0+ivLSClFtpXDh7lfKycnr07czxQ6eZPm8iZSUVNEiW9MvuKRPnZhdw4cwVvq2ZjXJGmpG+teZldv2yj+DgQH7btptFL8xiy1fbKcwvos/A7iQk1eP0ifPMXjCFxS/O4dL5axzae5yWbbR8/dkPUtOvrzezF0xmzcpPsFisXDp3jcL8Ig7ukYL7svdfpLLCSEhYEF17dsBqsZLUJJFff/qTtDuZTJ71aJ3n9L+D2zdTuXzhOk6HE41GTUCQv5uU/5+/H2DclBF/mUPjPxpI+v8F5z4DJAiCEIcUBB5Fqr3Uxq/AZKTaxyhgvyiKoiAIvwLfCoLwHlKxPQE4/c++QJvZirGoAr8oSTvo2o6TJPRsjqBQoPHQ4RnkU6dwLggCAXFhZF+8g6mkiph2DXHanLicTswVRmKTkim44Z6TDmsSh0qrRqFU4RMRSEXO/eJhWUYBzUd15fyWA9hNVhJ6NMdhtYNQ48Z45hY6X0+6zBvG7f0X7vvAG7TovAxUFVbyYIuTUqtDdLkkdV7RhcrggYDkEwKSC6I5/35cdlRXISgUGMKjMOfnyJRhpd6A2ssHhUqF02pG6x8MLqfk4a7WSEEjLApBqcJlt0qBQ6nCZbdhKbg/a9AFhcopO5Wnl3RulwutfyCWkiI0Pn4oDBoodS+Uqr18sBXlEeahZODg7ty6lU5oaJBbvwdA6w4taNSsIVqthpKiMta//wVBwQGMHj+MRbOX1HmJ2+0Oli15n0cfG45Or0WpUhIWEcrCWUvQajU0SK7P8UOnSWqUAAqB6PhIyssrOX/mCjevpRBbL5qxE4fx0epNWC1WFjw3kzUrP+HNl95j2Xsv4nA46NClDdGxEax79wuGjupX59lTKBVcv3yLj1ZvQm/Q8eik4Vy+cJ2zJy+S1CSR9LQscrLyaNm2KUcPnuLUsXNUlFcy88nHCIsMwWQ08+ZL79G9Tyf8A33pP6QHPr4+JDaM59zpy/Qd1IPg0EAWzHiJUeOHUFFe6cZ08/XzIT4hhoAgf0ZNGMJP3+7gp+928NxrT7Jz+16Ki0rp2a8zpcXliC4Xfv4+nDx6jh59OnHiyFksFqusTCyKIqJL5Jcf3Q2n7vmY+Pp5yfTgu3cyWLFmCdeu3sJhc7Bn5yESk+rx3aafMZstNGmexOgJQ1mz8hPGTBzGqnWv8foL71BSVMrM+ZPc5O179e/Cli+3uZ2zqtJIQW4RFeWV/PrTH0yeOZbvv/mVzj3a0bhZEuUlFbz31kc8+fR0uWlUqVRSXVXNu8s/ko9TUV7Jt5t+pv/QXmz/fict2zZl9+8HAWmWtnzJajZ++x4H9hxl29adeHoaSGgYT3KTBkTHRcoF+ofB5XKRcTdbomRHhBASGvQ3twW4cfU208YukFO8Gq2G+c/O4J2l98U727Rv/pfa/P5DYi2iKGb8vf/+b04siqIDSbvrT+AG8L0oitcEQXhDEIShNZt9BgTUFNOfAp6v2fca8D1SYf4PYO5fwdgSXS58wgJQajVEtUrAYbVzY9cZss7dxmm1c2HLQZIHtXPLiyf2bsH1nadoNLAd8Z0akXb4ilT7CPOn6YjO2M1W6ndvhlKjQqlW0aBPK6oKymvYWiLeYf40H9ON8Ob1UOs0BDeIojg1D49AH0pSc7m24yTVJZVUFZRzetNu8q9lkHPhDme+2kNcx0aotFJKpumIzlz95TgZZ1NBW8u5TaFAFxKOw1SNPjgUfUg42oAQbFXlOC0m9GGRiA8Rx3M5HNirKtz6TiTZeTuCRoPKwwunxYQoijgtJkw5GZiy01GoVDgsJkSHA6fNhlKrw/FAo6GluABtQBAKjUZuQnSYJOaN2sMTc34O9ooydMFhUNMzofb2RRAUuGxWtE4LjWMDuXrxOhqthjY1KQNBEBg2qj+xcZGsemMdn63/FqvVxqOTh9O+UyvMZgvBIe6Msm69OuLhoWfq7PHo9FqKCksoL62Q8ugmM+VlFZw6do70tCx8/Lw5sPsoabfTWb3iY5lCmp6ayfr3v2DkuMFYrTa+/+ZXetXY6167cpN33lzPopkv43C4MHjoaNaqcZ2O5pHjBvP5R99KEjfVZj7/6Fs6dmlDSGgQE6aNpGWbJtisNsIjQmhck2e/eS2FvbsOk59TxMrXPyA9LYvvNv1Mq3bNMFaZeOfNdWh1Wp5/7UlJY0qn5ZExA/AP8OXJZ6YTFhkCSPIob77/Iitf/wC7zU7Pvp15Z/1r0uwgMoSkxomMmjCEho3qE58QQ2JyPRY8P4v01Ex8/X0YN2Wkm/UvSD0lDzoIgiSLf/nCDda//wUbP/gai8XKnZS7HNl/khNHzzLokT58/tG3sgbZlYs3uHT+Gn0GdiO5SQNuXr8jj7wf9ARxuVwoHuKbrlIr6T+kJ4UFxax5+1MmzxiLWqXiiw3f8d5bH6HX64iICiU3R6rTxCfE4Otf13opKyOHoJAA+g3pgUIQ3GYAFrOFC2cu89m6bygtLiMzPYd3l33EzPmTeOH1Bfg95HggMb+2/7CLMQMeZ9KIuYwfMouL564+dNt72LvrsFud0Ga1ceHMFRo2kijF0bGRDBr+f+7G/+/gX+PG8jcgiuJOYOcDy16p9beFv8EME0VxGfdZZH8JLFUmcs6nkHLgIiFJ0bSa0AubyYKxoJyrv51AdIkISiWNhrTHaXeiVCvJuZhKWWYhFUnR5F1Lp373ZmSdTyGieT2Orf+N8GbxRLRIILRRDFUFZaQeuoyxSHp5Ft/JpckjnTAWlqNQKEjo3RKfiADKc0oISYqGJMnwSqXTcGWbu2WqpdJEVUEZbSb3pSK7GO9Q/5qAU4bNbKPZsLaoNAoE0YWtsgyVwUMa+YvSZzCEx1Cdk46gUGIIrctb0PgHgtMh1VaqjbI8vMNiRnSJUpd5aREumw2lwQN9WCTmvBycdhvW4gKJXuywU52TjkonyaGY83Ml/S9RRKFQUp2TKeuBKb28amZN0o/ZXlWB02rGMyYO0SViLS7EUps6bLfQt39nljz3Lq+tfJZHJw8nN6eAg7uP8ueOgzRtkczlC9flUbd/gC/evl48MmYA5WWVXL10g+atGtOhS2teXvyWXFTuO6g7fQd1x8/fl6oqIz99t4OqSiM9+nbm643fc+bkRWbOn+SWCwdp5Kuu6RvJSMuiT00g8fTyxGy24LA7eHfZep5eMpcj+08w7+nHuX0jjarKKrr17sS+P4/Uyd1brVaWrHiaW9dTadGmCT37dcFqsTIxcDQKhYK0lHRi4qN4adFyeZ8R4wbz7pvr5es7fvgM2Zl5dOnRTiYgqNQqnl0yl7ETHkGlVpGacpefv/uNlR+8wrlTl/ls3Wb6Du5B/QaxZGXmUVJYgtPp5OfvdpCdmcf8Z6fj4eXJirVLqKo0EhYRTFFBkZvo4oHdx3j+9fm8+sz9zv6Q0CCKi0pp3roxJ4+do32nVsTERWI1Wxn72CN4eOpJueVOcQc4e/Ii675YwdavfiE67v6zevXiDbr37sjBvVKP0J6dh5g0fQwb1nwpb+Mf6IdGq+FuaiZzn5qGUqVCqVDQrFVjCguKadO+Oe06taSyvMotMMXVj6lzHdGxkfQf0hOX08XoAY+7reszsFsdJ0mAG1du07Nv5zrLS0vKuHz+Olcv3USlUjJgWG+2f7+TosISXlq0nK+3rcP/IaQIQPZaqY3ysgqWvLUYY6WR+PqxsiLzX4V/ayD5fx3WSjMpBy8Cksii5F4v4HQ4adCnFcaiCmxGMykHLso2uvcgCAJV+WVEt26A3sdDKojbHWSdvU32uRTaTu3n5v0OUjCwmazc+OOMtOB8CrGdGuHh782Vn49KtF+Nilbje6Hx0GN7wKXOI9CH6uIKfKMCsZmthDWOJediKmqdBqexDIft/vYOoxF9cDjmgpwaSRQBXWAISp1B6kj39JaDhT4kHFtJoVzsltwMA7CVlaDSGRCdDqlz/Z5/tqkam8uFLjgEhUaLISIaa0mxrJ9lt1fgMJvQBQRhKS5AUCgRVCoEpRLR4UKhVqMPDJECTS24bDasxUVoAoJxmIxu6wSlEhDQ6bRodRqWLVktm0qdP3OFBc/NID4hlqMHT9KzTxc692zH3TsZhIQFo1Ip0Wo1XD5/neLCUjmItG7fHD9/X2ZNfBpRFPHx9WbOoqm8v+JjmjRPYu3bG+Xz37PkrQ1VTSqhbccWXL5wnejYCOxWu7yd3WbHx8eb4qJStm3dSVRMBB6eBvb9cRiTyf27BcmkqrS4jE0ff0dJURkdurRm3JQRMt318L4TjBw32I1d5u3tVSfIZaZn4+nV6/6zYHew78+jtOvUgm++2C4LDTZq2pCQ0EDMZotMaZ6zaCpnj19g3x/Ss9uhc2vMZiurV3wifWaVkpVrX6F1+xYc3n9SVgWOrRdFWWkZL76xkMKCIkRRSt9t+ngLrdo244mFUwiLCOXFhW/K1zXnqWmER9xnGnXu3o7mrRtjqjZz8/oduvXpSEZalkykOLz/BH0GdufVFU9TmF9MVEw4t26kMuepaVy9eIOQsCBCw4PJzcrn1vU7rLp+h1nzJ7Pu3c+wWm28+MZCNn74Nd9v/gWVSslXP6+XmU4JDeJZ/NIcVq/4WK6RvPHOcyQ0iMditvDcq0/y7rL1OBxOvLw9GTZmAJl3c0hPq01MhcDgurLvFouVz9Z9w9e1JPE7dGlN9z6dOLjnGFkZOZQUlf3NQNJ3cI86acOxjz1Ck2Z1deX+KvwnkPwd2MySC2BCz+aYSqu48N0BGj/SEVu1mayzt/AOCyCsSQwN+rRykzfxCPSWO9HFmv4QBNB5G7BUSumfqvxSBIVQJ43ksLgzYzwDvLn66/32GKfNweWfj9BsZGfOfLVHXh5YL4zqogqu7TiJQqWk5aM9qNe1KU6Hk7DkSESb+4tJdNhBocAQFYdot0nBRBQx5Wai9fVHGxiMxtcPl8OJy2ZxY0zZK8vRBYeh9Q/CaTWj0Orr6HU5LWZ0IeFUZ6SiCw6rI8J47/yCSo0+KJTq7AwM4dG4nA4kdXuXlAZ7QHlYodMhKJWoPDxxVN8PJtqAYDq08+XN917AbLLIQQSkHP3qFZ+w5K2nGT5mAHdTs5j/+Ivy+oHDeuPl5YnT6eT2zfv1lfadW/HhO/cteCvKK/n+m19ZsnwxGrVa7h3Zs/MQ46eMcBNUHPHoYI4eOk3TFsk8Omk4pSXlFBQU8dmH38jb6Aw6ueHtyP6TZGXkoNFqaN+5FYkN69GzXxcqK6r4fP03NGrakNj4aOZNex5RFImNj6JRs4ZkZ+bRf0hPnp+/lKmzx1FUUExUTIQsE6N8SGpHEAQUCneacnlZBWERoeTlFBAWEYJGo8ZqteFwOAkNDyY/VyI+bFjzpaQvllOAQiHQonUTTh+/wIwnH+PwPokI8On6zUyaMZauvTqQ1CiB/LxCsjNyWf2WFGxatWtOSFgQO7dLz29oeBCt2zVn8qh5btf05cdbeH3Vc/To25nsjFwiosPcvo8efTujN+iYt3ga505fpiCvqMZD/Sq//vgHs+ZP5stPtqJSq6ifGMeNq7cpKixh1vzJ8jH+/P0AHbu24c8dB8jNyZcDn8PhJCMtE4OHnsqKKsLCQxg/ZQQduramoqyK8MgQImq62nV6HY+MGUDHbm0oLS4jNFxaN3P+JM6eviQPHAKD/eW0a21k3s1m8+c/ui07ceQsTyycwsE9xwgJDcLX72+7mrds05SVH7zC+vc+x+FwMH3uRDp0/pseVH8J/hNI/g40Bi2+UUGILpGci6lEtUok8/QtmQJckVPMhS2H6DBzEM1GdqE8uwitlwGlWsXNP8+gUCqk1FReCQZ/L1pN6MW1305Snl1EZUE59bs3J2X/fT+KoMQIKmuxtgC3Qv49WCpNOG0OmjzSEXN5NRqDFpvJiqBUSJ3tDic5F+8Q2SqBiOb1CIgPw1aQXuc4gkIhBRGXC9FqkeTZg0NBoUIQBOxG6UX94OgfkGoeVguO6io8omLr3jyFVL/4e1BqdVIBPy8b0WHHUpyPyuCJSqfHUW1E5emFraJMrssISpVUxEdEHxqB02qVDK5cLmwV5ajM1YSH+HP2/A1Znv3+5SgIDQviyIFTco/JPez8ZS8rP3iF4qISYvKL5a70h3Uq372TQXZmLscPnWbgsN7s2Labu3cy8Pbx5MWlC1Gr1dgdDlwOJ4HB/qTevsviJ15l7uJpJCTGo1ar8PA0MHnmWNRqNXdupxEbF8WrK5/l1x92MWhEXz5Z8yWbCiS/88ioMNZtWsmdW+lUVFQyfe4E9AY9LpeLj9d+hd1mJzgkkCVvPU1BXiH1GsTRukMLcrLyyMrIJSY+imGjB7g1SY6eMJRjh8+4fa6ho/rz2fpveOrF2aTdycRqsRIXHy2Pthc/8Soul4v4+jGk3k7ni4+/4601L7Pp4+84cuAUOp2G519bgMViRaFU4OXtRbeeHbHarGRn5aFUKeVZ27lTF5nx5GOA1I/RuVs77tRqUrwHl8uFzW6nSYtkBg3rzfO1ZisAB3YfZfn7L/HOsvU0bFSfSTNGo9Vp+eO3fQDk5xXKkiT36lfhkaGUl92v0ekNOlq3by7Ls9xDUuNEiovKGD3gcew2O0HBAbz38VICgvwoLS7n8oXr3LieQnKTBty5mcZn6zZjtdqZMutRGtTUJlq2bcrmn9dz/ept9HodjZo1fKg9rtVqe6h0ksslojfoWfru8wSFBNRZfw8engYGDO1Fx65tcLlcf9cn5a/CfwLJ34HocNF8TDfOfyv1DniF+JJ1zl3bx2G1U5Kay40/ztB5zjAKb2WRduQKPhGBxLaX+kwcVhvNx3anKr+M4AaRNBrcntzLaXiH+tN4aAfMZUa8IwLQeOgpup3txtzSeOqklFqt58zg54VKp6GqqAybyYJKp0YURQpvZuEfG0pxam7NzAfObd7HgNcmoA0IchNJVBk8Uag1VBfkSrMDQYEuKAS7yYTGxw9beamkxhsaCYjYHggKgkqFo0wKMKLTidrHz91yNzgMW0U5INU3NH6Bbl4lKg8pSNiNleiDQjEX5EqeIjon1dnp6EMjZeOse2QGURSxlhVj0MXgcthx2a1ScNFoUWg0OM3VeHno2PXLPibPfJTP1t8f/c948jEsZgtJjRPdXAvv4c7tu3y27hvWfLqM3OwC9u46hF5fV0QvMakearUKnV5Lp+5tSUiK587Nu0TFhpObXUCXHu0oKS7jxUXL5JFofEIM5WUVBIcG8dj00SQm1+eVp1fKHdB6vY6VH7zCwOF9SL2V5ib5kZ2Vx5H9p+jQtTW//LCLPTsP4e3jxYSpI2mQVI+rl25KReMVH9OgUX0K8opo37kVnl4etO3QgsqKKiwWK0uWL6Ygr5DwyFAcTheR0WFUVRqxmC2MmzwcvV7HrPmT2Pz5T1yqKe7u+nUfy1e/xKVz13j97WdBENDqNFy7fJNV61/j6sUbHKlx7ntsxlg2rvtabiTs3KMdTZolsXHdZhx2BzFxkSx8fhbvvrkeTy8PGiTV5/E5E6QRf6URhUopNwDeQ2R0GOWlFaxd+Qmz5k+ukzoEyYVx9SdLycnKR6lUkpmWzeyFU3j5qbfY8fNu5i6exu0bqZw/fZkWbZrQb3APXlr8lrz/gCE98fbxJCwiBIVCwbgpI9BptUTFRfDG8+/I2xUVlrDk6RV06taWbz7/kfqJcYx4dBDlpRVu2z0773Xe+3gpvft3RaFQkNy0AclN6/b41EZUTDiNmjXk2qX7PkchYcF06NKaISP7EhQciM1mQ6P5+xa9Pr73lYmrqqopyCvEw0NPWMRf14h4D/+QZ/v/FvxXPdtLMwqoLqsi68wtss+l0LBf64fWQxoNaU/W2dskDWiL0+6gLKMQc0U1+Vfv4nK68A7zxycikKxzt2k6vDMpBy4S0TQejYeOG7vOoPHU4xMRQFjjOPKvpaP20BHSIApTaRUaTx2iS+Tar8dx2p1ovfS0mtCLm7vPUpp2X+I6onk9IlslkHrwMsWpuSQPbIvL6SL/egbtp/VFKZpRavU1joRqBJUaa3FBnVmDPjQCl82Gw2xC7ekFiAgKpSRTUqPEq/b0RuXpLQs9GiJjEZQqRLs0ghdFFy6nC5VWi7W0GEd1FWpvX1R6Ay6XE4VCiaBS47LbEJ0OBJUaS0EuWv9A7MYqXDYrKk8v9CERGDNS3Qyu9GGRkmWv1YKtvFROuWkDgnDZ7Zy5mkGlyUpZaQV2ux2Hw0l0bAS+ft68/cY6WrZpwrXLt7h1/X6/iUqt4tlX5lFeWgGCQFLjBLIzc0loEM/5M5f5ZO1XOGpmGC8tXcS+P4/QtmML3njhXRBFAoMDKCspJyQsiN4DutKwcSJZ6TnYrHaUKiXlpRUU5BfRvVdHqk1m7txKc2sYAxj4SG8UCgU5mXlcOHvFbd2YicNwOl389J37PvOfmcHaVffrNBu+XsWRA6f4piZNIggC85+dgU6nIzQ8iD927Gf/n0eJjolg1IShpKWk06ZDc/JzCnmvRrBx/jMz+HzDtxirpFRkm/YtSG7SgG59OiC6XFy/ksLVS5JOVLXJzIb3N+Hp5cGo8UPY9PEW+VqefGY6H6z61O16W7VtilanpXP3dkREh2O1WPHy9sThcFCQV4TT6eSLDd+Rn1tI/cQ4ZsybyK8//cmxQ6fpP6QnGXez3UQaff18GD5mIGqtmk/WfsWs+ZPZ+OHXvP3hK9hrgo5Op+XcqUtUVVWTevsuRYWlLHx+JqeOnSeuXjS/b9/DjHmPERIayM3rd1j/3heYTWZGTxyGudrMT1vcC+avLH+ajPQszp++jN6gIzg0mB0/u+vFtmnfnI83v4vqIU26fwtpdzL49gtJTLRV22ZMe2IcIWHBHNl/ks2f/4ifvw9TZ4+jZdumddhpDyLl1l2WvvgOF89excfXm5eWLqRX/64y+eP/Bv8sz/b/X8I/JgRREInv3JjCm1lknbtNQo/m3PzzfjAKaxyLw+agfo/muJxO1DoNBTczMRaWA9KPOa5jI67+ehxESD95g6CECFRaDcVpeQQnRVN8J4fgxEgu/XhYPm7uhTs0HdmFW7vPEd+1MW0m9cVusaHSqFGoFJSlF7hda87FVPzjJP+SsCax+MeFUplXSuOhHbCbbTjNpSjUatSe3pKGVUS01LnuckmWtjKtV8BhrsZZE0hEUZRf8kKNsZXTZsVpMdW4KYbjtFuxFRWg8w+UtLVQYi3JxWqzovbykXzbK8pQ6j3AZkGp1mAqyMFlsSCo1BjCIiVWWK10mKBQ4BJdeETF4rSYER0OqRjvcmE3ViHabW51G2tJEZqwKNa8v4nJMx/l/bc2UD8xjrGThlFeVkludj6TZ4zBz9+Xzt3b8dXGrVw8d43Q8GBmL5jM9Su3ZSOjkLAgJk0fw/bvdzFsdH8++PwtiovK0KjVpNxKY/fvBwkNC2bBczMxVlZz4+ptjh8+w+iJw1j/7ufMf24GSqWSn7dso7iolO59OpHUKIFffvqDQcP7yJ7stVFaLD0vnXu0qxNIWrVr5ibYeA8mk5lZCybjcrnw8vJErdHIQQSkGdyn6zbz8dfvsPePwxw7eBq7zU5qSjrvLf+I199+DqVKhd5DL+/zw7e/0ntAN7Z/L5EprTYbfQd1w1ht4sThMwiCQNdeHagsq0Sr1fDKW09z/PDpOsKJDzLOQCI9fLplNRdOX+b65Vt8v3k7pmozoycMxcfPm9PHLzB38eMEhwRy/NBp3l2+gd79u3Ls0Gl2/36QeYsfJzQ8mHOnLpHcJJE+A7uTevsu9RLjiI6NwOFw4HK5yLybw/YfdvLsq0/yx6/72bFtt9t1XL5wnRvXbkvyLolxuFwuzp665Eae2PzZD0x8fDT+gX5yvS08MpS8vAK2fLmNjt3a0qZDC7LS6/ZBe/t6ISgeLpXztxBfP4Zps8czZGQ/vL09iYyJYOcve1lSa/Z04shZvvzxQ5q1avQ3j2OqNrHq9Q9kl8+K8kqem7+Ub3/Z8JfIsdzDfwLJ/wEB0aFYzGa6PDkMY2EFKq2KNpP6YCyuQOdtwDPYl4IbWZz/bj9d5j7CsQ2/kdCjGSqdBqVKhSiKpB29Ktc6bNUWghIi8Qz2pbKgjODECEIbxZB+3F26zOV0YS430mx0VwRB4OzXe+UCflynRrSa2JusM7fcmhtddieXfjtJx5mDcNmdaAw6zn69F42HjnbjO+A0V6DQ6fGIjMGcl43LbkNQKtEFSZ7nLrutJkUkcdKdFjPqGkXe2k6E+rAoFCo1Gv9AbGWlKDQa9AGBmIsKMISES37qNTNde1UFosuFPiSM6sw02TRL8lkvwmW3YcrNRBcU6tYEqfELApuN6rwsxJrCuy5YKm4KCjuWcnfNJ4DKkjIK8orkH/6Qkf1w2J2sWfGxXO+IiY+k/+BehEeGMffp6fz5234qyqvc3PAK8oo4vP8EPv4+VFVV89LCZXTq3hadTseNq7d5dcUz5OcWsO6dz7BYrLTt0IJ3P3qdVUvXERkdTmCgPxkZOcxaMJnC/CJatmvK7InPAHDx7BWeWDSVQ3uPu1176/bN2LDmS/oO7Maw0f3Z8fMeBIXAkBF9qaioIiYukqu1Uh8A4ZEhrHztA6xWG4Ig8MTCKW5e5wYPPbPmT+KnLb+TmpLO2MceoaigmF9/+hO7zV7DzhIJCPLD08sDY1U1VZVGPD3v+390792Rq1duser1D+Sak94gHXdNDVNr9IShJDdJZOf2+/L0D3P6a9ioPsZKI0qVkg/fvV803/z5j8x7+nESGsTx6jMrUalVPDJ6AE1bJOHhZZDrHGtXbaR3/y68/8mbOO0ODu0/zp6dhzh9/ALT507kuy+3MePJiWRn5hIaHkxgkD/ZWbl1rqMgv4io6AiSkhPoN7gHN66lkJmeXWe7Y4dO06ptM/bsPIiHp4HHHh/N2lWfYrXaOLD7KNXGaiZOG83PW3a4edlMmDoKU7WZsycvsv2HXdRPjKNLj3ZYLDYCg/2JiYtC/cBs5fTx8yyaJdGnVSolq9a9xpe1Zngg1YxOHT//dwNJUWEJJ4+dc1smiiIZd7P/E0j+3dDp9ehi9XiE+oHNSWVBGaIoYi43cmXbMcqziohu24DK/FJcDie39kgdvU0e6cTVX467FdLqdWmCw2oj+3wKvlFBaAw6nC4nMe0aSr7wSiVpR69iKqtCqVJSkV1EzqU0N6+Su8eu4RHog39sCOXZxVirTIQkx1CSlofL4aTkbj4aDy3pJ65jqTSBIGCsdOLt64tCoZSDCNRY6xbk1Pi0O3GYqjGERWI3VaP1C8BhteIRFYe1tBjR6UDt4yfJx4siot2B02xCqdXitNtRqNS4XM46DC5HdRUOk5cURABcLswFubLvu+h0otBo0YdFyUZaglKB02ZHFxiCrbICp7kaU0EO+kDJflipN8j05HsoLTdiNlmIiomQGw337z7qVjTPSMtGq9Ow94/DNG7ekH6DunOypgO7Nq5dvsWajcv48bvfmL1wCju27cZsstBvcA9USgUb1nwpd8SfPnEBvwA/2nVqjcGg5YWFb+JwOIlPiOGpF57g/KkrTJoxhq8//QGHw8nVSzd57rX5bP7se0QRJkwdyfHDZ3DYHeTlFhARGcby1S9RXlZJVVUVJUUlTHtiPM8veFN2QmzSIpn0tGz5s4miyEerNzHv6cflQDLx8dF8uu4bKisk5pskPjiE2HrRpKdm4nQ62fzZD4ybMkKehYwaP4SKskpatW1Kp+7tyMspIC+nwI24YDaZyc7MlSXcf/jmV9ZsXMbM+ZPY/NkP2Gx2lEopGGyvKfJ7+3gxfe5EMu5mP/SlvePn3TRIri8V2K02vt/8C3OemsanH37NkJH9efyJCZQUl5GTlccLC5cyaFgfvvlc6oMpKizh1WffZs3GZWRn5RESGkzX3h1YvepjevbtzMWzV9FqNYRFhFCQV0TPvl2olxBLRloWLyx4kwaN6tO5+4N6sRAeEUzHrq2pnxhLo6YN2fb970ydPQ6Hw4HJaOLHb3/Dc5Eni1+eQ9qdDBx2B7HxUQSHBHJwzzFeemo5QcEBNGvZiBnjn8JqtaFSq3j1racZ+EgfOZgUFhTz4sJlcu3O4XCybetODLVmivdgMPx9FV8PTw83lt09+Af6/t39/rv4TyD5L0Cn04EOUClQ6TRovQwIgkCD3q3IvpCCtdLkZkJ199hVmo/uSsbpW1irTEQ0r4el0kRIZDSXfzpKp7lDcVrtpOy+RFmGlKpSKBU0HdGFazuO4xcdTGlGIZW5dVMhTpudtKNXSezdEmulCdElknLgIiAJ7F3bcYr4zo0JbxKP3WwlZf9lAuLDiGmbiMv+QAOTKCIoVdgqy3FaLRjConDZ7RjT76Dy8EQVGCo5FFZVolSrMWWn47JZJROq0AgcpmocleUotDqUGq3ksFirl0FQ1pKgr3XO+xsICEolzspybOWlGMKiqM68K9dGNH6BKFRSXcVSlAdI0ivVFrO8jUOtY/eeg/Qf2hNBUDB74WSUSiW52Xl17l1xYSkeHnpyswv46bsdTHviQWUeSfAwNyefxk2TWLX0Q3n5Z+u/YfFLc/D186GoVjrn+OHTrFy7hDlTnpOXpaVksOXrbbTt2JJ6CbEsfed5PL08yM8r4quNW2ndrhkIAg2S6qHRaamsqEKr1bL9h12UlpQRVy+GvJx8SkvKWfzyHOY+NQ2NVk1gkD9+/r5MH7fogVsq4uvvI+ssabUaOYjcw28//8mEqSO55O9L5t1sKiuqUCqVREaFMmPeYyQm16OirJLsrDzWv/8Frds1x2yu29NSbTShr/VSKy+roCCvkMemj6Fpi2Sy0nNIbtqALj3bU1RQQr3EWHKy8giPCHHz9biH8MhQiovcZ5l3bt0lPDKUlFtphEeG8kFNPWjIiH51ZP9FUeTiuauER4VSVSEZgM1ZMA2z2cJLby6iuLCEtDuZDB87iIQGcfz2859s/uxHmrVqRHLjBhiNJsIiQuQeGp1OS6du7Xi9ppC+cu0rKBQK1r/3OSA5NC58fpYsnnn9yi0USiUlRaWERYTITZCDR/Rl44dfywHfYXfw+vOrSG7agIQGkjtjaXFZndTgiaNnWfbeizw773V5mYengTYdW9S5d7URGOTPK289zfzHX5CD/6DhfWjwFxtn/SeQ/F9AZ9CjM+gx+3uh8zaACA36taYqv5RWE3py8YfDWCqqcdgcaLz0xHZIojS9gMwzt2R5eZVOQ2VeCQKCHERASmndPXaVVhP6UJFbQkVOMf6xoZSmu3tHKzVqXA4XftHBnPx0lyzkGNUqkaLUXBxWG35RQdzae16mKxen5lKWUUCzIU0RzZVo/AJQqDUINS9ytYcXuqBQTLmZ8ovfUW3EZMvEEBmDQqOhOjdLDhJOixlTTiZqL2+cVotMB9aHRkqFfLsNRImqay588IV+P4esDwnHabNiKy+V9LMqSt0K7LayYgzhUThtNkSnC9HpwJiZhi4oBAQFSo2GsrJKuvfrQkZ6Di8tehNvHy+Wv/8iXXp0YMtX7npLQSEBVJRXERQcQOrtdPbsPMSjk4az9evtiKJIZHQ4Hbq2wWyykJlRd/T8289/0rhZQw7sPiovS2wYT1VVdZ1tU27epWffLry4aJksdjh+ykhGTxjC7RtptGjThJKScnIy85g+dwIWs41FL8ziuflLuXZZSmV16dmBG1duy86Gi16YjVqtqWPqpFQqqSiros/Abvj7+xIQVLf5TalU0qhZQ8pKK9j+/U4mTB1JRGQI2Vn5eHjquXbpJp26teXmtRQcdgelJWWMeHQQl865S540bJTArl8lmq1KrSI/t5Cg4ECiYyJlEzGAmLhIJk0fw93UTKmpzt+HZi0bER4ZKvuh63RaBo/o69aRD5DQMJ4hI/pQWWGUzwWSlpWfv+SnLsmZhJGfV0hy40TupmbicDix2+xkZ+biH+jH6RMX2FOjg7Vn50HadWpJZFQ4AN16dmTtqo2oVEomPj4ag0GPWqPGx9ebD9+5TxaoqKhiz85D8r+LCks4tO84np4e1G8QR3papsxYq82uUqlVbvIlIM04igpK5EASEOjvJhAKyC6Mn29dw8G9x/D19aFzj3ZuGl02q42Mu9lYzBYiY8Jl2m+HLq3ZsmMjmenZ+Ph6k9Aw/u/2ofwz8J9A8t+A3kOP3kOP1WLFWFCOWqdF7+dJw76t8Azxw262cWHrQWwPKASr9RpaTehFxpmbBMWH1zludUklpXfz0HoZyLt6l2Yju2A1mqkurkChUpLQswX5V9OJ7ZCEy+6gzSQp6OASKbmbR8H1DEIbx6JQKd1k7wEKb2djd7XGMzgMe0UZNmuNJ4mHF2pfPwSFAoVai7PWDMJlt+GyWlFqdXUsc112G4LqPhtE4+MHoojKwxOl3oBCo5XUhQOCMBfkyjMRfbjEp/eIisNhMSHULFfq9LIsitt5nE4UKjX60HBJfl50YSnMk6yBQ8LRGEspyC6iIK+IFm2acuncVVLvZNKzb2ecTgfbvt+Fh4eBSTPGkJOVx+KXnpD7SQ7uOcbKD14hKjaCyvIqiotKWfv2RuYumoqnp0edawkI9HOzWDV46Bk1fqicdqqNcZOHs/btjW6Kud9u+olXVzzNsNH9KS4qQ6fX0qR5Qw7uOc4vP/5BcGggL76xEKvFioengfCIUM6cvMicp6aRevsuF85c5uK5KzzzyjzeenUNpcVl6PU6ps0Zz68/7iI9LUv2IHkw2EydPY4fNv9Kg+R6vPTmIjw9PcjJzsPpdGIxS2mlIwdOMnfx47z9+gd069WB44fP8MySefy8ZQcarZoJU0exZ5f0Ur1nmPXdl9soKylnyVuLGTluCN9uktJOFrOVhKR6vPH8O+TlFvD4nAl4eBtY9t6LZKRnY6yqJjgkkJCwIHz9vCkrlXo8omMj0Om1ePt4k3E3m9btm3P0oEQ1Pnb4NIuen0WjZg3w8vIk7U4Gg0f25U5KOhtWb5KeL72ORS/MRhAEOYjcw6lj5+nzZjcAzBZpAOZwON1YZzPmTaS0pBwAtUZNSXHdmtyVizcYMrIf32/+lbGPDeOn73bQuXs7kpokMmvBZF5+ajl2m122Sb4HtUbtJlkSFBLAKyue5vn5S6UaiVrF1FmPcvrEeUSXSNqdDKJiwt0UC8rLKvjyk618seE7XC4XDZLr8daaJdRPjEOpVJKYVE9Wbv5X4D+B5J8ArU6LNiYES5AX1cVVqPVarv1+miZDOhDVKoHUQ5flbfX+nvhGB3P+2/0k9GqBzWipc7ywJvEU3MxCUAg0HtKB2/svEpoUjV90a3S+HmScukF403j8Y0PIOn+byBYJeAX7cnv/BYwF5cR1bkxwQgTKv0E/FJBqI84aOq+gVKHx8cVaWozTYkHt5Y3GOxxLcSFa/0AQBBRqNSIPYaLUcj5Ue3mjUGvB5ZQaB10u7JUVILoQ9B4YImNxWS0IgoDociA6nQgKHU6rFa2PH9S4H6oMHjhM7qN7AQFzfja6oFD0IRHYyksQ1Bo0vn6Y83OxqbR889UvhEWEMGBoT7r0bI/NaiUnOx9BoWD63Il4e3sSnxhLaUk577+1wS3v77A5yM8r5Oua7nRBEDCZLegNOrx9vOQUkVKpZMiIfhw/fIZ31r1GdbUZbx9PSovLSGqcyJynprHlq22UFpcRGORPcpMGjJ44FKvFys1rd7h9I5XysgqUSiUfvvMZt26kolQomPPUNLbVMKXycwt544V3mDhtFPv+OMzwRwfx8VopVdK6fXMeGTOAZS+9z9mTl1ixZgnZmbl4eBr4aPUX5GTl07BRAn0GduOLj75l1IShlNaQEJIaJxIeEULsuMEsX7Ka8rIKBEFg7GOPkJqSzvnTlxnz2DCSGzcgKyuPOU9NJTQ8hI0fbubCmSs8vWQuDoeDU8fPMWrcELr2aE/anQw+/+g7yssqUCgUpKdlUVRYTNOWjbh8/hrjpoygtLiMu6mZrFr/KjarnfzsQs6fucy2rfdl9jp2a8vqT97k9s00yssqUKvVIIp89tE36PU66iXGyZIhDrsDm9VGYX6xLDl/YPdRWrVrRq/+Xdj3xxHMZgs3rqXQu0bj7EF4eHmgquldeRD1E+MIjwxFo9Vgs9qonxhLg4e8lJu1bMSta3ewWqy069QKH19vLl+4zsE9x2jbsQWrN77J7t8P8cySubz9xocYq6rR63W8seo5t8ZEm9XGnp0HGTNxGNGxEeRmF3Dzego+Pl78+tN9avGOn/fwzfaPiK0XzeXz19z6pG5dT+Xz9d/y2spn0DyE6PBX4z+B5J8IncGALtqA3seAZ4gvVXllxHZMxic8gNyr6XgH+xKUGImlopqqgjKsVWayz6fQ8tEeXPv9FDajmYgW9fEM9Cbr7C1Amp0kD2yLQqkk9chlNB5aGvZvS9GtLEozCkg9dAWNQUfh7RzUei31ujVFrddwedsxAhMiCKwfTvGd+8yV0MYx6DxVOGtJpmgDgjDl5ciCifbKckSXE0NYhFsqSxsYUiPGeD/dow8Ow1rjtKj28cdWVuwmXSJJxysxF+ahCw7DVl4KgoAuIBhbZSXW4kJUBk8QBDwiYqQifECwlMaSayQBcne9tbQIXWgk2sAQ7NVV2MvL0AaHceLQOfz8fbFarLJ/+7DRA7hy4TpptZwDg4IDeOnNRWytle6Kjo2UZpd6HQ2S63Pr+h1EUSQsPJj3V3zMxMdHYzFbsNsdJDVO4PvNv9CwUQIfr/2KlFtpKBQKps+bSE5OvvQynjiMeokxeHl5sfK1taSmpOMf6MfsBZNp1a45KpUCb19vWrVrRvvOrfEL8OVSLbVchULBuMnDCQoJZMxjjxAeGUpEVBg5WXmcPXmRXv26YLPZMZstHNx7jP27j+Lv78vAR/qQ1CiRvbsOce70ZW7dSOXMyYv4B/oREOhXIw45nT9/OyB3d4uiyJavtvHkM9M5c+IC3236meatGlE/MZZXnl5Br/5dJcOwu9m8+sxKvLw9iY6NwOBpYM3bG91qMH0H9eDqRcnBcdW6VykpLkOhUHBo33FWb3yT95ZvkF0cO3dvx/CxA9m2dSfBIYE0aZbEnVt3UamUdOzahrKSCm7fuEP/IT15ceEyZsyLwG6zM3uBJG/i6eXJwT3uwqXnTl3iiYVTOLTvBHOfmsbNaykc3ndcDmr3EF8/BrVGzdrP30Kt1jB87CC21ThmengamPPUVH75cRdTZo4lJCyYVu2a4R/gx+NzJsgzgNj4KNp1asl7yzfwzCvz+GLDd5w+LpE2Duw+SrfeHVn+/kv07NsFkAYAhfnF+Af6ERMX6aYYbjSaOHHkLDFxkZw/fZkLZ6/w+JwJfLlxq9vnq6o0knLrLlqdlosPqCsDHDlwUkrZ/p0u+L8K/wkkfwEMPl4YfLzwCvbDVFaFT2Qgel9PlGoVt/aeI6J5feI6NgKXSHlWEVovPVGtElDrNdjNNuxmqyRf4nKh85GomBe2HgQgpm1DLv9wGEu1mZg2Ep3vxh9naDK0I1VF5aj1WrkfJevMLXouHkHJ3TyK7uQTnBBOQJQP9tJCDJHR7nLuonvKSqHWSnWNWtNpa3EBHjH18Iiph+hwIDodOO12tH4BOGtmGrWDiLSP1LMi1njBawOCcFmtOK0W1N4+OCwmHNVVuOw21N6+kkqwUokhIhpcLlw2q8zaAqm/RBAETNnp8jns1VXodBoaNWsoF0MBQsPq+qcXFZZw53Y6cxZNQ6FSgCgFl/dXfER2Rh6Dh/elR59OCIKAX4Afs+dPZv/uo1SWV9KlVwdOHj3HxbNX6diljew9PnRUf/b/cYQ7tyWl2otnrzJ38TR+/2Uv6XckenZpcRlvv/4BM+dPZu/Ow+Rk5fHzFunl5ePrzQtLF7C7poA8fsoIjh0+I790lUoli16czdq3N2Kz2ki/m4XBQ09VpZGIqDAUgkDKrTQqyitp+lYyO3/ZS2JSPTp2bcMfv+2ntLhMpkRHRIVx45q7OgO4930U5pdw8uhZnnllLqJLUkD+4qNvOXPyIqHhwQwZ0Y+rF68z48nHuHb5Jqm30hk/dQQWi427dzJo3a4ZlRVVOBwONqz+kuCQQCxmq5sV8NGDp5g1fzK+/j48NmMMH6z6VE4Njhw3mIQGcRQVlWK3O3l1xdOo1WocdicKhQKnU/I/qT1TlB9jUWTE2EH89N0O2SlzxryJNGyUwKVzV2nZpgkJDevx3Lw3cDqd9OrfldkLJhMbH0V1tQmX08WKV9ew+OW5hIYFkdxU8rHJycqj76DudOnRDrPZSmpKOj99+5tk/tWwHqve+NDtOg7tPU7G3Swa1wgnRkaHExldN40tff9e9OjTmXOnL8kzFafTWfOZHxACVSk5tO/4Q+nVLdo0wcvH86Hn+Kvxn0DyF0LnZUDnZcButmGsMQDyDgvAaXdQmV9GUGIkwQ2iMJVVcefgJXk/n4hAkvq3xjcqGLVeS+bpm6i0arzDAwhtHItnsC/WKhNeoX6EN4sn91Iat/aco+GAtnWuQXRYCY7QEBqXgMtqRbRVIqjUKJRqlHoPqdP9IT7jCrVakix5AE6zCUGhwGExofUNwGExY7ea0foFPlQvSKwxukIQEBQKt9mMoFTWkkexojJ4oFCqqLqbAkgOitaSIjcPFG1gCC6rVbL6LcqXAp0oEh0exPWb6W7nFgRkYcXaSGgQx6ZPviOuXozcNe3t7UWzVv7s+m2f/OOdOX8Sn364mSefnkb7Lm2YN+0F2XPCUavmERoWJDfw3YPNZpeDyD04HE7sdjs3rt2me5+O8vKK8koy0rJki15vHy+3l67T6WT71p10792J3b8fICg4AGNVNSGhQYiiyOSZj2K3OwiLDKaywsjsBZKfeUxcFO06teLUsXMYPPSMnzIStVpFQoN4OQjeQ+0Uj1qj4vjhM4yfOoLP1n8rBc5ubVj0wiyCQgJ55ZmVLFm+mG8++5H+w3rSvFVj/vh1P6eO36dRjxo/hIy0bF5cuhC7zcEnH3zJg8jLyWf4mIF8vfF7t/rST9/tYNl7L9KqXTM+Xv0lKbfS8PbxYub8Sfz47W+kp0piis++8iSvP79Kfu6SGiVQUlxGQKCfm93yxg83ExYRwpvvPk/63Wyqq0107dWBA7uPsu+Pw7Tr2BKFSondZqe0uIwps8fx289/0qRZEi3aNOXEkbOsXrGBG1dTaNoimRnzHmPAkJ48MnoA3j5eXDhzpc5nA0kr6x+BUqlk/NQR3LmVRtOWjTh64BR7dh5k5LjBbK6lCBwZFUZiUj22fr0dX38fuvXqyKF9Uj9SYLA/Tyya+tBU3b8C/wkk/wKo9Rr8ooIxVVUT3aYBdpOVktRcTn3+B9FtG6DW64hqnUjWWWmkWJFTjMvpxDPUj5Of7ULv60nLcT3xDPLh6m8nKLx5X5o6sXdL4rs1wS8ymEs/HqZB31Zu5y7NKiM4Uis3GQLoAoKozrqLPiQc0dcPQaVyk40HSWVXqdXJdZR7UOoNuGw2NJ4+iKILXWAwzhp6qKBQuNVMQCri43KhD4vEUuJe+BedTkSXS95HdDhwKZQSy6sgp6aRMRyn1VLTlKjFXlmOy25H7e2DR3i03Pzo7etFXL0YDB562SXvyIFTDB870M0Xome/Lmz7fifjJo2QZzdRMREU5hdTWlLOEwuncPr4ec6evET9hFhmL5zCmZOXKCwo4dlX55GRKtWuwiKCZdvZhwVii9n60BGzpkam4sGgW15WyZiJw+jUtQ3qWqwfL29PBj7Sm4AAPwKC/AmPDCEg0J8nFk7BarWx/v0vePPdFzh1/DxrVn4snUOr4clnpvPJ2q94ZMxAxk0ZTlFBCXdu32Xjus2MeHQQn67/hpKiUpRKJZNnjuX6ldsIgsCo8UOIiAzD18+baqNJ9p4/euAURw+c4tHJw1n6zvOYTWZ69u+Cp6cHIWFBbkEEYNvWnUyfOxG7zYbBw8DsBZP54uMtsiAmQLPWTQgJDUSlVuFyutjx826ZueRyufhs3WY54FVWVPHum+uZ9/TjfLDqU0zVZnb9upcXli5k+9adtOvUkm69OlBUWIparWLA0F5uTK+8nALOnrrMR+9/AUD3Pp3kmsvxI6dp3qoJP377Gz6+3vz2825cLheDhvXm0vlrHNx7jK49O9Ktl+R1smzJ+7y97lWat2wMSBL5TVokc+XC/abiNh1bEhNXV6DxbyE2Ppo1ny4nLyefZi0akXE3G18/b5q1bMSpY+eIT4ilU7e2RESFSfWXFZ/QuUc75jw1DafDSWJSvOTY+W/CfwLJvxAGLw8MXh5YzRaSA7yI69QYm9GM1stARIt6+MeGkn8tnYC4UDyD/TjzxW5EUaS6uIKyjAIUSoVbEAFI2X+R7k+N5PrO0zisdqxVZjfRxyu/nqLfS2NRKZ24HA4UKhW2inJEpwNTbib60AhM2Rl4xtTD6Sk1DQpKJbbiYrRBodIL3W6X2VGWwjw5KKm9fFB5eSMICqqz7iKo1HhEx2MpyJVSV17eqPQeOK0WVD5+uIoL69wTamYr2oBgREHAVpSPy2aVu9hNuZmoff2kJsb8YrQBQSi1OmxlJTgNFjyi43FYzHjoPUFMYe2ny7lx9TYZd7MpKiyh98DutOvYkmtXbuHl7cmdW3f547f9XLt0k579u9CoaUNWvLZWHhGfOnaO+c/N4JHRA1i76lOyM3PpO6gHWZm5fFdj26pSKXl52WKeeXUeP3z9C/l5hTRpkcSVCzfkj1VeVsGcRVN5+40P5RnRkBH9OHPiAsPHDiQsMpRZ8ydjrDLy47e/ERMbyZ6dB9n3xxHmPzsDQRAIiwhh3OThfLlxK8WFpSQ3acCo8YNZtuR9edYUERWKUqng51rB0ma18dUnW1m98U2KC0vZtGEL1UYT4yaPIPNuNh+88ymPjB6Ip7cHCkHA28ezRkixHlqdlssXr/P4nAmcOXmxztd159Zd6ifGuUm2LHhuZp3tnE4nTVsm8/OW39n3x2HUGjXjp47kkdEDWLV0HSPGDSIgwJcli9+irLQCvUHPjLkT+Xnr7+Rk5aHX69yMsaRHRXRLwaXcukvDRglERofRoUtrnntyqRyIWrZtythJj7D1q+2AlJ47c+K+0vbBPceYu3gaB/cco3mrJvTq35V9fx7h6sUbjJk4jPiEGAoLSvAL8OXE4TOkp2WhVCoZPnYgeTkFlBTdFyj18/dl+fsvsWfnQQ7vP0GP3p3oPbAbPr5eZGfmknE3G71eR3xiLL61hBVrf65qowmDh57EGnpv89aN5fX9Bvdw275X/678+ftBObgnNU5k8Ig+dY77r8R/Asm/AVq9Dq1eh8FLj8VkxW60SKNyl0hM+yTMpVWc3bxXHrX6x4ai9tBiqTLVOZbocuG0OajMkwredw5don7XpkS2rI9Kq8EnMhAFdkSnC1t5SZ3GQNHlQhsQjCk/R651OEzVqHx8weVC4+0nmUYJguS3br3/Q7ZXVUh+71o1Cq0W0enEUVWBUqtD7eWNvdqIuSAXhVqDKEoe7LVlUECQ3BQ1Goneq1TI/iP2qooa+ZZQFCoVLpcLlcETV43GliE8GpfLKVGBERGClRgMOp6d9zqlJeVERIXxzJK5XDx7hYioUL77cptb+qSosISAIH/upmbWoe3+9tNu5iyczLDR/bHZ7ETHRLDk6RXyeofDyScffEXnHu3o2a8zeoOeISP6cmjfCdLuZNCidROCQ4MoLS5j3aaV5OUU4O3jRUlxKU2Ehlw4e012CgwJDeLtD19lz86D2Go65Xf9so8lyxdjrKpmzcpPZHbZ9Su3+O4rOwOG9uK3n/7EP9CPidNG1/Gov/f5yssqeX7+Uvk5OrD3KBOmjaJJi2QO/HlUThlNmjGGD9/5jGatGtGidRMCg/zZ98cRWndojl6v45GxA/Hy9kQhCNRvGM/yJavdzlVWWi43Qd7DvabEfX9I9Tq7zc6XH2/h6Zfn8Nbql/Dx9eblmiACUrf8+vc/Z/bCKbicLu6kpBMY7C+bjN1D7dpA+86tOHrgFGqNmh+++dWtD+P86cv0G9xDUklulMDJo2c5+0BgtNnsJDdpQHKTRGLiIlm/aSVpdzJ465U1bP16O63bN+fcqUuyOZXT6eTHb39j3tPT8fJ2r0XExEUyfe5Epj0xHkWNHP21yzd5YtKzMrGh94BuvPD6fIJq2Ttn3M1i+/e7OLDnKG07tGDMxEeo3yCuzvfpfq4o1m9ayd07mYhAfP1oAgLr9gz9K/FvCSSCIPgDW4FYIB0YI4pi2QPbNAc+ArwBJ7BMFMWtNes2Ad2Ae9XiKaIoXvzrr/yfC41Bj8agx+Zhlex2RRGn3UFRcQV6Xw+qi6VUU0hSFIIgYC4zovXSY626z7gKqB9O9sVUQpOiuXv8uiwMqff1JK5jMqLThbNaUvxVqDVu/SEACo1W6mxXKAABhd6AWqlE7elNdUYatfXrBZUKjV8A1pL7MwtRFCUZeS8fFCo1osvlpssFoNTpcFnMqD290QWH4TCb0Hj7SIcWRZQ6PUqXC9HlQqHWyH0rhvAoTDVeJYBkAxwZg8tul2ZAShX6kDBERLLzy3jmyaVyUMjJymPZy+/TZ1B3nA6XLGdyD6HhwVSUVjxUoVWpUODh5cEXG77D4XDy6ltPM2PeRCrKq9j5y16MVdWERUje5Xt3HsLL25PkJon06NuJ+PoxCIKCHT/vpl2nlixfslo2merQpTWjxg8hJDSQGU8+hlql4siBk3y/+RdGPjqY5xYsBSQ7WJVKha+/jxtFGSDlRhqLnp9NaHgwJqOJj1ZvYsa8iXVqQa3aNuPcqUtyEJkxbyIXz11j3tTnUamUjJsyggnTRhESGsiNayk8sXAKqSkZ7Ni2mwlTR3Hh7BXi6sfwyoqneW/ZRxQVlqBUKpnx5ETqN4jjdC3jsO+//oWVH77CT9/t4Na1O7Tr3JJW7ZqzacN3de7t7RupdOnZngvnrsp9GvfgcEjOg5+u24yp2syMeRNZ+/ZG+R4MHdmP65clNmP7zq2JiApj5/a99B/Sk+tXbtU5163rdwgOCeTi2ctkZbhrbimVShIaxFOYVyS/2AVBwGKyyM9KUuNEfvzm17rPh1LxN1/294KI2Wzhw3c+d/M+2bvrEIOH96FnP4nJVVVRxevPreJsjcNlWkoGRw+e5osf1hIS+vetcQMC/f/twaM26tqn/WvwPLBPFMUEYF/Nvx+ECZgkimIjoD+wWhAE31rrnxFFsXnNfxf/6gv+K6HRa/GPDsYzzB+Np56I5vVo2K+NnHt3OV2ILpE7hy7RsF8bQpJj0Pl4ENU6kcQezUk7chmNp56wJnEk9GxOQo/m+MWEoNSoEQQBl0KLvbICrV8AgkIpn1ft5VNjUSuiUKlwmquxFuRKaSOzCTcTFJAVeGUoFCh1OkSXE2txIeaCXCko6O838QlKJWpPHxymalw2mxSMfPww5WRiys2kOuuuJDVvNlGdnY42MBgQQKBG9fd+ABBdTuw1Mi5ATXouC1wiORk5dWYWRYUlRMdGYrfaeOaVebKMto+vN5Omj2Hb9zvx9fWuU6AcOrIfaSkZLH5pDivWLuGz9d+w8cPNHNxzjNkLpxAVE0GPPp14/blVHDt0mj9+28/86S9htzkJCg4AXIyfOpL0tCw5iICk3lpWWsFvP/3Jxg++5qPVm0hsWA+tToNCKfD4E+OZvWAyLdo0ZucveynIq5sK9PH1RqlUsvmzH6msMDJx2ijycwt5eflTDBgm2edGRIUxedaj8kstOjaCosJSObXjcDj5+tMfsFltrFr6IYIgUF1tIiYugmeWzOOLGl97u83GVxu3ymrFTqeTDau/ZODQXm7X5HS5MFebUamUvPnuCxTmF7Ply23E1ouuc/1BIYGUl1ZiMOjrjOoVCgVVVUYK8oqoqjSyY9se1m96m2eWzGXZ6pdwiSIxcVG8/4kUcDd+8DUg6aK1atuszrnCI0OplxhLYX4JL76xUN4mOCSQBc/N5Lef/mTYmIHExkdx4shZpo1dwOI5r5LUOJHJM8dy904GDR9Sd4irH4P//8E8qqrSyNVLN+osv+fACJCZkSMHkXvIzswl4wF73v8J+HeltoYB3Wv+/hI4CDxXewNRFG/X+jtXEIRCIAgo/5dc4b8Bnn5eePp5YTWaMVVU03XhcKoKytF46KSgIopc+vEwgfXCCWsUg9VkIediKohwa/c5kga0ofhODkUp0ugr8/RNIprXI7FXM9R6A+aCXMkoSqVCUCixV5VjKcxDHxqJpSDXzQnxYQVkQamUA4lSp0cXHIYpNxOVwVNiURXkAiJKvQGNtw9izUzH5XSgNHjgsllAUEi9JLVgLy9FFRKOIAjYysvwiI4DQaizHYDLZpUYZbVIAKLTiZ+fl9ToWKuIrTfoCY8MZcH0F2mQXJ9VH76KKIrk5RTw0epN9OjdkcSk+ry74Q327jxEeXkFvft3Q2fQ8dLCZQwe0Zfjh8/IUh6FBcV88PZGlq95mQ3vb3K7LpvVxvnTlwgKCcDXzxedTiMbRNXGlYs35IAmiiI/bdnBkuWLycrMlfWZ5jw1jZNHz6JUKug/pCd//LZf/k4mzxjLB29/wgtvLOD08fOsrykeA4yaMIQX3ljAret32LPzII2bN8TTy4PGzZLc6gP3kJOVh0arZc3KT9Ab9DRunoQgCFRWGomICqX3wG789vPuOvs57A5GjR/C4X0nCA0PZuCw3gQE+dG7f3fupmbQZ2B3/tyxn5ZtmnDx7BU5fZWYVA9RhMrKKvJyCnj65TksW7Iam9WGQqFg6uxxaDUaXnh9ARXllTicTkqKSzmw9zhjJg5j5/a9KJQK3mryMsNGD5A8Vn7dT1FBMX0GdSfjbjbnz1xGoVAwavwQfH19+HTdN8x88jEaNW3AB1+8RVFBMTq9DkGEEWMH4e3rxfUrt5gz+VmcTidarYYd23YzYGgvSkrKeOzx0WSkZ8v06ZGPDqb5Q9R3nU4nmenZVBtNhEeG4u/vS7feHfn1AS/1eon3ZzIqlarOMwv8l3xM/l/Bv+uKQ0RRvBea84GQv7exIAhtAQ1QOxm8TBCEV6iZ0Yii+FBfV0EQZgIzAaKj646Q/l+E1lOP1lOP3WJF46GjNC0frbeeNpP7cvWX4xSn5oIACT1bcPLT+30SCqVSDiL3kHMxFd/IIPxjg/GPDMFlteCoqsRhMSE6nOiCQzFmpqIPDHELJLaKMok9da+mIQjogsJwVBvRBYXisllxWS2IDkdNA6NLkpUvL8NeWe52DfrQCNTevgiAQq1xS43dg6BUog0MkUbHWXdBBH1ouJvrIoDK0xtLsbsXi6BQEOajZc7CKayrebHeezHt+Hk3Hbu24dih0yyc+TLzn52B3qDnpWVP8eev+5g79TkUCgXjp46k7+DuHDt4mp2/7MViseIX4CsHkXuwWm116MTydQgCB/cc5/D+EyQm1aNNhxZyfv0ewiNCKH6IH4nBYOCZJfOorKyS/dSPHTpN5x7tmLt4GjabnfqJcXy0ehPpqZncfOEdZj45ye0YP327g1nzJ/Hzlt8RBIGBj/Tm+dfmU1lRhdVqcxsNAwQGB9B7QFe69+5IRHQ46WmZRESHM+PJxygtKmPvrsMyVdg/wJcBQ3th8DQQHBqEWqOmQVJ9bHY7Xj5eFOaXsGrpOjmVM3BYL65euskjYwYSGBSATq+lorwSnU7L1cs3ia8Xw42rt3lr9ctUVlSh0aoxVhhJS83k8oVrBAUHcuzQaT755h36DOjKpXNXeeaVeXj7ePLJh1+RcjONqJgIXl/1HNlZuZw9dZFeA7rSpoMkanj04Cni68dQXlpBZUUVLpcLT0+Ph0re3L6RxqDhfYiKCaeq0oivrw/nz15m6qxxbNv6O4OH90Gv1xMcEkD7Lq3xD/Bz299UbeLnLb/z/oqPsdvsxNaL5u0PX2XaE+NJT8vi8vlrqNQqZj05ica1pNxj4qMYNX4IP9RKn3Xo2ob4+jEPfb7+X8ZfFkgEQdgLPMzj8aXa/xBFURQE4W8SrgVBCAO+BiaLotw19wJSANIAnyDNZt542P6iKH5Ssw2tW7f+H2UHqdZp8YsMxuDnSXVxJbZqG/V7NMdpsyMolGj0WvR+HphKpAK1oHx4plIURWxGE9ZiGxr/QFSeXig0GpQ6vTQacjpxVBvRh0ZgLStBEAQ0vv44HQ48ouNxWswo9QbMuVm47DbsSP0ctloBw2GsxBAZ+1CdLKfNisNoxGWzSDL0Or2bKRVIswpJBuV+Kuhe0LKUFIIodeCrPDyh1jmUOj0uhx2V005Scj3mPf04VqsNjUbN79v3kp6aycz5k0hNSWfAkF4YDHqqqoxkZ+ZyoKYz2uVysfmzH/Dz98HT21PO3TsdTvQGvWyJew/ZGbn0G9LDrRdDo9UQEhYkzxBu30hl4LDeNG7ekKsXJfHFISP7AWKduoex0sj7KyTqrp+/D8tXvyzTmO8xc5o0TyItJZ30VKk3xWF34HiQOCGKMvM6ODQQq8XGgT1HSWrSgN4DunLhzGX5szVpnoTNZic7I5egkAAO7jnKvj+O8MHnK/D19ebiuav8vm0vz702j00fb6FH3858tfF7yssqSGqcwIx5j5GZkUPD5Pqkp2Xx+7Y9bvWAnb/s4/VVUpKhrLSCO5fSyMrIJSs9m+dfX8AXG75Do1Xj6eXJp+s2y/s1bZFM30E9OHboNGs/Xc67yzbI9Y8xE4dxeP8JWSI9KyOHN154h0cnDcfPz7dOU2B6WiajJwylqtLIu8vWM2r8UOIeSLel3LqLj58XhfnFbrOHCdNGYTDoOXHkLCeO3Deye2X5YqJiItyOcfPaHd6ude701ExWvfEhaz9dzvovVpKdlYtOpyMqNsLNh0Sn0zJ74RTadmzJhTOXSW7SgDYdmv/lAot/Bf6yQCKKYu+/tU4QhAJBEMJEUcyrCRQP4YWCIAjewO/AS6Ionqx17HtDK6sgCF8AT/8TL/3/OWg9DGg9DFhNZvR+HjhsDgSFQFV+KckD20l1EIcT74gAPEN8MRaUy/sG1AujIreE4PgAwInDWOmWMlJ5eqP29pX6M0QRQ0Q0TosFnA4USiUuux1LYR6CWo3WLxBBoUCp1WGuRQOGe2kvFWovb6wP9p5otAieYC21YK8oxxAWgbW8VGpuVKnQBYZiLS1C4+tePLRXVeC0mPGIiAFRxOVy4nK6METESDL2Gg0uux1zQR4qgwdqrYMP3/nM7Rhe3p4YDHqGjurP5s9+wFRtlmm0D6YVbl1LoWHjRFmJddev+5gycywb1nwpbzfy0cEc2nccLy9PVn7wCrt+3YfBoGfgI71565U1budWq1XExEXRqWs7FAqBi+eu0qtfF/n4KpWSKbPGcbDG5CqhQTx9B3cnP6+QVete4+M1X5JyM43OPdrTpHkSq2uCzT14POBXkZhUj0bNGrDw+VnEJ8Rw4vAZnE4XTruD0qJSho0agEarQalUkJWRi91uI6FBHBvWfkVVpZE1ny7jwO4jHNh9lLj6Mby68mkunLvK7AWTefLxF+WZ2I2rKWz88Gsio8P5ePWXrP18Odev1O2WL8wvJiIylA6dWxIVHY7ZZMbT25Ob11Jo17kVSY0SeH7Bm277XL5wnUkzxjDx8dGYTGa3IrqPn3cdn41qo4nY+ChMDwT7e+tCw4NRq1W8t3wD589cYf2mlbJK7pWLN3hq9is8sWgKJ4+6W3B/v/kXomIj6hzzxNGzjJow1G3ZgzM9gDMnLlBWWk5kdDjJvn/bsz0oOIB+g3vUofj+T8O/K7X1KzAZWFHz/18e3EAQBA2wDfhKFMUfH1h3LwgJwCNA3WT0/0JoDXq0Bj2m8ipsJiseAd4St76smhNf7aHFuO40faQzRbezKbqTQ0BsKBpPPQY/D9QKK2qvgJo6xn04jJXoQ8KxV5aj9fXHYTbjspiw1ehn6cOjQBAQ7XZJ5t3bF6elHEFwn/1I8vMZaLz90AYEyxIvKJU4qo0o1GpJH6uyHFtVpexjotRoJdn6v9HYR80yc2GebMOr1OlRe/lQnZ2B2tMbfXQ8edkFlJcb6dCltdsIcsa8xwiNCObZufd9Ha5fucXu3z1p16mV2wskpl40CAJPL5nDW6+uJS+ngEN7j/PuR29QVlqOf4AvxYUlBAYHoFAIZKVn03tAVzQaDcYqI/4BPvJLpUFyfc6dvsz+P4+4fZyQsCCGjRlAXL1ojFXVXDp/jQtnr0gF/H6dWf/eF8x/dgYbVm8iqXEiI8cPwWK2oNNr3VJqPfp2JqlxIr36deHyhes0b92Yho0SsJit/LHjAJMeH43LJRIcHIDeoMdoNJGXV8Afv0r1lvadWxMY5E9goL8kzhgazPbvd3JgtzRLKzt9mRtXbrNm4zLOnLpYJ51342oKnXu0Z8/OQ6TdzqBV+2acfsAgLCQsiMqKSo4ePE3KzTQaN2vIy4vfQllTZ5s+d+JDFZPNZiv+ej2V5ZV11qk16joMPL1Bh06nrbMurn4MdruDnKx8fHy9uXbpJjev3aFdp5ZcvXSTP3/bT0FeIXnZBQ+eBrvNjp9v3ZnBvdRZbdSm895DYlK9f5tcyb8D/65AsgL4XhCEx4EMYAyAIAitgdmiKE6vWdYVCBAEYUrNfvdovt8IghCEJGR7EZj9L736fzMMvl4YfL1w2h1U5BahD/AivFk8dw5cIiAujNLMQup1bozT7sBushKUEI6zPP+hEiYA1HST28pL0PoFYim7n8O3FuXjERGDtawEtZc35oJcVAZPNP6BaPz8cTkcKDVaLMUFUuMiIqJLxFpaJHu6a4LCJLVfl0tS9rWYcBiNWIry0Pj4odBInff2aiMavwA5iKFQoKup3dwLIlBjAezpjaBUYjdWcrewiktXU6k2mkhoEE/Ltk2x2ezodTr27DzIxOmj63zkk0fPMf/ZGXIgSWgQj8PuYO3KT3hs+mjmPDUVf39fMtNzeOfNdeRm5xMZHU6/wT34YsN3BAT60WtAV2x2B5+t+4b1X65k7uLpLJq1BLPJjIeHAWOVsc55KyuMVBvNdO7els2f/UBcvVgA+g/tyRcbviMmLpI7t+9SkCdJ4t/D868vYNn7L5JxN5vgkEACg/xZMONlEhrG03dQd7r17kRZaTkOu4N5i6cxv9YMQqVWseDZGRQXlrLh61WILhEPLwPVRjNpd9L56P1NvLdhqRxE7sFstpCVmftQXaeAIH+5a//iuStMnj6G/JwCMtNzpJnW7HHkZOVx4vAZ+gzsRnKTRO7UpALvSepfu3yTth1bcPr4fSKAl7cnNqudzZ//QFLjRPz8feRi/e4dB5g+ZwIf1UjFA4wYN5ivP/uB/JxC3nz3Bd5b/hEFeUU0SK7P0JH9WL3yE5q1bER8/RgunL3C5QvXMJvM7Nl1CGdNijE0PLiOCkGzlo1ISIqnUbOGXKuxOG7bsSXtOrbE6XTKwRCgYeMEHp30CFtqmh89PA28tHQRPj51mw//t+LfEkhEUSwBej1k+Vlges3fm4HND25Ts67nX3qB/0OgVKvwjwnDbraRPKAtxuIKFEoFCHDpx8NovfQkDWhHysErhCVHoRJVdeoTCrVGMqzKSJVcCh+oXbjsdkz5OXhE10N02mXRRUk0UUDj5y/1p5hNknQ8khEVSHRhp9WMraocR5U0ulQaPNH6+HGv3GWrKMMjKg57ZTn2aiNKne5+XUZnwFZeIvmbeHrhtFhkOrDTapHO6zRTbrTw7rKPeHzuBL769Ps696myvKrOsth60fj6+fDaimcoyC+iIL9I9qP48ZvfeG3ls3z7xU9u9MzszFx8/bxZ9MIsQkKD2LDmS1q3b47T6eTi2ats27qTVeteJSs9B09vT1wul9tLEqQO68sXrjFn8nM8PmcCUbHhTJ09DqVS0nqKjY8iJjaSmfMnISBwYM9Rbt9I5cyJ85hNVjp1a0NZSTnLl6zG5XIREhpETHwUGXez8A/wpTC/mN07D7rNIBx2Bzev36Ewv4jbN9JIuZlKUuNEvvtyG17enryz/nWyMnLw8DTUMWFyuZzkZOXRZ2B39uw8SERUGE6ni/FThvPpOknGPKlxIn/+fpDZC6eQlZ5DfP1Yvty4RfaYv3zhOjOfnETjFsls/+F+HeLowVO8tWYJsfHRHNxzjAbJ9Rk/ZQTpd7M4ceQsF89e5fVVz1FYUIzD7sDL2xO/QF/mPzsDPz8ffP198PH15rknlxIWIXm0v7LiGYoLijlx5KxsFVC/QRx7dh6ie++OXLl4k7LSCjp3a4dLdHH6+Hnycwt4YuEU9v5xmJSbabTp0JzhYwaiUqlY98VKMu5mYbXYKMwv4qnZr9Cqpms+oaEkLe/r682C52YxeEQ/KiuqiI6NIDo2ss4z978Z//N4Zv9BHaj1GvxjQ/GJDKKqsIwojYaoVomoNGpJRVSvIvNsCqHJ0YQmBuOoNuKoNqLUG1AZPHGajKBQSOkltbrO8VUenrisZhzm6hra8L2iqoitrASVwROP6HhAwJxXi6WkUCAolHIQAXCajLg8PHHarVIhvbgQW2U5oihKTosmE9YaKRVDeBTagCAcxioc1VVofHwRFEosRfkodXrs1dXow6O5eegPXC4XWek5DxUkDAj0o2vPDhzefwKQuqPHTBjKytfWMu2J8W4jXAAEAf9APwIfIsednZXHH7/up3X75sxeOIVXajreBYUCrV5LRVklpaXlVFUaMZvMzF08jV2/7kOpVDJx2mjsdgc+Pt6MmTiMj1Zv4sU3FpDUOBFfP2/8/H3p0LUNX36yldzsfJRKJaMnDsXXz5v6ifF8vuFbjFVG+g/thcvlYvDwvhQVlsid5nqDnudefRK73V7nuq0WKxqNBovFSkx8FO8u+4i5T01j/+4jfP/NLxirqpkyaxzr3r1fY0pu0gBvH292/36QAUN7sWLtEs6evIinlyde3l40bZFMvyE90Ol0ZGXmcmjPcXr270J5eYUcRO7h200/sfSd5xkzcRg/b9mBw+GkR59O+Pn7EBYZQoeubcjOzOXVZ99m6bsvoFQqMZstiCJs37pTVlaOrRfNoudn8d7yj8i4m02jZg1ZsfZlSorL2LFtD79v30N0TASPTR9FYsN6OJ1OwiJDaNy0Idev3iYyKozvvtzGt1/8RPNWjVi17jWWvvQeGWlZtOvUigFDe3Hl4g2OHTpDVEw4Jw6fobKiCrVGzVeffk96aiapKekcPnCSr35aR2h4MCDNQpq2SK5z3//dSE/NZO8fhzl3+jK9+namc4/28jX/M/GfQPK/CEqVEt/wQLxD/SlJy8NWbcVutuDppyOpczQKvUF+Sas8PHFaLZjzs9EGh6IPDkd0OVFotGgCgrHVUHQVGi1avwCqs+5iCI+p8Ux3h91Yhei0IyDInekgFdkfnOGAJPuu9vLBVlqMLigEpcEDXK4a2m+NU6LBA0GjxZyfIxf1HaZqlHoDutBIFCoVuoBArMWFeHlJlM69fxxm8YtP8OXGreTnFqLRapg8YyyZ6TkolAoWPj8LnU5LWWkFn330LVarDYfDgX+Ar1uX9fAxA3ntubd58pnp7N112E3KOyQ0iIrySvb9cZikxgkolAp69utCZUUlU2eP4+03PqSivBK1Rs3cp6ah0qhY8MxMgsMDmTP5OcpqzhMUHMDsBZMpKiwhP/cGB3YfY/nql9iwZpNMOXY6nWz5chuvrniG08fPY7fZuXzhOnOemkLDRgnUS4xlx7b7fR5mk5mtX29n8qxHOXbwtNs979a7E/UTczFWGtHqNDidTqxWGy1aN+HQvuNkpucwaFhvnnrxCYoKS6R7WhNQO9ZQUp+fv1Q+nlarYeUHr3Dj2m1+3vI77Tq2IrZeNOmpmUTF1JVLV6tVeHp50LxVI3z8vFEICs6fuUxhQTFrVnzitu369z/niUVTOLjnOKkpd+UgAtKL8cTRs5iqzcTVj6Fn384smPESxqpqomMjWPjcTL7+/EfKy6tYW+PxDtCuU0sGD+/Lmy+9J9sAXzx3jd+37SEw0I/01ExOHj0rpzoHPtKbp554ldTb6YBEJ1/04mxZ7t5ms5OeJs0CHQ4nhgeID/8voLCgmEWzlpCakg7AsYOnGDq6Py+/+dQ/XSX4P4HkfyEUCgVB9SNwOV2UZxeh9VDjLLfhqKpE6xeApbhA7g5X6vSy++A96EMi8IxLwFFtrElPWRGdTsyFuaj0hjrBQaXTYymqqDHDisJhrpZYVjYbai+fWjOYmu0NnpKab40Vr5RWU6EPDgOFEoVajVBTpK/NDANJxl7j6+/mR9K0Ubyc416z8hOGjupPm/bNcTid2Gw2bt9I4+CeYxzcc4yJ00ZRWlpOWUk53r5e6A163v7wNQ7tO0bq7XSatkgmNSWdnKw8vtv0M0+9OJvVKz7By9uTsY894mam5OXtxawFk0m5kUrjZkm8/9YGKmoKxHabnTUrP+Gd9a+xaeMW+g7qIQcRnV7LwhdnS9bIxWUEhwYydfajuFyuOt7oAMWFJbKS7aQZY7ibmkVYeIhMRKiNtJR0CvOKWPj8LA7vO4Faq2boiH78vHUHWem5DB87iNj4KGbMm0hCw3iMxmpMJguZ6Tm8+fL7TJgyEv8AX8IiQlCrVVw4e4WJ00a7vZRB6qe5dP4qAYH+PDppOH/8tp8d23YjCAIjxw9m2Kj+/FKLTjvmsUc4eew8arVK7kgH6NqrAyDNNHr06YTdZmfPzkO0bNuM+olxct2hNu7cuktkdDidurflw3c+k2t/mek5/PTdDqbPncjqtza47XPq2HkGPdJHDiL38MuPf/D+x0u5cPaqXLvxD/Alvl6MHERAooj/+uMfTJg6Em8fL3Ky8ki5mUZWejY/fvcbE6eNJqlJAgqFkvCIEHR6XZ3r/u/CVG2msKAYg4ee4IcU+B9EWkqGHETu4bcf/2Ty9LEkNIz/p17bfwLJ/2IolAr8Y6ReT4eXR405lIA2SMBaUoigUKALCqM6yz0VZC7MxTO2vuSJDujDIkEQcFktKP0CEFRquVah1OpQ6LSILulHaC7KRx8chihKsisKrQ5VtVFudrzHuLJXlOKyWXGaqtEFhmApypcZZR5RcZKcysNYXFDHNz7MAB98upx9u49gqjYTFRNORXklmz//ifS0TJq1aiQ39TVIro/NYqNLjw4U5BbgG+CLQiGlJiorqvh8w3cyk8hqtZGXU8DiF5/AZDKz6eMtcqAAacbw/nLphRUWGepWHAepryP1djrJjRPlWU1Cw3jmPT2dVW98KHtmJDSMp3P3diQmKUlu0qCObpSipj+oWatGOJ0uVr72ASD1gjyIdp1asX/3Ea5euslTL8wmvn4Mc6c+L78kP177Ja+9/SybPt6KWqNi6qxxtGzbhOKiEqKiw9HqNZQUlxIeGYper+OTNV/hcooPZVdVVZpo27ElP2/5XZaHF0WRH7/5jeXvv4h/oB852fl06Nyaq5dvSqrXTdypsH7+vgwbPQCVUsm3X/yERqth/JSRiKLIju17SGqcUIea27RFMj988yutOzSvQyBJTUnH09Pg5pF+//uq20iq1WmIrRfNVz9/yNWLNzF46GnSIpkzD9S2AEpLyklqnMCz8+63rIWGBzNpxlju3L7Lmy+/j8VsofeArix4bhbRD6EP/98i7U4G77y5nqMHThIQ5M9LSxfSrVdHWSXh4fjXtc39u7S2/oN/MVRabQ1DSoPaywd9aCQeEbE4bXU94xFFXHabJOUuCFiK8vGIikPl6YW1vAR9aASG8Cj0IeHoQsLcqMCiw465IBfRYcdSVorLbkcbFIJHVBweUXHow6Mx5WTU+MObsZYW4TAZUXv71jq9KNVXVGrUPu5dxCpPbxwPzFI03r4oBZGft/zOgT3HeHfZR3y+4Tvad5G8WS6du8bn67+lKL8YrVaDKIq8sGApq1d+wmvPvs2bL79P81aNuXE1xe2FOXrCUCoqqljx2lpUKqWcW9Yb9Cx6YRY7t++Rt60sr3roKFFQCHh6eeIXIFFJx00eztEDJ92Ml1JupuFwODm8/wSPzxmPn/992unoCUMJCw/hrTUvs+DZGWz9eru87siBk0x7Yjz6mtFvi9ZNGDqqPwaDnsefGI/RaOKjNZvkIHIPxw6eIio2HFO1mXXvfY7T4aRZi0acP3OFj9d8xebPfuS5J9/g8sUbrNm4jJjYMPoM6u52DIVCQat2TREEgYtn67LvU2+nc+XiDVJv3UWn19G1ZwcSGtZDUCiolxArb1daUkZEZCg/bdmB1WqjqtLIx2u/JC0lnbKScqqNJrr36SRv37NfZxo1bUhVpVH2dqmNwGB/KiuMtGnvTtNVa9SEhgfzyJiBbsufffVJYuIi8fXzxT/QD7vDQVFhCWqNqs5AZuSjg/hq4w9uy/JzC1GrVXyx4TvMJjOiKLJn5yG2fv3/tXee4VGUXRi+Z3s2vZNeIAkJEHrvvTfpXZo0AUEUEFRURFQE6UhHQap0EKT33iEQCCWQDoT0TTa7me/HJgtLwPIhoDL3dXGxOzsz++4mmTPve855no3PVUD4q+h02Xw/+QcO7zO10j28n8z7gz59purzkwQG+Rfqkm/5VqNnLj2+KMJzS0L/g1SoUEE8ffr0H+/4BmDQZeXnMgQy7t60uMs3CSs6obS1Q8zLI8+Qi1ytITspAZlCQW5mOqLBgNLeEZWDE3m5uQiY/NTzcnNROTihsLZGEGQYc3KQq9XkGY3kPEhE5eBssYxWgMbNw6T75eFtEo3M1plkWdyKIMgUGDLSUNjYmhSCZXJyUpJNxQJWWmQKBakPH/Hu0EkWHhY9+3XA3sGeY4dO07hlXX5csIb42ATqNKxOYJA/82f8aN73829HY+9gx7qVW8hMz6Jlu0ZERkThF+jLw/vJXLtyg7qNa6C1skKukKNQyImMuImIyKG9x7gXHcf4iSOYOH4aGemZyOVyevbryPEjZ2jauj52djbExyai0qjY/9sRLp23FPSr06AapcqEsWHtdpq0qEexkACcnB1Rq1UkxCcx45v5tO3UnPWrttG4eV0UKgX37sRy+cI1Rn86lEsXrhIVeZuIS5H0f7cHW37ZSY06lYm6ftus1VVApx5t2L3joFn2vVuf9ji7ODLjG8vlK41GzfhJ7zN32mLKVgynXMVwNq/bgdbais492xIZcYOoG9EYDUZ2/3rA4thRHw8hLy8PN3cXfP292bJ+JyuXrkdjpWHsZ8PIE0UMuQacXR35YfqPREZEWRzfrHUDylUsxcTx06hYtSzlK4UDJlmRHxespn6TWtg72BMVecscXBVKBZ9+9QGxMXHY2Npw+tg5Du49jl/+Ul7Ko1RKlQ4jOzvHNAPz8yI4tCgPk5IZ3PtDbkfdpd+Q7qxZvglnVyfe6tyczWt3kJycQtuOzfD192LyhBlm4zTzZx0/mCkT51hsc3FzYs32Rbi4PluhNzs7h6jI2yTEJeLu4Uax4ACstM9eDrsXHUvzWl0Lbf9q+niat/l9H5LbN+/y27b9nDp2jobNalOrXlU8vH5XkeqZCIJwRhTFCs97XVraekNRWGkRNVYYs7Ox8QkkK+4uebl6ZCo1VkW8IE8kNzPD3N0uKJSonVzIyXc5VDk6I1Mo0cXHoLCxRf/oocnoSmtjOk6pRJcYh5WHN6LRSF5Otqlz/TmrVWCabeSmpz3OwYgi2YnxWPsEoHZxM0niZ2ehT31kGkvyffTJpvFo7ByYPutjfpi3mmtXb9KxWyt0WdnIFXL6DOrC0L4fme/Md/96kLrGPMLLhnEx39UuK1OHtY01bTs2Y9+uI3z16Qyq1arImRMXiY2Jxz/Ql1nfLuRRcioTvxvL6p+2c+bEBeRyOa3aN8bTuwiRETcZPWEYd+/EoJDL2bX9AMGhRfHydufDd7+gVJniVK5envCyYYUCSXBYMVQaFTHRcSycvZwPP3kXhULBvHyZ+DIVSnHz+h069WjD/Jk/kpmRRUhYMbr3bc+l8xHMn/kTarWKBs1q4+PvRZkKJVmxZB0jxg7kwO6j6HSmmae9gx2lyoayZvnjHuAiHm5kZRb2usnJ0ePi6khiwgOKlwji689mUrZCKbJ1OXzywdd069OO37bu47NvRnPpQgSJcaafRYOmtRHz8pj65VwAAoP86NmvE70HdMHZzYkfZvxIXEwCgiAw8L1eePl4FAokzq5OHNp/gqEf9GPPjkOcOHqWbr3bsXHNr1y9fMN8w1ChShm+m/s5iQn3SU9NZ/bURSQlPGDwyN5mvbX4uES+/XwWn08Zg7evB85PXdwjLkdyO98WWS6Xk5aaTlpqOrO+XUit+tUoXb4EHl7uLJy9nGatG7Du5y3mY5UqJe4ehauggooXxdpG+8zfc4PBwKa1v/Ll+GnmbaPGD6FLr7bPXKrSWmsp4ulWqKvfwfFxn0rsvXgiLkWSlppBsZAAwkoGo1QpCSjqy4BhPRkwrOfTp/1bkQLJG4wgCCisrMDKCoVWi1Gvx5Cjw6jTIVOrUWhtEAQBmcpoSuzK5agcnQHRtPpqMJg8Q+LugShaijUKzlh7B5i8RYx6ch49QDQY0Pr4Iy8oOc5H5eCEXGuNXGNF5t1bPE2ewUDuowcYMkz9IConVwy6TItEfG5aCnZuHvTq8xZ37iUxavCn5mWqQSPeLrS8c2D3UfoM6srFcxHI5XLUGjVXLl6jcvVyCIKAIdfAjchb1GtYg58WrTXnLQKK+XHxXARn8vtLjEYjG1ZvZ9zEEcTei2fPrwdo0KwOsXfj6T+sB0q5ggljvjX1mpy5Qp2GNXAr4krtBtU4sPsogiDQ8q1GlCodyvj3v6JokD8t25n6EfT6XOo3qcW6lVsYNKI31yNuMnnCDPNniIyIYu/OQ7i6OVPE040e/Tqy7uct7Nyylxp1q9Cl11tM+2oeA997G5VKidpKg6OTHRGXrvP++MGs/nEjSqWCosH+qNQqbGytyUjPNJ+/Zr0qpKakUaNOZXZu2UtOdo5FviIzI4uRHw1i26ZdNG3ZAFd3J9zcXdFaaxny9ofm/W7diObk0bP4+Htx9VKkuSpNFEXmTlvKzMVfcTy/EgvA3dMVa2srDuw+yulj5/lm1idEXb/NwlnLKVU2jHeG9WTBzJ8QRZGIS5FYaTVcPHOFitXL0bFba3z8PZk9dYlJl+y3wwDY2dvi4eVOcnIqBoMRV3dnYu7Goc/JRfFEybv8Cb06U5Bwxdpai4urk+mmKi+P7n3ac3DvMTy9PejQrRVGo8HCatdKa8WQkX3MS45PE307hq8/m2mxbdpX86hSozzBoUUL7e/s4sj4iSMZ3n8cWmsrjEYjlaqVM0vcx96LZ1i/j8x5KkEQmLFwErUbVHvm+78MpEAiAeQ3JipVyJRKDLLMfEsQEWNODtmJcShsbE0Ohw8SUFjbIddo0CUlIFepUGht0D/ReW7l6YOYl0du2iOzcZa2iDeZMdHo4mOwcvdCtHfAmJWJTG2SyDdkpCPXaJ4p6CjIZOYgAoDRUChPAmDU60nJ0LFq2XqLXEeBL8eTuLo5o8vSEVQ8kM692nL3zj2WzFuFPtdgmmF4uePm4YqLqxM3b9zh6MFTaK21vD9uMJM+nlbofDeu3SL6dgyCYPpD9g3wZNWy9fQe2IWKVcpwcN9xGjatjUwuJyc7B32OnoHDeyGKcOLoGSKvRpGr19OmY1OmTppnTiK7uDnxVucWRFy8/kxlgpNHzzF5xsc0a92A4e+MNyf19+86gj5HT9vOLfhl1Vbe/2gQZ05eYPmiE7i5u1KrXhXGTBiKXCEnJjqO/XuOMnhEbw7tO86dW/eoUqM8zds05LftB2jUvA7zn6i0evLncunCVSpULk16Wga3o+6SqzfgF1C4Ge/yhatobbRoNIUvrlcv32Dg8F7Y2tmSnZ2NRqPGydmR9z8ebHKQ/GIW0bdMy6HXIqIIL1eClu0acy86lqat6vPx+1/RsXtrIiNuUDwsiMyMrELyKgOG9eK7L+Zw7PBpnF2dGDFmACuXradsxXC8fIrw9axPmDZpHnGxiYSVCuHWjTsMGdmHBbOXk/zgEQqlwrRUefgMiQn3qd+4Jj36dTD7spevVIYb126i02UTWMyPgN9R8E15lGpRUg6mG5KE+CSCQ4uSlHCf7Owc3Iq4mst0K9cox9yfvuXcqYuoVSoqVitnNra6evm6OYiAKUBPmTib0uVK4OBUWOblZSAFEgkL5Co1cpXpl9egyyIvX2E2Ny0Foz4HKzdPDJnpyBQqNM6u5CGiUGnMMiYKrTVG3WOtLgC5lRZRELD28SPz3h2TR0mewZS8f3gflYMzOQ/ikKk0WBXxJDPmjjlno7RzxJhjGViM2ToUWmuL4AWQ8DCdn5duJCnB0qExIe6+xR2jIAj0GdyVB0kPeatTc1RKJa5upkS5WqXg3KnLZm+QQe+9TXBoMcLLhhESVoyfl6wjKCSwkLx8QDFfHiWnEF42jCsXrpInipw7dRkXt120bN+YmvWq8NPCtSQ/TCErM4uL5yIsNME8vNwZNX4Iq37aaBEwHiQlm++8K1QtU+jn5evvRWZGFnExCYUuTscOnaZazYpUq1WRzIwsflxg6vq/dSOac6cuMuGbD7Gzs8XH38usMly+UjgVKpfh9PEL+Ab4oFIq8PByp23HZkx7oqRWEAQ8vd0pXa4En37wtUU125fTxqFSq9Dn6PH198LZxQnfAC/yDEasrK2ws7dl0IjeuLo5c+n8Vays1AgyGV+On2pWRhYEgSlzJpCY8MAcRAq4ePYK7bu2QJ+Tiy4rGyutFXO/X4qbuwtRkbeZPONjgsOKcWT/CaJvx1C3UQ3OnrxIyTLFCS9XArlCzukT5+nZvxOTP51u8oy30jDk/T7s2LKX7n3bY29vx1efTjf7kBhyDSye+zPfz59Irj6X0FLBFt3rru7OuD6jgfVZeHi6W0i/ANjYWnPvTiybf9nB1C/n8ig5lYbNajP0g/74BXhz+fw1Bvf8wPz9aK2tWLJmBqElg0l/YhZZQFLiQ7Jznums8VKQAonEc1FYaRHzFYGzHyRhzNaRp9cjs9KSm5GGoJCjUKjJiruH2skFQaFAobUm447lerdRl4XKzgHRaETt4k5OwTKVIENpZ4+Q77+Rp88mKzYajbMbCAJyldqU2H+q+sWYk43G3YvcrEzE/GCSK1ex67cTHDt4irc6NbdoYvtl5RZmLZlMUsJ9cnJy0WVlsfqnjdy6EQ1A09YNKFu+JL0HdqFocCBR124yfPQ7aLQaLp+LYNvG3ZQIL861iCiOHjzF++MHc+XSNbOfeLlK4dy6Ec2u7QfYtf0A3Xq3p2GT6pQqHcq+XUfQZ+uZ+/1SEuPvI5PJTM6B+UGtAA8Pd8LCQ0h9VFio0MpKQ0hYMZyc7GnQtDZnT12kdbsmqNQqylcpzY2Im7i6ORMcWpTa9auCIHDs4Cli78UTF5vIqmUbuHz+Ko1b1GXn1n2AKf/x6GEKG1ZvZ8Dwnjg42pPyKJUzJy9y5uRFAAy5uSxfvI79u4/Ss38nho9+h03rdmBra02jFnVZ9/NWqteqaBFEAJYvXMOg995GJpOZEsrxSYSWDMbVzQU7exvKVijFjG/mc/dOLFVqVqB2/R7s2LrPQl5fFEU2rdthUbH1JPExiezYsodd2/fz1fTx3Ii8hUqlJPpWDForK0qGF6dkuMn7IzIiiqT4+8yZtsRcFly1ZgXOn7n82DNel820r37gx/WzKVm6OFHXb1tU1pl/94xGGrf86wpNkVejOH7oNGmp6VSrXYmZi79i9NDPib2XgLuHKz37dUQExo/8ynzMb9v2m7xzvhjBjwvXWHw/WZk6Duw+SmjJYIKCAwpZLbfv2iLfrfPVIJX/SvwuSq01akdnrH0DsfEvBiolcrVVfimxBmQywCTSmJ0UjyGr8N0RAHK5SfjRzp68gjslMY/c1BQEueX9jEGXhUGXhVGfjTErE6W1rWmJrOBUVloEmYDMuQg3k/UkC9bMWLCJeTOXk5qSRlLSA7r1aY+dvS1FPN14d1Rfzp28yNIfVpGty2bGNwvNQQTg8L7jePt6cvzwaU4fP4+TqxNzv1/K3dsxHNhjklXx9vXgVn5z16xvF9K0VQMGDOvF5Bkf4+nlTrYuh/5De9ChWyu2bvyNzMxs7ic95OaNO9zIF2EEuBF5i/ByYRaJWCcXR6ysNaxftZX23VpafBcymckf3MXNiSlfzqVp6/qMGDuQNSs28cOMZQzvNw6tjZbAIF9KlwtjyQ+rWDznZ3z8vBg9YRi/bt4NwOUL1wp5cVhprbC1syYhLokPPh5i8VqVGhW4cc0UjGPuxqHX6wkrFUxIaFHsHe2YPvkHYp5hbwyQlpZBYDE/Fs5ezpb1Ozl17ByTP53B9Ws3ycvLY+x7E7l7x+Q7c/zQaRbNWUGN2pULnScnW4+AwIefDmXAsF70GdQVaxst9ZvUNGugGY1Gbl6/w4KZPzH7u8U4OTuSnp7OmZMXOXn0LA8fJGNta81v2/Zb9JYcO3QaJ2cHi/czGo08Sk5BEAQcnRzw8vEoNKY/8lJ/Ftev3qRPx+F89+VcFsxaTp+Ow0lPy+DdUf3pP7QHNetWYfZUS3/3An7dvIeUlFTzzOhJCtQYQkoEMXPxVwQW88PaRkvP/h3p2ru9hbDky0aakUj8KRRqNahNF3Njrh7RYIC8PHJ1mSbr23x9J6MuC4W1LYbMxzkNmVKFTG6qy895kIjW04fsB4kIMjlKGztyUpLRuBYxaX3JZORmZpjKelVqVI7O5Kanmf3lZSo1hqwMjLm5yEUj6zfswcfPiy0bdpvfb9uGXdSsW5nBI/vg4urE8cOncXVz5lFyqllOBUwXy0pVy/LoUQoPHyRTsnQojk4OzPxmAfocPXH3EvAP9OHyhWtcvnCVKtUrcPdOLDk5en7KF4f8cuo4SoQXZ/VPG9myfifuHq68M7QnUddvMXvKYvoM7oqNjdZC4nz+jB/p2a8jLm7OpKakkZWpY+Gs5TRqUZe0lHR69u/Inh0HcXB0oGnr+iTG38cv0IeAor4kxiUxZ9oSs7iiLkvH1C/n8N5HA1m74nE10a+b9+Dt62lxF1uwng8mifekxAfs2XGIPTsOMX3BRKYv+JKE+PukpaZxO+ou2zc9/k4DivoSey+BowdPmS/IhoysfEl9y7vhdl1acDsqulBT4E8L11AyvLj5e7Cxtab3wC48vP+Ik0fPMGLsQLZt3MWtqGh8/Lzo2L0Va5Zv4kS+PL2dvS2Tvh/H3p2mMYNJqsXe0RZXd2dCwopRpkIJFs1dSXZWNsGhgaxdsZmBw9+2mKEWkJmRZV6CA9NyWsFdvIurE59PGc17/ceTnpaBTCZj+Oh3KBby7I5wg8GALtPkt/J0/8nJY2ctvgtRFJk3fRnvjX6HscMfy84on2Gx6+PriZWVhq6923HhrKXqQf0mNc3H1axbhfCyYWTrcnBxc3qlQQSkQCLxfyBXqkCpwqhQggBKK2v0KQ8xZOuQqdQotNbIrbQYMtKQW1mj0FqTFX8PKzcPsuLuIddoUTo4oU9+QFb8PZQ2dsjUGgwZaeTkl/MadZkIaQrU+RVaakdn9KmP0Di7mnI4hlz0iXH07NGCg0cuUiw4wHyxUCgVVK1ZkR1b9lA8LIjAYn74+nvTrksL4uOSCAkrRrYum2IhARbSH6XLl6B915ZmGY3D+08w/ssR3LwRTey9BOwcbKleuxJHDpxEoVTQ9e122NpbM+Pb+eYZR2L8fWZ9u5Aho/piNBpZPOdn3h83kB59O7B47s8ApKdlkK0zVUDt2v64/8LL24N505fi7OpE1RoVSE/L4PvJP9B7YBf0OXqq1KiAwWi0qKwC8PTx4OhT2lpguoCFlgzi5NFz1G1UA12Wjp79O2Fnb0Ou3mDhTLhmxRZatGmAX4A3G9f8yo4te8yv+QV64+jkwNmTF+ncq62FxMml81eYPH08K5dtIDU1jXadW2BtoyUhvrBXnUwmQ6l6fMl5e0Bnlv6wyuIi+8WUMaSlpXP10nUeJaeYgwhAWmo6K5duMBc09OjXAUdHB2LvxjNgWC9s7WwYO3yi+ecnbBR4f/xgbkTeolqtiuZlvQLCSoWYq9FlMhlT5kwgNSWdg3uOEVDUl4pVyrJq63xiYxJwdLInINAX1TMk9a9fvcmKpb9w7uRFGjStRZuOzS262rMyCuvNpadl4O3rRd/B3Vj6wyqzNH2FyqXNsy2FQs7oCUOxd7Cjeu1KTJw6lsVzf0aj0TBweC/Cy1l6x9s72GHvUOitXglSIJH4v5ErlciVDhgNBtRKJSpDLiICuthoRES0nr7k6fXk5epRO7qgS4hDplKTl5tLXmb645mKQkGePsfCuRFMMvQAeTnZ6BJi0Xr6YMif8RRUdrmr8mhevxyVqoQTF3efpCST1EdKsumiZmtvy4rFa6lao6JZJr5TzzZUqFKG8SMmWbzfhTNXeKtTCzQaNRorDf3e7c6ZkxcZOqovao0alVpJlerlqFi1LJkZmTg42nPlYmQhaRSdLtucNzAtl6Ry4shZvvhuLPeTHpJnNBISWox7d2MpGuRv1kNydnWidLkSXDh7xUKMUaNRk5Geyf2kh9SqX62Q/W9ycgq16lVlz1MmWsXDglCqVNRtWAMbOxt0WTrs7G2IjUlgx6Y9tO3YDHtHOxBF4mISsLGzIfLqDVq3b0ypsqEc2X+CkNBihIQV5dqVG1jbaDl1/DzDPuhPamoaNjbWlK1YiltRd3jnvV7kGfI4sv849RrXRGutNeddCujSqy2ZmTo+/ORdEuLvo8vSFZq1/Lx0Pe4erpw4fAbHp7zRwVShNP/n70hNSee7iXPM/Scubk40bdXAQktLFEX27zpCtZoVCC9XgqTEB5w7dQm1WmWS7lfIGTSyNx6e7lhpNVy5FMmiWcsxGIzY2dvyw/LvKBEeUsha90niYhMZ8vZoEhNMvwMLZ6/g6uUopsyZYF6+rFStLLJplrO2Xv074e7hyuCRvWnxViP0Obl4+3nSun0TIq9GkZ6WSUBRX3M5sJ29La3aNaFuwxrIZLLn9qi8LqRAIvHCyBUK5ArTr5KYZ0TmE4AhKwNDRjoKaxt0DxLJy9Uj11ihdnIhKz4GRBFrv6JgNJIZcweNaxFTr8pTJa5PLhMYs7PJTU8hz2BEoS34QxLR5uXgZwUBxT246eXOZ2OmmL3NQ0sFU6VaeU4dP0f3vh1Yu3wTq3/cSLEg/0ICfmC68I/9fDgPkpKZPWUROl02W9ebLupd327HD3uW0alnGxbMWs7Yz4fj6uaMRqMmO9uyQkaZ35ugUqvw8feitkqFUqlgzneLCAkLwsPTnbw8kdBSwfQd0g0QyNXrGTSiN1+On8a96FjUahV9BnVFJhNITUln3c9byM3NZfSnQ5n0yffoc/QolAo6dGmJWqPGy8fD7NDo6uZMseKBpCanMm3yD+blm94Du1K/cU2Ciwey9IdVJMbfR6VWMWRkH2xstKjVapKSkilRMpg7N++yZcNOFKrmeHoVYdGcFfTs15HsnBystBoUSlP/TczdeL6bOJecHD3h5UpQNDiAowdO0rN/R2LvxvPgQTJ1G9Xg7u0Y3h/4CQBFPF358JNhhb5/XZYOjUaNTpdt0XBXQN1GNSgWHMCJI2ctmhiVSiXpaYV9Z/Q5erz9PLly8TrhZcOoVNVkTPXr5j34BvpQxMONyxeusvvXg9ja2TB89DtMnTSPtNR05k1fypQ5E1Crn6+Uezsq2hxECjhy4AT37sZSPMzU51GidCjzfvqWhXNW8OhhCj36dTQXESiVSgvJGFtb62c6Lppft/tnui5KgUTib8WU97BFplJhyEgnNyPd3N0OmIOIwsbO5FWSP7PITU9F7ehMTvLj0l2ZSm0uPzZtMNn35ulzMMplWLl7okuKB1FEkMlJFlVs37jVHEQArl66Tr2GNbh84RpinsiMxV+R+iiNtNR0KlQpw+nj58372trZ4OzqyOQJM+jRp6O5G7yAzb/soE2HpkRdv0O1WhXx8fNi87od9HqnMz/MWGber2P31hzedxxrGy2DRvRm9bKN+Ph7E1DUF1s7Wxo1r8O4kY9nQzs272H46Hfw9vNk4vipVKxShqat65NnzGPrhl0M/aAfC2eZlqE2rd3B9as3+X7+RBLjk0iIu8+W9b8RF5NA555tcHJxxNrGGrciLiQ/eMScaUss7oR/WriaWvWrsH7lVvNMSp+jZ9pX85i1eDK2djakp2WgzzWwYfU2atWvRvlKpZHJBdw9XJn7/VLkcjmCAJ16tuV+4gNzWTGYSnNd3ZwoWTaU6ZNNSsyde7YlPjbRvLQHppLsXdv3UyK8OFcuPvYuadi8DquWbQDgzIkLdHn7LVMA1edStmI4vQd0QalUFkryx8cmEljMD0EQLMqnm7dpyMkjZylXqTQfjfjSvD28XAky0zNZPPdnsjJ1NGvTgIy0TPbvOkL12pU4vP8EERdNneKubs8PJM/S+5LL5eYbCTDlMKrUqEDZiuEYX1ByPje3IBdj/cz+qNeFFEgkXgpylRrRyggKBQZdlimvAlh7+yHI5ejTUsi4E4W1jz9g6g2RqzVo3DxMfSL5pccFCsSCXI5MLkc0GlHZOyC30pKTnoHG0xcRGdF341g0f0WhZSaAiMvX6Tu4G6kpaQzrM5acHD3ValWkU482eHi5c+TASQKL+dG0VX2OHzpDfEziM+9uVWqVyTtdFPHwKsHtqLvs2LKX4NCivDuqLznZOfgH+pKbm4u9ox0Vq5bFwdGe1h2b4u3rwbSv5jNt/heFJNkNBiNJiQ/w8HInJjqOmGjLstPMjCxmLP6KyIibCAjIFTIunL2Ch6c782f+aL5wrljyC13efotNa3cwbHR/HJ3sCwkHGgxGEuPvcyOycPI5KfEB6Wnp2DnYcTvqDh9Pep91K7cwdvgX1G9SiyEj+3Ll4jXuJz7EL9CbsFIhFo1wBRw/fIawUiHUqlcVXVY2H4/6ilr1qhba7+K5K4wcO4g1KzaTmpJGx+6tSU1NMy93HT14iqysbL6e/jHunm74B/qY78iLBvubg14BMXfj+fjLkezacQBdVjYt32pMxKVIflm5Fb9AH4v3rteohoVEycql63l7QGcO7TtOh24tObz/BJWqlyMn+/d7MYoGBRS6Ienetz2+z1gOU6tV8AI2IDcib7Fi8S+cPn6OOg2r075rS/wDff/4wFfAawkkgiA4AasBf+AO0FEUxUL1bYIgGIFL+U/viqLYKn97ALAKcAbOAD1EUSy8TiHxWlFYaREUCnLTU5GpNWSnJKPU2iDIZejzNbuyH97H2tufrPgY9KmPkGus0LgUIc9oRKFSYVXE26TPJYroHiSicfNArrHCkJGOYGVN5v1EFLnZuGrk9OjVmsOHz5n1swooGuRPifAQ+ncdad528uhZGjWvS5nyJahZtwpGo5Gd2w5gY2O6WzQYjTi5OFqUXXbu2Zb01HQqVS3L6RPnuZ0/87l+9aZZiTWsVAiOTvYcOWBKfqvVKnoN6IzBYKRF24YsnbcS0Vi4Q91ozOPOrRiq1qjAsSdkSARBID0tA2OukTu3TKZRRw+e49C+4xQLDmDMZ8NZ9eMGUpJTaN2hKUHFA7G3t2Xjmu30HdS10MXW0cme7OwcvHyKEHvPsqkyPT2DdT9vJTi0KA2a1uKTD742V1it+3kLGemZVKlRAScnB7z9PFk4+ydatG1c6LMEh5pyKpWql2PKF7ORyWQU8SwsFFilZkUO7j1G6/ZNOHnsLMvmr2LoB/3p9253jh44hX+gD8GhRfn4g8n88ttSi2UdvwAf5iz9mhWLfyHiciT1GtU0NQQKMHLsIKZNnsfXn800z1yCQ4uZK7Tad2mBja01/Yf2QKNRs3PrPq5fvcnBvcdo3LIuCXH3KV+5ND5+Xn+o4Ovk4sAX343l9LFzXL92i7IVSlK2YvgfyLv/dRLj7zO0z1hzI+yPC9Zw8VwEsxZNxs7B9m99r/+H1zUjGQPsEUVxsiAIY/Kfj37GfjpRFMs8Y/vXwDRRFFcJgjAP6AvMfWmjlfi/kStVaJzd0CXGmaqtZAKC/PEfmTEznaxsHVoPH5CZ8iB5Bj15ubno4u+icfcymW+JIhpHF/SPHpKdFI/SwQlRl4k8OwMRkBsN+NnIUTWqzsVzVzl++DSCIFCvcQ1ysnO4n/jQYlzd+3ZgwawfLS6mE77+EHtHO7au/42fFqyh75BuZOtyiItJICgkkBNHz3LiyBmOHznLxO/GsH/30UKft0R4iNnS19nVCYVcjiiKWFlp+OazmSiUCoaO6se505fMxygUcspVDEehkOMX4I3RaOTksXM4uTjSo28HNqzZjlqtIrRkEDY21nTv24HWHZryKDkVtVrJgGE9OXvyIr9t228uKGjXpSVR100yHz8tWkvsvXh8/b3o2b8Ts6Ysos/ALiycs4Iy5UtgZ2eLt68nMpmMUmVCCSsVQk52jjmIFLBr+wF8/DxZPHclfQZ35ezJS3j7elGtVgWOHjQFP2sbLe27tkSr1ZCaYprV5eXlcfP6bVq0bcS2jbsQRZGwUiGElwklL0+0WHIaNfhTxnw2jC6925Kelsn2DbuoUacyzi6PE++iKHLiyBlOHjtHYLA/NetVwdnFiaLBfrh7uGE0GnnvwwH8tHgtaSnpdOjeitLlwpj30xT27zrMraho1q3caj7fkPf7kPzwEa5uztSuV40rF6+RkpJKti4bT+8if/g77uVdBK8OTf9wvxfhzq27hdQUzp++zN3oWEo6FH+p7/1neF2BpDVQJ//xMmA/zw4khRBM2dd6QIGu8jJgAlIg+cei0Fhh4xNAnsFgWp7Ky7PQ1BKNBnIe3Ufl5IyYl4egVkN+X0rOwyQ03v4YUpLJTX1c1SVXqcl+usorz4iHix2BxXwpU95UGnn6xAUqVimDLtsy36HVWhW6I18892c+mfw+oz8dyr5dhzl+6DTd+7Rnz85D7Ny6z3x3euPaTS6cjUCtVlKlRgWzkGG9RtUpHlaMA7uPMGLsQGLuxmE0GCgWHIB1/rq4IdfAb9v2M/KjgZzK9z+vWrMCmRmZ/LZtP5Wql6NjjzaUqViKtNR0ls1fba586ta7HSePnmPiuGmIokiz1g0ILRmMvaOMiEvXzRca/2K++Pp7Mff7pYwaN5jqtSth72CLWq3mxNGzPEh6SOTVm4yfOIIrFyM5e/IiDk4mhYGdW/eZpNWf0QBnZ29DRkYWzdo0wMbaVOywed0OGjarw7ezJxAVeZu8PCNfT5jJ2wM6IwiCOWexc+s+wsuGMWr8ENQaJaePX+CXlVupUbdwI+KubftNMibHL9C8bUN69O1gWhbK58yJ87zbZ6x5tmFja03/d3twPfImIaFF2ffbEVJT0mjVvgnlKoab5dkrVC5Nrl5vkdMB0919207NqVqzApERUZw/e4UmLepSrXYlFIp/xur/s8qOBUF4Zo7mdfC6viV3URQLzL8TgOcJ5GsEQTgNGIDJoihuxLSclSKKYkEWNgb4+6zIJF4KgkyGXGX6YxDkcqx9A9GnJJObkYbKzgGFjR3pUVdRaG2QKVUobGyRq9WIRiNyQUD/tAGXKD6zyisjQ0dOTi4yuRyDwUDVmhVITHxIyfAQs/ugIAjPXLJ4lJzC/l1HcXN3oXT5ksTHJJCZpbNI3heQm5uLnb0dKpWS4R/2xzfAm5NHz3F4/wlGTxjG2Pe+NF/oNq7dwTezPjVfVK9cvMbVy9dp2a4RTVs24Oihk+aL25EDJ/nsm9EWXikAoSWDyRNFtqzfad62dcNvBIcGcvTASQKL+VKzbmVEUcTZxYn0tHQ692iDSxFnKlcvh0wm4/q1m1SsUoab129TvEQQG9Zs59Bek1nSudOX8Avwpu+Qbly/dhMbG2uCigda5EB6D+xCnjEPQ66Bi+cjKFuhFOdOX+Lyhau4uDiy6ZcdOLs4IcgEUh6lcnDPMYZ+0I8fF6wh5VEqOTl6RES++mQGRqORUR8PMXdnP4mTiyN3bt0jLy+PLb/spELl0mal27y8PFYv32SRbM9Iz+RedCwVq5bh3T5jydXnolQp8fbz5O6dGPT6XEqXDaNEeHEyn9PTUa9xDcpWKEX12pXoM6iw98frJrCYH7XqVTXPdsHkK+MXWFgg83Xw0gKJIAi7gWfNC8c9+UQURVEQhOe5a/mJohgrCEIgsFcQhEtAYR2B3x/HO8A7AL6+/4zElIRplqIo4oWY52EScTQYTE2MWQW2vAJWnt7IrLTo01JQ2thayMbrUx+hcS1iTsYDoNaS+CAVO3sb5s9YhsFgpFzFcNp3a8mkT6ZTs24V6jSohq2dDa7uzsjlcgt5+dYdmnBwz1Hu3omleIkgevbriEwmo17jGuzdedi8X/nKpbl7O4Yinm5UrFoGewc7Pv3wGzLSM/Hwcsfe0d7iQpeXl8fW9Tv5fMpo4mMS+XXLXuJjEmjUrC6JifcL3SHv/vUAvQd2Ydn81eTl5eHq5kynHm3Ys+Ngoe9x/66jyOQyi2SvIAgMHN6LbRt3U612Re7ejiFbl8PcaUuxsbXm40nvcy861hxECoi+HYO9gx2H953g6uXr9B3clU492pCUcB9HJwdsHWw5e/w861dvRyaT0e/d7pw7fYk6DatTMrw4Gq3GVD0V5I+jkz13bt1j+aK1tGjbEB9/b7RaDd9NmotKpaRD9/bY2GgRBMEij6PRqCldviQnjpzlnXwPjaysbJIfPMLJxZE8Yx73nxLlBEhNSUNjpTEvx739Tic2rfmVpMQH5u9k5uKv8Av0RqFUWAhclq0UTnDxwOdbO/8DsHewY/yXIzl78iLXIm4QXjaMshVK/W5p8qvkpQUSURQbPO81QRASBUHwEEUxXhAED6BwG6zpHLH5/98SBGE/UBb4BXAQBEGRPyvxBmJ/Zxzzgflgckj8fz+PxMtByC9hlCkUWHv7k3nvNsZsHYJchkyhQq7RIhNk5KanonJ0QZ/yEAQBlYMjgkKJVRFvcnNzyck1kpiczo2ouxTxcOOL78Yil8nQ6XJQyOUo5HI2rtkOmDSmPvxkCCPHDWLr+t94kPSQRi3q4uPrZdaAunblBomJ90lJTqVpqwYUDfLnVlQ05SqGEx+bSFZmFlqthvkzf6Jzr7bmbnO1WvXMSp/09Ew2rd3BuVOXeG/MO3j5enDy+Fns7AonSk8fP0+lauV4Z2gPfPy9iLwSxfyZP1KrfuHKp5ASxTjwVK5GFEXy8kTu3onh2KEzGA0GfPy9cHJ2IPlhCmdPXsTNw7VQqSyALiub8pVLE3EpEq1WyzefzzK5X+YHxp79O+Lq5kxKShp+Ad68m9+suXzRWq5eeexO2alnG0qWCeXy+assX7wOgKk/fMHa7YvI0es5tOcYE0Z/i42tNT37dcRozMPVzQl7R3smfzqdQe+9zcwpC80eJQd2H+GLKWNw93CjQ/fWnD11yWLcNepUMfeVyOVyBJnMHEQKvpPpX89nyerpzF7yNRPHTeVedCyVq5WjTsPq/LplL42a1cHeoXDvyj+FIp5uNGvTgGZtnntpfW28rkLkzUCv/Me9gE1P7yAIgqMgCOr8xy5AdSBCNP3m7wPa/97xEv8+FFZabANDsAsKwy4oDLWDIwqVCqWtHRrXIqicnLH28Ufj5Ir+UTK6uLvoEmIQDHpURj2Z6Zkkxd/H3dMVmUzGZ2On8OmHXzPpk+kWYoi6LB2H953Azt6GLr3aMuaz4QQU9eGrT6eb97HSWlG2QinsHGw5fvgMdg521G9SC11WNjH34gkI8mPB7BW07dQcrfXjLuM7t+4RViq40GerXrsSt2/epXS5EqxYvA6tVkvjZnVwdHKw8Gh3c3dhwtcfIgiQEH+fqZPmYWNnQ8u3GlO1RgWLLmt3T1dKlw0rFAyCij+Wuc8zGpk3fRm6rGw+/foDbO1s2PXrAezsbMxaTQUEhxbFkGsgtGQQLdo2IitLhz5Hb9G4uX3TbmrVr0b3vh24eyuWudOWkpWRZRFEANat2EL/d7vTuGU9qtSowLezP6VildK4e7iitdKwIF+eJSM9kznTlrBg1k8gCBQLCaB67Urs3LbPwtL2+OEzXDp/jVx9LmElQ5g2fyLBoUXxC/Dmoy/e48zJ81hprVAoFSgU8meKSaYkp6LPNS13fvLVKPoP7YEgk/HN57P4Yux3nDx6ttAxEn+O15UjmQysEQShLxANdAQQBKECMFAUxX5AKPCDIAh5mALeZFEUC+o6RwOrBEGYCJwDFr3qDyDxcpApFMiekeCUKZWolEoy7t228DoByNPrkTu5IUtIo3zl0gx5ezQ2ttZ8+Mm7ZGbqMOQa8PH35ONJIzEa81AoFfj5e/PRiC/NfSfderejfdeWHNxzjAbNalOzbhUmfPgNd27dM7/PB5+8iz43l9i78Xj5eDDyo0GMG/Elg0b0RmttZb7wbf5lJxOnfsSGVVvJNRipU78acrmMZq0bcPXydWrndzVnZWVzP+kB4ye9z5kTF0hPSye8XEm+GPcdGemZppzFoK7MnLKQAcN6cisqmsYt66JUKHBxdcbR2Z6JH03j7YGduHD2CpcvXKNM+ZIEhxZl9neLCSoeaNa8Wv3jRr6cNo4pcyZw5sRFBJmMJi3rUbxEMBfPXSGsZDAlShdn7YrN/LhwNUHFAwktGVTo56DRaGjUvA7xsYk8fJjMvOVTiH2O3HpaSjrFgvzxCfDmi3FTadaqPj36dcLe3hYnJweL0uq8vDysbbS4e7jSrmtLhvX9qPDvgExg4sfT2Lx2B07ODgx5vy8Z6ZlkZmYRWiIYFzcXZiz8kp1b9uHr711ITLJL77fMHuorlqxj/64jFuffsGY7DZvVKfS+En/Ma5mRiKL4UBTF+qIoBomi2EAUxeT87afzgwiiKB4VRbGUKIql8/9f9MTxt0RRrCSKYjFRFDuIovjqHFwkXisq+8L6S3lWNnz+yUyuXb3FudOXEEWR9LQMHiWnMvXLOShVSsYOm8gXH01l0sff8+vmPaSmpFGuYrj5HLt/PYi1jZavpo/Hzt6WS+evWgQRgB+mLyMrIyu/MWwdPy9Zx9jPh7N2+SaGj36Hdl1aULl6ORo2q8OeHQdRKBW0bNeY65E3OX74DD8tXMPp4+dZ/eNGvv9qHrqsbFYt28Dodz9DJpPRpkMzJo2fZl4mi74dw/pV25ixaBJ7dhxi1bINpKdl4OTsiNpKRW6ugdGfDeXalSjsHeyYNPUjatWvypUL1+jWpz0161Zh45pfAfD0KYKXbxEO7TvO/Jk/cvbEBSIuXud65C38A33wC/TmXnQch/edICtTx4UzV0hPy7SYLQEMGN4LP39PbGytERDYsHobVtbaQt4XdRtW5/iRM/yyaiunjpwlLSWdVT9uZP7MZVhpNXw08T1Kl38sOljE042SpUM5dvAUkz7+nio1ylucLygkkCMHTrJh1TaMRiP3kx4yYfQ3OLs4kpWpY/um3SybvwqNlRXN2zakTIUSzFoymVJlilPE040RYwfSql0T8/l8fD0L/R79nqaWxO/zz6htk5D4kyisbdF6+6FLiAMxD42rB4eOXya8fEkWzPyRbn1MK55WWiuyMrOoUbcKW37ZaaGFderoOZq2rEej5nW4cjGScpXC0Wqt2LF5L/YOdqxYvI5uvdsVeu+sLB2aJ3y4L1+4RqeebRj6QT8MBiNlK5TidtRdbt24Q1DxQIwGI1cvRtK4eV1GDPjY4lzXIqJIS0vH0dmBR8mpLF+0Fg9Pt0LLVFHXb3Pq2Hkunb8KmGYWvQd0oWylUvy0YC0pj1Lp0vstECFbn4u7uyu9B3flk1FfczvK5LmiVClp1qYBSfEPUCgUuLo54+zqxOJ5jyVLFAo5386ewNBRfZk/azm6LB2ZGRn0H9qTuHvxpKWmExjkz9b1O/EP9OHg3mNsXrcDMAXhUeOHEHHpGlcuRlK9ViXyxDx+Xrqe98YMYO60Jeb32bp+F/Ua12T61/PxC/Dh+/kTycnJIbRkCO4eLowbOYnIiCjqNqxOxaplOXXsXL57YV/GjfySp7kbHcOi2SsoW7EUTVrWo0/Hx/pd777fl1lLvkYUKeQ90rxtQ9av3maW49daW1kEGom/hhRIJP5VyORyNE6uqGwdEBFNDY/Wt0i5Ec2j5FSOHTxFuy4tWb9qK67urmSkZ1lUNBWQ8igNjZUV7415h8iIm2atrPS0DHJy9MjkcqysNBZ6W41b1OXw/hMW54m7l8Dc75cCpmqu3oO68MP0ZezYsheNRk3/oT3MTopPExMdR9tOzfn+qx8wGo3kPmWVCyZV26cdCNet3EJuroFTx88B8PmYKQwY3gul0iSeqdVa0a1Pex49eIQ+NxeNRs3MbxfSqFkdvHw8qNuoBjs277E4p8FgNM2AVm+jS6+2yBVyvH09+fSDr7Gzt8XG1prNv5hKj1u1j2Hbhl3mY/U5eiZ9PI0ZiybRvksrLpy7QmpKGpOnf8y3X8wyX6wBHJ3tObL/JLduRHPrRjTHD51mxaZ5+Af6oNfr0ViZqpDmTV9GjTqVGTCsF0EhAZSvHI6Xj4dZQaCAosX82bjnR3Ky9fRoO8jitVnfLaJG3cqElQop9L2GlQrhx/WzibgUCaLpeVDxZ3uNSPwx/xzVLwmJv4BMqTTrd5WtGE5IaFHKVQznzMmLRF2/xeCRvbGx1WJlpaZqzQrm4xQKOd16t8PV3RlRzCMrU8f6NdvMr+/ffZQWbRuycukvDPuwP5Wrl8Pb15N+Q7oRXNwk/VFAo+Z1cHRxpHrtStRpUJ23Ordg3c9buHzBJEKYnZ3DzG8X4uBkT616VSzGHxJWjLjYRPb/dpgKVUoDEBeTQLsuLcz7KFVKRo0bwvaNuy2OtbO3JS3VMrj8umk3foE+1G9cE6PRwMSPvmP21MUsnbeKn5euR0Ag+WEKS+atpH6TWlg/YfBVgFwmIy7GJK64YOZPxETHolKrSEtNt+iqdnV3offALrzVubmFnHlGeiZuRVwIKh6Ik7MD9o52uD2lZNv17Xbs2LLX/FynyyYq0tSrolKp6D2gi/m1w/tPsPSHlXj6emDvYMeo8YNRKB4bNpUsXZxSZUPxD/QhJyenkAIz8Mw+lQKCQgJp3b6pWVpG4v9HmpFI/OuxtbOhYbM6ePl6MnfqEg7sOcqDpId0ebsdWhtrOnRrTVamjiMHTjJ4ZB9+WbmVFUt+AcDX35uefTvy3ZdzAJMseHjZMHr068jmX3ZSp0F13hnai5XLfqFE6eKMnjCMC2ev4B/oQ6nSoRw9eBIHRzuq1a5ESnIKJw6fKTS+O7fu0WdwN8LLluD0iQsUDfJDrVGzZN5KQksG42yjRaVWEVDUl32/HWbI+33Q63MpX6k0Pn6e2NnbWuhl9R3SjSlfzLZ4D2tba4JCArDSakw+3iGBtOti8mPJMxpRqpScPXmRuJgE9u48SI++7fnio6nm451cHPHy9bDoq9m1/QB9B3ezWJr67JsP+eXnLezafgB3D1cGDu/F2hWbycnOwdXdhSKebri4OVG6bAmstBqmzPmMiEuRPEpOxdffizlTl5CWaimI+aQuVXDxQBavmc6tG9HcT3xInYbVzDOKilXLsmLTPG7euIONjTXFSwRRxNMNMOVY3D1cLUQ7NRo13s/IhUj8/QhPr8n+l6lQoYJ4+vTpP95R4l+LTmfySs/JziEhLgk7O1uKhvgjE2TcT3rAxjW/smjOCotj+r/bgw1rtvMgyVQNVsTTjQUrpnL79j0WzPiRW1HR1K5fjWZt6jNh9BSsrDS07dSURXN+tli2+X7+RBbNWc6l89cszv/Fd2M4tPc45SqWYvumPdyLjuVRsqmv9tPJH2DINVDEy40fF6zm1LHz2NrZMHRUP2o3qo6HhxvRt2M4efQsCXFJuBVxwcPTnY9GfGlxQZ72g0mht4Bzpy/x0XtfEnsvPt/N8S2MRiOxd+PZv/sok6aN41FyCrei7mJjq8XewY4iRVxJTk7hp4VrSU/PoFnrBhTxcEOQCWRn5+Dp5c7Rg6csXB1lMhmffTsag8FAifDi5GTn8OOC1dyLjqNDt1bUa1QD5/xKKYC1KzbzxUffmZ97+3gwf+VUvH08OX3iAhM+/Ia7d2IILRHEJ5NHUSL8z+tIXTwXwfj3v+LOzbu4F3Hls29HU7VmhX90o+G/BUEQzoiiWOG5r0uBROJNok+n4YVyJtVrV6JJq3rcvH6HgKK+lK0YTk52Dh2b9bNIfru5u1C3UQ02rNlO9z7tLfw1wCRj0W9IdyaOn2ouBa5WqwL1mtRi4kdTCSjmR7suLTiw+yi6LB11Glbn/JnLJrHABtUoXa4kcffiUalVODrZY6W1slC8vRl1hwtnrjDzm4V0ebstKY/SyM3RU7JMKKGlggkubnLTy0jPYHCvDzl/xtLj+91RffHy8eDi2QhsbLUsmLUcFzcnsjJ1ZGXq6NKrLe+NGcjlC1d5+OARs6YsNDdoAvR/tzuL5vxcSF5myPt9mf3dIj7/dgxfjp9q0Xfy3pgBFpIjGRmZXDhzheOHz+Dt60Hl6uXxD/Th7p0YOjXvbxGYi3i6sWLjPJOq758k+WEKD+8nP3NZ7a9gMBiIuHSdKxevobW2IrxsCQKKvrnKGH8USKSlLYk3isYt6hYKJGGlgvl6wkymzP2MajUrArBv1+FCFVRJiQ+wc7BFJgiIeYVvwHJy9Fw8F8GMhZNISnxA7L0ErLQaFswweZzfjopmzneL+OCTYezZeYCFs5ab1/VLlyuBs4sjD+8n892Xc0yeHuEhjP1sGOFlTWWyq5ZuwNXNmTqNqpNnzKNEeAix9+JJiE8iLS0dpVJJQFFfHiWnFgoigHn5aeznw/lgyAQAi0KAa1eiUKmVOLs4cmjfcTKeuKgDZGfrcXV3fobni+m7SIhLLOQ6uWTeSlq0bYhbEVcAbGysqV67EtVrV7LYL+ZuvEUQMZ0vifjYhL8USJycHQpVaP0/nDlxgYE9PjAv9Tm7OrFo1fcEFvN74XP/F5GS7RJvFLXrV6Nt5+bIZDJkMhlvdW5O2QrhLFr1PZWrlTPv555/4XsSZ1cn8owi2dk52NrbFFJkbd2+CfWb1KR85dI0a92AKjXKo1DI8fV/3J8QUMyf3b/u5/C+ExbJ4a0bdpGaksaoIRM4dui0SdzxwjUG9xpNzN04U29MeibWNlqOHzqFMc/U8Hdg91Fmf7eYbRt2c+HsFTLSM7Czt31m8tjWzha/AJPI35PLYAW06tAEuVxOYJA/DZrUZsLXH9KuSwsUSgWhJYNo1qo+474YYbFUFF6uhDkR/yznP41GjfyJBPnzeJatrkqtwtb+1VvL6rKymfP9Uot80cP7yZw5ceGVj+XfgjQjkXijKOLpxkefD6dn344IAnj7ej5TojswyJ+xnw3nm89nYTSa7FHfebcHiQn3GTDMVGr7yaT3ObT/BPcTH9C+S0vKVQ7H0+uxTmnpciUoXa4EwcUDudRrNNnZOSQl3Ce8XJjZ+KqAkqWLEx+XVEhpOC01nbt3YvD29aRZq3oM7z8eo9FIYDF/pn451+wXHnEpkulfzye4eCBhpUL48NOhjBzwsTlJ375rS6rWrGgOkJWrlWPYB/1ZMOsnjHl59OjX0exiqMvKJjk5haXzVyIA38z6lLLlS+Hs6khuroEVm+YSFXmb7Owcrl6+zobV2/H196ZMhZKFzMCGftgfZxcn/oiAon70f7c7C/IthQHeHzcIX//H6rb3omN5kJSMi5vTS20e1Ofm8uAp/xrAnNeSKIwUSCTeONRqNUWD/X93H41GTftuLSlfpTSPHqbi4eXGvbtxfP3ZTHOOoGa9KnwxZQx29nYWZalP4+BoT5/BXTEa8pDLZfj4e7F3xyFzEHB2daLFW41RyGWFlGkBbGxNd+WpqRnmu2S9Xm8+voCH95NJTU3n2MFTzP1+KZ17tsHRyQEnF0eUSgUhYUXN+zo6O9B3SDeatq6PKIKHlxtyuekznDl5gWF9x5r3PXPiIvN+mkI114oolQpKlg6lZOlQMjOyKFUmjFbtmuAb4I2rmzMLV07jyP6TxMclULNuVcqUL/m733MBVloNbw/oQrValUhMuI+XjwfOLo4c3HOMtLQMnJ0dmP7NAiIjorC20TLp+3HUaVD9pSTS7e1t6dKrLd98Pstie/lK4c85QkIKJBISz0GpVJoT2AAeXkX4efM8bt+8h4ODLSElgnByLizZ8jS3ou4yZ+rjElqlSknXt9/CL8AHG1trSoQXx8fPk9xcA++O6sv3X/1g3rdj99YEBpnW5a2fWDoy5BoLaUkVGB290+19cvW5nD9zGYAqNSqgsVJTsnQoHl7uFvt7+XgUGu+6FZsLbduwZhuVqpVFr89FqzWNw9pGi7efJ/psvVnDqlhwAMWCA/7wO3kWtnY2lK9s6qmJj01k1OBPzR39MpmMEWMHcPdOLJkZWYwZ+gVrfl2IX4DP753y/6ZJq/oYDEaWL1qLvYMdg0f2pmTp1+9E+E9FCiQSEn8SpVJBWKmQZ3ZK/x72T63/5+pz2bL+N5aumYH/E5VASqWCtzo1p3zF0qSmpKJUqygeVgwbG1PzoKOTg9lMas3yTXTt3Y7li9aaj397QGfuJz60sMh1c3dBFEUqVilLZmYWMXfjcHN3eeZyXgEFMyDz+B3sqNuwBuNGTOJWVDTtOregdoOqXL96i+8mzSUlOZUub7/FW52a4e7hVuh8jx6mcPbURU4eO0dQSCCVq5fHx+/3+zuuXr5uDiJgEnVcs3wzDZvWYvMvO9HpskmKf/DSAomLqxMdu7emWEgAu3cc5ItxU+ncsy3tOrf4S8n/NwUpkEhIvGSCQ4vSrE0Dc4e6IAiMnzjCIoiASVdr1pSFHNx7nOJhQQz7sB/3kx6iUquwttZia2+Df1FfqtWuiF6fi7ePJx98PAQrrRU+fl6Elgzi8oXHF9++g7uRkZ7J1cvXSXmUwt4dh5gzbQnNWjdg4Hu9LPIPT9Kuawu2b9qFwWBaRuvetz2fjZmCLstU0jzpk++Jj09k745DRN+OAWDutCUoFAr6v9vd4lwGg4HlS9axYOZP5m1h4SHMXDgJ16fKc+/cusfVy9cxGo1k655yxAQSE+6b8y0qtQpntz/OvbwI505fZHCvD83P50xdjIODHZ17tX2p7/tvRAokEhIvGUdHe0Z/Oow2HZvx6GEKfgHeBIVYVlWlpqYxfuQkIi5dB+DyhauMGPAxXXq9RfTtewwe2YfEuCRKlSnOg6Rk1q/axqOHKXwyeRSt2jU2e4sHhxajas0KODo5cPzwGa5cNDVHXjwXQVipEJq0qsfWDb8hV8j59KtRKJSFLwHhZcNYsnYmB/ccRRAE3NxdzUGkgJVL1tOl11ss+WGledvqHzfQrktzi+W+mLtxLJm30uLYiIuRRF2/YxFIbkTeon+XEWZJk+Gj3ylkvFWvUQ2OHjqFUqXks28+NFegvSyeLogAWPvzZlq1b/LMCrU3GSmQSEi8Ahyd7KlSvfxzX4+7l2AOIgVkZmQhk8uwd7Dj87FTOJfvCqhWq5gy5zPcirgQczeeL8ZNpVhwADXrVMa/qC+fTxnDlYuRvNffwtWaiEuRZs2vbRt3Mei9t/H0LuyGLZfLzRVnALu27y+0j0qtKtQz4lbEBfVTS2ZGQ16h4gEwFQs8yYHdRy10sTb/spOPJ41k8ZyfSUi4T5v2TWjftRWpqem4ubvgF+htLg54WTxZgVeAX4APqickXSRMSIFEQuIfgJXWymTT+9TFWaFQ4FbElXU/bzFvy8nRs2ndr4SEFmP21MXm7auWbWDhyql4eBUhqVDToImCKicv7yLmpPkfERIWhLuHG4nxjx2xB733NjqdDhtbazLSM5HL5Qz9oD/WNpZikJ4+RWjUvC6/bdtn3ubk4kjRIH+L/e5FW7pl346K5tfNe1i2fhYGvQEXd2eUyld7Aa9WqxJu7i5my16NRk2vdzo9cxb3piN9IxIS/wB8/DwZ9mF/vn1CjLF2/WpcvXTdXLX1JIHF/Fk4e7nFtnvRsVy/dgsPryL4F/WlVr2qHNx7zPx6hSpluH7tJnK5nNEThuHwlGnV8/D19+KHn77l6MFT3L55Fx8/L/bvPsrtqGg+/3Y0mZk6Aov5ElqysMWwlZWGEWMGEFQ8kB2b9xBeLoyub7crJKZYr1ENNqzebrGtRdtGuLr9/zInL0pgkB+L10wn4tJ1DLkGgkOLEhxa9I8PfAORAomExD8AuVzOW51bEFoymNtR0eh02Vy+cI0dW/ZSqVrZQvv7BnhZdF4XUJAgt7Wz4aMv3qN67UocPXiKUmVD8fHz4u6dGLr36WDhTvhnCAzyR2Ol4b13xrPu5y3m3MUnH3zN6m0LfrdB0MvXgwHDetK9TzvUGrU5n/Mk5SqV5tPJo5g5ZRG5+lz6DOpKrfpV/9IYXwa+/t7PLUqQeIwk2igh8Q/k4YNkbkfdRQR8/Tw5evA0UybOJj0tg/pNajHyo0EsX7SWlcvWm4+xd7Bjxaa5hS58D+8nE3X9DkajkWLB/mbdq7/KmRMX6P2EA2EBC1dOo9IT8jIvwoOkhxjz8p4pUSPx+vhHijYKguAErAb8gTtAR1EUHz21T11g2hObigOdRVHcKAjCUqA2UKBZ8LYoiudf7qglJF4dzi5OFtIibTs1o3KN8uizcyji6YbGSkPvgV3wDfBi87qdlAgPoUP31s+8e3Z2dbKQcv9/cXC0L9R5L5fLcXRyeOFzF+DiJvVo/Bt5LTMSQRC+AZJFUZwsCMIYwFEUxdG/s78TEAV4i6KYlR9ItoqiuO6vvK80I5H4L5KdnYNKpUQme7karAaDgfUrtzFx/GNDrDGfDaNDt1avPBEu8Wr5R85IgNZAnfzHy4D9wHMDCdAe+FUUxazf2UdC4o1Eo1G/kvdRKBS07tiUEqWLkxifhLuHK0WDA6QgIvHaZiQpoig65D8WgEcFz5+z/15gqiiKW/OfLwWqAjnAHmCMKIqFDZtN+74DvAPg6+tbPjo6+u/7IBIS/wLiYxO5evk66ekZFA0KICSsGEqphFXiL/DaHBIFQdgNFO7ogXHAsicDhyAIj0RRfKb6nSAIHsBFwFMUxdwntiUAKmA+cFMUxc//aEzS0pbEm0Z8XCIjBnxMxMVIwCR+OHPxV9SsW+U1j0zi38RrW9oSRbHB814TBCFREAQPURTj84NC0vP2BToCGwqCSP654/Mf5giCsAQY9bcMWkLiP8bVS9fNQQRM4offfj6LUmXCnmkm9TpJT00nR59rVhKW+PfwuhwSNwO98h/3Ajb9zr5dAAuxnvzgU7As1ga4/PcPUULi309aanqhbQlxSeh0umfs/dfJ1eeSnV1YYPGvnuPQvuP07jScjs36Mn/mT4W8ViT+2byuhdLJwBpBEPoC0ZhmHQiCUAEYKIpiv/zn/oAPcOCp41cIguAKCMB5YOCrGbaExL+LokH+hcQP23RsiusLltkajUbOnbrEknkrefjgEd36tKdm3Sr/1yznyqVI3u09xjzGWVMWIgjQ/90eLzRGiVfHa5mRiKL4UBTF+qIoBomi2EAUxeT87acLgkj+8zuiKHqJopj31PH1RFEsJYpiSVEUu4uimPGqP4OExL+B4iWCmLFwEt6+nihVSjp2b0XP/p2e2V3+V7hyMZL+XUdyaN9xIi5FMm7El+z77dD/da6IS5E8natduWw9Dx88es4REv80pNINCYn/MEqVktoNqlGqbBjZ2Tm4ujn/LRVbZ05cKCTRsmTeSuo3roWdg+1fOpftU0ZaAK6uzpLK7r8IKZBISLwBODk7/K3n01gV7l3RWmuRK/76Ikd4uRJ4ehchLiYBMFWWDf2wP7Z2hQOMxD8TKZBISEj8ZcpXLm2WkC9g0HtvF5KR/zP4BXjzw/LvuHz+Kmlp6f+XnbHE60USbZSQkPi/iIyI4tC+46Q8SqV2vWqUKhf2yrrsJV4t/1SJFAkJiX85IWHFCAkr9rqHIfEP4HX1kUhISEhI/EeQAomEhISExAshBRIJCQkJiRdCCiQSEhISEi+EFEgkJCQkJF4IKZBISEhISLwQb1QfiSAI9zGJRP4VXIAHL2E4/3Skz/1mIX3uN4u/+rn9RFF0fd6Lb1Qg+X8QBOH07zXi/FeRPvebhfS53yz+7s8tLW1JSEhISLwQUiCRkJCQkHghpEDyx8x/3QN4TUif+81C+txvFn/r55ZyJBISEhISL4Q0I5GQkJCQeCGkQCIhISEh8UJIgeQPEAShgyAIVwRByBME4T9fJigIQhNBECIFQYgSBGHM6x7Pq0IQhMWCICQJgnD5dY/lVSEIgo8gCPsEQYjI/x0f/rrH9KoQBEEjCMJJQRAu5H/2z173mF4VgiDIBUE4JwjC1r/rnFIg+WMuA28BB1/3QF42giDIgdlAUyAM6CIIQtjrHdUrYynQ5HUP4hVjAN4XRTEMqAIMeYN+3jlAPVEUSwNlgCaCIFR5vUN6ZQwHrv6dJ5QCyR8giuJVURQjX/c4XhGVgChRFG+JoqgHVgGtX/OYXgmiKB4Ekl/3OF4loijGi6J4Nv9xOqaLi9frHdWrQTSRkf9Umf/vP195JAiCN9AcWPh3nlcKJBJP4gXce+J5DG/IheVNRxAEf6AscOI1D+WVkb/Ecx5IAnaJovgmfPbvgQ+BvL/zpFIgAQRB2C0IwuVn/Hsj7sYl3mwEQbABfgHeE0Ux7XWP51UhiqJRFMUygDdQSRCEkq95SC8VQRBaAEmiKJ75u88tebYDoig2eN1j+IcQC/g88dw7f5vEfxRBEJSYgsgKURTXv+7xvA5EUUwRBGEfphzZf7nYojrQShCEZoAGsBMEYbkoit1f9MTSjETiSU4BQYIgBAiCoAI6A5tf85gkXhKCIAjAIuCqKIpTX/d4XiWCILgKguCQ/9gKaAhce62DesmIojhWFEVvURT9Mf1t7/07gghIgeQPEQShrSAIMUBVYJsgCDtf95heFqIoGoB3gZ2YEq9rRFG88npH9WoQBGElcAwIEQQhRhCEvq97TK+A6kAPoJ4gCOfz/zV73YN6RXgA+wRBuIjpBmqXKIp/Wznsm4YkkSIhISEh8UJIMxIJCQkJiRdCCiQSEhISEi+EFEgkJCQkJF4IKZBISEhISLwQUiCRkJCQ+I/yV8RIBUGY9kT13nVBEFL+7PtIgURC4h+OIAg7BEFI+TvVWiXeGJbyJ8VIRVEcIYpimfxu/5nAn25QlQKJhMQ/n28x9XtISPwlniVGKghC0fybkzOCIBwSBKH4Mw7tAqz8s+8jBRIJiZeEIAj+giBcEwRhhSAIVwVBWCcIglYQhIqCIBzN98I4KQiCbf6+hwRBOJv/r1rBeURR3AOkv8aPIvHfYj4wVBTF8sAoYM6TLwqC4AcEAHv/7AklrS0JiZdLCNBXFMUjgiAsxqQcMBDoJIriKUEQ7AAdJgXahqIoZguCEITpbvA/b6Qm8WrJF+isBqw1KeQAoH5qt87AOlEUjX/2vFIgkZB4udwTRfFI/uPlwDggXhTFUwAFaruCIFgDswRBKAMYgeDXMFaJ/z4yICU/D/I8OgND/upJJSQkXh5PaxA9T6Z9BJAIlMY0E1G9zEFJvJnk37jcFgShA5iEOwVBKF3wen6+xBGT7tyfRgokEhIvF19BEKrmP+4KHAc8BEGoCJCfH1EA9phmKnmYEuvy1zJaif8UzxEj7Qb0FQThAnAFSxfUzsAq8S+KMEqijRISL4l818EdwGmgPBCBKUiUwFReaYUpP9IAkxrtL5hmMDuAIaIo2uSf5xBQHLABHmLKufxnVagl/n1IgURC4iWRH0i2iqL4n3bek5CQlrYkJCQkJF4IaUYiISEhIfFCSDMSCQkJCYkXQgokEhISEhIvhBRIJCQkJCReCCmQSEhISEi8EFIgkZCQkJB4If4HTAgxbHHdsVMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "map={'low':0,'medium':1,'high':2}\n",
        "high['RISK']=high['RISK'].map(map)\n",
        "high_x=high.drop(['BIRTH_DT','CUST_ADD_DT','NAME','CUSTOMER_ID'],axis=1)\n",
        "high_x_data=high_x.iloc[:,high_x.columns!='RISK']\n",
        "high_y=high_x['RISK']\n",
        "high_x_data=pd.get_dummies(high_x_data)\n",
        "print(high_x_data.shape,high_y.shape)\n",
        "kmeans_high= KMeans(n_clusters=3, init='k-means++',max_iter=50000,random_state=0).fit(high_x_data)\n",
        "reduced_data = PCA(n_components=pca_num_components).fit_transform(high_x_data)\n",
        "results_high= pd.DataFrame(reduced_data,columns=['pca1','pca2'],index=high.index)\n",
        "results_high['label']=kmeans_high.labels_\n",
        "sns.scatterplot(x=\"pca1\", y=\"pca2\", hue=kmeans_high.labels_, data=results_high)\n",
        "plt.title('K-means Clustering with 2 dimensions High Class')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "NOau6wGqkYX3",
        "outputId": "a377f1b5-08e1-45df-a7b4-da08f2839086"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-2e32ebedaaa9>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  high['RISK']=high['RISK'].map(map)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 22) (50000,)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# open figure/axis\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# plot // you need to specify the axis\n",
        "results_med.plot(x='pca1', y='pca2', kind='scatter', ax=ax, c='g',label='medium')\n",
        "results_high.plot(x='pca1', y='pca2', kind='scatter', ax=ax, c='b',label='high')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "dL-G2fbJ6pet",
        "outputId": "5924d814-1ff8-449a-beaf-3019f6c977e3"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='pca1', ylabel='pca2'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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bEVkhnjDkhB6xwkhHKn21YlzH8trVsGiR6bZyOEzjsWhR11Yf6aioqODuu+9mwoQJVFZWMmZM7mrXxaCoWlgiMgFYCBjAfUqpW+L2NwAnhd72Ab6slOoX2hcANob2bVVKnZXufuWuhRWeFNfvXB+ZrDv9nQmSH5mSqe5R2EUmIngDXoLBIC67C6UUiyctpnZobUpNpZtfuZnrXr4uozE5DWfEBRdfcR7No995lJOGnmQ59vhamIbTGhjabyhbd2+l3d9O7cG1lllb4fNsYiOogmkNbPj4eOOd6vvauLaRWctn4TSc+IP+shB0zCf50hXL9Do5ybl7TLfVkCGFNR5hWltbqaqqQinFZZddxqGHHkp9faI7t7vIRguraAZERAzgX8B4YDuwBpislHonyfFXADVKqWmh961Kqaps7lnOBiQ8Wdlt9oQgrEMc2A07SilLN1EyqpxV3Hn6nZxx6BkACcKJLbta8Aa8nLjkxIgbKea+NgdLvr2EQ/Y7JGn/kCpnFSPuGbF3lRF1bvzKAsAQgxtOvIGx/zOWc584N+aa0VQ6Ki0neSuBwFTHpzrPyhCEvy9VzipG3TvKcuUX/X2NPnezZzM1jTWRPu7J7lFOlLo8fjmIKTY0NLBkyRK8Xi81NTX8/ve/p0+f5NmIhSYbA1LMGMhY4H2l1AdKKS/wCDAxxfGTgfzl65UR6TJ4KpwVLD1vKa9OfZWJ/2v9LTTEwGFzxGxr9bZyxfNXcGDDgQz8zcBIdtSlz1zKoIZBHL/4eL55/zctjQeYLrK6ZXVUOasSXFu+gI/1O9dT01iTYDwAfnTsjxLGAxBQAea/Op9dHbsSrhlNm68tkoHzl/f/Eom7NK5ttJzUw8dPXTrVUvYkk2yq6CyymsYaSPLsFf6+RmeaNW1sSjAe8feITnHOZ51DoWomCplO25uor69nw4YNvPPOOzz00ENFNR7ZUswYyEBgW9T77cDXrQ4UkYOAocBLUZvdIrIW8AO3KKWWFmic3Uayp7lMMngG7TuIbbu3sfzfyy2Pcdgc+IN+nIYTp+Gk1dsKEGOQwk/7i5oXZTxmpRQPbngwJr7gEAcNExqY/fzshAkTwG24uerYq6h0VnLdqkTXVkegg6nPTCWgAhgYBEhegd7ub+fsx84mqII0TGhg/muJsZZoOgOd1DTWcP+k+2NWIpk00UoQfkxB+Ps6delUBu8zmLpn6iy/F+F7RLvd2v3tqKCij7NPTgrA0b9HKz9YWRA14WILTWaDUgoRKfYwyoJsPVLlkoV1PvCEUjHJ/AeFllQXALeLyFetThSR6SKyVkTWxhfplBLxT3ONaxsjT43JMnj6OvtSYa+gbqSpUnv2Y2dbTlJgTsp+5ScYCPLTb/40ZcFeNnQEOrjlzVti3FEBFWDVB6ss3Wkuw8XiSYsBGPs/Y3FKYlYXwB7fHnxBHwEC2G2pn3PCq4tZz8/CYSSuauLpDHQybem0hGLGZJlWnjYPz733XMI4KuwVuAwXfZ19cRkujht0nOW9TlpyUsJ2MI36oommsY6ubfAGvJEK/GzrHOKr6qcsm1KQmolyqZZ3u918+umnWU+MvRGlFJ9++ilud2JGYjKKGQM5FrhBKXVa6P1PAZRSv7A4thm4TCn1ZpJrPQA8q5R6ItU9SzUGksxv39fZNxJoBWIyTxomNDDyKyNT+uKTEX4a7W4c4mDVlFWs+nAV81+bj8Nw0OZtIxiffhWHIQZ2mz2pcQzTx94Hb9Cb1OUWz5xxczhpiDm5D9p3UKQeJVo3LFXsqcJewY0n3si1q67FbrPT5mvL6L5hXDYX267aRsuuloQYUjSZNtJK9nuUy7XSkW3MqFgxEp/Px/bt2+noyDw22Jtxu90ceOCBOByxD2Kl2FBqDXCoiAwFdmCuMi6IP0hEDgf2A/4atW0/YI9SqlNE+gPjgF91y6gLQDIXVXjCqltWx5bZW9gye0vCH+OaHWssz610VOIL+CzjD8UwHgB+5eekJSdFViuZBvydNidnDzubhzY9lPK4ZFXslY5Ky8n9ljdu4ZY39ib+hav2F01cxJiBY5IWTlY5qwgEAzSc1kD9ino6A51pjZvT5kxMJLAnL1SMJtM6h5ZdLWlXa7n2goknk3TaUmgh7HA4IlXemvxTNBeWUsoPXA6sADYDjyml3haRG0UkOiX3fOARFbtUGgasFZF/AKswYyCW2VvlQLoJxLAZPPfecwCRwrJwYLTKWZVwrtvuZu5xc7nrjLvSTihW2KQwvxYKZZl5lY72QHta45EMt+HmF6f8giu/fmX6+4T8+WH3ltWE3NfZlztPv5Mts7cwcsBIy8JKKxQKl80Vs63V28pTm58CiLjP+jr74rA5cIgj66LS9TvXW2bo5aMXjBWpZG8KKTmiKR10T/QSwNPmiRSxOQyHZaZVH0cflFI0nNZgFua9Nh+X3YU34KVuZB2L1i/CbrPT4e/AH/SjkqUI9UIq7BXM+vqsmNVGOmaOmslX9/sq16y8JuFaW2ZvAaB5ZzMTH5mYsJKyWvE4xMGC2gVc80Ls9cA0cosnLea/Hf+N1Ij4Aj5+dvzPMu4Bksx9dc+Z93D24Wdn1Asmn/3R1+xYkzS1OxP3WbFdX5pYStGFpSF2mS8i/Hjcj/lw14csbl4cc9wen+memfnnmZFt4YnrvnX3cdNJN3HtqmtzesLv6bT727ntr7clrT2x4p5191hu//G4H8dkNvlVbLxl2ohpHH/Q8Vzx/BUxDwIVzgqq+1TT19k34QGhI9DBtKXTEJEYd1iyHiBWk6uVG7Svsy8jvzIyo34fzTubE1aeXdGY6orkSCm4vjSZoQ1IEbFKhZz/2nyCwdRB5Xg6Ah3MeXFOyoZLvR1v0BuphQlPTLkY25+/8nPsNjv+oN8yWN20qYmrv3F1QiDfF/AxduDYpAF+w2YkbIuewK1UCKIn1ypnFR3+2JWQP+hPKW4Zn+ob/3m6ojGVq+RIMtHNUkwP1mgDUlSsnhrjJ4FM0cYjPQEVwGFzsPgsc3V3ybJLcvp+p8rychgOWr2tNJzWkCBZMqx6GIsmLuKSpy5JkGgJBAMJtQrxNSLRmWDh35mpS6eyY/cO5r08D1sopBmdDJBO6iXc6THemNqx0zChIUZiPltyURS2Et3Ml9quJv/oGEgRySTtUpNf7GLHsBk5pd1mQliVuH5FPYIQUAHmfHMOQ/sNjbTdtapIv/6E67Hb7Cx4bUHME/uIA0YwonFEVplzLsNF84xmS72vTH/nDAycdmdSN1IhYhTJxuY23Gyt31rSBqSnx2x0DKTEiG4AZRVY1RQGv/LjD/jTpt3myoVHXcjlz10eExv5+Ss/j7wWxDLB4eev/BynzYkgXHPcNcwYNYOnNj/FEXcfkfUYbGKLKA3Ek07VIEyAQNIq83zFKOIn3WRj+9nxPyvpSbk3x2y0ASkC0b9wHf4O3DY3HUFd6NQTuK/5vpT7U2XHhetEbnz5RtyGOyEDLFPa/e1UOa11RtOljCcjuso8nYRJJk/jVpNu7dDahLFV2CssEwkKRbYriXKSdCkE5SJl0mOIz4/vDHRq46GJwad8/Hjlj3M+32lzJl2BREu2WFHpqMRtuBPqW8LxmJZdLQkikuH+LBDb931QwyAa1zYm3CNZjQiQVE6mO8hFHLJcJF0KhV6BdCOeNg+PbHokIeDtNEzXRTioec4R5+RcOKfpGXSljsdms8VkT8U/Vcc30HLanRF5nKH7mlXb4QZhDsOB1+/l4qMv5pM9n1DlrIp0vgzTEeigylllWbk/888zQYhZRaRqS1usVr65riQK2SGxHNAGpJto2tjEJU8nZt+AKS1y/fHX84vXf4FNbDyy6ZEijFDTU/i///2/SOpv49pGFry+IMFV1LKrhRmjZjBj1IyYdN5Jj07a24hrQgMv/vtFHt/8OI3rGmlc18g5R5xDhb0ioeCw1dtKq7fVUvlg1vOzOPvwsyMTcbpJN5O6lXyTa6/1QnZILAd0FlY34GnzMLhhcFLtJ5fhwh/061RcTV4IZ4LNXj474XfOaTixYTNXFgEvC09fyIxRMywzoKz0u8CUyolOf46uzh/UMCghQcFlc/HatNdiKtCbNjUlTLrFDDx3tRJfZ2FpCkbLrhazUCyJffAFfGkVaTU9B0OMgj8szFo+yzLTLPzkHzYsM5+dCQr2c++XIHluZTwAphw9hSX/WGL5xH3zyTcnZBV2BjsTgvqTh09mxAEjWL1jNWMHjrVMOe5OurqSKMaqqRTQBqQbGNJvSMruelq3qneRqfFw2BwEVRCb2DBsRsZFj9nWFf3gzz/I6nfwyq9fyY0n3UjzzmYAagbURPZ1+BLHGHZxRVOKqa/Fir+UM9qAFBhPm4fmnc0p/0C1AdFYEa4OP+/I83j8nccLdp9sfv8uH3s5w6qHJU3DTdYRcki/ITH95Lsr9TVb11KuK4me7sJKhjYgBST8RxauSNZocuHhTQ/jkPSdFguF23Dzq/G/ovbgWoZVD0uasbT0vKW47K6EuMvc4+bGCFB2+DvyKtyYjK6ucjI1CqW4muoudB1IgQiLwrX725M2OtJoMsUqe6+QuO3uSC3G7RNu55gDjwFMmfbmnc2WtQ+Q2KzMbbj5zrDvJNQ+pRJuDPe66UrvkK72I8m0JqS39z3RK5ACYSUKp9GUA4Jw+4TbGfmVkRH1X5TZ2MtluMwVdTB2Re0L+KgZUEPDhAZmPT8LQwwUikUTF9HqbU0pn2K32SMB63w9zeealgvZ1YR05T49Ab0CKQCeNg8LXl9Q7GFoNDmhUNQvr6fKWUX9inpzIg0VD3YGOukIdBBQAZw2J1XOqkjF+MoPVnLFc1fQGehkj38PvoCP/3b+l8/bP0+ZROIP+hlxwIi8Ps13pcAvm+rybO6Tj5VVqaENSAGw+gXUaMoJh+Fg5Qcrk7Y3DmJmh/kCPmZ9fRYjDhjBxU9fHCML71d+Zj47k+8+/l2CBHGIA5fhsrze6h2r8yoLEi3Zkq0sSjZGIdP75CKTUg7oQsICkK5wUKMpdRw2B4YYGf8OGxgEkhU6RV1TBRV+EvupvPPDd+jfp39e2+pC7tlR2RY6prpPvtsFF4NkhYRFXYGIyAQReVdE3heRORb7p4iIR0Q2hL4ujdp3iYi8F/q6pHtHnpqVH6zUhYGassYX9GX1AJTOeISvaWU8wqnB1ZXV1NXUxeyrG1lnOclm6g6qrqxmzMAxWU/Uk4dPZsvsLay8aCVbZm9h8vDJKe+Z6j49WXCxaAZERAzgLuB04AhgsohYNT94VCk1IvR1X+jcLwHXA18HxgLXi8h+3TT0lIT9uLlIZms0vQ2HOPjGoG8A5t/OouZFMfvvW3dfwoTdXe6gaKPQlXvmQ3CxVOMnxVyBjAXeV0p9oJTyAo8AEzM89zTgBaXUZ0qpz4EXgAkFGmdW6PiHRmONzWK68Skf05ZOi7iA4v92OgIdNK4zJeE9bR7+8v5fuj1ttqvB/a7EY6C04yfFTOMdCGyLer8dc0URz3dE5HjgX0C9UmpbknMHWt1ERKYD0wEGDx6ch2GnJteGPRpNuRKvzpsMu2FHBVVCTUvYSMwYNcMyW+vGl2+kuk819SvqsYkt4V6FTpvNR6purjIppd6wqtSzsP4EDFFKfQ1zlbEk2wsope5VSo1WSo2uri78Nzz8tJEs20RjQVt/2DHa/F9TVgiCP5gY10h2bLLY4I2v3AjA7GNmJ+zzKR9XPn8l7f52yz728e6gfLt78tXzI5d4TKnHT4ppQHYAg6LeHxjaFkEp9alSKvxIch8wKtNzi0nt0FpmjOy+NpxlzcbzoWELPPiC+f/G84o9ol5PstRdKxQqJnU3FZ2BzqSSPr6gj+adzZw05CTL/VZ9RvrY+yS4gzLpiJgtXXVBdYVSb1hVTBfWGuBQERmKOfmfD1wQfYCIDFBK7Qy9PQvYHHq9AlgQFTg/Ffhp4YecnqaNTUxbNk2n8GZCW39Ytgj8fcwvgGWL4eAXofKT4o6tFxNUxcsgrBlQE6lCDxNWJY4noAIsnLAwkl6baUfEXCiWUm+pN6wqmgFRSvlF5HJMY2AAi5VSb4vIjcBapdQzwJUichbgBz4DpoTO/UxEbsI0QgA3KqU+6/YPEUf4F1gbjwzZNQQM717jAWD4zO3agPQq7LJ3Knpg0gPULavDJjaCKsiiiYt4c9ub3Ln6zphzOgOd1K+o5/iDjqfV28rn7Z9n1BExG+LrO4qhFlzKMvO6kDCPPLbpMb731Pe6XfiubGnrb7qtog2IfQ/UH6QNSA/AIQ5EJGljKjCbaymlMGwGfRx9Etruhl018YV4YSrsFQRVELfdTaffdJHFu9SqnFW8dPFLMR0RM6HQKrvlpOJbkoWEPYkrnruC8548TxuPbKj8BCZOM42Ga7f5/8Rp2nj0EHzKx/xT5lNhr0joSAjmqsOGjSBBfEFfTIosEAk4p0qNb/e30xnoZHfnbjoCHQldFQECwUDWMYNCq+z2FBVfrcabBzZ7NnPnmjvTH6hJ5KhHzZjHriHQr0UbjxLChs1U3s2gytyKCnsFJxx0Altmb6FlV0tE2Tfsy/cH/JYPXPEpsqlS4x3iiLmG2+HmO4d/h6ZNTTjtTgLBQE4xg0Kr7PYUFV9tQPLAyg9WFnsI5U3lJ9pwlCBdleMJqiBVzqpI7GDMwDGcPexsWna18Hn755z7xLns7tydcF50ltFmz2ZW71jNjSfdyHUvXZcQX4w3QK3eVp7651MYNoOfjPsJM0bNyGlCLnT2U6lnV2WKdmHlgQMqDyj2EDSakmTUvaMsK6cH7TvIclURnSJ7xXNXcMTdRzBl2RSueeEaxvzPGCrsFQnHuwxXjIvsC+8XtPvbWfBa7i0VCp26W8zU4Hyig+h5wNPm4Su3fkULKGo0FrgNN1vrt8a0te30d/J/h/0fT7/zdERg0W6zc/WxV3PVsVfxyZ5POOJuK2m8WCrsFaybvo7VO1Yz888z6fB3xOx7ZcorWQfPo/G0eWje2QyYKcbF7tleLJIF0bULKw9UV1bzwzE/1HEQjcaCjkAHN796M43rGmPa2T7+zuMxx/mDfm554xZu/9vtjBs8LqNrN5zWwLDqYeZ9/LHurXZ/u2XwPhuijV62mVKZGIdCpAZ3J9qFlQesVEQ1Gs1efrv6t3QGkncljKYj0MGLH76Y0bFhSY9tu7dZ7t/40cacZU26kilVygKI+UQbkDzQsqslK/kHTRfQulmaKBr+1pByQr946cVJJ/F0mlm56lD1lBTdTNAurDwwpN8QS5G3Hk9bfzP91vkFePsWPg134/mm9InhhYATJsyCARt0+m8vJjyhW0mggFmtHl75RKvYZlLEZ5Up1envTOsW6ykpupmgH5vzwJINWYsElxdWT/1hEcT7X4G7NsPiV9KLIXZl9RCtm9XZz/z/2XthyUotwtiLCRcJfrLnEyYdNgm7zY7bcOOwOXDb3DHHGjaDll0tGa8QojOl3IZ5LZvYkmaWhekpKbqZoA1IF/G0efjJyp8UexiFY+P5cNsWeOAl8/+N51mIIAoEQq+XLbY2EF1V3Q3rZsUg4N3XvO/SB8BzWG6fUVNWCEKVsyqS+nrjKzdyxN1H8Ng7j+EP+iMV6R3B2KB6q7eV9TvXZ+Waqh1aywMTH4iIOYb7cqRySfWUFN1M0C6sLtK8s7l803fDLqh+Leb76Grwtv6wcwQ89QAoF5Fi5KeWwIVngi2JZIvNv1cMMdrFZaW6+5UNmbu++rWYbqtkBFxwzwaYNMWsbtf0WNx2N0+e8ySD9h3Exo82WmY/+pV1j5L6FfWsm74uoxVC2M1lE1uCnlc6l1QpCyDmE21AusjW3VuLPYTc2Hg+LF0EtoA5MSvA2W6+PuqP8NbFoPyg4iZt5YSOfc0J24qA05zsw/EKCUDQYf4fzz0bwOg0r3XMb+Abtyc3JGHdrGWLTSPl7QtI1AECAbeWg+8hOGwOVFBFakRi9hkOVu9YzaRHJxEMZvfw5jActHpb00qkW0nDR5OJS6rcU3QzQbuwusgT7zxR7CFkT1t/c2UR6AO+vhB0mauMcGyh+fvmZBysInaSjuKY32BanWgUnBxqy/L0EvNavr7mtaIVdwH8FeZ2777m/2/MhVu3p3ZtHfWoqdR7ySlw5nSwdSSOISwHrylbbNiY+825lsYDzMl7/mvzTSHFYGapwWHCQfDJwyezbvo6fjvht6ybvi4hgN68s9kys7LSUdmjXVLZolcgXcDT5mHFByuKPYzs2TkicWURQxKjASBBeOJBsAcBH2AD8YFyg70dVs2HLcebq44EFBjtgAEqAMFooyKmEVuaZgUR1s367BCwKRK8hwHHXpecpiz55fhfMm/VPMt9TsPJ3OPm8qs3fpVT3x2lFKPuHUXdyDoWrV9kmYWVrClchb2Cp859qiAV6eWKXoF0gdvevK3YQ8iNlhNzOEmZX8oG9AF/FeDENAZuQPbGOP41iUQjJOZXoALEC8EKLJEgvHdG6kytSBC/Iuo+CgwtB1/u/Lr215xw0AmWjaEAUOAP+PnC+0VO1/cGvbT727lz9Z2WWVibPZuZumyqpfFYNHERpx5yas6NqfLZp71U0CuQHPG0ebjlzVuKPYzsaesPf60ncYJX4GgFXwq3leX2FKuVZNfw9917z/jz/ZXw57tMQzVxmnVA3KqToaMNzvs2HKKVkcuZwfsO5sl3nkxqILxBLz9/9ecZXcthcyAIDsORtk6r3d/OZc9dxjPvPpNQMV/pqOSpc5/i1ENOzexDxFFOjaOyRa9AcmTVh6uKPYTc2DkCVPxzg4KBb8B5Z8PwPxJZbcSQraHIFBX3JaYR8/eBpx+0Ts21yshSNrOoUFPWTH5yMr9885d5uZYv6MMmNiYPnxzTMjcZj7/zuKXcSlAFqRlQE7Mt0xVFT69KL6oBEZEJIvKuiLwvInMs9l8lIu+IyFsi8qKIHBS1LyAiG0Jfz3TvyOGjto+6+5ZdZ+P50PQni/iEwI5x8MfnYdP3iLibcibTc9PcJ+gwM7XiA+u6k2GPJd8p8R2BDu5rvi9pWm86nDYz5hJNNjpXucqhlAtFk3MXEQP4FzAe2A6sASYrpd6JOuYk4O9KqT0i8gPgRKXUeaF9rUqprKQ28ynnvtmzOSO56ZJh6zHwwMtmxlXJYuHSgr190iG2bmXnCPP/ARu08dAUBJfhwm13x/Rqj+/PXmGvYMvsLQAJdR+eNk/S48spEF+Kcu5jgfeVUh8AiMgjwEQgYkCUUtF+or8B3+vWEfYUnlsIq68o9ii6QBBe+jlsmAJ2L3grzLWzPVS3kixWotGkwGlz4g16qbBXEFRBFCqllta0pdNYdv4yS52rxrWNLHh9QUKcI1yVnqrmJB2l3DOkmAZkIBCtwbwd+HqK4+uA56Peu0VkLeAHblFKLbU6SUSmA9MBBg8e3JXxxtCVbmfdiuewkPEoVAwj34RXxFHj9VfCuh8QkUwBszI+XMyoiwd7FUd9+Sg2fryxS9dwiIM/Tf4Tg/YdxLbd29jVsYtLll6S8pyOQAerWlYlGBmv38uC1xdEZE4gVrixK1XppR6AL4sguoh8DxgN/Dpq80GhJdUFwO0i8lWrc5VS9yqlRiulRldX5896v7ntzbxdq6DsSGWTSw3BOi6SJlYSLh5s6w/v15pf6dKAtSR82fKvT/7Ftw//NrYuTF9iE2oG1LDhPxuY9Ogkpj0zjYAK4BAH+7j2wW24La+/8O8LaZjQEKNz9bPjf5aQdhwf5wj3hM925VHqAfhirkB2AIOi3h8Y2haDiNQCPwNOUEpFUiSUUjtC/38gIi8DNcC/CzngaOJ7M5csrfsXewSFx+uGnTVw3+t7CyRtPjjjMhh9X+yx8ZLw2v1VdnQGO3n6n0936RpOw8kjmx7hR3/5Eb7gXl03h83B4999nEH7DuKo3x2VkIxot9kZ+ZWRbJm9JbKieGrzUwlpx/lQ3y0HWfhirkDWAIeKyFARcQLnAzHZVCJSAzQCZymlPo7avp+IuEKv+wPjiIqddAdvf/J2d94uN9r6w8vzKR/3VSZYpRjb4c93mJXs4dVK0GnKva+9dO9hVpLwydSDo9Erlh5Hq7eVn6z8SYzxACLvn3znSQIqUb/NG/BG3FDhXuv1K+oTjms4rSHtJJ8uFbgcZOGLZkCUUn7gcmAFsBl4TCn1tojcKCJnhQ77NVAFPB6XrjsMWCsi/wBWYcZAus2ATHt6WnfdqmvsHAF+K0mRciQ6NhJnEJUtiTSLwPN37J34rSTh02lndVWGXpMTNmz8afKfuuSmiuar/b6KPc7hkkwocVfHLua/Nt9y38IJC2MMg1WabpWzipEDRqYcT3wq8M2v3pyyH0mpysIXLY23GOQjjdfT5uHLt345TyMqIBvPNwvxgnZ61grECovAe4QO+N7/mRXqVqnM4RRhqwB8W3/TaERXvKc6XpM3BMEudpQo/MHcajhywYaN5y98nnOfOJfdnbtj9s0ZN4df1P4iZlsuabpW5wC4DTeLJy1OCJJ3NQsrH1lcydJ4yyKIXkqURQFQW39TDTfooDyNR7YPNamC7C546Fl48HlY/Kbp2kKBdKYvQMxlxaLJCwqFT/nSGg9DjISVRVcwbAaVzkr2ePck7LNyHeWySrBatYCZ5ZWsM2K2Afgw2RQ95oLWwsqSeJ9kSfLq3CRquOVCPo1eSOX3g9Nir6uccM4ZcNjy5KdaSaZotd+SwipOAeZKIpeqdl/Qx8lLTrY898rnr2T4l4fjNJwxT/OTh09mxAEjWL1jNWMHjmVY9bCU97CKbYTJZ5A8OovLKr04H+gVSJa80vJKsYeQmjXfh7/PpjxXHvkgi9XLo0v3tuhNFiQ/7mYtmVJmXDD8AgybkfP53qDXOoAe9PLN+7/JCfefEPM037SxiVH3jmLW8llp+6VD7KolnnwGybtDRiXtCkRE9gGqlVL/jtv+NaXUW3kbSZnw/HvPpz+oWLT1h+fvovcaD8hKhyvoMlv0GoHEtN7odF8FfOOXMPr32niUAY++/WjSlUk+aA+YT/PTlk7DwIj0Dkn2lG8VgwgXFzaua2T+q/Nx2p05VamnojuyuFIG0UXkXOB24GPAAUxRSq0J7VuvlEqdalBidDWIXvIB9Pdr4Y9/oXcbECtSBdnj9Lfse2DGSGhcnxg8nzEy8x7umrLGEAOb2BLSfOOpsFckBMP3ce3DyotWMmbgmIwqyQspVdK0qSlBRiWXSvZctbDmAqOUUjtFZCzwBxH5qVLqaXrhLFUWAXSNBVn8qho+s3o/vt+IwlQGtnfsXakc/OJecUdtUMoOGzZcdpd1Oq+CVVNWseO/O7h46cWWMu9gnQocfsrPNAZRyN7pXZFRyYR0MRBDKbUTQCm1GjgJuFZEriT7VJmyp+Ql3J2txR5BmaHAFpflE3DAl/4Ffnfc9lAP93AB4tIlcJuuDykHDAwMSYyJnDf8PILKOtAeIMApD55CgADfH/n9jO4T3y+9VKTcu5LFlY50K5AvROSr4fhHaCVyIrAUODLvoylxnnznyWIPITUrbi32CMoPpcDmNVcWQTuM/D08+CJIgL093G3me3/l3vMCTmLEHbWgY8kSIGD5uNu0qQmXkby9QWegk2lLMysatuqXXg6V5F0l3QokJIG6F6XUF8AEoEzKsfPH1t1biz2E5HgOgx3foBd6FrMguvMhmCm+TjPl2e+Ek34G678f6u1eae7HBpecRNrvq64PKUvCrim7zY5DElPfM8nmchpOy37p5VBJ3lVSrkCUUv9Ist0HPFSQEZUw6//f+mIPITllpbpbLFL0eg+64aVfgBHn67b5wVtlxjyWLTYNRcABASOkvRVC14eUNX0cfZjytSn8ds1vY7YHgumzuWzYqB1aa7mv0DGIYpNRHYiIHCMia0SkVUS8oXayu9Of2XPwtHnY5d1V7GEk572Tiz2C8sfw7u0xEsZXCU3LzNf1B8HFteb/Z1+i60N6EL6Aj3vX35uw/fYJt7N40mLL1UkYp92ZMq5RyBhEscm0kPBOYDLwHlABXArcXahBlSJ//Mcfiz2E5LT1h7fDvcw1ORO0w8lzweggxs0VCKn2AgxcaxqKox6NNShWkvBaxbfk6evsS4W9grnHzcVlj314CIsiTh4+mTvOuAOnzYnbcCdcI1VcI53ibrbk+3pdJeNKdKXU+5hZWQGl1P2YcZBew+PvPF7sISRn5wi0qECuKHB8Ya4ijvojvLQAs8llHFYxjspP9hqUeLSKb1lwwVEXsGX2FmaMmmEZ8K5yVrHZs5lZy2fhDXrpCHRE9lc5q1LGNfKtQ1VoXatcyHTW2RPq2bFBRH4lIvVZnNsjaPm8pdhDSM72McUeQRkSCqYb7WYQ/fAnoPn7ZqpuoIqE1Vw2MY5c+45oup37m+/nkz2fxAS8w6sMEeHoe47mqN8dlVAH0tfZlztPv5Mts7dYFublu5tgqXYnzNQIXBQ69nKgDbOT4HcKNahS5KM9JVwD8vpVxR5BmRJyTwXcsOkiEl2AChyt2cc4tIpv2eANeqlprKFpUxOTh09m3fR1qJD7ssPfgS/os5RF8Qf9nHHoGUnjGlY1IHabPSZWko07qlRqSuLJVI33E8CrlOoAfi4iBpA8gbqH4Wnz5KTs2S209Qf//uj4R7Zk8P2ydcIpP4GvvgjV72Z+aa3iW1Z0BjqpW1bHiANGsPBvC5NWnYdxGa606bhWNSBfeL9g/X/WM2bgGBrXNjJr+SychhN/0J9WYqRUa0oyXYG8CETpOlABrMz/cEqTkg6gvzG72CPooSgICqyab+piZRPDqPzEXLHoLK2yIRAMcPQ9R9O4vjHtsU+c+0RaPanqymrLY+qX13PrG7cy888z6Qx08oX3i4zcUaVaU5LpCsStlIroZCilWkWkT6oTehKLmhcVewjJWXNp+mM0KQhnWwVCqryd5uohGOoj0hlaaGdbaX7wi3D+RPP1gA3aeJQ43mDmfX4+3fNpyv2eNg/NO5v541uJD542sTH3pbkJ2w2bkbYPSCnWlGRqQNpEZKRSaj2AiIwGrBsK90De/uTtYg8hOb79iz2CMifsyrJD0AvjfwpVH8Gf7ttrPGBvDCMTQxAtBR8tEa/pEVTYK/C0eZJmXtU9U4dNbJZGqd3XbukOz9QdVUjhxVzI1IU1G3hcRF4TkdeARzAD6l1CRCaIyLsi8r6IzLHY7xKRR0P7/y4iQ6L2/TS0/V0ROa2rYylL2vpj/gh1/CMvqAp48RZY+iB445r9ZBrD0BlYPRpDDKY/O90yjTY6U6rN12Z5vk2sp9yFpy8sKcOQKZkakI3APUAn4AEagS49locC8XcBpwNHAJNF5Ii4w+qAz5VShwANwC9D5x4BnI8p6DgBuDt0vd7Fw4+hjUc+EfD1NSd9G2BYxDDSFQfqDKwewaU1l+K0JfYtD6hAJI126tKpbPZsjuxL1uvcZbhwGS7mjJtDpbMyYf+ccXOYMWpGfj9AN5GpAXkQOAyYD9wB/C/why7eeyzwvlLqA6WUF3NVMzHumInAktDrJ4BTRERC2x9RSnUqpT4E3g9dr3ex47hij6DnYu+AyRNjK83jiwNf+WmiIdEZWD2C8QePp8KR2HI2ms5Apxl4X2cG3pP1OjfEQBD2r9ifDn9HzD634eaqY8s3DT9TAzJcKXWpUmpV6Ov7dF3OfSCwLer99tA2y2OUUn5gN7B/hucCICLTRWStiKz1eLIvuvnV67/K+pzuQ68+CkbAYQa/w5XmVq6pVfPNniDRGVo6A6tH8FHbR5bGIB5f0MfMZ2fSuLYxJlOqr7Nv5Jg9/j10BDq4ZuU1kW0V9goq7BUsnrS4YK6r7pA9ydSArBeRY8JvROTrQO69YbsRpdS9SqnRSqnR1dXZ/6BuePmG/A8qH+j4Rx4IS7v7iJV674yd9Nv6w3tngC2+vWmUTlb0SiQTnSxNSfPX7X9l0cRFKfuFRDNr+Sw8bR4mD5/MltlbuOP0O2KMSJhwjUlQBVk3fV1O7WUzobtkTzI1IKOAN0WkRURagL8CY0Rko4i8leO9d2BWtIc5MLTN8hgRsQP7Ap9meG5eaA+UaLLZr7cUewQ9AAl92aNeC+CEztAff9ht9dxvwbuP9WWy1cnSlDxNm5oYccAImmc0Z2REnIaT5p3NrNmxBoAzDj0Df9BCUy2Ey+6i1ZvfDqLhFcdmz+Zukz3JNI23EMKJa4BDRWQo5uR/PnBB3DHPAJdgGqzvAi8ppZSIPAM8LCK3Af8DHAqsLsAYS5jEYJ0mXwg8dwe4P4eli/Z2HQRiVHrD6BhHj2T1jtVcMuIS7p90P9OWTosRUoynM9DJxEcm4rK78Aa8LJq4iEUTF1G3zEzpjc/KyncVeTh92Gk46fB3YItbG4RlT7q7JzoASqktqb5yuXEopnE5sALYDDymlHpbRG4UkbNChy0C9heR94GrgDmhc98GHgPeAZYDlyllIVjTo0n+y6zJA0GXmc4biAukGu0w6m7rDC1Nj+KQLx0CmAV862esx2FL7AkS7oOugoqOQEfME3/t0FoaTmvAH/TjtpsCjW7DnVEVeTbxi3ihxc5AZ4LnpFCyJ5muQAqCUuo54Lm4bfOiXncA5yQ5dz5mVljBeOitEm66eENfuCFsM3UcpOsoYr+PYgbK4wlUwDF3wMk3mG6rfi3aeJQ5dpudQDAQEVEEM3MqOiW31dtKH0cfdnfu7aPX19mXO06/gwFVAzj3iXNj9jkMB807m6lfUR+jraVQrJ++nmHVwyzH4mnz0Li2kfmvzY9ZzaSKlYTTh9v9e41Ghb2CoArisrvwBXwFkz0pqgEpdb739PeKPYTU3GBoI5IvjmiCd88G8Uf1Q7fA3g7evqa4ojYcPQKHzZEQrwioAOt3msKHYJ2iG1bkBSyFDoGEiT1V7CPshgofH3aZhVczyQxAsvTh5hnNtHpbCyp70qt6evRIbjAwFfbDGUSanNj/fZg5As7/Nkh8tlUcOt7Ro4ie4KOpX1EfcSGlEjNMtq9mQE3GCrrRbqh40sm2J7v/sOphBW+lq1cgPYEb+ppppL+O7lmiVyRZ8ber4K9Xw1EPgYr3dStwfmG2vNXxjl5D2A21X8V+DOk3JEbMsMpZRau3NaKJVTu0lqXnLQWgZkBNZNIOB9IdhiOlK8nKDRUmk/hFsYQWRane89Q6evRotXZt5uUr8vMynISXPAUfTmRvSqomO+JjIZjG44zL4dDntPHoRTjEgd2w4zSckVhE7dBabvvrbdz219tw2V34g37qRtaxaP2imOOiYxaeNk/aid3T5uGg2w9KMCBuw83iSYsLVi+SKSKyTik1OmG7NiDJKUsDEubmz8DfL/SmjD9Ht2NhQGxeOPsCGPqKNiA9FIfNwaTDJ/HMP5/BbtjxB/woUTEuKIfNgVIKv0pe3wFmAHvL7C1ZrwKaNjXFrFbmHjc3opGVy8oiE8OVKdqAkL0BgTI3Iv84D55+GL0asajdSHmsxL0PYXhh0iW6srwH4hQnhs2gPdAeKRy0YcupkHgf1z6svGhlJACfDZs9m1m9YzVjB45lWPWwmPqOTDKywuR6XjK0ASE3AwJlbkQAmprg3fPo3UYkQ8RnSpbYAuCrIuF7Zt9jypPolUiPwYYtry2ro1cg0asASL2SiJ/0GyY0UL+8PiE9N93qxsodluuqKEwyA6KD6BmgrjeNbNkaksmTYe2L8Oy9URvL9LPknbgVh3MPnPNdaP0fePYu8FfFHi7BzBtLaUqW28bfht2wU2GvYPaK2Un7d2TCCYNP4JWtr0Te142so7qyOsYg7PHuQWxChb0iaZwknIUVnvhnPT8rQR4+k4pyq4B8USvRNSbqesU7P3yn2MPIjdH3wTVfhkF/Qaf8RhNnSH0hFd5DnwNl8eehbDqNt8wwiG0VVFdTR/036unfpz9XLr+yS8bj0ppLWf3/YlWUFq1flKBH5VM+vAFvUm0qq14iDsORcRpwNFZ1IYWqRNcGJEuGVQ9DXa/4+OqPiz2U7Kn8BOommLUjro/o3UbEyogqCFbAK9ea36tJ00A69x5rdMKEWeYKRHcYLAuuPuZqbjjxBoyofnMP/uNBbn3z1qR1F9mwZMOShF8jh+Fg9Y7Vls2loo+Jru2wmvQDwQALJyy0rD1JRaqalXyjYyBdxNPm4cu3fjmv1+w2Vv0MXrmRXhlklw7z8Sngttip4LJhZrV5W3/YOcLcvGsoLL9d9zovIyT0zyrG4TbcKQUSo8kmTlJhr2Dd9HWMundUUgNlFZO44rkruHPNnZH35xxxDnedcRef7PkkJrAeTapMK52FlWcKYUCi6XNjH9pVicq/J6OtP9zzV/jiq6ENvcSQ2PeYq4nn7oSgk4TMq0lTYMSDeze19Tdl3aP1sXRAvdfw69pfM+/leZYGIaw7FU6/XXj6QmaMmhGTltvubUdsgtvujhQUxsdArOpAHOJIGjvJd6ZVKnQQvRvYM28PnjYPg38zmA5VJmq5lZ/Ajw6FrcfAyvmw9SR6jhGxSMk1Qn+g4dVD3+3Q9FziqV/6V+z7cK/zaAMS7gOiDUhZ4rK5UCi8weSdB12GK2IQBu47kLpl1m6vm0+6mWtXXYvTcFK/vJ59XPskVIdD8iysZJXoPuWDwF6trbAuFpAQdE+nmVUIdAwkz1RXVtM+r52Pr/4Yl2TWzawkGPw3mHYK1Pye2O585YyFIRQF2PY2jDKSFIV547KvdK/znofAS5e8xJxxcyx3Gxg0z2iOFPOF5UrmfHMObsMdiS80TGhg3svz6Ax08oX3i5ggeXVldUSPKvp1PMkEEeMJx04a1zYmrlbSaGYVAm1ACkR1ZTUd8zp4ferrDNvfWrq5JJk4w/T/H3cT5W9AwoSNoZhKuwG3mdK89tLML6F7nfc4lFKM/8N4vvaVr1kakUpXZUQ599Y3bmXgbQM5+7GzWfi3hdw+4XZWXrSSLbO3MPIrI5Om22ZKOPDtNqxicnvxBXxUOauY/1piJ4tCZVqlQhuQAjNu8Djeufwd1PWKA/ocUOzhZEb1u3DK9fCdC4CoLKSyxSpJQOD5O2DfbSAWT37xLWpB9zrvYXiDXtr97UxZOoWLj744YfL2BXx83v45Fz55IdesvAZf0Eebr412fzv1K+ojrqh8pc1OHj6ZrfVbuemkmyIZVA5x4DScMdlUrd5WXPZE78bc4+Z2q/sKdBC929ns2cxlz17Gqq2rijqOjAlnIb05Gz44g54THwGc/4VzvwMtJ8Lrc4n5bDpA3qtYceEKPu34dG/Q29dOMGg2ZNrj35NwfKWjklWXrIrIlcTrWEUHtHPJhkpVwW4VcHcbbrbWby2YAdFZWJSGAYmm9oFaXtzyYrGHkTlvzIaVvwJlpzQMiYXwYTZIJxgBEAvZEtduc6UxsHR+XzSFY8WFKzj1kFPxtHn4zZu/4Zdv/jLl8Q6bgx1X7YiZsK0MRaaZUtkamVQGqxCUlAERkS8BjwJDgBbgXKXU53HHjAB+B+wDBID5SqlHQ/seAE4Awj0kpyilNqS7b6kZEDB/cS568iJWfLii2EPJDM9hcNdbQLTPt4sTedaEf2eDEFNlnE40UQF+cLZDwG6eG0iS6KBXID0KhzgwDIMOf2J2pEMc7PjRjsjT/aCGQTFtaK24/oTrueHEG1Iek6kmVa7puPms80hHMgNSrBjIHOBFpdShwIuh9/HsAS5WSh0JTABuF5F+UfuvUUqNCH1tKPSAC0V1ZTXLL17Ox1d/TM0BNcUeTnqq34XvXAzGHnC0guyBwS+T/xhJqrhLKKZhCxKTMXbIMrNaPBlGO1x2FFxyCkyeCEb8ZKLMz6QD5D2OeSfOY/HExVTYK6iwVwDgtDlxG26WnL0kYjyee+857LbU1Q0Om4NvHfqttPdMJk8SHVyP1sBKJnOSjFRZXd1FsepAJgInhl4vAV4GfhJ9gFLqX1Gv/5+IfAxUA7u6ZYTdTHVlNetnrmezZzNTl07l7//v78UeUnKOehQOftEMNPdrMSfalTcnxhG6gs1rdgf8xwWAy/q6wThX2vsTYfgfYPO5oZVFXA3I6bNMAwiw9vvg3Sf2evZ2OO/bphaWNh49Boc4mDFqRqRzYHRHwfDTe3gVYLfZ02pj2W32jALkmQTXu1P4sBAUawVygFJqZ+j1f4CU6UkiMhbTZ/LvqM3zReQtEWkQSV5wISLTRWStiKz1eNJb9WIzrHoYf/v+3/j46o959DuPUmlUFntI1lR+YsYHwhPtsbebE3C+EAWD/gZGNjLbApsuMmMa8Tja9tZstPU3JUniDcyEWXDISm08ehhi2/tzDj+1R/cLj14FfOH9InJspaMSl+HiguEX4DJc9HX2Takr5WnzsGbHmoz6qIfpTuHDQlAwAyIiK0Vkk8XXxOjjlBmESer/EJEBwB+AqUqp8GzyU+BwYAzwJeJWL3HXv1cpNVopNbq6uvQtepjqymrOHX4urde2cv3x1xd7OOkJ10kYHViKFGaFgpPnmpN8oA/WqxoF+Cy2h2o9ElR2K6FpGWw8b29VeTTOL8yVh6bHYbfZad7ZnHT/bX+9LaEoz2W4uOuMu1g4YSFP//PpmB4dVvGJpo1NHHT7QYz/w3gOuv0gmjY1AWZq7pbZWyI1I/HndqfwYSEoVhD9XeBEpdTOkIF4WSl1mMVx+2C6txYopZ5Icq0TgauVUmemu28pBtEzxdPm4QfP/oAn//lksYeSGs9hcM+GOJHCTjAUEIRARdwJVsahE753Jjz+OHT2i9quzPiEMuD0K8xNz96b5BpJsO+BGSOhcb3WtepFOGwOlnx7ScIEfusbt3LNymssz3l96uuM/8P4tEHwfDRw6s6AeC6UmhbWM8AlwC2h/5fFHyAiTuBp4MF44yEiA0LGR4BJwKaCj7jIVFdW88R5T+Bp83DPmnu4d+29bG/bXuxhJVL9rilEuGyxqRUVcJgrk3DMxPkF7B5kHvv2OdD8/dCJ0Sm0oeB2vHSI0Q7nT0qMUTx/B3uNUxpjYvjA29ccU/wYtfHosfiCPqYuncqIA0ZEFG0b1zYmNR5Ow8n7n72fUXwiH3GMsNRJuVGsFcj+wGPAYGALZhrvZyIyGpiplLpURL4H3A+8HXXqFKXUBhF5CTOgLsCG0Dmt6e5bziuQZJz96Nk8/c+niz2MRNr6xwbZk7H1GHjgZQhGhbHCq4EPTkmc5K2qvy1XPUmIXmlkOkZNj8FluLh/0v2MOGAER99zNL6glRvUdHu9NfOtBEn2Qq1ASp2SqgMpFj3RgAA0rmtk5rMziz2M3Nl4XnJDkckkv2M0PPhCorvL0QZBh1ku4uxIbYQ0ZUcfRx98AR8oqHBW0OnvRKnU6rpgri5QpDxOED66+iNWfrgypmCv4bQGRg4YmeBq6u7Cvu5GGxB6rgEB8ylo2T+Xcctrt/Dv3f9Of0Kp0ZXVgFWvDmOPWesRDozrlUaPwyEORIT5J8/nhCEn4A14OXnJyWkNSKasuHAFNQNqIgH4D3d9SP2K+qQFf6Uax8jHuLQBoWcbkGg8bR7ql9fz0KaHij2U7iPVKkbT4zlu0HGs+X9rEJEut6kNM2fcHBb+fWHEYPgDfrM/R4hycFPlq+mUNiD0HgMSxtPm4ZjfH8MHuz8o9lC6Bx3T0IRw2pwgZNZjQxwgxMRDHDYHhhgpW95WOipZfNZihu43tORWHZDf2EypSZlouoHqymr+PfvfvD71dWqH1BZ7OIUnvrhR03sRs99HJsw7cR5Lvr2ECnsFlY5KKuwVzDthnqVkejRtvjbOe/I8Trj/BA66/SAa1zXGFBIWgvhixVRkIqXSVfQKpBfhafMw98W53Nd8X7GHotGUBNFP5Js9m1m9YzVjB44FoKaxJkZU0Wk4ESV0BpPrrfV19sUf9BckiJ6tO0qvQDR5pbqymt+f9Xs+vvpjTjv4tGIPR6PpdhziwGW4Eqq+mzY2MereUcxaPoujf3c0IxpHYAtNj2EBxukjp6PSqCrEt7RNR6YrilxEF7ujyr1YhYSaIlJdWc3yi5az2bOZhX9fSOO6xmIPSaPpFgybwfoZ62OEFKMn58jTepScWlAFefHiFxn/h/EZZ3hlUkiYzYoik2JFq2yrycMnRwQkCxGn0SuQXsyw6mHcc+Y9fHz1x/z+zN/zg1E/wK6fKTRlhtPmRDKQszEwWDxpcYyQIljHCqJx2V2RqnTL/UZirCSdIGK2K4p0oovJtLigsLLv2oBoqK6s5tJRl3L3mXfz4HcejAQTNZpSx2FzoJSK9DN3GS6MmCZjRLZv/OFGaofWRlxGYfdRlbMqZbaW1++lwm4WKsZf8/Wpr/Pa1Ne451v3ZOUqsjJadps9aYA77I5yG24qHZW4DXfkHl3pKdJV9OOmJoboJW+Vs4oj7j6i2EPSaBJw4OCCr13AI5seoVN1mhXpITb+cCOvbnmVWctn4TSckaD2hv9sYNqyaRg2wzQYCvo4+9Dp7+T/Dvs//vTun3DanbR72xGb4La7afe2EyTI9GenEySIQxxUOCsi1ebjBo8DYMzAMZw97OyMXUVWK4ovvF+w/j/rGdJviPV1FJjyf3v/h+L2FNFZWJqUNG1q4qKnLiKgLHpsaDRFwmk4cdgcCc2fqpxVvHTxS4wZOCYmJgAw8DcDYwoB43Hb3fzsuJ8xY9QMAJp3NjPp0UkJWUxLz1tKzYCarCfn+BhF49pGZv45VoLIIQ7shj0hLpIqowoouBaXzsLS5MTk4ZPZ+aOdzBk3B6fNicuWOjdeo+kOvAGvZefAVm8r63euT5ism3c2pzQeAB3+Dha8tgAwXUb7VexnWUexX8V+WU/MVjGKkQNG0tfZN+Y4n/JZuqJS1XQUs6eIdmFp0lJdWc0van/BVcdeFXFtLX9vOVe9cFWxh6bRJHD5ny9PcF/t794/o3OjJ+V8dQu0yvKqW1bHuunr8Af9GY0n3VgKnW2VDL0C0WRMdDvQ+m/U8/B3Ho7kyms03YXD5ki534+fzkBnTE3GoH0HYbelf172+r183v45njZPVk/2qeo5kq0eWr2tMdd3G+6E48JGIpOxFDLbKhl6BaLJmfBTz6oPV9G4tpFVW1alLbTSaLpCX2dffjzuxyx4bQFKqZRaVWFsYmPb7m2Iik31tWFDEAKhog9DDIIEOfeJc2PiD+me7NPVc6RaPYwZOCbm+vHy8dFGolirjFToILomb3jaPDTvbOauNXfxzL+eKfZwND2QcHD4kz2fJEiNpGLOuDks/NtC2gOZK/VmEojOVC4km34hpSgLX2otbTU9kOrKak495FROPeRU3tj6BsfffzxBgsUelqYHUOmoJKiCkSfyll0tuO3ujA1Iw18bUmpYJSNdKmymKbTZrB7Kqb2tNiCagjBu8Dj++J0/MvXpqTn94Wp6F+FivkAgEHEpRbP4rMWcNPSkyMRq5RZKhd2wg5CxwQFo97dT5axKeUw2gfZ8GIZSW50UJQIqIl8SkRdE5L3Q//slOS4gIhtCX89EbR8qIn8XkfdF5FERSa5DoCkak4dPZttV25gzbk6xh6IpcWaMmsGfzv8TD3/nYbOXRxRuw83Q/YYmBIzDQeUKe0XkuAp7Bb+u/XWCvEggGMAm2U13bsNNq7c15THdmUKbSq6kWBQlBiIivwI+U0rdIiJzgP2UUj+xOK5VKZXwCCAijwFPKaUeEZF7gH8opX6X7r46BlI8Gtc1ctmfL0takOgQB0GCumCxl+K2u3EZrqw6/3naPKz6cBUftX3EyAEjcRrOyJO5VcwBzPTZTIPv2RTjFXplkE9p9lwotRjIRODE0OslwMtAggGxQswa/pOBC6LOvwFIa0A0xWPGqBmcffjZNO9sZlfHLqYsmxLzx2A37Lxw0QuccP8Jli4MTc/FEIMOfwcdfnNSdxpOXMqF3bATCAYsn+ibNjYxZdmUiPvIEIO7zrgrUoF+yH6HsG76uhjVXYARB4ygprEm6Vjs2Onj6pOQAZWOQsctiilXkopiGZADlFI7Q6//AxyQ5Di3iKwF/MAtSqmlwP7ALqVUuAJnOzCwkIPV5IdwkB0gQCDhCdFpOKlyVbG7c3eRR6rpLuzYqXBU8IX3i8i2cDotxGo+hQkX5kXHHgIqwMw/z2TN/1vDwxsfjqTUNkxoiBxTXVlNq7fVMvjusDm44/Q7stKz6k7yVdSYbwpmQERkJfAVi10/i36jlFIiksyPdpBSaoeIHAy8JCIbgaxmFxGZDkwHGDx4cDanagqIVVaKp82TVWBUU144xIESFam+doiDBbULuPala2OOCz9lhyf5qUunMuKAEQyrHgaYT+NWhgVgUfOimGvMfHZmTJfA2qG1Cb9jLsNF84zmyPVLyXCECcdaktWIFItixUDeBU5USu0UkQHAy0qpw9Kc8wDwLPAk4AG+opTyi8ixwA1KqbQt9nQMpPSJ911fcNQF/OEff8i4kY/GGpfNVfRsuAp7Beumr2Pb7m0AfLjrQ+pX1IOC9kA7FfYKgiqIDVtCvYbLcHH/pPsjwoKDGgZllVEVvv+W2VsSivXmHjeXGaNmFH0yzoRiZWGVmpjiM8AlodeXAMviDxCR/UTEFXrdHxgHvKNMi7cK+G6q8zXlyeThk80/8otWsmX2Fu476z62X7Wdm066Ka2EhSY5U2um8vszf1+Ue/d19o1kJw2rHsaph5xKzYAa6lfUm/pQIWMR7vxn1RuqM9AZERasrqxm4YSFWY8jHDMI/45dc+w1KKW49c1bSyarKR3FkCtJRbEMyC3AeBF5D6gNvUdERovIfaFjhgFrReQfmAbjFqXUO6F9PwGuEpH3MWMii7p19JqCEv9HUl1ZzbXHX8uOq3bw2wm/jaRtajJnyT+WMG7wOBzSdSPsMlxcf8L1GR8/dcRUlp63lNqhtZFtVvpQLrsLp+Fk0cRFll3+wgbA0+Zh5ICRXDn2yoRj7GLHZbgs6zfiYwYLXl9AR6Cj25sw9SSKYkCUUp8qpU5RSh2qlKpVSn0W2r5WKXVp6PWbSqmjlFJHh/5fFHX+B0qpsUqpQ5RS5yildKVaL6C6sprzh5+fsN2QxA505cCEr07otnuFxfvu+NYdXbpOOF4wtN/QjM/57erf8t3HvxvzlJ8yKKzASlLNF/Cxfuf6SC3E79f/HrvEhnEdhoMXL36RO0+/k1+P/3XS+oxU8uiazNGV6JqywiqYOPe4ufzqjV/FZPKUAy988EK33avD38GQfkP4vP1zKuwVMemgqRAkRiAzGAyy4aMNWT+ph382dcvqIiuRucfNZf6r83HanbG1Gs/UJcRrKuwVNExooH55fYwsutNw4sYduUbdyDrG/2F8TBbWyK+MTIgZlGpWU7mhDYim7IjP4AIijYAyJVy0Vkz14EIVTV541IU8svGRmHoaFVQ8tfkpZi+fnVERXeS8uO+PT/mYunQqyZJvLjzqQh7a+FDS6zkMB41rG1nw+gKchhMR4ZpvXBMJYq/ZsSah3qHSUclT5z4VafAUvc9td/P4dx9nv4r9qHJWMereUTEGpn55vWWxXalmNZUbupmDpiyJjpOEJwO34c74/EAwgEHpu76ycc85xME9Z97DrK/PosoVGwNwO9zMWj4rK+ORjM5AZ9KsuK8P/Dr3nHkPLsNFpaMyYb8v4GP+a/Njuu5FG3+rlUFQBakZUJN01VAzoIYxA8fQ6m3Nyi0Vn7CRTB1XkxxtQDQ9gsnDJ7O1fis3nXQTbntqQ2LDhl/58ZO6GxyA0+bEFvpXjFjLtcdfm1H2mctw8ewFzzJj1AzLibbD35FRQ6WuckDlAcwYNYNt9dtYdckq7vnWPTFxiLnHzcVljw2QR0/yqbSl0ulO5eKWKrWspnJD9wPR9Dg8bR4a1zVG/Otev5fZx86m5gBTwuLipRenrSFwGk5euvgl2rxtrPpwFQ1/a8CwGezx7ynYuOPjDXU1ddx31n00bWrikqcvwRdM3tM7XhcpXE8TDAbpDHbiMlwJnzn+fjZsOA1nzqsUQwx2/sgUmIgvEI12N6bSdAofW+WsSpAhCZOqFiKbvhuazElWB6INiKbHYjXRrNmxhlMePCVpwD267wSKSC/r7qDCXsELF73A+5+9z9iBYyOV0eHP0ryzmYfeeogHNz6YcO5NJ93EtcfHVnTf+satXLPymoRjq5xV+AN+ggRjntgr7BUsPW8puzp2ccnSS7I2JJePvZxvHPiNlN35IPkkn66zX6bks9iu1OTTi4U2IGgDorFWNQ3jNtwsO38ZNQPMlUqy4wqBQxwsOXtJZMK0mrg8bR4GNwxOmNidNicbZm5IMDhW1dqVjkruOuMuBlQN4Nwnzo3RHdvHtQ8rL1rJkH5DLO/jsDkQJGn8w224EZGMFGPjP1+x1WatyJdB6wmUWiW6RlMUov3o4ViJy3BRYa9g8aTFnHrIqZGOd/EB2UyZNmIaKy5cwZxxc3AbbvrY+6Q9x27YI+mtyfo+tOxqSYgfhKlprKFxXWPkfbLx+4N+zjj0DGoG1CSNF1RXVrN40uKYokOn4WTJt5ewYeYGyyI/AMNmJPTcSBbEjo89lFpdRliwMTrYrwsNE9FpvJpeR3QacDJfezYd7yodlQRUgClHT+HKr18ZWQmcesipXHXsVTTvbObMpjNTxjCiJ8vwxBV+Gg/XTgzpN4Q93sQYTHhFMPPZmaBgxmgzkB4WLYxm4ekLI58zVRrr5OGTGXHACFZ+sJIDKg+I6QZ4/6T7qVuW6NoLBAMJIoeZ1laUWl1Gqcqnlxp6BaLplYSfgIdVD7PMwrHK+Jkzbk6CjIrbcPPUuU+xdfZWfnfm72LcSOHrnHrIqcwYNSPleMKTZboncbFZq9CGmbV8Fp42D09tfipmQnbYHNzzrXtixpEqjbVpYxOj7h3FdauuY8qyKaz8cGXCeTeddBNuwx35/iyetDjn7nzJMqzAjFuFn/w9bZ6Y94Wi1AxaqaJjIBpNCuJ99blk+aSKuwCRyTKsNJssFtCyq4UTHjghZVymr7MvPxz9Q3755i9jtrsNN1vrt2bcXS/TeESyWE2ugefoc1d+sDImBlFXU8ei5kXdFpPQGV17KbWOhBpNWRDfac6qj0k6rNwhYAa/rzvhuhgp8VQV0p/s+SRtUN8X8HH7329P2G437Bm7X5K5b5p3NrNfxX5pP3ey7nyZGJbwudExiPA47lxzJ0CCa69QLqXwz7p5ZzNAJLlCsxdtQDSaLMm2famVOyS+iVE0tUNrWXreUsCctML3avW2UmFUxPTKMDAwDAOX4cIf9Ed0weKzrzr9nXgDXtbsWJPWAFiNt93bzqRHJ8U8/YfTnDNZEWSa0RQ2Mp+3f25pdKPpjphE/CqoN69CrNAuLI2mG8jUHRI/0UY3O9rs2UxNY02McQg3aQonAoB1+rHD5sAX9OEyXNjElnYijB6v1++1rBlRSsWk+qZyc2XiEov+7J3+zoR7xlPoNN9STC0uFjqNV6MpIpnoLlmljl636joGNwzmiuevYNS9oyJpsm7DHYmd9O/TP3KN6GB0tBZVOAOsM9CZUUpq9HiXnb8sIXnAJjYMW6y0S7K020xSdOM/e0egAxVUMUH1y8denlOAPldKLbW4FNEuLI2mm0jn+koWK+kIdHDn6jtjtikU66evZ8N/NnDQ7QcluFhqh9by3HvPcfnzl9PqbU24l92WPiYSHY+wEjiM914ky1LKJKPJ6rNXOCsiSrtht9u84+d1W2W4zsRKj16BaDQlQja1Jy67i227tyUtdquurOaMQ8/AF7CuPfEGvBlPhFYptg2nNfCz438Wk8abbEWQTgQx2WePVtqNTjLoLvHDTMbd29ErEI2mRIjOwMok2wpIWewW7h0+888zE85fOGFhVhNhdPbZ+p3rqV9Rb9nPI5PzrVYPpdqfI5esu96EDqJrNCVGvJpwuNPeovWLYibX2qG1GQV5G9c1Muv5WdhtdvxBPwtPX5i2sDHV2AoZWNbihaVJSYkpisiXgEeBIUALcK5S6vO4Y04CGqI2HQ6cr5RaKiIPACcAYSW4KUqpDenuqw2IppywEhyMn1wzze7K18S8Zscaxv9hvKUI45iBY3K+rqa0KTUD8ivgM6XULSIyB9hPKfWTFMd/CXgfOFAptSdkQJ5VSj2RzX21AdH0RLrzqV2ntvZOSi2NdyKwJPR6CTApzfHfBZ5XShWum49GU6bowLKmWBRrBbJLKdUv9FqAz8Pvkxz/EnCbUurZ0PsHgGOBTuBFYI5SyrLFnIhMB6YDDB48eNSWLVvy90E0mjIh36sUHavoXXS7C0tEVgJfsdj1M2BJtMEQkc+VUvsluc4A4C3gf5RSvqht/wGcwL3Av5VSN6Ybk3ZhaXojujGSpqt0u5iiUqo2xWA+EpEBSqmdIWPwcYpLnQs8HTYeoWvvDL3sFJH7gavzMmiNpodhJUpYaBFCTe+hWDGQZ4BLQq8vAZalOHYy0BS9IWR0wu6vScCm/A9Royl/ykmOo7t6fWjyR7EMyC3AeBF5D6gNvUdERovIfeGDRGQIMAh4Je78h0RkI7AR6A/c3B2D1mjKjULLceRr0k/WxldT2uhCQo2mh1Ooxkj5iq3o1ODSRzeU0mh6KYWQ48hnbEX3Hy9ftAHRaHoB2TbBSkc+J32telu+aDVejUaTNfmc9HVxYvmiVyAajSZr8q2eq1VvyxMdRNdoNDmjK9J7BzqIrtFo8k6+Yyua8kLHQDQajUaTE9qAaDQajSYntAHRaDQaTU5oA6LRaDSanNAGRKPRaDQ50avSeEXEA2TbUao/8EkBhlPq6M/du9Cfu3eR7ec+SCmVkG7XqwxILojIWqv8556O/ty9C/25exf5+tzahaXRaDSanNAGRKPRaDQ5oQ1Ieu4t9gCKhP7cvQv9uXsXefncOgai0Wg0mpzQKxCNRqPR5IQ2IBqNRqPJCW1A0iAi54jI2yISFJEen+4nIhNE5F0ReV9E5hR7PN2FiCwWkY9FZFOxx9JdiMggEVklIu+EfsdnFXtM3YWIuEVktYj8I/TZf17sMXUXImKISLOIPNvVa2kDkp5NwNnAq8UeSKEREQO4CzgdOAKYLCJHFHdU3cYDwIRiD6Kb8QM/UkodARwDXNaLft6dwMlKqaOBEcAEETmmuEPqNmYBm/NxIW1A0qCU2qyUerfY4+gmxgLvK6U+UEp5gUeAiUUeU7eglHoV+KzY4+hOlFI7lVLrQ6+/wJxUBhZ3VN2DMmkNvXWEvnp8RpGIHAh8C7gvH9fTBkQTzUBgW9T77fSSCaW3IyJDgBrg70UeSrcRcuVsAD4GXlBK9YbPfjvwYyCYj4tpAwKIyEoR2WTx1SuevjW9GxGpAp4EZiul/lvs8XQXSqmAUmoEcCAwVkSGF3lIBUVEzgQ+Vkqty9c1dUtbQClVW+wxlAg7gEFR7w8MbdP0UETEgWk8HlJKPVXs8RQDpdQuEVmFGQPryUkU44CzROQMwA3sIyJ/VEp9L9cL6hWIJpo1wKEiMlREnMD5wDNFHpOmQIiIAIuAzUqp24o9nu5ERKpFpF/odQUwHvhnUQdVYJRSP1VKHaiUGoL5t/1SV4wHaAOSFhH5tohsB44F/iwiK4o9pkKhlPIDlwMrMAOqjyml3i7uqLoHEWkC/gocJiLbRaSu2GPqBsYBFwEni8iG0NcZxR5UNzEAWCUib2E+OL2glOpyWmtvQ0uZaDQajSYn9ApEo9FoNDmhDYhGo9FockIbEI1Go9HkhDYgGo1Go8kJbUA0Go2mh5KNSKiINERl4/1LRHalO0cbEI2mxBGR5SKyKx/qqZpexwNkKBKqlKpXSo0IVeffAaQtLNUGRKMpfX6NWa+h0WSFlUioiHw19FCyTkReE5HDLU6dDDSlu742IBpNgRCRISLyTxF5SEQ2i8gTItJHRMaIyJuhXhSrRaRv6NjXRGR96Osb4esopV4EvijiR9H0LO4FrlBKjQKuBu6O3ikiBwFDgZfSXUhrYWk0heUwoE4p9YaILMas9J8JnKeUWiMi+wDtmIqw45VSHSJyKObTX49vYKbpXkLCmd8AHjeVbABwxR12PvCEUiqQ7nragGg0hWWbUuqN0Os/Aj8Ddiql1gCE1W9FpBK4U0RGAAHgf4swVk3PxwbsCsU5knE+cFmmF9NoNIUjXisomVx6PfARcDTmysNZyEFpeiehB5YPReQcMAU1ReTo8P5QPGQ/TF24tGgDotEUlsEicmzo9QXA34ABIjIGIBT/sAP7Yq5MgpgBc6Moo9X0KJKIhF4I1InIP4C3ie06ej7wiMpQJFGLKWo0BSLU5W85sBYYBbyDaRyOxEyTrMCMf9RiqsM+ibliWQ5cppSqCl3nNeBwoAr4FDOm0mNVoTXlgzYgGk2BCBmQZ5VSPbrTnab3ol1YGo1Go8kJvQLRaDQaTU7oFYhGo9FockIbEI1Go9HkhDYgGo1Go8kJbUA0Go1GkxPagGg0Go0mJ/4/xCv4KqCNaZ4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# open figure/axis\n",
        "fig1, ax1 = plt.subplots()\n",
        "\n",
        "# plot // you need to specify the axis\n",
        "results_med.plot(x='pca1', y='pca2', kind='scatter', ax=ax1, c='r',label='medium')\n",
        "results_low.plot(x='pca1', y='pca2', kind='scatter', ax=ax1, c='g',label='low')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "V0BYZL5a_geh",
        "outputId": "749f1eb5-e5c2-4f76-b9fa-6f77529552fa"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='pca1', ylabel='pca2'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# open figure/axis\n",
        "fig1, ax2 = plt.subplots()\n",
        "\n",
        "# plot // you need to specify the axis\n",
        "results_high.plot(x='pca1', y='pca2', kind='scatter', ax=ax2, c='b',label='high')\n",
        "results_low.plot(x='pca1', y='pca2', kind='scatter', ax=ax2, c='g',label='low')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "VslaEm2D_zVK",
        "outputId": "02769aae-0d9f-46cc-a4b7-749f0aef3d8a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='pca1', ylabel='pca2'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## find the cluster center for high risk class using Kmeans"
      ],
      "metadata": {
        "id": "zWIXAIlTBiez"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans_pca_high=KMeans(n_clusters=1, init='k-means++',max_iter=50000,random_state=0).fit(results_high[['pca1','pca2']])"
      ],
      "metadata": {
        "id": "CKcchvgJBl_a"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x2,y2=kmeans_pca_high.cluster_centers_[0][0],kmeans_pca_high.cluster_centers_[0][1]\n",
        "print(x2,y2)\n",
        "print(kmeans_pca_high.cluster_centers_)\n",
        "x_sum=results_high['pca1'].sum()\n",
        "y_sum=results_high['pca2'].sum()\n",
        "print(x_sum,y_sum)\n",
        "print(x_sum/results_high.shape[0],y_sum/results_high.shape[0])\n",
        "x_t,y_t=x_sum/results_high.shape[0],y_sum/results_high.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-tt1vZqCCNN",
        "outputId": "e470bf9a-cef9-4283-a553-dc71c750c67c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3578683137893712e-11 -3.901361196767538e-11\n",
            "[[ 1.35786831e-11 -3.90136120e-11]]\n",
            "-1.5497207641601562e-06 -2.332031726837158e-06\n",
            "-3.099441528320312e-11 -4.6640634536743164e-11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x='pca1',y='pca2',data=results_high)\n",
        "plt.scatter(x2,y2,marker = \"x\", s=150)\n",
        "plt.scatter(x_t,y_t,marker=\"o\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "tFCV4wtIHZoF",
        "outputId": "81e63731-ab98-49ad-d025-72d7cad9f6de"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa5761edee0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## model as elliptical eqns\n",
        "import numpy as np\n",
        "from skimage.measure import EllipseModel\n",
        "model = EllipseModel()\n",
        "model.estimate(results_high[['pca1', 'pca2']].values)\n",
        "\n",
        "# get ellipse parameters\n",
        "center_x, center_y = model.params[0:2]\n",
        "major_axis, minor_axis = model.params[2:4]\n",
        "angle = model.params[4]\n",
        "\n",
        "# print ellipse parameters\n",
        "print('Ellipse center: ({}, {})'.format(center_x, center_y))\n",
        "print('Major axis length: {}'.format(major_axis))\n",
        "print('Minor axis length: {}'.format(minor_axis))\n",
        "print('Angle (in degrees): {}'.format(np.rad2deg(angle)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSWmfhlqOvL9",
        "outputId": "7fac3dca-e51e-466e-b15d-dd78f30676b9"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ellipse center: (1521050.249385744, 133325.82748022277)\n",
            "Major axis length: 1166233.4203683767\n",
            "Minor axis length: 3561675.9036165024\n",
            "Angle (in degrees): 89.08823935721057\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1521050.249385743, 133325.82748022274"
      ],
      "metadata": {
        "id": "yyEXr49Cu2li"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_center=center_x+0.12e7"
      ],
      "metadata": {
        "id": "r7AKaIdgwmVl"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter('pca1','pca2',data=results_high)\n",
        "plt.scatter(new_center,center_y,marker=\"x\")\n",
        "plt.scatter(new_center,0.25e7,marker=\"x\")\n",
        "plt.scatter(0.83e7,center_y,marker=\"x\")\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "4kzXQ00-WxR0",
        "outputId": "ad7684ed-fe6b-4c46-c223-5b88837c9188"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p1=np.array([new_center,0.25e7])\n",
        "p2=np.array([0.83e7,center_y])\n",
        "center=np.array([new_center,center_y])\n",
        "height=2*np.linalg.norm(p1-center)\n",
        "width=2*np.linalg.norm(p2-center)\n",
        "print(height,width)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "leYJ7dmqwkn_",
        "outputId": "ecb7b6ca-e566-49b1-c7ef-aa673a6eb62e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4733348.345039555 11157899.501228511\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(height,width)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAFuN2DG5Gju",
        "outputId": "d22e46cc-3808-4ec1-8be1-a82ec317064b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11157899.501228511"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.patches import *\n",
        "fig, ax = plt.subplots()\n",
        "ellipse1=Ellipse((new_center,center_y),height=height+0.4e7,width=width+0.1e7,fill=False,angle=-10)\n",
        "ax.add_patch(ellipse1)\n",
        "ax.axis('equal')\n",
        "plt.scatter('pca1','pca2',data=results_high,color='r')\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "2KdjgvfWzAqk",
        "outputId": "559e6d4c-e6df-4d38-a30b-c066df871ef7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## remove all samples which lie with the ellipse boundary.\n",
        "results_med['distance'] = (np.square(results_med['pca1'] -center[0])/np.square(width)) +(np.square(results_med['pca2'] - center[1])/np.square(height))"
      ],
      "metadata": {
        "id": "k0QZs9jQYE6q"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_med"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "_50gR3r37Ovz",
        "outputId": "0df86063-7c4f-4be5-c62a-b2a3bb688843"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                pca1          pca2  label  distance\n",
              "60907  -1.327504e+06 -2.543648e+05      0  0.138363\n",
              "60908  -6.685232e+06 -1.413154e+06      1  0.817421\n",
              "60909  -4.127114e+06  8.247379e+05      1  0.398027\n",
              "60910  -4.699534e+06  1.422757e+06      1  0.516504\n",
              "60911  -1.009330e+06  5.526653e+05      0  0.119623\n",
              "...              ...           ...    ...       ...\n",
              "999871 -7.161306e+06  7.354874e+05      1  0.800618\n",
              "999872 -5.140966e+06  4.393013e+06      1  1.306357\n",
              "999873 -2.465794e+06  2.066363e+05      1  0.216333\n",
              "999874  1.897284e+07 -6.413333e+05      2  2.148258\n",
              "999875  2.916635e+07 -3.827903e+05      2  5.629246\n",
              "\n",
              "[350000 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e46c1b1-d91f-4e2e-be45-d97106640065\">\n",
              "    <div 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>pca1</th>\n",
              "      <th>pca2</th>\n",
              "      <th>label</th>\n",
              "      <th>distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60907</th>\n",
              "      <td>-1.327504e+06</td>\n",
              "      <td>-2.543648e+05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.138363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60908</th>\n",
              "      <td>-6.685232e+06</td>\n",
              "      <td>-1.413154e+06</td>\n",
              "      <td>1</td>\n",
              "      <td>0.817421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60909</th>\n",
              "      <td>-4.127114e+06</td>\n",
              "      <td>8.247379e+05</td>\n",
              "      <td>1</td>\n",
              "      <td>0.398027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60910</th>\n",
              "      <td>-4.699534e+06</td>\n",
              "      <td>1.422757e+06</td>\n",
              "      <td>1</td>\n",
              "      <td>0.516504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60911</th>\n",
              "      <td>-1.009330e+06</td>\n",
              "      <td>5.526653e+05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.119623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999871</th>\n",
              "      <td>-7.161306e+06</td>\n",
              "      <td>7.354874e+05</td>\n",
              "      <td>1</td>\n",
              "      <td>0.800618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999872</th>\n",
              "      <td>-5.140966e+06</td>\n",
              "      <td>4.393013e+06</td>\n",
              "      <td>1</td>\n",
              "      <td>1.306357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999873</th>\n",
              "      <td>-2.465794e+06</td>\n",
              "      <td>2.066363e+05</td>\n",
              "      <td>1</td>\n",
              "      <td>0.216333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999874</th>\n",
              "      <td>1.897284e+07</td>\n",
              "      <td>-6.413333e+05</td>\n",
              "      <td>2</td>\n",
              "      <td>2.148258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999875</th>\n",
              "      <td>2.916635e+07</td>\n",
              "      <td>-3.827903e+05</td>\n",
              "      <td>2</td>\n",
              "      <td>5.629246</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>350000 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e46c1b1-d91f-4e2e-be45-d97106640065')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7e46c1b1-d91f-4e2e-be45-d97106640065 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-7e46c1b1-d91f-4e2e-be45-d97106640065');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t=results_med[results_med['distance']>1]\n",
        "print(t.shape)\n",
        "plt.scatter(x='pca1',y='pca2',data=t,color='g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "BhQcw_iHYpHG",
        "outputId": "97d1faa7-6a90-483d-ccac-2be0de9ab7b0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(51982, 4)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa570ba4f40>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x='pca1',y='pca2',data=results_med)\n",
        "plt.scatter(x='pca1',y='pca2',data=results_high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "E0CJCWhR5_Sl",
        "outputId": "f9d7f22c-d999-4ef5-bcea-865dc7176ef8"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa57c7657c0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter('pca1','pca2',data=t,label='medium',color='g')\n",
        "plt.scatter('pca1','pca2',data=results_high,label='high')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "knrhFfveLkCX",
        "outputId": "ec8547af-5dce-4e5a-c084-9c2d27e78000"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa570a75dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## remove index from the medium dataset\n",
        "outliers=results_med[results_med['distance']<=1]\n",
        "outliers_idx=outliers.index"
      ],
      "metadata": {
        "id": "LM7ZikrT8rKo"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## remove them from med dataset\n",
        "new_med=med[~med.index.isin(outliers_idx)]"
      ],
      "metadata": {
        "id": "1ZCQyuVK-gQf"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_med"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "AMz6DsgK-3Yk",
        "outputId": "1fb9483e-9463-49f9-f113-7d0fd10c1a07"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         BIRTH_DT CUST_ADD_DT OCPTN_NM  RES_CNTRY_CA  CNTRY_OF_INCOME_CA  \\\n",
              "60916  1968-02-18  2001-10-07     High             1                   1   \n",
              "60921  1965-04-03  2006-12-03     High             1                   1   \n",
              "60937  1979-04-07  2000-04-11     High             1                   1   \n",
              "60949  1969-07-16  2018-05-23     High             1                   1   \n",
              "60957  1948-02-10  1996-04-01     High             1                   1   \n",
              "...           ...         ...      ...           ...                 ...   \n",
              "999856 1975-10-30  2007-05-16      Low             0                   1   \n",
              "999864 1966-08-02  2021-03-08     High             0                   1   \n",
              "999872 1976-10-07  1996-12-12      Low             0                   1   \n",
              "999874 1996-09-24  2022-03-25      Low             0                   1   \n",
              "999875 1940-02-20  1997-08-28      Low             0                   0   \n",
              "\n",
              "        PEP_FL  CASH_SUM_IN  CASH_CNT_IN  CASH_SUM_OUT  CASH_CNT_OUT  \\\n",
              "60916        0      19524.0            8       11534.0             8   \n",
              "60921        0       8739.0            5       76088.0            37   \n",
              "60937        0      60097.0           31       28577.0            16   \n",
              "60949        0      67636.0           24        6667.0            12   \n",
              "60957        0      10498.0           13       76117.0            50   \n",
              "...        ...          ...          ...           ...           ...   \n",
              "999856       1       1535.0            0       10149.0             5   \n",
              "999864       0      33510.0           17       21044.0            21   \n",
              "999872       0      21775.0           12       38097.0            27   \n",
              "999874       0      18043.0            6       87439.0            60   \n",
              "999875       0      13099.0            5       75244.0            34   \n",
              "\n",
              "        WIRES_SUM_IN  WIRES_CNT_IN  WIRES_SUM_OUT  WIRES_CNT_OUT  \\\n",
              "60916         428045            37        1546205            168   \n",
              "60921       24160866           104        6673232            158   \n",
              "60937         675417            53         282939            162   \n",
              "60949         419872            11        5679053            281   \n",
              "60957       25537840           324        7275861            273   \n",
              "...              ...           ...            ...            ...   \n",
              "999856        731749            24         120636              9   \n",
              "999864        396070            17         885841             36   \n",
              "999872       2858630           135        6257871            357   \n",
              "999874      27421298            92        8127016            225   \n",
              "999875      37140941            75       11209819            233   \n",
              "\n",
              "       COUNTRY_RISK_INCOME COUNTRY_RISK_RESIDENCY  RISK  \\\n",
              "60916                  Low                    Low     1   \n",
              "60921                  Low                    Low     1   \n",
              "60937                  Low                    Low     1   \n",
              "60949                  Low                    Low     1   \n",
              "60957                  Low                    Low     1   \n",
              "...                    ...                    ...   ...   \n",
              "999856                 Low                    Low     1   \n",
              "999864                 Low                    Low     1   \n",
              "999872                 Low                    Low     1   \n",
              "999874                 Low                    Low     1   \n",
              "999875                High                    Low     1   \n",
              "\n",
              "                                        NAME  GENDER  CUSTOMER_ID  \n",
              "60916   Antonette Ashburn Rollins Washington  Female       811633  \n",
              "60921                        Lee, Lisa Meads  Female        76681  \n",
              "60937             Hall, Barbara Frija Gooden  Female       811034  \n",
              "60949                       joe Hamm Morales    Male        60399  \n",
              "60957              Lamke, Helen Haskett Post  Female        70614  \n",
              "...                                      ...     ...          ...  \n",
              "999856         Koslow, Michael Dumas Mellish    Male       232539  \n",
              "999864                 Mettig, Jamie Douglas    Male       271080  \n",
              "999872                   Ramon Johnson Sandi    Male       836967  \n",
              "999874        Benningfield, Gina Lacy Fisher  Female      1000630  \n",
              "999875            Jack Huges Addison Barrett    Male       477516  \n",
              "\n",
              "[51982 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ad8afc4-8499-4cd5-a6d9-5d3601cb5f4b\">\n",
              "    <div 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>BIRTH_DT</th>\n",
              "      <th>CUST_ADD_DT</th>\n",
              "      <th>OCPTN_NM</th>\n",
              "      <th>RES_CNTRY_CA</th>\n",
              "      <th>CNTRY_OF_INCOME_CA</th>\n",
              "      <th>PEP_FL</th>\n",
              "      <th>CASH_SUM_IN</th>\n",
              "      <th>CASH_CNT_IN</th>\n",
              "      <th>CASH_SUM_OUT</th>\n",
              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>WIRES_SUM_OUT</th>\n",
              "      <th>WIRES_CNT_OUT</th>\n",
              "      <th>COUNTRY_RISK_INCOME</th>\n",
              "      <th>COUNTRY_RISK_RESIDENCY</th>\n",
              "      <th>RISK</th>\n",
              "      <th>NAME</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>CUSTOMER_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60916</th>\n",
              "      <td>1968-02-18</td>\n",
              "      <td>2001-10-07</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>19524.0</td>\n",
              "      <td>8</td>\n",
              "      <td>11534.0</td>\n",
              "      <td>8</td>\n",
              "      <td>428045</td>\n",
              "      <td>37</td>\n",
              "      <td>1546205</td>\n",
              "      <td>168</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Antonette Ashburn Rollins Washington</td>\n",
              "      <td>Female</td>\n",
              "      <td>811633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60921</th>\n",
              "      <td>1965-04-03</td>\n",
              "      <td>2006-12-03</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8739.0</td>\n",
              "      <td>5</td>\n",
              "      <td>76088.0</td>\n",
              "      <td>37</td>\n",
              "      <td>24160866</td>\n",
              "      <td>104</td>\n",
              "      <td>6673232</td>\n",
              "      <td>158</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Lee, Lisa Meads</td>\n",
              "      <td>Female</td>\n",
              "      <td>76681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60937</th>\n",
              "      <td>1979-04-07</td>\n",
              "      <td>2000-04-11</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>60097.0</td>\n",
              "      <td>31</td>\n",
              "      <td>28577.0</td>\n",
              "      <td>16</td>\n",
              "      <td>675417</td>\n",
              "      <td>53</td>\n",
              "      <td>282939</td>\n",
              "      <td>162</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Hall, Barbara Frija Gooden</td>\n",
              "      <td>Female</td>\n",
              "      <td>811034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60949</th>\n",
              "      <td>1969-07-16</td>\n",
              "      <td>2018-05-23</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>67636.0</td>\n",
              "      <td>24</td>\n",
              "      <td>6667.0</td>\n",
              "      <td>12</td>\n",
              "      <td>419872</td>\n",
              "      <td>11</td>\n",
              "      <td>5679053</td>\n",
              "      <td>281</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>joe Hamm Morales</td>\n",
              "      <td>Male</td>\n",
              "      <td>60399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60957</th>\n",
              "      <td>1948-02-10</td>\n",
              "      <td>1996-04-01</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10498.0</td>\n",
              "      <td>13</td>\n",
              "      <td>76117.0</td>\n",
              "      <td>50</td>\n",
              "      <td>25537840</td>\n",
              "      <td>324</td>\n",
              "      <td>7275861</td>\n",
              "      <td>273</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Lamke, Helen Haskett Post</td>\n",
              "      <td>Female</td>\n",
              "      <td>70614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999856</th>\n",
              "      <td>1975-10-30</td>\n",
              "      <td>2007-05-16</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1535.0</td>\n",
              "      <td>0</td>\n",
              "      <td>10149.0</td>\n",
              "      <td>5</td>\n",
              "      <td>731749</td>\n",
              "      <td>24</td>\n",
              "      <td>120636</td>\n",
              "      <td>9</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Koslow, Michael Dumas Mellish</td>\n",
              "      <td>Male</td>\n",
              "      <td>232539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999864</th>\n",
              "      <td>1966-08-02</td>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>33510.0</td>\n",
              "      <td>17</td>\n",
              "      <td>21044.0</td>\n",
              "      <td>21</td>\n",
              "      <td>396070</td>\n",
              "      <td>17</td>\n",
              "      <td>885841</td>\n",
              "      <td>36</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Mettig, Jamie Douglas</td>\n",
              "      <td>Male</td>\n",
              "      <td>271080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999872</th>\n",
              "      <td>1976-10-07</td>\n",
              "      <td>1996-12-12</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21775.0</td>\n",
              "      <td>12</td>\n",
              "      <td>38097.0</td>\n",
              "      <td>27</td>\n",
              "      <td>2858630</td>\n",
              "      <td>135</td>\n",
              "      <td>6257871</td>\n",
              "      <td>357</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Ramon Johnson Sandi</td>\n",
              "      <td>Male</td>\n",
              "      <td>836967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999874</th>\n",
              "      <td>1996-09-24</td>\n",
              "      <td>2022-03-25</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18043.0</td>\n",
              "      <td>6</td>\n",
              "      <td>87439.0</td>\n",
              "      <td>60</td>\n",
              "      <td>27421298</td>\n",
              "      <td>92</td>\n",
              "      <td>8127016</td>\n",
              "      <td>225</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Benningfield, Gina Lacy Fisher</td>\n",
              "      <td>Female</td>\n",
              "      <td>1000630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999875</th>\n",
              "      <td>1940-02-20</td>\n",
              "      <td>1997-08-28</td>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13099.0</td>\n",
              "      <td>5</td>\n",
              "      <td>75244.0</td>\n",
              "      <td>34</td>\n",
              "      <td>37140941</td>\n",
              "      <td>75</td>\n",
              "      <td>11209819</td>\n",
              "      <td>233</td>\n",
              "      <td>High</td>\n",
              "      <td>Low</td>\n",
              "      <td>1</td>\n",
              "      <td>Jack Huges Addison Barrett</td>\n",
              "      <td>Male</td>\n",
              "      <td>477516</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>51982 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ad8afc4-8499-4cd5-a6d9-5d3601cb5f4b')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-8ad8afc4-8499-4cd5-a6d9-5d3601cb5f4b 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-8ad8afc4-8499-4cd5-a6d9-5d3601cb5f4b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.concat([new_med,low,high])"
      ],
      "metadata": {
        "id": "GX5RSsvG_HEc"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save the data file"
      ],
      "metadata": {
        "id": "9F_OQ8YBm2RH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv('resampled_uoft_nodes_v3.csv')\n",
        "from google.colab import files\n",
        "files.download('resampled_uoft_nodes_v3.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "zR45bD7Dm12R",
        "outputId": "7eebf8d1-12dd-4a8a-c2c8-74157693315d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_fe6dcb57-ecb1-46bc-8eb7-d78d3c86ad1e\", \"resampled_uoft_nodes_v3.csv\", 91089032)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hN2ZNR0v_L0M",
        "outputId": "632bcaab-6e25-4e79-f20c-92b27129aa60"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(701982, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['RISK'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWk6-hcP_Nn_",
        "outputId": "cde6d74d-9f43-450b-ec21-9c7648cfbf80"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    600000\n",
              "1     51982\n",
              "2     50000\n",
              "Name: RISK, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#inverse map high,low,med\n",
        "map={'low':0,'medium':1,'high':2}\n",
        "map_2={0:'low',1:'medium',2:'high'}\n",
        "data['RISK']=data['RISK'].map(map_2)\n",
        "data['RISK'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVuw_5Pi_TWp",
        "outputId": "5a45049f-d87f-484a-bf15-411139e8daa2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "low       600000\n",
              "medium     51982\n",
              "high       50000\n",
              "Name: RISK, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Remove outliers using IQR"
      ],
      "metadata": {
        "id": "O7TKMvbkhRCj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Q1_low = low[c].quantile(0.25)\n",
        "# Q3_low = low[c].quantile(0.45)\n",
        "# IQR_low = Q3_low- Q1_low\n",
        "# df_low= low[~((low[c] < (Q1_low - 1.5*IQR_low)) |(low[c] > (Q3_low + 1.5 * IQR_low))).any(axis=1)]"
      ],
      "metadata": {
        "id": "rn_RsXBnHiv6"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df_low"
      ],
      "metadata": {
        "id": "1FGvjPk_H9r2"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Q1_med = med[c].quantile(0.20)\n",
        "# Q3_med = med[c].quantile(0.45)\n",
        "# IQR_med = Q3_med- Q1_med\n",
        "# print(IQR_med)\n",
        "# print(Q1_med - 1.5*IQR_med,Q3_med + 1.5 * IQR_med)\n",
        "# df_med= med[~((med[c] < (Q1_med - 1.5*IQR_med)) |(med[c] > (Q3_med + 1.5*IQR_med))).any(axis=1)]"
      ],
      "metadata": {
        "id": "E20-fqG8IPWC"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df_med"
      ],
      "metadata": {
        "id": "ARjjjq4AIbTa"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Q1_high = high[c].quantile(0.15)\n",
        "# Q3_high = high[c].quantile(0.95)\n",
        "# IQR_high = Q3_high- Q1_high\n",
        "# df_high= high[~((high[c] < (Q1_high - 1.5*IQR_high)) |(high[c] > (Q3_high + 1.5 * IQR_high))).any(axis=1)]"
      ],
      "metadata": {
        "id": "OFk7TleKIlk8"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df_high"
      ],
      "metadata": {
        "id": "uHF_zNiHI0Y5"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for i in c: \n",
        "#   upper_lim = low[i].quantile(.95)\n",
        "#   lower_lim = low[i].quantile(.05)\n",
        "#   print(\"For \",i,\"Upper_lim is\",upper_lim)\n",
        "#   print(\"For \",i,\"Lower lim is \",lower_lim)\n",
        "#   low = low[(low[i] < upper_lim) & (low[i] > lower_lim)]\n",
        "#   # print(low.shape)"
      ],
      "metadata": {
        "id": "dLvzfhspwTZ7"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # for i in c: \n",
        "#   upper_lim = med[i].quantile(.80)\n",
        "#   lower_lim = med[i].quantile(.05)\n",
        "#   print(\"For \",i,\"Upper_lim is\",upper_lim)\n",
        "#   print(\"For \",i,\"Lower lim is \",lower_lim)\n",
        "#   med = med[(med[i] < upper_lim) & (med[i] > lower_lim)]\n",
        "#   print(med.shape)"
      ],
      "metadata": {
        "id": "HH6b4kew1dt4"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for i in c: \n",
        "#   upper_lim = high[i].quantile(.98)\n",
        "#   lower_lim = high[i].quantile(.1)\n",
        "#   print(\"For \",i,\"Upper_lim is\",upper_lim)\n",
        "#   print(\"For \",i,\"Lower lim is \",lower_lim)\n",
        "#   high = high[(high[i] < upper_lim) & (high[i] > lower_lim)]\n",
        "#   print(high.shape)"
      ],
      "metadata": {
        "id": "hek5Peb11maS"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "xVAuiZkWJDB0",
        "outputId": "9eb7d702-97ef-4821-b120-98b58607716f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         BIRTH_DT CUST_ADD_DT OCPTN_NM  RES_CNTRY_CA  CNTRY_OF_INCOME_CA  \\\n",
              "60916  1968-02-18  2001-10-07     High             1                   1   \n",
              "60921  1965-04-03  2006-12-03     High             1                   1   \n",
              "60937  1979-04-07  2000-04-11     High             1                   1   \n",
              "60949  1969-07-16  2018-05-23     High             1                   1   \n",
              "60957  1948-02-10  1996-04-01     High             1                   1   \n",
              "...           ...         ...      ...           ...                 ...   \n",
              "999994 1970-08-21  2014-09-09      Low             0                   1   \n",
              "999995 1939-01-16  1994-03-19      Low             0                   1   \n",
              "999997 1958-09-09  2010-11-03     High             0                   1   \n",
              "999998 1983-09-15  2013-01-30     High             0                   1   \n",
              "999999 1962-01-23  2017-03-07     High             0                   0   \n",
              "\n",
              "        PEP_FL  CASH_SUM_IN  CASH_CNT_IN  CASH_SUM_OUT  CASH_CNT_OUT  \\\n",
              "60916        0    19524.000            8       11534.0             8   \n",
              "60921        0     8739.000            5       76088.0            37   \n",
              "60937        0    60097.000           31       28577.0            16   \n",
              "60949        0    67636.000           24        6667.0            12   \n",
              "60957        0    10498.000           13       76117.0            50   \n",
              "...        ...          ...          ...           ...           ...   \n",
              "999994       0    21161.905            8       30217.0            24   \n",
              "999995       0    18945.108           10       32210.0            21   \n",
              "999997       0     2351.255            2       15997.0            19   \n",
              "999998       0    27312.613            8        3399.0             5   \n",
              "999999       0     1421.363            1        1276.0             1   \n",
              "\n",
              "        WIRES_SUM_IN  WIRES_CNT_IN  WIRES_SUM_OUT  WIRES_CNT_OUT  \\\n",
              "60916         428045            37        1546205            168   \n",
              "60921       24160866           104        6673232            158   \n",
              "60937         675417            53         282939            162   \n",
              "60949         419872            11        5679053            281   \n",
              "60957       25537840           324        7275861            273   \n",
              "...              ...           ...            ...            ...   \n",
              "999994       6155329            66        3442076            474   \n",
              "999995       1186467            25        2550098            174   \n",
              "999997       3024271            68         948981            124   \n",
              "999998        943477             9         376429             12   \n",
              "999999        683797             5         617628             12   \n",
              "\n",
              "       COUNTRY_RISK_INCOME COUNTRY_RISK_RESIDENCY    RISK  \\\n",
              "60916                  Low                    Low  medium   \n",
              "60921                  Low                    Low  medium   \n",
              "60937                  Low                    Low  medium   \n",
              "60949                  Low                    Low  medium   \n",
              "60957                  Low                    Low  medium   \n",
              "...                    ...                    ...     ...   \n",
              "999994                 Low                   High    high   \n",
              "999995                 Low                   High    high   \n",
              "999997                 Low                   High    high   \n",
              "999998                 Low                   High    high   \n",
              "999999                High                   High    high   \n",
              "\n",
              "                                        NAME  GENDER  CUSTOMER_ID  \n",
              "60916   Antonette Ashburn Rollins Washington  Female       811633  \n",
              "60921                        Lee, Lisa Meads  Female        76681  \n",
              "60937             Hall, Barbara Frija Gooden  Female       811034  \n",
              "60949                       joe Hamm Morales    Male        60399  \n",
              "60957              Lamke, Helen Haskett Post  Female        70614  \n",
              "...                                      ...     ...          ...  \n",
              "999994                  bev Snell Hood [tern  Female       377454  \n",
              "999995                     Bret* Dove Gainey    Male       420884  \n",
              "999997        Bernadette Schofield Pace Mile  Female       731360  \n",
              "999998               Wasson, Russell Terrell    Male       837988  \n",
              "999999                      Daniel Mckeighan    Male       166357  \n",
              "\n",
              "[701982 rows x 20 columns]"
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              "      <th></th>\n",
              "      <th>BIRTH_DT</th>\n",
              "      <th>CUST_ADD_DT</th>\n",
              "      <th>OCPTN_NM</th>\n",
              "      <th>RES_CNTRY_CA</th>\n",
              "      <th>CNTRY_OF_INCOME_CA</th>\n",
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              "      <th>CASH_CNT_IN</th>\n",
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              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>WIRES_SUM_OUT</th>\n",
              "      <th>WIRES_CNT_OUT</th>\n",
              "      <th>COUNTRY_RISK_INCOME</th>\n",
              "      <th>COUNTRY_RISK_RESIDENCY</th>\n",
              "      <th>RISK</th>\n",
              "      <th>NAME</th>\n",
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              "      <th>60916</th>\n",
              "      <td>1968-02-18</td>\n",
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              "      <td>168</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>medium</td>\n",
              "      <td>Antonette Ashburn Rollins Washington</td>\n",
              "      <td>Female</td>\n",
              "      <td>811633</td>\n",
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              "    <tr>\n",
              "      <th>60921</th>\n",
              "      <td>1965-04-03</td>\n",
              "      <td>2006-12-03</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8739.000</td>\n",
              "      <td>5</td>\n",
              "      <td>76088.0</td>\n",
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              "      <td>24160866</td>\n",
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              "      <td>6673232</td>\n",
              "      <td>158</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>medium</td>\n",
              "      <td>Lee, Lisa Meads</td>\n",
              "      <td>Female</td>\n",
              "      <td>76681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60937</th>\n",
              "      <td>1979-04-07</td>\n",
              "      <td>2000-04-11</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>60097.000</td>\n",
              "      <td>31</td>\n",
              "      <td>28577.0</td>\n",
              "      <td>16</td>\n",
              "      <td>675417</td>\n",
              "      <td>53</td>\n",
              "      <td>282939</td>\n",
              "      <td>162</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>medium</td>\n",
              "      <td>Hall, Barbara Frija Gooden</td>\n",
              "      <td>Female</td>\n",
              "      <td>811034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60949</th>\n",
              "      <td>1969-07-16</td>\n",
              "      <td>2018-05-23</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>67636.000</td>\n",
              "      <td>24</td>\n",
              "      <td>6667.0</td>\n",
              "      <td>12</td>\n",
              "      <td>419872</td>\n",
              "      <td>11</td>\n",
              "      <td>5679053</td>\n",
              "      <td>281</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>medium</td>\n",
              "      <td>joe Hamm Morales</td>\n",
              "      <td>Male</td>\n",
              "      <td>60399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60957</th>\n",
              "      <td>1948-02-10</td>\n",
              "      <td>1996-04-01</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10498.000</td>\n",
              "      <td>13</td>\n",
              "      <td>76117.0</td>\n",
              "      <td>50</td>\n",
              "      <td>25537840</td>\n",
              "      <td>324</td>\n",
              "      <td>7275861</td>\n",
              "      <td>273</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>medium</td>\n",
              "      <td>Lamke, Helen Haskett Post</td>\n",
              "      <td>Female</td>\n",
              "      <td>70614</td>\n",
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              "      <td>2014-09-09</td>\n",
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              "      <td>1</td>\n",
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              "      <td>Low</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>bev Snell Hood [tern</td>\n",
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              "      <td>377454</td>\n",
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              "    <tr>\n",
              "      <th>999995</th>\n",
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              "      <td>18945.108</td>\n",
              "      <td>10</td>\n",
              "      <td>32210.0</td>\n",
              "      <td>21</td>\n",
              "      <td>1186467</td>\n",
              "      <td>25</td>\n",
              "      <td>2550098</td>\n",
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              "      <td>Low</td>\n",
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              "      <td>high</td>\n",
              "      <td>Bret* Dove Gainey</td>\n",
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              "      <th>999997</th>\n",
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              "      <td>2010-11-03</td>\n",
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              "      <td>2351.255</td>\n",
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              "      <td>high</td>\n",
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              "      <td>731360</td>\n",
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              "    <tr>\n",
              "      <th>999998</th>\n",
              "      <td>1983-09-15</td>\n",
              "      <td>2013-01-30</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>27312.613</td>\n",
              "      <td>8</td>\n",
              "      <td>3399.0</td>\n",
              "      <td>5</td>\n",
              "      <td>943477</td>\n",
              "      <td>9</td>\n",
              "      <td>376429</td>\n",
              "      <td>12</td>\n",
              "      <td>Low</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>Wasson, Russell Terrell</td>\n",
              "      <td>Male</td>\n",
              "      <td>837988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>1962-01-23</td>\n",
              "      <td>2017-03-07</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1421.363</td>\n",
              "      <td>1</td>\n",
              "      <td>1276.0</td>\n",
              "      <td>1</td>\n",
              "      <td>683797</td>\n",
              "      <td>5</td>\n",
              "      <td>617628</td>\n",
              "      <td>12</td>\n",
              "      <td>High</td>\n",
              "      <td>High</td>\n",
              "      <td>high</td>\n",
              "      <td>Daniel Mckeighan</td>\n",
              "      <td>Male</td>\n",
              "      <td>166357</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>701982 rows × 20 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-929da489-7407-42c9-94c4-12a698736ada')\"\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-929da489-7407-42c9-94c4-12a698736ada');\n",
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              "            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",
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            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering"
      ],
      "metadata": {
        "id": "6LJVZyolp7bh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in c:\n",
        "  data[i]=data[i]+1"
      ],
      "metadata": {
        "id": "1bYtlhzy4hry"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['cash_diff']=data['CASH_SUM_IN']-data['CASH_SUM_OUT'] #difference in cash\n",
        "data['wire_diff']=data['WIRES_SUM_IN']-data['WIRES_SUM_OUT'] # difference in wire\n",
        "data['cash_cnt_diff']=data['CASH_CNT_IN']-data['CASH_CNT_OUT'] # difference in cash counts\n",
        "data['wire_cnt_diff']=data['WIRES_CNT_IN']-data['WIRES_CNT_OUT'] # difference in wire counts\n",
        "data['avg_cash_transaction_out']=data['CASH_SUM_OUT']/data['CASH_CNT_OUT'] # per cnt cash out value\n",
        "data['avg_wire_transaction_out']=data['WIRES_SUM_OUT']/data['WIRES_CNT_OUT'] # per cnt wire out value\n",
        "data['avg_cash_transaction_in']=data['CASH_SUM_IN']/data['CASH_CNT_IN'] # per cnt cash in value\n",
        "data['avg_wire_transaction_in']=data['WIRES_SUM_IN']/data['WIRES_CNT_IN'] # per cnt cash out value\n",
        "data['Wcnt_in_per_month']=(data['WIRES_CNT_IN']/12) #cnt per month\n",
        "data['Wcnt_out_per_month']=(data['WIRES_CNT_OUT']/12)# cnt per month \n",
        "data['Ccnt_in_per_month']=(data['CASH_CNT_IN']/12)\n",
        "data['Ccnt_out_per_month']=(data['CASH_CNT_OUT']/12)\n",
        "data['monthly_cash_in']=data['CASH_SUM_IN']/12 #estimated monthly cash in\n",
        "data['monthly_cash_out']=data['CASH_SUM_OUT']/12 # estimated monthly cash out\n",
        "data['monthly_wire_in']=data['WIRES_SUM_IN']/12 \n",
        "data['monthly_wire_out']=data['WIRES_SUM_OUT']/12\n",
        "data['Ccnt_in_per_month']=data['monthly_cash_in']/data['Ccnt_in_per_month'] # estimated cash in per montly cnt\n",
        "data['Ccnt_out_per_month']=data['monthly_cash_out']/data['Ccnt_out_per_month']\n",
        "data['Wcnt_in_per_month']=data['monthly_wire_in']/data['Wcnt_in_per_month']\n",
        "data['Wcnt_out_per_month']=data['monthly_wire_out']/data['Wcnt_out_per_month']\n",
        "data['diff_year']=data['CUST_ADD_DT'].dt.year-data['BIRTH_DT'].dt.year # age when opened bank account\n",
        "data['age']=2023-data['BIRTH_DT'].dt.year #age of customer\n",
        "data['cust_age_bank']=2023-data['CUST_ADD_DT'].dt.year # number of years assocaited with bank\n",
        "data['total_money_left']=data['CASH_SUM_IN']+data['WIRES_SUM_IN']-data['CASH_SUM_OUT']-data['WIRES_SUM_OUT'] # left out money yearly\n",
        "data['IN_AVG']=(data['CASH_SUM_IN']+data['WIRES_SUM_IN'])/2 #avg in\n",
        "data['OUT_AVG']=(data['CASH_SUM_OUT']+data['WIRES_SUM_OUT'])/2 # avg out\n",
        "data['IN']=(data['CASH_SUM_IN']+data['WIRES_SUM_IN']) # total in \n",
        "data['OUT']=(data['CASH_SUM_OUT']+data['WIRES_SUM_OUT']) # total out"
      ],
      "metadata": {
        "id": "nWvTgNNv4lrO"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(['NAME','BIRTH_DT','CUST_ADD_DT','CUSTOMER_ID'],inplace=True,axis=1)"
      ],
      "metadata": {
        "id": "8KAl9cvR10x8"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Abk3LJtUDYFZ",
        "outputId": "dbe4557e-864f-434d-9bbb-8ed9d9143d48"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       OCPTN_NM  RES_CNTRY_CA  CNTRY_OF_INCOME_CA  PEP_FL  CASH_SUM_IN  \\\n",
              "60916      High             1                   1       0    19525.000   \n",
              "60921      High             1                   1       0     8740.000   \n",
              "60937      High             1                   1       0    60098.000   \n",
              "60949      High             1                   1       0    67637.000   \n",
              "60957      High             1                   1       0    10499.000   \n",
              "...         ...           ...                 ...     ...          ...   \n",
              "999994      Low             0                   1       0    21162.905   \n",
              "999995      Low             0                   1       0    18946.108   \n",
              "999997     High             0                   1       0     2352.255   \n",
              "999998     High             0                   1       0    27313.613   \n",
              "999999     High             0                   0       0     1422.363   \n",
              "\n",
              "        CASH_CNT_IN  CASH_SUM_OUT  CASH_CNT_OUT  WIRES_SUM_IN  WIRES_CNT_IN  \\\n",
              "60916             9       11535.0             9        428046            38   \n",
              "60921             6       76089.0            38      24160867           105   \n",
              "60937            32       28578.0            17        675418            54   \n",
              "60949            25        6668.0            13        419873            12   \n",
              "60957            14       76118.0            51      25537841           325   \n",
              "...             ...           ...           ...           ...           ...   \n",
              "999994            9       30218.0            25       6155330            67   \n",
              "999995           11       32211.0            22       1186468            26   \n",
              "999997            3       15998.0            20       3024272            69   \n",
              "999998            9        3400.0             6        943478            10   \n",
              "999999            2        1277.0             2        683798             6   \n",
              "\n",
              "        ...  monthly_wire_in  monthly_wire_out diff_year age cust_age_bank  \\\n",
              "60916   ...     3.567050e+04     128850.500000        33  55            22   \n",
              "60921   ...     2.013406e+06     556102.750000        41  58            17   \n",
              "60937   ...     5.628483e+04      23578.333333        21  44            23   \n",
              "60949   ...     3.498942e+04     473254.500000        49  54             5   \n",
              "60957   ...     2.128153e+06     606321.833333        48  75            27   \n",
              "...     ...              ...               ...       ...  ..           ...   \n",
              "999994  ...     5.129442e+05     286839.750000        44  53             9   \n",
              "999995  ...     9.887233e+04     212508.250000        55  84            29   \n",
              "999997  ...     2.520227e+05      79081.833333        52  65            13   \n",
              "999998  ...     7.862317e+04      31369.166667        30  40            10   \n",
              "999999  ...     5.698317e+04      51469.083333        55  61             6   \n",
              "\n",
              "       total_money_left        IN_AVG    OUT_AVG            IN        OUT  \n",
              "60916     -1.110170e+06  2.237855e+05   778870.5  4.475710e+05  1557741.0  \n",
              "60921      1.742028e+07  1.208480e+07  3374661.0  2.416961e+07  6749322.0  \n",
              "60937      4.239980e+05  3.677580e+05   155759.0  7.355160e+05   311518.0  \n",
              "60949     -5.198212e+06  2.437550e+05  2842861.0  4.875100e+05  5685722.0  \n",
              "60957      1.819636e+07  1.277417e+07  3675990.0  2.554834e+07  7351980.0  \n",
              "...                 ...           ...        ...           ...        ...  \n",
              "999994     2.704198e+06  3.088246e+06  1736147.5  6.176493e+06  3472295.0  \n",
              "999995    -1.376896e+06  6.027071e+05  1291155.0  1.205414e+06  2582310.0  \n",
              "999997     2.061644e+06  1.513312e+06   482490.0  3.026624e+06   964980.0  \n",
              "999998     5.909616e+05  4.853958e+05   189915.0  9.707916e+05   379830.0  \n",
              "999999     6.631436e+04  3.426102e+05   309453.0  6.852204e+05   618906.0  \n",
              "\n",
              "[701982 rows x 40 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3916ba36-11f8-44db-b319-8fb6eb154551\">\n",
              "    <div 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>OCPTN_NM</th>\n",
              "      <th>RES_CNTRY_CA</th>\n",
              "      <th>CNTRY_OF_INCOME_CA</th>\n",
              "      <th>PEP_FL</th>\n",
              "      <th>CASH_SUM_IN</th>\n",
              "      <th>CASH_CNT_IN</th>\n",
              "      <th>CASH_SUM_OUT</th>\n",
              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>...</th>\n",
              "      <th>monthly_wire_in</th>\n",
              "      <th>monthly_wire_out</th>\n",
              "      <th>diff_year</th>\n",
              "      <th>age</th>\n",
              "      <th>cust_age_bank</th>\n",
              "      <th>total_money_left</th>\n",
              "      <th>IN_AVG</th>\n",
              "      <th>OUT_AVG</th>\n",
              "      <th>IN</th>\n",
              "      <th>OUT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60916</th>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>19525.000</td>\n",
              "      <td>9</td>\n",
              "      <td>11535.0</td>\n",
              "      <td>9</td>\n",
              "      <td>428046</td>\n",
              "      <td>38</td>\n",
              "      <td>...</td>\n",
              "      <td>3.567050e+04</td>\n",
              "      <td>128850.500000</td>\n",
              "      <td>33</td>\n",
              "      <td>55</td>\n",
              "      <td>22</td>\n",
              "      <td>-1.110170e+06</td>\n",
              "      <td>2.237855e+05</td>\n",
              "      <td>778870.5</td>\n",
              "      <td>4.475710e+05</td>\n",
              "      <td>1557741.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60921</th>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8740.000</td>\n",
              "      <td>6</td>\n",
              "      <td>76089.0</td>\n",
              "      <td>38</td>\n",
              "      <td>24160867</td>\n",
              "      <td>105</td>\n",
              "      <td>...</td>\n",
              "      <td>2.013406e+06</td>\n",
              "      <td>556102.750000</td>\n",
              "      <td>41</td>\n",
              "      <td>58</td>\n",
              "      <td>17</td>\n",
              "      <td>1.742028e+07</td>\n",
              "      <td>1.208480e+07</td>\n",
              "      <td>3374661.0</td>\n",
              "      <td>2.416961e+07</td>\n",
              "      <td>6749322.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60937</th>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>60098.000</td>\n",
              "      <td>32</td>\n",
              "      <td>28578.0</td>\n",
              "      <td>17</td>\n",
              "      <td>675418</td>\n",
              "      <td>54</td>\n",
              "      <td>...</td>\n",
              "      <td>5.628483e+04</td>\n",
              "      <td>23578.333333</td>\n",
              "      <td>21</td>\n",
              "      <td>44</td>\n",
              "      <td>23</td>\n",
              "      <td>4.239980e+05</td>\n",
              "      <td>3.677580e+05</td>\n",
              "      <td>155759.0</td>\n",
              "      <td>7.355160e+05</td>\n",
              "      <td>311518.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60949</th>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>67637.000</td>\n",
              "      <td>25</td>\n",
              "      <td>6668.0</td>\n",
              "      <td>13</td>\n",
              "      <td>419873</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>3.498942e+04</td>\n",
              "      <td>473254.500000</td>\n",
              "      <td>49</td>\n",
              "      <td>54</td>\n",
              "      <td>5</td>\n",
              "      <td>-5.198212e+06</td>\n",
              "      <td>2.437550e+05</td>\n",
              "      <td>2842861.0</td>\n",
              "      <td>4.875100e+05</td>\n",
              "      <td>5685722.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60957</th>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10499.000</td>\n",
              "      <td>14</td>\n",
              "      <td>76118.0</td>\n",
              "      <td>51</td>\n",
              "      <td>25537841</td>\n",
              "      <td>325</td>\n",
              "      <td>...</td>\n",
              "      <td>2.128153e+06</td>\n",
              "      <td>606321.833333</td>\n",
              "      <td>48</td>\n",
              "      <td>75</td>\n",
              "      <td>27</td>\n",
              "      <td>1.819636e+07</td>\n",
              "      <td>1.277417e+07</td>\n",
              "      <td>3675990.0</td>\n",
              "      <td>2.554834e+07</td>\n",
              "      <td>7351980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999994</th>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21162.905</td>\n",
              "      <td>9</td>\n",
              "      <td>30218.0</td>\n",
              "      <td>25</td>\n",
              "      <td>6155330</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>5.129442e+05</td>\n",
              "      <td>286839.750000</td>\n",
              "      <td>44</td>\n",
              "      <td>53</td>\n",
              "      <td>9</td>\n",
              "      <td>2.704198e+06</td>\n",
              "      <td>3.088246e+06</td>\n",
              "      <td>1736147.5</td>\n",
              "      <td>6.176493e+06</td>\n",
              "      <td>3472295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999995</th>\n",
              "      <td>Low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18946.108</td>\n",
              "      <td>11</td>\n",
              "      <td>32211.0</td>\n",
              "      <td>22</td>\n",
              "      <td>1186468</td>\n",
              "      <td>26</td>\n",
              "      <td>...</td>\n",
              "      <td>9.887233e+04</td>\n",
              "      <td>212508.250000</td>\n",
              "      <td>55</td>\n",
              "      <td>84</td>\n",
              "      <td>29</td>\n",
              "      <td>-1.376896e+06</td>\n",
              "      <td>6.027071e+05</td>\n",
              "      <td>1291155.0</td>\n",
              "      <td>1.205414e+06</td>\n",
              "      <td>2582310.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999997</th>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2352.255</td>\n",
              "      <td>3</td>\n",
              "      <td>15998.0</td>\n",
              "      <td>20</td>\n",
              "      <td>3024272</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>2.520227e+05</td>\n",
              "      <td>79081.833333</td>\n",
              "      <td>52</td>\n",
              "      <td>65</td>\n",
              "      <td>13</td>\n",
              "      <td>2.061644e+06</td>\n",
              "      <td>1.513312e+06</td>\n",
              "      <td>482490.0</td>\n",
              "      <td>3.026624e+06</td>\n",
              "      <td>964980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999998</th>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>27313.613</td>\n",
              "      <td>9</td>\n",
              "      <td>3400.0</td>\n",
              "      <td>6</td>\n",
              "      <td>943478</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>7.862317e+04</td>\n",
              "      <td>31369.166667</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "      <td>5.909616e+05</td>\n",
              "      <td>4.853958e+05</td>\n",
              "      <td>189915.0</td>\n",
              "      <td>9.707916e+05</td>\n",
              "      <td>379830.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1422.363</td>\n",
              "      <td>2</td>\n",
              "      <td>1277.0</td>\n",
              "      <td>2</td>\n",
              "      <td>683798</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>5.698317e+04</td>\n",
              "      <td>51469.083333</td>\n",
              "      <td>55</td>\n",
              "      <td>61</td>\n",
              "      <td>6</td>\n",
              "      <td>6.631436e+04</td>\n",
              "      <td>3.426102e+05</td>\n",
              "      <td>309453.0</td>\n",
              "      <td>6.852204e+05</td>\n",
              "      <td>618906.0</td>\n",
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              "<p>701982 rows × 40 columns</p>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-3916ba36-11f8-44db-b319-8fb6eb154551 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-3916ba36-11f8-44db-b319-8fb6eb154551');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.loc[:, data.columns !='RISK']\n",
        "y=data['RISK']"
      ],
      "metadata": {
        "id": "eYd8FSDl2Cr3"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUBulJJh2T2W",
        "outputId": "2ae0b588-ab87-4ce9-e82e-4ee413385c42"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['OCPTN_NM', 'RES_CNTRY_CA', 'CNTRY_OF_INCOME_CA', 'PEP_FL',\n",
              "       'CASH_SUM_IN', 'CASH_CNT_IN', 'CASH_SUM_OUT', 'CASH_CNT_OUT',\n",
              "       'WIRES_SUM_IN', 'WIRES_CNT_IN', 'WIRES_SUM_OUT', 'WIRES_CNT_OUT',\n",
              "       'COUNTRY_RISK_INCOME', 'COUNTRY_RISK_RESIDENCY', 'GENDER', 'cash_diff',\n",
              "       'wire_diff', 'cash_cnt_diff', 'wire_cnt_diff',\n",
              "       'avg_cash_transaction_out', 'avg_wire_transaction_out',\n",
              "       'avg_cash_transaction_in', 'avg_wire_transaction_in',\n",
              "       'Wcnt_in_per_month', 'Wcnt_out_per_month', 'Ccnt_in_per_month',\n",
              "       'Ccnt_out_per_month', 'monthly_cash_in', 'monthly_cash_out',\n",
              "       'monthly_wire_in', 'monthly_wire_out', 'diff_year', 'age',\n",
              "       'cust_age_bank', 'total_money_left', 'IN_AVG', 'OUT_AVG', 'IN', 'OUT'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One hot encode"
      ],
      "metadata": {
        "id": "k2lfh36vDaow"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=pd.get_dummies(x)"
      ],
      "metadata": {
        "id": "7FZao8HG2XGt"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(X.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uynp_PwKDcbB",
        "outputId": "4250abed-cec4-4b2c-dc1f-c053690dd638"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(701982, 46)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "YUGQcqhGDgcT",
        "outputId": "386e7604-f374-4ef9-9f3f-60785ff0db96"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        RES_CNTRY_CA  CNTRY_OF_INCOME_CA  PEP_FL  CASH_SUM_IN  CASH_CNT_IN  \\\n",
              "60916              1                   1       0    19525.000            9   \n",
              "60921              1                   1       0     8740.000            6   \n",
              "60937              1                   1       0    60098.000           32   \n",
              "60949              1                   1       0    67637.000           25   \n",
              "60957              1                   1       0    10499.000           14   \n",
              "...              ...                 ...     ...          ...          ...   \n",
              "999994             0                   1       0    21162.905            9   \n",
              "999995             0                   1       0    18946.108           11   \n",
              "999997             0                   1       0     2352.255            3   \n",
              "999998             0                   1       0    27313.613            9   \n",
              "999999             0                   0       0     1422.363            2   \n",
              "\n",
              "        CASH_SUM_OUT  CASH_CNT_OUT  WIRES_SUM_IN  WIRES_CNT_IN  WIRES_SUM_OUT  \\\n",
              "60916        11535.0             9        428046            38        1546206   \n",
              "60921        76089.0            38      24160867           105        6673233   \n",
              "60937        28578.0            17        675418            54         282940   \n",
              "60949         6668.0            13        419873            12        5679054   \n",
              "60957        76118.0            51      25537841           325        7275862   \n",
              "...              ...           ...           ...           ...            ...   \n",
              "999994       30218.0            25       6155330            67        3442077   \n",
              "999995       32211.0            22       1186468            26        2550099   \n",
              "999997       15998.0            20       3024272            69         948982   \n",
              "999998        3400.0             6        943478            10         376430   \n",
              "999999        1277.0             2        683798             6         617629   \n",
              "\n",
              "        ...  OCPTN_NM_Low  OCPTN_NM_Moderate  COUNTRY_RISK_INCOME_High  \\\n",
              "60916   ...             0                  0                         0   \n",
              "60921   ...             0                  0                         0   \n",
              "60937   ...             0                  0                         0   \n",
              "60949   ...             0                  0                         0   \n",
              "60957   ...             0                  0                         0   \n",
              "...     ...           ...                ...                       ...   \n",
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              "999997  ...             0                  0                         0   \n",
              "999998  ...             0                  0                         0   \n",
              "999999  ...             0                  0                         1   \n",
              "\n",
              "        COUNTRY_RISK_INCOME_Low  COUNTRY_RISK_INCOME_Moderate  \\\n",
              "60916                         1                             0   \n",
              "60921                         1                             0   \n",
              "60937                         1                             0   \n",
              "60949                         1                             0   \n",
              "60957                         1                             0   \n",
              "...                         ...                           ...   \n",
              "999994                        1                             0   \n",
              "999995                        1                             0   \n",
              "999997                        1                             0   \n",
              "999998                        1                             0   \n",
              "999999                        0                             0   \n",
              "\n",
              "        COUNTRY_RISK_RESIDENCY_High  COUNTRY_RISK_RESIDENCY_Low  \\\n",
              "60916                             0                           1   \n",
              "60921                             0                           1   \n",
              "60937                             0                           1   \n",
              "60949                             0                           1   \n",
              "60957                             0                           1   \n",
              "...                             ...                         ...   \n",
              "999994                            1                           0   \n",
              "999995                            1                           0   \n",
              "999997                            1                           0   \n",
              "999998                            1                           0   \n",
              "999999                            1                           0   \n",
              "\n",
              "        COUNTRY_RISK_RESIDENCY_Moderate  GENDER_Female  GENDER_Male  \n",
              "60916                                 0              1            0  \n",
              "60921                                 0              1            0  \n",
              "60937                                 0              1            0  \n",
              "60949                                 0              0            1  \n",
              "60957                                 0              1            0  \n",
              "...                                 ...            ...          ...  \n",
              "999994                                0              1            0  \n",
              "999995                                0              0            1  \n",
              "999997                                0              1            0  \n",
              "999998                                0              0            1  \n",
              "999999                                0              0            1  \n",
              "\n",
              "[701982 rows x 46 columns]"
            ],
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              "\n",
              "  <div id=\"df-13094f30-db72-4f42-b164-5cb937022a1d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .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>RES_CNTRY_CA</th>\n",
              "      <th>CNTRY_OF_INCOME_CA</th>\n",
              "      <th>PEP_FL</th>\n",
              "      <th>CASH_SUM_IN</th>\n",
              "      <th>CASH_CNT_IN</th>\n",
              "      <th>CASH_SUM_OUT</th>\n",
              "      <th>CASH_CNT_OUT</th>\n",
              "      <th>WIRES_SUM_IN</th>\n",
              "      <th>WIRES_CNT_IN</th>\n",
              "      <th>WIRES_SUM_OUT</th>\n",
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              "      <th>OCPTN_NM_Moderate</th>\n",
              "      <th>COUNTRY_RISK_INCOME_High</th>\n",
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              "      <th>GENDER_Female</th>\n",
              "      <th>GENDER_Male</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>32</td>\n",
              "      <td>28578.0</td>\n",
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              "      <td>675418</td>\n",
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              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>999999</th>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>683798</td>\n",
              "      <td>6</td>\n",
              "      <td>617629</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>701982 rows × 46 columns</p>\n",
              "</div>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13094f30-db72-4f42-b164-5cb937022a1d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-13094f30-db72-4f42-b164-5cb937022a1d');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import *\n",
        "from sklearn.ensemble import *\n",
        "from sklearn.metrics import *"
      ],
      "metadata": {
        "id": "7oVn9h-q2XmY"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
      ],
      "metadata": {
        "id": "yvFVaWNE2lF-"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-WMrzqODkfc",
        "outputId": "60445509-a383-4008-c657-f80848f3339e"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(491387, 46) (210595, 46) (491387,) (210595,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueeJ_hLNAI83",
        "outputId": "673f1689-3c35-483c-c9d3-cc9de83a9cd8"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "low       419953\n",
              "medium     36470\n",
              "high       34964\n",
              "Name: RISK, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvihyurRALbV",
        "outputId": "97f3347c-a7fa-4f47-a6d5-5894a10b3407"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "low       180047\n",
              "medium     15512\n",
              "high       15036\n",
              "Name: RISK, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "rus = RandomUnderSampler()\n",
        "X_resampled, y_resampled=rus.fit_resample(X_train,y_train)"
      ],
      "metadata": {
        "id": "NBa3yHbh5pJk"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_resampled.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MhqxhcT2zYs",
        "outputId": "4add1092-a7cf-49ba-87e7-58f4b3b9e6b9"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "high      34964\n",
              "low       34964\n",
              "medium    34964\n",
              "Name: RISK, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9_ISskD21vh",
        "outputId": "e95f324d-1778-4710-c633-f17b8772b08e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "low       180047\n",
              "medium     15512\n",
              "high       15036\n",
              "Name: RISK, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1=HistGradientBoostingClassifier()\n",
        "model_1.fit(X_resampled,y_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nuuxbhvP24gz",
        "outputId": "87844288-c5e7-46ba-e329-9860ac20d612"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HistGradientBoostingClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model_1.predict(X_test)"
      ],
      "metadata": {
        "id": "KjBdyFlo3X2H"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruAf3fy73_wU",
        "outputId": "8dc2a82d-9ba6-493d-cf17-13290b16aeb2"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        high       0.92      0.89      0.90     15036\n",
            "         low       1.00      1.00      1.00    180047\n",
            "      medium       0.89      0.93      0.91     15512\n",
            "\n",
            "    accuracy                           0.99    210595\n",
            "   macro avg       0.94      0.94      0.94    210595\n",
            "weighted avg       0.99      0.99      0.99    210595\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxZfkjas4Cx2",
        "outputId": "0b3806c4-a39c-43e5-8975-7cfe361f8108"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 13347     30   1659]\n",
            " [   103 179876     68]\n",
            " [  1103     22  14387]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find Feature Importances"
      ],
      "metadata": {
        "id": "1HnwWyS5ogbE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_2=RandomForestClassifier()\n",
        "model_2.fit(X_resampled,y_resampled)\n",
        "y_pred_2=model_2.predict(X_test)\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(confusion_matrix(y_test,y_pred_2))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFwdsoQXBwdV",
        "outputId": "597ef7ad-b788-4890-a0dd-2a404903ab89"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        high       0.92      0.89      0.90     15036\n",
            "         low       1.00      1.00      1.00    180047\n",
            "      medium       0.89      0.93      0.91     15512\n",
            "\n",
            "    accuracy                           0.99    210595\n",
            "   macro avg       0.94      0.94      0.94    210595\n",
            "weighted avg       0.99      0.99      0.99    210595\n",
            "\n",
            "[[ 13170     35   1831]\n",
            " [    56 179906     85]\n",
            " [  1025     27  14460]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features=pd.DataFrame({'features':X_train.columns,'scores':model_2.feature_importances_})\n",
        "features.sort_values(['scores'],ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nhVpKaRJo6H7",
        "outputId": "9901b085-d4c7-4adf-9655-637bb5e4320c"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           features    scores\n",
              "15         avg_cash_transaction_out  0.091298\n",
              "22               Ccnt_out_per_month  0.072337\n",
              "17          avg_cash_transaction_in  0.070256\n",
              "7                      WIRES_SUM_IN  0.066943\n",
              "21                Ccnt_in_per_month  0.061699\n",
              "31                           IN_AVG  0.061008\n",
              "25                  monthly_wire_in  0.057445\n",
              "33                               IN  0.052008\n",
              "16         avg_wire_transaction_out  0.044864\n",
              "19                Wcnt_in_per_month  0.044034\n",
              "18          avg_wire_transaction_in  0.038637\n",
              "12                        wire_diff  0.037253\n",
              "30                 total_money_left  0.037126\n",
              "9                     WIRES_SUM_OUT  0.032302\n",
              "32                          OUT_AVG  0.029474\n",
              "20               Wcnt_out_per_month  0.029305\n",
              "26                 monthly_wire_out  0.025124\n",
              "34                              OUT  0.022347\n",
              "5                      CASH_SUM_OUT  0.014812\n",
              "3                       CASH_SUM_IN  0.014464\n",
              "23                  monthly_cash_in  0.014197\n",
              "24                 monthly_cash_out  0.011764\n",
              "11                        cash_diff  0.011513\n",
              "29                    cust_age_bank  0.008167\n",
              "10                    WIRES_CNT_OUT  0.007219\n",
              "14                    wire_cnt_diff  0.006882\n",
              "8                      WIRES_CNT_IN  0.006847\n",
              "2                            PEP_FL  0.005006\n",
              "13                    cash_cnt_diff  0.004833\n",
              "27                        diff_year  0.004592\n",
              "28                              age  0.004302\n",
              "6                      CASH_CNT_OUT  0.004160\n",
              "4                       CASH_CNT_IN  0.003714\n",
              "35                    OCPTN_NM_High  0.000661\n",
              "36                     OCPTN_NM_Low  0.000645\n",
              "45                      GENDER_Male  0.000638\n",
              "44                    GENDER_Female  0.000610\n",
              "0                      RES_CNTRY_CA  0.000365\n",
              "41      COUNTRY_RISK_RESIDENCY_High  0.000263\n",
              "42       COUNTRY_RISK_RESIDENCY_Low  0.000222\n",
              "37                OCPTN_NM_Moderate  0.000190\n",
              "1                CNTRY_OF_INCOME_CA  0.000180\n",
              "39          COUNTRY_RISK_INCOME_Low  0.000136\n",
              "38         COUNTRY_RISK_INCOME_High  0.000125\n",
              "43  COUNTRY_RISK_RESIDENCY_Moderate  0.000017\n",
              "40     COUNTRY_RISK_INCOME_Moderate  0.000015"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02d61eef-1447-483c-8d8f-3a41ee8f057b\">\n",
              "    <div 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>features</th>\n",
              "      <th>scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>avg_cash_transaction_out</td>\n",
              "      <td>0.091298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Ccnt_out_per_month</td>\n",
              "      <td>0.072337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>avg_cash_transaction_in</td>\n",
              "      <td>0.070256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>WIRES_SUM_IN</td>\n",
              "      <td>0.066943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Ccnt_in_per_month</td>\n",
              "      <td>0.061699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>IN_AVG</td>\n",
              "      <td>0.061008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>monthly_wire_in</td>\n",
              "      <td>0.057445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>IN</td>\n",
              "      <td>0.052008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>avg_wire_transaction_out</td>\n",
              "      <td>0.044864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Wcnt_in_per_month</td>\n",
              "      <td>0.044034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>avg_wire_transaction_in</td>\n",
              "      <td>0.038637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>wire_diff</td>\n",
              "      <td>0.037253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>total_money_left</td>\n",
              "      <td>0.037126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>WIRES_SUM_OUT</td>\n",
              "      <td>0.032302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>OUT_AVG</td>\n",
              "      <td>0.029474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Wcnt_out_per_month</td>\n",
              "      <td>0.029305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>monthly_wire_out</td>\n",
              "      <td>0.025124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>OUT</td>\n",
              "      <td>0.022347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CASH_SUM_OUT</td>\n",
              "      <td>0.014812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CASH_SUM_IN</td>\n",
              "      <td>0.014464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>monthly_cash_in</td>\n",
              "      <td>0.014197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>monthly_cash_out</td>\n",
              "      <td>0.011764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>cash_diff</td>\n",
              "      <td>0.011513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>cust_age_bank</td>\n",
              "      <td>0.008167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>WIRES_CNT_OUT</td>\n",
              "      <td>0.007219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>wire_cnt_diff</td>\n",
              "      <td>0.006882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>WIRES_CNT_IN</td>\n",
              "      <td>0.006847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PEP_FL</td>\n",
              "      <td>0.005006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>cash_cnt_diff</td>\n",
              "      <td>0.004833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>diff_year</td>\n",
              "      <td>0.004592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>age</td>\n",
              "      <td>0.004302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CASH_CNT_OUT</td>\n",
              "      <td>0.004160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CASH_CNT_IN</td>\n",
              "      <td>0.003714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>OCPTN_NM_High</td>\n",
              "      <td>0.000661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>OCPTN_NM_Low</td>\n",
              "      <td>0.000645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>GENDER_Male</td>\n",
              "      <td>0.000638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>GENDER_Female</td>\n",
              "      <td>0.000610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RES_CNTRY_CA</td>\n",
              "      <td>0.000365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>COUNTRY_RISK_RESIDENCY_High</td>\n",
              "      <td>0.000263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>COUNTRY_RISK_RESIDENCY_Low</td>\n",
              "      <td>0.000222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>OCPTN_NM_Moderate</td>\n",
              "      <td>0.000190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CNTRY_OF_INCOME_CA</td>\n",
              "      <td>0.000180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>COUNTRY_RISK_INCOME_Low</td>\n",
              "      <td>0.000136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>COUNTRY_RISK_INCOME_High</td>\n",
              "      <td>0.000125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>COUNTRY_RISK_RESIDENCY_Moderate</td>\n",
              "      <td>0.000017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>COUNTRY_RISK_INCOME_Moderate</td>\n",
              "      <td>0.000015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02d61eef-1447-483c-8d8f-3a41ee8f057b')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-02d61eef-1447-483c-8d8f-3a41ee8f057b 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-02d61eef-1447-483c-8d8f-3a41ee8f057b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oRZjT014pZ21"
      },
      "execution_count": 73,
      "outputs": []
    }
  ]
}