9_group_by.ipynb

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   "source": [
    "# ICS 434: DATA SCIENCE FUNDAMENTALS\n",
    "\n",
    "## Data Wrangling: Groupby & Split-Apply-Combine\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Overview\n",
    "\n",
    "\n",
    "* In this section, we will tackle the handy `groupby` method.\n",
    "\n",
    "* We also cover the split-apply-combine scheme to:\n",
    "\n",
    "  * Aggregate data in each group\n",
    "  * Transform data in each group\n",
    "  * Filter the data in each group\n",
    "  * Thin the data in each group"
   ]
  },
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   "outputs": [],
   "source": [
    "# from IPython.core.display import HTML\n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "<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>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NX531425</th>\n",
       "      <td>1255626040</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>30</td>\n",
       "      <td>135.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QG879256</th>\n",
       "      <td>1699761833</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>ALLOPURINOL</td>\n",
       "      <td>30</td>\n",
       "      <td>715.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FW363228</th>\n",
       "      <td>1538148804</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>LOSARTAN POTASSIUM</td>\n",
       "      <td>146</td>\n",
       "      <td>1056.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WD733417</th>\n",
       "      <td>1730200619</td>\n",
       "      <td>PSYCHIATRY</td>\n",
       "      <td>OLANZAPINE</td>\n",
       "      <td>13</td>\n",
       "      <td>28226.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XW149832</th>\n",
       "      <td>1023116894</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>PRAVASTATIN SODIUM</td>\n",
       "      <td>348</td>\n",
       "      <td>8199.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QT485324</th>\n",
       "      <td>1952359671</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>57</td>\n",
       "      <td>247.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA293426</th>\n",
       "      <td>1841235223</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>SEVELAMER CARBONATE</td>\n",
       "      <td>11</td>\n",
       "      <td>4869.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IF945618</th>\n",
       "      <td>1326095662</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>FLUTICASONE/SALMETEROL</td>\n",
       "      <td>20</td>\n",
       "      <td>7832.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH384257</th>\n",
       "      <td>1821126830</td>\n",
       "      <td>HEMATOLOGY/ONCOLOGY</td>\n",
       "      <td>ZOLPIDEM TARTRATE</td>\n",
       "      <td>14</td>\n",
       "      <td>65.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JY407340</th>\n",
       "      <td>1710986088</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>MECLIZINE HCL</td>\n",
       "      <td>47</td>\n",
       "      <td>861.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "            doctor_id            specialty              medication   \n",
       "unique_id                                                            \n",
       "NX531425   1255626040      FAMILY PRACTICE           METFORMIN HCL  \\\n",
       "QG879256   1699761833      FAMILY PRACTICE             ALLOPURINOL   \n",
       "FW363228   1538148804    INTERNAL MEDICINE      LOSARTAN POTASSIUM   \n",
       "WD733417   1730200619           PSYCHIATRY              OLANZAPINE   \n",
       "XW149832   1023116894      FAMILY PRACTICE      PRAVASTATIN SODIUM   \n",
       "QT485324   1952359671      FAMILY PRACTICE     HYDROCHLOROTHIAZIDE   \n",
       "NA293426   1841235223      FAMILY PRACTICE     SEVELAMER CARBONATE   \n",
       "IF945618   1326095662    INTERNAL MEDICINE  FLUTICASONE/SALMETEROL   \n",
       "PH384257   1821126830  HEMATOLOGY/ONCOLOGY       ZOLPIDEM TARTRATE   \n",
       "JY407340   1710986088    INTERNAL MEDICINE           MECLIZINE HCL   \n",
       "\n",
       "           nb_beneficiaries  spending  \n",
       "unique_id                              \n",
       "NX531425                 30    135.24  \n",
       "QG879256                 30    715.76  \n",
       "FW363228                146   1056.47  \n",
       "WD733417                 13  28226.97  \n",
       "XW149832                348   8199.48  \n",
       "QT485324                 57    247.01  \n",
       "NA293426                 11   4869.32  \n",
       "IF945618                 20   7832.46  \n",
       "PH384257                 14     65.21  \n",
       "JY407340                 47    861.67  "
      ]
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   ],
   "source": [
    "spending_df = pd.read_csv('data/spending_10k.csv', index_col=\"unique_id\", dtype={\"doctor_id\": \"object\"})\n",
    "spending_df.head(10)"
   ]
  },
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    "### `groupby` and `DataFrame` groups\n",
    "\n",
    "* The `groupby()` method is used to group the data using values from one or more columns.\n",
    "\n",
    "   * `groupby` takes as input one or more column labels, which it uses to group the data.\n",
    "\n",
    "```python\n",
    "df_1.groupby(\"X\")\n",
    "```\n",
    "\n",
    "<img src=\"https://www.dropbox.com/scl/fi/4v91kv7az7fuax63gaols/groupby.png?rlkey=qrusieta8kt7gz0yz6ukxwv0b&dl=1\" alt=\"drawing\" style=\"width:500px\">"
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    "### Identifying Groups from a GroupBy Object\n",
    "\n",
    "\n",
    "```python\n",
    "spending_df.groupby('specialty')\n",
    "```\n",
    "\n",
    "<img src=\"https://www.dropbox.com/scl/fi/xk87csuim45lffue9tbtw/group_by_specialty.png?rlkey=osksfgxq81i5hmhji62f6wtbp&dl=1\" alt=\"drawing\" style=\"width:700px\">\n",
    "\n",
    "* The `groupby` method returns an object of type `DataFrameGroupBy`\n",
    "  * This is not a `DataFrame` and does not, therefore, have the `DataFrame` methods discussed previously \n",
    "\n",
    "* We can get `DataFrameGroupBy`  element using a valid `groupby` column(s) value(s)\n"
   ]
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       "pandas.core.groupby.generic.DataFrameGroupBy"
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    "x = spending_df.groupby('specialty')\n",
    "type(x)"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
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       "    <tr>\n",
       "      <th>VG585760</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>11</td>\n",
       "      <td>82.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJ278932</th>\n",
       "      <td>1134139991</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>49</td>\n",
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       "      <th>TX420809</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
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       "      <td>19.56</td>\n",
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       "            doctor_id           specialty     medication  nb_beneficiaries   \n",
       "unique_id                                                                    \n",
       "VG585760   1801032297  ADDICTION MEDICINE    LAMOTRIGINE                11  \\\n",
       "GJ278932   1134139991  ADDICTION MEDICINE  BUSPIRONE HCL                49   \n",
       "TX420809   1801032297  ADDICTION MEDICINE      LORAZEPAM                14   \n",
       "\n",
       "           spending  \n",
       "unique_id            \n",
       "VG585760      82.62  \n",
       "GJ278932     817.88  \n",
       "TX420809      19.56  "
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "spending_by_specialty = spending_df.groupby('specialty')\n",
    "\n",
    "addiction_med_group = spending_by_specialty.get_group(\"ADDICTION MEDICINE\")\n",
    "addiction_med_group"
   ]
  },
  {
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   "source": [
    "### `groupby` and Group-Specific Processing\n",
    "\n",
    "* An ideal use-case for `groupby` consists of applying operations to each group independently.\n",
    "\n",
    "* For instance, to compute the total spending by `specialty`, we need to:\n",
    "  * Split the data by `specialty`.\n",
    "  * Sum the total `spending` for each type of `specialty`.\n",
    "  * Combine and return the sums for each group into as a new `DataFrame`.\n",
    "\n"
   ]
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    "\n",
    "### Split-Apply-Combine Paradigm\n",
    "\n",
    "* `groupby()` is often applied in the context of the data processing paradigm called \"split-apply-combine\".\n",
    "\n",
    "  * **Split**: you need to split the data into chunks defined using one or more columns.\n",
    "    * This is typically done using `groupby`.\n",
    "  * **Apply**: apply some operation to the chunks generated.\n",
    "    * Ex. Count the number of rows in each chunk, average the values for a specific column, etc.\n",
    "  * **Combine**: combine the results of the applied operation into a new `DataFrame`."
   ]
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   "source": [
    "### Split-Apply-Combine Example\n",
    "\n",
    "<img src=\"https://www.dropbox.com/scl/fi/21kbfipwtvfaalmgko011/split_apply_combine_example.png?rlkey=c0v7cv400091aqajg8j4odlsw&dl=1\" alt=\"drawing\" style=\"width:700px\">\n",
    "\n",
    "* The type of split-apply-combine applied here is referred to as *aggregation*.\n",
    "  * Aggregations refer to any operation that aggregates (reduces) group data to a single value."
   ]
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    "### The 3 ( or 3$\\frac{1}{2}$) Classes of Opearations on Groups\n",
    "\n",
    "\n",
    "* There are three formal classes of split-apply-combine operations that can be applied to group data.\n",
    "\n",
    "  * I include a variant ($\\frac{1}{2}$ a class) which I think is useful to better classify split-apply-combine operations.\n",
    "\n",
    "\n",
    "1\\.$~~$__Aggregations__ generate a single value for each group.\n",
    "   * E.g., sum the spending by specialty\n",
    "  \n",
    "2\\.$~~$ __Transformations__ convert the data and generate a group of the same size as the original group.\n",
    "   * E.g., convert the currency by country for some datasets that contains medication cost by country.\n",
    "\n",
    "3\\.$~~$ __Filters__ retain or discard a group based on group-specific boolean computations.\n",
    "   * E.g., drop specialty if the sum of spending is below some threshold\n",
    "\n",
    "3$\\frac{1}{2}$\\.$~$\"__Thinning__\" drops entries in a group based on some defined logic.\n",
    "  * E.g., compute how far off are the group values from the group mean."
   ]
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   "source": [
    "### The 3 ( or 3$\\frac{1}{2}$) Classes of Opearations on Groups\n",
    "\n",
    "\n",
    "* There are three formal classes of split-apply-combine operations that can be applied to group data.\n",
    "\n",
    "  * I include a variant ($\\frac{1}{2}$ a class) which I think is useful to better classify split-apply-combine operations.\n",
    "\n",
    "\n",
    "1\\.$~~$__Aggregations__ generate a single value for each group.\n",
    "   * E.g., sum the spending by specialty\n",
    "  \n",
    "2\\.$~~$ __Transformations__ convert the data and generate a group of the same size as the original group.\n",
    "   * E.g., convert the currency by country for some datasets that contains medication cost by country.\n",
    "\n",
    "3\\.$~~$ __Filters__ retain or discard a group based on group-specific boolean computations.\n",
    "   * E.g., drop specialty if the sum of spending is below some threshold\n",
    "\n",
    "3$\\frac{1}{2}$\\.$~$\"__Thinning__\" drops entries in a group based on some defined logic."
   ]
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   "source": [
    "### Aggregations\n",
    "\n",
    "- __Aggregations__ aggregate the data in each group, i.e., they reduce the data in each group to a single value. \n",
    "\n",
    "  * This includes, for instance, computing group sums, means, maximums, minimums, etc.\n",
    "\n"
   ]
  },
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   "source": [
    "<img src=\"https://www.dropbox.com/scl/fi/zuwu7z54488mlf5lwbroc/aggregate.png?rlkey=ojqjoebw6oq9llby2q6dxjo0j&dl=1\" alt=\"drawing\" style=\"width:800px\">"
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    "\n",
    "### Transforming Group Data\n",
    "\n",
    "* __Transformations__ will trnsform the data in a group-specific way.\n",
    "\n",
    "   * E.g., transform the value by dividing it by the group mean\n",
    "\n",
    "*  The number of entries per group resulting from a transformation is the same as the number of entries in the group before the transformation.\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "- The diagram below shows an example where the data in column \"Y\" is transformed by dividing it by the group sum.\n",
    "\n",
    "<img src=\"https://www.dropbox.com/s/nf8lg0lqk3yxf7k/transform_2.png?dl=1\" alt=\"drawing\" style=\"width:800px\">\n"
   ]
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   "source": [
    "### Filtering Group Data\n",
    "\n",
    "* Consist of dropping or retaining a group in a way that depends on a group-specific computation that returns `True` or `False`. \n",
    "\n",
    "* For instance, we can filter specialties that don't have enough entries or for which the mean `spending` is below a certain threshold.\n",
    "  * Groups are either retained or discarded. Groups that are retained are unmodified.\n"
   ]
  },
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    "- The diagram below shows an example where groups are filtered if their sum for column `Y` is less than 10.\n",
    "\n",
    "<img src=\"https://www.dropbox.com/scl/fi/a1futlo41lczaxrxggola/filter.png?rlkey=jid6gthqx7smrgqi3nt30libl&dl=1\" alt=\"drawing\" style=\"width:800px\">"
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   "source": [
    "### Thinning Group Data\n",
    "\n",
    "* Consist of reducing the number of entries using a group-specific operation.\n",
    "\n",
    "* Thinning can be useful to, for instance:\n",
    "  * Sub-sample the data at the group level (create a blanced dataset)\n",
    "  * Returning the top `n` entries in each group\n",
    "    \n",
    "    etc. \n",
    "\n",
    "\n",
    "  * As opposed to aggregating functions, thinning does not have to reduce the group into a single entry; although it could."
   ]
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    "<img src=\"https://www.dropbox.com/scl/fi/trqabjyyzsqf60ugephdz/thin.png?rlkey=tcvpzid60j2ecfqlefxm9s8ef&dl=1\" alt=\"drawing\" style=\"width:900px\">"
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    "\n",
    "### Aggregating the Data Using `groupby`\n",
    "\n",
    "* Aggregation is commonly used to compute summary statistics on each of the groups.\n",
    "\n",
    "* Some of the interesting/important summary aggregation methods `DataFrameGroupBy` objects have:\n",
    "\n",
    "|Methods           |        Decription                              |\n",
    "|:-----------------|:-----------------------------------------------|\n",
    "| `mean`, `median` | Computes the mean and the median in each group | \n",
    "| `min` , `max`    | computes the min and max in each group         | \n",
    "| `size`           | computes the number of values in each group    | \n",
    "\n",
    "* The function applies to all valid columns that are available after using the `groupby` argument as an index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Aggregating the Data Using `groupby` -- Cont'd \n",
    "\n",
    "- The functions above all use the same syntax:\n",
    " \n",
    "```python\n",
    "spending_df.groupby('specialty').sum()\n",
    "# or\n",
    "spending_df.groupby('specialty').min()\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### Aggregating the Data Using `groupby`\n",
    "\n",
    "* Aggregation is commonly used to compute summary statistics on each of the groups.\n",
    "\n",
    "* Some of the interesting/important summary aggregation methods `DataFrameGroupBy` objects have:\n",
    "\n",
    "|Methods           |        Decription                              |\n",
    "|:-----------------|:-----------------------------------------------|\n",
    "| `mean`, `median` | Computes the mean and the median in each group | \n",
    "| `min` , `max`    | computes the min and max in each group         | \n",
    "| `size`           | computes the number of values in each group    | \n",
    "\n",
    "* The function applies to all valid columns that are available after using the `groupby` argument as an index\n",
    "\n",
    "* The functions above all use the same syntax:\n",
    " \n",
    "```python\n",
    "spending_df.groupby('specialty').sum()\n",
    "# or\n",
    "spending_df.groupby('specialty').min()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>F</td>\n",
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       "   A  B  C\n",
       "0  1  1  A\n",
       "1  1  2  B\n",
       "2  2  3  C\n",
       "3  2  4  D\n",
       "4  3  5  E\n",
       "5  3  6  F"
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       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>CD</td>\n",
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       "      <th>3</th>\n",
       "      <td>11</td>\n",
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       "    B   C\n",
       "A        \n",
       "1   3  AB\n",
       "2   7  CD\n",
       "3  11  EF"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame({\"A\":[1, 1, 2, 2, 3, 3], \"B\":[1, 2, 3, 4, 5, 6], \"C\":[\"A\", \"B\", \"C\", \"D\", \"E\", \"F\"]})\n",
    "display(df)\n",
    "\n",
    "df.groupby(\"A\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NX531425</th>\n",
       "      <td>1255626040</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>30</td>\n",
       "      <td>135.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QG879256</th>\n",
       "      <td>1699761833</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>ALLOPURINOL</td>\n",
       "      <td>30</td>\n",
       "      <td>715.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FW363228</th>\n",
       "      <td>1538148804</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>LOSARTAN POTASSIUM</td>\n",
       "      <td>146</td>\n",
       "      <td>1056.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WD733417</th>\n",
       "      <td>1730200619</td>\n",
       "      <td>PSYCHIATRY</td>\n",
       "      <td>OLANZAPINE</td>\n",
       "      <td>13</td>\n",
       "      <td>28226.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XW149832</th>\n",
       "      <td>1023116894</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>PRAVASTATIN SODIUM</td>\n",
       "      <td>348</td>\n",
       "      <td>8199.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id          specialty          medication   \n",
       "unique_id                                                      \n",
       "NX531425   1255626040    FAMILY PRACTICE       METFORMIN HCL  \\\n",
       "QG879256   1699761833    FAMILY PRACTICE         ALLOPURINOL   \n",
       "FW363228   1538148804  INTERNAL MEDICINE  LOSARTAN POTASSIUM   \n",
       "WD733417   1730200619         PSYCHIATRY          OLANZAPINE   \n",
       "XW149832   1023116894    FAMILY PRACTICE  PRAVASTATIN SODIUM   \n",
       "\n",
       "           nb_beneficiaries  spending  \n",
       "unique_id                              \n",
       "NX531425                 30    135.24  \n",
       "QG879256                 30    715.76  \n",
       "FW363228                146   1056.47  \n",
       "WD733417                 13  28226.97  \n",
       "XW149832                348   8199.48  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADDICTION MEDICINE</th>\n",
       "      <td>180103229711341399911801032297</td>\n",
       "      <td>LAMOTRIGINEBUSPIRONE HCLLORAZEPAM</td>\n",
       "      <td>74</td>\n",
       "      <td>920.06</td>\n",
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       "      <td>1104888403100389785118614997411720080062179085...</td>\n",
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       "      <td>1673</td>\n",
       "      <td>142804.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC ELECTROPHYSIOLOGY</th>\n",
       "      <td>1710150693103321883918210567711538130034166958...</td>\n",
       "      <td>RIVAROXABANLISINOPRILRIVAROXABANFLECAINIDE ACE...</td>\n",
       "      <td>1041</td>\n",
       "      <td>225543.62</td>\n",
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       "      <th>CARDIAC SURGERY</th>\n",
       "      <td>16896272671609804855</td>\n",
       "      <td>INSULIN GLARGINE,HUM.REC.ANLOGHYDROCODONE/ACET...</td>\n",
       "      <td>33</td>\n",
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       "      <th>CARDIOLOGY</th>\n",
       "      <td>1013915552175038231316191814271801882386156846...</td>\n",
       "      <td>ESOMEPRAZOLE MAGNESIUMPOTASSIUM CHLORIDEPRAVAS...</td>\n",
       "      <td>29638</td>\n",
       "      <td>1915787.90</td>\n",
       "    </tr>\n",
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       "      <th>CERTIFIED CLINICAL NURSE SPECIALIST</th>\n",
       "      <td>1528385762152838576216897420741437306941182115...</td>\n",
       "      <td>METOPROLOL SUCCINATEMETFORMIN HCLROPINIROLE HC...</td>\n",
       "      <td>1146</td>\n",
       "      <td>114057.40</td>\n",
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       "      <th>CERTIFIED NURSE MIDWIFE</th>\n",
       "      <td>16290689601942202890</td>\n",
       "      <td>MIRABEGRONMEDROXYPROGESTERONE ACETATE</td>\n",
       "      <td>58</td>\n",
       "      <td>14763.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLINIC/CENTER</th>\n",
       "      <td>17606802431730415829</td>\n",
       "      <td>ESOMEPRAZOLE MAGNESIUMMIRTAZAPINE</td>\n",
       "      <td>23</td>\n",
       "      <td>3110.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLINICAL PSYCHOLOGIST</th>\n",
       "      <td>1235211798</td>\n",
       "      <td>ZOLPIDEM TARTRATE</td>\n",
       "      <td>83</td>\n",
       "      <td>495.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                                             doctor_id   \n",
       "specialty                                                                                \n",
       "ADDICTION MEDICINE                                      180103229711341399911801032297  \\\n",
       "ALLERGY/IMMUNOLOGY                   1104888403100389785118614997411720080062179085...   \n",
       "ANESTHESIOLOGY                       1972509974140783819618918500121760454805118461...   \n",
       "CARDIAC ELECTROPHYSIOLOGY            1710150693103321883918210567711538130034166958...   \n",
       "CARDIAC SURGERY                                                   16896272671609804855   \n",
       "CARDIOLOGY                           1013915552175038231316191814271801882386156846...   \n",
       "CERTIFIED CLINICAL NURSE SPECIALIST  1528385762152838576216897420741437306941182115...   \n",
       "CERTIFIED NURSE MIDWIFE                                           16290689601942202890   \n",
       "CLINIC/CENTER                                                     17606802431730415829   \n",
       "CLINICAL PSYCHOLOGIST                                                       1235211798   \n",
       "\n",
       "                                                                            medication   \n",
       "specialty                                                                                \n",
       "ADDICTION MEDICINE                                   LAMOTRIGINEBUSPIRONE HCLLORAZEPAM  \\\n",
       "ALLERGY/IMMUNOLOGY                   DEXLANSOPRAZOLEMOMETASONE FUROATEALBUTEROL SUL...   \n",
       "ANESTHESIOLOGY                       HYDROCODONE/IBUPROFENATENOLOLHYDROMORPHONE HCL...   \n",
       "CARDIAC ELECTROPHYSIOLOGY            RIVAROXABANLISINOPRILRIVAROXABANFLECAINIDE ACE...   \n",
       "CARDIAC SURGERY                      INSULIN GLARGINE,HUM.REC.ANLOGHYDROCODONE/ACET...   \n",
       "CARDIOLOGY                           ESOMEPRAZOLE MAGNESIUMPOTASSIUM CHLORIDEPRAVAS...   \n",
       "CERTIFIED CLINICAL NURSE SPECIALIST  METOPROLOL SUCCINATEMETFORMIN HCLROPINIROLE HC...   \n",
       "CERTIFIED NURSE MIDWIFE                          MIRABEGRONMEDROXYPROGESTERONE ACETATE   \n",
       "CLINIC/CENTER                                        ESOMEPRAZOLE MAGNESIUMMIRTAZAPINE   \n",
       "CLINICAL PSYCHOLOGIST                                                ZOLPIDEM TARTRATE   \n",
       "\n",
       "                                     nb_beneficiaries    spending  \n",
       "specialty                                                          \n",
       "ADDICTION MEDICINE                                 74      920.06  \n",
       "ALLERGY/IMMUNOLOGY                               1063   189174.06  \n",
       "ANESTHESIOLOGY                                   1673   142804.73  \n",
       "CARDIAC ELECTROPHYSIOLOGY                        1041   225543.62  \n",
       "CARDIAC SURGERY                                    33    12432.92  \n",
       "CARDIOLOGY                                      29638  1915787.90  \n",
       "CERTIFIED CLINICAL NURSE SPECIALIST              1146   114057.40  \n",
       "CERTIFIED NURSE MIDWIFE                            58    14763.47  \n",
       "CLINIC/CENTER                                      23     3110.16  \n",
       "CLINICAL PSYCHOLOGIST                              83      495.95  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df.groupby('specialty').sum().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### Applying Functions to Group Columns\n",
    "\n",
    "* The method `agg()` can be used where complex or custom aggregation logic is required.\n",
    "* Note that `agg` applies to a `DataFrameGroupBy` object.\n",
    "  *  The method `agg` takes a function (or a list of functions) and uses it (them) to aggregate the group's column(s).\n",
    "\n",
    "* For example, we can use `sum_spending_CAD` to return the sum of the spending in Canadian dollars.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```python\n",
    "def sum_spending_CAD(x):\n",
    "    return x.sum() * 1.27\n",
    "\n",
    "spending_by_specialty['spending'].agg(sum_spending_CAD)\n",
    "```\n",
    "\n",
    "\n",
    "* `agg` can take a dictionary of functions to aggregate on.\n",
    "    * Required for aggregating more than one column \n",
    "\n",
    "    ```python \n",
    "    spending_by_specialty.agg({'nb_beneficiaries': sum,\n",
    "                               'spending': sum_spending_CAD)\n",
    "    ```\n",
    "\n",
    "* `agg` can take a list of functions to apply to each column to aggregate on.\n",
    "  \n",
    "    `spending_by_specialty.agg([min, max, sum])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specialty</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADDICTION MEDICINE</th>\n",
       "      <td>74</td>\n",
       "      <td>1168.4762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLERGY/IMMUNOLOGY</th>\n",
       "      <td>1063</td>\n",
       "      <td>240251.0562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANESTHESIOLOGY</th>\n",
       "      <td>1673</td>\n",
       "      <td>181362.0071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC ELECTROPHYSIOLOGY</th>\n",
       "      <td>1041</td>\n",
       "      <td>286440.3974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC SURGERY</th>\n",
       "      <td>33</td>\n",
       "      <td>15789.8084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nb_beneficiaries     spending\n",
       "specialty                                               \n",
       "ADDICTION MEDICINE                       74    1168.4762\n",
       "ALLERGY/IMMUNOLOGY                     1063  240251.0562\n",
       "ANESTHESIOLOGY                         1673  181362.0071\n",
       "CARDIAC ELECTROPHYSIOLOGY              1041  286440.3974\n",
       "CARDIAC SURGERY                          33   15789.8084"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum_spending_CAD(x):\n",
    "    return x.sum() * 1.27\n",
    "\n",
    "# format\n",
    "(\n",
    "    spending_by_specialty.agg({'nb_beneficiaries': sum, 'spending': sum_spending_CAD})\n",
    "                         .head(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">spending</th>\n",
       "      <th colspan=\"3\" halign=\"left\">nb_beneficiaries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specialty</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADDICTION MEDICINE</th>\n",
       "      <td>920.06</td>\n",
       "      <td>19.56</td>\n",
       "      <td>817.88</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLERGY/IMMUNOLOGY</th>\n",
       "      <td>189174.06</td>\n",
       "      <td>109.80</td>\n",
       "      <td>52389.61</td>\n",
       "      <td>1063</td>\n",
       "      <td>11</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANESTHESIOLOGY</th>\n",
       "      <td>142804.73</td>\n",
       "      <td>35.33</td>\n",
       "      <td>34073.91</td>\n",
       "      <td>1673</td>\n",
       "      <td>12</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC ELECTROPHYSIOLOGY</th>\n",
       "      <td>225543.62</td>\n",
       "      <td>69.85</td>\n",
       "      <td>89101.54</td>\n",
       "      <td>1041</td>\n",
       "      <td>12</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC SURGERY</th>\n",
       "      <td>12432.92</td>\n",
       "      <td>442.91</td>\n",
       "      <td>11990.01</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            spending                   nb_beneficiaries       \n",
       "                                 sum     min       max              sum min   \n",
       "specialty                                                                     \n",
       "ADDICTION MEDICINE            920.06   19.56    817.88               74  11  \\\n",
       "ALLERGY/IMMUNOLOGY         189174.06  109.80  52389.61             1063  11   \n",
       "ANESTHESIOLOGY             142804.73   35.33  34073.91             1673  12   \n",
       "CARDIAC ELECTROPHYSIOLOGY  225543.62   69.85  89101.54             1041  12   \n",
       "CARDIAC SURGERY             12432.92  442.91  11990.01               33  15   \n",
       "\n",
       "                                \n",
       "                           max  \n",
       "specialty                       \n",
       "ADDICTION MEDICINE          49  \n",
       "ALLERGY/IMMUNOLOGY         297  \n",
       "ANESTHESIOLOGY             419  \n",
       "CARDIAC ELECTROPHYSIOLOGY  188  \n",
       "CARDIAC SURGERY             18  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note that sum, min and max here are functions\n",
    "spending_by_specialty[['spending', 'nb_beneficiaries']].agg([sum, min, max]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specialty</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADDICTION MEDICINE</th>\n",
       "      <td>11</td>\n",
       "      <td>817.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLERGY/IMMUNOLOGY</th>\n",
       "      <td>11</td>\n",
       "      <td>52389.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANESTHESIOLOGY</th>\n",
       "      <td>12</td>\n",
       "      <td>34073.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC ELECTROPHYSIOLOGY</th>\n",
       "      <td>12</td>\n",
       "      <td>89101.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC SURGERY</th>\n",
       "      <td>15</td>\n",
       "      <td>11990.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nb_beneficiaries  spending\n",
       "specialty                                            \n",
       "ADDICTION MEDICINE                       11    817.88\n",
       "ALLERGY/IMMUNOLOGY                       11  52389.61\n",
       "ANESTHESIOLOGY                           12  34073.91\n",
       "CARDIAC ELECTROPHYSIOLOGY                12  89101.54\n",
       "CARDIAC SURGERY                          15  11990.01"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_by_specialty.agg({'nb_beneficiaries': min,\n",
    "                           'spending': max}).head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"2\" halign=\"left\">nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>specialty</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADDICTION MEDICINE</th>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>817.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLERGY/IMMUNOLOGY</th>\n",
       "      <td>11</td>\n",
       "      <td>1063</td>\n",
       "      <td>52389.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANESTHESIOLOGY</th>\n",
       "      <td>12</td>\n",
       "      <td>1673</td>\n",
       "      <td>34073.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC ELECTROPHYSIOLOGY</th>\n",
       "      <td>12</td>\n",
       "      <td>1041</td>\n",
       "      <td>89101.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIAC SURGERY</th>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>11990.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          nb_beneficiaries        spending\n",
       "                                       min   sum       max\n",
       "specialty                                                 \n",
       "ADDICTION MEDICINE                      11    74    817.88\n",
       "ALLERGY/IMMUNOLOGY                      11  1063  52389.61\n",
       "ANESTHESIOLOGY                          12  1673  34073.91\n",
       "CARDIAC ELECTROPHYSIOLOGY               12  1041  89101.54\n",
       "CARDIAC SURGERY                         15    33  11990.01"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_by_specialty.agg({'nb_beneficiaries': [min, sum],\n",
    "                           'spending': max}).head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Transforming  the Data in `groupby`\n",
    "\n",
    "- As opposed to aggregations, which reduce the data into a single value, transformations modify the data but don't change the `shape` (dimension) of the groups\n",
    "\n",
    "- Transformations are useful for applying operations that are group specific"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Transforming the Data in `groupby` -- Cont'd\n",
    "\n",
    "\n",
    "- The example below computes the percent contribution of each entry to each specialty by applying a transformation that normalizes the entry's spending over the total spending in that specialty. \n",
    "\n",
    "<img src=\"https://www.dropbox.com/s/xwomvq1cs90jpg1/transform_spending.png?dl=1\" alt=\"drawing\" style=\"width:1200px\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unique_id\n",
       "VG585760     82.62\n",
       "GJ278932    817.88\n",
       "TX420809     19.56\n",
       "Name: spending, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_by_specialty[\"spending\"].get_group(\"ADDICTION MEDICINE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Applying a Transformation\n",
    "\n",
    "- Applying a transformation is done using the method called `transform`.\n",
    "\n",
    "\n",
    "- The method `transform` takes as input a function name, which it calls on each group of the `DataFrameGroupBy` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unique_id\n",
       "NX531425    0.002075\n",
       "QG879256    0.010985\n",
       "FW363228    0.010855\n",
       "WD733417    1.614442\n",
       "XW149832    0.125835\n",
       "QT485324    0.003791\n",
       "NA293426    0.074728\n",
       "IF945618    0.080474\n",
       "PH384257    0.005987\n",
       "JY407340    0.008853\n",
       "Name: spending, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_function(x):\n",
    "    return (x / x.sum() ) * 100\n",
    "    \n",
    "spending_by_specialty['spending'].transform(my_function).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NX531425</th>\n",
       "      <td>1255626040</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>30</td>\n",
       "      <td>135.24</td>\n",
       "      <td>0.002075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QG879256</th>\n",
       "      <td>1699761833</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>ALLOPURINOL</td>\n",
       "      <td>30</td>\n",
       "      <td>715.76</td>\n",
       "      <td>0.010985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FW363228</th>\n",
       "      <td>1538148804</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>LOSARTAN POTASSIUM</td>\n",
       "      <td>146</td>\n",
       "      <td>1056.47</td>\n",
       "      <td>0.010855</td>\n",
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       "    <tr>\n",
       "      <th>WD733417</th>\n",
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       "      <td>PSYCHIATRY</td>\n",
       "      <td>OLANZAPINE</td>\n",
       "      <td>13</td>\n",
       "      <td>28226.97</td>\n",
       "      <td>1.614442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XW149832</th>\n",
       "      <td>1023116894</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>PRAVASTATIN SODIUM</td>\n",
       "      <td>348</td>\n",
       "      <td>8199.48</td>\n",
       "      <td>0.125835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id          specialty          medication   \n",
       "unique_id                                                      \n",
       "NX531425   1255626040    FAMILY PRACTICE       METFORMIN HCL  \\\n",
       "QG879256   1699761833    FAMILY PRACTICE         ALLOPURINOL   \n",
       "FW363228   1538148804  INTERNAL MEDICINE  LOSARTAN POTASSIUM   \n",
       "WD733417   1730200619         PSYCHIATRY          OLANZAPINE   \n",
       "XW149832   1023116894    FAMILY PRACTICE  PRAVASTATIN SODIUM   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "NX531425                 30    135.24      0.002075  \n",
       "QG879256                 30    715.76      0.010985  \n",
       "FW363228                146   1056.47      0.010855  \n",
       "WD733417                 13  28226.97      1.614442  \n",
       "XW149832                348   8199.48      0.125835  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df[\"spending_pct\"] = spending_by_specialty['spending'].transform(my_function)\n",
    "\n",
    "spending_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>VG585760</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>11</td>\n",
       "      <td>82.62</td>\n",
       "      <td>8.979849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJ278932</th>\n",
       "      <td>1134139991</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>49</td>\n",
       "      <td>817.88</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX420809</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LORAZEPAM</td>\n",
       "      <td>14</td>\n",
       "      <td>19.56</td>\n",
       "      <td>2.125948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id           specialty     medication  nb_beneficiaries   \n",
       "unique_id                                                                    \n",
       "VG585760   1801032297  ADDICTION MEDICINE    LAMOTRIGINE                11  \\\n",
       "GJ278932   1134139991  ADDICTION MEDICINE  BUSPIRONE HCL                49   \n",
       "TX420809   1801032297  ADDICTION MEDICINE      LORAZEPAM                14   \n",
       "\n",
       "           spending  spending_pct  \n",
       "unique_id                          \n",
       "VG585760      82.62      8.979849  \n",
       "GJ278932     817.88     88.894203  \n",
       "TX420809      19.56      2.125948  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df[spending_df['specialty'] == \"ADDICTION MEDICINE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GJ278932</th>\n",
       "      <td>1134139991</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>49</td>\n",
       "      <td>817.88</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VG585760</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>11</td>\n",
       "      <td>82.62</td>\n",
       "      <td>8.979849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX420809</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LORAZEPAM</td>\n",
       "      <td>14</td>\n",
       "      <td>19.56</td>\n",
       "      <td>2.125948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XY715196</th>\n",
       "      <td>1376691626</td>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>FLUTICASONE/SALMETEROL</td>\n",
       "      <td>102</td>\n",
       "      <td>52389.61</td>\n",
       "      <td>27.693866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL492570</th>\n",
       "      <td>1962588053</td>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>OMALIZUMAB</td>\n",
       "      <td>12</td>\n",
       "      <td>29153.71</td>\n",
       "      <td>15.411051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UJ888112</th>\n",
       "      <td>1003897851</td>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>MOMETASONE FUROATE</td>\n",
       "      <td>55</td>\n",
       "      <td>20759.04</td>\n",
       "      <td>10.973513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO641321</th>\n",
       "      <td>1255301404</td>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>FLUTICASONE/SALMETEROL</td>\n",
       "      <td>35</td>\n",
       "      <td>14277.61</td>\n",
       "      <td>7.547340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JU235992</th>\n",
       "      <td>1003812595</td>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>MOMETASONE FUROATE</td>\n",
       "      <td>50</td>\n",
       "      <td>13559.50</td>\n",
       "      <td>7.167737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WE196352</th>\n",
       "      <td>1720080062</td>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>FLUTICASONE/SALMETEROL</td>\n",
       "      <td>37</td>\n",
       "      <td>12594.63</td>\n",
       "      <td>6.657694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EW891894</th>\n",
       "      <td>1104888403</td>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>DEXLANSOPRAZOLE</td>\n",
       "      <td>32</td>\n",
       "      <td>12411.92</td>\n",
       "      <td>6.561111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id           specialty              medication   \n",
       "unique_id                                                           \n",
       "GJ278932   1134139991  ADDICTION MEDICINE           BUSPIRONE HCL  \\\n",
       "VG585760   1801032297  ADDICTION MEDICINE             LAMOTRIGINE   \n",
       "TX420809   1801032297  ADDICTION MEDICINE               LORAZEPAM   \n",
       "XY715196   1376691626  ALLERGY/IMMUNOLOGY  FLUTICASONE/SALMETEROL   \n",
       "DL492570   1962588053  ALLERGY/IMMUNOLOGY              OMALIZUMAB   \n",
       "UJ888112   1003897851  ALLERGY/IMMUNOLOGY      MOMETASONE FUROATE   \n",
       "GO641321   1255301404  ALLERGY/IMMUNOLOGY  FLUTICASONE/SALMETEROL   \n",
       "JU235992   1003812595  ALLERGY/IMMUNOLOGY      MOMETASONE FUROATE   \n",
       "WE196352   1720080062  ALLERGY/IMMUNOLOGY  FLUTICASONE/SALMETEROL   \n",
       "EW891894   1104888403  ALLERGY/IMMUNOLOGY         DEXLANSOPRAZOLE   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "GJ278932                 49    817.88     88.894203  \n",
       "VG585760                 11     82.62      8.979849  \n",
       "TX420809                 14     19.56      2.125948  \n",
       "XY715196                102  52389.61     27.693866  \n",
       "DL492570                 12  29153.71     15.411051  \n",
       "UJ888112                 55  20759.04     10.973513  \n",
       "GO641321                 35  14277.61      7.547340  \n",
       "JU235992                 50  13559.50      7.167737  \n",
       "WE196352                 37  12594.63      6.657694  \n",
       "EW891894                 32  12411.92      6.561111  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df.sort_values(['specialty', 'spending_pct'], ascending=[True, False]).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### More Complex Transformations\n",
    "\n",
    "* As you may have noted above, drugs are still duplicated across `doctor_ids` within the same `specialty`.\n",
    "  *  Ex., FLUTICASONE/SALMETEROL is prescribed by at least three doctors whose specialty is ALLERGY/IMMUNOLOGY.\n",
    "  \n",
    "* To see the percent spending by drug column, we need to group on both the `specialty` and the `medication` and then sum the `spending_pct` computed previously.\n",
    "\n",
    "```python\n",
    "medication_spending_pct =  spending_df.groupby([\"specialty\", \"medication\"])[\"spending_pct\"].sum()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specialty           medication                \n",
       "ADDICTION MEDICINE  BUSPIRONE HCL                 88.894203\n",
       "                    LAMOTRIGINE                    8.979849\n",
       "                    LORAZEPAM                      2.125948\n",
       "ALLERGY/IMMUNOLOGY  ALBUTEROL SULFATE              3.553199\n",
       "                    AMOXICILLIN/POTASSIUM CLAV     0.196089\n",
       "Name: spending_pct, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct = spending_df.groupby([\"specialty\", \"medication\"])[\"spending_pct\"].sum()\n",
    "medication_spending_pct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('ADDICTION MEDICINE',                 'BUSPIRONE HCL'),\n",
       "            ('ADDICTION MEDICINE',                   'LAMOTRIGINE'),\n",
       "            ('ADDICTION MEDICINE',                     'LORAZEPAM'),\n",
       "            ('ALLERGY/IMMUNOLOGY',             'ALBUTEROL SULFATE'),\n",
       "            ('ALLERGY/IMMUNOLOGY',    'AMOXICILLIN/POTASSIUM CLAV'),\n",
       "            ('ALLERGY/IMMUNOLOGY',                'AZELASTINE HCL'),\n",
       "            ('ALLERGY/IMMUNOLOGY',                  'AZITHROMYCIN'),\n",
       "            ('ALLERGY/IMMUNOLOGY',               'DEXLANSOPRAZOLE'),\n",
       "            ('ALLERGY/IMMUNOLOGY',                 'DILTIAZEM HCL'),\n",
       "            ('ALLERGY/IMMUNOLOGY',            'DOXAZOSIN MESYLATE'),\n",
       "            ...\n",
       "            (           'UROLOGY',            'SILDENAFIL CITRATE'),\n",
       "            (           'UROLOGY', 'SULFAMETHOXAZOLE/TRIMETHOPRIM'),\n",
       "            (           'UROLOGY',                     'TADALAFIL'),\n",
       "            (           'UROLOGY',                'TAMSULOSIN HCL'),\n",
       "            (           'UROLOGY',                  'TESTOSTERONE'),\n",
       "            (           'UROLOGY',          'TOLTERODINE TARTRATE'),\n",
       "            (           'UROLOGY',    'TRAMADOL HCL/ACETAMINOPHEN'),\n",
       "            (           'UROLOGY',                  'TRIMETHOPRIM'),\n",
       "            (  'VASCULAR SURGERY',    'ACETAMINOPHEN WITH CODEINE'),\n",
       "            (  'VASCULAR SURGERY',                      'APIXABAN')],\n",
       "           names=['specialty', 'medication'], length=2779)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pandas `MultiIndex`\n",
    "\n",
    "* When we group by more than one column, the result is a (Multindex). \n",
    "  *  I.e., the index that involves more than one column\n",
    "* Indexing into a `Series` or `DataFrame` that has a MultiIndex requires a tuple with at least one value\n",
    "\n",
    "<img src=\"https://www.dropbox.com/s/vnbuq8zmdf267ef/multi_indx_example.png?dl=1\" alt=\"drawing\" style=\"width:200px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "0  1  1  1\n",
       "1  1  2  2\n",
       "2  2  1  3\n",
       "3  2  2  4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1, 1, 1], [1, 2, 2], [2, 1, 3], [2, 2, 4]], columns=[\"A\", \"B\", \"C\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(1, 1),\n",
       "            (1, 2),\n",
       "            (2, 1),\n",
       "            (2, 2)],\n",
       "           names=['A', 'B'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index([\"A\", \"B\"])\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.89420255200748"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct[('ADDICTION MEDICINE', 'BUSPIRONE HCL')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medication\n",
       "BUSPIRONE HCL    88.894203\n",
       "LAMOTRIGINE       8.979849\n",
       "LORAZEPAM         2.125948\n",
       "Name: spending_pct, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct[('ADDICTION MEDICINE', )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medication\n",
       "ALBUTEROL SULFATE                  3.553199\n",
       "AMOXICILLIN/POTASSIUM CLAV         0.196089\n",
       "AZELASTINE HCL                     3.646451\n",
       "AZITHROMYCIN                       0.100500\n",
       "DEXLANSOPRAZOLE                    6.561111\n",
       "DILTIAZEM HCL                      0.293666\n",
       "DOXAZOSIN MESYLATE                 0.104872\n",
       "ENALAPRIL MALEATE                  0.318553\n",
       "FLUTICASONE PROPIONATE             2.842509\n",
       "FLUTICASONE/SALMETEROL            41.898900\n",
       "HYDROXYZINE HCL                    0.135473\n",
       "IRBESARTAN                         0.058042\n",
       "LEVOCETIRIZINE DIHYDROCHLORIDE     1.200698\n",
       "MOMETASONE FUROATE                18.141250\n",
       "MOMETASONE/FORMOTEROL              2.566123\n",
       "OMALIZUMAB                        15.411051\n",
       "PREGABALIN                         2.679754\n",
       "RANITIDINE HCL                     0.091477\n",
       "TRIAMCINOLONE ACETONIDE            0.200281\n",
       "Name: spending_pct, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct[('ALLERGY/IMMUNOLOGY', )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct[('ALLERGY/IMMUNOLOGY', )].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### More Complex Transformations -- Cont'd\n",
    "\n",
    "* The multi-index is sometimes inconvenient to work with, or we may have a better column to index on\n",
    "\n",
    "* We can reset (drop) the index using the method `reset_index`\n",
    "  * `reset_index` converts the MutliIndex into columns and uses a range index (0 to n-1)\n",
    "  * Allows us to sort on `specialty` and `spending_pct` as we did earlier\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specialty           medication                \n",
       "ADDICTION MEDICINE  BUSPIRONE HCL                 88.894203\n",
       "                    LAMOTRIGINE                    8.979849\n",
       "                    LORAZEPAM                      2.125948\n",
       "ALLERGY/IMMUNOLOGY  ALBUTEROL SULFATE              3.553199\n",
       "                    AMOXICILLIN/POTASSIUM CLAV     0.196089\n",
       "Name: spending_pct, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df.groupby([\"specialty\", \"medication\"])[\"spending_pct\"].sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>8.979849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LORAZEPAM</td>\n",
       "      <td>2.125948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>ALBUTEROL SULFATE</td>\n",
       "      <td>3.553199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>AMOXICILLIN/POTASSIUM CLAV</td>\n",
       "      <td>0.196089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            specialty                  medication  spending_pct\n",
       "0  ADDICTION MEDICINE               BUSPIRONE HCL     88.894203\n",
       "1  ADDICTION MEDICINE                 LAMOTRIGINE      8.979849\n",
       "2  ADDICTION MEDICINE                   LORAZEPAM      2.125948\n",
       "3  ALLERGY/IMMUNOLOGY           ALBUTEROL SULFATE      3.553199\n",
       "4  ALLERGY/IMMUNOLOGY  AMOXICILLIN/POTASSIUM CLAV      0.196089"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct = spending_df.groupby([\"specialty\", \"medication\"])[\"spending_pct\"].sum().reset_index()\n",
    "medication_spending_pct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2779, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>8.979849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LORAZEPAM</td>\n",
       "      <td>2.125948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>FLUTICASONE/SALMETEROL</td>\n",
       "      <td>41.898900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>MOMETASONE FUROATE</td>\n",
       "      <td>18.141250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>OMALIZUMAB</td>\n",
       "      <td>15.411051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>DEXLANSOPRAZOLE</td>\n",
       "      <td>6.561111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>AZELASTINE HCL</td>\n",
       "      <td>3.646451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>ALBUTEROL SULFATE</td>\n",
       "      <td>3.553199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ALLERGY/IMMUNOLOGY</td>\n",
       "      <td>FLUTICASONE PROPIONATE</td>\n",
       "      <td>2.842509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             specialty              medication  spending_pct\n",
       "0   ADDICTION MEDICINE           BUSPIRONE HCL     88.894203\n",
       "1   ADDICTION MEDICINE             LAMOTRIGINE      8.979849\n",
       "2   ADDICTION MEDICINE               LORAZEPAM      2.125948\n",
       "12  ALLERGY/IMMUNOLOGY  FLUTICASONE/SALMETEROL     41.898900\n",
       "16  ALLERGY/IMMUNOLOGY      MOMETASONE FUROATE     18.141250\n",
       "18  ALLERGY/IMMUNOLOGY              OMALIZUMAB     15.411051\n",
       "7   ALLERGY/IMMUNOLOGY         DEXLANSOPRAZOLE      6.561111\n",
       "5   ALLERGY/IMMUNOLOGY          AZELASTINE HCL      3.646451\n",
       "3   ALLERGY/IMMUNOLOGY       ALBUTEROL SULFATE      3.553199\n",
       "11  ALLERGY/IMMUNOLOGY  FLUTICASONE PROPIONATE      2.842509"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medication_spending_pct.sort_values([\"specialty\", \"spending_pct\"], ascending=[True, False]).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Filtering Groups\n",
    "\n",
    "- Filtering a group is done using the method called `filter`.\n",
    "\n",
    "\n",
    "- The method `filter` takes as input a function name, which it calls on each group of the `DataFrameGroupBy` object.\n",
    "  - The function must return either `True` or `False`.\n",
    "  - Groups for which the function returns `False` are dropped.\n",
    "\n",
    "\n",
    "- The resulting` DataFrame` has its entries in the same order as the original `DataFrame`.\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "0  1  1\n",
       "1  1  2\n",
       "2  2  3\n",
       "3  2  4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1, 1], [1, 2], [2, 3], [2, 4]], columns=[\"A\", \"B\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "2  2  3\n",
       "3  2  4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"A\").filter(lambda x: x[\"B\"].sum() > 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['FAMILY PRACTICE', 'INTERNAL MEDICINE', 'PSYCHIATRY',\n",
       "       'HEMATOLOGY/ONCOLOGY', 'OPHTHALMOLOGY', 'NEUROLOGY',\n",
       "       'NURSE PRACTITIONER', 'NEPHROLOGY', 'DENTIST', 'SPECIALIST',\n",
       "       'GENERAL PRACTICE', 'INTERVENTIONAL CARDIOLOGY',\n",
       "       'OBSTETRICS/GYNECOLOGY', 'PHYSICIAN ASSISTANT', 'CARDIOLOGY',\n",
       "       'ENDOCRINOLOGY', 'RHEUMATOLOGY', 'OPTOMETRY',\n",
       "       'STUDENT IN AN ORGANIZED HEALTH CARE EDUCATION/TRAINING PROGRAM',\n",
       "       'PULMONARY DISEASE'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(spending_df['specialty'].unique()))\n",
    "spending_df['specialty'].unique()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['FAMILY PRACTICE', 'INTERNAL MEDICINE', 'PSYCHIATRY',\n",
       "       'HEMATOLOGY/ONCOLOGY', 'OPHTHALMOLOGY', 'NEUROLOGY',\n",
       "       'NURSE PRACTITIONER', 'NEPHROLOGY', 'GENERAL PRACTICE',\n",
       "       'INTERVENTIONAL CARDIOLOGY', 'OBSTETRICS/GYNECOLOGY',\n",
       "       'PHYSICIAN ASSISTANT', 'CARDIOLOGY', 'ENDOCRINOLOGY',\n",
       "       'RHEUMATOLOGY', 'OPTOMETRY', 'PULMONARY DISEASE', 'DERMATOLOGY',\n",
       "       'INTERVENTIONAL PAIN MANAGEMENT', 'PSYCHIATRY & NEUROLOGY'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filter_on_spending(x):\n",
    "    return x['spending'].sum() > 50_000\n",
    "\n",
    "\n",
    "high_spending_df = spending_df[[\"specialty\", 'spending']].groupby('specialty').filter(filter_on_spending)\n",
    "\n",
    "print(len(high_spending_df['specialty'].unique()))\n",
    "high_spending_df['specialty'].unique()[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Thinning Groups\n",
    "\n",
    "* Thinning the data consists in reducing the number of entries in a group\n",
    "\n",
    "* As opposed to aggregating functions, thinning does not have to reduce the group into a single entry\n",
    "  * Although it could reduce it to a single entry\n",
    "\n",
    "* Thinning can be used, for instance, to return only the top 3 entries in each category, or to randomly sample a small subset of entries from each category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Thinning Methods and `apply`\n",
    "\n",
    "* Pandas offers a few methods for thinning the data\n",
    "  * Ex. `nlargest`, `nsmallest`\n",
    "        \n",
    "* However, thinning  is most often carried out using a method  called `apply` \n",
    "\n",
    "* The  method `apply` takes as input a function that it calls on each group of the `DataFrameGroupBy` object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specialty           unique_id\n",
       "ADDICTION MEDICINE  GJ278932       817.88\n",
       "                    VG585760        82.62\n",
       "                    TX420809        19.56\n",
       "ALLERGY/IMMUNOLOGY  XY715196     52389.61\n",
       "                    DL492570     29153.71\n",
       "                                   ...   \n",
       "UROLOGY             XR127617     97962.00\n",
       "                    IW761955     74482.73\n",
       "                    RY513742     28282.76\n",
       "VASCULAR SURGERY    KL360002      4309.59\n",
       "                    CU764903        57.59\n",
       "Name: spending, Length: 201, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_by_specialty['spending'].nlargest(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specialty           unique_id\n",
       "ADDICTION MEDICINE  TX420809       19.56\n",
       "                    VG585760       82.62\n",
       "                    GJ278932      817.88\n",
       "ALLERGY/IMMUNOLOGY  HQ120242      109.80\n",
       "                    HN843226      173.05\n",
       "                                  ...   \n",
       "UROLOGY             VI631849       36.07\n",
       "                    UK512406       79.76\n",
       "                    IE290947       93.30\n",
       "VASCULAR SURGERY    CU764903       57.59\n",
       "                    KL360002     4309.59\n",
       "Name: spending, Length: 201, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_by_specialty['spending'].nsmallest(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A\n",
       "0  1\n",
       "1  2\n",
       "2  3\n",
       "3  4"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data_frame = pd.DataFrame({\"A\": [1, 2, 3, 4]})\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "Name: A, dtype: int64\n",
      "----\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.639016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.655537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.964405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A\n",
       "0  1.000000\n",
       "1  2.639016\n",
       "2  4.655537\n",
       "3  6.964405"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# temp\n",
    "data_frame = pd.DataFrame({\"A\": [1, 2, 3, 4]})\n",
    "\n",
    "def some_function(x):    \n",
    "    print(type(x))\n",
    "    print(x)\n",
    "    print(\"----\")\n",
    "    # does somet\n",
    "    return x**1.4\n",
    "\n",
    "\n",
    "data_frame.apply(some_function, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specialty           unique_id\n",
       "ADDICTION MEDICINE  GJ278932       817.88\n",
       "                    VG585760        82.62\n",
       "                    TX420809        19.56\n",
       "ALLERGY/IMMUNOLOGY  XY715196     52389.61\n",
       "                    DL492570     29153.71\n",
       "                                   ...   \n",
       "UROLOGY             XR127617     97962.00\n",
       "                    IW761955     74482.73\n",
       "                    RY513742     28282.76\n",
       "VASCULAR SURGERY    KL360002      4309.59\n",
       "                    CU764903        57.59\n",
       "Name: spending, Length: 201, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# implementation of nlargest using apply\n",
    "spending_by_specialty['spending'].apply(lambda x: x.sort_values(ascending=False).head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Sub-sampling a `DataFrame` \n",
    "\n",
    "* Subsampling is the process of selecting a subset of the original data.\n",
    "\n",
    "* This can be achieved using the DataFrame mthod called `sample`. \n",
    "  * Two parameters are relevant in this scenario, `n` the number of samples to randomly select or `frac` a portion of the data to return.\n",
    "\n",
    "```python\n",
    " spending_df.sample(frac=0.001)\n",
    "```\n",
    "\n",
    "* Subsampling is an ideal way to handle datasets that are unbalanced across some factor(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YS172954</th>\n",
       "      <td>1770571838</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>GLIPIZIDE</td>\n",
       "      <td>440</td>\n",
       "      <td>3305.98</td>\n",
       "      <td>0.050736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS851454</th>\n",
       "      <td>1164528105</td>\n",
       "      <td>GENERAL PRACTICE</td>\n",
       "      <td>ALCOHOL ANTISEPTIC PADS</td>\n",
       "      <td>37</td>\n",
       "      <td>105.57</td>\n",
       "      <td>0.021940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GX264896</th>\n",
       "      <td>1336578848</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>ATORVASTATIN CALCIUM</td>\n",
       "      <td>106</td>\n",
       "      <td>1601.09</td>\n",
       "      <td>0.134224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QP977634</th>\n",
       "      <td>1063454247</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>FLUTICASONE/SALMETEROL</td>\n",
       "      <td>23</td>\n",
       "      <td>11769.98</td>\n",
       "      <td>0.120930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC396067</th>\n",
       "      <td>1538163951</td>\n",
       "      <td>DENTIST</td>\n",
       "      <td>CLINDAMYCIN HCL</td>\n",
       "      <td>17</td>\n",
       "      <td>127.98</td>\n",
       "      <td>0.943923</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK624878</th>\n",
       "      <td>1912228149</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>PREGABALIN</td>\n",
       "      <td>19</td>\n",
       "      <td>10195.27</td>\n",
       "      <td>0.104751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LE565596</th>\n",
       "      <td>1700833191</td>\n",
       "      <td>PSYCHIATRY</td>\n",
       "      <td>CLOZAPINE</td>\n",
       "      <td>77</td>\n",
       "      <td>5350.47</td>\n",
       "      <td>0.306020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XQ119106</th>\n",
       "      <td>1821031469</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>POTASSIUM CHLORIDE</td>\n",
       "      <td>17</td>\n",
       "      <td>561.80</td>\n",
       "      <td>0.008622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR193268</th>\n",
       "      <td>1750382446</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>PRAVASTATIN SODIUM</td>\n",
       "      <td>98</td>\n",
       "      <td>3391.67</td>\n",
       "      <td>0.101569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HM352832</th>\n",
       "      <td>1205860970</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>METOCLOPRAMIDE HCL</td>\n",
       "      <td>11</td>\n",
       "      <td>55.08</td>\n",
       "      <td>0.000845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id            specialty               medication   \n",
       "unique_id                                                             \n",
       "YS172954   1770571838      FAMILY PRACTICE                GLIPIZIDE  \\\n",
       "MS851454   1164528105     GENERAL PRACTICE  ALCOHOL ANTISEPTIC PADS   \n",
       "GX264896   1336578848  PHYSICIAN ASSISTANT     ATORVASTATIN CALCIUM   \n",
       "QP977634   1063454247    INTERNAL MEDICINE   FLUTICASONE/SALMETEROL   \n",
       "EC396067   1538163951              DENTIST          CLINDAMYCIN HCL   \n",
       "...               ...                  ...                      ...   \n",
       "SK624878   1912228149    INTERNAL MEDICINE               PREGABALIN   \n",
       "LE565596   1700833191           PSYCHIATRY                CLOZAPINE   \n",
       "XQ119106   1821031469      FAMILY PRACTICE       POTASSIUM CHLORIDE   \n",
       "HR193268   1750382446   NURSE PRACTITIONER       PRAVASTATIN SODIUM   \n",
       "HM352832   1205860970      FAMILY PRACTICE       METOCLOPRAMIDE HCL   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "YS172954                440   3305.98      0.050736  \n",
       "MS851454                 37    105.57      0.021940  \n",
       "GX264896                106   1601.09      0.134224  \n",
       "QP977634                 23  11769.98      0.120930  \n",
       "EC396067                 17    127.98      0.943923  \n",
       "...                     ...       ...           ...  \n",
       "SK624878                 19  10195.27      0.104751  \n",
       "LE565596                 77   5350.47      0.306020  \n",
       "XQ119106                 17    561.80      0.008622  \n",
       "HR193268                 98   3391.67      0.101569  \n",
       "HM352832                 11     55.08      0.000845  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return 1% of the data, i.e., 100 entries\n",
    "spending_df.sample(frac=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OY253416</th>\n",
       "      <td>1093011074</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>LISINOPRIL/HYDROCHLOROTHIAZIDE</td>\n",
       "      <td>12</td>\n",
       "      <td>114.26</td>\n",
       "      <td>0.009579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQ176933</th>\n",
       "      <td>1760454805</td>\n",
       "      <td>ANESTHESIOLOGY</td>\n",
       "      <td>SUMATRIPTAN SUCCINATE</td>\n",
       "      <td>25</td>\n",
       "      <td>581.18</td>\n",
       "      <td>0.406975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QC384488</th>\n",
       "      <td>1831296474</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>INSULIN ASPART</td>\n",
       "      <td>11</td>\n",
       "      <td>10732.12</td>\n",
       "      <td>0.110267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ233943</th>\n",
       "      <td>1326031006</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>LISINOPRIL</td>\n",
       "      <td>422</td>\n",
       "      <td>3373.08</td>\n",
       "      <td>0.051766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ238381</th>\n",
       "      <td>1326154485</td>\n",
       "      <td>DENTIST</td>\n",
       "      <td>PENICILLIN V POTASSIUM</td>\n",
       "      <td>19</td>\n",
       "      <td>161.63</td>\n",
       "      <td>1.192110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OV111616</th>\n",
       "      <td>1124060215</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>VALSARTAN</td>\n",
       "      <td>16</td>\n",
       "      <td>1359.76</td>\n",
       "      <td>0.070977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LI372052</th>\n",
       "      <td>1346228467</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>FINASTERIDE</td>\n",
       "      <td>28</td>\n",
       "      <td>503.65</td>\n",
       "      <td>0.005175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LI529666</th>\n",
       "      <td>1790731107</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>WARFARIN SODIUM</td>\n",
       "      <td>95</td>\n",
       "      <td>1033.71</td>\n",
       "      <td>0.015864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS682391</th>\n",
       "      <td>1710074273</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>DEXLANSOPRAZOLE</td>\n",
       "      <td>24</td>\n",
       "      <td>8459.95</td>\n",
       "      <td>0.129833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZC138035</th>\n",
       "      <td>1669456448</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>METHADONE HCL</td>\n",
       "      <td>22</td>\n",
       "      <td>1033.45</td>\n",
       "      <td>0.010618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id            specialty                      medication   \n",
       "unique_id                                                                    \n",
       "OY253416   1093011074  PHYSICIAN ASSISTANT  LISINOPRIL/HYDROCHLOROTHIAZIDE  \\\n",
       "EQ176933   1760454805       ANESTHESIOLOGY           SUMATRIPTAN SUCCINATE   \n",
       "QC384488   1831296474    INTERNAL MEDICINE                  INSULIN ASPART   \n",
       "NJ233943   1326031006      FAMILY PRACTICE                      LISINOPRIL   \n",
       "AJ238381   1326154485              DENTIST          PENICILLIN V POTASSIUM   \n",
       "OV111616   1124060215           CARDIOLOGY                       VALSARTAN   \n",
       "LI372052   1346228467    INTERNAL MEDICINE                     FINASTERIDE   \n",
       "LI529666   1790731107      FAMILY PRACTICE                 WARFARIN SODIUM   \n",
       "TS682391   1710074273      FAMILY PRACTICE                 DEXLANSOPRAZOLE   \n",
       "ZC138035   1669456448    INTERNAL MEDICINE                   METHADONE HCL   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "OY253416                 12    114.26      0.009579  \n",
       "EQ176933                 25    581.18      0.406975  \n",
       "QC384488                 11  10732.12      0.110267  \n",
       "NJ233943                422   3373.08      0.051766  \n",
       "AJ238381                 19    161.63      1.192110  \n",
       "OV111616                 16   1359.76      0.070977  \n",
       "LI372052                 28    503.65      0.005175  \n",
       "LI529666                 95   1033.71      0.015864  \n",
       "TS682391                 24   8459.95      0.129833  \n",
       "ZC138035                 22   1033.45      0.010618  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df.sample(n=10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specialty\n",
       "ADDICTION MEDICINE                                                 3\n",
       "ALLERGY/IMMUNOLOGY                                                24\n",
       "ANESTHESIOLOGY                                                    30\n",
       "CARDIAC ELECTROPHYSIOLOGY                                         20\n",
       "CARDIAC SURGERY                                                    2\n",
       "                                                                  ..\n",
       "STUDENT IN AN ORGANIZED HEALTH CARE EDUCATION/TRAINING PROGRAM    40\n",
       "THORACIC SURGERY                                                   2\n",
       "UNKNOWN PHYSICIAN SPECIALTY CODE                                   4\n",
       "UROLOGY                                                           59\n",
       "VASCULAR SURGERY                                                   2\n",
       "Length: 75, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df.groupby('specialty').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='specialty'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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az9uy0op6a/369V5vKyLy5JNPyqRJk4xRxd6wqu40W2+Z6Uiwooy6ceOGfPvttzJjxgz5z3/+Iw0aNJCJEydKw4YNPZomzGxZLWL+eEjJ23PLijrDLVMzOv4P6dixo165csXr7UuXLm3qYS9Hjx7V9u3bG+8LFCjgMNlrrly59Ndff3WbzoULF/S7777TVatW6a1bt1RV9caNGzpx4kTNmzevhoWFudw+PDzcqwdbuHPy5Ek9cOCAqaegZkTKCVpTv9xN3Lphwwa9efOmJfkwM+Fq/vz5dfPmzcb7999/X8uXL2+8//zzzx3ee+LOnTv6ww8/6AsvvKCBgYH60EMPaY8ePVxuY3Zy63z58mlkZKRHx29qV69edXhIz8CBAx2+v759++r169e9zltGfPXVVxoWFqa1a9f26ImaVrt165bu3btXr127lmbZ1atXde/evW7PL7MTY5vl7OnAyRYtWqRly5a9J3m5fPlymu9yz5492rhxY48mZDbzUAh3T0HctWuXNmrUyG06ZcuW1SJFiph6oJIVabhy9epVj9YzM/m82adLPwhSnxv79+93eJDC/v37XW4/bNgwj7/rzJKUlOTwIJ9PP/3U4TecPHnyPWsDpJZ64nV3Ro0apYGBgdq+fXuvn7pq9tx67bXXNDAwUN99912jPXcv8xATE+PRyx2z7evatWvr6dOnvd6+Y8eOevnyZZfrpPcAvtRu3rypQ4cO1cDAQO3du7f+/fffGXqa74EDB3Ty5Mk6depUjY+PV9W7bfbo6GjNkiWLlixZ0um2rib9v379un7wwQeaN29et/tg1ty5c01tHxMTc9/aH1YzU+9YdW6ZYVV7rF69epojRw6dPXu2VVnLMDNl5YNQb8XHx+snn3xivG/Tpo3DQ2eef/55o8zIbGbrLSsfiOSNnDlzaqFChfSdd97RI0eOePXkdTNltZXMnFv3os6wqVrQvf0A++mnnzxaz91cfJcuXZKvvvpKunbtKiIiL730kly/ft1Y7uvrK9OnTzduwU3txo0bcvjwYSlbtqyI3I2E37hxw2H7rl27Oo2i9+rVS4KCgmTkyJEicnc+g3feecfoIVuwYIEULFhQPvvsM6f7sHnzZmnUqJEkJCSIzWaTJ554QmbNmiVNmzaVO3fuSHR0tHTo0MFl1Pqvv/6SgIAAYzjx999/7zDE2tfXVxo1auR0+9mzZ0t8fLzD6LrXXntNZsyYISIixYsXl1WrVjkd9fPKK6/IlClTjJEse/fulVKlSmX4Fp3ff//do/UKFSqU5m/btm2TixcvSoMGDYy/zZkzR4YOHWrcpvzxxx97NALp9u3b8t5778nIkSOlcuXKaW61WrdundNtg4KC5LfffjO+q1q1akmVKlXk/fffFxGRY8eOScWKFTM872iyixcvGrdT79271+l6Pj4+EhcX5/UooeHDh8vgwYO9etDM1KlT5bvvvpPly5eLyN3zonTp0sZcH7/++qv0799fevXq5TSNDh06uP0cm81mHKPOZM2a1ete3z/++MOj9Vzd5hsTEyOTJ0+Wbdu2penBvn37tkRGRkp0dLTLHriffvrJoSy8fPmyw8gCHx8fl7f+7Nq1S/r27StLly5Nc8tBQkKCNG3aVCZOnCjly5d3msb06dNl9erV4u/vL2+++aZERkbKunXrpE+fPnL48GFp27atTJ061en2c+bMcbosJWcjXv78809p2bKlbN26VXx9faV79+4yfPhwef311+Wrr76S5557Tvr06SNRUVEu0z9y5IgULVrUeH/gwAGHKQN8fX2ldOnSTrdfs2aN8T106tRJHn74Yfn1119l4MCBsnz5cqlTp46sXLnSZR46depkuvfbijTS888//8iUKVNk3LhxbudzTfb555/L66+/Lvny5UtTVh4/ftzpdmbLqPT8888/smDBArl69arUqVPH4bfODP/5z3+kd+/esmPHDhG5W9Zdu3bNOD9tNpusWrVKateune72VpQxzmzcuFGuXr0qUVFRkiNHDqfrffnllzJ16lTZuHGjiNzdh+zZsxu/5V9//SUTJ06Ujh07Ok3jn3/+MUYNph7RmpiYKBs2bJB69eq5rX979+7t8H7KlCny8ssvp7ml3tmcivny5ZNp06aZuoXf7LlVpkwZmTNnjssRNn/++afLkf6dOnWSiRMnuizXv/nmG3n++ee9yqMnzLavr127JuvWrTNuoxw0aJDD/IG+vr7y/vvvuxxtc+vWLTl8+LD4+/s73Ma2dOlSeeedd+TXX3/1eE7C1atXS8OGDR3qTnUzbcx3330nLVq0kJs3b4qIyMMPPyzTp0+Xli1bSpkyZaRPnz7G/qXn5s2bMmzYMKPO6N+/vzRt2lRmzZolQ4YMEZvNJt27d5dBgwY5TcOKttCxY8dkxIgRMnPmTBG5W54kz58ncve32LRpk9Mpm8xes1lRzlkxFZFI5tQ7nqpZs6bbdWw2m/z4448u1zHbHhMRqVOnjtu7tZzdHWJVvWWmrLSi3rpz547cuXPHod1y7tw5+eyzz+Tq1avy7LPPytNPP+10+w8++EBiY2Nl3rx5Rh7q1atn1IFbtmyRVq1aybBhw5x/CU54Wn8nM1tvbdy4UZ566qn78qAUEXG41kxvVGVml9UpJSQkyJo1a+TkyZNis9mkSJEiUrt2bbe3bCerU6eOxMTEeHU3nxV1hlumQpT/BdIbqZZyxJqPj4/6+vq6TWfcuHH60ksvGe9DQkK0RYsW2q5dO23Xrp0WL15chw4d6nT7L774QqtWreqwff78+bVw4cJauHBhDQkJ0c8//9zp9qVLl9Z169Y5bJ+y133Dhg366KOPutyHmjVr6osvvqi//PKL9urVS202mxYpUkRnz56td+7ccbltsuXLl+tjjz3mkI/U36mrHt/KlSvrzJkzjfc//PCD+vn56bx583TXrl0aFRWlHTt2dLp96p7B0NDQDI0+sEL9+vV19OjRxvt9+/apn5+fdurUST/88EMNDw93eSwk279/v1aoUEELFy7s8Nt6KiIiQrdt26aqqrdv39Zs2bI5jOo7ePCgZsuWLcPpZlT16tVN9aCdOXNGBw8ebLx/6qmntEKFCsbriSeecDpK4emnn9bFixcb71OfF3PnztXKlSu7/PymTZs6fTVp0kSDgoI8GrlmZqRRyvIo5Ssj5dTTTz+tX331ldPlCxYs0GeeecZlGnv27NGGDRsa70NCQhzy4+vrq9u3b3e6fevWrfW9995zunzEiBEO5Whq48aNU39/f61YsaIGBwdrcHCwjhgxQsPCwnTYsGF64cIFl/lXVc2ePbvTV44cOTQgIMDl7/nSSy9puXLl9OOPP9bq1aurj4+PPv7449q+fXs9fvy4289P9tNPP+kTTzxhvE/+LlOWlWvWrEl325iYGLXZbBoWFqY2m01z586tc+fO1dDQUG3Xrp3+8ssvHuXBit5vM2kkJSXp4MGD9YknntCoqCj99ttvVVV15syZmi9fPo2IiNCRI0e63Y+4uDht3LixZs+e3dLRG56OoO/bt6/27NnTeJ+UlKSPPfaY+vv7q91u16xZszqMKk+tevXqWqNGDZevmjVrusxDq1atHEZshoSE6MaNG/XkyZN64sQJ7dWrlzZv3tzp9laUMWPHjtV33nnHeH/nzh2tV6+ecUznzZvXZTlXq1Yt/fLLLx32IWV5/emnn2r16tVd5mHixIkuv6tatWrpxx9/7DINVdVq1app9erVXb5q1KjhdPsLFy44jHZYsWKFLl261Hh99913bvNg9vxMSkpyuuzs2bPavXt3zZIli9t83Lx5U/fv36+HDx92+PuSJUu0XLlyGhAQkO5227ZtcxgVlLod+c8//+iCBQvcfr7Z9vVnn32mjRs3dtg+MjLS+B3Dw8N1/PjxTrc/cOCAFilSxDgPmjVrpnFxcVq1alW12+3ap08fj+9OWLRokebJk0dr1Kiha9eu1Q0bNji8nKlcubL27NlTL1++rB9++KHabDYtVqyY21HtyQYNGqTZsmXTFi1aaHh4uPr5+elrr72mxYoV05iYGL1x44bbNKxoC0VHR+ugQYOM9yEhITp27Fhj5F2DBg20S5cuTrd3d5eRu3LKinKuadOm+tZbbxnvjx8/rkFBQVq3bl3t2bOnhoSE6IQJE1ymoXq3fknv2urmzZtuR84mu3btmi5dulTHjRunH3zwgS5btizdu1VSi46Odvrq0KGDR7+lFe2xlLy5O8SK31PVXFlpRb3Vrl077dy5s/E+MTFRCxQooLlz59Zy5cqpn5+frlixwun2lSpVclieOg+LFy92uDZPj9n6O5nZemvr1q36/fffO/xt9uzZWrhwYc2dO7d27tzZ6WjmwoULa5EiRVy+Hn74YZf5T10mO3s5Y7asTjZ37ly12+1pyrns2bPr/PnzPUrDzN18VtQZ7vzrg4yXLl1K93XmzBkdMGCABgUFaenSpd2mY/YEN1tIhYSE6IkTJ4z30dHRDkNdT5486bYxGRYWZhQgV69eVR8fH124cKHLbVJr3LixQzA09X6MGTNGGzRo4HT7nDlzOgw7f/311x0ujNavX6+FCxd2un3qYdYZvcUpZT5TVnYbN250KNQSExO1a9eu6W4bHh6uO3bsMN4PHjxYn3rqKeP9woUL3Q6VtuJWq9atW2vjxo31jz/+0A8//FBDQkIcbjn65ptvtFy5ci7TePfdd92+XAWNnLl+/brGxMTolClT3N5y8fbbb2u3bt2M9yEhIdqzZ08dNmyYDhs2TCMjI7VPnz7pbpu6UsyVK5fDeXL48GGvA61LlizRUqVKafbs2XXUqFFu1zcTVIqNjU33tWfPHqOcyp07t8vPz507t8O+p3b8+HHNlSuXyzQ6duzoEPQJCQnRL774Qjds2KDr16/Xtm3b6ssvv+x0+4cfflj37t3rdPm+ffu0SJEiTpeXKFFCZ8yYoap3ywKbzaa1atWy5DaQM2fOaJcuXdTf31/r1avndL2IiAjdtGmTqt5tfNpsNo9+/9TMBIXKly9vfOaCBQvUZrPp448/rkePHs1QHsxevJtNw4pGjLtpCC5evOj2VpGYmJg0F4WdO3c2LlJKlizpMohQunRpXbp0qfF+5syZmiNHDuMisl27dg7B+dSsuOB75JFHdMuWLcb71HXf7t27NV++fE63t6KMqVChgkPjd+HChRoUFKSbNm3Sv//+Wxs1aqQvvPCC0+0feughjY2NdboPBw8e1Bw5crjMQ6VKlVxO2bJ8+XKtVKmSyzSsYLbDVdX8+Xnp0iVt06aN5sqVS/Ply6eTJk3S27dv69tvv61BQUH6xBNPOLQ702MmwOau0zcuLs6jDjqz7WuznY1NmjTRmjVr6vLly7VVq1Zqs9m0aNGi+u6773rcPouPj9fWrVtr1qxZdeLEiR5tk5LdbjeCvDdv3lRfX980F+KuPPLII8Z3EBsbqzabTVu1amXJ1D4ZaQuZHRBh9prNinLOqqmIVqxYoXPmzHH42/DhwzUwMFB9fX21Tp06evHiRafbL126VHPnzp0mCJE7d26vpq26efOmTpw4UXPnzq2PPvqoy05pVevaY6dPn9YqVaqoj4+P+vv7a69evfTq1avatm1b9fPz0xYtWjjtpLPi91Q1V1ZaUW8VLVpUV61aZbyfPHmy5suXTy9duqSqqv3793cZAwgLC3PoBKpYsaKeOnXKeH/s2DHNmjWryzyYrb+Tma23zAzUmThxotNXdHS0x4NC3Dl//rzTZWbLatW7Ux75+fnpq6++qrGxsfrPP//o9evXddeuXdq2bVv19/d3OOacMdPBlpl1RrJ/fZAxtdu3b+v06dM1f/78WrBgQZ05c6ZHcymYPcHNFlLZsmUzRq2lZ9u2bRoaGupyH9IL0B05csTlNqkVKlTIIcCWej/27dvnssAPCgrSkydPGu/LlSvn0CD7/fffXQZLrQoymmkcBwYGOjS6n3rqKX3//feN9ydOnNCQkBCXn2/F3JbHjh3TRx55RH18fNTPz8+hZ0lV9bnnntPo6GiXaTz22GNOXxUqVNDg4GC3BbbZUT7ly5fX1atXG+9T/6YrV67UUqVKpbttlixZXM7leOjQIQ0MDHSZ/9Q2bdqkTz31lAYHB2v//v1dNgJTMjvSKLU1a9ZoxYoVNTQ0VIcOHeq25zs4ONhlgG/v3r0aHBzsMo3ixYvrTz/95LAPKX+LrVu3asGCBZ1uHxgY6HK03/Hjx12e30FBQfr7778b7wMCAnTr1q0u8+xOYmKiDhkyxKh83Y0a9vHx0bNnzxrvg4OD9eDBgxn+XDNBoZCQEON7vH37tvr5+Xk1/40Vvd9m0rCiEeNubsvY2Fi3ZZTZEfShoaEOdWWrVq0cRiXs2bPHZYAvPRm94MuSJYtD3blo0SKHORZPnjzpdMSZMxktY7Jnz+5wLrRr186h02HLli2aP39+p9sHBgY6BMrPnz/v0P46cuSI233Inj27QxmR2u+//67Zs2d3mYaqapEiRVzOSeSO2Q5XVfPnZ9euXTV//vzap08fLV26tPr4+GiDBg20Ro0aHpcXZgJs7tpjcXFxarPZ3ObBbPvabGdj3rx5jTnT4+Pj1Waz6bRp09zmOyV3c0u7K6fS+y4z0qkUEBDg8J0FBgbqnj17PN4+Pd60hawYEJGSt9dsKWW0nMuSJYtDG79mzZoOIxuPHj2qdrvd7efWqFFDJ0+ebLz/+eef1cfHR4cPH66LFi3SEiVKOJ17/eeff1Z/f38jABcfH6/x8fH6888/a/PmzTUgIMBluzq1efPm6cMPP6z58uXTKVOmeFQHW9Ues+rukGQZ/T1VzZWVVtRbwcHBDvvarFkz7d69u/H+wIEDbq+dXd3Bsm/fPg0KCnKZB7P1dzKz9ZYVA3VS+vvvvzU6OloDAwO1atWqDu3ujLhz546uWLFCmzVr5vL3NFtWq9797p9//nmny1u0aOHwHA5nzHSwZUadkdr/VJBx0aJFWrx4cc2ZM6eOGzcuQ5MLmz3BzRZSUVFROmLECKfL33vvPY2KinK6XPXuxfPRo0c1ISFBL126pKGhobp3794MTXaaOoiwY8cOh9Eox48fd7kfJUqU0EWLFqnq3duNfH19defOncbybdu2uZxo1Gaz6fr1641bULNmzaorVqxwuC3VVaAlZTruGsfOGoQFCxY0hkUnJSVpUFCQrl271li+b98+t71aVtxqpXr3oT2xsbH6559/plkWGxvr9UXUnj17tF69eurv7+/y1hZV86N87Ha7w3efPJIi2YkTJ5yeW48++qh+8803TtNesGCBPvLIIy7zn2z//v3auHFj9fPz0w4dOjgUvp4wO9Io2c6dO7V27doaGBiob7zxhscTJJcvX14//fRTp8unTJnitvc9ODjYIZAxfvx4h+P0999/dxm0zZ8/v/7www9Ol3///fcuGzFWdSKo3j03P/zwQw0LC9PixYt7NHG/6t1yMmUvZsqAX0aYCQpZ9T1Y0fttJg0rGjHuRkJ7EmQ0O4Lebrc75KNw4cLGCA/Vu2VURi6cvbngy507t65fv97p8vXr17sdqZzM2zIma9asDsdh8eLFHTq33HUSFixY0OUtYcuWLXPZiaF691xI2WZIbefOnW47+VTNTz5vtsNV1fz5WbBgQWN0/LFjx9Rms+mbb76Zof0wE2Az045KyWz72mxno81mc2hzZM2aNc2t4+68//77Lh8okdzJ4ioPZtq26f0W3tRbqubaQlYMiEhm5ppN1ftyzqqpiHLnzq27d+823vfq1cvhLooVK1Y4HdXZoEEDfe2115ym/dprr7ntxFC926FWvnx5zZYtm7733nsZesCSVe0Qq+4O8fb3VDVXVlpRb+XMmVMPHDhgvM+XL5/OmzfPeH/s2DGXZVzp0qVd3rExc+ZMp4Mxkpmtv5OZrbesGKijencqgeHDh6vdbtfy5cu7/I1cOXbsmA4ZMkTz58+v2bNn15deeskhcJeaFXGIokWLOr2zTfVuIL1o0aJu826mg83KOsOZ/4kg44YNGzQyMlKDg4N10KBBxvDkjDB7gpstpKZNm6bBwcHpBp+WLVumwcHBbhuGqee2cPbelXz58rk8MVatWqXh4eFOl48cOVLDw8P1vffe0+rVq6e57WHChAlaq1Ytt/vgzVOhU6fjbeP4tdde06ioKP3pp5+0d+/eGhYW5jDfx7x58xxum02PFbdamR2JkZ7jx4/rSy+9pH5+ftqyZUu3F/iq5kf5ZM2a1aEhltru3budVlg9e/bUUqVKpTvnxLVr17RUqVIOoyzT88cff2i7du3Uz89PmzZt6tWoNVXzI42OHDmiLVu2VF9fX23dunWGG3NjxozRsLCwdCu32NhYDQsL0zFjxrhMI0eOHEZjMD2bNm1yGUBv166dPv300+kuu3Pnjj799NParl07p9vbbDYdMWKE8dS+LFmy6Ntvv52hJwnfuXNHY2JitGDBghoREaFTp07N0NMEbTabli1b1pgT1NfXV0uXLu0wT2iFChXcpmMmKGSz2XTOnDlGp0Ny+Z6yIyJlYN8ZK3q/zaRxLxoxngQZzY6gj4yM1A8//FBV716A+/j4OOzHhg0btFChQm7zauaCr3Hjxi57tl999VW3Txw3W8aUL19eZ82apap3vzObzeZw4fTzzz/rQw895HT79u3ba5UqVdJddufOHY2KinLbex8ZGelwq1Vqo0aN0sjISJdpqJoPMprtcFU1f376+fk5dDC6Sy89ZgJsVgUZzbavzXY2pu5YCg0NvefllNm2rc1m0y5duhhzcQUEBGiHDh0c5udyNmoumRVtISsGRJi9ZjNbzlkxFZHq3TZhypGAlSpVcmiDnTx50undJdmzZ3foGEtt7969Lkdsb9u2TatXr65ZsmTR6OjoDM+fqGpNe0zV/N0hZn9PVXNlpRX1Vo0aNXTgwIGqendqJR8fHz1z5oyxfPXq1S7LqLfeeksLFCjg8D0mO3PmjBYoUECHDBniMg9m6+9kZustswN1bt26pZ9++qmGh4dr4cKFdc6cOR4/VyLZ9evXde7cuVqtWjUNDAzUxo0bq6+vr0fHhBVxiKxZs7q9I8PdnWeq5jrYrKgz3Lk/j/a5hxo2bCg//vijtG/fXpYsWSLh4eFepdOsWTN56623pG7dumnSOHv2rAwdOtTp00pF7j75d8SIEdKwYcM0y1RVRo0aJbVq1XK6fefOnWXdunXSpEkTKVGihBQvXlxsNpv8+uuvcvjwYWnRooV07tzZ5T6sX7/ezV66V7VqVfnoo4+cPsHyo48+cvmk7gEDBsi1a9dk8eLFEh4eLl9//bXD8p9//llat27tdPsTJ054l3ELDR8+XJo3by7VqlWTkJAQmT17tgQEBBjLZ86cKXXr1nWZxtSpU6V79+4Ofzt69Kg8/PDDIiIyduxYmTlzpssnOp48edLp068y6q+//pJ3331Xpk2bJk8//bRs3rw53Se9pcfHx8fhKYpbt26Vt99+23ifPXt2iY+Pd7p98eLFZfPmzVKhQoV0l//nP/9xeNpjSoMHD5aFCxdK8eLFpXv37lKsWDHjvJg8ebLcunXL7VMAk8+lPn36SJUqVeTIkSNy5MiRNOs9++yzLtMJDQ2VEydOGE8kb968ucPyEydOOH1iWLdu3WTGjBlSo0YN2blzpzz22GMuPys9vXr1kh9++EEqVqwotWvXlhIlSojNZpNDhw7J2rVr5amnnnL5lG0RkQoVKsiSJUvkqaeeSnf54sWLnf5OIiJvvfWWVKxYUSIjI6VPnz7Gd3vo0CH58MMP5bfffpNZs2Y53b5gwYIyffp04314eLjMnTvXYR2bzSY9e/Z0mkb58uXl2LFj0qNHD4mOjpbg4GC5evVqmvWc/RZDhw51eP/cc885/SxXIiMjZc6cOVK9evV0l8fExEhkZKTT7V999VWH9126dHF4b3Px9LtkDz/8sOzevVvKlCmT7vKdO3dKkSJFMjWNd955R4KDg0VE5MaNGzJ8+HCPn+BrlUKFCsmuXbukUKFC8tdff8mBAwccnuQYFxeXJk8p9evXT1q3bi0rVqyQAwcOSMOGDR32+fvvv5cnn3zS6fbbt2+XAQMGyNatW+X111+XtWvXSq5cuTK0D71795batWtLWFiY9OvXz3hi6fnz52XMmDEyb948Wb16tdPtrShjunbtKt27d5f//Oc/snXrVomKipJSpUoZy9etW+eyfBgyZIg8/vjjEhkZKX379nUorz/44AM5fPiw26fDd+jQQXr37i2lS5dO8wTH5cuXy/Dhwz0+ng4ePOj2yeblypVL9+85c+aUY8eOGcfBE0884bD8yJEjkjNnTpdpmz237ty5I/7+/sZ7X1/fDD/x02azOTxt08fHxyFNd1J+h6oqv/76q/E04b/++sujNMy2rxs2bCjvvPOONGrUKM0TpK9fvy7vvvuuNGrUyOn2qmociyIiV65ckQoVKjh8LyIiFy9e9Gh/vGG2bVu1alU5fPiw8b5KlSpy/Phxh3WS988ZK9pC7du3l+joaClfvnya73z58uUyevRomThxotPtzV6zWVHOjRgxQurUqSOFCxcWHx8f+eijjxzOq7lz53r09OaIiAg5dOiQ8YTtvXv3yoQJE4zlf//9t1E3pvbPP/+4fMKs3W53+bTzypUrS1BQkHTt2lUKFy4sX375ZbrruWpLWdEeS+br62v838fHx+WT3lOy4vcUMVdWWlFvvf3229KwYUNZuHChnD17Vtq1ayf58uUzln/77bdO290iIv3795dFixZJsWLFpG3btg55mDdvnjz00EMyYMAAl3kwW38nM1tv1a9fXwYOHChjxoyRJUuWSHBwsDzzzDPG8n379skjjzyS7rYLFy6Ut956SxISEmTw4MHStWtXh+tvT3Tr1k3mz58vxYsXl5dfflkWLVokYWFh4u/vn6bMT48VcYhr1665PAcCAwPln3/+cZtO/vz5Zf/+/VK8ePF0l+/bt8/pU92tqDPcsWnK6MC/kI+Pj/j5+UnWrFldflnuGg+XL1+WyMhIOX36tNMTfPv27cbj5FM7duyYPP7441KiRAmnhdSuXbvk0UcfdZmP+fPny/z58+W3334TEZGiRYtK69atpVWrVm6+CWvs2bNHoqKipEmTJtK/f38j+HP48GEZM2aMrFixQjZv3iyPP/74PcmPt3x8fGT48OESEhIiIneDn/369TMu/C5fvizvvPOOy4v4hIQECQkJcag8Re4eSyEhIS4LvsKFC8s333xjXJiEhobK3r17jSDjL7/8IrVq1ZLz58+73Ie4uDjjYtMbV69elQ8++EDGjx8vjz76qIwaNcptgDS1ypUrS8uWLaV3795y4MABKVeunBw9etSoZDZu3CivvvqqnDx5Mt3tx40bJ6NHj5b169enuaDbu3ev1KxZUwYOHCj9+vVLd/sTJ05I165dZc2aNUaw02azSZ06deSTTz4xvlNnPKlUPAnoNGnSRHLnzi0zZ85Md3m7du3kr7/+ku+++y7dPGTJkkVKlCjh8jN2797tcvnNmzdlwoQJ8uWXX8qRI0eMi6c2bdpIdHS028p40aJF0qpVK5k4caJ07drV+G5u374tn3zyifTp00e+/PJLl8HvnTt3Srt27eTgwYNGmauqUqpUKZk1a5bHwWtvpfw90yvzVdXl7/nHH39I/vz5PTouXFm/fr3Url1bevfunW5QaNKkSbJ69WqPLla89fbbb8vs2bNl+/bt6V68R0ZGyiuvvCLDhw/PlDSqV6/uUSPFVQfYRx995HLbP//8Uz744AOX5+eoUaPko48+km7dusm6devkwoULsn//fmP5xIkT5bvvvpO1a9c6TWPt2rWyYsUKCQ8Plx49ejhcHL777rtSvXp1qVatWrrb+vj4SFBQkHTp0kUKFy7s9DPcXax98skn0qtXL7l165Zky5ZNbDabJCQkiJ+fn3z44YdpOq5S58GKMmbGjBny3XffSXh4uAwdOtThmOjWrZvUrl07TQdLStu3b5d27drJr7/+6lA+lChRQmbNmuUy8J7s5Zdfli+//NKhw/XQoUPy22+/ScuWLeWrr75ym4aPj4/YbDZJrwmc/HdX5USrVq3k2rVrsmzZsnSXN27cWLJmzSoLFixwmgez56ePj480aNBAAgMDReRuEKdmzZppLp4XL17sNA8+Pj5it9uN3+LSpUuSLVs2jwJsZr/DZGbb1+fOnZPHHntMAgICnHY27tmzR/LmzZvu9rNnz3aZv2SpO35SSkxMdLntvn37pFq1apZ1DGcGq9pCrVu3lgULFjgdELFw4UKXeTBzzWZlW+rgwYOSO3duiYiIcFi2d+9eKVCggNtOhAEDBsiyZctk8ODB8v3338vmzZvl+PHjxjXDtGnTZM6cObJp06Y025YvX16io6Olffv26aY9c+ZMmThxouzbty/d5YULF3Zb99pstjRBhczg4+MjZcqUET+/u+Oa9u3bJyVKlEjTHk3vN7Hq9zRbVlpRbx08eFDWrFkj4eHh8sILLzicb9OmTZPIyEgpX7680+3j4+Nl0KBBsnDhQrl06ZKI3B3E0bJlSxk5cqTb41HEfP0tYr7eunDhgjRv3lx+/vlnY6BOs2bNjOW1atWSypUry4gRI9Jsm9yWat26tcsgvKuORj8/PxkwYIAMHDjQoU7x9/eXvXv3OgReM4uPj4/Mnj3baef2pUuXpH379m7L2jfffFPWrl0ru3btSreD7YknnpDatWvLpEmTLMt7Rvzrg4xWNB6SmT3BrSikzFi4cKE0bdrUKNhPnjwpBQoUMCq8a9euyeTJk6V///4u01m6dKl06tQpTSWfI0cO+fzzz6Vp06aZkn8RcVqhpuZs9EEyTypgkcwbOZklSxY5dOiQEYjbuXOnlC9f3uhpO3HihJQoUcJlT6WPj4+sW7fO7XHn6rsIDw+Xy5cvS48ePaR169ZOvxNXaSxatEhat24tzzzzjBw4cEAqVaoky5cvN5YPGDBATpw44bRhefPmTaldu7Zs3rxZ6tSp49AoXbNmjURFRcmPP/7odnTFxYsX5ejRoyIi8uijj3pU4VrJTFDp3Xff9egzUo+yywwDBgyQcePGSWhoqDz88MNis9nk2LFjcuXKFendu7eMGzfOo3T27NkjR48eNQKd3vZAZ9TGjRs9Ws9ZQMjX11fOnj1rKnifzExQyApmL96tSsMMdyMtk7kqq+/cuSNDhw41Gtfjx4+XkiVLGstfeOEFqV+/vnTs2NHrfMbGxjo9xq284Dt16pR88803xgijokWLyvPPPy8FChRwud2DVMaI3C0fUu6DJyMoUlq4cGG6nSktW7b0aHsfHx/Zvn275M6d2+V6ySPT08u/2Q5Xs+dWu3btPGrHuBo9bqaN/Pvvv3u0rbPvMCWz7WsznY0//fSTVKlSxQiCeCM54OqMu4Dr2LFjpUePHhIUFGTkKTIy0giKXL58WQYMGCCffPKJ13m8l7wdEGH2mu1elHO//PKLzJgxw+WITJG711RdunQx6p1p06Y5jNiqUaOG1K9fP90RaBMmTJDhw4fL3Llz09wBt2LFCnn11VdlyJAhbu9OeRCY+U2s+j2tKCtFzNdbzty+fVuWL1/u0fWzqsqFCxdERCR37tymR5tllFVtQm8G6njScW2z2WTdunVOl3/55Zcya9Ys2bJlizRq1Ejatm0r9evXl6CgII+CjFaU1VZ16JjtYMt0pm62/h91584dPXfunJ47dy7D8wCo3p1jbsGCBbpgwQKXc9Gl5/Tp0zpp0iR94403tHv37vrRRx/p6dOnPdrWzBOVU7t69aouXrxYx4wZo2PGjNHFixd7NL9U9uzZNUeOHG5fzriaCyHlnAiZqVmzZh69XDE7t6WqNfNCpLeNN3NcrlmzRqOjo3X06NEO8xCqqg4bNszl3HSqd+flGDVqlJYvX16DgoI0KChIy5Urp6NGjfJ4su/4+HjdsWOH7ty5U+Pj4z3axmpTpkzRgIAA9fHxMY51Hx8fDQgI0I8//vi+5MkbW7Zs0Z49e2qDBg20QYMG2rNnT6+f2JYRDRo0cJh/afjw4Q6/5V9//ZWhp855w+xcban98ccfOn78eO3atat27dpVx48f7zDpdXq6du3q8MTEOXPmOLyPj4/3aNJ3VdWLFy9qly5dNEeOHMa5nSNHDu3SpYv+/fff9yyN9Ozbty/DD6p4kFy6dEmnTJmijz/+eKbXO/8Ndu3a5XZeyJQuXLhg+bzCnrLiPF+yZInmypXLYW5rHx8fDQsL02+//dajNDLr3PpvZbZ9/ffff+u2bdt027ZtHn9/qdvG3tiwYYNHL0/zkNH2ecmSJR32t3Pnzg7zTJ47d87t/LtwLiEhQT/77DOtVKmS2mw2tw/RM+v27dv6/PPPq81m0xIlShjXFcWLF1cfHx9t3rx5hp+yndJff/2lEyZMcLmOVe2x33//3VReHzRW1luHDh3Sfv36aZ48edTf39+jbfbu3atff/21fvPNNy7n7cyojNTf/4Z668SJE/rOO+9owYIFjXrckwdEWhlLscLx48e1Xr16DtfwPj4+Wq9ePZfzl96LOuNfP5IxM+zbt09+++03sdlsUqxYMSlbtmyG0/jrr7/EZrNJWFiYx9t88skn0rt3b7lx44bY7XZRVUlMTJSAgAAZP368dOvWzeX2qW+vTX2L7rlz5yQiIiJTb+cw20tpVc95zZo1ZfHixZI9e3aP0kvJ2e0LqbnqEbPiViuzIzFErB2JcL+cPHlS3njjDVm1apXDCIb69evL5MmTXd6eKCJOf4PU3M3JmMzbkUZm5ciRw6MezcycV6p3794erefsVobUowizZcsmsbGxGSqj3N22lszZrRZWTENglhXfQ2pqQe+3FWkkJibKV199JTNmzJCdO3dKuXLlJDY2NsPp3E/r1q2TmTNnyuLFi6VQoULSokULadGihWWjGlz5+uuv5auvvjLaIEWLFpU2bdq4nMJA5O6IalfH9K1bt2T37t0u55YUEVmzZo2sXr1a/P39pVOnTvLwww/Lr7/+KgMHDpTly5dLnTp1ZOXKlU63v3TpkgwZMkQWLFhgzNWbI0cOadWqlQwfPtxtnWz2/E7myXl+4cIFt/XrtWvXZNWqVQ7lfd26dTM8N6I355YVo663b98uFStWNEaT6P+NuEuWlJQkS5cuTXeE6B9//OHRZxQsWNDj/JhtX1+6dEmOHj0qNptNHnnkEY/aeA9CmW+2fZ56+/TqjHz58smdO3ec5sHKttCff/4pixYtcvgtmzdvLg899JDL7cwcj5lh48aNMmPGDFm0aJH8888/0q9fP+nUqZPbqa2ssmDBAqO8FxEpVqyYtGrVyqspslRVVq9eLTNmzJClS5dKtmzZjDInPVa1Q6y8O8RbZvNgtt5K6erVq7JgwQKZMWOGbN26VWrUqCGtWrWSpk2bupyrefv27dKxY0c5ePCgw7VO6dKlZcaMGR5NRWS2/k7Nm3rL3e3YyVxN82ElVZVVq1bJzJkzZdmyZZIrVy5p3ry506l6HoRYSnoyejefFXWGO//6B79YeeFt9gQ3U0itWLFCevbsKdHR0dKnTx9jwtizZ8/KuHHj5M0335TChQun+2AZq926dUsmTJiQ7oXOm2++6fK2Vne3pd+8eVPOnj3rdLlVwa4NGzbIjRs3vNrW3XB6TwwYMECioqLkhRdeSPdWq7Vr18rmzZvdplOwYEFTFbcV36dVt7Bfv35d1qxZ49AorV27tjEkPT2nTp2SypUri7+/v7z//vtSsmRJUVU5dOiQfPrppxIVFSU7duxwOvGtiHh0e4Inw9aTFShQIMO3sFSoUMGjcsrVvDPubt3JiB07djic38WKFZPWrVunebhBevnz5FYGZ1L3eXnTB5Y9e3ZTt62JiHz++efGfK3OeDLZuYh3QSErvofUfvnlF9OdY2bSSO9i7csvv3R7seZuQvVkrh4MYUU74PTp0xITEyMzZ86Uq1evSsuWLeXmzZuyaNEit7fXuJtXMpmrY+rOnTvSunVr+frrr6VYsWJSokQJUVU5cOCAvPjii/LCCy/IV1995XQ/8+XL53CRVbJkSVm1apURAPr7778lKirK5Xkxe/Zsad++veTMmVMuXrwon3/+udHB2aJFC9m7d6/TyeBF7n6/UVFR8ueff8pLL73kUF7HxMTIjz/+KJs3b5YcOXI4TcOK81vk7nQJ6d2Oparyww8/yOeffy4rVqxwOWWJiEhwcLDDfFLe8ubcsqJciIqKcjgu7Ha7w4XGpUuXpHXr1ukGdZxNA5AyMGSz2eTWrVtu82G2fW22s/Fe33KY2dI7Ntzto1VtIWcDIvr16+d2QISZ41HEmrbU2bNnZdasWUZZ37p1a9m4caNERUXJK6+84nGA0Yq8vPjii/Liiy969HnOnDx5UmbOnCkxMTFG2btixQqpUaOGy+2saoeYKaes+A7N5sGKektEZMuWLfL555/LwoULpWjRovLSSy/Jtm3b5KOPPnLbhjh48KDUqlVLSpYsKfPmzXPIw4QJE6RWrVqydetWl+mYrb/T40295eohe+6YHciQnuR6on79+nLx4kWZM2eOxMTEeJnDjPG20zg9Pj4+xrQd3swp702d4c6/Psho1YW32RPcbCE1duxYGThwYJqJVPPlyyfjx4+X4OBgGTNmTKYHGa9fvy516tSRLVu2SO3ataVq1arGEwWTJzlevXq1x08OS+3gwYPy+OOPO23EPPTQQ1KzZk2pUaOG1KhRw+P5uh40FSpUkAULFkinTp3S9NbkyJFD5s+f/0A8PGfx4sUybNgwl4HExx57zPTk78uWLZNOnTqleRplrly5ZMaMGdKkSZN0txs6dKgUL15cVq1a5XDMNWvWTHr16iX169eXoUOHyowZM5x+tplemvR4U2lYMY9pkSJFTM8rJXL3KXYffPCBhISEyMMPPyyqKhs2bJCJEydK3759ZcyYMU633bBhg6nPtoKrh4h46rPPPkszT0xKNg+eqGg2KGQVK3q/vU3Diou1N9980+kym80mV69elVu3brkMMpptBzRs2FA2bdokjRs3lo8//ljq168vvr6+8tlnn3m0fconijrj7piaOHGirF27VpYtW5bmqcrLli2T9u3by6RJkyQ6Ojrd7VOXz6dPn04T/HF3ITZhwgQZOXKkDBw4UBYuXCitWrWSCRMmyJ49e5w+DTKl9957TwICAuTYsWNp5gh67733pG7duvLee++5/L6sOL/TS+f48eMyc+ZMmT17tly5ckUaNWok8+fPd5mGmQ7XZFacn2Z4Ekhwdlzs2bPHaZrz58+Xjz76yG1njYj59rUVnY1vv/220yf9JnN14epuTkYRzwOu94sVbSGzAyLMHI8i1rWlXnjhBZkyZYrUqVPH64fAmcmL2RHbSUlJsnjxYvn8889l8+bN0qBBAxk/fry0bt1aBg4ceE8ebmGFzJzj31NW1FulSpWSa9euSZs2bWTbtm3G9z9w4ECP8jB06FCpU6eOLFq0yKGcqVChgrRu3VqaN28uw4YNc/lQJbP1d0pm6i0zA3Wc1TkpmWlT58yZU5555hmXDwG0gpXXB2Y72DKVqZut/4c8//zz2qxZs3TniLlz5442bdpUX3jhBafbv/nmm1qmTBmNi4tLs+zs2bNatmxZjY6Odrp9aGio/vrrr06X//rrrxoSEuJyH2w2m86ZM0eXLl2qS5cu1eDgYJ02bZrxfvbs2W7nEXj77be1YMGCunfv3jTLYmNjtWDBgjp06FCXabgSGxvrMg/vvfee1qpVS4ODg9XHx0cLFSqk7du31zlz5uipU6c8/hybzaZHjx7VhIQEl6/0nDlzRgcPHmy8f+qpp7RChQrG64knnvB4nkxv57ZUVa1evbolcw9OmzZNn3/+eW3durVu3bpVVVV//PFHfeyxxzQoKEhfe+01l9ufPHnSo5czP//8s/r7+2uLFi108+bNGh8fr/Hx8frzzz9r8+bNNSAgQDdv3pzutvny5dP//Oc/TtPeuHGj5suXz4Nvwbzbt29ry5Yt1WazafHixfW5557TZ599VosVK6Y+Pj764osvejXHlKesmFcqJiZGs2TJoh9//LHeuHHD+PuNGzd00qRJmiVLFp09e7bT7YsUKWJqrhofHx+HOUFCQkL0+PHjxvt7MdeJVXMyfvjhh5ozZ05dvnx5mmVLly7VnDlzOp0TKXUeQkJCvJrz5cCBAxoSEqKVKlXSL7/8Uvfs2aO7d+/WL774Qp944gkNDQ3VAwcOZFoagYGB+vLLL+vKlSsd5mTy8/Nz+7nunDlzRrt06aL+/v5ar149U2nduHFDf//9d6fLfX19tVevXvrbb785/N2K/fBU2bJldcaMGU6Xf/7551qmTBmny604plKej7dv31Y/Pz+X88ylVqhQIV25cqXT5T/88IMWKlTIZRqzZ8/2eJ5ed65fv65z587VatWqaWBgoDZu3Fh9fX31l19+cbvttWvX9KmnnlIfHx+tW7euvvnmm9qzZ0+tW7eu+vj46DPPPKPXr193mYbZ8zN1m87ZyxWryppka9as0YoVK2poaKgOHTrUYS5ZZ8y2r9u3b69Vq1ZN9/u+du2aVq1aVTt06OB0e5vNplWqVNHq1as7fdWoUcPlPixZssTpq3///hoUFKRZsmRxmYcRI0bopEmTjLr27bffNt4PHz7c5e/wINSdqqpVq1bVIUOGOF0+ZMgQrVq1qtPlVh+P3ihWrJgWLlxYBw8erIcOHTL+fi/L++R51Zy93M2XHhYWps8884xOnTpVL168aPw9I/tg1TGV+th29spMZspKK+otf39/bdu2ra5evdqhnPP098iVK5fu2LHD6fLt27drrly5XKZhtv5OZkW70pWDBw9qkSJFvN7eE6tXr9a+ffvqoEGDjPLl0KFD+txzzxnzGTpjtqxWNXd9kNIff/yhefPm1fz58+vIkSP122+/1cWLF+uIESM0f/78Gh4e7jQ2ci/qjH99kPHixYv60UcfpRswunTpktNlqZk9wc0WUlmzZnU5geexY8c0a9asTperOj7kw9uHphQtWlS/+eYbp8sXLlyoRYsWdZmGK+6CjMlu3LihGzdu1HfffVdr1qypQUFB6uPjo48++qjboJiquQr8rbfe0m7duhnvQ0JCtGfPnjps2DAdNmyYRkZGap8+fTzfaQtdv35dY2JidMqUKWkuiNMzbtw49ff314oVK2pwcLAGBwfriBEjNCwsTIcNG6YXLlxwm8a7776b5mEvGdGgQQOXv9lrr73m9CEXAQEBLoPLp06d0oCAAJefv3PnTq1evbrTMqJ69eoaGxvrMg1Vc5XG9evXdenSpZqYmJhmWUJCgi5dutTthbUVwbFKlSrp+PHjnS7/8MMPtVKlSpmWB5vNpg0bNjQmOffz89O6desa7xs2bOi2fPjzzz+1T58+Tn/Pvn37ptvZk8yKYK2quaCQzWbTLl26aK9evbRXr14aEBCgHTp0MN536dLFo3LS7MW72TQy42ItMTFRhwwZoiEhIRoZGanr1q3zKp2U3NU7mzdv1k6dOmm2bNn0ySef1I8//ljPnz9/Ty86s2TJ4jIQevLkSbdBDLMX7+7ScMeT8jowMNBlGladn127dtUcOXJo5cqVdfLkyUbniKe/qRUdrmbPTyvadFYFdXbu3Km1a9fWwMBAfeONNzL0G5ltX5vtbLT6YV/JDh06pE2bNlVfX1995ZVXXJ6/hQoV0sKFC7t9udqHsmXLGp3dvr6+Wrp0aeN92bJl3f6OVrSFzA6IMHs8WtGWUlXdtGmTtm/fXkNCQvTxxx/X8ePHq5+fnx48eNDttlbkxeyDhLJnz65Vq1bVadOmOfyeGamzrGiPJadToEABl8e1s6CSVb+nmbLSinrr9OnTOnz4cH3kkUc0IiJC+/Tpo7t371Z/f3+Pfo/AwECXDwz8448/3ObBbP2dzIp2pSvu2mMJCQnpPkjo9u3bHsVzYmJi1GazaVhYmNpsNs2dO7fOnTtXQ0NDtV27dm47Gc2W1armO42Tmelgs6LOcOdfH2R877339Pnnn3e6/IUXXtDhw4e7TcfsCW62kHryySfdXvw/+eSTTpdbxYqCzhVPg4ypXbx4UYcMGaLZsmXzuNJbvHixVxV4+fLldfXq1cb71AX1ypUrtVSpUm7zcPPmTR07dqxWqFBBs2bNqiEhIVqhQgUdN26cwygyZ/r27as9e/Y03iclJeljjz2m/v7+arfbNWvWrE5HACYrUaKEUdCtX79ebTab1qpVK0MjJM1e9GXPnt3lE9L27t2r2bNnT3dZ4cKFTfcwtm7dWt977z2ny0eMGKEvvfSSyzRUzVUaEydO1Jo1azrdtlatWm6fTm2z2Rx6pbwRHBzstjMjODjYZR7MHAvt2rXz6OVKnz59tHPnzk6Xd+nSRfv37+/1Pty6dcujJ8eaCQpVq1bN5aia5Jc7VvR+m03Dios11bvl24cffqhhYWFavHhxj54C6ClP652rV6/qjBkz9KmnnlJ/f3/18fHRiRMnpnsBlNKPP/6oJUuWdHrxXqpUKd24caPLNHLkyJFuQCvZvn37NEeOHE6X+/j4GKP3L126pKGhobp3715j1P5vv/3mUUDK1d0Q7kbORUREuAwG/fTTTxoREeE2D1YEhHx9fXXw4MFpfjtPL8Kt6HA1e25Z8V3YbDZdv3697t27V/fu3atZs2bVFStWGO9//PFHl8fFkSNHtGXLlurr66utW7f26qL1XrSvXXU2WhW4Tvbnn39qp06d1N/fXxs3buzRyFizkju53b1csaItZHZAhNnj0Yq2VEqXL1/WadOmaeXKldVms2n16tV12rRpHrW1zOTF7Ijt69ev67x587RGjRoaFBSkzZs318WLF3sc1FK1pj2maq6csur3NJMHK+qtlH788Ud96aWXNCgoSG02m/br108PHz7scpvixYu7rG++/vprLVasmMs0zNbfyaxoV7riqj22ePFiLVq0aLoDW65evarFihXTZcuWuUy/fPnyOmrUKFVVXbBggdpsNn388cf16NGjXuc5o8x2Gicz08FmRZ3hzr8+yFi+fHldu3at0+Vr167Vxx57zG06Zk9ws4VUTEyMBgUF6ZQpU/TmzZvG32/evKmTJ0/WoKAgnTVrluudsEDu3Ll1586dTpdv375dc+fO7XR5ckPB2WvBggUeXexdv35d165dq2+99ZY+/fTTGhgYqMWLF9fOnTvrvHnz3G5vpsKx2+0OjahmzZo5jIw6ceKE28e+W3GrVenSpR0qhJkzZ2qOHDn05MmTeufOHW3Xrp02bNjQZRpBQUEOBV1AQIBxy7SnzF7oZMmSxeXt1CdPnnT6fb755ptatmzZdBt8586d03Llyumbb77p8vMffvhhtxfvngzdN1NpVKpUyWXFuHz5cpcjCFXTjn5z9nIlNDTUYcRZar/++quGhoa6zEPKCwRnr8xUunRpl2Xtzz//7LITYNiwYek2YA4dOqT9+vXTPHnyqL+/v9t8mA0KWcGKTiGrOpa8vVi7c+eOxsTEaMGCBTUiIkKnTp2qt27dcvt5GeFN59avv/6q/fr10/DwcM2SJYs2adLE6bpNmjRx2Uk4adIkbdq0qcvPa9iwob7++utOl3fp0sVleZ969L6z966YHTnXoUMHrVq1qiYlJaVZ9s8//2i1atVc3taanAeznSmqql988YXWrl1bs2bNqi1bttTly5frzZs3PQ4yPgjnlhXBseTfzNlv6eo37dq1qwYEBGi9evV0z549XufBbPvabGejVYHrS5cuGbdGR0VF6U8//WQ6zXvJiraQ2QERZo5HVWvaUs4cPHhQ+/Tpo3ny5FE/Pz+365vJi5WB76NHj+qQIUM0f/78arPZtE2bNrp69WrL61FnzOyLVb+nmTxYUW+l59KlSzplyhStWLGiMarMmXfeeUcLFiyYbofFvn37tFChQvrOO++4/DwrRr6r3t/BRnXq1NHp06c73XbGjBlat25dl+lbddu4GVZdH1hxN19m+tcHGUNCQlxe+P/+++8uL5qTmT3BrSik+vTpozabTbNly2YMZ00euedqPsdkt27dchgx9umnnzrMhzF58uR0hyCn1LJlS23evLnT5c2bN3d7e4+ZBsQ777yjzzzzjGbJkkVLlSqlXbt21fnz5+vZs2dd5ju9fHhb4WTNmlV3797tdPnu3bvd3rpuxa1WoaGheuTIEeN9q1atHEZw7dmzx+18hFYMnzd70VeuXDmdOXOm0+UzZsxwWvlevHhRixYtqqGhodq1a1fjWO7SpYuGhoZq0aJF9e+//3b5+YGBgQ7zUKR2/Phxj3qUzFQa2bNnd1tOORvNmcyKeaWqV6+ub731ltPlQ4YM0WrVqrnMg5nz2wrBwcFuv0tXozFTunLlis6YMUOrVKmiPj4+WqtWLZ0+fbpH0wiYDQpZwYrebyvSSC0jF2tly5bV4OBgHTBggJ49ezZD8+d6ytsR9Kr/f2SrqyBjwYIFXY7cPHTokBYoUMDl5yTPXfvCCy/otm3bjBGJW7Zs0eeff179/f1106ZNTrc3e/udFU6dOqV58+bVggUL6pgxY4yRE6NGjdICBQponjx5XF68qKa9hc/Zy1MnTpww6uNcuXKpj4+PR6NkzXa4qpo/tzxpx7gL/pmZU9lms2lQUJDDnNTpvdwx274229kYExNjep7PMWPGaM6cObVUqVK6ZMmSDG9/7do1h6lWBg4c6NA52LdvX7cdz2ZZ0RYyOyDC7BzfVrSl3Ll586YuWrTI7Xpm8mI28J3eSMjbt2/r999/ry1atNCAgAANCwvzOv2MMHN3iFW/p5my0op6y509e/Zojx49nC6/fv26VqlSRX19fbV+/fpGuVCvXj319fXVqKioTC8fkmVGmzAlV+2xfPnyOVz3pnbkyJFMv+61oqy26vrAirv5MtO/Pshot9t1y5YtTpdv2bJF7Xa723TMnuBWFVJbtmzRnj17aoMGDbRBgwb65ptvuty/lL744guHCZdDQkI0f/78xvwBISEh+vnnn7tMI3nC18jISF2wYIExMumrr77SJ598UkNCQnT//v1OtzfbgLDZbFqoUCH99NNPTT1gonDhwl5v//jjj+vkyZOdLp80aZLbhrUVt1rZ7XaHeRcLFy7scLvuiRMn3DYIbTbXE9h6MiFz6nkdMnqhMX78eM2ZM6euWLEizbLvvvtOw8LCXPaMX7x4UV9//XXNkSOHEdDKkSOHdunSxaPfOH/+/PrDDz84Xf79999r/vz53aZjptIICQlxecG6c+dOjx7sZLbne/ny5err66v9+vVzGJ179uxZ7du3r/r5+aU752TKPOzYscPr89uKhyqFhYW5vPV048aNbhvXmzdv1g4dOhhTGHzwwQfq6+ubofn3zASF4uPj9ZNPPjHet2nTxiGA8vzzz3s0pYEVvd9WpOGMJxdrqQPVGZ0AX9W6EfTO3Llzx+W5FxgY6LZh7ElHxuLFi41AWMpXWFiYy/rkQXL8+HGtX7++Q2dE8kTrrr6jZDabTV988UXTt/CldufOHf3hhx/0hRde0MDAQH3ooYdcXvSZ7XBVNX9utWvXLt1b9ZNHyFSoUCFTO3Wsut3KbPvabGdj165dHR5QM2fOHIf38fHxTueFTmaz2TQ4OFifffZZr4Lfn332mTZu3Nh4n9zOTu4gDA8Pd9kOOnr0qLZv3954X6BAAc2RI4fxypUrl8u5ElWtawuZHRBhhhVtqQULFjgMCDlx4oTDqL+rV6/qmDFjMjUvZjvv3Y3cO3/+vH744Ycu07DqIZdm7g6x4vdUNV9Wmq233LXNb968qdu2bXO5TlJSko4ePVrLly+vQUFBGhQUZNz6a9XD0Dxhtt7Knj27Q9mU+hUaGur0t8iSJYvLu60OHjzo0XWvmdvGzZbVquY7jZOZ6WCzos5wx6b6f8+7/peqUaOGREZGyujRo9NdPmDAANm+fbusX7/ebVo3btyQCRMmyFdffSW//fabiIgUK1ZMWrVqJb169ZLAwECX2584cUK6desmq1evdnjMeJ06dWTy5Mny6KOPZnDv/r9Dhw5Jo0aN5Pjx407XqVOnjnTo0EFat24tIiKhoaGyd+9eefjhh0VE5LPPPpMFCxa4/S62bt0qHTt2lEOHDhmPV1dVKVGihHz++edSpUoVr/dDRCQ2NlYee+yxdJetXLlSNmzYIBs2bJA9e/ZIsWLFpHr16lKtWjWpVq2a5M6d29Rne2LcuHEyevRoWb9+vZQrV85h2d69e6VmzZoycOBA6devn9M0smTJIkeOHJECBQqku/zUqVNStGhR+eeff5ymUblyZWnZsqX07t1bDhw4IOXKlZOjR49KkSJFRERk48aN8uqrr8rJkyedplG4cGHjN3TGZrO5PK58fHykT58+EhIS4jKdoUOHpvv3O3fuyIsvviiLFi2S4sWLS8mSJUVE5ODBg3LkyBFp2rSpfP311+Lj4+MyfVWVCxcuiIhI7ty5xWazyblz52Tq1KnyzjvvON2uffv2cvToUfnPf/6TbppVq1aVRx99VGbNmuXy8zdv3izVq1eXpk2bSt++faVEiRKiqnLo0CH58MMPZenSpbJ+/Xp56qmn0mxbuXJladasmQwYMCDdtEePHi1LliyRrVu3Ov18X19fOXv2rOTJk8dlPt35+OOPpW/fvnLr1i2x2+0iIpKQkCC+vr4yduxYiY6Odrqtj4+PxMXFeZ2Ht99+Wy5evChTpkwRkbtlVIcOHSRnzpwiIvLDDz/I008/LR988IHTNBo1aiQREREyffr0dJd36tRJzpw5I99//326y0uVKiXXrl2TNm3ayMsvvyylSpUSERF/f3/Zu3ev8d4T3377rbz22mty8eJFh7/nyJFDpk6dKi1atEh3u3HjxsnevXtl3rx5InL3e6hXr56EhoaKiMiWLVukVatWMmzYMJef/88//0itWrVk27ZtUqdOHYdza+3atfLkk0/KunXrJEuWLJmSxpkzZ2T8+PHyzjvvSLZs2RyWJSQkyPDhw6Vv376SN29ep5+/ceNGl/uYrFq1ak6X+fj4iM1mk/SaO8l/t9lscvv27XS3Dw4Olt9//92oX+rXry+zZs2SfPnyiYjIuXPnJCIiwun2jzzyiHzwwQfSrFmzdJcvXrxY+vbt67KcTXbt2jVZtWqVHDlyRETutkHq1q0r/v7+cvbsWSlYsKDbNLw1Z86cdP9ut9ulePHiUqJECY/Tio+PN/bh0UcflZw5c0p8fLwsX75cXnnlFafbmS1jUlJVOXr0qNy8eVOKFSsmfn5+IiJy8eJFmTNnjsyaNUv27t2b7rYHDx6UyMhIKV26tPTu3dvY94MHD8qECRPk4MGDsnXrVildurTTz7fi/Exp3bp1MnPmTFm8eLEUKlRIWrRoIS1atJAKFSq43C4xMdE4P7///nu5deuWsczX11caNWrk0eebYbZ9HR8fL4MHD5YFCxbIpUuXREQke/bs0rJlSxkxYoSEhYU53TZ13ZktWzaJjY012sbuzm8RkXbt2rltS4mI03ZE1apVpVevXkYZkbp9Pm/ePJkyZYps2bIl3e2jo6MlODhYRo4caWz/zjvvGPu0YMECKViwoHz22WdO82ZVW0jk7nXCV1995VBOtWrVSipXruxyuz/++MNt2iLitJzLjLaUN8eD2bz4+PhIgwYN3B73ixcvTvfvVpSTVrTHUrt69aosWLBAZsyYIVu3bpUaNWpIq1atpGnTppIrV64061vxe6bH27LS23or9TFVsmRJWbVqlXEce3pMOXPq1CkZOnSozJw50+k6VtXfZuut2bNne/Q5r776apq/lSxZUoYMGSIvv/xyutvMnTtXRowYIb/++qvTdN1dS4qIy/ag2bI6mbfXBynFx8dLZGSkxMXFycsvv+zQDvnyyy8lPDxctm7dapyzKVlRZ7hlKkT5X+Cbb75RPz8//fjjjx16om7duqUfffSR+vv7WzKB/B9//OEQEXbl4sWLum3bNt22bZvRu3rx4kWdPXu215/vye1eDz30kMOT4VIPET548GCG5gjbs2ePLliwQBcsWGAMM79y5YrbCezT403Pe2Jioq5YsUL79++vlSpV0oCAAC1VqpS+8cYbbretUaOG25ezyYZv3LihVatWVT8/P23QoIFGR0drr169tEGDBurn56fPPPOM2we3WHGr1TfffKP+/v5as2ZNzZs3r0PPiqpq//79TT3hy1NWzWc0f/58fe6557RkyZJasmRJfe655/Srr74ylaYn58XRo0fVbrfrk08+qQsWLNDY2Fjdu3evzp8/XytVqqR2u92jnkpV70caTZ06VbNmzZruKMFly5Zp1qxZderUqS4/26rfQfXuyOvx48dr165dtWvXrjphwgSPRlqbzYMVD1Vat26d+vr6ap8+fRxGY8bFxWnv3r3V19dXf/zxR6fb+/v7a9u2bXX16tUOT8/z9knCV69e1cWLF+uYMWN0zJgx+u233+rVq1f1xo0bTm8DevLJJx1G9qb+HhYvXuzRXMKq1vR+e5uG2YfwWMWKEfTunnhqs9mcbt+9e3ctU6aM0ycAlilTxuWoOU+4K+vSGwWa3suV7Nmzp/vy8/NTHx8fbdy4sduH4JjZh+T9sKKcO3HihJYrV87Y70KFCumuXbsylMaWLVu0VKlSDqNsbTablixZUn/++WeP0jB7fp46dUrff/99LVKkiObJk0e7d++eobJq+fLlDmVJSEhImtHDnrSR9+7dq19//bV+8803ls+7m5H2dfKo4nPnzhnld1xcnL777rtOt/Hk/M7saT7y5s3rcBdQrly59MSJE8b7w4cPa7Zs2ZxuX7p0aV23bp3xPvU+bNiwQR999FGXebCyLeTMwYMHXc7rmHqUekbnjs2MtpS3x4OZvJgdsW3F3LVWPeRS1fu7Q6z4PZOZLSud8aTeMtuGsCIPVtbfmT2qMuVUCykNHjxYCxYs6NC2T3b27FktWLCgw+jbzGC2rE7J2fVBRnh7N58VdYY7//ogo+rdgzJ56P5jjz3mMHR/wIABlnyGmTmd7tX2gYGBDk9POn/+vMMcjEeOHDE9QWhG9yPlU7ZKlCihQ4YMcTnfYXpu3bqlmzdv1oEDB3r8dOno6Ginrw4dOmhQUJDLdJKSknTUqFEOBWy5cuU8LmCtuNVKVXXNmjUaHR2to0ePTlMwDRs2TNevX+9ye0+eeupu8nKrn8yY2tGjR93OJeiMp8fjjh07tHTp0mkuFkuXLq3bt2/P0Gd6E1RSVX3ppZeMC9SmTZtqs2bNtESJEurj46OtWrVy+7lWzCvljrvvs3r16hl6MnlqVjxUSfXu7QyBgYHq4+Nj3Jrh4+OjgYGBDrchp+f06dM6fPhwfeSRRzQiIkL79Omju3fvztBTGT3h6rsMCwtzeNpgxYoVHSZ3dveETk9l5OLdmzTMPoQnpdOnT+ukSZP0jTfe0O7du+tHH33k0a1annI1d53Zi864uDiNiIjQAgUK6JgxY3TJkiW6dOlSHT16tBYoUEAjIiLSbTRnhLtz02azaeHChXXo0KG6ZMkSpy9v3L59W7dv367lypXTPn36eLsLXl2seatly5ZarFgx/eKLL3TRokVauXJlrx8IYWWHa0ruzs8GDRpoaGiotm7dWr/77jujIz0jF85NmjRxmCYn9bE9ZswYl7cKb9u2TcuUKZMmKFS2bNkM153OZHb72OogY3x8vO7YsUN37tzpcX2YJUsWl7emHTp0yOXDFEJCQhwudKOjox0uMD19WqmVbaH0uPstfH19tVChQjp06FDduXOnxsbGpvtyxWxbysrjwdu8mC3nrJi71qr2WMmSJbVQoUI6aNAgh3LJ03LK7O+pak1Z6YxVQcb7FUOwqv5OZqZdeeDAAe3du7fmyZMn3eWJiYlaunRpY2qMiRMn6qRJk/T111/X0NBQLVWqlKmOTk+YLaszS0Y72KyqM1z5nwgyqt5tCPXs2VMbNmxozGXobv6DjPhvCDIWLFgw3Xnvki1btkwLFizodR48zYfZ3qTbt2/rtm3bdPTo0Vq/fn1j/oaCBQvqq6++qjExMV7l/ebNmzpx4kTNnTu3Pvroo6ZH0blidm5Lq1jx1FMrR9Clx8y5kdFtd+/erQsXLnS4WLSKJ3lZsGCBPvfcc1qqVCljNOeCBQs8Sn/nzp1avXp1pwHj6tWru22cuxMbG+uyt/XixYv60UcfOc2Ds2XJrHioUrLTp0/r+PHjtVu3bsZoTFdPYUtPyk4Qm82m/fr1cwj+meHqeAgKCkp3vptk+/bt86hxbyYPVqRh1UN4pkyZooGBgWqz2TR79uxqt9vVZrNpYGCgTpkyxeu8ezqC3ooLhP/H3pvH1Zi//+PP+5z2nURCCi2EMEjMmIpQtuwiVNbsCRlmxpp1LFnGMlosg7IMsu87WQuVfSe70iJU1++PHuf8OnXu5Zz7znjP9/N8PO5H3ee6X6/7dW/X69pe1/Xo0SPy8fEpZYzx8fFREfS0Bd+zvHjxIg0bNowsLCyoYcOGtGzZMnr//r3o8xbH4cOHyyzhuwInTpxgjXLQBJUrV1YpdPP06VOSyWSUm5srum+isv+2iIoMMqGhoSq5mYk0U5yrV69Oly5dUu6XfLevX7/OuqpCIcc0adKENm3aRNeuXaOrV6/S33//TY0bNyZTU1NJHDP/K0bGhw8fkq+vL8nlcqWBTi6XU/v27Xm/8Vq1anGudoiLi6OaNWuy0s3MzDh1msTEREFFLhUoK1mI71mkp6fT3LlzydnZmSpVqkRhYWGcRbPYIEaWktogpM1YxDrvpchdK5U8JsXqEDHPk0gaXsmG/3UjowJi529tx5KVlUV//fUXNWvWjORyObVo0YJTL83IyKCQkBAqX768UpYqX748hYSECHLqiC2CK5ZXE0kT4CMEXM9C6jlDHf6fMTKWNf4XjIxBQUHUvHlztbTCwkJyd3cXHdXCNw6x3iQfHx8yMzMjhmGoSpUq1LdvX1q7dq3GFZFLYuPGjVSjRg2qXLlyqap4bMjNzaVdu3bRggUL6I8//qDdu3drpKCIXWrFV8xAsXFBiqqnjx49UhEcpMa3NDKWJcp6LH369KEZM2aw0iMiIqhv376izsF3DTNnzqTu3buz0nv06EGzZs1ipUtRVKksoDBG/fDDD8pCR2LBdS9dXFw402dER0cLjgDUdgxS9CFFEZ49e/Yol7+/ePFC+fuLFy8oNDSUdHR0OJ1n6qBpBL1MJlNZemZqaqpShVUTBeH9+/d08eJFSkxMlNTIJ/RZfvr0iTZs2EBeXl5kZGREvXr1UlkSJwYPHz4UXLldHYRcg1TCOcMwpaJHjY2NJTH4En0bI+O5c+do0KBBZGZmRk2bNqVly5bR69evNVKcS1YUvnTpkkq6lwcPHrCucOnevTt16dJF7fxfWFhIfn5+kqRs+RZGxqFDhyoLzujp6VFwcLByf+jQobznf/LkCVWqVImqVq1Ks2fPpn/++Yd27NhBERERVLVqVbK2tuZ0co0ePZrq1KnDmlKhTp06NHr0aNb27u7uFBERwUqfMWMGubu7c17Dt4Amz/L06dMUHBxMpqam5ObmRmvWrOFU/qUCX2GIdevWSSLLcVVV5nPeX79+nbNiuhTOf6nksW+1OoQLUvBKNghN83Hv3j1lgQ9TU1NKTk6mzMxMyszMpDt37vzrRkax87emYzl9+jQNGDCATExMqF69eiSXywUVO1GgsLCQXr9+rRK5JwRii+CK5dVE0gT4CAHXs/gWc4aO9tkc/zdw/fp1QceVLODxvaFcuXKcSaWLJ+pmw5QpU9CoUSO4ublh/PjxcHR0BMMwuHXrFv744w/cvn2bNTGsVDh06BBGjx6NkJAQODg4aNze3NwcCxYsgKenp1btS+LAgQOYNGkSHj58iPHjx2PcuHEwNjbmbbd7924MGjQIb9++Vfm9QoUKiIqKQseOHXn7aNasGVJSUpCUlKSS6LxBgwbIycnBqVOn0LJlS9b2DRo0EFXMAChKNqyrq8tK19HRURZTYYPQJL5cxVe0xbhx4zjpfGMX0ocCixYtEnScNvj48aOg40oWzyiOCxcusCbHBoCOHTti7dq1Go9NE2zbtg0LFy5kpQ8dOhTjx4/HlClT1NJ79+6N33//HT/99JPaokrTp0/HpEmTOMewe/duQWPt1KmToOOAIr4zfPhwDB8+HElJSZzJtaVAly5d8Ouvv6JNmzawtrZWoaWnp2Pq1KmcSca/F7i5uWHDhg2sfGz9+vVo2rQpZx/z58/HpEmTMGvWLJXfK1eujEWLFsHIyAjz5s2Dr68vZz/Pnj1DbGwsoqOjkZOTg549e+Lr16/Yvn07bzEfIlLOlwCQnZ2Nhg0bKhOIq+PBbChXrhyaNGki+HgF+GSZ27dvC+rHwMAAAQEBCAgIwMOHDzFw4EC0a9cOb968UZscXBPcv38fVatWZaUvXbqUs/3z5895zxEZGYnBgwer5YXm5uYYOnQoFi1ahJ9++omzH4ZhSiWAl8lkGj3Lfxvu7u5wd3dHZGQktmzZgujoaIwbNw6FhYU4fPgwqlWrpiwWxYby5cvj/v37yoJxjRs3VqHfvXuX9b04ceIE9u/fr1Y2ZRgGkydP5v0uvwe0bNlS5ftp3rx5qSJMXLIYUFTYzsnJCQcPHlQpeNClSxeEhoaiXbt2mDp1KqKiotS2nzx5MuLj4+Hk5ISRI0eqyOfLly9Hfn4+Jk+ezHr+oKAgjB07Fq6urqUK9SQkJGDu3LlYsmQJ5zV8D7JQcfz444/48ccfMXv2bPj7+2PYsGHo1q0bJ5+SQpYCShedGDp0qMq+kCI/bLh16xaio6Oxbt06fPjwAV++fCl1zPHjx0td58ePH7F582ZERUXh8uXLnHqrmPEpIIU8BgBVqlTBlClTMGXKFGXBlRYtWiA/Px+xsbEYNGgQHB0d1baV6nmK4ZVSzFsKGaL4fvEiMwp9jQ1du3bl7F9R7EoM+OZvqTB//nxER0cjOzsb/v7+OHPmDFxdXaGrq4ty5coJ7odhGK2KvcbExGDYsGEqv508eVKlCO7GjRsxcOBAte3F8mqg6PuZN28eK71NmzYaFVTSBlLMGXz4zxsZuQwxCvAZYgDxH7hYJiX2QQNFFS4PHz6MwMBA9OrVq1Rl6EOHDvFWuOZT4B8+fMhJP336NKKjo9G4cWM4OzujX79+6NWrl+Br2Lx5s+BjuXDx4kWEh4fjwoULGDZsGI4cOaK2spk6nDt3Dt27d0enTp0QFhamUllr4cKF6N69O06cOAF3d3dB/TVo0KBUNe179+7B09OT873ku9dCUKVKFdy4cYP1uV+/fl1ZQZUN//zzDyuNYRjcvn0beXl5ZWJkvHbtGu8xfMqBkD6kENi4YGFhwXkOIQbj58+fcyqUJiYmSE9P5xwHn0CXlZXFSb9//z6n8d/BwQH3799npY8dOxZ79uzBDz/8AG9vbzg5OSkn78OHD8Pd3Z2zujUA+Pn5cdIBYTyfDQ0aNODl54A4o9DEiROxfft2ODo6ol+/fipCzMaNG1GlShVOg/L3gvHjx8Pb2xvm5uaYMGGCsor0q1evMH/+fMTGxuLQoUOcfVy7dg1r1qxhpffr1w+RkZGcffj6+uLMmTPo0KEDli1bhnbt2kEulwuumiekmioX+OQHBdgqhQLCnUpCoDC4xsbG4tOnT5gwYQKvksYFIsK1a9cQFhbG6WBbvHgxb1981bGTkpIwd+5cVrpQ4byk4RgobTwGUKry4/cIIyMjBAcHIzg4GLdv30ZUVBTmzp2LSZMmwdvbm1Nua9myJZYuXYrWrVurpS9dupR1Ds3KyuKsDG9tbc07ZwDi5WuxzsYTJ05w0oXgwIEDiI+PV1tR1dDQEDNnzkTv3r1Z21eqVAnnzp1DSEgIJk2apPzOGYaBt7c3/vzzT857PXjwYBw7dgwdO3aEs7Ozytx5+/ZtdOvWDYMHD+a8BilkISkCIhQ4d+4coqOjsXXrVjg5OWHFihWwsLDgbCOFLFVYWCh4jEKhrqpyREQEq7zy888/K/8/efIkoqKisH37duTl5WHChAnYtGkTp74mhbNECnmsJLy8vODl5YXMzEz8/fffiI6Oxh9//IG6deuqlZmkeJ7FoQ2vlGLeOn78uKDxscHc3JyXrq3jWej8LRUmT56M8PBwzJgxA3K5XKO2DRs2FCTnXL16lZWWlpbG6Vj++eefOY2EYnk1IE2Aj1hIMWfw4T9vZJTCEAOI/8DFMil1pdy1QdOmTZGamqoSPefg4KDiUeGCUAWeDVJ43gsLCxEbG4sdO3bg0aNHYBgG9vb26N69O/r16yeIATVr1gyGhoYICQmBnZ0dNm3apPa40aNHl/pt1qxZCAoKwurVq1V+b968OZo3b46hQ4di5syZ2LdvH+84xODDhw+ljJOawtfXF7///jt8fHxKCcefPn3C1KlT0aFDB84+2ATTpKQkTJo0CTdv3uRkVHyTRm5uLitN7MQtVR+AOKOSFGOwsrLC7du3lREpJXHr1i1eQ7pQgY4NcrkcL168YOVlL168KBU9VBy6uro4fPgwFi1ahC1btiiVPwcHB8ycOROhoaGcEzMgXkEQIsQwDIMrV65wHiPGKGRqaoqzZ8/il19+webNm5VKtoWFBfr06YPZs2fz8klAGu+3mD48PT2xYsUKjBkzBosXL4aZmRkYhkFmZiZ0dXWxbNkyeHl5cfZfWFjI+cx1dXV5FSqxEfRi518++UEIxMoyX758wT///IOoqCicPn0aPj4+WLJkCXx9fTm/yeJgMyBkZ2ejoKAA7dq1w7Rp01jbSyGPSSWcizUci3W4AmUTneLk5IT58+djzpw5SEhI4I26Dg8Ph7u7O3r06IGJEycqo21u376NefPm4ciRIzh37pzatnZ2drh48SKqVaumlp6YmIjq1avzjlmsfC2Fs1Es3r17Bzs7O1Z6jRo18O7dO84+7O3tceDAAbx//x737t0DANSqVUtwhPHmzZvRuXNnbNmyRSlvODg44Pfff+c0cCoghRwiNiAiPT0d69evR0xMDD58+IC+ffvi3LlzcHFxEdReKnlOKpw/fx5r165FfHw8HBwc0LdvXyQmJmLp0qWcho709HTExMQoI+/9/f1x8uRJuLu7o3///rwBIeoiITWFFPIYG4SuDinL5ymUV0oxbxU3GmsDsfMVIH7+VkDsvDVjxgzExsZiw4YN8Pf3R79+/VC3bl3e8wLCbBB8ePv2LUxMTJT7Dx48gKWlpXJfV1cXOTk5nH2I5dVSBPgA4h1sYucMPjD0v7Q25P+AT58+4fDhw7hz5w4YhoGjoyNat24NQ0NDSfq/dOmSVku5xEDhTdqwYQMyMjI4Pe9EhA4dOmD//v1wdXWFs7MziAhpaWm4ceMGOnXqhJ07d/Ke087OTpAhoeSSGaCIUZ86dQr16tVT2+769ev4+eef8eHDB95xsCE5ORmNGjXi9M7JZDI0bNgQgwYNQp8+fbRSZF+9eoVGjRpBLpdj5MiRSk9GWloaVqxYgYKCAly9epXXK1McDx8+xG+//Ya4uDh07doVs2bN4lTsp0+fLqjfqVOn8h7z9u1bMAyjMmF8K8hkMtHL18UgKCgI9+7dw+nTp0vRiAgtW7ZErVq1OIWVkydPCjoXm8Dk6ekJNzc31kij8PBwXLx48btTBIpDqvfx8ePHgvrhU8KJSCkoWFlZaRRVGxQUJOg4rndCij6eP3+O+Ph43Lt3TxlB1r17d0FLc9zc3NC7d2+EhoaqpS9atAhxcXFITExk7eP8+fOIjo5GfHy8SgS9jY0NkpOTeZdLq0NeXh7i4uKQk5MDb29vSdJ3lCUsLS1hamqKAQMGoF+/fqhYsaLa47giGtlSY5iZmcHZ2VkZ1V+WqFmzJv744w906dJFLX3Hjh0YP3682rlbSggxzPLxeym+LSmwa9cuDBo0qFTUZrly5bB27VpWxW7q1KmIjY3F3r17SymJN27cQMeOHTFgwADBPPXfwowZMwQdx7Uaw97eHqtWrULbtm3V0g8cOIBhw4bh0aNH2gwRQFE6ku7du2vd/n8Benp6sLGxwYABA9CpUydWI1ZZpri6d+8eMjMz8cMPPyh/O3r0KGbNmoWcnBz4+fnxLocEgDp16iA3Nxd9+vRBQECAcp7R1dXlnXcMDAzQo0cPBAQEwNvbW8lvhLQFigz879+/h4+Pj/K39evXY+rUqcprWLZsGfT19Xmv4/8gHoWFhSgsLISOzv8f2/Xq1SusWrUKOTk56NSpE3788UdBfWVkZODevXtgGAY1a9bkje5VQKr5W6p56+TJk4iOjsb27dtRs2ZNpKSk4OTJk2jRooWg/rVF9erVsXLlStZUHgkJCRg5cqRgGV4d+Hj1qFGjcOLECVy6dEltgE/Tpk3h6enJu2rK09NT0Hj+NZ1LVEbH/yHcuXOHFixYQCNGjKCRI0fSwoULtS4W8uHDB7p06RJdvnxZUCUjqbBr1y6ysrJSVlNSbFZWVrR7927B/WRlZZUqUHLt2jXq0KHDv1okQ5EEuWPHjqzHREdHk6mpKR07dqwU7ejRo2RqaspZMEEKGBgY0KNHj1jpjx49El35VUji3OKJjA0NDalv375q7wsfpKp6+ubNGxo5ciTp6emRl5cXXbx4UeOxaIMPHz7Q8OHDydLSUllAx9LSkkaMGCH4+8zOzqbffvuNXFxcyNjYWJmIePr06ZSTkyOoj0ePHgnauJCRkUFbt25VFhPavn07ZzXm4rh37x6Zm5tT06ZNKS4ujpKSkig5OZm2bNlCTZo0IXNzc7p7966gvrTFtm3bSEdHh5YtW6Ys6kRU9G0vXbqUdHV1aevWrVr3/+LFCxoxYoSgY48ePUojRoyg9u3bU4cOHWjUqFGcRUj+l/Dp0ydasGDBvz2Mb4LY2FgyNDQsVZDr69evtHz5cjI0NKSYmBhBfeXk5FBUVBS1aNGCdHV1SSaT0ZIlS+jjx4+c7caPH6+SyPvz58/UoEED0tXVJXNzczI2NqZz584JGkNhYSG9efOG3r59K+j4krh48SKFhoYq3+vQ0FCV6sBsKC4zKPhk8U3xe1nj69evNH/+fGrYsKGS1zZs2JAWLFigUnCEDSNHjqS6deuyJl2vW7cujRo1SvB4xBZw+zfRpUsX3q1r166C+srJyaEdO3bQvHnzaN68ebRjxw7Kzs7mbPPp0ydq3rw5yeVyateunbJQStu2bUkul5O7u7va58QFKeRrTb8vRSHBhg0bUoMGDdRufAUuxowZQ/Xq1VMpEKXAq1evqH79+pyFOoiKvo2bN2/S7du3VX7fuXMn1a9fn7UAjxBcuXKF2rdvz3ucFLIQkfbflTo+VVLnEcKnxMhSfn5+9Ouvvyr3Hzx4QIaGhtSmTRsaPXo0mZiY0OLFi3n7EVNV2dHRkezs7Gjy5MmUlpamUVsionbt2tHcuXOV+9evXycdHR0aNGgQLVy4kKytrWnq1Km8/XBBqDym+H64tkaNGnH2IeZ5EonnlWLnrcDAQBo8eLBy/+PHj1StWjWysrKi+vXrCypgJ6Z6/feMjx8/0sqVK6lp06bKeWPhwoW87d68eaOcKzTh91IUwRXLq1++fEk2NjZUrVo1mjdvHu3cuZN27dpFc+fOpWrVqpGNjU2pwnTfGkLnDC78P2FknD17Nuno6JBMJiNra2uqVKkSyWQy0tXV1UhRE/uBi2FSZ8+eJV1dXerWrRudO3eOPnz4QB8+fKCzZ89S165dSU9Pj1fJefr0KTVv3lx57aGhoZSTk0P9+vUjHR0dZd9CEB8fT126dCEXFxeqW7cudenSRZTxQCi8vb1pzpw5rPSIiAhq06ZNmY6hfv36FB0dzUqPiorirT6rqFLHti1ZskSwwpebm0uxsbH0888/k0wmoxo1atCsWbM4Kxmqg7ZVT7Ozs2natGlkZmZGjRo1ooMHD2p03uJITk6mrVu30rZt23grYxMRvXv3jhwdHcnY2JiGDBlCixcvpkWLFtHgwYPJ2NiYnJ2dea/l8+fP9MMPP5C+vj75+fnRpEmTKDw8nDp16kR6enrUrFkzQUKEWGzYsIHMzc1LCdQWFha0ZcsWQX1cunSJXFxcSgnoLi4uGhl9nz17RpGRkUqnzNKlS+nZs2eC2k6ePJkYhiEzMzOlcGlmZkYymYzCw8N526ekpNDy5ctp9erVSiXzzZs3NHbsWDIwMKDatWvz9jF06FBiGIbKly9PzZo1Izc3NypfvjzJZDIaOXKkoOuQCtoahd68eUN79uyhgwcPKg22X758oSVLllClSpV4qzKXhBTK+7/lYAsLC1O+UwqlRPFOjR07lrf948ePS1UfvHXrFk2YMIGsra3JwMCA07nl4uJCu3btUu5HR0dTuXLl6NGjR1RYWEiBgYHk6+vLOYb09HTq168fmZubK79NCwsLCgoKEixMTpgwgRiGIVNTU3J1daX69euTiYkJyWQymjhxImfbEydOCNrKErm5udSiRQuSyWTUpk0bGjNmDI0ePZratGlDMpmMfvrpJ16jlJTCuVSOWymgzbcVGBgoaBMLLt7/+fNnmjt3Lrm6upKhoSEZGhqSq6srzZkzh/Ly8gSfQ6x8LcbZ6OPjQwYGBtS5c2fatWuXioNMKN6/f08ODg5kampKISEhFBkZSZGRkTR06FAyNTUlBwcHevfuHWv7lJQUsre3V469S5cu9PLlS2rZsiWZm5tTWFgYPXnyhHMMhw4dovHjx9Mvv/yiDKJIS0ujzp07k0wmo7Zt23K2l0oWEvNdSeGsFStLVa1aVUUXmjlzJrm6uir3165dq7LPBrFVlc+cOUNBQUFkYmJCjRo1okWLFpGOjg6lpqbytrW2tlaRMyZPnkwtWrRQ7sfHxwuSpaSQx6ZNmyZoY4MUsrEYXinFvOXg4KCiGy1fvpwqV65MGRkZREQ0ceJE8vDwYG0vtnp9WUFqmVBRNd3Kyor1mJs3b9JPP/1UylHq6elJt27d4j3HvXv3lFXG4+PjlQEZcXFx1KRJEzIzM+MMyJCCVxNJF+BTHJo62MTOGXz4zxsZjx07RjKZjKZOnapibHj37h399ttvJJfLBUW3iP3AxTIpHx8fGjJkCCt9yJAh5OPjw3kNffv2pfr169OyZcvIw8ODZDIZNWrUiIKCgujBgwe894CIqKCggHr27EkMw5CTkxN17tyZOnXqRI6OjiSTyahXr16cpeTFepMqVapE165dY6VfvXqVKlWqxHsdPj4+SuZORDRr1iwVBvn27VvWyXPRokVUvnx5tV6nPXv2kKWlJWdpeiIqNVmq27SJKrl37x5NmTKFqlWrRjo6OrzvhBSoVKkSGRkZUXh4uJJZq9u4kJiYSHXr1i3FbOvVq8dpHBszZgzVrVtXrVKZnp5O9erV4zVCKIw26iantLQ0qlSpEi1dupSzj+LQxqh05coV0tHRoQEDBlBSUhLl5eXRp0+f6MqVK9SvXz/S1dWlpKQkwWO4du0axcfHU1xcHOf3og4rVqwgfX19pRCnEO709fVpxYoVgvpITEyk0aNHk6+vL/n4+NCYMWMoMTGRt11CQgLp6ekp34GaNWvSsWPHqEKFCuTh4UEJCQm8fezYsYP09PQoJiZGhRcVFBRQVFQU6enpqRiMSkKI150vqkUBbY1CZ8+eJQsLC+V30LRpU0pJSSEHBweqWbMmLVu2THBUiRTe7+/Bg37+/HkaPXo0+fj4KN+p8+fPC2ork8no1atXamlCIuhNTU1VhM7evXurRCVcu3aNKleuzNo+MzOT7O3tycrKisaOHUurVq2ilStX0qhRo6hChQrk4OBAWVlZnNcQGxtLBgYGtGzZMhVF/8uXLxQZGUkGBgZlHsXPFgVZfJPL5aztf/vtN7K1tVU7HyQlJZGtra2g6BophHMpHLdE4h2u38O3xYb09HQaOXIkGRgYlOl5xMrXUjgbX7x4QbNnzyZHR0eytramiRMnClJYi+P9+/c0bNgwKleunPK9LFeuHA0dOpRX8evYsSN5eXlRQkIC9e7dmxiGIQcHB5o+fTpvpDVREX9gGIYsLS2VBr0NGzaQqakpBQYG0o0bN3j7kEIWkuq70hZSyFIGBgYqRgIvLy+VyEbFqhFNcPToUerbty8ZGhoSwzA0YcKEUlFQbMjKyqI1a9ZQs2bNiGEY8vDwoDVr1qiNmlVAX19f5RpatGhBM2fOVO4/fPiQTExMOM8rhTwmFlLLxtpAinnLyMhIRc/u0qWLisM7JSWF07AWFBRELVu2ZI3gb9myJQUHB3OOQez8XRxlPW+xOTPS09PJ0tKSnJ2dacmSJXTgwAHav38/LVy4kJydncnKyopV1iuOxMREql27dqmAjNq1a9OFCxc424rl1SWhbYCPAto62KSYM/jwnzcy9uzZk9M4N3jwYOrduzdvP2I/cLFMysLCgq5fv85KT05OJgsLC85rsLGxoTNnzhBR0YfKMAxnVKA6LFy4kMqXL692ctm1axeVL1+ecxmBWM+7rq4uvXjxgpX+/PlzQUtKSiqdpqamKsvnX758yWrkKygooO7duxPDMOTs7Kw0jjo5OZFMJqOuXbtSQUEB7xjKCllZWbRq1Spl5FZZo6RhVN0+1zhSUlLIxMSEmjRpQps2baJr167R1atX6e+//6bGjRuTqakpq9e3evXqdODAAda+9+/fT9WrV+ccf8uWLWn58uWs9KVLl1LLli05+1BAW6NSYGAgde/enZXerVs33vB9Lrx//56WLl3K633fs2cPyeVyCgsLU/nOXrx4QaGhoYKWdHDh7du3nPyhWbNmNHr0aMrKyqKFCxcSwzDk6Oio0TLnjh070qRJk1jpEydOpE6dOrHSi3vWp06dSnp6ejR69GjBXncFxBiFvLy8qFevXnTjxg0KDQ0lhmHI3t6e1q1bx+nEKQkpvN/fqwddEzAMI0jwZIO5uTnduXNHuW9nZ0dRUVHK/YcPH3IaYmbMmEG1atViXUpZq1YtioiI4BxDkyZNOJ1XCxcupCZNmrDS4+Li6PPnzypjLh6xlZOTQ/PmzeMcw86dO1m3iRMnkqGhIed9cHBwoG3btrHS4+PjycHBgXMMxcEmnBdfVs8GsY5bsQ5Xou/j2/rw4QP16dOHKlSoQJUrV6bIyEgqKCig3377jQwNDalx48a0adOmMh2DWPlaCmdjcZw8eZICAwPJ1NSUmjdvrvHy+cLCQnr16hW9evVKML+uVKkSXblyhYiKngnDMLRmzRrB51REjxIVfesMw1CjRo3o3r17gvuQQhYS+13NmzdP5X6fPHlSJSL248ePFBISwtpeClnKxsZG6RQtKCggMzMzFZ0nNTWVzMzMOPtgQ0ZGBq1YsYJ++OEHYhiGddUT2/L01NRUCgsLo4oVK5KOjg7reWxtbZVy0+fPn8nQ0JCOHDmipF+/fp3KlSvHOVYp5LGS0HSJa1nLxkIgxbxVvnx5FT2mcuXKtHHjRuX+/fv3OdNsVa5cmU6fPs1KP3nyJKejk0j8/K2A2HlLEeXNtbE5MyZOnEiNGjVinSsaNWrEKf+XxNWrVykuLo7i4uLo6tWrgtqI5dVSQoyDTYo5gw//eSOjnZ0d54d56tQpsrOz4+1H7AculklJkQdQJpNRenq6ct/IyEhQ2H1x1KtXT0W5Kom1a9dS3bp1NepTE8hkMk7vHZdxsDhKKp0mJiaCjYwKbNmyhTp37ky1a9em2rVrU+fOnWnz5s0CrqJscOLECerfvz8ZGxuTmZkZDRo0SHCkjxiIXd7SvXt36tKli1phvLCwkPz8/KhHjx5q2+rp6XFOZk+fPiV9fX3O8VeoUIFu3rzJSr9x4wZVqFCBsw8icUYlBwcHOnz4MGvfhw8f1kj5Lt6ud+/eZGBgQFWrVlXJK6cOLVu2pClTprDSp0yZItjgqkBhYSEdOHCAevToQXp6epz30tzcXOnd//r1K8nlctq3b59G56tSpQpn1GRiYiJVqVJFcH8leYNQiDEKWVpaKt/JnJwckslkFB8fr/EYpPB+S9HHvw2xRkY3NzdljqCbN2+STCZTiUo4ceIEpzPDzc2NN8VGs2bNOMdgZGTE+R7ev3+fjIyMWOliHGtcSEtLIz8/P5LL5dS/f396/Pgx67Elo2tK4smTJ7z8mgspKSkUGhpKFStW5D1WrONWrMOVSPy3JWRlSJcuXTjHEBISQlWrVqWwsDBycXFRRoR6enryLp+3sLCgcuXK8W58ECtfS+FsLI7c3Fxat24dNW3alAwNDTXK/aYtGIZRMZIaGxsLjnQjKpqnFDypoKCAdHR0NE5/IIUsJPa7EsunpJCl/P39qUOHDvTkyRNauHAhmZiYqOQn3bZtG9WvX5+zDyG4du0aa/5Yruh7oiL5aPv27az0IUOGkLu7O506dYrGjRtHlpaWKk6mjRs3UuPGjTnHJ4U8poC2S1ylko3F8Eop5i1PT0+l8evUqVMkk8lUHPmHDh2imjVrsrYXoutok7NVk/lbAbHzlp2dHe9mb2+vtm3Dhg0pLi6Ote/NmzcLXmmkgDY5fMXwaikhxsEmxZzBh/+8kdHQ0JD3wxRiuRf7gYtlUlLkASxpoCv+ggmFgYEBJxN69OgR5/3Mz8+n5ORktZ7hnJwcSk5O5owCZBiGfH19WScIX1/fb2ZkZMO9e/fI09NT0LFil1o9efKEZsyYQTVq1CCGYahFixYUHR3Nm7D9e0KFChU4lxNfvHiRVbC1sbHhdSLY2Nhwnl9HR0fF+F4SL168IF1dXc4+iMQZlYyNjTm/q8ePH3MaEEoeO23aNKpevboyfJ7LwVEcpqamnALfrVu3eJfYKPDw4UP67bffqFq1aiSTySggIIAOHz7Mme9K3XepqVdNX1+fM4fYs2fPNFr+p62RUYxRSN190KZojxTebzF9JCYmqjzvko6EvLw8ToFRKjAMQxEREbzeczZs27aNdHV1ycvLiypVqkQdOnRQoU+cOJHVEUJEVK5cOc7vKi0tjdcYY2pqqlIAoCRu3bpFpqamrHSp57znz5/ToEGDSFdXlzp06CBoaY2VlRVdvnyZlX7x4kXOJWPqkJWVRX/99Rc1a9aM5HI5tWjRgjddCZF4x60UDlex32fJFSB6enrUrVs3jXIy2traKpX4+/fvE8MwvAVKFIiNjRW08UGsfC2Fs5FItZhe48aNacWKFd8s92xJ+dzU1FQj+Zzv+xYCKWQhsd+VWD4lhSz14MEDqlmzJslkMtLR0aE///xThd65c2eNImO1gVjH2OvXr+nHH39UrqzZsWOHCt3Ly4smT56s0Ri0kceIxC1xlUo2FsMrpZi3jh07RgYGBlSjRg0yNDQsZYQLCQmh/v37s7a3s7OT1JGizfytgBRypbbgK2B59+5dQakMxOTwFcurpYQYB5sUcwYfdPjrT/9vIy8vD3p6eqx0XV1dfPnyhbcfGxsbpKSkoGrVqmrpN2/eROXKlVnbm5mZ4fXr16hWrZpa+suXL2FmZsbaPjAwEOPHj0elSpVKlV3fu3cvJk6ciClTpnBeAxGhVatW0NEpeuyfPn1Cx44dS92fq1evsvZhaGiIjIwM2NraqqV//PgRhoaGrO03bNiA5cuXIzExsRRNX18fwcHBGDt2LAICAtS2HzBgAGvfCvTv35/3GIZhwDBMqd+kQHZ2Nk6ePMl5TGFhIfz9/bF161Y4OjrC2dkZRISUlBT06tULPXr0wObNmznH5O3tjePHj8PKygr9+/dHcHAwnJycJLkGTTB//nyMGjVK+dxPnToFNzc36OvrAwCysrIQHh6OP//8U237rKwsVKpUibV/a2trZGVlqaW1a9cOU6ZMweHDh0u9x58/f8Zvv/2Gdu3acY6/sLAQcrmclS6TyVBQUMDZBwCkpKSgc+fOrHQ/Pz/89ttvamm5ubkwMDBgbauvr4+8vDzO88fHx2Pt2rU4e/YsfH19ERkZCR8fHxgbG6N27dq84weK7oWuri4rXVdXF0TESv/8+TN27NiBtWvX4ty5c/Dx8cGiRYvg7++PX375BXXq1OEdQ2pqKl6+fAmgiGfdvn0bOTk5KsfUr1+ftf2XL184eb6Ojo4gni8Wcrmc8zxfv35lfe8YhkFWVhYMDAxARGAYBrm5ufj48aPKcVxzBgC8e/cOdnZ2rPQaNWrg3bt3ZdaHu7s70tPTUbFiRQCAubk5kpKSUKNGDQBARkYG/P390bNnT84xSIFVq1ZxfucMw2D06NFqad26dcO+ffuwd+9etGnTBqNGjVKhGxkZYfjw4ax9f/z4ERYWFqx0CwuLUs+2JH744Qf8/fffmDlzplr6hg0b0KhRI84+pEBmZiZmz56NZcuWoUGDBjh69Ch++uknQW09PT0xe/ZsbN++XS197ty58PDwENTXmTNnsHbtWmzfvh329vZITU3FyZMn0aJFC0HtHR0dcezYMQQFBamlHz16FLVq1WJtf/fuXbRu3ZqV3rp1a4wcOZJzDGK/z5iYGJX9bdu2Yf78+crvSwhevHih5Ms1atSAgYEBBg0aJKitvb09mjdvrpQptYVY+bpChQp49OgRa/uHDx/C0tKStf38+fMRExODd+/eoW/fvjhz5gzq1aun2UWIBBHB0dFRKfNlZ2ejYcOGkMlkKse9f/+etY+DBw/C3NwcQNFcfvToUdy8eVPlmE6dOrG2l0IWEvtdiYUUspS9vT3S0tKQmpoKKysr2NjYqNCnT5/O+q4Vh5eXF+8xDMPg6NGjrDRtYWVlhdOnTyMzMxMmJialnuvWrVthYmLC249YeQwAFi9ejOrVq+Ps2bMqz6Zdu3YICQnBjz/+iMWLF2POnDml2krxPAFxvFKKecvT0xNXrlzB4cOHYW1tjR49eqjQGzRogKZNm7K279y5MyZMmIBGjRrByspKhfb69WuEh4fDz8+P91rEzN8KSCFXaousrCxOudfU1BTZ2dmcfbx//x7u7u54/vw5+vbti9q1a4OIkJaWhtjYWBw9ehTnzp1DuXLl1LaXgldLhfT0dLi4uLDS69atq/x+1UHsnMGH/7yREQDWrl3LykzZjBclIfYDF8ukxowZg3PnzqFDhw5wcnJSGg1SU1Nx9+5d+Pn5YcyYMZzXMHXq1FLXpCnc3d2xcuVKrFy5Ui19xYoVcHd3Z20fFRWF8ePHqxVk5HI5Jk6ciOXLl7MaGUtOFNqCiBAYGKg0hOXl5WHYsGEwNjYGUGQsKUssWbIER44cwe7du9GhQwcV2u7duxEUFITIyEiMHTuWtQ9DQ0Ns374dHTp04BQMyxq//PILAgMDlUbGDh06qBgScnNzsXr1alYjo52dHS5evMhqgE9MTET16tXV0qZPn47GjRvDwcEBI0aMgLOzM4Ci7+LPP//E58+fsWHDBs7xlzS+l0R+fj5newXEGJUAVWZfEhkZGbzn79OnDyZOnIjt27fD1NSU93h1cHFxwa5duxAaGqqWvnPnTs4JrUqVKqhTpw4CAgKwbds25STt7+8veAytWrVSMWQqvg+GYZQGNz5F57fffoORkZFaWm5uruCxiIEYo5BCiCm+37BhQ5V9IfdBrPIuto+SBml1Bmouo7WUuHz5stLYqQ1at27NalQqObeWBBGVEkCLQ/FucyEsLAx+fn74/PkzwsLClI6Zly9fYuHChViyZAn++ecfnqsQh/nz52PevHmwtrbG5s2bNZYhpk6dCjc3NzRr1gzjxo1T4deLFy9GamoqLly4wDuG6OhoZGdnw9/fH2fOnIGrqyt0dXVZlQJ1EOu4FetwBaT5PsWipGNJLpcr5SA+eHp6qjgRtIVY+Vqss3HSpEmwtbVFz549wTAMq5y5aNEi4RelIaSQbUs64YcOHaqyzzdnSCELSREQIRZiZSmgyKHq6uqqlsb2e0mcOHEC1atXR/v27Tmdt2zgehYKcAWFAGC9D+XLlxc8BrHy2OHDhzFp0iS1xkJDQ0NMmDAB8+fPV2tkBKR5nmIgxbwFAHXq1GF1tA8ZMoR3DPv27UPNmjUREBCgMoZNmzbB2toav//+O2cfYudvBcTOW8eOHcPIkSNx4cKFUgbDzMxMNG/eHCtXrkTLli3Vtlc44NXh48ePvLLUjBkzoKenh/v375cKcJkxYwbatGmDGTNmYPHixWrbS8GrExIS0LFjR9H9iHWwiZ0z+MDQt5Lw/yXY2dkJ8gY9fPiQk/7hwwe4ubnh5cuXrB/4hQsXWBl3amoq3Nzc4OLiwsmkuBR4AIiLi8PmzZtx584dAEVew969e6N379681ygFzp07Bw8PD/j5+WH8+PHKCLy0tDQsXLgQu3btwvHjx1mjCSpWrIiLFy+yekEePnyIpk2b4s2bN2V4FUWCkJD3QhtmkpycjEaNGnF+mPXr18fYsWMRHByslh4VFYUlS5bgxo0bGp//W0Mmk+Hly5dKRcPU1BTJyclKI+OrV69gY2PDej+mTp2K2NhY7N27F3Xr1lWh3bhxAx07dsSAAQMwffp0te0fPnyI4cOH49ChQ8rJhWEYeHt7Y/ny5bxec7Z+1Y2TC56envjxxx9ZjUq//vorzpw5gxMnTpSicRkgFOBj9kOGDEF8fDxcXFzQr18/9OrVC+XKlYOuri6Sk5MFRRGuW7cOISEh+OOPPzBkyBClgJufn4/Vq1djwoQJ+PPPPxEYGKi2fbly5VC/fn0EBASgV69eSgFC6BgeP37MO0YArEZnAPDw8BD0bR8/flzt70uXLlXZDw8Px4QJE1ChQgWV39mi3hTYs2cP/Pz8MG7cOE6jUEknAwDeSGgFfv75Z0762LFjcezYMRw9elSt8u7t7Q1PT08sWbKkTPoQyxu4cPLkSeTk5MDd3Z3XuCSXy0UZQ65fvy7oOLaIDplMBnNzc9b3kojw8eNH3vuwbNkyjB8/Hvn5+UqlKzMzE3K5HPPnz+d0SslkMqxbt07Zzt/fH0uWLFG+lxkZGQgKCuIcg0wmg6GhIVq3bs3pMNmxYwcr7cKFCxg4cCDS0tKU94OI4OzsjLVr16J58+asbYGiSOTw8HDMmDFDZQya8DmgyLjWq1cvbN++ndVxu3XrVlbe3L59e9ja2rI6XIcNG4anT59i7969rGOQ4vssjpLflxDIZDL4+PgoHa4JCQnw8vIqZWhU90xLft/aQqx8/ezZMzRu3Bj6+vqszsbLly+zOjKFzBkMw+DYsWMirvL7hxSykNjvSiaTYdasWcrAkJLzb1ZWFn7//XdWPiWFLDVu3DjePgB+o/P8+fMRGxurjJANDg4uJeOyQSaTISwsjDfakO1ZdO3aVdB5uHi1FPIYUBSpf/nyZVZZ/N69e2jcuLFag6EUz1MdNOWVYuet3bt3CzoPV9TYhw8fMHnyZMTFxSnvlYWFBXr27ImIiAhOYxIgzfwNiJ+3OnXqBE9PT9ZghqVLl+L48eNqnaYymYyTVwsxfNvZ2WH16tVo27atWvqBAwcwbNgwPHr0iLUPsdDX10dAQAAiIyMFRRSzYeDAgbh37x6rg61t27aoWbMmoqKixA5ZK/znjYxSQuwHLpZJfS/4559/MGTIkFKhwOXKlcPq1avRrVs31rbGxsY4f/48qzJ2/fp1uLu7lwrHV0CKibOsIcTIaGhoiNu3b7NGQTx+/BjOzs749OkTax/fy70Qa0jIy8tDq1atkJiYCG9vbxWh9MiRI2jatCmOHTvGuWQCKPo+7969CwCoVauWYE+tVBBjVJIKnz59Qnx8PKKjo5GYmIi2bdti7969SEpKEizcjh8/HosWLYKpqSlq1qwJALh//z6ys7MxevRoVu8eUPQst2/fjqioKFy4cAE+Pj5Kg2NSUpJgA8C/CXt7e95jGIbBgwcPeI8TYxSSAmKVd7F9SGFkXLBgAbKzs5UKMBHBx8cHhw4dAlDkuDp69Cing06sMUQh2KoTl4REdKxbt07QeYSkA3n27Bm2bt2q5HWOjo7o1q0bqwFFASmUNSmdc0lJSSrO0gYNGvC2AYDZs2cjNjYWeXl58Pf3R79+/VC3bl2NjYwKaOu4FetwBaT5PotDGyMj27LWklD3TGUyGV69elVK0dQGYuVrsc5GsUhMTMT79+/h4+Oj/G39+vWYOnUqcnJy4Ofnh2XLlimNuf91aPtdSRUYIgaenp68x2hidD5//jyio6MRHx8PJycnBAcHo0+fPpzLPsXOWWK+a6nB5+R79eoVqlSpInjVkBTQhlcC2s9bUhpLiUgZiGNlZSV4Wb1U87fYeat69eo4cOAAaxqnW7duoU2bNnjy5EkpmhQOeH19fdy/f581+u/Zs2eoVauWoGX42iI5ORlBQUH48OEDYmNjeQMG2CDWwVbW+D8joxbQ9gNX4Nq1ayoKghAmxZevSQGuScve3l7tWM3NzeHk5ITx48ejcePGgs6Tm5uLgwcPqlxHmzZtWJcoKtCgQQMMGzYMw4YNU0v/888/sWbNGiQlJamlSzVxFhQUICUlBQ4ODqWWNOXm5uLevXuoW7eu2omhYcOGnM88NzcXd+/e5ZwsypcvjxMnTrAaW2/cuIGff/6ZM6fD9yJESGFI+PLlCxYvXqxWKA0NDS1Twfz169ecglx+fj6uXr3KmS9FgX/bqFQcd+/eRXR0NNavX4/s7Gy0b98e3bt3F2ScvnDhAjZv3qzyfffu3RvNmjUTfP779+8jJiYG69atw/Pnz+Hv74/AwEB4eXmxelFPnTql9ndzc3PUqlVL8DK+7wnaGIXUCVdA0X1gWzbEBrHKu5g+ZDIZjh07phQ2mzdvjvj4eKVw9/btW3h7e3PyhkaNGiE8PBy9evUCUJRLasCAATh8+DBq166N/v37w8jICPHx8ax9TJ8+HRMmTOCdn9ggVUTH/6FIljExMSk1txYWFiI7O5s3z6gCJ0+eRHR0NLZv346aNWsiJSVFo5yMUkCMw1UBMd9nyeiYktGpCojJqcQFmUyGIUOG8H5XmiwzFitfS+FsfPv2LRiGEcQbFfDx8YGHhwfCw8MBFMlwjRo1QmBgIGrXro0FCxZg6NChmDZtmtr2bBE6ZmZmcHJywsSJEznn7vXr16v9XSHfK5RPLkgpC/0fSiM3Nxdbt27FihUrkJqaihcvXrDyO7HR91JAKnlMLpfjzp07rM6IV69ewdnZWdSyTD6UNa+8dOkSmjRpovX4+PDp0yccPnwYnp6epdIhffz4ESdOnEDbtm2/mRNDzLxlYGCAmzdvcka21qtXjzPIRgyqVKmCuLg4/Pjjj2rpp0+fRu/evfH8+XO1dLG8WoH8/HzMmjULc+fOxYgRIzBlypRS6RGEyEPaOtikmDP48J83Mvr6+mLz5s1KxSwiIgIjRoxQJmF/9+4dfvrpJ6Smpv6Lo+SHFCHCkZGRan/PyMjApUuXcPDgQRw6dEiQF09bzJ8/H/Pnz8exY8dKGdiSk5PRqlUrTJw4ERMnTiyzMQBAbGyssgBNSYNHQUEB3NzcWAvQSLGkRIqlVt8LxC5vOXXqlNbJ46WI5iwpzNWuXRsHDx5URplquqRTG6PS8OHDMX/+fOU93LBhA7p06aLcz8jIQJ8+fbBv3z5BYyiOwsJC7N27F1FRUdi/f3+Z5xtVd/6DBw8iKioKCQkJMDExYU0KzeXtlcvlCAkJwcKFCznzG9WpUwdnzpxRKpdDhgxBRESEUsB9/fo17OzsvlluRm3Axe+trKwwceJEQUu6njx5gmrVqimj7cQo74DmBgCxEYBAkcHm3LlzSq93UFAQ8vPzlblWL1y4gB49euDp06esfYhd7iwWFy9exA8//KCcaxTXrcDnz5+xa9cuzgI4Uiy3+rfxzz//IDw8HElJSaUMU7m5uWjYsCH++OMPjXIVZWVl4e+//0ZMTAyuXLmCpk2bonv37oKXPLLh6tWr+P3337Fnzx7O47R1uJaENt9nWS0lFAqZTAZ3d3fOQltCIr6+BwU6IyMDU6ZMQVxcHD58+ACgiPf07t0bs2bN4izcBACVK1dGQkKC0lE/ZcoUnDx5EmfOnAFQ5ByZOnUqq66xa9cu1nFdvHhR6bArWTBCAbaUEdnZ2SgsLISvry82bdrEma9ZalkIKCqIV/x4uVzOmxZKDMpSlhKLM2fOIDo6Glu3boWLiwuOHz/OmrdVqlQEJfH48WPk5OTA2dmZl39IIY8p+tFWf5XqeUrBK7OzsyGXy1WeWVJSEn777Tfs27evTI2kkZGR2L17N2uRoNatW6NLly4YMWJEmY1BAbFyZc2aNfHHH3+gS5cuauk7duzA+PHj1a4Wio+Ph5+fn3LOefToEapVq6aUrXJzc7F8+XJOG4LYJcZieXVJHDp0CL6+vipystB8p8WhqYNNijmDD/95I2PJSdPMzEylKIXQSdPT01NQzhY2BiA2x4dUObq4MHPmTBw5coTzXCXzlbGBLV/Z169f0aZNG5w5cwatW7eGs7MzGIZBWloajhw5ghYtWuDw4cNaJUnWBD/99BNGjBjBunQjPj4ey5cvZ/XkiYUUS63YoIkQIQXELm8R47GVIppTSCRm5cqVUVhYqPH4hEIqPsWFT58+Yfny5ZgwYYLWfezYsQPTpk0TbLQpibdv32LlypWsVbYzMzPV/q6YvCdMmIAhQ4Zg8uTJrOco+TzV3Uuu5yk2KbUCV65cwfjx47Fr1y61/fj5+WHJkiVqk8gnJyer7VNxH+bOnYuIiAjWiHAFpIiGEGMAkCIC0MTEBNevX1c+P2dnZ4wZMwYhISEAigReJycnTq+3FMZOoCg6eNeuXXj06BEYhoG9vT38/Px4l1xJ8X2LVZIKCgqQmpqqrJq7atUqlUJVCqWxLOeMNm3aoGfPnqzVi6OjoxEXF4eDBw9q1f+NGzcQFRWFTZs24fXr17zHHz58GIcOHYKuri4GDRqEGjVq4NatW5g0aRISEhLg7e2NAwcOaDUWAHj+/DmqVKmidfu8vDwsX74c48eP17oPPrDJtopohhEjRrA6yKQyhIhVoNlyW5dEdHS02t+5qo1u2rQJ1apV46w2ChRF59y9e1d5r3788Ue0a9cOv/76K4AiZbhevXqCi02WxIoVK7B+/XokJiZq1K6wsBBXrlzBoEGD4O3tjT/++IP1WClkodOnT2PcuHG4dOmSso/c3FyVCJuDBw+yFtH69OkTjh49qkwr88svv6g4RuVyOWbOnMmaPkcqWSonJwfz5s3Djh07VPh99+7dMX78eMFOhBcvXiA2NhaxsbH4+PEjAgICEBwcLCg/ta2trdYVptetW4cPHz6orJ4ZMmSI0nDi5OSEgwcPcjq/pZDHAHH667eQjfnw7Nkz9OrVCxcuXIBcLsfIkSMxa9YsDBs2TFlAJSwsjLPoqQLHjh1T+07xyZNNmzbFb7/9xuqA27NnD2bMmIGLFy9qdY2aQKxcOWrUKJw4cQKXLl0q9R1/+vQJTZs2haenp1p7gxTvQ1kvMdaEV+/YsQMhISFwcXFRG8koxqajwLZt29C9e3fBx2syZ/CC/uNgGIZevXql3DcxMaH79+8r91++fEkymYy3n7Fjx7JuwcHBZGhoyNmPh4cH7+bp6SnuYkUiNTWVLC0tOY+xs7Pj3ezt7Tn7+PLlC82bN49cXV3JyMiIDA0NydXVlebNm0efP3/mbBsUFCRo44OVlRU9fPiQlf7gwQOqUKECbz/Jycm0detW2rZtGyUnJ/MeXxw7duygChUqkEwmU9ksLS1p27ZtvO1jY2Np8eLFKr8NHjxY2U/t2rXpyZMnGo3p30DJb/TfPr+2POLkyZOCtrIcw5s3b2jPnj108OBBys/PJ6Ki723JkiVkbW3N+30TEa1Zs4a6d+9O/v7+dOHCBSIiOnr0KDVo0IAMDQ1pyJAhvH2oQ3p6Oo0aNYoMDAy0ak9EtHPnTqpTpw7nMWLvZceOHWnRokWs9MjISPLz8+Mdq7+/P82YMYOVHhERQX379uXtRx02bNhArq6uvMdJ8W0tWbKEvLy8WOmtWrWi5cuXq6Vdu3ZN1LmJiFxdXSkmJoaIiB4/fkwMw1BKSoqSfvbsWapSpQpnH48ePRK0cWH27Nmko6NDMpmMrK2tqVKlSiSTyUhXV5cWLFjA2VbIO8kwDGcfYvH3339Ty5YtVcZQtWpV5bxtYmJCa9euLdMxVK5cme7evctKv3v3LlWuXFnUOZ4/f04jRozgPS42NpYYhiFLS0tiGIasrKxow4YNZGpqSoGBgXTjxg2tx5Cenk4jR44UxOu4eHalSpUE8WwxYJNrAwMDqV69emRsbMz6HctkMknm7iZNmtDu3btZ6QkJCdSkSRNWOsMwZGdnR126dCE/Pz/WjQ1jxoyhunXr0suXL0vR0tPTqV69ejR27FjOa7C1tVXO758/fyZDQ0M6cuSIkn79+nUqV64cZx9cuHPnDllYWGjd/vDhw+To6Mh5jBRySO/evSkyMlKlj5MnT9KjR4/o4cOHFBoaSl27dmVtv2rVKurQoYNKezc3N6WeZG1tzTk/S3ENnz9/ph9++IH09fXJz8+PJk2aROHh4dSpUyfS09OjZs2a0ZcvXzj7ICLy8fEhAwMD6tSpE+3cuZO+fv3K20aB0NBQQRsbmjVrRtHR0cr9/fv3k46ODm3cuJGuXLlC7u7uNHDgQMHjUQch8phYSCUbi0Hfvn2pfv36tGzZMvLw8CCZTEaNGjWioKAgevDggeB+hg4dSgzDUPny5alZs2bk5uZG5cuXJ5lMRiNHjuRsa2FhQY8fP2alP378WBR/0ARi5cqXL1+SjY0NVatWjebNm0c7d+6kXbt20dy5c6latWpkY2OjlherO7e278ODBw+oXbt2JJPJiGEYYhiGZDIZtW3bllNGEQIhvPrDhw/k7+9PxsbGtGTJElHn+/r1K928eZNu376t8vvOnTupfv36pKenp1W/QuYMPvyfkVEEg/r69SstWbKErKysqFatWrR582bR42VDQUFBqQnq5cuXNG3aNJowYQKdPn1a9DmEGBn/bYgVJhUwMjLiNAomJyeTkZERKz0xMZHq1q1bikHVq1ePLl68KPh6cnJyaMeOHTRv3jyaN28e/fPPP5STkyOo7bcQIoSioKCAoqKiqH379uTi4kJ169alTp060bp166iwsJCzLcMw9Pr1a9FjKCwspDdv3tDbt281aicVj1C8B+o2heFXLpeX2RjOnj1LFhYWyvM1bdqUUlJSyMHBgWrWrEnLli3jfbcWLFhAurq69MMPP5CRkREZGRlRREQEWVpa0rRp0+jNmzec7T98+EB9+vShChUqUOXKlSkyMpIKCgrot99+I0NDQ2rcuDFt2rSJsw8uPHz4kIyNjTmPEXsvbW1tKTU1lZWelpZG1apV4x1rjRo1OHnM9evXeR0ybLh//z6ZmpryHieFkVGMAYBhGGrUqBH9+eeflJGRodX5V61aRcbGxhQcHEx16tSh5s2bq9BnzpypopSWBY4dO0YymYymTp1K79+/V/7+7t07+u2330gul7M6EIi+D0WpdevWKt9eyTGsXLmSPDw8ynQMBgYGlJaWxkpPTU0VZJhLSUmh5cuX0+rVq+nDhw9EVGSsGzt2LBkaGlLt2rV5+3B1daU5c+YQEVFcXJzyXb13756ga5GC14nl2ZcvXyYPDw/KzMwsRcvIyCAPDw9KSkoSdD1sGD58OPn4+KilSeUgFKtAh4SEULly5cjV1ZUiIyPp3bt3Gp2/evXqdODAAVb6/v37qXr16px9DBkyhNzd3enUqVM0btw4srS0VHGab9y4kRo3bqzRuIojOTmZrK2ttW7/8OFDTpmWSBo+VbNmTTp//jxrH1evXuV0JPz000+0Y8cO1vYbNmygZs2alek1KAz8t27dKkVLS0ujSpUq0dKlSzn7UIzFxsaGGjRoQA0bNmTd1EFIYAoXvy5fvjxdv35duT9s2DAV4+7x48fJzs6O9xq4IEQeK45nz55RZGQkjRgxgkaOHElLly6lZ8+ecbaRau4UwyttbGzozJkzRFTkdGAYRjl3CMWOHTtIT0+PYmJiVHQihe6kp6dHu3btYm1vYmJCly9fZqVfvnyZTExMNBqTtpCC7z969Ih8fHxK6dA+Pj6cAUBSy1Lv37+nxMRESkxM1HjeYIMQXl25cmVyc3NTy2M0QUpKCtnb2yt1zC5dutDLly+pZcuWZG5uTmFhYVoHGwmZM/jwnzcyymQyFQOGiYmJiudBW+F+48aNVKNGDapcuTKtWLFCIw9VcXz9+pWysrJ4jwsMDKTBgwcr9z9+/EjVqlUjKysrql+/Puno6NDevXu1GoMCM2bMoJ9//llUH3xITExUeuuJqJQBKi8vj+Li4ljbixUmFXB1daWVK1ey0lesWMEaKZSSkkImJibUpEkT2rRpE127do2uXr1Kf//9NzVu3JhMTU1VIm3UwdPTU6kcaYtvIUQIQWFhIfn6+hLDMNSgQQPq3bs39erVi+rXr08Mw1Dnzp052zMMQ0OHDtXaY5uenk79+vUjc3NzJaO1sLCgoKAgVm9YcchkMrp37x5lZmZSRkYGmZqaUnJyMmVmZlJmZibduXNHEI/IyMhQu7148YLCw8PJ0NCQXFxcWO+B2InTy8uLevXqRTdu3KDQ0FBiGIbs7e0FGXoVcHZ2pqioKCIqen8YhqFWrVoJfldDQkKoatWqFBYWRi4uLkqhwdPTk06cOCGoDy6cPXuW1zAnlufr6+vzRlsJMYTo6+tzerkfPHigdVTn5cuXBRk6GYahiIgIioyM5Ny4IMYAcO7cORo0aBCZmZmRoaEh9e3bl44dO8Y77pJYu3Yt+fn50bBhwyg9PV2FFhISoqKUqkNOTg4NHz6cbGxsyMrKivz9/XkN5sXRs2dPzgjewYMHU+/evVnpUgrGR48epREjRlD79u2pQ4cONGrUKE4DpwJVqlRRUaJKjiE1NVVUpBVR0XVMnz6dle7s7EwbNmxgpa9fv56cnJw4z5GQkEB6enpKxaRmzZp07NgxqlChAnl4eFBCQoKgsRbnCwUFBaSjo6MRj5KC14nl2WUZLa0Al8IUGxtLeXl5ovonkkaBzsvLo02bNlHr1q3JyMiIevToQQcOHBB0H/X09Ojp06es9KdPn5K+vj5nH69fv6Yff/yRGIYhU1PTUjzJy8uLJk+ezDsWNowcOZLV2CsER44c4Y1KkUIWMjAwUIkK3759u4qh/NGjR5yRNZUqVaKbN28q9ytUqKBidLh9+zaZmZmxtpeC17Zs2ZI1Op+IaOnSpSpR4WyYNm2aoK0sYGhoqPIc6tevrxIx9fjxY1GrSoiEyWMKrFixgvT19YlhGLKwsCBzc3NiGIb09fVpxYoVrO2kmjvF8EqZTKYidxgZGXE6otWhY8eONGnSJFb6xIkTqVOnTqx0Nzc3mjt3Lit9zpw55ObmptGYtIUUcqUC79+/p4sXL1JiYqKKA5fr3N/CYbt161at2wrh1TNnzhQUDc2Hjh07kpeXFyUkJFDv3r2JYRhycHCg6dOn08ePH0X1LWTO4MN/3sjIMAz5+vpSly5dqEuXLqSjo0Nt2rRR7vv6+mr0Qu7fv59cXV3JzMyMZsyYQdnZ2YLa7d27l9avX6/y26xZs0hfX5/kcjl5e3tzfmAODg508OBB5f7y5cupcuXKyuiQiRMn8kYhsDGCGTNmUMeOHUlHR4eOHj3Key3qotY6duwoSDAuubzG1NRUYwYhRphUYN68eWRpaak20igpKYksLS1p3rx5att2796dunTpovZ8hYWF5OfnRz169OA8vxSeoG8hRAhBdHQ0mZqaqjUeHD16lExNTWndunWs7RmGoebNm2uVSiAzM5Ps7e3JysqKxo4dS6tWraKVK1fSqFGjqEKFCuTg4MBrxC8eaajwqqnb1xQFBQX0119/UdWqVcnW1paio6OpoKCAdQzFDa16enoUHBys3B86dCjvGCwtLZXCeU5ODslkMoqPj9dozIaGhioGJT09PeWSaSGwtbWlw4cPE1FRtB3DMDRmzBiNxsCGV69ekaenJ290LsMwVK9ePWWUgFwuJxcXF+V+vXr1OO9ljRo1OI1W27dvFyRYV61alfbv389K37dvH1WtWpW3n5L4/Pkz9ezZk5fHEBXdi2rVqolKbyGFASA3N5diY2Pp559/JplMRjVq1KBZs2ZxKvZSYvz48WRkZESDBw9W8obu3bsLbm9nZ8e5WuDUqVOcDh2GYej48eOUnJxMycnJZGxsTHv37lXuHz16VBCPEbPcSl9fXyVK7/Xr1yr86O7du1ovq1EgKSmJ8zomT55Mtra2rMtSbW1teQ0xzZo1o9GjR1NWVhYtXLiQGIYhR0dHQYbW4uBTVvggBa8Ty7PLMlpaAa6lX7t27RK08UFqBfrRo0c0bdo0qlGjBlWrVo1XBrCxseH9vm1sbASdOyMjQ8WRrsC7d+840wGxOVeDg4Opfv36ZGxsTFevXhU0huIoLCykK1eukKurK4WFhXEeK4UsZGVlRcePH2elHz9+nDMVkYGBAWd0T1paGqfBVwpZqkKFCiqGzpK4ceOGoHRKZYl79+5xpthydnam7du3E1FRlLdcLleZxxMTE6lSpUpan1+oPEZEtGfPHpLL5RQWFkYvXrxQ/v7ixQsKDQ3lDJKR4nkSieOVfI5rIahSpQolJiay0hMTEznTvqxevZqMjY3VOtF2795NxsbGtHr1akFj0dZRqYAUciUXCgsLWfVjhmFo/fr1yrnFyMiI1qxZo9xft26doPdBzBJjKXi1VKlGKlWqRFeuXCGiotUVDMPQmjVrRPWpyZzBh/984RcpCkMARdUhw8PDceHCBQwbNgxTpkxRVs8VAi8vL3Tr1k2ZuPrcuXP46aefMGPGDNSuXRtTpkyBj48Pa+EXY2Nj3Lx5E/b29gCKqupWqVIFy5YtA1CUsNTDw4Mz2bmibUmYmZnB2dkZ48aNg5ubG+d1EBE6duyIffv2wdXVVaVgyY0bN9CpUyfs3LmTtb3URTYeP36M2NhYrF+/Hl+/fkVqaqqy6hgXxBSgsbKywv79+5VVBEvi0qVL8PX1VVbcUgcpEqbXrl0bERER6Nq1K96+fQtra2skJibihx9+AFD0znbq1AkvX77U+hxC0KZNG3h5eWHSpElq6bNnz8bJkydZk/mLuRczZ87E+vXrce7cOWX1YAVev36NFi1aICgoiDMxdVkUVdqxYwcmT56MN2/e4JdffsGoUaM4K2N6eHgISvB9/PhxVpq6b+vatWuoVauW4HHzfZ980NXVxePHj2FjYwMAMDIywsWLF1G3bl1B7Rs2bKj2PmRmZuLZs2eoXbs2Dh06xPmuiK3+LiYpdXEEBQXh3r17OH36dCkaEaFly5aoVauW2rmHrWp6ZmYmbt68CR0dHZw+fZr3uUjBZ5o1a4YuXbogPDxcLX3u3LnYuXMnLly4IKi/+/fvIyYmBuvXr0d6ejq8vb05K0O+ePECixYtwu+//662gM6sWbMwfvx4VKpUibWPmjVrIiIiQlno6+LFi2jRogXy8vKUVQm5YGRkhDt37qBq1apq6c+ePYODgwNr8RkpCs/8888/6N27N1avXo0BAwYov5PCwkLExsYiJCQEW7duZa0uXb16daxcuRK+vr5q6QkJCRg5cqTgYj3qkJycjEaNGrFeR1ZWFtzd3fHkyRMEBATAyclJOe/+/fffqFatGi5cuMBZzdDCwgIXL16Eo6Mj8vPzYWBggISEBPj4+Gg0VplMhnXr1sHc3BwA4O/vjyVLlpR6j9jup1hepxiDGJ5tYGCAtLQ0Vtnu4cOHqFOnDmdRJD5wJbGXqrr1mjVrMG7cOGzZskVZ8EOBhIQE+Pv7Y9GiRRgyZIigMT958kRZbOPLly+4desWp1wottooIL4Ygqenp9rfFfL58OHDOQtklStXTu3cmZ2djYKCArRr1w5xcXGc90EKWahjx46wsrJiLbITGBiIt2/fslZtd3BwwNy5c9GtWze19Pj4eEyePBn37t1TS5dCltLV1cXTp09hbW2tlp6eno7q1aurFM7SBmIKO/Hx2jlz5mDp0qUYPnw4jh07hjdv3uDmzZtK+pIlS7Bnzx4cOXKE9RxSyGNA0fvy008/YdasWWrpv/76K06fPq32/ZPieQLieKVMJkPdunWVRTmuX78OZ2fnUrzi6tWrnOe/f/8+ayGw58+fo1atWpy8OiAgAJs2bYKzs7PK3Hnnzh307NkTmzdvZm2rwLBhw7BmzRqUK1cOjo6OICLcvXsXGRkZGD58uNKmwAWxcqWRkREeP36s1NnatWuHmJgYVK5cGQB38RYp5pzU1FR06NBBKet07twZK1euRM+ePZGcnIxBgwZhzJgxrIVfxPJqxXVIUTRNJpMhPT1dKbeYmJjg6tWrcHR05G0rxZzBBx3+Q/63wWc8FIpmzZrB0NAQISEhsLOzw6ZNm9Qex1ZV+ebNm1i4cKFyf9u2bfD29saUKVMAFDGgMWPGsBoZDQwMVJjPhQsXsGDBAhV6dnY25zWwVffVBLGxsTh16hSOHj1a6kM7duwY/Pz8sH79evTv31/rc2hSTY1hGKWSpkn1X11dXRw6dAiLFy/Gpk2bcOrUKRARHB0dERERgbFjx7JWuM7KyuJUaK2trQVVEMzKymKtkKdASaW6OPr3748RI0YgJSUFx44dg7Ozs9LACBQZsjVReLTF9evXMX/+fFa6j48Pp0FG2+p5ALB3715Mnjy5lIERACpWrIhffvkFf/31F6eRUYrqXQqcPHkS4eHhuHHjBsaMGYPw8HClEsuFEydOiD43wzDKd0phtMjNzcXHjx9VjuN6pwBg7dq1ykklPz8fsbGxpRwqbHyusLBQ5buRy+UwNjYWfA1+fn5qf1dM3m3atOE1CrEZD4Xi119/xY4dO+Do6IiRI0eqCHMrVqxAQUGBkm/z9fPDDz/Azc0NYWFhKv0sXLgQd+7cYZ2f2N6ZatWqoXv37ujbty/vcwTEfVsKBAcHY9y4cXBxcVFrAJg1axbrvKUONWvWxKRJk1CtWjVMnjyZt5LwokWL8PHjR7XXa25ujqysLCxatAjz5s1j7ePp06f46aeflPtNmzaFjo4OXrx4IaiCYF5eXimFojh0dXU5FU4p5t6YmBiMGzcOgYGBKr/LZDIEBwfj9u3biIqKYjWKtWrVChEREWqNjESEOXPmoFWrVqLHyQVTU1OcPXsWv/zyC+Li4vDhwwcARcJuQEAAZs+ezWlgBIoqmltYWAAAdHR0YGhoKEigVocBAwao7A8dOlRln0tZEcvrFP2L4dlWVla4ffs2q+J869YtXmf47t271f6emZmJS5cuISoqCrGxsWqP0UTm4sKQIUNw6tQpdOrUiVWB5jMwfv78GTt27EB0dDTOnDmDDh06YPny5WjXrh2vYjp9+nQ0btwYDg4OrNVGN2zYwNmH2HgNPiMJH5YsWaL2d8XcWbt2bd4+pJCFxo0bh9atW8PS0hITJkxQKtGvX7/GvHnzsHHjRhw6dIi1va+vL37//Xe0b99erZNv+vTpaN++PWt7KWSpwsJCTjlDJpMJrmb89u1bJCYmQldXF61atYJcLsfXr1/x559/Ys6cOcjPzy+T6vHh4eHIzc3Fjh07YG1tja1bt6rQz549C39/f84+pJDHAODatWtYs2YNK71fv36IjIxUS5PieQLieGVJmbJz584an//Lly+cMoSOjg6v0Xrjxo3o1KkTNm3ahDt37oCI4OTkhOnTp6Nnz568Y/jnn38QExOD6OhoVkelt7c3qwyhgFi5Mi8vT4Vfnj17tpRxlY2fSjHnTJo0Cfb29li6dCn+/vtvxMXF4ebNmwgICMCePXt4ZRCxvFoBKeRzhmFU5jeZTMZquygJKeYM3vH91yMZpYKdnR3vC8EwDB48eKCWZmhoiNu3b8PW1hZAkZLTvXt3TJw4EUBRRF6dOnWQk5Ojtr2Xlxfc3NwwZ84cnD59Gh4eHnj27JnS8n/48GGEhISweve4kJ+fj7y8PEHWaqmj1tRFMvKVn1cnTAYFBQkSJhW4c+eO1oqJs7MzIiIiWD2t27Ztw5QpU3D79m3WPhSRLWwQEtlSWFiIqVOnYs+ePbC2tsaiRYtUmEKPHj3Qrl07DBw4UMBVaQ89PT08fvxY+S6WxIsXL2Bvb4/Pnz+rpfN5dN69e4cNGzZg7NixpWjly5fH+fPn4eTkpLbtrVu30Lx5c7x//17YxYiAr68vjh49iqCgIEybNo3VC15WKPlOKd6hkvtc75RYPieTyeDj46OM2kxISICXl1cp5XvHjh281/Nv4tGjRxg+fDgOHjyoFHYYhkHbtm3x559/ws7OTlA/ly9fRmBgIFJTU5X3lYhQp04dxMTEoEmTJmV1CQD4v62CggIkJCSwKhMKSOFBB4qM8NHR0di+fTvkcjl69uyJgQMHolmzZqxt6tati1WrVuHHH39USz937hwGDx6MlJQU1j7kcjlevnyp4owwNTXF9evXWZWO4pDJZJg1axbrHJmVlYXff/9dsOKpDapWrYodO3agadOmaukXL15E165d8ezZM7X0+/fvo1GjRnB2dsb48ePh6OgIhmFw69Yt/PHHH7h9+zauXLmiUeRzSfBF1xQHEeHt27cgIlhZWQkWuGUyGY4dO4by5csDAJo3b474+PhSUab169fX/AI0gBS8TizPFhMtXXwM6mBqaqp8V3r06KH2mODgYERGRvIqZUIRHx+PTZs24e7du0qnb58+fXgV6OHDh2PLli2wtbVFUFAQAgICYGlpqdG5Hzx4gBEjRuDQoUMqPN/b2xvLly/n/S6kikwpicePHyMnJwfOzs6C5dt/G3/++SdCQ0ORn58PMzMzMAyDzMxM6OjoYOHChRg5ciRr21evXqFBgwbQ09PDyJEjVfjU8uXLkZ+fj2vXrnE6+sWiZORaSeTn5yMlJYWXz507dw7t27dHZmYmGIZB48aNERMTAz8/PxQWFmLs2LEIDg6GkZGRxmPUhNf+2zAxMcH169dZV148ePAA9evX5w2UEQMpeKUYyGQyDBkyhPVZ5+bm4q+//irT59mpUye4uLhgzpw5aunh4eG4desWdu3axdmPWLlSCjuAGFhbW2Pfvn1o1KgRMjIyUL58eaxevRqDBw8W1a8mvJrvfVCAz4Evk8lgbm6ulBsyMjJgZmZW6vzfQgdWh/+8kTE4OFjQcWyh/VKhZs2a+PPPP9G2bVtkZ2fD0tISx44dQ4sWLQAUhVm3bduWdYnt8ePH4evrCxsbG6Snp8Pf319l6cbw4cORk5ODdevWsY5h3759ePfuHfr166f8LSIiAjNnzkR+fj68vLwQFxeHcuXKsfZhbW2NAwcOoEGDBmrp165dg4+PD+sSXT4F4e3bt/D29mZlLlIIk0DR8nNbW1t06tQJfn5+cHd3F9x26tSpiI2Nxd69e0tFCt64cQMdO3bEgAEDOJdtymQybN++XXkf2CBllF1ZQZ0CXxx8E8a6devQu3dvleXERIRDhw4hKioKu3btgpmZmdpvQ0dHB8+fP2cVOF++fImqVasiPz+fc/xCwDfhyWQy6OjowNjYmFNhVsfsZ8yYIWgMv//+OyutLJZ9awqp0lMUR15eHuLi4pCTkwNvb284ODhwHu/l5SWo32PHjqn9fdmyZRg1ahQA4MOHD7h37x6ICA4ODpy8kQtJSUkqijMb/+TDyZMnkZOTA3d3d0FjmT59OiZMmFBKkLl16xaio6Oxbt06fPjwQdCyL20NAE+fPlUuXXz48CGaN2+OgQMHomfPnoIiv4yNjZGWlqZ00JXEkydPULt2bVYHHVDaIASoNwqxGYSEGN8B9ojFJ0+e8LYFwHqNgDTLrS5evIjAwEDcunVLxejt7OyMmJgY3nQp48aN46S/efMGmzZtKlNFSYql51JACl4nlmffv38fP/zwA5ycnFijpS9fvizKcMwFsUuEpYJMJoOtrS3r8k4FhDi3Pnz4gLt37wIAatWqxSujFR9D8eX3bGCLElLw4uLO1CFDhijlfCcnJxw8eJA18rqwsBCFhYUqhrFXr15h1apVyMnJQceOHVWiudVBKlkIKOL727ZtU95LBwcHdO/eXVDk+MOHDxESEoLDhw+XMvj++eefnGlCpJClxKZcUaBVq1awsrLCr7/+iujoaCxZsgR2dnaYNm0a+vXrJyqSSRsjY3FZqk2bNlrxBU3lMQBwc3ND7969ERoaqpa+aNEixMXFqU3JIMXzBP59XinVsm8xEOuoVECsXCnGyHjq1Cne6wSAli1bstLELDEGxPNqxRjc3d05o1sB/veBy+ZTHCVXbQDSzBl8+M8bGWUyGapXr46GDRtyLmf4559/ynQc4eHh2L17NyZPnox9+/bh3LlzePDggXJSX7NmDdavX48zZ86w9pGSkoIjR47A2toaPXr0ULFUr1mzBk2bNuVUXsXmhQSkiVoToyBIJUzm5eXh8OHD2LVrF/bs2QMiQocOHdC5c2e0adOGcxlzXl4eWrVqhcTERHh7eyujB1NTU3HkyBE0bdoUx44d4+yjrLze/wbUKfDF8fnzZxw4cECQMPTo0SNER0cjNjYWz58/R9++fdG/f394enqqFYDFGjgV469evToGDBiAhg0bsh7Ht0RCDLOXyWSwsbFBxYoVWfkUwzCcOV/+C5gwYQK+fPmiXDrz5csXNG3aFKmpqTAyMkJ+fj4OHz7M6RRQPM/27dtzLhtYvHix2t/Lly+PH374ATExMaw5+MoaCxYsQHZ2tlLZISL4+Pgol5lVrFgRR48ehYuLi+A+c3JyEBcXh6ioKFy4cAGenp7o3bs3/Pz8NMovrAm8vb1x/PhxWFlZoX///ggODmaNOmZDhQoVsGPHDlah8dSpU8q8tGwoC+O3JijOu4orzcV/4zOMyWQyvHr1ShSvUyApKQl37twBUKT8c/G94mDLRVQSbIKxp6enoEjpo0ePstKF5ozky4m0fv16tb+bm5vDyclJuWT2e8e/GS0tlRwTHx8PPz8/pcL16NEjVKtWTfnd5ObmYvny5cqVPyURGBgoSIFn+76lMJaKzRXm7u6OIUOGKHnVgQMH0LFjR8TGxqJ27doYOXIk6tSpg7Vr16ptHxQUBF1dXeWy1KysLLi4uCAvLw+VK1dGamoqdu3axZqTVXENUshCUuH9+/fK1VnFDb75+fmsUYbfkyxVoUIFnDx5Ei4uLsjNzYWpqSm2bNnCGhlcHHw6Tm5uLu7evcv6PqmTpdzc3JCSkiJYlpJCHgOKZOOQkBD88ccfGDJkiPLZ5efnY/Xq1ZgwYQL+/PPPUqlAAGmfp7a80t7eXu2zUMwV48ePZ83RLxX4Vr8BRfeBK6BCCkdlSWgjV5bU28zMzJCcnKxcVcKXk7H4s1MHPlmK7/x8EMurFdfxb9sApJgz+PCfNzIWj3wLDg5GQECAYM9kcbB5UxRMpk2bNpxCRm5uLoYOHapc2rpmzRoVC7GnpyfatWvHmlw/ISEBHTt21HjcxVGxYkUcPHhQKTyMGzcOqampOHDgAICiSMcxY8YoPY/qINaoI1ZBECtMqgMR4fz589i9ezd2796Nx48fo3Xr1ujcuTM6dOiglgl8+fIFixcvxubNm5XKmqOjo9Jbx1XkA5CGwbBNfMXBMAzu37+v9TmEQKwCr1j+vnbtWpw7dw4+Pj7o06cP/P39kZycjDp16rD2WTJUvCSICB8/fuSccC5duoTo6Ghs2bIF9vb2CA4ORt++fbWOWtMGvr6+OH78ONq2bYvg4GC0b99ecFSBAkSEP/74Azt37sTXr1/RunVr/P7777x5P0uOY/PmzcpojIiICIwYMUKZA+3du3f46aefkJqaytrH48ePcejQIXz9+hUeHh6cz68k6tati9mzZyujPWJiYhAWFoZr164pefjr16+xd+9e1j7mz5+P2NhYvHv3Dn379kVwcLBGuUlfvHiBIUOG4OzZs1i6dKlK5LcmEOOBb9SoEcLDw9GrVy8AwNatWzFgwAAcPnwYtWvXRv/+/WFkZIT4+Hje/s+fP4+1a9ciPj4eDg4O6Nu3L8LDw3H9+nWNno026NSpEwYOHIgOHTpo/D4r0L59e9jY2OCvv/5SSx80aBBevHjBWTzm34aOjg6qVq2KwMBAdOzYkVVBdnV1Ze2jLJdbffjwARs3bkRUVBSSkpI0bi8UbJEsQFGuxc2bN+Pz58/fZAkgG3/Pzs5GYWEhfH19sWnTJsmWAqsDW4Srubm5oFy+xeVCbaOlExMT8f79e5XCOevXr8fUqVORk5MDPz8/LFu2TK1Mw2f4FoqSRj4zMzMkJSV9s+VzUshjYvuwtLTEiRMnUK9ePQBASEgIXr9+je3btwMoyk0XFBTEGi3t6OiI5cuXo02bNgCKCvZEREQgLS0N5ubmCA8Px8WLFzkjY6SQhaSINGJDamoq1q5di7///huvXr1Se4wUspRUEFPYSWw0pRSylBR9KDB+/HgsWrQIpqamqFmzJoCi6MLs7GyMHj2a1ekr1fMUwyvZ8kVmZGTg0qVLOHjwIA4dOiTYCacNuJYwnzt3DsuWLQMRcRoIpXRUipEr+Zb4cultlpaWMDU1RWBgIPr168dqyOSaP8UuMRbLqwF+x9aHDx+QkJDAW9+CzfhsZmYGJycnTJw4kbWQpBRzBh/+80ZGQDWHnyJHxsCBA9GmTRvB4epsXr2MjAw8f/4cLi4uOHjwYJlZpfX19REQEIDIyEitK/2IzQsJSBu19r3i7t272L17N3bt2oXExEQsWrRIGf0pFezt7XH58mWtlnorwDbxAUWRAKtXr/5mCpsYVKhQAXXq1EFAQAB69OihFGh1dXV5jYxiogdLIi8vD9u2bUNMTAwuXLiAjh07YuDAgfD29hZ2ISKRnp6uXFb68eNHjSO/5syZg19//RWtWrWCoaEhDh48iP79+3Mm3C4JscreqVOn4Ovri9zcXABFxpV169bxJhhXwMzMDFevXlUK4f7+/jA1NVVeQ1JSEnx9ffHixQvevs6fP4/o6GjEx8fDyckJwcHB6NOnj6CCKUBRkatx48bBw8MDv/76aynDEF/ON65IEIZhcPv2beTl5am9l+XKlcO5c+eUUdJBQUHIz89XFh+4cOECevTogadPn3KOoU6dOsjNzUWfPn0QEBCg/JaEfFsKSOFBF4Pjx4/D29sbY8eOxYQJE5RLXF69eoX58+cjMjIShw4dErxMvji+Vb6zly9fYt26dYiNjcWHDx8QEBCAgQMHapRYuyyWWx05cgRRUVHYuXMnKlSogK5du3LOK3y4ceMGoqKiWBOKq0N+fr5SuDU3N8fMmTOVVcDVITc3FxMmTFBxpixdulSyaNzCwkJcuXIFgwYNgre3N/744w+1x7EJ7iWhSU7G4rCyssLEiRM5l6gr5MKlS5dqXHRGAR8fH3h4eCgd3Ddu3ECjRo0QGBiI2rVrY8GCBRg6dCimTZumdvxcTj4F+PJBlXWOrrS0NLRv354zl7BYI6PYaEgjIyOkpaUpHeyurq4IDg7GmDFjABQZpJ2cnFiNCMbGxrh586YyGqdr166oUqWKslpsamoqPDw88Pr1a96xiJGFuPio4j3RZL7Izs7Gli1bEBUVhUuXLqFZs2bo1q0bp8NCrCwlFeRyOe7cuQMrKysQEapVq4YzZ86UyuksVCbRBFLIUlLKY0CRQ0NdYAZXTmZAmucpBa9kw8yZM3HkyBHB6S+kwq1bt/DLL78gISEBffv2xcyZMzlTrkjlqBQrV4rR2758+YJ//vkH0dHROH36NHx9fTFw4EC0a9dOsD1HrN4ollcD/HOO0HQIO3fuVHvdGRkZuHjxImJiYrBu3Tq10dNSzhmsoP/H8OjRI5o2bRrVqFGDqlWrRllZWaL7fPHiBXl4eNDAgQNZj3n16hVnH/n5+ZSYmMhKT0pKooYNG5KdnR2dOHFCq3HWqFGDDhw4QEREWVlZpKenR2fOnFHSr1y5QhUqVODsIzAwUNDGhpycHBo+fDjZ2NiQlZUV+fv705s3b7S6nm+Bt2/f0p07dyTvNzc3l3bt2kUfP34sRcvMzKRdu3ZRXl6exv2+e/eOxo4dS/r6+tSyZUs6f/68FMMtU1hYWFDLli1pzZo1lJmZqfxdR0eHUlJSRPf/9etXjds8ePCAPD09SSaT0bt37wS1sbCwoHLlyvFuQnDy5EkKDAwkU1NTat68OeXm5vK2cXR0pBUrVij39+/fT/r6+lRYWCjonEREDMOo8CoTExO6f/++cv/ly5ckk8lY27ds2ZI6dOhAz58/p/fv39PQoUOpatWqgs9vbm6u8r3Z2dlRVFSUcv/hw4dkYGAguD+iIp4TGxtLTZo0IWNjY5V3jA+HDx8muVxOMpmMGIZR+astrl27Rm3btiVdXV0aOnSo2mOMjY1V7ruTkxP9+eefyv3Hjx8Lug+6urrUr18/OnTokMp7oMm3tXPnTtZt4sSJZGhoyDqWLl26CNr4sGrVKtLX1yeZTKb8zmQyGenr66vcFzbExsbS4sWLVX4bPHgwyWQykslkVLt2bXry5Img+yEWp0+fpuDgYDI1NSU3Nzdas2YNFRQUfJNzExW9O9OmTaPq1auTpaUlyWQy2rZtm9b9ZWZm0qpVq6hJkybEMAy5uroKbrtx40aqUaMGVa5cmVasWCGIV48fP56MjIxo8ODBNGrUKKpQoQJ1795d6/Gz4fDhw+To6MhKLynz6OnpUbdu3QTLQkRFcp267cSJEzR//nwqX748rVy5krO9WLnQ2tqaLl26pNyfPHkytWjRQrkfHx9PtWvXVtuWYRiKjIyk2NhYzo0PYucdPiQlJXG2ZxiG1q9fT7t27eLcNLkGTeHs7Ezbt28nIqI3b96QXC6ny5cvK+mJiYlUqVIl1vbly5dX4emVK1emjRs3Kvfv379PhoaGGo9LU1koIyND7fbixQsKDw8nQ0NDcnFx4e3n9OnTNGDAADIxMaF69eqRXC5X0VeEQhtZSiooZAXFxrZfFpBClpJKHiup9/Xu3VtrvU/b5ykFr2RDamoqWVpaStonF54/f06DBg0iXV1d6tChA924cUNQu59//pk8PDx4Nz5IIVdKgSdPntD06dOpRo0aVKVKFZo8ebJW+p6mEMuriYpkUi4dn2/OEorly5dT06ZN1dLKas4ojv/njIyPHz+m6dOnk729PVWpUkUSIyMR0ZkzZ8je3p6VLpPJVAQQZ2dnevz4sXJfiBD19etXmjp1Kunr69O4cePo3bt3lJmZqbJxYeLEieTs7Ezr16+n3r17k62tLeXn5yvpq1evVhEuywLfSkHgw+XLl8nDw0PtPcvIyCAPDw9KTk5W21YKY1JkZCR5eXmx0lu1akXLly8XfD25ubk0a9YsMjc3J1dXV9q7d6/gtv82Pn36RBs3biRPT08yNDSkrl270o4dO0hXV1fUhJWSkkLjxo2jihUrCm7z9OlTmjlzJtWsWZNsbGwoPDxc8KTFp2gJVbiIip7nunXrqGnTpmRoaCjIMKavr6/CUwoLC0lPT4+ePXsm6JxE4pW9cuXKqQg82dnZJJPJ6P3794LO7+bmRgsXLiQiops3b5JMJqMHDx4o6SdOnKDq1asLvRwiKlJWgoKCyMTEhNzc3AQLpQsXLiQjIyMaMGAA3bt3jx49eqSyaYoHDx5Q3759SUdHh3r27MnpvHB1daWYmBgiKpqzGIZR+RbOnj1LVapU4T3ns2fPaNasWcr3OSwsjK5evSr620pLSyM/Pz+Sy+XUv39/lfeuOAYMGCDKKVXyWhYtWkTDhw+nkJAQWrx4MT19+lRQ22bNmlF0dLRyf//+/aSjo0MbN26kK1eukLu7O6eTsCzw8uVLjR0ZfLh48SIrLS4ujry9vcnIyIi6d+9OO3fupM+fP2utGJw4cYL69etHRkZGJJPJKDw8nO7evSuo7f79+8nV1ZXMzMxoxowZlJ2dLfi8NWrUoM2bNyv3ExMTSUdHR0WWkQIPHz4kIyMjwceX5JVSYMOGDbxGW7Fyob6+voqBvUWLFjRz5kzl/sOHD8nExERtW7GGNbZ+/g0jI9/Gd/7AwEC1TmOhmD17NllbW9OMGTPIw8OjlCFu8eLF1KpVK9b2np6eNGnSJCIiOnXqFMlkMnrx4oWSfujQIapZs6bg8YiRhYqjoKCA/vrrL6patSrZ2tpSdHQ0p1Nl3rx55OTkRFWqVKHx48dTUlISEWlvwNBGlpIKJ06cELSpQ4MGDahhw4a8GxukkKWkkseK632jR48WpfeJeZ5ieSUbvpWRMSMjQ+nYdXd3p1OnTpX5OdVBCrkyPj6e+vTpQz169KDVq1eLGo82QSFizi+WVwuBVEbGO3fukIWFhVqa1HOGOvw/YWTMy8ujTZs2UevWrcnAwIC6d+9Oe/fulTR64OHDh2RsbMxKFyJEMQwj6FwHDx5URtdo4hHLycmhgIAAsrCwIGdn51IMysPDg+bOnStoDFzYunUrK+1bKQh88Pf3pxkzZrDSZ82aRX379lVLK24wiomJIQMDA5o/f75GxqTGjRvT7t27WekJCQnUpEkT3uvIz8+nlStXkrW1NdnZ2dH69es1ilz73nDv3j2aMmUKVa1alRiGoT59+tChQ4cEvx9ZWVn0119/UbNmzUgul1OLFi1o0aJFnG0+f/5MW7ZsIW9vbzIwMKAuXbpQQkLCN40uUuDcuXM0aNAgMjMzo8aNG9OKFSvow4cPgtoyDEOvX79W+U1TxVcmk6n0YWJioiJU8il76pTOkn1wYdu2baSrq0teXl5UqVIl6tChgwp94sSJ1KNHD95+nj9/ThEREeTg4ECVKlWisLAwwcLP/fv3qUWLFmRtbU07d+4U1IYLb968oZEjR5Kenh55eXlxGoIUWLVqFRkbG1NwcDDVqVOHmjdvrkKfOXNmqXvDh6NHj1Lfvn3J0NCQGIahCRMm0O3btzXqQ1sPelniypUr1L59e85jypcvT9evX1fuDxs2jLp27arcP378ONnZ2ZXZGIvj7NmzNHDgQDIzM6MmTZrQypUrNeI1WVlZpQzl165dow4dOnB+m3K5nH755ZdShhBNlPcXL15QREQE1axZk6ytrSk0NJQuXbokuI/ExETy8PAgAwMDGjt2rFbRLLq6uqUcJwYGBpJHoh45coQzkrEkysLIeP/+fTI1NRV0rLZyoa2tLZ08eZKIiuZCQ0NDOnLkiJJ+/fp1VqdpSee5tigZSWhkZERr1qxR7q9bt67MjYxir6OgoKCUEe7ly5c0bdo0mjBhAp0+fZq3/a+//koNGjSgdu3aUWpqqgq9e/futHbtWtb2x44dIwMDA6pRowYZGhpScHCwCj0kJIT69+/POQapZaHt27eTk5MTlS9fnhYsWCBodY5cLqfJkyeXkvk0NTKKkaWEQBPnLRdKymwKTJs2TdDGBilkKankMSn0Pimfp7a8kg0zZsygn3/+mfOYK1euaNW3AvPmzaPy5ctTnTp1JJFLpYI2cuXq1auJYRhydHSk+vXrk0wmUxq7hCIvL4/+/vtvatWqFRkZGVGPHj1o//79gtqKPb9YXi0EUhkZk5OTydraWi1NijmDD/95I2NISAiVK1eOXF1dacmSJfT27dsyOc/OnTs5lwBI5andvn07VaxYkTw9PenIkSOCPGJS4+vXr3Tz5s1SjGTnzp1Uv3596crRlQABAABJREFU0tPTY237rRQEPtSoUYM1UpGoSLDmikwtDm2UCwsLC9boH6Ki6CU274MCcXFx5ODgQBUrVqQlS5bQ58+fNRrD94yCggLat28fdevWjfT09Hi9hGKW1pQvX56qV69Ov//+O929e7eUZ1OMh7M4Xrx4QSNGjFBLmzdvHjk7O5OVlRWNHTtWxSAiFAzD0NChQyk0NFS56enpUXBwsMpvfH34+voql7Hq6OhQmzZtlPu+vr68ytrx48cpOTlZuRkbG9PevXtVfuPC4cOHaezYsTR37lzKyclRoU2bNo2OHz/O2d7Hx4cMDAyoU6dOtHPnTo2jL4yNjalbt26i0zhkZ2fTtGnTyMzMjBo1akQHDx7UqP3atWvJz8+Phg0bRunp6Sq0kJAQ2rFjh1bjysjIoBUrVtAPP/xADMNQvXr1BLXRxoMulRHi0KFDNH78eJo8ebKS16alpVHnzp1JJpNR27ZtOdsbGhqqRJ/Wr1+flixZotznW34u1oDw4sULmjt3Ljk5OVHFihUpNDSUbt68ydmmJJ4+fUrNmzcnmUxGurq6FBoaSjk5OdSvXz/S0dGhbt260blz51jbDx48mMzNzal58+a0cuVKZXSxJsq7vr4+BQQE0IEDB1QMD0L7YBiGjIyMKDQ0lCIjI1k3LpR0hBBp5sjgQ2FhIV25coVcXV0pLCxMcLuyMDJevnyZqlWrxnucGLlwyJAhym963LhxZGlpqSJLbNy4kRo3bqy2rZSRjGIjCbnAp7BJwacCAwNp8ODByv2PHz9StWrVyMrKiurXr086OjplvsIkJSWFlixZQlu2bCllGFy9ejVdu3aNs71UstCJEyfIzc2NjIyM6JdffqGMjAzB16BwDlarVo0mTpyodGQJ5TFSyFJcSE9Pp5EjR2qctqU4CgsLae/evdSlSxdOfUks+GQpITqjWHmMSJzeJ/Xz1IZXss1TM2bMoI4dO5KOjg4dPXqU87y6uro0Y8YMrQ32irmzU6dOWqeeqV27tkqk3+DBg1Xm0levXmm9PFYTubJu3br066+/KvdjYmJYo+VLIjExkYYNG0YWFhbUsGFDioyM1HgliJjzSwUu+ScyMpImTpwoiZFx5MiR5OPjw0oXO2fw4T9f+EUmk8HW1hYNGzbkTArKlZwbKKp8qA6ZmZm4dOkSwsLCMGjQIEyZMoV1HGISW2dkZGD48OHYvXs3IiIilAlGvzVSU1PRoUMHZZXozp07Y+XKlejZsyeSk5MxaNAgjBkzBtWqVVPbXl11alNTU1y/fl1w+XgpYGBggLS0NNZzPnz4EHXq1OFM3KpAyWcpBKampjhx4gR++OEHtfQrV67Aw8MDWVlZrH3IZDIYGhrC39+fM3H0okWLBI/re8SbN2+wYcMGtQnw58+fj+joaGRnZ8Pf3x8BAQFwdXXVuLCFAup4BBGBYRhBSedTU1Nx/Phx6OrqomfPnrCwsMDbt28RERGBVatWwd7eXm1lZgWf6tChA/T09Fj753qWQgpDMAyDY8eOsdLFVm9XFDJQN60ofhd6L7WFTCZD5cqVUbFiRc5ruXr1qtrfN27ciICAANHjsLa2RlZWFkaNGgV/f3/WsfAVkClLJCUlITo6GkuXLmU9Zv78+Zg3bx6sra0xe/ZsdO7cWXD/UhRUWLduHYKCglC+fHm8f/8eFSpUwKJFizB8+HB069YNYWFhvNXDa9eujYiICHTt2hVv376FtbU1EhMTlfz34sWL6NSpE16+fKm2fVBQEHR1dZUJ77OysuDi4oK8vDxUrlwZqamp2LVrF3x9fdW219PTg42NDQYMGIBOnTpBV1dX7XFc70JAQABu3LiBwYMHY/v27Th16hQaNGgAV1dX/Pbbb4Lmz0+fPiE+Ph7R0dFITExE27ZtsXfvXiQlJQmqwO7k5IQvX76gT58+6NevH5ydnQEIT/huZ2cniEexFegA1BefS0hIgJeXl0pCfz6Zrly5cmrHkp2djYKCArRr1w5xcXGCC+1pIwdw4cuXL+jXrx+IiLWKvBRy4Zs3b9C1a1ecPXsWJiYmiI2NVSlq06pVKzRr1gwRERGl2ubn5yM1NRUODg4wNDRUoeXm5uLevXuoW7dumRZVAtifZfFx5uTksM47UvCpb1Gps6whhSzk6+uLo0ePIigoCNOmTYO1tbVWYzl58iSio6Oxfft21KxZEykpKTh58iRatGjBew1iZamMjAyMGDEChw4dgq6uLiZNmoSRI0di2rRp+OOPP+Di4oJx48YJLminwIMHDxAdHY1169YhOzsb7du3R7du3dClSxfBfXz58gVfvnzRugBocSQlJQmuQi8GYvQ+KZ4nII5Xso3RzMwMzs7OGDduHNzc3Dj72LdvH4YOHQobGxts2LABjo6Ogs8PiJfNgdJ8Tl1hx8qVK6OwsFCjsZUEn1xpbGyMGzduKM9bUFAAQ0NDPHnyhJdfKN6HAQMGsOrPAJQV0aU+v1QQau/gqlANgLUwXGZmJi5fvoz79+/j9OnTnIUoyxL/eSOjFB8mwF0FkGEYDB06FEuWLGFVHviqjL169QrOzs6sk7eNjQ1sbW2xbt26b14hrTg6deqEnJwchIaG4u+//0ZcXBxq1aqFgIAAhIaGwtTUlLO9WAVh+PDhmD9/vnKC3bBhA7p06aLcz8jIQJ8+fbBv3z7OcVSrVg1//fUX2rVrp5a+f/9+DBkyhLd6K6CdctGsWTN06dJFWdGxJObOnYudO3fiwoULrH1IYVT6X4eOjg7Cw8MxY8YMyOVy5e+aGBmFVoT7+eefOel79uxBt27d8PXrVwBAjRo18Ndff6Fnz56oW7cuwsLC0KFDB7Vt/yvPUuF84IOiKltZYPr06YKOmzp1qtrfCwsLUVhYqFJN+tWrV1i1ahVycnLQqVMn/Pjjj7z9l1TYik+138rgKgUUzozWrVurfGMloY5nS6G8N2jQAL1798akSZMQHx+P3r17o2HDhoiPj0fNmjUF9TFnzhwsXboUw4cPx7Fjx/DmzRvcvHlTSV+yZAn27NmDI0eOqG0v1oCgTnkvKXrxvQtVqlRBfHw8WrRogZcvX8LGxgazZ8/GpEmTBN2Dkrh79y6io6Oxfv16pcLbvXt33qrJZ8+eRVRUFLZu3QpHR0cEBARg4sSJuH79ukbVsrVFUFCQoOP4ZDq2CpMKxZHvWnbv3q2y7+/vjyVLliirnyvApeyw3evMzEzcvHkTOjo6OH36NKtsIaVcmJmZCRMTk1Lf+Pv372FiYqJWwY+NjcXy5cuRmJhYql1BQQHc3NwwduxYSZw2XBBbLTQoKAhLly7llV+58E0qdZYxpJCFZDIZdHR0YGxszCnT8FUcVyArKwt///03YmJicOXKFTRt2hTdu3dnVa6lkKWGDx+OhIQE9OrVCwcOHEBaWhratm2LvLw8TJ06lVcWLA5Fpe61a9fiwoUL8Pb2xv79+wU5dmJiYnD16lU0a9YMffv2xS+//IJFixYhPz8fXl5e2LJlCywtLQWPBSj6zv/++29ERUUhKSnpm8gfYvQ+qWTj70GHzszMxJgxY7Bt2zbMmTMHo0aN+qbnFxvsVFbjUDcWrrZ84JOlxJz/e4Onp6fa3xVyzPDhw8tU3+LDf97IKBXYJl8zMzM4ODjwepVKGikVCmbJfbYPY9asWfjll184lbxvAWtra+zbtw+NGjVCRkYGypcvj9WrV2Pw4MGC2otVEORyOdLT0zk9MUKYZFBQEO7du4fTp0+XohERWrZsiVq1avEqKoB2zGnNmjUYN24ctmzZUsrwlJCQAH9/fyxatAhDhgwR3Of/KoTeN3XRLbNnz0ZsbCzy8vLg7++Pfv36oW7duhoZGaWCu7s7mjZtioiICKxZswbjx4+Hg4MD/vrrL7Rs2fKbjUMMSn5f/y9CbNSaAt+DwdXLy4v3GIZhcPToUVa6GEedTCbDunXrYG5uztmWyxBTPOKhsLAQ+vr6OHLkiEaKXmFhIaZOnYo9e/bA2toaixYtUjEi9ejRA+3atcPAgQPVthdrQJDiXZDL5Xj+/LnS025sbIzLly+LNuwVFhZi7969iIqKwv79+/H582dB7bKzs7F582ZlVOTPP/+MPn36wM/PTyVi5XvF+vXr0atXLxXFVxNIoeywyUMKBaFv376cqxSklgszMjJw7949MAyDmjVrwsLCgvP4H3/8ESNHjkTv3r3V0uPj47F8+XKcOnWKsx8+ugJi5tH8/HwVx1FxFBYWYuHChdi5cye+fv2K1q1b4/fff4eBgYHg/i0tLXH69GmlzGFjY4MFCxagb9++AIrkl7p16yI3N1fra/hfgFiDLxdu3LiBqKgobNq0qUyNtdWrV0dUVBRat26NBw8eoFatWhg9ejSWLFmiUT/Dhw/Hli1b4OTkhICAAPTu3RuWlpaC5NOIiAhERESgefPmuHbtGnr27ImdO3di7NixkMlkWLp0KTp06ICVK1cKGsuxY8cQHR2NHTt2oHr16ujWrRu6dev2TSKcpHIMicH3okMDwLZt29C7d28YGxurderwQVM+rYBURkaxcqVMJsOsWbNU7Cbh4eGYMGECKlSooPxt9OjRvOfRBv/2+YXg3bt32LBhA8aOHfuvjUEK/J+R8RtBqmipfxsymQzp6elKb72JiQmuXr2qcei3mPNLwSTv37+PH374AU5OTggLC4OTkxMYhkFaWhoWLlyIO3fu4PLly6hVq1aptiU9qCtWrEBAQEApRZovfD8gIACbNm2Cs7Ozyvnv3LmDnj17YvPmzbz3478AmUyG6tWro0+fPpzGLa7lDdourZESFhYWuHjxIhwdHZGfnw8DAwMkJCTAx8fnm41BLKSIPPtfx39h2ZsCim+rffv2rFH2ALB48eIyOz8fNPU6/xse5+/BgFByyVlZpBl5/fq1Vt9+WloaoqKisGHDBrx//14Zza0O+fn5WLx4MTZv3ow7d+6AYRg4ODigT58+GDNmDOd7KiX+qw6VvLw8xMXFIScnB97e3nBwcOBt8+jRI4wYMQIHDx5URtgyDIN27dph+fLlyhU3JVGxYkVcvHiRlf7w4UM0bdoUb9684Tw/F59QODgYhkF+fj7vtZREamoqoqKisHHjRrx69UrtMXPmzMGvv/6KVq1awdDQEAcPHkT//v2VjiYh8PLygpubG+bMmYPTp0/Dw8MDz549Q+XKlQEAhw8fRkhICO7du6fxNfwvITs7W5KlvFz4+vVrmfIJXV1dPH78GDY2NgAAIyMjXLx4UVBKieJQrLSZNGmSSpSsECOjg4MDZsyYAX9/f1y+fBlubm6Ii4tD9+7dARSttho2bBinA+vZs2eIjY1FdHQ0cnJy0LNnT6xateqbO+C/VwjllXXq1MGZM2dQvnx5AMCQIUMQERGhnItfv34NOzs7QfP/pUuX0L9/fzAMg7CwsFKODy7ju7Z8WgE+GUKo/ixWrpQibYoY/NvnZwMR4dChQ4iKisKuXbtgZmbGO3d+7/g/I6NAPHnyRNBxtra2ZXJ+vpwzCghdgqAtSjIpMzMzJCcni1Z0Hj9+jJycHDg7O3MKnFKGe1++fBmBgYFITU1VWcJWp04dxMTEoEmTJmrbsYUnF4fQpa3x8fHYtGkT7t69CyKCo6Mj+vTpg549e/K2lXLi+zcRHx+PmJgYnDhxAj4+PggODoavr69WeZw0XVojJdS9m0lJSYKXdH4P+D8jo/TL3i5duqTWoNK4ceMyuwYF5s+fj9jYWLx79w59+/ZFcHCwxopScWjqQZfifSoZDanNslQFPn36hMOHD6s8C29v71L55EriezAgyGQy1K1bV6mUXL9+Hc7OzqWWsbLlGv1W0WK7d+9mXQb86dMneHt74/z582jdujVq164NIsKtW7dw5MgRtGjRAocOHdIogkxbSMXrPn/+jPz8fJVlf0LBZ9TNz8/H1atX0bRpU7X0CRMm4MuXL4iMjARQlK/Nzc0NKSkpMDIyQn5+Pg4fPgx3d3fWczx9+hRNmjSBrq4uhg8frnwmaWlpWLlyJfLz83Hp0iVUrVq1VFtjY2OcP3+eNZfo9evX4e7ujpycHK7bgMzMTLW/5+bmIjIyEkuXLkWNGjVUUhxwITs7G1u2bEFUVBQuXbqEZs2aoVu3bggNDVV7vJOTE8aMGYPhw4cDAA4cOAA/Pz98+vRJkNwNAMePH4evry9sbGyQnp4Of39/REVFKenDhw9HTk4OZ6Tfx48fYWJiUkr2KSgoQE5ODmdU6/cCe3t7rFu3Tms+sn79et5jGIZBv379tOpfCKRy6GzatAkxMTE4f/482rdvj379+qFdu3YwNDTkNfTp6+vj3r17yvz2+vr6uH79unKp7/Pnz2Fvb48vX76obe/r64szZ86gQ4cO6Nu3L9q1awe5XP6vrPL5HiCGV0qRyzA/Px9Tp07FH3/8gREjRmD27NkazXNi+HTx6+CSIfLz85GSksKrP0stV/4vQkpe/ejRI0RHRyM2NhbPnz9H37590b9/f3h6en4XkbeiIKpszP9DKFnqXlHtrvhvXJWA2Kq0Ca3aFhsbq9xiYmLIwMCA5s+fr/J7bGws5zUcPXqUateurfY8GRkZVKdOHd6qoQzDkIWFBZUrV47KlStHDMOQubm5cl+xcV3H4sWLVX4bPHiw8j7Wrl2bs+KYVFW6i+Pq1asUHx9PcXFxoispfUuUvBempqal7gXDMP/G0LTCs2fPaNasWVSrVi2qXLkyhYeH0507d7Tu7/r16zRmzBiysrKScJTsKFlZWV1VZb7Kyv82GIah9evX065duzi3fwt8VUKlQPny5VWqWFauXJk2btyo3L9//77gCnwTJkwghmHI1NSUXF1dqX79+mRiYkIymYwmTpwoqI/CwkJ68+YNvX37VrMLKYZz587RoEGDyMzMjJo0aUIrV67UqGL6w4cPydfXl+RyuZJXy+Vyat++PT18+JC1nRRVW6WqPLtr1y6ysrIq1dbKyop2797N2fbYsWNkYGBANWrUIENDQwoODlahh4SEUP/+/UVdJx+mTZsmaGMD3/1TPFMuPH/+nMLCwlhliPHjx9PLly9Z2//2229ka2urlg8mJSWRra0tTZ06lXMMUoFhmFJVqjXBmzdvyNfXl3R0dEgmk5G7u7vG1aVLfh/Ozs70+PFj5T6fPOPi4qLCj6Ojo6lcuXL06NEjKiwspMDAQPL19eUcQ1BQELVs2ZI+ffpUipabm0stW7Ys9b4r4OrqSitXrmTte8WKFeTq6sp5fnUoKCigv/76i6pWrUq2trYUHR0tqCrr6dOnacCAAWRiYkL16tUjuVxOZ86c4W2nr6+vct8LCwtJT0+vVEVcPoip1Lljxw5ycHAoVcGXiCgnJ4ccHR05+dT79+9p6dKlrN8mG01qTJgwgXR1dWncuHGUl5encXsLCwvWrVy5cqSnp1fmMgDDMOTr66us2Kujo0Nt2rTRqJJvcTx8+JB+//13srW1pQoVKpBMJqOtW7fyjkGMriOXyyk0NLSUDC20SjcR8c7dX79+pcTEREF9/dsQwyul0Dvr1atH9vb2gip6q4MYPq2AWBmiJLSVKy9cuED79u1T+W3dunVkZ2dHVlZWNHjwYK14x7eCWF5NRJSXl0ebNm0iLy8vMjAwoC5dutDWrVs1+j7LGi9evKARI0aI6uP/jIwCIZfLqXr16jR16lS6fPkyJSUlqd3YUNIoWXITqigpUJLJCUHHjh1p0aJFrPTIyEjy8/Pj7KOkUZNtY0OzZs0oOjpaub9//37S0dGhjRs30pUrV8jd3Z0GDhzI2p5hGBo6dCiFhoZSaGgo6enpUXBwsHJ/6NChZS6AEBUZjdUJvQUFBRoJcs+ePaPIyEgaMWIEjRw5kpYuXSpYsC0Lg+v3ghMnTpCHhwfJZDJ6//691v08f/5cNJMUCsU3zKbEa/qNa4orV66I7kMqg05ZISkpqcwN556enjRp0iQiIjp16hTJZDJ68eKFkn7o0CGqWbMmbz+xsbFkYGBAy5Ytoy9fvih///LlC0VGRpKBgQGtW7eOtX16ejr169ePzM3NlfOEhYUFBQUFcRpyuJCTk0OxsbHUpEkTMjY2FsSrnjx5QpUqVaKqVavS7Nmz6Z9//qEdO3ZQREQEVa1alaytrenp06dq25bkUerwLRw7Z8+eJV1dXerWrRudO3eOPnz4QB8+fKCzZ89S165dSU9Pj86dO8fZx82bN7U2IHwPyMjIULu9ePGCwsPDydDQkFxcXDj7CAsLo8GDB7PShw4dymk8d3BwoG3btrHS4+PjycHBgf9iJEBJQwLbxoZBgwZRpUqVKCIighYuXEgODg7UunVrjcfAN4dz8TtTU1O6e/eucr93794qz+fatWtUuXJlzjFUrlyZTp8+zUo/efIkax/z5s0jS0tLVqOxpaUlzZs3j/P8JbF9+3ZycnKi8uXL04IFCwQpm/PmzSMnJyeqUqUKjR8/XimLC1XY1BmctZGxxcDb25v++usvVnpUVBS1adOGlT5jxgzq3r07K71Hjx40a9YsUWMUivPnz1Pt2rWpTp06ksglREUK79ChQ0lXV5fatm0rSZ9sCAwMFLRpisLCQtq/fz/16NGD9PX1qUqVKjRq1Ci1x/I5rY8ePcopixU3ADVt2pSWLVtGr1+/1siIIdYJ8j1BDK+UQtcaOHAgZWVlcR7DZXgWw6fLGprKlW3btqW5c+cq969fv046Ojo0aNAgWrhwIVlbW38zZ6M2EMuriYgsLS3pp59+otWrV6vouN/ayJiSkkLLly+n1atX04cPH4ioyIE6duxYMjAwoNq1a4vq//+MjAKRnp5Oc+fOJWdnZ6pUqRKFhYVRamqq4PYnTpxQbsePHydDQ0P6+++/VX7XxMOhjQBka2vLOea0tDSqVq2aRn2qw9evX1lp5cuXp+vXryv3hw0bRl27dlXuHz9+nOzs7Fjb//zzz+Th4cG78UFhlOTb1EEKLwZRkZdfX19fGR1qbm5ODMOQvr4+rVixgrf9f9HI+OnTJ9qwYQN5enqSoaEh9erVi1fJ4GKShoaGvEyya9euoqLEFHj06JGgTR2kEMR1dXVpxowZgiI+2CDEKCQGYiMRv0Uko1RRa02aNOF06ixcuJCaNGmilpaZmUn29vZkZWVFY8eOpVWrVtHKlStp1KhRVKFCBXJwcOAVWNXh9OnTFBQURCYmJuTm5ka5ubm8bcR40AMDA+njx4+lfs/IyKAVK1ZQw4YNvwmP8vHxoSFDhrDShwwZQj4+PmU+jrKCIopJk6gxbaLFXFxcOBWds2fPUp06dVjp+vr6nCsVnjx5Qvr6+vyDp6JIgOzsbEHHqgPDMNSrVy+tDQnVqlWjvXv3KvfT0tJILperOBSEjEHMHG5ubq4SqWRnZ0dRUVHK/YcPH5KBgQHnGPT09FidBERET58+JT09PbW0L1++kIeHB+no6FC7du1o7NixFBoaSu3atSMdHR36+eefBd+PEydOkJubGxkZGdEvv/xCGRkZgtoRFQUBTJ48mfLz81V+18TIWNx5rc6BzSYPKsAX/c+3CqBy5coqRpCSuHv3LqcRwdXVlY4cOcJKP3LkCDVo0IDzGvhQWFgoWD7Iy8uj8ePHk4GBAXXs2FHrKMCPHz/SlClTlHPWsWPHtB3+d4V3797R4sWLqX79+mrpUjmtc3JyKCoqilq0aEG6urokk8loyZIlaudldWMQ4wT5niCGV8pkMhUnhImJCT148EC5L1TX+vr1K928eZNu376t8vvOnTupfv36rHyWSByfFopPnz7RggULNG6nqVxpbW1Nly5dUu5PnjyZWrRoodyPj48XbdwqS4jl1URFEdstW7akNWvWqBhlv6WRMSEhgfT09JR8pWbNmnTs2DGqUKECeXh4UEJCguhz/OeNjCdPnhS0aYLTp09TcHAwmZqakpubG61Zs0ZjxV6sl1Sb9vr6+rwfBp9AyoWUlBQaN24cVaxYkfUYQ0NDFUNL/fr1acmSJcr9x48fixqDUAgxVHp6eqptK4UXY8+ePSSXyyksLEwlSurFixcUGhpKOjo6KgqMOkg18X0PuHDhAg0ePJjMzMyoYcOGtGzZMkERjFIwSXd3d6pUqZIgw3BZQQoD4d69e6lq1arUtGnTUkKMUEixvJULYiMRhRgZpUgLIUXUmpGRESePvn//PhkZGamlzZgxg2rVqqV2OeerV6+oVq1aFBERwTsGoqJo3oiICHJwcFA6yDQRYqT0oB89epT69u1LhoaG5OzsTFOmTKGrV69ytgkJCVExqK5fv15l/8OHD7wGQgsLCxXnVkkkJyeThYUFK33dunVqt507d1JaWhrnuYVAW+P54cOHqXfv3mRgYEBVq1al0aNHC2qnTbQYUdE7XTySpSQeP37M+k4TEVlZWdHly5dZ6RcvXuRNbyHFMmUi8Q4VuVyuMncTlZZvNB2DpkZGNzc3WrhwIREV8SyZTKYiA5w4cYKqV6/OOQY7Ozs6cOAAK33//v2cfXz58oXmzZtHrq6uZGRkRIaGhuTq6krz5s2jz58/c55bAR8fH9LT06OhQ4dSenq6oDbFoeBv1apVo4kTJ9KNGzeISLjCJsR5zSYPKiB2FYCBgQEnL0lNTeWUjU1MTHi/TVNTU85rMDQ0VJlz2rZtq/KOayJTZmZmUv/+/cnQ0JACAgI0jgL8/PkzLVy4kCwtLcnJyYl3ebECd+7cod69e7PO//7+/qJ0r4KCAtq9ezd17txZ6z6EQIzTmg23bt2iCRMmkLW1tdL4y4XvIZBh3rx5KkarkydPqsxXHz9+pJCQEN5+xPBKhmGoXr161LBhQ2rYsCHJ5XJycXFR7terV4/3PqSkpJC9vb1yVUqXLl3o5cuX1LJlSzI3N6ewsDBOB5xYPq3AmzdvaM+ePXTw4EGlU+bLly+0ZMkSqlSpEllaWvL2QSROrizpbGzRogXNnDlTuf/w4UMyMTER1BdRkXE0NjaWVqxYISrFllCI5dVERWPeuHGjMqCma9eutGPHDtLV1f1mRsZmzZrR6NGjKSsrixYuXEgMw5Cjo6PGNjEu/OeNjFLkIWLDy5cvydPTk2QyGb17906jtv+GkbFGjRq0Y8cOVvr27dvJ3t5eoz6zsrLor7/+ombNmpFcLqcWLVpwRu84OzvT9u3biaiI2cnlchWlIzExkSpVqqTRGIiKPETaRPZoAym8GC1btqQpU6aw0qdMmUItW7bk7EOKie97QJ06dahChQo0evRojXMWSsEkCwsLaf78+cqoNSEeXnUQI9xKYSBUnGfAgAFkbGxMS5cu1bj9vx3JyJez9vTp07zvtBRpIYjER0qZmppyCiK3bt1iVfrc3NxU0kqURFRUFDVr1ox3DD4+PmRgYECdOnWinTt3ckaZs0GsB/3p06c0c+ZMsre3p4oVK9LIkSNFLdlSl3uW750wMDDgVMgePXrEmWeTLUeYwtDVoUMHrfkGkWbG98ePH9O0adOoevXqZGlpSTKZjHMJcnGIiRYjKlriw8VbT548yamk9OzZU2XlQkl07dqVevTowTkGKZYpE4l3qJR08hEVvZvFFVchfdy7d48yMzMpIyODTE1NKTk5Wcnv7ty5w/lub9u2jXR1dcnLy4sqVapEHTp0UKFPnDiR936OGTOG6tWrx+rQqF+/Po0ZM0bwNZWEEIcMwzCkq6urku9b3caHEydOUP/+/cnY2Jjq168vOCdjSYjNgasNnJ2dacOGDaz09evXk5OTEyvd3Nyczp8/z0o/f/48mZubc45Bqsi1gwcPKuUZTZ0whYWFFBsbS7a2tmRjY0OrV68uFaHKhcGDB9OECRNY6RMnTqRhw4ZpNCaiIvlu0qRJVLlyZTIwMBBkZBSyWmrcuHEaj0UBbVN05Ofn0z///PM/YWSUYv4nUuWVFStWpPbt26vQuXilFLkMO3bsSF5eXpSQkEC9e/cmhmHIwcGBpk+fLkh2kIJPnz17liwsLJT2j6ZNm1JKSgo5ODhQzZo1admyZWpX6JWEWLnS1tZWKUd8/vyZDA0NVaKwr1+/zsrvx48fr+JM/fz5MzVo0IB0dXXJ3NycjI2NeVPfiIVYXl0S9+7doylTplDVqlWJYRjq06cPHTp0SCO+pw3Mzc2V+ubXr19JLpeXypUpFv95I6MUeYhK4uzZszRw4ECVZKdlHclYcmJSt5SDbznHyJEjqW7duqzL3urWrcuaH6QktE2wPXv2bLK2tqYZM2aQh4dHqXu/ePFiatWqFWv7vXv30vr161V+mzVrFunr65NcLidvb29ROfyEQAovhqmpKd26dYuVfuvWLV5PjtRJfP8tMAxDJiYmWikYUjLJtLQ0atasGVWvXp0WLlxIkZGRKhsfxAq3Yg2ExbF161aSy+VkZmamkaLGtrxVKvAZGaXIXSs2LYRUkVIeHh7066+/stKnTJlCP//8s1pauXLlOPlDWlqaIKWbYRiysbGhBg0aKJ0P6jYuiPGg+/j4kKmpKfn7+9OePXuUQpMmRkYpFJ369evzGm3r1asnaDzFUVBQQBcvXqT69etTWFiYxu0VEBLJGBcXR97e3mRkZETdu3ennTt30ufPnwXfS7HRYkREvr6+NGjQIFb6wIEDOaNKU1JSlEuq4uLilPnFNm/eTE2bNiUTExO6efMm5xikWKZMxO9QURQO42pf3MmnztHH922xFRDUhN8dPnyYxo4dS3Pnzi2lIE6bNo2OHz/O2f79+/fk4OBApqamFBISopzvhg4dSqampuTg4KCxE13TdAhic32XxMePH2nlypXUtGlTksvl5O7uroxiYsOHDx9o+PDhSsO9TCYjS0tLGjFihDINCxeCgoJEzZ2TJ08mW1tbtfl209PTydbWliZPnsza3sPDg8LDw1npEydO5E0lJAWvHTJkCOnr69P06dO1UpLr1atHRkZGFB4eTunp6RoXyXRycqKLFy+y0i9fvkyOjo6CxpKbm0uxsbH0008/KZcaR0ZGCg5oKBkNq6OjQ25ubhpFyJbEt0w1ItYJIgWkNHQeOXKExo4dWyo6kkgYrxSDSpUqKdMiffjwgRiGoTVr1ghuLwWf9vLyol69etGNGzcoNDSUGIYhe3t7WrduHRUWFgoei1i5csiQIeTu7k6nTp2icePGkaWlpUrU+8aNG6lx48Zq20pR7Kwkbt68qVKck08GEcur2VBQUED79u2jbt26kZ6enuCoUm2h7tu6d++etOcgIvq3K1x/SxQWFiI6OhrTp0+HTCbDtGnTMGDAgFJlyEsiPT0d69evR0xMDD58+IC+ffti4MCBcHFx0WocpqamuH79Ouzt7QUd7+npyXsMwzD4/9h7z7Aoku/9+54hSAYDBhQxEsy6rqLrKphQVMQMCAZwdTGioKisWYyIGRNRTKBgwBwQEyImRAVUDKwJTIBEJZznhX/6YWC6pyegrr/v57r6xUx1V1fPdFdXnTrnPtHR0azlGRkZ6NChA5SUlDB16lSYmJhAIBAgOTkZW7duRUlJCe7cuYM6deqw1rFmzRoEBgYiNzcX9vb2cHR0RNu2baGiooJ79+6hRYsWnG0sLS3FokWLcPz4cdStWxe+vr4wMzNjykeMGIF+/frBxcVF7PE9e/bEsGHDMGXKFABAbGws/vzzTyxduhRmZmbw8vJC//794evry9mOpUuXcpaXsXDhwkrflZ3H0dFR7DGhoaHw9vZGSkoKa71aWlpITExEkyZNxJY/e/YMbdq0QW5uLmsd//77Lxo0aCDx3v3ZCQkJ4bXf2LFjK30nFAqRnp6O2rVrA/j2XCUkJKBp06YytcXf3x9///036tWrB2VlZeZ7gUCAZ8+ecR5ramqK0NBQ/P7772LLb9++DQcHBzx69IiznkOHDsHOzg6amppQUlISKfv06ZPEa7h58ybGjBkDgUAAd3d3kesAxP+OiuLz58+c5YmJiejRowdKSkrEll+6dInXeXr06MFapqamhgcPHqBZs2Ziy1NTU9G6dWsUFBSILf/rr78QFRWF6dOnQ01NDdu3b4eRkRHOnTvHq21lHD9+HLa2tpg1axbc3d2ZfjU9PR3r1q3Dhg0bcPjwYQwcOLDSscrKynj9+jVrX5yeno4GDRqguLiYsw1Llizh1dZFixaxlrm5uSE6OhoXLlyAvr6+SNm7d+/Qp08fWFpaYsOGDZWOVVZWxvTp0+Hq6ormzZsz3/N9XwDin/F79+4xfWdGRgYMDAxY7ykAWL9+PZYvX47Q0FBYW1uLlJ04cQJjx46Fl5cXZs6cKbE94jh//jymTJki8dlm4969e+jQoQPnNSgrK2POnDmYN28etLW1me/5/pZCoRDKysrQ1NSEQCBg3Y+rj7l48SL69OkDNzc3zJ49m7k/MzIysGbNGmzcuBFnz55Fz549WeuIi4uDi4sLkpOTmXYQEUxNTeHv74+uXbtyXoeysjJevnyJevXqMd9paGggOTkZRkZGnMeW59KlS/jjjz9E+sfPnz9j//79CAgIwK1bt9CmTRskJCSIPV4Rz5Yi+jtFkJmZifnz5yMsLAxZWVkAAD09PYwcORLe3t6oWbMmr3qio6MREBCAw4cPw8jICMOGDcOwYcPQvn17zuNyc3OhpaUl72WI5f79+wgICMC+ffvw7t07sft8+vQJXbp0wevXrzF69GiYmZmBiJCcnIx9+/bB0NAQsbGxqF69Out5lJSU8PbtW6afkpacnBx06dIF//77LxwdHUXG53v37oWhoSHi4uJEnv3yREREwM7ODuvXr4erqyszdigpKYGfnx/c3d2xb98+DB8+nLUNiuhrW7Vqhd27d6NDhw6s+7x+/Rr169dnbUMZ4vopIoJAIGBtg7q6OlJSUlj7grS0NJiZmSE/P5+1ffHx8fD390dYWBiMjY3h6OgIOzs7NGjQgPd7SxwVf09piI6ORmBgICIjI6V6tuRBKBSK/Adlv33Fz1z3gyLaIO89CQAFBQXw8PDAkSNHUFRUhN69e2PTpk2oVatWlbW9PEKhEG/fvmXemVpaWrhz5w6MjY151yFvP12rVi1cunQJLVu2RH5+PrS1tXHgwAGMGDFCqmuR9933/v17DB06FNeuXYOWlhZCQkIwZMgQprxXr14wNzeHt7d3pWN1dHRw584dZnxvb28PbW1t7Ny5EwCQkJAAa2trvHnzhrVdV65cwaxZs3Dz5k0A3+6p/Px8lJnDBAIBzpw5g969e4s9Xt6+mg/v379HaGgoZs2aJXMdkhAKhYiOjkaNGjUAAF27dkV4eDgaNGggsl+bNm1kP4lCTZY/ObLqEBF900wzMjKihQsX0q1bt0Ss3uU3NiqKHisrK1Pfvn1lFkOWlRcvXlD//v1FBIWFQiH179+fnj9/LvF4eQW25UVfX19Ew2vmzJkimeZOnDhBzZo1k1hPu3btWLf27duThoYG6+qYIlYxOnXqJDEpRKdOnTjrqGr9vP8CkjLwSXouy0hPT6eBAweSnp6eVB4T5ZE3JJPomyaZqakpmZmZkb+/v1SeHEVFRTR//nxSVVWlmTNnivVYloQkT0JJ8hKK8ESUhLhwkfLIKwuhKE8pIqJNmzaRqqoqCYVCxpNUKBSSiooKrV+/nvU4caGY5fmeeqvyrKArIsOlIjwZSkpKaPjw4SQQCMjU1JR535qYmJBQKKShQ4fKpYf6/PlzTi1CRcgA/PXXX6Srq0tdu3albdu2MR77fH9LRXmLbd++napVq8ZkOy+7p6tVq0Z+fn4Sjy/j7t27FBYWRmFhYVKF/SkiTLkiMTEx5OTkxLz3PT09OSVRfhby8vJo8uTJZGBgQPr6+mRvb0/v37+Xub6yxB4ZGRm8PVvklUMg+uYtrUgdKHFw9d8zZsygVq1asY7pWrduTW5ubpz1K0JqJCsri1xdXalGjRrM+LxGjRrk6urKy5ty/vz5JBAISEdHhxnL6ujoMPe0JCo+WxWfKz59LZcO59u3b2nq1KmckT4Vk2GybWzUqVOHLly4wFp+/vx5iZJMSkpK5ObmVimaQN55jrQRbIp4tuRB3v9CESjKk9HDw4M0NDTor7/+ounTp1OtWrU4s7GXp8zjlGvr2bMnZx2Sni1pkKWfJhL/W/7I91xWVpZYb+ePHz+y9iOKSHZmZ2cnEp2mpaVFly5dohcvXtDz589p5syZnLIuZW2Xp6/+GVBUcinOcxD9+p6Mly5dgqenJ+7fv48ZM2bA09MTurq6UtUhbnWt4k/HtaIzbtw4Tu+BMoKCgljLcnJyEBcXh6KiInTq1EmuFZjMzEykpqaCiNC8eXNmhba4uLiS51N5VqxYgeDgYBQWFsLe3h5OTk5o1aqVVJ4p8qCuro5Hjx6hYcOGAIBOnTph+PDhmDNnDoBvq5QtWrRAXl6eTPUnJCRg7ty5iI6OhrOzM7Zv315pH0WsYoSEhMDV1RU+Pj6YOHEi85sXFxdjx44dmD17Nvz8/DBu3DjWOiqu8P2qvH37Ft7e3tiyZUulsrKVVnHdWNn3klZaDxw4gKlTp6J9+/YIDAyEoaGhTO2sW7cu9u3bx+rFc+HCBYwePRrp6emVyoqLi7Fo0SL4+PhgypQpWLFiBdTU1KQ6f5nna1BQkFjPl+TkZAwYMIDTI/PIkSOs/VRsbCw2b94MImL1AqwqzxwiwqlTp+Dv748TJ07gy5cvrPtOmzYNMTExuHnzZqXfsKCgAJ06dYKlpSU2bdok9nhFeUqV8fLlSxw6dAhPnjwBABgbG2PYsGGc95lQKISuri7rf0FE+Pz5s8TV+3fv3nH2D8XFxbhz5w46derEWY+8K+j5+fk4cOAAAgMDER8fj5KSEvj6+sLZ2Vniaq9QKMTEiROhoaEBANi6dSscHR2Zd3h+fj527drFy5siLCwM+/btE/kv7OzsYGdnJ/FYLi5cuIDJkyezejJW9AipCJ9+Cvh2/4aHhyMwMBA3btyAlZUVTpw4gYSEBLRq1UquawAkv//LeP36NcLDw5kxhLGxMYYPH15pBbwqEAqFaNWqlUg7ExMTYWpqClVVVea7O3fucNbz9u1bBAUFITAwEHl5ebC3t4eDgwO6dOnCayyjiGdL0n0BfHuPsXksl40TRo8eDTU1Nezfvx8WFhY4ePAgZ53SUFhYiC1btsDDw6NSmbW1Na5evYqBAwdi9OjR6NevH5SUlKQeD86ZMwcbNmzAtGnTsGLFClSrVk2qNrL15eURCASYNm2a2LJGjRphx44dsLKyElt++vRp/P3333jx4gVr/UKhEBkZGZW8vWWBiPDhwwcQEfT19XnNHcqIj4/H3r17RZ5NBwcHiX08UPm9k5WVBR0dHWb+w+e9k52djcmTJ+Ps2bNQUVHB3LlzMXXqVCxevBg+Pj5o2bIlZs2aBXt7e97XVJH379+z/s4jR45EUVERDh8+LLZ88ODBUFVV5XxG+vbti7i4OAwaNAhOTk6wsrKCQCCQe54jjSejop4teeD6nb8XQqEQy5cvZzydPT09MXv2bGb+m5OTg4ULF0p8dzZt2hTe3t7Muz4+Ph5//PEHCgsLK0UMVYQrwqHM+/3Lly+cbZD0bJXBJ1pJVpSUlPD48WPo6+uDiGBoaIirV6+iUaNGIvvp6OhUWRvkxdzcHCNHjsSsWbPw8OFDtGnTBqmpqUxU6KVLlzB27FjOvrpZs2bYs2cPzM3NAVR+Lu/evYsBAwZwekOWIWtfXb16dV77VuX9kJaWxms/WeY9ZfzyRkZra2tcuHAB48ePx+LFi1G3bl2Z6vkefwYXiYmJ6N+/P96+fQvgWydw6NAhVndeaUlKSoK/vz/27t2LjIwMiftfunQJgYGBiIiIQNOmTfHw4UMm/Kgqadq0Kfz8/GBlZYXc3FzUrFkT0dHRzHnv3LkDKysrvH//Xqp6nz9/jgULFiAsLAxDhw7F8uXLRcL7KpKdnY158+YhLCwMmZmZAL51GqNGjcKKFSugp6cn8ZweHh7w9fWFtrY2E9779OlT5ObmYvr06Vi/fj3n8b+SkTEpKQkXL16EiooKRo4cCT09PXz48AHe3t7Yvn07GjdujKSkpErHKeK51NTUxKpVq1gnIHyRZ3CrCAPhhAkTsGHDBtawMz4hmeJISUnBvHnzEBUVhdGjR2PZsmWMkV8WpBm4Pnv2DIGBgQgJCUFubi4GDBiAYcOGiYRWVEReWQglJSWkp6eLtFFHRwf37t3jLW8BAPPnz4etrS2vyV1F5JEQKE/FED4zMzOcOXOG+f/4hBqVD2UkIqZvlXbyW8ajR48QEBCA0NBQZGVloU+fPjh27Bjr/hYWFrzOc/HiRdYySWH8ZUg7uCYi3L17F87Ozujduzd8fHzE7lcVxvcnT54gMDAQu3fvZp6N4cOHY+jQobzrKCMpKQkBAQHYs2cPr/f/j0QRYcrAN1mFESNGwNHREX369GEme3wn8Yp4to4ePcpaxmdRR56Jc3k+fPiAGzduQEVFBb169YKSkhKKiorg5+eHlStXori4GB8+fKh0nCLkEMqIi4uDs7MzBAIBQkNDOcNtK1KxXy5bJOIreVKtWjU8ffqU1Uj+6tUrNGvWDIWFhaxtkLQwVEZVThrlRRHvncmTJyMqKgqjRo3C6dOnkZycDCsrKxQWFmLRokUyh/7zXWi8e/cuunTpgoEDB2LOnDkwMTEB8G0cs2bNGpw4cQKxsbES76+XL18iKCgIQUFBKCgowKhRo+Dn54fExEQRiSdpkMbIqMhnS1ZUVVVhY2MDFxcX9OvXT6b3vbw0atSI13mfP3/OWa6qqornz5+LhOmrq6vj8ePHMjkWFBcXY+vWrfD29oauri6WLVvGuVipqDGdPCgq/L1x48Zi/xNdXV2YmJjAw8MDHTt2VFzDyxEREQF7e3v8+eefePjwIX7//XdERUUx5Z6ennj+/DnCw8NZ66goqRAZGYl+/foxC9lpaWkwNjbmdGaQl5/hfuBDQkIC2rVrJ/Pxv7yRURE6RIpAXr0Wa2trZGZmYt26dVBTU8OSJUvw6NEjTt0/SeTm5uLAgQMICAjAzZs3YW5ujmHDhkmlS5WTk4O9e/ciKCgIt2/fZjwLq0pHwNPTE8eOHcP8+fNx8uRJxMbG4tmzZ8yAeufOndi9ezeuXr3Kq74PHz5gyZIl2LlzJ7p164ZVq1axauqV4e7ujlWrVkFFRUWuFWfg28B6//79lTxrylZYuKi4wsfG9OnTpWrT9+b48eMYNmwYioqKAABNmjTBrl27MHLkSLRq1Qru7u5idesUxePHj1G3bl1oaWlVWlUsKSlBXl4eL+ODPIPbqjIQylPHmzdvsGjRIoSEhMDKygorV66U2VtKGk/EwsJCHDp0CP7+/oiLi0OfPn1w6tQpqby10tLS4OrqijNnzojorFhZWcHPz6/Sym15FOUpNX78eJw4cQJKSkoYNGgQbG1t0atXL6m9dOSBj55RvXr1UFpaylqHmpoaLC0tYWNjg8GDB8PAwEAhbSspKUFUVBQCAwM5jYyKQF5PQrZV59zcXJSUlKBfv34ICwuTS1dOVq+R0tJSnDhxAgEBATh16hTvgbEs73++/5ONjQ2v/X4kJiYm+Pr1KxwcHODk5ARTU1MA0mlcyvtsiUOaRR1FTJxjY2MxYMAAZGdnQyAQoGPHjggKCoKtrS1KS0vh5uYGZ2dnZgJWnuvXryMwMBDh4eEwNTWFk5MTRo0aBQMDA5kMIV++fME///yDLVu2oE+fPpW8aiMjI3nVI632Xf369REWFoZu3bqJLb9y5Qrs7Ozw+vVr1jqEQiE2bNggMUqKbdLYvn17XuNHtvdOYmKixGMBbn0tvp7MXBgZGSEgIAC9e/fGs2fP0KxZM0yfPl2sZi8fZFloPH78OJydnfHx40eR72vWrAl/f3+p+6dz584hMDAQR44cgaGhIYYPH47hw4dLNFRW/E+k0TyT99m6fPkyr2vr3r07a9n+/fsRFBSE6Oho1K1bF+PHj8e4ceNk1jz/kYhbPJY2N0IZe/fuxcKFC1FQUIB//vlHJBpNHhTx/HGhqMXOjRs3iv0+KysLN2/exJkzZ3D27FleuSRk4fz58zhx4gTq1q2LadOmibyblixZgh49esDCwoL1+Nq1ayM8PJx1n5iYGIwYMYLVWUnevvpnJzs7G3v37oW/vz/u3bsn17zzlzcyKspa/O+///Kqh20wKK/XWe3atXHy5ElmdeDjx4+oXbs2srOzpZ7YXL16Ff7+/oiIiGA8xBThhfjgwQMEBARg7969rALb8pKfn49JkyYxiWN27tyJP//8kym3tLREv3794OnpyVlPXl4efHx84Ovri2bNmmHlypXo27cvrzY0adIE6urq2LNnT5WKLktCKBSiQYMGnB4LfBKW/Gi6dOmCTp06wdvbGzt37oSHhweaN2+OXbt2cQ6AFMXhw4fh6emJhISEShOp/Px8tG/fHj4+Phg0aJDEuhQ9uC3jexoZs7OzsWLFCmzevBnt2rXD6tWrRZ4xaZB2gjB58mQcOHAAJiYmjNh6zZo1ZV69Z5OF4EJRnlLAN+PV1atXERUVhWPHjuH169fo06cPbGxsMHDgwCoXHVeEaHpaWhqOHTuGo0eP4sqVK2jTpg1jcJRLEPo7Un5wTUSwtraGv79/peQDbINrtnGEjo4OTE1NZfZskcb4zgdJIbyAfO9/PknGqjoRgCK5du0aAgICcPDgQSbBw5w5c3h5KykqIUEZsizqKGLi3KtXL+jr6+Off/5BYGAgNmzYgEaNGmHx4sVwcnLiNZmSRw6hPJ8/f8a0adNw8OBBDBs2rNKEm0tOqDzSGhldXFyQmpqKc+fOiSwkAd8Mn1ZWVmjatCkCAgJY65B3jC/ve4dLOqYMSc9m7dq1MWbMGLi4uMjcp6moqCAtLY1ZjNLQ0EB8fLxUC5SKWGgsKCjA6dOnRcLG+/btK9ZYzpfMzEzs2bMHgYGBSExMlPhsK0LOR9Zni6uvLnumuaQYyvPy5UtmHJeWlobu3btjwoQJGDZsmNSyPtKyYcMGjBkzhklOIStCoRD9+/cXWeSNiopCz549oampyXzHtZBx+vRpzJ07F8+fP4eHhwdmzZolcqys/JciCfiwbNkynD9/nrdRUxqWLl0KDw8PuZ7jQYMGQV9fH4GBgWLLx40bhw8fPuD48eNiyxUxRyjrS8aOHVvJiSU7Oxu7d+8WW1aVVElyKbkUHf8PUTF5QcUEB5IEMuUVhRZ3vJaWllTCsatXryYTExOqX78+eXh4UEJCAhFJJ2Z84cIFMjMzo+zs7EplWVlZZGZmRufOnePdph9FnTp1SENDgzw9PSkhIUGqRD5lYuvVqlWjpUuXypUwgI2IiAhq3bo15z6KEBr/GdDV1aVHjx4R0bfkJUpKSnTy5Mnvdv4+ffrQrl27WMsDAgKob9++vOvLz8+nyMhIWrNmDa1evZoOHz5MeXl5crUxISFBbgFePnWsXr2aatSoQS1atKAjR47IdJ6CggIKDQ2lHj16ULVq1WjgwIGkpKRE9+/fl3hsWWKpz58/i3z/PcXOq5KkpCRavXo1de3alVRVVenPP/+ktWvX0qtXr0T2a9SoETVu3Jhza9KkicTzKUo0vYysrCzat28fjRo1inR1dalRo0Y0ffp0unDhglgB74qJzcRtkgS2lyxZwmuTBmkF+BXN06dPycvLixo0aEB6eno0evRozmRFRES3bt0iCwsL1nevhYUFZ5IrRbz/q5pPnz5RSEgI5z5sz0a7du1o1KhRdPPmTanPm5OTQzt37iRzc3MSCARkYWFBO3fu5Ey+pKhnKysri+bMmUPq6urUpUsXunz5Mu92CwQCsra2lphUkIuaNWvSgwcPiOjb2EYoFFJ4eDjvNlQkJSWFZs+eTXXr1iU1NTUaNGgQr+POnDlDDRo0oE6dOlFycrLM5yeSLcFGnTp1qGHDhrR69Wo6evQoHT16lFauXEmGhoZUu3Zt+vfffznr+NGJ+F68eMFr42LFihVkbGxMQqGQzM3Nyd/fn3JycqRqR8UEF9LOU1xdXal69epkbm5OW7ZsoQ8fPhDRz9VPERHdvn1b4j6K+E/KI82zlZWVJXZ78+YNeXp6krq6OrVs2ZL3ucs4f/48jR49mjQ0NEhPT49cXV2lrkMa9PT0qFq1ajRixAg6c+aMVIlOyjNu3Dhemzhu3LhBFhYWpKamRm5ubnIl1yojJyeHdu3aRebm5qSkpER//PEHZzJQRRAWFiaSUOX58+ci47a8vDxavXq13OdJSkqimjVryl2POBTRz0ZHR5NQKCQPDw+RujIyMmjWrFmkpKTEmTxKESxdupQz8dCIESNo+fLlVdoGoqpPLvXLezKWUVBQgHPnzuHx48cQCAQwNjZG7969oa6uzut4ZWVlNGjQAOPGjcOgQYNYXZrbtm0r9nuhUIiQkBCJoRRsnk7lBVsBsIq2clm9lZWV4enpiaVLl4p4v0njJWRjYwNLS0vWkKpNmzbh4sWLrLp0LVq0wNWrV5lVqYkTJ8Lb25u5rnfv3qFRo0bIz8+X2BZxcImUl6diIp/yjwHfFcaLFy/CxcUF+vr6mDt3biWPQklea7t27WLEsWfMmIHOnTsjOjoa7u7uePToEZycnLBjxw7W4+UNwf9ZEOcRkpCQ8N1CMgwMDHD58mU0a9ZMbHlqaiq6d+/OSwS4quDjhShJSLi4uBh5eXkSxanV1dXRu3dvTg9ZttVeeT0R9+3bh6CgIFy/fh0DBgyAk5MT+vXrB3V1dd59FF9NOr6hd1XF+/fvERUVhaNHj+LPP/8U6bPYwlEA4MWLF9ixY4dEoXFAstB3RkYGTE1NZfI8Ky4uRnR0NOOlmZOTg82bN2P06NHMPuPHj+dVF5eHklAohIGBAWrXrs3qpSMQCKQKTZHW06mM169fIyIiQmQcMXTo0EoekeKQ1zvHwcEBZmZmWLBggdhyb29vJCcnY8+ePWLLFfH+d3Z2xsaNG6XyTpMGPv1cVYdqlfcq+fTpEyPjURFFPFtr1qzB6tWrUbduXaxYsQKDBw+Wqq2Ker4qvn/v3r3L+j7kS5kcQlBQEKf2JABMmjQJwcHB8PLygpeXl1R6kuKQ5fl+/vw5k7CEykls9OnTB1u2bJH4eyhSI/vDhw948eIFBAIBGjVqJDGxlqK5cuUKAgMDcejQIQDA8OHDMWHCBN7ezuU9xsR5iwHs79+yfmru3Lki/QzffopPEiBAPhkhvlEhBQUF8PDwwJEjR1BUVITevXtj06ZNckcwyCI1UlpaisDAQCxZsgRCoRCLFy/G2LFjeXmniyMiIgITJ05EVlZWlXquf/nyBYcOHUJQUBAuXryI+vXrM2Hb0oY5y0rZuHjSpEmcUjt87ilZIwl2797Nq61jxoxhLas4Z9TR0UFCQoLM3vdsJCcn488//xSr4ysviupn/fz8MHPmTBQXF0NHRwcCgQDZ2dlQVlbGunXrMHXqVNZjCwsLmTFGxbHQ58+fERMTAysrK05ppHbt2mHdunXo1auX2PILFy7Aw8MDd+/ele0CefA9kkv9nzAyHjt2DBMmTKh0w9eqVQsBAQG8wiDT09MREhKC4OBgZGZmwtHRUaqQAnnDjMRpSlE50VY+RjFFZIY2MjLC6dOnWa87JSUFffv2ZQ0vr9hBiOvkJOkYySpSXh5FJfI5evQohg0bVqm9kv4LHx8fzJ8/H23atEFycjIAwMvLC76+vpg2bRqmTJkicSDyqyR+EQqFiI6OZgzP0ujWKAJ1dXXcvXuX0eSqSHJyMjp06MAqvl+e4uJirF+/Hvv372cMEc2bN4eDgwNmzJgBFRUVsccpwkCoCGmIcePG8QqPY5u0yjtBKOPFixcICgpCcHAw8vPz8enTJ4SFhWH48OESj1XExPt7wCeZT3k+ffqEZcuWYdu2bejcuTNWr14tUbtVEULfd+7c4ZWE4e7duyguLpaoaSst1tbWuHjxIqysrODs7IwBAwb8ECOEn58fZs2aha9fv0JXV5fJtKqqqgpfX19MnjyZ9VhFyAA0bdoUhw8fZu0H79+/j8GDB7PeT4p4/1f1wpYiZCEUFapVXFyMY8eOsS5aKOLZkndRRxFUZcbRiIgILFmyRKJeYKtWrbB7926pkr2Up2JipwYNGsh8DZmZmYxGdrNmzeQO05SGhw8fwtXVFdeuXRP5vkePHti2bRuj88zFkydPcPToUcZI2bhxY9ja2kq9oAJ8kxU6cOAAgoODce3aNTRv3hwuLi6YM2cO6zHyvn/lXWjkY3iSV0aIbz/1PbK/8yEyMhLz58/H+/fvMW/ePEybNk0mbeiycVlISAhevXoFS0tLuLi4cCY8USRl59+9ezdevnwJCwsLTJgwAUOGDKlSrWs+yWck3VNr1qxBYGAgcnNzYW9vD0dHR7Rt25b3+5dL6kcgECAvLw/FxcUS3zeKlPhgY9myZbhw4QJiYmLkqkccQqEQGRkZCsl6/vLlSxw6dIjp75s3b47hw4dL1DPeuHEjjh07hgsXLogt7927N2xtbTkNldra2nj48CGrxN6///6LVq1a8U5aKAvfI7nUL29kjI2NhYWFBWxsbODu7s4Yx5KSkrBu3TocP34cMTEx6NKlC+86r169iqCgIBw8eBAtWrSAi4sLXFxcOA2J8hqEFJmdUp7M0Gpqanjw4AGn11fr1q1ZDTLydnLyiJQrkoKCAnh6emLnzp2YN28evLy8pBLsNTMzw+zZs+Hs7IyYmBj07NkTPXv2xKFDh3hlpga+6ULMnj27yq+1qpFHt6bMQ1meFSUzMzN4eXnB0dFRbHloaCi8vb0lJlkqKChAnz59cP36dfTu3RtmZmYgIqSkpOD8+fP4448/cPbsWbEaNv+VTGOSUIQnYnmICGfOnGFW7GvVqoWhQ4fy9lb4mZHGG8LX1xdr165Fo0aNsGLFClhbW/M6hyLeG6qqqliwYAG8vLxk9npgg6+h9e3btwgODkZwcDA+f/6MMWPGwNnZmdekWxzS6tadOHECgwcPhpubG9zd3VGvXj2mXWvXrsXmzZtx9OhR1v9FEcZ3NTU1JCcns7b5+fPnaNGihcTFEHne/1W9sKUIIyNfLwpJyYAAbs0yRTxb8i7qlCcrKwupqakQCARo2rQp73GEvMZSSREZY8aMwfbt2znb8PXrVzx69AitW7cGAGzfvh1fv35lypWUlODq6sra/ygqa6o8ODs7S9xHIBCw6jqmp6ejVatW0NfXx99//w1TU1MQEZKSkrBr1y58/PgRDx484Hz2Vq5ciYULF6K0tJTx/H7//j2UlJSwYsUKiRE+XJw4cQJjxoypcs+1MuRZaKxq+PZT8mR/5xORIRAIEBERwVp+6dIleHp64v79+5gxYwY8PT0lRtNVpLCwEAcPHkRQUBAuX76M+vXrY9y4cRg/fjynV19Vc/78eQQFBeHIkSNQU1OrpIP+s6GISAJxvH37FkuWLEFgYCB69uyJ06dPs+6rKCMj2/g7OzsbN2/exKlTp3DmzBn07NlTpmviQlxiRnFUZdKVTp06YcGCBawOasePH8fSpUsRHx/PWoeenh5Onz7N6igQFxeHfv36ISsrSxFNFouiE7eJ45c3MlpbW8PQ0JA17HTSpEl4+fIlTp48KXXdGRkZsLe3x6VLl/D+/XvOFc+fMbRVlszQTZs2hY+PD2vihsjISHh4eLBOGuXt5BQhUg7Il4kvNjYWY8eORbVq1RASEoLffvuNV13l0dDQQEpKCrOKUa1aNVy+fBmdO3fmXYe8yYh+FuTxKuWzojRkyBBMmTKFtV4vLy/s2bMH8fHxqFOnjkhZeno6OnfuDEdHR3h7e3O2b+HChQgJCUFUVFSl++bevXuwsbHB+PHjsXjxYs56fnb4JJeoignCp0+fsHv3bgQHByMhIUHmegDg0KFDP3yyImmiUlJSgl27dmHJkiVQU1PD0qVL4ejoKFUGe76roFwePidPnsSkSZNgYGCA0NBQGBsb8z6/JGQxKl2+fBlBQUGIiIhA69atcf78eYmyJxUnbNKG8PXo0QN//vknli9fLrb8n3/+wZUrV1gNT4owvhsaGmLXrl3o16+f2PJTp05h4sSJePnypcS6ANne/4r0IhDH9zQycoXwxsbGYvPmzQAgs3TL9+LFixeYMmUKzpw5IxLm269fP2zZskWiIUAeY6kiIjKAb8/Hjh07mLZoa2tDT0+PmUh++PABGzZsgIuLi8KvQVFwZTsuKSnB+fPnOSUuPD09cf78eVy7dq3SQmRBQQG6deuGvn37YuXKlWKPv3jxInr37o0FCxZgxowZjOfTp0+fsGHDBqxYsQLR0dFSJdPLz89HWFgYgoKCcO3aNTRt2hTOzs6YO3cu7zrKU1paihMnTiAgIABHjhzhdUxVLDS+fv2al8QFG3z7KXmyv1f0CN23bx8GDRpUaTGdbQHC2toaFy5cYMacdevW5TyfOCZOnIjw8HAUFhZi8ODBcHZ2Rt++faUag1QV5ZNUVKtWDZmZmT+6SZwoIpKgPDk5OVi9ejU2btyIli1bYuXKlRIlQhRlZGRb6CxLhDdr1iyp5rLSIBQK4e7uLjHhLVfSFXkzr1evXh337t3j9EJs27Yt5z1paWmJzp07Y9WqVWLLPT09ER8fj4sXL/JqqzwoKnGbWBSi7PgTo6enR4mJiazl9+7dIz09PanqvHbtGrm4uJCOjg79/vvvtG3bNonJPxSVpOPVq1e0ceNGmjJlCk2dOpU2bdpUKWmALCQmJtKMGTNIX1+fc7+pU6dSq1atqKCgoFJZfn4+tWrViqZNm8Z6vCRRaEli6YoSKS9L1FOWxEfcxtYOFRUVcnd3p8LCQqnPW/78XKLx0lxDxa3890pKSjK38b/A77//TseOHWMtj4qKot9//52zjs+fP1PLli1JW1ubXF1dacOGDbRx40b6+++/SVtbm1q0aFEpEYk4mjdvTocOHWItDw8Pp+bNm0usR1b09PSoevXqEjcu1NXVRZ5PKysrevPmDfNZ2kQhpaWldOrUKRoxYgRVq1aN6tevz9k/SOLWrVs0YMAAifsVFRXRgwcPmIRCZRw5coTatGlDqqqqMrdBUXAl4gkLC6PmzZtT7dq1acOGDSJi3dLA1kdU3CSRlZVFY8eOJU1NTdq0aZNMbRGHLAmN8vPzKSQkhDp16kTq6upiE6FURB7RdyIibW1tSklJYS1PSUkhLS0tie14/vw5LVy4kBo2bEi1atUioVBIBw8elHhc2TV069ZNbFlpaSl169aN8xq44Pv+FwgEvPoZNjZu3Mi5zZkzR+4EV0uXLqUePXrIdGxycjLZ2tqSkpISjRkzhtLS0lj35fNsVfX7999//6U6depQgwYNaMWKFXT48GGKjIwkb29vatCgAdWtW5devnxZZec3NTWlgIAAIiK6ePEiCQQC6tWrF2VmZkpVT69evWjfvn3M54rjoW3btpGFhQXr8SEhIXKNx6qSI0eOUIsWLUhPT49WrlzJul/79u0pLCyMtXz//v3Uvn171vKRI0fSxIkTWcv/+usvsrOz49Xmy5cv0/jx40lbW5s0NDRozJgxdOnSJV7HiuPx48c0d+5cqlevHqmpqdHgwYNlqufjx4+0fv16atOmjUzHv337lqZOnUpqamqc+2VnZ3NuV65c4dVPVZzvEEmfCKf8cdLMEQQCAamoqEjsr7lo3bo1bdiwgT5+/Ch1e6uCFy9e0OLFi6lRo0akpKRElpaWtGfPHrHz0Z+VmJgYGjNmDGlqalKbNm1ISUmJrl69yvv4L1++0Lp166hmzZpkYmLCe/xA9O2e2L17N5PYSkNDg3bu3Ml8DgkJkfv9W9UowpYiad4v6d2tpaVFt27dYi2/deuWxPHgoUOHSFlZmTZv3iySfKe4uJg2bdpEKioqUv23ikLWxG1s/PKejOrq6khJSWHV1ktLS4OZmZnE1eq3b99i9+7dCAoKQmZmJkaPHg0XFxe0bNmSVzvGjx+PTZs2yWUVlkcTii9FRUWsunHAt5WODh06QElJCVOnToWJiQkEAgGSk5OxdetWlJSU4M6dO5U8wsqo6OqcmJgIU1NTqKqqAvimg/Tw4UNObUpFiJTL4z13+fJlqVaDxSEUCrF8+XJmNcbT0xOzZ8+utOrPJSR87949sd8TEQ4cOIBNmzZBS0sL7969k6utVc2aNWswbdo0xhupzKOzLMQ5JycHnp6e8PPzq3SsIlaUgG9u/vPmzUNYWBizb/Xq1TFq1CisWLGCV+iZmpoanjx5wrpC/fLlSzRv3hyFhYVir4PP6vCnT59YyxQRcs1npVOSZiobfD0Rz507x4TfTZgwAU2aNEFKSgrmzp2LqKgo9OnThzMkJCkpCQMHDmSe8cGDB2Pbtm0YOXIk7t27hwkTJmDGjBms/1N0dDSmTp2KuLi4Sh5+2dnZ6Nq1K7Zv344///xT6t+gPFzeEGVabfb29pxehr6+vpznKO/hQ0SwtraGv79/JS8Ovh4+hw4dgp2dHTQ1NSuFe3Hdm2xI47lWPrTD2NgY48ePh4ODA++wUHnQ0tJCYmIiq67Zs2fP0KZNG+Tm5vKqj2Twznn69Cl+++03mJiYwN3dXeTdu27dOjx+/Bi3bt2SK2GHpPe/UCjEhg0bJIbcsfUxfMPTnz9/zlpWFaFab968waJFixASEgIrKyusXLlSYjIePp6QRMRLy1dWnJ2d8fTpU5w5c0as91u/fv3QrFkz1hBdgD0iQldXV+L/rIiIDOCbhuKJEyeYxIkV3zvJycn4448/WPuYnzFS6Nq1a/D09MTdu3cxdepUzJ07l1NXTU9Pj/P5TU1NRceOHVnD5xo3bozQ0FB069ZNbPmVK1cwZswYzmerzNvq6dOn6NixI5ydnSW+g9goKChAeHg4AgICEBcXh5KSEqxfvx7Ozs4SvZDkISsrC1OmTGHGEHPnzsXUqVOxePFi+Pj4oGXLlpg1axbs7e1Z65AkpUA8w+8rJsEBxHvR89FclVZH+FeR4CksLERERAQCAwNx6dIl1KtXD2PHjoWzs7NMOqM/C9JGEhARdu/ejYULF6K4uBiLFi2Ci4uLVPrU8uaG+BlQRF+fnZ0t9vv8/Hxs3LgRmzZtQpMmTfDgwQOx+5mbm2PIkCHw9PQUW75q1SocOXIEcXFxnO3w8vLCypUroa2tjSZNmkAgEODp06fIzc3F7NmzWb0cvweyJJcSxy9vZGzbti3c3NxYxYjLQm4lhc+qqqrCwMAAY8eOhY2NDetAnE2UvbS0FKWlpSI6AhkZGdi+fTvy8vJgY2PDOjgA5NeEAvhlXRMIBJg2bRrnPmlpaXB1da0UnmNlZQU/Pz/O8JwlS5ZIbAPA7upclSLl5cnMzERUVJTYTF2fPn1Cfn6+SGKShw8fwsfHB3l5ebC1tYWDgwNn/YoQEhbH+fPnMXfuXDx+/BizZs2Ch4dHlQ7oFIE8Gc+0tbURExPDGrJ++/ZtWFhYICcnh1dbiAgfPnwAEUFfX1+qsJDatWvj1KlTrG25efMmBgwYINbo+7MMCKtaGPr27dtYtGgRjh8/LrY8JCQE48ePR40aNfDp0yfUqlWLWUAZNmwY3N3dJU7+bWxskJeXh5kzZ2Lv3r0ICwtDs2bN4OjoiJkzZ0pc6LGxsYGlpSVmzpwptnzTpk24ePEiDh8+zFmPPMl8LCwseN170oZSyJpVGfh2/44ZMwYCgQDu7u6VNHFkuTf5GBnXrFmDoKAgfPz4EaNHj4azszOj3fa96Ny5M+zs7FjvCV9fX4SFheHGjRtS111mfA8KCmJdOCrj1q1bGDduHJKSkkSSvrVo0QJBQUGcSXf4ZKcUCARwcnJiLf8Zko0pMlQrOzsbK1aswObNm9GuXTusXr1arsWDlJQUzJs3D1FRURg9ejSWLVtWpXIlBgYGCA8PZx07Xr58GXZ2dnjz5g1rHVwGFX19fcyZM4d14ivpfcEXNTU1PHz4EE2bNgUAvH//HjVr1mQmxampqWjZsiW+fPnCqx0/kocPH2Lu3Lk4ffo0xowZgyVLllRKYicOSZPnjIwM1K9fn1UjVENDA48fP2Y916tXr9C8eXNOo7e+vj6T1JJPxntxxMfHw9/fH2FhYTA2NmYSXTVo0IBXWOjSpUslnkMgEGDBggViyyZPnoyoqCiMGjUKp0+fRnJyMqysrFBYWIhFixbx1q3ng6S6FJmETp53NxvFxcWc2nZc0hnlkbTYKQ96enooLCzEwIED4eLiAisrK4VrQ/9o7t+/j4CAAOzbt4/VKaRNmzZ4+vQppk2bBjc3N1Ytfnnnvnxo0aIFrl69ykjETZw4Ed7e3oyMyrt379CoUaMqkRrh09ffvHlTqgSE0mZe37lzJ2bNmoUDBw5g4MCBImVRUVGwt7eHr68vJk6cKPHc8fHx2Lt3L1JTU0FEMDY2hoODAzp16sS7/VVFRkYGduzYgYULF8peiVx+kP8BfH19qUaNGnTixIlKZcePH6eaNWuSr6+vxHrEudPyDa8l+hbq9NdffzGfP3/+TIaGhqSvr09t2rQhZWVlsW0so3v37uTl5cVa7uXlRd27d+e8hkaNGkncGjduzFlHeT59+kTx8fF048YN+vTpE+/j5KFiiBLbZ3nhCuOzs7OjmTNnMp8zMjKoevXq1LJlS7KxsSEVFRXavXu33G2Qhlu3blHv3r2pWrVqNGXKFIWE5n8vJIWOc4Xodu7cmVatWsVa98qVK6lz586KaywHI0eOpKFDh7KWDx06lEaMGPFd2iIr8vwXZZw9e5Y8PDxo3rx5zLHJyck0ePBgEgqFZGVlxXps27ZtmZCysLAwEggE1KFDB0pNTeV9DXXq1KHbt28TEVFmZiYJBALauXMn7+MbNmxISUlJrOXJyclkaGgosZ7g4GBe2/dEFlmGoqIimj9/PqmqqtLMmTOlCk2SFKqlra0t8X4SCARkZGREU6ZMoZkzZ7JuVUlwcDCpq6vT1q1bqaioiPm+qKiItmzZQurq6hQUFFSlbSjP3bt3KTw8nMLCwuju3bu8jhEIBKStrU3Vq1cnPT09sZuk8DmhUFil75YPHz7Q+vXrq6z+8qxevZpq1KhBLVq0oCNHjshV1+vXr2nChAmkoqJCAwcOpPv37yuoldyoqqpyhkO/fPlSojREQkKC2C0mJobWrFlDNWrUoG3btok9ViAQkLe3NxPurqamRgsWLKgUBi+Jhg0bco5/jx07Rg0bNmQtFwgElcJSvzf//vsvjRs3jpSVlcnW1pbzHSIOoVBIqamprCG6jx8/5uwrJYUR8nl3f/36Vao2i0NJSYnc3NwqyUsoKyvTw4cPJR4vEAiofv361L59e2rXrp3YjStsvGHDhnTu3DkiInr69CkJBAKaMWOGXNf0MyDLu5uNhw8f0qxZs6h27dqc+/Xo0YMsLCw4N0tLS4W0iY1169bR+/fvq/QcPwtcz584G4S0c9/x48fzkn6SRMW+Rltbu9I8QSAQyH0ecbx48YJKS0spJyeH8vPzRcru3r1LAwcOlMoGEBERQSYmJlSjRg1au3Ytb9mN0aNHk0AgIDMzM7K1taUhQ4aQqakpCYVC3rIUPzuyyBlVhH863P8oM2bMQGxsLAYOHAgTExOR7NJPnjyBra0tZsyYIbEerhADPly7dg1btmxhPu/evRvFxcV48uQJdHV14enpibVr17J6It69exc7d+5krd/JyQkbN27kbIO811CR6tWrS7VawMWlS5eQl5eHLl26cIaUfA8RVEnExcWJrDzu3r0bNWrUQEJCApSVleHj44OtW7dyeoQA3zxQUlNTUVRUBGNjY6myU5eRmpoKLy8vREREYOTIkUhKSvpPhxBIi7OzM2bNmoWWLVuKXVFavny5xFVWS0tLXl6lbMllyli0aBE6d+4Mc3NzzJo1C6ampgC+9TXr169HUlKSRPd5eeD7v3N5xwoEApHfouJnSVT0RPT39xfxRLx37x6nh8TTp08xatQoAMDw4cOhpKQEX19fxsOFD+/evWPCgfX09KChoSGV4H9GRgZnyKiysjLev38vsR4+nn1sXimSKFv53rBhg0zHS0OHDh2Qm5uLs2fPSp04QRHt6969OwQCAR4+fMi6T1UL0Y8dOxb379/H1KlTMW/ePOZ+LAttmT59OsaNG8d6vLzeORVp164d2rVrx2vfMszMzJCRkQFHR0c4OzuzRl1wQVUQ+EJEOHv2LAICAnD06FHo6OjAzc2N9/EfPnyAQCBAzZo1pTrv3Llzoa6ujmbNmiEkJITVm5wrlLGiJ+SFCxfkllGQBgMDAzx8+JDVe+3BgwdM1AsbZSHK4ujRowfq1asHHx8f/P3335XKGzZsiF27djGf69ati9DQUJF9BAIBp+wL8C2hn7e3t9jxLxFh5cqV6NWrF2cd48aNEwlLFQefsFRZKZMvcHd3R9euXfHkyRM8efKk0n42NjZij6f/58XCBlXImC0Of39/1sgVPtEc27Ztk7gPwC3j07NnTwQEBODdu3dwcnKClZWVVP1zv379cPHiRSZce8CAAVKFhL5584bxlmzSpAnU1NQwYcIE3scD8kkIKIqKIYqlpaW4cOFCpRBOtvupIrm5uThw4AACAgJw8+ZNmJubS0zgExMTI1WbqwK+3pTfk/Xr1+PPP/9Ex44dAXx7NmfOnMk53pE3kkARc9+QkBCsWrVK/mQeFRA3LqiqMZmSkhK6deuGuLg4RrZt+fLl+Pvvv7F//34MHjwYV69elViPvJnX9+zZAxsbG+zbtw+PHz8GEcHExARLlizByJEjJR4vj0zYf4lfPly6jLCwMOzfvx+PHz8GABgbG8POzg52dnbf5fyampp48OABE+ozdOhQ1K9fn8lgmJSUBAsLC1ZXaUVrQv0o1q5di9zcXCZsmojQv39/nD17FsC3kNMLFy7w1rqsKrjC+CrqfFpbW6Nly5ZYu3YtAODx48fo0qULPn78yFr/ixcvMHjwYGbAYGhoiIiICKkyVU+ePBkBAQGwtLTEqlWrpJ50/izIG6Lr6OiIffv2wdTUVESn7PHjxxg5ciT279/PeX62EEjgW3be/fv3c2aFLE9cXBxcXFyQnJwsEs5oamoKf39/dO3aVexxijAQCoVCGBkZwcHBgTOUgGtRRSgUQldXl2l7VlYWdHR0mLAB+n86sGy/Rbt27WBnZ4e5c+ciPDwcdnZ2aN++PcLDw3kZChURfqekpIT09HQmdENHRwf37t3jrQfXtGlT+Pj4sGYLjYyMhIeHh9RSBuVJSkpCQEAA9uzZg4yMDF7HlN2LAQEBuHXrFtq0aSN1lm1tbW0kJiby/i0AYMKECdi4cWOlTMyKoKioCG/fvq3SkFJFEhcXh/379zPGg7JxhLm5Oedx7du3Zy0TCAR49OgRCgsLq1wL6caNGwgMDGQkBFxcXDB69GjeIVbFxcVISkpC8+bNK2X0zs/PR2pqKlq1asUrnO3FixcIDAxEcHAwXr9+jdGjR2PMmDGwtLSUaFTIysqCl5dXJQ1dOzs7LF++nJdO57hx43hNhNhCGdesWYPVq1ejbt26WLFiBQYPHiyxLkXj5uaG6OhoXLhwoVLG73fv3qFPnz6wtLSUy9j/7NkztGvXjnemell4+vQpOnToAFNTU3h4eMDY2BgCgQApKSnw8fHBo0ePcPv2bVa9QqFQiJEjR0rMMs8nLFVW5NU7kzdEl48ED8DtbMDnvcBHxufly5cICgpCUFAQCgoKMGrUKPj5+SExMZFx9ODi7du3CA4ORnBwMD5//owxY8bA2dkZJiYmEo+t+P6X5Z0nj4SAolCUft7Vq1fh7++PiIgING7cGElJSbh06RL++OMPifU3adIEN2/elHoBR5EoQq9c0bRu3RpPnz5F06ZNsXz5cgQEBODy5cuseqkAOJ1nBAIB8vLyUFxcXKVjAEXJSlS1rBIXjo6OuH//Pv766y9ERETg8uXLaNeuHdq2bYsFCxbwes4VkXmdizI9Q1tbW9Z95JEJ+15Io5nOxv8ZI6O8SNJsLIPNO6BmzZq4cuUKs8JmYGCAtWvXYvTo0QC+DeRatWrFqmGgKE2o0tJSBAcHIzIyEi9evIBAIEDjxo0xfPhwODk5VblHSIcOHeDp6cl4Kx08eBBjx47FuXPnYGZmhjFjxkBDQwPh4eFij/9eK4xcD1edOnVw9uxZxgOgVq1a2LFjB4YNGwYAePLkCdq3b89p8B01ahQSEhKwaNEiqKmpYe3atSgpKUF8fDzvNgqFQqipqTEec2zcuXOHd50/AklJcHJycrBw4ULOji48PBz79u3DkydPRHQt+KwoiaO4uBhbt26Ft7c3dHV1sWzZMqkWJO7evStiiJBkAFaEgTA8PBxBQUGIiYlB//794ezsDGtra6n0a+TVhiw/oC8tLUW1atVw/vx53h5wQqEQISEhzLNsb2+PDRs2VEokxbV6L8lQWgbboHTatGmIiYnBzZs3xSZT6NSpEywtLXlp3JZHnCfBsGHDOI3cwLfJZ0BAACIiIlBYWIjZs2djwoQJvBJ8DB06VOSzONF5gNvDJysrC/v374erqysAYPTo0SK6XkpKSti1a5dMCVgUMYj5L5OQkIC5c+ciOjoazs7O2L59+3c5b0FBAQ4ePIigoCDEx8fD1tYWgYGBEj3BgoODsWXLFty4caOSIbCkpASdO3eGm5sbHB0dxR7/5csXREZGwt/fH7Gxsejfvz8cHBxgb2/PS68N+PbcdunShTFMmpmZgYiQnJyMffv2wdDQELGxsZyTOkVQlpypd+/enEbRqvSey8zMROfOnZGeng5HR0cR7/l9+/ahbt26iIuLY7SzZOH27dsYMmQI69hLEREZwDdtqnHjxiElJaXSAl1QUBCnzubPpMn4P0Q5d+4cAgMDceTIERgaGmL48OEYPnw4OnTowOv4y5cvIygoCBEREWjdujXOnz/PaUyumGxFlncemzZuVlYW4uPjsWrVKnh7e4v17v1ZWLNmDQIDA5Gbmwt7e3s4Ojqibdu2UFFR4d3X/gzP1c+iV16RwsJCbNy4EfPmzYOWlhZu3rzJywhekbdv32LJkiUIDAxEz549OZMaAsDr168RERGBx48fQyAQwNjYmHFakoRQKERGRkalBSlpkWTIr0oDWf369REeHo4//vgD6enpMDAwwIoVKyR65ZZHKBRCWVkZmpqanDYPaQ3XKSkpCAwMREhICDIzM/H161fONvwoQy1f/mdk5AHf1VdJK/llK1tcPxfXqlLPnj3RuXNnrFy5EleuXIGFhQVevXrFhLKcO3cOrq6uSE1NFXt8SEgIXF1d4ePjg4kTJzIDueLiYuzYsQOzZ8+Gn58fZ8gWEWHQoEE4efIk2rZtC1NTU2Zwfv/+fdjY2ODIkSOcv4O8VK9eHbGxscxq5vjx41FcXMyE2MTFxWHEiBF4+fKl2OMVtcIoyUDw+vVr+Pj4iP0/Bw0ahNq1a2PXrl2IjIzE6NGjkZ6ezkxqTpw4AQ8PDyQnJ7PWb2BggP379zPGl1evXsHIyAi5ubkSV+PLkDeJzs+CIlbgFcnevXuxcOFCFBQU4J9//hF53qoKRRgIy3j9+jXjBZCXl4cxY8bAxcUFzZs3r4KWiyKvJ6IiVu/lHZRmZGSgQ4cOTChGee/YrVu3oqSkBHfu3Klk+GRDFk+Ct2/fIigoCIGBgcjLy4O9vT0cHBzQpUsX3hMEQDHC8z4+PkhISMCePXsAfPtPraysmJCb69evw87ODosXL+Z1rvLwHcTk5eVh9erVYhfHPDw8WEXQFYW8i4wVef78ORYsWICwsDAMHToUy5cv/y7PZ0UuX76MRYsW4fLly/jw4YNEw1y3bt0wdepU1gWX8PBwbNmyBZcvXxZbXqtWLbRo0QKOjo4YMWIEcz5pJr5ubm64cOECzp8/X+kZTE9PR9++fdGrVy+sX79eYl3yIK8npKLIzMzE/PnzERYWxnjS6OnpYeTIkfD29pbLC+nr169wcnICEYld+FVEREZFyi/QNW/enNMLuIyfMbu0tISHh8PW1haqqqoAvv22hoaGjAE7Pz8fW7ZswZw5c6qsDT179kRkZKRMC0aSyMzMxJ49exAYGIjExETeE9eyBZGtW7fi/v37SE9P55yvfY/ncs+ePcx78WdFWVkZnp6eWLp0qcgiyH/NyHj58mV07dq1ysfgXPj7+0NfX1/EW72goAA9e/bE169f8fbtW/j4+EhM+FmenJwcrF69Ghs3bkTLli2xcuVKWFpach7j5+eHWbNm4evXr9DV1WUii1RVVRlZIi4qLsCzIcm4JhQK0apVK+Y/SUxMhKmpKdN3FRcX4+HDh1ViIFNSUsLr168Z70NNTU3cunWLl4d0GYo0XOfl5SEsLAwBAQGIi4uDpaUl7OzsYGtryzjKiONnMDJKspW8f/8e+/btk68Ncik6/gdgE0mVNlHIixcveG1sREdHk5qaGjVp0oTU1dXJ2dlZpNzV1ZXGjBnD2QZ3d3cSCASko6ND7du3p/bt25OOjg4JhUJyc3OTeA2BgYGkra1N0dHRlcouXLhA2traFBISIrEeedDU1BQRiDUxMSE/Pz/mc1paGqmpqbEeL49IeXn4JMFp1KiR2GPv3r1LNWvWJFVVVRIKhfTPP/+IlDs6OtKkSZM4zy8QCCg9PV3kO01NTXr+/LnEtv+P/x82kfSKGx9OnTpFbdu2JR0dHVq6dCnl5uZK3Z7c3FxasGABtWzZkjQ1NUlLS4tat25NS5Ysoby8PInHv3r1ipYvX07NmjWjevXqkaenJz1+/FjqdpQRExNDFhYWJBQKpUrOlJ+fT0ePHqW1a9eSj48PHTt2rJLIsjgEAgHt3r2bjh49SkePHiUNDQ3auXMn87lsq0ouXbokkpxDFl68eEH9+/cXSfAlFAqpf//+vJ/R1atXk4mJCdWvX588PDwoISGBiPgJ4FerVo0cHR3p9OnTVFJSwnzPVzxfkfz+++8iSRkqCtBHRkZSu3btZKqbj7D0ly9f6LfffqNq1aqRra0tzZ07lzw9PcnGxoZUVVXJ3NxcIQkLuCj7/ysme+Ob+K2M9+/f09SpU0lVVZV69uxJ8fHxCmvjp0+feL2/X716Rd7e3kwfM3v2bEpOTuZ1Dn19fc77/9mzZ1SrVi3Wcj09PerevTvt3LlTpF+W5r42MjKi06dPs5afOnWKjIyMeNX1K1FaWkoZGRmUkZFBpaWlvI8bMmSI2K1nz55Uu3ZtMjAwYE04MXLkSDI2Nqa9e/dSREQEmZub0++//y51283NzWnVqlW878OKSEp68j1wdXWlnJwc5vPu3btFPmdmZlL//v1Zj6+YVElcMgWuPqZ///6UlZXFfF6+fDllZmYynz98+EBmZmac1/C9fseyxGxcxMbG0oQJE0hHR4c6duxIW7duFbmeH8nTp09JW1u7Ss8h7/3k7e1NzZs3J0NDQ5ozZw6TjEqavlYgENDFixfp3r17nFtVUtXJxvhgampKV69eZT5//fqVrKysqFu3bpSdnU2hoaHUsWNHXnV9+fKF1q1bRzVr1iQTExM6ePAgr+OOHz9OSkpK5O7uTm/evGG+f/PmDc2cOVNi8liib//nxo0b5U5GuHjxYl5bVSAUCkWSfGlpadGzZ8+q5FxcxMbGkrOzM2lpaVH79u3Jx8eHlJSUpHq25E2yKS+SkjqVbfLwyxsZY2JieG2S2LFjB719+1autjx8+JA2bNhABw4cEJk4ltXPJ0vk9evXafr06dS/f3/q378/zZgxg65fv87r/H369GEyt4rD29ub+vbty6suWWnbti2ThTMtLY0EAoHIQ3nt2jWqX7++zPWHhoZS27Zt5WylZN69e0dHjhyhuLi4SmXHjx+X2OlV7CiJvg0qf0Rn+TNQUlJCAQEBNGDAAGrZsiW1atWKbGxsKCQkhHPCpIhFhBs3bpCFhQWpqamRm5ubzJnsFG0MkdVASERUUFBAoaGhZGlpSerq6jRq1CjeWdOOHj1K+vr6lYwo+vr6dOzYMc5juYww0hhj5EGRg9JPnz5RfHw83bhxQ+r/QElJiebPn0/FxcUi3/MZ5BsbG1OjRo1o/vz5IhPvH2FkrFmzJj169Ij5/Ntvv4lktH369ClpamrKVDcfI+OGDRuoTp06lbKVEn3L9F2nTh3atGmTTOfni7yLjLm5ubR48WLS0dGhDh060JkzZxTeRkm/ZVhYGPXr14/U1dXJ1taWjh49WunelISGhgbnpPLevXukoaHBWl5QUEB79uxh+qWhQ4dSZGQkqaio8L6v+WRUrlatGq+6fnUKCgpo7dq1nPuMGzdO7DZ9+nTy8/PjXKSrV6+eyPj55cuXJBQKeS1IlSc4OJiGDh1KWlpa1Lx5c/Lw8KArV67wNpbGxMTIvbAkL/IaCeWddMp7fnFtqAok9VOrV68mU1NT0tfXJzc3N0pMTJSq/u9hlLp16xYZGhpW6TkU8X8SfXs2xowZQ5qamtSmTRtSUlISMZhxwbW4Jo2Tjjz8DAsI6urqlJaWRkTfFnPs7OyoT58+jONAamoqqaurc9ZRWlpKwcHB1LBhQzIwMKAdO3ZI9f7t3r07eXl5sZZ7eXlR9+7dOev4GX5LeREIBNS6dWvG0UpJSYlatmzJfC7bqhIzMzMyMjKiefPmiYxbpDXge3t708aNG2njxo2kpqZGCxYsYD4vX768yp+t78Evn106LS0No0aNkqg1JIn9+/dj+vTpaNu2LQYPHozBgwdLnZykRYsWrC7qEydO5FWHubm5RJF5NhITE7FmzRrW8v79+0utMyYtrq6umDp1Kq5cuYK4uDh06dJF5DeJjo7mFR7DRteuXXklZIiOjsbUqVMRFxdXKfQiOzsbXbt2xfbt21kzRZaFfRUVFaG4uFjElX/AgAESz0//TzewvNt6bm4u2rdvLxIyyuW23r59e15hIT+7JiP9vzD+U6dOoW3btmjdujUTxj9u3DhERkayhvErIuOaubk51NXV4erqikaNGmHfvn1i95OUIXPbtm149eoV7t27V0mbJSUlBRYWFti+fTumTZvGWU9hYSEOHTqEwMBA3LhxAyNGjOAdDnrjxg0EBAQgLCwMTZs2hbOzMyIiInjrk8XGxmL48OGwsbGBu7s7E4KQlJSEdevWYfjw4YiJiUGXLl3EHl9aWsrrPGxMnjwZa9asYfQ5Q0NDMWTIEOZzVlYWHBwccPLkSdY6SIEKINWrV8fvv/8u07FLly5FcHAwQkNDYW9vDycnJ87M2uV59OgRrl27hoCAAPz+++8wNjZmdO6qWje3Ivn5+SLaMrdu3RIpz8vLY/3fJYUZP3r0SOL5IyMjsWDBArF6R6ampvDy8sKhQ4ckPlfyEBISIldYdtOmTZGTk4Np06bB3t4eAoFA7G8jS8ZnvtjZ2aFhw4aYOXMm6tSpgxcvXmDr1q2V9uPq55o3b47Y2FjWdl69epUz7FtNTQ2jR4/G6NGj8fTpUwQFBWH69OkoLi6Gt7c3xo0bh549e3JqHNaqVQsvXrxgzaj8/PnzH5qo4Hvz4cMH3LhxAyoqKujVqxeUlJRQVFQEPz8/rFy5EsXFxfDw8GA9Xp6w0fT0dBFN6AYNGkBdXR0ZGRlo1KgR73rGjh2LsWPH4suXL7hw4QKOHj2KkSNHoqioCAMGDMDgwYNhZWXF+vzdu3ePVUevPJLe4fJQ8b2jyPfQ9zx/Tk5OJS3iivBNFCULc+fORcOGDTFy5EgIBALW+9PX11fs91X9u3/9+hVr1qyReR7GF0X9nz169ECPHj2wZcsW7N27F0FBQejRowc6deqE4cOHSwyZvHHjhtwafvLyvcc8FalTpw6OHz8OZ2dnTJ48GV++fMHx48dFpA0k/UZt27bF06dPMW3aNLi5uUFDQwN5eXmV9mN7tu7evYudO3ey1u/k5ISNGzdytuFH/46KoKL8lywJ1+RNJpSamgo7OztYWlpKFaZdnoYNG2LXrl3M57p16zKyceX3+a/zy2syKlKrJTMzEydOnMCxY8dw5swZ1KpVC4MHD4aNjQ26d+/OqSemiMkzF5GRkVi8eDHnpE5VVRVpaWmMDmRF3rx5g8aNG+PLly8ytYEvAQEBOH78OOrWrYtFixaJZHaaPHkyevfuXSlhAV8kiZSXYWNjA0tLS9akC5s2bcLFixdx+PDhSmXidIgiIyN5C1kDitGE+FU0GYOCgjBjxgwcPXq0kiZJdHQ0bG1tsWXLFowZM6bSsbt375Z7EYGPJiSfjIo9evTAyJEjMWXKFLHlmzdvxqFDh1izSIozEI4ePZq3gbBly5Z49+4dHBwc4OLiIpPBwtraGoaGhtixY4fY8kmTJuHly5cy91OSUETGNXnFrfn2PXwTOly6dAmBgYGIiIhA06ZN8fDhQ97ZHYFviw/79+9njM49evSAg4MDbG1tv8vgv1WrVpgzZ47Y5w/49vz6+Pjg4cOHlcq4tIzLvpeksamvr4+YmBjWRb0HDx7A0tIS79+/53lF0iPvOKL82KDi78H3d5CEJH1LRfRza9aswZo1axAdHV2pf7l37x569eqFOXPmSKUbV1paijNnziAgIABRUVHQ0tLCx48fWfd3cXFBamoqzp07x0zyyvjy5QusrKzQtGlTBAQE8G7Df5XY2FgMGDAA2dnZEAgE6NixI4KCgmBra4vS0lK4ubnB2dmZ0zj+7t07zvu6uLgYd+7cQadOnSqVVUwAAHzrs+/duydVNl82bty4gWPHjuHYsWN4+vQpevbsiXnz5lXqOyue6+XLl6hXr57I4i+fd7g8yKux9aOPL6uDq4/4Hv2UhYUFr34qOjpabJkidATZxgDZ2dl48OABlJWVceXKFd5a07JQlZpt9+/fR0BAAPbt24d3797xbsOPQCgUYuLEiRIX+NiMzopg3bp1mD17NpSVlWFgYIB27doxCQrfvHmDAQMGoGPHjiJGo4pUHANURNKzpaWlhcTERNZ77tmzZ2jTpg1nwlFF/Z//dQcXeeffZbr3QUFBKCgogL29PUaPHo3OnTsjISGBt2b6j8bZ2Vns97q6ujAxMYGjoyNjo5KVX97IWFWd5NevXxEdHY1jx44hKioK+fn5GDBgAGxsbNC/f/9KmcwUMXnetWsXzp49CxUVFcyYMQOdO3dGdHQ03N3d8ejRIzg5ObEaB8raUHFAWJ6fIZuRPEgSKS+PkZERTp8+zboKkZKSgr59+4o1VioiM/T/+P/p27cvevbsyZodbMWKFbh06RLOnDlTqexnEnyXxxiiCAOhUCiEpqYmlJWVZc6YVr16dVy+fBmtW7cWW56YmIgePXogMzNTbLm8iymKmijJMyhVRLIUceTk5DCeBLdv3+btSVCe5ORkBAQEIDQ0FJ8+fUJRUZFUbZCFBQsWICQkBPHx8SILQsC3BDWdO3fGmDFjsHz58krHpqWl8TqHkZERa5mKigpevnxZ6dzl22BkZMSZyU9e5B1HKOJ3kMT3yNRdVFSEvn374urVq+jduzdMTU2ZpEjnz5/HH3/8gXPnzkFFRUWm+j98+IDdu3dzPhOvXr1Cx44dUa1aNUyZMkUko7Kfnx++fPmCW7duwdDQUKY2/Jfo1asX9PX18c8//yAwMBAbNmxAo0aNsHjxYjg5OfGaCFZ8h5qZmeHMmTOMFwVXnysukUBWVhZ0dHR4R2Tw5enTpzh27BiToZgLaROOKQJFGAnLDBcAYG9vjw0bNjDJjbKysjB+/HjW4xWR8VUoFCIiIkJiNvKyhIWyUNX9VMXfkQ0bGxvWMrYxgI6ODkxNTTF69Ogq9eYEvk9iiKKiIs6++mcxMnbp0qXSglJ5uIzOiuLKlSsQCoVo3749bGxscP36dTRs2BDPnz9Hw4YNERsby5nog825oCJsz1bnzp1hZ2fH6hjj6+uLsLAw3Lhxg9d5ypOWloa8vDyYmprySr5Y3sGFiLBy5Ur8/ffflfqNqnZwSUxMZLJsN2/evEojQdiIjo5GYGAgIiMjUVhYCA8PD0yYMAHGxsYSjyUipKamoqioCMbGxt89udGQIUPEfp+VlYWHDx9CRUVF7sWU/xNGRkWkbJfErVu3cOzYMRw9ehTDhw/HggULKrVDnheGj48P5s+fjzZt2jBZi728vODr64tp06ZhypQpnB1cWRv69+/P6vX15csXnD59+ocaGe/cuYOFCxfi+PHjYssVtcKopqaGBw8eoFmzZmLLU1NT0bp1axQUFFQqU0Rm6P/x/1O3bl2cPn0a7dq1E1t+9+5d9O/fH+np6ZXKfoZBUBnyGEMUYSBUhHesuro6UlJSWI0daWlpMDMzQ35+vthyeRdTFGVk/BkGpVzw9SRgo6ioCFFRUTJ7fEtDTk4OOnfujFevXsHJyYmReUhJScGePXtQv359xMfHM9mmpSUhIYH12Qd+jsWx7zWO4EKSlMnr16/h4+NT5e/voqIirF+/Hvv27cOTJ08Y6Q8HBwe4ublxPndlGWbHjh0rVqZk9+7dYssq8vz5c0yePBlnz55lvEIFAgH69OmDLVu2sL7XfzVq1aqFS5cuoWXLlsjPz4e2tjYOHDiAESNG8K6DT59br149sZIIiszSWZHCwkKEhYUhLy8Pffv2leo//VFGxvKLW1u3boWjoyNj7MrPz8euXbs43318YJOmqDi+j4qKQs+ePRmHBz7je0WMpz5//sxZXrZQWZVGRknI6435PZD3fuIjfSUQCDhlRiwtLXH48OEqyTbOl59pjF8GEeHUqVO4f/8+6tevj2HDhlX53C8kJASurq7w8fHBxIkTGYNUcXExduzYgdmzZ8PPzw/jxo3jrCMzMxNubm7MdxMnTmS8/k1MTHDmzBmpF+i+d38bHx8PFxcXJCUlibz/W7ZsyUgMfW+ys7Oxd+9eBAYG4s6dO2jVqhVnZKm4iMiIiAj89ttv36vJnBQUFGDMmDEQCAQSnba4+D9hZOQyrJUhKfTN3d0dK1eu5BxAlyFudUjeybOZmRlmz54NZ2dnxMTEoGfPnujZsycOHTrE+wVQVR460nLu3DnGI3PChAlo0qQJUlJSMHfuXERFRaFPnz44ffq02GMVtcLYtGlT+Pj4sFryIyMj4eHhITa8RigU4u3bt8wKM/DNlf3BgwdS6RDJQ79+/bBw4UJ07dqVc7+cnBz4+flBS0uLNYz3RyNPGL8iJv/W1tbYv38/M3jz9vbGlClTmOfq48eP+PPPP5GUlMRZjzzGkKqcrJWnon5oRdq2bQs3NzfW56zMW4bt5fmzhHxV9aD00KFDEr1p+MDmSSBpolZGVXtTlJGZmYl58+YhPDwcWVlZAAA9PT2MHDkSK1askOj5UpGyAZm/vz/u3bsn8f9s1aoV631bXFyMhw8fVrmRkasNZcgaHsRH7oRv+Onz589laoOi4DIaL1u2DImJiTh48KDY8pEjR6Jdu3aYP38+r3NlZmbiyZMnAIBmzZpJfR/+1xHXX969e1cqg9z38JiSxOzZs/H161dGU+zr16/o3LkzHj58CA0NDRQXF+PcuXOsWsAV+RFGRj5hvoBidKTFoYjxPZ93p6QxxPcIueZCEe//wsJCnD17FpaWlpUWzz5//oyYmBhYWVnJrfXPhbz3E5/3hSQJAUUtCsnDzxStpAhev36NiIgIxgPP2NgYQ4cORf369SUe6+HhAV9fX2hra6Np06YAvnl45+bmYvr06Vi/fj3n8V26dMHEiROZvuL06dMYNGgQgoODYWZmhqlTp6JFixbw9/eX6pq+Z3+blJSEzp07w8zMDDNnzoSZmRmj4b9+/Xo8evQIcXFxnCHLjRs35iXJ8PTpU5namJCQgM2bN3NKtvwXIiJv3bqFoUOHSpSf4+KXT/wCfHsA5F1lOHz4ME6fPo09e/ZITEwia6gQF2lpaejduzeAby8fFRUVeHt7S7XCVNXGQz6EhIRg/PjxqFGjBj59+gR/f3/4+vpi8uTJGDZsGO7du8eZHEFR12BtbY2FCxeif//+lUSuCwoKsGjRIgwcOFDssQKBoNJqqVAo/K5C3yNGjMDIkSOhra0NGxsbdOzYEQYGBlBTU0NmZiaSkpJw9epVnDx5EgMHDsTatWu/W9ukpaSkhHPQqqSkhOLiYtbycePGybWIcObMGRED5urVq2Fvb888W8XFxbwSVBARevXqxWkMYYOP8ZDreEkkJSUhICAAe/bsQUZGBut+48aNg4eHB+rUqQNra2uRshMnTmDOnDnw8vKSuR18WLhwIbN6//XrV3h7e4us3ktCEeLWZf+5ioqKSNjD0aNHsXDhQqSkpEg0MsqTXEpPT++HTtQqUr16dWzfvh3btm1jwv319fWl/q3Lh5YYGRlh2LBhErXz+ITcDBs2TKp2yIKVlZVc+jR85E64+NHGQy74Go0jIiKwbt061nomTZoEDw8P3kbG6tWri9UKVNQiwM+OQCBgEnWU9Qn5+fmVFim+12KErJw6dQorVqxgPu/duxdpaWl48uQJGjZsCGdnZyxfvhwnTpz4ga3kJiYmpkrrLykpQVRUFGxtbcWWK2JsbGRkxJp0KSkpCf7+/ti7dy/nGKKqjKh8UcT7f8eOHTh27JjYkGodHR1s2rQJL1++rNKFe3nvJ0W8L7Zu3Yp79+6J9XbU1dXFlStX8Pnz5yodE/5KflB+fn6YNWsWvn79Cl1dXRARPn/+jNmzZzPzYC58fHwwfPhw7N+/n1lc6969O+zs7HglInr8+DE6duzIfD569ChsbGwwevRoAN+kqfguVvwoFi1ahD59+iAiIkLkWW/fvj3s7e0xdOhQLF68mNP7rrwnZ0VevHiBHTt2yJyborCwENHR0RLfVVeuXBGJiOzUqROMjIxQUFDw00RE1qhRg3EqkJX/E0bGTZs2yb0K8uDBA8yePRtdunSBl5cXvLy8eIc3lCHP5LmwsFDEGKaqqloloVtVPThfv349VqxYgblz5yI8PBx2dnZYv3497t69y6zMfA/++ecfREZGwtjYGFOnToWJiQmjLbV161aUlJSwvjgVkRlaXlxcXODk5IRDhw4hLCwMu3btYjoDgUCAFi1awMrKCrdv3xablfVngog4DYWSOnt5FxEUlcWvqowhfA2EFcnNzcWBAwcQEBCAmzdvwtzcnFX3sowZM2YgNjYWAwcOhImJiUh26SdPnsDW1hYzZsyQ+hr40r17dxGDrrhs8d27d+esQ9L/9/HjR4SGhrIONJKSkjBw4EBGR2/w4MHYtm0bRo4ciXv37mHChAmscg7l2bBhA/766y+xE3xdXV1MmjQJvr6+Yo2M5SdqRARra2v4+/vzWu2uSu7fvy+yAs+m3VmeV69eITg4GIGBgcjLy2OyxkZERPASyP5ZElfNnj1b5nFERbmTo0ePSi13IglJ93VVEB0djYCAABw+fJiX0fjp06ec2aebN2/Oy3tAEYsAvwJlY5Hyn8svgvNZjBBnqMzNzWUMlXy9quXh33//FekLzp49i+HDhzOyHTNmzKi06FWeim2seA1l/OzGVnGkpKQgMDCQCXXk0p5NS0vD2bNnUVRUBAsLC6kTEFQ0TMkyhpBHr1ER8Bm/SZLo2Lt3byXJq/K4ublh6dKlPzw66ObNm1UaGnro0CFei0JVaWQMCgqSqK/5X+DEiROYPn063Nzc4O7uzkRuvX37FmvXrsWMGTPQqFEjzn4OAMzNzWXObF5QUCDSB8bGxook/2jSpIlYWaqfiZiYGJw6dUrsYoJAIMD8+fMl/obi5jCfPn3CsmXLsG3bNnTu3BmrV69mPf7r169YsmQJs2g8Z84c2NraIigoCF5eXhAIBBLnSenp6YyeNAA0aNAA6urqyMjI+G4RkZKIjY2V2y7zyxsZFZWyXUNDA1u3bsXw4cPh4uKC48ePY+7cuZVW/djEhBUxefb392c8KYqLixEcHFxpYjJ9+nTOOn704Pzp06cYNWoUAGD48OFQUlKCr68v7xvZ0tKSl5vzhQsXOPepU6cOYmNj4erqinnz5onoOlhZWcHPz08kHLo8ilgxlib8ng1VVVU4ODjAwcEBwDdvkoKCAtSsWbNKvGmrCj5efGyZbQHFLCIoAkUaQ2QZ3Jdx9epV+Pv7IyIiAo0bN0ZSUhLvbMZCoRAHDx5EWFgY9u/fj5SUFACAqakpFi9eDDs7O4l1yLOYoghvEHGDUiLC2bNnERAQgKNHj0JHR4fVGDN37lw0btwYmzZtwt69exEWFoYHDx7A0dERx48f5609eO/ePc6BSt++feHj4yO2rOJETUlJCebm5t81/K88smrgWFtb4+rVqxg4cCA2b96Mfv36QUlJCdu3b/+ezZcbeccRAQEB2L59u4jcSXR0NFJTU+XSu5LmvlYU8hiNlZSU8ObNGyapSEXevHkjcfFWUYsAvwKK8Brja6isSipGgsTFxYkYefT09FiTjZWVl2+jLMZWRZCXl4fVq1cjMjISL168gEAgQOPGjTF8+HB4eHhITEZWvp6wsDAEBAQgLi4OlpaW8Pb2ZvViBIDLly/D2tqaeccqKysjJCQE9vb2Ul+HPGOIMuQJC5WHsWPHil10lkai48mTJ2jbti1reZs2bRhPsqomNzcXSkpKIteUkJCABQsW4OTJk6zXwSeSYtu2bZzzTkUtCslD48aNeSUzkTR//tGsWbMGc+fOrZQgr169evD19YWGhgZWr17NaiDjklIpD1fyEyMjI9y+fRtGRkb48OEDHj58iG7dujHl6enpvAy6FfU+ZbVHyEJOTg7r3Bz4pvGfk5PDu76CggL4+vpi7dq1aNSoESIjIyUaKRcvXoytW7eiT58+uHbtGkaMGMGM7VauXAkHBweJc/CfISKS7Z7Kzs7GzZs3sWLFCrEJHaWCfnEEAgFlZGQotM4jR46QkpISCQQCkU0oFCr0POUxMjKiRo0acW6NGzfmrOPhw4fUuHFjEgqFJBQKaciQIZSenk7du3cnXV1dcnd3p3///bfKroGo8v+hpaVFT58+5X28m5sb6+bs7Ezq6upS/w+fPn2i+Ph4unHjBn369EmqY2WlcePG1KJFC7pz5853Od+vilAolPv5FgqF9O7dO+azlpYWPXv2jPmcnp5epc92ea5cuUJjx44lLS0tat26NSkpKdHVq1d5Hbt69WoyMTGh+vXrk4eHByUkJBARkbKyMj18+LAqm83Qo0cPsrCwkLh9L54/f04LFiwgQ0NDEgqF5OTkROfOnaPi4mLWY+rUqUO3b98mIqLMzEwSCAS0c+dOqc9drVo1evLkCWv5kydPSE1NjVdd0vaTiuThw4ekpaVFv//+O+3bt4/u3r1Ld+7cob1791LHjh1JW1ub9f5SUlKimTNn0uPHj0W+53tPWllZ0bVr1yTu9/nzZ1q1ahVt2bKF30VJibzjCHV1dUpLS2M+q6qqUlxcnMz1yXJfK4L+/fuTtrY22dvb0/Hjx5nz8f0/LSwsyNPTk7V8zpw5EvuHQYMGUc+ePSkqKors7OxIIBBQ8+bNacmSJfT582fpLuh/UExMDK+tKuncuTOtW7eOiIgePHhAQqFQ5B0cExNDRkZGP/U1fPnyhX777TeqVq0a2dra0ty5c8nT05NsbGxIVVWVzM3N6evXr5x1xMbGkrOzM2lpaVH79u3Jx8eHlJSUeD1b3bt3p4EDB9Lr16/p06dPNGnSJGrQoIFU16CoMcTWrVupWrVqJBAISE9Pj3R1dUkgEFC1atVo69atnMempaXx2vhy4cIFGj16NKmrq5OpqSl5eXlJHHdraWnRrVu3WMtv3bpFWlpavNsgCy9fvqSuXbuSUCgkFRUVmjlzJuXl5ZGTkxMpKyvTsGHDKDY2lvX4QYMGka+vL2v5xo0bydbWlrMNurq6dP36ddby69evk66ursRrkYeyuXXF+XbZ90KhkJSUlKq0DTk5OXLXoa2tTSkpKazlKSkpnPcU1+/A1waxYsUKqlu3Li1dupQsLCyoZcuWIuXr16+nXr16SbwWSbYIPvYIWTExMaFDhw6xlh88eJCMjY0l1lNcXEzbtm2junXrUqNGjWj37t1UWlrKqw1NmzalyMhIIiJKSEgggUBAdnZ2VFRUxO8iiJi+sXr16swmEAhIV1dX5LuqhOueql27Nq1Zs0buc/zynowXL15UmBh4QUEBPD09sXPnTixYsABeXl7fLeX4ixcv5K5DUR468nLmzBlmtaS0tBQXLlxgMiyVweYRKk7Ytri4GFu3boW3tzfq16+PZcuWSdWe6tWrf/dsVIoIv/8fitFroQrh2oWFhfj7779FsjLyQR4v2zVr1iAwMBC5ubmwt7fH1atX0bZtW6ioqKB69eq8zj9//nx4enpi6dKlrLpK8iIp+7u8nohLly7ltd/ChQtZy758+YLIyEj4+/sjNjYW/fv3h6+vL+zt7TF37lyJ3lbv3r1jvC309PSgoaEhUwhY/fr1cf/+fdYkDImJiazJjn4m5NHAuXLlCgIDA9GxY0eYmprCycmJ8WTnw8+iPfvs2TOoqKigtLS0Uj9dUlKCvLw8zlBMRcidyHtfS0rIAHzrn7i0X8+ePYvp06fD1dWV08OFjalTp8LOzg4NGjSAq6sr00+VlJTAz8+PyVrNRXx8PE6ePIkOHTqgW7duCAsLw+zZs/HXX39J3Z7/OmyC7Lq6urxDDH90eCvwTYrA3t4eJ06cwMOHD2FtbS2SuOLkyZNitTfL+BmuYdu2bXj16hXu3btXSaImJSUFFhYW2L59O2s23xYtWiA/Px8ODg64ceMG8zzzjWC4f/8+Ll++DAMDAwDAunXrsGvXLmRmZn7XMYS8YaGNGjUS209ROY9aSf2UvBIdLVu2xPnz51kzvZ47dw4tW7aUWI88zJ07F7m5udi4cSMiIiKwceNGXLp0CW3btsXjx48lJnaRJ5KijPbt2+PIkSOs4bmHDx+WmKNAXtg8mPPz87Fx40Zs2rSpyiM8WrdujZCQELm8JUtLSzm921RUVDjnM4rQ2PT09ER+fj4iIyNRt27dSgnYrl27xsvzmU9bXr9+LXM7uRg1ahRmzZoFExOTSvkb7t+/Dw8PD4nRceHh4fjnn3+QnZ2N+fPnw9XVVaqowpcvXzI2g7Zt20JVVRWenp5S2YN+hhwZbP+jrq6u4jLKy22m/Mm5cOECmZmZUXZ2dqWyrKwsatGiBV2+fFliPdeuXaNmzZpRy5YtOVe42FiyZAmvjQ1LS0vKzMyU+rzlUZSHjjxwrcLI4hG6Z88eatKkCdWrV4+2bt0q1UrCz0B0dDQ1btyYOnXqRJGRkXT06FGR7X9wExMTI/d/Pm7cOF6bJOTxslVSUqL58+dX8kSSxoPA29ubmjdvToaGhjRnzhy6f/++1HUQEZ09e5Y8PDxo3rx5jPdccnIyDR48mIRCIVlZWfGuS1ratWvHurVv3540NDQk9g81a9akP//8k3bs2CHimcz3d6jo2aqtrS3iVcOXqVOnUqtWraigoKBSWX5+PrVq1YqmTZvGq66K3rXfk1q1atHNmzdZy+Pj46lWrVqcdeTl5VFAQAD98ccfpKKiQkKhkDZs2MDL8+zLly+0d+9esrGxYVZ7y94TrVq1Ind3d04PAUUQGRlJzZs3p7y8vEpleXl5ZGxsTMeOHWM9XiAQkLe3N23cuJE2btxIampqtGDBAuZz2caFvPf1kSNHWLc5c+aQurq6RM/a2NhYmjBhAuno6FCnTp1o8+bN9O7dO6n6mPnz55NAICAdHR3mudbR0SGhUMjp5ViGQCCg9PR05rOmpiY9evSI17l/Ncp781Tc6tSpw3gHylqHJE8hMzMz+vjxI/P5r7/+Euk7MzIySF1dnde1nDt3jtzc3GjVqlWVnrPFixfTxYsXWY8tLS2lNWvWUNeuXen333+nefPmie13q5Lu3btzelJv2rSJunfvzlquoqJCTk5OdPbsWRGPGr7Pljhva2nfG4oYQ3Tv3p28vLxYy728vDh/h4SEBLHb3bt3ydPTk9TV1UlfX5/1eHm9rYmIduzYQZqamhQVFVWp7NixY6SpqUk7duzgVZesGBgYMFEsb9++JYFAQCtXruR9vCIiKQ4dOkTKysq0efNmkbFpcXExbdq0iVRUVOjgwYO826QISkpKaNeuXdSgQQNq2LAhBQYGUklJSZWec/bs2aSiokKzZs2iwsJCmero1KkTp2fpunXrqFOnTqzlS5YsETv++Nl4+/YtTZs2jXeUjrQUFBRQ165dSUlJifr160czZ86kmTNnkpWVFSkpKVGXLl0k9v0CgYA0NDTIxcWFOV7cxnV8xYjMHzU+/9n55Y2MNjY2cruME5HcHYy8k2dFhH3/SoPzU6dOUdu2bUlHR4eWLl1Kubm5P7pJMvMjwu9/FeLi4ujkyZMi34WEhFCjRo1IX1+f/vrrL5mfWUVQVFREGzZsIH19fWrWrBnt379f7H6KMhASfTO8jhkzhjQ1NalNmzZShVwHBweTQCCgmjVrkkAgIH19fQoNDSVtbW0aN24c0y425F1MYePu3btkZWVFKioqNGnSJM599fT0qHv37rRz506RxSVpJmvlwxjEhTDwCWNIT08nAwMDMjQ0pNWrV9ORI0fo6NGjtGrVKjI0NCQDAwOR/rg8Q4YMEdmUlZWpb9++lb7/HlSrVo1TRuPff/+latWq8a4vJSWFZs+eTXXr1iU1NTUaNGiQVO3Jysqit2/fSgw9VCR9+vShXbt2sZYHBARQ3759WcsVIXci730tjuTkZLK1tSUlJSUaM2YM7zBEeYzGREQ3btyg6dOnk7W1NfXv359mzJhBN27c4HWsohYBfgXYDDIxMTG0Zs0aqlGjBm3bto2zDnmMzxXHpdra2iKyDunp6SQQCBRzsRysWLGChEIh9enTh2xsbKhatWr0119/Vfl5y1OrVi168OABa/n9+/c5F2NevXpFy5cvp6ZNm5KBgQG5u7vTnTt3SEVFhfd76+LFi3Tv3j1m09TUpBMnToh8xwd5xhDyhoWK49y5c/Tbb7+RtrY2LVq0iDN8VV6JjjJGjx5NAoGAzMzMyNbWloYMGUKmpqYkFArJzs5OqvbLglAopLdv3zKfNTQ0KCkpiffxTZo0YUI6xREREcErpFXeRSFFEhERQSYmJlSjRg1au3btdx3bX79+nczMzKhFixaMs440BAcHk7q6eiVnmKKiItqyZQupq6tTUFAQ6/GKkIZSFJmZmeTg4EC1atWievXq0caNG6mkpIQWLFhA6urq1LFjR9q3b1+Vnf/Lly+0atUqatu2Lamrq5O6ujq1bduWVq5cyeue4CPrZGlpyXq8QCCgSZMmMcZIVVVVcnZ25m2kZKOgoICCg4Np69atlfqvquDWrVtkYWHB6oRnYWHBSGbIyi9vZGzYsCFnx5ycnEyGhoYS67l06ZLI58+fP1N2djazyarZwHfyrAgj468wOL9x4wZZWFiQmpoaubm50fv37390k2QmPz+fpk2bRtWqVaPFixf/57wwfwb69etHq1atYj4nJiaSsrIyTZgwgdatW0d169alRYsW/ZC2yeJlK8/gviLZ2dm0bds26tSpE7PCJ8mzpexFTUQUFhZGAoGAOnToQKmpqbzOqQhPxPI8e/aMRo8eTcrKyjRy5EheL96CggLas2cPWVpakrq6Og0dOpQiIyN5T9aCg4N5bXx48eIF9e/fX0T3RCgUUv/+/en58+esxynKu1YRyKuB8/nzZzp79iydOHFCpL8uLi6mw4cPS21k/BHUq1dPoldIvXr1qrQN8t7X5Xn9+jVNmDCBVFRUaODAgRIXD7iQ12gsLYpaBPi/QGhoKLVt21bq4/ganyVpbPPVM378+DHZ2dmxTnbs7e05NWmNjY1FtP5OnTpF1apV462xpQiUlZVFjEIVefPmDamoqPCqq7yOoEAgoNmzZ0t0CJCkXSfLwvXnz5+lHkNoampy/ldPnz4lTU1NXue/desW9e7dm6pVq0ZTpkzhNQdShLd1GWFhYTR48GBq0aIFmZmZ0eDBgyksLEyqOmRFkla4JBQZSSHPopAiiImJoc6dO5OGhgbNmzePsrKyvtu5y1NYWEgeHh7Me07aRV93d3fGYNu+fXsRg62bmxvnsYqwAVTUAGTbJOHq6koNGjQgd3d3atmyJTOmtbS0rHLt258BeY2UREQeHh40ffp05vOXL1+oXbt2pKKiQrq6uqSpqcmpuaoI7O3taenSpazl3t7eNHr0aLnO8csbGRUlvn/37l2ytrZmPmtpaVUKJ4mPj+fdLmknzwKBgFJTU0UMm+I2SXX86MF5SEiI2O3IkSOUnJws8fgyN+eZM2dWCjXjG3ZW1aSmpkrsYCSF3+fl5VUybFfkxo0bIiEMFQfUhYWF321A9KOoW7euSCjn/Pnz6Y8//mA+h4eHk5mZGWcdb968ofnz5zOf//jjD2YA0L59e+rYsSO9evWKd5sU4WUri4Hw6dOnrJOqxMREmjFjBmeYEZHoQLakpISUlZUVMmiQxhORiOj9+/c0depUUlVVpZ49e0rVt5YnNTWVvLy8qEGDBiQQCMjBwYHOnj3LmSDj0qVLCjf4/4jkUopi4cKF1LBhQ7GGqMTERDIyMqKFCxeKPfbevXtkYGDATHJ1dXXp3LlzVd1khaOmpsb5fkpKSqqy8CBxyHJfE30z2JR5p3Xp0oWXVAxfyozGNjY2rPuU96ji2rhQ5CLAr87Tp09JW1ub9/7SGp8VZWT866+/aPbs2azlc+bMob///pu1vFq1aiKG0NLSUlJVVZXqvS0vFY1CFZElgVxWVhZt3bqVfvvtNxIIBNS6dWvWfV+8eMFrkxW+Ywh5w0KJvs3LRo4cSUpKShINzGzI6239oyn7v8vGoUpKStSyZUuRsWn79u1Zj5cnkuJnon///qSqqkqTJk3iNOJ/D7Kzs2nMmDGkrq5Ojo6OMi36Xr9+naZPn079+/dnDLZcyXXKEAgEnP0LHxT17mzYsCEzjnv69CkJBAKaMWOGXG2ThvDwcHJwcKARI0ZUuWxBVdGyZUsRSbTAwECqXr06vXjxgkpLS2ncuHEiNqeqoEmTJpzjrcTERLkT+AiIvmO+7B9A06ZN4ePjgyFDhogtj4yMhIeHB549e8ZZz4QJE9C0aVPMmzcPAKCtrY0dO3agfv36ICIEBgaCiBAaGspZz4cPH7BkyRLs3LkT3bp1w6pVq3glHZEk2k7/TxS5pKSEdZ+QkBCJ5wEgUTRVHtgEqHNzc1FaWgpra2vs27ePNQkNmyh0eQQCgcT/syq5d+8eOnTowPlfqKqqYvr06fD29mYSjkhbh5KSEt6+fYvatWsDAHR0dJCQkMCIIGdkZMDAwICzjv86ampqePLkCQwNDQEA3bp1Q79+/fDPP/8A+JYwqXXr1sjJyWGtY8GCBfj06RO2bt0K4Nuz7ezszCSMOnXqFLp16yZRJDs+Ph6enp6Ii4vD33//DS8vL9SqVYvXdTx79gyNGzcWe2/fv38fAQEB2LdvH969e8daR8X7YdSoUdi0aRPq1KnD7FNUVMQpPi0UCpGens7Uoa2tjXv37sksrP38+XMsWLAAYWFhGDp0KJYvX86ZMCIvLw8+Pj7w9fVFs2bNsHLlSvTt21emc5entLQUZ86cQUBAAKKioqCtrY0PHz6I3bfi7/h/ncLCQvTq1Qs3btxAnz59YGZmBgBISkrC+fPn0alTJ0RHR4skNinD2toamZmZWLduHdTU1LBkyRI8evQIKSkp3/sy5MLMzAxeXl5wdHQUWx4aGgpvb2/W67K2tsb+/fuZZBze3t6YMmUKI6798eNH/Pnnn0hKSpKqXdLc12vWrMHq1atRt25drFixAoMHD5bqXHyIiIjAkiVLkJiYKLa8bBzDNeyUNI75H/y5ffs2hgwZwpogpozs7GysWLECmzdvRrt27bB69Wr8+eefEutXUlJCeno6k8RIW1sbiYmJTFIKvmMQU1NThIaGso6Fb9++DQcHBzx69EhsuVAoREZGhkgyJXnfXdIiFArRqlUrVvH/4uJiPHz4kPO3SEtLw9mzZ1FUVAQLCwuRJCUJCQkIDAzEpk2bFN52vuTl5eH69evo3bs36z4hISFwdXWFj48PJk6cyPwexcXF2LFjB2bPng0/Pz+MGzdO7PGTJ09GQEAALC0tsWrVKrRr107udj969AgBAQEIDQ1FVlYW+vTpg2PHjrHuX1paitLSUpH/MiMjA9u3b0deXh4GDRrE6/mQhyVLlvDab9GiRaxlaWlpcHV1xZkzZ5g+VyAQwMrKCn5+fmjUqBFn3ZcvXxb7va6uLpo1a8YkR6xKhEIhlJWVoampyTn3+/TpU5W24+zZs3BxcYGBgQFCQkJgampapeeriKT+pYw7d+5UeVtUVFSQlpbGJJnS0NBAfHx8pUQsVcHOnTvx999/o3nz5lBTU8ODBw8wZ84crFy5kncds2bN4rWfr68vr/0+fPgAgUCAmjVr8m6Djo4O7ty5wySGtLe3h7a2Nnbu3AngW39vbW2NN2/e8K5TWtTU1JCcnMyaROr58+do0aIFCgoKZD7HL29knDZtGmJiYnDz5s1Kk6CCggJ06tQJlpaWEl/cpqam2LVrF/NiqTiAuXHjBkaOHIm0tDSxx8s7eRYKhYiIiJCYKVveLHvFxcXfLWN2eUpLS3H79m1MmDABffr0kWjQ+ZnhYyC8fPkyZ6YyPnVIMghlZGSgXr16KC0tlfFKfn6MjIwQGhqK7t274+vXr9DT00NUVBR69eoF4JuBrkePHpwDkHbt2mHt2rXo06cPgMq/45kzZzBr1iw8fPiQsy1CoRDq6uqYNGkS5+Bt+vTplb77WQyEQqEQISEhjDHE3t4eGzZsEGkHwJ79vQxZF1Pq1q2LnJwcTJs2Dfb29qyDyjZt2vC8osq8f/8eoaGhrAONir+jrAwdOpTXfpGRkXKd53vw9etXrF+/Hvv378fjx48BAMbGxrCzs8PMmTPFLpQAQO3atXHy5El07NgRwDdjWu3atZGdnQ0tLa3v1n558fLywp49exAfH1/pWUhPT0fnzp3h6OgIb29vscd/jwUhPve1uro6evfuzZk5VtL9uGvXLpw9exYqKiqYMWMGOnfujOjoaLi7u+PRo0cYM2YMtm/fLvZYtvFRRYyMjHjtBwA5OTkiRkuhUPifureqiq9fv8LJyQlEJDbzexnyGJ8rTnwTExNhamrKZOrkY1gDAHV1daSkpLD+72lpaTAzM0N+fj5rOyZOnAgNDQ3mu61bt8LR0VEkyzbfSaMsyGsUunz5MqytrZlrVFZWRkhICK9sr+W5efMm008LBAI0b94cDg4OTB8sD3zGpQDg4eEBX19faGtro2nTpgCAp0+fIjc3F9OnT8f69etZjxUKhVBTU5NoxJHFmFJSUoKoqCgEBgZyGhnHjx8PFRUVZsKfk5ODli1borCwEPXq1UNSUhKOHj3KmiH7ZyMzMxOpqakgIjRv3px3tnGhUMhapqSkBFdXV6xbt45zXCovP4NzzKRJkxASEoL58+fDy8tLpszrkhZ6ymjYsKHY74VCIdzd3SW+27iMzhWR9d0paXGpKmndujVsbW2xbNkyAEBwcDCmTZvG6UhSEUtLS177Xbx4kbUsKysLXl5eCAsLYzKgV69eHXZ2dli+fLnE7Mx6enq4efMm43TRuHFjLFiwAM7OzgC+OciYmZnJZeCThKGhIXbt2oV+/fqJLT916hQmTpyIly9fynyOX97ImJGRgQ4dOkBJSQlTp06FiYkJBAIBkpOTsXXrVpSUlODOnTuVJg4V0dTURFJSEjMIWr9+PVxcXKCjowPgWwdibGyMwsJCscfLO3lW1MSXjaSkJAQEBGDPnj3IyMioknPw4fz585gyZQrrivV/Ab4DMXnr4GNk/NU9GSdNmoT79+9j9erVOHLkCEJCQvDmzRtmkrN3715s2LABN2/eZK1DT08Pd+7cYX63oUOHYtu2bUyf8OLFC7Ro0YJ1glOGPF62ijIQKqIOSXB5GiliMaX8ecq/nso+V7WnkzivGFkYP348r/2CgoLkOs+P5uXLl1i0aBECAwMrlYl7byl6QFpcXIw3b96wDs4VQU5ODrp06YJ///0Xjo6OIuOIvXv3wtDQEHFxcawe+D9DXz1u3DiJ/RPAfT/6+Phg/vz5aNOmDZKTkwF8M8D6+vpi2rRpmDJlCm/vbVlJSEiAl5cXTpw4AeDbb1m+bxYIBLh+/TqvRY3/OmwLGdnZ2Xjw4AGUlZVx5coVzneAPMZnRXhbAd/Gx/v27UPPnj3Fll+4cAGjR49Genq62HILCwte797o6Ghe7f0R9OjRAzo6OtixYwfU1dUxb948nDhxQqoJ3pw5c+Dj4wMtLS00adIERIRnz54hPz8fHh4eWL16tVxtlGZsGxcXh/379+PJkycA/v9FKXNzc87jFHVPyYOxsTG2bNnCjF22bt0Kb29vJCcnQ1dXF56enoiPj+c0QvwKZGdni/0+KysL8fHxmD17NiZOnIj58+d/55Z9X1q1aoXdu3ejQ4cOrPu8fv0a9evXZy0v37eW9yot/x3X2FYRNgBFvTuFQiH69+/PLC5HRUWhZ8+elTxbq2IBXVNTE/fv32feaSUlJVBXV8e///6LunXrKvx84vj06RO6dOmC169fY/To0TAzMwMRITk5Gfv27YOhoSFiY2M5jfnm5uYYOXIk48DSpk0bpKamMuPiS5cuYezYsXjx4kWVXcf48eORmpqKK1euVCojInTv3h3NmjWTa47y/V3WvjN16tRBbGwsXF1dMW/ePLEu45IMjABQrVo1vHr1ijEyzpw5U6T85cuXIquoFSkLc1yzZg3Wrl0rNlzoe4cJ5ebm4sCBAwgICMDNmzdhbm6OuXPnfrfzi6NZs2Z49erVD23D//jvsHz5cgwdOhQ9evSAlpYWgoODGQMjAAQGBko0chUXF4sMpiq+GDMzM3kZ36ryZcAHgUBQaaLFx6hQHnm9Xps2bVppMUVc6CTbYsrz58/lOj/wbTWRz3VzebcuWLCAsz8HJHvF/NeNh3z59OkTQkJCxBoZBQIBcnJymCiCsoF0Tk4OPn/+zOxXtlgnCw8fPpR7UUcS2trauHbtGubNm1dp5drR0RErVqxgNTAqCjY5hfIIBAI8ffpUbFlwcLDcbQgICMD27dvh7OyMmJgY9OzZE9HR0UhNTZW4cl+eJ0+e4OjRo3jx4gUEAgEaN24MW1tbXgsimzdvRrdu3US+Cw0NFZGu2bRpk0Tpml+B8l565TE0NMTw4cMxevRoic/WmDFjpH5PlKEoQ0/37t2xefNmViPjpk2bOMNTY2JiRD7LEr72o7l//z4uX77MhCCuW7cOu3btQmZmJi/Ps5CQEGzevBmbNm3CpEmTGO+yoqIibNu2DZ6enmjZsiXGjBlTpdexdOlSeHh4wNzcXKJBURzy3lN8IggEAgEiIiJYy1+/fi0i63LhwgUMGzaMed7Gjh3707/fFRFJwda/6OrqwsjICKqqqpg/f36VGhnj4+Px22+/MUa6sjFEGV++fMHRo0cxcuTIKmvDnTt3ROYV5UlPT4e3tzf8/f05vc4EAgEaNGiAcePGYdCgQVJHDMraR5dHUe/Oil6jbDIyVUFBQYGIt6WSkhKqVasm0QlEGsokqjZs2CC2fOnSpVBVVcXTp08r2Y+WLl2Kvn37YunSpZwe27Nnz4a9vT1OnDiBhw8fwtraWmTh/eTJk+jUqZNCroeNf/75B7/99hs6d+4Md3d3kcXzdevW4fHjx3L3c7+8kRH4Fnpz8uRJmV3GAaB9+/Y4cuQI/vjjD7HlkZGRaN++PevxfCbPZRMXcRgZGcnkoi2Oq1evwt/fHxEREWjcuDGSkpJw6dIl1mv7njx9+hQNGjT40c3gpH379pwdPp/OjitUA+BvbElKSmJW94kIKSkpyM3NBQBWba5fCX19fVy5coUJv6z4jBw8eFCi+7+JiQliY2NZn98rV67A2NhYYlvk0V1ThIGQiDBu3DhmdbGwsBB///23VKuLzs7O2Lhxo8wGE67FFD6eiNKESbLBNjCQhvv377MOKgHFDPj+L0BElZ4dImKete/hmaoodHV14efnh61bt+LDhw8gIujr6/O6FxTxfLu5ubGWvXjxAjt27MCXL1+kqlNa0tLSGD02CwsLqKiowNvbWyoD48qVK7Fw4UKUlpaidu3aICK8f/8ec+fOxYoVK+Dh4cF5/LVr1yrpuZmbmzMGSnV19SqdcP5MKMLQoQjjc0UuXbqEvLw8dOnShdc4e968eejSpQuGDx+OOXPmwMTEBACQkpKCNWvW4MyZM4iNjeWsQ97wNXno168fFi5ciK5du3Lul5OTAz8/P2hpaWHKlCkiZVlZWSJeSpqamtDQ0EBWVhav33Dr1q1YsWIFpk6dKvK9iooKpk+fjuLiYmzZsqXKjYxLlizB33//LXGRrqpgM4xJg5qamojBKC4uDmvXrhUpLxtn/6wo4neQRNu2bXlLYMhKly5dRKRGdHV1RaRGsrKyYG9vX6V9fkFBAcaPH8/IhMydOxdTp07F4sWL4ePjg5YtW4pdZC3Pq1evEBISguDgYGzfvh2Ojo5wcXFhNK4lwRV0WlpaihMnTiAgIABHjhxh3U9R784fbWD39/cXmdcVFxcjODhYJIpCnCwVF58/f8b+/fsREBCAW7ducUoyHTlyBDt27BDroFa3bl2sWbMGf//9N6eRcdiwYTh58iROnDiBvn37Ytq0aSLlGhoamDx5slTXIC1NmzbF+fPnMW7cONjZ2TFjUiJCixYtcO7cOUYzUlZ++XBpRREREQE7Ozts2LABrq6ujGdTSUkJ/Pz84O7ujn379mH48OFS1ZudnY29e/ciICAACQkJrJOtgoICnDt3DpaWlpUMAJ8/f0ZMTAysrKxYtbGAbxP/wMBA5Obmwt7eHo6Ojmjbti1UVFRw7949EZHp7w0R4e7du3B2dkbv3r1/ak1GRYRzyBuWWlYHm4j+9wot/ZnIyspCamoqBAIBmjZtyntSsXbtWqxatQoXL16s9GK5d+8eevbsiblz52L27Nmc9VQMZ5BGd00R4QeKCM+VN+mJvJpra9aswbRp06Curg7gm05V586dmd8lJycHnp6e8PPzk6l9fFCUNMXbt2+xZcsWRqevW7duIgsQSkpKOHLkCGeIzX8BrhC6S5cu8apDHi1hRchTSOLdu3ec90NxcTHu3LnDuvIs6fn+8uULTp8+LfU1fPr0CcuWLcO2bdvQuXNnrF69mtV7SBGeLfJKMly8eBG9e/fGggULMGPGDMZ48unTJ2zYsAErVqxAdHQ0p16xvNI1vxLy3pfysnbtWuTm5jJjIiJC//79cfbsWQDfNFkvXLiAli1bSqzr+PHjcHZ2xsePH0W+r1mzJvz9/Tl1gBURviYPAQEBWLRoEbS1tWFjY4OOHTvCwMAAampqyMzMRFJSEq5evYqTJ09i4MCBWLt2LZOsrgyhUIjo6GgR3fWuXbsiPDxcZOGdbfJbMYywIs+ePUPr1q2Rl5fHeh18Fr9nzZollYyPtEhaxC+jKhNc9OzZE507d8bKlStx5coVWFhY4NWrV6hXrx4A4Ny5c3B1dUVqamqVteG/QGxsLBwdHas00ebPoD0/efJkREVFYdSoUTh9+jSSk5NhZWWFwsJCLFq0SOrxy9WrVxEUFISDBw+iRYsWcHFxgYuLC+e8MC0tDYaGhiL7PHnyBIGBgQgJCUFmZiasrKw4jYy/wrtT0clfL126hICAAERERKCwsBCzZ8/GhAkTOI1r1apV43SIevXqFZo1a/ZT/44VSUhIwJMnTxjHAEUk3AL+DxgZFSm+7+npibVr10JbWxtNmjRhQpNyc3Mxa9YskZUuSURHRyMwMBCRkZEwMjLCsGHDMGzYMFZvqk2bNuHo0aO4cOGC2PLevXtjyJAhlVZHy6OsrAxPT08sXbpUxOPrexoZ2UIZc3NzUVJSgn79+iEsLOx/wu08qAoR/f8iL168wJQpUypl0OvXrx+2bNkiMYNeUVERevfujdjYWPTp04dxGU9JScG5c+fQpUsXXLhwQaK4tTy6az+Lfl9Va79K4nskyHj79i28vb2xZcsWXm2QFUVlLf/Z+R5Gvh99/or3hJmZGc6cOcPoQEq6LxWhh1iegoIC+Pr6Yu3atWjUqBFWrFghMQmBIvoYoVCI5cuXM+9nT09PzJ49u5IOI5sXwahRo6Cnp4cdO3aILZ84cSJycnKwf/9+1jbUqFEDUVFRrJEX165dw6BBg6o82+jPgLz3pbx06NABnp6eGDVqFIBvkQNjx47FuXPnYGZmhjFjxkBDQ4Mz8Ux5CgoKcPr0aSbiyNjYGH379pXoFefm5oYLFy7g/PnzYhMz9e3bF7169eL0LJGXr1+/4tChQwgLC8OVK1eQlZUF4NtYpEWLFrCyssJff/3FeGlWRN5FYx0dHcTHx7MmTHn06BF+//13EZkKcW2QBJ/Fb3k0jRWpyUhE+Pjxo9Sh8xcvXoS1tTUMDAzw9u1b2NvbIyAggCmfPHky8vLyeCcl+ZlIS0tDXl4eTE1Nef3fbLx79w52dnZo0qQJ/P39FdhCUX4GPWMjIyMEBASgd+/eePbsGZo1a4bp06fLHTWTkZEBe3t7XLp0Ce/fv5eY2BX41keGh4cjICAAcXFxKCkpwfr16+Hs7Cxx3vy/d+c33r59i6CgIAQGBiIvLw/29vZwcHBAly5deNlC6tevj7CwsEqh52VcuXIFdnZ2eP36dVU0v8opLi5GYWGhQuwwv3y4tCJdxlevXo0hQ4aIiBn/+eefsLe356U98urVKwQHBzM39siRI1FUVISIiAiJN3VoaCgWLlzIWu7m5oalS5dyGhmXLl2K4OBghIaGwt7eHk5OTt8l5Xx52DplHR0dmJqa8nYdz87Oxrlz50Q0nXr37i2XtpciyMzMxJ49exjPVHnIz8/nHFz/6sZDPrx8+RLm5uZQUVHBsmXLRDwYtm3bhi5duuDmzZucIfgqKio4d+4cfH19ceDAAUbfqXnz5li2bBlmzpxZpdnzgB8fflAeeUKB5fVErDjBknUNLCkpCRcvXoSKigpGjhwJPT09fPjwAd7e3ti+fTtn0hFJ5/z48SNCQ0M5w1eBb95qFReeZsyYwQyOzc3NMWvWrJ/eyChpoa5sMl1ViNP0LM/3SBJW8Z549eoViouLOfcpj6JCUktKSrBr1y4sWbIEampq2Lx5MxwdHRVqwOSiYcOG2LVrF/O5bt26lfSbBAIBq5ExPj6eU+/JyclJYjinvNI1vxLy3pfy8vz5cxHPupMnT2LYsGHMf/PPP/9gxIgRvOtTV1fHkCFDpG6HIsLX5EVVVRUODg5wcHAA8G2MWlBQgJo1a/IaP8irR/zbb79h7969TNbVioSGhnImrgDk12Quo1evXhI159g8ERWh85meno45c+bg2LFjTNZZHR0dDBkyBCtXrpSow29paYnbt2/j3LlzqFu3bqV7uF27dujcubPc7ZREaWkpgoODERkZKTLXGT58OJycnDj7/TLvtvLjlIkTJzLGUhMTE5w5c6aSR2152LxKs7Oz8erVK5iZmeHAgQOyX+B/hDdv3jBz9CZNmkBNTQ0TJkyQub7Y2FgEBgbi4MGDMDExwdatWyVGXsXHx8Pf3x9hYWEwNjaGo6MjDh48iAYNGqB37968DEL/e3d+o3HjxhgxYgS2bt2KPn36SG1s79evH7y8vHDu3LlKskpfvnzBggULWDM2/0ycPHkSHz9+hJOTE/Odt7c3li1bhuLiYvTs2RNhYWFyRQD88kZGRU/eZRUztra2xtWrVzFw4EBs3rwZ/fr1g5KSErZv387r+NTUVLRt25a1vE2bNozhk40ygd5Lly4hMDAQ5ubmaNq0KYiIUw9SkVQUjJWFPXv2YOrUqZVWZHV1dbF9+3ZmVf17cv78eUYPo1atWrw9aMVRWFiIrVu3Yu3atazZFPkQGRmJxYsXS5yk/5dZtGgRM1gqSy4BAEOGDMHMmTPRr18/LFq0SGQVWhyqqqqYO3euXImPFKG79jNgbGwssd1sK53z5s3DuHHjGCPjwIEDRTwR8/PzsWPHjioNdz5+/DiGDRuGoqIiAN8Mn7t27cLIkSPRqlUrHDx4EAMHDmQ9PigoqNLiFBHh7NmzCAgIwNGjR6GjoyPRyPjixQs0bdqU+dynTx+R8HcTExOFJLqpaiQt1Onq6lapzle7du14efj8aLjaoAjv2PDwcPzzzz/Izs7G/Pnz4erqyqkbKg18NZ3kTW6VkZHB6VneuHFjie+8yZMnw87ODo0aNRIrXbN582bs27dPrnb+SlTls1FUVCQi0XP9+nXMmDGD+WxgYMBLG3rTpk28zsdmvH779i1nSHarVq3kGkvJgq6urlRODvIuGru7u8PW1hZfvnyBu7s7Y0hLT0/HunXrsGHDBhw+fFiuc/DFysqqSqKR+Czif/78GV27dkVubi7Gjx8PU1NTEBGSkpKwf/9+XL16FXfu3JHYvhYtWrA6f7i4uCAqKopzTiYvRAQbGxucPHkSbdu2RevWrZkF9HHjxiEyMpKzr96+fTsmTpzIfD59+jSCgoKwe/dumJmZYerUqViyZAmnF6Ktra3Y78ucQvr27auwXAFc/Gjt+dLSUpGFAiUlpUpSRpJ4+/Ytdu/ejaCgIGRmZmL06NGIjY3lJSUBfJNOmDZtGuLj41m9oSXxK7w7b9y4gU+fPqF///7Md7t378aiRYuQl5cHW1tbbN68mVM6zsjICFevXkXDhg1hZGTE6v3NxpIlS9CxY0c0b94cU6ZMYY5PSkqCn58fvnz58p9IPOfj44Nhw4Yxn2NjY7Fw4UIsXboUZmZm8PLywrJlyyQmueSE/g9RWlpK79+/pw8fPii87oiICGrdujVruZKSEs2cOZMeP34s8r2ysjI9fPhQYv1aWlp069Yt1vJbt26RlpYW/wYT0efPn2nbtm3UqVMnUlJSoi5dutC6deukquN7c/v2bVJWVqaxY8dSQkICFRYWUkFBAd2+fZucnJxIRUWFEhISvktb0tLSaPHixWRkZEQ1a9YkoVBIhw4d4nXsly9faP78+dSxY0fq0qULHT58mIiIAgMDqV69emRgYEArVqyQWM/OnTtp+PDhZG9vT3FxcUREdOHCBWrXrh2pq6vTxIkTZb6+/wL16tWjK1eusJZfunSJ6tWrJ9c53rx5Q1OmTJG4n0AgIGtraxoyZAgNGTKElJWVqW/fvsxna2trEgqFcrWlqhEIBLRx40YKDg7m3LiOz8jIYD5raWnR06dPmc/p6emcv4G8xxMRmZub0/Tp0yknJ4fWrVtHAoGAjI2N6dKlS5zHieP58+e0YMECMjQ0JKFQSE5OTnTu3DkqLi6WeKympibduXOHtfzOnTukqakpdZv+r/HixQteW1Wi6Pta1jZoaGiQi4sLzZw5k3WThsePH9PcuXOpXr16pKamRoMHD5Z4TGlpKT1+/JgePnxIRUVFUl8D1+/A5/kmIpozZw4JBALS0dGhdu3aUfv27UlHR4eEQiF5eHhI1ab/MoroL+Whbdu2FBQURETfxkMCgUBkPHvt2jWqX7++xHoaNWokcWvcuDHr8QYGBpzjgMuXL5OBgQH/C/uBxMfH08yZM2nAgAE0cOBAmjlzJt28eZPXsZs2bSJVVVUSCoVUvXp1ql69OgmFQlJRUaH169dLPD4kJETsduTIEUpOTubVBkX0dRU5d+4c2dnZkZqaGjVo0ICmT5/Ouu/SpUupWbNm9O7du0plGRkZ1KxZM/L29papHcnJyTR79myqXbs2qaioyFQHXwIDA0lbW5uio6MrlV24cIG0tbUpJCSE9fgaNWpQYmIi8/nvv/+moUOHMp8vXrxIjRo1UmyjqwCBQEBCoZAEAkGlrez7qh5XSxrbl21cqKiokJGRES1cuJBu3bpF9+7dE7ux0adPH9LW1iYHBwc6deoUlZaWEhF/G0IZ//V3Z79+/WjVqlXM58TERFJWVqYJEybQunXrqG7durRo0SKJ9Vy9epXGjx9PWlpa1KFDB/L19SVlZWVKSkri1Y5nz55Rv379RO5NoVBIVlZW9OTJE1kv77uir68vMkeZOXMmWVlZMZ9PnDhBzZo1k+scv7wmIyC/63wZu3btYrJLzZgxA507d0Z0dDTc3d3x6NEjODk5sWoNXb9+HYGBgQgPD4epqSmcnJwwatQoGBgY8NIAMDc3x5AhQ+Dp6Sm2fNWqVThy5Aji4uJ4XUtFylK279u3j8kQWxWU6c5wIRAIKoX8lDF+/Hjk5ubi4MGDYsuHDx8OHR0diZm+5CE8PBz+/v64du0arK2t4ejoiP79+0NTU5O3tuX8+fMZV+1r167hw4cPcHZ2RkxMDObPnw8HBweJITY+Pj6YP38+2rRpg+TkZACAl5cXfH19MW3aNEyZMqWSVtavBh8B3qZNm0rMusonvFZcVujy/Cy6ivIgryajvPo5kjTfcnJysHDhQk79HT09PcTHx8PY2BjFxcVQU1NDVFSUyMonF1++fEFkZCT8/f0RGxuL/v37w8HBAfb29lJp1/72229wdnZmlbDYtGkTgoODq1TAXtHImlypqklISFCYULU4lJSU8PjxY+jr64OIYGhoiKtXrzJeeRkZGTA1NeW8r+XVOrWwsOD17oyOjubcRx5NpxcvXmDw4MF48OABAMDQ0BARERH47bffeF1Dxee7Inye7zLi4uJEpGuaN2/OW7rmV0He+1JeduzYAXd3d4waNQpxcXHQ09PDtWvXmPLly5fjxo0biIqKqpLzl+Hi4oLU1FTW8DUrKys0bdpUYkTDj2bOnDnw8fGBlpYWmjRpAiLCs2fPkJ+fDw8PD6xevVpiHa9evcLBgweZ58LY2BjDhg3jDIstgy0sLjc3F6WlpbC2tsa+ffsqJZ8sj6I0jf/9918EBQUhKCgIubm5yMzMRHh4uIjnjTjMzc0xadIk1vFYYGAgdu3ahevXr/NqR15eHsLCwpj+0tLSEnZ2drC1ta3S8XXfvn2ZpIPiWLFiBS5duoQzZ86ILdfQ0EBycjLjIdu2bVs4Ozsznsb//vsvTExMRLJoS4skfWtF8DNozytCU7l8SG75LL7lkaR3+vLlS+aZKCgowKhRo+Dn54fExETeUmPAf/vdWa9ePURFRaFjx44Avs15L126hKtXrwL4pgu8aNEiifO1MnJzc7F//34EBgbixo0b6NGjBxwcHGBra8tLVzYzM5P5HZs1a8ZLV/NnQV1dHY8ePWI0nDt16oThw4djzpw5AL49ey1atOBMFiYRuUyU/wGys7OpcePGpK+vT25ubrR9+3batm0bTZs2jWrVqkXNmzennJwcifWsXbuWVFRU6LfffiMNDQ3S0NAgb29vqlmzJi1evJjev3/Pqz15eXkUEBBAf/zxB6moqJBQKKQNGzbQ58+fOY/bsWMHaWpqUlRUVKWyY8eOkaamJu3YsYNXG7j4+vWr3HVwceTIEdZtzpw5pK6u/v+xd95RUSTf239mAAWVZFgxo0hSzK7xawBBFBMqItEAJsyrICKGVUHXrIjsoswAZnDBgAEDmFnFBChBRQEjKhIUCRLq/cOX/jkwCSZjf87h7PZUh9tjT3XVrXufS1RVVXker6+vTy5fvsyz/fLly0RfX18SplMoKSkRLy+vGv9mtVlR0tPTI5GRkYQQQhISEgiDwSB2dna1igwxMjIiLBaLEPJjVZLBYJARI0aQvLw8oc+h6Ojq6pLo6Gie7RcuXCAdOnTge46oqCjSoEEDajVKT0+PxMbGkubNm5Phw4dz/c3VV5hMpkhRCKJG1nTo0EGoqJba2pCeni70PTRr1owMGTKEBAYGktzcXOrz2q4Yb926lTRt2pTr6nRCQgJp2rQp2bp1q9DnkyUZGRnEysqKKCkpESaTSZhMJlFSUiJjxowhGRkZQp1D3JkE+fn5ZN++faRXr15SiWSouu+foyeqb/M7/uDBg+T06dN8/yTJ3bt3yezZs4mGhgbp27cv2b17N8nOzq7Vc21ra0sMDAzIkSNHSEREBBkwYAD5/fffhbZBHL/vwMBA8v79e6GvWZ8R9bkUB0FBQcTa2prMmzevxr+Lm5sbiYiIEMt13rx5w7Pt9evXpGXLlqR9+/Zky5Yt1O9p8+bNpF27duS3334jr169EosdkiIkJISoqqqSvXv3cozDv3//Tvbs2UNUVVX5Rq5JkoqKChIfH0+6d+9Oli9fznff6u/f2vb5YWFhxMLCgjRq1IjY2NiQU6dOkdLSUqH7KW1tbZKWlsazPTU1lWhraws8T1xcHHFxcSFNmjQhvXr1Itu3bydKSkq1GgOIQsuWLcmjR494tj98+JC0bNmSZ7uRkRH12/v06RNRUlLiyIa7e/cu3+OrSE5OJv7+/iQwMJCaW3z69IksXbqUqKqqEmNjY+FuqI7w+w4UCXFnZFy6dImK7tXX1ydeXl7kwYMHErwD+aBhw4YcffngwYPJxo0bqe2MjIxaZ3VWkZKSQpYvX05+++03oqysLLKt/Bg+fDgxNTXl+2dmZiZRGzp16kTNn79+/UoaNGhAbt26RbU/ePCANG/eXKRr1Hsno7hC5yXh0ElLSyMeHh5ER0eHqKqqknHjxvHd39HRkTAYDGJsbEysra3JxIkTiZGREWEymcTOzk7g9WJiYoixsTEpKCio0Zafn0+6dOlCbty4Uef7qSupqanE2tqaKCkpkWnTppGsrCye+zZu3Jhve1ZWFmnUqJEkzKSYPXs20dTUJIMGDSJ///035YiozWStQYMG5PXr19R2w4YNa/0yVVNT4/guGjRoQKVM/yosWbKEdOvWjefvu3v37mTJkiV8zyHO9FpFR9RUJwaDQXx9fcmePXuoidGaNWuobR8fH6k4hK5evUqlnzRu3JicO3dO6LQULS0tMnToULJ//36OvrK2Tsbv37+ToUOHEmVlZTJ69GiydOlS8scff5DRo0cTZWVlMmTIEIkv6oiDV69ekZYtW5K2bduSTZs2kZMnT5LIyEji6+tL2rZtS3R0dDj6suq8f/+eODs7E01NTcr5oaWlRWbOnEmys7NrbU9MTAxxdHQkampqxMjIiHh7e/NNSxcH165dE+qPF9xSvbilftWG2k7elZSUyNKlS2tMwGvzXLdq1YrjPl+/fk2YTCYpKioS2g5RGT58OGnYsCHp168f8fX1ldqkXx4R9blUBN6/f08WLlzId/GZENmnrwkTrMCP33//nezcuZNn+44dO/g69AUtYIhjIePy5cvEwMCA7z6ZmZkkNzeXzJ8/n5IRYjKZpFmzZmTBggUC50yiLuIrKSnxfa+8f/+eKCkp8T2HsbEx6dChA/Hy8uK4Zm3HAKKgoqJC3r17x7P97du3pEGDBjzbN23aRHR0dMiGDRvI8OHDSdeuXTnad+3aRUaMGMHXBnlYgGcwGKR3794kICCA5OfnS/x63BB18V0YcnNz67SIkJubS/z8/EjPnj0FjiG2bNnC8a6+fv06KSkpoba/fPlC3Nzcam2DNGnfvj01NystLSVqamrkypUrVHtSUpJQiwj8KCsrI1u2bOHZXj1NntcfP5YuXcrzz8XFhaipqUl8rrRixQpiZGREDh48SOzs7Ej79u05pKACAwPJ4MGDRbpGvXcy9u/fn7DZbJ7tLBaLDBgwQOB5JOnQKS8vJydPnhToZCTkxyrfhAkTSJcuXYixsTGZMGECCQsLE+o648aN4zuI2bNnD7G2thbablF5+/YtmTVrFlFRUSFjx44ljx8/FniMuDSdRKWoqIiEhISQoUOHkoYNG5Lx48cTJSUloe6BEO7RVi9fvqyVDYKixn4FcnNzib6+PlFXVydubm6UM2vu3LlEXV2d6Ovrk8+fP/M9h6amJnn69Ckh5MfLRUlJiZw/f14a5isMmZmZJDk5mVRUVPDdTxyRSqIiqoZPcXExOXz4MDE1NSVqampk0qRJJDIykqioqNR6glFaWko2b95MevToQdTU1Iiamhrp3r072bx5M8fgTp6ZOXMmGTp0KCkuLq7RVlRURIYOHUpcXFy4HiuuTILXr1+TjRs3ko4dO5LffvuNLFy4UKoTPlERl05ZXl5enSfv4tB0YjAYNSbwjRs3FjqaVVzk5uaSQ4cOkSlTphANDQ3SqVMn8scff5CrV68K7KNopMeDBw/ImDFjBO6Xl5dHHBwcSPPmzUmrVq3Inj17SEVFBVmzZg1RU1Mjffv2JUePHhXqmrm5ueTu3bvk7t27At/94kRXV1ekhclGjRrxHb+9ePGC7wI6r3edKAsZ1cnIyBC4iP/582diYGBAGjduTObMmUN27dpFdu7cSWbPnk0aN25MjIyMODIEqiPqIj6TyeS66FyFMHMEFRUV4uzsTC5dukT1k7WxQRyIeh8VFRVk9erVpGfPnmTUqFE1tOZsbGxIUFAQXxvkYQE+Li6OzJo1i2hoaBA1NTXi6OjIVadSkkhCZ7Q6CQkJIv8+BUUyVneWqqurS1XDVxzMmTOHDBw4kNy4cYMsW7aMNGvWjJSWllLthw8fJn379hXqXF+/fq2xQPro0SMybtw4vt/DjBkzOP4aNGhAJk+eXOPz2lJWVkZ2795NWrRoQTp37kyOHTtW63PUhm/fvhEnJyeipaVFjIyMagSZDR8+nEP/si7UeyejuELn64NDp3379nxFTVNTU0m7du0kbkd+fj6VGl3VWQiLoLSz0NBQqXeSVeL5rVu3JhoaGsTe3l5gihCDwSBz586lxPobNGhAXFxcaiXiLyhqrOqvvpObm0vmzZtHtLW1qcG0trY2mTt3rlCRPqKm19YnQkJCagjEz549m3JoGBsbSzTtrKioiGN1fOXKlRy/B3d3d67Orp8RZ1pKeno68fb2Jm3btiUMBoM4ODiQS5cuCVX4pb4gSnElcWQSjB49mqirqxN7e3ty9uxZ6ruX5oRvzZo15Nu3b9Q2v0kyN8QRCSHq5J2QH1Gp69evJ7q6uqRly5Zk8eLFtRI75zbxVVdXF3qBbPTo0RzRKD4+PhzO0ZycnFqn35WWlpILFy4QNzc30rZtW9K0aVPi7OxMTpw4QQoLC2t1LkWjeno0tz9BUVuicunSJeLu7k68vLyoMXFqaiqZMGECFUkoiKp/u+XLl5OuXbsSJpNJRo8eTUxNTRUmEtPDw4OoqKiQZcuW1WkBSV1dnW9xlbS0NKKuri70+SQxR7ly5YrASMYlS5YQExMTrtGE79+/J926dSNLly7lew5RFvEZDAbR0tKiCt9U/9PS0hI4R3jz5g3x8fEhenp6pHXr1mT58uXk4cOHdVporCvVi41U/xNUSFDUyFpC5GsBvuqZGDZsGGEymaRTp07Ex8eHbxaFuJAHJyOvQjHCZugQIvtCYeLg48eP5H//+x9hMBhEXV2dkh2rwszMjKxatYrvOV6/fk0GDRpEFcX6448/yLdv34izszNRVlYmkydPJnFxcULbJI6+9vDhw6RTp06kVatWZN++fbUuqiev1PvCL8rKynj79i3P4i7Z2dlo27Ytz0IjVQgqSFDF4sWLxWN4NcLDw2FtbU2JWmdmZqJdu3ZQUlICABQVFcHf358S7OSGqqoqnjx5gs6dO3NtT09PR7du3UQSAhbE1q1bsWXLFujo6GDTpk2YMGFCrY7/WTyXF4LEcyVFZWUlzp07BxaLhQsXLvAtNiIOEX9dXV2hzvHy5Uv+htcTCCH49OkTAKBFixZCCTUDP56p2NhYSrB30KBBCA8Pr1FMpnv37uI1WA4ZOHAg5syZQ4mmR0dHY9y4cQgJCYGxsTEWLlyILl26ICgoSCLXDwwMxNmzZ6lCAerq6ujatSvU1NQAAGlpaVixYgX++OMPiVyfF5WVlbh48SJYLBaioqKgrq6OnJwcoY4tLi7G5cuX8ezZMzAYDBgYGMDc3Jy6J3lHlOJK4hDhV1ZWxuLFi+Hm5gZ9fX3qcxUVlVoV4hGF6sUMNDQ0kJCQQBU0EoQ4Cr8sXboUMTExuHLlSo3xTHZ2NkaOHIkRI0Zg165dQp3v8uXLYLPZOHXqFNq1awcbGxvY2Nigd+/efO9DU1OTo2/Nz8+HhoYGx7s5NzeX6/GCvkdBhaGE4f79+zhz5gxOnz4NGxsbrFmzps7nkndOnz7Nsy0uLg579+4FIURiY7rQ0FDMnDkTTZs2RW5uLpo3b46dO3di/vz5mDx5MpYvXw4TExOB5+nQoQNYLBbMzc3x8uVLdO7cGYsXL8bu3bslYrekuHPnDlxcXMBgMHDo0CG+v6XqmJqa4n//+x82btzItX316tW4desWrl27JtT5qhddEwVCCB49egQXFxeYm5tj+/btPPfV1dVFYGAgLC0tubZHR0dj3rx5yMzMFOraz58/B5vNxsGDB1FYWIgxY8bAxsYGkyZN4rp/aGioUOedPn26UPvFxsaCzWYjMjISJSUlcHd3x6xZs2BgYCDU8XVF1EKCHTt2RGhoKIYOHVpnG7gV8ktISICenl6dzykOXrx4geDgYBw8eBDv37+HhYUFzp8/L7HrMZlMhIaGQlNTk+9+48ePr/M1EhMT0bt3b77F4xgMRo1iMT8jaO4ramFGeaKgoABNmjShfCBV5ObmokmTJjUKgP2Mk5MTHj9+jNmzZyMiIgI3btxAz5490aNHD6xZswYdO3aslS2i9LXR0dFYuXIlMjIy4O7ujmXLlqFx48a1Po+4ycvLw+HDh8FisZCQkFD3E8nSwykNxBE6T4hwaYAdO3YUp+kciCPMuVOnTjW8/j8TEREh0Xsg5MdKSqNGjcj48ePrrGWgCEh61YtGfIiaXlufaNq0KUlKSqK2582bRyZNmkRtX716lW+6s6iRiEOGDOHoo6qvEB46dEigvIWkdWc+fvxIduzYIdS+p0+fJi1atKjxXLVo0YKcOXOmzjZIE1GKK4kjk+DndKl+/fqRvXv3ko8fP0o1klHUTIYZM2YILO4miA4dOohc5IobtdF0CgkJEeqPF+KKpCgoKOCaFl1RUcGho6oImqfipjYa16LSo0cPsnnzZkLIDymfKv202mYCKCsrk7dv31LbampqQkvPyBslJSXE3d2d0lkXdmwbFRVFlJSUiIeHB0cU4Pv374m7uztRVlaulQZeXaJreEUAVhWptLKyEhghV11vvDqvX78mDRs2rJVdhPz4bZ85c4ZMmDCBrxahpKgqNNanTx/CYDBIt27dpG5DbRA1spYQ0fWtJcnXr1/JP//8Q5o2bSoVnW9xaypXR1AkozgydOpDJKM4aN26NVXg5P3794TBYFDvsbpQl7727t27ZPjw4URVVZUsXbpU6OLBkuby5ctUQaG2bduSxYsXi3Q+ZbG5PeUUQggMDAx4RjURIQM5hV11kxTV7RTW7p+xsrLC2rVrMXr0aKiqqnK0FRcXY926dRg7dqxIdgpi2rRpQkeYySsPHjyAu7s7Tp8+DQ0NDY62goICWFtbY8+ePSJFrYiDz58/49ChQ1i6dKlM7ZAkpqamQkVzxsTE8GzPyMgQt1kKS3FxMcczHRcXBxcXF2q7U6dOyM7O5nn8wYMHcfbsWaof8ff3rxGJ2Lp1a56RiM+ePeOIEFBVVeWIkOrXrx8WLFjA9x68vLwwY8YM6ppjx47liJYqKipCYGAgAgIC+J6HFy1atMCyZcsE7hcXFwcbGxuMHz8ey5cvh7GxMQAgJSUFO3bsgI2NDa5du4aBAwfWyQ5pMWHCBHh4eKB3795o0aIFR9vHjx/h6ekJa2trrsd++fIFWlpaPM+tpaWFL1++8L3+wIEDMXDgQOzZswfHjx8Hm83GsmXLUFlZicuXL6Ndu3ZQV1ev7W1JFV7RJrXh/fv36Nq1K892ExMTvr/NyspK7NixA6dOnUJZWRnMzc2xdu1aaGtrY9GiRVi0aBEePnzI1wZho38kycmTJ+Hp6YmEhAQ0atSIo62kpAS///47tm/fjnHjxkFFRUVGVkqfd+/eYd26dQgNDYWlpSUSEhKEiiIUhRcvXmDq1KkAABsbGygpKWHnzp21jnSqrKzk+LdSUlKSi2iOulBaWoqPHz+CwWBAU1MTysrCTbPGjh2LXbt2wd3dHTt27KCipgoKCqCkpIRt27ZJfHzOK3JUQ0MDRkZG1DuMH82bN0dmZibPyPeMjAw0a9aM5/FFRUXw8PDg6Kf8/PzQvHlzjBs3DuPGjcPHjx95Hh8fH48+ffpQEU6EEI4xYmlpKU6fPg1bW1ue57C2tsasWbNgZWVFjT80NTUxf/58zJ8/HwkJCWCz2Xy/B1mzdetWTJo0CS4uLoiOjq51ZG0VI0aM4JhvVj2DVVF10s4cu379OthsNiIiIqCkpARbW1u4urpK/LqiZiL4+fnxbX/79i3f9g4dOtT52j8TFBREZWSWl5cjJCSEysj8+vWrWK4h72RnZ1PvKB0dHaipqdU6q1JUBgwYADU1Nbi5uUFXVxdHjx7lup+ksmN/5tWrVwgODkZwcDAKCwuRl5eH8PBwTJ48WeRz1/t0aXGHzssKcYQ5f/jwAb1794aSkhIWLlwIQ0NDMBgMpKamYt++faioqMDDhw95ppbLA/Pnz8fWrVupTvLQoUOYOHEitZ2fnw8HBweJhs47ODjA2NiYZxqWr68vUlNTcfjwYZ7n6NKlC27dukWl6M6ZMwe+vr7UJP7jx4/Q1dVFUVFRrWwjhODSpUtgsViUE7Qqhbg+wi9t9suXLzh27BhKS0sVIvxfHjA2Noavry8mTZqEnJwc6Ojo4O7du+jTpw+AHwP48ePH83RmDB06FH/88QcmTpwIoGY/dfjwYezbt49neqyamhoSEhJgaGjItT0tLQ09e/ZESUkJz3sQta/U1tYWaiGEV0poFVZWVmjXrh0CAwO5ts+dOxevX7+WaF8lDvLy8tC/f39kZ2fDyckJRkZGAH44S48ePQodHR3cuXOH6st+RklJCdnZ2TWck1UI89569eoV2rVrx/Fv8vTpU7BYLBw6dAj5+fmwsLDAmTNnRLxT3igpKeHZs2do0aIFCCFo164dbt26BV1dXY79qi86VVGV6sQPBoPBV7alTZs2CAsLw//+9z+u7Tdv3oSdnR3PycrmzZuxevVqjBgxAmpqarh48SKmTZuG/fv387VLnFR/HtTV1ZGUlESlJwnzPIwcORK2traYNWsW13Y2m42wsDBcvHhR/DcghxQUFGDTpk3Yu3cvevbsiS1btmDIkCFSubagvrY25xk9ejQaNmwIAIiKioKZmVkNR2NkZKR4DJcQly5dgqurK1q3bo3Q0FCqr6wNb968wYkTJ/D8+XMAgIGBASZPnox27drV6jwaGhpITEysdeqfqLi6uiI9PR2XL1+ukbJYWloKS0tL6OnpgcVicT3ew8MDAQEBcHR0hJqaGo4ePYrhw4fjxIkTQl1fHJIMlpaWiImJwW+//YYZM2Zg5syZHFId0uDnxV1eMBgMnt9jFaWlpVi9ejX8/f1hYWFRw+nN7zeVlZUllK3icoDx4vXr1wgJCUFISAgyMjIwaNAguLq6wtbWViqLEdWfqbog7O9QUNDD8+fPcfr0aWRmZoLBYKBjx46wtrYWqs8VRmJLGBsUHUHjEEFUH2va29tj9+7dNXwn/NLn5UHuLDw8HEFBQbh9+zasrKzg5OSE0aNHo3HjxmKTIqr3TkZxYWVlhWPHjlGri76+vliwYAEVpfH582cMGTIEKSkpErm+uLQUsrKy4ObmhosXL1KrUwwGA5aWlggICKgxaZI3pKHpJAg9PT2cPHmSp0bf48ePMWHCBL6dQ/V/T2730apVK1RWVgplU2ZmJthsNkJCQvD27Vs4Ojpi2rRpMDU1raFZUd8pLy/Hvn374OvrC01NTWzcuBF2dnY899+6dSsWLVpERb7duHED/fv3pyY8X79+haenZ50j3xSJzZs3w8/PD/Pnz0dsbCw+ffqEJ0+eUO27d+/G2bNnceXKFa7H6+joICYmhoq4atGiBe7du0f1K8+ePcPvv/+OgoICrsfr6+vjr7/+4rmCFh4ejlWrViE9PZ3nPYjaV4prYUpbWxs3btxAt27duLYnJSVh2LBhyMvLE+p6siQvLw+rVq1CWFgY8vPzAfyIQrS1tYWvry/PyBRuGn4/QwjBly9f+PbX/Ab4FRUViIqKApvNlqiTsbqTsHp0jKCIjlOnTvH8DoTVzhN18m5oaIglS5Zg/vz5AH5oAVlbW6O4uFhq2QWCnEmlpaWIjo7m+zy0bt0aN27c4KstPXToULx79078NyBniKpxLSrVtcrqMtkCgBkzZgj1DIojIlhSzJ07F6GhoVi1ahW8vb2lPu6qvjjGTSsV4L84RgjB9u3ba0Q7V8984sebN2/Qt29fNGzYEAsWLOBYlAoICEBpaSnu37/P02mqp6cHX19faswWHx+PwYMHo6SkRKjvVJj3vzBj6zdv3iA4OBihoaHIyMjA4MGDMWvWLEyZMkUqespVC7XcqKiowJUrV4RaQP/y5QsWLVqEEydOYPLkyTWcjPx+Uxs2bIC7u3uNiHFpYmFhgatXr6JFixaYNm0aXFxceC5CSwphNJUTEhLQs2dPidqxefNmrF27FpWVlfjtt98oHXolJSVs2rQJ7u7uEr1+fYHJZMLExIT6LSQlJcHIyKjGuIpXZoc814aoDcrKylixYgW8vLw4soHEqXf+yzgZCSF48OABh/e/V69eQg+uZe3cEjSYy8/Px8yZM4W+fl5eHtLT00EIgb6+PrS1tSVit7iRB+FaVVVVpKam8lz1yMjIQJcuXfhOGMVxH6WlpYiMjERQUBDi4uIwevRoODg4wN7eXmoFEeSNI0eOYO3atSguLsbq1asxZ84cgelKsv5tyxOVlZVYt24dzp49Cx0dHezcuZMjRWrKlCmwtLTkGUUkaiTikiVLcOXKFTx48ICrpEPfvn1hbm6OPXv28LwHeegjgB/fRVpaGs9V/qysLBgbG9c6Wlna/BxJSGpZXEkcDltxFE0RlevXrwu137Bhw4Q+Z1paGry8vBAVFQVHR0ds3LgR7du357m/qJN3VVVVPHv2jLoGIQSqqqp4+fIl2rRpI7TdoiBqMQPgx+/q0aNHPKPEUlNT0bt3b4kWsJMXmEwm1NTUYG5uztcBI6kIwPoy2RIHJiYmOHjwIN+U1Ldv3/L8rQm7SMLLYSuOvlZc0c4ZGRmYP38+Ll26xBHMYGFhAX9/f54LBADQoEEDZGRkcHxPampqePbsmVDRnJJ4/1+9ehVsNhsnT56EkpIS7Ozs4OLigv79+wt9DnFx+vRprFq1Cu/evYOnpydWrlzJc19RI2vFEcEnKuPHj4erqyvGjh0rs4CJmTNnws/Pr4YsS0FBAY4cOYKgoCAkJiZKtJ+7evUqzM3NsWbNGixZsoSas+fm5mL37t3YtGkTYmNjRSr0A/Dvo+oL69evF2q/devWSdgS3khD7mzOnDkIDw9H165d4ezsjKlTp0JbW1u8RRVFUnRUEGJjY0nHjh05ijswmUyip6dHrl+/LtQ5ZC2YKozwLIPBkNj15QVZ/zsQQkjbtm3JhQsXeLafP3+etG3blu85xHEfzZo1I0OGDCGBgYEkNzeX+lyaBRHkhQsXLpAePXoQDQ0NsmHDBlJYWCj0sfLwTCkSZWVlPNs6d+5M/v33X57tYWFhRE9Pj2d7dnY20dHRIe3btydbt24lp06dIqdPnyZbtmwh7dq1I61ateIQxecGg8Egvr6+ZM+ePWTPnj1EVVWVrFmzhtr28fER6d/z3bt3ZMGCBQL36969O2Gz2TzbWSyW3IvHE1Kz6Ji0qf77VHTevn1LZs2aRVRUVMjYsWNrVeDi5cuXZNSoUTXGMpaWluT58+d8j2UwGDWK4NVFsFzWGBkZkUOHDvFsP3jwIDE0NJSiRbJj+vTpZMaMGQL/5B1Z9zHioLS0lGfb+/fvycKFC4mqqirPfXgVnRO2uMT169f5vpuFwcDAgOzbt4/avnDhAmnYsCGprKys0/lyc3PJ3bt3yd27d8nnz5+FOoZbsc4mTZqQly9fCnW8JMdzX758oYqNKCkp1ekcdeXWrVtk8ODBpFGjRmTFihUcY35uzJkzhzRs2JCsX7+elJeX1+maivDurayslLqNMTExxNHRkaipqREjIyPi7e1NHj58yPeY9evXc/3bvXs3uXDhAtdCZj9ja2tL5syZw7N99uzZxM7Ork73Q4hwfVR9ISsrS+D3zY+ZM2eKXMiPG5WVlSQ6OppMmTKFNGjQgDRv3lzs16hOUVERCQkJIUOHDiUNGzYk48ePJ0pKSmIrvFbvC7+kp6dj7Nix6N+/P3bt2gUjIyMQQpCSkgI/Pz9YWVkhKSmpTqXHpYmwabM0ksfc3By+vr4YNWpUjTZCCDZt2gRzc3O+52AwGDWigGqbslZRUUGd51dLia4iPj4enp6euHPnDubNm4crV65QIsY04iUlJQUsFguHDx/Ghw8fuO5TVVxqzJgxXCMR169fjzFjxvC8RsuWLREXFwc3NzesXLmyRhREQECAQM3Y9u3b48CBA9S2jo4ODh06VGMfQfd69epVqKiowNbWFlpaWsjJyYGvry/++ecfobRbZsyYAXd3d7Rs2RJWVlYcbefOncOKFSvg7e0t8DyyhshBssPPYuW8kIZAtihU186LiYmptXZex44dceHCBeTl5VGabZ07d+aqh8mNNWvWcKS+ff/+nZKVqGLnzp21sknaTJo0Cd7e3rCwsKjRF2RnZ2P16tVwcnKSkXXSJSQkRNYmiAV56GNEpbi4GDNnzsSlS5egoqKClStXYuHChfjzzz+xfft2dO3alW+xkOpj/NrqW5qamoocdZaVlcVRXMbS0hKEELx7965O0U3a2tro169frY4hhGDGjBmUpALwo6DTvHnzOPT3+EXnpqSkULrRhBCkpaWhsLAQAJCTk1Mre6p4+fIlpQtYUFAgcIwvLpKTk7Fy5UpER0dj2rRpOH78OM+iOj9z+/ZtxMXF1anYy8/IulBno0aNkJWVRennjRo1CsHBwWjVqhWAH/r10shMefPmDUJCQsBms/Ht2zfY2tqirKwMERERQkV8nTx5kuvn+fn5ePv2Lbp27YqLFy/y/P3Gx8fXGMf+jLOzM6ZNm8bXhvz8fCxYsKDOfVR9oWPHjiL1laGhofjrr7/EVnCQm9zZuXPnYGpqKpbz80NNTQ3Tp0/H9OnT8fz5c7DZbNy/fx+DBw/GmDFjYGNjg0mTJtX5/PU+XXrhwoVITU3lWl2WEAJzc3N06dIFe/fu5XsecQiWS5IPHz4gMDAQa9eulcn1xYWg+2AymZgzZw41Udq3bx+cnJyoSVJRUREOHDgg0X+HFy9eoE+fPjA0NMTy5cs5Cujs2LEDz549w/379/mmhAjShCgvL0dycjLf+ygpKUFERARYLBbu3LmD0aNHw8nJCVOnTkVCQsIvkS5dlS42d+5cvnqi/BwQ8pJeK48UFhbi+PHjYLFYuHfvHgYMGIDJkyfzLLjz4cMH9OzZEw0aNMDChQthYGAABoOBtLQ0+Pv7o7y8HI8ePRKquFRubi6lvVgbR4qonD17FpMnT0ZZWRmAHxW1Dxw4AFtbW5iYmGD58uVCVfmsrKzE1KlTERERAUNDQ47q0s+fP4e1tTVOnDghVMqhLBElXbljx45CiVu/ePGC7/Xbtm3LdyFF0gLZohZukbV2HgAMHz5cqHuIjY3lu8+3b9+wZcsWREZGcsjP2NjYCNTvEqaYAQC+E52vX79i4MCBePXqFZycnDjev0eOHEG7du1w584dua84Xh84ePAg1881NTVhaGgodHqmPEgiiMr8+fMRFRWFqVOnIjo6GqmpqbC0tERJSQnWrVtXKykFoPZORnF8h0wmEx8+fOAo1FXXYj51RVRJhaq+mtvUtrYVkYuLi3HixAkEBwfjxo0baN++PWbOnImZM2fWuhBPbXn9+jXWrl2Lw4cPY+zYsdi0aZNQ1b2r+P79ew2NudpSfZ7CC17adeJAXBqbomBlZYVbt25h7NixcHR0xKhRo6CkpCS2tNL379/DwcEBenp6CAoK4rpPo0aN8OzZM54O5jdv3kBfX5+vTIi4+yhFRdS+Uhx9rTzInfHSEa2srMS5c+fAYrFw4cIFlJaW1vka9d7JaGJigs2bN2PcuHFc26OiouDl5cVR3IAb4hAslySJiYno3bu3wjtCBN2HMBMl4Id+hSS5f/8+ZsyYgZSUFMoeQgi6dOmC4OBg/P7773yPF7cmxIsXLyiR6rdv38Le3h4zZsyAmZlZvY5yFEeFLiaTCR8fHypSytPTEx4eHlRE5NevX7F27VqF/23Vhlu3biEoKAgRERHo2LEjUlJScP36dQwePFjgsRkZGXBzc8Ply5e5RiLKe9T4wIED0a9fP/j6+mL//v1wd3eHvr4+Dhw4UCe9m7CwMBw7dgzPnj0D8KNaqJ2dHd9iRPJE9d8HL7g58vlpZ2ZmZiIwMFCgeL08OCBOnz7Ns02Ywi3i0M4TdjVZkhV4v3//jkGDBuHJkycYPXo0lRmSmpqK6Oho9O7dGzdu3ICKigrX45lMJjp06IBevXrxjV7jFfVRRUFBAby8vBAWFkYVTtLW1sbUqVOxadMmqiAfjWThpeVdWFiIyspKWFlZ4ejRowIdvtU1x3khqICMLOnQoQNYLBbMzc3x8uVLdO7cGYsXL8bu3bvrdL66OBmrOwhrS/VFfKDmQj4g39HO4qiIHBcXh+DgYISHh+P79++wtraGq6ur1KIXgR9OJQaDgUWLFmHQoEE89+P1m1i2bJlQ1+H3b8lkMrF8+XKB735JatfJQxCAsrIyFi9eDDc3N44q4+LUrrt9+zacnZ15zlUEjYOE+R7E3UcpKuJwMora1zZv3hxdunSBk5MTpkyZQr1LxaqHKAAmk4levXph1qxZcHBw4Pr+/fjxo0hj73rvZNTQ0EBSUhLPKKeMjAx0794dX79+5XsecQiWS5Jfxckobzx69IgqoGNgYCDx6mKCqKysxMWLF8FisRAVFYUmTZrg8+fPMrVJ3hHGUQn86CvqO1u3bgWbzUZhYSHs7e3h5OSEHj161OnFJ6tIxOLiYsTExFDRhl5eXhwrcUpKSti4cSPPiplaWlqIj4+HgYEBysvLoaqqiqioKIwePVqsdr548QKzZ88WGDkma8QdSZibm4uNGzfi77//Rv/+/bFlyxYMGDCA5/7yID7PjdoUbhFH9Vx5qMC7Z88ebN68GdevX69R3CktLQ3Dhw+Ht7c3Fi1axPX4+fPn4/jx42jfvj1cXFzg5OQkUr9ACEFOTg4IIUIVIqKRDpWVlXjw4AFmzZoFCwsLbN++ne/+9aGAjIqKCrKystC6dWsAP5xE8fHxMDExqdP56uJkrO4g5AY/p5K4op0VHSaTiR49esDV1RWOjo4yKYwp6m+ieqrlrVu30KdPH47K2IL+LeVhgU8enIz//fcf2Gw2wsPDYWRkRBXJaN26tdgcQpmZmTAxMaHS+qsjaLFXmGAIcfdRioooC+dVx2tqagrsK3Nzc3m2aWtro3v37lT2oYaGBgDpOhl/fq7LysowadIkuLq6ijVNu947GcXh/VcEFM05x4v6ch/yQE5ODg4ePCj0iiYNjbKyMjw9PbFhwwYOp5I0X3yiEhgYiLNnzyIqKgrAj0Fp165dqcF1WloaVqxYwTPlm9ugNiEhAXp6emK1U1H6OnFNNIqLi7Fz505s27YNurq62LRpUw2tSkleX1y8e/cO69atQ2hoKCwtLbF58+ZfZpA+bNgw2NraYsGCBVzb9+7di3///ZdvNe6qNCE2m424uDiMGTMGrq6uGDlyJO0krGdcuXIFCxYswNOnT/nuJ2+/8bogSFKptmhoaCAxMVHo45lMJgYOHMg3RfZXcBAWFRXBw8MDp06dQllZGczNzeHn51crre6HDx+KrGUob9Ql7V0eFviq/66q/y6kOYcvKirC8ePHwWazER8fj4qKCuzcuRMuLi4iy3OcPn0a3t7ePLMqxREMIe4+SlERdeGcyWRi9+7dAiPvp0+fzrNNnuTOiouLER4ejuDgYNy8eRO6urpwcXHB9OnThdJ/5ccv4WSMjY3luVqek5MDCwsLkTuo1NRUjBkzRqK6UPxQlAmrIATdhzhSAERFHDaYmZkJdQ5+A8K8vDwcPnwY06dPp1ZBqigoKMDBgwe5ttUnRI1ao+Fk06ZNCAkJQUlJCezt7eHs7AwTExOFcjIOHToUf/zxByZOnAig5uD68OHD2LdvH/777z+ux1d/ZwwaNAjh4eE1Xrbdu3cXyU5F6bNFnWhUVFTgwIEDWL9+PVRVVbFhwwY4OTkJ7VBav349PDw8BEboSJrqhVu2bNlS68ItoiAPE74WLVrg2rVr6Nq1K9f2J0+ewNTUFJ8+fRLqfFlZWQgJCcHBgwdRVlaGlJQUgdEFvXr1EurZkaROGI1wZGZmomvXrvj27Rvf/eTh2RYVQZJKVfCSM9DW1uZ4rvPz86GhoVEjoo1XdEx9cNSKAw8PDwQEBMDR0RGqqqo4duwYhg8fjhMnTsjaNLFSUVGBqKgoWFtbC7V/XZyM8vBMVY8aq/67IITgy5cvUh9HPX36FCwWC4cOHUJ+fj4sLCxw5swZnvt/+fKF6+cFBQW4d+8eli9fjlmzZkm0GKCofVR9QR40GX9GnuTOqmw5ePAg3r9/DwsLC5w/f77O56v31aUBYMSIEQJFgEXl+/fvQmuB1AVBji1hB/WyRtT7ePTokcBrSDoaQhw2XLt2DR06dMCYMWN46lcJwt/fH0lJSVxT0zQ1NXHz5k18/foVq1atqtP5FYGDBw/i7NmzlJPR39+/RtRa69ateUatVZ1DGARVbqsPrFq1CqtWrcL169fBZrMxYMAA6OnpgRBCaZ/JO8+ePYOBgQG1raqqyjFR69evH89IrCqqvzOqnq/aCsfXBwStQ/Kb7ISHh2P16tUoKCjAqlWr4ObmVmsx+okTJ1Jp9/wQ1enLj58Ltxw7dqzWhVvEoacoD+vB+fn5aNasGc/2Zs2aoaCgQOjzMRgM6jclrHC/sJNqGtnz4sULoSIh5OHZFpXqUSu1rXAuqi6aOMa98rCILyqRkZFgsViU5rGTkxMGDx6MioqKeqFPnpaWBjabjdDQUOTl5eH79+8Su1ZGRgZX3bny8nKUlJQIXBASB7KSIBOEoaEhtm7dis2bNyMqKkpgVWYtLS2ev1EGg4G5c+dixYoVdbbn8+fPOHToEJYuXcpzH1H7KJofiNvHoKenBx8fH2zYsIGSOxs7dizU1dWRk5Mj1msJY8vKlSvRrl07rFq1ChcvXhTpfPU+klEcIsDCIOmoFGFz5CVd8ERU6st9iMrWrVsREhKCz58/w9HRES4uLrVOuevZsyd27NiBESNGcG2PiYmBu7u7UE5RRUXUqDXgx6pUkyZNoKyszHOyw2Aw+Opr1Fe+fPmCo0ePgs1m48GDB+jfvz9sbGzkOgVfTU0NCQkJNTTjqkhLS0PPnj1RUlLCtb2+vDPEBa9IQmEmO1UFT+zt7flGVAsSnxdXtdC6ImrhFnFoOks6qiQvLw9RUVF8F1Oqp1tVR5jUtZ/Tpasqds6cOROjRo2S+0rrNMJBCMGjR4/g4uICc3NzgZqMM2fOhJ+f3y9dEfzGjRsYNGiQwEq+vBCmfygqKuIbES7M+FzeU64bNGiAjIwMtGnThvpMTU0Nz549k3hFaEnx7ds3hIWFUamVpqamsLOzg7W1tdBp4HWJZDx//jw+f/4MZ2dn6jNfX19s3LgR5eXlMDMzQ1hYmEw0KxUNXhIiGhoa0NfXr5PDlhCCS5cugcVi4fTp09DQ0FCYgCNZIm+RjNz49OkTDh06JNW5VlVwSUREBJSUlGBrawtXV1e+mumCqPdORmHgVca7NijKhFHRqaysrFcTkZ+FVw0NDeHi4gIHBwehUpzV1dWRnJzMs9jAq1evYGJiwjNMvz6go6ODmJgYKn2vRYsWuHfvHlXo6dmzZ/j999/5Rtd07doVHz58gJOTE1xcXCQaEaXIPHnyBCwWC0eOHMHHjx9lbQ5P9PX18ddff2Hy5Mlc28PDw7Fq1SqhouNEQVBaZ1FREZ4/f65Q74zaTnaEKSQA8F9UkpbTlx/yUHRF0hV4hRnDMJlMmJiY8HSElJeXIzk5mec5fi78MnPmTDg5OfGNjKSRb6qn+FZRWFiIiooKjBo1CmFhYQIn0OHh4bC2tqainDMzM9GuXTvKoV9UVAR/f3+RIn3kAX6VOkVNGQ8NDYWdnR2VCvkzJSUl2LdvH7Zt24bs7Ow6nV9R4LYQoqjac//99x+CgoIQHh4OfX19ODo6wtPTE0lJSQKla5KSkji26yL7YmZmhsmTJ1OZH3FxcRgyZAg2bNgAY2NjeHt7Y/To0TKNbH3//j18fX3h7+8vsWsIk4nAYDAQEREhMRt+JjMzE2w2GyEhIXj79i0cHR0xbdo0mJqa1otoXUnTqlUr3Lt3T2S9wfrA69evERISgpCQEGRkZGDQoEFwdXWFra1tjTT6uvDLOhkLCgpw5MgRBAUFITExUeSJHu1kFA+PHz8Gi8XimTpSfSDm4eEBLy8vqVWuBYANGzYItd/atWuFPmdRURFOnDiBffv2ISUlBe/evRPoaNTS0kJ0dDTPVYY7d+5g1KhRyM/PF9oORUPUqLUq7t69CzabjbCwMHTu3JmqKlif9Sy5IYzG5dq1a+U64mTJkiW4cuUKHjx4UEOLs7i4GH379oW5uTn27NnD9fitW7di0aJFVMr9jRs30L9/f2ry9vXrV3h6eiIgIICvHevXrxfK3nXr1gm1nywRZbJDIx4kXYFXmDGMqM80k8lE+/btBTrg+aWNm5qaClUBNyYmRihbaepOaGgo1881NDRgZGQEY2Njoc5TfVynoaGBhIQEqVaQFZVGjRohKyuLcm6NGjUKwcHBaNWqFQDB9yBqdMz379+xfv16XLp0CSoqKlixYgWsra0RHBwMb29vMBgMLFy4EF5eXnW7QQWhuu4cwF17ThTdOWnM97p06YKioiI4ODjAycmJes8Kq48tjgyA3377DRcvXkSvXr0A/EinT0lJQXR0NIAfkY5LlizB8+fP63qbQpGSkoKrV69CRUUFtra20NLSQk5ODnx9ffHPP/+gY8eOSElJkdj1xZGJIGpBoqoMgKCgIMTFxWH06NFwcHCAvb29wuilywvDhw/HnTt30KNHD0yYMAETJkzgqTMtKXgt0FVHkhl0FhYWuHr1Klq0aIFp06bBxcWF51y6rvwSmow/ExsbCzabjcjISHTo0AGTJ08Gi8USeJygB6K8vFycZtagS5cuuHXrFuVMmzNnDnx9fakBzcePH6Grq4uioiKJ2iEJvnz5gmPHjoHFYuH+/ft8V9aqvzADAwPh5uYmVSfjyZMnebYxGAw8ffoUJSUltXIyPnz4ENevX0dqaipVaEMQvXr1wqlTp3g6GU+ePEkNDuorbdu2xZMnT3h2jElJSUKtVvXv3x/9+/fH7t27ceLECQQHB8Pd3R3W1tZgs9lcowPqI4I0Lp8+fSpQ41LWrFq1iooMXrhwIQwMDMBgMJCWlgZ/f3+Ul5fz1Sn18vLCjBkzqHseO3Ysx4S3qKgIgYGBAp2MiuA8FIafJzt3796lBrMrV64U+dyCFpUA8VQLrS/IWoRf1Gd62rRpIusZ8cs6qRpL/LwwQiM5+FXPrA3Vx3WKGPtQUlLCYfft27dRXFzMsY+g+xLlt/Hnn39i3759sLCwwO3btzFlyhS4uLjg2rVr2Lx5MxwcHASOKyWxgC5tuD2TktCek/Qzmp6eDjs7O5iamgrtrP8ZflWGheXr168ckea3bt2CjY0Ntd21a1e8e/dO5Ovw4+zZs5g8eTLKysoA/FgEPnDgAGxtbWFiYoITJ05Q41VJIY4MhXXr1iEkJISjIJGbm5vQBYnatGmDLl26wMnJCf/++y+Vom5vby+ybb8a165dQ15eHs6dO4czZ85gy5YtaN68OSZMmIDx48dj6NChEs+YFFWDVxyoqakhIiICY8eOlVgE7C/hZHzz5g1CQkLAZrPx7ds32NraoqysDBEREUJ7/2X9QKSlpXE4Mo8fP46VK1dSTkZCiMBoLXnj+vXrYLFYiIiIQElJCTw8PHD06FF07txZ6HPIYjDKS+MwISEBK1euxJMnTzB79myB53n37h0Vpvzlyxc4OTlxTOIFsXDhQtjZ2aFt27Zwc3OjOomKigoEBARg165dOHr0qPA3poBYWVlh7dq1GDNmDNeotfXr12PMmDFCn09NTQ3Tpk2Drq4u1q1bh+PHj8Pf3/+XcTIeOXKkhgPx6NGjNTQu5dnJ2LJlS8TFxcHNzQ0rV66k+ggGgwELCwsEBASgZcuWPI+XxIQ3KSkJz549A4PBgL6+vkKl5Is62alObRaVANEH5+JAHIVbREXSBc2EgV+6J/BjsfXhw4fo168f1/aQkBCRbdi1axfX6+7btw++vr5o06YNNm7cKPJ1aGjEjaDf8Jo1a/hqJgK89WvDw8MREhKCiRMnIjExEb169cKXL1+QnJwstM6jJBbQpY20CoVIuj/OyMhASEgI3NzcUFxcDHt7ezg6Ogp9XXHIh7Ru3Rqpqalo3749CgsLkZiYyNH/fv78WeDzKiq+vr6YN28efH19sX//fri7u2PevHmIiIjA0KFDJXptcSJqQaKKigqqUBqdEi062tracHJygpOTE75//47Y2FicOXMGzs7OKCoqwpgxYzB+/HiMHj1aLGnD1RHXAp0o8KuGLi7qvZPRysqKEhffu3cvRo0aBSUlJfzzzz+1Oo+gB6KsrAzv378XxdRawSsEXt55//49goODKYevvb09rl+/joEDB2LatGm1cjDKCxkZGVizZg3CwsIwadIkJCcnQ19fn+8xVlZWuHr1KkaOHIlt27ZhzJgxtRb8njx5MlasWIHFixfD29sbnTp1AoPBwIsXL1BYWAgPDw+OVcf6iKhRaz/z9u1bhIaGIjg4GN++fYOTkxP+/vvvX0rUWhyVmeWBjh07Ijo6Grm5uZT2YufOnaUa8QwA8fHxcHV1RUpKCoezs2vXrmCxWPj999+lak9dEHWyU0VdF5XkoVqoIB1EaSCqs9vPz49v+9u3bwWeo1WrVhxprcbGxrh48SKlC/z582cMHDhQqmmtR44cwdq1a1FcXIw///wTc+bMqXPxDJraUZWSyQ8GgyHxTJ/6wuPHjyldSm7w+65fv35NvU969OiBBg0awNPTs1a/BXEtoNOITps2beDt7Q1vb28qA2/w4MEoLy9HSEgIZs2axTFWq444MgBsbGywdOlSrFq1CufPn4eOjg5H5tT9+/fFnl5ZndTUVISGhqJJkyZYvHgxVqxYgd27d0vVwSiORcbXr19jyJAh1Ha/fv2grKyMd+/eCVWQ6P3794iIiACLxcKSJUswevRoODk5KcS8X95p0KABRo0ahVGjRiEgIAD379/HmTNnsHHjRqSmpmLNmjViv2ZeXh4OHz6M6dOn15DlKigowMGDB7m2KRr1fiR26dIlLF68GG5ubgIdP6KQkpJCazIKQceOHTFlyhQqraMuIclr166lVs++f/8OX1/fGpNAaQgR5+TkYP369di/fz/+97//IS4uTminQXR0NFq1aoVXr15h/fr1PLWuHj58yPc8vr6+mDBhAo4cOYL09HQQQjB06FA4ODjwjCapT4gatQb8iAAIDg7G9evXYWlpiR07dmDMmDG/5GphQUEBx6SkeqW6yspKhUpFbNq0qcx+BykpKRgxYgSMjY1x+PBhGBsbgxCC1NRU7Nq1CyNGjMCdO3fkXktHlMmOOBaVRB2ciwNpRcfwY/r06VQKf13gFgFYHV5FxKqo7uh88+ZNDQeStLILoqOjsXLlSmRkZMDd3R3Lli2TSMQBDW/4Rb7FxcVh7969Qj8PFy9epMZxlZWViImJwZMnTwBAIXSlq6KMeG0Lw8mTJ+ssh1BWVsbhoFRRURF5caQuC+j1AUHFEr9+/SolS35gZmYGMzMzqpYAm83G9u3bYWJiUqPASxXiyABYt24d3r17h8WLF0NHRweHDx/mGBcfO3YM48aNE/n++PHlyxdoaWkBAJSVlaGmpsbXuSoJqv+Ojh49inHjxtVKm7yioqLGAoKysrLQCzCqqqpwdHSEo6MjXrx4geDgYCxevBjl5eXw9fXFjBkzYGZm9kvOW8RN37590bdvX2zYsIFK0xc3/v7+SEpKwqJFi2q0aWpq4ubNm/jy5Qu8vb0lcn1pUe8Lv/xcvdfIyAjOzs6YOnUqWrduLVaxVEkLAVevmFa9WpoiCGMDgKGhIb5//w4HBwc4OzvDyMgIgPBixsJUK2UwGIiNjRWbzdX59u0btm/fjp07d6Jz587YvHkzRo4cWatz1KeiEPICr6i18vJyvqv5VcUIHB0d+TokFy9eLF6D5RB5qcwsS5hMJnx8fKiKqJ6envDw8KAiAL5+/Yq1a9cK7GunTJmCiooKRERE1OizCCGYNGkSVFRUEB4eLpkbkSA/T3YePnzIc7KjqqqKKVOmwMnJiWNRSdj+Hqhf1UIVnerFKdTV1ZGYmCjVAh3x8fHw9PTEnTt3MG/ePHh7e/+S+pzySlpaGry8vBAVFQVHR0ds3LhRoPNa0kWNpAGTyYSmpibV1+fn50NDQ4O6N0IIvnz5wvMeRK0uzWQyMWfOHGoBft++fXBycqrTAnz1BfS//vpLIaLuxYWgCF1hiqZImoSEBLDZbJ4R6np6evD19aUyAOLj4zF48GCUlJQolCOKyWQiNjaWGs/XpUq2uKn+3hMGSRQkqqysxMWLF8FisRAVFYUmTZrg8+fPQh//qyKM9iyDwZBIBGMVPXv2xI4dOzBixAiu7TExMXB3d+cZXa4o1HsnYxVFRUU4fvw42Gw24uPjUVFRgZ07d8LFxUUslVIl7WRkMpkwMTGhnCVJSUkwMjKiVkbKy8uRnJws14OwKm7fvg0Wi4UTJ07AwMAATk5OWLFiBZKSksSi+SVpdHR08PXrVyxatAj29vY8ByOKpLtWH0lJSUFQUBCOHDmCDx8+8NxPV1dXKMf1y5cvxW2i3CFqZeb6gDDPAyBYWL1Fixa4cOEC+vbty7X93r17sLKyqhEtqmjwm+yIuqgESKdaqCIg6dTUz58/49ChQ1i6dClfG2TtZGQymVBTU8PcuXOhq6vLc79fYVFInnj37h3WrVuH0NBQWFpaYvPmzTAxMZG1WVKDV6Xt6vCSXhKmunRRURFPDTxxLMCLYwG9PnD9+nWh9hs2bJiELak7DRo0QEZGBtq0aUN9pqamhmfPnkktA0AciKNKtripi5NRHBWq+ZGTk4O///5boo6x+gK/gqg/a89K8plSV1dHcnIyzwW4V69ewcTERGBUtbzzyzgZf+bp06dgsVg4dOgQ8vPzYWFhIbIApqSdjPUx8q2wsBDHjh0Dm83G3bt3MWzYMDg4OMDa2pojakXe+HnVvfrLT1wvvZKSEvj7+8Pd3V0kW381CgsLcfz4cbBYLNy7dw8DBgzA5MmT5bpQiTzx4cMH9OzZEw0aNOCpcfno0SOBKeg0P6L4nj9/znNA//r1a+jr6ytcwa7aIuqikmgBURwAAK7KSURBVKQH54rCqVOneDoRfk5NrV7Rlh+EEFy6dAksFgunT5+GhoYGX6e3kpISnj17hhYtWoAQgnbt2uHWrVuUs+/Dhw8wMjKS6OCcXhSSLwoKCrBp0ybs3bsXPXv2xJYtWzjkDYTBxcUFe/bsEcuCv6ISGhoKOzs7rkXmSkpKsG/fPmzbtg3Z2dkSs4FeQBeeT58+yfU8RRwZAKampkL1tTExMSLZyo+srCyh9hNHoRthqYuTUZJkZ2dj06ZNOHDgQK3e/zScVGnPxsbGwsXFpda1O2qDlpYWoqOjOTROf+bOnTsYNWqUQkiF8OOXdDJWUVFRgaioKLDZbIFORl66F1WkpaXB3t5eISIJ5ZHU1FTK8Zubm8tTB2HZsmVCnU+Smozieunl5OTg7t27UFFRwYgRI6CkpISysjIEBARg8+bNKC8vR05OjjhMrvfcunULQUFBiIiIQMeOHZGSkoLr169j8ODBsjZN4cjIyICbmxsuX77MVeNSXgZW8o6RkRF8fX15pp7/+++/8Pb2xtOnT6VsWe0wMzMTuI8wEw1FXVSSZ+qSmgoAmZmZYLPZCAkJwdu3b+Ho6Ihp06bB1NSUbypd9WjKqgW16tv0OOjXYOvWrdiyZQt0dHSwadMmTJgwoU7nETVVWB75+vUrxwI0k8mkJDi48f37d6xfvx6XLl2CiooKVqxYAWtrawQHB8Pb2xsMBgMLFy6El5eXUNfPyckBg8FAs2bNhLZZGgvoigwhBBcuXEBQUBDOnTsn1/rU4sgA4Lc4/+XLFxw7dgylpaW/3PMgCydjfn4+FixYQPUPK1euxMKFC/Hnn39i+/bt6Nq1K5YtWwZ7e3up2VRfqK496+PjI3HtWVNTU/Tv3x9//fUX13ZPT0/Ex8fj6tWrErVD0vzSTsbawC9ku4pf+eUrLsrLy3HmzBme1bzkQZNRHMTFxWHMmDEoKCgAg8FA3759ERwcDGtra1RWVmLp0qVwcXHhmRpD84OtW7eCzWajsLAQ9vb2cHJyQo8ePWqVjmllZYVjx45R2kW+vr5YsGABJTb9+fNnDBkyBCkpKZK8FblD1pWZZUVxcTFiYmIwduxYAICXlxfHZEJJSQkbN26skU5enSrh9XPnztVIG3z8+DHGjRuH6dOnCx2lLiuYTCY6dOiAMWPGQEVFhed+whQWqULYRSV+ZGVl4du3bzAyMqpTATFFpi6pqaWlpYiMjERQUBDi4uIwevRoODg4wN7eXui+sj6kEdKIj6rUdXNzc77OaUFSBsKkCss7CQkJ8Pb2xrlz5wD8cEQUFRVR7QwGA//99x9PbcNVq1ZRBRFv376NnJwcuLi44Nq1a1i1ahUcHBz49r/AD0eEt7c3wsLCkJeXBwDQ1taGnZ0dfHx8qDENL+QxakweePnyJdhsNkJDQ1FYWIgxY8Zg8uTJmDhxoqxN44mkMgDKy8uxb98+quDmxo0bKd1HSSCOKtmiUj0Iyd7eHrt3766R0TN+/HiJ2TB//nxERUVh6tSpiI6ORmpqKiwtLVFSUoJ169bR79w6IEvt2YiICNjZ2WHXrl1wc3Oj3p8VFRUICAjA8uXLcfToUdjY2EjFHklBOxmFRJiXb15eHnr27Cl5YxSYd+/eYefOnVi7di3Xsu0+Pj5wd3eX63RMQVGtVfBLKRkxYgRatGiB1atXg81mY/fu3dDV1cWff/4JZ2fnWlcl5IakU/jlAWVlZXh6emLDhg0ck5zaFpb4OYpCQ0MDCQkJUtUZo5EfAgMDcfbsWURFRQH4MVns2rUrVdk3LS0NK1asEJiCX1JSghEjRuDu3buwsLCgUoNTUlJw5coV9OvXD7GxsQKdlbJm69atCAkJwefPn+Ho6AgXFxexaa0JWlQCfqQR5uXlcWgFzpkzBywWC8AP3ceLFy8qlM5UXRElNbV58+bo0qULnJycMGXKFGhrawOoXV8pD/AqdFAdWpNR8syYMUOosYogRwaTycSHDx8UOqLZ1dUVnTt3piIN1dXVERgYiDZt2oAQAjabDUIIDh06xPX4zp07Y9u2bZg4cSISExPRq1cvTJ06FYcOHeJbuK6K3NxcDBw4kIpMNjY2BiEEqampOHr0KNq1a4e4uDjqd19XEhISfol5TklJCf79918EBQXhzp07sLCwwIULF5CQkPBLaY3+zJEjR7B27VoUFxdj9erVmDNnjlDPpih4eHggICCAo0r28OHDa1UlW1TkoTBVhw4dwGKxYG5ujpcvX6Jz585YvHgxdu/eLbFr1lfkRXvW29sbmzdvhrq6Ojp16gQGg4EXL16gsLAQHh4ePKMcFQpCIxL5+flk3759pHfv3oTJZMraHLln+fLlZPbs2Tzb586dS1asWMGzvWPHjiQnJ0cSpgkNg8EgTCaTMBgMnn+CnoVmzZqRJ0+eEEII+fbtG2EymSQ8PFysdiYkJBAGgyHWc8obvr6+RF9fn7Rr146sWLGCPH78mBBCiLKyMklOThbqHAwGg3z48IHabtKkCXnx4gW1nZ2dTf+2fyGGDBlCIiMjqe3qz8OhQ4fIgAEDhDpXaWkp+euvv0iPHj2ImpoaUVNTIz169CCbN28mJSUlYrddksTFxZFZs2YRDQ0N8vvvv5O///6bFBQUSPy6AwYMIGw2m9q+cOECUVZWJocPHyYPHjwgAwcOJK6urhK3Q9Zs2bKFNG3alHTp0oWcOnWq1sdraWmRoUOHkv3793P8u9Wmr6yoqCBlZWUcn2VnZ5M///yTeHh4kJs3b9bartqiq6sr8K9jx44St4NGfDAYDKKlpUW0tbX5/skzhoaG5MaNG9R29ffGnTt3SPv27Xke36BBA/L69Wtqu2HDhuTRo0dCX3/JkiXExMSEZGdn12h7//496datG1m6dKnQ5/uZqnlOr169fomxkJubG9HW1iYDBgwg/v7+1JyjNn2lqIwePZrk5+dT2z4+PiQvL4/azsnJIcbGxlKx5cKFC6RHjx5EQ0ODbNiwgRQWFkrluoQQ0qlTJ3Ls2DFq++7du0RZWZmUl5dLzQZ5QFlZmbx9+5baVlNTo+Y7NLWjZcuWpFGjRsTT05MkJCSQxMRErn/S4O7du2Tx4sXEysqKjB49mixZsoTcvXtXKteWBnQkYx2JjY0Fm81GZGQkOnTogMmTJ2Py5Ml8qxbRACYmJvjnn3/wv//9j2t7XFwcZs+ejeTkZK7t8pBWI46UEm5VOh89eoTOnTuLxUbg14hkrOL69etgs9mIiIiAnp4ekpOThdZklIeKqTTyg46ODmJiYtC1a1cAP6pE37t3jypu8ezZM/z+++8oKCiQoZWyo6ioCCdOnMC+ffuQkpKCd+/e1YhKFyfNmjXDtWvX0K1bNwCAm5sbPn78iIiICADAtWvXMHPmTIHVvhUdUVNTS0pKEBERARaLhTt37mD06NFwcnLC1KlTkZCQIFQk48yZM6GiooL9+/cD+KE717VrV5SUlKBVq1ZISUnB6dOnYWVlVbebpPklYTKZ2L17NyVZwgtelZnlgcaNGyMlJYUa9+3atQuurq5U3/jq1SsYGBjwLPTFbRxSmyIdurq6CAwMhKWlJdf26OhozJs3D5mZmULf0686z6nKkFm5ciVHMSJpRn3LQ4ZNfHw8PD09cefOHcybNw/e3t5STVMG6k+VbFGpXsintv0Dzf9Ba89KD8nGOdcz3rx5g5CQELDZbHz79g22trYoKytDRESEwqQayZqMjAy+wvRt27at1SBIFgjSo8nLy0NUVBSmTZvGcx8Gg4GvX79CVVWV6tCKiopqlKuX5OS9PjFs2DAMGzYM/v7+OHLkCIKDgzFs2DD069cPNjY2fAsGMRiMGilf4khXp1FMCgoKOFKAqlfbraysFErwPS8vD4cPH8b06dO5SkMcPHiQa5u88/DhQ1y/fh2pqakwMTERqBMmKsXFxRzfUVxcHFxcXKjtTp06SbTiqrwwbdo0kfolVVVVODo6wtHRES9evEBwcDAWL16M8vJy+Pr6YsaMGTAzM+PrwLx9+zb8/f2p7YMHD6K8vBzPnz+HpqYmPD09sW3bNpk6GV+/fo1169aBzWbLzAaa2mNnZ6fQmowNGzbEmzdvqPFhdTmN169fC9TYXrt2LbXP9+/f4ePjU8Pxyquo4fv376mFMW6YmJgI1U/S85wf/VpwcDBatWqFMWPGwNnZGaNGjZKqDdXjf2QRDzRgwACoqanBzc0Nurq6OHr0KNf9JClNUVFRgQYNGnB8pqysjPLycoldszoPHjyAu7s7Tp8+zXUsZ21tjd27d6NHjx4Ss4EQghkzZlCFfEpKSjBv3jyOIj6AYP1bGsjFgrQ4ZNcUAdrJKCRWVla4desWxo4di71792LUqFFQUlKSaInzn4mNjcXChQtx584drp3coEGD8M8//witzyQr1NTUkJmZydPRmJmZSWmf8SIlJUXgYEmWP8xXr15h5syZfJ2MhBAYGBhwbP+8OizMSkp1h2R1vn79Wgur6wfq6uqYN28e5s2bh8ePH4PFYuGvv/7i62QU9PKW5wqCNOKnbdu2ePLkCQwNDbm2JyUloW3btgLP4+/vj6SkJCxatKhGm6amJm7evIkvX77A29tbZJslzbt37xASEoKQkBB8+fIFTk5OuHv3rlQmnR06dMCDBw/QoUMH5OTkIDk5mSMSPjs7W2AEVH0gJCREbOfS09ODj48PNmzYgIsXL4LFYmHs2LFQV1dHTk4Oz+Pevn3LUXUxJiYGkydPpr7/6dOn17qQgLjJzc1FaGgo7WRUIOrDol6vXr1w6tQpntkTkZGRfCMAhw4diqdPn1LbgwYNwsuXLzn24fc9NW/eHJmZmTzfTRkZGQIrTct6niMvODg4wMHBAZmZmQgODsaCBQtQVFSEyspKpKSk/DLO1vbt24PBYODkyZM892EwGBJ1MlYfnwPcHWySdK7t2LEDZmZmXBeENTU1YWFhgW3btuHw4cMSs6F6FLeTk5PErlXfkYfCVT179vwlignT6dJCoqysjMWLF8PNzY1jkC2t8Pnx48fD1NSUZ7EBPz8/XL16le/LQB4YM2YMWrdujQMHDnBtnzVrFt69e4fz589zbedX5VtewpyFSVMWR5XOqu+CF/LwXcgDZWVlfKOtJFWFj0YxWbJkCa5cuYIHDx7UKMpSXFyMvn37wtzcHHv27OF7np49e2LHjh0YMWIE1/aYmBi4u7vj0aNHYrNdElhZWeHq1asYOXIkXFxcMGbMGLGJvRNC8OnTJ74RTJs3b4afnx/mz5+P2NhYfPr0CU+ePKHad+/ejbNnz+LKlStisUmR+fjxY52jwT59+oRDhw7xXZBp1qwZbt68SY13WrdujW3btsHR0RHAjyqsJiYmHFV1pc2vJBNSX5AHGRxRqaoWunv3bri5uVEpedKqFurq6or09HRcvny5RuRXaWkpLC0toaenRxXM4oas5znyCiEEFy9eBJvNxpkzZ9C8eXNMmjRJ6CJUdUFQeuyvIuMjD+NzPT09nDx5kmfwyuPHjzFhwoQaiwI08s29e/dw7NgxPHv2DAwGA/r6+nBwcEDfvn0lfm1xyK4pArSTUUj+++8/sNlshIeHw8jICM7Ozpg6dSpat24tlZdvhw4dEB0dTVUorU5aWhpGjhyJV69eSdQOUbl69SosLCywdOlSeHh4UFWkP3z4gK1bt2LPnj24dOkSzMzMuB7PZDIRHx8vsAqhLH+Y0prkiMNRqegIM8hjMBhco8loaLjx4cMH9OzZEw0aNMDChQthYGAABoOBtLQ0+Pv7o7y8HI8ePaL6Ll6oq6sjOTmZZ9T2q1evYGJiIjAiWdYwmUy0atUKv/32G99FjYcPH9b4rFGjRsjKyqL661GjRlFpaIBwE6XKykqsW7cOZ8+ehY6ODnbu3MnxHpwyZQpGjRoFV1fXut6iQiCO71JUzMzM0L9/f2zevBk3b97E8OHD8ebNG8qGy5cvw83NDenp6RKzQRC0k7H+kJWVhW/fvsHIyEioCq+ypkougFu10GXLlmHbtm1CnysnJwcMBkNg9GEVb968Qd++fdGwYUMsWLAARkZGAH5k/gQEBKC0tBT379/nq2Mn63mOIpCbm4uDBw8iJCQECQkJErsOk8nE6NGjqQi+qKgomJmZcWTYREdH0/2cFFBVVUVqaipP/cOMjAx06dIFxcXFUraMpq6sWLEC27dvR5MmTdCpUycQQvDy5UsUFRXB3d0dW7ZskbWJ9QLayVhLioqKcPz4cbDZbMTHx6OiogI7d+6Ei4sLh0CwuFFVVcWTJ094FgZJT09Ht27dFKKTCwwMxJIlS1BWVgYNDQ0wGAwUFBRARUUFu3btgpubG89jFWHFuzaTnLdv3yIiIoJaSTEwMMCkSZM4RI5F4dOnTwIdsoqMMKLHDAaDXmGkqRUZGRlwc3PD5cuXqahpBoMBCwsLBAQEUOLr/NDS0kJ0dDQGDBjAtf3OnTsYNWoU8vPzxWm62Fm/fr1Q+61bt67GZ8IUVWrVqhUqKyvFZ3A9RdTvsmPHjgLTUqucIry4evUqrKys0Lp1a7x//x729vYckVHz58/Ht2/fEBoaWtvbExu0k1HxCA0NRV5eHpYuXUp9NmfOHOrZMjQ0xMWLFxWi0MOdO3dw7NgxPH/+HACgr68Pe3t7nu+Bn8nPz4e3tzfCwsKQl5cHANDW1oadnR18fHygpaXF9/iMjAzMnz8fly5dqvHe8vf3F7qwoKzmOYrCgwcPqIUvSSEPEXwAUF5ejl27dnGN+FqyZInENZnlgXbt2uHAgQM8dTkvXLiAOXPm4PXr11K2jKYuhIaGYt68edi2bRvmzp1LPcNlZWX4+++/4enpicDAQL6SZ6KSm5uLoqIiDnmL5ORkbN++Hd++fYO1tTUcHBwkdn1pQTsZReDp06dgsVg4dOgQ8vPzYWFhgTNnzkjkWnp6eti+fTsmTpzItT0yMhLu7u4K40x5+/YtwsPDkZ6eTukT2tjYCNQ6kwcno6Doubdv32L79u0CJzkBAQFYtmwZvn//Dk1NTRBC8OXLFzRo0AA7d+7E/Pnz62QfIQQXLlxAUFAQzp07R2sK0tDUkdzcXCoqq3PnzmjatKnQx5qamqJ///7466+/uLZ7enoiPj4eV69eFYut8og4K7cXFxfj8uXLHBMdCwsLgRq+9QVRv0t+6f2ZmZkIDAxEaWmpwH+LlJQUXL58GTo6OpgyZQpHhNn+/fvRr18/9OzZs5Z3JzyTJk3i256fn4/r16/TTkYFYuDAgZgzZw7lWImOjsa4ceMQEhICY2NjLFy4EF26dEFQUJCMLZUcubm5GDhwIN6+fQtHR0cYGxuDEILU1FQcPXoU7dq1Q1xcHLS1tQWeKy8vj3Jy1va9VR1pznPkicuXL+PSpUtQUVHBrFmz0KlTJ6SlpWHlypWIioqChYUFoqOjZW2mRCkuLoaFhQX+++8/mJubU89kWloarly5gsGDB+PSpUs1ZGXqGzNnzkR6ejpu3rxZo40QgqFDh6Jz5860pJKC0K9fP9jb2/OUn9u5cyeOHz+O+Ph4idlgb2+PVq1aUYW8Pn78CCMjI7Ru3Rp6enq4cOECWCwWnJ2dJWaDVCA0IlNeXk5OnjxJxo0bJ7FrLFy4kJiYmJDi4uIabUVFRcTExIQsWrRIYteXF4YPH07y8vJ4tr97944sWLBAojbo6uoK9cePs2fPEiUlJbJ8+XLy7t07Dvv/+OMPoqysTM6dO1cru168eEG8vb1J27ZtiZaWFnF0dCSRkZF1ukcaGhrR+Pfff4mysjLZu3cvKS8vpz4vLy8nfn5+REVFhZw4cUKGFgrHhw8f+LaXlZWRu3fvcm1jMBgcxzdp0oS8ePGC2s7OziZMJlOgDadPnyYtWrQgDAaD469FixbkzJkzQt6JYiOu7/JnPn/+TJYuXUoaNmxIhg4dSv777z+x2SspZsyYIdQfjeLQtGlTkpSURG3PmzePTJo0idq+evWqwDGVvBMREUG6devGs33JkiXExMSEZGdn12h7//496datG1m6dKkkTeSLNOY58kJISAhhMBikWbNm1Hvm0KFDRF1dncyYMYM8fvxY1iZKhTVr1pD27duTxMTEGm0JCQmkffv2ZN26ddI3TMqkp6cTTU1N0q9fPxIWFkYSEhJIYmIiOX78OPn999+JpqYmef78uazNpBGSRo0acYydqvPixQvSqFEjidqgq6tLrl69Sm1v27aN6OnpkbKyMmq7f//+ErVBGtCRjArChw8f0Lt3bygpKWHhwoUwNDQEg8FAamoq9u3bh4qKCjx8+FCgTpisEXYFdPz48TzbUlJScPXqVaioqMDW1hZaWlrIycmBr68v/vnnH3Ts2BEpKSniMlkiDBs2DEOGDIGPjw/X9tWrV+PmzZsCdRdLSkrw77//IigoCHfu3IGFhQUuXLiAhIQEmJiYSMJ0uaOyshIhISGIjIxEZmYmGAwGOnbsCBsbGzg7O9eLypU0iom3tzc2b97MVaPLw8ODZ5SjPKGkpIT3799TEXTGxsa4ePEipTXJL4Kuuni9hoYGEhMTayVeHxcXh+HDh2P8+PFYvnw5pceYkpKCHTt24OzZs7h27RoGDhwo1vuWN8TxXVZRXFyMnTt3Ytu2bdDV1cWmTZtgZWUl8LgbN24IZevQoUOF2o+GBvihN5qamkppaffo0QMuLi5YsmQJgB/6tYaGhnIvB3TgwAEq+m3JkiXo378/YmNjsXz5cjx9+hTOzs4IDAzkeqyuri4CAwNhaWnJtT06Ohrz5s1DZmamBO+ABvhRtM3Ozg4rV65EeHg47Ozs0KtXL4SHh0NPT08qNri4uAjch8Fg8C3kIyoGBgbYvHkzJk+ezLX9xIkT8Pb2xrNnzyRmg7xw//59zJgxAykpKdScghCCLl26IDg4GL///ruMLaQRFg0NDcTHx1O6tdV5+vQpfv/9d4nqpaupqSEtLY1651lZWaFr166Ubu+zZ88wcOBAfP78WWI2SAPayahAZGVlwc3NDRcvXuTQW7G0tERAQAB0dXVla6AQCCPeza8i8tmzZzF58mSUlZUBADp16oQDBw7A1tYWJiYmWL58OcaOHStWmyWBhoYG7t27B0NDQ67tT58+Rd++ffH161ee55g/fz6OHz8OQ0NDODk5wc7ODs2aNfulKgESQjBu3DicP38ePXr0gJGREZVi9PjxY4wfPx6nTp2StZk0vzDx8fE4cuQIhzSEg4MD+vXrJ2vThEIULUAmkwlNTU1qUJ6fnw8NDQ3qPUD+v0QEP8eYlZUV2rVrx3NyPnfuXLx+/Rrnz58X6T7lHXF8lxUVFThw4ADWr18PVVVVbNiwAU5OTkIvxPB7f1edg8FgoLy8XNjbqjUVFRVITk6Gvr5+jVT5oqIipKenw8TERCEKhdD8wNjYGL6+vpg0aRJycnKgo6ODu3fvok+fPgB+9KHjx49Hdna2jC3lzfbt27Fq1Sp0794dqampAH4sMu3cuROLFi3CggUL0Lx5c57HN2zYEC9evOApGfTmzRt07twZJSUlErEfkA/HljzwcyXnyspKNGzYEFeuXJFqEUVe0ljAjz7wypUrQslbiIKqqiqeP3/OUwv19evX0NfXl+gzKQ9ERUVh3LhxAICEhAQ8f/6cGstJUhqERjKYmprif//7HzZu3Mi1ffXq1bh16xauXbsmMRtatmyJS5cuoUePHgCA5s2bIzAwkHLoP3/+HL169UJhYaHEbJAGyrI2gEZ4OnTogPPnzyMvL4+asOrr61MaLeXl5VBWlu9/UlEF/n19fTFv3jz4+vpi//79cHd3x7x58xARESG16InY2FgsXLgQd+7cgYaGBkdbQUEBBg0ahL///puvPZWVlXwFk1VUVCDI/79//354enpi5cqVv6wYd0hICG7cuIGYmBiYmppytMXGxsLa2hoHDx6UqIAvDQ0/+vXrpzAOxbrCy0klDo2i//77j2+lvwULFkh18icrRP0uw8PDsXr1ahQUFGDVqlVwc3NDgwYNanWOqmIU1SkqKsKePXvg5+cnVFEkUTh06BD8/f1x9+7dGm0NGzaEi4sLli5dCicnJ4naQSM+pk2bhgULFiA5ORmxsbEwMjKiHIzAj2hmec/MYLFY+Oeff+Di4oJr167BzMwMsbGxSE9PF1iwBfgxyczMzOTpZMzIyBC60nRdCQkJQYcOHdCrVy+B48/6zLdv36gqzkwmE6qqqlIvOnTy5Emun58+fRqrVq1Cw4YNsXbtWonaoKGhgY8fP/K89+zs7BpzoPqIjY0NnJyc4Ofnh549e9KORQVn+fLlsLa2RmlpKZYvX05lgGZnZ2PHjh3YvXs3z9+fuOjXrx/8/Pxw4MABREZG4uvXrzAzM6Panz17phCFzgQigxRtGjGTnJxM/vjjD/Lbb7/J2hSJo6mpSZ4+fUoI+aEFpqSkRM6fPy9VG8aNG0d27tzJs33Pnj3E2tqa7zn69evH9xw7duwg/fr143uOI0eOEHNzc9K4cWNia2tLoqKiSFlZGVFWVibJycn8b6KeYGFhQTZv3syz3dfXl4wcOVKKFtHQ/GDLli2kqKiI2r5+/TopKSmhtr98+ULc3NxkYVqtkIQWYG1QVVUlmZmZPNszMzOJmpqaxK4vL1Rp9dQVBoNBGjVqRFxdXckff/zB8682VFRUkAMHDpC2bduS9u3bEzabTSoqKkSyUxD/+9//yLFjx3i2h4WFkSFDhkjUBhrxUlFRQVavXk169uxJRo0aRVJSUjjabWxsSFBQkIysEw41NTWSlZVFbTdo0IDcuXNH6ONdXFzI0KFDSWlpaY22kpISMmzYMOLi4iIWW3nh5uZGtLW1SY8ePciePXvI58+fJXo9eYXBYJCDBw+S06dPk9OnT5NGjRqR/fv3U9tVf9Lk1q1bZPDgwaRRo0ZkxYoVJDc3V+LXtLW15dBGrc6kSZPIlClTJG6HrElISCC9evUiurq65Nq1a7I2h0YM+Pn5kQYNGhAmk0m0tbWJtrY2YTKZREVFhezatUvi13/48CFp1qwZZcPq1as52p2cnMjcuXMlboekodOlFZTCwkIcP34cLBYL9+7dw4ABAzB58mSe1ZLkBVE1nbil7iUkJEhNJwX4EVEaHR1NaYNVJy0tDSNHjsSrV694niM0NBRubm7Yvn075syZQ0WglpeXIzAwEB4eHggICMCMGTME2pOZmYng4GCEhISgqKgIubm5CAsLg42NTZ3uT5HQ0dFBdHQ0z5XFR48eYfTo0XKdZkVTP6muZaihoYGEhIQ6VVaWJUpKSnj27BlatGgBQgjatWuHW7duUfIcHz58gJGREdf7yMvLw+HDhzF9+nSuUd8HDx7k2vYzPXr0wNKlS6nKs9Vhs9nYvXs3kpKS6n6TCsBvv/2GadOmwdXVlee7hx/Dhw8XmBbNYDAQGxsr1PkiIyOxatUqfPr0CV5eXli0aBEaNmxYa7tqy2+//Yb4+Hie8jAZGRno168fPn36JHFbaGiqECQrIYg3b96gb9++aNiwIRYsWEDphaWkpCAgIAClpaW4f/++xKNbSktLERkZCTabjbi4OIwZMwaurq4YOXLkL6NvLaqskzhJTk7GypUrER0djWnTpmH9+vU8o13FTUpKCvr374+uXbti2bJlHM/krl27kJKSgjt37qBr165SsUeWlJeXw8fHB3/99RcWLFgAb2/vGpmDv0JUZ33izZs3OHHiBJ4/fw7ghwbp5MmTpRZB+OnTJ8TFxUFHRwf9+/fnaDt37hy6dOlCaW4rLDJ2ctLUkps3b5Lp06eTJk2akG7duhElJSVy69YtWZslNNWrg/78x2QyCZPJJEpKSnyPv3r1KklMTCSJiYmkcePG5Ny5c9R21Z8kadiwId9KYs+fPyeqqqoCz7N8+XLCYDCIhoYG6dWrF+nVqxfR0NAgTCazTlUEKysryYULF8iUKVNIw4YNSZs2bep9xXEVFRWO6tzVefv2LWnQoIEULaKh+YGsIwDFxc99M5PJ5LnNjQ0bNhAbGxue554yZQrx8fHhe/2dO3eSpk2bknPnztVoO3v2LGnWrBnfqPD6wqZNm4iBgQFhMplkwIABJCgoiHz9+lXqdly7do3079+fNGrUiHh5eZH8/HypXr9Ro0Z83/GJiYkSrwxJI3kKCwsJi8Ui/v7+ClG5lcFgEF9fX7Jnzx6yZ88eoqqqStasWUNtV/3x4+XLl2TUqFFUv1rVt1paWsrkO8jMzCR//vkn6dSpE2nXrp1M+ptflVevXpEZM2YQZWVlYm1tXSO6V1r8999/pEuXLhzvfQaDQYyNjcnt27dlYpMsuXjxIlFSUhJ6DERDww0/Pz9ZmyAV6EhGBWHr1q1gs9koLCyEvb09nJyc0KNHD4Ur8lFQUMD18+qaTk+ePOG6H5PJBIPB4KoXU/W5pFcY9fT0sH37dp7CzJGRkXB3d8fLly8FnuvOnTs4duwYx0qKnZ0dBgwYIJKNubm5OHjwIIKDg5GYmCjSueSZ6hVXq6Mo0WI09Q9hCqYowrMpqMJ9Fdx0EXv27IkdO3ZgxIgRXI+JiYmBu7s7Hj16xPO8lZWVmDp1KiIiImBoaMhRXfr58+ewtrbGiRMnfplCHzdv3gSbzca///4L4Ide1KxZszB48OBanScnJwcMBqNWOm9WVlaIiYnBzJkz8eeff0JHR6dW1xQHPXv2xLx58zBv3jyu7QEBAdi/fz8SEhKkaxhNnXn16hWcnZ3x8OFDDBgwACwWCxYWFtS4SE1NDRcuXJDrquW6urpCRQoLMy7My8uj7r1z585o2rSpWGysLa9evUJISAhCQkLw/ft3pKWloUmTJjKx5VejUaNGYDAYWLRoEQYNGsRzv/Hjx0vFnoSEBKqK9K9a8CQyMhJubm7o2rUr10jGX0Ebuj5w5swZofaT5G+radOm6NOnD4KDg6UWmSwLaCejgqCsrAxPT09s2LABSkpK1OeK5mSsTmVlJdhsNtavXw8mk4k///wT06dP5zlhzMrKEuq8VWXhJcGiRYtw7do13Lt3D6qqqhxtxcXF6NevH0xNTeHn5ycxG6r48uULmjRpUuP7qqysRGFhYb0P32cymRg9ejTPNL3S0lJER0fLvSOHpv5RX5yMX758EWo/bn2Nuro6kpOT0b59e67HvHr1CiYmJkJdIywsDMeOHeOY6NjZ2cHOzk4o++ob3759w/HjxxESEoLbt29DX18frq6uWLFiBc9j8vPz4e3tjbCwMKqIi7a2Nuzs7ODj4yOwQAWTyYSysjIaN27M16GSm5tbp3sShq1bt2Lr1q2IjY1F9+7dOdoSExMxYsQIrFixgu/3QCNf2Nra4vXr11iwYAFOnDiBZ8+eQU9PDywWC0wmE/Pnz8fnz5+FTuWnqTs/p0vfunULY8eOxcyZMzFq1KhfZiFn/vz52Lp1K+VQPXToECZOnEht5+fnw8HBAefPn5eYDfKSsv2rzzGAH//e8+fPx5kzZ+Dr64slS5bI2iQaEZCH39a7d+8wZ84c3L59G35+fnB2dpbYtWQJ7WRUEDZt2oSQkBCUlJTA3t4ezs7OMDExUWgno6w0nUTlw4cP6N27N5SUlLBw4UIYGhqCwWAgNTUV+/btQ0VFBR4+fEhVrOLGtGnTsG/fPqoqdNW/Ib+K09U5efIkPD09kZCQgEaNGnG0FRUVoVevXti+fTvGjRtXtxtVAHjptFVHHFVuaWhqA5PJhI+PDzUx8fT0hIeHB5o3bw4A+Pr1K9auXSv3Tsaq6HFBcLsPLS0tREdH84zMvnPnDkaNGoX8/HxRzfylOXfuHKZNm4b8/Hyez1Nubi4GDhyIt2/fwtHREcbGxiCEIDU1FUePHkW7du0QFxcHbW1tntcJDQ0Vyp7p06fX6T6EoaysDCNHjsStW7dgbm4OIyMj6v175coVDB48GJcvX67Vu5RGtujo6ODMmTPo168fcnNz0bx5c9y+fRsDBw4E8H/O45ycHBlbWr+ZP38+jh8/jvbt22PmzJlwcnKSeEVreaS+6CmLCj3H+EHr1q3Rvn17hIaGwtDQUNbm0NQjQkJCsGzZMgwfPhyrV6+uER1bfSFV0aCdjArG9evXwWazERERAT09PSQnJ+P69eu1TpWSJdevX4enpyceP36MJUuWwNPTE5qamkIdu3XrVixatAhqamoAfhSS6d+/P+Wc/Pr1Kzw9PREQECAx+4EfEZVubm64ePEilbrNYDBgaWmJgIAAnqL0VQgaxAjDyJEjYWtri1mzZnFtZ7PZCAsLw8WLF4U+Jw0NjXgQJn0O+FGoQp75OV2aEAIrKysEBQWhTZs2HPtxSxUyNTVF//798ddff3E9t6enJ+Lj43H16lWe1xclkrI+U1RUhLCwMAQHB+P27dvQ09ODi4sLVq5cyXX/pUuXIiYmBleuXKmxAJadnY2RI0dixIgR2LVrlzTMF4mysjLs2rULR48exfPnz0EIgYGBARwcHLB06VI0aNBA1ibS1AIlJSW8e/eOei6bNGmCpKQkhXLqbNiwgevnmpqaMDQ0xMiRI+U+EpDJZKJ9+/bo1asX33dXZGSkFK2SPvUlC0FU6DnGD3x8fODl5cWRRUijuLi4uGDPnj1UkI+suXLlCkaNGgVCCCX5Jg3pN2lAOxkVlK9fv+LIkSMIDg7GgwcP0K9fP9jY2GDZsmWyNo0vomo6ydsKY15eHtLT00EIgb6+Pt8okJ8RtRIh8GN17caNG+jcuTPX9vT0dAwdOhTv3r0T+pw0NDQ0/KhNXxUREQE7Ozvs2rULbm5u1CC9oqICAQEBWL58OY4ePQobGxue5xAUSVlfBmPCcvPmTQQHB+Pff/9FRUUFbGxs4OrqKlCvTldXF4GBgbC0tOTaHh0djXnz5iEzM5PnOeLj49GnTx/q37Hqu6+itLQUp0+fhq2tbe1vjOaXpT44dXr16sX18/z8fLx9+xZdu3bFxYsXqXuUR2bMmCHUwlh9zwyRh+dRHnTj6DkGb0pKShAWFoZv377BwsIC+vr6sjaJRkiq+xFkyc6dO7FmzRpMmTIFa9asqRHJKEnpN2lAOxnrAY8fPwaLxcLRo0fx8eNHWZvDF1E1neTh5S8OxOFkVFNTw6NHj2BkZMS1PTU1Fb1790ZxcbFYbKahoaGpbV/l7e2NzZs3Q11dHZ06dQKDwcCLFy9QWFgIDw8PnlGOVYgSSVmfqJJMefHiBfr27QsXFxfY29sLHcHZsGFDvHjxgqfI+Js3b9C5c2eUlJTwPIe8LfLR1A+YTCbmzJlDpWTu27cPTk5OVIZLUVERDhw4oLDP1fv37+Hg4AA9PT0EBQXJ2hwaAcjDPEMedOPoOcYPPDw88P37d+zZswcA8P37d/Tv3x/Jyclo1KgRysvLcfnyZUregUa+qf77lgUvX77EtGnT8OLFC/zzzz+YMGGCzGyRJMqCd6GRd7p164bdu3dj27ZtsjZFIPV9BbQ2pKSkIDs7G8CPyXNaWhoKCws59uGnx6Crq4v79+/zHADcv39f4VdBaGgUlYMHDwq137Rp0yRsiWzx9fXFhAkTcOTIESrqe+jQoXBwcEC/fv0EHl/deaikpIQBAwbUakGmPrBr1y44OTnB1dUVJiYmtT6+efPmyMzM5OlkzMjIEKi/Vn1NmtsaNb1uTVNbhg4diqdPn1LbgwYNqlGFWZ4rSwuiVatW8PHxqbfi/vWRtWvXUk7v79+/w9fXl8PpLWkqKyslfg1B0HOMH1y4cAGbNm2ito8cOYKsrCw8f/4c7du3h4uLC3x8fHDu3DkZWklTG4SJ2JYk3bt3x6hRo3Dq1ClKp70+QkcyKgjCVCpmMBhYtGiRFKyRHfKwwigOqlIAuf38hNVj8Pb2xuHDhxEfH89VY6t///5wcnKCr6+v2O2noaHhDz/pBAaDgW/fvqG8vFzu+6rqqKurIykpCR07dpTZ9Wsb9V0fKCsr41vMJDIyEn/++SeSkpK4tru6uiI9PR2XL1+uoVlYWloKS0tLqqIvL+rL+5dGvsnJyQGDwahXRUcyMzNhYmJSYyGZRv4YPny4UE4IflrC9QF6jvEDDQ0NPHz4kEobt7e3h7q6Ovbv3w8ASEhIgJWV1S+ZNq6IMJlMaGpqCvyN88qoFAeHDx+Gk5OTxM4vL9CRjAqCMGLsv4KTEQCCgoKoiq3l5eUICQnhqNiqCIij2MPKlStx+vRp6Ovrw8nJiaPK9ZEjR9CuXTueRQBoaGgkS15eHtfP379/j/Xr14PNZsPCwkLKVtWeSZMmcWyXlJRg3rx5aNy4Mcfn3IoB8HJ4VUfRK+hJAxUVFRw4cACXLl2CiooKlixZgv79+yM2NhbLly/H06dP+UZKrV+/Hn379oW+vj4WLFhARaekpKQgICAApaWlOHTokLRuh4aGg/z8fHh7eyMsLIzqO7W1tWFnZwcfHx9oaWnJ1kARSUxMFFgQkEY+uHbtmqxNwIMHD+Du7o7Tp0/XkMQoKCiAtbU1du/ejR49ekjMBnqO8QMmk8kREHLnzh2sWbOG2tbS0uI53qORT9avXy90wVlJMH78eKGKGip6QUPayaggyHsFUmHR1tYWaoWQ1wpC+/btceDAAWpbR0enxsSoffv2ohkpBcSRYqCuro7bt2/Dy8urxsDcyckJmzZtkpvqWTQ0vzpfv37Fli1bsGfPHqoIgKmpqazNEkj1gVhtVl979uzJM2K7irroSsk61UUWbN++HatWrUL37t2RmpqK06dPw9vbGzt37sSiRYuwYMECvmk3bdu2xX///Yf58+fDy8uL+jdhMBiwsLCAv78/2rVrJ9AOfjIfOTk5YrhT/ixfvhybN2+mK0jXI3JzczFw4EC8ffsWjo6OMDY2BiEEqampCAkJQUxMDOLi4oQurCcLeE0YCwoKcO/ePSxfvpxnlV4amurs2LEDZmZmXJ0MmpqasLCwwLZt23D48GGJ2UDPMX5gZGSEqKgoLFu2DMnJyXj16hXH2C0rK6tGpCeNfGNnZydTTUYtLa1foqAhnS5NI1VCQ0OF2m/69OkStkR2vHr1qlaO0Ldv39YoclAdQghycnJACEGLFi1+yUk4DY088v37d/j7+2PTpk1o3rw5fHx8+FZTrk9kZWUJtR+/RZfqkZRRUVEwMzMTKpKyPmFsbAwPDw+4uLjg2rVrMDMzg5mZGf79999aR3nl5eXh+fPnAIDOnTujadOmQh0nDpkPUenUqRPU1NRw+PBhnhV9aRSLpUuXIiYmBleuXOGaljly5EiMGDFCqIweWVH12+AGg8HA3LlzsXv3br6SBzTywbJly4Tab+fOnRKzQU9PDydPnuQZ5f/48WNMmDChhnappPiV5xgRERGwt7fHkCFDkJycjN9//x1RUVFUu6enJzIyMhAeHi5DK2mERR6qS/8qBQ1pJ6MCUVlZiZCQEERGRiIzMxMMBgMdO3aEjY0NnJ2d60WnX1ZWhvfv3ytENGJdadmyJcaPH4/Zs2fzLHxQUFCA8PBw7NmzB3Pnzv0l0uBpaOoThBAcPHgQa9euRXl5OdatWwdXV1coKSnJ2jSFYubMmULtV9+LijVq1AhpaWnUu7Fhw4a4ceMG+vfvLzUbxOE0FpWioiJ4eHiAxWLB29sb3t7eQlVipZFfdHV1ERgYCEtLS67t0dHRmDdvHjIzM6VrWC34edL4MxoaGtDX16ckfuQZeUjRlQeqZxjcunULffr0gZqaGvUZg8FAbGysxGxQVVVFamoqT+3jjIwMdOnSRaKVnT9+/MjXEVNeXo6HDx8KVcBN0bly5QrOnTsHHR0dLFq0iCoKBPxIvR02bBiGDx8uOwNphIZfdem8vDwcPnwYLBYLCQkJUrOpvmqN005GBYEQgnHjxuH8+fPo0aMHjIyMqHSSx48fY/z48Th16pSszRSZxMRE9O7dm2ckRGxsLBYuXIg7d+5wHQQNGjQIf//9t1xXIszNzcWmTZvAZrOhoqKCvn37onXr1lBVVUVeXh5SUlKQnJyMvn37YvXq1Rg9ejTX8/Tq1Usox/LDhw/FfQs0NDQC6N69O168eIFFixZh6dKlHIPSn1F0zRV+VDmETp06hbKyMpibm8PPz69eV9OTFIKKrgiiekQoLxQlIvTq1atwdXVFixYtsHLlyhrO+/Hjx8vIMpra0rBhQ7x48YJn5fM3b96gc+fOKCkpkbJlvxYODg4wNjbm0Jv7mU2bNiElJUWiKbryiCwcAO3atcOBAwcwatQoru0XLlzAnDlz8Pr1a4nZUD3iy9jYGBcvXqQWuuhCXzT1hStXroDFYlHVnidNmoQ9e/ZI7fr11clIazIqCCEhIbhx4wZiYmJqrLLFxsbC2toaBw8exLRp02RkoXTYvXs3Zs+ezVOnZO7cudi1a5dcOxmbNm2K7du3w8fHB+fPn8fNmzeRmZmJ4uJiNG/eHI6OjrC0tISJiQnf81hbW0vHYBoamlrz5MkTAMDWrVuxbdu2Gu31RXOFH+vWrUNISAgcHR2hqqqKY8eOwc3NDSdOnJC1aQoJv6JnVSxevJjrsRoaGiJnOzx//hxr165FYGAg10U+Nzc3+Pj4SGWgbGpqil27dmHy5MmYPHkyR1t9/13VN5o3b47MzEyeTsaMjAy5rzSdm5uLoqIijntITk7G9u3b8e3bN1hbW8PBwUGGFgrm7t27fAt5jBs3DkFBQVK06NfF3Nwcvr6+XJ2MhBBs2rQJ5ubmErWhegzSmzdvUF5eznef+gi9WFo/efXqFYKDgxEcHIzCwkLk5eUhPDy8xniCpu7QkYwKwsiRI2FmZsZzALBp0yZcv34dFy9elLJl4kVQJGOHDh0QHR0NY2Njru1paWkYOXIkXr16JUkz5YJXr16hbdu2dKoYDY0cwit9rjqKrrnCDz09Pfj6+sLOzg4AEB8fj8GDB6OkpIROG68lurq6Ap2EDAZDohpdc+bMgZaWFrZu3cq13dPTE1++fMHff/8tMRsAoLi4GJ6enti/fz+8vLzg7e0NZWV6zVxRcXV1RXp6Oi5fvlyjoE9paSksLS2hp6cHFoslIwsFY29vj1atWlE6fR8/foSRkRFat24NPT09XLhwASwWi28FeFkjDym68ogsooxevHiBPn36wNDQEMuXL+eo7Lxjxw48e/YM9+/fR+fOnSVmg6Do+V8lktHDwwMBAQEci6XDhw+nF0sVlPDwcAQFBeH27duwsrKCk5MTRo8ejcaNGyMxMRFdunSRuk3q6upISkri2fcqKvSoTEFISkriObAHgNGjR8PPz0+KFsmGDx8+8BXOVlZWxqdPn6Rokezo2LGjzMVraWhouKOnp8czMudX4fXr1xgyZAi13a9fPygrK+Pdu3dCVTKm+T9E1aMTh9j5jRs3cOjQIZ7ttra2Eo/WiouLw/Tp09GwYUPcvn0bffr0kej1aCTP+vXr0bdvX+jr62PBggUwMjIC8KOSeUBAAEpLS/k+d/LAnTt3OHRhDx48iKZNmyIhIQHKysrYvn079u3bJ9dOxhYtWuDp06c8J7ppaWl09JaU0NPTw5UrVzBjxgzY2dlRC0yEEHTp0gWXL1+WqIOR5v+IjIwEi8WiFkudnJwwePBgVFRU0IulCoiDgwNWrFiBiIgImVVHry5fU1JSgnnz5tW7goa0k1FByM3NrVF172datmyJvLw8KVpUN5KSkvi2P336lG97mzZt8PjxY54v16SkJLRq1arO9ikSdBAyDY38YmJigr1798r1pFLSVFRU1IhMUlZWrpFyRSN5xPG+yMrK4uukbN68uUQ1wgBg+PDhWLx4MXx9fdGwYUOJXotGOrRt2xb//fcf5s+fDy8vL+pZZTAYsLCwgL+/v9wvSmRnZ3M452JjYzFx4kQqwnb8+PHYvHmzrMwTCnlI0ZUHqs9TCCFIS0tDYWEhx+e8Kj+Li759++LJkyd49OgR0tPTQQiBgYEBevbsKdHrVsFgMPD161eoqqpS8i6FhYX48uULAFD/re/Qi6X1CxcXFwQEBOD69etwdnbG1KlToa2tLVUbNDU1ObadnJyken1pQadLKwhKSkrIzs5GixYtuLYrStg6k8kEg8HgOuGp+pyfntKiRYtw7do13Lt3D6qqqhxtxcXF6NevH0xNTX+JqE5+FbJoaGhkS0BAAFauXAkLCwvs379f7jXFJAGTycTo0aM5nEFRUVEwMzPjWLFV9NVaaWBlZYVjx45Rg1NfX18sWLAAWlpaAIDPnz9jyJAhSElJ4Xq8ON4XOjo6OHr0KMzMzLi2x8TEwNHREdnZ2XW+hiDMzMxw9uxZnoWUaBSbvLw8PH/+HADQuXNnNG3aVMYWCUfLli1x6dIlqvJy8+bNERgYSOl7PX/+HL169arhqJIn5CFFVx4QdZ5SX6j6Hqqouu/q2/X9e+A2/66v6a2/CsXFxQgPDwebzcbdu3dhaWmJc+fOISEhQWA9BBrhoZ2MCgK3ydrPlJaWIjo6Wu47+6ysLKH269ChA9fPP3z4gN69e0NJSQkLFy7kGATt27cPFRUVePjwId+oz/oCk8mEj48PVQiAF7wKAdDQ0EiWjIwMuLq6IiUlBfv37//lKt7OnDlTqP1+TjOk4U71dGcNDQ0kJCQIrY/FZDIRGhpaYwW9OvyeUVtbW5SVleHkyZNc2ydMmIAGDRpIVKtKHGnfNDTiZty4cfjtt99w4MABREZGUs72qgiZc+fOwd3dHampqTK2lD/379/HjBkzkJKSUiNFNzg4GL///ruMLZQ8os5TxMGyZcuE2q9KA1QS0LrSP6AXS+s3z58/B5vNxsGDB1FYWIgxY8bAxsamRkqzuMnKysKlS5dQVlaG4cOHy0QLUtLQTkYFgZ6s/R9ZWVlwc3PDxYsXOdJqLC0tERAQAF1dXdkaKARRUVEYN26cSOdgMplo27YtX00QSRcCoKGhEYy/vz/++OMPGBsb1yhQ8fDhQxlZRaNIiCrCL0yBMEFRKY8ePcLAgQMxduxYrFixAoaGhgB+aLVt3boV586dQ1xcHHr37l3b2xMaOoKfRh5JSEiAubk5vn79ivLycqxatQobN26k2p2dndG4cWP8888/MrRSeGSVokvzA1NTU4H7MBgMxMbGSsGaXxt6/v1rUFlZiXPnzoHFYuHChQsoLS2V2LVu3LgBKysrFBUVAfghIxQaGgp7e3uJXVMW0E5GGqny/PlzrF27FoGBgdDQ0OBoKygogJubG3x8fISq4paXl0cNgvT19aWuqSAKDRs2hJOTE/bs2SMwEpEX9GSLhkb+ycrKoiJD5syZU8PJuG7dOhlZJjuysrLw7ds3GBkZCeX8ohGPk1Ec74uzZ8/CxcUFnz9/5vi8WbNmCAoKkni0LpPJxIcPH3hKx9DQyIpPnz4hLi4OOjo66N+/P0fbuXPn0KVLFzq9UgEoKiqCh4cHTp06hbKyMpibm8PPz48uelONhw8fYu3atTh79qysTaGhESsfP36U6Nx62LBh0NDQQGBgINTU1ODl5YVz585JXNNa2tBOxnrEv//+CxsbG1mbwZc5c+ZAS0uLZ6VsT09PfPnyBX///beULZMuiYmJmDlzJvLy8hASElKndAM6bYyGRr45cOAAli9fDnNzcwQGBv5yjpHQ0FDk5eVh6dKl1Gdz5swBi8UCABgaGuLixYu0eLoQVNeFqq4JJcjJKM73RXFxMaKjozkinUaOHCkVnUQmkwkTE5Mazvrq0BHCNDS1Qx5SdOUBDw8PBAQEwNHREaqqqjh27BiGDx8uURkIeeXy5cu4dOkSVFRUMGvWLHTq1AlpaWlYuXIloqKiYGFhgejoaFmbSUMjNA8ePIC7uztOnz7NNdjJ2toae/bskWhhp6ZNm+LGjRuU/uO3b9+goaGBnJwchQqYEgRdXVqBKC8vx9OnT6GiogIDAwPq89OnT2Pt2rVIS0uTeyfjjRs3cOjQIZ7ttra2cHBwkKJFsqFHjx6Ij4+Hj48PLC0tsWDBAnh7e9eYOFXvAH+GXh+goZFfRo0ahfj4ePj7+2PatGmyNkcm/PPPP5gzZw61HR0djeDgYBw8eBDGxsZYuHAh1q9fj6CgIBlaqRgQQjBjxgxKF6qkpATz5s2jNKEEpfaI832hpqaGiRMniu18tcXS0rLOGQA0NDTcefTokcB9fi78UV+JjIwEi8WCnZ0dgB+VXwcPHoyKigq+8kTi5tu3b9iyZQsiIyORmZkJBoOBjh07wsbGBu7u7hJf1AkNDcXMmTPRtGlT5ObmIigoCDt37sT8+fMxefJkJCYm0kUyaBSOHTt2wMzMjOv8WlNTE+bm5ti6dSsOHz4sMRvy8/M5FnwbN26MRo0aIT8/v145GelIRgUhJSUFY8eOpQSJJ0yYgL///hu2trZITEzErFmzsGTJErmPCFFTU0NaWhpPweSsrCwYGxtTOgW/ApcuXYKVlRXHJFCYqm3r16+Hh4cHXWWThkYOGTFiBGbMmAFnZ2cAgJeXF4cjSElJCRs3boSqqqqsTJQ4zZo1w7Vr19CtWzcAgJubGz5+/IiIiAgAwLVr1zBz5kxkZGTI0kyFYMaMGUJN8HnpQs2cORN+fn5QV1evsw1+fn5C7SfJYmO0TAgNDY0kadCgATIyMtCmTRvqMzU1NTx79kxqc6zv379j0KBBePLkCUaPHg0jIyMQQpCamoro6Gj07t0bN27cgIqKisRs6NmzJ+zs7LBy5UqEh4fDzs4OvXr1Qnh4OPT09CR2XRoaSaKnp4eTJ0/yjFR8/PgxJkyYINF6BkwmE7GxsWjatCn12aBBgxAeHo62bdtSn0kymlIa0JGMCsLKlSvRsWNH+Pn54ciRIwgLC8OTJ0/g5OSEs2fPijRxkCaampp48eIFTydjeno63+i9+kZkZCTc3NwwdOhQrpGM/Jg4cSLS09MF7qfonRQNjSIyZcoUhIeHU05Gf39/dO3aFWpqagB+FMto3bo1/vjjD1maKVGKi4s5+vO4uDi4uLhQ2506dUJ2drYsTFM4QkJCRDpeHKL0u3btErgPg8GQqJNRkKP19evXWLduHdhstsRsoKGhqb9UVFSgQYMGHJ8pKyujvLxcajb8/fffePPmDRITE6kCW1WkpaVh+PDh+Oeff7Bo0SKJ2fDixQtMnToVAGBjYwMlJSXs3LmTdjDSKDRv377l6zNp0qQJ3r9/L3E7RowYUSPDZOzYsWAwGEIFGikCtJNRQYiPj8f58+fRu3dv/O9//0NYWBg8PDwwe/ZsWZtWK4YOHYq9e/fCzMyMa7ufnx+GDBkiZaukT35+PubPn48zZ87A19cXS5YsqfU5evbsSXVG1alPnRQNjSJy9OjRGg7Eo0ePUoU6Dh8+jH379tVrJ2OHDh3w4MEDdOjQATk5OUhOTsb//vc/qj07OxuampoytFBxEFVTkclkCnTQMRgMvhNpeYg4FZR8k5ubi9DQUNrJSCNXfPv2DQ8ePMDQoUNlbQpPaE3GH1SXpgBqylMAP4IEJEVkZCTWrFlTw8EIAEZGRvD29sa///4rUSfjt2/fqPtlMplQVVWV+2w5GhpBtGjRAk+fPuVZhCstLU3iRZ7kYSwlDWgno4Lw8eNHKnRfS0sLjRo1qlOxEFnj5eWFgQMHwsbGBitWrKBeoGlpadi6dSsuXryIuLg4GVspebp06YL27dvjwYMHXAcRwvCrdFI0NIrIs2fPOLRzVVVVOSop9+vXDwsWLJCFaVJj2rRpWLBgAZKTkxEbGwsjIyP06dOHao+Li6M1nYREVGWbyMhInk7GuLg47N27V+RrSCOKMCMj45croESj+KSnp8PU1FSuF32razLeunULffr0oaLvgV9Dk3H69Ok1PnNycpKqDSkpKRg+fDjPdlNTU2zYsEHidly8eJFaCKysrERMTAyePHnCsc/48eMlbgcNjbgwNzeHr68vRo0aVaONEIJNmzbB3Nxcojbwyuasb9CajApC9cqSGhoaSExM5OmJl2fOnj0LFxcXfP78mePzZs2aISgo6Jd4Yfn4+MDLy4uniHRJSQn8/f3h7u4uZctoaGjEgZqaGhISEnguIqSlpaFnz54oKSmRsmXSo7KyEuvWrcPZs2eho6ODnTt3wtjYmGqfMmUKRo0aBVdXVxlaqRhIQoswLS0NXl5eiIqKgqOjIzZu3Ij27dvX+XyJiYno3bu3TB0p8mADDU11FPG5VFdXR2JiIhV9TyM9VFRU8Pr1a+jo6HBtf//+PTp06IDv379LzIafF0V5QWdL0SgaL168QJ8+fWBoaIjly5fD0NAQDAYDqamp2LFjB549e4b79++jc+fOsjZV4aEjGRUEQggMDAyoVcTCwkL06tWrxksgNzdXFubViqoCNtHR0UhPT6fubeTIkb9MEZPVq1cjJycHd+/ehYqKCkaMGAElJSWUlZUhICAAmzdvRnl5uUhOxsjISPz5559ISkoSo+U0NDTC0LZtWzx58oSnkzEpKYlD4Lk+wmQysXHjRmzcuJFr+4kTJ6RskWLzc1QJL4RZpHv37h3WrVuH0NBQWFpaIiEhgY4opaGhoZETKisr+VayZjKZEnfuVVZWSvT8NDSyQE9PD1euXMGMGTNgZ2dH+VUIIejSpQsuX75MOxjFBO1kVBDEIdouT6ipqWHixImyNkNmxMXFYcyYMSgoKACDwUDfvn0RHBwMa2trVFZWYvXq1RwFEnhx4MABXLp0CSoqKliyZAn69++P2NhYLF++HE+fPqWKTtDQ0EgXKysrrF27FmPGjKlRQbq4uBjr16/HmDFjZGSddCkuLsbly5fx7NkzMBgM6Ovrw8LCgiMNj0Yw3NL4fkZQVElBQQE2bdqEvXv3omfPnoiJiVE4DeRJkybxbc/Pz5eOITQ0NPUSQX1MFZLUZCSEYMSIETyLQUqzCA0NTX2jb9++ePLkCR49esQR7NSzZ09Zm1avoJ2MCoKgyQVAv3QUiTVr1sDS0hKrV68Gm83G7t27MXbsWPz5559wdnYWSvdm+/btWLVqFbp3747U1FScPn0a3t7e2LlzJxYtWoQFCxZIXLyWhoaGO6tWrUJ4eDgMDQ2xcOFCKhI9LS0N/v7+KC8vx6pVq2RtpsQ5c+YMZs2ahZycHI7PmzdvDhaLhXHjxsnIMsVDlHTprVu3YsuWLdDR0cGxY8cwYcIEMVsnHQRFcmpqamLatGlSsoaG5gdnzpzh205raCsO8lCMbN26dQL3mTx5shQsoaGpv/Tq1Qu9evWStRn1FlqTsR6QkpICFouFw4cP48OHD7I2h0YImjdvjuvXr6Nr164oKiqCuro6jh8/jilTpgh9DmNjY3h4eMDFxQXXrl2DmZkZzMzM8O+//0JLS0tyxtPQ0AhFRkYG3NzccPnyZaqoBoPBgIWFBQICAuq91lVcXByGDx+O8ePHY/ny5ZQeY0pKCnbs2IGzZ8/i2rVrGDhwoIwtlX/EUV1aTU0N5ubmfNPw+EXnCBNFeP36dVqji+aXQxj9OkC+U1CrS+sMGjQI4eHhNWQ9unfvLk2zaGhoaMQGryxBTU1NGBoawsnJCU2aNJGyVfUT2smooBQWFuL48eNgsVi4d+8eBgwYgMmTJ+OPP/6QtWk0QlBdxF9dXR2PHj2qlQ5Eo0aNkJaWRgn1N2zYEDdu3ED//v0lYjMNDU3dyM3NRXp6OgCgc+fOaNq0qYwtkg5WVlZo164dAgMDubbPnTsXr1+/xvnz56VsmeIhauGXGTNmCBUhz0+aZebMmUJdS1byLpWVlTh37hxYLBZOnTolExtoaBQVJpMJBoPBtcp81ed0oY8f/Pvvv7CxsZHY+T9+/Mi3ry8vL8fDhw/Rr18/idlAQ1Mf4SXVlp+fj+TkZKioqODmzZsSDQIwNTUVOB5jMBiIiYmRmA3SgHYyKhi3bt1CUFAQIiIi0LFjR6SkpOD69esYPHiwrE2jqQVKSkp49uwZWrRoAUII2rVrh1u3bkFXV5djPw0NDZ7n4OaopCsB0tDQyAva2tq4ceMGunXrxrU9KSkJw4YNQ15enpQtUzxmzpwJPz8/qKury9oUueP58+dgs9kIDQ1FXl4eLC0taScjjVxRUVGBqKgoWFtby9oUnmRlZQm1X4cOHSRsiewpLy/H06dPoaKiAgMDA+rz06dPY+3atUhLS0NpaanErl89ct3Y2BgXL16kggo+fPiA1q1b0w5fGhoxUlxcjGnTpoHBYCA8PFxi1+EXEPblyxccO3YMpaWlCv/7pjUZFYStW7eCzWajsLAQ9vb2uHXrFnr06AEVFRVoa2vL2rxaU1BQgMuXLyMzMxMMBgMdO3aEubk5X6dafaJKZPbn7Z91IYRdMQ4KCqLCusvLyxESElJDh3Hx4sVitJyGhoZGOEpKSvj26ZqamhKdqNUnpBEdKCh6Rp4oLi5GeHg4WCwW7ty5g4qKCuzatQsuLi50qhON3JCWlsbhAP/+/busTeJJaGgo3N3d0ahRI1mbIlNSUlIwduxYyuk6YcIE/P3337C1tUViYiJmzZqFs2fPStSG6vE/b968qaG7L60YIUIIHjx4wDFf69Wrl1CR8TQ0ioSamho8PT2FLv5UV3bt2lXjs/Lycuzbtw++vr5o06YNNm7cKFEbpAEdyaggKCsrw9PTExs2bODQU1JRUUFiYiK6dOkiQ+tqx+HDh7Fw4UJ8+fKF43NNTU38888/mDp1qowskx7Xr18Xar9hw4bxbNPV1RUq3Prly5e1so2GhoZGHPTo0QNLly7lmWZbVfSquhYYTU2qUhn5wWAweBaAa9SoEbKystCiRQsAwKhRoxAcHIxWrVoBUJzImPj4eAQFBSEsLAwGBgZwcnKCnZ0d2rZtq3BjIZr6ybdv3xAWFkY5wE1NTWFnZwdra2u5LsYnqu5rfWH8+PH49u0b/vjjDxw5cgRhYWHo3LkznJyc8Mcff0glmlxQppK0+uurV6/C1dUVWVlZHLrSHTt2BJvNxtChQyV6fRoaafPy5Uv07Nmzho9Ckhw5cgRr165FcXExVq9ejTlz5vCsLK9IKP4d/CJs2LABISEhOHToEOzt7eHs7AwTExNZm1VrHj58iJkzZ8LR0RF//PEHjIyMQAhBSkoKdu/eDWdnZxgZGaFHjx6yNlWi8HMeCktmZqbohtDQ0NBIiBkzZsDd3R0tW7aElZUVR9u5c+ewYsUKeHt7y8g6xSIyMpKnkzEuLg579+7lG9lSUlLC0X779m0UFxdz7KMIa86DBg3CokWLEB8fD0NDQ1mbQ0ND8d9//yEoKAjh4eHQ19eHo6Mj7t69Cz8/P4VwfivC718axMfH4/z58+jduzf+97//ISwsDB4eHpg9e7asTZMq6enpGDt2LPr3749du3ZxzNf8/PxgZWWFpKQkWqKJpl4RFxcHPT09qVwrOjoaK1euREZGBtzd3bFs2TI0btxYKteWBrSTUUFYtWoVVq1ahevXr4PNZmPAgAHQ09MDIUSh9Kz27t0La2trhISEcHzeu3dvHDx4EEVFRdizZw/YbLZsDJQS4eHhsLa2RoMGDQD8cBi2a9eOilItKiqCv78/VqxYIUszaWhoaOrMkiVLEBcXh7Fjx8LQ0JCjuvTz589hbW2NJUuWyNhKxYCblltaWhq8vLwQFRUFR0dHkdNrFCH9zczMDCwWCx8/foSzszMsLS0Vwm6a+k2XLl1QVFQEBwcH3L17l3Iqrly5UsaW1Q76t/RDNqJNmzYAAC0tLTRq1EgsgQG1gcFg4OvXr1BVVaXkkwoLC6noKmlEWe3evRsDBgyoUXzCyMgIEydOhLm5OXbt2oW9e/dK3BYaGnHBK3OmoKAA9+7dw6ZNm+Dj4yNRG+Lj4+Hp6Yk7d+5g3rx5uHLlilxHudcVOl1aQfn69SuOHDmC4OBgPHjwAP369YONjQ2WLVsma9P4YmBggICAAJibm3Ntv3LlCubPn49nz55J2TLpUj0tRUNDAwkJCbVKhYiNjcXChQtx586dGrpnBQUFGDRoEP7++286nYGGhkamhIWF4dixY1S/bmBgADs7O9jZ2cnYMsXk3bt3WLduHUJDQ2FpaYnNmzcLzGyQl/Q7cfD69WsEBwcjODgYxcXFmDp1KgICApCUlEQ5smlopEmDBg1gZ2cHZ2dnmJubU846RZI0YjKZMDExEZim9/DhQylZJBuUlJSQnZ1NSUtoaGggMTERHTt2lJoN1eUxqhyN1bcl2V+bmJhg8+bNGDduHNf2qKgoeHl54cmTJxKzgYZG3FT9tri5v1q0aAF3d3d4eHhI3AY1NTXMnTu3RsHXn1H0mgq0k7Ee8PjxY7BYLBw9ehQfP36UtTl8adKkCVJSUqgKadV59eoVjI2N8e3bNylbJl3EMeEbP348TE1NeVap8vPzw9WrV3Hy5Enx3wANDQ0NjVQpKCjApk2bsHfvXvTs2RNbtmzBkCFDhDpW0MRZkZyMP3P58mWw2WycOnUK7dq1g42NDWxsbNC7d29Zm0bzC/H27VuEhIRQjm97e3s4Ojqif//+SEhIUBgn4/LlywUWTlq3bp2ULJINTCYTmpqalFMvPz8fGhoaYDKZHPvl5uZKzAZx6LaLioaGBpKSkng6QTIyMtC9e3d8/fpVYjbQ0IibqoJO1dHU1ISWlpZUbPhVairQTsZ6RFlZGVRUVGRtBl+qO9eqo6gTndoiDidjhw4dEB0dzTNyIy0tDSNHjsSrV6/EfwM0NDQ0AhA2pYtfBWqaH2zduhVbtmyBjo4ONm3ahAkTJtTqeEETZ0IIvnz5orDv3ry8PBw+fBhsNhtJSUkKex80ik9sbCzYbDYiIyNRUlICd3d3zJo1CwYGBrI2jS+Cxue/CqGhoULtN336dAlbIlvo+RoNDY0o0E5GBUGY1Nh//vlH6KgGWcFkMhEaGgpNTU2u7fn5+Zg5c2a9f2mJw8moqqqKJ0+eoHPnzlzb09PT0a1btxri/jQ0NDTSQFBFZGmkfNUXqtJrzM3NKe1ebkRGRnL9/FeaOD98+JCOZKSROQUFBThy5AjYbDYePnwIExMTnnpg8gBdXZrmZ5hMJmJjY9G0aVOu7Tk5ObCwsKDf3zQKSWxsLCIjI5GZmUlVTLexsZGKxFh6ejrPuXt9gi78oiDs3r0bs2fP5hrxoampiblz52Lnzp1y72QEBE9ifhXh6YsXL1LO1srKSsTExFDaJvn5+QKPb9OmDR4/fsyzo0pKSkKrVq3EZi8NDQ1Nbbh69Sr1/4QQWFlZISgoiBLVpxGeadOmifRudHR0FKi1pggI46SpD/dJo/hoampi/vz5mD9/PhISEuS+QAa/mJPKykqcO3cOLBYLp06dkp5RMiA+Ph59+vShFnOq6yGWlpbi9OnTsLW1lZgN/BaSfkbSDr4RI0ZwfS6qNO1+lfkaTf1i3rx52L9/P7S1tWFgYABCCOLi4rBv3z7Mnz9f4n21gYEB2rRpA1NTU+qPnzajokJHMioIdGps/aK6tgs3BEX4LFq0CNeuXcO9e/egqqrK0VZcXIx+/frB1NQUfn5+IttLQ0NDIyrVI7ZppMdvv/2GadOmwdXVVaGLo/ATba+Cjo6lkSdKSkoQEBCArVu3Ijs7W9bm8CQrKwvt2rXjGJ8+f/4cbDYboaGhyMvLg6WlZb13MoqjMKOoMJlMdOjQAdOnT0evXr147ldb2YzawEu7rjodOnSQmA00NOLm5MmTsLOzQ2BgIKZPn045yisrKxESEgI3NzecOHEC48ePl5gNN2/exPXr13Ht2jX8999/KCkpQfv27WFmZkY5HevDYjztZFQQ6NRYmup8+PABvXv3hpKSEhYuXAhDQ0MwGAykpqZi3759qKiowMOHD9GyZUtZm0pDQ0NDOxllyObNmxESEoL09HT069cPs2bNwtSpUwUWeZA36IkvjTzy/ft3rF+/HpcuXYKKigpWrFgBa2trBAcHw9vbGwwGAwsXLoSXl5esTRVIcfH/a+/O42s61///v9dOQqKSiHmeRQyltKX4GIuUojXUGG3FqZqrjtZY2qr2nB5FHZSSxNQaqmiVY4yhOCmKGGIWQ1UMlSDEkGT//uiv+3tCQtpk75WsvJ6Ph8fpWvdiv5tTZF/7vq8rQUuXLlVISIgiIiKUlJSkyZMnKzg4ONv9efFXpKedUbFixZScnOy0DLt371ZoaKgWL16scuXKKTg4WD169JCfn5/TXhPICdq1a6dq1arpk08+SXV9+PDhOnr0qL777juX5Ll//77++9//asuWLdqyZYsiIiJ09+5dVaxYUceOHXNJBmehyJhNVKhQQRMnTlT79u1TXV++fLmGDRuW5ScR9e/fX59++qnjG5UFCxaoffv2juu4uDh1795da9asMTNmtnH27Fn169dP69atc+zsMAxDgYGBmjFjhiW3XwPInigy/nUdOnRI13Np9WT8w48//qjQ0FAtW7ZMktSpUyf97W9/U4MGDTKcEcipRo0apenTp6tFixbasWOHrl69quDgYG3ZskWjRo1S9+7ds/xgxl27dmnOnDlasmSJ/P39FRQUpK5du6pkyZKKjIzMFhOyM0Nm9EzPLHfu3NGyZcsUFhamiIgItW3bVr1791aLFi2c/trpPRlXunRpJycBMk/JkiW1fPly1alTJ9X1Xbt2qUOHDvrll19cmishIUHbt2/XunXrNHv2bMXHx2f7ExkUGbMJqxyNzQrHELKCpKQkRUVF6cknn5QkzZw5U/fu3XOsu7m5qV+/fuk6Vi39Plnz5MmTstvtqlSpEp92AshyvL29deDAAZUrV87sKNlOr1690vVcWFhYup67deuWFi9erLlz52rHjh2qVKmSevfurXfffTcjMZ3uxIkTGjt2rGbNmpXqELx+/frpo48+opANl6pYsaL+9a9/qX379oqMjFStWrXUpUsXLViwINv0CHV3d9egQYPUt29fVa5c2XHfw8ODIqNJRcb/FR0drd69e2vr1q26cuVKmgNZMktag9v+txejYRhKTEx0ag4gM3l6eurUqVNpHke+cOGCKlas6PSToXfu3NHOnTu1efNmR32nXLlyaty4sRo1aqTGjRtn+yPTFBmzCascjc2qf3m72tdff61Zs2Zp69atkn7/OuTLl8/xzejVq1c1ZcoU9e7d28yYAPCXPbj7btWqVWrWrJmeeOKJFPcft/sOzrV69Wq9+uqriouLy/J/9/bp00f58uXTp59+mur68OHDdePGDX3xxRcuToacLHfu3Dp16pRKliwp6fc3shEREXrqqafMDfYntGzZ0rFbrmfPngoMDJRhGDmyyPi/U5Xr16+vpUuXOv6/dfVU5V9++UVz587V3LlzlZCQoJ49e+qjjz5yevE6MjIy1ft2u12LFy/W1KlTlTdvXl2+fNmpOYDMZLPZdOnSJRUqVCjVdVfUIRo3bqzdu3erQoUKjoJi48aNs3wN58/KHh+vQUWKFNHOnTvVr18/jRw5MtWjsVb7j9PKwsLC1Ldv3xT3tm7d6ii2zpw5UwsXLqTICCDb8vX1TXEdFBRkUhI86Pbt21qyZInCwsK0Y8cOVahQQe+8847ZsR5r27ZtWrBgQZrrnTt3Vvfu3V2YCPi9r1auXLkc1x4eHg/9+ZfVrV+/XufPn1dYWJj69eunhIQEdenSRZJy3BThB6cqt2nTRpLrpirfu3dPK1asUEhIiH788Ue1atVKU6ZMUevWrdN9wimjatas+dC9jRs3asSIETp+/LjeffddDRs2zCVZgMz03nvvKU+ePKmu3b592+mvv3PnThUrVkxNmzZVkyZN1KhRIxUsWNDpr+tq7GTMhrLz0Vh2Mv6uZMmSWr16teMv8Qe/DkeOHFGDBg107do1M2MCACzkxx9/VFhYmJYtW6akpCR16tRJvXv3VqNGjcyOli5eXl46evRomoNdzp49qypVqrjkjQLwB5vNpj59+jjeuE6fPl1BQUEPFRonTZpkRry/ZMOGDQoNDdXKlStVqlQpderUSZ06dVLt2rXNjuZUWWG4VIECBeTt7a3XXntNPXv2dLxnetCDLSOc5eeff9aIESP0448/6m9/+5vGjh2bZiYgK2vSpEm6PiTYvHmz0zLcunVLP/74o7Zs2aLNmzdr//798vf3V+PGjdWkSRM1btw4zZ2W2QlFRrjU474Ru337tmbPnm35IqOnp6cOHz6sChUqSJKuXLmiAgUKOD6hPHnypKpVq6a7d++aGRMAYAEff/yx5s6dq1OnTumZZ55RcHCwunXr5rI3qZmlaNGi+vrrr9WsWbNU1zdt2qQePXooJibGxcmQk6XnjathGAoPD3dRoswTGxurhQsXKjQ0VAcOHLD89+dZwf/uVnxUX0Rn/39x8uRJjR49Wt9++606d+5Mv1vACW7evKnt27c7+jNGRkaqUqVKOnTokNnRMoQiI1wqK3yCkBWUKVNGX3zxhVq3bp3q+qpVqzRw4MB0f6IKAEBaChUqpKCgIPXu3VvVq1c3O85f1rlzZ92/f18rVqxIdf2ll15Srly59M0337g4GZC9JScn67PPPtPKlSt1//59NW/eXGPHjk0xbHLv3r2W38l4+/ZtvfPOOym+DlOnTnXpccY/+rU/TuPGjZ2WoX///goJCVHTpk31j3/8I1v1FwWyk+TkZO3evVubN2/W5s2btX37dt25cyfbf6BDkREwQXBwsI4dO6YdO3Y8tGa329WgQQMFBAQoNDTUhHQAACu5f/++PDw80lxfvny53n//fR04cMCFqf68ffv2qV69emrTpo3effddxxTco0eP6tNPP9Xq1au1c+dOyxdCkLVdvXpVhmGoQIECZkdJt08++URjxozR888/Ly8vL61bt06vvvqqvvzyS7OjudQ777yjGTNmqEePHvL09NSiRYvUpEmTHPfBhc1mk6enpwICAh753N69e12UCMi4qlWravv27Y7BTn369NGECRMcx5MvX76ssmXLOrXlSnJysvbs2eM4Lr1jxw7dunVLJUqUUNOmTR0/nNmSwRUoMgImOHXqlGrXrq2AgAANGzZM/v7+MgxDR48e1cSJE3Xs2DH9/PPPqlixotlRAQAWMHv2bK1fv14eHh566623VLduXYWHh+vvf/+7jh07pp49e2rWrFlmx3ysH374QcHBwfrtt99S3C9QoIDmzJmjdu3amZQMOVlcXJxGjx6tJUuWKDY2VpLk5+enrl276qOPPlK+fPnMDfgYlStX1ltvvaX+/ftLktauXauXX35ZCQkJOWrwS4UKFTRhwgR17dpVkrRr1y41aNBAd+7ckZubm8npXOeDDz5I13Pjxo1zchIg8zw4G8LHx0f79+9PMRuiWLFiSk5OdloGHx8f3bp1S8WKFVOTJk3UpEkTNW3a1NFCzSooMsKlhg4dmq7nslNz7L9q165dev3113X06FHHN3B2u10BAQEKCwtT3bp1TU4IALCCiRMnatSoUapRo4aOHDkiSRo9erQmTZqkQYMGacCAAdlqumFCQoLWrl3rGILn7++vli1bpjkxEnCma9euqV69erpw4YJ69OihKlWqyG6368iRI/r6669VqlQp7dy5M0sPavT09NTx48dVunRpSb9/P+rp6anTp0+rRIkSJqdznVy5cik6OjrFv7OXl5eOHz+uUqVKmZgMQEZlhQG0s2bNUtOmTeXv7++018gK3M0OgJxl3759j30mp3xiWqdOHUVFRWn//v06fvy4JKlSpUqqVauWyckAAFYSEhKimTNnKjg4WFu2bFGzZs0UHh6ukydPZvkdVqnx8vJS+/btzY4BSJI+/PBD5cqVS6dOnVKRIkUeWmvZsqU+/PBDTZ482aSEj3fv3j15eXk5rg3DUK5cuXLcAMKkpCTlypUrxT13d3clJiaalCjr+WMYUEhIiPbv3292HCBbefPNNx3/HBcXp5MnT8owDFWoUCFbfj+WFnYyAlnU7t279eyzz5odAwCQzeXJk0dHjx517FLKnTu3tm3blu12zLdu3VqLFi2Sr6+vJGnChAkaMGCA4xvz3377TQ0bNlRUVJSJKZHTlC1bVrNmzVJgYGCq62vXrlXfvn115swZ1wb7E2w2m/r06ZNiN/D06dMVFBTk+P0mWf+kkc1mU6tWrZQ7d27HvVWrVqlZs2Z64oknHPeWL19uRjxTbdy4USEhIVq5cqUKFiyoDh066PPPPzc7FpBubm5uiomJcfRg9Pb21oEDB1SuXDlJrtnJKElnzpzRgAEDtG7dOv1RijMMQy+88IKmTZumsmXLOvX1XYEiI1wqOTlZNpvN7BhZRnx8vNzc3FJ8erx//3699957WrNmTbafLAUAMN/jjghlF25ubrp48eIj+ym54g0C8L9y586tU6dOqWTJkqmu//LLL6pYsaLu3Lnj4mTp16RJk8eeJDIMQ+Hh4S5KZI5evXql67mwsDAnJ0nbkSNH9OKLL+r06dNOf61z584pLCxMYWFhio+PV2xsrJYuXaqOHTs6/bWBzGaz2VS9enW5u/9+mPfAgQMKCAhw7F5OTEzU4cOHnfo9xPnz5/Xss8/Kw8ND/fv3T9Fe44svvlBiYqJ2796d5t8n2QXHpeFSHh4eKd4gvPPOOxo5cqRjylNO8csvv6hLly6KiIiQm5ubBg4cqI8++kh9+/bVokWL9NJLL2n79u1mxwQAWMScOXOUN29eSb9/Iz137tyH+jAOHjzYjGjp9uDn4nxOjqygYMGCOnPmTJpvCqOjo7P8pOktW7aYHSFLMLN4mF737t3T2bNnnfoaS5cu1Zw5c7Rjxw61bt1an3/+uVq1aqUnnnhCVapUceprA87y4KCil1566aFnnF1AHzdunCpXrqx169bJ09PTcb99+/Z6++239cILL2jcuHEKCQlxag5nYycjXOpxU51yiqCgIB08eFBvvPGGvv32W23btk1PPfWUatasqffee8+xbRsAgIwqW7ZsunYpuWJnTEZkhabtwIN69+6tkydPasOGDQ/187t7964CAwNVoUKFbPWm8erVqzIMI8sXR3OiyMhI1a5d26l/zrm7u+vdd9/VyJEj5e3t7bjv4eGhyMhIVa1a1WmvDVhZ8eLFtXTpUv3f//1fquvbtm1T165d9euvv7o4WeZiJyNMlVNr3Js3b9bSpUvVoEEDderUScWLF9crr7yiESNGmB0NAGAxWbkX3J9hGMZDxdKcMiwOWdcHH3ygZ555RpUqVdKAAQMUEBAgSYqKitKMGTN09+5dLViwwOSUjxcXF6fRo0dryZIlio2NlST5+fmpa9eu+uijjyw1lACPFhwcrBkzZmjr1q3q2bOnunTpkqWnowPZxW+//fbInovly5fXb7/95rpATkKRETBBTEyMKlSoIEkqWrSovLy8Ut2yDQAAfme32/X66687hjLcuXNHffv2dQxkyGmTcJE1lCxZUv/973/Vv39/jRw5MkUj/xYtWmjatGkqVaqUySkf7dq1a6pXr54uXLigHj16pOgTNnfuXG3atEk7d+6k0JRDfPnll/r888+1dOlShYaGasiQIQoMDJTdbldycrLZ8YBsq3jx4jp8+HCa7TUOHTqkYsWKuThV5uO4NFzqwel1qU2uk6w/ve5x060AAMgsVpnKnB2GMiBni42N1YkTJyRJFStWzDY9x4cMGaJNmzZp48aNKlKkSIq1mJgYtWzZUs8//7wmT55sUsKcw8/P75E7tBMTE3Xr1i2XtoU4ceKEQkNDNX/+fMXHx+vFF19Up06d1KFDB5dlAKxgyJAhCg8P16ZNmxx1gD9cvnxZLVq0UNOmTTVlyhRzAmYSioxwKabX/e5x063+sHfvXjPiAQAshKnMAB6lbNmymjVrlgIDA1NdX7t2rfr27WuZ1gtZ2bx589L13GuvvebkJA9LTk7W6tWrFRISov/85z/sHgf+pNjYWNWtW1cxMTEKCgpK0V7j66+/VtGiRRUREZFtPqBKC0VGwAQffPBBup57cAoWAAB/lpUGppw9e1br16/X/fv31aRJEwYQAJkgd+7cOnXqVJpH+H755RdVrFhRd+7ccXEypCYxMdGxUcEsly9fdvydAiD9YmNjNWrUKC1ZskRxcXGSpHz58qlz586aMGGCJQZu0ZMRMAHFQwAA/pxt27apdevWun37tqTfJ6DOmzdP3bp1MzkZkL0VLFhQZ86cSbPIGB0dbYk3vtldVFSUQkJCtHDhQl26dMnULBQYkd1kldYxfn5++uKLLzRjxgxduXJFklSoUCFLDbJjJyNcaujQoel6zuo9GQEAcJXH9QHOLjsZGzduLB8fH82aNUteXl4aOXKkVq9erfPnz5sdDcjWevfurZMnT2rDhg0Pte65e/euAgMDVaFCBYWEhJiUMOeKj4/X4sWLFRISot27d+u5555Tx44d9fbbb5sdDchWaB3jOuxkhEvt3bs3XT0Zra5cuXKp/nv6+vqqcuXKGjZsmJ555hkTkgEArMYqU5kPHjyobdu2qXjx4pKkzz77TLNnz1ZsbCxTb4EM+OCDD/TMM8+oUqVKGjBgQIo+YTNmzNDdu3e1YMECk1PmLNu3b9ecOXP07bffqly5coqKitLWrVvVoEEDs6MB2dKDe+vM2GvXtGnTdNVCNm3a5KJEzkGRES61ZcsWsyNkCUOGDEn1flxcnHbv3q169epp/fr1atq0qWuDAQAs59VXX03xTW1QUFCqz2R1cXFxKY7oPfHEE8qTJ4/i4uIoMgIZULJkSf33v/9V//79NXLkSMebb8Mw1KJFC02bNk2lSpUyOWXO8Omnnyo0NFTx8fHq1q2btm/frpo1a8rDw4M/54Bs7qmnnkpz7caNG1q0aFG2+eD3UTguDZcqX768du/eTV+Xxxg/frw2btyorVu3mh0FAIAswWazKTw8PMXUxfr162vp0qUpesnVqFHDjHiAJcTGxurEiROSpIoVK2b7KafZjbu7u4YPH64PP/xQbm5ujvseHh6KjIx0ybCr2NhYLVy4UK+99pp8fHxSrF2/fl3z589PdQ3IyrJq65jExERNnz5dEyZMkK+vr8aPH6+uXbu6NENmo8gIl3pwwiVSd+TIETVs2FBXr141OwoAIJt7sA9RdmWz2WQYRqpHnP64bxgG/ZSATGa323XlypVs/2dIdvDxxx9r7ty5unPnjrp166aePXuqevXqLi0yjh8/XgcOHNA333yT6nrnzp1Vs2ZNjR492ulZgMxis9nUqlUrR+uYVatWqVmzZilax6xdu9al30N89dVXGjt2rBISEjRmzBj16dPH9MnxmSH7/xsAAAAgTVb5PDk6OtrsCIAl5cmTR2fPnnXs8HnhhRcUFhamYsWKSZIuX77MQAQXGTVqlEaNGqWtW7cqNDRUzz33nCpUqCC73a7Y2FiXZPj222/12Wefpbn+5ptvatiwYRQZka289tprKa7NbB2zdu1ajRgxQtHR0Ro2bJiGDh3qKHZaAUVGuFxUVJRiYmIe+UxOP+q0bNkyVa9e3ewYAABkGWXKlDE7AmBJd+7cSfFhxI4dO5SQkJDiGat8WJFdNG7cWI0bN9a0adP01VdfKSwsTI0bN1adOnXUqVMnDR061GmvferUKVWqVCnN9UqVKunUqVNOe33AGcLCwsyOoF27dmn48OGKiIhQ3759tXHjRhUsWNDsWJmO49JwKY46/W7q1Kmp3r9+/bp2796t//znP1q3bp2aNWvm4mQAAKux2WyaN2+efH19H/lcu3btXJTor7l27Zpu376dov/i4cOHNXHiRN26dUsvv/yyunfvbmJCIHt6sJ2Rt7e3IiMjVb58eUnm9SpDSgcPHlRISIi+/vprXb582Wmvky9fPq1du1bPPfdcqusRERF64YUXFBcX57QMgBmWLVumTp06Oe3Xt9ls8vLy0ptvvqmyZcum+dzgwYOdlsEVKDLCpWw2m3bt2uU4jpEWq+9W+KPB7IN8fHwUEBCgoUOHqm7dui5OBQCwIpvN9thnssMHfN26dVOxYsU0adIkSb8f4QwICFDx4sVVoUIF/ec//1FISIh69uxpclIge6HImL3cv39fHh4eTvv1mzZtqrp16+of//hHquvDhw/Xrl27tHnzZqdlAJwhMTFRx44dk4eHh/z9/R33v/vuO40dO1ZHjx516nTnsmXLyjCMRz5jGIZOnz7ttAyuwHFpuFzp0qVzfONo+koBAFzJCkPXIiIiUhx3mj9/vvLnz6/9+/fL3d1dEydO1PTp0ykyAn+SYRgp3vg+eA3XCQ8P18CBAxUREZHqZOf69etr5syZatiwodMyDBw4UF27dlXJkiXVr18/x5TrpKQkzZgxQ5MnT9bXX3/ttNcHnCEqKkpt2rTR2bNnJUkvvfSSvvjiC3Xu3FmRkZH629/+ph9++MGpGc6cOePUXz+rePxH2wBcJjExUfHx8WbHAABYiFWKBTExMSlOAoSHh6t9+/aOSYzt2rXTiRMnzIoHZFt2u13+/v7Knz+/8ufPr/j4eNWqVctxHRAQYHbEHGPKlCl64403HiowSpKvr6/efPNNx25uZ+nYsaPeffddDR48WPnz51etWrVUu3Zt5c+fX0OGDNHQoUOdeqQUcIYRI0aoXLly+u6779S5c2etXLlSDRs21PPPP6/z589r4sSJKlWqlFMzHDp06LHPpLWDODvhuDRcqmnTplqxYoXy5cuX6vrFixc1YcIETZs2zbXBXGzNmjX67bffUuy2mDBhgsaPH6/ExEQ1a9ZMS5YskZ+fn4kpAQBW8OBRyOyqSJEiWr9+vWrWrClJKliwoGbNmqWOHTtKkk6cOKFatWrxYR3wJ82bNy9dzz04nRWZr0yZMlq7dq2qVKmS6vrRo0fVsmVLnTt3zulZdu3apa+++konT550FKK7d++uOnXqOP21gcxWtGhRrVmzRrVr11ZcXJzy58+vWbNm6Y033nBZhhIlSmjHjh1p9mP85z//qbFjxzr1yLYrcFwaLrV582ZFRUXpq6++koeHhzp37qx8+fLp6tWrmjBhgmbOnJlmv0IrmThxouNNkSTt3LlTY8eO1YcffqgqVapo9OjRGj9+vNM/qQQAWN9rr70mLy8vs2NkWJ06dTR16lTNnj1by5cv182bN1MMSDt+/LjTdyEAVlSuXDnVr1/fsSsY5rl06dIj+y26u7vrypUrLslSp04dCoqwjMuXL6tEiRKSfh9ulCdPHjVu3NilGRo2bKgWLVpox44dD33w+69//UvvvfeevvrqK5dmcgb+JoFL/fDDD+rYsaPu378vSfr00081e/Zsde7cWdWrV9c333yjNm3amJzS+Q4dOqTPPvvMcb1s2TK1aNFCo0ePliR5enrqrbfeosgIAMiw/+1jmJ2NHz9ezZs318KFC5WYmKhRo0al2PG/ePFil79hAKygadOmunjxYrbf7WwFJUqU0MGDB1WxYsVU1w8cOKBixYo5NcOBAwfS9VyNGjWcmgPITIZhpBiEZ7PZnDpAKTULFy5U27Zt1bJlS23dulW+vr6SpM8++0yjRo3SggUL9Morr7g0kzNwXBouVa9ePdWpU0cTJkzQl19+qWHDhqlSpUqaPXu2GjVqZHY8l/Hy8tKxY8dUunRpSb9/UtipUye9++67kqSzZ8+qatWqunXrlpkxAQAWYLPZ0jXNMDEx0UWJ/rorV65o586dKlq0qOrWrZtibfXq1apatWqOOBEBZCartFSwgkGDBmnLli3avXu3PD09U6wlJCSoTp06atq0qaZOneq0DH/8nfGoMoFhGEwbR7Zis9nk6+vr+H4oLi5OPj4+KQqPknTt2jWn5khISFCLFi1kGIY2bNigmTNn6p133tG8efPUvXt3p762q1BkhEvly5dPu3btkr+/vxITE+Xp6alVq1apVatWZkdzqQoVKmjGjBkKDAxUfHy8ChQooPDwcDVo0ECStHfvXgUGBrrsOAQAwLpWrlyZZpFx586d+ve//y273a6EhAQXJwOQFdhsNl26dEmFChUyO0qOd+nSJdWuXVtubm4aOHCgKleuLMMwdOTIEU2fPl1JSUnau3evihQp4rQMf0zffZwyZco4LQOQ2bJS79nr16+rcePGun//vo4fP66wsDAFBQU5/XVdhSIjXOrBT0q9vb21f/9+VahQweRkrjV8+HB9//33GjVqlNasWaOdO3fq9OnTcnNzkyR9+eWXmj9/vrZv325yUgCAFR09elQjR47UqlWr1KNHD40fP96xuz6rSu/OncGDBzs5CWAtNptNffr0UZ48eR75HG18XOPs2bPq16+f1q1b59hNaBiGAgMDNWPGjDSHRmSWDz/8UMOGDXvsfw+A1SQmJjq1N+3333/v+OeLFy/qrbfeUrt27R4qMLZr185pGVyBIiNcymazKTw8XPnz55ck1a9fX0uXLlXJkiVTPGf1Hh+3b9/Wm2++qR9++EFFixbVl19+qYYNGzrWmzZtqhdeeEHDhw83MSUAwGp+/fVXjRs3TvPmzVNgYKA++eQTVa9e3exY6ZKeY9CGYej06dMuSANYh81mU7169ZQrV640nzEMQ+Hh4S5MhdjYWMdk50qVKqXoQetMbm5u9OhEjhIVFaWQkBAtXLhQly5dctrrPHg0OzVWaEVAkREu9ageH3/ct8JvLAAAspLr16/r448/1r///W899dRT+uc//5niwy0AORc9GfG/+O8BOUF8fLwWL16skJAQ7d69W88995w6duyot99+2+xo2R7TpeFS0dHRZkcAACBH+fTTT/XPf/5TRYsW1aJFi/TSSy+ZHekvadasmZYvX658+fKZHQWwlMcNhoLrdOjQIV3PLV++3Kk5+G8CVrV9+3bNmTNH3377rcqVK6eoqCht3brVMRsBGcdORgAAAAuz2Wzy8vJS8+bNHb1/U+PsN60Zxe4awDn4vZV19OrVK13PhYWFOS2DzWZT9erVH9ubbu/evU7LAGS2Tz/9VKGhoYqPj1e3bt0UFBSkmjVrysPDQ5GRkapatarTM2zbti1dzzVq1MjJSZyLnYxwqU8//VSDBg2Sl5eXpN9/o9WtW1e5c+eWJN28eVPDhw/XjBkzzIwJAIBlvPrqq+xKAZCmsLAw+fr6mh0Dcm7x8M8IDAxU3rx5zY4BZJpRo0Zp+PDh+vDDDx/5gaszNWnSJM21P75PMwxDiYmJLkrkHOxkhEs92EjYx8dH+/fvV/ny5SVJly5dUvHixenJCAAAUrDZbDpx4oQKFSr0yOd8fHxclAiwhpMnT+r69et6+umnHfc2bdqkjz76SLdu3dLLL7+sUaNGmZgQZ8+e1a1btxQQEJCu4REZwc5WWNHHH3+suXPn6s6dO+rWrZt69uyp6tWru3Qn4/Xr11O9f/v2bX3++eeaOnWqypcvr0OHDjk9izM5908o4AEP1rSpcafu/PnzCg4ONjsGAABZir+/v/z8/FL9kS9fPpdNXwWs5J133tHKlSsd19HR0Wrbtq1y5cqlevXq6ZNPPtGUKVNMy5eTzJs376GvdZ8+fVS+fHk9+eSTql69us6fP+/UDOx8hxWNGjVKx48f14IFCxQTE6PnnntONWvWlN1uV2xsrEsy+Pr6pvjh7e2tb775RnXq1NGiRYs0ffp0HThwwCVZnImdjHCpBz8Z8/b2VmRkJDsZHxAZGanatWvn+K8DACDjssoggYyy2Wz69ttvlT9//kc+17hxYxclAqyhVKlSWrp0qerVqydJ+uijj7Rs2TLt379fkhQSEqJ///vfjms4T7169dSnTx9Hb8a1a9eqbdu2mjt3rqpUqaKBAweqatWqmjNnjtMysJMROcHNmzf11VdfKSwsTD///LPq1KmjTp06aejQoS55/eXLl2vUqFG6cuWKRo4cqUGDBjlayGV39GQEAACwMCv1WmvQoAFvfIFMdvXqVZUsWdJxvXnzZrVt29Zx3aRJE/397383I1qOc/z4cT3zzDOO6++++07t2rVTjx49JP1+5DO9w2H+qujo6FTbUiQmJurOnTv0aoQleHt7q2/fvurbt68OHjyokJAQ/eMf/3B6kXHr1q0aPny4Dh48qLfeekvDhw+31PdpEkVGmGDOnDmOv5wSExM1d+5cFSxYUNLvnygAAIDMk1UGCTjbkSNH9OKLL+r06dNmRwGylfz58+vixYsqVaqUkpOTtWfPHr399tuO9Xv37tHiyEUSEhJS9JXduXNnihZK5cuXV0xMjFMzHD58WNu2bVPPnj0d9yZMmKDx48crMTFRzZo105IlS2hPAct48sknNWXKFP3rX/9y6uu0bt1amzZtUq9evbRy5UoVLVrUqa9nFoqMcKnSpUtr9uzZjuuiRYtqwYIFDz0DAADwv8qUKfPIiZD37t3T2bNnXZgIsIbGjRtr/PjxmjFjhr755hslJyeradOmjvWoqCiVLVvWvIA5SJkyZfTzzz+rTJkyunr1qg4fPqz/+7//c6zHxMQ4fdfTxIkT1bFjR8f1zp07NXbsWH344YeqUqWKRo8erfHjx2vSpElOzQFkpvDwcA0cOFAREREPDYi7fv266tevr5kzZ6phw4ZOy7B27Vq5u7tryZIlWrp0aZrPXbt2zWkZXIEiI1zqzJkzZkfIEh7XHysuLs41QQAAyCaio6PNjgBY0oQJE9SiRQuVLVtWNptNU6dO1RNPPOFYX7BggZo1a2Ziwpzj1Vdf1YABA3T48GGFh4crICAgxdTvnTt3qnr16k7NcOjQIX322WeO62XLlqlFixYaPXq0JMnT01NvvfUWRUZkK1OmTNEbb7zxUIFR+r2tzJtvvqlJkyY5tciYU06WUGQETPC4TyB9fX316quvuigNAAAAcqpy5crpyJEjioqKUqFChVS8ePEU6x988EGKno1wnuHDh+v27dtavny5ihYtqm+++SbF+o4dO9StWzenZrh586YKFCjguN6+fbs6derkuK5WrZp+/fVXp2YAMltkZKT++c9/prnesmVLTZw40akZXnvtNaf++lkF06XhUunZpvzFF1+oUaNGJiUEAADZUWRkpGrXrq2kpCSzowDZ2tWrV2UYRopCE1wjPj7e9MEqFSpU0IwZMxQYGKj4+HgVKFBA4eHhatCggSRp7969CgwM1JUrV0zNCfwZnp6eOnTokCpWrJjq+smTJ/Xkk08qISHBxcmsx2Z2AOQs6dmmPHnyZBOSZR3JyclatWqVXn75ZbOjAACQZfj5+Sl//vxp/nDmESfA6uLi4jRgwAAVLFhQRYoUUeHChVWwYEENHDiQNj4u9OSTT2rbtm2mZujUqZOGDBmiBQsW6I033lDRokX13HPPOdb37NmjypUrm5gQ+PNKlCihgwcPprl+4MABFStWzKkZbDab3NzcHvrh5+en5557TsuXL3fq67sKx6XhUllhm3JWdeLECYWGhmrevHmKjY1VYGCg2ZEAAMgypkyZYnYEwJKuXbumevXq6cKFC+rRo4eqVKkiu92uI0eOaO7cudq0aZN27tzJNGEXeOWVV9S8eXMNGjRIH3/8sXLnzu3yDOPGjdOvv/6qwYMHq2jRolq4cGGKoVuLFi1S27ZtXZ4LyIjWrVtr7NixatWqlTw9PVOsJSQkaNy4cWrTpo1TM6xYsSLV+3Fxcdq1a5eCgoI0b948vfLKK07N4Wwcl4ZLsU05pYSEBC1dulQhISGKiIhQUlKSJk+erODgYNOPSgAAAMD6hgwZok2bNmnjxo0qUqRIirWYmBi1bNlSzz//fI4/beQqERERCg4OlmEYWrBggWrXrm12JCDbu3TpkmrXri03NzcNHDhQlStXlmEYOnLkiKZPn66kpCTt3bv3oT8DXWn69OmaP3++fvrpJ9MyZAaKjHCpChUqaOLEiWrfvn2q68uXL9ewYcN0+vRpFydzrV27dmnOnDlasmSJ/P39FRQUpK5du6pkyZKKjIxU1apVzY4IAACAHKBs2bKaNWtWmqdo1q5dq759++rMmTOuDZaD3b17V2PGjNG0adPUokULubunPIBo1rHK2NhYLVy4UCEhIdq/f78pGYC/6uzZs+rXr5/WrVunP8pghmEoMDBQM2bMUNmyZU3Nd+LECdWpU0exsbGm5sgojkvDpbLCNuWsoH79+ho0aJB27dpFTxMAANLBZrPJMIyH7vv4+Khy5cp699131aFDBxOSAdnbxYsXVa1atTTXq1evrpiYGBcmwt27d3X58mUZhiFfX9+HioyutnHjRoWEhGjlypUqWLAgf9YiWypTpozWrFmj2NhYnTx5Una7XZUqVcoyrSASEhIeqpFkRxQZ4VJjxozR8uXL5e/vn+Y25dGjR5sd0+maNWumkJAQXb58WT179lRgYGCqb5wAAMDvckovI8DVChYsqDNnzqhkyZKprkdHRzNp2oXWr1+v3r17q3jx4tq7d68CAgJMyXHu3DmFhYUpLCxM8fHxio2N1dKlS9WxY0dT8gCZxc/PT88++6zZMR4ye/Zs1apVy+wYGcZxabhcVt+m7Crnz593/MWdkJCgLl26aMaMGTpw4ICqVKlidjwAALIVq/QyAlytd+/eOnnypDZs2KBcuXKlWLt7964CAwNVoUIFhYSEmJQw53jzzTc1b948jRo1SqNHj04xcMVVli5dqjlz5mjHjh1q3bq1goKC1KpVKz3xxBO0dQIyYOjQoanev379uvbs2aNTp07pxx9/zPaFRoqMME1W3aZshg0bNig0NFQrV65UqVKl1KlTJ3Xq1IlGzwAApJNVehkBrvbLL7/omWeeUe7cuTVgwADHzrmoqCjNmDFDd+/e1Z49e1SqVCmTk1pf9erVNX/+fFPfA7i7u+vdd9/VyJEj5e3t7bjv4eFBkRHIgKZNm6Z638fHRwEBAerfv7/KlCnj4lSZjyIjYILg4GB9/vnnKf7ilv5fM+XQ0FAdOHBASUlJJiUEACB7OXDggAIDA3Xx4kWzowDZTnR0tPr376/169enOGnUokULTZs2TRUrVjQ5Yc5w7969h3aTulqfPn20dOlSVatWTT179lSXLl3k5+dHkRFAulBkBEzg5uamixcvqnDhwmk+s3fvXnYyAgCQToMGDdKpU6e0Zs0as6MA2VZsbKxOnDghSapYsaLy589vcqKcJa3jlA+aNGmSU3MkJCRo6dKlCg0N1U8//aTAwECtXr1a+/fvV/Xq1Z362gCyN4qMgAlsNptiYmIeWWQEAAD/T07pZQQg53rwOOX27dv19NNPy8vLy3HPMAyFh4e7LNOJEycUGhqq+fPnKz4+Xi+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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spending_df.groupby('specialty').size().plot.bar(figsize=(16,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x1232892b0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_by_specialty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XD201183</th>\n",
       "      <td>1477513802</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>SILDENAFIL CITRATE</td>\n",
       "      <td>13</td>\n",
       "      <td>2015.01</td>\n",
       "      <td>0.105179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SB421912</th>\n",
       "      <td>1295735793</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>DABIGATRAN ETEXILATE MESYLATE</td>\n",
       "      <td>42</td>\n",
       "      <td>15949.07</td>\n",
       "      <td>0.832507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JS147591</th>\n",
       "      <td>1265480891</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>15</td>\n",
       "      <td>76.10</td>\n",
       "      <td>0.003972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HM795848</th>\n",
       "      <td>1881689347</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>METOPROLOL SUCCINATE</td>\n",
       "      <td>36</td>\n",
       "      <td>2233.23</td>\n",
       "      <td>0.116570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JE924200</th>\n",
       "      <td>1083645576</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>OMEPRAZOLE</td>\n",
       "      <td>25</td>\n",
       "      <td>499.21</td>\n",
       "      <td>0.026058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OB754099</th>\n",
       "      <td>1255495701</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>NATEGLINIDE</td>\n",
       "      <td>12</td>\n",
       "      <td>816.58</td>\n",
       "      <td>0.042624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KB183636</th>\n",
       "      <td>1689679490</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>NIFEDIPINE</td>\n",
       "      <td>14</td>\n",
       "      <td>203.50</td>\n",
       "      <td>0.010622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR122692</th>\n",
       "      <td>1356324115</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>PRASUGREL HCL</td>\n",
       "      <td>13</td>\n",
       "      <td>5776.43</td>\n",
       "      <td>0.301517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT926266</th>\n",
       "      <td>1619064706</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>PROPRANOLOL HCL</td>\n",
       "      <td>15</td>\n",
       "      <td>263.68</td>\n",
       "      <td>0.013764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LC463557</th>\n",
       "      <td>1639156805</td>\n",
       "      <td>CARDIOLOGY</td>\n",
       "      <td>DOXAZOSIN MESYLATE</td>\n",
       "      <td>14</td>\n",
       "      <td>360.56</td>\n",
       "      <td>0.018820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id   specialty                     medication   \n",
       "unique_id                                                          \n",
       "XD201183   1477513802  CARDIOLOGY             SILDENAFIL CITRATE  \\\n",
       "SB421912   1295735793  CARDIOLOGY  DABIGATRAN ETEXILATE MESYLATE   \n",
       "JS147591   1265480891  CARDIOLOGY                  METFORMIN HCL   \n",
       "HM795848   1881689347  CARDIOLOGY           METOPROLOL SUCCINATE   \n",
       "JE924200   1083645576  CARDIOLOGY                     OMEPRAZOLE   \n",
       "OB754099   1255495701  CARDIOLOGY                    NATEGLINIDE   \n",
       "KB183636   1689679490  CARDIOLOGY                     NIFEDIPINE   \n",
       "BR122692   1356324115  CARDIOLOGY                  PRASUGREL HCL   \n",
       "IT926266   1619064706  CARDIOLOGY                PROPRANOLOL HCL   \n",
       "LC463557   1639156805  CARDIOLOGY             DOXAZOSIN MESYLATE   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "XD201183                 13   2015.01      0.105179  \n",
       "SB421912                 42  15949.07      0.832507  \n",
       "JS147591                 15     76.10      0.003972  \n",
       "HM795848                 36   2233.23      0.116570  \n",
       "JE924200                 25    499.21      0.026058  \n",
       "OB754099                 12    816.58      0.042624  \n",
       "KB183636                 14    203.50      0.010622  \n",
       "BR122692                 13   5776.43      0.301517  \n",
       "IT926266                 15    263.68      0.013764  \n",
       "LC463557                 14    360.56      0.018820  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_by_specialty.get_group(\"CARDIOLOGY\").sample(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specialty</th>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ADDICTION MEDICINE</th>\n",
       "      <th>GJ278932</th>\n",
       "      <td>1134139991</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>49</td>\n",
       "      <td>817.88</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX420809</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LORAZEPAM</td>\n",
       "      <td>14</td>\n",
       "      <td>19.56</td>\n",
       "      <td>2.125948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJ278932</th>\n",
       "      <td>1134139991</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>49</td>\n",
       "      <td>817.88</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJ278932</th>\n",
       "      <td>1134139991</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>49</td>\n",
       "      <td>817.88</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJ278932</th>\n",
       "      <td>1134139991</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>BUSPIRONE HCL</td>\n",
       "      <td>49</td>\n",
       "      <td>817.88</td>\n",
       "      <td>88.894203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               doctor_id           specialty     medication   \n",
       "specialty          unique_id                                                  \n",
       "ADDICTION MEDICINE GJ278932   1134139991  ADDICTION MEDICINE  BUSPIRONE HCL  \\\n",
       "                   TX420809   1801032297  ADDICTION MEDICINE      LORAZEPAM   \n",
       "                   GJ278932   1134139991  ADDICTION MEDICINE  BUSPIRONE HCL   \n",
       "                   GJ278932   1134139991  ADDICTION MEDICINE  BUSPIRONE HCL   \n",
       "                   GJ278932   1134139991  ADDICTION MEDICINE  BUSPIRONE HCL   \n",
       "\n",
       "                              nb_beneficiaries  spending  spending_pct  \n",
       "specialty          unique_id                                            \n",
       "ADDICTION MEDICINE GJ278932                 49    817.88     88.894203  \n",
       "                   TX420809                 14     19.56      2.125948  \n",
       "                   GJ278932                 49    817.88     88.894203  \n",
       "                   GJ278932                 49    817.88     88.894203  \n",
       "                   GJ278932                 49    817.88     88.894203  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We sample only 10% of the data in each category\n",
    "\n",
    "def sample_10p(x):\n",
    "    return x.sample(n=10, replace=True)\n",
    "    \n",
    "    \n",
    "spending_by_specialty.apply(sample_10p).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
       "      <th>nb_beneficiaries</th>\n",
       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NX531425</th>\n",
       "      <td>1255626040</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>30</td>\n",
       "      <td>135.24</td>\n",
       "      <td>0.002075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QG879256</th>\n",
       "      <td>1699761833</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>ALLOPURINOL</td>\n",
       "      <td>30</td>\n",
       "      <td>715.76</td>\n",
       "      <td>0.010985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FW363228</th>\n",
       "      <td>1538148804</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>LOSARTAN POTASSIUM</td>\n",
       "      <td>146</td>\n",
       "      <td>1056.47</td>\n",
       "      <td>0.010855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WD733417</th>\n",
       "      <td>1730200619</td>\n",
       "      <td>PSYCHIATRY</td>\n",
       "      <td>OLANZAPINE</td>\n",
       "      <td>13</td>\n",
       "      <td>28226.97</td>\n",
       "      <td>1.614442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XW149832</th>\n",
       "      <td>1023116894</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>PRAVASTATIN SODIUM</td>\n",
       "      <td>348</td>\n",
       "      <td>8199.48</td>\n",
       "      <td>0.125835</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZK471712</th>\n",
       "      <td>1669695367</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>METFORMIN HCL</td>\n",
       "      <td>66</td>\n",
       "      <td>467.35</td>\n",
       "      <td>0.007172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM412803</th>\n",
       "      <td>1497790877</td>\n",
       "      <td>GASTROENTEROLOGY</td>\n",
       "      <td>DICYCLOMINE HCL</td>\n",
       "      <td>66</td>\n",
       "      <td>479.59</td>\n",
       "      <td>0.024190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NN242894</th>\n",
       "      <td>1528254570</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>AMOXICILLIN</td>\n",
       "      <td>22</td>\n",
       "      <td>77.55</td>\n",
       "      <td>0.001190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ER452896</th>\n",
       "      <td>1477571818</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>ALBUTEROL SULFATE</td>\n",
       "      <td>74</td>\n",
       "      <td>3824.68</td>\n",
       "      <td>0.039296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WH309636</th>\n",
       "      <td>1144242090</td>\n",
       "      <td>FAMILY PRACTICE</td>\n",
       "      <td>DIAZEPAM</td>\n",
       "      <td>12</td>\n",
       "      <td>25.36</td>\n",
       "      <td>0.000389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id          specialty          medication   \n",
       "unique_id                                                      \n",
       "NX531425   1255626040    FAMILY PRACTICE       METFORMIN HCL  \\\n",
       "QG879256   1699761833    FAMILY PRACTICE         ALLOPURINOL   \n",
       "FW363228   1538148804  INTERNAL MEDICINE  LOSARTAN POTASSIUM   \n",
       "WD733417   1730200619         PSYCHIATRY          OLANZAPINE   \n",
       "XW149832   1023116894    FAMILY PRACTICE  PRAVASTATIN SODIUM   \n",
       "...               ...                ...                 ...   \n",
       "ZK471712   1669695367    FAMILY PRACTICE       METFORMIN HCL   \n",
       "QM412803   1497790877   GASTROENTEROLOGY     DICYCLOMINE HCL   \n",
       "NN242894   1528254570    FAMILY PRACTICE         AMOXICILLIN   \n",
       "ER452896   1477571818  INTERNAL MEDICINE   ALBUTEROL SULFATE   \n",
       "WH309636   1144242090    FAMILY PRACTICE            DIAZEPAM   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "NX531425                 30    135.24      0.002075  \n",
       "QG879256                 30    715.76      0.010985  \n",
       "FW363228                146   1056.47      0.010855  \n",
       "WD733417                 13  28226.97      1.614442  \n",
       "XW149832                348   8199.48      0.125835  \n",
       "...                     ...       ...           ...  \n",
       "ZK471712                 66    467.35      0.007172  \n",
       "QM412803                 66    479.59      0.024190  \n",
       "NN242894                 22     77.55      0.001190  \n",
       "ER452896                 74   3824.68      0.039296  \n",
       "WH309636                 12     25.36      0.000389  \n",
       "\n",
       "[10000 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>doctor_id</th>\n",
       "      <th>specialty</th>\n",
       "      <th>medication</th>\n",
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       "      <th>spending</th>\n",
       "      <th>spending_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specialty</th>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ADDICTION MEDICINE</th>\n",
       "      <th>VG585760</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>11</td>\n",
       "      <td>82.62</td>\n",
       "      <td>8.979849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VG585760</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>11</td>\n",
       "      <td>82.62</td>\n",
       "      <td>8.979849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX420809</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LORAZEPAM</td>\n",
       "      <td>14</td>\n",
       "      <td>19.56</td>\n",
       "      <td>2.125948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX420809</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LORAZEPAM</td>\n",
       "      <td>14</td>\n",
       "      <td>19.56</td>\n",
       "      <td>2.125948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VG585760</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>ADDICTION MEDICINE</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>11</td>\n",
       "      <td>82.62</td>\n",
       "      <td>8.979849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">VASCULAR SURGERY</th>\n",
       "      <th>CU764903</th>\n",
       "      <td>1780885574</td>\n",
       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>ACETAMINOPHEN WITH CODEINE</td>\n",
       "      <td>16</td>\n",
       "      <td>57.59</td>\n",
       "      <td>1.318700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KL360002</th>\n",
       "      <td>1710981444</td>\n",
       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>APIXABAN</td>\n",
       "      <td>14</td>\n",
       "      <td>4309.59</td>\n",
       "      <td>98.681300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KL360002</th>\n",
       "      <td>1710981444</td>\n",
       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>APIXABAN</td>\n",
       "      <td>14</td>\n",
       "      <td>4309.59</td>\n",
       "      <td>98.681300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KL360002</th>\n",
       "      <td>1710981444</td>\n",
       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>APIXABAN</td>\n",
       "      <td>14</td>\n",
       "      <td>4309.59</td>\n",
       "      <td>98.681300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KL360002</th>\n",
       "      <td>1710981444</td>\n",
       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>APIXABAN</td>\n",
       "      <td>14</td>\n",
       "      <td>4309.59</td>\n",
       "      <td>98.681300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>750 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               doctor_id           specialty   \n",
       "specialty          unique_id                                   \n",
       "ADDICTION MEDICINE VG585760   1801032297  ADDICTION MEDICINE  \\\n",
       "                   VG585760   1801032297  ADDICTION MEDICINE   \n",
       "                   TX420809   1801032297  ADDICTION MEDICINE   \n",
       "                   TX420809   1801032297  ADDICTION MEDICINE   \n",
       "                   VG585760   1801032297  ADDICTION MEDICINE   \n",
       "...                                  ...                 ...   \n",
       "VASCULAR SURGERY   CU764903   1780885574    VASCULAR SURGERY   \n",
       "                   KL360002   1710981444    VASCULAR SURGERY   \n",
       "                   KL360002   1710981444    VASCULAR SURGERY   \n",
       "                   KL360002   1710981444    VASCULAR SURGERY   \n",
       "                   KL360002   1710981444    VASCULAR SURGERY   \n",
       "\n",
       "                                              medication  nb_beneficiaries   \n",
       "specialty          unique_id                                                 \n",
       "ADDICTION MEDICINE VG585760                  LAMOTRIGINE                11  \\\n",
       "                   VG585760                  LAMOTRIGINE                11   \n",
       "                   TX420809                    LORAZEPAM                14   \n",
       "                   TX420809                    LORAZEPAM                14   \n",
       "                   VG585760                  LAMOTRIGINE                11   \n",
       "...                                                  ...               ...   \n",
       "VASCULAR SURGERY   CU764903   ACETAMINOPHEN WITH CODEINE                16   \n",
       "                   KL360002                     APIXABAN                14   \n",
       "                   KL360002                     APIXABAN                14   \n",
       "                   KL360002                     APIXABAN                14   \n",
       "                   KL360002                     APIXABAN                14   \n",
       "\n",
       "                              spending  spending_pct  \n",
       "specialty          unique_id                          \n",
       "ADDICTION MEDICINE VG585760      82.62      8.979849  \n",
       "                   VG585760      82.62      8.979849  \n",
       "                   TX420809      19.56      2.125948  \n",
       "                   TX420809      19.56      2.125948  \n",
       "                   VG585760      82.62      8.979849  \n",
       "...                                ...           ...  \n",
       "VASCULAR SURGERY   CU764903      57.59      1.318700  \n",
       "                   KL360002    4309.59     98.681300  \n",
       "                   KL360002    4309.59     98.681300  \n",
       "                   KL360002    4309.59     98.681300  \n",
       "                   KL360002    4309.59     98.681300  \n",
       "\n",
       "[750 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We select 10 samples of the data in each category\n",
    "\n",
    "def sample_10p(x):\n",
    "    return x.sample(n=10, replace=True)\n",
    "    \n",
    "    \n",
    "spending_subsampled_df = spending_df.groupby('specialty').apply(sample_10p)\n",
    "spending_subsampled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th rowspan=\"5\" valign=\"top\">ADDICTION MEDICINE</th>\n",
       "      <th>VG585760</th>\n",
       "      <td>1801032297</td>\n",
       "      <td>LAMOTRIGINE</td>\n",
       "      <td>11</td>\n",
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       "      <td>8.979849</td>\n",
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       "    <tr>\n",
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       "      <th rowspan=\"5\" valign=\"top\">VASCULAR SURGERY</th>\n",
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       "      <td>APIXABAN</td>\n",
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       "      <td>4309.59</td>\n",
       "      <td>98.681300</td>\n",
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       "    <tr>\n",
       "      <th>KL360002</th>\n",
       "      <td>1710981444</td>\n",
       "      <td>APIXABAN</td>\n",
       "      <td>14</td>\n",
       "      <td>4309.59</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>750 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               doctor_id                  medication   \n",
       "specialty          unique_id                                           \n",
       "ADDICTION MEDICINE VG585760   1801032297                 LAMOTRIGINE  \\\n",
       "                   VG585760   1801032297                 LAMOTRIGINE   \n",
       "                   TX420809   1801032297                   LORAZEPAM   \n",
       "                   TX420809   1801032297                   LORAZEPAM   \n",
       "                   VG585760   1801032297                 LAMOTRIGINE   \n",
       "...                                  ...                         ...   \n",
       "VASCULAR SURGERY   CU764903   1780885574  ACETAMINOPHEN WITH CODEINE   \n",
       "                   KL360002   1710981444                    APIXABAN   \n",
       "                   KL360002   1710981444                    APIXABAN   \n",
       "                   KL360002   1710981444                    APIXABAN   \n",
       "                   KL360002   1710981444                    APIXABAN   \n",
       "\n",
       "                              nb_beneficiaries  spending  spending_pct  \n",
       "specialty          unique_id                                            \n",
       "ADDICTION MEDICINE VG585760                 11     82.62      8.979849  \n",
       "                   VG585760                 11     82.62      8.979849  \n",
       "                   TX420809                 14     19.56      2.125948  \n",
       "                   TX420809                 14     19.56      2.125948  \n",
       "                   VG585760                 11     82.62      8.979849  \n",
       "...                                        ...       ...           ...  \n",
       "VASCULAR SURGERY   CU764903                 16     57.59      1.318700  \n",
       "                   KL360002                 14   4309.59     98.681300  \n",
       "                   KL360002                 14   4309.59     98.681300  \n",
       "                   KL360002                 14   4309.59     98.681300  \n",
       "                   KL360002                 14   4309.59     98.681300  \n",
       "\n",
       "[750 rows x 5 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_subsampled_df.drop(\"specialty\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>750 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            doctor_id           specialty                  medication  \\\n",
       "unique_id                                                               \n",
       "VG585760   1801032297  ADDICTION MEDICINE                 LAMOTRIGINE   \n",
       "GJ278932   1134139991  ADDICTION MEDICINE               BUSPIRONE HCL   \n",
       "TX420809   1801032297  ADDICTION MEDICINE                   LORAZEPAM   \n",
       "GJ278932   1134139991  ADDICTION MEDICINE               BUSPIRONE HCL   \n",
       "TX420809   1801032297  ADDICTION MEDICINE                   LORAZEPAM   \n",
       "...               ...                 ...                         ...   \n",
       "CU764903   1780885574    VASCULAR SURGERY  ACETAMINOPHEN WITH CODEINE   \n",
       "KL360002   1710981444    VASCULAR SURGERY                    APIXABAN   \n",
       "KL360002   1710981444    VASCULAR SURGERY                    APIXABAN   \n",
       "KL360002   1710981444    VASCULAR SURGERY                    APIXABAN   \n",
       "CU764903   1780885574    VASCULAR SURGERY  ACETAMINOPHEN WITH CODEINE   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "VG585760                 11     82.62      8.979849  \n",
       "GJ278932                 49    817.88     88.894203  \n",
       "TX420809                 14     19.56      2.125948  \n",
       "GJ278932                 49    817.88     88.894203  \n",
       "TX420809                 14     19.56      2.125948  \n",
       "...                     ...       ...           ...  \n",
       "CU764903                 16     57.59      1.318700  \n",
       "KL360002                 14   4309.59     98.681300  \n",
       "KL360002                 14   4309.59     98.681300  \n",
       "KL360002                 14   4309.59     98.681300  \n",
       "CU764903                 16     57.59      1.318700  \n",
       "\n",
       "[750 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_subsampled_df.reset_index((0,), drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>1710981444</td>\n",
       "      <td>APIXABAN</td>\n",
       "      <td>14</td>\n",
       "      <td>4309.59</td>\n",
       "      <td>98.681300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KL360002</th>\n",
       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>1710981444</td>\n",
       "      <td>APIXABAN</td>\n",
       "      <td>14</td>\n",
       "      <td>4309.59</td>\n",
       "      <td>98.681300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CU764903</th>\n",
       "      <td>VASCULAR SURGERY</td>\n",
       "      <td>1780885574</td>\n",
       "      <td>ACETAMINOPHEN WITH CODEINE</td>\n",
       "      <td>16</td>\n",
       "      <td>57.59</td>\n",
       "      <td>1.318700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>750 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    specialty   doctor_id                  medication  \\\n",
       "unique_id                                                               \n",
       "VG585760   ADDICTION MEDICINE  1801032297                 LAMOTRIGINE   \n",
       "GJ278932   ADDICTION MEDICINE  1134139991               BUSPIRONE HCL   \n",
       "TX420809   ADDICTION MEDICINE  1801032297                   LORAZEPAM   \n",
       "GJ278932   ADDICTION MEDICINE  1134139991               BUSPIRONE HCL   \n",
       "TX420809   ADDICTION MEDICINE  1801032297                   LORAZEPAM   \n",
       "...                       ...         ...                         ...   \n",
       "CU764903     VASCULAR SURGERY  1780885574  ACETAMINOPHEN WITH CODEINE   \n",
       "KL360002     VASCULAR SURGERY  1710981444                    APIXABAN   \n",
       "KL360002     VASCULAR SURGERY  1710981444                    APIXABAN   \n",
       "KL360002     VASCULAR SURGERY  1710981444                    APIXABAN   \n",
       "CU764903     VASCULAR SURGERY  1780885574  ACETAMINOPHEN WITH CODEINE   \n",
       "\n",
       "           nb_beneficiaries  spending  spending_pct  \n",
       "unique_id                                            \n",
       "VG585760                 11     82.62      8.979849  \n",
       "GJ278932                 49    817.88     88.894203  \n",
       "TX420809                 14     19.56      2.125948  \n",
       "GJ278932                 49    817.88     88.894203  \n",
       "TX420809                 14     19.56      2.125948  \n",
       "...                     ...       ...           ...  \n",
       "CU764903                 16     57.59      1.318700  \n",
       "KL360002                 14   4309.59     98.681300  \n",
       "KL360002                 14   4309.59     98.681300  \n",
       "KL360002                 14   4309.59     98.681300  \n",
       "CU764903                 16     57.59      1.318700  \n",
       "\n",
       "[750 rows x 6 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that we first need to drop the column\n",
    "spending_subsampled_df = spending_subsampled_df.drop(\"specialty\", axis=1).reset_index((0,))\n",
    "spending_subsampled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='specialty'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spending_subsampled_df.groupby('specialty').size().plot.bar(figsize=(16,4))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
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 "nbformat_minor": 4
}