hw1.ipynb

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    "# Initialize Otter\n",
    "import otter\n",
    "grader = otter.Notebook(\"hw1.ipynb\")"
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   "source": [
    "# CPSC 330 - Applied Machine Learning \n",
    "\n",
    "## Homework 1: Programming with Python  \n",
    "\n",
    "**Due date: See the [Calendar](https://htmlpreview.github.io/?https://github.com/UBC-CS/cpsc330-2024W1/blob/master/docs/calendar.html)**. "
   ]
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   "source": [
    "### About this assignment:\n",
    "The main purpose of this assignment is to check whether your programming knowledge is adequate to take CPSC 330. This assignment covers two python packages, `numpy` and `pandas`, which we'll be using throughout the course. For some of you, Python/numpy/pandas will be familiar; for others, it will be new. Either way, if you find this assignment very difficult then that could be a sign that you will struggle later on in the course. While CPSC 330 is a machine learning course rather than a programming course, programming will be an essential part of it.\n",
    "\n",
    "Also, as part of this assignment you will likely need to consult the documentation for various Python packages we're using. This is, of course, totally OK and in fact strongly encouraged. Reading and interpreting documentation is an important skill, and in fact is one of the skills this assignment is meant to assess. That said, do not use Large Language Model tools such as ChatGPT to complete your assignment; it would be self-deceptive and by doing so you will only be hurting your own learning. \n",
    "\n",
    "For Python refresher, check out [Python notes](https://ubc-cs.github.io/cpsc330-2023W1/docs/python_notes.html) and [Python resources](https://ubc-cs.github.io/cpsc330-2024W1/docs/resources.html#python-resources). "
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   "source": [
    "### Set-up\n",
    "\n",
    "In order to do this assignment and future assignments, you will need to set up the CPSC 330 software stack, which is Python and Jupyter. For software install help, see [here](https://ubc-cs.github.io/cpsc330-2024W1/docs/setup.html). Once you have the software stack installed, you should be able to run the next cell, which imports some packages needed for the assignment.\n",
    "\n",
    "Setting up the software stack can be frustrating and challenging. But remember that it is an integral part of becoming a data scientist or machine learning engineer. This is going to be a valuable skill for your future self. Make the most of the tutorials available this week, as the TAs are ready to assist you with the setup. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Points\n",
    "------\n",
    "\n",
    "Each question or sub-question will have a number of points allocated to it, which is indicated right below the question. "
   ]
  },
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   "source": [
    "<br><br>"
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   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "## Instructions\n",
    "rubric={points}\n",
    "\n",
    "**PLEASE READ:** \n",
    "1. Before submitting the assignment, run all cells in your notebook to make sure there are no errors by doing `Kernel -> Restart Kernel and Clear All Outputs` and then `Run -> Run All Cells`. \n",
    "2. Notebooks with cell execution numbers out of order or not starting from \"1\" will have marks deducted. Notebooks without the output displayed may not be graded at all (because we need to see the output in order to grade your work).\n",
    "3. Follow the [CPSC 330 homework instructions](https://ubc-cs.github.io/cpsc330-2024W1/docs/homework_instructions.html), which include information on how to do your assignment and how to submit your assignment.\n",
    "4. Upload your solution on Gradescope. Check out this [Gradescope Student Guide](https://lthub.ubc.ca/guides/gradescope-student-guide/) if you need help with Gradescope submission. \n",
    "5. Make sure that the plots and output are rendered properly in your submitted file. If the .ipynb file is too big and doesn't render on Gradescope, also upload a pdf or html in addition to the .ipynb so that the TAs can view your submission on Gradescope.\n",
    "\n",
    "_Points:_ 6"
   ]
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   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
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    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "## Exercise 1: Loading files with Pandas\n",
    "rubric={points}\n",
    "\n",
    "When working with tabular data, you will typically be creating Pandas dataframes by reading data from .csv files using `pd.read_csv()`. The documentation for this function is available [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)."
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   "source": [
    "In the \"data\" folder in this homework repository there are 6 different .csv files named `wine_#.csv/.txt`. Look at each of these files and use `pd.read_csv()` to load these data so that they resemble the following:\n",
    "\n",
    "![](img/hw1-table.png)\n",
    "\n",
    "You are provided with tests that use `df.equals()` to check that all the dataframes are identical. If you're in a situation where the two dataframes look identical but `df.equals()` is returning `False`, it may be an issue of types. Try checking `df.index`, `df.columns`, or `df.info()`."
   ]
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   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_1\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 12"
   ]
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     "text": [
      "   Bottle         Grape     Origin  Alcohol    pH Colour   Aroma\n",
      "0       1    Chardonnay  Australia    14.23  3.51  White  Floral\n",
      "1       2  Pinot Grigio      Italy    13.20  3.30  White  Fruity\n",
      "2       3   Pinot Blanc     France    13.16  3.16  White  Citrus\n",
      "3       4        Shiraz      Chile    14.91  3.39    Red   Berry\n",
      "4       5        Malbec  Argentina    13.83  3.28    Red  Fruity\n",
      "   Bottle         Grape     Origin  Alcohol    pH Colour   Aroma\n",
      "0       1    Chardonnay  Australia    14.23  3.51  White  Floral\n",
      "1       2  Pinot Grigio      Italy    13.20  3.30  White  Fruity\n",
      "2       3   Pinot Blanc     France    13.16  3.16  White  Citrus\n",
      "3       4        Shiraz      Chile    14.91  3.39    Red   Berry\n",
      "4       5        Malbec  Argentina    13.83  3.28    Red  Fruity\n",
      "   Bottle         Grape     Origin  Alcohol    pH Colour   Aroma\n",
      "0       1    Chardonnay  Australia    14.23  3.51  White  Floral\n",
      "1       2  Pinot Grigio      Italy    13.20  3.30  White  Fruity\n",
      "2       3   Pinot Blanc     France    13.16  3.16  White  Citrus\n",
      "3       4        Shiraz      Chile    14.91  3.39    Red   Berry\n",
      "4       5        Malbec  Argentina    13.83  3.28    Red  Fruity\n",
      "   Bottle         Grape     Origin  Alcohol    pH Colour   Aroma\n",
      "0       1    Chardonnay  Australia    14.23  3.51  White  Floral\n",
      "1       2  Pinot Grigio      Italy    13.20  3.30  White  Fruity\n",
      "2       3   Pinot Blanc     France    13.16  3.16  White  Citrus\n",
      "3       4        Shiraz      Chile    14.91  3.39    Red   Berry\n",
      "4       5        Malbec  Argentina    13.83  3.28    Red  Fruity\n",
      "   Bottle         Grape     Origin  Alcohol    pH Colour   Aroma\n",
      "0       1    Chardonnay  Australia    14.23  3.51  White  Floral\n",
      "1       2  Pinot Grigio      Italy    13.20  3.30  White  Fruity\n",
      "2       3   Pinot Blanc     France    13.16  3.16  White  Citrus\n",
      "3       4        Shiraz      Chile    14.91  3.39    Red   Berry\n",
      "4       5        Malbec  Argentina    13.83  3.28    Red  Fruity\n",
      "   Bottle         Grape     Origin  Alcohol    pH Colour   Aroma\n",
      "0       1    Chardonnay  Australia    14.23  3.51  White  Floral\n",
      "1       2  Pinot Grigio      Italy    13.20  3.30  White  Fruity\n",
      "2       3   Pinot Blanc     France    13.16  3.16  White  Citrus\n",
      "3       4        Shiraz      Chile    14.91  3.39    Red   Berry\n",
      "4       5        Malbec  Argentina    13.83  3.28    Red  Fruity\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"data/wine_1.csv\", sep=\",\")\n",
    "df2 = pd.read_csv(\"data/wine_2.csv\", sep=\",\", skiprows=1)\n",
    "df3 = pd.read_csv(\"data/wine_3.csv\", sep=\",\", skipfooter=2, engine='python')\n",
    "df4 = pd.read_csv(\"data/wine_4.txt\", sep=\"\\t\")\n",
    "df5 = pd.read_csv(\"data/wine_5.csv\", sep=\",\").drop(columns=[\"Add more columns\"])\n",
    "df6 = pd.read_csv(\"data/wine_6.txt\", sep=\"\\t\", skiprows=1, usecols=range(1,8), skipfooter=2, engine='python')\n",
    "print(df1)\n",
    "print(df2)\n",
    "print(df3)\n",
    "print(df4)\n",
    "print(df5)\n",
    "print(df6)\n"
   ]
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      "text/plain": [
       "Ellipsis"
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    "..."
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     "text": [
      "All tests passed.\n"
     ]
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   "source": [
    "for i, df in enumerate([df2, df3, df4, df5, df6]):\n",
    "    assert df1.equals(df), f\"df1 not equal to df{i + 2}\"\n",
    "print(\"All tests passed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
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   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
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    "toc-hr-collapsed": true
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   "source": [
    "## Exercise 2: The Titanic dataset\n",
    "\n",
    "The file *data/titanic.csv* contains data of 1309 passengers who were on the Titanic's unfortunate voyage. For each passenger, the following data are recorded:\n",
    "\n",
    "* survival - Survival (0 = No; 1 = Yes)\n",
    "* class - Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "* name - Name\n",
    "* sex - Sex\n",
    "* age - Age\n",
    "* sibsp - Number of Siblings/Spouses Aboard\n",
    "* parch - Number of Parents/Children Aboard\n",
    "* ticket - Ticket Number\n",
    "* fare - Passenger Fare\n",
    "* cabin - Cabin\n",
    "* embarked - Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "* boat - Lifeboat (if survived)\n",
    "* body - Body number (if did not survive and body was recovered)\n",
    "\n",
    "In this exercise you will perform a number of wrangling operations to manipulate and extract subsets of the data.\n",
    "\n",
    "_Note: many popular datasets have sex as a feature where the possible values are male and female. This representation reflects how the data were collected and is not meant to imply that, for example, gender is binary._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
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   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.1\n",
    "rubric={points}\n",
    "\n",
    "Load the `titanic.csv` dataset into a pandas dataframe named `titanic_df`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": false
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   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.1\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 1"
   ]
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     "text": [
      "      pclass  survived                                             name  \\\n",
      "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
      "1          1         1                   Allison, Master. Hudson Trevor   \n",
      "2          1         0                     Allison, Miss. Helen Loraine   \n",
      "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
      "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
      "...      ...       ...                                              ...   \n",
      "1304       3         0                             Zabour, Miss. Hileni   \n",
      "1305       3         0                            Zabour, Miss. Thamine   \n",
      "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
      "1307       3         0                              Zakarian, Mr. Ortin   \n",
      "1308       3         0                               Zimmerman, Mr. Leo   \n",
      "\n",
      "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
      "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
      "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
      "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
      "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
      "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
      "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
      "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
      "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
      "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
      "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
      "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
      "\n",
      "       body                        home.dest  \n",
      "0       NaN                     St Louis, MO  \n",
      "1       NaN  Montreal, PQ / Chesterville, ON  \n",
      "2       NaN  Montreal, PQ / Chesterville, ON  \n",
      "3     135.0  Montreal, PQ / Chesterville, ON  \n",
      "4       NaN  Montreal, PQ / Chesterville, ON  \n",
      "...     ...                              ...  \n",
      "1304  328.0                              NaN  \n",
      "1305    NaN                              NaN  \n",
      "1306  304.0                              NaN  \n",
      "1307    NaN                              NaN  \n",
      "1308    NaN                              NaN  \n",
      "\n",
      "[1309 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "titanic_df = pd.read_csv(\"data/titanic.csv\", sep=\",\")\n",
    "print(titanic_df)"
   ]
  },
  {
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    "nbgrader": {
     "grade": true,
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success\n"
     ]
    }
   ],
   "source": [
    "assert set(titanic_df.columns) == set(\n",
    "    [\n",
    "        \"pclass\",\n",
    "        \"survived\",\n",
    "        \"name\",\n",
    "        \"sex\",\n",
    "        \"age\",\n",
    "        \"sibsp\",\n",
    "        \"parch\",\n",
    "        \"ticket\",\n",
    "        \"fare\",\n",
    "        \"cabin\",\n",
    "        \"embarked\",\n",
    "        \"boat\",\n",
    "        \"body\",\n",
    "        \"home.dest\",\n",
    "    ]\n",
    "), \"All required columns are not present\"\n",
    "assert len(titanic_df.index) == 1309, \"Wrong number of rows in dataframe\"\n",
    "print(\"Success\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
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   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.2\n",
    "rubric={points}\n",
    "\n",
    "The column names `sibsp` and `parch` are not very descriptive. Use `df.rename()` to rename these columns to `siblings_spouses` and `parents_children` respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
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   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.2\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-99e694773668d8a1",
     "locked": false,
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     "solution": true
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    "tags": []
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   "outputs": [],
   "source": [
    "titanic_df.rename(columns={\n",
    "    \"sibsp\":\"siblings_spouses\",\n",
    "    \"parch\":\"parents_children\"\n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-6c0798bceeeab962",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success\n"
     ]
    }
   ],
   "source": [
    "assert set([\"siblings_spouses\", \"parents_children\"]).issubset(\n",
    "    titanic_df.columns\n",
    "), \"Column names were not changed properly\"\n",
    "print(\"Success\")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
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   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": false,
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   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.3\n",
    "rubric={points}\n",
    "\n",
    "We will practice indexing different subsets of the dataframe in the following questions.\n",
    "\n",
    "Select the column `age` using single bracket notation `[]`. Then, use the type() function in Python to display the data type of the object."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "editable": false
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   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.3\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
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   "execution_count": 10,
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     "grade": true,
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "age_column = titanic_df[\"age\"]\n",
    "print(type(age_column))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-5b123152df1d576a",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.4\n",
    "rubric={points}\n",
    "\n",
    "Now select the `age` using double bracket notation `[[]]`. Then, use the type() function in Python to display the data type of the object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.4\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-0d32262f000b09f4",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "age_column_2 = titanic_df[[\"age\"]]\n",
    "print(type(age_column_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-5b9513513d0b0e22",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.5\n",
    "rubric={points}\n",
    "\n",
    "Select the columns `pclass`, `survived`, and `age` using a single line of code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.5\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-d3fdbdd8c0af6b49",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      pclass  survived      age\n",
      "0          1         1  29.0000\n",
      "1          1         1   0.9167\n",
      "2          1         0   2.0000\n",
      "3          1         0  30.0000\n",
      "4          1         0  25.0000\n",
      "...      ...       ...      ...\n",
      "1304       3         0  14.5000\n",
      "1305       3         0      NaN\n",
      "1306       3         0  26.5000\n",
      "1307       3         0  27.0000\n",
      "1308       3         0  29.0000\n",
      "\n",
      "[1309 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "selected_columns = titanic_df[[\"pclass\", \"survived\", \"age\"]]\n",
    "print(selected_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-a7b37913a746262d",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.6\n",
    "rubric={points}\n",
    "\n",
    "Use the `iloc` method to obtain the first 5 rows of the columns `name`, `sex` and `age` using a single line of code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.6\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-a59f3080f818f34a",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              name     sex      age\n",
      "0                    Allen, Miss. Elisabeth Walton  female  29.0000\n",
      "1                   Allison, Master. Hudson Trevor    male   0.9167\n",
      "2                     Allison, Miss. Helen Loraine  female   2.0000\n",
      "3             Allison, Mr. Hudson Joshua Creighton    male  30.0000\n",
      "4  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female  25.0000\n"
     ]
    }
   ],
   "source": [
    "selected_iloc = titanic_df.iloc[:5, [titanic_df.columns.get_loc(\"name\"), titanic_df.columns.get_loc(\"sex\"), titanic_df.columns.get_loc(\"age\")]]\n",
    "print(selected_iloc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-37f2418e03a50b60",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.7\n",
    "rubric={points}\n",
    "\n",
    "Now use the `loc` method to obtain the first 5 rows of the columns `name`, `sex` and `age` using a single line of code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.7\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-01f92751cc57fded",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              name     sex      age\n",
      "0                    Allen, Miss. Elisabeth Walton  female  29.0000\n",
      "1                   Allison, Master. Hudson Trevor    male   0.9167\n",
      "2                     Allison, Miss. Helen Loraine  female   2.0000\n",
      "3             Allison, Mr. Hudson Joshua Creighton    male  30.0000\n",
      "4  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female  25.0000\n"
     ]
    }
   ],
   "source": [
    "selected_loc = titanic_df.loc[:4, [\"name\", \"sex\", \"age\"]]\n",
    "print(selected_loc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-07036a85b1202239",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.8\n",
    "rubric={points}\n",
    "\n",
    "How many passengers survived (`survived = 1`) the disaster? Hint: try using `df.query()` or `[]` notation to subset the dataframe and then `df.shape` to check its size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.8\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-5677d8dc540bc7ec",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "survived_passengers = titanic_df.query(\"survived == 1\")\n",
    "num_survived = survived_passengers.shape[0]\n",
    "print(num_survived)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-560ca2c33cf74784",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.9\n",
    "rubric={points}\n",
    "\n",
    "How many passengers that survived the disaster (`survived = 1`) were over 60 years of age?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.9\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-123d66686c410308",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "survived_passengers_over_60 = titanic_df.query(\"survived == 1 and age > 60\")\n",
    "num_survived_over_60 = survived_passengers_over_60.shape[0]\n",
    "print(num_survived_over_60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-64aa731d50112f88",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.10\n",
    "rubric={points}\n",
    "\n",
    "What was the lowest and highest fare paid to board the titanic? Store your answers as floats in the variables `lowest` and `highest`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.10\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-54dc8e011e41a6b5",
     "locked": false,
     "schema_version": 3,
     "solution": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "512.3292\n"
     ]
    }
   ],
   "source": [
    "lowest = titanic_df[\"fare\"].min()\n",
    "highest = titanic_df[\"fare\"].max()\n",
    "print(lowest)\n",
    "print(highest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-2f06f5b42b270b44",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.11\n",
    "rubric={points}\n",
    "\n",
    "Sort the dataframe by fare paid (most to least)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.11\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      pclass  survived                                               name  \\\n",
      "183        1         1                             Lesurer, Mr. Gustave J   \n",
      "302        1         1                                   Ward, Miss. Anna   \n",
      "49         1         1                 Cardeza, Mr. Thomas Drake Martinez   \n",
      "50         1         1  Cardeza, Mrs. James Warburton Martinez (Charlo...   \n",
      "113        1         1                         Fortune, Miss. Mabel Helen   \n",
      "...      ...       ...                                                ...   \n",
      "363        2         0                              Campbell, Mr. William   \n",
      "384        2         0                     Cunningham, Mr. Alfred Fleming   \n",
      "581        2         0                         Watson, Mr. Ennis Hastings   \n",
      "410        2         0                   Frost, Mr. Anthony Wood \"Archie\"   \n",
      "1225       3         0                                 Storey, Mr. Thomas   \n",
      "\n",
      "         sex   age  siblings_spouses  parents_children    ticket      fare  \\\n",
      "183     male  35.0                 0                 0  PC 17755  512.3292   \n",
      "302   female  35.0                 0                 0  PC 17755  512.3292   \n",
      "49      male  36.0                 0                 1  PC 17755  512.3292   \n",
      "50    female  58.0                 0                 1  PC 17755  512.3292   \n",
      "113   female  23.0                 3                 2     19950  263.0000   \n",
      "...      ...   ...               ...               ...       ...       ...   \n",
      "363     male   NaN                 0                 0    239853    0.0000   \n",
      "384     male   NaN                 0                 0    239853    0.0000   \n",
      "581     male   NaN                 0                 0    239856    0.0000   \n",
      "410     male   NaN                 0                 0    239854    0.0000   \n",
      "1225    male  60.5                 0                 0      3701       NaN   \n",
      "\n",
      "            cabin embarked boat   body  \\\n",
      "183          B101        C    3    NaN   \n",
      "302           NaN        C    3    NaN   \n",
      "49    B51 B53 B55        C    3    NaN   \n",
      "50    B51 B53 B55        C    3    NaN   \n",
      "113   C23 C25 C27        S   10    NaN   \n",
      "...           ...      ...  ...    ...   \n",
      "363           NaN        S  NaN    NaN   \n",
      "384           NaN        S  NaN    NaN   \n",
      "581           NaN        S  NaN    NaN   \n",
      "410           NaN        S  NaN    NaN   \n",
      "1225          NaN        S  NaN  261.0   \n",
      "\n",
      "                                           home.dest  \n",
      "183                                              NaN  \n",
      "302                                              NaN  \n",
      "49    Austria-Hungary / Germantown, Philadelphia, PA  \n",
      "50                      Germantown, Philadelphia, PA  \n",
      "113                                     Winnipeg, MB  \n",
      "...                                              ...  \n",
      "363                                          Belfast  \n",
      "384                                          Belfast  \n",
      "581                                          Belfast  \n",
      "410                                          Belfast  \n",
      "1225                                             NaN  \n",
      "\n",
      "[1309 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "sorted_by_fare = titanic_df.sort_values(by=\"fare\", ascending=False)\n",
    "print(sorted_by_fare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.12\n",
    "rubric={points}\n",
    "\n",
    "Save the sorted dataframe to a .csv file called 'titanic_fares.csv' using `to_csv()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.12\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-4be555e930c36650",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sorted_by_fare.to_csv(\"data/titanic_fares.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.13\n",
    "rubric={points:3}\n",
    "\n",
    "Create a scatter plot of fare (y-axis) vs. age (x-axis). Make sure to follow the [guidelines on figures](https://github.com/UBC-CS/cpsc330-2024W1/blob/master/docs/homework_instructions.md#figures). You are welcome to use pandas built-in plotting or `matplotlib`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.13\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5+eoACdvAgSlTprBo0SLGjBmjbpeXl8fZZ59NSUmJ02O5es3Y6seZc3h4OIMGDWLfvn0uWhzn4osv5vrrr2fNmjVUVFQQHx8PwM6dO/npp5+Ij49nxowZ6vaDBg3ikksu4c033yQzM5PTTjuNadOmMXnyZCZMmIDZ7N6vzldffRVAHTBhIy4ujlmzZvHhhx/yyiuv8OCDD6qfFRQUIIQgNjaWpKQkh8cdPXp0p7T6+nr2798PwP/7f/+vy3I1NTVx5MgRlybslmgHGdhJfI7g4GAmT57M5MmTWbx4MRs2bOA3v/kNDQ0NPPzwwzz//PPA8fnvAIcTBjvjyiuvZPfu3YwfP56lS5cyZswYYmJiCAgIoK2tTf2vu6mtrQWgpaVFDa6c0dTU5DC9r3Ndpaamqjf83lJfXw+gTmzriISEBAoKCjh69Gif8uqYZ3x8vNNRlrYvJnfl2RO++uor/vrXv2Iymbjvvvs499xzSU1NJSQkBIPBwJ133sk999zjdMSns3q1tZmKigoqKiq6LINtWpee8NJLL3U7WbTVamXOnDmUlJRw5plncvvttzNixAiioqIwmUwUFBSQlZXl8mjW9nXpjISEhB4HdlFRUZxxxhm89957vPnmm1x33XXAr1P4XHLJJZ2CtZdffpkTTjiBF154gc8++4zPPvtMLdttt93GLbfcgtHY94klvv/+e3bv3k1AQACXXHJJp88vu+wyPvzwQ1auXMn999+v5tnQ0AAcD3ad4egzW7sBur3XQO/ajsS7yOlOJD7Pqaeeqt6ov//+ezXddlNztsxXR0pKSvjyyy8JCQnho48+Yvbs2SQkJBAQEADQacoMd2Kb6HXQoEGI469IdPlPq9g8ysvLnW5z+PBhoOsvpN7kWVFR4fTcuDvPnvDaa68BcOutt/LnP/+ZE044gdDQUDUI7W27snlfdtll3baX/lpX9fvvv6egoIDU1FTefvttpkyZQmxsLCaTCei5W/u6dEZXbasrLr30UsB+PkbbKg+2z9oTFBTEkiVLOHToELt27eJf//oX55xzDkeOHOHWW2/lkUce6VU5OvLyyy8Dx1ediI2N7TQdkq0Xr7i42G7KI1vAbwuGHeHoh0z7SaVbWlq6bTv+sB603pCBncQvsD2Ca2lpUdNsj982bdrk0jFsc9ENGzaMmJiYTp9v27bN6b7dLWbe3edZWVkEBARQWlraL496PUV2djZw/BGXI44ePap+2du2he7PT1dkZmZiNBppbm5W5zzsiG2uuPZ5egpbL+jEiRMdft5Vu+oK2+Pu7du392p/d2BzO+mkkxyu3NFTN1v9OFuVor6+vtfzx5177rmEhYXxzTffcODAATUoHTx4MJMmTepy32HDhvH//t//47///S/PPPMMgPpkoC+0traq6xTHxsaSkJDg8J8tGGs/p52t3VdWVlJaWurw+LbH9e2JjIxUH93KdX/9ExnYSTRPZWVlt71UGzduBFAnTwU488wzCQgIYNOmTS49crCtPlBeXu4wv/bvtzjb19lji+4+DwkJYfbs2SiKwhNPPNFtWbXK7NmzAXjzzTcpKyvr9Pm//vUvmpubSU1NtVsrtLvz0xVhYWFq0PTkk092+ryxsZEXXnjBrnyexOZm6zVsz2effdbrwG7y5MnExcWxbdu2fuuR646u3FpbW3nsscd6dLxZs2YBxx9fO/px8MILL9j9eOsJwcHBnH/++QghWLVqldpzN2/evB79sLC9s9vVO4Wu8vHHH1NZWUloaCj79++nrKzM4b///Oc/ALz99tscO3YMON77PH78eIBOE63D8Ueu7777rsN8bRMx97R+JL6BDOwkmufVV19lzJgxPP/88xw5csTus5qaGv72t7+pLx9fddVV6mcDBw7khhtuAI7fyGzvyNgoKSnh73//u/r3iBEjiI6O5tChQ9xzzz1qcNfU1MTNN99MXl6e0zLaegzXr1/v8PP4+HjCw8MpLy9XB3V05O677yYwMJBly5Zx//33dwpySktLefzxx3n22WedlsPbnH766YwbN47m5mYuvfRSu8dmn332GUuXLgXgz3/+s92XqW1ljp07d3b7vpgjbr/9dgCeeeYZVq5cqaYfPXqUyy+/nIqKCoYMGcK8efN65dUXbAMb7r//frt3wzZv3szvfvc7goKCenXcoKAgtf1ecsklvPPOO51+kGzfvp3bb7/dpR82vcE2kOCbb75RHynC8aDisssucxjwdUVmZibnnXceQgiuuOIKu965devWsWTJEvXViN5ge6z52muvqWtLdxywALBmzRpuvfXWTsFlfX09//jHPwA48cQT7T577LHHetzGbD1wF1xwQZfr7k6fPp2BAwdSX19vt8TYLbfcAhy/d9hWrQCorq5m3rx5Th/T3n777cTExLBixQpuueWWTq+rVFVV8e9//5tly5a57CLREP097FYi6SuPPfaY3dQmaWlp4uSTTxZZWVl2U58sXry4075NTU3ivPPOU7dJSkpSV2VwtPLEU089pW6bmJgoxo4dKyIiIoTBYFCnTHB02bz88svqZyNHjhRTp04VU6dOFXl5eeo2thnig4KCxNixY9Vt2vP222+rqyUEBQWJMWPGiJNPPtluRvjbb7/dbp/uplrpju4mKHZEdytPJCcnq1OonHjiiSIzM1Mt/4IFC+zmsLNx+umnCzi+4sb48ePF1KlTO01P0hXtV55ISUkRY8eOVSdEjo6OFt9//32nfTwxQXFtba26koLFYhGjRo0SQ4cOFYA44YQT1DnFOh7H1bK1946JiRHjxo0TJ554ooiJiVHTO07O3RXdrTzRkcWLF9tNO3TSSSeJ4OBgERAQIP75z386bSfOrqXi4mJ1Oo6AgACRm5srsrOz1alferryRHtaW1tFfHy8mvfw4cMdbvfOO++o28THx4uxY8eKnJwc9dqMjIwUW7ZssdvHVl+uTtlTXV2tTv3z6aefdrv9n/70J4dTtrRfeSI9PV2MHTtWBAcHi+joaHHHHXcI6Dz3pRBCbNiwQZ3fMSAgQIwaNUqMHz9epKenq/fGnlx/Eu0gAzuJ5mlpaRFr164Vt956q5g4caIYPHiwsFgsIiQkRGRlZYnLL79cfP311073VxRFvPbaa2L69OkiJiZGWCwWMXjwYHHWWWc5nLj11VdfFWPGjBEWi0VERUWJ008/Xf1idPZlJIQQjz/+uBg9erQIDg52OInq0aNHxc033yyGDBmizk3l6Fj79+8XN998sxg2bJgIDg4WYWFhYujQoeKCCy4QK1as6LSsmtYCOyGOz7+1ePFikZWVJQIDA0VERISYMmWKeOWVVxwGdUIIUVZWJq688koxaNAgYTabe1wmIY5PrDpz5kwRHR2tLgt37bXX2k362h5PrTxRUlIiLr/8chEXFycsFotIS0sTt9xyi6itrXVahp6U7ZtvvhHz588XKSkpwmKxiJiYGHXpsQ8//FC0tLS47NXTwE5RFPHYY4+JYcOGCYvFIuLi4sQ555wjNm3a1GU76epaKi8vF9dee60YOHCgCAwMFFlZWeLvf/+7aGlpUc97bwI7IYS47rrr1LzbT7rdnsrKSvHEE0+Ic845R6SlpYmQkBARGRkpRo8eLW677TaHk2/3NLCzLSGWmJjYadUOR2zZskXA8ZVI2udvtVrFM888I0aNGiUCAwNFfHy8mDNnjsjPz1d/qDqauFuI4+f5jjvuEDk5OSIsLEwEBweLzMxMccYZZ4hnnnnGbuUYie9gEELDQ+wkEolEIpH0ihtvvJGnnnqKRx99lIULF3q7OBIPId+xk0gkEonEz6ivr+ett94C6HbUr8S/kIGdRCKRSCQ+ymOPPcbWrVvt0oqLi7nooosoKyvjxBNPZNy4cd4pnMQryEexEolEIpH4KNOmTWP9+vVERkaSnp5Oc3Mzv/zyC4qiEBcXx5dffsnIkSO9XUyJB5FLikkkEolE4qPcfPPNREdH8+OPP/LLL78ghCAjI4MzzjiD2267jUGDBnm7iBIPI3vsJBKJRCKRSPwE+Y6dRCKRSCQSiZ8gH8U6QVEUSkpKCA8P79M6lhKJRCKRSCR9QQjB0aNHSUpKwmjsuk9OBnZOKCkpISUlxdvFkEgkEolEIgHg4MGDJCcnd7mNDOycEB4eDhw/iREREW4/vqIolJeXM2DAgG6jb39Cr96gX3e9eoN+3fXqDdJdj+6e8K6rqyMlJUWNTbpCBnZOsD1+jYiI6JfAzmq1UlZWRnh4OCaTye3H1yp69Qb9uuvVG/TrrldvkO56dPektyuvhuknpJZIJBKJRCLxc2RgJ5FIJBKJROInyMDOSxgMBuLi4nQ34lav3qBfd716g37d9eoN0l2P7lrzlhMUO6Guro7IyEhqa2v75R07iUQikUgkElfoSUwie+y8hKIoHDx4EEVRvF0Uj6JXb9Cvu169Qb/uevUG6a5Hd615y1GxbkAIgdVqpa2tzeV9rFYrR48epbGxUXejh/ToDf7hHhAQ0OOyCyFoaGhAjw8H9OquV2+Q7np015q3DOz6gBCCmpoaKioqsFqtPd63ra2NoqIizTyX9wR69Qb/cY+KiiIxMdGnHSQSicRfkYFdHygrK6Ompkad685sNrv8ZSeEoLm5mcDAQF19QerVG3zfXQjBsWPHKC8vB2DgwIFeLpFEIpFIOiIDu15itVqpra0lPj6euLi4Hu8vhFAfa/nil3xv0as3+Id7cHAwgDrLuiuPZY1GI4mJibqaid6GXt316g3SXY/uWvOWgV0vaW1tRQhBaGhor/Y3GAyYzfo7/Xr1Bv9xDwkJAY5fA64EdgaDgaioqH4ulWPa2tpYs2YNZWVlJCYmMn36dMxmM4qiUFBQQG1tLZGRkWRmZvbLTdmb7t7EH7x720b8wb236NVda96+/y3jZXrb8yKEoKWlBYvF4rO9N71Br97gP+49LbuiKOzfv58hQ4Z49Bft66+/zj8ef4qS6gasmDBhJSk6lEvOO5vyqhp2F5XQ0qpgCTAyNDWJy+fPIzc3161l8Ja7t/F177y8PF5euapXbcTX3fuCXt215u39EugYrQyN9jR69QZ9utsCWk+OGHv99de5bcn9HA4eQspZNzLiyntIOetGDrVFcM9jz7K+oJrUafPJnXcLqdPms7PWzLKHniAvL8+t5fCGuxbwZe+8vDyWPfQEO2vNvWojvuzeV/TqrjVvGdhJHLJ8+XIMBoP6z2w2k5yczFVXXUVxcbG3i+dXXHnllXbnOjQ0lCFDhnDuuefy0ksv0dzc3Otjf/TRRyxZssR9hfUB2tra+MfjT6EkjWToWX8gYlAmJksw4QPTCYqIJiBtHPVhKUQOSMIcYCEqMZnc2XOpD03ilZWrdRl8S46jKAovr1xFfWgSubPnEpWYLNuIxOeQgZ2kS1566SW+/fZbPv/8c66++mpef/11Jk+eTENDg7eL5lcEBwfz7bff8u233/LBBx/w97//ndDQUK6++mpOOukkDh061KvjfvTRRyxdutTNpdU2a9asoaS6gaTc0zEYfr3FNR4pprm+juhR0zjW0kZxya8/UAwGA+ljTuWXomIKCgq8UWyJBigoKGB3UQnpY07t9MqBbCMSX0G+Y+dFLBaLt4vQLSNHjmTs2LEAnHbaaVitVu6++27effddLrvssl4d0xe83U1jYyNBQUFO3Y1GIxMmTLBLu/zyy7nqqqs4++yzufjii9m0aZMniup2jEYjycnJHnv3pKysDCsmQuMH2aW3NTagCAiKS6G5rpJjx47ZfR4WE09Lq0Jtba3byuJpd63gq961tbW0tCqExQ5w+LkrbcRX3d2BXt215q2NUugQg8HgdNoLRVHYs2cPmzdvZs+ePZrq9rcFH0VFRQAsXbqU8ePHExMTQ0REBCeeeCIvvvhip3cN1q5dy7Rp04iLiyMsLIzU1FQuuugiuy/Xf/7zn+Tk5BAWFkZ4eDjDhg3jr3/9q91xysrKuOaaa0hOTsZisZCWlsbSpUvtVv3Yv38/BoOBhx56iEceeYS0tDTCwsI45ZRTHAZHzz//PNnZ2QQGBnLCCSewcuVKrrzySoYMGWK3XUtLC8uWLWPYsGEEBgYSHx/PVVddRUVFhd12Q4YM4eyzz+btt98mNzeXoKAgli5d2mWdO2PWrFlcffXVfPfdd3z11Vdq+urVq5k1axYDBw4kODiY4cOH8+c//9muJ/XKK6/k6aefBrB71Lt//34Ann76aaZMmcKAAQMIDQ1l1KhRPPjgg7S2trpcPlcwGAyEhYV5bMBIYmIiJqw0VNi/MmAODsVogKbKgxgQ6uheG/VVFVgCjERGRrqtLJ521wq+6h0ZGYklwEj9kXKHn7vSRnzV3R3o1V1r3rLHzks4m6y2L6OxPIHtEUR8fDxwPIi65pprGDx4MACbNm3ixhtvpLi4mL/97W/qNmeddRaTJ0/mxRdfJDQ0lPLycj799FNaWloICQlh1apVXHfdddx444089NBDGI1GCgoK2Llzp5p3WVkZJ598Mkajkb/97W9kZGTw7bffsmzZMvbv389LL71kV9ann36aYcOG8dhjjwHwf//3f5x55pns27dPvTE/99xzXHPNNVx00UU8+uij1NbWsnTp0k7vtSmKwnnnncfXX3/NbbfdxsSJEykqKuKuu+5i2rRp/PDDD+ocbwA//vgju3bt4s477yQtLY3Q0NBeT1B87rnn8swzz/DVV18xZcoUAPLz8znzzDNZuHAhoaGh/PLLLzzwwAN8//33rF27VvVtaGjgrbfe4ttvv1WPZ5tYuLCwkPnz55OWlobFYmHbtm3cc889/PLLL/z73/92uXzdYbVaKSwsJCMjwyNLqU2fPp2k6FBK8tYSnpSuPo4Njh1EYFgE1T+vIz4rh0FJv/boCSHYu3UDI1IHkZmZ6bayeNpdK/iqd2ZmJkNTk9i5dQO5s+faXaeuthFfdXcHenXXmrcM7LxIx14t22is+tAk0qfNJyx2APVHytm5dQPLHnqCOxff5PHgzrYGblNTE+vXr2fZsmWEh4dz7rnnAtgFU4qiMG3aNIQQPP744/zf//0fBoOBLVu20NTUxD/+8Q9Gjx5NU1MTQUFBdo9yv/nmG6KionjiiSfUtOnTp9uVZcmSJVRXV7Njxw41kJw+fTrBwcEsXryYW2+9lRNOOEHdPjw8nA8++EC90JKSkjj55JP5+OOPmTdvHoqicNdddzF+/Hjeeustdb9TTz2VzMxMkpKS1LQ33niDTz75hP/85z9ceOGFanpOTg7jxo1j+fLl/PGPf1TTy8vL2blzJ9nZ2WqaEKJXo6ZSU1MBKCkpUdPuvPNOu+NOmjSJ4cOHM3XqVH766SdGjx5NRkYGCQkJAJ0e8wI88sgj6v8risLkyZOJjY3lqquu4uGHHyY6OrrHZXWGJ3udzWYzt958A7ctuZ/dH75AUu7phMQN4lhlMU111bQe3EnYwBhqy0sIi4mnvqqCvVs3EH6shAV/vMntj1O01OPuSXzR22g0cvn8ecdHv366mvQxp/aqjfiiu7vQq7uWvDX3KHbJkiV2j40MBgOJiYnq50IIlixZQlJSEsHBwUybNo0dO3bYHaO5uZkbb7yRuLg4QkNDOffcc3v98rmn0OporAkTJhAQEEB4eDhnn302iYmJfPzxx2rAsHbtWmbMmEFkZCQmk4mAgAD+9re/ceTIEXXpqTFjxmCxWPh//+//sWLFCvbt29cpn5NPPpmamhouvfRS3nvvPSorKztt88EHH3DaaaeRlJREW1ub+u+MM84AYP369Xbbn3XWWXa/nkaPHg38+hh59+7dlJWVMWfOHLv9Bg8ezKRJkzrlHRUVxTnnnGOX95gxY0hMTGTdunV2248ePdouqOsLjoLBvXv3Mn/+/OOPHf933qdOnQrArl27XDpuXl4e5557LrGxseoxLr/8cqxWK3v27HFL2b3FpZdeyoNL/kxC434OfvgkO1fcwcEPnyTFXMcdC69lamY0RetWkrf6UYrWrWREpJU7Fnn+h5NEe+Tm5nLn4ps4IbJNthGJT6LJHrsRI0bwxRdfqH+3/3J+8MEHeeSRR1i+fDnZ2dksW7aMmTNnsnv3bsLDwwFYuHAh77//PqtWrSI2NpZFixZx9tlns2XLFk10kzpCHY01bb7z0VjrVlJQUOC2gMEVXn75ZYYPH47ZbCYhIcFufdDvv/+eWbNmMW3aNJ5//nn1vbd3332Xe+65h8bGRgAyMjL44osvePDBB7nhhhtoaGggPT2dm266iZtvvhmABQsW0NbWxvPPP89FF12EoiiMGzdOrV+Aw4cP8/777xMQEOCwrB2DwdjYWLu/AwMDAdRyHTlyBEANUtuTkJBgF4AePnyYmpoap4MfOubtznVUbYGorQexvr6eyZMnExQUxLJly8jOziYkJISDBw9y4YUXqn5dceDAASZPnszQoUN5/PHHGTJkCEFBQXz//fdcf/31Lh1D61x66aVccsklXl15QuKb5ObmkpOTI9uIxCfRZGBnNpvteulsCCF47LHHuOOOO9THYStWrCAhIYGVK1dyzTXXUFtby4svvsgrr7zCjBkzAHj11VdJSUnhiy++YPbs2R516QpboAHuGY3VHwwfPlwdFduRVatWERAQwAcffEBQUJCa/u6773badvLkyUyePJm2tjY2b97M008/zcKFC0lISGDevHkAXHXVVVx11VU0NDTw1Vdfcdddd3H22WezZ88eUlNTiYuLY/To0dxzzz0Oy9P+0akr2AK/w4cPd/qsrKzM7u+4uDhiY2P55JNPHB7L9qPChrN36NrXuav897//BWDatGnA8V7SkpIS1q1bp/bSAdTU1Lh8zHfffZeGhgbefvtt9VEvwNatW3tcvu4wGo2kpaV55UvRbDY7vOaNRqNHfiB5092b+IN3b9uIP7j3Fr26a81bG6XoQH5+PklJSaSlpTFv3jz27t0LwL59+ygrK2PWrFnqtoGBgUydOpWNGzcCsGXLFlpbW+22SUpKYuTIkeo2WqH9l787RmN5GtvExe17QRsbG3nllVec7mMymZgwYQJPPfUUcHyQQUdCQ0M544wzuOOOO2hpaVEftZ999tls376djIwMxo4d2+lfTwO7oUOHkpiYyBtvvGGXfuDAgU5t5eyzz+bIkSNYrVaHeQ8dOtSlPHs6aurzzz/nhRdeYOLEiZx66ql2x+gYJP7rX//qtH/HXsqO5Wh/DCEEzz//fI/K5yr+sEZub9Gru169QbrrES15a6ck/2P8+PG8/PLLZGdnc/jwYZYtW8bEiRPZsWOH2ovS8dFZQkKC+riqrKwMi8XS6cXvhISETr0w7WlubrYbCVlXVwccHzxgtVqB41+GRqMRRVGwWq3qy/BCCAwGg8N3oZylAzQ1NalfrBkZGQwdPPB/o7HmOBmNlURGRoZ6vJ7m2ZN029+OXvi3bX/mmWfyyCOPMH/+fK6++mqqqqp46KGHVCfbvs8++yxffvklZ555JikpKRw9epTXXnsNOD74QQjB1VdfTXBwMKeeeiqJiYmUlZVx//33ExkZqfYYLl26lM8//5yJEydy4403MnToUJqbm9m3bx8ff/wx//znP0lOTu6y7O3TDQYDS5Ys4dprr+Xiiy/mqquuoqamhr///e8MHDhQ/fUlhGDu3Lm89tprnHnmmdx0002cfPLJBAQEcOjQIdatW8e5557LBRdc4PAc2s5X+1GxHc+7oijqyNWWlhaKior45JNPeOONNxg+fDirV69Wj3nKKacQHR3Ntddey9/+9jcsFguvvfYa27Zt6+Q3cuRIAO6//37OOOMMTCYTOTk5zJgxA4vFwqWXXsqtt95KU1MTzz77LNXV1V2eu45tw3Zt2HyMRiNCCLt3QRVFUUeMtf9F2/56ap+Xs3Sj0YjBYHCa3r4stnRb/q6km0ymTmV35uRq2a1WKwUFBWRlZREQEOAXTt2l29L27NljN0rQ151crSfbCMmsrKxOP+R81amrsrdPt7X3zMxMLBaLXzi1x1k9CSE6tff+cHIVzQV2thfhAUaNGsUpp5xCRkYGK1asUEf2dbxYbF9iXdHdNvfdd5/DGfoLCwsJCwsDjveqDRw4kMOHD1NdXU1bWxvNzc2YzWYCAgJobW21azgBAQGYzWZaWlrsKsVisWA0GrFarXbB5G8vncu9jzzFlo9WkTZmImHR8dRXV1D08ybCGoq55KpraGlpUc9BUFAQiqKoaXC8EQQGBmK1Wu3mIzOZTFgsFvWl/47pHctuK69tRKwjp4kTJ/Lss8/yyCOPcO655zJo0CCuvPJK4uPj+eMf/0hzczNCCMaMGcMnn3zCXXfdxeHDh9U50959912mTJlCU1MTEyZM4NVXX+XNN9+kurqa2NhYJk6cyHPPPaf2Ug4YMICvv/6a+++/n3/84x8UFxcTHh7OkCFDmDFjBsHBwTQ1NaneVqvVruy2X1SKoqjpl19+OUIIHn74YS688EJSU1NZtGgRH3zwgbp0ms1j9erVPP3006xatYr7778fs9nMoEGDOPXUU9VHNraAyJZ3x3qynfv29WS1WmlsbGTixInA8VUo4uPjGTlyJE8//TTz5s1Tp1JpbW0lNDSUt99+mz//+c8sWLCA0NBQzjnnHFasWMHEiRPVujSbzVx88cV89dVX/POf/+Tuu+9GCMG+fftIS0tj5cqVLF26lIsuuojY2FguvfRSrr/+es4//3xaWlrU0cu2gNSGrb0dO3ZM/UFla9fp6enU1tba/Yiylb2qqkoNHMH+emr/ikFcXBxxcXEUFxfbzcuXmJhIVFQU+/fvt2vzycnJhIWFUVhYaHedpaWlYTabyc/Ppz1ZWVm0tbXZvUNpe+zW0NBgN9DKmVNoaCgpKSlUVVXZvV/Z0UlRFKqqqqiqqiIhIcEvnFypp/DwcGpqaigoKFC/JH3dydV6UhRF/Wd72uTrTuBaPdna+759+xg6dKhfOLlST6mpqTQ3N9u1d3c7tb/XdodBaGXV2i6YOXMmmZmZ3HrrrWRkZPDjjz/ajUw677zziIqKYsWKFaxdu5bp06dTVVVl12uXk5PD+eef73R5JUc9drZKiYiIAOwj6sbGRoqKikhLSyMoKKhPPXYd57F7ZeUqdheV0tymEGg2MmxIEr+9dG6n0Vj92WPnzvT22IKE9u/kaaGM7dNramoYOnQo5513Hs8//3yfF3Z21GNnG/Htbdfe0NTUxP79+xkyZIjdYBLZYyd77GxlkT12ssdO9ti516mmpobo6Ghqa2vVmMQZmuux60hzczO7du1i8uTJpKWlkZiYyOeff64GOS0tLaxfv54HHngAgJNOOomAgAA+//xzdRqL0tJStm/fzoMPPug0n8DAQIcvtptMpk4jaY1Go7qCgO0fOH9/ylF6+8d07T8/8cQTGTNmjMujsXqSpzfTtVSW9ullZWXcc889nHbaacTGxlJUVMSjjz7K0aNHWbhwYZfH6Qntj9HbNuOJdFdoX3ZHo8ydpduuG0fpjuhpurMR7z1J741Td2U0Go3q3/7i5Eq67fgd8/BlJ1fryXaN+5OTq+ntr3N/cWqPo7JbrVan7b2/nRyhucBu8eLFnHPOOQwePJjy8nKWLVtGXV0dV1xxBQaDgYULF3LvvfeSlZVFVlYW9957LyEhIcyfPx843s35+9//nkWLFhEbG0tMTAyLFy9m1KhR6ihZreCs18rW5euvOPP2BoGBgezfv5/rrruOqqoqQkJCmDBhAs8++ywjRoxwe35acvcURqORrKysHt2Y/AW9uuvVG6S7Ht215q25wO7QoUNceumlVFZWEh8fz4QJE9i0aZM6JcNtt91GY2Mj1113HdXV1YwfP57PPvvMbrqJRx99FLPZzJw5c2hsbGT69OksX75cc3PYufJuoD+iJe/o6Gjef/99j+WnJXdP0tbW5nQOQH9Hr+569Qbprkd3LXn7xDt23qCuro7IyEinz7ObmprUl9B70wsjhFBfTtfTF71evcF/3Hva9q1WK/n5+WRlZWnux1V/o1d3vXqDdNejuye8u4tJ2qONfkOJRCKRSCQSSZ+RgZ1EIpFIJBKJnyADuz7SlyfZvvw4ri/o1Rv8w703bV4rLxV7A72669UbpLse0ZK3fMfOCd09z7Y9U7dNKiiR6IUjR45QXl5Odna2rt6jkUgkEm/Rk3fsNDcq1lcwmUxERkZSUVFBc3MzERERmM1ml3tkbJMc2iYf1At69QbfdxdCcOzYMcrLy4mKinI5qBNC0NDQQGhoqE969wW9uuvVG6S7Ht215i0Duz6QmJhIcHAw5eXl6tqyriKEoK2trUfBoD+gV2/wH/eoqCgSExNd3l5RFA4dOqS7kXKgX3e9eoN016O71rxlYNcHDAYDUVFRREZGYrVa7dZg7Q6r1UpRURGpqamaaAieQq/e4B/uAQEBPlt2iUQi0QMysHMDBoMBs9msLjLvClarFaPRSFBQkK6+KPXqDfp2l0gkEoln0M4wDp1hMBiwWCw+/UiuN+jVG/Trrldv0K+7Xr1BuuvRXWveclSsE3oyAkUikUgkEomkv5ArT/gAQghqamr6NA+eL6JXb9Cvu169Qb/uevUG6a5Hd615y8DOSyiKQllZGYqieLsoHkWv3qBfd716g37d9eoN0l2P7lrzloGdRCKRSCQSiZ8gAzuJRCKRSCQSP0EGdl7CYDBoZpZqT6JXb9Cvu169Qb/uevUG6a5Hd615y1GxTpCjYiUSiUQikWgBOSrWB1AUhcrKSs28bOkp9OoN+nXXqzfo112v3iDd9eiuNW8Z2HkJIQSVlZWaGR7tKfTqDfp116s36Nddr94g3fXorjVvGdhJJBKJRCKR+AkysJNIJBKJRCLxE2Rg5yUMBgORkZGaGUXjKfTqDfp116s36Nddr94g3fXorjVvOSrWCXJUrEQikUgkEi0gR8X6AIqiUFpaqplRNJ5Cr96gX3e9eoN+3fXqDdJdj+5a85aBnZcQQlBbW6uZUTSeQq/eoF93vXqDft316g3SXY/uWvOWgZ1EIpFIJBKJnyADO4lEIpFIJBI/QQZ2XsJgMBAXF6eZUTSeQq/eoF93vXqDft316g3SXY/uWvOWo2KdIEfFSiQSiUQi0QJyVKwPoCgKBw8e1MwoGk+hV2/Qr7tevUG/7nr1BumuR3etecvAzksIIWhoaNDMKBpPoVdv0K+7Xr1Bv+569Qbprkd3rXnLwE4ikUgkEonET5CBnUQikUgkEomfIAM7L2E0GklMTMRo1FcV6NUb9OuuV2/Qr7tevUG669Fda95yVKwT5KhYiUQikUgkWkCOivUBFEVh7969mhlF4yn06g36dderN+jXXa/eIN316K41bxnYeQkhBC0tLZoZReMp9OoN+nXXqzfo112v3iDd9eiuNW8Z2EkkEolEIpH4CTKwk0gkEolEIvETZGDnJYxGI8nJyZoZReMp9OoN+nXXqzfo112v3iDd9eiuNW+ztwugVwwGA2FhYd4uhsfRqzfo112v3qBfd716g3TXo7vWvLURXuoQq9XKnj17sFqt3i6KR9GrN+jXXa/eoF93vXqDdNeju9a8ZWDnRbQyNNrT6NUb9OuuV2/Qr7tevUG66xEtecvATiKRSCQSicRPkIGdRCKRSCQSiZ8glxRzQn8vKWab0NBisWAwGNx+fK2iV2/Qr7tevUG/7nr1BumuR3dPeMslxXwEs1mfg5L16g36dderN+jXXa/eIN31iJa8ZWDnJRRFIT8/X1MvXHoCvXqDft316g36dderN0h3PbprzVsGdhKJRCKRSCR+ggzsJBKJRCKRSPwEGdhJJBKJRCKR+AlyVKwTPDEqVlEUjEaj7kYP6dEb9OuuV2/Qr7tevUG669HdE95yVKyP0NbW5u0ieAW9eoN+3fXqDfp116s3SHc9oiVvGdh5CUVR2Ldvn2ZG0XgKvXqDft316g36dderN0h3PbprzVsGdhKJRCKRSCR+ggzsJBKJRCKRSPwEGdh5EaNRn6dfr96gX3e9eoN+3fXqDdJdj2jJWzslccB9992HwWBg4cKFapoQgiVLlpCUlERwcDDTpk1jx44ddvs1Nzdz4403EhcXR2hoKOeeey6HDh3ycOm7xmQykZ2djclk8nZRPIpevUG/7nr1Bv2669UbpLse3bXmrdnAbvPmzTz33HOMHj3aLv3BBx/kkUce4amnnmLz5s0kJiYyc+ZMjh49qm6zcOFC3nnnHVatWsWGDRuor6/n7LPPxmq1elrDKUII6uvr0dtsM3r1Bv2669Ub9OuuV2+Q7np015q3JgO7+vp6LrvsMp5//nmio6PVdCEEjz32GHfccQcXXnghI0eOZMWKFRw7doyVK1cCUFtby4svvsjDDz/MjBkzyM3N5dVXX+Xnn3/miy++8JZSJxRF4dChQ5oZReMp9OoN+nXXqzfo112v3iDd9eiuNW+ztwvgiOuvv56zzjqLGTNmsGzZMjV93759lJWVMWvWLDUtMDCQqVOnsnHjRq655hq2bNlCa2ur3TZJSUmMHDmSjRs3Mnv2bId5Njc309zcrP5dV1cHgNVqVXv6DAYDRqMRRVHsInNn6bbJCh2lw/HG0L4XsX16e5ylm0wmdWLEjmVxlu5q2XvjZDAYOvWKdiy71WpV9+1YRl916i7d5mRzt1qtfuPkSj3Z/r/jMXzZydWy2+pcURRMJpNfOHWXbiuLzd9fnFytJ6vVqt7fXHXVulNXZe/4fWb7r784tceZE3Ru7/3h5CqaC+xWrVrFjz/+yObNmzt9VlZWBkBCQoJdekJCAkVFReo2FovFrqfPto1tf0fcd999LF26tFN6YWEhYWFhAERGRjJw4EAOHz5MbW2tuk1cXBxxcXEUFxfT0NCgpicmJhIVFcX+/ftpaWlR05OTkwkODqa6upqCggK1EaWlpWE2m8nPz7crQ1ZWFm1tbezbt09NMxqNZGdn09DQYPf+oMViIT09ndraWjvf0NBQUlJSqKqqorKyUk13p1NYWBiFhYV2DbCjk6IoVFVVqRe/Pzi5Wk8HDhygqqqKgoICgoKC/MLJlXoKDg4GoKqqiurqar9wcrWebO29qqqKhIQEv3BypZ7Cw8Opqamxu7/5upOr9WQL5BVFYe/evX7hBK7Vk62979u3j6FDh/qFkyv1lJqaSnNzs117d7eTLcZxBU0tKXbw4EHGjh3LZ599Rk5ODgDTpk1jzJgxPPbYY2zcuJFJkyZRUlLCwIED1f2uvvpqDh48yCeffMLKlSu56qqr7HrfAGbOnElGRgbPPvusw7wd9djZKsW2fIc7fz0IIdi7dy+pqalqQ/C3X66Oyq4oCkVFRaSlpanH93Wn7tJtTm1tbRQVFZGamorJZPILJ1fqSQjBgQMHGDx4sN1yO77s5GrZbe19yJAhmM1mv3DqLr2r+5svO7laT4qicODAAYYMGUJHfNWpq7K3T7e199TUVAICAvzCqT1d9djt27ePwYMHq/u626mmpobo6GiXlhTTVGD37rvvcsEFF9iNLGn/2Gr37t1kZmby448/kpubq25z3nnnERUVxYoVK1i7di3Tp0+nqqrKrtcuJyeH888/32GvnCP6e61YiUQikUgkElfw2bVip0+fzs8//8zWrVvVf2PHjuWyyy5j69atpKenk5iYyOeff67u09LSwvr165k4cSIAJ510EgEBAXbblJaWsn37dnUbLSCEoKamBg3F1R5Br96gX3e9eoN+3fXqDdJdj+5a89bUO3bh4eGMHDnSLi00NJTY2Fg1feHChdx7771kZWWRlZXFvffeS0hICPPnzweOP7/+/e9/z6JFi4iNjSUmJobFixczatQoZsyY4XEnZyiKQllZGeHh4ZqZ+8YT6NUb9OuuV2/Qr7tevUG669Fda96aCuxc4bbbbqOxsZHrrruO6upqxo8fz2effUZ4eLi6zaOPPorZbGbOnDk0NjYyffp0li9frokTLpFIJBKJRNJfaD6wW7dund3fBoOBJUuWsGTJEqf7BAUF8eSTT/Lkk0/2b+EkEolEIpFINISm3rHTEwaDgdDQULtRgnpAr96gX3e9eoN+3fXqDdJdj+5a89bUqFgtIUfFSiQSiUQi0QI+OypWTyiKQmVlZY9mk/YH9OoN+nXXqzfo112v3iDd9eiuNW8Z2HkJIQSVlZWaGR7tKfTqDfp116s36Nddr94g3fXorjVvGdhJJBKJRCKR+AkysJNIJBKJRCLxE2Rg5yUMBgORkZGaGUXjKfTqDfp116s36Nddr94g3fXorjVvOSrWCXJUrEQikUgkEi0gR8X6AIqiUFpaqplRNJ5Cr96gX3e9eoN+3fXqDdJdj+5a85aBnZcQQlBbW6uZUTSeQq/eoF93vXqDft316g3SXY/uWvOWgZ1EIpFIJBKJnyADO4lEIpFIJBI/QQZ2XsJgMBAXF6eZUTSeQq/eoF93vXqDft316g3SXY/uWvOWo2KdIEfFSiQSiUQi0QJyVKwPoCgKBw8e1MwoGk+hV2/Qr7tevUG/7nr1BumuR3etecvAzksIIWhoaNDMKBpPoVdv0K+7Xr1Bv+569Qbprkd3rXnLwE4ikUgkEonET5CBnUQikUgkEomfIAM7L2E0GklMTMRo1FcV6NUb9OuuV2/Qr7tevUG669Fda95yVKwT5KhYiUQikUgkWkCOivUBFEVh7969mhlF4yn06g36dderN+jXXa/eIN316K41bxnYeQkhBC0tLZoZReMp9OoN+nXXqzfo112v3iDd9eiuNW8Z2EkkEolEIpH4CTKwk0gkEolEIvETZGDnJYxGI8nJyZoZReMp9OoN+nXXqzfo112v3iDd9eiuNW+ztwugVwwGA2FhYd4uhsfRqzfo112v3qBfd716g3TXo7vWvLURXuoQq9XKnj17sFqt3i6KR9GrN+jXXa/eoF93vXqDdNeju9a8ZWDnRbQyNNrT6NUb9OuuV2/Qr7tevUG66xEtecvATiKRSCQSicRPkIGdRCKRSCQSiZ8glxRzQn8vKWab0NBisWAwGNx+fK2iV2/Qr7tevUG/7nr1BumuR3dPePckJun1qNj8/Hw2btzIoUOHqKysJCQkhPj4eEaNGsXEiRMJDg7u7aF1g9msz0HJevUG/brr1Rv0665Xb5DuekRL3j0qycGDB3n++edZvnw5xcXFAJ2W0DAYDJjNZmbNmsU111zDWWedpavI3VUURSE/P5+srCxMJpO3i+Mx9OoN+nXXqzfo112v3iDd9eiuNW+XAruKigr+9re/8eKLL9LW1kZWVhYLFizgpJNOIiEhgZiYGBobG6mqqmL37t1s2rSJtWvX8tFHH5GVlcWDDz7Iueee298uEolEIpFIJLrGpcAuIyMDk8nEzTffzG9/+1tycnK63aehoYG33nqLF198kQsuuICHHnqIP/3pT30usEQikUgkEonEMS4Fdrfccgt/+tOfiIyMdPnAoaGhXHHFFVxxxRWsW7eO2traXhdSIpFIJBKJRNI9clSsEzwxKlZRFIxGo67eQdSrN+jXXa/eoF93vXqDdNejuye8exKTyHnsvEhbW5u3i+AV9OoN+nXXqzfo112v3iDd9YiWvGVg5yUURWHfvn2aWobEE+jVG/Trrldv0K+7Xr1BuuvRXWvePQ7s9u/fr0510p7169dz8sknExYWxrBhw/j3v//tlgJKJBKJRCKRSFyjR4FdWVkZGRkZ3HPPPXbp27dvZ9asWWzbto1hw4ZRWlrK1Vdfzccff+zWwkokEolEIpFInNOjwO6bb75BCMFll11ml/7www9jtVr55JNP+OGHH9i1axcDBgzgwQcfdGth/Q2jUZ9PwvXqDfp116s36Nddr94g3fWIlrx7NCr2scceY9GiRVRUVBATE6OmJyUlkZmZyVdffaWm3XHHHbzwwgscPnzYvSX2EP09KlYikUgkEonEFdy+VmxaWhoGg4GamhoAxo4da/d5WVkZ9fX1pKenq2lHjx6lqqqK9PR0hBAYDAb27t3bQxX/RQhBQ0MDoaGhuhsWrkdv0K+7Xr1Bv+569Qbprkd3rXm71He4bt06vvzyS2644QYA3nvvPb788ku+/PJL/vKXv2AwGHjkkUfUtC+//JLbb7+dkJAQvvzyS3V/ya8oisKhQ4c0M4rGU+jVG/Trrldv0K+7Xr1BuuvRXWveLvXYpaamAjBu3DiEEKxbt44bb7wRgM8//xyj0cj5559PXFycuk9jYyODBg1S95VIJBKJRCKR9C8uBXY2Zs+ezeDBg/nTn/7Ep59+Sk1NDd9++y3z5s2zC+oAPv30U0488US3FlYikUgkEolE4pweDeOwWCy8+eabZGdn89FHH/Hdd99x4YUX8s9//tNuu++++46NGzdy0UUXubWw/oTBYMBisWjiebwn0as36Nddr96gX3e9eoN016O71rx7vVZsRUUFERERBAYGdvqstraWmpoakpOTMZlMfS6kN5CjYiUSiUQikWgBj6wVGx8f7zCoA4iMjCQ1NdVngzpPIISgpqaGXsbVPotevUG/7nr1Bv2669UbpLse3bXmrZ0Z9XSGoiiUlZVpZhSNp9CrN+jXXa/eoF93vXqDdNeju9a8XQrsHnroIRobG3udyebNm+XyYhKJRCKRSCT9jEuB3V133UVaWhp33323y5MMt7a28u6773LWWWcxYcIEduzY0aeCSiQSiUQikUi6xqXpTnbv3s1f//pXlixZwpIlS8jNzWXChAmceOKJJCQkEB0dTWNjI1VVVeTn5/P999/z9ddfU1NTw+DBg3n11Ve59NJL+9vFpzAYDJqZpdqT6NUb9OuuV2/Qr7tevUG669Fda949GhWbn5/Ps88+yyuvvEJlZaVDCSEERqORKVOmcM0113DhhRcSEBDg1kJ7AjkqViKRSCQSiRbot1GxWVlZPPzwwxw+fJi8vDyeeuop/vznP3P11Vdz0003cffdd/Pee+9RWVnJ2rVrmTt3bo+Dun/+85+MHj2aiIgIIiIiOOWUU+zezxNCsGTJEpKSkggODmbatGmdHvM2Nzdz4403EhcXR2hoKOeeey6HDh3qUTn6G0VRqKys1MzLlp5Cr96gX3e9eoN+3fXqDdJdj+5a8+7RyhM2DAYDOTk55OTkuLs8JCcnc//995OZmQnAihUrOO+888jLy2PEiBE8+OCDPPLIIyxfvpzs7GyWLVvGzJkz2b17N+Hh4QAsXLiQ999/n1WrVhEbG8uiRYs4++yz2bJli2amYBFCUFlZSXR0tLeL4lH06g36dderN+jXXa/eIN316K41b81Nd3LOOedw5plnkp2dTXZ2Nvfccw9hYWFs2rQJIQSPPfYYd9xxBxdeeCEjR45kxYoVHDt2jJUrVwLHJ0d+8cUXefjhh5kxYwa5ubm8+uqr/Pzzz3zxxRdetpNIJBKJRCLpPzQX2LXHarWyatUqGhoaOOWUU9i3bx9lZWXMmjVL3SYwMJCpU6eyceNGALZs2UJra6vdNklJSYwcOVLdRiKRSCQSicQf6dWj2P7m559/5pRTTqGpqYmwsDDeeecdTjjhBDUwS0hIsNs+ISGBoqIiAMrKyrBYLJ26RBMSEigrK3OaZ3NzM83NzerfdXV1wPHg0mq1AscfQRuNRhRFsZth2lm60WjEYDA4TQ8PD7d7Jm80Ho+zOz6nd5ZuMpkQQtil28riLN3VsvfWyXaunJVdURT1kXnHMvqqU3fp7Z1sde5PTh3L0jFdCEFkZCRCCLt8fdnJ1bLb6ty2jT84dZfe1f3N151cqSdFUdSX21111bpTV2Vvn97+HucvTu1x5mQwGIiIiOh3J1fRZGA3dOhQtm7dSk1NDf/5z3+44oorWL9+vfp5x9G4Qohuhxl3t819993H0qVLO6UXFhYSFhYGHF8qbeDAgRw+fJja2lp1m7i4OOLi4iguLqahoUFNT0xMJCoqiv3799PS0qKmJycnExYWRkNDA4WFhWp6WloaZrOZ/Px8uzJkZWXR1tbGvn371DSj0Uh2djYNDQ12A0MsFgvp6enU1tbaBbKhoaGkpKRQVVVFZWWlmu5up8LCQrsG6MwpMTGRlpYWv3JytZ6OHj3qd07QfT1VVlb6nZOr9RQYGOh3Tt3VU3Nzs939zR+celJPgN85uVpPDQ0NfufUXT2FhITYtXd3O9k6r1yhR9OdeIsZM2aQkZHB7bffTkZGBj/++CO5ubnq5+eddx5RUVGsWLGCtWvXMn36dKqqqux67XJycjj//PMdBm/guMfOVim2X1/u/PUghKC0tJQBAwaovw787Zero7IrikJ5eTmJiYnq8X3dqbt0m1NbWxvl5eUMGDAAk8nkF06u9thVVFQQHx9v9+PKl5160mNXXl5OQkICZrPZL5y6S+/q/ubLTj3psauoqOj0ZMmXnboqe8ceO9s9LiAgwC+c2uOsnuD408L4+Hh1X3c71dTUEB0d7dJ0J5rsseuIEILm5mbS0tJITEzk888/VwO7lpYW1q9fzwMPPADASSedREBAAJ9//jlz5swBoLS0lO3bt/Pggw86zSMwMJDAwMBO6SaTqdNIWlvFdaQn6YqicPToURITEzsd39nIXUfpBoOhR+nuKHtX6a6U3ebd07Jr2am7dNtFa3Nvf/H7spMr6VarldraWjWg7WvZteDUkzLa6tzV7bsrY0/TvXE9uev+5ixd6/eIuro6EhIS/MrJ1fT27d1fnNrjqOxWq9Vpnfe3kyM0F9j99a9/5YwzziAlJYWjR4+yatUq1q1bxyeffILBYGDhwoXce++9ZGVlkZWVxb333ktISAjz588Hjndz/v73v2fRokXExsYSExPD4sWLGTVqFDNmzPCynUQikUgkEkn/obnA7vDhwyxYsIDS0lIiIyMZPXo0n3zyCTNnzgTgtttuo7Gxkeuuu47q6mrGjx/PZ599pr6QD/Doo49iNpuZM2cOjY2NTJ8+neXLl2tmDjuJRCKRSCSS/sCld+zS09N7d3CDwe5lQl+iv5cUUxSFqqoqYmJietTF6uvo1Rv0665Xb9Cvu169Qbrr0d0T3j2JSVwK7IYMGdJpRGlLSwulpaUAmM1mYmNjOXLkCG1tbQAMHDgQi8ViN6rEl5BrxUokEolEItECbl8rdv/+/ezbt0/9l5eXx8CBA5kyZQpff/01TU1NlJaW0tTUxFdffcWUKVNISkpi69at7vDxSxRF4eDBgz2am8Yf0Ks36Nddr96gX3e9eoN016O71rx71Wd4++2309TUxJo1a5g0aZLdcPZTTz2VL774gmPHjnH77be7tbD+hBCChoYGXOgw9Sv06g36dderN+jXXa/eIN316K41714Fdu+99x5nn32208EIZrOZs88+m/fee69PhZNIJBKJRCKRuE6vAru6ujq7GZMdUVtb2+02EolEIpFIJBL30avAbsSIEaxatcrpiNf8/HxWrVrFyJEj+1Q4f8ZoNNpNVKsX9OoN+nXXqzfo112v3iDd9eiuNe9eLSn23nvvccEFFxAWFsbvf/97Tj31VAYMGEB5eTlff/01//73v2loaOCdd97h3HPP7Y9y9ztyVKxEIpFIJBIt4PZRsR0577zzWL58OQaDgccff5w5c+Ywbdo05syZwxNPPIHRaOSll17y2aDOEyiKwt69ezUzisZT6NUb9OuuV2/Qr7tevUG669Fda969Xnni8ssv54ILLuDdd99l27Zt1NbWEhkZSU5ODuedd57s5eoGIQQtLS2aGUXjKfTqDfp116s36Nddr94g3fXorjXvPi0pFh4ezoIFC1iwYIG7yiORSCQSiUQi6SXaeNNPIpFIJBKJRNJnXOqxe/nllwG44IILCA8PV/92hcsvv7x3JfNzjEYjycnJmhlF4yn06g36dderN+jXXa/eIN316K41b5dGxRqNRgwGA7t27SI7O1v9uyuEEBgMBqxWq9sK60nkqFiJRCKRSCRaoCcxiUs9dv/+978xGAwMHDgQgJdeeqnvpdQ5VquVwsJCMjIynK7g4Y/4u7eiKBQUFKiDiTIzM9Vfce5yd5ZHV3m7i97k4e913hV6ddert6Io7Nmzh5KSEpKSktSOEHfn0d/XeW/Ra71rzdulwO7KK6+0+/uKK67oj7LoDq0MjfY07vD25s3NWd55eXm8vHIVu4tKaGlVsAQYGZqaxOXz55Gbm6vu2xcPZ3mcfOIYvv9xa5d59xVX/JyhtbbuyfbjDXd3+vX2WFqr8/7Gdn3kHyxj9PCh/LRrN1kpiVw+fx45OTluqY++XIOeQm/1bkNL3n0aFSuReANv3ty6Cqze+uAz6kOTSJ82n7DYAdQfKWfn1g0se+gJ7lx8E6NHj+6TR15eHsseeqJTHpu+eIu3/ns/qSdNY5STvPt6Xpzl7c48PIUvfDn2BXf6+fu5chftr4+MKXMZmBBETfwYdm79hsV/vYuBA2Kpamzr0zn0p2tQ0r9oo/9WInER281tZ62Z1GnzyZ13C6nT5rOz1syyh54gLy/P83nXmLjrgUcpaTSSO3suUYnJmAMsRCUmkzt7LvWhSbyycrXdL7qeeiiKwssrV1EfmmSXR+SAJKyKQEkaiWnQSKISBnWbd09xlrc78/AU3mw/nsCdfv5+rtyFo+vDaDQRlZhMUtZodh44zPfFLaROu7TX59CfrkFJ/9PrwO7gwYNcc801ZGRkEBwcjMlk6vTPbJYdgs4wGo2kpaVp5t0IT9EXb2/e3LrKO/2U33AsYjDHGuqhw1gkg8FA+phT+aWomL1795KWlgbQY4+CggJ2F5WQPuZUu4FL1WUHqK6uJin3dGobjlFbV+sw74KCgl67O8vb1Ty00ta90X486e5Ov74eSyt17gk6Xh8CKBORKIrCjm8+IyT9RIKzxmEIDu91ffT1GvQUeqr39mjNu1el2Lt3LyeeeCIvvvgiYWFhNDc3M3jwYLKzszGZTAghGD16NJMnT3Z3ef0KvQa+vfX25s2tq7xbW1sJSx5GQ2MT1WUHOu0bFhNPS6tCbW0tZrO5Vx61tbW0tCqExQ6w2775WD2KIgiNH4SiHJ/93FnevcVZ3j3JQwtt3Vvtx1Pu7vRzx7G0UOeewNH1YcX4vx9dVQwYMQkhsLs2e1of7rgGPYVe6r0jWvLuVWC3dOlSamtrWbNmDdu2bQPgqquuYteuXezfv59zzjmHhoYG3nzzTbcW1p9QFIX8/HzddZ33xdubN7eu8rZYLFjComhtbqH5WH2nz+urKrAEGImIiCA/P79XHpGRkVgCjNQfKbfbNjAkDKPRQENFMUajAYvF4jDvyMjI3mh3mbereWilrXuj/XjS3Z1+fT2WVurcE3S8PgzAIEM1rY0NKIrAGBTm8NrsSX309Rr0FHqq9/ZozbtXgd0XX3zBmWeeydSpU9U023R4SUlJvPHGGwDccccdbiiiRHIcb97cuso7MiKSYFppqq/BEhxq95kQgr1bNzAsdRAZGRkARERE9NgjMzOToalJ7N26wW49wujEwURHR1OSt5bI0BAiI37dp33emZmZvXZ3lrc78/AEvvLl2Fvc6efv58qdOLs+AoNDMRrhSNEvRIWF2l2b0LNz6C/XoMQz9Cqwq6ysZNiwYerfZrOZY8eOqX8HBgYyc+ZMPvjgg76XUCL5H968uXWZNxBQX0aksYn9P22ipuwQbS3N1JQdIu/T1YQfK2HB/Lnq+xcZGRk99jAajVw+fx5hDSXkfbpazaO2vAST0YCxdDvW4u3UHC7uMu/e4Cxvd+bhCfz9y9Gdfv5+rtyJo+tDsVoxYKS1rpLGvVvIykiHdo+0e3oO/eUalHiGXrWCuLg4Ghoa7P7ev3+/3TZms5mampq+lE0iscObN7fu8k401rH0r7cxIspK0bqV5K1+lKJ1KxkRaeWORfbTEPTWIzc3lzsX38QJkW12eZySEc+Dd/2ZcYOCus27tzjL25159Df+/uXoTj9/P1fupv31ceCr1ZTu2MSBDW9y6qgsssKtHNiyps/n0B+uQYlncGlJsY5MmzaNkJAQPvroIwDOP/981q1bx48//kh6ejoVFRWMGTOG2NhYfvrpJ7cX2hP095JiQggURXFpeTZ/wh3ejubWGpY6iAXz53plHrv2eXc1mWtH9956+NrKE1pr655sP95wd6dfb4+ltTr3FLZ3rWzXR1ZWFtu2bXNre9PyyhN6rXdPePckJulVYPfAAw+wZMkSSktLiYqKYt26dUyfPp3g4GCGDx9OQUEBdXV1PPvss1x99dW9FvEmngjsWlpasFgsursA3OGtxZUnusORuxZWCOjvY2mxrXuq/XjL3ZttwRbcVFdXEx0dTVZWlmYCD0/Q39e5ltHite4JPOHd74FdXV0du3btYvjw4WoGb775JkuWLGHv3r2kpqZy4403cv311/fOQAP0d2BntVrJz88nKytLE2vLeQq9ekP/umt5tQFZ5/px72pZLb08KtRbnbdHr+6e8O5JTNKrnwxtbW1kZWXZHfySSy5hx44dNDY28ssvv/h0UCeR+BJytQGJFmjfdgZPmcvAkRMYPGWubDsSiYfpUWD39ttvk5GRQXx8PPHx8WRmZvLuu+/2U9EkEkl3aGm1AYl+6WpZLdl2JBLP4nJg9/XXX3PJJZewb98+QkJCCA4OZu/evVxyySVs3LixP8vot/jjOxauoFdvcL+71lYbcIasc//GUdtROP5fLS155Sn0UOfO0Ku7lrxdLskjjzyCEIIXXniBuro6jh49ynPPPYfVauWRRx7pzzL6JSaTSV2CTU/o1Rv6x11Lqw04Q9a5/7t3bDsCAyUiGvG/4E5LS171N3qpc0fo1V1r3i4Hdt999x2/+c1v+N3vfofBYMBgMPCHP/yB2bNnyx67XiCEoL6+vtPkn/6OXr2hf9x9YbUBWef+79657QgCaeX49N36WqlCL3XuCL26a83b5cCuoqKCnJycTuljxoyhsrLSrYXSA4qicOjQId29c6JXb+gfd19YbUDWuf+7d2w7BiDecBQD+lupQi917gi9umvN2+XAzmq1Ehwc3Ck9KCgIq9Xq1kJJJBLXkKsNSLSAs2W1ZNuRSDyP2dsFkEgkfcO21NDLK1exe91Kde65EamDWPDHni015M5jSfRF+7aT/9VqooYP5cCu3YxIGSjbjkTiQXoU2D311FOsWrXKLs32GPaEE07otL3BYGDHjh19KJ7/YjAYdDc7N+jXG/rXPTc3l5ycHLfMbu/OY4Gscz2529pOfn4+paWlXDHvIt2tPKG3Om+PXt215u3yyhN9WY7GF+nvlSckEolEIpFIXKFfVp5QFKVX/ySOEUJQU1OjmVE0nkKv3qBfd716g37d9eoN0l2P7lrz1k//uMZQFIWysjLdBb/+4K0oCnv27GHz5s3s2bPHZRd/cO8N/uDtiTrvbR7uwp35+0Od9xbprj93rXnLwRMSSQ+wLXK+u6hEHVgwNDVJV4uc6w1P1Lm325W385dIJO5D9thJJC7SfpHz1GnzyZ13C6nT5stFzv0YT9S5t9uVt/OXSCTuRQZ2XsJgMBAaGqqZUTSewle9HS1ybg6w9GiRc1917yu+6u2JOndHHt52dISv1rk7kO76c9eatwzsvITRaCQlJUVX0wCA73o7WuTchquLnPuquyt09X6Wr3p7os7dkUdf6K/8fbXO3YG73D3xzqW789BrvWvNW75j5yUURaGqqoqYmBjNNAZP4KveHRc574gri5z7qnt3dPd+lq96e6LO3ZFHX+iv/H21zt2BO9x99b1Ovda71ry9XwKdIoSgsrJSM8OjPYWvende5NweVxY591X3rnDl/Sxf9fZEnbsjj77QX/n7ap27g766+/J7nXqtd615y8BOInGBjouct0dvi5zb8Pb7Yf2NJ+rc2+3K2/lL7PHENeXv162kD4FdW1sbjz76KCeffDIRERGYzb8+1d26dSvXXXcde/bscUshJRJv42iR87aWZl0vcu7q+1mFhYVeKmHf8ESde7tdeTt/iT2eeOfS2+91SvqfXr1j19jYyKxZs9i4cSNxcXFERETQ0NCgfp6WlsZLL71ETEwMy5Ytc1th/QmDwUBkZKRmRtF4Cl/2br/I+e51K9X3UkakDnJpkXNfdneEq+9n1dXVkZKS4pPenqjzvubRV/ojf39r6z2hL+6eeOeyP/PQa71rzbtXgd29997LN998w/3338+tt97K0qVLufvuu9XPIyMjmTp1Kp9++qkM7JxgNBoZOHCgt4vhcXzd27bIeUFBAbW1tURGRpKZmelSj4avu3ek/ftZUYnJnT63vZ8VFRXl096eqPO+5OEO3J2/v7X1ntAXd1evqb68c9mfeei13rXm3avAbvXq1UybNo3bbrsNwGGUmp6eLie27AJFUTh8+DAJCQm6eszhTW9FUdzyxWU0GsnOzu5V/r5S566cK9v7WTu3bmDMzEuoOXyQ5mP1BIaEEZWQwt6tGxiROoj09HRKS0t75d3XOvNEnXeVR0/qvLd5uIvetuuOtLW18cUXX3DkyBFiY2OZMWOG3as6/k5frvP211Tu7Ll23622dx5HuOm9zv7Iw5fuce5Ea969utoOHDjABRdc0OU2ERER/TZE3x8QQlBbW8uAAY67w/0Vb3lrYckkX6lzV8+V7f2sxX+9i9X3rscQGA7mAGhrRTQfJXNgDAv+uBSDwdAr777WmRamjHBHnWuh7brK66+/zj8ef4ryuiZmnz6VT9euZ0DE3dx68w1ceuml3i6eR+hLnduuqWUPPUHep6tJH3MqYTHx1FdVsHfrhuPvPP7xJre819kfefjKPc7daM27V4FdeHg4FRUVXW5TWFhIfHx8rwolkbgT29D++tAk0qfNJyx2APVHytm5dQPLHnqCOxf3/3tMvkJvzpXBZMYSOxhTYgam4EisjbVYy/aCsdGj5XDn/u4q4+jRo/s9D6203ddff53bltyPkjSSlEmnEz88geTAERz8cS23LbkfQDfBXV/wxDuX3n6vU9K/9CqwmzBhAu+//776WKAjhw4d4qOPPuL888/va/kkkj7RcWi/7bGDbWh/3qereWXlanJycjTRhe5NenqubNubkoZz4aw51B2to6WlBYvFQkR4BHmfvcErK1fzwMiR/VoOd+/vzjL21N3THu6ira2Nfzz+FErSSIae9QfMBgNGUxMRSRkMHZjO7g9f4KHHn+KSSy7R1WPZ3uKJdy69/V6npP/oVQ3eeuutVFVVMWPGDDZu3EhbWxsAx44dY82aNcyaNYvW1lZuueUWtxbWnzAYDMTFxWlmFI2n8LS3lob2a73Oe3qu7LY3GomMjCI+fgCRkVEYjEZ1+7179/bIu691pqUpI3rq7mkPd7FmzRpKqhtIyj0dg8GIAhxqNKMABoORpNzTKa5uYM2aNd4uar/jruvc9s7juHHjyM7O7peAy915aP0e119ozbtXP52mTJnC008/zU033cTkyZPV9PDwcABMJhPPPPMMJ510kntK6YcYjUbi4uK8XQyP42lvby/Z1B6t13lPz1VPpjsZOnRov5XD3fu7s4w9de9NHlp4l7msrAwrJkLjBwEgMFDcGKB+HhI3CCsmysrKvFVEj6H167w/0au71rx7HZ5fe+21bNu2jRtuuIFx48aRkZFBbm4u1157LXl5efzhD39wZzn9DkVROHjwoO5m9/a0t7eXbGqP1uu8p+fK1e0jIiJ65N3XOvNEnfeXe2/y8ETb7Y7ExERMWGmoKAbAiGBYeAtGjq9mcayyGBNWEhMTvVlMj6D167w/0au71rx7Fdh99dVXbN26leHDh/P444+zadMm9uzZww8//MDTTz/NiBEj3F1Ov0MIQUNDg2bWlvMUnvbW0pJJWq/znp4rV7dPT0/vkXdf60xLS4H11N3THu5i+vTpJEWHUpK3FiEUDEBkgBUDIIRCSd5aBkWHMn36dG8Xtd/R+nXen+jVXWvevQrsTjvtNJ5//nl3lwWA++67j3HjxhEeHs6AAQM4//zz2b17t902QgiWLFlCUlISwcHBTJs2jR07dtht09zczI033khcXByhoaGce+65HDp0qF/KLNEucskk1+npueqvc9vX4/rLUmC+1HbNZjO33nwDxpLt7P7wBepKClHa2qgrKWT3hy9gLN3O4ptvkAMnJBIPYBC9CDEHDhzIvHnzePTRR91eoN/85jfMmzePcePG0dbWxh133MHPP//Mzp07CQ0NBeCBBx7gnnvuYfny5WRnZ7Ns2TK++uordu/erb7n98c//pH333+f5cuXExsby6JFi6iqqmLLli2YTKZuy1FXV0dkZCS1tbVERES43dNqtZKfn09WVpZL5fEXvOXtaC6wYamDWDB/rseG9vtKnff0XHW3fW+9+1pnnqjz/nL3tIe7cDSPXUJEEIt1NI+dr1zn/YFe3T3h3ZOYpFeB3ZVXXslPP/3Eli1b+n0USEVFBQMGDGD9+vVMmTIFIQRJSUksXLiQ22+/HTjeO5eQkMADDzzANddcQ21tLfHx8bzyyivMnTsXgJKSElJSUvjoo4+YPXt2t/n2d2Bnm9BQS+vLeQJventi9v6u8KU67+m56mr7vnhrZeWJ3ubhrjr3dtvtCbaVJyorK4mLi9PdyhO+dJ27G726e8K73wO7kpISTjnlFGbNmsUDDzxATExMrwvbHQUFBWRlZfHzzz8zcuRI9u7dS0ZGBj/++KPdr9XzzjuPqKgoVqxYwdq1a5k+fTpVVVVER0er2+Tk5HD++eezdOnSTvk0NzfT3Nys/m1buLyqqko9iQaDQZ2/q/1pc5ZuNBoxGAxO061Wq10Z2i9D5Eq6yWRCCGGXbiuLs3RXyy6dpJN0kk7SSTpJJ2041dTUEB0d7VJg16ufUb/97W+Jiori3//+N6+++ippaWkkJCQ4nGupL/MWCSG45ZZbOPXUUxn5v4k+bcPlExIS7LZNSEigqKhI3cZisdgFdbZtnA23v++++xwGfIWFhYSFhQHHR6kNHDiQw4cP200xEBcXR1xcHMXFxTQ0NKjpiYmJREVFsX//flpaWtT05ORkQkJC2Lx5s12En5aWhtlsJj8/364MWVlZtLW1sW/fPjXNNv9QQ0OD3buDFouF9PR0amtr7VxDQ0PVQLWyslJNd6dTWFgYhYWFdo27o5MQgpqaGsaOHYuiKH7h5Go9HTx4kJqaGqKioggMDPQLJ1fqKTg4GKvVSlhYGFVVVX7h5Go92dp7RkYGAwYM8AsnV+opIiKCLVu2EBYWpt7ffN3J1XoSQmAymUhLS6OwsNAvnMC1erK195iYGIYOHeoXTq7U05AhQ9i1a5cahPWHky2+cYVe9di5+gjAUYTcE66//no+/PBDNmzYQHJyMgAbN25k0qRJlJSUMHDgQHXbq6++moMHD/LJJ5+wcuVKrrrqKrseOICZM2eSkZHBs88+2ykvT/fYKYrC7t27yczMVJ/J+9Kvh97+IrJarRQUFJCdnY3JZPILp+7SbU6tra0UFBSQmZmJ2Wz2CydX6klRFAoLC8nIyLC7d/iyk6tlt7X3rKwsAgIC/MKpu3Rb2p49e8jIyLC7v/myk6v1ZLVaKSwsJCsrq1Nnh686dVX29um29p6ZmYnFYvELp/Y4qychRKf27nM9dh1l+4Mbb7yR//73v3z11VdqUAeo8yCVlZXZBXbl5eVqL15iYiItLS1UV1fb9dqVl5czceJEh/kFBgYSGBjYKd1kMnV6GdJZYNubdEfHd/bypaN0g8HQo3R3lt3VMnZMtzXUnpZdy07dpdvKbqtzW17+4ORqus3d1TL6ilN3ZTQajerf/uLkSrrt+H25vzlL1/o9oqv7m6Ptbfto2cnV9PbXub84tcdR2a1Wq9P23t9ODrd1eUsPIYTghhtu4O2332bt2rWkpaXZfZ6WlkZiYiKff/65mtbS0sL69evVoO2kk04iICDAbpvS0lK2b9/uNLCTSCQSiUQi8XU0N1Tp+uuvZ+XKlbz33nuEh4erz+EjIyMJDg7GYDCwcOFC7r33XrKyssjKyuLee+8lJCSE+fPnq9v+/ve/Z9GiRcTGxhITE8PixYsZNWoUM2bM8KaeitFoJDk5uUdRuD+gV2/Qr7tevUG/7nr1BumuR3eteffqHTsbhw4d4ssvv6SkpKTT+2xwvMvy//7v/3pWIIPjocIvvfQSV155JXC8V2/p0qX861//orq6mvHjx/P000+rAywAmpqauPXWW1m5ciWNjY1Mnz6dZ555hpSUFJfK0d/TnUgkEolEIpG4Qr9PdwJw66238vjjj9u9jCiEUAMz2//3ZfCEN/HEBMW2F8r1NpGjHr1Bv+569Qb9uuvVG6S7Ht094d2TmKRX/YbPP/88Dz/8MKeddhpvvfUWQgiuuOIKXn/9da699lrMZjMXX3wxa9eu7ZWAXvDEIBQtoldv0K+7Xr1Bv+569Qbprke05N2rd+yee+45hgwZwscff6w+Ux4yZAhz585l7ty5zJkzh5kzZzJnzhy3FlYikUgkEolE4pxe9dj98ssv/OY3v7F7UbCtrU39/6lTp3LWWWfx0EMP9b2EEolEIpFIJBKX6PUQjqioKPX/Q0NDOXLkiN3nQ4cOZceOHb0umL9jNBpJS0vTzCgaT6FXb9Cvu169Qb/uevUG6a5Hd61596oUgwYNsltSIyMjg++++85um+3btxMaGtq30vk5eloYuz169Qb9uuvVG/TrrldvkO56REvevQrsJk2axKZNm9S/zzvvPPLy8rj22mv58MMP+ctf/sLHH3/MlClT3FZQf0NRFPLz8zX1wqUn0Ks36Nddr96gX3e9eoN016O71rx7FWIuWLCAkpISioqKSE1N5dZbb+WDDz7gueee4/nnn0cIwZAhQ/jHP/7h7vJKJBKJRCKRSJzQq8Bu2rRpTJs2Tf07LCyMTZs28d5771FYWEhqairnnHOOfBQrkUgkEolE4kFcDuxMJhNLliyxW0niu+++47vvvuOmm24iICCAiy++uF8KKZFIJBKJRCLpHpffsRNC0HGRik8++YQ//elPbi+UHjAajWRlZWlmFI2n0Ks36Nddr96gX3e9eoN016O71ry1UQqd0n7uPz2hV2/Qr7tevUG/7nr1BumuR7TkLQM7L6EoCvv27dPMKBpPoVdv0K+7Xr1Bv+569Qbprkd3rXlrZ+IViUTid9imASguLgYgOztbfVyhKAoFBQXU1tYSGRlJZmamZh5l+Dry3LqOPFcSf0MGdhKJpF/Iy8vj5ZWryD9YxujhQ/lp126yUhK5fP48AF5euYrdRSW0tCpYAowMTU3i8vnzyM3N9XLJfRvbeZfntnvkuZL4Iz0K7F599VW7iYkLCgoAOPPMMx1ubzAY+PDDD/tQPP9Gr78K9eoN+nHPy8tj2UNPUB+aRMaUuSQMCGZw/Bh2bv2GW//vboS1DXPKKNKnzScsdgD1R8rZuXUDyx56gjsX3+RXX6qerPP2593b51brbb0/z5XW3fsTvbprydsgOg51dUJvCm0wGLBarT3eTwvU1dURGRlJbW0tERER3i6OROIzKIrCotv/ws5aM7mz52IwGNTPhKLw9rMP0HLkIHP/+iRGk+nXz4Qg79PVjIi08tAD92rqRukLdHne5bm1Q54ria/Rk5jE5R67ffv29blgkl8RQtDQ0EBoaKjdTcXf0as36Me9oKCA3UUlpE+b/z9PQSBtNGOm7mgdpsQMDPXV1Bw+SEzSEHU/g8FA+phT+WXdSgoKCsjOzvaag7vwZJ13Pu+/4ulzq/W23p/nSuvu/Yle3bXm7XJgl5qa2p/l0B2KonDo0CGysrIwteu18Hf06g36ca+traWlVSEsdgAABiDecJRiEU1LSwumoAgwB9B8rL7TvmEx8bS0KtTW1nq41P2DJ+u843nviCfPrdbben+eK6279yd6ddeat+xjlkgkbiUyMhJLgJH6I+WdPrNYLFib6qCtlcCQsE6f11dVYAkwEhkZ6Ymi+hVdnXeQ57Y98lxJ/BkZ2EkkEreSmZnJ0NQk9m7d0Gm1mojwCKxlhYjmeqISUuw+E0Kwd+sGhqUOIjMz05NF7hZFUdizZw+bN29mz549Ls1X1X6ql/z8/H6f46qr867lc+sNXDlXQwcnoShKj+pcItECcroTL2EwGLBYLJp4Hu9J9OoN+nE3Go1cPn8eyx56grxPV5MxZhJx8SHUVByicOs3DA5qQiRGs/XzN0kfcyphMfHUV1Wwd+sGwo+VsOCPN2nqhfXeTInx61QvpWSlpZG/bx9ZKQP7dRqNjufdm+dW6229u3NlPfQzFfGx3PK3e3o8DYrW3fsTvbprzdvlUbF6wxujYp1NlCkn0NQf7qxzb7WfvLw8Vrz2Onk7dtPY2ExwcCAnjhjG5Zc5nsduWOogFsyf6/SL0xsedlNijDlVnRJj79YNhDWUOJwSozf7uLvMPT23esXRuYoJMlNWVYspabhX6k8icURPYhIZ2DmhvwM7IYT6BWUwGJz2Cpx84hi+/3Gr30yg2dFbT7jq7s5JU705AastsNv2y17Cw8M5evQoOcPSueKyS8nNze1RoOYNj95MidF5HwihhWNYEAKPTaPh7R+DvnSdtz9X4eHhPPvCv9lVF9DraVB8yd3d6NXdE949iUlkt4+XUBSFsrIyFEVRf+HvrDWTOm0+ufNuIXXafDYVVnLbkvvZXNxkl76z1nz8EUJenrc1ekx7b73hiruzttCbOnfnsXqKLe9ddQEMP/N3zLx4AcPP/B276gLUvI1GI9nZ2YwbN85uqTGteKhTYow51fmUGEXF6kTtjvYxADGGBgxd7NMfuHpu+wtfus7bnyuj0cieA6U9qvOO+JK7u9Gru9a8ZWDnZRRF4eWVq6gPTSJ39lyiEpMxB1iIHJCEVREoSSMxDRpJVMIgzAEWohKTyZ09l/rQJF5ZuVozDUnSd5y1hd7UuTuP5Q4Po9Hkcx69mRJDS1OOSFyj/cCYn376ieYWK2GxAxCKQlXJfkoLtlNVsh+hKLL+JD6BHDzhZQoLCx1OlFlddoDq6mqSTjqb2oZ6autqiYyMAvxzIleJeydN9eZktY4mKPZFj/ZTYkQlJnf63NGUGL3ZR+I9Oj7ib2tpomhfAYZP3qCuupLq6ioURWA0GoiOjiF1eK6sP4nmkT12XsJgMBAaGkpdXZ3DX/jNx+pRFEFo/CAURdDS0mL3ua/+crR56+n9Cxvdubuzt8ebPUeO8m4ioFd5e9OjN9OHONrH5q6nKUd84Tp39Ih/+Fm/R8Rn8+3Hb3K47hjxk+Yw+OzriZ80h1pjGBv++woxQeYu688X3PsLvbprzVsGdl7CaDSSkpJCVFSUw4kyA0PCMBoNNFQUYzQeH0rdHl/95W/z1uOo3u7c3TlpqjcnYO2Yt8BApQhHYOhx3t70sE2JEdZQQt6nq6kpO0RbSzM1ZYfI+3T18elD5s+1q8+O+1SXFVPWbKG6rNjpPv6I1q9zp4/4E5JJGDWZwCFjOHa0DmNQGAajCVNIBIEpo7DEDcZg7PrLW+vu/Yle3bXmrY1S6BBFUaisrCQ9Pd1hr0B04mCio6MpyVtLZGgIkRG/fnH58i9/m7ce3w3szt2dE8x6c7LaznkLImgEhE95AOTm5nLn4ps4IbKNonUryVv9KEXrVjIi0sodixxPe9F+nwPrX6d8/ascWP96l/v4G1q/zp0NjKmtq6VZESSPnQEtjRz5ZROVhT9xtKSAmEA4Zdb5HDnW2u3gCS279yd6ddeat3zHzksIIaisrCQ6OtrpRJkmowFj6XasxXHUHE7W/ESurtDeW2905+7OCWa9OVmtowmKkwYEcbC8ksKt3/iMh43c3FxycnJ6NH2IbZ89e/ZQXFzMoEGDvDI61Vto/Tp39oi/paUFRRFEJ6fTEBHBiMxUogZlYLFYiIyIpK21hbwfPury0b/W3fsTvbprzVsGdhrA9gv/5ZWr2L1upTpP1ykZgxh3yZ+Pz2PXLn1E6iAW/FEfv/z1hrO20Js6d+ex+uKR/9VqooYP5cCu3YxIGehTHjZsU2L0dJ+srCwAsrKydBPU+QLOBrlYLBaMRgPHKo+/AhM7YCAx8b8Gf776CoxEX8jATiN01Sswd+5cufKEjsjNzWXUqFGsWbOGsrIyEhMTmT59OmZzzy/XrtpVV5PYumOC2469VpfPvbDXvVa96TXrysPbE/hK+g9X6tb2iH/n1g12ExFHRkQSGRpCwQ9rGRQTQ3TiYHUf26P/ET74CoxEX8jAzksYDIZOs1Q76xXoTW9BT/HUF50jb73gqrujVRY++WJtr1dZcNR+ulrJATov99XbVR5seUdGRpKQkGDXpnra5np6HXh7NRebX0VFBQaDQe2188S15u3A1VvXuasrlHT1iN9avB1jyXZM0WOpLS/p8aN/eY/Tn7vWvOWSYk7wxlqx3sKby05J7PHEOqNd5WEt2YWwtmFOGdWv62T2d5tz5vjzhk8o2rKO5BFjGTPjYo/7eSKo9Pb17M21iXuzrq+jdXXHnZjTqZ7kersSbyLXinUD/R3YKYrC4cOHO/VieBpPL1iuFW9v0J17b9Ym7U0ZnOahKLz97AO0HDnI3L8+idFkckv+Hb37u805dRSCjZu+o+CHdQyKCuL0y27C8D8Pd51fsL+mMsZMIiU+goMVdeR98R8O7fiB1JOmMerU33g8aPfEAva2QCn/QCkDExIoPXyYrMED+z2o7Mu14+7H9fIepz93T3jLtWJ9ANuiwd6Mq72xXJMWvL1Fd+69WZu0p3SVR93ROkyJGRgCw6g5fNBt+bf39kSb62oqi9qGYyTlnk51VRXVZQfc4tceR34RZqtHlgj05vJrYD/h7+Cp8xg99QwGT53nkbWJ+3LtOFtXt7fr7cp7nP7cteYtAzsd44lAQuI6nlhloas8WlpaMAVFgDmA5mP1/ZK/J9pcd1NZhMQdX82lo2N/+tXYlgjMPZ3ahmPU1v2ahyeC9v6+nt25PnBvkGv0SiS/IgM7HSNvhtrCE6ssdJWHxWLB2lQHba0EhoT1S/6eaHPOHDtOZdHRsT/9mhsb+n2JQG9ez97+kejNFUokEq0hAzsvYTAYiIuL8+ooGm/cDLXg7S26c/fEKgtd5RERHoG1rBDRXE9UQorb8m/v7Yk258zRNpVFSd5aop1MZdHX89t5OTWoE8FYgkP7fYlAbwY3HYNKm7ft7Pf3j0Rvr1DSHnmP05+71rxlYOcljEYjcXFxXn3B1Bs3Qy14e4vu3HuzNmlvyuA0j8/eYHBQExmJ0Wz9/E235d/e2xNtzqnj4eJfp7IwQG15idvPb2c/A3UEE5WY2u9LBHozuOkcVB73phfrA/cGT1w7PSmLvMfpy11r3nJUrBM8MSrWttSQpkbFdpizyd1rW2rF2xu46u5sCgZ3TrXQVR7QeR67vuTf0dtTbc5bU1l0HBU7JD6M/RX1x0fF7vyB1BP/Nyq2H7w9fT3b6Dgq1WiAWEM9R0QYisBtI467wxPXTnfIe5z+3D3hLac7cQP9HdhZrVby8/PJysrC1G5aCW/gyZuhlrw9TU/cvT2JrTvzd+TtqTbnrZUn1Gk/DpYxevhQftq1m+yUgR6ZH81bwU3HgHb4gCB2lTep6wP3V1DZEW9Pzizvcfpz94S3DOzcgJ4CO/DczdCZt7vy9/ZNvSu0Vueeor/rvDe0tbW5Zcm2rlAURV1ObdCgQeqUGd4O2vsTZwGtnib21et1Dvp111pgJ5cUkwCeWbbMGe6aKd/bM+5Leoa32tzrr7/OPx5/ipLqBqyYMGElKTqUW2++gUsvvdRt+RiNRrKysgDU5cRs6f3t7a1z6871gSUSSe+QPXZO6O8eO9uEhlpaX84TdPR210z53p5x3xVknXvf+/XXX+e2JfejJI0kKfd0QuMH0VBRTEneWowl23lwyZ/dGtxpyd2T6NUbpLse3T3hLR/FugE9rRXrLdy1hJYnluKS+D5tbW2cfOpUDgcPYehZf8Bg+LUtCKGw+8MXSGzcz3cb1rv9saxEIpH0BbmkmA+gKAp79+7tt5nYtUp7b3dNaurtyVFdxV11bnt3a/PmzezZs0fzbciZt6c91qxZQ0l1A0m5p9sFdQAGg5Gk3NMprm5gzZo1bstTXue+693b9ukP7r1Fr+5a85Y/S72EEMdnn9dbh2l7b3fNlO8rK2i4o8598T1CR97e8CgrK8OKidD4QQ4/D4kbhBUTZWVlbstTXue+6d2X9unr7n1Br+5a85Y9dhKv4a6Z8vtrxn2t9Yy1X2Q9ddp8cufdQuq0+R5ZZN2deMsjMTERE1YaKoodfn6sshgTVhITE3t0XK21E0/hr97+cp1J9IvssZN4DdtM+Tu3bnD4btzerRsY4cJM+e46Tnvy8vJY8drr5O3YTWNjM8HBgeSOGMoVl11KTk6Ox6eS6LjIus3Rtsh63qereWXlanJycjT9HqE3PaZPn05SdCgleWsJT0rv9I5dSd5aBkWHMn36dJeP2VU70WoPqjvwxZ5jV/CX60yib2TL9BJGo5Hk5GTd3Rzae7trGSB3LyeUl5fH4r/exbtrNnKguoWKNgsHqlt4d81GrrlhIZdf9XsW3nk3f7n3MRbeeTeLbv+LS7/i+1LnvvIeoSPae3vTw2w2c+vNN2As2c7uD1+grriAtuZG6ooL2P3hCxhLt7P45htcHjjRVTtZ/Ne7yMvL88vr3JUeLV/1dkf79FV3d6BXd615yx47L2EwGAgLC/N2MTxOR+/c3FzuXHzT8V//61aqv/5HpA5iwR9dn6LEXcdRFIWHHn2cnQcOEzF0ArHDJhAYGUdzbSVlW79kx49fUNYWwllX3EhYbAL1R8rZuXUDyx56otspVfpS577yHmFHOk6UW11d7VUP21Qm/3j8KQ5++KQ6j92g6FAW3+X6VCddtZMjv2xi5+5NPPzoE7y8/EWfv87b12F4eDgrXnu9yx6tl19bRXBwMEePHtXcJOHd4Y7rTK/3dtCvu9a8ZWDnJaxWK4WFhWRkZOhuhu6O3rZJTfv6eNMdx9mzZw8bf8gjJGMigyZeoH5xBccMJCB5FEG1NTQ11UBgKOYAS48e0fSlztu/RxiVmNzp8/5eZL03tF+FYER2Jjv2FBAfFkhjfY1XPS699FIuueSSPq084bSdxCYxaOIFtLW2sPGHjfzyyy+YzWafvc47PnJta2miaF8BY8+72mGPVkTsQN5973l+2r2Xsbmj2bGngKyURJ95ROuO60yv93bQr7vWvH3jZ5Sf4i8vG/cUR962mfLHjRvXp5nq+3qc7du3c6zVwIARk+y+uJob62ltbSZ61DRam5uoLN6vftaTR4i9rXPbe4R7t27oNPLK9h7hsB6+R9iftH9cN3jKXJJzJjF4ylxKDHFUVFSw9Yu3vOphNpuZPXs2V1xxBbNnz+7xvHXO2gkcbw8DRkyiodXAzp07ffY6d/TIdcAp59MSk8XP366hbO8uu+3L9u7i52+/oCUmi/gJ56p17kuDDtx1nflqnbsDvbpryVsGdhJJR0wmjEH23erWtlaEAHPkADAYEW3Ndp/39yNEd79H2J90fAE9KjEZo9F0vHfzN/MYcMIpHMn/kR8/WaVpj25x0E5sGANDQQO/3HuLozo0B1iIGzSEuFGn0hqeyM5vPkP878tMKAo7NnxKa/hA4kadSmxS6q91Pnsu9aFJvLJytaa+/BzhS9eZROIM+ShWImnHyJEjCQs0c6ToFwaNnAAc740xmQMwGOBY2X7MRiOx8fZTYnjiEaK73iPsb9QX0KfN/19v1q89HwaDgVGn/obtVUUkKRUUadijK5y1k+MIjhzYTVhgACeccIK3itgnOtfhcSIjIokOD6M5LpWqvZuoLjtATNIQqssOUF19hID0CUSHhxEZEQnUAO16tNetpKCgwGtrUruKr1xnEokzNBfYffXVV/zjH/9gy5YtlJaW8s4773D++eernwshWLp0Kc899xzV1dWMHz+ep59+mhEjRqjbNDc3s3jxYl5//XUaGxuZPn06zzzzDMnJnd+Z8BZGo5G0tDTd/fLTund2djanjDmBr/ZuoSIijsj4gQQEBgMGrM2N1O/NI3ngQKIHpqr7uDqlijvc3fU+Yn/S8QV0AZSJSDW8C4uJJzA4lD/+vz8QHR2tWY+ucNZOWpsbqa0opXHvFqbknsDQoUNpa2vzGS8bTgcRGAxkZ2ZQV1fHoSPlVB7cS0TcQCoP7qX2SDnJJ1jIzsxAGAyd6lyLg3uc0ZfrTOv3uP5Er+5a89ZcYNfQ0EBOTg5XXXUVF110UafPH3zwQR555BGWL19OdnY2y5YtY+bMmezevZvw8HAAFi5cyPvvv8+qVauIjY1l0aJFnH322WzZskUTLzba0Ot6lFr2NhqNLFp4E2X/dzcHdqylNTEDU1AE1qY6DMW/YKn4hdCkU6gtLyEsJp76qgr2bt1w/BHNH2/q9sJ2h7vtPUKt4ugFdGu7tz5svZvR0dGa9uiKrtqJtWwvWeFWbrn5RoxGo6bbuzO6GkQQGxdHdkoCtSYrlT+tpXbP97Q21RNuUshOTiA2Lg4QDutcS4N7uqMv15kv1rm70Ku7lrwNQitrYDjAYDDY9dgJIUhKSmLhwoXcfvvtwPHeuYSEBB544AGuueYaamtriY+P55VXXmHu3LkAlJSUkJKSwkcffcTs2bNdyrsnC+72BqvVSn5+PllZWZoKNvsbX/G2TTy7dVchjc2tBAcGkHtCJiefOIbvf9xqNzHrsNRBLJg/t9tHNL7i3lcURWHR7X9hZ62Z3NlzMRpgkKGaYhGNIiDv09WMiLTy0AP3auYXbm9x1k5so0B9tc471mHHSb/zPl3NCZFtXPP7qzh69Cjh4eE8+8K/2VUX4Pd13h2+WufuQK/unvDuSUyinRDTBfbt20dZWRmzZs1S0wIDA5k6dSobN27kmmuuYcuWLbS2ttptk5SUxMiRI9m4caPTwK65uZnm5l9fiK+rqwOOV5jVagWOB5pGoxFFUexGTDlLNxqNGAwGh+lw/OZpO3bH9PY4SzeZTAgh7NJtZXGW7mrZe+NkMBjsfByV3Wq1qvt2LKOWnEaPHs3Do0dTWFhITU0NERERZGRkYDQamTNnjvqIpn26bV9nTjZ3q9XaJyfb0HrbhZ6VlQXg1nrqLr2rejIYDCy4dC73Pfo0Wz9bTXzSEMKSIjhQvIfK0v1ENpXx22uvV/fzVNvri5Oz9JycHP4xcqTD+rDdOxRFQVEUTCaTTzjZymKrw22fvcGQnEmEx8RRX1XBvp82EtlUxoJrr2fYsGHq9pfPn3e8zj9dTfqYSQwcEEht+UH2/vStWudGo9Ghk6Io5OfnU1dXp15Tth6Qnjq1tbV1e330533ParWq9zdX608r9z1nTl2VveP3me2//uLUHmdOQKf67g8nV/GpwM62OHdCQoJdekJCAkVFReo2FouF6OjoTtt0tbj3fffdx9KlSzulFxYWqhMPRkZGMnDgQA4fPmz3rkhcXBxxcXEUFxfT0NCgpicmJhIVFcX+/ftpaWlR05OTkwkODqa6upqCggK1EaWlpWE2m8nPz7crQ1ZWFm1tbezbt09Nsz0maGho4NChQ2q6xWIhPT2d2tpaO9/Q0FBSUlKoqqqisrJSTXenU1hYGIWFhXYNsKOToihUVVWpF7/WnbKzs9V1MAsLC1WnzMxM1cmW3l09HThwgKqqKgoKCggKCuqV07fffssPP+ZRWXMUqyI4XFFJRHAAF513DnFxcW6rJxu9bXshISHMPe8Mvtn0PUV7vqGwIobiffvJHZ7JxJlnEBISwuHDhz3a9vrq1F09RUREEBERgdFopLS0lNraWrW9V1VVkZCQ4FNOISEhXPe73/LNpu/YuG4lgxITSYyPYeiIREaPnM7gwYMBVCfb9mvXf03eV6sZOvkUYqtryP7f9rbHmh2d6uvrWfnGW4SGR2JVBCajgbiocCZPmsiIESN65PT999+z6fvN6vXRcKwBo9LGnAvPt/tO6M/7ni2QVxSFvXv39ns9ecIJXGt7tva+b98+hg4d6hdOrtRTamoqzc3Ndt/n7nayxTiu4FOPYjdu3MikSZMoKSlh4MCB6nZXX301Bw8e5JNPPmHlypVcddVVdr1vADNnziQjI4Nnn33WYV6OeuxslWLr9nTnrwdFUdi9ezeZmZlq161eeuxsI+NsPRi+7tQ+3VHPQ0BAAEIIWltbKSgoIDMzE7PZ3GOnbdu2cc/DT9IQlkTa6ImExQ6g/kg5hVu/IaKxlL/86QZycnI6OTnq4et42bu77eXl5XHfo09TH5LIgIzRnJAYws6yY5QX/kTYsTL+8qfrGTNmjE/8Gu9L27O196ysLAICAnzSSQjB3r17O/VcO9seID8/n0OHDpGUlERWVhZGo9Ghk61N14cmkTFmktqm9/20kZCjxdyx6Ea1TXfndPxYT9AQNqjT9RF+rJS/3vLr9dHfPXaFhYVkZWV1muPQn+7ljtJt7T0zMxOLxeIXTu1xVk9CCPbs2WM3QbG7nWpqatTBZn71KDYx8fgUE2VlZXaBXXl5udqLl5iYSEtLC9XV1Xa/0MrLy5k4caLTYwcGBhIYGNgp3WQydXpmbqv0jvQk3Wg0MnToULXSOubpCEfpBoOhR+nuKHtX6d2VvaO3PzjZ6G5h9ICAgE517qqTohyfV+xoyEByZ85R949MTFFXvnj19TfUYMnGtm3berRYuzvanhCCV15fTV1wIrmz5mIwQC2QlAkDM4bbldWRa1fnID8/3+koRUdlsfW0tt/Hdu6clb3j9l2ld1f29u29J65dObVPVxSl25Gb7rieHA0iUBSFPXv2OMx76NChakDn7P6mKAqvvL660/JkkYkp5CTMcdqmHZX91+sjqUfXR3/cI2w9O47cHW3vzKm7dK3c99qnd2zv/uDUEUdlF0I4rfP+dnKETwV2aWlpJCYm8vnnn6tfSi0tLaxfv54HHngAgJNOOomAgAA+//xz5syZA0BpaSnbt2/nwQcf9FrZHdHW1obFYvF2MTyOP3rbZumvD00ifdp8tbeg/TqyY8aM6bW7s3nFwPk8Ya6UqT/m5HI0j50JhTaMTsvaHd0Fze7Yx9n2jgbMdJd3e/qrvffmnHgy7+68e9OmPXEsd+CP9zhX0au7lrw1Nzypvr6erVu3snXrVuD4gImtW7dy4MABDAYDCxcu5N577+Wdd95h+/btXHnllYSEhDB//nzg+PPr3//+9yxatIg1a9aQl5fHb3/7W0aNGsWMGTO8aGaPoijqOwl6Qovetp6HzZs3q+/T9XR/R7P0d5x1v/17KD2lp4uTu1qm/qiH9mUVikJNyX5CqwuoKdmPUJQez2lmC1B31JiIHD6FxHGziRw+hR01JqdLVTlaDit12nyny1s5235zcRO3LbmfTYWVLh2nI/3V3nvq5+m8XfHuaZvuCnceq69o8R7nKfTqrjVvzfXY/fDDD5x22mnq37fccgsAV1xxBcuXL+e2226jsbGR6667Tp2g+LPPPlPnsAN49NFHMZvNzJkzR52gePny5boafi1xDXf0erjaW2AbZNEbero4uTd7MGxl3b/1W4p25VFXV8Pp40bx5eafiYiIInV4rstzmtkC1JJGI9aGMvbt2YWiCIxGA9HR0Rw1Gnll5WpycnLs3otpH9Ta/G1Bbd6nq+32cbp9wiBMg0ailFZiFRA5IAmD0ej0OJ6ip37eyPuBkSO7PVZP27SnjiWR+Dqa67GbNm2aOlS8/b/ly5cDx7+UlixZQmlpKU1NTaxfv56RHW4iQUFBPPnkkxw5coRjx47x/vvvk5KS4gUbiZZxV6+Hq70Ftil0ekP7xckVq5Wqkv2UFmynqmQ/itXK3q0bGDo4CUVR2Lx5Mz/99BPNLVav9GBkZmYSHWRiw39fodYYRvwpFxOZPY74Uy6m1hjGhv++QkyQuduF1OF4gPr91u1UlJVSa4wgftIcBp99PfGT5lBrjKCirJTvtv5MQUGB3T67i0pIH3MqCGF3rhDieFBbVKzu42z7oj3bqamvJyn3dKqrqqguO6DmoQbHRcXs2bOnTz2+PaV9eZ0F7bv2H+Lzzz93e5lcyfuXomKXfsS0b9MdB18IcXw1l2HdrObSk2O1vz48UU/9RV+fMEj8H8312OmJ/vyV78pL1d5CC+VwZ6+Hq70FtqkweoPReHxx8sV/vYtV96zDKkwoHP9lZjJYSQw1U56RxjV/up3GxmYQCuXlZcRs/ZbMcVOdlqmnPRiutiuDwUBA3GACU0ZhCgnHKoyYQsIJTBmF5UgJBqPjl8o7Ul1dzaGDhwgcPpVBE89X6yk4NolBE8/n0Ddvc2jXeqqrq9V9bIF2Q10VWz57i+rqqna9fDEMG3+6XVDrbPu2tlaONbcweOJ5KIqg+Vi9XdnCYuI5UlHJ3++5n5pmq9MeX3e39+5+SDTUVrHz55+5+5FnCAoJc+u7dz35ERMVFdXlsWxtetlDT5D36WrSx5zaaTWXy665wWF7c9QOuzqW9dDPVMTHcsvf7vHI+4j9dY/z5nuVrqKF+7s30JK3DOy8hMlk6reXeLV88fend09w56NKW2/Bzq0bHM7Sb1tH1jZqqi/U19dztO4oVksoBpMFYW2BpqPUlFRz8MhRAqMSwGyBtlbqmuHL1c8SFhNPYsavi9G3L5MrvSE2XG1XBQUFVDW2MXHW+ZTVNlJTXMgnB48HVjFhoZww63yObPvEpXNbU1NDszAwIC3HYT1FpuVwcOdX1NTUqOmRkZE01tew8b+vYUrMJn7SDAIj42iureTIL9+x8f3XSAg1qEGts+3rK0so2vIlRV+/TbBJobG+ltKC7QSGhBGdOJj92zZxsPgQQcnDOWHaTKeDU/rrUbejHxJle3ex8f3XsA7IYsjplzBwSKZbB8y4+iMmKirKJe/c3FzuXHzT8Xa1bqXarkakDmLcabN4ddUbPRrM4uhY8UFmyoxmKgKTSB9/ar8PIurJPa4nP8C9NRiqJ2jl/u5ptOYtAzsvIYSgoaGB0NBQp0Pie4PWL/7+8u4pfXnZuqe9BbZ1ZA0GA/X19b1yVxSFhx59nANVDcSPOY2IjBMxh0XTVl/Nge8/o3rH15gCo0nImYExIBCltQVDcQHlP63n0xfu59wblxEel+BSb4gjetKubOc2JXMYaeYAautqMFubaTMFEhkRRVtrC3k/fOTSY+CoqCiCg4JpamklAgG0P2+CppZWgoOC7XqH0tPTaTxaQ2PwEIZOPA+D4bjT8V6+89j94Qs01u2jra2NzZs3ExoayrG6ahpD0uy2j0hMJf6kMzjwyfM0lebz47pPUAQYjQaioqIp/uVHwgaPZsI5C6gpP0RF0R4CQ8IYM/MStn7+Jq+sXM3o0aNpbGx0a3t39kNCKAo7NnxCY2A0mWOnkZI5HAwGu17oFa++TllZGeXl5SQmJjJ9+vRu17hs397Dw8PJHjyQXd38iMnIyHC5refm5jJq1CjWrFlDWVkZiYmJxMTEcP9jz3Rqb5s3fMJb/72f5BFjGTPDcTt8+IH77Mr77Av/piLIM+8j2qbkqaqqIiYmRp3yxRE9+QHuzfcqe4JW7u82PPXkSmveMrDzEoqicOjQIbeuLecLF39/ePeG3r5s3dXN2FnPw4I/3qSuG9rRva2tze4LzdkX7Z49e9j4Qx4hGRMZNPECtW5bAgIxJY8ksPowx8r3c/CHzzEYzRgNEBgWSXhKNsf2/sjOD/6FJTi8294Qd3ypdDy3UZFR/1s3NAqBoUePgaOjo0kemEBNbSmVBwOIiEskIDCY1uZG6irLoLaU5IEJdnNW7t27l9CoeGLiMzlyaG+nfYJDQik/WM11t/4fZksQbS1NVBypJjhuZKftG4t+QjlWTUDyKIJHTCdmcBYNFcUc+uFT6mpqSM2J4suVT3V63Js6PJdfCr9TZ7R3Z3t39gjz0K48ivJ3MeDEWQzNyoR2XzAGgwFraxsvvfoar733MZgsmLCSFB3KrTffwKWXXuowL0ftPSbYTFv5EfI+xemPGMDhde7oi3bbtm2seO118nbsprGxmeCgQOqqKwgaNJypFzoezNJqVThSsp+Du7YQGh1HzvSL2LbmP2o7tPWe7Nmzhz0HSj0yiMh2rvIPljF6+FB+2rWbrJREh9eUKz+UcnJy1HNVVVXFL/uLST/tMk1M5+IMrdzfwbNPrrTkDTKw8yu0NpeTlnH18Wn7R5Wu3Izb9xZ09wvx9ddf5x+PP0VJdQNWTF1+0W7fvp1jrQbSR0yyK2vTsaO0HDmEaGvFMnAY4WNmEjownbajRziav5m26sMYAwK5+MwZnHLKKURGRnL06FHufeSpfvtS6Xxuf93elcfAHXuIThqZzQ8lNZgCkqktKVADqKjQEKzWGsaNGmZ3rNraWgKDQzl5wkQKiw5S024fc8MRlOpiROIIBpxyHqlZJ1CUv5P8WgNKdTHm6iKOttSjKAKDQdB6YCux2SdhiEqmreUYVUW/YDQaSBk1icKaCvJ/3EDs6GmdHvf+/O0XRBqa1RVI3I2jR5jHjlYRYoaTJ0wktt3ycgA7vv6IzWv/i3HIeAaOnUFMcjoNFcWU5K3ltiX3A3Rqc7b2fjRkILHDp2AKDMTa3Ex5yT4MplpiGw+x8/1njwdjwYGcOGIYl7f7EdMRZ0HinoK9lB+zYggMB7OFtiPHqCmvJarpFw7v+4XE9OHH67WultqGYwQGh7Lj6/fZvfV7MAVgUKyEBj7HiIkz+aWoxO7+5qlpUNqfq/hhpxKWFEGkiGdH8b5OPdqu/FB6+NEniBsQz54DpbS0KjQdq+dg0X7iT6py+EPUk9O5+AJaf3LV38jAzo/Q0lxOWseVF7cX/PGmXk2h4UrQ/Prrr3PbkvtRkkaSMvF0QuMHdftFi8mEMSjMLkmxttF0cCeBSUMxxw0mICIOo9mCJXogMePOoXJTG8eKd5OYmMi4ceNQFIVFt/+lX79UOp7bjDGTGDggiJryQ/9b3sn+3LbH2Zd/YPURzKGhpGTmYA6NoK2hjoqCbYQb61gw/0q7Y9l6DC0GhVPGn0xtXS0tLS0EmM1seX8FhtjBxA7KwmwyUVG0B7PJROzIiTQU52OuK+bkKdNpbWvjWNVhNu8MIGr4eFob6xk1PIvAwEAsFguKVaHwq7cQMUOIGzODoNDjwZttUEfR+tVUFG5U15nuD3JzczsF4I+/8CoWg/0oSaWtjc0frcacPIrosedgMbfSUFaEOTiU7DN+x56P/81Djz/FJZdcovYWdzfNTNMxharKAiJiEjCYLRiMZgTOV6d0/EV7mI9Xv0RF4UHiThhP0oRzCYyMo678EE1566k78DObP1zJ2dcvxWA00tLSQtWeH6jc9T2WzAnEjJpGSEIazVXF1Gxfz+a1/yUlPsbu/uaJaVDan6u2+lIKf95C6IRcNm3KIyYhiaMm+yl5uvsBHhE7kLX/+S9Z42dywv/OVen+AvZ/+CYb//sap55vIjAklOZj9er7nnI6l1/xhSdX/Y0M7LyEwWDAYrG49Xm8L8zl1B/evaWrF7cX/PF4r5VtyaT2vVbCamXf9u9oqK4kNDqOISPHu9QbanO3Wq384/GnUJJGkjX7Smr2bqW8pABLRAxZs68k/9Plnb5oR44cSVigmSNFvzBo5ATUd80aahDWViwDhyJaG7HWV3Gs/gimoFAsMUkEJGZhsgSr8zz29Utl8oUBag+KDUftqv25zf9qFYFpaRzYt48RKUnqo+mOdNdDFNdcQtWWg/b1dF3nY3XsMYyMjAKgqmQ/1dVVWCMHU711DZt3BaAoYDRCU3MrpgEZVFWVojTWEZ80hNLacqwKNLW0EhseSvKgZPXxZlXxPowGE8aEdJS21g7nEQLiUmk9kNfv7d22fBUc/0L75Iu1nXqh92//jobmVkKiB1Hz/dsctbYef1fwf4/rowZlUrx5B2vWrGH27NlAu2lmGsT/BpTMVHsky7Z+SWX+LoLiU5g1da46QGNXhx5fm7ezL1qCwhGJwwlqMyAwERSdiMFoJCRuEFE5M6kzmigu+Jaqkv3EJqdjNhqp3LUJy+AxRI49l5DIaEzmAEIS0wlKSKP0i39zqOAbQkNDnbYFV3rme4rtXB0qr6Gh2UpAWCQ1TVbq26B69y5CA018d7RMvTd09QNcKAr7dvyAYcBQMiedSdSA40tlpmQOJ2P8bHZ9+Taf/vtBwgYMVt/3jI6OxmQ0cEpG3zzcgRbu7954cqUF7/bIwM5LGI1G0tPT3XpMT9zE+kp/ePeFjr0e7d/7WXT7X9SeI1uvVb35Awp+/JaG5laE0aQ+Bjpp1kXd9oba3D/99FNKqhsIiQnlx38tps3aBkYzKG2YTWbiho+nuKTB7os2OzubU8acwFd7t1AREUdk/EACAoNpa2nEADRXFNFyaDtNAZbjkYUAgyWIwLgUIiIiiImJAbru1e3uS6Xgh3Xs+OZTEoYMxfC/X7pdtStn59bRr+TueohMlmgGJMTxf7+/iqNHj3Z5LGe9sZUH91J5aB+GI0eIGjKKsEGZGAMsKK0tWIsLqCnMQzTVUXlwLxFxA2luqKeh6jChtaVkjZxCVWmR2kvS1HAUS0AAhsBQaitKaK2rBOV4PbYIAyGBFgIGJFBfX8/w4cM7lbE/cOZdvj+flsYGKPqJ0OShRGafjDk8Vn1cX7l7My1NzZSVlanHcjrNTMxAApJHYamrQxyrIjAixm5Fk/a9IbbrfM+ePQ6/aKurqrAKA9GjpnH0x4/4/+y9Z3hc133u+9tl9vSGXgkQAHvvRYXqkrudWNFVnGo751wnjp2T2Intk1ynNycncUl3kuMi27Jky5IlW5WS2EmIBCsIovc2g+l11/thgCGGBChSjbLN9wufB5yZvdcua73rX943OzOGq7IRRVGwKzacTRvI9B4jNNJHeUML0eEuEGScLVuRZRlJtl0cuyDgbttObOgkQ0NDrF69+orXZLHI/GtBNBqlv6eHjOzFvXwnvmXb6PSWE7xpDYmedhLdR+jv6SlK8lxpAx6dHGZmegpf29ZS73JBoNxuoaWiqO5aKufVe4517EWcOMu2+z9zXSNQ80sodF2/bhJb1yNz9XZb124Qu+sEy7KKi90bxfLfikns9eLNGPdiuNqOqPlRD1g4bTQx2EvXQ//Csecex922g6qNd2Evqy+mgfb94Ossrau8YjR0buyTk5OkE3ESp17G3ryRwKpbsQXr0KLjJM/vY/LUy0imWrLQiqLI7/3OJ5j8oz9j+NxetJpWJIeP7PQAZmKK3IUDKA2rkVx+BNPCEgXMbJJs71Fayj3F5oLXuqisWNZGZHKUoRPPMnTmGA2rNl3VcyWKIsuWLXvVe36lCNFM11GMyW6OJSf5mCiybdu2Ra/xHBaKxqrZJFYmhqOmBT0TZ7xjb0nkyh6oINs/TOjUC8S7j2GTBRq8Ikaom1NPjxCNRotk06nIZKNTVNROk+zoYCKVwkJAwMLj8bB6y26koA+fz0csFntLnvfFxp0MT2CpaexVTVRsf2/xPObS9dMHH0EbOk1V1cWFcDGZmXw2habl8a26mejhR8jGQlBdA5RGQ3p6eqiursbv9195oRUEZH8VpgV6Nj33RwI+P9lEDNMwSEdn0NU800O9hc+7/QiAoeuIkoRpGGi5DHZfObLdyfT09Ktek0sbm14PIpEIiXQGz4ZbKN/2HkQBapwWk3IN5dveg6HmSJ5+hkgkAlx5A55LJ0mn4jRW1eH3XZxLLNNk9OxRgm2bMbw1JfWebVtvwxiroP3EKR544IHrMr/PlVB0D4/j8/hIpBIsX3J9JLauR+bqrVzXrgY3iN11gmmaTE5O4vV639Aumjd7Enu9eLPGfSlea0fUYmmj+qXLMS2wt2zHs+pmnNUtCALFNNDYMzpTY8dpbm6+4m9PTk5SXl5OPpXAtfYOym/9laJYr716KUplM2HTJHNuLxWXFMBv2rSJL/zZH/G1h77NyfOdZPMaZTaJlKSRtdlQB49jWhSjf6IAijuAaGrF3eRrWVQAyisq2LZzNy+efpahg98ndPqlq36uruaevxYh4lfDpRHDcDjMb3zy0yQj03iWbiS47u6SyFVmYhCv18tnP/kxKioq8Pv9tLe385k//VvMurXUbXl3SS2kmggTPneIyk13UjNPfibRd4LTLz3BnVtW09LSQl9f35v+vF9p3KFQiF/6f/8XcrCupFMWAEFADNSiON00NTUV/7yYzIyhF9K4ksuPLEkol3CI+dEQy7Lwer2LLrTBsjJskkhmchBRANl5MYVqd9gRk1PYRYPc6Bk6Hh4kNTOJbOl4hDySIqFm4lhWYUiKTUa0siiyQE1NzatekzdS+iKVSiHaXTibCiRYFCxW+g2mczIWQiHy2LWfVKogcH2lDXjf8X3Y9Cx15b6SexWdHCYajVC+5d2X1Xv6fX5iUw3XrTFu/ka4dc+DrKp2cH4qR+fJg9elUeF6ZK7eqnXtanGD2P0U4s2cxH4S8Ho6oharzxjpO49pGnhX3oSWz6Kl49jcvmK0wL98G+nQBV588cVi+nQxCIKA5PLhbNl6mQODIAo4W7aS62tnfHyc9vb2kvu3UMH8b//+54hPhnC0bMGz6laUsjrUyDip8/vI9R8nZQ/S399fFEi+1kVlDnbBZNXyNj750V+irKzsVZ0AruV5ey1CxFeD+dHYo0ePYugGjoZluFbtQXK6ESQJyVuJa9Ue9FwaY+goZWVlxUaTf/mP/6Rpy21I9WuJp1PFKEnrllu5MNGF4anC3rgOh7+iIJEiSeSv0V3jzcD8cbe3t9OwZAlRLGJD53FX1CM73Oi5NOnwGDIWlY1LSKfTxe8vJjNjaBp6PgvRcdwuJ3a3t+S4811W5rDYQhvw+6kqC9DzyktIloXk8mOZBlo+Szw0gTpymnfetps//OwfkEwmcbvd/NKvf5Sp7mOseOdH0XQN0zQRRRGbbOPCscepD7q58847X/WavJEQBAG7w4Vsd5BLJXA4nUAhopjLZpEUB3aHq+S5XmwDvm1JHc3OjYSGOmleua74nXwmhWFaC9Z7wvVrjLt0IywKIArR69qo8JOQuXqzcYPY/ZTizZrE3u7QdZ0vfuWfGcvA+p07rtm4fbG0UToRBUHCU9NMavQCajqGYejFaEFZ8wr6jiol6dPFEA6Hsbs8yG4/+WQMm8OFINuwdA0tl8HMJMBQ+aevfYeKH710WbRx/r09fPgwUxOTuJfdjG/XB8kOniE/0Y3kDhK86RdIiCKTPQeYmZkpHv9aFxWYt9Ntrufuu+8uuXZvhF7UaxEivlbEYjEsm53q1dsxZUoiPnabTPXq7UyPnSySxzmSv+62XyRQXV/srlUUBSMVZSRQhbxkI25yJOdJqsx317gaz9Q3G36/n4a6GhqWNtA3MUNq+Gwxbey222htbQBxuCQ11dbWxtZ1K2gfiyHKdYQ6D6HlstjsTpySQmqwg4bqaizTKrpxBKobSwSK58Z+pYXWFulDCXWhVCxhpvsYksOHkUtgTPazzGvwe7/zCVauXFk8r0//zm/z+3/811z40Vep23QHrop6MqER+mfrzD71+c+8quDyG421a9fid9sRElMongBaJoGet6FlNBRJJp+cwu92XOZpfqX63kuv1fx6z+Xrb7ts43W9GuMu3whf7Iy+nhJbb/fM1ZuNG8TuOkEQhLeNSvVbiTdz3B0dHXzpn/6Fpw+0Yw/UMvPofxMMlrHm5nupaVl1VRPNYmkjty+IgIUWncBuV6isrEC2uxBFEUVRSIz1IWEsmAa6dOzV1dU4FAmPkCdvaWipaJHKGJERct37cTRtoPmOD7JszcYraszt3bsXTXJgV5xMffdPsSwTRAlMg8Sxx/C0biEnO+ns7GTXrl3Fc7mWReVKO92rFVp9tXv+WoSIrxVz5NFCpLqyClVTixEfxaYwnY6UkMcSki8Ixe5agInQKMg27P5yVq9ahd2uFEmf3+cvumskEglqa2vf8vd8QbeI6Cjvf8/9jE+Mk8lkcLlc1NXWcfK5Ry5LTc2RsdOf+zx9549jyE4sUSZv6piZBPnIBGP+CsKTEyDbQNew8knaasv45Y/9CZIkldzzxRba7U31/Naf/xFHj3cUywucdhubNrUtuDGYkwD6whe/wshTXy7qP9YH3Xzq859ZVGj5zUSxsWnwNM7yWnyBCvKiRNBjIxULo46c5tZNqxecbxbagC90rebqPZ1GjLLy8pLPX8/GuIU2wjkuNrVcT4mttzJz9XZbz28Qu+sEURRpbGy83qfxluPNGvccwZg0vQS2vJuadTejJqPMdB3lyFMPs/NdD1DTsupVJ5rF0kZL2lbj8XiInnmJqg178PjKirtmyzIZ79h7xTTQ/LHX1tZSF3Qz1X2M5e/8CNlsFl3XkUSRifPPIXqrqGhbR1VF5WU2VZdqzIWnJ1CTMaJn9uJetgv3qluKjRjp8/uJdx1CULNX1TQC17bTvRa9qFe75/MjRFcrRHytKCGPowXyaJ8lj+GpkcvI45WKsO0uD+gaRjaO3a6UkD4o9Ux9q9/zK7lFnHyu4BZR09xMKhLi5HOPXDE1JUgySvkSpJpWJKcfIxsn3fMKYjKKUt2Ko3Fl8e/GZD+IWWDh9/xKC+0DDzxw1Qvwgw8+yP33339Vji1vBRZqbDoyVhp5/N1P/vY1EYqFrtWcsPhim65rsQh8o3DpO2IhELYupuevt8TWW5W5erut5zeI3XWCaZpFP8Gf5lz/pXgzxj2fYKzfeS+HX+nAMIxi4f3YoR/QefBZqptXvOpEc6W0UXnAR7TjEHFZwOd2FdJA4UIh/dWkgeaP/dOf/HghpfTUVylfugab00NicpDwwDncDasRx07z4vdOlthU+YIV7D3ySonGXPeZk/TGQYtN4Vy2E6VqKQKgVC1FLm/Esgwy5/aWdDy+Gq52p3u1elE9PT2Ul5df8Z7PXffhv/sSqdjAVQkRXyuulTxeqQg7UN2IlU9iTPbj85Y6S8yPoLS0tBAOh9+y93yxCGr/yQOv6hZxaZTvaw99G6luFT93zy+QSCYuijwnxxhzldG09TZWr1qJphUilT6vj45nv8s3vvUw69atIxaLXfW4r3UBlmX5VWtZ30rMb2w61XWeivIywrEIGze1vubO0IWuyWKbrmuxCHwjsZDLjI8cCRxYFm8Lia23Am+39fwGsbtOsCyLcDj8ulJLP4l4M8Y9n2AE/AECHjfR8CQVja0IgkD5yh2EDn6XyMQQQ2eOvupEs1jU6o51TTywZz3f/cEPGXjs79BNC1kUWFIV5NNXkQaaP/YHH3yQwcFB/vGf/43+7mNYog1Ly2HmU4huJ9byHXiqmoo6a7HpQfr2/RjJW12iMeetqMHZth1bdJx090EEm4Kl5hDtLmxltbhat6EOHL/mFMHVLLTXohdlWdar3vOS6378yVcVIr5WXCt5fLUi7LbaMhCzdDz73UXT1oIgvGXv+fwNzsa77yc2NVIS8d337S/T1dO1oFvEpVE+Xc0xNNDL1vf9BoIoloo8x6LUbXk38XQKQYDKyov3f36pA1Ay7jfSu/NKzTpvlfH7pZjbEHV3dzM2NkZ9fX2xYemNPsZCkbzrYZ+1kMtMXZWDkenwq7rM/DTh7bae3yB2N/ATj0troZa3tXLi9FnCI32FWi1PEDWX4/Tex2hwc9lEs9BCcKUatMlwhCMd50jnVNwOhZ2b1pQUeF8NOjo6eOVsNxvv+0Wcta2guMjEwhzf+yNS0/0IY73Ex/qKOmuy3YnuKANDRbFdrGHJ5rLINgX8NaSPfR8zEUZQHGBZCDYHzsa12F0eQqHQG3a953A1elE2WSASiZBMJgFedaF7o+tiLr23GzZsuCbyeOXU9J8AXDFtvZBn6huJ+eObc0fxte3kxYe+TDQaKUYkFQkSM9NY/lpW77m/xC3iU5/7PIIkI9WtKhKDgn/u45w5/AKe8qqi20g+U/DRdVfWExnqQlXVkvOZI/OXeuS+kd6dVyKIwFtm/L4Q5nQbAZYtW/amEJpL3UZezSLwze5KLXWZeZjAqhUMn7/Amsban4lGhbcjbhC7G/iJx6UEo7yigs3r19Ld20dsvJdcLEQ+NsGqtY184uMfK5lorrRIbNiwoeQ4HR0dxZ3x5g/+dnFx6rrGxWmx2rRYLMbZs+fIzIyhSk4qt70bm6+gsxbrOow2PoAoWpiZOAQLThIulwsjPkluog9Hw2pcK27CVr4EMxUhN9RBrns/splfsKnjtUQ2FizKX0Qv6uTzj5If6+dvv/RvbFq/ho4z51i/ahm/+qEHL0v9XSqdMjAwUKyfamlpeU2L0pXu7d//zV9d9dhfjWyuW7fuutR7XTq+XCZFX3cX9v4x7E0bqLzproLIcyxE//7HyKZzVLY14QhUFN0iNt59Pw//5cso5U383D2/UHQUqahvpmLdzaRGLxTLGARRxO7yIIoC6dAYgmCRiUwxEZ8u8Sy9lMy3tbW9Yd6dVyKIn/6jP8MydOTGdT8zxu/Xwz5rIVwarfyVB37uDY9W3sDV4waxu04QBOFto1L9VuLNGPdCtVDlFRXsKi8nFo9x+rlH2XXrDv7jX/6pZMG90iLxqc99ntqqciJZvUAKZJHJkX7sdSvZ83OvbXGaG3t/f/+Ck7Gaz5Gf7MW1dDNyTStaKoaeiSPYnLhW3Uo+k0LvP0YunSx+p66mFn2yGzlQj7NpPZ7qpYh2J4LPR7ChhbFn/xMpNoSmaXzta18rEo8zZ85cc2TjSkX5Hc9Qko48+fyj9J/Yj+wJEpdUXBMxhqIq/S8c4sy5Lj76Kw9y7MTJy45fVRbgkcefZDyaLnY81gXdfPqTH7+mjsc3MkIEi6emF7omTz+/t3gd36jn/VISvFD6bXywl/Ph/0s6NsmKW9fgLK8rnLvLh7NtO5bDR2LwLDb5vcXfjU2NINi9SDUtJJKJYsrV7/MT9HrIVzQR6T9CdHKYsrpmgjVLCAaDDO57FLss0t4557dbsH7LJSLYMiG+8l/forammompKSq9DrqHxlj/gY+/LvJxxVTzXR/ksX//AurMCA98+H7EWZHY66Gn9lbO7dfDPmsxzL0jfr+f6urqnylS93Zbz28Qu+sEURSpra293qfxluPNGPer1ULVSkk+8ZufKCF1V+rorFu2nqcPPs+I5uXOn/sQnvJqJgZ7OT31CI7JCSb7OrG73EXf0GDNkuLi1N3djSiKC0Z25sY+Ojq64GScjYUwdQ1XRR2Js3vJAIIoI2Bhc3txljcSQyCbThS/E5sexWm3o5bVY+YziCLIkoSeSxMPj0EuRTqb49d/57MgKUgYBO0CXq+P4JqbryhRcjV1PHNF+RW5MYbmSTPEes5g2b14lu+kfOVO0v4KquvDzHQd4fSZl/nM5/+clXveV/JbLzz6b/Sf68C7YheN77q7xOXh9//4rzFNk23btl217+xitWYnn3tk0UX+WqKYV0seX+/zfhl5XGST4ausw7t8J6mJPqZPv4y3rg1BFDF0reiAkA4NlER885kUyDYkp780rTpb0pBIJBidmS7656YiIXKJCJmRTqQVuwhsuXifBvc9Smakk+W776Xxtg/gKa9Cnpnm9P4f0zcwTGsismDK/mrJx1x0yte647JUs9vtRlPcCHYPsakRyuqa5w3lrdVTeyvn9uthn3Ul3FjX3h64QeyuE0zTZGpq6mduZ/NmjftaBSkXS2FYpsm5g8/iatmMs2EFgtOLbFNwBCoIrrqJePcxnvmvv8VTtaRQ/zbbsbpyxx3MhML86V/8NbG8sWAUbG7sPp9vwclYEQE1Q6LrCErFEgKrbsIWqMZIzZDpO05q4BSCoTJ05ijltc1FY3stm6TM70PXNFLD54ris8Z4J1qoH2XZTdRuvYuyhhbSoTEGDv+I4b4O7tzmLR5/fmTj7//hS5RXVnCys7vQPemwk4iGcNSvWjRaWeXX+aOP/DrJZJJwOMxv/u5n8C7fTv3uDyAJ0OTSGZJrcex6P9GJESIT3TRvuwtTTRMa6samOJkeHcS2dBtlOz6Ar6YaEPDVt+Gta+Hsd/6W3/3M/2bLjptRDeuKEcb5BGDvN7/E9PgIuqYi2xSq6hppXr2Zrr6jly3yHR0dfO2hb9Nx7kKxa3TTmhXF1PF8XK3Uy7p16wiFQiXP+2LkUdf1y1K6Z86cWdC3eG6TMTXQVax/0zQVp8uN0LKJaPsTTJ89gMNfjppX0XJpHEEfTqeTfPaiw8R82RZZFOk/eZB0NIw7WEHz2h0sb6wmJuoMH36CkaM/xuFQkNNhlu++F2/btqIbhyBY2GURacUuvG3bCFbXERCy2GrqWX/3BxkaG+fUi09Q17a2mO6dw5XIx/xrNTQ0RHg6RP/0C5jBJXhWrrnYXDTWS6z/NE5FKpDVS/BGR65erXnjrZrbr4d91pVwY117e4z7BrG7TpgzDb4WGYqfBryZ476WwvvFUhhznoxVu36edCxcjGIoikI+Mk4mMgWBJvxb3oW3pqloUv/Sd/+d7MwYjoZVrL7t7gWjN+vXrycej9Pa2rrgZKw43ZjZBHL1cjxrbscRrESUJCR/Nazag5qO40iNs7JMLkbHtFwKr2SyacVSmlesZWx8jEwmg0NR2PtvT2Bv3kjZtvfhr65EUuw4KhsJ7vg5LFGi/elHWLX7XsTZSKYgCPjKa3nmoYdx+CuR3QGQFfSZDLHpOIFcVwmRmPtOy8abufDStxBFkW3btvHoo4+S0QVa1tyEIBRIZpVDZzgjo+bSKHUryEz0svfr/wc1n0fXVHQ1RzQ0ReWae9EMY1bs1w5AarwPU8ujV65CWX07axYQbZ5PvOLxODOhMJ193yORziI6vQiym7yu0Xehk9BoP9U+Z8ki39HRwac+93n6JiMIdi/ICkRVhmZTx3/3l39ScoxrqW0Cis/7YnV/C6agAy6qK8sx69aXPCdzm4z89EBJ/ZuiKNjtNoRshqnwCCOHf4iguBCwEEQBqWE1drutQOZmMSfbEj25l8fbnySd17FECcE0cNv/DY/Pj98pIwgi6UyGXC5LIp5i2x3radu6o+jGkYlM0d5pI7DlbuLpFPFEnPqARcxyEfAHqF+5hcGDTxCZGKK8fmnx+FciH5d16uazXDh/Bnv9KhxiuLS5yOnBcvpIT/dhUxxExgdLIupvZOTq1bp738q5/e1mn3VjXXt7jPsGsbuBnypcrR7WYimMua4/0eFBFGdQFAUAn8dLdqQToaIZV8MqbL5yRFnBWV5H3a73cnq4F8vmZud7frlIlC6N3vzNrKXQYpNxIjyFCbgql+Cwy6THujByGSSHC3dFPYHGFZixXv7gU7+LLMvFBoZ//ep/cX6ok6bla3CJBpKoMd13jlROI9i2HbvdVhyHaZogCATX3cb00+cZPHuUlo03AbPRykPPkMGOt3kTNetuwe6vIDE9Sq7jZRLDZ2h/6lu8+7f+pCTqsmA0RJIQHRcJxBwMXUNPx1GTUaYnJGy+CgTZjaqaCE4fic79SC4/ZnlZ8ZwmT76IrW4lireyYD4/W/i/WO2U1+tlZKiPlODGs2I3vmXbkL2FJpRETzuRC4fQouN4vd7iNfm7f/gincNT+FYUUsd2f8UsaT9C54Uj/P0/fImv/9//LB7jamub5neHLpa6ffm5x+g+9B1cjatpeteHi6nN0ePPc6H9ILveubqEPCqKgiSJ+JduIHL8yWL9m9/nR0rPMHbsBVxN66jfdjeuigb0VJSZrmNMn3oJn1NBQERX88XF32NmmZ4OYW/ZTsW623BUNpILjRA5+RzhC4fwlVfiq/aD4kTLZohnDNqf/V5Jt+xEfBrTpNgtq6kqzDkQCAJrNmxmeP/3OL33MTbd/QuvydFkfLCXU5MZ4uM94CyjbNcHi/c12dOOPj6Ank5w5MmHyGnGPA3IIJIosKv19Ueurib9vn79+td1jGvF290+63rJz/ws4waxexvhp+UFuFK3Y09PD2NjY0Cp9MUbNfar/Z35KYy5Oqx8JkU2FUcQLGaGuqiuKMNIRZkIjZJNxRGxcNevQFdzGJpWNCyPjA+hVLciWTli06OL1vfM9w1daDLOJCP4vF4cZprI/m9jGjoIIlgmaUmmvGkFZVXVpNNptm3bVvytX/3Qg3zqc5/n4b98eTbaZCM9M0kuMQP5NAGfnznvVVEUEQSQfFVYokQ6Gi7+TmR8kLH+bpwt26jb9T7sdgcArop6AhvuJiFKjPYcov/kQVy+4KLRkLVr1+Kxy8wMdVG/dmfJdRcFkXjnfgSHG1fbDvwrdyJ7y8mEx4icPUB+5AyxU8+yZPVmALIzY+RTcdytu1ATIVwu14LXdn5a1TRNMtkcyoqdlG97z8WIaLCW8m3vQc0kSV94uUByge7ubg690oGrdTf1uz9Q/HxB4PoD6JrKoVcO0d3dXZS1uVapF9M0+dpD374sdeuvqiPlacS2dBsOlx1vbQuCKOKrb6M5WEtXLs/ZQ8+y+d5fIB4eL0ShnG78bieRrIZhWsXUo2VZqJO9SP5q3C2b8FY1oTicaJKEp2UT+cg4vuQgQy9/G00vpLNXNdYybJn4V+6mbMcH0AwDLZtG9gSxVzWRm+jBrFxO7e33Yw9UkQqPox5/kcjQqRKSP79bVhQFbIrCfN9Qu2DS1tRA67xo87U6mjj8FbhbtmApXjRdR/SUI0g2JG8lrlV7yEwNoc4ME8oJNG5/d5Egj80KiG+7/zOvaz692vT731ziCftW4K20z7oWvJHahTdw9bhB7K4TBEGgoqKiODn8tLwAi41j++aNHDtxku7hCSrKyghHIixfUvuGak9dyzWci5pdSojQNSLDXdhCk9iWtLL3WBbTtNByaRLTowQb1uJ2+VCjE4Qj44iigEcU0CtqUOMDV6zvSSQStLS0lPhnzp+MI5EIf/qFLzEy1okt0IS9cR2SO4iRjpIfOUNm6DTlAfuC6aRLrZ+EyBQ5+2mS5/ai1tVjry9oaymKgmKTSUwOFtJtwYrib4SGe9FMqF6+DbvdXvy7oijYFRuSp5xsMs6BH34Hp69s0WhI0Tuz/zjT3jIcElyIi0STJrHQOHoyjGf1Hsp3vh9x9lq4a5aiCQUykOs7hp6Ywu5oRs+mMS0LNZ/DbbdRX1e/4LWdHy3s7OxEcvrwtmwin05ic7gQJQnTMNByGbwtm0gMn6Czs5PVq1dz9uxZMtrF1HHJdRUEqtbcRH/XYc6ePVskdq9W23Ty+UdRxwf48n99i4ryMoZHRhns7ykI/s777Nj4GBlVJ7juNtKnniY7M4arsmBNJMsy3uU7CD9/kse//IeYsqMYhXI6nWQlD+nIFPl0Cl3NM3q+g+nRAarW3Y6/3E9q4qKzRpnHzep3fpDEyaf55Ed/ibKyMvx+P319fXznyWdp3Ho3vppqVLXgn6vlUkyPduJetgNbdSuGzYkoK/hqmqjc+g6mTJOxgVeIjA9S3tBS7JYd69hL29bb8Pn8JKw8FhfTrdvWreRv/+rP6e/vf02OJqqaB0kmsOZmYsefIjPejS1QiyCAIovI6LiaNlK3didarlD7J4oCbVtvwxiroP3EKR544IHXTHauNv3e399fMre/VXir7LOuhPnr2hvdmf52xqXr+fXGDWJ3nSCKIhUVhUX1p+UFWGwcR55/lEef+GuattzGuj0P4imvwvUGa0+91mu4kBemV7IT6TnBlLuMpls/WEgvDfeQOPY0sbP7uO2Dv05t6+qi6btlwUt7nwVdK6lfmsN839C5ez6H+ZOxrut89vN/iuFpZuU9v4Sma7Mm9bXYVm3gwo++Si45SEtLS/H7c1GEy62fbLzwopdQ72kmT76Ip7Z1Nn0q4PN6GX/5EIKaJlBRPy8tdxBBFPBV1DAX4Zu9SsipEKnedpQl6/FtvI2aFRsXjYbMeWf2fOL36HnyKxiKmx7JhmVoGLFJRFHCt2wr6iWky6Yo2BtXk+09RujcEez+anKJMJnwGLaZYTbsuf2qC+9lxU55dR0500LNxLGsgrWvYpPxVdeSUewln18sdQwg2t0wK58x/74tVtt08vlHGe18habNt7Hk5vvwlFeR7emkO3a54G8mk8FCwFHZQNICfV5jg6IoyKaKrumkHRUs2fHOkhRx4twByhWdWOfLhE6/RCYZwSXDTbfsoaqmtlj/pigKfp8fXVPpeOVHlJWVFSO+hw8fxkDCXVkPCMW6xvREH4aax79iF2o8PJtaLTwLQX+ATMsmpnqOMnjmGP6qelKREJIoIE6cxRirIDbVgF5WSSoyVpJulWX5NTuaKIodWZKRPGXYbDYCLgV3RcHCSY9NMabmcS/dyKYNG7A77CVjj001vO6u2GtJv69YseI1HeMnHXPr2rX4SF/vqOIbgfnr+dsBN4jddYJpmoyNjVFbW/tT8QIs9iL7q+owTAuzbi1S/VqC1XWUi2lmaurZdM8v8P1//ZvXrT31WiaRRQmRTaHzfCOG5EBWk6jZBPmhJKIgULV6J8mhc4ydO8aKjTuLJMMyTYzJPqx8ikB1qRH0pb6hIyMj1NfXLzie/v5+3IFKyirbmBnrLzGpn5kaoayhDVcoTX9/f3FxKokizLN+Ati4fh0HomFmOvfh7zxE2bItRW9bJdTFps1rGDnw3WJ0c9OSciJjTlKTA/grCl2pc+OLnD+EUt6ArWIJsixdVTTE6w9Qbq9BrlzKxrZ6TvaOkRruJNxzAregI0qlpMvlUHBU1ZIQBWZO/JhEzzFES8ejx6lRcrS0ti56befXTs2lglOTA9Sv2YmqqbMEWUSxKYydO4LHbmPtbMrs8tTxfFJrMTN8oeTzc1gonW6TBdTxAZo238bNP/8RRAHKhRSZ+qYFBX9drkJzQy40ioCFlk2QGO5CdrpxBGtJdh3EXr8S19JNSC4fgighuXzYG9fhnBlnS7OHP/zsH5BMJolEInzxq99EEQp1lPOfBViYBNfU1CBhkA6N4a1tITszhp5NkwuPYgkCyA7Amk2tFmB3OCivriUsSYTP7acjPIhiE9nVWs+2+z/DsRMn6dn3bZrqGxgZG2VNY9011Xotlua22xXcLifp6Dh6Potss2NXFLR8lvDQBdA1fOXV2B32y8b+RnTFXq20iM/nu+J7/tOMuXUtk8m8LYST3yrMjfvtcs9vELvrBMuySKfTJQszlkVkYmhBfbTFXoC3S13eYmmKQpfpRW/JuW45gEQygVTTipCKvi7tqdeivr4YIYrHY8TTWRq23k3s+A9Zs6QaV1k1iqKgaRpH0Bk68SxDZ47RsGpTsQB8iSOHVRPk5HOPXNE3NJ1OY1nWpUOYPXYcu9PN9p276R0cYrrzEFoui83hpKq+idaduxl+vrdkcbpSFKG1rQ1DU3nh3POMPPMfjOz9xkVv2z//Ix544IGSZ6elpYVf+8hvsK//OCFfBf7KWmx2J4mxXpKhCVwrdtFYW8OmTRuLxu8LRUPmk+afv+cXSCbjNCkpylrWYhj38Mi//i1TJ55jw4f+N5qhX0K6+qmrDPKZj38URVGoqamhrKyMv/7Hf77qrr/5qeC5ccwR5NDkMNn+49y6aXXxWVjo87bZz8dDE5d9fj4WSqd/8avfpOnm+2afRQsHGn5fYEHB3/q6elyKzMSRHyCkQox3vISFgCiAJImkJvqp2voOaoNe4uOXpFbveT+RU08Xu5FN0+Tp5/dek/TFnXfeSV3QzdC+R3H4guRTCUwLLC2HPjNK4tzLeBtX4PHN98C0SE0OUhX08hd/9FkqKipK5p4HHnjgdfmlLpbm9vv8VAT9RI6+gMflRMunCfedRhQFAm4HMSuPz2bh911eqvBGdMVerbRIS0sLfX19i77nP82YW9cSicTbRjj5rcDcuN8u9/wGsbvOmHsB0okIrzzzyGWaW6t23kleNTh9+vRl5O2tqsu7GvK4GMG41FtyfrecqqpIDh/IttelPTX/2JZpEp0cLiHHC/3O/O+Yus7g2aOko2F0wYauS/gr6omYoEhCicn5tp27efH0swwd/D6h0y/NKwD/I+D1+YbORQQSoz1kuo+THBlAU1VsioInPU5SVi9bnF4tiiCmQgSdNnx1LRiIJd62C9Xk/N7vfILJP/ozhs/tRatpRXL4SI11YWTiBP0+1q9bSyAQuOJ9Wog0uwULvzOAZUHj6q307nuMsbOHqFq+aQHStYaPfOQjJc/YtXT9zaWCLx2HkUtgTPazzGvwO7/9WyXP9P/6xMeZ/PxfLPr53/3kby9KTuZfx/b2dlR9gQVtnuDvSHiKgVNHSMcjGKqKMHICdawTZ+s2PBvvxlHRSC5c6Eo11SxtS+rYvnPHoqnVuev+WqQvZFnm/ve9m7/4x39FW7qN4Ib7Zo8/TL79KTJd+/H4/MRHe8DUQZTJmxTv07333nvZdbmSX+rVzCVXGocxdhZ56jwNq7fQ2lyH7PahpxNM90wSt+nYUpNYXBJzfYP03N5u0iJvZyym1TmHt1o4+WcNN4jddYbP5yObivHit/+ZVE4HUSpMTHmNRNc5hs8fR9ZzfOW/88iKo6QZ4dEnn120puxzv/txvF7v647kXS15XDR9coVuOUVRMHKJK9amzXUWtre3LzqOuWMPnjzM0PmOEkX6YLCMplWbUGwiXq+X7u7uYmTFJgt0PP1dzh16jnRewxIlMDQMXSWz4XYkUbjsvOyCyarlbSUF6PPP6fV0prW1tRF0SDzz8L9iKm5Epw/B4SJnFPTXBs60c9/N20oWpytFESb7OjnwxDfw1q9l96yDxqt5227atIkv/Nkf8bWHvs3J851k8xouLYfkkVnbUvDhXeg+zZ+kr1iLJAhs3rGbscOPk+3aT9jMXxWJutauv4XG4bTb2LSpje2bN/KNbz98mRDxRz50P0ePd1z2+WvZKM1/D/xVdcQmh/A5VaLZOGU1TVRKWfojY3TsfRxkO5JlYGVj1K/ajNyylUwiRD4RRsCivG0jKdEi3HMC67b7riq1eq3SF6ZpMh2JsXz3vSTd9STGukgOnkK02alYsY2YliZ88GESF5YgzNZJSmqGZfXl/O4nP3dNc8q1bEQXG8f2pnp+6z2FdO+F408W/762qZ5f/9zv8+iTz76ppOvVru98v1TgZ9YvdTGtTrg+wsk/a7hB7K4TRFGkpqYGt9vNzOQo4XAa0R1AsMTCdtMCU8uTik7hrV/Gynd+GG9FTYG8deznqb/5B8pb1i3oBPDyQ1/kf/zWJ6hubEHVX1+X6ZUaEuaTx8UM4RfqlotYGhbg8/quWJs211n4xa9+84rjmCNEe5/4Br4VOy+an88WmR944htsWlrNv371v+genijaMnWdeoXJmRdxr7qFqrV7sJfVk4+MMnPqRUaPPU1Dc6Hbb/459Z88wJrmeu6+++5rmrDnIhUzMzMIgnBZJGMO0UiEnG7hXLqWwMrdKIFK1FiIWNchcj1HiEYjJZ+fH0U48fR3KK9biqQo6Pk8h5/4GkrFEvbc/xEClZXA1dUvXkqi5mvlNa9c96qT9KUk3wIilrsofmEXTFa2NrO0tpyu/qOkszm8Tge7t21c0OVh/livpR5nITKYTCb5/T/8k0WFiP/2zz/Pb76ODdEc0T7y/KMYpkUsFqOmPMjkTBSbYDI11EvVmpvZdvf7UNw+otPjvPLijxGNDDetX45u95LJZHC5XNTV1fHSs45rFva9FhI8F11tXbmDoc4T5GYm0A0DWZJwZe1kURGaNuCuqEO02RFMHVs+gdeZv+J1mJvf5o75WpqbrjSOS8sI5v6+cuXKN13PbbHzOnXqFL/3B5+le3gcn8dHIpVg+ZKfPGWD14O5+y7L8s9UdPPS5/164waxu04QBIFAIEBXVxeRWALTkhBMC9HhRJAULEPFUrMIigtTUxEcnqIwa8uu+zh3oRtnOkWx8nwWUwNdhCYnyCm1rNr6bmqb215Tl+mrNSQsRB4XM4S/UrfcYrVp8zsLm2Y7C680DkEQsFUsKUiEXFJkztA5OvuGMGrXFheVZHiSYwNhyAzhXLoRe8USREnCXtFE2c6fw9BVpodOEB0fxldVe1UT0qtJvbxapKK7u5vz/UOUr9mNZ91daLqBmkkh2J1UbbmHlCJyvu9YiZ4aFBaaD777Hr7wxa9wet+PMJBAz6Nlkuy46Z1UzJK6+dfq1eoXLyVRv/qhBy8jj4aqMjM+gC87UXJNFooiZih0W84RkqaqIMGyMlzTMQSbA6fd9qrP5GvB/HGYpskv/9qHryhE/A9f/EqJEPFrOd72zRt59Im/xqxbS93mdyFW1uObHmNg/2OousWaHXtYsmw1AJJNIbh61kXi0HPc/qHfLun8vVZh32vFnEtH//QLWGVNNN55d+GaxEL073+MVCKBzRCxRBNTEpElkWBFFWnJdcXGprn5DV5bc9P863k1z+ccrkQG38h65EuPX0Jc9/zkKhu8Xsy/72934eQ3EvPH/XbADWJ3nWCaJoODg5w+fZpkMo0UqMPZuhXH0i3InnL0ZIhM3yvk+trJzowSGuknMGvcrWkanoaVpEfPFIuwYdY54MAziNVtBKuW4ghUvKpK/2K4UkPCYuRxzhC+PDtK5w//tZjm2rlmJdvndcstW7qUkYGB2W65y2vTLu0sfLWFoLe3l0hWZ/c972cyniU2r8g86HaSd7tJuwO07LqPQKBQBJ42BOwt2xD8tWR7jhYkN/K5gstDZT31m+9gbOI8Jx79R/zlNQumWy7drS8UkWg/8DSPPvHXNKzZyqa7HqStyk3vdJrOkwcvi3oePnyYtAqtG/bgqryoKSbO2kVlxD3097aX6KlBYVF59MlnqVy3h9VtG7C5fYTGhunY/yxD516hbumyEhswuPbi5YXIo4RBfdDNr3/y4yWT9KW1SK0bb6Kt0kVvKEPfyYMYo2eYFGVCjjpWveujxWt1/k1eBF+LEPG1wjRNjp04SdOW25Dq15LKpGizxmhPR/Eu24ZVWct4ZzsrNu0s2oAt5CIxh2sR9p3DtaQ8vV4v01PjiK27WbLzveQi46TG+zEBIVCHOdqD6akguO4mFJen4Ms6PYQ52cPR5OQVm7oGBwdpbm5+Tc1NrwcLkb43sx75UuIqClAlJJiuqS/OV19/6Ds4nU6SyeTbRjz4zcD8+y6KIps2bWLdunWXeSDL8k8X9bh03NcbP11X9ycIlmWhqirj4+PoCPhaNhPc9t7i/8tKHbL3HQiYJE/8iMT0MLAVmBWZ9QTI5tWSpoM5n9PAlt1ouVTRRgpefQK9dDcbjUYXrJOaTx4DlU3k8zlCQ93YXR423n0/+779Zbp6uvCVVSPICoIoY2GxcuXKS7rlHiypP7lyZ+FFLDSOuZquxraVLJVtJUXmRirK2BHwNK5E07Ti72QyGRBEnJWNzFw4hJmNI8h2REHA9AWpXL0Lu9vDz7/zbu68887L0i3zF4jlS2oJTU+TcteXRiSq65Hq12JOhDGsgvSLXYovGvWMzoRIJyJkotO4KhuLmmJzWEhPbbFoiNPtYSSWvUxeYw7XWrw8Rx4r1t5KQ6AKSxARLJNcbJpHn3yWlStXliyQ83frPfseJrBqBcPnL7C6oYZQZTkh+xsn73O1kZjXIkR8rZgjMetu+0UC1fUkEjGalBQZewVnewZwN7USPvy9EhuwgMfNTEZDN0ymh3qKjT+B6sZrEvaF15byFG0ODMFG39P/ST4Vx7TANFTS4/3IwTqMbIrJUy+BKCMKYPf4we5ndKSTaDS64HWYm9/mPDSvZ4fkm60TejlxtbBRaJSa819+7Af/zsH2jsKcM1vTeaWyg59UzL/vsDChfvr5vT916elLx329cYPYXWcIgoBod+No3ly62AgigiDgbN5M6uxLpKbGmOg9W+j0rG7EicZUKobN7iwaXsdDE+iGSU7VKHM7i3ZY87tDc3mdp59+msOHDxd3T2fOnOFrD327pJh8aV0V2VTssmaIOfKoNDYwffwZ0ucURFFGFAUUCRIz0xjeagI1Kyhz2BFFmXOx9GU+iovVmAEkk0nymnHFhWB+p/BcI8Tcuc4vMp8IjaKpKi5vsIToulwutOg4qcl+bLUrCWy4A1fN0qLv5ND+72PlUmzatKko5rrYAtFx+Dl6jh7npp/fWnIP44k48XSGuk13ED3+JLHJYRrqC0Rqoajn+GAvg5mvM7jveyguN7LdjZ5NIzvdOMvrFtRTW1Qux+km4HGRr2hipu8wA6cP4/T4SwjDlYqX55Mlr9fL1x76NuNZESM9RbSnq8SHMymKC5Kxud36888/Tzwe59Ydm1myZAmf+uO/omXHzYuS9vMvPsRzzz23YHPKpbjmSMysELFlmkXNtsL1rV+QOC90PS49J13XixGJeDxOLq8Xuq0Ng8hgJ+UBkdREEiiQc+MSAresZSmjP3yY6Z6TvJJJIrs8oGtY+SRttWX88sf+5KqEfV9LyjOZTOJ0OBnv70CuXo5/2x0ogUrSE/2o5jPkJ3tw1K/Ev/29OMpri+9Hfuw8VjZLLBa74jnBtdmvXalJ6mqx0LObctcVrQPnb0RPPvfI69YJvRJxnew/T/uzjzKTNtBTJjano6Sm8+/+8k9+qgjOfPy0CO//JOIGsbvOqKurK3SKSiK5qQEsXUWye5CDNViWiRqZwMyn6T13gqG+C7MyKA1o+Rz2fITn/u/fFe2w9EyKyFgfOhK47Tz9UrhEOkW0TLqO7uPMubMgKUgYBO0CoiCSFp2lxeST5zHTUcznH+XWB3+76KUaD02QjkcIR17GUd1CoHUNos2OqeUZP3uYxOQ4TIzSPnIBJFvBtsouU1nfxNcf+g6/YbczMTEBXOwYu3Rx1tUcQwO9lJ08TNu2PZdds8FTR+jvPnexU1gWmRoZIHbJudpdHmx2J7lUjBpLLdG3qqupRZ/sRvTX42xaj6OiAVFWUIK1BLe9h7HIFPbIALfffjtw5UWz7aZ30jswzGDncVo23URsepR8JkU6p2IYxqx0ikU+mwb8i6bMl7Stom3nvZx/+Qec/e7f4wxUY1omoiAhOj3YLJW7LtFTm1tU0okIx599tKQj2Ol0ktNFprpOkgyNIylOREASDJbVlfPgb/wRzz333GUpkoXuR8/5s4jeClyt26i86e55tWlHMSa7OZKYuIyMnTp1qhCxG5lk/aoVnD5/AZ9NYCYUZs0ipD0dj9B55gx/9n/+GYfLg00WqPY5uev2PWzfvr1kwb/WhWNOiHjsxF7M1EwxOjUXhRI95bgVGbfbXUIw5saxEHns6urib/7hSwyNT6PpOpIgYOp5pmMpZibHyOsmyj23ceDZl9DVLPaqpegzQ3TszyPZHIUNkWgx038BT9M6PMs2IbkKDijGZD+I2aueS+aTfMswGJiV8HEHK2heu2PBiL3X6yWdjOJt2l5S1ykqLhzNGwpuHLqKUlZTfD/Ktr2HyXwWbbofn8/3qud1tfZrr9YkdTVYbC5p3XEfLz705QU75rv6jl5VFmMxsrkYcbVMk2NPfotILI6zbRu1W27HU1FXUtP59//wpddV0/l2xc+S88SVPNCvF24Qu+sEURRpaGjAsizcskV8/0MgO7D0HJZhIDq92GvbSHXuQ3T6yBkCal6DbJ6Z0HHsWpLamirU4Dw7rEycdF4j1HmERFkNos1RlE6JnD5ObmYCV9M6arbegc3lQcukGH7lBbKjF6jedCeNO95RUkwePXeAgfbnmRi4UCSPWiZJuP8c9voViGqa8Y4XMS3A1EmHx9HVHI7W7QRaNmBz+dFzSdIj5+k7d4Tx7pMcbO8gGAiQU/NsWrOCHVs2LSDbMsX497/JgSe+gaeskprW1cXrVpTwqFvLqnddlPBIHHianoM/ZqizA3uguuj7auWT2PORy/StYtOjOO121LJ6zHwG09AxDQM9lyYdHsNZXkeNLc7g4CDLly+/Yp2Q3W7H17yKsVN7+fF//CVZVcc0LUxTJ51TMdPxgtSL003I8hJZLGUuCNQHHJxLTGNaAqpuIChOdD2POTWE08ryzk+V6rv5/X6yqRiHnngIsXoZnpVrEG0KpqYy091O6Nw+kB3kTAlBF7AMFUnNMJCN8Ssf+R9E81axXq4u6Ob+972bV852l9yPwe5OTk9kMBLT1DStwVleB4AjWENw2RYGhjqZOH+Sz/7JXyMrdpxOOw2VQSZDEaSGtZSvuJkZtxPvikpG+zoZGTvO4MnDtG65pURzMJ9Jc+iHD2FULaP5jvsRLZ1Te5/gRGcfzxzqoKWxjiqfg03r17Jt2zaefn7vNS0cy5cvZ0VjNftOvoCjbQfuZbuQFAeGmiM90Ufm2JPU+Ox8+b++VdIQNDE9s6Dl3f/8+O9woaeXnEFBmsbuxjA0tEyG8+378ay6BXdNM6fiDhwrbyE5fJ5Y/2ncTeuo2HU/nqp6UrNNFbolsufud1PXtqZYRuDz+uh49rt841sPs27dugVTsfPJx9DQEHnVYKTrBK88/SipdLpQK2dZOBWZldtvw4zGLkufijYH9ppWaqqqiy4dmZhJJmzDtWwHqZM/Ro1MIlUtKfrtuutXwGA7iUTisrlt7pyi0Si9vb0sW7bsquzXFmqS+szv/CaRSOSq6rPmiH7SVUv5qluR7HZmpifJhDXa9/6QQOtG6m/6hZJNyZnDz+MX8pelga8lEnwpcUWAkOVlZnyI8cELKM3bqNz6DnzVBTeXN7Km8+2GuXWtv7//Z8J5Yv5z4nS4yOYybwuP9xvE7jpBEAQ8Hg+ZTAanw0FGUkAUEWxOTMnASM4QHzwJFsiBGrRcFstMI4gigiSTNQQiKZVf+vTvMTpwgXQiistTw7G+Y6RDDlTNwOZ0IMgKpq6iGWksyYYWGWbq7CEsBDBN1OgESt0KrPKlOMpqEASxOPGkw+NEu0I4fDWI/ioQbcj5LHZdQp0aIB+oxVm3AtMwMEwDIa9DNoWRjpHsOVZIJwOWZaDqBrrgYCxlEjJyoGsMvXCI7z/xFPVrd14i29LInvs/yjPf+hde/NZXuO8jn8FbUU0yPMWL3/ryghIeyzbtpvv4AQxfLY7W0qhH0OfEHh0oWVTCI/1o2SRlfh+aqhI9tw9TyyPa7PjLq1i3+ybir8wUJ/wrpVv8Pj9ODIZCk8i1K7C5bej5NLLdBYrK4P7v07S0BcuEwd4Li6bMFYebrv1PIcgK7qZNuJs3YPOWY2SiqCPnMEZO8tSPny2x7mppaSGbjJHQfSjRKaY7D6OreSSbgpqKYiDjbtmK3+VAUzPY7C4yoRFGBjvxLNtO/ZatRZI/OXCOv/7yv9O0cgP3/c/fZujcMUbOHyerg71lC2ZsgomOvSRHu0mMdJGLTWNKDnIzY+Q1kzOnTyHJIjaHmxM2F5KpUpuBs0deQsvnsNkd1DUvRw7Wc/D7/0n/2VeYGOwu/J/iwMinsfx1rNx9N5mZcU688ARC2RJa3vtbjB5/kY7TL6JreZ7aux/F9h8Ips6Od/8KpqZxdv+TJEIT+CprWXvLu4sLR2dnJ0ePHmV4eJiGhgb8gQCuYBWZ6UFSFw5hankEWSlEqtU8Of8yfKtuRnY4MfJ5jh7YS2pyivvu+FAxGhOoaWDDnT/Plx/5dwzJiWvFTpz1KxHtHoxcivTgaTJd+0n1HSM7dp4ZCsqNZi6JrawRZ/MG9FySyFAXpqEXmyqGTx1m9OQB4qExAtX17Hngt2nZeDNHH/sKH/2fH2NgfLpEd2/Hlk0ceeUEh9pPkE5lkCWRsfFRMvtexHQGESwBU8th6SrZtEX7i0/hsNv5w8//Mbfs3sWOHTvw+XxUVNWgORTCo31FG7v87LsouwOF5//8AZJ9LmRPAH/LeiSnC8vloqurq4R0nTlzhv/+xkO8uO8gqWQKj9fD7bfexK//8of4w099olDyMdtY5XAo5KPhRZukfvRP/x/v/rn7EZwBDOHi5uPTn/w4999/f0lB/u23387Xv/Wd2XKBSQa6zxc3V7lkAgL1mJaAqWukxvuRnW7qdr6X4f2PEOo7hNfrLb7PCxFEI5/n3PjAgpHg+c1Cx3/8bRSHG9PQmBq8QDavU9uyiaA/wHzZ5Lmazt7Og3zzm99k2bJllxHXxSKG81P/87/zdnAhmlvXfhacJ97OqeYbxO46wTAMent7eejhRwg0rSY+MoJY1oSjZSuytwI9Pkni1HNkug9hpGeQnH4E2YalaxjZBJZlEQ1N8vRX/4qsZmCaFoaaY+LCCXAFcbZuxdWyBZu3HC0xQ/z0s5iZGFL5Uryb34WzspH01CD5jucw0jGykXFSkWm85TWFE7SsAtEpa8QuWqgzw+iGiWUYoOUQFCfxroOkRs8jiBKmrmHkUgiCgOwpw7flHSj+avREmEj74wiChFKzjMrd7+Oe9Us4PBhn/MxBQidfxDExcplsS0VlJbvveT/t3/0S55/6d2wOD1ouhWTqbLvn/SUSHpZpcu7gs3iXb8dTv4L1q5djt9tLoh5V6gTlPo3u2c5CLZfCK5k0ewWio6No8/S7ArYMYqqspLngSnVClmWRHj6HKUpMHHsK0e4pRgzNfBrBpjDceYK9ms7tOzdxct9Rpoe70ZEQPA72HssW799Uzymcy3ZQvvk+yssCSJKEKNZjW7OJoZeVy3b4/f39WKKN1HgPWj6P5PAgyDa0dBIjncRUcyRPP0/G6S2ek54MI3rK0TWVyTMHQRARBZCdHszypQycO87X/7+PkMnrRdFmLZdDdPlQQ4NI7gCIBbFaI5PAVLMIkoTgKcOSFfK6ih4fxdLypFNJHMEq3n3XbTz5wsucO/oidsVOOhoiHJpCcvkLwrfpCFo6hhyLMHEIzoWm0EUFuzhDz5P/TnywE0tWkFw+RMmGpquYaoKXHv5nDvzgv8HuBckGhsa+736VTXe+l/Gzp9lz133kRAeWaANDxUhGQADDEhHdfgRJLkRsk2EEUSYyeJ5nHvoXPGVVyIpCKpvHXr6EzkPPUb10ZbEBZeDMITTNwNGwHCxI97YXHgbTRE/OgACyuwxHsIZ33baDp146Si42iR6fIjfZS8v2bZSVl5FKpujsHWDs5I8YGexE9pQhyApDA/10vPBD6ttWkorOMDgeRvGWFUslep9+mf/6v1/HFG0ITh+IMpg62VQWI5tEVPNI7gCCzVHYYGUS6DmNjKFz8Ewfh84N4vraI1R5FWS7k1UN1UzE0kUbO0G2YZMcZM68QG74DHpkDEFWsEyd6LHHcLh8aJER/vzLXy0p60jEYswkUkjuIO+59w5++Mxeev7ra7zwwov84Wc+BYAgygiyRV7ViSbStDYtuyyq03ngxwz2nEeq30zTtnsI1DWTDhV8jn/rd/+Az/3xn5LFXow2lzslNN3A9NXNRq7XItoU1GyaePcZsqNnCU/2ko1OIdgcJel3yeYsHncuhXgpQbyaetKta5fzj//8b+QEO++86w4OnD6OnpxBCA9gX7P5sjVgpvsE8dAY//zN7yPZnSXEtajHd0nEsKoswCOPP8l4NH1ZpH06EnvTXYheDYZh0NfXh9fr/al2nlioE7pWiDExrxP6eqaabxC764hwOMyFoXEs06SsZQOOVbeSVzUsS0d0+0FxIDo8OFq24FqyHtHhwcylyI2dJ9NzGE3NE8NNw033YfdXEOo7gy2jYyTD2OuWo8gSVmICSSxohDnbtmOrbEZQnIiygs1bgXPZLvTQIPnxLtTc7uK5ZWfGyERDGJpKzllJ/a492P0VRCeGie/9DkZ6EEfLVjxr7kApb0CdGSV9/mWyAx0gK0UiKrq8BS052V5YQE0DIx3FMi2qtr6DeCxKaPgEkfFByhtaSq5PY+sKQi2tfOxDP0dTUxNDQ0P8y0Pfp7GtNG0x19BRtevnScfC2O32Ehuwlo03M/TSt/jfH/0woigWi6r/9C/+ihdfegLfip0X9bvmiRrfuWV1sblgfrplrgh7LoVoGgajXR2YuoFzxU24V96KEqxDjY6TOP4E6th5hIbVWMEl2LwV2OqW4zAlZno6EFbvoHzlLYg2hcT0GHIOtGQEMzaKu7Ge+bv8hbo2o9EoI4P9GLqIa9lO7HXLZyNHSeLHHkdIz+BctgvPmj0oZY3kw8OF+9RzFD0do+LOX8dVVo2enCHefQy9/yxaNoetpY2qDXdgL6snGx5h4oWvoc0M41xxK/b6lUhOL6aaITt4kvzACeSaZXhW3oLo8GLmkuRGO8n1HsFyeHGt3IOrthXPKshEp0hfOIRpgXf5TbhX3oTNX40WmyB+4kfkxzqZUUVc6+/Fs3QTVj7N+MvfAlnB0bgG/9b3YQvUokZGibc/hjrWhdy8BXfLRmSnHz0bJzfSyZEffRcEAc+qW6hedzuOykYyk4OEjjyGOt6FY+lmHHWrMNU0iDJabJL8wHFstctxrboJm2LH5XaSunCCfHSUiXS4RIpk6HQ7gs2BpauInrJCPZqkYKgZ1I4fI9rdOJduwb/mZnzLawjoLSS6j5HtOUyq5xiW+QtUVlahKApD//LHZGYm8Ky9C/eqPYX3KTJKuvNlxroPIgCBTfdRvfam2Wc0ROcT/07WEHG3bKVi893YyxrITA8zc+p5sj1HkMsaCNz8IeRgPXp8nNj+h9BnRnC0bKVi491I7iBWYpLYYAepC4fI/OA/CNa1kAtPoxsmsiSSCY2TmB7HufwmvGtuxeavQYtPkTzzAsm+V3A1rqb57g/hrqwnHRqj98APSSUmsJU3U3n7L1G2fgmV4ipiZ16mp+cIv/2/PsWW9/xqUeJmqKeT0acf58zhF/CUVxUleUxdp/1HD6M0bSKw9d24KyuQFCe++jYyUwOEzuzH8C6nce12FHch2hzqPU303H48pgu3I0x8rK/geWuaaLkMZj6DUr0M7+b7cNW2ocZCxLsOofd1UOdxkEwWmlt6e3s5dvIsobSFVLN8wXrShWRevv3tb/PP//c7yG030bbuFupX17DUsYah9ucZO/YUTp+fmk13FT8/2fE8Q4cex7niFhp33kdwHnH9nc98nubmJgIrtpdEgl584uv0dnwHz7LtNL7rvuJ1Hzn2NH/xj/9K26bd7Hrvr1z3yJFpmj/1zhMLdUKLsxLsb4dU8w1idx2Ry+VIxOKkc1C99Wac5Re1y2LhSfKDJ1HqVoBpkOk9CkKB+YtOD7byJvTEFL5Vu4s1T6LTh7N1G7nRTiIvfQ2bt6Lg72gaqIkZfNs/gCDbMLWCarysKIXi7bqV5IZPY6ZjxXPTMgly0SncK3bTePMHsNsdADjLajANHeey7TiWbsVZ3YRosyNWNSG43weSjdzAcYyt70S2vOiZREGvq7KJxLEfMHEwT8yxm4FDJxEUF0qgjuSgSGik7zJil4qEsNsk1q9fz/Lly/H7/di/+/hlu8A5P1rR4UEUZ0q6X+Fi2D+ZTBY7XOf04RYTNVZmxhHEi5PRXLrlU5/7PA//5cvFmkN0jWx0glwigmfDPVTc8ZFi4FF2tpLtroC6lRj5DHp4ECNVgRkdQcEk560g3NNBNhEDUULPZTE0DZu3jNxAB9aa7aXnsEDXZjgcJpFMY1+6CVEQyA2cAMAyDYz4BI6mDfh2P4DscCGKEvbqpQjuIAgC2aHTmHqe3NQAksONf9N9pMb7ME2Dst0fxOUNAOCqacbMJVHqV2HoKqkzz4GugU0Bmwu5rAE9OkG6az+YOoIkI3jKEd1laJERUl37UJtlkuf3oesmUqAGKzpO5a0PoDjcWJaF6fGQ6vLhXLYToXIpuppHi02gq3lsDWsRHF4QJWyVTYhC4b5ZCCj1qzDVDJm+48XFQ3R4wOHBXt1K3d0fQZq9Zo6apQh2d+E72QTZ/nYQpULzj2Fgq27FEmU8zevQYtOoeo7A+rtInd9Poms/udTFejILC8s0kLzlGKkI6dBQ4bnS8xjxKZTa5dib1iH7qhAkEVuwBu/G+8DQSZ56mvhoN6xeg0u2kZrow73mTgK3/RqiJAEC9tpl2KqWYmpZ1MleGne9F2lW/kZwBsjHpnAu34l707uwV1QjyTZsFQ24N74DEMhPdCNVNCLa7NiUZkw1i3PZLtwb7kUuq0JR7GiSyNJl6+iKhxntO03GXU/djg/grqwnNTXKzN6HEd1ZvMu24qtrLUTmXS60xHoQZWTRwlu7FEGUsFc04NzwDgxLQJ3qRalqRpBFHDVtlFe2YVoWsc6XadpyB4GqagAq6pupWHfzZZI8g2ePks5rVKy6GVEUixEPU9cZOfRD7HXLMU2diTMHEEUJUQDR7kIM1JKaHsHetInguruRveXkY9NMH30SMRVBrmgE2VEU/a7ccg+pMyKZoWPFVGw0GmV0ZBT7qj3U737/JVqH72f04PcZPf9ySZ2irut84Ytfwaxby4p3fRRZEFDsOWpa16A6gkwcEBh46RECbVuxu73k00kGXvoujiXrqb35g1TUFzZwvvo2PDXNvPIvZxnM2vm1u++ffR7AX1nLTCyBbek2/GtuxVfXCoKAr64V/+pbyOsWM7EE/spaBEm67k0KP+2+utdbwufVcIPYXSeYpkk2myWViJBJCyjeMizTQo9Po2fTpHpOYBoa6CpixRJkhxewQJQwskksYwBBsqHGpsiECrIYRiqBnphGn5lLbYqITh+mmkGQbaQ79+FoWofYUDDoluweZJsdTc1hYaHGpokPnQNRJjx0AUsQ8DWvw26/qKeWnhoAXcW94haMXJL81ACiKIEkIygu3CtuIjd0inTnfoza5RhqFjU6gR4axNG4msDmu3C3NhEwWoh1t5Me6EDPZwvnOw8L7eoWi5plU3EEwWJmqIuayoqS7ldYOOw/X9R4fgrK5nBSVd/E6nvez8yppy/bcQmSjK2sEdMdBNEGpoZlSoiuaezVLYjziJgWmUSLjIEooVS14F2/m7JVzVSIbcQOP40x3o8cqEWuX4Pk8GDTVKzQEHp4mEwaIj2vYHP6XlXuxLLZMTUVyVuOZ+O9yJ5yMsNnsCyw1BxaeAi5bgWIEpgmogBKVQvpC4eYfvY/CouiZCukOV1+JE8QIzFNXk1h5NKkx3sx1CxExzHzGUSHu+COkk9jRMawNBXRU46RjhZqKgUJKxnBTIZRmjbibFiBraIJ14pbyUYnyQ8WyGe6/xRZNYUan0aQbOiZBM6mjWR6j5AzDbIj5zDULKZh4Fq2i+zwaVJnngdBxMgm0RNhbO4gckUjNncQQRSxRKmQ/hJERE8Z2dgkVnQCLRVBy2bRYpPIdjdKRROO5g0olUsxkjOkuw+hhQYxMnFy00O4q5rITfWhWAa2mmWkug4SGulFEEXsLg9lNU0IooieCOFcsg7HhsJ1z092k+58GTObRJsZwapcAsgIiAiiiKN1K+nz+0iO9TDRe5bjP/42oiuAe9WtiILI/AitpWZxNKzBSIQYPXuQysZl6Nk0keHzmJaFZ9WtCIKArqlIsg09n0MUJdyr9pCfuEB+6DTuZdvJDZ0By8K9ag+iJKPnszicLiwLTMNAlmXsTRupXrkZLZciMtSFmsvibNuGUlGPOnAcvboJENHTMQRBwLtmD6kTT5KYHMRf10oikUAQJdyr96BOdpMbPAsNBWkjAaswLwye5PzBp7n5A78KFGpTg14P+YomIv1HihHRdDSMJUqIrgCKTS5u1KJ9J1CzSWRnAFtFE/4VO3GV16AnZ4hdOIqZ6UAqa8C9fAdKsEAebZ4AzrbtiC4/2lQfZdvvwuENFkS/bQpjNa1o42eK1zwWi5G3BKqWbliw6N+/dAMjnftKZF5eeOEFxqNpGnffgSCIUDTPE6goryCzfAfTw6cZ3vcw/pZNxPsL0X3/8h1UlFeU3PNcZALFW45Q2cL4xDgNDQWrxeHeTlKpFMFd70BTc+SzKewuL/lsCk1TCa67jejhRxju7aRpxbri+V7PyNFPs/PE1Uj4XM9U8w1idx0w10kzNDFDOJkjFo1z+qE/x0jH0POF+jVLlLFyqUKkp+cIICBIUqHGDauw+EbGGT/yJGHFgWkZYFmkJocQHW5cy3biXr4TJVCHGp8kfeEQucEOMn3teOpasTJxLAQMLUeu7zh6eJjxE88hKi4sQ8NKzSCZOg6Xq+TcrXwGRBFTzZI+92JhxyXKYFkINjtSWSNmLkl2sAM9Moqpq+SmBnA0rMa9fDdKWQPtETtyoIaybe8hn06QH+lkcrALh9t3Rauq+VGz7/zFSxiWhAmIQGJ6GHc6zS3/43MltXqLhf3ndlyKBNmeI+RChRSUIYlkMxMoO+4s2XHN1VSklSBBySIUHi6mrOySha2iifxkH9ba22YndzBySfRkGPeqPTjadiD5fJxJuhB9lSiN67ElZtCm+skPn0aQZERBQBJtaIJAcuwCQwd/iOzygmmCKCKb+cvkTtLpNJgmjroVONffhTrcSW74LIaaQVm6BSs1Q7bnKI6aVrAKKXk1NEz6wgEkpw9LELAsC0PLo4VHMQ0VTIuZI99HlB0ggBoPYaYiCA43zpW34F55C7ZAHVp0lET7D8gNnMRSkyBLIClgqJjZBKauoY13kUrN8OTEORKJJBYWkrcSbeg008/8C5LLD5JcqNdLzqCGBnC37cTVvB5bsB4tPk2m/zjJ00+jRyfRw8MINgVLzWHEpxFMEz0xTV4UC2LYpgGWgejykxs6yfjQKQRJBlHC1PLosSnkpZtxrbkDQRQRFQf4q3CvvYts9wFSZ/ei51KIigPLArfTTqx3GDU5w6kDL6CcOl6M0ppqDqWqBff6+xAlEQQRyVuBs20n2sww+dFOtLbtHJgQMUyjcJ2jUxi5FL1nTjA2PER8chhTzWHp2qxHtHnxZTN1RHcAS9eYfuVpkj3HMa3Z+5FNYGkqOOeRD8sEQUQuq0MQZcxMDCww0hEESUIuq8fMJRAoEDpBADU2iWFa2OuW09zURE1NIWswNjZG8kI//jW3ED/6GH5RxV5WR8LMkhUl7BWNJBFQM4UUpmkYIAjYyuoQRAk1OcOhKbHQMW9Z2AI1CLJCNhG+eL6CwPK2AikcnZkmPNKPr6IWy6Iw70XHCKzYyBzxycdnMNQ87roVuNfdjd3tQpRllGAt/rW3kwmNFmoYk1EUbzmiJBXmUyycSzeiTnajRafwl1ej5bOEp0Zw2RVsVdXFVGwgEMDpcJJTNXwlffQAFjlVw+lwlthHTU5OYiDhrqwvvPfA6bgdA7A7HNS1rCSq2HGFL+AWU2jhMWyKQl3rKuwOR8n8qmfTCDY7otNXEFGfe88TUSwEHBWNZCd6MPSC2Lqha1gWOCsbsBBIJ0o7nt/qyJEoiixdurQ4Z1+Lb/FPEhbqhJ60/BQe9+ufar5B7N5izO+kad39c9y96+d5+AufIdZ/CtldhmCzY2VTmNkkguLCiE3gbNuOe+0d2Px1aPFxMuf3k+09hplLYuazaDYHguTE1HKIdhdYFkp1K7K/GksUsQVq8Ky9EyMRItvXzszh7yHYHAiWgWjqWJkYwbbN+JeuR3B6sbJJ9KleYv0n0UbOEHa48FXUYLMXfGzNTILUyR+hVC3Fs2I3SqAWLRkifuIp0qefwdG4FtfynSjljeixKbA50RMhjPgEyGvI6ha6bqDmMtgrl4DPS6LnOC+eab+iVdUcUqkUyUQSQ3EXfXUFuwd1qp+zzzzExrs++Kph/0tlQgJr1xZlQmLTQxz64UNUu4Xijmt+7Y1Q1YajoRrL1BFEmWQshDU5jhoaIj89hL2yGUEQMLJpECXs9asQBbDZbKiGgKHmUCOjBWJQvxL/pntwz4ojR868jD50FqWqBefqW3FWtaIlpsn0tmNND/DOe0vlTiorK5HsTkw1S+jhz2NZFoIoYRkqppbDteIWjGyS7ODpAomSZZJnnsNIRhAVF6LTV5DF0VXQVcxsEkvNYVu2Hdey7ciectKjnYgOH1p8CteyXdirmgEBuawePRNHdHlxtm7HverWAhmLjZE49hjq2HkcS7fgXncXroolSLHCs5s6uxfJ5cPRthOlrAZBsGFZGmpkgnz/KxhqBnugGsNQkbxlBb/kTBLXit141tyBrawRNTRAumsfud525IolBG75JZRgHVpsgvT5faTP7wPLwrnqFjyrb8MWrCM/PUDq/MvkR86R6T2CvaoVPRlGUByYuXSh6cWysPJZTDWLZRpE+06R7T2Kq3kj7mUbUXzlhW7roXPIM2Ekuxsrn8S0OQpNAaIMWNhrV5AfPY8WnSRtd2FYoIcGSBx/DNnlx7B7yegCeCqQhDjxw99Bsv8aSnVr8d6KsoI63l1Igy9dT3DjXcjectJj3Zgdz5E4/gSedXchlxccaWSbQj6XQYuMYZl6oY7OMgv/GgZaZBTJ6UVW7Gi5DIpNRsipGFoexenB7XYVxb3z+TzChT6QHZgWCLqGw+FEcxeuUX5mBAELxVVIYdodDkim0GbGsEwD0V1G1piNXQkCanQCDJWq+uaSd7m8ooLljdXEJYPw6b3Eu48hS+AVVfKDHSjrt1/8sGUi2hwotSsK8kHzZE8ELJS65eiJacRsHC0TL0Qk1VyhMcjuRpYkcuERwgKFZgiPm5rKahIRX/E9DwaDNNRWE4tPEB6xFec9LZ8lEZ6E+AQNtdUEg8HisWtqapAwSIfG8NUXFnLVuEgIjdQMbofC73/yN1m/fj2nT5/mf//NlzGSYfAFSq6H7HRjaXnMbALXvE212xdEwCIXHkEQQJILmzRJtiEIkAuNFiKjvmDJ712PyNGlcjSL+fr+JOPyVPNNeMsqSUZC9J88eN1TzTeI3VuI+Z00m+/9BRrEGN/73vdR4yE8a+7EsWI3ZiqCHhpCDQ+Sn+grRABW3IxS0YQgiNgrm7GVL0FPTGOqOTwrduKsXgqygpFJkBzrJT9yltSZ55HdvkKaTHFgZGKY2QTOli1Urr0Jf+MK1Pg0k+3PoOYz7LpEQ8vr8fLYv/0t6aHT1K3aQmLWfxUMyKeQqlvwrL8bm9ODhYnkCSIIIo4l61Gql1JRXoapxTDsErRtJT9eOC916Tr2tPl4vi+FkUng0JLkJRuB1TezaeNN2Nw+tHSCUO8pHn3yWZYvX170UvV6vXzh//wjw5E0lRtvx9e6GdkTRE9FSfSdIHruALGeVxiU8mi6dcWw/3yZEEc8VCy2LnTL+cipFj4jhq7rtLe3Mzg4yMjwCKq3jsypl9ENvdiJKEsSgs2DNjOMlo6ixaYw0lGMfBokG6aeR7IM7FqCdVKaF2dmyI2cQ6ldhlLZjGAa5KYGEO1u7EvW4syk0OOTeJwOpFwYp0Nm6c33YozV037iVIncydq1axH0POnuwyhNG5FsdjA0kGzo+QyZcy9iYcIs6Te1HOpoJ5IniKNlC/aqJpDsYKioMyNkzu/HtKBi6zuQXT4sy8IKVGG2bUeOTZLpPohod2LlM+STIbTpAZxLN+NceRNyRQOCpCCXLwFRwrlsF0rTekiHua1Z5JnxBO6dP0/6/D7ksnq0qR60qd6SSLQcrCfddZCG5ZvIJmcwJIVU50s4W7fh3nAv6Hm0yBCCTcG94R0IgkR+sgdb5RIEUUapWopc3khu5Ay2YB2+3Q9gsxcWyEJ9YQAznyHR/ji2sjqsfBbLULEQsM1Ke6S6DmDkszjcHrSR07iql7J8x52sXrUSTSu8H7nsrTz+X3nMcB+yuQ09qxbjO5IoYhoqpqGj9x1hT32eH58aJdpzHAwD/6rdNG29A9kTREtG6D38DNnBU8SP/5DKd36iGPG1bHYyvUdRapbh3/JuJJcXQZJw1i3HGYuQG+0k09dOrrwG9DyC7MDUTTLn9wECypK1WJaJrXE1YJE5vw/PpvsKZM8yCPjK0MmRT8zgN7LU19UX34/6unrcdhuJkfMIWMhON0ChW1jsJHFuH4rLi6+mGShslKampguEGgF38zrurDV5fkwgr2lkLhyCXJI1t7y75D20LIvoUCfvvec2/udHfr3opdre3s5n/vRvufDUV6nbdAeuivpChzZgGhoOe6ndniDJhQizZSKbOZz5KKahIkgKOjr6zCABv4+tW7fgKqsu6Zi/tNxj67oVtI/FkGwNxOf5TgfcLgwjxrZ1K0siMXfeeSd1QTfjHXvx1rUgCwJby3K8EnGgWybjHXupL3Pzq7/6q8iyzKZNm/in//iv4ufn7jeAo6wWNTmDHOqnrrau+PclbavxeDxEz7xE1YY92J2ewjPt9GCzKUyffgmPx8OStouan9cjcjQn1rts2bJibetPKy61TVy/agUj5y+wprH2uqeabxC7txCXdtKYus6xpx/BVrMMC4HIj74EmIhKoUhekES0yAhGPoWZTxeiA5aFpWXBAlv1UrKhYbKjF7BMvZCCdPoR3QHU8S4SR+cicxZ6bKLQKVu/FpssYcQmEOZpaI2fa6equg49l0ZyeRC8Pjbf/i72f+PvMIaOs6RpFaYgkpkJM+xyIweqsZJhZLsdye4kO9mHngxjb1hLtvcYQ6efxTQ0RMmG6AlSsWonqeQEUrgPackynGqUQJmXyZ4hHI1rWH/3BwkECrtNyzRxuZwc/v5X+ZUP/wZLlq1GMyz0fJaO40fxrig1ccfjx1vdhK6p5PsO8YkPf4iKiopiQXQymaS7u5uWlpaiyGskEsEUZLRUFPx1+LfdiRKoLHbLZcNnGDHS/NrHfgcEkVwuy9TEGERiBXFbf1WhmcUyUePTGAMnMNJxQk/+Y6ERAwHLNNDTMRJHv4fdG6DH1Fm2ZxuDz79MfmYSOVBL+uxecrKtSG4MQ8PVvAkrMUGjGzwBF25fkCVtq4mHGi7TZrPZbFi6iuAMku0+iCjZiudlqDkEBJxL1uLdcA+O2jYyQ2cQJBk1NIQaGiQ/chpLyyPY7MjBepTKZnJjnYQ7D2MzchjZBLquI1S0YAvUEDv6PbTwMJahY8zWspmZGMnjT4GWLRBVhwcjn8EWqCW+7+vYbDbywbuI7n8BNZ/DVLOY6RiOli0X07qxcdJd+8mcewm0LAMvfAPR7sJSc5jJMGYuReqVxwvnKopYpoEg21HqV5Ab7yJ9+nmU6qWINie6lkVyl6HUrkCPTiKVNyDIhTStHh5GCw8jCgLkC7WnAmDm05j5DK7WbUiigNp3lMY1GxhJzlC1eSsrlrWVpN8gRlnLekJnXsKemqCsfvlsDaNB1vIztPebqOPnyWTCaNE6YqeeR42MI3srKGtoIR+ZID3SheIro3LrvUybBumuAyS7DuFu2Yw6M0byxFNYarZQOhGfJKdmihFqxeEkL4pk+zqYTIYLNml6HiM5gxafRq5oInPq2cKzYOogO8j2HEbExLn2Fsob28jPjDJ24gWkdIggKUzDYOj04aJbxdoVy9n7vf9Gj46j5bPo+SyZ8Bj54dPke4+iNK8jOTGAq6K+8Pezz5LtOYxcVk+m+wh5TzOprkFSo11oA+20NTdx+sXHFiyk/5WPfaJEpHdOvf8LX/wKI099GQMJS82giCZEhkkpCookIWBhIaAaBmYqjJRLkL5wiMi5wt8FLBx2O4auYRN1fF4/Xr+fVCREx+GnL4uszEVihv/uS6RiAzS2bUB2+9BnN5teMcEv/+KvlURiZFnm05/8OL//x3/Nhae+SuPmOzB91STG+xg5sRdx4iyf+vxnipGsSz8/R1wz4UJXrMvK0uzMc/K5R0quVXnAR7TjEHFZwOd2Fb8T79yPNtBO+abdxEMTP1VNCm93zKWa5zzQf+WBn7vhPPGzhvmdNFMDXUTHTmDIDvTwEOroOQS7C9HuK+w+nV6EsnrUkXOk2p9E2vkBQER0OMHQMXMpzFwaQ8shuvwINg+WoaJHxjBSEWxVrXiX70QJ1mBmEiT6T6ElwjgCYVas2oxgauimwrAoIjia6N77EGMD3YUanVkLslU77yTo9zPTdYy+08cKk6uWw8qmWNZQTUTPkB7txEJAi4xCLknuwn7UVBTZ4UWQ7ZiGhjY1yFQyTLCimvUrltJYX8PNlW1oyRhdP5qm+ab3EphNAU32n+fcgWeYGhshGp4sGJKHU2y7734i8RTCZJZ8MkpqvBdv/bLitZ1v4p5Op6mpqeHf/vO/i7pO+WyadCyE0xvA6QmQSycZ6OvFs2I3gS33FK2UBLsTT8MyUhcOkjJtjKVMFKcDVdPAFcTIJEj3HSetZcE0sARxti6xQDgEdxnI0qwZtIIIqJO9CFo9gr8SS5QxZQei4iB9fj+O5g0427YiSnYMNVtIF3YdwIxN0HngR4iSDdmm0DN7Py6cPc3Nt99NZtbdQhAKdTnkBhDLGkDLYxmznanuIEI6iqblSXa+SOLEUwiKEyFQjzXRQ3b4DOJsd6cgSuTCYyhuL2YmQeTF/0aULk4PpvEsksONaXNihocLZFJSwFOJnolDKoIoymST4VkttzDaxAVMxQepScxMHC06ieEIIooSkrcC/+4HMeKTqOEhRLsTuawBQXEUtBxntcZMBCzvNLnBEyi1K3GvvhVRtmMZKmpkjMyFg2ixKeJHvwcICIoD0V+LqKtILj+WmiY72oml5RBkO/Fjj2HpeWytOxEyUcx8GtHjQXSXoQ+eIDt4gupbH0Q1cox3vIDTHWT7zt2UlZUVPZnn/JprG5YQPWNiV5NMH/oe+WQUuzeIoGbQwiO4Vt2Oq7wG0ekFQ0Xy14Ch0fPYlxBtCqLNXmjGsbtwNq8nicDMj75IRJJBELG0HHWtq7nvPe+nb2iYcDSKYZpIokhAUTHUCGbjOmy+MkTZgWVqSB4/hpEkPtlDMj5ZJJtWLsXKliVY2igzB79JQrhY8vDB//VxHnvyaf7tf/08ps1VjESLWoZKvxvJLTL4g/+DblrIosCSqiA3fejnOdh+oki6JAyWBN1s3rGB/UdfIXrgW2QD9xE98DSoaT76yw/y4Q9/uESg2Om0s3nNSn7lY59g3bp1PPPMMyWiuw8++GCJEHFVVRVPP7+XZ070MX28k5iaxxIkBMtAUew4RQPNyCMF6mladwv2shrykUlCZ/djDXewalUrw/u+c1kR/0LHLhb9H3+y9PO/WYjEXCoG/MADDwDwhS9+hdEf/zOh/B5G975Mjc/Bpz7/GR544AG6u7sX/Pz8a1gfdPOpv/qTizp285oO7ljXxAN71vPI409e9p0P/s7/W9Cx+ylrUvhJgCiKLFtWWIuu5IH+VuIGsXsLMddJM3jyMOfbX2bHprU4W7YUFtSaNuTKZrTQAEYqhpkMY+SzSGV1aFO9ZM6+UIjGUBAO1kJDyMEaXG3bcbdtR/aWoydnSHTuI3V+P2p4hETngaKgqikpCAgkz+3lfHaWMJk68UgELZfCMAUEU0K2u8jrGn0XOhk+fxxTy7Pqpnewdd1ObG4f0elxXnnxx4S62rnlA7+KbveSyWTQkrU83/EjdMGGe/luvKtvRSmrR4uOk+zcR7r7MOH+c8hYOO0OrHiK0y8+hmhqrN2wGQSByf7zHHnqYcxgA1Lbbso3lmPqKqnBE+x75Ks0rNuJq3UbVmKKqVMv4altLY4PCnIglihy7Ngx/umrX0Pz1bP21v8HTZA5duQQkXwvznSU3Xe+k0QmhxDWyCdmcOsJ7JVNmKaJAPTu/wbIDuyNa/C1rMbl9ZOMzRBJPIMWm8LS80iKsxAhNXT0dAxZcYDNgZWOIHiCiDYHpqGDWmg2sdWtonLz3XiXVVGRbyB89gDp7iNkh8+hhQaLUTOcQYzoJKLiJC84kGRH8X50t79EKjKNJdkKQsSzkTlB17HyKazYREFmxOHG0lWsxBSmlsccPgX+qqKAr9ZzpEB0FAeSuyCIa+kqVjqCGo4XflNxIiouEEUwTaxcEiM+iaA4kbzlhVpMXUWPjaEbGp71d+NZfw+Sy4c2UyBcqZM/RrDnsQWrkdwBbP4KjOgUpppDCw2ROPgQZi4NWGBBdvg0guJEHTyBbncVhIu1PEYmjiApqFM9WNkYsqcM0eZA1zXyo10ImIiyrzgObfRMISXnDeB2uQuuLZaFqecLHeOSjdz5vcjOgoSKEZ9AzyaRnH7IJti1uhVrbRtDe/M4FBuJ0R7OPPfdy3xGg/Ut5MPDdD31VXD6CgLioz1o8WlEpx8z1Ed6uhuz6Q4kxYmRz802GDQgOlwgSAVinokTPvI4YGHzV86OQ0NWk0haCkUw2bVjB/FEHFVVsckyx3/4NSRvBY5UDCU7gwnIkoRCntGMilKxBJuvHEG2Y+l5zHScUDLL3//FZ6isrCwhMY888gjDkyFsTZtxtW3H5q8qNK30thMdOcH2jWsJZAr2eG6Hws5Na/i1X/pF/uEf/qHEAaGsrIzP/H9/RlXzSjTRgcNXRrChDZuZo7N3iK6uLuCiQLEgylhYPP3003zkYx+/THT305/8OA8++CD33ntv8R2PxWJ8/8lncdatpW7lLmz+SrR4iOj5w6QuHKR25Waqt95GPJ0hOzWIKAq0bb0No7aClfWOknRvW1sbDz/88KLH/vu/+asFi/6vZDd27MDLPP/888Tjce5/9z3cddddnDlzht/7g88u+vmFXCSARZsOPv3pT79tnSdu4O0BwbIu0Zm4AQASiQR+v594PH5VRtdXA9M0+d3f/ww/eOEQ9uYtaO4KpjpeRKleiuDwkzrxw0IjwKy9mJmKgiBgX7IeV9N6bN5yTCNPbqyH1NkXcLZupfre3yxqnVmGTqL/FLF9XwfAFqhGVFyFBS02iZlL42nZSOst78db00Q+Nk3nc98mM3gGZ9M6qna+H3uwIFab6Glnpv1J3FVL+I0//nJRTwnL4tCRo/S+8hL1AQd3fOgTCKJIaLCHr33+N3Cuvp2yPb+MzWYrjtsyLcIv/jfpsy+wY/dNuLxBZAmqvU56x6ZZ+97/ib+qjr3f/BJx0UvyJcgAAE6+SURBVEvFxjuZGLiAaZgkug4imRpqMoKo5zAdfpxLN2NN99Byx/+Dq7Jx7ij0vfx9Ysd+gK+skpxYsAYLBssw/A3owSbKG1oYO/Q4ASvFmrsf4IX9h8lMD+Iwc5S1bkRLRTFNnaEDj6PUrijUIuVTWFahozExeAbBsrAUJ5IgYFkmgiBiICDNauBJNcsgNYMgSohOL4YloI2cxrZ0Ky5vADGXwHIFML01xPd+FUPNzpLEwiiMXAZBFHCuvgNZTWHk4tjcAeTyJqLtj2NpKkKgGtE0i92yei6DaOSwte5AEgoSJ4LiwLAE9MHjCP4anPXLZw8hkJ0Zx5zqwd68CefSTYgOH2YuUajZOvciiCJSoAbJ7gZBAsvASCcw0zNIdauwuXzFY5hIaAPt6E4/7trl6IkpJGeATOc+ZG8Ae+sOlGANsmxD1zXU6CS53qMYyTC+3Q/gaFiNICvoiWlS517GmBnB3roVR/1qBFnGUHNkug6hT/eiNK4rNKJgIXsCpHraUUfOYKtbiXv57oK0TzZBbqyTbNd+LEGkavcHcVY1IcgK6akhEv0nUUfPgsOH5HAXHU9MQ8dKhjDyGXa991do2bSbvpcewSdbnB4O41uxk/KVO0tErCeO/ohsMoZ71c24V9xcaNCYGSXddYBs71Gk8iW4l+9C8QTQTZP0+X1oEz24Vu/Bs/YOBNmOEZ8mfuz7aNP9OJftxLvmNhwVS8jNDJM8t49sz2Fq6+r4xT/972LpQWR8kB//998Tz2i4G1bSvO1O7P5KcrFpzv/462RGz+Nq2ULljvdg91cW3+f4uX0s8Ymc6TheJA+6rrP95j1MOZtZft+HSU4PoWaShaYIXeX8D/4JT3UTH/jVj+GtqCE1M03/yQN40uMlwremafLLv/Zh9h4/j2fZdmS7BxEDEwk9nyJ6+kU8isSKW95Fy6ZbiiK6R597jO5Dz+BqXE3TrR8siu6Od+xFHD/L3/7xZ3jwwQeLx/i9P/gs7WM5pPq1xNOZItF2yhKDr7xAlVfhHR/9LIlUslgv7Pf5iU2NMfTSt/jHP/+jYiH/t7/9bX7/j/8ao3YN5UvXFK31ZgbOIU2cKzn2HEpspDbeXBzH/GuycePGok7myZMnr/j5K3nhvpFE7a0gfXP1dXPHeLtEr94KWJZVvOeXyuS8UbgWTnIjYvcWQhRFbrt5N488+RyKuxybmsbKp8iPd5MfO48crMXmrS901gkiVrCW/Oh59MgoOUNFtTvBNNA1DVvVUixNxdDzSLK90IWpq6TPPI9od+FoXIu7bRu2QC16aobEqWdRJ3oQ/TXYvGWIsoLo9OFYsgFTcoCRxxaoRpBsSN5KHC3bsE8OYuXiJBJxAsGywiAEgRXL2ohMjjJ04lmGzhyjYdUmzh95FtHpw718Z4H8mBbCrJSGaWi4V95EfrCD27Zv5N3vfjfBYJDW1lY+/dn/TefJAzSt20E0GqHyprswDZ1ceIzU8FnsVUspX70bbC7Sgx3kQyOkeo8hGCrZWAhneR1aPsvkuaPMnHwBf8smLLsbn78C2e4mnI4TO/4STbd+ECxwVS5hvP0JKvtO43Y5yKhZRl/5MWNHnixIZQgi6IVrbCpuSExi6bmCjAcylpmDZAwDA4piKxKmoxAZUScHgYLkRoEUiQhOJ/orPyAnSvi8HhLJFJZpzgocmxi5+UbqNizRInP8B5c9P4IzAFiY02MYQkF4upBqEzBEC+PU8yBYs8cWwRLA7oD4WbSx8wU5jNnPy+W1pEPjpM++AIYOkgzLb0NAQHIF0HQBbboLjHyhucJXC5qK0X0UVRIvjg+pcAniYdKhgaLkBqaJoebJjl8g13MYr8dBMpXDcAawOb0YqRkSfcdJde4DPYcg2TFME0d5PenYDOmz/wRarvBbrnIUXwXZyX6yPUcK0jqKE8qbsJc1oMWnSZ1+FksvRD2FYENBONnpJnzhGFb7k6DnQZQxHX4EJNCyWOKcbpyFpalYSGBZnD2ylwsnDmPm0wTcCpa3HrluBaHzR9BSUWyeILa65VhlZ5BFhdq7fp304GnUgXawe3BtvA8jWyDKWVFA9rhJZ3KokQnkYB2CK4ChqVjpGKaWR42O41q2C9eW92BER0ldOIDkLqP89l9lBouJc3s5+sNv4PL4MQ2NeHiS6eF+/OvuYMnu9xE9f4BcdBrLZkeoX4PT4cMwdXLhUbIj57B5yghueQeGmmPk9DM899xzvOMd7wAuarCVt7XR+/R/kpkZx9B1RElCS0ax17QiLVnHxMQ4uXQCu8vDxrvv5+Rzj5QI33Z3d3PolQ4MRyWTJ19GV7N4PR6SqRSy4sDIZbCCK2jaeheRsW5Gzh/H5S8n4arFtnQbDpcdb20Lgijiq2/DW9fChae+yt998Svcf//9yLJcrFFed9svEqiuL0YwFUUhn1eJhidJj54hOjVSdAixTJPIxBDpeIRYNFYUFp4TFc5763CIMH3ucGnzlLeu5NhwuY3Upd62c2LA69evR9d1ZFm+4uev5IW7mKXYlSzCFiNvV4owvlFp2vnHkCQbuq5S7XNy1+172L59+xtGJN/OUUld1y8Tx79euEHs3mLU19fT0LiEjBZnY7nFSC5RIGrBWmzlS3CvvQPJ4SE7cILs0CkEuxtbeSPutbcje8sx01Hyw6fJjJzHzERRpwZQKpeAIJEPDaFFxnCvugXXyltwe33IDhemTcRcvousy09u7ALZ1E04vQGSkWksBLwrd5M6+SMy493YArUIAtgEA8+StWR6DhEeG7xI7ChIFGzbuZsXTz/L0MHvEzr9EuG+zoJ5fUUDiAUF/jmIooi7oo6YTcHr9RIIBGhra0OSpGLL+Om9j6HmcijeIOlEjGR/B/aaVip3vK+YZpA95VS2rmf48JMkz77IzIVjJCcGsEyN6NmDyA4nmfEesppZSGuaOqIgIniCTLT/mNC5QyTH+8jHpjj6w2+imxAb7Cy4Dzg9hdSqZWGYKfTwIEh2BMVOYUAGaPki0REUZ0EaAyCfwcqlsEiBZC94qc79VjaFlYmB7MDm9vLO976LR594Ci0ZL5ANQQblYuQITQNTA4RiE0ThXwMrGytcUNleqMEURSzTxMwmL/6WzQ7M+618CpAKf8Mq/J5poocGEOxu5GDtxRRm30EsNYOeTYJUsIMTxMK5GYkx0LKguJDdfhClQgNFOlYgYLIDyRMoaMaZBno6jpWNYalZZIeT++64j0e//wMID2EFqpB9lRiJKQSbAjY7gmVCLkFuYGLeMYLF31LHpxDsLuRiqlJFn+4hp+YQ3UEMpxeEQpTbmOhDUuzoiTCmNo4gy4WaQ1PHmhnCMnXkqqWFyPiskLcg2TAzMUS7h7whoOkWWDLj4RhiaIZwd0chNVy4Mxi6hlJWj5lL0/cfn5hNqwrFnbuAVZCSsSzu3r6O7z32ROGRyCbInH6O9ImnkNyBQjQ4n8HSVWYe/5tC6p5ZcXGHB1frFixR4djj/41pcxWMi0wDPZskN3yGjq88B7JjdpOgomeT2MobUMOj5Ppemb3vAoJsx9u8Dh2JkydPFond5OQk+VyeiY7nyedyWIaBZRkICOhaHn28h1x4lFcGynA4XcU0dNOqTXT1HS0K3549e5aZSJS8EUV0+rFXLOEdd+zi8b2HyUWnCu9BaJxH/urjaJqOCYXn0DAIrNlDPhUmOzNWjMALgkjdpjsYeerLvPDCC9x7772lav+CUJRmAYjHYyieANm8Sj6TKoxttl43Go2g5nLkYxP863/8J4qiMD09zdBECCFgx3AECa67p1jOkuxpx5yZZDAUKh4bSpvfsCwiE0MXay5rlhTFgHt6eornVWo7dRFX8sJdzFLsShZhi5G37Zs38uiTz76pRvUlEl63PUijkuaRR7/Pic6zPHOog7amBrauW/G6ieRbQVBfK0zTZGBg4G3TDXyD2L3F8Pv9yOhkhs9ia74Vz+rbIViP7CkjN3yG9Jnn8ay/B/eG+9DjU1haDkfLViqra7F7g8hyPdKqDZx/5pukLxzEbmYRjXxB1DM8hCDbCuk1UcBmdyBJEqplIogCrrYdqBPdJIfOYWbi5LNpBEHAXdmAqtgJuBTcFWWIooil5clFJrBME2seSZuDXTBZtbyNT370lygrK+PkyZP83p98ATUySmDpOgzTLGiqCQKSKBIb6EWyDNatW1fyO3Mt41/8yj8z2DnB5JkDCJaFaGq4mzZc3C2rOQQBXG4PLo+fNCaZgVOkJRuCoZFPhLFkB65luylbthN7RSN6bLJQ39dzlGw2XrAcs7vB4SWVzqHGpwppR7cPyRko1EjpWsHZIKUh2GzI7uBF4mOGwdQQFGX27zZMXcXAQpBsBXKgZRHtntk6Nw0QMSwD0WZHcvkR7W5kpxc9n8XSBQTZhuQpRFAtXUVPzmDl1QKJ8ZYjyAqmlsNIRRFkBUFWwDQQ7e6C5qGWL9TA2RyAheQKINpmfysVwcqpCHZH8beKx1CzeNbdhXvVbdjKG8j0HCF58keY2SSmmgXLRHJ6ERVnYbE3/v/2zjy8qurc/5+9zz7zlIQkZCJkIIwyBMKMOOCAtV69VqtYFa3eatUWp1artw63Tr+2Wuu1otbhWr0qWofaq1ZRUZQZJBAZQkhCICEDGU+SkzPu9ftjJ8eEBESrhJ6sz/PwPLr2Onuv79o5a79n7XeIGjkRk0eguVMM3W0NKF4rREJG4mFnIpo7GQC1vZFQXRmKpmF2J2GyuzF7U4i2NiB0HZMzAZM7xfA1U01GURXbASKtdZjcyZiTMo1arOEAalsD0fYDmDPG4xpn1KONBnwEq3fQVfopQgHzsBGAINS4F5PVhsnuNfKpRYKG/6LNafioqiaina3ovgOoXmOHWkTDiM4WFFXDMXoWzvEnYk/OQrQfoGnzP/Dv/AxFs6I6E4yo1LARqRturjZyACoKJocDzZ0CCoSbqol2NBtGc3cAiuZJRjFZMDkTiHa2IkJ+RDSKqlkxOTx07lyJarFjciXFIl+jna20bfi7ETHtGo4tYTiq2UIkFCDQVEdn/R6s2ZNImH4O5qQsgk378G38G4HKz1HNduNvXVURQT96lw/f7o0oisL7H65g4cKFFBYWkpqaSsDXRNTfiRCGn6qqmYlGwuj+NqN84KgZpMw9l8SMkbGaqSVrPsCrBGOJbyORCF3tbZiSsnCMnYdnVBGO/GEkhnLx7VqPb/O7hJr3QVIG5sQMTGYberCLaHsjrV+swOpOMoKAeuFIziSKibq6utjaeahs/16PFzth6jtasdidvfx1R5AydwHtPh+OUAv7RTv3/P4RJhaMpMvfRcL4sSRNPytmeFkS00mafhaNQtBWW8b+/ftj1+gxLDt9zWx6/6/9fC7HzjyZUFjH5/Ph8Xjw+XwxQ1ToOi11ewn6OzBb7Kx/++UBa+EeqqTY4UqE9Xk93Nt42/wpb/+/PzAsbyInnHvoHcZ/ptzYwbuYB/bsZP++nQRcaeSffRbtPh+hUAvbW9v/KUPykBoHqR7usY407I4yPfnTIq48HGn5mNx+FPcwzEmZaMNG0LH5bfy7VuPypmBJG2U8BLra0NTev1AF3pyJdO5ag+hoJDnvOBSzGbXGhq+nvBcASnd0puFDZLFY0cwWRudkkTl6IoFAgFXrNhE4YKR/sLsTsdnsxhXMZiIdzRDsJCk5rY+GWH6knExOPfVUVFWlsLCQh/77MepLV2NJHoHV7sJkMqFHo/g7fXSUriZrmIsFCxZQWVnZ53yFhYU89cRS/uPqa9jV6SNt1AQ6KhOwuhMIdPgw2+wEWuvRTCYadm6ko6oE75hZFJ14BimZ2dRX7+Gzd14j3FpHwoR5KIlZ6MLIW2ZOGUlXzU5EOIijYDb2MXOweNOItNfj2/IhgfL1aMkjSTj+4lgC6Ob3lqKAUVtz3AnGDkhTNW2fPU+kucZIdzJuPponlUhbPf5dq41XhGYbrjFz0BLSsWZNAHQ6Pn+HwJ7NmDPG4hk7F2t2Np5ZbihdTXh/KbbcqTiPW4CWkEaktZ6OLe8R3LsFS+Y4PNPORnV6Ce79glBDBcGabaAahrs5NbfbcFTo2r2OUG0p9rwi7GPmYHIZO7u+jW8RrivDljcd13ELMA/LItxUTeeOT+gqW4N/9wYST76CcEMlwZptOMbOJ9Kyn+C+Emx5Rdjzp6O5k4j62+ncvoJA+UZM3uE4x86na/c6bCMmIHSdYPU2rOljsKQVoJhMqFYnbRveROtoxpY/He/EBTgnZJMUHoWv228s3FRDwryLsOdNQ9GsRNoP0Ll1OXqwC2vmODzTz8bkSiba0WgkHN65ikhjFV2VjtgDSo9GUexebCk5uGadR3h/qRF0kDzCMLytTlSL3UjX0VSNbeQkQgcq6SpdjSkxk8QTLsWcmEW4tYbOLz6iq/JzsDiwDMvE4fYgXG6aik2odi/2vGm4Jp6M5koheKCCroqNdJVvAtWEe8KJmJNzDO0WO22b3kJEw1izJuDIKMCeO4bEUybQsuEtgtU7jNeu445HhLsIt+wnWFOKCPqxZIzBM/Pc7vQv3YmWv/gQ1ebGOXYew8bPidU/PVD8EV2VmxFCYB6ei6qYsAzPJeL3GeMdNQN7/nQjgl4PE64rJ1hbipaQTnh4TuxhOGLECCKhIJhsRrWacfMxJ6QTbt1PR8lHdFVuAlXDmZSKqlliNVOrPlnGgfLVsZRCbW1tCJMFe950PFPPRFMBVUdLGI6rcCG+re+heVJwjp6Da/RMNHcyIV8j7bvW4S9dZaxBB+Wm8zfWYCJKWpqx/hyc7b9PYXnA3FGHVw1QuWUNDXsriCZkklp4Cu1N9VgJM7loFknDhrH5vWWsWb8RNIvh4zlA6TB71nh8W95H17+sBNI7qbkpbTQpc0/p5XO5LpbUvMf/yePxxILlqnZsjhmCwc42WhrrSZ5wWp9auHDokmI94zq4RNjhXg/nzV7IttJd2Ds7vnwjcJhzfRMO3sXcvmo50yZPIHP28eiomBxe2vcHySuaS8Xa976RIXmkr8CPdj3cYxk5C0eZiooKnAkpJGbmEw52oZqM10QiGu4ufTPVyB3WuK/7dZsJwkHM1r6lZ2x2BxZNI1KznbaKTbRXbScabIdwF3rTXhx2K2F/GwFfM3o0jFnTCDVUYtFMZOfkkZKSyoisEaQmJdBRuhpdCCM9hB4l1NVB475yaKwgQQuzp2QtrXXVREJBWuuq2fzeMiM/0kVfJsrVNI07fvVLLAd20rJqGc0lK2jetormkhW0rHoFy4Gd/PrWX6Bp2oBfPk3T+Pm1PyVNbae+dDMaOi41jCnSRXv1LkLN+zHpQboqNuLOHsvwKSeSPXo8aRlZ2DzJ2PKn48ifjm/HZ0ZGfSGIhkNE/D6i/jbso2bimnEu1mEj0OxOVFcKrsIzsRfMRg90og3LQrXaUBPTiXY0Yi+YjWfuRVjS8lAtNrSULPSQH3vBbFyF38OcnI1qc2LNHIv3hMuwjpyEYtJwz/sR1rRRRNvqMSWkYxkxAUvmOKK+BgKVm+iq30OgqphIaz2WzHFYR07Bkl6AanWgJWcZCYoLZqM6EzGn5aJ3tmBJy8dzwmIUiwNLag7OaWdhzRiD2ZuK6jJe59lHzcQ+bj62rPGYbC5M3uGxJMH20bOxpOejWGxY0kfhPfFyQ3dXG51ttXTu/Axzcg7OyacRqivDljsN75wLsWWNR7W6MLmSuudqFtG2Brr2GZUxnJMXEm1vNMrFTTwF28hJmJyJRKNhRDiIvWA2jnEnYh2eS1TRsKZ9eW3QsR53IubEdFSHG9WRgFBU7AUzsWZPRLE4Y7uZluGjMNlcWDPH4px0KokLrsIz41xMdjcmmwstZSS6v43Avi+wjpyIZ8YPMKfkoNpc2Atm4Zn5Ayxp+Ua5sskLDe3BTlRXIorFijkpC1fR2dhHzSS453NUPYDJZCLQUktgXwn2gpm4unfFhIhiSc0jYf5lRhBPJIhj4inY86Ziciaih4OAgn3UTKxZEzDZ3egWFybXMEAxkkJnT0TzpKAlpGOye7FmjMFeMAvV5kZLzES12LCm5uKe8QM0TyrW9NEkFZ2JJTEdVbOgOBJxTDgZx6gZRJqrCTXsQdejhBoqiTbvw14wE8+s81FMJkyuRBx5RXhm/xBb1niivkZGTF1AhzOD519cxtatW0ExYR81A+/ci7CkjESx2Iw5md49J9Vf4G//slSVooA5eSSK2R5ra25uxmR348gvQggdISASVRACgnUVRtm7nEJck07DnJiOopmxJqXjKfwe9rxpCFWjq6kudj7Rk9g30cmCBQuAL3PMuTr3s/m9Zf3WpDTVx923/ZJM0Uh9VSmq3UNHbTmJFsHUSccxLDk5ZtB0RFQ0k0IoGIhVdo1dGwgFurBYrYwf/2XC354f5V3WRDLnnI19WEYvY/dsuqyJBNpbYyW18vPzSbSZ+Oyt52lTXaTM/SHZ378WT+H3sI6cTHv5JqLN+/r4ZR2qpFgPB5cIixlWU+b1M1DD4TCurLF0dgVoqdv7lef6JvR+Pd5St5eW1mYswzJRurcWzFY7ui4Ih8OGIVlVw+7du7/WNQ6nMWagfoPzftscS0blsTOSIUJbWxtWu5NpM2aztbbT8NMKB9GDXd3lfxJQECjREAQ7jQeQyYTF5uxjdGn+Rgpyszl5YjbDW7bj3PMpI6L15KR6MDWUYon4SXI5SE7ykuR24rTbCFRuwhL1oygmY0Gsr8HcXI7lwE7sapSmXetpKN1E4861BLatYFyiygN3/ycTEqJUffwim5f9gaqPX2SCN8rtN/Xf+l60aBFLrrwEreZz2ta/QfOaV2lb/wZazSaWXHkJixYtwmQyMXr06AH9EHpeyxZle8C3n/oN72IJNJOVYGXWtCkUFmRjs5ixp+WT6Hbh9XSXyVEUVFXDnj+dSKePaFs9NpsVVYGuik2omhV7fhEqRt43EQ0bFTlMRsF0hCC4ZysA/i3LUa1Oo2C6akS+At3Hle5C6kY+v57XbISNYu0mh4dIzU60xHQEgnB9OXpHE0SjWNJGYZu0kM+s07FNPB1r+miIRon6Goj6GlBUE9HmGlTNgnPcCYhAB6G6CkQ0jMmTjN5Wj8mRgDWtAN13AIRAS0wn0lQNkTDOcfNRohH0oB/FYiPcUAnRMM5xJ6AInaj/ywANVVVxjjsBzT2Mtjd/H6tIEtpbAoqCo2B27Me9YrEZtVdVE85x80HohBursOVNI9JSiwj5sY+aZSzjegSTJ5lIwx6jvNG4E0APEwp0sbxGISIUVFXBMe54NFcS7Z++2H37TETb6hDhLpxjj0dRTegBw09Kj0boqtiIPa8IW/50TBYHimY2An/yp2PPKyJUt5twUw1EgjjypkM0bLz2NlsRIb+xA5NXhO5vR/cZtXsVRSGw1yj+LnQdVVFj1w7u2w5AZ+UWVJMZ59jjURUFPRJGCN2oLyui2POnY7K5CO4tQUEY2tvqIRLCOXZet5tjmA9qNQJN+xEhP86x80BE0YOd3a/ugygmDee4+YhQJ5GWGmNN0HWiLfsNwza9gHB7c+z+6dGI4VpRMAvFZCbStA/NZCK4twTV5sI59niIBBGRMCZ3MiCMHehRM0FE2V+xM/Yw/PTTT1EdXtzjDI1C1xHRCCIa6Z6TeSiqRtvu4j7rj8NqIblXjVWn04lJM2OyGnk2I5EIy6sFkUiEcEMFqsWOLbfQ8CnURbe7p0BRBPa8aahWB82lG4gEu/DV7Kb07aeMxL5LrutToqpnjRjvjQy4Ji1atIif/uRK8nLzmD1nLnOmT2X2zBkMS06OncOVZOSTTE9OQm/aS2vVDkKdPvRolFCnj9aqHejN+8jNymDYsGGxz/X8KE/KGkVTdQWhro7YnDRVV5CUNQpHQgpVVVWxNU5RFMzJ2VhHTDQSl6smrAmp2HIKMSdmEKjcbMxHN4cqKdbDwSXC+vgdHoTFYsHiSiDcy+/wcOf6JvR+PR70dxAOR9ncmUC027ALB7tQVQWLxfKNDcnDaYSjXw93IA73XBsM4vpV7GOPPcbvfvc7amtrmTBhAg8//DDHH3/8oI6p54tgUaKcfvws3v90HQ2dEYLRIAQU9EAbqqpid3loKluL3tlM+shcOurK+5W2mTFrGr+9/55YNQWv10t7ezu33Hkve7d9RDgtH5PNQzTgI1pXwRh3hPyRY/sk6ZwxMpNr7/k16zZtpnjHdrqCYexWM4WFo2JOqRdccMERRSJt3ryZjV/sYsrCi7Cn54PFASE/XbXlbPxiF5s3b2bKlCl0dnbidDoHDAvvyeR94rw5/Pef/4dI517GTzoFV1IqFZtX0dbcQNYEC6NH5cdeLTidDkyqgslsJRqNEO70YUnKxGo2I/xtYDKheVNwdD98RHdQQ0Qo3QXTTUQ7u6Pl2hpQNAtaUnd5JQEoCtHOFhS1p5B6u5FqpKf0UzQS87+K+H2YOlqItNSimG0E9pdhyxqPOTUXsyuJFJeZhkAS9oJZRBr3EKzZiTVnMpqiooeCoJrQkjKMFBxdPlSLDVWzEQ52opgtqA6vEcUKRmLkkBE5ak7KJFRfYYzF4kAPdKAoKlpSJsHanYQb9xIxWw0fv8R0Q7dmIdLeDORhcg0jsPcLQ6M3lZ78K4piiv23lpDWvYMcwuQaZryqR8HkTkYEu320zDb0cBeoxrVDDeUQjZBsEzQFu1+ZJWaiaBairbWGESOi6KFA97lSjBqaPelffA2IoB/b5GlE2uqMvHpg1OlVjB2wYM12dH+LEXDgSEBEAoYvomIy5koITC6jqoke7sKSNgoUE5HGfQRrd6FoFlSr08jPZ9JQQl3GNUJdYDKjeVKMlEEt+9E7W9ES0jB5kjF5UlBMFqIdrYQa9xINdBJprUMI0NwpRDpa0NsP4G7ZRd2BeqPdm0YoUI6iGzkOFT2CUFS0hHRAQQ90ouuR2PUVzYLJ7jFq+XajmjQjvsJsM35cCB09Euz+sdE93lCX8YPHbDWmUgGzJ9kwmiPB2MNQVVXj792dgtliNsoGCoHATDgSQHMno5jMBJv301i+tU+N1dZGF1u3bmX79u3Y7XYsqk64oRJn/jB0XWeYVacpAKbuhMsmqwPNZOoTWKWpKjgNg6dt11q2H9jzZaLeO/unG+m9RhxqTUpMTCTB48Sm0ifAooeO5gMkuJ2MzpzM1uYAHZF2OvZ+EatU4bBouCwBTpw1rU8prp4f5TNmzaG8ah+tvcqNJbqc5M2aw94PdtPa2kpHRwc1NTU0d0WYc9o51LV1fdlfAYsqiCZnE967uU/QyKFKisHAJcK+jt/hV53rm9D79fjIiTMxqQr2QBNBl/H63NdYR6LLGUs5800MycNphMGph3swQojDPteONnFr2C1btozrr7+exx57jLlz5/LEE09wxhlnsH37drKzswdtXD1fhJ1bVzPh9FOZNGEcG7duoz0SIBCKGL5EHa00b34PtW4HozLSSFd8pIzMHbC0jaZp/fwjfvebX/Pc/740gKF28yEXxMMZb0dSxPlQfhAAYvL0mB/EcccdR3V19WGjh1RV5ZxzzmHkyJHd2ddfIhTWCQc6cJt0RmcN7/MLPDMjE5fdgq+hEhWBLnQCvmajwlpiCr5wkEhbA+aMnB57AbvLRXtLM+GmaoQexeQyon61hFSCVcWEm6uxJI/oTgJsGAZC7y6k7nCDao5FrComDb2zlai/lUDFBoJ7NqOH/Ag9QqSlDrVgJoqioGlmpqfovNdqJqQoWDPGE6gqMYxPoaNarOhdbYSbjV0b1e6BaAg9EjCSL4dD6P42NO9wY85DfhSr3dhFa64BjJqZQkSNElNCx797LYGKTYbB0r3DqNo9aMNGICIhTB5jHqMdTaiuRCO4oq0Oq2usUWpLGFUphB415ioaRbXbiXY0YXJ4AUGkrRaTzYNiMqGHA6hmO+i6UY0E0MxmpqcIlldDBJVwS41x7YR0I1Y3FDCqMwhBuHGPEYhhN/yURCSMUBQjEbUQqI4EFEAxGXkSe+psmhPTiTTtI9K6Hy0x3YgU1o00LgKItjcZ2i12/GVriXY0Em6sQu9sBQRoFswpOYhoBM2bihAC67AREF1J567VRBqrEOFgrEqHandj8gwnGvAR3r+DaGOlscvmbyXqO0DbpreIttaiKTB1tIu39m4m3FhF1+51mD3JOJwugm2NOFwu/J1+wk37UACLy4upJxeWN5n2cJCovxXN+uXujdXuxO9rIVBbCgLcWaOx2my4ssbQuWstoQN7MCdlGGXTIkYgjgIEWutB6GRl58UehlOmTEF74z1CTXsx2RyoJrMRba0Yfyuhpn0oIsqUohmkj5kYq7H6zmN3Ul9WTPHWbUaqDhFFBNpp2/R3TO5huJMzmJVu44PKAJ1WOyIcINxUzbARBehCxAKrVEWlsXwjTquJB26/E6vV2i+f26HWiEOtSYf1xevlH/yjC8/nvocepcPWhT03H8x2CHfRVVuOW4T7uJpA7x/lOrNnzuiTbqW34eLxeKiuro4FT4wYNZZczdyn/4EDB1i7fhO+5jra9u7E4kn+ypJiA5UIO1K/wz1b12Iymb/1cmM9r8fv+f0j7Nm6FpfdRi61fNHlpLWxAXPEz+jxxyHgGxuSR3Q/j2I93IHQdf0rn2tHk7g17B566CGuuOIKrrzySgAefvhh3nvvPZYuXcr9998/aOPq+SLc99Cj1O3ehurMYdK40Wzb8jk1Ozah7i0hM9HLxIIEbn3yr7FcSIcqbTMQX/WLdqAF8UiMt8NxcB3c3vR21C0vLz/icx6sw+128/hTz7Cjajs5YyfGrqOoKpMmjOeDZX8m6mvA67Rjc9sJttTTfmAPorOZYOUmurJGY7W7UE0mTJoFoUfx7/wUhI4lcyxCj2KfcCK+dW/g37ESbe5FmDQFAdhyCvGtfRX/jpU4pyxEs1uN0l2aBcw2OnetQe/yYRl7PNaMsag2B1F/O/7tK+gqW4t91AyUrLHd0Z9G2SY90tXtsGQ3jMukTEJN1QR3rESxubCk5RGuKyfqa0RNzCDqbyVYV4Z19GyIhIm01KINy4KqLXTuWIl9zFxjty7YiTk1Fz0coGPzu9jzp+EcdwIm1zCiHU34yzfSsfldwm0NeM/+BfqeEvw7P0PLHIMeDuIvW4OWmovJZDF2BAFd6PhLP0PoEVRvOl3lG3BMPAXF4qBr9wYc3a98I20HMCWlIyrBv2Ml9jHzUC12I78eoOtR/Ds+JdLRjLtgBhFfg1HGzp0MioK/bA3WrAlG7eNoxIj4DQcI1Vd0p1KJdL8qDKMHO4m2NxkPMG8KIYeHroqNOCeeRtTfZvi7qRp6NIy/YhOqw02kq5OO4n9gGzkZT9HZRtqV9ib8FRvoLPmAaHsjumqis3E/QtOIdDTRWfIBjjFzcYyagWr3Eu1opquq2MidF+jEnDYKR8EMo05tWz1tG9+iq3QVjoJZJEyYiy07Ffe0VPSST2gv/geuCScSdHkxhf0kZuSwd2cx/tLPwOrA7B1uvOqPhNHNNqKdLYRqSunKGI3JYkOzOYkEOtE7mgmUr0eEuwydkRAmhwe9qx1/2Rq8M89DMZmItB/AbM4iGgnTVb7eKBRfMJ7iD/7KhJGZLF68mIf++zHqdq/DMiwLYbbGklIruk7XrjVo0S6yckbj6a6x+s7zD1KxbTPuMbPJKjo1llS4euNyWrd/hm/lX2DCfAKeMbSXliLqduNUQoT3FtOSmIYrJcvQ0dVB24FqAhWbmDN5PFdeeeW34qfU29jY/N6yQxpHvYu4l+78+CvX14MNjN67gb0NjPz8fMrLy2PBEz07Tb37e70JdDbVs3ZDBw1rXuPApne/sqTYQCXCvkprmurj2tt+yfrPi7+zcmO953HT3g6C9Xuo2rGG4QWFTJg8FVMk8KVf9jcwJI/0fh5LPm6DTVwadqFQiE2bNnHrrbf2aT/ttNNYvXr1II3qSwoLC/nVDdfy6arVFK9cRiAYYZimMn7sMBZc9et+CR0PZ6Qdin/WUPu6HKkfRE8qgCPlYB2Lf7RowC+4r3wTY9wRWsNQv/zJvnUUL7+YV9/5kNbVr+AYNQPNk0LEd4CuXWvpKluDKTGDUPXOWLvJmYB/1xoAHGONigLhlv2oFgf+XWsQIopz7Hw0byqRtgY6S1cR2r8Te14RlrRRqGarsWtlE1hzCgnt30Wgaguu3CkI3UPU32n4ntVXEu1qI+o7gO4dTqStnuDeLwhUbcGaNY5QXQUmu5dg9TZCW5ejB/2EGvbQueFNtJRciIZBUVAUE1271xm59vQIJmcSkc4moh3NWIfnYc2aaOTdUxQUix1LWj7h+jJCDeV0rvgfY2L1CJbGPdhScwhWbsYnwJ5fhMnuJdLeaESB7l6PyZWEyWylq3Iz0c4WTM4EgtXb0ENdWFJzQY8au2XRsDFXegSzpiDSRxKsr8K37VOjPRImuPVDRHoBqsOL3uVD9/sI7StB9/vQO418aFF/K5GWWgIVm7BkH0dw3xdfVsMI+onUbEftaiXatA+LdzidlZ+j+9uwJGUYhe/LNxBpribctA9z+jg6NryGOSkT5+g5aHaX4Rtnc2DPHEe0aR9KsIXA2pfwY0IlisuqoQ/LwpI+2jDiMZIjW4aPIly3m4iqYM0cByjo3WXaVFXFnluIPXMsFpsLVVXRLDYjJ12oA3/JcqyaijtpOJ01O7A07sJXsQFzWgH+vduMaGtfA4GKTdijnaSobfi2f0hrSl6suoY4UEFSqJ4O4afhnUcQqhlFD+PUBNF9xXQoKpYRx4HSRqh5P+GGCkRjJVMWfJ/iD/4aexhaLBbu+NUvueE/f0PnhjcMv0F3MtH2RrrKN2Bp2MH0okns++wVQmEdzQT1ZcW4x8xm3L9dFdsx9WSOYlxGHjsAvXIdWf4y7O1OsgPlzDhhAjOnXczDjz/DngF0jHFHuOfuO77VB3Mfo+0wBs1X/Qg+eC36OgZGfn7+YXeaoi3VXHr+2Zy+4CQaGhqOuKTYN9F6pO40/8x8T548mV27drF161ZE5CNqW7az94MvvhVD8kjvp8QgLkuK7d+/n8zMTFatWsWcOXNi7ffddx/PPfccpaWl/T4TDAYJBr/0/fD5fIwYMYLm5uaYIaIoCqqqonfnaOvhUO095UUGahdCUF5eTiQSob29HY/HEyvB0jvEvqc/0K/dZDLFEqIePJZDtR/p2L+upt27d3PDr+8h58RFffwgenq01VWzd+UyfnfnrZjN5ljk2DfRtGXLFl54+RV27d1PIBjFajExdqTxamXChAmsWLGC+vp6hg8fzkknnYSmabzyyiv8/o+PsrfRB6oZTdEZkexh1rQpfLZ2I7uqDd86E1FGZaaSkuBi5dqNhDUHZpsdEQljjvjJy0pj157qWLseCRNtb8LsSsQxZSEmi9NIiKsYvk9mVRARCm0lH+LOm8oJU8fy2a56IqEgnZWb6SrfiOZJxmQ26oMKfwuEuggrZizeFCNXXjhIpL0Zi91JfsFYavbtIRARYNLQwyEsqlGcvaOjA9WRgGo2E/G3owtIKjwdHAmo3Tsx0WiEaDhIgjlKPg1s3/gZHR0d2Gw2/u3fz2N/Szv7Kiup3ruHAGYiQsGEzriR6Zz/72exb389b75r1MJEj5CWNRKnw0FHWzNNbe1EdXDarZz3/dMp37WDj9esR3MmcdLxc/ho5Sp0fyuzpk7ipJNOYuPmrZSU7SEQjuCy2zjjpLnMmj6NT1ev5aU33yYqFKxmlZNnFdHSGUQk51MbUAmjYVOjDLfp0LCbYV4HFTWNBMIRCAcwqwKT3UVLawf19bVEAl2oFhsmBcxWKzknLaI9BF3hCEIoKIrAabOQMzwJylfxgzOMv5lIJMIb732MnjuH8v2N+IMhww9L6KBHCHS2E96zCW/BdFTXMDSzGfxttJZtJHX22QQ7fDhsFqbnJPL5vnZsNhvWqJ+9n7zK8JREFM2O1aIxZVw+qUkJvPb3d9jX0EbUyPBNdqqXXyy5jjFjxvCXF19m/ZbttHcGcNgszJk2kUsWXcC4ceN48cUX2bt3L9nZ2Vx00UW8+eabPPjfj7GvoQ1/KGLkwtQUMkfkMCI7izHZGVy86Iex0lcAr7zyCg8+8ieqDrQhFA2zKhiR7OGG637K+eefT0VFBa2trZSUlHDn7x8j63vX4MrIR6AYQUnd31Xf/nKq/u9R7vnldeTl5ZGWlhYzIrZs2cJz//sSG7buiNWdnTllApcsuoDCwsLvZN0TQsTG7vF4yM/Pj61h33TdKy4u7l5/agkEI1jMKuNysvjRheczefJkdF1n79695OTksGXLFu7/w5/ocKaTO2kOzm5DcM/W1bj9tdx6/bVMnjz5W1nLv67Wwz2fFEUhGo0e0f3o3a7rOlVVVYwYMYKqqqpDjuWbPp90Xae8vByfz0dCQgJ5eXl9DObvQlNvDnWfACorK8nOzo599tt+5ra2tpKYmHhEJcXi2rBbvXo1s2fPjrXfe++9PP/887Fi1L256667uPvuu/u1b9iwAZfLBRg+Funp6dTW1vaJwElOTiY5OZl9+/bR2fllks20tDQSEhKoqKggFPrS+TkrKwuXy8WuXbv6/IHk5uaiaVqfzOUABQUFRCKRPvnfenayOjo6qK6ujrVbLBby8vJobW2NJfYEI2ptxIgRNDY20tjYGGv/tjRlZmZyx3/dgy15BJmjJ8YW+jrhNYyDilWk2HROO2UBiqL805qEEHR0GJFemqZhs9liX7BDaUpMTKS4uJimpiY8Hg95eXmoqkpKSgobN26kubkZt9vdp/3xxx/HZDIxbNgwZs+ejaZppKen86c//QlN00hISEDTNIp37KY5fSYjR2Ti8tcSDocxm80oJo1XPttBcNsHnHHGGVjcSSgiiq+pnu0bV/GTSxdRW1sb+9KeeeaZjBo1ivLych599FHa29txu91cddVVqKpKfX09YBRD9/v9uFwucnNzOXDgAEIIiouLaW1tpaGhgR01zZx1yU/RiFLefb92NHQxbc4JZGvt1G9bw4J5M8nMzIwZ2uvXrycQCGCxWGhtbaW5uZnhw4fHFiwhBC0tLZjNZqxWKz6fj2AwiNVqxWQyoWmaEY1nsaAoCuFwmLfffps1a9YwdepUzj333FgdYY/HQ0dHB/X19aiqSlJSEoqikJSURGtrK7W1tWiaRlJSEnV1dXz0yads2l7GmPw8XA4byQluJh03gaKiImpra6mursZqtZKYmEhLSwsmkwmXy0VlZSV+v59AIMDOymqi407HYlJxdtbE7pPT7WFfyEnFO3/mmsU/JDMzk5qaGv7vg5VoU/4NjymK1lkf698lNF75eAsZnWWcePLJOBOSiepR9tcd4O9vvsEJP/wP3MEDpCQlYrGaadNt6I5kEvVWWrd/StHEcXi9XpKTk5k4cSJ79uwhEAhQUVFBZ2cnHo+HU089lYqKithC39zcjMlkIiEhIeajdqg1oicTfmNjIykpKbjdbkKhEDabjbS0NPLz8/utETabjdLSUhobG/F6vbHvQe/v08qVK3n/03WkTDmZ/SEbNV1mxrpDeM3GA1OPRFj+0lJ+vviHHH/88f3WPYfDwdq1a+nq6sJms5GUlEReXt6/3LrXcz9UVWXYsGGIbr/BHnqv5bW1tWz9YhuNre2sK96Oy2HlhBlTmHTcBNLT048ZTb3vU7w9n+JB05YtW5gyZcrQNexCoRAOh4NXX32Vf//3f4+1L1myhOLiYj755JN+nznaO3YALS0teDye2AL9bf16GIwdu56C1/c99Gjs16krKYX25gNUFK/CG6jj1uuvZdKkSbFfWz3nOZY1HcmvvLKyMm6+635GzL+AhLQs+noYQsWOEja++ggjszJJS8/A19HO6BH9d02+TU2H30FVaKvbx96Vy/j9Xb/qU6z7u/jbA2IGam++riaA8vLyfrsA39Z9aqkzCsU/9F+3U1BQ0N3/PkbMX0RCWmaf/m1trXz04XJC5Zs47UdXk9hdm7R5/x4+fPVZkoq+TyTQwayiKaR7HXRiAdQB530wdhi+6ffp/fff58obbyfrjMPs2L39KM8+fD8zZszot74di5q+7TVCCEF7ezterzfWt/dOU2+j+V9F0+HuR+92IUTM3UbTtLjQ1JtD3aeeHTW32/2l7/cg7tjFpY+dxWJh2rRpLF++vI9ht3z5cs4+++wBP2O1WrEelPkcjBt5cJTLoXwTvk57NBqloaEBr9fb7/yHiqoZqF1RlK/V/m2M/VDthYWF3HbjdX0iWb/0g7iOwsLCPrpVVT3mNcFX34/Ro0dTMCKN7cWrKDz9gj4Z3oUQtFRt56xTT+Q/Ll9MbW0tmZmZjB49Ona970JTQUFBt3/PqgH9e8qLVzFhRHqfcRyJ1t4c6X2KRqPU1dXhdrv/aa2H8xv9Z+9Tj/N7z5wY/dNjPlK9+7vdXqJ15UQD7XiGZ8dMm4S0kSQmJlL9+UeMKjoRryeBRKUVv7CiH2bev+nf3pG0f5vfp1NOOYVUz2/Y9/lHjEk3XoPp3dqF0Nn3+UdkJDg46aSTqKys/KfXt6Oh6dtoP/jvvb6+Ho/HE2s3mUyMHTt2wM9+1diPBU1H2t57fYf40HQwA419oHv+Tcf4ddsH7HvEPf/FuPHGG3nqqad45pln2LFjBzfccAN79+7l6quvHuyhxTWFhYU8+P/u5+F7fs39t13Pw/f8mt//v/vi2rlVVQ+fEd/t38+lF13ImDFjyMzM7LNDNphjOjidQ7zzdefksP3ff4VsW4D8tESKl78aO9bWsB+TqqDWfkG05gta62vQo9G4mXdN0/jFkutQ939B6dtP4avZ/ZVJhSUSydElLl/F9vDYY4/x29/+ltraWo477jj+8Ic/MH/+/CP6bM+W+ZFse34TotEoZWVlx0zem6NFPOvevHmzsVtZtT+2Wzl2ZCaXXHRBbLfyaGv/qjEdDY61e/515+Rw/YEBj02fOpn1nxdTtq+OSePGsHVHKaNHpB/Vef8ueemll/jdHx9lf0tnnwj0m5dcx6JFi465e340kdqHnvajofvr2CRxbdj9M3zXhp2u69TU1JCZmfkv++v9mxDvunVdP2RagcHSfrgxHa3rH2v3/OvOyVfd14GO6bpOWVkZdXV1pKWlHZWd2qNJJBLhww8/jOnrnarjWLznRwupfehpPxq6pWH3LfBdG3YSiUQikUgkR8LXsUmGjkl9jKHrOo2Njf2ibuKdoaobhq72oaobhq72oaobpPahqP1Y0y0Nu0FCCEFjY2O/dA7xzlDVDUNX+1DVDUNX+1DVDVL7UNR+rOmWhp1EIpFIJBJJnCANO4lEIpFIJJI4QRp2g4SiKHi93j6JY4cCQ1U3DF3tQ1U3DF3tQ1U3SO1DUfuxpltGxR4CGRUrkUgkEonkWEBGxf4LoOs6tbW1x0wUzdFiqOqGoat9qOqGoat9qOoGqX0oaj/WdEvDbpAQQtDW1nbMRNEcLYaqbhi62oeqbhi62oeqbpDah6L2Y023NOwkEolEIpFI4gRZqfkQ9FjePp/vOzl/NBqlo6MDn8835GrqDUXdMHS1D1XdMHS1D1XdILUPRe1HQ3ePLXIku4LSsDsE7e3tAIwYMWKQRyKRSCQSiURi2CZer/ewfWRU7CHQdZ39+/fjdru/kxBmn8/HiBEj2Ldv35CKuh2qumHoah+qumHoah+qukFqH4raj4ZuIQTt7e1kZGSgqof3opM7dodAVVWysrK+8+t4PJ4h9QXoYajqhqGrfajqhqGrfajqBql9KGr/rnV/1U5dDzJ4QiKRSCQSiSROkIadRCKRSCQSSZwgDbtBwmq1cuedd2K1Wgd7KEeVoaobhq72oaobhq72oaobpPahqP1Y0y2DJyQSiUQikUjiBLljJ5FIJBKJRBInSMNOIpFIJBKJJE6Qhp1EIpFIJBJJnCANu0HgscceIzc3F5vNxrRp0/j0008He0jfOitXruSss84iIyMDRVF48803+xwXQnDXXXeRkZGB3W7nxBNPZNu2bYMz2G+R+++/n+nTp+N2u0lNTeWcc86htLS0T5941b506VImTZoUy+U0e/Zs3n333djxeNV9MPfffz+KonD99dfH2uJV+1133YWiKH3+paWlxY7Hq26AmpoaLr74YoYNG4bD4WDKlCls2rQpdjxetefk5PS754qicO211wLxqzsSifCf//mf5ObmYrfbycvL47/+67/QdT3W55jRLiRHlZdfflmYzWbx5z//WWzfvl0sWbJEOJ1OUVVVNdhD+1Z55513xO233y5ee+01AYg33nijz/EHHnhAuN1u8dprr4mSkhJxwQUXiPT0dOHz+QZnwN8Sp59+unj22WfFF198IYqLi8WZZ54psrOzRUdHR6xPvGp/6623xNtvvy1KS0tFaWmpuO2224TZbBZffPGFECJ+dfdm/fr1IicnR0yaNEksWbIk1h6v2u+8804xYcIEUVtbG/vX0NAQOx6vupubm8XIkSPFZZddJtatWycqKyvFBx98IHbv3h3rE6/aGxoa+tzv5cuXC0CsWLFCCBG/uu+55x4xbNgw8X//93+isrJSvPrqq8LlcomHH3441udY0S4Nu6PMjBkzxNVXX92nbezYseLWW28dpBF99xxs2Om6LtLS0sQDDzwQawsEAsLr9YrHH398EEb43dHQ0CAA8cknnwghhpZ2IYRITEwUTz311JDQ3d7eLgoKCsTy5cvFCSecEDPs4ln7nXfeKSZPnjzgsXjWfcstt4h58+Yd8ng8az+YJUuWiPz8fKHrelzrPvPMM8WPf/zjPm3nnnuuuPjii4UQx9Y9l69ijyKhUIhNmzZx2mmn9Wk/7bTTWL169SCN6uhTWVlJXV1dn3mwWq2ccMIJcTcPbW1tACQlJQFDR3s0GuXll1+ms7OT2bNnDwnd1157LWeeeSannHJKn/Z4115WVkZGRga5ublceOGFVFRUAPGt+6233qKoqIjzzz+f1NRUCgsL+fOf/xw7Hs/aexMKhXjhhRf48Y9/jKIoca173rx5fPjhh+zatQuALVu28Nlnn/G9730POLbuuawVexRpbGwkGo0yfPjwPu3Dhw+nrq5ukEZ19OnROtA8VFVVDcaQvhOEENx4443MmzeP4447Doh/7SUlJcyePZtAIIDL5eKNN95g/PjxsYUtXnW//PLLfP7552zYsKHfsXi+5zNnzuQvf/kLo0ePpr6+nnvuuYc5c+awbdu2uNZdUVHB0qVLufHGG7nttttYv349P//5z7FarVx66aVxrb03b775Jq2trVx22WVAfP+t33LLLbS1tTF27FhMJhPRaJR7772XRYsWAceWdmnYDQKKovT5fyFEv7ahQLzPw3XXXcfWrVv57LPP+h2LV+1jxoyhuLiY1tZWXnvtNRYvXswnn3wSOx6Puvft28eSJUt4//33sdlsh+wXj9rPOOOM2H9PnDiR2bNnk5+fz3PPPcesWbOA+NSt6zpFRUXcd999ABQWFrJt2zaWLl3KpZdeGusXj9p78/TTT3PGGWeQkZHRpz0edS9btowXXniBF198kQkTJlBcXMz1119PRkYGixcvjvU7FrTLV7FHkeTkZEwmU7/duYaGhn5WfjzTEzUXz/Pws5/9jLfeeosVK1aQlZUVa4937RaLhVGjRlFUVMT999/P5MmT+eMf/xjXujdt2kRDQwPTpk1D0zQ0TeOTTz7hkUceQdO0mL541H4wTqeTiRMnUlZWFtf3PD09nfHjx/dpGzduHHv37gXi/3sOUFVVxQcffMCVV14Za4tn3b/4xS+49dZbufDCC5k4cSKXXHIJN9xwA/fffz9wbGmXht1RxGKxMG3aNJYvX96nffny5cyZM2eQRnX0yc3NJS0trc88hEIhPvnkk3/5eRBCcN111/H666/z0UcfkZub2+d4PGsfCCEEwWAwrnUvWLCAkpISiouLY/+Kior40Y9+RHFxMXl5eXGr/WCCwSA7duwgPT09ru/53Llz+6Ux2rVrFyNHjgSGxvf82WefJTU1lTPPPDPWFs+6/X4/qtrXZDKZTLF0J8eU9qMaqiGJpTt5+umnxfbt28X1118vnE6n2LNnz2AP7Vulvb1dbN68WWzevFkA4qGHHhKbN2+OpXV54IEHhNfrFa+//rooKSkRixYtiouQ+J/+9KfC6/WKjz/+uE9KAL/fH+sTr9p/9atfiZUrV4rKykqxdetWcdtttwlVVcX7778vhIhf3QPROypWiPjVftNNN4mPP/5YVFRUiLVr14rvf//7wu12x9azeNW9fv16oWmauPfee0VZWZn43//9X+FwOMQLL7wQ6xOv2oUQIhqNiuzsbHHLLbf0OxavuhcvXiwyMzNj6U5ef/11kZycLH75y1/G+hwr2qVhNwj86U9/EiNHjhQWi0VMnTo1lgojnlixYoUA+v1bvHixEMIIDb/zzjtFWlqasFqtYv78+aKkpGRwB/0tMJBmQDz77LOxPvGq/cc//nHs7zolJUUsWLAgZtQJEb+6B+Jgwy5etffk6TKbzSIjI0Oce+65Ytu2bbHj8apbCCH+/ve/i+OOO05YrVYxduxY8eSTT/Y5Hs/a33vvPQGI0tLSfsfiVbfP5xNLliwR2dnZwmaziby8PHH77beLYDAY63OsaFeEEOLo7hFKJBKJRCKRSL4LpI+dRCKRSCQSSZwgDTuJRCKRSCSSOEEadhKJRCKRSCRxgjTsJBKJRCKRSOIEadhJJBKJRCKRxAnSsJNIJBKJRCKJE6RhJ5FIJBKJRBInSMNOIpFIJBKJJE6Qhp1EIpF8Q6qrq3E4HPz2t78d7KF856xYsQJFUXjnnXcGeygSieQwyMoTEonkmOPSSy/l+eefZ/jw4VRXV6Np2mAPaUAuvfRS/vGPf1BZWYnT6Rzs4XznnHjiiRw4cICtW7diMpkGezgSiWQA5I6dRCI5pvD5fLz22msoikJ9fT1vv/32YA9pQHbt2sULL7zANddcMySMOoCbb76Z7du389JLLw32UCQSySGQhp1EIjmmeOmll/D7/dx0000oisLTTz892EMakCeeeAIhBBdffPFgD+WosXDhQlJSUnj88ccHeygSieQQSMNOIpEcUzz99NNYLBZ+9atfMXfuXN555x1qa2sP2f/111+nqKgIu93O8OHD+Y//+A9aWlrIyckhJyenX/9QKMRDDz3E1KlTcTqduN1ujj/+eN56660jHqOu6/zlL3+hsLCQUaNGxdrb29txu91MmDBhwM9Fo1EyMjJISUkhFArF2oUQPPPMM8ydOxePx4PD4aCoqIhnnnmm3zn279/PnXfeyaxZs0hNTcVqtZKTk8M111xDQ0NDv/6XXXYZiqJQUVHBH/7wByZMmIDVauWyyy4DIBAI8OCDDzJ58mS8Xi8ul4v8/HwWLVpESUlJn3NpmsY555zDqlWrKCsrO+L5kkgkRw9p2EkkkmOGkpISNmzYwJlnnklSUhKXXnop0WiU5557bsD+zzzzDD/4wQ8oLy/n0ksvZfHixaxZs4ZTTz2VcDjcr38wGOT000/npptuAuCKK67g4osvpqqqirPPPptHH330iMa5detWGhsbmTVrVp92t9vNokWL2L59O6tXr+73ubfffpva2loWL16MxWIBiO36XXHFFTQ2NnLRRRdx5ZVX0tnZyRVXXMHNN9/c5xwrV67kwQcfZPjw4SxatIif/exn5Ofns3TpUmbPnk1bW9uAY/7Zz37GPffcw7Rp07j++uuZNGkSAIsXL45d4/LLL+faa69lxowZrFixgk2bNvU7z+zZswH46KOPjmiuJBLJUUZIJBLJMcKSJUsEIF5//XUhhBCtra3CZrOJgoKCfn1bWlqEy+USbrdblJeXx9rD4bA45ZRTBCBGjhzZ5zO33XabAMRdd90ldF2Ptft8PlFUVCQsFouoqan5ynH+6U9/EoD485//3O/Yhg0bBCAuv/zyfsf+7d/+TQBix44dsbYnn3xSAOKKK64Q4XA41h4MBsVZZ50lALFx48ZYe319vWhvb+937ueee04A4p577unTvnjxYgGIrKwsUVVV1edYa2urUBRFFBUViUgk0udYJBIRLS0t/a6zZcsWAYhLL7203zGJRDL4yB07iURyTBAKhXjhhRdITEzkzDPPBMDr9XL22WdTVlbGypUr+/T/29/+RkdHB1deeSV5eXmxdk3T+M1vftPv/Lqus3TpUkaNGsUdd9yBoiixY263mzvuuINQKMTrr7/+lWOtrq4GYPjw4f2OFRUVMXXqVF555RXa29tj7XV1dbzzzjvMmzePsWPHxtofffRRnE4njz76aJ/oX4vFwr333gvQJ1ghNTUVl8vV77qXXHIJHo+HDz74YMAx/+IXvyA7O7tPm6IoCCGwWq39olxNJhMJCQn9ztOjuWcOJBLJscWxmUNAIpEMOd58802ampq4+uqrY68pwUgpsmzZMp555hnmz58fa9+yZQsAc+bM6XeuGTNm9EuRUlpaSktLCxkZGdx99939PnPgwAEAdu7c+ZVjbWpqAiAxMXHA41dddRVXXXUVL730Ej/5yU8A+J//+R8ikQhXXnllrJ/f76ekpISMjAweeOCBfufpeZ188Jhef/11nnjiCT7//HNaWlqIRqOxY/v37x9wTDNmzOjX5vF4WLhwIf/4xz+YOnUq5513HscffzwzZ87scw96k5SUBEBjY+OAxyUSyeAiDTuJRHJM0BMocMkll/RpP/3000lLS+PVV1/lkUcewePxAEZaFICUlJR+51JVleTk5D5tzc3NAGzbto1t27YdchydnZ1fOVa73Q5AV1fXgMcvuugibrrpJp566qmYYffMM8/g9Xo5//zzY/1aWloQQlBTUzOgsTnQmB588EFuvvlmUlJSOO2008jKyoqN5+GHHyYYDA54joF2FwH++te/ct999/HSSy9x++23A8YO5o9//GPuu+8+HA5Hn/49mg9ul0gkxwbSsJNIJIPOvn37WL58OQBz5849ZL+XX345Zij1GHg9O2290XWdxsZGMjMzY209/X/wgx/w17/+9Z8ab48x2WMsHozL5eKiiy7iySefZOvWrTQ3N1NWVsY111zTxyDqGdO0adPYuHHjV143Eonwm9/8hoyMDIqLi/sYtUKIw1bA6P3quTdOp5N7772Xe++9l8rKSlasWMHjjz/OH//4R7q6unjiiSf69O/RPJBBLZFIBh9p2EkkkkHn2WefRdd15s2bx5gxY/odD4VCPP/88zz99NMxw27y5MkArF69mvPOO69P//Xr1xOJRPq0jRs3Do/Hw8aNGwmHw5jN5m883okTJwIcNuXHVVddxZNPPslTTz1FS0sLQJ/XsGDsjI0bN44dO3bQ2to6oE9bbxobG2lra2PBggX9DKuNGzcecgfxSMnNzSU3N5dFixaRmprKW2+91c+wKy0tBb6cA4lEcowxyMEbEolkiKPrusjJyRGKooiKiopD9issLBSAKCkpEUL0jYrt/bnDRcXecsstAhA///nPRSgU6neNkpISUV9f/5Vjbm5uFqqqirPOOuuw/aZNmyYSExOF3W4X06ZNG7DP0qVLBSDOO+880dHR0e94RUWFqKysFEIIEY1Ghd1uFzk5OaKzs7PPeGbOnDmg5p6o2J5z9KahoUGsW7euX3tNTY0wm80iNze337G7775bAOK99947jHKJRDJYyB07iUQyqHz44Yfs2bOHk046idzc3EP2u/zyy9m8eTNPP/00f/jDH0hISOChhx7iJz/5CVOnTuWCCy7A6/XyzjvvYLVaycjIQFX7Bv7ffffdfP755zzyyCO8/fbbnHDCCaSkpFBTU0NJSQlbtmxhzZo1pKamHnbMiYmJzJ8/n08//ZRgMIjVah2w31VXXRXbYTx4t653n7Vr1/Lcc8+xatUqTjnlFDIyMqivr2fnzp2sW7eOF198kZycHFRV5ZprroklFD7rrLPw+Xy8++67jBw5koyMjMOO+2BqamqYOXMmEyZMYOrUqWRmZtLU1MTf/vY3wuEwv/zlL/t9Zvny5TH9EonkGGSwLUuJRDK0ufDCCwUgnn/++cP2a2xsFBaLRSQnJ4tgMBhrf/XVV0VhYaGwWq0iNTVVXHnllaKpqUm4XC4xefLkfueJRCLiiSeeEHPnzhUej0dYrVaRnZ0tFi5cKJYuXTrgrtlAvPTSSwIQr7322iH7tLe3C7PZLBwOh2hrazvs+ZYtWyZOOeUUkZiYKMxms8jMzBQnnniiePDBB8WBAwdi/UKhkLj33ntFQUFBbOw33nijaG9vFyNHjvxaO3YtLS3irrvuEvPnzxfp6enCYrGIjIwMsXDhwgF35Pbs2SMURRHXX3/94SdHIpEMGooQQgyybSmRSCTfKrt376agoIAf/vCHLFu27Du5RigUYvTo0YwbN4533313wD7r169n5syZXH755QOWB/tX44477uCBBx5gx44d5OfnD/ZwJBLJAMgExRKJ5F+WlpaWfuk9urq6uOGGGwA455xzvrNrWywW7rvvPv7xj3+wbt26Afv8/ve/B+Dqq6/+zsZxtGhtbeWRRx7hpz/9qTTqJJJjGOljJ5FI/mX55JNPuOKKKzjttNPIzs6msbGRjz76iD179nDyySdzwQUXfKfXX7RoEdXV1X2S9e7du5cXX3yRbdu28eqrr7Jw4cIBkwP/q7Fnzx6uv/56fvaznw32UCQSyWGQr2IlEsm/LGVlZfz6179m9erVsXx2o0aN4oILLuDmm2/GZrMd9TF9/PHHnHTSSbhcLk4++WSeeOIJ0tLSjvo4JBLJ0EQadhKJRCKRSCRxgvSxk0gkEolEIokTpGEnkUgkEolEEidIw04ikUgkEokkTpCGnUQikUgkEkmcIA07iUQikUgkkjhBGnYSiUQikUgkcYI07CQSiUQikUjiBGnYSSQSiUQikcQJ0rCTSCQSiUQiiRP+P+tekAmS9VVMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sorted_by_fare[\"age\"], sorted_by_fare[\"fare\"], alpha=0.6, edgecolor=\"k\", label=\"Passenger Data\")\n",
    "\n",
    "plt.xlabel(\"Age (years)\", fontsize=14)\n",
    "plt.ylabel(\"Fare Paid ($)\", fontsize=14)\n",
    "plt.title(\"Scatter Plot of Fare Paid vs. Age\", fontsize=16)\n",
    "plt.legend(fontsize=12)\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5) \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 2.14\n",
    "rubric={points}\n",
    "\n",
    "Create a bar chart of `embarked` values. \n",
    "\n",
    "> Make sure to name the axes and give a title to your plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_2.14\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embarked_counts = sorted_by_fare[\"embarked\"].value_counts()\n",
    "\n",
    "plt.bar(embarked_counts.index, embarked_counts.values, color=\"skyblue\", edgecolor=\"k\")\n",
    "\n",
    "plt.xlabel(\"Embarkation Port\", fontsize=14)\n",
    "plt.ylabel(\"Number of Passengers\", fontsize=14)\n",
    "plt.title(\"Bar Chart of Embarked Values\", fontsize=16)\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)  \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3: Treasure Hunt\n",
    "\n",
    "In this exercise, we will generate various collections of objects either as a list, a tuple, or a dictionary. Your task is to inspect the objects and look for treasure, which in our case is a particular object: **the character \"T\"**. \n",
    "\n",
    "**Your tasks:**\n",
    "\n",
    "For each of the following cases, index into the Python object to obtain the \"T\" (for Treasure). \n",
    "\n",
    "> Please do not modify the original line of code that generates `x` (though you are welcome to copy it). You are welcome to answer this question \"manually\" or by writing code - whatever works for you. However, your submission should always end with a line of code that prints out `'T'` at the end (because you've found it). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "\n",
    "letters = string.ascii_uppercase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first one is done for you as an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('nothing',\n",
       " {0: 'A',\n",
       "  -1: 'B',\n",
       "  -2: 'C',\n",
       "  -3: 'D',\n",
       "  -4: 'E',\n",
       "  -5: 'F',\n",
       "  -6: 'G',\n",
       "  -7: 'H',\n",
       "  -8: 'I',\n",
       "  -9: 'J',\n",
       "  -10: 'K',\n",
       "  -11: 'L',\n",
       "  -12: 'M',\n",
       "  -13: 'N',\n",
       "  -14: 'O',\n",
       "  -15: 'P',\n",
       "  -16: 'Q',\n",
       "  -17: 'R',\n",
       "  -18: 'S',\n",
       "  -19: 'T',\n",
       "  -20: 'U',\n",
       "  -21: 'V',\n",
       "  -22: 'W',\n",
       "  -23: 'X',\n",
       "  -24: 'Y',\n",
       "  -25: 'Z'})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (\"nothing\", {-i: l for i, l in enumerate(letters)})\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example answer**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T\n"
     ]
    }
   ],
   "source": [
    "print(x[1][-19])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: In these questions, the goal is not to understand the code itself, which may be confusing. Instead, try to probe the types of the various objects. For example `type(x)` reveals that `x` is a tuple, and `len(x)` reveals that it has two elements. Element 0 just contains \"nothing\", but element 1 contains more stuff, hence `x[1]`. Then we can again probe `type(x[1])` and see that it's a dictionary. If you `print(x[1])` you'll see that the letter \"T\" corresponds to the key -19, hence `x[1][-19]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 3.1\n",
    "rubric={points}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "# Do not modify this cell\n",
    "x = [\n",
    "    [letters[i] for i in range(26) if i % 2 == 0],\n",
    "    [letters[i] for i in range(26) if i % 2 == 1],\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_3.1\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T\n"
     ]
    }
   ],
   "source": [
    "print(x[1][9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 3.2\n",
    "rubric={points}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "# Do not modify this cell\n",
    "np.random.seed(1)\n",
    "x = np.random.choice(list(set(letters) - set(\"T\")), size=(100, 26), replace=True)\n",
    "x[np.random.randint(100), np.random.randint(26)] = \"T\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_3.2\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T\n"
     ]
    }
   ],
   "source": [
    "result = np.where(x == \"T\")\n",
    "row = result[0][0]\n",
    "col = result[1][0]\n",
    "print(x[row][col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- BEGIN QUESTION -->\n",
    "\n",
    "#### 3.3\n",
    "rubric={points}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "# Do not modify this cell\n",
    "n = 26\n",
    "x = dict()\n",
    "for i in range(n):\n",
    "    x[string.ascii_lowercase[i]] = {\n",
    "        string.ascii_lowercase[(j + 1) % n]: [[letters[j]] if j - 2 == i else None]\n",
    "        for j in range(n)\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Your solution_3.3\n",
    "    \n",
    "</div>\n",
    "\n",
    "_Points:_ 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T\n"
     ]
    }
   ],
   "source": [
    "for outer_key, inner_dict in x.items():\n",
    "    for inner_key, value in inner_dict.items():\n",
    "        if value and value[0] and value[0][0] == \"T\":\n",
    "            print(value[0][0])\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<!-- END QUESTION -->\n",
    "\n",
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before submitting your assignment, please make sure you have followed all the instructions in the Submission Instructions section at the top. \n",
    "\n",
    "Well done!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/eva-well-done.png)"
   ]
  }
 ],
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