01-dask-bags/02-bag-basics.ipynb

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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Bag-basics\" data-toc-modified-id=\"Bag-basics-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Bag basics</a></span><ul class=\"toc-item\"><li><span><a href=\"#Getting-the-data-and-creating-the-bag\" data-toc-modified-id=\"Getting-the-data-and-creating-the-bag-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Getting the data and creating the bag</a></span></li><li><span><a href=\"#persist\" data-toc-modified-id=\"persist-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span><code>persist</code></a></span></li><li><span><a href=\"#filter\" data-toc-modified-id=\"filter-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span><code>filter</code></a></span></li><li><span><a href=\"#map\" data-toc-modified-id=\"map-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span><code>map</code></a></span></li><li><span><a href=\"#Using-map-and-predefined-functions\" data-toc-modified-id=\"Using-map-and-predefined-functions-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Using <code>map</code> and predefined functions</a></span></li><li><span><a href=\"#flatten\" data-toc-modified-id=\"flatten-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span><code>flatten</code></a></span></li><li><span><a href=\"#compute\" data-toc-modified-id=\"compute-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span><code>compute</code></a></span></li></ul></li></ul></div>"
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   "cell_type": "markdown",
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   "source": [
    " # Bag basics\n",
    " \n",
    "[Introduction to Dask, by Anderson Banihirwe](https://github.com/andersy005/dask-notebooks)\n",
    "\n",
    "This notebook will introduce basic but essential `Dask.Bag` operations. At the same time we will introduce different concepts such as peristence in Dask.\n",
    "\n",
    "\n",
    "## Getting the data and creating the bag\n",
    "\n",
    "\n",
    "\n",
    "As we did in our first notebook, we will use the reduced dataset (10 percent) provided for the KDD Cup 1999, containing nearly half million network interactions. The file is provided as a Gzip file that we will download locally.\n",
    "\n",
    "If you previously downloaded this dataset, the following program won't download it again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import os\n",
    "import pathlib\n",
    "DATASET_DIR = pathlib.Path('../data')\n",
    "DATASET_FILEPATH = pathlib.Path('../data/kddcup.data_10_percent.gz')\n",
    "if not DATASET_DIR.exists():\n",
    "    os.mkdir(DATA_DIR)\n",
    "    f = urllib.request.urlretrieve(\n",
    "        \"http://kdd.ics.uci.edu/databases/kddcup99/kddcup.data_10_percent.gz\",\n",
    "        DATASET_FILEPATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this file to create our dask bag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask\n",
    "import dask.bag as db\n",
    "# Progress Bar\n",
    "from dask.diagnostics import ProgressBar\n",
    "pbar = ProgressBar()\n",
    "pbar.register()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = db.read_text(DATASET_FILEPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n"
     ]
    },
    {
     "data": {
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       "('0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,9,9,1.00,0.00,0.11,0.00,0.00,0.00,0.00,0.00,normal.\\n',\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Take a look at a few elements\n",
    "raw_data.take(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dask.bag<bag-fro..., npartitions=1>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## `persist`\n",
    "\n",
    "It is often ideal to load, filter, and shuffle data once and keep this result in memory. Afterwards each of the several complex queries can be based off of this in-memory data rather than have to repeat the full load-filter-shuffle process each time. To do this, we can use `.persist()` method to tell dask to persist the data in memory. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 11.6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dask.bag<bag-fro..., npartitions=1>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.persist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ```filter```\n",
    "\n",
    "\n",
    "This method can be applied to bags in order to keep just elements that satisfy a certain condition. More concretely, a function is evaluated on every element in the original bag. The new resulting bag will contain just those elements that make the function return True.\n",
    "\n",
    "For example, imagine we want to count how many normal. interactions we have in our dataset. We can filter our raw_data bag as follows.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_raw_data = raw_data.filter(lambda x: 'normal' in x)\n",
    "normal_count = normal_raw_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1.1s\n",
      "There are 97278 'normal' interactions\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} 'normal' interactions\".format(normal_count.compute()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.8s\n",
      "There are 494021 'total' interactions\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} 'total' interactions\".format(raw_data.count().compute()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a total of 494021 in our 10 percent dataset. Here we can see that 97278 contain the `normal.` tag word.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `map`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using the map method, we can apply a function to every element in our bag. Python's lambdas are specially expressive for this particular.\n",
    "\n",
    "In this case we want to read our data file as a CSV formatted one. We can do this by applying a lambda function to each element in the bag as follows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_data = raw_data.map(lambda x: x.split(\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "[########################################] | 100% Completed |  0.1s\n",
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      "  'normal.\\n'])\n"
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   ],
   "source": [
    "pprint(csv_data.take(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, dask operations are lazily evaluate, i.e the computation happens once we call the dask to do so (i.e. take in this case). What if we take a lot of elements instead of just the first few?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  6.2s\n"
     ]
    }
   ],
   "source": [
    "samples = csv_data.take(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that it takes longer. The map function is applied now in a distributed way to a lot of elements in the bag, hence the longer execution time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `map` and predefined functions\n",
    "\n",
    "Of course we can use predefined functions with map. Imagine we want to have each element in the bag as a key-value pair where the key is the tag (e.g. normal) and the value is the whole list of elements that represents the row in the CSV formatted file. We could proceed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_interaction(line):\n",
    "    elems = line.split(\",\")\n",
    "    tag = elems[41][:-2]\n",
    "    return (tag, elems)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_csv_data = raw_data.map(parse_interaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.1s\n",
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    }
   ],
   "source": [
    "pprint(key_csv_data.take(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was easy, wasn't it?\n",
    "\n",
    "In our notebook about working with key-value pairs we will use this type of bag to do data aggregations (e.g. count by key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `flatten`\n",
    "\n",
    "This method concatenate nested lists into one long list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  7.2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "74889749"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "raw_data.flatten().count().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `compute`\n",
    "\n",
    "`compute` method computes the dask collection it is applied to.  Basically it will get all the elements in the lazily evaluate bag into memory for us to work with them. For this reason it has to be used with care, specially when working with large bags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 34.2s\n"
     ]
    },
    {
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       " '0,tcp,http,SF,219,1337,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0.00,0.00,0.00,0.00,1.00,0.00,0.00,39,39,1.00,0.00,0.03,0.00,0.00,0.00,0.00,0.00,normal.\\n',\n",
       " '0,tcp,http,SF,217,2032,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0.00,0.00,0.00,0.00,1.00,0.00,0.00,49,49,1.00,0.00,0.02,0.00,0.00,0.00,0.00,0.00,normal.\\n',\n",
       " '0,tcp,http,SF,217,2032,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0.00,0.00,0.00,0.00,1.00,0.00,0.00,59,59,1.00,0.00,0.02,0.00,0.00,0.00,0.00,0.00,normal.\\n',\n",
       " '0,tcp,http,SF,212,1940,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0.00,0.00,0.00,0.00,1.00,0.00,1.00,1,69,1.00,0.00,1.00,0.04,0.00,0.00,0.00,0.00,normal.\\n',\n",
       " '0,tcp,http,SF,159,4087,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,5,0.00,0.00,0.00,0.00,1.00,0.00,0.00,11,79,1.00,0.00,0.09,0.04,0.00,0.00,0.00,0.00,normal.\\n',\n",
       " '0,tcp,http,SF,210,151,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,8,89,1.00,0.00,0.12,0.04,0.00,0.00,0.00,0.00,normal.\\n',\n",
       " '0,tcp,http,SF,212,786,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,8,99,1.00,0.00,0.12,0.05,0.00,0.00,0.00,0.00,normal.\\n']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.compute()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a last example combining all the previous, we want to compute all the normal interactions as key-value pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 13.3s\n"
     ]
    }
   ],
   "source": [
    "# get data from file and repartition it in 8 partitions and pe\n",
    "raw_data = db.read_text(DATASET_FILEPATH).repartition(8)\n",
    "\n",
    "# parse into key-value pairs\n",
    "key_csv_data = raw_data.map(parse_interaction)\n",
    "\n",
    "# filter normal key interactions and persist them \n",
    "normal_key_interactions = key_csv_data.filter(lambda x: x[0] == \"normal\")\n",
    "\n",
    "# Compute all\n",
    "all_normal = normal_key_interactions.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97278\n"
     ]
    }
   ],
   "source": [
    "print(len(all_normal))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This count matches with the previous count for normal interactions. The new procedure is more time consuming. This is because we retrieve all the data with compute and then use Python's len on the resulting list. Before we were just counting the total number of elements in the bag by using count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: **At its core, dask is a graph execution engine. It allows us to construct a prescription for the computation we want to carry out.**\n",
    "As a result, we can use `.visualize()` method to render the computation of an object’s task graph using graphviz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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tx40bd+vWLYqipFKphYXFN998QzoagML++OOPmTNndurUqUmTJv379w8MDIyKipJtilAUJRKJpFIpn8+fN28eNkVAA4wYMaLimqfy8nLZ1X5VNkX4fP7IkSNjYmKwKQLqD/sioCFMTEx8fX21tLQoipJIJPn5+cuXL2/dunVQUJD63FQUQE4ikYiiKIlEEh0dHRISQjoOAElNmjSRnYV05coVb29v7BQCcffu3aMoimGYU6dOXb9+nXQc4K4dO3bI7oOhpaWFfRFghZycnKCgoNatW8+ZMycjI4NhGIlEQlEUTdM//vgjLvcHNvL399+7d29ycrLsV8NlZWWyVlfQ0tLq37//1q1bCQUEUKapU6fWvdvB4/HGjRt38uRJ2YnLAGoO+yKgOZYsWVL592Visfj9+/dr1qxp3bp1YGBgXl4ewWwACpFdL0JRlFQqXbx48bVr18jmASDIyMhI9r1dLBZHRUXhDH0g7v79+7LzMAQCweLFi0nHAY7at2+fn5+f7M9isdjc3JxsHoC63bt375tvvmnbtu3PP/9cUFBQ+R70PB6vbdu2U6ZMIZsQoGHWrVtXx3sFAkHLli2joqIEAjzcFzSBvr7+3LlzZSvh6mianjlz5sGDB2v7AAB1g30R0Bxt2rTx9vau8v1XLBaXlJRs2rSpdevW/v7+2dnZpOIByK9iX0Rm3Lhx7969IxUGgKwmTZpU/FkqlQYHB+MiKiDr3r17snNCxWLxP//8c/bsWdKJgHOioqJmzJhR8WtlqVSK60VAbb17927AgAHOzs7h4eHl5eXVb4nJMMyaNWvwW2NgqSlTpvTp06fGAvN4PG1t7b/++svU1FT1wQAaQ3l5edu2bWu8gp+m6aVLl4aGhvJ4+FUzsAbKCholMDCwylWrMmKxuLS0dOfOnXZ2dkVFRaoPBqCQyvsiEokkNzfXy8ur+qPMALig8r6IzH/+85+jR48SCQNAUdTt27crFht8Pn/RokX4/gyqdOLEiYkTJ1b5lQT2RUBtMQyTnJzM5/NrfEgYj8eztbWdOHGi6oMBKAVN06GhobWtBKKiohwcHFQcCaAx5OXlBQcHt2vXzs/Pb8CAAdWvCAkODg4ODq54+ggAK2BfBDSKra2tu7t7bZfsMQwzY8YMQ0NDFacCUFRZWVnlkyzKy8svXrxY9zXaAJqq+r4IRVFeXl5Xr15VfRgAkUiUnp5e8VeJRJKWlnbw4EGCkYBTzp8/P2HCBIZhsC8CbNGiRYu4uDhDQ0M+n1/9vQzDrF27FicXA3vl5eXt2LFDV1e3yiUjNE1v2bLliy++IBUMQFnS09P9/f3btm27du1aDw+PJ0+erF+/vvJWN03TISEhS5cuJRgSoGGw/gBNs3z5crFYXP3tPB7P399/y5Ytqo8EoCiRSFTlPAupVLp8+fILFy6QigRASvV9EYZhpFLpyJEjHzx4QCQScFlSUlKVK1MZhgkMDBSJRKQiAXdcvHhx9OjREomkyqaIQCBo1qwZqVQA9XJ0dDx//ryOjk6VrRE+n+/k5OTm5kYqGMCnYBgmIiLC3t7+5MmTmzdvbtasWcVPcAKBwMfHp+IpUAAsFR8f7+npaWdnd/r06RUrVjx79iwkJMTKyqpbt269e/fm8/k0TfP5/AMHDqDtwFLYFwFN06VLl2HDhlU/WePbb7/dvHkzqVQACqlyvYgMTdMTJkzAM3KAa2q8XkQikYhEoqFDhz5//lz1kYDL7t+/X+X3egzD5OTk7N69m1Qk4Ih//vlnzJgxEomk+t1aTExMcNsKUHM9e/acPn06TdOVuyqRSNatW4f2Ahvdu3evX79+M2bMmDhxYkpKyqxZszZt2iR7l5aWVvfu3cPCwsgmBGiwsrKyqKiozz//vH///q9evTp06FBaWlpAQICxsXHFxyxevFgikWhpaZ08eXLSpEkE0wJ8CuyLgAYKCgqqfMkITdMCgaC4uLjGZ0MBqKGysrLqPyJKJJKioiJPT88aH6IDoKm0tLR0dHSqv10sFufl5Q0fPvzjx4+qTwWcVX1fhKIoiUSycuXKgoICIpGAC5KSkoYMGSISiWq8hb2ZmZnqIwEoZN26dTt37pw+fbrs/GKKogQCweeffz5ixAjS0QAU8/79e39//+7du/P5/ISEhJCQkKZNm1IU9fXXX/fu3ZuiqJYtW0ZHR2tra5NOCqCwt2/frlu3zsbGZvLkyVZWVv/88098fPz48eOrr37Hjh07ffr0mJiYUaNGEYkKoBTYFwEN1KtXrz59+sguGaFpevHixSdPnty3b5+vry+ejAqsUONzKSmKKisri4+P37hxo4rzAJBlYGBQ/Y0CgUAikRQWFubn56s+EnBWQkJCWVlZ9bcXFRXhslRoPEVFRbIn5NX4GIbWrVurPBGAvBiGWbx48ffffx8WFrZ79+79+/fL9kXEYnFwcDDpdAAKkN04y87O7sCBA7/88svly5c7d+5c8V6apsPCwhwcHP766y9TU1OCOQEaIC0tzd/f39raWvYQkfT09CNHjvTq1au2j+fz+Xv27BkwYIAqQwIoHfZFQDP98MMPYrGYpully5atX79+5MiRx48fj4yMnD17Nq4aAfVX/XoR2WVPFEUJhUKhUEgoFwAZst8GVtDS0qJpum/fvocPH3769GmrVq1IBQMOun//fo1vF4vF69evf/funYrzAEf07NnzxYsXv//+u62tLUVRlU/bFAgEbdq0IRcNoC4SicTX1zckJGTv3r0zZ86kKGrChAlhYWE0Tbu4uAwaNIh0QAB53b9/v3///tOmTRs+fHhqaqqvr2/16/sdHBwSExMdHByIJARomPj4+DFjxgiFwrNnz/78888vX74MCQmxtLQknQtAFQT1fwgACw0fPnzkyJG9e/desWKF7C1ffPHFsWPHPDw8KIravXs37mML6qysrEy2gUfTNI/Hk0gk7du3/+abbzw8PNq3b086HYCqyR4xIjtLWl9fv7i4+OjRo+7u7qRzAee8ePGi8s2ytLW1pVKp7NadBgYG9vb2uKsbNB4tLS0fHx9vb++pU6ceOXJEdlPv8vJyPp+P+2iBehKLxTNmzDh8+HBUVFTlh6vPmDHD1NS08on2AOrs/fv3K1eu3Llzp7Oz840bN3r06EE6EYASiESiw4cPb9iwITExUXbCmbu7e/X7ZQFoNuyLgMY6e/ZslbeMGjXq4MGDEydOpGk6NDQUWyOgtsrLy2WXjPTq1WvChAkHDhwQCoVLly4lnQuADBMTE4qiunbt6ufnN2HChM8///z06dPYFwHVe/78OUVRNE23bNnS3t6+uLg4Pz8/NDRUKBRaWFiQTgecUFZWdv78+YULF3711Vfr168/duyYSCQyNzcnnQugKpFINHHixJiYmNOnTw8ZMqTKe8eOHUskFYBCGIaJjIxcunRpeXn5pk2bFixYUOPNDAHY5fXr17t27dq5c2dBQcGECRMOHjzo5OREOhQAGTTuKQRcc+zYsUmTJs2dO3fr1q2kswDULCUlRXY1q+w3HVu2bFm9evXr16/x+D7gpsTERLFY3LVrV9lfd+3atWjRohcvXjRv3pxsMOAahmHS0tIsLS319fUpitq2bduqVatyc3NJ5wIOCQsL8/Pze/r0qWwr7unTp3v27Jk2bRouJwW1Ulxc7ObmlpCQcObMGdmTqAFY58GDB/Pmzbt+/frkyZM3b96MR4aABrh3796uXbsiIiKaNGkybdq0BQsW4BFlwHHYFwEuOnr06KRJk+bPn79lyxbSWQDq9/z5cysrq7Nnz44YMYJ0FgDyioqKWrduHRQU9J///Id0FuC08+fPDx8+/O3bt/hdCagGwzAODg59+/b99ddfSWcBqFV+fv6oUaMyMjL+/vvvLl26kI4DoLDi4uINGzasXbvWyclp586dn3/+OelEAJ9EKpXGxsaGhIScOXOmY8eOc+fO9fX11dPTI50LgDxcAwhcNG7cuEOHDu3YsWPhwoWkswDUz9LSslu3bsePHycdBEAtGBoaTp48+ZdffpFKpaSzAKcJhUKKolJSUkgHAa44efJkSkoKtoRBnb1+/drFxeXly5dXr17FpgiwUXR0tFAo3L59+/r162/evIlNEWC1oqKisLAwR0fHYcOGlZaWyhYS/v7+2BQBkMG+CHDUuHHjDh48uH379kWLFpHOAlA/Dw+PEydOyJ7uCwDz589PT0+/ePEi6SDAaZaWloaGhtgXAZXZsGHDmDFjOnXqRDoIQM2ysrL69+9fWloaHx/fsWNH0nEAFJOamjps2DA3NzcXF5fU1FR/f388TQTYKzs7OygoyMrKys/Pr3v37g8fPoyJiRkzZgyeswtQGb7LA3eNHz/+wIED27ZtW7x4MeksAPXw8PB49+5dfHw86SAAaqFTp079+vXbtWsX6SDAaTRN29rapqamkg4CnHDz5s3r168vWbKEdBCAmmVkZAwaNEhLSys2NtbS0pJ0HAAFFBcXBwUFde7cOTc399q1axEREbhDJrDXnTt3fHx8rKysdu/evWDBghcvXkRERDg4OJDOBaCOBKQDAJDk6enJMMzXX39N0/SGDRtIxwGoVceOHZ2cnI4dOzZo0CDSWQDUwpw5c3x8fF68eNGmTRvSWYC7hEIhrhcB1Vi7dm2PHj369etHOghADR49ejRs2LBWrVqdO3fOxMSEdBwABURHR8+bN6+oqGj9+vXz58/n8/mkEwE0hFQqPXPmzLZt2y5cuODs7Lxjxw4fHx9dXV3SuQDUGq4XAa6bMGFCZGTkli1bli5dSjoLQF3c3d1PnDjBMAzpIABqwcPDw8TEBA8fBrLs7OywLwIqkJaWdurUqcDAQNJBAGpw+/btgQMHduzYMTY2FpsiwCJpaWnDhw8fO3bsoEGDZDfOwqYIsFFhYWFISEj79u3d3Nx0dXVjYmLu3Lnj6+uLTRGAemFfBICaOHHib7/9tmnTpoCAANJZAGrl4eHx8uXLf//9l3QQALWgra09ffr0X3/9tby8nHQW4C6hUPj06dPS0lLSQUDDbdq0ydraeuzYsaSDAFR1+fJlV1fXXr16/fXXX0ZGRqTjAMilpKQkKCjIycnp7du3shtntWjRgnQoAIU9ffo0MDDQyspq+fLlw4YNe/ToUXR09JAhQ0jnAmAN7IsAUBRFTZ069bffftu4cWNQUBDpLAA1c3JysrOzO3bsGOkgAOpi9uzZb968OXnyJOkgwF1CoVAikTx58oR0ENBkb968iYyMXLJkCU5kBnVz5syZkSNHjho16sSJE3p6eqTjAMglOjra3t5+27Zt69evv3XrVu/evUknAlBYQkKCj4+Pra1tRESEn5/fs2fPdu/eLRQKSecCYBnsiwD8n2nTpv36668//vjj6tWrSWcBqNlXX3119OhR3EoLQKZt27YjR47E09eBIFtbWz6fj1tpQaPavn27oaHhlClTSAcB+B9//PHHV1995e3tfeDAAS0tLdJxAOr3+PHjESNGjB07duDAgSkpKbhxFrCORCKJjo7u06dP9+7dk5KS9uzZk5WVFRQU1Lx5c9LRAFgJ+yIA/yW7JcuqVat+/PFH0lkAauDu7p6ZmXnv3j3SQQDUxZw5c2JjYx89ekQ6CHCUrq6ulZUV9kWg8ZSUlISGhs6fPx8n44NaCQsLmzx58qxZs0JDQ3k8/FYB1F3FjbNev34dHx8fERFhZmZGOhSAAgoKCkJCQmxsbNzc3ExMTOLj42/fvu3j4yMQCEhHA2AxvH4A/sf06dMZhvH19aVpevny5aTjAPyP7t27W1lZHT9+3NnZmXQWALUwcuTIjh07hoWFhYSEkM4CHCUUClNTU0mnAI21Z8+ekpKSuXPnkg4C8F87duzw8/NbunRpcHAw6SwA9YuOjl6wYMGHDx/WrVs3f/58XCMC7JKenr5t27Y9e/bw+fypU6cuXLjQysqKdCgADYEzOwCqmjFjxu7du3/44Yc1a9aQzgLwP2ia/uqrr6KiokgHAVAXNE3PnDkzPDy8uLiYdBbgKKFQiOtFoJFIJJKQkJBp06aZmpqSzgLwf9atW+fn57dx40ZsioD6e/LkyRdffDF27NgBAwbgxlnAOvHx8Z6ennZ2dqdPn16xYsWzZ89CQkKwKQKgRNgXAajBzJkzd+/evWLFip9//pl0FoD/4eHhkZqampycTDoIgLqYPn26SCQ6dOgQ6SDAUXZ2dikpKXjyEzSGo0ePZmZmfvvtt6SDAFAURTEMs2jRou+//z4sLGzhwoWk4wDURXbjLEdHx1evXl29ejUiIsLc3Jx0KAC5lJWVRUVFff755/3798/IyNi7d29qampAQICxsTHpaACaBvsiADX75ptvQkNDly9fvnbtWtJZAP6rT58+FhYWx44dIx0EQF2YmpqOGzdu586dpIMARwmFwqKiopcvX5IOAhpoy5Yt7u7uHTp0IB0EgJJIJL6+vjt27Dh48ODMmTNJxwGoS3R0tIODw9atW9etW3f79u2+ffuSTgQgl7dv365bt87Gxmby5MlWVlb//PMPHiIC0KiwLwJQK19f3y1btnz//fe4SBzUB4/HGzt27PHjx0kHAVAjc+bMuXfv3o0bN0gHAS4SCoUUReFWWqB0cXFx//77L87KB3VQXl7u5eUVGRl5+PBhT09P0nEAavXkyZNRo0aNHTu2f//+qamp/v7++IUysEJaWpq/v7+1tfXatWs9PDzS09OPHDnSq1cv0rkANBz2RQDq4u/vv3nz5u+++27dunWkswD8Hw8Pj7t376anp5MOAqAu+vTp89lnn+3atYt0EOAiMzMzExMT7IuA0m3YsGHgwIH4nQgQJxKJPD09z5w5c/r0aTc3N9JxAGr28ePHoKAgJyenjIyM8+fP48ZZwBbx8fFjxowRCoVnz579+eefX758GRISYmlpSToXACdgXwSgHt9+++2WLVu+++67bdu2kc4CQFEUNWjQIFNT0xMnTpAOAqBGZs2adfjw4bdv35IOAlxkZ2eXmppKOgVolMTExHPnzi1ZsoR0EOC64uLi0aNHX758+cKFC0OGDCEdB6Bmshtnbdy4MSAg4P79++gqqD+RSBQREeHk5NS/f//8/PzDhw+npKT4+/sbGBiQjgbAIdgXAajft99+u2nTpm+//Xb79u2kswBQAoFgzJgxeMQIQGVeXl66urrh4eGkgwAXCYVCXC8CyrVx40Y7O7uRI0eSDgKclp+fP2TIkIcPH166dAmXLoF6Sk9PHz169Jdffung4JCcnBwUFKStrU06FEBdXr9+HRQU1KZNm2+++cbZ2fnBgwfx8fHjx4/n8/mkowFwDvZFAOTyn//8Z+PGjf7+/jt27CCdBYByd3f/999/nz9/TjoIgLowNDT09vYODQ2VSqWkswDn2NnZYV8ElOjly5eHDh0KCAjg8fDDGhDz+vXrQYMGvXr16urVq507dyYdB6Aq2Y2zHB0dnzx5cv78+ejoaNx6CNTcvXv3Zs2aZW1tvWvXrhkzZmRkZMguGSGdC4C7sNQGkNfChQs3bNjg5+e3c+dO0lmA64YOHWpkZHTy5EnSQQDUyNy5czMyMmJiYkgHAc4RCoUvX74s/J6xRAAAIABJREFUKCggHQQ0REhIiKmpqZeXF+kgwF1ZWVn9+/cXiUTx8fEdO3YkHQegqso3znrw4MHQoUNJJwKolVQqvXDhwpgxYz777LNLly4FBwdnZmYGBwe3bt2adDQArsO+CIACFi1atH79+gULFvzyyy+kswCn6ejojBo1CrfSAqjM3t5+wIABePo6qJ5QKGQYJi0tjXQQ0ASFhYW//vqrv78/bgUDpKSmpvbr109bWzs2NhYn4IO6efHihaenJ26cBaxQVFQUFhbm6Og4bNiw0tLSkydPyh4ioqenRzoaAFAURQlIBwBgmcWLFzMMM3/+fIqi5s6dSzoOcJeHh8eECRPevHljZmZGOguAupgzZ87kyZMzMzOtra1JZwEOsbGx0dHRSUlJ6d69O+kswHqhoaESicTX15d0EOCoR48eDRs2rHXr1n/99ZeJiQnpOAD/VVZWtmvXru+//75169Z///33sGHDSCcCqFV2dvbu3bu3b99eXFzs6ekZFRXl4OBAOhQAVIXrRQAUtmTJkuDg4Pnz5+OsZCBo5MiRurq6uJUWQGXu7u7m5ua//fYb6SDALQKBoH379qmpqaSDAOuVl5dv377d19fX2NiYdBbgolu3bg0cONDW1vbixYvYFAG1cvHixS5duixbtmzx4sUPHjzApgiorTt37vj4+FhZWYWGhi5YsODFixcRERHYFAFQT9gXAWiIpUuX/vDDD/PmzQsNDSWdBThKX19/+PDhx48fJx0EQI1oaWlNnz49LCxMJBKRzgLcIhQK8eh1+HQHDx7Mycnx8/MjHQS46NKlS4MHD+7du/fZs2eNjIxIxwH4Py9evPDx8RkyZEiHDh1kN87S0dEhHQqgKqlUGh0dPXTo0G7duiUmJu7YsSMzMzMoKMjU1JR0NACoFfZFABooKChoxYoVc+fO3b17N+kswFHu7u4XL17Mz88nHQRAjfj6+ubn5//555+kgwC32NnZYV8EPt2WLVsmTpzYtm1b0kGAc06fPj1y5MhRo0YdP34cN74HNVFeXh4SEmJvb3/jxo1z585FR0fj2yOoocLCwpCQkPbt27u5uenq6sbExNy5c8fX11dXV5d0NACoB/ZFABpu1apVy5cvnzt3bkREBOkswEVffvkln88/ffo06SAAasTS0nLUqFG4zyGomJ2dXVpamlgsJh0EWOyvv/66f//+woULSQcBzvnjjz/c3d19fHwOHDigpaVFOg4ARVFUbGys7MZZixYtevjw4fDhw0knAqjq6dOngYGBVlZWy5cvHzZs2KNHj6Kjo4cMGUI6FwDIC/siAJ9k9erVy5Ytmz59emRkJOkswDlGRkaurq64lRZAFXPmzLl8+fLDhw9JBwEOEQqFZWVlmZmZpIMAi23YsGH48OFdu3YlHQS4JSwsbPLkybNnzw4NDeXx8PsBIO/ly5c+Pj6DBw9u3759UlISbpwFaighIcHHx8fW1jYiIsLPz+/Zs2e7d+8WCoWkcwGAYrDuAfhUP/74Y2Bg4LRp0/bv3086C3COu7v7uXPnCgsLSQcBUCPDhg2ztbXFTQ5Blezt7SmKwqPXocHu378fFxe3ZMkS0kGAW7Zv3z579uwlS5Zs27aNpmnScYDrZDfOEgqF//zzz9mzZ6Ojo62srEiHAvgviUQSHR3dp0+f7t27JyUl7dmzJysrKygoqHnz5qSjAUBDYF8EQAl++umngICAqVOnYmsEVMzNzU0sFp87d450EAA1QtO0r69vREREQUEB6SzAFU2aNLGwsMAjRqDB1q5d26VLF1dXV9JBgEPWrVvn7++/cePG4OBg0lkAqLi4uK5du3733XeLFi1KTEwcOXIk6UQA/1VQUBASEmJjY+Pm5mZiYhIfH3/79m0fHx+BQEA6GgA0HPZFAJRjzZo1S5cunTp16oEDB0hnAQ4xMTEZMGDAsWPHSAcBUC/Tp0+XSCSHDh0iHQQ4RCgU4noRaJjMzMxjx44FBATghH1QDYZhFi1a9P333//66694pA0Q9+rVKx8fH1dXVxsbG9w4C9RNenq6v79/q1atfvjhBzc3t4yMjOjo6L59+5LOBQBKgI1NAKX5+eefGYaZMmUKTdNeXl6k4wBXeHh4BAQElJaW6urqks4CoC6aNWs2fvz4X375ZdasWaSzAFcIhcLExETSKYCVNm/ebGFhMW7cONJBgBMkEsmsWbMiIyMPHTo0fvx40nGA08rLy3/55ZcVK1aYmZmdOXPmiy++IJ0I4L/i4+O3bdt2/PhxKyurFStWzJo1y9jYmHQoAFAm7IsAKNPatWsZhvHx8aFpetKkSaoPIJVKs7KyUlNT09PTCwsL8/Pzi4uLi4qKiouLtbW1DQwMmjVrZmBgYGhoaGlpaWtra2dnh5Nx2M7d3X3BggUxMTFjxoyp8i70Abhszpw5vXr1un79ep8+fer+yNzc3NTU1MePH+fk5FS8RvLz8ymKqniNGBgYtGzZ0tbW1tbW1sTERCVfAbCMnZ3dkSNHrl+/ji5pqkb6XpGXl/f777//9NNPWlpajfwVgPKxbq1VVlbm7e198uTJI0eOjB07lmASzmJdZxrPpUuX5s+fn5GRsXTp0sDAQJzjJT+sXRtVWVnZyZMnN27cePPmzW7duu3du9fLywv3ywLQSDTDMKQzAGgUhmG+/fbbnTt37t+/f+LEiSr4dElJSbGxsfHx8SkpKWlpaaWlpRRFmZiYNG3a1NjYWLYkMjQ0LCsrKyoqev/+vWzZlJ2dLZFIeDyelZWVnZ1djx49XFxcevfujfUoG/Xr169Dhw779u1DHwAq6969u729fWRkZJW3v379+tKlS5cuXXrw4EFqampubi5FUbq6uq1atTIwMJC9RmSng1W8RoqKirKzs2UvKFNTUzs7u86dOw8cONDFxcXMzEz1XxqoicpdevTo0YcPHyh0SYOo5nvFTz/9tHnz5qysLENDQ1V+ddAwrF5riUSiCRMmXLx48c8//xw8eLDKPi/HsbozjSQ7OzsgIGD//v2jRo3atm1bu3btSCdSd1i7qsbbt2/37t27ffv2N2/euLm5LVy4sFevXqRDAUAjwr4IgPIxDOPv7x8aGnrkyBE3N7fG+BR5eXnHjx+/cOHCpUuXXr9+bWxs3L9/f0dHR1tbW6FQaGdn16xZs7qPIBKJHj9+nJqampaWlpycfP369fT0dF1d3T59+ri6urq7u9vb2zdGcmgMP/3009q1a0eNGnXlyhX0AaDCb7/9Nm/evOfPn5uZmYnF4piYmL/++is2NjYpKYnP5/fs2fOzzz4TCoWyM+natm1b9539GYbJyspKS0tLTU1NSUm5c+fOrVu3JBKJg4ODq6vryJEjhw4dyufzVfbVASnokmZT8fiKRCJra+vp06evWbNGJV8fNJAGrL2Liorc3Nzu3Llz9uxZ/KZPBTSgM41BLBbv3Lnzhx9+MDU13bZt26hRo0gnUl9Yb6jS48ePd+zY8dtvv2lpaU2ZMmXx4sWWlpakQwFAo8O+CECjYBjGz89v9+7dyt0aKSsr++uvvyIiIs6cOcPn82Wne7i4uDg7O3/6giYrKys2NjY2NvbixYuvXr3q0aOHt7f3xIkTW7RooZTwoHQVfTh9+jRN0y4uLq6urugDQIWPHz+2adPm66+/5vP5hw4dev36tbOzs+xlMmDAgE8/NbuwsPDq1auxsbFxcXF3795t2bLlpEmTfHx8unTpopT8oG7u3bsXERGBLmkqIuMbFhbm5+f39OlTCwsLpXwVoFwas/bOz8//4osvnj59ev78+c6dOyv9+FBBYzrTGC5fvjx//vz09HTcOKtuWG+oUnx8/Lp1686cOdO+ffv58+fPnDnTwMCAdCgAUBUGABqHVCqdN2+etrb2n3/++elHe/ny5eLFi01NTXk8nqur6++//15QUPDph62RRCK5ePHi1KlTjYyMtLS03Nzcrl+/3kifCxoGfQCol0gk+vXXX83NzSmK6tChQ1BQUHp6euN9uidPnqxcubJ9+/YURXXu3HnPnj0ikajxPh2okqxLTk5O6JJGIju+FhYW06dPb7xPBw2mSWutnJyczp07W1lZpaWlKfGwUIUmdUbpXr165e3tTdP06NGjG/V7LKthvaFKpaWl4eHhjo6OFEX17dv3yJEjYrGYdCgAUDXsiwA0IqlUOnfuXG1t7ZMnTzb4IE+fPp0zZ46Ojk6rVq3WrFmTlZWlxIR1Ky4u3r9/f9++fSmKcnV1vXjxoso+NdQGfQCoV0lJSUhIiKWlpba29vTp0+Pj46VSqWo+tVQqjY+Pnz59ura2dtu2bbdv315SUqKaTw2NAV3SbOowvgKBoFWrVhhftaJha63MzMyOHTva2dmp8gvhGg3rjHKVl5dv3bq1SZMmbdq0CQ8PJx1HTanDfMSd9UZOTs7KlStNTU21tbW9vb0fPHhAOhEAEIN9EYDGJZVK58yZo62tferUKUX/7cuXL6dOnaqlpdWuXbtdu3aVlpY2RkJ5xMXFDRkyhKKo3r17X758mVQMjkMfAOpVVla2adMmMzMzfX19f3//58+fk0qSlZW1YMECPT29li1bbt26tby8nFQSaBh0SbNhfKFGmrfWSklJsbS0dHBwePXqlRLjQQXN64xyXb582cnJSVtb28/Pr7CwkHQcdYT5SJXu3r3r6+urq6trZmYWEBDw4sUL0okAgDDsiwA0OqlUOnv2bIW2RsrLyzdv3mxkZGRjYxMeHq4mi5IbN26MGDGCpmlvb++cnBzScTgEfQCQx+XLlx0cHPT09AIDA1+/fk06DsMwTE5OzpIlS3R1dTt37nz16lXScUBe6JJmw/hCdRq51kpMTLSwsOjRo8e7d+8aIx7HaWRnlCg7O1t246zBgwcnJycTTKLOMB+phkQiiYmJGT16NE3Ttra2W7du1fhrYgBATtgXAVAFqVQ6a9YsbW3t6Ojoej/41q1b3bt319LS8vPzKyoqUkE8hZw6dcra2trY2Hjr1q24BacKoA8A9crNzfX19VXbn72fPHnyxRdfyH5JoSY/9EJt0CXNhvGFGmnkWuvmzZsmJiaDBg1qvEdccJlGdkZZZDfOatq0aevWrXHjrNpgPlKNwsLC3bt329vb0zQ9ZMiQU6dOqewGZQDACtgXAVARqVTq6+uro6Nz+vTp2j6mrKxs8eLFNE0PHz5cnZ+LWFxcHBgYqK2t3a9fP4KX+mo89AFAHtHR0SYmJm3btj1+/DjpLHU5evSopaWlqanpmTNnSGeBmqFLmg3jC9Vp6lorLi7OyMho9OjRHz9+VEE8TtHUzijL1atXO3fuLNsxwo2zaoP5SAVevXq1cuXK5s2b6+joeHt7JyYmkk4EAOoI+yIAqiORSHx8fHR0dGpcWDx79qx3794GBga///67yqM1xMOHDzt16sTSdZL6Qx8A6lVWVrZo0SKapqdNm6aGZ2tWV1hYOGXKFJqmly5dqiY33AAZdEmzYXyhRpq61oqOjtbV1Z00aVJZWZnKsnGEpnZGKd69e+fn58fj8VxdXZOSkhr707EU5iMVSEhI8Pb21tLSMjc3X7ly5du3b0knAgD1hX0RAJUSi8Xe3t56enoXLlyo/PZTp041b95cKBQ+ePCAVLYGKCkp+eabb2ia9vPzE4lEpONoDvQBoF5ZWVl9+vTR09MLCwsjnUUx4eHhBgYGPXr0yMjIIJ0FGAZd0nQYX6iRpq61Dh48qKWlNWvWLIlEovp4mk1TO/PpJBJJeHi4iYkJbpxVN8xHjUoikZw6dWrIkCEURXXt2nX37t24YA4A6oV9EQBVE4vFX3/9tb6+/sWLF2Vv2bp1K4/H8/b2ZsU5I9XJ1kmurq4fPnwgnUUToA8A9bp9+7aZmZm9vf3Dhw9JZ2mI5ORkJycnc3PzhIQE0lm4Dl3SbBhfqJGmrrVCQ0N5PN6CBQtwA32l09TOfLpbt2717NlTduMsPMymDpiPGk9BQcHWrVutra15PN7o0aNjYmJIJwIA1sC+CAABFVsjFy5cCAgIoGl6w4YNpEN9Etk6r3v37qx+LBtxUqkUfQCoV2xsbJMmTQYPHszqH7+LiopGjBhhaGh47tw50lm4C13SbBhfqE6D11rbtm2jaTogIIBsMM2jwZ35RLm5ubIbZ7m4uDx69Egpx9RUmI8aSUZGRkBAQLNmzQwNDX19fdXwCfYAoOawLwJARnl5uaenp4GBgUAg2Lt3L+k4SpCent6hQwcbG5vHjx+TzsJKYrF45syZ6ANA3Y4fP66rq+vl5aUBt00XiUSTJk3S1tY+dOgQ6SxchC5pNowvVKfBa63g4GCapjdv3kw6lKbR4M58ynFkN84yNTVt1aoVbpxVL8xHjeH27dve3t4CgcDCwmLlypW5ublk8wAAS2FfBICM8vJyNzc3PT099Tnb4tO9evWqS5curVu3Tk9PJ52FZcrLy7/66isDAwP0AaAO+/bt4/F43377rcbcIUQikcjOtdy3bx/pLNyCLmk2jC9Up8FrLSMjIz6fz5YngbOIBnfmU9bnt2/f/vzzz3HjLDlhPlIusVh86tSp3r17UxTVrVu38PBwtjwNHgDUE/ZFAAiQSqXTp0/X19e/du0a6SxK9v79+88++6xDhw64gZL80AcAeZw6dUogECxbtox0EOULDAwUCATR0dGkg3AFuqTZML5QnWavtezt7Vu2bIm1lnJpdmcatj6vuHHWwIEDExMTGymeJsF8pEQfPnzYunVr27ZtZQ8RiY+PV9mnBgANhn0RAAICAgK0tLTOnDlDOkijePPmjZ2dXefOnfPz80lnYQf0AaBe//zzj4GBga+vL+kgjUIqlc6cOVNPT+/q1auks2g+dEmzYXyhRlhrgaLQmcpkN85q0aKFhYVFeHi4xlz60KgwHynLkydP/Pz8DAwMmjRp4ufnl5mZ2difEQC4A/siAKq2ZcsWmqYjIiJIB2lET548admy5eDBg0UiEeks6g59AKjXw4cPmzVr5u7uLhaLSWdpLOXl5V9++WXz5s1xAmajQpc0G8YXaoS1FigKnaksISGhV69eAoHAz8/vw4cPqonHdpiPlOLq1avjx4/n8/k2NjbBwcHY+gUApcO+CIBKnT9/nsfjbdy4kXSQRnf37l0jI6N58+aRDqLW0AeAeuXn59vY2AwcOPDjx4+kszSukpKSvn37dujQAb90aCTokmbD+EKNsNYCRaEzFfLy8vz8/Ph8/oABAx4+fKjKbKyG+egTiUSiI0eO9OzZEw8RAYDGhn0RANXJycmxsLDw9PQkHURFjhw5QlHUwYMHSQdRU+gDgDwmTZpkbm6enZ1NOogqyL4tjB8/nnQQzYQuaTaML1SHtRYoCp2RkUqluHFWg2E+arA3b94EBwe3adNGS0tr/Pjx//zzj1IOCwBQG+yLAKiIRCIZMmQI107umz17trGxcXp6Oukgagd9AJDH9u3beTzehQsXSAdRnbi4OD6fHxoaSjqIpkGXNBvGF6rDWgsUhc7I3Llzp3fv3rhxVsNgPmqYtLQ0Pz8/fX39pk2b+vn5ZWVlKSseAEAdsC8CoCJBQUG6urp3794lHUSlPn782LVr1x49euBmx1WgD6SzAAskJCTo6OisXr2adBBV++GHH3R1de/du0c6iOZAl0gHaVwYX9JB1BTWWqSzsA86k5+fL7txVv/+/R88eEA6GvtgPmrAv7169ero0aNpmu7QocPWrVuLioqUHg8AoDY0wzAUADSyBw8edOvWbevWrfPmzSOdRdUeP37s7Oy8bNmyZcuWkc6iLtAH9AHqJRaLu3fvbmJiEhMTw+PxSMdRKYlE4urqWlRUdPPmTT6fTzoO66FLmt0ljK9mj2+DYa2FtZai0Jlhw4ZdvXpVIBBs2LBh8uTJNE2TzsUymI8Umo9EItHhw4c3bNiQmJjYt29ff39/d3d3zGUAoGqkN2YANJ9UKh04cGCPHj0kEgnpLGSsXbtWT08vIyODdBC1gD6gDyCPzZs36+jopKSkkA5CRlpamo6Ozvbt20kH0QTokmZ3CeOr2ePbMFhrYa2lKHRm7dq1fD5/6tSp79+/J52FrTAfyTkf5eTkrFy50tTUVFtb29vbG1cmAQBBuF4EoNHt3bvX19f31q1bzs7OpLOQUVZW1rVrV2tr67Nnz5LOQh76gD5AvXJycoRCob+//6pVq0hnIWbZsmU7d+5MTk5u1aoV6Swshi5RGt0ljC+l0ePbYFhrYa2lKHQGnflEmI8oOeaj+/fv//LLLxEREU2aNJk2bdqCBQtat26t4pAAAJVhXwSgceXl5QmFQi8vr61bt5LOQtKVK1cGDRp08uTJMWPGkM5CEvoggz5A3SZOnHjjxo2kpCR9fX3SWYj5+PGjg4ND3759IyMjSWdhMXSJ0uguYXwpjR7fhsFaSwZrLfmhMzLozKfAfETVPh9JpdLY2NiQkJAzZ8507Nhx7ty5vr6+enp6pHICAFTAvghA41q8ePH+/ftTU1ObNm1KOgthXl5eCQkJSUlJXL5tKPpQAX2A2vz777+9evWKjo4ePXo06SyEnThxwsPD49atW926dSOdhZXQpQoa2SWMbwWNHN8Gw1qrAtZackJnKqAzDYP5qEKV+aioqOjgwYNbt25NSUkZPHiwn5+f7BHrpGMCAPwf7IsANKK8vDxra+ugoKCFCxeSzkJeenq6UCiMjIycOHEi6SxkoA+VoQ9Qmy+//PLNmzc3btwgHUQt9OzZ09LS8tixY6SDsBK6VJnmdQnjW5nmjW/DYK1VGdZa8kBnKkNnGgbzUWWy+WjHjh27d+/evn17cXGxp6dnQECAg4MD6WgAAFVhXwSgEf3www+//PJLZmamoaEh6SxqwcvL6/79+w8fPuTxeKSzEIA+VMHxPkCNHjx40LVr11OnTuGEOxnZaXcPHjxwdHQknYVl0KUqNKxLGN8qNGx8GwxrrSqw1qoXOlMFOqMozEdVyOYjHo9nbm4+f/58X19fExMT0qEAAGqGfRGAxlJQUGBtbb1w4cLly5eTzqIukpKSnJycjh8/PnbsWNJZVA19qI7LfYDaTJw4MSUl5e7du7jEXoZhmC5dunTu3Hn//v2ks7AMulSFhnUJ41uFho1vw2CtVR3WWnVDZ6pDZxSF+agK2XzUrFmzmJgYbW1t0nEAAOqCUwAAGsvu3bulUun8+fNJB1EjnTp1Gjt2bHBwMOkgBKAP1XG5D1Cj9PT0o0ePLlu2DD9YVqBp+rvvvvvjjz+ePn1KOguboEvVaVKXML7VadL4NhjWWtVhrVU3dKY6dEYhmI+qk81H165de/nyJeksAAD1wL4IQGPZu3evj4+PsbEx6SDqxd/f/8aNG48ePSIdRNXQhxpxtg9Qo3379rVs2dLDw4N0EPXi6elpbm4eHh5OOgiboEs10pguYXxrpDHj22BYa9UIa606oDM1Qmfkh/moRpiPAIAtsC8C0Chu3ryZkpLi4+NDOojaGTBgQLt27bh2kwf0oTbc7APUiGGYAwcOfP3113w+n3QW9cLn8ydNmhQeHo57n8oJXaqNZnQJ41sbzRjfBsNaqzZYa9UGnakNOiMnzEe14fh8BAAsgn0RgEYRGRlpb2/fvXt30kHUDk3TXl5ekZGREomEdBbVQR9qw80+QI2uXLny9OnTyZMnkw6ijqZOnZqZmXnt2jXSQdgBXaqDBnQJ41sHDRjfBsNaqzZYa9UGnakNOiMnzEd14PJ8BAAsgn0RAOUrLy//448/vL29SQdRU1OmTHn58uWlS5dIB1ER9KFuXOsD1CYyMvKzzz5zcnIiHUQdOTo6du7cOTIyknQQdkCX6qABXcL41kEDxrdhsNaqG9Za1aEzdUNn5IH5qA6cnY8AgF2wLwKgfBcvXszNzcWZI7Xp2LFjjx49Dh8+TDqIiqAPdeNaH6BGEonk2LFjeJnU4euvvz569CjO3KwXulQvVncJ41svVo9vg2GtVTestapDZ+qGztQL81G9uDkfAQC7YF8EQPkuXrzYqVOntm3bkg6ivkaMGBEbG0s6hYqgD/XiVB+gRgkJCe/fvx85ciTpIOprxIgReXl59+7dIx1E3aFL9WJ1lzC+9WL1+DYY1lr1wlqrCnSmXuhM3TAf1Yub8xEAsAv2RQCULy4uztXVlXQKtebi4pKenv7s2TPSQVQBfagXp/oANYqNjTU3NxcKhaSDqC9HR0dzc/O4uDjSQdQdulQvVncJ41svVo9vg2GtVS+stapAZ+qFztQN81G9uDkfAQC7YF8EQMnev39/7949FxcX0kHUWu/evfX09LiwSEIf5MGdPkBt4uLiBg8eTNM06SDqi6bpgQMH4mVSL3SpXqzuEsa3Xqwe34bBWkseWGtVhs7IA52pG+ajenFwPgIA1sG+CICSXbp0iWGYAQMGkA6i1nR1dXv37s2FRRL6IA/u9AFqVFZWdu3aNfx6ol4uLi5XrlwpLy8nHUR9oUtyYmmXML5yYun4NhjWWvLAWqsydEYe6EwdMB/JiWvzEQCwDvZFAJTsxo0bjo6OJiYmpIOouwEDBly/fp10ikaHPsiJI32AGj18+LC4uLh///6kg6i7gQMHFhUVJSYmkg6ivtAlObG0SxhfObF0fBsMay05Ya1VAZ2REzpTG8xHcuLafAQArIN9EQAlS0lJ6dSpE+kULNCpU6enT5+KRCLSQRoX+iAnjvQBapSSkqKtrd2+fXvSQdRdx44dtbS0UlNTSQdRX+iSnFjaJYyvnFg6vg2GtZacsNaqgM7ICZ2pDeYjOXFtPgIA1sG+CICSpaam2tnZkU7BAnZ2dhKJJCMjg3SQxoU+yIkjfYAapaamdujQQSAQkA6i7gQCgY2NDX62rAO6JCeWdgnjKyeWjm+DYa0lJ6y1KqAzckJnaoP5SE5cm48AgHWwLwKgTGKxOCMjQ33W2b169Vq6dCnpFDXr2LEjj8dLSUkhHaQRoQ/y40IfoDYaXKZAAAAgAElEQVRq9esJdX6ZUBRlZ2eHny3rgC7Jj41dwvjKj43j2zBYa8kPay0ZdEZ+6ExtMB/JjzvzEQCwEfZFAJQpIyOjrKyM4CLpxYsXlf/arl07XV3d2t5Llp6eXps2bTR7kYQ+yI8LfYDapKSk4GUiJ1tbW/x6og7okvzY2CWMr/zYOL4Ng7WW/LDWkkFn5IfO1Abzkfy4Mx8BABthXwRAmTIzMymKateuHanP7uXlVfkthw4dWr16dW3vJa5Dhw6y/2OaCn1QiMb3AWrz7NkzvEzk1L59e7xM6oAuyY+NXcL4yo+N49swWGspBGstCp1REDpTI8xH8uPOfAQAbIT7IQIoU0FBAU3TTZo0Uf2nfvny5ejRoyUSSQPeS0qTJk0KCgpIp2hE6INCNL4PUCOGYQoLC42NjVX/qdn4MmnatCleJrVBlxTCui5hfBXCuvFtMKy1FIK1FoXOKAidqQ7zkUK4Mx8BABvhehEAZSosLNTX1+fz+Uo52o4dO7y9vefOnaurq0v/fxRFlZaWrl+/fubMmT169Bg6dGhiYiJFUfv27Xv06FFOTs6cOXMoipJKpVFRUVOnTh04cGD199Z2kJKSkgMHDnh5efXt2/fGjRufffaZtbX1tWvX0tLSvvrqqxYtWtjb2yckJCjlq6MoysjIqLCwUFlHU0Pog0I0vg9Qo+LiYqlUamhoqJSjceFlIpFIPn78qKwDahJ0SSGs6xLGVyGsG98Gw1pLIVhrUeiMgtCZ6jAfKYQ78xEAsBIDAMqzbds2c3NzpRxq+/btfD4/NzeXYZi1a9dSFLVo0SLZu7755puUlBTZn4cNG2Zubl5QUMAwDEVRQqGw4ghZWVmV31LlvTUeRCqVPnnyhKKopk2bnjlzJikpiaIoa2vrDRs2fPjw4e7duxRFDRo0SClfIMMw8+bNGzhwoLKOpobQB4VofB+gRtnZ2RRFXbly5dMPxYWXyaVLlyiKysnJUdYBNQm6pBDWdQnjqxDWjW+DYa2lEKy1GHRGQehMdZiPFMKd+QgA2Aj7IgDKtGbNmg4dOijlUF9++SWPxysrK2MYRnZmR69evRiG+ffff6tvcJ4+fZqptgyq8pbKf67jIFU+snXr1pU3UM3MzIyNjZXyBTIMExgY6OzsrKyjqSH0QSEa3weoUVpaGkVRd+/e/fRDceFlcufOHYqiHj9+rKwDahJ0SSGs6xLGVyGsG98Gw1pLIVhrMeiMgtCZ6jAfKYQ78xEAsBHuowWgTB8/ftTX11fKoYYOHSqVSs+cOUNRlK6uLkVRrq6uFEXdunXL0dGxyit51KhRCh1c/oMYGRlV/mvz5s3fv3/f8K/qf+nr65eUlCjraGoIfVCIxvcBaiQbdD09vU8/FEdeJhRFFRcXK+uAmgRdUgjruoTxVQjrxrfBsNZSCNZaFDqjIHSmOsxHCuHOfAQAbITnrgMok56enrIWjvPnz9fT05sxY8a1a9ceP368evXqZcuWURSVm5ubkZFRUlJSeUEvlUp5PAW2OZVykE9XJYDmQR8UovF9gBrJBl0pNx3myMuEoigDAwNVflK2QJcUwrouYXwVwrrxbTCstRSCtRaFzigInakO85FCuDMfAQAb4XoRAGUyNDQsKipSyqEkEkliYuKNGzc2bNjw559/rlixQvZ4QKFQWFJSsm7duoqPTE5O3rFjB0VRNE2LxeLaDlj5vXUcRJUKCwurnJyiYdAHhWh8H6BGsqdWKuWRnhx5mVDVTusDGXRJIazrEsZXIawb3wbDWkshWGtR6IyC0JnqMB8phDvzEQCwEa4XAVAmIyMjpayQKIr6+eefo6OjnZycMjIymjRpYmpqamNjw+fzx44da2Njs3r16hcvXgwePDg5OfnmzZtHjx6lKKp9+/bZ2dnPnz+3tLSkKEq24i8oKJAdsPJ76zhIaWkpRVEMw8j+VXl5uexQsvWf7L3KOs2ksLCwSZMmn34ctYU+KETj+wA1kv2YpJTfUHDkZULhZ8taoEsKYV2XML4KYd34NhjWWgrBWotCZxSEzlSH+Ugh3JmPAICVGABQnqioKNk5Gp9+qJiYGHNz88qv1hYtWhw7doxhmMzMzC+//LJ58+YtW7b09fV9+/at7J989913FhYWso8pLi7+7rvvZP9w8+bNBQUFld9b20Fev369cOFCiqJ0dHQuXLjw999/CwQCiqL8/Pxyc3O3b99O0zRFUevXr3/37t2nf41fffXVpEmTPv04agt9UIjG9wFqJPuh6/Dhw59+KC68TA4ePMjn8z/9OBoJXVII67qE8VUI68a3wbDWUgjWWgw6oyB0pjrMRwrhznwEAGyEfREAZfr7778pisrLy/v0Q+3du3f9+vWyP0skkufPn0dERJiZmX36kdWHq6urr68v6RSNCH1QiMb3AWrTtGnTsLCwTz8OF14moaGhzZo1I51CfaFL8mNjlzC+8mPj+DYM1loKwVqLQWcUhM7UCPOR/LgzHwEAG+E+WgDKZG1tTVHU06dPmzVr9inHWbduXWBgYG5uruyvPB6vTZs2/fr1a9269aeHVB/p6elDhw4lnaIRoQ8K0fg+QG2srKwyMjI+8SDceZnIvrFAjdAl+bGxSxhf+bFxfBsGay2FYK1FoTMKQmdqhPlIftyZjwCAjfDcdQBlsrGx0dLSSk1N/cTjxMfHUxQVGhpasU66c+dOYGDg/v37PzWi2vj48ePz58/t7OxIB2lE6IP8uNAHqI2dnR1eJnJKTU3Fy6QO6JL82NgljK/82Di+DYO1lvyw1pJBZ+SHztQG85H8uDMfAQAbYV8EQJkEAoGNjc2nL5LCw8MXLFiwZ8+eNm3a9O3b19PT886dO/v37+/UqZNScqqDx48fS6VSzV4koQ/y40IfoDZK+dmSCy8TCj9b1gddkh8bu4TxlR8bx7dhsNaSH9ZaMuiM/NCZ2mA+kh935iMAYCOaYRjSGQA0ytixY/X19Q8dOkQ6iLqLioqaNGlScXGxjo4O6SyNCH2QE0f6ADXav3//jBkziouLZU99hNqIxWJ9ff3w8PBJkyaRzqKm0CU5sbRLGF85sXR8GwxrLTlhrVUBnZETOlMbzEdy4tp8BACsg+tFAJRMKBQmJSWRTsECycnJ7dq10/hFNvogJ470AWokFArLyso+/TbNGu/Jkyfl5eU4564O6JKcWNoljK+cWDq+DYa1lpyw1qqAzsgJnakN5iM5cW0+AgDWwb4IgJL16tUrMTExLy+PdBB1d+XKld69e5NO0ejQBznV2wepVJqTk3Pv3r1r166pLBWoRufOnQ0MDK5cuUI6iLq7fPmyoaGhk5MT6SDqC12SE0u7hPGVE0vHt8Gw1pITR9be8kBn5ITO1AbzkZy4Nh8BAOtgXwRAyVxcXGiaxiKpbiKR6Pr16y4uLqSDNDr0QR6yPgwcODA7O/vOnTunT5/+9ddfV61aNXv27NGjR3ft2rVFixba2toWFhbOzs79+vXLzs4mHRmUSVtbu2/fvnFxcaSDqLu4uLj+/ftraWmRDqK+0CU5sbRLGF85sXR8GwxrLXlwZ+0tD3RGHuhMHTAfyYlr8xEAsA72RQCUzNjYuEuXLlgk1e369esfP37kwjobfZDHn3/++fHjR19f31atWnXr1m3MmDFz585du3bt3r17z5w5c//+/Xfv3kkkEtkHN2/evGXLlmQDg9K5uLjExcXhmWd1YBjm0qVLXPi2+YnQpXqxuksY33qxenwbBmsteXBn7S0PdEYe6EzdMB/Vi4PzEQCwDvZFAJTP1dU1NjaWdAq1FhcX1759e2tra9JBVAF9qNeDBw+MjIwYhqFpWvYWsVgsEonKy8urfCSfzx80aFDFh4HGcHFxyc7OTklJIR1EfSUmJr5+/drV1ZV0EHWHLtWL1V3C+NaL1ePbYFhr1YtTa295oDP1QmfqhvmoXtycjwCAXbAvAqB8rq6ujx49ev78Oekg6uvcuXPcWSGhD/WKiYmZOHHi5cuXra2tBQJBHR9J0/SgQYNUlQtUp1u3bsbGxufOnSMdRH39/fffzZs379q1K+kg6g5dqheru4TxrRerx7fBsNaqF6fW3vJAZ+qFztQN81G9uDkfAQC7YF8EQPkGDx5sYmJy4MAB0kHU1OPHj2/fvu3p6Uk6iIqgD3Wr6EP//v0fPXq0aNEiHo9X2+6IWCx+9OhRTExMUVGRinNCoxIIBO7u7niZ1GH//v3jxo3j8/mkg6g7dKlerO4SxrderB7fBsNaq25cW3vLA52pGzpTL8xH9eLmfAQA7IJ9EQDl09bW9vT0jIiIIB1ETYWHh1tYWHDnTqPoQ90q90FPTy84ODghIcHe3r7GNbS+vv6FCxeGDRvWrFmznj17Lly48MSJE2/fvlV5alA+b2/vhISEhw8fkg6ijh49enT//n1vb2/SQdgBXaqDBnQJ41sHDRjfhsFaq25cW3vLA52pGzojD8xHdeDsfAQA7IJ9EYBG4e3tnZycnJCQQDqI2mEY5uDBg19//TWnzhxBH2pTYx+6du16586dNWvWaGlpVb5whMfjDRo06MmTJ9nZ2cePH3d1db1y5cq4cePMzMzat2/v4+MTFhb26NEjEl8HKMHAgQPbtWuH0+5qtG/fPisrq759+5IOwg7oUh00oEsY3zpowPg2GNZateHm2lse6Ext0Bk5YT6qA5fnIwBgEeyLADSKXr162dnZ4RSk6q5cufL06VOunTmCPtSmtj4IBIKAgICkpKRevXrxeP83Vckeuk5RVMuWLceMGRMcHHz79u0PHz7ExMR4e3tnZ2f7+/s7Ojq2atXK09MzJCQkISFBKpWq+CuCBqNp2svL68CBAxKJhHQW9SKRSA4ePOjj40PTNOks7IAu1UYzuoTxrY1mjG+DYa1VG26uveWBztQGnZET5qPacHw+AgAWwb4IQGOZNm1aeHj4+/fvSQdRL9u2bevZs6ejoyPpIKqGPtSo7j506NDh8uXLW7Zs0dPTEwgE5eXlAwYMqPIxhoaGQ4YMCQoKiomJKSwsvH37tr+//8ePH1etWtW9e3djY+OhQ4cGBQVduHBBJBI1/hcEn2Tq1Kmyi4FIB1EvUVFRr1+/njJlCukgbIIu1UhjuoTxrZHGjG+DYa1VI86uveWBztQInZEf5qMaYT4CALagGYYhnQFAMxUUFFhbWy9atOj7778nnUVdJCUlOTk5HTt2zM3NjXQWVUMfqpO/D5mZmTNnzkxISHjz5o2WlpY8B5dIJCkpKdeuXYuPj798+XJWVpa+vr6zs3O/fv369u3bv39/Y2NjZXwRoGQTJkxITU29e/cuzi+TYRjG3Ny8qKjIxsbG3NzczMysefPmJiYmzZs3r/6HiourgEKXqmEYpkuXLk5OTppxxw+MbxUaNr4Ng7VWdZxdeycnJz958qSkpOTDhw+FhYXFxcXFxcXv378vLi4uLCz88OFDXl6e7I1v3rzx8PCIiooiHVldcLYzVRQUFNy6dauoqKi0tPTDhw8fP34sLS3Nz88vLS39+PHjhw8fSkpKCgoKCgoK0tPTeTxeXl4e5iMZzEcAwCLYFwFoRCtWrNi1a1dmZqahoSHpLGrBy8vr/v37Dx8+5OYv79CHKhTtQ1FRUYP/12VkZMTHx8u2SZKTk3k8np2dXb9+/YYMGTJo0KAWLVo07LCgdPfv33d2dj516tTo0aNJZ1ELJ06c8PDw0NbWll3wRNO0QCDg8XgMw4jF4ip3ijMyMvLz8/vpp58IhVUv6FIVsi7dv3/fycmJdBYlwPhWoWHjW6/8/Pzs7OycnJxXr169efPmxYsXb968yczMTEpK+vDhQ2ZmpqWlJemMaoGza+927dplZmZWTJqyN0qlUolEUmXqbN68OcMwWVlZWJ/LcLYzVfz0008rVqyQ/VlLS4umadm2B8MwsjVY5d+k0TSN+agC1+YjAGA17IsANKLc3Fxra+tVq1YtXLiQdBby0tPThUJhZGTkxIkTSWchA32ojGAfcnJybt26de3atQsXLty9e1cqldrY2PTt21d2KYmDg4OK80AVY8aMefv27Y0bN0gHUQs9e/a0tLQMCAgYNmxYSUlJeXl53R+/YcOGxYsXqyab+kOXKpN16dixY6SDKA3GtzLNG9/KPnz4sHLlymfPnr169erFixfv3r0rKyuTvYvH4wkEAj6fX15eLhaLKYoSCATBwcGLFi0iGlktcHntHRAQsHXr1oqe1EggEPTo0SMyMrJr165Yn8twuTNVPHz4sHPnznV/DI/H4/P5+/fvj4yMxHxUQbPnIwDQMNgXAWhcixcvPnDgQEpKStOmTUlnIWzkyJExMTFWVlbm5uaWlpbm5uYtWrRo2bKl7A/m5ubm5uYGBgakYzYu9KGCl5dXQkJCUlISn88nGKOoqOjGjRsVl5KUlpZaWFjINkj69evn7OzM8XPliLhx40bv3r2jo6Nx2t2ff/7p7u5+69atbt26JScnu7q65ubm1rE1oqOj8+rVq+bNm6sypDpDlypU7hLpLEqD8a2gkeNbWVZWVrt27aqc5l8dj8fz8fFp3rz5wYMHsdai1GatRURGRkaHDh3q+F0Hn88fPXr0oUOH9PT0sD6vwOXOVNezZ8+EhITavvMIBAJ9ff3o6OgBAwZgPqqg8fMRAGgaBgAaU25ubosWLfz9/UkHIezy5cs0TXfq1Knim49AINDW1q6y5tbV1W3VqpWzs7OXl1d5eTnp1MqHPsjI+nDq1CnSQf5HWVnZ7du3g4ODR48e3axZM4qijIyMhgwZ8v/Yu/O4pq70f+A3JGGXVZbIjoAsAoawSUAtpWNL8dup39GO7TBtnZbaqY1bLdN2WmacLtqxFrC1Wlstndaq3+m0Q6WLW1V2IYREdjSAsu9hJ+vvjzPml0FrrQI3CZ/3H7zywgt58J7k3pznnOfJyMg4derUxMQE3QHOIWvXrvXy8hodHaU7EDqNjY35+PikpqZqv9Pc3Ozr6/tTLXZMTU03bNhAY8D6CWNJc7OxZDRwfjVGfX51/eY3v7l1gzETE5Onn35apVLhXovQz3ut2XTPPfewWKybjhYGg/H888+rVCpyJMYMgTEzxaFDh35qgRSbzfbw8KitrdUejOuRZs5cjwDAmCAvAjDjPvroIyaTWVFRQXcgtFEoFKGhof/zP/8zOjp63333/dRHFF0uLi5GmRfRYDzojAe6A7kVpVJZVVV14MCB1NRUUqPc0tKSz+enp6fn5uYODAzQHaCR6+josLW1fe211+gOhE4vvfSSjY1Ne3u77jf7+voiIiJuOjnIYDDOnDlDV7R6C2NJ8xNjyTjg/GqM+vzqEolEt+hpbGJi8uyzz6rVanIw7rUM4l5rRpWVld133303jhnSJeLtt9+ecjzGDMbMFF1dXbt27TI3N7/xDYfFYkVERHR1dekej+uRZs5cjwDAmCAvAjDj1Gr1smXLoqKitIuS5pqdO3daWFhIpVKNRjM5Ofnwww/fems2i8W68eOK0cB40B0PhuLKlSs5OTlpaWlkzxOTyQwODk5LSzt+/Hh3dzfd0Rmnd955x8zMrK6uju5A6NHQ0GBmZrZ3794b/2l4eHjFihVTEsxMJtPT05PFYq1ZsyY/P3/2A9ZnGEs/NZaMA86vcZ9fXQkJCTddW8NgMF588UXdI3GvZYj3WtNiYGBg7969S5YsoSgqODiYbP/VzZ+x2exjx47d+IMYM3N2zEwhl8u//vrrhx56iM1m29raxsbGTlmMwmQyyWq/G38W16O5cz0CAKOBvAjAbKisrGSxWO+//z7dgdCgsbHRysrq9ddf135HqVSmpqbeom2DhYWFca/Hx3jQHQ8Gp6OjIzc3Nz09ncfjkWHs6+ubmpp64MCBqqoquqMzHgqFIiwsLDExcQ7OUCiVyhUrVnC5XKVSedMDJiYmbkww5+bmHj9+nM/nUxTF5XIPHDgwNjY2y5HrJ4ylW4wlI4Dza9znl+js7MzIyJg3b95N7xvT09Nv/BHcaxn0vdYdKC8vT0tLs7KyMjc3X7NmzalTp9Rq9auvvqqd1GaxWDY2NhcuXPip34AxM9fGzBQ1NTXp6ekuLi4mJiZ8Pv/AgQMjIyNisXhKFla3AtsUuB7NhesRABgZ5EUAZslrr71mbm4uEonoDmRWjY+Pc7ncyMjIyclJ3e+r1eqNGzfetB4Cm82eCyXyMR7ojmV6DA0NnTp1KiMjIykpiWy053A4a9asyczMLC8vn4MfiqZXeXm5mZnZ3/72N7oDmW1/+ctffvb9QalUrl+/nmTmGAyGt7e3dryVl5enpqay2WxnZ+f09PRr167NStR6DWOJ7kBmFs4v3YHMoNra2j/84Q9mZmaurq5vvfVWSEjIlJTwjh07fupnca9FdyyzoaOjIzMzc/HixRRF8Xi8zMzM/v5+7b9evXqVXChJN4j6+vpb/zaMGbpjocHAwMCBAwfIshIPD4/09PQrV67oHkAWQjEYDBMTk5/dDIHrEd2BAAD8MsiLAMwSlUp17733+vn5yWQyumOZPX/84x/nzZt30w8harV669atN6ZGyA73Rx99tLi4ePYDnjUYD8YHbdtnQlZWlomJyenTp+kOZPb8+OOPTCbzgw8++Nkj1Wr1li1bfuqDekdHR0ZGhpOTk6mp6Zo1a4qKimYmXoOBsWTccH6Nj1AoTE1NZTKZvr6+mZmZZAPc559/rr11ZDAY77777i1+A+61jJhKpTp16tSaNWtIsaO0tLSfag2SnJxMUdSSJUs6Oztv59dizMwRZAilpqZaWlrq7jG68ciPP/6Yoihzc/N///vft/ObcT0CADAgyIsAzJ7Ozk5XV9e1a9fSHcgsOX78OEVRR44cucUxO3funLJZZO3atcePH1+6dClFUREREQcOHLhp/VYjgPFgxG7dtn1wcJDuAA3J6tWrXVxcOjo66A5kNnR1dZEtR7f/I2+++aa7u/vQ0NBN/3ViYiInJycsLIwspM3JyZHL5dMUrOHBWDJuOL/GQa1Wnzp1KiUlhZQEzMnJ0a3KolAo3NzcSFLkdubgcK9lfK5du7Zz505vb29yXfvZTwoXL17cuHHjyMjIbf5+jBmjV19fn5GRoTuEfuomihgdHf3tb3978eLF238KXI8AAAwF8iIAs+qHH34wMTHZvXs33YHMuMrKShsbm+eee+5nj9y1a5furhGhUEi+T8oEm5ub29nZCQSCpqammY2YDhgPc0Rtbe3Bgwd///vf+/j4kArXMTEx27Zt+/rrr3t6euiOTt8NDAz4+PgsX758fHyc7lhm1vj4eEJCwgwtU83Pz1+zZg2LxeJwOBkZGXNz4GEsGTecX0Mnl8tzcnJCQkIoiuLz+bm5uTc97L333jMxMTl06NBt/lrcaxmHsbGxI0eO3HfffSYmJhwO56WXXrp8+fIMPRfGjFEaHBw8ePCgtl7WK6+80tDQMEPPhesRAIChQF4EYLbt3r2bwWD84x//oDuQGSSVSjkczr333nubZWrfe+89BoPBYDASEhKm/FNnZ+fOnTs9PDxMTEySkpJyc3NvusHZcGE8zDW3bttuZMN7uojFYjs7u//93/814l6OSqXy4YcfdnBwqKqqmrlnkUql6enpDg4OZmZmqampEolk5p5LP2EsGTecXwM1PDycmZnp4eHBZrPXrFlTVlZ2i4NVKpVUKv1Fvx/3WoZLrVYXFBQ8/fTTtra2LBZr1apV//73vxUKxUw/L8aM0ZiYmMjNzU1NTbWysjIzM1uzZk1ubu4sDCFcjwAADALyIgA02L59O5vN/vbbb+kOZEb09PQsWrQoLCxsYGDg9n/q8OHDTCbz+++/v+m/KpXK3NzcpKQkiqICAgIyMzOHh4enKV76YTzMWf39/SdOnHjppZcSEhJI23YXF5eHH354z549JSUlc7ne0Y2Ki4stLS3T0tLoDmSmbNy40cLCIj8/fxaea3h4+MCBA9pF2cePHzfiD+03wlgybji/hqWrqysjI8PBwcHa2logELS0tMzQE+Fey+C0trbu3LnT39+foqjg4OCdO3fOclUijBmDplKp8vPzBQLB/PnzTUxM+Hx+ZmbmLG+WxfUIAED/IS8CQAO1Wv34449bWVkZX2txmUwWFRW1cOHC2+ltOMXtNF0QiUR/+MMfLCwsbG1tBQLBpUuX7ihM/YLxABqNRqFQlJeXZ2ZmrlmzxtnZmbTb4fF4AoHg+PHjvb29dAdIv6+++orJZL766qt0BzL9Xn31VRaL9VNFY2aItog/g8Hw9fXduXNnf3//bAZAI4wl44bzaxCuXLkiEAgsLCycnJwyMjL6+vpm9Olwr2UoxsfHjx8/npKSwmQy7e3t09LS6Jp1xZgxUFVVVenp6RwOR5tRa29vpysYXI8AAPQc8iIA9FAoFKtWrbK2tj558iTdsUybzs7OiIgIDoczcwV/ib6+vrfffnvhwoUURcXExHz44Ye3bpen/zAeYIorV67k5OQIBAIej0ca8OiW26I7Otp89NFHJiYm27ZtM5qCY2q1euvWrb+oVv60a2hoEAgEVlZW8+bNS0tLq6mpoSuS2YSxZNxwfvVZRUVFamoqk8n08fHJzMy8ddPsaYR7LX1GVvenpaXNmzfP1NQ0JSXl+PHjtNd3wpgxIDU1NRkZGQEBARRFeXt7p6en19fX0x2URoPrEQCAfkNeBIA2CoVi/fr1pqamR44coTuWaSCVSv39/X19fRsbG2ftSUlvdktLS3Nz8zVr1pw6dcpw7zgxHuCnyGSyU6dOZWRkJCUlkXJbHA4nJSVl586d+fn5tM8azLLPP//c1NT0scceM4I6Y3r1qpfJZJmZmd7e3sbazOlGGEvGDedXD+Xn56ekpFAUtWTJkpycnFko8T+F0fxPEsZxr9XS0rJz506y2oms7u/q6qI7qP8PY0bPtba2ZmZmkm7qCxYsEAgE+fn5+nYDg+sRAIDeQl4EgE5qtUM6XNsAACAASURBVPrFF19kMBh///vf6Y7lrkgkkgULFvB4PFo+yQwODh44cCAiIoKiqEWLFunbB6rbh/EAP0sul2vLbc2fP5+iKCsrKz6fn56enpubO0fqIJ05c8bGxiYpKcmgN4qNjIw88MADVlZW3333Hd2x/H8qlYo0c2IwGP7+/pmZmSMjI3QHNYMwlowbzq+ekMvlx48fj4yMJD2N6K27gnstPTEwMHDw4MH4+HgGg8HhcF544QW93Q6LMaOHBgYGcnJySL01Ozu71NTU2emmfsdwPQIA0E/IiwDQLzMzk8FgpKamGuj0U05OjpWVVWJiokwmozcSUk/W0dGRyWQmJSUdP35cn++PfwrGA9w+Um4rLS0tODiYwWAwmczg4OC0tLScnBypVEp3dDOorKzMyckpKCjIQJsM1dbWhoaGOjo66m3VcpFIRHbjkWZOzc3NdEc0UzCWjBvOL72Gh4czMzM9PT1NTExSUlIuXrxId0T/gXstuoyPj+fm5qamplpaWpqZmZF6WQaxiB5jRh/09fUdOnTowQcfNDU1tbCwWLt27ddffz0xMUF3XLcF1yMAAD2EvAiAXvj3v//t4OBgcPdJY2NjAoGAwWAIBAL9KeYzNjaWk5OzbNkyBoPh4eHx2muvNTU10R3UL4PxAHego6MjNzc3PT2dz+ebmpqScltr1qzJzMwsLy9XqVR0BzjNWlpa4uLiLCwsDh48SHcsvwyZm4iKitL/3FV3d/fOnTs9PDzInOapU6fojmhGYCwZN5xfWnR3d2dkZDg6OlpZWaWlpTU0NNAd0VS415pNSqWStA+xsbExMTHh8/mZmZk9PT10x/XLYMzQpbu7+8MPP1y5ciWbzTYzM1u1atWnn35qiBsvcD0CANA3yIsA6Ivm5ubY2FgrK6ucnBy6Y7ktVVVVwcHBjo6OJ06coDuWm2toaMjIyPD09KQoisfjZWZmdnd30x3U7cJ4gLsxMjJy5syZv/71rytXrrSxsaEoys7OLjk5+fXXXz937pyBLnW8kVwu37p1K4PBWL9+vUH8USMjI0888QSDwdi+fbtBLI8lSA0cUryby+UeOHBgbGyM7qCmGcaSccP5nU1SqVQgEFhaWs6fPz8jI6O3t5fuiH4S7rVmQXl5uUAgcHFx0bYPaW9vpzuoO4cxM5t6e3tJsSw2m21ubp6SkpKTkzM4OEh3XHcF1yMAAL2CvAiAHpHL5S+88AKDwbj//vv1uRve6OjoSy+9ZGpqGh8ff/XqVbrD+RkKhSI3N/eRRx6xsLAwMzN7+OGHv/zyS4PYcI3xANNCqVSKRKK9e/euW7fOw8ODoigWixUZGSkQCL744ouWlha6A7xbubm5jo6OXl5eX331Fd2x3MqXX37p4eExf/78vLw8umO5Q+Xl5ampqWw229nZOT09/dq1a3RHNM0MZSxlZmay2WxbW1vDHUu0yM3NtbGxYbPZWVlZdMdyK4b7XiESiVJTU1kslre3d2Zm5ujoKN0R/Tzca82QqqqqjIwMbTf1jIwMPdwzdGcwZmba5cuXd+/enZCQYGJiYmVltWbNmmPHjg0PD9Md13QylPsNw70eAQDcJuRFAPROWVlZZGQkm80WCAR6uIokNzfX29vb1tY2MzNTqVTSHc4vIJPJDh8+nJSURBr0PfXUU6dPn9b/PwHjAaZXW1ubttyWmZkZKbeVkpKyc+fO/Px8g0gZ3qivry8tLY3BYNx77721tbV0hzPV5cuXk5OTSV1yI2h22tHRkZGR4eTkZGpqumbNmqKiIrojmk6GMpYoijIxMXnjjTeMr0TeDFGpVG+88YaJiQlFUfp/fg3uvSI/Pz8lJYWiqPDw8JycHIPr7oZ7renS3Ny8c+fOoKAgiqI8PDwEAkF+fj7dQc0IjJnppVarL168+PLLL4eEhFAU5eDg8Lvf/e7LL780vv2pWoZyv2Fw1yMAgF8EeREAfaRQKPbs2TNv3jxfX1/9+XhZUlKycuVKcnvU2dlJdzh3rq2tbffu3RERERRFOTs7b9iw4ezZs/r8mQHjAWaIXC4vLy/PzMxcs2aNk5MTRVFsNpvH4wkEguPHjxvcp6Bz586FhIRYWFikp6frSfCdnZ3bt283NzcPDQ01srmhiYmJnJyc8PBwUqgwJyfHmKor6P9YcnNzI6mRpKQkAyoRSZfu7u777ruPJEXc3Nz0//zSHc7tUqlUubm5UVFRFEXx+fzc3Fy1Wk13UHcI91p34+rVq3v27CEjwcXF5fnnny8qKjLcwXCbMGbuHmk8IxAI3N3dKYry8vJKS0vLzc010D4odwDXIwAAeiEvAqC/WltbH3/8cRaL5evre+DAARpvEM+fP3/fffdRFBUbG3vu3Dm6wph2jY2Nb7zxBpnXc3Fx+eMf/3ju3Dm9TZBgPMBMa2trO378uEAg4PF4ZALR4Dq3y+Xyd955x9nZ2dLScvPmza2trXRFcu3aNVJh38XFJTMzU0+mS2ZCfn7+mjVrWCwWh8PJyMgwuD66P0XPx9L69evZbDbJZTo7O1+4cIGu8PRfaWmpm5sb+e9isVjr16/X6P351X8kM+rv729iYpKSklJaWkp3RNMD91q/iFQqzczM5PP5DAbD1tY2NTU1NzfXmHLktwNj5g60tbV99NFHq1evJj3wIiIi/vKXv4hEIrrjogeuRwAANEJeBEDfSaXSZ555xszMzN3dfefOnbN5qzQ2NvbFF18kJCRQFLVixYpTp07N2lPPsqamJvK5jqIoR0fH1NTU48eP62cdW4wHmB1DQ0P5+fk7d+5MSUmxt7enKMra2prP56enp+fm5vb19dEd4K2Mjo5mZma6ubmZmZk9/fTTxcXFs/nsRUVFTz/9tJmZmYeHR3Z2thGXgNAllUrT09MdHBzMzMxSU1MlEgndEU0PvR1Ln332GUleUhTFZDJNTEwyMjIMInk5m9RqdWZmJpPJZDKZ5P/KxMTk888/1x6gt+dXn/X09GRkZDg6OpIXe319Pd0RTT/ca90a6R3C4/FIvSOSDjHQOpzTBWPmZymVyoKCgpdffpnL5TIYDEtLywcffPD99983gkZ30wLXIwAAWiAvAmAYWltbt2zZ4uDgQIpm5OTkzNysvUqlOnfu3Pr1621tbVks1qpVq+bOFtq6urq33norNjbWxMTE0tLy4Ycf/uSTT3p7e+mOayqMB5hNSqWyqqoqJycnLS0tODiYwWAwmczg4ODU1NQDBw5UVVXpZ62MiYmJ/fv3BwcHUxQVEBCwY8eOpqammXs6qVS6Y8cOf39/iqJCQkI+/PDDuVMFQmt4ePjAgQOkODifzz9+/Lje7sD7RfRwLLW3t5MuI1omJiYrVqzQ/6ops6anp2flypXa7JFWW1vblCP18Pzqp6amJrKU2NbWViAQtLe30x3RzMK91hQkHbJo0SKKotzd3Um9o7m2O+TWMGZu1NHR8emnn/72t791cHCgKGrhwoUbN2787rvvMPN+U7geAQDMMoZGo6EAwEBMTk7m5eX94x//+Pbbb9ls9ooVK+65557ExMTw8PAbP/n/Uq2trWfPnj179uyZM2daW1sjIiJSU1PXrVvn4uIyLcEblo6Ojtzc3K+//vrHH39UKpVxcXH333///fffT5Y40R3df2A8AC06OzvLysqEQmFhYWFBQcHExISLi0tUVBSPx4uPj+fz+RYWFnTH+F+EQuE//vGPL774oqenh8fjJSYmJiYmxsfHW1lZ3eVvHh0dzc/P//HHH8+ePSsUCp2dndetW5eamkraF81ZGo3mzJkzWVlZeXl5Pj4+aWlpaWlpZNeRodOrseTn53flyhXd77DZbAcHh3/+85/x8fF3GY+hu3jx4urVq7u7uxUKhe73/fz8Ghsbf+qn9Or86hWxWPzOO+988cUXbm5uW7Zseeqpp+7+/8RQzPF7LZVKVVxc/H//939ffvllW1ubj4/PqlWr1qxZQ2pn0R2dnprjY4aiqLGxsaKiotOnT58+fbqiooLJZMbExKxatSopKYlsM4KfhesRAMDsQF4EwCD19fV9+eWXp0+fPnfuXE9Pj6OjY0JCQkhISGBg4KJFiwICAmxtbW/9G+RyuVQqraura2hoqKurKywsbGhoMDMzW7p0aWJi4urVq8mCXxgeHv7uu+++/fbbH374obOz08XFhSRI7rvvPkdHR7qj+w+MB6CLUqkUi8UFBQVCofD8+fNXr15lsVjh4eF8Pp/H4y1btszb25vuGP9DqVT+8MMP33333dmzZ2tra9lsdkxMDI/HW7RoEXmZkJ6ft9ba2lpfX9/Q0FBfXy8UCktLSxUKRXBw8D333JOcnPyrX/2KxWLNwt9iKC5fvrx3795Dhw4xGIx169Zt3rw5KCiI7qCmgZ6MpY0bNx48eFAul+t+k8lkajSaV1999bXXXrv7CThDpNFosrOzt23bRlGUSqXS/Sc2m/3MM8/s3bv31r9BT86vnigoKNi1a1deXl5oaOi2bdseffRRQ4l82s2pe62JiYmCgoJvvvnm2LFjXV1dwcHBq1atSklJQc71F5lTY0alUlVWVpJcSH5+/uTkpK+vb1JSUlJS0sqVK0kfEfilcD0CAJhpyIsAGDaNRnPp0qWzZ88WFBTU1dU1NjaSKRIXFxdbW9t58+bZ2dlZW1ubmppSFDUwMDAyMjIyMjI0NNTe3q5UKhkMhqenZ0BAQFRUVGJiYlxcnL4t9NYfGo2msrLy+++///7774uKijQaTWRkJFm8oz8L5DEegF5XrlwpKioqLi4uLCysrq5WqVTe3t5Lly6NiYmJjY3lcrlk7NGuo6Pjxx9/PHfunEQiaWhoGBgYoCjK2tra2dnZ1tbW+jqKokauk8lk3d3dIyMjFEXZ29sHBASEh4cvX748MTHR1dWV5r9Hvw0NDR0+fDgzM/Pq1auJiYkCgSAlJcVoFhrTOJb+9a9//eY3v7npnbyJicmvfvWrY8eOzbWpqKGhoUceeeTkyZNqtfrGf2UwGP/85z9Xr159+79wzr5XqNXqvLy8N954o7S0lHSWMqaX7V0y4nutrq6ub7/9Ni8v7+TJk6Ojo9HR0atXr169evXChQvpDs2wGeuYUSqVQqHwwoUL58+fz8/PHxoa8vDwILmQe++9V6+2sBiBOXs9AgCYUciLABgVlUrV3NxM1oOQ+yFybz05OWliYmJvb6+9Z/L09CTLTCwtLemO2vDIZDKyHurs2bO667YSExNjYmL0Z9ENxgPQaHh4uKSkpLi4uKSkpKSkZGBgwNzcPCIiIjY2NjY2dunSpbezxm12dHd3k8WYfX19g4OD2s+TFEVpXyN2dnaOjo4BAQFBQUFOTk50h2x4yDRrdnb2mTNn/Pz8nnvuOaMsxTObY6m/v3/+/Pk3vZMnndhLS0u5XO6d/zEGSCQSxcTEqNXqKTtFCAaD0dvbS2rc35m58F4xOTl57NixN998s7GxMTk5+ZVXXomNjaU7KL1m6PdaGo2moqIiLy/vxIkTQqHQ1NT0nnvuSUlJeeihh9zc3OiOzjgZ9JhRKBRlZWXnz5+/cOFCYWHh8PCwi4vL8uXLly9fnpSUFBAQQHeAc8VcuB4BAMwC5EUAAO5KZ2dnfn7+6dOnT5482dzcbGlpGRcXR5ZKcbncuVnGBOBG7e3tpB+JUCgsKyuTy+UcDofH4+ltVxKYIZWVlR988MFnn33GZrMff/zxrVu3enl50R2UoQoNDa2qqpryTSaTGRAQ8Pnnn8+1pAhRXV39u9/97tKlSzemRkJDQyUSCS1RGQSZTPbJJ5/s2rWrv79/7dq1r7zyCumwDUaJdID45ptv/vWvf7W2tjo7O69cuXLVqlX333//vHnz6I4O9Aspmnr69OmCgoL8/HyZTObi4rJs2TI+nx8fHx8REYHNZAAAYKCQFwEAmDZSqZR8Zjh79mxbW9u8efNiYmJIjgSfGQC0RkdHRSIR6dx+4cKFrq4uFosVEBBAEiQ8Hi84OBivF+PW09Nz6NCh999/v62tLTk5edOmTUlJSXQHZXi2b9+enZ2tbTHCYrHUavX27dt37NihJzXraKFUKt95550///nP5DH5pqmp6aZNm95++21aQ9NTLS0te/bs+fjjj5lM5hNPPJGenr5gwQK6g4IZ0dzcfPLkyW+++ebUqVMKhYLL5SYlJaWkpKCPOkwxPj5eWlp6/vz58+fPl5SUjI+Pu7u7r1ixYvny5cuWLcO+EAAAMA7IiwAATD+NRiORSM6ePXv27Nnz588PDw+7ubklJibee++9iYmJHh4edAcIoEfa29tJjqSgoKC8vHxyctLW1jYqKorkSBISEuzs7OiOEWaEQqH4+uuvs7KyCgsLuVzuhg0bUlNTsXPo9n377bcPPvggecxkMu3t7RUKRW1tLYfDoTcw2nV0dAQFBZmbm/f29mo3jnz77bcPPPAAvYHpG4lEsnv37qNHjzo6Oj7zzDNbtmz52UbQYHBIQ+xvvvmGVMqysrK65557SB91JMBA1/DwcGlpaUFBAbklm5iY4HA48fHxSUlJfD5ff3q8AwAATBfkRQAAZhb5OEr6keAzBsCtKRQKiURCym0VFBQ0NTUxmcxFixZpy20FBQWhPJ3xEQqFWVlZR48etbe3f/LJJzdu3Kg/7Wf02cjIiJ2dnUajMTExee211zZt2sTj8QIDA7/55hu6Q6PZ6tWrKysrKyoq9u7du2PHDrVazWAwBgcHSU9aoCiqoKBg165deXl5ixcvfuGFF9atW8dms+kOCqZTR0fH999///333586dWpgYCAgICAlJeXBBx9MSEjAuQata9eukU4h+fn5NTU1Go0mKCgoISEhPj4+ISEBhS4BAMC4IS8CADB7JiYmioqKLly4cP78+dLSUrInnWxIX7ZsWWBgIN0BAugX3a0kFRUV4+PjNjY20dHRZCsJn8+/mxbKoG86Ozv379+/b98+mUz20EMPbd68OS4uju6g9N3y5cv7+/s///zzsLAwiqIKCwuXLVv2ySefpKam0h0abT799NMnn3zy7Nmzy5cvpyhKIpE89thjDg4O58+fpzs0+qnV6ry8vDfffLOkpITP56enp6ekpKCAktFQKBSFhYUkHSKRSMzMzJYtW/bAAw88+OCD/v7+dEcH+kIqlWo3hdTU1LBYrPDwcNIs5J577pk/fz7dAQIAAMwS5EUAAOih28OwoKBgcHDQ2dk5OjqabCVBz3aAKZRKZX19vbZ5e01NDUVRvr6+JEcSHx+PV41xmJycPHbs2J49e8RiMY/HEwgEWMl+C3K5nMVi6Y58gUDw6aefVlVVzc09Nx0dHYsXL/7d736XlZWl/aZarVYqlXO55wp1/ZX11ltvNTQ0JCcnv/zyy0uXLqU7KJgenZ2dJ0+ePHHixMmTJ2Uyma+vL2luhybqQIyPjwuFwqKiIpIO6e/vnzdvXlxcHJ/PT0hIiI6OtrS0pDtGAAAAGiAvAgBAP1Jri3xWOXPmDPm4Qnq28/n8mJgYzAkCTNHR0VFSUlJcXFxSUiIUCsfGxmxtbWN0YMGjoSsoKMjOzv7qq6+cnJzS0tI2btyIc3o7xsbGlixZEhAQcOLECbpjocHDDz8sFoslEglKZmkNDQ0dPnz47bff7u3tfeSRR1566aWgoCC6g4K7NTExUVBQQMq0CoVCS0vLuLi4pKSkVatWBQcH0x0d0K+1tbWoqKi4uLi4uLiiokKhUHA4HLIpJCEhITw8nMlk0h0jAAAAzZAXAQDQLyqVSiKRkFpb+fn5vb29NjY28fHxy5Yt4/P5kZGR5ubmdMcIoF/I7quSkpKSkpLS0tLGxkaKovz8/EiCJDo6msvlzvGl4oarqanpwIEDBw8eHB0dXbt27fbt20NDQ+kOSt8VFRUlJCQcPnz497//Pd2xzKqcnJz169drK2gBqU2XmZmpVquffPLJF1980c3Nje6g4K5IpVKSC/n++++Hh4fJ1pCUlJRf/epXZmZmdEcHdLpxWy3p0Ebas/F4PDQ1BAAAmAJ5EQAA/aXRaGpqas6fP3/hwoULFy50dHSYmppGRkYuXbo0Pj4+Li7O2dmZ7hgB9M7Q0JBEIiFTAyUlJb29vSwWKyAgQDs1EBwcjHr6hmVkZOTIkSPZ2dnV1dV8Pn/Tpk2rV6/GWtdb2Lx58yeffHLp0iUPDw+6Y5kl7e3tixcvfvzxx9999126Y6FfVVXV3r17c3Jy7O3tn3nmmc2bN9vZ2dEdFNyh/v7+s2fPnjp16tSpU01NTba2tqRG1v333z83y+WBlkwmKysrIzvOi4qKxsbGdNuwJSQk4IUPAABwC8iLAAAYjPb2djLVW1hYKBKJ1Go1h8MhU71orgDwU7QvHKFQWF5ePjk5aWtrGxUVRWYN4uLiHB0d6Y4RbotGozlz5kxWVlZeXp6Pj09aWlpaWpq9vT3dcekjUk3L398/Ly+P7lhmya9//etLly6JxeI5XkGroKBg165deXl5Cxcu3Lhx4zPPPINtpoZILpcXFRWdOnWKlMmiKCoyMjIpKWnlypVLly5lsVh0Bwj0mLIppLa2VqPRoNcaAADAnUFeBADAII2MjFRWVpLPRaRtO2lJQnIkcXFx6KAIcCOFQiGRSMhswo3N23k8XnR0NCpu6b/Lly/v3bv30KFDDAZj3bp1mzdvRr+EGxUXFyckJHz00UdPPPEE3bHMuMOHDz/11FM//vjjsmXL6I6FHmq1Oi8v76233iouLsaeKsOlLZP1ww8/DA0NaTuo33vvvQ4ODnRHB/SQSqWl14lEosnJSXt7e203tdjYWKwPAAAAuDPIiwAAGDylUklyJEVFRYWFhW1tbWw2OyIiIi4ujs/nx8XFcTgcumME0EednZ1lZWUkR1JYWDgwMGBlZbVkyRKSI4mPj/f19aU7RvhJpJt0Zmbm1atXExMTBQJBSkoKKqTp2rJly+HDh42+mhapoPXEE0/s2bOH7lhoIJfLjx49unPnzvr6+uTk5D/96U98Pp/uoOAX6O7uPn/+/OnTp7/77rtr165ZW1vHxsaig/pcRhY/kTuTCxcudHV1kXKg5M6Ez+cHBQVhUwgAAMDdQ14EAMDY6FYNunjxokKh4HA42nlePp9vYWFBd4wAekelUtXV1WlzJJWVlSqVSvvaQZ1uvUWWyWdnZ585c8bPz++555576qmnrKys6I5LL4yNjXG5XC8vrx9++MGIM0bJycn19fUSiWSunffh4eFDhw79/e9/7+npeeSRR/70pz9hGt1QjI6OXrhwgZTJunTpkqmpaVxc3H333ZeUlMTj8bDRZ66ZnJwUi8Xl5eXl5eUXL16sra1Vq9Wenp5kO0h0dDSPx8PdOwAAwLRDXgQAwJgNDQ2VlpaWlJSQ3fe9vb1sNpvL5Wp33/v5+dEdI4A+0q7WFAqFFy5caGlpYTKZixYt0qYYUcJb31RWVn7wwQefffYZm81+/PHHt27d6uXlRXdQ9CPVtD788MP169fTHcuM+Pjjj9PS0s6dO5eQkEB3LLOnq6vrgw8+yMrKUqlUTz755Pbt29F/W/8plUqxWEzKZOXn509OTmrLZK1cudLGxobuAGH2KBSKS5culV9XVVWlUChsbW0jIiK0t+jY7Q0AADDTkBcBAJhDdLeSCIXCiYkJNKAGuB0tLS3a/GJFRQV57URHR5OFnNHR0c7OznTHCBRFUT09PYcOHXr//ffb2tqSk5M3bdqUlJREd1A027Zt20cffXTp0iVPT0+6Y5lmbW1toaGh69ev3717N92xzBLSXOfDDz+0tbXdsGHDpk2b0FpAn6lUKpFIdPbs2bNnzxYUFIyOjnp5eSUmJiYmJt57772Y+J47SL904XUVFRXj4+PW1tbh4eHabamojgUAADDLkBcBAJijpjSgrq2t1Wg0aEAN8LO0sxsky1hXV6dWq1FxS68oFIqvv/46KyursLCQy+Vu2LAhNTV1zhYhmZycjIiIWLBgwcmTJ42smlZycrJUKhWJRHPh5BYUFGRnZ//rX//y9vZ+/vnnn3nmGXNzc7qDgpvTtk8/c+ZMf3+/k5PTihUrkpKS+Hx+SEgI3dHBbNAtzikUCkUi0djYGJvN9vf31xa2DQwMRM00AAAAGiEvAgAAFEVRnZ2dpOJWSUlJeXn5yMiItbV1RERE5HV+fn5GNqEGMC2GhoYkEoluf1TdiltIMdJLKBRmZWUdPXrU3t7+ySef3Lhx49wsN1RSUhIfH79///6nnnqK7limzcGDBzds2HD+/Pn4+Hi6Y5lBGo3mxIkTu3btKiws5PF4AoHgsccew1yqHiJbcm9sn56UlBQREYE7qLmgvb1d26WsuLh4dHRUmwjB/QAAAIAeQl4EAACmUqlU1dXVpaWlZWVl2qrHdnZ2kTpQuB/gprTTIkKhsKCgYHBw0MrKasmSJdppkeDgYEyQzbLOzs79+/fv27dPJpM99NBDmzdvjouLozuo2bZ9+/YPP/zQaKpptbW1LV68+Omnn3777bfpjmWmyOXyo0eP7tq1q7a29t577xUIBKtWraI7KPgvbW1t586d+/HHH8+ePdvU1GRpacnn80mZLLRPnwt0r/hFRUX9/f0sFisgIEB7xY+MjMS+LgAAAL2FvAgAAPwMhULR0NCg/eBXVlYml8vt7OxCQkJIKYD4+HjUyAa40ZQyGuS1o9vUJzY21snJie4w54rJycljx47t2bNHLBaTdffr1q1js9l0xzVLJicneTyeq6vrqVOnDD0zp9FokpOTm5qajLWC1vDw8KFDh3bv3t3V1fXb3/42PT0dxZf0R2tr67lz586fP3/+/PnGxkZTU9Po6GiSC4mNjTUzM6M7QJgpGo1GKpVWVFSQZukVFRWDg4MsFiskJES7bCgsLAw7QgAAAAwF8iIAAPDLjI6OikQi7VTvjc0VYmJi0IMa4EY3bepDXjuk1DiPxzPKSV59Q/o0fPXVV05OTmlpaRs3bpw/fz7dN6NHXAAAIABJREFUQc2G0tJSPp//wQcfPP3003THclc+/PDDZ5991igraHV1dX3wwQfZ2dkKhWL9+vUvvPCCh4cH3UEB1dHRUVBQcPr06YKCgpqaGhaLFR4eTvqFLF++3MbGhu4AYUboLgyqqakRiUR9fX1TSmVGRERYWlrSHSkAAADcCeRFAADgrshkMrIQnqyea25upijKx8eHy+VyudyIiAgul4vdJAA3kslkZWVlJE1SWlra09OjW38jPj6ey+WamJjQHabRampqOnDgwMGDB0dHR9euXbt9+/bQ0FC6g5pxL7744r59+yQSia+vL92x3KGWlpawsLANGzbs2rWL7lim05UrV7Kzsw8ePDhv3rxnn31WIBA4ODjQHdSchlzIHNTT01NZWVlZWSkWiysrK+vr65VKpZWV1eLFi5dcFxoaamVlRXekAAAAMA2QFwEAgOnU29tbXl4uFApFIlFFRUVTUxNFUa6urtzrIiIiDHc+DmDmkDLlhYWFBQUFIpFobGzM2to6PDxcuygVhXRmwsjIyJEjR7Kzs6urq/l8/qZNm1avXm3EXQFINS0XF5fTp08bYjUtjUbzwAMPtLS0VFRUGM3mqoqKiszMzCNHjnh5eQkEgrS0NKP50wyOtnf6lFwISYfgvBgf3QYhNTU1UqmUoih7e/vg4GDtxTcwMNCILwoAAABzGfIiAAAwg4aGhiQSifYD56VLl+RyuY2NTWhoqHaqNzQ0FLWYAXQplcr6+nrywiksLBSJRFOq1cXFxTk6OtIdpvHQaDRnzpzJysrKy8vz8fFJS0tLS0uzt7enO64ZUVFRERsbu3fv3meeeYbuWH6x/fv3b9y4saioKDo6mu5Y7pZ21J04cYLL5W7evPmxxx7D9Ovsq62tzc/Pz8/Pv3DhwtWrV83NzWNiYlasWLFixYrY2Fg0zTYmUxrmicXikZERJpPp5eWlTYRERkZilzMAAMAcgbwIAADMnrGxMbFYLLqOpEksLCzCwsK0RbcWL16MaQgAXSMjI5WVlbprWimK4nA42q4kkZGReNVMi8uXL+/du/fQoUMMBmPdunWbNm0KDg6mO6jp96c//em9994Ti8ULFy6kO5ZfoKWlJTQ09LnnnnvrrbfojuWuKBSKL7744u233ya7lNLT01etWkV3UHOISqUSi8UkEZKfn9/T02NlZbV06dKEhITly5fHxMTg7dRoDAwMVFdXa6+e9fX1KpXK2tp60aJF2kQIl8tFXSwAAIC5CXkRAACgje6i+JqamoqKiv7+ft2FeyEhIcHBwcHBwYZY7wVghnR0dJBqdUKhsLi4uK+vj81m+/v7a9MkQUFBaExyN4aGhg4fPpyZmXn16tXExESBQJCSkmJM70KTk5ORkZHOzs4GVE1Lo9Hcf//9V69eFYlEhjttPTIy8vHHH7/zzjudnZ2//vWvX3zxxcjISLqDmhOUSqVYLC4oKCBlsgYGBubNmxcTE0MKZEVFRZmZmdEdI9wtsh2kqqpKJBKRBiGdnZ0URbm7u4eHhy9ZsiQ8PJzL5fr6+uISCQAAABTyIgAAoD/UanVjY6NYLBaLxRKJRCKRXL16laIoBwcH0ugyLCwsPDw8JCTEcCfFAKYdKYhP+rcLhcKJiQltqbr4+PiEhARXV1e6YzRIarU6Ly8vOzv7zJkzfn5+zz333FNPPWU0y4pJNa2srKxnn32W7lhuy759+zZt2lRYWGigFbS6u7v37du3d+9euVy+fv36bdu2eXp60h2UkRsdHRWJRCQRUlhYOD4+7urqGhkZGR8fn5SUxOVyMTlu6Nrb22tqasiOkJqampqamvHxcRaLFRAQQBbW8Hi86OhoFxcXuiMFAAAAfYS8CAAA6C+ZTHbp0iXth97KysrR0dEplaBDQkJ8fHwMZckzwIzS7sEimZK6uropjUni4+ONtW3GzBGLxfv27fvss8/YbPbjjz++detWLy8vuoOaBi+//HJ2drZBVNNqbm4OCwt7/vnn33jjDbpj+cWkUmlWVtbBgwetra3/+Mc/CgQCBwcHuoMyWh0dHUVFRYWFhYWFhRUVFUql0s/PLyEhYdmyZQkJCfo/1OEWSFEs7T0h6Q5CURSHwyFZEPI1IiLC0tKS7mABAADAACAvAgAABkOlUjU2Nkokkp/aULJ48eLQ0NDg4OB58+bRHSwA/YaHh8ViMUmT5Ofnd3Z2MpnMRYsWadMkqB5z+3p6eg4dOvT++++3tbUlJydv2rQpKSmJ7qDuyuTkZFRU1Pz588+cOaPPqWWNRrNy5crW1taKigrD2iwoEonefffdI0eOeHp6btq06emnn8Z07bRTqVTV1dUFBQXFxcWFhYVNTU1MJjM0NDQ+Pj4+Pn7ZsmXooW2gZDLZ5cuXtXtBLl261NXVRVGUvb29NgXC4/HCw8NxywcAAAB3BnkRAAAwYAMDA5LrxGJxTU3N6Ogog8Hw8vJafF1ISEhQUBAmfwHa29u17WcLCwsHBgbYbHZYWBjpSsLj8dDL52cpFIqvv/46KyursLCQy+Vu2LAhNTXVwsKC7rjukEgkiomJeffdd5977jm6Y/lJ77333pYtW4qKiqKiouiO5XYVFBTs2rXrxIkTS5Ys2bJly6OPPspisegOyniMjIxUVlaSXXFFRUX9/f3W1tbh4eGkxxJ2xRkiuVze2NioWxSrqalJo9HY2tr6+flpEyFRUVGoDAkAAADTBXkRAAAwKj9VbNrT01P7uTokJGTx4sXIlMBcplKp6urqtGmS8vLyyclJW1vbxYsXk7nFmJgYZ2dnusPUX0KhMCsr6+jRo/b29k8++eTGjRvd3d3pDupO/PnPf3733XfFYrGfnx/dsdxEU1NTWFjY5s2b//a3v9Edy88jPWl27NhRXl7O5/PT09NXrVpFd1BGQreR0sWLFxUKBYfDIW9WPB4vJiaGzWbTHSPcLqVSefXqVe2tWnV1Nan6aGpq6ufnp90LgkKpAAAAMKOQFwEAAGOmUCgaGxurqqqqqqqqq6svXboklUpVKpW5uXlQUBBJkJA9JV5eXvjsDXPW2NiYSCS6eJ1UKqUoauHChdHXcblcw90VMXM6Ozv379+/b98+mUz20EMPbd68OS4uju6gfhm5XB4ZGWljY3PhwgV9a0OtVqsTExN7enqEQqGeV9AaGRn5+OOP9+zZ09rampyc/NprrxnQ7hb9RLolkVxIfn5+c3Mz6adNciHLli3z9vamO0a4LePj43V1dXV1dbW1tXV1dTU1NQ0NDQqFgs1mkwbpoaGh5Kuvr6++vQsBAACAEUNeBAAA5hbdWg3ka21trUaj0V2lGBIS4uvrGxISouczcQAzRCaTXbp0icxIXrx4sbu7G41JbmFycvLYsWN79uwRi8U8Hk8gEKxbt86AVq9XVlZGR0fv2bNn48aNdMfyX/bu3bt169bi4uLIyEi6Y/lJPT0977///nvvvTcyMrJ27dpXX33V39+f7qAM1dDQ0MWLFwsKCgoLC4uKisbGxmxsbKKjo0l1LD6fj+ys/uvr66utra2tra2vr6+urq6vr29paVGr1Ww228/PLygoKDAwkCRCFi1aZGpqSne8AAAAMHchLwIAAHOdTCarra0lZRxqampqa2vJZ3hTU1N/f/+goKCgoKDg4ODAwMDAwEBkSmAOkkqlZWVlZWVl5eXlQqFwZGTE3Nx8yZIlUVFRUVFRkZGRixYtwiJfiqIKCgqys7O/+uorJyentLS0jRs3zp8/n+6gbsurr75K8jr6U02LVNDasmXLjh076I7l5pqamjIzMz/66CNLS8vnnnvu+eefd3R0pDsowyOVSkl1rMLCQpFIpFarfX19SXWs+Ph4LpeL9xZ9NjAwoF1lUlNTI5VKyXZDMzOzhQsXYq0JAAAA6DPkRQAAAKYaGxsjNR+0yZIrV64oFAomk+nt7R0cHEwWPIaEhAQGBtrY2NAdL8Cs0s5jCoXCioqK8fFx0vRYu5tkjvdvb2pqOnDgwMGDB0dHR9euXfvCCy+EhYXRHdTPUCgUsbGxFhYWelJNi1TQGhgYKCsr08MV5ZWVlXv27Pniiy/c3d03b9789NNPW1pa0h2UwVAoFBKJhGwKOXfuXE9PD5vNDgsLI5tCVqxY4eTkRHeMcBPapiAk/1FdXS2RSIaHhymKsre39/X11e3i5u3trQ/vJAAAAAC3gLwIAADAz1MoFNeuXdNdFFlXVzc6Okr993QAeRAYGMhkMukOGWA2kB4A2v7tZWVlcrmc9G8ny70TEhJcXV3pDpMGIyMjR44cyc7Orq6u5vP5mzZtWr16tT6/M4jF4qioqN27dwsEArpjobKysl544YWSkhIej0d3LP+loKBg165dJ06cCA8P37p166OPPspisegOygC0tLSUlJSUlpYWFxdXVFTI5XIOhxMXF8fn85cuXcrj8Qyo7twcMTk5efnyZd2NINXV1RMTExRFcTgc7Q0P6dM2N9/kAQAAwNAhLwIAAHAnVCpVc3MzaSJaX1/f0NBQV1fX3d1NUZSZmVlAQMCiRYvI18DAwICAADs7O7pDBphx2pXgJE1SV1enVqs5HI52K8nSpUsNpbTUtNBoNGfOnMnKysrLy/Px8UlLS0tLS7O3t6c7rpvLyMjYvXt3ZWUlvR0ySAWtbdu2/eUvf6ExDF1qtTovL+9vf/tbWVkZn89PT09PSUmZy5uiftbQ0FBZWVnpdV1dXSwWKyQkJO46X19fumOE/1Aqlc3NzY2NjfX19Y2NjQ0NDfX19deuXaMoytTUlNzGBAYGBgcHk8fo8gIAAADGAXkRAACAaTMwMEASJPXXXb58eXJykqIoFxeXwMBAkiwhD3x8fPR58TjA3RsaGpJIJNrdJDU1NRRFadMk8fHxcXFxc6QA0eXLl/fu3Xvo0CEGg7Fu3bpNmzYFBwfTHdRUSqUyNjbWzMzswoULdL07qdXqe+65RyaTXbx4UR8qaE1OTh47duz111+/cuVKcnLyq6++Gh0dTXdQ+kilUtXV1ZFXurZTiG5ONCEhAesD9EFra2tjYyPJfxBSqVShUFAU5ezsHHAdaa7m6+uLGxUAAAAwVsiLAAAAzKz29nZtDQryoKmpSaPRsNlsDw8P3+tIPQovLy/MQYCxGhwcLC8vJ7tJLl682N3dzWQyFy1apJ05jYyMNO7GvENDQ4cPH87MzLx69WpiYqJAINC3bQdisTg6OnrXrl2bN2+mJYB33333xRdf1IcKWr29ve+999577703MjKydu3aP//5zwEBAfSGpG/a29u1Wc+CgoLBwUErK6slS5ZoEyE+Pj50xzinDQwMSK8jdyANDQ2kI4i2L7r2JiQ0NNTFxYXukAEAAABmD/IiAAAAs00mk5FFmo06BgcHqetTFf7X+fn5+fv7u7u769XMKcC00J1ULSoq6u/vZ7PZ/v7+2t0kXC7XKDv3kqJM2dnZZ86c8fPze+6555566ikrKyu64/qPv/71r2+//bZIJJr9NIBUKg0LC3vxxRdfe+21WX5qXc3Nze++++5HH33EZrMff/zxP/3pTxwOh8Z49Mfo6KhIJNImQpqamqakNqOjo/Vhl88cJJfLW1tbtcsvtCiK0i7C0DZC8/X19fHxwX0FAAAAzHHIiwAAAOiF3t5e3TTJ5cuXGxsbh4aGKIqysLAgCRJtpmThwoVubm6Y1ABj0t7eXlhYSHaTVFRUjI+PW1tbh4eHa6dcg4ODjWzMi8Xiffv2ffbZZ2T+fevWrV5eXnQH9Z9qWqampvn5+bO5fU2tVq9YsYL0paCrC7dYLH7nnXe++OILNze3LVu26FW+iha61bGEQmFZWRlpma59VcbHx+ttvxxjJZfLp7QDaWxsbG1t1Wg0TCbT09MzICDA399/0aJF/v7+AQEBnp6e2IcKAAAAcCPkRQAAAPTXjUUwtMkSU1NTd3d33/8WFBQ0R7o1gHFTKpX19fXa2djy8vLJyUlbW9vFixeTqdj4+HijWcLf09Nz6NCh999/v62tLTk5edOmTUlJSfSGJJFIoqKidu7cuWXLlll70j179qSnp5eWlkZERMzak2oVFBTs2rUrLy8vNDR027Ztjz76KIvFmv0w9EFHR0d5ebm2U8jAwIBudSwejxcSEkJ3jHPF5ORkW1ub7j2AVCq9evWqUqmkKMre3l63DifuAQAAAAB+EeRFAAAADEx7e/uVK1euXLkilUqvXNfb20tRFJPJdHd3X6jD19d34cKFtra2dEcNcOcUCoVEIiFbSYRCYV1d3ZSWzkuXLp0/fz7dYd4VhULx9ddfZ2VlFRYWcrncDRs2pKamWlhY0BXPjh073nrrLaFQODv94evr67lc7ksvvfTqq6/OwtNpkZpmb7zxRmlpKZ/PT09P17eOL7NgZGREJBKVXnft2jUTE5OgoKDo6OiYmJiYmJjFixfP2SzRrOnu7tZe0C9fvkwedHd3UxRlYmIy5cpO6m3OmzeP7qgBAAAADBjyIgAAAMZAJpPppkmI1tZWlUpFUdT8+fO1aRJvb28vLy9vb29PT08UggdDNDw8LBaLtbtJampqKIrSpkni4+OXLl1quOWPhEJhVlbW0aNH7e3tn3zyyY0bN7q7u89+GEqlcunSpSwWq6CgYKaL8KjV6uXLl4+MjFy8eHHWKmhNTk4eO3bszTffbGxsTE5OfuWVV2JjY2fnqWmnUCgaGhq0O0IqKytVKpWdnV1kZCSfz+fxeHw+38HBge4wjRbZCarbC+Ty5csymYzS6QWiKzAw0HDf0AAAAAD0FvIiAAAARotUIdemScj8S3Nz88jICEVRJiYmHA7Hx8fH29tbmyxBvgQMzuDgYHl5OdlNUlZW1tXVNaUXdGRkpLm5Od1h/jKdnZ379+/ft2+fTCZ76KGHNm/eHBcXN8sx1NTU8Hi8119/fdu2bTP6RLt3737llVfKy8tDQ0Nn9IkImUz2ySef7Nq1q7+/f+3ata+88sqiRYtm4XlpJJfLJRJJ+XXV1dVKpdLe3j5Sh6enJ91hGhvSC13632pra8fGxiiKMjc3X7BgwZR26F5eXugFAgAAADA7kBcBAACYcwYGBtrb2zs6OnQna7TrVSmdquUEh8NZsGABlqyCQWhvb9duJSkuLu7r62OxWAEBAdrdJEuWLDGUmUeyp2HPnj1isZjH4wkEgnXr1s1mT/LXX3/99ddfr6iomLlqWqSC1iuvvPLKK6/M0FNotbS0fPDBB/v379doNE888UR6evqCBQtm+klpMaVfekVFxfj4uLW1dXh4uDZfGBwcPNcqhs0QpVLZ2tra3Nzc1NREvpIHbW1t5LO2i4vLwoUL/fz8dAthOTk50R04AAAAwJyGvAgAAAD8B/IlYGTUanVDQ0NZWVlZWVl5eblIJJqYmLC2tiabSIiFCxfq/+xwQUFBdnb2V1995eTklJaWtnHjxtnpp6JUKuPi4phM5gxV0yIVtEZHR0tLS2c03yORSHbv3n306FFHR8dnnnlmy5Ytxtd1qb29vbCwkGycEolEY2NjbDY7LCyMlMbi8XhBQUEmJiZ0h2nANBpNR0dH03XNzc0kC3Lt2jXSCN3CwsLHx4fswiQPSArE2tqa7tgBAAAAYCrkRQAAAOBnIF8CxkGhUFRVVWnTJNXV1QqFws7OjsfjRUVFkWSJt7c33WHeBJ/P7+zsVCqVMplseHjY1tbW3t5+dp5aLpePj4/PXBZBJpNZWFjMdO2+1tZWiqLs7OysrKx002Curq6FhYUz+tQzR3drVFFRUX9/P5vN9vf31+4IiY6ORlHEO0NagEilUt1rX11d3ejoKKXTBYRc7LTXPm9vb2SeAAAAAAwF8iIAAABwh/r6+pqbm1taWpr/2/DwMHW9f4m3t7eHh4e7u7u7u7uHh4ebm5uHh4erqysmj4B2ut2nSW8SuVxua2u7ePFi7cxySEgI3WFSFEUxGIxHH300LCyMoii5XK5Wqw2uYwq9RkZGpmREKIqSSCRHjhwxoE9DuomQkpKS3t5e3RpxBtpKh14DAwPabR+6JbBI/oPFYrm7u5PNH9otIN7e3gsWLMAlDAAAAMDQIS8CAAAA06yvr0+bLGlpabl69WpbW1tra2tnZye58WCz2RwOx8PDQ5spQcoEaCeXyy9dukTKEAmFwrq6OrVabWdnFxkZSSoRRUVFubq60hIbg8E4duzY2rVraXl2Y3X8+PFHHnlEnz8NSaXSiooKkUhUUVFRXl7e29vLZDKDg4O1VeDCw8PNzMzoDtMAaPd/6G4BuXLlyuDgIDlgyq5HwtPTk8Vi0Rs5AAAAAMwQ5EUAAABgligUip6eHjIhpZ2ZIg9aWlpUKhU5jMxPaeuTaB9gigpm08jISGVlpXZ5PkmTcDgc7dr8mJgYZ2fn2QkGeZGZoG95EdIsXSQSkURIZWXl4OAgk8kMDAyMiIgg20G4XK6lpSXdkeqp0dHRlpYWkownmpubr1692t7eTvp/mJqauru7e3p6enp6ent7kwdeXl5eXl5ILwEAAADMNciLAAAAAP2QMgE9NzQ0JJFItGmS2tpajUajmyZZunTpzPVCR15kJtCeF5lSya2ysnJ0dHRKaSwul4tGTVOQzR9Tul61t7drtySam5tr237otgDx8vJiMpl0hw8AAAAAegF5EQAAANBrcrm8o6Pj2rVr165da2tr032gnQVjsViurq6enp7u7u4LFixwc3NzdXVdsGAB+WpnZ0f3HwHGRiaTXbp0STujXVNTQ1GUbpqEz+c7ODhM19MhLzITZj8vMjIyUl9fX11drR05ExMTpqamfn5+6BEyRX9/f1tb29WrV1tbW8mDa9euka+Tk5OUTgsrTx2ko5WtrS3d4QMAAACAvkNeBAAAAAyVQqFob2+fkjLp6OhobW3t7u6Wy+XkMHNzc7JkWJsscXNzc3Z2dnd3d3Z2dnFxofevACMwMDAgFApJb5Ly8vLOzk6KojgcTnx8POlNEhERcTflj5AXmQmzkBeZkj+rr69XqVQ2NjahoaHaREhgYOCc3cTQ19dHch6kBxV5QL4zNjZGjrGxsSE9qEgJLFL2imTB2Ww2vfEDAAAAgOFCXgQAAACM0/j4eEdHB6m1MuVrW1ubTCbTHmlvb08SJzd+9fDwwNQb/FLt7e3aqfCLFy92d3czmcxFixbxdFhYWNz+L0ReZCbMRF5kYGBAdzsIqbdmb28fHBysPfXBwcEMBmMan1TPkbfiG2skNjY2Dg0NkWO0la+mlElcuHAhNvwBAAAAwExAXgQAAADmoptmTQYGBsgDbYUu6mZZE3t7e/LAy8vL2tqa3j8E9J9umqS4uLivr29KD4mfLZ2EvMhMuPu8iFKprK+vF4vFYrG4srJSLBZ3dXVRFOXj48PlciMiIrhcLpfL5XA40xe1PlIqlV1dXaS2Ifna2tqq3cY3Pj5ODrOzs3N3dyc7P9zc3Dw9Pd3c3MguELyRAgAAAMAsQ14EAAAAYKqJiYn+/v6f2m7S1dWlVqvJkebm5to0yU0fuLq6mpiY0PvngF7RTZMUFhYODAyw2Wx/f39tmiQ6OtrU1FT3R5AXmQl3kBeRyWQSiUSbCKmqqpqYmGCz2UFBQeHh4eHh4SQRYm9vP3Nh06Wvr4+0etJNfrS3t095S3RyciLdnhYsWEByHtrkBxrIAwAAAID+QF4EAAAA4JeZnJzs6upqa2vr6urq6Ojo7u7u6enp6uoiD7q7u/v6+rQHm5mZOTk5kUYmTk5OTk5OHA6HPHB1dXV2dnZycpoyCQ5zilQqJY1JhEKhSCQaGxtjs9lhYWGkMQmPxwsKCmIymciLTLvbyYuQJFZNTQ2pjlVXV6dWq21tbRcvXhwSEqKtjvWLqqLprcnJSZL80Ja60n69du3a8PAwOUybDNZWu9J+9fT0nDdvHr1/BQAAAADA7UBeBAAAAGD6DQwM6BbmGhgY0H08pcEJNp0AoVQqq6ury8vLy8vLhUKhWCyWy+U2NjZDQ0PIi0y7G/MiCoWioaFBmwgpLS3t6emhKIrD4ZD8B8mFGG6DEJlM1t7e3t3d3dra2t3dTWoGdnR0dHZ2krcmchiLxXJxcfHw8HB1dSVf3d3dFyxYsGDBAjc3N1tbW3r/CgAAAACAu4e8CAAAAAANhoeHOzo6enp6puw16ezs7LlOe7Cpqen8+fMdHR0dHR3nz5/v5OTk+N/mz58/f/58zFcaGblcfunSpfLy8g0bNiAvMu1IXuTkyZOkNYhYLK6rq1MqlZaWlosXL16yZAkpjRUWFmYoeyDUajV5D7lp8qOjo0Pb6oPFYjk7Oy9YsMDV1dXV1ZU80CY/XFxckIgFAAAAAOOGvAgAAACAPlIqlVOyJn19fX19fb29vb29veRBX1/f5OSk9kfYbPaUfIluBkWbWXF0dKTx74I7gP4iM4HkRSiKsre311bE4vF4gYGBTCaT7uhubmhoiNTu6+rq6uzs7O7u1hb0I99XqVTkSEtLS5Lt4HA4HA7HxcXFzc3N2dnZzc3NxcXF2dnZQLe8AAAAAABMCxbdAQAAAADATbBYLDKheevDRkZGdJMlulpaWsrLy8njsbEx7Y+YmJjcdLvJ/PnzHRwc7K+zs7MzlGXyAHesu7vbycmJ7ij+Y3BwkGQ7Ojs7u7q6enp6Ojo6yAOyBWRiYkJ7sLOzs7OzM3mXCAkJ4XA4ZOeHs7Ozu7u7tbU1jX8IAAAAAICeQ14EAAAAwIBZW1tbW1t7eXnd+rCJiYn+/v6B/0b6nfT09DQ0NJDvdHV1qdVq3R8kvU9uB5qggCGataSIQqHovU5bLo9kO7q7u8luD+32LwaD4ezs7OTkROpc+fn5kT0fTk5OCxYscHJycnZ2ZrHwUQ4AAAAA4A7hZhoAAADA+Jmbm5POAbc+TKFQDNzM4OAgeXDt2jWJREIeDw8P6/4si8Uiu0xukTuxtbW1s7OztbW1sbFhs9kz+RcDzJ6JiQmS8CB7O2583N2l/fJcAAAgAElEQVTdre1qTlEUk8l0cnJycnIi2Y6AgACyz4Ps/yAZEb2t5QUAAAAAYASQFwEAAACA/2Cz2WRy9jaPHx8fv2ke5cYkim7nA60bN6NYWFjcdIcK+b6Liwsmi2dBd3f3+fPnGxsbX375ZbpjoZ/uICdbrG583NnZqdu10d7ensPhkKFLKlyRxwsWLCCPsdsDAADg/7F3p/FVVYfegFfIJIRAmGR0ICgkggqttVgEFHCqXqwTzjiXiojVWkXFCSs4ISqiOACKzOrVq6Io0xXBgFoVqxJAASUEmUMgYQhJ3g+nby5lEsJwgPM8H/zts8/ea//P4YvJP2stgOjyv+MAAJRTxYoVK1as+KvTUEIIxcXFq1atWr16dV5e3urVq/Pz8/Pz88sOIieXL19ediZycotBKlSoEJlxUqVKlSpVqkTmnUQO0tLSUlJSUlJSUlNTq1atGjmOvJuSklKxYsW98wXsj+bOnfvOO+/85S9/adq06cCBA88888xduj07O/vZZ58dMGBARkZGWS/SsmXLNm3aPPbYYzs/Tps2be64445zzjnn/ffff/jhh6dNmxY5//333999991Tp06Ni4vr0KHDk08+Wbdu3YKCgp1PO3jw4HHjxjVu3HjJkiXt2rW79NJLQwjFxcX33HPPzTffXL9+/V36vHPnzo1M6Vi2bNnSpUsjMzwii1wtXbp0+fLlm+/NU7FixZo1a0a6w5o1a5544om1atWqXbt22Q49tWvXrlq16i4FAAAA9j29CAAAe118fHzkF8e7dNfq1as3b0oiZUleXt7mZ3755ZfIZQUFBQUFBfn5+VuPEylUUlNTI31JWlpa5cqVI8fVqlWLHFSuXHmLcuWQQw6JnD+Alvz6+OOPX3zxxVdeeaW0tDQnJ2fz3+nvpIyMjL59+w4YMGDzkw0bNjzkkEPKXubk5DRo0GDH4/zwww9HHXVUCOHHH39s1KhR5OSsWbN69ux59dVXP/DAA08++eRrr722bNmyCRMmJCYm7mTahx56aPDgwV999VVaWlpeXl6LFi2WLVvWvXv3+Pj4O++88/rrr3/iiScaNmy485+3cePGkYOyiUqRWR1HH330FpM8IrNA4uLidn5wAABg/6QXAQBgP1W1atVy/PV9fn5+pCNZvXr1mjVrIsd5eXlr166NHK9atSpysGTJkry8vMjxmjVrVq9evcW28xGRWuWQQw6pWLFi1apVk5KSIhVLUlJStWrVkpOTK1WqlJqampycXKVKlUqVKiUnJ6elpSUnJ5fVKtWqVYuLi0tLS9sT38p2zZo1q3Pnzl999VWkyElPT2/SpEk5xklOTt7izMiRI8uOFyxY0Llz5ylTpuxghMh3G+knNu9Fxo8fP3z48Mj0ncGDB7/77rszZswIISQlJe1M2oULFz700EO9evWKfJNpaWk33HDDXXfddfnll9eoUaNatWr3339/x44dp0+fnpKSspMf9ssvv6xVq1bNmjU3L34AAICDm14EAICDSmT5rPLdu27dusikk/z8/PXr10eqlA0bNuTl5a1fv37dunWrV6/euHFjpG7ZsGHDvHnzIufz8/M3bNgQOb9x48btjR+pWOLj46tUqZKQkJCampqYmFi5cuVIuRKpXiKbqUT6lUj7sjPJS0tLr7jiimuuuaZ69eqRM8ccc0xkxsYetGjRonPOOWfrrWK28OOPPzZo0CDSr8ybN69Tp06R8927d9/8sk2bNl133XU7n3bYsGFFRUXt27cvO9OuXbt77rln0KBBd9xxRwjhuOOOa9So0d///vfnnntuJz9RixYtdvJKAADgoKEXAQCAf4vUEru63tfWVq1atWHDhsLCwvz8/KKiotWrVxcXF+fn52/atGnNmjVFRUVr167duHFjpFwpLCxct27d+vXrV65cuWHDhkizsnbt2qKiojVr1mzatGlnnvjOO+98+eWXm69/ddNNN0WaiS+++KJbt24nnHBC9erV+/Tpk5eX969//euNN9548803p0+fftNNN02ePLlevXoPPvjg+eefv8WwJSUlb7755tixY+fPn//xxx+/8sor3333XVpa2o033vj8889vHWPAgAHdunWLHJctOfXuu+9eeeWVy5cvr1GjRtmV991331NPPVXWi5Sl3YGpU6eGEDZfwuuwww4LIcycObPszBlnnNG9e/fbb789PT19x6MBAAAxSy8CAAB7WLVq1fbgaDuzp8Xo0aPj4uJOOOGEsjOnnXZa5ODyyy9fvnz59OnTQwhz5sxZu3btihUrnnvuuXXr1j388MO33HLLBRdc0KVLlwsuuGDatGl/+MMfNh+2QoUKLVu27NSpU0ZGRgjhnnvu6dmzZ506dbZZioQQ/vKXv1x//fUPP/zwihUrnnzyyU2bNlWvXn3BggXVq1cvqz3efvvtfv36TZkyJbLQVqQaKUu7A7m5ueE/v9vI5Jj58+eXnTnppJM2bdo0ZsyYHj16/OqAAABAbKoQ7QAAAMDuysrKqlq1akLCNv7sadWqVStXrnzmmWdKS0vvvffeSpUqnX322ZGZFo888kjr1q0vvfTShx56KITQv3//rW+PXLmT4uPjk5OTs7OzmzdvnpycnJOTU7Nmzbp1624+F+SUU04ZOHDgs88+u2TJkuuvv/7VV1/dycEjy6Nt3hJFjjdfuKx27dohhE8++WTnMwMAALFGLwIAAAe8X375ZXuTVJ5//vnU1NRbbrnlxBNPXLt2bWpqagihQoUKIYRKlSpFrunYsWMIYe7cubv63Mz/1Lp168zMzPfee69Xr16ZmZmnnXbaypUrMzMz77777rJb0tLSMjMzb7rpphdeeCGEMHTo0J18VmTOSl5eXtmZVatWhRDq1au3+eAhhCVLluzqBwEAAGKHdbQAAOCAFx8fv73t0C+44IIWLVp07dr1ww8/bN269UsvvXTVVVdtcU2kWtilqSERs2bN2uLMunXrUlNT58yZU7Fixfvuu6+wsPCJJ57Y5r3nnntuCGEnN5YPITRt2jSEkJubG5kUEkJYvHhxCOHkk08uu2Zn1hwDAABinPkiAABwwKtbt+7mEyk2d//996enp48bN27kyJFFRUU9e/bc+poVK1aEEDp06PCrD4qLi9vxVvA//PBDgwYNKlasGDk+6qijtndlpNX44x//+KsPjbjyyivT0tImT55cdmbSpElJSUmXXXZZ2ZnIDJI6ders5JgAAEAM0osAAMABr23btmvWrFm7du3Wbz3xxBORyuTCCy+sWrVq/fr1y94qm2IyYcKE3/72t126dAkhrFu3LoSwfv36yFuRMfPz8yMvGzVqtHjx4oULF24vyZw5c5o0aRI53qIX6dev3+DBg1evXh1C2LBhw5133nnxxRd369Zt89v79u3btGnTUaNGbT1ytWrV7rrrroEDB0YirVmz5sUXX+zZs2eDBg3Krlm+fHn4zxkkAAAAW9CLAADAAa9z586lpaVZWVlbv1VYWNi+fftHH3306quvbt269eaVw1NPPbVixYply5YtXrz4448/TkhImD9/fo8ePUIICxYsePrpp3Nzc3v37h1CyM3N7dev35o1ay666KIqVap8/vnn20uyRS/SqFGjsrfy8/P79OnTsGHDrl273nnnnd26dRs1atQWK1/NmzcvOzv79ttv3+bgd9xxR48ePbp27dqzZ8/rrrvu73//+7333rv5BdOmTYuPj+/UqdOvfmMAAEDMiistLY12BgAAYLvi4uJGjx79q7/rP/vssxs3btyvX7+dGTMzMzM7O3v//Flgzpw5nTt3nj59ejnu7dixY506dV588cVfvXLMmDEXX3zx/vkNAAAAe5X5IgAAcDAYMmTI+++/v2TJkmgH2S2FhYX9+/d/+eWXy3HvjBkz5syZ07dv3z2eCgAAOJjoRQAA4GBw6KGHvvnmm7feemthYeGvXlxQUFD23/3KvHnzevfu3axZs129cfHixQ8//PCECRNSU1P3RjAAAOCgoRcBAICDRLNmzR5++OEBAwbs4JqCgoJ77rknsnF69+7dy7dc1d7TrFmzchQbmzZtGjp06PDhwzffgx0AAGCb7C8CAAD7tZ3cX4RdYn8RAACIWeaLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsSKutLQ02hkAAIDtio+Pr1WrVkpKSrSD/LpNmzaFEBISEqId5NcVFBQsW7asuLg42kEAAIB97QD4iQUAAGLZ8OHDf/rpp2in2CkDBgwIIfz5z3+OdpCdcsQRR0Q7AgAAEAXmiwAAAHvAzz//fOSRR4YQ5s2bFzkAAADYD9lfBAAA2ANGjBiRkJCQkJAwatSoaGcBAADYLvNFAACAPSAzMzM7OzuEkJGRMWvWrGjHAQAA2DbzRQAAgN01a9asSCkSQsjOzv7222+jmwcAAGB79CIAAMDuGjZsWGJiYuQ4KSnJUloAAMB+yzpaAADA7jriiCN+/vnnspcNGjT4+eef4+LiohgJAABgm8wXAQAAdktWVtbmpUgIIScnZ8aMGdHKAwAAsAN6EQAAYLeMHDkyKSlp8zNJSUkjR46MVh4AAIAdsI4WAABQfsXFxYceeujKlSu3OF+tWrWlS5cmJCREJRUAAMD2mC8CAACU38SJE7cuRUIIq1atmjx58r7PAwAAsGN6EQAAoPxGjBixxSJaEYmJiSNGjNj3eQAAAHbMOloAAEA5rV+/vmbNmgUFBdt8t1KlSsuXL69YseI+TgUAALAD5osAAADlNHXq1O2VIiGEwsLCadOm7cs8AAAAv8p8EQAAoJw2bNjwwQcfFBUVRV7269cvhHDrrbdGXiYmJp511lnJyclRywcAALAVvQgAALBndOrUKYQwZsyYaAcBAADYLutoAQAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr4kpLS6OdAQAAOMAsW7bs66+/nj17dnZ29rx581avXl1QULBo0aIQQv369VNSUqpWrZqenp6Zmdm4cePmzZvXqlUr2pEBAABC0IsAAAA7aePGjR999NH48eMnT5787bfflpaW1qxZs0mTJkcddVRaWlpKSkqVKlVCCPn5+QUFBXl5eXPnzp09e/aKFSvi4uKOPfbYU0899bTTTjv99NMTExOj/VEAAIDYpRcBAAB+xWeffTZ06NBRo0atWrXq+OOPP/XUU9u1a9eyZcsaNWr86r0rVqzIysqaPHnypEmTvvnmm+rVq19yySWdO3f+3e9+tw+SAwAAbEEvAgAAbNfUqVMfeOCBiRMnZmZmdurU6aqrrmrYsGG5R1u0aNEbb7zxyiuvfP31161atbrzzjvPOeecuLi4PRgYAABgx+y7DgAAbMOUKVNOOOGENm3aJCcnf/LJJ99///0DDzywO6VICKF+/fq33HLLV199NWXKlNTU1I4dO5544omffPLJnsoMAADwq/QiAADAf1i6dOlVV111yimn1K5d+5///OfYsWNPPvnkPfuI1q1bf/DBB1988UWNGjXatm17zTXXLFu2bM8+AgAAYJusowUAAPyft99++7rrrktJSXn66afPO++8ffDEN95449Zbb123bt3gwYM7duy4D54IAADEMvNFAACAEELYuHHjX//61/PPP/+CCy6YNWvWvilFQggXXnjhrFmzzj333D/96U+33Xbbxo0b981zAQCA2GS+CAAAEFauXPlf//Vf//rXvwYOHHjZZZdFJcNrr73WtWvX5s2bv/POO9WqVYtKBgAA4KCnFwEAgFiXk5Nz5plnrl279oMPPsjMzIxiku++++6ss86qWrXquHHj6tevH8UkAADAwco6WgAAENPmz58f2VZ92rRp0S1FQghNmzadNm1acXFxq1atFixYEN0wAADAQcl8EQAAiF1Lly49+eSTK1euPGHChOrVq0c7zr+tWLGiffv269ev/+STT2rVqhXtOAAAwEFFLwIAADEqPz//1FNPzc/Pnzp1au3ataMd5z8sXrz45JNPrlmz5sSJEytXrhztOAAAwMHDOloAABCjrr322kWLFn344Yf7WykSQqhbt+6HH364YMGCG264IdpZAACAg4peBAAAYtGzzz771ltvDRs2LD09PdpZtu2oo44aNWrU66+//sILL0Q7CwAAcPCwjhYAAMScr7/+umXLlnfdddf9998f7Sy/4r777nv88cdnzJhx3HHHRTsLAABwMNCLAABAbCkpKWnZsmVycvLHH39cocL+PoO8uLi4TZs2JSUl06ZN2//TAgAA+z8/VwAAQGwZOHDgV199NWDAgAOiZoiPj3/hhRf++c9/Dh48ONpZAACAg4H5IgAAEENWrFhx9NFHX3/99Y899li0s+yC2267bejQoXPnzq1WrVq0swAAAAe2A+APxAAAgD3l6aefjo+Pv++++6IdZNc88MADJSUl/fv3j3YQAADggKcXAQCAWJGfn//ss8/+9a9/rVy5crSz7JoqVap079796aefXrNmTbSzAAAABza9CAAAxIqBAweWlJTcdNNN0Q5SHt27dy8qKnrhhReiHQQAADiw6UUAACBWvPzyy1dffXVaWlq0g5RH9erVr7766pdffjnaQQAAgAObXgQAAGJCVlbW3Llzr7rqqmgHKb8rr7xy9uzZn3/+ebSDAAAABzC9CAAAxIRhw4Y1bdq0RYsW0Q5Sfr/73e8yMzOHDh0a7SAAAMABTC8CAAAx4Z133rn44oujnWJ3derU6d133412CgAA4ACmFwEAgIPf3Llzc3Jy2rdvH+0gu6tdu3Y//fTTvHnzoh0EAAA4UOlFAADg4Ddp0qTKlSv/7ne/2wfPWrp06euvv967d++9MXjLli1TUlImTZq0NwYHAABigV4EAAAOftOmTfvDH/6QmJi4tx+UnZ3dq1evTp06vfbaa2UnW7Zseccdd+yR8ZOSkk466aSpU6fukdEAAIAYpBcBAICD36xZs5o1a7YPHpSRkdG3b98tTjZs2PCQQw4pe5mTk7M7j2jatGl2dvbujAAAAMQyvQgAABz85syZ06RJk33zrOTk5C3OjBw5slevXpHjBQsWXHbZZbszfpMmTWbPnr07IwAAALFMLwIAAAe5xYsX5+fnN27cONpBwqJFi84555xly5btziBNmjTJy8tbsmTJnkoFAADEFL0IAAAc5JYuXRpCqFevXvlu/+KLL1q2bNmtW7f77rsvMTGxoKBg+vTpt99+e8OGDZcsWXLhhRfWqFHj2GOP/e///u+t7y0pKXn99devvvrqtm3bhhBeeeWV77777pdffrnxxhvL/XHq1q0bQtjNcgUAAIhZcaWlpdHOAAAA7EVTp05t3br1okWLyleNNGnSZPny5StWrAghXHLJJU8//fQXX3xx0UUXrVu37uabb77oootycnK6dOmyZs2ayO7uIYS4uLiMjIxZs2aFEBYuXHj44YeXvdz8rfLJyck57LDDPv3005NOOqncgwAAADHLfBEAADjIrV27NoSQmppavttXrVq1cuXKZ555prS09N57761UqdLZZ5992GGHhRAeeeSR1q1bX3rppQ899FAIoX///lvfHrlyD6pSpUoIIT8/f88OCwAAxAi9CAAAHOTWr18fQjjkkEPKd/vzzz+fmpp6yy23nHjiiWvXro30KxUqVAghVKpUKXJNx44dQwhz587dM4l3qGLFiiGEdevW7YNnAQAABx+9CAAAHOQiRUJhYWH5br/gggu+/vrrM84444svvmjduvWrr7669TWRFbr2+NSQbSooKAghpKSk7INnAQAABx+9CAAAHOQiMzwiq2mVw/3335+enj5u3LiRI0cWFRX17Nlz62siu4906NDhV0eLi4vbtGlT+ZJErFmzJuzGsmAAAECM04sAAMBBLlIhlHtDjieeeCIvLy+EcOGFF1atWrV+/fplbxUXF0cOJkyY8Nvf/rZLly7h/69wFVm8K/z/Pqbs6Y0aNVq8ePHChQvLFyboRQAAgN2jFwEAgINc3bp1Qwg5OTnlu72wsLB9+/aPPvro1Vdf3bp161GjRpW99dRTT61YsWLZsmWLFy/++OOPExIS5s+f36NHjxDCggULnn766dzc3N69e4cQcnNz+/Xrt2bNmosuuqhKlSqff/55uT9OpFOJfCgAAIBdFVdaWhrtDAAAwN5Vs2bNBx988KabbtpTA2ZmZmZnZ0flp4lnnnnmH//4x9KlS/f9owEAgIOA+SIAAHDwa9KkyezZs6OdYs+YPXt2RkZGtFMAAAAHKr0IAAAc/I455phvvvlmDw5YUFBQ9t997JtvvsnMzNz3zwUAAA4OehEAADj4tW7devr06ZEd0XdTQUHBPffcE9nko3v37tOnT9/9MXdeYWHh559/3qZNm335UAAA4GBifxEAADj45ebm1q9ff8KECe3bt492lt3y4YcfnnnmmTk5OfXr1492FgAA4IBkvggAABz86tWrd/TRR48fPz7aQXbXxIkTMzIylCIAAEC56UUAACAmnHfeeaNGjSopKYl2kPIrKSkZNWrU+eefH+0gAADAAUwvAgAAMeGqq6766aefpk6dGu0g5fe///u/CxcuvOyyy6IdBAAAOIDpRQAAICYcc8wxv/nNb1599dVoBym/oUOHnnDCCU2bNo12EAAA4ACmFwEAgFjRpUuXESNG5ObmRjtIeeTk5IwaNapLly7RDgIAABzY9CIAABArrrrqqho1ajz55JPRDlIejz/+eK1atTp37hztIAAAwIFNLwIAALEiOTn5b3/728CBA5cuXRrtLLtm8eLFL7300h133JGUlBTtLAAAwIEtrrS0NNoZAACAfaSwsDAjI6Ndu3avvPJKtLPsgiuuuGLatGnff/99xYoVo50FAAA4sJkvAgAAMaRSpUr9+vUbOnTo//7v/0Y7y8765JNPRowY8fTTTytFAACA3We+CAAAxJw//vGPP//88+eff77/Nw2FhYUnnHBCo0aN3n333WhnAQAADgbmiwAAQMx57rnncnNzb7nllmgH+XU333zzkiVLBgwYEO0gAADAQUIvAgAAMefII48cOnToyy+/PGzYsGhn2ZFRo0YNGTJk0KBBhx9+eLSzAAAABwnraAEAQIy65ZZbBg0aNHHixN///vfRzrINWVlZHTp0uPHGG5944oloZwEAAA4eehEAAIhRxcXFF1100ZQpUz755JPMzMxox/kP33//fevWrf/whz+89dZbCQkJ0Y4DAAAcPPQiAAAQuwoLCzt06LBo0aLJkyenp6dHO86//fjjj+3atTv88MM/+uij/X9neAAA4MBifxEAAIhdlSpVeu+992rVqtWqVauvv/462nFCCOHLL79s1arVoYce+s477yhFAACAPU4vAgAAMa169eqTJ09u1qzZKaecMmHChOiG+fDDD0855ZTjjz9+8uTJ1apVi24YAADgoKQXAQCAWJeamjp27Nizzz77zDPPfPDBB0tKSvZ9huLi4vvuu++Pf/zjueee++6771auXHnfZwAAAGKB/UUAAIB/GzBgwN/+9reTTz55yJAhhx122D577k8//XTNNddkZWU9+eSTN9544z57LgAAEIPMFwEAAP7tpptu+vTTT3NycjIzMx977LGioqK9/cSNGzf26dPnmGOOWbx4cVZWllIEAADY2/QiAADA//nNb34zc+bMHj16PPDAA8cff/yYMWP20rJaJSUlo0aNOv744//xj3/cfffdM2fObN68+d54EAAAwOb0IgAAwH9ITk7u2bPnd999d9xxx1166aVNmzZ99dVX169fv6fGX7du3SuvvHLMMcdcccUVLVq0+O677+65556kpKQ9NT4AAMAO2F8EAADYrtmzZ/fp02fEiBGVKlW68MILO3fu3KpVq/j4+HIMVVxc/Mknn7z22mtvvvlmYWHhFVdc0aNHj8aNG+/xzAAAADugFwEAAH7FL7/8MnLkyNdee+2rr75KS0tr27Ztu3btWrZs2aRJk6pVq+7gxry8vDlz5mRlZU2aNGnKlCl5eXm/+c1vrrzyyksvvbR27dr7LD8AAEAZvQgAALCzsrOzx48fP2nSpI8//njVqlUhhNq1azdu3DgtLa1y5cqRtbA2bty4du3aSCOyZMmSEEL16tXbtm176qmnnn766U2aNInyZwAAAGKbXgQAANhlJSUlCxYsmD17dnZ29o8//pifn79mzZopU6aEENq2bVu5cuUqVao0atQoIyOjSZMmRx55ZIUKtjYEAAD2C3oRAABgD1i6dGm9evVKS0tzc3OtkQUAAOy3/NEWAACwB4wePTouLq5ChQpjxoyJdhYAAIDt0osAAAB7wKuvvlpcXFxcXDx06NBoZwEAANgu62gBAAC766effmrYsGHkh4u4uLgff/yxYcOG0Q4FAACwDeaLAAAAu2v48OEJCQmR44SEhFGjRkU3DwAAwPaYLwIAAOyujIyM2bNnl708+uij58yZE8U8AAAA22O+CAAAsFu++eabzUuREMLcuXP/9a9/RSsPAADADuhFAIb1VIkAACAASURBVACA3TJy5MjExMTNzyQlJY0cOTJaeQAAAHbAOloAAED5lZaWHn744Tk5OVucr1evXk5OTlxcXFRSAQAAbI/5IgAAQPl9+umnW5ciIYTc3NysrKx9nwcAAGDH9CIAAED5bb2IVkRiYqKltAAAgP2QdbQAAIBy2rRpU+3atVeuXLnNd9PS0pYtW5aQkLCPUwEAAOyA+SIAAEA5TZs2beXKlYmJiclbSUxMzMvLmzZtWrQzAgAA/Ad/ugUAAJRT8+bNn3zyyY0bN0ZeDh8+PIRw+eWXR14mJSU1b948auEAAAC2xTpaAADAntGpU6cQwpgxY6IdBAAAYLusowUAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArIgrLS2NdgYAANgv/PLLL717996wYUO0gxyovvnmmxDCcccdF+0gB6rk5OS77767Tp060Q4CAAAHM70IAAD82+uvv96pU6cOHTpEOwgxasKECWPGjLnooouiHQQAAA5mCdEOAAAA+4vI3wyNHz8+2kGIUXFx/nANAAD2OvuLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAA7En5+fnRjrDvrF69escXLF269PXXX+/du/f+EAYAACDoRQAAoNzmzp3bt2/fgoKCI488cuzYsY8++mjr1q1r1Kixt5/bv3//Hj16tGvXrk2bNnPmzNnbj9vahg0bevfu/Yc//GHHHzY7O7tXr16dOnV67bXXoh4mhNCmTZv33nsvhPD++++3atWq7PygQYNatGiRmpravHnzIUOGRE5G/lnHjRu3MxkGDx7cqVOnnj173nDDDSNHjoycLC4u7tGjx6JFi8rzqQAAgL0mIdoBAADggPTxxx+/+OKLr7zySmlpaU5OzoYNG2655ZbHHnts06ZNe/W5zzzzzD333JOXl7d27dprr702KpMkkpOTb7vttr59+xYXF+/gsoyMjL59+w4YMGB/CBNC+OGHH4466qgQwo8//tioUaPIybvuuisnJ+eGG26YM2fOiy++eO211xYUFHTr1i0xMTEnJ6ewsPBXAzz00EODBw/+6quv0tLS8vLyWrRosWzZsu7du8fHx995553XX3/9E0880bBhw93/pAAAwB5hvggAAOyyWbNmde7cuX///omJiUlJSenp6U2aNDnkkEMOPfTQvf3o559/vn79+vHx8VWrVn3zzTd/97vf7e0nbtNOftjk5OT9JExBQcGSJUsi/URZL5KTk7Nw4cLXXnuta9euTz311Ntvvx1CePrpp0MIZf+sOx524cKFDz30UJcuXdLS0kIIaWlpN9xww1133bVixYoQQrVq1e6///6OHTsWFBTsiQ8KAADsAXoRAADYNaWlpVdcccU111xTvXr1yJljjjkmMhFhH1i4cGFcXNy+edbB5Mcff2zQoEGkp5k3b16kF/npp5/69u1bds3pp59eq1atpUuXRl7uzD/rsGHDioqK2rdvX3amXbt2hYWFgwYNirw87rjjGjVq9Pe//33PfhwAAKDcrKMFAAC75p133vnyyy83Xx7qpptu2nxixA8//HDbbbdNnTr1qKOOeuyxx0455ZQQwty5c+++++5GjRrl5uYuWLBgwIABxx57bOT6Z599dsaMGampqYMHD96wYUPkZGlp6RbPHTt27HvvvVdQUPDLL7/ceOONJSUl55577vjx499+++1p06Zddtll8+fPnzlzZoUKFR5++OEKFSps3Ljx22+/bdas2b333puWllZYWPjWW2+NHTs2UgZ07dp15cqVw4cPr1Wr1p133jl16tSaNWsOGzbst7/97fY++Lp163r27Ll27dpDDz20uLh48zkQ69evf+aZZ+bMmTNz5sy0tLR+/fo1a9Zs6xG2+SUMHz78z3/+c2Fh4SOPPHL77bfHx8ePGDHimmuuefHFF6+66qrtjbyDMFsYMGBAt27dIsdlldK777575ZVXLl++fItdSTZu3Ni6dett/rNu09SpU0MIDRo0KDtz2GGHhRBmzpxZduaMM87o3r377bffnp6evuPRAACAfaEUAAAoLS0tLR09evTO/B/ypZdeGhcXV1RUtPVbGRkZIYS//vWv48ePf+GFF1JSUuLj47/55pvS0tKjjz66UaNGpaWlRUVFaWlpzZo1i9zSv3//+Pj4FStWlJaW9unTJ4Twt7/9bQdPDyFkZGSUlpZu2LDh008/rVSpUgihT58+EyZMuP7663/55ZfGjRs/8MADkYuXLl3auHHj9PT0vLy8kpKSH374IYRQtWrVsWPHfv/99yGEI4888vHHH1+9evVXX30VQjjllFO299xNmzb9/ve/v+GGGyIvf/zxx4SEhLKv64YbbsjOzo4cn3766bVr187Pz98i8A6+hJ49e4YQvvvuu8jLn3/++bzzztvByDsOs3Xy9evX33vvvV27dl2/fv3atWuTkpJyc3PXr1+/xZXTpk2rWLHil19+uYPvfwvNmzcPIaxbt67sTGRLkpNOOqnsTOS77dOnz6+OFkIYPXr0zj8dAAAoB+toAQDArsnKyqpatWrkF/Hb1KtXrw4dOvz5z3/u3bt3cXFxZKWmG2+88R//+EcIIT4+vkaNGrNnz45cPH78+NLS0tTU1BDCf/3Xf4UQpk2btjMxkpKSTjrppMjshC5durRv3/6ll17q37//nDlzunTpErmmVq1aPXv2nDdvXu/evePi4iKLR9WtW/ePf/xjZmZm/fr1FyxYcPvtt1epUqV58+aHHnro119/vb3HDRw4cMaMGbfddlvkZXp6etnsh88+++yll17KyMiIi4uLi4v76KOPlixZMmXKlK0H2d6XcOutt6ampj711FORl8OHD7/uuut2MPIOwmwtPj4+OTk5Ozu7efPmycnJOTk5NWvWrFu37hZzQYqLi+++++7Bgwe3aNHi1777/1OlSpWw2TSUsuONGzeWnaldu3YI4ZNPPtn5YQEAgL3HOloAALBrfvnll7p16+7ggkjJEUL405/+dMstt0RmZtx6660FBQXPPffcypUrN2zYUFRUFLnmtNNOe+edd8aOHfunP/3pkEMOCSG0a9cu8lZmZubmw86aNWvrZ0V+C1+tWrXIy0inUhYghNCmTZsQwqeffrqDnBHVq1fPzs4ue7nF0xs3bhxCOPLII8vOVKjw77+y+vzzz5s1a/avf/1rW1/Gf9jel1C9evWbb775iSeeeOCBB+rVqzdx4sTIhhzbG/ncc8/dXpitk9esWXP58uU//fRTVlbWk08+WVBQsHLlyszMzPPOO693795llz344IPt27e/5JJLfvVTbC4jI2PKlCl5eXmR8iOEsGrVqhBCvXr1yq6JbMm+ZMmSXRoZAADYS/QiAACwa+Lj44uLi3fmysjvyg8//PAQwueff37xxRc/99xzXbt2HT58eNk13bp1q1ix4nXXXTdt2rS5c+f26tXr7rvvjry1zSJkxyL1wIIFC5o2bbp5hqpVq+7qUFs8/YQTTgghrFixon79+ltcuWLFinnz5hUWFkYW9YooKSnZvKuI2N6XEEK47bbbnnnmmaeeeuriiy8+8cQT4+PjdzDyokWLthdm6+QhhHXr1qWmps6ZM6dixYr33XdfYWHhE088sfkF7733XkpKyp133rmjb2RbIt9zbm5uWS+yePHiEMLJJ59cds3ms0kAAICos44WAADsmrp16+bl5e3MlQsXLgwhnHPOOSGEzp07FxUVnXnmmSGEkpKSsmuKi4u//fbb6dOnP/7442+//fa9994bqQTKJzI7ZOzYsVtk6NChQ7nHjIhsnbL5yJu/VVhY+Oijj5admTVr1rPPPrv1ldv7EkIINWrUuPHGGwcOHPjMM89ce+21Ox55B2G26YcffmjQoEHFihUjx0cdddTm744fPz4nJ2fzUiQrK2snR77yyivT0tImT55cdmbSpElJSUmXXXZZ2ZnIDJI6ders5JgAAMBeZb4IAADsmrZt2w4ePHjt2rWVK1fe4q3IzIBVq1ZFFrbq16/fueeee/XVV4cQFi9enJ+fP378+GXLlkVqlc8++6xevXpDhgx59913jz322Hnz5lWpUqVmzZrp6enbq0Yiv2HffO+K9evXhxAKCgpSUlJCCHfccccbb7zRv3//zp07R34RP2DAgFatWnXr1q3s4tLS0si9kWWsyj5I5N1tzvMIIfz9738fPXr03XfffcQRR7Rp02b69Om5ubkhhAULFpx77rnp6em9evXKyclp3779rFmzPvvsszfeeCOEsG7durKRd/AlNGjQIITwt7/97Zlnnvn5558j+6CEELY3ctu2bbcXZvPFtcrMmTOnSZMmkeMffvihrHcJIUycOPGRRx45//zzBwwYEPly5s2bl5KSctJJJ5Vd07dv38GDB997771br7JVrVq1u+66a+DAgX/+858rV668Zs2aF198sWfPnpFPFLF8+fLwnzNIAACAKIp/4IEHop0BAAD2C999990bb7zxq/+HnJaWNmTIkFNPPbXs1/dlmjRpkpeX99prr2VlZU2YMCEzM/Pxxx+PlCVVqlSZOnXqN998c/nll6enp8+YMePnn3++8MILExISRo0aNWLEiGHDhg0aNKh///7PP/98o0aNttgkI4Tw7bffPvLII1988cXq1avj4uKSk5OHDBny1ltvhRCWL19+2GGH1alTJzExsXPnzqtWrRo4cODMmTMnTpyYlpb20ksvJSYmLl26tFevXllZWWvXrj3ppJN++OGH5557rqSkpKCg4Pe///2gQYNGjRoVQqhcuXJGRsbm61ZF1KlT59RTT505c+azzz776quv1qlTZ82aNWeddVa9evUaNWp03nnnzZs376OPPpo4cWKDBg0GDBhQrVq1+fPn/+Mf/5gxY0ZeXl61atUyMjIOPfTQbX4JkZkclStXnjFjxpVXXnncccdFHpqQkPCnP/1p65F3EOaII47Yet2q//mf/0lOTj7rrLNCCHfeeWePHj0ixVVWVtZZZ501Z86c9/+/Dz74YMaMGUOGDCnbsiWEMHz48HHjxmVlZZXt9L65Vq1apaSkDBgw4J///OeQIUOuvPLKW2+9dfML3nrrrXHjxg0cOHDzMbfpwQcfvOiii8rWQAMAAPaGuLI/FgMAgBg3ZsyYiy++eGf+D/nss89u3Lhxv379dv+hQ4YMWb58eWSb8ZKSktzc3MmTJ99+++0xuE13YWHh8ccf/80330Rqkv3KnDlzOnfuPH369HLc27Fjxzp16rz44ou/emVcXNzo0aM7depUjqcAAAA7yf4iAACwy4YMGfL+++/vfnXx6KOPXnvttdddd13kZYUKFRo0aHDyySdvczvxg96AAQNuvvnm/bAUKSws7N+//8svv1yOe2fMmDFnzpy+ffvu8VQAAED56EUAAGCXHXrooW+++eatt95aWFi4O+NMnTo1hDBw4MAVK1ZEznz55Zc9evQYNmzYHkh5gJgxY8bxxx9/9NFHP//883/5y1+iHWcb5s2b17t372bNmu3qjYsXL3744YcnTJiQmpq6N4IBAADloBcBAIDyaNas2cMPPxzZrLvcXn311ZtvvnnQoEENGjRo1apVp06dvvzyy2HDhh1zzDF7Kuf+LyUlJT8/v0KFCiNGjEhKSop2nG1o1qxZOYqNTZs2DR06dPjw4ZvvwQ4AAESd/UUAAODfdn5/Edgb7C8CAAD7gPkiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArEiIdgAAANi/dOnSJdoRDlSbNm0KISQk+CkDAADYf/mJBQAA/u3YY489/fTT582bF+0gB6qZM2eGEI4//vhoBzlQnX766ccee2y0UwAAwEEurrS0NNoZAACAA97KlStr165dWlq6ZMmSGjVqRDsOAADAttlfBAAA2APeeOONyB9dvfnmm9HOAgAAsF16EQAAYA8YOnRoaWlpaWnp0KFDo50FAABgu6yjBQAA7K7c3NwGDRpEfriIi4v7+eefGzRoEO1QAAAA22C+CAAAsLtGjBgRHx8fOU5ISBg9enR08wAAAGyP+SIAAMDuOu6447799tuy+SLHHnvszJkzox0KAABgG/QiAADAbpk9e3ZGRsYWJ7///vvMzMyo5AEAANgB62gBAAC7ZeTIkYmJiZufSUpKspQWAACwfzJfBAAA2C3p6enz58/f4uQRRxyxYMGCaMQBAADYEfNFAACA8vviiy+2LkVCCD/99NM///nPfZ8HAABgx/QiAABA+Y0cOTIpKWnr80lJSSNHjtz3eQAAAHbMOloAAEA5lZSU1K1bd+nSpdt8t1atWosXL46Pj9/HqQAAAHbAfBEAAKCcPv3006VLlyYmJiZvJTExcdmyZVlZWdHOCAAA8B8Soh0AAAA4UGVkZPTs2bOoqCjycvz48SGE0047LfIyMTExIyMjauEAAAC2xTpaAADAntGpU6cQwpgxY6IdBAAAYLusowUAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArNCLAAAAAAAAsUIvAgAAAAAAxAq9CAAAAAAAECv0IgAAAAAAQKzQiwAAAAAAALFCLwIAAAAAAMQKvQgAAAAAABAr9CIAAAAAAECs0IsAAAAAAACxQi8CAAAAAADECr0IAAAAAAAQK/QiAAAAAABArEiIdgAAAOAAVlRUlJubu3r16oKCgtWrV4cQsrKyUlJSqlatWq9evcTExGgHBAAA+A9xpaWl0c4AAAAcMAoKCqZOnfrxxx9/99132dnZ8+fPLyoq2uaViYmJ6enpGRkZTZs2bdu2batWrVJSUvZxWgAAgC3oRQAAgF+3ePHikSNH/vd///dnn31WVFSUmZnZvHnzJk2aNGnSpGHDhlWrVk1JSalcuXIIYe3atZG5I/Pnz8/Ozp49e/bMmTNnzZqVlJR04oknnn/++ZdeemmdOnWi/YEAAIAYpRcBAAC2q6Sk5O2333755Zc/+uij1NTU8847r0OHDqeeemrdunV3aZzFixdPmjRpwoQJb731VkFBwemnn3799defe+65FSrY8hAAANin9CIAAMA2bNq0aeTIkX369Jk9e/ZZZ53VuXPnjh07HnLIIbs57Pr16//nf/5n6NCh48aNy8jIuOuuuy655JKEBBsfAgAA+4heBAAA2NK4ceO6d+++YMGCSy+99K677srIyNjjj5g1a1afPn1GjhyZnp7ev3//008/fY8/AgAAYGsmrQMAAP9n4cKFF1544VlnndWiRYs5c+a8+uqre6MUCSFkZmYOHTp09uzZxx577BlnnNGpU6ecnJy98SAAAIDN6UUAAIB/e+utt44//viZM2d+8MEHo0ePPvLII/f2E9PT0994441JkyZ9++23TZs2HT169N5+IgAAEOP0IgAAQFi/fn3Xrl3PP//8yy+//Ntvvz3zzDP35dNPPfXUL7/88vLLL7/kkku6deu2YcOGffl0AAAgpthfBAAAYt2KFSvOPvvs7OzsQYMGXXDBBVFMMmbMmBtuuKFp06bvvfde9erVo5gEAAA4WOlFAAAgpi1cuPCMM85Yt27dhx9+2Lhx42jHCdnZ2WeccUblypXHjRt32GGHRTsOAABwsLGOFgAAxK4f/x97dxpeZXWoDXhtQhIQIgGjooDKJMERbWuRUaaqxWo9YKwDTlUsrS1VL4ejqC0WirNFKVacEE0U9KtUtKgMgiAqiCIgYQYDYYYIZGBKvh/xUIqAmEC25L3vX3nf7LXWs/WPXk/WWgsXtm7dumrVqpMnT/4hlCIhhPT09A8//DAWi7Vu3XrRokXxjgMAAFQ29osAAEBErVy5sk2bNrVr137vvfdSU1PjHee/bNiwoXPnzhs3bpw0adLRRx8d7zgAAEDloRcBAIAo2rhx4znnnLNp06YfbPGwZs2atm3bJicnT5gw4YdW2wAAAIcu52gBAEDklJSUXHbZZatXrx4zZswPsxQJIRx55JFvv/326tWrr776an/OBQAAHCh6EQAAiJyHHnro3XffHT58+PHHHx/vLPvSqFGjf/7zn//+978ff/zxeGcBAAAqCb0IAABEy5QpU/r06TNgwIBWrVrFO8t3a9myZd++fe+8885PPvkk3lkAAIDKwP0iAAAQIVu3bj399NMbNWo0atSoWCwW7zj7pbi4+Pzzz1+2bNnnn3+emJgY7zgAAMChzX4RAACIkIceeuirr74aNGjQoVKKhBCqVKkyZMiQpUuXPvbYY/HOAgAAHPL0IgAAEBVLlizp37//Pffcc8IJJ8Q7y/dz3HHH3XnnnX379v3qq6/inQUAADi0OUcLAACi4tprr/3www9nzpyZlJQU7yzf25YtW04++eRzzjnnmWeeiXcWAADgEGa/CAAAREJOTk5mZubdd999KJYiIYTk5OT//d//ffHFF5cuXRrvLAAAwCHMfhEAAIiE3/3ud6NGjVqwYMGhe3X5tm3bmjZteuGFFw4cODDeWQAAgEOV/SIAAFD5ff31188///wdd9xx6JYiIYTExMRbb7312Wef3bhxY7yzAAAAhyq9CAAAVH6vvvpqSUnJ5ZdfHu8g5XXVVVeVlJS89tpr8Q4CAAAcqpyjBQAAlV/btm0bNGiQmZkZ7yAHQEZGxpo1a8aPHx/vIAAAwCHJfhEAAKjkli5dOnny5CuvvDLeQQ6Mq666asKECTk5OfEOAgAAHJL0IgAAUMm9++671atX79SpU7yDHBhdunSpVq3amDFj4h0EAAA4JOlFAACgkhs/fnybNm2Sk5PjHeTASE5ObtWqlXO0AACAstGLAABAJTdhwoQOHTrEO8WB1KFDh7Fjx8Y7BQAAcEjSiwAAQGWWk5OTm5vbpk2bClirZcuWt99++65vnnjiiTvvvLNjx47t2rWbN2/egVqobdu2ubm5y5cvP1ATAgAA0VE13gEAAICDKDs7O4TQvHnzClirYcOG1apV2/k4cODAu+++Oy8vb/Pmzdddd93XX399oBZKT08PIcydO7devXoHak4AACAi9CIAAFCZzZ07t06dOkcccUQFrJWVlbXr4+DBg+vVq5eQkFCrVq3XX3/9AC501FFH1alTZ+7cuR07djyA0wIAAFHgHC0AAKjM5s+f36xZs7gsnZOTE4vFDtLkTZs2PYAHcwEAANGhFwEAgMpszZo1Rx99dBkGFhcXT5gw4eabb27YsGFubu4555xz/PHH5+XlFRUVPfjgg9dff/1PfvKTLl26zJo1q/TDI0aMuOaaa9q3bx9CeOutt3r16pWfn79y5cpevXr16tXr6aefrlGjRiwWe+CBB3bs2BFCyMzMTE5OHjp0aNm+19FHH7127dqyjQUAAKLMOVoAAFCZbd68OTU1tQwDt2/fnpSU9PTTTxcUFLz44ov33HPPK6+8kpiY+Ic//OHWW28t3YNy7rnndu7cef78+SkpKS1btszIyCi9+aNr165du3Z96qmn6tatO3jw4NIJc3Jy/vKXv/ziF79ISEgIIbRt27Zr165XX3112b5XSkrKpk2byjYWAACIMvtFAACgMtu0aVNKSkoZBiYlJZ199tkNGjQIIdx4442dOnUaMmTI7NmzhwwZkp6eHovFYrHYu+++u2rVqokTJ4YQSj+5DzfffHNKSsrjjz9e+vjyyy//+te/LkOwUikpKRs3bizzcAAAILL0IgAAUJkVFhZWq1atzMNLLwipXbt26ePUqVNPOeWUkv/WtWvX/ZmqTp06v//974cOHZqbmxtCGDt27HnnnVfmYIcddlhBQUGZhwMAAJGlFwEAgMqsevXqhYWFB2q2devWLVq0aLdCori4eD+H33LLLUlJSY8//vinn3561llnlR6oVTYFBQU1atQo83AAACCy9CIAAFCZpaSkbN68+UDNlp6eXlBQ8MADD+x8M2fOnCeffHI/hx9xxBG9evV66qmnBg4ceN1115UnSZnPBwMAACLOvesAAFCZlfN+8qKiohBCfn5+6eaMiy66qFGjRn379l22bFmnTp3mzJnzySefvPbaayGE0vZl550fGzZsCCFs3bp1twlvvfXWgQMHfvXVV40bNy5zqtKFatWqVZ4ZAACAaLJfBAAAKrO0tLRVq1aVYWBBQcH999+/ZMmSEMItt9zy+eefhxCSk5PHjRt34YUXvvHGG7feeuvq1atffvnllJSUgoKC/v37dmC3TAAAIABJREFUhxByc3Mfe+yxKVOm3HXXXSGEJUuW9O3b94svvtg57dFHH92lS5fy3LheavXq1WlpaeWcBAAAiKBYSUlJvDMAAAAHy6BBg+69995169bFO8g3CgoKTj/99C+++KJ69erlmad27dp//etff/Ob3xyoYAAAQETYLwIAAJVZs2bN1q9fv3bt2ngH+cagQYN+//vfl7MUWbVqVV5eXrNmzQ5UKgAAIDrcLwIAAJVZaXkwZ86ctm3bxjHGxx9/3LNnz4KCgh07dmRnZ5dzttIZTjzxxAMRDQAAiBb7RQAAoDJr0KBBvXr1Jk2aFN8YNWrU2LhxY5UqVTIzM5OSkso52wcffFCvXr169eodkGwAAECkuF8EAAAquSuvvHL16tXvvvtuvIMcMJ06dapfv/7QoUPjHQQAADj02C8CAACVXIcOHSZPnrxly5Z4BzkwioqKpkyZ0qFDh3gHAQAADkl6EQAAqOS6dOlSWFj43nvvxTvIgfHuu+9u2bKlc+fO8Q4CAAAckpyjBQAAlV/79u2POeaYV155Jd5BDoDu3buvX79+3Lhx8Q4CAAAckuwXAQCAyq9Hjx4jR47My8uLd5Dy2rBhw6hRo3r06BHvIAAAwKFKLwIAAJVfRkZGlSpVXnrppXgHKa9hw4YlJCR069Yt3kEAAIBDlXO0AAAgEn7/+9+PHDlywYIFSUlJ8c5SRtu2bWvatOkvf/nLxx9/PN5ZAACAQ5X9IgAAEAm33377qlWrXn755XgHKbvnn38+Nzf3lltuiXcQAADgEGa/CAAARMX1118/YcKEWbNmJScnxzvL91ZUVHTSSSd16dLlH//4R7yzAAAAhzD7RQAAICruu+++lStXPvzww/EOUhYDBgxYu3btvffeG+8gAADAoU0vAgAAUdGgQYM+ffr069dv8eLF8c7y/SxcuPCBBx7405/+VK9evXhnAQAADm3O0QIAgAjZtm1bixYtGjRo8Pbbb1epcmj8mVRxcfHPfvaz1atXT58+vWrVqvGOAwAAHNoOjf8RAgAADojExMTnn39+/PjxDz74YLyz7K/+/ftPmjTp+eefV4oAAADlZ78IAABEzqOPPnrHHXeMHz++TZs28c7yHSZMmNCpU6fHH3/8pptuincWAACgMtCLAABA5JSUlFx00UVTp06dPHlyo0aN4h1nrxYsWNC6deu2bdu+9tpr8c4CAABUEnoRAACIok2bNnXo0GHDhg2TJ0+uW7duvOPswZo1a1q3bp2amjpu3LiaNWvGOw4AAFBJ6EUAACCiVq9e3bp165SUlDFjxtSpUyfecf7LunXrOnfuXFhYOGnSpLS0tHjHAQAAKg/3rgMAQEQdddRR77777vr169u2bZuTkxPvOP/x1VdftW3bNi8vb/To0UoRAADgwNKLAABAdDVs2HDy5MlVqlRp3br1l19+Ge84IYQwa9asVq1aJSYmTp48+YQTToh3HAAAoLLRiwAAQKTVq1dv4sSJxx9/fMuWLTMzM+MbZtiwYWeffXbjxo0nTJhw7LHHxjcMAABQKelFAAAg6mrXrj127Njrrrvuyiuv7NmzZ2FhYcVnKCgouP7666+++uqePXuOGTMmNTW14jMAAABR4N51AADgG//617+uvfba1NTUgQMHdu3atcLWffPNN3v37r1+/fpnnnmme/fuFbYuAAAQQfaLAAAA37jwwgtnzZr105/+9IILLrj44ovnz59/sFecO3fuhRdeeOGFF7Zq1So7O1spAgAAHGx6EQAA4D+OOeaYzMzMsWPHzp07t3n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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_key_interactions.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.6.4 64bit [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]"
        },
        {
         "module": "IPython",
         "version": "6.2.1"
        },
        {
         "module": "OS",
         "version": "Linux 4.13.0 32 generic x86_64 with debian stretch sid"
        },
        {
         "module": "dask",
         "version": "0.17.0"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.6.4 64bit [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]</td></tr><tr><td>IPython</td><td>6.2.1</td></tr><tr><td>OS</td><td>Linux 4.13.0 32 generic x86_64 with debian stretch sid</td></tr><tr><td>dask</td><td>0.17.0</td></tr><tr><td colspan='2'>Sat Feb 17 01:47:13 2018 CST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.6.4 64bit [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] \\\\ \\hline\n",
       "IPython & 6.2.1 \\\\ \\hline\n",
       "OS & Linux 4.13.0 32 generic x86\\_64 with debian stretch sid \\\\ \\hline\n",
       "dask & 0.17.0 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Sat Feb 17 01:47:13 2018 CST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.6.4 64bit [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]\n",
       "IPython 6.2.1\n",
       "OS Linux 4.13.0 32 generic x86_64 with debian stretch sid\n",
       "dask 0.17.0\n",
       "Sat Feb 17 01:47:13 2018 CST"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext version_information\n",
    "%version_information dask"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "186px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}