Understanding and Handling Merge conflict in Git

[NimaSarajpoor]

I am trying to understand merge conflict and how to handle it. I read atlasian guide and also watch youtube and read articles online.

In most cases, the conflict is easy to detect. For instance, a few lines are modified/updated and it is visually easy to understand that by looking at the file that include merge conflict marker.

However, there are a lot of metadata in jupyter notebook when merge conflicts exist. I opened the notebook in a JSON editor (please see below where I copied only a part of the notebook )

{
 "cells": [
  {
<<<<<<< HEAD
   "cell_type": "markdown",
   "id": "73312d87",
   "metadata": {},
   "source": [
    "# Summarizing Time Series Data with Snippets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e88f834",
   "metadata": {},
   "source": [
    "This tutorial is adapted from the [Matrix Profile XIII](https://www.cs.ucr.edu/~eamonn/Time_Series_Snippets_10pages.pdf) paper and replicates Figures 7,8 and 13.\n",
    "\n",
    "One of common analysis query on a data is \"Show me some representative/typical data\". Sometimes, it is useful to summarize a time series into a few representative patterns (snippets) as it can be visualized more easily and some new insights might be discovered. \n",
    "\n",
    "For instance, let's consider a simple problem: ?\n",
    "\n",
    "The \"snippet-finder\" algorithm proposed in the paper can be utilized to summarize a time series into a user-defined number of snippets. \n",
    "\n",
    "To demonstrate the usefulness of Snippets, we first create a toy data used in the original paper and will use the [STUMPY](https://pypi.org/project/stumpy/) library to discover the snippets in the data. This tutorial replicates the figures 7,8 and 13 of the original paper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e5e116",
   "metadata": {},
   "source": [
    "## Snippets vs. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4c34af",
   "metadata": {},
   "source": [
    "### - Clustering: \n",
    "Snippet is a different task than clustering. In a single time series, one can first extract subsequences (with a user-defined time window) and then perform clustering on the set of subsequences. However, as comprehensively studied in Eamonn Keogh and Jessica Lin (2005), clustering the subsequences of single time series leads to meaningless result. In some cases where there is a periodic behaviour (e.g. electricity demand), the clustering can be performed. However, algorithms specialized in clustering the electricity demand data into seasons, for instance, are more complex and requires more domain knowledge (for example: the subsequences must be exactly one-week long.) However, the snippets algorithm is domain-free and it can be used to better explore and analyze data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c505f5e6",
   "metadata": {},
   "source": [
    "### - Semantic Segmentation:\n",
    "The snippets are different than semantic segmentation proposed in [Matrix Profile VIII](https://www.cs.ucr.edu/~eamonn/Segmentation_ICDM.pdf). In semantic segmentation, the task is to find boundary locations, not in explaining them, or producing representative patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f77e100",
   "metadata": {},
   "source": [
    "### - Shapelets:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd47dd07",
   "metadata": {},
   "source": [
    "There are two main differences between Time Series Snippets and [Shapelets](https://link.springer.com/article/10.1007/s10618-017-0519-9). For one, the shapelets are supervised; however, the snippets are unsupervised. For another, the shapelets are usually shorter whereas the snippets are preferred to be longer to capture the behavior of the time series at a higher level."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4345de90",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "Let's import the packages that we'll need to load, analyze, and plot the data."
   ]
  },
  {
||||||| merged common ancestors
=======
   "cell_type": "markdown",
   "id": "73312d87",
   "metadata": {},
   "source": [
    "# Summarizing Time Series Data with Snippets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e88f834",
   "metadata": {},
   "source": [
    "This tutorial is adapted from the [Matrix Profile XIII](https://www.cs.ucr.edu/~eamonn/Time_Series_Snippets_10pages.pdf) paper and replicates Figures 7,8 and 13.\n",
    "\n",
    "One of common analysis query on a data is \"Show me some representative/typical data\". Sometimes, it is useful to summarize a time series into a few representative patterns (snippets) as it can be visualized more easily and some new insights might be discovered. \n",
    "\n",
    "For instance, let's consider a simple problem: ?\n",
    "\n",
    "The \"snippet-finder\" algorithm proposed in the paper can be utilized to summarize a time series into a user-defined number of snippets. \n",
    "\n",
    "To demonstrate the usefulness of Snippets, we first create a toy data used in the original paper and will use the [STUMPY](https://pypi.org/project/stumpy/) library to discover the snippets in the data. This tutorial replicates the figures 7,8 and 13 of the original paper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0ed2ac",
   "metadata": {},
   "source": [
    "## Snippets vs. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8390d6e",
   "metadata": {},
   "source": [
    "### - Clustering: \n",
    "Snippet is a different task than clustering. In a single time series, one can first extract subsequences (with a user-defined time window) and then perform clustering on the set of subsequences. However, as comprehensively studied in Eamonn Keogh and Jessica Lin (2005), clustering the subsequences of single time series leads to meaningless result. In some cases where there is a periodic behaviour (e.g. electricity demand), the clustering can be performed. However, algorithms specialized in clustering the electricity demand data into seasons, for instance, are more complex and requires more domain knowledge (for example: the subsequences must be exactly one-week long.) However, the snippets algorithm is domain-free and it can be used to better explore and analyze data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "564298bb",
   "metadata": {},
   "source": [
    "### - Semantic Segmentation:\n",
    "The snippets are different than semantic segmentation proposed in [Matrix Profile VIII](https://www.cs.ucr.edu/~eamonn/Segmentation_ICDM.pdf). In semantic segmentation, the task is to find boundary locations, not in explaining them, or producing representative patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3643c0e1",
   "metadata": {},
   "source": [
    "### - Shapelets:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a081d0ad",
   "metadata": {},
   "source": [
    "There are two main differences between Time Series Snippets and [Shapelets](https://link.springer.com/article/10.1007/s10618-017-0519-9). For one, the shapelets are supervised; however, the snippets are unsupervised. For another, the shapelets are usually shorter whereas the snippets are preferred to be longer to capture the behavior of the time series at a higher level."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4345de90",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "Let's import the packages that we'll need to load, analyze, and plot the data."
   ]
  },
  {
>>>>>>> 5f004a2b9512b7353b1d3c45205ac4dbfa1decb0
   "cell_type": "code",
   "execution_count": 28,
   "id": "6cbb4ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import stumpy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import resample, resample_poly\n",
    "\n",
    "plt.style.use('stumpy.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0fb4d9",
   "metadata": {},
   "source": [
    "## Retrieve the Data\n",
    "\n",
    "It is the same toy data set used in the original paper. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0fb4d9",
   "metadata": {},
   "source": [
    "## Retrieve the Data\n",
    "\n",
    "It is the same toy data set used in the original paper. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "43ab8d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
<<<<<<< HEAD
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.347609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.350937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.357541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.367308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.380056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        toy\n",
       "0  0.347609\n",
       "1  0.350937\n",
       "2  0.357541\n",
       "3  0.367308\n",
       "4  0.380056"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://zenodo.org/record/4662217/files/snippets.csv?download=1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c887ef84",
   "metadata": {},
   "source": [
    "## Visualizing the Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a31fb9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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jZ1CGjQi2ualzakgtOOGw9hDeBtIbw00OFiHMoRd0D1P9gmn1DO79Czj3NbUwBdPqGdz2L4Ba6AiCIAiCILKABCAiM1o9wxMqhmGbcnqBF8Nqw6shTWEKhwkYvPrVqX6hsPauoHuY6hdM0BpIDpZwWlpw/XTTRs8gEZIgCIIgCCIpJAARmdEM2PzUiuRgCaOpGSgqMorq6G1JDqBwmDul7rZ7+akUFMgStdAF0fQEyNH6sa/T9RdMM8ABRPULJ6h+dWqDJQiCIAiCSA0JQERmBLU/kIARjuMe4G++aYx5OEEOKkmSKIMlhFZACyLgiLhUPzGGaaFnWAEOlgKFaIfQ6hneYcEwNc/FRzUkCIIgCIJICglARGa0eqZQwOi3gNHLuwinfsGn39SCIyZIAAIogyWMsPpRC1gwYS109ZKClmbAsigIX0SUNZBESIIgCIIgiOSQAERkRmALmPfyTlOERAS3j1D7QxheC1OAg4A2j2KCQsgBql8YTY3VT9RCp8K2gbZOIrgIZw0kFyRBEARBEMS4SC0ASZJ0UJKkz0uS9LwkSc9KkvQvs/jBiO1HSxNPAat7Y7hp8yMiqIWuqMooKrK3ySRGafUMVIsKFFnifk4CRjBBIdrs6+QAEtNvoStwP6+Riy8Q27YDQ7SZu5TqRxAEQRAEkZwsHEAGgJ+xbfsmAF8P4KckSbo5gz+X2EbYto1mN2iCi7Mpopd3MUEOKsAZZU71ExNWvwYJGIEwd5mwjZMEtEA2w+pXJgdLED3DgmHZoZMkqX4EQRAEQRDJSS0A2bZ9ybbtx9x/3gTwPID9af9cYnsR9vJOp7fhBLWAAU4NKUNJTLNnUv1S0J+iJnYAdXULhmldzR9r2xAlRNv/64hBorQgAlQ/giAIgiCINGSaASRJ0hEAdwL4apZ/LpF/om5+NinDRkjQFDDAqSHVT0xo/cjBEkirZ0CWgEpBnGHj/DoS0XiEhWhTjlcwketH9zBBEARBEERiMhOAJEmqA/hrAD9t2/YG5/N3SZL0NUmSvra4uJjVtyVyQtgEHFmWUC0qdHobQNAEHICmWIXRDBghDTj1o82jGFY/SeJnKHk5XpRDxSXMwUIhxsH06ycQIItUP4IgCIIgiLRkIgBJklSAI/78uW3bH+T9Gtu2/8C27btt2757165dWXxbIkeEvbwDQLWo0gQcAbZto6UFt4BVS1S/IIJCtAHn+utoVD8RQQG8gFM/AOiQAMQlzMFSddfGDt3DXMIOERRZQrkg0z1MEARBEASRgiymgEkA/hjA87Zt/2b6H4nYjoRNEAKASpFe3kW0NRO2HVK/gkyb7wDCBIxKQYFmUoaNiJYW3ELHWsPadA9zaWlMwODXkOoXTJiABjg1pPoRBEEQBEEkJwsH0OsA/EMAb5Ik6Qn3f9+RwZ9LbCOaEV7eqwUVbRIwuETZ/FSLKm1+AmiGtNBVi+4GnBwYXJo9E/Uyf4Q54KsfXYNcmj0DBUVCSeULQFS/YNgzpEFrIEEQBEEQxNgQv2lFxLbtLwPgh0YQrxjCQqABoFJU0NHJfcEjSgtdpaigS+KFEKcFLLh+ANDVTEwECB2vVKLWj1qY+IQ60Nj1R/XjEskBRGsgQRAEQRBEKjKdAka8conmYFGohUmAl38REGJcpfYHIaZlo6NHdABRDbm0QkK0+xlAVD8eYSHkRUWGIkvkghQQyUVaVKh+BEEQBEEQKSABiMiEpitgBDqASMAQ4jmAyiECmm7Ctu2r9WNtG1gGVXAINAlAQTRDQ7SpfkG0egYaAfevJEkk4gbQF8GDc6iofgRBEARBEMkhAYjIBM8BFPTyXlTIPSAgSgtduajAtoEutdGNEKl+BdbCRA4CHmEtTF79yIHBpRWSQQU49zCtgXxamoFyQYaqiF9LKq4IThAEQRAEQSSDBCAiE5q98Jd3x75PL+88ooVoMwcGbcCHaXajhWgD5GAR0QwdA08OoCA2Q+oH0BoYxGY32IEGUP0IgiAIgiDSQgIQkQltLTj/AnA24HR6y4dtaiJlsFANR2iHjOAG+gIGOTBG0U0LummHtt8AdP2J6GhGYP0Ap4ZUPz4dzfDWOBGVgkr3L0EQBEEQRApIACIyoa2Z3pQbEeUCtT+IYK6eoBpWSMAQwgSgSiF8ChNtwEfx6hdw/cmyhHJBputPQJQ1sEotYELamumJtCKq1AJGEARBEASRChKAiEzo6qbnEBBRLSrQTAuGSRk2w7DRxkE1rBSoBUeEV78IDhaq3yhR6gdQCG8QUdbACk2xEtLRTS9nSgTVjyAIgiAIIh0kABGZEPX0FgDadII7QlszocoSimpwhhL7tcQgrCZB1yDVT0yU+jmfq1Q/AVHWwEqB6ieiE6l+Crq6BcuiSYgEQRAEQRBJIAGIyISOFu30lv1aYpBORPeA82vpBHyYThQHlXf9Uf2G6Wjh9QPYFCaq3zC2bUe6h6mFSUzU+rFfSxAEQRAEQcSHBCAiEzp6tNNbgAQgHp1I+SFuCLRGLXTDdCJkKBUVGYos0eaRAxN1KqFB7pRhw6NnWLBtql8aoq2BJAARBEEQBEGkgQQgIhPivLxTC8QoHT1O/ciBMUwUB5AkSahShg0XJiqGOoCoflz6DqrgR2qFBCAh0VyQTASnGhIEQRAEQSSBBCAiE9qRWsDYGHMSMIZpa+GbnzKN4RbCRImwa7BMG3Au3hS6SC1gVL9h2nFCtHUTtk0ZNsNEmaJGQe4EQRAEQRDpIAGIyIRuhBYwcgCJ6cZyAFH9hunoJkqq0+IVRLVIDhYenYgCBtWPj+cAitACZlo2NJqEOAK5IAmCIAiCIMYPCUA7FNu28aufPI6//0cPYbWljf37RXGwbLcMoL985By+6/e+ghcub479e3V2YP0eOLGE7/q9r+Azz10Z+/eK0oIIODXcLg6W00st/MAfPIQ/+OKJsX+v6GPg1W1z/W12dfyT9z6Kf/vBp8Y+NSp6iLYjEHW3QY6XZdn4hY88ix/+k4ex2dXH+r1My4ZmWDGC8LfHNUgQBEEQBJE3SADaoTx0cgW/f/8JfOXlZfyPz7w41u/lTcAJOf3eTi/vCxtd/PsPP4Mnzq3h33/46bF/vygjpGVZQrkgb4v6mZaNf/vBp/HEuTX8zF89iZ4x3p+5o5mohmwege0VwvvLH38eD55cxi9//DhOLbXG+r28MfA7aIrVu798Gp945jLe//A5fOKZy2P9XqwmkV2Q26AN9rPHF/CnD5zG/S8s4n9/YbwiZNz6bZd7mCAIgiAIIm+QALRD+dDj59Eoq/jmm3bjY09fgjnGE/CeES1Adju1MH386UvQTAvvvGs/Hjm9igtrnbF+v64enqEEOJPAtkP7w2NnV3FmuY133rkf6x0dX3pxaazfr6ObKEdwAG2X+q13dHz2+AK+6459kCTgb5+8ONbvF68FLP/1A4APPn4er7t2FrsbJXzkyQtj/V6sfmH38HZaAz/0+HnsbpTwumtn8ZEnL441tyiqg2o71Y8gCIIgCCKPkAC0A7FtG196aQmvv3YO33HrXiw1Nby0ML42Js89ELZ5LKgDvz7PfOmlJRyereIf33cMAPDVk8tj/X5RHEDA9pnC9KUXFyFLwM+/5SaUVBkPjrl+nYj1K2+T+j14YgmmZeMHX30Yr9o3iQdPjL9+kgSU1OBHQrmgoKtbY2+pSsvZ5TbOLLfxzTfN40037sZDJ1fG+jN3XFEs7Bosb5M2TtOy8eWXlvDG63fhW2/Zg3MrnbGK4FEzlLZL/QiCIAiCIPIKCUA7kMVmD5fWu7j36AzuOjQNAHj87NrYvl+UEdwAUC46l1tnGzgInjy/jnuOzODa3XXUS+pY6wdEG4EMbJ8x0k+cX8eNeyYwVy/h1v2TePzs6li/X9T6bZcWpifPr0OVJdxxcAp3HZrCU+fXxuriYxlUkhQeog3kv43zyfNrAIB7j87gzkNTWO/oOLU8vja6qGvgdnGwnFluYaNr5O4ZUi2yQ4T8P0MIgiAIgiDyCAlAO5AXLzcBADfsaeDwbBWTlQKevrA+tu/HBJ2w9pGi4kxpyvvmcbnZw1Kzhxv3NKDIEl61f2Ks9QNYiHHw6TewfQSMFy9v4sY9DQDAbQem8OzFjbE6MNpx6pfzzTfg1O/YrjqKqozbDkyhpZk4tdQc2/drR5jiB2wfAejFK5tQZAnHdtVx24EpAMAzY7yHI7sgt1H9AODGPRO4YU8DRUUec/2iOaj69ct/iDZBEARBEEQeIQFoB3L88gYA4Ib5BiRJwrW763h5YXybx44WLQNIkiRUt0ELzgvu5uf6eUfAuG53AycWmmPLwDBMC5oZPgEH2B4tYOttHZc3urjeFYCum6+jZ1hjbSHp6iYqhfDlbLs4qI5f3hyoH4Cx3sNdLVoGFRPZ8l7DFy5v4shsFeWCgmt21SBLwImxroFuBlCEKWrOr8+3g+X45U1IEnDt7joKioyjc7XxPkMiZiiVVBmSlP/6EQRBEARB5BUSgHYgL17ZxFy9iNl6CQBw7a76WDc/7YgOIAAoFRRv5HReefEyO/12NuDX7q5js2dgYbM3lu/XD+ANvx3L26F+bt7UDfP9+gHjFTDaWrQWsPI2GAO/2dVxYa3jXX/Hdl2d+kXLUHLbOHNewxevbOIGt34lVcGhmSpeXhy/ABTaBruN6nd4puqt6dfurl+V+oVdg5Ikoazm/x4mCIIgCILIKyQA7UBeuNL03CsAcGx3DcstDettfSzfL+oEIcDZAHVzbt9/4UoTU9UCdjUcAY1twE+MaQPUr194C5NTv3xvfl5wBTTmYLl2zPUD3AygKPVTFRiWDcPM7zX4kiv0sHu4VlKxd7KMk4vjzbCJJKCpzq/J8zXY0UycWWkPrIHX7q6PvX4FRUJBCQ/RBpD/NfDy5uAzZFcN51ba0Izx/Nw77RlCEARBEASRV0gA2mHYto2Xrgy+vO+fqgIALq6PpwUn6uk3sE0cLG79WCDu/ukKAODiWncs368bsYUO6E9hyjMvXtlEo6Ri32QZADBVLaBaVMZWP6AfYhwGc2B0x7SRzQLmQLth4B6ujO3+BZwNeJQWsO0gYLy80IRtj9ZvnC2I7YgtdP365XcN7BkmTi+3PQcV4KyBlg1c2RjPPbzTniEEQRAEQRB5hQSgHcZis4e2ZuLoXM372t4pZyN+cUwbIHZ6G3WMed5f3s8st3F01lc/V8i4NKb6tfVoAajA9qnf4bmqJ6BJkoS9k2VcGpOAYds2OhFDjJnDIM81PLPShipLnvAIAHunKmMX0OK0gOW7fo7T58jAGljBZtdAszee7Jhu1OtvGwhoF1Y7MC0bRwbWQCaCj/cZEs0BpORawCUIgiAIgsgzJADtMC6sOi/o+6f6m8d97OV9fTwbyHas09t82/e7uomlZm9g810uKJipFcdWv512+n1hrTNw/QHAvqnK2OqnmRZMy462edwGLUwXVjvYO1WGIvdHsu+bLOPyendsk9ScFrqd4WDx1kC/gDZuETeiA62k5l9AY04pf/32uYcIl2gNJAiCIAiC2NaQALTDYC4B/8v7rkYJqiyNbfPTjX16m9+Xd7bBGRUwyuM7/dai169UkHN9+m3bNi6udby2Q8a+ycrY6henha7kOVjyW8OLAgFNMy0st7SxfE+nhS5KBpUrAOX4Gry41kGjrGKiXPC+xuo5rjawqBlUsiyhqMq5XgPZfeq/Bj0H0JhcfPEPEfJbP4IgCIIgiDxDAtAO48JaG8CgAKTIEuYnyrk4vS2p+c6w4bkHAGcDNK4WJq/9IWIIr2ZYY3OCpGWtraOtmaP1mypjqdkbS4hs3PYRIP8OjGEBzXOwjPEajDaFbns4WIYFtL3uv49rDezqJiqFaI/Tsiqjl/M1UJaAPe41BzhB5JOVAi6NKwdNN1FSZcg+15uIsqrkun4EQRAEQRB5hgSgHcaF1Q4apcHTb8BxsIzr9LutmygqMtSQCTiAs4Hs5XjzyDv9BpwWnHFtftoRRyADfQGjl1MHhtc+MlUe+Pq+yQrsMYXItrXoGUr9+uXzGtRNC1c2uqP1mxpvBktbM1CNNIXOrV+O7+Hzq6MC0HyjBFkabwtYlPoB+W9hOr/WwfxEeWSi2d7J8bkg2xEzqADXAZTT+5cgCIIgCCLvkAC0w7iw1h1xXwDOBnJs7gHN9JwBYZQLiufYyCPn10ZPvwGnfps9AxtdPfPvyeoRbYpQvh0YfQFoqAVsjC04sernZrB0tHwKaJfXu7DsUQdaXwDKXkCzLBtd3Yo1xSrP9/DFtc5I/VRFxvxEGRfGJOJ2Ik4BA/K/BvJaEAE2iW5M9dOjZSgBbv20/NaPIAiCIAgiz5AAtMPgtT8ATgvTuEJkO1q0AFkg//kNF0Wn36yFZAwbyDgZQP0MlnzWUNhCNzW+Fqa4AbLA9hPQpqsFlFR5LPVj11IkB5Wa7wylza6Oja4hWAPHN4kuaog2kP818AJHQAOce3ichwjR65fvHDmCIAiCIIg8QwLQDuPCattzC/jZO1mGbtpjCZF1RnBHbH/YBhlAovoBYxIw9DgtYMzBks8N0IW1DsoFGdPVwRbEfv3GIKDFqt/2END2DbWASZLkChhjFCAjCGiqIkOVpdwKGExA49/Djgg+DjqaiWoMB0te10DTsnFprSus31pbH8t/+/gCWj7rRxAEQRAEkXdIANpBtHoGNroG9+V9rl4CAKyMQQBqx2h/qBSd01vbzmeI8aV1vgA07voB/RHlQVQ8B0s+N0CsfpI0GOZaLaqoFhWsNMdYvwjX4HaoH8AXMObqpbFef7EcGHmtn+vQ49evOLYpam3NiFm/fApoS80eDMsW1g/AWGrY1gxUI0yhA9gggXzWjyAIgiAIIu+QALSDWNjsAQB2N0ojn83U3Jf3Zi/z79vV4wR4KrBtQDPzuYFc2OyF1C/7zU9XdzKUokzAKeXcwbKwwa8f4NRwHJvHbgIHVV43kAubPUyUVa6YNVMrju36A6I5gIB8h/AubDoCEP8eLmG9o0Mfw9rT1a2YLUw5Xf82gp4hztfG8Qzp6BbKMepHU8AIgiAIgiCSQQLQDmKRCUAToy/v4z69jbp5LOU4Q6TVM9DWTO7mZ6KsoqBI42mh02IEoKr5zrBZbPawu1HmfjZbL43p+ovuYCnlPANocbOH3RNB9RvH5jueAJRnBwZbA3dx7uFZdw1czfgaNEwLmmnFuIfzOwlxsSkW0Fj9xiJCaiYqkQcJyNBMC+YY8uwIgiAIgiB2OiQA7SDY6Tdv8zNOB1An5uk3kM8x0gsBm0dJkjBbK42lfvFGSDu3bB5PwG3bxsJGj1s/AJitFcdz/bkCUJQWEq9+eXVgbPawq86v31y9iJWWlnmQOxPQorr4KsX8OjCCHFSz7hq4lLGAESeDCsh3CxhzAPHu4TnmABqHiKsbkddAJrT1cupCIwiCIAiCyDMkAO0gPAcQx4ExVS1Clsbz8t6J4QAq5ziDZWGDnX7zHRjjbGEqRz79zq+DpaWZ6Oh8BxXABKDxhJADQLkYXsOiIkOS8lk/gDmAxC10lg2sdfRMv2e/ftt/ilWYgwpA5i6qTowMKufX5TfEOMhBNVMf4yGCZsWoX36fIQRBEARBEHmHBKAdxMJmD6osYapSGPlMkaWxCRidWBlAbgtYDk9vF5vizQ/gtECMLQA1sgMovxlATEAT188JMc46ALyjmVBkCUUlfDmTJMmdRJe/+tm2jYXNrtAB5AkYGW/AOzEdQGU1v2O4gxxUrIUp6yDtRA6gHNdvslJAiRNIXysqKKny2A4RYj9DcngPEwRBEARB5B0SgHYQi5tO+40oTHhmjC04kU9v3Y1FHseYBwWgAs4UpvG00MXIACrkN0MpyIEGOA4gzbSw2TMy/b6sfsOTx0SUC7K3ac8TzZ6Brm4JHUBz42phijEGHnAEjDzev0Cwg2rcLWA7pn6C9U+SJMzVS1jKeA20bTvmGug+Q3J4DxMEQRAEQeQdEoB2EKIJVgwnw2Y8DqC4GUB5PL1dbPZQUCRMchxUwBhbmDQzevtNjkOgFwJCyIHxhci2YwiQQH7HmAdlUAF9B1DWDpY2EzBitYDlr37MQSVaAyfKBaiylLmIyzKUot/DMnqGlbkTLgsWNrvC6w9w7uGsr7+eYcGyo19/pRyvgQRBEARBEHmHBKAdBHMAiZgZw8u7blrQTRvVuA6WHIbwLmz0MFcPcFDVi+joJtpa9g6W6PXLb/6Flx8iaMFhQeQrGWewdGO0IAL5DeHt10+cQQVkn2HTjekAKuW0hYk5qERroCxLmK5lvwZ2vRDy6PUD8hlEvtgMeYaMQQTvxnZQ5dcFSRAEQRAEkXdIANpBLG52sUvQfgM4LSRZ2/c7sd0D+T29DXIPAL4pOGNwsEQ//c5v/sXCpuOgmqryHVRzrjCUdQtOO0YIOZB/B5DIQTVdLUCSxlG/mBtwNZ9TwBZCWhABx8U3tvpFnaKW0zWQTfELd5GOx0EVJ0MJyOckSYIgCIIgiLxDAtAOwTAtLLe0EPt+CRtdA1qGJ8/dmJufPAtAYQ4qr4UpawdBjBY6WZZQUvM5hWnRDeAVZfGMqwWso1uR6wc4DoI8jpAOc1CpiozpajFzB1VHN1FUZKgRQrSB/E4BC5pgxZirl7KfApYgBBrIn4Nls2egZ4gdVAAw5wbhZ9m+lvgQIYf3MEEQBEEQRN4hAWiHsNrWYdvALneTzYO1kKy2s9uAx3YPuPb9PDoIllua51LhMa4Wpo4WPQAVyG8L03Krh7mQ9hFgHPWL6QDK6RSw5WYPiix2UAHja8GJJ6DltX5OXcLu4cyngCUYAw/kTwSPWr+eYaGVYYh1/BByagEjCIIgCIJICglAO4Q1V9SZrokFILaxXO/omX3fxKffOTu9tW0ba20tpH7OZ1nWz7ZttGNn2OQzhHe1rWO6Kq5fSVVQKSiZ1g9wM5R2UP2CpplNVQqZ1y9+C52cywyvVW8NFAtoU9Xs69dfA9VIvz6va+DqFj9DIjuAKASaIAiCIAgiMSQA7RDYqfZMwAacTbdaa2f38t6Offqdz5f3Zs+AbtpXvX7ONKDo9QNcB0bONo8AsNrSPJePiMlKIdP6Ae4UsB3gYHHqJxYvgPHUr6Nb8QQ0VYFp2dDNfIlAq+4aGCRCTroCmmVl18KU1AWZNxFyNdYzJDsXVSdhBlDe6kcQBEEQBLEdIAFohxDp9LaSvYOlG/f02w0x7mj5enlfbTk1CarfRNn5O2Z6+h1z8wPkt4VptaUFbr6B8Tgwulr0KWqAs4Hs5LB+K+3w+k2Ow8GiGbEESObUyFsNV9oaGmUVhYAso8lKAbbt5N1kBasDE3bCYA6WToZtVFngHSIErIGTY3iGxD1EYEJb3q4/giAIgiCI7QAJQDuEFVfACHp5Z/b9LE9v455+q4oMVZZy52BZabPNj9iBoSoyGmU1UwdGJ+YIZCCfLUyaYWGzZ0RzsGTdwhQ7wyZ/9QOiO6jG0cIUp36lnLr4otTPa+PM8h52W+iCWvf8lLZxCxhzAGVZv7hj4Es5zVAiCIIgCILYDpAAtENgL+9BAbITlXHmN0S/lPLYghOlfQRwNkAbYzj9jrsBz1v9omRQAdnXD4gfol1SlVyOkF4NyaACHBef066YnYDV1mJmKKn5DHJfCcmgAnwCRsZrYFwBEsjfGPOVlo6iIqMW8HcZRwZQfwx8NBdpSZUhSfmrH0EQBEEQxHaABKAdwkpLQ62ooKSKX94bJRWylHELmCdgRHt5B5gAlLPNY0QBaKqarYMl7uk3+7V5C+FlDqpI9cvQPWBZNnpGvDHwlWL+MpQsy8ZqWw/MXwGAyYpzn2UponU0M3YGFZA/B4bTghjsQPNckJ0sM2ys2PcvkL8Mm9WWhulaIdDJ5GUAjeMQIWINJUlCSc1nEDlBEARBEETeIQFoh+C8vAdvHmVZwkTGIbJtzcnSiNvClLfT2yjtDwAL4R1DC11MB0He6hdVQHNawDLcfCdpoVMV6KYNM8Mg4LRsdg2Ylh3uAHLrm/UGPN4UtXwKGCsR1sBxBLl3dCPm/ZtPAS1KBlW1qKCgSNnWjz1DdkCQO0EQBEEQRN4hAWiHsNIOz78Ash8j3XE3gXE3kHlzYKy0NCiy5AU9i5iqFMfSQhe7fjnb/KxGyKACHAGjq1uZ/fzJ6pe/DJEoGVSAEwINZB9EHlfABfKZYRPmoJoaRwtY3Ba6nApAUTKUJEnC5BjWQEWWUFCiZSgB+Q3CJwiCIAiCyDskAO0QokxgAoDJajFb94BmQJKcXIao5DGEd9U9/Q4Lcs16ClMn5gQcwNn85G0CjtcCFiEEGsiuhSlR/XK4AY/joAIyDjGOnWGTv/p1dRNtzQx1AI0jB60du4WOCWj5WgNXImRQAU4b4nrGLXTVGCHaQD6fIQRBEARBENuBTAQgSZLeLUnSgiRJz2Tx5xHxieoAmqwUsJ5hC1NHN2NNwAHyeXq70tJC3RcAawHTYdvZtA91dKf9IWoAKpDPzU+cEG0guxamvgMoXv2AfG3Ao9ZvqjKODJuYDiA1fy1gq+3wEeaAI16VC3KmbZzduC10av4ENCBahhLguPiybqErx6gfkE8XJEEQBEEQxHYgKwfQnwL4toz+LCIBq63wCThA9i1g7ZibRyCfL+9x6mdYtpfdk5aO5myi47Xg5K9+Ky0NjbKKghK8pGQ9RaifoRRvCh2Qrw34SkQBI2sHkG5aMCx7+7fQRRTQgOzbOOOugbIsoajkS8Q1LRtrnfAQcsA9RNjCFjrAnYSYIwGXIAiCIAhiu5CJAGTb9hcBrGTxZxHx6Rkmmj0jkoMl6ylWcdtHAGcD2cnR5geIkaFUzdbB0k4QgFoqKOgZVmYupCxYjZxB5YYYZyRgsBawSiG6A4hNyutkJOJlgecAihpinLGAlqSFLk9tiFEzqIDsJ9ElWQNLBTlXAtp6R4dth19/gCOCZztIIMEhgip7EygJgiAIgiCI6Fy1DCBJkt4lSdLXJEn62uLi4tX6tq8I2Mt4tPyGAjY6OqyMJiDFbR8BXAEjR5sfINoUNSB7B0aSMfDMgdHL0Qn4StQMqowzWFgLXdwpaoAjnOaFlbaGoiKjFvL3UBUZ9ZKaWf26CVroSqx+ObqHo4ZoA04O0FaGaAP5c/ExB1UUAW3CfYZkRbJDhPwNEiAIgiAIgtgOXDUByLbtP7Bt+27btu/etWvX1fq2rwi8l/eIG3DLBjZ7RibfO+4IaSB/GUCWZUeaIAQAk8zBklEGS1szocoSinFCtHOYIRLVAcSmWGWVwcJa6Lb7GHNHgCxEytJycry2roWuktP6AVFbwLKehJhgDcyZA4hlKEWqX7WAzZ4B3czmv38SAa2SMwGNIAiCIAhiu0BTwHYAUdtHgP4UnKxOcONOwAGczWae8hs2ujqsiO0PExXHKZHZFCs9weanmMcNeLQMpUZJhSRlef25DqAYNazkMQMoYv2AbB0s/Ra67T0FbKWlQZL6DrMgsqyfbdvJ7uGCkqv7N5YDqOzUeLOb3SFCfAdVvjKUCIIgCIIgtgskAO0AogbIAsBE2RUwutm1kMS27+fMAdTf/ETYPLqbn42sNj+amWACTj5DeKPUT5Yl1ItqZvVjNUg2Bj4/G8ioDioAaJTVDDffrIUuegtYQZGhyFKuWnBW2xomKwWoISHkQLb1c7K4kGyKVZ7qF+MQoeE+Q5oZroGJWsBytP4RBEEQBEFsF7IaA/9+AA8CuEGSpPOSJP1YFn8uEY047Q8NV8DI8uU9fvuD8/KelxDjVZahFKl+GW9+ErbQAcjNBrKjmejoZqTNI+DUsJlhCyIQtwUsfwLaajtaBhXgiLiZtXAmmEIHuCG8ORLQVlrRWjgBZw1s9oxMctCYg6oau375EjDYGhilhg1PBN86FyQJQARBEARBEMmIfuwbgG3bP5DFn0MkY8WdgMMmVAXBBIysTsCTtICVCzIsG9BNG0U1PPNk3KzGaH+ol7KtX9IAWSA/DhaWHxJnA76Z0eYxzRSrvAhogHMNxqvfZibfN4mABuRvAx5XQAOApmZ4jr6ktFmIe4IpYFmtIVmw2tZQLsiR/h4TGT9DkojgpUK+2ogJgiAIgiC2C9QCtgNYbWuYKKsoRGp/cPMbetm1gCXZPAL52YCvxAhAVRUZ1aKSmYCRdIQ0kB8Hy0qM9hEg6xYmEyXVaUmKSj9EOx8bSNOysdbRt6R+LEMpvoibtwyb6BlKWYrgXoZSjBY6IH8CWlwHFYBMRdzYLXSqAs2wMptmSRAEQRAE8UqBBKAdgJO/cvU3P4Dz8p5kDDwAdLV8bIDiOICAjAWMFA6gTk42kKsxMqiAMdRvmwto6x0dtg3MRHDwAf0WuixaKLsJHUClvE2xiphBBWQrYCQJ0QbyJwA5U+iu/jPEtGxohoVqIb6ABuTnEIEgCIIgCGK7QALQDiBO+0OWL+/eBJy4p9/uyPNeTiz8K20NRVWOvAlulAuZOajaSTKUXAeLlpf6xcigArJtAetoZuz8lVLerr/YDqoCTMv22t/S0E4qYKhKbupn2zZWtmgNTNxCp8q5uX8BZw2Mf4iQgYDmtdDFexVhOV55qiFBEARBEMR2gASgHUAc+35JVVBU5UwCPFkLSFIHUC8np7csf0WSorUR1UvZOVi6evwMpbw5WLbSQdXW47ePSJKEkiqjl5f6JXBQAdkKGElcVHm5f9uaCc2wtqSFKWkLXd4ybFZbWiwBF9jaFrpSzto4CYIgCIIgtgskAO0A4tj3AXeK0BaefjMHRl5e3lda0fNXAGcDntUY8yQOoNw5WNo6JAmYrERvwclMQEtQP8CpYW7ql8BBBWTXwiRJ/WsqKo6AlrP6bYGAlriFTlVyI0AC8dqIi6qMkipnMokuaQtdfw3MTw0JgiAIgiC2AyQA7QBW2hqmI+aHANltwNnpd9IMm7xswFdj1m8iyxamBCOQ2el3burX0jBZKUQOYm6UVWimlYmDKUkGFeBcg3nZPCZxUAHIRIRkLXRR3W+MXNUvRog7kG39ErfQFfIjQOqmhY2uEbl+QHbPkDRT6ID8rIEEQRAEQRDbBRKAtjkdzURXt2I5WJwWpixawNwR3EkdLDk5AY/roMo6xDj2BJxCzurXjt6CCGTfwhS3/QZgIcb52DzGmUIHAI1Sdhks7QRT6ADnHs5L/VbbTh0ih0CXMnRQJW2hUxUYlg3D3PoarsWsH8DWwOxa6JI6gPLSBksQBEEQBLFdIAFom+Plh8TcgGfjAHJPb7d7CG8CASOLzY9hWtDM+BNwcucAihHAC2QcIpu4BSxHDpaWhkpBiSwiZJnB0tUSCmg5qx8QXUArF2SospRxhs32XQM9B9UWiOBMQEuag5aH+hEEQRAEQWwnSADa5sTNvwCyEzCSb37yEwJtmBbWO3EzgAro6hb0lKf3LAQ27gScgiJBkvLjAFpp6fHaR0oZhsgmaKED8pZho0du/wKyd1DtlAylqDWUJCn7NXAbi+Be/bZABE+ToQQgN/cwQRAEQRDEdoEEoG1O3AlCgCNgNLdwglA5R6e36x0dtg3MxMpQcjbgaWvotT/EnIAjSVKuxnCvtrTY7SMA0MwiRFY3Y9cPYBk2OalfW8N0ovpl0YKTJkMpP/WTJSebKypZroEFRUJBiTvGPD8i+GqSQ4RSVjlyaZ8hW18/giAIgiCI7QQJQNucuBOEgAzt+0lPvwtshO/Wv7wna3/IxsHS1VwHUOIMm62vn23bTgh5ovpl48BI6gDKQ/0A5x6Oc//WiiokKUMHVeIMoHzVT44YQg5k2wabNIMKyIeDZSXRIcIWP0NoDDxBEARBEEQiSADa5sSdIAS4p9+aAcuyU33vtGPg8+AgWGmxANR4IdoAsJFSwGjrzgZqO7fgtDUTmmElCoFOO4XJtu0d0cK02o4+ghsAZFlyg9yz2YAnFSDzVL84AiSQnYDRTXz9uQJGDhws7BkyFXuS5FaGaJMDiCAIgiAIIgkkAG1zVto6JAmYrMQZY67CtoGmlraFKekI5PzkNyRxUE1klMGS9PQbYCG8OapfjA34REYOKs20YFp2IgdLnsaYx3UAAU4N0wqQAMsAStBCpyowczLFaqUVL8QdcASMLOqXvIUuRw6glo56SfVEqSg0yipamgkz7SGCluwQgcbAEwRBEARBJIMEoG3OakvDVKUAJWb7A5BBC1PK09s8tJAkzVAC0rcwJQ3RBpwNZK7qF2MDXs9oCliqFrqcjDHXTQubXSPW9Qdk24KTpoWpm4MN+GpLj5WhBGQ7xSpJBlWeJvnFzaACssvxYocI5RjiE5CvZwhBEARBEMR2ggSgMfPhxy/g7l/6DL7lN7+AZy+uZ/7nx81fAbITMLyX95gbSFWWIEvRNj+PnlnBN/76/bj3v34Gn3r2cqKfM4ikGUpABg4gffwOoHMrbbzz97+C237h7/B/vnAi9vcJI4kDSJEl1IpK6vqxFrpkGTbRHEDNnoF/+ueP4pb/+En82w8+lbnjJUkGFZDhJL+ULUxhk+gsy8Yvf/x5vOo//R3+0bsf9tqNsmQlZgsd4Diossugiv8YjSNgvO+rZ3HXf/k0vv1/fgkvLzRjf68wkjioJjJ6hnR1E+WCHCu/CaAx8ARBEARBEEkhAWiMPHV+DT/zV09i/1QZm10D73rPo97kp6xYTdT+kJ2AkWQCjiRJkTbgy80efuI9j8KwLMzUivjn738cp5ZaaX7kEdbaGioFJZaI0MjIwZJ0Ag7AMmyC62eYFn7qfY/hpYUmbt43gV/5xHF85rkriX5WEUkcVEA2GSKpWugiZtj84keexSefuYzXHJvF+x8+h/91f7Yi2lrbzaBK0MKUTYixkfj6A8I34O958DT+4Isncc+RaTx0Yhn/9oNPJ/o5Rdi2jdUELXSNsopmz4Btp89BSypAAuH1e+jkMn7+Q0/j6FwNCxtdvOvPvgYtY9FjLWGGEpD+GZK0hY7GwBMEQRAEQSSDBKAxYVk2/vX/ewpz9SLe82Ovxm//wJ24sNbB+756NtPvs9LaOgdQ0gBZgLUwBb+8/9LHnkeza+CP/tE9eM+P3gtZkvDbn30p0fcTsdLSE4kXwBY7gCLU708fOI2nzq/jv73zNrznR1+Na3bV8Gt/90LqTa8fL0Q7wQY8i80jkLSFTgl1Xzzw8hL+6tHz+CffcAx/9EP34NtftQf/+wsnsN5O7xxh9B1UV7+FybJsdHUr8Rh4INjBcnGtg//+yRfwjTfswrt/+B788zddi08+exlPnV9L+iOPsNkzYFh2ohY6ywZaWroWIkfASJChFGGMuWZY+Dd//RQOz1bx3h97NX7t792Gk4stfPCx84l/Xh4r7WQZSkA2a2CSDCpFllBQpFyEaBMEQRAEQWwnSAAaE595/gqOX97Ez3/HTZisFHDv0Rncc2Qaf/7Vs5luwFcTvbxnF2KcZPMNhLfgnF1u42+euIAfed0R3LCngd0TZXzfPQfxsacuYa2dXRtJkvyLoiqjpMrYTJl/kTRDCXAyM4Lq1zNM/O8vnMTrr53DW27bi6Iq4yfvO4YXrmzi0TOriX/mYVZbGhRZ8q6pqGQhYHRTtdA5DqCge/F3P/8y9kyU8S++6ToAwE9947VoaSb+5skLyX5gDkmm+AHZtIAx98m4HEDv/vIpaKaF//z2V0GSJPzI64+iWlTw5w9lJ4KvJmjhBLITwbtpHUABIu5Hn7qIM8tt/MfvvBmVooJvvGE3bto7gT/P+BDByVBK+gxJeYjgtoAloaQq5AAiCIIgCIKICQlAY+IPv3QS+6cqeMute72vfc/XHcCppRaevbiRyfdw2h8SvLyXshnD3U54eguEt+C8+yunoMgSfuR1R72vffddB6CZFj6VYRtTkglMQDYtTO2EE3AAt34Bm5+/efwilpo9/OQbj3lfe8tte1EuyPjoU5fi/7ACVtoapquF2Bke9a2unyrDtgHd5AtAz1xYxwMnlvEjrzvibdZftX8SN+5p4KNPZls/IL6AUS85LWBpxGTWjprUgQaIBaCNro4PPHIO33nbXhycqQIA6iUV3/aqPfjEM5cyy1JaSSig1UtZtTAZqKYK0eaLuLZt4w+/dArX7a7jTTfuBuC0zn73Xfvx9IV1nFnOphW2Z5ho9gxMxxgBD/iD3LfyECG8DZYgCIIgCIIYhASgMfDyQhOPnF7FD732MFRfPs433uC8yH/xpcVMvk9LM6GZFmZit49k1wKWZIIQ4DpYBAJGVzfx14+dx3fcuhd7Jsve11+1fwK7GiV88cVs6ge4DqqYm0fAGQWfVkDrJJyAA4SHQL//kbO4fr6O1107632tVlLx9dfMZnb9AUiUvwJk4wBiLXRJrsH+GGn+BvIDj5xFSZXx/fceGvj6m27cjcfOrmYSIAz0HSxTMTfgjbIKw23hSkonpQMNEIdAf/TJS2j2jAEBF3Dqt9E18OT5bALx04RoAxm1waaqH/+/31Pn1/H8pQ38yOuOQpL64uo33TQPAJmtgSyDaivrV03QQgc49zCFQBMEQRAEQcSDBKAx8KHHz0OWgO+6Y//A13dPlHHT3onMXt6Ttj+UCzJUWcqkBSeJ+wJwM2wEm+/PHV/AZtfAd991YODrkiThvut24csvL8G0smmjS+oAqmckYCSZgAOwMeb8+p1aauHxs2t4510HBjaPAHDfdbtwcrGFcyvtRD/zMEkyqIBsBDT290/qAHL+jNENpGZY+OhTl/DmW/ZgsjIozNx3/S4Ylo0HTiwn+IlHWWnpqJdUz2UUlYkMNuBpQ7QB8Rj4Dz1+Hsd21XD7gcmBr7/+2jnIEvCFjNbA5BlUzn/X9NeglawFLMRB9aHHL6CoynjLbXsHvn5ktoqDMxV84cWl+D8sB89BlXAKWBYu0nIKBxCNgScIgiAIgogHCUAZY1k2Pvz4Rbz+ul3YPVEe+fy+6+fw6JlVtFLmxwDJ2x8kyclsaWbQ/pA0BLqkiluYPvT4BexulPC6a+dGPrvv+jmstXU8fSG9g0A3LWx2jUQOIKd+WxmiLT79/vDjFyBJwNvv2Dfy2X3X7wKQnQstSQYV4GzAm72tnKImdgDd/8IC1to63nnn/pHP7jo0jVpRyU7ETZBBBfhcfCnWkU4qAU3sADq30sYjp1e5AuRUtYjbDkxlL4LHrCET0NKsgYZpQTOThWgXFXEItG5a+NsnL+JbbpofESCZCP7giaVMpoH16xfvHi6pMgqKhGbaHDTNTNRCBzhZbOQAIgiCIAiCiAcJQBnzyOkVXFjrcDePAPDaY3PQTRtPZjAJZyVh+wOQ0RjuhKffABMwRjc/qy0N97+wgLffsQ8KxxnzmmNOS9PXTq8k+r4D3ytN/Urpx3C3tRQZSoL8C9u28eEnLuC1x2axd7Iy8vmxXTXsbpTw6OlsgqBXEmRQAU4OVVe3Um1imYMlSQtJkAPjQ49fwFy9iDdcNypAFlUZ9xydySxIe6WVVEBLn8HSTuEAKgfU78OPOyHZPAESAF57bBbPXFjPxL2x0tZQUCQv0ycqWUyxSiOgybKEosqf5PfFFxex3NLwjoBnSEsz8cLlzdjfdxj2DEl2iJBBjpdupHyGkABEEARBEAQRBxKAMuZDj19AtajgzbfMcz+/bb/TEvHkufQOlqQtYIATgpr29LaT0gHE2/x89KmL0E0b77jzAOd3AbsbZeybLGeSIbLqto/EDUAFnBaw1KffqSbgOPUbDgF+7Owqziy3R9oPGZIk4bYDU3giAwHStm3HwZKwfgBSOeG8DKBi/BoyB8uwCLHe1vHZ5xfw1tv3DeR3+bntwBRevLLphSinwXEAJbt/gXQOljQZQKL62baNDz1+AfcencGB6Sr39952YAqGZWcShr/a0jBVLY44jcJg118aF5qX4ZXGBckRcT/4+AVMVwueW2+Y29y2uizu4dTPkNQ5aMkPEagFjCAIgiAIIj4kAGVIVzfxsacv4dtu2SN0dkzXijg8W8VTWTiAEuY3ANll2CQXgPgOoA8+fgE37mng5n0Twt97+8Gpra9fBpuftpb89Lvk1l0bmqb0wccuoFyQ8e237uX9NgDA7QcmcXKxhY2Up/cbXQOmZSdqofMEjDQCkGZClvrtNHEQOYA+9vQlaKaFdwoESAC44+AkLBt45kJ6ASOpAyhLASPLMfBPnl/HyaWW0AEJAHccnAKAzO7hJPWrFhRIUkYCWqo1cLB+G10dn3nuCt56+z4UVf51fWC6gtlaEU+dW0v0ff2wDKW4IeRADg4RyAFEEARBEAQRGxKAMuSzzzvhxe+8S7x5BJwT8CczeHlfbWtQZMlrB4lDI4OX93bqEb6DL+/98GLx5hFw6ndmue2dXiclVQtYWUVTM2ClCKPu6Mkn4PA24D3DxEefuoRvvWVPYEvM7e4G/OmULqo07oGsWpiqRTW2+wPw1W/Ihfahx8/jut11vGq/WIC87cAUAGRzDycM0W6UMmhhStNC52UoDdXvsfMoqjK+4zaxALlnsozdjVJma2CSDCVZllAvqqkylFgLXeIgfE4O2ieevoSeYQU+QxwX32QmbcSrbQ0TZRWFBCJq2kME27adNTBV/cgBRBAEQRAEEQcSgDLkg4+dx56JspdTI+L2A5O4uN7FwmY31fdbaemYrhYSTZHKqoUpjYOFt3mUJeDtgvYlxu0HnRaIp1IGQScN0Qac02/bdqbYJKWjW8kn4LAx5r4N5OePL2C9o0cQIN02xJQbyKT5IQBQdwWMVA4g3UzcfsMbA39muSUML/YzVy9h/1Qldf26uomWZiarXzkDB1WaFrrCaIixZlj4yJMX8eab570pUSIcF1/6Ns6VlpaofoC7BmbgAEp6D5c5kxD/+rELuIYzPW2Y2w9O4aWFZuphAmnql/YQQTMtWHbyFrpyQckkCJsgCIIgCOKVBAlAGbHU7OELLy7i7Xfyw4v9sPamtCGeqwlHmAPpW5h004Ju2oknuAznN1iWjQ8+fgGvu3YO85zpaX5u2etsjo5fSteCwxwsidofMpgi1NGMVPUDBjNYPvjYBexqlPC6EAFyqlrE/qkKjl9Kf/0ByRxU2bQwGancA8DgGPgPudPTvutOfnixn1v2TeB4yvt3rc0yqOLXr1Zy/t7prj/mYEniABqt3/0vLGC1rYc6+ACnfqeWW97PkJTVtp5uDUzZggggxT2sDAi451baePjUCt555/5QV9st+yZh28CLV1LewwkzqID0hwidDBxUlAFEEARBEAQRDxKAMuIjT1yEYdmB2SGMG+YbANILQCspX96zGCGd3AE02AL28OkVnF/tRNo8TlYLmJ8o4YWUm5+VtoZ6SfXaWeLQz7BJIWCkcVANtYAtN3v4/AsL+K47xOHFfq6fr6fePKbNUALST2FKk78C9B0slmXjg4+Jp6cNc/18A6eWWtwcq6j0HWjxBciSqqCoytk4gARZM0FIkuSO4e7//f/6sfPu9DR+eLGf6+cbsG3g5YVm7O/NMC0ba+2UDqAMBIx0a2C/fh9yp6d9V0B+EuP6+TqA9AJQ0gwlIP0hQvoMJRoDTxAEQRAEERcSgDLAsmy858HTuPPQFG7Y0wj99bP1EubqxfSntyle3hslFZphJd7Apt38lFXHvs+mWP3pV05jslLAt96yJ9Lvv36+kUn9kuSHANlk2HRSZCgNtzC976tnoZs2vu+eg5F+//V7Gjix2IRuJt9A9TOU4tewkUELU5oMquEx5p9/YQFnV9r4vnsORfr91+9pwLRsnFxsJfr+gK9+Ke7htBk2RUWOJBjyKPsybM6vtvHp567gu7/uQKQ8metdETzNPbzR0WHZyetXL6XLsGmnGAMPOGsgu/40w8J7HzqDN1w3J5ye5ufgdBXlgowXLicX0IDkGVRA+kOEdtpnCIVAEwRBEARBxIYEoAz47PEFnF5u48defzTy77l+voEXrqR8eW/ryV/eS2wMd0oBKPEEl/4G/NxKG5967jJ+8NWHIrej3DDfwEtXmjBThDCvtPUUp98ZZNhoaRws/RacnmHiPQ+dwRuv34Vrd4cLkIBTP920cXopjYCho6BIgYHTIrIYY97NwgHkbuL/+MunsHeyjG9/VTQB8oYMBIw0GVRA+gybNBleAMvxcur3fx84DUmS8EOvORLp9x6ZraKoyKnqt5oigwpwg9xT3L/dtGPgC/0Q4489fRELm73IzxBZljIRwVdSOKgyO0TIqI2YIAiCIAiCCIcEoJTopoVf+7vjODhTwbdFdK8AjgD00pXNxFOkbNvGaltL1D4CAHU3pDXpBrKT8vS7vwG38Kt/9wJURY68eQQcB0bPsHB2pZ3o+wPAWooWukbKDCDbttFONQGn7wB695dPY3Gzh3fdd03k388cGGna6FgGVZIpXNWiO4Y7ZQtT4vr5BMgvvriIB04s40dedyTyNKSjczWospSqjXMtxRQ6IL2A0U4xghvoT7E6t9LGex48g7fethf7psLb5wBAVWQc211Pd/2lrF9WLUxJMpSAfgtTVzfxW59+CTfMN3BfhPY5hnOIkLx+Hc1EV7dSOaiAFIcIaduIVQWGZcNI4WIkCIIgCIJ4pUECUEr+8Esn8eKVJv7DW26O1Upx/XwDbc3EhbVOou+70TVgWnbql/fNhBk27ZSn36wF52NPX8LfPnkR/+SNx7BnMjj82U8WOUpp8y8AJG6B6BkW7FQTcJz6PXdxA7/zuZfwLTfP43XXzkX+/dfurkOWgBfT1i/h5luSpPQtOJqZfIqa66C6vN7FL3zkWRyZreKHXnsk8u8vqjKu2VVL6QBy7r2pSkIRN7WAYSUW0ADn2t3o6viPf/MMZEnCv/62G2P9/hvm6ymvP6d+aVx8aVsQgeQOlnJBwWbXwO987iWcXWnjP7315lgTHW+Yb2Bxs+eFscel76DaokOElCHQbA1MKkARBEEQBEG8EiEBKCELm138yw88jl/95At4y6178S03z8f6/cd21QAAJxO24KRtH0nrYOmmDPC84+AUAODnP/Q0btzTwE++8Vis33+NW79TKVqYVlLkX6StX9r2h+vnGyiqMn7pY8+jpMr4T2+9OdbvLxcUHJiuJr7+ALd+CTffQPox0t1ULXQKbt47gT/68imcWWnjl995a+ww8GO76inr18NkpZA4g6deKqQLcteMxAIkANx5cAqfeX4Bn39hET/37TdGdv8wju2q4+J6N/EksJVWD0CyDCqgHwKd1IXJHCylBCHagLMGXljr4Pc+fwLfe/cBvDaGgAsAx3Zn8wzZrocIdx6aBgD81aPnEv1+Avj0c1fwgYfPell8RDyeubCO3/v8y6mGAbySWdzs4Vc/eRzLzd5W/yjbEs2w8JuffhEvpWwFfiXz7i+fwgMvL231j7Ft+dhTl/Chx89v9Y9BJIAEoIQ0SgU8cmoFP/GGo/iN7709dhvM0Tnn5T1pBgt7YCbODymlC+FtpxghDThjjL/37gN4+x378N4ff3XsNoBGuYC5eilx/TqaibZmJq5fLWX90rbQ1Uoq/n/fcj3eeP0u/MU/fk2k4NhhjszVcHo5uYCx3NIwU08uAKXNsEnTQgcAP/3N1+HeIzN49w/fg9cei7f5Bpz6nVtpJ25BWWppmE14/QGsBSzdFLo09fuJ+67BnYem8MvvuBX/6DWHY//+I+4aeGYl2TW41HQEjNlaKdHvb7AWJi2piOu00MVx7fh5510H8PXXzOCnvvEY/us7bo39+4/MpnuGLLnPkNmE93BWhwhJnyH3HJnGq4/O4Jc//jweO7vK/TUfevw8vvTSYqI/HwDuf2EBrRQiaxps28b//MxLWNjoju17/NT7HsPPffBpfOARvoj24Ill/NXXkgtsj59dxcWELucs+MDDZ/H0+fWx/fm/9ekX8Wt/9wL+y0ef435+eqmF3/nsS4kFtpOLTTx/aSPNj5iKz7+wgE8/d2Vsf/5fPXoOv3//CfzQnzzMFeI3ujr++yePo51wjV5q9vDQyeW0P2Zinr+0gT976MzY/vwHTy7jtz/7Er7lt76I9fbou4Bl2fidz76EMwnf8zTDwqeevbxlAvFKS8NvfuqFxIc0YSw1e/jPH30OP/hHX8Xxy/z77AMPn8XDp1YSf4/PPHdly7LqTMvGb3zqBS9uYBz81Psew7/6iyfxt09e5H5+/wsL+Ijgsyg8cnplrM/AVzIkACWkUlTwpX/zJvy7t9yc6ARzV6OEWlFJ7GBZdk9v5+rJNj/1lFOY+vkNyS+hX/2e2/E/v//OxH+Ho3PVFPVzNj9zCTc/BUVGuZB8DHfa/AsA+Mk3HsP//dF7vTyfuFwzV8OpxVbih/tys4e5FAJGPaUDKE2INgC8+ZY9+MuffA3eeH303BU/R2dr0E07cRvnSjN5Cx2QQQtYiilqgONC+9A/fR1+8NWHEuVAMRH8VMJJaistDdWikvjvkMUamKZ+k5UCPvCu1+Bnv/XGyNlTfg7OVKHIUuI1kDmAkgpoaQ8R0o6BlyQJf/RDd2OuXsJvffrFkc/X2zr+1V88iX/4xw8n+vPPr7bxw3/yCH72/z0p/DUPvLw0tpfTC2sd/NZnXsRnnl8Yy5/f7BnQ3Clqv/XpF0cmQtq2jR/4w4fws//vqcTPiHf8/gO471c/L/z82YvrqYPMg/jljz+Pv/ja2bH9+cx9976vnuU+B972u1/Gb3z6RZxfTfaMeNNvfAHf/j+/JPz8wlon1eY0jD/4wkn8/v0vj+3Pf/aCs+l+5sIGPv/C6HX+yx97Hv/r/hP4xNOXE/35P/iHD+H7/+AhoYDQ6hn41LPJ/uwo/L9Hz+OXBOJgFjxzoS9uvufB0yOff+GlRfzGp1/Er3z8eKI//3c+9xLe9WeP4ssCh4xt2/joUxfHlsN2/wsL+O3PvZzKaR3E0776/c/PvDTy+ZWNLn7ug0/j+//gwUR//pPn1vDj7/kafvnjzwt/zRdeXPSexVnz0sImfudzL+OLL43H4eR/9v06R6gzLRs//CeP4F+8//HE3+Pv/e8H8dbf/bLw8yfPreHkYrqBSq9USABKgZLw5BdwXl6PzNVSv7ynmeACJB9j3nFPZCoJT2+z4OhcDacSnmz065ds8wO4LThb1AKWBUdmq2hpJhYT2K9108JG10hXv3LyFibTstEzrFQb8LQcTdmGuNLSErsvgH4LU9LNWTulgJYW5gBKcw+nFdCA5A6Wra5fQZFxcLqSfg1MeA2mFdDSjoEHHCfoP/j6w/jSS0sj9yFvQ+lnYbOL+37183j0DH8Dzdbo45f4AoVt2/jBP/oq3vm/Hkjwk4fDxJlhYSYrWH7e33/1ISxs9kacHi8t9F+qVwXugrf/3lfwF48ECyxGwOn9W377y3jzb30xzo8dC820oBvjcQ+0NQOnllr4/nsOwgbw/q8O1sFwn5EAhE7bf/3/nsQvfOTZxD/DN/7a/fje/5NscxoFzbTGdv0BjkPmm2/ajV2NEt7LccowcWtJ8I7yR186iR/8w4eEz8AX3Um7oveMf/PXT+Fdf/bo2DaQmjH++h2aqeL1187h/Q+fHRFiPvVssHvr/hcW8KbfuB+bXb6T+Jw7ZGVxk1//jz51Cf/sfY/jD790KsFPH86410DmrvuBew/hU89dwZUhMf9TIe63cyttvP6/fw7PXeS7h9j+4OUF/vXV1U380Lsfxo/+6SNxf/RIePUzxlQ/3zPkzHJ7RCh88vya9888F1TPMPFt/+OLQvcQ++9+ZUO8R3n7730Fb/qNL8T90QmQALSlHN1CASj16XceBIy5GhY3e8KHVxDLKesHpJvClIUDKC1Hd9UBJHNgrKbcPAJuBlCC/3ZA+gyqLGAtOGlcfOkESBW66QhhSUg7Bj4t9ZKK3Y1SYgfQcsoWOrYGJhUht7p+gLMGpqlfQZG8w4C4pD1EyOoe/v57DkKVJbz/4cENuL91hpcz9fT5dZxdaeOfvY9/OsnWaJGA0dWd+y6puyMM3bTd/x/Pyztrq3n7Hfuxf6qCP//q4AbcXz/eGrfW0fHkuTX8m79+mvvnj3PjGxXdtMf2c7Br6uZ9E/imG3fjA48MbsD9oo+oTfMvv3Yef/rAaax3kj0HtTHXWB+jgAY47bdz9RJ+4N5DuP/FRZxf7U91Na2+u1YkoP3Sx57HAyeW8cS5tcDvw2uPAvob86TP0DB004JlO3+XcdDWTExVHRH84noX978w2O7K7uGL6/w16j0PnsHJxRY+/AR/A67IzhZRtAYywWRhczwuSHbvjm0N7JlQZAk/+cZrYFo2/mKoFZbVT5Vl7n/Dr55awfnVDv7dh/lrIENUvw33/ffEmATI8dfPeYZ8/z2HMFsrjjxD/MIYb2LzlfUejl/exD8XOIS2qv36lQIJQFvI0bkazq+2PZU2DstNDbWikjhAs1JQIEspTr9TZthkwTUsQ2Q5/ij4ZS8/JG0LTroA1K2s31GWIZLAQbDcyqh+qTOotq5+c/UiGiU1UQaLZdlYbafPAALSOTC2sn5Auhyq5WYvnYCbgQNoq+t31K1fEhcYq1+S9j0gCweQAVWWUEwYos3YPVHGN9ywG3/zxIWBl/TnfRPmeDlT7Oe+tM7fvLDrQrR520i49jOubHTxpl+/H2cFzy/20j6uTT7blBRVGd/zdQfwwInlgRPw533OJ16GCAthB/inu+N+eTctG+/4/a8InV6mZcO07LHVj10Xiizh7919EEtNDV850c+b8dfvNOe/sf+eFTkEGGEbuKQZKT/zl0/iD754Qvj5uB0spmU79fu6A7Bt4G+fvOR9dnq55Qkzp5f498g+d3LsMwIHBkMksLFrI6lA83++cAI/+1fiFlFtzBtww63fN920GzO1Ij78xAXvM8uyPZffqSX+M2J+wqnfcxf5OVmsM9kw+fVhf78kLcwA8LnjV/CO3/+KsP7amEVwVr/DszV8/TUz+PATFwbqdNwVgDTTwiWOiMbC3y+t8Z8hLF9QdH9udJzPk9bvzHILb/qN+4VtyJox/voBzkH2d925H58/vjggtvpzlXiHCMu+Zwjv+kwzJTgKXd3EW377S2Nto80zJABtIUfnarBs4NxqAgGj1UvlvmBjuJO+vHc1E5KUfAJOFngtJAk24OzlNVWIcYr6dVJOwMmC/dMVFBQJpwQvV0EwAS1VC06KEGi24djK+nltnAkEyPWODtOyt7SFqaObW1o/wM2hSuGCTNeCmN4FudX1OzpXQ1szhRb9INLWL+0hQkezMnPwvf2Ofbiy0fNe5GzbxjMX1nHT3gkAfAcGCxEXwZxhos1JEuepn7954gJOLrXwfznZHUDflTAuBwbb1KmyhLfdsc/dgPedAE9fWMP183XIUnj9Vjkho2Ev72mDZTc6Oh4/u4af/sAT3M/HffqtW/36fcMNu9Aoq/gb3wb86QvrKCoyrtlV49bP7zxcDckA2QhxCHUTTiH768fO45cD8mE00xqry0g3baiyhIMzVdx1aGqwfm549817J4SHBOwKEtWPxTSsdfifs3sgaUjvr3ziOP7qUfEEJHa4OzYR17SgyhIKioy33LoXn3n+iie8nlhsoqObuGnvBDa7BreNk70HizJoWP1EYrd/DUnCv/zAE3j87Jrw+vbqN7Y10PJ+9rffsR8nF1t41hUTddPCc5c2fM+Q0fe85ZBnCHs2ihxA7BmStH7/9wHHwSUKWda8Q4Qx1c9y/nxVlvD2O/ZBMy188tm+iPv0+XXcuMfJKOWtgf768ZzYYe9macO1Ty05/73/w4efSfXnbFdIANpCjqQIQV1paYnDOxmNcvIMG5Z/kfT0OAsOzyQXgJZbGoqKnLj9AXA2kIkzgHTn9yWdgJMFiizh0EwVp5bi20/ThmgDjoDR0sxEp29pp9BlhZPjlaR+roMqZf2AdALGVrbQAU79lppabDeFbdtYbmmprz8gnYC21Q4g1oaYJCQzbf3SHiJ0dAPljOr3zTfNo1pUvBfhU0strLQ0fPdd+91/H3159ztYeGtQ2Mv7unt6G5QFuNHVhS+p7NRXJFCMW8Aw3Zd3RZZwbFcdr9o/4QlAhmnhibNrePXRWRyYrnJFbv+mkbeBDLsuorTddHVTKLSx/y6i+vTdF+PZ/JgmcwDJKKkKvv1Ve/CpZ/sTfx49s4pbD0zi+t0Nbk7Xim/zs8IR0PwCWViLGK/FEXCcB2lGrOtjzgByHEDOffD2O/bj+OVNLxT80TOrqBUVvPmWeVxa73L/jkw4CBMwRPVjLXudgI2kKH8oCt49PKYWM+ZgAYC33bEPXd3ysry+dsaZjNhfA3kuPqduqy1+fdi6GFa/IAfLSksTOmCK7u8T1X/ca6C/ft/+qj0oKJL3DHn+0ga6utWvH9cF6dRPtNY1Qw4RWEZYUP3WO7qwS6SgOj+7SGDUx5yhZHhroIRb90/iyGzVq19HM/HsxQ18442OO43ngvTftzwRN8xFGmX/1dFM4Z9TUJz65aFdeSsgAWgLYS1MiVpwmunaRwDmYEl2itnRt37zWCkq2DdZTtSCwyYwpRGwGqkcQM6Cs9U1PDpXF9qrg8giRJu1MCUZw53FFLosODpXw4XVjmcFjkraDC/Al2GTQMDQTQuGZW+5gMEmgcW9h1uaCc2wUraAFQAkzwDKg4CWtH5A+hBtIN0hQifDFrpKUcGbb57Hx5++hJ5h4gG3FecbbtiNuXqRWx//yydvg9N/eee/HIad3hqmhdt+4VP4qT9/jPt5XwDibw6uxubH+TncDeTt+/Dk+XWcWmrhqQvraGkm7j4yjcOzVcHpbX9jzNtA+p+NPBEsiuj7+v/+ebzmVz7H/YxtekTtKWPf/Fhs89t3EDR7Bj77/AI2uzqeOr+Gu49M48hcDedW2iMBvf72B97mx78pXgsTgAQb6H/xgcfxdb/0mUQxA4DjPhuXgAY4NWT1+45b90KW4LmAHjixhLsOT+MaN6twuI2zq5touaIQz4EG9O/NNUEGEHNxiQS0P//qGdz9S58RjggPo5/jNSYR0rK9deTrDk1j/1TFawN74MQy5upFfMMNzpTTIAcGT4AEgFbPqYuofj33mhb97V64vIm7/sun8X5BUDz72UUb9PGL4P36TVWLeOP1u/CRJy7CtGzvGfKW2/aiXJAFLkjnHm72DO47IFsDwxxA7B4YpqubuP0XP4Wf++unuJ8zAU24Bo5ZgDS9Z4gMSZLwtjv2e63Ej5xegWHZuCfgGbLkWwN5Iq7/3YzfIhb+DLn9Fz8lHDTALitd8Izf6ZAAtIVMVYuYqhYSnd5m8fJeTxlivNXtD4DjIEh6+r2V9WMBnFu/gazi9HIrdobASkuDLAFTlULi753GgZGHFjrAqZ9l96dlRIVtntK4+JiAkeQabOemfslcfKx+ae7hWsn5u6dxAG31/btvqoKiIidzQTYzWANTHCJkPUXtnXcdwHpHx98+eQmfeOYSjs7VcGxXDUdm+dMi/fbzIAdL0tNblgfz2eP8jBr28i7anIe1j9i2jWv+7cfwR186yf08jH6GjfNzvO32/VBlCX/24Bl84ulLbmvTbidnipMhstwKdrD41yVeiwfLvxCxsNHFUrMnXN/C6sM23UHixzt+/yuJRxT7M4AA4OuvmcW+yTLe8+BpfPb5BeimjW+5aR5H56rQTRsXh3JClkMcQP6/d1IH0EefuhT4eRjOFDVx/X7hI8/ivl/9fKI/G+hnAAHArkYJ33DDbvzFI+fx9Pl1nFhs4Vtunu9nFQ6tccshDjQg3AGkhziAfumjzvjupc3gVh/RAVDYFKsPP34BR37uY977YFz8DhZZlvCOO/fjCy8u4vjlDXz++AK+6cZ5HJqpOW2cvDXQcwDx/37sGhS1aLFnp8jlyEKBRTmdzMESdo+L6vfSlU0c+bmPCadwheGvH+A8Qy5vdPHp567gE09fwq37J7F3soIjs/w2Tv91xxPJ2N9LVB+2BqqCZ8hnn3eeHcPTtRiFsGdIiIDWM0wc+bmP4S+Hwq+jYgytge+4cz9sG/jzh87gE89cQrWo4LXH5nB0lp/1uBLSRux/N+Pdoxsh726nllrQTMsLkx+m32JIAhCxBRyZrXEDFoOwbdsRgFLY9wEWYrz1p7dpODIXv36AO0Eoo/olyTLwJuBsdQvJXA09w8JlQYiciOWWhulqEXLC3mUgXQZLHlrogH4LTlwXVSYtYF794m/Au3o+WugOzVQhSVtTP1WRUSkoyV2Q2tZPAVNkCYdmq7FdpD3DRLNnpHeRpjxEyLJ+b7huDjfvncD/91dP4isvL+O779rv5XTxXt4XfLlJ3JdP9+8lEhg8B5Dg9JYFKl+zq8b9nL00J20B6+rOhKFf/vjz3M/D0IfyO/ZMlvFdd+7Hu79yCn/4pVN48y3zmKwUcGS2hs2eMbDhBobqxxPQfO8WPJed//R22B0DAIshrTdhL+1RRkg/fnZNmJ8RxnD9FFnCj7/hGnz11Ap++i+ewNG5Gu46NN2fFjl0j8apH+89zS868DZH/veSuA5Vhm4EZwD96QOnudN9omDbtpcBxPjH912DpWYPb/3dL6NSUPDtr9qLI3NVAKNtnP7gW5EDiP23F61RzDkhEshYXcMcAswpM0w/g4X/+3/1k07+UliWjAh/hg0A/KPXHEZZVfBt/+NLaPYMfM/dB1BUZeyfrowcEnR10xPGVtv8Ni1WN5FLlrk/RfVjosik4KCQieBCASgkw4YJnH/37GXu52EM1+/NN8/j6FwNP/neR/Hk+XWv/Ut0iOC/h7mHCG59RC7ZMBcpm6523Xyd+3lYG3FfJOfXb8Edr/4/PvMi9/Mw2LrNfv6jczV82y178Nufexnvf/gcvvO2vSgXFByZq3HbOAfrN/oe5neG8dbAMAeQKByboZnu/T1Gl2OeIQFoizk8W409xWqzZ0AzLcylzACql9Xk7Q85GIEMAIdnqlht67HHqK60eplsfgwr2Rjuju6MnxRZP68WLEcp7jW43OxlIqAByVqY8tJCd9h9uT8T8yWYvSxMV7cmw4Y9iLe6ha5cULBnosyd0hTEijfFL/0amErAyIEL8vBM/GfIiiegpaxfikOEbsb1kyQJv/l9t+P6+Truu34XfvT1RwE4L6ULm72RU/YrG10voDLo5b2r83NQ2Lqlyvx7qBcSUBkm8HgTcAQBqKyFSlTDL7+0hGM//3GsCTbH/gwgxs9/x02498gMbtk3gX/77TcB6Lv0hg9arqx3ccN8A5IUngHEf3kPPt3t6tEEHuHnKTOArmx0ceTnPobHzq5yPx92UAHOBvytt+/DkdkqfuWdt0KWJXH9NrqQJeD6+Tp38zNQP15Aqr9+nA24/72EV0ue6DaMliIDyLZt3P1Ln8F7HzrD/ZzpDf76vfqaWfz0N1+HvZNl/OLbbsGuRgmNcgFz9SK3fgBw454GtwVRNy3v7y1ao8IcQIywezlpCxObpis6SPvhP3lY2P4DDDqoAGci4n//ntuwf6qCn3jDUdxzZAYAO2geFtCczfeNexqwbH5LJqubaNot24CL6mfZwSHbkVvABPd6J+Qg9df/7gV85+98ifsZMFo/VZHxW993B67ZVcO33DyPH3j1IQDw2jiH3aBX1vvPEK6I6/69RIdMbA0URVGErYHMOBTmghRdf6x+oiy+Tz5zCTf9h08KBT7PAeTbx/zi22/BHQencPvBKfzMm28A0M+7HX7Pu7wRrX6A6BCh/zWegNkNeUb0By28Mh1AW3v8S+DwbA1/++RFaIYVeRzuSgYTmAA3wyZlCPRWwzbgZ5fbuPXAZOTf52QApQzR9gkYcVtp2pqJ6haHaAOOAAk4L6evOTYb+fdlkx+S3AHENnNb7UKbrhbQKKuxXWgrLQ2NsppqBDarXxIRl7WAVQpb/whIIoJnkaEEOPdwEgHStu1chEADzhr44Mll2LYdeT3JYoof4AhoSaZYAs41uHcyeQspjxv3TOBT/+qNA19ja9zppTZu3udMdLEsGwubPbzmmlkcv7zJffn0b4haPQNTQ2JtO2XIcS/EoRLmYGFtGaLNz//54gmYlo0nzq3hG27YPfI5e3n3nz7P1Ir4y598zcCv60/bbOPrDs94X7+y2cW+qTIWNrtcB4a/bYS3xvsFuY5uolEevBbCXCthn4dtvsOcuw+ddFr4/vjLp3DXD06PfO5NwFEGN5C/8wN3Dvy6XY0SqkVlxIFxZaOLuXoJuxolQf2CBbS2FuwA6vk2j7xa+TeNvLXDtm1opgXbHt0oR8GynYyUf//hZ/APvv7wyOe8+gHAT3/z9fjpb75+4GtHZkenRV5xBYyb907gE8+MOkD867roHYNdAaECUMi9HNrCJBBx2cZaJMbd/8IiAOC/ffdt3M8Nyx4RoN92+z687fZ9A187OlfDhx67MPDf+YrrLrl57wSOX97ESksbWePYGij6+7EMJlH9WN1E9St4DqBkLXTeQZbg/ft3P/8y9+sMw5cBxLjj4BQ+9zPfMPC1I7OsjbODgzPO86TVM7DZM/r1C1gD2SHC8Pdi+ZcigZHdt6KlKkzg0Qwz8HP231VUv9/41Ivo6CbOrrRxgyvU+DE5z5D5iTI+/FOvG/h1/jbOG/dMeF9f2OjiVfsncWKxGVg/gL8G+oXDnmGNPAvDhNteSBvxToccQFvM4RknQ+R8jJdoZsXOpAUs6Rj4vDiAmIARw0HAwgNTO1hSCBhd3cxsAk4a9k05o+DjOliWM5hCV2cZNgk24HkYAw84JzdJBIylZnoHWkmVocpSMgdQTloQAceFFrt+bnhgFvdwkvu3Zzgbozzcw4dnq84o+BjTarwWui08RLhaOXJem6ZPpF1q9WBaNm7c67zUDrc3AYMvnzyRkG3ARafbYSNqw+z5uhn8csryD0T3MHMIitpTvJf3gAk0AHBgugJFlkba6C6v9zA/UcZ0rcivXzda/QCgq43+Hf0CBi+HKWoLmKh+YZv6fv341zdv88PDeUaMtiFe3uhhz2QZ09ViqADJE/kHBCDOCb1/NDzPSeCvH28Db1q2t/FM4gIK+z1R6wc4IuRwm+vljS4KioRju+vo6OZIDcLuX9u2vZ8hLCMp7F4Oc7CEXYNJXWqmZQtbUP3w2jgvrzsCEBtzHiTiitZ4VjdR/VjdxA4g52cX1y+agyXsPUaUcRlV2DzCySr0HGh7xS7S4UOEYVjd2sL6BWfUeAKG4POw+rH7QnSQVQuZNNs/RAh+hhzmtHHato3LG13smQhaA4NFXP+6xcvRCnMARWkT3smQALTF9AWM6BuglYxe3utlFe0UY7jzcPp9aIY5WBIIaKkDUJMLGHmpnyJLODBdxdkEDows3ANAsgyb/hj4ra/h4Zla7ByELOonSVJiAaOTo/odmq1iqdkLHfnpZ6WpoVyQU2cYJW1h8q6/HLggD7nPkDj3MBuBnk0IdL5z5Lgv7+vO3//wbA2VgpLo5ZO1b3R0k+smYS/nos2HFtLeEOZgCWsBY/eGaMqi7hvhG0RBkXFgujKQgaGbFpZbjgA0I3p574TUz7fpaevBGTc8B0sv5KU9av1EsE1lWyCgRa0fwIYtjGbY7G6UMVMrJjr9HnZQDRPqAPJdd7w/3y9KJDkhF00+Gv480gZ8toorG4NtnFfc+rH34OEa+v/78t4xNNOKLACFiYUiF27YBpxhJJxCpJtWRAGDuSCDBIzBGhmm5Tl8RH8/9t9DVD/PASRoZWIO6KQZQOz7KiHOV5HLVx/KABJxlDOxmTnQbnAdLVwByLcGBongYgeV6f5/sEs0NINK1EbsuUj571FsUIYopNwwo4m4E+UCZmuDbZwbXQNd3XKeIbWioH7RDxH4a2D/a7xnNKtfgi3wjoAEoC3GyxCJMcVlxTv9Tp/fACQM4dXyMQWsVlKxq1GK1YLTzw/JKMMmgYCRhxHSjEMz8UJkDdPCWlvf2gygnDiAAEfE5Y35DWKlpaW+f4HkAoZ3cpaT+gHxRNyVDBxoQHIBI08Oqr7DJYYIztbAtM+QFIcIVytDqe4+I3ibnz0TwRtw1mYZdHprWvxR2ezUO+z0NnyCUMjLu6CG3su70MEyGOAZxPAUnMXNHmwbngNIdPrdCHDRdMIcLH4BI8TBwqMvsInqF3zfs/2CSEAzI55+A079hp8Rlze62DNZwkytiPWOPvL8YAJGo6yG14+XoTQgoHEcVn4BiPPn+0WfsIwM3v0f9jyMunkE+iKuf1jAlY0udk+UPBF7WIRk/30bJZW7QQ6rnx+RgMEQHV5EdRiIrtEwTMuOfP8Cow6Wkip7nw3Xj72XseuPt4HuhAkYOhMwgusregbrYS1g7p8fJqCJMkKjOoB2N0qoFBSuA2j/VAWTlYLQxec9QzjrSJgAxNbAsGeEsH6RDxH4axg7RBC9Y5qWBUkSZ1j5OTJX49Zvnrkgec/gkGfIgIuUuwb2/948QfqVOv2LQQLQFjNXL6JaVGI5gDKz76eawpQPBwsQPwR1OaP2Ea9+CTfgedg8As7p2tnlduRpZqvuZIesBLSkAmRJlWPnEoyDw7NVGJaNS+vRJ6k5LXTp6gc4NUyWARTcPnI1YS+gZ2O0cWYxxQ9wg/AThWiz+m19htL+qQpkCTgbRwRvaSgoEibK6R1UQNIcL/Oq1e/IUJsmm3rITh95L+/rHR37pyoARC04IQ4M9+VSZEMPa1EKG+G7GdICxuz7LcHpfBwHxtE5p02TPSM8AW2y5DiABALaPrd+YQ4gfv18L/chDhYeYe6LMAcQ+32izb0ow4bHkdkaDMv2xhF3dRNrbR3zrgPItkc3qesdHYosYXejFF4/Xgi0T7TgbY781x1PIPLXN0jgdD4f/f1hbU39+kUT0IDBIO0rGz1PwAVGHRjsv+++qUqi688vaoW1gIkEorB7mBE2ZUz0+50x5uH1OzhThSJLA2vglQ3Hwceeo8MiOLse909VYNn8GrVDWry8NVBUnxCHUJhLkl33YdeaSAAyLDvS9cdr9e+vgewQYfR7+J8hvH0Cm2arGRZXRGVroLB+ZrDAFiZAes8QwSECe74H1i/iO/iRoVHwngDUKAkdQOshz5CO/xnMbSMOFoheqdk/DBKAthhJknBoJl4LznJTQ7WopHY/pGlhypWDZbYaqwWnH4C6tQ6q/NTP6Q9f5TzAeCx77SPp6qfIEqpFZdsLaIdiTlKzLDuTFjDAESHTZCjl4Ro8lMABtNzqZVO/pA6gnEyhAxwb/b6pSrxDhKaG6WoxdQh90kME07KhGdZVq9/wGF82gWmuXnQcLJy1b6Pre/kMs59zHSzuy3mIvV+Y3+C6AkSfh7WAlVXn66L/Nqa7aRoOJuVxZLaKZs/AkvvsZC/vuxuOA2i1pY8cIKx3dMw1iigqMt++72v74p7ehjiA/KIFz20SJrBthEwOZRsyUUBt3AwboO/AYBOY2Ok3wBEwOgYmyirq5QJfgNRDrr8wB5CvprxrzL9pDJqCB/BP18NcgYnq57+H17ueAw0YzbBhm9Z9U+Xw9hHuFLXg+vmvd/EUpuAWHIYRImCIrlXTsiNNkuW1cbL8lUpBQUmVhQLa3skygOA1UJxhEyxQ9EN4RW2WwQIGuwdEbrOS22K21hFNQowuYBydG3RBXt7oolZUUC+pmK6OOoB000JbM71nSGiOV0Abp7B+IRlBoQ4g97oSiWDMRRrkoIrigAScNlh/GyfLoNozWcZ0rSBsods35V5/YW3EHIeV/77lr4HBwu5OhwSgHHB4thrz5T2bzU/SDBbLsnMzAhlwMlgurXdDT2kYWU0QShMCnZcpaoDjoAJGx9SKyGqCEJC8BSdX9YsZRL7R1WFa9pbXD8iHgDFRLmC6WoiXg9bMRkBjGUpR3W8Mz0GVg/oB8SepZSWgJT1E6LfQXZ1XkCNzNSxu9rx75cpGF7saJaiKjBnOy3vPMNHVrf7mJ0ELTmQHUMKXdy//QrCBMd1rWhiw6m7Ao+x/jgxlYLD8C+f0uwDNlxfC2OgamCgXUCsp3HeMzsDLezoHEG8DHrV+IpirQJR/EScDaDiDhU1gCnOwTFQKbtA6r37RM4DCTr+D6if63F8/nkMjNPcmRv2G2zjZBCaWQQXwBDRXwJiqhF5/SernF73EIm5EB1DI56INeNQMIGC0jXNho4v5yTIkSeI6MFgLnUjAYCI+ED4FLMwBFCqCCwQyw7vH+Z/XQhwsseo352Q9su+5sNHDvPt84NWPiY5BAlpYG2y4AyhY5GZ1EdWPrbsiAa2oOO83a8L6xXAADbVxLmy6Irh7D6919BHReKOrY65eQkGRuCJu+D0c3QEU9x1wJ5DJ25ckSd8mSdILkiS9LEnSz2XxZ76SODLrLCyisMhhFps97G5kk38BxM9gYYt6HtofgP7L1bmIG8iFTaf3Oav2hyQtJHmZogbEz2BZdBfu3RMZXIPlZC1MeXIA7Zkoo6jKkeu34NWvnPp718uFbZ9hAzgutKgCpG3b7hqYQf1KBZiWLXzBEpG3+h2OUT/AuYezuf6SHSJ443uv0jPEC/F0N0CXXPcAANfBwn95z8IBZFp2oENFGPAZsrnptzgFb46C8htUWYrkAjs65GC5tO5MYJqpFj0Hy2gGi46JcgH1Mj+DJbx+YRlAZuDnelj9QkOKgze3cTKAdtVLqBUVL6eLtQvPT/QdQMMOFq9+ApE/zD3g3/CEtXiFZSxxW7x8bUu8Fia/QMLNCPKm0EV0YMzWvM1jv4WzhIlKAbLEuf66TgvdfKPsjeH2M9DCmcRBNeAuCA7hTSsACV18CRwstm17E5jm3X0EbwqTv4UOGF1H/PXrJnQAsfqI1sBeSP3Y0iVqoSuzkGnhGhijfkNtnJfWO5h330F4GTYbnaH6JcmwiZgBJGqh86bMCerL1jjRWsg+D3qGKBHv3+FpnJfWO5isFFAuKJgWtMFudBwRvF7i56DFyQAKWwPDQut3IqkFIEmSFAC/B+DbAdwM4AckSbo57Z/7SuLQbBWaYXkPtTAWNrLZ/CS17/cDZPNhIIs7CWxhs4fdE6XU7Q8lVUZBkRJvwPPiHjg4U4Ukxakfs/+nF4CSjpHu5sgBJMtOG2fUDTiz/2cl4iYSIDUTktS3SG81wxktQay2deimnU39ysmC3PPUQgc4Lr7Vth6aa8JY2NzaQ4SrXb/hl8/Tyy1vAMNMtYjNnjHwMuhvHwH49v2ubmKy4jigghxAAN8F1J/QEnw6Lsz/cL8uCkBlL7Til/toAaiAkwOi+kbBn15q4eBMFbIs9TNEOBvIyWoB9VKBf3rrqx/v5T2sBSfMwcJ+j2nZqUKKRRWKkwEkSdJACCqr46GZqs8BNJoBNFlxBDR+fohTn8lKQVC/EAfQgIMq+HScF1Lsb1vitTD568u7hvsh5NGeQYdnq14LE6vf4dkaFFnCVHU0yJ210ImC3EPrF+ag8tdP0CIWeQpYSAuYUOSNmAEEOPVraSYWmz1c3uiiq1s47Aq7s/XR+rE18MA0X8Dw1298DqDgNZCZNkT1Y+/4QWtglAwgYLSN8/Ry2zt8Zg4gv4tkpH4ha2BQDlpXtwKnWIW1IIqfIbb7/8EOoqBnTFwHUH8NbOPIbL9+wOAzRHddpd4aKBgDH1g/vwgeInK/EgOhs3j7vxfAy7Ztn7RtWwPwAQBvz+DPfcVwOGaGCBMw0uJl2MR8eWfKf9oRzFlxeOjlPozFzWwENEmSUo2RzkuIdrmgYM9EOXIL08JGD5WC4l0/aUg6xjxP9QPiBZEvNjMU0MpqbPcF4NavoKQWQbPi8EwVF9c6kR7CWTrQGonXQOfFIi/X4OEYo+Aty3bXwGyuPyD+IcLVrh+rz+mlFnqGifOrHc/VwssQYae3U5Vi4OkjC3IPcgAB/KyBqC1goS/3gnwR9rno5d6MYd9XFRkHfdMiTy21cM1cHQC4GTashW6irLo5W/wWHK9+IRlASTbgYRk2UV0Zoq15nAwbYDAE9dRSC/smy6gUFUxVnQ3MiIOga2CiogodQB3NhCw5G3BeC12oA8gn+vDrF5xxE15fn0AU5ACKsYFkbZwnF506Htvl3sPV0QwR1kInatVn9+xsrcivX5gAGXr9+es3+vf3O/559fNv+IX3cIIN+OmlNk659buGrYE8BxBroZsUCEC++hm+djD/zx/qAErZBsvuwXCBLYP6+Z4ha20NKy2tvwbWiugZ1sB1xA5j9kwEtxGzNZB/DweL3GGHCGH1C3OZhrXYRZ2iBjj7zbl6f2LzqaUWrtnl1G+G8wxmhwYTZVV8iBCjfrw24p4ZfA/vdLIQgPYDOOf79/Pu1waQJOldkiR9TZKkry0uLmbwbXcO/Rac8A14Vzex3tGxK4sR0kkdQO6NVs7J5me6WkCjrEYOgs7q9BtILmB0NDM39QPgOlii129XI72DCkg+xrytm7kYAc847LZxRukj9hxAWbTglFSuvT2Mdo5a6ACnBcyygfOr4dcgc6BlsgYmDHL3MpRyUsM4IvhqW4Nh2diVoQMo6SHC1XIA1UoqdjdKOL3cdqdZ9Tc/vNPHDfbyyTbg3BYww/u9iRxAzL4vejkPOf3WPYdP2OYo/ek34GyATi85reqnllu4Zpe4ft7Le8DpbVszPPGNO8Elxgac97m/7SHJlKqwEOM4GTaA06p+frUD3bRwcrGJo279ygUFtaLCzbDxt4ANP1tYDl61qAjyQ6ILaOFTwMIEoOAQaN4GPG79mGB7ZrmFk0tNTFcLmHLFR36GjVO/hmCNZ2v4TK2YLEPJX7+AzTkgyEgaaKHjCES+L/EENNt2nG1xWugA5xlxwnVhHPWtgTwBjU2hA3gtYE5NpgVroGHZ3t8h1AEUIlCIQrSNkDXO+3XCNtroGUC7Gv02zhOLQ/XjiOAsQ2mqWuSKuLZtDz5DwnLQAoLww1yk4gylYAGN3cNBDqGoDj7ACYI+vdRGVzdxYc13CMOtnyOgeTlonEOEtu5/hoS4SMMcQK/AiWBZCEC8u2fkarNt+w9s277btu27d+3alcG33Tnsm6qgoEiRQlCXmtmdfteKyez7bOGv5WTzwxvRGMSCGwCaBSJlOgjDtKCZllf/PBCnflm5BwCnfskENCN39Wu79uowFjYdB1UW9w/bgIuCXkV0NDM3Dj7AH6QdQQDKUkArJxMwOjlzAMVpg2XXaCYZSikPEa5m/a6br+O5ixt49uI6AOCGPQ0A4GbYeC+f5WD7eZAANJhhI96gawbf3h/m8PFawEIygsT2fSvy6TcAXDffwMuLTTx7cQOaYeGGebd+AQ4qT8AQnN7WSiqKqjwwEYwR5gAKy2DRQgSKsBaw8Iyg6BlAAHDd7gZMy8ZzFzdw/PImbpif8D7j5VD5HSy8MdxtzUSlqKJcUBJlAIVNyAlz+PhFCd41NiBwBAhEUQWM63Y7bgHnHt7w7l+AOViG8kOYg0qwxrMJUjO1oiB/JbqDiptBFSKgDbbQJa9/1Hv4wHQFJVXGcxc38NzFdUyUVc+dMl0tYqNrDHyf4RY6kYDGHBjDNQyrn//rogybsBaw/hoYdi+ndwBJkoRrd9fx3CWnfoDvGcJbA7tMwOAfIvQMC5bdF9BDJyHyHCwhOXKhLWBWyDMkpI3YtKzI9y8AXLu7geOXN/D0hcH6eQ6gFqd+Ac9gvws3rH5cB1BIDtpOJwsB6DyAg75/PwDgYgZ/7isGRZZwYDraKHgvQDaDl3dFllArKtv+9Btw2uiiOqg2ukZmAoZImQ6CvXjkZfMIOA6CpWYvkpCwsNnNRIAEnBaSzYi5JX7y1gJ2KEYLTlYZVIAvwyaBAyNP9YvTwtRfAzPMsEncwpQPEa3m2qsj1W8jP4cIV7N+9x6ZxfOXN/DZ5xdQL6m4fn7w5XOF+/JeQK00GlTfM0zopo059xrscEfQmt7mIrSFJMChEpbfEL45Sm/fB4B7j8xAMyz80ZdPAgC+7vA0AOcZqMqS0EFVK6ncUerNnoF6yXGw8EJke4YVWD//18JacMIEDN4AjrBNJcuwiVrDe4/OAAD++Mun0DMsr36A68Bo81voRC67lq9+QQ4gVZa4m5uw+vmvSd41NCBQhGUEcUOiWf2ibUOO7apjplbE519YwHMXNwLrB/QdQDXBGs/edeYaJcEUOl/9QjaPSVrkjBjXJ6/+TECLWj9VkXH3kWk8dHIZj55ZxV2HpyG71+5MzWlDXGv338XCWuj89QNGW3DC6mfbdj8EWugAChax02YsxV4Dj87gibNr+MrLy9jdKHn5Pqx+XAeLNwkxXv2AoWdIgINF6AAKqU/4M8b5uhmYQRW9fq8+OoONroH3PnQGAHDXIece9hxA7VEHFXsG8wYJtHoGpqoFqLKUqn4APwdtp5OFAPQIgOskSToqSVIRwPcD+EgGf+4risOz1Uj2ffbynpmDRRAwGETeMoAAp37nVzuhL22LGQpoQLIWsHYvhwJajElgC5u9TNpvAAjt7WE4p5/5qV8/ZDaCA2Ozm6kACSRrYcpT/XbVS6gWlUhr4OJmD9Wi4r3Yp6GR0AHU1gwUVTnWy8+4ORL1GeKugVncw0kPEVqsBewqXoNvvGEXbBv46FOX8OqjM95/u2n35d1/+sgCPCcFY7jZyyirIXcDrlvBIcch+QOeQyg0v0F0uhviAIqRAQQA914zg5Iq42+euIg9E2XvmSFJkuNgaQvqJ8gpa/VM1EsqKgEOFla/MIcPV8CIIXBwp1iFtZW49S1EPAHfN1XBdbvr+MiTFyFLwD1H+wLGcAYL2/yw+gF8AaNeDq4f4GygeKffofWJKFACovoFC0jsa4WI16AsS7jvujl8/OnLMCwbX3/NrPcZc1ANh/Cy+xfgC2iS5DhYOro58g7C6ueERMcX0AYFIk6AbwyHWhYZSgDwxut34fjlTbx4pYnXDNUPwMg9PFkpoKQqKCryiMjPBAzRGuivH3fzHSF/hU36E7WAeQ4XgUOF/TcVTXgyLBuFGG2wb7x+NzTTwiefvYyvv2bWO8TjTfJb7+hQZQnVooJ6ucC5f4eeIQIHS+AayOpjikKinc9FU8DC6hfmIo3joAKA1147C0WW8DdPXMS1u+vePrZSVFApKMJnsGjYifMMKQSsgWH1C3ZB7nRSC0C2bRsA/hmAvwPwPIC/tG372bR/7iuNwzOOAyhsI7yY4QQmAMKAwSDy1gIGOAKGYdneeFUR3uYnIwdLkgwbJqDlqoXJDSI/GxIE3dVNbHaNTNpvAAjt7WG0NSMTASAr9k9VIEvA2Ygb8CwFSCCZAJQnB5AkOZPUojmAshPQ0mQA5Wn9AxwXWpQcNG+KX1ZrYIJDBLZZqJWuXg1vPzCJN17vtJ//2BuOel/v5w/4Tr87BoqKjJIquyHQg+uTt/lhDiDOBqejm5isspfP+AJF5Akuwpf3YAdQ3AygiXIBP/Z6p24//oajAw7GmWpRePotyilr9Zw13Hl5H/07dn31423AwwWKOA4MjsNFsOkZ/v1xROB//k3XQZKA77pz/8AzYNjBMuBAK/LbfJs9pw26XBQLQCVVRqWgJMq/CK9fiENooEUsyMESvX4//oZrUCkouGnvBF53bM77+kzVCSL2b7KHHSy8+tWLKipFBbY9eo96U66qhVT37/A/Mwwr+HM9zEHl/p44LTjfd/chzE+U0Cir+N67+40b/Awbx0EFOOs0r35A38EyfA12ffXTTGvEZTdYP/77X6hDhbUoCcecB2fYGDEygADgddfO4u7D05AleGshAO4kP3b9SZKEBmeQQHPkGTJag07YGhgyCZHVLzQDKGxMfECbcZwMoN2NMv7+qw8BAH7C9wwGWA7VYP0Ax0XKO0SwLBstzXFBlosK95Bl4BlMU8BGyGQHZdv2xwF8PIs/65XKodkaNnsGVtu6t5jwWNzsQZaA2awcGBxlOoxODlvADs30A+4OunkYPDIX0JI4gPJYv4gOIOagyi5DqX86F9VRZlk2urqVmxHcAFBUZeybqkTKsFnc6OG+67KvXxzamhm4zmwFh2erXrhiEPkR0PIjQAKOiPuhxy+gGxKQvrjZQ72kZvbzpzlEqBauXg0lScIf/9DdWOvomPM9PwuKjEZZHclvaJRVZ9IjZ41np5FBU6w6mumGXLYEIbLBGRl9AUgk4LifC15czZD8hjgjfBk/+6034Idfd2TEPTZdKwxksLCX94YrAAHMru/Uy7ZtNDUD9ZKbYcNpoetopivOtcJPb0Ne7kMdFrwx5REdQHE2QG+7fR9ed2zWO5VmDGfYMAGtUe7fp8NrfLNnYM9EGZUCv4Wu4wb9lwoy3wFkBtcvzpSvoDHvgCAEOmYGEAC8av8kHvr5b0JJlb32JcDnYGlpmCgXoBkWurqFRkkVivzNbl+ABDCybrJ7dqpSGGiNYgy6B0brO1Afzj06mAEUFqKdjQNoslrA/f/fN0K3LE/cAQbrx9joGph3D/p4a2BzyAE0vMaxYPf+PWwNvPNGCeANE8E9kTtgjQv7PE79JEnC+9/19djsGgPvTxPlAhRZGnHxMfdevaR6784MT0Bj9Ru6h013slrkNdCwUFIHn/vR6xf8DBF/Hi8DCAB+8W234J+96Vr+M4STI9fwHSIYpuUdWrR1E7aN/iGCYBJiYP3Mwfq90siiBYzIgCMRJ4EtbPYwUytl1nrAs7eHkccWsCNzWyNgNATWxCDYZiFPDozJSgHT1UKogOFNYMqqfgJ7exB5rB/gtIGFXX8dzcRmz9jS+jk/R74ygID+JDVeHoefJXcKXRaI7O1hdHQjVwIu4KyBdoRJagsZ1g9IeIigb40IrirygPjDmB2agrPa0rxNkWM/H2oBc5+BXgvOkIBh2zY6uompitgBFJrB4n7NtGzuRCov3yHMvi+c8BLv9BtwNkC7G+WR/LJhBwvbCE3X+Bkiba3/8l4VOFg6vhYwfguOb4x5AgeLFuoAcr5m2+A6s+NmADFm66UR59VsvYhmz/D+TmwjNO1OEAL4LWCsfm2BAFkpKCipfAeQ/2u8/IsBAY3TghNPIOIJGKx+8bYhk5XCiMA9Wxt0sHj1qxWFOWUtzUDNzVACRjNY2IZyulpM5AAKm6IW1oIYVt+4GUCMSlEZEH8Afg7a4Bo4Ouyk74Lkj+Hu+AQ0YFQkCwvgtSzbN+UrnUAUNOUq7v1bUOSRwzNZljBdLQzWr6157lKegBZWv+5w/ZLkeIVNkgwdJBB2CBG/fqJnyPSQi3SlraGoyKgVFd8hQr9GrH7eGigQwaeiPkNIACK2iqgZLJfWu9g7mc3pN5Ds9LaVswk4ADDfKKOoyqEtEJfWuygoEuZq2TkweoYVa/FgC1ee6gc4LrQwAZK12GV1DSZxsLS0vNavGlq/yxtZ1895uMV1ALVy1gIGOJOsNMPyasTDtp02zz1ZroGCjJIgWr181g8ATi8Fr4GX17ve9JcsSHKI0OoZUGUJRTUfryDDGTbLTc3bVPJyypq+l88KR8BgL+bM8SIaI83ySdK04AjbG0ImvBhm/Jd3ETNDU6yWmhoaJRUlVeE6MFrD9QtysKiy8PTbqx8348f2/XNwC1NYixNPgEviwBDBNorMabLUdGo5Vy8Jc8qaPTPw9LujOwJQuSCon2lCkSVUCkqy+g04fBKEQJsZ1m8ow4ZNy52rFyELcsqabgYVE5OG72HWljhZLfDvX7cmjZLKrc9ge05wfbgh2iEtdOO4/tg9bFo2Vtoa5ty68oadNHsmCorkibSj9XMFDG8NHKwRu+aS1g+IEoRvD/z/yOcxM4CCGM7xWmpqmKuLDxHY9ThRLqCoyOH1S7QGBgtA7BoSObCM0EOE+C5SEbO8Z3C96LhwPRG8X0NWP89FKhDBWf2SPEN2Ovl4+yJwYLoKSQoXgC6udbBvKuPNT4L8BlkCSjl5eQccBf7QTBWnl4I34BfXOtgzWR6wD6dB1F8eRH8Ecn4cVICTQxXl+gOcQMssSJLBkuf6rbZ1r/2BR+b1807X423AndPhnNUvggi+1tbR0c3M6gcky/Hq5FBAO+wGkYe5+JxnSMb12+YZVDPVIpabvpfPVg+z9f7p7XBOGVvvG94GfCg/RGMv7wEZQKblbe7DNuBBGTbC01nPISR+ec9q8zNTdV7emVCy0tIw49v8AIMCRtNXv7IoA8hzsMjCENnA+g04WMQtSEBwi83wPzNMy4YsIZN3CTZFiF2D7CR8plYUPiNbPafFpFxQnJHSQz8ja2kS1s+wUFRkFFWZn2ET4mAJn7IW3IIXdwx8ECzDZrR+zkEf7z3XH6INjIYYexlAleAMoEZZTZQB5P9aUMbP8D8zWItiFvUrqjIaJRXLbt3W2hpsu+8MEjlY/ALacAvT6Bo4+LknoEWpH+dz27Z9GT/iKV9AcItTViL4dK3o1Q8AVlq9fv04hwjsGVIvqygXZE4L3VD9BPdw0Brod/DwXIx6iECme/ULaqHLSECrFbHS9NdPG7j+AP4hAhskIMoAapRVyJI4R86rX8gAoZ1Ifnbwr3DKBQV7Jso4ExDCa9v2WF7ek4xArhbVTMZYZ8nhmfAQ1ItrXeybzLZ+QDwBo51DBxXgtCFeXOsEKuEX17polNQRC3FSkowxz2v9oowyv+AKQPszuoerBQWSFM8BZNu2G6Kdr/qxSWpBQeT9+m2tC7KtR8+sulpMV52JN0FB5Lpp4cpGN9v6JTxEyFP95uolb0AAMPTyyRMwuoMOlpGXd521jwSHQLP1L2jCC8DfQIed7oba+zPc/Mw1SrBsRzgDXAFt6OXd/54x4KASZQB5DhZFeHobVL/BDXjIFKYELThxA1CDYG2JrMV6paWhpMoD0w79a5RhWujoJmpuiDEw6hBgDqqg+hVVRwASCZSMMAGD14Y4EGIclAGUwTU457bRsHuYCUGz9cENuJ9m1xisHyfEuFxwQ7QD8lfqZTW0RSy0frwWxIgh5Vnew179mIDmXpc1ziGJP8QdEIdAi9ZAf/2StNANTqETrXHBLUy6aWXmYNlVL3kRE7Ztu8+QvgA5fIjgXwOrRVU4RU1UP9Yi118Dw2oobuMUT4oMaQEz42cAiZirl7DZM7w6LDd7wc/gEBcu4NQwaA3shTxDdjokAOWIw7PBDoyNjoGWZma2eQTgpqvHG8Odx/wLwDkBPxMySe3CWifz+gFxBYyrPwI5Codma7BCMkQuZCxANlgLUwIBLW/1OzzbDyIXcXGtA0mCF66YFlmWUC/GE3F7hgXLzl/99k6WocoSTgesgVk7qADn5SxuBlC7Z+aufpIk4fBcNbB+Vza6sOyM65fgEKGVswyq+ckylls96KYTNLna1jHrvrw3OKePwwJGe0jA6E/Ace3nghYw9mLLD4GO5rAQnc56p98BGUBZbX7Yeraw0d+As0EVvDHc/fopgRlALMRYdPodXL/oLV5BGSvOr+VnAGW1+Wb1u+K2vy41e5irlyBJEkqqjIIiDZ5++6boiTJs2KCEkioLJwgVVWfSXbgDKKTFKywEOnAKWPptSLXoTApacOvHBIw5bwNe4IYY1938EICfAcQESN0czeFiG8Z6KZkDyF+T0OuPs0H3HFQZiZDzEyVccVv8mYA2N+BgGaxPv37OPSjKAJoUtMGye7ZeUmFwcs5iOcw49TctG2wrENTClOU9fGWjC9u2sdE1oJv2QAsYIHiGuCLkcI5XWP2YQMv+bP41GOYitYS/F+hfY+ZVcFDtGVoDl1uaJ4zzDhFYHlDdFYCGrz/dtKCbdt9FKhC5g+q30yEBKEccngkOkb0wjs1PSYVtjy7eQeQx/wJwBLSObo6k7TNMy8bljW7G9UsuYOTpBBzwteAEuKjG0YIIIFaGSB5DyIF+BkvQPXxxrYPdjVKm2SdxHRj9CUz5uodVRcaB6Uqgg2ocAlAjaQtTzuoHOM+QIBfkxTXn5SrT+iU5RNDyJaDtmSjDtp0hAatuDstswMs7e/msFRV3jHlYAOrgy6Xpnt42XCelaAPJ3q15n7MNuGnZ3OD0UPt+hhlA7OX98nr/5X3YAdQSvLyXORk2bNJj2Q0x5uVf9AyrXz9ehojhq1/IGHjuGPOBDBt+i1hWp9/zXv2cdxe/A41lYPjXeH/7Q1nUwqSZXv24p996vwWsF+JQSdbiFFw/9nuyEiH3TJS9/LjlZg+qLGGi4lx7vDXeCYEOygDqtyAC4hDjhjt1bBj295MlQAsJ0eYJFHqIgMau2SzvYa9+rpOPtXHyxnA3XQcQq89I/bTgNZDVU7QG+usX5pDiCWSDLXajv5+1kA0Hsidlz2QJbXfIh7+FEwA3x6vVM1ApKFBkibsGhtZP719/AD/IXfetgUH3qOgZEeYiTTJJUgTLdbzsimjLzf4ayA4RWgMCmnM91koKdxIiE8xYED6vRaynW6gHPIN3OiQA5YhDs1UsNXvCPJlxnX4D8QWMvG2+Ad8oc8EGaGGzC9Oyx1S/OAJGf2HKE4dnwluYsm5BZG1IO6GFrlZSMVcvhdQvWwESiN/ClFcBDXCDyANawC6ud1FUZW9zmQW8fIMw2u7mIW8cmq3i/GqbG1oLjOkZkuAQoa2Z3nSePLBn0jlpvLzR9U4gdzfcEcgcB0tLM1AuyFAVGeXiaIYNe9mcqBQgSZzTW1/7g//fh38Nq1GSKUJeSHSAQyhzAWOjC920sNTsYffEUP0CQqCHHSpss+OFGIscQIEZSjHqF+IQEjkwstr8sDWNbcAvr3cxP9EfVDG8RrWGHGgAv4XJaX/gO4B6poVSQUZR4TuAeq5DqKjIyaZ8hWXYZJgBBDgbyMuuA+3yRhe7GyUvpmBYQLNte6SFidfGWS72BaCREGOzfw8HTfmrFdVELUxRx8Rn6eJb2OjBtm1cces471sDu7o18Pdg9ZNliZ9h47UwCRwsIWugv37hIfkhGV6cz9nHWbsgr6x3PSF8PmANZCHuAFBJUL+eK/gEtYD1fGtgUA2DBB4gOEcuOweVs95d2ehio2ugo5ve1+ocAa3pdwAFHMKUiywIn/8MaATUb6dDAlCOYBkYIgfBxXX35T3jKWBAvBamjp4v+z6D1U8UBM02P3szzg8B4tbP6S3PauHMil2NEioFRdjC1NFMrLb1TDePJVVBUZXjjYHPqQAEODlKYS1gWWZQAfEFjE5OW+gAp35nlsRtnKyFM8v8sUQh0Hq+HCyMI7NV6KbtrXXD9F2kY3DxxREhc1a/eZ+DxXtOuM9Z9pLuX6M2u4bn/qxyTh9ZKHS1yLefs83ORFB+g2mh6grk4S044s+FGUEZBnjO1YuQJad+ThuEr36cMdyslixEW3Nb7xgd7/RWDnQAsfYoUQtYv37BDqAkG0jdtDNpX2LMT5Rx2X3HG550WCuq3PoNhBhzHSyy0AHEQqBF7RG6YaOoOPUNFchCW5zE9c2yBcer39pQ/YYOSXqG0x7SKPczgEYyWHwtYM7v4QsYjRJfAGJ/52pJCW9B5Dr8ojmEshRxNdPCckvDpbUOSqrsBRDXuA4Mw3Nm8CbRdXUTkgTPhTUs4o6sgUMOFlYzcf1CWugiOoSydkFeWu/iknsd7hl6hgy3wTLxoVpUhRlKokECYc8Q27aH1kDxPaiZFvedK2yKmm5amTmo5rn1c96VeTlo/hBtXgZQV+sfIogcQJphBT6DdzokAOWIo3OOgHFiscn9/OKaO8K8ns0Ic4CfbxBG3ia4MA5MV1BQJJxY5G/AL7jtD+PIAIpTv1YvfwGygGM1PzpXE9aPCZBZ1g9gY6S3fwsdgMD62bbtZihlt/kG2IjRGNefLz8ibxydq2GzZwjbOC+No37leBk2mrt5yGML2NG5OgDgZcEz5NJ6B1PVQqb3ThIRvN3L1yHCgWnH/XhupY1L7uktOygQ2ffr7v3j5DcM/t07Pvt5mTOhxDu9DcxvsLwX3yQZNnrIGPgsM4BURca+qQrOrfrq525+eGO4/Q4Wdh34X+C9+hUDHECmIwCJHCy6affrxx2zHbLBDs0Iyq5+AHBwpoJzqx20NQPrHR17fQcFwy04/hYwYYaNbwy8aAJOSXUEIk0Qol1UZRRUWRBCHBJSHLIBzzrD5uB0FVc2eujqJi6td7B3arB+/jHc3vVXVFAtiDNsKgUngwrgjzEvKE77jqh+gHONJ2qR89c3IEMps/rNDK6B+3wHLQ3OGt/qmd47RLWoBmYoAaMh5WEZNl79iipXIBtsoROHZDu/dvwOKq9+vjWQHfbVOYcIjoPKqU25MJphw9bAekmFKktiB5WgfiwDqb8GBteQ5xr2TxHjkaULslEuYKpacK4/1qoecIjQ6hmQJecZ60wBG5yEOPgMFjiAfC5bagEjtpRrdtWgyBJeurLJ/fz8aht7JyuZjTAHfBk2cRws7sKeNwqKjGvm6oH1A7JvfwB2Rv0A4IY9jYD6Zd8+AsR3sOQ1RBtw6rfU7GHVNw6UsdTU0DOszOvXSFq/nI2BB4Ab5hsAgBev8AWM86vZO6gabohn1CkQeXZQXT/vCEBB93Dm9Ut4iJCn+k1WCpiuFnDG3fwUFKkfICs4fWQv1mXeGHhmPxeM4e6PkA7OsAm074dswMNGIGeZAQQ4DtzTy77Nj2+dG84pa/UMSJLjkOJlsLB7rJ9hw69P0Bjz0PqFtOAMhvCONwMIcIYInF1p+9o0+0L3cJtvX8BQUeYIaICbAVTsZwANn/D7p4CJ6ldQJBQUWZChFNyCOBACHTCBKKsasgxDT8CYHKxfS+vXwMvwKqkoFwUZNqyFThU7gLzrT3D/AqyFKViAFAmM3q8NcMBkdQ8fYVNMV9q4uN7xBFxAPIa7vwbyW5gGMpRC1kCRw8UR0IIdevwW2GAB0ptCl1UG0EQZRVXG2eW2d9DCnnG8Q4Rmz/DWJ+4kSeZgEblIzaFniODzoDZYI0SEDH2GZNgCBvQH+fQPYZxniMI5RGD1kySJOwmxf4jguiBFhwgFx+VIY+CJLaVcUHB4tirc/Jxaankuoazov9xGz7Bp5TT/AgCum6/jxQX+5ufUYgu7GiXv75wF1aI7hjvm5ieP7gvAqd+l9S42OKHMp1xXwTiuwWQOoPzV8DpPwBi9Bk+5rYlbXb88t9AF1a/ZM7Cw2cPRXeNZA1u9aAIQc3vkcQ2cqhaxu1EKfoZkXr8Ehwh6vjKAAPby2cK5lTb2T/UPWtgL9nqnvyY2fZufCsfh09X8DpbRFqYop7eW3b9HRae3bHPF3eB4LWA2196fZQYQ4GzAz7r1AwYFoIlyYbR+7OWdOQR8Ilp36PRWZN8veg4WQQsYq5/A4cPqx69v+JSmLB1Ah2er0AwLD59aBdB3pQFOltRg/QbzLwAMtCFalo2eYXn1s+xRkZBNARMJGLppoaC4GUCC+rJhBmEh2kFTrrKqIROAHjm9ip5hDdVPhWnZ3nta0+egKioyZE5OlxdCLnAA9Yx+RpJmjLbQeC1MRSXQfVFSk2UsZZ2hdHCmCkkCTi+1cW6lgwPTg/cv0F8DbdtGUzO89YvbguPWT+gAClkDNV/9eC1KrGZFYf2urgNIliUcmnEiAM6G1A9gLtJ+BpBoDHxZ4CINqx9zPbI1UHSPszUwSUZQ1mvgkdkqzqy0cHaljYIiYb7R73YZWQO7g89gYLCN01+/UkEeuf4M04Jp2SgqivAZstMhAShnXL+7wd38WJaNk4stXJPxy3uSMeZ5m+Di5/r5Bs6tdEbG8gLAyaUWrsl4880mdMRqf9BNVHK2+WFcv9vZgPMcBCcWW2iUVW+0ZVbEHSPd1kw3myB/yxdzYPDu4ZOugHZsVz3T71kvjY64DSLPAtpcvYjpaiGwftfMZVy/cjwBI8/1A5w1kFe/nmHi3Eobx3JwiNDO2Rh4ALhmroaXrjTx/KUN3LCn4X29qMqol1SstvuuvpbWz79gY8z9GxS//ZznAPJGIHv5A/z8C1ZbUQZQxROIgqcM8ez9RoYZQIAjbK+2dXzppUUcnKkMHLRMV4uD9esNbh6BQQdG19cCJnIA9QzTy5DjZtz4RvyKTr/Z9+Y7BIIdLFk7qNjBwEeevADAuY8Z09Ui1lqcFjBBBhDb7LD8C4A3xcp5jhYVcYtdURVnAOmmjZIqQ5LCx3SLHFRAdg6W4fr57+EpN0h3zZ3w19L69WMiJG8DPlA/zgacCWiWPfp39Lc4BQk8laISmOEFCDJsWAZQRvUrFxTsm6zgwZNLWGr2cMOeCe8zlkOz5t7Dbc2EbcMnYAjqVxQ7gIbXwNEWMOfvzzb5w/VlNa0UBPUbyPASO9iyvodfutLE8UsbuGG+Xz82DGBteA0MyAAaeYYIQrJFIdDDLXbDzxA2Bc1bAwNqKHIA6WZ2U9QAp34XVjt47Mwqju2qD/zZU9XiYP20fv14a+Bg/UYdQJpPQBQ9Q3Y6+dtBvcK5fr6O08utEbX38kYXHd0cw+YxYQZQTluY2Ab85YXRE/ATi01ck3H9gPhjpNs9I8f1E7fgnFxy6pdlAC/gtjDFEiCN3AqQeybKaJRUbv1OLDZRVOWxtdDxRkHz8KaA5dDBIkkSrhMIGCfdbKVjY3IAbUYUMNq9fE7xY1w3X8dLV5oj18OZ5TYsG5mvgXEPEUx3xHfe7uE7D01hYbOHk0st3Ojb/ADOBohtHoGh08eiAtOyB05QhzOARAGyE4LND3uZrwYIGLppec8RbgtEaAtTthk2dx6aBgA8dHIlvH6+/At2HfgPbYbzG4bfhwzTgmUj0MGiGZavfvyMEK9+CTbgTv2ye4W+7cAUZMmp3/6pCibd8c+AU7/NnuFdB+x9o1ZSuBlAnQEHGhtjPtpiU1Kd03FR/dhBC78Fx/1cFnxuBm/As86wmaoWcc1cDQ+dXAEA3LhnUEAD4ImQ7H2jfw+raHNa6Nj1B/A32CW1L3CMOFjYJLticIhxtaDwp4CFCJAs5DjLa/DOQ1Ne/W7y16/G6ucKaL0I9dOHQ7T59fHWQJP/ucjF59WvqAgcPtEEyGzXwCmcXGphYbOHm/b266fIEiYrBa9+wKCLlDcGvqubUGTJy5kKrZ8gY0n0DGF/f7YGBrUZXo0cOcB5hlg28PDpFdy0d/AZMl0drp858AwGhhxAQ2ugyIEWlCO30yEBKGdcN9+AZY8GQbPNT9YOIF46fRCWZaOj5zMEGui3kLxweXADudLSsNbWM988AqP5BmHkNUQbcIK0KwVlpH6Acw1m7R4Akowxz2/9HAGjjhcEAsbR2Vrm09+YE6HFcb3x8BwsORUwrncFjGHL98nFJmTJGXWeJbz+/CA8AS2vLr75Bjq66WV2MTwH1ZgEtKj3MNvc5+0evvvIjPfP9x6dGfhs2MHif/ksc+zn7J+dkN3REF4vnyE0ADXYvh90ejsQksrLd8jYwfKq/f0X9nuOTA98xquf3z0A8OsnygAaOL0VhkD3BR7R5xXB5nL4a6Ix3Fm20NVLqifO3s2pH9B3sDR7BgqKhJKq9DOAtNHT77LPwcJrIQmrX8EVgLjXn+H8/VVFEoRoh2coSVK2DgxWt/1TFc/1AzibR6AvYPhbwAAnJ2R4A97xHCwh9RMIQLppQZGdzTs/JNsnEIW0IF4NBxUA3ONbA2/ZP+n9c79+roA2XL+CjM7Q+wcT0IoKa6HjCxReBpDOXwM9B8uQy3GgfmHXX2ALXXbbYH/9/M8TgLcGGgNroGZaA38PVj9JklBUR0XwsBw5dk3VPJcov76VoGcMGyRg8duIs84AuvPQlPfP94TUb2AQQ4Ejgoc5gIxBBxAJQMSWc/M+5yXq2QsbA19ngtC1GZ/eFt0X1Kgv713DsX7m0T0AOEGU1aKCZy8O1m9c7TdAfAGjo5u5rZ8sS7hxbwPPXlwf+HqrZ+DSehfHdo+hfrFDjPMrAAHOPfzcxY2RtouTSy0c2z0eARKIvgFv+05G8sjNeyex2TNwZrk98PUTiy0cmql6L+RZEVfAYCed1ZzmeN3snpw9M3QPn/AOEbK9h+MeIuRVQLtxTwM/8roj+OabduO1x2YHPpsaOn30v3yy//dfP13dRLkgQ5Yl7hhu9jLqtTANv7wPCUTDGxzLzQgqF8QChmnZKLgChWhMd5YCRklV8F/f8Srcun8S33/voYHPpmqOA6gfwts//ebdf52BFrDRzU+/fjJKggkvuml7AZ8iB0a5IBbQBuonbKHLVsz/z2+7BdfP1/FPv+Haga8Pt+D468eytIavPwADU6x4LSSlgM2Pbtq+KWD8zaEqM4cQ7/qzvM0/v8UpW/cAAPyzb7wON+5p4D+99eaBr/dbwPr1AzBQw+H1v6Obbv6KqH6mVz+AswE3bc9BFZSvUi4ogilpztcKAoEt6wwgAPierzuA11wzi5/91hsGHGiVgrNOeS10vhBt9v/DGXod3Qkhl2XJaTMcrt/QQYAmGgMvCHL3WugKSqB7paBIwSHQWQqQh6fxA/cexFtv34c7Dk4NfOZ3QRqmha7eD6lnbsh2b1DAYOtTieMAYs+U0ClqpeD6idZAy50iFrQGZp0BNFEu4N+/5SbceWgK77xr/8Bnwy7Sli9Em/0dW7xniLsGjrbA+p4hAhfpTidfb2AEjs7WMFFW8fi5NXzvPQe9r59cbKJeUrGrkd0IeEYjxhjkvOdfKLKEW/dP4vGzqwNfH5eDCnAyRPzhZGG0tfy2gAHAHQen8P6Hz3ongEA/wDjrDCXAzbCJ5aAycrd59HPHwWm896GzOLHY9FrqNMPC2ZU23nLr3sy/38AkusmQXwznZEmW4FnX8wZ7cXri3BqO+K63cbVwxhXQ8hyiDQA37Z1AUZXx+NlVfIfveju52ML8RLYh+ED8Q4S81k+SJPynt97C/Wy6WsRZN9xYNy10dNMLv+6/fA6+vLNTyXJBxkpr8OWyH1Apo8TJYNGGT2+H2yNCBCKgL3DopiGcopO1G/Hvv/ow/v6rD498fbpahGZa7gAEZyT37Gxt4O/gdzAOChjO5se2ba/9mNn5y67DQDQlrKCIBQrNtDBXcd6neC10uuWvH7++WdfvtdfO4VP/6o0jX58ZasHZ7PbdA4rsZNgMbH7YBKGC4m3cRkOOnQ2mqkgB9ZMgCzJ+WAZQz5C4LUxO/ZyNFX9KWPb1OzRbxSd/+r6Rr3sOFnc6J2tXrftESH/9TMuG5oZoixxALOSYiVy8e7igSCgGZCgBzvUvEuAA5xoPahHLsoa1kor3v+vrR74uSRJmqkVf/Zzr0F+/4fW/q5uYn3DurxKnjbNrWM76p/JdeuyaZOLIcA0HxsRzrz+fwCYQIIFs6ydJEn7lnbdxP5uuFnFlw5lu5TmoykMiuGZg0r1WHQeac22VOSI4c5U2BBlAvZFnCN9B1c9YGqqvr366acAwbQxvW5w1MNv3yB9/wzX48TdcM/L1mZqTAWRZNmRZctbA4foNrIHuM8Kd5Dd8f/pDooucnL5XAvncAbyCkWUJtx+cwhPn1ga+fvzyJq7dnX3+ChBvipDXW55nAePQFJ67tDGwYB6/vIlyQR6YDJEVjZKKJmdqloh2L78h2oDTh9vVrYE2sOPuP183P4YMpbIKzYw+hjtvI6SH8QSMs2ve104sNmFa9ljqxx6CUUXcltafwJNHrp+vo1JQBtZAzbBwcrGF68bgQGMtdFEzbNhGoVrIpwhZVGXcsm9i5BnywpUNXLe7wf9NKYlziMCEkrwJQEFMVwve5oeJ/dO1QQFo2IHBnpE8B5BfwOBlsHgTXsrB7Q+shrzTWd2y+qe73Clh2WbYBDHcQrLW1r22pprnoOJk2PjGSPtrxDY/5ULwGPOSKs6wGZgSxvn9hml5/w35AalXs36DGTZrbc37GuA6MPwCGguBLoodQI6AIZ6i1nMzbgqKzA8Z9wlsopDdIIdV1iHkQTA3CxPQ1joaFFny8lNqQwJQP4RcDqif4/IrFfgOlp5hoVRg9RM7gKrCjCCfiBfkcLlKNfS7INc4a2CrZ4wE4Q+ugbz6ia/P4SlXIgGoWuLXjzmkKgUlMCMtaxeaCL+Dhf0/Wxd5DpaBZwjHATQw5YqzBnoh0OWQ+jEH1tA9bvgcVoAoZ87yHELjZqpahGX339P8ayB7hogPEUYPCfrPEEWYI7fTIQEoh9x5cAovXN7wrPKWZeOZC+u47UCE4/0ExGnBYQ6gPI5AZtx5cAq6aeO5S/02sKfOr+GWfZOZnzgB8VrAbNtGW8/vGHjAqR+AgQ3kU+fXUCsqOJrxBCZgyMESAWeEdH7rd81cDQ3Xxcd46rzzz7fuz/4ebsStX84FNFWRceuByYH6vXB5E5pp4dYxrIGxHUA5bwEDHBHy6Qvr3kteVzdx/NLmWOoHxDxE0PPZAhbEVLWIja4Bw7S8NhK2qaxz7eeWz74/mgHknU6qCjeDpX/6HWzfFwkYpmvfDxIwsrbvB8FacFZbThvYWkfHVC24fkB/BDIwOIbbXz/e5sdyQ7mLanCGTZCAFjYlLOsMoCCGW8BW27r3NcBpQ+QJaOWCjDLHwWLbttdiwjY/wxkfPd0Mrp9poaA6GUCiEOj+mPitrZ+qyJgoqwMC5FSl4B2CDL/DRclQ6uimd/8C/BaxosJa6PgCJHPiBk1gEk0JYwKHcpVqOO2bwsTqOFVx7ut6SYVh2QM16Gj9FqZyQR6ZotbRBusXtgYGCRi2PTrp0J9xc7UygILwZ9gwAY3dw7x3kIH6qZz6DTtYRlrsQuo3NCZ+eI3zBCBBzhxrQx7HnooHE8tW2ho0w0JLMzE19AxuDrlwnRBtZw3U3LHv/s8B9xBB4CLd6ZAAlEPuODQFy+5vwF9ebKKlmbjtwNRYvl+cl3emsOZ5A3nHQScI8LEzThuYZlh45uKYBbSI9WOLUJ43PwemK5irF736Ac61+Kr94xPQgOgb8FYv3y1gsizhjoNTI/VrlFUcmc1HBlDe3Rd3HpzCcxfXvY3ME+ecWt4+hjWwUlAgSztnDDzgCEBd3cLzrgj+7MUNGJaN23N0iJDn+g3DXj7XO7rv9NY9fSxyBAzfyzv39Nv993KR72DphZx+9yfkuJ8HjEjm/X7AbQG7iptHwNk4dnQTmmF5m0d2//HyG1g+AzA4xtxzULkhvSMOKl9IdEGRhA4MUb4I4Dqo1IApYWNoYRLRr59z7a139IGQ42EHy4CAwXFYsH8uCxxW7N+DxsBrLCRaFmUE2a5DSNAiNoYMoCCma8W+g6Wte602gOMgaHEFNN8Yc56DpRg8BaykyijIEldgC2tR9FrAVFEL2NV1sEzXCgMCGtAXMNiB3PA12F8DOQ4MwxocEx/iABp2qLBfXykIplz56xcgsF21+lULaGsmeobZF9CqfQENCKgf1wHUX+N5GTbDY+BHHULsOSwQiFgLmMoXiEz7atev/wxZ67j1qzEHEO8ZbPkcVKP36HCbMYVAE7ngniMzKCgSvvjiEgDggZed/3/10GSSrKiXCpHt+15+Q45bwPZMlnFsVw1feHERgLP57uoWXn10NuR3JqNeUtHSzJETCB55HyENOH3Mrzk2hy++tATLsrHe1vHMhXW8+pox1S/mGOm8O1gA4HXXzuGFK5u4vO70fD9wYhmvPjoDeZwCWowQ3kqOBTTAqZ9u2njo5DIAp377Jss4MF3J/HtJkhTLxdd2f1054zDqLHntsTkAwJdecp4dD55w/n94skZWxDpEYGtgzu9hP/4xyKtDm58GR4Dt6v01inf63Rs6vR0VgFynrejlfGhK2LDDgm1uWIDt1QoxFuFvARtuf5AkCbWh+88fou2NkdZHX97Lqqh+zr97LUw8AcOwfPXjO1j69eO34Fyt+lWLjlNi1efAmKr4HUCj9QOc9wy2TvmvwZ6//UHkwND9LXSijCDWAiYOeVZlfovYODKAgpjyOVjWOoMtdPVSgd8C5heAeBlA7vUH8B0szEEFjN6Dmml5IdHc9hovJFrQYjeGDKAgnPoxAU1z3GXuvVl3J1H5RbTBNVDhZlANhGgL1kDRGPh+hpJgTLzlq19Afa9m/QBHPFtnz5AKE9D4LsiKzwHEqx8gHmPOrtf+M2RYgHTrJ8gI8gQ0tgYKPs86A0iE3wW5NlS/alGBxDlE8DuogEEX30AGEI2BJ/JCo1zAvUdn8HfPXoZt2/jcC4s4PFvFwZns82uc76ei2YuWYdM/vc33BvJNN+7GV0+uYK2t4XPHFyBLwGvGJGCwDUCUMdzeBKGcb37edOMuLDV7ePTsKr7w0iIsG3j9tXNj+V6NmA6gtp5/B8ubbtwNAPi7Zy/jxGITZ5bbY6yf8xCME+Se5xY6wBnDXS0q+OQzl9HVTXz55SW87tq5seUWNcqFyAJk2x3POg4xLyt2NUq4/cAkPvHMJQDA544v4JZ9E5itZz9EAIh5iLBNW8AA9vLpbCL7+QP809uw/AuAtTCNZgR5IdAlwcu7197AF4jMoQkvw5/btu22gF2tl/f+5qd/+j0oYAw7qCo+9wAw6ADq+TOAlNEJL/4RvwVBi5I2kFHDFyj69RM5gK5O/SRJcjJEWrpzKNPRPQEN4NcPEGcA9TOoxBk3mhmeoVRSZbcFjO8wUxVZ3CJ2Fa8/wM3xYgJaSx8S0BQ0tX6GjX+CkCRJXAdLz3AzgAIcLCV3ihrAF3GLqgxVlgJbwErCEOOrmwE0XS1greO2cPoyvIDRSYi6aUE37YF7WJgBxEKgOS5H1ScAi1vA3BDjkZDj/hoYVN+tcLCsDj1DeC1MXf8ayMuwMZwWTVmWuA6hsEEB+tAzZETg8YVAB31+tTKApn1txMMuXEmSUC+OiuAsRJvldA2ugf1niHN9Rssg3UmQAJRT3nHnAZxaauG9Xz2LL7+0iLfdvm9s3yvO6a03wjfH+ReAUz/NtPDur5zGBx87j2+4YfeA5TdL4jgwOl798r35efPNe1ArKvjTB07j/V89i32TZXzd4emxfC+vhSnGBjzvm8fr5xu4Zd8E3vvQGfzZg2egyNLARKYs8UJU4wgYOReAygUF33nbXnz0qYv4swfPYLNr4G13jHkNjCqC5zzDi/GOO/fjmQsbeN9Xz+Kxs2tjfYYkOUTIuwjpp+9g6b98TnoBnqNj4AdbwBx7vuVzAHR1J/+joEjcAMrhFjDhiF/RhBern38BjOZjmFd58zPlcwB5p99DLUzDGSzDAlCX5wAKcVCVQsZwF9npuUDgENXP+drVC0AF+hkiG10dtg1MRmgBG3SwCBxUQgeQ6TmoRFOqCooMVZH5LUquA6igyIL6Xb0MIMCtX0vcQmfb/bXJL6ABznXGD9EWO4AcAUjxHEAjQe6G2yKn8h1WhmVBcUeoizKogKubAWRaNja6Blbb+sCY+OFJfn4HFcAPwu+5IeRBDqqSz0E1moPWzwDy/zvDC4EuKoH1u3oZQGwSnfMMkSRgojL0DPENk+n4HFQlVeG4SC3P2eI4WEY/B/oH1GEuUlELHfsZhl1q3vW3hQLaVHXwGoxziNB3UFEINJEzvvO2vTg8W8V/+PAzqBZV/OCrD43te7H8huEeZR7bJb/h5n0T+KYbd+O3P/sSFjZ7+AnOWMGsiJPB0t4GLXSAs5j+6OuP4mNPXcKDJ5fx42+4ZmwLfZwMIMO0oBlW7q8/APjnb7oWLy008acPnMY77tyP3RPlsXwfVZFRKSiRN+CdbZABBADvuu8YNNPCf/3487hl34TX1jQO4mTYbIcWRAD4nrsPYn6ihJ//0NOYrhbw9+4+OLbvlWiS5DaoIcP/8rnc0qDKkudcdDZ50uDp7VD7AzB0+uiefkuSxLWfa77TSVUedViwzaQopHhkgsvI6e3V3TwWFBmNkoq1to5ld5oaG20OMAFoMMCzPFK/wc/ZZzwBiP17yd1g8jaHlg1XwJAELTa2L0RbkKF0VVuYCkP1G9z8DNcPGArRDqgfwMsIcQUyVTTGvD/mXFQfVZEch4tgCtPVr58G27ax3OqN1A/ou/j89QGcDSQ3BNovYIyIuKYrMErcz1mIdkEWh2irshOyLWrhBK6miNt3Qa60eiP3L9B/h/Pq52uDHQnCd9dARZagyBLXxceuP4DXwjQkAAXkoAU5gK5+C5iGlZaGyUrB+959AW1oDfSFaHcFDioA/DVwyAEkygiqiAQgq18/gOcAurrXX6OsQpYcF+mKuwZOD1yDQzlevkME3iABagED8n2M/gqmXFDwf3/kXrzv4bN4883z2DuZffYFo15SoZtOgn85RJjoCxj5v3R+43tvxx9/+RRu2NPAa46Np/0L6AsYUVpIttMI5H/xTdehUVahyjJ+6LVHxvZ94owx3y4tdADwrbfswW/8vdtxdqWNn7hvfAIkEE/AaGn5DtFmXLu7jj/54Xvx5ZeX8A9fc3isL2r1kuq19oTR6hnbYv2rl1S898dejb/82jm89fZ9Ay/smX8v3yFCWJtefw3Mfw0Z7EVzpaVhYaOL+YnywN9z+PSxrZmeyO8/fWQv212j/3JaVGW024P3ridgCDJsNO/0O3hMvKjF6Wq/vANODZdbGq5sOLlo842+IF4vKSOnt9XiUP0GXt6jBaB6GSuC0++C4rbgCEKKRQIacHUzgABHMHvxymbk+rEJU7oZUL+iDBvO381fQ9u2vRamrigDyA2BVkUh0KaFgixuIWMOoavFTLWIlmZicbOHrm5hfsJfv76AsRu+rEt2DQ614OjuMI9KQIaSZlqY9jlYePcwc1DxBDTPYSWL6wdcPQGDCWbOPdzDvb5M0uEQ4+GsUJ4DyD/mXCSCDzqo+BlA/RYwsQhuWPbIs4n9+qt1DbLn73JLw+WN7sD9W3JbAYddpFWfA8i0bOeeUfp5Nux5wnOwDIdojz4jhuo3IqANHyLw63u12mBlWcJ01XmGsGt+l6+lfWSSn+Z3UIkdQKxNlgQgIlccmavh57/jprF/H3+IZZgA1NkGU8AYU9UifubNN4z9+/BCQEWw/IvtUL+CIuNd9x0b+/dhGTbRWui2j3tAkiR899cduCrfq1FSd1SINuP1183h9deNz/nDqJdVnFttR/q1fmt23rluvoF/95abx/59Yh0i6AaKqnxVT//TUi+paJRUXFrrOC/vE4NZSrXioADU7BneySt/jLk14C4YzRcJzrDpZwTx7flhIdAsI+hqZrDsnSzj0loH840SygUZE5X+62etqGK52b//ml3DCy/lOVj6p7fM4WPDsmwvl4uJHaUCmwLGz1ASteABzgaHZUeIWkiu1uYHcIZbfOHFxb4ANNnfQNZKKjq66QUrb3ad60+SJN/1x6mfqoD91f0CkeE6pPoCGidDyXBCjFVFQkcXj4FXZLFD6GrWb++Uc4j6uDtd1y8A9R1ATl3YYRTbPJdVRRgg288XGW3BKQYJQIYVWF/DsqAqEgoBDivg6gkYeyac+l1c62Bhsyuon1M39i7SXwNHHUBdwz/lit/GGVY/QNwCxu7Z/j1sD7RsXm0H0Fy9CFWWcGm9gysb3YH7lwXhs/pZlj30DHFFH8NC3a1HRze9gPeSKg/cv0D/eqwUnSmLovoJQ7TNfgYVMPoMYfW9mm2ceybLuLTegW3bmKsXPfcdMHoIs9kzsH/KqTHfAeQekqg0Bp54BRMnw6atmd5LE+FQjyFgePkXOc8AupqUC85LYpQWpn5+CNXPT9wx3Nspf+Vq0IiVg7Y9MoCuJrFE8G16/e2fruCCKwDtmRxs5/SfPtq2jZZmeMGoojHmLHyX38Jkep/xBIqwAFRzKMBTtDm6mi/vA/UbclANn942e4b3XuKFFPtf3g1OC5Pv79h3ACncDCB/SHTQlKpAB9BVzgDaP1VBWzNx/PImAGAPx8HCMlhavvqpsgRZGm1BBJzNXb9+/etzMERbnKFUUGRhRs1ACLQgA+hq1w8AHjuzCgAD9/BwjhfbSNZ81+Bg/friQpADqBgSAu1MUZP4U8As1gImcx1qTOwb12CEYfa7EzifvrAO3bSxxyeC14uDIcatIQGN7wCyPGGjyLtHzUEBjbcGylJfHBnJQRtysIyukW6I9lXKAFIVGXsmy7iw2sHl9e5A/YDBNZA53fvPkNFJfgP1C3AAhbkgq4IpYcMtYKYgA+hquvj2T1Wc+m0MCpDAaI5cyyegiRxA7P7iPYNfCdAunoiVwdL2BWsRDv0MoOgCBtWwjzeGO1IL3fZxUF1NotbPsmzXwUICmp9YY+A1E5Vt0AJ2NYnTBrsdQtx57J+q4PxqB1fWeS+firf5bmsmbBuBDqCe7/S2GOAA8qYwCUKgw0Ykh21+rqYL68BUBVc2uji/2uG+vPtPb1ua7/Rb5TmA+vVhG3B/DZlYxEKeRZsf5mDhZYToVn+Dxfv8ao8xPzDdFzAaJXXgEGnYgeGvH3MBdfXR+rEpasBQ/Qx/fZ0MoOGMSN11+KgchxXgOlhkCQWBwHa1M4BY/R5lAhBPQBsRgNgYaYEDyDfGXDQFzMsA4rTgMAHStkeDxp0WOdnNCNr6+k1WCmiU1EABzX/9+b/OdQC5IeOAYA3UrYH7e1Tgsbz6AeI1zhOIBC7Jq1nD/VMV/P/be/NgW7KrzO/bOZ3pnnvPffNUg6pKVaoqqaRCRaOBQRNICFkCjGjhSaY7UNMBMtAdDQgiMOFwuNvgdoeJtt3GAUE7gqbV7oZo7B6AJhyGiAa3xBBIQhKaqoqa33iHM+W0/UfmztxT5jn3vlfvnsxcvwjFu+/ke6rzVmbuzLX2t771zI0Zrh0ulesPUCf56QVIWQEkWERJoc6xjoGPUzAxaMBSYNOfIVVtwlUFthOJn9hE2FvY4xfaC0BVCiDZQylOuTKooQtQAYgoPVjWenlvhn/I3eRIyc9SjECmAobMVs9bywNo3iAPoLvJugUMip+drb6HWZhYp9XoZGsgxU/maCrSuJEF3Cu7A3zhpQNMwwT37A6VY7IJb7H7nT9XxSjaecXurdXDRlNgVO/eVpg86wWgiuTnbu7eXtkdIuXAn/7lLdxzyoyfbOA5XcZS/LJ/wzzUkh/PyUZ05/9GOQESapZeVfzicvfflhwlKQfn5c57pcLlLscPAD719E1cscQPKK+9w2VS3JNAdh3MK1qYbCbQ5fWXKahsBYooLj1srAoW2cR4AzyALuz04TDg08/chO8yewtTWMYvcJ3i/A+C6vj1LAo0IEvAa1uYchNtvzA5NltwPJflLXT2KXR3M35AloB/6umsAHRFWgM9NxulLV9/QKkM7WvxA0wTY9sY80xBZY9P6aFUbbIN1BXB764HEJDF7E+evYWUw3IPlybGh5qCSsRpHsoqUrWAYfNQCtxsjbRNmlvVQrc6fne/jfjK7rBQQZrPENUE+nAZF4MaivhJ16AwcQfKdb5rk8CoAESUHixrKoAo+VE5koKqSMCpiCYz7q+nYGnKFLq7zVZ/PQ8gip+do6ogKX4qpZH7eirIJsbv8cs7xc9vuLKjHJN3b3X/EKEWm0nXlpz8WEf8xll7QzZG2+IBtMIAVR8Db/g33GUDTyCbzCl4w2U9fi7CfMIjkG2mFPHL/w2zsCJ+rinvlxVAvmd6rBQeQJ5dASSSn8KDxTrm/O562Dx0bqtQQ7zh8rZybKtoYcoTyEWkFoACt1j7AXsBQ1UASQU0z64ACPMpVoHr2BVUsslxxRSru6ke8F0Hj1zI4vbIhbFiY6Cv/4fLSGnzHfguZkubgqpsAdM9WAwT46oWMKfieMpr4xcldzd+APD4pey+9V2Gh8+PlWPyJpSuYBn6HsI4LdapJOWIEq4k4IaCJVLHwNviF7ilQqhKwVI1KTEq1sC7F8PHa9ZAuYVJvAuL61Ks8/IamKlIy/jZVKTi3rb5oOkm0JWbCBVj4MX5OKn4vb4mflGSYhmn5fUnniFLNX6yAi37jApARMc4SguTLKsjMlyHYRi4a7cwyX3LRMa6Chb9xYLIGB81flSAVDiKhw2tgSZHMXKfLuNGXn/fIE29eeyimoDLLUz6PSYSSSUB16aAVflfMMZqPWz6vgNmMfg0DDwr/B3upgfLo1LMnrhivrwDWexi8fIeiOTREr9IjR+gK4DkFjpmJJdlC1gW3yqFlO+ySo+gu+0B1PddPHxhCwDwxnsmyjERK3H/TZeqT9ko8IwCGlBOUQOqFEB2DxbOeXaN5lPUqlqUPDcbc15lon23/FcE4h5+45WJ8rnRQrdMivdicXwWSfGLy/g5eZG2agx8lYdNaQKdXUPGPZorpLIC5GbF7/JkYPiAjmoKQMUamF938vUH2BUsyyQtFGiAxWNJUqABtiJ36dOU/f5kPYAAKJPTXntuSzlW1wI2CirWQGkKmK0AFOQFDuskyVjdDDTXwHXjd/fWwDdJ694b9U2YICsyhnFqiZ8ooGnP4EArACXqRkzbad5bGHHHOYp8f6pJi4mM9QsYWfzulnFfU9jqe7gxXT2GW5fGEhnrjuEu4ten+Mmsa+SephyzkNZAna0jFNAOlwkuT169kfSvFveeGuKn3/8oHjw3MgqA8vp/qO9+5y+Zsj/BPJTaH2pGIAPIp1xVFDC8TEGgm+yKl/e+Z989PwkPINdh+IXvexKHi1h5kQfKWB0uYzj5+iWSRmHSOVUKGKoBKqAm2PIUtToPIJFAhnpyIymkPIsCC7j7HkAA8DMfeBz/4WvX8eE336N8PjIULGqR2lAASQVEWwFN9wAC1GtItMgVLWDWFi8OryZ+d1sBBADf//b7Meq5+P63v0b5XBQZD6UWHLlIPQh0BVA5hQ4w7+E0V7j0PAdB0cJkqszkAoZp1J7FJytAbkb83v/ERXzt+hTf9eRl45hcBD/UiuBlG2eC7b6vKNCATMVnXwPrPIA4fI9VtoAVHjae3SPoJDxsHru4jb/z3kfwpnsmxXkX2OJXp4LUp4CFmsl2KCmA7B5AvPjvyr8XGG3EFfG9m/Hr+y7+hw+/EQzZhFMZuYgrnhVCGWlXkUpt2BVFxrZDb7FEsfu9jgfL4TLGpXyaAlGy1V/Pw0aebkKUbPU8PHt99RhufboEkbHV85GkHIsorfVXofjZWVcFWb5YUPxkjtJCN13GxTOnSTDG8APf/ID1WOY/kBVghQ+BLt9X/BuiVJkClvJ8x19qZwoK+f46JsZ2BVBlC9gJeAABwAffeMn6uTzFSoxyl++xUeBq8VMLaIC9gFHloaQoXCxjyiNJIWVTCAF33wMIyBQEsopAYJgYh+p7xqhX0QLm2aeo6fED1AKFSBaDvEBkj0+mYPEcR0m8BCfhYXPf6RH+zntfZ3zuOAyjwFUUGPr1NwvtLWCAqWAJE0v8DBUaz4/nBSKLgqWYEmadUnf347fV8/AT7zPjlx1zlRamge8WxQFRCBLxlQuQQBYn/RopFVTVHkBqC5hZwHCdcmKx3sKUiDbPuxhDx2H4oXc+ZD2mtNCFmoKlZ1GwrJgCtowTqQXMsgauGCRQtNCJSYipXQF0Nz2AAOB73nzF+rn8DqJPWxbXkb4G9r1qFWkXoD4UAj0vk/GuowDKChjN8294tVl3jPThgtpHbIzXLaAtqAXMxroeLLo6gchY18id4mdnfIRBApk6oV3PkFHPQ8qzXVlRRBT35NAi31/GsgeQfQqTSGxs/g3iRdUvChz1I3yrCkR308OmjkLBsohL/wupSDgMPCV+c2kCTjEm3tLCJDxYKj2A8hHJtuQRyJIbz2LCKxQwmxa/aZgVIQ8XagFj4JvxC1wHjsOsI6ZtLWDyNRhqCiq9uAGUJsZerYfS5iihR9I73KFkQg4Ag8DDPEqKKUFzTQGkjzkvCmhujQl0nBbXH2BXWDQtfqL4PQ31+KlroCjmymugrUWu5zr5qHuLB1CsmkDbiriiAJkdr1AA3cUWpjpGeQFN3L+A7CNnbwGT47eM1Ul9YW5CDgC+Z6rw5E0Em9F4YQKdDzHQx8CfhAdQHfIaWDyDNSN8fQ2sewZ3gc14ehEnCmOsaCFZBflf2Fk7fiHFz8a6Y8wPw1h5KSUyxlICVUcpLW5XAn67rOsBpI8HJjKKTYQ1VZBtWwPH/ayF8GARF0WwehPjtHb3cRmXCiGbB1ChwKhQCOhj4KsKRN6GJD9yAfFgYXl5D1zNADUtWjvsCiDZxNj0ZxHxEAlk1QQhL1cANSl+iyhFnHKtgGbGr1fbQifi5xYm0KGiACo9lDyXGeoA8Wd8NxvTXdUCprfBnCTZJlR27R1oBTRRxJ1rHjZym6bNQ6nnSx42NhNjj1W2gEVJCt/J4pcVHM0Cxia9B437fnHv7i/KCUyAWQRfFT+gXAOrfND0MfBGC1jClQJRlcLF35Ai7rjvI+VZjPbzZ8hY30TIn6+c8zw+pQqSc1XpqZpA21WknsPgOMzq4yUKQv2KZ8hJeADVIa+BevwAsYmwoo2YCkBEF1k7Add2RoiMo8Svie0PrzZbPR/zKLGabcpMl+qLBZGxbgtO2Z7iv+rfqUms64Omj7clMopNhBXxEyaNWw00ga5jd5jdTzdnIW7NsiRokn8mjHJNA8/qBDyUFEA2D5tCAZQn2FUKlsLAU29/OKEWsCp2h5knlBw/8RlgacGxGHhaTYzd0gNITqAjTaFRawJtaXHatPj1fRcD38XNaYhb88xLT4mfpQVsUNNCVyqo7B5A8vWXTQGrbpHzLS2KQBbDTYkfkMXr5jS79m7NQuP6A8oChlBLydegtYDmVnvYiDHmvmP3CBIKqrIFykzAN0V9AWRr4M383t2bRcX6B5hTrER8lPhVjDEH8jVQV0Hm8atuAUuV+OkqqpPwQatDfobszSP0facovhQtYPl1J5QqtUb4sgKowmNJFBDritz9ChPok/AAqqN4hkxD7BXP4PIeHmpr4FJuI7Y8g7sAFYAIAFkCtKoFR4zWa9vL+51gq+evt/u9aOYEnFcbUVScSkaLNqiFzk7hYbOygJE9GEnBorKuiTFNUatmHSP8woOqZQU08fJ5axbh1iwbwS1erhljiglvnGQKjUIBVOFhI4o3Vbu3DstevjN5v32CS7UCaNNe3kXyE+HW3CwA2Ux4dQWV3kLnuyyf0JTtjidSAliYQHvM2mIn2h1cx94esWnxA8oEvChADsoEfOB7lR5KIkGuMoG2ewCpBchUiy+QmxS7rGYMfLpR8ZsMA9yaR0hTjr25WsAYaAUMwwPIrVIAZQo0oLqFqapFTJg8izZD25SmTSqgTYYB9hcRkpTj1jxUk2/JBBqQ4iebFBsKoERaAy0qx5gXBVrAHj9Pjl+FwmVTYjhRniEhJoMyfj0vm/Yo1kDDhNyqIpXGnFufIby4923xLU20KzYRpDbZTUDcryJ+gPoMGRo+XvU+cl1gM84cceKM19i9pRHc1Yz7XiF/rYNa6OwIVc9qD5uE4meh8LBZkYALBQsVMFREPFZ52ByQB1UlWz2vsx5K5ctnpsDYGagKO3kMd2mAqhcwZA+WpJhM4nvm7qxofwBgnwKmmUAbyfmGvbxv9304LI9f/vK+IyXgo0Abwy1PcBEeNkr8ZA8lU4FRmPQKDxvD30K0hzD4jkUhtGHJI5AXMCQFlRK/nlv4AwFq/BhjxhhpqweQrQXMq2mxyVuYqsbAZ1PCNid+u0Mft2YhDpYxUg7lHtYVQKWJdpmAK/GTrq+qAo8wgfYqFD5xWppAZ7+3mxxvCrtDH5wDe/OsCDkZqPcvAEz1+ElroO6/ot/DVhNozy3jZ4mP55QKK9MIPwVjKEznTxp9E0EuQDLG8meIVkAzniHqNVgqgEwPIBE/AHmR1r7GDYKqTYTN8gDaHUkq0nwTYbuuBSyWniG++QzpApvx9CdOnHV2b2kEdzUifnqftg6ZaNs5igKD4mcyXlMBNF3GGAbuxrz0bApuPgVmbQULrYEG477X2SlqZQuT+fIOqLuPpQGqXsBQE/DSA4iZ8v2klO9bp4ClZYLOmN0gVfzdTcBxGHYGftH+4DAorb7GGHOLgaeuwOjpCpeKFibfpvARBZ5iyprZXiKObwq7Iz9vocsKaLKCYBC44Ly8xhaSCTlQmsgKRKyEiTYAhLIJdFx6KBUtOFKCnaYcKUdpYmzxAIqSzfIA2h0F2fVnaR/RTYwXcZIZDLviHlbjt4wkBZBFXcA5L1vAqkyg8wKZKJIZJr0b5qGkt3HuKAoqoQCyK6h6nqvEJ05SpByKh43VA8hlUguYxQTaLc+RaXLMN8b/B9DaiOeRsYkwCFzM8yK4zYQcsKyBdR5AcVq0d/pOtUm0+G8Y8S181DbjGTIKXPguK57B476n3B9D2yTJGhVuF9icq584Ubb6q1uYCv+Qlsn37wRb/XIKTBWcc0zDhOJnYV0PFn28LZGxvgcQxa+KdTxsigIG3cMGR2kBa5sCSE1+QqMAJBcwCgPUmgKGPAXMLt+v3x2XX87rFEKblABNhkHx8r4z8JUi9VBrAZuv8G9YxkkZPzEGumKMuTV+0hj4TCFkJt/i+KaQKYDKFjrFg0WbIjQPy+QHMKcwCY+bwHMQWFqYSgWQ1IIjXb9l/HKFldUEOi3UGZvAzsDHIkrx0v4CgNpCp3vYzMNUKaBVjoF3XasHkCiWBbnJuH48+30K33OkAoZNYbVB8cuvt6sHSxwuY6UAqZtA6wWMwHOwTNT1T3wufrXGx3UqTbTjhOcKP6EQMuO3KQVwoIzfrbwIadtEmOotYPoggUSdRFdOAbMX0HzpeFUL2ND3lN+XxzerCM4Yw84gU0HqLZxAHr/8+uOcW58hNAWM6CTryfeFf0i7Xt7vBOsUMBZRiiTlFD8L5RjzFdcgeQBZWVdBdUAFoEpIBXl7bPX9lQW0ckJWu1R8g8BFz3OKBFxOfgC1BUzIzI0CRoWBp24wK46XCiB7cgTII37N5Cf7u5uTAE3yFpxb80hRXwCmfF+ZYlVhYlx3XPGwscVPNomui98GFdAyDyDThBwAhmJE8rJsQxTxASweNlKLV+Ba1AVa/AA1wZan1Hl1LWAbkjwCZRH36WtTAFr8LAqgvhS/nlYAEgqgrIBWbbLt1xQooryA4de1OG3Q/Svi98x1M37Ci6yuBSyUxpiHegHIdRBq7TliDRQKqaoCkV8zBn6TWhDFMyPzQQuNZ8hQaQHT4udaWsCkTYSezQRaVpHaxsALo+nAbgIt4r1JMSzXQHv8hAIoTFJwLiuoSAFEdJh15PuHxQShdr283wnGaxQwKHms5ihjzCl+Jj0v22lcVcQlD6pqtvr+ygLkdBnDdVjxwkCUrKcAau8UurPjHq4eLHH9MMSpkfryqSqA7BNcdBPjnpL8WAw8vVK+ryfYaoHDIu/fQAXL2S0Rv6URP7mFLkmz9plV8dMVQPYCUD6lqsoDyLO3gBXJz0bFr4+bswgv7s3R950iPoA5xnwpTQEDTA+WooVJLmDICiBlCpjZolQeZ9YpdUA+hnqT4jfuAQA+/9I+ACjXYFkAEi1MiaEAUjyoEjHm3MmNnJmizpDvz6oCRVbAYEWR0dbitElj4Iv4vXgAQI2f4zAMfFdqAVMLGD3tGitNyKunhEX5mPeyhc6moCoVamYL02bFL/AcTIY+Xt5f4MY0xKkt2xpY1UJX8QyRFFbmFDCuTQEz48NYdg87zGJCXqhMNyeGxTN4aj6DMwVQhYk7KYCILrPV87CIUmMRkCn9L9r38n67rKMAIv+Qao7mAUTxs7G1jgcLxa+Scc/D4Qoj98NFFj/GNidx2RQyI/x1W8Dat4lwaWeAv3j5AHvzCJcmA+WYPIZbn+Bie3mXFUA932KQmmgjfPURybLHjcXgU07gN4VLkwGevznH87fmlvh5iFOOME4NBVURv6gifjaPpUSNT2TEN1GPVxTYgg2K38VJHwDwx8/exKXJQFmjChPepb2A0fPcou0LyOLDWLa7X16f6nGgnAIGaGPiiylrjnXCkPjzGxW/nSx+n376JgAo12AxhrsYA5+a8asw0c6OO+r1KcenYkpYoXCpMInetALG+XEPDgM+/cwNALCugUIBJGJVtQZa42dZAwMvK645zIzfMlZNtG0Kqk2KHwBc3Bngz57bQ5RwI35yEXwRVzxD9DXQLddIPX7CQwmwF4CW+fXFGKtVmW7SJsKlyQAv3FrgBcszRFZQLSsKkFQAIjrJuK++INg4XLT35f12WceDpa0TcO4E435WVKwroKVp5qFE8bOz1VvtYXNALXSVZCrI1VPUqIBmZ9zzsIzTWhn1QYuL4JcmfXzuhf3iZ5mB72G21A083fxY9usikv0byhG+Pc9FknJlBzuSFC6ey4wCRSh5ANnk/Zvm3wAAlycDTMMEz1yfWeJXKjAKE22vbIHzXVYkRYCqoLIVMETBTHiE6MlhaXJcET/JJHpTuJwnPJ99fr/4WTDQxnBn/hfld+8HbjGdDijjxxgrVARycimSwV7FFCu9xUn3DylMojeohU7E7DPP7+H0KFAKPIUJ9LIifr5bqAoAWcFiL2AU8asooAEwTaItCoxNar/xXAcXtvv47PPZGmi7BueSB5XDygJqX1sDxb3aqymCywVE+yS/VClQ2hRAm6RAA4DLkz4+8/xe8bOMogAK1WdIP78+1TUwKe7dnu8aE67kSZK2ezSKeekzZ2mDFdfrJq2BlyYDvLS/wLXD0Bq/ME4RJ6nxDNavv66wOWeOOFGKMdI1CSS1MFVTeNhQ/I7F0HfBWH0LXVsnCN0p1mrBCWmKWhXrFNCyFjqKn42tNTYR2moCDag73nryM+q5mOUvl7oSVLx8ygMEVAWLpQVHGfFrttiI5Igxlu/u2luYNslEVo7fpR0zfkCmwCiHUZRK5L6vTnjRFVTis+J4kk1xcsSY94rkJmvhqTaJ3qQEsjZ+gapgmS4TRck98J0iqQQ0/xAxYchiAq1OATOP97wsAU9SrkxI3cT4TYZ+UWi8qCePmom23oo+8F2tgKsrWNQpV4UCz2PlmHfLPSxayKqOb5qCRVyDnsOKljCB7IN2mLeiC5VaUQQP1RawqvgBWQzFvz+wroG88EAD7FPUNjV+QKYGkpHHwOu5RBm/7Hia8uLfD5QeVco9KKtIbWuc9IxxHbNAFG9gG7Fc9NHjV7RxRokRv57nwGFUACI6yniNFpw2v7zfLuP8ZYridzwch2ErqE/AxYs/xc/O1lotOKSgqmKr7632AApJQVXFOirI6TIuJi+1jftOD4ufr+wOlWOyB1BphJ3Fa6ipMwDR3lAt748S2b/BbLHJkqPsxdxzGRLLBJzs2OacBzV+unqgLGAc5G2uciFbH/G7TFJFQQXAUGDILXQpz7yFyuNyixhTjgGbOUVNtDABtvgJE944n0Yaa/HzMIskk+04ReBp7RGRrQBUjtmW2xAjyR+kKHBIMYyTzUseGWPFNXhlot6/npt5IYkYiVZggVBniATbKGD4qkeQen3ZFUClx40ZP/H7TSqgAcC9efzOb/eLwotAXgP1VvQyOc/iK5uQA0JBpStYeDHFyrOtgfkaWiqo9Hs43SgFFQDcd3pU/HzZcg/PKwpAukm5LX4pV2MQymugZx8D79e0iG2iEf69p8r46WugmOQ3D5MizxDxYyzzqJqFVAAiOsjWGgWMwzBGr6Uv77dL4WFT4yFCCqB6VnnYFPGjEdxWxmtOsaL42RHxk3fJdA4W5KFUxXhNFeS4pfF7xyPnip8v7OgKAq+Qn+sbAQPNoFfs3hYm0DYPG6XAY9+9LZIjx2xxEonAJiWQj13cLn5+64OnlWNDqQXMthEw8F1FQbWMEmWKGqB52GgFMsA+5lxMubIlj/Lf3QT6vlsoAd796HnlmFxknIUJOLfEL9TaR2rit9RavAB9CpgaP0BVYBQtdBuUPALlNfgtj5w1jg0Dt2gB0zcCBoGLlJeJd1i0gJVTmnSDXkCPXxmfJOVIcoVKVQtT1gK2WfH7loezuD1yYWwckz1s9Pj1tSK4bEIOmCblnHNtipWpchT3eGECbTF636QCOAC863XlM2S7bxtjnj07jGeIpiLVWxBtJsfKJEmLClKJb40R/iYVcd98327x85P37irHxBo4XcZFHGU19yBQnyFdoJ1vYsSRKQsY9R5AlPzYEQvJOh5AFEM7q1qYyvhRC46Nrb6Hw6vV8Qtzf5atgK4/G1t9D5xnu2hVKp/pMlZ22omStTYRWjyF7vx2Hz/1/tfh8Us7xrGihSlKCiNU8VnfU5Mf2+4tYJrwjv0sjoGlQCHv7la93Iu/uyk4DsM//E+exCxMit1awVBqARNxUlpwpPYIYB0FVXk8sCgEQlnB4pjqAtkEeZP41R/4BvzeX1zFY5e2lc/lFjCbF+EgUD1sQslDyctNdlUFVXmNBpYCTygrhJyyQDSA2k4mipSbwo+/73XYHvj4zjddNo7JLTj6NM0iAQ8TpV2pysNGNhEv42cpoElTrIwCR26CvEm89/EL+C/fdj9+4JsfMI4NAw83pnMAphfh0LevgYqHksWDSpgYB67do0aesqbHL05LE+RN4TVnRvjb3/ow3vbQaePYMMgG9SQpx+Eyya6dPD66x5dx/YlNhKj0MIyk66eqTTiQnyG6R1CuoNqkgRiB5+Dvf/iN8FxWxEYgq6TKidbqGjjvmAKonW9ixJEpPIBWyPfb+vJ+u/Q8F4HnrIwfQCbaVaxqYSriRwUMK6s8bKgFsR65gFFXAKL42Skn+VWr+Noev49984PWz2UT2cNlDN9lxUu54zD0fUca0a2qB2weNsIfBMgVPrUGqTUmxhvWAvGBJy5ZPx8WBYxSAaR6sDiqB0tkM4HWCkBSfIDclyW3LRHxFCbRVfHbpN1vAPi6e3fxddrONyBdf2Fs3YjK2h/0FrAsPowxY8qVEh/HoqCyHFcUQLkaY5M8qIBMufezH3zcemwgmfAaHkCSim+Cslgr+yipLWB5AcMrFVI2BVpQawKdbpwCqO+7lfGTTYyzFjDTZLtcA4UJdNnGaWuhK02MbSpIXty/QJUJ9GbFDwA+/u7XWj8fSjHSvQh1H7nSRLt6ypW8Bla3EZfHjetvA1sQAeA/fvMV6+dDqQhuexfWVZBdYPOufuJEGK+jAKIR0rWMVyTgxRQ1KmBYWaUAKrwzqIXJyioPG2qhq2cdI/cDWgMrWWeQwMGivS1gdYyUAoZ5Dckvn8t8BHndGHPZwDObgFPt32A7LpIh3adjUxlJu7fWl3dNvh9KBbJenhypBTTZPyRPwG0tTB7LW8D05Gfz/C/qCPJx7HL8jN1vzYS8J+2g93zHiB+QKXjsBQzzuJyAb+IUtVWM8hamOEmxiFKjgAaoCgzfzUzGgdKEV6C0yDmmwkf2ULIV0MTvNzEBr2KoKKgSe/yiahVkVfwAMQnRbpJdtoCZCpdNK4DXMexpzxDpPU54RRXxs3hQyZ8DaoHH1uaatRGXzxCrCXlD1j9AVpHG5RrYl9dAr3MtYM05e8SrSmngWb17uz+PsT3o3sv7umytGCO9v4gx7nvFSwGhMu7XF9D2c38lvTeayBj3Mp8R3SxRsDen+NUxLtZA+zWYphyHyxjbA4qfjXUGCewvuvkMKf0HMgNKXQU1lF4+bf4XAAwFQTkBx2x/kP0drCN8Uw7f3Sz5fh0i+ZkuY3sLk6+2gC2jRPFfAWweQGX8AK2FSfJoyVrANt//YhXDwKuJX9YClqbCxLiMHyA8bNQCG5D9++0tYEl+vDQxjqwm282Ln9WDSjPhXcapGj/Nw0aOn2+Ln3z9VZlEp5s3BayOUeAWibeusjXit8IDSG/BzKaAWdZAj0ktYKaCqmnxA7JnyMEyNjaSlU0E3QPItfjISUVwmwJI9VgyjzetACniNV0mxSaVHMOB75ACiOgmwyAbw12XgO/NI+xQ8lPJqhacfYpfLasUQPt5AWNnSDG0sdUrH3A2RAGNrkE7q3zQDhYxOKf4VVFsIqxYA7tYQBP/5v1FZFXS9qWXT/3l3eZhE2q7t/YJLjUGnnGzkp/t/N7cn1e0MAXmGG5991tPIGWPJMDeguM5uQKoYkx8o2I48LC/qGihy5PLRVxeg4GmANKvPyCbgmYz0Q7jskBmMzGOihbEpsUvwmFoehEWY7ilFhwlfl5F/PIx5Q6rMiFntQqWJhXQtgc+ZmGCKEnzKXSmAmihmRjLKsg45cU1VChcZAWQxSPJdx04eXwNBVXasPjlG3f788iuIg3MAlCtj1yclC1gljHwyiZClUKoYesfkD2Dp8sYA99VFLD6IIEu0JyzR7yqMMaw1atvIdlfRKQeqGFV/PbmFL86tnr+yuSRMZCJcQVb+bVVFUNRQOuiAmMdVqkgSwUaxc9GsYmwoojbxTVwkhetb80iYwIOoCqARBIkkqLi5T1RCxhBIc+37d7Ku7uO0t4E5P4NDVKibvU8uA7DrXlofXkfSh42acqxjFMzfob/hT4FTDWBDlwHjDH7FLWihak5MZwMAtyahVYvwqFmIjsPk6IoBMD0AMoNYB2H1Sp8ZA8WtcWpeQqgLH6RtQVRH8M9D8vrD1jTwyZV729x3OaxxHk2JaxJBTSxBu7lBQw1fqU/C1C2gg1zo/uihSmPQdlimN/DlgKG4vPlmGvgJk4Bq6N4hljiB+QtdlF5/wJlYddWBJcLiPYpX2X8XIsCSFahNoHJIABQ/wyWfdC6wG2dPcbYhxljn2OMpYyxp+7UlyJOhlUeNl3dvV2XdVqYKPmuZqvv4TCMCxm6zn7uH0ItdHZKI/eKAsY8uza7mICvwyoPm6KFjtZAK8UmQkX8kpTjYBl3UkElXj735lHmg9Q35fvi5VMkP8XLezHBxW7gGdgMUOMUPUm+nzR895Yxhp2BXx0/afdbqFhE/MoWsFVTwOzHPdcSv7RUcDSFnYGPW/MIB3kheyw9B4SJrEjAF1GiFTDMKVaBVGAEoBiRywUOcVyOoVATNCl+k6G4/qrjJxdx1QKa3cNGvodjS4EskMbAJ0qBrXktiGLdf2lvgSjhyj0sirTzUC2C9wNVwSJiWManvMdtY8plFWRiVUk2J35yAc22BvalFjBzE8Hmg6bew1YTbalFzFRQpY0qgPf9bOLerXmYtaJb4idPQuwCt7v6fhbAdwP4vTvwXYgTpm4KU5ykmIZJJ1/e12Xc9yuTbyBLwCl+1Yx72RjuaUUVfn8eUftXDdsrTIyLFjCKoZXxuvGje7iScU0BSBTHu1hAK3dvQ9yYhjg1DJTj/cDFPH/5LHZvfX33VlIQxGoLU8qhFM5lA0/XMVvEmubfAACTgY9bsyiL30iLn/TyLuInVBnZFCtHix9XWujEZ8Vx2UTbcZCkHJxLBYwNnaJWx05ewLgxzdaxifQc0Ftw5tYCkN2DyqsooAFli5P8GVC2gzXpGtwZ+ljGKV64tQAA5R4uWuiq4qePgRctclKRUZ2iJplAWzyqChPyBhXQxHPz6etTAGr8xCTEhaRgcZg6RQ0oi7hlC51dBZmkmUJK9rDRW5g2cYpaHTtiE2EW4sbMXAPlSYjmJoL6DElTnrfAyR5ANYMEHIsRfsNMtBlj2Bn62J9HuDkNsavFbxhQC9iR4Jx/nnP+xTv1ZYiTZdz3iyRHZ1+8vFP7QyXjvleoLGxQC109IgHfr1FgUPyqGUs94jaoha4eoQCqXAPJRHsldc+Q0oS8e9df33cReA72ZhUvn76LRWh/ebcrWKQWr7zQEyZqAq6M8LUYyDYp+QHKAsbNWYjdofnyHiYp4iQt4teXEvBA92DR4iM+E+jxyz4zFRpNSsAnAz+7/mYhtvueor4ZamO49RYwmwmvGT+1hS47VppAxxYFS5OuQVHA+Nq1rICxOyqfA2YLmBY/1600gc5+VRUYxXFJQaVOqWteAXKS37Nfuyrip9/DntICNgy8wqQ+0HzQIik+gBk/eYqf7Xj2Z5pVBBfX3/VpiL15ZFkDyxamogiet9AZ8dMUjLb4KD5prllAa5qKFFA3EfT4DQKXWsBeLRhjH2OMfZox9umrV6/erf8scQR2hz5uzlYkPx3cvV2XyTDA/iIy5OKCPWqhq0W8INychtbjVECrR/YZsbE3j6iFrgbPdTDue5XxK1roqI2zkt2Rj1uz6vsX6K6CajLw8crBEtMwMXdvAxezSH151xVAYtebc668nIsCkZpAqgoNm4GsbFLbBEQLmE0BJI+R1uMHmB426hQwS/wsCqHYkoAHDUqARAvY9Zr4yQl4nQdQGJceSkX8tBHT4pjdBLqZHkAA8HReAJJjqI+Bn1kVQOoUOkBtAQtjs8CYmWibCiDdQ6gJ6AU02zUoCpCzMFEKuLqCRY+f7zpKAVJvsbN53MRp2qj7N/AcDAMXz16fgXMzfn1fUpFWtNDpCio5fnHKDRWpcrxm0EBT2MkLQDdnIU6N1PcQfRJiF1h59hhj/44x9lnL/z50lP8Q5/wXOedPcc6fOnv27PG/MfGqMRkGK1/eKQGvZnfog/Nyp1smSlLMwoTiV8PuigLG/rybI6TXReyo3ay8h2mE+Sp2h0FN/KgIvoosfrSJYGMy9PFVoR7QW8B8F/NQfXk3PIAK/wtRfGD58SqTYzHhxTbCN22UegDICmg3ZyFuziJFfQFkLXRAloDPI1sBSG9hKk207QapkgeQYyqAmtjCNBn6SFKOZ2/MDPVFX1IARUmKKOFm/CI1fkJ9oV+f4jiQKTBsJtBxAz2UxAbLV65N0fMcJT6GB1BottBFSZlg61PkbNcnIEy0zQJl3EAF1WRQxg+wrYGO4mEzCNQpaoC5Bhbx880WRUAy2baYQDe1DbaIn2UTYZ4rWAwPIF/co9nnuoeUbrINaG2ytmdI2sD4DfNnyDSyxg8oPeS6wMpsinP+nrvxRYiT59SoOvkRRQ3yD6nmlJSA69V54YuxQwWMSk6tKGDszaPOqgfWYRS4CFynNgGn+NWzO6ouYOzNIzjUQldL3SbCXsdb6M6N+/jU0zcAwNh9HEpjzA0FkLb7LX4ViXegGaSKn+snvDRrAg4AnNvu46W9BeKUGx5KQ4sCaBioCgw5Pss4LePnmgapcvxEEqQoWNLmteCcG/cBAF94cR/f+NAZ5ZiI1SJMjOQRyBJIOTlcRmlxXVZdf4BoAatWUDUpgTw37gHI4ndqFBTtSUB2jzpMmqJmUVABWYLdd0o1lRzD6vjZWuiaV4DcGfgIXAdfeHEfgKlgkSchzvUCmq8WGcs1MC8AudXxy34118CwYR5AAHB2u1/Gz7IGzrVnSF97hoR6/HyzzVj8nVDykascA9+0+I37+L0vXUOYpEb8ZBXfsCPveM06e8SrymToYxGlxeIhQxOEViNamGwJUNd3v9ehaAGrUWDQ9VcNYwyTYX0LDsWvnt2hX92COI8w7vvUQlfD7jCTWMuGuYKum5BfngyKBMbwH8ingHHODQ+bnubfIP4/+n51Ai7L87MWMH33tlkTcADgyu4AUcLBuX33G8gLQEX7Q3UL0zJOjhA/MwEXCiq5CLDpXNkdAMiuHyN+UguYPX6qh1IWv+y46zC4DkOYmAoWz7G3MMWagqMJXJbjp92/jDGlhWkeVbQwRWUC7jmsKMIGnmN4UAG5CbSlha6cotac689xGC7vZmugw8xW4IE0xcpmQg7IRXCxBpZFcNuUtdo1UBqD3hSuyM8QvYVJmoQ4ixL4LlMUZoBUQIvUZ4heIAK0QQMWH7kmKqiu7A6Kf2PdM6Qr3Nbqyxj7LsbYcwDeCuBfMcZ+6858LeIk2K1JwMv2h25URo+DaGG6OTUVBF3f/V6HSU38ihY6KqDVUtfClHlQ0f1bR30LGLUgrmJ3GCDOx73rlJsI3YyhSMAB4DVnRsqxQeAi5dmu6yLKJuCIl3LGWOYRkqgFIEPBopsYC4NUp2rCS3OSb0CN3/2W+AFZAcOmYNFNjBUFkEh+pAKGYoBqmWLVzPgNi59t1x+QJT+LvBVxuDJ+5b8/0BUYCUfgOmCsTEJtJr1NUlANA69QrejxA4CBZGK8CBNFgRboBYxodfyyv8esLYh6gaMpiHv4vtOjYjqcIPNBsyuoAr2AoSmA9AKabrLtWdbArAjezPgBWQxlBoFbqaAy46c9Q4o1UI2hUAa5LXyGPKCvgZqPVxe43Slgv8E5v8I573HOz3PO33unvhhx9ykKGJYEqGgBowS8ElFAu1FTQOvq7vc6+K6Dcc+zFyDp+luLydC3FtCALAGn+NUzyRUsNqgFcTVlEdf+DHEYMOqIvFrnsvTyeTZvJxEUY7jDrNA98F2jxaRQD0RCvq/t7spTrmL55d0xBhNEDRvhCwCXJ2UB43UXxsqxMn5JkYTXedjICbgtfqoHUPZrok2xatru9znpmnvkfEX8oqQwIx/oCiDJG0MuoAGWMedS/ESin1hamJqWQIoC0CPa9QcAgyAbw805N02gLQl4z6+JX2HS68JxGBxmj1/TWpguT7I1UE++gdyEN6xoAdMVVPmv8hqo37+AagItx49z3sg2WPkZIqaWCga+iyjhiPJNBHUKnYif3kas+XhpPl9BsYlgKqiiJiqopPg9rN3D+iTELtCsq594VSlbmMwEaH8ewXOYsigTKuUUJlsBg1ro1mFSMUVIjIYnBUY9q0yM6fqrZ3cY4HAZK7uxgv05xW8VpY+X5RmyyKYgdrWF7hseOA0AuO/00GgdKhQsUWzsfgOqyelCJD+6B4vSglNO+fJduwl005JvWXVxYbuvHLO1MA21AobY3eacYxEnlvipHjWB1gKmmxg3LX6Ow/DA2SyGr7toLwDNwuopavKauIiSWgVLpg4Q/iF5/OQWurR5LWAA8PYHs3v44fNbxjHRxhklHEnK1QKaxcNmVfyAcoy556omxnHSvBYwAHjX684BqFJQSZMQazyUgNLkWFaw6AbGgNQC5qhjzkUxyG/Ys+htD2beXW+8smMcUyYhagVIxlhWJCviV68ASlKOlENpAdM3EeK0eR5Kj17cLn7W3+X0SYhdgLIpomBVC9j2wG9Uz/vdZqvnwXNYZfIDUAFjFVVThPaphW4tdkc+bj5DLXTHpZhENw8L01TB/iLCA2fMF3+ipM7Hq+sFtMuTAf7tj34T+p65iTKUplgttBHIgOphU2UCrSswFBNo4+W9eQqWwHPwh594N17aXxjvIfLubWGAqiWQ1w+za1L4CPV8LfnR4idaFW0mxnEDFVQA8H/98DfiT569pbSDAVmBIXAd1UPJmKKWgnMOxliuYJEKGBYPFjn5BjQPmwaaGAPAz/xHj+Mdj5zDN732jHFsEHiYR2ll/ADVw0YpAK3wsPEd1cRYXItNU7B82+MX8Gs/8BY8dmnbOJZ52OSTEKvGwEdl/OTPA9dFkvLMm0tql/WlInicqAo+oHnxe+jcFv7vj3+jVYk8kIzc9fgBmoo0VlWkotgtrkHj+nMds4WugSrIYeDh93/8ndZJzV30AKJslCgoW8BsHjbUPrKKzITXPgWHWujWg+J3e4j4iRd1AbXQrYesgtQLQNQCtprdGhUkeVABr7tgJj6AOkb6cBkbbXJygqgnP3p7SZpyxClXEvAk5cqakLWANSv5AYALO31c2Okbn8vxmy5zBVDFGHiz/cEsoIWx6QGkJ5BNU68AwKjn4RstxQugHMMt4jfqmR4iYZLmxchEbQHTPIJCeYS0a48fgMZNEXIdhnfmKhadge9gESaY5v5nozoPm0hroctjKgg1k2zPddQpdEkzFSwA8NZcRaUz8MtJiNNQXQNtLXTy5/IYc891pAKG8ABSW5iEmqppCioAeP1lU/0DqAqgaRhj1NOfIS50E2gjfvlxm4eSbgIdNVAFCQD3nBriHsvncgGtKzTv7BGvGsXurcW/4dYsJPXAGuxWeLDcmkXwXWqhW8Xu0Ld6KN2iAsZa7A59xCnHoWbCS/Fbj8LHS1sDOee4NYvIw2sFhYrUtgbOI0wGgfE5oSqADpcxxppRtlrAEP4XdgVLmdyUu9+AaSIbeM1LfqpQ4xdhGLjK7r5coDDi55oFIMVEu8LEuInJYx3DwCviBwBjSa1nmyLUVxRArhG/QI9fqh4HmqcAqmMYeJhFcfHstcYvT7wX0hQ6oFoBFEj3cGTzUPLak8INg3IS4uFCXQMLjxpJBalMUatQsMhtnEoLZwOn0K1iKBnh6/ED7M+QfsUaWHhQeWUBMtJ95OLmeQDV0cUWsPZc/cRtE3gORoFrle9fPwxxZkQv76uo8mC5frjE6VGPWuhWsDsMcMuSPF4/XAIATm/1jGNESZWPl2h/OL1F93AdVT5e0zDBMk5xmtbAWrI2YbsC6PphSNdfBQNNAbRlfXnXTKA1BUuxexvryY+thal5/g11yPL9g0VsGKTKJru6eqCYsqZMYUoNDyBlDHyaNq59ZBViitBh7rcnx7DwsInKGMoKFmMKU5wq6gFAGwOfihac9rwPiTHmB3m7/5a1gCFPAauPH6ApWJQWujx+DVQAVdH3s0mI+4sYccrV+PkrpqhVrYFSEVJf/4B2XX/9I62B5hQ1wKYAklvoNB+5lq2BXWwBa8/ZI+4Iu6PAagJ9fbqkl/c12B3Zpwhdn1Lysw67wwAHy9gwLb1+GMJhwIQULLWcqvBgKQpoIyqg1VFlYkwFyPVwHYbJwLe2EYsiOGEiXj6ny+zlfax5JfU8V0m+ARQKAn13PNIMYu1jpJvn31CH8FWaLWMcWBVUpYmxXkATP8stOPLutiiURVoLTpuSb6A0MRYDF1QFhmoSq5sY61PWFA+gmjHwTWsBq2MQuJiF2f0LVMRPNoH29fip8WGsnKDmGR42zZyiVodQsLyyvwCgKqh0hY8xRU1TqOnx8TQPpShtZgtiHaLldbZM8jXQfIaYbcSu8mupADJNtFOetRcLooQ3sgWximHecjgL4xV/sj205+on7gi7w8Da/pDt3tLL+yp2h4G1hen64ZLitwa7I+FDpRUwpkucGvU6O0FoXUT89Hv4Wv77M1SErKWqBewaKajWxrYGzsME0zCh+FUgzLH3F1Hl7m3dBBzAYuDplckPoI6RjtO0VcmP4zCM+x72F3EWPyP5sU1R0xQYVSbGriV+DZyitortgYf9edbC5LvMKPAAWfGMc46FpsDo6QqWxPRQUuPH4TqsVc/z7b6P/XlUFoCke7jvqwUKmwn00hI/oRj3HKYp0NqnoBJr4HO35gDU+DlOptIT8TOm0GkFINNDSY2faHFqVfwG8jMkqmgB058h9kmIhgdQMclPU5G2aA0cBS5chxUTm7tAe84ecUc4O+7h6sFS+Wx/nkkyqf1hNWfHPVw/XBojE69RC91anM2LZPo1eO0wpOLFGpzdygxS9fgJBcsuXYO1DAIXWz2vMn5nSMGykjOWZ8j1aR4/uoetiNbDvVn28r6tvbxnyU/VBBxRAEryXytawDQFi98iDyAgi+HePMKhLX7SFCt9Ao44brSAaQU00wS6XfHbGWTxy5JHdeKrrLAQyaGswLCNMS/9Q8wWuihJW6egmgx9TMOkaH9VFSy6h41mAp3Hj/MsRlHM0ZOSa09rYWqjAkj4Ez53My8A2e7hyF5A0xVWkb4G6i10afviJ54h1w+XWESpUkADRPx0HznNCD8/Lq6vXs0mQlON8KtgjGG771knhLWV9pw94o5wfruHV/Tku3h5p+RnFee2+0h5mTACuYJqusSZMcVvFee2swKGfg1mCipKHldxbju7xsz4hdgd+q16YL9anNu2FTBIAbQu57f7lgJaHj8qoFnZ6nlwHYZrh0ss49Tu36C1gBkKoEQzQJVGIAMwTGTb5AEEZAnkrVloV1B5DjjPkha9gAaYHiyR5AFUZQLdpt1vAJgMAtyahzi0xq8sYFTGL1YLjEX8Klro2vYsKgoYuYKl1sMmtitYRJumbEIOZAm43sIJtKuFSRQwnrs5AwDrPazGzz6lDpBVkKWCxRq/FhVx664/QPeRUwtklfEz2jjlNrr2GeFPhkExMKULtGf1IO4IZ8d9XJ8ulWo5Gciuz7mxmYDPwgSLiAxk16GIX94HLrg+DSl5XIO+72K771niRy2I63Ju3MPLevzygu4puodXYo3fVHgoUfxsMMawM/BrXt6lEb6agkWfMBTp7Q+OqQCKW6hgmQyCXMFSV8BIjAJa9rPuwWIZY6614LQtfjtDvyZ+pUJAHyEtfjamqOXxcRwGh+km0Gmr2m8AqYBxYw7GSk8WwLxHl1GqeQCpJtH6lDnf1U2g29fCpCuAagsYcUX8NAWLLxUhVRNoYaLdnhR44LsIXAfP3cjjZ1kDq6aoGQoqiwk0UF53ScrBebviB2RtdKQAIjrL+e0eOC89LwAykD0KooAhJ0BlAY3it4pCwbJvKggoeVyPc9t9vLxvttBRAXI9zm/3TRXkYYhxzyvGphLVnBv3slG0y7KXXjxPSEVazc7Al9ofTA+bUNu9LeT5rgOHVe/e+p6pwGjbBBcgVwDNIxzaDFD9MsERSaIxhltKbpKUS8mPJX4tVVAtohTXpqHpH+KXCoGyAFntoZRNASvj47tO6xVA24NSwbLV8xR/I1GgKE20zSlggDqFSY2fpmBpoQfQzlAtAG0b97CrrIH9GgWQ7gHke3r82ldAY4xhe+AXCirbGhhKbcR934xf4aGkmUD7mgIoamEBEsiGzOxZPFzbSrtWYOK2OTfOWnDkAgYZyK7PeUsL0zXa/V6bnudiMvTx8kF5/S3y0ciUPK5H1sZpKlgofushFCzCjwGgKX5HQayB9iI4xbCKnYGPr75yCAA4NTILQAthYhwnCDxH8WiRE3DdwDPIfxUv9Zzz1k1wAbIE8pX9JQ6XsTV+ALCIUywsCiDZw0ZvH9EnEGV/poUKoLyA8dWrh4bSUcRqEaWSiXZ9C53cwhS46vE4SVt3/U2K+E2N+IkEW5jvLqLE8KAC5CJu2UIHZAm4Hj+gXS1g8vUHmH6FPc8p46dNUdPv0aINVipgRPL9G7fPAwjIVGhfvToFYKqVs/hVmGhXxS9fA/UiuFBDtnENJAUQ0VnOWzxEyEB2fUSSbUt+yEB2Pc6P+4oCqPBfoetvLc6NTQUQFTDW5/x2H8s4LcYhAzTF7yiUbZzqM2Tgu8WoVcJkZ+DjIFdNiSKaoB+4mIdZ8jMPk2JkskCW9+sGqHpyWb68t+v1b2fgF6ozI375bvc8TDDPx/zKMczip7aPVMVP/Jk2xg8ADhZxdfyipLgO5Xu557naGHi1gGGbstZGBRoAHCzN+LkOQ+A5mEcJkjTzoRr65pj4oo1TU1DZ4gdAKbI1nZ7nou87RQui3sLU913Mo3INHMgtdtqUtbLAkxdxtSlrbV4DxTPkgnYNDny3KKDNwwQDaf3zXAeuwypbwHSFUFsLaEJF2hXadfaI28amALp+GGJCBrJrEXgOTo8CawGNEvD1OLfdw8vW+FECvg7CxLiYKJKkuDWLqIVzTc5afKiuUwvd2pRG7lL8qAC5ErH5Apgv76PAwzJOkaQcszBR/EWAcsoVICmAiilheftJfrzwv2jZ8/y8NGThwo4ZPwCYhTFmRQHD3sIUasmNTQEUp7x98ZOuOSN+vSxWs2WMmaWApiuAshYwu0INyFqY2tY+osRPu3+BbMz0bJkURQw9foA6hlue0tczCkDCw6adMZTXQsGo5xb37kwrguv3aJikYCwrvAGWKWstbWGS43ZOi+Go52Ga37t6/ABVpbdqDSxb6Nq2BvZwaxYVa1zbadfZI26bM1sBGNMUQNMlJT9H4Oy4pySP18hA9kicG/dxldpHjs25cR9hXvQBgBs0wepI2No4yUR7fWw+XtdIQbWS+8+Mip/1Z4V4WZ+FsbF7C1S0MOkKFu3lvW3y/dec3Sp+1hUYZfySIokcaAl4sbut+Yfo8cv+TPtamF4jXX96AUOoVWZhgllkiZ/rIEo40lSaYqUrWIwWsHalH6OeV2we2AoYw8DLr78sudTjB6gtOPXxa6eCRVyDegESAAa+pxSABpICrcpDSbTJ6lPW4hZOUQPK+Nn8CjMFUIo05ZhFavwAdUx8qD1DerqKtIhf29bA7Bny2ef3T/ib3B3adfUTt43nOjg9UgsYL+0tCmUQsZpzmonsS/sL7Ax8MpBdk3PbPbxysCxeJl/Kr8VzY0og10GfRPfSHsXvKOhG7mGc4vo0pPitSfby6Sgq0pf3FxS/FbzmdJmAy/4+QJksigRSb6XrefLurerPUKoLspf7uKXqATl+egFooBXQXIeZLUqGibYWv1hNwNumHpA9HlfFD1hDwSLH13WM+MkKl7Yg/kV6/IAshvOoPn5LSYVWG79UVbi0BVF4PG/JNzIFUFY8m4exPX7iHo45etr9DchtsO1UAN2Xr4FysVAg4pW1ccZWFansQQVY2mBbriK9/8wQAPC9/9sfFBunbaZdZ4+4I5zfVsf4vnBrgcu7gxP8Rs3i/LhXJN1AHr8JxW9dzo97iFOOG7kb/wu35nAdZpVVEybi5VMUzl7IR0vTPbwepYlxWUDjnOK3LowxnN/uF22cnHM8f3NOa+AKXndxGwDwn73lXuNY2cKUYGpTAEm7t6KA0StawPSX9/b5hwDl/fnI+XHhxyIY9eT4ZcmPXGSTx8CLJEgkPZ7DwJiaVMVp+zyAGGN45PwYAPDkvRPlWOA58F2WxS/3GLF62EgKFsMkWm8Ba5n6AgDe/eg5AMB7H79gHBsFLqbLBNOlWQCyjYGvjV8Lp6gBwBvvmQAAPvTkZePYMMhawDjPFCzW+ElroG5CDsgKoXYqqB7LnyE/8E0PGMeG+Ro4zdtgTR85R7l/gXIN7IyKNFdQPXXfLnaH/oo/3XzIkZEwuDwZ4GvXMif5ME7x8gEVMI7C5d0BXjlYYhEl6Psunr85xz2nhif9tRrD5d0sVs/dnOPMVg/P35zjwna/dbsNrxYiERLjQJ/PC0BXJnQNrsOo52EyLMepPncr+/UKrYFrc3kyKOK3P48xDRNcoQJaLa85M8Lv//g7rXESBZ/pMlMQ6O2cPauCxW7gWcj7W5aAuw7DH3ziXdZph8IwdrZMrC10tviJpJExZigwdIVGW/jk33gLXIdZ1coDP0vA55YWsJ6eIGoFCmNKmOYR1BZ+5gOP42996yNFK5jMIDdyn0eiBay6hWll/FrYgggA3/vUPfiWh8/ikuVZOww8zJYxFlEKziuuP+keVj2odB+0dhYwXn95B7//4++05htDxQjfvolgthFrkxCTRDverjVwGHj49z/5Lpzf7hsq3DbSrrNH3BHuPzPCMzdmSFNOu9/H4P5chvmXN2bZ7vetOSU/R+D+09nD65nrWRHyuVukHjgKF7f7CDwHz1zPCxg359jqedgeUL1/Xe47PSri9/xNUlAdFTl+ooBG9/Bq7jk1tL54yvL9mdb+AFS9vGv+DYZ8v30vuBd3BtakRPZQqjRALSbcmOoA2SMIyE2gW5iAT4YBxn37znfmYVNtog1kCXiSciQpN1qYlloLUxsVQIPAtRZ/gDx+kT1+ZgFNK2C4rtZC174pakBWxLUVf4BcARSVHkpyC5PNBFq/f+XjbW1hAlC52az7oNnWQLkFEZAGCVTFr4Vr4KXJoHWtlVW07+onbpt7Tw0L5c8zN7IknAoY63NvUcCY4cY0xOEypvgdAfEAEwnks9dnFL8j4DgM9+wOigLaszey+HVhR+NOcd+pYbH2PXtjlrUgWowpCTv3nR7ixjTE/iLCs/l9fGWXFGjHZSi1gGUjkHUPINd8ea8cA9/OCS51FC1gUWIYyAJAzy/HwItdbrmFpOeZJrxdih8ADPMpTIWJtm9vwSlHlNdMAWuhh9IqhvkUMHv8VngAWVro2qZeWcUw8MA5cDO3BpB90ByHwXeZEj/d4wuQFCyihakjiT5QtoBlRVyLj5zvKgU0wPQA0lvE2qYA6hp09ggD0Qf5lVem+NLLhwCA154bn+RXahTCjPKr1w7x5Vfy+J2n+K1L33dxaaePr1w9xP4iwkv7C4rfEXnNmRG+ejUrYHzplQM8dG5rxd8gZO4/M8LzN+dYRAm+9PIh7js9LJIcYjVCBfnVq1N8KV8DHzw3qvsrRA3F7u0yNvwvADVBXOReNn2/YoRvSye41NHzHDCWt4BFFgWVa4mf5iEijnPOEaXtbGGqQ3iwzMMYfd+B46gFHiBLHBd5i1jfUwscuolx0LHksYxfjYKquAYTpQ1PtICJMeZx0k4FVR0iXtfyqbB1kxAXUYqeRSG07IACqApZATSPLG2wljWw0kcuba+KtEt05+on1ubR3Ejsz1/cw5deOcRk6CsTIoh6dkcBLu708ecv7BfJz2spAT8Sj13axp+/sF8W0Ch+R+Kxi9v4ytVD3JiGeO7mnAq4R+Sxi9tIOfCFlw7wpVcO6Po7Io9fyp8h+Rp4ZXdg7DgS6yObQK9qYRIJuChYmv4i3du9ZYxhVIzhthfQUp4l1kIJ1NMT8Dx+ScrBebfiB6gtYPq9LCeIRfLo13kAdVEBpLfQWTyA8hgtYtUE2vS4aecUtToGRQEoGy5QVwRfxok9fhUeN11AxGt/HiNKuHUKmFj7llGCwHMK1bhx/cXde4a0ETp7hMGpUYBLO3189vl9fPb5PTxyfkztI0fk8Uvb+Mzze/js83vY7nu4SO0jR+KxSzv4ytVDfOprNwAAj1ygAsZReOzSDlIO/PofPwfOKX5HRRQw/vCr1/H09RkeubB9wt+oWVzZHWC77xVr4Ovo+rstRPJzsIgQxql9Cph4eY9TOKx6jHnUYg+gOgaBW4wxH1iSHyCLna6gEse7vvstK1jq4ieuQ1kBpJtoR2k7PWzqEPETHjbyPVwoVKIyAe/XeNxkJtDdip8ogl/Lp0vWroFRaty/gEUF2aFrUBQcRQHNagKdiAJaqiogjSlgIn7dWgPbRneufuJIvPn+U/hXn3kRn3l+D2978MxJf53G8dT9p/CVq1N88tN/ibc+eJoKaEfkqft2kXLg537ri7iyOyAPoCPydfdNwBjw3/3rz4Mx4C0PnDrpr9QoruwOcGG7j5//rS8iSTne9uDpk/5KjYIxhjff8U8NowAAGqZJREFUt4tPfupZfO3aFG+lZ8htMe5nL+8v7i8AAFs93QPIMdpHxDNHH2Med1ABBGQxPFjGOFjE1vgBQsFiKWDYpqx1LAEf930cLGIcLFfFTxTQ7AoqIGvB6VILIpDFL0550cI0kk2g81jJCqC6AkbcQQ+l1Wtg6WGziM0WOsBcA9toYlxFEb+99Z8hAvP6E/Hr1hrYNujsEVa+4w0XkeRV3nc/eu6Ev03zeP/rLwIAOAfe8+j5E/42zeOtD57GwHeRpBzvefQ8FdCOyLlxH19//ymkHPj6+05hMqQWzqPAGMP78zVw3PPw5L2Tk/5KjeM7nriE/BGCdz5y9mS/TMPp+y76voOnr2W+Xvr9LBco9ORHH2NeKFg6lPwAwGTgY28WYW8eWeMHCA8bSwFDjl9HFVSTgY9bsxB7swiToTopLLAV0GpawNo6xaoOEbOnr00x7nvKv19W+ET5JDW9AAmoRu5dS77l+AHAbt0aGCWGAg0wFSxdmfYEADsDNX7WZ0hVAajCR65ra2DboKZ8wsq3PXYeP/Lu12Lc9/D6yzsn/XUax72nh/j573kCn3thH9/9dVdO+us0Dt918EsffQr//I+fw4+95+GT/jqN5O9+9xvwC7/7JXz8XQ+d9FdpJP/Vux/CdBnjO564SAbQx+BDb7qEr1w9xKXJAA+cJQ+l22UyCIrJiJOBmYAvi5d3Vb6vHxcKlu4l4AGevznH4TI2CxhSglO0MFW0gEUdnKIGZAn43jzCjVmIB8+qhu5qC52In5xAuloLWPemWIl79unrU+P6E7EIK+NnJuDdi19WsBBr4I7lHpZNjOsVVJmJe5c2Fn3XwVbPw9PXRQHIjN+yIn6e68BhMCZJdk1F2jaoAERYcRyGH/tWSrxvhw8/dQ8+fNJfosG87aEzeNtD1DpyXB48u4X/6SNPnvTXaCyTYYD//nueOOmv0Vh818FPvO91J/01WsNk6Fe+vMtj4BdRohgYZ8fL3XGRBPW8br28TwY+/v1XrmU/6/HL47WME2kCjqzAcLE3jwBI8etY8rMz8JFy4LmbM7z53l3lWDEGPk6wiN38s+ox5vqY7i4gChbPXJ8ZUzkZY+jlRdoqE22gvPbCOC0+6woifk9fn8J1GMZ6C5PvqGtgjYKqi9cfkN3DxSaCsQY6Sgudvukl38Pi1y7GsE3Q2SMIgiAIgthgdgZ+kRxWyfc551hqE4QAdXdcJEFdTCAr4+eaChalgOFakp+OxU/EbBGlmIz0AqSkoLIpWLQx5l0sYAgFyzxKjOQbKFV6VhNtSWEFZPdw1+I37nlwWH79DXxDvSMrWJa6h5JL8QOyos88vz+NFjpJpaebaGfHy/h2dQ1sG3T2CIIgCIIgNhj5hd1UAGWvclHCDf8GQBuRHHVz91aJ36CmgJEXJxzJHyQzSC2nrAHdS352pWtOFDMEiodSbE5R08dIL+Oke/GTimY2Tz6h0qtVAMnx69j96zisiJve/gXUe9joY+CXUTcLQPIauGNrI67wkcuOu8qUMPF3iOZCZ48gCIIgCGKDkYs++su7SHAWcWLfvfVMBVDP79brnxy/qgLaIkpzA1lL/LreQneE+GWf2T1s4iRFytE5XzW5aKYXIIEsHososXoA9TQPoDBOOxc/oIybrl4ByvgVKsiaKVZh0s34icJZ33eMAo+YApamHIvIjI88Jayra2DboLNHEARBEASxwVzY6QPIXrp1881hkPlhzMOk2r9BK2B0TUFwYbtf/Hx23FOODXM/kXkUYxmbHkrUQgdc2BkUP5/d0uJXXH9x0QJW5WHT1fj1facwbtavPwAYBi7mYVK0gOkeSkC3PYCAcg3Urz8AGPVczKOkUKdY45d0O34X8zXwlKWANupla948SrCMEmODwLaJ0MUYtgkygSYIgiAIgthg3pBP47RNXhkG2cv7LEzy9ocaD6COyvefuDIpfpaLQYAevxUKqo4W0C7tlDF74h51MmzgOfAchllYFh91DyAgSxzdOCuCdC1+jDEk+fjxN94zMY4PA7e4/oCK+HW8AHRlNytCPnpx2zhWxm/1FLVlR02g33Alu28Hgal+GuRF3OIZom8iSM+QZUfXwLZBZ48gCIIgCGKDES/v3/LwWeOYeKGfLuPcALVmgktHd2/Pb2eqgXHPMwxkB35ZAFrGluTHVgDqWPwYY/ByX6Rz475xXCTgVhNjKQHvavyA8t59k1SMFAw0BVBVAU382sX4icLPoxfHxrGB72GWr38A7GPMO24C/fil7Bny1gdPG8eGxRoYGybaAOB7zJiipq+jRLMgBRBBEARBEMQGc27cx7/4m2+zJj8j0YITVezeSmPMu7p7yxjDb//YNxv+SQAwylvAZss4VwBVt9B12QD19378nZiFifXYMPAwC2Msomxak2h3AlQFi5MnjV2M3y9835P48iuHVhPjUeDhpf2FpAAyp1h1XcHy0bfej4fPj/E2SwFj1HMxkz2Uattgu2dCDgAPndvCJz/2FqsCTbSAlSrSmjbYjirQ2gYVgAiCIAiCIDacN9+3a/18ILUwzcPEkPir8v2ks7u3D583i2eA1AIW5fGzJD9RwpGm3OrR0hUuTQaVx4QCaB5l8ZOvL3mMufi8i/Eb9308eW/1PTzP718AyjWot4At47ST8XMchrc/dMZ6bBC44By4OYuK38voLUxbvW6mv9/wgFk8A+QWsLi4h2WUSZIdLaC1DTqDBEEQBEEQDaUoYCxjHC7jYjdXII8xDzuaPNbR8xwwlplo2+Int+CUE3C6N0WojmEvKwAdLuJCUSWwx4+uQRlRQDtcxgCgxLAooCUpOOd0D1sQLUyv7C8AwHINuoV6j+JnIp4h1w9DpLwifgnFr03QGSQIgiAIgmgoogXs+rTq5d3p/AScOhhjGAUepssE06VZwOjJBYyOeiitYuhnLWCHYYytvhY/2QOI4mdlGHiYhnFRAJIVKj03S87DOEWUZEbSFD8VMcnvlYMlABgKH32MOcVPRRSAyvjVT0Kk+DUfOoMEQRAEQRANZaC9vI/1AhD5N6xkELiYRzEOljHG/QoFi2xi3EEPljqEAmi6jI3k2x4/UlDJiDHw02UMz2GVY+CpgGbHLGDUFMGTbnoo1TEMtAKaXsTVVKQUv+ZDZ5AgCIIgCKKhiOTnav7yblUA0e5tLaIFZ7qMC0WVgKZYraZoYVqsWQCi+CkMAxdxynFzFmGkTaqjAuRq9DVQL2BkRXCpgEHXn4LxDNHXQFKRtg46gwRBEARBEA1FGHZePajyv9AUQJQ8GgwDD4eLGLMwqfawoQJGJcPAkzyUajyAkkT5jMgQCoyrBwujgOY6DK7DECaJdP2RgkpGjh8AbNkKGKSCrKQsAOXxq1OR0iZCK+imDTpBEARBEEQLcByGge9Wt4BpY8zp5d1kGLi4epjHr6oFLEnzSVaA53Rvilodw8DF4TKG49iTRyBLvJn2GZEhtzDZJlSJBFxMoaN7WEXE7+V9oYKsHgO/jFNqQdRY1QKmTAGLaBOhDdAZJAiCIAiCaDCToY/nb84BWBRA0hhzmuBiZzKojx9QerBkU8OoACQzGfg4WETYn69oAcuT8J5P16DMZOgDAJ6/OTeKF0AWwyjhNEWtgskgAAA8f2uOvu/A0woUug8aXX8qrsMw7nnVa6BcAEpS9HwqoDUdugMIgiAIgiAazO4wwPVpCMD+8g6UY7hJPWCyO1odv2XeAka73ya7owApB/bmUXX8cgUVQAognd1hVsC4Pg2N+AFZDJexFD+6hxV2R1kB7cY0tCuo8gIG55xMoCuQ18BaE21aA1sBnUGCIAiCIIgGc2oUFD/bRiADeQtTkpJ/iAU1fmb7CIC8BYfiZ6MufvIYc1Kw2Km7f4FSwUJTwOxs9Tz4bqbKqyugUfyq2ZWuwXoVaUL3bwugM0gQBEEQBNFgxMs7Y+VuuEApYEQJ+vTybiAUGABwZqunHFMKaFGCPrWPGMjxO63FT77+FlHmYUMtJCpy8q1ff0A+hjsp49enIqQCY6woop2WYikIivhlBaA+XX8Gp/I2xL7vYBRUFMHzGFILXfOhM0gQBEEQBNFgxMv7ma0eelpyKHvYzKMEg4CSH51TUtHs4s5AORZICpZ5lBRT14gSWcFyaaLFz1IAGtI1qDAZSNffpG8cz1qYkiJ+dA+biCKkfv0BQC9XUBXxo3vYQBQhL+0MDI+znlIAojWwDdAUMIIgCIIgiAYjXt7PjU31gJyAz8KEkm8LioJFUxDI8ZtHFD8bsoLlslbAkMeY8ziboOaTh4iCbFp82VLAEB42s5AKaFWIe5jidzxO1RTQ6BnSPqgARBAEQRAE0WAePLsFAHj62tQ4JhRByzjFIkyo/cHCA3n8AMBx7LvfyzjBjOJn5azUtqQrqIAshssoRcI5qQdWcGHbVAD1cg+beUgKlirEJDVbe6EeP7qHTR48l62BL+7NjWNiDVxECakgWwKV4AmCIAiCIBrMex+/gHHPw4+857XGMbFbOwtjenmv4KFzW3jzfbv44BsvGcfK+GUtONR+YxJ4Dr7/7ffj/tNDqwnvMHAxiyh+dfyDv/pGuA7Dw+fHxrFB4BXXH0AFDBsff1e29r3jkbPGsUHgYR5mxYvs9xQ/nQ+96RJ8l+Gvf+MDxrFBkN3Tt2YRAKBP8Ws8pAAiCIIgCIJoMIHn4M9+9tsM7wagLGDszSPEKSf5fgX//Affao9fXtCYhwnmYYLhLsXPxs984DH8zAcesx4bBh5myxgAJd9VfNeTV/Cdb7psvQZHgYuX9uZFAYPuYZPHLm3ja3/3/ZXxC5MUB4usgEHxMxkGHv7iv/32yvgBwLXDZfZnqQDZeKgARBAEQRAE0XBsL+5AOdL3+mEIgNQDVVTFTyimprmCiuJnpyp+QK4AovallVReg3n8ZtTCVEtd/IByDaRr0E5V/IaB+gyhIm7zua0WMMbYzzPGvsAY+zPG2G8wxiZ36HsRBEEQBEEQt0mR/EyXyu+J9XAdhr7vlC1glDweGVEAoil0x2OUt4DNowSB58B1qotthElRBM/XQCqgHY2h9gyh+DWf2/UA+h0Ar+ecPwHgLwB84va/EkEQBEEQBHEnGOm7t/TyfmSyBDzGLKQC0HEY9TxMw5gKaMdk2HMxXcZY0PV3LMoCBilYjsOoRwqqtnFbBSDO+W9zzuP8t38I4MrtfyWCIAiCIAjiTjDsCf+G7OWd/C+OziBwMV0mNAb+mAx8F/O8hYmSx6Mz9D0s4xQHy5iuv2MgWpiuHeRrIF2DR6KIX/EMIQeZpnMnp4D9NQD/5g7+/xEEQRAEQRC3gUh2SL5/fEaBh5uzEJzTBJzjIBRA1AJ2PIQC48Y0pALaMRhRG+xtUahIi/jREPGms7KExxj7dwAuWA79NOf8X+Z/5qcBxAB+teb/52MAPgYA995777G+LEEQBEEQBLE+nusg8ByS798Gw56LG1OK33EZBi5mywQ9j1P8joFswksF3KMjJvmJe7jnUQHjKOgm2nQNNp+VBSDO+XvqjjPGPgrgAwDezTnnNf8/vwjgFwHgqaeeqvxzBEEQBEEQxJ1jFLjFCF/a/T46w8DF165OAVAB6DgMAxfTMEacunT9HYOhNIb70mRwwt+meRTxO1hi4Lu1E+sIk8Bz4LusfIbQGth4bncK2PsA/ASAD3LOZ3fmKxEEQRAEQRB3imHg4cW9BQBgdxic8LdpHsPAwwt5/CZD/4S/TfMYBh4WUYq9eYTJgOJ3VEQB48W9BcXvGIj4vbC3oPv3mMjPkAk9QxrP7Wrg/iGAMYDfYYz9KWPsH92B70QQBEEQBEHcIYSHCEAFjOMwklQrVEA7OvL1tzui+B0VMcYcoPgdh5FkWkz37/EQayBjwA4VIRvPbdl4c84fulNfhCAIgiAIgrjznB71ABzCcxi2ejTB5aic3uoVP1MCfnSy6y+DEvCjc3qrjNkuFXCPzM7Ah+swJCnH7ojidxxOb/Xwwt6iiCXRbMgFiyAIgiAIosWcHWcJ+O4oIP+LYyDiB5CC6jhQ/G6Ps1ty/KiAdlQch+FMXkSj+B2P4hlC8WsFVAAiCIIgCIJoMeXLOyXfx0FJwAeUAB2Vc9ukALod5JhR/I7HuXEfAK2Bx0WsgVTAbQdUACIIgiAIgmgxogAU0PjjYyErWCiGR0cuoFEB4+g4UssNFTCOh2ijo+vveIg1kPx/2gE9xQiCIAiCIFqMmBxEyc/xkAtAxNGRrztZDUQcnfvPjE76KzSSZZQCAF5D8TsWW/3MO+4UeaC1AnICJAiCIAiCaDHveOQcPvzmK/iJb3/dSX+VRvLg2S185OvvwUffdv9Jf5VG4jgMH3/XQ3jy3gn6vrv6LxAGP/c9T2C2jPHoxe2T/iqN5OPvfghvuLKDD73p8kl/lUby7a+/gK9dneKnvuPRk/4qxB2Acc7v+n/0qaee4p/+9Kfv+n+XIAiCIAiCIAiCIAiirTDG/ohz/pTtGLWAEQRBEARBEARBEARBtBwqABEEQRAEQRAEQRAEQbQcKgARBEEQBEEQBEEQBEG0HCoAEQRBEARBEARBEARBtBwqABEEQRAEQRAEQRAEQbQcKgARBEEQBEEQBEEQBEG0HCoAEQRBEARBEARBEARBtBwqABEEQRAEQRAEQRAEQbQcKgARBEEQBEEQBEEQBEG0HCoAEQRBEARBEARBEARBtBwqABEEQRAEQRAEQRAEQbQcKgARBEEQBEEQBEEQBEG0HCoAEQRBEARBEARBEARBtBzGOb/7/1HGrgJ45q7/h18dzgC4dtJfgjgR6Nx3Ezrv3YXOfXehc99d6Nx3Fzr33YXOfXdpy7m/j3N+1nbgRApAbYIx9mnO+VMn/T2Iuw+d+25C57270LnvLnTuuwud++5C57670LnvLl0499QCRhAEQRAEQRAEQRAE0XKoAEQQBEEQBEEQBEEQBNFyqAB0+/ziSX8B4sSgc99N6Lx3Fzr33YXOfXehc99d6Nx3Fzr33aX15548gAiCIAiCIAiCIAiCIFoOKYAIgiAIgiAIgiAIgiBaDhWAjglj7H2MsS8yxr7MGPvJk/4+xJ2FMXYPY+z/YYx9njH2OcbYj+Sf/yxj7HnG2J/m/3u/9Hc+kV8PX2SMvffkvj1xuzDGnmaMfSY/x5/OPzvFGPsdxtiX8l93pT9P574FMMYeke7tP2WM7TPGfpTu+3bCGPtlxtgrjLHPSp8d+T5njL05Xy++zBj7BcYYu9v/FmJ9Ks77zzPGvsAY+zPG2G8wxib55/czxubSvf+PpL9D571hVJz7I6/vdO6bR8W5/6R03p9mjP1p/jnd9y2iJqfr7POeCkDHgDHmAvifAXw7gMcAfB9j7LGT/VbEHSYG8Lc5548CeAuAH5LO8T/gnL8p/9+/BoD82EcAPA7gfQD+l/w6IZrLO/NzLEZB/iSA3+WcvxbA7+a/p3PfIjjnXxT3NoA3A5gB+I38MN337eNXkJ03mePc5/8rgI8BeG3+P/3/k9gsfgXmOfodAK/nnD8B4C8AfEI69hXp3v9B6XM6783jV2A/T0dd3+ncN49fgXaeOOd/VXrm/wsAvy4dpvu+PVTldJ193lMB6Hj8FQBf5px/lXMeAvinAD50wt+JuINwzl/knP9x/vMBgM8DuFzzVz4E4J9yzpec868B+DKy64RoDx8C8I/zn/8xgO+UPqdz3z7ejewF8JmaP0PnvsFwzn8PwA3t4yPd54yxiwC2Oed/wDNTxf9D+jvEBmI775zz3+acx/lv/xDAlbr/DzrvzaTinq+C7vkWUXfucxXH9wL4tbr/Dzr3zaQmp+vs854KQMfjMoC/lH7/HOqLA0SDYYzdD+BJAP9f/tEP5zLxX5bkgnRNtAsO4LcZY3/EGPtY/tl5zvmLQPYwAXAu/5zOfTv5CNSXQbrvu8FR7/PL+c/650Rz+WsA/o30+9cwxv6EMfb/Msa+Kf+Mznu7OMr6Tue+fXwTgJc551+SPqP7voVoOV1nn/dUADoetn4/GqfWQhhjW8hkoT/KOd9HJv17EMCbALwI4O+LP2r563RNNJe3c86/Dlmb5w8xxr655s/SuW8ZjLEAwAcB/J/5R3TfE1Xnmq6BFsEY+2lk7QK/mn/0IoB7OedPAvhbAP4JY2wbdN7bxFHXdzr37eP7oG740H3fQiw5XeUftXzWqnufCkDH4zkA90i/vwLghRP6LsSrBGPMR7ZQ/Crn/NcBgHP+Muc84ZynAP53lO0edE20CM75C/mvryDzgPkrAF7O5Z9CBvxK/sfp3LePbwfwx5zzlwG67zvGUe/z56C2C9E10FAYYx8F8AEA/2ku70feAnA9//mPAHwFwMOg894ajrG+07lvEYwxD8B3A/ik+Izu+/Zhy+nQ4ec9FYCOx6cAvJYx9pp8p/gjAH7zhL8TcQfJ+4F/CcDnOef/o/T5RemPfRcAMU3gNwF8hDHWY4y9Bpkx2H+4W9+XuHMwxkaMsbH4GcC3ITvPvwngo/kf+yiAf5n/TOe+fSi7gXTfd4oj3ee5bPyAMfaW/LnxX0h/h2gIjLH3AfgJAB/knM+kz88K80/G2APIzvtX6by3h6Ou73TuW8d7AHyBc1609tB93y6qcjp0+HnvnfQXaCKc85gx9sMAfguAC+CXOeefO+GvRdxZ3g7gPwfwGZaPhQTwU8gmvr0JmeTvaQB/AwA4559jjP0zAH+OTD7+Q5zz5C5/Z+LOcB7Ab+STHT0A/4Rz/m8ZY58C8M8YY38dwLMAPgzQuW8bjLEhgG9Ffm/n/Bzd9+2DMfZrAN4B4Axj7DkA/zWAv4ej3+d/E9mEmQEy7xjZP4bYMCrO+ycA9AD8Tr72/2E++eebAfw3jLEYQALgBznnwkiWznvDqDj37zjG+k7nvmHYzj3n/Jdg+v0BdN+3jaqcrrPPe5arXAmCIAiCIAiCIAiCIIiWQi1gBEEQBEEQBEEQBEEQLYcKQARBEARBEARBEARBEC2HCkAEQRAEQRAEQRAEQRAthwpABEEQBEEQBEEQBEEQLYcKQARBEARBEARBEARBEC2HCkAEQRAEQRAEQRAEQRAthwpABEEQBEEQBEEQBEEQLYcKQARBEARBEARBEARBEC3n/wcOIwiFk/RSDwAAAABJRU5ErkJggg==\n",
||||||| merged common ancestors
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIAAAAFlCAYAAACTGZPMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A

Is that a typical thing in resolving merge conflicts?!

Because it is really messy and hard to understand what has been changed. Even an image/png is shown as a lot of characters. I copied a small part of it below:

============================================================

So, I took a different approach. I decided to copy my notebook file somewhere else. Then, I deleted it from Snippets_Tutorial branch (let's call it ST here) of my local repo (by using git rm <file>). I thought that I could do merge with no conflict as there is no such file in the 'ST' and it can be fetched and merged from origin Snippets_Tutorial. But, again, I got merge conflict after running git pull origin Snippets_Tutorial! No idea why!

Then, I decided to take another approach! I copy back my tutorial notebook to my local repo while I was at Snippets_Tutorial branch. This time, I decided to delete the cells inside my notebook. In other words, instead of deleting the notebook, I deleted the cells. Now, I think there should be no conflict for merge by running git pull origin Snippets_Tutorial. But, I was wrong. I got conflict message!!

======================================================================================

To sump up, this is what I can understand:

At local repo, I created Snippets_branch. I did some changes and then did pull request. Then, origin repo accepts this PR and this branch will be appeared there.
(1) However, I cannot find such branch on Stumpy repo (original repo)

Now, the owner or any other contributor made some changes to the file. This file is the one to which they have access through the Snippets_Tutotrial branch. That is the branch created after I did Pull Request in the first place. Right?
(2) How can I see the content of this file? Because I cannot pull that file into my Snippets_Tutorial branch before resolving the conflicts. I cannot see it through the origin repo avaialble on GitHub. If fact, the only branch of the origin repo is main.

======================================================================

I hope I explained things clearly. If not and further elaboration is required, please let know.

Thanks

[seanlaw]

I am trying to understand merge conflict and how to handle it. I read atlasian guide and also watch youtube and read articles online.

In most cases, the conflict is easy to detect. For instance, a few lines are modified/updated and it is visually easy to understand that by looking at the file that include merge conflict marker.

However, there are a lot of metadata in jupyter notebook when merge conflicts exist. I opened the notebook in a JSON editor (please see below where I copied only a part of the notebook )

{
 "cells": [
  {
<<<<<<< HEAD
   "cell_type": "markdown",
   "id": "73312d87",
   "metadata": {},
   "source": [
    "# Summarizing Time Series Data with Snippets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e88f834",
   "metadata": {},
   "source": [
    "This tutorial is adapted from the [Matrix Profile XIII](https://www.cs.ucr.edu/~eamonn/Time_Series_Snippets_10pages.pdf) paper and replicates Figures 7,8 and 13.\n",
    "\n",
    "One of common analysis query on a data is \"Show me some representative/typical data\". Sometimes, it is useful to summarize a time series into a few representative patterns (snippets) as it can be visualized more easily and some new insights might be discovered. \n",
    "\n",
    "For instance, let's consider a simple problem: ?\n",
    "\n",
    "The \"snippet-finder\" algorithm proposed in the paper can be utilized to summarize a time series into a user-defined number of snippets. \n",
    "\n",
    "To demonstrate the usefulness of Snippets, we first create a toy data used in the original paper and will use the [STUMPY](https://pypi.org/project/stumpy/) library to discover the snippets in the data. This tutorial replicates the figures 7,8 and 13 of the original paper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e5e116",
   "metadata": {},
   "source": [
    "## Snippets vs. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4c34af",
   "metadata": {},
   "source": [
    "### - Clustering: \n",
    "Snippet is a different task than clustering. In a single time series, one can first extract subsequences (with a user-defined time window) and then perform clustering on the set of subsequences. However, as comprehensively studied in Eamonn Keogh and Jessica Lin (2005), clustering the subsequences of single time series leads to meaningless result. In some cases where there is a periodic behaviour (e.g. electricity demand), the clustering can be performed. However, algorithms specialized in clustering the electricity demand data into seasons, for instance, are more complex and requires more domain knowledge (for example: the subsequences must be exactly one-week long.) However, the snippets algorithm is domain-free and it can be used to better explore and analyze data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c505f5e6",
   "metadata": {},
   "source": [
    "### - Semantic Segmentation:\n",
    "The snippets are different than semantic segmentation proposed in [Matrix Profile VIII](https://www.cs.ucr.edu/~eamonn/Segmentation_ICDM.pdf). In semantic segmentation, the task is to find boundary locations, not in explaining them, or producing representative patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f77e100",
   "metadata": {},
   "source": [
    "### - Shapelets:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd47dd07",
   "metadata": {},
   "source": [
    "There are two main differences between Time Series Snippets and [Shapelets](https://link.springer.com/article/10.1007/s10618-017-0519-9). For one, the shapelets are supervised; however, the snippets are unsupervised. For another, the shapelets are usually shorter whereas the snippets are preferred to be longer to capture the behavior of the time series at a higher level."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4345de90",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "Let's import the packages that we'll need to load, analyze, and plot the data."
   ]
  },
  {
||||||| merged common ancestors
=======
   "cell_type": "markdown",
   "id": "73312d87",
   "metadata": {},
   "source": [
    "# Summarizing Time Series Data with Snippets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e88f834",
   "metadata": {},
   "source": [
    "This tutorial is adapted from the [Matrix Profile XIII](https://www.cs.ucr.edu/~eamonn/Time_Series_Snippets_10pages.pdf) paper and replicates Figures 7,8 and 13.\n",
    "\n",
    "One of common analysis query on a data is \"Show me some representative/typical data\". Sometimes, it is useful to summarize a time series into a few representative patterns (snippets) as it can be visualized more easily and some new insights might be discovered. \n",
    "\n",
    "For instance, let's consider a simple problem: ?\n",
    "\n",
    "The \"snippet-finder\" algorithm proposed in the paper can be utilized to summarize a time series into a user-defined number of snippets. \n",
    "\n",
    "To demonstrate the usefulness of Snippets, we first create a toy data used in the original paper and will use the [STUMPY](https://pypi.org/project/stumpy/) library to discover the snippets in the data. This tutorial replicates the figures 7,8 and 13 of the original paper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0ed2ac",
   "metadata": {},
   "source": [
    "## Snippets vs. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8390d6e",
   "metadata": {},
   "source": [
    "### - Clustering: \n",
    "Snippet is a different task than clustering. In a single time series, one can first extract subsequences (with a user-defined time window) and then perform clustering on the set of subsequences. However, as comprehensively studied in Eamonn Keogh and Jessica Lin (2005), clustering the subsequences of single time series leads to meaningless result. In some cases where there is a periodic behaviour (e.g. electricity demand), the clustering can be performed. However, algorithms specialized in clustering the electricity demand data into seasons, for instance, are more complex and requires more domain knowledge (for example: the subsequences must be exactly one-week long.) However, the snippets algorithm is domain-free and it can be used to better explore and analyze data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "564298bb",
   "metadata": {},
   "source": [
    "### - Semantic Segmentation:\n",
    "The snippets are different than semantic segmentation proposed in [Matrix Profile VIII](https://www.cs.ucr.edu/~eamonn/Segmentation_ICDM.pdf). In semantic segmentation, the task is to find boundary locations, not in explaining them, or producing representative patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3643c0e1",
   "metadata": {},
   "source": [
    "### - Shapelets:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a081d0ad",
   "metadata": {},
   "source": [
    "There are two main differences between Time Series Snippets and [Shapelets](https://link.springer.com/article/10.1007/s10618-017-0519-9). For one, the shapelets are supervised; however, the snippets are unsupervised. For another, the shapelets are usually shorter whereas the snippets are preferred to be longer to capture the behavior of the time series at a higher level."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4345de90",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "Let's import the packages that we'll need to load, analyze, and plot the data."
   ]
  },
  {
>>>>>>> 5f004a2b9512b7353b1d3c45205ac4dbfa1decb0
   "cell_type": "code",
   "execution_count": 28,
   "id": "6cbb4ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import stumpy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import resample, resample_poly\n",
    "\n",
    "plt.style.use('stumpy.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0fb4d9",
   "metadata": {},
   "source": [
    "## Retrieve the Data\n",
    "\n",
    "It is the same toy data set used in the original paper. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0fb4d9",
   "metadata": {},
   "source": [
    "## Retrieve the Data\n",
    "\n",
    "It is the same toy data set used in the original paper. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "43ab8d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
<<<<<<< HEAD
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.347609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.350937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.357541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.367308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.380056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        toy\n",
       "0  0.347609\n",
       "1  0.350937\n",
       "2  0.357541\n",
       "3  0.367308\n",
       "4  0.380056"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://zenodo.org/record/4662217/files/snippets.csv?download=1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c887ef84",
   "metadata": {},
   "source": [
    "## Visualizing the Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a31fb9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
||||||| merged common ancestors
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIAAAAFlCAYAAACTGZPMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A

Is that a typical thing in resolving merge conflicts?!

Because it is really messy and hard to understand what has been changed. Even an image/png is shown as a lot of characters. I copied a small part of it below:

============================================================

So, I took a different approach. I decided to copy my notebook file somewhere else. Then, I deleted it from Snippets_Tutorial branch (let's call it ST here) of my local repo (by using git rm <file>). I thought that I could do merge with no conflict as there is no such file in the 'ST' and it can be fetched and merged from origin Snippets_Tutorial. But, again, I got merge conflict after running git pull origin Snippets_Tutorial! No idea why!

Then, I decided to take another approach! I copy back my tutorial notebook to my local repo while I was at Snippets_Tutorial branch. This time, I decided to delete the cells inside my notebook. In other words, instead of deleting the notebook, I deleted the cells. Now, I think there should be no conflict for merge by running git pull origin Snippets_Tutorial. But, I was wrong. I got conflict message!!

======================================================================================

To sump up, this is what I can understand:

At local repo, I created Snippets_branch. I did some changes and then did pull request. Then, origin repo accepts this PR and this branch will be appeared there.
(1) However, I cannot find such branch on Stumpy repo (original repo)

Now, the owner or any other contributor made some changes to the file. This file is the one to which they have access through the Snippets_Tutotrial branch. That is the branch created after I did Pull Request in the first place. Right?
(2) How can I see the content of this file? Because I cannot pull that file into my Snippets_Tutorial branch before resolving the conflicts. I cannot see it through the origin repo avaialble on GitHub. If fact, the only branch of the origin repo is main.

======================================================================

I hope I explained things clearly. If not and further elaboration is required, please let know.

Thanks

@ninimama Are you able to upload the notebook here (not Copy/Paste - just drag and drop the file in a comment box)?

[NimaSarajpoor]

@seanlaw

temp_files.zip

It contains two files: one of them is the version I have on my local repo (before git pull ... ) and the other one is the file that contains merge conflict markers.

[seanlaw]

So, I think we need to clarify the definitions. In this case, when I say "upstream", I am referring to any place upstream to your local branch. This may mean a local parent OR it may mean as high up(stream) as TDAmeritrade/stumpy.git but, in any case, a merge conflict means that somebody (could be you) changed the SAME file as you did locally in this branch. The only difference is that they likely committed it and pushed it before you did. Also, when you say to "push to the original repo", are you referring to the TDAmeritrade/stumpy repo or Ninimama/stumpy repo? Technically, on Github, you can't "push" to TDAmeritrade/stumpy (unless you had the correct maintainer permissions) so you can only submit a PR. However, you CAN commit/push to your own fork at Ninimama/stumpy. So, for clarity, we should try to be explicit where possible as it might help reduce any ambiguity. I will try to do this below.

Hopefully, when I said "you caused the divergence", that my written tone didn't come across as accusatory. When I look at the commit history for docs/Tutorial_Time_Series_Snippets.ipynb (here in the TDAmeritrade/stumpy repo), it says that the last time that somebody changed that file was April 6th (it was me!). This means that when you forked/cloned TDAmeritrade/stumpy to Ninimama/stumpy, you received that April 6th version of the tutorial. Now, if you didn't make any changes to that tutorial file in ANY of your local branches (and sub-branches), then it is impossible for there to be a merge conflict for that tutorial file (as there is nothing to merge for that tutorial file). Does this make sense?

So, does that mean I myself somehow change the file from A to B_upstream at the upstream? I thought I am just doing changes locally and then push those changes towards the original repo. I am trying to understand if, by any chance, I made a git command that mess up things.

So, while TDAmeritrade/stumpy is indeed "upstream" and it is possible that somebody may have changed A to B_upstream in the TDAmeritrade/stumpy repo, we have verified that this was not the case (as we discussed/looked above). Note that it is possible that there were OTHER file changes in TDAmeritrade/stumpy but as long as you didn't change those same files locally then it is impossible to have a merge conflict. Now that we've eliminated TDAmeritrade/stumpy as having changed Tutorial_Time_Series_Snippets.ipynb, we go down to the next layer which is to look at your fork Ninimama/stumpy.

The Ninimama/stumpy fork shows that you had committed/pushed changes to the tutorial to your fork around 20 days ago. Now, these changes have not been merged into TDAmeritrade/stumpy yet (there is only a PR). The following is probably where I have the least visibility but I'm going to guess that:

1. After you created the Ninimama/stumpy fork, you cloned it to your local machine
2. Locally, you created a branch called Snippets_Tutorial
3. Locally, you committed some changes to the snippets tutorial in Snippets_Tutorial
4. You pushed those changes from your local branch upstream to your Github Ninimama/stumpy fork (this is what happened 20 days ago)
5. You submitted a PR for this change

Locally, this fork is your "source-of-truth" and all downstream (not upstream) child branches should be relative to this fork.

I want to preface this but saying that I'm not a a git expert so I may be missing a lot or not have the right knowledge to answer the question.

Now, following what I am seeing in your image above where you are currently in the Snippets_Tutorial branch, when you did git status it only showed MERLIN_datasets and papers_stumpy as untracked files (btw, this is somewhat suspect to since these untracked files should not exist in this branch since they seem unrelated to Snippets_Tutorial). However, it does NOT say anything about docs/Tutorial_Time_Series_Snippets.ipynb having been modified. If that file was never modified since 20 days ago (as per above) then there can be no future merge conflicts due to that file. I should mention that I'm not sure if git pull origin Snippets_Tutorial is the right thing to do here but I don't know enough about this to be helpful. However, since I am seeing Auto-merging docs/Tutorial_Time_Series_Snippets.ipynb, this implies that you have committed changes to docs/Tutorial_Time_Series_Snippets.ipynb in your local Snippets_Branch. If not, then there should be nothing to merge. What do you see when you do git log in your local Snippets_Tutorial branch?

The latest git commit hash in your git log should be 5f004a2b9512b7353b1d3c45205ac4dbfa1decb0.

[NimaSarajpoor]

@seanlaw
Thanks a lot for the time you are putting into this.

Please give me some time to go through it and give a proper response.

[NimaSarajpoor]

@seanlaw: UPDATE

=========================================================================

So, I think we need to clarify the definitions. In this case, when I say "upstream", I am referring to any place upstream to your local branch. This may mean a local parent OR it may mean as high up(stream) as TDAmeritrade/stumpy.git but, in any case, a merge conflict means that somebody (could be you) changed the SAME file as you did locally in this branch

Got it! Thanks!!!

Also, when you say to "push to the original repo", are you referring to the TDAmeritrade/stumpy repo or Ninimama/stumpy repo? Technically, on Github, you can't "push" to TDAmeritrade/stumpy (unless you had the correct maintainer permissions) so you can only submit a PR

Thanks for clarifying this. I thought after the PR, git push origin <file> not only pushes my commit to fork but also automatically to the originAL repo (i.e. TDAmeritrade/stumpy). So, it does NOT push to the originAL repo but there is just no need to do any other PR as you can SEE the changes in my fork after the first PR for that branch.

Hopefully, when I said "you caused the divergence", that my written tone didn't come across as accusatory. When I look at the commit history for docs/Tutorial_Time_Series_Snippets.ipynb (here in the TDAmeritrade/stumpy repo), it says that the last time that somebody changed that file was April 6th (it was me!). This means that when you forked/cloned TDAmeritrade/stumpy to Ninimama/stumpy, you received that April 6th version of the tutorial. Now, if you didn't make any changes to that tutorial file in ANY of your local branches (and sub-branches), then it is impossible for there to be a merge conflict for that tutorial file (as there is nothing to merge for that tutorial file). Does this make sense?

Yes.

Note that it is possible that there were OTHER file changes in TDAmeritrade/stumpy but as long as you didn't change those same files locally then it is impossible to have a merge conflict.

I created the Snippet_Regime as a sub-branch in Snippet_Tutorial to change snippets.py and aampdist_snippets.py modules. I also changed the snippet tutorial notebook file, and then I merge that branch into Snippet_Tutorial.
(I should say that I cannot see that sub-branch when I do git log. It seems that Snippet_Regime is a branch of main! Not sure if I understand that correctly. I provided the screenshot of git log below)

where you are currently in the Snippets_Tutorial branch, when you did git status it only showed MERLIN_datasets and papers_stumpy as untracked files (btw, this is somewhat suspect to since these untracked files should not exist in this branch since they seem unrelated to Snippets_Tutorial).

I added those files when I was in MERLIN_Tutorial branch, but didn't do git add. I think that's why it appears with git status. To make sure, I created a test file in one branch and didn't do git add. Then, I switch to another branch and I realized the git status shows that file.

However, it does NOT say anything about docs/Tutorial_Time_Series_Snippets.ipynb having been modified. If that file was never modified since 20 days ago (as per above) then there can be no future merge conflicts due to that file

So, it does NOT say anything because I already did git add and git commit. So, I modified it locally without doing git push (based on what I understand and if I remember correctly)

However, since I am seeing Auto-merging docs/Tutorial_Time_Series_Snippets.ipynb, this implies that you have committed changes to docs/Tutorial_Time_Series_Snippets.ipynb in your local Snippets_Branch. If not, then there should be nothing to merge

You are correct. But, as you said, I probably change the SAME file (on its PARENT) outside the branch. I am trying to figure out when I did such thing. Because I think I probably change it when I was at its sub-branch Snippets_Regime (so not its PARENT). But maybe I am wrong, and I mistakenly created the Snippet_Regime branch from the main branch.

What do you see when you do git log in your local Snippets_Tutorial branch?

The latest git commit hash in your git log should be 5f004a2

According to the image above, the commit you mentioned is the one you can see because I did git push. Right? But, I haven't pushed the other commits yet to the origin repo. I highlighted the commit 5f004 you mentioned (please see the bottom of image). I also highlighted another commit right after the time I got the merge conflict error and those commits after that is my attempt to solve the conflict.

[seanlaw]

@ninimama I think now is the time to step through each commit and see which files were changed along the way. This command might help you see when the tutorial file was altered:

https://stackoverflow.com/questions/424071/how-do-i-list-all-of-the-files-in-a-commit

Having said that, even if you have a merge conflict, you should always be able to replace the conflicting file with a copy of one of the two parents and the conflict should be resolved?

[NimaSarajpoor]

@seanlaw
Sorry for the late response. I got a little busy with my research, and I was also dealing with this merge conflict issue from time to time.

I FINALLY resolve it by getting help from stackoverflow and Advance_Merging In below, I am just explaining what I did as it might help others:

After getting into the merge conflict state (when doing git pull origin my_branch), we can do:
git show :1:my_file > my_file_common
This command will copy the version of the file that is common in ours and theirs versions that are having conflict. Now, I can replace it with the actual file in the branch and repeat the command git pull origin my_branch. Now, we can merge files
(FYI: I just got one weird message that there is no atom.exe file and I need to do git commit to finish the merge).
Now, after that, I can open both the ours version of the file AND the new file in the branch (which is actually the merge of common file and theirs version of the file). And, as you said before, I manually add changes.