{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Getting Started\n",
    "\n",
    "The following examples match the code from the _Getting Started_ section of the `README.md`.\n",
    "\n",
    "### Simplest Example\n",
    "\n",
    "In the simplest case, you can pass the x/y coordinates to the plot function as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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      },
      "text/plain": [
       "HBox(children=(VBox(children=(Button(button_style='primary', icon='arrows', layout=Layout(width='36px'), style…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import jscatter\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.rand(500)\n",
    "y = np.random.rand(500)\n",
    "\n",
    "jscatter.plot(x, y, axes_grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas example\n",
    "\n",
    "If your data is stored in a Pandas dataframe, you can reference columns via their name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e73d128b52a4391843eda42fa11bd65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Button(button_style='primary', icon='arrows', layout=Layout(width='36px'), style…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = np.random.rand(500, 4)\n",
    "\n",
    "df = pd.DataFrame(data, columns=['mass', 'speed', 'pval', 'group'])\n",
    "df['group'] = df['group'].map(lambda c: chr(65 + round(c)), na_action=None)\n",
    "\n",
    "jscatter.plot(data=df, x='mass', y='speed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advanced example\n",
    "\n",
    "Often you want to customize the visual encoding, such as the point color, size, and opacity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84fe978d439845b3aa4559c220716f13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Button(button_style='primary', icon='arrows', layout=Layout(width='36px'), style…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jscatter.plot(\n",
    "  data=df,\n",
    "  x='mass',\n",
    "  y='speed',\n",
    "  size=8, # static encoding\n",
    "  color_by='group', # data-driven encoding\n",
    "  opacity_by='density', # view-driven encoding\n",
    "  legend=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, we chose a static point size of `8`. In contrast, the point color is data-driven and assigned based on the `group` value. The point opacity is view-driven and defined dynamically by the number of points currently visible in the view.\n",
    "\n",
    "Also notice how jscatter uses an appropriate color map by default based on the data type used for color encoding. In this examples, jscatter uses the color blindness safe color map from [Okabe and Ito](https://jfly.uni-koeln.de/color/#pallet) as the number of categories is less than `9`.\n",
    "\n",
    "You can of course customize the color map and many other parameters of the visual encoding as shown next.\n",
    "\n",
    "### Functional API example\n",
    "\n",
    "The flat API (from above), can get overwhelming when you want to customize a lot of properties. Therefore, jscatter provides a functional API that groups properties by type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a76f5bca71fe462c97396b51b2bf465c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Button(button_style='primary', icon='arrows', layout=Layout(width='36px'), style…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter = jscatter.Scatter(data=df, x='mass', y='speed')\n",
    "scatter.selection(df.query('mass < 0.5').index)\n",
    "scatter.color(by='mass', map='plasma', order='reverse')\n",
    "scatter.opacity(by='density')\n",
    "scatter.size(by='pval', map=[2, 4, 6, 8, 10])\n",
    "scatter.legend(True)\n",
    "scatter.height(300)\n",
    "scatter.background('black')\n",
    "scatter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatter plot is **interactive**! It supports pan-and-zoom, hover, click, and lasso interactions:\n",
    "\n",
    "- **Pan**: Click and drag your mouse.\n",
    "- **Zoom**: Scroll vertically.\n",
    "- **Select a single dot**: Click on a dot with your mouse.\n",
    "- **Select multiple dots**: While pressing <kbd>SHIFT</kbd>, click and drag your mouse. All items within the lasso will be selected. You can also click once into the void and a translucent circle will appear. When you mousedown into this circle and then drag the mouse you can also lasso-select points!\n",
    "- **Deselect**: Double-click onto an empty region.\n",
    "- **Rotate**: While pressing <kbd>ALT</kbd>, click and drag your mouse.\n",
    "\n",
    "Moreover, you can also update the plot dynamically with Python after having called `scatter.show()`. For instance, the following call will swap the x and y axes. The transition is animated to better follow the change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<jscatter.jscatter.Scatter at 0x169584e80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter.xy(x='speed', y='mass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, all arguments are optional. If you specify an argument, the function will act as a setter and change the property. If you call a function without any arguments it will act as a getter and return the property (or properties). For example, `scatter.selection()` will return the _currently_ selected points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   2,   4,   5,   6,   7,   8,  10,  14,  15,  20,  22,  23,\n",
       "        24,  26,  28,  29,  30,  34,  35,  38,  42,  44,  45,  47,  50,\n",
       "        55,  56,  58,  59,  61,  66,  68,  69,  70,  73,  74,  75,  76,\n",
       "        77,  78,  83,  84,  85,  87,  90,  91,  92,  94,  97,  98,  99,\n",
       "       105, 107, 109, 111, 112, 113, 115, 118, 121, 122, 123, 125, 127,\n",
       "       129, 131, 133, 134, 136, 138, 139, 141, 142, 145, 146, 148, 149,\n",
       "       151, 152, 154, 155, 156, 161, 162, 167, 168, 170, 171, 172, 173,\n",
       "       180, 182, 183, 186, 190, 192, 193, 194, 195, 196, 197, 199, 200,\n",
       "       203, 207, 208, 212, 213, 216, 217, 218, 220, 221, 222, 227, 228,\n",
       "       230, 231, 233, 241, 242, 243, 244, 245, 247, 248, 250, 251, 252,\n",
       "       255, 257, 260, 261, 262, 266, 267, 270, 275, 278, 280, 288, 289,\n",
       "       290, 291, 295, 296, 303, 304, 305, 309, 311, 313, 314, 315, 316,\n",
       "       317, 318, 320, 322, 325, 330, 331, 333, 334, 335, 336, 338, 339,\n",
       "       341, 346, 347, 348, 350, 351, 352, 353, 354, 355, 359, 363, 364,\n",
       "       365, 367, 368, 371, 372, 376, 377, 378, 379, 382, 385, 386, 390,\n",
       "       391, 393, 394, 395, 397, 399, 402, 404, 405, 406, 415, 417, 418,\n",
       "       420, 422, 423, 424, 425, 426, 434, 435, 436, 442, 444, 446, 447,\n",
       "       449, 451, 452, 454, 457, 459, 460, 462, 465, 466, 468, 472, 474,\n",
       "       475, 476, 477, 478, 485, 487, 491, 492, 495, 497, 498, 499],\n",
       "      dtype=uint32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter.selection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Linking Multiple Scatter Plots\n",
    "\n",
    "Ever wanted to explore multiple scatter plots in sync? `jscatter.link()` allows to link the view, selection, and hover interaction. For more details, see [linking.ipynb](linking.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd1ccf120a674443b527f7074effda0b",
       "version_major": 2,
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      },
      "text/plain": [
       "GridBox(children=(HBox(children=(VBox(children=(Button(button_style='primary', icon='arrows', layout=Layout(wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jscatter.link([\n",
    "    jscatter.Scatter(x=x, y=y),\n",
    "    jscatter.Scatter(x=x, y=y)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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FIp95mTIAIIIIAAArMIcHHBIYIAAgggEHXB9EVJR8ZMs0TSvIg++S6KGZruEEAg9wJRn9suaT5Pcz/1JIgAAgggMJsAX4azCfF7BBBAIP8CURdM7vHC8ZhTd+t/uUcVwxvfRZL8cuVWSRtO4tjAK/TF7E93CCCQH4Goz+2rJO2SnxTJBAEEEEAAgdYEuLhozY1WCCCAQJ4E3OOE7rHCqe0cSdPeUhdjsuGLsx9J2i7G/jPZlV8eH5NM+AL1gcArvDCTCRE0Ap0RWCqptgC8iqSnOjNUY73u/sOHX7jSE4/fN7W3tfr9SLGwWWOt29rrGEknhnrgvL4tUhojgAACCORFgC/EvMwkeSCAAALZEThbkrsQPEnSFdkJO/5Ia9/8F9V74BX4no6fnR6zKXClpIURoXf1byTqb9hIxw17hRMSYF5N0nGSbpF0XgLjMQQCCCCAAAKZEOjqyUEmhAgSAQQQQACBDgjMVuRyQ1Lo6gA8XWZVIJVrUtX7O87j3+5Qefx0K3Po1AFkbHX/4eKC87N6QDUT99DYxLK7nocH+z/fTDv2RQABBBDojgCFru64MyoCCCCAQI8LzFboMrLnDXsDB/Y4E+kjMCVQrbPQelfPZev8Hf898AobzTZ1pbHK9sbq+8sLR9I3hr3CQbO168bvhxZXNrBV/T08dh4LerU5lq55cH3z9JO31/7MrrTKS0Z2XOOObswDYyKAAAIINCbQ1ZODxkJkLwQQQACBDAh8VdLqk2t7uXV02GYRWHGhyxwdeP0ng4gAAssFXiLptpDHGZIO66aRP1r5tYy2qo1hlZXs6hfuOPDo5M9K7ubMyf//D0nrT+0b+dhjtfqG4YULftvNnKLG9suVCyS9O/y7vBe6eumOvbQdc8SDAAIItCNAoasdPdoigAACCKwt6e4Qw+69vvZWI4eFPzbxVVn7wdp9jZmz2/DgPLcWERsCCMwUcGtS/ViSeyvsIZLKaUDyRytHyuhkI/u36lpPbzay9XqPT8a1qaQ/RsS47Pw7uohirgm8/p3SkFdtDENjlSOs1RcodD0rkPcCX9qOP+JBAAEEmhWg0NWsGPsjgAACCNQKpHLdnKxMUWlsYj9jrVvj5tGqtMOlXuHXWYmdOBFIgcDLJovqK0vaR9J1KYipNgT3yNvyO7hqfrGCQpc9PfAGphXAO52TP1a5VlZbTo1Tr4gTVZhLQcHnF5LeJOlbkvaL24o7uuIWpT8EEEAgGQEKXck4MwoCCCDQLYFRSa+U9ClJF3YgCApdHUClSwQQmFVg14g7R91jjO5xxrRsd0laNyKYuoWupAtHpfL42UbmfeEY6xa7RscXyZiipLMDr3Bwl6Gjvn9ivbbZ7fJ/r7HySk/9uzbPp55eec0rdl/zwS7nzvAIIIAAAisQiPXLAGkEEEAAgVQJhC8CfiRpu5gjXCJpXr0LuZjHojsEEEBgSsC+eNs9tcXHvrZc5JknHtX33v6SNJ3bujuN3B1H4W15jP7oxDky9kBZXRcUC9PW+kpiqjN8x9JnJX2c754kjhLGQAABBLInkKaTgezpETECCCCQXoFfSdo6oYuAcEGtIGkivTREhkDzAkPlytlW1j0adVngDbyj+R5oEbOA9cuVqBLS8cFg4dMxj9VOd4dKOn2ygyclrdpOZ3G3zXCh6zJJeyT0HRc3O/0hgAACCHRYgEJXh4HpHgEEEOiSwNOS5nIR0CV9hs2VQErXJsqVcQvJ3O6XK1HrX/078AprtdBfTzYZGrtvV2uXXhFK/urAK+yccpCXS/oL33EpnyXCQwABBLokQKGrS/AMiwACCHRY4DhJx3MR0GFluk+DwCmSPlwTiFuTLuoCuOVYowpd1pj3jAz2X9BypzRsWyDybiSrS4NiodR25z3UwdsXjXt9fcat5yjJnBx4/UdnJP2vSqpduH+hpMk8MpIBYSKAAAIIdESAQldHWOkUAQQQSIXAPyS9uCaSN0j6bSoiIwgE4hFwx/RvIrqK9fwmstBlq6eMFBd8NJ406KUVgVK5coWR3KL0y7ekF3NvJW7aIIAAAggggEBnBWI9EexsqPSOAAIIIIAAAikUCCS5O2jc47IbSfpnKzHueWVl07lzdFbV2gdGigPurW6NbD+XtE03Cl1PP/X0vMv3WPf+RoLs0D61jye7Rc/dunw9t/nle98q9X3CWP1+uFg4sucASBgBBBBAAAEEZghQ6OKgQAABBBBAAIFWBR6T9JxQ4+dKcj9vePPL9x4l9X2+tkHfnOr6l+y8wN2VuKLtQkn7RuwQ6/mNX664O7e+VDPOlwKvcETDCca/Y/gFEG6EOZKq8Q9FjwgggAACCCCAQLYEYj0RzFbqRIsAAggggAACbQpEFVxukPT6Zvpt881vUTHk/fwmKucRSX49d788cZ5k95/8/eWBV4h6Y10z08a+CCCAAAIIIIBAKgXyfiKYSnSCQgABBBBAICcCUQWXeyWt3Ux+bRa63FDucckXSbpM0tubGTuj+0a5f0/SnlH5+OXKNyS9d/rv7OLAGxjMaP6EjQACCCCAAAII1BWg0MXBgQACCCCAAAKtCkQVXFyxxRVdGt4iC13G/DYY7HeLzbPNFIhyX03S43UKXVH7i4XbObQQQAABBBBAII8CFLryOKvkhAACCCCAQDICq0h6omaocyS9r9mh33nN+HOffNo8UtuOIsysirXFq50kXVOvRQx3zM0aDDsggAACCCCAAAJpEaDQlZaZIA4EEEAAAQQQQKADAn65coGkd4e6/mHgFbbvwHB0OSkQKjD+J/AKzwcHAQQQQAABBDovQKGr88aMgAACCCCAAAIIdFXAL09cLNm9JoO4JvAK7i4wttYE3iPJPVZ7iqRbo7rwxyYelLUvCP3u2sArbN3akLRCAAEEEEAAgUYFKHQ1KsV+CCCAAAIIIJAqgbeP3rttn+m7UtJKsnYoKA6MpipAgsmjQHi9s4sk7RNOlMdFMz31x06+1MLN7cmZzoTgEUAAgR4VoNDVoxNP2ggggAACywXeJOkXNR7/lrRWin1cYWczSSdIcmti9eS219jEflVrz5+evDk68Pq5MO3JIyKRpN0dXB+OGGnG+TSFrkTmoxODRL24geulTkjTJwIIINBBAT64O4hL1wgggAACmRCIurD5gqSjUhh9ONbfSNoyhXF2PCQKCR0nbmcAVwx6TNLZ7XSSprZ+ufL7xQe+4TWPjP8zKqyZha7RyskyOrJ2Z2PM/sOD/aHibJqy7PlYFkpy/5AQ3rhe6vlDAwAEEMiaAB/cWZsx4kUAAQQQqCswrfhh9UhQLDyvAa6oQpdrlrbvyKsl7chF2LMCFLoaOLKT3+W1km4MDTtHUjX5UOId0R1vN11yiv78ncgbBiM/K0pjE/uZybsOq9XqLpcuXHBVvFHRW8wCn5N0NJ+xMavSHQIIINAFgbSdxHeBgCERQAABBPIgEFn4MPpxMFh42yz5RRW6fi3pjSlzub/OI5U9+V3ulydukuwmoTl6MPAKa6Zs3vISjntj4EOhZMLHXlaKxk3PydTnS+AVwm1PlPSppjukQRoF1pZ0N4WuNE4NMSGAAALNCfTkyXFzROyNAAIIIJAFgTbu8LlC0q6zXMCngeAjkr7cKxdh/milIqN+l68x9r3DgwPnhnMPz3ngFeqe1/jliluHza3H9uxm7fuC4kDPrnHWwgEdVcR6WtLKNX3lvtBV69Zn+l5/yeD8G1qwpEl6BS6UtG9NeF+TdEh6wyUyBBBAAIEoAQpdHBcIIIAAArkQaKPQ5fLf9WV7vP/MV+9//DpTGEa6YNgrvCdlODdJqr2L6c2hhfRTFm5r4UTNZZ/0xku8grvTrumtVB5faGRmrL2zosJY04Pkv0EjRaxG9smk1FB5/K9WZuPlwRv9IxgsrJ/JZAgaAQQQQACBnAtQ6Mr5BJMeAggg0KhAadH4+jJ9pxlTnQi8gQMabVez36mS3iLpNEmJL7jslye+ItkPTYu7r29hsMv80UZyiSquzJlbfeXFOy34SyPt2Sc+gTpFy3sDr+AeLWp6GypP/NzKbhNuSKGrKcpGilivlvSHUK+5OdcsjU28xliV+oy5nDu5mjp22BkBBBBAAIFEBXJz8pGoGoMhgAACORPwy5WPSfpibVqrrGRXv3DHgUcbTHXZRfALN36dtjzi67LWPv3ceeu+ZmS3Be4OJLedJOkpSZ9usL/Zdlsiad7kTltLutb9/9KiyjvNHH1LVg9bO2eHkeK862fraOr3UcUVY3XRcLGwT6N9sF88ArEXusaWfMHa6hHh6Ch0rXi+Ih4NDTfYUNJt8cw6vSDQdYEzah5TPFbSZ7oeEQEggAACCLQkQKGrJTYaIYAAAvkSqLOQ+6PBYGH1BjL9gqQjNvT212YHu5dW/Xf717VXHfXrk/YLv6bM3ZVzbwP91tsl6s6S50h6oo0+o9/iZ3VUUCy4/NiaECiNTezUZ+2Z1ph7/2MeettVu2z0ZBPNI+einUcX3dgzjnFr7wqKAy9qJq5e2rdOsfG2wCv8QJKbz+l3T/YSDrnmUSBw/1YSSuwEScflMVlyQgABBPIuQKEr7zNMfggggEADAm2ub/UrSVv75UrkSBFvKXP7tfP9E1XoWixpsIFU6+4SZcAdP82L+qPjX5UxH6xtOdes/D8XDa75z2Z6a2Qx+mb6c/sOlStnW2O2VXXpaUFxwZnNtu+l/dv8TOglqp7M1S9XviTpo1PJWztni2buoE0hWiOP5qYwbEJCAAEEEIgSaOdCA1EEEEAAgZwI1Lmj67pgsLBVAym+XdJIlwtdP5K0XQOxrnAXv1z5omQOMra6eLg4sFe7/fVi+zoFkmrgFeb0okdWc6bQldWZqx/3UHn8XCvj1l98eqk1r7is2H9rK1mWxv71GmNX+n24bcb/YYBCVysHA20QQACBlApQ6ErpxBAWAgggkKTAHuXxl8yVmbbWTpMXLTeUFt2zuembXst4tHKXxg54Xdz/0BJ1QbKupLuTNGOsaAEKJPk4MvzRysEyOqs2m74+s9klu/TPKHDkI+N8Z1EqV+4ykvucXL5ZYxaMDPaPN5t5abRyjTHaIWeFrj9JemUopxH31HOzPuyPAAIIINB9AQpd3Z8DIkAAAQRyIxCxePWCiPW44vjuqS12vUPSxblBzHgiUYUua215pDhQzHhqmQjfHxvfW7ZvN1l7cVAsXNFO0KUr736ZmTv3PFvVo4/MeXhhs2uttTM2beMViHzZhnT7sFfYoNmR/HLl65IOylmhy6Vzs6SXTub1M0nbNmvD/ggggAAC6RCI42IjHZkQBQIIIIAAAgh0XWC/n9hVH3t04vHaQJq8O7DrOWQ1AH+sUpWdtv7dksAr9CedT7ioYozZf3iw//yk42C8/wpEP55ung4G+1duxYk1DVtRow0CCCCAQFICFLqSkmYcBBBAAIFUCPjlyoOSXjAVDEWYVEwLQbQp4JcrO0q6OtxN0se3Pzr+UxnzlnAcz8hu8D1v4PY20+x489Kie99i+vp++t+B7E2BN/CKjg/c4QEi7+gyOnh4sHB2q0P75crUHVCXBl4h/MbCVrulHQIIIIAAAm0LUOhqm5AOEEAAgdQJLJI09ZjY3pIuSV2EXQrIL1d+I+kNtcNb6d4Rr7B2l0JiWARiERgarXzZGn2k64WucqXeot5KuujWCmz0I372w8PewKmt9JemNqHcKE6laXKIBQEEEEAgVgEKXbFy0hkCCCDQdYF/SHpxKIqdo+706HKkbtV6twjyPEk/kfS/ScTDQulJKDNGNwSGyuOvtDJuQe1pW9LFJX+08gsZvSnKIOlYWpmHOp8R9wVeYX4r/dEGAQQQQAABBJIXoNCVvDkjIoAAAp0UyMor0qPi7Ph3EoWuTh569N1tgRlFJqMrgsHC7knHleW/s+iXKeiqkWJhl6Qdo8bzRyt3y2jyDtS+/w28+e4fCtgQQAABBBBAoEag4xcVaCOAAAIIJCrQVKHLH60slVHfZIRnB17h4ASi/Yak90aM0/HvpNLoxKHG2NNrx7a5CEb+AAAgAElEQVTWfn6kOPDxBPJmCAR6RiCri5VHPd6cljvRIotwfdX+kV0WLOmZA4tEEUAAAQQQaECg4xcVDcTALggggAAC8QlEFbrcuj1fCQ8RedeFNYcGxf4z4wsnsqeypMFuFLrcmP5YZU9ZXer+v5U5cMTrP6/D+dI9Aj0nsNvld6yx8kqr/XsqcfvYklVG/Fc+lQWIUnn8fZI5ts/od8ODhd3SEnOdO+V+GXiFbdISI3EggAACCCCQBgEKXWmYBWJAAAEE4hV4RpJbA8tt35R0QFT3XXy8aCNJt3Sr0BUvNb0hgECvCPijlSNldPJkvo8HXmG1JHOP/Mw25rfBYP+0F2wkGVMKxrpe0usn43hU0uopiIkQEEAAAQS6LEChq8sTwPAIIIBAtwS6WOhyKR8j6cSa3F8u6a/dsmBcBBBAYEUCpbGJnYy1V4X3SfKxxshHF+f0bTyy8/ybe3T2LpLk3ixcu90/+ZKTHiUhbQQQQAABJ0Chi+MAAQQQ6FGBUrlyj5EWTE/fbh94Az/sURLSTqmAXx4fk4xbDPxbgVfYL6VhElaOBfxyxT2GuUY3C11u7OnFLntA4A24u3Z7dWtqTcpeRSJvBBBAoBcFKHT14qyTMwIIIDApUBqtLDZGOy/7T2OGgsH+ABwEEhIoSloUGmvGeUlWFzVPyJBhEhIojVYqxqi/24WuhNLNyjAUurIyU8SJAAIIJCxAoSthcIZDAAEEsirglys/kMyLrJZ+ZMRbMJbVPIg7NQJRF6mLa19UUBodP9wYc2oKiwtugXK3LpB7ycN9qRGNJ5BHJD13siu3yPkv4+k2273sOTa+xRxrrkvhsZht2Paid28JPivUhXvRSKm9bmmNAAIIIJB1AQpdWZ9B4kcAAQQ6LLDwyiXPW3VO9eFpwxhzbjDY/94ODv0CSb+R9BJJH5bU6TdBdjAVuq4jMOvdGP5Y5SxZuYvZaVuS6yJFxB6O+xuSDsrJLEfNyaqSnsxJfm2l4Y+N7y1r3LpQ7hbYvwZev1tbkK27Au+ZfOmKi+ILko7qbjiMjgACCCCQBgEKXWmYBWJAAAEEUizglysTkuYnWGwYkHRvaLxpd/qkmIvQGheIKqrcKGnzqS72uHrJgrnPVO9J8NibLfrP17mQzsv5VNScuMdLd50Nht8jgAACCCCAAAJpEcjLiVlaPIkDAQQQyJ1AF97O6B4Fe2EEZBa+s14syRVr1pJ00uTbJXN3TMSU0CcmjWq7m7lG19iSk2WrR/53p64uwH3b5F2GYYIsHJuNTNusj5OuoJPathdKelcjA7IPAggggAACCCAQt0BeTszidqE/BBBAAIFJgaHRynet0TvCIB18fGzWR9q6PTl+ueJeYe+KWZLVRFAsFCS9SNI/Q7H9VtIbuh1vnfGvlLRw8ne+pJEuxOkKmp+VdIckd7dU2rcT6xQv83I+FfW35+bogVkmJqqde7T53LRPKPEhgAACCCCAQP4E8nJilr+ZISMEEEAgRQL+2MRTsnalqZD6lj65ziW7vmjGI2UxhewWHz88oq9UfGeVRsf/bIx5xbT4rG4MioX1oh7xdIv5xOQSZzf/kOTuPqvddpD0gzgHyWlf4aKOW5D+IznKtTY/t+i+K4jOtkUVupZKmjtbQ36PAAIIIIAAAgjELZDGk++4c6Q/BBBAAIHsCfxd0gY1Ya8m6fE0pLGCRznrhZfG79rU3zWXhrlOKIbwXKwvyRUis7RFHU93S1o3S0kQKwIIIIAAAgjkQyCNJ9/5kCULBBBAAIFcCqyg0HW8JPe/8Fb7XeseD3x7zQ47SbqmC1AUurqAHjGke8nCzrMcM+mIdMVRjEtyj+/WbhtKcmuasSGAAAIIIIAAAokKUOhKlJvBEEAAAQSyLuCPjn9Wxnx8Wh5GxweDhU9L+qWkN9b8rvZ71j0aGFXU6sZ3cVSh68OS3GOjbBEC/tjEGbL2kJpfHRF4hS+1idWtgqMrQt0air3d4/BiSXtN9vk6Sb9r0eaPkjatafszSdu22BfNEEAAAQQQQKAHBdo9qelBMlJGAAEEEOh1AX9s4hBZe4ZzMEYHDw8Wzm7A5NeStorYr2Pfxf7YxKWyds+pMUMvEKjWrB92vqT9G8ihpV1KYxOvMdZ+QNLPA6/wnZY66WKj0uh96xiz9F/hEGJ4IYNb5H3NJI+JybGiCmx3RqzbFru6X65cJcndyfjwE0v71l206/z/1AyyWZ0CWcf+RmJPkA5XKFAaqxxhrL5Qu1MMf0eoI4AAAgggME2AEwcOCAQQQAABBJIR+IYk9ya68NaR7+LS6MShxtjTO1CcaUqrNFa5xli5u9mWb1m7sB0qj59uZQ7tgKVbe+7RUL/u7ZPT7xhsSryhnbtyJ1nUY7+hY+GLkj6W1N9IQ1I52Kk0WvmjMc/eJWesXjNcLLi75rqyRT/6bT4beP2f7EpADIoAAgggkEuBjpxc51KKpBBAAIEeFPDHKgfL6qwsFylSNm3hAoMrcqzeiRhXsJZYot/9kXFYHRUUC9Pu6uiEQVx9Do2NH2itOacDha6pLoclrSPJPT7627jiXkE/UYUu97O+To5d51i4LCgWptate7Mk96hieEv0mO2kQdJ9R5k/bpc+d7S49mNJx+LGq3MMTATFQniNt26Ex5gIIIAAAjkR4MQhJxNJGggggEAnBKIuSozMScNe/zGdGK9H+vzR5JpDX5Z0ZKdyLpUr9xhpQQeLMw2FXqfg9svAK2zTUAcp2amBu5FSEmlDYQxJuiS0pyu4hu8ua6izRneqcyxcG3iFrWv6CD/OeXvoDayNDsd+dQpLVvanI97AW7sBFF3oMucGxf6ou127ESJjIoAAAgjkQIBCVw4mkRQQQACBTgnUuTC9P/AK8zo1Jv3GI1BaNL6+6TOuSFCz2X8E3sD68YzQWC9Rx1CfzIGXeP3nNdZDevbyy5ULJO1urT1/pDjwofRE1lIkcyWdKelBSUe11EOTjaKOBWurW4wUF1wf6uptknaT5O50cy94SPvm1t7bQ9Klkn6TpmDrPCp4Y+D1b96NOCf/ht5dO3bWHmXuhhtjIoAAAgg0J0Chqzkv9kYAAQR6SqBOoes7gVd4Z09BZDTZ0hXjrzJzzf+58I3MecNe/4FJpxJe38pa/WekWHh+0nEwXvcF9rri/vWqc59xi94v26y1nx8pDnR6PbJOJ/5PSS+qGeQOSS/p9KCN9h9ZaO4zm12yS//vG+0j7v2Gxpa8tGqXHtknc+OwV/ha3P3THwIIIIAAAhS6OAYQQAABBOoK+OXKRZL2rt2Bf33PzQHjFn/+TE02N0rqyl0euRElkV4TyMRbIkPFro8FXsE9Nt2z2zQPq0uDYqHUsxgkjgACCORUgEJXTieWtBBAAIG4BPZaPPHaalVHG6PfDw/2uzfCseVDIGpB8oWSRmNIzz2+9YbJftzbJg+KoU+6QCBtAp+W9KmIoDi/TttMTcYTve6k/fCwN3BqSkMmLAQQQACBFgT4Im4BjSYIIIAAAgjkQCCq0PUDSTu0mdtNkjYJ9eEKpFl/RK1NFprnUOAVkv5MoSs7M5uWt9FmR4xIEUAAgWwKUOjK5rwRNQIIIIAAAu0KRBW63Ns0T2qz46h+XZecc7QJS/PGBPzyxAOSXdPtbaUPjHiFsxpr2dJef5D06pqWPALcEmMyjeoszv9g4PUvO17YEEAAAQTyIcBJZz7mkSwQQAABBBBoVuBWSRuGGsVxXpB4ocsfHb9Txqw3lQvryDV7KORn/zpv+XzzJV7/L/KTJZm0KlAqV24zoZcFGNlXDXsDUXfmtToM7RBAAAEEuiwQxwltl1NgeAQQQACBpAVKwV3PMaut/NjUuEb6xrBXYB2mpCei/fHeIekDkn4k6bj2u1vWw+8kuUW6a7fjJbn1jGLf/PL4mGR2CXX8ZOAVVo19MDpMvUCdR9PGA6+wIPXB5yPAr0vaRtIpks5LY0p+uXKBpHe72EzVbjW8cOC6NMZJTAgggAACrQtQ6GrdjpYIIIBAzwpEXkz2zdk92GXeFUmh+OXx0yRzmBvPWrvXSHFgOKmxGWdWAbfW13aTe7k3vH1s1hYt7sCaOy3C5bRZnePhnsArrNNuyn65svwuSNNnThrepd896sv2X4Hw3Zw3S9oYIAQQQAABBJIWoNCVtDjjIYAAAjkQqHMxeXfgFdZNIr3S6PgPjTFvqx3L9pm9Rnbpp9iVxASkaIwuFroOlXR6DcX9kualiKYnQ4l8dNHM2fKSwXnuTaAtb3654u5gfc60zxzZg0a8AfdWUbZn/xbc30R441qDowMBBBBAIHEBvnwSJ2dABBBAIPsCdYoL1wdeYYsksuticSOJ9BijCYGh0co+1ug7oSbnBF7hfU1008quUWuRfUTSV1rpjDbxCfjlyr8kTd7BZfYLvP5vtds7nzmzCkY9suwaca0xKx07IIAAAgjELcCXT9yi9IcAAgj0gIBfHn9CMqvUpmofe2q1EX+9x5NIn4vOJJSzM4Y/OrGLjB1bdlVtzIeGB/u/mkD0UYWu2yIW+F9RKG4to5/X7BDHWy8TSL33huAzZ9Y531vSRRF7ca0xKx07IIAAAgjELcCXT9yi9IcAAgj0iIA/WhmR0dslPWWrSzcaWbj2nUmlXipXqmbmnQJfC7zCIUnFkLFxPiHpJEl/krRpxmJPa7hRha6zJR3cRMBRfbi3R7o7ktiSFXiPpG/WDHm+pP2n/tsfq9wvq7VqQzJ9evfwLoVvJxtmqkcL39Xl/JwjGwIIIIAAAokKUOhKlJvBEEAAAQRaFfDLlasl7bi8vZGVffaxGGvtT0eKA29tte+ct/u9pNeEcuT7v/1J/76k7dt0jSp0XSzJvQ2TLVmBqLlwRWFXHF62+eVKTSHHHB14/ScnGyKjIYAAAggggEAjApzoNqLEPggggAACXRUojVZ2N0bfCwcReAW+x2afmagL+BslbT57U/aYReB/J98oeb2k41vQipobVzw5uoW+aNKeQNRcuJdb7NVet7RGAAEEEEAAgaQFuEBIWpzxEEAAAQSaFhgam7jRWvvaGApdB0g6t6Yf1+cfmg4oWw2iLuCfkjRtjbVspZRItMdKOqFmpNslbRDzyFFzw7lZzMgNdhc1F6yZ1iAeuyGAAAIIIJAmAU6m0jQbxIIAAgggECkwVK6cb6X9Zit0+eXKVZJ2krHnB4MDy9fXmWz3XEmPRAyQ9+/CqAv4j0o6hcNthQJRbvtK+m7MbtdI2kHS3yVtFHPfLXW3z+L7n/909ZlxSc+x0q9HvMIbW+ooW40oOmZrvogWAQQQQACBugJ5P7ln6hFAAAEEciIQ8dazSuAVBqbSi3orWujRxnMkHdiDha4XSfpnTd7XSdoqJ4dFJ9OIKnz8StKbOjloGvpu4G8pDWF2IoYvS3q/JFcw37MTA9AnAggggAACCHRegEJX540ZAQEEEEAgJoHSaGWxkbaR6ftC4M0/carb0mjlCGP0hfAwoULXZyV9vAcLXTHp91w3UYUu9yjjcXmWGBqrfNtavXOWv6U8E5Bb4wJfk1SQdDhvCm0cjT0RQAABBDovQKGr88aMgAACCCDQYQG/PHGeZMOPKipisXoeT+rwXOSo+xsiFuzP/XlTaaxyjbHLHqWctvHihxwd2e2nsrKkJ0Pd8Dh0+670gAACCCAQk0DuT9hicqIbBBBAAIEUC+wxds+L59o5/2jw4vzfktaQ9CdJm6Y4ra6H5pfHy1baoM/MOWJ4cH656wElH8CgpMMk/UzS55IfPvkRh8aWvNTa6s0N/i0lHyAjpkHgIUnPjwiE64o0zA4xIIAAAgiILyQOAgQQQACB1AgMjU2caq11j8Es257pm7ve93Z54b8aCXBorPJla/WRqX2N1buHi4VFkm6UtL6kT0pyjy+yzSZgrfHHJqq1uxmrC4eLhXfN1pTfZ1+gtGj8aNNnlhf2bHXpi0cWrn1n9jMjg5gEou6MdV1zXRETMN0ggAACCLQnwBdSe360RgABBBCISaC0eMlbTLX603B3bTwytZak+0P9XStp65hCzm03frly+2RxcFqObcxFbq1IDIEeFHAvtNgiIm+uK3rwYCBlBBBAII0CfCGlcVaICQEEEOhBAb9c+b2k18RY6HJvGnRvHAxvfPfNcnxFvXXPNaHQlek/zHVCC4a7R1GLmc6I4LspEL6ra2NJMx557WaAjI0AAggg0LsCnOz37tyTOQIIIJAqAX9sYpGsnXHh3UZxhcdrWpxhf2ziHFl7YIxFxxYjodmkQPhYfoOk3zapE/X34F7gcH6T/bA7AggggAACCCCQagEKXameHoJDAAEEeksg6k6iNgpdx0k6PkKQ774GDiu/XHlM0nOmdl1qzUsvK/bf2kBTdolX4BZJG8VwHEcVuu6TND/ecOmtFwVKv77rOeaBld1nxtR2S+AVXtaLFuSMAAIIINB9AU72uz8HRIAAAgggMClQ+snE6uZR6x45dOtrXR54hT3axLlB0uY1fTxP0iNt9klzBJIUiOvOxKh+WLMuyZnM8VjRjzvbrwTewPIXhKQ9/b3GxreoWuPWH5vaPhV4hRPTHjfxIYAAAgjMFKDQxVGBAAIIIIAAAgikV+ABSWtGhNfsOdxdktYN9dNsH+lVIrKuCrSwrp97i+d6NUF3/ViMyqFvzsrrX7Lzmv/oKi6DI4AAAgg0LdD1L5WmI6YBAggggAACCCDQWwLhu7HeIeniFghOkfRhSXdI2kTSky30QRMEZgjUKXQtCbxCfwTXRZL2jqF4G+tMROZgdVlQLLw91oHoDAEEEECg4wIUujpOzAAIIIAAAggggEDbAp+UNG+yUNV2Z3TQWwJ+uXKvpAFZXRcUC1vFnb1frlwtacfafuesqoGLtytUIsaK63HcWNOILHQZfSUYLGTm8ctYQegMAQQQyLAAha4MTx6hI4AAAggggAACCCCwIoGYX/JRdyh/0fgH1WfcXYO3PfX0yltcsfuaD9bZOTOFrjZehsJBiQACCCDQRQEKXV3EZ2gEEEAAAQQQ6A0Bf7RyrYy2dNlaq61HigW3EDwbAh0V8MeWnCxbPTI8SJcLOG+U9MtQTO5xxn06itFA56XRyjXGaAf3eG/gFV7SQBN2QQABBBBIoQCFrhROCiEhgAACCCCAQH4EIhe5XrlvnUt2mH9PfrIkkzQKDI1Vvm2t3pmyQpcLZ1NJP5O0hqRDJZ2ZRj9iQgABBBDIpgCFrmzOG1EjgAACCCCAQEYE6izU/afAK7iL/TxuiyQVaxJ7laQ/5zHRtOe09+g985aaOUtSWOhKOx3xIYAAAghkWIBCV4Ynj9ARQAABBBBAIP0CdQpddwZe4cXpj77pCPeq80ZIzjmbpoynQWl0/HBjzKlTvS1duvT1l+269g3x9N61XlyR+I81o39O0ie6Fg0DI4AAAgikSoCTjlRNB8EggAACCKRdwC9X3Noybo0ZGWN+PzzYv1naYya+7gpEF7rs9oE38MPuRtaR0W+XtH5Ez5xzdoS7ZzuNWtDeFb/+1LMiJI4AAgggsFyAkw4OBgQQQAABBBoU8MsTZckO1u5upF8Pe4VlhS82BOoJ1Ba7rHTCiFc4LqdaiyXtTKGre7O795WVwtI5Gp+KwBgdOTxY+GL3IurIyFGFrh9IyxaSZ0MAAQQQ6HEBCl09fgCQPgIIIIBA4wJ1HkFTl99g1ngCKduzNHrvG4zp+82ysIxuDgYLG88Wol+u/ELSm/67n70v8Abmz9auxd+/IrS21Jgkr8W+eqlZuAhRkTTQSwA1uYYtWjr33uOyfyyYu8qq/yeZedZWTxkpLvhoPc+ozyk7p2/jkZ3n35yjOYgqdJ0n6cAc5UgqCCCAAAItCrT0ZdviWDRDAAEEEEAg0wIUuuKbvn0W3//8p6vPPBTucbaiYZ052CnwCtfEF93ynqIupveRdFEHxspbl3+Q9GpJ7k6iI/OWXIP53CHpf0L7PizpBQ22X7Zb6eq71jLPrHz/tDZWNwfF6MJwZKFL+tGIV9iumXFTvm/U3ybXNSmfNMJDAAEEkhLgCyEpacZBAAEEEGhX4PuStq/p5B11Fr1ud5y67f3yxFck+6HQDt8KvMJ+ku6UtF7N746Q9KWOBZPxjv2xiZ/K2reE02ip0GXMucFg/3s7QBJ1Mf3PiOJFB4amyxwIRB0/Lq2mzr/9cuVWSRs2+LdyiV+uDIX3NdYODxcH3IsC8rSNTt5h+a/QZ2+eciQXBBBAAIEWBJr6om2hf5oggAACCCAQh4BbF6sc0VHi32Ol0fHPGWOOXhaLtacFxYHDJR0g6dw0xBcHdhJ9+OWJGyS7eYMX78t3S8EdXe7OsZ2SMOr1McKPqdYUQcMFpN0kXZlCr7gKXdWo4lidorD1y+5J0enbbAXkFNoREgIIIIAAAi0LJH6B0HKkNEQAAQQQ6GWB0LpMyynS8j12o6TXUuhq/BDd88rKpnPm6I/NXpAnvEbXPZIWhGJMyzHXOHYG9xwaq3zZWn0k4vhYJGlhRv7W3inp26FYPyjp9GamZKg8/iEr85UG/1aWFde2O+UqrfXSzWRtVYv2faWefOh+jttm0NkXAQQQQCDTAnzpZXr6CB4BBBDoGYFTJbk7p8JbWr7H3DpEH0txfI0eKMOSXi7pmCTukBkqjx9mZU6bCq7Ru05KV05saObafUzfnMXDO8/7baPJtbjfFyS5x1D/b3LNqRa7oVkzAitYD69eNx39LPBHx++TMS+cGtxaUxop9l/aQE5bSLpc0lxJ7pHCnzTQZsYupdHKNcb8942Cqz33sedc8Nb1n4joK+ouMvc3FvX52UootEEAAQQQQCD1Ah09KUh99gSIAAIIIJAlgfAF3ISkQooSCMd3raStUxTfbKGE43cX53vM1ojfZ0NgaNGSzWxf1b2V7uEnlvZ5i3ad/580R76CQpcrbL4+IvaOndP6o+PHyJgTw2M2WphN2NkV1q6rGfNRSauHYthB0tTLG745+eh1wmEyHAIIIIAAAp0T6NhJQedCpmcEEEAAgR4WuFjStpLcHV4np9DhU5LeOBnfVSmMr15Iv5H0hriLB8++LW6Vv0h2QLKnB96Ae2yLLWEBv1z5aPjFCKbat/nwwvnukdtUbqVF977F9PX9dFpwxvwsGOx3f//houxxkk7oVCJ+ueLeeLhWuP/AW1Znz9q5tHsBxHRX6U+SNu2UH/0igAACCCCQtEDWvpyT9mE8BBBAAAEE5Jcr35G0z7Ll55c+s8nIruvcnDOWWBbNrjXZ7/w7Vn1s/mqPh5zuDLzCi3Nml/p0VnB3VOzngaWxif2M1aut0ckjg/3j7eD4YxNvlrU/W1ZNMn1fHB6cf2RNfx+XtIGkT0qaufp6OwOH2g6VJy6zsjPubpwsdLl1xGasnxXj8OGuXAHdvQzBrVW2awvjxP633kIMNEEAAQQQQKCjArGf4HQ0WjpHAAEEEEAgYQG/XFkiaV7tsKusZAsX7jjgHp3Myxb7GmN+eeL3kn1NGCilj3vlZR4j80iq0DVznPzcxRfObfx3P9bPj9vbeV8g6T0JHUBRRapmz+UpdCU0WQyDAAIIINA9gWa/HLsXKSMjgAACCDQi8AFJZ9bs6NZhcf/6z9aiQJ0iwS2BV3hZi12mtdkDktasCW6r0Fo/TcXtlysPS3oeha6m2Dqyc51j+E+BV4jtcbWhcuV8K+2X5/ne7ODP2TU2eJX+Onyq7r3hh1OpvlTSrR2ZuOmd7ivpwohxmj2XP7bOY57N9pNAygyBAAIIIIBAawJ8qbXmRisEEEAgrQJR/1r/Nkk/TmvAaY+rTpHALfD83OWxV+3hwcKB5W8PTHtOScTnj46/V8Z8I8bCxzmSDpzs725J6yaRR17GCB/Hcd9Z55crD0l6fozznUb690v6Wk1g7vg+KKFAPy3JrQEY3lo5l78i9NhjK30klDbDIIAAAggg0LwAX2zNm9ECAQQQ6JZASdL+ktwaLfWKKlGFLrf/Lk0Evb6k20P79+z3Rb3HvsKeS+3STS4rrv23Jpxzv6s/Or5IxhSnEn3q6ZXXvGL3NR9sIXG3DtNnQu2i3ibXQtc0iUPAH534qoyd8bKBuAtqccSa0T7cZ3A1pkJXRgkIGwEEEEAAgcYEevbCpTEe9kIAAQRSI3CnpPUaKD5FFbrcnQDHN5FJVB9uUekFTfSRq11DxS63ML17jGjaZqVfjHiFN+cq8fQkw7pC6ZmLupHMLAqbowOvP41vR82AZmSI4bdnukfVz8pqMsSNAAIIIIBApwQodHVKln4RQACBeAWiLvQPkPTN0DDhdZbcr5v9rKeoMMvcRd3lZa0tjxQHlt+9FO/093xvHJM9fwgAEJfAHovvX3du9ZlRyaxm+qrvHt5l4Lq4+qYfBBBAAAEE0iDQ7MVPGmImBgQQQKAXBaIu9L8bdWeRJPev/odIcqslv68FLIoKLRS6eESrhSOt8SburXbhou7NkjZuvAv2RAABv7zkrVJ12pqN1uqjI8XCKegggAACCCCQFwEKXXmZSfJAAIG8C0QVn94i6ecdSHx3Sd8L9buGJLfYNNukwFC58mcrvcL9p13at/HIrvNd4YWtcwIfkvSVye6bXXeuc1HRMwIZEqi35iCF+gxNIqEigAACCMwqQKFrViJ2QAABBFIhcKYktx7L1Ha9pC06HJl7u9jDko7u8Dh0332Bd0h6maQTJT3T/XCIIC8CpdHxw01f37xgsP/YvOSU5TwodGV59ogdAQQQQKBRAQpdjUqxHwIIIIAAAvkUCN8t6N5w+Nl8pkpWSQrMKKpU9fZgYeGyJGNgrOkCfnn8JslsEnbhji6OFAQQQACBPAlQ6MrTbJILAggggAACzQlcLmm3iCacHzTnyN4hAb9ccW9qLVBQSd+hES5AUuRK3xwREQIIIIBAewKcyLbnR2sEEEAAAQSyJuDWX3PrsK1o4/wga7Oasnh5RC5lE9K9cKfN7wsAACAASURBVG6S5O4ge1zSCyQ93b1QGBkBBBBAoFcEOJHtlZkmTwQQQKBJAX90vChjDrOyPxvxBk5qsnlcu79L0rdqOuN7qz3ZD0r6agNd4NwAUpu7RL30ITfuQ+XKbVZ6SdiIu4faPGqy1TzqJSq5OcazNRVEiwACCPSWAF82vTXfZIsAAgg0JOCXK7+TtFntzl24QF1b0t0RAfPd1dAsRu4UdeEZ3tG93bCRYljrUdDSCUTNxU8lvTUvPDPu6jLmLcFgfyfeFJsXsrzlEXWMf0nSEXlLlHwQQAABBNIlwMVCuuaDaBBAAIFUCEQ9dmSkE4a9wnEJBni1pB0pdMUqHrlukiTOB2JlbqizekVH5qIhPnbKgEDUMX6+pP0zEDshIoAAAghkWICTqQxPHqEjgAACnRKos77OrwKv8KZOjRnRb721pPjuan0SNpD091Dz2yW5n7MlKxBVBPh9+E7KZENiNARiFYg6xp8n6ZFYR6EzBBBAAAEEQgJcLHBIIIAAAgjMEIgqdFlj9xkZHLgoYS7WeIkffFNJf5zs9lxJ741/CHpsQOAwSadxXtaAFLtkVcAtQu8Wo5/a3J1c7o4uNgQQQAABBDoqQKGro7x0jgACCGRTYKg88Ukr+5ma6P8eeIWNupBNbVHGrde1bhdiYEgEOiWwsqTPSfpnRNGrU2PSLwLNCPxB0qsnG7jC7OHNNGZfBBBAAAEEuiFAoasb6oyJAAIIIIAAAggg0KrAtpJ+UtOYRz5blVxxu7si/nHhE5PF2c6MSK8IIIAAAgjEIEChKwZEukAAAQQQQMAvV34p6Y1TEnPsnE0uLs77GzIIIBC7QNQjzR+QdFbsI/V2h7wwobfnn+wRQACBzApQ6Mrs1BE4AggggEBaBPyxiUNk7RnheAKvwPdsWiaJOPIkEFWAuUfSOnlKMgW5UOhKwSQQAgIIIIBA8wKcgDdvRgsEEEAAAQSmCfjliX9Ldg0KXRwYCCQiEFWAWSRp10RG751BbpW0YSjdj0j6ShYJ/PLEw5J1b318OPAKL8hiDsSMAAIIINCYAIWuxpzYCwEEEEAAgboC/mjlFzJ6E4UuDhIEEhFwbw11L6qo3XrtnPZaSVvWAGwl6boI/ZUmH+l8WJIrUjW7/VzSNpON3AtKjm22gzTsH/UmYe64TcPMEAMCCCDQGYFeOynojCK9IoAAAgj0vMDMCyl7U+ANvKLnYQBAoDMCh0r6pKQbJBU7M0Rqez1I0tcjoguf15ckBaH9XijpgdRm1oHAhsaWHGht9Zxw1xS6OoBNlwgggEBKBCh0pWQiCAMBBBBAoLMCpdHxzxljjpZ022rPfeyVF7x1/SfiHrFUnvitkX2tlT1uxBs4Ke7+6Q8BBBCQ5NYjW9BAoYs1tiT55cpHJX2JQhd/OwgggEDvCFDo6p25JlMEEECgZwVK5cotRtqoFiAY7O+TMfUuBHvWisQRQCD1Aosl7Uyhq/F5qvPo4lQH20r6WeO9sScCCCCAQNoFKHSlfYaIDwEEEECgbYGoixxZ3RgUC5u33TkdIIAAAskLhIv0N0kKPyrN2ykn58W/8p43a86c5cWsHx9Z1H03XV87a1wTJX8MMyICCCDQMQE+1DtGS8cIIIAAAmkRiCx0SY8GXmH1tMRIHAgggECTAj+S9DpJJ0Y9midpPUl3hvqM+9z/SEmfl+QKba+V9HSTOSS9O49zJi3OeAgggEAXBOL+sutCCgyJAAIIIJAjgcsk7TGZzysl/SWO3KIKXUY6ZNgrfC2O/ukDAQQQ6EEB95bHLUJ5ryrpyRRb3CfJLcgf3rgmSvGkERoCCCDQrAAf6s2KsT8CCCCAQKcE/iMpfIfVgKRKuwPucdmSBXNXqboFnKe2HwZeYft2+6U9Aggg0MMCUXdH/U3SJik3Cce9o6TvpzxmwkMAAQQQaEKAQlcTWOyKAAIIINBRgaiLpr9KenlHR6VzBBBAAIFWBLL8GOBBktacfOyyldxpgwACCCCQYgEKXSmeHEJDAAEEekwg6qLpwcmLkR6jIF0EJH90/L0yfbtZY747Mjj/IkwQSJlA1Gf2MZJOSlmc4XDc3b0LJn/4kKQ1Uh4v4SGAAAIINClAoatJMHZHAAEEEOiYQNRF096SLunYiHSMQEoFItaVezjwCi9IabiE1ZsCBUnjNanfKCntb7K9VtKWoem6RdLLenMKyRoBBBDIpwCFrnzOK1khgAACWRWoLXadI+l9WU2EuBFoVWBobIlnbXU03D7wCpy3tYpKOwSeFcjy45bMIQIIIIBAgwKcMDUIxW4IIIAAAp0XKI1WFhujnSevR8YCb8Dr/KiMgEC6BPyxyimy+jCFrnTNC9HkQoBCVy6mkSQQQACBFQtQ6OIIQQABBBBIhYA/NrFI1hanBWN0RTBY2D0VARIEAgkJlBbf+3JT7fsLha6EwBnm65I2lXSCpKtzzvGJiDXEviTpiJznTXoIIIBATwlQ6Oqp6SZZBBBAIL0CEWsSLQuWx7XSO2dE1jkBv1z5gaTtpkYwRhcNDxb26dyI9NyjAuE7nK6UtFvOLQ6U5B6Nd9vhkk7Leb6khwACCPScAIWunptyEkYAAQTSKUChK53zQlQIIJBbAXf31o4R2XF9kNspJzEEEECgNwT4IuuNeSZLBBBAIPUCfrlykST3lsXa7TuBV3hn6oMnQAQQQCB7Ao9LWpVCV/YmjogRQAABBFYsQKGLIwQBBBBAIDUC/ljlW7J6lwvIyFww7PW/JzXBEQgCCCCQL4HPSPokha58TSrZIIAAAgi46wg2BBBAAAEEEEAg2wLnSjpgMoUJSYUVpTM0NnG0tXbIGDM8PNj/+WynTvStCAyNVQ6qVo17q+u3Ror9l7bSR07aPCTp+TW5OJOxnORGGggggAACPSpAoatHJ560EUAAAQQQyInAxyV9NpTLU5JWicovai04XniQkyOhwTRmHgPmnsDrX6fB5uyGAAIIIIAAAikXoNCV8gkiPAQQQAABBBBYoUD4rXFTO884x/HL49tJxr3NcNpGoat3jjB/bHxvWePWA+QY6J1pJ1MEEEAAgR4ToNDVYxNOuggggECTArVFBPeIl7t7hg2BNAk0XuhavORYVasnhIM3MmdY2UPdz60xrx0Z7P9DowmWRicONca+WUuXnhHsuvbPG23Hft0R8EfHz5Mx+9cbfY6ds8nFxXl/6050jIoAAggggAACcQhQ6IpDkT4QQACBfApEFRCKksr5TJesMiqwr6QLQ7HfIull4XxKV9+1lnlm5ftny9NozsuHvXl/nW2/iMcgfx54hbfM1o7fd09gaNH4lrbPXLuiCLjDr3vzw8gIIIAAAgjEIUChKw5F+kAAAQTyKRBV6FoiqT+f6ZJVhgU+IOnMyfgXSxqsl4s/Ov5NGbPit3ka81Aw2L/Gijz80cpxMjo+vA9FkvQfRX658mtJW9WLlDlM/xwSIQIIIIAAAisSoNDF8YEAAgggUE8gqtAVeacMhAhkVSBqcXqXy2zFDn90/FoZsyWFrqzOvFQaHf+hMeZtzGF255DIEUAAAQQQiBKg0MVxgQACCCDQTKFrfUn/gAyBvAhEFrqs/URQHPjcinL0y+P7S+Y8iiTZPhIi5v9fgVdYL8VZnSHppZKOkXR9iuMkNAQQQAABBLomQKGra/QMjAACCGRC4GeS3jwZ6TaSfpmJqAkSgSYEQsWOqwOvsHMjzf1yZVxS4b/7ms8GXv8nG2nLPukR8MuV/5P0KsmeHHgDR6cnshmRhO+y/Yakg1IcL6EhgAACCCDQFQEKXV1hZ1AEEEAAAQR6S6B0zYPrm6efvL0269keD+wtodiydS+MWFTT25clfSy23umoWwK/kfSGiMG7dS5fW3Rzd/m6u33ZEEAAAQQQSIVAt74cU5E8QSCAAAIIIIBAMgJ11sJ6KPAKK1z0PZnoujtKabSyuzH63rNR2B8G3sD2bUQUtbaee9Tt1jb6pGn3BaLm1UXVjXP5qFjOlnRw95mIAAEEEEAAge58OeKOAAIIIIAAAj0m0Oqi73ln8hdX3qjqjEeC/x14hbVazD2qCDEsaa8W+6NZOgTOlXRARChpKXR1q+iWjtkhCgQQQACBVAl048sxVQAEgwACCORQ4B2SvjuZ1/cl7ZjDHEkpYwIUuqInzB+tPCaj54R/28ZjnVGFrs9IOjZjh0yz4R4l6fM1jdz6gts220nK9w/PrXtj5I+7EHPUMfa4pNW6EAtDIoAAAgggMEOAQhcHBQIIIJAvgZ0kXRVK6V5Ja+crTbLJmoBfrriC69W1cdvHHlljxN/goazlEme8HSgARhUheuF8Lyrv7ST9KM75oq9lApdL2i1ksaGk2/BBAAEEEEAgDQK9cOKTBmdiQAABBJISSNM6LknlzDgZEhgaq3xZVo8Me4XjMhR2x0L1yxP7S/a88ABt3NHlunJ3dLo7O6+VtHXHgk9Xx1GffWVJbnF+tvYELpW052QXT0paVdIHJJ0u6Z+Tx5h7A+mKtudLqi1qf07SJ9oLi9YIIIAAAghEC1Do4shAAAEEMi6w95WVwtI5uknSWoFXqJdNRz7vS2OVxcZq52cHtRcH3oC7uE7FFr5Tps3CQSpyIoh8CvjlidMke9hUdhyrLc1zVKHr46HHGVvquMcbucdePxlh0Ox3StT8bCPNWJ+ux7lJHwEEEEAgDoFmv6TiGJM+EEAAAQRiEnjnNePPffJp88hUd38f+6ZuPMtd283YYv+898sVt/7XtLfDGemCYa/wnpjSa7kbv1z5k6RXhjvoYAHhREm+pIslHd9y4DREIB0Cd0paryaUUUkL0xFa3SjcI9oDod/G/rkXHn2oPHGYlT1t6ufW2q+OFAc+lHKrZsKL6y7hqH7+T9KrmwmGfRFAAAEEEGhEoOMnAI0EwT4IIIAAAq0JlEYrfzRGm9a2/t3XjtJtiy+Y+tHUYyatDbCCVh1YWyi2GBOOrVfXRIptvugoVQIz1lKbjG7aOWPt35iROWbY6z8pBVkcJOn9kn4g6Ygk4on6rOlb2rfOJbvOvyeJ8RMYo5OFLjdPOySQA0MggAACCPSYAIWuHptw0kUAgXwJlEYrDxuj54Wz6uCdS8uHSriY1NTEJRibW/9nUURwfL82NWPsnCKBb0l614qO6ci/L7P0LcHg2j9PUR6JhFLns+ZrgVc4JJEAOj/I2yWNhIa5UdLmTQ7NPwg0CcbuCCCAAAKtC3Ai3rodLRFAAIGuC3RoIeuG8pq+PtdkE2vODYr9722ogw7uVBqtbGWMfh0a4mOBV/hyzMO6RxaPodAVsyrddVPALTruFh8Pb8vPGesUd/4eeIWNuhl4N8aOtLB6f1AsfL0b8XRozH0lXTjZt3vRgfvvVjZ3B5d7E6Yrer1I0r9a6YQ2CCCAAAIIzCZAoWs2IX6PAAIIpFygVK5cZqQ9psK0j5nnjfj9y9ft6mT4/ljlctlnXzOflvW5pvLde/Seec+YOeU+afWqre4/UlxwfQcs5klasqKiQAfGpEsEOi3wjKQ5NYPcIOn1U/9dp9DlHpNeRdIjgVeYcZdppwPuVv9RFkncUdutfBkXAQQQQACBLAhQ6MrCLBEjAggggECaBb4hqfYuttMlfTDNAROb5I9VTpbVkTUW7wy8wnewWS6wk7TsjarflvS7Wpd6jwbX7tNLxR5/tHKWjN3fmL7Lhwf79+IYQgABBBBAAIHuClDo6q4/oyOAAAIIINDzAv7o+DeM0TZV6Ssj3oArHHZ0K43et44xS2c8NtVLxZl2gUvlid8a2dfV7cdWPx0UF/AG0nahaY8AAggggAACTQtQ6GqajAYIIIAAAgisWGDb438yt//1r7hT1i6QtVcExYHdMYsWmHF3kNXfg2Jn13ryRyfOkbEHhiPqUqHrJkmbTMZylqQPZO1Yib7Dy5weeP3c2Zi1ySTeGQJDYxOHW2tPXfYLawaDYv9imBBAAAEE0i1AoSvd80N0CCCAAAIZFGDdnsYmzS+PnyaZw5IuOPnlysckfTHpcSNU7pU0EPr5sZI+05hgOvbieE/HPBBF/AJ+uRJ+NF3Gat/hYsEtys+GAAIIIJBSAQpdKZ0YwkIAAQQQyKbA0Oj4JdaYoRQUUVIP6I9NXC9rly9yPhVwEndWpaQ4494+F7Vl6vxsaLTyamv0h6lEjPr2HvbmX5L6A5AAGxVw6w76kr7rlysfXt7ImKFgsD+YrZPwenh2qdloZNf+v8/WLg2/r7ceXRKfUWnInxgQQACBrApk6kQqq8jEjQACCCDQOwJD5cqvrLR1CgtdN0t6qaQnJL1A0lPdnhV/bMKXtcPdsvLLle9L2t6YvguHB+e/qwseuSh0dcGNIZMTWH6M+uXKjFHn2KXzLy6ufV+9cPYaW/K6qq3+tlt/4+0yUehqV5D2CCCAQHcEKHR1x51REUAAAQRyKjC0eHxLWzXXpuzCLqqgkopzAH90/HoZs/yuLmOr+w8XF5yf08MjnNbfJL0s9MPDJZ3WI/n3YpruzqhwcTcVf4sRk7GnpEunfh5V6LLWlEeK/cV6E+mXK7+Q9KaUfR42fNz55Yr7h4FVpjewiwNvYLDhTtgRAQQQQCBxgbR+sSYOwYAIIIAAAgjEJTA0VvmytfrIVH/GPP2y4cF1b4mr/xb6iSp0fUHSUS30RZN4BX4k6X8nuzxO0gnxdk9vKROI+lu8TtJWKYvThePWivvkigpdrhAWeIVSKPYLJe3rfrbKC+Y9uut3//LccG5ZevRv2l1d1t4VFAdelMK5IiQEEEAAgRoBCl0cDggggAACCORfIOri+jxJM948mH8KMkSgqwJZelx1DUn/XlGhK6Jg9VlJH68VXuX5L9SuF7mXi/53y1Khq6tHC4MjgAACCLQkQKGrJTYaIYAAAgggkCmBqIvr1SQ9nqksCBaB7AtE/S3+XtJmKU3tVEnucdpl2x6X3vH43FVXe477/9aY144M9i9/CcHkLpGFPL88vkQy82d71DFtBuE3w1KgS9sMEQ8CCCAQLUChiyMDAQQQQACB/AtsIqn2lop3S/p2/tMmQwRSJ3CApHNDUeXpfDxLd6yt8ODwxyp7yv53jbKpnSl2pe5vioAQQACBGQJ5+mJlehFAAAEEEEAgWYHai9qqpDnJDs9oCLQk8FdJG0v67tRaUi310l6j4yXdJck9Qpyn7RBJZ4QSulHS5llL0i9X7pe0VjhuCl1Zm0niRQCBXhSg0NWLs07OCCCAAAIItC8QdefGVZJ2ab9rekCgYwKpfQNpxzJOvuMjJLmXXbjtCkm7Jx9C+yP65crNkl5Koat9S3pAAAEEkhag0JW0OOMhgAAC+RX4kqSPSrpNkntU7un8pkpmbomeOgqcW3B4JCow7a14klZwx835kvaLCI5jNtEZy8Zgeyy+f9251WfcXXc1m30q8AZWyUYGRIkAAgj0rgBf7L0792SOAAIIxCnwT0nhV67zHROncPr6otCVvjnpuYjCRa4pgDrFrp9JejOFrp47TFpOuFResrlR9QbXgbW6aqRY4I7VljVpiAACCCQnwEVIctaMhAACCORZIKro8WtJb8xz0nnNzS9XLpxcu+jJvjnVjS/ZecE/InKd9ja2yd+/StKf8+pCXukTaLLQtaWkayl0pW8eiQgBBBBAAIE4BSh0xalJXwgggEDvCkQVuu6TNL93SbKZuT9WuV92+gLMdpWV1hjZfq2HIjJaKOnrkiYkvU2SW7yZDYHEBJosdLm4zpT0gZoAXy9p2R07bAgggAACCCCQDwEKXfmYR7JAAAEEui0QVeg6QNI3ux0Y4zcnUKdw8KfAK2zaXE/sjUDnBUrlezY3mjOtUGWrZpuRhf2/7PzojIAAAggggAACaRSg0JXGWSEmBBBAIHsC60m6sybsxZIGs5cGEUcXusy/A69/LXQQSKuAPzp+joz6Am/AFdjZEEAAAQQQQKCHBSh09fDkkzoCCCCAAAJhgahCl7Had7hY+C5aCCCAAAIIIIAAAgikXYBCV9pniPgQQACBbAu4xZ8/Lemvkj6U7VR6I/r3nX3DSg+us95TU9kaqwuHi4V39Ub2ZIkAAggggAACCCCQdQEKXVmfQeJHAAEE0itwvqT9QuHxvZPe+SIyBBBAAAEEEEAAAQQyL8AFR+ankAQQQACB1ApELVD/U0lvTW3EBIYAAiqNjv/ZGPOKZynMWOD1e7AggAACCCCAAAJZEaDQlZWZIk4EEEAgewJRha5lV87ZS4WIEYhVoPZvwz0mukqsvbfR2fQi1/KOvhV4hfDdmW2MQlMEEEAAAQQQQKBzAlxsdM6WnhFAAIFeF4gqdJ0p6dBehyH/nha4Q9L/hAT+IumVaVCJfuumFHiFVJwzhuNLS1xpmDtiQAABBBBAAIFnBVJx0sJkIIAAAghkTmAvSRdPRv2kpFUjMhiUVA79nO+dzE01AccskOo7HeModA2NTZxqrT18yi2uYpRfroxLKkyfD3N34PWvG/Mc0R0CCCCAAAIIZFiAC44MTx6hI4AAAl0S2EjSLRFj853SpQlh2EwJrKjQVfu74ySdkHRmfnmiLFlXpP7vVq2eGCxc8KlGYvHH7vVl+4bD+8ZR7IqjCNdIDuyTCwH3DzHuH2RukjS53lwu8iIJBBBAAIEGBLgoaQCJXRBAAAEEpgn8TtJmFLoaOio23NDb/9bNDv7csp2f+s+/dcXeG/Pd2xBdbnc6RtKJoew+KunLERm/RdLPk5bwy5ULJL172bhNFLnc7n658rCk51HoSnrWGK9GIKqYzOcuhwgCCCDQQwJ86PfQZJMqAgggEJPADf/P3p3HSU6Vaxx/Ts0MuAFeZTo9oCAKLqC4AKKoiIooVBoErTSLiriAIKgXEQUFQZRFQBQBWVQUZemUbNPpEVdEURRxB1y4KsgyXT0oyqIs033uJ0P3UJ1OTdeSVCWpX/6C6Zxz3vd70lXJ28mJpK0odM2vObDlK+wOx182Z8ck7m6Zf3T2yLDAPpK+MR2fJ6kqKe7i/BZJz85wHnNCqwTjtxqZjdModFWC2oFGOivat5X5YdUd4G2ueTpQ0o017nfpbEkHpjssvSOAAAIIZEWAQldWZoI4EEAAgfwIbCLprxS65p+w3S76g1173adQ6Jqfij3iC12/kPTSPOFUrprY1Ky0YYHusc3oQb/sPD6JPAr++GK0QBP+QeFXSbj1WR9xha7vSHpDnzmQLgIIINC3AhS6+nbqSRwBBBDoSGAPSZdO9xA+qrReR70VtPFu37jJrv3k9Sl0ZXR+h4OJQ6zs6avCM+Z0vzywegH1HoQcd3H+VEn/7EEsHQ1ZWXrXK01pwY/DTqzVd6pDTmIFhhwVuq6WtMOsct+aVf/U4O49ztVbPxrjfpfCx+1/3XpXtEAAAQQQyKMAX555nDViRgABBBDIhcAz3/i2M7Y++JT3RYPl0cXeT19ltHaoMXPWxbrcd52wiNur7WeStp0efGtJ4Xp4bHUCw0HtLBt5BC2Djy6G660dGjNxazrvzvTbOHN2EL5c0k/rYv68pA/mLAfCRQABBBDoQIBCVwd4NEUAAQQQQGA+gTee9aMPr7vRcz6zaj9rb/eHBjearw0/T18gR3cGpY+RsxG8sYmvyNr9psO+ynednTOWQjtFq381uDOWc/WMTS7hIIAAAghkX4Avz+zPEREigAACCCCAQMICFLoSBqW7eoF2Cl2rSuERxrdKunDm3/YIxp+5UOYvq/cp6ZX+Ls5PoEcAAQQQQACB2QIUujgiEEAAAQQQQKDvBLygFr71MHz74erNSndUXefpfYdBwkkL7CTp25FOfyTp1U0M9AlJ4cJ+4XpxU/X7xxVneQy6CVF2QQABBBDoOwEKXX035SSMAAIIINDnAndLChc5n9leEVnPpm94KkHtR0Z61XTCNd91BvsmeRJNWyBcgP+q6UESWSOqwV2Ix/muc3TaydA/AggggAACeRKg0JWn2SJWBBBAAAEEOhM4UdJHYrrgfKAzV1ojkLpAbKHLmCP98sAJqQ/OAAgggAACCORIgBPbHE0WoSKAAAIIINChQLtrB3U4LM0RQKBTgSI9uuiNTRwva49YZWKnDvaHlpzZqQ/tEUAAAQQQmBGg0MWxgAACCCCAQP8IhAtXbxeTLucD/XMMkGmOBbyg9oikhavqQ2svenL19U/5d97S8YLa1yWFC+2v3qzV4dUh5+S85UK8CCCAAALZFODENpvzQlQIIIAAAgikJRC9q2uppN3SGox+EUAAgXoB3njK8YAAAgggkLYAha60hekfAQQQQACB7AlcImkLSeEi1pdnLzwiQgCBogpQ6CrqzJIXAgggkB0BCl3ZmQsiQQABBBBAAAEEEECg0AKVYPxWI7PxrCSNrvDLzu6FTpzkEEAAAQS6JkChq2vUDIQAAggggEBbAvWPGl4sae+2eqERAgggkBEBL6hNSFo8Hc6vfNfZKiOhEQYCCCCAQAEEKHQVYBJJAQEEEECgsAJxb0ncX9J5hc2YxBBAAAEEEEAAAQQQ6ECAQlcHeDRFAAEEEOi+gBcsf41U+sH0yHf7rjNzV0D3g0l/xLhC10pJi9IfmhEQQGAegTslbVC3z9Mkhf9WyM0LamdLOiBMzhgzMlIe2LOQiZIUAggggEDuBSh05X4KSQABBBAorsBeV9acyQUafyxDc7VkXxPN2Hedon6fxRW6/i5p9vo2xT0EyAyBrAp8TtIHYoIr5GeRN1r7oozeOytfY0b98sCuWZ0g4kIAAQQQ6F+BQn4Z9+90kjkCCCBQLIFGb+fqcaErWnxK87v0LklLIvmGRa6w2MWGAAJdEoh8Fv3Gd50XNRg6zc+DLmU7d5hW3pToBbXDJJ0804u19qbq0ODzexY8AyOAAAII9J1AIb+M+24WSRgBBBAoqEAGC11xd1jdK2m9FKfgAklvm+5/S0m/T3EsukYAgYiAF9T+I+nx9f985Vu3eOChf939xBisREl4rgAAIABJREFUQp5bt1jomvs5acywXx7wObgQQAABBBDohkAhv4y7AccYCCCAAALpC+Sk0BVC8H2a/uHACAj0RCDuc2jykYd06e4bReO5UtKbehJkyoN6Qe3rkt46exhzte8OvDY6dIPP7Z/4rvPKlMOkewQQQAABBFYJcGLOgYAAAgggkFkBL6jdImnTSIDh3U3hRdeqrcvrc8Xd0cX3aWaPIAJDoHOBee5mulbSZpI+LCm8+7KwWyWoXWqkPVYlaPUDf8h5XVyycV5G5tMj7sDHC4tDYggggAACmRKg0JWp6SAYBBBAAIGogDdaO0lGh0v623/t5PNHhzYIHyPq1RberXF5ZPBtJN3Qq4AYFwEE0hXwRmtVGb2lfhRjte/IkJOnwta/Ja07ncP3Je2YllpldPxGY8wW9f13+Q8SaaVGvwgggAACORGg0JWTiSJMBBBAAIHMCITrcX1N0sOSvMxEld9AtoopFHJ+kt/5LGTk3uj4cTLm0TuSpqaG/V2X5Gm9qX/FrCN4rqQD0posb2zCk7XvNzI/5E6utJTpFwEEEECgkQAnkhwbCCCAAAIIINBLgbjHQcO3SoZvl2RDAIHOBXjkunNDekAAAQQQyJEAha4cTRahIoAAAgggUEABLsILOKmklCmBfv0du7WuYP5tSW/M1KwQDAIIIIBAagIUulKjpWMEEEAAAQQQaEIg7iL8fknrNNGWXRBAYH6BsMizU2S3/SWdN3/T3O7xN0nPiER/vqR35jYjAkcAAQQQaFqAQlfTVOyIAAIIIIBA/gQqY8vfYWzpTEl3rVxY2v6yNy5enrEswre4XRqJifOTjE0S4eRe4CxJB05n8R5JX8p9RmtOoF/vYiv4tJIeAggg0JwAJ5LNObEXAggggAACuRMYHq1dYI3eVh+40YLNR9z1/5DBZO6tu4vrJZJ+3WmMlaB2oJGe4LvOqZ32RftiC3hjEx/XlD1SJV3nl53XFTvbvsiOQldfTDNJIoAAAvECFLo4MhBAAAEEECiogBfUYi/2fNfJ2vd/XJyLJK2MTM3OkpZN/9s9kp4SN3U7XH31woEHNn9kVoFvYWmrkTcu/lVBp5q0OhDwgtptkjaq7yKDvyMdZNiXTcPPifDzon77iKTP9KUGSSOAAAJ9JpC1E90+4yddBBBAAAEE0hPIeaErkDRUp7MkfPwyRmvOucx8eQ+P1l5ojf2kzII/+uXF4cUvWx8LxB8v5pu+O1DpY5YipB6uQfbu6UQOl3RyEZIiBwQQQACB+QUodM1vxB4IIIAAAgjkUiDuAt5YXTQy5OyTsYTi7ui6WtJr6+L8iaTtOi10VYLaWeaxtYpWdcfdOxk7GrocTuzviXTLiOs8u8uhMBwCCCCAAAIIJCBAoSsBRLpAAAEEEEAgiwIxF/CTvusszGCscYWuTSTdWhfrzyRt22mhK/buHatr/SHnVRl0IaQuCMQdE9bYfarlwYu6MDxDIIAAAggggEDCAhS6EgalOwQQQAABBLIg4I3WDpfRSdFYMnz3Un2x652Szo/E/ixJ/9dMoSvcJ1q8sMZuWS0P/n6+xxqzMHfE0F2BPYOJV03J/mj1qNb+wB8aZEH67k4DoyGAAAIIIJCYAIWuxCjpCAEEEEAAgewIVEZry4yZsxhz3h/T21vShXXKq89jKqPjHzAyu8qYC3x34GuNZiL+MTXz5RF3YGYtn+xMYmeR1K9P1HDh/s6GoDUCCCCAAAIIIJA9AQpd2ZsTIkIAAQS6IuAFtQckPWFmsAzf6dMVj6INUgmWl41K4YLus7YizvOc4pUx//bLA0+Om9PhsXHXWjNa/7MCmny4wdvlsnLeF87NYZJ+LenSov3ukQ8CCCCAAAII9FYgKyc8vVVgdAQQQKDPBCpjtSuM1W79UATp5dR6Qe0wY3TfSNk5pxdxeMHELyX7kpmxjfTJEdf5RC9iSWtMb2zCk7UjHMuzBOLWPAt3yMJ53+ckfSAyX1mIK61DtLD9Dl+14iV25dQvC140Luz8kRgCCCBQZAFOLIo8u+SGAAIINBBgnaJ0D409g9p2U1L4lsDVWwHvGkoXscneh4PaWTbyFsWwaZ97Z7nQFRfbGZIOaXLK2S0jAvHfI+Za3x3gxQ4ZmSPCQAABBPpVgEJXv848eSOAQF8LrKHQFXX5rKQP9TVWG8nH+9qHfXdw7Ta6o8kaBIZHay+0Rr+J7tLnhS5PUvQut79KChf07/UWV+iakOT0OjDGb02AP5i05sXeCCCAAALdE6DQ1T1rRkIAAQQyI7DXt2pbTk7qt/UBTfzuJ7/44ZF7bBMTJN8VLc4cF4Atgk3vXrnRrmVuW3GmkblnpLz48GZ78YLadyXtWLf/eb7r7N9s+7T3G15650tsaWH9I173+66zTsrjvkfSudNjfEvSLimP12z3cYWu0yQd2mwH7JcNgQafc+O+6yzJRoREgQACCCDQrwJcvPTrzJM3Agj0vcAeV61Ysmil/YEN76Qweq9fHjhO0rMpdHV+aMReAFr91x9yVi/+3/koxeqhMjq+tzGm/o2KWnuRfdLX3zAYvjQh11v8mx7tGSPuYD8+rvdpSUdGJpTz0Rwe4V5QC+/2PaU+9D6/kzKHs0jICCCAQDEFOLEo5rySFQIIINCqQPgWtHsaNOK7ogXN4aXLt7Gl0vXRJv16AeiNTXxF1u4XelhpZdV1FsVxNljvZ9J3Bxa2wJ/JXbnDL5PTQlAJCOxx6YolCx83daqZ0vKRIYfH3BMwpQsEEEAAgc4FuHjp3JAeEEAAgSIINFq8+muS3lGEBLuVQ1xRw8ruVnUHl3YrhqyME3fHRxhbXNGvyMWg2GPC6nfVIeeFWZkr4kAAAQQQQAABBIoiQKGrKDNJHggggEBnAnGFrnDdo50667b/WscWbIx+75edLftNY57i1bsk7S3pMklnNtj3b77rPDPvbsOj45dYY4br8+jXO/zyPpfEjwACCCCAAALZF6DQlf05IkIEEECgGwJxha5vSHpbNwbP+RgXS3rO9LpDVzV4BK/quwPhm/D6altDoethSWvVY1SumtjMrLS3FLUYNDw27lprjpfMn3x3oJKhA+Gjkk6YjmelpNhHSxvFOxwsP8iaBVtPTk6eeumuS27KUF6EggACCCCAAAJ9KkChq08nnrQRQACBiEBcoasr3xHeWO29xpqtS7Z0ysVD6/8xZzMTdbvIC2qupHWLWrBpZX680YldZOxYpE14l9YmMf2EbtF9WxmOfVsXeKOk8I2M0a2p3/25hUxzie8O7NV6GLRAAAEEEEAAAQSSE2jqRCa54egJAQQQQCDDAuHdNJtOx7expL+nHWvMHT8X+q7z1rTHTaj/X0t6UVyRwAsmTpPsByX9ynedrRIaL5fdeGPje8mai+qD910nLpeTJR2eyyTzG/Q/Jf1PO4Uub6x2hqzeF23LI5n5PRiKGPnw2IoDNDX1rHsX3HvUt3bZ7KEi5khOCCCAAAJzBSh0cVQggAACCPREwAtqZ0o6KMcXyo0W8Oe7NTKp0YJmg0JXWGT9S08Oxv4d9C5JS9oqdAXjt0lmoxz//vbvrPdJ5jF/SPmw7zqn9En6pIkAAgj0tQAn4309/SSPAAII9E6gEtRuN9LTcnyhfIY0944WSXy3zlPo+vmpB+u2q6v1ewWShnp3NPbtyOHdhje0V+hacZQ09ckc//727aT3Q+KVoPYjI72K47MfZpscEUAAgbkCnIxzVCCAAAIINBL4vaTnT//w6ZLuSJLKG60dI6NPxFyI1P/TbZKekeS4Cff1X0mPq+tzmwaFg4SHzVd387x9MV/JFC/at0iaqTqGi8nP/M7Pm+mceTXmSL88MLOw/bzt2QGBtARmjs1vvfcVuu+O/6sf5nmS8rYWZFpM9IsAAggUVoBCV2GnlsQQQACBjgS6sjh99EL59xccrz/4n48G/gZJ3+koGxr3VKAyNvEOY+359UFYY7eslgfDYiobAghkUKD+89lOLdq4uutTous2Rr8nnijpP0mkMjxa28canSSrP/lDzuta7dMLJsZuvviUXW688DNxTbn+aRWU/RFAAIGcCfBBn7MJI1wEEECgSwJxha4RSXumPH7cuBdI2jflcek+ZYE9r7jz6VMLF35ZRg/4ZWf3Fob7gaTX1O1/rWIeSWqhP3ZFAIF5BOLuwoy8aCC8w3fDmG46vrbwgtqVknat77utlxwYY2Vjl1LsOEYOIAQQQACBbAvwQZ/t+SE6BBBAoFcCcVcH4V1V4d1VaW5x44aPVl2a5qD0nWmBuGPiOZL+nOmoCQ6BHAvEPm5sdaw/5BwznVZqL+NoMHZ4Z9dzWyQN15+Le+st1z8tQrI7AgggkDcBPujzNmPEiwACCHRHIO4i5pmS/pby8L+U9JK6MaYkLUh5zNx2P+uC0OpAf8g5O7fJNA487lgMXwRwSAFzTTqlhyStVdfpiyT9NulB6K94AnHFJivz6ao78PGeFLoktXVXlxT9/PiRpFcXb8bICAEEEECgXoBCF8cDAggggECcwJMl3VP3g7CoEBYXurFtPf2o4rckLevGgHkcI+5C1Mi+YMQdvDGP+awh5tTuHCmYUzSdcyTtH5Mj534Fn/gk0mvi0cXNYu6qfKukCzsdP/azzeizI2XnQ232fc30SxY+JqmIfwxok4VmCCCAQHEFONkp7tySGQIIIIBAgQUavMnwV77rxD2qk2eJ6JstZ3LhHGbNs0qBMM9HfY9j3/2yO566aK1Fd8+EMVkyL7l0l4Ffx4T1dUlPkvRuSf9IIuzKlRObmgX2ltV9GbPcLw9skETfLfZRmr4DMnwT6ZGSeKNoi4DsjgACCPRKgJPEXskzLgIIIIAAAh0INCh0/cR3nVd20G0Wm4avTftwTGCcw8SgeMHEDZLd6qoDX6V7b49dxgy3LB7lxJQ1gbDINRkJKny0PrzjmA0BBBBAIOMCnOxkfIIIDwEEEEAAgTiBuELXAju5+OKhDVbfhVEgubi7kziHiUxw9JjwXSd6CIR337y9QMcFqSCQlkC4lt2WFNjT4qVfBBBAIF0BThLT9aV3BBBAAAEEUhPwgvG/SeYZqwYwk6/2yxuECy0XcVtX0r+nE7tL0oZFTLLTnOKKnz/51H4P3/mzZTdLOkLSVZ2O0c32lWD8aiOzw8yYJZl3X+IOfLmbMTBW3wqEd3OFd3VFN66d+vaQIHEEEMiTAB/WeZotYkUAAQQQaFfgs5J2l/Q1Sce02wntWhI4SNKZdS02l/SHlnpg51gBL5g4UdZuakqlI0fKi1c/n9jgcdb/+q7zhLxRVoLlZaNSEI27zTfv5S194u29wOGSTqLQ1fuJIAIEEECgHQEKXe2o0QYBBBAouMBwUDvI1hUprNVnq0Ntv/Gq11o89tb9GVhf0gouEpOHjxazrNXJ1SEnvChXXKHLWu1RHXIuTz6SdHv0xiZ+KGtfTaErXeeM9n6WpI0kHSbpjz2M8eeSXlo3/tMl3dHDeBgaAQQQQKBJAQpdTUKxGwIIINBPAnEXzCsfKm1w2ZsXL8+ZQ/i6+3BdoujG91+6E3mhpL1xTxbZC2rhYtgvWVPxpxLUHjHSwlX7WB3rDzm5vIOxMlr7jDFzX0LAHV3JHlMp9vZtSZtMF6uWtjBO9A8Tx0k6uoX27IoAAggggIA40ecgQAABBPIlEP6V+7bpkFN7A1TsI1BGZ/pl5+B8celzkj4QV3DxRmt3ymj6lfWl1/ru4qtzlluWwz1N0gcpdCU7RQ0eTVQaxZ/hYOILVvax33ejn/ll5+XJZrTm3mLyXe67zvTvbDcjYawWBNaW9GBk/wsk7dtEH+F3W/gdF924XmkCj10QQAABBB4T4IuDowEBBBDIl0BXHsOLfQRKOqjqOl/MF5c2lnRrNGYvqM1Jw5amBqq7LIl73G7elIeXrniJLU0dL6v/84dyVwycN782d+jKsdpmbLls5gXjI5LxosGnUeiK+wwoyWx/iTvw427ieaPj4eLzr7Eqfb46NPD5bo7NWG0J3C7paW0Wq+I+M8KuuF5paypohAACCPSvAF8c/Tv3ZI4AAvkTCNfZeVObFxAtZRt3kZvGxXRLQbW/8/mS3lHX/NNeUPtYTHfX+q7zqlaHGQ7Gv2BlZt3plmOrVtNf0/7h43P/lLSOpF/HPXKX5GD90lf0d9NYvWFkyPlO0vk3uHvsQt91wseB2RBoJNBJsWqZpJ278R3H9CGAAAIIFFuAQlex55fsEECgWALXSNq+WxcBXlA7V1bvknS5P+S8pUiU8Y9mml/45YH6hYebSrlBQeA3vuu8uKkO2AmBDArEH9fmXb478JUMhktIGRDYZ9k/1r3jVz/45b/+dtOmv/3ynKXhmr3miBbKtpL0qwykRwgIIIAAAjkSaPZLJ0cpESoCCCBQWIHNJd3UrUJXYRUbvZ1uQem51Z0X/6nVvLu5blKrsbE/Au0KeGMTt8vaWY+gcadiu5rFbzc8Nv5ua8159Zl+801P09TKR8J/ekbd2pLFxyBDBBBAAIGeC1Do6vkUEAACCCDQkkD4uvWT61qsJ+nelnpg51UCswtU9l2+O9jWnSpxhS5j9PWRsvN2qBHIs0BldHxYMu8slcyykTLrY+V5LtOOvUHB/wHfdZ6U9tj0jwACCCCAQFSAQhfHBAIIIIAAAh0IVJateLWZmvphfRfc+dIBaLaari/pw5J+I+nibIVGNH0g8ClJ+0sK166qX2cwc6lzZ2vmpoSAEEAAgb4WoNDV19NP8ggggAAC7Qh4o+PflzGvnWlr7dQh1aElZ7TTF20yKxC+4e/9kehSOW/ac9nEiyenps6VMf/UA9fsUvW8ycyqEFi3BHL11tLYN/Vae1N1aPD53QJjHAQQQAABBGYEUjlhgxcBBBBAAIGiClTGaq83VnPecsddXIWb8bhCw7mSDkgyUy+Y+IhkT6zv006ufG51tw1bXi8uybjoq+cCccdfeHfhKT2PLCaA4eDu51lN3lz/Iz4TszhTxIQAAgj0hwCFrv6YZ7JEAAEEEEhIwAtqV0l6Q7Q7LuoSAs5ON3GFhn9KemqSIfLIV5Kaheor7vi7SNI+hcqSZBBAAAEEEEhBgEJXCqh0iQACCCBQXIFKUDvWSEdT6CruHE9nFldoCB9n/GCSmVPoSlKzUH3FHX8vlPS7QmVJMggggAACCKQgQKErBVS6RAABBBAotsCc4oTRhF92nGJn3XfZhcXMYyNZJ37eFP/WTnPDSHlgm74TJ+F6gUMknV73D9zNxfGBAAIIIIBAkwKJn7A1OS67IYAAAgggkGuB4aB21pS0ozHmDL88UH9Bmuu86oOfW9AzB/vlgTMLk2AGEjnmarvw5gcmHqkPhcdgMzAxhIAAAggggAACuRWg0JXbqSNwBBBAAAEE0hNgLbL0bOkZgYQFbpG06XSfKyUtSrj/VLuLFtTtf8w6VW/g/lQHpXMEEEAAgUILUOgq9PSSHAIIIIAAAu0JsHZUe260QqDLAssk7RwZc4WkgS7H0dZw3lhtSlZzrke4q7EtThohgAACCEwLUOjiUEAAAQQQQACBOQLc0cVBgUAuBOIWrQ8Dz8U5PgX1XBxjBIkAAgjkTiAXX4K5UyVgBBBAAAEECiAwcxHqu3PW2X+LpEsLkCIpINA1geHL7ni2XWvRnx4b0PzSdwe27jAACl0dAtIcAQQQQKB4AhS6ijenZIQAAggggEBiAk/aYJPr7r/rby+L6ZBziMSU6agfBBrcvfRh33VO6SD/AyWdFWn/JUnv6aDPrjV98+jtmy0wa/151oDGvNovD/yoa0EwEAIIIIBA4QQ4SS3clJIQAggg0JrA8LKJne2UDdd5CbdbfdfZpLUe2LvgArm+Y6Tgc0N6ORKILXQZPeiXncd3mMbeki6c7uMISSd22F/Xm3vBxGkydpFfdg7u+uAMiAACCCBQOAEKXYWbUhJCAAEEmheoLB3fxJTMX6Mt6hYCvkzSBpI+IOnnzffMngUSuFbSK2Ly4RyiQJNMKukLNLij63rfdbZNf3RGQAABBBBAoH8EOEntn7kmUwQQ6DMBb2z8f2XNHla6qOo6X4xLvzJa+60x2jKm0LWepH9H/v0kSR/tM0bSfVQgelfXpyQdBQ4CCDQv4AUTN0h2q/oWbb5dMPz9C9+0eK6kc5qPgD0RQAABBBDoDwEKXf0xz2SJAAJ9JhBz58CU7zoLogzeaO2PMnpOTKHrXknrxrDxvdFnx1JdumGRc9PpAtfy/mVIJPNTJR1a19OIpD0T6bnDTobHJvaz1n5lphuzYMFLR3Ze/xcddkvzaQEvqB0mo+Nk9bs27+SKFp3/IWl9gBFAAAEEEEDgMQEuWDgaEEAAgYIJDI+tcK2dGo0pXs35zH/z2F1bL7AL5lzExrxlb/V1b8G4NLy0dsCUsYcYU/qB7w68v2j5kU8mBeLWPQuLiH/pZbQV365lnjDxUDOfHb2Ms4/HDj+fPh+TP+fzzR8UV0vaYXr38Fh/XPNN2RMBBBBAIC8CfDHmZaaIEwEEEGhSYHi09hlr9OFmL1aHg9pBVjpzZv/pR2nCxenDR2OiW6G+N7ygdpukjeqTbPNRoiZnh90KKBAuJP6fSF7z/Z7EFbpOn14Lr2dEXjBxpmQPavazo2eB9u/A4Z1/XtE/l1Oc3rMlHRDp/7+SnpDimHSNAAIIINADgflOxHoQEkMigAACCHQi0MQC8812H70Yf7qkO5ptnIf9GiwOvSp0I/11xHWelYc8iLGnAu28lTKuzV6SLullJt5Y7ROyOoZCVy9nYY1jhy+FCF8OMWurLFs+UN1lyYrMRp2dwNr5Xc1O9ESCAAIIINC0AIWupqnYEQEEEMiPgBfUwrcl7r46YmPP98uD78xPBt2JdE2FrjACa8x+1fLAV7sTDaPkVKCdi+e4Npk4J4v7neAux+wcmYs33/a/K27++erH7bbY60PaYp/DxRw1NUft/K421TE7IYAAAghkSyATJ1XZIiEaBBBAAIF+EZiv0CXZX/ru4Nb94kGebQm0e/EcPl78dkmXSzq6rZFTauQFtfDuoPUle6PvDr4gpWHotg2BRp9ZFLqawtxXUvQPF9+R9IamWrMTAggggEBuBCh05WaqCBQBBBBAIGmByujEwcbYLzTq1xidPFJ2Dk96XPorlMD2kq6JZPRcSX8qVJYkkwmBDBe6ogXfcC2xaibQZgfxNkkXTP/TGZIOyWCMhIQAAggg0KEAha4OAWmOAAIIIFAMAW90/HoZs019NtwlUYy57VIWZ0l6pNcLyncpV4bpkcBbr1qx5OGVU3dFhn+b7zrf6FFI4bDh2PvEjM91Rg8nhaERQACBfhbgC6ifZ5/cEUAAAQRmCVSC8V2NMR+wVtdWXecT8CCAAALdEPCCiZsl+7xwLGu1R3XICR9pbbh5wfi5xmjdqbXWOqD6+qf8uxsxrmGMdh/f7XHYDI8AAgggUFQBCl1FnVnyQgABBBBAAIGmBCqjE28xZmp7o6kvjrgb/KGpRuyEQEICcY8jTk7qhZfu5vwuoSHS7uZqSTvEDMJ1Rtry9I8AAgggECvAFxAHBgIIIIAAAm0IeGMTD8vaRdNNT/Vd57A2uqFJjwXmFhnsmO8Ouj0Oi+H7SCCu0GWk20Zc5xk5Yoje1XWyJNY3zNEEEioCCCBQJAEKXUWaTXJBAAEEEhCIXHRd7rvOHgl0W6guYi9MS9p3ZBdnZpHjlvP1gol3SvbLqxtaXecPOdu13BENmhbwgtqHJJ0SbcDabE0TZnJHL6jdJGnzuuAO813n1EwGK6nBAvM133UGsxpzg7iOlPSc6beI3paz2AkXAQQQQKBAAhS6CjSZpIIAAgh0KhB7wWVKh/rlxad12neR2qfx5rMGd3W8ecR1LiuSXZZy8YLajyW9kkJXlmals1iGgxV7Wk1dnKc5jfvdL1m74yVDg9/vTIPWCCCAAAII9KcAha7+nHeyRgABBGIF0ijgFJE6viCoB/2y8/h2821gf53vcldXu6bztasE4/sbmXPyVBSZL6d+/7k3WvupjF6etzn1gtqkpFIYt7U6vDrkhI/+sSGAAAIIIIBAGwIUutpAowkCCCBQVIH4You5x3cHnlLUnNvJywsm7pXsOvVtbam0Q3WXxde001/YpkGh6zTfdQ5tt0/azS/gjdX+K6vHzexpZc+tuoMHzN+SPbIo4I1NHC9rj0i70DU8Nv7uKWv2M9YE/tDACVm0aBRTZXT8A8bo3ZLC9eg+mqfY1xBr+Dt79vTPPyXpqILkRRoIIIAAAm0IUOhqA40mCCCAQFEFhoOJP1vZzWYXcKa2qO6y5Oai5txuXpWg9iMjvSpsb0tmz+ouAyPt9jVd6PqbpFmLT7NWVCeitI0IPEHS3yU9VdKVkt5UVKGYovHvfdfZspl8K8uWLzZT5kip9HvfHfhKXBsvqP1L0nr1P8vL76oX1B6QFB4Lq7e8xL6G+Qv/GBBdgy1cL3HfZuacfRBAAAEEiidAoat4c0pGCCCAQEcCw8HEl6zsu8JOSqa0zSXlxTd01CGNmxaoBLUDjfRBSdf4rrN/0w17sOPM+lZW9stVdzC8O4QtowJeMH6kZD49E151aImsnQr/t7DngV4w/mmptL2mJr/g77rEb2ZqvKAWvpggfEHBGotAcXdfWmP2q5YHvtrMOL3cp8FagO8bcZ2zehlX3djvk3RGJJb5jtPoGx9nms+0i/58vv4yQkEYCCCAAALtCvBB364c7RBAAAEEEOhTgbiL5QLcFVLI2RxeunwbWypdH03Od51CF7ramcwGjw9f7LvO3vX9xe5n7fn+0OA72xm3m20a5PgN33Xe1s041jBWXNHqJElresRyTYWuCUmLY8bjGigjE04YCCCAQBoCfMinoUqfCCCAAAIIFFTAC2rhItmHxRROMn9OMRzU/mylzWQ19eBU6clLd1t8X0GnaXVaXlD7taQXUeiaf6bji0D2Yd8dXHt/Jv3OAAAgAElEQVS+QpdVaeuqu/iX84/S2z0avXAkjCojxeq4olX4qOj/rEEufBR318jPb5e0UfhkeYN2mf+86u2RwugIIIBAvgX4kM/3/BE9AggggAACXRWojI6PGmPcvBW6+vUuNG+09n0ZvZZC1/y/JrGPJMa8nMALJk6TbPiI8fRmv+e7g6+ff4Te71EJasca6ei4SKx0WdV13tzjKOMKU9+QNN8dZ7+R9MLp2O+RNPMCFQpdPZ5QhkcAAQR6IUChqxfqjIkAAggggEBOBSpjEy8y1oZ3Cc3aMnI3SEPV2LWJjP38SHmwrmAxt/mcdsYc6Zfz85a9im/XMk+YeChmvsIXH9yW08MwlbC9sRX/Kzv12frOs35ctwvR6M6uDOQbrnMWXUS+k+uVbSRFH93dS9Il7drRDgEEEEAg+wKdfHFkPzsiRAABBBBAAIHEBbzR2hdl9N7VHS8wZX/ngWWJD5Rgh7EX9sZc4JcHGr6ZzQtq50p6T5aLel4w8WnJHjkTo7XaozrkXF4f83Bw1/OsFjz65lRrf+EPDb40QVq6yqFAg0LXVb7r7JyBdLaYXpMrvEsr+jbFdsJbKOlSSY+bLqKN73C1XTjwwMQjq39vjFlSLQ+Mt9M5bRBAAAEEsidAoSt7c0JECCCAAAIIIJCwQNyF/YOTpXXXtE6XF9T+O31xPCuaDNz1siqePa+48+lTCxf+PcuFuISnsevdDY9OnGCNfXQhdGNu98sD4bpPud+80YkjZOzx9Ylk5bjuBm6/PsrcDVvGQAABBLIgQKErC7NADAgggAACCCCQqsAewfgzF8r85bFBzDt8d+BraxrUG6t9TVZvz2ohyQtqcY95ZWVR8VTnsxudV4Lx/Y3MObPGspryh5wF3Rg/7TF2XmbXftJk7ZhSqfTnkfLA+WmP1+P+w8d0VxcpvaA2Jxxr7BHV8uCJPY6T4RFAAAEEEhCg0JUAIl0ggAACCCCAQCYEnirp7kgkHZ3rRO/8sNaeWB0aPCIL2Xqj4x+XMcdltRCXBaNOYsjwOladpNWPbb8o1T1qLSmu0CVjjvLLA5/qRyByRgABBIom0NHJX9EwyAcBBBBAoC2BcP2T1WudSDpb0oFt9UQjBDoTiHvD2p8lPaeTboeD8T2t9AL7xNIJ1dcM3N9JX0m35RGspEUf649CV3q2Xe55zudCXKGrnx7d7LI/wyGAAAJdF6DQ1XVyBkQAAQQKJxBXXNhd0hWFy5SEsi4QdyyGMRf6fKcS1H5ipO0kVX3X8bI+SXmJ79ECp7l4VrxG//TLTnjnIFt+BO6T9KT6cB//FEdDF/xu9T9NTuqFl+7mPPYP+cmNSBFAAAEEYgQKfeLHjCOAAAIIdEUgrrhwl6QNuzI6g2RNoP54eFDS47sYYF8Wurro23dDDQfjH7Qyp00nfr3vOtv2HUL+E95Y0q2RNMI/xIR/kGFDAAEEECigAIWuAk4qKSGAAAJdFogrLvxS0tYzcXjLVgxpamrpqv83OtYvO8d0OcZcDeeNTfxR1j72uN2kXunv5vwkB0nEHQtXS3ptl2J/t6TzImM9TdKdXRo/s8NEH8Obkl7xTdf5aWYDTjgwL6h9V9KOM91a6aaq6zw/4WHoLrsC60sKj/ew6PWB6UfssxstkSGAAAIIdCRAoasjPhojgAACCEh6SNJaEYl1JK1ay8gbW+7JlkZm/dzoUr/svAW9uQLe6PLDZUonRX+Sk/VjsnBH1eD0Rey/Je3LMSYNBxPnW9l35PSYSmQK49bbmjT2ZZeWB3+eyAB0kmeBt0naQtLxku7NcyLEjgACCCDwqACFLo4EBBBAAIEkBC6VtIeksNCxqaS/znTKgs6t8XpBLSzQrJvTokQWCl2tged4by+ohW+YXL1eVKNiKL+Dq96yN/fYNOZ0vzwQ3t2Txy2az9qSHs5jIj2OOep4gqQjexwTwyOAAAIIdChAoatDQJojgAACCKxZgIvs1o6Qymjte8bodTktdJ0q6dBI7DtLuqo1BfaeT8ALapfFrTEUV+yqjNV+a6y2zOkxNR9FUz+P+xyyk9qpupsTPtKYty2QVI4JmvP61mbyIkl74dgaGnsjgAACeRDgCzEPs0SMCCCAQI4FvKD2dUlvnZWCkfXLTinHaaUaevSi3Eo/rbrOK1IdNLnO95T0NUnhnWk7SfpNcl131NMPJL2mrofjJB3dUY89bNxqATlm/3N813lvD1Po6tCVoPZnI21WN+j9vuuEj1jncePOyWRm7RFJCyl0JYNJLwgggECWBCh0ZWk2iAUBBBAoqIAX1MJixwtn0svJelM9nQ1vbCJcvP9F1urYqjvw5Z4Gk//BXynpx0W6oG210BXm7o1NnKepqc2szBHVIee6XkzrcDC+p5W5eHrsf/mu8z/diqOydOKVpmTDO3iW+q7z7W6Nm8I4FLqSQQ0/V99ZpM+FZFjoBQEEEMi/AIWu/M8hGSCAAAIIIIDAmgXClyF4RbqgrYzWXm7MqrfIrd6s7Jer7mD45slMbsPB3c+zmrw5GhyF75an65mS/hJp9XlJF0haJumJksIF1q9ouef+axAtGn5Y0in9x0DGCCCAQLEEKHQVaz7JBgEEEEAAAQTmChwi6fQiFbrCXCrfWvEcMzl1tWQWGzu5/8jQkvOzPPnDQe1G++jb7WZtFLramrXHSwofxw3fMnqQpEWSroz09ClJR7XVe3802lzSTXWphgXC//RH6mSJAAIIFFuAQlex55fsEEAAAQQQQOBRgeidG+EbCxeD0z0BLxi/STJhcYFCV/LsPM441/Sbkt48/c9xd2rFmXFtlPyxSY8IIIBA1wX4MO86OQMigAACCCCAQI8EPikpfAvkuZLO61EMfTvsXlf9Y4vJlStvpNCVyiFAoWs26/WStolIHyzpzOl/C+92+1jMTHBtlMrhSacIIIBAdwX4MO+uN6MhgAACCCCAAAJ9KzA8WtvHGn1jFYDV/f5Qbt98mLU5/Lmkl1K4WS0wX+GPQlfWjmDiQQABBBIUoNCVICZdIYAAAggggAACCCCQskB9Eef3kracHi9a3Onn8/w1FbrCN33+UdIAhcGUj1S6RwABBHok0M9fgD0iZ1gEEEAAAQQQQAABBNoSiCvgnCXpfW31VtxGEzFr8H1G0pGSVjZI+3GSHiouCZkhgAAC/SNAoat/5ppMEUAAAQQQQAABBPItMN8jefnOLtnob5O00XSXF0vaWxKPeCZrTG8IIIBAJgUodGVyWggKAQQQQKCPBcKLsbKkCyR9u48dSB0BBOYKUOjq7KiIu9Mr7JFros5caY0AAghkSoAP9UxNB8EggAACCPS5QPQi9i+SNu1zE9JHAIHHBJZNvzm03iRchP4XIDUl8FZJX4/Zk2uipvjYCQEEEMiHAB/q+ZgnokQAAQQQKL5ARZLPBVjxJ5oMEehQIFxn6tOS/iNpO0m/7bC/vDR/h6Tz64J9haSfthF8+Dkbft7ObOtL+kcb/dAEAQQQQCCjAhS6MjoxhIUAAggg0HcCX5b0TgpdfTfvJIwAAvMLLGiwiDzXMvPbsQcCCCDQdwJ8OfTdlJMwAggggEBGBV4p6ccUujI6O/FhHS9pE0kflRQufM2GAALpCJwu6RA+H9PBpVcEEECgaAIUuoo2o+SDAAIIJCtwjKRP1HV5wvTr2ZMdhd5mBMJHkLas47hE0l4Z5YmudbOrpNGMxppGWNH11D4Z+V1JY0z6XIOAF9TukfTkcBcr3VJ1nWcDVhiBj0n6FIWuwswniSCAAAKpClDoSpWXzhFAAIHcC8S94evxkh7MfWYk0KlA3LHRL+cVv5f0fC66Oz2EkmvvBbW/Tt9dV9/p93zXeX1yo9BTjwX6+TOnx/QMjwACCORLoF9OSPM1K0SLAAIIZEcg7sIivMvr2OyESCQ9EAjn/+g+LvTE/V6EHJxX9eBgDIf0glrsnPiu0405OVlSSdKHepR+Pw27XNKgpLDYXH/3az8ZkCsCCCCAwDwC3fjyZxIQQAABBPIrEHfxuIeky/ObEpEnIHCApLP7uNC1VNJQH+efwCGUbBc9KnQ9T9LNkUy2kvSrZLPLT2+VoHa0Cf8QYnWXLZmtquWB8fxET6QIIIAAAkURoNBVlJkkDwQQQCAdgTmFLi+orRrJmNLXR8qL357OsPSaA4F+f4womv+Okr6fg3lbY4h7BrXtpqSfrN7J6jP+kPORtPPyxsa/Imv2k+zVvjv42lbH88ZqX5TVe2e1s/qIP+R8ptW+WtifO/vqsLyg9lNJL6/3s8YsyXqxa4er7cKBBybulrSekX4+4jova+EYYFcEEEAAgQwKUOjK4KQQEgIIIJAxgXMk7f3MnfZ50tbv/+ys0BYsWvC8i9+w/h8zFi/hdE8gnPvnSAoX0X9R94ZlpLQE4u6MsqWpLaq7LIneuZRYCHFjtvPIoReMHymZT4eBWdkDqu7guYkFGd8Rha7Zha44j+W+62zQ5Xn4gKTwLY1NbUkdf00Nxk4IIIAAAl0RoNDVFWYGQQABBPIvEPtokLXn+0OD78x/dmSAAAKhQGyhy9qgOjQY96hmImixny1Gx/hlJ+trAVLomr/QpXaKli0cWOHbTo+K2b+paxxvbOIMWfu+aPuUY24hPXZFAAEEEGhHoKkvgXY6pg0CCCCAQLEE4i9GzcF+eeDMYmVKNgj0r0D8Wlf2C747+P60VOLHNJf47sBeaY2ZYL/RYlcW3kobjWlvSRcnmHNcV+G6ZC8Of/D6z31X/7Pp9DrxRsf6ZSd8gUlaW7gGmNN2oSuYqEr2LRS60poe+kUAAQR6I0ChqzfujIoAAgjkToDHO3I3Zf0S8JsiL0f4sKRT+iX5pPPsxe953JjGlJ4zUl7856Tz64P+LpIUVyBM85x/zp1tu4/cokVPXPd633W27cD8DEnvkXRleLNhg36+Imm/dgtdldHbNzRmrTsodHUwSzRFAAEEMiiQ5pdeBtMlJAQQQACBTgS80YnjZOy7JTvqu4P7d9IXbRFISCDu8bF1Jd2XUP99100lqF1qpD2s7B+r7mD4ZsFUt8roxFuMsdXVg1h7uj80GK6zxNa6QC8ep4wbM1yY/hWth//YURDTttF1S3T88O618C62prbhYMUHraZOm9mZtSebYmMnBBBAINMCFLoyPT0EhwACCCBQIIGXSjpL0gpJOxcor16nEneRHd4JckivA2N8BHog8ANJr2mhSJREiHG/gzdKekEHncf1uaZF5j8kaStJJ02/HKODoWmKAAIIIJB3AQpdeZ9B4kcAAQSKJRBeyOwpKby7Y/YrHvOd5yckRdep2VjS3/OdVnvRRx9Vm2fh5+gFb/TcJe6C+DBJp7YXHa06EFgpaUFd+5Hp3+cOuqRpGwLR34kTJB3ZRj9rbLLn6Pjrpoy5oMFbFV8m6ecdjBn3e32eJO4k7gCVpggggEC/CFDo6peZJk8EEEAg+wJxFzZF+Z7qxeNEmZxxb2zidln7tNnBmXt8d+ApMQH/RdIzI/8+FSmmFPm4yeQcNgjqXZK+FPOzovwO52kuwljDteqeLSksst+VdPDe6PgRMub4mX59d9Z68B+dvrOqk2Hjfq83kXRrJ53SFgEEEECgPwQ4+eiPeSZLBBBAoGOBymjt5cYoXHdFkhnz3QG3405ndxB3YfMGSd9JeJxedJfHQtcG04tAP0HSvpJuSAIu/g17UoO7upp1+7aknSSFb34LH19i677AdyXtSKGr+/C9GLHF3+N2Qjxw+lHvmbbh230Pbqcj2iCAAAII9J8Aha7+m3MyRgABBFoWqFw5salZYG+JNLzfd511Wu6scYO4osZxko5OcIxedRWX248lbd9qQJWx8b1L1hxvpT/6rvPGVts3uX+4xk+41k/9tqb1cZrsVmrxArnZQlfT47NjagIHSDo7pnfONVMj713HLf4e9y5QRkYAAQQQ6EsBTj76ctpJGgEEEGhNwAvGb5PMRtFW86yt1NogUlxRY4mk8VY7yuD+T4p5C2DL38FeMB5IplyfX8JzMNN1agUmL5jYV7Jfrc/BSgdVXeeLMfP2JkmXR/79/ZK+kME5JqS5v8NXSgrnkK1gAnGFLmv1u+qQ88KCpUo6CCCAAAI5FGj5JDuHORIyAggggECHAl5Qe0BS+AjbrC3hIsvXJL29boCWXhHfYYq5aN7Fi8vUCl0h9J5jd287ZScvk1QqyXiXuAPh3W2NtudJukLSWpL2kWYen83FlPU8SG+09n0ZbS3pON91TulCQOHjiztIOrdfX7bQBeNMDNHiSyUyETNBIIAAAgj0hwCFrv6YZ7JEAAEEOhLwxlbsJTt1UcqFro5i7IfGXXxc6E/TC1lHWQtx3uAFtaMkHWKMvjtSdsLiWSG3OceL0c1+2dmikMmSFAIIIIAAAgggMC1QiBNWZhMBBBBAIH0BL6iFiwEfNDNSwndzpZ9AAUZocEfXydUh5/AU0ove1VWIcwYvqN0v6Yn1XkU8liujtWXGaGeK0yn8ZtAlAggggAACCGRaoBAnrZkWJjgEEEAAAQQSEnjz0uVbLCiVbqzrbrnvOuHbEdmaFIi/K84c77sDH2uyi1zs5gW1uySFa9zN2opY1MvFhBAkAggggAACCHRNgEJX16gZCAEEEEhU4KWSfl7X44WS3proCHSGQPcEAkkzi+zfLWlxWkM3ePzzKt915tz9lFYM3eh3eKx2gLVz34JIoasb+oyxBoE/SHpu3c+vlfQqScslDdb9eyrXKN5Y7c2y+mbdOOf5rrM/M4YAAgggUCyBVL5EikVENggggEAmBeIWC3+DpO9kMtriBvUcSedI+hdvl2t7ksM3KB4caX2PpKe03eMaGsY+/jk1tUN11yXXpDFeL/v0xmrXyeplMzGYKfvykV0Hf9bLmBi7rwW2kXR9KOAFtdUQjzxw7yOXD2+2KCKzUlL03zrGi/39N+bF1fLAbzrunA4QQAABBDIjQKErM1NBIAgggEBLAnGFrl9Kq96uxtYdgfdL+nxkqM0k/V93hi/MKKm+4TGqVBmbeIex9vzVxR+jK0fKzpsKo0kiCGRX4FRJh9YXuWZCveMngX56wruikddfp4Rvad19eocfSnpNO2nG3tFpdIVfdmb6bqdb2iCAAAIIZEyAQlfGJoRwEEAAgSYF4ooD4VsRc/kGuVkXH6XS0f4ui4+LXpDY0tRAdZclK5r06cZuXS3QdCOhHo2BY4/g+2DY70t6bV2e4Rsnb+6DvLOa4vaSrokrdIUB+67TqND1jZjvtraKXf2yRl9WDwDiQgABBLolQKGrW9KMgwACCCQr8KCktSNd5vIzvcGaSeFzLXOuejK2vhAFmmSO6fdK+mKkq7YuYpMJh14KIvBuSefF5JLLz8mCzEmYxgovqK0fl0+k0BV+Jsy85Texz9q475uMfa8UaKpJBQEEEOidAF/2vbNnZAQQQKBTgTOnLwTCtUVe3GlnvWrfoNAVG07GLkjiLr7CNyK+oFeWOR43vKANj+dwCx8rfGeOc+k49MrS8Y+akjlhpiM7NblxddcN/t5xx/3VQVgsH6DQlb1JL3/pF2NPHNxol/rIbvz6iYfcPHJauF5fuL1P0ll1P0+s0BX2WQlqZxnpQBl91y87O2VPiIgQQAABBDoVoNDVqSDtEUAAAQQ6Eui00LXH2F0bL7QLbp0JYvqugLUkPdJRYOEF0bLx15opEz7+tGqLFNoeJ+m/kTE6+l4dDiY+JmtvHRlywrdosvWhwPDYimdbO/WnaOoZK/ImOjNeUDtb0gGSxiNv3pNkv+C7g+F6eK1uP2iwjlNHv6OtBsH+8QLDo8v3s6YUvsijZkraYWQX5y9rsAqLXgdGfr5U0m74IoAAAgggECfAlz3HBQIIIIBATwVi34Kl0tZGUzdEAnub7zrhWi2ztgaPooT7dPQdt/Myu/Y6UxPhI6KztjQKDsPBiv2tpsKLvtVbGuP0dKIZvCmByljt28Zqzl0mRT0evKA2IWnxmnA6yD16J1BYTNm0qYlgp6wJ1Be7LpG0V9YCJB4EEEAAgewIdHQRkJ00iAQBBBBAIM8C3tjE9bI2fPW87ALzqurOA9eG/+0F4+cao3Wn1lrrgOrrn/LvaI7e2MRxsvbjMcWo8J86+o6rBLVLjbRHTN8d9Rs3T7F3tVld6w85r8rDvHrB+E2S2VzWfskfGnxPHmLOaozDY7ULrNXbunHcZcGgmTs6Oyh0hSmGb6N9iaRPSvpEFnImBgQQQAABBBBIVyDxk/V0w6V3BBBAAAEEHhPwgvETJfORQha65j4q2e7U/2p6DbfbJD2j3U4atYspVEz6rrMw6XH6qb9+WjC7C4Wufjp0yBUBBBBAAAEEOv1rN4IIIIAAAgj0WqAIjy7G39Flr/GHBnfo0DduEedO/8i1TNLOM3Gt9aT19KZL/jwrzA7vwOkw5fw332fZP9Z9ZGpluF7V463006rrvCL/WcVn0ESh6yzfdcLFydkQQAABBBBAAIGmBDo92W1qEHZCAAEEEMiEwAslhW9orN9y/z0wvHT5NrZUun4mqSv2eo4evu9fieQ1z2L0iU3q8NjEftbar9R3mFCxKK7QFd4B95kOgp/T5ys+dr42fPljL1FLKPYOQmyvqTdWu1zWbFSykx+6ZGjJD+fp5TRJk5IOa280WoUCu182PrBoLRO+IXFmq8pOfVUqbWP/e//nqt6z5jyyjFxbAm+U9K26luFbTg9uqycaIYAAAgggkHGBRC4EMp4j4SGAAAIIPCoQV/QI3yi4Y5NAn5q+MPpOuHxWXBtvbMVJslOHr/6Ztbf7Q4MbNdk/uyUvEDfnn5P0vx0MNafPjbZ/k152+GNr6eet0FW50a5lbp14aJaJ1Zn+kBNXCNhW0s8ifs+VNOdNiR0Y0xSBpAXiPgteJOm3SQ9EfwgggAACCPRagEJXr2eA8RFAAIHuCcRd6ISjN/Nd8Iik6LpLc9rFPoZkSnv75cUXdy9NRqoTiJvzBZKmOlCa0+d2H/2SnvbKoVVdmqmVW43sumG4LlhuNi+oLZc0GA24QcGuk9+j3JgQaOEE4o7bUUm7Fi5TEkIAAQQQ6HuBZi5u+h4JAAQQQKAgAnEXOuEF/gZN5BfXNrzD66j6tnGFLmvtD6tDg69pYgx2SV4gdP9BXbfvkjTrEck2hrxaUv3aYQ+Ga0m10U9mmjRaJ4pCV2amiEA6F4j7DD+VR287h6UHBBBAAIHsCVDoyt6cEBECCCCQlsDHJR0X6bzZ74G4i6RA0qO38UxvsYWuKXtEddfBE9NKin5XC9TP0SclfSJFm80kvUNS+BjrNSmO05WuvaDmS6pEB0ux0FU/V3dLWtyVRBmknwXSeDFFP3uSOwIIIIBAhgWavcDJcAqEhgACCCDQgsCzJH1W0p2SDmqhXdxF0naSrqvvYzio/cxK4RpGq7e8rdfUgkmWdo2bn3Dx6W9nKcgsxxIt0i47YLu77r/zL1VJH4zE/QRJD0T+rZXzqbi5ulTSW7LsQ2yFEDh/ukAdrjH38gQyCn8XwrvC7or5I0oC3dMFAggggAAC7Qm0cmLW3gi0QgABBBAogsC+kr5aX7+SNByXWGXZ3a81k5MHWU39sDq05IwiJJ+DHOKKJ+HF54Y5iD1rIaZ95wtrfGVtxomnHYEDJZ3VQcG3nTFpgwACCCCAQFMCFLqaYmInBBBAoPgCXlA7TNLJqzI15lK/PMAdJvmZ9rjiyR8kbZ6fFDITaZxlWOi9IKEIKXQlBEk3PRWIO47/LmnjnkbF4AgggAACCDT5pi2gEEAAAQQKLuCNTbxP1s6++8qYX/jlgZcWPPWipBd30fkMSbcVJcEu5hFn+QVJ728QQ/hShvskndRkjF+W9M7IvuFjZOHjZGzZEniqpA9JCt8i+s1shdbzaCjY9nwKCAABBBBAoJEAd3RxbCCAAAIIqMW3ziGWTYFwvbSXTYe2vaQfZzPMzEcVdwG/iaRbI5G/TtL3Iv/2OEkPNZHhfpLOk1STFL4Z889NtGGX7gqEhf/3RYZcfd4c/czcvDyw4BhjprobYk9Hi/s9uV6RNRp7GiGDI4AAAgj0rQCFrr6dehJHAAEEHhOg0MXRgMBqgWMlHV3nEa5NFxamoht3tBT7oImb38+HLydI+PNyK0k3TFNeKelNOWF9laQfNSoE5iQHwkQAAQQQKKgAha6CTixpIYAAAq0IeGO1k2R1eKTNCt91Blrpp4j7ekFtXJJTl9u1vuuEF3ls/S1AoavY89/wBQ8JFrrC9ayidwqGj8GuW2xaskMAAQQQQCBdAQpd6frSOwIIIJAbAS+oXSRpr0cDtn/33cG+X1S4cmXt9WaBvhOdRN91+P7MzZGdWqBxhRCKFKlxd73juPn9jKSPJFjo+quk8LHY6Jbo58vOy+za60xNPDg9yH2+61BI6/rhxIAIIIAAAt0USPSLtJuBMxYCCCCAQCYEXiHp2rpIfl63TlQmAuwkiOGgdo6V9qfQ1YliYds+T9LNkew4ryrOdJ8g6aNx8zs8escLrVn0m/qfmamVW43sumG4aH0r2/2SnhjTINHjKK4wR7G+lWliXwQQQACBvAkk+kWat+SJFwEEEECgY4G4ux4OkHRuxz1noIPh0dpO1ujbFLoyMBmEgEDGBLxg4jRjZEbKAx9sM7S9JV2YZqHLCyY+ItkT+Qxrc4ZohgACCCCQSwEKXbmcNoJGAAEEMiMQV+i6U9LTMhNhh4F4wfgKyaxf1811vuts12G3NEcAAQRCgXM0+67RRM/NK8HEp4zsxyh0cbAhgAACCPSTQKJfpv0ER64IIIAAAqsE4gpdgaShIvm8eWx824XW7CJTunCkvPjPRcqNXBDoUKD+M+A9kr7UYX80T1iARxcTBqU7BBBAAIHMC1DoyvwUESACCCAgVYLavUZaZ7qydF81O4sJh2sUhWsV1W98t/TRQesFtcMknbwqZaNz/LLz3j5KP5pqtPC7taRfFnA70CUAABZ8SURBVNgjrtD9Qkm/K3DOuUvtLaPjrysZ872ZwI0WbD7irv+H3CVCwAgggAACCDQpwMVIk1DshgACCPRKoBLUbjTSFvXjW+mmqus8v1cxRcb9kKSPS/qFpJ3aiGlAUo1iWRtyPW7ijdYOl9FJs45Na4Pq0GCh7uhrkjksJLwuZt8in2vFFbpulPSCJs3YDQEEEEAAAQQQSFygyCdfiWPRIQIIINALgQRfZd+L8JsZM+5i+XJJezTTuN19vNHaMSqZ51mz4NjqLk+Nvj2v3W77ql0fHJutzGfccRy2L/K5VlzOv5b0klbg2LfvBcI/lpwyrbCbpKV9LwIAAggggEBHAkU++eoIhsYIIIBAVgT6oJjQ9QLBHFM7dbY/tOTArMx5XuLog2Ozlam4vcFLGIp8rhX3u/sMSbe1Ase+fS0Qrun2rojA2yV9va9VSB4BBBBAoCOBIp98dQRDYwQQQCArApWgdrSRjq2Px0qfqLrOJ7MSY4dxxF0s3ytpvQ77jW1eGa1925i5j1j6rsN3YovgXjDxVcnuG2l2q+86m7TYVVF2jx7L4SO9ny5Kcg3yuEPShtM/K0talma+0eKqfaJZp/qagfvTHJO+UxXo+h86Us2GzhFAAAEEMiHASX0mpoEgEEAAgTULeMHEOyX75Uf3Mu/y3YGvFMjsdEmHRPJ5iqR70siRu5CSVfWC2ncl7fhor/bvvju4cbIj5K63z0/f2RUu0v+33EWf4YC9sdpNsto8GiJF6gxP2vyhUeia34g9EEAAAQRaFKDQ1SIYuyOAAAIIpCIQFko+J+lOSW9IZYTpTr2gdrakA7hYTlOZvhGQ9lh2x9MWTi0KH+mc3uxpvjt4aLs2FKnblct0u/9IenwkwkBSP77QItMTRXAIIIBAngQodOVptogVAQQQQCARgTmPP1mzZ3VoYCSRzukEAQRWCcQVpqY0uf033Q1+3A4Rha521HLRZtVdXS/e/9PabNd3zwS83HedDXIRPUEigAACCGROgEJX5qaEgBBAAIGOBRZJeriul69K2q/jXukAAQTiBPaWdOH0D74o6SCYHhWILUxZ/cofcrZqx6iydHwTUzJ/rW9rpPeNuM5Z7fRHm+wIVEbHP2CMCe/qrd8mfddZmJ0oiQQBBBBAIC8CFLryMlPEiQACCDQvELfmyV6SLmm+C/ZEAIEmBOqLXDO7Xy3ptU20LfwuDe7AWuq7zm6dJO8FE75kn2wX2QOqbxhkHbROMDPSlrv1MjIRhIEAAggURIBCV0EmkjQQQACBOoG4Qtc/JK2PEgIIJCrAQtpr4IwrXsQtHO8FtZ9KenldVzXfdQYTnSk6y7QAha5MTw/BIYAAArkToNCVuykjYAQQQGBegbiL799KetG8LdmhbwS8YPw2yWwUJmwX2WemcWeMNzb+v7Lms6tQrXb3h5wrmgWeeWmAkW5bb3Bgs3O3No8027aL+1HomgfbC2pXStrVWv3roanSRkt3W3xftElckaO0YMGOl+y8/ve7OJcM1UMBb2x8L1lzUSSEO33XeVoPw2JoBBBAAIGcClDoyunEETYCCCCwBoF/SVov8vPw/+9FDYFQoNk7bTrRGg5q51hp//o+Ssbsd0l5IFwzbo2bF9TmHMNxdwLN108Xfv4FSQdHxpmUxLpCLeDH3s1jdLZfdg5soRt2jQh4Qe0wK+1ekr4x4jrh+nGZ3oaD8UOszOmrgrT2Gn9ocIdMB0xwCCCAAAKZFaDQldmpITAEEECgI4Fwcexw/aDwLphnS7q1o95oXCiBuMKCMfr6SNl5e1KJdvIoUlxba+33q0ODOyYVX4L9fFfSTFwrJYUvg0hyu0PShtMdhgXA/0my8yz0xR1dyc9CjCkLuyfPTI8IIIAAAhkVoNCV0YkhLAQQQAABBNISaHAHzRV+2dk9qTGTLnRJ9lbfHdwkqfhy0k907aow7D9Jem5O4m8qTNboaoqp6Z0qo7XdjdFl0QYZvSuy6bzYEQEEEEAAgWYFKHQ1K8V+CCCAAAIIPCoQfndO1WGEi2bX8oQTe8dUaWqgusuSFUnl4Y3W7pPRkyL9XeW7zs7zjRF/R9dUpTq05JvztS3Yz/tmDbA3j05stqBk9y6pNHZJefENBZvHrqbjBbVwXbz/pdDVVXYGQwABBBDIkACFrgxNBqEggAACCORCIK74kKvv012vXLHO4xZMrV6zzVjtOzLkXJC0/uyClf277w5u3MwYe4/ds/FK+3Dd47bmEt8d2KuZtgXbp28KXW3OW7iwfX0x9b2Szmmzr9SaeWO162T1sscGsDf67uAL0hqwctXEpmalvaXXhS4vqIVv+31KGIeRPjniOp9IK2f6RQABBBBAoF4gVyfmTB0CCCCAAAIZEIgrPhwp6YQMxEYIxRL4mKRPRVI6VdJhxUqzrWyOkHR8TMvMndvG3qFozIur5YHftJV5E40qQe0KI+1Wt+t5vuvMejlEE920vUuDdfb2qQ4NRt+s2PYYNEQAAQQQQKCRQOZOBpgqBBBAAAEEMi4QV+g6RtKxGY+b8PIpEBYnZu5SCh9H+1w+00g86vpF+us7z9y5bfzLH8yJI+WBsFhXyK2TNfoKCUJSCCCAAAJdFcjcyUBXs2cwBBBAAAEEWhfI/aOLradMCwQyJ/ALSVvHRJW5c9v4u5u0XXXIuS5zqgkF1KDQxZsfE/KlGwQQQACBNQtk7mSACUMAAQQQQCDjAhtIurMuxhdLSu0RpIxbEB4CvRBotHbZqKRdexHQmsb0glq4Xtamq/cxZrlfHgg/Rwq7eaPjD8mYteoTtMbsXC0PXFXYpEkMAQQQQCAzAhS6MjMVBIIAAggggAACCCAwj8AuksZi9tle0o+zqrfn2N3bWk16UyVzeXXngWuzGmeScXnBxC8l+5KwT2Pse0bKg19Ksn/6QgABBBBAoJEAhS6ODQQQQACBtATOl/QOSd+VtFNag9BvegKVpXdtZEoLbqsfwXcdzh3SI6fn+QXCBejj1rbiuJzfru/2GA5qR8uoNlJ2Mvc2zr6bDBJGAAEEuijASUEXsRkKAQQQ6CMB1rEqwGTHLqIt88MRd+A1BUiPFPIpsKGkcCH66DbvOW3liuWbm4Wlm6Yb3uy7zhb5JCDq+QSGg4mKlfUp0s8nxc8RQACBYgrMe1JQzLTJCgEEEEAgZYG4QteRkk5IeVy6T1CAN6cliElXSQpcLGnPug6/IuldaxzAWuONTUxF9+EOxSSnJTt9NfjsutV3nU2yEyWRIIAAAgikJUChKy1Z+kUAAQS6IOAF4ydaa++oDi05Y2Y4b2ziCCO7rV05+Vl/tw1+1IUw4oaIK3RdIGnfHsXDsG0INLhYfMB3nSe10R1NEOiZgBeMj0kmXN9r1kaha86UfEBSSdJpPZusBAamSJ8AIl0ggAACORag0JXjySN0BBDoX4FKML6/kZm15kh4wRZzcn+V7zo790AqrtD1LEl/7UEsDNmmgBfUjpL0yfrmD06W1l262+L72uySZgj0RMALauEC8K+g0NWQf1DS8shPnynpbz2ZsA4HjS10Wf3JH3Ke22HXNEcAAQQQyIEAha4cTBIhIoAAAlGBBn+t/oekp2bkQm5vSRfWxRLeHXAoM5k/geFg/PlWOt0Ys3yk7OyTvwyIGAFpr2/f/dzJRyb/kJHPxyxOSdwfJ8I4c3mtUBmbeKOx9lv10Ny9l8XDjpgQQACBdARy+eWVDgW9IoAAAvkRaPRYRlwGnNznZ16JFAEE0hMYDiYOsbKnz4xg/3PNwqrnTaY3Yq56LlShK1fyBIsAAgggkLgAha7ESekQAQQQSF8gttBldKusnhEdPVLoCteoGavbJ1yPZfWFXzORe0HtT5Kevfpi0dp9qkODFzXTln0QQAABBDIpUKhC1/Cy8ZfZKXNdvXT0jz5eMHGxZFe/1IA/CmXyuCQoBBBAoC0BCl1tsdEIAQQQ6K3A8Nj4btaaK6In8V5Q+5ek9Vb/uykd6pcX1y8qHHcx80RJ/2kmo+Gx2gHW6ux5imnNdMU+CCCAQKcCv5W0pbTqM+nATjvr8/ZPlnRPxGBDSXfl0aXBXc8X+q7z1jCf4bHaodbqVL7L8ji7xIwAAgjML0Cha34j9kAAAQSKJBBX6PqCpPc3k6Q3WrtFRptycdCMFvsggECKAtHPsoclrZ3ieHSdI4H53ro4389zlCqhIoAAAgjECFDo4rBAAAEE+ksgrtB1uKSTm2EYDmrnW+kdFLqa0WIfBBBISSB8VHovzmtT0i1At3GFLCPdNOI6zw/T84LaQ5LW4rusAJNNCggggAAnBBwDCCCAQN8LxBW6WvqjR/QCwsreUHUHt+l7WQAQQKBbAjdI2orz2m5x528cb7R2hozeVx95/RpccWt4SfqL7zpz7ljOX/ZEjAACCCDQ0sUNXAgggAAChRD4oaRXS7pJ0qq/bre6eWO1s2X1MmvsZ6plFqJv1Y/9EUCgI4GdJH2bQldHhoVvXBld/lJTKp0gu6qAtX80YS8Y31Ey3w3/3RhdNFJ29ik8CgkigAACfSJAoatPJpo0EUAAAQQQQACBAglcIOltdfmEd5WGd3qxIYAAAggggECfC1Do6vMDgPQRQAABBBBAAAEEEEAAAQQQQACBoghQ6CrKTJIHAggggAACCCDQZwLeWO3NsvpmXdqP+K4zZ5HxPmNJI92fSdp2uuPPSfrfNAahTwQQQAABBJIQoNCVhCJ9IIAAAggggAACCHRdIO7terL6jD/kfKTrwRR3wFskRRdpP0rSp4qbMpkhgAACCORZgEJXnmeP2BFAAAEEEEAAgT4WiCt0WdmHqu7g4/qYJenU497WG47BdUTS0vSHAAIIIJCIAF9QiTDSCQIIINA7AS+YuFOyG9RF8DXfdd7Ru4gYGQEEmhGofPef65UeeuRXVnqmlf141R38dDPt2Ocxgdg7umTHfHfQxSkxAQpdiVHSEQIIIIBANwQodHVDmTEQQACBlAS8pXe9WaUF9evTrBrJdx0+35swryydeKUp2UMl83PfHTipiSbsgkAiAm+6/J4nr7Xo4XvqO7OyP6+6gy9LZIA+6WR4bOISa+1wfbp8/iU++X+S9OxIrx+TdHziI9EhAggggAACCQhwIZQAIl0ggAACvRLwgtqVknaNjs+F3vwzUhmtLTNGO3OBPL9Vm3tE7wI5TdKhbfZVuGZeUPubpGfwu9v51O4ZTLxqSvYoa6d+Xx1a8qHOe8xFD9Hfr1dK+kmKkYd9bzfd/ymSPpziWHSNAAIIIIBARwIUujriozECCCDQWwFvdPw9MuZcLpZbn4e4R56MzBkj7sAhrfeWuRbfkvTG6ajCQuholyM8TtLHY8bkvGMaJf6RO+7G7PJxmtfhLpS0N79feZ0+4kYAAQQQSFuAE860hekfAQQQSFkg5oL5Ot91Zv7ynvLo+e0+vtClW0ZcJ/qITt6SvFvSUyNBh8fDdV1M5C+SnsmFeGPxSlA72kjHUqTu4lFZnKFYM6s4c0kmCCCAAAIpCFDoSgGVLhFAAIFuC3hjE56mpl5vtfC86tD613d7/DyOF/u2NqsPVYecz+Yxn7qY4y6C/yPpiV3M60RJH6HQtWZxb2zielm7zcxei0r3rnfhLpvd28V5Yqh8Cvxe0vP5/crn5BE1AggggED6AhS60jdmBAQQQKCvBSr+jWuZJyz+sox50C8PvCcrGF5QC9fyCdeamdnu9V1nvazE10EcWbnbIxpH+IjtAR3kRVME1ijgBbWrJL0h3Mka8+JqeeA3BSaL/n6dLukDBc6X1BBAAAEEEGhagEJX01TsiAACCCDQqoA3Oj4kY5bWt7OTK59b3W3D8C1e+0n6yvTPviHpba323+P9d5f0bknhxfUXehzLLOKYWMIC45d6EOObJIV3LIUL0YePVLIhkIqAF9SmJM06r10wqcGLd3NqqQyYjU7PkvQsSUdI+lUPQqq/czNcnL7+Dwc9CIchEUAAAQQQeFSAQhdHAgIIIIBA4gJeUPukpKMadey7zlslhcWt+u0Xkl6aeDDpdPh/0xeY9b1n6Tu1/m6PL0o6KB0Ges2AQHgH0/mS7pO0i6RwfbS+2xos7n+j7zov6DuM7iT8VUn7RoY6RjHrznUnHEZBAAEEEEDgMYEsnZQzLwgggAACBRDwxiZ+KGtfvaZUfNdp9OO8fC/FPR74XknnFGAKSSE/Ap+QFBYX6rfXSro6PykkE2mDQte47zpLkhmBXiICWXlEmolBAAEEEEBgjkBeLiiYOgQQQACBnAg0uOBcHb21uq865KzTIJ28fC/FXeRVJXk5mSbCLIYAxYbpeYx9uYTsblV3cNaj08WY9kxkUaRj7++Snj6tupskjplMHGIEgQACCLQvkJcLivYzpCUCCCCAwGqBypW115sF+s6qfzDmS2ksDj9foct3nfC7J+6tfCslLcrJdMVd5L1e0vdyEj9hFkOgSMWGjmck8tlzmu86h3bcKR00ErhZ0vMiPwwkDeWMLO536NmSbslZHoSLAAIIIFAnQKGLwwEBBBDoEwHvyru214IF10TS/a3vOi9KkiCu0GVM6eSR8uLDI+NcJilc0D3c7pfU6C6vJMNLqq/wDWeH1HX2a0kvSapz+kGgSYG4i/TrJW3bZHt2Q6ATgdskbTTdwS8lbd1JZz1qG/c7dKMk1nbr0YQwLAIIIJCEAIWuJBTpAwEEEMiBQKM7rabvsEo0g9lj2et9d5AL70SF6QyB1QLRC3XO7Tg4EGheIK7Q9WdJz2m+C/ZEAAEEEMiaACdDWZsR4kEAAQRSEuhmoSulFOgWAQTCheCC8RWSWV/G3O6XB2buqMEGAQRaF4grdG0j6YbWu6IFAggggEBWBCh0ZWUmiAMBBBBIWaAS1I42Ma9+T+OOrpRToftsC/x/e3eM0kAQhXH8GxVrQYxi7wEEsdRagjZi7AURRRC8gF7ARpsQsNIqdq56AL2AFxC0MoqtlZiVISGsYQqRmWxm9586eW/m93bZ3Udmtv/B0b71rjXcQ45ndK6GNedwPPVjpEMp0La7VnZHtiupPpSjZFAIIIAAAn8WoNH1Zyq+iAACCMQvULttXSs1vc2CeUCOv6ZDNgO7V5lrzzfuNzwUaiN53Tdm5Kw/FOexB1xCIIAAAggggEBhBLjxLEwpmQgCCCCAAAK5C/AWwIAlqCVvRzI6ptEVELkT+kPSZCbLg6Sl4FlJgAACCCCAAAJeBGh0eWEkCAIIIIAAAghIotEV+DBg6WJgYGlF0p0jC/fMwelJgAACCCCAgB8BLtp+HImCAAIIIFBygfXkfW7M6DxV+jkxU1lrLJivEpJMSXrvm/eBpNMSWgSZci1prcqY615w873crM7eB0lWzqDnkrZodJWz+MwaAQQQQKAYAjS6ilFHZoEAAgjEKGDfFveSGbhtktglQ9F9Nm/fdtL09wbGqTHzV9XKY3ST8TPgi+7Srz1Jz35CDi5KLWlty5hGNiP7YHnzz/7rzzaDx71F9hOIf3T5cSQKAggggAACuQnQ6MqNnsQIIIBA6QVcy9yivC65lpPZ6tIcifMYd9bTqN6sTts3svH5v4DrnL+R1HtBxv9De/0le3R55SQYAggggAACgxWI8oFisERkQwABBBAIIHAo6cQRN8rrEo2uAEdIjiGd9UzVbq5Oj+Y4rCKkjmkPt0V19uu6lPRUBHzmgAACCCCAQFkEfgCh6zo5F7egAAAAAABJRU5ErkJggg=="
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